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'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 71.96339416503906, 'median': 45.0, 'min': -932.0, 'percentile_00_5': -93.0, 'percentile_99_5': 1052.0, 'std': 141.6230926513672}}}", + "preprocessed_dataset_folder": "/data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/nnUNetPlans_3d_fullres", + "preprocessed_dataset_folder_base": "/data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL", + "save_every": "50", + "torch_version": "2.5.0+cu121", + "unpack_dataset": "True", + "was_initialized": "True", + "weight_decay": "3e-05" +} \ No newline at end of file diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_0/progress.png b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_0/progress.png new file mode 100644 index 0000000000000000000000000000000000000000..ede3ef498996d4fa3115b433887da30e4422d587 --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_0/progress.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d2a869890a6e72a1933004cb4aa13af6a6f033d37534f5db8a4c859dd8d21e7a +size 1605236 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_0/training_log_2026_4_10_10_09_46.txt b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_0/training_log_2026_4_10_10_09_46.txt new file mode 100644 index 0000000000000000000000000000000000000000..0891043c55b9a2df59fa52600a82a12b22941d1c --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_0/training_log_2026_4_10_10_09_46.txt @@ -0,0 +1,28352 @@ + +####################################################################### +Please cite the following paper when using nnU-Net: +Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. +####################################################################### + +2026-04-10 10:09:46.637262: do_dummy_2d_data_aug: False +2026-04-10 10:09:46.642830: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json +2026-04-10 10:09:46.645968: The split file contains 5 splits. +2026-04-10 10:09:46.647418: Desired fold for training: 0 +2026-04-10 10:09:46.648944: This split has 387 training and 97 validation cases. +2026-04-10 10:09:53.562552: Using torch.compile... + +This is the configuration used by this training: +Configuration name: 3d_fullres + {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True} + +These are the global plan.json settings: + {'dataset_name': 'Dataset201_MSWAL', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.25, 0.75, 0.75], 'original_median_shape_after_transp': [261, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 71.96339416503906, 'median': 45.0, 'min': -932.0, 'percentile_00_5': -93.0, 'percentile_99_5': 1052.0, 'std': 141.6230926513672}}} + +2026-04-10 10:09:54.831703: unpacking dataset... +2026-04-10 10:09:59.870421: unpacking done... +2026-04-10 10:09:59.892266: Unable to plot network architecture: nnUNet_compile is enabled! +2026-04-10 10:09:59.946899: +2026-04-10 10:09:59.948568: Epoch 0 +2026-04-10 10:09:59.950563: Current learning rate: 0.01 +2026-04-10 10:13:58.662392: train_loss 0.1961 +2026-04-10 10:13:58.667134: val_loss 0.0676 +2026-04-10 10:13:58.668455: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:13:58.670071: Epoch time: 238.72 s +2026-04-10 10:13:58.671510: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 10:14:00.961081: +2026-04-10 10:14:00.963151: Epoch 1 +2026-04-10 10:14:00.964983: Current learning rate: 0.01 +2026-04-10 10:15:41.772416: train_loss 0.0673 +2026-04-10 10:15:41.780198: val_loss 0.0544 +2026-04-10 10:15:41.782437: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:15:41.784690: Epoch time: 100.81 s +2026-04-10 10:15:42.809214: +2026-04-10 10:15:42.811194: Epoch 2 +2026-04-10 10:15:42.812474: Current learning rate: 0.01 +2026-04-10 10:17:23.571289: train_loss 0.0574 +2026-04-10 10:17:23.580215: val_loss 0.046 +2026-04-10 10:17:23.582174: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:17:23.584043: Epoch time: 100.76 s +2026-04-10 10:17:24.657196: +2026-04-10 10:17:24.659088: Epoch 3 +2026-04-10 10:17:24.660701: Current learning rate: 0.00999 +2026-04-10 10:19:05.305478: train_loss 0.0571 +2026-04-10 10:19:05.323451: val_loss 0.0348 +2026-04-10 10:19:05.325131: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:19:05.334989: Epoch time: 100.65 s +2026-04-10 10:19:06.376582: +2026-04-10 10:19:06.378241: Epoch 4 +2026-04-10 10:19:06.379593: Current learning rate: 0.00999 +2026-04-10 10:20:47.273031: train_loss 0.0615 +2026-04-10 10:20:47.278382: val_loss 0.0473 +2026-04-10 10:20:47.280292: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:20:47.282576: Epoch time: 100.9 s +2026-04-10 10:20:48.348310: +2026-04-10 10:20:48.350121: Epoch 5 +2026-04-10 10:20:48.351597: Current learning rate: 0.00999 +2026-04-10 10:22:28.976258: train_loss 0.0629 +2026-04-10 10:22:28.983766: val_loss 0.0443 +2026-04-10 10:22:28.985624: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:22:28.987194: Epoch time: 100.63 s +2026-04-10 10:22:30.005595: +2026-04-10 10:22:30.007837: Epoch 6 +2026-04-10 10:22:30.009481: Current learning rate: 0.00999 +2026-04-10 10:24:10.841049: train_loss 0.0635 +2026-04-10 10:24:10.847187: val_loss 0.0615 +2026-04-10 10:24:10.849414: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:24:10.851803: Epoch time: 100.84 s +2026-04-10 10:24:11.871608: +2026-04-10 10:24:11.882142: Epoch 7 +2026-04-10 10:24:11.883781: Current learning rate: 0.00998 +2026-04-10 10:25:52.868425: train_loss 0.0503 +2026-04-10 10:25:52.876285: val_loss 0.0472 +2026-04-10 10:25:52.878507: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:25:52.880925: Epoch time: 101.0 s +2026-04-10 10:25:53.934694: +2026-04-10 10:25:53.937456: Epoch 8 +2026-04-10 10:25:53.939001: Current learning rate: 0.00998 +2026-04-10 10:27:34.981565: train_loss 0.0655 +2026-04-10 10:27:34.989508: val_loss 0.0509 +2026-04-10 10:27:34.991567: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:27:34.993120: Epoch time: 101.05 s +2026-04-10 10:27:36.051541: +2026-04-10 10:27:36.053723: Epoch 9 +2026-04-10 10:27:36.055538: Current learning rate: 0.00998 +2026-04-10 10:29:17.410245: train_loss 0.0546 +2026-04-10 10:29:17.418863: val_loss 0.0572 +2026-04-10 10:29:17.421463: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:29:17.423435: Epoch time: 101.36 s +2026-04-10 10:29:18.409416: +2026-04-10 10:29:18.411855: Epoch 10 +2026-04-10 10:29:18.413715: Current learning rate: 0.00998 +2026-04-10 10:30:59.672086: train_loss 0.0539 +2026-04-10 10:30:59.680767: val_loss 0.0333 +2026-04-10 10:30:59.682866: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:30:59.685176: Epoch time: 101.26 s +2026-04-10 10:31:00.692303: +2026-04-10 10:31:00.694058: Epoch 11 +2026-04-10 10:31:00.696156: Current learning rate: 0.00998 +2026-04-10 10:32:41.641782: train_loss 0.0467 +2026-04-10 10:32:41.650508: val_loss 0.0467 +2026-04-10 10:32:41.652757: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:32:41.655378: Epoch time: 100.95 s +2026-04-10 10:32:42.667279: +2026-04-10 10:32:42.668964: Epoch 12 +2026-04-10 10:32:42.670377: Current learning rate: 0.00997 +2026-04-10 10:34:23.620686: train_loss 0.05 +2026-04-10 10:34:23.631334: val_loss 0.0718 +2026-04-10 10:34:23.633788: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:34:23.635990: Epoch time: 100.96 s +2026-04-10 10:34:24.682841: +2026-04-10 10:34:24.686345: Epoch 13 +2026-04-10 10:34:24.689196: Current learning rate: 0.00997 +2026-04-10 10:36:05.519042: train_loss 0.0517 +2026-04-10 10:36:05.526252: val_loss 0.036 +2026-04-10 10:36:05.528603: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:36:05.530607: Epoch time: 100.84 s +2026-04-10 10:36:06.565028: +2026-04-10 10:36:06.566833: Epoch 14 +2026-04-10 10:36:06.568322: Current learning rate: 0.00997 +2026-04-10 10:37:47.573041: train_loss 0.0421 +2026-04-10 10:37:47.579079: val_loss 0.0375 +2026-04-10 10:37:47.580758: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:37:47.582247: Epoch time: 101.01 s +2026-04-10 10:37:48.666808: +2026-04-10 10:37:48.668932: Epoch 15 +2026-04-10 10:37:48.670571: Current learning rate: 0.00997 +2026-04-10 10:39:29.788815: train_loss 0.0534 +2026-04-10 10:39:29.795064: val_loss 0.0429 +2026-04-10 10:39:29.796827: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:39:29.798525: Epoch time: 101.12 s +2026-04-10 10:39:30.848725: +2026-04-10 10:39:30.850467: Epoch 16 +2026-04-10 10:39:30.851982: Current learning rate: 0.00996 +2026-04-10 10:41:11.807034: train_loss 0.0458 +2026-04-10 10:41:11.813716: val_loss 0.0302 +2026-04-10 10:41:11.817070: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:41:11.819344: Epoch time: 100.96 s +2026-04-10 10:41:12.876158: +2026-04-10 10:41:12.878093: Epoch 17 +2026-04-10 10:41:12.879588: Current learning rate: 0.00996 +2026-04-10 10:42:54.058593: train_loss 0.0459 +2026-04-10 10:42:54.066181: val_loss 0.024 +2026-04-10 10:42:54.068451: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:42:54.070042: Epoch time: 101.18 s +2026-04-10 10:42:55.121432: +2026-04-10 10:42:55.123175: Epoch 18 +2026-04-10 10:42:55.124851: Current learning rate: 0.00996 +2026-04-10 10:44:36.169842: train_loss 0.0452 +2026-04-10 10:44:36.177083: val_loss 0.0231 +2026-04-10 10:44:36.179260: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:44:36.181365: Epoch time: 101.05 s +2026-04-10 10:44:38.146468: +2026-04-10 10:44:38.149218: Epoch 19 +2026-04-10 10:44:38.151374: Current learning rate: 0.00996 +2026-04-10 10:46:19.052259: train_loss 0.0386 +2026-04-10 10:46:19.058111: val_loss 0.0304 +2026-04-10 10:46:19.059953: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:46:19.061666: Epoch time: 100.91 s +2026-04-10 10:46:20.116194: +2026-04-10 10:46:20.118101: Epoch 20 +2026-04-10 10:46:20.119790: Current learning rate: 0.00995 +2026-04-10 10:48:01.484258: train_loss 0.0414 +2026-04-10 10:48:01.496010: val_loss 0.0623 +2026-04-10 10:48:01.506213: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:48:01.509201: Epoch time: 101.37 s +2026-04-10 10:48:02.572426: +2026-04-10 10:48:02.577996: Epoch 21 +2026-04-10 10:48:02.580078: Current learning rate: 0.00995 +2026-04-10 10:49:44.795735: train_loss 0.044 +2026-04-10 10:49:44.819131: val_loss 0.0424 +2026-04-10 10:49:44.839226: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:49:44.848970: Epoch time: 102.22 s +2026-04-10 10:49:45.850636: +2026-04-10 10:49:45.857558: Epoch 22 +2026-04-10 10:49:45.860208: Current learning rate: 0.00995 +2026-04-10 10:51:27.525023: train_loss 0.0492 +2026-04-10 10:51:27.533064: val_loss 0.0487 +2026-04-10 10:51:27.535233: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:51:27.537039: Epoch time: 101.68 s +2026-04-10 10:51:28.555004: +2026-04-10 10:51:28.557318: Epoch 23 +2026-04-10 10:51:28.558884: Current learning rate: 0.00995 +2026-04-10 10:53:09.735799: train_loss 0.0402 +2026-04-10 10:53:09.743104: val_loss 0.0456 +2026-04-10 10:53:09.745068: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:53:09.747035: Epoch time: 101.18 s +2026-04-10 10:53:10.715600: +2026-04-10 10:53:10.717412: Epoch 24 +2026-04-10 10:53:10.719244: Current learning rate: 0.00995 +2026-04-10 10:54:52.045018: train_loss 0.0429 +2026-04-10 10:54:52.050307: val_loss 0.0535 +2026-04-10 10:54:52.051856: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:54:52.053325: Epoch time: 101.33 s +2026-04-10 10:54:53.035557: +2026-04-10 10:54:53.037306: Epoch 25 +2026-04-10 10:54:53.038902: Current learning rate: 0.00994 +2026-04-10 10:56:34.835157: train_loss 0.0442 +2026-04-10 10:56:34.850466: val_loss 0.0311 +2026-04-10 10:56:34.852940: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:56:34.856444: Epoch time: 101.8 s +2026-04-10 10:56:35.854204: +2026-04-10 10:56:35.858340: Epoch 26 +2026-04-10 10:56:35.861680: Current learning rate: 0.00994 +2026-04-10 10:58:17.457206: train_loss 0.0424 +2026-04-10 10:58:17.474225: val_loss 0.0636 +2026-04-10 10:58:17.477676: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:58:17.483100: Epoch time: 101.61 s +2026-04-10 10:58:18.502491: +2026-04-10 10:58:18.504463: Epoch 27 +2026-04-10 10:58:18.506428: Current learning rate: 0.00994 +2026-04-10 10:59:59.637009: train_loss 0.0442 +2026-04-10 10:59:59.644537: val_loss 0.0411 +2026-04-10 10:59:59.663805: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:59:59.665980: Epoch time: 101.13 s +2026-04-10 11:00:00.731620: +2026-04-10 11:00:00.734594: Epoch 28 +2026-04-10 11:00:00.736745: Current learning rate: 0.00994 +2026-04-10 11:02:42.846074: train_loss 0.0456 +2026-04-10 11:02:42.861524: val_loss 0.0414 +2026-04-10 11:02:42.865072: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:02:42.867298: Epoch time: 162.12 s +2026-04-10 11:02:43.885234: +2026-04-10 11:02:43.887286: Epoch 29 +2026-04-10 11:02:43.889039: Current learning rate: 0.00993 +2026-04-10 11:04:24.415792: train_loss 0.045 +2026-04-10 11:04:24.421511: val_loss 0.0638 +2026-04-10 11:04:24.423554: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:04:24.425410: Epoch time: 100.53 s +2026-04-10 11:04:25.458132: +2026-04-10 11:04:25.460218: Epoch 30 +2026-04-10 11:04:25.461964: Current learning rate: 0.00993 +2026-04-10 11:06:13.231401: train_loss 0.0418 +2026-04-10 11:06:13.236732: val_loss 0.0433 +2026-04-10 11:06:13.239161: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:06:13.241727: Epoch time: 107.78 s +2026-04-10 11:06:14.287443: +2026-04-10 11:06:14.289294: Epoch 31 +2026-04-10 11:06:14.291241: Current learning rate: 0.00993 +2026-04-10 11:08:02.649905: train_loss 0.0498 +2026-04-10 11:08:02.657600: val_loss 0.0296 +2026-04-10 11:08:02.659516: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:08:02.660966: Epoch time: 108.37 s +2026-04-10 11:08:03.684999: +2026-04-10 11:08:03.687106: Epoch 32 +2026-04-10 11:08:03.689171: Current learning rate: 0.00993 +2026-04-10 11:10:14.255871: train_loss 0.035 +2026-04-10 11:10:14.261076: val_loss 0.0261 +2026-04-10 11:10:14.262745: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:10:14.264354: Epoch time: 130.57 s +2026-04-10 11:10:15.276683: +2026-04-10 11:10:15.278762: Epoch 33 +2026-04-10 11:10:15.280400: Current learning rate: 0.00993 +2026-04-10 11:11:55.882535: train_loss 0.0276 +2026-04-10 11:11:55.887844: val_loss 0.0391 +2026-04-10 11:11:55.889735: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:11:55.891464: Epoch time: 100.61 s +2026-04-10 11:11:55.893205: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 11:11:58.524074: +2026-04-10 11:11:58.526509: Epoch 34 +2026-04-10 11:11:58.528627: Current learning rate: 0.00992 +2026-04-10 11:13:39.304997: train_loss 0.035 +2026-04-10 11:13:39.310945: val_loss 0.0612 +2026-04-10 11:13:39.312966: Pseudo dice [0.0, 0.0, 0.0858, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:13:39.314691: Epoch time: 100.78 s +2026-04-10 11:13:39.316833: Yayy! New best EMA pseudo Dice: 0.0012 +2026-04-10 11:13:41.862159: +2026-04-10 11:13:41.864420: Epoch 35 +2026-04-10 11:13:41.866322: Current learning rate: 0.00992 +2026-04-10 11:15:22.231731: train_loss 0.0318 +2026-04-10 11:15:22.237042: val_loss 0.0745 +2026-04-10 11:15:22.238887: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:15:22.240925: Epoch time: 100.37 s +2026-04-10 11:15:23.279003: +2026-04-10 11:15:23.283886: Epoch 36 +2026-04-10 11:15:23.285970: Current learning rate: 0.00992 +2026-04-10 11:17:03.914943: train_loss 0.0467 +2026-04-10 11:17:03.919698: val_loss 0.0443 +2026-04-10 11:17:03.921935: Pseudo dice [0.0, 0.0, 0.0006, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:17:03.923407: Epoch time: 100.64 s +2026-04-10 11:17:04.960362: +2026-04-10 11:17:04.961919: Epoch 37 +2026-04-10 11:17:04.963451: Current learning rate: 0.00992 +2026-04-10 11:18:45.403151: train_loss 0.0298 +2026-04-10 11:18:45.408921: val_loss 0.0254 +2026-04-10 11:18:45.411239: Pseudo dice [0.0, 0.0, 0.017, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:18:45.413350: Epoch time: 100.45 s +2026-04-10 11:18:46.458332: +2026-04-10 11:18:46.460646: Epoch 38 +2026-04-10 11:18:46.462415: Current learning rate: 0.00991 +2026-04-10 11:20:27.118860: train_loss 0.0344 +2026-04-10 11:20:27.189227: val_loss 0.0344 +2026-04-10 11:20:27.191352: Pseudo dice [0.0, 0.0, 0.0133, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:20:27.193495: Epoch time: 100.66 s +2026-04-10 11:20:29.258210: +2026-04-10 11:20:29.260001: Epoch 39 +2026-04-10 11:20:29.261527: Current learning rate: 0.00991 +2026-04-10 11:22:10.038334: train_loss 0.0326 +2026-04-10 11:22:10.042969: val_loss 0.0064 +2026-04-10 11:22:10.044874: Pseudo dice [0.0, 0.0, 0.2176, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:22:10.047474: Epoch time: 100.78 s +2026-04-10 11:22:10.048970: Yayy! New best EMA pseudo Dice: 0.0042 +2026-04-10 11:22:12.603679: +2026-04-10 11:22:12.605961: Epoch 40 +2026-04-10 11:22:12.607706: Current learning rate: 0.00991 +2026-04-10 11:23:53.229490: train_loss 0.0318 +2026-04-10 11:23:53.233949: val_loss 0.0154 +2026-04-10 11:23:53.236178: Pseudo dice [0.0, 0.0, 0.0744, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:23:53.237978: Epoch time: 100.63 s +2026-04-10 11:23:53.239394: Yayy! New best EMA pseudo Dice: 0.0048 +2026-04-10 11:23:55.887342: +2026-04-10 11:23:55.889350: Epoch 41 +2026-04-10 11:23:55.891246: Current learning rate: 0.00991 +2026-04-10 11:25:36.643638: train_loss 0.0347 +2026-04-10 11:25:36.648027: val_loss 0.0351 +2026-04-10 11:25:36.649736: Pseudo dice [0.0, 0.0, 0.3392, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:25:36.651325: Epoch time: 100.76 s +2026-04-10 11:25:36.652950: Yayy! New best EMA pseudo Dice: 0.0092 +2026-04-10 11:25:39.241678: +2026-04-10 11:25:39.243828: Epoch 42 +2026-04-10 11:25:39.245645: Current learning rate: 0.00991 +2026-04-10 11:27:19.903015: train_loss 0.0302 +2026-04-10 11:27:19.909711: val_loss 0.0178 +2026-04-10 11:27:19.912938: Pseudo dice [0.0, 0.0, 0.0872, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:27:19.915144: Epoch time: 100.66 s +2026-04-10 11:27:19.917601: Yayy! New best EMA pseudo Dice: 0.0095 +2026-04-10 11:27:22.457481: +2026-04-10 11:27:22.460116: Epoch 43 +2026-04-10 11:27:22.461763: Current learning rate: 0.0099 +2026-04-10 11:29:03.619874: train_loss 0.0244 +2026-04-10 11:29:03.625284: val_loss 0.015 +2026-04-10 11:29:03.629251: Pseudo dice [0.0, 0.0, 0.1203, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:29:03.631622: Epoch time: 101.17 s +2026-04-10 11:29:03.634480: Yayy! New best EMA pseudo Dice: 0.0103 +2026-04-10 11:29:06.103301: +2026-04-10 11:29:06.105383: Epoch 44 +2026-04-10 11:29:06.107283: Current learning rate: 0.0099 +2026-04-10 11:30:47.269797: train_loss 0.0352 +2026-04-10 11:30:47.275146: val_loss 0.0154 +2026-04-10 11:30:47.277493: Pseudo dice [0.0, 0.0, 0.1122, 0.0, 0.0, 0.0, 0.0005] +2026-04-10 11:30:47.280015: Epoch time: 101.17 s +2026-04-10 11:30:47.281862: Yayy! New best EMA pseudo Dice: 0.0109 +2026-04-10 11:30:49.851209: +2026-04-10 11:30:49.855861: Epoch 45 +2026-04-10 11:30:49.857869: Current learning rate: 0.0099 +2026-04-10 11:32:31.116641: train_loss 0.0187 +2026-04-10 11:32:31.122044: val_loss 0.0296 +2026-04-10 11:32:31.123949: Pseudo dice [0.0, 0.0, 0.2041, 0.0, 0.0, 0.0, 0.2542] +2026-04-10 11:32:31.125858: Epoch time: 101.27 s +2026-04-10 11:32:31.127894: Yayy! New best EMA pseudo Dice: 0.0163 +2026-04-10 11:32:33.691213: +2026-04-10 11:32:33.694177: Epoch 46 +2026-04-10 11:32:33.695808: Current learning rate: 0.0099 +2026-04-10 11:34:14.824905: train_loss 0.0248 +2026-04-10 11:34:14.833767: val_loss 0.0166 +2026-04-10 11:34:14.835725: Pseudo dice [0.0, 0.0, 0.3994, 0.0, 0.0, 0.0, 0.2152] +2026-04-10 11:34:14.837642: Epoch time: 101.14 s +2026-04-10 11:34:14.839802: Yayy! New best EMA pseudo Dice: 0.0235 +2026-04-10 11:34:17.379595: +2026-04-10 11:34:17.382114: Epoch 47 +2026-04-10 11:34:17.383658: Current learning rate: 0.00989 +2026-04-10 11:35:58.462918: train_loss 0.0129 +2026-04-10 11:35:58.468005: val_loss 0.0204 +2026-04-10 11:35:58.469985: Pseudo dice [0.0, 0.0, 0.1585, 0.0, 0.0, 0.0, 0.1703] +2026-04-10 11:35:58.471821: Epoch time: 101.09 s +2026-04-10 11:35:58.474111: Yayy! New best EMA pseudo Dice: 0.0258 +2026-04-10 11:36:01.004999: +2026-04-10 11:36:01.007430: Epoch 48 +2026-04-10 11:36:01.008987: Current learning rate: 0.00989 +2026-04-10 11:37:42.114254: train_loss 0.0179 +2026-04-10 11:37:42.119100: val_loss 0.0034 +2026-04-10 11:37:42.120801: Pseudo dice [0.0, 0.0, 0.2238, 0.0, 0.0, 0.0, 0.2126] +2026-04-10 11:37:42.122391: Epoch time: 101.11 s +2026-04-10 11:37:42.124320: Yayy! New best EMA pseudo Dice: 0.0295 +2026-04-10 11:37:44.705211: +2026-04-10 11:37:44.708361: Epoch 49 +2026-04-10 11:37:44.710368: Current learning rate: 0.00989 +2026-04-10 11:39:25.871025: train_loss 0.0245 +2026-04-10 11:39:25.877684: val_loss -0.0049 +2026-04-10 11:39:25.879999: Pseudo dice [0.0, 0.0, 0.2759, 0.0, 0.0, 0.0, 0.3906] +2026-04-10 11:39:25.882244: Epoch time: 101.17 s +2026-04-10 11:39:27.419522: Yayy! New best EMA pseudo Dice: 0.0361 +2026-04-10 11:39:29.986756: +2026-04-10 11:39:29.990747: Epoch 50 +2026-04-10 11:39:29.993056: Current learning rate: 0.00989 +2026-04-10 11:41:10.996701: train_loss 0.0107 +2026-04-10 11:41:11.002027: val_loss 0.0216 +2026-04-10 11:41:11.004317: Pseudo dice [0.0, 0.0, 0.4559, 0.0, 0.0, 0.0, 0.3154] +2026-04-10 11:41:11.006701: Epoch time: 101.01 s +2026-04-10 11:41:11.008653: Yayy! New best EMA pseudo Dice: 0.0435 +2026-04-10 11:41:13.410599: +2026-04-10 11:41:13.413160: Epoch 51 +2026-04-10 11:41:13.414735: Current learning rate: 0.00989 +2026-04-10 11:42:54.685290: train_loss 0.0131 +2026-04-10 11:42:54.690393: val_loss 0.0187 +2026-04-10 11:42:54.692398: Pseudo dice [0.0, 0.0, 0.1864, 0.0, 0.0, 0.0, 0.2006] +2026-04-10 11:42:54.694550: Epoch time: 101.28 s +2026-04-10 11:42:54.696910: Yayy! New best EMA pseudo Dice: 0.0447 +2026-04-10 11:42:57.001291: +2026-04-10 11:42:57.004703: Epoch 52 +2026-04-10 11:42:57.007305: Current learning rate: 0.00988 +2026-04-10 11:44:38.107230: train_loss 0.0126 +2026-04-10 11:44:38.113480: val_loss 0.0011 +2026-04-10 11:44:38.115389: Pseudo dice [0.0, 0.0, 0.332, 0.0, 0.0, 0.0, 0.4527] +2026-04-10 11:44:38.117781: Epoch time: 101.11 s +2026-04-10 11:44:38.119918: Yayy! New best EMA pseudo Dice: 0.0514 +2026-04-10 11:44:40.753331: +2026-04-10 11:44:40.761392: Epoch 53 +2026-04-10 11:44:40.763600: Current learning rate: 0.00988 +2026-04-10 11:46:22.000731: train_loss 0.0006 +2026-04-10 11:46:22.006003: val_loss 0.0158 +2026-04-10 11:46:22.009295: Pseudo dice [0.0, 0.0, 0.3759, 0.0, 0.0, 0.0, 0.1287] +2026-04-10 11:46:22.011024: Epoch time: 101.25 s +2026-04-10 11:46:22.012931: Yayy! New best EMA pseudo Dice: 0.0535 +2026-04-10 11:46:24.582494: +2026-04-10 11:46:24.584665: Epoch 54 +2026-04-10 11:46:24.586152: Current learning rate: 0.00988 +2026-04-10 11:48:05.681705: train_loss 0.0175 +2026-04-10 11:48:05.688829: val_loss 0.0054 +2026-04-10 11:48:05.692074: Pseudo dice [0.0, 0.0, 0.4472, 0.0, 0.0, 0.0, 0.3165] +2026-04-10 11:48:05.694271: Epoch time: 101.1 s +2026-04-10 11:48:05.696107: Yayy! New best EMA pseudo Dice: 0.059 +2026-04-10 11:48:08.864271: +2026-04-10 11:48:08.866941: Epoch 55 +2026-04-10 11:48:08.868496: Current learning rate: 0.00988 +2026-04-10 11:49:50.144624: train_loss 0.0052 +2026-04-10 11:49:50.150393: val_loss -0.0251 +2026-04-10 11:49:50.152897: Pseudo dice [0.0, 0.0, 0.3844, 0.0, 0.0, 0.0, 0.3162] +2026-04-10 11:49:50.154804: Epoch time: 101.28 s +2026-04-10 11:49:50.156924: Yayy! New best EMA pseudo Dice: 0.0631 +2026-04-10 11:49:52.764810: +2026-04-10 11:49:52.771893: Epoch 56 +2026-04-10 11:49:52.773687: Current learning rate: 0.00987 +2026-04-10 11:51:33.831833: train_loss -0.0047 +2026-04-10 11:51:33.837840: val_loss -0.0108 +2026-04-10 11:51:33.840834: Pseudo dice [0.0, 0.0, 0.4583, 0.0, 0.0, 0.0393, 0.4017] +2026-04-10 11:51:33.843133: Epoch time: 101.07 s +2026-04-10 11:51:33.845472: Yayy! New best EMA pseudo Dice: 0.0697 +2026-04-10 11:51:36.229043: +2026-04-10 11:51:36.232131: Epoch 57 +2026-04-10 11:51:36.233962: Current learning rate: 0.00987 +2026-04-10 11:53:17.410335: train_loss -0.0067 +2026-04-10 11:53:17.414944: val_loss -0.0078 +2026-04-10 11:53:17.416945: Pseudo dice [0.0, 0.0, 0.3673, 0.0, 0.0, 0.3075, 0.346] +2026-04-10 11:53:17.418985: Epoch time: 101.18 s +2026-04-10 11:53:17.421432: Yayy! New best EMA pseudo Dice: 0.0773 +2026-04-10 11:53:20.102407: +2026-04-10 11:53:20.105267: Epoch 58 +2026-04-10 11:53:20.107043: Current learning rate: 0.00987 +2026-04-10 11:55:01.370839: train_loss -0.0061 +2026-04-10 11:55:01.376071: val_loss -0.0196 +2026-04-10 11:55:01.378154: Pseudo dice [0.0, 0.0, 0.4867, 0.0, 0.0, 0.3615, 0.4173] +2026-04-10 11:55:01.380436: Epoch time: 101.27 s +2026-04-10 11:55:01.382274: Yayy! New best EMA pseudo Dice: 0.0876 +2026-04-10 11:55:03.956103: +2026-04-10 11:55:03.958448: Epoch 59 +2026-04-10 11:55:03.960731: Current learning rate: 0.00987 +2026-04-10 11:56:44.923131: train_loss -0.0074 +2026-04-10 11:56:44.929837: val_loss -0.0013 +2026-04-10 11:56:44.931746: Pseudo dice [0.0, 0.0, 0.3937, 0.0, 0.0, 0.3633, 0.3584] +2026-04-10 11:56:44.933751: Epoch time: 100.97 s +2026-04-10 11:56:44.935626: Yayy! New best EMA pseudo Dice: 0.0948 +2026-04-10 11:56:47.306327: +2026-04-10 11:56:47.310097: Epoch 60 +2026-04-10 11:56:47.311687: Current learning rate: 0.00986 +2026-04-10 11:58:28.344868: train_loss -0.018 +2026-04-10 11:58:28.349674: val_loss -0.015 +2026-04-10 11:58:28.351444: Pseudo dice [0.0, 0.0, 0.494, 0.0, 0.0, 0.2698, 0.473] +2026-04-10 11:58:28.352933: Epoch time: 101.04 s +2026-04-10 11:58:28.354436: Yayy! New best EMA pseudo Dice: 0.103 +2026-04-10 11:58:30.744460: +2026-04-10 11:58:30.747236: Epoch 61 +2026-04-10 11:58:30.749024: Current learning rate: 0.00986 +2026-04-10 12:00:12.794343: train_loss -0.0144 +2026-04-10 12:00:12.801204: val_loss 1e-04 +2026-04-10 12:00:12.803492: Pseudo dice [0.0, 0.0, 0.2564, 0.0, 0.0, 0.3195, 0.262] +2026-04-10 12:00:12.805508: Epoch time: 102.05 s +2026-04-10 12:00:12.807542: Yayy! New best EMA pseudo Dice: 0.1047 +2026-04-10 12:00:15.229828: +2026-04-10 12:00:15.231735: Epoch 62 +2026-04-10 12:00:15.233564: Current learning rate: 0.00986 +2026-04-10 12:01:56.471121: train_loss -0.0131 +2026-04-10 12:01:56.478883: val_loss -0.0363 +2026-04-10 12:01:56.481560: Pseudo dice [0.1122, 0.0, 0.2398, 0.0, 0.0, 0.5126, 0.3724] +2026-04-10 12:01:56.484293: Epoch time: 101.24 s +2026-04-10 12:01:56.486962: Yayy! New best EMA pseudo Dice: 0.1119 +2026-04-10 12:01:59.125280: +2026-04-10 12:01:59.127142: Epoch 63 +2026-04-10 12:01:59.128746: Current learning rate: 0.00986 +2026-04-10 12:03:40.130718: train_loss -0.011 +2026-04-10 12:03:40.136769: val_loss -0.0395 +2026-04-10 12:03:40.139219: Pseudo dice [0.0356, 0.0, 0.4842, 0.0, 0.0, 0.2694, 0.4412] +2026-04-10 12:03:40.141705: Epoch time: 101.01 s +2026-04-10 12:03:40.144066: Yayy! New best EMA pseudo Dice: 0.1183 +2026-04-10 12:03:42.759354: +2026-04-10 12:03:42.762027: Epoch 64 +2026-04-10 12:03:42.764049: Current learning rate: 0.00986 +2026-04-10 12:05:23.790593: train_loss -0.0168 +2026-04-10 12:05:23.797034: val_loss -0.0042 +2026-04-10 12:05:23.799939: Pseudo dice [0.0156, 0.0, 0.333, 0.0, 0.0, 0.4192, 0.5237] +2026-04-10 12:05:23.801762: Epoch time: 101.03 s +2026-04-10 12:05:23.803329: Yayy! New best EMA pseudo Dice: 0.1249 +2026-04-10 12:05:26.237732: +2026-04-10 12:05:26.240782: Epoch 65 +2026-04-10 12:05:26.242974: Current learning rate: 0.00985 +2026-04-10 12:07:07.366981: train_loss -0.0282 +2026-04-10 12:07:07.371922: val_loss -0.0143 +2026-04-10 12:07:07.373522: Pseudo dice [0.0346, 0.0, 0.1891, 0.0, 0.0, 0.5755, 0.4865] +2026-04-10 12:07:07.375412: Epoch time: 101.13 s +2026-04-10 12:07:07.377463: Yayy! New best EMA pseudo Dice: 0.1308 +2026-04-10 12:07:09.825369: +2026-04-10 12:07:09.828156: Epoch 66 +2026-04-10 12:07:09.829942: Current learning rate: 0.00985 +2026-04-10 12:08:50.826861: train_loss -0.0216 +2026-04-10 12:08:50.833969: val_loss -0.0501 +2026-04-10 12:08:50.836190: Pseudo dice [0.0663, 0.0, 0.4776, 0.0, 0.0, 0.3385, 0.6486] +2026-04-10 12:08:50.838310: Epoch time: 101.0 s +2026-04-10 12:08:50.841074: Yayy! New best EMA pseudo Dice: 0.1396 +2026-04-10 12:08:53.478094: +2026-04-10 12:08:53.480171: Epoch 67 +2026-04-10 12:08:53.482001: Current learning rate: 0.00985 +2026-04-10 12:10:34.655350: train_loss -0.0257 +2026-04-10 12:10:34.660859: val_loss -0.0184 +2026-04-10 12:10:34.663136: Pseudo dice [0.3242, 0.0, 0.1732, 0.0, 0.0, 0.3894, 0.321] +2026-04-10 12:10:34.665011: Epoch time: 101.18 s +2026-04-10 12:10:34.667178: Yayy! New best EMA pseudo Dice: 0.1429 +2026-04-10 12:10:37.393498: +2026-04-10 12:10:37.397159: Epoch 68 +2026-04-10 12:10:37.399466: Current learning rate: 0.00985 +2026-04-10 12:12:18.775629: train_loss -0.0243 +2026-04-10 12:12:18.784672: val_loss -0.0517 +2026-04-10 12:12:18.787215: Pseudo dice [0.1752, 0.0, 0.5459, 0.0, 0.0, 0.3268, 0.495] +2026-04-10 12:12:18.790243: Epoch time: 101.39 s +2026-04-10 12:12:18.792862: Yayy! New best EMA pseudo Dice: 0.1506 +2026-04-10 12:12:21.536434: +2026-04-10 12:12:21.539046: Epoch 69 +2026-04-10 12:12:21.540910: Current learning rate: 0.00984 +2026-04-10 12:14:02.692097: train_loss -0.0289 +2026-04-10 12:14:02.696972: val_loss -0.0223 +2026-04-10 12:14:02.698694: Pseudo dice [0.2955, 0.0, 0.1638, 0.0, 0.0, 0.4642, 0.3961] +2026-04-10 12:14:02.700567: Epoch time: 101.16 s +2026-04-10 12:14:02.702045: Yayy! New best EMA pseudo Dice: 0.1544 +2026-04-10 12:14:05.331456: +2026-04-10 12:14:05.334280: Epoch 70 +2026-04-10 12:14:05.336039: Current learning rate: 0.00984 +2026-04-10 12:15:46.306262: train_loss -0.0294 +2026-04-10 12:15:46.313961: val_loss -0.0635 +2026-04-10 12:15:46.316647: Pseudo dice [0.1982, 0.0081, 0.3848, 0.0, 0.0, 0.5736, 0.3829] +2026-04-10 12:15:46.318881: Epoch time: 100.98 s +2026-04-10 12:15:46.321055: Yayy! New best EMA pseudo Dice: 0.1611 +2026-04-10 12:15:48.825708: +2026-04-10 12:15:48.828236: Epoch 71 +2026-04-10 12:15:48.830038: Current learning rate: 0.00984 +2026-04-10 12:17:30.863072: train_loss -0.0252 +2026-04-10 12:17:30.867808: val_loss -0.0436 +2026-04-10 12:17:30.869592: Pseudo dice [0.2274, 0.0, 0.4792, 0.0, 0.0, 0.5454, 0.6371] +2026-04-10 12:17:30.871384: Epoch time: 102.04 s +2026-04-10 12:17:30.873046: Yayy! New best EMA pseudo Dice: 0.172 +2026-04-10 12:17:33.548671: +2026-04-10 12:17:33.550759: Epoch 72 +2026-04-10 12:17:33.552358: Current learning rate: 0.00984 +2026-04-10 12:19:14.718137: train_loss -0.0118 +2026-04-10 12:19:14.724310: val_loss -0.0146 +2026-04-10 12:19:14.726677: Pseudo dice [0.3192, 0.0, 0.2433, 0.0, 0.0, 0.4809, 0.4236] +2026-04-10 12:19:14.728668: Epoch time: 101.17 s +2026-04-10 12:19:14.730467: Yayy! New best EMA pseudo Dice: 0.1757 +2026-04-10 12:19:17.437298: +2026-04-10 12:19:17.440311: Epoch 73 +2026-04-10 12:19:17.441975: Current learning rate: 0.00984 +2026-04-10 12:20:58.483723: train_loss -0.0392 +2026-04-10 12:20:58.493243: val_loss -0.0233 +2026-04-10 12:20:58.495325: Pseudo dice [0.3911, 0.0, 0.5025, 0.0, 0.0, 0.1079, 0.3603] +2026-04-10 12:20:58.497249: Epoch time: 101.05 s +2026-04-10 12:20:58.499408: Yayy! New best EMA pseudo Dice: 0.1776 +2026-04-10 12:21:01.144140: +2026-04-10 12:21:01.146461: Epoch 74 +2026-04-10 12:21:01.148236: Current learning rate: 0.00983 +2026-04-10 12:22:42.362388: train_loss -0.045 +2026-04-10 12:22:42.368865: val_loss -0.0426 +2026-04-10 12:22:42.370417: Pseudo dice [0.3265, 0.0, 0.2382, 0.0, 0.0, 0.475, 0.4486] +2026-04-10 12:22:42.372104: Epoch time: 101.22 s +2026-04-10 12:22:42.374112: Yayy! New best EMA pseudo Dice: 0.1811 +2026-04-10 12:22:45.016885: +2026-04-10 12:22:45.019384: Epoch 75 +2026-04-10 12:22:45.021395: Current learning rate: 0.00983 +2026-04-10 12:24:26.136050: train_loss -0.049 +2026-04-10 12:24:26.140725: val_loss -0.0552 +2026-04-10 12:24:26.142393: Pseudo dice [0.2639, 0.1705, 0.4556, 0.0, 0.0, 0.5273, 0.5351] +2026-04-10 12:24:26.144086: Epoch time: 101.12 s +2026-04-10 12:24:26.145551: Yayy! New best EMA pseudo Dice: 0.1909 +2026-04-10 12:24:28.598909: +2026-04-10 12:24:28.601356: Epoch 76 +2026-04-10 12:24:28.603048: Current learning rate: 0.00983 +2026-04-10 12:26:09.809822: train_loss -0.0492 +2026-04-10 12:26:09.814465: val_loss -0.0409 +2026-04-10 12:26:09.816429: Pseudo dice [0.0814, 0.2957, 0.5033, 0.0, 0.0, 0.623, 0.3816] +2026-04-10 12:26:09.817887: Epoch time: 101.21 s +2026-04-10 12:26:09.819369: Yayy! New best EMA pseudo Dice: 0.1987 +2026-04-10 12:26:12.398858: +2026-04-10 12:26:12.401667: Epoch 77 +2026-04-10 12:26:12.403775: Current learning rate: 0.00983 +2026-04-10 12:27:53.669406: train_loss -0.0428 +2026-04-10 12:27:53.676105: val_loss -0.0397 +2026-04-10 12:27:53.680257: Pseudo dice [0.0339, 0.0745, 0.4514, 0.0, 0.0, 0.2302, 0.6314] +2026-04-10 12:27:53.682702: Epoch time: 101.27 s +2026-04-10 12:27:53.685125: Yayy! New best EMA pseudo Dice: 0.1992 +2026-04-10 12:27:56.443472: +2026-04-10 12:27:56.446101: Epoch 78 +2026-04-10 12:27:56.448221: Current learning rate: 0.00982 +2026-04-10 12:29:37.612375: train_loss -0.0396 +2026-04-10 12:29:37.617641: val_loss -0.0705 +2026-04-10 12:29:37.619429: Pseudo dice [0.1471, 0.1085, 0.3392, 0.0, 0.0, 0.5391, 0.7292] +2026-04-10 12:29:37.621345: Epoch time: 101.17 s +2026-04-10 12:29:37.622776: Yayy! New best EMA pseudo Dice: 0.2059 +2026-04-10 12:29:40.190472: +2026-04-10 12:29:40.193919: Epoch 79 +2026-04-10 12:29:40.195792: Current learning rate: 0.00982 +2026-04-10 12:31:21.262613: train_loss -0.0524 +2026-04-10 12:31:21.267970: val_loss -0.0648 +2026-04-10 12:31:21.269789: Pseudo dice [0.0484, 0.2011, 0.561, 0.0, 0.0, 0.2702, 0.7284] +2026-04-10 12:31:21.271307: Epoch time: 101.08 s +2026-04-10 12:31:21.273009: Yayy! New best EMA pseudo Dice: 0.2111 +2026-04-10 12:31:23.904181: +2026-04-10 12:31:23.907532: Epoch 80 +2026-04-10 12:31:23.909515: Current learning rate: 0.00982 +2026-04-10 12:33:05.363973: train_loss -0.0522 +2026-04-10 12:33:05.369755: val_loss -0.0409 +2026-04-10 12:33:05.372240: Pseudo dice [0.4739, 0.5679, 0.4891, 0.0, 0.0, 0.4717, 0.1795] +2026-04-10 12:33:05.374318: Epoch time: 101.46 s +2026-04-10 12:33:05.377401: Yayy! New best EMA pseudo Dice: 0.2212 +2026-04-10 12:33:08.085365: +2026-04-10 12:33:08.087854: Epoch 81 +2026-04-10 12:33:08.089375: Current learning rate: 0.00982 +2026-04-10 12:34:49.255293: train_loss -0.0481 +2026-04-10 12:34:49.260652: val_loss 0.005 +2026-04-10 12:34:49.262419: Pseudo dice [0.0017, 0.1669, 0.0578, 0.0, 0.0, 0.1827, 0.4737] +2026-04-10 12:34:49.263967: Epoch time: 101.17 s +2026-04-10 12:34:50.329513: +2026-04-10 12:34:50.331773: Epoch 82 +2026-04-10 12:34:50.333730: Current learning rate: 0.00982 +2026-04-10 12:36:31.757154: train_loss -0.0509 +2026-04-10 12:36:31.763341: val_loss -0.035 +2026-04-10 12:36:31.767086: Pseudo dice [0.1819, 0.1742, 0.4892, 0.0013, 0.0, 0.204, 0.3884] +2026-04-10 12:36:31.769881: Epoch time: 101.43 s +2026-04-10 12:36:32.749515: +2026-04-10 12:36:32.751547: Epoch 83 +2026-04-10 12:36:32.753283: Current learning rate: 0.00981 +2026-04-10 12:38:13.972824: train_loss -0.0385 +2026-04-10 12:38:13.980520: val_loss -0.0536 +2026-04-10 12:38:13.982555: Pseudo dice [0.0309, 0.0442, 0.5388, 0.0026, 0.0, 0.4392, 0.6761] +2026-04-10 12:38:13.985377: Epoch time: 101.23 s +2026-04-10 12:38:14.989488: +2026-04-10 12:38:14.991141: Epoch 84 +2026-04-10 12:38:14.992688: Current learning rate: 0.00981 +2026-04-10 12:39:56.225331: train_loss -0.0518 +2026-04-10 12:39:56.229961: val_loss -0.0645 +2026-04-10 12:39:56.231523: Pseudo dice [0.4139, 0.185, 0.634, 0.0, 0.0, 0.4013, 0.4942] +2026-04-10 12:39:56.233025: Epoch time: 101.24 s +2026-04-10 12:39:56.234714: Yayy! New best EMA pseudo Dice: 0.2236 +2026-04-10 12:39:58.894505: +2026-04-10 12:39:58.897331: Epoch 85 +2026-04-10 12:39:58.899334: Current learning rate: 0.00981 +2026-04-10 12:41:39.996097: train_loss -0.0391 +2026-04-10 12:41:40.001120: val_loss -0.0547 +2026-04-10 12:41:40.002627: Pseudo dice [0.206, 0.4574, 0.661, 0.0011, 0.0, 0.4476, 0.5738] +2026-04-10 12:41:40.004163: Epoch time: 101.1 s +2026-04-10 12:41:40.005977: Yayy! New best EMA pseudo Dice: 0.2348 +2026-04-10 12:41:42.748323: +2026-04-10 12:41:42.750931: Epoch 86 +2026-04-10 12:41:42.752615: Current learning rate: 0.00981 +2026-04-10 12:43:23.811111: train_loss -0.055 +2026-04-10 12:43:23.817381: val_loss -0.0553 +2026-04-10 12:43:23.819302: Pseudo dice [0.4271, 0.1325, 0.3386, 0.0158, 0.0, 0.5827, 0.517] +2026-04-10 12:43:23.820974: Epoch time: 101.07 s +2026-04-10 12:43:23.823203: Yayy! New best EMA pseudo Dice: 0.2401 +2026-04-10 12:43:26.556684: +2026-04-10 12:43:26.559054: Epoch 87 +2026-04-10 12:43:26.560717: Current learning rate: 0.0098 +2026-04-10 12:45:07.647538: train_loss -0.0545 +2026-04-10 12:45:07.652292: val_loss -0.0548 +2026-04-10 12:45:07.653720: Pseudo dice [0.2357, 0.5119, 0.6145, 0.0055, 0.0, 0.4659, 0.6789] +2026-04-10 12:45:07.655685: Epoch time: 101.09 s +2026-04-10 12:45:07.657505: Yayy! New best EMA pseudo Dice: 0.252 +2026-04-10 12:45:10.293809: +2026-04-10 12:45:10.295980: Epoch 88 +2026-04-10 12:45:10.297605: Current learning rate: 0.0098 +2026-04-10 12:46:52.232525: train_loss -0.0588 +2026-04-10 12:46:52.239816: val_loss -0.0756 +2026-04-10 12:46:52.242195: Pseudo dice [0.2529, 0.5043, 0.5581, 0.0005, 0.0, 0.3963, 0.734] +2026-04-10 12:46:52.244340: Epoch time: 101.94 s +2026-04-10 12:46:52.246204: Yayy! New best EMA pseudo Dice: 0.2617 +2026-04-10 12:46:54.894458: +2026-04-10 12:46:54.897575: Epoch 89 +2026-04-10 12:46:54.899453: Current learning rate: 0.0098 +2026-04-10 12:50:02.252697: train_loss -0.0637 +2026-04-10 12:50:02.265809: val_loss -0.0675 +2026-04-10 12:50:02.268111: Pseudo dice [0.3553, 0.5961, 0.5302, 0.0553, 0.0, 0.3602, 0.6147] +2026-04-10 12:50:02.270429: Epoch time: 187.36 s +2026-04-10 12:50:02.272282: Yayy! New best EMA pseudo Dice: 0.2714 +2026-04-10 12:50:05.046241: +2026-04-10 12:50:05.048474: Epoch 90 +2026-04-10 12:50:05.050150: Current learning rate: 0.0098 +2026-04-10 12:51:45.797126: train_loss -0.067 +2026-04-10 12:51:45.802284: val_loss -0.0586 +2026-04-10 12:51:45.804349: Pseudo dice [0.434, 0.2099, 0.4152, 0.0, 0.0, 0.4686, 0.1444] +2026-04-10 12:51:45.806303: Epoch time: 100.75 s +2026-04-10 12:51:46.802569: +2026-04-10 12:51:46.804591: Epoch 91 +2026-04-10 12:51:46.806071: Current learning rate: 0.0098 +2026-04-10 12:53:36.369009: train_loss -0.068 +2026-04-10 12:53:36.375643: val_loss -0.0699 +2026-04-10 12:53:36.377971: Pseudo dice [0.198, 0.1958, 0.4799, 0.0018, 0.0, 0.6083, 0.5524] +2026-04-10 12:53:36.380511: Epoch time: 109.57 s +2026-04-10 12:53:37.394875: +2026-04-10 12:53:37.396628: Epoch 92 +2026-04-10 12:53:37.398345: Current learning rate: 0.00979 +2026-04-10 12:55:18.422948: train_loss -0.0717 +2026-04-10 12:55:18.428742: val_loss -0.054 +2026-04-10 12:55:18.431214: Pseudo dice [0.2899, 0.4166, 0.1928, 0.0603, 0.0, 0.3761, 0.5318] +2026-04-10 12:55:18.433035: Epoch time: 101.03 s +2026-04-10 12:55:19.454638: +2026-04-10 12:55:19.456722: Epoch 93 +2026-04-10 12:55:19.458445: Current learning rate: 0.00979 +2026-04-10 12:57:00.382098: train_loss -0.053 +2026-04-10 12:57:00.388626: val_loss -0.0356 +2026-04-10 12:57:00.390696: Pseudo dice [0.1635, 0.0783, 0.204, 0.0036, 0.0, 0.4514, 0.1465] +2026-04-10 12:57:00.393135: Epoch time: 100.93 s +2026-04-10 12:57:01.436827: +2026-04-10 12:57:01.439654: Epoch 94 +2026-04-10 12:57:01.442467: Current learning rate: 0.00979 +2026-04-10 12:58:59.274504: train_loss -0.0708 +2026-04-10 12:58:59.280036: val_loss -0.0546 +2026-04-10 12:58:59.282310: Pseudo dice [0.4012, 0.1834, 0.6616, 0.0036, 0.0, 0.3883, 0.5942] +2026-04-10 12:58:59.284696: Epoch time: 117.84 s +2026-04-10 12:59:00.482289: +2026-04-10 12:59:00.484098: Epoch 95 +2026-04-10 12:59:00.485823: Current learning rate: 0.00979 +2026-04-10 13:00:41.496984: train_loss -0.0674 +2026-04-10 13:00:41.502855: val_loss -0.0585 +2026-04-10 13:00:41.504950: Pseudo dice [0.4799, 0.1402, 0.4845, 0.0017, 0.0, 0.4165, 0.4602] +2026-04-10 13:00:41.507651: Epoch time: 101.02 s +2026-04-10 13:00:42.491143: +2026-04-10 13:00:42.493919: Epoch 96 +2026-04-10 13:00:42.496261: Current learning rate: 0.00978 +2026-04-10 13:02:23.434330: train_loss -0.0634 +2026-04-10 13:02:23.440355: val_loss -0.0552 +2026-04-10 13:02:23.442425: Pseudo dice [0.1407, 0.0996, 0.4498, 0.0, 0.0, 0.5625, 0.5086] +2026-04-10 13:02:23.444220: Epoch time: 100.95 s +2026-04-10 13:02:24.462281: +2026-04-10 13:02:24.464527: Epoch 97 +2026-04-10 13:02:24.466816: Current learning rate: 0.00978 +2026-04-10 13:04:05.655503: train_loss -0.0625 +2026-04-10 13:04:05.661112: val_loss -0.0622 +2026-04-10 13:04:05.663052: Pseudo dice [0.3783, 0.1993, 0.424, 0.0108, 0.0, 0.5697, 0.744] +2026-04-10 13:04:05.665063: Epoch time: 101.2 s +2026-04-10 13:04:06.679832: +2026-04-10 13:04:06.682164: Epoch 98 +2026-04-10 13:04:06.683875: Current learning rate: 0.00978 +2026-04-10 13:05:47.710777: train_loss -0.074 +2026-04-10 13:05:47.716658: val_loss -0.0508 +2026-04-10 13:05:47.720088: Pseudo dice [0.0, 0.5278, 0.6554, 0.0, 0.0, 0.4067, 0.7088] +2026-04-10 13:05:47.722215: Epoch time: 101.03 s +2026-04-10 13:05:47.723920: Yayy! New best EMA pseudo Dice: 0.2771 +2026-04-10 13:05:50.363352: +2026-04-10 13:05:50.366698: Epoch 99 +2026-04-10 13:05:50.368936: Current learning rate: 0.00978 +2026-04-10 13:07:31.348123: train_loss -0.0553 +2026-04-10 13:07:31.353543: val_loss -0.0715 +2026-04-10 13:07:31.355434: Pseudo dice [0.264, 0.5118, 0.5686, 0.0451, 0.0, 0.5617, 0.6503] +2026-04-10 13:07:31.357055: Epoch time: 100.99 s +2026-04-10 13:07:32.910472: Yayy! New best EMA pseudo Dice: 0.2865 +2026-04-10 13:07:35.506013: +2026-04-10 13:07:35.508836: Epoch 100 +2026-04-10 13:07:35.510733: Current learning rate: 0.00977 +2026-04-10 13:09:16.563065: train_loss -0.0739 +2026-04-10 13:09:16.568790: val_loss -0.0665 +2026-04-10 13:09:16.571139: Pseudo dice [0.3126, 0.4292, 0.6444, 0.0262, 0.0, 0.4119, 0.3854] +2026-04-10 13:09:16.573209: Epoch time: 101.06 s +2026-04-10 13:09:16.575133: Yayy! New best EMA pseudo Dice: 0.2894 +2026-04-10 13:09:19.189679: +2026-04-10 13:09:19.191533: Epoch 101 +2026-04-10 13:09:19.193408: Current learning rate: 0.00977 +2026-04-10 13:11:00.246394: train_loss -0.0697 +2026-04-10 13:11:00.252743: val_loss -0.0824 +2026-04-10 13:11:00.255006: Pseudo dice [0.3226, 0.2654, 0.4659, 0.0, 0.0, 0.4983, 0.5984] +2026-04-10 13:11:00.257072: Epoch time: 101.06 s +2026-04-10 13:11:00.259555: Yayy! New best EMA pseudo Dice: 0.2912 +2026-04-10 13:11:02.856802: +2026-04-10 13:11:02.859572: Epoch 102 +2026-04-10 13:11:02.861590: Current learning rate: 0.00977 +2026-04-10 13:12:43.946609: train_loss -0.0831 +2026-04-10 13:12:43.951590: val_loss -0.0801 +2026-04-10 13:12:43.953223: Pseudo dice [0.4148, 0.5014, 0.5906, 0.0853, 0.0, 0.4582, 0.5277] +2026-04-10 13:12:43.954545: Epoch time: 101.09 s +2026-04-10 13:12:43.955933: Yayy! New best EMA pseudo Dice: 0.2989 +2026-04-10 13:12:46.569986: +2026-04-10 13:12:46.571840: Epoch 103 +2026-04-10 13:12:46.573451: Current learning rate: 0.00977 +2026-04-10 13:14:27.674362: train_loss -0.0588 +2026-04-10 13:14:27.679250: val_loss -0.0575 +2026-04-10 13:14:27.680723: Pseudo dice [0.5416, 0.2588, 0.4245, 0.1187, 0.0, 0.5513, 0.532] +2026-04-10 13:14:27.682168: Epoch time: 101.11 s +2026-04-10 13:14:27.683647: Yayy! New best EMA pseudo Dice: 0.3037 +2026-04-10 13:14:30.209714: +2026-04-10 13:14:30.212697: Epoch 104 +2026-04-10 13:14:30.214450: Current learning rate: 0.00977 +2026-04-10 13:16:11.179808: train_loss -0.072 +2026-04-10 13:16:11.185947: val_loss -0.0685 +2026-04-10 13:16:11.188204: Pseudo dice [0.4736, 0.4889, 0.4081, 0.0001, 0.0, 0.6162, 0.5001] +2026-04-10 13:16:11.191432: Epoch time: 100.97 s +2026-04-10 13:16:11.193644: Yayy! New best EMA pseudo Dice: 0.3089 +2026-04-10 13:16:13.801562: +2026-04-10 13:16:13.803481: Epoch 105 +2026-04-10 13:16:13.804952: Current learning rate: 0.00976 +2026-04-10 13:17:54.873596: train_loss -0.0784 +2026-04-10 13:17:54.878399: val_loss -0.046 +2026-04-10 13:17:54.881040: Pseudo dice [0.3853, 0.4066, 0.5822, 0.0063, 0.0, 0.5345, 0.4246] +2026-04-10 13:17:54.882630: Epoch time: 101.08 s +2026-04-10 13:17:54.884477: Yayy! New best EMA pseudo Dice: 0.3114 +2026-04-10 13:17:58.274976: +2026-04-10 13:17:58.277436: Epoch 106 +2026-04-10 13:17:58.279154: Current learning rate: 0.00976 +2026-04-10 13:19:39.330762: train_loss -0.0848 +2026-04-10 13:19:39.339781: val_loss -0.0879 +2026-04-10 13:19:39.342613: Pseudo dice [0.4478, 0.1938, 0.5023, 0.1371, 0.0, 0.6551, 0.5785] +2026-04-10 13:19:39.344243: Epoch time: 101.06 s +2026-04-10 13:19:39.347263: Yayy! New best EMA pseudo Dice: 0.3162 +2026-04-10 13:19:41.686769: +2026-04-10 13:19:41.689347: Epoch 107 +2026-04-10 13:19:41.691115: Current learning rate: 0.00976 +2026-04-10 13:21:22.952979: train_loss -0.0739 +2026-04-10 13:21:22.958228: val_loss -0.0789 +2026-04-10 13:21:22.960524: Pseudo dice [0.5851, 0.5647, 0.503, 0.0, 0.0, 0.5643, 0.4407] +2026-04-10 13:21:22.962162: Epoch time: 101.27 s +2026-04-10 13:21:22.963623: Yayy! New best EMA pseudo Dice: 0.3225 +2026-04-10 13:21:25.565255: +2026-04-10 13:21:25.567714: Epoch 108 +2026-04-10 13:21:25.569932: Current learning rate: 0.00976 +2026-04-10 13:23:06.700052: train_loss -0.0756 +2026-04-10 13:23:06.707533: val_loss -0.0947 +2026-04-10 13:23:06.709880: Pseudo dice [0.5069, 0.2395, 0.5262, 0.1708, 0.0, 0.5412, 0.6] +2026-04-10 13:23:06.712116: Epoch time: 101.14 s +2026-04-10 13:23:06.714923: Yayy! New best EMA pseudo Dice: 0.3272 +2026-04-10 13:23:09.141901: +2026-04-10 13:23:09.144651: Epoch 109 +2026-04-10 13:23:09.146277: Current learning rate: 0.00975 +2026-04-10 13:24:50.107969: train_loss -0.0681 +2026-04-10 13:24:50.115809: val_loss -0.0963 +2026-04-10 13:24:50.117631: Pseudo dice [0.7023, 0.4016, 0.6517, 0.0023, 0.0, 0.5905, 0.7423] +2026-04-10 13:24:50.119390: Epoch time: 100.97 s +2026-04-10 13:24:50.121365: Yayy! New best EMA pseudo Dice: 0.3386 +2026-04-10 13:24:52.819469: +2026-04-10 13:24:52.822028: Epoch 110 +2026-04-10 13:24:52.823829: Current learning rate: 0.00975 +2026-04-10 13:26:33.952690: train_loss -0.0841 +2026-04-10 13:26:33.957786: val_loss -0.0618 +2026-04-10 13:26:33.959428: Pseudo dice [0.4535, 0.1571, 0.6208, 0.0143, 0.0, 0.5417, 0.6161] +2026-04-10 13:26:33.961533: Epoch time: 101.14 s +2026-04-10 13:26:33.963225: Yayy! New best EMA pseudo Dice: 0.3391 +2026-04-10 13:26:36.598068: +2026-04-10 13:26:36.600863: Epoch 111 +2026-04-10 13:26:36.603320: Current learning rate: 0.00975 +2026-04-10 13:28:17.906312: train_loss -0.0917 +2026-04-10 13:28:17.913145: val_loss -0.0982 +2026-04-10 13:28:17.914820: Pseudo dice [0.355, 0.3946, 0.4704, 0.0304, 0.0, 0.6965, 0.6925] +2026-04-10 13:28:17.916408: Epoch time: 101.31 s +2026-04-10 13:28:17.917858: Yayy! New best EMA pseudo Dice: 0.3429 +2026-04-10 13:28:20.537358: +2026-04-10 13:28:20.539988: Epoch 112 +2026-04-10 13:28:20.541712: Current learning rate: 0.00975 +2026-04-10 13:30:01.660731: train_loss -0.0805 +2026-04-10 13:30:01.665812: val_loss -0.0602 +2026-04-10 13:30:01.669878: Pseudo dice [0.2319, 0.1748, 0.4008, 0.0001, 0.0, 0.6638, 0.6087] +2026-04-10 13:30:01.672359: Epoch time: 101.13 s +2026-04-10 13:30:02.668962: +2026-04-10 13:30:02.670647: Epoch 113 +2026-04-10 13:30:02.672346: Current learning rate: 0.00975 +2026-04-10 13:31:43.957696: train_loss -0.0872 +2026-04-10 13:31:43.962730: val_loss -0.076 +2026-04-10 13:31:43.964718: Pseudo dice [0.2523, 0.3797, 0.683, 0.0049, 0.0, 0.5283, 0.4951] +2026-04-10 13:31:43.966592: Epoch time: 101.29 s +2026-04-10 13:31:44.959837: +2026-04-10 13:31:44.961783: Epoch 114 +2026-04-10 13:31:44.963374: Current learning rate: 0.00974 +2026-04-10 13:33:25.948504: train_loss -0.0834 +2026-04-10 13:33:25.953097: val_loss -0.0724 +2026-04-10 13:33:25.954689: Pseudo dice [0.4701, 0.4248, 0.4245, 0.0028, 0.0, 0.5144, 0.5945] +2026-04-10 13:33:25.956602: Epoch time: 100.99 s +2026-04-10 13:33:26.959852: +2026-04-10 13:33:26.962049: Epoch 115 +2026-04-10 13:33:26.963763: Current learning rate: 0.00974 +2026-04-10 13:35:08.071980: train_loss -0.0863 +2026-04-10 13:35:08.077554: val_loss -0.0708 +2026-04-10 13:35:08.080059: Pseudo dice [0.5727, 0.2888, 0.4633, 0.017, 0.0, 0.6323, 0.5639] +2026-04-10 13:35:08.082207: Epoch time: 101.12 s +2026-04-10 13:35:09.093335: +2026-04-10 13:35:09.095491: Epoch 116 +2026-04-10 13:35:09.097341: Current learning rate: 0.00974 +2026-04-10 13:36:50.216629: train_loss -0.0857 +2026-04-10 13:36:50.222724: val_loss -0.0969 +2026-04-10 13:36:50.224914: Pseudo dice [0.4069, 0.1248, 0.5901, 0.0142, 0.0, 0.7254, 0.5073] +2026-04-10 13:36:50.227231: Epoch time: 101.13 s +2026-04-10 13:36:51.247722: +2026-04-10 13:36:51.249745: Epoch 117 +2026-04-10 13:36:51.251359: Current learning rate: 0.00974 +2026-04-10 13:38:32.167171: train_loss -0.0853 +2026-04-10 13:38:32.172030: val_loss -0.05 +2026-04-10 13:38:32.174061: Pseudo dice [0.2315, 0.2476, 0.4697, 0.0026, 0.0, 0.6307, 0.5814] +2026-04-10 13:38:32.175866: Epoch time: 100.92 s +2026-04-10 13:38:33.184512: +2026-04-10 13:38:33.186333: Epoch 118 +2026-04-10 13:38:33.187989: Current learning rate: 0.00973 +2026-04-10 13:40:14.077100: train_loss -0.082 +2026-04-10 13:40:14.102030: val_loss -0.0688 +2026-04-10 13:40:14.105834: Pseudo dice [0.092, 0.4377, 0.5483, 0.037, 0.0, 0.5451, 0.74] +2026-04-10 13:40:14.107743: Epoch time: 100.9 s +2026-04-10 13:40:15.133564: +2026-04-10 13:40:15.135440: Epoch 119 +2026-04-10 13:40:15.137208: Current learning rate: 0.00973 +2026-04-10 13:41:56.065052: train_loss -0.0688 +2026-04-10 13:41:56.070174: val_loss -0.1048 +2026-04-10 13:41:56.072174: Pseudo dice [0.429, 0.4448, 0.7015, 0.0293, 0.0, 0.6258, 0.7388] +2026-04-10 13:41:56.074868: Epoch time: 100.93 s +2026-04-10 13:41:56.077065: Yayy! New best EMA pseudo Dice: 0.3469 +2026-04-10 13:41:58.379072: +2026-04-10 13:41:58.381610: Epoch 120 +2026-04-10 13:41:58.383030: Current learning rate: 0.00973 +2026-04-10 13:43:39.321145: train_loss -0.0877 +2026-04-10 13:43:39.326709: val_loss -0.0688 +2026-04-10 13:43:39.328635: Pseudo dice [0.2547, 0.0355, 0.5939, 0.0613, 0.0, 0.6889, 0.3367] +2026-04-10 13:43:39.330191: Epoch time: 100.95 s +2026-04-10 13:43:40.373497: +2026-04-10 13:43:40.375385: Epoch 121 +2026-04-10 13:43:40.376960: Current learning rate: 0.00973 +2026-04-10 13:45:30.577799: train_loss -0.0946 +2026-04-10 13:45:30.583504: val_loss -0.0868 +2026-04-10 13:45:30.585228: Pseudo dice [0.3492, 0.6322, 0.7334, 0.0272, 0.0, 0.5367, 0.7686] +2026-04-10 13:45:30.587020: Epoch time: 110.21 s +2026-04-10 13:45:30.588788: Yayy! New best EMA pseudo Dice: 0.3498 +2026-04-10 13:45:33.352901: +2026-04-10 13:45:33.355945: Epoch 122 +2026-04-10 13:45:33.357695: Current learning rate: 0.00973 +2026-04-10 13:47:36.306726: train_loss -0.0831 +2026-04-10 13:47:36.311371: val_loss -0.0434 +2026-04-10 13:47:36.312984: Pseudo dice [0.3415, 0.3466, 0.5117, 0.0, 0.0, 0.6289, 0.5354] +2026-04-10 13:47:36.314498: Epoch time: 122.96 s +2026-04-10 13:47:37.375443: +2026-04-10 13:47:37.377172: Epoch 123 +2026-04-10 13:47:37.378848: Current learning rate: 0.00972 +2026-04-10 13:49:18.228614: train_loss -0.0963 +2026-04-10 13:49:18.235526: val_loss -0.0969 +2026-04-10 13:49:18.238161: Pseudo dice [0.4025, 0.1543, 0.742, 0.1189, 0.0084, 0.5873, 0.7407] +2026-04-10 13:49:18.240281: Epoch time: 100.86 s +2026-04-10 13:49:18.242221: Yayy! New best EMA pseudo Dice: 0.3531 +2026-04-10 13:49:21.113409: +2026-04-10 13:49:21.115786: Epoch 124 +2026-04-10 13:49:21.117567: Current learning rate: 0.00972 +2026-04-10 13:51:02.186508: train_loss -0.1026 +2026-04-10 13:51:02.193729: val_loss -0.0611 +2026-04-10 13:51:02.195831: Pseudo dice [0.148, 0.389, 0.498, 0.0485, 0.1034, 0.6942, 0.4961] +2026-04-10 13:51:02.197956: Epoch time: 101.08 s +2026-04-10 13:51:04.287550: +2026-04-10 13:51:04.289287: Epoch 125 +2026-04-10 13:51:04.291136: Current learning rate: 0.00972 +2026-04-10 13:52:45.463308: train_loss -0.0887 +2026-04-10 13:52:45.468087: val_loss -0.1088 +2026-04-10 13:52:45.469848: Pseudo dice [0.4814, 0.2756, 0.5715, 0.17, 0.1185, 0.5969, 0.8072] +2026-04-10 13:52:45.471478: Epoch time: 101.18 s +2026-04-10 13:52:45.473108: Yayy! New best EMA pseudo Dice: 0.3597 +2026-04-10 13:52:48.120307: +2026-04-10 13:52:48.123333: Epoch 126 +2026-04-10 13:52:48.125454: Current learning rate: 0.00972 +2026-04-10 13:54:29.240994: train_loss -0.101 +2026-04-10 13:54:29.246767: val_loss -0.0225 +2026-04-10 13:54:29.248975: Pseudo dice [0.4518, 0.4021, 0.3598, 0.0, 0.0851, 0.3257, 0.6302] +2026-04-10 13:54:29.251009: Epoch time: 101.12 s +2026-04-10 13:54:30.298172: +2026-04-10 13:54:30.300645: Epoch 127 +2026-04-10 13:54:30.303431: Current learning rate: 0.00971 +2026-04-10 13:56:11.404416: train_loss -0.0956 +2026-04-10 13:56:11.411568: val_loss -0.07 +2026-04-10 13:56:11.413557: Pseudo dice [0.4534, 0.4295, 0.618, 0.0099, 0.2834, 0.5325, 0.508] +2026-04-10 13:56:11.415360: Epoch time: 101.11 s +2026-04-10 13:56:11.417622: Yayy! New best EMA pseudo Dice: 0.3609 +2026-04-10 13:56:13.823283: +2026-04-10 13:56:13.826308: Epoch 128 +2026-04-10 13:56:13.827778: Current learning rate: 0.00971 +2026-04-10 13:57:54.777177: train_loss -0.1127 +2026-04-10 13:57:54.782390: val_loss -0.0629 +2026-04-10 13:57:54.784371: Pseudo dice [0.2322, 0.6274, 0.2791, 0.1234, 0.1741, 0.7168, 0.7341] +2026-04-10 13:57:54.786045: Epoch time: 100.96 s +2026-04-10 13:57:54.788272: Yayy! New best EMA pseudo Dice: 0.366 +2026-04-10 13:57:57.440618: +2026-04-10 13:57:57.448004: Epoch 129 +2026-04-10 13:57:57.451189: Current learning rate: 0.00971 +2026-04-10 13:59:38.383909: train_loss -0.0946 +2026-04-10 13:59:38.389059: val_loss -0.1077 +2026-04-10 13:59:38.390979: Pseudo dice [0.5867, 0.5466, 0.4876, 0.0244, 0.2189, 0.7338, 0.7415] +2026-04-10 13:59:38.393290: Epoch time: 100.95 s +2026-04-10 13:59:38.395151: Yayy! New best EMA pseudo Dice: 0.3771 +2026-04-10 13:59:40.963642: +2026-04-10 13:59:40.966477: Epoch 130 +2026-04-10 13:59:40.968197: Current learning rate: 0.00971 +2026-04-10 14:01:23.204642: train_loss -0.1089 +2026-04-10 14:01:23.209395: val_loss -0.0965 +2026-04-10 14:01:23.211244: Pseudo dice [0.107, 0.5528, 0.3386, 0.0194, 0.1977, 0.531, 0.6294] +2026-04-10 14:01:23.213424: Epoch time: 102.24 s +2026-04-10 14:01:24.258759: +2026-04-10 14:01:24.260797: Epoch 131 +2026-04-10 14:01:24.262412: Current learning rate: 0.0097 +2026-04-10 14:03:12.190981: train_loss -0.1081 +2026-04-10 14:03:12.195971: val_loss -0.0793 +2026-04-10 14:03:12.198033: Pseudo dice [0.1601, 0.3814, 0.5276, 0.0126, 0.2449, 0.7665, 0.6532] +2026-04-10 14:03:12.199717: Epoch time: 107.94 s +2026-04-10 14:03:13.233626: +2026-04-10 14:03:13.235565: Epoch 132 +2026-04-10 14:03:13.237133: Current learning rate: 0.0097 +2026-04-10 14:04:54.169496: train_loss -0.1096 +2026-04-10 14:04:54.174434: val_loss -0.1169 +2026-04-10 14:04:54.177505: Pseudo dice [0.5261, 0.0627, 0.7827, 0.025, 0.1432, 0.6727, 0.6654] +2026-04-10 14:04:54.179236: Epoch time: 100.94 s +2026-04-10 14:04:54.181243: Yayy! New best EMA pseudo Dice: 0.3789 +2026-04-10 14:04:56.887030: +2026-04-10 14:04:56.890185: Epoch 133 +2026-04-10 14:04:56.892169: Current learning rate: 0.0097 +2026-04-10 14:06:38.144466: train_loss -0.0975 +2026-04-10 14:06:38.148810: val_loss -0.0585 +2026-04-10 14:06:38.150636: Pseudo dice [0.4788, 0.4619, 0.2796, 0.0307, 0.0974, 0.5971, 0.5814] +2026-04-10 14:06:38.152535: Epoch time: 101.26 s +2026-04-10 14:06:39.206259: +2026-04-10 14:06:39.208078: Epoch 134 +2026-04-10 14:06:39.209612: Current learning rate: 0.0097 +2026-04-10 14:08:20.248343: train_loss -0.0876 +2026-04-10 14:08:20.254837: val_loss -0.1088 +2026-04-10 14:08:20.257092: Pseudo dice [0.0957, 0.4301, 0.6677, 0.0008, 0.4261, 0.7456, 0.6599] +2026-04-10 14:08:20.259325: Epoch time: 101.05 s +2026-04-10 14:08:20.261503: Yayy! New best EMA pseudo Dice: 0.3826 +2026-04-10 14:08:22.923445: +2026-04-10 14:08:22.926013: Epoch 135 +2026-04-10 14:08:22.928285: Current learning rate: 0.0097 +2026-04-10 14:10:08.291135: train_loss -0.1122 +2026-04-10 14:10:08.295359: val_loss -0.113 +2026-04-10 14:10:08.297100: Pseudo dice [0.659, 0.4606, 0.6749, 0.0618, 0.141, 0.6405, 0.7875] +2026-04-10 14:10:08.299312: Epoch time: 105.37 s +2026-04-10 14:10:08.301163: Yayy! New best EMA pseudo Dice: 0.3933 +2026-04-10 14:10:10.978205: +2026-04-10 14:10:10.980511: Epoch 136 +2026-04-10 14:10:10.982438: Current learning rate: 0.00969 +2026-04-10 14:12:01.936421: train_loss -0.1147 +2026-04-10 14:12:01.942209: val_loss -0.0945 +2026-04-10 14:12:01.944132: Pseudo dice [0.1751, 0.1184, 0.301, 0.0718, 0.1711, 0.5361, 0.7034] +2026-04-10 14:12:01.945896: Epoch time: 110.96 s +2026-04-10 14:12:03.014801: +2026-04-10 14:12:03.016609: Epoch 137 +2026-04-10 14:12:03.017883: Current learning rate: 0.00969 +2026-04-10 14:13:43.959155: train_loss -0.1135 +2026-04-10 14:13:43.964383: val_loss -0.1045 +2026-04-10 14:13:43.966255: Pseudo dice [0.3124, 0.2428, 0.6615, 0.0957, 0.2387, 0.6504, 0.6225] +2026-04-10 14:13:43.968033: Epoch time: 100.95 s +2026-04-10 14:13:45.049905: +2026-04-10 14:13:45.051980: Epoch 138 +2026-04-10 14:13:45.053572: Current learning rate: 0.00969 +2026-04-10 14:15:26.117399: train_loss -0.116 +2026-04-10 14:15:26.121893: val_loss -0.1053 +2026-04-10 14:15:26.123770: Pseudo dice [0.4851, 0.2101, 0.4389, 0.1956, 0.2194, 0.6103, 0.7047] +2026-04-10 14:15:26.125371: Epoch time: 101.07 s +2026-04-10 14:15:27.184196: +2026-04-10 14:15:27.186045: Epoch 139 +2026-04-10 14:15:27.187793: Current learning rate: 0.00969 +2026-04-10 14:17:14.654730: train_loss -0.1088 +2026-04-10 14:17:14.659886: val_loss -0.0607 +2026-04-10 14:17:14.662441: Pseudo dice [0.3534, 0.517, 0.4634, 0.0004, 0.1773, 0.8289, 0.6151] +2026-04-10 14:17:14.664559: Epoch time: 107.47 s +2026-04-10 14:17:15.729172: +2026-04-10 14:17:15.747749: Epoch 140 +2026-04-10 14:17:15.750129: Current learning rate: 0.00968 +2026-04-10 14:19:00.832980: train_loss -0.1163 +2026-04-10 14:19:00.838816: val_loss -0.1039 +2026-04-10 14:19:00.841999: Pseudo dice [0.6507, 0.1132, 0.7512, 0.0246, 0.1968, 0.4723, 0.6671] +2026-04-10 14:19:00.844707: Epoch time: 105.11 s +2026-04-10 14:19:00.847203: Yayy! New best EMA pseudo Dice: 0.3933 +2026-04-10 14:19:03.598772: +2026-04-10 14:19:03.600628: Epoch 141 +2026-04-10 14:19:03.602506: Current learning rate: 0.00968 +2026-04-10 14:20:44.615564: train_loss -0.1266 +2026-04-10 14:20:44.620368: val_loss -0.0918 +2026-04-10 14:20:44.622522: Pseudo dice [0.5921, 0.1616, 0.4294, 0.0027, 0.2519, 0.4668, 0.6593] +2026-04-10 14:20:44.624193: Epoch time: 101.02 s +2026-04-10 14:20:45.732563: +2026-04-10 14:20:45.734542: Epoch 142 +2026-04-10 14:20:45.736623: Current learning rate: 0.00968 +2026-04-10 14:22:26.808858: train_loss -0.1169 +2026-04-10 14:22:26.813211: val_loss -0.1098 +2026-04-10 14:22:26.815187: Pseudo dice [0.2374, 0.1112, 0.5054, 0.0008, 0.3082, 0.7185, 0.4605] +2026-04-10 14:22:26.816887: Epoch time: 101.08 s +2026-04-10 14:22:28.766972: +2026-04-10 14:22:28.768719: Epoch 143 +2026-04-10 14:22:28.770511: Current learning rate: 0.00968 +2026-04-10 14:24:13.140477: train_loss -0.1124 +2026-04-10 14:24:13.146393: val_loss -0.0913 +2026-04-10 14:24:13.149231: Pseudo dice [0.3664, 0.5719, 0.6235, 0.2667, 0.3402, 0.7294, 0.5879] +2026-04-10 14:24:13.151629: Epoch time: 104.38 s +2026-04-10 14:24:13.153583: Yayy! New best EMA pseudo Dice: 0.3963 +2026-04-10 14:24:15.774287: +2026-04-10 14:24:15.776307: Epoch 144 +2026-04-10 14:24:15.778074: Current learning rate: 0.00968 +2026-04-10 14:25:57.367815: train_loss -0.1167 +2026-04-10 14:25:57.372653: val_loss -0.0034 +2026-04-10 14:25:57.374490: Pseudo dice [0.3977, 0.4421, 0.2786, 0.0227, 0.1449, 0.6586, 0.5399] +2026-04-10 14:25:57.376337: Epoch time: 101.6 s +2026-04-10 14:25:58.539105: +2026-04-10 14:25:58.540947: Epoch 145 +2026-04-10 14:25:58.542750: Current learning rate: 0.00967 +2026-04-10 14:27:39.482835: train_loss -0.1034 +2026-04-10 14:27:39.490467: val_loss -0.0793 +2026-04-10 14:27:39.494104: Pseudo dice [0.1398, 0.1788, 0.4072, 0.0808, 0.1956, 0.5707, 0.4262] +2026-04-10 14:27:39.496129: Epoch time: 100.95 s +2026-04-10 14:27:40.552094: +2026-04-10 14:27:40.554043: Epoch 146 +2026-04-10 14:27:40.555613: Current learning rate: 0.00967 +2026-04-10 14:29:24.249140: train_loss -0.108 +2026-04-10 14:29:24.254778: val_loss -0.0846 +2026-04-10 14:29:24.256535: Pseudo dice [0.4295, 0.2192, 0.5088, 0.02, 0.2607, 0.5547, 0.6641] +2026-04-10 14:29:24.258277: Epoch time: 103.7 s +2026-04-10 14:29:25.361848: +2026-04-10 14:29:25.363693: Epoch 147 +2026-04-10 14:29:25.365377: Current learning rate: 0.00967 +2026-04-10 14:31:06.451892: train_loss -0.1137 +2026-04-10 14:31:06.458513: val_loss -0.102 +2026-04-10 14:31:06.461066: Pseudo dice [0.5525, 0.3245, 0.4176, 0.0003, 0.1414, 0.7191, 0.7252] +2026-04-10 14:31:06.463063: Epoch time: 101.09 s +2026-04-10 14:31:07.511269: +2026-04-10 14:31:07.513019: Epoch 148 +2026-04-10 14:31:07.514953: Current learning rate: 0.00967 +2026-04-10 14:33:31.906657: train_loss -0.1166 +2026-04-10 14:33:31.911924: val_loss -0.1024 +2026-04-10 14:33:31.914850: Pseudo dice [0.538, 0.5539, 0.5725, 0.0817, 0.288, 0.4903, 0.6074] +2026-04-10 14:33:31.917159: Epoch time: 144.4 s +2026-04-10 14:33:33.116302: +2026-04-10 14:33:33.118193: Epoch 149 +2026-04-10 14:33:33.119842: Current learning rate: 0.00966 +2026-04-10 14:35:16.499356: train_loss -0.1178 +2026-04-10 14:35:16.504949: val_loss -0.1089 +2026-04-10 14:35:16.510271: Pseudo dice [0.2277, 0.2459, 0.6615, 0.0022, 0.1942, 0.5363, 0.7026] +2026-04-10 14:35:16.512324: Epoch time: 103.39 s +2026-04-10 14:35:19.404517: +2026-04-10 14:35:19.406755: Epoch 150 +2026-04-10 14:35:19.408430: Current learning rate: 0.00966 +2026-04-10 14:37:01.542713: train_loss -0.1139 +2026-04-10 14:37:01.549369: val_loss -0.1104 +2026-04-10 14:37:01.552505: Pseudo dice [0.5593, 0.3002, 0.7102, 0.0004, 0.1389, 0.6335, 0.6247] +2026-04-10 14:37:01.554657: Epoch time: 102.14 s +2026-04-10 14:37:02.625857: +2026-04-10 14:37:02.627610: Epoch 151 +2026-04-10 14:37:02.629463: Current learning rate: 0.00966 +2026-04-10 14:38:43.590544: train_loss -0.1186 +2026-04-10 14:38:43.596068: val_loss -0.1068 +2026-04-10 14:38:43.598083: Pseudo dice [0.5385, 0.601, 0.5407, 0.0, 0.2645, 0.7191, 0.623] +2026-04-10 14:38:43.600420: Epoch time: 100.97 s +2026-04-10 14:38:43.603748: Yayy! New best EMA pseudo Dice: 0.3996 +2026-04-10 14:38:46.285024: +2026-04-10 14:38:46.287921: Epoch 152 +2026-04-10 14:38:46.290075: Current learning rate: 0.00966 +2026-04-10 14:40:27.161027: train_loss -0.1264 +2026-04-10 14:40:27.165860: val_loss -0.0911 +2026-04-10 14:40:27.167754: Pseudo dice [0.2849, 0.2041, 0.6356, 0.0038, 0.2277, 0.6423, 0.7176] +2026-04-10 14:40:27.169672: Epoch time: 100.88 s +2026-04-10 14:40:28.247685: +2026-04-10 14:40:28.250145: Epoch 153 +2026-04-10 14:40:28.251900: Current learning rate: 0.00966 +2026-04-10 14:42:52.994452: train_loss -0.1221 +2026-04-10 14:42:53.000947: val_loss -0.1051 +2026-04-10 14:42:53.003042: Pseudo dice [0.5221, 0.2852, 0.5517, 0.0167, 0.1837, 0.7729, 0.6211] +2026-04-10 14:42:53.005188: Epoch time: 144.75 s +2026-04-10 14:42:53.007018: Yayy! New best EMA pseudo Dice: 0.4008 +2026-04-10 14:42:55.839912: +2026-04-10 14:42:55.842107: Epoch 154 +2026-04-10 14:42:55.844115: Current learning rate: 0.00965 +2026-04-10 14:44:36.888032: train_loss -0.1293 +2026-04-10 14:44:36.892332: val_loss -0.1213 +2026-04-10 14:44:36.894103: Pseudo dice [0.6392, 0.1708, 0.6221, 0.023, 0.2039, 0.7412, 0.8409] +2026-04-10 14:44:36.896236: Epoch time: 101.05 s +2026-04-10 14:44:36.897939: Yayy! New best EMA pseudo Dice: 0.407 +2026-04-10 14:44:39.626005: +2026-04-10 14:44:39.628039: Epoch 155 +2026-04-10 14:44:39.629632: Current learning rate: 0.00965 +2026-04-10 14:46:20.549504: train_loss -0.1077 +2026-04-10 14:46:20.553654: val_loss -0.0908 +2026-04-10 14:46:20.555279: Pseudo dice [0.6097, 0.1574, 0.5967, 0.0001, 0.2627, 0.3116, 0.5875] +2026-04-10 14:46:20.556784: Epoch time: 100.93 s +2026-04-10 14:46:21.630996: +2026-04-10 14:46:21.632794: Epoch 156 +2026-04-10 14:46:21.634624: Current learning rate: 0.00965 +2026-04-10 14:48:02.471129: train_loss -0.1088 +2026-04-10 14:48:02.479106: val_loss -0.1245 +2026-04-10 14:48:02.482411: Pseudo dice [0.8134, 0.4958, 0.6995, 0.0382, 0.3449, 0.7308, 0.6481] +2026-04-10 14:48:02.484952: Epoch time: 100.84 s +2026-04-10 14:48:02.487673: Yayy! New best EMA pseudo Dice: 0.416 +2026-04-10 14:48:04.831635: +2026-04-10 14:48:04.834461: Epoch 157 +2026-04-10 14:48:04.836355: Current learning rate: 0.00965 +2026-04-10 14:49:45.796894: train_loss -0.1193 +2026-04-10 14:49:45.802249: val_loss -0.0924 +2026-04-10 14:49:45.804309: Pseudo dice [0.6025, 0.383, 0.6547, 0.0202, 0.169, 0.4738, 0.7423] +2026-04-10 14:49:45.805704: Epoch time: 100.97 s +2026-04-10 14:49:45.807469: Yayy! New best EMA pseudo Dice: 0.4179 +2026-04-10 14:49:48.462925: +2026-04-10 14:49:48.465581: Epoch 158 +2026-04-10 14:49:48.467446: Current learning rate: 0.00964 +2026-04-10 14:51:29.379009: train_loss -0.1277 +2026-04-10 14:51:29.383926: val_loss -0.1145 +2026-04-10 14:51:29.385877: Pseudo dice [0.5949, 0.3242, 0.5018, 0.0306, 0.2796, 0.7363, 0.7602] +2026-04-10 14:51:29.389003: Epoch time: 100.92 s +2026-04-10 14:51:29.390623: Yayy! New best EMA pseudo Dice: 0.4222 +2026-04-10 14:51:31.913366: +2026-04-10 14:51:31.916731: Epoch 159 +2026-04-10 14:51:31.919295: Current learning rate: 0.00964 +2026-04-10 14:53:12.591617: train_loss -0.1253 +2026-04-10 14:53:12.598339: val_loss -0.1293 +2026-04-10 14:53:12.600515: Pseudo dice [0.5542, 0.3129, 0.6008, 0.0005, 0.362, 0.593, 0.761] +2026-04-10 14:53:12.603864: Epoch time: 100.68 s +2026-04-10 14:53:12.605735: Yayy! New best EMA pseudo Dice: 0.4255 +2026-04-10 14:53:17.630077: +2026-04-10 14:53:17.632977: Epoch 160 +2026-04-10 14:53:17.634809: Current learning rate: 0.00964 +2026-04-10 14:54:58.447703: train_loss -0.1223 +2026-04-10 14:54:58.453492: val_loss -0.0738 +2026-04-10 14:54:58.456098: Pseudo dice [0.1901, 0.1246, 0.638, 0.0468, 0.2885, 0.7142, 0.6814] +2026-04-10 14:54:58.458351: Epoch time: 100.82 s +2026-04-10 14:54:59.550622: +2026-04-10 14:54:59.553613: Epoch 161 +2026-04-10 14:54:59.556032: Current learning rate: 0.00964 +2026-04-10 14:56:40.557819: train_loss -0.1216 +2026-04-10 14:56:40.562725: val_loss -0.0966 +2026-04-10 14:56:40.564782: Pseudo dice [0.5485, 0.2001, 0.5966, 0.0086, 0.1786, 0.7281, 0.5357] +2026-04-10 14:56:40.566537: Epoch time: 101.01 s +2026-04-10 14:56:41.656762: +2026-04-10 14:56:41.658470: Epoch 162 +2026-04-10 14:56:41.659825: Current learning rate: 0.00963 +2026-04-10 14:58:22.677295: train_loss -0.111 +2026-04-10 14:58:22.683253: val_loss -0.1268 +2026-04-10 14:58:22.686399: Pseudo dice [0.6893, 0.0338, 0.7334, 0.1379, 0.217, 0.7241, 0.813] +2026-04-10 14:58:22.688677: Epoch time: 101.02 s +2026-04-10 14:58:23.764796: +2026-04-10 14:58:23.766959: Epoch 163 +2026-04-10 14:58:23.768923: Current learning rate: 0.00963 +2026-04-10 15:00:04.673057: train_loss -0.1339 +2026-04-10 15:00:04.685700: val_loss -0.0846 +2026-04-10 15:00:04.689118: Pseudo dice [0.5957, 0.6586, 0.6131, 0.0955, 0.1414, 0.4144, 0.5869] +2026-04-10 15:00:04.692058: Epoch time: 100.91 s +2026-04-10 15:00:04.694248: Yayy! New best EMA pseudo Dice: 0.4269 +2026-04-10 15:00:07.411325: +2026-04-10 15:00:07.414837: Epoch 164 +2026-04-10 15:00:07.416869: Current learning rate: 0.00963 +2026-04-10 15:01:48.334976: train_loss -0.1146 +2026-04-10 15:01:48.339509: val_loss -0.0965 +2026-04-10 15:01:48.341197: Pseudo dice [0.4245, 0.2253, 0.6886, 0.0, 0.2598, 0.4238, 0.6575] +2026-04-10 15:01:48.343297: Epoch time: 100.93 s +2026-04-10 15:01:49.392924: +2026-04-10 15:01:49.394993: Epoch 165 +2026-04-10 15:01:49.396640: Current learning rate: 0.00963 +2026-04-10 15:03:30.490921: train_loss -0.1264 +2026-04-10 15:03:30.497737: val_loss -0.1116 +2026-04-10 15:03:30.500581: Pseudo dice [0.4581, 0.039, 0.5859, 0.048, 0.3146, 0.5299, 0.7297] +2026-04-10 15:03:30.503108: Epoch time: 101.1 s +2026-04-10 15:03:31.553565: +2026-04-10 15:03:31.556084: Epoch 166 +2026-04-10 15:03:31.557882: Current learning rate: 0.00963 +2026-04-10 15:05:12.639906: train_loss -0.1072 +2026-04-10 15:05:12.645312: val_loss -0.1087 +2026-04-10 15:05:12.646935: Pseudo dice [0.37, 0.262, 0.5859, 0.3783, 0.3099, 0.7206, 0.5396] +2026-04-10 15:05:12.648481: Epoch time: 101.09 s +2026-04-10 15:05:13.742472: +2026-04-10 15:05:13.744302: Epoch 167 +2026-04-10 15:05:13.745973: Current learning rate: 0.00962 +2026-04-10 15:06:54.892001: train_loss -0.1311 +2026-04-10 15:06:54.898177: val_loss -0.1295 +2026-04-10 15:06:54.903401: Pseudo dice [0.6924, 0.3934, 0.5033, 0.0, 0.432, 0.702, 0.8616] +2026-04-10 15:06:54.906060: Epoch time: 101.15 s +2026-04-10 15:06:54.908105: Yayy! New best EMA pseudo Dice: 0.4312 +2026-04-10 15:06:57.354367: +2026-04-10 15:06:57.356604: Epoch 168 +2026-04-10 15:06:57.358153: Current learning rate: 0.00962 +2026-04-10 15:08:38.500247: train_loss -0.1067 +2026-04-10 15:08:38.509257: val_loss -0.059 +2026-04-10 15:08:38.511403: Pseudo dice [0.0687, 0.163, 0.5139, 0.0274, 0.2498, 0.3905, 0.7434] +2026-04-10 15:08:38.514272: Epoch time: 101.15 s +2026-04-10 15:08:39.584176: +2026-04-10 15:08:39.585905: Epoch 169 +2026-04-10 15:08:39.587489: Current learning rate: 0.00962 +2026-04-10 15:10:20.731027: train_loss -0.116 +2026-04-10 15:10:20.736187: val_loss -0.099 +2026-04-10 15:10:20.738147: Pseudo dice [0.2055, 0.4965, 0.5608, 0.0769, 0.3309, 0.6215, 0.6994] +2026-04-10 15:10:20.739873: Epoch time: 101.15 s +2026-04-10 15:10:21.806902: +2026-04-10 15:10:21.809143: Epoch 170 +2026-04-10 15:10:21.811228: Current learning rate: 0.00962 +2026-04-10 15:12:02.863197: train_loss -0.1201 +2026-04-10 15:12:02.867490: val_loss -0.0993 +2026-04-10 15:12:02.869059: Pseudo dice [0.1147, 0.2373, 0.7439, 0.0007, 0.3387, 0.6999, 0.5268] +2026-04-10 15:12:02.870556: Epoch time: 101.06 s +2026-04-10 15:12:03.944407: +2026-04-10 15:12:03.946032: Epoch 171 +2026-04-10 15:12:03.947716: Current learning rate: 0.00961 +2026-04-10 15:13:45.092281: train_loss -0.1199 +2026-04-10 15:13:45.097236: val_loss -0.1115 +2026-04-10 15:13:45.099362: Pseudo dice [0.7266, 0.1655, 0.7239, 0.0946, 0.3276, 0.7181, 0.7915] +2026-04-10 15:13:45.101542: Epoch time: 101.15 s +2026-04-10 15:13:46.171329: +2026-04-10 15:13:46.172876: Epoch 172 +2026-04-10 15:13:46.174304: Current learning rate: 0.00961 +2026-04-10 15:15:27.330611: train_loss -0.1417 +2026-04-10 15:15:27.337243: val_loss -0.1079 +2026-04-10 15:15:27.339149: Pseudo dice [0.5887, 0.5811, 0.6591, 0.0001, 0.2233, 0.6563, 0.6213] +2026-04-10 15:15:27.341575: Epoch time: 101.16 s +2026-04-10 15:15:28.411023: +2026-04-10 15:15:28.413048: Epoch 173 +2026-04-10 15:15:28.414847: Current learning rate: 0.00961 +2026-04-10 15:17:09.882564: train_loss -0.1231 +2026-04-10 15:17:09.887711: val_loss -0.107 +2026-04-10 15:17:09.890343: Pseudo dice [0.3827, 0.5589, 0.7418, 0.0628, 0.4025, 0.2977, 0.5625] +2026-04-10 15:17:09.892742: Epoch time: 101.47 s +2026-04-10 15:17:10.948840: +2026-04-10 15:17:10.950544: Epoch 174 +2026-04-10 15:17:10.952835: Current learning rate: 0.00961 +2026-04-10 15:18:52.038203: train_loss -0.1213 +2026-04-10 15:18:52.042922: val_loss -0.134 +2026-04-10 15:18:52.044911: Pseudo dice [0.4377, 0.1138, 0.7323, 0.0596, 0.369, 0.1996, 0.8116] +2026-04-10 15:18:52.046706: Epoch time: 101.09 s +2026-04-10 15:18:53.094722: +2026-04-10 15:18:53.096510: Epoch 175 +2026-04-10 15:18:53.098126: Current learning rate: 0.00961 +2026-04-10 15:20:34.272487: train_loss -0.134 +2026-04-10 15:20:34.279117: val_loss -0.1381 +2026-04-10 15:20:34.281615: Pseudo dice [0.5971, 0.5195, 0.6643, 0.0264, 0.2205, 0.5946, 0.7918] +2026-04-10 15:20:34.284568: Epoch time: 101.18 s +2026-04-10 15:20:34.287070: Yayy! New best EMA pseudo Dice: 0.4321 +2026-04-10 15:20:36.776526: +2026-04-10 15:20:36.779366: Epoch 176 +2026-04-10 15:20:36.781163: Current learning rate: 0.0096 +2026-04-10 15:22:17.865672: train_loss -0.136 +2026-04-10 15:22:17.871081: val_loss -0.1095 +2026-04-10 15:22:17.873255: Pseudo dice [0.4037, 0.3166, 0.7347, 0.001, 0.3443, 0.6417, 0.5814] +2026-04-10 15:22:17.876302: Epoch time: 101.09 s +2026-04-10 15:22:18.977949: +2026-04-10 15:22:18.980595: Epoch 177 +2026-04-10 15:22:18.982627: Current learning rate: 0.0096 +2026-04-10 15:24:00.247992: train_loss -0.1359 +2026-04-10 15:24:00.253248: val_loss -0.1126 +2026-04-10 15:24:00.254917: Pseudo dice [0.3197, 0.2791, 0.6392, 0.0, 0.3269, 0.7711, 0.8183] +2026-04-10 15:24:00.256894: Epoch time: 101.27 s +2026-04-10 15:24:00.259425: Yayy! New best EMA pseudo Dice: 0.4339 +2026-04-10 15:24:03.002231: +2026-04-10 15:24:03.004710: Epoch 178 +2026-04-10 15:24:03.006532: Current learning rate: 0.0096 +2026-04-10 15:25:44.994081: train_loss -0.1413 +2026-04-10 15:25:44.999296: val_loss -0.1227 +2026-04-10 15:25:45.000955: Pseudo dice [0.4838, 0.1029, 0.4402, 0.6918, 0.2692, 0.6101, 0.692] +2026-04-10 15:25:45.002724: Epoch time: 101.99 s +2026-04-10 15:25:45.004784: Yayy! New best EMA pseudo Dice: 0.4375 +2026-04-10 15:25:47.697894: +2026-04-10 15:25:47.700994: Epoch 179 +2026-04-10 15:25:47.702834: Current learning rate: 0.0096 +2026-04-10 15:27:29.061298: train_loss -0.1338 +2026-04-10 15:27:29.067741: val_loss -0.1052 +2026-04-10 15:27:29.069978: Pseudo dice [0.6655, 0.4097, 0.7328, 0.3539, 0.3111, 0.5819, 0.3796] +2026-04-10 15:27:29.071928: Epoch time: 101.37 s +2026-04-10 15:27:29.073639: Yayy! New best EMA pseudo Dice: 0.4428 +2026-04-10 15:27:31.834875: +2026-04-10 15:27:31.837746: Epoch 180 +2026-04-10 15:27:31.839454: Current learning rate: 0.00959 +2026-04-10 15:29:13.021461: train_loss -0.1307 +2026-04-10 15:29:13.030381: val_loss -0.1173 +2026-04-10 15:29:13.032893: Pseudo dice [0.4734, 0.5113, 0.5239, 0.0, 0.2974, 0.4075, 0.5681] +2026-04-10 15:29:13.035761: Epoch time: 101.19 s +2026-04-10 15:29:14.117981: +2026-04-10 15:29:14.119725: Epoch 181 +2026-04-10 15:29:14.121279: Current learning rate: 0.00959 +2026-04-10 15:30:55.292030: train_loss -0.1307 +2026-04-10 15:30:55.298294: val_loss -0.0962 +2026-04-10 15:30:55.300642: Pseudo dice [0.4269, 0.3091, 0.6572, 0.0345, 0.3257, 0.7119, 0.5566] +2026-04-10 15:30:55.302385: Epoch time: 101.18 s +2026-04-10 15:30:56.381314: +2026-04-10 15:30:56.383248: Epoch 182 +2026-04-10 15:30:56.385142: Current learning rate: 0.00959 +2026-04-10 15:32:37.438889: train_loss -0.1339 +2026-04-10 15:32:37.444372: val_loss -0.1157 +2026-04-10 15:32:37.446108: Pseudo dice [0.7381, 0.4504, 0.4313, 0.2798, 0.3421, 0.7161, 0.526] +2026-04-10 15:32:37.448149: Epoch time: 101.06 s +2026-04-10 15:32:37.450333: Yayy! New best EMA pseudo Dice: 0.4436 +2026-04-10 15:32:39.818474: +2026-04-10 15:32:39.821189: Epoch 183 +2026-04-10 15:32:39.823116: Current learning rate: 0.00959 +2026-04-10 15:34:21.278952: train_loss -0.135 +2026-04-10 15:34:21.284031: val_loss -0.1012 +2026-04-10 15:34:21.286123: Pseudo dice [0.4337, 0.0753, 0.1923, 0.0002, 0.4463, 0.6224, 0.7189] +2026-04-10 15:34:21.291505: Epoch time: 101.46 s +2026-04-10 15:34:22.370983: +2026-04-10 15:34:22.373029: Epoch 184 +2026-04-10 15:34:22.374981: Current learning rate: 0.00959 +2026-04-10 15:36:03.414286: train_loss -0.1394 +2026-04-10 15:36:03.420158: val_loss -0.1038 +2026-04-10 15:36:03.422619: Pseudo dice [0.3929, 0.2209, 0.4289, 0.0248, 0.3673, 0.6873, 0.5339] +2026-04-10 15:36:03.424539: Epoch time: 101.05 s +2026-04-10 15:36:04.474980: +2026-04-10 15:36:04.477139: Epoch 185 +2026-04-10 15:36:04.478879: Current learning rate: 0.00958 +2026-04-10 15:37:45.432541: train_loss -0.1203 +2026-04-10 15:37:45.437647: val_loss -0.1079 +2026-04-10 15:37:45.441295: Pseudo dice [0.6414, 0.5219, 0.5214, 0.0001, 0.2254, 0.7547, 0.4962] +2026-04-10 15:37:45.443818: Epoch time: 100.96 s +2026-04-10 15:37:46.509904: +2026-04-10 15:37:46.511786: Epoch 186 +2026-04-10 15:37:46.513436: Current learning rate: 0.00958 +2026-04-10 15:39:27.538686: train_loss -0.1045 +2026-04-10 15:39:27.543891: val_loss -0.0872 +2026-04-10 15:39:27.545789: Pseudo dice [0.3623, 0.483, 0.5701, 0.0683, 0.2144, 0.5666, 0.6714] +2026-04-10 15:39:27.547715: Epoch time: 101.03 s +2026-04-10 15:39:28.616632: +2026-04-10 15:39:28.618850: Epoch 187 +2026-04-10 15:39:28.621041: Current learning rate: 0.00958 +2026-04-10 15:49:52.539367: train_loss -0.1233 +2026-04-10 15:49:52.545681: val_loss -0.1236 +2026-04-10 15:49:52.548236: Pseudo dice [0.5747, 0.3286, 0.585, 0.3018, 0.1876, 0.3919, 0.7253] +2026-04-10 15:49:52.550592: Epoch time: 623.93 s +2026-04-10 15:49:53.745300: +2026-04-10 15:49:53.747721: Epoch 188 +2026-04-10 15:49:53.750890: Current learning rate: 0.00958 +2026-04-10 16:21:52.468462: train_loss -0.1339 +2026-04-10 16:21:52.478632: val_loss -0.1154 +2026-04-10 16:21:52.481191: Pseudo dice [0.6152, 0.606, 0.735, 0.0204, 0.3099, 0.3169, 0.7074] +2026-04-10 16:21:52.483947: Epoch time: 1918.73 s +2026-04-10 16:21:53.651677: +2026-04-10 16:21:53.653603: Epoch 189 +2026-04-10 16:21:53.655267: Current learning rate: 0.00957 +2026-04-10 16:23:34.414334: train_loss -0.1455 +2026-04-10 16:23:34.421284: val_loss -0.0473 +2026-04-10 16:23:34.423889: Pseudo dice [0.7105, 0.1762, 0.4749, 0.0211, 0.2173, 0.3415, 0.6097] +2026-04-10 16:23:34.426212: Epoch time: 100.77 s +2026-04-10 16:23:35.527617: +2026-04-10 16:23:35.529690: Epoch 190 +2026-04-10 16:23:35.531388: Current learning rate: 0.00957 +2026-04-10 16:25:16.579664: train_loss -0.1366 +2026-04-10 16:25:16.585610: val_loss -0.1086 +2026-04-10 16:25:16.587276: Pseudo dice [0.6745, 0.1086, 0.5768, 0.0008, 0.3832, 0.7691, 0.5821] +2026-04-10 16:25:16.589126: Epoch time: 101.06 s +2026-04-10 16:25:17.729678: +2026-04-10 16:25:17.731869: Epoch 191 +2026-04-10 16:25:17.733712: Current learning rate: 0.00957 +2026-04-10 16:26:58.582590: train_loss -0.1421 +2026-04-10 16:26:58.590714: val_loss -0.1207 +2026-04-10 16:26:58.593476: Pseudo dice [0.3546, 0.4669, 0.7747, 0.3344, 0.3189, 0.6983, 0.5262] +2026-04-10 16:26:58.598131: Epoch time: 100.86 s +2026-04-10 16:26:59.700148: +2026-04-10 16:26:59.703650: Epoch 192 +2026-04-10 16:26:59.705769: Current learning rate: 0.00957 +2026-04-10 16:28:41.020794: train_loss -0.134 +2026-04-10 16:28:41.026944: val_loss -0.1087 +2026-04-10 16:28:41.029605: Pseudo dice [0.5802, 0.099, 0.7241, 0.4184, 0.2942, 0.6331, 0.5434] +2026-04-10 16:28:41.032645: Epoch time: 101.32 s +2026-04-10 16:28:42.134389: +2026-04-10 16:28:42.136487: Epoch 193 +2026-04-10 16:28:42.138064: Current learning rate: 0.00956 +2026-04-10 16:30:23.048271: train_loss -0.1325 +2026-04-10 16:30:23.054245: val_loss -0.1225 +2026-04-10 16:30:23.056100: Pseudo dice [0.585, 0.5056, 0.4218, 0.0386, 0.3567, 0.6852, 0.7315] +2026-04-10 16:30:23.058161: Epoch time: 100.92 s +2026-04-10 16:30:24.168046: +2026-04-10 16:30:24.169792: Epoch 194 +2026-04-10 16:30:24.171422: Current learning rate: 0.00956 +2026-04-10 16:32:05.142944: train_loss -0.1456 +2026-04-10 16:32:05.149329: val_loss -0.1128 +2026-04-10 16:32:05.153527: Pseudo dice [0.642, 0.3954, 0.6163, 0.3133, 0.1591, 0.6952, 0.6223] +2026-04-10 16:32:05.156214: Epoch time: 100.98 s +2026-04-10 16:32:05.158449: Yayy! New best EMA pseudo Dice: 0.4483 +2026-04-10 16:32:07.974174: +2026-04-10 16:32:07.976920: Epoch 195 +2026-04-10 16:32:07.979063: Current learning rate: 0.00956 +2026-04-10 16:35:36.198012: train_loss -0.1415 +2026-04-10 16:35:36.204176: val_loss -0.1485 +2026-04-10 16:35:36.206961: Pseudo dice [0.6996, 0.2574, 0.7815, 0.0021, 0.362, 0.7619, 0.8099] +2026-04-10 16:35:36.209567: Epoch time: 208.23 s +2026-04-10 16:35:36.211848: Yayy! New best EMA pseudo Dice: 0.4559 +2026-04-10 16:35:39.037996: +2026-04-10 16:35:39.040633: Epoch 196 +2026-04-10 16:35:39.043317: Current learning rate: 0.00956 +2026-04-10 16:37:20.802523: train_loss -0.1313 +2026-04-10 16:37:20.807115: val_loss -0.1291 +2026-04-10 16:37:20.809589: Pseudo dice [0.6391, 0.6521, 0.6979, 0.0001, 0.3467, 0.7821, 0.5546] +2026-04-10 16:37:20.812167: Epoch time: 101.77 s +2026-04-10 16:37:20.814242: Yayy! New best EMA pseudo Dice: 0.4628 +2026-04-10 16:37:23.289975: +2026-04-10 16:37:23.292361: Epoch 197 +2026-04-10 16:37:23.294038: Current learning rate: 0.00956 +2026-04-10 16:39:04.993361: train_loss -0.1422 +2026-04-10 16:39:05.000774: val_loss -0.13 +2026-04-10 16:39:05.002524: Pseudo dice [0.3305, 0.2318, 0.6873, 0.3526, 0.2988, 0.8035, 0.7605] +2026-04-10 16:39:05.004468: Epoch time: 101.71 s +2026-04-10 16:39:05.006852: Yayy! New best EMA pseudo Dice: 0.466 +2026-04-10 16:39:07.471016: +2026-04-10 16:39:07.474603: Epoch 198 +2026-04-10 16:39:07.476648: Current learning rate: 0.00955 +2026-04-10 16:40:48.659982: train_loss -0.1252 +2026-04-10 16:40:48.664387: val_loss -0.1118 +2026-04-10 16:40:48.666221: Pseudo dice [0.4156, 0.6079, 0.279, 0.0004, 0.1052, 0.6818, 0.5331] +2026-04-10 16:40:48.668116: Epoch time: 101.19 s +2026-04-10 16:40:49.774417: +2026-04-10 16:40:49.776236: Epoch 199 +2026-04-10 16:40:49.777954: Current learning rate: 0.00955 +2026-04-10 16:42:31.396361: train_loss -0.1331 +2026-04-10 16:42:31.401690: val_loss -0.1201 +2026-04-10 16:42:31.403551: Pseudo dice [0.49, 0.1582, 0.4625, 0.0016, 0.2805, 0.6289, 0.6397] +2026-04-10 16:42:31.406236: Epoch time: 101.63 s +2026-04-10 16:42:33.871910: +2026-04-10 16:42:33.875444: Epoch 200 +2026-04-10 16:42:33.877798: Current learning rate: 0.00955 +2026-04-10 16:44:15.403570: train_loss -0.1408 +2026-04-10 16:44:15.409667: val_loss -0.12 +2026-04-10 16:44:15.413187: Pseudo dice [0.6036, 0.3588, 0.6249, 0.0, 0.3394, 0.6252, 0.66] +2026-04-10 16:44:15.415455: Epoch time: 101.53 s +2026-04-10 16:44:16.521866: +2026-04-10 16:44:16.524583: Epoch 201 +2026-04-10 16:44:16.526560: Current learning rate: 0.00955 +2026-04-10 16:45:57.576982: train_loss -0.136 +2026-04-10 16:45:57.584493: val_loss -0.1119 +2026-04-10 16:45:57.586822: Pseudo dice [0.5166, 0.1819, 0.6671, 0.0285, 0.2912, 0.8015, 0.824] +2026-04-10 16:45:57.589372: Epoch time: 101.06 s +2026-04-10 16:45:58.715894: +2026-04-10 16:45:58.717950: Epoch 202 +2026-04-10 16:45:58.719798: Current learning rate: 0.00954 +2026-04-10 16:47:40.735848: train_loss -0.1258 +2026-04-10 16:47:40.742137: val_loss -0.0769 +2026-04-10 16:47:40.745074: Pseudo dice [0.5992, 0.5948, 0.4616, 0.0003, 0.4806, 0.6736, 0.6953] +2026-04-10 16:47:40.747870: Epoch time: 102.02 s +2026-04-10 16:47:41.849527: +2026-04-10 16:47:41.851720: Epoch 203 +2026-04-10 16:47:41.854086: Current learning rate: 0.00954 +2026-04-10 16:49:22.797379: train_loss -0.1226 +2026-04-10 16:49:22.803142: val_loss -0.0994 +2026-04-10 16:49:22.805501: Pseudo dice [0.6083, 0.5604, 0.5953, 0.0008, 0.3653, 0.5423, 0.5044] +2026-04-10 16:49:22.807483: Epoch time: 100.95 s +2026-04-10 16:49:23.909896: +2026-04-10 16:49:23.911725: Epoch 204 +2026-04-10 16:49:23.913604: Current learning rate: 0.00954 +2026-04-10 16:51:05.145246: train_loss -0.124 +2026-04-10 16:51:05.152968: val_loss -0.119 +2026-04-10 16:51:05.156981: Pseudo dice [0.5423, 0.4684, 0.6883, 0.0003, 0.2523, 0.7637, 0.6049] +2026-04-10 16:51:05.160516: Epoch time: 101.24 s +2026-04-10 16:51:06.277388: +2026-04-10 16:51:06.281676: Epoch 205 +2026-04-10 16:51:06.286216: Current learning rate: 0.00954 +2026-04-10 16:52:47.347532: train_loss -0.1386 +2026-04-10 16:52:47.353985: val_loss -0.1356 +2026-04-10 16:52:47.365026: Pseudo dice [0.7345, 0.2492, 0.7119, 0.6434, 0.2952, 0.6887, 0.7965] +2026-04-10 16:52:47.367616: Epoch time: 101.07 s +2026-04-10 16:52:47.375934: Yayy! New best EMA pseudo Dice: 0.4717 +2026-04-10 16:52:50.176144: +2026-04-10 16:52:50.178960: Epoch 206 +2026-04-10 16:52:50.180780: Current learning rate: 0.00954 +2026-04-10 17:00:12.200620: train_loss -0.1448 +2026-04-10 17:00:12.207360: val_loss -0.1171 +2026-04-10 17:00:12.209763: Pseudo dice [0.2336, 0.6539, 0.4599, 0.0007, 0.4535, 0.7057, 0.6103] +2026-04-10 17:00:12.212793: Epoch time: 442.03 s +2026-04-10 17:00:13.366888: +2026-04-10 17:00:13.386446: Epoch 207 +2026-04-10 17:00:13.389199: Current learning rate: 0.00953 +2026-04-10 17:01:54.046265: train_loss -0.1507 +2026-04-10 17:01:54.053869: val_loss -0.1438 +2026-04-10 17:01:54.057514: Pseudo dice [0.6536, 0.3566, 0.7684, 0.3088, 0.3545, 0.6299, 0.8089] +2026-04-10 17:01:54.076086: Epoch time: 100.68 s +2026-04-10 17:01:54.080332: Yayy! New best EMA pseudo Dice: 0.4776 +2026-04-10 17:01:56.804436: +2026-04-10 17:01:56.806993: Epoch 208 +2026-04-10 17:01:56.809046: Current learning rate: 0.00953 +2026-04-10 17:03:37.958589: train_loss -0.1416 +2026-04-10 17:03:37.964520: val_loss -0.133 +2026-04-10 17:03:37.966696: Pseudo dice [0.67, 0.3637, 0.6036, 0.7261, 0.2771, 0.7173, 0.7249] +2026-04-10 17:03:37.969332: Epoch time: 101.16 s +2026-04-10 17:03:37.971647: Yayy! New best EMA pseudo Dice: 0.4881 +2026-04-10 17:03:40.285288: +2026-04-10 17:03:40.287858: Epoch 209 +2026-04-10 17:03:40.289449: Current learning rate: 0.00953 +2026-04-10 17:05:21.250817: train_loss -0.1428 +2026-04-10 17:05:21.255642: val_loss -0.1429 +2026-04-10 17:05:21.260826: Pseudo dice [0.469, 0.4212, 0.5807, 0.6976, 0.4659, 0.7857, 0.7626] +2026-04-10 17:05:21.262606: Epoch time: 100.97 s +2026-04-10 17:05:21.264405: Yayy! New best EMA pseudo Dice: 0.4991 +2026-04-10 17:05:23.859448: +2026-04-10 17:05:23.862589: Epoch 210 +2026-04-10 17:05:23.864733: Current learning rate: 0.00953 +2026-04-10 17:07:47.215909: train_loss -0.1399 +2026-04-10 17:07:47.222903: val_loss -0.1123 +2026-04-10 17:07:47.225162: Pseudo dice [0.4086, 0.3942, 0.5651, 0.0089, 0.4487, 0.6517, 0.5934] +2026-04-10 17:07:47.227076: Epoch time: 143.36 s +2026-04-10 17:07:48.532527: +2026-04-10 17:07:48.534421: Epoch 211 +2026-04-10 17:07:48.536343: Current learning rate: 0.00952 +2026-04-10 17:14:03.973125: train_loss -0.1446 +2026-04-10 17:14:03.986206: val_loss -0.1279 +2026-04-10 17:14:03.988341: Pseudo dice [0.0542, 0.3971, 0.7076, 0.2015, 0.3817, 0.7356, 0.7731] +2026-04-10 17:14:03.990483: Epoch time: 375.44 s +2026-04-10 17:14:05.034715: +2026-04-10 17:14:05.036593: Epoch 212 +2026-04-10 17:14:05.038317: Current learning rate: 0.00952 +2026-04-10 17:15:46.081861: train_loss -0.1461 +2026-04-10 17:15:46.093058: val_loss -0.1096 +2026-04-10 17:15:46.095843: Pseudo dice [0.7576, 0.597, 0.7142, 0.0073, 0.3657, 0.608, 0.6512] +2026-04-10 17:15:46.097852: Epoch time: 101.05 s +2026-04-10 17:15:47.153391: +2026-04-10 17:15:47.155107: Epoch 213 +2026-04-10 17:15:47.156841: Current learning rate: 0.00952 +2026-04-10 17:17:28.048204: train_loss -0.1415 +2026-04-10 17:17:28.054565: val_loss -0.1209 +2026-04-10 17:17:28.056958: Pseudo dice [0.6632, 0.5056, 0.7398, 0.0045, 0.1894, 0.6984, 0.6047] +2026-04-10 17:17:28.059196: Epoch time: 100.9 s +2026-04-10 17:17:29.946510: +2026-04-10 17:17:29.948503: Epoch 214 +2026-04-10 17:17:29.950298: Current learning rate: 0.00952 +2026-04-10 17:19:10.847766: train_loss -0.1471 +2026-04-10 17:19:10.854994: val_loss -0.1177 +2026-04-10 17:19:10.857249: Pseudo dice [0.2343, 0.4483, 0.6951, 0.0028, 0.1695, 0.5328, 0.6225] +2026-04-10 17:19:10.860913: Epoch time: 100.9 s +2026-04-10 17:19:11.904105: +2026-04-10 17:19:11.917592: Epoch 215 +2026-04-10 17:19:11.919633: Current learning rate: 0.00951 +2026-04-10 17:20:52.851978: train_loss -0.1402 +2026-04-10 17:20:52.858374: val_loss -0.0785 +2026-04-10 17:20:52.860819: Pseudo dice [0.4644, 0.014, 0.6676, 0.0002, 0.3285, 0.6739, 0.7146] +2026-04-10 17:20:52.863451: Epoch time: 100.95 s +2026-04-10 17:20:53.915149: +2026-04-10 17:20:53.917094: Epoch 216 +2026-04-10 17:20:53.918745: Current learning rate: 0.00951 +2026-04-10 17:22:34.648028: train_loss -0.1516 +2026-04-10 17:22:34.653456: val_loss -0.0664 +2026-04-10 17:22:34.655622: Pseudo dice [0.6919, 0.5364, 0.6928, 0.0267, 0.3718, 0.778, 0.5978] +2026-04-10 17:22:34.658358: Epoch time: 100.74 s +2026-04-10 17:22:35.692523: +2026-04-10 17:22:35.698079: Epoch 217 +2026-04-10 17:22:35.699941: Current learning rate: 0.00951 +2026-04-10 17:24:16.928897: train_loss -0.1553 +2026-04-10 17:24:16.936338: val_loss -0.133 +2026-04-10 17:24:16.938838: Pseudo dice [0.6676, 0.343, 0.7187, 0.155, 0.2809, 0.3513, 0.6951] +2026-04-10 17:24:16.941300: Epoch time: 101.24 s +2026-04-10 17:24:18.000376: +2026-04-10 17:24:18.002342: Epoch 218 +2026-04-10 17:24:18.004018: Current learning rate: 0.00951 +2026-04-10 17:25:59.107587: train_loss -0.1432 +2026-04-10 17:25:59.113631: val_loss -0.1024 +2026-04-10 17:25:59.115930: Pseudo dice [0.3916, 0.352, 0.7222, 0.0363, 0.2402, 0.6496, 0.6761] +2026-04-10 17:25:59.118997: Epoch time: 101.11 s +2026-04-10 17:26:00.162599: +2026-04-10 17:26:00.165189: Epoch 219 +2026-04-10 17:26:00.166973: Current learning rate: 0.00951 +2026-04-10 17:27:41.352148: train_loss -0.1476 +2026-04-10 17:27:41.359326: val_loss -0.1131 +2026-04-10 17:27:41.361652: Pseudo dice [0.4473, 0.5409, 0.6522, 0.0043, 0.3228, 0.7548, 0.4565] +2026-04-10 17:27:41.364148: Epoch time: 101.19 s +2026-04-10 17:27:42.424118: +2026-04-10 17:27:42.426024: Epoch 220 +2026-04-10 17:27:42.428118: Current learning rate: 0.0095 +2026-04-10 17:29:23.405000: train_loss -0.1377 +2026-04-10 17:29:23.410600: val_loss -0.0684 +2026-04-10 17:29:23.413238: Pseudo dice [0.645, 0.4537, 0.536, 0.0057, 0.2923, 0.7403, 0.6693] +2026-04-10 17:29:23.415612: Epoch time: 100.98 s +2026-04-10 17:29:24.514492: +2026-04-10 17:29:24.518388: Epoch 221 +2026-04-10 17:29:24.521467: Current learning rate: 0.0095 +2026-04-10 17:31:05.315458: train_loss -0.1454 +2026-04-10 17:31:05.336415: val_loss -0.1311 +2026-04-10 17:31:05.341166: Pseudo dice [0.005, 0.5625, 0.7502, 0.0883, 0.566, 0.6738, 0.7586] +2026-04-10 17:31:05.345624: Epoch time: 100.8 s +2026-04-10 17:31:06.393471: +2026-04-10 17:31:06.395530: Epoch 222 +2026-04-10 17:31:06.397654: Current learning rate: 0.0095 +2026-04-10 17:32:47.894491: train_loss -0.1365 +2026-04-10 17:32:47.903600: val_loss -0.112 +2026-04-10 17:32:47.905996: Pseudo dice [0.5041, 0.2988, 0.661, 0.3558, 0.5908, 0.4282, 0.4383] +2026-04-10 17:32:47.908239: Epoch time: 101.5 s +2026-04-10 17:32:48.937781: +2026-04-10 17:32:48.939604: Epoch 223 +2026-04-10 17:32:48.941322: Current learning rate: 0.0095 +2026-04-10 17:34:30.768538: train_loss -0.1451 +2026-04-10 17:34:30.774506: val_loss -0.1207 +2026-04-10 17:34:30.777730: Pseudo dice [0.0206, 0.4061, 0.6226, 0.4736, 0.458, 0.7618, 0.7708] +2026-04-10 17:34:30.780058: Epoch time: 101.83 s +2026-04-10 17:34:31.845039: +2026-04-10 17:34:31.846915: Epoch 224 +2026-04-10 17:34:31.848651: Current learning rate: 0.00949 +2026-04-10 17:36:13.101823: train_loss -0.1486 +2026-04-10 17:36:13.110437: val_loss -0.1086 +2026-04-10 17:36:13.113653: Pseudo dice [0.6282, 0.1163, 0.7414, 0.0012, 0.2692, 0.5934, 0.557] +2026-04-10 17:36:13.116539: Epoch time: 101.26 s +2026-04-10 17:36:14.179638: +2026-04-10 17:36:14.181668: Epoch 225 +2026-04-10 17:36:14.185057: Current learning rate: 0.00949 +2026-04-10 17:37:55.162318: train_loss -0.1435 +2026-04-10 17:37:55.168895: val_loss -0.1432 +2026-04-10 17:37:55.170959: Pseudo dice [0.5239, 0.1604, 0.7661, 0.2328, 0.315, 0.529, 0.6941] +2026-04-10 17:37:55.173229: Epoch time: 100.99 s +2026-04-10 17:37:56.245761: +2026-04-10 17:37:56.247529: Epoch 226 +2026-04-10 17:37:56.249072: Current learning rate: 0.00949 +2026-04-10 17:39:37.249037: train_loss -0.1305 +2026-04-10 17:39:37.255698: val_loss -0.0967 +2026-04-10 17:39:37.258465: Pseudo dice [0.6874, 0.3815, 0.6306, 0.0, 0.2047, 0.4225, 0.7146] +2026-04-10 17:39:37.260564: Epoch time: 101.01 s +2026-04-10 17:39:38.320756: +2026-04-10 17:39:38.323045: Epoch 227 +2026-04-10 17:39:38.325836: Current learning rate: 0.00949 +2026-04-10 17:41:19.832234: train_loss -0.1489 +2026-04-10 17:41:19.839190: val_loss -0.0864 +2026-04-10 17:41:19.843130: Pseudo dice [0.6396, 0.0559, 0.6028, 0.0002, 0.2627, 0.173, 0.4089] +2026-04-10 17:41:19.847845: Epoch time: 101.51 s +2026-04-10 17:41:20.902855: +2026-04-10 17:41:20.911908: Epoch 228 +2026-04-10 17:41:20.922487: Current learning rate: 0.00949 +2026-04-10 17:43:02.603471: train_loss -0.1423 +2026-04-10 17:43:02.611559: val_loss -0.0705 +2026-04-10 17:43:02.615121: Pseudo dice [0.3654, 0.617, 0.5235, 0.1193, 0.3433, 0.3307, 0.4821] +2026-04-10 17:43:02.617582: Epoch time: 101.7 s +2026-04-10 17:43:03.658752: +2026-04-10 17:43:03.661486: Epoch 229 +2026-04-10 17:43:03.671784: Current learning rate: 0.00948 +2026-04-10 17:44:44.984817: train_loss -0.1373 +2026-04-10 17:44:44.992648: val_loss -0.1223 +2026-04-10 17:44:44.996745: Pseudo dice [0.6759, 0.3414, 0.7012, 0.4639, 0.3849, 0.6731, 0.506] +2026-04-10 17:44:44.999241: Epoch time: 101.33 s +2026-04-10 17:44:46.035792: +2026-04-10 17:44:46.038396: Epoch 230 +2026-04-10 17:44:46.040624: Current learning rate: 0.00948 +2026-04-10 17:46:27.322342: train_loss -0.1442 +2026-04-10 17:46:27.328607: val_loss -0.1125 +2026-04-10 17:46:27.330486: Pseudo dice [0.6115, 0.2182, 0.6888, 0.2264, 0.4062, 0.4392, 0.6645] +2026-04-10 17:46:27.333312: Epoch time: 101.29 s +2026-04-10 17:46:28.385565: +2026-04-10 17:46:28.388290: Epoch 231 +2026-04-10 17:46:28.391282: Current learning rate: 0.00948 +2026-04-10 17:48:09.144145: train_loss -0.1433 +2026-04-10 17:48:09.155126: val_loss -0.1327 +2026-04-10 17:48:09.157896: Pseudo dice [0.6514, 0.4462, 0.7183, 0.3302, 0.4158, 0.6918, 0.7494] +2026-04-10 17:48:09.161604: Epoch time: 100.76 s +2026-04-10 17:48:10.199636: +2026-04-10 17:48:10.202260: Epoch 232 +2026-04-10 17:48:10.204317: Current learning rate: 0.00948 +2026-04-10 17:49:52.240015: train_loss -0.1415 +2026-04-10 17:49:52.245643: val_loss -0.0781 +2026-04-10 17:49:52.248074: Pseudo dice [0.6961, 0.2609, 0.6815, 0.0213, 0.1148, 0.7423, 0.7732] +2026-04-10 17:49:52.250763: Epoch time: 102.04 s +2026-04-10 17:49:53.332589: +2026-04-10 17:49:53.334901: Epoch 233 +2026-04-10 17:49:53.337754: Current learning rate: 0.00947 +2026-04-10 17:51:35.139258: train_loss -0.1497 +2026-04-10 17:51:35.146655: val_loss -0.1349 +2026-04-10 17:51:35.149345: Pseudo dice [0.6015, 0.4821, 0.6777, 0.2664, 0.3145, 0.5986, 0.6025] +2026-04-10 17:51:35.152059: Epoch time: 101.81 s +2026-04-10 17:51:36.202274: +2026-04-10 17:51:36.205037: Epoch 234 +2026-04-10 17:51:36.207299: Current learning rate: 0.00947 +2026-04-10 17:53:17.115723: train_loss -0.1561 +2026-04-10 17:53:17.121217: val_loss -0.0988 +2026-04-10 17:53:17.124327: Pseudo dice [0.5055, 0.5665, 0.693, 0.0704, 0.3352, 0.5344, 0.7613] +2026-04-10 17:53:17.127542: Epoch time: 100.92 s +2026-04-10 17:53:19.065469: +2026-04-10 17:53:19.067100: Epoch 235 +2026-04-10 17:53:19.068705: Current learning rate: 0.00947 +2026-04-10 17:55:01.142950: train_loss -0.1523 +2026-04-10 17:55:01.153467: val_loss -0.1023 +2026-04-10 17:55:01.156082: Pseudo dice [0.6052, 0.1725, 0.7378, 0.1188, 0.3904, 0.6214, 0.7508] +2026-04-10 17:55:01.159487: Epoch time: 102.08 s +2026-04-10 17:55:02.208450: +2026-04-10 17:55:02.210656: Epoch 236 +2026-04-10 17:55:02.212444: Current learning rate: 0.00947 +2026-04-10 17:56:43.167862: train_loss -0.1297 +2026-04-10 17:56:43.174793: val_loss -0.0949 +2026-04-10 17:56:43.178745: Pseudo dice [0.4575, 0.4339, 0.5775, 0.0199, 0.2937, 0.657, 0.7812] +2026-04-10 17:56:43.181308: Epoch time: 100.96 s +2026-04-10 17:56:44.212905: +2026-04-10 17:56:44.214741: Epoch 237 +2026-04-10 17:56:44.216412: Current learning rate: 0.00947 +2026-04-10 17:58:26.237055: train_loss -0.1519 +2026-04-10 17:58:26.243882: val_loss -0.0955 +2026-04-10 17:58:26.246722: Pseudo dice [0.5406, 0.5625, 0.7473, 0.0004, 0.2474, 0.514, 0.6775] +2026-04-10 17:58:26.249148: Epoch time: 102.03 s +2026-04-10 17:58:27.296236: +2026-04-10 17:58:27.298625: Epoch 238 +2026-04-10 17:58:27.300284: Current learning rate: 0.00946 +2026-04-10 18:00:08.335262: train_loss -0.1483 +2026-04-10 18:00:08.342774: val_loss -0.148 +2026-04-10 18:00:08.345284: Pseudo dice [0.7656, 0.2954, 0.7647, 0.1475, 0.2065, 0.6954, 0.6829] +2026-04-10 18:00:08.347628: Epoch time: 101.04 s +2026-04-10 18:00:09.381192: +2026-04-10 18:00:09.383033: Epoch 239 +2026-04-10 18:00:09.384781: Current learning rate: 0.00946 +2026-04-10 18:01:51.015023: train_loss -0.148 +2026-04-10 18:01:51.022954: val_loss -0.1365 +2026-04-10 18:01:51.025232: Pseudo dice [0.4312, 0.0771, 0.5923, 0.4927, 0.34, 0.8028, 0.7329] +2026-04-10 18:01:51.027641: Epoch time: 101.64 s +2026-04-10 18:01:52.095721: +2026-04-10 18:01:52.098923: Epoch 240 +2026-04-10 18:01:52.102007: Current learning rate: 0.00946 +2026-04-10 18:03:33.424062: train_loss -0.1564 +2026-04-10 18:03:33.432260: val_loss -0.08 +2026-04-10 18:03:33.439619: Pseudo dice [0.6588, 0.4927, 0.6231, 0.0605, 0.4328, 0.7327, 0.6735] +2026-04-10 18:03:33.442287: Epoch time: 101.33 s +2026-04-10 18:03:34.510652: +2026-04-10 18:03:34.513538: Epoch 241 +2026-04-10 18:03:34.516731: Current learning rate: 0.00946 +2026-04-10 18:05:16.510397: train_loss -0.1414 +2026-04-10 18:05:16.517031: val_loss -0.1304 +2026-04-10 18:05:16.519094: Pseudo dice [0.4509, 0.0641, 0.6611, 0.0275, 0.3631, 0.5065, 0.8055] +2026-04-10 18:05:16.521302: Epoch time: 102.0 s +2026-04-10 18:05:17.594962: +2026-04-10 18:05:17.598881: Epoch 242 +2026-04-10 18:05:17.601197: Current learning rate: 0.00945 +2026-04-10 18:06:59.456410: train_loss -0.1636 +2026-04-10 18:06:59.469764: val_loss -0.1253 +2026-04-10 18:06:59.473285: Pseudo dice [0.5711, 0.616, 0.697, 0.5568, 0.3378, 0.6462, 0.691] +2026-04-10 18:06:59.476599: Epoch time: 101.86 s +2026-04-10 18:07:00.566209: +2026-04-10 18:07:00.568599: Epoch 243 +2026-04-10 18:07:00.570203: Current learning rate: 0.00945 +2026-04-10 18:08:42.416908: train_loss -0.1499 +2026-04-10 18:08:42.423864: val_loss -0.0855 +2026-04-10 18:08:42.427579: Pseudo dice [0.7146, 0.5678, 0.703, 0.0155, 0.2116, 0.429, 0.7135] +2026-04-10 18:08:42.430856: Epoch time: 101.85 s +2026-04-10 18:08:43.486913: +2026-04-10 18:08:43.488968: Epoch 244 +2026-04-10 18:08:43.491072: Current learning rate: 0.00945 +2026-04-10 18:10:24.593837: train_loss -0.1565 +2026-04-10 18:10:24.600181: val_loss -0.1117 +2026-04-10 18:10:24.602776: Pseudo dice [0.4524, 0.4229, 0.6682, 0.2156, 0.4619, 0.744, 0.5592] +2026-04-10 18:10:24.605113: Epoch time: 101.11 s +2026-04-10 18:10:25.676280: +2026-04-10 18:10:25.679208: Epoch 245 +2026-04-10 18:10:25.682140: Current learning rate: 0.00945 +2026-04-10 18:12:07.035896: train_loss -0.1397 +2026-04-10 18:12:07.043038: val_loss -0.1234 +2026-04-10 18:12:07.046057: Pseudo dice [0.6546, 0.4545, 0.7997, 0.0722, 0.3431, 0.4361, 0.7687] +2026-04-10 18:12:07.049391: Epoch time: 101.36 s +2026-04-10 18:12:08.102956: +2026-04-10 18:12:08.106017: Epoch 246 +2026-04-10 18:12:08.109751: Current learning rate: 0.00944 +2026-04-10 18:13:48.821491: train_loss -0.1597 +2026-04-10 18:13:48.828921: val_loss -0.1345 +2026-04-10 18:13:48.831398: Pseudo dice [0.5302, 0.3511, 0.6774, 0.3073, 0.19, 0.8038, 0.6716] +2026-04-10 18:13:48.833983: Epoch time: 100.72 s +2026-04-10 18:13:49.895780: +2026-04-10 18:13:49.898625: Epoch 247 +2026-04-10 18:13:49.900984: Current learning rate: 0.00944 +2026-04-10 18:15:30.905142: train_loss -0.1445 +2026-04-10 18:15:30.910991: val_loss -0.1412 +2026-04-10 18:15:30.913069: Pseudo dice [0.6657, 0.5125, 0.6037, 0.7571, 0.4811, 0.4523, 0.7227] +2026-04-10 18:15:30.915178: Epoch time: 101.01 s +2026-04-10 18:15:30.920583: Yayy! New best EMA pseudo Dice: 0.5018 +2026-04-10 18:15:33.510448: +2026-04-10 18:15:33.513177: Epoch 248 +2026-04-10 18:15:33.515948: Current learning rate: 0.00944 +2026-04-10 18:17:15.263630: train_loss -0.1482 +2026-04-10 18:17:15.271334: val_loss -0.1134 +2026-04-10 18:17:15.274646: Pseudo dice [0.5164, 0.317, 0.7538, 0.0039, 0.3158, 0.498, 0.3148] +2026-04-10 18:17:15.276943: Epoch time: 101.76 s +2026-04-10 18:17:16.335481: +2026-04-10 18:17:16.339724: Epoch 249 +2026-04-10 18:17:16.341558: Current learning rate: 0.00944 +2026-04-10 18:18:56.614870: train_loss -0.1352 +2026-04-10 18:18:56.621082: val_loss -0.1062 +2026-04-10 18:18:56.624339: Pseudo dice [0.7661, 0.4309, 0.6639, 0.0005, 0.2444, 0.8195, 0.7431] +2026-04-10 18:18:56.626862: Epoch time: 100.28 s +2026-04-10 18:18:59.115420: +2026-04-10 18:18:59.118569: Epoch 250 +2026-04-10 18:18:59.120636: Current learning rate: 0.00944 +2026-04-10 18:20:40.367330: train_loss -0.1511 +2026-04-10 18:20:40.374290: val_loss -0.1377 +2026-04-10 18:20:40.378560: Pseudo dice [0.5138, 0.2729, 0.7037, 0.5512, 0.3302, 0.4349, 0.7876] +2026-04-10 18:20:40.381459: Epoch time: 101.26 s +2026-04-10 18:20:41.434727: +2026-04-10 18:20:41.436913: Epoch 251 +2026-04-10 18:20:41.438895: Current learning rate: 0.00943 +2026-04-10 18:22:23.249264: train_loss -0.1631 +2026-04-10 18:22:23.258628: val_loss -0.1528 +2026-04-10 18:22:23.260664: Pseudo dice [0.814, 0.0453, 0.7712, 0.4447, 0.4205, 0.7903, 0.7809] +2026-04-10 18:22:23.264798: Epoch time: 101.82 s +2026-04-10 18:22:23.268263: Yayy! New best EMA pseudo Dice: 0.5043 +2026-04-10 18:22:25.816774: +2026-04-10 18:22:25.820042: Epoch 252 +2026-04-10 18:22:25.822280: Current learning rate: 0.00943 +2026-04-10 18:24:07.095498: train_loss -0.1597 +2026-04-10 18:24:07.107530: val_loss -0.1279 +2026-04-10 18:24:07.112939: Pseudo dice [0.6862, 0.0593, 0.7073, 0.3082, 0.3533, 0.5211, 0.7011] +2026-04-10 18:24:07.117683: Epoch time: 101.28 s +2026-04-10 18:24:08.172059: +2026-04-10 18:24:08.174103: Epoch 253 +2026-04-10 18:24:08.175780: Current learning rate: 0.00943 +2026-04-10 18:25:49.008593: train_loss -0.1547 +2026-04-10 18:25:49.014779: val_loss -0.1282 +2026-04-10 18:25:49.017813: Pseudo dice [0.6806, 0.4955, 0.7562, 0.1538, 0.189, 0.714, 0.8085] +2026-04-10 18:25:49.019877: Epoch time: 100.84 s +2026-04-10 18:25:49.022637: Yayy! New best EMA pseudo Dice: 0.5057 +2026-04-10 18:25:52.559415: +2026-04-10 18:25:52.562647: Epoch 254 +2026-04-10 18:25:52.564992: Current learning rate: 0.00943 +2026-04-10 18:27:33.210423: train_loss -0.154 +2026-04-10 18:27:33.217542: val_loss -0.155 +2026-04-10 18:27:33.220537: Pseudo dice [0.7239, 0.3717, 0.7895, 0.0148, 0.3752, 0.7808, 0.853] +2026-04-10 18:27:33.222606: Epoch time: 100.65 s +2026-04-10 18:27:33.224780: Yayy! New best EMA pseudo Dice: 0.5109 +2026-04-10 18:27:35.712073: +2026-04-10 18:27:35.716703: Epoch 255 +2026-04-10 18:27:35.719155: Current learning rate: 0.00942 +2026-04-10 18:29:16.555642: train_loss -0.1604 +2026-04-10 18:29:16.560717: val_loss -0.1092 +2026-04-10 18:29:16.563270: Pseudo dice [0.3253, 0.2758, 0.5746, 0.0154, 0.2159, 0.4654, 0.3736] +2026-04-10 18:29:16.565098: Epoch time: 100.85 s +2026-04-10 18:29:17.672771: +2026-04-10 18:29:17.675724: Epoch 256 +2026-04-10 18:29:17.678674: Current learning rate: 0.00942 +2026-04-10 18:30:58.156794: train_loss -0.1436 +2026-04-10 18:30:58.162654: val_loss -0.121 +2026-04-10 18:30:58.165305: Pseudo dice [0.6116, 0.3647, 0.7678, 0.6372, 0.2418, 0.222, 0.6034] +2026-04-10 18:30:58.167283: Epoch time: 100.49 s +2026-04-10 18:30:59.245051: +2026-04-10 18:30:59.246847: Epoch 257 +2026-04-10 18:30:59.248532: Current learning rate: 0.00942 +2026-04-10 18:32:40.322840: train_loss -0.1679 +2026-04-10 18:32:40.330499: val_loss -0.1168 +2026-04-10 18:32:40.333390: Pseudo dice [0.232, 0.3642, 0.5621, 0.6163, 0.374, 0.4802, 0.7696] +2026-04-10 18:32:40.337158: Epoch time: 101.08 s +2026-04-10 18:32:41.397140: +2026-04-10 18:32:41.401382: Epoch 258 +2026-04-10 18:32:41.404389: Current learning rate: 0.00942 +2026-04-10 18:34:22.189571: train_loss -0.1438 +2026-04-10 18:34:22.195247: val_loss -0.1229 +2026-04-10 18:34:22.197372: Pseudo dice [0.7342, 0.5369, 0.6857, 0.0024, 0.2832, 0.4927, 0.689] +2026-04-10 18:34:22.199920: Epoch time: 100.8 s +2026-04-10 18:34:23.259959: +2026-04-10 18:34:23.265094: Epoch 259 +2026-04-10 18:34:23.269270: Current learning rate: 0.00942 +2026-04-10 18:36:04.188239: train_loss -0.1441 +2026-04-10 18:36:04.195795: val_loss -0.14 +2026-04-10 18:36:04.198359: Pseudo dice [0.4442, 0.2981, 0.7375, 0.0695, 0.4276, 0.8016, 0.8555] +2026-04-10 18:36:04.201431: Epoch time: 100.93 s +2026-04-10 18:36:05.274073: +2026-04-10 18:36:05.277319: Epoch 260 +2026-04-10 18:36:05.280951: Current learning rate: 0.00941 +2026-04-10 18:37:46.604620: train_loss -0.1532 +2026-04-10 18:37:46.612355: val_loss -0.1396 +2026-04-10 18:37:46.615503: Pseudo dice [0.7255, 0.5739, 0.5876, 0.6511, 0.3916, 0.8625, 0.8339] +2026-04-10 18:37:46.620312: Epoch time: 101.33 s +2026-04-10 18:37:47.698770: +2026-04-10 18:37:47.707944: Epoch 261 +2026-04-10 18:37:47.723017: Current learning rate: 0.00941 +2026-04-10 18:39:28.471837: train_loss -0.1677 +2026-04-10 18:39:28.478798: val_loss -0.1463 +2026-04-10 18:39:28.481492: Pseudo dice [0.5101, 0.3643, 0.7882, 0.7624, 0.4043, 0.612, 0.7318] +2026-04-10 18:39:28.484657: Epoch time: 100.78 s +2026-04-10 18:39:28.486912: Yayy! New best EMA pseudo Dice: 0.5192 +2026-04-10 18:39:31.250275: +2026-04-10 18:39:31.252689: Epoch 262 +2026-04-10 18:39:31.254606: Current learning rate: 0.00941 +2026-04-10 18:41:12.009790: train_loss -0.168 +2026-04-10 18:41:12.016802: val_loss -0.0794 +2026-04-10 18:41:12.020406: Pseudo dice [0.2345, 0.3105, 0.5646, 0.0685, 0.3152, 0.5362, 0.6644] +2026-04-10 18:41:12.027369: Epoch time: 100.76 s +2026-04-10 18:41:13.107257: +2026-04-10 18:41:13.123089: Epoch 263 +2026-04-10 18:41:13.127162: Current learning rate: 0.00941 +2026-04-10 18:42:53.604992: train_loss -0.1684 +2026-04-10 18:42:53.615096: val_loss -0.1387 +2026-04-10 18:42:53.618645: Pseudo dice [0.7297, 0.5621, 0.6096, 0.0031, 0.2747, 0.6903, 0.8336] +2026-04-10 18:42:53.620994: Epoch time: 100.5 s +2026-04-10 18:42:54.693143: +2026-04-10 18:42:54.695410: Epoch 264 +2026-04-10 18:42:54.697729: Current learning rate: 0.0094 +2026-04-10 18:44:35.370638: train_loss -0.1506 +2026-04-10 18:44:35.379457: val_loss -0.1012 +2026-04-10 18:44:35.382733: Pseudo dice [0.6416, 0.6144, 0.6861, 0.0513, 0.3953, 0.8372, 0.7009] +2026-04-10 18:44:35.387328: Epoch time: 100.68 s +2026-04-10 18:44:36.452026: +2026-04-10 18:44:36.454936: Epoch 265 +2026-04-10 18:44:36.456906: Current learning rate: 0.0094 +2026-04-10 18:46:18.294109: train_loss -0.1375 +2026-04-10 18:46:18.299580: val_loss -0.1172 +2026-04-10 18:46:18.301852: Pseudo dice [0.6528, 0.5466, 0.7021, 0.1023, 0.2921, 0.6983, 0.5502] +2026-04-10 18:46:18.304092: Epoch time: 101.85 s +2026-04-10 18:46:19.372795: +2026-04-10 18:46:19.379804: Epoch 266 +2026-04-10 18:46:19.384781: Current learning rate: 0.0094 +2026-04-10 18:48:00.360002: train_loss -0.1508 +2026-04-10 18:48:00.367490: val_loss -0.1011 +2026-04-10 18:48:00.370811: Pseudo dice [0.7639, 0.1674, 0.607, 0.6013, 0.1805, 0.5088, 0.6597] +2026-04-10 18:48:00.374707: Epoch time: 100.99 s +2026-04-10 18:48:01.445546: +2026-04-10 18:48:01.447915: Epoch 267 +2026-04-10 18:48:01.452485: Current learning rate: 0.0094 +2026-04-10 18:49:41.935152: train_loss -0.1535 +2026-04-10 18:49:41.943094: val_loss -0.1241 +2026-04-10 18:49:41.946136: Pseudo dice [0.2942, 0.4666, 0.8163, 0.1009, 0.3537, 0.6825, 0.7077] +2026-04-10 18:49:41.949529: Epoch time: 100.49 s +2026-04-10 18:49:43.009934: +2026-04-10 18:49:43.012129: Epoch 268 +2026-04-10 18:49:43.014265: Current learning rate: 0.00939 +2026-04-10 18:51:23.848037: train_loss -0.159 +2026-04-10 18:51:23.856221: val_loss -0.1331 +2026-04-10 18:51:23.859034: Pseudo dice [0.3268, 0.3951, 0.7755, 0.1437, 0.1904, 0.5068, 0.8027] +2026-04-10 18:51:23.861576: Epoch time: 100.84 s +2026-04-10 18:51:24.949857: +2026-04-10 18:51:24.952913: Epoch 269 +2026-04-10 18:51:24.956685: Current learning rate: 0.00939 +2026-04-10 18:53:06.144479: train_loss -0.1669 +2026-04-10 18:53:06.170719: val_loss -0.1245 +2026-04-10 18:53:06.174323: Pseudo dice [0.6786, 0.2947, 0.7586, 0.3662, 0.3436, 0.6039, 0.7629] +2026-04-10 18:53:06.199525: Epoch time: 101.2 s +2026-04-10 18:53:07.265963: +2026-04-10 18:53:07.268220: Epoch 270 +2026-04-10 18:53:07.270268: Current learning rate: 0.00939 +2026-04-10 18:54:48.531827: train_loss -0.1512 +2026-04-10 18:54:48.541807: val_loss -0.144 +2026-04-10 18:54:48.544441: Pseudo dice [0.6038, 0.4021, 0.5977, 0.0093, 0.405, 0.4758, 0.5406] +2026-04-10 18:54:48.547116: Epoch time: 101.27 s +2026-04-10 18:54:49.605048: +2026-04-10 18:54:49.608812: Epoch 271 +2026-04-10 18:54:49.613452: Current learning rate: 0.00939 +2026-04-10 18:56:31.202867: train_loss -0.161 +2026-04-10 18:56:31.209982: val_loss -0.0949 +2026-04-10 18:56:31.212394: Pseudo dice [0.6079, 0.3023, 0.723, 0.0285, 0.3394, 0.6744, 0.659] +2026-04-10 18:56:31.215903: Epoch time: 101.6 s +2026-04-10 18:56:32.273866: +2026-04-10 18:56:32.275656: Epoch 272 +2026-04-10 18:56:32.277874: Current learning rate: 0.00939 +2026-04-10 18:58:14.303591: train_loss -0.1436 +2026-04-10 18:58:14.313958: val_loss -0.1315 +2026-04-10 18:58:14.317720: Pseudo dice [0.684, 0.5514, 0.7345, 0.1467, 0.3261, 0.3938, 0.8292] +2026-04-10 18:58:14.320939: Epoch time: 102.03 s +2026-04-10 18:58:15.377041: +2026-04-10 18:58:15.379309: Epoch 273 +2026-04-10 18:58:15.383057: Current learning rate: 0.00938 +2026-04-10 18:59:56.752319: train_loss -0.1598 +2026-04-10 18:59:56.758751: val_loss -0.0933 +2026-04-10 18:59:56.762874: Pseudo dice [0.7215, 0.149, 0.4186, 0.0111, 0.3646, 0.631, 0.7053] +2026-04-10 18:59:56.765940: Epoch time: 101.38 s +2026-04-10 18:59:57.819694: +2026-04-10 18:59:57.821905: Epoch 274 +2026-04-10 18:59:57.823615: Current learning rate: 0.00938 +2026-04-10 19:01:39.184271: train_loss -0.156 +2026-04-10 19:01:39.193297: val_loss -0.0973 +2026-04-10 19:01:39.196203: Pseudo dice [0.282, 0.2645, 0.6476, 0.2403, 0.1801, 0.7297, 0.5476] +2026-04-10 19:01:39.199466: Epoch time: 101.37 s +2026-04-10 19:01:40.266298: +2026-04-10 19:01:40.269602: Epoch 275 +2026-04-10 19:01:40.271981: Current learning rate: 0.00938 +2026-04-10 19:03:22.221254: train_loss -0.1474 +2026-04-10 19:03:22.228908: val_loss -0.1245 +2026-04-10 19:03:22.230792: Pseudo dice [0.45, 0.6152, 0.7083, 0.113, 0.3731, 0.8096, 0.6915] +2026-04-10 19:03:22.233490: Epoch time: 101.96 s +2026-04-10 19:03:23.345568: +2026-04-10 19:03:23.347714: Epoch 276 +2026-04-10 19:03:23.349512: Current learning rate: 0.00938 +2026-04-10 19:05:04.903066: train_loss -0.1629 +2026-04-10 19:05:04.908977: val_loss -0.1336 +2026-04-10 19:05:04.911838: Pseudo dice [0.7851, 0.1275, 0.7683, 0.3946, 0.3626, 0.4609, 0.7105] +2026-04-10 19:05:04.914231: Epoch time: 101.56 s +2026-04-10 19:05:06.024504: +2026-04-10 19:05:06.028882: Epoch 277 +2026-04-10 19:05:06.033967: Current learning rate: 0.00937 +2026-04-10 19:06:46.566178: train_loss -0.1494 +2026-04-10 19:06:46.573001: val_loss -0.084 +2026-04-10 19:06:46.576004: Pseudo dice [0.7312, 0.4192, 0.3046, 0.0001, 0.2488, 0.6047, 0.3908] +2026-04-10 19:06:46.578485: Epoch time: 100.54 s +2026-04-10 19:06:47.657579: +2026-04-10 19:06:47.659667: Epoch 278 +2026-04-10 19:06:47.661392: Current learning rate: 0.00937 +2026-04-10 19:08:29.088323: train_loss -0.1615 +2026-04-10 19:08:29.095037: val_loss -0.1354 +2026-04-10 19:08:29.097450: Pseudo dice [0.7139, 0.6845, 0.5379, 0.3106, 0.3177, 0.5303, 0.7657] +2026-04-10 19:08:29.099885: Epoch time: 101.43 s +2026-04-10 19:08:30.200669: +2026-04-10 19:08:30.202831: Epoch 279 +2026-04-10 19:08:30.206683: Current learning rate: 0.00937 +2026-04-10 19:10:11.636514: train_loss -0.1506 +2026-04-10 19:10:11.643101: val_loss -0.1245 +2026-04-10 19:10:11.645018: Pseudo dice [0.6927, 0.6021, 0.5593, 0.0021, 0.4011, 0.3459, 0.7034] +2026-04-10 19:10:11.647053: Epoch time: 101.44 s +2026-04-10 19:10:12.718877: +2026-04-10 19:10:12.721423: Epoch 280 +2026-04-10 19:10:12.723797: Current learning rate: 0.00937 +2026-04-10 19:11:53.432075: train_loss -0.1455 +2026-04-10 19:11:53.441072: val_loss -0.1534 +2026-04-10 19:11:53.444077: Pseudo dice [0.6794, 0.6181, 0.6894, 0.3119, 0.3006, 0.3115, 0.802] +2026-04-10 19:11:53.448509: Epoch time: 100.72 s +2026-04-10 19:11:54.524506: +2026-04-10 19:11:54.526699: Epoch 281 +2026-04-10 19:11:54.528830: Current learning rate: 0.00937 +2026-04-10 19:13:35.737369: train_loss -0.1556 +2026-04-10 19:13:35.744051: val_loss -0.1227 +2026-04-10 19:13:35.747092: Pseudo dice [0.5867, 0.1806, 0.7599, 0.0488, 0.469, 0.8059, 0.5868] +2026-04-10 19:13:35.751105: Epoch time: 101.22 s +2026-04-10 19:13:36.806615: +2026-04-10 19:13:36.808528: Epoch 282 +2026-04-10 19:13:36.810390: Current learning rate: 0.00936 +2026-04-10 19:15:18.250040: train_loss -0.171 +2026-04-10 19:15:18.256561: val_loss -0.1176 +2026-04-10 19:15:18.259247: Pseudo dice [0.5995, 0.4592, 0.7001, 0.0102, 0.2802, 0.6614, 0.8161] +2026-04-10 19:15:18.261843: Epoch time: 101.45 s +2026-04-10 19:15:19.329437: +2026-04-10 19:15:19.332418: Epoch 283 +2026-04-10 19:15:19.334239: Current learning rate: 0.00936 +2026-04-10 19:17:00.772864: train_loss -0.1638 +2026-04-10 19:17:00.781426: val_loss -0.1305 +2026-04-10 19:17:00.784871: Pseudo dice [0.5695, 0.4898, 0.7397, 0.4865, 0.5346, 0.6434, 0.7442] +2026-04-10 19:17:00.787599: Epoch time: 101.45 s +2026-04-10 19:17:01.860414: +2026-04-10 19:17:01.863477: Epoch 284 +2026-04-10 19:17:01.865695: Current learning rate: 0.00936 +2026-04-10 19:18:43.148907: train_loss -0.1463 +2026-04-10 19:18:43.160459: val_loss -0.1206 +2026-04-10 19:18:43.163161: Pseudo dice [0.5289, 0.5688, 0.7964, 0.5077, 0.466, 0.4284, 0.5681] +2026-04-10 19:18:43.166250: Epoch time: 101.29 s +2026-04-10 19:18:44.236556: +2026-04-10 19:18:44.239742: Epoch 285 +2026-04-10 19:18:44.242585: Current learning rate: 0.00936 +2026-04-10 19:20:24.786249: train_loss -0.1461 +2026-04-10 19:20:24.793224: val_loss -0.1039 +2026-04-10 19:20:24.796944: Pseudo dice [0.7377, 0.5124, 0.7618, 0.0562, 0.2239, 0.8043, 0.781] +2026-04-10 19:20:24.799345: Epoch time: 100.55 s +2026-04-10 19:20:25.861978: +2026-04-10 19:20:25.865677: Epoch 286 +2026-04-10 19:20:25.867685: Current learning rate: 0.00935 +2026-04-10 19:22:06.318831: train_loss -0.1521 +2026-04-10 19:22:06.328342: val_loss -0.1148 +2026-04-10 19:22:06.331549: Pseudo dice [0.5545, 0.5581, 0.6381, 0.3915, 0.3681, 0.569, 0.4796] +2026-04-10 19:22:06.334852: Epoch time: 100.46 s +2026-04-10 19:22:07.401569: +2026-04-10 19:22:07.404917: Epoch 287 +2026-04-10 19:22:07.407156: Current learning rate: 0.00935 +2026-04-10 19:23:48.525974: train_loss -0.158 +2026-04-10 19:23:48.533399: val_loss -0.0902 +2026-04-10 19:23:48.535821: Pseudo dice [0.7562, 0.3924, 0.5885, 0.0385, 0.5441, 0.5081, 0.6929] +2026-04-10 19:23:48.539092: Epoch time: 101.13 s +2026-04-10 19:23:49.625880: +2026-04-10 19:23:49.628388: Epoch 288 +2026-04-10 19:23:49.630844: Current learning rate: 0.00935 +2026-04-10 19:25:31.038023: train_loss -0.1555 +2026-04-10 19:25:31.045053: val_loss -0.1408 +2026-04-10 19:25:31.048004: Pseudo dice [0.7803, 0.3374, 0.7831, 0.4617, 0.5507, 0.7422, 0.6458] +2026-04-10 19:25:31.051215: Epoch time: 101.42 s +2026-04-10 19:25:31.054490: Yayy! New best EMA pseudo Dice: 0.5219 +2026-04-10 19:25:33.713395: +2026-04-10 19:25:33.716400: Epoch 289 +2026-04-10 19:25:33.719042: Current learning rate: 0.00935 +2026-04-10 19:27:14.876724: train_loss -0.1656 +2026-04-10 19:27:14.884934: val_loss -0.1325 +2026-04-10 19:27:14.887786: Pseudo dice [0.6331, 0.3476, 0.6581, 0.2872, 0.4129, 0.6034, 0.6276] +2026-04-10 19:27:14.891607: Epoch time: 101.17 s +2026-04-10 19:27:15.977635: +2026-04-10 19:27:15.980462: Epoch 290 +2026-04-10 19:27:15.982349: Current learning rate: 0.00935 +2026-04-10 19:28:57.880866: train_loss -0.1461 +2026-04-10 19:28:57.889923: val_loss -0.134 +2026-04-10 19:28:57.893864: Pseudo dice [0.7349, 0.4548, 0.6923, 0.0063, 0.3123, 0.72, 0.8682] +2026-04-10 19:28:57.896998: Epoch time: 101.91 s +2026-04-10 19:28:57.899613: Yayy! New best EMA pseudo Dice: 0.5227 +2026-04-10 19:29:00.462179: +2026-04-10 19:29:00.465122: Epoch 291 +2026-04-10 19:29:00.467122: Current learning rate: 0.00934 +2026-04-10 19:30:42.413122: train_loss -0.1675 +2026-04-10 19:30:42.419920: val_loss -0.1422 +2026-04-10 19:30:42.422414: Pseudo dice [0.5672, 0.2482, 0.7633, 0.0515, 0.328, 0.7871, 0.5755] +2026-04-10 19:30:42.426181: Epoch time: 101.95 s +2026-04-10 19:30:43.718916: +2026-04-10 19:30:43.721421: Epoch 292 +2026-04-10 19:30:43.723522: Current learning rate: 0.00934 +2026-04-10 19:32:26.021003: train_loss -0.1556 +2026-04-10 19:32:26.027460: val_loss -0.1036 +2026-04-10 19:32:26.032079: Pseudo dice [0.6124, 0.3099, 0.6362, 0.5741, 0.2143, 0.6986, 0.5837] +2026-04-10 19:32:26.034990: Epoch time: 102.31 s +2026-04-10 19:32:27.141057: +2026-04-10 19:32:27.144442: Epoch 293 +2026-04-10 19:32:27.157783: Current learning rate: 0.00934 +2026-04-10 19:34:07.859611: train_loss -0.1501 +2026-04-10 19:34:07.867469: val_loss -0.1135 +2026-04-10 19:34:07.871350: Pseudo dice [0.6935, 0.641, 0.6631, 0.0546, 0.3755, 0.7713, 0.6033] +2026-04-10 19:34:07.873618: Epoch time: 100.72 s +2026-04-10 19:34:08.984217: +2026-04-10 19:34:08.989042: Epoch 294 +2026-04-10 19:34:09.000023: Current learning rate: 0.00934 +2026-04-10 19:35:49.825895: train_loss -0.1527 +2026-04-10 19:35:49.832001: val_loss -0.1414 +2026-04-10 19:35:49.835135: Pseudo dice [0.5715, 0.551, 0.4596, 0.1944, 0.4041, 0.6643, 0.7819] +2026-04-10 19:35:49.838397: Epoch time: 100.84 s +2026-04-10 19:35:50.968291: +2026-04-10 19:35:50.970392: Epoch 295 +2026-04-10 19:35:50.972422: Current learning rate: 0.00933 +2026-04-10 19:37:32.932551: train_loss -0.1625 +2026-04-10 19:37:32.942834: val_loss -0.1154 +2026-04-10 19:37:32.947091: Pseudo dice [0.5249, 0.2471, 0.6324, 0.62, 0.4055, 0.8005, 0.534] +2026-04-10 19:37:32.950089: Epoch time: 101.97 s +2026-04-10 19:37:34.049693: +2026-04-10 19:37:34.052396: Epoch 296 +2026-04-10 19:37:34.055182: Current learning rate: 0.00933 +2026-04-10 19:39:16.021641: train_loss -0.1427 +2026-04-10 19:39:16.028256: val_loss -0.0935 +2026-04-10 19:39:16.031431: Pseudo dice [0.5508, 0.3974, 0.6044, 0.1811, 0.3759, 0.6941, 0.6311] +2026-04-10 19:39:16.034869: Epoch time: 101.98 s +2026-04-10 19:39:17.138328: +2026-04-10 19:39:17.140663: Epoch 297 +2026-04-10 19:39:17.142373: Current learning rate: 0.00933 +2026-04-10 19:40:58.661355: train_loss -0.1518 +2026-04-10 19:40:58.669093: val_loss -0.1159 +2026-04-10 19:40:58.672323: Pseudo dice [0.7682, 0.1863, 0.6542, 0.5611, 0.2171, 0.5177, 0.631] +2026-04-10 19:40:58.674139: Epoch time: 101.53 s +2026-04-10 19:40:59.773401: +2026-04-10 19:40:59.778741: Epoch 298 +2026-04-10 19:40:59.788898: Current learning rate: 0.00933 +2026-04-10 19:42:41.107293: train_loss -0.168 +2026-04-10 19:42:41.114220: val_loss -0.1075 +2026-04-10 19:42:41.116516: Pseudo dice [0.5199, 0.3394, 0.473, 0.0004, 0.4823, 0.7712, 0.8472] +2026-04-10 19:42:41.118502: Epoch time: 101.34 s +2026-04-10 19:42:42.227329: +2026-04-10 19:42:42.231476: Epoch 299 +2026-04-10 19:42:42.233406: Current learning rate: 0.00932 +2026-04-10 19:44:23.542942: train_loss -0.1586 +2026-04-10 19:44:23.548723: val_loss -0.071 +2026-04-10 19:44:23.550770: Pseudo dice [0.5083, 0.214, 0.6623, 0.0251, 0.3605, 0.3467, 0.6433] +2026-04-10 19:44:23.553192: Epoch time: 101.32 s +2026-04-10 19:44:26.306506: +2026-04-10 19:44:26.308918: Epoch 300 +2026-04-10 19:44:26.311350: Current learning rate: 0.00932 +2026-04-10 19:46:07.598283: train_loss -0.1686 +2026-04-10 19:46:07.604709: val_loss -0.1249 +2026-04-10 19:46:07.607335: Pseudo dice [0.5026, 0.1463, 0.7668, 0.0004, 0.4664, 0.7939, 0.6252] +2026-04-10 19:46:07.609696: Epoch time: 101.29 s +2026-04-10 19:46:08.786152: +2026-04-10 19:46:08.788588: Epoch 301 +2026-04-10 19:46:08.791033: Current learning rate: 0.00932 +2026-04-10 19:47:49.902426: train_loss -0.1588 +2026-04-10 19:47:49.909820: val_loss -0.1241 +2026-04-10 19:47:49.912501: Pseudo dice [0.7224, 0.5508, 0.6691, 0.0764, 0.4766, 0.6838, 0.7066] +2026-04-10 19:47:49.917463: Epoch time: 101.12 s +2026-04-10 19:47:51.055007: +2026-04-10 19:47:51.057443: Epoch 302 +2026-04-10 19:47:51.060806: Current learning rate: 0.00932 +2026-04-10 19:49:32.833579: train_loss -0.1607 +2026-04-10 19:49:32.841057: val_loss -0.1311 +2026-04-10 19:49:32.843467: Pseudo dice [0.6707, 0.2712, 0.6028, 0.0147, 0.3109, 0.5408, 0.7152] +2026-04-10 19:49:32.845653: Epoch time: 101.78 s +2026-04-10 19:49:33.950750: +2026-04-10 19:49:33.953430: Epoch 303 +2026-04-10 19:49:33.956312: Current learning rate: 0.00932 +2026-04-10 19:51:15.606637: train_loss -0.1626 +2026-04-10 19:51:15.615363: val_loss -0.128 +2026-04-10 19:51:15.623717: Pseudo dice [0.3448, 0.4317, 0.7347, 0.0051, 0.3045, 0.4154, 0.7048] +2026-04-10 19:51:15.626920: Epoch time: 101.66 s +2026-04-10 19:51:16.728283: +2026-04-10 19:51:16.731878: Epoch 304 +2026-04-10 19:51:16.734424: Current learning rate: 0.00931 +2026-04-10 19:52:58.448421: train_loss -0.1627 +2026-04-10 19:52:58.458081: val_loss -0.1321 +2026-04-10 19:52:58.460733: Pseudo dice [0.6678, 0.4104, 0.7594, 0.0737, 0.4743, 0.7667, 0.699] +2026-04-10 19:52:58.464266: Epoch time: 101.72 s +2026-04-10 19:52:59.584489: +2026-04-10 19:52:59.586562: Epoch 305 +2026-04-10 19:52:59.589322: Current learning rate: 0.00931 +2026-04-10 19:54:41.267489: train_loss -0.1631 +2026-04-10 19:54:41.280027: val_loss -0.1009 +2026-04-10 19:54:41.283213: Pseudo dice [0.7909, 0.1841, 0.6217, 0.0984, 0.2807, 0.6095, 0.7165] +2026-04-10 19:54:41.287100: Epoch time: 101.69 s +2026-04-10 19:54:42.390133: +2026-04-10 19:54:42.392020: Epoch 306 +2026-04-10 19:54:42.393949: Current learning rate: 0.00931 +2026-04-10 19:56:23.798879: train_loss -0.1568 +2026-04-10 19:56:23.805829: val_loss -0.1308 +2026-04-10 19:56:23.809562: Pseudo dice [0.775, 0.4498, 0.5092, 0.0013, 0.3124, 0.7286, 0.8677] +2026-04-10 19:56:23.812219: Epoch time: 101.41 s +2026-04-10 19:56:24.893957: +2026-04-10 19:56:24.896957: Epoch 307 +2026-04-10 19:56:24.900118: Current learning rate: 0.00931 +2026-04-10 19:58:07.207133: train_loss -0.1589 +2026-04-10 19:58:07.213217: val_loss -0.1184 +2026-04-10 19:58:07.216048: Pseudo dice [0.7188, 0.5189, 0.3865, 0.001, 0.4173, 0.8636, 0.8069] +2026-04-10 19:58:07.218951: Epoch time: 102.32 s +2026-04-10 19:58:08.352231: +2026-04-10 19:58:08.356721: Epoch 308 +2026-04-10 19:58:08.360898: Current learning rate: 0.0093 +2026-04-10 19:59:49.352944: train_loss -0.165 +2026-04-10 19:59:49.359551: val_loss -0.1417 +2026-04-10 19:59:49.362530: Pseudo dice [0.5963, 0.3448, 0.5562, 0.418, 0.2573, 0.8213, 0.7871] +2026-04-10 19:59:49.364969: Epoch time: 101.0 s +2026-04-10 19:59:50.468665: +2026-04-10 19:59:50.472027: Epoch 309 +2026-04-10 19:59:50.474085: Current learning rate: 0.0093 +2026-04-10 20:01:32.290923: train_loss -0.1649 +2026-04-10 20:01:32.297287: val_loss -0.1545 +2026-04-10 20:01:32.300269: Pseudo dice [0.8044, 0.2554, 0.5784, 0.2914, 0.5149, 0.8303, 0.8195] +2026-04-10 20:01:32.302570: Epoch time: 101.83 s +2026-04-10 20:01:33.411994: +2026-04-10 20:01:33.414495: Epoch 310 +2026-04-10 20:01:33.416532: Current learning rate: 0.0093 +2026-04-10 20:03:14.980706: train_loss -0.1675 +2026-04-10 20:03:14.985563: val_loss -0.1188 +2026-04-10 20:03:14.987743: Pseudo dice [0.7337, 0.5624, 0.5693, 0.0, 0.3792, 0.4336, 0.6673] +2026-04-10 20:03:14.989762: Epoch time: 101.57 s +2026-04-10 20:03:16.114384: +2026-04-10 20:03:16.117087: Epoch 311 +2026-04-10 20:03:16.119523: Current learning rate: 0.0093 +2026-04-10 20:04:57.677279: train_loss -0.1605 +2026-04-10 20:04:57.688373: val_loss -0.0997 +2026-04-10 20:04:57.690634: Pseudo dice [0.7149, 0.2858, 0.576, 0.0003, 0.288, 0.8199, 0.72] +2026-04-10 20:04:57.693202: Epoch time: 101.57 s +2026-04-10 20:04:59.842830: +2026-04-10 20:04:59.844657: Epoch 312 +2026-04-10 20:04:59.847123: Current learning rate: 0.0093 +2026-04-10 20:06:40.446108: train_loss -0.1756 +2026-04-10 20:06:40.451780: val_loss -0.1488 +2026-04-10 20:06:40.455539: Pseudo dice [0.5409, 0.5638, 0.6376, 0.556, 0.4102, 0.7369, 0.7824] +2026-04-10 20:06:40.457898: Epoch time: 100.61 s +2026-04-10 20:06:41.580583: +2026-04-10 20:06:41.583193: Epoch 313 +2026-04-10 20:06:41.585153: Current learning rate: 0.00929 +2026-04-10 20:08:23.460691: train_loss -0.1667 +2026-04-10 20:08:23.469172: val_loss -0.1388 +2026-04-10 20:08:23.472740: Pseudo dice [0.5995, 0.2195, 0.7334, 0.6341, 0.4545, 0.3006, 0.8041] +2026-04-10 20:08:23.476165: Epoch time: 101.88 s +2026-04-10 20:08:24.585126: +2026-04-10 20:08:24.587334: Epoch 314 +2026-04-10 20:08:24.589644: Current learning rate: 0.00929 +2026-04-10 20:10:05.548279: train_loss -0.1649 +2026-04-10 20:10:05.554960: val_loss -0.1528 +2026-04-10 20:10:05.557396: Pseudo dice [0.8052, 0.2604, 0.6439, 0.0002, 0.4394, 0.7638, 0.5661] +2026-04-10 20:10:05.559942: Epoch time: 100.97 s +2026-04-10 20:10:06.664754: +2026-04-10 20:10:06.667300: Epoch 315 +2026-04-10 20:10:06.669427: Current learning rate: 0.00929 +2026-04-10 20:11:47.746324: train_loss -0.1596 +2026-04-10 20:11:47.753592: val_loss -0.1355 +2026-04-10 20:11:47.755995: Pseudo dice [0.5047, 0.3079, 0.719, 0.006, 0.3413, 0.5681, 0.7981] +2026-04-10 20:11:47.759279: Epoch time: 101.08 s +2026-04-10 20:11:48.896454: +2026-04-10 20:11:48.899861: Epoch 316 +2026-04-10 20:11:48.902340: Current learning rate: 0.00929 +2026-04-10 20:13:29.983271: train_loss -0.1588 +2026-04-10 20:13:29.992554: val_loss -0.1611 +2026-04-10 20:13:29.995352: Pseudo dice [0.7893, 0.4777, 0.7197, 0.6026, 0.3793, 0.6103, 0.8476] +2026-04-10 20:13:29.998314: Epoch time: 101.09 s +2026-04-10 20:13:30.001000: Yayy! New best EMA pseudo Dice: 0.5231 +2026-04-10 20:13:32.732687: +2026-04-10 20:13:32.734392: Epoch 317 +2026-04-10 20:13:32.736321: Current learning rate: 0.00928 +2026-04-10 20:15:14.411910: train_loss -0.1617 +2026-04-10 20:15:14.419133: val_loss -0.1219 +2026-04-10 20:15:14.446358: Pseudo dice [0.3578, 0.4308, 0.7785, 0.7626, 0.4289, 0.5059, 0.6073] +2026-04-10 20:15:14.450527: Epoch time: 101.68 s +2026-04-10 20:15:14.455462: Yayy! New best EMA pseudo Dice: 0.5261 +2026-04-10 20:15:17.285324: +2026-04-10 20:15:17.287688: Epoch 318 +2026-04-10 20:15:17.289590: Current learning rate: 0.00928 +2026-04-10 20:16:58.906904: train_loss -0.1649 +2026-04-10 20:16:58.914693: val_loss -0.1047 +2026-04-10 20:16:58.917963: Pseudo dice [0.7764, 0.2551, 0.5469, 0.1831, 0.2724, 0.6478, 0.5289] +2026-04-10 20:16:58.920744: Epoch time: 101.62 s +2026-04-10 20:17:00.002925: +2026-04-10 20:17:00.005638: Epoch 319 +2026-04-10 20:17:00.007825: Current learning rate: 0.00928 +2026-04-10 20:18:41.426319: train_loss -0.149 +2026-04-10 20:18:41.432927: val_loss -0.1281 +2026-04-10 20:18:41.435632: Pseudo dice [0.5894, 0.3179, 0.6492, 0.1305, 0.4884, 0.7188, 0.7373] +2026-04-10 20:18:41.438741: Epoch time: 101.43 s +2026-04-10 20:18:42.537750: +2026-04-10 20:18:42.540380: Epoch 320 +2026-04-10 20:18:42.542946: Current learning rate: 0.00928 +2026-04-10 20:20:23.711113: train_loss -0.1634 +2026-04-10 20:20:23.719776: val_loss -0.1499 +2026-04-10 20:20:23.722427: Pseudo dice [0.4863, 0.3084, 0.8184, 0.4446, 0.4939, 0.8144, 0.7646] +2026-04-10 20:20:23.725847: Epoch time: 101.18 s +2026-04-10 20:20:23.728548: Yayy! New best EMA pseudo Dice: 0.5264 +2026-04-10 20:20:26.555038: +2026-04-10 20:20:26.565379: Epoch 321 +2026-04-10 20:20:26.568085: Current learning rate: 0.00927 +2026-04-10 20:22:07.713432: train_loss -0.1657 +2026-04-10 20:22:07.720568: val_loss -0.13 +2026-04-10 20:22:07.724056: Pseudo dice [0.4065, 0.5631, 0.6853, 0.3949, 0.3435, 0.8406, 0.7959] +2026-04-10 20:22:07.726941: Epoch time: 101.16 s +2026-04-10 20:22:07.729690: Yayy! New best EMA pseudo Dice: 0.5313 +2026-04-10 20:22:10.414444: +2026-04-10 20:22:10.416724: Epoch 322 +2026-04-10 20:22:10.418932: Current learning rate: 0.00927 +2026-04-10 20:23:52.436120: train_loss -0.1674 +2026-04-10 20:23:52.441597: val_loss -0.1313 +2026-04-10 20:23:52.444393: Pseudo dice [0.6065, 0.6067, 0.7418, 0.0127, 0.3497, 0.3205, 0.7541] +2026-04-10 20:23:52.447309: Epoch time: 102.02 s +2026-04-10 20:23:53.556719: +2026-04-10 20:23:53.559146: Epoch 323 +2026-04-10 20:23:53.561403: Current learning rate: 0.00927 +2026-04-10 20:25:34.652624: train_loss -0.1685 +2026-04-10 20:25:34.659322: val_loss -0.145 +2026-04-10 20:25:34.662038: Pseudo dice [0.8315, 0.427, 0.7226, 0.0228, 0.4115, 0.6881, 0.654] +2026-04-10 20:25:34.664532: Epoch time: 101.1 s +2026-04-10 20:25:35.755187: +2026-04-10 20:25:35.758049: Epoch 324 +2026-04-10 20:25:35.761129: Current learning rate: 0.00927 +2026-04-10 20:27:18.000508: train_loss -0.1558 +2026-04-10 20:27:18.008963: val_loss -0.1069 +2026-04-10 20:27:18.012213: Pseudo dice [0.7138, 0.2306, 0.7197, 0.1326, 0.2842, 0.6044, 0.7679] +2026-04-10 20:27:18.015383: Epoch time: 102.25 s +2026-04-10 20:27:19.109033: +2026-04-10 20:27:19.111500: Epoch 325 +2026-04-10 20:27:19.114062: Current learning rate: 0.00927 +2026-04-10 20:28:59.945625: train_loss -0.1598 +2026-04-10 20:28:59.954441: val_loss -0.1375 +2026-04-10 20:28:59.957381: Pseudo dice [0.567, 0.4363, 0.5046, 0.0059, 0.5065, 0.6443, 0.711] +2026-04-10 20:28:59.960449: Epoch time: 100.84 s +2026-04-10 20:29:01.074568: +2026-04-10 20:29:01.076914: Epoch 326 +2026-04-10 20:29:01.078853: Current learning rate: 0.00926 +2026-04-10 20:30:41.761794: train_loss -0.1529 +2026-04-10 20:30:41.768306: val_loss -0.0836 +2026-04-10 20:30:41.771164: Pseudo dice [0.581, 0.2876, 0.6498, 0.0679, 0.3775, 0.8137, 0.6296] +2026-04-10 20:30:41.773128: Epoch time: 100.69 s +2026-04-10 20:30:42.859981: +2026-04-10 20:30:42.862504: Epoch 327 +2026-04-10 20:30:42.865057: Current learning rate: 0.00926 +2026-04-10 20:32:23.653907: train_loss -0.1596 +2026-04-10 20:32:23.660149: val_loss -0.1299 +2026-04-10 20:32:23.662442: Pseudo dice [0.7456, 0.5362, 0.7131, 0.8209, 0.291, 0.6852, 0.7192] +2026-04-10 20:32:23.665664: Epoch time: 100.8 s +2026-04-10 20:32:24.769343: +2026-04-10 20:32:24.771618: Epoch 328 +2026-04-10 20:32:24.774020: Current learning rate: 0.00926 +2026-04-10 20:34:05.830244: train_loss -0.1503 +2026-04-10 20:34:05.841118: val_loss -0.133 +2026-04-10 20:34:05.844930: Pseudo dice [0.8161, 0.4783, 0.6922, 0.0976, 0.4179, 0.6043, 0.7049] +2026-04-10 20:34:05.848701: Epoch time: 101.06 s +2026-04-10 20:34:06.950695: +2026-04-10 20:34:06.952583: Epoch 329 +2026-04-10 20:34:06.954273: Current learning rate: 0.00926 +2026-04-10 20:35:48.146473: train_loss -0.1454 +2026-04-10 20:35:48.154830: val_loss -0.0917 +2026-04-10 20:35:48.158164: Pseudo dice [0.6468, 0.2037, 0.5303, 0.0001, 0.2761, 0.8427, 0.5923] +2026-04-10 20:35:48.161345: Epoch time: 101.2 s +2026-04-10 20:35:50.147206: +2026-04-10 20:35:50.149020: Epoch 330 +2026-04-10 20:35:50.150766: Current learning rate: 0.00925 +2026-04-10 20:37:31.343186: train_loss -0.1627 +2026-04-10 20:37:31.349945: val_loss -0.122 +2026-04-10 20:37:31.353302: Pseudo dice [0.7704, 0.0467, 0.6346, 0.0233, 0.3621, 0.7485, 0.8371] +2026-04-10 20:37:31.355654: Epoch time: 101.2 s +2026-04-10 20:37:32.449243: +2026-04-10 20:37:32.451142: Epoch 331 +2026-04-10 20:37:32.453079: Current learning rate: 0.00925 +2026-04-10 20:39:13.966934: train_loss -0.164 +2026-04-10 20:39:13.974053: val_loss -0.1235 +2026-04-10 20:39:13.976600: Pseudo dice [0.6397, 0.4592, 0.6453, 0.7301, 0.3812, 0.6448, 0.8309] +2026-04-10 20:39:13.978908: Epoch time: 101.52 s +2026-04-10 20:39:15.082700: +2026-04-10 20:39:15.084991: Epoch 332 +2026-04-10 20:39:15.087122: Current learning rate: 0.00925 +2026-04-10 20:40:55.708487: train_loss -0.1571 +2026-04-10 20:40:55.714370: val_loss -0.1417 +2026-04-10 20:40:55.717077: Pseudo dice [0.4858, 0.1947, 0.6936, 0.1801, 0.2506, 0.7316, 0.8807] +2026-04-10 20:40:55.720927: Epoch time: 100.63 s +2026-04-10 20:40:56.837607: +2026-04-10 20:40:56.840407: Epoch 333 +2026-04-10 20:40:56.842087: Current learning rate: 0.00925 +2026-04-10 20:42:37.288435: train_loss -0.1521 +2026-04-10 20:42:37.294369: val_loss -0.133 +2026-04-10 20:42:37.297198: Pseudo dice [0.7751, 0.5211, 0.7193, 0.0128, 0.3223, 0.4149, 0.8314] +2026-04-10 20:42:37.299769: Epoch time: 100.45 s +2026-04-10 20:42:38.424730: +2026-04-10 20:42:38.426924: Epoch 334 +2026-04-10 20:42:38.429131: Current learning rate: 0.00925 +2026-04-10 20:44:19.630034: train_loss -0.1389 +2026-04-10 20:44:19.637415: val_loss -0.1131 +2026-04-10 20:44:19.639791: Pseudo dice [0.7049, 0.4056, 0.7277, 0.0017, 0.2377, 0.3175, 0.6534] +2026-04-10 20:44:19.641956: Epoch time: 101.21 s +2026-04-10 20:44:20.777962: +2026-04-10 20:44:20.780352: Epoch 335 +2026-04-10 20:44:20.782583: Current learning rate: 0.00924 +2026-04-10 20:46:02.874665: train_loss -0.1537 +2026-04-10 20:46:02.881373: val_loss -0.1308 +2026-04-10 20:46:02.883443: Pseudo dice [0.3622, 0.2166, 0.676, 0.0017, 0.2665, 0.6307, 0.7118] +2026-04-10 20:46:02.886135: Epoch time: 102.1 s +2026-04-10 20:46:04.026873: +2026-04-10 20:46:04.030861: Epoch 336 +2026-04-10 20:46:04.033946: Current learning rate: 0.00924 +2026-04-10 20:47:45.496115: train_loss -0.1463 +2026-04-10 20:47:45.504384: val_loss -0.1628 +2026-04-10 20:47:45.507040: Pseudo dice [0.5489, 0.126, 0.8221, 0.6934, 0.5449, 0.7621, 0.7998] +2026-04-10 20:47:45.510207: Epoch time: 101.47 s +2026-04-10 20:47:46.642572: +2026-04-10 20:47:46.645669: Epoch 337 +2026-04-10 20:47:46.648402: Current learning rate: 0.00924 +2026-04-10 20:49:27.534001: train_loss -0.1677 +2026-04-10 20:49:27.539607: val_loss -0.1246 +2026-04-10 20:49:27.542212: Pseudo dice [0.4485, 0.6166, 0.7173, 0.0652, 0.4119, 0.3536, 0.4409] +2026-04-10 20:49:27.545696: Epoch time: 100.89 s +2026-04-10 20:49:28.703836: +2026-04-10 20:49:28.705913: Epoch 338 +2026-04-10 20:49:28.709111: Current learning rate: 0.00924 +2026-04-10 20:51:09.513940: train_loss -0.1652 +2026-04-10 20:51:09.521556: val_loss -0.069 +2026-04-10 20:51:09.524697: Pseudo dice [0.6916, 0.3859, 0.4475, 0.0081, 0.2684, 0.7274, 0.5753] +2026-04-10 20:51:09.527795: Epoch time: 100.81 s +2026-04-10 20:51:10.652984: +2026-04-10 20:51:10.656334: Epoch 339 +2026-04-10 20:51:10.658904: Current learning rate: 0.00923 +2026-04-10 20:52:52.331254: train_loss -0.1658 +2026-04-10 20:52:52.342808: val_loss -0.1015 +2026-04-10 20:52:52.346486: Pseudo dice [0.7284, 0.424, 0.7821, 0.0507, 0.4601, 0.7434, 0.681] +2026-04-10 20:52:52.349058: Epoch time: 101.68 s +2026-04-10 20:52:53.460087: +2026-04-10 20:52:53.463107: Epoch 340 +2026-04-10 20:52:53.465450: Current learning rate: 0.00923 +2026-04-10 20:54:34.707990: train_loss -0.1759 +2026-04-10 20:54:34.714893: val_loss -0.1273 +2026-04-10 20:54:34.717125: Pseudo dice [0.4198, 0.1822, 0.5788, 0.0812, 0.4393, 0.2848, 0.8761] +2026-04-10 20:54:34.719425: Epoch time: 101.25 s +2026-04-10 20:54:35.846465: +2026-04-10 20:54:35.848839: Epoch 341 +2026-04-10 20:54:35.850772: Current learning rate: 0.00923 +2026-04-10 20:56:17.988785: train_loss -0.1665 +2026-04-10 20:56:17.998168: val_loss -0.1472 +2026-04-10 20:56:18.000969: Pseudo dice [0.6836, 0.6024, 0.5792, 0.0, 0.4003, 0.8775, 0.6601] +2026-04-10 20:56:18.003548: Epoch time: 102.15 s +2026-04-10 20:56:19.109953: +2026-04-10 20:56:19.112740: Epoch 342 +2026-04-10 20:56:19.119343: Current learning rate: 0.00923 +2026-04-10 20:58:00.459999: train_loss -0.1735 +2026-04-10 20:58:00.467118: val_loss -0.127 +2026-04-10 20:58:00.470335: Pseudo dice [0.508, 0.3113, 0.7485, 0.0083, 0.4941, 0.7758, 0.6747] +2026-04-10 20:58:00.473588: Epoch time: 101.35 s +2026-04-10 20:58:01.590629: +2026-04-10 20:58:01.594495: Epoch 343 +2026-04-10 20:58:01.598557: Current learning rate: 0.00922 +2026-04-10 20:59:42.915304: train_loss -0.1589 +2026-04-10 20:59:42.922850: val_loss -0.1587 +2026-04-10 20:59:42.925323: Pseudo dice [0.6342, 0.471, 0.7217, 0.2743, 0.3352, 0.6763, 0.8314] +2026-04-10 20:59:42.929229: Epoch time: 101.33 s +2026-04-10 20:59:44.052670: +2026-04-10 20:59:44.054780: Epoch 344 +2026-04-10 20:59:44.057566: Current learning rate: 0.00922 +2026-04-10 21:01:25.734904: train_loss -0.1659 +2026-04-10 21:01:25.744468: val_loss -0.1235 +2026-04-10 21:01:25.748457: Pseudo dice [0.3461, 0.3465, 0.3469, 0.0066, 0.3607, 0.7779, 0.7724] +2026-04-10 21:01:25.750929: Epoch time: 101.69 s +2026-04-10 21:01:26.918638: +2026-04-10 21:01:26.921426: Epoch 345 +2026-04-10 21:01:26.923648: Current learning rate: 0.00922 +2026-04-10 21:03:08.688842: train_loss -0.1495 +2026-04-10 21:03:08.694736: val_loss -0.1311 +2026-04-10 21:03:08.697478: Pseudo dice [0.3193, 0.5264, 0.5441, 0.3619, 0.3789, 0.6371, 0.6411] +2026-04-10 21:03:08.699837: Epoch time: 101.77 s +2026-04-10 21:03:09.829112: +2026-04-10 21:03:09.831465: Epoch 346 +2026-04-10 21:03:09.833381: Current learning rate: 0.00922 +2026-04-10 21:04:51.015591: train_loss -0.1631 +2026-04-10 21:04:51.023099: val_loss -0.1059 +2026-04-10 21:04:51.026236: Pseudo dice [0.5835, 0.4747, 0.5738, 0.0443, 0.4787, 0.6598, 0.8509] +2026-04-10 21:04:51.028586: Epoch time: 101.19 s +2026-04-10 21:04:52.145390: +2026-04-10 21:04:52.147405: Epoch 347 +2026-04-10 21:04:52.149179: Current learning rate: 0.00922 +2026-04-10 21:06:34.789078: train_loss -0.1618 +2026-04-10 21:06:34.801383: val_loss -0.1221 +2026-04-10 21:06:34.803978: Pseudo dice [0.6467, 0.5263, 0.7386, 0.2125, 0.2394, 0.8073, 0.5249] +2026-04-10 21:06:34.808562: Epoch time: 102.65 s +2026-04-10 21:06:35.942294: +2026-04-10 21:06:35.944037: Epoch 348 +2026-04-10 21:06:35.945778: Current learning rate: 0.00921 +2026-04-10 21:08:17.997377: train_loss -0.1782 +2026-04-10 21:08:18.004639: val_loss -0.1048 +2026-04-10 21:08:18.007029: Pseudo dice [0.4733, 0.6765, 0.6387, 0.0293, 0.3099, 0.6559, 0.7499] +2026-04-10 21:08:18.010148: Epoch time: 102.06 s +2026-04-10 21:08:19.120077: +2026-04-10 21:08:19.122324: Epoch 349 +2026-04-10 21:08:19.124384: Current learning rate: 0.00921 +2026-04-10 21:10:00.216450: train_loss -0.1505 +2026-04-10 21:10:00.223960: val_loss -0.088 +2026-04-10 21:10:00.226483: Pseudo dice [0.4529, 0.0685, 0.6877, 0.0293, 0.3663, 0.4904, 0.6895] +2026-04-10 21:10:00.228858: Epoch time: 101.1 s +2026-04-10 21:10:03.743862: +2026-04-10 21:10:03.746191: Epoch 350 +2026-04-10 21:10:03.747986: Current learning rate: 0.00921 +2026-04-10 21:11:45.661083: train_loss -0.1541 +2026-04-10 21:11:45.667408: val_loss -0.1446 +2026-04-10 21:11:45.671737: Pseudo dice [0.5333, 0.4476, 0.7385, 0.0345, 0.3483, 0.6941, 0.8246] +2026-04-10 21:11:45.675355: Epoch time: 101.92 s +2026-04-10 21:11:46.791609: +2026-04-10 21:11:46.793862: Epoch 351 +2026-04-10 21:11:46.797204: Current learning rate: 0.00921 +2026-04-10 21:13:27.760757: train_loss -0.1415 +2026-04-10 21:13:27.767313: val_loss -0.125 +2026-04-10 21:13:27.770351: Pseudo dice [0.5315, 0.6061, 0.7035, 0.7559, 0.2898, 0.4256, 0.5358] +2026-04-10 21:13:27.775906: Epoch time: 100.97 s +2026-04-10 21:13:28.909376: +2026-04-10 21:13:28.912239: Epoch 352 +2026-04-10 21:13:28.914377: Current learning rate: 0.0092 +2026-04-10 21:15:10.282427: train_loss -0.1641 +2026-04-10 21:15:10.289256: val_loss -0.0641 +2026-04-10 21:15:10.292268: Pseudo dice [0.6822, 0.1741, 0.5158, 0.1189, 0.4216, 0.4089, 0.5651] +2026-04-10 21:15:10.295055: Epoch time: 101.38 s +2026-04-10 21:15:11.411266: +2026-04-10 21:15:11.413846: Epoch 353 +2026-04-10 21:15:11.415719: Current learning rate: 0.0092 +2026-04-10 21:16:54.037430: train_loss -0.1663 +2026-04-10 21:16:54.043366: val_loss -0.1239 +2026-04-10 21:16:54.046458: Pseudo dice [0.5635, 0.4288, 0.5742, 0.0709, 0.2519, 0.8301, 0.6276] +2026-04-10 21:16:54.050440: Epoch time: 102.63 s +2026-04-10 21:16:55.184496: +2026-04-10 21:16:55.186502: Epoch 354 +2026-04-10 21:16:55.188842: Current learning rate: 0.0092 +2026-04-10 21:18:36.699112: train_loss -0.1735 +2026-04-10 21:18:36.706851: val_loss -0.1376 +2026-04-10 21:18:36.711001: Pseudo dice [0.169, 0.4655, 0.813, 0.6804, 0.2985, 0.8141, 0.7684] +2026-04-10 21:18:36.713747: Epoch time: 101.52 s +2026-04-10 21:18:37.840787: +2026-04-10 21:18:37.842884: Epoch 355 +2026-04-10 21:18:37.844508: Current learning rate: 0.0092 +2026-04-10 21:20:22.661530: train_loss -0.1603 +2026-04-10 21:20:22.668939: val_loss -0.1402 +2026-04-10 21:20:22.674498: Pseudo dice [0.3423, 0.5259, 0.7485, 0.0, 0.4655, 0.8041, 0.7965] +2026-04-10 21:20:22.687551: Epoch time: 104.82 s +2026-04-10 21:20:23.862067: +2026-04-10 21:20:23.865396: Epoch 356 +2026-04-10 21:20:23.867531: Current learning rate: 0.0092 +2026-04-10 21:22:05.716101: train_loss -0.1638 +2026-04-10 21:22:05.723127: val_loss -0.1394 +2026-04-10 21:22:05.726813: Pseudo dice [0.47, 0.0739, 0.6873, 0.1703, 0.2958, 0.7428, 0.7872] +2026-04-10 21:22:05.730294: Epoch time: 101.86 s +2026-04-10 21:22:06.852898: +2026-04-10 21:22:06.855065: Epoch 357 +2026-04-10 21:22:06.857502: Current learning rate: 0.00919 +2026-04-10 21:23:51.838222: train_loss -0.166 +2026-04-10 21:23:51.847457: val_loss -0.1184 +2026-04-10 21:23:51.850519: Pseudo dice [0.7973, 0.4539, 0.7431, 0.0367, 0.3119, 0.7318, 0.4369] +2026-04-10 21:23:51.853925: Epoch time: 104.99 s +2026-04-10 21:23:53.016006: +2026-04-10 21:23:53.018005: Epoch 358 +2026-04-10 21:23:53.020690: Current learning rate: 0.00919 +2026-04-10 21:25:34.172321: train_loss -0.1789 +2026-04-10 21:25:34.180089: val_loss -0.1398 +2026-04-10 21:25:34.182516: Pseudo dice [0.8177, 0.1816, 0.8118, 0.3798, 0.2984, 0.8257, 0.8385] +2026-04-10 21:25:34.185893: Epoch time: 101.16 s +2026-04-10 21:25:35.297548: +2026-04-10 21:25:35.301903: Epoch 359 +2026-04-10 21:25:35.304260: Current learning rate: 0.00919 +2026-04-10 21:27:16.274963: train_loss -0.1632 +2026-04-10 21:27:16.280425: val_loss -0.145 +2026-04-10 21:27:16.282694: Pseudo dice [0.3701, 0.6822, 0.7231, 0.243, 0.2751, 0.4013, 0.8393] +2026-04-10 21:27:16.284430: Epoch time: 100.98 s +2026-04-10 21:27:17.415579: +2026-04-10 21:27:17.418455: Epoch 360 +2026-04-10 21:27:17.420665: Current learning rate: 0.00919 +2026-04-10 21:28:59.076543: train_loss -0.1758 +2026-04-10 21:28:59.084740: val_loss -0.081 +2026-04-10 21:28:59.088136: Pseudo dice [0.5289, 0.5325, 0.7687, 0.0152, 0.3557, 0.6854, 0.7027] +2026-04-10 21:28:59.092536: Epoch time: 101.66 s +2026-04-10 21:29:00.226696: +2026-04-10 21:29:00.229418: Epoch 361 +2026-04-10 21:29:00.233143: Current learning rate: 0.00918 +2026-04-10 21:30:41.044024: train_loss -0.1546 +2026-04-10 21:30:41.050266: val_loss -0.1426 +2026-04-10 21:30:41.053149: Pseudo dice [0.722, 0.3336, 0.6212, 0.001, 0.4533, 0.7593, 0.7567] +2026-04-10 21:30:41.057141: Epoch time: 100.82 s +2026-04-10 21:30:42.173621: +2026-04-10 21:30:42.177218: Epoch 362 +2026-04-10 21:30:42.180258: Current learning rate: 0.00918 +2026-04-10 21:32:23.473021: train_loss -0.1494 +2026-04-10 21:32:23.480388: val_loss -0.1174 +2026-04-10 21:32:23.483607: Pseudo dice [0.3381, 0.2874, 0.8279, 0.0263, 0.0808, 0.5177, 0.8153] +2026-04-10 21:32:23.487090: Epoch time: 101.3 s +2026-04-10 21:32:24.581455: +2026-04-10 21:32:24.584948: Epoch 363 +2026-04-10 21:32:24.587439: Current learning rate: 0.00918 +2026-04-10 21:34:06.033835: train_loss -0.166 +2026-04-10 21:34:06.040020: val_loss -0.1435 +2026-04-10 21:34:06.045447: Pseudo dice [0.4312, 0.1135, 0.4043, 0.765, 0.5754, 0.5134, 0.899] +2026-04-10 21:34:06.047915: Epoch time: 101.46 s +2026-04-10 21:34:07.172409: +2026-04-10 21:34:07.175134: Epoch 364 +2026-04-10 21:34:07.178122: Current learning rate: 0.00918 +2026-04-10 21:35:48.496956: train_loss -0.1647 +2026-04-10 21:35:48.502155: val_loss -0.1172 +2026-04-10 21:35:48.505270: Pseudo dice [0.4389, 0.5717, 0.6839, 0.0056, 0.4088, 0.5521, 0.792] +2026-04-10 21:35:48.508725: Epoch time: 101.33 s +2026-04-10 21:35:49.629949: +2026-04-10 21:35:49.632671: Epoch 365 +2026-04-10 21:35:49.634829: Current learning rate: 0.00917 +2026-04-10 21:37:31.402709: train_loss -0.1575 +2026-04-10 21:37:31.408927: val_loss -0.1269 +2026-04-10 21:37:31.411132: Pseudo dice [0.7454, 0.6182, 0.7021, 0.0014, 0.3567, 0.6506, 0.6972] +2026-04-10 21:37:31.417814: Epoch time: 101.78 s +2026-04-10 21:37:32.538825: +2026-04-10 21:37:32.541857: Epoch 366 +2026-04-10 21:37:32.544517: Current learning rate: 0.00917 +2026-04-10 21:39:14.228665: train_loss -0.1655 +2026-04-10 21:39:14.239798: val_loss -0.1109 +2026-04-10 21:39:14.243377: Pseudo dice [0.2235, 0.1875, 0.6623, 0.0056, 0.3749, 0.3948, 0.4446] +2026-04-10 21:39:14.246005: Epoch time: 101.69 s +2026-04-10 21:39:15.370646: +2026-04-10 21:39:15.373293: Epoch 367 +2026-04-10 21:39:15.375339: Current learning rate: 0.00917 +2026-04-10 21:40:56.575909: train_loss -0.1732 +2026-04-10 21:40:56.583083: val_loss -0.1437 +2026-04-10 21:40:56.585804: Pseudo dice [0.1456, 0.5379, 0.7969, 0.1503, 0.4479, 0.8244, 0.864] +2026-04-10 21:40:56.590030: Epoch time: 101.21 s +2026-04-10 21:40:57.726995: +2026-04-10 21:40:57.729557: Epoch 368 +2026-04-10 21:40:57.733262: Current learning rate: 0.00917 +2026-04-10 21:42:39.436218: train_loss -0.1717 +2026-04-10 21:42:39.444229: val_loss -0.1461 +2026-04-10 21:42:39.448703: Pseudo dice [0.7985, 0.6097, 0.5245, 0.2259, 0.4182, 0.8273, 0.847] +2026-04-10 21:42:39.451584: Epoch time: 101.71 s +2026-04-10 21:42:41.447953: +2026-04-10 21:42:41.451468: Epoch 369 +2026-04-10 21:42:41.453393: Current learning rate: 0.00917 +2026-04-10 21:44:22.767182: train_loss -0.1673 +2026-04-10 21:44:22.778243: val_loss -0.1201 +2026-04-10 21:44:22.782532: Pseudo dice [0.4666, 0.437, 0.8033, 0.0037, 0.4465, 0.681, 0.7659] +2026-04-10 21:44:22.785676: Epoch time: 101.32 s +2026-04-10 21:44:23.919307: +2026-04-10 21:44:23.922864: Epoch 370 +2026-04-10 21:44:23.926553: Current learning rate: 0.00916 +2026-04-10 21:46:04.740021: train_loss -0.1592 +2026-04-10 21:46:04.747808: val_loss -0.1317 +2026-04-10 21:46:04.750744: Pseudo dice [0.4364, 0.1898, 0.5508, 0.7891, 0.2793, 0.5029, 0.4366] +2026-04-10 21:46:04.754119: Epoch time: 100.82 s +2026-04-10 21:46:05.864369: +2026-04-10 21:46:05.866685: Epoch 371 +2026-04-10 21:46:05.868403: Current learning rate: 0.00916 +2026-04-10 21:47:47.162664: train_loss -0.1505 +2026-04-10 21:47:47.169982: val_loss -0.1636 +2026-04-10 21:47:47.173075: Pseudo dice [0.7156, 0.3979, 0.7858, 0.3496, 0.2738, 0.8183, 0.8058] +2026-04-10 21:47:47.176258: Epoch time: 101.3 s +2026-04-10 21:47:48.302272: +2026-04-10 21:47:48.304604: Epoch 372 +2026-04-10 21:47:48.307018: Current learning rate: 0.00916 +2026-04-10 21:49:29.940133: train_loss -0.1593 +2026-04-10 21:49:29.949106: val_loss -0.1243 +2026-04-10 21:49:29.953436: Pseudo dice [0.3512, 0.1525, 0.7216, 0.1225, 0.2714, 0.7542, 0.6344] +2026-04-10 21:49:29.957345: Epoch time: 101.64 s +2026-04-10 21:49:31.077966: +2026-04-10 21:49:31.080403: Epoch 373 +2026-04-10 21:49:31.084026: Current learning rate: 0.00916 +2026-04-10 21:51:12.614078: train_loss -0.1564 +2026-04-10 21:51:12.619169: val_loss -0.0901 +2026-04-10 21:51:12.621530: Pseudo dice [0.6585, 0.4711, 0.6846, 0.0208, 0.5197, 0.7295, 0.4743] +2026-04-10 21:51:12.624208: Epoch time: 101.54 s +2026-04-10 21:51:13.757506: +2026-04-10 21:51:13.760822: Epoch 374 +2026-04-10 21:51:13.762901: Current learning rate: 0.00915 +2026-04-10 21:52:55.635070: train_loss -0.1543 +2026-04-10 21:52:55.646554: val_loss -0.1514 +2026-04-10 21:52:55.650239: Pseudo dice [0.5007, 0.5939, 0.8177, 0.0015, 0.228, 0.7647, 0.8604] +2026-04-10 21:52:55.653156: Epoch time: 101.88 s +2026-04-10 21:52:56.767153: +2026-04-10 21:52:56.769381: Epoch 375 +2026-04-10 21:52:56.771565: Current learning rate: 0.00915 +2026-04-10 21:54:37.380528: train_loss -0.164 +2026-04-10 21:54:37.387526: val_loss -0.1353 +2026-04-10 21:54:37.390708: Pseudo dice [0.7505, 0.1691, 0.6102, 0.3448, 0.2169, 0.6452, 0.7672] +2026-04-10 21:54:37.393075: Epoch time: 100.62 s +2026-04-10 21:54:38.507644: +2026-04-10 21:54:38.509877: Epoch 376 +2026-04-10 21:54:38.512570: Current learning rate: 0.00915 +2026-04-10 21:56:20.277079: train_loss -0.1641 +2026-04-10 21:56:20.287146: val_loss -0.0995 +2026-04-10 21:56:20.290068: Pseudo dice [0.4754, 0.5776, 0.6401, 0.0005, 0.4236, 0.7839, 0.633] +2026-04-10 21:56:20.292265: Epoch time: 101.77 s +2026-04-10 21:56:21.404773: +2026-04-10 21:56:21.407469: Epoch 377 +2026-04-10 21:56:21.410089: Current learning rate: 0.00915 +2026-04-10 21:58:03.418943: train_loss -0.1669 +2026-04-10 21:58:03.428934: val_loss -0.1342 +2026-04-10 21:58:03.432142: Pseudo dice [0.6993, 0.3413, 0.6244, 0.6328, 0.3606, 0.2425, 0.5946] +2026-04-10 21:58:03.436026: Epoch time: 102.02 s +2026-04-10 21:58:04.552409: +2026-04-10 21:58:04.556348: Epoch 378 +2026-04-10 21:58:04.560770: Current learning rate: 0.00915 +2026-04-10 21:59:45.326243: train_loss -0.172 +2026-04-10 21:59:45.334739: val_loss -0.105 +2026-04-10 21:59:45.337522: Pseudo dice [0.4577, 0.3032, 0.5334, 0.0016, 0.2717, 0.7166, 0.8981] +2026-04-10 21:59:45.340163: Epoch time: 100.78 s +2026-04-10 21:59:46.454179: +2026-04-10 21:59:46.457994: Epoch 379 +2026-04-10 21:59:46.461230: Current learning rate: 0.00914 +2026-04-10 22:01:27.084966: train_loss -0.1736 +2026-04-10 22:01:27.092093: val_loss -0.0959 +2026-04-10 22:01:27.095697: Pseudo dice [0.6451, 0.2363, 0.6577, 0.1067, 0.4384, 0.7254, 0.5067] +2026-04-10 22:01:27.097931: Epoch time: 100.63 s +2026-04-10 22:01:28.221364: +2026-04-10 22:01:28.225153: Epoch 380 +2026-04-10 22:01:28.228747: Current learning rate: 0.00914 +2026-04-10 22:03:10.446501: train_loss -0.1685 +2026-04-10 22:03:10.454565: val_loss -0.1472 +2026-04-10 22:03:10.457929: Pseudo dice [0.2943, 0.4184, 0.5561, 0.2529, 0.431, 0.7638, 0.8319] +2026-04-10 22:03:10.462050: Epoch time: 102.23 s +2026-04-10 22:03:11.565683: +2026-04-10 22:03:11.568477: Epoch 381 +2026-04-10 22:03:11.570702: Current learning rate: 0.00914 +2026-04-10 22:04:52.913903: train_loss -0.1725 +2026-04-10 22:04:52.920285: val_loss -0.1636 +2026-04-10 22:04:52.922952: Pseudo dice [0.7673, 0.3182, 0.7542, 0.4511, 0.4408, 0.7863, 0.8141] +2026-04-10 22:04:52.925652: Epoch time: 101.35 s +2026-04-10 22:04:54.065034: +2026-04-10 22:04:54.067591: Epoch 382 +2026-04-10 22:04:54.070344: Current learning rate: 0.00914 +2026-04-10 22:06:35.959860: train_loss -0.1587 +2026-04-10 22:06:35.968139: val_loss -0.1628 +2026-04-10 22:06:35.970285: Pseudo dice [0.6135, 0.3218, 0.7119, 0.0156, 0.4628, 0.6986, 0.8693] +2026-04-10 22:06:35.972643: Epoch time: 101.9 s +2026-04-10 22:06:37.084375: +2026-04-10 22:06:37.087345: Epoch 383 +2026-04-10 22:06:37.091283: Current learning rate: 0.00913 +2026-04-10 22:08:20.071657: train_loss -0.1628 +2026-04-10 22:08:20.080003: val_loss -0.155 +2026-04-10 22:08:20.082429: Pseudo dice [0.6863, 0.4233, 0.7363, 0.0, 0.4316, 0.7479, 0.7797] +2026-04-10 22:08:20.086061: Epoch time: 102.99 s +2026-04-10 22:08:21.203987: +2026-04-10 22:08:21.206187: Epoch 384 +2026-04-10 22:08:21.209052: Current learning rate: 0.00913 +2026-04-10 22:10:03.954443: train_loss -0.1525 +2026-04-10 22:10:03.974059: val_loss -0.1372 +2026-04-10 22:10:03.976521: Pseudo dice [0.6649, 0.1963, 0.453, 0.5636, 0.4364, 0.5146, 0.7812] +2026-04-10 22:10:03.979633: Epoch time: 102.75 s +2026-04-10 22:10:05.103546: +2026-04-10 22:10:05.105732: Epoch 385 +2026-04-10 22:10:05.108012: Current learning rate: 0.00913 +2026-04-10 22:11:46.870347: train_loss -0.1537 +2026-04-10 22:11:46.878818: val_loss -0.1254 +2026-04-10 22:11:46.881821: Pseudo dice [0.8122, 0.3884, 0.7526, 0.0153, 0.4692, 0.5254, 0.8249] +2026-04-10 22:11:46.885724: Epoch time: 101.77 s +2026-04-10 22:11:48.027734: +2026-04-10 22:11:48.029813: Epoch 386 +2026-04-10 22:11:48.031431: Current learning rate: 0.00913 +2026-04-10 22:13:28.563374: train_loss -0.1619 +2026-04-10 22:13:28.571198: val_loss -0.1196 +2026-04-10 22:13:28.574961: Pseudo dice [0.2108, 0.5206, 0.6695, 0.5652, 0.2629, 0.5985, 0.6207] +2026-04-10 22:13:28.577006: Epoch time: 100.54 s +2026-04-10 22:13:29.707967: +2026-04-10 22:13:29.710430: Epoch 387 +2026-04-10 22:13:29.713713: Current learning rate: 0.00912 +2026-04-10 22:15:11.347278: train_loss -0.1641 +2026-04-10 22:15:11.355145: val_loss -0.1103 +2026-04-10 22:15:11.359128: Pseudo dice [0.704, 0.4176, 0.6813, 0.0001, 0.4611, 0.6759, 0.6859] +2026-04-10 22:15:11.361739: Epoch time: 101.64 s +2026-04-10 22:15:12.472002: +2026-04-10 22:15:12.474053: Epoch 388 +2026-04-10 22:15:12.476465: Current learning rate: 0.00912 +2026-04-10 22:16:53.913682: train_loss -0.1667 +2026-04-10 22:16:53.925888: val_loss -0.1226 +2026-04-10 22:16:53.928738: Pseudo dice [0.4146, 0.4899, 0.5224, 0.1152, 0.3234, 0.5361, 0.818] +2026-04-10 22:16:53.931337: Epoch time: 101.44 s +2026-04-10 22:16:55.954076: +2026-04-10 22:16:55.957016: Epoch 389 +2026-04-10 22:16:55.959508: Current learning rate: 0.00912 +2026-04-10 22:18:37.012002: train_loss -0.1772 +2026-04-10 22:18:37.019089: val_loss -0.1239 +2026-04-10 22:18:37.021475: Pseudo dice [0.5093, 0.3591, 0.6494, 0.7877, 0.4131, 0.7248, 0.531] +2026-04-10 22:18:37.023895: Epoch time: 101.06 s +2026-04-10 22:18:38.171388: +2026-04-10 22:18:38.174291: Epoch 390 +2026-04-10 22:18:38.177105: Current learning rate: 0.00912 +2026-04-10 22:20:19.677858: train_loss -0.1616 +2026-04-10 22:20:19.683203: val_loss -0.147 +2026-04-10 22:20:19.685098: Pseudo dice [0.465, 0.2522, 0.592, 0.07, 0.4071, 0.8177, 0.7707] +2026-04-10 22:20:19.687585: Epoch time: 101.51 s +2026-04-10 22:20:20.806088: +2026-04-10 22:20:20.808415: Epoch 391 +2026-04-10 22:20:20.810510: Current learning rate: 0.00912 +2026-04-10 22:22:02.460113: train_loss -0.1516 +2026-04-10 22:22:02.466260: val_loss -0.1564 +2026-04-10 22:22:02.468859: Pseudo dice [0.6355, 0.6128, 0.7391, 0.3097, 0.4224, 0.5562, 0.8761] +2026-04-10 22:22:02.470881: Epoch time: 101.66 s +2026-04-10 22:22:03.610232: +2026-04-10 22:22:03.612510: Epoch 392 +2026-04-10 22:22:03.614623: Current learning rate: 0.00911 +2026-04-10 22:23:44.903380: train_loss -0.1715 +2026-04-10 22:23:44.914579: val_loss -0.1439 +2026-04-10 22:23:44.919725: Pseudo dice [0.5637, 0.4707, 0.6715, 0.001, 0.4455, 0.7569, 0.6417] +2026-04-10 22:23:44.923402: Epoch time: 101.3 s +2026-04-10 22:23:46.040978: +2026-04-10 22:23:46.042985: Epoch 393 +2026-04-10 22:23:46.044502: Current learning rate: 0.00911 +2026-04-10 22:25:27.751380: train_loss -0.177 +2026-04-10 22:25:27.761852: val_loss -0.1599 +2026-04-10 22:25:27.764617: Pseudo dice [0.7777, 0.7794, 0.6872, 0.3547, 0.3093, 0.7979, 0.835] +2026-04-10 22:25:27.767478: Epoch time: 101.71 s +2026-04-10 22:25:27.771033: Yayy! New best EMA pseudo Dice: 0.5321 +2026-04-10 22:25:30.596003: +2026-04-10 22:25:30.599012: Epoch 394 +2026-04-10 22:25:30.601061: Current learning rate: 0.00911 +2026-04-10 22:27:12.878736: train_loss -0.1727 +2026-04-10 22:27:12.886081: val_loss -0.1244 +2026-04-10 22:27:12.887891: Pseudo dice [0.4394, 0.5249, 0.7322, 0.0067, 0.2569, 0.7302, 0.8947] +2026-04-10 22:27:12.889957: Epoch time: 102.29 s +2026-04-10 22:27:14.017498: +2026-04-10 22:27:14.019603: Epoch 395 +2026-04-10 22:27:14.022377: Current learning rate: 0.00911 +2026-04-10 22:28:56.445631: train_loss -0.1698 +2026-04-10 22:28:56.452719: val_loss -0.1162 +2026-04-10 22:28:56.455163: Pseudo dice [0.2535, 0.5012, 0.5434, 0.0289, 0.328, 0.8377, 0.6877] +2026-04-10 22:28:56.458570: Epoch time: 102.43 s +2026-04-10 22:28:57.592164: +2026-04-10 22:28:57.594993: Epoch 396 +2026-04-10 22:28:57.597832: Current learning rate: 0.0091 +2026-04-10 22:30:39.550268: train_loss -0.1683 +2026-04-10 22:30:39.563761: val_loss -0.1353 +2026-04-10 22:30:39.567548: Pseudo dice [0.6513, 0.3293, 0.8052, 0.0979, 0.407, 0.6936, 0.6976] +2026-04-10 22:30:39.570129: Epoch time: 101.96 s +2026-04-10 22:30:40.691923: +2026-04-10 22:30:40.694359: Epoch 397 +2026-04-10 22:30:40.697257: Current learning rate: 0.0091 +2026-04-10 22:32:22.734915: train_loss -0.1731 +2026-04-10 22:32:22.741615: val_loss -0.1081 +2026-04-10 22:32:22.744846: Pseudo dice [0.7648, 0.5202, 0.7492, 0.0005, 0.5423, 0.6479, 0.709] +2026-04-10 22:32:22.747666: Epoch time: 102.05 s +2026-04-10 22:32:23.871053: +2026-04-10 22:32:23.873522: Epoch 398 +2026-04-10 22:32:23.875975: Current learning rate: 0.0091 +2026-04-10 22:34:06.865120: train_loss -0.1614 +2026-04-10 22:34:06.877731: val_loss -0.1308 +2026-04-10 22:34:06.882596: Pseudo dice [0.274, 0.3658, 0.8512, 0.5043, 0.3419, 0.7169, 0.6282] +2026-04-10 22:34:06.888932: Epoch time: 103.0 s +2026-04-10 22:34:08.007790: +2026-04-10 22:34:08.011192: Epoch 399 +2026-04-10 22:34:08.015655: Current learning rate: 0.0091 +2026-04-10 22:35:50.389222: train_loss -0.1281 +2026-04-10 22:35:50.397595: val_loss -0.0681 +2026-04-10 22:35:50.401077: Pseudo dice [0.4549, 0.3536, 0.144, 0.0023, 0.2885, 0.2375, 0.6421] +2026-04-10 22:35:50.404067: Epoch time: 102.38 s +2026-04-10 22:35:53.292514: +2026-04-10 22:35:53.299725: Epoch 400 +2026-04-10 22:35:53.305139: Current learning rate: 0.0091 +2026-04-10 22:37:35.900311: train_loss -0.1469 +2026-04-10 22:37:35.908878: val_loss -0.1249 +2026-04-10 22:37:35.913363: Pseudo dice [0.5783, 0.21, 0.6436, 0.0135, 0.414, 0.3382, 0.6085] +2026-04-10 22:37:35.916158: Epoch time: 102.61 s +2026-04-10 22:37:37.041998: +2026-04-10 22:37:37.044765: Epoch 401 +2026-04-10 22:37:37.047189: Current learning rate: 0.00909 +2026-04-10 22:39:20.023695: train_loss -0.1591 +2026-04-10 22:39:20.032620: val_loss -0.1627 +2026-04-10 22:39:20.036248: Pseudo dice [0.4506, 0.5452, 0.7822, 0.194, 0.541, 0.7969, 0.7654] +2026-04-10 22:39:20.039853: Epoch time: 102.99 s +2026-04-10 22:39:21.185432: +2026-04-10 22:39:21.192290: Epoch 402 +2026-04-10 22:39:21.197495: Current learning rate: 0.00909 +2026-04-10 22:41:03.988029: train_loss -0.1772 +2026-04-10 22:41:03.995327: val_loss -0.1488 +2026-04-10 22:41:03.997252: Pseudo dice [0.7461, 0.131, 0.7962, 0.0027, 0.381, 0.6925, 0.8684] +2026-04-10 22:41:03.999783: Epoch time: 102.81 s +2026-04-10 22:41:05.150940: +2026-04-10 22:41:05.153772: Epoch 403 +2026-04-10 22:41:05.155638: Current learning rate: 0.00909 +2026-04-10 22:42:47.571476: train_loss -0.1605 +2026-04-10 22:42:47.578030: val_loss -0.1498 +2026-04-10 22:42:47.580585: Pseudo dice [0.8405, 0.2316, 0.7534, 0.0, 0.3818, 0.8126, 0.8634] +2026-04-10 22:42:47.583251: Epoch time: 102.42 s +2026-04-10 22:42:48.705590: +2026-04-10 22:42:48.708654: Epoch 404 +2026-04-10 22:42:48.711374: Current learning rate: 0.00909 +2026-04-10 22:44:30.278127: train_loss -0.1533 +2026-04-10 22:44:30.288870: val_loss -0.1147 +2026-04-10 22:44:30.291417: Pseudo dice [0.4851, 0.5496, 0.7693, 0.2976, 0.3329, 0.4216, 0.6202] +2026-04-10 22:44:30.296470: Epoch time: 101.58 s +2026-04-10 22:44:31.431292: +2026-04-10 22:44:31.433691: Epoch 405 +2026-04-10 22:44:31.437375: Current learning rate: 0.00908 +2026-04-10 22:46:12.066596: train_loss -0.1796 +2026-04-10 22:46:12.073938: val_loss -0.1226 +2026-04-10 22:46:12.076064: Pseudo dice [0.6669, 0.3937, 0.6846, 0.0081, 0.518, 0.7998, 0.7658] +2026-04-10 22:46:12.078874: Epoch time: 100.64 s +2026-04-10 22:46:13.188089: +2026-04-10 22:46:13.190972: Epoch 406 +2026-04-10 22:46:13.193776: Current learning rate: 0.00908 +2026-04-10 22:47:54.306274: train_loss -0.1653 +2026-04-10 22:47:54.313869: val_loss -0.1376 +2026-04-10 22:47:54.316674: Pseudo dice [0.7421, 0.1692, 0.758, 0.3737, 0.4911, 0.3186, 0.7676] +2026-04-10 22:47:54.319316: Epoch time: 101.12 s +2026-04-10 22:47:55.435639: +2026-04-10 22:47:55.437809: Epoch 407 +2026-04-10 22:47:55.440001: Current learning rate: 0.00908 +2026-04-10 22:49:37.045201: train_loss -0.1706 +2026-04-10 22:49:37.052418: val_loss -0.1539 +2026-04-10 22:49:37.054726: Pseudo dice [0.6263, 0.7074, 0.7011, 0.2242, 0.4254, 0.7561, 0.7622] +2026-04-10 22:49:37.057169: Epoch time: 101.61 s +2026-04-10 22:49:39.054152: +2026-04-10 22:49:39.056646: Epoch 408 +2026-04-10 22:49:39.058781: Current learning rate: 0.00908 +2026-04-10 22:51:20.179048: train_loss -0.1757 +2026-04-10 22:51:20.186655: val_loss -0.1261 +2026-04-10 22:51:20.189069: Pseudo dice [0.5026, 0.3162, 0.5913, 0.6782, 0.3063, 0.6266, 0.7142] +2026-04-10 22:51:20.191719: Epoch time: 101.13 s +2026-04-10 22:51:21.305598: +2026-04-10 22:51:21.309085: Epoch 409 +2026-04-10 22:51:21.311574: Current learning rate: 0.00907 +2026-04-10 22:53:02.241740: train_loss -0.1669 +2026-04-10 22:53:02.247842: val_loss -0.1123 +2026-04-10 22:53:02.250044: Pseudo dice [0.2104, 0.6493, 0.4827, 0.0704, 0.435, 0.7624, 0.748] +2026-04-10 22:53:02.251754: Epoch time: 100.94 s +2026-04-10 22:53:03.380911: +2026-04-10 22:53:03.383300: Epoch 410 +2026-04-10 22:53:03.385384: Current learning rate: 0.00907 +2026-04-10 22:54:44.212253: train_loss -0.1734 +2026-04-10 22:54:44.224374: val_loss -0.1401 +2026-04-10 22:54:44.227901: Pseudo dice [0.7903, 0.5668, 0.6691, 0.1317, 0.3401, 0.4315, 0.7001] +2026-04-10 22:54:44.232187: Epoch time: 100.83 s +2026-04-10 22:54:45.291333: +2026-04-10 22:54:45.295681: Epoch 411 +2026-04-10 22:54:45.298820: Current learning rate: 0.00907 +2026-04-10 22:56:26.876880: train_loss -0.1579 +2026-04-10 22:56:26.883323: val_loss -0.1123 +2026-04-10 22:56:26.886886: Pseudo dice [0.4378, 0.3912, 0.5711, 0.0271, 0.1859, 0.6722, 0.7808] +2026-04-10 22:56:26.888795: Epoch time: 101.59 s +2026-04-10 22:56:27.936790: +2026-04-10 22:56:27.939559: Epoch 412 +2026-04-10 22:56:27.941914: Current learning rate: 0.00907 +2026-04-10 22:58:09.882318: train_loss -0.1875 +2026-04-10 22:58:09.889803: val_loss -0.1377 +2026-04-10 22:58:09.892299: Pseudo dice [0.7441, 0.5519, 0.631, 0.1992, 0.4077, 0.7943, 0.8291] +2026-04-10 22:58:09.896363: Epoch time: 101.95 s +2026-04-10 22:58:10.955814: +2026-04-10 22:58:10.959061: Epoch 413 +2026-04-10 22:58:10.961322: Current learning rate: 0.00907 +2026-04-10 22:59:53.002683: train_loss -0.1628 +2026-04-10 22:59:53.011885: val_loss -0.1386 +2026-04-10 22:59:53.014744: Pseudo dice [0.6748, 0.4807, 0.7959, 0.3781, 0.2461, 0.7197, 0.4686] +2026-04-10 22:59:53.017982: Epoch time: 102.05 s +2026-04-10 22:59:54.081811: +2026-04-10 22:59:54.084464: Epoch 414 +2026-04-10 22:59:54.087102: Current learning rate: 0.00906 +2026-04-10 23:01:35.604890: train_loss -0.1595 +2026-04-10 23:01:35.611782: val_loss -0.1212 +2026-04-10 23:01:35.616261: Pseudo dice [0.639, 0.3969, 0.4198, 0.0001, 0.4187, 0.6476, 0.8251] +2026-04-10 23:01:35.619013: Epoch time: 101.53 s +2026-04-10 23:01:36.703555: +2026-04-10 23:01:36.706104: Epoch 415 +2026-04-10 23:01:36.708059: Current learning rate: 0.00906 +2026-04-10 23:03:17.806526: train_loss -0.1602 +2026-04-10 23:03:17.813365: val_loss -0.121 +2026-04-10 23:03:17.817277: Pseudo dice [0.494, 0.3698, 0.5231, 0.1705, 0.3931, 0.2137, 0.6729] +2026-04-10 23:03:17.821009: Epoch time: 101.11 s +2026-04-10 23:03:18.864336: +2026-04-10 23:03:18.867231: Epoch 416 +2026-04-10 23:03:18.870157: Current learning rate: 0.00906 +2026-04-10 23:05:01.620272: train_loss -0.1589 +2026-04-10 23:05:01.626327: val_loss -0.1087 +2026-04-10 23:05:01.628345: Pseudo dice [0.6483, 0.3319, 0.5904, 0.0004, 0.5217, 0.7063, 0.7289] +2026-04-10 23:05:01.631403: Epoch time: 102.76 s +2026-04-10 23:05:02.701926: +2026-04-10 23:05:02.707042: Epoch 417 +2026-04-10 23:05:02.711026: Current learning rate: 0.00906 +2026-04-10 23:06:44.649419: train_loss -0.1454 +2026-04-10 23:06:44.660914: val_loss -0.1406 +2026-04-10 23:06:44.664586: Pseudo dice [0.1118, 0.0831, 0.7912, 0.6662, 0.364, 0.5704, 0.8689] +2026-04-10 23:06:44.668702: Epoch time: 101.95 s +2026-04-10 23:06:45.745199: +2026-04-10 23:06:45.747365: Epoch 418 +2026-04-10 23:06:45.749809: Current learning rate: 0.00905 +2026-04-10 23:08:27.088238: train_loss -0.1635 +2026-04-10 23:08:27.095115: val_loss -0.1485 +2026-04-10 23:08:27.098117: Pseudo dice [0.5751, 0.6386, 0.7674, 0.0669, 0.4776, 0.629, 0.7969] +2026-04-10 23:08:27.101078: Epoch time: 101.35 s +2026-04-10 23:08:28.159222: +2026-04-10 23:08:28.162274: Epoch 419 +2026-04-10 23:08:28.164802: Current learning rate: 0.00905 +2026-04-10 23:10:09.852059: train_loss -0.1625 +2026-04-10 23:10:09.858653: val_loss -0.1561 +2026-04-10 23:10:09.861058: Pseudo dice [0.4431, 0.7292, 0.7563, 0.4841, 0.3614, 0.7224, 0.7926] +2026-04-10 23:10:09.864247: Epoch time: 101.7 s +2026-04-10 23:10:10.945921: +2026-04-10 23:10:10.948517: Epoch 420 +2026-04-10 23:10:10.952472: Current learning rate: 0.00905 +2026-04-10 23:11:52.067645: train_loss -0.163 +2026-04-10 23:11:52.073960: val_loss -0.1357 +2026-04-10 23:11:52.077444: Pseudo dice [0.6149, 0.2696, 0.7978, 0.366, 0.2043, 0.8236, 0.5517] +2026-04-10 23:11:52.079664: Epoch time: 101.13 s +2026-04-10 23:11:53.153846: +2026-04-10 23:11:53.155687: Epoch 421 +2026-04-10 23:11:53.157706: Current learning rate: 0.00905 +2026-04-10 23:13:34.278469: train_loss -0.1623 +2026-04-10 23:13:34.284966: val_loss -0.1601 +2026-04-10 23:13:34.288266: Pseudo dice [0.8186, 0.4094, 0.7619, 0.5878, 0.6266, 0.6076, 0.5697] +2026-04-10 23:13:34.291750: Epoch time: 101.13 s +2026-04-10 23:13:35.355481: +2026-04-10 23:13:35.358548: Epoch 422 +2026-04-10 23:13:35.361711: Current learning rate: 0.00905 +2026-04-10 23:15:15.787697: train_loss -0.1737 +2026-04-10 23:15:15.795721: val_loss -0.1398 +2026-04-10 23:15:15.798286: Pseudo dice [0.4561, 0.6607, 0.7695, 0.2762, 0.2584, 0.7193, 0.6824] +2026-04-10 23:15:15.800653: Epoch time: 100.44 s +2026-04-10 23:15:15.802831: Yayy! New best EMA pseudo Dice: 0.5322 +2026-04-10 23:15:18.507600: +2026-04-10 23:15:18.509686: Epoch 423 +2026-04-10 23:15:18.512093: Current learning rate: 0.00904 +2026-04-10 23:16:59.721965: train_loss -0.1659 +2026-04-10 23:16:59.728543: val_loss -0.1106 +2026-04-10 23:16:59.730742: Pseudo dice [0.5998, 0.4642, 0.746, 0.0094, 0.4566, 0.5659, 0.5489] +2026-04-10 23:16:59.733180: Epoch time: 101.22 s +2026-04-10 23:17:00.838285: +2026-04-10 23:17:00.840706: Epoch 424 +2026-04-10 23:17:00.842581: Current learning rate: 0.00904 +2026-04-10 23:18:41.370439: train_loss -0.1742 +2026-04-10 23:18:41.377762: val_loss -0.1706 +2026-04-10 23:18:41.380129: Pseudo dice [0.5188, 0.3772, 0.684, 0.4192, 0.3847, 0.6664, 0.8911] +2026-04-10 23:18:41.382655: Epoch time: 100.54 s +2026-04-10 23:18:42.440982: +2026-04-10 23:18:42.442733: Epoch 425 +2026-04-10 23:18:42.444477: Current learning rate: 0.00904 +2026-04-10 23:20:23.975586: train_loss -0.186 +2026-04-10 23:20:23.982331: val_loss -0.1235 +2026-04-10 23:20:23.985006: Pseudo dice [0.8, 0.2373, 0.7818, 0.2611, 0.3546, 0.5728, 0.7014] +2026-04-10 23:20:23.988467: Epoch time: 101.54 s +2026-04-10 23:20:25.052691: +2026-04-10 23:20:25.054677: Epoch 426 +2026-04-10 23:20:25.058952: Current learning rate: 0.00904 +2026-04-10 23:22:06.419197: train_loss -0.1749 +2026-04-10 23:22:06.425342: val_loss -0.1527 +2026-04-10 23:22:06.429848: Pseudo dice [0.7132, 0.2877, 0.7044, 0.4775, 0.4726, 0.6289, 0.8142] +2026-04-10 23:22:06.432222: Epoch time: 101.37 s +2026-04-10 23:22:06.434756: Yayy! New best EMA pseudo Dice: 0.5363 +2026-04-10 23:22:09.150703: +2026-04-10 23:22:09.153259: Epoch 427 +2026-04-10 23:22:09.155658: Current learning rate: 0.00903 +2026-04-10 23:23:50.982059: train_loss -0.1831 +2026-04-10 23:23:50.988972: val_loss -0.1519 +2026-04-10 23:23:50.991505: Pseudo dice [0.5712, 0.2671, 0.7283, 0.1658, 0.2678, 0.755, 0.7365] +2026-04-10 23:23:50.993893: Epoch time: 101.83 s +2026-04-10 23:23:52.074016: +2026-04-10 23:23:52.076165: Epoch 428 +2026-04-10 23:23:52.078442: Current learning rate: 0.00903 +2026-04-10 23:25:33.285051: train_loss -0.1787 +2026-04-10 23:25:33.290876: val_loss -0.1418 +2026-04-10 23:25:33.293280: Pseudo dice [0.8212, 0.5031, 0.4849, 0.0003, 0.3352, 0.7392, 0.7282] +2026-04-10 23:25:33.295053: Epoch time: 101.21 s +2026-04-10 23:25:34.367188: +2026-04-10 23:25:34.369426: Epoch 429 +2026-04-10 23:25:34.371190: Current learning rate: 0.00903 +2026-04-10 23:27:15.817168: train_loss -0.1745 +2026-04-10 23:27:15.824664: val_loss -0.1421 +2026-04-10 23:27:15.828722: Pseudo dice [0.7623, 0.2474, 0.6535, 0.7503, 0.3364, 0.2591, 0.833] +2026-04-10 23:27:15.831593: Epoch time: 101.45 s +2026-04-10 23:27:16.917051: +2026-04-10 23:27:16.919405: Epoch 430 +2026-04-10 23:27:16.921234: Current learning rate: 0.00903 +2026-04-10 23:28:57.608509: train_loss -0.1545 +2026-04-10 23:28:57.615745: val_loss -0.1074 +2026-04-10 23:28:57.617915: Pseudo dice [0.6737, 0.5087, 0.6977, 0.0001, 0.3009, 0.7074, 0.8399] +2026-04-10 23:28:57.620307: Epoch time: 100.69 s +2026-04-10 23:28:58.694391: +2026-04-10 23:28:58.696884: Epoch 431 +2026-04-10 23:28:58.699304: Current learning rate: 0.00902 +2026-04-10 23:30:39.439405: train_loss -0.173 +2026-04-10 23:30:39.445119: val_loss -0.1229 +2026-04-10 23:30:39.448193: Pseudo dice [0.6776, 0.2772, 0.5696, 0.007, 0.326, 0.2712, 0.742] +2026-04-10 23:30:39.450877: Epoch time: 100.75 s +2026-04-10 23:30:40.512646: +2026-04-10 23:30:40.514439: Epoch 432 +2026-04-10 23:30:40.517716: Current learning rate: 0.00902 +2026-04-10 23:32:22.758968: train_loss -0.1615 +2026-04-10 23:32:22.766809: val_loss -0.1305 +2026-04-10 23:32:22.768867: Pseudo dice [0.384, 0.4515, 0.6585, 0.0346, 0.2543, 0.8839, 0.7114] +2026-04-10 23:32:22.771971: Epoch time: 102.25 s +2026-04-10 23:32:23.883323: +2026-04-10 23:32:23.885701: Epoch 433 +2026-04-10 23:32:23.887689: Current learning rate: 0.00902 +2026-04-10 23:34:06.881292: train_loss -0.1695 +2026-04-10 23:34:06.888997: val_loss -0.1384 +2026-04-10 23:34:06.892232: Pseudo dice [0.3988, 0.3935, 0.7733, 0.5839, 0.3124, 0.7557, 0.8084] +2026-04-10 23:34:06.894548: Epoch time: 103.0 s +2026-04-10 23:34:07.974586: +2026-04-10 23:34:07.976722: Epoch 434 +2026-04-10 23:34:07.980064: Current learning rate: 0.00902 +2026-04-10 23:35:49.141007: train_loss -0.1718 +2026-04-10 23:35:49.151844: val_loss -0.1223 +2026-04-10 23:35:49.154606: Pseudo dice [0.6019, 0.5232, 0.6467, 0.0187, 0.3641, 0.7432, 0.7989] +2026-04-10 23:35:49.157413: Epoch time: 101.17 s +2026-04-10 23:35:50.232213: +2026-04-10 23:35:50.235224: Epoch 435 +2026-04-10 23:35:50.237493: Current learning rate: 0.00902 +2026-04-10 23:37:30.843052: train_loss -0.179 +2026-04-10 23:37:30.849212: val_loss -0.1438 +2026-04-10 23:37:30.851077: Pseudo dice [0.3519, 0.3552, 0.4517, 0.0529, 0.4574, 0.6408, 0.6914] +2026-04-10 23:37:30.853727: Epoch time: 100.61 s +2026-04-10 23:37:31.918844: +2026-04-10 23:37:31.920870: Epoch 436 +2026-04-10 23:37:31.923035: Current learning rate: 0.00901 +2026-04-10 23:39:13.314770: train_loss -0.154 +2026-04-10 23:39:13.324900: val_loss -0.0958 +2026-04-10 23:39:13.334101: Pseudo dice [0.558, 0.544, 0.5828, 0.0008, 0.3137, 0.7619, 0.6006] +2026-04-10 23:39:13.336829: Epoch time: 101.4 s +2026-04-10 23:39:14.439753: +2026-04-10 23:39:14.443803: Epoch 437 +2026-04-10 23:39:14.445975: Current learning rate: 0.00901 +2026-04-10 23:40:54.539364: train_loss -0.1686 +2026-04-10 23:40:54.547838: val_loss -0.1259 +2026-04-10 23:40:54.550402: Pseudo dice [0.6536, 0.422, 0.7827, 0.0028, 0.4019, 0.8031, 0.5871] +2026-04-10 23:40:54.553362: Epoch time: 100.1 s +2026-04-10 23:40:55.616980: +2026-04-10 23:40:55.619627: Epoch 438 +2026-04-10 23:40:55.621894: Current learning rate: 0.00901 +2026-04-10 23:42:36.593377: train_loss -0.1587 +2026-04-10 23:42:36.601808: val_loss -0.1261 +2026-04-10 23:42:36.606172: Pseudo dice [0.6098, 0.1613, 0.7073, 0.0341, 0.3492, 0.745, 0.6145] +2026-04-10 23:42:36.609185: Epoch time: 100.98 s +2026-04-10 23:42:37.695300: +2026-04-10 23:42:37.697891: Epoch 439 +2026-04-10 23:42:37.699951: Current learning rate: 0.00901 +2026-04-10 23:44:18.234671: train_loss -0.146 +2026-04-10 23:44:18.239812: val_loss -0.1259 +2026-04-10 23:44:18.242378: Pseudo dice [0.5228, 0.5111, 0.5689, 0.3104, 0.2922, 0.6426, 0.672] +2026-04-10 23:44:18.245100: Epoch time: 100.54 s +2026-04-10 23:44:19.330251: +2026-04-10 23:44:19.332231: Epoch 440 +2026-04-10 23:44:19.333964: Current learning rate: 0.009 +2026-04-10 23:45:59.939070: train_loss -0.1593 +2026-04-10 23:45:59.950395: val_loss -0.1611 +2026-04-10 23:45:59.953914: Pseudo dice [0.4509, 0.5274, 0.6614, 0.5824, 0.5148, 0.7144, 0.8184] +2026-04-10 23:45:59.957571: Epoch time: 100.61 s +2026-04-10 23:46:01.028872: +2026-04-10 23:46:01.031800: Epoch 441 +2026-04-10 23:46:01.033895: Current learning rate: 0.009 +2026-04-10 23:47:42.148530: train_loss -0.1662 +2026-04-10 23:47:42.154341: val_loss -0.1153 +2026-04-10 23:47:42.157757: Pseudo dice [0.7079, 0.6092, 0.6374, 0.1301, 0.4641, 0.8762, 0.7003] +2026-04-10 23:47:42.160196: Epoch time: 101.12 s +2026-04-10 23:47:43.226891: +2026-04-10 23:47:43.228593: Epoch 442 +2026-04-10 23:47:43.230056: Current learning rate: 0.009 +2026-04-10 23:49:24.066184: train_loss -0.1817 +2026-04-10 23:49:24.072243: val_loss -0.116 +2026-04-10 23:49:24.075347: Pseudo dice [0.5985, 0.563, 0.6848, 0.0006, 0.4154, 0.5399, 0.6426] +2026-04-10 23:49:24.080979: Epoch time: 100.84 s +2026-04-10 23:49:25.134059: +2026-04-10 23:49:25.136072: Epoch 443 +2026-04-10 23:49:25.138216: Current learning rate: 0.009 +2026-04-10 23:51:06.180729: train_loss -0.1835 +2026-04-10 23:51:06.190904: val_loss -0.1342 +2026-04-10 23:51:06.196365: Pseudo dice [0.6317, 0.6397, 0.6978, 0.755, 0.1592, 0.7842, 0.7115] +2026-04-10 23:51:06.203382: Epoch time: 101.05 s +2026-04-10 23:51:07.265712: +2026-04-10 23:51:07.267517: Epoch 444 +2026-04-10 23:51:07.269090: Current learning rate: 0.009 +2026-04-10 23:52:48.124023: train_loss -0.1723 +2026-04-10 23:52:48.138478: val_loss -0.1371 +2026-04-10 23:52:48.141401: Pseudo dice [0.4848, 0.1809, 0.6208, 0.6211, 0.2973, 0.7864, 0.6375] +2026-04-10 23:52:48.145713: Epoch time: 100.86 s +2026-04-10 23:52:49.218245: +2026-04-10 23:52:49.220987: Epoch 445 +2026-04-10 23:52:49.223347: Current learning rate: 0.00899 +2026-04-10 23:54:29.891618: train_loss -0.1707 +2026-04-10 23:54:29.898460: val_loss -0.1464 +2026-04-10 23:54:29.900605: Pseudo dice [0.7, 0.4038, 0.7279, 0.1058, 0.5783, 0.7858, 0.8043] +2026-04-10 23:54:29.902944: Epoch time: 100.68 s +2026-04-10 23:54:30.961454: +2026-04-10 23:54:30.963609: Epoch 446 +2026-04-10 23:54:30.965405: Current learning rate: 0.00899 +2026-04-10 23:56:12.211299: train_loss -0.1693 +2026-04-10 23:56:12.218580: val_loss -0.1372 +2026-04-10 23:56:12.220815: Pseudo dice [0.6553, 0.631, 0.5277, 0.5327, 0.3332, 0.8226, 0.7967] +2026-04-10 23:56:12.223084: Epoch time: 101.25 s +2026-04-10 23:56:12.225685: Yayy! New best EMA pseudo Dice: 0.5433 +2026-04-10 23:56:14.900623: +2026-04-10 23:56:14.903830: Epoch 447 +2026-04-10 23:56:14.906667: Current learning rate: 0.00899 +2026-04-10 23:57:56.498856: train_loss -0.1643 +2026-04-10 23:57:56.506185: val_loss -0.1272 +2026-04-10 23:57:56.508412: Pseudo dice [0.7774, 0.3424, 0.5715, 0.6055, 0.3789, 0.5621, 0.6145] +2026-04-10 23:57:56.511086: Epoch time: 101.6 s +2026-04-10 23:57:56.512949: Yayy! New best EMA pseudo Dice: 0.544 +2026-04-10 23:57:59.268225: +2026-04-10 23:57:59.270756: Epoch 448 +2026-04-10 23:57:59.272535: Current learning rate: 0.00899 +2026-04-10 23:59:40.588056: train_loss -0.1779 +2026-04-10 23:59:40.595830: val_loss -0.034 +2026-04-10 23:59:40.597796: Pseudo dice [0.4785, 0.484, 0.6574, 0.0002, 0.4485, 0.8414, 0.6039] +2026-04-10 23:59:40.600344: Epoch time: 101.32 s +2026-04-10 23:59:41.684498: +2026-04-10 23:59:41.686422: Epoch 449 +2026-04-10 23:59:41.688687: Current learning rate: 0.00898 +2026-04-11 00:01:28.200406: train_loss -0.1908 +2026-04-11 00:01:28.228719: val_loss -0.142 +2026-04-11 00:01:28.239087: Pseudo dice [0.5689, 0.3139, 0.6595, 0.0342, 0.4047, 0.714, 0.8743] +2026-04-11 00:01:28.248552: Epoch time: 106.52 s +2026-04-11 00:01:31.530456: +2026-04-11 00:01:31.534319: Epoch 450 +2026-04-11 00:01:31.537210: Current learning rate: 0.00898 +2026-04-11 00:03:12.464636: train_loss -0.1844 +2026-04-11 00:03:12.471911: val_loss -0.1417 +2026-04-11 00:03:12.474129: Pseudo dice [0.678, 0.5136, 0.7354, 0.0685, 0.4867, 0.6991, 0.641] +2026-04-11 00:03:12.476918: Epoch time: 100.94 s +2026-04-11 00:03:13.559551: +2026-04-11 00:03:13.565348: Epoch 451 +2026-04-11 00:03:13.567415: Current learning rate: 0.00898 +2026-04-11 00:04:57.507352: train_loss -0.1745 +2026-04-11 00:04:57.519361: val_loss -0.1171 +2026-04-11 00:04:57.522745: Pseudo dice [0.6468, 0.3988, 0.6352, 0.0081, 0.1917, 0.532, 0.7722] +2026-04-11 00:04:57.526973: Epoch time: 103.95 s +2026-04-11 00:04:58.607404: +2026-04-11 00:04:58.611310: Epoch 452 +2026-04-11 00:04:58.615930: Current learning rate: 0.00898 +2026-04-11 00:06:39.852907: train_loss -0.1559 +2026-04-11 00:06:39.871384: val_loss -0.1075 +2026-04-11 00:06:39.873670: Pseudo dice [0.6567, 0.3813, 0.6907, 0.0508, 0.2324, 0.4071, 0.646] +2026-04-11 00:06:39.876651: Epoch time: 101.25 s +2026-04-11 00:06:40.943160: +2026-04-11 00:06:40.944934: Epoch 453 +2026-04-11 00:06:40.946632: Current learning rate: 0.00897 +2026-04-11 00:08:23.454577: train_loss -0.1546 +2026-04-11 00:08:23.473799: val_loss -0.1579 +2026-04-11 00:08:23.477971: Pseudo dice [0.6633, 0.4937, 0.7508, 0.2772, 0.4229, 0.6995, 0.8461] +2026-04-11 00:08:23.482556: Epoch time: 102.51 s +2026-04-11 00:08:24.554043: +2026-04-11 00:08:24.557169: Epoch 454 +2026-04-11 00:08:24.559304: Current learning rate: 0.00897 +2026-04-11 00:10:05.296707: train_loss -0.1698 +2026-04-11 00:10:05.307221: val_loss -0.1368 +2026-04-11 00:10:05.309702: Pseudo dice [0.6204, 0.482, 0.6416, 0.0068, 0.2819, 0.5843, 0.8253] +2026-04-11 00:10:05.315584: Epoch time: 100.75 s +2026-04-11 00:10:06.391723: +2026-04-11 00:10:06.393932: Epoch 455 +2026-04-11 00:10:06.395892: Current learning rate: 0.00897 +2026-04-11 00:11:47.044089: train_loss -0.1714 +2026-04-11 00:11:47.052864: val_loss -0.1379 +2026-04-11 00:11:47.056306: Pseudo dice [0.6953, 0.517, 0.7146, 0.1235, 0.4344, 0.8381, 0.6009] +2026-04-11 00:11:47.063106: Epoch time: 100.66 s +2026-04-11 00:11:48.155067: +2026-04-11 00:11:48.156892: Epoch 456 +2026-04-11 00:11:48.158589: Current learning rate: 0.00897 +2026-04-11 00:13:29.876642: train_loss -0.1557 +2026-04-11 00:13:29.887206: val_loss -0.1309 +2026-04-11 00:13:29.891184: Pseudo dice [0.0531, 0.4506, 0.7001, 0.1857, 0.4251, 0.5768, 0.8155] +2026-04-11 00:13:29.894673: Epoch time: 101.72 s +2026-04-11 00:13:30.996500: +2026-04-11 00:13:30.999563: Epoch 457 +2026-04-11 00:13:31.002604: Current learning rate: 0.00897 +2026-04-11 00:15:13.094171: train_loss -0.1614 +2026-04-11 00:15:13.102556: val_loss -0.1607 +2026-04-11 00:15:13.105727: Pseudo dice [0.4832, 0.3514, 0.7695, 0.2481, 0.3619, 0.7162, 0.8053] +2026-04-11 00:15:13.110280: Epoch time: 102.1 s +2026-04-11 00:15:14.187164: +2026-04-11 00:15:14.189134: Epoch 458 +2026-04-11 00:15:14.191746: Current learning rate: 0.00896 +2026-04-11 00:16:55.068249: train_loss -0.1669 +2026-04-11 00:16:55.076271: val_loss -0.1232 +2026-04-11 00:16:55.078950: Pseudo dice [0.6589, 0.4914, 0.6448, 0.003, 0.4359, 0.6497, 0.3096] +2026-04-11 00:16:55.081059: Epoch time: 100.88 s +2026-04-11 00:16:56.178622: +2026-04-11 00:16:56.180344: Epoch 459 +2026-04-11 00:16:56.182613: Current learning rate: 0.00896 +2026-04-11 00:18:36.678864: train_loss -0.1694 +2026-04-11 00:18:36.688298: val_loss -0.1073 +2026-04-11 00:18:36.691173: Pseudo dice [0.2951, 0.433, 0.5819, 0.0754, 0.6179, 0.7327, 0.7534] +2026-04-11 00:18:36.693492: Epoch time: 100.5 s +2026-04-11 00:18:37.775450: +2026-04-11 00:18:37.778811: Epoch 460 +2026-04-11 00:18:37.782379: Current learning rate: 0.00896 +2026-04-11 00:20:19.786618: train_loss -0.1662 +2026-04-11 00:20:19.794835: val_loss -0.1169 +2026-04-11 00:20:19.798036: Pseudo dice [0.6713, 0.2997, 0.7947, 0.0024, 0.5088, 0.793, 0.6317] +2026-04-11 00:20:19.802078: Epoch time: 102.01 s +2026-04-11 00:20:20.866760: +2026-04-11 00:20:20.870309: Epoch 461 +2026-04-11 00:20:20.872559: Current learning rate: 0.00896 +2026-04-11 00:22:01.887048: train_loss -0.1598 +2026-04-11 00:22:01.893520: val_loss -0.0862 +2026-04-11 00:22:01.895727: Pseudo dice [0.4188, 0.3974, 0.5966, 0.0002, 0.483, 0.3852, 0.7744] +2026-04-11 00:22:01.898439: Epoch time: 101.02 s +2026-04-11 00:22:02.982847: +2026-04-11 00:22:02.985269: Epoch 462 +2026-04-11 00:22:02.986762: Current learning rate: 0.00895 +2026-04-11 00:23:43.686697: train_loss -0.1676 +2026-04-11 00:23:43.693589: val_loss -0.1324 +2026-04-11 00:23:43.695867: Pseudo dice [0.6402, 0.7244, 0.6816, 0.0831, 0.3526, 0.7574, 0.6322] +2026-04-11 00:23:43.698933: Epoch time: 100.71 s +2026-04-11 00:23:44.760236: +2026-04-11 00:23:44.762555: Epoch 463 +2026-04-11 00:23:44.765051: Current learning rate: 0.00895 +2026-04-11 00:25:25.980817: train_loss -0.1836 +2026-04-11 00:25:25.988633: val_loss -0.138 +2026-04-11 00:25:25.991282: Pseudo dice [0.6334, 0.1067, 0.7354, 0.0709, 0.1898, 0.5607, 0.8244] +2026-04-11 00:25:25.993552: Epoch time: 101.22 s +2026-04-11 00:25:27.049936: +2026-04-11 00:25:27.052710: Epoch 464 +2026-04-11 00:25:27.054759: Current learning rate: 0.00895 +2026-04-11 00:27:08.590837: train_loss -0.1672 +2026-04-11 00:27:08.598053: val_loss -0.0822 +2026-04-11 00:27:08.600538: Pseudo dice [0.6755, 0.3157, 0.7204, 0.0159, 0.0007, 0.7409, 0.6147] +2026-04-11 00:27:08.603213: Epoch time: 101.54 s +2026-04-11 00:27:09.720385: +2026-04-11 00:27:09.722647: Epoch 465 +2026-04-11 00:27:09.724519: Current learning rate: 0.00895 +2026-04-11 00:28:50.364964: train_loss -0.1656 +2026-04-11 00:28:50.372262: val_loss -0.1522 +2026-04-11 00:28:50.375144: Pseudo dice [0.6714, 0.208, 0.5618, 0.4942, 0.3399, 0.738, 0.7123] +2026-04-11 00:28:50.377904: Epoch time: 100.65 s +2026-04-11 00:28:51.481888: +2026-04-11 00:28:51.485774: Epoch 466 +2026-04-11 00:28:51.488683: Current learning rate: 0.00895 +2026-04-11 00:30:32.591638: train_loss -0.1722 +2026-04-11 00:30:32.600471: val_loss -0.1379 +2026-04-11 00:30:32.604087: Pseudo dice [0.7835, 0.1663, 0.5664, 0.4931, 0.3787, 0.8514, 0.7163] +2026-04-11 00:30:32.608557: Epoch time: 101.11 s +2026-04-11 00:30:34.564433: +2026-04-11 00:30:34.567149: Epoch 467 +2026-04-11 00:30:34.569141: Current learning rate: 0.00894 +2026-04-11 00:32:15.673980: train_loss -0.1591 +2026-04-11 00:32:15.681199: val_loss -0.1378 +2026-04-11 00:32:15.683862: Pseudo dice [0.5312, 0.2239, 0.7984, 0.0007, 0.3953, 0.829, 0.6831] +2026-04-11 00:32:15.687433: Epoch time: 101.11 s +2026-04-11 00:32:16.828842: +2026-04-11 00:32:16.832310: Epoch 468 +2026-04-11 00:32:16.834693: Current learning rate: 0.00894 +2026-04-11 00:33:57.808055: train_loss -0.1804 +2026-04-11 00:33:57.818179: val_loss -0.113 +2026-04-11 00:33:57.820686: Pseudo dice [0.5291, 0.5494, 0.6951, 0.0001, 0.3702, 0.7353, 0.7795] +2026-04-11 00:33:57.823706: Epoch time: 100.98 s +2026-04-11 00:33:58.915089: +2026-04-11 00:33:58.918120: Epoch 469 +2026-04-11 00:33:58.921055: Current learning rate: 0.00894 +2026-04-11 00:35:39.575694: train_loss -0.1814 +2026-04-11 00:35:39.582432: val_loss -0.0986 +2026-04-11 00:35:39.585411: Pseudo dice [0.6328, 0.3904, 0.6077, 0.0055, 0.3215, 0.2013, 0.6358] +2026-04-11 00:35:39.588302: Epoch time: 100.66 s +2026-04-11 00:35:40.657465: +2026-04-11 00:35:40.659491: Epoch 470 +2026-04-11 00:35:40.661548: Current learning rate: 0.00894 +2026-04-11 00:37:21.633649: train_loss -0.1721 +2026-04-11 00:37:21.639773: val_loss -0.1686 +2026-04-11 00:37:21.642143: Pseudo dice [0.6284, 0.5897, 0.7246, 0.0055, 0.3743, 0.7524, 0.7373] +2026-04-11 00:37:21.644997: Epoch time: 100.98 s +2026-04-11 00:37:22.718611: +2026-04-11 00:37:22.722340: Epoch 471 +2026-04-11 00:37:22.724347: Current learning rate: 0.00893 +2026-04-11 00:39:03.241580: train_loss -0.1777 +2026-04-11 00:39:03.249229: val_loss -0.1427 +2026-04-11 00:39:03.251411: Pseudo dice [0.6663, 0.5992, 0.4994, 0.0195, 0.4546, 0.5013, 0.715] +2026-04-11 00:39:03.254132: Epoch time: 100.53 s +2026-04-11 00:39:04.333103: +2026-04-11 00:39:04.335455: Epoch 472 +2026-04-11 00:39:04.337528: Current learning rate: 0.00893 +2026-04-11 00:40:44.803455: train_loss -0.1859 +2026-04-11 00:40:44.811653: val_loss -0.1546 +2026-04-11 00:40:44.814149: Pseudo dice [0.7427, 0.3948, 0.6842, 0.4936, 0.2513, 0.7149, 0.8137] +2026-04-11 00:40:44.817243: Epoch time: 100.47 s +2026-04-11 00:40:45.872643: +2026-04-11 00:40:45.874416: Epoch 473 +2026-04-11 00:40:45.876034: Current learning rate: 0.00893 +2026-04-11 00:42:26.515863: train_loss -0.1697 +2026-04-11 00:42:26.528933: val_loss -0.1578 +2026-04-11 00:42:26.531321: Pseudo dice [0.6875, 0.5419, 0.6613, 0.3023, 0.5365, 0.4877, 0.6063] +2026-04-11 00:42:26.533688: Epoch time: 100.65 s +2026-04-11 00:42:27.620745: +2026-04-11 00:42:27.622961: Epoch 474 +2026-04-11 00:42:27.624709: Current learning rate: 0.00893 +2026-04-11 00:44:08.363581: train_loss -0.174 +2026-04-11 00:44:08.370896: val_loss -0.1297 +2026-04-11 00:44:08.374592: Pseudo dice [0.5422, 0.4864, 0.7365, 0.008, 0.4158, 0.6384, 0.811] +2026-04-11 00:44:08.377484: Epoch time: 100.75 s +2026-04-11 00:44:09.462382: +2026-04-11 00:44:09.464387: Epoch 475 +2026-04-11 00:44:09.466461: Current learning rate: 0.00892 +2026-04-11 00:45:50.504948: train_loss -0.1757 +2026-04-11 00:45:50.512057: val_loss -0.1384 +2026-04-11 00:45:50.514526: Pseudo dice [0.5838, 0.5374, 0.7122, 0.0153, 0.3356, 0.7178, 0.6719] +2026-04-11 00:45:50.518730: Epoch time: 101.05 s +2026-04-11 00:45:51.591537: +2026-04-11 00:45:51.594131: Epoch 476 +2026-04-11 00:45:51.596312: Current learning rate: 0.00892 +2026-04-11 00:47:32.155894: train_loss -0.157 +2026-04-11 00:47:32.163691: val_loss -0.1143 +2026-04-11 00:47:32.166341: Pseudo dice [0.2562, 0.2691, 0.6081, 0.2283, 0.5022, 0.7331, 0.6141] +2026-04-11 00:47:32.169344: Epoch time: 100.57 s +2026-04-11 00:47:33.255471: +2026-04-11 00:47:33.258684: Epoch 477 +2026-04-11 00:47:33.260633: Current learning rate: 0.00892 +2026-04-11 00:49:13.900861: train_loss -0.1704 +2026-04-11 00:49:13.909982: val_loss -0.1416 +2026-04-11 00:49:13.912337: Pseudo dice [0.4762, 0.5808, 0.5257, 0.6697, 0.3792, 0.7373, 0.5774] +2026-04-11 00:49:13.917076: Epoch time: 100.65 s +2026-04-11 00:49:14.996934: +2026-04-11 00:49:14.998791: Epoch 478 +2026-04-11 00:49:15.000875: Current learning rate: 0.00892 +2026-04-11 00:50:55.492099: train_loss -0.1717 +2026-04-11 00:50:55.498862: val_loss -0.0674 +2026-04-11 00:50:55.500885: Pseudo dice [0.675, 0.5776, 0.6862, 0.0001, 0.3077, 0.1629, 0.571] +2026-04-11 00:50:55.503599: Epoch time: 100.5 s +2026-04-11 00:50:56.590491: +2026-04-11 00:50:56.592859: Epoch 479 +2026-04-11 00:50:56.594985: Current learning rate: 0.00892 +2026-04-11 00:52:37.040734: train_loss -0.1717 +2026-04-11 00:52:37.046126: val_loss -0.1493 +2026-04-11 00:52:37.048899: Pseudo dice [0.8105, 0.6023, 0.6982, 0.6744, 0.4036, 0.7956, 0.653] +2026-04-11 00:52:37.051181: Epoch time: 100.45 s +2026-04-11 00:52:38.132225: +2026-04-11 00:52:38.134672: Epoch 480 +2026-04-11 00:52:38.138903: Current learning rate: 0.00891 +2026-04-11 00:54:18.778622: train_loss -0.1843 +2026-04-11 00:54:18.785596: val_loss -0.1057 +2026-04-11 00:54:18.787727: Pseudo dice [0.7852, 0.6039, 0.7667, 0.0002, 0.2264, 0.5026, 0.7791] +2026-04-11 00:54:18.790717: Epoch time: 100.65 s +2026-04-11 00:54:19.861908: +2026-04-11 00:54:19.863976: Epoch 481 +2026-04-11 00:54:19.865851: Current learning rate: 0.00891 +2026-04-11 00:56:00.578100: train_loss -0.175 +2026-04-11 00:56:00.584671: val_loss -0.1264 +2026-04-11 00:56:00.587066: Pseudo dice [0.7528, 0.5572, 0.6518, 0.0272, 0.5572, 0.7945, 0.7134] +2026-04-11 00:56:00.589110: Epoch time: 100.72 s +2026-04-11 00:56:01.674951: +2026-04-11 00:56:01.676941: Epoch 482 +2026-04-11 00:56:01.679324: Current learning rate: 0.00891 +2026-04-11 00:57:42.109851: train_loss -0.1696 +2026-04-11 00:57:42.115167: val_loss -0.1509 +2026-04-11 00:57:42.118321: Pseudo dice [0.7904, 0.4484, 0.7838, 0.0015, 0.606, 0.7609, 0.8313] +2026-04-11 00:57:42.120528: Epoch time: 100.44 s +2026-04-11 00:57:43.188978: +2026-04-11 00:57:43.190575: Epoch 483 +2026-04-11 00:57:43.192243: Current learning rate: 0.00891 +2026-04-11 00:59:23.618973: train_loss -0.1733 +2026-04-11 00:59:23.625406: val_loss -0.1497 +2026-04-11 00:59:23.628109: Pseudo dice [0.3305, 0.1964, 0.726, 0.0117, 0.4803, 0.6885, 0.7489] +2026-04-11 00:59:23.631789: Epoch time: 100.43 s +2026-04-11 00:59:24.721480: +2026-04-11 00:59:24.723761: Epoch 484 +2026-04-11 00:59:24.725542: Current learning rate: 0.0089 +2026-04-11 01:01:05.345962: train_loss -0.1727 +2026-04-11 01:01:05.353176: val_loss -0.1549 +2026-04-11 01:01:05.354798: Pseudo dice [0.5239, 0.163, 0.751, 0.6942, 0.2665, 0.7463, 0.8428] +2026-04-11 01:01:05.359203: Epoch time: 100.63 s +2026-04-11 01:01:06.443524: +2026-04-11 01:01:06.445159: Epoch 485 +2026-04-11 01:01:06.446933: Current learning rate: 0.0089 +2026-04-11 01:02:46.832913: train_loss -0.1619 +2026-04-11 01:02:46.838177: val_loss -0.1253 +2026-04-11 01:02:46.840190: Pseudo dice [0.3137, 0.2071, 0.5698, 0.4397, 0.3497, 0.8533, 0.721] +2026-04-11 01:02:46.842345: Epoch time: 100.39 s +2026-04-11 01:02:47.920491: +2026-04-11 01:02:47.922125: Epoch 486 +2026-04-11 01:02:47.923548: Current learning rate: 0.0089 +2026-04-11 01:04:28.547251: train_loss -0.1814 +2026-04-11 01:04:28.553362: val_loss -0.1545 +2026-04-11 01:04:28.555772: Pseudo dice [0.7356, 0.1938, 0.6316, 0.5989, 0.4845, 0.879, 0.5022] +2026-04-11 01:04:28.558790: Epoch time: 100.63 s +2026-04-11 01:04:30.528343: +2026-04-11 01:04:30.531127: Epoch 487 +2026-04-11 01:04:30.533056: Current learning rate: 0.0089 +2026-04-11 01:06:11.215935: train_loss -0.1731 +2026-04-11 01:06:11.221427: val_loss -0.1219 +2026-04-11 01:06:11.223005: Pseudo dice [0.7247, 0.0614, 0.6211, 0.0196, 0.3388, 0.6478, 0.7899] +2026-04-11 01:06:11.226175: Epoch time: 100.69 s +2026-04-11 01:06:12.378595: +2026-04-11 01:06:12.381058: Epoch 488 +2026-04-11 01:06:12.382672: Current learning rate: 0.00889 +2026-04-11 01:07:52.742522: train_loss -0.1587 +2026-04-11 01:07:52.748107: val_loss -0.1497 +2026-04-11 01:07:52.752260: Pseudo dice [0.6666, 0.3179, 0.5507, 0.0263, 0.3555, 0.6197, 0.7091] +2026-04-11 01:07:52.756181: Epoch time: 100.37 s +2026-04-11 01:07:53.843248: +2026-04-11 01:07:53.845228: Epoch 489 +2026-04-11 01:07:53.847065: Current learning rate: 0.00889 +2026-04-11 01:09:34.908421: train_loss -0.1669 +2026-04-11 01:09:34.914162: val_loss -0.1386 +2026-04-11 01:09:34.915912: Pseudo dice [0.7474, 0.4318, 0.7528, 0.0, 0.4876, 0.8232, 0.5673] +2026-04-11 01:09:34.918040: Epoch time: 101.07 s +2026-04-11 01:09:36.009980: +2026-04-11 01:09:36.011765: Epoch 490 +2026-04-11 01:09:36.013403: Current learning rate: 0.00889 +2026-04-11 01:11:16.296524: train_loss -0.1668 +2026-04-11 01:11:16.303592: val_loss -0.1217 +2026-04-11 01:11:16.305935: Pseudo dice [0.7451, 0.1718, 0.6576, 0.1089, 0.4062, 0.7887, 0.7089] +2026-04-11 01:11:16.309402: Epoch time: 100.29 s +2026-04-11 01:11:17.392050: +2026-04-11 01:11:17.393947: Epoch 491 +2026-04-11 01:11:17.397485: Current learning rate: 0.00889 +2026-04-11 01:12:58.441328: train_loss -0.1571 +2026-04-11 01:12:58.447462: val_loss -0.1247 +2026-04-11 01:12:58.449322: Pseudo dice [0.673, 0.2969, 0.6405, 0.6091, 0.2886, 0.6114, 0.848] +2026-04-11 01:12:58.451204: Epoch time: 101.05 s +2026-04-11 01:12:59.542691: +2026-04-11 01:12:59.544645: Epoch 492 +2026-04-11 01:12:59.546538: Current learning rate: 0.00889 +2026-04-11 01:14:40.188091: train_loss -0.1652 +2026-04-11 01:14:40.193681: val_loss -0.1586 +2026-04-11 01:14:40.195795: Pseudo dice [0.789, 0.3709, 0.7011, 0.1656, 0.4778, 0.8223, 0.7844] +2026-04-11 01:14:40.198118: Epoch time: 100.65 s +2026-04-11 01:14:41.312253: +2026-04-11 01:14:41.314742: Epoch 493 +2026-04-11 01:14:41.316698: Current learning rate: 0.00888 +2026-04-11 01:16:22.153620: train_loss -0.1744 +2026-04-11 01:16:22.160380: val_loss -0.1398 +2026-04-11 01:16:22.162389: Pseudo dice [0.8097, 0.0545, 0.833, 0.0031, 0.4536, 0.207, 0.7973] +2026-04-11 01:16:22.164864: Epoch time: 100.84 s +2026-04-11 01:16:23.254837: +2026-04-11 01:16:23.256746: Epoch 494 +2026-04-11 01:16:23.258747: Current learning rate: 0.00888 +2026-04-11 01:18:04.371768: train_loss -0.1702 +2026-04-11 01:18:04.377967: val_loss -0.1383 +2026-04-11 01:18:04.379784: Pseudo dice [0.6549, 0.1485, 0.7392, 0.0699, 0.5578, 0.8384, 0.7416] +2026-04-11 01:18:04.382679: Epoch time: 101.12 s +2026-04-11 01:18:05.472919: +2026-04-11 01:18:05.474845: Epoch 495 +2026-04-11 01:18:05.476581: Current learning rate: 0.00888 +2026-04-11 01:19:46.326085: train_loss -0.1869 +2026-04-11 01:19:46.333069: val_loss -0.139 +2026-04-11 01:19:46.335132: Pseudo dice [0.4532, 0.4426, 0.7447, 0.4222, 0.5374, 0.5154, 0.7534] +2026-04-11 01:19:46.337402: Epoch time: 100.86 s +2026-04-11 01:19:47.422639: +2026-04-11 01:19:47.424717: Epoch 496 +2026-04-11 01:19:47.426509: Current learning rate: 0.00888 +2026-04-11 01:21:28.169855: train_loss -0.1779 +2026-04-11 01:21:28.179989: val_loss -0.1429 +2026-04-11 01:21:28.182356: Pseudo dice [0.2376, 0.4911, 0.7561, 0.1551, 0.388, 0.7081, 0.7701] +2026-04-11 01:21:28.188692: Epoch time: 100.75 s +2026-04-11 01:21:29.306104: +2026-04-11 01:21:29.308031: Epoch 497 +2026-04-11 01:21:29.310687: Current learning rate: 0.00887 +2026-04-11 01:23:10.095554: train_loss -0.1708 +2026-04-11 01:23:10.101975: val_loss -0.1002 +2026-04-11 01:23:10.104031: Pseudo dice [0.5511, 0.2928, 0.7625, 0.0874, 0.2259, 0.8201, 0.542] +2026-04-11 01:23:10.106469: Epoch time: 100.79 s +2026-04-11 01:23:11.213993: +2026-04-11 01:23:11.215869: Epoch 498 +2026-04-11 01:23:11.217948: Current learning rate: 0.00887 +2026-04-11 01:24:51.961260: train_loss -0.2077 +2026-04-11 01:24:51.967912: val_loss -0.1943 +2026-04-11 01:24:51.969893: Pseudo dice [0.364, 0.0885, 0.6174, 0.1607, 0.15, 0.8051, 0.6836] +2026-04-11 01:24:51.972503: Epoch time: 100.75 s +2026-04-11 01:24:53.065163: +2026-04-11 01:24:53.066937: Epoch 499 +2026-04-11 01:24:53.068831: Current learning rate: 0.00887 +2026-04-11 01:26:33.603540: train_loss -0.2432 +2026-04-11 01:26:33.609643: val_loss -0.2799 +2026-04-11 01:26:33.611355: Pseudo dice [0.0, 0.0, 0.8128, 0.0074, 0.2371, 0.6432, 0.7243] +2026-04-11 01:26:33.614147: Epoch time: 100.54 s +2026-04-11 01:26:36.255381: +2026-04-11 01:26:36.257638: Epoch 500 +2026-04-11 01:26:36.259337: Current learning rate: 0.00887 +2026-04-11 01:28:16.707332: train_loss -0.3193 +2026-04-11 01:28:16.713866: val_loss -0.3168 +2026-04-11 01:28:16.715949: Pseudo dice [0.0, 0.0, 0.6347, 0.0, 0.0, 0.0, 0.5774] +2026-04-11 01:28:16.718729: Epoch time: 100.46 s +2026-04-11 01:28:17.777641: +2026-04-11 01:28:17.779277: Epoch 501 +2026-04-11 01:28:17.780903: Current learning rate: 0.00887 +2026-04-11 01:29:58.343759: train_loss -0.3315 +2026-04-11 01:29:58.349317: val_loss -0.2296 +2026-04-11 01:29:58.351161: Pseudo dice [0.0, 0.0, 0.5817, 0.0, 0.0, 0.0, 0.6208] +2026-04-11 01:29:58.353746: Epoch time: 100.57 s +2026-04-11 01:29:59.433239: +2026-04-11 01:29:59.435672: Epoch 502 +2026-04-11 01:29:59.438481: Current learning rate: 0.00886 +2026-04-11 01:31:39.905126: train_loss -0.2973 +2026-04-11 01:31:39.911769: val_loss -0.3477 +2026-04-11 01:31:39.913996: Pseudo dice [0.0, 0.0, 0.6705, 0.0, 0.0, 0.0, 0.6381] +2026-04-11 01:31:39.916827: Epoch time: 100.47 s +2026-04-11 01:31:40.979110: +2026-04-11 01:31:40.980959: Epoch 503 +2026-04-11 01:31:40.982699: Current learning rate: 0.00886 +2026-04-11 01:33:21.599124: train_loss -0.2999 +2026-04-11 01:33:21.606235: val_loss -0.2597 +2026-04-11 01:33:21.609012: Pseudo dice [0.0, 0.0, 0.4125, 0.0, 0.0, 0.0, 0.2789] +2026-04-11 01:33:21.611615: Epoch time: 100.62 s +2026-04-11 01:33:22.694439: +2026-04-11 01:33:22.696613: Epoch 504 +2026-04-11 01:33:22.698513: Current learning rate: 0.00886 +2026-04-11 01:35:03.549286: train_loss -0.353 +2026-04-11 01:35:03.555645: val_loss -0.342 +2026-04-11 01:35:03.557654: Pseudo dice [0.0, 0.0, 0.7298, 0.0, 0.0, 0.0, 0.1299] +2026-04-11 01:35:03.559851: Epoch time: 100.86 s +2026-04-11 01:35:04.631395: +2026-04-11 01:35:04.633567: Epoch 505 +2026-04-11 01:35:04.635681: Current learning rate: 0.00886 +2026-04-11 01:36:44.892815: train_loss -0.3211 +2026-04-11 01:36:44.901184: val_loss -0.2765 +2026-04-11 01:36:44.903317: Pseudo dice [0.0, 0.0, 0.6655, 0.0, 0.0, 0.0, 0.1849] +2026-04-11 01:36:44.905668: Epoch time: 100.26 s +2026-04-11 01:36:45.980331: +2026-04-11 01:36:45.982577: Epoch 506 +2026-04-11 01:36:45.984489: Current learning rate: 0.00885 +2026-04-11 01:38:26.563205: train_loss -0.2881 +2026-04-11 01:38:26.569086: val_loss -0.3087 +2026-04-11 01:38:26.570932: Pseudo dice [0.0, 0.0, 0.5399, 0.0, 0.0, 0.0, 0.7545] +2026-04-11 01:38:26.574147: Epoch time: 100.59 s +2026-04-11 01:38:28.586973: +2026-04-11 01:38:28.589069: Epoch 507 +2026-04-11 01:38:28.591964: Current learning rate: 0.00885 +2026-04-11 01:40:09.063500: train_loss -0.3409 +2026-04-11 01:40:09.070173: val_loss -0.3199 +2026-04-11 01:40:09.071896: Pseudo dice [0.0, 0.0, 0.582, 0.0, 0.0, 0.0, 0.0637] +2026-04-11 01:40:09.074638: Epoch time: 100.48 s +2026-04-11 01:40:10.392943: +2026-04-11 01:40:10.394916: Epoch 508 +2026-04-11 01:40:10.396517: Current learning rate: 0.00885 +2026-04-11 01:41:50.987852: train_loss -0.327 +2026-04-11 01:41:50.993609: val_loss -0.3641 +2026-04-11 01:41:50.995716: Pseudo dice [0.0, 0.0, 0.7796, 0.0, 0.0, 0.0, 0.1134] +2026-04-11 01:41:50.998098: Epoch time: 100.6 s +2026-04-11 01:41:52.072275: +2026-04-11 01:41:52.074246: Epoch 509 +2026-04-11 01:41:52.076177: Current learning rate: 0.00885 +2026-04-11 01:43:32.658059: train_loss -0.3408 +2026-04-11 01:43:32.663477: val_loss -0.3408 +2026-04-11 01:43:32.665549: Pseudo dice [0.0, 0.0, 0.6473, 0.0, 0.0, 0.0, 0.2263] +2026-04-11 01:43:32.667612: Epoch time: 100.59 s +2026-04-11 01:43:33.737596: +2026-04-11 01:43:33.740267: Epoch 510 +2026-04-11 01:43:33.741832: Current learning rate: 0.00884 +2026-04-11 01:45:14.251643: train_loss -0.3212 +2026-04-11 01:45:14.256889: val_loss -0.3041 +2026-04-11 01:45:14.258743: Pseudo dice [0.0, 0.0, 0.6046, 0.0, 0.0, 0.0, 0.652] +2026-04-11 01:45:14.260647: Epoch time: 100.52 s +2026-04-11 01:45:15.353010: +2026-04-11 01:45:15.354648: Epoch 511 +2026-04-11 01:45:15.356173: Current learning rate: 0.00884 +2026-04-11 01:46:55.799175: train_loss -0.2993 +2026-04-11 01:46:55.805782: val_loss -0.3555 +2026-04-11 01:46:55.808862: Pseudo dice [0.0, 0.0, 0.6912, 0.0, 0.0, 0.0, 0.6432] +2026-04-11 01:46:55.811456: Epoch time: 100.45 s +2026-04-11 01:46:56.898132: +2026-04-11 01:46:56.899949: Epoch 512 +2026-04-11 01:46:56.901753: Current learning rate: 0.00884 +2026-04-11 01:48:37.371489: train_loss -0.3282 +2026-04-11 01:48:37.378892: val_loss -0.3256 +2026-04-11 01:48:37.380981: Pseudo dice [0.0, 0.0, 0.752, 0.0, 0.0, 0.0, 0.1524] +2026-04-11 01:48:37.383685: Epoch time: 100.48 s +2026-04-11 01:48:38.456611: +2026-04-11 01:48:38.458283: Epoch 513 +2026-04-11 01:48:38.459977: Current learning rate: 0.00884 +2026-04-11 01:50:18.838455: train_loss -0.3505 +2026-04-11 01:50:18.843697: val_loss -0.308 +2026-04-11 01:50:18.845983: Pseudo dice [0.0, 0.0, 0.6717, 0.0, 0.0, 0.0, 0.0265] +2026-04-11 01:50:18.849112: Epoch time: 100.39 s +2026-04-11 01:50:19.913819: +2026-04-11 01:50:19.915762: Epoch 514 +2026-04-11 01:50:19.917925: Current learning rate: 0.00884 +2026-04-11 01:52:00.175901: train_loss -0.2915 +2026-04-11 01:52:00.183225: val_loss -0.2532 +2026-04-11 01:52:00.185222: Pseudo dice [0.0, 0.0, 0.3223, 0.0, 0.0, 0.0, 0.0] +2026-04-11 01:52:00.187285: Epoch time: 100.27 s +2026-04-11 01:52:01.266045: +2026-04-11 01:52:01.267979: Epoch 515 +2026-04-11 01:52:01.270391: Current learning rate: 0.00883 +2026-04-11 01:53:41.598802: train_loss -0.3158 +2026-04-11 01:53:41.604696: val_loss -0.2347 +2026-04-11 01:53:41.606950: Pseudo dice [0.0, 0.0, 0.7007, 0.0, 0.0, 0.0, 0.0] +2026-04-11 01:53:41.609752: Epoch time: 100.34 s +2026-04-11 01:53:42.684185: +2026-04-11 01:53:42.686373: Epoch 516 +2026-04-11 01:53:42.688632: Current learning rate: 0.00883 +2026-04-11 01:55:23.120974: train_loss -0.3518 +2026-04-11 01:55:23.127063: val_loss -0.3614 +2026-04-11 01:55:23.129705: Pseudo dice [0.0, 0.0, 0.6035, 0.0, 0.0, 0.0, 0.7277] +2026-04-11 01:55:23.132521: Epoch time: 100.44 s +2026-04-11 01:55:24.197370: +2026-04-11 01:55:24.199271: Epoch 517 +2026-04-11 01:55:24.201187: Current learning rate: 0.00883 +2026-04-11 01:57:04.533197: train_loss -0.3472 +2026-04-11 01:57:04.540072: val_loss -0.3554 +2026-04-11 01:57:04.542139: Pseudo dice [0.0, 0.0, 0.6291, 0.0, 0.0, 0.0, 0.3083] +2026-04-11 01:57:04.544402: Epoch time: 100.34 s +2026-04-11 01:57:05.622955: +2026-04-11 01:57:05.625200: Epoch 518 +2026-04-11 01:57:05.626900: Current learning rate: 0.00883 +2026-04-11 01:58:46.034250: train_loss -0.3571 +2026-04-11 01:58:46.040260: val_loss -0.3768 +2026-04-11 01:58:46.042191: Pseudo dice [0.0, 0.0, 0.7333, 0.0, 0.0, 0.0, 0.5299] +2026-04-11 01:58:46.044209: Epoch time: 100.41 s +2026-04-11 01:58:47.102805: +2026-04-11 01:58:47.105201: Epoch 519 +2026-04-11 01:58:47.107055: Current learning rate: 0.00882 +2026-04-11 02:00:27.666768: train_loss -0.2837 +2026-04-11 02:00:27.672969: val_loss -0.2785 +2026-04-11 02:00:27.675461: Pseudo dice [0.0, 0.0, 0.4815, 0.0, 0.0, 0.0, 0.0] +2026-04-11 02:00:27.678583: Epoch time: 100.57 s +2026-04-11 02:00:28.760806: +2026-04-11 02:00:28.762464: Epoch 520 +2026-04-11 02:00:28.763990: Current learning rate: 0.00882 +2026-04-11 02:02:09.243731: train_loss -0.3023 +2026-04-11 02:02:09.248936: val_loss -0.3713 +2026-04-11 02:02:09.251071: Pseudo dice [0.0, 0.0, 0.5588, 0.0, 0.0, 0.0, 0.7693] +2026-04-11 02:02:09.253833: Epoch time: 100.49 s +2026-04-11 02:02:10.329523: +2026-04-11 02:02:10.331333: Epoch 521 +2026-04-11 02:02:10.332955: Current learning rate: 0.00882 +2026-04-11 02:03:50.678804: train_loss -0.3254 +2026-04-11 02:03:50.685013: val_loss -0.2836 +2026-04-11 02:03:50.687313: Pseudo dice [0.0, 0.0, 0.5983, 0.0, 0.0, 0.0, 0.5165] +2026-04-11 02:03:50.689970: Epoch time: 100.35 s +2026-04-11 02:03:51.750990: +2026-04-11 02:03:51.752875: Epoch 522 +2026-04-11 02:03:51.754526: Current learning rate: 0.00882 +2026-04-11 02:05:32.052748: train_loss -0.3355 +2026-04-11 02:05:32.059233: val_loss -0.2188 +2026-04-11 02:05:32.061411: Pseudo dice [0.0, 0.0, 0.4661, 0.0, 0.0, 0.0, 0.5042] +2026-04-11 02:05:32.063759: Epoch time: 100.3 s +2026-04-11 02:05:33.154804: +2026-04-11 02:05:33.156523: Epoch 523 +2026-04-11 02:05:33.158094: Current learning rate: 0.00882 +2026-04-11 02:07:13.671257: train_loss -0.3164 +2026-04-11 02:07:13.677418: val_loss -0.2663 +2026-04-11 02:07:13.679772: Pseudo dice [0.0, 0.0, 0.3575, 0.0, 0.0, 0.0, 0.2517] +2026-04-11 02:07:13.681903: Epoch time: 100.52 s +2026-04-11 02:07:14.774400: +2026-04-11 02:07:14.776410: Epoch 524 +2026-04-11 02:07:14.777874: Current learning rate: 0.00881 +2026-04-11 02:08:55.128663: train_loss -0.3465 +2026-04-11 02:08:55.135454: val_loss -0.3619 +2026-04-11 02:08:55.137648: Pseudo dice [0.0, 0.0, 0.7041, 0.0, 0.0, 0.0, 0.6627] +2026-04-11 02:08:55.140512: Epoch time: 100.36 s +2026-04-11 02:08:56.227027: +2026-04-11 02:08:56.228794: Epoch 525 +2026-04-11 02:08:56.230272: Current learning rate: 0.00881 +2026-04-11 02:10:36.594579: train_loss -0.3543 +2026-04-11 02:10:36.602128: val_loss -0.3759 +2026-04-11 02:10:36.604342: Pseudo dice [0.0, 0.0, 0.4212, 0.0, 0.0, 0.0, 0.7839] +2026-04-11 02:10:36.607643: Epoch time: 100.37 s +2026-04-11 02:10:37.713979: +2026-04-11 02:10:37.715692: Epoch 526 +2026-04-11 02:10:37.717452: Current learning rate: 0.00881 +2026-04-11 02:12:18.269234: train_loss -0.3618 +2026-04-11 02:12:18.276086: val_loss -0.3248 +2026-04-11 02:12:18.278419: Pseudo dice [0.0, 0.0, 0.6748, 0.0, 0.0, 0.0, 0.8215] +2026-04-11 02:12:18.280826: Epoch time: 100.56 s +2026-04-11 02:12:19.406722: +2026-04-11 02:12:19.408581: Epoch 527 +2026-04-11 02:12:19.410429: Current learning rate: 0.00881 +2026-04-11 02:14:00.705097: train_loss -0.3472 +2026-04-11 02:14:00.711639: val_loss -0.3103 +2026-04-11 02:14:00.715492: Pseudo dice [0.0, 0.0, 0.7139, 0.0, 0.0, 0.0, 0.7452] +2026-04-11 02:14:00.718304: Epoch time: 101.3 s +2026-04-11 02:14:01.844896: +2026-04-11 02:14:01.846564: Epoch 528 +2026-04-11 02:14:01.848318: Current learning rate: 0.0088 +2026-04-11 02:15:42.318400: train_loss -0.339 +2026-04-11 02:15:42.324669: val_loss -0.3473 +2026-04-11 02:15:42.327287: Pseudo dice [0.0, 0.0, 0.6254, 0.0, 0.0, 0.0, 0.7042] +2026-04-11 02:15:42.330272: Epoch time: 100.48 s +2026-04-11 02:15:43.432488: +2026-04-11 02:15:43.434768: Epoch 529 +2026-04-11 02:15:43.437037: Current learning rate: 0.0088 +2026-04-11 02:17:23.811769: train_loss -0.3382 +2026-04-11 02:17:23.818948: val_loss -0.3657 +2026-04-11 02:17:23.821098: Pseudo dice [0.0, 0.0, 0.7688, 0.0, 0.0, 0.0, 0.7106] +2026-04-11 02:17:23.823478: Epoch time: 100.38 s +2026-04-11 02:17:24.886914: +2026-04-11 02:17:24.889210: Epoch 530 +2026-04-11 02:17:24.891084: Current learning rate: 0.0088 +2026-04-11 02:19:05.392476: train_loss -0.3647 +2026-04-11 02:19:05.398894: val_loss -0.3823 +2026-04-11 02:19:05.401006: Pseudo dice [0.0, 0.0, 0.758, 0.0, 0.0, 0.0, 0.8132] +2026-04-11 02:19:05.403346: Epoch time: 100.51 s +2026-04-11 02:19:06.473835: +2026-04-11 02:19:06.475785: Epoch 531 +2026-04-11 02:19:06.477383: Current learning rate: 0.0088 +2026-04-11 02:20:46.974445: train_loss -0.3553 +2026-04-11 02:20:46.979740: val_loss -0.3589 +2026-04-11 02:20:46.982040: Pseudo dice [0.0, 0.0, 0.8257, 0.0, 0.0, 0.0, 0.5569] +2026-04-11 02:20:46.984473: Epoch time: 100.5 s +2026-04-11 02:20:48.063499: +2026-04-11 02:20:48.079287: Epoch 532 +2026-04-11 02:20:48.083491: Current learning rate: 0.00879 +2026-04-11 02:22:28.889163: train_loss -0.3059 +2026-04-11 02:22:28.894763: val_loss -0.2924 +2026-04-11 02:22:28.897742: Pseudo dice [0.0, 0.0, 0.2115, 0.0, 0.0, 0.0, 0.4167] +2026-04-11 02:22:28.900059: Epoch time: 100.83 s +2026-04-11 02:22:30.042521: +2026-04-11 02:22:30.058028: Epoch 533 +2026-04-11 02:22:30.059836: Current learning rate: 0.00879 +2026-04-11 02:24:10.600342: train_loss -0.3122 +2026-04-11 02:24:10.607176: val_loss -0.3737 +2026-04-11 02:24:10.609021: Pseudo dice [0.0, 0.0, 0.6185, 0.0, 0.0, 0.0, 0.5662] +2026-04-11 02:24:10.611132: Epoch time: 100.56 s +2026-04-11 02:24:11.681985: +2026-04-11 02:24:11.683739: Epoch 534 +2026-04-11 02:24:11.685523: Current learning rate: 0.00879 +2026-04-11 02:25:51.980880: train_loss -0.3074 +2026-04-11 02:25:51.987494: val_loss -0.3154 +2026-04-11 02:25:51.989542: Pseudo dice [0.0, 0.0, 0.684, 0.0, 0.0, 0.0, 0.7594] +2026-04-11 02:25:51.991804: Epoch time: 100.3 s +2026-04-11 02:25:53.069726: +2026-04-11 02:25:53.073359: Epoch 535 +2026-04-11 02:25:53.075340: Current learning rate: 0.00879 +2026-04-11 02:27:33.614240: train_loss -0.339 +2026-04-11 02:27:33.619215: val_loss -0.3785 +2026-04-11 02:27:33.621072: Pseudo dice [0.0, 0.0, 0.627, 0.0, 0.0, 0.0, 0.7071] +2026-04-11 02:27:33.623178: Epoch time: 100.55 s +2026-04-11 02:27:34.702939: +2026-04-11 02:27:34.705714: Epoch 536 +2026-04-11 02:27:34.707395: Current learning rate: 0.00879 +2026-04-11 02:29:15.240213: train_loss -0.3094 +2026-04-11 02:29:15.247624: val_loss -0.2995 +2026-04-11 02:29:15.249901: Pseudo dice [0.0, 0.0, 0.6148, 0.0, 0.0, 0.0, 0.0] +2026-04-11 02:29:15.252332: Epoch time: 100.54 s +2026-04-11 02:29:16.336441: +2026-04-11 02:29:16.338887: Epoch 537 +2026-04-11 02:29:16.340643: Current learning rate: 0.00878 +2026-04-11 02:30:56.574687: train_loss -0.3143 +2026-04-11 02:30:56.580266: val_loss -0.318 +2026-04-11 02:30:56.581847: Pseudo dice [0.0, 0.0, 0.6558, 0.0, 0.0, 0.0, 0.0] +2026-04-11 02:30:56.583819: Epoch time: 100.24 s +2026-04-11 02:30:57.657727: +2026-04-11 02:30:57.659798: Epoch 538 +2026-04-11 02:30:57.661856: Current learning rate: 0.00878 +2026-04-11 02:32:38.159697: train_loss -0.3265 +2026-04-11 02:32:38.166232: val_loss -0.3199 +2026-04-11 02:32:38.168547: Pseudo dice [0.0, 0.0, 0.6828, 0.0, 0.0, 0.0, 0.8003] +2026-04-11 02:32:38.171127: Epoch time: 100.5 s +2026-04-11 02:32:39.258227: +2026-04-11 02:32:39.259935: Epoch 539 +2026-04-11 02:32:39.261863: Current learning rate: 0.00878 +2026-04-11 02:34:19.706604: train_loss -0.3301 +2026-04-11 02:34:19.711297: val_loss -0.2913 +2026-04-11 02:34:19.713468: Pseudo dice [0.0, 0.0, 0.5601, 0.0, 0.0, 0.0, 0.754] +2026-04-11 02:34:19.715595: Epoch time: 100.45 s +2026-04-11 02:34:20.788382: +2026-04-11 02:34:20.790114: Epoch 540 +2026-04-11 02:34:20.791974: Current learning rate: 0.00878 +2026-04-11 02:36:20.230845: train_loss -0.3328 +2026-04-11 02:36:20.236537: val_loss -0.3219 +2026-04-11 02:36:20.238914: Pseudo dice [0.0, 0.0, 0.5221, 0.0, 0.0, 0.0, 0.7614] +2026-04-11 02:36:20.241565: Epoch time: 119.45 s +2026-04-11 02:36:21.362877: +2026-04-11 02:36:21.365749: Epoch 541 +2026-04-11 02:36:21.367705: Current learning rate: 0.00877 +2026-04-11 02:38:01.601307: train_loss -0.3024 +2026-04-11 02:38:01.606373: val_loss -0.2831 +2026-04-11 02:38:01.608300: Pseudo dice [0.0, 0.0, 0.3304, 0.0, 0.0, 0.0, 0.5144] +2026-04-11 02:38:01.610558: Epoch time: 100.24 s +2026-04-11 02:38:02.694336: +2026-04-11 02:38:02.696532: Epoch 542 +2026-04-11 02:38:02.698229: Current learning rate: 0.00877 +2026-04-11 02:39:43.115819: train_loss -0.3441 +2026-04-11 02:39:43.120902: val_loss -0.3084 +2026-04-11 02:39:43.122311: Pseudo dice [0.0, 0.0, 0.7169, 0.0, 0.0, 0.0, 0.6476] +2026-04-11 02:39:43.124421: Epoch time: 100.42 s +2026-04-11 02:39:44.201255: +2026-04-11 02:39:44.203917: Epoch 543 +2026-04-11 02:39:44.205701: Current learning rate: 0.00877 +2026-04-11 02:41:24.542837: train_loss -0.3629 +2026-04-11 02:41:24.548255: val_loss -0.3097 +2026-04-11 02:41:24.550135: Pseudo dice [0.0, 0.0, 0.4468, 0.0, 0.0, 0.0, 0.6058] +2026-04-11 02:41:24.552254: Epoch time: 100.34 s +2026-04-11 02:41:25.653116: +2026-04-11 02:41:25.655206: Epoch 544 +2026-04-11 02:41:25.656975: Current learning rate: 0.00877 +2026-04-11 02:43:05.888242: train_loss -0.3667 +2026-04-11 02:43:05.895396: val_loss -0.3459 +2026-04-11 02:43:05.897579: Pseudo dice [0.0, 0.0, 0.7483, 0.0, 0.0, 0.0, 0.7635] +2026-04-11 02:43:05.900830: Epoch time: 100.24 s +2026-04-11 02:43:06.977028: +2026-04-11 02:43:06.979379: Epoch 545 +2026-04-11 02:43:06.980983: Current learning rate: 0.00876 +2026-04-11 02:44:49.751127: train_loss -0.3445 +2026-04-11 02:44:49.756066: val_loss -0.2443 +2026-04-11 02:44:49.758383: Pseudo dice [0.0, 0.0, 0.6595, 0.0, 0.0, 0.0, 0.0] +2026-04-11 02:44:49.760595: Epoch time: 102.78 s +2026-04-11 02:44:50.855583: +2026-04-11 02:44:50.857254: Epoch 546 +2026-04-11 02:44:50.858892: Current learning rate: 0.00876 +2026-04-11 02:46:31.199795: train_loss -0.3211 +2026-04-11 02:46:31.205241: val_loss -0.3274 +2026-04-11 02:46:31.207521: Pseudo dice [0.0, 0.0, 0.5466, 0.0, 0.0, 0.0, 0.0] +2026-04-11 02:46:31.209507: Epoch time: 100.35 s +2026-04-11 02:46:32.479815: +2026-04-11 02:46:32.481581: Epoch 547 +2026-04-11 02:46:32.483188: Current learning rate: 0.00876 +2026-04-11 02:48:13.738075: train_loss -0.333 +2026-04-11 02:48:13.744172: val_loss -0.3445 +2026-04-11 02:48:13.746126: Pseudo dice [0.0, 0.0, 0.7889, 0.0, 0.0, 0.0, 0.2773] +2026-04-11 02:48:13.748601: Epoch time: 101.26 s +2026-04-11 02:48:14.820367: +2026-04-11 02:48:14.822883: Epoch 548 +2026-04-11 02:48:14.825234: Current learning rate: 0.00876 +2026-04-11 02:49:55.131870: train_loss -0.3094 +2026-04-11 02:49:55.137767: val_loss -0.3282 +2026-04-11 02:49:55.139961: Pseudo dice [0.0, 0.0, 0.6118, 0.0, 0.0, 0.0853, 0.0] +2026-04-11 02:49:55.142146: Epoch time: 100.31 s +2026-04-11 02:49:56.289700: +2026-04-11 02:49:56.291474: Epoch 549 +2026-04-11 02:49:56.293195: Current learning rate: 0.00876 +2026-04-11 02:51:36.672952: train_loss -0.3204 +2026-04-11 02:51:36.678634: val_loss -0.358 +2026-04-11 02:51:36.680522: Pseudo dice [0.0, 0.0, 0.686, 0.0, 0.0, 0.6987, 0.5034] +2026-04-11 02:51:36.682476: Epoch time: 100.39 s +2026-04-11 02:51:39.345130: +2026-04-11 02:51:39.347302: Epoch 550 +2026-04-11 02:51:39.348985: Current learning rate: 0.00875 +2026-04-11 02:53:19.561486: train_loss -0.3512 +2026-04-11 02:53:19.566034: val_loss -0.3217 +2026-04-11 02:53:19.567952: Pseudo dice [0.0, 0.0, 0.4365, 0.0, 0.0, 0.1985, 0.6117] +2026-04-11 02:53:19.570122: Epoch time: 100.22 s +2026-04-11 02:53:20.647878: +2026-04-11 02:53:20.649745: Epoch 551 +2026-04-11 02:53:20.651262: Current learning rate: 0.00875 +2026-04-11 02:55:01.016802: train_loss -0.3368 +2026-04-11 02:55:01.021552: val_loss -0.3216 +2026-04-11 02:55:01.023281: Pseudo dice [0.0, 0.0, 0.6308, 0.0, 0.0, 0.4, 0.5893] +2026-04-11 02:55:01.025412: Epoch time: 100.37 s +2026-04-11 02:55:02.102138: +2026-04-11 02:55:02.110398: Epoch 552 +2026-04-11 02:55:02.112084: Current learning rate: 0.00875 +2026-04-11 02:57:17.408310: train_loss -0.3449 +2026-04-11 02:57:17.412883: val_loss -0.325 +2026-04-11 02:57:17.414922: Pseudo dice [0.0, 0.0, 0.5233, 0.0, 0.0, 0.0011, 0.5298] +2026-04-11 02:57:17.416933: Epoch time: 135.31 s +2026-04-11 02:57:18.485370: +2026-04-11 02:57:18.487284: Epoch 553 +2026-04-11 02:57:18.489075: Current learning rate: 0.00875 +2026-04-11 02:58:58.846047: train_loss -0.3313 +2026-04-11 02:58:58.850964: val_loss -0.2566 +2026-04-11 02:58:58.852787: Pseudo dice [0.0, 0.0, 0.7194, 0.0, 0.0, 0.6319, 0.5843] +2026-04-11 02:58:58.854823: Epoch time: 100.36 s +2026-04-11 02:58:59.927170: +2026-04-11 02:58:59.928766: Epoch 554 +2026-04-11 02:58:59.930413: Current learning rate: 0.00874 +2026-04-11 03:00:40.323435: train_loss -0.3277 +2026-04-11 03:00:40.329444: val_loss -0.3164 +2026-04-11 03:00:40.332340: Pseudo dice [0.0, 0.0, 0.8158, 0.0, 0.0, 0.0059, 0.408] +2026-04-11 03:00:40.336006: Epoch time: 100.4 s +2026-04-11 03:00:41.413732: +2026-04-11 03:00:41.415802: Epoch 555 +2026-04-11 03:00:41.417724: Current learning rate: 0.00874 +2026-04-11 03:02:21.910866: train_loss -0.3005 +2026-04-11 03:02:21.917939: val_loss -0.365 +2026-04-11 03:02:21.920094: Pseudo dice [0.0, 0.0, 0.7592, 0.0, 0.0, 0.0493, 0.6731] +2026-04-11 03:02:21.922349: Epoch time: 100.5 s +2026-04-11 03:02:22.996957: +2026-04-11 03:02:22.998666: Epoch 556 +2026-04-11 03:02:23.000352: Current learning rate: 0.00874 +2026-04-11 03:04:03.271372: train_loss -0.3362 +2026-04-11 03:04:03.276658: val_loss -0.3018 +2026-04-11 03:04:03.278939: Pseudo dice [0.0, 0.0, 0.5608, 0.0, 0.0, 0.2972, 0.2719] +2026-04-11 03:04:03.281029: Epoch time: 100.28 s +2026-04-11 03:04:04.356684: +2026-04-11 03:04:04.370865: Epoch 557 +2026-04-11 03:04:04.373827: Current learning rate: 0.00874 +2026-04-11 03:05:44.545131: train_loss -0.3025 +2026-04-11 03:05:44.552552: val_loss -0.3375 +2026-04-11 03:05:44.555652: Pseudo dice [0.0, 0.0, 0.7782, 0.0, 0.0, 0.0, 0.0249] +2026-04-11 03:05:44.558066: Epoch time: 100.19 s +2026-04-11 03:05:45.635973: +2026-04-11 03:05:45.638011: Epoch 558 +2026-04-11 03:05:45.639987: Current learning rate: 0.00874 +2026-04-11 03:07:25.861717: train_loss -0.2981 +2026-04-11 03:07:25.867450: val_loss -0.2857 +2026-04-11 03:07:25.869387: Pseudo dice [0.0, 0.0, 0.3992, 0.0, 0.0, 0.5095, 0.5598] +2026-04-11 03:07:25.871461: Epoch time: 100.23 s +2026-04-11 03:07:26.958813: +2026-04-11 03:07:26.960814: Epoch 559 +2026-04-11 03:07:26.962710: Current learning rate: 0.00873 +2026-04-11 03:09:08.056000: train_loss -0.2991 +2026-04-11 03:09:08.062049: val_loss -0.2478 +2026-04-11 03:09:08.063894: Pseudo dice [0.0, 0.0, 0.1224, 0.0, 0.0, 0.0266, 0.0] +2026-04-11 03:09:08.067002: Epoch time: 101.1 s +2026-04-11 03:09:09.123841: +2026-04-11 03:09:09.125747: Epoch 560 +2026-04-11 03:09:09.127226: Current learning rate: 0.00873 +2026-04-11 03:10:49.606586: train_loss -0.2831 +2026-04-11 03:10:49.612892: val_loss -0.3043 +2026-04-11 03:10:49.614549: Pseudo dice [0.0, 0.0, 0.4844, 0.0, 0.0, 0.0, 0.0] +2026-04-11 03:10:49.617023: Epoch time: 100.49 s +2026-04-11 03:10:50.694563: +2026-04-11 03:10:50.696390: Epoch 561 +2026-04-11 03:10:50.698006: Current learning rate: 0.00873 +2026-04-11 03:12:31.328403: train_loss -0.2994 +2026-04-11 03:12:31.334292: val_loss -0.2871 +2026-04-11 03:12:31.336643: Pseudo dice [0.0, 0.0, 0.3772, 0.0, 0.0, 0.1145, 0.3768] +2026-04-11 03:12:31.338875: Epoch time: 100.64 s +2026-04-11 03:12:32.428680: +2026-04-11 03:12:32.430290: Epoch 562 +2026-04-11 03:12:32.431940: Current learning rate: 0.00873 +2026-04-11 03:14:12.930675: train_loss -0.3447 +2026-04-11 03:14:12.936271: val_loss -0.2623 +2026-04-11 03:14:12.938058: Pseudo dice [0.0, 0.0, 0.2827, 0.0, 0.0, 0.8087, 0.4598] +2026-04-11 03:14:12.940514: Epoch time: 100.51 s +2026-04-11 03:14:14.018218: +2026-04-11 03:14:14.020061: Epoch 563 +2026-04-11 03:14:14.021760: Current learning rate: 0.00872 +2026-04-11 03:15:54.435360: train_loss -0.3331 +2026-04-11 03:15:54.460380: val_loss -0.3731 +2026-04-11 03:15:54.463322: Pseudo dice [0.0, 0.0, 0.6827, 0.0, 0.0, 0.1718, 0.5535] +2026-04-11 03:15:54.471532: Epoch time: 100.42 s +2026-04-11 03:15:55.552460: +2026-04-11 03:15:55.554528: Epoch 564 +2026-04-11 03:15:55.556103: Current learning rate: 0.00872 +2026-04-11 03:17:35.879139: train_loss -0.3118 +2026-04-11 03:17:35.885839: val_loss -0.3201 +2026-04-11 03:17:35.887875: Pseudo dice [0.0, 0.0, 0.71, 0.0, 0.0, 0.0307, 0.013] +2026-04-11 03:17:35.889947: Epoch time: 100.33 s +2026-04-11 03:17:36.957333: +2026-04-11 03:17:36.959264: Epoch 565 +2026-04-11 03:17:36.960700: Current learning rate: 0.00872 +2026-04-11 03:19:17.154824: train_loss -0.335 +2026-04-11 03:19:17.161889: val_loss -0.3421 +2026-04-11 03:19:17.164158: Pseudo dice [0.0, 0.0, 0.6258, 0.0, 0.0, 0.2679, 0.242] +2026-04-11 03:19:17.167014: Epoch time: 100.2 s +2026-04-11 03:19:18.258977: +2026-04-11 03:19:18.260802: Epoch 566 +2026-04-11 03:19:18.262478: Current learning rate: 0.00872 +2026-04-11 03:20:58.428370: train_loss -0.3295 +2026-04-11 03:20:58.435683: val_loss -0.3769 +2026-04-11 03:20:58.438356: Pseudo dice [0.0, 0.0, 0.6415, 0.0, 0.0, 0.5288, 0.6354] +2026-04-11 03:20:58.440769: Epoch time: 100.17 s +2026-04-11 03:21:00.417147: +2026-04-11 03:21:00.419238: Epoch 567 +2026-04-11 03:21:00.420927: Current learning rate: 0.00871 +2026-04-11 03:22:40.832740: train_loss -0.3197 +2026-04-11 03:22:40.839219: val_loss -0.3236 +2026-04-11 03:22:40.841034: Pseudo dice [0.0, 0.0, 0.7232, 0.0, 0.0, 0.6566, 0.6593] +2026-04-11 03:22:40.843370: Epoch time: 100.42 s +2026-04-11 03:22:41.933799: +2026-04-11 03:22:41.936202: Epoch 568 +2026-04-11 03:22:41.937587: Current learning rate: 0.00871 +2026-04-11 03:24:22.199848: train_loss -0.3242 +2026-04-11 03:24:22.205309: val_loss -0.3454 +2026-04-11 03:24:22.207319: Pseudo dice [0.0, 0.0, 0.792, 0.0, 0.0, 0.3304, 0.463] +2026-04-11 03:24:22.209714: Epoch time: 100.27 s +2026-04-11 03:24:23.293590: +2026-04-11 03:24:23.295668: Epoch 569 +2026-04-11 03:24:23.297152: Current learning rate: 0.00871 +2026-04-11 03:26:03.511249: train_loss -0.3236 +2026-04-11 03:26:03.520944: val_loss -0.3567 +2026-04-11 03:26:03.524585: Pseudo dice [0.0, 0.0, 0.5855, 0.0, 0.0, 0.3648, 0.3721] +2026-04-11 03:26:03.526871: Epoch time: 100.22 s +2026-04-11 03:26:04.612991: +2026-04-11 03:26:04.614726: Epoch 570 +2026-04-11 03:26:04.616734: Current learning rate: 0.00871 +2026-04-11 03:27:44.672474: train_loss -0.3238 +2026-04-11 03:27:44.677945: val_loss -0.2385 +2026-04-11 03:27:44.680407: Pseudo dice [0.0, 0.0, 0.1144, 0.0, 0.0, 0.0033, 0.0241] +2026-04-11 03:27:44.682555: Epoch time: 100.06 s +2026-04-11 03:27:45.764770: +2026-04-11 03:27:45.766409: Epoch 571 +2026-04-11 03:27:45.768023: Current learning rate: 0.00871 +2026-04-11 03:29:26.027079: train_loss -0.2958 +2026-04-11 03:29:26.032019: val_loss -0.3168 +2026-04-11 03:29:26.034099: Pseudo dice [0.0, 0.0, 0.6173, 0.0, 0.0, 0.0, 0.6827] +2026-04-11 03:29:26.036732: Epoch time: 100.27 s +2026-04-11 03:29:27.099548: +2026-04-11 03:29:27.101380: Epoch 572 +2026-04-11 03:29:27.102933: Current learning rate: 0.0087 +2026-04-11 03:31:07.352391: train_loss -0.2999 +2026-04-11 03:31:07.358168: val_loss -0.3503 +2026-04-11 03:31:07.360018: Pseudo dice [0.0, 0.0, 0.562, 0.0, 0.0, 0.0, 0.3511] +2026-04-11 03:31:07.362076: Epoch time: 100.26 s +2026-04-11 03:31:08.450173: +2026-04-11 03:31:08.452012: Epoch 573 +2026-04-11 03:31:08.453519: Current learning rate: 0.0087 +2026-04-11 03:32:48.276007: train_loss -0.3186 +2026-04-11 03:32:48.282899: val_loss -0.3621 +2026-04-11 03:32:48.285269: Pseudo dice [0.0, 0.0, 0.6773, 0.0, 0.0, 0.6163, 0.2486] +2026-04-11 03:32:48.287565: Epoch time: 99.83 s +2026-04-11 03:32:49.376929: +2026-04-11 03:32:49.378746: Epoch 574 +2026-04-11 03:32:49.380568: Current learning rate: 0.0087 +2026-04-11 03:34:29.507267: train_loss -0.3482 +2026-04-11 03:34:29.513696: val_loss -0.3251 +2026-04-11 03:34:29.517024: Pseudo dice [0.0, 0.0, 0.6803, 0.0, 0.0, 0.5603, 0.392] +2026-04-11 03:34:29.520180: Epoch time: 100.13 s +2026-04-11 03:34:30.601496: +2026-04-11 03:34:30.603372: Epoch 575 +2026-04-11 03:34:30.604996: Current learning rate: 0.0087 +2026-04-11 03:36:10.892273: train_loss -0.3249 +2026-04-11 03:36:10.898338: val_loss -0.2433 +2026-04-11 03:36:10.900524: Pseudo dice [0.0, 0.0, 0.7129, 0.0, 0.0, 0.3553, 0.0] +2026-04-11 03:36:10.903438: Epoch time: 100.29 s +2026-04-11 03:36:11.999888: +2026-04-11 03:36:12.002542: Epoch 576 +2026-04-11 03:36:12.004591: Current learning rate: 0.00869 +2026-04-11 03:37:52.182931: train_loss -0.3032 +2026-04-11 03:37:52.188768: val_loss -0.2543 +2026-04-11 03:37:52.191657: Pseudo dice [0.0, 0.0, 0.8088, 0.0, 0.0, 0.0439, 0.0] +2026-04-11 03:37:52.194137: Epoch time: 100.19 s +2026-04-11 03:37:53.274338: +2026-04-11 03:37:53.276008: Epoch 577 +2026-04-11 03:37:53.277674: Current learning rate: 0.00869 +2026-04-11 03:39:33.574238: train_loss -0.3337 +2026-04-11 03:39:33.580224: val_loss -0.3248 +2026-04-11 03:39:33.582031: Pseudo dice [0.0, 0.0, 0.5184, 0.0, 0.0, 0.0002, 0.0] +2026-04-11 03:39:33.584230: Epoch time: 100.3 s +2026-04-11 03:39:34.671373: +2026-04-11 03:39:34.673843: Epoch 578 +2026-04-11 03:39:34.675985: Current learning rate: 0.00869 +2026-04-11 03:41:15.014493: train_loss -0.2609 +2026-04-11 03:41:15.019759: val_loss -0.2808 +2026-04-11 03:41:15.021939: Pseudo dice [0.0, 0.0, 0.4572, 0.0, 0.0, 0.0, 0.0] +2026-04-11 03:41:15.024482: Epoch time: 100.35 s +2026-04-11 03:41:16.130885: +2026-04-11 03:41:16.132930: Epoch 579 +2026-04-11 03:41:16.134723: Current learning rate: 0.00869 +2026-04-11 03:42:56.300143: train_loss -0.3096 +2026-04-11 03:42:56.306178: val_loss -0.2772 +2026-04-11 03:42:56.308607: Pseudo dice [0.0, 0.0, 0.6805, 0.0, 0.0, 0.0, 0.0] +2026-04-11 03:42:56.311064: Epoch time: 100.17 s +2026-04-11 03:42:57.406922: +2026-04-11 03:42:57.409572: Epoch 580 +2026-04-11 03:42:57.411356: Current learning rate: 0.00868 +2026-04-11 03:44:37.684905: train_loss -0.3024 +2026-04-11 03:44:37.689680: val_loss -0.3351 +2026-04-11 03:44:37.692098: Pseudo dice [0.0, 0.0, 0.8135, 0.0, 0.0, 0.0007, 0.0] +2026-04-11 03:44:37.695088: Epoch time: 100.28 s +2026-04-11 03:44:38.783024: +2026-04-11 03:44:38.784964: Epoch 581 +2026-04-11 03:44:38.786393: Current learning rate: 0.00868 +2026-04-11 03:46:19.259391: train_loss -0.3365 +2026-04-11 03:46:19.265154: val_loss -0.3755 +2026-04-11 03:46:19.267213: Pseudo dice [0.0, 0.0, 0.673, 0.0, 0.0, 0.3975, 0.5747] +2026-04-11 03:46:19.269707: Epoch time: 100.48 s +2026-04-11 03:46:20.360651: +2026-04-11 03:46:20.362983: Epoch 582 +2026-04-11 03:46:20.364480: Current learning rate: 0.00868 +2026-04-11 03:48:00.799967: train_loss -0.3531 +2026-04-11 03:48:00.805530: val_loss -0.3533 +2026-04-11 03:48:00.807856: Pseudo dice [0.0, 0.0, 0.7897, 0.0, 0.0, 0.6775, 0.5953] +2026-04-11 03:48:00.810017: Epoch time: 100.44 s +2026-04-11 03:48:01.905415: +2026-04-11 03:48:01.907681: Epoch 583 +2026-04-11 03:48:01.909999: Current learning rate: 0.00868 +2026-04-11 03:49:42.196057: train_loss -0.3076 +2026-04-11 03:49:42.203145: val_loss -0.3032 +2026-04-11 03:49:42.206498: Pseudo dice [0.0, 0.0, 0.6051, 0.0, 0.0, 0.4856, 0.5875] +2026-04-11 03:49:42.210590: Epoch time: 100.29 s +2026-04-11 03:49:43.318074: +2026-04-11 03:49:43.320035: Epoch 584 +2026-04-11 03:49:43.322006: Current learning rate: 0.00868 +2026-04-11 03:51:23.694489: train_loss -0.3287 +2026-04-11 03:51:23.699879: val_loss -0.3552 +2026-04-11 03:51:23.702606: Pseudo dice [0.0, 0.0, 0.6343, 0.0, 0.0, 0.5482, 0.3769] +2026-04-11 03:51:23.705537: Epoch time: 100.38 s +2026-04-11 03:51:24.782472: +2026-04-11 03:51:24.784361: Epoch 585 +2026-04-11 03:51:24.785946: Current learning rate: 0.00867 +2026-04-11 03:53:05.066307: train_loss -0.3459 +2026-04-11 03:53:05.072236: val_loss -0.3919 +2026-04-11 03:53:05.074259: Pseudo dice [0.0, 0.0, 0.6886, 0.0, 0.0, 0.5645, 0.7259] +2026-04-11 03:53:05.076541: Epoch time: 100.29 s +2026-04-11 03:53:06.156528: +2026-04-11 03:53:06.158541: Epoch 586 +2026-04-11 03:53:06.160459: Current learning rate: 0.00867 +2026-04-11 03:54:46.488375: train_loss -0.3254 +2026-04-11 03:54:46.495618: val_loss -0.3543 +2026-04-11 03:54:46.498135: Pseudo dice [0.0, 0.0, 0.6744, 0.0, 0.0, 0.3185, 0.1294] +2026-04-11 03:54:46.500419: Epoch time: 100.33 s +2026-04-11 03:54:47.597972: +2026-04-11 03:54:47.599775: Epoch 587 +2026-04-11 03:54:47.601427: Current learning rate: 0.00867 +2026-04-11 03:56:29.512581: train_loss -0.3362 +2026-04-11 03:56:29.518378: val_loss -0.3433 +2026-04-11 03:56:29.520266: Pseudo dice [0.0, 0.0, 0.5487, 0.0, 0.0, 0.4745, 0.7493] +2026-04-11 03:56:29.522648: Epoch time: 101.92 s +2026-04-11 03:56:30.860556: +2026-04-11 03:56:30.862365: Epoch 588 +2026-04-11 03:56:30.863991: Current learning rate: 0.00867 +2026-04-11 03:58:11.141497: train_loss -0.3284 +2026-04-11 03:58:11.149235: val_loss -0.383 +2026-04-11 03:58:11.151314: Pseudo dice [0.0, 0.0, 0.8079, 0.0, 0.0, 0.0085, 0.748] +2026-04-11 03:58:11.153809: Epoch time: 100.28 s +2026-04-11 03:58:12.236375: +2026-04-11 03:58:12.238383: Epoch 589 +2026-04-11 03:58:12.239924: Current learning rate: 0.00866 +2026-04-11 03:59:52.714837: train_loss -0.2964 +2026-04-11 03:59:52.720588: val_loss -0.3722 +2026-04-11 03:59:52.722847: Pseudo dice [0.0, 0.0, 0.7346, 0.0, 0.0, 0.0829, 0.6311] +2026-04-11 03:59:52.725480: Epoch time: 100.48 s +2026-04-11 03:59:53.835741: +2026-04-11 03:59:53.837974: Epoch 590 +2026-04-11 03:59:53.839807: Current learning rate: 0.00866 +2026-04-11 04:01:34.344053: train_loss -0.3178 +2026-04-11 04:01:34.349795: val_loss -0.3098 +2026-04-11 04:01:34.352793: Pseudo dice [0.0, 0.0, 0.5785, 0.0, 0.0, 0.5873, 0.163] +2026-04-11 04:01:34.355365: Epoch time: 100.51 s +2026-04-11 04:01:35.456712: +2026-04-11 04:01:35.458694: Epoch 591 +2026-04-11 04:01:35.460233: Current learning rate: 0.00866 +2026-04-11 04:03:15.860265: train_loss -0.3337 +2026-04-11 04:03:15.866007: val_loss -0.3744 +2026-04-11 04:03:15.867913: Pseudo dice [0.0, 0.0, 0.6864, 0.0, 0.0, 0.6065, 0.5688] +2026-04-11 04:03:15.869978: Epoch time: 100.41 s +2026-04-11 04:03:16.964628: +2026-04-11 04:03:16.966303: Epoch 592 +2026-04-11 04:03:16.968411: Current learning rate: 0.00866 +2026-04-11 04:04:57.167236: train_loss -0.3431 +2026-04-11 04:04:57.172235: val_loss -0.3586 +2026-04-11 04:04:57.174193: Pseudo dice [0.0, 0.0, 0.5698, 0.0, 0.0, 0.7419, 0.6184] +2026-04-11 04:04:57.176576: Epoch time: 100.21 s +2026-04-11 04:04:58.262478: +2026-04-11 04:04:58.264671: Epoch 593 +2026-04-11 04:04:58.266568: Current learning rate: 0.00866 +2026-04-11 04:06:38.685755: train_loss -0.3476 +2026-04-11 04:06:38.691643: val_loss -0.3523 +2026-04-11 04:06:38.693831: Pseudo dice [0.0, 0.0, 0.712, 0.0, 0.0, 0.4675, 0.7354] +2026-04-11 04:06:38.696225: Epoch time: 100.43 s +2026-04-11 04:06:39.800890: +2026-04-11 04:06:39.803317: Epoch 594 +2026-04-11 04:06:39.805710: Current learning rate: 0.00865 +2026-04-11 04:08:20.045293: train_loss -0.3656 +2026-04-11 04:08:20.050779: val_loss -0.3954 +2026-04-11 04:08:20.052732: Pseudo dice [0.0, 0.0, 0.8094, 0.0, 0.0, 0.4812, 0.8229] +2026-04-11 04:08:20.054523: Epoch time: 100.25 s +2026-04-11 04:08:21.158241: +2026-04-11 04:08:21.160773: Epoch 595 +2026-04-11 04:08:21.162879: Current learning rate: 0.00865 +2026-04-11 04:10:01.329297: train_loss -0.3279 +2026-04-11 04:10:01.334574: val_loss -0.327 +2026-04-11 04:10:01.336398: Pseudo dice [0.0, 0.0, 0.7127, 0.0, 0.0, 0.2445, 0.1154] +2026-04-11 04:10:01.338466: Epoch time: 100.17 s +2026-04-11 04:10:02.435224: +2026-04-11 04:10:02.437560: Epoch 596 +2026-04-11 04:10:02.439253: Current learning rate: 0.00865 +2026-04-11 04:11:42.706245: train_loss -0.3482 +2026-04-11 04:11:42.711315: val_loss -0.3814 +2026-04-11 04:11:42.712948: Pseudo dice [0.0, 0.0, 0.5929, 0.0, 0.0, 0.757, 0.6195] +2026-04-11 04:11:42.714946: Epoch time: 100.27 s +2026-04-11 04:11:43.822752: +2026-04-11 04:11:43.824473: Epoch 597 +2026-04-11 04:11:43.825924: Current learning rate: 0.00865 +2026-04-11 04:13:24.525044: train_loss -0.3506 +2026-04-11 04:13:24.532914: val_loss -0.3146 +2026-04-11 04:13:24.535790: Pseudo dice [0.0, 0.0, 0.5875, 0.0, 0.0, 0.5811, 0.2885] +2026-04-11 04:13:24.539394: Epoch time: 100.71 s +2026-04-11 04:13:25.666463: +2026-04-11 04:13:25.668687: Epoch 598 +2026-04-11 04:13:25.670389: Current learning rate: 0.00864 +2026-04-11 04:15:06.142617: train_loss -0.3207 +2026-04-11 04:15:06.149021: val_loss -0.2087 +2026-04-11 04:15:06.151189: Pseudo dice [0.0, 0.0, 0.3143, 0.0, 0.0, 0.0, 0.0] +2026-04-11 04:15:06.153440: Epoch time: 100.48 s +2026-04-11 04:15:07.264176: +2026-04-11 04:15:07.266393: Epoch 599 +2026-04-11 04:15:07.268971: Current learning rate: 0.00864 +2026-04-11 04:16:48.087668: train_loss -0.2831 +2026-04-11 04:16:48.095290: val_loss -0.2444 +2026-04-11 04:16:48.097809: Pseudo dice [0.0, 0.0, 0.3101, 0.0, 0.0, 0.3778, 0.0] +2026-04-11 04:16:48.100387: Epoch time: 100.83 s +2026-04-11 04:16:50.859851: +2026-04-11 04:16:50.863519: Epoch 600 +2026-04-11 04:16:50.865986: Current learning rate: 0.00864 +2026-04-11 04:18:30.810201: train_loss -0.3392 +2026-04-11 04:18:30.816382: val_loss -0.2977 +2026-04-11 04:18:30.819486: Pseudo dice [0.0, 0.0, 0.3475, 0.0, 0.0, 0.5553, 0.5418] +2026-04-11 04:18:30.822391: Epoch time: 99.95 s +2026-04-11 04:18:31.942704: +2026-04-11 04:18:31.944716: Epoch 601 +2026-04-11 04:18:31.946263: Current learning rate: 0.00864 +2026-04-11 04:20:12.196771: train_loss -0.3245 +2026-04-11 04:20:12.202973: val_loss -0.2712 +2026-04-11 04:20:12.205971: Pseudo dice [0.0, 0.0, 0.2651, 0.0, 0.0, 0.5847, 0.1148] +2026-04-11 04:20:12.208481: Epoch time: 100.26 s +2026-04-11 04:20:13.313664: +2026-04-11 04:20:13.315889: Epoch 602 +2026-04-11 04:20:13.317768: Current learning rate: 0.00863 +2026-04-11 04:21:53.525979: train_loss -0.3138 +2026-04-11 04:21:53.532780: val_loss -0.3512 +2026-04-11 04:21:53.535020: Pseudo dice [0.0, 0.0, 0.6046, 0.0, 0.0, 0.5146, 0.5158] +2026-04-11 04:21:53.537889: Epoch time: 100.22 s +2026-04-11 04:21:54.679004: +2026-04-11 04:21:54.682084: Epoch 603 +2026-04-11 04:21:54.683946: Current learning rate: 0.00863 +2026-04-11 04:23:34.784706: train_loss -0.3365 +2026-04-11 04:23:34.790158: val_loss -0.264 +2026-04-11 04:23:34.792127: Pseudo dice [0.0, 0.0, 0.5276, 0.0, 0.0, 0.2755, 0.6487] +2026-04-11 04:23:34.794180: Epoch time: 100.11 s +2026-04-11 04:23:35.873251: +2026-04-11 04:23:35.874841: Epoch 604 +2026-04-11 04:23:35.876301: Current learning rate: 0.00863 +2026-04-11 04:25:16.060824: train_loss -0.3141 +2026-04-11 04:25:16.067884: val_loss -0.3388 +2026-04-11 04:25:16.070520: Pseudo dice [0.0, 0.0, 0.6046, 0.0, 0.0, 0.5942, 0.5411] +2026-04-11 04:25:16.072951: Epoch time: 100.19 s +2026-04-11 04:25:17.175206: +2026-04-11 04:25:17.177473: Epoch 605 +2026-04-11 04:25:17.179883: Current learning rate: 0.00863 +2026-04-11 04:26:57.721476: train_loss -0.3303 +2026-04-11 04:26:57.726930: val_loss -0.3448 +2026-04-11 04:26:57.729243: Pseudo dice [0.0, 0.0, 0.7242, 0.0, 0.0, 0.221, 0.0367] +2026-04-11 04:26:57.732264: Epoch time: 100.55 s +2026-04-11 04:26:58.841718: +2026-04-11 04:26:58.843468: Epoch 606 +2026-04-11 04:26:58.845411: Current learning rate: 0.00863 +2026-04-11 04:28:39.914943: train_loss -0.3412 +2026-04-11 04:28:39.920443: val_loss -0.3716 +2026-04-11 04:28:39.922190: Pseudo dice [0.0, 0.0, 0.6979, 0.0, 0.0, 0.6662, 0.8004] +2026-04-11 04:28:39.924273: Epoch time: 101.08 s +2026-04-11 04:28:41.020285: +2026-04-11 04:28:41.023301: Epoch 607 +2026-04-11 04:28:41.025186: Current learning rate: 0.00862 +2026-04-11 04:30:21.267845: train_loss -0.3537 +2026-04-11 04:30:21.272997: val_loss -0.2003 +2026-04-11 04:30:21.274973: Pseudo dice [0.0, 0.0, 0.5391, 0.0, 0.0, 0.2593, 0.4925] +2026-04-11 04:30:21.277386: Epoch time: 100.25 s +2026-04-11 04:30:22.384888: +2026-04-11 04:30:22.386924: Epoch 608 +2026-04-11 04:30:22.388891: Current learning rate: 0.00862 +2026-04-11 04:32:02.630589: train_loss -0.3116 +2026-04-11 04:32:02.636933: val_loss -0.3322 +2026-04-11 04:32:02.639163: Pseudo dice [0.0, 0.0, 0.5545, 0.0, 0.0, 0.2596, 0.3001] +2026-04-11 04:32:02.641248: Epoch time: 100.25 s +2026-04-11 04:32:03.717206: +2026-04-11 04:32:03.719942: Epoch 609 +2026-04-11 04:32:03.721996: Current learning rate: 0.00862 +2026-04-11 04:33:43.930609: train_loss -0.2924 +2026-04-11 04:33:43.938892: val_loss -0.3465 +2026-04-11 04:33:43.941082: Pseudo dice [0.0, 0.0, 0.6448, 0.0, 0.0, 0.0, 0.3317] +2026-04-11 04:33:43.943731: Epoch time: 100.22 s +2026-04-11 04:33:45.055151: +2026-04-11 04:33:45.056985: Epoch 610 +2026-04-11 04:33:45.058395: Current learning rate: 0.00862 +2026-04-11 04:35:25.640878: train_loss -0.3216 +2026-04-11 04:35:25.647348: val_loss -0.3229 +2026-04-11 04:35:25.650348: Pseudo dice [0.0, 0.0, 0.6691, 0.0, 0.0, 0.4045, 0.6547] +2026-04-11 04:35:25.653244: Epoch time: 100.59 s +2026-04-11 04:35:26.752944: +2026-04-11 04:35:26.754726: Epoch 611 +2026-04-11 04:35:26.756348: Current learning rate: 0.00861 +2026-04-11 04:37:07.093093: train_loss -0.3474 +2026-04-11 04:37:07.100016: val_loss -0.2958 +2026-04-11 04:37:07.103192: Pseudo dice [0.0, 0.0, 0.3458, 0.0, 0.0, 0.4839, 0.0689] +2026-04-11 04:37:07.105615: Epoch time: 100.34 s +2026-04-11 04:37:08.208249: +2026-04-11 04:37:08.210576: Epoch 612 +2026-04-11 04:37:08.212686: Current learning rate: 0.00861 +2026-04-11 04:38:48.574511: train_loss -0.3146 +2026-04-11 04:38:48.581060: val_loss -0.3712 +2026-04-11 04:38:48.582995: Pseudo dice [0.0, 0.0, 0.6662, 0.0, 0.0, 0.6554, 0.6963] +2026-04-11 04:38:48.585823: Epoch time: 100.37 s +2026-04-11 04:38:49.676153: +2026-04-11 04:38:49.678503: Epoch 613 +2026-04-11 04:38:49.680393: Current learning rate: 0.00861 +2026-04-11 04:40:29.953840: train_loss -0.3137 +2026-04-11 04:40:29.959697: val_loss -0.3318 +2026-04-11 04:40:29.961805: Pseudo dice [0.0, 0.0, 0.6189, 0.0, 0.0, 0.5999, 0.0833] +2026-04-11 04:40:29.963759: Epoch time: 100.28 s +2026-04-11 04:40:31.060692: +2026-04-11 04:40:31.063517: Epoch 614 +2026-04-11 04:40:31.065267: Current learning rate: 0.00861 +2026-04-11 04:42:11.315504: train_loss -0.3452 +2026-04-11 04:42:11.320081: val_loss -0.358 +2026-04-11 04:42:11.322338: Pseudo dice [0.0, 0.0, 0.3811, 0.0, 0.0, 0.4883, 0.4004] +2026-04-11 04:42:11.324606: Epoch time: 100.26 s +2026-04-11 04:42:12.429384: +2026-04-11 04:42:12.431291: Epoch 615 +2026-04-11 04:42:12.433212: Current learning rate: 0.0086 +2026-04-11 04:43:52.636075: train_loss -0.3288 +2026-04-11 04:43:52.641429: val_loss -0.3133 +2026-04-11 04:43:52.643136: Pseudo dice [0.0, 0.0, 0.6891, 0.0, 0.0, 0.5669, 0.6327] +2026-04-11 04:43:52.644811: Epoch time: 100.21 s +2026-04-11 04:43:53.981272: +2026-04-11 04:43:53.983141: Epoch 616 +2026-04-11 04:43:53.985045: Current learning rate: 0.0086 +2026-04-11 04:45:34.361570: train_loss -0.3291 +2026-04-11 04:45:34.368767: val_loss -0.2434 +2026-04-11 04:45:34.370577: Pseudo dice [0.0, 0.0, 0.4924, 0.0, 0.0, 0.1401, 0.5572] +2026-04-11 04:45:34.373194: Epoch time: 100.38 s +2026-04-11 04:45:35.468270: +2026-04-11 04:45:35.469860: Epoch 617 +2026-04-11 04:45:35.471779: Current learning rate: 0.0086 +2026-04-11 04:47:15.692863: train_loss -0.3156 +2026-04-11 04:47:15.699480: val_loss -0.3014 +2026-04-11 04:47:15.701816: Pseudo dice [0.0, 0.0, 0.5302, 0.0, 0.0, 0.6296, 0.3994] +2026-04-11 04:47:15.704138: Epoch time: 100.23 s +2026-04-11 04:47:16.795769: +2026-04-11 04:47:16.797305: Epoch 618 +2026-04-11 04:47:16.799289: Current learning rate: 0.0086 +2026-04-11 04:48:56.956389: train_loss -0.3542 +2026-04-11 04:48:56.961328: val_loss -0.3613 +2026-04-11 04:48:56.963532: Pseudo dice [0.0, 0.0, 0.6855, 0.0, 0.0, 0.5249, 0.3162] +2026-04-11 04:48:56.965562: Epoch time: 100.16 s +2026-04-11 04:48:58.060337: +2026-04-11 04:48:58.062324: Epoch 619 +2026-04-11 04:48:58.064091: Current learning rate: 0.0086 +2026-04-11 04:50:38.452528: train_loss -0.3555 +2026-04-11 04:50:38.458973: val_loss -0.3706 +2026-04-11 04:50:38.461355: Pseudo dice [0.0, 0.0, 0.7584, 0.0, 0.0, 0.6432, 0.6077] +2026-04-11 04:50:38.464041: Epoch time: 100.4 s +2026-04-11 04:50:39.557698: +2026-04-11 04:50:39.559515: Epoch 620 +2026-04-11 04:50:39.560998: Current learning rate: 0.00859 +2026-04-11 04:52:19.813956: train_loss -0.3314 +2026-04-11 04:52:19.818977: val_loss -0.3029 +2026-04-11 04:52:19.820797: Pseudo dice [0.0, 0.0, 0.7591, 0.0, 0.0, 0.2764, 0.064] +2026-04-11 04:52:19.823315: Epoch time: 100.26 s +2026-04-11 04:52:21.086039: +2026-04-11 04:52:21.087682: Epoch 621 +2026-04-11 04:52:21.089422: Current learning rate: 0.00859 +2026-04-11 04:54:01.594450: train_loss -0.3206 +2026-04-11 04:54:01.600905: val_loss -0.3644 +2026-04-11 04:54:01.603333: Pseudo dice [0.0, 0.0, 0.7174, 0.0, 0.0, 0.0333, 0.4022] +2026-04-11 04:54:01.606780: Epoch time: 100.51 s +2026-04-11 04:54:02.726464: +2026-04-11 04:54:02.728179: Epoch 622 +2026-04-11 04:54:02.729877: Current learning rate: 0.00859 +2026-04-11 04:55:43.023550: train_loss -0.3339 +2026-04-11 04:55:43.031049: val_loss -0.369 +2026-04-11 04:55:43.034223: Pseudo dice [0.0, 0.0, 0.7356, 0.0, 0.0, 0.0, 0.6437] +2026-04-11 04:55:43.037423: Epoch time: 100.3 s +2026-04-11 04:55:44.132136: +2026-04-11 04:55:44.134343: Epoch 623 +2026-04-11 04:55:44.136331: Current learning rate: 0.00859 +2026-04-11 04:57:24.650870: train_loss -0.3177 +2026-04-11 04:57:24.660292: val_loss -0.3795 +2026-04-11 04:57:24.663295: Pseudo dice [0.0, 0.0, 0.2429, 0.0, 0.0, 0.764, 0.3604] +2026-04-11 04:57:24.667138: Epoch time: 100.52 s +2026-04-11 04:57:25.783612: +2026-04-11 04:57:25.785881: Epoch 624 +2026-04-11 04:57:25.787598: Current learning rate: 0.00858 +2026-04-11 04:59:05.989133: train_loss -0.3352 +2026-04-11 04:59:05.994175: val_loss -0.318 +2026-04-11 04:59:05.996098: Pseudo dice [0.0, 0.0, 0.5015, 0.0, 0.0, 0.421, 0.5275] +2026-04-11 04:59:05.998389: Epoch time: 100.21 s +2026-04-11 04:59:07.098506: +2026-04-11 04:59:07.100742: Epoch 625 +2026-04-11 04:59:07.102364: Current learning rate: 0.00858 +2026-04-11 05:00:47.561221: train_loss -0.3045 +2026-04-11 05:00:47.566950: val_loss -0.3772 +2026-04-11 05:00:47.571417: Pseudo dice [0.0, 0.0, 0.6717, 0.0, 0.0, 0.6829, 0.6527] +2026-04-11 05:00:47.573956: Epoch time: 100.47 s +2026-04-11 05:00:48.695852: +2026-04-11 05:00:48.699882: Epoch 626 +2026-04-11 05:00:48.711872: Current learning rate: 0.00858 +2026-04-11 05:02:30.000108: train_loss -0.3343 +2026-04-11 05:02:30.005290: val_loss -0.3155 +2026-04-11 05:02:30.007316: Pseudo dice [0.0, 0.0, 0.6056, 0.0, 0.0, 0.4094, 0.5966] +2026-04-11 05:02:30.009424: Epoch time: 101.31 s +2026-04-11 05:02:31.113025: +2026-04-11 05:02:31.115507: Epoch 627 +2026-04-11 05:02:31.117017: Current learning rate: 0.00858 +2026-04-11 05:04:12.280321: train_loss -0.3272 +2026-04-11 05:04:12.286083: val_loss -0.3522 +2026-04-11 05:04:12.287991: Pseudo dice [0.0, 0.0, 0.428, 0.0, 0.0, 0.0, 0.5681] +2026-04-11 05:04:12.290058: Epoch time: 101.17 s +2026-04-11 05:04:13.408902: +2026-04-11 05:04:13.410776: Epoch 628 +2026-04-11 05:04:13.412712: Current learning rate: 0.00858 +2026-04-11 05:05:53.513293: train_loss -0.3053 +2026-04-11 05:05:53.519111: val_loss -0.3343 +2026-04-11 05:05:53.522005: Pseudo dice [0.0, 0.0, 0.6121, 0.0, 0.0, 0.3438, 0.6977] +2026-04-11 05:05:53.524507: Epoch time: 100.11 s +2026-04-11 05:05:54.660071: +2026-04-11 05:05:54.663919: Epoch 629 +2026-04-11 05:05:54.666109: Current learning rate: 0.00857 +2026-04-11 05:07:34.884940: train_loss -0.2912 +2026-04-11 05:07:34.897301: val_loss -0.3624 +2026-04-11 05:07:34.900017: Pseudo dice [0.0, 0.0, 0.6985, 0.0, 0.0, 0.429, 0.386] +2026-04-11 05:07:34.903163: Epoch time: 100.23 s +2026-04-11 05:07:36.244969: +2026-04-11 05:07:36.247519: Epoch 630 +2026-04-11 05:07:36.249803: Current learning rate: 0.00857 +2026-04-11 05:09:16.678437: train_loss -0.332 +2026-04-11 05:09:16.684441: val_loss -0.3314 +2026-04-11 05:09:16.687010: Pseudo dice [0.0, 0.0, 0.7193, 0.0, 0.0, 0.4672, 0.5203] +2026-04-11 05:09:16.691014: Epoch time: 100.44 s +2026-04-11 05:09:17.788057: +2026-04-11 05:09:17.789993: Epoch 631 +2026-04-11 05:09:17.792077: Current learning rate: 0.00857 +2026-04-11 05:10:58.021563: train_loss -0.2932 +2026-04-11 05:10:58.028464: val_loss -0.3159 +2026-04-11 05:10:58.030684: Pseudo dice [0.0, 0.0, 0.7718, 0.0, 0.0, 0.6744, 0.0021] +2026-04-11 05:10:58.033184: Epoch time: 100.24 s +2026-04-11 05:10:59.128579: +2026-04-11 05:10:59.130514: Epoch 632 +2026-04-11 05:10:59.132400: Current learning rate: 0.00857 +2026-04-11 05:12:39.484200: train_loss -0.3063 +2026-04-11 05:12:39.490200: val_loss -0.3392 +2026-04-11 05:12:39.492801: Pseudo dice [0.0, 0.0, 0.4389, 0.0, 0.0, 0.2625, 0.0149] +2026-04-11 05:12:39.495252: Epoch time: 100.36 s +2026-04-11 05:12:40.619551: +2026-04-11 05:12:40.621844: Epoch 633 +2026-04-11 05:12:40.624598: Current learning rate: 0.00856 +2026-04-11 05:14:21.003244: train_loss -0.3087 +2026-04-11 05:14:21.009802: val_loss -0.252 +2026-04-11 05:14:21.012148: Pseudo dice [0.0, 0.0, 0.4445, 0.0, 0.0, 0.0, 0.0112] +2026-04-11 05:14:21.014846: Epoch time: 100.39 s +2026-04-11 05:14:22.127418: +2026-04-11 05:14:22.129199: Epoch 634 +2026-04-11 05:14:22.131023: Current learning rate: 0.00856 +2026-04-11 05:16:02.462587: train_loss -0.2663 +2026-04-11 05:16:02.470028: val_loss -0.3158 +2026-04-11 05:16:02.472208: Pseudo dice [0.0, 0.0, 0.3264, 0.0, 0.0, 0.006, 0.5795] +2026-04-11 05:16:02.474872: Epoch time: 100.34 s +2026-04-11 05:16:03.575801: +2026-04-11 05:16:03.577879: Epoch 635 +2026-04-11 05:16:03.579501: Current learning rate: 0.00856 +2026-04-11 05:17:43.996927: train_loss -0.3168 +2026-04-11 05:17:44.002463: val_loss -0.3341 +2026-04-11 05:17:44.004467: Pseudo dice [0.0, 0.0, 0.6658, 0.0, 0.0, 0.0003, 0.4995] +2026-04-11 05:17:44.006832: Epoch time: 100.42 s +2026-04-11 05:17:45.106997: +2026-04-11 05:17:45.109009: Epoch 636 +2026-04-11 05:17:45.110707: Current learning rate: 0.00856 +2026-04-11 05:19:25.368220: train_loss -0.3273 +2026-04-11 05:19:25.373402: val_loss -0.2689 +2026-04-11 05:19:25.375790: Pseudo dice [0.0, 0.0, 0.3297, 0.0, 0.0, 0.4864, 0.528] +2026-04-11 05:19:25.378298: Epoch time: 100.26 s +2026-04-11 05:19:26.476371: +2026-04-11 05:19:26.477989: Epoch 637 +2026-04-11 05:19:26.479386: Current learning rate: 0.00855 +2026-04-11 05:21:06.646260: train_loss -0.3597 +2026-04-11 05:21:06.651925: val_loss -0.3349 +2026-04-11 05:21:06.654948: Pseudo dice [0.0, 0.0, 0.6057, 0.0, 0.0, 0.4716, 0.4055] +2026-04-11 05:21:06.657190: Epoch time: 100.17 s +2026-04-11 05:21:07.748055: +2026-04-11 05:21:07.750142: Epoch 638 +2026-04-11 05:21:07.752644: Current learning rate: 0.00855 +2026-04-11 05:22:48.293871: train_loss -0.3592 +2026-04-11 05:22:48.299744: val_loss -0.3598 +2026-04-11 05:22:48.302458: Pseudo dice [0.0, 0.0, 0.6419, 0.0, 0.0, 0.7509, 0.6828] +2026-04-11 05:22:48.305115: Epoch time: 100.55 s +2026-04-11 05:22:49.417459: +2026-04-11 05:22:49.419217: Epoch 639 +2026-04-11 05:22:49.420891: Current learning rate: 0.00855 +2026-04-11 05:24:29.680059: train_loss -0.3475 +2026-04-11 05:24:29.684866: val_loss -0.3297 +2026-04-11 05:24:29.686781: Pseudo dice [0.0, 0.0, 0.656, 0.0, 0.0, 0.4618, 0.546] +2026-04-11 05:24:29.688965: Epoch time: 100.27 s +2026-04-11 05:24:30.799786: +2026-04-11 05:24:30.801368: Epoch 640 +2026-04-11 05:24:30.802957: Current learning rate: 0.00855 +2026-04-11 05:26:10.922819: train_loss -0.3695 +2026-04-11 05:26:10.928712: val_loss -0.3047 +2026-04-11 05:26:10.930434: Pseudo dice [0.0, 0.0, 0.8422, 0.0, 0.0, 0.6551, 0.6262] +2026-04-11 05:26:10.934154: Epoch time: 100.13 s +2026-04-11 05:26:12.015786: +2026-04-11 05:26:12.017565: Epoch 641 +2026-04-11 05:26:12.019179: Current learning rate: 0.00855 +2026-04-11 05:27:52.404565: train_loss -0.3643 +2026-04-11 05:27:52.410580: val_loss -0.396 +2026-04-11 05:27:52.412689: Pseudo dice [0.0, 0.0, 0.754, 0.0, 0.0, 0.7651, 0.6791] +2026-04-11 05:27:52.414983: Epoch time: 100.39 s +2026-04-11 05:27:53.524918: +2026-04-11 05:27:53.527003: Epoch 642 +2026-04-11 05:27:53.528649: Current learning rate: 0.00854 +2026-04-11 05:29:33.756099: train_loss -0.3566 +2026-04-11 05:29:33.761547: val_loss -0.4059 +2026-04-11 05:29:33.763764: Pseudo dice [0.0, 0.0, 0.6224, 0.0, 0.0, 0.867, 0.6974] +2026-04-11 05:29:33.765924: Epoch time: 100.23 s +2026-04-11 05:29:34.845746: +2026-04-11 05:29:34.847296: Epoch 643 +2026-04-11 05:29:34.848920: Current learning rate: 0.00854 +2026-04-11 05:31:15.295455: train_loss -0.3448 +2026-04-11 05:31:15.300994: val_loss -0.3696 +2026-04-11 05:31:15.303124: Pseudo dice [0.0, 0.0, 0.4824, 0.0, 0.0, 0.5242, 0.657] +2026-04-11 05:31:15.305349: Epoch time: 100.45 s +2026-04-11 05:31:16.442901: +2026-04-11 05:31:16.446315: Epoch 644 +2026-04-11 05:31:16.448362: Current learning rate: 0.00854 +2026-04-11 05:32:56.883363: train_loss -0.3325 +2026-04-11 05:32:56.889145: val_loss -0.3182 +2026-04-11 05:32:56.891120: Pseudo dice [0.0, 0.0, 0.4172, 0.0, 0.0, 0.0, 0.5755] +2026-04-11 05:32:56.893288: Epoch time: 100.44 s +2026-04-11 05:32:57.977471: +2026-04-11 05:32:57.979278: Epoch 645 +2026-04-11 05:32:57.980828: Current learning rate: 0.00854 +2026-04-11 05:34:38.350287: train_loss -0.3264 +2026-04-11 05:34:38.355249: val_loss -0.3631 +2026-04-11 05:34:38.357066: Pseudo dice [0.0, 0.0, 0.8021, 0.0, 0.0, 0.0, 0.7121] +2026-04-11 05:34:38.359378: Epoch time: 100.38 s +2026-04-11 05:34:39.454894: +2026-04-11 05:34:39.457441: Epoch 646 +2026-04-11 05:34:39.459507: Current learning rate: 0.00853 +2026-04-11 05:36:20.865334: train_loss -0.327 +2026-04-11 05:36:20.870159: val_loss -0.2619 +2026-04-11 05:36:20.872303: Pseudo dice [0.0, 0.0, 0.6372, 0.0, 0.0, 0.0095, 0.6472] +2026-04-11 05:36:20.874283: Epoch time: 101.41 s +2026-04-11 05:36:21.987998: +2026-04-11 05:36:21.989871: Epoch 647 +2026-04-11 05:36:21.991461: Current learning rate: 0.00853 +2026-04-11 05:38:02.276011: train_loss -0.3192 +2026-04-11 05:38:02.281646: val_loss -0.3638 +2026-04-11 05:38:02.283629: Pseudo dice [0.0, 0.0, 0.5173, 0.0, 0.0, 0.4221, 0.6949] +2026-04-11 05:38:02.285856: Epoch time: 100.29 s +2026-04-11 05:38:03.353930: +2026-04-11 05:38:03.355970: Epoch 648 +2026-04-11 05:38:03.357578: Current learning rate: 0.00853 +2026-04-11 05:39:43.628571: train_loss -0.3523 +2026-04-11 05:39:43.633206: val_loss -0.3664 +2026-04-11 05:39:43.634935: Pseudo dice [0.0, 0.0, 0.4782, 0.0, 0.0, 0.6104, 0.6921] +2026-04-11 05:39:43.636853: Epoch time: 100.28 s +2026-04-11 05:39:44.732301: +2026-04-11 05:39:44.734110: Epoch 649 +2026-04-11 05:39:44.735773: Current learning rate: 0.00853 +2026-04-11 05:41:25.077183: train_loss -0.3557 +2026-04-11 05:41:25.082932: val_loss -0.3342 +2026-04-11 05:41:25.085134: Pseudo dice [0.0, 0.0, 0.5424, 0.0, 0.0, 0.752, 0.4981] +2026-04-11 05:41:25.087661: Epoch time: 100.35 s +2026-04-11 05:41:27.792519: +2026-04-11 05:41:27.794594: Epoch 650 +2026-04-11 05:41:27.796063: Current learning rate: 0.00852 +2026-04-11 05:43:08.181518: train_loss -0.3245 +2026-04-11 05:43:08.187008: val_loss -0.3551 +2026-04-11 05:43:08.188771: Pseudo dice [0.0, 0.0, 0.7072, 0.0, 0.0, 0.2715, 0.5583] +2026-04-11 05:43:08.191282: Epoch time: 100.39 s +2026-04-11 05:43:09.288994: +2026-04-11 05:43:09.291057: Epoch 651 +2026-04-11 05:43:09.293326: Current learning rate: 0.00852 +2026-04-11 05:44:49.483727: train_loss -0.3364 +2026-04-11 05:44:49.489434: val_loss -0.3585 +2026-04-11 05:44:49.491221: Pseudo dice [0.0, 0.0, 0.7001, 0.0, 0.0, 0.2534, 0.757] +2026-04-11 05:44:49.493072: Epoch time: 100.2 s +2026-04-11 05:44:50.584006: +2026-04-11 05:44:50.586172: Epoch 652 +2026-04-11 05:44:50.587956: Current learning rate: 0.00852 +2026-04-11 05:46:30.913279: train_loss -0.3501 +2026-04-11 05:46:30.918629: val_loss -0.3187 +2026-04-11 05:46:30.920338: Pseudo dice [0.0, 0.0, 0.6239, 0.0, 0.0, 0.4608, 0.6751] +2026-04-11 05:46:30.922495: Epoch time: 100.33 s +2026-04-11 05:46:32.031342: +2026-04-11 05:46:32.034608: Epoch 653 +2026-04-11 05:46:32.037445: Current learning rate: 0.00852 +2026-04-11 05:48:12.334705: train_loss -0.3492 +2026-04-11 05:48:12.339534: val_loss -0.3266 +2026-04-11 05:48:12.341289: Pseudo dice [0.0, 0.0, 0.6936, 0.0, 0.0, 0.0133, 0.3798] +2026-04-11 05:48:12.343174: Epoch time: 100.31 s +2026-04-11 05:48:13.448196: +2026-04-11 05:48:13.450029: Epoch 654 +2026-04-11 05:48:13.451511: Current learning rate: 0.00852 +2026-04-11 05:49:54.181504: train_loss -0.3236 +2026-04-11 05:49:54.187007: val_loss -0.3165 +2026-04-11 05:49:54.189563: Pseudo dice [0.0, 0.0, 0.5956, 0.0, 0.0, 0.7018, 0.7043] +2026-04-11 05:49:54.191414: Epoch time: 100.74 s +2026-04-11 05:49:55.302680: +2026-04-11 05:49:55.304610: Epoch 655 +2026-04-11 05:49:55.306102: Current learning rate: 0.00851 +2026-04-11 05:51:35.599327: train_loss -0.3121 +2026-04-11 05:51:35.605823: val_loss -0.285 +2026-04-11 05:51:35.607654: Pseudo dice [0.0, 0.0, 0.5959, 0.0, 0.0, 0.0239, 0.0] +2026-04-11 05:51:35.609823: Epoch time: 100.3 s +2026-04-11 05:51:36.706713: +2026-04-11 05:51:36.708562: Epoch 656 +2026-04-11 05:51:36.710131: Current learning rate: 0.00851 +2026-04-11 05:53:16.948673: train_loss -0.3045 +2026-04-11 05:53:16.953548: val_loss -0.3193 +2026-04-11 05:53:16.955100: Pseudo dice [0.0, 0.0, 0.4373, 0.0, 0.0, 0.406, 0.0] +2026-04-11 05:53:16.956976: Epoch time: 100.24 s +2026-04-11 05:53:18.050514: +2026-04-11 05:53:18.052265: Epoch 657 +2026-04-11 05:53:18.053757: Current learning rate: 0.00851 +2026-04-11 05:54:58.280586: train_loss -0.3341 +2026-04-11 05:54:58.289186: val_loss -0.353 +2026-04-11 05:54:58.291963: Pseudo dice [0.0, 0.0, 0.5688, 0.0, 0.0, 0.8346, 0.6554] +2026-04-11 05:54:58.294733: Epoch time: 100.23 s +2026-04-11 05:54:59.416525: +2026-04-11 05:54:59.418529: Epoch 658 +2026-04-11 05:54:59.420343: Current learning rate: 0.00851 +2026-04-11 05:56:39.492284: train_loss -0.3439 +2026-04-11 05:56:39.499096: val_loss -0.3795 +2026-04-11 05:56:39.504030: Pseudo dice [0.0, 0.0, 0.5734, 0.0, 0.0, 0.7029, 0.4856] +2026-04-11 05:56:39.508687: Epoch time: 100.08 s +2026-04-11 05:56:40.614276: +2026-04-11 05:56:40.616329: Epoch 659 +2026-04-11 05:56:40.618189: Current learning rate: 0.0085 +2026-04-11 05:58:20.710152: train_loss -0.3517 +2026-04-11 05:58:20.715330: val_loss -0.3564 +2026-04-11 05:58:20.717613: Pseudo dice [0.0, 0.0, 0.6517, 0.0, 0.0, 0.5438, 0.6747] +2026-04-11 05:58:20.719691: Epoch time: 100.1 s +2026-04-11 05:58:21.827133: +2026-04-11 05:58:21.831280: Epoch 660 +2026-04-11 05:58:21.835241: Current learning rate: 0.0085 +2026-04-11 06:00:02.068205: train_loss -0.3146 +2026-04-11 06:00:02.075053: val_loss -0.3107 +2026-04-11 06:00:02.076908: Pseudo dice [0.0, 0.0, 0.579, 0.0, 0.0, 0.4979, 0.6994] +2026-04-11 06:00:02.079285: Epoch time: 100.24 s +2026-04-11 06:00:03.202893: +2026-04-11 06:00:03.205226: Epoch 661 +2026-04-11 06:00:03.207011: Current learning rate: 0.0085 +2026-04-11 06:01:43.402941: train_loss -0.2972 +2026-04-11 06:01:43.407958: val_loss -0.3492 +2026-04-11 06:01:43.409520: Pseudo dice [0.0, 0.0, 0.7017, 0.0, 0.0, 0.587, 0.0] +2026-04-11 06:01:43.411900: Epoch time: 100.2 s +2026-04-11 06:01:44.524863: +2026-04-11 06:01:44.526877: Epoch 662 +2026-04-11 06:01:44.528408: Current learning rate: 0.0085 +2026-04-11 06:03:24.658468: train_loss -0.3064 +2026-04-11 06:03:24.665210: val_loss -0.3302 +2026-04-11 06:03:24.667469: Pseudo dice [0.0, 0.0, 0.7073, 0.0, 0.0, 0.2752, 0.0] +2026-04-11 06:03:24.670915: Epoch time: 100.14 s +2026-04-11 06:03:25.772350: +2026-04-11 06:03:25.774406: Epoch 663 +2026-04-11 06:03:25.776750: Current learning rate: 0.0085 +2026-04-11 06:05:05.943107: train_loss -0.3098 +2026-04-11 06:05:05.948678: val_loss -0.3251 +2026-04-11 06:05:05.950440: Pseudo dice [0.0, 0.0, 0.6772, 0.0, 0.0, 0.6568, 0.1596] +2026-04-11 06:05:05.952388: Epoch time: 100.17 s +2026-04-11 06:05:07.058407: +2026-04-11 06:05:07.060161: Epoch 664 +2026-04-11 06:05:07.061918: Current learning rate: 0.00849 +2026-04-11 06:06:47.237607: train_loss -0.3333 +2026-04-11 06:06:47.243326: val_loss -0.3545 +2026-04-11 06:06:47.246099: Pseudo dice [0.0, 0.0, 0.6622, 0.0, 0.0, 0.5436, 0.7565] +2026-04-11 06:06:47.248317: Epoch time: 100.18 s +2026-04-11 06:06:48.347988: +2026-04-11 06:06:48.350267: Epoch 665 +2026-04-11 06:06:48.352151: Current learning rate: 0.00849 +2026-04-11 06:08:28.617771: train_loss -0.3664 +2026-04-11 06:08:28.624936: val_loss -0.2479 +2026-04-11 06:08:28.627711: Pseudo dice [0.0, 0.0, 0.2456, 0.0, 0.0, 0.0006, 0.4896] +2026-04-11 06:08:28.629896: Epoch time: 100.27 s +2026-04-11 06:08:30.614632: +2026-04-11 06:08:30.617316: Epoch 666 +2026-04-11 06:08:30.619541: Current learning rate: 0.00849 +2026-04-11 06:10:10.966034: train_loss -0.3301 +2026-04-11 06:10:10.972150: val_loss -0.339 +2026-04-11 06:10:10.974450: Pseudo dice [0.0, 0.0, 0.6972, 0.0, 0.0, 0.2198, 0.3055] +2026-04-11 06:10:10.977144: Epoch time: 100.35 s +2026-04-11 06:10:12.071613: +2026-04-11 06:10:12.074318: Epoch 667 +2026-04-11 06:10:12.076038: Current learning rate: 0.00849 +2026-04-11 06:11:52.491199: train_loss -0.3188 +2026-04-11 06:11:52.496762: val_loss -0.3794 +2026-04-11 06:11:52.498447: Pseudo dice [0.0, 0.0, 0.7252, 0.0, 0.0, 0.7524, 0.6257] +2026-04-11 06:11:52.500427: Epoch time: 100.42 s +2026-04-11 06:11:53.640768: +2026-04-11 06:11:53.642678: Epoch 668 +2026-04-11 06:11:53.644391: Current learning rate: 0.00848 +2026-04-11 06:13:34.013144: train_loss -0.3575 +2026-04-11 06:13:34.022867: val_loss -0.3647 +2026-04-11 06:13:34.025347: Pseudo dice [0.0, 0.0, 0.6954, 0.0, 0.0, 0.2717, 0.6523] +2026-04-11 06:13:34.028176: Epoch time: 100.38 s +2026-04-11 06:13:35.161879: +2026-04-11 06:13:35.163998: Epoch 669 +2026-04-11 06:13:35.166357: Current learning rate: 0.00848 +2026-04-11 06:15:15.786153: train_loss -0.3328 +2026-04-11 06:15:15.793925: val_loss -0.3754 +2026-04-11 06:15:15.796763: Pseudo dice [0.0, 0.0, 0.7352, 0.0, 0.0, 0.3618, 0.71] +2026-04-11 06:15:15.801242: Epoch time: 100.63 s +2026-04-11 06:15:16.968995: +2026-04-11 06:15:16.970938: Epoch 670 +2026-04-11 06:15:16.972650: Current learning rate: 0.00848 +2026-04-11 06:16:57.211143: train_loss -0.3476 +2026-04-11 06:16:57.217026: val_loss -0.3129 +2026-04-11 06:16:57.219604: Pseudo dice [0.0, 0.0, 0.7088, 0.0, 0.0, 0.0341, 0.0141] +2026-04-11 06:16:57.222370: Epoch time: 100.25 s +2026-04-11 06:16:58.338794: +2026-04-11 06:16:58.341355: Epoch 671 +2026-04-11 06:16:58.343348: Current learning rate: 0.00848 +2026-04-11 06:18:38.632888: train_loss -0.341 +2026-04-11 06:18:38.639110: val_loss -0.3353 +2026-04-11 06:18:38.640878: Pseudo dice [0.0, 0.0, 0.5289, 0.0, 0.0, 0.4006, 0.6469] +2026-04-11 06:18:38.644616: Epoch time: 100.3 s +2026-04-11 06:18:39.765663: +2026-04-11 06:18:39.767894: Epoch 672 +2026-04-11 06:18:39.770315: Current learning rate: 0.00847 +2026-04-11 06:20:20.055921: train_loss -0.3478 +2026-04-11 06:20:20.063168: val_loss -0.299 +2026-04-11 06:20:20.065428: Pseudo dice [0.0, 0.0, 0.5901, 0.0, 0.0, 0.756, 0.4322] +2026-04-11 06:20:20.069039: Epoch time: 100.29 s +2026-04-11 06:20:21.200493: +2026-04-11 06:20:21.202225: Epoch 673 +2026-04-11 06:20:21.203958: Current learning rate: 0.00847 +2026-04-11 06:22:01.429993: train_loss -0.3476 +2026-04-11 06:22:01.437188: val_loss -0.1948 +2026-04-11 06:22:01.439708: Pseudo dice [0.0, 0.0, 0.4708, 0.0, 0.0, 0.7776, 0.6055] +2026-04-11 06:22:01.442426: Epoch time: 100.23 s +2026-04-11 06:22:02.560484: +2026-04-11 06:22:02.562579: Epoch 674 +2026-04-11 06:22:02.564292: Current learning rate: 0.00847 +2026-04-11 06:23:42.855124: train_loss -0.3531 +2026-04-11 06:23:42.866015: val_loss -0.3773 +2026-04-11 06:23:42.867971: Pseudo dice [0.0, 0.0, 0.5921, 0.0, 0.0, 0.6697, 0.5232] +2026-04-11 06:23:42.870259: Epoch time: 100.3 s +2026-04-11 06:23:43.993402: +2026-04-11 06:23:43.995490: Epoch 675 +2026-04-11 06:23:43.997146: Current learning rate: 0.00847 +2026-04-11 06:25:24.198126: train_loss -0.3304 +2026-04-11 06:25:24.205378: val_loss -0.2496 +2026-04-11 06:25:24.209068: Pseudo dice [0.0, 0.0, 0.3405, 0.0, 0.0, 0.4589, 0.0059] +2026-04-11 06:25:24.211509: Epoch time: 100.21 s +2026-04-11 06:25:25.350605: +2026-04-11 06:25:25.352303: Epoch 676 +2026-04-11 06:25:25.354324: Current learning rate: 0.00847 +2026-04-11 06:27:05.534427: train_loss -0.3472 +2026-04-11 06:27:05.539658: val_loss -0.3125 +2026-04-11 06:27:05.542182: Pseudo dice [0.0, 0.0, 0.49, 0.0, 0.0, 0.4918, 0.8513] +2026-04-11 06:27:05.544896: Epoch time: 100.19 s +2026-04-11 06:27:06.678564: +2026-04-11 06:27:06.680415: Epoch 677 +2026-04-11 06:27:06.682060: Current learning rate: 0.00846 +2026-04-11 06:28:46.656572: train_loss -0.3426 +2026-04-11 06:28:46.661271: val_loss -0.3551 +2026-04-11 06:28:46.663120: Pseudo dice [0.0, 0.0, 0.2966, 0.0, 0.0, 0.3576, 0.6029] +2026-04-11 06:28:46.665218: Epoch time: 99.98 s +2026-04-11 06:28:47.772406: +2026-04-11 06:28:47.774540: Epoch 678 +2026-04-11 06:28:47.776218: Current learning rate: 0.00846 +2026-04-11 06:30:28.014081: train_loss -0.3462 +2026-04-11 06:30:28.020176: val_loss -0.2602 +2026-04-11 06:30:28.022274: Pseudo dice [0.0, 0.0, 0.1101, 0.0, 0.0, 0.5634, 0.7931] +2026-04-11 06:30:28.024269: Epoch time: 100.24 s +2026-04-11 06:30:29.154061: +2026-04-11 06:30:29.155612: Epoch 679 +2026-04-11 06:30:29.157299: Current learning rate: 0.00846 +2026-04-11 06:32:09.347495: train_loss -0.3693 +2026-04-11 06:32:09.353628: val_loss -0.3814 +2026-04-11 06:32:09.355643: Pseudo dice [0.0, 0.0, 0.7207, 0.0, 0.0, 0.7112, 0.7258] +2026-04-11 06:32:09.357723: Epoch time: 100.2 s +2026-04-11 06:32:10.471554: +2026-04-11 06:32:10.473557: Epoch 680 +2026-04-11 06:32:10.475713: Current learning rate: 0.00846 +2026-04-11 06:33:50.591148: train_loss -0.3584 +2026-04-11 06:33:50.596096: val_loss -0.2136 +2026-04-11 06:33:50.597719: Pseudo dice [0.0, 0.0, 0.6442, 0.0, 0.0, 0.5086, 0.5275] +2026-04-11 06:33:50.599535: Epoch time: 100.12 s +2026-04-11 06:33:51.699386: +2026-04-11 06:33:51.701224: Epoch 681 +2026-04-11 06:33:51.702574: Current learning rate: 0.00845 +2026-04-11 06:35:31.719703: train_loss -0.3392 +2026-04-11 06:35:31.724764: val_loss -0.3693 +2026-04-11 06:35:31.726733: Pseudo dice [0.0, 0.0, 0.7023, 0.0, 0.0, 0.6392, 0.5104] +2026-04-11 06:35:31.729086: Epoch time: 100.02 s +2026-04-11 06:35:32.849933: +2026-04-11 06:35:32.851587: Epoch 682 +2026-04-11 06:35:32.853065: Current learning rate: 0.00845 +2026-04-11 06:37:12.859457: train_loss -0.3384 +2026-04-11 06:37:12.865932: val_loss -0.3529 +2026-04-11 06:37:12.867959: Pseudo dice [0.0, 0.0, 0.6056, 0.0, 0.0, 0.7484, 0.6293] +2026-04-11 06:37:12.870548: Epoch time: 100.01 s +2026-04-11 06:37:13.989972: +2026-04-11 06:37:13.991863: Epoch 683 +2026-04-11 06:37:13.993717: Current learning rate: 0.00845 +2026-04-11 06:38:54.261351: train_loss -0.3143 +2026-04-11 06:38:54.266335: val_loss -0.3514 +2026-04-11 06:38:54.268341: Pseudo dice [0.0, 0.0, 0.6736, 0.0, 0.0, 0.5141, 0.2483] +2026-04-11 06:38:54.270526: Epoch time: 100.27 s +2026-04-11 06:38:55.400674: +2026-04-11 06:38:55.402447: Epoch 684 +2026-04-11 06:38:55.404154: Current learning rate: 0.00845 +2026-04-11 06:40:35.746943: train_loss -0.3277 +2026-04-11 06:40:35.752667: val_loss -0.3534 +2026-04-11 06:40:35.754388: Pseudo dice [0.0, 0.0, 0.6388, 0.0, 0.0, 0.8164, 0.4482] +2026-04-11 06:40:35.756804: Epoch time: 100.35 s +2026-04-11 06:40:36.877948: +2026-04-11 06:40:36.879609: Epoch 685 +2026-04-11 06:40:36.881087: Current learning rate: 0.00844 +2026-04-11 06:42:18.230352: train_loss -0.3346 +2026-04-11 06:42:18.236499: val_loss -0.334 +2026-04-11 06:42:18.239089: Pseudo dice [0.0, 0.0, 0.6195, 0.0, 0.0, 0.3859, 0.4866] +2026-04-11 06:42:18.241441: Epoch time: 101.36 s +2026-04-11 06:42:19.366889: +2026-04-11 06:42:19.368803: Epoch 686 +2026-04-11 06:42:19.370356: Current learning rate: 0.00844 +2026-04-11 06:43:59.641343: train_loss -0.3407 +2026-04-11 06:43:59.647831: val_loss -0.3761 +2026-04-11 06:43:59.649775: Pseudo dice [0.0, 0.0, 0.6806, 0.0, 0.0, 0.7738, 0.6721] +2026-04-11 06:43:59.652482: Epoch time: 100.28 s +2026-04-11 06:44:00.769167: +2026-04-11 06:44:00.771171: Epoch 687 +2026-04-11 06:44:00.773666: Current learning rate: 0.00844 +2026-04-11 06:45:41.009758: train_loss -0.3028 +2026-04-11 06:45:41.014740: val_loss -0.2788 +2026-04-11 06:45:41.016582: Pseudo dice [0.0, 0.0, 0.095, 0.0, 0.0, 0.0071, 0.0348] +2026-04-11 06:45:41.019559: Epoch time: 100.24 s +2026-04-11 06:45:42.154200: +2026-04-11 06:45:42.155936: Epoch 688 +2026-04-11 06:45:42.157449: Current learning rate: 0.00844 +2026-04-11 06:47:22.511753: train_loss -0.2945 +2026-04-11 06:47:22.518076: val_loss -0.3478 +2026-04-11 06:47:22.520686: Pseudo dice [0.0, 0.0, 0.5168, 0.0, 0.0, 0.4787, 0.5217] +2026-04-11 06:47:22.522744: Epoch time: 100.36 s +2026-04-11 06:47:23.632927: +2026-04-11 06:47:23.635862: Epoch 689 +2026-04-11 06:47:23.637872: Current learning rate: 0.00844 +2026-04-11 06:49:03.979190: train_loss -0.3286 +2026-04-11 06:49:03.985044: val_loss -0.321 +2026-04-11 06:49:03.986750: Pseudo dice [0.0, 0.0, 0.7739, 0.0, 0.0, 0.3678, 0.0517] +2026-04-11 06:49:03.988997: Epoch time: 100.35 s +2026-04-11 06:49:05.119018: +2026-04-11 06:49:05.121470: Epoch 690 +2026-04-11 06:49:05.123321: Current learning rate: 0.00843 +2026-04-11 06:50:45.404732: train_loss -0.3477 +2026-04-11 06:50:45.409741: val_loss -0.3843 +2026-04-11 06:50:45.411314: Pseudo dice [0.0, 0.0, 0.7565, 0.0, 0.0, 0.6057, 0.1379] +2026-04-11 06:50:45.413170: Epoch time: 100.29 s +2026-04-11 06:50:46.519534: +2026-04-11 06:50:46.525684: Epoch 691 +2026-04-11 06:50:46.530283: Current learning rate: 0.00843 +2026-04-11 06:52:26.873209: train_loss -0.296 +2026-04-11 06:52:26.878398: val_loss -0.3154 +2026-04-11 06:52:26.881401: Pseudo dice [0.0, 0.0, 0.298, 0.0, 0.0, 0.1353, 0.2014] +2026-04-11 06:52:26.883827: Epoch time: 100.36 s +2026-04-11 06:52:28.019524: +2026-04-11 06:52:28.021186: Epoch 692 +2026-04-11 06:52:28.022872: Current learning rate: 0.00843 +2026-04-11 06:54:08.301729: train_loss -0.3122 +2026-04-11 06:54:08.306198: val_loss -0.3991 +2026-04-11 06:54:08.307907: Pseudo dice [0.0, 0.0, 0.7033, 0.0, 0.0, 0.5854, 0.6659] +2026-04-11 06:54:08.310032: Epoch time: 100.29 s +2026-04-11 06:54:09.443395: +2026-04-11 06:54:09.445090: Epoch 693 +2026-04-11 06:54:09.446726: Current learning rate: 0.00843 +2026-04-11 06:55:49.853000: train_loss -0.3659 +2026-04-11 06:55:49.861401: val_loss -0.2475 +2026-04-11 06:55:49.863697: Pseudo dice [0.0, 0.0, 0.6016, 0.0, 0.0, 0.5873, 0.5639] +2026-04-11 06:55:49.868586: Epoch time: 100.41 s +2026-04-11 06:55:51.006074: +2026-04-11 06:55:51.008352: Epoch 694 +2026-04-11 06:55:51.009677: Current learning rate: 0.00842 +2026-04-11 06:57:31.259653: train_loss -0.3495 +2026-04-11 06:57:31.264898: val_loss -0.3853 +2026-04-11 06:57:31.266976: Pseudo dice [0.0, 0.0, 0.7012, 0.0, 0.0, 0.7568, 0.5794] +2026-04-11 06:57:31.269240: Epoch time: 100.26 s +2026-04-11 06:57:32.390376: +2026-04-11 06:57:32.392034: Epoch 695 +2026-04-11 06:57:32.393728: Current learning rate: 0.00842 +2026-04-11 06:59:12.485332: train_loss -0.3572 +2026-04-11 06:59:12.490937: val_loss -0.3031 +2026-04-11 06:59:12.493112: Pseudo dice [0.0, 0.0, 0.706, 0.0, 0.0, 0.7723, 0.6487] +2026-04-11 06:59:12.494979: Epoch time: 100.1 s +2026-04-11 06:59:13.605937: +2026-04-11 06:59:13.608036: Epoch 696 +2026-04-11 06:59:13.609779: Current learning rate: 0.00842 +2026-04-11 07:00:53.838355: train_loss -0.3178 +2026-04-11 07:00:53.843104: val_loss -0.3135 +2026-04-11 07:00:53.844741: Pseudo dice [0.0, 0.0, 0.5628, 0.0, 0.0, 0.582, 0.7393] +2026-04-11 07:00:53.846835: Epoch time: 100.24 s +2026-04-11 07:00:54.975072: +2026-04-11 07:00:54.976901: Epoch 697 +2026-04-11 07:00:54.978581: Current learning rate: 0.00842 +2026-04-11 07:02:35.143214: train_loss -0.3505 +2026-04-11 07:02:35.147520: val_loss -0.3056 +2026-04-11 07:02:35.148944: Pseudo dice [0.0, 0.0, 0.6028, 0.0, 0.0, 0.3687, 0.0132] +2026-04-11 07:02:35.150776: Epoch time: 100.17 s +2026-04-11 07:02:36.278039: +2026-04-11 07:02:36.279664: Epoch 698 +2026-04-11 07:02:36.281030: Current learning rate: 0.00841 +2026-04-11 07:04:16.541387: train_loss -0.2972 +2026-04-11 07:04:16.547002: val_loss -0.3257 +2026-04-11 07:04:16.548740: Pseudo dice [0.0, 0.0, 0.6615, 0.0, 0.0, 0.0, 0.0] +2026-04-11 07:04:16.550941: Epoch time: 100.27 s +2026-04-11 07:04:17.674898: +2026-04-11 07:04:17.676494: Epoch 699 +2026-04-11 07:04:17.678061: Current learning rate: 0.00841 +2026-04-11 07:05:57.936315: train_loss -0.3025 +2026-04-11 07:05:57.941872: val_loss -0.2792 +2026-04-11 07:05:57.943534: Pseudo dice [0.0, 0.0, 0.5255, 0.0, 0.0, 0.719, 0.0023] +2026-04-11 07:05:57.945992: Epoch time: 100.26 s +2026-04-11 07:06:00.624974: +2026-04-11 07:06:00.627973: Epoch 700 +2026-04-11 07:06:00.631389: Current learning rate: 0.00841 +2026-04-11 07:07:40.919407: train_loss -0.3479 +2026-04-11 07:07:40.924147: val_loss -0.3673 +2026-04-11 07:07:40.926549: Pseudo dice [0.0, 0.0, 0.7079, 0.0, 0.0, 0.6236, 0.5519] +2026-04-11 07:07:40.928501: Epoch time: 100.3 s +2026-04-11 07:07:42.055409: +2026-04-11 07:07:42.056910: Epoch 701 +2026-04-11 07:07:42.058286: Current learning rate: 0.00841 +2026-04-11 07:09:22.324455: train_loss -0.3031 +2026-04-11 07:09:22.329326: val_loss -0.3536 +2026-04-11 07:09:22.331130: Pseudo dice [0.0, 0.0, 0.6706, 0.0, 0.0, 0.006, 0.1093] +2026-04-11 07:09:22.333097: Epoch time: 100.27 s +2026-04-11 07:09:23.449506: +2026-04-11 07:09:23.451370: Epoch 702 +2026-04-11 07:09:23.453113: Current learning rate: 0.00841 +2026-04-11 07:11:03.864103: train_loss -0.3337 +2026-04-11 07:11:03.873955: val_loss -0.3728 +2026-04-11 07:11:03.876253: Pseudo dice [0.0, 0.0, 0.7247, 0.0, 0.0, 0.14, 0.5863] +2026-04-11 07:11:03.879202: Epoch time: 100.42 s +2026-04-11 07:11:05.005971: +2026-04-11 07:11:05.007553: Epoch 703 +2026-04-11 07:11:05.009338: Current learning rate: 0.0084 +2026-04-11 07:12:45.889308: train_loss -0.3525 +2026-04-11 07:12:45.901488: val_loss -0.3591 +2026-04-11 07:12:45.904162: Pseudo dice [0.0, 0.0, 0.4569, 0.0, 0.0, 0.6452, 0.3777] +2026-04-11 07:12:45.906434: Epoch time: 100.89 s +2026-04-11 07:12:47.044252: +2026-04-11 07:12:47.045987: Epoch 704 +2026-04-11 07:12:47.047474: Current learning rate: 0.0084 +2026-04-11 07:14:28.444122: train_loss -0.3199 +2026-04-11 07:14:28.451609: val_loss -0.2614 +2026-04-11 07:14:28.454259: Pseudo dice [0.0, 0.0, 0.5162, 0.0, 0.0, 0.4185, 0.1275] +2026-04-11 07:14:28.456709: Epoch time: 101.4 s +2026-04-11 07:14:29.576882: +2026-04-11 07:14:29.578474: Epoch 705 +2026-04-11 07:14:29.580412: Current learning rate: 0.0084 +2026-04-11 07:16:09.747206: train_loss -0.2998 +2026-04-11 07:16:09.753580: val_loss -0.3306 +2026-04-11 07:16:09.755694: Pseudo dice [0.0, 0.0, 0.6002, 0.0, 0.0, 0.3632, 0.0806] +2026-04-11 07:16:09.758575: Epoch time: 100.17 s +2026-04-11 07:16:10.895257: +2026-04-11 07:16:10.896978: Epoch 706 +2026-04-11 07:16:10.899497: Current learning rate: 0.0084 +2026-04-11 07:17:51.240604: train_loss -0.3039 +2026-04-11 07:17:51.245488: val_loss -0.3173 +2026-04-11 07:17:51.247156: Pseudo dice [0.0, 0.0, 0.5313, 0.0, 0.0, 0.5562, 0.6386] +2026-04-11 07:17:51.249183: Epoch time: 100.35 s +2026-04-11 07:17:52.366951: +2026-04-11 07:17:52.368807: Epoch 707 +2026-04-11 07:17:52.370486: Current learning rate: 0.00839 +2026-04-11 07:19:32.727559: train_loss -0.3472 +2026-04-11 07:19:32.732544: val_loss -0.3346 +2026-04-11 07:19:32.734304: Pseudo dice [0.0, 0.0, 0.5819, 0.0, 0.0, 0.1357, 0.7516] +2026-04-11 07:19:32.736169: Epoch time: 100.36 s +2026-04-11 07:19:34.125606: +2026-04-11 07:19:34.127933: Epoch 708 +2026-04-11 07:19:34.129266: Current learning rate: 0.00839 +2026-04-11 07:21:14.375115: train_loss -0.3502 +2026-04-11 07:21:14.381006: val_loss -0.3859 +2026-04-11 07:21:14.383954: Pseudo dice [0.0, 0.0, 0.7055, 0.0, 0.0, 0.7013, 0.5843] +2026-04-11 07:21:14.386560: Epoch time: 100.25 s +2026-04-11 07:21:15.504416: +2026-04-11 07:21:15.506017: Epoch 709 +2026-04-11 07:21:15.507434: Current learning rate: 0.00839 +2026-04-11 07:22:55.664407: train_loss -0.333 +2026-04-11 07:22:55.669756: val_loss -0.3275 +2026-04-11 07:22:55.671512: Pseudo dice [0.0, 0.0, 0.7571, 0.0, 0.0, 0.0081, 0.7856] +2026-04-11 07:22:55.673843: Epoch time: 100.16 s +2026-04-11 07:22:56.801000: +2026-04-11 07:22:56.802670: Epoch 710 +2026-04-11 07:22:56.804103: Current learning rate: 0.00839 +2026-04-11 07:24:36.959152: train_loss -0.2551 +2026-04-11 07:24:36.966425: val_loss -0.3551 +2026-04-11 07:24:36.969901: Pseudo dice [0.0, 0.0, 0.6428, 0.0, 0.0, 0.2511, 0.0] +2026-04-11 07:24:36.972629: Epoch time: 100.16 s +2026-04-11 07:24:38.103637: +2026-04-11 07:24:38.105574: Epoch 711 +2026-04-11 07:24:38.107143: Current learning rate: 0.00839 +2026-04-11 07:26:18.311086: train_loss -0.3139 +2026-04-11 07:26:18.316447: val_loss -0.348 +2026-04-11 07:26:18.318638: Pseudo dice [0.0, 0.0, 0.7006, 0.0, 0.0, 0.5489, 0.7057] +2026-04-11 07:26:18.321382: Epoch time: 100.21 s +2026-04-11 07:26:19.450560: +2026-04-11 07:26:19.452367: Epoch 712 +2026-04-11 07:26:19.454033: Current learning rate: 0.00838 +2026-04-11 07:27:59.646667: train_loss -0.3531 +2026-04-11 07:27:59.652958: val_loss -0.3544 +2026-04-11 07:27:59.654825: Pseudo dice [0.0, 0.0, 0.6552, 0.0, 0.0, 0.8157, 0.8418] +2026-04-11 07:27:59.657202: Epoch time: 100.2 s +2026-04-11 07:28:00.791568: +2026-04-11 07:28:00.793845: Epoch 713 +2026-04-11 07:28:00.795756: Current learning rate: 0.00838 +2026-04-11 07:29:41.063560: train_loss -0.347 +2026-04-11 07:29:41.069647: val_loss -0.4024 +2026-04-11 07:29:41.071538: Pseudo dice [0.0, 0.0, 0.634, 0.0, 0.0, 0.7585, 0.7283] +2026-04-11 07:29:41.073954: Epoch time: 100.28 s +2026-04-11 07:29:42.195483: +2026-04-11 07:29:42.197303: Epoch 714 +2026-04-11 07:29:42.198633: Current learning rate: 0.00838 +2026-04-11 07:31:22.547953: train_loss -0.3417 +2026-04-11 07:31:22.552634: val_loss -0.3295 +2026-04-11 07:31:22.554709: Pseudo dice [0.0, 0.0, 0.5554, 0.0, 0.0, 0.1013, 0.0806] +2026-04-11 07:31:22.556923: Epoch time: 100.36 s +2026-04-11 07:31:23.670391: +2026-04-11 07:31:23.672350: Epoch 715 +2026-04-11 07:31:23.673769: Current learning rate: 0.00838 +2026-04-11 07:33:04.019528: train_loss -0.3062 +2026-04-11 07:33:04.024651: val_loss -0.3601 +2026-04-11 07:33:04.026402: Pseudo dice [0.0, 0.0, 0.6367, 0.0, 0.0, 0.6668, 0.1268] +2026-04-11 07:33:04.028314: Epoch time: 100.35 s +2026-04-11 07:33:05.153390: +2026-04-11 07:33:05.161714: Epoch 716 +2026-04-11 07:33:05.163647: Current learning rate: 0.00837 +2026-04-11 07:34:45.552546: train_loss -0.3358 +2026-04-11 07:34:45.558853: val_loss -0.3271 +2026-04-11 07:34:45.561505: Pseudo dice [0.0, 0.0, 0.6959, 0.0, 0.0, 0.0, 0.006] +2026-04-11 07:34:45.564255: Epoch time: 100.4 s +2026-04-11 07:34:46.707182: +2026-04-11 07:34:46.709631: Epoch 717 +2026-04-11 07:34:46.711430: Current learning rate: 0.00837 +2026-04-11 07:36:27.349779: train_loss -0.3172 +2026-04-11 07:36:27.355018: val_loss -0.261 +2026-04-11 07:36:27.356733: Pseudo dice [0.0, 0.0, 0.7673, 0.0, 0.0, 0.0, 0.0] +2026-04-11 07:36:27.359136: Epoch time: 100.65 s +2026-04-11 07:36:28.489696: +2026-04-11 07:36:28.492001: Epoch 718 +2026-04-11 07:36:28.494329: Current learning rate: 0.00837 +2026-04-11 07:38:09.307632: train_loss -0.3342 +2026-04-11 07:38:09.313370: val_loss -0.219 +2026-04-11 07:38:09.315303: Pseudo dice [0.0, 0.0, 0.5762, 0.0, 0.0, 0.0, 0.2991] +2026-04-11 07:38:09.317708: Epoch time: 100.82 s +2026-04-11 07:38:10.477208: +2026-04-11 07:38:10.479399: Epoch 719 +2026-04-11 07:38:10.481511: Current learning rate: 0.00837 +2026-04-11 07:39:50.802294: train_loss -0.3458 +2026-04-11 07:39:50.809863: val_loss -0.3711 +2026-04-11 07:39:50.812041: Pseudo dice [0.0, 0.0, 0.3096, 0.0, 0.0, 0.6413, 0.5902] +2026-04-11 07:39:50.814457: Epoch time: 100.33 s +2026-04-11 07:39:51.937081: +2026-04-11 07:39:51.939500: Epoch 720 +2026-04-11 07:39:51.942093: Current learning rate: 0.00836 +2026-04-11 07:41:32.511638: train_loss -0.3478 +2026-04-11 07:41:32.517652: val_loss -0.3101 +2026-04-11 07:41:32.520604: Pseudo dice [0.0, 0.0, 0.6158, 0.0, 0.0, 0.4702, 0.1021] +2026-04-11 07:41:32.522797: Epoch time: 100.58 s +2026-04-11 07:41:33.651658: +2026-04-11 07:41:33.653711: Epoch 721 +2026-04-11 07:41:33.656164: Current learning rate: 0.00836 +2026-04-11 07:43:14.199552: train_loss -0.3496 +2026-04-11 07:43:14.208955: val_loss -0.3196 +2026-04-11 07:43:14.211095: Pseudo dice [0.0, 0.0, 0.656, 0.0, 0.0, 0.0139, 0.3267] +2026-04-11 07:43:14.215305: Epoch time: 100.55 s +2026-04-11 07:43:15.344513: +2026-04-11 07:43:15.349074: Epoch 722 +2026-04-11 07:43:15.352339: Current learning rate: 0.00836 +2026-04-11 07:44:55.873348: train_loss -0.3263 +2026-04-11 07:44:55.900929: val_loss -0.3555 +2026-04-11 07:44:55.903193: Pseudo dice [0.0, 0.0, 0.6994, 0.0, 0.0, 0.4401, 0.346] +2026-04-11 07:44:55.905859: Epoch time: 100.53 s +2026-04-11 07:44:57.028470: +2026-04-11 07:44:57.030558: Epoch 723 +2026-04-11 07:44:57.032788: Current learning rate: 0.00836 +2026-04-11 07:46:37.815889: train_loss -0.3291 +2026-04-11 07:46:37.823852: val_loss -0.3742 +2026-04-11 07:46:37.826324: Pseudo dice [0.0, 0.0, 0.452, 0.0, 0.0, 0.4431, 0.6964] +2026-04-11 07:46:37.829533: Epoch time: 100.79 s +2026-04-11 07:46:39.915272: +2026-04-11 07:46:39.917539: Epoch 724 +2026-04-11 07:46:39.919745: Current learning rate: 0.00836 +2026-04-11 07:48:20.226600: train_loss -0.3311 +2026-04-11 07:48:20.234020: val_loss -0.2399 +2026-04-11 07:48:20.235994: Pseudo dice [0.0, 0.0, 0.2884, 0.0, 0.0, 0.3095, 0.6875] +2026-04-11 07:48:20.239300: Epoch time: 100.31 s +2026-04-11 07:48:21.366904: +2026-04-11 07:48:21.372870: Epoch 725 +2026-04-11 07:48:21.386608: Current learning rate: 0.00835 +2026-04-11 07:50:01.990900: train_loss -0.2991 +2026-04-11 07:50:01.998135: val_loss -0.277 +2026-04-11 07:50:02.000421: Pseudo dice [0.0, 0.0, 0.6245, 0.0, 0.0, 0.0, 0.0] +2026-04-11 07:50:02.003087: Epoch time: 100.63 s +2026-04-11 07:50:03.128150: +2026-04-11 07:50:03.130535: Epoch 726 +2026-04-11 07:50:03.132545: Current learning rate: 0.00835 +2026-04-11 07:51:44.250544: train_loss -0.2971 +2026-04-11 07:51:44.259077: val_loss -0.3226 +2026-04-11 07:51:44.261903: Pseudo dice [0.0, 0.0, 0.287, 0.0, 0.0, 0.0, 0.0758] +2026-04-11 07:51:44.264728: Epoch time: 101.13 s +2026-04-11 07:51:45.406909: +2026-04-11 07:51:45.409353: Epoch 727 +2026-04-11 07:51:45.411547: Current learning rate: 0.00835 +2026-04-11 07:53:25.798798: train_loss -0.3404 +2026-04-11 07:53:25.805502: val_loss -0.3497 +2026-04-11 07:53:25.807411: Pseudo dice [0.0, 0.0, 0.5397, 0.0, 0.0, 0.3854, 0.5627] +2026-04-11 07:53:25.811089: Epoch time: 100.39 s +2026-04-11 07:53:26.928972: +2026-04-11 07:53:26.930734: Epoch 728 +2026-04-11 07:53:26.932644: Current learning rate: 0.00835 +2026-04-11 07:55:07.092944: train_loss -0.3592 +2026-04-11 07:55:07.100505: val_loss -0.3766 +2026-04-11 07:55:07.103185: Pseudo dice [0.0, 0.0, 0.6689, 0.0, 0.0, 0.6804, 0.7611] +2026-04-11 07:55:07.105878: Epoch time: 100.17 s +2026-04-11 07:55:08.240469: +2026-04-11 07:55:08.243311: Epoch 729 +2026-04-11 07:55:08.246898: Current learning rate: 0.00834 +2026-04-11 07:56:48.657689: train_loss -0.3572 +2026-04-11 07:56:48.664305: val_loss -0.3194 +2026-04-11 07:56:48.666338: Pseudo dice [0.0, 0.0, 0.4124, 0.0, 0.0, 0.4443, 0.4849] +2026-04-11 07:56:48.670395: Epoch time: 100.42 s +2026-04-11 07:56:49.814843: +2026-04-11 07:56:49.816865: Epoch 730 +2026-04-11 07:56:49.820461: Current learning rate: 0.00834 +2026-04-11 07:58:30.591704: train_loss -0.3011 +2026-04-11 07:58:30.597641: val_loss -0.3218 +2026-04-11 07:58:30.600689: Pseudo dice [0.0, 0.0, 0.5237, 0.0, 0.0, 0.3729, 0.2692] +2026-04-11 07:58:30.603295: Epoch time: 100.78 s +2026-04-11 07:58:31.730270: +2026-04-11 07:58:31.732698: Epoch 731 +2026-04-11 07:58:31.734797: Current learning rate: 0.00834 +2026-04-11 08:00:12.322435: train_loss -0.3188 +2026-04-11 08:00:12.331111: val_loss -0.3535 +2026-04-11 08:00:12.334345: Pseudo dice [0.0, 0.0, 0.5519, 0.0, 0.0, 0.7519, 0.7683] +2026-04-11 08:00:12.336983: Epoch time: 100.6 s +2026-04-11 08:00:13.450601: +2026-04-11 08:00:13.453631: Epoch 732 +2026-04-11 08:00:13.457251: Current learning rate: 0.00834 +2026-04-11 08:01:54.141043: train_loss -0.353 +2026-04-11 08:01:54.146508: val_loss -0.2018 +2026-04-11 08:01:54.148407: Pseudo dice [0.0, 0.0, 0.7019, 0.0, 0.0, 0.0431, 0.5114] +2026-04-11 08:01:54.150649: Epoch time: 100.69 s +2026-04-11 08:01:55.263110: +2026-04-11 08:01:55.266014: Epoch 733 +2026-04-11 08:01:55.269358: Current learning rate: 0.00833 +2026-04-11 08:03:35.600503: train_loss -0.2874 +2026-04-11 08:03:35.611450: val_loss -0.3134 +2026-04-11 08:03:35.615079: Pseudo dice [0.0, 0.0, 0.6634, 0.0, 0.0, 0.0281, 0.0717] +2026-04-11 08:03:35.619975: Epoch time: 100.34 s +2026-04-11 08:03:36.745818: +2026-04-11 08:03:36.747888: Epoch 734 +2026-04-11 08:03:36.750881: Current learning rate: 0.00833 +2026-04-11 08:05:17.663140: train_loss -0.3338 +2026-04-11 08:05:17.670897: val_loss -0.2842 +2026-04-11 08:05:17.673031: Pseudo dice [0.0, 0.0, 0.7317, 0.0, 0.0, 0.1611, 0.5265] +2026-04-11 08:05:17.676328: Epoch time: 100.92 s +2026-04-11 08:05:18.819737: +2026-04-11 08:05:18.822603: Epoch 735 +2026-04-11 08:05:18.825125: Current learning rate: 0.00833 +2026-04-11 08:06:59.138715: train_loss -0.3468 +2026-04-11 08:06:59.145650: val_loss -0.3393 +2026-04-11 08:06:59.147932: Pseudo dice [0.0, 0.0, 0.7632, 0.0, 0.0, 0.41, 0.6731] +2026-04-11 08:06:59.150225: Epoch time: 100.32 s +2026-04-11 08:07:00.257339: +2026-04-11 08:07:00.259250: Epoch 736 +2026-04-11 08:07:00.261165: Current learning rate: 0.00833 +2026-04-11 08:08:40.955833: train_loss -0.3207 +2026-04-11 08:08:40.965188: val_loss -0.3269 +2026-04-11 08:08:40.970304: Pseudo dice [0.0, 0.0, 0.6789, 0.0, 0.0, 0.0117, 0.1642] +2026-04-11 08:08:40.974476: Epoch time: 100.7 s +2026-04-11 08:08:42.090840: +2026-04-11 08:08:42.092777: Epoch 737 +2026-04-11 08:08:42.095249: Current learning rate: 0.00833 +2026-04-11 08:10:22.535070: train_loss -0.323 +2026-04-11 08:10:22.541512: val_loss -0.3703 +2026-04-11 08:10:22.543757: Pseudo dice [0.0, 0.0, 0.7448, 0.0, 0.0, 0.373, 0.7219] +2026-04-11 08:10:22.546137: Epoch time: 100.45 s +2026-04-11 08:10:23.662841: +2026-04-11 08:10:23.664959: Epoch 738 +2026-04-11 08:10:23.667051: Current learning rate: 0.00832 +2026-04-11 08:12:03.996243: train_loss -0.3535 +2026-04-11 08:12:04.002574: val_loss -0.3604 +2026-04-11 08:12:04.004786: Pseudo dice [0.0, 0.0, 0.6509, 0.0, 0.0, 0.5328, 0.5872] +2026-04-11 08:12:04.007982: Epoch time: 100.34 s +2026-04-11 08:12:05.139043: +2026-04-11 08:12:05.141597: Epoch 739 +2026-04-11 08:12:05.144101: Current learning rate: 0.00832 +2026-04-11 08:13:45.480672: train_loss -0.3417 +2026-04-11 08:13:45.490525: val_loss -0.3143 +2026-04-11 08:13:45.493904: Pseudo dice [0.0, 0.0, 0.5716, 0.0, 0.0, 0.6617, 0.6673] +2026-04-11 08:13:45.497177: Epoch time: 100.34 s +2026-04-11 08:13:46.650663: +2026-04-11 08:13:46.652716: Epoch 740 +2026-04-11 08:13:46.654752: Current learning rate: 0.00832 +2026-04-11 08:15:26.911608: train_loss -0.3437 +2026-04-11 08:15:26.921530: val_loss -0.3097 +2026-04-11 08:15:26.935228: Pseudo dice [0.0, 0.0, 0.5935, 0.0, 0.0, 0.7635, 0.6751] +2026-04-11 08:15:26.940590: Epoch time: 100.26 s +2026-04-11 08:15:28.205549: +2026-04-11 08:15:28.207579: Epoch 741 +2026-04-11 08:15:28.209639: Current learning rate: 0.00832 +2026-04-11 08:17:08.482150: train_loss -0.3459 +2026-04-11 08:17:08.490306: val_loss -0.3038 +2026-04-11 08:17:08.492413: Pseudo dice [0.0, 0.0, 0.7224, 0.0, 0.0, 0.8205, 0.6674] +2026-04-11 08:17:08.495159: Epoch time: 100.28 s +2026-04-11 08:17:09.808171: +2026-04-11 08:17:09.810083: Epoch 742 +2026-04-11 08:17:09.812204: Current learning rate: 0.00831 +2026-04-11 08:18:50.008080: train_loss -0.368 +2026-04-11 08:18:50.015513: val_loss -0.4227 +2026-04-11 08:18:50.018524: Pseudo dice [0.0, 0.0, 0.7755, 0.0, 0.0, 0.7725, 0.8638] +2026-04-11 08:18:50.021566: Epoch time: 100.2 s +2026-04-11 08:18:51.171634: +2026-04-11 08:18:51.173509: Epoch 743 +2026-04-11 08:18:51.176546: Current learning rate: 0.00831 +2026-04-11 08:20:32.904629: train_loss -0.3553 +2026-04-11 08:20:32.911532: val_loss -0.3609 +2026-04-11 08:20:32.915433: Pseudo dice [0.0, 0.0, 0.6178, 0.0, 0.0, 0.4944, 0.5714] +2026-04-11 08:20:32.918932: Epoch time: 101.74 s +2026-04-11 08:20:34.049541: +2026-04-11 08:20:34.051816: Epoch 744 +2026-04-11 08:20:34.054008: Current learning rate: 0.00831 +2026-04-11 08:22:14.437611: train_loss -0.36 +2026-04-11 08:22:14.443086: val_loss -0.3358 +2026-04-11 08:22:14.447798: Pseudo dice [0.0, 0.0, 0.4254, 0.0, 0.0, 0.7934, 0.4838] +2026-04-11 08:22:14.450375: Epoch time: 100.39 s +2026-04-11 08:22:15.577621: +2026-04-11 08:22:15.580030: Epoch 745 +2026-04-11 08:22:15.582501: Current learning rate: 0.00831 +2026-04-11 08:23:55.707277: train_loss -0.3502 +2026-04-11 08:23:55.723119: val_loss -0.2427 +2026-04-11 08:23:55.726819: Pseudo dice [0.0, 0.0, 0.3602, 0.0, 0.0, 0.1247, 0.5336] +2026-04-11 08:23:55.730018: Epoch time: 100.13 s +2026-04-11 08:23:56.855798: +2026-04-11 08:23:56.857773: Epoch 746 +2026-04-11 08:23:56.859766: Current learning rate: 0.0083 +2026-04-11 08:25:37.077162: train_loss -0.3245 +2026-04-11 08:25:37.084005: val_loss -0.2747 +2026-04-11 08:25:37.086077: Pseudo dice [0.0, 0.0, 0.7279, 0.0, 0.0, 0.2697, 0.1647] +2026-04-11 08:25:37.088456: Epoch time: 100.22 s +2026-04-11 08:25:38.231014: +2026-04-11 08:25:38.233014: Epoch 747 +2026-04-11 08:25:38.235187: Current learning rate: 0.0083 +2026-04-11 08:27:18.443073: train_loss -0.3419 +2026-04-11 08:27:18.451924: val_loss -0.3955 +2026-04-11 08:27:18.454706: Pseudo dice [0.0, 0.0, 0.6095, 0.0, 0.0, 0.7616, 0.5967] +2026-04-11 08:27:18.457651: Epoch time: 100.22 s +2026-04-11 08:27:19.572785: +2026-04-11 08:27:19.575212: Epoch 748 +2026-04-11 08:27:19.577556: Current learning rate: 0.0083 +2026-04-11 08:28:59.868755: train_loss -0.3026 +2026-04-11 08:28:59.875535: val_loss -0.2362 +2026-04-11 08:28:59.877521: Pseudo dice [0.0, 0.0, 0.2326, 0.0, 0.0, 0.3142, 0.0] +2026-04-11 08:28:59.879694: Epoch time: 100.3 s +2026-04-11 08:29:01.003415: +2026-04-11 08:29:01.005158: Epoch 749 +2026-04-11 08:29:01.007192: Current learning rate: 0.0083 +2026-04-11 08:30:41.640540: train_loss -0.2315 +2026-04-11 08:30:41.651467: val_loss -0.2315 +2026-04-11 08:30:41.653690: Pseudo dice [0.0, 0.0, 0.5942, 0.0, 0.0, 0.0, 0.0] +2026-04-11 08:30:41.667667: Epoch time: 100.64 s +2026-04-11 08:30:44.441316: +2026-04-11 08:30:44.443302: Epoch 750 +2026-04-11 08:30:44.445566: Current learning rate: 0.0083 +2026-04-11 08:32:24.717486: train_loss -0.2953 +2026-04-11 08:32:24.723298: val_loss -0.2646 +2026-04-11 08:32:24.725618: Pseudo dice [0.0, 0.0, 0.6243, 0.0, 0.0, 0.0, 0.0] +2026-04-11 08:32:24.728162: Epoch time: 100.28 s +2026-04-11 08:32:25.859718: +2026-04-11 08:32:25.861908: Epoch 751 +2026-04-11 08:32:25.864048: Current learning rate: 0.00829 +2026-04-11 08:34:06.006388: train_loss -0.3246 +2026-04-11 08:34:06.011561: val_loss -0.3587 +2026-04-11 08:34:06.013837: Pseudo dice [0.0, 0.0, 0.6038, 0.0, 0.0, 0.0, 0.0] +2026-04-11 08:34:06.016308: Epoch time: 100.15 s +2026-04-11 08:34:07.127993: +2026-04-11 08:34:07.129745: Epoch 752 +2026-04-11 08:34:07.131851: Current learning rate: 0.00829 +2026-04-11 08:35:47.359469: train_loss -0.303 +2026-04-11 08:35:47.365832: val_loss -0.3374 +2026-04-11 08:35:47.368166: Pseudo dice [0.0, 0.0, 0.6573, 0.0, 0.0, 0.2059, 0.0] +2026-04-11 08:35:47.370193: Epoch time: 100.23 s +2026-04-11 08:35:48.493093: +2026-04-11 08:35:48.495510: Epoch 753 +2026-04-11 08:35:48.498367: Current learning rate: 0.00829 +2026-04-11 08:37:28.785730: train_loss -0.3215 +2026-04-11 08:37:28.792956: val_loss -0.3572 +2026-04-11 08:37:28.795821: Pseudo dice [0.0, 0.0, 0.7552, 0.0, 0.0, 0.584, 0.0] +2026-04-11 08:37:28.798211: Epoch time: 100.3 s +2026-04-11 08:37:29.923576: +2026-04-11 08:37:29.925677: Epoch 754 +2026-04-11 08:37:29.927771: Current learning rate: 0.00829 +2026-04-11 08:39:10.244557: train_loss -0.3459 +2026-04-11 08:39:10.250903: val_loss -0.2505 +2026-04-11 08:39:10.254609: Pseudo dice [0.0, 0.0, 0.5138, 0.0, 0.0, 0.3567, 0.0] +2026-04-11 08:39:10.256953: Epoch time: 100.32 s +2026-04-11 08:39:11.387854: +2026-04-11 08:39:11.389869: Epoch 755 +2026-04-11 08:39:11.392057: Current learning rate: 0.00828 +2026-04-11 08:40:51.596257: train_loss -0.3275 +2026-04-11 08:40:51.609850: val_loss -0.3563 +2026-04-11 08:40:51.612388: Pseudo dice [0.0, 0.0, 0.5852, 0.0, 0.0, 0.6043, 0.0] +2026-04-11 08:40:51.615314: Epoch time: 100.21 s +2026-04-11 08:40:52.717800: +2026-04-11 08:40:52.719930: Epoch 756 +2026-04-11 08:40:52.722124: Current learning rate: 0.00828 +2026-04-11 08:42:33.045411: train_loss -0.3397 +2026-04-11 08:42:33.051257: val_loss -0.2614 +2026-04-11 08:42:33.053807: Pseudo dice [0.0, 0.0, 0.5661, 0.0, 0.0, 0.488, 0.0] +2026-04-11 08:42:33.056534: Epoch time: 100.33 s +2026-04-11 08:42:34.203794: +2026-04-11 08:42:34.205649: Epoch 757 +2026-04-11 08:42:34.207549: Current learning rate: 0.00828 +2026-04-11 08:44:14.893768: train_loss -0.3592 +2026-04-11 08:44:14.901942: val_loss -0.3914 +2026-04-11 08:44:14.906524: Pseudo dice [0.0, 0.0, 0.7103, 0.0, 0.0, 0.7959, 0.0] +2026-04-11 08:44:14.909147: Epoch time: 100.69 s +2026-04-11 08:44:16.042181: +2026-04-11 08:44:16.044628: Epoch 758 +2026-04-11 08:44:16.047215: Current learning rate: 0.00828 +2026-04-11 08:45:56.946529: train_loss -0.3598 +2026-04-11 08:45:56.955333: val_loss -0.3759 +2026-04-11 08:45:56.958698: Pseudo dice [0.0, 0.0, 0.7085, 0.0, 0.0, 0.8749, 0.6689] +2026-04-11 08:45:56.962536: Epoch time: 100.91 s +2026-04-11 08:45:58.104136: +2026-04-11 08:45:58.106244: Epoch 759 +2026-04-11 08:45:58.108535: Current learning rate: 0.00827 +2026-04-11 08:47:38.201766: train_loss -0.3704 +2026-04-11 08:47:38.208341: val_loss -0.3431 +2026-04-11 08:47:38.210518: Pseudo dice [0.0, 0.0, 0.6633, 0.0, 0.0, 0.406, 0.3442] +2026-04-11 08:47:38.212629: Epoch time: 100.1 s +2026-04-11 08:47:39.374019: +2026-04-11 08:47:39.375885: Epoch 760 +2026-04-11 08:47:39.377997: Current learning rate: 0.00827 +2026-04-11 08:49:19.942909: train_loss -0.3497 +2026-04-11 08:49:19.949785: val_loss -0.3293 +2026-04-11 08:49:19.952610: Pseudo dice [0.0, 0.0, 0.7091, 0.0, 0.0, 0.472, 0.6374] +2026-04-11 08:49:19.955248: Epoch time: 100.57 s +2026-04-11 08:49:21.079096: +2026-04-11 08:49:21.081317: Epoch 761 +2026-04-11 08:49:21.083537: Current learning rate: 0.00827 +2026-04-11 08:51:02.189727: train_loss -0.36 +2026-04-11 08:51:02.215970: val_loss -0.3876 +2026-04-11 08:51:02.219128: Pseudo dice [0.0, 0.0, 0.6472, 0.0, 0.0, 0.5305, 0.7581] +2026-04-11 08:51:02.223295: Epoch time: 101.11 s +2026-04-11 08:51:03.352707: +2026-04-11 08:51:03.356844: Epoch 762 +2026-04-11 08:51:03.361757: Current learning rate: 0.00827 +2026-04-11 08:52:44.626094: train_loss -0.3201 +2026-04-11 08:52:44.632695: val_loss -0.3363 +2026-04-11 08:52:44.634866: Pseudo dice [0.0, 0.0, 0.4468, 0.0, 0.0, 0.6199, 0.2925] +2026-04-11 08:52:44.637916: Epoch time: 101.28 s +2026-04-11 08:52:45.777406: +2026-04-11 08:52:45.780027: Epoch 763 +2026-04-11 08:52:45.782964: Current learning rate: 0.00827 +2026-04-11 08:54:26.325147: train_loss -0.3451 +2026-04-11 08:54:26.333177: val_loss -0.3525 +2026-04-11 08:54:26.335280: Pseudo dice [0.0, 0.0, 0.7027, 0.0, 0.0, 0.4887, 0.009] +2026-04-11 08:54:26.339078: Epoch time: 100.55 s +2026-04-11 08:54:27.506903: +2026-04-11 08:54:27.508832: Epoch 764 +2026-04-11 08:54:27.511047: Current learning rate: 0.00826 +2026-04-11 08:56:07.881058: train_loss -0.3167 +2026-04-11 08:56:07.894089: val_loss -0.368 +2026-04-11 08:56:07.911704: Pseudo dice [0.0, 0.0, 0.721, 0.0, 0.0, 0.0, 0.7366] +2026-04-11 08:56:07.916292: Epoch time: 100.38 s +2026-04-11 08:56:09.109584: +2026-04-11 08:56:09.111426: Epoch 765 +2026-04-11 08:56:09.113964: Current learning rate: 0.00826 +2026-04-11 08:57:49.542449: train_loss -0.3072 +2026-04-11 08:57:49.551028: val_loss -0.3135 +2026-04-11 08:57:49.552878: Pseudo dice [0.0, 0.0, 0.6119, 0.0, 0.0, 0.6119, 0.4755] +2026-04-11 08:57:49.555699: Epoch time: 100.44 s +2026-04-11 08:57:50.703194: +2026-04-11 08:57:50.705443: Epoch 766 +2026-04-11 08:57:50.719424: Current learning rate: 0.00826 +2026-04-11 08:59:30.907949: train_loss -0.3549 +2026-04-11 08:59:30.916286: val_loss -0.3714 +2026-04-11 08:59:30.919780: Pseudo dice [0.0, 0.0, 0.5567, 0.0, 0.0, 0.7275, 0.7535] +2026-04-11 08:59:30.922702: Epoch time: 100.21 s +2026-04-11 08:59:32.077575: +2026-04-11 08:59:32.079540: Epoch 767 +2026-04-11 08:59:32.081556: Current learning rate: 0.00826 +2026-04-11 09:01:12.401171: train_loss -0.3295 +2026-04-11 09:01:12.406901: val_loss -0.303 +2026-04-11 09:01:12.410434: Pseudo dice [0.0, 0.0, 0.4719, 0.0, 0.0, 0.1175, 0.5931] +2026-04-11 09:01:12.412877: Epoch time: 100.33 s +2026-04-11 09:01:13.547803: +2026-04-11 09:01:13.550054: Epoch 768 +2026-04-11 09:01:13.552171: Current learning rate: 0.00825 +2026-04-11 09:02:53.795144: train_loss -0.3586 +2026-04-11 09:02:53.801898: val_loss -0.304 +2026-04-11 09:02:53.804468: Pseudo dice [0.0, 0.0, 0.5341, 0.0, 0.0, 0.4579, 0.6117] +2026-04-11 09:02:53.807096: Epoch time: 100.25 s +2026-04-11 09:02:54.953135: +2026-04-11 09:02:54.956107: Epoch 769 +2026-04-11 09:02:54.958974: Current learning rate: 0.00825 +2026-04-11 09:04:35.258816: train_loss -0.3317 +2026-04-11 09:04:35.268314: val_loss -0.3687 +2026-04-11 09:04:35.271164: Pseudo dice [0.0, 0.0, 0.5226, 0.0, 0.0, 0.5644, 0.7644] +2026-04-11 09:04:35.273592: Epoch time: 100.31 s +2026-04-11 09:04:36.419319: +2026-04-11 09:04:36.427567: Epoch 770 +2026-04-11 09:04:36.429792: Current learning rate: 0.00825 +2026-04-11 09:06:16.632993: train_loss -0.362 +2026-04-11 09:06:16.639585: val_loss -0.3041 +2026-04-11 09:06:16.641964: Pseudo dice [0.0, 0.0, 0.6167, 0.0, 0.0, 0.5558, 0.6465] +2026-04-11 09:06:16.645100: Epoch time: 100.22 s +2026-04-11 09:06:17.785340: +2026-04-11 09:06:17.788129: Epoch 771 +2026-04-11 09:06:17.791301: Current learning rate: 0.00825 +2026-04-11 09:07:57.988349: train_loss -0.3545 +2026-04-11 09:07:57.994249: val_loss -0.401 +2026-04-11 09:07:57.996970: Pseudo dice [0.0, 0.0, 0.7266, 0.0, 0.0, 0.7691, 0.8465] +2026-04-11 09:07:58.000134: Epoch time: 100.21 s +2026-04-11 09:07:59.167929: +2026-04-11 09:07:59.170375: Epoch 772 +2026-04-11 09:07:59.174514: Current learning rate: 0.00824 +2026-04-11 09:09:39.309438: train_loss -0.3668 +2026-04-11 09:09:39.316344: val_loss -0.3439 +2026-04-11 09:09:39.318335: Pseudo dice [0.0, 0.0, 0.7756, 0.0, 0.0, 0.7323, 0.724] +2026-04-11 09:09:39.321461: Epoch time: 100.14 s +2026-04-11 09:09:40.485052: +2026-04-11 09:09:40.487487: Epoch 773 +2026-04-11 09:09:40.490000: Current learning rate: 0.00824 +2026-04-11 09:11:20.696378: train_loss -0.3252 +2026-04-11 09:11:20.704001: val_loss -0.3416 +2026-04-11 09:11:20.706698: Pseudo dice [0.0, 0.0, 0.7158, 0.0, 0.0, 0.5644, 0.0787] +2026-04-11 09:11:20.709788: Epoch time: 100.21 s +2026-04-11 09:11:21.854326: +2026-04-11 09:11:21.857613: Epoch 774 +2026-04-11 09:11:21.860472: Current learning rate: 0.00824 +2026-04-11 09:13:02.224904: train_loss -0.3648 +2026-04-11 09:13:02.233511: val_loss -0.365 +2026-04-11 09:13:02.235697: Pseudo dice [0.0, 0.0, 0.5566, 0.0, 0.0, 0.5386, 0.6315] +2026-04-11 09:13:02.238387: Epoch time: 100.37 s +2026-04-11 09:13:03.398005: +2026-04-11 09:13:03.400194: Epoch 775 +2026-04-11 09:13:03.402882: Current learning rate: 0.00824 +2026-04-11 09:14:43.611979: train_loss -0.3651 +2026-04-11 09:14:43.618283: val_loss -0.3531 +2026-04-11 09:14:43.620049: Pseudo dice [0.0, 0.0, 0.7268, 0.0, 0.0, 0.5515, 0.4129] +2026-04-11 09:14:43.622983: Epoch time: 100.22 s +2026-04-11 09:14:44.765266: +2026-04-11 09:14:44.767692: Epoch 776 +2026-04-11 09:14:44.770376: Current learning rate: 0.00824 +2026-04-11 09:16:25.122886: train_loss -0.3508 +2026-04-11 09:16:25.129143: val_loss -0.3735 +2026-04-11 09:16:25.132140: Pseudo dice [0.0, 0.0, 0.6576, 0.0, 0.0, 0.5153, 0.5803] +2026-04-11 09:16:25.134460: Epoch time: 100.36 s +2026-04-11 09:16:26.304258: +2026-04-11 09:16:26.306697: Epoch 777 +2026-04-11 09:16:26.309091: Current learning rate: 0.00823 +2026-04-11 09:18:06.679474: train_loss -0.3701 +2026-04-11 09:18:06.685901: val_loss -0.3746 +2026-04-11 09:18:06.689282: Pseudo dice [0.0, 0.0, 0.5328, 0.0, 0.0, 0.6653, 0.5628] +2026-04-11 09:18:06.691946: Epoch time: 100.38 s +2026-04-11 09:18:07.835720: +2026-04-11 09:18:07.838078: Epoch 778 +2026-04-11 09:18:07.840168: Current learning rate: 0.00823 +2026-04-11 09:19:48.663480: train_loss -0.3028 +2026-04-11 09:19:48.670356: val_loss -0.3032 +2026-04-11 09:19:48.672318: Pseudo dice [0.0, 0.0, 0.6623, 0.0, 0.0, 0.0, 0.2074] +2026-04-11 09:19:48.675873: Epoch time: 100.83 s +2026-04-11 09:19:49.830122: +2026-04-11 09:19:49.832272: Epoch 779 +2026-04-11 09:19:49.834576: Current learning rate: 0.00823 +2026-04-11 09:21:30.155294: train_loss -0.3143 +2026-04-11 09:21:30.160270: val_loss -0.3136 +2026-04-11 09:21:30.162182: Pseudo dice [0.0, 0.0, 0.6943, 0.0, 0.0, 0.0, 0.0079] +2026-04-11 09:21:30.164470: Epoch time: 100.33 s +2026-04-11 09:21:31.315431: +2026-04-11 09:21:31.317672: Epoch 780 +2026-04-11 09:21:31.319919: Current learning rate: 0.00823 +2026-04-11 09:23:11.708465: train_loss -0.3466 +2026-04-11 09:23:11.716099: val_loss -0.38 +2026-04-11 09:23:11.718819: Pseudo dice [0.0, 0.0, 0.7335, 0.0, 0.0, 0.6338, 0.4608] +2026-04-11 09:23:11.721967: Epoch time: 100.4 s +2026-04-11 09:23:12.893223: +2026-04-11 09:23:12.898532: Epoch 781 +2026-04-11 09:23:12.903344: Current learning rate: 0.00822 +2026-04-11 09:24:53.316821: train_loss -0.3672 +2026-04-11 09:24:53.324523: val_loss -0.38 +2026-04-11 09:24:53.328591: Pseudo dice [0.0, 0.0, 0.737, 0.0, 0.0, 0.4683, 0.5921] +2026-04-11 09:24:53.331205: Epoch time: 100.43 s +2026-04-11 09:24:55.397371: +2026-04-11 09:24:55.399591: Epoch 782 +2026-04-11 09:24:55.402398: Current learning rate: 0.00822 +2026-04-11 09:26:35.697898: train_loss -0.3274 +2026-04-11 09:26:35.707634: val_loss -0.2981 +2026-04-11 09:26:35.711159: Pseudo dice [0.0, 0.0, 0.6387, 0.0, 0.0, 0.377, 0.4301] +2026-04-11 09:26:35.714483: Epoch time: 100.3 s +2026-04-11 09:26:36.877182: +2026-04-11 09:26:36.879170: Epoch 783 +2026-04-11 09:26:36.881313: Current learning rate: 0.00822 +2026-04-11 09:28:17.093675: train_loss -0.2864 +2026-04-11 09:28:17.104380: val_loss -0.2548 +2026-04-11 09:28:17.106897: Pseudo dice [0.0, 0.0, 0.4279, 0.0, 0.0, 0.2117, 0.1787] +2026-04-11 09:28:17.110139: Epoch time: 100.22 s +2026-04-11 09:28:18.261529: +2026-04-11 09:28:18.263315: Epoch 784 +2026-04-11 09:28:18.265270: Current learning rate: 0.00822 +2026-04-11 09:29:58.901001: train_loss -0.3654 +2026-04-11 09:29:58.908706: val_loss -0.271 +2026-04-11 09:29:58.911538: Pseudo dice [0.0, 0.0, 0.5801, 0.0, 0.0, 0.7245, 0.5745] +2026-04-11 09:29:58.914893: Epoch time: 100.64 s +2026-04-11 09:30:00.074192: +2026-04-11 09:30:00.076131: Epoch 785 +2026-04-11 09:30:00.078886: Current learning rate: 0.00822 +2026-04-11 09:31:40.433042: train_loss -0.3267 +2026-04-11 09:31:40.440178: val_loss -0.3966 +2026-04-11 09:31:40.442406: Pseudo dice [0.0, 0.0, 0.7934, 0.0, 0.0, 0.667, 0.7544] +2026-04-11 09:31:40.444989: Epoch time: 100.36 s +2026-04-11 09:31:41.586531: +2026-04-11 09:31:41.589631: Epoch 786 +2026-04-11 09:31:41.592854: Current learning rate: 0.00821 +2026-04-11 09:33:21.708819: train_loss -0.3501 +2026-04-11 09:33:21.714404: val_loss -0.3058 +2026-04-11 09:33:21.716213: Pseudo dice [0.0, 0.0, 0.7371, 0.0, 0.0, 0.5082, 0.628] +2026-04-11 09:33:21.718431: Epoch time: 100.13 s +2026-04-11 09:33:22.848082: +2026-04-11 09:33:22.850397: Epoch 787 +2026-04-11 09:33:22.853171: Current learning rate: 0.00821 +2026-04-11 09:35:02.923894: train_loss -0.3361 +2026-04-11 09:35:02.933149: val_loss -0.3544 +2026-04-11 09:35:02.935953: Pseudo dice [0.0, 0.0, 0.6711, 0.0, 0.0, 0.7615, 0.5257] +2026-04-11 09:35:02.939986: Epoch time: 100.08 s +2026-04-11 09:35:04.093510: +2026-04-11 09:35:04.095555: Epoch 788 +2026-04-11 09:35:04.097720: Current learning rate: 0.00821 +2026-04-11 09:36:44.596079: train_loss -0.3571 +2026-04-11 09:36:44.603522: val_loss -0.3442 +2026-04-11 09:36:44.605818: Pseudo dice [0.0, 0.0, 0.597, 0.0, 0.0, 0.6768, 0.2446] +2026-04-11 09:36:44.608351: Epoch time: 100.51 s +2026-04-11 09:36:45.769953: +2026-04-11 09:36:45.772186: Epoch 789 +2026-04-11 09:36:45.774268: Current learning rate: 0.00821 +2026-04-11 09:38:26.089271: train_loss -0.3681 +2026-04-11 09:38:26.095641: val_loss -0.3758 +2026-04-11 09:38:26.097616: Pseudo dice [0.0, 0.0, 0.6998, 0.0, 0.0, 0.6283, 0.5966] +2026-04-11 09:38:26.100730: Epoch time: 100.32 s +2026-04-11 09:38:27.234107: +2026-04-11 09:38:27.236562: Epoch 790 +2026-04-11 09:38:27.239217: Current learning rate: 0.0082 +2026-04-11 09:40:07.220874: train_loss -0.3669 +2026-04-11 09:40:07.228287: val_loss -0.3716 +2026-04-11 09:40:07.231464: Pseudo dice [0.0, 0.0, 0.6253, 0.0, 0.0, 0.6822, 0.6653] +2026-04-11 09:40:07.234705: Epoch time: 99.99 s +2026-04-11 09:40:08.375847: +2026-04-11 09:40:08.377718: Epoch 791 +2026-04-11 09:40:08.379844: Current learning rate: 0.0082 +2026-04-11 09:41:48.593020: train_loss -0.3779 +2026-04-11 09:41:48.599894: val_loss -0.3096 +2026-04-11 09:41:48.604168: Pseudo dice [0.0, 0.0, 0.6522, 0.0, 0.0, 0.5792, 0.668] +2026-04-11 09:41:48.609563: Epoch time: 100.22 s +2026-04-11 09:41:49.758489: +2026-04-11 09:41:49.762886: Epoch 792 +2026-04-11 09:41:49.765138: Current learning rate: 0.0082 +2026-04-11 09:43:29.875507: train_loss -0.363 +2026-04-11 09:43:29.882944: val_loss -0.2002 +2026-04-11 09:43:29.885740: Pseudo dice [0.0, 0.0, 0.6348, 0.0, 0.0, 0.6153, 0.0865] +2026-04-11 09:43:29.890374: Epoch time: 100.12 s +2026-04-11 09:43:31.045085: +2026-04-11 09:43:31.047493: Epoch 793 +2026-04-11 09:43:31.049881: Current learning rate: 0.0082 +2026-04-11 09:45:11.100589: train_loss -0.3185 +2026-04-11 09:45:11.113746: val_loss -0.3484 +2026-04-11 09:45:11.119811: Pseudo dice [0.0, 0.0, 0.7038, 0.0, 0.0, 0.2013, 0.586] +2026-04-11 09:45:11.125081: Epoch time: 100.06 s +2026-04-11 09:45:12.259237: +2026-04-11 09:45:12.265261: Epoch 794 +2026-04-11 09:45:12.271938: Current learning rate: 0.00819 +2026-04-11 09:46:52.546580: train_loss -0.3439 +2026-04-11 09:46:52.552738: val_loss -0.325 +2026-04-11 09:46:52.554739: Pseudo dice [0.0, 0.0, 0.2011, 0.0, 0.0, 0.0261, 0.8426] +2026-04-11 09:46:52.556946: Epoch time: 100.29 s +2026-04-11 09:46:53.717112: +2026-04-11 09:46:53.718989: Epoch 795 +2026-04-11 09:46:53.721173: Current learning rate: 0.00819 +2026-04-11 09:48:33.957552: train_loss -0.3444 +2026-04-11 09:48:33.965754: val_loss -0.4064 +2026-04-11 09:48:33.968550: Pseudo dice [0.0, 0.0, 0.7697, 0.0, 0.0, 0.8005, 0.7617] +2026-04-11 09:48:33.972302: Epoch time: 100.24 s +2026-04-11 09:48:35.131349: +2026-04-11 09:48:35.133340: Epoch 796 +2026-04-11 09:48:35.135523: Current learning rate: 0.00819 +2026-04-11 09:50:15.305074: train_loss -0.3584 +2026-04-11 09:50:15.312498: val_loss -0.3173 +2026-04-11 09:50:15.315036: Pseudo dice [0.0, 0.0, 0.496, 0.0, 0.0, 0.4872, 0.627] +2026-04-11 09:50:15.317210: Epoch time: 100.18 s +2026-04-11 09:50:16.482274: +2026-04-11 09:50:16.484336: Epoch 797 +2026-04-11 09:50:16.486849: Current learning rate: 0.00819 +2026-04-11 09:51:57.006400: train_loss -0.3336 +2026-04-11 09:51:57.013185: val_loss -0.3104 +2026-04-11 09:51:57.016567: Pseudo dice [0.0, 0.0, 0.5764, 0.0, 0.0, 0.5655, 0.6481] +2026-04-11 09:51:57.019487: Epoch time: 100.53 s +2026-04-11 09:51:58.181762: +2026-04-11 09:51:58.183730: Epoch 798 +2026-04-11 09:51:58.186038: Current learning rate: 0.00819 +2026-04-11 09:53:38.309257: train_loss -0.3533 +2026-04-11 09:53:38.316601: val_loss -0.3775 +2026-04-11 09:53:38.319686: Pseudo dice [0.0, 0.0, 0.7018, 0.0, 0.0, 0.6989, 0.624] +2026-04-11 09:53:38.322305: Epoch time: 100.13 s +2026-04-11 09:53:39.470926: +2026-04-11 09:53:39.473508: Epoch 799 +2026-04-11 09:53:39.476322: Current learning rate: 0.00818 +2026-04-11 09:55:19.579906: train_loss -0.3256 +2026-04-11 09:55:19.594647: val_loss -0.231 +2026-04-11 09:55:19.610523: Pseudo dice [0.0, 0.0, 0.2917, 0.0, 0.0, 0.0, 0.3681] +2026-04-11 09:55:19.612935: Epoch time: 100.11 s +2026-04-11 09:55:22.376403: +2026-04-11 09:55:22.378940: Epoch 800 +2026-04-11 09:55:22.381177: Current learning rate: 0.00818 +2026-04-11 09:57:03.306482: train_loss -0.3305 +2026-04-11 09:57:03.313021: val_loss -0.3744 +2026-04-11 09:57:03.315204: Pseudo dice [0.0, 0.0, 0.6426, 0.0, 0.0, 0.5525, 0.7919] +2026-04-11 09:57:03.318356: Epoch time: 100.93 s +2026-04-11 09:57:04.484928: +2026-04-11 09:57:04.487312: Epoch 801 +2026-04-11 09:57:04.489691: Current learning rate: 0.00818 +2026-04-11 09:58:44.666841: train_loss -0.3252 +2026-04-11 09:58:44.672599: val_loss -0.2889 +2026-04-11 09:58:44.674895: Pseudo dice [0.0, 0.0, 0.6305, 0.0, 0.0, 0.5388, 0.6763] +2026-04-11 09:58:44.677192: Epoch time: 100.18 s +2026-04-11 09:58:45.837068: +2026-04-11 09:58:45.839613: Epoch 802 +2026-04-11 09:58:45.842013: Current learning rate: 0.00818 +2026-04-11 10:00:26.141719: train_loss -0.3408 +2026-04-11 10:00:26.149103: val_loss -0.3444 +2026-04-11 10:00:26.152090: Pseudo dice [0.0, 0.0, 0.7376, 0.0, 0.0, 0.4714, 0.5354] +2026-04-11 10:00:26.155183: Epoch time: 100.31 s +2026-04-11 10:00:27.306431: +2026-04-11 10:00:27.309649: Epoch 803 +2026-04-11 10:00:27.313419: Current learning rate: 0.00817 +2026-04-11 10:02:07.506083: train_loss -0.3538 +2026-04-11 10:02:07.514652: val_loss -0.357 +2026-04-11 10:02:07.517702: Pseudo dice [0.0, 0.0, 0.5369, 0.0, 0.0, 0.7156, 0.6193] +2026-04-11 10:02:07.522017: Epoch time: 100.2 s +2026-04-11 10:02:08.675101: +2026-04-11 10:02:08.677466: Epoch 804 +2026-04-11 10:02:08.682492: Current learning rate: 0.00817 +2026-04-11 10:03:48.996968: train_loss -0.3527 +2026-04-11 10:03:49.004701: val_loss -0.3475 +2026-04-11 10:03:49.007184: Pseudo dice [0.0, 0.0, 0.6526, 0.0, 0.0, 0.8262, 0.3825] +2026-04-11 10:03:49.010597: Epoch time: 100.32 s +2026-04-11 10:03:50.157605: +2026-04-11 10:03:50.160051: Epoch 805 +2026-04-11 10:03:50.162711: Current learning rate: 0.00817 +2026-04-11 10:05:30.308007: train_loss -0.3026 +2026-04-11 10:05:30.319069: val_loss -0.3395 +2026-04-11 10:05:30.321815: Pseudo dice [0.0, 0.0, 0.5007, 0.0, 0.0, 0.5556, 0.5175] +2026-04-11 10:05:30.324719: Epoch time: 100.15 s +2026-04-11 10:05:31.479203: +2026-04-11 10:05:31.481204: Epoch 806 +2026-04-11 10:05:31.483618: Current learning rate: 0.00817 +2026-04-11 10:07:11.600506: train_loss -0.3109 +2026-04-11 10:07:11.607342: val_loss -0.3595 +2026-04-11 10:07:11.609927: Pseudo dice [0.0, 0.0, 0.7559, 0.0, 0.0, 0.6704, 0.0549] +2026-04-11 10:07:11.612669: Epoch time: 100.12 s +2026-04-11 10:07:12.763968: +2026-04-11 10:07:12.766454: Epoch 807 +2026-04-11 10:07:12.768677: Current learning rate: 0.00816 +2026-04-11 10:08:53.240844: train_loss -0.3302 +2026-04-11 10:08:53.248081: val_loss -0.3737 +2026-04-11 10:08:53.252807: Pseudo dice [0.0, 0.0, 0.765, 0.0, 0.0, 0.8283, 0.7822] +2026-04-11 10:08:53.255588: Epoch time: 100.48 s +2026-04-11 10:08:54.396258: +2026-04-11 10:08:54.399034: Epoch 808 +2026-04-11 10:08:54.401557: Current learning rate: 0.00816 +2026-04-11 10:10:34.528103: train_loss -0.3393 +2026-04-11 10:10:34.538444: val_loss -0.331 +2026-04-11 10:10:34.541118: Pseudo dice [0.0, 0.0, 0.5246, 0.0, 0.0, 0.2261, 0.4264] +2026-04-11 10:10:34.546084: Epoch time: 100.14 s +2026-04-11 10:10:35.704940: +2026-04-11 10:10:35.707489: Epoch 809 +2026-04-11 10:10:35.713688: Current learning rate: 0.00816 +2026-04-11 10:12:16.013312: train_loss -0.3398 +2026-04-11 10:12:16.019006: val_loss -0.3203 +2026-04-11 10:12:16.021120: Pseudo dice [0.0, 0.0, 0.5096, 0.0, 0.0, 0.4399, 0.1907] +2026-04-11 10:12:16.023524: Epoch time: 100.31 s +2026-04-11 10:12:17.171524: +2026-04-11 10:12:17.173710: Epoch 810 +2026-04-11 10:12:17.176310: Current learning rate: 0.00816 +2026-04-11 10:13:57.395350: train_loss -0.3296 +2026-04-11 10:13:57.401248: val_loss -0.3183 +2026-04-11 10:13:57.403361: Pseudo dice [0.0, 0.0, 0.3503, 0.0, 0.0, 0.7827, 0.7652] +2026-04-11 10:13:57.406082: Epoch time: 100.23 s +2026-04-11 10:13:58.557062: +2026-04-11 10:13:58.559724: Epoch 811 +2026-04-11 10:13:58.562286: Current learning rate: 0.00816 +2026-04-11 10:15:38.886497: train_loss -0.3586 +2026-04-11 10:15:38.892925: val_loss -0.4125 +2026-04-11 10:15:38.895875: Pseudo dice [0.0, 0.0, 0.7511, 0.0, 0.0, 0.7719, 0.7486] +2026-04-11 10:15:38.898617: Epoch time: 100.33 s +2026-04-11 10:15:40.046036: +2026-04-11 10:15:40.048341: Epoch 812 +2026-04-11 10:15:40.050894: Current learning rate: 0.00815 +2026-04-11 10:17:20.693343: train_loss -0.3429 +2026-04-11 10:17:20.700680: val_loss -0.2628 +2026-04-11 10:17:20.703142: Pseudo dice [0.0, 0.0, 0.7829, 0.0, 0.0, 0.3408, 0.6102] +2026-04-11 10:17:20.706248: Epoch time: 100.65 s +2026-04-11 10:17:21.906543: +2026-04-11 10:17:21.908818: Epoch 813 +2026-04-11 10:17:21.910876: Current learning rate: 0.00815 +2026-04-11 10:19:01.984346: train_loss -0.3525 +2026-04-11 10:19:01.991564: val_loss -0.2713 +2026-04-11 10:19:01.994288: Pseudo dice [0.0, 0.0, 0.4401, 0.0, 0.0, 0.2007, 0.5549] +2026-04-11 10:19:01.998754: Epoch time: 100.08 s +2026-04-11 10:19:03.177870: +2026-04-11 10:19:03.180711: Epoch 814 +2026-04-11 10:19:03.183604: Current learning rate: 0.00815 +2026-04-11 10:20:43.503582: train_loss -0.3214 +2026-04-11 10:20:43.514730: val_loss -0.3194 +2026-04-11 10:20:43.517362: Pseudo dice [0.0, 0.0, 0.6847, 0.0, 0.0, 0.4163, 0.5856] +2026-04-11 10:20:43.520713: Epoch time: 100.33 s +2026-04-11 10:20:44.690878: +2026-04-11 10:20:44.692810: Epoch 815 +2026-04-11 10:20:44.695016: Current learning rate: 0.00815 +2026-04-11 10:22:24.934738: train_loss -0.356 +2026-04-11 10:22:24.941873: val_loss -0.2759 +2026-04-11 10:22:24.943872: Pseudo dice [0.0, 0.0, 0.3156, 0.0, 0.0, 0.5632, 0.818] +2026-04-11 10:22:24.947299: Epoch time: 100.25 s +2026-04-11 10:22:26.097592: +2026-04-11 10:22:26.099943: Epoch 816 +2026-04-11 10:22:26.102245: Current learning rate: 0.00814 +2026-04-11 10:24:06.379551: train_loss -0.3394 +2026-04-11 10:24:06.386536: val_loss -0.3473 +2026-04-11 10:24:06.389488: Pseudo dice [0.0, 0.0, 0.552, 0.0, 0.0, 0.5688, 0.0257] +2026-04-11 10:24:06.392648: Epoch time: 100.29 s +2026-04-11 10:24:07.539807: +2026-04-11 10:24:07.541790: Epoch 817 +2026-04-11 10:24:07.544075: Current learning rate: 0.00814 +2026-04-11 10:25:47.534819: train_loss -0.2859 +2026-04-11 10:25:47.541641: val_loss -0.3706 +2026-04-11 10:25:47.544390: Pseudo dice [0.0, 0.0, 0.5754, 0.0, 0.0, 0.6224, 0.4639] +2026-04-11 10:25:47.547061: Epoch time: 100.0 s +2026-04-11 10:25:48.756168: +2026-04-11 10:25:48.758578: Epoch 818 +2026-04-11 10:25:48.761019: Current learning rate: 0.00814 +2026-04-11 10:27:29.064346: train_loss -0.3463 +2026-04-11 10:27:29.074934: val_loss -0.3118 +2026-04-11 10:27:29.078910: Pseudo dice [0.0, 0.0, 0.3301, 0.0, 0.0, 0.4426, 0.5052] +2026-04-11 10:27:29.082501: Epoch time: 100.31 s +2026-04-11 10:27:30.240892: +2026-04-11 10:27:30.243058: Epoch 819 +2026-04-11 10:27:30.245625: Current learning rate: 0.00814 +2026-04-11 10:29:10.283570: train_loss -0.3496 +2026-04-11 10:29:10.290652: val_loss -0.2913 +2026-04-11 10:29:10.292783: Pseudo dice [0.0, 0.0, 0.2928, 0.0, 0.0, 0.3156, 0.3776] +2026-04-11 10:29:10.296161: Epoch time: 100.05 s +2026-04-11 10:29:12.263724: +2026-04-11 10:29:12.266225: Epoch 820 +2026-04-11 10:29:12.269016: Current learning rate: 0.00813 +2026-04-11 10:30:52.550479: train_loss -0.3034 +2026-04-11 10:30:52.557070: val_loss -0.3418 +2026-04-11 10:30:52.559879: Pseudo dice [0.0, 0.0, 0.6461, 0.0, 0.0, 0.1422, 0.5691] +2026-04-11 10:30:52.562840: Epoch time: 100.29 s +2026-04-11 10:30:53.641716: +2026-04-11 10:30:53.643714: Epoch 821 +2026-04-11 10:30:53.646062: Current learning rate: 0.00813 +2026-04-11 10:32:33.973468: train_loss -0.3034 +2026-04-11 10:32:33.981260: val_loss -0.3165 +2026-04-11 10:32:33.983515: Pseudo dice [0.0, 0.0, 0.5682, 0.0, 0.0, 0.5468, 0.0161] +2026-04-11 10:32:33.985923: Epoch time: 100.33 s +2026-04-11 10:32:35.066384: +2026-04-11 10:32:35.068379: Epoch 822 +2026-04-11 10:32:35.071199: Current learning rate: 0.00813 +2026-04-11 10:34:15.121732: train_loss -0.3322 +2026-04-11 10:34:15.128246: val_loss -0.1239 +2026-04-11 10:34:15.130507: Pseudo dice [0.0, 0.0, 0.5672, 0.0, 0.0, 0.1923, 0.1881] +2026-04-11 10:34:15.134144: Epoch time: 100.06 s +2026-04-11 10:34:16.221854: +2026-04-11 10:34:16.224181: Epoch 823 +2026-04-11 10:34:16.226314: Current learning rate: 0.00813 +2026-04-11 10:35:56.871967: train_loss -0.3042 +2026-04-11 10:35:56.878656: val_loss -0.3372 +2026-04-11 10:35:56.881120: Pseudo dice [0.0, 0.0, 0.6131, 0.0, 0.0, 0.0, 0.3389] +2026-04-11 10:35:56.883762: Epoch time: 100.65 s +2026-04-11 10:35:57.968537: +2026-04-11 10:35:57.970543: Epoch 824 +2026-04-11 10:35:57.972729: Current learning rate: 0.00813 +2026-04-11 10:37:37.815851: train_loss -0.3228 +2026-04-11 10:37:37.823520: val_loss -0.3154 +2026-04-11 10:37:37.826843: Pseudo dice [0.0, 0.0, 0.5563, 0.0, 0.0, 0.3296, 0.1158] +2026-04-11 10:37:37.830804: Epoch time: 99.85 s +2026-04-11 10:37:38.908587: +2026-04-11 10:37:38.910702: Epoch 825 +2026-04-11 10:37:38.913214: Current learning rate: 0.00812 +2026-04-11 10:39:19.053295: train_loss -0.3498 +2026-04-11 10:39:19.059592: val_loss -0.3638 +2026-04-11 10:39:19.063203: Pseudo dice [0.0, 0.0, 0.5656, 0.0, 0.0, 0.2918, 0.1507] +2026-04-11 10:39:19.065849: Epoch time: 100.15 s +2026-04-11 10:39:20.150089: +2026-04-11 10:39:20.152699: Epoch 826 +2026-04-11 10:39:20.155216: Current learning rate: 0.00812 +2026-04-11 10:41:00.342422: train_loss -0.317 +2026-04-11 10:41:00.353656: val_loss -0.343 +2026-04-11 10:41:00.358005: Pseudo dice [0.0, 0.0, 0.6328, 0.0, 0.0, 0.5236, 0.4975] +2026-04-11 10:41:00.361949: Epoch time: 100.2 s +2026-04-11 10:41:01.464330: +2026-04-11 10:41:01.467627: Epoch 827 +2026-04-11 10:41:01.470268: Current learning rate: 0.00812 +2026-04-11 10:42:41.666070: train_loss -0.3286 +2026-04-11 10:42:41.671587: val_loss -0.335 +2026-04-11 10:42:41.673785: Pseudo dice [0.0, 0.0, 0.6741, 0.0, 0.0, 0.4938, 0.4054] +2026-04-11 10:42:41.676312: Epoch time: 100.2 s +2026-04-11 10:42:42.769599: +2026-04-11 10:42:42.771279: Epoch 828 +2026-04-11 10:42:42.773881: Current learning rate: 0.00812 +2026-04-11 10:44:23.024704: train_loss -0.3445 +2026-04-11 10:44:23.030407: val_loss -0.3552 +2026-04-11 10:44:23.032167: Pseudo dice [0.0, 0.0, 0.7087, 0.0, 0.0, 0.8005, 0.7311] +2026-04-11 10:44:23.034781: Epoch time: 100.26 s +2026-04-11 10:44:24.122815: +2026-04-11 10:44:24.124941: Epoch 829 +2026-04-11 10:44:24.126914: Current learning rate: 0.00811 +2026-04-11 10:46:04.240163: train_loss -0.3522 +2026-04-11 10:46:04.248115: val_loss -0.345 +2026-04-11 10:46:04.252794: Pseudo dice [0.0, 0.0, 0.749, 0.0, 0.0, 0.667, 0.7262] +2026-04-11 10:46:04.255918: Epoch time: 100.12 s +2026-04-11 10:46:05.323332: +2026-04-11 10:46:05.325248: Epoch 830 +2026-04-11 10:46:05.327462: Current learning rate: 0.00811 +2026-04-11 10:47:45.568432: train_loss -0.3681 +2026-04-11 10:47:45.577664: val_loss -0.3762 +2026-04-11 10:47:45.581357: Pseudo dice [0.0, 0.0, 0.5869, 0.0, 0.0, 0.6211, 0.6968] +2026-04-11 10:47:45.584834: Epoch time: 100.25 s +2026-04-11 10:47:46.679694: +2026-04-11 10:47:46.681805: Epoch 831 +2026-04-11 10:47:46.684094: Current learning rate: 0.00811 +2026-04-11 10:49:27.160780: train_loss -0.3158 +2026-04-11 10:49:27.167954: val_loss -0.3294 +2026-04-11 10:49:27.169928: Pseudo dice [0.0, 0.0, 0.5602, 0.0, 0.0, 0.4604, 0.5191] +2026-04-11 10:49:27.173455: Epoch time: 100.48 s +2026-04-11 10:49:28.250631: +2026-04-11 10:49:28.252550: Epoch 832 +2026-04-11 10:49:28.254575: Current learning rate: 0.00811 +2026-04-11 10:51:08.388477: train_loss -0.3514 +2026-04-11 10:51:08.395078: val_loss -0.375 +2026-04-11 10:51:08.399151: Pseudo dice [0.0, 0.0, 0.6005, 0.0, 0.0, 0.6601, 0.5984] +2026-04-11 10:51:08.402223: Epoch time: 100.14 s +2026-04-11 10:51:09.495128: +2026-04-11 10:51:09.497171: Epoch 833 +2026-04-11 10:51:09.500265: Current learning rate: 0.0081 +2026-04-11 10:52:49.857168: train_loss -0.3188 +2026-04-11 10:52:49.863221: val_loss -0.3518 +2026-04-11 10:52:49.865206: Pseudo dice [0.0, 0.0, 0.6377, 0.0, 0.0, 0.1901, 0.2093] +2026-04-11 10:52:49.867203: Epoch time: 100.37 s +2026-04-11 10:52:50.969388: +2026-04-11 10:52:50.971443: Epoch 834 +2026-04-11 10:52:50.974720: Current learning rate: 0.0081 +2026-04-11 10:54:30.976571: train_loss -0.3238 +2026-04-11 10:54:30.983390: val_loss -0.3112 +2026-04-11 10:54:30.985338: Pseudo dice [0.0, 0.0, 0.439, 0.0, 0.0, 0.1836, 0.0] +2026-04-11 10:54:30.987589: Epoch time: 100.01 s +2026-04-11 10:54:32.050542: +2026-04-11 10:54:32.052573: Epoch 835 +2026-04-11 10:54:32.054796: Current learning rate: 0.0081 +2026-04-11 10:56:12.156322: train_loss -0.3368 +2026-04-11 10:56:12.162240: val_loss -0.3623 +2026-04-11 10:56:12.164382: Pseudo dice [0.0, 0.0, 0.636, 0.0, 0.0, 0.2314, 0.4964] +2026-04-11 10:56:12.166683: Epoch time: 100.11 s +2026-04-11 10:56:13.272210: +2026-04-11 10:56:13.274381: Epoch 836 +2026-04-11 10:56:13.276950: Current learning rate: 0.0081 +2026-04-11 10:57:53.452211: train_loss -0.3269 +2026-04-11 10:57:53.462326: val_loss -0.3683 +2026-04-11 10:57:53.464246: Pseudo dice [0.0, 0.0, 0.6003, 0.0, 0.0, 0.6192, 0.626] +2026-04-11 10:57:53.467326: Epoch time: 100.18 s +2026-04-11 10:57:54.560072: +2026-04-11 10:57:54.562252: Epoch 837 +2026-04-11 10:57:54.564607: Current learning rate: 0.0081 +2026-04-11 10:59:34.850267: train_loss -0.3477 +2026-04-11 10:59:34.860022: val_loss -0.3826 +2026-04-11 10:59:34.863108: Pseudo dice [0.0, 0.0, 0.7073, 0.0, 0.0, 0.6407, 0.5831] +2026-04-11 10:59:34.866703: Epoch time: 100.29 s +2026-04-11 10:59:35.937409: +2026-04-11 10:59:35.940786: Epoch 838 +2026-04-11 10:59:35.943437: Current learning rate: 0.00809 +2026-04-11 11:01:16.196970: train_loss -0.3504 +2026-04-11 11:01:16.205873: val_loss -0.3861 +2026-04-11 11:01:16.208405: Pseudo dice [0.0, 0.0, 0.6804, 0.0, 0.0, 0.8254, 0.4914] +2026-04-11 11:01:16.211582: Epoch time: 100.26 s +2026-04-11 11:01:17.299138: +2026-04-11 11:01:17.301845: Epoch 839 +2026-04-11 11:01:17.304524: Current learning rate: 0.00809 +2026-04-11 11:02:57.585225: train_loss -0.2979 +2026-04-11 11:02:57.591547: val_loss -0.2695 +2026-04-11 11:02:57.593328: Pseudo dice [0.0, 0.0, 0.4601, 0.0, 0.0, 0.4253, 0.6845] +2026-04-11 11:02:57.595865: Epoch time: 100.29 s +2026-04-11 11:02:58.669568: +2026-04-11 11:02:58.672043: Epoch 840 +2026-04-11 11:02:58.674721: Current learning rate: 0.00809 +2026-04-11 11:04:40.059941: train_loss -0.3235 +2026-04-11 11:04:40.067087: val_loss -0.3401 +2026-04-11 11:04:40.069831: Pseudo dice [0.0, 0.0, 0.6583, 0.0, 0.0, 0.397, 0.4761] +2026-04-11 11:04:40.073358: Epoch time: 101.39 s +2026-04-11 11:04:41.164390: +2026-04-11 11:04:41.166454: Epoch 841 +2026-04-11 11:04:41.168549: Current learning rate: 0.00809 +2026-04-11 11:06:21.531910: train_loss -0.346 +2026-04-11 11:06:21.538288: val_loss -0.3733 +2026-04-11 11:06:21.540952: Pseudo dice [0.0, 0.0, 0.7293, 0.0, 0.0, 0.7219, 0.6019] +2026-04-11 11:06:21.543758: Epoch time: 100.37 s +2026-04-11 11:06:22.786991: +2026-04-11 11:06:22.788957: Epoch 842 +2026-04-11 11:06:22.790998: Current learning rate: 0.00808 +2026-04-11 11:08:03.415109: train_loss -0.3664 +2026-04-11 11:08:03.421479: val_loss -0.3023 +2026-04-11 11:08:03.423927: Pseudo dice [0.0, 0.0, 0.7337, 0.0, 0.0, 0.2216, 0.2637] +2026-04-11 11:08:03.427159: Epoch time: 100.63 s +2026-04-11 11:08:04.530573: +2026-04-11 11:08:04.534168: Epoch 843 +2026-04-11 11:08:04.536971: Current learning rate: 0.00808 +2026-04-11 11:09:44.943527: train_loss -0.3332 +2026-04-11 11:09:44.950551: val_loss -0.3558 +2026-04-11 11:09:44.952303: Pseudo dice [0.0, 0.0, 0.7021, 0.0, 0.0, 0.6574, 0.7328] +2026-04-11 11:09:44.954171: Epoch time: 100.42 s +2026-04-11 11:09:46.023052: +2026-04-11 11:09:46.025182: Epoch 844 +2026-04-11 11:09:46.027810: Current learning rate: 0.00808 +2026-04-11 11:11:26.220116: train_loss -0.3657 +2026-04-11 11:11:26.229264: val_loss -0.4016 +2026-04-11 11:11:26.231809: Pseudo dice [0.0, 0.0, 0.6335, 0.0, 0.0, 0.643, 0.7638] +2026-04-11 11:11:26.234785: Epoch time: 100.2 s +2026-04-11 11:11:27.328557: +2026-04-11 11:11:27.330346: Epoch 845 +2026-04-11 11:11:27.332626: Current learning rate: 0.00808 +2026-04-11 11:13:07.445497: train_loss -0.3654 +2026-04-11 11:13:07.452184: val_loss -0.3483 +2026-04-11 11:13:07.454911: Pseudo dice [0.0, 0.0, 0.5754, 0.0, 0.0, 0.6976, 0.5909] +2026-04-11 11:13:07.458326: Epoch time: 100.12 s +2026-04-11 11:13:08.546317: +2026-04-11 11:13:08.548249: Epoch 846 +2026-04-11 11:13:08.550141: Current learning rate: 0.00807 +2026-04-11 11:14:48.846664: train_loss -0.3326 +2026-04-11 11:14:48.852852: val_loss -0.3496 +2026-04-11 11:14:48.855086: Pseudo dice [0.0, 0.0, 0.5366, 0.0, 0.0, 0.6997, 0.6409] +2026-04-11 11:14:48.858652: Epoch time: 100.3 s +2026-04-11 11:14:49.958194: +2026-04-11 11:14:49.960227: Epoch 847 +2026-04-11 11:14:49.962625: Current learning rate: 0.00807 +2026-04-11 11:16:30.076608: train_loss -0.3476 +2026-04-11 11:16:30.094667: val_loss -0.3492 +2026-04-11 11:16:30.098600: Pseudo dice [0.0, 0.0, 0.563, 0.0, 0.0, 0.7489, 0.6224] +2026-04-11 11:16:30.111618: Epoch time: 100.12 s +2026-04-11 11:16:31.205011: +2026-04-11 11:16:31.206903: Epoch 848 +2026-04-11 11:16:31.209100: Current learning rate: 0.00807 +2026-04-11 11:18:11.427225: train_loss -0.2996 +2026-04-11 11:18:11.434220: val_loss -0.2727 +2026-04-11 11:18:11.436817: Pseudo dice [0.0, 0.0, 0.3988, 0.0, 0.0, 0.0944, 0.1484] +2026-04-11 11:18:11.440737: Epoch time: 100.23 s +2026-04-11 11:18:12.527404: +2026-04-11 11:18:12.529134: Epoch 849 +2026-04-11 11:18:12.531149: Current learning rate: 0.00807 +2026-04-11 11:19:53.098513: train_loss -0.2987 +2026-04-11 11:19:53.105362: val_loss -0.3399 +2026-04-11 11:19:53.108238: Pseudo dice [0.0, 0.0, 0.4901, 0.0, 0.0, 0.1467, 0.3376] +2026-04-11 11:19:53.111229: Epoch time: 100.57 s +2026-04-11 11:19:55.832295: +2026-04-11 11:19:55.834518: Epoch 850 +2026-04-11 11:19:55.836458: Current learning rate: 0.00807 +2026-04-11 11:21:36.143932: train_loss -0.3406 +2026-04-11 11:21:36.150109: val_loss -0.3077 +2026-04-11 11:21:36.151918: Pseudo dice [0.0, 0.0, 0.6535, 0.0, 0.0, 0.7792, 0.6935] +2026-04-11 11:21:36.154261: Epoch time: 100.31 s +2026-04-11 11:21:37.231795: +2026-04-11 11:21:37.234355: Epoch 851 +2026-04-11 11:21:37.236642: Current learning rate: 0.00806 +2026-04-11 11:23:17.721709: train_loss -0.3358 +2026-04-11 11:23:17.729039: val_loss -0.3605 +2026-04-11 11:23:17.731377: Pseudo dice [0.0, 0.0, 0.7425, 0.0, 0.0, 0.2744, 0.6247] +2026-04-11 11:23:17.734919: Epoch time: 100.49 s +2026-04-11 11:23:18.836118: +2026-04-11 11:23:18.838635: Epoch 852 +2026-04-11 11:23:18.841525: Current learning rate: 0.00806 +2026-04-11 11:24:59.088383: train_loss -0.3463 +2026-04-11 11:24:59.094555: val_loss -0.3676 +2026-04-11 11:24:59.096925: Pseudo dice [0.0, 0.0, 0.6908, 0.0, 0.0, 0.0, 0.6876] +2026-04-11 11:24:59.100064: Epoch time: 100.26 s +2026-04-11 11:25:00.192449: +2026-04-11 11:25:00.194704: Epoch 853 +2026-04-11 11:25:00.198123: Current learning rate: 0.00806 +2026-04-11 11:26:40.528709: train_loss -0.3396 +2026-04-11 11:26:40.534745: val_loss -0.3409 +2026-04-11 11:26:40.537697: Pseudo dice [0.0, 0.0, 0.5106, 0.0, 0.0, 0.7632, 0.7388] +2026-04-11 11:26:40.540366: Epoch time: 100.34 s +2026-04-11 11:26:41.657629: +2026-04-11 11:26:41.659551: Epoch 854 +2026-04-11 11:26:41.666941: Current learning rate: 0.00806 +2026-04-11 11:28:22.436073: train_loss -0.3478 +2026-04-11 11:28:22.442797: val_loss -0.3955 +2026-04-11 11:28:22.444783: Pseudo dice [0.0, 0.0, 0.7683, 0.0, 0.0, 0.4961, 0.6689] +2026-04-11 11:28:22.447425: Epoch time: 100.78 s +2026-04-11 11:28:23.529792: +2026-04-11 11:28:23.531608: Epoch 855 +2026-04-11 11:28:23.534375: Current learning rate: 0.00805 +2026-04-11 11:30:04.370648: train_loss -0.3587 +2026-04-11 11:30:04.380206: val_loss -0.3116 +2026-04-11 11:30:04.382893: Pseudo dice [0.0, 0.0, 0.7493, 0.0, 0.0, 0.6248, 0.2606] +2026-04-11 11:30:04.385782: Epoch time: 100.84 s +2026-04-11 11:30:05.461202: +2026-04-11 11:30:05.463222: Epoch 856 +2026-04-11 11:30:05.465233: Current learning rate: 0.00805 +2026-04-11 11:31:45.757357: train_loss -0.2842 +2026-04-11 11:31:45.763730: val_loss -0.3042 +2026-04-11 11:31:45.765718: Pseudo dice [0.0, 0.0, 0.5258, 0.0, 0.0, 0.0243, 0.4691] +2026-04-11 11:31:45.769744: Epoch time: 100.3 s +2026-04-11 11:31:46.868564: +2026-04-11 11:31:46.870689: Epoch 857 +2026-04-11 11:31:46.873110: Current learning rate: 0.00805 +2026-04-11 11:33:26.981615: train_loss -0.3391 +2026-04-11 11:33:26.989023: val_loss -0.3477 +2026-04-11 11:33:26.991428: Pseudo dice [0.0, 0.0, 0.4346, 0.0, 0.0, 0.7323, 0.8265] +2026-04-11 11:33:26.994066: Epoch time: 100.12 s +2026-04-11 11:33:28.104192: +2026-04-11 11:33:28.106504: Epoch 858 +2026-04-11 11:33:28.108935: Current learning rate: 0.00805 +2026-04-11 11:35:08.565241: train_loss -0.2834 +2026-04-11 11:35:08.571429: val_loss -0.3656 +2026-04-11 11:35:08.573540: Pseudo dice [0.0, 0.0, 0.7343, 0.0, 0.0, 0.5321, 0.5055] +2026-04-11 11:35:08.576900: Epoch time: 100.46 s +2026-04-11 11:35:09.666716: +2026-04-11 11:35:09.668786: Epoch 859 +2026-04-11 11:35:09.670951: Current learning rate: 0.00804 +2026-04-11 11:36:49.952664: train_loss -0.3266 +2026-04-11 11:36:49.960826: val_loss -0.3903 +2026-04-11 11:36:49.963275: Pseudo dice [0.0, 0.0, 0.7049, 0.0, 0.0, 0.5328, 0.8488] +2026-04-11 11:36:49.965935: Epoch time: 100.29 s +2026-04-11 11:36:51.048506: +2026-04-11 11:36:51.050402: Epoch 860 +2026-04-11 11:36:51.052744: Current learning rate: 0.00804 +2026-04-11 11:38:32.164541: train_loss -0.3356 +2026-04-11 11:38:32.171825: val_loss -0.3603 +2026-04-11 11:38:32.175938: Pseudo dice [0.0, 0.0, 0.6002, 0.0, 0.0, 0.4409, 0.575] +2026-04-11 11:38:32.179058: Epoch time: 101.12 s +2026-04-11 11:38:33.263202: +2026-04-11 11:38:33.266031: Epoch 861 +2026-04-11 11:38:33.271399: Current learning rate: 0.00804 +2026-04-11 11:40:13.521359: train_loss -0.2901 +2026-04-11 11:40:13.527735: val_loss -0.2309 +2026-04-11 11:40:13.530397: Pseudo dice [0.0, 0.0, 0.4031, 0.0, 0.0, 0.0, 0.0528] +2026-04-11 11:40:13.533018: Epoch time: 100.26 s +2026-04-11 11:40:14.620107: +2026-04-11 11:40:14.622199: Epoch 862 +2026-04-11 11:40:14.625792: Current learning rate: 0.00804 +2026-04-11 11:41:55.931711: train_loss -0.3182 +2026-04-11 11:41:55.937793: val_loss -0.3666 +2026-04-11 11:41:55.940655: Pseudo dice [0.0, 0.0, 0.7354, 0.0, 0.0, 0.0, 0.1495] +2026-04-11 11:41:55.943172: Epoch time: 101.31 s +2026-04-11 11:41:57.029433: +2026-04-11 11:41:57.031471: Epoch 863 +2026-04-11 11:41:57.033973: Current learning rate: 0.00804 +2026-04-11 11:43:37.227143: train_loss -0.3053 +2026-04-11 11:43:37.239920: val_loss -0.3462 +2026-04-11 11:43:37.242246: Pseudo dice [0.0, 0.0, 0.5301, 0.0, 0.0, 0.0, 0.723] +2026-04-11 11:43:37.244837: Epoch time: 100.2 s +2026-04-11 11:43:38.353323: +2026-04-11 11:43:38.355217: Epoch 864 +2026-04-11 11:43:38.358588: Current learning rate: 0.00803 +2026-04-11 11:45:18.639806: train_loss -0.3597 +2026-04-11 11:45:18.646274: val_loss -0.3293 +2026-04-11 11:45:18.648772: Pseudo dice [0.0, 0.0, 0.5195, 0.0, 0.0, 0.1361, 0.7675] +2026-04-11 11:45:18.651697: Epoch time: 100.29 s +2026-04-11 11:45:19.968910: +2026-04-11 11:45:19.971109: Epoch 865 +2026-04-11 11:45:19.974521: Current learning rate: 0.00803 +2026-04-11 11:47:00.189795: train_loss -0.3751 +2026-04-11 11:47:00.216248: val_loss -0.3879 +2026-04-11 11:47:00.218904: Pseudo dice [0.0, 0.0, 0.697, 0.0, 0.0, 0.7353, 0.391] +2026-04-11 11:47:00.221859: Epoch time: 100.22 s +2026-04-11 11:47:01.307218: +2026-04-11 11:47:01.308971: Epoch 866 +2026-04-11 11:47:01.311816: Current learning rate: 0.00803 +2026-04-11 11:48:41.537335: train_loss -0.3587 +2026-04-11 11:48:41.544609: val_loss -0.3727 +2026-04-11 11:48:41.546663: Pseudo dice [0.0, 0.0, 0.6791, 0.0, 0.0, 0.6551, 0.7214] +2026-04-11 11:48:41.548830: Epoch time: 100.23 s +2026-04-11 11:48:42.626002: +2026-04-11 11:48:42.628002: Epoch 867 +2026-04-11 11:48:42.630435: Current learning rate: 0.00803 +2026-04-11 11:50:23.051872: train_loss -0.354 +2026-04-11 11:50:23.058076: val_loss -0.278 +2026-04-11 11:50:23.060416: Pseudo dice [0.0, 0.0, 0.4828, 0.0, 0.0, 0.5281, 0.6418] +2026-04-11 11:50:23.062734: Epoch time: 100.43 s +2026-04-11 11:50:24.144319: +2026-04-11 11:50:24.147102: Epoch 868 +2026-04-11 11:50:24.150815: Current learning rate: 0.00802 +2026-04-11 11:52:04.300871: train_loss -0.345 +2026-04-11 11:52:04.307488: val_loss -0.3557 +2026-04-11 11:52:04.310227: Pseudo dice [0.0, 0.0, 0.585, 0.0, 0.0, 0.5568, 0.4166] +2026-04-11 11:52:04.312962: Epoch time: 100.16 s +2026-04-11 11:52:05.405376: +2026-04-11 11:52:05.407421: Epoch 869 +2026-04-11 11:52:05.409691: Current learning rate: 0.00802 +2026-04-11 11:53:45.794235: train_loss -0.3374 +2026-04-11 11:53:45.801497: val_loss -0.2831 +2026-04-11 11:53:45.803652: Pseudo dice [0.0, 0.0, 0.4981, 0.0, 0.0, 0.2571, 0.5896] +2026-04-11 11:53:45.807333: Epoch time: 100.39 s +2026-04-11 11:53:46.884132: +2026-04-11 11:53:46.886363: Epoch 870 +2026-04-11 11:53:46.888556: Current learning rate: 0.00802 +2026-04-11 11:55:27.135051: train_loss -0.3487 +2026-04-11 11:55:27.143757: val_loss -0.355 +2026-04-11 11:55:27.147040: Pseudo dice [0.0, 0.0, 0.7461, 0.0, 0.0, 0.5546, 0.602] +2026-04-11 11:55:27.150177: Epoch time: 100.25 s +2026-04-11 11:55:28.256281: +2026-04-11 11:55:28.261368: Epoch 871 +2026-04-11 11:55:28.264422: Current learning rate: 0.00802 +2026-04-11 11:57:08.564973: train_loss -0.3382 +2026-04-11 11:57:08.571813: val_loss -0.2755 +2026-04-11 11:57:08.576270: Pseudo dice [0.0, 0.0, 0.6252, 0.0, 0.0, 0.2787, 0.1517] +2026-04-11 11:57:08.580177: Epoch time: 100.31 s +2026-04-11 11:57:09.668341: +2026-04-11 11:57:09.670337: Epoch 872 +2026-04-11 11:57:09.672843: Current learning rate: 0.00801 +2026-04-11 11:58:50.317432: train_loss -0.3442 +2026-04-11 11:58:50.325945: val_loss -0.3435 +2026-04-11 11:58:50.328750: Pseudo dice [0.0, 0.0, 0.6509, 0.0, 0.0, 0.645, 0.0808] +2026-04-11 11:58:50.332309: Epoch time: 100.65 s +2026-04-11 11:58:51.431679: +2026-04-11 11:58:51.433909: Epoch 873 +2026-04-11 11:58:51.436046: Current learning rate: 0.00801 +2026-04-11 12:00:31.493779: train_loss -0.3303 +2026-04-11 12:00:31.500867: val_loss -0.2678 +2026-04-11 12:00:31.503134: Pseudo dice [0.0, 0.0, 0.5927, 0.0, 0.0, 0.0479, 0.0012] +2026-04-11 12:00:31.506104: Epoch time: 100.07 s +2026-04-11 12:00:32.588871: +2026-04-11 12:00:32.590779: Epoch 874 +2026-04-11 12:00:32.593322: Current learning rate: 0.00801 +2026-04-11 12:02:12.866314: train_loss -0.2815 +2026-04-11 12:02:12.874035: val_loss -0.2289 +2026-04-11 12:02:12.876980: Pseudo dice [0.0, 0.0, 0.4825, 0.0, 0.0, 0.1032, 0.101] +2026-04-11 12:02:12.880987: Epoch time: 100.28 s +2026-04-11 12:02:13.987174: +2026-04-11 12:02:13.989198: Epoch 875 +2026-04-11 12:02:13.991388: Current learning rate: 0.00801 +2026-04-11 12:03:53.921965: train_loss -0.3034 +2026-04-11 12:03:53.933719: val_loss -0.3141 +2026-04-11 12:03:53.938979: Pseudo dice [0.0, 0.0, 0.6104, 0.0, 0.0, 0.0591, 0.2785] +2026-04-11 12:03:53.941597: Epoch time: 99.94 s +2026-04-11 12:03:55.041512: +2026-04-11 12:03:55.043973: Epoch 876 +2026-04-11 12:03:55.047061: Current learning rate: 0.00801 +2026-04-11 12:05:35.301676: train_loss -0.3225 +2026-04-11 12:05:35.307263: val_loss -0.3712 +2026-04-11 12:05:35.309534: Pseudo dice [0.0, 0.0, 0.7525, 0.0, 0.0, 0.5489, 0.6262] +2026-04-11 12:05:35.311882: Epoch time: 100.26 s +2026-04-11 12:05:36.417790: +2026-04-11 12:05:36.420147: Epoch 877 +2026-04-11 12:05:36.422472: Current learning rate: 0.008 +2026-04-11 12:07:16.554128: train_loss -0.3478 +2026-04-11 12:07:16.561940: val_loss -0.3801 +2026-04-11 12:07:16.564967: Pseudo dice [0.0, 0.0, 0.6225, 0.0, 0.0, 0.6291, 0.5426] +2026-04-11 12:07:16.568156: Epoch time: 100.14 s +2026-04-11 12:07:17.659058: +2026-04-11 12:07:17.661883: Epoch 878 +2026-04-11 12:07:17.664334: Current learning rate: 0.008 +2026-04-11 12:08:57.822451: train_loss -0.3646 +2026-04-11 12:08:57.837738: val_loss -0.3091 +2026-04-11 12:08:57.840366: Pseudo dice [0.0, 0.0, 0.5825, 0.0, 0.0, 0.612, 0.4681] +2026-04-11 12:08:57.843807: Epoch time: 100.17 s +2026-04-11 12:08:58.924457: +2026-04-11 12:08:58.926854: Epoch 879 +2026-04-11 12:08:58.929683: Current learning rate: 0.008 +2026-04-11 12:10:39.139679: train_loss -0.346 +2026-04-11 12:10:39.148472: val_loss -0.3389 +2026-04-11 12:10:39.150991: Pseudo dice [0.0, 0.0, 0.4862, 0.0, 0.0, 0.7323, 0.4318] +2026-04-11 12:10:39.153374: Epoch time: 100.22 s +2026-04-11 12:10:40.243356: +2026-04-11 12:10:40.245746: Epoch 880 +2026-04-11 12:10:40.248008: Current learning rate: 0.008 +2026-04-11 12:12:21.314145: train_loss -0.3271 +2026-04-11 12:12:21.320891: val_loss -0.324 +2026-04-11 12:12:21.323171: Pseudo dice [0.0, 0.0, 0.7257, 0.0, 0.0, 0.6333, 0.8244] +2026-04-11 12:12:21.325537: Epoch time: 101.07 s +2026-04-11 12:12:22.418058: +2026-04-11 12:12:22.420407: Epoch 881 +2026-04-11 12:12:22.423187: Current learning rate: 0.00799 +2026-04-11 12:14:02.853030: train_loss -0.3259 +2026-04-11 12:14:02.860151: val_loss -0.3653 +2026-04-11 12:14:02.868110: Pseudo dice [0.0, 0.0, 0.7528, 0.0, 0.0, 0.1533, 0.0698] +2026-04-11 12:14:02.870423: Epoch time: 100.44 s +2026-04-11 12:14:03.959369: +2026-04-11 12:14:03.961501: Epoch 882 +2026-04-11 12:14:03.963975: Current learning rate: 0.00799 +2026-04-11 12:15:44.326724: train_loss -0.3632 +2026-04-11 12:15:44.337017: val_loss -0.3777 +2026-04-11 12:15:44.340699: Pseudo dice [0.0, 0.0, 0.636, 0.0, 0.0, 0.5908, 0.7006] +2026-04-11 12:15:44.343692: Epoch time: 100.37 s +2026-04-11 12:15:45.442205: +2026-04-11 12:15:45.444502: Epoch 883 +2026-04-11 12:15:45.446825: Current learning rate: 0.00799 +2026-04-11 12:17:26.152573: train_loss -0.3568 +2026-04-11 12:17:26.158529: val_loss -0.3811 +2026-04-11 12:17:26.160576: Pseudo dice [0.0, 0.0, 0.6247, 0.0, 0.0, 0.4983, 0.8153] +2026-04-11 12:17:26.162781: Epoch time: 100.71 s +2026-04-11 12:17:27.256519: +2026-04-11 12:17:27.259260: Epoch 884 +2026-04-11 12:17:27.261694: Current learning rate: 0.00799 +2026-04-11 12:19:07.495504: train_loss -0.3629 +2026-04-11 12:19:07.502544: val_loss -0.3514 +2026-04-11 12:19:07.505604: Pseudo dice [0.0, 0.0, 0.6768, 0.0, 0.0, 0.8174, 0.5233] +2026-04-11 12:19:07.508773: Epoch time: 100.24 s +2026-04-11 12:19:08.614797: +2026-04-11 12:19:08.617239: Epoch 885 +2026-04-11 12:19:08.619430: Current learning rate: 0.00798 +2026-04-11 12:20:49.314787: train_loss -0.3526 +2026-04-11 12:20:49.321061: val_loss -0.2903 +2026-04-11 12:20:49.322896: Pseudo dice [0.0, 0.0, 0.5727, 0.0, 0.0, 0.2205, 0.7561] +2026-04-11 12:20:49.325258: Epoch time: 100.7 s +2026-04-11 12:20:50.405930: +2026-04-11 12:20:50.408340: Epoch 886 +2026-04-11 12:20:50.410417: Current learning rate: 0.00798 +2026-04-11 12:22:30.610712: train_loss -0.3525 +2026-04-11 12:22:30.617536: val_loss -0.3063 +2026-04-11 12:22:30.619802: Pseudo dice [0.0, 0.0, 0.6556, 0.0, 0.0, 0.1043, 0.3774] +2026-04-11 12:22:30.622221: Epoch time: 100.21 s +2026-04-11 12:22:31.734481: +2026-04-11 12:22:31.736929: Epoch 887 +2026-04-11 12:22:31.739121: Current learning rate: 0.00798 +2026-04-11 12:24:11.842474: train_loss -0.3065 +2026-04-11 12:24:11.863245: val_loss -0.3511 +2026-04-11 12:24:11.865531: Pseudo dice [0.0, 0.0, 0.7533, 0.0, 0.0, 0.477, 0.3377] +2026-04-11 12:24:11.868670: Epoch time: 100.11 s +2026-04-11 12:24:12.964507: +2026-04-11 12:24:12.968574: Epoch 888 +2026-04-11 12:24:12.971459: Current learning rate: 0.00798 +2026-04-11 12:25:53.024559: train_loss -0.3644 +2026-04-11 12:25:53.032396: val_loss -0.3211 +2026-04-11 12:25:53.034706: Pseudo dice [0.0, 0.0, 0.7092, 0.0, 0.0, 0.4558, 0.6277] +2026-04-11 12:25:53.037639: Epoch time: 100.06 s +2026-04-11 12:25:54.139506: +2026-04-11 12:25:54.142022: Epoch 889 +2026-04-11 12:25:54.144326: Current learning rate: 0.00798 +2026-04-11 12:27:34.410165: train_loss -0.3392 +2026-04-11 12:27:34.417370: val_loss -0.3519 +2026-04-11 12:27:34.420021: Pseudo dice [0.0, 0.0, 0.7766, 0.0, 0.0, 0.3817, 0.4184] +2026-04-11 12:27:34.423155: Epoch time: 100.27 s +2026-04-11 12:27:35.505658: +2026-04-11 12:27:35.511042: Epoch 890 +2026-04-11 12:27:35.513397: Current learning rate: 0.00797 +2026-04-11 12:29:15.438038: train_loss -0.3502 +2026-04-11 12:29:15.444101: val_loss -0.2757 +2026-04-11 12:29:15.446219: Pseudo dice [0.0, 0.0, 0.4475, 0.0, 0.0, 0.0045, 0.0005] +2026-04-11 12:29:15.448763: Epoch time: 99.94 s +2026-04-11 12:29:16.537692: +2026-04-11 12:29:16.539716: Epoch 891 +2026-04-11 12:29:16.542002: Current learning rate: 0.00797 +2026-04-11 12:30:56.779337: train_loss -0.2866 +2026-04-11 12:30:56.787416: val_loss -0.3135 +2026-04-11 12:30:56.790491: Pseudo dice [0.0, 0.0, 0.5924, 0.0, 0.0, 0.0171, 0.1098] +2026-04-11 12:30:56.793428: Epoch time: 100.24 s +2026-04-11 12:30:57.898253: +2026-04-11 12:30:57.900663: Epoch 892 +2026-04-11 12:30:57.903410: Current learning rate: 0.00797 +2026-04-11 12:32:38.032887: train_loss -0.3305 +2026-04-11 12:32:38.042880: val_loss -0.3584 +2026-04-11 12:32:38.045164: Pseudo dice [0.0, 0.0, 0.4424, 0.0, 0.0, 0.5476, 0.3354] +2026-04-11 12:32:38.047588: Epoch time: 100.14 s +2026-04-11 12:32:39.126214: +2026-04-11 12:32:39.128836: Epoch 893 +2026-04-11 12:32:39.131639: Current learning rate: 0.00797 +2026-04-11 12:34:18.991848: train_loss -0.3467 +2026-04-11 12:34:18.997439: val_loss -0.3159 +2026-04-11 12:34:18.999220: Pseudo dice [0.0, 0.0, 0.4768, 0.0, 0.0, 0.708, 0.7047] +2026-04-11 12:34:19.001547: Epoch time: 99.87 s +2026-04-11 12:34:20.070112: +2026-04-11 12:34:20.071998: Epoch 894 +2026-04-11 12:34:20.073989: Current learning rate: 0.00796 +2026-04-11 12:36:00.063263: train_loss -0.3376 +2026-04-11 12:36:00.068309: val_loss -0.3449 +2026-04-11 12:36:00.070097: Pseudo dice [0.0, 0.0, 0.4886, 0.0, 0.0, 0.6487, 0.7157] +2026-04-11 12:36:00.072887: Epoch time: 100.0 s +2026-04-11 12:36:01.154513: +2026-04-11 12:36:01.156961: Epoch 895 +2026-04-11 12:36:01.159476: Current learning rate: 0.00796 +2026-04-11 12:37:41.220286: train_loss -0.3437 +2026-04-11 12:37:41.227264: val_loss -0.3377 +2026-04-11 12:37:41.229848: Pseudo dice [0.0, 0.0, 0.5812, 0.0, 0.0, 0.755, 0.6169] +2026-04-11 12:37:41.232746: Epoch time: 100.07 s +2026-04-11 12:37:42.328616: +2026-04-11 12:37:42.330861: Epoch 896 +2026-04-11 12:37:42.333737: Current learning rate: 0.00796 +2026-04-11 12:39:22.347930: train_loss -0.3591 +2026-04-11 12:39:22.355031: val_loss -0.3671 +2026-04-11 12:39:22.357451: Pseudo dice [0.0, 0.0, 0.7974, 0.0, 0.0, 0.4473, 0.7619] +2026-04-11 12:39:22.361178: Epoch time: 100.02 s +2026-04-11 12:39:23.481938: +2026-04-11 12:39:23.484311: Epoch 897 +2026-04-11 12:39:23.487348: Current learning rate: 0.00796 +2026-04-11 12:41:04.560714: train_loss -0.3612 +2026-04-11 12:41:04.567050: val_loss -0.2897 +2026-04-11 12:41:04.569218: Pseudo dice [0.0, 0.0, 0.5095, 0.0, 0.0, 0.5012, 0.626] +2026-04-11 12:41:04.571895: Epoch time: 101.08 s +2026-04-11 12:41:05.719790: +2026-04-11 12:41:05.721808: Epoch 898 +2026-04-11 12:41:05.726270: Current learning rate: 0.00795 +2026-04-11 12:42:45.871952: train_loss -0.3543 +2026-04-11 12:42:45.878825: val_loss -0.3303 +2026-04-11 12:42:45.882117: Pseudo dice [0.0, 0.0, 0.6708, 0.0, 0.0, 0.2487, 0.6587] +2026-04-11 12:42:45.885116: Epoch time: 100.16 s +2026-04-11 12:42:46.986349: +2026-04-11 12:42:46.988175: Epoch 899 +2026-04-11 12:42:46.990276: Current learning rate: 0.00795 +2026-04-11 12:44:26.990286: train_loss -0.3462 +2026-04-11 12:44:26.998179: val_loss -0.2666 +2026-04-11 12:44:27.000430: Pseudo dice [0.0, 0.0, 0.2845, 0.0, 0.0, 0.6599, 0.543] +2026-04-11 12:44:27.002599: Epoch time: 100.01 s +2026-04-11 12:44:30.557910: +2026-04-11 12:44:30.559887: Epoch 900 +2026-04-11 12:44:30.562109: Current learning rate: 0.00795 +2026-04-11 12:46:10.704368: train_loss -0.3596 +2026-04-11 12:46:10.712595: val_loss -0.3394 +2026-04-11 12:46:10.714938: Pseudo dice [0.0, 0.0, 0.6529, 0.0, 0.0, 0.2912, 0.4396] +2026-04-11 12:46:10.718695: Epoch time: 100.15 s +2026-04-11 12:46:11.811723: +2026-04-11 12:46:11.814709: Epoch 901 +2026-04-11 12:46:11.816785: Current learning rate: 0.00795 +2026-04-11 12:47:52.048887: train_loss -0.3781 +2026-04-11 12:47:52.054867: val_loss -0.2979 +2026-04-11 12:47:52.056885: Pseudo dice [0.0, 0.0, 0.5845, 0.0, 0.0, 0.5902, 0.535] +2026-04-11 12:47:52.059554: Epoch time: 100.24 s +2026-04-11 12:47:53.131389: +2026-04-11 12:47:53.133443: Epoch 902 +2026-04-11 12:47:53.136452: Current learning rate: 0.00795 +2026-04-11 12:49:33.249240: train_loss -0.3455 +2026-04-11 12:49:33.256731: val_loss -0.3512 +2026-04-11 12:49:33.259170: Pseudo dice [0.0, 0.0, 0.5502, 0.0, 0.0, 0.6253, 0.5507] +2026-04-11 12:49:33.262167: Epoch time: 100.12 s +2026-04-11 12:49:34.351568: +2026-04-11 12:49:34.353892: Epoch 903 +2026-04-11 12:49:34.356386: Current learning rate: 0.00794 +2026-04-11 12:51:14.644792: train_loss -0.3506 +2026-04-11 12:51:14.651683: val_loss -0.3225 +2026-04-11 12:51:14.654594: Pseudo dice [0.0, 0.0, 0.3925, 0.0, 0.0, 0.2931, 0.675] +2026-04-11 12:51:14.658841: Epoch time: 100.3 s +2026-04-11 12:51:15.763520: +2026-04-11 12:51:15.765512: Epoch 904 +2026-04-11 12:51:15.768297: Current learning rate: 0.00794 +2026-04-11 12:52:56.005791: train_loss -0.3388 +2026-04-11 12:52:56.011045: val_loss -0.3733 +2026-04-11 12:52:56.012836: Pseudo dice [0.0, 0.0, 0.5234, 0.0, 0.0, 0.7423, 0.6194] +2026-04-11 12:52:56.015271: Epoch time: 100.25 s +2026-04-11 12:52:57.098328: +2026-04-11 12:52:57.100744: Epoch 905 +2026-04-11 12:52:57.103652: Current learning rate: 0.00794 +2026-04-11 12:54:37.462911: train_loss -0.3453 +2026-04-11 12:54:37.473367: val_loss -0.3749 +2026-04-11 12:54:37.476585: Pseudo dice [0.0, 0.0, 0.6964, 0.0, 0.0, 0.7298, 0.4591] +2026-04-11 12:54:37.479738: Epoch time: 100.37 s +2026-04-11 12:54:38.541084: +2026-04-11 12:54:38.543433: Epoch 906 +2026-04-11 12:54:38.545805: Current learning rate: 0.00794 +2026-04-11 12:56:18.752815: train_loss -0.3557 +2026-04-11 12:56:18.760961: val_loss -0.2228 +2026-04-11 12:56:18.763686: Pseudo dice [0.0, 0.0, 0.3513, 0.0, 0.0, 0.3213, 0.6289] +2026-04-11 12:56:18.766192: Epoch time: 100.21 s +2026-04-11 12:56:19.861151: +2026-04-11 12:56:19.863644: Epoch 907 +2026-04-11 12:56:19.865675: Current learning rate: 0.00793 +2026-04-11 12:58:00.093082: train_loss -0.3538 +2026-04-11 12:58:00.099445: val_loss -0.3059 +2026-04-11 12:58:00.102170: Pseudo dice [0.0, 0.0, 0.6234, 0.0, 0.0, 0.4769, 0.6287] +2026-04-11 12:58:00.106359: Epoch time: 100.23 s +2026-04-11 12:58:01.199227: +2026-04-11 12:58:01.201612: Epoch 908 +2026-04-11 12:58:01.203731: Current learning rate: 0.00793 +2026-04-11 12:59:41.609153: train_loss -0.3528 +2026-04-11 12:59:41.619279: val_loss -0.4003 +2026-04-11 12:59:41.623328: Pseudo dice [0.0, 0.0, 0.5672, 0.0, 0.0, 0.7204, 0.7086] +2026-04-11 12:59:41.627314: Epoch time: 100.41 s +2026-04-11 12:59:42.735820: +2026-04-11 12:59:42.739482: Epoch 909 +2026-04-11 12:59:42.741802: Current learning rate: 0.00793 +2026-04-11 13:01:22.967737: train_loss -0.3529 +2026-04-11 13:01:22.972984: val_loss -0.3511 +2026-04-11 13:01:22.975543: Pseudo dice [0.0, 0.0, 0.6615, 0.0, 0.0, 0.6861, 0.7887] +2026-04-11 13:01:22.978431: Epoch time: 100.24 s +2026-04-11 13:01:24.066161: +2026-04-11 13:01:24.068339: Epoch 910 +2026-04-11 13:01:24.070282: Current learning rate: 0.00793 +2026-04-11 13:03:04.381979: train_loss -0.3618 +2026-04-11 13:03:04.387590: val_loss -0.3819 +2026-04-11 13:03:04.389963: Pseudo dice [0.0, 0.0, 0.6049, 0.0, 0.0, 0.8328, 0.6568] +2026-04-11 13:03:04.392579: Epoch time: 100.32 s +2026-04-11 13:03:05.468925: +2026-04-11 13:03:05.470745: Epoch 911 +2026-04-11 13:03:05.472768: Current learning rate: 0.00792 +2026-04-11 13:04:46.154504: train_loss -0.3776 +2026-04-11 13:04:46.161398: val_loss -0.3885 +2026-04-11 13:04:46.163622: Pseudo dice [0.0, 0.0, 0.6457, 0.0, 0.0, 0.8356, 0.5802] +2026-04-11 13:04:46.167289: Epoch time: 100.69 s +2026-04-11 13:04:47.267189: +2026-04-11 13:04:47.269943: Epoch 912 +2026-04-11 13:04:47.271797: Current learning rate: 0.00792 +2026-04-11 13:06:27.623568: train_loss -0.3649 +2026-04-11 13:06:27.629210: val_loss -0.3659 +2026-04-11 13:06:27.631363: Pseudo dice [0.0, 0.0, 0.6004, 0.0, 0.0, 0.376, 0.795] +2026-04-11 13:06:27.633679: Epoch time: 100.36 s +2026-04-11 13:06:28.711739: +2026-04-11 13:06:28.713513: Epoch 913 +2026-04-11 13:06:28.715315: Current learning rate: 0.00792 +2026-04-11 13:08:09.160092: train_loss -0.3476 +2026-04-11 13:08:09.167288: val_loss -0.3458 +2026-04-11 13:08:09.170460: Pseudo dice [0.0, 0.0, 0.5734, 0.0, 0.0, 0.3512, 0.7598] +2026-04-11 13:08:09.173594: Epoch time: 100.45 s +2026-04-11 13:08:10.259097: +2026-04-11 13:08:10.261409: Epoch 914 +2026-04-11 13:08:10.263217: Current learning rate: 0.00792 +2026-04-11 13:09:51.379253: train_loss -0.3639 +2026-04-11 13:09:51.390224: val_loss -0.384 +2026-04-11 13:09:51.392915: Pseudo dice [0.0, 0.0, 0.7678, 0.0, 0.0, 0.7553, 0.6632] +2026-04-11 13:09:51.395463: Epoch time: 101.12 s +2026-04-11 13:09:52.470505: +2026-04-11 13:09:52.473057: Epoch 915 +2026-04-11 13:09:52.475730: Current learning rate: 0.00792 +2026-04-11 13:11:32.924330: train_loss -0.3399 +2026-04-11 13:11:32.932337: val_loss -0.3158 +2026-04-11 13:11:32.934572: Pseudo dice [0.0, 0.0, 0.2377, 0.0, 0.0, 0.1704, 0.1129] +2026-04-11 13:11:32.938041: Epoch time: 100.46 s +2026-04-11 13:11:34.024922: +2026-04-11 13:11:34.027018: Epoch 916 +2026-04-11 13:11:34.029852: Current learning rate: 0.00791 +2026-04-11 13:13:15.238868: train_loss -0.2975 +2026-04-11 13:13:15.245492: val_loss -0.3758 +2026-04-11 13:13:15.247686: Pseudo dice [0.0, 0.0, 0.7184, 0.0, 0.0, 0.6395, 0.6591] +2026-04-11 13:13:15.250789: Epoch time: 101.22 s +2026-04-11 13:13:16.316841: +2026-04-11 13:13:16.318998: Epoch 917 +2026-04-11 13:13:16.321684: Current learning rate: 0.00791 +2026-04-11 13:14:57.235742: train_loss -0.335 +2026-04-11 13:14:57.241989: val_loss -0.2036 +2026-04-11 13:14:57.244337: Pseudo dice [0.0, 0.0, 0.6346, 0.0, 0.0, 0.5904, 0.283] +2026-04-11 13:14:57.246895: Epoch time: 100.92 s +2026-04-11 13:14:58.564391: +2026-04-11 13:14:58.567240: Epoch 918 +2026-04-11 13:14:58.569814: Current learning rate: 0.00791 +2026-04-11 13:16:39.159255: train_loss -0.3604 +2026-04-11 13:16:39.164736: val_loss -0.2992 +2026-04-11 13:16:39.167069: Pseudo dice [0.0, 0.0, 0.5778, 0.0, 0.0, 0.6141, 0.1846] +2026-04-11 13:16:39.169339: Epoch time: 100.6 s +2026-04-11 13:16:40.254946: +2026-04-11 13:16:40.257843: Epoch 919 +2026-04-11 13:16:40.260355: Current learning rate: 0.00791 +2026-04-11 13:18:20.483492: train_loss -0.3641 +2026-04-11 13:18:20.490716: val_loss -0.3529 +2026-04-11 13:18:20.493646: Pseudo dice [0.0, 0.0, 0.7196, 0.0, 0.0, 0.5276, 0.7016] +2026-04-11 13:18:20.496460: Epoch time: 100.23 s +2026-04-11 13:18:21.592269: +2026-04-11 13:18:21.594170: Epoch 920 +2026-04-11 13:18:21.596383: Current learning rate: 0.0079 +2026-04-11 13:20:01.830552: train_loss -0.3673 +2026-04-11 13:20:01.839874: val_loss -0.386 +2026-04-11 13:20:01.843411: Pseudo dice [0.0, 0.0, 0.6141, 0.0, 0.0, 0.7923, 0.8488] +2026-04-11 13:20:01.846405: Epoch time: 100.24 s +2026-04-11 13:20:03.833986: +2026-04-11 13:20:03.836961: Epoch 921 +2026-04-11 13:20:03.840162: Current learning rate: 0.0079 +2026-04-11 13:21:44.355609: train_loss -0.3775 +2026-04-11 13:21:44.362705: val_loss -0.3106 +2026-04-11 13:21:44.364889: Pseudo dice [0.0, 0.0, 0.7112, 0.0, 0.0, 0.6274, 0.3817] +2026-04-11 13:21:44.367287: Epoch time: 100.52 s +2026-04-11 13:21:45.690107: +2026-04-11 13:21:45.694001: Epoch 922 +2026-04-11 13:21:45.696231: Current learning rate: 0.0079 +2026-04-11 13:23:25.959630: train_loss -0.3664 +2026-04-11 13:23:25.964556: val_loss -0.3952 +2026-04-11 13:23:25.968278: Pseudo dice [0.0, 0.0, 0.8013, 0.0, 0.0, 0.7001, 0.864] +2026-04-11 13:23:25.970357: Epoch time: 100.27 s +2026-04-11 13:23:27.040869: +2026-04-11 13:23:27.042800: Epoch 923 +2026-04-11 13:23:27.044812: Current learning rate: 0.0079 +2026-04-11 13:25:07.419238: train_loss -0.3766 +2026-04-11 13:25:07.425550: val_loss -0.2318 +2026-04-11 13:25:07.427629: Pseudo dice [0.0, 0.0, 0.7228, 0.0, 0.0, 0.7821, 0.5065] +2026-04-11 13:25:07.431330: Epoch time: 100.38 s +2026-04-11 13:25:08.510034: +2026-04-11 13:25:08.515701: Epoch 924 +2026-04-11 13:25:08.517999: Current learning rate: 0.00789 +2026-04-11 13:26:49.561860: train_loss -0.3757 +2026-04-11 13:26:49.571062: val_loss -0.3939 +2026-04-11 13:26:49.574032: Pseudo dice [0.0, 0.0, 0.7764, 0.0, 0.0, 0.7565, 0.7388] +2026-04-11 13:26:49.577103: Epoch time: 101.05 s +2026-04-11 13:26:50.657796: +2026-04-11 13:26:50.660715: Epoch 925 +2026-04-11 13:26:50.663101: Current learning rate: 0.00789 +2026-04-11 13:28:30.918050: train_loss -0.3503 +2026-04-11 13:28:30.925285: val_loss -0.3406 +2026-04-11 13:28:30.927562: Pseudo dice [0.0, 0.0, 0.7126, 0.0, 0.0, 0.7874, 0.3748] +2026-04-11 13:28:30.930979: Epoch time: 100.26 s +2026-04-11 13:28:32.028899: +2026-04-11 13:28:32.031204: Epoch 926 +2026-04-11 13:28:32.035197: Current learning rate: 0.00789 +2026-04-11 13:30:12.290540: train_loss -0.3492 +2026-04-11 13:30:12.296854: val_loss -0.2794 +2026-04-11 13:30:12.300176: Pseudo dice [0.0, 0.0, 0.3937, 0.0, 0.1447, 0.2674, 0.0675] +2026-04-11 13:30:12.302534: Epoch time: 100.26 s +2026-04-11 13:30:13.373251: +2026-04-11 13:30:13.375247: Epoch 927 +2026-04-11 13:30:13.377372: Current learning rate: 0.00789 +2026-04-11 13:31:53.796689: train_loss -0.2999 +2026-04-11 13:31:53.806898: val_loss -0.341 +2026-04-11 13:31:53.809996: Pseudo dice [0.0, 0.0, 0.5513, 0.0, 0.0, 0.5808, 0.6734] +2026-04-11 13:31:53.812641: Epoch time: 100.43 s +2026-04-11 13:31:54.894836: +2026-04-11 13:31:54.897125: Epoch 928 +2026-04-11 13:31:54.899633: Current learning rate: 0.00789 +2026-04-11 13:33:35.147538: train_loss -0.3357 +2026-04-11 13:33:35.158610: val_loss -0.3691 +2026-04-11 13:33:35.161104: Pseudo dice [0.0, 0.0, 0.6873, 0.0, 0.0, 0.5795, 0.4886] +2026-04-11 13:33:35.163935: Epoch time: 100.26 s +2026-04-11 13:33:36.244301: +2026-04-11 13:33:36.246473: Epoch 929 +2026-04-11 13:33:36.248456: Current learning rate: 0.00788 +2026-04-11 13:35:16.289278: train_loss -0.3601 +2026-04-11 13:35:16.295865: val_loss -0.3784 +2026-04-11 13:35:16.298274: Pseudo dice [0.0, 0.0, 0.5856, 0.0, 0.0, 0.6748, 0.4765] +2026-04-11 13:35:16.301128: Epoch time: 100.05 s +2026-04-11 13:35:17.388020: +2026-04-11 13:35:17.390800: Epoch 930 +2026-04-11 13:35:17.393313: Current learning rate: 0.00788 +2026-04-11 13:36:57.626297: train_loss -0.3615 +2026-04-11 13:36:57.632400: val_loss -0.3662 +2026-04-11 13:36:57.635070: Pseudo dice [0.0, 0.0, 0.5805, 0.0, 0.0, 0.0455, 0.6966] +2026-04-11 13:36:57.639200: Epoch time: 100.24 s +2026-04-11 13:36:58.712773: +2026-04-11 13:36:58.715523: Epoch 931 +2026-04-11 13:36:58.718110: Current learning rate: 0.00788 +2026-04-11 13:38:38.872012: train_loss -0.3459 +2026-04-11 13:38:38.879559: val_loss -0.3336 +2026-04-11 13:38:38.882830: Pseudo dice [0.0, 0.0, 0.5133, 0.0, 0.0, 0.4945, 0.6296] +2026-04-11 13:38:38.886917: Epoch time: 100.16 s +2026-04-11 13:38:39.969718: +2026-04-11 13:38:39.971803: Epoch 932 +2026-04-11 13:38:39.974220: Current learning rate: 0.00788 +2026-04-11 13:40:20.221292: train_loss -0.3131 +2026-04-11 13:40:20.230967: val_loss -0.3196 +2026-04-11 13:40:20.233343: Pseudo dice [0.0, 0.0, 0.6725, 0.0, 0.0, 0.28, 0.5788] +2026-04-11 13:40:20.236143: Epoch time: 100.25 s +2026-04-11 13:40:21.313607: +2026-04-11 13:40:21.316259: Epoch 933 +2026-04-11 13:40:21.319195: Current learning rate: 0.00787 +2026-04-11 13:42:01.616045: train_loss -0.3359 +2026-04-11 13:42:01.623405: val_loss -0.3487 +2026-04-11 13:42:01.626266: Pseudo dice [0.0, 0.0, 0.1196, 0.0, 0.0, 0.5798, 0.6913] +2026-04-11 13:42:01.629098: Epoch time: 100.31 s +2026-04-11 13:42:02.732490: +2026-04-11 13:42:02.734603: Epoch 934 +2026-04-11 13:42:02.737095: Current learning rate: 0.00787 +2026-04-11 13:43:43.070385: train_loss -0.3328 +2026-04-11 13:43:43.077303: val_loss -0.3498 +2026-04-11 13:43:43.081519: Pseudo dice [0.0, 0.0, 0.5583, 0.0, 0.0, 0.6663, 0.5142] +2026-04-11 13:43:43.086100: Epoch time: 100.34 s +2026-04-11 13:43:44.158172: +2026-04-11 13:43:44.160429: Epoch 935 +2026-04-11 13:43:44.162687: Current learning rate: 0.00787 +2026-04-11 13:45:24.444208: train_loss -0.3464 +2026-04-11 13:45:24.451079: val_loss -0.3432 +2026-04-11 13:45:24.453728: Pseudo dice [0.0, 0.0, 0.4912, 0.0, 0.0, 0.8024, 0.6327] +2026-04-11 13:45:24.456228: Epoch time: 100.29 s +2026-04-11 13:45:25.555458: +2026-04-11 13:45:25.558683: Epoch 936 +2026-04-11 13:45:25.561518: Current learning rate: 0.00787 +2026-04-11 13:47:05.818491: train_loss -0.3615 +2026-04-11 13:47:05.824878: val_loss -0.3672 +2026-04-11 13:47:05.827112: Pseudo dice [0.0, 0.0, 0.4745, 0.0, 0.0137, 0.6258, 0.7257] +2026-04-11 13:47:05.830511: Epoch time: 100.27 s +2026-04-11 13:47:06.905530: +2026-04-11 13:47:06.908122: Epoch 937 +2026-04-11 13:47:06.910556: Current learning rate: 0.00786 +2026-04-11 13:48:47.363062: train_loss -0.3641 +2026-04-11 13:48:47.370469: val_loss -0.4103 +2026-04-11 13:48:47.373175: Pseudo dice [0.0, 0.0, 0.687, 0.0, 0.3343, 0.781, 0.7608] +2026-04-11 13:48:47.382090: Epoch time: 100.46 s +2026-04-11 13:48:48.483127: +2026-04-11 13:48:48.485413: Epoch 938 +2026-04-11 13:48:48.488564: Current learning rate: 0.00786 +2026-04-11 13:50:29.084667: train_loss -0.326 +2026-04-11 13:50:29.092537: val_loss -0.3063 +2026-04-11 13:50:29.094686: Pseudo dice [0.0, 0.0, 0.7234, 0.0, 0.0, 0.044, 0.4327] +2026-04-11 13:50:29.097117: Epoch time: 100.6 s +2026-04-11 13:50:30.161921: +2026-04-11 13:50:30.164608: Epoch 939 +2026-04-11 13:50:30.168146: Current learning rate: 0.00786 +2026-04-11 13:52:10.570054: train_loss -0.3398 +2026-04-11 13:52:10.579350: val_loss -0.3327 +2026-04-11 13:52:10.582031: Pseudo dice [0.0, 0.0, 0.6185, 0.0, 0.3069, 0.4879, 0.56] +2026-04-11 13:52:10.586179: Epoch time: 100.41 s +2026-04-11 13:52:11.671127: +2026-04-11 13:52:11.674105: Epoch 940 +2026-04-11 13:52:11.676246: Current learning rate: 0.00786 +2026-04-11 13:53:51.850138: train_loss -0.3289 +2026-04-11 13:53:51.856667: val_loss -0.3287 +2026-04-11 13:53:51.859503: Pseudo dice [0.0, 0.0, 0.6248, 0.0612, 0.0, 0.7115, 0.6231] +2026-04-11 13:53:51.862077: Epoch time: 100.18 s +2026-04-11 13:53:52.950001: +2026-04-11 13:53:52.952459: Epoch 941 +2026-04-11 13:53:52.954499: Current learning rate: 0.00786 +2026-04-11 13:55:34.180407: train_loss -0.3575 +2026-04-11 13:55:34.187624: val_loss -0.3689 +2026-04-11 13:55:34.190095: Pseudo dice [0.0, 0.0, 0.6465, 0.0, 0.0927, 0.7336, 0.3675] +2026-04-11 13:55:34.193547: Epoch time: 101.23 s +2026-04-11 13:55:35.279543: +2026-04-11 13:55:35.281542: Epoch 942 +2026-04-11 13:55:35.285026: Current learning rate: 0.00785 +2026-04-11 13:57:16.364459: train_loss -0.3761 +2026-04-11 13:57:16.375138: val_loss -0.3986 +2026-04-11 13:57:16.385038: Pseudo dice [0.0, 0.0, 0.6258, 0.0, 0.3477, 0.8024, 0.7703] +2026-04-11 13:57:16.389830: Epoch time: 101.09 s +2026-04-11 13:57:17.457421: +2026-04-11 13:57:17.459318: Epoch 943 +2026-04-11 13:57:17.461551: Current learning rate: 0.00785 +2026-04-11 13:58:57.681768: train_loss -0.3301 +2026-04-11 13:58:57.688531: val_loss -0.3773 +2026-04-11 13:58:57.692322: Pseudo dice [0.0, 0.0, 0.6227, 0.0, 0.2577, 0.7484, 0.2145] +2026-04-11 13:58:57.696651: Epoch time: 100.23 s +2026-04-11 13:58:58.783155: +2026-04-11 13:58:58.785465: Epoch 944 +2026-04-11 13:58:58.787656: Current learning rate: 0.00785 +2026-04-11 14:00:39.067737: train_loss -0.3142 +2026-04-11 14:00:39.074017: val_loss -0.3348 +2026-04-11 14:00:39.076059: Pseudo dice [0.0, 0.0, 0.6294, 0.0, 0.0, 0.0851, 0.703] +2026-04-11 14:00:39.079029: Epoch time: 100.29 s +2026-04-11 14:00:40.168556: +2026-04-11 14:00:40.170456: Epoch 945 +2026-04-11 14:00:40.172509: Current learning rate: 0.00785 +2026-04-11 14:02:20.571462: train_loss -0.3028 +2026-04-11 14:02:20.577702: val_loss -0.2092 +2026-04-11 14:02:20.580716: Pseudo dice [0.0, 0.0, 0.5896, 0.0, 0.0, 0.0954, 0.0493] +2026-04-11 14:02:20.583842: Epoch time: 100.41 s +2026-04-11 14:02:21.664502: +2026-04-11 14:02:21.667184: Epoch 946 +2026-04-11 14:02:21.670233: Current learning rate: 0.00784 +2026-04-11 14:04:01.927923: train_loss -0.3086 +2026-04-11 14:04:01.934393: val_loss -0.2995 +2026-04-11 14:04:01.936559: Pseudo dice [0.0, 0.0, 0.2277, 0.0, 0.0, 0.0546, 0.1209] +2026-04-11 14:04:01.939282: Epoch time: 100.27 s +2026-04-11 14:04:03.196428: +2026-04-11 14:04:03.198801: Epoch 947 +2026-04-11 14:04:03.201210: Current learning rate: 0.00784 +2026-04-11 14:05:43.507234: train_loss -0.2729 +2026-04-11 14:05:43.514910: val_loss -0.2351 +2026-04-11 14:05:43.517332: Pseudo dice [0.0, 0.0, 0.5195, 0.0, 0.0, 0.1835, 0.0] +2026-04-11 14:05:43.519644: Epoch time: 100.31 s +2026-04-11 14:05:44.605361: +2026-04-11 14:05:44.608178: Epoch 948 +2026-04-11 14:05:44.610656: Current learning rate: 0.00784 +2026-04-11 14:07:24.684407: train_loss -0.3112 +2026-04-11 14:07:24.692498: val_loss -0.3202 +2026-04-11 14:07:24.696293: Pseudo dice [0.0, 0.0, 0.7381, 0.0, 0.0, 0.1142, 0.0] +2026-04-11 14:07:24.699510: Epoch time: 100.08 s +2026-04-11 14:07:25.753823: +2026-04-11 14:07:25.755918: Epoch 949 +2026-04-11 14:07:25.757943: Current learning rate: 0.00784 +2026-04-11 14:09:06.365724: train_loss -0.3423 +2026-04-11 14:09:06.373375: val_loss -0.365 +2026-04-11 14:09:06.376199: Pseudo dice [0.0, 0.0, 0.6437, 0.0, 0.0, 0.7836, 0.0] +2026-04-11 14:09:06.379051: Epoch time: 100.62 s +2026-04-11 14:09:09.089108: +2026-04-11 14:09:09.091664: Epoch 950 +2026-04-11 14:09:09.094202: Current learning rate: 0.00783 +2026-04-11 14:10:49.413822: train_loss -0.3411 +2026-04-11 14:10:49.419720: val_loss -0.2365 +2026-04-11 14:10:49.423439: Pseudo dice [0.0, 0.0, 0.3328, 0.0, 0.2113, 0.1556, 0.0] +2026-04-11 14:10:49.425992: Epoch time: 100.33 s +2026-04-11 14:10:50.501706: +2026-04-11 14:10:50.504552: Epoch 951 +2026-04-11 14:10:50.506512: Current learning rate: 0.00783 +2026-04-11 14:12:30.783912: train_loss -0.2654 +2026-04-11 14:12:30.789389: val_loss -0.3092 +2026-04-11 14:12:30.791564: Pseudo dice [0.0, 0.0, 0.4594, 0.0, 0.0, 0.0, 0.0] +2026-04-11 14:12:30.794083: Epoch time: 100.29 s +2026-04-11 14:12:31.875316: +2026-04-11 14:12:31.877515: Epoch 952 +2026-04-11 14:12:31.879651: Current learning rate: 0.00783 +2026-04-11 14:14:12.199393: train_loss -0.3314 +2026-04-11 14:14:12.207475: val_loss -0.2736 +2026-04-11 14:14:12.209900: Pseudo dice [0.0, 0.0, 0.4344, 0.0, 0.4786, 0.0047, 0.0] +2026-04-11 14:14:12.212499: Epoch time: 100.33 s +2026-04-11 14:14:13.295122: +2026-04-11 14:14:13.297531: Epoch 953 +2026-04-11 14:14:13.300116: Current learning rate: 0.00783 +2026-04-11 14:15:53.534143: train_loss -0.3503 +2026-04-11 14:15:53.541361: val_loss -0.3828 +2026-04-11 14:15:53.543629: Pseudo dice [0.0, 0.0, 0.551, 0.0, 0.0, 0.3952, 0.4497] +2026-04-11 14:15:53.546417: Epoch time: 100.24 s +2026-04-11 14:15:54.697001: +2026-04-11 14:15:54.699830: Epoch 954 +2026-04-11 14:15:54.702486: Current learning rate: 0.00783 +2026-04-11 14:17:34.826658: train_loss -0.3026 +2026-04-11 14:17:34.834916: val_loss -0.3371 +2026-04-11 14:17:34.837224: Pseudo dice [0.0, 0.0, 0.6957, 0.0, 0.0, 0.1777, 0.4103] +2026-04-11 14:17:34.839934: Epoch time: 100.13 s +2026-04-11 14:17:35.943949: +2026-04-11 14:17:35.946067: Epoch 955 +2026-04-11 14:17:35.948477: Current learning rate: 0.00782 +2026-04-11 14:19:16.207233: train_loss -0.2894 +2026-04-11 14:19:16.214571: val_loss -0.3488 +2026-04-11 14:19:16.216916: Pseudo dice [0.0, 0.0, 0.6915, 0.0, 0.0, 0.0584, 0.7332] +2026-04-11 14:19:16.219210: Epoch time: 100.27 s +2026-04-11 14:19:17.548504: +2026-04-11 14:19:17.550557: Epoch 956 +2026-04-11 14:19:17.553137: Current learning rate: 0.00782 +2026-04-11 14:20:57.656194: train_loss -0.334 +2026-04-11 14:20:57.661469: val_loss -0.272 +2026-04-11 14:20:57.663144: Pseudo dice [0.0, 0.0, 0.5925, 0.0, 0.0033, 0.2361, 0.0188] +2026-04-11 14:20:57.665853: Epoch time: 100.11 s +2026-04-11 14:20:58.754539: +2026-04-11 14:20:58.757195: Epoch 957 +2026-04-11 14:20:58.759658: Current learning rate: 0.00782 +2026-04-11 14:22:39.442164: train_loss -0.3125 +2026-04-11 14:22:39.449288: val_loss -0.375 +2026-04-11 14:22:39.452089: Pseudo dice [0.0, 0.0, 0.5395, 0.0, 0.0017, 0.501, 0.7708] +2026-04-11 14:22:39.454838: Epoch time: 100.69 s +2026-04-11 14:22:40.584537: +2026-04-11 14:22:40.586653: Epoch 958 +2026-04-11 14:22:40.588609: Current learning rate: 0.00782 +2026-04-11 14:24:21.160728: train_loss -0.3167 +2026-04-11 14:24:21.169155: val_loss -0.3332 +2026-04-11 14:24:21.171783: Pseudo dice [0.0, 0.0, 0.6347, 0.0, 0.1775, 0.2793, 0.0] +2026-04-11 14:24:21.175027: Epoch time: 100.58 s +2026-04-11 14:24:22.262457: +2026-04-11 14:24:22.265131: Epoch 959 +2026-04-11 14:24:22.269363: Current learning rate: 0.00781 +2026-04-11 14:26:02.655790: train_loss -0.3027 +2026-04-11 14:26:02.663200: val_loss -0.332 +2026-04-11 14:26:02.665577: Pseudo dice [0.0, 0.0, 0.5127, 0.0, 0.3516, 0.1823, 0.0] +2026-04-11 14:26:02.669821: Epoch time: 100.4 s +2026-04-11 14:26:03.819381: +2026-04-11 14:26:03.821763: Epoch 960 +2026-04-11 14:26:03.824579: Current learning rate: 0.00781 +2026-04-11 14:27:44.178374: train_loss -0.3049 +2026-04-11 14:27:44.184701: val_loss -0.2852 +2026-04-11 14:27:44.187860: Pseudo dice [0.0, 0.0, 0.5572, 0.0, 0.0664, 0.2258, 0.0] +2026-04-11 14:27:44.190223: Epoch time: 100.36 s +2026-04-11 14:27:45.296597: +2026-04-11 14:27:45.298934: Epoch 961 +2026-04-11 14:27:45.301307: Current learning rate: 0.00781 +2026-04-11 14:29:25.507776: train_loss -0.3402 +2026-04-11 14:29:25.514329: val_loss -0.2967 +2026-04-11 14:29:25.516567: Pseudo dice [0.0, 0.0, 0.3769, 0.0, 0.0, 0.6623, 0.017] +2026-04-11 14:29:25.519769: Epoch time: 100.21 s +2026-04-11 14:29:26.637512: +2026-04-11 14:29:26.639967: Epoch 962 +2026-04-11 14:29:26.642356: Current learning rate: 0.00781 +2026-04-11 14:31:07.825052: train_loss -0.3273 +2026-04-11 14:31:07.832528: val_loss -0.3127 +2026-04-11 14:31:07.835309: Pseudo dice [0.0, 0.0, 0.5774, 0.0, 0.0, 0.4221, 0.5984] +2026-04-11 14:31:07.839551: Epoch time: 101.19 s +2026-04-11 14:31:08.941536: +2026-04-11 14:31:08.944174: Epoch 963 +2026-04-11 14:31:08.947683: Current learning rate: 0.0078 +2026-04-11 14:32:49.475185: train_loss -0.3315 +2026-04-11 14:32:49.495265: val_loss -0.2955 +2026-04-11 14:32:49.506523: Pseudo dice [0.0, 0.0, 0.6783, 0.0, 0.0, 0.5331, 0.0024] +2026-04-11 14:32:49.510117: Epoch time: 100.54 s +2026-04-11 14:32:50.625913: +2026-04-11 14:32:50.628004: Epoch 964 +2026-04-11 14:32:50.630048: Current learning rate: 0.0078 +2026-04-11 14:34:31.428548: train_loss -0.2813 +2026-04-11 14:34:31.434468: val_loss -0.3028 +2026-04-11 14:34:31.436471: Pseudo dice [0.0, 0.0, 0.6204, 0.0, 0.0, 0.524, 0.1564] +2026-04-11 14:34:31.438971: Epoch time: 100.81 s +2026-04-11 14:34:32.544483: +2026-04-11 14:34:32.546780: Epoch 965 +2026-04-11 14:34:32.549199: Current learning rate: 0.0078 +2026-04-11 14:36:13.027287: train_loss -0.3193 +2026-04-11 14:36:13.032946: val_loss -0.3976 +2026-04-11 14:36:13.035278: Pseudo dice [0.0, 0.0, 0.6897, 0.0, 0.0, 0.7305, 0.6156] +2026-04-11 14:36:13.039921: Epoch time: 100.49 s +2026-04-11 14:36:14.312358: +2026-04-11 14:36:14.314557: Epoch 966 +2026-04-11 14:36:14.316827: Current learning rate: 0.0078 +2026-04-11 14:37:54.653328: train_loss -0.3372 +2026-04-11 14:37:54.659633: val_loss -0.3509 +2026-04-11 14:37:54.661529: Pseudo dice [0.0, 0.0, 0.7463, 0.0, 0.0, 0.7256, 0.3713] +2026-04-11 14:37:54.663846: Epoch time: 100.34 s +2026-04-11 14:37:55.762465: +2026-04-11 14:37:55.764285: Epoch 967 +2026-04-11 14:37:55.766241: Current learning rate: 0.0078 +2026-04-11 14:39:37.081714: train_loss -0.3626 +2026-04-11 14:39:37.090366: val_loss -0.3356 +2026-04-11 14:39:37.092674: Pseudo dice [0.0, 0.0, 0.7135, 0.0, 0.0, 0.6368, 0.285] +2026-04-11 14:39:37.095385: Epoch time: 101.32 s +2026-04-11 14:39:38.209376: +2026-04-11 14:39:38.211337: Epoch 968 +2026-04-11 14:39:38.213374: Current learning rate: 0.00779 +2026-04-11 14:41:18.562159: train_loss -0.3304 +2026-04-11 14:41:18.568892: val_loss -0.3515 +2026-04-11 14:41:18.571746: Pseudo dice [0.0, 0.0, 0.7887, 0.0, 0.0, 0.4724, 0.551] +2026-04-11 14:41:18.573963: Epoch time: 100.36 s +2026-04-11 14:41:19.680814: +2026-04-11 14:41:19.682709: Epoch 969 +2026-04-11 14:41:19.684907: Current learning rate: 0.00779 +2026-04-11 14:42:59.985097: train_loss -0.3638 +2026-04-11 14:42:59.990555: val_loss -0.3712 +2026-04-11 14:42:59.992738: Pseudo dice [0.0, 0.0, 0.6229, 0.0, 0.0, 0.5973, 0.4768] +2026-04-11 14:42:59.995170: Epoch time: 100.31 s +2026-04-11 14:43:01.068629: +2026-04-11 14:43:01.070514: Epoch 970 +2026-04-11 14:43:01.072703: Current learning rate: 0.00779 +2026-04-11 14:44:41.392007: train_loss -0.3446 +2026-04-11 14:44:41.398968: val_loss -0.244 +2026-04-11 14:44:41.401419: Pseudo dice [0.0, 0.0, 0.5909, 0.0, 0.0, 0.2503, 0.0001] +2026-04-11 14:44:41.403937: Epoch time: 100.33 s +2026-04-11 14:44:42.504273: +2026-04-11 14:44:42.506652: Epoch 971 +2026-04-11 14:44:42.509856: Current learning rate: 0.00779 +2026-04-11 14:46:22.700036: train_loss -0.2975 +2026-04-11 14:46:22.707844: val_loss -0.2894 +2026-04-11 14:46:22.710335: Pseudo dice [0.0, 0.0, 0.3778, 0.0, 0.0, 0.5802, 0.0] +2026-04-11 14:46:22.713029: Epoch time: 100.2 s +2026-04-11 14:46:23.835125: +2026-04-11 14:46:23.837505: Epoch 972 +2026-04-11 14:46:23.840029: Current learning rate: 0.00778 +2026-04-11 14:48:04.173180: train_loss -0.3396 +2026-04-11 14:48:04.179341: val_loss -0.3228 +2026-04-11 14:48:04.182604: Pseudo dice [0.0, 0.0, 0.5902, 0.0, 0.0, 0.7095, 0.153] +2026-04-11 14:48:04.184924: Epoch time: 100.34 s +2026-04-11 14:48:05.262433: +2026-04-11 14:48:05.264388: Epoch 973 +2026-04-11 14:48:05.266422: Current learning rate: 0.00778 +2026-04-11 14:49:45.581134: train_loss -0.3514 +2026-04-11 14:49:45.588553: val_loss -0.3536 +2026-04-11 14:49:45.590916: Pseudo dice [0.0, 0.0, 0.6922, 0.0, 0.0, 0.5797, 0.6479] +2026-04-11 14:49:45.593125: Epoch time: 100.32 s +2026-04-11 14:49:46.681378: +2026-04-11 14:49:46.683320: Epoch 974 +2026-04-11 14:49:46.685469: Current learning rate: 0.00778 +2026-04-11 14:51:27.053511: train_loss -0.3624 +2026-04-11 14:51:27.060169: val_loss -0.3353 +2026-04-11 14:51:27.063061: Pseudo dice [0.0, 0.0, 0.6109, 0.0, 0.0, 0.7813, 0.6445] +2026-04-11 14:51:27.065718: Epoch time: 100.38 s +2026-04-11 14:51:28.150188: +2026-04-11 14:51:28.152488: Epoch 975 +2026-04-11 14:51:28.154815: Current learning rate: 0.00778 +2026-04-11 14:53:08.358999: train_loss -0.3613 +2026-04-11 14:53:08.364774: val_loss -0.3578 +2026-04-11 14:53:08.367196: Pseudo dice [0.0, 0.0, 0.6881, 0.0, 0.0, 0.5708, 0.5972] +2026-04-11 14:53:08.370316: Epoch time: 100.21 s +2026-04-11 14:53:09.458531: +2026-04-11 14:53:09.460843: Epoch 976 +2026-04-11 14:53:09.463282: Current learning rate: 0.00777 +2026-04-11 14:54:49.609066: train_loss -0.3424 +2026-04-11 14:54:49.615780: val_loss -0.3217 +2026-04-11 14:54:49.618047: Pseudo dice [0.0, 0.0, 0.5487, 0.0, 0.0412, 0.3091, 0.5778] +2026-04-11 14:54:49.620836: Epoch time: 100.15 s +2026-04-11 14:54:50.711537: +2026-04-11 14:54:50.715549: Epoch 977 +2026-04-11 14:54:50.717466: Current learning rate: 0.00777 +2026-04-11 14:56:30.891621: train_loss -0.3454 +2026-04-11 14:56:30.898522: val_loss -0.2538 +2026-04-11 14:56:30.900962: Pseudo dice [0.0, 0.0, 0.7001, 0.0, 0.0, 0.6085, 0.5762] +2026-04-11 14:56:30.903689: Epoch time: 100.18 s +2026-04-11 14:56:32.015507: +2026-04-11 14:56:32.017552: Epoch 978 +2026-04-11 14:56:32.019515: Current learning rate: 0.00777 +2026-04-11 14:58:12.212339: train_loss -0.3716 +2026-04-11 14:58:12.219417: val_loss -0.3142 +2026-04-11 14:58:12.222208: Pseudo dice [0.0, 0.0, 0.5818, 0.0, 0.0, 0.1005, 0.4326] +2026-04-11 14:58:12.226229: Epoch time: 100.2 s +2026-04-11 14:58:13.308553: +2026-04-11 14:58:13.311411: Epoch 979 +2026-04-11 14:58:13.313851: Current learning rate: 0.00777 +2026-04-11 14:59:53.507564: train_loss -0.3521 +2026-04-11 14:59:53.515110: val_loss -0.3368 +2026-04-11 14:59:53.517046: Pseudo dice [0.0, 0.0, 0.6304, 0.0, 0.0, 0.3291, 0.6577] +2026-04-11 14:59:53.519505: Epoch time: 100.2 s +2026-04-11 14:59:54.631468: +2026-04-11 14:59:54.633232: Epoch 980 +2026-04-11 14:59:54.635249: Current learning rate: 0.00777 +2026-04-11 15:01:34.903981: train_loss -0.3349 +2026-04-11 15:01:34.910324: val_loss -0.3304 +2026-04-11 15:01:34.913206: Pseudo dice [0.0, 0.0, 0.2518, 0.0, 0.0, 0.2147, 0.5637] +2026-04-11 15:01:34.916602: Epoch time: 100.28 s +2026-04-11 15:01:36.014729: +2026-04-11 15:01:36.016676: Epoch 981 +2026-04-11 15:01:36.018569: Current learning rate: 0.00776 +2026-04-11 15:03:16.230140: train_loss -0.3491 +2026-04-11 15:03:16.236757: val_loss -0.2283 +2026-04-11 15:03:16.243207: Pseudo dice [0.0, 0.0, 0.6983, 0.0, 0.0, 0.6652, 0.6025] +2026-04-11 15:03:16.247491: Epoch time: 100.22 s +2026-04-11 15:03:17.330961: +2026-04-11 15:03:17.333209: Epoch 982 +2026-04-11 15:03:17.335374: Current learning rate: 0.00776 +2026-04-11 15:04:57.331665: train_loss -0.3395 +2026-04-11 15:04:57.337502: val_loss -0.3148 +2026-04-11 15:04:57.339274: Pseudo dice [0.0, 0.0, 0.6177, 0.0, 0.0, 0.5032, 0.0006] +2026-04-11 15:04:57.341631: Epoch time: 100.0 s +2026-04-11 15:04:59.289664: +2026-04-11 15:04:59.291948: Epoch 983 +2026-04-11 15:04:59.294466: Current learning rate: 0.00776 +2026-04-11 15:06:39.506314: train_loss -0.3577 +2026-04-11 15:06:39.511976: val_loss -0.3686 +2026-04-11 15:06:39.513812: Pseudo dice [0.0, 0.0, 0.7499, 0.0, 0.0, 0.5011, 0.7454] +2026-04-11 15:06:39.515731: Epoch time: 100.22 s +2026-04-11 15:06:40.593515: +2026-04-11 15:06:40.595444: Epoch 984 +2026-04-11 15:06:40.597027: Current learning rate: 0.00776 +2026-04-11 15:08:20.814702: train_loss -0.355 +2026-04-11 15:08:20.821187: val_loss -0.2651 +2026-04-11 15:08:20.822971: Pseudo dice [0.0, 0.0, 0.5044, 0.0, 0.0, 0.7207, 0.711] +2026-04-11 15:08:20.825260: Epoch time: 100.22 s +2026-04-11 15:08:21.910760: +2026-04-11 15:08:21.912638: Epoch 985 +2026-04-11 15:08:21.914322: Current learning rate: 0.00775 +2026-04-11 15:10:02.253251: train_loss -0.383 +2026-04-11 15:10:02.258913: val_loss -0.3043 +2026-04-11 15:10:02.260968: Pseudo dice [0.0, 0.0, 0.6316, 0.0, 0.0487, 0.7546, 0.7472] +2026-04-11 15:10:02.263429: Epoch time: 100.35 s +2026-04-11 15:10:03.367550: +2026-04-11 15:10:03.369476: Epoch 986 +2026-04-11 15:10:03.371704: Current learning rate: 0.00775 +2026-04-11 15:11:43.694814: train_loss -0.2951 +2026-04-11 15:11:43.700977: val_loss -0.3378 +2026-04-11 15:11:43.702827: Pseudo dice [0.0, 0.0, 0.6082, 0.0, 0.2154, 0.119, 0.0723] +2026-04-11 15:11:43.704859: Epoch time: 100.33 s +2026-04-11 15:11:44.793444: +2026-04-11 15:11:44.795837: Epoch 987 +2026-04-11 15:11:44.797835: Current learning rate: 0.00775 +2026-04-11 15:13:25.146284: train_loss -0.3374 +2026-04-11 15:13:25.153939: val_loss -0.3423 +2026-04-11 15:13:25.156016: Pseudo dice [0.0, 0.0, 0.5799, 0.0, 0.3383, 0.5008, 0.3793] +2026-04-11 15:13:25.158552: Epoch time: 100.36 s +2026-04-11 15:13:26.238732: +2026-04-11 15:13:26.240531: Epoch 988 +2026-04-11 15:13:26.242135: Current learning rate: 0.00775 +2026-04-11 15:15:06.590935: train_loss -0.3447 +2026-04-11 15:15:06.596750: val_loss -0.2601 +2026-04-11 15:15:06.598885: Pseudo dice [0.0, 0.0, 0.383, 0.0, 0.3417, 0.6028, 0.8223] +2026-04-11 15:15:06.601337: Epoch time: 100.36 s +2026-04-11 15:15:07.690493: +2026-04-11 15:15:07.692308: Epoch 989 +2026-04-11 15:15:07.693947: Current learning rate: 0.00774 +2026-04-11 15:16:47.971407: train_loss -0.3592 +2026-04-11 15:16:47.977014: val_loss -0.3879 +2026-04-11 15:16:47.979622: Pseudo dice [0.0, 0.0, 0.6713, 0.0, 0.0, 0.5245, 0.5643] +2026-04-11 15:16:47.982277: Epoch time: 100.28 s +2026-04-11 15:16:49.081162: +2026-04-11 15:16:49.083732: Epoch 990 +2026-04-11 15:16:49.085801: Current learning rate: 0.00774 +2026-04-11 15:18:29.413062: train_loss -0.351 +2026-04-11 15:18:29.421857: val_loss -0.2411 +2026-04-11 15:18:29.423758: Pseudo dice [0.0, 0.0, 0.4919, 0.0, 0.2176, 0.437, 0.7387] +2026-04-11 15:18:29.426020: Epoch time: 100.33 s +2026-04-11 15:18:30.519326: +2026-04-11 15:18:30.521124: Epoch 991 +2026-04-11 15:18:30.523079: Current learning rate: 0.00774 +2026-04-11 15:20:11.053772: train_loss -0.3136 +2026-04-11 15:20:11.063336: val_loss -0.3598 +2026-04-11 15:20:11.065501: Pseudo dice [0.0, 0.0, 0.5005, 0.0, 0.0, 0.6708, 0.4951] +2026-04-11 15:20:11.067859: Epoch time: 100.54 s +2026-04-11 15:20:12.173790: +2026-04-11 15:20:12.176066: Epoch 992 +2026-04-11 15:20:12.178271: Current learning rate: 0.00774 +2026-04-11 15:21:52.521155: train_loss -0.3023 +2026-04-11 15:21:52.528744: val_loss -0.2388 +2026-04-11 15:21:52.530751: Pseudo dice [0.0, 0.0, 0.6848, 0.0, 0.0, 0.0, 0.0002] +2026-04-11 15:21:52.532816: Epoch time: 100.35 s +2026-04-11 15:21:53.622460: +2026-04-11 15:21:53.624377: Epoch 993 +2026-04-11 15:21:53.626043: Current learning rate: 0.00774 +2026-04-11 15:23:33.932057: train_loss -0.2835 +2026-04-11 15:23:33.937545: val_loss -0.3336 +2026-04-11 15:23:33.940542: Pseudo dice [0.0, 0.0, 0.5366, 0.0, 0.0, 0.0, 0.0] +2026-04-11 15:23:33.943225: Epoch time: 100.31 s +2026-04-11 15:23:35.031127: +2026-04-11 15:23:35.033050: Epoch 994 +2026-04-11 15:23:35.034865: Current learning rate: 0.00773 +2026-04-11 15:25:15.397168: train_loss -0.2944 +2026-04-11 15:25:15.405237: val_loss -0.3395 +2026-04-11 15:25:15.407538: Pseudo dice [0.0, 0.0, 0.7734, 0.0, 0.0, 0.0005, 0.0] +2026-04-11 15:25:15.410444: Epoch time: 100.37 s +2026-04-11 15:25:16.512849: +2026-04-11 15:25:16.515065: Epoch 995 +2026-04-11 15:25:16.516901: Current learning rate: 0.00773 +2026-04-11 15:26:56.764271: train_loss -0.3307 +2026-04-11 15:26:56.770792: val_loss -0.35 +2026-04-11 15:26:56.772568: Pseudo dice [0.0, 0.0, 0.6828, 0.0, 0.0, 0.4619, 0.0] +2026-04-11 15:26:56.774842: Epoch time: 100.25 s +2026-04-11 15:26:57.872088: +2026-04-11 15:26:57.874547: Epoch 996 +2026-04-11 15:26:57.876778: Current learning rate: 0.00773 +2026-04-11 15:28:38.239682: train_loss -0.3625 +2026-04-11 15:28:38.246323: val_loss -0.3936 +2026-04-11 15:28:38.248770: Pseudo dice [0.0, 0.0, 0.6949, 0.0, 0.139, 0.7187, 0.6643] +2026-04-11 15:28:38.251125: Epoch time: 100.37 s +2026-04-11 15:28:39.337150: +2026-04-11 15:28:39.339026: Epoch 997 +2026-04-11 15:28:39.340755: Current learning rate: 0.00773 +2026-04-11 15:30:19.676736: train_loss -0.3454 +2026-04-11 15:30:19.686274: val_loss -0.3849 +2026-04-11 15:30:19.688917: Pseudo dice [0.0, 0.0, 0.6964, 0.0, 0.0144, 0.6551, 0.3326] +2026-04-11 15:30:19.691409: Epoch time: 100.34 s +2026-04-11 15:30:20.795917: +2026-04-11 15:30:20.797657: Epoch 998 +2026-04-11 15:30:20.799321: Current learning rate: 0.00772 +2026-04-11 15:32:01.083627: train_loss -0.353 +2026-04-11 15:32:01.090657: val_loss -0.2949 +2026-04-11 15:32:01.092421: Pseudo dice [0.0, 0.0, 0.3033, 0.0, 0.0, 0.6277, 0.6959] +2026-04-11 15:32:01.094741: Epoch time: 100.29 s +2026-04-11 15:32:02.205069: +2026-04-11 15:32:02.207295: Epoch 999 +2026-04-11 15:32:02.209180: Current learning rate: 0.00772 +2026-04-11 15:33:42.458044: train_loss -0.3411 +2026-04-11 15:33:42.479438: val_loss -0.3383 +2026-04-11 15:33:42.482130: Pseudo dice [0.0, 0.0, 0.7229, 0.0, 0.0, 0.6797, 0.7063] +2026-04-11 15:33:42.485850: Epoch time: 100.26 s +2026-04-11 15:33:45.186020: +2026-04-11 15:33:45.188254: Epoch 1000 +2026-04-11 15:33:45.190377: Current learning rate: 0.00772 +2026-04-11 15:35:25.419911: train_loss -0.3631 +2026-04-11 15:35:25.427860: val_loss -0.3702 +2026-04-11 15:35:25.431038: Pseudo dice [0.0, 0.0, 0.6335, 0.0, 0.0, 0.8446, 0.5523] +2026-04-11 15:35:25.433352: Epoch time: 100.24 s +2026-04-11 15:35:26.528359: +2026-04-11 15:35:26.530497: Epoch 1001 +2026-04-11 15:35:26.532346: Current learning rate: 0.00772 +2026-04-11 15:37:06.732383: train_loss -0.3425 +2026-04-11 15:37:06.750441: val_loss -0.3264 +2026-04-11 15:37:06.757360: Pseudo dice [0.0, 0.0, 0.628, 0.0, 0.0, 0.7337, 0.672] +2026-04-11 15:37:06.760042: Epoch time: 100.21 s +2026-04-11 15:37:07.845003: +2026-04-11 15:37:07.847092: Epoch 1002 +2026-04-11 15:37:07.848791: Current learning rate: 0.00771 +2026-04-11 15:38:48.070838: train_loss -0.3263 +2026-04-11 15:38:48.076410: val_loss -0.3564 +2026-04-11 15:38:48.078614: Pseudo dice [0.0, 0.0, 0.547, 0.0, 0.0996, 0.2361, 0.8229] +2026-04-11 15:38:48.080807: Epoch time: 100.23 s +2026-04-11 15:38:50.030410: +2026-04-11 15:38:50.032283: Epoch 1003 +2026-04-11 15:38:50.034539: Current learning rate: 0.00771 +2026-04-11 15:40:30.264821: train_loss -0.3313 +2026-04-11 15:40:30.270885: val_loss -0.1985 +2026-04-11 15:40:30.273072: Pseudo dice [0.0, 0.0, 0.4544, 0.0, 0.2166, 0.5065, 0.632] +2026-04-11 15:40:30.275805: Epoch time: 100.24 s +2026-04-11 15:40:31.616981: +2026-04-11 15:40:31.619380: Epoch 1004 +2026-04-11 15:40:31.621548: Current learning rate: 0.00771 +2026-04-11 15:42:11.978710: train_loss -0.2937 +2026-04-11 15:42:11.984347: val_loss -0.3248 +2026-04-11 15:42:11.986437: Pseudo dice [0.0, 0.0, 0.6213, 0.0, 0.0, 0.4987, 0.4082] +2026-04-11 15:42:11.988866: Epoch time: 100.36 s +2026-04-11 15:42:13.089723: +2026-04-11 15:42:13.091640: Epoch 1005 +2026-04-11 15:42:13.094005: Current learning rate: 0.00771 +2026-04-11 15:43:53.393446: train_loss -0.3043 +2026-04-11 15:43:53.399650: val_loss -0.3045 +2026-04-11 15:43:53.401942: Pseudo dice [0.0, 0.0, 0.2403, 0.0, 0.0, 0.0, 0.0812] +2026-04-11 15:43:53.404941: Epoch time: 100.31 s +2026-04-11 15:43:54.502844: +2026-04-11 15:43:54.504573: Epoch 1006 +2026-04-11 15:43:54.506252: Current learning rate: 0.0077 +2026-04-11 15:45:34.924346: train_loss -0.2886 +2026-04-11 15:45:34.931453: val_loss -0.3092 +2026-04-11 15:45:34.933516: Pseudo dice [0.0, 0.0, 0.6325, 0.0, 0.0, 0.0024, 0.1997] +2026-04-11 15:45:34.935674: Epoch time: 100.42 s +2026-04-11 15:45:36.049250: +2026-04-11 15:45:36.050917: Epoch 1007 +2026-04-11 15:45:36.052424: Current learning rate: 0.0077 +2026-04-11 15:47:16.466496: train_loss -0.3171 +2026-04-11 15:47:16.471541: val_loss -0.3547 +2026-04-11 15:47:16.473156: Pseudo dice [0.0, 0.0, 0.7209, 0.0, 0.0, 0.2665, 0.5657] +2026-04-11 15:47:16.475214: Epoch time: 100.42 s +2026-04-11 15:47:17.574222: +2026-04-11 15:47:17.577035: Epoch 1008 +2026-04-11 15:47:17.579535: Current learning rate: 0.0077 +2026-04-11 15:48:57.712652: train_loss -0.3588 +2026-04-11 15:48:57.718876: val_loss -0.3467 +2026-04-11 15:48:57.721217: Pseudo dice [0.0, 0.0, 0.7602, 0.0, 0.0, 0.4647, 0.3325] +2026-04-11 15:48:57.723416: Epoch time: 100.14 s +2026-04-11 15:48:58.804518: +2026-04-11 15:48:58.808533: Epoch 1009 +2026-04-11 15:48:58.811588: Current learning rate: 0.0077 +2026-04-11 15:50:39.193807: train_loss -0.3294 +2026-04-11 15:50:39.199630: val_loss -0.3015 +2026-04-11 15:50:39.201508: Pseudo dice [0.0, 0.0, 0.188, 0.0, 0.0175, 0.2121, 0.3572] +2026-04-11 15:50:39.203791: Epoch time: 100.39 s +2026-04-11 15:50:40.299600: +2026-04-11 15:50:40.301250: Epoch 1010 +2026-04-11 15:50:40.302994: Current learning rate: 0.0077 +2026-04-11 15:52:20.596359: train_loss -0.3274 +2026-04-11 15:52:20.602842: val_loss -0.3644 +2026-04-11 15:52:20.605758: Pseudo dice [0.0, 0.0, 0.4932, 0.0, 0.1581, 0.6796, 0.5695] +2026-04-11 15:52:20.608064: Epoch time: 100.3 s +2026-04-11 15:52:21.694924: +2026-04-11 15:52:21.696904: Epoch 1011 +2026-04-11 15:52:21.698510: Current learning rate: 0.00769 +2026-04-11 15:54:01.917392: train_loss -0.3524 +2026-04-11 15:54:01.923132: val_loss -0.3914 +2026-04-11 15:54:01.924878: Pseudo dice [0.0, 0.0, 0.6694, 0.0, 0.0022, 0.761, 0.4965] +2026-04-11 15:54:01.927037: Epoch time: 100.23 s +2026-04-11 15:54:03.007723: +2026-04-11 15:54:03.009391: Epoch 1012 +2026-04-11 15:54:03.011135: Current learning rate: 0.00769 +2026-04-11 15:55:43.297618: train_loss -0.3651 +2026-04-11 15:55:43.304824: val_loss -0.3404 +2026-04-11 15:55:43.307081: Pseudo dice [0.0, 0.0, 0.7171, 0.0, 0.1067, 0.7485, 0.7449] +2026-04-11 15:55:43.310241: Epoch time: 100.29 s +2026-04-11 15:55:44.416319: +2026-04-11 15:55:44.419074: Epoch 1013 +2026-04-11 15:55:44.421962: Current learning rate: 0.00769 +2026-04-11 15:57:24.638080: train_loss -0.3606 +2026-04-11 15:57:24.643905: val_loss -0.3296 +2026-04-11 15:57:24.646108: Pseudo dice [0.0, 0.0, 0.6317, 0.0, 0.0662, 0.6853, 0.2532] +2026-04-11 15:57:24.648354: Epoch time: 100.22 s +2026-04-11 15:57:25.755690: +2026-04-11 15:57:25.757779: Epoch 1014 +2026-04-11 15:57:25.759710: Current learning rate: 0.00769 +2026-04-11 15:59:48.769555: train_loss -0.3278 +2026-04-11 15:59:48.778491: val_loss -0.315 +2026-04-11 15:59:48.781184: Pseudo dice [0.0, 0.0, 0.584, 0.0, 0.0, 0.1019, 0.5164] +2026-04-11 15:59:48.784419: Epoch time: 143.02 s +2026-04-11 15:59:49.953100: +2026-04-11 15:59:49.954786: Epoch 1015 +2026-04-11 15:59:49.956455: Current learning rate: 0.00768 +2026-04-11 16:05:57.863981: train_loss -0.3368 +2026-04-11 16:05:57.869381: val_loss -0.3822 +2026-04-11 16:05:57.871386: Pseudo dice [0.0, 0.0, 0.5737, 0.0, 0.0, 0.4512, 0.5297] +2026-04-11 16:05:57.873854: Epoch time: 367.91 s +2026-04-11 16:05:59.032621: +2026-04-11 16:05:59.035192: Epoch 1016 +2026-04-11 16:05:59.037424: Current learning rate: 0.00768 +2026-04-11 16:10:24.539416: train_loss -0.3494 +2026-04-11 16:10:24.545883: val_loss -0.3613 +2026-04-11 16:10:24.548408: Pseudo dice [0.0, 0.0, 0.3806, 0.5577, 0.4316, 0.411, 0.6934] +2026-04-11 16:10:24.551033: Epoch time: 265.51 s +2026-04-11 16:10:25.742612: +2026-04-11 16:10:25.745065: Epoch 1017 +2026-04-11 16:10:25.747175: Current learning rate: 0.00768 +2026-04-11 16:27:43.294026: train_loss -0.3515 +2026-04-11 16:27:43.299876: val_loss -0.355 +2026-04-11 16:27:43.302480: Pseudo dice [0.0, 0.0, 0.4424, 0.0, 0.3182, 0.5374, 0.4489] +2026-04-11 16:27:43.304705: Epoch time: 1037.55 s +2026-04-11 16:27:44.458700: +2026-04-11 16:27:44.460813: Epoch 1018 +2026-04-11 16:27:44.462520: Current learning rate: 0.00768 +2026-04-11 16:44:30.431924: train_loss -0.2906 +2026-04-11 16:44:30.439356: val_loss -0.2982 +2026-04-11 16:44:30.442876: Pseudo dice [0.0, 0.0, 0.3585, 0.0, 0.0, 0.0, 0.0038] +2026-04-11 16:44:30.445718: Epoch time: 1005.98 s +2026-04-11 16:44:31.548029: +2026-04-11 16:44:31.550255: Epoch 1019 +2026-04-11 16:44:31.552012: Current learning rate: 0.00767 +2026-04-11 16:46:11.461345: train_loss -0.29 +2026-04-11 16:46:11.467762: val_loss -0.3317 +2026-04-11 16:46:11.470529: Pseudo dice [0.0, 0.0, 0.7204, 0.0, 0.11, 0.0, 0.0] +2026-04-11 16:46:11.475181: Epoch time: 99.92 s +2026-04-11 16:46:12.572064: +2026-04-11 16:46:12.573958: Epoch 1020 +2026-04-11 16:46:12.576249: Current learning rate: 0.00767 +2026-04-11 16:47:54.208620: train_loss -0.3131 +2026-04-11 16:47:54.213709: val_loss -0.3323 +2026-04-11 16:47:54.215474: Pseudo dice [0.0, 0.0, 0.5737, 0.0, 0.0, 0.0, 0.1178] +2026-04-11 16:47:54.217701: Epoch time: 101.64 s +2026-04-11 16:47:55.322056: +2026-04-11 16:47:55.323808: Epoch 1021 +2026-04-11 16:47:55.325424: Current learning rate: 0.00767 +2026-04-11 16:49:35.644946: train_loss -0.3007 +2026-04-11 16:49:35.650470: val_loss -0.2265 +2026-04-11 16:49:35.652688: Pseudo dice [0.0, 0.0, 0.6847, 0.0, 0.0, 0.0004, 0.4618] +2026-04-11 16:49:35.655332: Epoch time: 100.33 s +2026-04-11 16:49:36.770523: +2026-04-11 16:49:36.772228: Epoch 1022 +2026-04-11 16:49:36.773977: Current learning rate: 0.00767 +2026-04-11 16:51:16.974652: train_loss -0.3319 +2026-04-11 16:51:16.979792: val_loss -0.3201 +2026-04-11 16:51:16.982946: Pseudo dice [0.0, 0.0, 0.707, 0.0, 0.0, 0.5414, 0.4585] +2026-04-11 16:51:16.985266: Epoch time: 100.21 s +2026-04-11 16:51:18.103395: +2026-04-11 16:51:18.105433: Epoch 1023 +2026-04-11 16:51:18.108504: Current learning rate: 0.00767 +2026-04-11 16:52:59.344674: train_loss -0.3402 +2026-04-11 16:52:59.350969: val_loss -0.3694 +2026-04-11 16:52:59.353374: Pseudo dice [0.0, 0.0, 0.4159, 0.0936, 0.0, 0.2434, 0.8025] +2026-04-11 16:52:59.355784: Epoch time: 101.24 s +2026-04-11 16:53:00.466190: +2026-04-11 16:53:00.468554: Epoch 1024 +2026-04-11 16:53:00.481047: Current learning rate: 0.00766 +2026-04-11 16:54:40.800582: train_loss -0.3044 +2026-04-11 16:54:40.807587: val_loss -0.3334 +2026-04-11 16:54:40.810266: Pseudo dice [0.0, 0.0, 0.638, 0.0, 0.243, 0.1291, 0.6124] +2026-04-11 16:54:40.812950: Epoch time: 100.34 s +2026-04-11 16:54:41.954121: +2026-04-11 16:54:41.955902: Epoch 1025 +2026-04-11 16:54:41.957432: Current learning rate: 0.00766 +2026-04-11 16:56:22.296000: train_loss -0.3391 +2026-04-11 16:56:22.302158: val_loss -0.293 +2026-04-11 16:56:22.304105: Pseudo dice [0.0, 0.0, 0.6954, 0.0441, 0.2837, 0.2605, 0.6254] +2026-04-11 16:56:22.306358: Epoch time: 100.34 s +2026-04-11 16:56:23.396298: +2026-04-11 16:56:23.398016: Epoch 1026 +2026-04-11 16:56:23.399678: Current learning rate: 0.00766 +2026-04-11 16:58:03.760953: train_loss -0.3419 +2026-04-11 16:58:03.766209: val_loss -0.3734 +2026-04-11 16:58:03.768278: Pseudo dice [0.0, 0.0, 0.6618, 0.6416, 0.0, 0.4575, 0.0858] +2026-04-11 16:58:03.770665: Epoch time: 100.37 s +2026-04-11 16:58:04.875612: +2026-04-11 16:58:04.877252: Epoch 1027 +2026-04-11 16:58:04.878687: Current learning rate: 0.00766 +2026-04-11 16:59:45.151569: train_loss -0.3381 +2026-04-11 16:59:45.156805: val_loss -0.3368 +2026-04-11 16:59:45.159120: Pseudo dice [0.0, 0.0, 0.4298, 0.0, 0.0, 0.4137, 0.7322] +2026-04-11 16:59:45.161579: Epoch time: 100.28 s +2026-04-11 16:59:46.254902: +2026-04-11 16:59:46.256546: Epoch 1028 +2026-04-11 16:59:46.258159: Current learning rate: 0.00765 +2026-04-11 17:01:26.543489: train_loss -0.3478 +2026-04-11 17:01:26.549080: val_loss -0.3146 +2026-04-11 17:01:26.552941: Pseudo dice [0.0, 0.0, 0.6402, 0.0003, 0.2395, 0.5911, 0.4664] +2026-04-11 17:01:26.555432: Epoch time: 100.29 s +2026-04-11 17:01:27.648090: +2026-04-11 17:01:27.650274: Epoch 1029 +2026-04-11 17:01:27.652206: Current learning rate: 0.00765 +2026-04-11 17:03:07.934188: train_loss -0.3511 +2026-04-11 17:03:07.939089: val_loss -0.3356 +2026-04-11 17:03:07.940830: Pseudo dice [0.0, 0.0, 0.5686, 0.0, 0.2206, 0.6316, 0.7466] +2026-04-11 17:03:07.944831: Epoch time: 100.29 s +2026-04-11 17:03:09.051817: +2026-04-11 17:03:09.054226: Epoch 1030 +2026-04-11 17:03:09.056095: Current learning rate: 0.00765 +2026-04-11 17:04:49.295586: train_loss -0.3555 +2026-04-11 17:04:49.302194: val_loss -0.2734 +2026-04-11 17:04:49.304830: Pseudo dice [0.0, 0.0, 0.398, 0.0, 0.0, 0.0258, 0.8277] +2026-04-11 17:04:49.307874: Epoch time: 100.25 s +2026-04-11 17:04:50.424987: +2026-04-11 17:04:50.427143: Epoch 1031 +2026-04-11 17:04:50.429150: Current learning rate: 0.00765 +2026-04-11 17:06:30.887422: train_loss -0.3589 +2026-04-11 17:06:30.894518: val_loss -0.3746 +2026-04-11 17:06:30.897050: Pseudo dice [0.0, 0.0, 0.7871, 0.0, 0.0, 0.6138, 0.781] +2026-04-11 17:06:30.899811: Epoch time: 100.47 s +2026-04-11 17:06:32.013384: +2026-04-11 17:06:32.015643: Epoch 1032 +2026-04-11 17:06:32.017734: Current learning rate: 0.00764 +2026-04-11 17:08:12.383002: train_loss -0.3625 +2026-04-11 17:08:12.391149: val_loss -0.3336 +2026-04-11 17:08:12.393736: Pseudo dice [0.0, 0.0, 0.6673, 0.6948, 0.0, 0.0118, 0.5122] +2026-04-11 17:08:12.396174: Epoch time: 100.37 s +2026-04-11 17:08:13.493416: +2026-04-11 17:08:13.504611: Epoch 1033 +2026-04-11 17:08:13.506527: Current learning rate: 0.00764 +2026-04-11 17:09:53.913184: train_loss -0.3461 +2026-04-11 17:09:53.920933: val_loss -0.3528 +2026-04-11 17:09:53.922711: Pseudo dice [0.0, 0.0, 0.6708, 0.34, 0.0, 0.0958, 0.0011] +2026-04-11 17:09:53.924836: Epoch time: 100.42 s +2026-04-11 17:09:55.024593: +2026-04-11 17:09:55.026237: Epoch 1034 +2026-04-11 17:09:55.027862: Current learning rate: 0.00764 +2026-04-11 17:11:35.278428: train_loss -0.3529 +2026-04-11 17:11:35.287583: val_loss -0.2735 +2026-04-11 17:11:35.289855: Pseudo dice [0.0, 0.0, 0.5261, 0.0, 0.0, 0.6194, 0.113] +2026-04-11 17:11:35.292280: Epoch time: 100.26 s +2026-04-11 17:11:36.404420: +2026-04-11 17:11:36.406447: Epoch 1035 +2026-04-11 17:11:36.408046: Current learning rate: 0.00764 +2026-04-11 17:13:16.697312: train_loss -0.3322 +2026-04-11 17:13:16.703954: val_loss -0.3388 +2026-04-11 17:13:16.706087: Pseudo dice [0.0, 0.0, 0.6615, 0.0, 0.0, 0.3739, 0.4869] +2026-04-11 17:13:16.708431: Epoch time: 100.3 s +2026-04-11 17:13:17.814507: +2026-04-11 17:13:17.816822: Epoch 1036 +2026-04-11 17:13:17.818429: Current learning rate: 0.00764 +2026-04-11 17:14:57.899166: train_loss -0.3427 +2026-04-11 17:14:57.905314: val_loss -0.2839 +2026-04-11 17:14:57.907180: Pseudo dice [0.0, 0.0, 0.6466, 0.0, 0.155, 0.4591, 0.7162] +2026-04-11 17:14:57.909566: Epoch time: 100.09 s +2026-04-11 17:14:59.020497: +2026-04-11 17:14:59.022121: Epoch 1037 +2026-04-11 17:14:59.023659: Current learning rate: 0.00763 +2026-04-11 17:16:39.455355: train_loss -0.3296 +2026-04-11 17:16:39.461514: val_loss -0.3145 +2026-04-11 17:16:39.463160: Pseudo dice [0.0, 0.0, 0.6071, 0.0406, 0.0, 0.3244, 0.7916] +2026-04-11 17:16:39.465651: Epoch time: 100.44 s +2026-04-11 17:16:40.552138: +2026-04-11 17:16:40.554175: Epoch 1038 +2026-04-11 17:16:40.555964: Current learning rate: 0.00763 +2026-04-11 17:18:20.921649: train_loss -0.3335 +2026-04-11 17:18:20.928073: val_loss -0.3321 +2026-04-11 17:18:20.930078: Pseudo dice [0.0, 0.0, 0.6194, 0.0, 0.0, 0.3671, 0.5143] +2026-04-11 17:18:20.932598: Epoch time: 100.37 s +2026-04-11 17:18:22.035671: +2026-04-11 17:18:22.038047: Epoch 1039 +2026-04-11 17:18:22.040011: Current learning rate: 0.00763 +2026-04-11 17:20:02.301421: train_loss -0.3177 +2026-04-11 17:20:02.307606: val_loss -0.2659 +2026-04-11 17:20:02.309685: Pseudo dice [0.0, 0.0, 0.5836, 0.0, 0.0, 0.1291, 0.3928] +2026-04-11 17:20:02.312130: Epoch time: 100.27 s +2026-04-11 17:20:03.414801: +2026-04-11 17:20:03.416589: Epoch 1040 +2026-04-11 17:20:03.418248: Current learning rate: 0.00763 +2026-04-11 17:21:53.018230: train_loss -0.3164 +2026-04-11 17:21:53.025306: val_loss -0.319 +2026-04-11 17:21:53.027489: Pseudo dice [0.0, 0.0, 0.537, 0.0006, 0.0, 0.0126, 0.3106] +2026-04-11 17:21:53.030058: Epoch time: 109.61 s +2026-04-11 17:21:54.158924: +2026-04-11 17:21:54.160975: Epoch 1041 +2026-04-11 17:21:54.162641: Current learning rate: 0.00762 +2026-04-11 17:23:54.845589: train_loss -0.3002 +2026-04-11 17:23:54.853175: val_loss -0.2784 +2026-04-11 17:23:54.856020: Pseudo dice [0.0, 0.0, 0.5173, 0.0, 0.0, 0.0, 0.4601] +2026-04-11 17:23:54.858730: Epoch time: 120.69 s +2026-04-11 17:23:55.963906: +2026-04-11 17:23:55.965930: Epoch 1042 +2026-04-11 17:23:55.967523: Current learning rate: 0.00762 +2026-04-11 17:25:35.981358: train_loss -0.3203 +2026-04-11 17:25:35.988777: val_loss -0.3419 +2026-04-11 17:25:35.991075: Pseudo dice [0.0, 0.0, 0.72, 0.0, 0.0, 0.0, 0.005] +2026-04-11 17:25:35.993195: Epoch time: 100.02 s +2026-04-11 17:25:37.110811: +2026-04-11 17:25:37.112525: Epoch 1043 +2026-04-11 17:25:37.114145: Current learning rate: 0.00762 +2026-04-11 17:27:18.055656: train_loss -0.3305 +2026-04-11 17:27:18.061334: val_loss -0.3611 +2026-04-11 17:27:18.063257: Pseudo dice [0.0, 0.0, 0.7087, 0.0, 0.0, 0.0, 0.3287] +2026-04-11 17:27:18.065700: Epoch time: 100.95 s +2026-04-11 17:27:20.169482: +2026-04-11 17:27:20.171314: Epoch 1044 +2026-04-11 17:27:20.172953: Current learning rate: 0.00762 +2026-04-11 17:29:00.598020: train_loss -0.3428 +2026-04-11 17:29:00.604106: val_loss -0.3622 +2026-04-11 17:29:00.606108: Pseudo dice [0.0, 0.0, 0.6825, 0.0, 0.0, 0.145, 0.7543] +2026-04-11 17:29:00.608052: Epoch time: 100.43 s +2026-04-11 17:29:01.702407: +2026-04-11 17:29:01.704603: Epoch 1045 +2026-04-11 17:29:01.706511: Current learning rate: 0.00761 +2026-04-11 17:30:42.044207: train_loss -0.3632 +2026-04-11 17:30:42.051597: val_loss -0.3721 +2026-04-11 17:30:42.053690: Pseudo dice [0.0, 0.0, 0.7187, 0.1097, 0.0, 0.6011, 0.5486] +2026-04-11 17:30:42.056068: Epoch time: 100.34 s +2026-04-11 17:30:43.156858: +2026-04-11 17:30:43.158470: Epoch 1046 +2026-04-11 17:30:43.161405: Current learning rate: 0.00761 +2026-04-11 17:32:23.499699: train_loss -0.3661 +2026-04-11 17:32:23.506925: val_loss -0.364 +2026-04-11 17:32:23.509275: Pseudo dice [0.0, 0.0, 0.4919, 0.5286, 0.0, 0.7947, 0.4494] +2026-04-11 17:32:23.512224: Epoch time: 100.35 s +2026-04-11 17:32:24.610809: +2026-04-11 17:32:24.612557: Epoch 1047 +2026-04-11 17:32:24.614444: Current learning rate: 0.00761 +2026-04-11 17:34:04.897919: train_loss -0.345 +2026-04-11 17:34:04.904548: val_loss -0.3434 +2026-04-11 17:34:04.906842: Pseudo dice [0.0, 0.0, 0.7208, 0.0, 0.0, 0.7915, 0.631] +2026-04-11 17:34:04.909492: Epoch time: 100.29 s +2026-04-11 17:34:06.019524: +2026-04-11 17:34:06.022546: Epoch 1048 +2026-04-11 17:34:06.024679: Current learning rate: 0.00761 +2026-04-11 17:35:46.304368: train_loss -0.3283 +2026-04-11 17:35:46.312640: val_loss -0.3275 +2026-04-11 17:35:46.314869: Pseudo dice [0.0, 0.0, 0.6613, 0.0, 0.2922, 0.4372, 0.2195] +2026-04-11 17:35:46.317200: Epoch time: 100.29 s +2026-04-11 17:35:47.527112: +2026-04-11 17:35:47.530862: Epoch 1049 +2026-04-11 17:35:47.532834: Current learning rate: 0.00761 +2026-04-11 17:37:27.791179: train_loss -0.332 +2026-04-11 17:37:27.798091: val_loss -0.3604 +2026-04-11 17:37:27.799922: Pseudo dice [0.0, 0.0, 0.803, 0.0, 0.0, 0.6687, 0.7939] +2026-04-11 17:37:27.802243: Epoch time: 100.27 s +2026-04-11 17:37:30.619949: +2026-04-11 17:37:30.622717: Epoch 1050 +2026-04-11 17:37:30.625019: Current learning rate: 0.0076 +2026-04-11 17:39:11.043024: train_loss -0.367 +2026-04-11 17:39:11.048895: val_loss -0.339 +2026-04-11 17:39:11.051010: Pseudo dice [0.0, 0.0, 0.7128, 0.0, 0.0, 0.3299, 0.6326] +2026-04-11 17:39:11.053567: Epoch time: 100.43 s +2026-04-11 17:39:12.140292: +2026-04-11 17:39:12.142070: Epoch 1051 +2026-04-11 17:39:12.143849: Current learning rate: 0.0076 +2026-04-11 17:40:52.429606: train_loss -0.3691 +2026-04-11 17:40:52.435062: val_loss -0.3832 +2026-04-11 17:40:52.436829: Pseudo dice [0.0, 0.0, 0.7195, 0.0, 0.0, 0.6702, 0.7275] +2026-04-11 17:40:52.439176: Epoch time: 100.29 s +2026-04-11 17:40:53.550757: +2026-04-11 17:40:53.552519: Epoch 1052 +2026-04-11 17:40:53.554782: Current learning rate: 0.0076 +2026-04-11 17:42:33.863286: train_loss -0.389 +2026-04-11 17:42:33.869518: val_loss -0.3183 +2026-04-11 17:42:33.871605: Pseudo dice [0.0, 0.0, 0.7039, 0.0, 0.0, 0.5949, 0.6677] +2026-04-11 17:42:33.874047: Epoch time: 100.32 s +2026-04-11 17:42:34.976020: +2026-04-11 17:42:34.980898: Epoch 1053 +2026-04-11 17:42:34.982732: Current learning rate: 0.0076 +2026-04-11 17:44:15.149079: train_loss -0.3193 +2026-04-11 17:44:15.154613: val_loss -0.3165 +2026-04-11 17:44:15.156392: Pseudo dice [0.0, 0.0, 0.7298, 0.0, 0.0, 0.0192, 0.7432] +2026-04-11 17:44:15.158400: Epoch time: 100.18 s +2026-04-11 17:44:16.261444: +2026-04-11 17:44:16.263471: Epoch 1054 +2026-04-11 17:44:16.265456: Current learning rate: 0.00759 +2026-04-11 17:45:56.317333: train_loss -0.2758 +2026-04-11 17:45:56.324713: val_loss -0.2931 +2026-04-11 17:45:56.326822: Pseudo dice [0.0, 0.0, 0.1162, 0.0, 0.0, 0.5456, 0.6655] +2026-04-11 17:45:56.329090: Epoch time: 100.06 s +2026-04-11 17:45:57.434571: +2026-04-11 17:45:57.436443: Epoch 1055 +2026-04-11 17:45:57.438326: Current learning rate: 0.00759 +2026-04-11 17:47:37.622880: train_loss -0.2838 +2026-04-11 17:47:37.628891: val_loss -0.3001 +2026-04-11 17:47:37.631270: Pseudo dice [0.0, 0.0, 0.2106, 0.0, 0.0, 0.1003, 0.2227] +2026-04-11 17:47:37.633753: Epoch time: 100.19 s +2026-04-11 17:47:38.754165: +2026-04-11 17:47:38.756371: Epoch 1056 +2026-04-11 17:47:38.759012: Current learning rate: 0.00759 +2026-04-11 17:49:19.059915: train_loss -0.2898 +2026-04-11 17:49:19.066668: val_loss -0.3243 +2026-04-11 17:49:19.068750: Pseudo dice [0.0, 0.0, 0.4061, 0.0, 0.0, 0.2721, 0.0] +2026-04-11 17:49:19.071514: Epoch time: 100.31 s +2026-04-11 17:49:20.178936: +2026-04-11 17:49:20.181149: Epoch 1057 +2026-04-11 17:49:20.183233: Current learning rate: 0.00759 +2026-04-11 17:51:00.426961: train_loss -0.2866 +2026-04-11 17:51:00.432548: val_loss -0.352 +2026-04-11 17:51:00.434533: Pseudo dice [0.0, 0.0, 0.6547, 0.0043, 0.1324, 0.2881, 0.0] +2026-04-11 17:51:00.436817: Epoch time: 100.25 s +2026-04-11 17:51:01.553004: +2026-04-11 17:51:01.555305: Epoch 1058 +2026-04-11 17:51:01.557145: Current learning rate: 0.00758 +2026-04-11 17:52:41.710898: train_loss -0.3366 +2026-04-11 17:52:41.716303: val_loss -0.2613 +2026-04-11 17:52:41.718576: Pseudo dice [0.0, 0.0, 0.6315, 0.0, 0.0, 0.1418, 0.0] +2026-04-11 17:52:41.720537: Epoch time: 100.16 s +2026-04-11 17:52:42.828924: +2026-04-11 17:52:42.832308: Epoch 1059 +2026-04-11 17:52:42.834041: Current learning rate: 0.00758 +2026-04-11 17:54:23.139947: train_loss -0.3404 +2026-04-11 17:54:23.144862: val_loss -0.3551 +2026-04-11 17:54:23.146650: Pseudo dice [0.0, 0.0, 0.6172, 0.0, 0.0, 0.5206, 0.4274] +2026-04-11 17:54:23.148782: Epoch time: 100.31 s +2026-04-11 17:54:24.238290: +2026-04-11 17:54:24.240139: Epoch 1060 +2026-04-11 17:54:24.242199: Current learning rate: 0.00758 +2026-04-11 17:56:04.477847: train_loss -0.3414 +2026-04-11 17:56:04.482993: val_loss -0.3815 +2026-04-11 17:56:04.485116: Pseudo dice [0.0, 0.0, 0.7683, 0.4112, 0.0, 0.4425, 0.5018] +2026-04-11 17:56:04.487048: Epoch time: 100.24 s +2026-04-11 17:56:05.600435: +2026-04-11 17:56:05.602447: Epoch 1061 +2026-04-11 17:56:05.604051: Current learning rate: 0.00758 +2026-04-11 17:57:46.003059: train_loss -0.3443 +2026-04-11 17:57:46.008058: val_loss -0.3204 +2026-04-11 17:57:46.010040: Pseudo dice [0.0, 0.0, 0.7027, 0.0, 0.0, 0.2461, 0.5744] +2026-04-11 17:57:46.012450: Epoch time: 100.41 s +2026-04-11 17:57:47.124899: +2026-04-11 17:57:47.126739: Epoch 1062 +2026-04-11 17:57:47.128399: Current learning rate: 0.00758 +2026-04-11 17:59:27.397759: train_loss -0.3707 +2026-04-11 17:59:27.405263: val_loss -0.3566 +2026-04-11 17:59:27.407219: Pseudo dice [0.0, 0.0, 0.5412, 0.3877, 0.0, 0.6164, 0.7944] +2026-04-11 17:59:27.409318: Epoch time: 100.28 s +2026-04-11 17:59:28.517076: +2026-04-11 17:59:28.518806: Epoch 1063 +2026-04-11 17:59:28.520356: Current learning rate: 0.00757 +2026-04-11 18:01:08.885487: train_loss -0.3201 +2026-04-11 18:01:08.889971: val_loss -0.3638 +2026-04-11 18:01:08.891975: Pseudo dice [0.0, 0.0, 0.5625, 0.0, 0.0, 0.035, 0.4222] +2026-04-11 18:01:08.894110: Epoch time: 100.37 s +2026-04-11 18:01:10.969966: +2026-04-11 18:01:10.971707: Epoch 1064 +2026-04-11 18:01:10.973384: Current learning rate: 0.00757 +2026-04-11 18:02:51.218561: train_loss -0.328 +2026-04-11 18:02:51.228466: val_loss -0.3182 +2026-04-11 18:02:51.230421: Pseudo dice [0.0, 0.0, 0.6143, 0.0, 0.0, 0.664, 0.5354] +2026-04-11 18:02:51.233434: Epoch time: 100.25 s +2026-04-11 18:02:52.342109: +2026-04-11 18:02:52.344047: Epoch 1065 +2026-04-11 18:02:52.345751: Current learning rate: 0.00757 +2026-04-11 18:04:32.580866: train_loss -0.306 +2026-04-11 18:04:32.585377: val_loss -0.2572 +2026-04-11 18:04:32.587313: Pseudo dice [0.0, 0.0, 0.6326, 0.011, 0.0, 0.1756, 0.0336] +2026-04-11 18:04:32.589467: Epoch time: 100.24 s +2026-04-11 18:04:33.702942: +2026-04-11 18:04:33.705287: Epoch 1066 +2026-04-11 18:04:33.707010: Current learning rate: 0.00757 +2026-04-11 18:06:14.120416: train_loss -0.3422 +2026-04-11 18:06:14.126015: val_loss -0.2753 +2026-04-11 18:06:14.128823: Pseudo dice [0.0, 0.0, 0.612, 0.0724, 0.026, 0.8683, 0.1302] +2026-04-11 18:06:14.131072: Epoch time: 100.42 s +2026-04-11 18:06:15.235140: +2026-04-11 18:06:15.237456: Epoch 1067 +2026-04-11 18:06:15.239813: Current learning rate: 0.00756 +2026-04-11 18:07:55.622922: train_loss -0.3227 +2026-04-11 18:07:55.631092: val_loss -0.2934 +2026-04-11 18:07:55.633098: Pseudo dice [0.0, 0.0, 0.5208, 0.0, 0.0, 0.0203, 0.2625] +2026-04-11 18:07:55.636595: Epoch time: 100.39 s +2026-04-11 18:07:56.755698: +2026-04-11 18:07:56.757896: Epoch 1068 +2026-04-11 18:07:56.759504: Current learning rate: 0.00756 +2026-04-11 18:09:37.030676: train_loss -0.3223 +2026-04-11 18:09:37.037036: val_loss -0.3286 +2026-04-11 18:09:37.039331: Pseudo dice [0.0, 0.0, 0.4861, 0.0, 0.1569, 0.4139, 0.5878] +2026-04-11 18:09:37.042493: Epoch time: 100.28 s +2026-04-11 18:09:38.152186: +2026-04-11 18:09:38.154185: Epoch 1069 +2026-04-11 18:09:38.155900: Current learning rate: 0.00756 +2026-04-11 18:11:18.543920: train_loss -0.3406 +2026-04-11 18:11:18.548798: val_loss -0.3636 +2026-04-11 18:11:18.550735: Pseudo dice [0.0, 0.0, 0.6776, 0.5564, 0.3171, 0.4382, 0.5959] +2026-04-11 18:11:18.552810: Epoch time: 100.39 s +2026-04-11 18:11:19.660220: +2026-04-11 18:11:19.662053: Epoch 1070 +2026-04-11 18:11:19.663845: Current learning rate: 0.00756 +2026-04-11 18:12:59.940715: train_loss -0.3662 +2026-04-11 18:12:59.947007: val_loss -0.3482 +2026-04-11 18:12:59.950296: Pseudo dice [0.0, 0.0, 0.6408, 0.0, 0.0, 0.5289, 0.691] +2026-04-11 18:12:59.953187: Epoch time: 100.28 s +2026-04-11 18:13:01.095168: +2026-04-11 18:13:01.097412: Epoch 1071 +2026-04-11 18:13:01.098927: Current learning rate: 0.00755 +2026-04-11 18:14:41.408378: train_loss -0.3603 +2026-04-11 18:14:41.418259: val_loss -0.3653 +2026-04-11 18:14:41.420484: Pseudo dice [0.0, 0.0, 0.6205, 0.2898, 0.2758, 0.4783, 0.553] +2026-04-11 18:14:41.422920: Epoch time: 100.32 s +2026-04-11 18:14:42.537711: +2026-04-11 18:14:42.539709: Epoch 1072 +2026-04-11 18:14:42.541415: Current learning rate: 0.00755 +2026-04-11 18:16:22.777762: train_loss -0.3417 +2026-04-11 18:16:22.782893: val_loss -0.3603 +2026-04-11 18:16:22.785451: Pseudo dice [0.0, 0.0, 0.7479, 0.0, 0.3207, 0.7493, 0.2716] +2026-04-11 18:16:22.788617: Epoch time: 100.24 s +2026-04-11 18:16:23.899296: +2026-04-11 18:16:23.902121: Epoch 1073 +2026-04-11 18:16:23.904795: Current learning rate: 0.00755 +2026-04-11 18:18:04.129348: train_loss -0.2957 +2026-04-11 18:18:04.134546: val_loss -0.2655 +2026-04-11 18:18:04.136559: Pseudo dice [0.0, 0.0, 0.3884, 0.0, 0.0, 0.0, 0.0] +2026-04-11 18:18:04.138637: Epoch time: 100.23 s +2026-04-11 18:18:05.252447: +2026-04-11 18:18:05.254441: Epoch 1074 +2026-04-11 18:18:05.256085: Current learning rate: 0.00755 +2026-04-11 18:19:45.494543: train_loss -0.2754 +2026-04-11 18:19:45.500371: val_loss -0.3124 +2026-04-11 18:19:45.502382: Pseudo dice [0.0, 0.0, 0.797, 0.0, 0.0, 0.0, 0.0] +2026-04-11 18:19:45.504339: Epoch time: 100.25 s +2026-04-11 18:19:46.591704: +2026-04-11 18:19:46.593976: Epoch 1075 +2026-04-11 18:19:46.596359: Current learning rate: 0.00755 +2026-04-11 18:21:26.585800: train_loss -0.3231 +2026-04-11 18:21:26.592218: val_loss -0.3455 +2026-04-11 18:21:26.594068: Pseudo dice [0.0, 0.0, 0.8372, 0.0, 0.0, 0.3648, 0.0] +2026-04-11 18:21:26.597068: Epoch time: 100.0 s +2026-04-11 18:21:27.699589: +2026-04-11 18:21:27.701383: Epoch 1076 +2026-04-11 18:21:27.703248: Current learning rate: 0.00754 +2026-04-11 18:23:07.926381: train_loss -0.3285 +2026-04-11 18:23:07.931062: val_loss -0.3303 +2026-04-11 18:23:07.932959: Pseudo dice [0.0, 0.0, 0.7445, 0.0, 0.0, 0.7382, 0.0] +2026-04-11 18:23:07.935284: Epoch time: 100.23 s +2026-04-11 18:23:09.055538: +2026-04-11 18:23:09.057363: Epoch 1077 +2026-04-11 18:23:09.059070: Current learning rate: 0.00754 +2026-04-11 18:24:49.281324: train_loss -0.3432 +2026-04-11 18:24:49.286810: val_loss -0.3284 +2026-04-11 18:24:49.289096: Pseudo dice [0.0, 0.0, 0.4734, 0.0, 0.0, 0.6599, 0.0] +2026-04-11 18:24:49.291581: Epoch time: 100.23 s +2026-04-11 18:24:50.392755: +2026-04-11 18:24:50.394715: Epoch 1078 +2026-04-11 18:24:50.396297: Current learning rate: 0.00754 +2026-04-11 18:26:30.763623: train_loss -0.3276 +2026-04-11 18:26:30.769857: val_loss -0.188 +2026-04-11 18:26:30.772337: Pseudo dice [0.0, 0.0, 0.2229, 0.0, 0.0, 0.6572, 0.4747] +2026-04-11 18:26:30.774654: Epoch time: 100.37 s +2026-04-11 18:26:31.870355: +2026-04-11 18:26:31.877034: Epoch 1079 +2026-04-11 18:26:31.881910: Current learning rate: 0.00754 +2026-04-11 18:28:12.521019: train_loss -0.2611 +2026-04-11 18:28:12.526290: val_loss -0.3638 +2026-04-11 18:28:12.528518: Pseudo dice [0.0, 0.0, 0.8122, 0.0, 0.0, 0.3699, 0.6212] +2026-04-11 18:28:12.530824: Epoch time: 100.65 s +2026-04-11 18:28:13.640222: +2026-04-11 18:28:13.642237: Epoch 1080 +2026-04-11 18:28:13.644117: Current learning rate: 0.00753 +2026-04-11 18:29:53.905519: train_loss -0.3416 +2026-04-11 18:29:53.911272: val_loss -0.3722 +2026-04-11 18:29:53.913038: Pseudo dice [0.0, 0.0, 0.5649, 0.0, 0.0, 0.5769, 0.4722] +2026-04-11 18:29:53.914858: Epoch time: 100.27 s +2026-04-11 18:29:55.006782: +2026-04-11 18:29:55.009383: Epoch 1081 +2026-04-11 18:29:55.011440: Current learning rate: 0.00753 +2026-04-11 18:31:35.164074: train_loss -0.3371 +2026-04-11 18:31:35.168809: val_loss -0.3667 +2026-04-11 18:31:35.170631: Pseudo dice [0.0, 0.0, 0.7291, 0.0, 0.0, 0.576, 0.5822] +2026-04-11 18:31:35.172846: Epoch time: 100.16 s +2026-04-11 18:31:36.282927: +2026-04-11 18:31:36.284692: Epoch 1082 +2026-04-11 18:31:36.286199: Current learning rate: 0.00753 +2026-04-11 18:33:16.444038: train_loss -0.3389 +2026-04-11 18:33:16.449689: val_loss -0.2833 +2026-04-11 18:33:16.451418: Pseudo dice [0.0, 0.0, 0.6813, 0.0, 0.0025, 0.7084, 0.5936] +2026-04-11 18:33:16.453608: Epoch time: 100.16 s +2026-04-11 18:33:17.565640: +2026-04-11 18:33:17.567424: Epoch 1083 +2026-04-11 18:33:17.568957: Current learning rate: 0.00753 +2026-04-11 18:34:57.751916: train_loss -0.3249 +2026-04-11 18:34:57.758041: val_loss -0.3158 +2026-04-11 18:34:57.759992: Pseudo dice [0.0, 0.0, 0.3943, 0.0, 0.0, 0.4502, 0.0] +2026-04-11 18:34:57.762506: Epoch time: 100.19 s +2026-04-11 18:34:58.863228: +2026-04-11 18:34:58.866198: Epoch 1084 +2026-04-11 18:34:58.868236: Current learning rate: 0.00752 +2026-04-11 18:36:39.985814: train_loss -0.3434 +2026-04-11 18:36:39.991608: val_loss -0.3186 +2026-04-11 18:36:39.995333: Pseudo dice [0.0, 0.0, 0.4132, 0.0, 0.27, 0.8264, 0.5495] +2026-04-11 18:36:39.997889: Epoch time: 101.13 s +2026-04-11 18:36:41.085227: +2026-04-11 18:36:41.087725: Epoch 1085 +2026-04-11 18:36:41.089762: Current learning rate: 0.00752 +2026-04-11 18:38:21.312309: train_loss -0.3444 +2026-04-11 18:38:21.318513: val_loss -0.3565 +2026-04-11 18:38:21.320408: Pseudo dice [0.0, 0.0, 0.6729, 0.0, 0.0, 0.2132, 0.419] +2026-04-11 18:38:21.323073: Epoch time: 100.23 s +2026-04-11 18:38:22.432427: +2026-04-11 18:38:22.434231: Epoch 1086 +2026-04-11 18:38:22.436035: Current learning rate: 0.00752 +2026-04-11 18:40:02.690068: train_loss -0.3547 +2026-04-11 18:40:02.695479: val_loss -0.3693 +2026-04-11 18:40:02.697716: Pseudo dice [0.0, 0.0, 0.7286, 0.0, 0.0, 0.6504, 0.5837] +2026-04-11 18:40:02.699740: Epoch time: 100.26 s +2026-04-11 18:40:03.813292: +2026-04-11 18:40:03.815432: Epoch 1087 +2026-04-11 18:40:03.817328: Current learning rate: 0.00752 +2026-04-11 18:41:44.248881: train_loss -0.3621 +2026-04-11 18:41:44.256005: val_loss -0.3419 +2026-04-11 18:41:44.258373: Pseudo dice [0.0, 0.0, 0.7045, 0.0432, 0.2482, 0.4955, 0.8075] +2026-04-11 18:41:44.260552: Epoch time: 100.44 s +2026-04-11 18:41:45.359480: +2026-04-11 18:41:45.361875: Epoch 1088 +2026-04-11 18:41:45.363819: Current learning rate: 0.00751 +2026-04-11 18:43:25.626106: train_loss -0.3476 +2026-04-11 18:43:25.631216: val_loss -0.2812 +2026-04-11 18:43:25.633128: Pseudo dice [0.0, 0.0, 0.5569, 0.0, 0.0, 0.4073, 0.7277] +2026-04-11 18:43:25.635260: Epoch time: 100.27 s +2026-04-11 18:43:26.750881: +2026-04-11 18:43:26.752718: Epoch 1089 +2026-04-11 18:43:26.754514: Current learning rate: 0.00751 +2026-04-11 18:45:07.023652: train_loss -0.3399 +2026-04-11 18:45:07.030855: val_loss -0.2864 +2026-04-11 18:45:07.032825: Pseudo dice [0.0, 0.0, 0.4752, 0.0, 0.0, 0.1248, 0.5187] +2026-04-11 18:45:07.035457: Epoch time: 100.28 s +2026-04-11 18:45:08.138297: +2026-04-11 18:45:08.140356: Epoch 1090 +2026-04-11 18:45:08.142076: Current learning rate: 0.00751 +2026-04-11 18:46:48.355822: train_loss -0.3039 +2026-04-11 18:46:48.361578: val_loss -0.3151 +2026-04-11 18:46:48.363331: Pseudo dice [0.0, 0.0, 0.6672, 0.0, 0.0, 0.6032, 0.405] +2026-04-11 18:46:48.366283: Epoch time: 100.22 s +2026-04-11 18:46:49.461490: +2026-04-11 18:46:49.463849: Epoch 1091 +2026-04-11 18:46:49.466038: Current learning rate: 0.00751 +2026-04-11 18:48:29.813198: train_loss -0.3165 +2026-04-11 18:48:29.820315: val_loss -0.27 +2026-04-11 18:48:29.822835: Pseudo dice [0.0, 0.0, 0.5106, 0.0072, 0.0, 0.5617, 0.3033] +2026-04-11 18:48:29.824935: Epoch time: 100.35 s +2026-04-11 18:48:30.937717: +2026-04-11 18:48:30.939585: Epoch 1092 +2026-04-11 18:48:30.942987: Current learning rate: 0.00751 +2026-04-11 18:50:11.332806: train_loss -0.3115 +2026-04-11 18:50:11.338413: val_loss -0.3377 +2026-04-11 18:50:11.340404: Pseudo dice [0.0, 0.0, 0.5592, 0.0, 0.0, 0.5346, 0.3457] +2026-04-11 18:50:11.342640: Epoch time: 100.4 s +2026-04-11 18:50:12.458182: +2026-04-11 18:50:12.460064: Epoch 1093 +2026-04-11 18:50:12.462013: Current learning rate: 0.0075 +2026-04-11 18:51:52.752528: train_loss -0.3598 +2026-04-11 18:51:52.757890: val_loss -0.3842 +2026-04-11 18:51:52.760818: Pseudo dice [0.0, 0.0, 0.5213, 0.0, 0.0, 0.7888, 0.565] +2026-04-11 18:51:52.763366: Epoch time: 100.3 s +2026-04-11 18:51:53.869405: +2026-04-11 18:51:53.871221: Epoch 1094 +2026-04-11 18:51:53.872836: Current learning rate: 0.0075 +2026-04-11 18:53:34.249386: train_loss -0.3643 +2026-04-11 18:53:34.254896: val_loss -0.3473 +2026-04-11 18:53:34.257288: Pseudo dice [0.0, 0.0, 0.6922, 0.0, 0.4509, 0.534, 0.8183] +2026-04-11 18:53:34.260748: Epoch time: 100.38 s +2026-04-11 18:53:35.370238: +2026-04-11 18:53:35.371974: Epoch 1095 +2026-04-11 18:53:35.374207: Current learning rate: 0.0075 +2026-04-11 18:55:15.632687: train_loss -0.3426 +2026-04-11 18:55:15.637530: val_loss -0.3205 +2026-04-11 18:55:15.639338: Pseudo dice [0.0, 0.0, 0.2877, 0.0892, 0.0, 0.6535, 0.735] +2026-04-11 18:55:15.641714: Epoch time: 100.27 s +2026-04-11 18:55:16.755367: +2026-04-11 18:55:16.757198: Epoch 1096 +2026-04-11 18:55:16.758847: Current learning rate: 0.0075 +2026-04-11 18:56:57.047446: train_loss -0.3496 +2026-04-11 18:56:57.052987: val_loss -0.3704 +2026-04-11 18:56:57.054933: Pseudo dice [0.0, 0.0, 0.7436, 0.0009, 0.0, 0.7873, 0.6597] +2026-04-11 18:56:57.057311: Epoch time: 100.3 s +2026-04-11 18:56:58.171505: +2026-04-11 18:56:58.173845: Epoch 1097 +2026-04-11 18:56:58.175447: Current learning rate: 0.00749 +2026-04-11 18:58:38.468327: train_loss -0.3008 +2026-04-11 18:58:38.473294: val_loss -0.3093 +2026-04-11 18:58:38.474989: Pseudo dice [0.0, 0.0, 0.4715, 0.0, 0.0, 0.0164, 0.473] +2026-04-11 18:58:38.477349: Epoch time: 100.3 s +2026-04-11 18:58:39.588186: +2026-04-11 18:58:39.589879: Epoch 1098 +2026-04-11 18:58:39.591373: Current learning rate: 0.00749 +2026-04-11 19:00:19.952622: train_loss -0.3105 +2026-04-11 19:00:19.959222: val_loss -0.3259 +2026-04-11 19:00:19.961892: Pseudo dice [0.0, 0.0, 0.6548, 0.0, 0.0, 0.4191, 0.5686] +2026-04-11 19:00:19.964993: Epoch time: 100.37 s +2026-04-11 19:00:21.074441: +2026-04-11 19:00:21.076248: Epoch 1099 +2026-04-11 19:00:21.078303: Current learning rate: 0.00749 +2026-04-11 19:02:01.345506: train_loss -0.3051 +2026-04-11 19:02:01.350726: val_loss -0.2791 +2026-04-11 19:02:01.353030: Pseudo dice [0.0, 0.0, 0.5267, 0.0, 0.0, 0.1703, 0.6229] +2026-04-11 19:02:01.355842: Epoch time: 100.27 s +2026-04-11 19:02:04.064847: +2026-04-11 19:02:04.067288: Epoch 1100 +2026-04-11 19:02:04.069350: Current learning rate: 0.00749 +2026-04-11 19:03:44.532031: train_loss -0.3375 +2026-04-11 19:03:44.539791: val_loss -0.3291 +2026-04-11 19:03:44.542696: Pseudo dice [0.0, 0.0, 0.5458, 0.0, 0.0, 0.5091, 0.7656] +2026-04-11 19:03:44.545402: Epoch time: 100.47 s +2026-04-11 19:03:45.646973: +2026-04-11 19:03:45.648793: Epoch 1101 +2026-04-11 19:03:45.650569: Current learning rate: 0.00748 +2026-04-11 19:05:25.952716: train_loss -0.3068 +2026-04-11 19:05:25.957755: val_loss -0.3807 +2026-04-11 19:05:25.959646: Pseudo dice [0.0, 0.0, 0.5764, 0.0, 0.0, 0.6534, 0.7587] +2026-04-11 19:05:25.962001: Epoch time: 100.31 s +2026-04-11 19:05:27.157992: +2026-04-11 19:05:27.159858: Epoch 1102 +2026-04-11 19:05:27.161641: Current learning rate: 0.00748 +2026-04-11 19:07:07.609612: train_loss -0.2982 +2026-04-11 19:07:07.616875: val_loss -0.2898 +2026-04-11 19:07:07.619919: Pseudo dice [0.0, 0.0, 0.6129, 0.0, 0.0, 0.0, 0.0] +2026-04-11 19:07:07.622720: Epoch time: 100.45 s +2026-04-11 19:07:08.723280: +2026-04-11 19:07:08.725200: Epoch 1103 +2026-04-11 19:07:08.726855: Current learning rate: 0.00748 +2026-04-11 19:08:49.009898: train_loss -0.3238 +2026-04-11 19:08:49.015108: val_loss -0.3454 +2026-04-11 19:08:49.017377: Pseudo dice [0.0, 0.0, 0.3718, 0.0, 0.0, 0.8113, 0.486] +2026-04-11 19:08:49.019431: Epoch time: 100.29 s +2026-04-11 19:08:50.964017: +2026-04-11 19:08:50.965783: Epoch 1104 +2026-04-11 19:08:50.967525: Current learning rate: 0.00748 +2026-04-11 19:10:31.444699: train_loss -0.3639 +2026-04-11 19:10:31.455813: val_loss -0.3384 +2026-04-11 19:10:31.462996: Pseudo dice [0.0, 0.0, 0.452, 0.0, 0.0, 0.0325, 0.6547] +2026-04-11 19:10:31.469415: Epoch time: 100.48 s +2026-04-11 19:10:32.573424: +2026-04-11 19:10:32.575500: Epoch 1105 +2026-04-11 19:10:32.577158: Current learning rate: 0.00748 +2026-04-11 19:12:12.829620: train_loss -0.3313 +2026-04-11 19:12:12.835206: val_loss -0.3402 +2026-04-11 19:12:12.837179: Pseudo dice [0.0, 0.0, 0.6048, 0.3952, 0.0, 0.3076, 0.2985] +2026-04-11 19:12:12.839404: Epoch time: 100.26 s +2026-04-11 19:12:13.966105: +2026-04-11 19:12:13.967988: Epoch 1106 +2026-04-11 19:12:13.969799: Current learning rate: 0.00747 +2026-04-11 19:13:54.191347: train_loss -0.3225 +2026-04-11 19:13:54.198440: val_loss -0.3171 +2026-04-11 19:13:54.200975: Pseudo dice [0.0, 0.0, 0.5799, 0.0, 0.0, 0.5782, 0.7763] +2026-04-11 19:13:54.203233: Epoch time: 100.23 s +2026-04-11 19:13:55.312626: +2026-04-11 19:13:55.314718: Epoch 1107 +2026-04-11 19:13:55.316750: Current learning rate: 0.00747 +2026-04-11 19:15:35.749753: train_loss -0.3406 +2026-04-11 19:15:35.755249: val_loss -0.3256 +2026-04-11 19:15:35.757127: Pseudo dice [0.0, 0.0, 0.6141, 0.0, 0.0, 0.5686, 0.7032] +2026-04-11 19:15:35.759361: Epoch time: 100.44 s +2026-04-11 19:15:36.878388: +2026-04-11 19:15:36.880106: Epoch 1108 +2026-04-11 19:15:36.881706: Current learning rate: 0.00747 +2026-04-11 19:17:17.506399: train_loss -0.3246 +2026-04-11 19:17:17.513684: val_loss -0.3795 +2026-04-11 19:17:17.515781: Pseudo dice [0.0, 0.0, 0.7235, 0.0, 0.0, 0.5831, 0.7202] +2026-04-11 19:17:17.518504: Epoch time: 100.63 s +2026-04-11 19:17:18.615370: +2026-04-11 19:17:18.617294: Epoch 1109 +2026-04-11 19:17:18.619243: Current learning rate: 0.00747 +2026-04-11 19:18:58.966979: train_loss -0.3332 +2026-04-11 19:18:58.973908: val_loss -0.3398 +2026-04-11 19:18:58.976335: Pseudo dice [0.0, 0.0, 0.5486, 0.0, 0.0, 0.3035, 0.4807] +2026-04-11 19:18:58.979096: Epoch time: 100.35 s +2026-04-11 19:19:00.099887: +2026-04-11 19:19:00.103597: Epoch 1110 +2026-04-11 19:19:00.106609: Current learning rate: 0.00746 +2026-04-11 19:20:40.483794: train_loss -0.3702 +2026-04-11 19:20:40.489497: val_loss -0.3953 +2026-04-11 19:20:40.491099: Pseudo dice [0.0, 0.0, 0.6212, 0.7272, 0.0, 0.7932, 0.8285] +2026-04-11 19:20:40.493488: Epoch time: 100.39 s +2026-04-11 19:20:41.610816: +2026-04-11 19:20:41.612868: Epoch 1111 +2026-04-11 19:20:41.614614: Current learning rate: 0.00746 +2026-04-11 19:22:21.880155: train_loss -0.3626 +2026-04-11 19:22:21.885275: val_loss -0.37 +2026-04-11 19:22:21.887399: Pseudo dice [0.0, 0.0, 0.8222, 0.0, 0.0, 0.1294, 0.786] +2026-04-11 19:22:21.889639: Epoch time: 100.27 s +2026-04-11 19:22:23.018515: +2026-04-11 19:22:23.020753: Epoch 1112 +2026-04-11 19:22:23.022792: Current learning rate: 0.00746 +2026-04-11 19:24:03.295264: train_loss -0.3508 +2026-04-11 19:24:03.299830: val_loss -0.3784 +2026-04-11 19:24:03.301434: Pseudo dice [0.0, 0.0, 0.7273, 0.0, 0.0, 0.6957, 0.697] +2026-04-11 19:24:03.303534: Epoch time: 100.28 s +2026-04-11 19:24:04.400957: +2026-04-11 19:24:04.402809: Epoch 1113 +2026-04-11 19:24:04.404519: Current learning rate: 0.00746 +2026-04-11 19:25:44.907935: train_loss -0.3665 +2026-04-11 19:25:44.913913: val_loss -0.3726 +2026-04-11 19:25:44.915527: Pseudo dice [0.0, 0.0, 0.6054, 0.0, 0.0, 0.5171, 0.7402] +2026-04-11 19:25:44.919090: Epoch time: 100.51 s +2026-04-11 19:25:46.032926: +2026-04-11 19:25:46.034725: Epoch 1114 +2026-04-11 19:25:46.036525: Current learning rate: 0.00745 +2026-04-11 19:27:26.432473: train_loss -0.3486 +2026-04-11 19:27:26.437117: val_loss -0.3597 +2026-04-11 19:27:26.438821: Pseudo dice [0.0, 0.0, 0.4748, 0.2195, 0.0, 0.7686, 0.6373] +2026-04-11 19:27:26.441782: Epoch time: 100.4 s +2026-04-11 19:27:27.565773: +2026-04-11 19:27:27.567810: Epoch 1115 +2026-04-11 19:27:27.569776: Current learning rate: 0.00745 +2026-04-11 19:29:08.109647: train_loss -0.3744 +2026-04-11 19:29:08.115726: val_loss -0.3629 +2026-04-11 19:29:08.117997: Pseudo dice [0.0, 0.0, 0.4416, 0.2597, 0.0, 0.2266, 0.5638] +2026-04-11 19:29:08.119952: Epoch time: 100.55 s +2026-04-11 19:29:09.243138: +2026-04-11 19:29:09.245260: Epoch 1116 +2026-04-11 19:29:09.247045: Current learning rate: 0.00745 +2026-04-11 19:30:49.613493: train_loss -0.3754 +2026-04-11 19:30:49.619022: val_loss -0.3947 +2026-04-11 19:30:49.621788: Pseudo dice [0.0, 0.0, 0.56, 0.0, 0.0, 0.8057, 0.7339] +2026-04-11 19:30:49.624449: Epoch time: 100.37 s +2026-04-11 19:30:50.754156: +2026-04-11 19:30:50.756372: Epoch 1117 +2026-04-11 19:30:50.757986: Current learning rate: 0.00745 +2026-04-11 19:32:31.154760: train_loss -0.3595 +2026-04-11 19:32:31.164729: val_loss -0.2895 +2026-04-11 19:32:31.167011: Pseudo dice [0.0, 0.0, 0.5661, 0.0, 0.0, 0.7278, 0.6491] +2026-04-11 19:32:31.169526: Epoch time: 100.4 s +2026-04-11 19:32:32.288344: +2026-04-11 19:32:32.290231: Epoch 1118 +2026-04-11 19:32:32.291782: Current learning rate: 0.00745 +2026-04-11 19:34:12.711678: train_loss -0.3391 +2026-04-11 19:34:12.717102: val_loss -0.3916 +2026-04-11 19:34:12.718833: Pseudo dice [0.0, 0.0, 0.7098, 0.0, 0.0, 0.5417, 0.8042] +2026-04-11 19:34:12.721548: Epoch time: 100.43 s +2026-04-11 19:34:13.828697: +2026-04-11 19:34:13.830608: Epoch 1119 +2026-04-11 19:34:13.832110: Current learning rate: 0.00744 +2026-04-11 19:35:54.310034: train_loss -0.3015 +2026-04-11 19:35:54.317452: val_loss -0.3625 +2026-04-11 19:35:54.319632: Pseudo dice [0.0, 0.0, 0.68, 0.4418, 0.0, 0.6284, 0.6602] +2026-04-11 19:35:54.322412: Epoch time: 100.48 s +2026-04-11 19:35:55.430767: +2026-04-11 19:35:55.433106: Epoch 1120 +2026-04-11 19:35:55.434737: Current learning rate: 0.00744 +2026-04-11 19:37:35.959846: train_loss -0.3582 +2026-04-11 19:37:35.964883: val_loss -0.343 +2026-04-11 19:37:35.966813: Pseudo dice [0.0, 0.0, 0.6689, 0.0, 0.0, 0.7017, 0.5304] +2026-04-11 19:37:35.968956: Epoch time: 100.53 s +2026-04-11 19:37:37.076630: +2026-04-11 19:37:37.078496: Epoch 1121 +2026-04-11 19:37:37.080050: Current learning rate: 0.00744 +2026-04-11 19:39:17.413520: train_loss -0.3761 +2026-04-11 19:39:17.420873: val_loss -0.3036 +2026-04-11 19:39:17.423007: Pseudo dice [0.0, 0.0, 0.7803, 0.0, 0.0, 0.7529, 0.5549] +2026-04-11 19:39:17.425237: Epoch time: 100.34 s +2026-04-11 19:39:18.537455: +2026-04-11 19:39:18.539503: Epoch 1122 +2026-04-11 19:39:18.541752: Current learning rate: 0.00744 +2026-04-11 19:40:58.877110: train_loss -0.3753 +2026-04-11 19:40:58.882353: val_loss -0.2973 +2026-04-11 19:40:58.883982: Pseudo dice [0.0, 0.0, 0.5832, 0.0, 0.0, 0.4032, 0.7285] +2026-04-11 19:40:58.886494: Epoch time: 100.34 s +2026-04-11 19:40:59.982477: +2026-04-11 19:40:59.984146: Epoch 1123 +2026-04-11 19:40:59.986027: Current learning rate: 0.00743 +2026-04-11 19:42:40.324189: train_loss -0.3309 +2026-04-11 19:42:40.329854: val_loss -0.2713 +2026-04-11 19:42:40.331982: Pseudo dice [0.0, 0.0, 0.5948, 0.0, 0.0, 0.1193, 0.719] +2026-04-11 19:42:40.334048: Epoch time: 100.34 s +2026-04-11 19:42:41.463713: +2026-04-11 19:42:41.465810: Epoch 1124 +2026-04-11 19:42:41.467284: Current learning rate: 0.00743 +2026-04-11 19:44:22.781458: train_loss -0.3262 +2026-04-11 19:44:22.787184: val_loss -0.3189 +2026-04-11 19:44:22.789308: Pseudo dice [0.0, 0.0, 0.7436, 0.0831, 0.0, 0.539, 0.7559] +2026-04-11 19:44:22.791652: Epoch time: 101.32 s +2026-04-11 19:44:23.894746: +2026-04-11 19:44:23.896522: Epoch 1125 +2026-04-11 19:44:23.898455: Current learning rate: 0.00743 +2026-04-11 19:46:04.354962: train_loss -0.3422 +2026-04-11 19:46:04.360908: val_loss -0.3739 +2026-04-11 19:46:04.362653: Pseudo dice [0.0, 0.0, 0.6166, 0.0, 0.0, 0.7373, 0.5123] +2026-04-11 19:46:04.364487: Epoch time: 100.46 s +2026-04-11 19:46:05.488992: +2026-04-11 19:46:05.490895: Epoch 1126 +2026-04-11 19:46:05.492688: Current learning rate: 0.00743 +2026-04-11 19:47:45.733471: train_loss -0.3827 +2026-04-11 19:47:45.738316: val_loss -0.3515 +2026-04-11 19:47:45.740014: Pseudo dice [0.0, 0.0, 0.7336, 0.0333, 0.0, 0.6291, 0.8262] +2026-04-11 19:47:45.742149: Epoch time: 100.25 s +2026-04-11 19:47:46.843992: +2026-04-11 19:47:46.845734: Epoch 1127 +2026-04-11 19:47:46.847219: Current learning rate: 0.00742 +2026-04-11 19:49:27.131891: train_loss -0.3646 +2026-04-11 19:49:27.156700: val_loss -0.3758 +2026-04-11 19:49:27.158514: Pseudo dice [0.0, 0.0, 0.662, 0.0, 0.0, 0.4463, 0.7555] +2026-04-11 19:49:27.160797: Epoch time: 100.29 s +2026-04-11 19:49:28.267734: +2026-04-11 19:49:28.269742: Epoch 1128 +2026-04-11 19:49:28.271409: Current learning rate: 0.00742 +2026-04-11 19:51:08.489588: train_loss -0.3838 +2026-04-11 19:51:08.495121: val_loss -0.3335 +2026-04-11 19:51:08.497158: Pseudo dice [0.0, 0.0, 0.6337, 0.0155, 0.0, 0.7691, 0.5093] +2026-04-11 19:51:08.499561: Epoch time: 100.22 s +2026-04-11 19:51:09.624493: +2026-04-11 19:51:09.626695: Epoch 1129 +2026-04-11 19:51:09.628450: Current learning rate: 0.00742 +2026-04-11 19:52:49.759869: train_loss -0.3538 +2026-04-11 19:52:49.764737: val_loss -0.2425 +2026-04-11 19:52:49.766474: Pseudo dice [0.0, 0.0, 0.6733, 0.012, 0.0, 0.5615, 0.6475] +2026-04-11 19:52:49.768907: Epoch time: 100.14 s +2026-04-11 19:52:50.887455: +2026-04-11 19:52:50.889318: Epoch 1130 +2026-04-11 19:52:50.891038: Current learning rate: 0.00742 +2026-04-11 19:54:31.262214: train_loss -0.3573 +2026-04-11 19:54:31.268121: val_loss -0.1725 +2026-04-11 19:54:31.270121: Pseudo dice [0.0, 0.0, 0.352, 0.0109, 0.0, 0.5856, 0.0639] +2026-04-11 19:54:31.272647: Epoch time: 100.38 s +2026-04-11 19:54:32.393188: +2026-04-11 19:54:32.395180: Epoch 1131 +2026-04-11 19:54:32.396941: Current learning rate: 0.00741 +2026-04-11 19:56:12.569220: train_loss -0.3592 +2026-04-11 19:56:12.573951: val_loss -0.365 +2026-04-11 19:56:12.575825: Pseudo dice [0.0, 0.0, 0.2593, 0.0, 0.0, 0.6405, 0.841] +2026-04-11 19:56:12.578162: Epoch time: 100.18 s +2026-04-11 19:56:13.702861: +2026-04-11 19:56:13.704859: Epoch 1132 +2026-04-11 19:56:13.706708: Current learning rate: 0.00741 +2026-04-11 19:57:53.946272: train_loss -0.3551 +2026-04-11 19:57:53.950916: val_loss -0.3234 +2026-04-11 19:57:53.952698: Pseudo dice [0.0, 0.0, 0.4936, 0.0, 0.0, 0.6184, 0.461] +2026-04-11 19:57:53.955276: Epoch time: 100.25 s +2026-04-11 19:57:55.075734: +2026-04-11 19:57:55.077571: Epoch 1133 +2026-04-11 19:57:55.079360: Current learning rate: 0.00741 +2026-04-11 19:59:35.373944: train_loss -0.3737 +2026-04-11 19:59:35.379175: val_loss -0.3995 +2026-04-11 19:59:35.381727: Pseudo dice [0.0, 0.0, 0.7728, 0.5302, 0.0, 0.8411, 0.6896] +2026-04-11 19:59:35.383717: Epoch time: 100.3 s +2026-04-11 19:59:36.508913: +2026-04-11 19:59:36.510818: Epoch 1134 +2026-04-11 19:59:36.512674: Current learning rate: 0.00741 +2026-04-11 20:01:16.726292: train_loss -0.3571 +2026-04-11 20:01:16.732266: val_loss -0.3813 +2026-04-11 20:01:16.734297: Pseudo dice [0.0, 0.0, 0.6387, 0.2841, 0.0, 0.5254, 0.7246] +2026-04-11 20:01:16.736978: Epoch time: 100.22 s +2026-04-11 20:01:17.863536: +2026-04-11 20:01:17.865245: Epoch 1135 +2026-04-11 20:01:17.867473: Current learning rate: 0.00741 +2026-04-11 20:02:58.154739: train_loss -0.3826 +2026-04-11 20:02:58.161046: val_loss -0.3754 +2026-04-11 20:02:58.163793: Pseudo dice [0.0, 0.0, 0.7551, 0.6455, 0.0, 0.7508, 0.5804] +2026-04-11 20:02:58.166304: Epoch time: 100.29 s +2026-04-11 20:02:59.301883: +2026-04-11 20:02:59.304145: Epoch 1136 +2026-04-11 20:02:59.305753: Current learning rate: 0.0074 +2026-04-11 20:04:39.796894: train_loss -0.3751 +2026-04-11 20:04:39.802315: val_loss -0.3958 +2026-04-11 20:04:39.804692: Pseudo dice [0.0, 0.0, 0.5852, 0.0972, 0.0, 0.7518, 0.8112] +2026-04-11 20:04:39.806915: Epoch time: 100.5 s +2026-04-11 20:04:40.922303: +2026-04-11 20:04:40.924042: Epoch 1137 +2026-04-11 20:04:40.925751: Current learning rate: 0.0074 +2026-04-11 20:06:21.236399: train_loss -0.3668 +2026-04-11 20:06:21.244363: val_loss -0.3029 +2026-04-11 20:06:21.247092: Pseudo dice [0.0, 0.0, 0.6845, 0.0, 0.0, 0.6002, 0.6489] +2026-04-11 20:06:21.249859: Epoch time: 100.32 s +2026-04-11 20:06:22.351277: +2026-04-11 20:06:22.353158: Epoch 1138 +2026-04-11 20:06:22.354683: Current learning rate: 0.0074 +2026-04-11 20:08:02.735336: train_loss -0.3504 +2026-04-11 20:08:02.742879: val_loss -0.3095 +2026-04-11 20:08:02.745088: Pseudo dice [0.0, 0.0, 0.6421, 0.0606, 0.0, 0.2835, 0.4647] +2026-04-11 20:08:02.747916: Epoch time: 100.39 s +2026-04-11 20:08:03.853653: +2026-04-11 20:08:03.855487: Epoch 1139 +2026-04-11 20:08:03.857533: Current learning rate: 0.0074 +2026-04-11 20:09:44.158618: train_loss -0.359 +2026-04-11 20:09:44.163347: val_loss -0.3649 +2026-04-11 20:09:44.164950: Pseudo dice [0.0, 0.0, 0.794, 0.4488, 0.0, 0.5575, 0.6821] +2026-04-11 20:09:44.167734: Epoch time: 100.31 s +2026-04-11 20:09:45.273955: +2026-04-11 20:09:45.276286: Epoch 1140 +2026-04-11 20:09:45.278217: Current learning rate: 0.00739 +2026-04-11 20:11:25.662172: train_loss -0.3401 +2026-04-11 20:11:25.667535: val_loss -0.3364 +2026-04-11 20:11:25.669374: Pseudo dice [0.0, 0.0, 0.3507, 0.3635, 0.0, 0.6828, 0.5006] +2026-04-11 20:11:25.671698: Epoch time: 100.39 s +2026-04-11 20:11:26.789477: +2026-04-11 20:11:26.791269: Epoch 1141 +2026-04-11 20:11:26.792872: Current learning rate: 0.00739 +2026-04-11 20:13:07.143650: train_loss -0.3145 +2026-04-11 20:13:07.149256: val_loss -0.3419 +2026-04-11 20:13:07.151569: Pseudo dice [0.0, 0.0, 0.6248, 0.0, 0.0, 0.3039, 0.4944] +2026-04-11 20:13:07.153751: Epoch time: 100.36 s +2026-04-11 20:13:08.269535: +2026-04-11 20:13:08.271295: Epoch 1142 +2026-04-11 20:13:08.273056: Current learning rate: 0.00739 +2026-04-11 20:14:48.605847: train_loss -0.3361 +2026-04-11 20:14:48.610651: val_loss -0.2808 +2026-04-11 20:14:48.612530: Pseudo dice [0.0, 0.0, 0.7626, 0.0, 0.0, 0.3237, 0.688] +2026-04-11 20:14:48.614596: Epoch time: 100.34 s +2026-04-11 20:14:49.744803: +2026-04-11 20:14:49.746721: Epoch 1143 +2026-04-11 20:14:49.748369: Current learning rate: 0.00739 +2026-04-11 20:16:30.274161: train_loss -0.3383 +2026-04-11 20:16:30.282267: val_loss -0.2746 +2026-04-11 20:16:30.285466: Pseudo dice [0.0, 0.0, 0.7405, 0.0, 0.0, 0.7222, 0.7914] +2026-04-11 20:16:30.289855: Epoch time: 100.53 s +2026-04-11 20:16:31.427229: +2026-04-11 20:16:31.429506: Epoch 1144 +2026-04-11 20:16:31.431354: Current learning rate: 0.00738 +2026-04-11 20:18:11.917055: train_loss -0.3485 +2026-04-11 20:18:11.922345: val_loss -0.3582 +2026-04-11 20:18:11.924201: Pseudo dice [0.0, 0.0, 0.6519, 0.3324, 0.0, 0.6271, 0.7545] +2026-04-11 20:18:11.926157: Epoch time: 100.49 s +2026-04-11 20:18:14.112043: +2026-04-11 20:18:14.114063: Epoch 1145 +2026-04-11 20:18:14.116179: Current learning rate: 0.00738 +2026-04-11 20:19:54.494368: train_loss -0.3442 +2026-04-11 20:19:54.502473: val_loss -0.3592 +2026-04-11 20:19:54.504243: Pseudo dice [0.0, 0.0, 0.6414, 0.0, 0.0, 0.7974, 0.8303] +2026-04-11 20:19:54.506444: Epoch time: 100.39 s +2026-04-11 20:19:55.659505: +2026-04-11 20:19:55.661733: Epoch 1146 +2026-04-11 20:19:55.663592: Current learning rate: 0.00738 +2026-04-11 20:21:35.881737: train_loss -0.3548 +2026-04-11 20:21:35.887762: val_loss -0.286 +2026-04-11 20:21:35.890032: Pseudo dice [0.0, 0.0, 0.637, 0.0, 0.0, 0.2338, 0.474] +2026-04-11 20:21:35.892127: Epoch time: 100.23 s +2026-04-11 20:21:37.035997: +2026-04-11 20:21:37.037804: Epoch 1147 +2026-04-11 20:21:37.039709: Current learning rate: 0.00738 +2026-04-11 20:23:17.355038: train_loss -0.3488 +2026-04-11 20:23:17.361007: val_loss -0.3635 +2026-04-11 20:23:17.362978: Pseudo dice [0.0, 0.0, 0.6012, 0.0, 0.0, 0.5686, 0.4941] +2026-04-11 20:23:17.366089: Epoch time: 100.32 s +2026-04-11 20:23:18.521783: +2026-04-11 20:23:18.523801: Epoch 1148 +2026-04-11 20:23:18.526229: Current learning rate: 0.00738 +2026-04-11 20:24:58.927498: train_loss -0.3424 +2026-04-11 20:24:58.932647: val_loss -0.2819 +2026-04-11 20:24:58.934803: Pseudo dice [0.0, 0.0, 0.6715, 0.1351, 0.0, 0.0107, 0.6432] +2026-04-11 20:24:58.937274: Epoch time: 100.41 s +2026-04-11 20:25:00.089858: +2026-04-11 20:25:00.091850: Epoch 1149 +2026-04-11 20:25:00.093654: Current learning rate: 0.00737 +2026-04-11 20:26:40.416846: train_loss -0.3 +2026-04-11 20:26:40.421539: val_loss -0.3062 +2026-04-11 20:26:40.423240: Pseudo dice [0.0, 0.0, 0.5687, 0.0, 0.0, 0.0003, 0.0] +2026-04-11 20:26:40.425328: Epoch time: 100.33 s +2026-04-11 20:26:43.192204: +2026-04-11 20:26:43.194382: Epoch 1150 +2026-04-11 20:26:43.196389: Current learning rate: 0.00737 +2026-04-11 20:28:23.488086: train_loss -0.2969 +2026-04-11 20:28:23.493099: val_loss -0.2932 +2026-04-11 20:28:23.495411: Pseudo dice [0.0, 0.0, 0.6999, 0.0, 0.0, 0.5002, 0.0596] +2026-04-11 20:28:23.497614: Epoch time: 100.3 s +2026-04-11 20:28:24.634428: +2026-04-11 20:28:24.636999: Epoch 1151 +2026-04-11 20:28:24.639320: Current learning rate: 0.00737 +2026-04-11 20:30:05.013873: train_loss -0.3207 +2026-04-11 20:30:05.020701: val_loss -0.3379 +2026-04-11 20:30:05.023559: Pseudo dice [0.0, 0.0, 0.6913, 0.0, 0.0252, 0.5106, 0.4804] +2026-04-11 20:30:05.026069: Epoch time: 100.38 s +2026-04-11 20:30:06.145788: +2026-04-11 20:30:06.147670: Epoch 1152 +2026-04-11 20:30:06.149226: Current learning rate: 0.00737 +2026-04-11 20:31:46.371312: train_loss -0.335 +2026-04-11 20:31:46.376115: val_loss -0.2665 +2026-04-11 20:31:46.378041: Pseudo dice [0.0, 0.0, 0.6982, 0.0, 0.5281, 0.37, 0.3106] +2026-04-11 20:31:46.380188: Epoch time: 100.23 s +2026-04-11 20:31:47.509164: +2026-04-11 20:31:47.511286: Epoch 1153 +2026-04-11 20:31:47.512851: Current learning rate: 0.00736 +2026-04-11 20:33:27.769081: train_loss -0.3291 +2026-04-11 20:33:27.776570: val_loss -0.3192 +2026-04-11 20:33:27.779241: Pseudo dice [0.0, 0.0, 0.6298, 0.0, 0.0, 0.0601, 0.4522] +2026-04-11 20:33:27.782013: Epoch time: 100.26 s +2026-04-11 20:33:28.920727: +2026-04-11 20:33:28.923047: Epoch 1154 +2026-04-11 20:33:28.924906: Current learning rate: 0.00736 +2026-04-11 20:35:09.283704: train_loss -0.328 +2026-04-11 20:35:09.290360: val_loss -0.3263 +2026-04-11 20:35:09.292284: Pseudo dice [0.0, 0.0, 0.5344, 0.0, 0.0, 0.0613, 0.0] +2026-04-11 20:35:09.294731: Epoch time: 100.37 s +2026-04-11 20:35:10.449759: +2026-04-11 20:35:10.451641: Epoch 1155 +2026-04-11 20:35:10.453335: Current learning rate: 0.00736 +2026-04-11 20:36:50.630425: train_loss -0.3337 +2026-04-11 20:36:50.635793: val_loss -0.2752 +2026-04-11 20:36:50.637589: Pseudo dice [0.0, 0.0, 0.3214, 0.0, 0.0, 0.1014, 0.4857] +2026-04-11 20:36:50.639611: Epoch time: 100.18 s +2026-04-11 20:36:51.773217: +2026-04-11 20:36:51.774998: Epoch 1156 +2026-04-11 20:36:51.776606: Current learning rate: 0.00736 +2026-04-11 20:38:32.208105: train_loss -0.3202 +2026-04-11 20:38:32.212830: val_loss -0.3732 +2026-04-11 20:38:32.214562: Pseudo dice [0.0, 0.0, 0.7665, 0.0, 0.0, 0.4781, 0.6422] +2026-04-11 20:38:32.216720: Epoch time: 100.44 s +2026-04-11 20:38:33.351281: +2026-04-11 20:38:33.353240: Epoch 1157 +2026-04-11 20:38:33.354959: Current learning rate: 0.00735 +2026-04-11 20:40:13.725543: train_loss -0.3658 +2026-04-11 20:40:13.731262: val_loss -0.3689 +2026-04-11 20:40:13.734089: Pseudo dice [0.0, 0.0, 0.683, 0.4859, 0.3316, 0.3781, 0.7153] +2026-04-11 20:40:13.736415: Epoch time: 100.38 s +2026-04-11 20:40:14.854700: +2026-04-11 20:40:14.856468: Epoch 1158 +2026-04-11 20:40:14.858208: Current learning rate: 0.00735 +2026-04-11 20:41:55.151017: train_loss -0.3427 +2026-04-11 20:41:55.156350: val_loss -0.3536 +2026-04-11 20:41:55.158470: Pseudo dice [0.0, 0.0, 0.7242, 0.0, 0.0048, 0.5089, 0.4875] +2026-04-11 20:41:55.160631: Epoch time: 100.3 s +2026-04-11 20:41:56.299523: +2026-04-11 20:41:56.301311: Epoch 1159 +2026-04-11 20:41:56.302968: Current learning rate: 0.00735 +2026-04-11 20:43:36.776504: train_loss -0.3601 +2026-04-11 20:43:36.784024: val_loss -0.3572 +2026-04-11 20:43:36.786506: Pseudo dice [0.0, 0.0, 0.4634, 0.0, 0.2165, 0.5377, 0.3773] +2026-04-11 20:43:36.788683: Epoch time: 100.48 s +2026-04-11 20:43:37.935887: +2026-04-11 20:43:37.937815: Epoch 1160 +2026-04-11 20:43:37.939648: Current learning rate: 0.00735 +2026-04-11 20:45:18.318576: train_loss -0.362 +2026-04-11 20:45:18.324006: val_loss -0.3346 +2026-04-11 20:45:18.326215: Pseudo dice [0.0, 0.0, 0.5649, 0.1047, 0.0, 0.7913, 0.4971] +2026-04-11 20:45:18.328407: Epoch time: 100.39 s +2026-04-11 20:45:19.454050: +2026-04-11 20:45:19.456030: Epoch 1161 +2026-04-11 20:45:19.457988: Current learning rate: 0.00735 +2026-04-11 20:46:59.944234: train_loss -0.3695 +2026-04-11 20:46:59.949443: val_loss -0.2903 +2026-04-11 20:46:59.950977: Pseudo dice [0.0, 0.0, 0.7275, 0.0, 0.2554, 0.6177, 0.0164] +2026-04-11 20:46:59.953408: Epoch time: 100.49 s +2026-04-11 20:47:01.083546: +2026-04-11 20:47:01.085860: Epoch 1162 +2026-04-11 20:47:01.087370: Current learning rate: 0.00734 +2026-04-11 20:48:41.636848: train_loss -0.3477 +2026-04-11 20:48:41.642704: val_loss -0.3369 +2026-04-11 20:48:41.644707: Pseudo dice [0.0, 0.0, 0.6958, 0.0, 0.0315, 0.8429, 0.4491] +2026-04-11 20:48:41.646986: Epoch time: 100.56 s +2026-04-11 20:48:42.787317: +2026-04-11 20:48:42.789063: Epoch 1163 +2026-04-11 20:48:42.790704: Current learning rate: 0.00734 +2026-04-11 20:50:23.046936: train_loss -0.3209 +2026-04-11 20:50:23.051833: val_loss -0.2995 +2026-04-11 20:50:23.053777: Pseudo dice [0.0, 0.0, 0.2369, 0.0, 0.0, 0.0, 0.2457] +2026-04-11 20:50:23.056347: Epoch time: 100.26 s +2026-04-11 20:50:24.195934: +2026-04-11 20:50:24.197956: Epoch 1164 +2026-04-11 20:50:24.200365: Current learning rate: 0.00734 +2026-04-11 20:52:05.639333: train_loss -0.3209 +2026-04-11 20:52:05.645139: val_loss -0.3755 +2026-04-11 20:52:05.647543: Pseudo dice [0.0, 0.0, 0.7187, 0.0, 0.0, 0.0, 0.7038] +2026-04-11 20:52:05.649756: Epoch time: 101.45 s +2026-04-11 20:52:06.776692: +2026-04-11 20:52:06.778997: Epoch 1165 +2026-04-11 20:52:06.780826: Current learning rate: 0.00734 +2026-04-11 20:53:47.161564: train_loss -0.3438 +2026-04-11 20:53:47.172581: val_loss -0.3544 +2026-04-11 20:53:47.174804: Pseudo dice [0.0, 0.0, 0.6137, 0.0, 0.0, 0.0, 0.5459] +2026-04-11 20:53:47.177561: Epoch time: 100.39 s +2026-04-11 20:53:48.329347: +2026-04-11 20:53:48.331959: Epoch 1166 +2026-04-11 20:53:48.334149: Current learning rate: 0.00733 +2026-04-11 20:55:28.712186: train_loss -0.3202 +2026-04-11 20:55:28.718590: val_loss -0.2649 +2026-04-11 20:55:28.720340: Pseudo dice [0.0, 0.0, 0.6282, 0.0, 0.0, 0.3223, 0.011] +2026-04-11 20:55:28.722200: Epoch time: 100.39 s +2026-04-11 20:55:29.851218: +2026-04-11 20:55:29.853228: Epoch 1167 +2026-04-11 20:55:29.854759: Current learning rate: 0.00733 +2026-04-11 20:57:10.120636: train_loss -0.3523 +2026-04-11 20:57:10.126127: val_loss -0.3672 +2026-04-11 20:57:10.128027: Pseudo dice [0.0, 0.0, 0.6672, 0.0, 0.0, 0.7491, 0.7738] +2026-04-11 20:57:10.131078: Epoch time: 100.27 s +2026-04-11 20:57:11.279436: +2026-04-11 20:57:11.281406: Epoch 1168 +2026-04-11 20:57:11.283048: Current learning rate: 0.00733 +2026-04-11 20:58:51.840908: train_loss -0.3795 +2026-04-11 20:58:51.845825: val_loss -0.3762 +2026-04-11 20:58:51.847860: Pseudo dice [0.0, 0.0, 0.721, 0.1966, 0.0, 0.6999, 0.7261] +2026-04-11 20:58:51.850560: Epoch time: 100.56 s +2026-04-11 20:58:52.983528: +2026-04-11 20:58:52.985616: Epoch 1169 +2026-04-11 20:58:52.987360: Current learning rate: 0.00733 +2026-04-11 21:00:33.327323: train_loss -0.3511 +2026-04-11 21:00:33.332237: val_loss -0.3587 +2026-04-11 21:00:33.334430: Pseudo dice [0.0, 0.0, 0.5485, 0.0, 0.0, 0.3493, 0.3944] +2026-04-11 21:00:33.336573: Epoch time: 100.35 s +2026-04-11 21:00:34.469294: +2026-04-11 21:00:34.470899: Epoch 1170 +2026-04-11 21:00:34.472635: Current learning rate: 0.00732 +2026-04-11 21:02:14.788634: train_loss -0.3042 +2026-04-11 21:02:14.795287: val_loss -0.3518 +2026-04-11 21:02:14.797717: Pseudo dice [0.0, 0.0, 0.7112, 0.0719, 0.0, 0.5767, 0.1538] +2026-04-11 21:02:14.800592: Epoch time: 100.32 s +2026-04-11 21:02:16.039979: +2026-04-11 21:02:16.042493: Epoch 1171 +2026-04-11 21:02:16.044592: Current learning rate: 0.00732 +2026-04-11 21:03:56.320998: train_loss -0.2672 +2026-04-11 21:03:56.326521: val_loss -0.3396 +2026-04-11 21:03:56.328264: Pseudo dice [0.0, 0.0, 0.2608, 0.573, 0.0, 0.3615, 0.6691] +2026-04-11 21:03:56.330162: Epoch time: 100.28 s +2026-04-11 21:03:57.494375: +2026-04-11 21:03:57.496171: Epoch 1172 +2026-04-11 21:03:57.497812: Current learning rate: 0.00732 +2026-04-11 21:05:37.653225: train_loss -0.3373 +2026-04-11 21:05:37.658238: val_loss -0.2597 +2026-04-11 21:05:37.660110: Pseudo dice [0.0, 0.0, 0.6956, 0.0, 0.0, 0.3412, 0.6955] +2026-04-11 21:05:37.662393: Epoch time: 100.16 s +2026-04-11 21:05:38.795450: +2026-04-11 21:05:38.797898: Epoch 1173 +2026-04-11 21:05:38.799595: Current learning rate: 0.00732 +2026-04-11 21:07:18.912299: train_loss -0.3679 +2026-04-11 21:07:18.917447: val_loss -0.3491 +2026-04-11 21:07:18.919326: Pseudo dice [0.0, 0.0, 0.7046, 0.0, 0.0, 0.5659, 0.5682] +2026-04-11 21:07:18.921203: Epoch time: 100.12 s +2026-04-11 21:07:20.042802: +2026-04-11 21:07:20.045037: Epoch 1174 +2026-04-11 21:07:20.046951: Current learning rate: 0.00731 +2026-04-11 21:09:00.207955: train_loss -0.3091 +2026-04-11 21:09:00.213820: val_loss -0.3037 +2026-04-11 21:09:00.215693: Pseudo dice [0.0, 0.0, 0.5903, 0.0, 0.0, 0.3351, 0.2434] +2026-04-11 21:09:00.217747: Epoch time: 100.17 s +2026-04-11 21:09:01.379149: +2026-04-11 21:09:01.380884: Epoch 1175 +2026-04-11 21:09:01.382419: Current learning rate: 0.00731 +2026-04-11 21:10:41.484539: train_loss -0.3197 +2026-04-11 21:10:41.490052: val_loss -0.3105 +2026-04-11 21:10:41.492149: Pseudo dice [0.0, 0.0, 0.4512, 0.0, 0.0, 0.2414, 0.0307] +2026-04-11 21:10:41.494512: Epoch time: 100.11 s +2026-04-11 21:10:42.614381: +2026-04-11 21:10:42.616266: Epoch 1176 +2026-04-11 21:10:42.618075: Current learning rate: 0.00731 +2026-04-11 21:12:22.838143: train_loss -0.3163 +2026-04-11 21:12:22.843387: val_loss -0.3618 +2026-04-11 21:12:22.845383: Pseudo dice [0.0, 0.0, 0.6722, 0.0, 0.0, 0.7224, 0.5726] +2026-04-11 21:12:22.847674: Epoch time: 100.23 s +2026-04-11 21:12:23.987777: +2026-04-11 21:12:23.990749: Epoch 1177 +2026-04-11 21:12:23.992682: Current learning rate: 0.00731 +2026-04-11 21:14:04.234900: train_loss -0.3374 +2026-04-11 21:14:04.240729: val_loss -0.3806 +2026-04-11 21:14:04.243048: Pseudo dice [0.0, 0.0, 0.4486, 0.0, 0.0, 0.7605, 0.7518] +2026-04-11 21:14:04.245260: Epoch time: 100.25 s +2026-04-11 21:14:05.372717: +2026-04-11 21:14:05.374434: Epoch 1178 +2026-04-11 21:14:05.376112: Current learning rate: 0.00731 +2026-04-11 21:15:45.442624: train_loss -0.3695 +2026-04-11 21:15:45.447340: val_loss -0.3917 +2026-04-11 21:15:45.448979: Pseudo dice [0.0, 0.0, 0.6166, 0.7257, 0.0, 0.6989, 0.556] +2026-04-11 21:15:45.451065: Epoch time: 100.07 s +2026-04-11 21:15:46.566374: +2026-04-11 21:15:46.568086: Epoch 1179 +2026-04-11 21:15:46.569724: Current learning rate: 0.0073 +2026-04-11 21:17:26.831038: train_loss -0.3467 +2026-04-11 21:17:26.836801: val_loss -0.343 +2026-04-11 21:17:26.839110: Pseudo dice [0.0, 0.0, 0.6579, 0.0, 0.0, 0.7914, 0.615] +2026-04-11 21:17:26.841444: Epoch time: 100.27 s +2026-04-11 21:17:27.974275: +2026-04-11 21:17:27.976103: Epoch 1180 +2026-04-11 21:17:27.977994: Current learning rate: 0.0073 +2026-04-11 21:19:08.100430: train_loss -0.3623 +2026-04-11 21:19:08.107469: val_loss -0.3254 +2026-04-11 21:19:08.109877: Pseudo dice [0.0, 0.0, 0.6597, 0.0, 0.0, 0.4652, 0.7499] +2026-04-11 21:19:08.113514: Epoch time: 100.13 s +2026-04-11 21:19:09.262965: +2026-04-11 21:19:09.265009: Epoch 1181 +2026-04-11 21:19:09.266909: Current learning rate: 0.0073 +2026-04-11 21:20:49.446076: train_loss -0.3567 +2026-04-11 21:20:49.452356: val_loss -0.3882 +2026-04-11 21:20:49.454346: Pseudo dice [0.0, 0.0, 0.6607, 0.0621, 0.0, 0.7631, 0.7572] +2026-04-11 21:20:49.456403: Epoch time: 100.19 s +2026-04-11 21:20:50.586132: +2026-04-11 21:20:50.588351: Epoch 1182 +2026-04-11 21:20:50.590008: Current learning rate: 0.0073 +2026-04-11 21:22:30.957300: train_loss -0.3446 +2026-04-11 21:22:30.962989: val_loss -0.3344 +2026-04-11 21:22:30.965641: Pseudo dice [0.0, 0.0, 0.4863, 0.0, 0.0, 0.7039, 0.019] +2026-04-11 21:22:30.968527: Epoch time: 100.37 s +2026-04-11 21:22:32.100582: +2026-04-11 21:22:32.103985: Epoch 1183 +2026-04-11 21:22:32.105945: Current learning rate: 0.00729 +2026-04-11 21:24:12.344050: train_loss -0.3316 +2026-04-11 21:24:12.349099: val_loss -0.3177 +2026-04-11 21:24:12.351059: Pseudo dice [0.0, 0.0, 0.6453, 0.0, 0.0, 0.3005, 0.1239] +2026-04-11 21:24:12.353057: Epoch time: 100.25 s +2026-04-11 21:24:13.479108: +2026-04-11 21:24:13.480994: Epoch 1184 +2026-04-11 21:24:13.482780: Current learning rate: 0.00729 +2026-04-11 21:25:54.522987: train_loss -0.3371 +2026-04-11 21:25:54.529194: val_loss -0.3326 +2026-04-11 21:25:54.531210: Pseudo dice [0.0, 0.0, 0.5224, 0.0, 0.0, 0.0119, 0.5037] +2026-04-11 21:25:54.533429: Epoch time: 101.05 s +2026-04-11 21:25:55.689235: +2026-04-11 21:25:55.691249: Epoch 1185 +2026-04-11 21:25:55.692967: Current learning rate: 0.00729 +2026-04-11 21:27:35.972940: train_loss -0.3127 +2026-04-11 21:27:35.981635: val_loss -0.3336 +2026-04-11 21:27:35.983510: Pseudo dice [0.0, 0.0, 0.4874, 0.0, 0.0, 0.1356, 0.5306] +2026-04-11 21:27:35.986575: Epoch time: 100.29 s +2026-04-11 21:27:37.119182: +2026-04-11 21:27:37.121014: Epoch 1186 +2026-04-11 21:27:37.122794: Current learning rate: 0.00729 +2026-04-11 21:29:17.582426: train_loss -0.333 +2026-04-11 21:29:17.589299: val_loss -0.3628 +2026-04-11 21:29:17.591968: Pseudo dice [0.0, 0.0, 0.7911, 0.0, 0.0, 0.7254, 0.6007] +2026-04-11 21:29:17.594450: Epoch time: 100.47 s +2026-04-11 21:29:18.758740: +2026-04-11 21:29:18.760922: Epoch 1187 +2026-04-11 21:29:18.762831: Current learning rate: 0.00728 +2026-04-11 21:30:59.133910: train_loss -0.3398 +2026-04-11 21:30:59.143175: val_loss -0.3833 +2026-04-11 21:30:59.146972: Pseudo dice [0.0, 0.0, 0.4785, 0.0, 0.0, 0.7258, 0.8572] +2026-04-11 21:30:59.150457: Epoch time: 100.38 s +2026-04-11 21:31:00.352344: +2026-04-11 21:31:00.354567: Epoch 1188 +2026-04-11 21:31:00.356868: Current learning rate: 0.00728 +2026-04-11 21:32:40.662439: train_loss -0.3545 +2026-04-11 21:32:40.669372: val_loss -0.3899 +2026-04-11 21:32:40.670998: Pseudo dice [0.0, 0.0, 0.5501, 0.5209, 0.0, 0.7561, 0.6818] +2026-04-11 21:32:40.673412: Epoch time: 100.31 s +2026-04-11 21:32:41.791105: +2026-04-11 21:32:41.793166: Epoch 1189 +2026-04-11 21:32:41.795084: Current learning rate: 0.00728 +2026-04-11 21:34:22.173925: train_loss -0.3566 +2026-04-11 21:34:22.178808: val_loss -0.2515 +2026-04-11 21:34:22.180879: Pseudo dice [0.0, 0.0, 0.4909, 0.0308, 0.0, 0.7752, 0.8225] +2026-04-11 21:34:22.183409: Epoch time: 100.39 s +2026-04-11 21:34:23.318677: +2026-04-11 21:34:23.320507: Epoch 1190 +2026-04-11 21:34:23.322143: Current learning rate: 0.00728 +2026-04-11 21:36:03.587496: train_loss -0.3753 +2026-04-11 21:36:03.592892: val_loss -0.4124 +2026-04-11 21:36:03.594984: Pseudo dice [0.0, 0.0, 0.7094, 0.0, 0.0, 0.8226, 0.8439] +2026-04-11 21:36:03.597614: Epoch time: 100.27 s +2026-04-11 21:36:04.734888: +2026-04-11 21:36:04.736848: Epoch 1191 +2026-04-11 21:36:04.738880: Current learning rate: 0.00728 +2026-04-11 21:37:44.996907: train_loss -0.3808 +2026-04-11 21:37:45.004139: val_loss -0.4144 +2026-04-11 21:37:45.006152: Pseudo dice [0.0, 0.0, 0.6447, 0.0, 0.3036, 0.7283, 0.8722] +2026-04-11 21:37:45.009076: Epoch time: 100.27 s +2026-04-11 21:37:46.176275: +2026-04-11 21:37:46.177985: Epoch 1192 +2026-04-11 21:37:46.179887: Current learning rate: 0.00727 +2026-04-11 21:39:26.438491: train_loss -0.3788 +2026-04-11 21:39:26.443665: val_loss -0.3683 +2026-04-11 21:39:26.446656: Pseudo dice [0.0, 0.0, 0.6595, 0.0, 0.1077, 0.1173, 0.8385] +2026-04-11 21:39:26.449913: Epoch time: 100.27 s +2026-04-11 21:39:27.584311: +2026-04-11 21:39:27.587959: Epoch 1193 +2026-04-11 21:39:27.589671: Current learning rate: 0.00727 +2026-04-11 21:41:07.958581: train_loss -0.3519 +2026-04-11 21:41:07.963524: val_loss -0.3078 +2026-04-11 21:41:07.965429: Pseudo dice [0.0, 0.0, 0.5847, 0.0, 0.0075, 0.8712, 0.5524] +2026-04-11 21:41:07.967389: Epoch time: 100.38 s +2026-04-11 21:41:09.097740: +2026-04-11 21:41:09.099460: Epoch 1194 +2026-04-11 21:41:09.101020: Current learning rate: 0.00727 +2026-04-11 21:42:49.421401: train_loss -0.3652 +2026-04-11 21:42:49.426702: val_loss -0.2516 +2026-04-11 21:42:49.429206: Pseudo dice [0.0, 0.0, 0.2497, 0.0, 0.2663, 0.4593, 0.0028] +2026-04-11 21:42:49.431297: Epoch time: 100.33 s +2026-04-11 21:42:50.567268: +2026-04-11 21:42:50.569638: Epoch 1195 +2026-04-11 21:42:50.571532: Current learning rate: 0.00727 +2026-04-11 21:44:30.911512: train_loss -0.3429 +2026-04-11 21:44:30.917754: val_loss -0.35 +2026-04-11 21:44:30.920098: Pseudo dice [0.0, 0.0, 0.5473, 0.0154, 0.1928, 0.3639, 0.0915] +2026-04-11 21:44:30.923261: Epoch time: 100.35 s +2026-04-11 21:44:32.070602: +2026-04-11 21:44:32.073234: Epoch 1196 +2026-04-11 21:44:32.075963: Current learning rate: 0.00726 +2026-04-11 21:46:12.384890: train_loss -0.3223 +2026-04-11 21:46:12.391333: val_loss -0.296 +2026-04-11 21:46:12.393873: Pseudo dice [0.0, 0.0, 0.5694, 0.4556, 0.1816, 0.4373, 0.5143] +2026-04-11 21:46:12.396448: Epoch time: 100.32 s +2026-04-11 21:46:13.547704: +2026-04-11 21:46:13.549417: Epoch 1197 +2026-04-11 21:46:13.551327: Current learning rate: 0.00726 +2026-04-11 21:47:53.840455: train_loss -0.338 +2026-04-11 21:47:53.848015: val_loss -0.3609 +2026-04-11 21:47:53.850438: Pseudo dice [0.0, 0.0, 0.5622, 0.2776, 0.2972, 0.7012, 0.3693] +2026-04-11 21:47:53.853792: Epoch time: 100.3 s +2026-04-11 21:47:55.020397: +2026-04-11 21:47:55.022032: Epoch 1198 +2026-04-11 21:47:55.023885: Current learning rate: 0.00726 +2026-04-11 21:49:35.436824: train_loss -0.3586 +2026-04-11 21:49:35.442881: val_loss -0.3644 +2026-04-11 21:49:35.444954: Pseudo dice [0.0, 0.0, 0.7369, 0.0, 0.0, 0.7111, 0.7279] +2026-04-11 21:49:35.448368: Epoch time: 100.42 s +2026-04-11 21:49:36.563648: +2026-04-11 21:49:36.566517: Epoch 1199 +2026-04-11 21:49:36.569174: Current learning rate: 0.00726 +2026-04-11 21:51:16.998484: train_loss -0.3631 +2026-04-11 21:51:17.005790: val_loss -0.2356 +2026-04-11 21:51:17.008361: Pseudo dice [0.0, 0.0, 0.4689, 0.0, 0.2421, 0.8082, 0.7271] +2026-04-11 21:51:17.011227: Epoch time: 100.44 s +2026-04-11 21:51:19.833878: +2026-04-11 21:51:19.835996: Epoch 1200 +2026-04-11 21:51:19.838221: Current learning rate: 0.00725 +2026-04-11 21:53:00.224563: train_loss -0.3733 +2026-04-11 21:53:00.232016: val_loss -0.3952 +2026-04-11 21:53:00.234518: Pseudo dice [0.0, 0.0, 0.8588, 0.0, 0.0106, 0.6853, 0.7642] +2026-04-11 21:53:00.237464: Epoch time: 100.39 s +2026-04-11 21:53:01.367500: +2026-04-11 21:53:01.369704: Epoch 1201 +2026-04-11 21:53:01.371749: Current learning rate: 0.00725 +2026-04-11 21:54:41.759023: train_loss -0.2927 +2026-04-11 21:54:41.765418: val_loss -0.3206 +2026-04-11 21:54:41.767283: Pseudo dice [0.0, 0.0, 0.4995, 0.0, 0.0, 0.3647, 0.5235] +2026-04-11 21:54:41.771194: Epoch time: 100.39 s +2026-04-11 21:54:42.923930: +2026-04-11 21:54:42.925875: Epoch 1202 +2026-04-11 21:54:42.928188: Current learning rate: 0.00725 +2026-04-11 21:56:23.859910: train_loss -0.3159 +2026-04-11 21:56:23.869314: val_loss -0.3418 +2026-04-11 21:56:23.872399: Pseudo dice [0.0, 0.0, 0.6224, 0.0005, 0.4412, 0.7094, 0.5733] +2026-04-11 21:56:23.876492: Epoch time: 100.94 s +2026-04-11 21:56:25.013504: +2026-04-11 21:56:25.016479: Epoch 1203 +2026-04-11 21:56:25.018551: Current learning rate: 0.00725 +2026-04-11 21:58:05.423644: train_loss -0.3249 +2026-04-11 21:58:05.430230: val_loss -0.3036 +2026-04-11 21:58:05.433627: Pseudo dice [0.0, 0.0, 0.6093, 0.0, 0.0, 0.6254, 0.2643] +2026-04-11 21:58:05.436162: Epoch time: 100.41 s +2026-04-11 21:58:07.630975: +2026-04-11 21:58:07.633286: Epoch 1204 +2026-04-11 21:58:07.636831: Current learning rate: 0.00724 +2026-04-11 21:59:47.989624: train_loss -0.3425 +2026-04-11 21:59:47.995557: val_loss -0.3455 +2026-04-11 21:59:47.998214: Pseudo dice [0.0, 0.0, 0.7166, 0.0, 0.0, 0.5721, 0.372] +2026-04-11 21:59:48.000718: Epoch time: 100.36 s +2026-04-11 21:59:49.160258: +2026-04-11 21:59:49.162184: Epoch 1205 +2026-04-11 21:59:49.164574: Current learning rate: 0.00724 +2026-04-11 22:01:29.613420: train_loss -0.3124 +2026-04-11 22:01:29.618746: val_loss -0.3262 +2026-04-11 22:01:29.620702: Pseudo dice [0.0, 0.0, 0.1531, 0.0, 0.0, 0.3397, 0.0] +2026-04-11 22:01:29.622888: Epoch time: 100.46 s +2026-04-11 22:01:30.742743: +2026-04-11 22:01:30.744650: Epoch 1206 +2026-04-11 22:01:30.746971: Current learning rate: 0.00724 +2026-04-11 22:03:11.015036: train_loss -0.316 +2026-04-11 22:03:11.023506: val_loss -0.3632 +2026-04-11 22:03:11.028375: Pseudo dice [0.0, 0.0, 0.5754, 0.0, 0.0, 0.6037, 0.0] +2026-04-11 22:03:11.030632: Epoch time: 100.28 s +2026-04-11 22:03:12.194337: +2026-04-11 22:03:12.196516: Epoch 1207 +2026-04-11 22:03:12.198514: Current learning rate: 0.00724 +2026-04-11 22:04:52.429617: train_loss -0.3323 +2026-04-11 22:04:52.437067: val_loss -0.3627 +2026-04-11 22:04:52.438967: Pseudo dice [0.0, 0.0, 0.6921, 0.0, 0.0, 0.672, 0.0] +2026-04-11 22:04:52.441240: Epoch time: 100.24 s +2026-04-11 22:04:53.603103: +2026-04-11 22:04:53.605057: Epoch 1208 +2026-04-11 22:04:53.607587: Current learning rate: 0.00724 +2026-04-11 22:06:33.939701: train_loss -0.3568 +2026-04-11 22:06:33.945003: val_loss -0.274 +2026-04-11 22:06:33.947251: Pseudo dice [0.0, 0.0, 0.4564, 0.0, 0.2738, 0.8794, 0.4947] +2026-04-11 22:06:33.949704: Epoch time: 100.34 s +2026-04-11 22:06:35.124665: +2026-04-11 22:06:35.126453: Epoch 1209 +2026-04-11 22:06:35.128482: Current learning rate: 0.00723 +2026-04-11 22:08:15.695502: train_loss -0.3855 +2026-04-11 22:08:15.701623: val_loss -0.3334 +2026-04-11 22:08:15.703631: Pseudo dice [0.0, 0.0, 0.7586, 0.0, 0.0, 0.7153, 0.7961] +2026-04-11 22:08:15.705874: Epoch time: 100.57 s +2026-04-11 22:08:16.844422: +2026-04-11 22:08:16.847200: Epoch 1210 +2026-04-11 22:08:16.850164: Current learning rate: 0.00723 +2026-04-11 22:09:57.590239: train_loss -0.3666 +2026-04-11 22:09:57.596455: val_loss -0.3893 +2026-04-11 22:09:57.599076: Pseudo dice [0.0, 0.0, 0.7152, 0.8238, 0.0, 0.5657, 0.7533] +2026-04-11 22:09:57.601386: Epoch time: 100.75 s +2026-04-11 22:09:58.764123: +2026-04-11 22:09:58.766994: Epoch 1211 +2026-04-11 22:09:58.770246: Current learning rate: 0.00723 +2026-04-11 22:11:39.510800: train_loss -0.2947 +2026-04-11 22:11:39.518214: val_loss -0.3561 +2026-04-11 22:11:39.520503: Pseudo dice [0.0, 0.0, 0.7266, 0.3402, 0.0, 0.2031, 0.2001] +2026-04-11 22:11:39.522919: Epoch time: 100.75 s +2026-04-11 22:11:40.674068: +2026-04-11 22:11:40.675951: Epoch 1212 +2026-04-11 22:11:40.678077: Current learning rate: 0.00723 +2026-04-11 22:13:21.346691: train_loss -0.3176 +2026-04-11 22:13:21.353721: val_loss -0.3394 +2026-04-11 22:13:21.356475: Pseudo dice [0.0, 0.0, 0.2379, 0.0, 0.0, 0.6617, 0.6366] +2026-04-11 22:13:21.360144: Epoch time: 100.68 s +2026-04-11 22:13:22.540436: +2026-04-11 22:13:22.542728: Epoch 1213 +2026-04-11 22:13:22.544803: Current learning rate: 0.00722 +2026-04-11 22:15:03.060216: train_loss -0.3307 +2026-04-11 22:15:03.067614: val_loss -0.3488 +2026-04-11 22:15:03.070168: Pseudo dice [0.0, 0.0, 0.6655, 0.0, 0.0, 0.3561, 0.5701] +2026-04-11 22:15:03.073421: Epoch time: 100.52 s +2026-04-11 22:15:04.251115: +2026-04-11 22:15:04.253448: Epoch 1214 +2026-04-11 22:15:04.255608: Current learning rate: 0.00722 +2026-04-11 22:16:44.520854: train_loss -0.357 +2026-04-11 22:16:44.529258: val_loss -0.3402 +2026-04-11 22:16:44.531734: Pseudo dice [0.0, 0.0, 0.4812, 0.0, 0.0, 0.5454, 0.3518] +2026-04-11 22:16:44.534025: Epoch time: 100.27 s +2026-04-11 22:16:45.689110: +2026-04-11 22:16:45.691864: Epoch 1215 +2026-04-11 22:16:45.693760: Current learning rate: 0.00722 +2026-04-11 22:18:26.113860: train_loss -0.3292 +2026-04-11 22:18:26.119235: val_loss -0.3077 +2026-04-11 22:18:26.120888: Pseudo dice [0.0, 0.0, 0.3753, 0.0, 0.0, 0.2236, 0.2595] +2026-04-11 22:18:26.123669: Epoch time: 100.43 s +2026-04-11 22:18:27.269251: +2026-04-11 22:18:27.271667: Epoch 1216 +2026-04-11 22:18:27.273777: Current learning rate: 0.00722 +2026-04-11 22:20:07.756052: train_loss -0.3331 +2026-04-11 22:20:07.761874: val_loss -0.3765 +2026-04-11 22:20:07.763944: Pseudo dice [0.0, 0.0, 0.6755, 0.0, 0.0, 0.6421, 0.4458] +2026-04-11 22:20:07.766502: Epoch time: 100.49 s +2026-04-11 22:20:08.906506: +2026-04-11 22:20:08.918272: Epoch 1217 +2026-04-11 22:20:08.929969: Current learning rate: 0.00721 +2026-04-11 22:21:49.110592: train_loss -0.3667 +2026-04-11 22:21:49.117098: val_loss -0.316 +2026-04-11 22:21:49.119003: Pseudo dice [0.0, 0.0, 0.7081, 0.0007, 0.0, 0.6981, 0.7159] +2026-04-11 22:21:49.122619: Epoch time: 100.21 s +2026-04-11 22:21:50.258252: +2026-04-11 22:21:50.260585: Epoch 1218 +2026-04-11 22:21:50.262620: Current learning rate: 0.00721 +2026-04-11 22:23:30.566572: train_loss -0.367 +2026-04-11 22:23:30.576285: val_loss -0.3751 +2026-04-11 22:23:30.579170: Pseudo dice [0.0, 0.0, 0.7627, 0.1484, 0.0, 0.4415, 0.593] +2026-04-11 22:23:30.587588: Epoch time: 100.31 s +2026-04-11 22:23:31.722531: +2026-04-11 22:23:31.724544: Epoch 1219 +2026-04-11 22:23:31.726674: Current learning rate: 0.00721 +2026-04-11 22:25:12.126250: train_loss -0.341 +2026-04-11 22:25:12.139054: val_loss -0.4058 +2026-04-11 22:25:12.141582: Pseudo dice [0.0, 0.0, 0.6378, 0.0, 0.0, 0.7775, 0.8405] +2026-04-11 22:25:12.144759: Epoch time: 100.41 s +2026-04-11 22:25:13.281078: +2026-04-11 22:25:13.283028: Epoch 1220 +2026-04-11 22:25:13.285232: Current learning rate: 0.00721 +2026-04-11 22:26:53.965031: train_loss -0.3412 +2026-04-11 22:26:53.971679: val_loss -0.315 +2026-04-11 22:26:53.974981: Pseudo dice [0.0, 0.0, 0.6777, 0.0, 0.0, 0.1384, 0.0136] +2026-04-11 22:26:53.977418: Epoch time: 100.69 s +2026-04-11 22:26:55.132417: +2026-04-11 22:26:55.136044: Epoch 1221 +2026-04-11 22:26:55.138426: Current learning rate: 0.00721 +2026-04-11 22:28:42.180201: train_loss -0.3283 +2026-04-11 22:28:42.185838: val_loss -0.3411 +2026-04-11 22:28:42.187624: Pseudo dice [0.0, 0.0, 0.5037, 0.0, 0.426, 0.2143, 0.6482] +2026-04-11 22:28:42.189703: Epoch time: 107.05 s +2026-04-11 22:28:43.363605: +2026-04-11 22:28:43.365632: Epoch 1222 +2026-04-11 22:28:43.367733: Current learning rate: 0.0072 +2026-04-11 22:30:23.509897: train_loss -0.3365 +2026-04-11 22:30:23.518237: val_loss -0.2555 +2026-04-11 22:30:23.520458: Pseudo dice [0.0, 0.0, 0.2681, 0.0, 0.1948, 0.2031, 0.6075] +2026-04-11 22:30:23.522902: Epoch time: 100.15 s +2026-04-11 22:30:24.655780: +2026-04-11 22:30:24.657801: Epoch 1223 +2026-04-11 22:30:24.660369: Current learning rate: 0.0072 +2026-04-11 22:32:04.894835: train_loss -0.3314 +2026-04-11 22:32:04.900707: val_loss -0.3526 +2026-04-11 22:32:04.902900: Pseudo dice [0.0, 0.0, 0.6185, 0.0, 0.0189, 0.2067, 0.7719] +2026-04-11 22:32:04.906293: Epoch time: 100.24 s +2026-04-11 22:32:06.048582: +2026-04-11 22:32:06.050439: Epoch 1224 +2026-04-11 22:32:06.052774: Current learning rate: 0.0072 +2026-04-11 22:33:47.929063: train_loss -0.3523 +2026-04-11 22:33:47.937398: val_loss -0.3703 +2026-04-11 22:33:47.940140: Pseudo dice [0.0, 0.0, 0.7351, 0.0, 0.3032, 0.7243, 0.5459] +2026-04-11 22:33:47.947764: Epoch time: 101.88 s +2026-04-11 22:33:49.101588: +2026-04-11 22:33:49.103823: Epoch 1225 +2026-04-11 22:33:49.106080: Current learning rate: 0.0072 +2026-04-11 22:35:29.681979: train_loss -0.3576 +2026-04-11 22:35:29.690040: val_loss -0.3205 +2026-04-11 22:35:29.692224: Pseudo dice [0.0, 0.0, 0.5809, 0.0, 0.068, 0.1233, 0.3972] +2026-04-11 22:35:29.695110: Epoch time: 100.58 s +2026-04-11 22:35:30.854083: +2026-04-11 22:35:30.856937: Epoch 1226 +2026-04-11 22:35:30.859311: Current learning rate: 0.00719 +2026-04-11 22:37:11.745016: train_loss -0.2897 +2026-04-11 22:37:11.765957: val_loss -0.3448 +2026-04-11 22:37:11.771580: Pseudo dice [0.0, 0.0, 0.5321, 0.0, 0.0, 0.0, 0.3961] +2026-04-11 22:37:11.774653: Epoch time: 100.89 s +2026-04-11 22:37:12.924090: +2026-04-11 22:37:12.925987: Epoch 1227 +2026-04-11 22:37:12.928234: Current learning rate: 0.00719 +2026-04-11 22:38:53.311146: train_loss -0.3165 +2026-04-11 22:38:53.316273: val_loss -0.3374 +2026-04-11 22:38:53.318019: Pseudo dice [0.0, 0.0, 0.3473, 0.0, 0.2512, 0.0, 0.7445] +2026-04-11 22:38:53.320307: Epoch time: 100.39 s +2026-04-11 22:38:54.457280: +2026-04-11 22:38:54.459538: Epoch 1228 +2026-04-11 22:38:54.462000: Current learning rate: 0.00719 +2026-04-11 22:40:35.092119: train_loss -0.3187 +2026-04-11 22:40:35.101845: val_loss -0.3383 +2026-04-11 22:40:35.106525: Pseudo dice [0.0, 0.0, 0.7097, 0.3588, 0.2389, 0.0, 0.0] +2026-04-11 22:40:35.110272: Epoch time: 100.64 s +2026-04-11 22:40:36.270805: +2026-04-11 22:40:36.272988: Epoch 1229 +2026-04-11 22:40:36.275729: Current learning rate: 0.00719 +2026-04-11 22:42:16.828280: train_loss -0.3172 +2026-04-11 22:42:16.836174: val_loss -0.2682 +2026-04-11 22:42:16.839462: Pseudo dice [0.0, 0.0, 0.7364, 0.0589, 0.3413, 0.0, 0.0] +2026-04-11 22:42:16.842989: Epoch time: 100.56 s +2026-04-11 22:42:17.987350: +2026-04-11 22:42:17.989406: Epoch 1230 +2026-04-11 22:42:17.991976: Current learning rate: 0.00718 +2026-04-11 22:43:58.298792: train_loss -0.3337 +2026-04-11 22:43:58.306975: val_loss -0.3555 +2026-04-11 22:43:58.309263: Pseudo dice [0.0, 0.0, 0.8052, 0.6405, 0.0, 0.0, 0.3554] +2026-04-11 22:43:58.311713: Epoch time: 100.31 s +2026-04-11 22:43:59.452981: +2026-04-11 22:43:59.457524: Epoch 1231 +2026-04-11 22:43:59.459781: Current learning rate: 0.00718 +2026-04-11 22:45:39.874440: train_loss -0.3405 +2026-04-11 22:45:39.881394: val_loss -0.3147 +2026-04-11 22:45:39.884150: Pseudo dice [0.0, 0.0, 0.7383, 0.0, 0.343, 0.0, 0.6626] +2026-04-11 22:45:39.888093: Epoch time: 100.42 s +2026-04-11 22:45:41.022380: +2026-04-11 22:45:41.024713: Epoch 1232 +2026-04-11 22:45:41.027232: Current learning rate: 0.00718 +2026-04-11 22:47:21.394308: train_loss -0.378 +2026-04-11 22:47:21.401114: val_loss -0.2921 +2026-04-11 22:47:21.403621: Pseudo dice [0.0, 0.0, 0.7317, 0.037, 0.3286, 0.6419, 0.3571] +2026-04-11 22:47:21.406489: Epoch time: 100.37 s +2026-04-11 22:47:22.541579: +2026-04-11 22:47:22.543968: Epoch 1233 +2026-04-11 22:47:22.547047: Current learning rate: 0.00718 +2026-04-11 22:49:02.887518: train_loss -0.3895 +2026-04-11 22:49:02.893639: val_loss -0.3819 +2026-04-11 22:49:02.896609: Pseudo dice [0.0, 0.0, 0.7788, 0.1817, 0.2356, 0.6039, 0.7337] +2026-04-11 22:49:02.898956: Epoch time: 100.35 s +2026-04-11 22:49:04.036072: +2026-04-11 22:49:04.038159: Epoch 1234 +2026-04-11 22:49:04.040528: Current learning rate: 0.00717 +2026-04-11 22:50:44.379349: train_loss -0.3514 +2026-04-11 22:50:44.404382: val_loss -0.3789 +2026-04-11 22:50:44.407206: Pseudo dice [0.0, 0.0, 0.4335, 0.5786, 0.0, 0.7009, 0.6044] +2026-04-11 22:50:44.409558: Epoch time: 100.35 s +2026-04-11 22:50:45.543710: +2026-04-11 22:50:45.546208: Epoch 1235 +2026-04-11 22:50:45.548647: Current learning rate: 0.00717 +2026-04-11 22:52:25.811378: train_loss -0.3254 +2026-04-11 22:52:25.819735: val_loss -0.3033 +2026-04-11 22:52:25.822433: Pseudo dice [0.0, 0.0, 0.4298, 0.0, 0.0, 0.2437, 0.4044] +2026-04-11 22:52:25.825152: Epoch time: 100.27 s +2026-04-11 22:52:26.964365: +2026-04-11 22:52:26.966974: Epoch 1236 +2026-04-11 22:52:26.969261: Current learning rate: 0.00717 +2026-04-11 22:54:07.573890: train_loss -0.3053 +2026-04-11 22:54:07.581373: val_loss -0.2666 +2026-04-11 22:54:07.584254: Pseudo dice [0.0, 0.0, 0.4923, 0.0, 0.0, 0.0, 0.7287] +2026-04-11 22:54:07.587044: Epoch time: 100.61 s +2026-04-11 22:54:08.729113: +2026-04-11 22:54:08.731724: Epoch 1237 +2026-04-11 22:54:08.734132: Current learning rate: 0.00717 +2026-04-11 22:55:49.242936: train_loss -0.3109 +2026-04-11 22:55:49.250082: val_loss -0.2685 +2026-04-11 22:55:49.254307: Pseudo dice [0.0, 0.0, 0.5669, 0.0, 0.0, 0.0882, 0.6636] +2026-04-11 22:55:49.256798: Epoch time: 100.52 s +2026-04-11 22:55:50.402016: +2026-04-11 22:55:50.404361: Epoch 1238 +2026-04-11 22:55:50.406580: Current learning rate: 0.00717 +2026-04-11 22:57:30.997376: train_loss -0.3397 +2026-04-11 22:57:31.004479: val_loss -0.2936 +2026-04-11 22:57:31.007288: Pseudo dice [0.0, 0.0, 0.5786, 0.0, 0.2347, 0.241, 0.68] +2026-04-11 22:57:31.009994: Epoch time: 100.6 s +2026-04-11 22:57:32.188897: +2026-04-11 22:57:32.192436: Epoch 1239 +2026-04-11 22:57:32.195227: Current learning rate: 0.00716 +2026-04-11 22:59:12.519709: train_loss -0.3496 +2026-04-11 22:59:12.526903: val_loss -0.3861 +2026-04-11 22:59:12.529683: Pseudo dice [0.0, 0.0, 0.7035, 0.0, 0.3472, 0.7484, 0.7484] +2026-04-11 22:59:12.533303: Epoch time: 100.33 s +2026-04-11 22:59:13.667986: +2026-04-11 22:59:13.670195: Epoch 1240 +2026-04-11 22:59:13.673313: Current learning rate: 0.00716 +2026-04-11 23:00:54.370153: train_loss -0.3689 +2026-04-11 23:00:54.376587: val_loss -0.3675 +2026-04-11 23:00:54.378640: Pseudo dice [0.0, 0.0, 0.5277, 0.0, 0.1123, 0.8063, 0.7568] +2026-04-11 23:00:54.380961: Epoch time: 100.71 s +2026-04-11 23:00:55.548737: +2026-04-11 23:00:55.550625: Epoch 1241 +2026-04-11 23:00:55.552879: Current learning rate: 0.00716 +2026-04-11 23:02:35.876927: train_loss -0.3675 +2026-04-11 23:02:35.882879: val_loss -0.2968 +2026-04-11 23:02:35.885087: Pseudo dice [0.0, 0.0, 0.5301, 0.0, 0.1668, 0.5955, 0.7581] +2026-04-11 23:02:35.888056: Epoch time: 100.33 s +2026-04-11 23:02:37.033417: +2026-04-11 23:02:37.035413: Epoch 1242 +2026-04-11 23:02:37.037489: Current learning rate: 0.00716 +2026-04-11 23:04:17.380240: train_loss -0.3556 +2026-04-11 23:04:17.386176: val_loss -0.3584 +2026-04-11 23:04:17.389203: Pseudo dice [0.0, 0.0, 0.6812, 0.0, 0.1542, 0.6855, 0.8115] +2026-04-11 23:04:17.391557: Epoch time: 100.35 s +2026-04-11 23:04:18.536285: +2026-04-11 23:04:18.538217: Epoch 1243 +2026-04-11 23:04:18.540270: Current learning rate: 0.00715 +2026-04-11 23:05:58.861894: train_loss -0.367 +2026-04-11 23:05:58.869275: val_loss -0.3892 +2026-04-11 23:05:58.871794: Pseudo dice [0.0, 0.0, 0.5282, 0.0, 0.1941, 0.7166, 0.8205] +2026-04-11 23:05:58.874853: Epoch time: 100.33 s +2026-04-11 23:06:01.012563: +2026-04-11 23:06:01.014875: Epoch 1244 +2026-04-11 23:06:01.017629: Current learning rate: 0.00715 +2026-04-11 23:07:41.747901: train_loss -0.3425 +2026-04-11 23:07:41.755781: val_loss -0.3391 +2026-04-11 23:07:41.758048: Pseudo dice [0.0, 0.0, 0.4118, 0.0183, 0.0, 0.5212, 0.6605] +2026-04-11 23:07:41.760808: Epoch time: 100.74 s +2026-04-11 23:07:42.906701: +2026-04-11 23:07:42.908534: Epoch 1245 +2026-04-11 23:07:42.911039: Current learning rate: 0.00715 +2026-04-11 23:09:23.477631: train_loss -0.328 +2026-04-11 23:09:23.484063: val_loss -0.3335 +2026-04-11 23:09:23.486038: Pseudo dice [0.0, 0.0, 0.7308, 0.0009, 0.0, 0.4084, 0.556] +2026-04-11 23:09:23.488759: Epoch time: 100.57 s +2026-04-11 23:09:24.623266: +2026-04-11 23:09:24.625321: Epoch 1246 +2026-04-11 23:09:24.627364: Current learning rate: 0.00715 +2026-04-11 23:11:04.875985: train_loss -0.3319 +2026-04-11 23:11:04.881339: val_loss -0.3058 +2026-04-11 23:11:04.883088: Pseudo dice [0.0, 0.0, 0.6165, 0.0, 0.0, 0.0137, 0.626] +2026-04-11 23:11:04.884973: Epoch time: 100.26 s +2026-04-11 23:11:06.001525: +2026-04-11 23:11:06.003171: Epoch 1247 +2026-04-11 23:11:06.005082: Current learning rate: 0.00714 +2026-04-11 23:12:46.391504: train_loss -0.3601 +2026-04-11 23:12:46.397800: val_loss -0.3012 +2026-04-11 23:12:46.400646: Pseudo dice [0.0, 0.0, 0.6561, 0.0107, 0.0, 0.5315, 0.6074] +2026-04-11 23:12:46.404013: Epoch time: 100.39 s +2026-04-11 23:12:47.534377: +2026-04-11 23:12:47.536695: Epoch 1248 +2026-04-11 23:12:47.539108: Current learning rate: 0.00714 +2026-04-11 23:14:27.789410: train_loss -0.3571 +2026-04-11 23:14:27.794500: val_loss -0.3035 +2026-04-11 23:14:27.796601: Pseudo dice [0.0, 0.0, 0.4517, 0.0, 0.0, 0.7813, 0.6341] +2026-04-11 23:14:27.798990: Epoch time: 100.26 s +2026-04-11 23:14:28.955715: +2026-04-11 23:14:28.957616: Epoch 1249 +2026-04-11 23:14:28.959712: Current learning rate: 0.00714 +2026-04-11 23:16:09.303470: train_loss -0.3652 +2026-04-11 23:16:09.311040: val_loss -0.37 +2026-04-11 23:16:09.313726: Pseudo dice [0.0, 0.0, 0.6208, 0.5001, 0.2102, 0.6367, 0.7698] +2026-04-11 23:16:09.316670: Epoch time: 100.35 s +2026-04-11 23:16:12.134156: +2026-04-11 23:16:12.136416: Epoch 1250 +2026-04-11 23:16:12.138426: Current learning rate: 0.00714 +2026-04-11 23:17:52.916999: train_loss -0.3719 +2026-04-11 23:17:52.925280: val_loss -0.2982 +2026-04-11 23:17:52.927881: Pseudo dice [0.0, 0.0, 0.58, 0.0348, 0.5252, 0.6644, 0.4881] +2026-04-11 23:17:52.930714: Epoch time: 100.79 s +2026-04-11 23:17:54.083515: +2026-04-11 23:17:54.085945: Epoch 1251 +2026-04-11 23:17:54.088940: Current learning rate: 0.00714 +2026-04-11 23:19:34.576456: train_loss -0.3667 +2026-04-11 23:19:34.583614: val_loss -0.3501 +2026-04-11 23:19:34.586013: Pseudo dice [0.0, 0.0, 0.6395, 0.0, 0.0, 0.521, 0.1162] +2026-04-11 23:19:34.588641: Epoch time: 100.5 s +2026-04-11 23:19:35.747843: +2026-04-11 23:19:35.749828: Epoch 1252 +2026-04-11 23:19:35.751953: Current learning rate: 0.00713 +2026-04-11 23:21:16.324197: train_loss -0.3412 +2026-04-11 23:21:16.332487: val_loss -0.2989 +2026-04-11 23:21:16.337182: Pseudo dice [0.0, 0.0, 0.7332, 0.0, 0.0, 0.6319, 0.8159] +2026-04-11 23:21:16.340223: Epoch time: 100.58 s +2026-04-11 23:21:17.480555: +2026-04-11 23:21:17.482912: Epoch 1253 +2026-04-11 23:21:17.485116: Current learning rate: 0.00713 +2026-04-11 23:22:57.710008: train_loss -0.3272 +2026-04-11 23:22:57.715919: val_loss -0.2952 +2026-04-11 23:22:57.719027: Pseudo dice [0.0, 0.0, 0.6933, 0.0504, 0.0065, 0.5194, 0.6653] +2026-04-11 23:22:57.722147: Epoch time: 100.23 s +2026-04-11 23:22:58.852509: +2026-04-11 23:22:58.854346: Epoch 1254 +2026-04-11 23:22:58.856383: Current learning rate: 0.00713 +2026-04-11 23:24:39.272457: train_loss -0.3204 +2026-04-11 23:24:39.278878: val_loss -0.2649 +2026-04-11 23:24:39.280917: Pseudo dice [0.0, 0.0, 0.4983, 0.1215, 0.0048, 0.0236, 0.4] +2026-04-11 23:24:39.284134: Epoch time: 100.42 s +2026-04-11 23:24:40.415562: +2026-04-11 23:24:40.417933: Epoch 1255 +2026-04-11 23:24:40.420601: Current learning rate: 0.00713 +2026-04-11 23:26:21.336673: train_loss -0.3483 +2026-04-11 23:26:21.342110: val_loss -0.3387 +2026-04-11 23:26:21.344239: Pseudo dice [0.0, 0.0, 0.5062, 0.1289, 0.2566, 0.2503, 0.487] +2026-04-11 23:26:21.346628: Epoch time: 100.92 s +2026-04-11 23:26:22.562258: +2026-04-11 23:26:22.564768: Epoch 1256 +2026-04-11 23:26:22.567462: Current learning rate: 0.00712 +2026-04-11 23:28:02.785120: train_loss -0.3395 +2026-04-11 23:28:02.791225: val_loss -0.2236 +2026-04-11 23:28:02.793705: Pseudo dice [0.0, 0.0, 0.5061, 0.0, 0.2281, 0.7278, 0.4022] +2026-04-11 23:28:02.797847: Epoch time: 100.23 s +2026-04-11 23:28:03.965548: +2026-04-11 23:28:03.967472: Epoch 1257 +2026-04-11 23:28:03.969681: Current learning rate: 0.00712 +2026-04-11 23:29:44.220554: train_loss -0.3235 +2026-04-11 23:29:44.226208: val_loss -0.2963 +2026-04-11 23:29:44.228355: Pseudo dice [0.0, 0.0, 0.6826, 0.0, 0.0, 0.6823, 0.0031] +2026-04-11 23:29:44.230768: Epoch time: 100.26 s +2026-04-11 23:29:45.374360: +2026-04-11 23:29:45.376314: Epoch 1258 +2026-04-11 23:29:45.378283: Current learning rate: 0.00712 +2026-04-11 23:31:25.776850: train_loss -0.3311 +2026-04-11 23:31:25.783315: val_loss -0.2239 +2026-04-11 23:31:25.788048: Pseudo dice [0.0, 0.0, 0.5, 0.0, 0.198, 0.5503, 0.5925] +2026-04-11 23:31:25.791074: Epoch time: 100.41 s +2026-04-11 23:31:26.937483: +2026-04-11 23:31:26.939449: Epoch 1259 +2026-04-11 23:31:26.941350: Current learning rate: 0.00712 +2026-04-11 23:33:07.291384: train_loss -0.3601 +2026-04-11 23:33:07.299182: val_loss -0.2759 +2026-04-11 23:33:07.304181: Pseudo dice [0.0, 0.0, 0.412, 0.0613, 0.082, 0.8024, 0.8296] +2026-04-11 23:33:07.308597: Epoch time: 100.36 s +2026-04-11 23:33:08.481850: +2026-04-11 23:33:08.483680: Epoch 1260 +2026-04-11 23:33:08.485809: Current learning rate: 0.00711 +2026-04-11 23:34:49.220202: train_loss -0.3715 +2026-04-11 23:34:49.226300: val_loss -0.3168 +2026-04-11 23:34:49.228256: Pseudo dice [0.0, 0.0, 0.6639, 0.0, 0.0279, 0.7278, 0.6969] +2026-04-11 23:34:49.230402: Epoch time: 100.74 s +2026-04-11 23:34:50.372695: +2026-04-11 23:34:50.374524: Epoch 1261 +2026-04-11 23:34:50.376609: Current learning rate: 0.00711 +2026-04-11 23:36:31.012005: train_loss -0.314 +2026-04-11 23:36:31.018474: val_loss -0.3585 +2026-04-11 23:36:31.020321: Pseudo dice [0.0, 0.0, 0.5774, 0.3714, 0.4575, 0.0026, 0.6175] +2026-04-11 23:36:31.022524: Epoch time: 100.64 s +2026-04-11 23:36:32.174987: +2026-04-11 23:36:32.176815: Epoch 1262 +2026-04-11 23:36:32.178887: Current learning rate: 0.00711 +2026-04-11 23:38:12.581999: train_loss -0.3305 +2026-04-11 23:38:12.587918: val_loss -0.359 +2026-04-11 23:38:12.589934: Pseudo dice [0.0, 0.0, 0.3353, 0.0, 0.0297, 0.6472, 0.8237] +2026-04-11 23:38:12.592143: Epoch time: 100.41 s +2026-04-11 23:38:13.733867: +2026-04-11 23:38:13.735945: Epoch 1263 +2026-04-11 23:38:13.737795: Current learning rate: 0.00711 +2026-04-11 23:39:54.080225: train_loss -0.2998 +2026-04-11 23:39:54.088453: val_loss -0.3238 +2026-04-11 23:39:54.090462: Pseudo dice [0.0, 0.0, 0.4972, 0.0, 0.1064, 0.3656, 0.4283] +2026-04-11 23:39:54.093776: Epoch time: 100.35 s +2026-04-11 23:39:56.315865: +2026-04-11 23:39:56.318381: Epoch 1264 +2026-04-11 23:39:56.320650: Current learning rate: 0.0071 +2026-04-11 23:41:36.675704: train_loss -0.339 +2026-04-11 23:41:36.683972: val_loss -0.3148 +2026-04-11 23:41:36.686763: Pseudo dice [0.0, 0.0, 0.606, 0.0, 0.0586, 0.5785, 0.058] +2026-04-11 23:41:36.690331: Epoch time: 100.36 s +2026-04-11 23:41:37.841080: +2026-04-11 23:41:37.843293: Epoch 1265 +2026-04-11 23:41:37.845261: Current learning rate: 0.0071 +2026-04-11 23:43:18.203423: train_loss -0.3323 +2026-04-11 23:43:18.210911: val_loss -0.3379 +2026-04-11 23:43:18.214617: Pseudo dice [0.0, 0.0, 0.7188, 0.5712, 0.0118, 0.3267, 0.5592] +2026-04-11 23:43:18.217541: Epoch time: 100.37 s +2026-04-11 23:43:19.368381: +2026-04-11 23:43:19.370342: Epoch 1266 +2026-04-11 23:43:19.372751: Current learning rate: 0.0071 +2026-04-11 23:44:59.910594: train_loss -0.3338 +2026-04-11 23:44:59.916881: val_loss -0.2833 +2026-04-11 23:44:59.919312: Pseudo dice [0.0, 0.0, 0.4316, 0.0, 0.2931, 0.4192, 0.2891] +2026-04-11 23:44:59.922341: Epoch time: 100.55 s +2026-04-11 23:45:01.073382: +2026-04-11 23:45:01.075625: Epoch 1267 +2026-04-11 23:45:01.077446: Current learning rate: 0.0071 +2026-04-11 23:46:41.724391: train_loss -0.3417 +2026-04-11 23:46:41.730326: val_loss -0.3753 +2026-04-11 23:46:41.732208: Pseudo dice [0.0, 0.0, 0.6538, 0.0, 0.0196, 0.7374, 0.7645] +2026-04-11 23:46:41.734724: Epoch time: 100.65 s +2026-04-11 23:46:42.867940: +2026-04-11 23:46:42.870160: Epoch 1268 +2026-04-11 23:46:42.872313: Current learning rate: 0.0071 +2026-04-11 23:48:23.333020: train_loss -0.3844 +2026-04-11 23:48:23.338721: val_loss -0.3208 +2026-04-11 23:48:23.341157: Pseudo dice [0.0, 0.0, 0.6362, 0.0, 0.0509, 0.7413, 0.5084] +2026-04-11 23:48:23.344481: Epoch time: 100.47 s +2026-04-11 23:48:24.491886: +2026-04-11 23:48:24.494063: Epoch 1269 +2026-04-11 23:48:24.496279: Current learning rate: 0.00709 +2026-04-11 23:50:04.801113: train_loss -0.3645 +2026-04-11 23:50:04.807534: val_loss -0.3406 +2026-04-11 23:50:04.809819: Pseudo dice [0.0, 0.0, 0.6326, 0.0, 0.2324, 0.5131, 0.7719] +2026-04-11 23:50:04.812518: Epoch time: 100.31 s +2026-04-11 23:50:05.954519: +2026-04-11 23:50:05.956342: Epoch 1270 +2026-04-11 23:50:05.958370: Current learning rate: 0.00709 +2026-04-11 23:51:46.463989: train_loss -0.3372 +2026-04-11 23:51:46.473029: val_loss -0.3689 +2026-04-11 23:51:46.475626: Pseudo dice [0.0, 0.0, 0.4184, 0.238, 0.0, 0.6685, 0.8434] +2026-04-11 23:51:46.478371: Epoch time: 100.51 s +2026-04-11 23:51:47.623770: +2026-04-11 23:51:47.625712: Epoch 1271 +2026-04-11 23:51:47.628055: Current learning rate: 0.00709 +2026-04-11 23:53:28.133704: train_loss -0.3516 +2026-04-11 23:53:28.142360: val_loss -0.3772 +2026-04-11 23:53:28.144855: Pseudo dice [0.0, 0.0, 0.5459, 0.0271, 0.0, 0.7991, 0.3492] +2026-04-11 23:53:28.148573: Epoch time: 100.51 s +2026-04-11 23:53:29.293195: +2026-04-11 23:53:29.296018: Epoch 1272 +2026-04-11 23:53:29.299005: Current learning rate: 0.00709 +2026-04-11 23:55:09.969600: train_loss -0.3552 +2026-04-11 23:55:09.978287: val_loss -0.3653 +2026-04-11 23:55:09.980464: Pseudo dice [0.0, 0.0, 0.688, 0.0, 0.0963, 0.5983, 0.3605] +2026-04-11 23:55:09.983256: Epoch time: 100.68 s +2026-04-11 23:55:11.142191: +2026-04-11 23:55:11.144263: Epoch 1273 +2026-04-11 23:55:11.146346: Current learning rate: 0.00708 +2026-04-11 23:56:51.882784: train_loss -0.3 +2026-04-11 23:56:51.891853: val_loss -0.362 +2026-04-11 23:56:51.894132: Pseudo dice [0.0, 0.0, 0.7024, 0.0, 0.0, 0.5354, 0.7169] +2026-04-11 23:56:51.899209: Epoch time: 100.74 s +2026-04-11 23:56:53.057448: +2026-04-11 23:56:53.059455: Epoch 1274 +2026-04-11 23:56:53.061895: Current learning rate: 0.00708 +2026-04-11 23:58:33.450463: train_loss -0.3352 +2026-04-11 23:58:33.456449: val_loss -0.2959 +2026-04-11 23:58:33.458707: Pseudo dice [0.0, 0.0, 0.7107, 0.0, 0.0121, 0.5902, 0.6393] +2026-04-11 23:58:33.460963: Epoch time: 100.4 s +2026-04-11 23:58:34.617735: +2026-04-11 23:58:34.619668: Epoch 1275 +2026-04-11 23:58:34.621686: Current learning rate: 0.00708 +2026-04-12 00:00:15.242583: train_loss -0.3575 +2026-04-12 00:00:15.249562: val_loss -0.3714 +2026-04-12 00:00:15.251827: Pseudo dice [0.0, 0.0, 0.6956, 0.0, 0.217, 0.6995, 0.6814] +2026-04-12 00:00:15.254118: Epoch time: 100.63 s +2026-04-12 00:00:16.409688: +2026-04-12 00:00:16.411844: Epoch 1276 +2026-04-12 00:00:16.414142: Current learning rate: 0.00708 +2026-04-12 00:02:00.591476: train_loss -0.3669 +2026-04-12 00:02:00.600779: val_loss -0.3768 +2026-04-12 00:02:00.603733: Pseudo dice [0.0, 0.0, 0.6468, nan, 0.31, 0.7078, 0.2763] +2026-04-12 00:02:00.609410: Epoch time: 104.18 s +2026-04-12 00:02:01.771644: +2026-04-12 00:02:01.776038: Epoch 1277 +2026-04-12 00:02:01.791332: Current learning rate: 0.00707 +2026-04-12 00:03:43.376455: train_loss -0.3378 +2026-04-12 00:03:43.390526: val_loss -0.3291 +2026-04-12 00:03:43.393792: Pseudo dice [0.0, 0.0, 0.7082, 0.012, 0.4314, 0.7217, 0.7629] +2026-04-12 00:03:43.397636: Epoch time: 101.61 s +2026-04-12 00:03:44.555403: +2026-04-12 00:03:44.558487: Epoch 1278 +2026-04-12 00:03:44.561664: Current learning rate: 0.00707 +2026-04-12 00:05:24.855336: train_loss -0.3799 +2026-04-12 00:05:24.863324: val_loss -0.2491 +2026-04-12 00:05:24.865664: Pseudo dice [0.0, 0.0, 0.3681, 0.0475, 0.1909, 0.371, 0.2955] +2026-04-12 00:05:24.868953: Epoch time: 100.3 s +2026-04-12 00:05:26.009187: +2026-04-12 00:05:26.011223: Epoch 1279 +2026-04-12 00:05:26.013305: Current learning rate: 0.00707 +2026-04-12 00:07:07.063082: train_loss -0.3711 +2026-04-12 00:07:07.070530: val_loss -0.3722 +2026-04-12 00:07:07.072307: Pseudo dice [0.0, 0.0, 0.7125, 0.1199, 0.4462, 0.6429, 0.8497] +2026-04-12 00:07:07.075251: Epoch time: 101.06 s +2026-04-12 00:07:08.235147: +2026-04-12 00:07:08.238019: Epoch 1280 +2026-04-12 00:07:08.240477: Current learning rate: 0.00707 +2026-04-12 00:08:49.219297: train_loss -0.3791 +2026-04-12 00:08:49.225207: val_loss -0.3196 +2026-04-12 00:08:49.227108: Pseudo dice [0.0, 0.0, 0.7328, 0.0, 0.0, 0.1264, 0.0357] +2026-04-12 00:08:49.229481: Epoch time: 100.99 s +2026-04-12 00:08:50.406775: +2026-04-12 00:08:50.408900: Epoch 1281 +2026-04-12 00:08:50.412789: Current learning rate: 0.00707 +2026-04-12 00:10:31.040217: train_loss -0.3147 +2026-04-12 00:10:31.049070: val_loss -0.2388 +2026-04-12 00:10:31.052071: Pseudo dice [0.0, 0.0, 0.4748, 0.0, 0.0, 0.0614, 0.1409] +2026-04-12 00:10:31.054674: Epoch time: 100.64 s +2026-04-12 00:10:32.215625: +2026-04-12 00:10:32.218062: Epoch 1282 +2026-04-12 00:10:32.220494: Current learning rate: 0.00706 +2026-04-12 00:12:13.639259: train_loss -0.3143 +2026-04-12 00:12:13.645962: val_loss -0.3398 +2026-04-12 00:12:13.648627: Pseudo dice [0.0, 0.0, 0.6544, 0.0, 0.0, 0.5616, 0.5211] +2026-04-12 00:12:13.651356: Epoch time: 101.43 s +2026-04-12 00:12:14.836457: +2026-04-12 00:12:14.838439: Epoch 1283 +2026-04-12 00:12:14.841394: Current learning rate: 0.00706 +2026-04-12 00:13:55.162367: train_loss -0.3083 +2026-04-12 00:13:55.169000: val_loss -0.3594 +2026-04-12 00:13:55.171341: Pseudo dice [0.0, 0.0, 0.6749, 0.0, 0.0062, 0.7456, 0.6909] +2026-04-12 00:13:55.174326: Epoch time: 100.33 s +2026-04-12 00:13:57.427724: +2026-04-12 00:13:57.430113: Epoch 1284 +2026-04-12 00:13:57.432508: Current learning rate: 0.00706 +2026-04-12 00:15:37.847338: train_loss -0.335 +2026-04-12 00:15:37.853207: val_loss -0.2575 +2026-04-12 00:15:37.855022: Pseudo dice [0.0, 0.0, 0.5219, 0.0, 0.4098, 0.2675, 0.7274] +2026-04-12 00:15:37.858176: Epoch time: 100.42 s +2026-04-12 00:15:39.032560: +2026-04-12 00:15:39.035946: Epoch 1285 +2026-04-12 00:15:39.038320: Current learning rate: 0.00706 +2026-04-12 00:17:19.254651: train_loss -0.3506 +2026-04-12 00:17:19.262151: val_loss -0.344 +2026-04-12 00:17:19.264893: Pseudo dice [0.0, 0.0, 0.5947, 0.0, 0.0, 0.1566, 0.5747] +2026-04-12 00:17:19.268360: Epoch time: 100.23 s +2026-04-12 00:17:20.410896: +2026-04-12 00:17:20.413272: Epoch 1286 +2026-04-12 00:17:20.415671: Current learning rate: 0.00705 +2026-04-12 00:19:00.745632: train_loss -0.3206 +2026-04-12 00:19:00.751712: val_loss -0.3693 +2026-04-12 00:19:00.753716: Pseudo dice [0.0, 0.0, 0.6874, 0.0, 0.0, 0.098, 0.8279] +2026-04-12 00:19:00.755903: Epoch time: 100.34 s +2026-04-12 00:19:01.913805: +2026-04-12 00:19:01.915793: Epoch 1287 +2026-04-12 00:19:01.918432: Current learning rate: 0.00705 +2026-04-12 00:20:42.623234: train_loss -0.347 +2026-04-12 00:20:42.629441: val_loss -0.3963 +2026-04-12 00:20:42.631997: Pseudo dice [0.0, 0.0, 0.4921, 0.0, 0.0, 0.7614, 0.7221] +2026-04-12 00:20:42.634819: Epoch time: 100.71 s +2026-04-12 00:20:43.822117: +2026-04-12 00:20:43.824031: Epoch 1288 +2026-04-12 00:20:43.826223: Current learning rate: 0.00705 +2026-04-12 00:22:24.754043: train_loss -0.3861 +2026-04-12 00:22:24.765356: val_loss -0.3277 +2026-04-12 00:22:24.768164: Pseudo dice [0.0, 0.0, 0.484, 0.0, 0.0884, 0.4901, 0.8001] +2026-04-12 00:22:24.770344: Epoch time: 100.93 s +2026-04-12 00:22:25.939407: +2026-04-12 00:22:25.941425: Epoch 1289 +2026-04-12 00:22:25.943671: Current learning rate: 0.00705 +2026-04-12 00:24:06.358869: train_loss -0.3593 +2026-04-12 00:24:06.365966: val_loss -0.325 +2026-04-12 00:24:06.368146: Pseudo dice [0.0, 0.0, 0.6863, 0.0027, 0.0, 0.8172, 0.6491] +2026-04-12 00:24:06.370543: Epoch time: 100.42 s +2026-04-12 00:24:07.517040: +2026-04-12 00:24:07.520150: Epoch 1290 +2026-04-12 00:24:07.523171: Current learning rate: 0.00704 +2026-04-12 00:25:50.306455: train_loss -0.3346 +2026-04-12 00:25:50.312573: val_loss -0.2891 +2026-04-12 00:25:50.314497: Pseudo dice [0.0, 0.0, 0.6412, 0.0, 0.0, 0.4254, 0.7234] +2026-04-12 00:25:50.316862: Epoch time: 102.79 s +2026-04-12 00:25:51.484434: +2026-04-12 00:25:51.486536: Epoch 1291 +2026-04-12 00:25:51.488778: Current learning rate: 0.00704 +2026-04-12 00:27:32.697348: train_loss -0.3625 +2026-04-12 00:27:32.704109: val_loss -0.3723 +2026-04-12 00:27:32.707101: Pseudo dice [0.0, 0.0, 0.7332, 0.8621, 0.0, 0.7262, 0.6451] +2026-04-12 00:27:32.709882: Epoch time: 101.22 s +2026-04-12 00:27:33.851010: +2026-04-12 00:27:33.853579: Epoch 1292 +2026-04-12 00:27:33.856258: Current learning rate: 0.00704 +2026-04-12 00:29:14.490805: train_loss -0.3572 +2026-04-12 00:29:14.496959: val_loss -0.3531 +2026-04-12 00:29:14.499025: Pseudo dice [0.0, 0.0, 0.6321, 0.0, 0.0, 0.629, 0.4907] +2026-04-12 00:29:14.503002: Epoch time: 100.64 s +2026-04-12 00:29:15.693794: +2026-04-12 00:29:15.697070: Epoch 1293 +2026-04-12 00:29:15.700318: Current learning rate: 0.00704 +2026-04-12 00:30:56.077758: train_loss -0.3532 +2026-04-12 00:30:56.085761: val_loss -0.3673 +2026-04-12 00:30:56.088908: Pseudo dice [0.0, 0.0, 0.7339, 0.0, 0.0, 0.4181, 0.7428] +2026-04-12 00:30:56.091506: Epoch time: 100.39 s +2026-04-12 00:30:57.261740: +2026-04-12 00:30:57.263929: Epoch 1294 +2026-04-12 00:30:57.266876: Current learning rate: 0.00703 +2026-04-12 00:32:37.417964: train_loss -0.2721 +2026-04-12 00:32:37.427030: val_loss -0.2963 +2026-04-12 00:32:37.429899: Pseudo dice [0.0, 0.0, 0.6444, 0.0, 0.0, 0.1224, 0.0] +2026-04-12 00:32:37.432870: Epoch time: 100.16 s +2026-04-12 00:32:38.599694: +2026-04-12 00:32:38.601890: Epoch 1295 +2026-04-12 00:32:38.604007: Current learning rate: 0.00703 +2026-04-12 00:34:18.929408: train_loss -0.2916 +2026-04-12 00:34:18.936439: val_loss -0.315 +2026-04-12 00:34:18.938520: Pseudo dice [0.0, 0.0, 0.5503, 0.0, 0.0, 0.5029, 0.0] +2026-04-12 00:34:18.942265: Epoch time: 100.33 s +2026-04-12 00:34:20.084043: +2026-04-12 00:34:20.085929: Epoch 1296 +2026-04-12 00:34:20.088026: Current learning rate: 0.00703 +2026-04-12 00:36:00.375617: train_loss -0.3471 +2026-04-12 00:36:00.381557: val_loss -0.3612 +2026-04-12 00:36:00.384500: Pseudo dice [0.0, 0.0, 0.5239, 0.0, 0.0, 0.2953, 0.0] +2026-04-12 00:36:00.386895: Epoch time: 100.29 s +2026-04-12 00:36:01.764617: +2026-04-12 00:36:01.766451: Epoch 1297 +2026-04-12 00:36:01.768439: Current learning rate: 0.00703 +2026-04-12 00:37:42.086322: train_loss -0.3706 +2026-04-12 00:37:42.092376: val_loss -0.3285 +2026-04-12 00:37:42.094403: Pseudo dice [0.0, 0.0, 0.7628, 0.1637, 0.0, 0.314, 0.0] +2026-04-12 00:37:42.099430: Epoch time: 100.32 s +2026-04-12 00:37:43.303010: +2026-04-12 00:37:43.305535: Epoch 1298 +2026-04-12 00:37:43.308207: Current learning rate: 0.00703 +2026-04-12 00:39:23.587675: train_loss -0.3407 +2026-04-12 00:39:23.594790: val_loss -0.186 +2026-04-12 00:39:23.598414: Pseudo dice [0.0, 0.0, 0.4936, 0.0, 0.0, 0.3169, 0.0154] +2026-04-12 00:39:23.601925: Epoch time: 100.29 s +2026-04-12 00:39:24.753981: +2026-04-12 00:39:24.755854: Epoch 1299 +2026-04-12 00:39:24.758127: Current learning rate: 0.00702 +2026-04-12 00:41:05.239728: train_loss -0.3681 +2026-04-12 00:41:05.246708: val_loss -0.3042 +2026-04-12 00:41:05.248828: Pseudo dice [0.0, 0.0, 0.7731, 0.0, 0.2121, 0.3516, 0.7539] +2026-04-12 00:41:05.251736: Epoch time: 100.49 s +2026-04-12 00:41:08.066511: +2026-04-12 00:41:08.068515: Epoch 1300 +2026-04-12 00:41:08.070730: Current learning rate: 0.00702 +2026-04-12 00:42:48.474041: train_loss -0.3391 +2026-04-12 00:42:48.481915: val_loss -0.3158 +2026-04-12 00:42:48.484199: Pseudo dice [0.0, 0.0, 0.4217, 0.0, 0.0, 0.7028, 0.6756] +2026-04-12 00:42:48.487708: Epoch time: 100.41 s +2026-04-12 00:42:49.623161: +2026-04-12 00:42:49.625249: Epoch 1301 +2026-04-12 00:42:49.627685: Current learning rate: 0.00702 +2026-04-12 00:44:29.976064: train_loss -0.3077 +2026-04-12 00:44:29.983078: val_loss -0.1922 +2026-04-12 00:44:29.986914: Pseudo dice [0.0, 0.0, 0.4312, 0.0, 0.0, 0.2988, 0.2081] +2026-04-12 00:44:29.991005: Epoch time: 100.36 s +2026-04-12 00:44:31.151393: +2026-04-12 00:44:31.153791: Epoch 1302 +2026-04-12 00:44:31.155953: Current learning rate: 0.00702 +2026-04-12 00:46:12.050320: train_loss -0.3148 +2026-04-12 00:46:12.058302: val_loss -0.3413 +2026-04-12 00:46:12.062590: Pseudo dice [0.0, 0.0, 0.5612, 0.0, 0.0, 0.324, 0.68] +2026-04-12 00:46:12.065970: Epoch time: 100.9 s +2026-04-12 00:46:13.243662: +2026-04-12 00:46:13.245764: Epoch 1303 +2026-04-12 00:46:13.248217: Current learning rate: 0.00701 +2026-04-12 00:47:54.497145: train_loss -0.3406 +2026-04-12 00:47:54.504788: val_loss -0.3511 +2026-04-12 00:47:54.507308: Pseudo dice [0.0, 0.0, 0.6483, 0.65, 0.0, 0.4295, 0.6728] +2026-04-12 00:47:54.509994: Epoch time: 101.26 s +2026-04-12 00:47:55.660999: +2026-04-12 00:47:55.663082: Epoch 1304 +2026-04-12 00:47:55.665451: Current learning rate: 0.00701 +2026-04-12 00:49:36.034749: train_loss -0.3368 +2026-04-12 00:49:36.042208: val_loss -0.2742 +2026-04-12 00:49:36.044584: Pseudo dice [0.0, 0.0, 0.6227, 0.0887, 0.0, 0.2329, 0.6569] +2026-04-12 00:49:36.046805: Epoch time: 100.38 s +2026-04-12 00:49:37.242053: +2026-04-12 00:49:37.244515: Epoch 1305 +2026-04-12 00:49:37.246799: Current learning rate: 0.00701 +2026-04-12 00:51:17.788563: train_loss -0.3347 +2026-04-12 00:51:17.795039: val_loss -0.3298 +2026-04-12 00:51:17.797079: Pseudo dice [0.0, 0.0, 0.5479, 0.0, 0.0, 0.2241, 0.2651] +2026-04-12 00:51:17.799629: Epoch time: 100.55 s +2026-04-12 00:51:18.955899: +2026-04-12 00:51:18.958131: Epoch 1306 +2026-04-12 00:51:18.962387: Current learning rate: 0.00701 +2026-04-12 00:52:59.539403: train_loss -0.3222 +2026-04-12 00:52:59.614469: val_loss -0.2295 +2026-04-12 00:52:59.617200: Pseudo dice [0.0, 0.0, 0.6057, 0.0, 0.0, 0.4497, 0.6284] +2026-04-12 00:52:59.619939: Epoch time: 100.59 s +2026-04-12 00:53:00.780378: +2026-04-12 00:53:00.782406: Epoch 1307 +2026-04-12 00:53:00.784329: Current learning rate: 0.007 +2026-04-12 00:54:42.599281: train_loss -0.3308 +2026-04-12 00:54:42.604907: val_loss -0.344 +2026-04-12 00:54:42.607227: Pseudo dice [0.0, 0.0, 0.6681, 0.0, 0.0, 0.7255, 0.7226] +2026-04-12 00:54:42.609530: Epoch time: 101.82 s +2026-04-12 00:54:43.852348: +2026-04-12 00:54:43.854268: Epoch 1308 +2026-04-12 00:54:43.856312: Current learning rate: 0.007 +2026-04-12 00:56:24.586782: train_loss -0.3601 +2026-04-12 00:56:24.593072: val_loss -0.3845 +2026-04-12 00:56:24.595385: Pseudo dice [0.0, 0.0, 0.6807, 0.0, 0.0, 0.6293, 0.7573] +2026-04-12 00:56:24.597810: Epoch time: 100.74 s +2026-04-12 00:56:25.759161: +2026-04-12 00:56:25.761395: Epoch 1309 +2026-04-12 00:56:25.763461: Current learning rate: 0.007 +2026-04-12 00:58:06.508991: train_loss -0.3517 +2026-04-12 00:58:06.516784: val_loss -0.3637 +2026-04-12 00:58:06.519378: Pseudo dice [0.0, 0.0, 0.6042, 0.0, 0.0, 0.5578, 0.5532] +2026-04-12 00:58:06.522185: Epoch time: 100.75 s +2026-04-12 00:58:07.689090: +2026-04-12 00:58:07.691298: Epoch 1310 +2026-04-12 00:58:07.693487: Current learning rate: 0.007 +2026-04-12 00:59:48.236335: train_loss -0.3494 +2026-04-12 00:59:48.244928: val_loss -0.3463 +2026-04-12 00:59:48.247857: Pseudo dice [0.0, 0.0, 0.7095, 0.0, 0.0, 0.4649, 0.8154] +2026-04-12 00:59:48.250686: Epoch time: 100.55 s +2026-04-12 00:59:49.422617: +2026-04-12 00:59:49.428007: Epoch 1311 +2026-04-12 00:59:49.430553: Current learning rate: 0.00699 +2026-04-12 01:01:31.852107: train_loss -0.3608 +2026-04-12 01:01:31.860805: val_loss -0.3587 +2026-04-12 01:01:31.864039: Pseudo dice [0.0, 0.0, 0.6765, 0.0, 0.0, 0.127, 0.6938] +2026-04-12 01:01:31.866595: Epoch time: 102.43 s +2026-04-12 01:01:33.020982: +2026-04-12 01:01:33.023166: Epoch 1312 +2026-04-12 01:01:33.025364: Current learning rate: 0.00699 +2026-04-12 01:03:15.601131: train_loss -0.359 +2026-04-12 01:03:15.613441: val_loss -0.3674 +2026-04-12 01:03:15.616518: Pseudo dice [0.0, 0.0, 0.688, 0.0, 0.0, 0.4999, 0.7036] +2026-04-12 01:03:15.620440: Epoch time: 102.58 s +2026-04-12 01:03:16.814932: +2026-04-12 01:03:16.817878: Epoch 1313 +2026-04-12 01:03:16.821940: Current learning rate: 0.00699 +2026-04-12 01:05:01.317209: train_loss -0.3717 +2026-04-12 01:05:01.325051: val_loss -0.371 +2026-04-12 01:05:01.327960: Pseudo dice [0.0, 0.0, 0.6709, 0.0, 0.0, 0.7588, 0.7333] +2026-04-12 01:05:01.330774: Epoch time: 104.51 s +2026-04-12 01:05:02.541168: +2026-04-12 01:05:02.543441: Epoch 1314 +2026-04-12 01:05:02.545786: Current learning rate: 0.00699 +2026-04-12 01:14:22.375993: train_loss -0.3633 +2026-04-12 01:14:22.386051: val_loss -0.3864 +2026-04-12 01:14:22.389081: Pseudo dice [0.0, 0.0, 0.6539, 0.356, 0.0, 0.671, 0.8674] +2026-04-12 01:14:22.392524: Epoch time: 559.84 s +2026-04-12 01:14:23.590517: +2026-04-12 01:14:23.593832: Epoch 1315 +2026-04-12 01:14:23.600036: Current learning rate: 0.00699 +2026-04-12 01:38:43.205715: train_loss -0.3704 +2026-04-12 01:38:43.234573: val_loss -0.3671 +2026-04-12 01:38:43.245285: Pseudo dice [0.0, 0.0, 0.7238, 0.0, 0.0, 0.7571, 0.8931] +2026-04-12 01:38:43.255002: Epoch time: 1459.62 s +2026-04-12 01:38:44.748269: +2026-04-12 01:38:44.753351: Epoch 1316 +2026-04-12 01:38:44.757073: Current learning rate: 0.00698 +2026-04-12 02:07:26.533683: train_loss -0.3822 +2026-04-12 02:07:26.542284: val_loss -0.3861 +2026-04-12 02:07:26.546031: Pseudo dice [0.0, 0.0, 0.4325, 0.3359, 0.0, 0.7447, 0.8011] +2026-04-12 02:07:26.549515: Epoch time: 1721.79 s +2026-04-12 02:07:27.805468: +2026-04-12 02:07:27.808313: Epoch 1317 +2026-04-12 02:07:27.811774: Current learning rate: 0.00698 +2026-04-12 02:11:41.357481: train_loss -0.3754 +2026-04-12 02:11:41.369416: val_loss -0.3255 +2026-04-12 02:11:41.373247: Pseudo dice [0.0, 0.0, 0.5416, 0.0978, 0.0, 0.742, 0.7401] +2026-04-12 02:11:41.376741: Epoch time: 253.56 s +2026-04-12 02:11:42.559278: +2026-04-12 02:11:42.568597: Epoch 1318 +2026-04-12 02:11:42.581405: Current learning rate: 0.00698 +2026-04-12 02:19:39.522372: train_loss -0.3656 +2026-04-12 02:19:39.541781: val_loss -0.3814 +2026-04-12 02:19:39.549088: Pseudo dice [0.0, 0.0, 0.694, 0.0, 0.0, 0.6762, 0.8156] +2026-04-12 02:19:39.555244: Epoch time: 476.97 s +2026-04-12 02:19:40.835566: +2026-04-12 02:19:40.843058: Epoch 1319 +2026-04-12 02:19:40.852427: Current learning rate: 0.00698 +2026-04-12 02:40:39.093227: train_loss -0.3546 +2026-04-12 02:40:39.102962: val_loss -0.3936 +2026-04-12 02:40:39.106385: Pseudo dice [0.0, 0.0, 0.736, 0.5856, 0.0, 0.7843, 0.6544] +2026-04-12 02:40:39.109279: Epoch time: 1258.26 s +2026-04-12 02:40:40.284436: +2026-04-12 02:40:40.287685: Epoch 1320 +2026-04-12 02:40:40.290151: Current learning rate: 0.00697 +2026-04-12 02:42:21.416055: train_loss -0.349 +2026-04-12 02:42:21.422575: val_loss -0.3598 +2026-04-12 02:42:21.425170: Pseudo dice [0.0, 0.0, 0.656, 0.0, 0.0, 0.6763, 0.449] +2026-04-12 02:42:21.428365: Epoch time: 101.13 s +2026-04-12 02:42:22.615629: +2026-04-12 02:42:22.617809: Epoch 1321 +2026-04-12 02:42:22.620170: Current learning rate: 0.00697 +2026-04-12 02:44:49.755186: train_loss -0.3555 +2026-04-12 02:44:49.765243: val_loss -0.3551 +2026-04-12 02:44:49.768999: Pseudo dice [0.0, 0.0, 0.5089, 0.0, 0.0, 0.66, 0.7278] +2026-04-12 02:44:49.772828: Epoch time: 147.14 s +2026-04-12 02:44:50.975048: +2026-04-12 02:44:50.979131: Epoch 1322 +2026-04-12 02:44:50.982646: Current learning rate: 0.00697 +2026-04-12 02:46:32.522335: train_loss -0.3398 +2026-04-12 02:46:32.530217: val_loss -0.3549 +2026-04-12 02:46:32.533368: Pseudo dice [0.0, 0.0, 0.5384, 0.0, 0.2398, 0.5609, 0.3645] +2026-04-12 02:46:32.536787: Epoch time: 101.55 s +2026-04-12 02:46:34.784141: +2026-04-12 02:46:34.787231: Epoch 1323 +2026-04-12 02:46:34.790521: Current learning rate: 0.00697 +2026-04-12 02:48:15.395220: train_loss -0.3596 +2026-04-12 02:48:15.405483: val_loss -0.3363 +2026-04-12 02:48:15.408217: Pseudo dice [0.0, 0.0, 0.5932, 0.1231, 0.0, 0.516, 0.2654] +2026-04-12 02:48:15.411282: Epoch time: 100.61 s +2026-04-12 02:48:16.595256: +2026-04-12 02:48:16.597453: Epoch 1324 +2026-04-12 02:48:16.599717: Current learning rate: 0.00696 +2026-04-12 02:49:57.423104: train_loss -0.322 +2026-04-12 02:49:57.429806: val_loss -0.2782 +2026-04-12 02:49:57.433050: Pseudo dice [0.0, 0.0, 0.6586, 0.0518, 0.0, 0.7865, 0.3202] +2026-04-12 02:49:57.436317: Epoch time: 100.83 s +2026-04-12 02:49:58.606377: +2026-04-12 02:49:58.608683: Epoch 1325 +2026-04-12 02:49:58.611983: Current learning rate: 0.00696 +2026-04-12 02:51:39.801810: train_loss -0.3464 +2026-04-12 02:51:39.809570: val_loss -0.358 +2026-04-12 02:51:39.812968: Pseudo dice [0.0, 0.0, 0.6023, 0.2819, 0.0, 0.7409, 0.5477] +2026-04-12 02:51:39.817376: Epoch time: 101.2 s +2026-04-12 02:51:40.976068: +2026-04-12 02:51:40.978698: Epoch 1326 +2026-04-12 02:51:40.982007: Current learning rate: 0.00696 +2026-04-12 02:53:22.007246: train_loss -0.3241 +2026-04-12 02:53:22.015357: val_loss -0.3215 +2026-04-12 02:53:22.018181: Pseudo dice [0.0, 0.0, 0.745, 0.0, 0.0, 0.7203, 0.5769] +2026-04-12 02:53:22.022162: Epoch time: 101.03 s +2026-04-12 02:53:23.197738: +2026-04-12 02:53:23.200069: Epoch 1327 +2026-04-12 02:53:23.202549: Current learning rate: 0.00696 +2026-04-12 02:55:03.943909: train_loss -0.3107 +2026-04-12 02:55:03.952363: val_loss -0.3149 +2026-04-12 02:55:03.955511: Pseudo dice [0.0, 0.0, 0.6544, 0.0, 0.0, 0.6206, 0.0] +2026-04-12 02:55:03.958562: Epoch time: 100.75 s +2026-04-12 02:55:05.120631: +2026-04-12 02:55:05.122581: Epoch 1328 +2026-04-12 02:55:05.125362: Current learning rate: 0.00696 +2026-04-12 02:56:45.930210: train_loss -0.3427 +2026-04-12 02:56:45.937912: val_loss -0.3848 +2026-04-12 02:56:45.941079: Pseudo dice [0.0, 0.0, 0.5973, 0.0, 0.0, 0.8038, 0.0] +2026-04-12 02:56:45.945226: Epoch time: 100.81 s +2026-04-12 02:56:47.119319: +2026-04-12 02:56:47.122210: Epoch 1329 +2026-04-12 02:56:47.126319: Current learning rate: 0.00695 +2026-04-12 02:58:29.111850: train_loss -0.3345 +2026-04-12 02:58:29.126761: val_loss -0.334 +2026-04-12 02:58:29.130598: Pseudo dice [0.0, 0.0, 0.5151, 0.0, 0.0, 0.6945, 0.0014] +2026-04-12 02:58:29.133546: Epoch time: 102.0 s +2026-04-12 02:58:30.300070: +2026-04-12 02:58:30.302651: Epoch 1330 +2026-04-12 02:58:30.305208: Current learning rate: 0.00695 +2026-04-12 03:00:11.775921: train_loss -0.2919 +2026-04-12 03:00:11.783073: val_loss -0.2152 +2026-04-12 03:00:11.786227: Pseudo dice [0.0, 0.0, 0.3138, 0.0, 0.0, 0.0, 0.0] +2026-04-12 03:00:11.789990: Epoch time: 101.48 s +2026-04-12 03:00:12.952674: +2026-04-12 03:00:12.955294: Epoch 1331 +2026-04-12 03:00:12.957912: Current learning rate: 0.00695 +2026-04-12 03:01:54.310293: train_loss -0.3387 +2026-04-12 03:01:54.317729: val_loss -0.4074 +2026-04-12 03:01:54.320054: Pseudo dice [0.0, 0.0, 0.7293, 0.0, 0.0, 0.6973, 0.6824] +2026-04-12 03:01:54.323740: Epoch time: 101.36 s +2026-04-12 03:01:55.503307: +2026-04-12 03:01:55.507297: Epoch 1332 +2026-04-12 03:01:55.511042: Current learning rate: 0.00695 +2026-04-12 03:03:37.347833: train_loss -0.3543 +2026-04-12 03:03:37.357352: val_loss -0.3338 +2026-04-12 03:03:37.360464: Pseudo dice [0.0, 0.0, 0.3549, 0.0189, 0.0182, 0.1627, 0.3709] +2026-04-12 03:03:37.364185: Epoch time: 101.85 s +2026-04-12 03:03:38.549955: +2026-04-12 03:03:38.552805: Epoch 1333 +2026-04-12 03:03:38.556798: Current learning rate: 0.00694 +2026-04-12 03:05:20.202224: train_loss -0.3669 +2026-04-12 03:05:20.209671: val_loss -0.3415 +2026-04-12 03:05:20.211949: Pseudo dice [0.0, 0.0, 0.7199, 0.0031, 0.0171, 0.3878, 0.5719] +2026-04-12 03:05:20.216004: Epoch time: 101.66 s +2026-04-12 03:05:21.401329: +2026-04-12 03:05:21.403657: Epoch 1334 +2026-04-12 03:05:21.406117: Current learning rate: 0.00694 +2026-04-12 03:07:02.938099: train_loss -0.3728 +2026-04-12 03:07:02.944723: val_loss -0.3872 +2026-04-12 03:07:02.947429: Pseudo dice [0.0, 0.0, 0.7026, 0.2057, 0.267, 0.654, 0.715] +2026-04-12 03:07:02.950152: Epoch time: 101.54 s +2026-04-12 03:07:04.119039: +2026-04-12 03:07:04.121352: Epoch 1335 +2026-04-12 03:07:04.124084: Current learning rate: 0.00694 +2026-04-12 03:08:45.814776: train_loss -0.324 +2026-04-12 03:08:45.826063: val_loss -0.204 +2026-04-12 03:08:45.829289: Pseudo dice [0.0, 0.0, 0.0263, 0.0, 0.0, 0.0, 0.0] +2026-04-12 03:08:45.833410: Epoch time: 101.7 s +2026-04-12 03:08:47.042496: +2026-04-12 03:08:47.048850: Epoch 1336 +2026-04-12 03:08:47.053310: Current learning rate: 0.00694 +2026-04-12 03:10:29.132931: train_loss -0.2845 +2026-04-12 03:10:29.143070: val_loss -0.2603 +2026-04-12 03:10:29.146039: Pseudo dice [0.0, 0.0, 0.5192, 0.0, 0.0, 0.2269, 0.1718] +2026-04-12 03:10:29.149514: Epoch time: 102.09 s +2026-04-12 03:10:30.353289: +2026-04-12 03:10:30.355963: Epoch 1337 +2026-04-12 03:10:30.358195: Current learning rate: 0.00693 +2026-04-12 03:12:12.317557: train_loss -0.3273 +2026-04-12 03:12:12.325731: val_loss -0.2906 +2026-04-12 03:12:12.328982: Pseudo dice [0.0, 0.0, 0.5869, 0.0, 0.0, 0.0822, 0.0904] +2026-04-12 03:12:12.332041: Epoch time: 101.97 s +2026-04-12 03:12:13.524046: +2026-04-12 03:12:13.526718: Epoch 1338 +2026-04-12 03:12:13.531456: Current learning rate: 0.00693 +2026-04-12 03:13:54.506003: train_loss -0.3341 +2026-04-12 03:13:54.517967: val_loss -0.3691 +2026-04-12 03:13:54.521514: Pseudo dice [0.0, 0.0, 0.7134, 0.0, 0.0, 0.4084, 0.0002] +2026-04-12 03:13:54.524837: Epoch time: 100.99 s +2026-04-12 03:13:55.744181: +2026-04-12 03:13:55.749813: Epoch 1339 +2026-04-12 03:13:55.752737: Current learning rate: 0.00693 +2026-04-12 03:15:38.391545: train_loss -0.3296 +2026-04-12 03:15:38.400728: val_loss -0.2513 +2026-04-12 03:15:38.406435: Pseudo dice [0.0, 0.0, 0.6398, 0.0, 0.0, 0.6412, 0.5701] +2026-04-12 03:15:38.410348: Epoch time: 102.65 s +2026-04-12 03:15:39.580541: +2026-04-12 03:15:39.584120: Epoch 1340 +2026-04-12 03:15:39.592310: Current learning rate: 0.00693 +2026-04-12 03:17:20.593089: train_loss -0.3638 +2026-04-12 03:17:20.600402: val_loss -0.3788 +2026-04-12 03:17:20.605653: Pseudo dice [0.0, 0.0, 0.6651, 0.0, 0.0, 0.729, 0.5521] +2026-04-12 03:17:20.608262: Epoch time: 101.02 s +2026-04-12 03:17:21.810764: +2026-04-12 03:17:21.812793: Epoch 1341 +2026-04-12 03:17:21.815298: Current learning rate: 0.00692 +2026-04-12 03:19:02.549068: train_loss -0.3472 +2026-04-12 03:19:02.556396: val_loss -0.37 +2026-04-12 03:19:02.559474: Pseudo dice [0.0, 0.0, 0.6896, 0.5184, 0.0, 0.5879, 0.1969] +2026-04-12 03:19:02.562341: Epoch time: 100.74 s +2026-04-12 03:19:03.748647: +2026-04-12 03:19:03.750744: Epoch 1342 +2026-04-12 03:19:03.752808: Current learning rate: 0.00692 +2026-04-12 03:20:45.327140: train_loss -0.3318 +2026-04-12 03:20:45.335615: val_loss -0.2835 +2026-04-12 03:20:45.339527: Pseudo dice [0.0, 0.0, 0.4966, 0.0, 0.0, 0.5461, 0.2789] +2026-04-12 03:20:45.343678: Epoch time: 101.58 s +2026-04-12 03:20:47.918093: +2026-04-12 03:20:47.920742: Epoch 1343 +2026-04-12 03:20:47.923736: Current learning rate: 0.00692 +2026-04-12 03:22:29.776227: train_loss -0.2981 +2026-04-12 03:22:29.785206: val_loss -0.3 +2026-04-12 03:22:29.787739: Pseudo dice [0.0, 0.0, 0.6411, 0.0, 0.0, 0.0, 0.281] +2026-04-12 03:22:29.791684: Epoch time: 101.86 s +2026-04-12 03:22:30.973904: +2026-04-12 03:22:30.976349: Epoch 1344 +2026-04-12 03:22:30.978794: Current learning rate: 0.00692 +2026-04-12 03:24:12.441077: train_loss -0.3255 +2026-04-12 03:24:12.448459: val_loss -0.3556 +2026-04-12 03:24:12.451507: Pseudo dice [0.0, 0.0, 0.691, 0.0, 0.0, 0.0, 0.7574] +2026-04-12 03:24:12.454553: Epoch time: 101.47 s +2026-04-12 03:24:13.651961: +2026-04-12 03:24:13.656283: Epoch 1345 +2026-04-12 03:24:13.659012: Current learning rate: 0.00692 +2026-04-12 03:25:55.382532: train_loss -0.3442 +2026-04-12 03:25:55.399036: val_loss -0.238 +2026-04-12 03:25:55.402425: Pseudo dice [0.0, 0.0, 0.576, 0.0, 0.0, 0.0, 0.0708] +2026-04-12 03:25:55.406993: Epoch time: 101.73 s +2026-04-12 03:25:56.649889: +2026-04-12 03:25:56.654583: Epoch 1346 +2026-04-12 03:25:56.657899: Current learning rate: 0.00691 +2026-04-12 03:27:37.166739: train_loss -0.3195 +2026-04-12 03:27:37.176406: val_loss -0.3507 +2026-04-12 03:27:37.178930: Pseudo dice [0.0, 0.0, 0.5711, 0.0, 0.0, 0.003, 0.1645] +2026-04-12 03:27:37.182221: Epoch time: 100.52 s +2026-04-12 03:27:38.395008: +2026-04-12 03:27:38.398652: Epoch 1347 +2026-04-12 03:27:38.402558: Current learning rate: 0.00691 +2026-04-12 03:29:19.624604: train_loss -0.3219 +2026-04-12 03:29:19.633195: val_loss -0.3464 +2026-04-12 03:29:19.637260: Pseudo dice [0.0, 0.0, 0.7284, 0.0, 0.0, 0.1243, 0.0] +2026-04-12 03:29:19.640720: Epoch time: 101.23 s +2026-04-12 03:29:20.848387: +2026-04-12 03:29:20.851283: Epoch 1348 +2026-04-12 03:29:20.853933: Current learning rate: 0.00691 +2026-04-12 03:31:01.574240: train_loss -0.3301 +2026-04-12 03:31:01.579520: val_loss -0.3964 +2026-04-12 03:31:01.581547: Pseudo dice [0.0, 0.0, 0.7138, 0.0, 0.0, 0.7605, 0.1775] +2026-04-12 03:31:01.584302: Epoch time: 100.73 s +2026-04-12 03:31:02.784001: +2026-04-12 03:31:02.786070: Epoch 1349 +2026-04-12 03:31:02.788363: Current learning rate: 0.00691 +2026-04-12 03:32:44.944680: train_loss -0.3065 +2026-04-12 03:32:44.953727: val_loss -0.2929 +2026-04-12 03:32:44.958216: Pseudo dice [0.0, 0.0, 0.4462, 0.3801, 0.0, 0.0036, 0.0006] +2026-04-12 03:32:44.961933: Epoch time: 102.16 s +2026-04-12 03:32:48.018058: +2026-04-12 03:32:48.020177: Epoch 1350 +2026-04-12 03:32:48.022565: Current learning rate: 0.0069 +2026-04-12 03:34:28.344182: train_loss -0.3068 +2026-04-12 03:34:28.351362: val_loss -0.2757 +2026-04-12 03:34:28.353764: Pseudo dice [0.0, 0.0, 0.578, 0.0, 0.0, 0.2183, 0.2869] +2026-04-12 03:34:28.356105: Epoch time: 100.33 s +2026-04-12 03:34:29.733385: +2026-04-12 03:34:29.735715: Epoch 1351 +2026-04-12 03:34:29.738183: Current learning rate: 0.0069 +2026-04-12 03:36:11.329560: train_loss -0.3267 +2026-04-12 03:36:11.339530: val_loss -0.3163 +2026-04-12 03:36:11.342575: Pseudo dice [0.0, 0.0, 0.5798, 0.0, 0.0, 0.0, 0.4417] +2026-04-12 03:36:11.345677: Epoch time: 101.6 s +2026-04-12 03:36:12.519768: +2026-04-12 03:36:12.523773: Epoch 1352 +2026-04-12 03:36:12.527382: Current learning rate: 0.0069 +2026-04-12 03:37:54.304695: train_loss -0.3435 +2026-04-12 03:37:54.313375: val_loss -0.3548 +2026-04-12 03:37:54.316118: Pseudo dice [0.0, 0.0, 0.5986, 0.0, 0.0, 0.1695, 0.8406] +2026-04-12 03:37:54.320045: Epoch time: 101.79 s +2026-04-12 03:37:55.533281: +2026-04-12 03:37:55.535996: Epoch 1353 +2026-04-12 03:37:55.539748: Current learning rate: 0.0069 +2026-04-12 03:39:37.804794: train_loss -0.3423 +2026-04-12 03:39:37.816665: val_loss -0.3789 +2026-04-12 03:39:37.819774: Pseudo dice [0.0, 0.0, 0.7191, 0.0, 0.0, 0.2017, 0.6608] +2026-04-12 03:39:37.824272: Epoch time: 102.27 s +2026-04-12 03:39:39.019874: +2026-04-12 03:39:39.022892: Epoch 1354 +2026-04-12 03:39:39.027595: Current learning rate: 0.00689 +2026-04-12 03:41:21.090451: train_loss -0.3399 +2026-04-12 03:41:21.098173: val_loss -0.3337 +2026-04-12 03:41:21.101048: Pseudo dice [0.0, 0.0, 0.374, 0.0, 0.0, 0.2423, 0.7125] +2026-04-12 03:41:21.105005: Epoch time: 102.07 s +2026-04-12 03:41:22.301491: +2026-04-12 03:41:22.303688: Epoch 1355 +2026-04-12 03:41:22.306508: Current learning rate: 0.00689 +2026-04-12 03:43:03.611555: train_loss -0.3427 +2026-04-12 03:43:03.621072: val_loss -0.3882 +2026-04-12 03:43:03.624433: Pseudo dice [0.0, 0.0, 0.7382, 0.0, 0.0, 0.7667, 0.7117] +2026-04-12 03:43:03.627843: Epoch time: 101.31 s +2026-04-12 03:43:04.827156: +2026-04-12 03:43:04.833239: Epoch 1356 +2026-04-12 03:43:04.840182: Current learning rate: 0.00689 +2026-04-12 03:44:46.199832: train_loss -0.3832 +2026-04-12 03:44:46.208496: val_loss -0.3726 +2026-04-12 03:44:46.210920: Pseudo dice [0.0, 0.0, 0.628, 0.1232, 0.0, 0.6126, 0.3496] +2026-04-12 03:44:46.213727: Epoch time: 101.38 s +2026-04-12 03:44:47.414833: +2026-04-12 03:44:47.417483: Epoch 1357 +2026-04-12 03:44:47.420029: Current learning rate: 0.00689 +2026-04-12 03:46:29.318701: train_loss -0.361 +2026-04-12 03:46:29.326147: val_loss -0.1881 +2026-04-12 03:46:29.329680: Pseudo dice [0.0, 0.0, 0.3496, 0.0, 0.0, 0.1071, 0.4577] +2026-04-12 03:46:29.332628: Epoch time: 101.91 s +2026-04-12 03:46:30.542275: +2026-04-12 03:46:30.544351: Epoch 1358 +2026-04-12 03:46:30.547140: Current learning rate: 0.00688 +2026-04-12 03:48:12.802599: train_loss -0.3643 +2026-04-12 03:48:12.809039: val_loss -0.3619 +2026-04-12 03:48:12.811260: Pseudo dice [0.0, 0.0, 0.7303, 0.0, 0.0, 0.7182, 0.5395] +2026-04-12 03:48:12.813809: Epoch time: 102.26 s +2026-04-12 03:48:13.984934: +2026-04-12 03:48:13.987586: Epoch 1359 +2026-04-12 03:48:13.990330: Current learning rate: 0.00688 +2026-04-12 03:49:55.127793: train_loss -0.3548 +2026-04-12 03:49:55.135740: val_loss -0.3405 +2026-04-12 03:49:55.138078: Pseudo dice [0.0, 0.0, 0.5362, 0.0, 0.2522, 0.6397, 0.8311] +2026-04-12 03:49:55.140865: Epoch time: 101.15 s +2026-04-12 03:49:56.400262: +2026-04-12 03:49:56.402065: Epoch 1360 +2026-04-12 03:49:56.404887: Current learning rate: 0.00688 +2026-04-12 03:51:37.098299: train_loss -0.3593 +2026-04-12 03:51:37.107103: val_loss -0.3442 +2026-04-12 03:51:37.109543: Pseudo dice [0.0, 0.0, 0.5514, 0.0, 0.0, 0.4936, 0.7982] +2026-04-12 03:51:37.112417: Epoch time: 100.7 s +2026-04-12 03:51:38.300505: +2026-04-12 03:51:38.302912: Epoch 1361 +2026-04-12 03:51:38.305954: Current learning rate: 0.00688 +2026-04-12 03:53:21.973423: train_loss -0.337 +2026-04-12 03:53:21.981108: val_loss -0.3428 +2026-04-12 03:53:21.983451: Pseudo dice [0.0, 0.0, 0.55, 0.0, 0.3849, 0.675, 0.4691] +2026-04-12 03:53:21.986773: Epoch time: 103.68 s +2026-04-12 03:53:23.190196: +2026-04-12 03:53:23.192329: Epoch 1362 +2026-04-12 03:53:23.194550: Current learning rate: 0.00688 +2026-04-12 03:55:05.311033: train_loss -0.3378 +2026-04-12 03:55:05.323306: val_loss -0.3008 +2026-04-12 03:55:05.335891: Pseudo dice [0.0, 0.0, 0.6675, 0.1363, 0.3887, 0.4983, 0.4594] +2026-04-12 03:55:05.338743: Epoch time: 102.12 s +2026-04-12 03:55:06.802207: +2026-04-12 03:55:06.804347: Epoch 1363 +2026-04-12 03:55:06.806764: Current learning rate: 0.00687 +2026-04-12 03:56:47.535982: train_loss -0.357 +2026-04-12 03:56:47.541716: val_loss -0.3945 +2026-04-12 03:56:47.544008: Pseudo dice [0.0, 0.0, 0.7182, 0.76, 0.0311, 0.7076, 0.7618] +2026-04-12 03:56:47.546054: Epoch time: 100.74 s +2026-04-12 03:56:48.736730: +2026-04-12 03:56:48.739399: Epoch 1364 +2026-04-12 03:56:48.741746: Current learning rate: 0.00687 +2026-04-12 03:58:29.330466: train_loss -0.3608 +2026-04-12 03:58:29.340178: val_loss -0.3081 +2026-04-12 03:58:29.343664: Pseudo dice [0.0, 0.0, 0.6365, 0.0, 0.1944, 0.7684, 0.6378] +2026-04-12 03:58:29.347404: Epoch time: 100.6 s +2026-04-12 03:58:30.526231: +2026-04-12 03:58:30.528424: Epoch 1365 +2026-04-12 03:58:30.534273: Current learning rate: 0.00687 +2026-04-12 04:00:10.892196: train_loss -0.3284 +2026-04-12 04:00:10.897732: val_loss -0.3535 +2026-04-12 04:00:10.900356: Pseudo dice [0.0, 0.0, 0.512, 0.2041, 0.2757, 0.8357, 0.3928] +2026-04-12 04:00:10.903184: Epoch time: 100.37 s +2026-04-12 04:00:12.092926: +2026-04-12 04:00:12.094757: Epoch 1366 +2026-04-12 04:00:12.096769: Current learning rate: 0.00687 +2026-04-12 04:01:52.700058: train_loss -0.3726 +2026-04-12 04:01:52.706309: val_loss -0.3717 +2026-04-12 04:01:52.708371: Pseudo dice [0.0, 0.0, 0.5161, 0.7081, 0.2372, 0.8095, 0.5938] +2026-04-12 04:01:52.711241: Epoch time: 100.61 s +2026-04-12 04:01:53.945952: +2026-04-12 04:01:53.948796: Epoch 1367 +2026-04-12 04:01:53.951121: Current learning rate: 0.00686 +2026-04-12 04:03:34.666171: train_loss -0.3224 +2026-04-12 04:03:34.676325: val_loss -0.2085 +2026-04-12 04:03:34.678522: Pseudo dice [0.0, 0.0, 0.448, 0.0196, 0.0306, 0.1336, 0.3269] +2026-04-12 04:03:34.681750: Epoch time: 100.72 s +2026-04-12 04:03:35.925901: +2026-04-12 04:03:35.928055: Epoch 1368 +2026-04-12 04:03:35.930431: Current learning rate: 0.00686 +2026-04-12 04:05:16.545155: train_loss -0.3542 +2026-04-12 04:05:16.552292: val_loss -0.4133 +2026-04-12 04:05:16.554607: Pseudo dice [0.0, 0.0, 0.7548, 0.0, 0.3944, 0.7707, 0.8238] +2026-04-12 04:05:16.557437: Epoch time: 100.62 s +2026-04-12 04:05:17.742997: +2026-04-12 04:05:17.749625: Epoch 1369 +2026-04-12 04:05:17.753004: Current learning rate: 0.00686 +2026-04-12 04:06:58.788250: train_loss -0.3762 +2026-04-12 04:06:58.796052: val_loss -0.2582 +2026-04-12 04:06:58.800511: Pseudo dice [0.0, 0.0, 0.3019, 0.0, 0.0, 0.7117, 0.4777] +2026-04-12 04:06:58.805054: Epoch time: 101.05 s +2026-04-12 04:06:59.991619: +2026-04-12 04:06:59.994666: Epoch 1370 +2026-04-12 04:06:59.998779: Current learning rate: 0.00686 +2026-04-12 04:08:40.849443: train_loss -0.3804 +2026-04-12 04:08:40.856776: val_loss -0.3856 +2026-04-12 04:08:40.859326: Pseudo dice [0.0, 0.0, 0.6097, 0.0, 0.4037, 0.6954, 0.7918] +2026-04-12 04:08:40.862891: Epoch time: 100.86 s +2026-04-12 04:08:42.081988: +2026-04-12 04:08:42.084976: Epoch 1371 +2026-04-12 04:08:42.087464: Current learning rate: 0.00685 +2026-04-12 04:10:22.971103: train_loss -0.3453 +2026-04-12 04:10:22.979143: val_loss -0.3067 +2026-04-12 04:10:22.982286: Pseudo dice [0.0, 0.0, 0.4403, 0.0, 0.1316, 0.0148, 0.6228] +2026-04-12 04:10:22.985388: Epoch time: 100.89 s +2026-04-12 04:10:24.179831: +2026-04-12 04:10:24.182320: Epoch 1372 +2026-04-12 04:10:24.184515: Current learning rate: 0.00685 +2026-04-12 04:12:04.648110: train_loss -0.2956 +2026-04-12 04:12:04.654152: val_loss -0.3434 +2026-04-12 04:12:04.656065: Pseudo dice [0.0, 0.0, 0.2992, 0.5001, 0.0, 0.4271, 0.4811] +2026-04-12 04:12:04.658231: Epoch time: 100.47 s +2026-04-12 04:12:05.846633: +2026-04-12 04:12:05.849177: Epoch 1373 +2026-04-12 04:12:05.851758: Current learning rate: 0.00685 +2026-04-12 04:13:47.785211: train_loss -0.3373 +2026-04-12 04:13:47.794694: val_loss -0.3629 +2026-04-12 04:13:47.797359: Pseudo dice [0.0, 0.0, 0.8087, 0.7328, 0.0184, 0.6958, 0.6414] +2026-04-12 04:13:47.800905: Epoch time: 101.94 s +2026-04-12 04:13:48.989234: +2026-04-12 04:13:48.991516: Epoch 1374 +2026-04-12 04:13:48.994163: Current learning rate: 0.00685 +2026-04-12 04:15:30.102565: train_loss -0.3603 +2026-04-12 04:15:30.109113: val_loss -0.3683 +2026-04-12 04:15:30.111765: Pseudo dice [0.0, 0.0, 0.6177, 0.0, 0.0, 0.0836, 0.6457] +2026-04-12 04:15:30.114206: Epoch time: 101.12 s +2026-04-12 04:15:31.302547: +2026-04-12 04:15:31.305010: Epoch 1375 +2026-04-12 04:15:31.307408: Current learning rate: 0.00684 +2026-04-12 04:17:12.073183: train_loss -0.357 +2026-04-12 04:17:12.083972: val_loss -0.3015 +2026-04-12 04:17:12.089648: Pseudo dice [0.0, 0.0, 0.7772, 0.0, 0.2698, 0.6694, 0.4701] +2026-04-12 04:17:12.093288: Epoch time: 100.77 s +2026-04-12 04:17:13.268069: +2026-04-12 04:17:13.270121: Epoch 1376 +2026-04-12 04:17:13.272460: Current learning rate: 0.00684 +2026-04-12 04:18:54.188578: train_loss -0.3606 +2026-04-12 04:18:54.198971: val_loss -0.3976 +2026-04-12 04:18:54.201473: Pseudo dice [0.0, 0.0, 0.5631, 0.0, 0.3705, 0.6796, 0.8408] +2026-04-12 04:18:54.205091: Epoch time: 100.92 s +2026-04-12 04:18:55.381284: +2026-04-12 04:18:55.383278: Epoch 1377 +2026-04-12 04:18:55.385263: Current learning rate: 0.00684 +2026-04-12 04:20:36.457543: train_loss -0.3681 +2026-04-12 04:20:36.466556: val_loss -0.4158 +2026-04-12 04:20:36.469833: Pseudo dice [0.0, 0.0, 0.6951, 0.0, 0.0059, 0.8553, 0.7868] +2026-04-12 04:20:36.473345: Epoch time: 101.08 s +2026-04-12 04:20:37.678557: +2026-04-12 04:20:37.681104: Epoch 1378 +2026-04-12 04:20:37.684005: Current learning rate: 0.00684 +2026-04-12 04:22:18.493249: train_loss -0.3648 +2026-04-12 04:22:18.501711: val_loss -0.3551 +2026-04-12 04:22:18.505512: Pseudo dice [0.0, 0.0, 0.6284, 0.0, 0.2563, 0.6471, 0.7657] +2026-04-12 04:22:18.508712: Epoch time: 100.82 s +2026-04-12 04:22:19.694330: +2026-04-12 04:22:19.696444: Epoch 1379 +2026-04-12 04:22:19.699124: Current learning rate: 0.00684 +2026-04-12 04:23:59.951685: train_loss -0.3713 +2026-04-12 04:23:59.958940: val_loss -0.3754 +2026-04-12 04:23:59.961559: Pseudo dice [0.0, 0.0, 0.5809, 0.2988, 0.0, 0.762, 0.7416] +2026-04-12 04:23:59.964773: Epoch time: 100.26 s +2026-04-12 04:24:01.152438: +2026-04-12 04:24:01.154798: Epoch 1380 +2026-04-12 04:24:01.156981: Current learning rate: 0.00683 +2026-04-12 04:25:41.803963: train_loss -0.3378 +2026-04-12 04:25:41.812646: val_loss -0.3571 +2026-04-12 04:25:41.815540: Pseudo dice [0.0, 0.0, 0.7369, 0.0, 0.0, 0.5922, 0.6741] +2026-04-12 04:25:41.818977: Epoch time: 100.65 s +2026-04-12 04:25:43.028103: +2026-04-12 04:25:43.030481: Epoch 1381 +2026-04-12 04:25:43.033024: Current learning rate: 0.00683 +2026-04-12 04:27:24.057038: train_loss -0.3575 +2026-04-12 04:27:24.067782: val_loss -0.3808 +2026-04-12 04:27:24.070672: Pseudo dice [0.0, 0.0, 0.5594, 0.0, 0.0, 0.7033, 0.6022] +2026-04-12 04:27:24.074969: Epoch time: 101.03 s +2026-04-12 04:27:26.470060: +2026-04-12 04:27:26.472738: Epoch 1382 +2026-04-12 04:27:26.475754: Current learning rate: 0.00683 +2026-04-12 04:29:06.812518: train_loss -0.3502 +2026-04-12 04:29:06.819715: val_loss -0.3876 +2026-04-12 04:29:06.822958: Pseudo dice [0.0, 0.0, 0.6769, 0.0, 0.0, 0.8249, 0.6704] +2026-04-12 04:29:06.825682: Epoch time: 100.35 s +2026-04-12 04:29:08.036495: +2026-04-12 04:29:08.038993: Epoch 1383 +2026-04-12 04:29:08.041175: Current learning rate: 0.00683 +2026-04-12 04:30:49.708886: train_loss -0.3706 +2026-04-12 04:30:49.716781: val_loss -0.3723 +2026-04-12 04:30:49.721208: Pseudo dice [0.0, 0.0, 0.7204, 0.0, 0.0, 0.5617, 0.6578] +2026-04-12 04:30:49.726257: Epoch time: 101.68 s +2026-04-12 04:30:50.966207: +2026-04-12 04:30:50.968279: Epoch 1384 +2026-04-12 04:30:50.970262: Current learning rate: 0.00682 +2026-04-12 04:32:31.558679: train_loss -0.3118 +2026-04-12 04:32:31.567043: val_loss -0.1868 +2026-04-12 04:32:31.570837: Pseudo dice [0.0, 0.0, 0.4994, 0.0, 0.0, 0.5186, 0.6917] +2026-04-12 04:32:31.574968: Epoch time: 100.6 s +2026-04-12 04:32:32.789557: +2026-04-12 04:32:32.791524: Epoch 1385 +2026-04-12 04:32:32.794271: Current learning rate: 0.00682 +2026-04-12 04:34:14.535469: train_loss -0.3436 +2026-04-12 04:34:14.544887: val_loss -0.3016 +2026-04-12 04:34:14.547511: Pseudo dice [0.0, 0.0, 0.6066, 0.0, 0.1244, 0.5638, 0.5954] +2026-04-12 04:34:14.550884: Epoch time: 101.75 s +2026-04-12 04:34:15.763322: +2026-04-12 04:34:15.765610: Epoch 1386 +2026-04-12 04:34:15.767807: Current learning rate: 0.00682 +2026-04-12 04:35:56.802027: train_loss -0.3714 +2026-04-12 04:35:56.809965: val_loss -0.2222 +2026-04-12 04:35:56.813004: Pseudo dice [0.0, 0.0, 0.5059, 0.0001, 0.3391, 0.2304, 0.387] +2026-04-12 04:35:56.817351: Epoch time: 101.04 s +2026-04-12 04:35:58.030972: +2026-04-12 04:35:58.033362: Epoch 1387 +2026-04-12 04:35:58.036498: Current learning rate: 0.00682 +2026-04-12 04:37:39.024308: train_loss -0.318 +2026-04-12 04:37:39.032919: val_loss -0.3302 +2026-04-12 04:37:39.035669: Pseudo dice [0.0, 0.0, 0.6052, 0.0304, 0.0, 0.3484, 0.2323] +2026-04-12 04:37:39.038570: Epoch time: 101.0 s +2026-04-12 04:37:40.213964: +2026-04-12 04:37:40.215975: Epoch 1388 +2026-04-12 04:37:40.218060: Current learning rate: 0.00681 +2026-04-12 04:39:20.451270: train_loss -0.3296 +2026-04-12 04:39:20.457621: val_loss -0.3606 +2026-04-12 04:39:20.459507: Pseudo dice [0.0, 0.0, 0.7346, 0.0, 0.0267, 0.5777, 0.2218] +2026-04-12 04:39:20.461728: Epoch time: 100.24 s +2026-04-12 04:39:21.866410: +2026-04-12 04:39:21.868388: Epoch 1389 +2026-04-12 04:39:21.872935: Current learning rate: 0.00681 +2026-04-12 04:41:02.175912: train_loss -0.3543 +2026-04-12 04:41:02.183175: val_loss -0.3809 +2026-04-12 04:41:02.185286: Pseudo dice [0.0, 0.0, 0.6303, 0.0, 0.0, 0.8046, 0.8203] +2026-04-12 04:41:02.189596: Epoch time: 100.31 s +2026-04-12 04:41:03.413874: +2026-04-12 04:41:03.415828: Epoch 1390 +2026-04-12 04:41:03.418387: Current learning rate: 0.00681 +2026-04-12 04:42:43.633327: train_loss -0.3633 +2026-04-12 04:42:43.639925: val_loss -0.2858 +2026-04-12 04:42:43.645081: Pseudo dice [0.0, 0.0, 0.5464, 0.0009, 0.0, 0.4044, 0.7673] +2026-04-12 04:42:43.648375: Epoch time: 100.22 s +2026-04-12 04:42:44.844163: +2026-04-12 04:42:44.847055: Epoch 1391 +2026-04-12 04:42:44.849896: Current learning rate: 0.00681 +2026-04-12 04:44:25.120144: train_loss -0.3651 +2026-04-12 04:44:25.127385: val_loss -0.3759 +2026-04-12 04:44:25.129802: Pseudo dice [0.0, 0.0, 0.7422, 0.7016, 0.0, 0.6247, 0.8308] +2026-04-12 04:44:25.132544: Epoch time: 100.28 s +2026-04-12 04:44:26.309375: +2026-04-12 04:44:26.311182: Epoch 1392 +2026-04-12 04:44:26.313424: Current learning rate: 0.0068 +2026-04-12 04:46:06.869422: train_loss -0.3497 +2026-04-12 04:46:06.877011: val_loss -0.3108 +2026-04-12 04:46:06.879207: Pseudo dice [0.0, 0.0, 0.5391, 0.0, 0.0, 0.5016, 0.4224] +2026-04-12 04:46:06.881797: Epoch time: 100.56 s +2026-04-12 04:46:08.074922: +2026-04-12 04:46:08.076865: Epoch 1393 +2026-04-12 04:46:08.079079: Current learning rate: 0.0068 +2026-04-12 04:47:48.372301: train_loss -0.3737 +2026-04-12 04:47:48.380400: val_loss -0.343 +2026-04-12 04:47:48.382855: Pseudo dice [0.0, 0.0, 0.5475, 0.0938, 0.0, 0.7081, 0.5233] +2026-04-12 04:47:48.385524: Epoch time: 100.3 s +2026-04-12 04:47:49.572402: +2026-04-12 04:47:49.574964: Epoch 1394 +2026-04-12 04:47:49.577889: Current learning rate: 0.0068 +2026-04-12 04:49:29.860757: train_loss -0.3719 +2026-04-12 04:49:29.868806: val_loss -0.375 +2026-04-12 04:49:29.871130: Pseudo dice [0.0, 0.0, 0.7456, 0.0, 0.0, 0.7345, 0.7652] +2026-04-12 04:49:29.873997: Epoch time: 100.29 s +2026-04-12 04:49:31.047064: +2026-04-12 04:49:31.049470: Epoch 1395 +2026-04-12 04:49:31.052246: Current learning rate: 0.0068 +2026-04-12 04:51:11.507069: train_loss -0.3883 +2026-04-12 04:51:11.516147: val_loss -0.3736 +2026-04-12 04:51:11.518325: Pseudo dice [0.0, 0.0, 0.6816, 0.2444, 0.0, 0.066, 0.538] +2026-04-12 04:51:11.521023: Epoch time: 100.46 s +2026-04-12 04:51:12.701083: +2026-04-12 04:51:12.703885: Epoch 1396 +2026-04-12 04:51:12.706655: Current learning rate: 0.0068 +2026-04-12 04:52:53.478246: train_loss -0.3305 +2026-04-12 04:52:53.488548: val_loss -0.2936 +2026-04-12 04:52:53.491851: Pseudo dice [0.0, 0.0, 0.5398, 0.0, 0.0, 0.2611, 0.634] +2026-04-12 04:52:53.495031: Epoch time: 100.78 s +2026-04-12 04:52:54.684225: +2026-04-12 04:52:54.686297: Epoch 1397 +2026-04-12 04:52:54.688322: Current learning rate: 0.00679 +2026-04-12 04:54:35.101194: train_loss -0.3749 +2026-04-12 04:54:35.108871: val_loss -0.3664 +2026-04-12 04:54:35.111947: Pseudo dice [0.0, 0.0, 0.6488, 0.0, 0.0, 0.6394, 0.786] +2026-04-12 04:54:35.115028: Epoch time: 100.42 s +2026-04-12 04:54:36.306905: +2026-04-12 04:54:36.308964: Epoch 1398 +2026-04-12 04:54:36.310792: Current learning rate: 0.00679 +2026-04-12 04:56:16.879762: train_loss -0.343 +2026-04-12 04:56:16.885387: val_loss -0.3032 +2026-04-12 04:56:16.887474: Pseudo dice [0.0, 0.0, 0.673, 0.0172, 0.0, 0.691, 0.4731] +2026-04-12 04:56:16.889827: Epoch time: 100.58 s +2026-04-12 04:56:18.098116: +2026-04-12 04:56:18.100371: Epoch 1399 +2026-04-12 04:56:18.102573: Current learning rate: 0.00679 +2026-04-12 04:57:59.523430: train_loss -0.3623 +2026-04-12 04:57:59.531362: val_loss -0.3725 +2026-04-12 04:57:59.533927: Pseudo dice [0.0, 0.0, 0.733, 0.0, 0.0, 0.5477, 0.7144] +2026-04-12 04:57:59.537540: Epoch time: 101.43 s +2026-04-12 04:58:02.467951: +2026-04-12 04:58:02.471315: Epoch 1400 +2026-04-12 04:58:02.474360: Current learning rate: 0.00679 +2026-04-12 04:59:42.714934: train_loss -0.35 +2026-04-12 04:59:42.722762: val_loss -0.3023 +2026-04-12 04:59:42.725222: Pseudo dice [0.0, 0.0, 0.7117, 0.0126, 0.0341, 0.6459, 0.5343] +2026-04-12 04:59:42.728769: Epoch time: 100.25 s +2026-04-12 04:59:43.955292: +2026-04-12 04:59:43.957832: Epoch 1401 +2026-04-12 04:59:43.960143: Current learning rate: 0.00678 +2026-04-12 05:01:25.892055: train_loss -0.355 +2026-04-12 05:01:25.901887: val_loss -0.3254 +2026-04-12 05:01:25.904746: Pseudo dice [0.0, 0.0, 0.6892, 0.0, 0.3191, 0.3881, 0.7423] +2026-04-12 05:01:25.909983: Epoch time: 101.94 s +2026-04-12 05:01:27.140329: +2026-04-12 05:01:27.144696: Epoch 1402 +2026-04-12 05:01:27.149808: Current learning rate: 0.00678 +2026-04-12 05:03:08.145363: train_loss -0.3698 +2026-04-12 05:03:08.176147: val_loss -0.3647 +2026-04-12 05:03:08.183592: Pseudo dice [0.0, 0.0, 0.4655, 0.0, 0.0, 0.7698, 0.6696] +2026-04-12 05:03:08.192316: Epoch time: 101.01 s +2026-04-12 05:03:09.412874: +2026-04-12 05:03:09.415901: Epoch 1403 +2026-04-12 05:03:09.419010: Current learning rate: 0.00678 +2026-04-12 05:04:50.700863: train_loss -0.3597 +2026-04-12 05:04:50.709205: val_loss -0.3818 +2026-04-12 05:04:50.713176: Pseudo dice [0.0, 0.0, 0.4733, 0.0, 0.0, 0.7692, 0.7195] +2026-04-12 05:04:50.716445: Epoch time: 101.29 s +2026-04-12 05:04:51.930560: +2026-04-12 05:04:51.933125: Epoch 1404 +2026-04-12 05:04:51.935893: Current learning rate: 0.00678 +2026-04-12 05:06:32.947502: train_loss -0.35 +2026-04-12 05:06:32.955465: val_loss -0.2901 +2026-04-12 05:06:32.957762: Pseudo dice [0.0, 0.0, 0.5963, 0.0, 0.0, 0.3563, 0.5851] +2026-04-12 05:06:32.960865: Epoch time: 101.02 s +2026-04-12 05:06:34.181081: +2026-04-12 05:06:34.183328: Epoch 1405 +2026-04-12 05:06:34.186160: Current learning rate: 0.00677 +2026-04-12 05:08:15.907074: train_loss -0.3303 +2026-04-12 05:08:15.918335: val_loss -0.3106 +2026-04-12 05:08:15.921917: Pseudo dice [0.0, 0.0, 0.7281, 0.0199, 0.0, 0.7062, 0.6452] +2026-04-12 05:08:15.924815: Epoch time: 101.73 s +2026-04-12 05:08:17.126338: +2026-04-12 05:08:17.128486: Epoch 1406 +2026-04-12 05:08:17.130601: Current learning rate: 0.00677 +2026-04-12 05:09:58.362939: train_loss -0.3529 +2026-04-12 05:09:58.368869: val_loss -0.3634 +2026-04-12 05:09:58.371034: Pseudo dice [0.0, 0.0, 0.7011, 0.0, 0.0, 0.7117, 0.6216] +2026-04-12 05:09:58.374002: Epoch time: 101.24 s +2026-04-12 05:09:59.584534: +2026-04-12 05:09:59.586902: Epoch 1407 +2026-04-12 05:09:59.588929: Current learning rate: 0.00677 +2026-04-12 05:11:39.915626: train_loss -0.3604 +2026-04-12 05:11:39.924068: val_loss -0.3893 +2026-04-12 05:11:39.927686: Pseudo dice [0.0, 0.0, 0.7772, 0.0, 0.0, 0.8101, 0.763] +2026-04-12 05:11:39.931040: Epoch time: 100.33 s +2026-04-12 05:11:41.185624: +2026-04-12 05:11:41.187905: Epoch 1408 +2026-04-12 05:11:41.190516: Current learning rate: 0.00677 +2026-04-12 05:13:22.042906: train_loss -0.3483 +2026-04-12 05:13:22.050041: val_loss -0.3579 +2026-04-12 05:13:22.052489: Pseudo dice [0.0, 0.0, 0.4748, 0.0, 0.0, 0.6921, 0.6349] +2026-04-12 05:13:22.055292: Epoch time: 100.86 s +2026-04-12 05:13:23.269856: +2026-04-12 05:13:23.272228: Epoch 1409 +2026-04-12 05:13:23.274523: Current learning rate: 0.00676 +2026-04-12 05:15:03.691799: train_loss -0.3554 +2026-04-12 05:15:03.697905: val_loss -0.3403 +2026-04-12 05:15:03.701270: Pseudo dice [0.0, 0.0, 0.2957, 0.6632, 0.0, 0.4905, 0.3869] +2026-04-12 05:15:03.704501: Epoch time: 100.43 s +2026-04-12 05:15:04.905035: +2026-04-12 05:15:04.910898: Epoch 1410 +2026-04-12 05:15:04.913405: Current learning rate: 0.00676 +2026-04-12 05:16:45.278413: train_loss -0.349 +2026-04-12 05:16:45.286971: val_loss -0.3105 +2026-04-12 05:16:45.289465: Pseudo dice [0.0, 0.0, 0.6951, 0.0, 0.0, 0.5193, 0.6708] +2026-04-12 05:16:45.292681: Epoch time: 100.38 s +2026-04-12 05:16:46.522480: +2026-04-12 05:16:46.524781: Epoch 1411 +2026-04-12 05:16:46.530415: Current learning rate: 0.00676 +2026-04-12 05:18:26.785967: train_loss -0.375 +2026-04-12 05:18:26.795416: val_loss -0.3462 +2026-04-12 05:18:26.797988: Pseudo dice [0.0, 0.0, 0.7329, 0.1402, 0.0, 0.3822, 0.4781] +2026-04-12 05:18:26.800934: Epoch time: 100.27 s +2026-04-12 05:18:27.998257: +2026-04-12 05:18:28.000305: Epoch 1412 +2026-04-12 05:18:28.002698: Current learning rate: 0.00676 +2026-04-12 05:20:10.204430: train_loss -0.3591 +2026-04-12 05:20:10.211454: val_loss -0.3561 +2026-04-12 05:20:10.214112: Pseudo dice [0.0, 0.0, 0.5312, 0.1167, 0.0, 0.8158, 0.5647] +2026-04-12 05:20:10.216704: Epoch time: 102.21 s +2026-04-12 05:20:11.412798: +2026-04-12 05:20:11.415200: Epoch 1413 +2026-04-12 05:20:11.417786: Current learning rate: 0.00676 +2026-04-12 05:21:52.051400: train_loss -0.382 +2026-04-12 05:21:52.062957: val_loss -0.367 +2026-04-12 05:21:52.066031: Pseudo dice [0.0, 0.0, 0.6711, 0.0, 0.0, 0.4943, 0.4165] +2026-04-12 05:21:52.069276: Epoch time: 100.64 s +2026-04-12 05:21:53.246229: +2026-04-12 05:21:53.248250: Epoch 1414 +2026-04-12 05:21:53.250681: Current learning rate: 0.00675 +2026-04-12 05:23:33.403765: train_loss -0.3608 +2026-04-12 05:23:33.412368: val_loss -0.333 +2026-04-12 05:23:33.416407: Pseudo dice [0.0, 0.0, 0.6001, 0.354, 0.0, 0.7081, 0.7005] +2026-04-12 05:23:33.419351: Epoch time: 100.16 s +2026-04-12 05:23:34.601291: +2026-04-12 05:23:34.603969: Epoch 1415 +2026-04-12 05:23:34.607112: Current learning rate: 0.00675 +2026-04-12 05:25:14.821820: train_loss -0.3703 +2026-04-12 05:25:14.828309: val_loss -0.3702 +2026-04-12 05:25:14.830662: Pseudo dice [0.0, 0.0, 0.5693, 0.4311, 0.0002, 0.6956, 0.8149] +2026-04-12 05:25:14.833466: Epoch time: 100.22 s +2026-04-12 05:25:16.013619: +2026-04-12 05:25:16.015963: Epoch 1416 +2026-04-12 05:25:16.019229: Current learning rate: 0.00675 +2026-04-12 05:26:57.181115: train_loss -0.3676 +2026-04-12 05:26:57.188118: val_loss -0.3656 +2026-04-12 05:26:57.190919: Pseudo dice [0.0, 0.0, 0.689, 0.0, 0.0113, 0.7377, 0.3186] +2026-04-12 05:26:57.193992: Epoch time: 101.17 s +2026-04-12 05:26:58.406338: +2026-04-12 05:26:58.409006: Epoch 1417 +2026-04-12 05:26:58.411591: Current learning rate: 0.00675 +2026-04-12 05:28:39.259021: train_loss -0.3251 +2026-04-12 05:28:39.267040: val_loss -0.3805 +2026-04-12 05:28:39.270973: Pseudo dice [0.0, 0.0, 0.7601, 0.0, 0.0461, 0.2713, 0.7702] +2026-04-12 05:28:39.273652: Epoch time: 100.86 s +2026-04-12 05:28:40.473374: +2026-04-12 05:28:40.476128: Epoch 1418 +2026-04-12 05:28:40.479677: Current learning rate: 0.00674 +2026-04-12 05:30:22.439202: train_loss -0.3719 +2026-04-12 05:30:22.446156: val_loss -0.3818 +2026-04-12 05:30:22.448031: Pseudo dice [0.0, 0.0, 0.6962, 0.0, 0.2933, 0.814, 0.7813] +2026-04-12 05:30:22.451018: Epoch time: 101.97 s +2026-04-12 05:30:23.649598: +2026-04-12 05:30:23.651268: Epoch 1419 +2026-04-12 05:30:23.653551: Current learning rate: 0.00674 +2026-04-12 05:32:03.901490: train_loss -0.3724 +2026-04-12 05:32:03.907302: val_loss -0.3008 +2026-04-12 05:32:03.910446: Pseudo dice [0.0, 0.0, 0.0493, 0.2717, 0.2406, 0.3011, 0.7869] +2026-04-12 05:32:03.913109: Epoch time: 100.25 s +2026-04-12 05:32:05.137790: +2026-04-12 05:32:05.139670: Epoch 1420 +2026-04-12 05:32:05.141806: Current learning rate: 0.00674 +2026-04-12 05:33:45.518288: train_loss -0.3013 +2026-04-12 05:33:45.526256: val_loss -0.3227 +2026-04-12 05:33:45.528869: Pseudo dice [0.0, 0.0, 0.5369, 0.0, 0.0, 0.0967, 0.6623] +2026-04-12 05:33:45.531825: Epoch time: 100.38 s +2026-04-12 05:33:47.807498: +2026-04-12 05:33:47.809648: Epoch 1421 +2026-04-12 05:33:47.812149: Current learning rate: 0.00674 +2026-04-12 05:35:28.804487: train_loss -0.2827 +2026-04-12 05:35:28.809727: val_loss -0.3607 +2026-04-12 05:35:28.812569: Pseudo dice [0.0, 0.0, 0.5291, 0.0, 0.0, 0.0009, 0.452] +2026-04-12 05:35:28.815243: Epoch time: 101.0 s +2026-04-12 05:35:30.012963: +2026-04-12 05:35:30.015022: Epoch 1422 +2026-04-12 05:35:30.017392: Current learning rate: 0.00673 +2026-04-12 05:37:10.842993: train_loss -0.3723 +2026-04-12 05:37:10.851548: val_loss -0.2849 +2026-04-12 05:37:10.854502: Pseudo dice [0.0, 0.0, 0.4848, 0.0463, 0.0, 0.8161, 0.4321] +2026-04-12 05:37:10.857346: Epoch time: 100.83 s +2026-04-12 05:37:12.055856: +2026-04-12 05:37:12.058026: Epoch 1423 +2026-04-12 05:37:12.060360: Current learning rate: 0.00673 +2026-04-12 05:38:52.930505: train_loss -0.3533 +2026-04-12 05:38:52.937293: val_loss -0.3392 +2026-04-12 05:38:52.940792: Pseudo dice [0.0, 0.0, 0.6556, 0.0, 0.295, 0.5766, 0.2798] +2026-04-12 05:38:52.943567: Epoch time: 100.88 s +2026-04-12 05:38:54.133337: +2026-04-12 05:38:54.136631: Epoch 1424 +2026-04-12 05:38:54.140277: Current learning rate: 0.00673 +2026-04-12 05:40:35.432643: train_loss -0.3645 +2026-04-12 05:40:35.445509: val_loss -0.3771 +2026-04-12 05:40:35.448240: Pseudo dice [0.0, 0.0, 0.5698, 0.1737, 0.086, 0.7216, 0.741] +2026-04-12 05:40:35.451788: Epoch time: 101.3 s +2026-04-12 05:40:36.688758: +2026-04-12 05:40:36.690885: Epoch 1425 +2026-04-12 05:40:36.693332: Current learning rate: 0.00673 +2026-04-12 05:42:17.353706: train_loss -0.3425 +2026-04-12 05:42:17.361652: val_loss -0.3499 +2026-04-12 05:42:17.363919: Pseudo dice [0.0, 0.0, 0.7002, 0.4924, 0.1363, 0.1205, 0.3251] +2026-04-12 05:42:17.366590: Epoch time: 100.67 s +2026-04-12 05:42:18.557224: +2026-04-12 05:42:18.559880: Epoch 1426 +2026-04-12 05:42:18.562939: Current learning rate: 0.00673 +2026-04-12 05:43:59.000648: train_loss -0.352 +2026-04-12 05:43:59.008270: val_loss -0.3467 +2026-04-12 05:43:59.010476: Pseudo dice [0.0, 0.0, 0.4627, 0.0403, 0.2836, 0.1915, 0.5863] +2026-04-12 05:43:59.012806: Epoch time: 100.45 s +2026-04-12 05:44:00.197743: +2026-04-12 05:44:00.200119: Epoch 1427 +2026-04-12 05:44:00.202565: Current learning rate: 0.00672 +2026-04-12 05:45:40.660080: train_loss -0.3315 +2026-04-12 05:45:40.666054: val_loss -0.2866 +2026-04-12 05:45:40.668057: Pseudo dice [0.0, 0.0, 0.744, 0.0, 0.0, 0.3739, 0.16] +2026-04-12 05:45:40.670860: Epoch time: 100.47 s +2026-04-12 05:45:41.857529: +2026-04-12 05:45:41.859690: Epoch 1428 +2026-04-12 05:45:41.862085: Current learning rate: 0.00672 +2026-04-12 05:47:22.121055: train_loss -0.3502 +2026-04-12 05:47:22.126728: val_loss -0.3841 +2026-04-12 05:47:22.128848: Pseudo dice [0.0, 0.0, 0.6403, 0.0, 0.0, 0.6587, 0.6974] +2026-04-12 05:47:22.130844: Epoch time: 100.27 s +2026-04-12 05:47:23.308905: +2026-04-12 05:47:23.310893: Epoch 1429 +2026-04-12 05:47:23.313388: Current learning rate: 0.00672 +2026-04-12 05:49:03.925639: train_loss -0.3256 +2026-04-12 05:49:03.933742: val_loss -0.3354 +2026-04-12 05:49:03.936198: Pseudo dice [0.0, 0.0, 0.7842, 0.0, 0.0, 0.5631, 0.2376] +2026-04-12 05:49:03.939936: Epoch time: 100.62 s +2026-04-12 05:49:05.142820: +2026-04-12 05:49:05.145346: Epoch 1430 +2026-04-12 05:49:05.147478: Current learning rate: 0.00672 +2026-04-12 05:50:45.902309: train_loss -0.3548 +2026-04-12 05:50:45.907171: val_loss -0.3408 +2026-04-12 05:50:45.925236: Pseudo dice [0.0, 0.0, 0.6608, 0.1152, 0.0, 0.6112, 0.3638] +2026-04-12 05:50:45.927910: Epoch time: 100.76 s +2026-04-12 05:50:47.143735: +2026-04-12 05:50:47.145837: Epoch 1431 +2026-04-12 05:50:47.148031: Current learning rate: 0.00671 +2026-04-12 05:52:28.216188: train_loss -0.3701 +2026-04-12 05:52:28.225072: val_loss -0.3431 +2026-04-12 05:52:28.229210: Pseudo dice [0.0, 0.0, 0.7418, 0.0, 0.2115, 0.7729, 0.595] +2026-04-12 05:52:28.232311: Epoch time: 101.08 s +2026-04-12 05:52:29.436449: +2026-04-12 05:52:29.438633: Epoch 1432 +2026-04-12 05:52:29.442494: Current learning rate: 0.00671 +2026-04-12 05:54:09.868189: train_loss -0.3601 +2026-04-12 05:54:09.875332: val_loss -0.3712 +2026-04-12 05:54:09.877779: Pseudo dice [0.0, 0.0, 0.6836, 0.6979, 0.1324, 0.6739, 0.8523] +2026-04-12 05:54:09.879992: Epoch time: 100.43 s +2026-04-12 05:54:11.068018: +2026-04-12 05:54:11.070529: Epoch 1433 +2026-04-12 05:54:11.072813: Current learning rate: 0.00671 +2026-04-12 05:55:51.773025: train_loss -0.3788 +2026-04-12 05:55:51.782196: val_loss -0.3486 +2026-04-12 05:55:51.784697: Pseudo dice [0.0, 0.0, 0.5534, 0.4745, 0.3695, 0.6733, 0.5989] +2026-04-12 05:55:51.788995: Epoch time: 100.71 s +2026-04-12 05:55:53.014322: +2026-04-12 05:55:53.016383: Epoch 1434 +2026-04-12 05:55:53.018439: Current learning rate: 0.00671 +2026-04-12 05:57:33.610027: train_loss -0.3555 +2026-04-12 05:57:33.616856: val_loss -0.3728 +2026-04-12 05:57:33.620037: Pseudo dice [0.0, 0.0, 0.6851, 0.0, 0.3599, 0.7022, 0.7608] +2026-04-12 05:57:33.623042: Epoch time: 100.6 s +2026-04-12 05:57:34.831314: +2026-04-12 05:57:34.833439: Epoch 1435 +2026-04-12 05:57:34.835617: Current learning rate: 0.0067 +2026-04-12 05:59:15.301274: train_loss -0.3655 +2026-04-12 05:59:15.310755: val_loss -0.319 +2026-04-12 05:59:15.314307: Pseudo dice [0.0, 0.0, 0.6121, 0.0, 0.0003, 0.6871, 0.6751] +2026-04-12 05:59:15.318693: Epoch time: 100.47 s +2026-04-12 05:59:16.520321: +2026-04-12 05:59:16.523209: Epoch 1436 +2026-04-12 05:59:16.525724: Current learning rate: 0.0067 +2026-04-12 06:00:56.755625: train_loss -0.3497 +2026-04-12 06:00:56.764196: val_loss -0.3335 +2026-04-12 06:00:56.766791: Pseudo dice [0.0, 0.0, 0.4714, 0.0, 0.0096, 0.5023, 0.4163] +2026-04-12 06:00:56.770006: Epoch time: 100.24 s +2026-04-12 06:00:57.958495: +2026-04-12 06:00:57.960951: Epoch 1437 +2026-04-12 06:00:57.963079: Current learning rate: 0.0067 +2026-04-12 06:02:38.177599: train_loss -0.3671 +2026-04-12 06:02:38.188690: val_loss -0.3483 +2026-04-12 06:02:38.190597: Pseudo dice [0.0, 0.0, 0.6703, 0.2414, 0.0, 0.644, 0.6456] +2026-04-12 06:02:38.193002: Epoch time: 100.22 s +2026-04-12 06:02:39.375326: +2026-04-12 06:02:39.378596: Epoch 1438 +2026-04-12 06:02:39.380915: Current learning rate: 0.0067 +2026-04-12 06:04:20.313561: train_loss -0.3504 +2026-04-12 06:04:20.321493: val_loss -0.3066 +2026-04-12 06:04:20.324044: Pseudo dice [0.0, 0.0, 0.5412, 0.1455, 0.0, 0.4724, 0.5824] +2026-04-12 06:04:20.326512: Epoch time: 100.94 s +2026-04-12 06:04:21.521089: +2026-04-12 06:04:21.524719: Epoch 1439 +2026-04-12 06:04:21.526973: Current learning rate: 0.00669 +2026-04-12 06:06:02.072602: train_loss -0.3123 +2026-04-12 06:06:02.082137: val_loss -0.3569 +2026-04-12 06:06:02.084388: Pseudo dice [0.0, 0.0, 0.5772, 0.3719, 0.0594, 0.0336, 0.7828] +2026-04-12 06:06:02.087983: Epoch time: 100.55 s +2026-04-12 06:06:03.281722: +2026-04-12 06:06:03.283590: Epoch 1440 +2026-04-12 06:06:03.285697: Current learning rate: 0.00669 +2026-04-12 06:07:44.479937: train_loss -0.3378 +2026-04-12 06:07:44.489773: val_loss -0.3528 +2026-04-12 06:07:44.492364: Pseudo dice [0.0, 0.0, 0.72, 0.0, 0.4286, 0.4224, 0.4277] +2026-04-12 06:07:44.494750: Epoch time: 101.2 s +2026-04-12 06:07:46.755955: +2026-04-12 06:07:46.757795: Epoch 1441 +2026-04-12 06:07:46.759924: Current learning rate: 0.00669 +2026-04-12 06:09:27.068359: train_loss -0.3575 +2026-04-12 06:09:27.076303: val_loss -0.3529 +2026-04-12 06:09:27.078047: Pseudo dice [0.0, 0.0, 0.3436, 0.6322, 0.2192, 0.3693, 0.7621] +2026-04-12 06:09:27.081004: Epoch time: 100.32 s +2026-04-12 06:09:28.267515: +2026-04-12 06:09:28.269523: Epoch 1442 +2026-04-12 06:09:28.271463: Current learning rate: 0.00669 +2026-04-12 06:11:08.813266: train_loss -0.3685 +2026-04-12 06:11:08.821987: val_loss -0.3224 +2026-04-12 06:11:08.823963: Pseudo dice [0.0, 0.0, 0.3455, 0.0, 0.0131, 0.5714, 0.3653] +2026-04-12 06:11:08.827066: Epoch time: 100.55 s +2026-04-12 06:11:10.013393: +2026-04-12 06:11:10.015265: Epoch 1443 +2026-04-12 06:11:10.017384: Current learning rate: 0.00669 +2026-04-12 06:12:50.699465: train_loss -0.3259 +2026-04-12 06:12:50.708821: val_loss -0.2225 +2026-04-12 06:12:50.711117: Pseudo dice [0.0, 0.0, 0.2531, 0.0, 0.0818, 0.3936, 0.0473] +2026-04-12 06:12:50.713974: Epoch time: 100.69 s +2026-04-12 06:12:51.916252: +2026-04-12 06:12:51.918377: Epoch 1444 +2026-04-12 06:12:51.920624: Current learning rate: 0.00668 +2026-04-12 06:14:32.201252: train_loss -0.3303 +2026-04-12 06:14:32.208913: val_loss -0.3446 +2026-04-12 06:14:32.211825: Pseudo dice [0.0, 0.0, 0.4853, 0.0, 0.3542, 0.2571, 0.4512] +2026-04-12 06:14:32.214903: Epoch time: 100.29 s +2026-04-12 06:14:33.386783: +2026-04-12 06:14:33.389561: Epoch 1445 +2026-04-12 06:14:33.392725: Current learning rate: 0.00668 +2026-04-12 06:16:14.235238: train_loss -0.3436 +2026-04-12 06:16:14.242562: val_loss -0.3358 +2026-04-12 06:16:14.245386: Pseudo dice [0.0, 0.0, 0.5402, 0.0, 0.1677, 0.1576, 0.1471] +2026-04-12 06:16:14.248065: Epoch time: 100.85 s +2026-04-12 06:16:15.464154: +2026-04-12 06:16:15.466386: Epoch 1446 +2026-04-12 06:16:15.469540: Current learning rate: 0.00668 +2026-04-12 06:17:55.693549: train_loss -0.3335 +2026-04-12 06:17:55.700559: val_loss -0.3453 +2026-04-12 06:17:55.702895: Pseudo dice [0.0, 0.0, 0.6632, 0.0, 0.0003, 0.7104, 0.6423] +2026-04-12 06:17:55.707072: Epoch time: 100.23 s +2026-04-12 06:17:56.925895: +2026-04-12 06:17:56.928284: Epoch 1447 +2026-04-12 06:17:56.930719: Current learning rate: 0.00668 +2026-04-12 06:19:37.279225: train_loss -0.3398 +2026-04-12 06:19:37.289497: val_loss -0.3802 +2026-04-12 06:19:37.291875: Pseudo dice [0.0, 0.0, 0.6318, 0.0, 0.2217, 0.6812, 0.6883] +2026-04-12 06:19:37.297455: Epoch time: 100.36 s +2026-04-12 06:19:38.487798: +2026-04-12 06:19:38.489771: Epoch 1448 +2026-04-12 06:19:38.492078: Current learning rate: 0.00667 +2026-04-12 06:21:20.439448: train_loss -0.3654 +2026-04-12 06:21:20.447056: val_loss -0.366 +2026-04-12 06:21:20.449406: Pseudo dice [0.0, 0.0, 0.6403, 0.0, 0.1189, 0.7417, 0.7536] +2026-04-12 06:21:20.452964: Epoch time: 101.95 s +2026-04-12 06:21:21.665445: +2026-04-12 06:21:21.667837: Epoch 1449 +2026-04-12 06:21:21.670399: Current learning rate: 0.00667 +2026-04-12 06:23:01.897420: train_loss -0.3404 +2026-04-12 06:23:01.902742: val_loss -0.2476 +2026-04-12 06:23:01.904911: Pseudo dice [0.0, 0.0, 0.5053, 0.0174, 0.4617, 0.4371, 0.0911] +2026-04-12 06:23:01.907496: Epoch time: 100.23 s +2026-04-12 06:23:04.745127: +2026-04-12 06:23:04.747271: Epoch 1450 +2026-04-12 06:23:04.749871: Current learning rate: 0.00667 +2026-04-12 06:24:45.596355: train_loss -0.3367 +2026-04-12 06:24:45.604406: val_loss -0.3883 +2026-04-12 06:24:45.607213: Pseudo dice [0.0, 0.0, 0.8036, 0.0, 0.4139, 0.6311, 0.5627] +2026-04-12 06:24:45.610453: Epoch time: 100.85 s +2026-04-12 06:24:46.818424: +2026-04-12 06:24:46.821611: Epoch 1451 +2026-04-12 06:24:46.824602: Current learning rate: 0.00667 +2026-04-12 06:26:27.572173: train_loss -0.3616 +2026-04-12 06:26:27.583946: val_loss -0.3475 +2026-04-12 06:26:27.587847: Pseudo dice [0.0, 0.0, 0.669, 0.1465, 0.0, 0.7155, 0.7992] +2026-04-12 06:26:27.590993: Epoch time: 100.76 s +2026-04-12 06:26:28.821140: +2026-04-12 06:26:28.823395: Epoch 1452 +2026-04-12 06:26:28.826044: Current learning rate: 0.00666 +2026-04-12 06:28:09.479205: train_loss -0.3756 +2026-04-12 06:28:09.487512: val_loss -0.3932 +2026-04-12 06:28:09.490244: Pseudo dice [0.0, 0.0, 0.6871, 0.0, 0.0, 0.666, 0.5332] +2026-04-12 06:28:09.493778: Epoch time: 100.66 s +2026-04-12 06:28:10.716880: +2026-04-12 06:28:10.719330: Epoch 1453 +2026-04-12 06:28:10.721694: Current learning rate: 0.00666 +2026-04-12 06:29:51.116477: train_loss -0.3657 +2026-04-12 06:29:51.123953: val_loss -0.3864 +2026-04-12 06:29:51.126071: Pseudo dice [0.0, 0.0, 0.7563, 0.0, 0.0054, 0.8155, 0.8068] +2026-04-12 06:29:51.129645: Epoch time: 100.4 s +2026-04-12 06:29:52.325632: +2026-04-12 06:29:52.328904: Epoch 1454 +2026-04-12 06:29:52.333267: Current learning rate: 0.00666 +2026-04-12 06:31:32.420676: train_loss -0.3708 +2026-04-12 06:31:32.430521: val_loss -0.4065 +2026-04-12 06:31:32.433806: Pseudo dice [0.0, 0.0, 0.5797, 0.0, 0.5048, 0.6936, 0.6851] +2026-04-12 06:31:32.436790: Epoch time: 100.1 s +2026-04-12 06:31:33.625494: +2026-04-12 06:31:33.627634: Epoch 1455 +2026-04-12 06:31:33.629992: Current learning rate: 0.00666 +2026-04-12 06:33:13.984618: train_loss -0.3754 +2026-04-12 06:33:13.991605: val_loss -0.3849 +2026-04-12 06:33:13.995923: Pseudo dice [0.0, 0.0, 0.729, 0.5436, 0.2823, 0.6137, 0.8226] +2026-04-12 06:33:13.998857: Epoch time: 100.36 s +2026-04-12 06:33:15.210634: +2026-04-12 06:33:15.213581: Epoch 1456 +2026-04-12 06:33:15.216962: Current learning rate: 0.00665 +2026-04-12 06:34:55.814132: train_loss -0.347 +2026-04-12 06:34:55.834777: val_loss -0.3671 +2026-04-12 06:34:55.837168: Pseudo dice [0.0, 0.0, 0.7305, 0.1057, 0.0, 0.7723, 0.8281] +2026-04-12 06:34:55.840012: Epoch time: 100.61 s +2026-04-12 06:34:57.034633: +2026-04-12 06:34:57.038020: Epoch 1457 +2026-04-12 06:34:57.041665: Current learning rate: 0.00665 +2026-04-12 06:36:37.681570: train_loss -0.327 +2026-04-12 06:36:37.691703: val_loss -0.3136 +2026-04-12 06:36:37.694957: Pseudo dice [0.0, 0.0, 0.5545, 0.0, 0.0, 0.0, 0.7202] +2026-04-12 06:36:37.698153: Epoch time: 100.65 s +2026-04-12 06:36:38.901986: +2026-04-12 06:36:38.904628: Epoch 1458 +2026-04-12 06:36:38.907227: Current learning rate: 0.00665 +2026-04-12 06:38:19.171596: train_loss -0.3378 +2026-04-12 06:38:19.177779: val_loss -0.3819 +2026-04-12 06:38:19.181499: Pseudo dice [0.0, 0.0, 0.5943, 0.0, 0.0, 0.5216, 0.5259] +2026-04-12 06:38:19.184476: Epoch time: 100.27 s +2026-04-12 06:38:20.380420: +2026-04-12 06:38:20.382400: Epoch 1459 +2026-04-12 06:38:20.384598: Current learning rate: 0.00665 +2026-04-12 06:40:00.826646: train_loss -0.3512 +2026-04-12 06:40:00.836370: val_loss -0.2761 +2026-04-12 06:40:00.839843: Pseudo dice [0.0, 0.0, 0.4102, 0.0, 0.1932, 0.7475, 0.065] +2026-04-12 06:40:00.844023: Epoch time: 100.45 s +2026-04-12 06:40:03.196541: +2026-04-12 06:40:03.198587: Epoch 1460 +2026-04-12 06:40:03.201229: Current learning rate: 0.00665 +2026-04-12 06:41:43.886237: train_loss -0.3283 +2026-04-12 06:41:43.893566: val_loss -0.3383 +2026-04-12 06:41:43.896341: Pseudo dice [0.0, 0.0, 0.6217, 0.0, 0.0, 0.5334, 0.7867] +2026-04-12 06:41:43.899351: Epoch time: 100.69 s +2026-04-12 06:41:45.141678: +2026-04-12 06:41:45.143832: Epoch 1461 +2026-04-12 06:41:45.145494: Current learning rate: 0.00664 +2026-04-12 06:43:25.447032: train_loss -0.3455 +2026-04-12 06:43:25.453946: val_loss -0.3437 +2026-04-12 06:43:25.457261: Pseudo dice [0.0, 0.0, 0.6739, 0.0, 0.0, 0.5747, 0.7393] +2026-04-12 06:43:25.460371: Epoch time: 100.31 s +2026-04-12 06:43:26.672909: +2026-04-12 06:43:26.675143: Epoch 1462 +2026-04-12 06:43:26.677364: Current learning rate: 0.00664 +2026-04-12 06:45:07.925549: train_loss -0.3382 +2026-04-12 06:45:07.935346: val_loss -0.37 +2026-04-12 06:45:07.938223: Pseudo dice [0.0, 0.0, 0.6995, 0.1296, 0.0, 0.7237, 0.5984] +2026-04-12 06:45:07.941528: Epoch time: 101.26 s +2026-04-12 06:45:09.133374: +2026-04-12 06:45:09.135413: Epoch 1463 +2026-04-12 06:45:09.137723: Current learning rate: 0.00664 +2026-04-12 06:46:49.470312: train_loss -0.3503 +2026-04-12 06:46:49.476674: val_loss -0.3172 +2026-04-12 06:46:49.479110: Pseudo dice [0.0, 0.0, 0.5768, 0.0, 0.0, 0.0278, 0.7118] +2026-04-12 06:46:49.485311: Epoch time: 100.34 s +2026-04-12 06:46:50.735494: +2026-04-12 06:46:50.738873: Epoch 1464 +2026-04-12 06:46:50.740873: Current learning rate: 0.00664 +2026-04-12 06:48:31.513280: train_loss -0.3493 +2026-04-12 06:48:31.520641: val_loss -0.3543 +2026-04-12 06:48:31.523960: Pseudo dice [0.0, 0.0, 0.4791, 0.0, 0.2259, 0.2199, 0.675] +2026-04-12 06:48:31.527033: Epoch time: 100.78 s +2026-04-12 06:48:32.747747: +2026-04-12 06:48:32.750176: Epoch 1465 +2026-04-12 06:48:32.752229: Current learning rate: 0.00663 +2026-04-12 06:50:13.028665: train_loss -0.3541 +2026-04-12 06:50:13.035170: val_loss -0.1807 +2026-04-12 06:50:13.037591: Pseudo dice [0.0, 0.0, 0.1987, 0.0694, 0.0345, 0.0153, 0.5955] +2026-04-12 06:50:13.040002: Epoch time: 100.28 s +2026-04-12 06:50:14.230017: +2026-04-12 06:50:14.232021: Epoch 1466 +2026-04-12 06:50:14.234385: Current learning rate: 0.00663 +2026-04-12 06:51:55.468004: train_loss -0.3153 +2026-04-12 06:51:55.474117: val_loss -0.3197 +2026-04-12 06:51:55.476197: Pseudo dice [0.0, 0.0, 0.634, 0.0, 0.2976, 0.5125, 0.3849] +2026-04-12 06:51:55.478470: Epoch time: 101.24 s +2026-04-12 06:51:56.699708: +2026-04-12 06:51:56.701855: Epoch 1467 +2026-04-12 06:51:56.703683: Current learning rate: 0.00663 +2026-04-12 06:53:37.453085: train_loss -0.3343 +2026-04-12 06:53:37.461900: val_loss -0.3755 +2026-04-12 06:53:37.464558: Pseudo dice [0.0, 0.0, 0.7765, 0.0, 0.0, 0.6935, 0.6361] +2026-04-12 06:53:37.468645: Epoch time: 100.76 s +2026-04-12 06:53:38.680556: +2026-04-12 06:53:38.683262: Epoch 1468 +2026-04-12 06:53:38.685086: Current learning rate: 0.00663 +2026-04-12 06:55:19.397986: train_loss -0.3511 +2026-04-12 06:55:19.404983: val_loss -0.3694 +2026-04-12 06:55:19.407324: Pseudo dice [0.0, 0.0, 0.7887, 0.0, 0.0946, 0.6423, 0.3632] +2026-04-12 06:55:19.410106: Epoch time: 100.72 s +2026-04-12 06:55:20.647544: +2026-04-12 06:55:20.649982: Epoch 1469 +2026-04-12 06:55:20.652172: Current learning rate: 0.00662 +2026-04-12 06:57:00.833103: train_loss -0.3518 +2026-04-12 06:57:00.838884: val_loss -0.3067 +2026-04-12 06:57:00.840917: Pseudo dice [0.0, 0.0, 0.6369, 0.0, 0.0361, 0.772, 0.6307] +2026-04-12 06:57:00.843558: Epoch time: 100.19 s +2026-04-12 06:57:02.033645: +2026-04-12 06:57:02.035989: Epoch 1470 +2026-04-12 06:57:02.038103: Current learning rate: 0.00662 +2026-04-12 06:58:42.488041: train_loss -0.3719 +2026-04-12 06:58:42.495293: val_loss -0.3926 +2026-04-12 06:58:42.497847: Pseudo dice [0.0, 0.0, 0.7816, 0.0, 0.1342, 0.4613, 0.8327] +2026-04-12 06:58:42.500417: Epoch time: 100.46 s +2026-04-12 06:58:43.727182: +2026-04-12 06:58:43.730519: Epoch 1471 +2026-04-12 06:58:43.732566: Current learning rate: 0.00662 +2026-04-12 07:00:24.228182: train_loss -0.3676 +2026-04-12 07:00:24.234119: val_loss -0.3505 +2026-04-12 07:00:24.236117: Pseudo dice [0.0, 0.0, 0.4927, 0.1158, 0.0977, 0.7762, 0.5182] +2026-04-12 07:00:24.238658: Epoch time: 100.5 s +2026-04-12 07:00:25.452699: +2026-04-12 07:00:25.455014: Epoch 1472 +2026-04-12 07:00:25.456763: Current learning rate: 0.00662 +2026-04-12 07:02:06.282299: train_loss -0.3279 +2026-04-12 07:02:06.288436: val_loss -0.3587 +2026-04-12 07:02:06.290656: Pseudo dice [0.0, 0.0, 0.6522, 0.0, 0.1853, 0.8066, 0.6621] +2026-04-12 07:02:06.293133: Epoch time: 100.83 s +2026-04-12 07:02:07.498755: +2026-04-12 07:02:07.501824: Epoch 1473 +2026-04-12 07:02:07.504725: Current learning rate: 0.00661 +2026-04-12 07:03:47.852633: train_loss -0.3481 +2026-04-12 07:03:47.859605: val_loss -0.3645 +2026-04-12 07:03:47.863063: Pseudo dice [0.0, 0.0, 0.4617, 0.2855, 0.0163, 0.5036, 0.5405] +2026-04-12 07:03:47.865580: Epoch time: 100.36 s +2026-04-12 07:03:49.050846: +2026-04-12 07:03:49.054078: Epoch 1474 +2026-04-12 07:03:49.055810: Current learning rate: 0.00661 +2026-04-12 07:05:29.244002: train_loss -0.3732 +2026-04-12 07:05:29.252304: val_loss -0.3863 +2026-04-12 07:05:29.254759: Pseudo dice [0.0, 0.0, 0.7475, 0.0, 0.3519, 0.8011, 0.6534] +2026-04-12 07:05:29.257488: Epoch time: 100.2 s +2026-04-12 07:05:30.447824: +2026-04-12 07:05:30.450047: Epoch 1475 +2026-04-12 07:05:30.451895: Current learning rate: 0.00661 +2026-04-12 07:07:10.918360: train_loss -0.385 +2026-04-12 07:07:10.926789: val_loss -0.3005 +2026-04-12 07:07:10.930271: Pseudo dice [0.0, 0.0, 0.2791, 0.0, 0.159, 0.7185, 0.8162] +2026-04-12 07:07:10.932817: Epoch time: 100.47 s +2026-04-12 07:07:12.173354: +2026-04-12 07:07:12.176260: Epoch 1476 +2026-04-12 07:07:12.179207: Current learning rate: 0.00661 +2026-04-12 07:08:52.335607: train_loss -0.3586 +2026-04-12 07:08:52.341993: val_loss -0.3401 +2026-04-12 07:08:52.344699: Pseudo dice [0.0, 0.0, 0.6107, 0.0, 0.4519, 0.6712, 0.7897] +2026-04-12 07:08:52.347573: Epoch time: 100.17 s +2026-04-12 07:08:53.539056: +2026-04-12 07:08:53.541424: Epoch 1477 +2026-04-12 07:08:53.543380: Current learning rate: 0.0066 +2026-04-12 07:10:33.809771: train_loss -0.3702 +2026-04-12 07:10:33.817149: val_loss -0.3638 +2026-04-12 07:10:33.819421: Pseudo dice [0.0, 0.0, 0.7307, 0.0, 0.0558, 0.562, 0.3866] +2026-04-12 07:10:33.822294: Epoch time: 100.27 s +2026-04-12 07:10:35.024046: +2026-04-12 07:10:35.025929: Epoch 1478 +2026-04-12 07:10:35.027518: Current learning rate: 0.0066 +2026-04-12 07:12:15.549796: train_loss -0.3723 +2026-04-12 07:12:15.559350: val_loss -0.3298 +2026-04-12 07:12:15.561433: Pseudo dice [0.0, 0.0, 0.5198, 0.0, 0.1364, 0.2652, 0.6489] +2026-04-12 07:12:15.563790: Epoch time: 100.53 s +2026-04-12 07:12:16.788468: +2026-04-12 07:12:16.790851: Epoch 1479 +2026-04-12 07:12:16.792782: Current learning rate: 0.0066 +2026-04-12 07:13:57.032539: train_loss -0.3734 +2026-04-12 07:13:57.039966: val_loss -0.362 +2026-04-12 07:13:57.042287: Pseudo dice [0.0, 0.0, 0.6295, 0.0661, 0.3628, 0.759, 0.8186] +2026-04-12 07:13:57.044806: Epoch time: 100.25 s +2026-04-12 07:13:59.260708: +2026-04-12 07:13:59.262871: Epoch 1480 +2026-04-12 07:13:59.264601: Current learning rate: 0.0066 +2026-04-12 07:15:39.529290: train_loss -0.3824 +2026-04-12 07:15:39.537264: val_loss -0.2916 +2026-04-12 07:15:39.539315: Pseudo dice [0.0, 0.0, 0.6888, 0.0, 0.167, 0.4857, 0.6458] +2026-04-12 07:15:39.541605: Epoch time: 100.27 s +2026-04-12 07:15:40.727559: +2026-04-12 07:15:40.729543: Epoch 1481 +2026-04-12 07:15:40.732106: Current learning rate: 0.0066 +2026-04-12 07:17:21.195824: train_loss -0.3932 +2026-04-12 07:17:21.203445: val_loss -0.2704 +2026-04-12 07:17:21.205770: Pseudo dice [0.0, 0.0, 0.5699, 0.0, 0.0, 0.6731, 0.6656] +2026-04-12 07:17:21.208955: Epoch time: 100.47 s +2026-04-12 07:17:22.414860: +2026-04-12 07:17:22.417272: Epoch 1482 +2026-04-12 07:17:22.419377: Current learning rate: 0.00659 +2026-04-12 07:19:02.658742: train_loss -0.349 +2026-04-12 07:19:02.674294: val_loss -0.3524 +2026-04-12 07:19:02.676790: Pseudo dice [0.0, 0.0, 0.7492, 0.0, 0.3047, 0.0758, 0.2261] +2026-04-12 07:19:02.680894: Epoch time: 100.25 s +2026-04-12 07:19:03.881653: +2026-04-12 07:19:03.883899: Epoch 1483 +2026-04-12 07:19:03.886112: Current learning rate: 0.00659 +2026-04-12 07:20:44.262717: train_loss -0.3425 +2026-04-12 07:20:44.270288: val_loss -0.2366 +2026-04-12 07:20:44.272243: Pseudo dice [0.0, 0.0, 0.618, 0.0, 0.389, 0.0132, 0.2673] +2026-04-12 07:20:44.274280: Epoch time: 100.38 s +2026-04-12 07:20:45.488474: +2026-04-12 07:20:45.490294: Epoch 1484 +2026-04-12 07:20:45.491992: Current learning rate: 0.00659 +2026-04-12 07:22:25.747019: train_loss -0.337 +2026-04-12 07:22:25.754213: val_loss -0.3818 +2026-04-12 07:22:25.756266: Pseudo dice [0.0, 0.0, 0.5723, 0.323, 0.3924, 0.6705, 0.6796] +2026-04-12 07:22:25.759433: Epoch time: 100.26 s +2026-04-12 07:22:26.945452: +2026-04-12 07:22:26.947295: Epoch 1485 +2026-04-12 07:22:26.948903: Current learning rate: 0.00659 +2026-04-12 07:24:07.617229: train_loss -0.3481 +2026-04-12 07:24:07.626104: val_loss -0.2887 +2026-04-12 07:24:07.628440: Pseudo dice [0.0, 0.0, 0.7228, 0.059, 0.1652, 0.4469, 0.6065] +2026-04-12 07:24:07.631994: Epoch time: 100.67 s +2026-04-12 07:24:08.855883: +2026-04-12 07:24:08.857741: Epoch 1486 +2026-04-12 07:24:08.859504: Current learning rate: 0.00658 +2026-04-12 07:25:49.040500: train_loss -0.3828 +2026-04-12 07:25:49.057768: val_loss -0.3884 +2026-04-12 07:25:49.061372: Pseudo dice [0.0, 0.0, 0.6702, 0.0, 0.3071, 0.7735, 0.8357] +2026-04-12 07:25:49.063618: Epoch time: 100.19 s +2026-04-12 07:25:50.292172: +2026-04-12 07:25:50.294066: Epoch 1487 +2026-04-12 07:25:50.296126: Current learning rate: 0.00658 +2026-04-12 07:27:30.568997: train_loss -0.3663 +2026-04-12 07:27:30.576396: val_loss -0.3367 +2026-04-12 07:27:30.579014: Pseudo dice [0.0, 0.0, 0.385, 0.0, 0.0, 0.4196, 0.6773] +2026-04-12 07:27:30.581426: Epoch time: 100.28 s +2026-04-12 07:27:31.791275: +2026-04-12 07:27:31.794113: Epoch 1488 +2026-04-12 07:27:31.796471: Current learning rate: 0.00658 +2026-04-12 07:29:11.878832: train_loss -0.3299 +2026-04-12 07:29:11.887139: val_loss -0.2755 +2026-04-12 07:29:11.890091: Pseudo dice [0.0, 0.0, 0.0397, 0.0, 0.0, 0.1037, 0.6207] +2026-04-12 07:29:11.893295: Epoch time: 100.09 s +2026-04-12 07:29:13.088946: +2026-04-12 07:29:13.090894: Epoch 1489 +2026-04-12 07:29:13.092535: Current learning rate: 0.00658 +2026-04-12 07:30:53.813242: train_loss -0.3185 +2026-04-12 07:30:53.819961: val_loss -0.3112 +2026-04-12 07:30:53.822029: Pseudo dice [0.0, 0.0, 0.6228, 0.0, 0.0899, 0.3034, 0.6425] +2026-04-12 07:30:53.824678: Epoch time: 100.73 s +2026-04-12 07:30:55.068077: +2026-04-12 07:30:55.070387: Epoch 1490 +2026-04-12 07:30:55.072370: Current learning rate: 0.00657 +2026-04-12 07:32:35.316455: train_loss -0.3632 +2026-04-12 07:32:35.323415: val_loss -0.3471 +2026-04-12 07:32:35.325743: Pseudo dice [0.0, 0.0, 0.4821, 0.0066, 0.3345, 0.2766, 0.7396] +2026-04-12 07:32:35.327932: Epoch time: 100.25 s +2026-04-12 07:32:36.559089: +2026-04-12 07:32:36.561371: Epoch 1491 +2026-04-12 07:32:36.563275: Current learning rate: 0.00657 +2026-04-12 07:34:16.683570: train_loss -0.3417 +2026-04-12 07:34:16.689446: val_loss -0.3709 +2026-04-12 07:34:16.691730: Pseudo dice [0.0, 0.0, 0.7427, 0.0, 0.3876, 0.6628, 0.1731] +2026-04-12 07:34:16.694128: Epoch time: 100.13 s +2026-04-12 07:34:17.892021: +2026-04-12 07:34:17.894059: Epoch 1492 +2026-04-12 07:34:17.895932: Current learning rate: 0.00657 +2026-04-12 07:35:57.958493: train_loss -0.3766 +2026-04-12 07:35:57.963964: val_loss -0.3352 +2026-04-12 07:35:57.965868: Pseudo dice [0.0, 0.0, 0.6703, 0.0, 0.1189, 0.7897, 0.1051] +2026-04-12 07:35:57.967945: Epoch time: 100.07 s +2026-04-12 07:35:59.174018: +2026-04-12 07:35:59.176318: Epoch 1493 +2026-04-12 07:35:59.178666: Current learning rate: 0.00657 +2026-04-12 07:37:40.254581: train_loss -0.3549 +2026-04-12 07:37:40.262040: val_loss -0.3826 +2026-04-12 07:37:40.264847: Pseudo dice [0.0, 0.0, 0.63, 0.0, 0.0396, 0.6984, 0.7543] +2026-04-12 07:37:40.267228: Epoch time: 101.08 s +2026-04-12 07:37:41.498561: +2026-04-12 07:37:41.500530: Epoch 1494 +2026-04-12 07:37:41.502996: Current learning rate: 0.00656 +2026-04-12 07:39:21.755764: train_loss -0.352 +2026-04-12 07:39:21.762951: val_loss -0.2867 +2026-04-12 07:39:21.765041: Pseudo dice [0.0, 0.0, 0.634, 0.0, 0.3282, 0.5024, 0.2592] +2026-04-12 07:39:21.767999: Epoch time: 100.26 s +2026-04-12 07:39:22.999331: +2026-04-12 07:39:23.002285: Epoch 1495 +2026-04-12 07:39:23.004426: Current learning rate: 0.00656 +2026-04-12 07:41:03.451741: train_loss -0.348 +2026-04-12 07:41:03.458757: val_loss -0.3573 +2026-04-12 07:41:03.461022: Pseudo dice [0.0, 0.0, 0.6744, 0.0, 0.0124, 0.5871, 0.6249] +2026-04-12 07:41:03.464542: Epoch time: 100.46 s +2026-04-12 07:41:04.692058: +2026-04-12 07:41:04.693914: Epoch 1496 +2026-04-12 07:41:04.696770: Current learning rate: 0.00656 +2026-04-12 07:42:45.569597: train_loss -0.339 +2026-04-12 07:42:45.577862: val_loss -0.3467 +2026-04-12 07:42:45.587386: Pseudo dice [0.0, 0.0, 0.6305, 0.0, 0.3596, 0.1596, 0.3127] +2026-04-12 07:42:45.590838: Epoch time: 100.88 s +2026-04-12 07:42:46.800924: +2026-04-12 07:42:46.802967: Epoch 1497 +2026-04-12 07:42:46.804764: Current learning rate: 0.00656 +2026-04-12 07:44:27.010894: train_loss -0.2931 +2026-04-12 07:44:27.018703: val_loss -0.3323 +2026-04-12 07:44:27.021298: Pseudo dice [0.0, 0.0, 0.5038, 0.0179, 0.0, 0.4405, 0.7093] +2026-04-12 07:44:27.024557: Epoch time: 100.21 s +2026-04-12 07:44:28.248502: +2026-04-12 07:44:28.250322: Epoch 1498 +2026-04-12 07:44:28.251960: Current learning rate: 0.00656 +2026-04-12 07:46:08.474083: train_loss -0.3222 +2026-04-12 07:46:08.482342: val_loss -0.3485 +2026-04-12 07:46:08.485218: Pseudo dice [0.0, 0.0, 0.655, 0.0, 0.2674, 0.377, 0.2789] +2026-04-12 07:46:08.488121: Epoch time: 100.23 s +2026-04-12 07:46:09.656964: +2026-04-12 07:46:09.659038: Epoch 1499 +2026-04-12 07:46:09.661008: Current learning rate: 0.00655 +2026-04-12 07:47:50.507986: train_loss -0.3471 +2026-04-12 07:47:50.516937: val_loss -0.3725 +2026-04-12 07:47:50.519387: Pseudo dice [0.0, 0.0, 0.6944, 0.0, 0.2821, 0.7002, 0.3628] +2026-04-12 07:47:50.521586: Epoch time: 100.85 s +2026-04-12 07:47:53.385535: +2026-04-12 07:47:53.387510: Epoch 1500 +2026-04-12 07:47:53.389383: Current learning rate: 0.00655 +2026-04-12 07:49:33.617416: train_loss -0.3646 +2026-04-12 07:49:33.623549: val_loss -0.3698 +2026-04-12 07:49:33.625594: Pseudo dice [0.0, 0.0, 0.5418, 0.0, 0.0171, 0.7618, 0.6768] +2026-04-12 07:49:33.629249: Epoch time: 100.23 s +2026-04-12 07:49:34.869897: +2026-04-12 07:49:34.872539: Epoch 1501 +2026-04-12 07:49:34.874541: Current learning rate: 0.00655 +2026-04-12 07:51:15.040234: train_loss -0.333 +2026-04-12 07:51:15.047036: val_loss -0.2776 +2026-04-12 07:51:15.049202: Pseudo dice [0.0, 0.0, 0.5982, 0.0, 0.2359, 0.6502, 0.614] +2026-04-12 07:51:15.052780: Epoch time: 100.17 s +2026-04-12 07:51:16.248822: +2026-04-12 07:51:16.250726: Epoch 1502 +2026-04-12 07:51:16.252368: Current learning rate: 0.00655 +2026-04-12 07:52:56.366906: train_loss -0.353 +2026-04-12 07:52:56.372494: val_loss -0.3281 +2026-04-12 07:52:56.374315: Pseudo dice [0.0, 0.0, 0.7152, 0.0, 0.3491, 0.6801, 0.7613] +2026-04-12 07:52:56.376239: Epoch time: 100.12 s +2026-04-12 07:52:57.568635: +2026-04-12 07:52:57.570463: Epoch 1503 +2026-04-12 07:52:57.572157: Current learning rate: 0.00654 +2026-04-12 07:54:38.005245: train_loss -0.3588 +2026-04-12 07:54:38.011393: val_loss -0.3346 +2026-04-12 07:54:38.013426: Pseudo dice [0.0, 0.0, 0.7264, 0.098, 0.2562, 0.2443, 0.4364] +2026-04-12 07:54:38.016547: Epoch time: 100.44 s +2026-04-12 07:54:39.227099: +2026-04-12 07:54:39.229100: Epoch 1504 +2026-04-12 07:54:39.230791: Current learning rate: 0.00654 +2026-04-12 07:56:19.558818: train_loss -0.3619 +2026-04-12 07:56:19.564568: val_loss -0.3327 +2026-04-12 07:56:19.568283: Pseudo dice [0.0, 0.0, 0.7249, 0.0926, 0.0734, 0.5969, 0.6431] +2026-04-12 07:56:19.571614: Epoch time: 100.33 s +2026-04-12 07:56:20.775008: +2026-04-12 07:56:20.777977: Epoch 1505 +2026-04-12 07:56:20.780283: Current learning rate: 0.00654 +2026-04-12 07:58:01.000290: train_loss -0.3614 +2026-04-12 07:58:01.008691: val_loss -0.3473 +2026-04-12 07:58:01.010672: Pseudo dice [0.0, 0.0, 0.5023, 0.53, 0.0, 0.6723, 0.7728] +2026-04-12 07:58:01.013766: Epoch time: 100.23 s +2026-04-12 07:58:02.208950: +2026-04-12 07:58:02.212069: Epoch 1506 +2026-04-12 07:58:02.214926: Current learning rate: 0.00654 +2026-04-12 07:59:42.364920: train_loss -0.36 +2026-04-12 07:59:42.371130: val_loss -0.4194 +2026-04-12 07:59:42.373234: Pseudo dice [0.0, 0.0, 0.7674, 0.0, 0.1619, 0.6003, 0.8033] +2026-04-12 07:59:42.375467: Epoch time: 100.16 s +2026-04-12 07:59:43.555788: +2026-04-12 07:59:43.558495: Epoch 1507 +2026-04-12 07:59:43.560437: Current learning rate: 0.00653 +2026-04-12 08:01:23.979740: train_loss -0.3845 +2026-04-12 08:01:23.987089: val_loss -0.2884 +2026-04-12 08:01:23.989958: Pseudo dice [0.0, 0.0, 0.6878, 0.0565, 0.3152, 0.7244, 0.6537] +2026-04-12 08:01:23.995607: Epoch time: 100.43 s +2026-04-12 08:01:25.218877: +2026-04-12 08:01:25.220979: Epoch 1508 +2026-04-12 08:01:25.224106: Current learning rate: 0.00653 +2026-04-12 08:03:05.695105: train_loss -0.3487 +2026-04-12 08:03:05.703874: val_loss -0.3778 +2026-04-12 08:03:05.706598: Pseudo dice [0.0, 0.0, 0.7564, 0.0, 0.2382, 0.6701, 0.3964] +2026-04-12 08:03:05.709637: Epoch time: 100.48 s +2026-04-12 08:03:06.932080: +2026-04-12 08:03:06.935279: Epoch 1509 +2026-04-12 08:03:06.937990: Current learning rate: 0.00653 +2026-04-12 08:04:47.165790: train_loss -0.3891 +2026-04-12 08:04:47.174469: val_loss -0.346 +2026-04-12 08:04:47.177050: Pseudo dice [0.0, 0.0, 0.4373, 0.3467, 0.0, 0.4382, 0.5872] +2026-04-12 08:04:47.180267: Epoch time: 100.24 s +2026-04-12 08:04:48.378174: +2026-04-12 08:04:48.383425: Epoch 1510 +2026-04-12 08:04:48.385446: Current learning rate: 0.00653 +2026-04-12 08:06:28.720578: train_loss -0.3729 +2026-04-12 08:06:28.727290: val_loss -0.3683 +2026-04-12 08:06:28.729832: Pseudo dice [0.0, 0.0, 0.5572, 0.2953, 0.0, 0.4289, 0.817] +2026-04-12 08:06:28.732485: Epoch time: 100.35 s +2026-04-12 08:06:29.931912: +2026-04-12 08:06:29.934131: Epoch 1511 +2026-04-12 08:06:29.935734: Current learning rate: 0.00652 +2026-04-12 08:08:10.423821: train_loss -0.3471 +2026-04-12 08:08:10.432010: val_loss -0.3886 +2026-04-12 08:08:10.434165: Pseudo dice [0.0, 0.0, 0.6944, 0.0, 0.0, 0.7124, 0.7974] +2026-04-12 08:08:10.437011: Epoch time: 100.49 s +2026-04-12 08:08:11.660659: +2026-04-12 08:08:11.662628: Epoch 1512 +2026-04-12 08:08:11.664953: Current learning rate: 0.00652 +2026-04-12 08:09:52.089308: train_loss -0.3631 +2026-04-12 08:09:52.095669: val_loss -0.3497 +2026-04-12 08:09:52.098166: Pseudo dice [0.0, 0.0, 0.6877, 0.0209, 0.0044, 0.7552, 0.7471] +2026-04-12 08:09:52.100338: Epoch time: 100.43 s +2026-04-12 08:09:53.282652: +2026-04-12 08:09:53.284470: Epoch 1513 +2026-04-12 08:09:53.286475: Current learning rate: 0.00652 +2026-04-12 08:11:33.604521: train_loss -0.3693 +2026-04-12 08:11:33.609944: val_loss -0.4062 +2026-04-12 08:11:33.612797: Pseudo dice [0.0, 0.0, 0.8088, 0.0, 0.4195, 0.8457, 0.7647] +2026-04-12 08:11:33.615460: Epoch time: 100.32 s +2026-04-12 08:11:34.803778: +2026-04-12 08:11:34.805475: Epoch 1514 +2026-04-12 08:11:34.807204: Current learning rate: 0.00652 +2026-04-12 08:13:15.126824: train_loss -0.3547 +2026-04-12 08:13:15.132905: val_loss -0.3505 +2026-04-12 08:13:15.134655: Pseudo dice [0.0, 0.0, 0.6362, 0.0, 0.0, 0.791, 0.7267] +2026-04-12 08:13:15.137077: Epoch time: 100.33 s +2026-04-12 08:13:16.324820: +2026-04-12 08:13:16.326685: Epoch 1515 +2026-04-12 08:13:16.328349: Current learning rate: 0.00652 +2026-04-12 08:14:56.504768: train_loss -0.3083 +2026-04-12 08:14:56.510402: val_loss -0.3713 +2026-04-12 08:14:56.512174: Pseudo dice [0.0, 0.0, 0.7814, 0.0, 0.0, 0.213, 0.8349] +2026-04-12 08:14:56.514543: Epoch time: 100.18 s +2026-04-12 08:14:57.716618: +2026-04-12 08:14:57.718412: Epoch 1516 +2026-04-12 08:14:57.720081: Current learning rate: 0.00651 +2026-04-12 08:16:37.905761: train_loss -0.3478 +2026-04-12 08:16:37.910848: val_loss -0.3302 +2026-04-12 08:16:37.912710: Pseudo dice [0.0, 0.0, 0.6799, 0.0, 0.0, 0.1764, 0.8252] +2026-04-12 08:16:37.914725: Epoch time: 100.19 s +2026-04-12 08:16:39.098227: +2026-04-12 08:16:39.100181: Epoch 1517 +2026-04-12 08:16:39.101919: Current learning rate: 0.00651 +2026-04-12 08:18:19.554129: train_loss -0.3554 +2026-04-12 08:18:19.561943: val_loss -0.3112 +2026-04-12 08:18:19.564003: Pseudo dice [0.0, 0.0, 0.7513, 0.0, 0.6009, 0.7415, 0.4566] +2026-04-12 08:18:19.566279: Epoch time: 100.46 s +2026-04-12 08:18:20.747159: +2026-04-12 08:18:20.749413: Epoch 1518 +2026-04-12 08:18:20.751057: Current learning rate: 0.00651 +2026-04-12 08:20:01.725281: train_loss -0.3702 +2026-04-12 08:20:01.733191: val_loss -0.3192 +2026-04-12 08:20:01.737889: Pseudo dice [0.0, 0.0, 0.5701, 0.0, 0.0, 0.2109, 0.1168] +2026-04-12 08:20:01.740552: Epoch time: 100.98 s +2026-04-12 08:20:02.937808: +2026-04-12 08:20:02.939893: Epoch 1519 +2026-04-12 08:20:02.941843: Current learning rate: 0.00651 +2026-04-12 08:21:43.648261: train_loss -0.3019 +2026-04-12 08:21:43.653351: val_loss -0.3139 +2026-04-12 08:21:43.655836: Pseudo dice [0.0, 0.0, 0.6769, 0.6545, 0.0, 0.0059, 0.0006] +2026-04-12 08:21:43.658339: Epoch time: 100.71 s +2026-04-12 08:21:44.853291: +2026-04-12 08:21:44.862627: Epoch 1520 +2026-04-12 08:21:44.865093: Current learning rate: 0.0065 +2026-04-12 08:23:25.344074: train_loss -0.3528 +2026-04-12 08:23:25.349682: val_loss -0.313 +2026-04-12 08:23:25.351624: Pseudo dice [0.0, 0.0, 0.4705, 0.0, 0.0, 0.5088, 0.8018] +2026-04-12 08:23:25.353838: Epoch time: 100.49 s +2026-04-12 08:23:26.547101: +2026-04-12 08:23:26.548990: Epoch 1521 +2026-04-12 08:23:26.550702: Current learning rate: 0.0065 +2026-04-12 08:25:07.110216: train_loss -0.3737 +2026-04-12 08:25:07.114923: val_loss -0.398 +2026-04-12 08:25:07.117249: Pseudo dice [0.0, 0.0, 0.725, 0.0, 0.1043, 0.8334, 0.5452] +2026-04-12 08:25:07.119323: Epoch time: 100.57 s +2026-04-12 08:25:08.321155: +2026-04-12 08:25:08.323111: Epoch 1522 +2026-04-12 08:25:08.324811: Current learning rate: 0.0065 +2026-04-12 08:26:48.901604: train_loss -0.3878 +2026-04-12 08:26:48.909964: val_loss -0.3753 +2026-04-12 08:26:48.915272: Pseudo dice [0.0, 0.0, 0.6623, 0.4312, 0.4162, 0.7267, 0.6567] +2026-04-12 08:26:48.917781: Epoch time: 100.58 s +2026-04-12 08:26:50.148024: +2026-04-12 08:26:50.150709: Epoch 1523 +2026-04-12 08:26:50.152853: Current learning rate: 0.0065 +2026-04-12 08:28:30.477025: train_loss -0.3702 +2026-04-12 08:28:30.485218: val_loss -0.3281 +2026-04-12 08:28:30.488050: Pseudo dice [0.0, 0.0, 0.646, 0.0, 0.0, 0.5141, 0.5142] +2026-04-12 08:28:30.490714: Epoch time: 100.33 s +2026-04-12 08:28:31.761693: +2026-04-12 08:28:31.763885: Epoch 1524 +2026-04-12 08:28:31.765651: Current learning rate: 0.00649 +2026-04-12 08:30:11.975671: train_loss -0.3233 +2026-04-12 08:30:11.981504: val_loss -0.3233 +2026-04-12 08:30:11.983597: Pseudo dice [0.0, 0.0, 0.4637, 0.0, 0.0, 0.7251, 0.7672] +2026-04-12 08:30:11.985990: Epoch time: 100.22 s +2026-04-12 08:30:13.259459: +2026-04-12 08:30:13.261245: Epoch 1525 +2026-04-12 08:30:13.263003: Current learning rate: 0.00649 +2026-04-12 08:31:53.564236: train_loss -0.3681 +2026-04-12 08:31:53.569665: val_loss -0.3241 +2026-04-12 08:31:53.571941: Pseudo dice [0.0, 0.0, 0.365, 0.6016, 0.3632, 0.543, 0.4369] +2026-04-12 08:31:53.574331: Epoch time: 100.31 s +2026-04-12 08:31:54.778523: +2026-04-12 08:31:54.780460: Epoch 1526 +2026-04-12 08:31:54.782174: Current learning rate: 0.00649 +2026-04-12 08:33:35.038217: train_loss -0.3776 +2026-04-12 08:33:35.046138: val_loss -0.341 +2026-04-12 08:33:35.049159: Pseudo dice [0.0, 0.0, 0.6255, 0.1373, 0.0, 0.4882, 0.4735] +2026-04-12 08:33:35.051993: Epoch time: 100.26 s +2026-04-12 08:33:36.273663: +2026-04-12 08:33:36.277165: Epoch 1527 +2026-04-12 08:33:36.279668: Current learning rate: 0.00649 +2026-04-12 08:35:16.608928: train_loss -0.355 +2026-04-12 08:35:16.616774: val_loss -0.4015 +2026-04-12 08:35:16.619107: Pseudo dice [0.0, 0.0, 0.7362, 0.4438, 0.0, 0.4332, 0.8405] +2026-04-12 08:35:16.622748: Epoch time: 100.34 s +2026-04-12 08:35:17.820149: +2026-04-12 08:35:17.822980: Epoch 1528 +2026-04-12 08:35:17.824936: Current learning rate: 0.00648 +2026-04-12 08:36:58.162077: train_loss -0.3594 +2026-04-12 08:36:58.168075: val_loss -0.3716 +2026-04-12 08:36:58.169888: Pseudo dice [0.0, 0.0, 0.7088, 0.358, 0.0205, 0.3358, 0.7122] +2026-04-12 08:36:58.172012: Epoch time: 100.34 s +2026-04-12 08:36:59.402403: +2026-04-12 08:36:59.404378: Epoch 1529 +2026-04-12 08:36:59.406218: Current learning rate: 0.00648 +2026-04-12 08:38:39.780025: train_loss -0.3762 +2026-04-12 08:38:39.785970: val_loss -0.415 +2026-04-12 08:38:39.788301: Pseudo dice [0.0, 0.0, 0.7969, 0.6153, 0.4496, 0.539, 0.7559] +2026-04-12 08:38:39.790574: Epoch time: 100.38 s +2026-04-12 08:38:41.005750: +2026-04-12 08:38:41.007761: Epoch 1530 +2026-04-12 08:38:41.009498: Current learning rate: 0.00648 +2026-04-12 08:40:21.339542: train_loss -0.3788 +2026-04-12 08:40:21.345584: val_loss -0.2557 +2026-04-12 08:40:21.348794: Pseudo dice [0.0, 0.0, 0.7182, 0.029, 0.0, 0.7807, 0.5399] +2026-04-12 08:40:21.351712: Epoch time: 100.34 s +2026-04-12 08:40:22.598272: +2026-04-12 08:40:22.600440: Epoch 1531 +2026-04-12 08:40:22.602464: Current learning rate: 0.00648 +2026-04-12 08:42:02.652889: train_loss -0.3724 +2026-04-12 08:42:02.659039: val_loss -0.2964 +2026-04-12 08:42:02.663244: Pseudo dice [0.0, 0.0, 0.5338, 0.0212, 0.0, 0.6141, 0.6633] +2026-04-12 08:42:02.666929: Epoch time: 100.06 s +2026-04-12 08:42:03.879721: +2026-04-12 08:42:03.881769: Epoch 1532 +2026-04-12 08:42:03.884148: Current learning rate: 0.00648 +2026-04-12 08:43:44.215445: train_loss -0.381 +2026-04-12 08:43:44.222264: val_loss -0.2814 +2026-04-12 08:43:44.224228: Pseudo dice [0.0, 0.0, 0.5, 0.0668, 0.3678, 0.5947, 0.7954] +2026-04-12 08:43:44.226763: Epoch time: 100.34 s +2026-04-12 08:43:45.454960: +2026-04-12 08:43:45.457067: Epoch 1533 +2026-04-12 08:43:45.458916: Current learning rate: 0.00647 +2026-04-12 08:45:25.692092: train_loss -0.3546 +2026-04-12 08:45:25.700817: val_loss -0.3087 +2026-04-12 08:45:25.704768: Pseudo dice [0.0, 0.0, 0.7076, 0.114, 0.4154, 0.03, 0.5661] +2026-04-12 08:45:25.707830: Epoch time: 100.24 s +2026-04-12 08:45:26.925627: +2026-04-12 08:45:26.927745: Epoch 1534 +2026-04-12 08:45:26.929615: Current learning rate: 0.00647 +2026-04-12 08:47:07.107038: train_loss -0.3422 +2026-04-12 08:47:07.112951: val_loss -0.3763 +2026-04-12 08:47:07.115111: Pseudo dice [0.0, 0.0, 0.5591, 0.6092, 0.2077, 0.6061, 0.8285] +2026-04-12 08:47:07.118044: Epoch time: 100.18 s +2026-04-12 08:47:08.324477: +2026-04-12 08:47:08.326739: Epoch 1535 +2026-04-12 08:47:08.328807: Current learning rate: 0.00647 +2026-04-12 08:48:48.726681: train_loss -0.3544 +2026-04-12 08:48:48.732978: val_loss -0.3383 +2026-04-12 08:48:48.734921: Pseudo dice [0.0, 0.0, 0.7044, 0.0, 0.2412, 0.5942, 0.6602] +2026-04-12 08:48:48.737528: Epoch time: 100.41 s +2026-04-12 08:48:49.943720: +2026-04-12 08:48:49.946219: Epoch 1536 +2026-04-12 08:48:49.948230: Current learning rate: 0.00647 +2026-04-12 08:50:30.486680: train_loss -0.3608 +2026-04-12 08:50:30.493760: val_loss -0.3934 +2026-04-12 08:50:30.496058: Pseudo dice [0.0, 0.0, 0.7137, 0.6161, 0.2708, 0.6868, 0.6642] +2026-04-12 08:50:30.499129: Epoch time: 100.55 s +2026-04-12 08:50:31.739156: +2026-04-12 08:50:31.741518: Epoch 1537 +2026-04-12 08:50:31.743772: Current learning rate: 0.00646 +2026-04-12 08:52:11.795481: train_loss -0.3547 +2026-04-12 08:52:11.802934: val_loss -0.3848 +2026-04-12 08:52:11.804782: Pseudo dice [0.0, 0.0, 0.669, 0.6729, 0.0, 0.7026, 0.6058] +2026-04-12 08:52:11.807166: Epoch time: 100.06 s +2026-04-12 08:52:14.169921: +2026-04-12 08:52:14.172231: Epoch 1538 +2026-04-12 08:52:14.174568: Current learning rate: 0.00646 +2026-04-12 08:53:54.438449: train_loss -0.363 +2026-04-12 08:53:54.445247: val_loss -0.3549 +2026-04-12 08:53:54.447845: Pseudo dice [0.0, 0.0, 0.5909, 0.0, 0.3301, 0.5398, 0.7261] +2026-04-12 08:53:54.450507: Epoch time: 100.27 s +2026-04-12 08:53:55.674001: +2026-04-12 08:53:55.675895: Epoch 1539 +2026-04-12 08:53:55.677586: Current learning rate: 0.00646 +2026-04-12 08:55:35.885972: train_loss -0.3282 +2026-04-12 08:55:35.893587: val_loss -0.3405 +2026-04-12 08:55:35.897071: Pseudo dice [0.0, 0.0, 0.5029, 0.0, 0.0, 0.0471, 0.5729] +2026-04-12 08:55:35.899390: Epoch time: 100.22 s +2026-04-12 08:55:37.155846: +2026-04-12 08:55:37.157882: Epoch 1540 +2026-04-12 08:55:37.159963: Current learning rate: 0.00646 +2026-04-12 08:57:17.992126: train_loss -0.307 +2026-04-12 08:57:17.998453: val_loss -0.3653 +2026-04-12 08:57:18.000790: Pseudo dice [0.0, 0.0, 0.7535, 0.0, 0.0, 0.6022, 0.5718] +2026-04-12 08:57:18.003558: Epoch time: 100.84 s +2026-04-12 08:57:19.234753: +2026-04-12 08:57:19.237670: Epoch 1541 +2026-04-12 08:57:19.240042: Current learning rate: 0.00645 +2026-04-12 08:58:59.498963: train_loss -0.3776 +2026-04-12 08:58:59.504992: val_loss -0.3929 +2026-04-12 08:58:59.507065: Pseudo dice [0.0, 0.0, 0.6749, 0.2252, 0.0, 0.6897, 0.8307] +2026-04-12 08:58:59.509413: Epoch time: 100.27 s +2026-04-12 08:59:00.713292: +2026-04-12 08:59:00.715322: Epoch 1542 +2026-04-12 08:59:00.717432: Current learning rate: 0.00645 +2026-04-12 09:00:41.089333: train_loss -0.3726 +2026-04-12 09:00:41.099502: val_loss -0.3796 +2026-04-12 09:00:41.103126: Pseudo dice [0.0, 0.0, 0.7806, 0.5416, 0.0, 0.6698, 0.5555] +2026-04-12 09:00:41.107716: Epoch time: 100.38 s +2026-04-12 09:00:42.353206: +2026-04-12 09:00:42.355881: Epoch 1543 +2026-04-12 09:00:42.358546: Current learning rate: 0.00645 +2026-04-12 09:02:22.571013: train_loss -0.352 +2026-04-12 09:02:22.579246: val_loss -0.3392 +2026-04-12 09:02:22.582225: Pseudo dice [0.0, 0.0, 0.7605, 0.0, 0.2558, 0.1297, 0.2602] +2026-04-12 09:02:22.584925: Epoch time: 100.22 s +2026-04-12 09:02:23.807105: +2026-04-12 09:02:23.808954: Epoch 1544 +2026-04-12 09:02:23.811222: Current learning rate: 0.00645 +2026-04-12 09:04:05.946977: train_loss -0.3504 +2026-04-12 09:04:05.954402: val_loss -0.3802 +2026-04-12 09:04:05.956766: Pseudo dice [0.0, 0.0, 0.7295, 0.0, 0.0466, 0.7658, 0.4948] +2026-04-12 09:04:05.959941: Epoch time: 102.14 s +2026-04-12 09:04:07.192229: +2026-04-12 09:04:07.197568: Epoch 1545 +2026-04-12 09:04:07.199788: Current learning rate: 0.00644 +2026-04-12 09:05:49.355415: train_loss -0.3478 +2026-04-12 09:05:49.361881: val_loss -0.2201 +2026-04-12 09:05:49.364440: Pseudo dice [0.0, 0.0, 0.2857, 0.0354, 0.3952, 0.0426, 0.6539] +2026-04-12 09:05:49.367174: Epoch time: 102.17 s +2026-04-12 09:05:50.592561: +2026-04-12 09:05:50.594949: Epoch 1546 +2026-04-12 09:05:50.596828: Current learning rate: 0.00644 +2026-04-12 09:07:31.761582: train_loss -0.3374 +2026-04-12 09:07:31.768053: val_loss -0.3031 +2026-04-12 09:07:31.775159: Pseudo dice [0.0, 0.0, 0.6576, 0.0, 0.4017, 0.2653, 0.4162] +2026-04-12 09:07:31.778383: Epoch time: 101.17 s +2026-04-12 09:07:33.042715: +2026-04-12 09:07:33.044773: Epoch 1547 +2026-04-12 09:07:33.046841: Current learning rate: 0.00644 +2026-04-12 09:09:13.266360: train_loss -0.3224 +2026-04-12 09:09:13.272192: val_loss -0.3567 +2026-04-12 09:09:13.274388: Pseudo dice [0.0, 0.0, 0.5024, 0.0, 0.2127, 0.4816, 0.8092] +2026-04-12 09:09:13.276384: Epoch time: 100.23 s +2026-04-12 09:09:14.488187: +2026-04-12 09:09:14.489942: Epoch 1548 +2026-04-12 09:09:14.491813: Current learning rate: 0.00644 +2026-04-12 09:10:54.734510: train_loss -0.3491 +2026-04-12 09:10:54.743440: val_loss -0.3471 +2026-04-12 09:10:54.745976: Pseudo dice [0.0, 0.0, 0.6735, 0.1496, 0.3204, 0.7298, 0.7523] +2026-04-12 09:10:54.749898: Epoch time: 100.25 s +2026-04-12 09:10:56.002475: +2026-04-12 09:10:56.005341: Epoch 1549 +2026-04-12 09:10:56.008341: Current learning rate: 0.00644 +2026-04-12 09:12:36.017321: train_loss -0.3739 +2026-04-12 09:12:36.022848: val_loss -0.3648 +2026-04-12 09:12:36.025037: Pseudo dice [0.0, 0.0, 0.6628, 0.0, 0.2404, 0.7078, 0.6005] +2026-04-12 09:12:36.027920: Epoch time: 100.02 s +2026-04-12 09:12:38.866265: +2026-04-12 09:12:38.868112: Epoch 1550 +2026-04-12 09:12:38.869875: Current learning rate: 0.00643 +2026-04-12 09:14:19.663154: train_loss -0.3669 +2026-04-12 09:14:19.668046: val_loss -0.363 +2026-04-12 09:14:19.669737: Pseudo dice [0.0, 0.0, 0.6828, 0.0, 0.0637, 0.8384, 0.7365] +2026-04-12 09:14:19.671733: Epoch time: 100.8 s +2026-04-12 09:14:20.910778: +2026-04-12 09:14:20.912619: Epoch 1551 +2026-04-12 09:14:20.914477: Current learning rate: 0.00643 +2026-04-12 09:16:01.105601: train_loss -0.3427 +2026-04-12 09:16:01.114082: val_loss -0.3807 +2026-04-12 09:16:01.117553: Pseudo dice [0.0, 0.0, 0.7142, 0.0, 0.0, 0.8183, 0.603] +2026-04-12 09:16:01.120327: Epoch time: 100.2 s +2026-04-12 09:16:02.355399: +2026-04-12 09:16:02.358193: Epoch 1552 +2026-04-12 09:16:02.361274: Current learning rate: 0.00643 +2026-04-12 09:17:42.452251: train_loss -0.3251 +2026-04-12 09:17:42.457239: val_loss -0.3156 +2026-04-12 09:17:42.458922: Pseudo dice [0.0, 0.0, 0.5298, 0.0, 0.2728, 0.6757, 0.7331] +2026-04-12 09:17:42.460945: Epoch time: 100.1 s +2026-04-12 09:17:43.659903: +2026-04-12 09:17:43.662012: Epoch 1553 +2026-04-12 09:17:43.663809: Current learning rate: 0.00643 +2026-04-12 09:19:23.908207: train_loss -0.3663 +2026-04-12 09:19:23.914919: val_loss -0.3918 +2026-04-12 09:19:23.918415: Pseudo dice [0.0, 0.0, 0.4518, 0.0, 0.2967, 0.8355, 0.8322] +2026-04-12 09:19:23.921694: Epoch time: 100.25 s +2026-04-12 09:19:25.147229: +2026-04-12 09:19:25.150256: Epoch 1554 +2026-04-12 09:19:25.152777: Current learning rate: 0.00642 +2026-04-12 09:21:05.293849: train_loss -0.3488 +2026-04-12 09:21:05.303465: val_loss -0.3341 +2026-04-12 09:21:05.306101: Pseudo dice [0.0, 0.0, 0.6382, 0.0, 0.3497, 0.7069, 0.2394] +2026-04-12 09:21:05.308828: Epoch time: 100.15 s +2026-04-12 09:21:06.507502: +2026-04-12 09:21:06.509531: Epoch 1555 +2026-04-12 09:21:06.511559: Current learning rate: 0.00642 +2026-04-12 09:22:46.780516: train_loss -0.3719 +2026-04-12 09:22:46.786562: val_loss -0.3748 +2026-04-12 09:22:46.788346: Pseudo dice [0.0, 0.0, 0.4379, 0.0, 0.3095, 0.7252, 0.4495] +2026-04-12 09:22:46.790903: Epoch time: 100.28 s +2026-04-12 09:22:48.031153: +2026-04-12 09:22:48.032898: Epoch 1556 +2026-04-12 09:22:48.034732: Current learning rate: 0.00642 +2026-04-12 09:24:28.411440: train_loss -0.3594 +2026-04-12 09:24:28.421432: val_loss -0.2936 +2026-04-12 09:24:28.424489: Pseudo dice [0.0, 0.0, 0.5379, 0.0, 0.3437, 0.4626, 0.5912] +2026-04-12 09:24:28.427830: Epoch time: 100.38 s +2026-04-12 09:24:30.760801: +2026-04-12 09:24:30.763059: Epoch 1557 +2026-04-12 09:24:30.764807: Current learning rate: 0.00642 +2026-04-12 09:26:11.049445: train_loss -0.3428 +2026-04-12 09:26:11.054961: val_loss -0.3535 +2026-04-12 09:26:11.058654: Pseudo dice [0.0, 0.0, 0.728, 0.0, 0.0, 0.8231, 0.657] +2026-04-12 09:26:11.061545: Epoch time: 100.29 s +2026-04-12 09:26:12.273343: +2026-04-12 09:26:12.275497: Epoch 1558 +2026-04-12 09:26:12.277625: Current learning rate: 0.00641 +2026-04-12 09:27:52.650284: train_loss -0.3442 +2026-04-12 09:27:52.655738: val_loss -0.3153 +2026-04-12 09:27:52.657611: Pseudo dice [0.0, 0.0, 0.5694, 0.0, 0.0, 0.0098, 0.5088] +2026-04-12 09:27:52.659577: Epoch time: 100.38 s +2026-04-12 09:27:53.875764: +2026-04-12 09:27:53.881411: Epoch 1559 +2026-04-12 09:27:53.885182: Current learning rate: 0.00641 +2026-04-12 09:29:34.067785: train_loss -0.3488 +2026-04-12 09:29:34.074751: val_loss -0.2764 +2026-04-12 09:29:34.077050: Pseudo dice [0.0, 0.0, 0.6771, 0.1677, 0.0071, 0.6758, 0.711] +2026-04-12 09:29:34.079828: Epoch time: 100.2 s +2026-04-12 09:29:35.288588: +2026-04-12 09:29:35.291540: Epoch 1560 +2026-04-12 09:29:35.293504: Current learning rate: 0.00641 +2026-04-12 09:31:15.702818: train_loss -0.3442 +2026-04-12 09:31:15.709305: val_loss -0.3602 +2026-04-12 09:31:15.711651: Pseudo dice [0.0, 0.0, 0.7858, 0.0, 0.0, 0.7455, 0.2644] +2026-04-12 09:31:15.713992: Epoch time: 100.42 s +2026-04-12 09:31:16.954178: +2026-04-12 09:31:16.956031: Epoch 1561 +2026-04-12 09:31:16.957911: Current learning rate: 0.00641 +2026-04-12 09:32:57.304566: train_loss -0.3637 +2026-04-12 09:32:57.310191: val_loss -0.3899 +2026-04-12 09:32:57.312319: Pseudo dice [0.0, 0.0, 0.4741, 0.0, 0.0551, 0.8083, 0.8166] +2026-04-12 09:32:57.314811: Epoch time: 100.35 s +2026-04-12 09:32:58.529896: +2026-04-12 09:32:58.531722: Epoch 1562 +2026-04-12 09:32:58.533505: Current learning rate: 0.0064 +2026-04-12 09:34:39.115373: train_loss -0.3475 +2026-04-12 09:34:39.123240: val_loss -0.3047 +2026-04-12 09:34:39.125958: Pseudo dice [0.0, 0.0, 0.6402, 0.0001, 0.11, 0.4684, 0.5054] +2026-04-12 09:34:39.128781: Epoch time: 100.59 s +2026-04-12 09:34:40.345361: +2026-04-12 09:34:40.347215: Epoch 1563 +2026-04-12 09:34:40.349212: Current learning rate: 0.0064 +2026-04-12 09:36:20.467576: train_loss -0.3576 +2026-04-12 09:36:20.473212: val_loss -0.3582 +2026-04-12 09:36:20.475090: Pseudo dice [0.0, 0.0, 0.591, 0.0, 0.2458, 0.1976, 0.7471] +2026-04-12 09:36:20.477511: Epoch time: 100.13 s +2026-04-12 09:36:21.710116: +2026-04-12 09:36:21.711957: Epoch 1564 +2026-04-12 09:36:21.714878: Current learning rate: 0.0064 +2026-04-12 09:38:02.443103: train_loss -0.3443 +2026-04-12 09:38:02.451527: val_loss -0.3735 +2026-04-12 09:38:02.454320: Pseudo dice [0.0, 0.0, 0.6473, 0.0, 0.0, 0.7577, 0.644] +2026-04-12 09:38:02.458367: Epoch time: 100.74 s +2026-04-12 09:38:03.690169: +2026-04-12 09:38:03.692501: Epoch 1565 +2026-04-12 09:38:03.695658: Current learning rate: 0.0064 +2026-04-12 09:39:43.927987: train_loss -0.3589 +2026-04-12 09:39:43.934176: val_loss -0.3802 +2026-04-12 09:39:43.936118: Pseudo dice [0.0, 0.0, 0.5337, 0.0, 0.0, 0.322, 0.6191] +2026-04-12 09:39:43.939126: Epoch time: 100.24 s +2026-04-12 09:39:45.166569: +2026-04-12 09:39:45.168558: Epoch 1566 +2026-04-12 09:39:45.170166: Current learning rate: 0.00639 +2026-04-12 09:41:25.539364: train_loss -0.3697 +2026-04-12 09:41:25.548230: val_loss -0.3404 +2026-04-12 09:41:25.550943: Pseudo dice [0.0, 0.0, 0.5714, 0.0, 0.0, 0.5953, 0.6594] +2026-04-12 09:41:25.554229: Epoch time: 100.38 s +2026-04-12 09:41:26.789544: +2026-04-12 09:41:26.791495: Epoch 1567 +2026-04-12 09:41:26.793546: Current learning rate: 0.00639 +2026-04-12 09:43:07.075212: train_loss -0.3717 +2026-04-12 09:43:07.082944: val_loss -0.3881 +2026-04-12 09:43:07.085488: Pseudo dice [0.0, 0.0, 0.6822, 0.7058, 0.0, 0.6668, 0.5737] +2026-04-12 09:43:07.088677: Epoch time: 100.29 s +2026-04-12 09:43:08.285056: +2026-04-12 09:43:08.287571: Epoch 1568 +2026-04-12 09:43:08.289425: Current learning rate: 0.00639 +2026-04-12 09:44:48.926341: train_loss -0.3716 +2026-04-12 09:44:48.932806: val_loss -0.3065 +2026-04-12 09:44:48.935031: Pseudo dice [0.0, 0.0, 0.7307, 0.0, 0.0961, 0.5378, 0.6837] +2026-04-12 09:44:48.938065: Epoch time: 100.64 s +2026-04-12 09:44:50.160035: +2026-04-12 09:44:50.162034: Epoch 1569 +2026-04-12 09:44:50.163956: Current learning rate: 0.00639 +2026-04-12 09:46:30.579657: train_loss -0.3576 +2026-04-12 09:46:30.585059: val_loss -0.298 +2026-04-12 09:46:30.587129: Pseudo dice [0.0, 0.0, 0.7309, 0.0, 0.0244, 0.7466, 0.6585] +2026-04-12 09:46:30.590277: Epoch time: 100.42 s +2026-04-12 09:46:31.775453: +2026-04-12 09:46:31.777486: Epoch 1570 +2026-04-12 09:46:31.779257: Current learning rate: 0.00639 +2026-04-12 09:48:11.848067: train_loss -0.3896 +2026-04-12 09:48:11.857130: val_loss -0.3798 +2026-04-12 09:48:11.859793: Pseudo dice [0.0, 0.0, 0.3045, 0.4611, 0.0, 0.8177, 0.7344] +2026-04-12 09:48:11.864390: Epoch time: 100.08 s +2026-04-12 09:48:13.081225: +2026-04-12 09:48:13.083409: Epoch 1571 +2026-04-12 09:48:13.086010: Current learning rate: 0.00638 +2026-04-12 09:49:53.483356: train_loss -0.3685 +2026-04-12 09:49:53.493697: val_loss -0.3717 +2026-04-12 09:49:53.496038: Pseudo dice [0.0, 0.0, 0.7622, 0.0, 0.0588, 0.7638, 0.7437] +2026-04-12 09:49:53.498841: Epoch time: 100.41 s +2026-04-12 09:49:54.729375: +2026-04-12 09:49:54.731497: Epoch 1572 +2026-04-12 09:49:54.735512: Current learning rate: 0.00638 +2026-04-12 09:51:34.989756: train_loss -0.3544 +2026-04-12 09:51:34.996927: val_loss -0.3741 +2026-04-12 09:51:34.999735: Pseudo dice [0.0, 0.0, 0.6082, 0.0, 0.1585, 0.5676, 0.7922] +2026-04-12 09:51:35.002439: Epoch time: 100.26 s +2026-04-12 09:51:36.231515: +2026-04-12 09:51:36.233997: Epoch 1573 +2026-04-12 09:51:36.236249: Current learning rate: 0.00638 +2026-04-12 09:53:16.461802: train_loss -0.3598 +2026-04-12 09:53:16.467922: val_loss -0.3853 +2026-04-12 09:53:16.469778: Pseudo dice [0.0, 0.0, 0.7726, 0.0, 0.0, 0.6507, 0.8416] +2026-04-12 09:53:16.472571: Epoch time: 100.23 s +2026-04-12 09:53:17.706309: +2026-04-12 09:53:17.709529: Epoch 1574 +2026-04-12 09:53:17.711609: Current learning rate: 0.00638 +2026-04-12 09:54:57.911645: train_loss -0.368 +2026-04-12 09:54:57.919030: val_loss -0.3556 +2026-04-12 09:54:57.922948: Pseudo dice [0.0, 0.0, 0.4807, 0.7393, 0.2246, 0.4616, 0.5655] +2026-04-12 09:54:57.926211: Epoch time: 100.21 s +2026-04-12 09:54:59.133503: +2026-04-12 09:54:59.136846: Epoch 1575 +2026-04-12 09:54:59.139288: Current learning rate: 0.00637 +2026-04-12 09:56:39.785090: train_loss -0.3447 +2026-04-12 09:56:39.791492: val_loss -0.339 +2026-04-12 09:56:39.795036: Pseudo dice [0.0, 0.0, 0.6252, 0.2184, 0.0362, 0.5673, 0.2691] +2026-04-12 09:56:39.797812: Epoch time: 100.65 s +2026-04-12 09:56:41.023285: +2026-04-12 09:56:41.025610: Epoch 1576 +2026-04-12 09:56:41.027441: Current learning rate: 0.00637 +2026-04-12 09:58:22.414853: train_loss -0.3348 +2026-04-12 09:58:22.429785: val_loss -0.3153 +2026-04-12 09:58:22.432357: Pseudo dice [0.0, 0.0, 0.617, 0.5555, 0.2807, 0.0629, 0.1635] +2026-04-12 09:58:22.435255: Epoch time: 101.39 s +2026-04-12 09:58:23.657268: +2026-04-12 09:58:23.659638: Epoch 1577 +2026-04-12 09:58:23.661696: Current learning rate: 0.00637 +2026-04-12 10:00:04.115581: train_loss -0.3143 +2026-04-12 10:00:04.120979: val_loss -0.2875 +2026-04-12 10:00:04.122817: Pseudo dice [0.0, 0.0, 0.1022, 0.0, 0.1102, 0.0, 0.0964] +2026-04-12 10:00:04.124726: Epoch time: 100.46 s +2026-04-12 10:00:05.335770: +2026-04-12 10:00:05.338274: Epoch 1578 +2026-04-12 10:00:05.340796: Current learning rate: 0.00637 +2026-04-12 10:01:45.950058: train_loss -0.3176 +2026-04-12 10:01:45.957301: val_loss -0.3262 +2026-04-12 10:01:45.961305: Pseudo dice [0.0, 0.0, 0.575, 0.2211, 0.0862, 0.0003, 0.7136] +2026-04-12 10:01:45.964338: Epoch time: 100.62 s +2026-04-12 10:01:47.188635: +2026-04-12 10:01:47.190825: Epoch 1579 +2026-04-12 10:01:47.192956: Current learning rate: 0.00636 +2026-04-12 10:03:27.578154: train_loss -0.366 +2026-04-12 10:03:27.584075: val_loss -0.4064 +2026-04-12 10:03:27.586465: Pseudo dice [0.0, 0.0, 0.7582, 0.7424, 0.3827, 0.7107, 0.6339] +2026-04-12 10:03:27.589004: Epoch time: 100.39 s +2026-04-12 10:03:28.843332: +2026-04-12 10:03:28.845474: Epoch 1580 +2026-04-12 10:03:28.847165: Current learning rate: 0.00636 +2026-04-12 10:05:08.959750: train_loss -0.3831 +2026-04-12 10:05:08.966737: val_loss -0.3578 +2026-04-12 10:05:08.969033: Pseudo dice [0.0, 0.0, 0.6303, 0.3847, 0.2384, 0.7493, 0.4199] +2026-04-12 10:05:08.972603: Epoch time: 100.12 s +2026-04-12 10:05:10.198525: +2026-04-12 10:05:10.202383: Epoch 1581 +2026-04-12 10:05:10.204520: Current learning rate: 0.00636 +2026-04-12 10:06:50.959163: train_loss -0.3627 +2026-04-12 10:06:50.968288: val_loss -0.3729 +2026-04-12 10:06:50.970707: Pseudo dice [0.0, 0.0, 0.6806, 0.0, 0.0, 0.2428, 0.7222] +2026-04-12 10:06:50.974755: Epoch time: 100.76 s +2026-04-12 10:06:52.211012: +2026-04-12 10:06:52.213847: Epoch 1582 +2026-04-12 10:06:52.216190: Current learning rate: 0.00636 +2026-04-12 10:08:32.330175: train_loss -0.3504 +2026-04-12 10:08:32.336908: val_loss -0.321 +2026-04-12 10:08:32.339951: Pseudo dice [0.0, 0.0, 0.6667, 0.228, 0.004, 0.7433, 0.7073] +2026-04-12 10:08:32.344077: Epoch time: 100.12 s +2026-04-12 10:08:33.569061: +2026-04-12 10:08:33.571345: Epoch 1583 +2026-04-12 10:08:33.573172: Current learning rate: 0.00635 +2026-04-12 10:10:13.843312: train_loss -0.3547 +2026-04-12 10:10:13.851966: val_loss -0.3478 +2026-04-12 10:10:13.857807: Pseudo dice [0.0, 0.0, 0.4382, 0.0, 0.1007, 0.4106, 0.6075] +2026-04-12 10:10:13.861473: Epoch time: 100.28 s +2026-04-12 10:10:15.062297: +2026-04-12 10:10:15.066289: Epoch 1584 +2026-04-12 10:10:15.074826: Current learning rate: 0.00635 +2026-04-12 10:11:55.312716: train_loss -0.3772 +2026-04-12 10:11:55.318915: val_loss -0.3153 +2026-04-12 10:11:55.320950: Pseudo dice [0.0, 0.0, 0.5596, 0.0, 0.0, 0.5872, 0.7025] +2026-04-12 10:11:55.323236: Epoch time: 100.25 s +2026-04-12 10:11:56.550670: +2026-04-12 10:11:56.552727: Epoch 1585 +2026-04-12 10:11:56.554455: Current learning rate: 0.00635 +2026-04-12 10:13:36.625127: train_loss -0.3495 +2026-04-12 10:13:36.632511: val_loss -0.3307 +2026-04-12 10:13:36.636518: Pseudo dice [0.0, 0.0, 0.6923, 0.1824, 0.0, 0.3761, 0.5636] +2026-04-12 10:13:36.639411: Epoch time: 100.08 s +2026-04-12 10:13:37.844110: +2026-04-12 10:13:37.848509: Epoch 1586 +2026-04-12 10:13:37.852215: Current learning rate: 0.00635 +2026-04-12 10:15:18.186091: train_loss -0.3665 +2026-04-12 10:15:18.192628: val_loss -0.291 +2026-04-12 10:15:18.195701: Pseudo dice [0.0, 0.0, 0.668, 0.0, 0.0737, 0.6645, 0.7422] +2026-04-12 10:15:18.214839: Epoch time: 100.35 s +2026-04-12 10:15:19.444043: +2026-04-12 10:15:19.446106: Epoch 1587 +2026-04-12 10:15:19.448263: Current learning rate: 0.00635 +2026-04-12 10:16:59.707028: train_loss -0.3415 +2026-04-12 10:16:59.713284: val_loss -0.3337 +2026-04-12 10:16:59.716188: Pseudo dice [0.0, 0.0, 0.4806, 0.3245, 0.0, 0.026, 0.4759] +2026-04-12 10:16:59.718738: Epoch time: 100.27 s +2026-04-12 10:17:00.958379: +2026-04-12 10:17:00.960191: Epoch 1588 +2026-04-12 10:17:00.962046: Current learning rate: 0.00634 +2026-04-12 10:18:41.273702: train_loss -0.3311 +2026-04-12 10:18:41.280874: val_loss -0.3285 +2026-04-12 10:18:41.284046: Pseudo dice [0.0, 0.0, 0.5564, 0.0, 0.0, 0.6061, 0.2625] +2026-04-12 10:18:41.287315: Epoch time: 100.32 s +2026-04-12 10:18:42.726283: +2026-04-12 10:18:42.728058: Epoch 1589 +2026-04-12 10:18:42.730239: Current learning rate: 0.00634 +2026-04-12 10:20:23.054819: train_loss -0.3166 +2026-04-12 10:20:23.059723: val_loss -0.3126 +2026-04-12 10:20:23.061447: Pseudo dice [0.0, 0.0, 0.4837, 0.0, 0.0, 0.5911, 0.4881] +2026-04-12 10:20:23.066767: Epoch time: 100.33 s +2026-04-12 10:20:24.269450: +2026-04-12 10:20:24.272399: Epoch 1590 +2026-04-12 10:20:24.274343: Current learning rate: 0.00634 +2026-04-12 10:22:04.505110: train_loss -0.3588 +2026-04-12 10:22:04.511804: val_loss -0.3829 +2026-04-12 10:22:04.514352: Pseudo dice [0.0, 0.0, 0.7106, 0.2111, 0.0, 0.6125, 0.8101] +2026-04-12 10:22:04.517456: Epoch time: 100.24 s +2026-04-12 10:22:05.762828: +2026-04-12 10:22:05.765025: Epoch 1591 +2026-04-12 10:22:05.767142: Current learning rate: 0.00634 +2026-04-12 10:23:45.933758: train_loss -0.3449 +2026-04-12 10:23:45.940578: val_loss -0.3547 +2026-04-12 10:23:45.942841: Pseudo dice [0.0, 0.0, 0.5448, 0.1688, 0.0, 0.321, 0.4399] +2026-04-12 10:23:45.945713: Epoch time: 100.17 s +2026-04-12 10:23:47.173394: +2026-04-12 10:23:47.175404: Epoch 1592 +2026-04-12 10:23:47.177318: Current learning rate: 0.00633 +2026-04-12 10:25:28.182359: train_loss -0.3732 +2026-04-12 10:25:28.188716: val_loss -0.3764 +2026-04-12 10:25:28.191101: Pseudo dice [0.0, 0.0, 0.6823, 0.0, 0.2747, 0.7752, 0.7528] +2026-04-12 10:25:28.196319: Epoch time: 101.01 s +2026-04-12 10:25:29.419137: +2026-04-12 10:25:29.421035: Epoch 1593 +2026-04-12 10:25:29.423288: Current learning rate: 0.00633 +2026-04-12 10:27:09.536838: train_loss -0.3718 +2026-04-12 10:27:09.544955: val_loss -0.3697 +2026-04-12 10:27:09.547867: Pseudo dice [0.0, 0.0, 0.6104, 0.0, 0.0, 0.5776, 0.8341] +2026-04-12 10:27:09.550338: Epoch time: 100.12 s +2026-04-12 10:27:10.781816: +2026-04-12 10:27:10.783723: Epoch 1594 +2026-04-12 10:27:10.785638: Current learning rate: 0.00633 +2026-04-12 10:28:51.110759: train_loss -0.3847 +2026-04-12 10:28:51.116529: val_loss -0.3778 +2026-04-12 10:28:51.118760: Pseudo dice [0.0, 0.0, 0.5768, 0.4132, 0.0606, 0.3878, 0.6607] +2026-04-12 10:28:51.121336: Epoch time: 100.33 s +2026-04-12 10:28:52.347141: +2026-04-12 10:28:52.348935: Epoch 1595 +2026-04-12 10:28:52.350735: Current learning rate: 0.00633 +2026-04-12 10:30:33.999912: train_loss -0.3641 +2026-04-12 10:30:34.008874: val_loss -0.3056 +2026-04-12 10:30:34.010957: Pseudo dice [0.0, 0.0, 0.4844, 0.0533, 0.0, 0.3852, 0.7271] +2026-04-12 10:30:34.013893: Epoch time: 101.66 s +2026-04-12 10:30:35.217852: +2026-04-12 10:30:35.221073: Epoch 1596 +2026-04-12 10:30:35.223727: Current learning rate: 0.00632 +2026-04-12 10:32:15.431788: train_loss -0.3392 +2026-04-12 10:32:15.438820: val_loss -0.3452 +2026-04-12 10:32:15.440965: Pseudo dice [0.0, 0.0, 0.5342, 0.2948, 0.2499, 0.6743, 0.6122] +2026-04-12 10:32:15.443811: Epoch time: 100.22 s +2026-04-12 10:32:16.644409: +2026-04-12 10:32:16.646336: Epoch 1597 +2026-04-12 10:32:16.648296: Current learning rate: 0.00632 +2026-04-12 10:33:57.243205: train_loss -0.3365 +2026-04-12 10:33:57.250961: val_loss -0.3382 +2026-04-12 10:33:57.253716: Pseudo dice [0.0, 0.0, 0.5799, 0.0, 0.0, 0.5495, 0.8354] +2026-04-12 10:33:57.258208: Epoch time: 100.6 s +2026-04-12 10:33:58.494136: +2026-04-12 10:33:58.496363: Epoch 1598 +2026-04-12 10:33:58.498603: Current learning rate: 0.00632 +2026-04-12 10:35:38.674098: train_loss -0.3526 +2026-04-12 10:35:38.681767: val_loss -0.3769 +2026-04-12 10:35:38.683951: Pseudo dice [0.0, 0.0, 0.728, 0.5279, 0.3217, 0.6636, 0.8014] +2026-04-12 10:35:38.686717: Epoch time: 100.18 s +2026-04-12 10:35:39.905768: +2026-04-12 10:35:39.908462: Epoch 1599 +2026-04-12 10:35:39.911268: Current learning rate: 0.00632 +2026-04-12 10:37:20.154325: train_loss -0.3438 +2026-04-12 10:37:20.159879: val_loss -0.1947 +2026-04-12 10:37:20.161883: Pseudo dice [0.0, 0.0, 0.1316, 0.0, 0.0, 0.7459, 0.4239] +2026-04-12 10:37:20.164096: Epoch time: 100.25 s +2026-04-12 10:37:23.124395: +2026-04-12 10:37:23.126894: Epoch 1600 +2026-04-12 10:37:23.129091: Current learning rate: 0.00631 +2026-04-12 10:39:03.446334: train_loss -0.3412 +2026-04-12 10:39:03.451644: val_loss -0.3686 +2026-04-12 10:39:03.454616: Pseudo dice [0.0, 0.0, 0.4885, 0.0, 0.0, 0.5243, 0.7438] +2026-04-12 10:39:03.457378: Epoch time: 100.32 s +2026-04-12 10:39:04.683667: +2026-04-12 10:39:04.685810: Epoch 1601 +2026-04-12 10:39:04.687842: Current learning rate: 0.00631 +2026-04-12 10:40:44.862400: train_loss -0.3368 +2026-04-12 10:40:44.870013: val_loss -0.2928 +2026-04-12 10:40:44.873592: Pseudo dice [0.0, 0.0, 0.1874, 0.0507, 0.0419, 0.048, 0.658] +2026-04-12 10:40:44.876415: Epoch time: 100.18 s +2026-04-12 10:40:46.101498: +2026-04-12 10:40:46.103509: Epoch 1602 +2026-04-12 10:40:46.106072: Current learning rate: 0.00631 +2026-04-12 10:42:26.780502: train_loss -0.3375 +2026-04-12 10:42:26.787704: val_loss -0.3428 +2026-04-12 10:42:26.790428: Pseudo dice [0.0, 0.0, 0.3469, 0.0, 0.3731, 0.523, 0.2362] +2026-04-12 10:42:26.793892: Epoch time: 100.68 s +2026-04-12 10:42:27.996107: +2026-04-12 10:42:27.998482: Epoch 1603 +2026-04-12 10:42:28.000218: Current learning rate: 0.00631 +2026-04-12 10:44:08.866466: train_loss -0.3359 +2026-04-12 10:44:08.875342: val_loss -0.3156 +2026-04-12 10:44:08.878290: Pseudo dice [0.0, 0.0, 0.538, 0.0, 0.2863, 0.1869, 0.1866] +2026-04-12 10:44:08.881058: Epoch time: 100.87 s +2026-04-12 10:44:10.121572: +2026-04-12 10:44:10.123296: Epoch 1604 +2026-04-12 10:44:10.125412: Current learning rate: 0.0063 +2026-04-12 10:45:51.742879: train_loss -0.3421 +2026-04-12 10:45:51.750947: val_loss -0.3515 +2026-04-12 10:45:51.753416: Pseudo dice [0.0, 0.0, 0.749, 0.1303, 0.1987, 0.5206, 0.6984] +2026-04-12 10:45:51.756119: Epoch time: 101.62 s +2026-04-12 10:45:53.009890: +2026-04-12 10:45:53.013622: Epoch 1605 +2026-04-12 10:45:53.017745: Current learning rate: 0.0063 +2026-04-12 10:47:33.262933: train_loss -0.3444 +2026-04-12 10:47:33.268384: val_loss -0.3037 +2026-04-12 10:47:33.270856: Pseudo dice [0.0, 0.0, 0.6158, 0.0, 0.303, 0.7087, 0.5887] +2026-04-12 10:47:33.272779: Epoch time: 100.26 s +2026-04-12 10:47:34.501530: +2026-04-12 10:47:34.503744: Epoch 1606 +2026-04-12 10:47:34.505534: Current learning rate: 0.0063 +2026-04-12 10:49:15.079990: train_loss -0.3389 +2026-04-12 10:49:15.087531: val_loss -0.342 +2026-04-12 10:49:15.089971: Pseudo dice [0.0, 0.0, 0.6263, 0.0005, 0.1393, 0.3707, 0.378] +2026-04-12 10:49:15.092542: Epoch time: 100.58 s +2026-04-12 10:49:16.343152: +2026-04-12 10:49:16.345262: Epoch 1607 +2026-04-12 10:49:16.347253: Current learning rate: 0.0063 +2026-04-12 10:50:57.221949: train_loss -0.3731 +2026-04-12 10:50:57.229153: val_loss -0.3739 +2026-04-12 10:50:57.231431: Pseudo dice [0.0, 0.0, 0.7176, 0.0, 0.3667, 0.6955, 0.7933] +2026-04-12 10:50:57.233954: Epoch time: 100.88 s +2026-04-12 10:50:58.454662: +2026-04-12 10:50:58.456957: Epoch 1608 +2026-04-12 10:50:58.459357: Current learning rate: 0.0063 +2026-04-12 10:52:39.847884: train_loss -0.4024 +2026-04-12 10:52:39.857052: val_loss -0.3718 +2026-04-12 10:52:39.860162: Pseudo dice [0.0, 0.0, 0.6714, 0.0, 0.2531, 0.629, 0.7235] +2026-04-12 10:52:39.862916: Epoch time: 101.4 s +2026-04-12 10:52:41.123041: +2026-04-12 10:52:41.125519: Epoch 1609 +2026-04-12 10:52:41.127673: Current learning rate: 0.00629 +2026-04-12 10:54:21.519248: train_loss -0.3786 +2026-04-12 10:54:21.526416: val_loss -0.3577 +2026-04-12 10:54:21.529873: Pseudo dice [0.0, 0.0, 0.7547, 0.0, 0.3652, 0.5747, 0.5326] +2026-04-12 10:54:21.534483: Epoch time: 100.4 s +2026-04-12 10:54:22.791977: +2026-04-12 10:54:22.794605: Epoch 1610 +2026-04-12 10:54:22.796870: Current learning rate: 0.00629 +2026-04-12 10:56:03.095620: train_loss -0.3795 +2026-04-12 10:56:03.103771: val_loss -0.3157 +2026-04-12 10:56:03.105927: Pseudo dice [0.0, 0.0, 0.2343, 0.0, 0.0, 0.4032, 0.7155] +2026-04-12 10:56:03.107979: Epoch time: 100.31 s +2026-04-12 10:56:04.363308: +2026-04-12 10:56:04.366146: Epoch 1611 +2026-04-12 10:56:04.368145: Current learning rate: 0.00629 +2026-04-12 10:57:44.781214: train_loss -0.353 +2026-04-12 10:57:44.791973: val_loss -0.3742 +2026-04-12 10:57:44.795786: Pseudo dice [0.0, 0.0, 0.5982, 0.0, 0.0, 0.6468, 0.7211] +2026-04-12 10:57:44.798837: Epoch time: 100.42 s +2026-04-12 10:57:46.007081: +2026-04-12 10:57:46.009193: Epoch 1612 +2026-04-12 10:57:46.011445: Current learning rate: 0.00629 +2026-04-12 10:59:26.921875: train_loss -0.3479 +2026-04-12 10:59:26.929828: val_loss -0.3062 +2026-04-12 10:59:26.932658: Pseudo dice [0.0, 0.0, 0.6542, 0.0, 0.0, 0.3988, 0.6071] +2026-04-12 10:59:26.935625: Epoch time: 100.92 s +2026-04-12 10:59:28.175390: +2026-04-12 10:59:28.177938: Epoch 1613 +2026-04-12 10:59:28.180822: Current learning rate: 0.00628 +2026-04-12 11:01:33.884435: train_loss -0.3666 +2026-04-12 11:01:33.892947: val_loss -0.3609 +2026-04-12 11:01:33.895984: Pseudo dice [0.0, 0.0, 0.7069, 0.0035, 0.0, 0.6981, 0.5302] +2026-04-12 11:01:33.900286: Epoch time: 125.71 s +2026-04-12 11:01:35.154036: +2026-04-12 11:01:35.155870: Epoch 1614 +2026-04-12 11:01:35.157740: Current learning rate: 0.00628 +2026-04-12 11:03:17.625537: train_loss -0.3949 +2026-04-12 11:03:17.630795: val_loss -0.388 +2026-04-12 11:03:17.633390: Pseudo dice [0.0, 0.0, 0.7467, 0.6278, 0.0, 0.7289, 0.7663] +2026-04-12 11:03:17.636440: Epoch time: 102.47 s +2026-04-12 11:03:18.906320: +2026-04-12 11:03:18.908375: Epoch 1615 +2026-04-12 11:03:18.910245: Current learning rate: 0.00628 +2026-04-12 11:04:59.109281: train_loss -0.3439 +2026-04-12 11:04:59.117209: val_loss -0.3309 +2026-04-12 11:04:59.119258: Pseudo dice [0.0, 0.0, 0.6335, 0.0, 0.4541, 0.2242, 0.8165] +2026-04-12 11:04:59.121700: Epoch time: 100.21 s +2026-04-12 11:05:00.331626: +2026-04-12 11:05:00.333916: Epoch 1616 +2026-04-12 11:05:00.335846: Current learning rate: 0.00628 +2026-04-12 11:06:40.586853: train_loss -0.3545 +2026-04-12 11:06:40.593724: val_loss -0.2056 +2026-04-12 11:06:40.596743: Pseudo dice [0.0, 0.0, 0.4882, 0.0, 0.0, 0.3508, 0.4727] +2026-04-12 11:06:40.599910: Epoch time: 100.26 s +2026-04-12 11:06:41.835970: +2026-04-12 11:06:41.837806: Epoch 1617 +2026-04-12 11:06:41.839935: Current learning rate: 0.00627 +2026-04-12 11:08:22.407864: train_loss -0.3508 +2026-04-12 11:08:22.417380: val_loss -0.3251 +2026-04-12 11:08:22.421170: Pseudo dice [0.0, 0.0, 0.5944, 0.3093, 0.0, 0.436, 0.6655] +2026-04-12 11:08:22.425254: Epoch time: 100.57 s +2026-04-12 11:08:23.664187: +2026-04-12 11:08:23.672065: Epoch 1618 +2026-04-12 11:08:23.675938: Current learning rate: 0.00627 +2026-04-12 11:10:03.943726: train_loss -0.3406 +2026-04-12 11:10:03.952496: val_loss -0.3376 +2026-04-12 11:10:03.956225: Pseudo dice [0.0, 0.0, 0.7389, 0.0, 0.0, 0.3593, 0.7698] +2026-04-12 11:10:03.960420: Epoch time: 100.28 s +2026-04-12 11:10:05.176391: +2026-04-12 11:10:05.179169: Epoch 1619 +2026-04-12 11:10:05.182917: Current learning rate: 0.00627 +2026-04-12 11:11:45.496706: train_loss -0.3595 +2026-04-12 11:11:45.505569: val_loss -0.3673 +2026-04-12 11:11:45.508133: Pseudo dice [0.0, 0.0, 0.6295, 0.0, 0.0, 0.0729, 0.6598] +2026-04-12 11:11:45.510309: Epoch time: 100.32 s +2026-04-12 11:11:46.761103: +2026-04-12 11:11:46.763153: Epoch 1620 +2026-04-12 11:11:46.765154: Current learning rate: 0.00627 +2026-04-12 11:13:27.445477: train_loss -0.3362 +2026-04-12 11:13:27.452178: val_loss -0.348 +2026-04-12 11:13:27.455154: Pseudo dice [0.0, 0.0, 0.6505, 0.0, 0.0, 0.4327, 0.4081] +2026-04-12 11:13:27.457911: Epoch time: 100.69 s +2026-04-12 11:13:28.700953: +2026-04-12 11:13:28.703173: Epoch 1621 +2026-04-12 11:13:28.705154: Current learning rate: 0.00626 +2026-04-12 11:15:08.971134: train_loss -0.3679 +2026-04-12 11:15:08.977698: val_loss -0.3853 +2026-04-12 11:15:08.980404: Pseudo dice [0.0, 0.0, 0.3103, 0.0, 0.1725, 0.7453, 0.6557] +2026-04-12 11:15:08.983831: Epoch time: 100.27 s +2026-04-12 11:15:10.199144: +2026-04-12 11:15:10.201350: Epoch 1622 +2026-04-12 11:15:10.203196: Current learning rate: 0.00626 +2026-04-12 11:16:50.663721: train_loss -0.3704 +2026-04-12 11:16:50.678636: val_loss -0.354 +2026-04-12 11:16:50.681049: Pseudo dice [0.0, 0.0, 0.6449, 0.3389, 0.3092, 0.7168, 0.6229] +2026-04-12 11:16:50.684205: Epoch time: 100.47 s +2026-04-12 11:16:51.953484: +2026-04-12 11:16:51.955860: Epoch 1623 +2026-04-12 11:16:51.957966: Current learning rate: 0.00626 +2026-04-12 11:18:32.307384: train_loss -0.388 +2026-04-12 11:18:32.313448: val_loss -0.4176 +2026-04-12 11:18:32.315767: Pseudo dice [0.0, 0.0, 0.7769, 0.3512, 0.5116, 0.7945, 0.854] +2026-04-12 11:18:32.318426: Epoch time: 100.36 s +2026-04-12 11:18:33.577809: +2026-04-12 11:18:33.579843: Epoch 1624 +2026-04-12 11:18:33.581753: Current learning rate: 0.00626 +2026-04-12 11:20:13.917599: train_loss -0.3537 +2026-04-12 11:20:13.923707: val_loss -0.3282 +2026-04-12 11:20:13.925867: Pseudo dice [0.0, 0.0, 0.5849, 0.0, 0.0, 0.0007, 0.1612] +2026-04-12 11:20:13.928457: Epoch time: 100.34 s +2026-04-12 11:20:15.137673: +2026-04-12 11:20:15.140896: Epoch 1625 +2026-04-12 11:20:15.143283: Current learning rate: 0.00626 +2026-04-12 11:21:55.392980: train_loss -0.3222 +2026-04-12 11:21:55.401950: val_loss -0.339 +2026-04-12 11:21:55.405156: Pseudo dice [0.0, 0.0, 0.687, 0.0, 0.0, 0.0, 0.6869] +2026-04-12 11:21:55.408348: Epoch time: 100.26 s +2026-04-12 11:21:56.656696: +2026-04-12 11:21:56.658643: Epoch 1626 +2026-04-12 11:21:56.660358: Current learning rate: 0.00625 +2026-04-12 11:23:36.963187: train_loss -0.2995 +2026-04-12 11:23:36.969415: val_loss -0.3428 +2026-04-12 11:23:36.971328: Pseudo dice [0.0, 0.0, 0.6327, 0.0, 0.0, 0.0, 0.5095] +2026-04-12 11:23:36.973659: Epoch time: 100.31 s +2026-04-12 11:23:38.184996: +2026-04-12 11:23:38.187727: Epoch 1627 +2026-04-12 11:23:38.190074: Current learning rate: 0.00625 +2026-04-12 11:25:18.393342: train_loss -0.3456 +2026-04-12 11:25:18.399546: val_loss -0.3643 +2026-04-12 11:25:18.403233: Pseudo dice [0.0, 0.0, 0.6186, 0.4839, 0.0, 0.5829, 0.7359] +2026-04-12 11:25:18.405859: Epoch time: 100.21 s +2026-04-12 11:25:19.621419: +2026-04-12 11:25:19.624304: Epoch 1628 +2026-04-12 11:25:19.627180: Current learning rate: 0.00625 +2026-04-12 11:26:59.977580: train_loss -0.3537 +2026-04-12 11:26:59.983246: val_loss -0.3405 +2026-04-12 11:26:59.985326: Pseudo dice [0.0, 0.0, 0.4354, 0.0, 0.0, 0.4048, 0.6196] +2026-04-12 11:26:59.987792: Epoch time: 100.36 s +2026-04-12 11:27:01.221479: +2026-04-12 11:27:01.223471: Epoch 1629 +2026-04-12 11:27:01.225226: Current learning rate: 0.00625 +2026-04-12 11:28:41.797805: train_loss -0.3491 +2026-04-12 11:28:41.806278: val_loss -0.3582 +2026-04-12 11:28:41.809357: Pseudo dice [0.0, 0.0, 0.5747, 0.0, 0.09, 0.6848, 0.6941] +2026-04-12 11:28:41.812126: Epoch time: 100.58 s +2026-04-12 11:28:43.015320: +2026-04-12 11:28:43.017501: Epoch 1630 +2026-04-12 11:28:43.020244: Current learning rate: 0.00624 +2026-04-12 11:30:23.323394: train_loss -0.335 +2026-04-12 11:30:23.328483: val_loss -0.3406 +2026-04-12 11:30:23.330315: Pseudo dice [0.0, 0.0, 0.5655, 0.0, 0.0, 0.3173, 0.1243] +2026-04-12 11:30:23.332452: Epoch time: 100.31 s +2026-04-12 11:30:24.543122: +2026-04-12 11:30:24.545079: Epoch 1631 +2026-04-12 11:30:24.546638: Current learning rate: 0.00624 +2026-04-12 11:32:04.688492: train_loss -0.3458 +2026-04-12 11:32:04.697375: val_loss -0.3655 +2026-04-12 11:32:04.699786: Pseudo dice [0.0, 0.0, 0.5604, 0.0, 0.0081, 0.8039, 0.5173] +2026-04-12 11:32:04.702205: Epoch time: 100.15 s +2026-04-12 11:32:05.932978: +2026-04-12 11:32:05.935235: Epoch 1632 +2026-04-12 11:32:05.936967: Current learning rate: 0.00624 +2026-04-12 11:33:46.394860: train_loss -0.3683 +2026-04-12 11:33:46.400007: val_loss -0.3813 +2026-04-12 11:33:46.402150: Pseudo dice [0.0, 0.0, 0.7671, 0.0, 0.2275, 0.5983, 0.6342] +2026-04-12 11:33:46.405026: Epoch time: 100.46 s +2026-04-12 11:33:47.629370: +2026-04-12 11:33:47.631727: Epoch 1633 +2026-04-12 11:33:47.633534: Current learning rate: 0.00624 +2026-04-12 11:35:28.794763: train_loss -0.3759 +2026-04-12 11:35:28.803359: val_loss -0.3503 +2026-04-12 11:35:28.805829: Pseudo dice [0.0, 0.0, 0.6389, 0.1343, 0.4582, 0.7783, 0.591] +2026-04-12 11:35:28.808582: Epoch time: 101.17 s +2026-04-12 11:35:30.052528: +2026-04-12 11:35:30.054743: Epoch 1634 +2026-04-12 11:35:30.056663: Current learning rate: 0.00623 +2026-04-12 11:37:10.374092: train_loss -0.3645 +2026-04-12 11:37:10.380182: val_loss -0.3724 +2026-04-12 11:37:10.382277: Pseudo dice [0.0, 0.0, 0.5156, 0.0, 0.2157, 0.5227, 0.6494] +2026-04-12 11:37:10.386082: Epoch time: 100.32 s +2026-04-12 11:37:11.636131: +2026-04-12 11:37:11.638329: Epoch 1635 +2026-04-12 11:37:11.640505: Current learning rate: 0.00623 +2026-04-12 11:38:52.005077: train_loss -0.3494 +2026-04-12 11:38:52.011802: val_loss -0.3509 +2026-04-12 11:38:52.014102: Pseudo dice [0.0, 0.0, 0.7341, 0.6872, 0.5483, 0.1745, 0.6296] +2026-04-12 11:38:52.016735: Epoch time: 100.37 s +2026-04-12 11:38:53.305259: +2026-04-12 11:38:53.307582: Epoch 1636 +2026-04-12 11:38:53.309281: Current learning rate: 0.00623 +2026-04-12 11:40:34.019731: train_loss -0.3435 +2026-04-12 11:40:34.027195: val_loss -0.3681 +2026-04-12 11:40:34.030282: Pseudo dice [0.0, 0.0, 0.5171, 0.0, 0.0183, 0.6732, 0.8323] +2026-04-12 11:40:34.032776: Epoch time: 100.72 s +2026-04-12 11:40:35.266726: +2026-04-12 11:40:35.269380: Epoch 1637 +2026-04-12 11:40:35.271515: Current learning rate: 0.00623 +2026-04-12 11:42:15.406585: train_loss -0.3567 +2026-04-12 11:42:15.412176: val_loss -0.3323 +2026-04-12 11:42:15.414157: Pseudo dice [0.0, 0.0, 0.5286, 0.0, 0.2647, 0.2247, 0.1808] +2026-04-12 11:42:15.416759: Epoch time: 100.14 s +2026-04-12 11:42:16.593228: +2026-04-12 11:42:16.595266: Epoch 1638 +2026-04-12 11:42:16.597115: Current learning rate: 0.00622 +2026-04-12 11:43:57.030244: train_loss -0.3362 +2026-04-12 11:43:57.040052: val_loss -0.2916 +2026-04-12 11:43:57.042503: Pseudo dice [0.0, 0.0, 0.6112, 0.0, 0.338, 0.4545, 0.6466] +2026-04-12 11:43:57.045006: Epoch time: 100.44 s +2026-04-12 11:43:58.213422: +2026-04-12 11:43:58.215611: Epoch 1639 +2026-04-12 11:43:58.217861: Current learning rate: 0.00622 +2026-04-12 11:45:38.449409: train_loss -0.3746 +2026-04-12 11:45:38.455977: val_loss -0.4055 +2026-04-12 11:45:38.459489: Pseudo dice [0.0, 0.0, 0.7802, 0.0, 0.3606, 0.7766, 0.801] +2026-04-12 11:45:38.462312: Epoch time: 100.24 s +2026-04-12 11:45:39.658924: +2026-04-12 11:45:39.661353: Epoch 1640 +2026-04-12 11:45:39.664251: Current learning rate: 0.00622 +2026-04-12 11:47:20.275369: train_loss -0.3893 +2026-04-12 11:47:20.282728: val_loss -0.3046 +2026-04-12 11:47:20.285399: Pseudo dice [0.0, 0.0, 0.7496, 0.0015, 0.3753, 0.7146, 0.5947] +2026-04-12 11:47:20.289271: Epoch time: 100.62 s +2026-04-12 11:47:21.479882: +2026-04-12 11:47:21.482892: Epoch 1641 +2026-04-12 11:47:21.485380: Current learning rate: 0.00622 +2026-04-12 11:49:01.813008: train_loss -0.3913 +2026-04-12 11:49:01.819579: val_loss -0.392 +2026-04-12 11:49:01.821899: Pseudo dice [0.0, 0.0, 0.791, 0.0, 0.3547, 0.6326, 0.7073] +2026-04-12 11:49:01.825143: Epoch time: 100.34 s +2026-04-12 11:49:02.998115: +2026-04-12 11:49:03.000427: Epoch 1642 +2026-04-12 11:49:03.002658: Current learning rate: 0.00621 +2026-04-12 11:50:43.128230: train_loss -0.3701 +2026-04-12 11:50:43.138318: val_loss -0.3321 +2026-04-12 11:50:43.140580: Pseudo dice [0.0, 0.0, 0.4395, 0.0, 0.4886, 0.7268, 0.6963] +2026-04-12 11:50:43.143478: Epoch time: 100.13 s +2026-04-12 11:50:44.378758: +2026-04-12 11:50:44.381458: Epoch 1643 +2026-04-12 11:50:44.383739: Current learning rate: 0.00621 +2026-04-12 11:52:24.485028: train_loss -0.3729 +2026-04-12 11:52:24.493163: val_loss -0.3958 +2026-04-12 11:52:24.495343: Pseudo dice [0.0, 0.0, 0.6972, 0.7194, 0.0, 0.4671, 0.8583] +2026-04-12 11:52:24.499000: Epoch time: 100.11 s +2026-04-12 11:52:25.688160: +2026-04-12 11:52:25.690298: Epoch 1644 +2026-04-12 11:52:25.692249: Current learning rate: 0.00621 +2026-04-12 11:54:05.718396: train_loss -0.3488 +2026-04-12 11:54:05.724988: val_loss -0.2803 +2026-04-12 11:54:05.728811: Pseudo dice [0.0, 0.0, 0.5793, 0.0483, 0.0, 0.2101, 0.2789] +2026-04-12 11:54:05.732057: Epoch time: 100.03 s +2026-04-12 11:54:06.922993: +2026-04-12 11:54:06.924961: Epoch 1645 +2026-04-12 11:54:06.926691: Current learning rate: 0.00621 +2026-04-12 11:55:47.149769: train_loss -0.3227 +2026-04-12 11:55:47.154746: val_loss -0.2695 +2026-04-12 11:55:47.156883: Pseudo dice [0.0, 0.0, 0.6673, 0.0041, 0.0, 0.7865, 0.1053] +2026-04-12 11:55:47.158946: Epoch time: 100.23 s +2026-04-12 11:55:48.344065: +2026-04-12 11:55:48.345992: Epoch 1646 +2026-04-12 11:55:48.347748: Current learning rate: 0.00621 +2026-04-12 11:57:28.442795: train_loss -0.3691 +2026-04-12 11:57:28.449225: val_loss -0.3164 +2026-04-12 11:57:28.451873: Pseudo dice [0.0, 0.0, 0.5895, 0.0003, 0.0, 0.4716, 0.6938] +2026-04-12 11:57:28.458209: Epoch time: 100.1 s +2026-04-12 11:57:29.665275: +2026-04-12 11:57:29.668305: Epoch 1647 +2026-04-12 11:57:29.670508: Current learning rate: 0.0062 +2026-04-12 11:59:09.940289: train_loss -0.3894 +2026-04-12 11:59:09.947325: val_loss -0.3536 +2026-04-12 11:59:09.949357: Pseudo dice [0.0, 0.0, 0.5749, 0.8016, 0.0, 0.3579, 0.6178] +2026-04-12 11:59:09.951982: Epoch time: 100.28 s +2026-04-12 11:59:11.138304: +2026-04-12 11:59:11.140224: Epoch 1648 +2026-04-12 11:59:11.142025: Current learning rate: 0.0062 +2026-04-12 12:00:51.348860: train_loss -0.366 +2026-04-12 12:00:51.356859: val_loss -0.4008 +2026-04-12 12:00:51.360334: Pseudo dice [0.0, 0.0, 0.7405, 0.0, 0.0, 0.3177, 0.8155] +2026-04-12 12:00:51.363667: Epoch time: 100.21 s +2026-04-12 12:00:52.557874: +2026-04-12 12:00:52.560283: Epoch 1649 +2026-04-12 12:00:52.562380: Current learning rate: 0.0062 +2026-04-12 12:02:32.578104: train_loss -0.389 +2026-04-12 12:02:32.583291: val_loss -0.3831 +2026-04-12 12:02:32.585428: Pseudo dice [0.0, 0.0, 0.6613, 0.0, 0.0, 0.7102, 0.8567] +2026-04-12 12:02:32.587665: Epoch time: 100.02 s +2026-04-12 12:02:35.418785: +2026-04-12 12:02:35.421979: Epoch 1650 +2026-04-12 12:02:35.423843: Current learning rate: 0.0062 +2026-04-12 12:04:15.564594: train_loss -0.3878 +2026-04-12 12:04:15.570985: val_loss -0.2575 +2026-04-12 12:04:15.573409: Pseudo dice [0.0, 0.0, 0.5939, 0.0, 0.0, 0.7405, 0.5571] +2026-04-12 12:04:15.577194: Epoch time: 100.15 s +2026-04-12 12:04:16.764771: +2026-04-12 12:04:16.767641: Epoch 1651 +2026-04-12 12:04:16.770040: Current learning rate: 0.00619 +2026-04-12 12:05:56.848812: train_loss -0.3517 +2026-04-12 12:05:56.855823: val_loss -0.3739 +2026-04-12 12:05:56.859164: Pseudo dice [0.0, 0.0, 0.6888, 0.6163, 0.0747, 0.4798, 0.6542] +2026-04-12 12:05:56.862898: Epoch time: 100.09 s +2026-04-12 12:05:58.033671: +2026-04-12 12:05:58.037802: Epoch 1652 +2026-04-12 12:05:58.040675: Current learning rate: 0.00619 +2026-04-12 12:07:38.160835: train_loss -0.3692 +2026-04-12 12:07:38.166248: val_loss -0.3719 +2026-04-12 12:07:38.168290: Pseudo dice [0.0, 0.0, 0.5834, 0.2647, 0.2545, 0.7179, 0.7613] +2026-04-12 12:07:38.170570: Epoch time: 100.13 s +2026-04-12 12:07:40.338192: +2026-04-12 12:07:40.340386: Epoch 1653 +2026-04-12 12:07:40.342228: Current learning rate: 0.00619 +2026-04-12 12:09:20.643704: train_loss -0.3656 +2026-04-12 12:09:20.649654: val_loss -0.332 +2026-04-12 12:09:20.651841: Pseudo dice [0.0, 0.0, 0.678, 0.1675, 0.2557, 0.6582, 0.8475] +2026-04-12 12:09:20.654213: Epoch time: 100.31 s +2026-04-12 12:09:21.832191: +2026-04-12 12:09:21.834158: Epoch 1654 +2026-04-12 12:09:21.836303: Current learning rate: 0.00619 +2026-04-12 12:11:02.718388: train_loss -0.3668 +2026-04-12 12:11:02.725952: val_loss -0.3534 +2026-04-12 12:11:02.730159: Pseudo dice [0.0, 0.0, 0.635, 0.0, 0.0, 0.5784, 0.3562] +2026-04-12 12:11:02.733259: Epoch time: 100.89 s +2026-04-12 12:11:04.101570: +2026-04-12 12:11:04.103537: Epoch 1655 +2026-04-12 12:11:04.105454: Current learning rate: 0.00618 +2026-04-12 12:12:44.535129: train_loss -0.3737 +2026-04-12 12:12:44.541320: val_loss -0.3695 +2026-04-12 12:12:44.543927: Pseudo dice [0.0, 0.0, 0.5757, 0.0, 0.1971, 0.7075, 0.5906] +2026-04-12 12:12:44.546646: Epoch time: 100.44 s +2026-04-12 12:12:45.724084: +2026-04-12 12:12:45.725975: Epoch 1656 +2026-04-12 12:12:45.727831: Current learning rate: 0.00618 +2026-04-12 12:14:26.403976: train_loss -0.3494 +2026-04-12 12:14:26.409640: val_loss -0.3482 +2026-04-12 12:14:26.411556: Pseudo dice [0.0, 0.0, 0.5457, 0.6242, 0.1924, 0.2384, 0.7384] +2026-04-12 12:14:26.413913: Epoch time: 100.68 s +2026-04-12 12:14:27.598815: +2026-04-12 12:14:27.600542: Epoch 1657 +2026-04-12 12:14:27.602429: Current learning rate: 0.00618 +2026-04-12 12:16:07.893800: train_loss -0.3638 +2026-04-12 12:16:07.899474: val_loss -0.3895 +2026-04-12 12:16:07.901598: Pseudo dice [0.0, 0.0, 0.7262, 0.6271, 0.1874, 0.505, 0.8264] +2026-04-12 12:16:07.903579: Epoch time: 100.3 s +2026-04-12 12:16:09.094504: +2026-04-12 12:16:09.096630: Epoch 1658 +2026-04-12 12:16:09.098507: Current learning rate: 0.00618 +2026-04-12 12:17:49.332230: train_loss -0.378 +2026-04-12 12:17:49.338534: val_loss -0.3231 +2026-04-12 12:17:49.340958: Pseudo dice [0.0, 0.0, 0.6127, 0.0, 0.3105, 0.7923, 0.561] +2026-04-12 12:17:49.344445: Epoch time: 100.24 s +2026-04-12 12:17:50.567738: +2026-04-12 12:17:50.569995: Epoch 1659 +2026-04-12 12:17:50.572170: Current learning rate: 0.00617 +2026-04-12 12:19:30.683086: train_loss -0.378 +2026-04-12 12:19:30.688426: val_loss -0.3748 +2026-04-12 12:19:30.690353: Pseudo dice [0.0, 0.0, 0.6563, 0.3597, 0.248, 0.6776, 0.6373] +2026-04-12 12:19:30.693076: Epoch time: 100.12 s +2026-04-12 12:19:31.884323: +2026-04-12 12:19:31.886501: Epoch 1660 +2026-04-12 12:19:31.889172: Current learning rate: 0.00617 +2026-04-12 12:21:12.808427: train_loss -0.3827 +2026-04-12 12:21:12.815877: val_loss -0.3455 +2026-04-12 12:21:12.818453: Pseudo dice [0.0, 0.0, 0.5181, 0.0, 0.1626, 0.3688, 0.4808] +2026-04-12 12:21:12.821328: Epoch time: 100.93 s +2026-04-12 12:21:14.018131: +2026-04-12 12:21:14.020243: Epoch 1661 +2026-04-12 12:21:14.022555: Current learning rate: 0.00617 +2026-04-12 12:22:54.288499: train_loss -0.35 +2026-04-12 12:22:54.299105: val_loss -0.34 +2026-04-12 12:22:54.301919: Pseudo dice [0.0, 0.0, 0.306, 0.0, 0.25, 0.7644, 0.6894] +2026-04-12 12:22:54.311396: Epoch time: 100.27 s +2026-04-12 12:22:55.507229: +2026-04-12 12:22:55.509224: Epoch 1662 +2026-04-12 12:22:55.510882: Current learning rate: 0.00617 +2026-04-12 12:24:36.034618: train_loss -0.343 +2026-04-12 12:24:36.040500: val_loss -0.3373 +2026-04-12 12:24:36.043113: Pseudo dice [0.0, 0.0, 0.2335, 0.0, 0.4145, 0.8199, 0.4081] +2026-04-12 12:24:36.045727: Epoch time: 100.53 s +2026-04-12 12:24:37.244448: +2026-04-12 12:24:37.246735: Epoch 1663 +2026-04-12 12:24:37.248907: Current learning rate: 0.00617 +2026-04-12 12:26:17.529633: train_loss -0.3629 +2026-04-12 12:26:17.537838: val_loss -0.3253 +2026-04-12 12:26:17.540894: Pseudo dice [0.0, 0.0, 0.1989, 0.0782, 0.0049, 0.5351, 0.7955] +2026-04-12 12:26:17.543552: Epoch time: 100.29 s +2026-04-12 12:26:18.767545: +2026-04-12 12:26:18.769514: Epoch 1664 +2026-04-12 12:26:18.771586: Current learning rate: 0.00616 +2026-04-12 12:27:58.875300: train_loss -0.3493 +2026-04-12 12:27:58.880557: val_loss -0.3372 +2026-04-12 12:27:58.882386: Pseudo dice [0.0, 0.0, 0.6677, 0.0, 0.0, 0.7731, 0.6449] +2026-04-12 12:27:58.885072: Epoch time: 100.11 s +2026-04-12 12:28:00.090492: +2026-04-12 12:28:00.092710: Epoch 1665 +2026-04-12 12:28:00.095697: Current learning rate: 0.00616 +2026-04-12 12:29:40.114932: train_loss -0.3594 +2026-04-12 12:29:40.146441: val_loss -0.3791 +2026-04-12 12:29:40.152219: Pseudo dice [0.0, 0.0, 0.6517, 0.0, 0.0, 0.5896, 0.6426] +2026-04-12 12:29:40.156412: Epoch time: 100.03 s +2026-04-12 12:29:41.370902: +2026-04-12 12:29:41.372978: Epoch 1666 +2026-04-12 12:29:41.374807: Current learning rate: 0.00616 +2026-04-12 12:31:21.573190: train_loss -0.375 +2026-04-12 12:31:21.578971: val_loss -0.38 +2026-04-12 12:31:21.581094: Pseudo dice [0.0, 0.0, 0.7241, 0.0, 0.0, 0.8496, 0.6986] +2026-04-12 12:31:21.583565: Epoch time: 100.21 s +2026-04-12 12:31:22.811107: +2026-04-12 12:31:22.814038: Epoch 1667 +2026-04-12 12:31:22.816318: Current learning rate: 0.00616 +2026-04-12 12:33:02.968650: train_loss -0.396 +2026-04-12 12:33:02.975970: val_loss -0.3997 +2026-04-12 12:33:02.980591: Pseudo dice [0.0, 0.0, 0.4836, 0.0, 0.0, 0.4647, 0.6988] +2026-04-12 12:33:02.984785: Epoch time: 100.16 s +2026-04-12 12:33:04.200137: +2026-04-12 12:33:04.204037: Epoch 1668 +2026-04-12 12:33:04.206280: Current learning rate: 0.00615 +2026-04-12 12:34:44.285931: train_loss -0.3671 +2026-04-12 12:34:44.291683: val_loss -0.299 +2026-04-12 12:34:44.293697: Pseudo dice [0.0, 0.0, 0.5794, 0.0, 0.0, 0.4223, 0.5623] +2026-04-12 12:34:44.296281: Epoch time: 100.09 s +2026-04-12 12:34:45.522891: +2026-04-12 12:34:45.524806: Epoch 1669 +2026-04-12 12:34:45.526715: Current learning rate: 0.00615 +2026-04-12 12:36:25.571260: train_loss -0.3618 +2026-04-12 12:36:25.577464: val_loss -0.3949 +2026-04-12 12:36:25.580276: Pseudo dice [0.0, 0.0, 0.6611, 0.0, 0.2115, 0.6941, 0.6447] +2026-04-12 12:36:25.583533: Epoch time: 100.05 s +2026-04-12 12:36:26.808746: +2026-04-12 12:36:26.810557: Epoch 1670 +2026-04-12 12:36:26.812584: Current learning rate: 0.00615 +2026-04-12 12:38:07.022412: train_loss -0.3788 +2026-04-12 12:38:07.029814: val_loss -0.3132 +2026-04-12 12:38:07.033095: Pseudo dice [0.0, 0.0, 0.5756, 0.0, 0.4615, 0.5745, 0.594] +2026-04-12 12:38:07.035927: Epoch time: 100.22 s +2026-04-12 12:38:08.275125: +2026-04-12 12:38:08.277207: Epoch 1671 +2026-04-12 12:38:08.278870: Current learning rate: 0.00615 +2026-04-12 12:39:48.490188: train_loss -0.376 +2026-04-12 12:39:48.495356: val_loss -0.3775 +2026-04-12 12:39:48.497646: Pseudo dice [0.0, 0.0, 0.6947, 0.4079, 0.3609, 0.51, 0.7068] +2026-04-12 12:39:48.500196: Epoch time: 100.22 s +2026-04-12 12:39:49.727957: +2026-04-12 12:39:49.729903: Epoch 1672 +2026-04-12 12:39:49.731903: Current learning rate: 0.00614 +2026-04-12 12:41:30.704620: train_loss -0.3567 +2026-04-12 12:41:30.711155: val_loss -0.3581 +2026-04-12 12:41:30.713308: Pseudo dice [0.0, 0.0, 0.5984, 0.0, 0.0, 0.5838, 0.7512] +2026-04-12 12:41:30.716007: Epoch time: 100.98 s +2026-04-12 12:41:31.915919: +2026-04-12 12:41:31.917822: Epoch 1673 +2026-04-12 12:41:31.919674: Current learning rate: 0.00614 +2026-04-12 12:43:12.077904: train_loss -0.3511 +2026-04-12 12:43:12.083898: val_loss -0.3233 +2026-04-12 12:43:12.086075: Pseudo dice [0.0, 0.0, 0.5596, 0.4768, 0.2734, 0.4818, 0.2445] +2026-04-12 12:43:12.089422: Epoch time: 100.16 s +2026-04-12 12:43:13.303627: +2026-04-12 12:43:13.305384: Epoch 1674 +2026-04-12 12:43:13.306888: Current learning rate: 0.00614 +2026-04-12 12:44:53.658131: train_loss -0.351 +2026-04-12 12:44:53.672363: val_loss -0.3664 +2026-04-12 12:44:53.677240: Pseudo dice [0.0, 0.0, 0.3523, 0.0, 0.0, 0.5314, 0.8671] +2026-04-12 12:44:53.680782: Epoch time: 100.36 s +2026-04-12 12:44:54.905928: +2026-04-12 12:44:54.908662: Epoch 1675 +2026-04-12 12:44:54.910435: Current learning rate: 0.00614 +2026-04-12 12:46:35.030353: train_loss -0.3759 +2026-04-12 12:46:35.035552: val_loss -0.3985 +2026-04-12 12:46:35.037705: Pseudo dice [0.0, 0.0, 0.6189, 0.0601, 0.2347, 0.6876, 0.876] +2026-04-12 12:46:35.040683: Epoch time: 100.13 s +2026-04-12 12:46:36.265010: +2026-04-12 12:46:36.267071: Epoch 1676 +2026-04-12 12:46:36.268833: Current learning rate: 0.00613 +2026-04-12 12:48:16.465275: train_loss -0.349 +2026-04-12 12:48:16.471122: val_loss -0.159 +2026-04-12 12:48:16.473161: Pseudo dice [0.0, 0.0, 0.7057, 0.0, 0.0, 0.5574, 0.6195] +2026-04-12 12:48:16.475633: Epoch time: 100.2 s +2026-04-12 12:48:17.701833: +2026-04-12 12:48:17.703626: Epoch 1677 +2026-04-12 12:48:17.705292: Current learning rate: 0.00613 +2026-04-12 12:49:57.836061: train_loss -0.3536 +2026-04-12 12:49:57.842441: val_loss -0.3844 +2026-04-12 12:49:57.845074: Pseudo dice [0.0, 0.0, 0.574, 0.0, 0.0, 0.8165, 0.7655] +2026-04-12 12:49:57.848679: Epoch time: 100.14 s +2026-04-12 12:49:59.074944: +2026-04-12 12:49:59.077188: Epoch 1678 +2026-04-12 12:49:59.079005: Current learning rate: 0.00613 +2026-04-12 12:51:39.242116: train_loss -0.3627 +2026-04-12 12:51:39.248829: val_loss -0.363 +2026-04-12 12:51:39.251031: Pseudo dice [0.0, 0.0, 0.5632, 0.5533, 0.0, 0.5665, 0.5877] +2026-04-12 12:51:39.253175: Epoch time: 100.17 s +2026-04-12 12:51:40.448162: +2026-04-12 12:51:40.450330: Epoch 1679 +2026-04-12 12:51:40.451968: Current learning rate: 0.00613 +2026-04-12 12:53:20.968517: train_loss -0.3363 +2026-04-12 12:53:20.974415: val_loss -0.404 +2026-04-12 12:53:20.976396: Pseudo dice [0.0, 0.0, 0.5893, 0.0, 0.5676, 0.4713, 0.7147] +2026-04-12 12:53:20.979212: Epoch time: 100.52 s +2026-04-12 12:53:22.202316: +2026-04-12 12:53:22.204750: Epoch 1680 +2026-04-12 12:53:22.206761: Current learning rate: 0.00612 +2026-04-12 12:55:02.475519: train_loss -0.3679 +2026-04-12 12:55:02.482157: val_loss -0.381 +2026-04-12 12:55:02.484340: Pseudo dice [0.0, 0.0, 0.7432, 0.5703, 0.0, 0.5052, 0.8364] +2026-04-12 12:55:02.486467: Epoch time: 100.28 s +2026-04-12 12:55:03.743964: +2026-04-12 12:55:03.745781: Epoch 1681 +2026-04-12 12:55:03.747403: Current learning rate: 0.00612 +2026-04-12 12:56:44.330400: train_loss -0.3558 +2026-04-12 12:56:44.336174: val_loss -0.3623 +2026-04-12 12:56:44.339985: Pseudo dice [0.0, 0.0, 0.6381, 0.2827, 0.2091, 0.7614, 0.6507] +2026-04-12 12:56:44.342058: Epoch time: 100.59 s +2026-04-12 12:56:45.569141: +2026-04-12 12:56:45.571251: Epoch 1682 +2026-04-12 12:56:45.573014: Current learning rate: 0.00612 +2026-04-12 12:58:25.909720: train_loss -0.3737 +2026-04-12 12:58:25.915129: val_loss -0.3742 +2026-04-12 12:58:25.917117: Pseudo dice [0.0, 0.0, 0.8028, 0.4319, 0.3871, 0.7309, 0.6575] +2026-04-12 12:58:25.919541: Epoch time: 100.34 s +2026-04-12 12:58:27.133475: +2026-04-12 12:58:27.136108: Epoch 1683 +2026-04-12 12:58:27.138754: Current learning rate: 0.00612 +2026-04-12 13:00:07.233758: train_loss -0.3689 +2026-04-12 13:00:07.240105: val_loss -0.3911 +2026-04-12 13:00:07.242401: Pseudo dice [0.0, 0.0, 0.7846, 0.0, 0.2583, 0.7026, 0.6182] +2026-04-12 13:00:07.245189: Epoch time: 100.1 s +2026-04-12 13:00:08.461527: +2026-04-12 13:00:08.463710: Epoch 1684 +2026-04-12 13:00:08.465798: Current learning rate: 0.00612 +2026-04-12 13:01:48.795596: train_loss -0.3551 +2026-04-12 13:01:48.810787: val_loss -0.4036 +2026-04-12 13:01:48.813753: Pseudo dice [0.0, 0.0, 0.7666, 0.0, 0.2868, 0.7486, 0.7713] +2026-04-12 13:01:48.816555: Epoch time: 100.34 s +2026-04-12 13:01:50.005673: +2026-04-12 13:01:50.008280: Epoch 1685 +2026-04-12 13:01:50.010686: Current learning rate: 0.00611 +2026-04-12 13:03:30.929940: train_loss -0.3814 +2026-04-12 13:03:30.936444: val_loss -0.2925 +2026-04-12 13:03:30.938791: Pseudo dice [0.0, 0.0, 0.7593, 0.032, 0.1322, 0.7869, 0.7832] +2026-04-12 13:03:30.941133: Epoch time: 100.93 s +2026-04-12 13:03:32.177579: +2026-04-12 13:03:32.179760: Epoch 1686 +2026-04-12 13:03:32.181842: Current learning rate: 0.00611 +2026-04-12 13:05:13.466105: train_loss -0.3776 +2026-04-12 13:05:13.472865: val_loss -0.358 +2026-04-12 13:05:13.476183: Pseudo dice [0.0, 0.0, 0.675, 0.0, 0.0, 0.6516, 0.5673] +2026-04-12 13:05:13.479072: Epoch time: 101.29 s +2026-04-12 13:05:14.720878: +2026-04-12 13:05:14.723617: Epoch 1687 +2026-04-12 13:05:14.726207: Current learning rate: 0.00611 +2026-04-12 13:06:54.960869: train_loss -0.3712 +2026-04-12 13:06:54.970973: val_loss -0.294 +2026-04-12 13:06:54.975587: Pseudo dice [0.0, 0.0, 0.5966, 0.0155, 0.4236, 0.6161, 0.6569] +2026-04-12 13:06:54.978467: Epoch time: 100.24 s +2026-04-12 13:06:56.237673: +2026-04-12 13:06:56.239716: Epoch 1688 +2026-04-12 13:06:56.241882: Current learning rate: 0.00611 +2026-04-12 13:08:36.220659: train_loss -0.3873 +2026-04-12 13:08:36.228940: val_loss -0.3488 +2026-04-12 13:08:36.230807: Pseudo dice [0.0, 0.0, 0.624, 0.0, 0.4276, 0.3422, 0.7131] +2026-04-12 13:08:36.232868: Epoch time: 99.99 s +2026-04-12 13:08:37.462920: +2026-04-12 13:08:37.465657: Epoch 1689 +2026-04-12 13:08:37.468402: Current learning rate: 0.0061 +2026-04-12 13:10:17.551816: train_loss -0.363 +2026-04-12 13:10:17.560358: val_loss -0.1856 +2026-04-12 13:10:17.563431: Pseudo dice [0.0, 0.0, 0.4579, 0.0, 0.0, 0.5246, 0.0] +2026-04-12 13:10:17.565958: Epoch time: 100.09 s +2026-04-12 13:10:18.771617: +2026-04-12 13:10:18.774766: Epoch 1690 +2026-04-12 13:10:18.776973: Current learning rate: 0.0061 +2026-04-12 13:11:58.848109: train_loss -0.3598 +2026-04-12 13:11:58.853837: val_loss -0.4074 +2026-04-12 13:11:58.855880: Pseudo dice [0.0, 0.0, 0.6027, 0.0, 0.2161, 0.6174, 0.8659] +2026-04-12 13:11:58.857790: Epoch time: 100.08 s +2026-04-12 13:12:00.055022: +2026-04-12 13:12:00.057104: Epoch 1691 +2026-04-12 13:12:00.058777: Current learning rate: 0.0061 +2026-04-12 13:13:40.696845: train_loss -0.3559 +2026-04-12 13:13:40.702484: val_loss -0.3433 +2026-04-12 13:13:40.704597: Pseudo dice [0.0, 0.0, 0.5841, 0.0669, 0.0311, 0.4504, 0.7781] +2026-04-12 13:13:40.707463: Epoch time: 100.64 s +2026-04-12 13:13:42.990585: +2026-04-12 13:13:42.992703: Epoch 1692 +2026-04-12 13:13:42.994467: Current learning rate: 0.0061 +2026-04-12 13:15:23.296562: train_loss -0.379 +2026-04-12 13:15:23.301838: val_loss -0.3907 +2026-04-12 13:15:23.303736: Pseudo dice [0.0, 0.0, 0.7521, 0.0, 0.285, 0.7851, 0.781] +2026-04-12 13:15:23.305858: Epoch time: 100.31 s +2026-04-12 13:15:24.507927: +2026-04-12 13:15:24.510459: Epoch 1693 +2026-04-12 13:15:24.513478: Current learning rate: 0.00609 +2026-04-12 13:17:04.645670: train_loss -0.386 +2026-04-12 13:17:04.652282: val_loss -0.386 +2026-04-12 13:17:04.654632: Pseudo dice [0.0, 0.0, 0.4403, 0.0, 0.2639, 0.6839, 0.4371] +2026-04-12 13:17:04.658041: Epoch time: 100.14 s +2026-04-12 13:17:06.064841: +2026-04-12 13:17:06.066921: Epoch 1694 +2026-04-12 13:17:06.068570: Current learning rate: 0.00609 +2026-04-12 13:18:46.960914: train_loss -0.375 +2026-04-12 13:18:46.967639: val_loss -0.3624 +2026-04-12 13:18:46.970109: Pseudo dice [0.0, 0.0, 0.4411, 0.32, 0.0991, 0.8255, 0.5822] +2026-04-12 13:18:46.972127: Epoch time: 100.9 s +2026-04-12 13:18:48.205617: +2026-04-12 13:18:48.207392: Epoch 1695 +2026-04-12 13:18:48.208962: Current learning rate: 0.00609 +2026-04-12 13:20:28.246565: train_loss -0.3933 +2026-04-12 13:20:28.252594: val_loss -0.2949 +2026-04-12 13:20:28.254734: Pseudo dice [0.0, 0.0, 0.3458, 0.0, 0.32, 0.7141, 0.7135] +2026-04-12 13:20:28.256959: Epoch time: 100.04 s +2026-04-12 13:20:29.475050: +2026-04-12 13:20:29.477479: Epoch 1696 +2026-04-12 13:20:29.479916: Current learning rate: 0.00609 +2026-04-12 13:22:09.736502: train_loss -0.3818 +2026-04-12 13:22:09.742118: val_loss -0.2626 +2026-04-12 13:22:09.744066: Pseudo dice [0.0, 0.0, 0.6922, 0.0036, 0.0, 0.8128, 0.6113] +2026-04-12 13:22:09.746401: Epoch time: 100.26 s +2026-04-12 13:22:10.979480: +2026-04-12 13:22:10.985204: Epoch 1697 +2026-04-12 13:22:10.991929: Current learning rate: 0.00608 +2026-04-12 13:23:51.351380: train_loss -0.3628 +2026-04-12 13:23:51.356723: val_loss -0.3902 +2026-04-12 13:23:51.358567: Pseudo dice [0.0, 0.0, 0.7019, 0.0, 0.2505, 0.7871, 0.8084] +2026-04-12 13:23:51.360996: Epoch time: 100.37 s +2026-04-12 13:23:52.575278: +2026-04-12 13:23:52.577252: Epoch 1698 +2026-04-12 13:23:52.578840: Current learning rate: 0.00608 +2026-04-12 13:25:32.675491: train_loss -0.3759 +2026-04-12 13:25:32.681264: val_loss -0.2384 +2026-04-12 13:25:32.683872: Pseudo dice [0.0, 0.0, 0.3946, 0.0, 0.3201, 0.6233, 0.7583] +2026-04-12 13:25:32.687324: Epoch time: 100.1 s +2026-04-12 13:25:33.936218: +2026-04-12 13:25:33.938362: Epoch 1699 +2026-04-12 13:25:33.940149: Current learning rate: 0.00608 +2026-04-12 13:27:14.785010: train_loss -0.3655 +2026-04-12 13:27:14.791581: val_loss -0.2179 +2026-04-12 13:27:14.794226: Pseudo dice [0.0, 0.0, 0.6833, 0.0182, 0.2346, 0.4157, 0.7232] +2026-04-12 13:27:14.796760: Epoch time: 100.85 s +2026-04-12 13:27:17.760071: +2026-04-12 13:27:17.762608: Epoch 1700 +2026-04-12 13:27:17.764735: Current learning rate: 0.00608 +2026-04-12 13:28:57.971709: train_loss -0.3606 +2026-04-12 13:28:57.978524: val_loss -0.3737 +2026-04-12 13:28:57.981263: Pseudo dice [0.0, 0.0, 0.4792, 0.0, 0.2114, 0.7033, 0.7287] +2026-04-12 13:28:57.983891: Epoch time: 100.21 s +2026-04-12 13:28:59.214540: +2026-04-12 13:28:59.216736: Epoch 1701 +2026-04-12 13:28:59.218639: Current learning rate: 0.00607 +2026-04-12 13:30:39.463355: train_loss -0.3727 +2026-04-12 13:30:39.471985: val_loss -0.3284 +2026-04-12 13:30:39.474553: Pseudo dice [0.0, 0.0, 0.505, 0.0, 0.0791, 0.3383, 0.7165] +2026-04-12 13:30:39.477261: Epoch time: 100.25 s +2026-04-12 13:30:40.696111: +2026-04-12 13:30:40.698069: Epoch 1702 +2026-04-12 13:30:40.700286: Current learning rate: 0.00607 +2026-04-12 13:32:20.900772: train_loss -0.3331 +2026-04-12 13:32:20.905674: val_loss -0.3301 +2026-04-12 13:32:20.907461: Pseudo dice [0.0, 0.0, 0.5993, 0.0, 0.0, 0.082, 0.5075] +2026-04-12 13:32:20.909816: Epoch time: 100.21 s +2026-04-12 13:32:22.125512: +2026-04-12 13:32:22.127264: Epoch 1703 +2026-04-12 13:32:22.128704: Current learning rate: 0.00607 +2026-04-12 13:34:02.105518: train_loss -0.3588 +2026-04-12 13:34:02.112767: val_loss -0.3241 +2026-04-12 13:34:02.117836: Pseudo dice [0.0, 0.0, 0.3858, 0.0, 0.0442, 0.7747, 0.7104] +2026-04-12 13:34:02.122664: Epoch time: 99.98 s +2026-04-12 13:34:03.353808: +2026-04-12 13:34:03.356967: Epoch 1704 +2026-04-12 13:34:03.360030: Current learning rate: 0.00607 +2026-04-12 13:35:43.544229: train_loss -0.3637 +2026-04-12 13:35:43.551018: val_loss -0.3515 +2026-04-12 13:35:43.553349: Pseudo dice [0.0, 0.0, 0.6454, 0.0, 0.019, 0.3704, 0.6534] +2026-04-12 13:35:43.561479: Epoch time: 100.19 s +2026-04-12 13:35:44.793935: +2026-04-12 13:35:44.795994: Epoch 1705 +2026-04-12 13:35:44.797778: Current learning rate: 0.00607 +2026-04-12 13:37:25.019545: train_loss -0.364 +2026-04-12 13:37:25.024787: val_loss -0.3447 +2026-04-12 13:37:25.026486: Pseudo dice [0.0, 0.0, 0.8375, 0.0238, 0.3207, 0.2322, 0.7473] +2026-04-12 13:37:25.028641: Epoch time: 100.23 s +2026-04-12 13:37:26.276275: +2026-04-12 13:37:26.278271: Epoch 1706 +2026-04-12 13:37:26.279946: Current learning rate: 0.00606 +2026-04-12 13:39:06.392788: train_loss -0.368 +2026-04-12 13:39:06.399055: val_loss -0.299 +2026-04-12 13:39:06.401196: Pseudo dice [0.0, 0.0, 0.4166, 0.0, 0.0052, 0.1677, 0.5543] +2026-04-12 13:39:06.403542: Epoch time: 100.12 s +2026-04-12 13:39:07.601876: +2026-04-12 13:39:07.603976: Epoch 1707 +2026-04-12 13:39:07.605998: Current learning rate: 0.00606 +2026-04-12 13:40:48.236838: train_loss -0.3241 +2026-04-12 13:40:48.244822: val_loss -0.3567 +2026-04-12 13:40:48.247936: Pseudo dice [0.0, 0.0, 0.7008, 0.172, 0.0, 0.0, 0.7627] +2026-04-12 13:40:48.250370: Epoch time: 100.64 s +2026-04-12 13:40:49.469499: +2026-04-12 13:40:49.471454: Epoch 1708 +2026-04-12 13:40:49.473174: Current learning rate: 0.00606 +2026-04-12 13:42:30.406245: train_loss -0.35 +2026-04-12 13:42:30.412513: val_loss -0.3662 +2026-04-12 13:42:30.415140: Pseudo dice [0.0, 0.0, 0.808, 0.4233, 0.1689, 0.0628, 0.7828] +2026-04-12 13:42:30.417799: Epoch time: 100.94 s +2026-04-12 13:42:31.646758: +2026-04-12 13:42:31.648849: Epoch 1709 +2026-04-12 13:42:31.651538: Current learning rate: 0.00606 +2026-04-12 13:44:12.641936: train_loss -0.3852 +2026-04-12 13:44:12.649478: val_loss -0.3804 +2026-04-12 13:44:12.652900: Pseudo dice [0.0, 0.0, 0.6307, 0.0, 0.3764, 0.7613, 0.6608] +2026-04-12 13:44:12.655658: Epoch time: 101.0 s +2026-04-12 13:44:13.900153: +2026-04-12 13:44:13.901948: Epoch 1710 +2026-04-12 13:44:13.903752: Current learning rate: 0.00605 +2026-04-12 13:45:54.179827: train_loss -0.3571 +2026-04-12 13:45:54.185766: val_loss -0.2879 +2026-04-12 13:45:54.187731: Pseudo dice [0.0, 0.0, 0.5019, 0.0, 0.289, 0.1176, 0.4294] +2026-04-12 13:45:54.190206: Epoch time: 100.28 s +2026-04-12 13:45:56.342726: +2026-04-12 13:45:56.345003: Epoch 1711 +2026-04-12 13:45:56.346748: Current learning rate: 0.00605 +2026-04-12 13:47:36.778848: train_loss -0.3412 +2026-04-12 13:47:36.784185: val_loss -0.3262 +2026-04-12 13:47:36.786158: Pseudo dice [0.0, 0.0, 0.664, 0.0, 0.123, 0.0826, 0.75] +2026-04-12 13:47:36.788376: Epoch time: 100.44 s +2026-04-12 13:47:37.999873: +2026-04-12 13:47:38.002618: Epoch 1712 +2026-04-12 13:47:38.005958: Current learning rate: 0.00605 +2026-04-12 13:49:18.166402: train_loss -0.3308 +2026-04-12 13:49:18.173217: val_loss -0.2813 +2026-04-12 13:49:18.175768: Pseudo dice [0.0, 0.0, 0.5296, 0.0, 0.2259, 0.0896, 0.3273] +2026-04-12 13:49:18.178720: Epoch time: 100.17 s +2026-04-12 13:49:19.393886: +2026-04-12 13:49:19.395634: Epoch 1713 +2026-04-12 13:49:19.397200: Current learning rate: 0.00605 +2026-04-12 13:50:59.766439: train_loss -0.3504 +2026-04-12 13:50:59.774289: val_loss -0.3799 +2026-04-12 13:50:59.776746: Pseudo dice [0.0, 0.0, 0.6713, 0.0, 0.216, 0.5311, 0.7864] +2026-04-12 13:50:59.779986: Epoch time: 100.38 s +2026-04-12 13:51:00.991863: +2026-04-12 13:51:00.994749: Epoch 1714 +2026-04-12 13:51:00.996880: Current learning rate: 0.00604 +2026-04-12 13:52:41.532890: train_loss -0.358 +2026-04-12 13:52:41.540113: val_loss -0.3518 +2026-04-12 13:52:41.543024: Pseudo dice [0.0, 0.0, 0.4762, 0.2925, 0.1308, 0.7331, 0.6342] +2026-04-12 13:52:41.546382: Epoch time: 100.54 s +2026-04-12 13:52:42.790377: +2026-04-12 13:52:42.794435: Epoch 1715 +2026-04-12 13:52:42.798993: Current learning rate: 0.00604 +2026-04-12 13:54:23.169685: train_loss -0.3662 +2026-04-12 13:54:23.175516: val_loss -0.4192 +2026-04-12 13:54:23.177554: Pseudo dice [0.0, 0.0, 0.7612, 0.5065, 0.2391, 0.4104, 0.6763] +2026-04-12 13:54:23.179497: Epoch time: 100.38 s +2026-04-12 13:54:24.364779: +2026-04-12 13:54:24.367010: Epoch 1716 +2026-04-12 13:54:24.369149: Current learning rate: 0.00604 +2026-04-12 13:56:04.551504: train_loss -0.3929 +2026-04-12 13:56:04.578794: val_loss -0.3619 +2026-04-12 13:56:04.580940: Pseudo dice [0.0, 0.0, 0.7068, 0.0, 0.4682, 0.3151, 0.5671] +2026-04-12 13:56:04.583494: Epoch time: 100.19 s +2026-04-12 13:56:05.853706: +2026-04-12 13:56:05.855941: Epoch 1717 +2026-04-12 13:56:05.858005: Current learning rate: 0.00604 +2026-04-12 13:57:46.564953: train_loss -0.375 +2026-04-12 13:57:46.572397: val_loss -0.371 +2026-04-12 13:57:46.574203: Pseudo dice [0.0, 0.0, 0.6184, 0.6964, 0.4162, 0.2293, 0.6664] +2026-04-12 13:57:46.577974: Epoch time: 100.71 s +2026-04-12 13:57:47.823133: +2026-04-12 13:57:47.836306: Epoch 1718 +2026-04-12 13:57:47.838193: Current learning rate: 0.00603 +2026-04-12 13:59:28.040398: train_loss -0.3819 +2026-04-12 13:59:28.046393: val_loss -0.3407 +2026-04-12 13:59:28.048331: Pseudo dice [0.0, 0.0, 0.793, 0.0, 0.076, 0.4733, 0.0553] +2026-04-12 13:59:28.050697: Epoch time: 100.22 s +2026-04-12 13:59:29.289807: +2026-04-12 13:59:29.291737: Epoch 1719 +2026-04-12 13:59:29.293292: Current learning rate: 0.00603 +2026-04-12 14:01:09.980255: train_loss -0.3686 +2026-04-12 14:01:09.985102: val_loss -0.3198 +2026-04-12 14:01:09.987357: Pseudo dice [0.0, 0.0, 0.6915, 0.0633, 0.2836, 0.7802, 0.4436] +2026-04-12 14:01:09.990091: Epoch time: 100.69 s +2026-04-12 14:01:11.258900: +2026-04-12 14:01:11.260858: Epoch 1720 +2026-04-12 14:01:11.262637: Current learning rate: 0.00603 +2026-04-12 14:02:52.393847: train_loss -0.3804 +2026-04-12 14:02:52.399431: val_loss -0.3583 +2026-04-12 14:02:52.401388: Pseudo dice [0.0, 0.0, 0.4406, 0.404, 0.5563, 0.6518, 0.5257] +2026-04-12 14:02:52.404063: Epoch time: 101.14 s +2026-04-12 14:02:53.620331: +2026-04-12 14:02:53.622088: Epoch 1721 +2026-04-12 14:02:53.623789: Current learning rate: 0.00603 +2026-04-12 14:04:33.717936: train_loss -0.3481 +2026-04-12 14:04:33.724766: val_loss -0.3158 +2026-04-12 14:04:33.727978: Pseudo dice [0.0, 0.0, 0.7002, 0.0946, 0.2826, 0.1122, 0.5694] +2026-04-12 14:04:33.732442: Epoch time: 100.1 s +2026-04-12 14:04:34.944864: +2026-04-12 14:04:34.961656: Epoch 1722 +2026-04-12 14:04:34.978525: Current learning rate: 0.00602 +2026-04-12 14:06:15.200225: train_loss -0.3681 +2026-04-12 14:06:15.208565: val_loss -0.3262 +2026-04-12 14:06:15.210533: Pseudo dice [0.0, 0.0, 0.5621, 0.0431, 0.3073, 0.7455, 0.7493] +2026-04-12 14:06:15.213635: Epoch time: 100.26 s +2026-04-12 14:06:16.418515: +2026-04-12 14:06:16.420870: Epoch 1723 +2026-04-12 14:06:16.422687: Current learning rate: 0.00602 +2026-04-12 14:07:56.690679: train_loss -0.3526 +2026-04-12 14:07:56.697103: val_loss -0.2667 +2026-04-12 14:07:56.699609: Pseudo dice [0.0, 0.0, 0.6309, 0.0323, 0.167, 0.5437, 0.3263] +2026-04-12 14:07:56.708744: Epoch time: 100.28 s +2026-04-12 14:07:57.928333: +2026-04-12 14:07:57.931308: Epoch 1724 +2026-04-12 14:07:57.934019: Current learning rate: 0.00602 +2026-04-12 14:09:37.942998: train_loss -0.3706 +2026-04-12 14:09:37.949490: val_loss -0.3257 +2026-04-12 14:09:37.953380: Pseudo dice [0.0, 0.0, 0.4134, 0.0, 0.2944, 0.6097, 0.8454] +2026-04-12 14:09:37.956339: Epoch time: 100.02 s +2026-04-12 14:09:39.163726: +2026-04-12 14:09:39.166866: Epoch 1725 +2026-04-12 14:09:39.169001: Current learning rate: 0.00602 +2026-04-12 14:11:19.647468: train_loss -0.3724 +2026-04-12 14:11:19.655946: val_loss -0.2869 +2026-04-12 14:11:19.658192: Pseudo dice [0.0, 0.0, 0.7867, 0.0714, 0.255, 0.4562, 0.3783] +2026-04-12 14:11:19.661212: Epoch time: 100.49 s +2026-04-12 14:11:20.893426: +2026-04-12 14:11:20.895792: Epoch 1726 +2026-04-12 14:11:20.898198: Current learning rate: 0.00602 +2026-04-12 14:13:01.133887: train_loss -0.3666 +2026-04-12 14:13:01.143123: val_loss -0.3432 +2026-04-12 14:13:01.146937: Pseudo dice [0.0, 0.0, 0.6613, 0.0935, 0.5273, 0.421, 0.743] +2026-04-12 14:13:01.149899: Epoch time: 100.24 s +2026-04-12 14:13:02.359131: +2026-04-12 14:13:02.361358: Epoch 1727 +2026-04-12 14:13:02.363143: Current learning rate: 0.00601 +2026-04-12 14:14:42.654558: train_loss -0.3477 +2026-04-12 14:14:42.659224: val_loss -0.3089 +2026-04-12 14:14:42.661428: Pseudo dice [0.0, 0.0, 0.3966, 0.0053, 0.0, 0.8058, 0.6965] +2026-04-12 14:14:42.663972: Epoch time: 100.3 s +2026-04-12 14:14:43.866868: +2026-04-12 14:14:43.868651: Epoch 1728 +2026-04-12 14:14:43.870127: Current learning rate: 0.00601 +2026-04-12 14:16:24.247401: train_loss -0.3836 +2026-04-12 14:16:24.253542: val_loss -0.2872 +2026-04-12 14:16:24.255928: Pseudo dice [0.0, 0.0, 0.379, 0.0593, 0.1886, 0.4601, 0.5356] +2026-04-12 14:16:24.258555: Epoch time: 100.38 s +2026-04-12 14:16:25.453971: +2026-04-12 14:16:25.456421: Epoch 1729 +2026-04-12 14:16:25.458731: Current learning rate: 0.00601 +2026-04-12 14:18:05.638329: train_loss -0.3572 +2026-04-12 14:18:05.645064: val_loss -0.3631 +2026-04-12 14:18:05.647529: Pseudo dice [0.0, 0.0, 0.6833, 0.001, 0.2152, 0.6378, 0.5116] +2026-04-12 14:18:05.650412: Epoch time: 100.19 s +2026-04-12 14:18:06.861434: +2026-04-12 14:18:06.865088: Epoch 1730 +2026-04-12 14:18:06.867477: Current learning rate: 0.00601 +2026-04-12 14:19:48.141467: train_loss -0.3091 +2026-04-12 14:19:48.147487: val_loss -0.3615 +2026-04-12 14:19:48.150396: Pseudo dice [0.0, 0.0, 0.6872, 0.0, 0.0523, 0.5821, 0.8076] +2026-04-12 14:19:48.153545: Epoch time: 101.28 s +2026-04-12 14:19:49.368935: +2026-04-12 14:19:49.370886: Epoch 1731 +2026-04-12 14:19:49.372537: Current learning rate: 0.006 +2026-04-12 14:21:29.556770: train_loss -0.3629 +2026-04-12 14:21:29.562137: val_loss -0.3136 +2026-04-12 14:21:29.564192: Pseudo dice [0.0, 0.0, 0.5913, 0.0, 0.305, 0.4169, 0.3478] +2026-04-12 14:21:29.566452: Epoch time: 100.19 s +2026-04-12 14:21:30.777653: +2026-04-12 14:21:30.779691: Epoch 1732 +2026-04-12 14:21:30.781476: Current learning rate: 0.006 +2026-04-12 14:23:11.017491: train_loss -0.375 +2026-04-12 14:23:11.022995: val_loss -0.3872 +2026-04-12 14:23:11.025354: Pseudo dice [0.0, 0.0, 0.6347, 0.8358, 0.3341, 0.6941, 0.4992] +2026-04-12 14:23:11.027893: Epoch time: 100.24 s +2026-04-12 14:23:12.252960: +2026-04-12 14:23:12.254723: Epoch 1733 +2026-04-12 14:23:12.256414: Current learning rate: 0.006 +2026-04-12 14:24:52.352791: train_loss -0.3987 +2026-04-12 14:24:52.358190: val_loss -0.3974 +2026-04-12 14:24:52.360030: Pseudo dice [0.0, 0.0, 0.8211, 0.0, 0.3756, 0.7266, 0.8356] +2026-04-12 14:24:52.362748: Epoch time: 100.1 s +2026-04-12 14:24:53.725465: +2026-04-12 14:24:53.727331: Epoch 1734 +2026-04-12 14:24:53.729223: Current learning rate: 0.006 +2026-04-12 14:26:33.697999: train_loss -0.368 +2026-04-12 14:26:33.703068: val_loss -0.3599 +2026-04-12 14:26:33.704926: Pseudo dice [0.0, 0.0, 0.6376, 0.0, 0.5731, 0.3165, 0.5747] +2026-04-12 14:26:33.707240: Epoch time: 99.98 s +2026-04-12 14:26:34.893925: +2026-04-12 14:26:34.895967: Epoch 1735 +2026-04-12 14:26:34.897645: Current learning rate: 0.00599 +2026-04-12 14:28:14.861882: train_loss -0.3641 +2026-04-12 14:28:14.869447: val_loss -0.3643 +2026-04-12 14:28:14.871349: Pseudo dice [0.0, 0.0, 0.5599, 0.6146, 0.1482, 0.6801, 0.7689] +2026-04-12 14:28:14.874457: Epoch time: 99.97 s +2026-04-12 14:28:16.104266: +2026-04-12 14:28:16.106832: Epoch 1736 +2026-04-12 14:28:16.109654: Current learning rate: 0.00599 +2026-04-12 14:29:56.465336: train_loss -0.3625 +2026-04-12 14:29:56.471170: val_loss -0.2514 +2026-04-12 14:29:56.474204: Pseudo dice [0.0, 0.0, 0.4721, 0.0095, 0.4701, 0.5087, 0.0056] +2026-04-12 14:29:56.476658: Epoch time: 100.36 s +2026-04-12 14:29:57.678684: +2026-04-12 14:29:57.681481: Epoch 1737 +2026-04-12 14:29:57.683619: Current learning rate: 0.00599 +2026-04-12 14:31:37.679103: train_loss -0.3532 +2026-04-12 14:31:37.684562: val_loss -0.346 +2026-04-12 14:31:37.687082: Pseudo dice [0.0, 0.0, 0.5738, 0.299, 0.1208, 0.506, 0.6114] +2026-04-12 14:31:37.689645: Epoch time: 100.0 s +2026-04-12 14:31:38.908001: +2026-04-12 14:31:38.910127: Epoch 1738 +2026-04-12 14:31:38.912671: Current learning rate: 0.00599 +2026-04-12 14:33:19.506576: train_loss -0.3729 +2026-04-12 14:33:19.513605: val_loss -0.3933 +2026-04-12 14:33:19.515946: Pseudo dice [0.0, 0.0, 0.6536, 0.0, 0.0127, 0.621, 0.5129] +2026-04-12 14:33:19.520888: Epoch time: 100.6 s +2026-04-12 14:33:20.749272: +2026-04-12 14:33:20.752408: Epoch 1739 +2026-04-12 14:33:20.754629: Current learning rate: 0.00598 +2026-04-12 14:35:01.184548: train_loss -0.3805 +2026-04-12 14:35:01.190174: val_loss -0.3751 +2026-04-12 14:35:01.192226: Pseudo dice [0.0, 0.0, 0.6529, 0.0, 0.0752, 0.6738, 0.8159] +2026-04-12 14:35:01.194446: Epoch time: 100.44 s +2026-04-12 14:35:02.413788: +2026-04-12 14:35:02.416386: Epoch 1740 +2026-04-12 14:35:02.418771: Current learning rate: 0.00598 +2026-04-12 14:36:42.418481: train_loss -0.3609 +2026-04-12 14:36:42.423512: val_loss -0.2424 +2026-04-12 14:36:42.425375: Pseudo dice [0.0, 0.0, 0.0313, 0.016, 0.0, 0.3301, 0.3153] +2026-04-12 14:36:42.427271: Epoch time: 100.01 s +2026-04-12 14:36:43.643538: +2026-04-12 14:36:43.646200: Epoch 1741 +2026-04-12 14:36:43.648190: Current learning rate: 0.00598 +2026-04-12 14:38:23.724903: train_loss -0.3041 +2026-04-12 14:38:23.730599: val_loss -0.3535 +2026-04-12 14:38:23.733150: Pseudo dice [0.0, 0.0, 0.6273, 0.0, 0.2479, 0.5479, 0.6227] +2026-04-12 14:38:23.736583: Epoch time: 100.08 s +2026-04-12 14:38:24.963262: +2026-04-12 14:38:24.965294: Epoch 1742 +2026-04-12 14:38:24.966803: Current learning rate: 0.00598 +2026-04-12 14:40:05.020289: train_loss -0.3148 +2026-04-12 14:40:05.030741: val_loss -0.3227 +2026-04-12 14:40:05.035401: Pseudo dice [0.0, 0.0, 0.4213, 0.0, 0.0, 0.7138, 0.5027] +2026-04-12 14:40:05.041632: Epoch time: 100.06 s +2026-04-12 14:40:06.250846: +2026-04-12 14:40:06.252856: Epoch 1743 +2026-04-12 14:40:06.254520: Current learning rate: 0.00597 +2026-04-12 14:41:46.651203: train_loss -0.3361 +2026-04-12 14:41:46.657766: val_loss -0.2594 +2026-04-12 14:41:46.660020: Pseudo dice [0.0, 0.0, 0.5603, 0.0, 0.0, 0.3193, 0.731] +2026-04-12 14:41:46.662705: Epoch time: 100.4 s +2026-04-12 14:41:47.872898: +2026-04-12 14:41:47.874637: Epoch 1744 +2026-04-12 14:41:47.876326: Current learning rate: 0.00597 +2026-04-12 14:43:28.025195: train_loss -0.3532 +2026-04-12 14:43:28.030127: val_loss -0.3668 +2026-04-12 14:43:28.032389: Pseudo dice [0.0, 0.0, 0.6697, 0.0119, 0.0, 0.7065, 0.467] +2026-04-12 14:43:28.034354: Epoch time: 100.16 s +2026-04-12 14:43:29.240788: +2026-04-12 14:43:29.243056: Epoch 1745 +2026-04-12 14:43:29.245023: Current learning rate: 0.00597 +2026-04-12 14:45:09.101350: train_loss -0.3944 +2026-04-12 14:45:09.109149: val_loss -0.4182 +2026-04-12 14:45:09.111777: Pseudo dice [0.0, 0.0, 0.8118, 0.0, 0.0, 0.7774, 0.7599] +2026-04-12 14:45:09.114360: Epoch time: 99.86 s +2026-04-12 14:45:10.314754: +2026-04-12 14:45:10.316766: Epoch 1746 +2026-04-12 14:45:10.318636: Current learning rate: 0.00597 +2026-04-12 14:46:50.307431: train_loss -0.3625 +2026-04-12 14:46:50.312633: val_loss -0.356 +2026-04-12 14:46:50.314308: Pseudo dice [0.0, 0.0, 0.6108, 0.5202, 0.0, 0.1888, 0.3634] +2026-04-12 14:46:50.316911: Epoch time: 100.0 s +2026-04-12 14:46:51.530780: +2026-04-12 14:46:51.533424: Epoch 1747 +2026-04-12 14:46:51.535443: Current learning rate: 0.00597 +2026-04-12 14:48:31.630571: train_loss -0.3525 +2026-04-12 14:48:31.635443: val_loss -0.3416 +2026-04-12 14:48:31.637053: Pseudo dice [0.0, 0.0, 0.7222, 0.1287, 0.0, 0.8381, 0.6632] +2026-04-12 14:48:31.639323: Epoch time: 100.1 s +2026-04-12 14:48:32.847357: +2026-04-12 14:48:32.850239: Epoch 1748 +2026-04-12 14:48:32.851882: Current learning rate: 0.00596 +2026-04-12 14:50:12.939195: train_loss -0.3732 +2026-04-12 14:50:12.947673: val_loss -0.3354 +2026-04-12 14:50:12.951243: Pseudo dice [0.0, 0.0, 0.665, 0.117, 0.2869, 0.6224, 0.5865] +2026-04-12 14:50:12.954963: Epoch time: 100.09 s +2026-04-12 14:50:14.175298: +2026-04-12 14:50:14.177966: Epoch 1749 +2026-04-12 14:50:14.180115: Current learning rate: 0.00596 +2026-04-12 14:51:54.336544: train_loss -0.3697 +2026-04-12 14:51:54.343268: val_loss -0.2866 +2026-04-12 14:51:54.345490: Pseudo dice [0.0, 0.0, 0.6134, 0.0502, 0.1561, 0.7558, 0.6446] +2026-04-12 14:51:54.348287: Epoch time: 100.16 s +2026-04-12 14:51:58.220208: +2026-04-12 14:51:58.222542: Epoch 1750 +2026-04-12 14:51:58.224633: Current learning rate: 0.00596 +2026-04-12 14:53:38.353621: train_loss -0.382 +2026-04-12 14:53:38.359121: val_loss -0.3418 +2026-04-12 14:53:38.361489: Pseudo dice [0.0, 0.0, 0.5716, 0.0, 0.0, 0.643, 0.5932] +2026-04-12 14:53:38.364073: Epoch time: 100.14 s +2026-04-12 14:53:39.579564: +2026-04-12 14:53:39.581735: Epoch 1751 +2026-04-12 14:53:39.583443: Current learning rate: 0.00596 +2026-04-12 14:55:19.733276: train_loss -0.3432 +2026-04-12 14:55:19.740177: val_loss -0.3288 +2026-04-12 14:55:19.743216: Pseudo dice [0.0, 0.0, 0.503, 0.0, 0.0, 0.4492, 0.5744] +2026-04-12 14:55:19.746333: Epoch time: 100.16 s +2026-04-12 14:55:20.979707: +2026-04-12 14:55:20.982116: Epoch 1752 +2026-04-12 14:55:20.984061: Current learning rate: 0.00595 +2026-04-12 14:57:01.120492: train_loss -0.3269 +2026-04-12 14:57:01.127235: val_loss -0.2733 +2026-04-12 14:57:01.129426: Pseudo dice [0.0, 0.0, 0.6214, 0.0, 0.1857, 0.1603, 0.0] +2026-04-12 14:57:01.132262: Epoch time: 100.14 s +2026-04-12 14:57:02.364463: +2026-04-12 14:57:02.366497: Epoch 1753 +2026-04-12 14:57:02.368112: Current learning rate: 0.00595 +2026-04-12 14:58:42.547586: train_loss -0.3596 +2026-04-12 14:58:42.553056: val_loss -0.3641 +2026-04-12 14:58:42.554947: Pseudo dice [0.0, 0.0, 0.5099, 0.0, 0.0, 0.7074, 0.6235] +2026-04-12 14:58:42.557230: Epoch time: 100.19 s +2026-04-12 14:58:43.757267: +2026-04-12 14:58:43.759648: Epoch 1754 +2026-04-12 14:58:43.761771: Current learning rate: 0.00595 +2026-04-12 15:00:23.835423: train_loss -0.3334 +2026-04-12 15:00:23.842954: val_loss -0.3369 +2026-04-12 15:00:23.845788: Pseudo dice [0.0, 0.0, 0.714, 0.0, 0.0227, 0.7909, 0.6451] +2026-04-12 15:00:23.850292: Epoch time: 100.08 s +2026-04-12 15:00:25.068971: +2026-04-12 15:00:25.071976: Epoch 1755 +2026-04-12 15:00:25.073807: Current learning rate: 0.00595 +2026-04-12 15:02:05.064335: train_loss -0.3646 +2026-04-12 15:02:05.070024: val_loss -0.2771 +2026-04-12 15:02:05.071848: Pseudo dice [0.0, 0.0, 0.3088, 0.0, 0.0, 0.4947, 0.6293] +2026-04-12 15:02:05.074123: Epoch time: 100.0 s +2026-04-12 15:02:06.284596: +2026-04-12 15:02:06.286506: Epoch 1756 +2026-04-12 15:02:06.288483: Current learning rate: 0.00594 +2026-04-12 15:03:46.485258: train_loss -0.3556 +2026-04-12 15:03:46.490123: val_loss -0.4121 +2026-04-12 15:03:46.492148: Pseudo dice [0.0, 0.0, 0.7079, 0.0, 0.0, 0.781, 0.5051] +2026-04-12 15:03:46.494465: Epoch time: 100.2 s +2026-04-12 15:03:47.685553: +2026-04-12 15:03:47.689830: Epoch 1757 +2026-04-12 15:03:47.692894: Current learning rate: 0.00594 +2026-04-12 15:05:27.662641: train_loss -0.3753 +2026-04-12 15:05:27.668732: val_loss -0.4066 +2026-04-12 15:05:27.672087: Pseudo dice [0.0, 0.0, 0.7653, 0.0, 0.3851, 0.4811, 0.8622] +2026-04-12 15:05:27.675126: Epoch time: 99.98 s +2026-04-12 15:05:28.881704: +2026-04-12 15:05:28.884161: Epoch 1758 +2026-04-12 15:05:28.885880: Current learning rate: 0.00594 +2026-04-12 15:07:08.825317: train_loss -0.3599 +2026-04-12 15:07:08.834301: val_loss -0.3903 +2026-04-12 15:07:08.839691: Pseudo dice [0.0, 0.0, 0.6633, 0.0, 0.2201, 0.7447, 0.7416] +2026-04-12 15:07:08.843306: Epoch time: 99.95 s +2026-04-12 15:07:10.064285: +2026-04-12 15:07:10.066789: Epoch 1759 +2026-04-12 15:07:10.069770: Current learning rate: 0.00594 +2026-04-12 15:08:50.778157: train_loss -0.3649 +2026-04-12 15:08:50.783808: val_loss -0.2991 +2026-04-12 15:08:50.786026: Pseudo dice [0.0, 0.0, 0.445, 0.044, 0.3434, 0.4006, 0.8536] +2026-04-12 15:08:50.789152: Epoch time: 100.72 s +2026-04-12 15:08:52.019909: +2026-04-12 15:08:52.022359: Epoch 1760 +2026-04-12 15:08:52.024754: Current learning rate: 0.00593 +2026-04-12 15:10:32.097906: train_loss -0.3531 +2026-04-12 15:10:32.102995: val_loss -0.321 +2026-04-12 15:10:32.105675: Pseudo dice [0.0, 0.0, 0.4258, 0.1994, 0.4282, 0.0573, 0.2046] +2026-04-12 15:10:32.107957: Epoch time: 100.08 s +2026-04-12 15:10:33.293306: +2026-04-12 15:10:33.295310: Epoch 1761 +2026-04-12 15:10:33.296998: Current learning rate: 0.00593 +2026-04-12 15:12:13.915598: train_loss -0.3472 +2026-04-12 15:12:13.925567: val_loss -0.3723 +2026-04-12 15:12:13.927739: Pseudo dice [0.0, 0.0, 0.6142, 0.1669, 0.0853, 0.8005, 0.5453] +2026-04-12 15:12:13.932630: Epoch time: 100.63 s +2026-04-12 15:12:15.157623: +2026-04-12 15:12:15.160515: Epoch 1762 +2026-04-12 15:12:15.163333: Current learning rate: 0.00593 +2026-04-12 15:13:55.332381: train_loss -0.3863 +2026-04-12 15:13:55.337888: val_loss -0.375 +2026-04-12 15:13:55.339965: Pseudo dice [0.0, 0.0, 0.6277, 0.0007, 0.1751, 0.484, 0.6027] +2026-04-12 15:13:55.342126: Epoch time: 100.18 s +2026-04-12 15:13:56.539310: +2026-04-12 15:13:56.541174: Epoch 1763 +2026-04-12 15:13:56.542834: Current learning rate: 0.00593 +2026-04-12 15:15:36.635043: train_loss -0.3539 +2026-04-12 15:15:36.641492: val_loss -0.3395 +2026-04-12 15:15:36.643723: Pseudo dice [0.0, 0.0, 0.6803, 0.0, 0.0, 0.4862, 0.5142] +2026-04-12 15:15:36.646466: Epoch time: 100.1 s +2026-04-12 15:15:37.867673: +2026-04-12 15:15:37.869629: Epoch 1764 +2026-04-12 15:15:37.871191: Current learning rate: 0.00592 +2026-04-12 15:17:18.528781: train_loss -0.327 +2026-04-12 15:17:18.534204: val_loss -0.3501 +2026-04-12 15:17:18.536390: Pseudo dice [0.0, 0.0, 0.6721, 0.0, 0.4695, 0.7158, 0.353] +2026-04-12 15:17:18.539930: Epoch time: 100.66 s +2026-04-12 15:17:19.760927: +2026-04-12 15:17:19.762679: Epoch 1765 +2026-04-12 15:17:19.764196: Current learning rate: 0.00592 +2026-04-12 15:19:00.050529: train_loss -0.331 +2026-04-12 15:19:00.056908: val_loss -0.3104 +2026-04-12 15:19:00.059449: Pseudo dice [0.0, 0.0, 0.6292, 0.0, 0.2306, 0.4588, 0.6079] +2026-04-12 15:19:00.062158: Epoch time: 100.29 s +2026-04-12 15:19:01.266017: +2026-04-12 15:19:01.268166: Epoch 1766 +2026-04-12 15:19:01.271193: Current learning rate: 0.00592 +2026-04-12 15:20:41.450072: train_loss -0.3811 +2026-04-12 15:20:41.455336: val_loss -0.3709 +2026-04-12 15:20:41.458741: Pseudo dice [0.0, 0.0, 0.7252, 0.131, 0.3483, 0.5772, 0.645] +2026-04-12 15:20:41.461543: Epoch time: 100.19 s +2026-04-12 15:20:42.677320: +2026-04-12 15:20:42.679249: Epoch 1767 +2026-04-12 15:20:42.681949: Current learning rate: 0.00592 +2026-04-12 15:22:22.923994: train_loss -0.3674 +2026-04-12 15:22:22.928521: val_loss -0.3812 +2026-04-12 15:22:22.930290: Pseudo dice [0.0, 0.0, 0.6811, 0.0, 0.4651, 0.8036, 0.7855] +2026-04-12 15:22:22.932404: Epoch time: 100.25 s +2026-04-12 15:22:24.126128: +2026-04-12 15:22:24.128013: Epoch 1768 +2026-04-12 15:22:24.129848: Current learning rate: 0.00592 +2026-04-12 15:24:04.691161: train_loss -0.3305 +2026-04-12 15:24:04.697166: val_loss -0.2651 +2026-04-12 15:24:04.699705: Pseudo dice [0.0, 0.0, 0.2326, 0.0, 0.0, 0.7257, 0.5508] +2026-04-12 15:24:04.702711: Epoch time: 100.57 s +2026-04-12 15:24:06.989299: +2026-04-12 15:24:06.992353: Epoch 1769 +2026-04-12 15:24:06.995640: Current learning rate: 0.00591 +2026-04-12 15:25:47.118458: train_loss -0.3469 +2026-04-12 15:25:47.124904: val_loss -0.3484 +2026-04-12 15:25:47.127582: Pseudo dice [0.0, 0.0, 0.5648, 0.0, 0.0, 0.8143, 0.5243] +2026-04-12 15:25:47.130167: Epoch time: 100.13 s +2026-04-12 15:25:48.341193: +2026-04-12 15:25:48.343706: Epoch 1770 +2026-04-12 15:25:48.345846: Current learning rate: 0.00591 +2026-04-12 15:27:28.512233: train_loss -0.3533 +2026-04-12 15:27:28.520468: val_loss -0.3441 +2026-04-12 15:27:28.522917: Pseudo dice [0.0, 0.0, 0.6712, 0.0, 0.0775, 0.2886, 0.726] +2026-04-12 15:27:28.525984: Epoch time: 100.17 s +2026-04-12 15:27:29.732131: +2026-04-12 15:27:29.734357: Epoch 1771 +2026-04-12 15:27:29.736311: Current learning rate: 0.00591 +2026-04-12 15:29:10.023419: train_loss -0.3764 +2026-04-12 15:29:10.030223: val_loss -0.3619 +2026-04-12 15:29:10.032164: Pseudo dice [0.0, 0.0, 0.6893, 0.0, 0.3973, 0.4019, 0.6203] +2026-04-12 15:29:10.034612: Epoch time: 100.29 s +2026-04-12 15:29:11.232372: +2026-04-12 15:29:11.235678: Epoch 1772 +2026-04-12 15:29:11.239858: Current learning rate: 0.00591 +2026-04-12 15:30:51.305070: train_loss -0.3319 +2026-04-12 15:30:51.310997: val_loss -0.2708 +2026-04-12 15:30:51.313359: Pseudo dice [0.0, 0.0, 0.4775, 0.0, 0.0, 0.5337, 0.2457] +2026-04-12 15:30:51.315381: Epoch time: 100.08 s +2026-04-12 15:30:52.512234: +2026-04-12 15:30:52.514302: Epoch 1773 +2026-04-12 15:30:52.516369: Current learning rate: 0.0059 +2026-04-12 15:32:32.717635: train_loss -0.3484 +2026-04-12 15:32:32.724736: val_loss -0.2799 +2026-04-12 15:32:32.727005: Pseudo dice [0.0, 0.0, 0.5126, 0.0, 0.1062, 0.6412, 0.4423] +2026-04-12 15:32:32.729527: Epoch time: 100.21 s +2026-04-12 15:32:33.946699: +2026-04-12 15:32:33.948653: Epoch 1774 +2026-04-12 15:32:33.950194: Current learning rate: 0.0059 +2026-04-12 15:34:14.056670: train_loss -0.3564 +2026-04-12 15:34:14.062882: val_loss -0.3122 +2026-04-12 15:34:14.064955: Pseudo dice [0.0, 0.0, 0.5523, 0.0, 0.2959, 0.4015, 0.3723] +2026-04-12 15:34:14.068119: Epoch time: 100.11 s +2026-04-12 15:34:15.294921: +2026-04-12 15:34:15.298835: Epoch 1775 +2026-04-12 15:34:15.301172: Current learning rate: 0.0059 +2026-04-12 15:35:55.472788: train_loss -0.3582 +2026-04-12 15:35:55.479199: val_loss -0.3912 +2026-04-12 15:35:55.481585: Pseudo dice [0.0, 0.0, 0.6947, 0.8029, 0.2004, 0.7983, 0.7681] +2026-04-12 15:35:55.484237: Epoch time: 100.18 s +2026-04-12 15:35:56.703290: +2026-04-12 15:35:56.705289: Epoch 1776 +2026-04-12 15:35:56.706908: Current learning rate: 0.0059 +2026-04-12 15:37:36.977791: train_loss -0.3617 +2026-04-12 15:37:36.983805: val_loss -0.329 +2026-04-12 15:37:36.986068: Pseudo dice [0.0, 0.0, 0.3791, 0.0994, 0.0, 0.5516, 0.0051] +2026-04-12 15:37:36.988703: Epoch time: 100.28 s +2026-04-12 15:37:38.200231: +2026-04-12 15:37:38.202950: Epoch 1777 +2026-04-12 15:37:38.204634: Current learning rate: 0.00589 +2026-04-12 15:39:18.509654: train_loss -0.3399 +2026-04-12 15:39:18.515548: val_loss -0.1988 +2026-04-12 15:39:18.518150: Pseudo dice [0.0, 0.0, 0.5207, 0.0132, 0.0215, 0.3019, 0.3733] +2026-04-12 15:39:18.522047: Epoch time: 100.31 s +2026-04-12 15:39:19.748114: +2026-04-12 15:39:19.750147: Epoch 1778 +2026-04-12 15:39:19.751945: Current learning rate: 0.00589 +2026-04-12 15:41:00.138118: train_loss -0.3717 +2026-04-12 15:41:00.143446: val_loss -0.3297 +2026-04-12 15:41:00.145528: Pseudo dice [0.0, 0.0, 0.5187, 0.0, 0.2214, 0.6718, 0.7707] +2026-04-12 15:41:00.147507: Epoch time: 100.39 s +2026-04-12 15:41:01.352425: +2026-04-12 15:41:01.354413: Epoch 1779 +2026-04-12 15:41:01.356170: Current learning rate: 0.00589 +2026-04-12 15:42:41.721416: train_loss -0.3867 +2026-04-12 15:42:41.726946: val_loss -0.3608 +2026-04-12 15:42:41.728899: Pseudo dice [0.0, 0.0, 0.6237, 0.0, 0.2183, 0.7423, 0.6125] +2026-04-12 15:42:41.731926: Epoch time: 100.37 s +2026-04-12 15:42:42.954808: +2026-04-12 15:42:42.957269: Epoch 1780 +2026-04-12 15:42:42.958960: Current learning rate: 0.00589 +2026-04-12 15:44:22.994319: train_loss -0.3664 +2026-04-12 15:44:23.000562: val_loss -0.2878 +2026-04-12 15:44:23.003726: Pseudo dice [0.0, 0.0, 0.6174, 0.0355, 0.3502, 0.565, 0.8665] +2026-04-12 15:44:23.006593: Epoch time: 100.04 s +2026-04-12 15:44:24.215187: +2026-04-12 15:44:24.217087: Epoch 1781 +2026-04-12 15:44:24.218781: Current learning rate: 0.00588 +2026-04-12 15:46:05.300377: train_loss -0.38 +2026-04-12 15:46:05.308684: val_loss -0.3947 +2026-04-12 15:46:05.311924: Pseudo dice [0.0, 0.0, 0.772, 0.4503, 0.0773, 0.787, 0.7268] +2026-04-12 15:46:05.315960: Epoch time: 101.09 s +2026-04-12 15:46:06.533659: +2026-04-12 15:46:06.535948: Epoch 1782 +2026-04-12 15:46:06.538186: Current learning rate: 0.00588 +2026-04-12 15:47:46.630428: train_loss -0.3838 +2026-04-12 15:47:46.636880: val_loss -0.2661 +2026-04-12 15:47:46.638725: Pseudo dice [0.0, 0.0, 0.6797, 0.0219, 0.1509, 0.7354, 0.6091] +2026-04-12 15:47:46.642986: Epoch time: 100.1 s +2026-04-12 15:47:47.832569: +2026-04-12 15:47:47.834680: Epoch 1783 +2026-04-12 15:47:47.836712: Current learning rate: 0.00588 +2026-04-12 15:49:28.072226: train_loss -0.3585 +2026-04-12 15:49:28.078630: val_loss -0.2911 +2026-04-12 15:49:28.081120: Pseudo dice [0.0, 0.0, 0.4638, 0.0, 0.3052, 0.3958, 0.303] +2026-04-12 15:49:28.083282: Epoch time: 100.24 s +2026-04-12 15:49:29.282539: +2026-04-12 15:49:29.284868: Epoch 1784 +2026-04-12 15:49:29.286570: Current learning rate: 0.00588 +2026-04-12 15:51:09.263925: train_loss -0.3605 +2026-04-12 15:51:09.269142: val_loss -0.3501 +2026-04-12 15:51:09.271146: Pseudo dice [0.0, 0.0, 0.1675, 0.0, 0.0, 0.6122, 0.5125] +2026-04-12 15:51:09.273603: Epoch time: 99.98 s +2026-04-12 15:51:10.477360: +2026-04-12 15:51:10.479331: Epoch 1785 +2026-04-12 15:51:10.481205: Current learning rate: 0.00587 +2026-04-12 15:52:50.640954: train_loss -0.3599 +2026-04-12 15:52:50.647458: val_loss -0.3626 +2026-04-12 15:52:50.651595: Pseudo dice [0.0, 0.0, 0.5486, 0.2439, 0.0, 0.7178, 0.2563] +2026-04-12 15:52:50.657323: Epoch time: 100.17 s +2026-04-12 15:52:51.893945: +2026-04-12 15:52:51.895879: Epoch 1786 +2026-04-12 15:52:51.897673: Current learning rate: 0.00587 +2026-04-12 15:54:32.041448: train_loss -0.3318 +2026-04-12 15:54:32.047254: val_loss -0.3794 +2026-04-12 15:54:32.049060: Pseudo dice [0.0, 0.0, 0.6366, 0.0, 0.0163, 0.5242, 0.7852] +2026-04-12 15:54:32.054713: Epoch time: 100.15 s +2026-04-12 15:54:33.248177: +2026-04-12 15:54:33.250832: Epoch 1787 +2026-04-12 15:54:33.252992: Current learning rate: 0.00587 +2026-04-12 15:56:13.435154: train_loss -0.3538 +2026-04-12 15:56:13.440733: val_loss -0.3705 +2026-04-12 15:56:13.442865: Pseudo dice [0.0, 0.0, 0.687, 0.8023, 0.347, 0.7008, 0.2941] +2026-04-12 15:56:13.445183: Epoch time: 100.19 s +2026-04-12 15:56:14.682381: +2026-04-12 15:56:14.685004: Epoch 1788 +2026-04-12 15:56:14.687461: Current learning rate: 0.00587 +2026-04-12 15:57:54.790287: train_loss -0.3321 +2026-04-12 15:57:54.796527: val_loss -0.3935 +2026-04-12 15:57:54.799455: Pseudo dice [0.0, 0.0, 0.6347, 0.4506, 0.2769, 0.4845, 0.8734] +2026-04-12 15:57:54.802430: Epoch time: 100.11 s +2026-04-12 15:57:56.020024: +2026-04-12 15:57:56.021883: Epoch 1789 +2026-04-12 15:57:56.025308: Current learning rate: 0.00587 +2026-04-12 15:59:37.221056: train_loss -0.3886 +2026-04-12 15:59:37.227692: val_loss -0.3996 +2026-04-12 15:59:37.229598: Pseudo dice [0.0, 0.0, 0.7556, 0.0094, 0.5288, 0.6755, 0.5938] +2026-04-12 15:59:37.233465: Epoch time: 101.2 s +2026-04-12 15:59:38.437869: +2026-04-12 15:59:38.441414: Epoch 1790 +2026-04-12 15:59:38.443380: Current learning rate: 0.00586 +2026-04-12 16:01:18.817682: train_loss -0.3794 +2026-04-12 16:01:18.824626: val_loss -0.3146 +2026-04-12 16:01:18.827019: Pseudo dice [0.0, 0.0, 0.6755, 0.0, 0.0, 0.8242, 0.6239] +2026-04-12 16:01:18.830230: Epoch time: 100.38 s +2026-04-12 16:01:20.213848: +2026-04-12 16:01:20.217616: Epoch 1791 +2026-04-12 16:01:20.219695: Current learning rate: 0.00586 +2026-04-12 16:03:00.447917: train_loss -0.3862 +2026-04-12 16:03:00.455430: val_loss -0.3259 +2026-04-12 16:03:00.458116: Pseudo dice [0.0, 0.0, 0.6911, 0.0, 0.1485, 0.6525, 0.836] +2026-04-12 16:03:00.460637: Epoch time: 100.24 s +2026-04-12 16:03:01.676323: +2026-04-12 16:03:01.678862: Epoch 1792 +2026-04-12 16:03:01.681493: Current learning rate: 0.00586 +2026-04-12 16:04:41.950311: train_loss -0.3846 +2026-04-12 16:04:41.955624: val_loss -0.3655 +2026-04-12 16:04:41.958856: Pseudo dice [0.0, 0.0, 0.6162, 0.8256, 0.244, 0.2385, 0.6789] +2026-04-12 16:04:41.961440: Epoch time: 100.28 s +2026-04-12 16:04:43.155625: +2026-04-12 16:04:43.157950: Epoch 1793 +2026-04-12 16:04:43.159629: Current learning rate: 0.00586 +2026-04-12 16:06:23.517743: train_loss -0.3703 +2026-04-12 16:06:23.522874: val_loss -0.3659 +2026-04-12 16:06:23.524926: Pseudo dice [0.0, 0.0, 0.6922, 0.4952, 0.2868, 0.3138, 0.7057] +2026-04-12 16:06:23.526976: Epoch time: 100.37 s +2026-04-12 16:06:24.731174: +2026-04-12 16:06:24.733211: Epoch 1794 +2026-04-12 16:06:24.735115: Current learning rate: 0.00585 +2026-04-12 16:08:05.289198: train_loss -0.3748 +2026-04-12 16:08:05.296470: val_loss -0.3798 +2026-04-12 16:08:05.298669: Pseudo dice [0.0, 0.0, 0.6068, 0.3533, 0.0696, 0.4066, 0.6433] +2026-04-12 16:08:05.301838: Epoch time: 100.56 s +2026-04-12 16:08:06.495475: +2026-04-12 16:08:06.503321: Epoch 1795 +2026-04-12 16:08:06.504997: Current learning rate: 0.00585 +2026-04-12 16:09:46.712814: train_loss -0.3879 +2026-04-12 16:09:46.720142: val_loss -0.3789 +2026-04-12 16:09:46.722315: Pseudo dice [0.0, 0.0, 0.6998, 0.0, 0.3395, 0.6272, 0.767] +2026-04-12 16:09:46.726522: Epoch time: 100.22 s +2026-04-12 16:09:47.959976: +2026-04-12 16:09:47.962283: Epoch 1796 +2026-04-12 16:09:47.964496: Current learning rate: 0.00585 +2026-04-12 16:11:28.143724: train_loss -0.3935 +2026-04-12 16:11:28.152137: val_loss -0.3048 +2026-04-12 16:11:28.156501: Pseudo dice [0.0, 0.0, 0.6316, 0.053, 0.3457, 0.2813, 0.659] +2026-04-12 16:11:28.164107: Epoch time: 100.19 s +2026-04-12 16:11:29.427281: +2026-04-12 16:11:29.429351: Epoch 1797 +2026-04-12 16:11:29.431446: Current learning rate: 0.00585 +2026-04-12 16:13:09.477591: train_loss -0.3875 +2026-04-12 16:13:09.484321: val_loss -0.3506 +2026-04-12 16:13:09.486384: Pseudo dice [0.0, 0.0, 0.7011, 0.0, 0.1129, 0.5511, 0.4642] +2026-04-12 16:13:09.488671: Epoch time: 100.05 s +2026-04-12 16:13:10.709700: +2026-04-12 16:13:10.712240: Epoch 1798 +2026-04-12 16:13:10.714282: Current learning rate: 0.00584 +2026-04-12 16:14:50.876245: train_loss -0.3923 +2026-04-12 16:14:50.883478: val_loss -0.4076 +2026-04-12 16:14:50.886053: Pseudo dice [0.0, 0.0, 0.6138, 0.6249, 0.4605, 0.5149, 0.634] +2026-04-12 16:14:50.888819: Epoch time: 100.17 s +2026-04-12 16:14:52.091364: +2026-04-12 16:14:52.093260: Epoch 1799 +2026-04-12 16:14:52.095018: Current learning rate: 0.00584 +2026-04-12 16:16:32.675289: train_loss -0.3536 +2026-04-12 16:16:32.681707: val_loss -0.3117 +2026-04-12 16:16:32.683921: Pseudo dice [0.0, 0.0, 0.6751, 0.0, 0.1201, 0.576, 0.6485] +2026-04-12 16:16:32.686511: Epoch time: 100.59 s +2026-04-12 16:16:35.616677: +2026-04-12 16:16:35.619727: Epoch 1800 +2026-04-12 16:16:35.622415: Current learning rate: 0.00584 +2026-04-12 16:18:15.633348: train_loss -0.3926 +2026-04-12 16:18:15.641221: val_loss -0.3572 +2026-04-12 16:18:15.652806: Pseudo dice [0.0, 0.0, 0.7108, 0.0, 0.2632, 0.7996, 0.6585] +2026-04-12 16:18:15.655520: Epoch time: 100.02 s +2026-04-12 16:18:16.868808: +2026-04-12 16:18:16.870983: Epoch 1801 +2026-04-12 16:18:16.873900: Current learning rate: 0.00584 +2026-04-12 16:19:57.350418: train_loss -0.3473 +2026-04-12 16:19:57.357453: val_loss -0.3756 +2026-04-12 16:19:57.359883: Pseudo dice [0.0, 0.0, 0.582, 0.0, 0.0, 0.5885, 0.5821] +2026-04-12 16:19:57.362411: Epoch time: 100.48 s +2026-04-12 16:19:58.570333: +2026-04-12 16:19:58.572756: Epoch 1802 +2026-04-12 16:19:58.574584: Current learning rate: 0.00583 +2026-04-12 16:21:38.817650: train_loss -0.3784 +2026-04-12 16:21:38.823666: val_loss -0.3484 +2026-04-12 16:21:38.826476: Pseudo dice [0.0, 0.0, 0.47, 0.0, 0.0081, 0.8012, 0.2328] +2026-04-12 16:21:38.829279: Epoch time: 100.25 s +2026-04-12 16:21:40.028434: +2026-04-12 16:21:40.030692: Epoch 1803 +2026-04-12 16:21:40.032464: Current learning rate: 0.00583 +2026-04-12 16:23:20.364089: train_loss -0.3583 +2026-04-12 16:23:20.369106: val_loss -0.3767 +2026-04-12 16:23:20.371450: Pseudo dice [0.0, 0.0, 0.4738, 0.0, 0.1701, 0.4898, 0.3293] +2026-04-12 16:23:20.373695: Epoch time: 100.34 s +2026-04-12 16:23:21.591916: +2026-04-12 16:23:21.594012: Epoch 1804 +2026-04-12 16:23:21.596004: Current learning rate: 0.00583 +2026-04-12 16:25:01.819441: train_loss -0.3665 +2026-04-12 16:25:01.824573: val_loss -0.3744 +2026-04-12 16:25:01.828032: Pseudo dice [0.0, 0.0, 0.7694, 0.0, 0.2677, 0.8315, 0.3724] +2026-04-12 16:25:01.830472: Epoch time: 100.23 s +2026-04-12 16:25:03.050262: +2026-04-12 16:25:03.052752: Epoch 1805 +2026-04-12 16:25:03.054977: Current learning rate: 0.00583 +2026-04-12 16:26:43.313777: train_loss -0.3705 +2026-04-12 16:26:43.318901: val_loss -0.3614 +2026-04-12 16:26:43.320700: Pseudo dice [0.0, 0.0, 0.2649, 0.0915, 0.1721, 0.6896, 0.6824] +2026-04-12 16:26:43.323720: Epoch time: 100.27 s +2026-04-12 16:26:44.506568: +2026-04-12 16:26:44.508293: Epoch 1806 +2026-04-12 16:26:44.510188: Current learning rate: 0.00582 +2026-04-12 16:28:24.761245: train_loss -0.3675 +2026-04-12 16:28:24.766600: val_loss -0.3899 +2026-04-12 16:28:24.768383: Pseudo dice [0.0, 0.0, 0.7378, 0.7364, 0.3341, 0.7728, 0.6752] +2026-04-12 16:28:24.770501: Epoch time: 100.26 s +2026-04-12 16:28:25.972155: +2026-04-12 16:28:25.973959: Epoch 1807 +2026-04-12 16:28:25.975801: Current learning rate: 0.00582 +2026-04-12 16:30:06.083329: train_loss -0.3894 +2026-04-12 16:30:06.089060: val_loss -0.2298 +2026-04-12 16:30:06.091234: Pseudo dice [0.0, 0.0, 0.6125, 0.0, 0.0692, 0.8071, 0.7242] +2026-04-12 16:30:06.094666: Epoch time: 100.11 s +2026-04-12 16:30:07.319719: +2026-04-12 16:30:07.321737: Epoch 1808 +2026-04-12 16:30:07.323663: Current learning rate: 0.00582 +2026-04-12 16:31:47.527363: train_loss -0.3742 +2026-04-12 16:31:47.534956: val_loss -0.3405 +2026-04-12 16:31:47.537307: Pseudo dice [0.0, 0.0, 0.6457, 0.0125, 0.1874, 0.7155, 0.3806] +2026-04-12 16:31:47.540257: Epoch time: 100.21 s +2026-04-12 16:31:49.854096: +2026-04-12 16:31:49.856392: Epoch 1809 +2026-04-12 16:31:49.858125: Current learning rate: 0.00582 +2026-04-12 16:33:30.363589: train_loss -0.3749 +2026-04-12 16:33:30.369351: val_loss -0.2944 +2026-04-12 16:33:30.371274: Pseudo dice [0.0, 0.0, 0.6362, 0.0019, 0.1271, 0.6409, 0.473] +2026-04-12 16:33:30.373705: Epoch time: 100.51 s +2026-04-12 16:33:31.592528: +2026-04-12 16:33:31.594636: Epoch 1810 +2026-04-12 16:33:31.596351: Current learning rate: 0.00581 +2026-04-12 16:35:11.846487: train_loss -0.3745 +2026-04-12 16:35:11.852569: val_loss -0.3746 +2026-04-12 16:35:11.854386: Pseudo dice [0.0, 0.0, 0.3472, 0.0775, 0.3767, 0.6329, 0.631] +2026-04-12 16:35:11.857518: Epoch time: 100.26 s +2026-04-12 16:35:13.057060: +2026-04-12 16:35:13.058864: Epoch 1811 +2026-04-12 16:35:13.060545: Current learning rate: 0.00581 +2026-04-12 16:36:54.095930: train_loss -0.3598 +2026-04-12 16:36:54.102971: val_loss -0.2637 +2026-04-12 16:36:54.105771: Pseudo dice [0.0, 0.0, 0.3688, 0.0, 0.266, 0.662, 0.6985] +2026-04-12 16:36:54.108638: Epoch time: 101.04 s +2026-04-12 16:36:55.321442: +2026-04-12 16:36:55.323241: Epoch 1812 +2026-04-12 16:36:55.325034: Current learning rate: 0.00581 +2026-04-12 16:38:35.610196: train_loss -0.3584 +2026-04-12 16:38:35.616545: val_loss -0.3766 +2026-04-12 16:38:35.618997: Pseudo dice [0.0, 0.0, 0.5505, 0.0, 0.1919, 0.5424, 0.6622] +2026-04-12 16:38:35.622414: Epoch time: 100.29 s +2026-04-12 16:38:36.850450: +2026-04-12 16:38:36.853162: Epoch 1813 +2026-04-12 16:38:36.855227: Current learning rate: 0.00581 +2026-04-12 16:40:17.291511: train_loss -0.3663 +2026-04-12 16:40:17.296997: val_loss -0.3458 +2026-04-12 16:40:17.299150: Pseudo dice [0.0, 0.0, 0.7038, 0.0, 0.0, 0.6444, 0.505] +2026-04-12 16:40:17.301377: Epoch time: 100.44 s +2026-04-12 16:40:18.529494: +2026-04-12 16:40:18.531627: Epoch 1814 +2026-04-12 16:40:18.533525: Current learning rate: 0.00581 +2026-04-12 16:41:58.909252: train_loss -0.3534 +2026-04-12 16:41:58.915294: val_loss -0.3062 +2026-04-12 16:41:58.917497: Pseudo dice [0.0, 0.0, 0.5273, 0.6149, 0.0, 0.0208, 0.0] +2026-04-12 16:41:58.919891: Epoch time: 100.38 s +2026-04-12 16:42:00.121074: +2026-04-12 16:42:00.123144: Epoch 1815 +2026-04-12 16:42:00.124882: Current learning rate: 0.0058 +2026-04-12 16:43:40.395893: train_loss -0.2973 +2026-04-12 16:43:40.402108: val_loss -0.3303 +2026-04-12 16:43:40.405632: Pseudo dice [0.0, 0.0, 0.6242, 0.0, 0.0, 0.5378, 0.0006] +2026-04-12 16:43:40.408530: Epoch time: 100.28 s +2026-04-12 16:43:41.626346: +2026-04-12 16:43:41.628504: Epoch 1816 +2026-04-12 16:43:41.630462: Current learning rate: 0.0058 +2026-04-12 16:45:21.863686: train_loss -0.3227 +2026-04-12 16:45:21.869366: val_loss -0.3156 +2026-04-12 16:45:21.871758: Pseudo dice [0.0, 0.0, 0.2766, 0.0, 0.0, 0.6601, 0.6712] +2026-04-12 16:45:21.874194: Epoch time: 100.24 s +2026-04-12 16:45:23.093433: +2026-04-12 16:45:23.096537: Epoch 1817 +2026-04-12 16:45:23.099047: Current learning rate: 0.0058 +2026-04-12 16:47:03.380175: train_loss -0.3175 +2026-04-12 16:47:03.387062: val_loss -0.3512 +2026-04-12 16:47:03.389347: Pseudo dice [0.0, 0.0, 0.719, 0.0, 0.0, 0.4257, 0.616] +2026-04-12 16:47:03.391995: Epoch time: 100.29 s +2026-04-12 16:47:04.600073: +2026-04-12 16:47:04.601831: Epoch 1818 +2026-04-12 16:47:04.603547: Current learning rate: 0.0058 +2026-04-12 16:48:44.723463: train_loss -0.3439 +2026-04-12 16:48:44.730476: val_loss -0.3623 +2026-04-12 16:48:44.733064: Pseudo dice [0.0, 0.0, 0.7018, 0.0, 0.0, 0.4926, 0.662] +2026-04-12 16:48:44.736258: Epoch time: 100.13 s +2026-04-12 16:48:45.941180: +2026-04-12 16:48:45.943407: Epoch 1819 +2026-04-12 16:48:45.945236: Current learning rate: 0.00579 +2026-04-12 16:50:26.177645: train_loss -0.3099 +2026-04-12 16:50:26.182555: val_loss -0.3505 +2026-04-12 16:50:26.184892: Pseudo dice [0.0, 0.0, 0.5239, 0.0, 0.1452, 0.6325, 0.4224] +2026-04-12 16:50:26.187767: Epoch time: 100.24 s +2026-04-12 16:50:27.385425: +2026-04-12 16:50:27.387243: Epoch 1820 +2026-04-12 16:50:27.389134: Current learning rate: 0.00579 +2026-04-12 16:52:07.446722: train_loss -0.3654 +2026-04-12 16:52:07.456170: val_loss -0.3578 +2026-04-12 16:52:07.458894: Pseudo dice [0.0, 0.0, 0.6202, 0.0, 0.2527, 0.6291, 0.8545] +2026-04-12 16:52:07.463826: Epoch time: 100.06 s +2026-04-12 16:52:08.679163: +2026-04-12 16:52:08.681471: Epoch 1821 +2026-04-12 16:52:08.683218: Current learning rate: 0.00579 +2026-04-12 16:53:48.669961: train_loss -0.3339 +2026-04-12 16:53:48.675415: val_loss -0.3154 +2026-04-12 16:53:48.677921: Pseudo dice [0.0, 0.0, 0.5145, 0.0, 0.4775, 0.4571, 0.2783] +2026-04-12 16:53:48.680216: Epoch time: 99.99 s +2026-04-12 16:53:49.886844: +2026-04-12 16:53:49.889220: Epoch 1822 +2026-04-12 16:53:49.890813: Current learning rate: 0.00579 +2026-04-12 16:55:30.817729: train_loss -0.3659 +2026-04-12 16:55:30.829077: val_loss -0.3603 +2026-04-12 16:55:30.838789: Pseudo dice [0.0, 0.0, 0.6753, 0.4384, 0.327, 0.4762, 0.3948] +2026-04-12 16:55:30.856495: Epoch time: 100.93 s +2026-04-12 16:55:32.090737: +2026-04-12 16:55:32.092539: Epoch 1823 +2026-04-12 16:55:32.094497: Current learning rate: 0.00578 +2026-04-12 16:57:12.373018: train_loss -0.3594 +2026-04-12 16:57:12.399163: val_loss -0.3242 +2026-04-12 16:57:12.402448: Pseudo dice [0.0, 0.0, 0.6083, 0.0738, 0.1226, 0.7458, 0.6663] +2026-04-12 16:57:12.405398: Epoch time: 100.29 s +2026-04-12 16:57:13.603978: +2026-04-12 16:57:13.606405: Epoch 1824 +2026-04-12 16:57:13.608720: Current learning rate: 0.00578 +2026-04-12 16:58:54.680695: train_loss -0.3773 +2026-04-12 16:58:54.688683: val_loss -0.3796 +2026-04-12 16:58:54.691987: Pseudo dice [0.0, 0.0, 0.6439, 0.0, 0.3689, 0.6816, 0.5124] +2026-04-12 16:58:54.695490: Epoch time: 101.08 s +2026-04-12 16:58:55.960673: +2026-04-12 16:58:55.962810: Epoch 1825 +2026-04-12 16:58:55.966286: Current learning rate: 0.00578 +2026-04-12 17:00:36.540450: train_loss -0.3912 +2026-04-12 17:00:36.549272: val_loss -0.3949 +2026-04-12 17:00:36.552178: Pseudo dice [0.0, 0.0, 0.6095, 0.5814, 0.478, 0.7599, 0.6504] +2026-04-12 17:00:36.557429: Epoch time: 100.58 s +2026-04-12 17:00:37.759485: +2026-04-12 17:00:37.761875: Epoch 1826 +2026-04-12 17:00:37.764101: Current learning rate: 0.00578 +2026-04-12 17:02:18.186941: train_loss -0.3688 +2026-04-12 17:02:18.195887: val_loss -0.2447 +2026-04-12 17:02:18.198199: Pseudo dice [0.0, 0.0, 0.7561, 0.0, 0.3195, 0.7711, 0.5305] +2026-04-12 17:02:18.201897: Epoch time: 100.43 s +2026-04-12 17:02:19.399228: +2026-04-12 17:02:19.401308: Epoch 1827 +2026-04-12 17:02:19.403926: Current learning rate: 0.00577 +2026-04-12 17:03:59.652412: train_loss -0.3269 +2026-04-12 17:03:59.658561: val_loss -0.3562 +2026-04-12 17:03:59.660755: Pseudo dice [0.0, 0.0, 0.6649, 0.0, 0.2172, 0.4697, 0.7136] +2026-04-12 17:03:59.663151: Epoch time: 100.26 s +2026-04-12 17:04:00.878453: +2026-04-12 17:04:00.881133: Epoch 1828 +2026-04-12 17:04:00.884351: Current learning rate: 0.00577 +2026-04-12 17:05:40.941759: train_loss -0.3592 +2026-04-12 17:05:40.950179: val_loss -0.3226 +2026-04-12 17:05:40.952712: Pseudo dice [0.0, 0.0, 0.5754, 0.0, 0.1057, 0.6932, 0.4737] +2026-04-12 17:05:40.956454: Epoch time: 100.07 s +2026-04-12 17:05:43.240694: +2026-04-12 17:05:43.244335: Epoch 1829 +2026-04-12 17:05:43.248817: Current learning rate: 0.00577 +2026-04-12 17:07:23.843786: train_loss -0.3756 +2026-04-12 17:07:23.852339: val_loss -0.3548 +2026-04-12 17:07:23.857921: Pseudo dice [0.0, 0.0, 0.6524, 0.0, 0.3013, 0.6082, 0.7488] +2026-04-12 17:07:23.860986: Epoch time: 100.61 s +2026-04-12 17:07:25.076777: +2026-04-12 17:07:25.079159: Epoch 1830 +2026-04-12 17:07:25.081779: Current learning rate: 0.00577 +2026-04-12 17:09:05.986578: train_loss -0.3819 +2026-04-12 17:09:05.994716: val_loss -0.2752 +2026-04-12 17:09:05.997788: Pseudo dice [0.0, 0.0, 0.528, 0.0, 0.03, 0.5847, 0.6733] +2026-04-12 17:09:06.000718: Epoch time: 100.91 s +2026-04-12 17:09:07.245801: +2026-04-12 17:09:07.249044: Epoch 1831 +2026-04-12 17:09:07.252178: Current learning rate: 0.00576 +2026-04-12 17:10:47.917673: train_loss -0.3716 +2026-04-12 17:10:47.924307: val_loss -0.3881 +2026-04-12 17:10:47.927413: Pseudo dice [0.0, 0.0, 0.5705, 0.1568, 0.1586, 0.8091, 0.7408] +2026-04-12 17:10:47.929765: Epoch time: 100.68 s +2026-04-12 17:10:49.139705: +2026-04-12 17:10:49.142642: Epoch 1832 +2026-04-12 17:10:49.146761: Current learning rate: 0.00576 +2026-04-12 17:12:29.055971: train_loss -0.3527 +2026-04-12 17:12:29.062293: val_loss -0.3555 +2026-04-12 17:12:29.064616: Pseudo dice [0.0, 0.0, 0.6359, 0.0479, 0.0, 0.6785, 0.7411] +2026-04-12 17:12:29.067652: Epoch time: 99.92 s +2026-04-12 17:12:30.285766: +2026-04-12 17:12:30.288048: Epoch 1833 +2026-04-12 17:12:30.290789: Current learning rate: 0.00576 +2026-04-12 17:14:10.450099: train_loss -0.3668 +2026-04-12 17:14:10.459074: val_loss -0.339 +2026-04-12 17:14:10.462582: Pseudo dice [0.0, 0.0, 0.7384, 0.0, 0.0, 0.177, 0.32] +2026-04-12 17:14:10.465716: Epoch time: 100.17 s +2026-04-12 17:14:11.705830: +2026-04-12 17:14:11.708114: Epoch 1834 +2026-04-12 17:14:11.710826: Current learning rate: 0.00576 +2026-04-12 17:15:52.145572: train_loss -0.3731 +2026-04-12 17:15:52.152117: val_loss -0.3859 +2026-04-12 17:15:52.154917: Pseudo dice [0.0, 0.0, 0.7476, 0.5213, 0.0024, 0.7949, 0.7555] +2026-04-12 17:15:52.158117: Epoch time: 100.44 s +2026-04-12 17:15:53.374236: +2026-04-12 17:15:53.376366: Epoch 1835 +2026-04-12 17:15:53.378737: Current learning rate: 0.00576 +2026-04-12 17:17:33.314374: train_loss -0.3667 +2026-04-12 17:17:33.322755: val_loss -0.271 +2026-04-12 17:17:33.324584: Pseudo dice [0.0, 0.0, 0.7379, 0.0, 0.0492, 0.437, 0.1998] +2026-04-12 17:17:33.327336: Epoch time: 99.94 s +2026-04-12 17:17:34.558427: +2026-04-12 17:17:34.560191: Epoch 1836 +2026-04-12 17:17:34.562186: Current learning rate: 0.00575 +2026-04-12 17:19:14.605993: train_loss -0.3637 +2026-04-12 17:19:14.613396: val_loss -0.3606 +2026-04-12 17:19:14.615730: Pseudo dice [0.0, 0.0, 0.6409, 0.0, 0.3072, 0.3502, 0.4636] +2026-04-12 17:19:14.618177: Epoch time: 100.05 s +2026-04-12 17:19:15.827299: +2026-04-12 17:19:15.830239: Epoch 1837 +2026-04-12 17:19:15.833012: Current learning rate: 0.00575 +2026-04-12 17:20:56.095405: train_loss -0.3647 +2026-04-12 17:20:56.104847: val_loss -0.333 +2026-04-12 17:20:56.107498: Pseudo dice [0.0, 0.0, 0.1523, 0.0, 0.1231, 0.7291, 0.6285] +2026-04-12 17:20:56.110435: Epoch time: 100.27 s +2026-04-12 17:20:57.312747: +2026-04-12 17:20:57.314639: Epoch 1838 +2026-04-12 17:20:57.316811: Current learning rate: 0.00575 +2026-04-12 17:22:37.813015: train_loss -0.3745 +2026-04-12 17:22:37.819778: val_loss -0.3582 +2026-04-12 17:22:37.822311: Pseudo dice [0.0, 0.0, 0.7511, 0.2419, 0.4273, 0.7038, 0.6229] +2026-04-12 17:22:37.825195: Epoch time: 100.5 s +2026-04-12 17:22:39.061755: +2026-04-12 17:22:39.064134: Epoch 1839 +2026-04-12 17:22:39.066818: Current learning rate: 0.00575 +2026-04-12 17:24:19.685649: train_loss -0.3691 +2026-04-12 17:24:19.693375: val_loss -0.3698 +2026-04-12 17:24:19.695846: Pseudo dice [0.0, 0.0, 0.348, 0.1457, 0.0, 0.7604, 0.5502] +2026-04-12 17:24:19.698509: Epoch time: 100.63 s +2026-04-12 17:24:20.898204: +2026-04-12 17:24:20.900184: Epoch 1840 +2026-04-12 17:24:20.902929: Current learning rate: 0.00574 +2026-04-12 17:26:01.069290: train_loss -0.3608 +2026-04-12 17:26:01.075065: val_loss -0.3562 +2026-04-12 17:26:01.077011: Pseudo dice [0.0, 0.0, 0.6992, 0.0134, 0.0, 0.5729, 0.7788] +2026-04-12 17:26:01.079202: Epoch time: 100.17 s +2026-04-12 17:26:02.269072: +2026-04-12 17:26:02.271161: Epoch 1841 +2026-04-12 17:26:02.273075: Current learning rate: 0.00574 +2026-04-12 17:27:42.608132: train_loss -0.3711 +2026-04-12 17:27:42.616988: val_loss -0.3753 +2026-04-12 17:27:42.620300: Pseudo dice [0.0, 0.0, 0.5803, 0.5, 0.0, 0.6271, 0.8081] +2026-04-12 17:27:42.627114: Epoch time: 100.34 s +2026-04-12 17:27:43.829476: +2026-04-12 17:27:43.832950: Epoch 1842 +2026-04-12 17:27:43.836335: Current learning rate: 0.00574 +2026-04-12 17:29:25.085706: train_loss -0.3907 +2026-04-12 17:29:25.092531: val_loss -0.3646 +2026-04-12 17:29:25.094692: Pseudo dice [0.0, 0.0, 0.7, 0.1378, 0.0, 0.6166, 0.746] +2026-04-12 17:29:25.097061: Epoch time: 101.26 s +2026-04-12 17:29:26.323432: +2026-04-12 17:29:26.325938: Epoch 1843 +2026-04-12 17:29:26.328081: Current learning rate: 0.00574 +2026-04-12 17:31:07.375712: train_loss -0.3914 +2026-04-12 17:31:07.382498: val_loss -0.3072 +2026-04-12 17:31:07.385859: Pseudo dice [0.0, 0.0, 0.7085, 0.0367, 0.2472, 0.2584, 0.5649] +2026-04-12 17:31:07.388348: Epoch time: 101.06 s +2026-04-12 17:31:08.590791: +2026-04-12 17:31:08.592799: Epoch 1844 +2026-04-12 17:31:08.595594: Current learning rate: 0.00573 +2026-04-12 17:32:49.051076: train_loss -0.3528 +2026-04-12 17:32:49.058437: val_loss -0.3267 +2026-04-12 17:32:49.060708: Pseudo dice [0.0, 0.0, 0.6002, 0.0, 0.2295, 0.5957, 0.5875] +2026-04-12 17:32:49.063289: Epoch time: 100.46 s +2026-04-12 17:32:50.262361: +2026-04-12 17:32:50.264469: Epoch 1845 +2026-04-12 17:32:50.266454: Current learning rate: 0.00573 +2026-04-12 17:34:31.128507: train_loss -0.3718 +2026-04-12 17:34:31.135298: val_loss -0.3871 +2026-04-12 17:34:31.138065: Pseudo dice [0.0, 0.0, 0.6337, 0.0, 0.3442, 0.8071, 0.7168] +2026-04-12 17:34:31.142580: Epoch time: 100.87 s +2026-04-12 17:34:32.385261: +2026-04-12 17:34:32.387274: Epoch 1846 +2026-04-12 17:34:32.389615: Current learning rate: 0.00573 +2026-04-12 17:36:13.718254: train_loss -0.3919 +2026-04-12 17:36:13.725910: val_loss -0.3447 +2026-04-12 17:36:13.728642: Pseudo dice [0.0, 0.0, 0.7555, 0.0387, 0.0042, 0.7489, 0.5818] +2026-04-12 17:36:13.732254: Epoch time: 101.34 s +2026-04-12 17:36:14.980630: +2026-04-12 17:36:14.984565: Epoch 1847 +2026-04-12 17:36:14.986934: Current learning rate: 0.00573 +2026-04-12 17:37:55.504196: train_loss -0.3751 +2026-04-12 17:37:55.510526: val_loss -0.3785 +2026-04-12 17:37:55.512618: Pseudo dice [0.0, 0.0, 0.6607, 0.4923, 0.4083, 0.7783, 0.7699] +2026-04-12 17:37:55.515337: Epoch time: 100.53 s +2026-04-12 17:37:56.730745: +2026-04-12 17:37:56.733376: Epoch 1848 +2026-04-12 17:37:56.735383: Current learning rate: 0.00572 +2026-04-12 17:39:37.108141: train_loss -0.3859 +2026-04-12 17:39:37.115066: val_loss -0.3123 +2026-04-12 17:39:37.117501: Pseudo dice [0.0, 0.0, 0.591, 0.0, 0.2495, 0.7075, 0.8442] +2026-04-12 17:39:37.120451: Epoch time: 100.38 s +2026-04-12 17:39:39.395132: +2026-04-12 17:39:39.397619: Epoch 1849 +2026-04-12 17:39:39.399850: Current learning rate: 0.00572 +2026-04-12 17:41:19.603253: train_loss -0.3943 +2026-04-12 17:41:19.612156: val_loss -0.4231 +2026-04-12 17:41:19.614292: Pseudo dice [0.0, 0.0, 0.7836, 0.12, 0.4094, 0.8506, 0.8513] +2026-04-12 17:41:19.617332: Epoch time: 100.21 s +2026-04-12 17:41:22.524971: +2026-04-12 17:41:22.527465: Epoch 1850 +2026-04-12 17:41:22.529630: Current learning rate: 0.00572 +2026-04-12 17:43:02.870953: train_loss -0.3645 +2026-04-12 17:43:02.877770: val_loss -0.2392 +2026-04-12 17:43:02.880587: Pseudo dice [0.0, 0.0, 0.1343, 0.0, 0.4495, 0.0, 0.5556] +2026-04-12 17:43:02.883324: Epoch time: 100.35 s +2026-04-12 17:43:04.088764: +2026-04-12 17:43:04.090851: Epoch 1851 +2026-04-12 17:43:04.093003: Current learning rate: 0.00572 +2026-04-12 17:44:44.161357: train_loss -0.3113 +2026-04-12 17:44:44.168000: val_loss -0.2999 +2026-04-12 17:44:44.170771: Pseudo dice [0.0, 0.0, 0.5772, 0.1559, 0.0, 0.0, 0.5526] +2026-04-12 17:44:44.173722: Epoch time: 100.08 s +2026-04-12 17:44:45.399458: +2026-04-12 17:44:45.401274: Epoch 1852 +2026-04-12 17:44:45.403518: Current learning rate: 0.00571 +2026-04-12 17:46:25.519967: train_loss -0.352 +2026-04-12 17:46:25.528272: val_loss -0.3329 +2026-04-12 17:46:25.530609: Pseudo dice [0.0, 0.0, 0.7121, 0.0021, 0.0, 0.0, 0.6366] +2026-04-12 17:46:25.533257: Epoch time: 100.12 s +2026-04-12 17:46:26.731078: +2026-04-12 17:46:26.733203: Epoch 1853 +2026-04-12 17:46:26.735618: Current learning rate: 0.00571 +2026-04-12 17:48:06.807570: train_loss -0.3508 +2026-04-12 17:48:06.817413: val_loss -0.241 +2026-04-12 17:48:06.821953: Pseudo dice [0.0, 0.0, 0.6134, 0.0, 0.2845, 0.0669, 0.5694] +2026-04-12 17:48:06.825248: Epoch time: 100.08 s +2026-04-12 17:48:08.049425: +2026-04-12 17:48:08.053000: Epoch 1854 +2026-04-12 17:48:08.056917: Current learning rate: 0.00571 +2026-04-12 17:49:48.771985: train_loss -0.3579 +2026-04-12 17:49:48.779077: val_loss -0.3872 +2026-04-12 17:49:48.782527: Pseudo dice [0.0, 0.0, 0.7788, 0.0, 0.1173, 0.4807, 0.7447] +2026-04-12 17:49:48.786285: Epoch time: 100.73 s +2026-04-12 17:49:49.995560: +2026-04-12 17:49:49.997954: Epoch 1855 +2026-04-12 17:49:50.000765: Current learning rate: 0.00571 +2026-04-12 17:51:30.623221: train_loss -0.3157 +2026-04-12 17:51:30.631191: val_loss -0.3334 +2026-04-12 17:51:30.633446: Pseudo dice [0.0, 0.0, 0.6229, 0.0, 0.1252, 0.0671, 0.6918] +2026-04-12 17:51:30.635847: Epoch time: 100.63 s +2026-04-12 17:51:31.864130: +2026-04-12 17:51:31.866156: Epoch 1856 +2026-04-12 17:51:31.868286: Current learning rate: 0.0057 +2026-04-12 17:53:12.241279: train_loss -0.3378 +2026-04-12 17:53:12.248317: val_loss -0.3495 +2026-04-12 17:53:12.251559: Pseudo dice [0.0, 0.0, 0.6087, 0.0, 0.3038, 0.3286, 0.6674] +2026-04-12 17:53:12.254187: Epoch time: 100.38 s +2026-04-12 17:53:13.491666: +2026-04-12 17:53:13.494051: Epoch 1857 +2026-04-12 17:53:13.496375: Current learning rate: 0.0057 +2026-04-12 17:54:54.845894: train_loss -0.3779 +2026-04-12 17:54:54.852957: val_loss -0.3705 +2026-04-12 17:54:54.855919: Pseudo dice [0.0, 0.0, 0.6007, 0.4447, 0.3005, 0.8062, 0.7678] +2026-04-12 17:54:54.858011: Epoch time: 101.36 s +2026-04-12 17:54:56.104784: +2026-04-12 17:54:56.108314: Epoch 1858 +2026-04-12 17:54:56.110849: Current learning rate: 0.0057 +2026-04-12 17:56:36.640940: train_loss -0.3766 +2026-04-12 17:56:36.649682: val_loss -0.3556 +2026-04-12 17:56:36.652357: Pseudo dice [0.0, 0.0, 0.3619, 0.0, 0.0, 0.1807, 0.7352] +2026-04-12 17:56:36.655355: Epoch time: 100.54 s +2026-04-12 17:56:37.886309: +2026-04-12 17:56:37.888804: Epoch 1859 +2026-04-12 17:56:37.891191: Current learning rate: 0.0057 +2026-04-12 17:58:18.659148: train_loss -0.3492 +2026-04-12 17:58:18.667642: val_loss -0.3264 +2026-04-12 17:58:18.670653: Pseudo dice [0.0, 0.0, 0.7032, 0.0, 0.0, 0.8302, 0.5205] +2026-04-12 17:58:18.675460: Epoch time: 100.78 s +2026-04-12 17:58:19.977847: +2026-04-12 17:58:19.980176: Epoch 1860 +2026-04-12 17:58:19.982447: Current learning rate: 0.0057 +2026-04-12 18:00:00.470211: train_loss -0.3857 +2026-04-12 18:00:00.476470: val_loss -0.3456 +2026-04-12 18:00:00.479458: Pseudo dice [0.0, 0.0, 0.7095, 0.1283, 0.1117, 0.6561, 0.7115] +2026-04-12 18:00:00.482084: Epoch time: 100.5 s +2026-04-12 18:00:01.699035: +2026-04-12 18:00:01.701255: Epoch 1861 +2026-04-12 18:00:01.703830: Current learning rate: 0.00569 +2026-04-12 18:01:42.980798: train_loss -0.402 +2026-04-12 18:01:42.986917: val_loss -0.3733 +2026-04-12 18:01:42.988909: Pseudo dice [0.0, 0.0, 0.6121, 0.0, 0.6108, 0.5345, 0.6769] +2026-04-12 18:01:42.991571: Epoch time: 101.28 s +2026-04-12 18:01:44.236760: +2026-04-12 18:01:44.239338: Epoch 1862 +2026-04-12 18:01:44.243974: Current learning rate: 0.00569 +2026-04-12 18:03:24.379617: train_loss -0.3874 +2026-04-12 18:03:24.388730: val_loss -0.3765 +2026-04-12 18:03:24.391391: Pseudo dice [0.0, 0.0, 0.7562, 0.1536, 0.3856, 0.748, 0.8026] +2026-04-12 18:03:24.394034: Epoch time: 100.15 s +2026-04-12 18:03:25.608222: +2026-04-12 18:03:25.610251: Epoch 1863 +2026-04-12 18:03:25.612620: Current learning rate: 0.00569 +2026-04-12 18:05:06.154045: train_loss -0.3884 +2026-04-12 18:05:06.161271: val_loss -0.3851 +2026-04-12 18:05:06.165404: Pseudo dice [0.0, 0.0, 0.5659, 0.3182, 0.1937, 0.7689, 0.8332] +2026-04-12 18:05:06.169549: Epoch time: 100.55 s +2026-04-12 18:05:07.400098: +2026-04-12 18:05:07.402365: Epoch 1864 +2026-04-12 18:05:07.405642: Current learning rate: 0.00569 +2026-04-12 18:06:47.575114: train_loss -0.399 +2026-04-12 18:06:47.582009: val_loss -0.3743 +2026-04-12 18:06:47.584321: Pseudo dice [0.0, 0.0, 0.4989, 0.0, 0.2279, 0.6061, 0.7549] +2026-04-12 18:06:47.586693: Epoch time: 100.18 s +2026-04-12 18:06:48.815611: +2026-04-12 18:06:48.819997: Epoch 1865 +2026-04-12 18:06:48.823973: Current learning rate: 0.00568 +2026-04-12 18:08:28.986488: train_loss -0.3951 +2026-04-12 18:08:28.992789: val_loss -0.2957 +2026-04-12 18:08:28.995144: Pseudo dice [0.0, 0.0, 0.5828, 0.0, 0.0906, 0.7926, 0.5419] +2026-04-12 18:08:28.998523: Epoch time: 100.17 s +2026-04-12 18:08:30.250739: +2026-04-12 18:08:30.252499: Epoch 1866 +2026-04-12 18:08:30.254607: Current learning rate: 0.00568 +2026-04-12 18:10:11.366429: train_loss -0.3859 +2026-04-12 18:10:11.374585: val_loss -0.3793 +2026-04-12 18:10:11.377005: Pseudo dice [0.0, 0.0, 0.6029, 0.0, 0.1018, 0.6924, 0.6896] +2026-04-12 18:10:11.379318: Epoch time: 101.12 s +2026-04-12 18:10:12.616919: +2026-04-12 18:10:12.619196: Epoch 1867 +2026-04-12 18:10:12.622844: Current learning rate: 0.00568 +2026-04-12 18:11:53.185152: train_loss -0.3892 +2026-04-12 18:11:53.192507: val_loss -0.3791 +2026-04-12 18:11:53.194969: Pseudo dice [0.0, 0.0, 0.7257, 0.0, 0.0, 0.7908, 0.7389] +2026-04-12 18:11:53.199500: Epoch time: 100.57 s +2026-04-12 18:11:54.428510: +2026-04-12 18:11:54.431012: Epoch 1868 +2026-04-12 18:11:54.433503: Current learning rate: 0.00568 +2026-04-12 18:13:35.995925: train_loss -0.3725 +2026-04-12 18:13:36.002677: val_loss -0.369 +2026-04-12 18:13:36.005456: Pseudo dice [0.0, 0.0, 0.6379, 0.0, 0.2726, 0.7403, 0.5836] +2026-04-12 18:13:36.008129: Epoch time: 101.57 s +2026-04-12 18:13:37.233564: +2026-04-12 18:13:37.235910: Epoch 1869 +2026-04-12 18:13:37.238348: Current learning rate: 0.00567 +2026-04-12 18:15:18.084999: train_loss -0.3951 +2026-04-12 18:15:18.096399: val_loss -0.3697 +2026-04-12 18:15:18.102991: Pseudo dice [0.0, 0.0, 0.7201, 0.0, 0.2589, 0.6677, 0.6971] +2026-04-12 18:15:18.107983: Epoch time: 100.85 s +2026-04-12 18:15:19.344817: +2026-04-12 18:15:19.348165: Epoch 1870 +2026-04-12 18:15:19.351167: Current learning rate: 0.00567 +2026-04-12 18:17:00.113066: train_loss -0.3986 +2026-04-12 18:17:00.122130: val_loss -0.3594 +2026-04-12 18:17:00.126366: Pseudo dice [0.0, 0.0, 0.5502, 0.0, 0.0, 0.6122, 0.548] +2026-04-12 18:17:00.130815: Epoch time: 100.77 s +2026-04-12 18:17:01.390206: +2026-04-12 18:17:01.392769: Epoch 1871 +2026-04-12 18:17:01.395880: Current learning rate: 0.00567 +2026-04-12 18:18:42.979331: train_loss -0.364 +2026-04-12 18:18:42.996330: val_loss -0.3239 +2026-04-12 18:18:42.998720: Pseudo dice [0.0, 0.0, 0.7388, 0.0027, 0.275, 0.4907, 0.7558] +2026-04-12 18:18:43.002246: Epoch time: 101.59 s +2026-04-12 18:18:44.219076: +2026-04-12 18:18:44.221464: Epoch 1872 +2026-04-12 18:18:44.223648: Current learning rate: 0.00567 +2026-04-12 18:20:24.510224: train_loss -0.3582 +2026-04-12 18:20:24.519373: val_loss -0.3783 +2026-04-12 18:20:24.521847: Pseudo dice [0.0, 0.0, 0.5965, 0.4907, 0.2098, 0.7307, 0.7323] +2026-04-12 18:20:24.524745: Epoch time: 100.29 s +2026-04-12 18:20:25.742253: +2026-04-12 18:20:25.744439: Epoch 1873 +2026-04-12 18:20:25.746570: Current learning rate: 0.00566 +2026-04-12 18:22:05.891813: train_loss -0.401 +2026-04-12 18:22:05.900103: val_loss -0.3957 +2026-04-12 18:22:05.902127: Pseudo dice [0.0, 0.0, 0.595, 0.5927, 0.4033, 0.7147, 0.6819] +2026-04-12 18:22:05.904769: Epoch time: 100.15 s +2026-04-12 18:22:07.123214: +2026-04-12 18:22:07.125676: Epoch 1874 +2026-04-12 18:22:07.129132: Current learning rate: 0.00566 +2026-04-12 18:23:47.307073: train_loss -0.3645 +2026-04-12 18:23:47.314616: val_loss -0.3182 +2026-04-12 18:23:47.317879: Pseudo dice [0.0, 0.0, 0.646, 0.0, 0.1198, 0.7382, 0.7681] +2026-04-12 18:23:47.320784: Epoch time: 100.19 s +2026-04-12 18:23:48.529882: +2026-04-12 18:23:48.531868: Epoch 1875 +2026-04-12 18:23:48.534377: Current learning rate: 0.00566 +2026-04-12 18:25:28.617464: train_loss -0.3516 +2026-04-12 18:25:28.626215: val_loss -0.3207 +2026-04-12 18:25:28.628766: Pseudo dice [0.0, 0.0, 0.6583, 0.0, 0.0917, 0.0, 0.7213] +2026-04-12 18:25:28.632065: Epoch time: 100.09 s +2026-04-12 18:25:29.829781: +2026-04-12 18:25:29.831952: Epoch 1876 +2026-04-12 18:25:29.834385: Current learning rate: 0.00566 +2026-04-12 18:27:10.295010: train_loss -0.3472 +2026-04-12 18:27:10.301255: val_loss -0.3743 +2026-04-12 18:27:10.304010: Pseudo dice [0.0, 0.0, 0.6907, 0.3863, 0.3979, 0.3075, 0.7862] +2026-04-12 18:27:10.307273: Epoch time: 100.47 s +2026-04-12 18:27:11.531126: +2026-04-12 18:27:11.533090: Epoch 1877 +2026-04-12 18:27:11.535492: Current learning rate: 0.00565 +2026-04-12 18:28:51.745788: train_loss -0.3855 +2026-04-12 18:28:51.753321: val_loss -0.3784 +2026-04-12 18:28:51.756110: Pseudo dice [0.0, 0.0, 0.7798, 0.0, 0.3153, 0.6773, 0.8116] +2026-04-12 18:28:51.758433: Epoch time: 100.22 s +2026-04-12 18:28:52.989319: +2026-04-12 18:28:52.991232: Epoch 1878 +2026-04-12 18:28:52.993309: Current learning rate: 0.00565 +2026-04-12 18:30:34.018217: train_loss -0.3638 +2026-04-12 18:30:34.027803: val_loss -0.3218 +2026-04-12 18:30:34.030570: Pseudo dice [0.0, 0.0, 0.5875, 0.0653, 0.2056, 0.7336, 0.7586] +2026-04-12 18:30:34.033083: Epoch time: 101.03 s +2026-04-12 18:30:35.264323: +2026-04-12 18:30:35.267175: Epoch 1879 +2026-04-12 18:30:35.270450: Current learning rate: 0.00565 +2026-04-12 18:32:15.571833: train_loss -0.3425 +2026-04-12 18:32:15.579677: val_loss -0.3375 +2026-04-12 18:32:15.582727: Pseudo dice [0.0, 0.0, 0.5465, 0.0, 0.0, 0.5512, 0.8373] +2026-04-12 18:32:15.585644: Epoch time: 100.31 s +2026-04-12 18:32:16.800852: +2026-04-12 18:32:16.803167: Epoch 1880 +2026-04-12 18:32:16.805820: Current learning rate: 0.00565 +2026-04-12 18:33:57.668017: train_loss -0.3659 +2026-04-12 18:33:57.679561: val_loss -0.3542 +2026-04-12 18:33:57.682252: Pseudo dice [0.0, 0.0, 0.7222, 0.0102, 0.0, 0.7206, 0.7786] +2026-04-12 18:33:57.685480: Epoch time: 100.87 s +2026-04-12 18:33:58.879224: +2026-04-12 18:33:58.881669: Epoch 1881 +2026-04-12 18:33:58.884640: Current learning rate: 0.00564 +2026-04-12 18:35:39.063766: train_loss -0.3772 +2026-04-12 18:35:39.071197: val_loss -0.3718 +2026-04-12 18:35:39.073919: Pseudo dice [0.0, 0.0, 0.3698, 0.0089, 0.0, 0.7478, 0.8259] +2026-04-12 18:35:39.076035: Epoch time: 100.19 s +2026-04-12 18:35:40.297770: +2026-04-12 18:35:40.299697: Epoch 1882 +2026-04-12 18:35:40.302003: Current learning rate: 0.00564 +2026-04-12 18:37:20.338617: train_loss -0.3895 +2026-04-12 18:37:20.346962: val_loss -0.357 +2026-04-12 18:37:20.348949: Pseudo dice [0.0, 0.0, 0.7124, 0.0466, 0.0, 0.7263, 0.5628] +2026-04-12 18:37:20.352514: Epoch time: 100.04 s +2026-04-12 18:37:21.554188: +2026-04-12 18:37:21.556173: Epoch 1883 +2026-04-12 18:37:21.558158: Current learning rate: 0.00564 +2026-04-12 18:39:01.836221: train_loss -0.3429 +2026-04-12 18:39:01.841501: val_loss -0.3555 +2026-04-12 18:39:01.843710: Pseudo dice [0.0, 0.0, 0.4998, 0.2597, 0.0, 0.576, 0.6782] +2026-04-12 18:39:01.846714: Epoch time: 100.29 s +2026-04-12 18:39:03.067354: +2026-04-12 18:39:03.069107: Epoch 1884 +2026-04-12 18:39:03.071444: Current learning rate: 0.00564 +2026-04-12 18:40:44.507657: train_loss -0.372 +2026-04-12 18:40:44.513654: val_loss -0.3736 +2026-04-12 18:40:44.515888: Pseudo dice [0.0, 0.0, 0.6973, 0.7962, 0.1599, 0.7167, 0.5937] +2026-04-12 18:40:44.519548: Epoch time: 101.44 s +2026-04-12 18:40:45.742083: +2026-04-12 18:40:45.744206: Epoch 1885 +2026-04-12 18:40:45.746438: Current learning rate: 0.00564 +2026-04-12 18:42:26.364426: train_loss -0.3981 +2026-04-12 18:42:26.372506: val_loss -0.3949 +2026-04-12 18:42:26.375005: Pseudo dice [0.0, 0.0, 0.7588, 0.0, 0.1145, 0.6503, 0.8524] +2026-04-12 18:42:26.378210: Epoch time: 100.63 s +2026-04-12 18:42:27.590777: +2026-04-12 18:42:27.592997: Epoch 1886 +2026-04-12 18:42:27.595313: Current learning rate: 0.00563 +2026-04-12 18:44:07.874047: train_loss -0.3537 +2026-04-12 18:44:07.883962: val_loss -0.3648 +2026-04-12 18:44:07.886098: Pseudo dice [0.0, 0.0, 0.5549, 0.3649, 0.0, 0.4354, 0.6199] +2026-04-12 18:44:07.889093: Epoch time: 100.29 s +2026-04-12 18:44:09.093359: +2026-04-12 18:44:09.095413: Epoch 1887 +2026-04-12 18:44:09.099713: Current learning rate: 0.00563 +2026-04-12 18:45:49.104220: train_loss -0.3678 +2026-04-12 18:45:49.110057: val_loss -0.3596 +2026-04-12 18:45:49.112085: Pseudo dice [0.0, 0.0, 0.5249, 0.4792, 0.0, 0.7386, 0.7923] +2026-04-12 18:45:49.114042: Epoch time: 100.01 s +2026-04-12 18:45:51.287775: +2026-04-12 18:45:51.290863: Epoch 1888 +2026-04-12 18:45:51.293676: Current learning rate: 0.00563 +2026-04-12 18:47:31.483696: train_loss -0.3695 +2026-04-12 18:47:31.493203: val_loss -0.3856 +2026-04-12 18:47:31.497004: Pseudo dice [0.0, 0.0, 0.8337, 0.6664, 0.0, 0.702, 0.8863] +2026-04-12 18:47:31.501158: Epoch time: 100.2 s +2026-04-12 18:47:32.709012: +2026-04-12 18:47:32.710892: Epoch 1889 +2026-04-12 18:47:32.712890: Current learning rate: 0.00563 +2026-04-12 18:49:12.804075: train_loss -0.3821 +2026-04-12 18:49:12.809900: val_loss -0.3777 +2026-04-12 18:49:12.811879: Pseudo dice [0.0, 0.0, 0.7186, 0.0002, 0.0, 0.6851, 0.3393] +2026-04-12 18:49:12.814022: Epoch time: 100.1 s +2026-04-12 18:49:14.023290: +2026-04-12 18:49:14.025823: Epoch 1890 +2026-04-12 18:49:14.028938: Current learning rate: 0.00562 +2026-04-12 18:50:54.517822: train_loss -0.3502 +2026-04-12 18:50:54.525463: val_loss -0.3703 +2026-04-12 18:50:54.528507: Pseudo dice [0.0, 0.0, 0.6685, 0.8889, 0.2899, 0.7212, 0.7113] +2026-04-12 18:50:54.532184: Epoch time: 100.5 s +2026-04-12 18:50:55.761264: +2026-04-12 18:50:55.763484: Epoch 1891 +2026-04-12 18:50:55.765984: Current learning rate: 0.00562 +2026-04-12 18:52:36.689669: train_loss -0.3718 +2026-04-12 18:52:36.696976: val_loss -0.3386 +2026-04-12 18:52:36.700027: Pseudo dice [0.0, 0.0, 0.5894, 0.0518, 0.4716, 0.5652, 0.6737] +2026-04-12 18:52:36.702534: Epoch time: 100.93 s +2026-04-12 18:52:37.919639: +2026-04-12 18:52:37.922198: Epoch 1892 +2026-04-12 18:52:37.925046: Current learning rate: 0.00562 +2026-04-12 18:54:18.687259: train_loss -0.373 +2026-04-12 18:54:18.699265: val_loss -0.376 +2026-04-12 18:54:18.702248: Pseudo dice [0.0, 0.0, 0.7383, 0.0, 0.2671, 0.2991, 0.8394] +2026-04-12 18:54:18.706325: Epoch time: 100.77 s +2026-04-12 18:54:19.933807: +2026-04-12 18:54:19.936096: Epoch 1893 +2026-04-12 18:54:19.940032: Current learning rate: 0.00562 +2026-04-12 18:56:00.275925: train_loss -0.3834 +2026-04-12 18:56:00.282256: val_loss -0.3863 +2026-04-12 18:56:00.285026: Pseudo dice [0.0, 0.0, 0.4657, 0.0, 0.2932, 0.7991, 0.6968] +2026-04-12 18:56:00.287142: Epoch time: 100.35 s +2026-04-12 18:56:01.501207: +2026-04-12 18:56:01.504318: Epoch 1894 +2026-04-12 18:56:01.507175: Current learning rate: 0.00561 +2026-04-12 18:57:41.699932: train_loss -0.3886 +2026-04-12 18:57:41.706037: val_loss -0.4097 +2026-04-12 18:57:41.708066: Pseudo dice [0.0, 0.0, 0.7684, 0.2128, 0.3165, 0.3723, 0.8304] +2026-04-12 18:57:41.710598: Epoch time: 100.2 s +2026-04-12 18:57:42.905251: +2026-04-12 18:57:42.907354: Epoch 1895 +2026-04-12 18:57:42.909655: Current learning rate: 0.00561 +2026-04-12 18:59:23.143094: train_loss -0.384 +2026-04-12 18:59:23.181234: val_loss -0.3134 +2026-04-12 18:59:23.184231: Pseudo dice [0.0, 0.0, 0.7528, 0.0, 0.1993, 0.4665, 0.5747] +2026-04-12 18:59:23.189528: Epoch time: 100.24 s +2026-04-12 18:59:24.403594: +2026-04-12 18:59:24.405779: Epoch 1896 +2026-04-12 18:59:24.407673: Current learning rate: 0.00561 +2026-04-12 19:01:05.039795: train_loss -0.3652 +2026-04-12 19:01:05.046522: val_loss -0.3822 +2026-04-12 19:01:05.048902: Pseudo dice [0.0, 0.0, 0.6529, 0.4462, 0.307, 0.808, 0.6709] +2026-04-12 19:01:05.052736: Epoch time: 100.64 s +2026-04-12 19:01:06.296703: +2026-04-12 19:01:06.298707: Epoch 1897 +2026-04-12 19:01:06.300774: Current learning rate: 0.00561 +2026-04-12 19:02:46.335905: train_loss -0.385 +2026-04-12 19:02:46.341894: val_loss -0.4152 +2026-04-12 19:02:46.344197: Pseudo dice [0.0, 0.0, 0.7121, 0.2162, 0.3103, 0.4325, 0.8134] +2026-04-12 19:02:46.346315: Epoch time: 100.04 s +2026-04-12 19:02:47.554060: +2026-04-12 19:02:47.555943: Epoch 1898 +2026-04-12 19:02:47.558197: Current learning rate: 0.0056 +2026-04-12 19:04:27.631849: train_loss -0.3769 +2026-04-12 19:04:27.640450: val_loss -0.3216 +2026-04-12 19:04:27.643638: Pseudo dice [0.0, 0.0, 0.63, 0.0651, 0.0, 0.0869, 0.8107] +2026-04-12 19:04:27.646148: Epoch time: 100.08 s +2026-04-12 19:04:28.848167: +2026-04-12 19:04:28.850162: Epoch 1899 +2026-04-12 19:04:28.852405: Current learning rate: 0.0056 +2026-04-12 19:06:09.745599: train_loss -0.3449 +2026-04-12 19:06:09.758895: val_loss -0.3883 +2026-04-12 19:06:09.761523: Pseudo dice [0.0, 0.0, 0.6887, 0.2079, 0.1267, 0.7045, 0.7547] +2026-04-12 19:06:09.773188: Epoch time: 100.9 s +2026-04-12 19:06:12.829745: +2026-04-12 19:06:12.832223: Epoch 1900 +2026-04-12 19:06:12.835465: Current learning rate: 0.0056 +2026-04-12 19:07:52.991132: train_loss -0.3556 +2026-04-12 19:07:53.000332: val_loss -0.3406 +2026-04-12 19:07:53.003562: Pseudo dice [0.0, 0.0, 0.6018, 0.0985, 0.369, 0.5154, 0.6534] +2026-04-12 19:07:53.007483: Epoch time: 100.16 s +2026-04-12 19:07:54.242819: +2026-04-12 19:07:54.245679: Epoch 1901 +2026-04-12 19:07:54.248951: Current learning rate: 0.0056 +2026-04-12 19:09:34.471985: train_loss -0.3676 +2026-04-12 19:09:34.478841: val_loss -0.3928 +2026-04-12 19:09:34.481143: Pseudo dice [0.0, 0.0, 0.4792, 0.0, 0.3934, 0.7872, 0.8457] +2026-04-12 19:09:34.484227: Epoch time: 100.23 s +2026-04-12 19:09:35.682928: +2026-04-12 19:09:35.685128: Epoch 1902 +2026-04-12 19:09:35.687235: Current learning rate: 0.00559 +2026-04-12 19:11:16.052407: train_loss -0.3784 +2026-04-12 19:11:16.059436: val_loss -0.3671 +2026-04-12 19:11:16.061874: Pseudo dice [0.0, 0.0, 0.6068, 0.5999, 0.4537, 0.4211, 0.3674] +2026-04-12 19:11:16.064655: Epoch time: 100.37 s +2026-04-12 19:11:17.302066: +2026-04-12 19:11:17.304483: Epoch 1903 +2026-04-12 19:11:17.307159: Current learning rate: 0.00559 +2026-04-12 19:12:57.357527: train_loss -0.3401 +2026-04-12 19:12:57.363499: val_loss -0.3722 +2026-04-12 19:12:57.367768: Pseudo dice [0.0, 0.0, 0.7792, 0.0, 0.4656, 0.2202, 0.7647] +2026-04-12 19:12:57.370110: Epoch time: 100.06 s +2026-04-12 19:12:58.605016: +2026-04-12 19:12:58.607399: Epoch 1904 +2026-04-12 19:12:58.610273: Current learning rate: 0.00559 +2026-04-12 19:14:39.188554: train_loss -0.3806 +2026-04-12 19:14:39.199123: val_loss -0.3483 +2026-04-12 19:14:39.202333: Pseudo dice [0.0, 0.0, 0.6803, 0.0711, 0.0, 0.5779, 0.6826] +2026-04-12 19:14:39.205896: Epoch time: 100.59 s +2026-04-12 19:14:40.432131: +2026-04-12 19:14:40.434467: Epoch 1905 +2026-04-12 19:14:40.437535: Current learning rate: 0.00559 +2026-04-12 19:16:20.608017: train_loss -0.4025 +2026-04-12 19:16:20.614176: val_loss -0.3961 +2026-04-12 19:16:20.616722: Pseudo dice [0.0, 0.0, 0.6479, 0.5007, 0.3546, 0.7618, 0.7136] +2026-04-12 19:16:20.619776: Epoch time: 100.18 s +2026-04-12 19:16:21.847209: +2026-04-12 19:16:21.849186: Epoch 1906 +2026-04-12 19:16:21.852405: Current learning rate: 0.00559 +2026-04-12 19:18:02.649689: train_loss -0.3737 +2026-04-12 19:18:02.657485: val_loss -0.3801 +2026-04-12 19:18:02.660299: Pseudo dice [0.0, 0.0, 0.7398, 0.8192, 0.0487, 0.5158, 0.7734] +2026-04-12 19:18:02.662753: Epoch time: 100.81 s +2026-04-12 19:18:03.890990: +2026-04-12 19:18:03.893939: Epoch 1907 +2026-04-12 19:18:03.897031: Current learning rate: 0.00558 +2026-04-12 19:19:44.110369: train_loss -0.3824 +2026-04-12 19:19:44.117945: val_loss -0.3666 +2026-04-12 19:19:44.119975: Pseudo dice [0.0, 0.0, 0.6216, 0.0, 0.0, 0.4624, 0.6747] +2026-04-12 19:19:44.122921: Epoch time: 100.22 s +2026-04-12 19:19:46.428333: +2026-04-12 19:19:46.431020: Epoch 1908 +2026-04-12 19:19:46.433718: Current learning rate: 0.00558 +2026-04-12 19:21:26.959045: train_loss -0.3674 +2026-04-12 19:21:26.966522: val_loss -0.3159 +2026-04-12 19:21:26.968911: Pseudo dice [0.0, 0.0, 0.5872, 0.0, 0.2571, 0.7652, 0.3588] +2026-04-12 19:21:26.972650: Epoch time: 100.53 s +2026-04-12 19:21:28.243768: +2026-04-12 19:21:28.246065: Epoch 1909 +2026-04-12 19:21:28.248470: Current learning rate: 0.00558 +2026-04-12 19:23:08.993952: train_loss -0.3523 +2026-04-12 19:23:09.002294: val_loss -0.3467 +2026-04-12 19:23:09.004501: Pseudo dice [0.0, 0.0, 0.7052, 0.085, 0.0, 0.3074, 0.6611] +2026-04-12 19:23:09.008060: Epoch time: 100.75 s +2026-04-12 19:23:10.257973: +2026-04-12 19:23:10.259934: Epoch 1910 +2026-04-12 19:23:10.262213: Current learning rate: 0.00558 +2026-04-12 19:24:50.376197: train_loss -0.3889 +2026-04-12 19:24:50.382683: val_loss -0.374 +2026-04-12 19:24:50.384881: Pseudo dice [0.0, 0.0, 0.5849, 0.0, 0.3083, 0.7432, 0.7839] +2026-04-12 19:24:50.387351: Epoch time: 100.12 s +2026-04-12 19:24:51.631329: +2026-04-12 19:24:51.633762: Epoch 1911 +2026-04-12 19:24:51.636328: Current learning rate: 0.00557 +2026-04-12 19:26:31.806758: train_loss -0.3908 +2026-04-12 19:26:31.816413: val_loss -0.3188 +2026-04-12 19:26:31.818978: Pseudo dice [0.0, 0.0, 0.6661, 0.0379, 0.4488, 0.6772, 0.614] +2026-04-12 19:26:31.821962: Epoch time: 100.18 s +2026-04-12 19:26:33.046711: +2026-04-12 19:26:33.049016: Epoch 1912 +2026-04-12 19:26:33.052460: Current learning rate: 0.00557 +2026-04-12 19:28:13.813143: train_loss -0.3555 +2026-04-12 19:28:13.821148: val_loss -0.2431 +2026-04-12 19:28:13.823380: Pseudo dice [0.0, 0.0, 0.4893, 0.0, 0.2748, 0.5481, 0.1793] +2026-04-12 19:28:13.827199: Epoch time: 100.77 s +2026-04-12 19:28:15.066598: +2026-04-12 19:28:15.069935: Epoch 1913 +2026-04-12 19:28:15.074125: Current learning rate: 0.00557 +2026-04-12 19:29:55.158445: train_loss -0.3855 +2026-04-12 19:29:55.166145: val_loss -0.3679 +2026-04-12 19:29:55.167989: Pseudo dice [0.0, 0.0, 0.5631, 0.0, 0.3477, 0.5916, 0.5106] +2026-04-12 19:29:55.170216: Epoch time: 100.09 s +2026-04-12 19:29:56.405883: +2026-04-12 19:29:56.407799: Epoch 1914 +2026-04-12 19:29:56.409996: Current learning rate: 0.00557 +2026-04-12 19:31:36.608726: train_loss -0.3602 +2026-04-12 19:31:36.615678: val_loss -0.3933 +2026-04-12 19:31:36.617738: Pseudo dice [0.0, 0.0, 0.6285, 0.0765, 0.0001, 0.6816, 0.8329] +2026-04-12 19:31:36.630585: Epoch time: 100.21 s +2026-04-12 19:31:37.896625: +2026-04-12 19:31:37.899376: Epoch 1915 +2026-04-12 19:31:37.902275: Current learning rate: 0.00556 +2026-04-12 19:33:18.285132: train_loss -0.3416 +2026-04-12 19:33:18.292739: val_loss -0.3915 +2026-04-12 19:33:18.295577: Pseudo dice [0.0, 0.0, 0.6709, 0.1469, 0.3329, 0.558, 0.7684] +2026-04-12 19:33:18.298801: Epoch time: 100.39 s +2026-04-12 19:33:19.523688: +2026-04-12 19:33:19.526001: Epoch 1916 +2026-04-12 19:33:19.528505: Current learning rate: 0.00556 +2026-04-12 19:35:00.318035: train_loss -0.3922 +2026-04-12 19:35:00.326088: val_loss -0.3818 +2026-04-12 19:35:00.329591: Pseudo dice [0.0, 0.0, 0.6651, 0.0, 0.0998, 0.5244, 0.7698] +2026-04-12 19:35:00.332043: Epoch time: 100.8 s +2026-04-12 19:35:01.590775: +2026-04-12 19:35:01.592872: Epoch 1917 +2026-04-12 19:35:01.595959: Current learning rate: 0.00556 +2026-04-12 19:36:41.646541: train_loss -0.3794 +2026-04-12 19:36:41.654551: val_loss -0.3599 +2026-04-12 19:36:41.656760: Pseudo dice [0.0, 0.0, 0.5176, 0.2238, 0.2025, 0.6742, 0.6903] +2026-04-12 19:36:41.660941: Epoch time: 100.06 s +2026-04-12 19:36:42.888101: +2026-04-12 19:36:42.890214: Epoch 1918 +2026-04-12 19:36:42.893621: Current learning rate: 0.00556 +2026-04-12 19:38:23.177235: train_loss -0.3515 +2026-04-12 19:38:23.182828: val_loss -0.2961 +2026-04-12 19:38:23.185253: Pseudo dice [0.0, 0.0, 0.5564, 0.0118, 0.3919, 0.7234, 0.6157] +2026-04-12 19:38:23.187926: Epoch time: 100.29 s +2026-04-12 19:38:24.422665: +2026-04-12 19:38:24.425003: Epoch 1919 +2026-04-12 19:38:24.427417: Current learning rate: 0.00555 +2026-04-12 19:40:04.693827: train_loss -0.377 +2026-04-12 19:40:04.700877: val_loss -0.3968 +2026-04-12 19:40:04.704052: Pseudo dice [0.0, 0.0, 0.4487, 0.189, 0.3884, 0.5789, 0.8193] +2026-04-12 19:40:04.706777: Epoch time: 100.27 s +2026-04-12 19:40:05.939009: +2026-04-12 19:40:05.940990: Epoch 1920 +2026-04-12 19:40:05.943308: Current learning rate: 0.00555 +2026-04-12 19:41:46.903385: train_loss -0.3917 +2026-04-12 19:41:46.909876: val_loss -0.3027 +2026-04-12 19:41:46.911889: Pseudo dice [0.0, 0.0, 0.7356, 0.0, 0.4067, 0.7584, 0.6193] +2026-04-12 19:41:46.914887: Epoch time: 100.97 s +2026-04-12 19:41:48.153640: +2026-04-12 19:41:48.155833: Epoch 1921 +2026-04-12 19:41:48.159394: Current learning rate: 0.00555 +2026-04-12 19:43:28.408125: train_loss -0.3773 +2026-04-12 19:43:28.413982: val_loss -0.3358 +2026-04-12 19:43:28.416203: Pseudo dice [0.0, 0.0, 0.8007, 0.0, 0.0, 0.4154, 0.8077] +2026-04-12 19:43:28.418557: Epoch time: 100.26 s +2026-04-12 19:43:29.644096: +2026-04-12 19:43:29.646541: Epoch 1922 +2026-04-12 19:43:29.649079: Current learning rate: 0.00555 +2026-04-12 19:45:09.744954: train_loss -0.3827 +2026-04-12 19:45:09.753262: val_loss -0.3445 +2026-04-12 19:45:09.755524: Pseudo dice [0.0, 0.0, 0.4164, 0.0, 0.1723, 0.4743, 0.7231] +2026-04-12 19:45:09.758120: Epoch time: 100.1 s +2026-04-12 19:45:10.977437: +2026-04-12 19:45:10.979480: Epoch 1923 +2026-04-12 19:45:10.981874: Current learning rate: 0.00554 +2026-04-12 19:46:51.222492: train_loss -0.3755 +2026-04-12 19:46:51.228180: val_loss -0.318 +2026-04-12 19:46:51.231936: Pseudo dice [0.0, 0.0, 0.5956, 0.1244, 0.0, 0.5525, 0.6786] +2026-04-12 19:46:51.235179: Epoch time: 100.25 s +2026-04-12 19:46:52.479657: +2026-04-12 19:46:52.481972: Epoch 1924 +2026-04-12 19:46:52.484077: Current learning rate: 0.00554 +2026-04-12 19:48:32.471822: train_loss -0.3715 +2026-04-12 19:48:32.479866: val_loss -0.3392 +2026-04-12 19:48:32.483357: Pseudo dice [0.0, 0.0, 0.5638, 0.0135, 0.3344, 0.671, 0.6116] +2026-04-12 19:48:32.485739: Epoch time: 100.0 s +2026-04-12 19:48:33.731168: +2026-04-12 19:48:33.733329: Epoch 1925 +2026-04-12 19:48:33.735570: Current learning rate: 0.00554 +2026-04-12 19:50:14.003771: train_loss -0.3684 +2026-04-12 19:50:14.018447: val_loss -0.3303 +2026-04-12 19:50:14.021385: Pseudo dice [0.0, 0.0, 0.7303, 0.185, 0.0, 0.5306, 0.3459] +2026-04-12 19:50:14.024394: Epoch time: 100.28 s +2026-04-12 19:50:15.243610: +2026-04-12 19:50:15.246501: Epoch 1926 +2026-04-12 19:50:15.248947: Current learning rate: 0.00554 +2026-04-12 19:51:55.473835: train_loss -0.3886 +2026-04-12 19:51:55.480504: val_loss -0.3218 +2026-04-12 19:51:55.483266: Pseudo dice [0.0, 0.0, 0.5789, 0.0, 0.0, 0.5321, 0.2868] +2026-04-12 19:51:55.486754: Epoch time: 100.23 s +2026-04-12 19:51:56.717592: +2026-04-12 19:51:56.719799: Epoch 1927 +2026-04-12 19:51:56.722085: Current learning rate: 0.00553 +2026-04-12 19:53:38.060013: train_loss -0.3606 +2026-04-12 19:53:38.070748: val_loss -0.3855 +2026-04-12 19:53:38.073707: Pseudo dice [0.0, 0.0, 0.7553, 0.2667, 0.0, 0.6058, 0.6424] +2026-04-12 19:53:38.076631: Epoch time: 101.35 s +2026-04-12 19:53:39.326189: +2026-04-12 19:53:39.328424: Epoch 1928 +2026-04-12 19:53:39.330480: Current learning rate: 0.00553 +2026-04-12 19:55:19.589265: train_loss -0.3846 +2026-04-12 19:55:19.594805: val_loss -0.3164 +2026-04-12 19:55:19.597007: Pseudo dice [0.0, 0.0, 0.6929, 0.0, 0.2009, 0.6872, 0.8145] +2026-04-12 19:55:19.599016: Epoch time: 100.27 s +2026-04-12 19:55:20.831614: +2026-04-12 19:55:20.834238: Epoch 1929 +2026-04-12 19:55:20.837401: Current learning rate: 0.00553 +2026-04-12 19:57:01.075826: train_loss -0.3844 +2026-04-12 19:57:01.085330: val_loss -0.2969 +2026-04-12 19:57:01.087451: Pseudo dice [0.0, 0.0, 0.6098, 0.0, 0.1372, 0.4287, 0.6713] +2026-04-12 19:57:01.090040: Epoch time: 100.25 s +2026-04-12 19:57:02.343259: +2026-04-12 19:57:02.345778: Epoch 1930 +2026-04-12 19:57:02.348236: Current learning rate: 0.00553 +2026-04-12 19:58:42.563089: train_loss -0.3814 +2026-04-12 19:58:42.569213: val_loss -0.3975 +2026-04-12 19:58:42.571855: Pseudo dice [0.0, 0.0, 0.6276, 0.0, 0.4212, 0.6934, 0.8389] +2026-04-12 19:58:42.574400: Epoch time: 100.22 s +2026-04-12 19:58:43.801468: +2026-04-12 19:58:43.803683: Epoch 1931 +2026-04-12 19:58:43.806306: Current learning rate: 0.00552 +2026-04-12 20:00:25.070947: train_loss -0.4045 +2026-04-12 20:00:25.077934: val_loss -0.1872 +2026-04-12 20:00:25.081241: Pseudo dice [0.0, 0.0, 0.4807, 0.0213, 0.2144, 0.3901, 0.7319] +2026-04-12 20:00:25.085165: Epoch time: 101.27 s +2026-04-12 20:00:26.309133: +2026-04-12 20:00:26.311251: Epoch 1932 +2026-04-12 20:00:26.313323: Current learning rate: 0.00552 +2026-04-12 20:02:06.556867: train_loss -0.3976 +2026-04-12 20:02:06.563822: val_loss -0.3047 +2026-04-12 20:02:06.567276: Pseudo dice [0.0, 0.0, 0.7036, 0.0, 0.4588, 0.6961, 0.4159] +2026-04-12 20:02:06.570059: Epoch time: 100.25 s +2026-04-12 20:02:07.792419: +2026-04-12 20:02:07.794249: Epoch 1933 +2026-04-12 20:02:07.796430: Current learning rate: 0.00552 +2026-04-12 20:03:48.029144: train_loss -0.3629 +2026-04-12 20:03:48.038497: val_loss -0.316 +2026-04-12 20:03:48.041070: Pseudo dice [0.0, 0.0, 0.7348, 0.0, 0.0, 0.582, 0.4857] +2026-04-12 20:03:48.044492: Epoch time: 100.24 s +2026-04-12 20:03:49.282427: +2026-04-12 20:03:49.285006: Epoch 1934 +2026-04-12 20:03:49.287231: Current learning rate: 0.00552 +2026-04-12 20:05:29.448443: train_loss -0.3622 +2026-04-12 20:05:29.455334: val_loss -0.2712 +2026-04-12 20:05:29.457289: Pseudo dice [0.0, 0.0, 0.681, 0.0456, 0.0, 0.7367, 0.7068] +2026-04-12 20:05:29.459882: Epoch time: 100.17 s +2026-04-12 20:05:30.679092: +2026-04-12 20:05:30.681064: Epoch 1935 +2026-04-12 20:05:30.683622: Current learning rate: 0.00552 +2026-04-12 20:07:12.208643: train_loss -0.3609 +2026-04-12 20:07:12.216036: val_loss -0.4044 +2026-04-12 20:07:12.219688: Pseudo dice [0.0, 0.0, 0.7341, 0.0408, 0.1254, 0.8124, 0.855] +2026-04-12 20:07:12.224416: Epoch time: 101.53 s +2026-04-12 20:07:13.462085: +2026-04-12 20:07:13.465458: Epoch 1936 +2026-04-12 20:07:13.468742: Current learning rate: 0.00551 +2026-04-12 20:08:53.679349: train_loss -0.3741 +2026-04-12 20:08:53.685807: val_loss -0.3205 +2026-04-12 20:08:53.687887: Pseudo dice [0.0, 0.0, 0.6048, 0.0, 0.0863, 0.8382, 0.8501] +2026-04-12 20:08:53.689992: Epoch time: 100.22 s +2026-04-12 20:08:54.913466: +2026-04-12 20:08:54.915337: Epoch 1937 +2026-04-12 20:08:54.917510: Current learning rate: 0.00551 +2026-04-12 20:10:35.080743: train_loss -0.4001 +2026-04-12 20:10:35.088455: val_loss -0.3124 +2026-04-12 20:10:35.091903: Pseudo dice [0.0, 0.0, 0.7747, 0.0616, 0.3568, 0.8007, 0.6201] +2026-04-12 20:10:35.095159: Epoch time: 100.17 s +2026-04-12 20:10:36.341968: +2026-04-12 20:10:36.344423: Epoch 1938 +2026-04-12 20:10:36.347301: Current learning rate: 0.00551 +2026-04-12 20:12:16.430691: train_loss -0.3782 +2026-04-12 20:12:16.436897: val_loss -0.3876 +2026-04-12 20:12:16.439303: Pseudo dice [0.0, 0.0, 0.7063, 0.6981, 0.1796, 0.7988, 0.7669] +2026-04-12 20:12:16.442168: Epoch time: 100.09 s +2026-04-12 20:12:17.664539: +2026-04-12 20:12:17.666557: Epoch 1939 +2026-04-12 20:12:17.669036: Current learning rate: 0.00551 +2026-04-12 20:13:58.168442: train_loss -0.3909 +2026-04-12 20:13:58.177386: val_loss -0.3749 +2026-04-12 20:13:58.181456: Pseudo dice [0.0, 0.0, 0.7992, 0.0, 0.3896, 0.8257, 0.8252] +2026-04-12 20:13:58.185003: Epoch time: 100.51 s +2026-04-12 20:13:59.423619: +2026-04-12 20:13:59.426719: Epoch 1940 +2026-04-12 20:13:59.429176: Current learning rate: 0.0055 +2026-04-12 20:15:39.931799: train_loss -0.3959 +2026-04-12 20:15:39.938936: val_loss -0.3849 +2026-04-12 20:15:39.941185: Pseudo dice [0.0, 0.0, 0.5294, 0.0, 0.3293, 0.6946, 0.7527] +2026-04-12 20:15:39.944402: Epoch time: 100.51 s +2026-04-12 20:15:41.190965: +2026-04-12 20:15:41.193411: Epoch 1941 +2026-04-12 20:15:41.195612: Current learning rate: 0.0055 +2026-04-12 20:17:21.120900: train_loss -0.3919 +2026-04-12 20:17:21.129122: val_loss -0.3674 +2026-04-12 20:17:21.133202: Pseudo dice [0.0, 0.0, 0.4209, 0.0, 0.0, 0.4823, 0.6919] +2026-04-12 20:17:21.136639: Epoch time: 99.93 s +2026-04-12 20:17:22.370310: +2026-04-12 20:17:22.373341: Epoch 1942 +2026-04-12 20:17:22.375899: Current learning rate: 0.0055 +2026-04-12 20:19:02.542792: train_loss -0.3658 +2026-04-12 20:19:02.555697: val_loss -0.3509 +2026-04-12 20:19:02.558947: Pseudo dice [0.0, 0.0, 0.4553, 0.1063, 0.0, 0.8194, 0.7988] +2026-04-12 20:19:02.563061: Epoch time: 100.18 s +2026-04-12 20:19:03.797848: +2026-04-12 20:19:03.800843: Epoch 1943 +2026-04-12 20:19:03.804144: Current learning rate: 0.0055 +2026-04-12 20:20:44.026345: train_loss -0.3355 +2026-04-12 20:20:44.035006: val_loss -0.3952 +2026-04-12 20:20:44.036840: Pseudo dice [0.0, 0.0, 0.773, 0.0, 0.0, 0.7349, 0.5912] +2026-04-12 20:20:44.040565: Epoch time: 100.23 s +2026-04-12 20:20:45.273735: +2026-04-12 20:20:45.275732: Epoch 1944 +2026-04-12 20:20:45.277730: Current learning rate: 0.00549 +2026-04-12 20:22:25.913435: train_loss -0.3968 +2026-04-12 20:22:25.919625: val_loss -0.3851 +2026-04-12 20:22:25.921936: Pseudo dice [0.0, 0.0, 0.7042, 0.0, 0.0, 0.8662, 0.7536] +2026-04-12 20:22:25.924913: Epoch time: 100.64 s +2026-04-12 20:22:27.160610: +2026-04-12 20:22:27.162709: Epoch 1945 +2026-04-12 20:22:27.165039: Current learning rate: 0.00549 +2026-04-12 20:24:07.473216: train_loss -0.402 +2026-04-12 20:24:07.479172: val_loss -0.3977 +2026-04-12 20:24:07.481554: Pseudo dice [0.0, 0.0, 0.7722, 0.7933, 0.2784, 0.739, 0.8756] +2026-04-12 20:24:07.484056: Epoch time: 100.32 s +2026-04-12 20:24:08.733840: +2026-04-12 20:24:08.736082: Epoch 1946 +2026-04-12 20:24:08.738307: Current learning rate: 0.00549 +2026-04-12 20:25:48.837596: train_loss -0.4035 +2026-04-12 20:25:48.843109: val_loss -0.3377 +2026-04-12 20:25:48.845288: Pseudo dice [0.0, 0.0, 0.6034, 0.0, 0.0, 0.7785, 0.2847] +2026-04-12 20:25:48.847619: Epoch time: 100.11 s +2026-04-12 20:25:51.122249: +2026-04-12 20:25:51.124560: Epoch 1947 +2026-04-12 20:25:51.126893: Current learning rate: 0.00549 +2026-04-12 20:27:31.327385: train_loss -0.3895 +2026-04-12 20:27:31.336961: val_loss -0.2911 +2026-04-12 20:27:31.339501: Pseudo dice [0.0, 0.0, 0.6189, 0.0, 0.0, 0.6407, 0.4455] +2026-04-12 20:27:31.343323: Epoch time: 100.21 s +2026-04-12 20:27:32.855969: +2026-04-12 20:27:32.857871: Epoch 1948 +2026-04-12 20:27:32.860072: Current learning rate: 0.00548 +2026-04-12 20:29:13.206029: train_loss -0.3724 +2026-04-12 20:29:13.216188: val_loss -0.4061 +2026-04-12 20:29:13.220130: Pseudo dice [0.0, 0.0, 0.6362, 0.0, 0.1395, 0.624, 0.6607] +2026-04-12 20:29:13.224900: Epoch time: 100.35 s +2026-04-12 20:29:14.471325: +2026-04-12 20:29:14.474083: Epoch 1949 +2026-04-12 20:29:14.476997: Current learning rate: 0.00548 +2026-04-12 20:30:54.878497: train_loss -0.3993 +2026-04-12 20:30:54.886210: val_loss -0.3649 +2026-04-12 20:30:54.888198: Pseudo dice [0.0, 0.0, 0.8227, 0.0, 0.1162, 0.7202, 0.6983] +2026-04-12 20:30:54.890471: Epoch time: 100.41 s +2026-04-12 20:30:57.854993: +2026-04-12 20:30:57.857790: Epoch 1950 +2026-04-12 20:30:57.860251: Current learning rate: 0.00548 +2026-04-12 20:32:37.818624: train_loss -0.3558 +2026-04-12 20:32:37.824310: val_loss -0.2943 +2026-04-12 20:32:37.826579: Pseudo dice [0.0, 0.0, 0.5833, 0.0, 0.2002, 0.3813, 0.0397] +2026-04-12 20:32:37.830528: Epoch time: 99.97 s +2026-04-12 20:32:39.056748: +2026-04-12 20:32:39.059039: Epoch 1951 +2026-04-12 20:32:39.061437: Current learning rate: 0.00548 +2026-04-12 20:34:19.643262: train_loss -0.3258 +2026-04-12 20:34:19.650436: val_loss -0.3481 +2026-04-12 20:34:19.652726: Pseudo dice [0.0, 0.0, 0.7222, 0.0, 0.0973, 0.5382, 0.0158] +2026-04-12 20:34:19.655724: Epoch time: 100.59 s +2026-04-12 20:34:20.865366: +2026-04-12 20:34:20.868201: Epoch 1952 +2026-04-12 20:34:20.870381: Current learning rate: 0.00547 +2026-04-12 20:36:01.565688: train_loss -0.3601 +2026-04-12 20:36:01.575052: val_loss -0.2659 +2026-04-12 20:36:01.578501: Pseudo dice [0.0, 0.0, 0.4742, 0.0, 0.0, 0.0087, 0.056] +2026-04-12 20:36:01.581730: Epoch time: 100.7 s +2026-04-12 20:36:02.803119: +2026-04-12 20:36:02.805401: Epoch 1953 +2026-04-12 20:36:02.807748: Current learning rate: 0.00547 +2026-04-12 20:37:43.046163: train_loss -0.3275 +2026-04-12 20:37:43.053150: val_loss -0.3684 +2026-04-12 20:37:43.058037: Pseudo dice [0.0, 0.0, 0.6374, 0.0, 0.0, 0.6325, 0.4402] +2026-04-12 20:37:43.061481: Epoch time: 100.25 s +2026-04-12 20:37:44.283525: +2026-04-12 20:37:44.285359: Epoch 1954 +2026-04-12 20:37:44.287404: Current learning rate: 0.00547 +2026-04-12 20:39:24.492409: train_loss -0.3637 +2026-04-12 20:39:24.497282: val_loss -0.3402 +2026-04-12 20:39:24.499281: Pseudo dice [0.0, 0.0, 0.6725, 0.0, 0.3131, 0.6048, 0.6669] +2026-04-12 20:39:24.501480: Epoch time: 100.21 s +2026-04-12 20:39:25.717670: +2026-04-12 20:39:25.719405: Epoch 1955 +2026-04-12 20:39:25.721370: Current learning rate: 0.00547 +2026-04-12 20:41:06.504498: train_loss -0.3712 +2026-04-12 20:41:06.511402: val_loss -0.4061 +2026-04-12 20:41:06.513341: Pseudo dice [0.0, 0.0, 0.6546, 0.4943, 0.2275, 0.4778, 0.7855] +2026-04-12 20:41:06.518544: Epoch time: 100.79 s +2026-04-12 20:41:07.804922: +2026-04-12 20:41:07.807002: Epoch 1956 +2026-04-12 20:41:07.809263: Current learning rate: 0.00546 +2026-04-12 20:42:48.711188: train_loss -0.3711 +2026-04-12 20:42:48.718802: val_loss -0.3571 +2026-04-12 20:42:48.720904: Pseudo dice [0.0, 0.0, 0.6947, 0.8424, 0.1789, 0.6863, 0.8347] +2026-04-12 20:42:48.724464: Epoch time: 100.91 s +2026-04-12 20:42:49.965488: +2026-04-12 20:42:49.967352: Epoch 1957 +2026-04-12 20:42:49.969543: Current learning rate: 0.00546 +2026-04-12 20:44:30.322310: train_loss -0.3872 +2026-04-12 20:44:30.331076: val_loss -0.3336 +2026-04-12 20:44:30.333203: Pseudo dice [0.0, 0.0, 0.7395, 0.0322, 0.2707, 0.5823, 0.6095] +2026-04-12 20:44:30.336310: Epoch time: 100.36 s +2026-04-12 20:44:31.577635: +2026-04-12 20:44:31.579499: Epoch 1958 +2026-04-12 20:44:31.581562: Current learning rate: 0.00546 +2026-04-12 20:46:11.817296: train_loss -0.3887 +2026-04-12 20:46:11.822693: val_loss -0.4203 +2026-04-12 20:46:11.824921: Pseudo dice [0.0, 0.0, 0.6643, 0.598, 0.3958, 0.7049, 0.8235] +2026-04-12 20:46:11.828005: Epoch time: 100.24 s +2026-04-12 20:46:13.043832: +2026-04-12 20:46:13.046434: Epoch 1959 +2026-04-12 20:46:13.049322: Current learning rate: 0.00546 +2026-04-12 20:47:53.337792: train_loss -0.3936 +2026-04-12 20:47:53.343925: val_loss -0.401 +2026-04-12 20:47:53.346924: Pseudo dice [0.0, 0.0, 0.5604, 0.5349, 0.5971, 0.637, 0.7324] +2026-04-12 20:47:53.349912: Epoch time: 100.3 s +2026-04-12 20:47:54.594077: +2026-04-12 20:47:54.596486: Epoch 1960 +2026-04-12 20:47:54.599636: Current learning rate: 0.00546 +2026-04-12 20:49:34.736294: train_loss -0.4081 +2026-04-12 20:49:34.743144: val_loss -0.3937 +2026-04-12 20:49:34.746111: Pseudo dice [0.0, 0.0, 0.5506, 0.254, 0.467, 0.3636, 0.5358] +2026-04-12 20:49:34.749148: Epoch time: 100.15 s +2026-04-12 20:49:35.968635: +2026-04-12 20:49:35.970811: Epoch 1961 +2026-04-12 20:49:35.973036: Current learning rate: 0.00545 +2026-04-12 20:51:16.107432: train_loss -0.3974 +2026-04-12 20:51:16.117443: val_loss -0.392 +2026-04-12 20:51:16.120574: Pseudo dice [0.0, 0.0, 0.6552, 0.0, 0.2717, 0.6801, 0.6196] +2026-04-12 20:51:16.123705: Epoch time: 100.14 s +2026-04-12 20:51:17.378800: +2026-04-12 20:51:17.380713: Epoch 1962 +2026-04-12 20:51:17.382689: Current learning rate: 0.00545 +2026-04-12 20:52:57.905953: train_loss -0.3907 +2026-04-12 20:52:57.913002: val_loss -0.2909 +2026-04-12 20:52:57.915025: Pseudo dice [0.0, 0.0, 0.4734, 0.0664, 0.1983, 0.3616, 0.4069] +2026-04-12 20:52:57.917358: Epoch time: 100.53 s +2026-04-12 20:52:59.189885: +2026-04-12 20:52:59.192031: Epoch 1963 +2026-04-12 20:52:59.194512: Current learning rate: 0.00545 +2026-04-12 20:54:39.540342: train_loss -0.3738 +2026-04-12 20:54:39.547787: val_loss -0.3144 +2026-04-12 20:54:39.550570: Pseudo dice [0.0, 0.0, 0.4085, 0.0, 0.0, 0.4354, 0.3139] +2026-04-12 20:54:39.553615: Epoch time: 100.35 s +2026-04-12 20:54:40.790137: +2026-04-12 20:54:40.792331: Epoch 1964 +2026-04-12 20:54:40.794253: Current learning rate: 0.00545 +2026-04-12 20:56:20.949870: train_loss -0.3255 +2026-04-12 20:56:20.956424: val_loss -0.2997 +2026-04-12 20:56:20.959288: Pseudo dice [0.0, 0.0, 0.615, 0.003, 0.0, 0.0783, 0.6399] +2026-04-12 20:56:20.962322: Epoch time: 100.16 s +2026-04-12 20:56:22.197738: +2026-04-12 20:56:22.199932: Epoch 1965 +2026-04-12 20:56:22.202204: Current learning rate: 0.00544 +2026-04-12 20:58:02.802645: train_loss -0.3477 +2026-04-12 20:58:02.829454: val_loss -0.3709 +2026-04-12 20:58:02.831788: Pseudo dice [0.0, 0.0, 0.728, 0.0, 0.3289, 0.4411, 0.6545] +2026-04-12 20:58:02.833979: Epoch time: 100.61 s +2026-04-12 20:58:04.068729: +2026-04-12 20:58:04.072130: Epoch 1966 +2026-04-12 20:58:04.076083: Current learning rate: 0.00544 +2026-04-12 20:59:45.406848: train_loss -0.3908 +2026-04-12 20:59:45.412486: val_loss -0.378 +2026-04-12 20:59:45.415122: Pseudo dice [0.0, 0.0, 0.695, 0.0, 0.0, 0.7173, 0.7222] +2026-04-12 20:59:45.418084: Epoch time: 101.34 s +2026-04-12 20:59:46.663608: +2026-04-12 20:59:46.665689: Epoch 1967 +2026-04-12 20:59:46.667829: Current learning rate: 0.00544 +2026-04-12 21:01:26.945757: train_loss -0.3887 +2026-04-12 21:01:26.951095: val_loss -0.2546 +2026-04-12 21:01:26.953444: Pseudo dice [0.0, 0.0, 0.4754, 0.0181, 0.0, 0.1078, 0.6995] +2026-04-12 21:01:26.956299: Epoch time: 100.29 s +2026-04-12 21:01:28.178442: +2026-04-12 21:01:28.180846: Epoch 1968 +2026-04-12 21:01:28.183246: Current learning rate: 0.00544 +2026-04-12 21:03:08.680485: train_loss -0.3503 +2026-04-12 21:03:08.687649: val_loss -0.3607 +2026-04-12 21:03:08.689927: Pseudo dice [0.0, 0.0, 0.4945, 0.0, 0.2186, 0.2466, 0.6679] +2026-04-12 21:03:08.692463: Epoch time: 100.51 s +2026-04-12 21:03:09.937932: +2026-04-12 21:03:09.940116: Epoch 1969 +2026-04-12 21:03:09.942438: Current learning rate: 0.00543 +2026-04-12 21:04:50.417053: train_loss -0.3698 +2026-04-12 21:04:50.422602: val_loss -0.2579 +2026-04-12 21:04:50.425374: Pseudo dice [0.0, 0.0, 0.4548, 0.0, 0.1139, 0.6654, 0.6473] +2026-04-12 21:04:50.428153: Epoch time: 100.48 s +2026-04-12 21:04:51.648358: +2026-04-12 21:04:51.650228: Epoch 1970 +2026-04-12 21:04:51.652196: Current learning rate: 0.00543 +2026-04-12 21:06:32.091517: train_loss -0.3761 +2026-04-12 21:06:32.097297: val_loss -0.3403 +2026-04-12 21:06:32.099905: Pseudo dice [0.0, 0.0, 0.6631, 0.0977, 0.2928, 0.5959, 0.5878] +2026-04-12 21:06:32.102189: Epoch time: 100.45 s +2026-04-12 21:06:33.363522: +2026-04-12 21:06:33.365373: Epoch 1971 +2026-04-12 21:06:33.367749: Current learning rate: 0.00543 +2026-04-12 21:08:13.640941: train_loss -0.3813 +2026-04-12 21:08:13.647430: val_loss -0.3751 +2026-04-12 21:08:13.652759: Pseudo dice [0.0, 0.0, 0.5353, 0.6982, 0.0, 0.3347, 0.7575] +2026-04-12 21:08:13.656744: Epoch time: 100.28 s +2026-04-12 21:08:14.922851: +2026-04-12 21:08:14.925304: Epoch 1972 +2026-04-12 21:08:14.928871: Current learning rate: 0.00543 +2026-04-12 21:09:55.140429: train_loss -0.3448 +2026-04-12 21:09:55.146805: val_loss -0.3076 +2026-04-12 21:09:55.148895: Pseudo dice [0.0, 0.0, 0.6701, 0.0, 0.1301, 0.6381, 0.2117] +2026-04-12 21:09:55.151186: Epoch time: 100.22 s +2026-04-12 21:09:56.405157: +2026-04-12 21:09:56.407194: Epoch 1973 +2026-04-12 21:09:56.409813: Current learning rate: 0.00542 +2026-04-12 21:11:36.966168: train_loss -0.3375 +2026-04-12 21:11:36.973254: val_loss -0.3417 +2026-04-12 21:11:36.975313: Pseudo dice [0.0, 0.0, 0.6614, 0.1546, 0.0, 0.7777, 0.5092] +2026-04-12 21:11:36.977996: Epoch time: 100.56 s +2026-04-12 21:11:38.229320: +2026-04-12 21:11:38.231610: Epoch 1974 +2026-04-12 21:11:38.233926: Current learning rate: 0.00542 +2026-04-12 21:13:18.550145: train_loss -0.3479 +2026-04-12 21:13:18.555595: val_loss -0.3583 +2026-04-12 21:13:18.558085: Pseudo dice [0.0, 0.0, 0.6848, 0.0, 0.0, 0.7289, 0.5781] +2026-04-12 21:13:18.560529: Epoch time: 100.32 s +2026-04-12 21:13:19.797391: +2026-04-12 21:13:19.799747: Epoch 1975 +2026-04-12 21:13:19.802167: Current learning rate: 0.00542 +2026-04-12 21:15:00.029058: train_loss -0.345 +2026-04-12 21:15:00.037292: val_loss -0.362 +2026-04-12 21:15:00.040566: Pseudo dice [0.0, 0.0, 0.637, 0.0, 0.0, 0.5066, 0.6143] +2026-04-12 21:15:00.042922: Epoch time: 100.23 s +2026-04-12 21:15:01.272012: +2026-04-12 21:15:01.273852: Epoch 1976 +2026-04-12 21:15:01.276323: Current learning rate: 0.00542 +2026-04-12 21:16:41.865516: train_loss -0.368 +2026-04-12 21:16:41.871725: val_loss -0.31 +2026-04-12 21:16:41.874175: Pseudo dice [0.0, 0.0, 0.6118, 0.0, 0.0, 0.364, 0.7411] +2026-04-12 21:16:41.876731: Epoch time: 100.6 s +2026-04-12 21:16:43.104051: +2026-04-12 21:16:43.106472: Epoch 1977 +2026-04-12 21:16:43.108561: Current learning rate: 0.00541 +2026-04-12 21:18:23.945068: train_loss -0.3728 +2026-04-12 21:18:23.952760: val_loss -0.362 +2026-04-12 21:18:23.957399: Pseudo dice [0.0, 0.0, 0.7278, 0.5376, 0.0, 0.5358, 0.3757] +2026-04-12 21:18:23.963385: Epoch time: 100.84 s +2026-04-12 21:18:25.197014: +2026-04-12 21:18:25.199545: Epoch 1978 +2026-04-12 21:18:25.204361: Current learning rate: 0.00541 +2026-04-12 21:20:05.236620: train_loss -0.3716 +2026-04-12 21:20:05.244226: val_loss -0.3776 +2026-04-12 21:20:05.247458: Pseudo dice [0.0, 0.0, 0.5882, 0.5487, 0.2815, 0.7527, 0.7652] +2026-04-12 21:20:05.250788: Epoch time: 100.04 s +2026-04-12 21:20:06.464396: +2026-04-12 21:20:06.466542: Epoch 1979 +2026-04-12 21:20:06.468758: Current learning rate: 0.00541 +2026-04-12 21:21:46.604246: train_loss -0.4042 +2026-04-12 21:21:46.612467: val_loss -0.3493 +2026-04-12 21:21:46.614863: Pseudo dice [0.0, 0.0, 0.6421, 0.0938, 0.3967, 0.6622, 0.6653] +2026-04-12 21:21:46.624753: Epoch time: 100.14 s +2026-04-12 21:21:47.852331: +2026-04-12 21:21:47.854983: Epoch 1980 +2026-04-12 21:21:47.857538: Current learning rate: 0.00541 +2026-04-12 21:23:28.582603: train_loss -0.3991 +2026-04-12 21:23:28.591840: val_loss -0.3614 +2026-04-12 21:23:28.594174: Pseudo dice [0.0, 0.0, 0.6877, 0.751, 0.399, 0.7042, 0.4656] +2026-04-12 21:23:28.596639: Epoch time: 100.73 s +2026-04-12 21:23:29.824030: +2026-04-12 21:23:29.826333: Epoch 1981 +2026-04-12 21:23:29.828588: Current learning rate: 0.0054 +2026-04-12 21:25:09.947155: train_loss -0.3996 +2026-04-12 21:25:09.954165: val_loss -0.3876 +2026-04-12 21:25:09.956689: Pseudo dice [0.0, 0.0, 0.7611, 0.0, 0.2323, 0.5154, 0.7007] +2026-04-12 21:25:09.959534: Epoch time: 100.13 s +2026-04-12 21:25:11.208903: +2026-04-12 21:25:11.210850: Epoch 1982 +2026-04-12 21:25:11.216645: Current learning rate: 0.0054 +2026-04-12 21:26:51.368640: train_loss -0.3603 +2026-04-12 21:26:51.375582: val_loss -0.3453 +2026-04-12 21:26:51.379666: Pseudo dice [0.0, 0.0, 0.6468, 0.2441, 0.0, 0.4384, 0.5706] +2026-04-12 21:26:51.385482: Epoch time: 100.16 s +2026-04-12 21:26:52.631070: +2026-04-12 21:26:52.636337: Epoch 1983 +2026-04-12 21:26:52.639100: Current learning rate: 0.0054 +2026-04-12 21:28:33.124819: train_loss -0.3696 +2026-04-12 21:28:33.131962: val_loss -0.3033 +2026-04-12 21:28:33.134510: Pseudo dice [0.0, 0.0, 0.6882, 0.0, 0.0706, 0.674, 0.6764] +2026-04-12 21:28:33.137473: Epoch time: 100.5 s +2026-04-12 21:28:34.395667: +2026-04-12 21:28:34.397497: Epoch 1984 +2026-04-12 21:28:34.399654: Current learning rate: 0.0054 +2026-04-12 21:30:14.583445: train_loss -0.3741 +2026-04-12 21:30:14.592819: val_loss -0.3275 +2026-04-12 21:30:14.598187: Pseudo dice [0.0, 0.0, 0.5778, 0.0, 0.3222, 0.6885, 0.816] +2026-04-12 21:30:14.601861: Epoch time: 100.19 s +2026-04-12 21:30:15.824970: +2026-04-12 21:30:15.827589: Epoch 1985 +2026-04-12 21:30:15.830659: Current learning rate: 0.0054 +2026-04-12 21:31:56.049049: train_loss -0.3682 +2026-04-12 21:31:56.054315: val_loss -0.3815 +2026-04-12 21:31:56.056492: Pseudo dice [0.0, 0.0, 0.2924, 0.0452, 0.0, 0.5363, 0.4587] +2026-04-12 21:31:56.058531: Epoch time: 100.23 s +2026-04-12 21:31:58.317888: +2026-04-12 21:31:58.319969: Epoch 1986 +2026-04-12 21:31:58.322205: Current learning rate: 0.00539 +2026-04-12 21:33:38.959025: train_loss -0.3918 +2026-04-12 21:33:38.964083: val_loss -0.2381 +2026-04-12 21:33:38.966217: Pseudo dice [0.0, 0.0, 0.6924, 0.0, 0.4372, 0.4119, 0.6796] +2026-04-12 21:33:38.968710: Epoch time: 100.64 s +2026-04-12 21:33:40.199061: +2026-04-12 21:33:40.200978: Epoch 1987 +2026-04-12 21:33:40.203054: Current learning rate: 0.00539 +2026-04-12 21:35:20.383918: train_loss -0.3612 +2026-04-12 21:35:20.391803: val_loss -0.3633 +2026-04-12 21:35:20.395251: Pseudo dice [0.0, 0.0, 0.4545, 0.0172, 0.4168, 0.7364, 0.571] +2026-04-12 21:35:20.398031: Epoch time: 100.19 s +2026-04-12 21:35:21.630791: +2026-04-12 21:35:21.632889: Epoch 1988 +2026-04-12 21:35:21.636685: Current learning rate: 0.00539 +2026-04-12 21:37:02.187819: train_loss -0.391 +2026-04-12 21:37:02.196160: val_loss -0.3852 +2026-04-12 21:37:02.198287: Pseudo dice [0.0, 0.0, 0.7593, 0.9044, 0.4653, 0.8334, 0.5435] +2026-04-12 21:37:02.201869: Epoch time: 100.56 s +2026-04-12 21:37:03.421600: +2026-04-12 21:37:03.423557: Epoch 1989 +2026-04-12 21:37:03.425646: Current learning rate: 0.00539 +2026-04-12 21:38:43.556983: train_loss -0.3747 +2026-04-12 21:38:43.565537: val_loss -0.323 +2026-04-12 21:38:43.567730: Pseudo dice [0.0, 0.0, 0.6619, 0.0, 0.0004, 0.4912, 0.4641] +2026-04-12 21:38:43.571152: Epoch time: 100.14 s +2026-04-12 21:38:44.832685: +2026-04-12 21:38:44.835984: Epoch 1990 +2026-04-12 21:38:44.838724: Current learning rate: 0.00538 +2026-04-12 21:40:24.978041: train_loss -0.3756 +2026-04-12 21:40:24.984888: val_loss -0.2887 +2026-04-12 21:40:24.987419: Pseudo dice [0.0, 0.0, 0.5806, 0.061, 0.0002, 0.8156, 0.4377] +2026-04-12 21:40:24.990294: Epoch time: 100.15 s +2026-04-12 21:40:26.230127: +2026-04-12 21:40:26.233093: Epoch 1991 +2026-04-12 21:40:26.236441: Current learning rate: 0.00538 +2026-04-12 21:42:06.711078: train_loss -0.3842 +2026-04-12 21:42:06.717372: val_loss -0.397 +2026-04-12 21:42:06.719401: Pseudo dice [0.0, 0.0, 0.7787, 0.0, 0.2644, 0.7007, 0.6675] +2026-04-12 21:42:06.721850: Epoch time: 100.48 s +2026-04-12 21:42:07.939250: +2026-04-12 21:42:07.941941: Epoch 1992 +2026-04-12 21:42:07.946569: Current learning rate: 0.00538 +2026-04-12 21:43:48.030829: train_loss -0.3715 +2026-04-12 21:43:48.037590: val_loss -0.3427 +2026-04-12 21:43:48.040075: Pseudo dice [0.0, 0.0, 0.6442, 0.0, 0.2972, 0.6822, 0.7699] +2026-04-12 21:43:48.042841: Epoch time: 100.09 s +2026-04-12 21:43:49.286864: +2026-04-12 21:43:49.288756: Epoch 1993 +2026-04-12 21:43:49.291187: Current learning rate: 0.00538 +2026-04-12 21:45:29.395848: train_loss -0.3433 +2026-04-12 21:45:29.403063: val_loss -0.3413 +2026-04-12 21:45:29.405174: Pseudo dice [0.0, 0.0, 0.7419, 0.0, 0.0, 0.29, 0.7479] +2026-04-12 21:45:29.408331: Epoch time: 100.11 s +2026-04-12 21:45:30.646960: +2026-04-12 21:45:30.649105: Epoch 1994 +2026-04-12 21:45:30.651313: Current learning rate: 0.00537 +2026-04-12 21:47:11.398427: train_loss -0.3712 +2026-04-12 21:47:11.407353: val_loss -0.3883 +2026-04-12 21:47:11.410618: Pseudo dice [0.0, 0.0, 0.4723, 0.5489, 0.0148, 0.7425, 0.7241] +2026-04-12 21:47:11.413079: Epoch time: 100.75 s +2026-04-12 21:47:12.676578: +2026-04-12 21:47:12.678534: Epoch 1995 +2026-04-12 21:47:12.680810: Current learning rate: 0.00537 +2026-04-12 21:48:53.063922: train_loss -0.3665 +2026-04-12 21:48:53.072415: val_loss -0.2901 +2026-04-12 21:48:53.075107: Pseudo dice [0.0, 0.0, 0.7303, 0.0, 0.22, 0.2215, 0.352] +2026-04-12 21:48:53.077986: Epoch time: 100.39 s +2026-04-12 21:48:54.332643: +2026-04-12 21:48:54.337651: Epoch 1996 +2026-04-12 21:48:54.339773: Current learning rate: 0.00537 +2026-04-12 21:50:34.722404: train_loss -0.3381 +2026-04-12 21:50:34.733405: val_loss -0.2613 +2026-04-12 21:50:34.739828: Pseudo dice [0.0, 0.0, 0.6426, 0.0, 0.0502, 0.374, 0.8446] +2026-04-12 21:50:34.747880: Epoch time: 100.39 s +2026-04-12 21:50:35.990355: +2026-04-12 21:50:35.995122: Epoch 1997 +2026-04-12 21:50:35.997876: Current learning rate: 0.00537 +2026-04-12 21:52:16.188070: train_loss -0.3829 +2026-04-12 21:52:16.195862: val_loss -0.3156 +2026-04-12 21:52:16.198132: Pseudo dice [0.0, 0.0, 0.6676, 0.0293, 0.0, 0.7132, 0.6297] +2026-04-12 21:52:16.201740: Epoch time: 100.2 s +2026-04-12 21:52:17.448627: +2026-04-12 21:52:17.451126: Epoch 1998 +2026-04-12 21:52:17.454200: Current learning rate: 0.00536 +2026-04-12 21:53:58.101088: train_loss -0.3574 +2026-04-12 21:53:58.107371: val_loss -0.3469 +2026-04-12 21:53:58.109675: Pseudo dice [0.0, 0.0, 0.5605, 0.0, 0.2362, 0.1047, 0.6893] +2026-04-12 21:53:58.112229: Epoch time: 100.66 s +2026-04-12 21:53:59.355247: +2026-04-12 21:53:59.357447: Epoch 1999 +2026-04-12 21:53:59.359662: Current learning rate: 0.00536 +2026-04-12 21:55:40.329536: train_loss -0.3782 +2026-04-12 21:55:40.337272: val_loss -0.328 +2026-04-12 21:55:40.341460: Pseudo dice [0.0, 0.0, 0.4525, 0.2594, 0.0914, 0.6789, 0.7455] +2026-04-12 21:55:40.345400: Epoch time: 100.98 s +2026-04-12 21:55:43.321974: +2026-04-12 21:55:43.324585: Epoch 2000 +2026-04-12 21:55:43.327668: Current learning rate: 0.00536 +2026-04-12 21:57:23.602790: train_loss -0.399 +2026-04-12 21:57:23.609354: val_loss -0.3401 +2026-04-12 21:57:23.612884: Pseudo dice [0.0, 0.0, 0.6097, 0.0895, 0.2689, 0.7018, 0.7834] +2026-04-12 21:57:23.615306: Epoch time: 100.28 s +2026-04-12 21:57:24.857601: +2026-04-12 21:57:24.860444: Epoch 2001 +2026-04-12 21:57:24.862651: Current learning rate: 0.00536 +2026-04-12 21:59:04.973697: train_loss -0.39 +2026-04-12 21:59:04.980413: val_loss -0.3854 +2026-04-12 21:59:05.003485: Pseudo dice [0.0, 0.0, 0.7724, 0.151, 0.2453, 0.2111, 0.8207] +2026-04-12 21:59:05.006033: Epoch time: 100.12 s +2026-04-12 21:59:06.266548: +2026-04-12 21:59:06.268665: Epoch 2002 +2026-04-12 21:59:06.271788: Current learning rate: 0.00535 +2026-04-12 22:00:47.256940: train_loss -0.4035 +2026-04-12 22:00:47.263583: val_loss -0.337 +2026-04-12 22:00:47.265889: Pseudo dice [0.0, 0.0, 0.6455, 0.0577, 0.0, 0.7573, 0.6757] +2026-04-12 22:00:47.268318: Epoch time: 100.99 s +2026-04-12 22:00:48.498476: +2026-04-12 22:00:48.500615: Epoch 2003 +2026-04-12 22:00:48.502782: Current learning rate: 0.00535 +2026-04-12 22:02:29.813355: train_loss -0.3798 +2026-04-12 22:02:29.822706: val_loss -0.3174 +2026-04-12 22:02:29.826266: Pseudo dice [0.0, 0.0, 0.7091, 0.1433, 0.0738, 0.6325, 0.8398] +2026-04-12 22:02:29.828820: Epoch time: 101.32 s +2026-04-12 22:02:31.052146: +2026-04-12 22:02:31.054109: Epoch 2004 +2026-04-12 22:02:31.056212: Current learning rate: 0.00535 +2026-04-12 22:04:10.968200: train_loss -0.3666 +2026-04-12 22:04:10.980819: val_loss -0.2891 +2026-04-12 22:04:10.983124: Pseudo dice [0.0, 0.0, 0.5069, 0.0, 0.3167, 0.1588, 0.5777] +2026-04-12 22:04:10.986549: Epoch time: 99.92 s +2026-04-12 22:04:13.256579: +2026-04-12 22:04:13.259488: Epoch 2005 +2026-04-12 22:04:13.263074: Current learning rate: 0.00535 +2026-04-12 22:05:53.654461: train_loss -0.3859 +2026-04-12 22:05:53.664040: val_loss -0.4106 +2026-04-12 22:05:53.666836: Pseudo dice [0.0, 0.0, 0.6184, 0.0, 0.1411, 0.8323, 0.7026] +2026-04-12 22:05:53.669458: Epoch time: 100.4 s +2026-04-12 22:05:54.905107: +2026-04-12 22:05:54.907701: Epoch 2006 +2026-04-12 22:05:54.910074: Current learning rate: 0.00534 +2026-04-12 22:07:35.797014: train_loss -0.3896 +2026-04-12 22:07:35.804977: val_loss -0.3429 +2026-04-12 22:07:35.807793: Pseudo dice [0.0, 0.0, 0.5864, 0.0, 0.3569, 0.7638, 0.7022] +2026-04-12 22:07:35.810109: Epoch time: 100.89 s +2026-04-12 22:07:37.043208: +2026-04-12 22:07:37.045130: Epoch 2007 +2026-04-12 22:07:37.047361: Current learning rate: 0.00534 +2026-04-12 22:09:17.132043: train_loss -0.3796 +2026-04-12 22:09:17.163753: val_loss -0.3588 +2026-04-12 22:09:17.166421: Pseudo dice [0.0, 0.0, 0.6873, 0.3133, 0.0, 0.737, 0.5956] +2026-04-12 22:09:17.168593: Epoch time: 100.09 s +2026-04-12 22:09:18.383986: +2026-04-12 22:09:18.385802: Epoch 2008 +2026-04-12 22:09:18.388153: Current learning rate: 0.00534 +2026-04-12 22:10:58.458404: train_loss -0.3816 +2026-04-12 22:10:58.464541: val_loss -0.3004 +2026-04-12 22:10:58.466862: Pseudo dice [0.0, 0.0, 0.6248, 0.0, 0.0, 0.769, 0.6964] +2026-04-12 22:10:58.469229: Epoch time: 100.08 s +2026-04-12 22:10:59.697751: +2026-04-12 22:10:59.700075: Epoch 2009 +2026-04-12 22:10:59.702358: Current learning rate: 0.00534 +2026-04-12 22:12:39.871453: train_loss -0.3776 +2026-04-12 22:12:39.879032: val_loss -0.3939 +2026-04-12 22:12:39.881034: Pseudo dice [0.0, 0.0, 0.7922, 0.4067, 0.0, 0.6752, 0.8044] +2026-04-12 22:12:39.883547: Epoch time: 100.18 s +2026-04-12 22:12:41.111988: +2026-04-12 22:12:41.114787: Epoch 2010 +2026-04-12 22:12:41.119654: Current learning rate: 0.00533 +2026-04-12 22:14:21.391869: train_loss -0.3854 +2026-04-12 22:14:21.400724: val_loss -0.4022 +2026-04-12 22:14:21.403863: Pseudo dice [0.0, 0.0, 0.4872, 0.0, 0.0, 0.6126, 0.7714] +2026-04-12 22:14:21.407652: Epoch time: 100.28 s +2026-04-12 22:14:22.631267: +2026-04-12 22:14:22.635185: Epoch 2011 +2026-04-12 22:14:22.637756: Current learning rate: 0.00533 +2026-04-12 22:16:02.920727: train_loss -0.3568 +2026-04-12 22:16:02.926319: val_loss -0.3276 +2026-04-12 22:16:02.928704: Pseudo dice [0.0, 0.0, 0.5805, 0.0, 0.0569, 0.4743, 0.8092] +2026-04-12 22:16:02.931014: Epoch time: 100.29 s +2026-04-12 22:16:04.163134: +2026-04-12 22:16:04.165261: Epoch 2012 +2026-04-12 22:16:04.167316: Current learning rate: 0.00533 +2026-04-12 22:17:44.291792: train_loss -0.391 +2026-04-12 22:17:44.300550: val_loss -0.2949 +2026-04-12 22:17:44.302970: Pseudo dice [0.0, 0.0, 0.6931, 0.0, 0.3251, 0.7606, 0.6599] +2026-04-12 22:17:44.305875: Epoch time: 100.13 s +2026-04-12 22:17:45.538377: +2026-04-12 22:17:45.540357: Epoch 2013 +2026-04-12 22:17:45.542739: Current learning rate: 0.00533 +2026-04-12 22:19:25.880093: train_loss -0.383 +2026-04-12 22:19:25.888176: val_loss -0.2985 +2026-04-12 22:19:25.891117: Pseudo dice [0.0, 0.0, 0.7143, 0.0, 0.0244, 0.2323, 0.8102] +2026-04-12 22:19:25.895078: Epoch time: 100.34 s +2026-04-12 22:19:27.136738: +2026-04-12 22:19:27.139049: Epoch 2014 +2026-04-12 22:19:27.141409: Current learning rate: 0.00533 +2026-04-12 22:21:07.786428: train_loss -0.3858 +2026-04-12 22:21:07.794014: val_loss -0.3727 +2026-04-12 22:21:07.796131: Pseudo dice [0.0, 0.0, 0.7439, 0.0, 0.2322, 0.7901, 0.7772] +2026-04-12 22:21:07.798615: Epoch time: 100.65 s +2026-04-12 22:21:09.030688: +2026-04-12 22:21:09.032621: Epoch 2015 +2026-04-12 22:21:09.035019: Current learning rate: 0.00532 +2026-04-12 22:22:49.143388: train_loss -0.3976 +2026-04-12 22:22:49.149772: val_loss -0.3611 +2026-04-12 22:22:49.153320: Pseudo dice [0.0, 0.0, 0.7933, 0.0686, 0.44, 0.4906, 0.8205] +2026-04-12 22:22:49.155593: Epoch time: 100.12 s +2026-04-12 22:22:50.382599: +2026-04-12 22:22:50.398756: Epoch 2016 +2026-04-12 22:22:50.401370: Current learning rate: 0.00532 +2026-04-12 22:24:31.186309: train_loss -0.4092 +2026-04-12 22:24:31.194640: val_loss -0.3948 +2026-04-12 22:24:31.197423: Pseudo dice [0.0, 0.0, 0.6384, 0.812, 0.1636, 0.7423, 0.6889] +2026-04-12 22:24:31.201110: Epoch time: 100.81 s +2026-04-12 22:24:32.457897: +2026-04-12 22:24:32.460577: Epoch 2017 +2026-04-12 22:24:32.463655: Current learning rate: 0.00532 +2026-04-12 22:26:12.664953: train_loss -0.3575 +2026-04-12 22:26:12.672512: val_loss -0.316 +2026-04-12 22:26:12.674944: Pseudo dice [0.0, 0.0, 0.6862, 0.0159, 0.1209, 0.5729, 0.5757] +2026-04-12 22:26:12.678063: Epoch time: 100.21 s +2026-04-12 22:26:13.930966: +2026-04-12 22:26:13.933349: Epoch 2018 +2026-04-12 22:26:13.935836: Current learning rate: 0.00532 +2026-04-12 22:27:53.996636: train_loss -0.3803 +2026-04-12 22:27:54.003867: val_loss -0.3313 +2026-04-12 22:27:54.006516: Pseudo dice [0.2273, 0.0, 0.8149, 0.0, 0.3327, 0.2263, 0.608] +2026-04-12 22:27:54.009592: Epoch time: 100.07 s +2026-04-12 22:27:55.255883: +2026-04-12 22:27:55.258045: Epoch 2019 +2026-04-12 22:27:55.260440: Current learning rate: 0.00531 +2026-04-12 22:29:35.310081: train_loss -0.3977 +2026-04-12 22:29:35.316119: val_loss -0.3904 +2026-04-12 22:29:35.318337: Pseudo dice [0.0006, 0.0, 0.6018, 0.0, 0.0, 0.6813, 0.8382] +2026-04-12 22:29:35.320548: Epoch time: 100.06 s +2026-04-12 22:29:36.556344: +2026-04-12 22:29:36.558207: Epoch 2020 +2026-04-12 22:29:36.560286: Current learning rate: 0.00531 +2026-04-12 22:31:16.632456: train_loss -0.3568 +2026-04-12 22:31:16.640436: val_loss -0.2281 +2026-04-12 22:31:16.642765: Pseudo dice [0.0, 0.0, 0.529, 0.0, 0.3481, 0.1345, 0.0869] +2026-04-12 22:31:16.646000: Epoch time: 100.08 s +2026-04-12 22:31:17.887669: +2026-04-12 22:31:17.890443: Epoch 2021 +2026-04-12 22:31:17.894233: Current learning rate: 0.00531 +2026-04-12 22:32:58.530441: train_loss -0.3462 +2026-04-12 22:32:58.537379: val_loss -0.3741 +2026-04-12 22:32:58.539784: Pseudo dice [0.0, 0.0, 0.5459, 0.6305, 0.4875, 0.6721, 0.7773] +2026-04-12 22:32:58.542656: Epoch time: 100.65 s +2026-04-12 22:32:59.794517: +2026-04-12 22:32:59.796510: Epoch 2022 +2026-04-12 22:32:59.798681: Current learning rate: 0.00531 +2026-04-12 22:34:39.968524: train_loss -0.384 +2026-04-12 22:34:39.978498: val_loss -0.3985 +2026-04-12 22:34:39.981435: Pseudo dice [0.0, 0.0, 0.7136, 0.1994, 0.4072, 0.8608, 0.8142] +2026-04-12 22:34:39.985536: Epoch time: 100.18 s +2026-04-12 22:34:41.218033: +2026-04-12 22:34:41.220347: Epoch 2023 +2026-04-12 22:34:41.223455: Current learning rate: 0.0053 +2026-04-12 22:36:22.024720: train_loss -0.3832 +2026-04-12 22:36:22.032958: val_loss -0.3661 +2026-04-12 22:36:22.035892: Pseudo dice [0.0, 0.0, 0.5828, 0.0, 0.0581, 0.6905, 0.6474] +2026-04-12 22:36:22.039106: Epoch time: 100.81 s +2026-04-12 22:36:23.270503: +2026-04-12 22:36:23.272887: Epoch 2024 +2026-04-12 22:36:23.276223: Current learning rate: 0.0053 +2026-04-12 22:38:03.387937: train_loss -0.386 +2026-04-12 22:38:03.396218: val_loss -0.3184 +2026-04-12 22:38:03.398869: Pseudo dice [0.0, 0.0, 0.2744, 0.0, 0.0, 0.3843, 0.2365] +2026-04-12 22:38:03.401690: Epoch time: 100.12 s +2026-04-12 22:38:05.748782: +2026-04-12 22:38:05.752450: Epoch 2025 +2026-04-12 22:38:05.755125: Current learning rate: 0.0053 +2026-04-12 22:39:45.965886: train_loss -0.3089 +2026-04-12 22:39:45.972494: val_loss -0.3363 +2026-04-12 22:39:45.975792: Pseudo dice [0.0, 0.0, 0.6158, 0.0, 0.0, 0.6096, 0.2949] +2026-04-12 22:39:45.979363: Epoch time: 100.22 s +2026-04-12 22:39:47.208920: +2026-04-12 22:39:47.211200: Epoch 2026 +2026-04-12 22:39:47.214545: Current learning rate: 0.0053 +2026-04-12 22:41:27.205134: train_loss -0.3133 +2026-04-12 22:41:27.212744: val_loss -0.3635 +2026-04-12 22:41:27.214964: Pseudo dice [0.0, 0.0, 0.6167, 0.6694, 0.0, 0.7322, 0.73] +2026-04-12 22:41:27.217875: Epoch time: 100.0 s +2026-04-12 22:41:28.458887: +2026-04-12 22:41:28.463552: Epoch 2027 +2026-04-12 22:41:28.467010: Current learning rate: 0.00529 +2026-04-12 22:43:08.421983: train_loss -0.3346 +2026-04-12 22:43:08.428725: val_loss -0.3507 +2026-04-12 22:43:08.431271: Pseudo dice [0.0, 0.0, 0.459, 0.0, 0.0, 0.0016, 0.4563] +2026-04-12 22:43:08.434862: Epoch time: 99.97 s +2026-04-12 22:43:09.669910: +2026-04-12 22:43:09.672132: Epoch 2028 +2026-04-12 22:43:09.676361: Current learning rate: 0.00529 +2026-04-12 22:44:49.735532: train_loss -0.3163 +2026-04-12 22:44:49.741631: val_loss -0.349 +2026-04-12 22:44:49.743944: Pseudo dice [0.0, 0.0, 0.6997, 0.0, 0.2159, 0.5233, 0.5754] +2026-04-12 22:44:49.747263: Epoch time: 100.07 s +2026-04-12 22:44:50.998283: +2026-04-12 22:44:51.000356: Epoch 2029 +2026-04-12 22:44:51.003068: Current learning rate: 0.00529 +2026-04-12 22:46:31.014259: train_loss -0.3596 +2026-04-12 22:46:31.022311: val_loss -0.4008 +2026-04-12 22:46:31.024359: Pseudo dice [0.0, 0.0, 0.7314, 0.0, 0.3128, 0.7118, 0.403] +2026-04-12 22:46:31.027349: Epoch time: 100.02 s +2026-04-12 22:46:32.273607: +2026-04-12 22:46:32.275528: Epoch 2030 +2026-04-12 22:46:32.278716: Current learning rate: 0.00529 +2026-04-12 22:48:12.829248: train_loss -0.3695 +2026-04-12 22:48:12.836987: val_loss -0.3484 +2026-04-12 22:48:12.839391: Pseudo dice [0.0, 0.0, 0.7339, 0.0, 0.2431, 0.7959, 0.7556] +2026-04-12 22:48:12.842300: Epoch time: 100.56 s +2026-04-12 22:48:14.058545: +2026-04-12 22:48:14.060363: Epoch 2031 +2026-04-12 22:48:14.063274: Current learning rate: 0.00528 +2026-04-12 22:49:54.173956: train_loss -0.3722 +2026-04-12 22:49:54.181124: val_loss -0.3395 +2026-04-12 22:49:54.184115: Pseudo dice [0.0, 0.0, 0.424, 0.4464, 0.1854, 0.6449, 0.7394] +2026-04-12 22:49:54.188390: Epoch time: 100.12 s +2026-04-12 22:49:55.427001: +2026-04-12 22:49:55.429403: Epoch 2032 +2026-04-12 22:49:55.431945: Current learning rate: 0.00528 +2026-04-12 22:51:35.870919: train_loss -0.3586 +2026-04-12 22:51:35.877863: val_loss -0.3239 +2026-04-12 22:51:35.880260: Pseudo dice [0.0, 0.0, 0.5567, 0.0, 0.1625, 0.309, 0.7322] +2026-04-12 22:51:35.882998: Epoch time: 100.45 s +2026-04-12 22:51:37.182192: +2026-04-12 22:51:37.187215: Epoch 2033 +2026-04-12 22:51:37.190080: Current learning rate: 0.00528 +2026-04-12 22:53:17.382087: train_loss -0.3287 +2026-04-12 22:53:17.388514: val_loss -0.3403 +2026-04-12 22:53:17.390943: Pseudo dice [0.0, 0.0, 0.6463, 0.0, 0.0004, 0.653, 0.2484] +2026-04-12 22:53:17.393519: Epoch time: 100.2 s +2026-04-12 22:53:18.651830: +2026-04-12 22:53:18.653574: Epoch 2034 +2026-04-12 22:53:18.655631: Current learning rate: 0.00528 +2026-04-12 22:54:59.187014: train_loss -0.3455 +2026-04-12 22:54:59.192640: val_loss -0.2538 +2026-04-12 22:54:59.194911: Pseudo dice [0.0, 0.0, 0.1021, 0.0, 0.0, 0.6408, 0.4709] +2026-04-12 22:54:59.197987: Epoch time: 100.54 s +2026-04-12 22:55:00.453573: +2026-04-12 22:55:00.455981: Epoch 2035 +2026-04-12 22:55:00.458275: Current learning rate: 0.00527 +2026-04-12 22:56:40.572443: train_loss -0.3426 +2026-04-12 22:56:40.578236: val_loss -0.3597 +2026-04-12 22:56:40.580217: Pseudo dice [0.0, 0.0, 0.6469, 0.0, 0.3897, 0.6115, 0.1532] +2026-04-12 22:56:40.582431: Epoch time: 100.12 s +2026-04-12 22:56:41.820322: +2026-04-12 22:56:41.822427: Epoch 2036 +2026-04-12 22:56:41.825242: Current learning rate: 0.00527 +2026-04-12 22:58:22.865883: train_loss -0.3395 +2026-04-12 22:58:22.873408: val_loss -0.3461 +2026-04-12 22:58:22.876023: Pseudo dice [0.0, 0.0, 0.6161, 0.0, 0.2028, 0.7435, 0.3965] +2026-04-12 22:58:22.879124: Epoch time: 101.05 s +2026-04-12 22:58:24.124966: +2026-04-12 22:58:24.129025: Epoch 2037 +2026-04-12 22:58:24.131539: Current learning rate: 0.00527 +2026-04-12 23:00:04.470144: train_loss -0.3524 +2026-04-12 23:00:04.476561: val_loss -0.3589 +2026-04-12 23:00:04.478734: Pseudo dice [0.0, 0.0, 0.2758, 0.0, 0.2445, 0.7747, 0.4083] +2026-04-12 23:00:04.481129: Epoch time: 100.35 s +2026-04-12 23:00:05.708318: +2026-04-12 23:00:05.711200: Epoch 2038 +2026-04-12 23:00:05.714151: Current learning rate: 0.00527 +2026-04-12 23:01:45.926935: train_loss -0.3766 +2026-04-12 23:01:45.934989: val_loss -0.3655 +2026-04-12 23:01:45.937907: Pseudo dice [0.0, 0.0, 0.672, 0.5871, 0.4546, 0.6745, 0.3369] +2026-04-12 23:01:45.940670: Epoch time: 100.22 s +2026-04-12 23:01:47.202813: +2026-04-12 23:01:47.205001: Epoch 2039 +2026-04-12 23:01:47.207277: Current learning rate: 0.00526 +2026-04-12 23:03:27.605656: train_loss -0.3869 +2026-04-12 23:03:27.611437: val_loss -0.3458 +2026-04-12 23:03:27.613651: Pseudo dice [0.4377, 0.0, 0.6947, 0.126, 0.361, 0.4735, 0.8437] +2026-04-12 23:03:27.616593: Epoch time: 100.41 s +2026-04-12 23:03:29.005926: +2026-04-12 23:03:29.008235: Epoch 2040 +2026-04-12 23:03:29.010377: Current learning rate: 0.00526 +2026-04-12 23:05:09.878607: train_loss -0.3777 +2026-04-12 23:05:09.884803: val_loss -0.3097 +2026-04-12 23:05:09.887335: Pseudo dice [0.0, 0.0, 0.5759, 0.0599, 0.2749, 0.3246, 0.7881] +2026-04-12 23:05:09.889914: Epoch time: 100.88 s +2026-04-12 23:05:11.134124: +2026-04-12 23:05:11.135978: Epoch 2041 +2026-04-12 23:05:11.137926: Current learning rate: 0.00526 +2026-04-12 23:06:51.255855: train_loss -0.3708 +2026-04-12 23:06:51.260902: val_loss -0.3436 +2026-04-12 23:06:51.263066: Pseudo dice [0.0, 0.0, 0.6273, 0.0, 0.3885, 0.4803, 0.7038] +2026-04-12 23:06:51.265723: Epoch time: 100.12 s +2026-04-12 23:06:52.518718: +2026-04-12 23:06:52.522026: Epoch 2042 +2026-04-12 23:06:52.524422: Current learning rate: 0.00526 +2026-04-12 23:08:32.621026: train_loss -0.3594 +2026-04-12 23:08:32.630008: val_loss -0.3352 +2026-04-12 23:08:32.633040: Pseudo dice [0.0, 0.0, 0.5614, 0.2047, 0.2041, 0.5381, 0.4859] +2026-04-12 23:08:32.636526: Epoch time: 100.11 s +2026-04-12 23:08:33.856090: +2026-04-12 23:08:33.858270: Epoch 2043 +2026-04-12 23:08:33.860368: Current learning rate: 0.00526 +2026-04-12 23:10:14.137706: train_loss -0.3709 +2026-04-12 23:10:14.146906: val_loss -0.3294 +2026-04-12 23:10:14.149488: Pseudo dice [0.0, 0.0, 0.4402, 0.0, 0.0, 0.737, 0.4158] +2026-04-12 23:10:14.152369: Epoch time: 100.28 s +2026-04-12 23:10:15.425501: +2026-04-12 23:10:15.427324: Epoch 2044 +2026-04-12 23:10:15.429390: Current learning rate: 0.00525 +2026-04-12 23:11:56.979313: train_loss -0.3382 +2026-04-12 23:11:56.987007: val_loss -0.3816 +2026-04-12 23:11:56.990207: Pseudo dice [0.0, 0.0, 0.6909, 0.0925, 0.0, 0.6994, 0.5477] +2026-04-12 23:11:56.992596: Epoch time: 101.56 s +2026-04-12 23:11:58.230001: +2026-04-12 23:11:58.231874: Epoch 2045 +2026-04-12 23:11:58.234271: Current learning rate: 0.00525 +2026-04-12 23:13:38.207707: train_loss -0.3715 +2026-04-12 23:13:38.214549: val_loss -0.2883 +2026-04-12 23:13:38.217054: Pseudo dice [0.0, 0.0, 0.7477, 0.0995, 0.3221, 0.702, 0.5246] +2026-04-12 23:13:38.219626: Epoch time: 99.98 s +2026-04-12 23:13:39.484448: +2026-04-12 23:13:39.489318: Epoch 2046 +2026-04-12 23:13:39.494789: Current learning rate: 0.00525 +2026-04-12 23:15:19.714892: train_loss -0.3766 +2026-04-12 23:15:19.724785: val_loss -0.3762 +2026-04-12 23:15:19.727059: Pseudo dice [0.0, 0.0, 0.7786, 0.0, 0.217, 0.3499, 0.8114] +2026-04-12 23:15:19.729052: Epoch time: 100.23 s +2026-04-12 23:15:20.884805: +2026-04-12 23:15:20.887379: Epoch 2047 +2026-04-12 23:15:20.889409: Current learning rate: 0.00525 +2026-04-12 23:17:01.040768: train_loss -0.3897 +2026-04-12 23:17:01.049110: val_loss -0.3722 +2026-04-12 23:17:01.051584: Pseudo dice [0.0, 0.0, 0.7497, 0.0, 0.4508, 0.754, 0.7662] +2026-04-12 23:17:01.056045: Epoch time: 100.16 s +2026-04-12 23:17:02.237840: +2026-04-12 23:17:02.239732: Epoch 2048 +2026-04-12 23:17:02.241705: Current learning rate: 0.00524 +2026-04-12 23:18:42.883034: train_loss -0.3956 +2026-04-12 23:18:42.889948: val_loss -0.398 +2026-04-12 23:18:42.892653: Pseudo dice [0.0, 0.0, 0.6477, 0.0083, 0.2074, 0.7278, 0.4664] +2026-04-12 23:18:42.896970: Epoch time: 100.65 s +2026-04-12 23:18:44.078655: +2026-04-12 23:18:44.080742: Epoch 2049 +2026-04-12 23:18:44.083857: Current learning rate: 0.00524 +2026-04-12 23:20:24.344002: train_loss -0.4089 +2026-04-12 23:20:24.351171: val_loss -0.3165 +2026-04-12 23:20:24.353028: Pseudo dice [0.0, 0.0, 0.6365, 0.0, 0.2414, 0.6713, 0.5511] +2026-04-12 23:20:24.356683: Epoch time: 100.27 s +2026-04-12 23:20:27.208102: +2026-04-12 23:20:27.210631: Epoch 2050 +2026-04-12 23:20:27.213682: Current learning rate: 0.00524 +2026-04-12 23:22:07.648360: train_loss -0.3908 +2026-04-12 23:22:07.655231: val_loss -0.371 +2026-04-12 23:22:07.658680: Pseudo dice [0.0, 0.0, 0.6221, 0.0802, 0.3752, 0.6009, 0.3457] +2026-04-12 23:22:07.662797: Epoch time: 100.44 s +2026-04-12 23:22:08.825239: +2026-04-12 23:22:08.829025: Epoch 2051 +2026-04-12 23:22:08.832439: Current learning rate: 0.00524 +2026-04-12 23:23:49.522806: train_loss -0.3762 +2026-04-12 23:23:49.530016: val_loss -0.3668 +2026-04-12 23:23:49.532389: Pseudo dice [0.0, 0.0, 0.7377, 0.0, 0.3066, 0.6869, 0.7786] +2026-04-12 23:23:49.535722: Epoch time: 100.7 s +2026-04-12 23:23:50.685755: +2026-04-12 23:23:50.688116: Epoch 2052 +2026-04-12 23:23:50.690531: Current learning rate: 0.00523 +2026-04-12 23:25:30.996831: train_loss -0.3545 +2026-04-12 23:25:31.004853: val_loss -0.3893 +2026-04-12 23:25:31.008892: Pseudo dice [0.0, 0.0, 0.7522, 0.0, 0.1427, 0.4976, 0.8604] +2026-04-12 23:25:31.011976: Epoch time: 100.31 s +2026-04-12 23:25:32.187039: +2026-04-12 23:25:32.189613: Epoch 2053 +2026-04-12 23:25:32.192039: Current learning rate: 0.00523 +2026-04-12 23:27:12.315020: train_loss -0.347 +2026-04-12 23:27:12.323824: val_loss -0.3652 +2026-04-12 23:27:12.326864: Pseudo dice [0.0, 0.0, 0.5311, 0.6715, 0.3399, 0.3439, 0.6506] +2026-04-12 23:27:12.329787: Epoch time: 100.13 s +2026-04-12 23:27:13.526787: +2026-04-12 23:27:13.528894: Epoch 2054 +2026-04-12 23:27:13.531176: Current learning rate: 0.00523 +2026-04-12 23:28:53.699767: train_loss -0.3633 +2026-04-12 23:28:53.707174: val_loss -0.3831 +2026-04-12 23:28:53.709779: Pseudo dice [0.0169, 0.0, 0.5976, 0.4017, 0.3716, 0.8628, 0.4832] +2026-04-12 23:28:53.712353: Epoch time: 100.18 s +2026-04-12 23:28:54.892826: +2026-04-12 23:28:54.894942: Epoch 2055 +2026-04-12 23:28:54.897780: Current learning rate: 0.00523 +2026-04-12 23:30:35.530042: train_loss -0.3813 +2026-04-12 23:30:35.538063: val_loss -0.3829 +2026-04-12 23:30:35.540863: Pseudo dice [0.0, 0.0, 0.5859, 0.4418, 0.2395, 0.79, 0.6598] +2026-04-12 23:30:35.544751: Epoch time: 100.64 s +2026-04-12 23:30:36.714029: +2026-04-12 23:30:36.716634: Epoch 2056 +2026-04-12 23:30:36.719025: Current learning rate: 0.00522 +2026-04-12 23:32:17.587597: train_loss -0.3942 +2026-04-12 23:32:17.596733: val_loss -0.3889 +2026-04-12 23:32:17.598853: Pseudo dice [0.0, 0.0, 0.7405, 0.0, 0.4405, 0.8076, 0.575] +2026-04-12 23:32:17.601230: Epoch time: 100.88 s +2026-04-12 23:32:18.790334: +2026-04-12 23:32:18.793358: Epoch 2057 +2026-04-12 23:32:18.796105: Current learning rate: 0.00522 +2026-04-12 23:33:59.685340: train_loss -0.3705 +2026-04-12 23:33:59.691089: val_loss -0.2934 +2026-04-12 23:33:59.692930: Pseudo dice [0.0, 0.0, 0.7413, 0.0215, 0.3475, 0.6231, 0.7364] +2026-04-12 23:33:59.696154: Epoch time: 100.9 s +2026-04-12 23:34:00.850235: +2026-04-12 23:34:00.852158: Epoch 2058 +2026-04-12 23:34:00.854357: Current learning rate: 0.00522 +2026-04-12 23:35:41.676669: train_loss -0.3874 +2026-04-12 23:35:41.683070: val_loss -0.4027 +2026-04-12 23:35:41.685721: Pseudo dice [0.4379, 0.0, 0.6839, 0.3089, 0.1906, 0.7593, 0.8091] +2026-04-12 23:35:41.688821: Epoch time: 100.83 s +2026-04-12 23:35:42.858598: +2026-04-12 23:35:42.862653: Epoch 2059 +2026-04-12 23:35:42.870132: Current learning rate: 0.00522 +2026-04-12 23:37:23.546891: train_loss -0.3875 +2026-04-12 23:37:23.553249: val_loss -0.3424 +2026-04-12 23:37:23.557474: Pseudo dice [0.0, 0.0, 0.615, 0.0, 0.324, 0.5903, 0.3404] +2026-04-12 23:37:23.559960: Epoch time: 100.69 s +2026-04-12 23:37:24.735651: +2026-04-12 23:37:24.737923: Epoch 2060 +2026-04-12 23:37:24.740540: Current learning rate: 0.00521 +2026-04-12 23:39:04.740273: train_loss -0.3663 +2026-04-12 23:39:04.748464: val_loss -0.3323 +2026-04-12 23:39:04.751227: Pseudo dice [0.0, 0.0, 0.1571, 0.0509, 0.3748, 0.7013, 0.7681] +2026-04-12 23:39:04.754456: Epoch time: 100.01 s +2026-04-12 23:39:05.925240: +2026-04-12 23:39:05.926994: Epoch 2061 +2026-04-12 23:39:05.928947: Current learning rate: 0.00521 +2026-04-12 23:40:46.299896: train_loss -0.3807 +2026-04-12 23:40:46.306785: val_loss -0.349 +2026-04-12 23:40:46.309340: Pseudo dice [0.0082, 0.0, 0.7362, 0.6098, 0.3915, 0.5572, 0.551] +2026-04-12 23:40:46.311755: Epoch time: 100.38 s +2026-04-12 23:40:47.471720: +2026-04-12 23:40:47.474056: Epoch 2062 +2026-04-12 23:40:47.476137: Current learning rate: 0.00521 +2026-04-12 23:42:27.710860: train_loss -0.3986 +2026-04-12 23:42:27.721248: val_loss -0.4143 +2026-04-12 23:42:27.724902: Pseudo dice [0.4104, 0.0, 0.802, 0.0, 0.3381, 0.7325, 0.8235] +2026-04-12 23:42:27.729236: Epoch time: 100.24 s +2026-04-12 23:42:28.907533: +2026-04-12 23:42:28.910181: Epoch 2063 +2026-04-12 23:42:28.912401: Current learning rate: 0.00521 +2026-04-12 23:44:08.889456: train_loss -0.3725 +2026-04-12 23:44:08.895879: val_loss -0.36 +2026-04-12 23:44:08.898247: Pseudo dice [0.0418, 0.0, 0.5007, 0.0, 0.2058, 0.7893, 0.7851] +2026-04-12 23:44:08.900808: Epoch time: 99.98 s +2026-04-12 23:44:10.071963: +2026-04-12 23:44:10.075854: Epoch 2064 +2026-04-12 23:44:10.078523: Current learning rate: 0.0052 +2026-04-12 23:45:51.100807: train_loss -0.3888 +2026-04-12 23:45:51.105580: val_loss -0.2975 +2026-04-12 23:45:51.107435: Pseudo dice [0.0758, 0.0, 0.6484, 0.0, 0.3457, 0.5853, 0.6709] +2026-04-12 23:45:51.110098: Epoch time: 101.03 s +2026-04-12 23:45:52.263970: +2026-04-12 23:45:52.266304: Epoch 2065 +2026-04-12 23:45:52.268742: Current learning rate: 0.0052 +2026-04-12 23:47:32.407587: train_loss -0.3627 +2026-04-12 23:47:32.413754: val_loss -0.3851 +2026-04-12 23:47:32.415826: Pseudo dice [0.0, 0.0, 0.6471, 0.0, 0.0068, 0.7405, 0.8544] +2026-04-12 23:47:32.419689: Epoch time: 100.15 s +2026-04-12 23:47:33.576182: +2026-04-12 23:47:33.578131: Epoch 2066 +2026-04-12 23:47:33.580291: Current learning rate: 0.0052 +2026-04-12 23:49:13.722502: train_loss -0.3653 +2026-04-12 23:49:13.728978: val_loss -0.3815 +2026-04-12 23:49:13.730568: Pseudo dice [0.0, 0.0, 0.7398, 0.0, 0.4201, 0.798, 0.7955] +2026-04-12 23:49:13.732906: Epoch time: 100.15 s +2026-04-12 23:49:14.895522: +2026-04-12 23:49:14.897691: Epoch 2067 +2026-04-12 23:49:14.900176: Current learning rate: 0.0052 +2026-04-12 23:50:54.890971: train_loss -0.375 +2026-04-12 23:50:54.898187: val_loss -0.3641 +2026-04-12 23:50:54.900152: Pseudo dice [0.0, 0.0, 0.6932, 0.0, 0.0, 0.8003, 0.542] +2026-04-12 23:50:54.902561: Epoch time: 100.0 s +2026-04-12 23:50:56.084256: +2026-04-12 23:50:56.086256: Epoch 2068 +2026-04-12 23:50:56.088336: Current learning rate: 0.00519 +2026-04-12 23:52:36.157669: train_loss -0.346 +2026-04-12 23:52:36.165346: val_loss -0.3851 +2026-04-12 23:52:36.169178: Pseudo dice [0.0, 0.0, 0.8094, 0.0639, 0.0, 0.6862, 0.6549] +2026-04-12 23:52:36.171535: Epoch time: 100.08 s +2026-04-12 23:52:37.344548: +2026-04-12 23:52:37.346663: Epoch 2069 +2026-04-12 23:52:37.349073: Current learning rate: 0.00519 +2026-04-12 23:54:17.382868: train_loss -0.3604 +2026-04-12 23:54:17.389843: val_loss -0.4008 +2026-04-12 23:54:17.391859: Pseudo dice [0.4441, 0.0, 0.5016, 0.7215, 0.1657, 0.6259, 0.7116] +2026-04-12 23:54:17.394625: Epoch time: 100.04 s +2026-04-12 23:54:18.554081: +2026-04-12 23:54:18.556765: Epoch 2070 +2026-04-12 23:54:18.559466: Current learning rate: 0.00519 +2026-04-12 23:55:58.718269: train_loss -0.3797 +2026-04-12 23:55:58.725726: val_loss -0.3719 +2026-04-12 23:55:58.728046: Pseudo dice [0.0, 0.0, 0.5284, 0.6659, 0.6346, 0.298, 0.7868] +2026-04-12 23:55:58.731244: Epoch time: 100.17 s +2026-04-12 23:55:59.916855: +2026-04-12 23:55:59.918793: Epoch 2071 +2026-04-12 23:55:59.920807: Current learning rate: 0.00519 +2026-04-12 23:57:40.077973: train_loss -0.3833 +2026-04-12 23:57:40.083253: val_loss -0.3699 +2026-04-12 23:57:40.085548: Pseudo dice [0.3843, 0.0, 0.6981, 0.0, 0.0, 0.6544, 0.7126] +2026-04-12 23:57:40.089144: Epoch time: 100.16 s +2026-04-12 23:57:41.286062: +2026-04-12 23:57:41.288560: Epoch 2072 +2026-04-12 23:57:41.290864: Current learning rate: 0.00518 +2026-04-12 23:59:21.777198: train_loss -0.3593 +2026-04-12 23:59:21.783974: val_loss -0.394 +2026-04-12 23:59:21.786077: Pseudo dice [0.0748, 0.0, 0.6884, 0.0, 0.0, 0.7937, 0.7605] +2026-04-12 23:59:21.789195: Epoch time: 100.49 s +2026-04-12 23:59:22.970962: +2026-04-12 23:59:22.972978: Epoch 2073 +2026-04-12 23:59:22.975303: Current learning rate: 0.00518 +2026-04-13 00:01:04.216026: train_loss -0.3825 +2026-04-13 00:01:04.224028: val_loss -0.3531 +2026-04-13 00:01:04.227703: Pseudo dice [0.5831, 0.0, 0.7415, 0.0278, 0.2724, 0.8328, 0.7997] +2026-04-13 00:01:04.231288: Epoch time: 101.25 s +2026-04-13 00:01:05.406069: +2026-04-13 00:01:05.408220: Epoch 2074 +2026-04-13 00:01:05.410486: Current learning rate: 0.00518 +2026-04-13 00:02:45.594135: train_loss -0.3729 +2026-04-13 00:02:45.600894: val_loss -0.3234 +2026-04-13 00:02:45.603725: Pseudo dice [0.4297, 0.0, 0.3164, 0.3288, 0.4362, 0.0404, 0.2833] +2026-04-13 00:02:45.607589: Epoch time: 100.19 s +2026-04-13 00:02:46.761944: +2026-04-13 00:02:46.764193: Epoch 2075 +2026-04-13 00:02:46.766238: Current learning rate: 0.00518 +2026-04-13 00:04:27.082372: train_loss -0.3518 +2026-04-13 00:04:27.090418: val_loss -0.381 +2026-04-13 00:04:27.092528: Pseudo dice [0.0, 0.0, 0.7333, 0.6109, 0.3496, 0.2596, 0.5017] +2026-04-13 00:04:27.095797: Epoch time: 100.32 s +2026-04-13 00:04:28.277848: +2026-04-13 00:04:28.279878: Epoch 2076 +2026-04-13 00:04:28.282356: Current learning rate: 0.00518 +2026-04-13 00:06:08.262774: train_loss -0.398 +2026-04-13 00:06:08.270359: val_loss -0.2169 +2026-04-13 00:06:08.273080: Pseudo dice [0.0, 0.0, 0.6615, 0.0, 0.5128, 0.6322, 0.619] +2026-04-13 00:06:08.275845: Epoch time: 99.99 s +2026-04-13 00:06:09.452960: +2026-04-13 00:06:09.455260: Epoch 2077 +2026-04-13 00:06:09.457607: Current learning rate: 0.00517 +2026-04-13 00:07:49.990308: train_loss -0.4046 +2026-04-13 00:07:49.998741: val_loss -0.3784 +2026-04-13 00:07:50.001109: Pseudo dice [0.2641, 0.0, 0.6832, 0.0, 0.0405, 0.7466, 0.7962] +2026-04-13 00:07:50.005439: Epoch time: 100.54 s +2026-04-13 00:07:51.177497: +2026-04-13 00:07:51.179549: Epoch 2078 +2026-04-13 00:07:51.181923: Current learning rate: 0.00517 +2026-04-13 00:09:31.370930: train_loss -0.4073 +2026-04-13 00:09:31.377692: val_loss -0.3783 +2026-04-13 00:09:31.379891: Pseudo dice [0.0, 0.0, 0.7057, 0.0, 0.311, 0.1584, 0.6322] +2026-04-13 00:09:31.382633: Epoch time: 100.2 s +2026-04-13 00:09:32.554665: +2026-04-13 00:09:32.556797: Epoch 2079 +2026-04-13 00:09:32.559011: Current learning rate: 0.00517 +2026-04-13 00:11:12.873866: train_loss -0.373 +2026-04-13 00:11:12.880083: val_loss -0.3818 +2026-04-13 00:11:12.882408: Pseudo dice [0.0, 0.0, 0.7287, 0.0, 0.1896, 0.6658, 0.8274] +2026-04-13 00:11:12.884573: Epoch time: 100.32 s +2026-04-13 00:11:14.041492: +2026-04-13 00:11:14.043342: Epoch 2080 +2026-04-13 00:11:14.045361: Current learning rate: 0.00517 +2026-04-13 00:12:54.134930: train_loss -0.3855 +2026-04-13 00:12:54.141658: val_loss -0.2734 +2026-04-13 00:12:54.143924: Pseudo dice [0.1141, 0.0, 0.6244, 0.0, 0.0725, 0.5662, 0.6643] +2026-04-13 00:12:54.146050: Epoch time: 100.1 s +2026-04-13 00:12:55.328401: +2026-04-13 00:12:55.330456: Epoch 2081 +2026-04-13 00:12:55.332934: Current learning rate: 0.00516 +2026-04-13 00:14:35.687354: train_loss -0.3807 +2026-04-13 00:14:35.693989: val_loss -0.374 +2026-04-13 00:14:35.696812: Pseudo dice [0.4597, 0.0, 0.5405, 0.3558, 0.2514, 0.5347, 0.5757] +2026-04-13 00:14:35.699224: Epoch time: 100.36 s +2026-04-13 00:14:36.898996: +2026-04-13 00:14:36.901548: Epoch 2082 +2026-04-13 00:14:36.904110: Current learning rate: 0.00516 +2026-04-13 00:16:16.968802: train_loss -0.3876 +2026-04-13 00:16:16.974458: val_loss -0.3754 +2026-04-13 00:16:16.976632: Pseudo dice [0.0, 0.0, 0.6479, 0.7573, 0.3998, 0.3167, 0.7604] +2026-04-13 00:16:16.978941: Epoch time: 100.07 s +2026-04-13 00:16:18.146324: +2026-04-13 00:16:18.148464: Epoch 2083 +2026-04-13 00:16:18.150488: Current learning rate: 0.00516 +2026-04-13 00:17:58.046980: train_loss -0.3607 +2026-04-13 00:17:58.052847: val_loss -0.3649 +2026-04-13 00:17:58.054982: Pseudo dice [0.0, 0.0, 0.6712, 0.0, 0.3138, 0.5898, 0.6986] +2026-04-13 00:17:58.057223: Epoch time: 99.9 s +2026-04-13 00:17:59.233508: +2026-04-13 00:17:59.235629: Epoch 2084 +2026-04-13 00:17:59.237754: Current learning rate: 0.00516 +2026-04-13 00:19:39.379133: train_loss -0.3724 +2026-04-13 00:19:39.387737: val_loss -0.3112 +2026-04-13 00:19:39.389599: Pseudo dice [0.0, 0.0, 0.7744, 0.0562, 0.1684, 0.5563, 0.8526] +2026-04-13 00:19:39.392433: Epoch time: 100.15 s +2026-04-13 00:19:41.551489: +2026-04-13 00:19:41.554159: Epoch 2085 +2026-04-13 00:19:41.556550: Current learning rate: 0.00515 +2026-04-13 00:21:21.612263: train_loss -0.3799 +2026-04-13 00:21:21.622698: val_loss -0.2909 +2026-04-13 00:21:21.625227: Pseudo dice [0.0, 0.0, 0.6029, 0.0, 0.1258, 0.2976, 0.6258] +2026-04-13 00:21:21.627938: Epoch time: 100.06 s +2026-04-13 00:21:22.804325: +2026-04-13 00:21:22.806950: Epoch 2086 +2026-04-13 00:21:22.809813: Current learning rate: 0.00515 +2026-04-13 00:23:02.706796: train_loss -0.3546 +2026-04-13 00:23:02.715858: val_loss -0.3395 +2026-04-13 00:23:02.718359: Pseudo dice [0.0, 0.0, 0.6124, 0.0, 0.2527, 0.6828, 0.6029] +2026-04-13 00:23:02.721083: Epoch time: 99.91 s +2026-04-13 00:23:03.944957: +2026-04-13 00:23:03.947775: Epoch 2087 +2026-04-13 00:23:03.950048: Current learning rate: 0.00515 +2026-04-13 00:24:44.006272: train_loss -0.3526 +2026-04-13 00:24:44.014383: val_loss -0.3746 +2026-04-13 00:24:44.017378: Pseudo dice [0.0, 0.0, 0.5973, 0.0, 0.0123, 0.3857, 0.5892] +2026-04-13 00:24:44.020042: Epoch time: 100.06 s +2026-04-13 00:24:45.192115: +2026-04-13 00:24:45.194029: Epoch 2088 +2026-04-13 00:24:45.196008: Current learning rate: 0.00515 +2026-04-13 00:26:25.992735: train_loss -0.3715 +2026-04-13 00:26:25.998888: val_loss -0.3289 +2026-04-13 00:26:26.000974: Pseudo dice [0.0, 0.0, 0.7385, 0.0594, 0.3989, 0.8133, 0.6464] +2026-04-13 00:26:26.003461: Epoch time: 100.8 s +2026-04-13 00:26:27.164180: +2026-04-13 00:26:27.174017: Epoch 2089 +2026-04-13 00:26:27.178346: Current learning rate: 0.00514 +2026-04-13 00:28:07.253647: train_loss -0.3857 +2026-04-13 00:28:07.259806: val_loss -0.3656 +2026-04-13 00:28:07.261714: Pseudo dice [0.0, 0.0, 0.6353, 0.0, 0.0, 0.7697, 0.6859] +2026-04-13 00:28:07.263833: Epoch time: 100.09 s +2026-04-13 00:28:08.440512: +2026-04-13 00:28:08.443125: Epoch 2090 +2026-04-13 00:28:08.444799: Current learning rate: 0.00514 +2026-04-13 00:29:48.525121: train_loss -0.3801 +2026-04-13 00:29:48.531396: val_loss -0.3885 +2026-04-13 00:29:48.533283: Pseudo dice [0.0, 0.0, 0.7606, 0.1256, 0.3825, 0.7804, 0.7676] +2026-04-13 00:29:48.535652: Epoch time: 100.09 s +2026-04-13 00:29:49.685640: +2026-04-13 00:29:49.687626: Epoch 2091 +2026-04-13 00:29:49.689250: Current learning rate: 0.00514 +2026-04-13 00:31:29.782566: train_loss -0.3833 +2026-04-13 00:31:29.788524: val_loss -0.3022 +2026-04-13 00:31:29.790664: Pseudo dice [0.0, 0.0, 0.4697, 0.004, 0.4943, 0.3315, 0.476] +2026-04-13 00:31:29.793302: Epoch time: 100.1 s +2026-04-13 00:31:30.958691: +2026-04-13 00:31:30.960607: Epoch 2092 +2026-04-13 00:31:30.962236: Current learning rate: 0.00514 +2026-04-13 00:33:11.317709: train_loss -0.3986 +2026-04-13 00:33:11.323072: val_loss -0.3628 +2026-04-13 00:33:11.325235: Pseudo dice [0.2827, 0.0, 0.5948, 0.1512, 0.2275, 0.6691, 0.3443] +2026-04-13 00:33:11.327966: Epoch time: 100.36 s +2026-04-13 00:33:12.489366: +2026-04-13 00:33:12.491399: Epoch 2093 +2026-04-13 00:33:12.493496: Current learning rate: 0.00513 +2026-04-13 00:34:52.648098: train_loss -0.3717 +2026-04-13 00:34:52.653815: val_loss -0.3586 +2026-04-13 00:34:52.655687: Pseudo dice [0.1612, 0.0, 0.4701, 0.0, 0.2338, 0.6937, 0.4468] +2026-04-13 00:34:52.657838: Epoch time: 100.16 s +2026-04-13 00:34:53.828851: +2026-04-13 00:34:53.830554: Epoch 2094 +2026-04-13 00:34:53.832178: Current learning rate: 0.00513 +2026-04-13 00:36:33.924763: train_loss -0.3694 +2026-04-13 00:36:33.930048: val_loss -0.3185 +2026-04-13 00:36:33.931762: Pseudo dice [0.0983, 0.0, 0.5728, 0.0, 0.1082, 0.5628, 0.5035] +2026-04-13 00:36:33.933967: Epoch time: 100.1 s +2026-04-13 00:36:35.117028: +2026-04-13 00:36:35.119047: Epoch 2095 +2026-04-13 00:36:35.120856: Current learning rate: 0.00513 +2026-04-13 00:38:15.124129: train_loss -0.3629 +2026-04-13 00:38:15.131840: val_loss -0.3828 +2026-04-13 00:38:15.134858: Pseudo dice [0.0938, 0.0, 0.5226, 0.0, 0.0, 0.8577, 0.8559] +2026-04-13 00:38:15.138403: Epoch time: 100.01 s +2026-04-13 00:38:16.319790: +2026-04-13 00:38:16.323273: Epoch 2096 +2026-04-13 00:38:16.325417: Current learning rate: 0.00513 +2026-04-13 00:39:56.500773: train_loss -0.3509 +2026-04-13 00:39:56.507299: val_loss -0.3692 +2026-04-13 00:39:56.509799: Pseudo dice [0.3334, 0.0, 0.6666, 0.0, 0.0, 0.49, 0.7349] +2026-04-13 00:39:56.514713: Epoch time: 100.18 s +2026-04-13 00:39:57.821144: +2026-04-13 00:39:57.823311: Epoch 2097 +2026-04-13 00:39:57.825505: Current learning rate: 0.00512 +2026-04-13 00:41:38.030501: train_loss -0.3645 +2026-04-13 00:41:38.036656: val_loss -0.402 +2026-04-13 00:41:38.038789: Pseudo dice [0.1732, 0.0, 0.7297, 0.0, 0.2773, 0.6781, 0.8745] +2026-04-13 00:41:38.041141: Epoch time: 100.21 s +2026-04-13 00:41:39.225881: +2026-04-13 00:41:39.227608: Epoch 2098 +2026-04-13 00:41:39.229444: Current learning rate: 0.00512 +2026-04-13 00:43:19.548886: train_loss -0.4015 +2026-04-13 00:43:19.555864: val_loss -0.4004 +2026-04-13 00:43:19.561206: Pseudo dice [0.6798, 0.0, 0.753, 0.0, 0.4079, 0.6545, 0.8387] +2026-04-13 00:43:19.566785: Epoch time: 100.33 s +2026-04-13 00:43:20.741838: +2026-04-13 00:43:20.744124: Epoch 2099 +2026-04-13 00:43:20.745885: Current learning rate: 0.00512 +2026-04-13 00:45:01.496636: train_loss -0.3834 +2026-04-13 00:45:01.504857: val_loss -0.2966 +2026-04-13 00:45:01.507649: Pseudo dice [0.0, 0.0, 0.555, 0.0, 0.0, 0.7121, 0.2344] +2026-04-13 00:45:01.510914: Epoch time: 100.76 s +2026-04-13 00:45:04.402817: +2026-04-13 00:45:04.404757: Epoch 2100 +2026-04-13 00:45:04.406890: Current learning rate: 0.00512 +2026-04-13 00:46:44.768870: train_loss -0.3718 +2026-04-13 00:46:44.775529: val_loss -0.3964 +2026-04-13 00:46:44.777767: Pseudo dice [0.259, 0.0, 0.6006, 0.6494, 0.514, 0.5672, 0.5439] +2026-04-13 00:46:44.780458: Epoch time: 100.37 s +2026-04-13 00:46:45.956506: +2026-04-13 00:46:45.958708: Epoch 2101 +2026-04-13 00:46:45.960539: Current learning rate: 0.00511 +2026-04-13 00:48:25.990478: train_loss -0.3596 +2026-04-13 00:48:25.998429: val_loss -0.3921 +2026-04-13 00:48:26.000574: Pseudo dice [0.5773, 0.0, 0.4833, 0.2294, 0.4071, 0.8137, 0.6954] +2026-04-13 00:48:26.003055: Epoch time: 100.04 s +2026-04-13 00:48:27.177917: +2026-04-13 00:48:27.179944: Epoch 2102 +2026-04-13 00:48:27.181629: Current learning rate: 0.00511 +2026-04-13 00:50:07.285507: train_loss -0.3941 +2026-04-13 00:50:07.290682: val_loss -0.2847 +2026-04-13 00:50:07.292905: Pseudo dice [0.3193, 0.0, 0.4492, 0.119, 0.0406, 0.6046, 0.5704] +2026-04-13 00:50:07.295217: Epoch time: 100.11 s +2026-04-13 00:50:08.468981: +2026-04-13 00:50:08.470706: Epoch 2103 +2026-04-13 00:50:08.472236: Current learning rate: 0.00511 +2026-04-13 00:51:48.530075: train_loss -0.3845 +2026-04-13 00:51:48.536534: val_loss -0.3811 +2026-04-13 00:51:48.539711: Pseudo dice [0.2885, 0.0, 0.6286, 0.6096, 0.3537, 0.3276, 0.5534] +2026-04-13 00:51:48.542028: Epoch time: 100.06 s +2026-04-13 00:51:49.710894: +2026-04-13 00:51:49.714253: Epoch 2104 +2026-04-13 00:51:49.718171: Current learning rate: 0.00511 +2026-04-13 00:53:29.847181: train_loss -0.3783 +2026-04-13 00:53:29.852662: val_loss -0.4043 +2026-04-13 00:53:29.854363: Pseudo dice [0.6926, 0.0, 0.7078, 0.4207, 0.5443, 0.7961, 0.5571] +2026-04-13 00:53:29.856637: Epoch time: 100.14 s +2026-04-13 00:53:31.030544: +2026-04-13 00:53:31.032368: Epoch 2105 +2026-04-13 00:53:31.034245: Current learning rate: 0.0051 +2026-04-13 00:55:12.010025: train_loss -0.399 +2026-04-13 00:55:12.015563: val_loss -0.37 +2026-04-13 00:55:12.017761: Pseudo dice [0.444, 0.0, 0.7225, 0.6658, 0.1911, 0.6316, 0.6404] +2026-04-13 00:55:12.021042: Epoch time: 100.98 s +2026-04-13 00:55:13.215027: +2026-04-13 00:55:13.216888: Epoch 2106 +2026-04-13 00:55:13.218519: Current learning rate: 0.0051 +2026-04-13 00:56:53.377783: train_loss -0.4189 +2026-04-13 00:56:53.385015: val_loss -0.3852 +2026-04-13 00:56:53.387104: Pseudo dice [0.0565, 0.0, 0.6681, 0.0, 0.3395, 0.6446, 0.784] +2026-04-13 00:56:53.389771: Epoch time: 100.17 s +2026-04-13 00:56:54.556705: +2026-04-13 00:56:54.560047: Epoch 2107 +2026-04-13 00:56:54.562300: Current learning rate: 0.0051 +2026-04-13 00:58:34.762058: train_loss -0.368 +2026-04-13 00:58:34.769811: val_loss -0.3929 +2026-04-13 00:58:34.772155: Pseudo dice [0.242, 0.0, 0.6504, 0.0, 0.4235, 0.6368, 0.6375] +2026-04-13 00:58:34.774692: Epoch time: 100.21 s +2026-04-13 00:58:35.945493: +2026-04-13 00:58:35.947475: Epoch 2108 +2026-04-13 00:58:35.949797: Current learning rate: 0.0051 +2026-04-13 01:00:16.225490: train_loss -0.3994 +2026-04-13 01:00:16.232995: val_loss -0.3852 +2026-04-13 01:00:16.235142: Pseudo dice [0.3942, 0.0, 0.7224, 0.3335, 0.3326, 0.8659, 0.4741] +2026-04-13 01:00:16.237287: Epoch time: 100.28 s +2026-04-13 01:00:17.410092: +2026-04-13 01:00:17.411856: Epoch 2109 +2026-04-13 01:00:17.413604: Current learning rate: 0.0051 +2026-04-13 01:01:57.682859: train_loss -0.3527 +2026-04-13 01:01:57.689592: val_loss -0.3662 +2026-04-13 01:01:57.691579: Pseudo dice [0.0, 0.0, 0.7364, 0.0, 0.0, 0.7434, 0.6248] +2026-04-13 01:01:57.693932: Epoch time: 100.28 s +2026-04-13 01:01:58.859496: +2026-04-13 01:01:58.861311: Epoch 2110 +2026-04-13 01:01:58.863142: Current learning rate: 0.00509 +2026-04-13 01:03:39.167055: train_loss -0.3849 +2026-04-13 01:03:39.174236: val_loss -0.3946 +2026-04-13 01:03:39.177646: Pseudo dice [0.0, 0.0, 0.7032, 0.7207, 0.1329, 0.7439, 0.835] +2026-04-13 01:03:39.180449: Epoch time: 100.31 s +2026-04-13 01:03:40.373576: +2026-04-13 01:03:40.375496: Epoch 2111 +2026-04-13 01:03:40.377137: Current learning rate: 0.00509 +2026-04-13 01:05:20.979586: train_loss -0.3827 +2026-04-13 01:05:20.986758: val_loss -0.338 +2026-04-13 01:05:20.988900: Pseudo dice [0.0, 0.0, 0.4637, 0.0553, 0.3712, 0.6942, 0.7127] +2026-04-13 01:05:20.992085: Epoch time: 100.61 s +2026-04-13 01:05:22.194585: +2026-04-13 01:05:22.196815: Epoch 2112 +2026-04-13 01:05:22.198989: Current learning rate: 0.00509 +2026-04-13 01:07:02.333638: train_loss -0.3895 +2026-04-13 01:07:02.342629: val_loss -0.3768 +2026-04-13 01:07:02.344396: Pseudo dice [0.4113, 0.0, 0.7058, 0.0, 0.4192, 0.6305, 0.6141] +2026-04-13 01:07:02.347059: Epoch time: 100.14 s +2026-04-13 01:07:03.519040: +2026-04-13 01:07:03.520714: Epoch 2113 +2026-04-13 01:07:03.522294: Current learning rate: 0.00509 +2026-04-13 01:08:43.693929: train_loss -0.3899 +2026-04-13 01:08:43.699216: val_loss -0.3252 +2026-04-13 01:08:43.700872: Pseudo dice [0.0369, 0.0, 0.5614, 0.0, 0.4568, 0.7158, 0.6863] +2026-04-13 01:08:43.702951: Epoch time: 100.18 s +2026-04-13 01:08:44.874126: +2026-04-13 01:08:44.875989: Epoch 2114 +2026-04-13 01:08:44.877671: Current learning rate: 0.00508 +2026-04-13 01:10:24.917897: train_loss -0.409 +2026-04-13 01:10:24.923378: val_loss -0.3559 +2026-04-13 01:10:24.925262: Pseudo dice [0.0, 0.0, 0.7687, 0.3499, 0.2654, 0.292, 0.6779] +2026-04-13 01:10:24.927556: Epoch time: 100.05 s +2026-04-13 01:10:26.095465: +2026-04-13 01:10:26.097242: Epoch 2115 +2026-04-13 01:10:26.098886: Current learning rate: 0.00508 +2026-04-13 01:12:06.206150: train_loss -0.3873 +2026-04-13 01:12:06.215996: val_loss -0.3537 +2026-04-13 01:12:06.218502: Pseudo dice [0.1689, 0.0, 0.6905, 0.0, 0.0, 0.7597, 0.7441] +2026-04-13 01:12:06.221137: Epoch time: 100.11 s +2026-04-13 01:12:07.378890: +2026-04-13 01:12:07.380933: Epoch 2116 +2026-04-13 01:12:07.382674: Current learning rate: 0.00508 +2026-04-13 01:13:47.417624: train_loss -0.374 +2026-04-13 01:13:47.424493: val_loss -0.3905 +2026-04-13 01:13:47.426578: Pseudo dice [0.0449, 0.0, 0.4518, 0.2143, 0.2609, 0.5821, 0.8593] +2026-04-13 01:13:47.429257: Epoch time: 100.04 s +2026-04-13 01:13:48.593910: +2026-04-13 01:13:48.596113: Epoch 2117 +2026-04-13 01:13:48.597625: Current learning rate: 0.00508 +2026-04-13 01:15:28.776997: train_loss -0.3657 +2026-04-13 01:15:28.783689: val_loss -0.3685 +2026-04-13 01:15:28.787847: Pseudo dice [0.0, 0.0, 0.7556, 0.0, 0.3057, 0.3107, 0.8269] +2026-04-13 01:15:28.790463: Epoch time: 100.19 s +2026-04-13 01:15:29.958464: +2026-04-13 01:15:29.961292: Epoch 2118 +2026-04-13 01:15:29.964279: Current learning rate: 0.00507 +2026-04-13 01:17:09.892605: train_loss -0.3673 +2026-04-13 01:17:09.898644: val_loss -0.2876 +2026-04-13 01:17:09.900550: Pseudo dice [0.0, 0.0, 0.716, 0.0, 0.3945, 0.3399, 0.856] +2026-04-13 01:17:09.902973: Epoch time: 99.94 s +2026-04-13 01:17:11.057974: +2026-04-13 01:17:11.059861: Epoch 2119 +2026-04-13 01:17:11.061487: Current learning rate: 0.00507 +2026-04-13 01:18:51.027733: train_loss -0.3614 +2026-04-13 01:18:51.034140: val_loss -0.3264 +2026-04-13 01:18:51.036538: Pseudo dice [0.2682, 0.0, 0.6024, 0.0026, 0.0074, 0.4474, 0.1172] +2026-04-13 01:18:51.039124: Epoch time: 99.97 s +2026-04-13 01:18:52.214417: +2026-04-13 01:18:52.216234: Epoch 2120 +2026-04-13 01:18:52.217883: Current learning rate: 0.00507 +2026-04-13 01:20:32.112351: train_loss -0.3462 +2026-04-13 01:20:32.117690: val_loss -0.3309 +2026-04-13 01:20:32.119527: Pseudo dice [0.4014, 0.0, 0.6312, 0.0, 0.2495, 0.7389, 0.1733] +2026-04-13 01:20:32.122056: Epoch time: 99.9 s +2026-04-13 01:20:33.313421: +2026-04-13 01:20:33.315278: Epoch 2121 +2026-04-13 01:20:33.317048: Current learning rate: 0.00507 +2026-04-13 01:22:13.339082: train_loss -0.3902 +2026-04-13 01:22:13.345487: val_loss -0.3432 +2026-04-13 01:22:13.348035: Pseudo dice [0.4977, 0.0, 0.6125, 0.0, 0.2286, 0.5903, 0.6728] +2026-04-13 01:22:13.350518: Epoch time: 100.03 s +2026-04-13 01:22:14.521786: +2026-04-13 01:22:14.524055: Epoch 2122 +2026-04-13 01:22:14.525834: Current learning rate: 0.00506 +2026-04-13 01:23:54.488372: train_loss -0.3872 +2026-04-13 01:23:54.493351: val_loss -0.3513 +2026-04-13 01:23:54.495217: Pseudo dice [0.389, 0.0, 0.3662, 0.1519, 0.3638, 0.7164, 0.5292] +2026-04-13 01:23:54.497804: Epoch time: 99.97 s +2026-04-13 01:23:55.679895: +2026-04-13 01:23:55.681641: Epoch 2123 +2026-04-13 01:23:55.683215: Current learning rate: 0.00506 +2026-04-13 01:25:35.688956: train_loss -0.391 +2026-04-13 01:25:35.694793: val_loss -0.2932 +2026-04-13 01:25:35.697213: Pseudo dice [0.0, 0.0, 0.5578, 0.0, 0.2005, 0.3453, 0.5768] +2026-04-13 01:25:35.700354: Epoch time: 100.01 s +2026-04-13 01:25:36.890173: +2026-04-13 01:25:36.891979: Epoch 2124 +2026-04-13 01:25:36.893676: Current learning rate: 0.00506 +2026-04-13 01:27:17.047058: train_loss -0.3863 +2026-04-13 01:27:17.054549: val_loss -0.3861 +2026-04-13 01:27:17.056671: Pseudo dice [0.5818, 0.0, 0.6165, 0.6115, 0.0, 0.8518, 0.7632] +2026-04-13 01:27:17.058687: Epoch time: 100.16 s +2026-04-13 01:27:18.226654: +2026-04-13 01:27:18.228430: Epoch 2125 +2026-04-13 01:27:18.230220: Current learning rate: 0.00506 +2026-04-13 01:28:58.247719: train_loss -0.3936 +2026-04-13 01:28:58.253903: val_loss -0.3882 +2026-04-13 01:28:58.258187: Pseudo dice [0.4278, 0.0, 0.7237, 0.0, 0.219, 0.6375, 0.6273] +2026-04-13 01:28:58.260842: Epoch time: 100.02 s +2026-04-13 01:29:00.451947: +2026-04-13 01:29:00.455181: Epoch 2126 +2026-04-13 01:29:00.457263: Current learning rate: 0.00505 +2026-04-13 01:30:40.703365: train_loss -0.4039 +2026-04-13 01:30:40.711190: val_loss -0.3124 +2026-04-13 01:30:40.713883: Pseudo dice [0.8398, 0.0, 0.6113, 0.055, 0.4117, 0.6468, 0.2803] +2026-04-13 01:30:40.716838: Epoch time: 100.25 s +2026-04-13 01:30:41.928156: +2026-04-13 01:30:41.929946: Epoch 2127 +2026-04-13 01:30:41.931808: Current learning rate: 0.00505 +2026-04-13 01:32:22.091697: train_loss -0.4006 +2026-04-13 01:32:22.096948: val_loss -0.3895 +2026-04-13 01:32:22.098573: Pseudo dice [0.4567, 0.0, 0.6094, 0.2464, 0.4249, 0.7552, 0.7038] +2026-04-13 01:32:22.100880: Epoch time: 100.17 s +2026-04-13 01:32:23.263608: +2026-04-13 01:32:23.265458: Epoch 2128 +2026-04-13 01:32:23.267440: Current learning rate: 0.00505 +2026-04-13 01:34:03.536727: train_loss -0.4112 +2026-04-13 01:34:03.543332: val_loss -0.325 +2026-04-13 01:34:03.545702: Pseudo dice [0.4959, 0.0, 0.1238, 0.0, 0.2691, 0.5582, 0.5441] +2026-04-13 01:34:03.548228: Epoch time: 100.28 s +2026-04-13 01:34:04.719663: +2026-04-13 01:34:04.721656: Epoch 2129 +2026-04-13 01:34:04.723608: Current learning rate: 0.00505 +2026-04-13 01:35:45.010386: train_loss -0.35 +2026-04-13 01:35:45.016159: val_loss -0.3595 +2026-04-13 01:35:45.018700: Pseudo dice [0.0, 0.0, 0.4358, 0.0, 0.1701, 0.8134, 0.3911] +2026-04-13 01:35:45.020723: Epoch time: 100.29 s +2026-04-13 01:35:46.208716: +2026-04-13 01:35:46.211684: Epoch 2130 +2026-04-13 01:35:46.214382: Current learning rate: 0.00504 +2026-04-13 01:37:26.376963: train_loss -0.3226 +2026-04-13 01:37:26.383225: val_loss -0.3239 +2026-04-13 01:37:26.385501: Pseudo dice [0.0067, 0.0, 0.6132, 0.0005, 0.3084, 0.295, 0.0] +2026-04-13 01:37:26.387633: Epoch time: 100.17 s +2026-04-13 01:37:27.562984: +2026-04-13 01:37:27.565047: Epoch 2131 +2026-04-13 01:37:27.566742: Current learning rate: 0.00504 +2026-04-13 01:39:07.730413: train_loss -0.353 +2026-04-13 01:39:07.736301: val_loss -0.3516 +2026-04-13 01:39:07.738075: Pseudo dice [0.0, 0.0, 0.5905, 0.0, 0.0, 0.7375, 0.4535] +2026-04-13 01:39:07.740858: Epoch time: 100.17 s +2026-04-13 01:39:08.894718: +2026-04-13 01:39:08.896652: Epoch 2132 +2026-04-13 01:39:08.898522: Current learning rate: 0.00504 +2026-04-13 01:40:48.960067: train_loss -0.3596 +2026-04-13 01:40:48.965425: val_loss -0.3546 +2026-04-13 01:40:48.967967: Pseudo dice [0.1196, 0.0, 0.7235, 0.0, 0.1633, 0.7881, 0.7084] +2026-04-13 01:40:48.970697: Epoch time: 100.07 s +2026-04-13 01:40:50.153894: +2026-04-13 01:40:50.155960: Epoch 2133 +2026-04-13 01:40:50.157887: Current learning rate: 0.00504 +2026-04-13 01:42:30.146804: train_loss -0.3947 +2026-04-13 01:42:30.151694: val_loss -0.3556 +2026-04-13 01:42:30.153417: Pseudo dice [0.0, 0.0, 0.6708, 0.0, 0.1194, 0.5093, 0.7322] +2026-04-13 01:42:30.155844: Epoch time: 100.0 s +2026-04-13 01:42:31.317312: +2026-04-13 01:42:31.319149: Epoch 2134 +2026-04-13 01:42:31.320909: Current learning rate: 0.00503 +2026-04-13 01:44:11.677014: train_loss -0.3751 +2026-04-13 01:44:11.687931: val_loss -0.3551 +2026-04-13 01:44:11.690912: Pseudo dice [0.0, 0.0, 0.3787, 0.0, 0.3288, 0.1637, 0.7514] +2026-04-13 01:44:11.693826: Epoch time: 100.36 s +2026-04-13 01:44:12.910922: +2026-04-13 01:44:12.912697: Epoch 2135 +2026-04-13 01:44:12.914490: Current learning rate: 0.00503 +2026-04-13 01:45:53.079212: train_loss -0.3617 +2026-04-13 01:45:53.084841: val_loss -0.3799 +2026-04-13 01:45:53.086744: Pseudo dice [0.0, 0.0, 0.7031, 0.0, 0.2467, 0.4968, 0.3899] +2026-04-13 01:45:53.089255: Epoch time: 100.17 s +2026-04-13 01:45:54.254552: +2026-04-13 01:45:54.256631: Epoch 2136 +2026-04-13 01:45:54.258553: Current learning rate: 0.00503 +2026-04-13 01:47:34.575056: train_loss -0.3969 +2026-04-13 01:47:34.581672: val_loss -0.3524 +2026-04-13 01:47:34.585020: Pseudo dice [0.5361, 0.0, 0.5794, 0.0856, 0.3763, 0.8382, 0.3607] +2026-04-13 01:47:34.587768: Epoch time: 100.32 s +2026-04-13 01:47:35.778468: +2026-04-13 01:47:35.780275: Epoch 2137 +2026-04-13 01:47:35.782370: Current learning rate: 0.00503 +2026-04-13 01:49:15.948787: train_loss -0.3878 +2026-04-13 01:49:15.955147: val_loss -0.2972 +2026-04-13 01:49:15.957381: Pseudo dice [0.2766, 0.0, 0.5041, 0.0244, 0.3184, 0.547, 0.6975] +2026-04-13 01:49:15.959869: Epoch time: 100.17 s +2026-04-13 01:49:17.149826: +2026-04-13 01:49:17.151589: Epoch 2138 +2026-04-13 01:49:17.153479: Current learning rate: 0.00502 +2026-04-13 01:50:57.621672: train_loss -0.4087 +2026-04-13 01:50:57.627954: val_loss -0.3664 +2026-04-13 01:50:57.630346: Pseudo dice [0.3371, 0.0, 0.4707, 0.0319, 0.3965, 0.8765, 0.8183] +2026-04-13 01:50:57.633092: Epoch time: 100.47 s +2026-04-13 01:50:58.800390: +2026-04-13 01:50:58.802672: Epoch 2139 +2026-04-13 01:50:58.804878: Current learning rate: 0.00502 +2026-04-13 01:52:38.946445: train_loss -0.414 +2026-04-13 01:52:38.952326: val_loss -0.4015 +2026-04-13 01:52:38.954756: Pseudo dice [0.0, 0.0, 0.5929, 0.0, 0.4948, 0.7143, 0.7511] +2026-04-13 01:52:38.957186: Epoch time: 100.15 s +2026-04-13 01:52:40.150331: +2026-04-13 01:52:40.152101: Epoch 2140 +2026-04-13 01:52:40.153926: Current learning rate: 0.00502 +2026-04-13 01:54:20.351361: train_loss -0.3978 +2026-04-13 01:54:20.357239: val_loss -0.3518 +2026-04-13 01:54:20.359051: Pseudo dice [0.0, 0.0, 0.739, 0.0841, 0.2722, 0.7007, 0.8231] +2026-04-13 01:54:20.361373: Epoch time: 100.2 s +2026-04-13 01:54:21.553284: +2026-04-13 01:54:21.555165: Epoch 2141 +2026-04-13 01:54:21.556902: Current learning rate: 0.00502 +2026-04-13 01:56:01.610993: train_loss -0.3921 +2026-04-13 01:56:01.618010: val_loss -0.3603 +2026-04-13 01:56:01.620028: Pseudo dice [0.0, 0.0, 0.6004, 0.1502, 0.2096, 0.7108, 0.7768] +2026-04-13 01:56:01.622430: Epoch time: 100.06 s +2026-04-13 01:56:02.801320: +2026-04-13 01:56:02.803243: Epoch 2142 +2026-04-13 01:56:02.804964: Current learning rate: 0.00502 +2026-04-13 01:57:43.032677: train_loss -0.393 +2026-04-13 01:57:43.040278: val_loss -0.3185 +2026-04-13 01:57:43.043808: Pseudo dice [0.0, 0.0, 0.6198, 0.0182, 0.2246, 0.6499, 0.5552] +2026-04-13 01:57:43.046859: Epoch time: 100.23 s +2026-04-13 01:57:44.218675: +2026-04-13 01:57:44.220628: Epoch 2143 +2026-04-13 01:57:44.222677: Current learning rate: 0.00501 +2026-04-13 01:59:24.206997: train_loss -0.3693 +2026-04-13 01:59:24.212730: val_loss -0.3828 +2026-04-13 01:59:24.215135: Pseudo dice [0.0, 0.0, 0.6868, 0.7172, 0.2638, 0.8321, 0.5109] +2026-04-13 01:59:24.217663: Epoch time: 99.99 s +2026-04-13 01:59:25.391396: +2026-04-13 01:59:25.393588: Epoch 2144 +2026-04-13 01:59:25.397464: Current learning rate: 0.00501 +2026-04-13 02:01:05.491768: train_loss -0.3845 +2026-04-13 02:01:05.497397: val_loss -0.3824 +2026-04-13 02:01:05.499421: Pseudo dice [0.0, 0.0, 0.551, 0.0548, 0.5253, 0.6512, 0.5741] +2026-04-13 02:01:05.502238: Epoch time: 100.1 s +2026-04-13 02:01:06.673879: +2026-04-13 02:01:06.675780: Epoch 2145 +2026-04-13 02:01:06.677528: Current learning rate: 0.00501 +2026-04-13 02:02:46.596798: train_loss -0.3944 +2026-04-13 02:02:46.603124: val_loss -0.3391 +2026-04-13 02:02:46.605550: Pseudo dice [0.0, 0.0, 0.5353, 0.1368, 0.282, 0.8268, 0.6491] +2026-04-13 02:02:46.608612: Epoch time: 99.93 s +2026-04-13 02:02:47.771574: +2026-04-13 02:02:47.774663: Epoch 2146 +2026-04-13 02:02:47.776632: Current learning rate: 0.00501 +2026-04-13 02:04:27.785879: train_loss -0.3998 +2026-04-13 02:04:27.792077: val_loss -0.3761 +2026-04-13 02:04:27.793915: Pseudo dice [0.0, 0.0, 0.8069, 0.581, 0.0, 0.7774, 0.8711] +2026-04-13 02:04:27.797373: Epoch time: 100.02 s +2026-04-13 02:04:30.010375: +2026-04-13 02:04:30.012109: Epoch 2147 +2026-04-13 02:04:30.013709: Current learning rate: 0.005 +2026-04-13 02:06:10.026291: train_loss -0.3788 +2026-04-13 02:06:10.031261: val_loss -0.3704 +2026-04-13 02:06:10.033037: Pseudo dice [0.0, 0.0, 0.6448, 0.1622, 0.3741, 0.699, 0.7946] +2026-04-13 02:06:10.035189: Epoch time: 100.02 s +2026-04-13 02:06:11.229516: +2026-04-13 02:06:11.231650: Epoch 2148 +2026-04-13 02:06:11.233229: Current learning rate: 0.005 +2026-04-13 02:07:51.228810: train_loss -0.4025 +2026-04-13 02:07:51.235472: val_loss -0.3548 +2026-04-13 02:07:51.237730: Pseudo dice [0.0, 0.0, 0.629, 0.0795, 0.297, 0.7415, 0.6986] +2026-04-13 02:07:51.240117: Epoch time: 100.0 s +2026-04-13 02:07:52.419564: +2026-04-13 02:07:52.422109: Epoch 2149 +2026-04-13 02:07:52.423995: Current learning rate: 0.005 +2026-04-13 02:09:32.425178: train_loss -0.3931 +2026-04-13 02:09:32.430653: val_loss -0.3893 +2026-04-13 02:09:32.432812: Pseudo dice [0.0, 0.0, 0.537, 0.5017, 0.0, 0.5143, 0.597] +2026-04-13 02:09:32.435390: Epoch time: 100.01 s +2026-04-13 02:09:35.241720: +2026-04-13 02:09:35.243544: Epoch 2150 +2026-04-13 02:09:35.245442: Current learning rate: 0.005 +2026-04-13 02:11:15.188882: train_loss -0.3768 +2026-04-13 02:11:15.193991: val_loss -0.2978 +2026-04-13 02:11:15.196151: Pseudo dice [0.0, 0.0, 0.633, 0.0, 0.1972, 0.5873, 0.5129] +2026-04-13 02:11:15.198536: Epoch time: 99.95 s +2026-04-13 02:11:16.362702: +2026-04-13 02:11:16.364406: Epoch 2151 +2026-04-13 02:11:16.365932: Current learning rate: 0.00499 +2026-04-13 02:12:56.269675: train_loss -0.3876 +2026-04-13 02:12:56.277968: val_loss -0.3808 +2026-04-13 02:12:56.280318: Pseudo dice [0.5177, 0.0, 0.8051, 0.0, 0.2862, 0.7828, 0.7656] +2026-04-13 02:12:56.282781: Epoch time: 99.91 s +2026-04-13 02:12:57.470082: +2026-04-13 02:12:57.472433: Epoch 2152 +2026-04-13 02:12:57.474350: Current learning rate: 0.00499 +2026-04-13 02:14:37.406089: train_loss -0.376 +2026-04-13 02:14:37.411989: val_loss -0.324 +2026-04-13 02:14:37.414307: Pseudo dice [0.3536, 0.0, 0.2637, 0.0, 0.1235, 0.7681, 0.6072] +2026-04-13 02:14:37.417289: Epoch time: 99.94 s +2026-04-13 02:14:38.581161: +2026-04-13 02:14:38.582812: Epoch 2153 +2026-04-13 02:14:38.584542: Current learning rate: 0.00499 +2026-04-13 02:16:18.573637: train_loss -0.3068 +2026-04-13 02:16:18.580329: val_loss -0.3586 +2026-04-13 02:16:18.582076: Pseudo dice [0.0, 0.0, 0.6647, 0.0, 0.1688, 0.475, 0.4355] +2026-04-13 02:16:18.584469: Epoch time: 100.0 s +2026-04-13 02:16:19.764406: +2026-04-13 02:16:19.766245: Epoch 2154 +2026-04-13 02:16:19.767878: Current learning rate: 0.00499 +2026-04-13 02:17:59.918690: train_loss -0.3451 +2026-04-13 02:17:59.926307: val_loss -0.3473 +2026-04-13 02:17:59.928432: Pseudo dice [0.0, 0.0, 0.5904, 0.0, 0.3612, 0.5967, 0.4801] +2026-04-13 02:17:59.931204: Epoch time: 100.16 s +2026-04-13 02:18:01.104347: +2026-04-13 02:18:01.106442: Epoch 2155 +2026-04-13 02:18:01.108205: Current learning rate: 0.00498 +2026-04-13 02:19:41.490308: train_loss -0.3749 +2026-04-13 02:19:41.497694: val_loss -0.3346 +2026-04-13 02:19:41.500375: Pseudo dice [0.0, 0.0, 0.4671, 0.141, 0.3555, 0.6345, 0.4812] +2026-04-13 02:19:41.505677: Epoch time: 100.39 s +2026-04-13 02:19:42.691272: +2026-04-13 02:19:42.693342: Epoch 2156 +2026-04-13 02:19:42.695589: Current learning rate: 0.00498 +2026-04-13 02:21:22.777049: train_loss -0.3922 +2026-04-13 02:21:22.782889: val_loss -0.2186 +2026-04-13 02:21:22.785768: Pseudo dice [0.0, 0.0, 0.7308, 0.0003, 0.1638, 0.6206, 0.6106] +2026-04-13 02:21:22.788461: Epoch time: 100.09 s +2026-04-13 02:21:23.946194: +2026-04-13 02:21:23.948334: Epoch 2157 +2026-04-13 02:21:23.950197: Current learning rate: 0.00498 +2026-04-13 02:23:04.173254: train_loss -0.3921 +2026-04-13 02:23:04.179007: val_loss -0.4187 +2026-04-13 02:23:04.181095: Pseudo dice [0.4844, 0.0, 0.5946, 0.8378, 0.3502, 0.7293, 0.7305] +2026-04-13 02:23:04.183228: Epoch time: 100.23 s +2026-04-13 02:23:05.335369: +2026-04-13 02:23:05.337613: Epoch 2158 +2026-04-13 02:23:05.339424: Current learning rate: 0.00498 +2026-04-13 02:24:45.621978: train_loss -0.3778 +2026-04-13 02:24:45.631262: val_loss -0.3101 +2026-04-13 02:24:45.634778: Pseudo dice [0.1907, 0.0, 0.7029, 0.0, 0.3902, 0.3323, 0.5651] +2026-04-13 02:24:45.637706: Epoch time: 100.29 s +2026-04-13 02:24:46.802752: +2026-04-13 02:24:46.804668: Epoch 2159 +2026-04-13 02:24:46.806416: Current learning rate: 0.00497 +2026-04-13 02:26:26.712837: train_loss -0.392 +2026-04-13 02:26:26.718087: val_loss -0.3547 +2026-04-13 02:26:26.719621: Pseudo dice [0.0763, 0.0, 0.727, 0.0, 0.1519, 0.7564, 0.7884] +2026-04-13 02:26:26.722035: Epoch time: 99.91 s +2026-04-13 02:26:27.894849: +2026-04-13 02:26:27.896753: Epoch 2160 +2026-04-13 02:26:27.898404: Current learning rate: 0.00497 +2026-04-13 02:28:08.103859: train_loss -0.3768 +2026-04-13 02:28:08.109692: val_loss -0.3317 +2026-04-13 02:28:08.111258: Pseudo dice [0.0821, 0.0, 0.6273, 0.0, 0.0, 0.4736, 0.6804] +2026-04-13 02:28:08.114122: Epoch time: 100.21 s +2026-04-13 02:28:09.292034: +2026-04-13 02:28:09.294023: Epoch 2161 +2026-04-13 02:28:09.295795: Current learning rate: 0.00497 +2026-04-13 02:29:49.437118: train_loss -0.3599 +2026-04-13 02:29:49.443542: val_loss -0.3745 +2026-04-13 02:29:49.446880: Pseudo dice [0.0962, 0.0, 0.5324, 0.6783, 0.0, 0.0526, 0.7425] +2026-04-13 02:29:49.450266: Epoch time: 100.15 s +2026-04-13 02:29:50.598783: +2026-04-13 02:29:50.600510: Epoch 2162 +2026-04-13 02:29:50.602197: Current learning rate: 0.00497 +2026-04-13 02:31:30.832310: train_loss -0.3775 +2026-04-13 02:31:30.839503: val_loss -0.3079 +2026-04-13 02:31:30.842066: Pseudo dice [0.4786, 0.0, 0.701, 0.0, 0.1975, 0.0317, 0.8078] +2026-04-13 02:31:30.844967: Epoch time: 100.24 s +2026-04-13 02:31:32.007228: +2026-04-13 02:31:32.011248: Epoch 2163 +2026-04-13 02:31:32.013803: Current learning rate: 0.00496 +2026-04-13 02:33:12.035108: train_loss -0.3888 +2026-04-13 02:33:12.042255: val_loss -0.3941 +2026-04-13 02:33:12.044676: Pseudo dice [0.0, 0.0, 0.6184, 0.0, 0.2356, 0.4514, 0.7759] +2026-04-13 02:33:12.047410: Epoch time: 100.03 s +2026-04-13 02:33:13.210664: +2026-04-13 02:33:13.213372: Epoch 2164 +2026-04-13 02:33:13.215301: Current learning rate: 0.00496 +2026-04-13 02:34:53.321811: train_loss -0.3751 +2026-04-13 02:34:53.330461: val_loss -0.38 +2026-04-13 02:34:53.333314: Pseudo dice [0.0, 0.0, 0.5243, 0.0, 0.3129, 0.7485, 0.6746] +2026-04-13 02:34:53.336453: Epoch time: 100.11 s +2026-04-13 02:34:54.505523: +2026-04-13 02:34:54.507831: Epoch 2165 +2026-04-13 02:34:54.509706: Current learning rate: 0.00496 +2026-04-13 02:36:34.953652: train_loss -0.322 +2026-04-13 02:36:34.959835: val_loss -0.2884 +2026-04-13 02:36:34.961944: Pseudo dice [0.0, 0.0, 0.1498, 0.0, 0.0, 0.3241, 0.0622] +2026-04-13 02:36:34.965260: Epoch time: 100.45 s +2026-04-13 02:36:36.154754: +2026-04-13 02:36:36.156741: Epoch 2166 +2026-04-13 02:36:36.158495: Current learning rate: 0.00496 +2026-04-13 02:38:16.069274: train_loss -0.2699 +2026-04-13 02:38:16.075284: val_loss -0.312 +2026-04-13 02:38:16.077533: Pseudo dice [0.0, 0.0, 0.6521, 0.0, 0.0, 0.0, 0.0] +2026-04-13 02:38:16.080349: Epoch time: 99.92 s +2026-04-13 02:38:18.244784: +2026-04-13 02:38:18.246804: Epoch 2167 +2026-04-13 02:38:18.248430: Current learning rate: 0.00495 +2026-04-13 02:39:58.274767: train_loss -0.3252 +2026-04-13 02:39:58.280760: val_loss -0.3459 +2026-04-13 02:39:58.282756: Pseudo dice [0.0, 0.0, 0.7112, 0.0, 0.0628, 0.4199, 0.6874] +2026-04-13 02:39:58.285262: Epoch time: 100.03 s +2026-04-13 02:39:59.457994: +2026-04-13 02:39:59.460437: Epoch 2168 +2026-04-13 02:39:59.462096: Current learning rate: 0.00495 +2026-04-13 02:41:39.687083: train_loss -0.365 +2026-04-13 02:41:39.692052: val_loss -0.2926 +2026-04-13 02:41:39.693784: Pseudo dice [0.1431, 0.0, 0.6875, 0.0024, 0.4953, 0.6454, 0.6587] +2026-04-13 02:41:39.695943: Epoch time: 100.23 s +2026-04-13 02:41:40.853547: +2026-04-13 02:41:40.855313: Epoch 2169 +2026-04-13 02:41:40.856865: Current learning rate: 0.00495 +2026-04-13 02:43:20.982695: train_loss -0.3748 +2026-04-13 02:43:20.989366: val_loss -0.3706 +2026-04-13 02:43:20.991801: Pseudo dice [0.0, 0.0, 0.8232, 0.0, 0.1424, 0.7265, 0.4749] +2026-04-13 02:43:20.994873: Epoch time: 100.13 s +2026-04-13 02:43:22.174725: +2026-04-13 02:43:22.177026: Epoch 2170 +2026-04-13 02:43:22.178806: Current learning rate: 0.00495 +2026-04-13 02:45:02.363899: train_loss -0.3843 +2026-04-13 02:45:02.369054: val_loss -0.1807 +2026-04-13 02:45:02.370710: Pseudo dice [0.0, 0.0, 0.6385, 0.0176, 0.0, 0.7692, 0.8109] +2026-04-13 02:45:02.373073: Epoch time: 100.19 s +2026-04-13 02:45:03.544354: +2026-04-13 02:45:03.546387: Epoch 2171 +2026-04-13 02:45:03.548196: Current learning rate: 0.00494 +2026-04-13 02:46:43.750628: train_loss -0.3819 +2026-04-13 02:46:43.756757: val_loss -0.3368 +2026-04-13 02:46:43.758613: Pseudo dice [0.3823, 0.0, 0.6379, 0.0, 0.0423, 0.4862, 0.5659] +2026-04-13 02:46:43.760460: Epoch time: 100.21 s +2026-04-13 02:46:44.940769: +2026-04-13 02:46:44.942624: Epoch 2172 +2026-04-13 02:46:44.944082: Current learning rate: 0.00494 +2026-04-13 02:48:24.964965: train_loss -0.3907 +2026-04-13 02:48:24.969960: val_loss -0.3804 +2026-04-13 02:48:24.971871: Pseudo dice [0.1918, 0.0, 0.6431, 0.0, 0.3239, 0.8437, 0.6953] +2026-04-13 02:48:24.974095: Epoch time: 100.03 s +2026-04-13 02:48:26.155634: +2026-04-13 02:48:26.157417: Epoch 2173 +2026-04-13 02:48:26.159776: Current learning rate: 0.00494 +2026-04-13 02:50:07.330181: train_loss -0.3856 +2026-04-13 02:50:07.338882: val_loss -0.3727 +2026-04-13 02:50:07.341190: Pseudo dice [0.0, 0.0, 0.3755, 0.6583, 0.4659, 0.2957, 0.8657] +2026-04-13 02:50:07.343626: Epoch time: 101.18 s +2026-04-13 02:50:08.527949: +2026-04-13 02:50:08.530345: Epoch 2174 +2026-04-13 02:50:08.533014: Current learning rate: 0.00494 +2026-04-13 02:51:48.762030: train_loss -0.3568 +2026-04-13 02:51:48.766804: val_loss -0.3758 +2026-04-13 02:51:48.768534: Pseudo dice [0.3555, 0.0, 0.6793, 0.0, 0.2789, 0.7701, 0.6881] +2026-04-13 02:51:48.770312: Epoch time: 100.24 s +2026-04-13 02:51:49.950711: +2026-04-13 02:51:49.952400: Epoch 2175 +2026-04-13 02:51:49.954392: Current learning rate: 0.00493 +2026-04-13 02:53:30.160875: train_loss -0.3914 +2026-04-13 02:53:30.166154: val_loss -0.3162 +2026-04-13 02:53:30.167840: Pseudo dice [0.0011, 0.0, 0.7192, 0.0415, 0.3601, 0.7421, 0.7547] +2026-04-13 02:53:30.170401: Epoch time: 100.21 s +2026-04-13 02:53:31.332389: +2026-04-13 02:53:31.334276: Epoch 2176 +2026-04-13 02:53:31.336186: Current learning rate: 0.00493 +2026-04-13 02:55:11.481177: train_loss -0.3341 +2026-04-13 02:55:11.485799: val_loss -0.3861 +2026-04-13 02:55:11.487550: Pseudo dice [0.5057, 0.0, 0.6955, 0.0, 0.2852, 0.0499, 0.7626] +2026-04-13 02:55:11.489630: Epoch time: 100.15 s +2026-04-13 02:55:12.644755: +2026-04-13 02:55:12.646507: Epoch 2177 +2026-04-13 02:55:12.647988: Current learning rate: 0.00493 +2026-04-13 02:56:52.722531: train_loss -0.3646 +2026-04-13 02:56:52.728223: val_loss -0.349 +2026-04-13 02:56:52.729983: Pseudo dice [0.5426, 0.0, 0.5593, 0.1069, 0.1909, 0.6964, 0.691] +2026-04-13 02:56:52.732155: Epoch time: 100.08 s +2026-04-13 02:56:53.906573: +2026-04-13 02:56:53.908539: Epoch 2178 +2026-04-13 02:56:53.910039: Current learning rate: 0.00493 +2026-04-13 02:58:33.996164: train_loss -0.3743 +2026-04-13 02:58:34.001184: val_loss -0.369 +2026-04-13 02:58:34.002964: Pseudo dice [0.5701, 0.0, 0.589, 0.0, 0.0, 0.687, 0.7462] +2026-04-13 02:58:34.005105: Epoch time: 100.09 s +2026-04-13 02:58:35.181653: +2026-04-13 02:58:35.184124: Epoch 2179 +2026-04-13 02:58:35.185921: Current learning rate: 0.00493 +2026-04-13 03:00:16.472752: train_loss -0.3725 +2026-04-13 03:00:16.477967: val_loss -0.349 +2026-04-13 03:00:16.479914: Pseudo dice [0.0, 0.0, 0.6532, 0.0, 0.0705, 0.8189, 0.4666] +2026-04-13 03:00:16.481932: Epoch time: 101.29 s +2026-04-13 03:00:17.659800: +2026-04-13 03:00:17.661994: Epoch 2180 +2026-04-13 03:00:17.664194: Current learning rate: 0.00492 +2026-04-13 03:01:57.741211: train_loss -0.3299 +2026-04-13 03:01:57.748817: val_loss -0.3546 +2026-04-13 03:01:57.751815: Pseudo dice [0.206, 0.0, 0.6563, 0.0, 0.1281, 0.5945, 0.5232] +2026-04-13 03:01:57.754488: Epoch time: 100.08 s +2026-04-13 03:01:58.968205: +2026-04-13 03:01:58.973013: Epoch 2181 +2026-04-13 03:01:58.974793: Current learning rate: 0.00492 +2026-04-13 03:03:39.043765: train_loss -0.3335 +2026-04-13 03:03:39.049804: val_loss -0.3099 +2026-04-13 03:03:39.051872: Pseudo dice [0.6214, 0.0, 0.6638, 0.0, 0.2016, 0.4001, 0.5047] +2026-04-13 03:03:39.054039: Epoch time: 100.08 s +2026-04-13 03:03:40.222979: +2026-04-13 03:03:40.224836: Epoch 2182 +2026-04-13 03:03:40.226878: Current learning rate: 0.00492 +2026-04-13 03:05:20.260053: train_loss -0.3482 +2026-04-13 03:05:20.265216: val_loss -0.3379 +2026-04-13 03:05:20.267051: Pseudo dice [0.0, 0.0, 0.408, 0.0, 0.0, 0.0348, 0.6676] +2026-04-13 03:05:20.270009: Epoch time: 100.04 s +2026-04-13 03:05:21.438823: +2026-04-13 03:05:21.440928: Epoch 2183 +2026-04-13 03:05:21.442817: Current learning rate: 0.00492 +2026-04-13 03:07:01.549284: train_loss -0.3463 +2026-04-13 03:07:01.555688: val_loss -0.3205 +2026-04-13 03:07:01.558409: Pseudo dice [0.4197, 0.0, 0.6608, 0.0, 0.2888, 0.2469, 0.6147] +2026-04-13 03:07:01.561691: Epoch time: 100.11 s +2026-04-13 03:07:02.755695: +2026-04-13 03:07:02.758877: Epoch 2184 +2026-04-13 03:07:02.761247: Current learning rate: 0.00491 +2026-04-13 03:08:42.994031: train_loss -0.3735 +2026-04-13 03:08:42.999448: val_loss -0.315 +2026-04-13 03:08:43.001422: Pseudo dice [0.499, 0.0, 0.5794, 0.0459, 0.3624, 0.6945, 0.7748] +2026-04-13 03:08:43.003688: Epoch time: 100.24 s +2026-04-13 03:08:44.187130: +2026-04-13 03:08:44.189006: Epoch 2185 +2026-04-13 03:08:44.190773: Current learning rate: 0.00491 +2026-04-13 03:10:24.329833: train_loss -0.3893 +2026-04-13 03:10:24.337452: val_loss -0.3502 +2026-04-13 03:10:24.339360: Pseudo dice [0.3917, 0.0, 0.6595, 0.0673, 0.4711, 0.6444, 0.7634] +2026-04-13 03:10:24.341623: Epoch time: 100.15 s +2026-04-13 03:10:25.517961: +2026-04-13 03:10:25.532120: Epoch 2186 +2026-04-13 03:10:25.534437: Current learning rate: 0.00491 +2026-04-13 03:12:05.661856: train_loss -0.355 +2026-04-13 03:12:05.666657: val_loss -0.349 +2026-04-13 03:12:05.668257: Pseudo dice [0.6338, 0.0, 0.378, 0.4407, 0.0, 0.7402, 0.21] +2026-04-13 03:12:05.670733: Epoch time: 100.15 s +2026-04-13 03:12:06.841656: +2026-04-13 03:12:06.843451: Epoch 2187 +2026-04-13 03:12:06.845198: Current learning rate: 0.00491 +2026-04-13 03:13:48.084393: train_loss -0.3642 +2026-04-13 03:13:48.090757: val_loss -0.3486 +2026-04-13 03:13:48.092863: Pseudo dice [0.5436, 0.0, 0.7423, 0.4401, 0.0, 0.3563, 0.7087] +2026-04-13 03:13:48.094943: Epoch time: 101.25 s +2026-04-13 03:13:50.366518: +2026-04-13 03:13:50.368526: Epoch 2188 +2026-04-13 03:13:50.370101: Current learning rate: 0.0049 +2026-04-13 03:15:30.649478: train_loss -0.349 +2026-04-13 03:15:30.655880: val_loss -0.2621 +2026-04-13 03:15:30.658897: Pseudo dice [0.1938, 0.0, 0.1297, 0.0304, 0.0, 0.6243, 0.6772] +2026-04-13 03:15:30.667351: Epoch time: 100.29 s +2026-04-13 03:15:31.876103: +2026-04-13 03:15:31.878533: Epoch 2189 +2026-04-13 03:15:31.880205: Current learning rate: 0.0049 +2026-04-13 03:17:12.113820: train_loss -0.3807 +2026-04-13 03:17:12.120087: val_loss -0.3544 +2026-04-13 03:17:12.121883: Pseudo dice [0.3657, 0.0, 0.624, 0.0, 0.3522, 0.8072, 0.7413] +2026-04-13 03:17:12.124205: Epoch time: 100.24 s +2026-04-13 03:17:13.283070: +2026-04-13 03:17:13.284859: Epoch 2190 +2026-04-13 03:17:13.286561: Current learning rate: 0.0049 +2026-04-13 03:18:53.364486: train_loss -0.3982 +2026-04-13 03:18:53.370150: val_loss -0.3783 +2026-04-13 03:18:53.372255: Pseudo dice [0.3793, 0.0, 0.7329, 0.0, 0.4345, 0.7035, 0.7512] +2026-04-13 03:18:53.374920: Epoch time: 100.08 s +2026-04-13 03:18:54.543321: +2026-04-13 03:18:54.545225: Epoch 2191 +2026-04-13 03:18:54.547193: Current learning rate: 0.0049 +2026-04-13 03:20:34.794760: train_loss -0.3917 +2026-04-13 03:20:34.800740: val_loss -0.2835 +2026-04-13 03:20:34.802378: Pseudo dice [0.5908, 0.0, 0.6293, 0.0076, 0.2213, 0.8025, 0.6317] +2026-04-13 03:20:34.804336: Epoch time: 100.25 s +2026-04-13 03:20:35.973513: +2026-04-13 03:20:35.975360: Epoch 2192 +2026-04-13 03:20:35.976982: Current learning rate: 0.00489 +2026-04-13 03:22:16.415058: train_loss -0.3853 +2026-04-13 03:22:16.420989: val_loss -0.3725 +2026-04-13 03:22:16.422892: Pseudo dice [0.5769, 0.0, 0.6351, 0.1791, 0.3271, 0.7246, 0.6945] +2026-04-13 03:22:16.426746: Epoch time: 100.44 s +2026-04-13 03:22:17.618598: +2026-04-13 03:22:17.620720: Epoch 2193 +2026-04-13 03:22:17.622470: Current learning rate: 0.00489 +2026-04-13 03:23:57.763425: train_loss -0.394 +2026-04-13 03:23:57.783619: val_loss -0.4134 +2026-04-13 03:23:57.787586: Pseudo dice [0.5419, 0.0, 0.8245, 0.0, 0.4108, 0.842, 0.7075] +2026-04-13 03:23:57.790236: Epoch time: 100.15 s +2026-04-13 03:23:58.966650: +2026-04-13 03:23:58.968688: Epoch 2194 +2026-04-13 03:23:58.970411: Current learning rate: 0.00489 +2026-04-13 03:25:39.122765: train_loss -0.408 +2026-04-13 03:25:39.128498: val_loss -0.3706 +2026-04-13 03:25:39.131102: Pseudo dice [0.1207, 0.0, 0.7516, 0.0, 0.0, 0.7682, 0.8682] +2026-04-13 03:25:39.133695: Epoch time: 100.16 s +2026-04-13 03:25:40.313343: +2026-04-13 03:25:40.315290: Epoch 2195 +2026-04-13 03:25:40.317132: Current learning rate: 0.00489 +2026-04-13 03:27:20.526995: train_loss -0.3769 +2026-04-13 03:27:20.532277: val_loss -0.3986 +2026-04-13 03:27:20.533940: Pseudo dice [0.2128, 0.0, 0.7268, 0.0, 0.0, 0.7654, 0.5693] +2026-04-13 03:27:20.536090: Epoch time: 100.22 s +2026-04-13 03:27:21.701211: +2026-04-13 03:27:21.703733: Epoch 2196 +2026-04-13 03:27:21.705570: Current learning rate: 0.00488 +2026-04-13 03:29:01.993668: train_loss -0.3942 +2026-04-13 03:29:01.998886: val_loss -0.3673 +2026-04-13 03:29:02.000918: Pseudo dice [0.7774, 0.0, 0.768, 0.0432, 0.0, 0.687, 0.8021] +2026-04-13 03:29:02.003082: Epoch time: 100.3 s +2026-04-13 03:29:03.167868: +2026-04-13 03:29:03.169713: Epoch 2197 +2026-04-13 03:29:03.171281: Current learning rate: 0.00488 +2026-04-13 03:30:43.160927: train_loss -0.3948 +2026-04-13 03:30:43.167899: val_loss -0.3793 +2026-04-13 03:30:43.170362: Pseudo dice [0.0, 0.0, 0.7841, 0.1467, 0.0, 0.4789, 0.8353] +2026-04-13 03:30:43.173918: Epoch time: 100.0 s +2026-04-13 03:30:44.375309: +2026-04-13 03:30:44.377128: Epoch 2198 +2026-04-13 03:30:44.379175: Current learning rate: 0.00488 +2026-04-13 03:32:24.622032: train_loss -0.3729 +2026-04-13 03:32:24.627962: val_loss -0.3766 +2026-04-13 03:32:24.629978: Pseudo dice [0.1697, 0.0, 0.6458, 0.3126, 0.4133, 0.4206, 0.783] +2026-04-13 03:32:24.632199: Epoch time: 100.25 s +2026-04-13 03:32:25.788328: +2026-04-13 03:32:25.789988: Epoch 2199 +2026-04-13 03:32:25.792028: Current learning rate: 0.00488 +2026-04-13 03:34:05.981260: train_loss -0.4012 +2026-04-13 03:34:05.987771: val_loss -0.3889 +2026-04-13 03:34:05.989834: Pseudo dice [0.4812, 0.0, 0.7582, 0.0112, 0.2402, 0.8383, 0.7007] +2026-04-13 03:34:05.992119: Epoch time: 100.2 s +2026-04-13 03:34:08.817901: +2026-04-13 03:34:08.820463: Epoch 2200 +2026-04-13 03:34:08.822350: Current learning rate: 0.00487 +2026-04-13 03:35:48.993882: train_loss -0.3773 +2026-04-13 03:35:48.999269: val_loss -0.2516 +2026-04-13 03:35:49.001293: Pseudo dice [0.1287, 0.0, 0.6631, 0.0307, 0.0107, 0.7419, 0.735] +2026-04-13 03:35:49.003895: Epoch time: 100.18 s +2026-04-13 03:35:50.256645: +2026-04-13 03:35:50.258682: Epoch 2201 +2026-04-13 03:35:50.260632: Current learning rate: 0.00487 +2026-04-13 03:37:30.287952: train_loss -0.3876 +2026-04-13 03:37:30.293291: val_loss -0.3945 +2026-04-13 03:37:30.295389: Pseudo dice [0.3812, 0.0, 0.7765, 0.2112, 0.286, 0.7257, 0.8625] +2026-04-13 03:37:30.298820: Epoch time: 100.03 s +2026-04-13 03:37:31.476220: +2026-04-13 03:37:31.478249: Epoch 2202 +2026-04-13 03:37:31.480013: Current learning rate: 0.00487 +2026-04-13 03:39:11.572946: train_loss -0.4031 +2026-04-13 03:39:11.577826: val_loss -0.3689 +2026-04-13 03:39:11.579668: Pseudo dice [0.7708, 0.0, 0.6624, 0.8209, 0.4048, 0.621, 0.7318] +2026-04-13 03:39:11.581919: Epoch time: 100.1 s +2026-04-13 03:39:12.761032: +2026-04-13 03:39:12.763349: Epoch 2203 +2026-04-13 03:39:12.765347: Current learning rate: 0.00487 +2026-04-13 03:40:52.825092: train_loss -0.4099 +2026-04-13 03:40:52.830524: val_loss -0.3921 +2026-04-13 03:40:52.832520: Pseudo dice [0.6731, 0.0, 0.7427, 0.0, 0.0, 0.6113, 0.8216] +2026-04-13 03:40:52.835128: Epoch time: 100.07 s +2026-04-13 03:40:54.003911: +2026-04-13 03:40:54.006581: Epoch 2204 +2026-04-13 03:40:54.008845: Current learning rate: 0.00486 +2026-04-13 03:42:34.437612: train_loss -0.3975 +2026-04-13 03:42:34.446016: val_loss -0.3737 +2026-04-13 03:42:34.448247: Pseudo dice [0.1375, 0.0, 0.6018, 0.0, 0.2524, 0.7874, 0.8102] +2026-04-13 03:42:34.451834: Epoch time: 100.44 s +2026-04-13 03:42:35.663201: +2026-04-13 03:42:35.665065: Epoch 2205 +2026-04-13 03:42:35.667877: Current learning rate: 0.00486 +2026-04-13 03:44:15.754596: train_loss -0.3753 +2026-04-13 03:44:15.759444: val_loss -0.3802 +2026-04-13 03:44:15.761487: Pseudo dice [0.4463, 0.0, 0.7012, 0.3015, 0.1786, 0.6143, 0.791] +2026-04-13 03:44:15.763425: Epoch time: 100.09 s +2026-04-13 03:44:16.953778: +2026-04-13 03:44:16.955644: Epoch 2206 +2026-04-13 03:44:16.957155: Current learning rate: 0.00486 +2026-04-13 03:45:56.960200: train_loss -0.4049 +2026-04-13 03:45:56.965748: val_loss -0.3604 +2026-04-13 03:45:56.967704: Pseudo dice [0.6944, 0.0, 0.6429, 0.0536, 0.4784, 0.6385, 0.8103] +2026-04-13 03:45:56.969529: Epoch time: 100.01 s +2026-04-13 03:45:58.134765: +2026-04-13 03:45:58.138203: Epoch 2207 +2026-04-13 03:45:58.140252: Current learning rate: 0.00486 +2026-04-13 03:47:38.574154: train_loss -0.4082 +2026-04-13 03:47:38.579182: val_loss -0.4189 +2026-04-13 03:47:38.580699: Pseudo dice [0.4506, 0.0, 0.7853, 0.0, 0.2883, 0.6239, 0.851] +2026-04-13 03:47:38.584138: Epoch time: 100.44 s +2026-04-13 03:47:39.755294: +2026-04-13 03:47:39.757232: Epoch 2208 +2026-04-13 03:47:39.759010: Current learning rate: 0.00485 +2026-04-13 03:49:20.884075: train_loss -0.414 +2026-04-13 03:49:20.891405: val_loss -0.3861 +2026-04-13 03:49:20.893477: Pseudo dice [0.6654, 0.0, 0.7323, 0.0, 0.2677, 0.8143, 0.8788] +2026-04-13 03:49:20.895873: Epoch time: 101.13 s +2026-04-13 03:49:22.068778: +2026-04-13 03:49:22.070519: Epoch 2209 +2026-04-13 03:49:22.072106: Current learning rate: 0.00485 +2026-04-13 03:51:02.135922: train_loss -0.4068 +2026-04-13 03:51:02.142248: val_loss -0.3404 +2026-04-13 03:51:02.144090: Pseudo dice [0.0, 0.0, 0.6229, 0.0, 0.1455, 0.7847, 0.8242] +2026-04-13 03:51:02.146306: Epoch time: 100.07 s +2026-04-13 03:51:03.326671: +2026-04-13 03:51:03.328959: Epoch 2210 +2026-04-13 03:51:03.330493: Current learning rate: 0.00485 +2026-04-13 03:52:43.434145: train_loss -0.3872 +2026-04-13 03:52:43.439500: val_loss -0.34 +2026-04-13 03:52:43.441158: Pseudo dice [0.0, 0.0, 0.5832, 0.0, 0.1999, 0.6017, 0.85] +2026-04-13 03:52:43.445104: Epoch time: 100.11 s +2026-04-13 03:52:44.609985: +2026-04-13 03:52:44.612031: Epoch 2211 +2026-04-13 03:52:44.613856: Current learning rate: 0.00485 +2026-04-13 03:54:24.975540: train_loss -0.3303 +2026-04-13 03:54:24.983507: val_loss -0.2379 +2026-04-13 03:54:24.985667: Pseudo dice [0.5258, 0.0, 0.3293, 0.0047, 0.0, 0.1283, 0.2644] +2026-04-13 03:54:24.990295: Epoch time: 100.37 s +2026-04-13 03:54:26.174185: +2026-04-13 03:54:26.176477: Epoch 2212 +2026-04-13 03:54:26.178362: Current learning rate: 0.00484 +2026-04-13 03:56:06.391844: train_loss -0.3341 +2026-04-13 03:56:06.398913: val_loss -0.3624 +2026-04-13 03:56:06.401826: Pseudo dice [0.0929, 0.0, 0.5875, 0.5417, 0.0, 0.6586, 0.7577] +2026-04-13 03:56:06.404987: Epoch time: 100.22 s +2026-04-13 03:56:07.600923: +2026-04-13 03:56:07.602916: Epoch 2213 +2026-04-13 03:56:07.604641: Current learning rate: 0.00484 +2026-04-13 03:57:47.628666: train_loss -0.3737 +2026-04-13 03:57:47.634728: val_loss -0.3876 +2026-04-13 03:57:47.636581: Pseudo dice [0.777, 0.0, 0.5577, 0.0, 0.0, 0.5263, 0.7631] +2026-04-13 03:57:47.638901: Epoch time: 100.03 s +2026-04-13 03:57:48.845292: +2026-04-13 03:57:48.846959: Epoch 2214 +2026-04-13 03:57:48.848452: Current learning rate: 0.00484 +2026-04-13 03:59:29.051734: train_loss -0.3261 +2026-04-13 03:59:29.058174: val_loss -0.3732 +2026-04-13 03:59:29.060361: Pseudo dice [0.2369, 0.0, 0.5541, 0.8782, 0.0, 0.4382, 0.6427] +2026-04-13 03:59:29.062705: Epoch time: 100.21 s +2026-04-13 03:59:30.234984: +2026-04-13 03:59:30.237070: Epoch 2215 +2026-04-13 03:59:30.239126: Current learning rate: 0.00484 +2026-04-13 04:01:10.264625: train_loss -0.3735 +2026-04-13 04:01:10.270405: val_loss -0.3722 +2026-04-13 04:01:10.272108: Pseudo dice [0.0, 0.0, 0.5909, 0.0, 0.1754, 0.6515, 0.3881] +2026-04-13 04:01:10.276445: Epoch time: 100.03 s +2026-04-13 04:01:11.451807: +2026-04-13 04:01:11.455209: Epoch 2216 +2026-04-13 04:01:11.457060: Current learning rate: 0.00484 +2026-04-13 04:02:51.588431: train_loss -0.3809 +2026-04-13 04:02:51.593004: val_loss -0.4014 +2026-04-13 04:02:51.594507: Pseudo dice [0.4246, 0.0, 0.7113, 0.3407, 0.2194, 0.8071, 0.5935] +2026-04-13 04:02:51.596554: Epoch time: 100.14 s +2026-04-13 04:02:52.758063: +2026-04-13 04:02:52.760328: Epoch 2217 +2026-04-13 04:02:52.762186: Current learning rate: 0.00483 +2026-04-13 04:04:32.841797: train_loss -0.3992 +2026-04-13 04:04:32.846724: val_loss -0.3901 +2026-04-13 04:04:32.848570: Pseudo dice [0.4826, 0.0, 0.6993, 0.5604, 0.0, 0.8227, 0.8499] +2026-04-13 04:04:32.850475: Epoch time: 100.09 s +2026-04-13 04:04:34.005502: +2026-04-13 04:04:34.007759: Epoch 2218 +2026-04-13 04:04:34.010023: Current learning rate: 0.00483 +2026-04-13 04:06:14.088216: train_loss -0.3731 +2026-04-13 04:06:14.093855: val_loss -0.3241 +2026-04-13 04:06:14.096187: Pseudo dice [0.4848, 0.0, 0.6114, 0.0594, 0.0, 0.5905, 0.6124] +2026-04-13 04:06:14.098534: Epoch time: 100.09 s +2026-04-13 04:06:15.266874: +2026-04-13 04:06:15.268820: Epoch 2219 +2026-04-13 04:06:15.270603: Current learning rate: 0.00483 +2026-04-13 04:07:55.363539: train_loss -0.3824 +2026-04-13 04:07:55.375623: val_loss -0.2451 +2026-04-13 04:07:55.378887: Pseudo dice [0.0, 0.0, 0.671, 0.0, 0.0, 0.6678, 0.8684] +2026-04-13 04:07:55.382436: Epoch time: 100.1 s +2026-04-13 04:07:56.552823: +2026-04-13 04:07:56.554824: Epoch 2220 +2026-04-13 04:07:56.556599: Current learning rate: 0.00483 +2026-04-13 04:09:36.986795: train_loss -0.4109 +2026-04-13 04:09:36.992188: val_loss -0.3851 +2026-04-13 04:09:36.994130: Pseudo dice [0.653, 0.0, 0.7927, 0.0, 0.4213, 0.6111, 0.5792] +2026-04-13 04:09:36.996251: Epoch time: 100.44 s +2026-04-13 04:09:38.182411: +2026-04-13 04:09:38.187555: Epoch 2221 +2026-04-13 04:09:38.189764: Current learning rate: 0.00482 +2026-04-13 04:11:18.290142: train_loss -0.416 +2026-04-13 04:11:18.296969: val_loss -0.3985 +2026-04-13 04:11:18.298691: Pseudo dice [0.4146, 0.0, 0.6974, 0.1099, 0.3063, 0.7077, 0.6592] +2026-04-13 04:11:18.301197: Epoch time: 100.11 s +2026-04-13 04:11:19.463639: +2026-04-13 04:11:19.465357: Epoch 2222 +2026-04-13 04:11:19.466926: Current learning rate: 0.00482 +2026-04-13 04:12:59.513614: train_loss -0.3948 +2026-04-13 04:12:59.520954: val_loss -0.3867 +2026-04-13 04:12:59.523287: Pseudo dice [0.3884, 0.0, 0.4024, 0.0, 0.3792, 0.8048, 0.6859] +2026-04-13 04:12:59.525491: Epoch time: 100.05 s +2026-04-13 04:13:00.700097: +2026-04-13 04:13:00.701798: Epoch 2223 +2026-04-13 04:13:00.703508: Current learning rate: 0.00482 +2026-04-13 04:14:40.574180: train_loss -0.4035 +2026-04-13 04:14:40.580522: val_loss -0.3856 +2026-04-13 04:14:40.582512: Pseudo dice [0.3091, 0.0, 0.6123, 0.2256, 0.4158, 0.6883, 0.8812] +2026-04-13 04:14:40.585341: Epoch time: 99.88 s +2026-04-13 04:14:41.749692: +2026-04-13 04:14:41.751388: Epoch 2224 +2026-04-13 04:14:41.753058: Current learning rate: 0.00482 +2026-04-13 04:16:21.741325: train_loss -0.415 +2026-04-13 04:16:21.747131: val_loss -0.3408 +2026-04-13 04:16:21.749839: Pseudo dice [0.0309, 0.0, 0.6346, 0.5295, 0.005, 0.6517, 0.6413] +2026-04-13 04:16:21.752446: Epoch time: 99.99 s +2026-04-13 04:16:22.939554: +2026-04-13 04:16:22.941245: Epoch 2225 +2026-04-13 04:16:22.942970: Current learning rate: 0.00481 +2026-04-13 04:18:03.092255: train_loss -0.3698 +2026-04-13 04:18:03.100806: val_loss -0.2872 +2026-04-13 04:18:03.103212: Pseudo dice [0.0806, 0.0, 0.6864, 0.0378, 0.1286, 0.6636, 0.6601] +2026-04-13 04:18:03.106426: Epoch time: 100.16 s +2026-04-13 04:18:04.265325: +2026-04-13 04:18:04.267085: Epoch 2226 +2026-04-13 04:18:04.268787: Current learning rate: 0.00481 +2026-04-13 04:19:44.482210: train_loss -0.3865 +2026-04-13 04:19:44.488422: val_loss -0.3436 +2026-04-13 04:19:44.491498: Pseudo dice [0.0, 0.0, 0.5889, 0.0454, 0.439, 0.7829, 0.5212] +2026-04-13 04:19:44.495391: Epoch time: 100.22 s +2026-04-13 04:19:45.660012: +2026-04-13 04:19:45.662054: Epoch 2227 +2026-04-13 04:19:45.663717: Current learning rate: 0.00481 +2026-04-13 04:21:25.799401: train_loss -0.3536 +2026-04-13 04:21:25.803906: val_loss -0.3653 +2026-04-13 04:21:25.805490: Pseudo dice [0.2691, 0.0, 0.6265, 0.4061, 0.1467, 0.1316, 0.7415] +2026-04-13 04:21:25.807341: Epoch time: 100.14 s +2026-04-13 04:21:26.972690: +2026-04-13 04:21:26.974351: Epoch 2228 +2026-04-13 04:21:26.976119: Current learning rate: 0.00481 +2026-04-13 04:23:07.317299: train_loss -0.3513 +2026-04-13 04:23:07.324499: val_loss -0.3459 +2026-04-13 04:23:07.326685: Pseudo dice [0.1821, 0.0, 0.4667, 0.728, 0.0, 0.3863, 0.5721] +2026-04-13 04:23:07.328861: Epoch time: 100.35 s +2026-04-13 04:23:08.506151: +2026-04-13 04:23:08.507831: Epoch 2229 +2026-04-13 04:23:08.509447: Current learning rate: 0.0048 +2026-04-13 04:24:49.763952: train_loss -0.3746 +2026-04-13 04:24:49.770134: val_loss -0.3295 +2026-04-13 04:24:49.772069: Pseudo dice [0.4243, 0.0, 0.3954, 0.0005, 0.0, 0.5257, 0.5066] +2026-04-13 04:24:49.774484: Epoch time: 101.26 s +2026-04-13 04:24:50.939908: +2026-04-13 04:24:50.942322: Epoch 2230 +2026-04-13 04:24:50.944173: Current learning rate: 0.0048 +2026-04-13 04:26:31.292196: train_loss -0.3753 +2026-04-13 04:26:31.300061: val_loss -0.4058 +2026-04-13 04:26:31.303458: Pseudo dice [0.3874, 0.0, 0.7995, 0.5746, 0.285, 0.6872, 0.8549] +2026-04-13 04:26:31.306194: Epoch time: 100.36 s +2026-04-13 04:26:32.477553: +2026-04-13 04:26:32.480099: Epoch 2231 +2026-04-13 04:26:32.482098: Current learning rate: 0.0048 +2026-04-13 04:28:12.916054: train_loss -0.3939 +2026-04-13 04:28:12.922314: val_loss -0.3906 +2026-04-13 04:28:12.924551: Pseudo dice [0.301, 0.0, 0.667, 0.0, 0.0, 0.7161, 0.8541] +2026-04-13 04:28:12.926719: Epoch time: 100.44 s +2026-04-13 04:28:14.115579: +2026-04-13 04:28:14.117612: Epoch 2232 +2026-04-13 04:28:14.119440: Current learning rate: 0.0048 +2026-04-13 04:29:54.311144: train_loss -0.3782 +2026-04-13 04:29:54.316351: val_loss -0.3639 +2026-04-13 04:29:54.318573: Pseudo dice [0.0, 0.0, 0.4101, 0.5209, 0.0, 0.6336, 0.6786] +2026-04-13 04:29:54.320696: Epoch time: 100.2 s +2026-04-13 04:29:55.472150: +2026-04-13 04:29:55.474545: Epoch 2233 +2026-04-13 04:29:55.476294: Current learning rate: 0.00479 +2026-04-13 04:31:35.511976: train_loss -0.4011 +2026-04-13 04:31:35.519801: val_loss -0.3975 +2026-04-13 04:31:35.521874: Pseudo dice [0.147, 0.0, 0.783, 0.3462, 0.1793, 0.8338, 0.7726] +2026-04-13 04:31:35.524344: Epoch time: 100.04 s +2026-04-13 04:31:36.688224: +2026-04-13 04:31:36.690468: Epoch 2234 +2026-04-13 04:31:36.692432: Current learning rate: 0.00479 +2026-04-13 04:33:16.774821: train_loss -0.3935 +2026-04-13 04:33:16.781256: val_loss -0.3458 +2026-04-13 04:33:16.782944: Pseudo dice [0.0, 0.0, 0.6721, 0.6102, 0.427, 0.1459, 0.0521] +2026-04-13 04:33:16.785687: Epoch time: 100.09 s +2026-04-13 04:33:17.969771: +2026-04-13 04:33:17.971872: Epoch 2235 +2026-04-13 04:33:17.973794: Current learning rate: 0.00479 +2026-04-13 04:34:58.233928: train_loss -0.3632 +2026-04-13 04:34:58.239383: val_loss -0.3588 +2026-04-13 04:34:58.241225: Pseudo dice [0.595, 0.0, 0.4453, 0.7282, 0.3847, 0.7954, 0.6776] +2026-04-13 04:34:58.243369: Epoch time: 100.27 s +2026-04-13 04:34:59.417208: +2026-04-13 04:34:59.419323: Epoch 2236 +2026-04-13 04:34:59.421167: Current learning rate: 0.00479 +2026-04-13 04:36:39.570142: train_loss -0.3826 +2026-04-13 04:36:39.578939: val_loss -0.3325 +2026-04-13 04:36:39.581065: Pseudo dice [0.0, 0.0, 0.5246, 0.1489, 0.3276, 0.8181, 0.6481] +2026-04-13 04:36:39.583597: Epoch time: 100.16 s +2026-04-13 04:36:40.760348: +2026-04-13 04:36:40.763486: Epoch 2237 +2026-04-13 04:36:40.765712: Current learning rate: 0.00478 +2026-04-13 04:38:21.131455: train_loss -0.3777 +2026-04-13 04:38:21.142195: val_loss -0.3547 +2026-04-13 04:38:21.145226: Pseudo dice [0.4866, 0.0, 0.3983, 0.1225, 0.0, 0.5885, 0.6253] +2026-04-13 04:38:21.149585: Epoch time: 100.37 s +2026-04-13 04:38:22.323505: +2026-04-13 04:38:22.325241: Epoch 2238 +2026-04-13 04:38:22.326960: Current learning rate: 0.00478 +2026-04-13 04:40:02.522604: train_loss -0.3723 +2026-04-13 04:40:02.527655: val_loss -0.3869 +2026-04-13 04:40:02.529381: Pseudo dice [0.286, 0.0, 0.8056, 0.0, 0.1801, 0.7824, 0.8114] +2026-04-13 04:40:02.531692: Epoch time: 100.2 s +2026-04-13 04:40:03.702337: +2026-04-13 04:40:03.704226: Epoch 2239 +2026-04-13 04:40:03.705719: Current learning rate: 0.00478 +2026-04-13 04:41:43.938261: train_loss -0.39 +2026-04-13 04:41:43.943392: val_loss -0.3414 +2026-04-13 04:41:43.946163: Pseudo dice [0.4488, 0.0, 0.7643, 0.0654, 0.2391, 0.4963, 0.7449] +2026-04-13 04:41:43.948169: Epoch time: 100.24 s +2026-04-13 04:41:45.116846: +2026-04-13 04:41:45.120233: Epoch 2240 +2026-04-13 04:41:45.122568: Current learning rate: 0.00478 +2026-04-13 04:43:25.403758: train_loss -0.3796 +2026-04-13 04:43:25.410017: val_loss -0.3018 +2026-04-13 04:43:25.411898: Pseudo dice [0.5207, 0.0, 0.3592, 0.1225, 0.0, 0.7662, 0.7256] +2026-04-13 04:43:25.414190: Epoch time: 100.29 s +2026-04-13 04:43:26.599191: +2026-04-13 04:43:26.601064: Epoch 2241 +2026-04-13 04:43:26.602736: Current learning rate: 0.00477 +2026-04-13 04:45:06.778561: train_loss -0.3414 +2026-04-13 04:45:06.783931: val_loss -0.367 +2026-04-13 04:45:06.785986: Pseudo dice [0.5879, 0.0, 0.7452, 0.0, 0.0, 0.2388, 0.6653] +2026-04-13 04:45:06.788230: Epoch time: 100.18 s +2026-04-13 04:45:07.950575: +2026-04-13 04:45:07.952484: Epoch 2242 +2026-04-13 04:45:07.954306: Current learning rate: 0.00477 +2026-04-13 04:46:48.129215: train_loss -0.3686 +2026-04-13 04:46:48.135801: val_loss -0.3768 +2026-04-13 04:46:48.137886: Pseudo dice [0.0, 0.0, 0.7721, 0.255, 0.2543, 0.7003, 0.6351] +2026-04-13 04:46:48.140210: Epoch time: 100.18 s +2026-04-13 04:46:49.317463: +2026-04-13 04:46:49.319209: Epoch 2243 +2026-04-13 04:46:49.320870: Current learning rate: 0.00477 +2026-04-13 04:48:29.572147: train_loss -0.4046 +2026-04-13 04:48:29.577414: val_loss -0.3915 +2026-04-13 04:48:29.579303: Pseudo dice [0.5747, 0.0, 0.7508, 0.0896, 0.3266, 0.3161, 0.7831] +2026-04-13 04:48:29.581528: Epoch time: 100.26 s +2026-04-13 04:48:30.757550: +2026-04-13 04:48:30.759547: Epoch 2244 +2026-04-13 04:48:30.761376: Current learning rate: 0.00477 +2026-04-13 04:50:11.105379: train_loss -0.3919 +2026-04-13 04:50:11.110587: val_loss -0.3794 +2026-04-13 04:50:11.112178: Pseudo dice [0.4345, 0.0, 0.7813, 0.0, 0.422, 0.7581, 0.7659] +2026-04-13 04:50:11.114705: Epoch time: 100.35 s +2026-04-13 04:50:12.282089: +2026-04-13 04:50:12.284341: Epoch 2245 +2026-04-13 04:50:12.286090: Current learning rate: 0.00476 +2026-04-13 04:51:52.555566: train_loss -0.411 +2026-04-13 04:51:52.561218: val_loss -0.315 +2026-04-13 04:51:52.563340: Pseudo dice [0.1165, 0.0, 0.8096, 0.3932, 0.315, 0.5926, 0.1405] +2026-04-13 04:51:52.565723: Epoch time: 100.28 s +2026-04-13 04:51:53.743049: +2026-04-13 04:51:53.744919: Epoch 2246 +2026-04-13 04:51:53.746777: Current learning rate: 0.00476 +2026-04-13 04:53:34.034039: train_loss -0.3839 +2026-04-13 04:53:34.039827: val_loss -0.3752 +2026-04-13 04:53:34.041848: Pseudo dice [0.021, 0.0, 0.569, 0.0, 0.3079, 0.8127, 0.8399] +2026-04-13 04:53:34.044344: Epoch time: 100.29 s +2026-04-13 04:53:35.218813: +2026-04-13 04:53:35.220999: Epoch 2247 +2026-04-13 04:53:35.222981: Current learning rate: 0.00476 +2026-04-13 04:55:15.554611: train_loss -0.4083 +2026-04-13 04:55:15.563042: val_loss -0.2921 +2026-04-13 04:55:15.566428: Pseudo dice [0.3335, 0.0, 0.7357, 0.023, 0.5389, 0.5548, 0.6142] +2026-04-13 04:55:15.568619: Epoch time: 100.34 s +2026-04-13 04:55:16.810888: +2026-04-13 04:55:16.812792: Epoch 2248 +2026-04-13 04:55:16.814467: Current learning rate: 0.00476 +2026-04-13 04:56:57.032099: train_loss -0.3939 +2026-04-13 04:56:57.036909: val_loss -0.3602 +2026-04-13 04:56:57.038554: Pseudo dice [0.6266, 0.0, 0.7441, 0.0133, 0.231, 0.5227, 0.6482] +2026-04-13 04:56:57.040421: Epoch time: 100.22 s +2026-04-13 04:56:58.212805: +2026-04-13 04:56:58.214530: Epoch 2249 +2026-04-13 04:56:58.215936: Current learning rate: 0.00475 +2026-04-13 04:58:38.327002: train_loss -0.3936 +2026-04-13 04:58:38.332041: val_loss -0.3941 +2026-04-13 04:58:38.334495: Pseudo dice [0.0639, 0.0, 0.5679, 0.0628, 0.0, 0.6078, 0.612] +2026-04-13 04:58:38.337339: Epoch time: 100.12 s +2026-04-13 04:58:42.123970: +2026-04-13 04:58:42.126499: Epoch 2250 +2026-04-13 04:58:42.128897: Current learning rate: 0.00475 +2026-04-13 05:00:22.289837: train_loss -0.3677 +2026-04-13 05:00:22.295787: val_loss -0.3954 +2026-04-13 05:00:22.298214: Pseudo dice [0.4757, 0.0, 0.8268, 0.0, 0.1228, 0.7831, 0.6816] +2026-04-13 05:00:22.302296: Epoch time: 100.17 s +2026-04-13 05:00:23.522187: +2026-04-13 05:00:23.524219: Epoch 2251 +2026-04-13 05:00:23.525825: Current learning rate: 0.00475 +2026-04-13 05:02:04.154583: train_loss -0.3879 +2026-04-13 05:02:04.160766: val_loss -0.3841 +2026-04-13 05:02:04.162917: Pseudo dice [0.5287, 0.0, 0.7511, 0.0, 0.1929, 0.7257, 0.5949] +2026-04-13 05:02:04.165129: Epoch time: 100.64 s +2026-04-13 05:02:05.357742: +2026-04-13 05:02:05.359557: Epoch 2252 +2026-04-13 05:02:05.361352: Current learning rate: 0.00475 +2026-04-13 05:03:45.933235: train_loss -0.4025 +2026-04-13 05:03:45.938207: val_loss -0.3064 +2026-04-13 05:03:45.940313: Pseudo dice [0.0, 0.0, 0.6832, 0.0, 0.291, 0.4123, 0.5534] +2026-04-13 05:03:45.942817: Epoch time: 100.58 s +2026-04-13 05:03:47.122333: +2026-04-13 05:03:47.124555: Epoch 2253 +2026-04-13 05:03:47.126513: Current learning rate: 0.00474 +2026-04-13 05:05:27.191491: train_loss -0.3868 +2026-04-13 05:05:27.196562: val_loss -0.3807 +2026-04-13 05:05:27.198397: Pseudo dice [0.4778, 0.0, 0.7261, 0.0, 0.301, 0.7392, 0.5459] +2026-04-13 05:05:27.200717: Epoch time: 100.07 s +2026-04-13 05:05:28.386804: +2026-04-13 05:05:28.388593: Epoch 2254 +2026-04-13 05:05:28.390235: Current learning rate: 0.00474 +2026-04-13 05:07:08.508036: train_loss -0.3861 +2026-04-13 05:07:08.516779: val_loss -0.41 +2026-04-13 05:07:08.519058: Pseudo dice [0.0577, 0.0, 0.7216, 0.0, 0.546, 0.7716, 0.8038] +2026-04-13 05:07:08.521589: Epoch time: 100.12 s +2026-04-13 05:07:09.700581: +2026-04-13 05:07:09.702759: Epoch 2255 +2026-04-13 05:07:09.704644: Current learning rate: 0.00474 +2026-04-13 05:08:50.084442: train_loss -0.3904 +2026-04-13 05:08:50.089896: val_loss -0.4147 +2026-04-13 05:08:50.092209: Pseudo dice [0.3968, 0.0, 0.7248, 0.6495, 0.4846, 0.7045, 0.7336] +2026-04-13 05:08:50.095477: Epoch time: 100.39 s +2026-04-13 05:08:51.271716: +2026-04-13 05:08:51.273882: Epoch 2256 +2026-04-13 05:08:51.275762: Current learning rate: 0.00474 +2026-04-13 05:10:31.439619: train_loss -0.384 +2026-04-13 05:10:31.445360: val_loss -0.4039 +2026-04-13 05:10:31.447458: Pseudo dice [0.6314, 0.0, 0.6273, 0.0, 0.4151, 0.6377, 0.7025] +2026-04-13 05:10:31.449552: Epoch time: 100.17 s +2026-04-13 05:10:32.636279: +2026-04-13 05:10:32.638972: Epoch 2257 +2026-04-13 05:10:32.640471: Current learning rate: 0.00473 +2026-04-13 05:12:12.820621: train_loss -0.3688 +2026-04-13 05:12:12.826469: val_loss -0.3619 +2026-04-13 05:12:12.828890: Pseudo dice [0.0, 0.0, 0.4296, 0.7468, 0.3662, 0.7909, 0.7413] +2026-04-13 05:12:12.830883: Epoch time: 100.19 s +2026-04-13 05:12:14.011496: +2026-04-13 05:12:14.013799: Epoch 2258 +2026-04-13 05:12:14.015739: Current learning rate: 0.00473 +2026-04-13 05:13:54.209617: train_loss -0.3769 +2026-04-13 05:13:54.215367: val_loss -0.3457 +2026-04-13 05:13:54.219791: Pseudo dice [0.0, 0.0, 0.6627, 0.0085, 0.4382, 0.6869, 0.5172] +2026-04-13 05:13:54.222021: Epoch time: 100.2 s +2026-04-13 05:13:55.398669: +2026-04-13 05:13:55.400643: Epoch 2259 +2026-04-13 05:13:55.404151: Current learning rate: 0.00473 +2026-04-13 05:15:35.607052: train_loss -0.3643 +2026-04-13 05:15:35.611901: val_loss -0.3793 +2026-04-13 05:15:35.613809: Pseudo dice [0.4829, 0.0, 0.6577, 0.3119, 0.4515, 0.6904, 0.7086] +2026-04-13 05:15:35.616259: Epoch time: 100.21 s +2026-04-13 05:15:36.788682: +2026-04-13 05:15:36.790359: Epoch 2260 +2026-04-13 05:15:36.792100: Current learning rate: 0.00473 +2026-04-13 05:17:17.078242: train_loss -0.364 +2026-04-13 05:17:17.085977: val_loss -0.3832 +2026-04-13 05:17:17.088195: Pseudo dice [0.2732, 0.0, 0.4559, 0.0, 0.4053, 0.5363, 0.7575] +2026-04-13 05:17:17.090548: Epoch time: 100.29 s +2026-04-13 05:17:18.280927: +2026-04-13 05:17:18.283205: Epoch 2261 +2026-04-13 05:17:18.285303: Current learning rate: 0.00473 +2026-04-13 05:18:58.392908: train_loss -0.3793 +2026-04-13 05:18:58.398202: val_loss -0.2879 +2026-04-13 05:18:58.400237: Pseudo dice [0.4232, 0.0, 0.5349, 0.0012, 0.3082, 0.7103, 0.3905] +2026-04-13 05:18:58.402330: Epoch time: 100.12 s +2026-04-13 05:18:59.565018: +2026-04-13 05:18:59.567234: Epoch 2262 +2026-04-13 05:18:59.568975: Current learning rate: 0.00472 +2026-04-13 05:20:40.062514: train_loss -0.3898 +2026-04-13 05:20:40.070519: val_loss -0.3318 +2026-04-13 05:20:40.072951: Pseudo dice [0.5025, 0.0, 0.7231, 0.0633, 0.5488, 0.451, 0.424] +2026-04-13 05:20:40.075135: Epoch time: 100.5 s +2026-04-13 05:20:41.261327: +2026-04-13 05:20:41.265131: Epoch 2263 +2026-04-13 05:20:41.266861: Current learning rate: 0.00472 +2026-04-13 05:22:21.521841: train_loss -0.3392 +2026-04-13 05:22:21.527954: val_loss -0.3357 +2026-04-13 05:22:21.530550: Pseudo dice [0.5703, 0.0, 0.6445, 0.1319, 0.2472, 0.8254, 0.7311] +2026-04-13 05:22:21.533734: Epoch time: 100.26 s +2026-04-13 05:22:22.713006: +2026-04-13 05:22:22.715375: Epoch 2264 +2026-04-13 05:22:22.717745: Current learning rate: 0.00472 +2026-04-13 05:24:02.851669: train_loss -0.363 +2026-04-13 05:24:02.857461: val_loss -0.2926 +2026-04-13 05:24:02.859481: Pseudo dice [0.3083, 0.0, 0.4952, 0.0, 0.1476, 0.6782, 0.3541] +2026-04-13 05:24:02.861798: Epoch time: 100.14 s +2026-04-13 05:24:04.047602: +2026-04-13 05:24:04.049522: Epoch 2265 +2026-04-13 05:24:04.051237: Current learning rate: 0.00472 +2026-04-13 05:25:44.270181: train_loss -0.3903 +2026-04-13 05:25:44.276114: val_loss -0.4212 +2026-04-13 05:25:44.278516: Pseudo dice [0.685, 0.0, 0.8131, 0.1309, 0.5334, 0.8456, 0.5329] +2026-04-13 05:25:44.280813: Epoch time: 100.23 s +2026-04-13 05:25:45.487141: +2026-04-13 05:25:45.489057: Epoch 2266 +2026-04-13 05:25:45.491042: Current learning rate: 0.00471 +2026-04-13 05:27:25.693725: train_loss -0.3747 +2026-04-13 05:27:25.699612: val_loss -0.4142 +2026-04-13 05:27:25.701859: Pseudo dice [0.4904, 0.0, 0.7334, 0.0, 0.3376, 0.8694, 0.6993] +2026-04-13 05:27:25.704514: Epoch time: 100.21 s +2026-04-13 05:27:26.881524: +2026-04-13 05:27:26.884056: Epoch 2267 +2026-04-13 05:27:26.885682: Current learning rate: 0.00471 +2026-04-13 05:29:07.097763: train_loss -0.386 +2026-04-13 05:29:07.104049: val_loss -0.3662 +2026-04-13 05:29:07.106743: Pseudo dice [0.2875, 0.0, 0.2797, 0.1823, 0.2615, 0.2763, 0.5003] +2026-04-13 05:29:07.109358: Epoch time: 100.22 s +2026-04-13 05:29:08.275176: +2026-04-13 05:29:08.277372: Epoch 2268 +2026-04-13 05:29:08.279070: Current learning rate: 0.00471 +2026-04-13 05:30:48.563379: train_loss -0.3448 +2026-04-13 05:30:48.569061: val_loss -0.2666 +2026-04-13 05:30:48.571305: Pseudo dice [0.0, 0.0, 0.4819, 0.0, 0.2485, 0.7906, 0.5078] +2026-04-13 05:30:48.573253: Epoch time: 100.29 s +2026-04-13 05:30:49.739068: +2026-04-13 05:30:49.740736: Epoch 2269 +2026-04-13 05:30:49.742221: Current learning rate: 0.00471 +2026-04-13 05:32:30.408289: train_loss -0.3439 +2026-04-13 05:32:30.413769: val_loss -0.3996 +2026-04-13 05:32:30.415649: Pseudo dice [0.0, 0.0, 0.6277, 0.0, 0.4289, 0.4787, 0.6619] +2026-04-13 05:32:30.418067: Epoch time: 100.67 s +2026-04-13 05:32:31.613868: +2026-04-13 05:32:31.615783: Epoch 2270 +2026-04-13 05:32:31.617917: Current learning rate: 0.0047 +2026-04-13 05:34:13.024454: train_loss -0.3793 +2026-04-13 05:34:13.030049: val_loss -0.3288 +2026-04-13 05:34:13.032399: Pseudo dice [0.0708, 0.0, 0.6816, 0.0, 0.3446, 0.7888, 0.638] +2026-04-13 05:34:13.034802: Epoch time: 101.41 s +2026-04-13 05:34:14.212212: +2026-04-13 05:34:14.214188: Epoch 2271 +2026-04-13 05:34:14.215556: Current learning rate: 0.0047 +2026-04-13 05:35:54.433075: train_loss -0.4104 +2026-04-13 05:35:54.440336: val_loss -0.3949 +2026-04-13 05:35:54.442454: Pseudo dice [0.7328, 0.0, 0.7748, 0.557, 0.2989, 0.6582, 0.5919] +2026-04-13 05:35:54.445801: Epoch time: 100.22 s +2026-04-13 05:35:55.627379: +2026-04-13 05:35:55.629844: Epoch 2272 +2026-04-13 05:35:55.631590: Current learning rate: 0.0047 +2026-04-13 05:37:35.735350: train_loss -0.4041 +2026-04-13 05:37:35.741433: val_loss -0.4128 +2026-04-13 05:37:35.743179: Pseudo dice [0.674, 0.0, 0.7727, 0.0, 0.055, 0.6662, 0.8575] +2026-04-13 05:37:35.745560: Epoch time: 100.11 s +2026-04-13 05:37:36.911798: +2026-04-13 05:37:36.913471: Epoch 2273 +2026-04-13 05:37:36.915046: Current learning rate: 0.0047 +2026-04-13 05:39:17.215287: train_loss -0.3921 +2026-04-13 05:39:17.220491: val_loss -0.3841 +2026-04-13 05:39:17.222908: Pseudo dice [0.4163, 0.0, 0.6092, 0.5741, 0.2844, 0.6847, 0.7879] +2026-04-13 05:39:17.225186: Epoch time: 100.31 s +2026-04-13 05:39:18.415411: +2026-04-13 05:39:18.417822: Epoch 2274 +2026-04-13 05:39:18.419792: Current learning rate: 0.00469 +2026-04-13 05:40:58.697882: train_loss -0.4072 +2026-04-13 05:40:58.702826: val_loss -0.3971 +2026-04-13 05:40:58.704778: Pseudo dice [0.609, 0.0, 0.7063, 0.0, 0.2845, 0.7251, 0.7894] +2026-04-13 05:40:58.707328: Epoch time: 100.29 s +2026-04-13 05:40:59.881327: +2026-04-13 05:40:59.883823: Epoch 2275 +2026-04-13 05:40:59.885444: Current learning rate: 0.00469 +2026-04-13 05:42:40.110156: train_loss -0.3963 +2026-04-13 05:42:40.115344: val_loss -0.4258 +2026-04-13 05:42:40.117392: Pseudo dice [0.6961, 0.0, 0.6554, 0.6617, 0.4415, 0.7674, 0.7911] +2026-04-13 05:42:40.119503: Epoch time: 100.23 s +2026-04-13 05:42:41.285280: +2026-04-13 05:42:41.287688: Epoch 2276 +2026-04-13 05:42:41.289456: Current learning rate: 0.00469 +2026-04-13 05:44:21.403455: train_loss -0.4078 +2026-04-13 05:44:21.410478: val_loss -0.3564 +2026-04-13 05:44:21.412564: Pseudo dice [0.6851, 0.0, 0.4642, 0.0, 0.3655, 0.7381, 0.4782] +2026-04-13 05:44:21.415616: Epoch time: 100.12 s +2026-04-13 05:44:22.601502: +2026-04-13 05:44:22.603488: Epoch 2277 +2026-04-13 05:44:22.605417: Current learning rate: 0.00469 +2026-04-13 05:46:02.839061: train_loss -0.3914 +2026-04-13 05:46:02.844178: val_loss -0.4165 +2026-04-13 05:46:02.846176: Pseudo dice [0.6763, 0.0, 0.5718, 0.0, 0.2123, 0.6863, 0.7111] +2026-04-13 05:46:02.848756: Epoch time: 100.24 s +2026-04-13 05:46:04.024580: +2026-04-13 05:46:04.026509: Epoch 2278 +2026-04-13 05:46:04.029711: Current learning rate: 0.00468 +2026-04-13 05:47:44.405063: train_loss -0.3825 +2026-04-13 05:47:44.411378: val_loss -0.2948 +2026-04-13 05:47:44.413419: Pseudo dice [0.2631, 0.0, 0.6877, 0.0477, 0.1666, 0.8293, 0.7423] +2026-04-13 05:47:44.415992: Epoch time: 100.38 s +2026-04-13 05:47:45.669187: +2026-04-13 05:47:45.671462: Epoch 2279 +2026-04-13 05:47:45.674240: Current learning rate: 0.00468 +2026-04-13 05:49:25.764859: train_loss -0.3906 +2026-04-13 05:49:25.770178: val_loss -0.3353 +2026-04-13 05:49:25.772028: Pseudo dice [0.4082, 0.0, 0.4342, 0.0, 0.1518, 0.7344, 0.7616] +2026-04-13 05:49:25.774359: Epoch time: 100.1 s +2026-04-13 05:49:26.944475: +2026-04-13 05:49:26.946434: Epoch 2280 +2026-04-13 05:49:26.948360: Current learning rate: 0.00468 +2026-04-13 05:51:07.125349: train_loss -0.388 +2026-04-13 05:51:07.131739: val_loss -0.2975 +2026-04-13 05:51:07.134089: Pseudo dice [0.6143, 0.0, 0.4776, 0.0, 0.083, 0.6591, 0.3647] +2026-04-13 05:51:07.136317: Epoch time: 100.18 s +2026-04-13 05:51:08.359306: +2026-04-13 05:51:08.361967: Epoch 2281 +2026-04-13 05:51:08.364666: Current learning rate: 0.00468 +2026-04-13 05:52:48.501338: train_loss -0.3752 +2026-04-13 05:52:48.509200: val_loss -0.3717 +2026-04-13 05:52:48.511560: Pseudo dice [0.4819, 0.0, 0.7217, 0.0748, 0.3611, 0.5168, 0.849] +2026-04-13 05:52:48.514303: Epoch time: 100.15 s +2026-04-13 05:52:49.699953: +2026-04-13 05:52:49.702581: Epoch 2282 +2026-04-13 05:52:49.704563: Current learning rate: 0.00467 +2026-04-13 05:54:29.664645: train_loss -0.4007 +2026-04-13 05:54:29.670918: val_loss -0.3791 +2026-04-13 05:54:29.673108: Pseudo dice [0.0, 0.0, 0.6006, 0.0, 0.2029, 0.5216, 0.8335] +2026-04-13 05:54:29.675820: Epoch time: 99.97 s +2026-04-13 05:54:30.851206: +2026-04-13 05:54:30.853166: Epoch 2283 +2026-04-13 05:54:30.854783: Current learning rate: 0.00467 +2026-04-13 05:56:11.193847: train_loss -0.381 +2026-04-13 05:56:11.200196: val_loss -0.328 +2026-04-13 05:56:11.202184: Pseudo dice [0.231, 0.0, 0.594, 0.0118, 0.3868, 0.6309, 0.5024] +2026-04-13 05:56:11.204469: Epoch time: 100.35 s +2026-04-13 05:56:12.360574: +2026-04-13 05:56:12.362479: Epoch 2284 +2026-04-13 05:56:12.364630: Current learning rate: 0.00467 +2026-04-13 05:57:52.421068: train_loss -0.3857 +2026-04-13 05:57:52.428450: val_loss -0.3917 +2026-04-13 05:57:52.430649: Pseudo dice [0.3852, 0.0, 0.7, nan, 0.1965, 0.3895, 0.4647] +2026-04-13 05:57:52.432656: Epoch time: 100.06 s +2026-04-13 05:57:53.622365: +2026-04-13 05:57:53.624621: Epoch 2285 +2026-04-13 05:57:53.626611: Current learning rate: 0.00467 +2026-04-13 05:59:33.715414: train_loss -0.4006 +2026-04-13 05:59:33.720688: val_loss -0.3926 +2026-04-13 05:59:33.722713: Pseudo dice [0.6237, 0.0, 0.7217, 0.5423, 0.2492, 0.7735, 0.5041] +2026-04-13 05:59:33.724962: Epoch time: 100.1 s +2026-04-13 05:59:34.867869: +2026-04-13 05:59:34.869775: Epoch 2286 +2026-04-13 05:59:34.871438: Current learning rate: 0.00466 +2026-04-13 06:01:14.997768: train_loss -0.4091 +2026-04-13 06:01:15.003521: val_loss -0.2941 +2026-04-13 06:01:15.006162: Pseudo dice [0.0, 0.0, 0.6993, 0.0029, 0.1688, 0.376, 0.2006] +2026-04-13 06:01:15.008530: Epoch time: 100.13 s +2026-04-13 06:01:16.173166: +2026-04-13 06:01:16.175036: Epoch 2287 +2026-04-13 06:01:16.177123: Current learning rate: 0.00466 +2026-04-13 06:02:56.462630: train_loss -0.3499 +2026-04-13 06:02:56.472968: val_loss -0.3727 +2026-04-13 06:02:56.474852: Pseudo dice [0.0, 0.0, 0.684, 0.8511, 0.2961, 0.627, 0.5404] +2026-04-13 06:02:56.481398: Epoch time: 100.29 s +2026-04-13 06:02:57.676265: +2026-04-13 06:02:57.678143: Epoch 2288 +2026-04-13 06:02:57.679871: Current learning rate: 0.00466 +2026-04-13 06:04:38.036685: train_loss -0.3641 +2026-04-13 06:04:38.044408: val_loss -0.32 +2026-04-13 06:04:38.048572: Pseudo dice [0.0, 0.0, 0.79, 0.0, 0.1071, 0.8646, 0.6546] +2026-04-13 06:04:38.051932: Epoch time: 100.36 s +2026-04-13 06:04:39.233641: +2026-04-13 06:04:39.236330: Epoch 2289 +2026-04-13 06:04:39.239166: Current learning rate: 0.00466 +2026-04-13 06:06:19.521435: train_loss -0.378 +2026-04-13 06:06:19.527453: val_loss -0.3293 +2026-04-13 06:06:19.529425: Pseudo dice [0.0, 0.0, 0.7775, 0.0628, 0.3776, 0.6207, 0.6104] +2026-04-13 06:06:19.531617: Epoch time: 100.29 s +2026-04-13 06:06:20.687639: +2026-04-13 06:06:20.689422: Epoch 2290 +2026-04-13 06:06:20.691301: Current learning rate: 0.00465 +2026-04-13 06:08:01.515391: train_loss -0.3877 +2026-04-13 06:08:01.521480: val_loss -0.352 +2026-04-13 06:08:01.523546: Pseudo dice [0.0, 0.0, 0.6197, 0.0812, 0.3511, 0.5689, 0.6058] +2026-04-13 06:08:01.526454: Epoch time: 100.83 s +2026-04-13 06:08:03.732081: +2026-04-13 06:08:03.734251: Epoch 2291 +2026-04-13 06:08:03.735868: Current learning rate: 0.00465 +2026-04-13 06:09:43.999650: train_loss -0.3768 +2026-04-13 06:09:44.004514: val_loss -0.3727 +2026-04-13 06:09:44.006469: Pseudo dice [0.0, 0.0, 0.7544, 0.4268, 0.5006, 0.7651, 0.6148] +2026-04-13 06:09:44.008728: Epoch time: 100.27 s +2026-04-13 06:09:45.167822: +2026-04-13 06:09:45.169986: Epoch 2292 +2026-04-13 06:09:45.171646: Current learning rate: 0.00465 +2026-04-13 06:11:25.305871: train_loss -0.3907 +2026-04-13 06:11:25.311738: val_loss -0.3672 +2026-04-13 06:11:25.313888: Pseudo dice [0.0, 0.0, 0.6052, 0.8, 0.3447, 0.7483, 0.5092] +2026-04-13 06:11:25.316383: Epoch time: 100.14 s +2026-04-13 06:11:26.482186: +2026-04-13 06:11:26.484349: Epoch 2293 +2026-04-13 06:11:26.486231: Current learning rate: 0.00465 +2026-04-13 06:13:06.576717: train_loss -0.4168 +2026-04-13 06:13:06.583723: val_loss -0.3088 +2026-04-13 06:13:06.586432: Pseudo dice [0.0, 0.0, 0.6049, 0.0018, 0.4459, 0.5599, 0.8099] +2026-04-13 06:13:06.590417: Epoch time: 100.1 s +2026-04-13 06:13:07.761038: +2026-04-13 06:13:07.763196: Epoch 2294 +2026-04-13 06:13:07.765215: Current learning rate: 0.00464 +2026-04-13 06:14:48.030844: train_loss -0.4149 +2026-04-13 06:14:48.036376: val_loss -0.396 +2026-04-13 06:14:48.039548: Pseudo dice [0.6302, 0.0, 0.6913, 0.0, 0.1824, 0.5157, 0.8469] +2026-04-13 06:14:48.042057: Epoch time: 100.27 s +2026-04-13 06:14:49.223605: +2026-04-13 06:14:49.225699: Epoch 2295 +2026-04-13 06:14:49.227731: Current learning rate: 0.00464 +2026-04-13 06:16:29.292630: train_loss -0.4031 +2026-04-13 06:16:29.299429: val_loss -0.3797 +2026-04-13 06:16:29.302078: Pseudo dice [0.6142, 0.0, 0.6904, 0.0, 0.3795, 0.6819, 0.8366] +2026-04-13 06:16:29.304735: Epoch time: 100.07 s +2026-04-13 06:16:30.480840: +2026-04-13 06:16:30.483950: Epoch 2296 +2026-04-13 06:16:30.487149: Current learning rate: 0.00464 +2026-04-13 06:18:10.691391: train_loss -0.3985 +2026-04-13 06:18:10.697258: val_loss -0.2593 +2026-04-13 06:18:10.699625: Pseudo dice [0.1952, 0.0, 0.8084, 0.0, 0.3413, 0.234, 0.5099] +2026-04-13 06:18:10.702049: Epoch time: 100.21 s +2026-04-13 06:18:11.880965: +2026-04-13 06:18:11.882881: Epoch 2297 +2026-04-13 06:18:11.884624: Current learning rate: 0.00464 +2026-04-13 06:19:51.988970: train_loss -0.3878 +2026-04-13 06:19:51.993957: val_loss -0.3646 +2026-04-13 06:19:51.996003: Pseudo dice [0.3878, 0.0, 0.743, 0.1272, 0.2183, 0.8472, 0.7134] +2026-04-13 06:19:51.998632: Epoch time: 100.11 s +2026-04-13 06:19:53.168162: +2026-04-13 06:19:53.170066: Epoch 2298 +2026-04-13 06:19:53.171845: Current learning rate: 0.00463 +2026-04-13 06:21:33.268692: train_loss -0.3971 +2026-04-13 06:21:33.275250: val_loss -0.2285 +2026-04-13 06:21:33.279796: Pseudo dice [0.1539, 0.0, 0.7302, 0.0, 0.1788, 0.5539, 0.7775] +2026-04-13 06:21:33.282572: Epoch time: 100.1 s +2026-04-13 06:21:34.453931: +2026-04-13 06:21:34.455724: Epoch 2299 +2026-04-13 06:21:34.457431: Current learning rate: 0.00463 +2026-04-13 06:23:14.465694: train_loss -0.375 +2026-04-13 06:23:14.471157: val_loss -0.3714 +2026-04-13 06:23:14.473135: Pseudo dice [0.6863, 0.0, 0.6648, 0.0, 0.2508, 0.7711, 0.5115] +2026-04-13 06:23:14.476259: Epoch time: 100.01 s +2026-04-13 06:23:17.355855: +2026-04-13 06:23:17.358253: Epoch 2300 +2026-04-13 06:23:17.360348: Current learning rate: 0.00463 +2026-04-13 06:24:57.495152: train_loss -0.3891 +2026-04-13 06:24:57.500104: val_loss -0.4084 +2026-04-13 06:24:57.501954: Pseudo dice [0.6936, 0.0, 0.57, 0.0, 0.0905, 0.6908, 0.7198] +2026-04-13 06:24:57.504317: Epoch time: 100.14 s +2026-04-13 06:24:58.680173: +2026-04-13 06:24:58.681868: Epoch 2301 +2026-04-13 06:24:58.683872: Current learning rate: 0.00463 +2026-04-13 06:26:38.837449: train_loss -0.3911 +2026-04-13 06:26:38.842919: val_loss -0.308 +2026-04-13 06:26:38.844984: Pseudo dice [0.3384, 0.0, 0.5723, 0.0224, 0.2085, 0.6051, 0.5319] +2026-04-13 06:26:38.847289: Epoch time: 100.16 s +2026-04-13 06:26:40.034389: +2026-04-13 06:26:40.036299: Epoch 2302 +2026-04-13 06:26:40.038260: Current learning rate: 0.00462 +2026-04-13 06:28:20.044576: train_loss -0.3745 +2026-04-13 06:28:20.050544: val_loss -0.2325 +2026-04-13 06:28:20.053379: Pseudo dice [0.0238, 0.0, 0.6186, 0.0109, 0.1642, 0.0, 0.4778] +2026-04-13 06:28:20.055763: Epoch time: 100.01 s +2026-04-13 06:28:21.228502: +2026-04-13 06:28:21.230422: Epoch 2303 +2026-04-13 06:28:21.232139: Current learning rate: 0.00462 +2026-04-13 06:30:01.278945: train_loss -0.3499 +2026-04-13 06:30:01.284859: val_loss -0.4016 +2026-04-13 06:30:01.286783: Pseudo dice [0.4289, 0.0, 0.7093, 0.0, 0.4867, 0.7545, 0.7458] +2026-04-13 06:30:01.289392: Epoch time: 100.05 s +2026-04-13 06:30:02.460365: +2026-04-13 06:30:02.462919: Epoch 2304 +2026-04-13 06:30:02.464945: Current learning rate: 0.00462 +2026-04-13 06:31:42.596832: train_loss -0.4107 +2026-04-13 06:31:42.602746: val_loss -0.4044 +2026-04-13 06:31:42.606475: Pseudo dice [0.1813, 0.0, 0.7271, 0.7399, 0.3873, 0.8579, 0.7531] +2026-04-13 06:31:42.608989: Epoch time: 100.14 s +2026-04-13 06:31:43.792121: +2026-04-13 06:31:43.794064: Epoch 2305 +2026-04-13 06:31:43.795623: Current learning rate: 0.00462 +2026-04-13 06:33:23.896623: train_loss -0.4116 +2026-04-13 06:33:23.903025: val_loss -0.3823 +2026-04-13 06:33:23.905886: Pseudo dice [0.0, 0.0, 0.7481, 0.0, 0.299, 0.74, 0.807] +2026-04-13 06:33:23.909620: Epoch time: 100.11 s +2026-04-13 06:33:25.066622: +2026-04-13 06:33:25.068860: Epoch 2306 +2026-04-13 06:33:25.071063: Current learning rate: 0.00461 +2026-04-13 06:35:05.326550: train_loss -0.3879 +2026-04-13 06:35:05.332570: val_loss -0.3588 +2026-04-13 06:35:05.334810: Pseudo dice [0.0, 0.0, 0.5976, 0.572, 0.4431, 0.7596, 0.5727] +2026-04-13 06:35:05.337698: Epoch time: 100.26 s +2026-04-13 06:35:06.514835: +2026-04-13 06:35:06.516845: Epoch 2307 +2026-04-13 06:35:06.519541: Current learning rate: 0.00461 +2026-04-13 06:36:46.751410: train_loss -0.3925 +2026-04-13 06:36:46.757005: val_loss -0.3651 +2026-04-13 06:36:46.759058: Pseudo dice [0.1408, 0.0, 0.8333, 0.248, 0.3361, 0.6945, 0.8541] +2026-04-13 06:36:46.761792: Epoch time: 100.24 s +2026-04-13 06:36:47.919770: +2026-04-13 06:36:47.921483: Epoch 2308 +2026-04-13 06:36:47.923104: Current learning rate: 0.00461 +2026-04-13 06:38:28.033778: train_loss -0.377 +2026-04-13 06:38:28.040767: val_loss -0.3633 +2026-04-13 06:38:28.043400: Pseudo dice [0.5576, 0.0, 0.6509, 0.0077, 0.2637, 0.6217, 0.4859] +2026-04-13 06:38:28.046401: Epoch time: 100.12 s +2026-04-13 06:38:29.240468: +2026-04-13 06:38:29.242332: Epoch 2309 +2026-04-13 06:38:29.244083: Current learning rate: 0.00461 +2026-04-13 06:40:09.289497: train_loss -0.3821 +2026-04-13 06:40:09.295416: val_loss -0.3672 +2026-04-13 06:40:09.300491: Pseudo dice [0.6481, 0.0, 0.573, 0.0, 0.303, 0.7023, 0.8143] +2026-04-13 06:40:09.303255: Epoch time: 100.05 s +2026-04-13 06:40:10.488421: +2026-04-13 06:40:10.490634: Epoch 2310 +2026-04-13 06:40:10.492272: Current learning rate: 0.00461 +2026-04-13 06:41:50.630146: train_loss -0.3727 +2026-04-13 06:41:50.635790: val_loss -0.3789 +2026-04-13 06:41:50.637984: Pseudo dice [0.5106, 0.0, 0.4508, 0.0, 0.2003, 0.3773, 0.7842] +2026-04-13 06:41:50.640439: Epoch time: 100.14 s +2026-04-13 06:41:51.802953: +2026-04-13 06:41:51.804641: Epoch 2311 +2026-04-13 06:41:51.806304: Current learning rate: 0.0046 +2026-04-13 06:43:33.087606: train_loss -0.347 +2026-04-13 06:43:33.092326: val_loss -0.3951 +2026-04-13 06:43:33.094149: Pseudo dice [0.6032, 0.0, 0.5765, 0.0, 0.23, 0.7516, 0.7994] +2026-04-13 06:43:33.097620: Epoch time: 101.29 s +2026-04-13 06:43:34.266016: +2026-04-13 06:43:34.268007: Epoch 2312 +2026-04-13 06:43:34.271030: Current learning rate: 0.0046 +2026-04-13 06:45:14.404408: train_loss -0.3846 +2026-04-13 06:45:14.410896: val_loss -0.3613 +2026-04-13 06:45:14.413333: Pseudo dice [0.349, 0.0, 0.6201, 0.0, 0.3442, 0.653, 0.6469] +2026-04-13 06:45:14.415915: Epoch time: 100.14 s +2026-04-13 06:45:15.587891: +2026-04-13 06:45:15.590384: Epoch 2313 +2026-04-13 06:45:15.592295: Current learning rate: 0.0046 +2026-04-13 06:46:55.733905: train_loss -0.3824 +2026-04-13 06:46:55.738857: val_loss -0.3494 +2026-04-13 06:46:55.740550: Pseudo dice [0.5911, 0.0, 0.5923, 0.144, 0.3167, 0.5019, 0.6729] +2026-04-13 06:46:55.743095: Epoch time: 100.15 s +2026-04-13 06:46:56.922629: +2026-04-13 06:46:56.924671: Epoch 2314 +2026-04-13 06:46:56.926534: Current learning rate: 0.0046 +2026-04-13 06:48:36.866111: train_loss -0.4021 +2026-04-13 06:48:36.871049: val_loss -0.4147 +2026-04-13 06:48:36.873369: Pseudo dice [0.4392, 0.0, 0.8501, 0.0, 0.392, 0.5673, 0.8249] +2026-04-13 06:48:36.876469: Epoch time: 99.95 s +2026-04-13 06:48:38.062341: +2026-04-13 06:48:38.064273: Epoch 2315 +2026-04-13 06:48:38.066156: Current learning rate: 0.00459 +2026-04-13 06:50:18.066334: train_loss -0.3889 +2026-04-13 06:50:18.072047: val_loss -0.3556 +2026-04-13 06:50:18.074151: Pseudo dice [0.0433, 0.0, 0.4379, 0.1497, 0.2491, 0.7276, 0.6758] +2026-04-13 06:50:18.076272: Epoch time: 100.01 s +2026-04-13 06:50:19.245018: +2026-04-13 06:50:19.246884: Epoch 2316 +2026-04-13 06:50:19.248605: Current learning rate: 0.00459 +2026-04-13 06:52:00.295759: train_loss -0.3909 +2026-04-13 06:52:00.304413: val_loss -0.4052 +2026-04-13 06:52:00.306829: Pseudo dice [0.4149, 0.0, 0.7215, 0.7128, 0.3404, 0.8046, 0.7825] +2026-04-13 06:52:00.309333: Epoch time: 101.05 s +2026-04-13 06:52:01.477948: +2026-04-13 06:52:01.480372: Epoch 2317 +2026-04-13 06:52:01.482827: Current learning rate: 0.00459 +2026-04-13 06:53:41.860433: train_loss -0.4052 +2026-04-13 06:53:41.865918: val_loss -0.4043 +2026-04-13 06:53:41.867689: Pseudo dice [0.2545, 0.0, 0.7914, 0.0, 0.3922, 0.6944, 0.7733] +2026-04-13 06:53:41.870190: Epoch time: 100.39 s +2026-04-13 06:53:43.044151: +2026-04-13 06:53:43.045894: Epoch 2318 +2026-04-13 06:53:43.047554: Current learning rate: 0.00459 +2026-04-13 06:55:23.032724: train_loss -0.4073 +2026-04-13 06:55:23.038584: val_loss -0.3395 +2026-04-13 06:55:23.040889: Pseudo dice [0.4929, 0.0, 0.7607, 0.0319, 0.0, 0.7662, 0.8041] +2026-04-13 06:55:23.043413: Epoch time: 99.99 s +2026-04-13 06:55:24.232407: +2026-04-13 06:55:24.234559: Epoch 2319 +2026-04-13 06:55:24.236277: Current learning rate: 0.00458 +2026-04-13 06:57:04.306892: train_loss -0.4088 +2026-04-13 06:57:04.313145: val_loss -0.4093 +2026-04-13 06:57:04.315400: Pseudo dice [0.5307, 0.0, 0.7937, 0.4559, 0.1723, 0.7802, 0.8998] +2026-04-13 06:57:04.318266: Epoch time: 100.08 s +2026-04-13 06:57:05.507217: +2026-04-13 06:57:05.509800: Epoch 2320 +2026-04-13 06:57:05.512391: Current learning rate: 0.00458 +2026-04-13 06:58:45.506816: train_loss -0.4024 +2026-04-13 06:58:45.513652: val_loss -0.4049 +2026-04-13 06:58:45.517272: Pseudo dice [0.6181, 0.0, 0.6503, 0.3835, 0.4441, 0.7511, 0.8605] +2026-04-13 06:58:45.520380: Epoch time: 100.0 s +2026-04-13 06:58:46.704662: +2026-04-13 06:58:46.707043: Epoch 2321 +2026-04-13 06:58:46.708873: Current learning rate: 0.00458 +2026-04-13 07:00:26.912786: train_loss -0.4043 +2026-04-13 07:00:26.918523: val_loss -0.3836 +2026-04-13 07:00:26.921895: Pseudo dice [0.4778, 0.0, 0.7095, 0.6176, 0.266, 0.671, 0.4811] +2026-04-13 07:00:26.924537: Epoch time: 100.21 s +2026-04-13 07:00:28.081795: +2026-04-13 07:00:28.083695: Epoch 2322 +2026-04-13 07:00:28.085480: Current learning rate: 0.00458 +2026-04-13 07:02:08.306941: train_loss -0.4202 +2026-04-13 07:02:08.312935: val_loss -0.3999 +2026-04-13 07:02:08.315093: Pseudo dice [0.6374, 0.0, 0.6529, 0.0, 0.4021, 0.8123, 0.3642] +2026-04-13 07:02:08.317635: Epoch time: 100.23 s +2026-04-13 07:02:09.678922: +2026-04-13 07:02:09.681088: Epoch 2323 +2026-04-13 07:02:09.682686: Current learning rate: 0.00457 +2026-04-13 07:03:49.760950: train_loss -0.4155 +2026-04-13 07:03:49.769460: val_loss -0.3735 +2026-04-13 07:03:49.772336: Pseudo dice [0.5431, 0.0, 0.4148, 0.7267, 0.2626, 0.6477, 0.6218] +2026-04-13 07:03:49.774992: Epoch time: 100.09 s +2026-04-13 07:03:50.948971: +2026-04-13 07:03:50.951609: Epoch 2324 +2026-04-13 07:03:50.954256: Current learning rate: 0.00457 +2026-04-13 07:05:30.991436: train_loss -0.383 +2026-04-13 07:05:30.996121: val_loss -0.4019 +2026-04-13 07:05:30.998404: Pseudo dice [0.6702, 0.0, 0.7987, 0.0323, 0.2666, 0.5236, 0.7575] +2026-04-13 07:05:31.000595: Epoch time: 100.05 s +2026-04-13 07:05:32.184351: +2026-04-13 07:05:32.186399: Epoch 2325 +2026-04-13 07:05:32.188002: Current learning rate: 0.00457 +2026-04-13 07:07:12.202463: train_loss -0.4024 +2026-04-13 07:07:12.208732: val_loss -0.3789 +2026-04-13 07:07:12.210710: Pseudo dice [0.0807, 0.0, 0.7695, 0.0, 0.3321, 0.1947, 0.6489] +2026-04-13 07:07:12.213164: Epoch time: 100.02 s +2026-04-13 07:07:13.400733: +2026-04-13 07:07:13.404530: Epoch 2326 +2026-04-13 07:07:13.407963: Current learning rate: 0.00457 +2026-04-13 07:08:55.354709: train_loss -0.3963 +2026-04-13 07:08:55.360754: val_loss -0.3579 +2026-04-13 07:08:55.363926: Pseudo dice [0.4065, 0.0, 0.6927, 0.0771, 0.0, 0.7294, 0.6992] +2026-04-13 07:08:55.366031: Epoch time: 101.96 s +2026-04-13 07:08:56.573452: +2026-04-13 07:08:56.575153: Epoch 2327 +2026-04-13 07:08:56.576805: Current learning rate: 0.00456 +2026-04-13 07:10:36.489458: train_loss -0.3972 +2026-04-13 07:10:36.495821: val_loss -0.3769 +2026-04-13 07:10:36.498031: Pseudo dice [0.4371, 0.0, 0.722, 0.7238, 0.0073, 0.4897, 0.6839] +2026-04-13 07:10:36.500390: Epoch time: 99.92 s +2026-04-13 07:10:37.686128: +2026-04-13 07:10:37.688063: Epoch 2328 +2026-04-13 07:10:37.689774: Current learning rate: 0.00456 +2026-04-13 07:12:17.615146: train_loss -0.4101 +2026-04-13 07:12:17.623888: val_loss -0.4052 +2026-04-13 07:12:17.627011: Pseudo dice [0.5778, 0.0, 0.7711, 0.4998, 0.46, 0.7857, 0.7573] +2026-04-13 07:12:17.629828: Epoch time: 99.93 s +2026-04-13 07:12:18.807896: +2026-04-13 07:12:18.809940: Epoch 2329 +2026-04-13 07:12:18.811970: Current learning rate: 0.00456 +2026-04-13 07:13:58.656921: train_loss -0.3795 +2026-04-13 07:13:58.662708: val_loss -0.3598 +2026-04-13 07:13:58.665380: Pseudo dice [0.1791, 0.0, 0.4294, 0.0916, 0.1098, 0.6621, 0.6233] +2026-04-13 07:13:58.667718: Epoch time: 99.85 s +2026-04-13 07:13:59.854256: +2026-04-13 07:13:59.856221: Epoch 2330 +2026-04-13 07:13:59.857898: Current learning rate: 0.00456 +2026-04-13 07:15:39.774431: train_loss -0.3791 +2026-04-13 07:15:39.782558: val_loss -0.3869 +2026-04-13 07:15:39.784534: Pseudo dice [0.4901, 0.0, 0.7439, 0.0, 0.2293, 0.5011, 0.7183] +2026-04-13 07:15:39.787265: Epoch time: 99.92 s +2026-04-13 07:15:40.950492: +2026-04-13 07:15:40.953119: Epoch 2331 +2026-04-13 07:15:40.955562: Current learning rate: 0.00455 +2026-04-13 07:17:20.925387: train_loss -0.3787 +2026-04-13 07:17:20.931341: val_loss -0.3095 +2026-04-13 07:17:20.933604: Pseudo dice [0.5414, 0.0, 0.564, 0.0, 0.3436, 0.6477, 0.7682] +2026-04-13 07:17:20.936247: Epoch time: 99.98 s +2026-04-13 07:17:23.203270: +2026-04-13 07:17:23.205281: Epoch 2332 +2026-04-13 07:17:23.207526: Current learning rate: 0.00455 +2026-04-13 07:19:03.345260: train_loss -0.4085 +2026-04-13 07:19:03.352485: val_loss -0.3986 +2026-04-13 07:19:03.355846: Pseudo dice [0.4798, 0.0, 0.6972, 0.0, 0.281, 0.7685, 0.7812] +2026-04-13 07:19:03.360273: Epoch time: 100.14 s +2026-04-13 07:19:04.529238: +2026-04-13 07:19:04.531212: Epoch 2333 +2026-04-13 07:19:04.532900: Current learning rate: 0.00455 +2026-04-13 07:20:44.786702: train_loss -0.3375 +2026-04-13 07:20:44.792575: val_loss -0.3532 +2026-04-13 07:20:44.795196: Pseudo dice [0.6597, 0.0, 0.7156, 0.0, 0.0, 0.0007, 0.1083] +2026-04-13 07:20:44.797535: Epoch time: 100.26 s +2026-04-13 07:20:45.975322: +2026-04-13 07:20:45.977396: Epoch 2334 +2026-04-13 07:20:45.978995: Current learning rate: 0.00455 +2026-04-13 07:22:26.729676: train_loss -0.353 +2026-04-13 07:22:26.737354: val_loss -0.379 +2026-04-13 07:22:26.740728: Pseudo dice [0.3155, 0.0, 0.6886, 0.5269, 0.1282, 0.362, 0.7413] +2026-04-13 07:22:26.743960: Epoch time: 100.76 s +2026-04-13 07:22:27.914527: +2026-04-13 07:22:27.916459: Epoch 2335 +2026-04-13 07:22:27.919040: Current learning rate: 0.00454 +2026-04-13 07:24:07.972557: train_loss -0.3828 +2026-04-13 07:24:07.977770: val_loss -0.3736 +2026-04-13 07:24:07.979527: Pseudo dice [0.328, 0.0, 0.6061, 0.0759, 0.4006, 0.6452, 0.503] +2026-04-13 07:24:07.981499: Epoch time: 100.06 s +2026-04-13 07:24:09.149216: +2026-04-13 07:24:09.151377: Epoch 2336 +2026-04-13 07:24:09.153101: Current learning rate: 0.00454 +2026-04-13 07:25:49.525753: train_loss -0.4212 +2026-04-13 07:25:49.538950: val_loss -0.4032 +2026-04-13 07:25:49.544372: Pseudo dice [0.5714, 0.0, 0.7298, 0.3165, 0.0801, 0.8638, 0.763] +2026-04-13 07:25:49.549520: Epoch time: 100.38 s +2026-04-13 07:25:50.722896: +2026-04-13 07:25:50.724709: Epoch 2337 +2026-04-13 07:25:50.726320: Current learning rate: 0.00454 +2026-04-13 07:27:30.883249: train_loss -0.4246 +2026-04-13 07:27:30.889389: val_loss -0.3715 +2026-04-13 07:27:30.891002: Pseudo dice [0.0, 0.0, 0.7405, 0.3422, 0.206, 0.8293, 0.8556] +2026-04-13 07:27:30.892875: Epoch time: 100.16 s +2026-04-13 07:27:32.057580: +2026-04-13 07:27:32.059744: Epoch 2338 +2026-04-13 07:27:32.061243: Current learning rate: 0.00454 +2026-04-13 07:29:12.179293: train_loss -0.3587 +2026-04-13 07:29:12.188778: val_loss -0.3535 +2026-04-13 07:29:12.191530: Pseudo dice [0.0, 0.0, 0.5018, 0.0, 0.0, 0.6302, 0.546] +2026-04-13 07:29:12.194604: Epoch time: 100.12 s +2026-04-13 07:29:13.373009: +2026-04-13 07:29:13.375110: Epoch 2339 +2026-04-13 07:29:13.376629: Current learning rate: 0.00453 +2026-04-13 07:30:53.501330: train_loss -0.3734 +2026-04-13 07:30:53.506537: val_loss -0.3179 +2026-04-13 07:30:53.509020: Pseudo dice [0.3398, 0.0, 0.6525, 0.0644, 0.0, 0.8426, 0.6786] +2026-04-13 07:30:53.511210: Epoch time: 100.13 s +2026-04-13 07:30:54.697665: +2026-04-13 07:30:54.699625: Epoch 2340 +2026-04-13 07:30:54.701820: Current learning rate: 0.00453 +2026-04-13 07:32:35.741248: train_loss -0.3852 +2026-04-13 07:32:35.747605: val_loss -0.3858 +2026-04-13 07:32:35.750752: Pseudo dice [0.0246, 0.0, 0.7033, 0.0, 0.0, 0.7089, 0.8437] +2026-04-13 07:32:35.753072: Epoch time: 101.05 s +2026-04-13 07:32:36.934908: +2026-04-13 07:32:36.936563: Epoch 2341 +2026-04-13 07:32:36.939108: Current learning rate: 0.00453 +2026-04-13 07:34:17.080258: train_loss -0.3628 +2026-04-13 07:34:17.086132: val_loss -0.2255 +2026-04-13 07:34:17.088451: Pseudo dice [0.4303, 0.0, 0.6698, 0.0, 0.0, 0.2768, 0.2296] +2026-04-13 07:34:17.091403: Epoch time: 100.15 s +2026-04-13 07:34:18.266533: +2026-04-13 07:34:18.268480: Epoch 2342 +2026-04-13 07:34:18.270293: Current learning rate: 0.00453 +2026-04-13 07:35:58.773787: train_loss -0.3786 +2026-04-13 07:35:58.781219: val_loss -0.3766 +2026-04-13 07:35:58.783375: Pseudo dice [0.043, 0.0, 0.7857, 0.0, 0.0, 0.7434, 0.6261] +2026-04-13 07:35:58.785668: Epoch time: 100.51 s +2026-04-13 07:35:59.958734: +2026-04-13 07:35:59.960935: Epoch 2343 +2026-04-13 07:35:59.962821: Current learning rate: 0.00452 +2026-04-13 07:37:39.961123: train_loss -0.3871 +2026-04-13 07:37:39.967606: val_loss -0.4081 +2026-04-13 07:37:39.969966: Pseudo dice [0.3228, 0.0, 0.6955, 0.2549, 0.0, 0.8055, 0.7494] +2026-04-13 07:37:39.972758: Epoch time: 100.01 s +2026-04-13 07:37:41.145276: +2026-04-13 07:37:41.147494: Epoch 2344 +2026-04-13 07:37:41.149243: Current learning rate: 0.00452 +2026-04-13 07:39:21.102097: train_loss -0.3804 +2026-04-13 07:39:21.107984: val_loss -0.3448 +2026-04-13 07:39:21.110501: Pseudo dice [0.3466, 0.0, 0.7394, 0.0035, 0.0, 0.6182, 0.6695] +2026-04-13 07:39:21.112918: Epoch time: 99.96 s +2026-04-13 07:39:22.509155: +2026-04-13 07:39:22.511610: Epoch 2345 +2026-04-13 07:39:22.513444: Current learning rate: 0.00452 +2026-04-13 07:41:02.931550: train_loss -0.3603 +2026-04-13 07:41:02.937762: val_loss -0.3824 +2026-04-13 07:41:02.940609: Pseudo dice [0.6886, 0.0, 0.7549, 0.6022, 0.0, 0.6444, 0.258] +2026-04-13 07:41:02.943512: Epoch time: 100.43 s +2026-04-13 07:41:04.163027: +2026-04-13 07:41:04.164940: Epoch 2346 +2026-04-13 07:41:04.166869: Current learning rate: 0.00452 +2026-04-13 07:42:44.217406: train_loss -0.3987 +2026-04-13 07:42:44.224318: val_loss -0.3932 +2026-04-13 07:42:44.226143: Pseudo dice [0.6807, 0.0, 0.6579, 0.6801, 0.0, 0.7532, 0.7132] +2026-04-13 07:42:44.228487: Epoch time: 100.06 s +2026-04-13 07:42:45.418808: +2026-04-13 07:42:45.420498: Epoch 2347 +2026-04-13 07:42:45.421984: Current learning rate: 0.00451 +2026-04-13 07:44:25.248649: train_loss -0.4005 +2026-04-13 07:44:25.254645: val_loss -0.4211 +2026-04-13 07:44:25.256623: Pseudo dice [0.0, 0.0, 0.6877, 0.0, 0.0, 0.8128, 0.8892] +2026-04-13 07:44:25.259039: Epoch time: 99.83 s +2026-04-13 07:44:26.440405: +2026-04-13 07:44:26.443419: Epoch 2348 +2026-04-13 07:44:26.445338: Current learning rate: 0.00451 +2026-04-13 07:46:06.957250: train_loss -0.4076 +2026-04-13 07:46:06.962924: val_loss -0.4034 +2026-04-13 07:46:06.965146: Pseudo dice [0.0, 0.0, 0.7674, 0.0, 0.3033, 0.6404, 0.6189] +2026-04-13 07:46:06.967731: Epoch time: 100.52 s +2026-04-13 07:46:08.180795: +2026-04-13 07:46:08.183044: Epoch 2349 +2026-04-13 07:46:08.187558: Current learning rate: 0.00451 +2026-04-13 07:47:48.055906: train_loss -0.4149 +2026-04-13 07:47:48.062079: val_loss -0.3983 +2026-04-13 07:47:48.063940: Pseudo dice [0.7013, 0.0, 0.6998, 0.0, 0.0736, 0.5717, 0.8947] +2026-04-13 07:47:48.066124: Epoch time: 99.88 s +2026-04-13 07:47:50.913574: +2026-04-13 07:47:50.915449: Epoch 2350 +2026-04-13 07:47:50.917544: Current learning rate: 0.00451 +2026-04-13 07:49:31.111568: train_loss -0.4117 +2026-04-13 07:49:31.118488: val_loss -0.3344 +2026-04-13 07:49:31.121373: Pseudo dice [0.7775, 0.0, 0.5346, 0.0186, 0.2203, 0.8396, 0.8232] +2026-04-13 07:49:31.123451: Epoch time: 100.2 s +2026-04-13 07:49:32.306834: +2026-04-13 07:49:32.308634: Epoch 2351 +2026-04-13 07:49:32.310288: Current learning rate: 0.0045 +2026-04-13 07:51:12.345208: train_loss -0.4132 +2026-04-13 07:51:12.351738: val_loss -0.3991 +2026-04-13 07:51:12.354952: Pseudo dice [0.0039, 0.0, 0.6733, 0.2689, 0.4403, 0.812, 0.7776] +2026-04-13 07:51:12.359664: Epoch time: 100.04 s +2026-04-13 07:51:13.536021: +2026-04-13 07:51:13.538326: Epoch 2352 +2026-04-13 07:51:13.540045: Current learning rate: 0.0045 +2026-04-13 07:52:54.538334: train_loss -0.4214 +2026-04-13 07:52:54.543412: val_loss -0.3432 +2026-04-13 07:52:54.545272: Pseudo dice [0.1299, 0.0, 0.3835, 0.1127, 0.5835, 0.718, 0.7464] +2026-04-13 07:52:54.547623: Epoch time: 101.01 s +2026-04-13 07:52:55.721626: +2026-04-13 07:52:55.723791: Epoch 2353 +2026-04-13 07:52:55.725465: Current learning rate: 0.0045 +2026-04-13 07:54:36.483887: train_loss -0.3517 +2026-04-13 07:54:36.491559: val_loss -0.3988 +2026-04-13 07:54:36.498968: Pseudo dice [0.0, 0.0, 0.7509, 0.7938, 0.2437, 0.6431, 0.7065] +2026-04-13 07:54:36.501874: Epoch time: 100.77 s +2026-04-13 07:54:37.668801: +2026-04-13 07:54:37.671123: Epoch 2354 +2026-04-13 07:54:37.673566: Current learning rate: 0.0045 +2026-04-13 07:56:17.714231: train_loss -0.3894 +2026-04-13 07:56:17.721791: val_loss -0.3989 +2026-04-13 07:56:17.724507: Pseudo dice [0.4825, 0.0, 0.5877, 0.542, 0.1206, 0.7391, 0.7784] +2026-04-13 07:56:17.726849: Epoch time: 100.05 s +2026-04-13 07:56:18.898564: +2026-04-13 07:56:18.900787: Epoch 2355 +2026-04-13 07:56:18.902862: Current learning rate: 0.00449 +2026-04-13 07:57:59.227061: train_loss -0.3639 +2026-04-13 07:57:59.233213: val_loss -0.3561 +2026-04-13 07:57:59.235164: Pseudo dice [0.3016, 0.0, 0.5277, 0.8644, 0.0, 0.7905, 0.5992] +2026-04-13 07:57:59.237376: Epoch time: 100.33 s +2026-04-13 07:58:00.406471: +2026-04-13 07:58:00.410374: Epoch 2356 +2026-04-13 07:58:00.414066: Current learning rate: 0.00449 +2026-04-13 07:59:40.583589: train_loss -0.3908 +2026-04-13 07:59:40.590425: val_loss -0.4091 +2026-04-13 07:59:40.593200: Pseudo dice [0.396, 0.0, 0.7418, 0.6241, 0.4086, 0.6366, 0.8545] +2026-04-13 07:59:40.596223: Epoch time: 100.18 s +2026-04-13 07:59:41.764478: +2026-04-13 07:59:41.766739: Epoch 2357 +2026-04-13 07:59:41.768856: Current learning rate: 0.00449 +2026-04-13 08:01:22.110778: train_loss -0.4092 +2026-04-13 08:01:22.118698: val_loss -0.3377 +2026-04-13 08:01:22.120943: Pseudo dice [0.7169, 0.0, 0.6489, 0.0858, 0.3234, 0.6056, 0.4355] +2026-04-13 08:01:22.125157: Epoch time: 100.35 s +2026-04-13 08:01:23.317285: +2026-04-13 08:01:23.320758: Epoch 2358 +2026-04-13 08:01:23.323421: Current learning rate: 0.00449 +2026-04-13 08:03:03.256632: train_loss -0.4001 +2026-04-13 08:03:03.263952: val_loss -0.4035 +2026-04-13 08:03:03.265821: Pseudo dice [0.3182, 0.0, 0.7778, 0.0, 0.6271, 0.7093, 0.4102] +2026-04-13 08:03:03.268311: Epoch time: 99.94 s +2026-04-13 08:03:04.468662: +2026-04-13 08:03:04.470942: Epoch 2359 +2026-04-13 08:03:04.473346: Current learning rate: 0.00448 +2026-04-13 08:04:44.473281: train_loss -0.3986 +2026-04-13 08:04:44.483657: val_loss -0.3146 +2026-04-13 08:04:44.487166: Pseudo dice [0.3271, 0.0, 0.5786, 0.0885, 0.457, 0.7893, 0.6936] +2026-04-13 08:04:44.489742: Epoch time: 100.01 s +2026-04-13 08:04:45.666299: +2026-04-13 08:04:45.668785: Epoch 2360 +2026-04-13 08:04:45.672894: Current learning rate: 0.00448 +2026-04-13 08:06:26.701384: train_loss -0.422 +2026-04-13 08:06:26.708304: val_loss -0.4068 +2026-04-13 08:06:26.710700: Pseudo dice [0.7883, 0.0, 0.7349, 0.3275, 0.4872, 0.7654, 0.6782] +2026-04-13 08:06:26.713103: Epoch time: 101.04 s +2026-04-13 08:06:27.887782: +2026-04-13 08:06:27.889817: Epoch 2361 +2026-04-13 08:06:27.891986: Current learning rate: 0.00448 +2026-04-13 08:08:08.280713: train_loss -0.3897 +2026-04-13 08:08:08.288353: val_loss -0.2624 +2026-04-13 08:08:08.291153: Pseudo dice [0.2956, 0.0, 0.2208, 0.0, 0.3418, 0.3607, 0.8316] +2026-04-13 08:08:08.293603: Epoch time: 100.4 s +2026-04-13 08:08:09.477116: +2026-04-13 08:08:09.479481: Epoch 2362 +2026-04-13 08:08:09.481635: Current learning rate: 0.00448 +2026-04-13 08:09:50.116130: train_loss -0.4083 +2026-04-13 08:09:50.126393: val_loss -0.3214 +2026-04-13 08:09:50.128528: Pseudo dice [0.0, 0.0, 0.4462, 0.0, 0.4439, 0.5461, 0.651] +2026-04-13 08:09:50.135046: Epoch time: 100.64 s +2026-04-13 08:09:51.340012: +2026-04-13 08:09:51.342556: Epoch 2363 +2026-04-13 08:09:51.345168: Current learning rate: 0.00447 +2026-04-13 08:11:31.743173: train_loss -0.4122 +2026-04-13 08:11:31.750431: val_loss -0.4215 +2026-04-13 08:11:31.753020: Pseudo dice [0.0, 0.0, 0.8486, 0.629, 0.4238, 0.5932, 0.8655] +2026-04-13 08:11:31.756383: Epoch time: 100.41 s +2026-04-13 08:11:32.948246: +2026-04-13 08:11:32.950169: Epoch 2364 +2026-04-13 08:11:32.952049: Current learning rate: 0.00447 +2026-04-13 08:13:13.611415: train_loss -0.4047 +2026-04-13 08:13:13.621891: val_loss -0.2924 +2026-04-13 08:13:13.625792: Pseudo dice [0.0, 0.0, 0.5973, 0.0046, 0.2797, 0.6273, 0.811] +2026-04-13 08:13:13.630777: Epoch time: 100.67 s +2026-04-13 08:13:14.832912: +2026-04-13 08:13:14.834762: Epoch 2365 +2026-04-13 08:13:14.836909: Current learning rate: 0.00447 +2026-04-13 08:14:55.160275: train_loss -0.3996 +2026-04-13 08:14:55.167261: val_loss -0.3536 +2026-04-13 08:14:55.169342: Pseudo dice [0.273, 0.0, 0.3585, 0.0, 0.0, 0.5584, 0.5362] +2026-04-13 08:14:55.171930: Epoch time: 100.33 s +2026-04-13 08:14:56.344705: +2026-04-13 08:14:56.346596: Epoch 2366 +2026-04-13 08:14:56.348495: Current learning rate: 0.00447 +2026-04-13 08:16:36.468554: train_loss -0.3765 +2026-04-13 08:16:36.474512: val_loss -0.383 +2026-04-13 08:16:36.476712: Pseudo dice [0.5066, 0.0, 0.6142, 0.4674, 0.2572, 0.7546, 0.8185] +2026-04-13 08:16:36.479130: Epoch time: 100.13 s +2026-04-13 08:16:37.648467: +2026-04-13 08:16:37.650361: Epoch 2367 +2026-04-13 08:16:37.652507: Current learning rate: 0.00447 +2026-04-13 08:18:18.480209: train_loss -0.3883 +2026-04-13 08:18:18.493986: val_loss -0.3612 +2026-04-13 08:18:18.496549: Pseudo dice [0.2958, 0.0, 0.7678, 0.0, 0.3652, 0.5091, 0.338] +2026-04-13 08:18:18.499672: Epoch time: 100.83 s +2026-04-13 08:18:19.684777: +2026-04-13 08:18:19.687400: Epoch 2368 +2026-04-13 08:18:19.690860: Current learning rate: 0.00446 +2026-04-13 08:20:00.016774: train_loss -0.3929 +2026-04-13 08:20:00.024352: val_loss -0.399 +2026-04-13 08:20:00.026461: Pseudo dice [0.6573, 0.0, 0.7456, 0.0, 0.2914, 0.8351, 0.8918] +2026-04-13 08:20:00.028978: Epoch time: 100.34 s +2026-04-13 08:20:01.232152: +2026-04-13 08:20:01.234636: Epoch 2369 +2026-04-13 08:20:01.237369: Current learning rate: 0.00446 +2026-04-13 08:21:41.392014: train_loss -0.3914 +2026-04-13 08:21:41.397519: val_loss -0.3766 +2026-04-13 08:21:41.400550: Pseudo dice [0.2962, 0.0, 0.5751, 0.1889, 0.2379, 0.6352, 0.5883] +2026-04-13 08:21:41.403382: Epoch time: 100.16 s +2026-04-13 08:21:42.587200: +2026-04-13 08:21:42.589560: Epoch 2370 +2026-04-13 08:21:42.591800: Current learning rate: 0.00446 +2026-04-13 08:23:23.147789: train_loss -0.3818 +2026-04-13 08:23:23.154260: val_loss -0.3396 +2026-04-13 08:23:23.156327: Pseudo dice [0.4157, 0.0, 0.7332, 0.0, 0.2932, 0.4202, 0.24] +2026-04-13 08:23:23.158725: Epoch time: 100.56 s +2026-04-13 08:23:24.327216: +2026-04-13 08:23:24.329311: Epoch 2371 +2026-04-13 08:23:24.331493: Current learning rate: 0.00446 +2026-04-13 08:25:05.664671: train_loss -0.3183 +2026-04-13 08:25:05.674650: val_loss -0.317 +2026-04-13 08:25:05.676890: Pseudo dice [0.4916, 0.0, 0.6215, 0.0, 0.2232, 0.4205, 0.0] +2026-04-13 08:25:05.681068: Epoch time: 101.34 s +2026-04-13 08:25:06.919771: +2026-04-13 08:25:06.922215: Epoch 2372 +2026-04-13 08:25:06.929302: Current learning rate: 0.00445 +2026-04-13 08:26:47.453563: train_loss -0.3938 +2026-04-13 08:26:47.461419: val_loss -0.3514 +2026-04-13 08:26:47.463626: Pseudo dice [0.0, 0.0, 0.7516, 0.1843, 0.0, 0.7653, 0.41] +2026-04-13 08:26:47.465919: Epoch time: 100.54 s +2026-04-13 08:26:49.763981: +2026-04-13 08:26:49.765991: Epoch 2373 +2026-04-13 08:26:49.768163: Current learning rate: 0.00445 +2026-04-13 08:28:29.787277: train_loss -0.4026 +2026-04-13 08:28:29.793160: val_loss -0.3822 +2026-04-13 08:28:29.795545: Pseudo dice [0.698, 0.0, 0.7386, 0.0, 0.3485, 0.6554, 0.7835] +2026-04-13 08:28:29.797468: Epoch time: 100.03 s +2026-04-13 08:28:30.965120: +2026-04-13 08:28:30.967599: Epoch 2374 +2026-04-13 08:28:30.970159: Current learning rate: 0.00445 +2026-04-13 08:30:12.427856: train_loss -0.3949 +2026-04-13 08:30:12.437503: val_loss -0.3868 +2026-04-13 08:30:12.440099: Pseudo dice [0.6216, 0.0, 0.6533, 0.1862, 0.3631, 0.839, 0.6506] +2026-04-13 08:30:12.442730: Epoch time: 101.47 s +2026-04-13 08:30:13.641012: +2026-04-13 08:30:13.644080: Epoch 2375 +2026-04-13 08:30:13.646580: Current learning rate: 0.00445 +2026-04-13 08:31:53.797829: train_loss -0.4017 +2026-04-13 08:31:53.807649: val_loss -0.3105 +2026-04-13 08:31:53.811374: Pseudo dice [0.5244, 0.0, 0.6382, 0.0733, 0.4204, 0.8088, 0.7053] +2026-04-13 08:31:53.814185: Epoch time: 100.16 s +2026-04-13 08:31:54.996415: +2026-04-13 08:31:54.998543: Epoch 2376 +2026-04-13 08:31:55.001761: Current learning rate: 0.00444 +2026-04-13 08:33:35.381030: train_loss -0.4179 +2026-04-13 08:33:35.388052: val_loss -0.3923 +2026-04-13 08:33:35.390448: Pseudo dice [0.6446, 0.0, 0.6822, 0.5556, 0.4515, 0.4051, 0.5665] +2026-04-13 08:33:35.393306: Epoch time: 100.39 s +2026-04-13 08:33:36.601130: +2026-04-13 08:33:36.603607: Epoch 2377 +2026-04-13 08:33:36.606799: Current learning rate: 0.00444 +2026-04-13 08:35:16.798594: train_loss -0.3911 +2026-04-13 08:35:16.807984: val_loss -0.3872 +2026-04-13 08:35:16.809907: Pseudo dice [0.4547, 0.0, 0.7034, 0.0056, 0.4209, 0.6175, 0.3654] +2026-04-13 08:35:16.812776: Epoch time: 100.2 s +2026-04-13 08:35:18.031276: +2026-04-13 08:35:18.033855: Epoch 2378 +2026-04-13 08:35:18.036486: Current learning rate: 0.00444 +2026-04-13 08:36:59.207345: train_loss -0.403 +2026-04-13 08:36:59.213276: val_loss -0.3804 +2026-04-13 08:36:59.215269: Pseudo dice [0.4697, 0.0, 0.5296, 0.2239, 0.4086, 0.7455, 0.875] +2026-04-13 08:36:59.218194: Epoch time: 101.18 s +2026-04-13 08:37:00.424628: +2026-04-13 08:37:00.426873: Epoch 2379 +2026-04-13 08:37:00.429382: Current learning rate: 0.00444 +2026-04-13 08:38:40.400820: train_loss -0.3858 +2026-04-13 08:38:40.406940: val_loss -0.3405 +2026-04-13 08:38:40.408980: Pseudo dice [0.625, 0.0, 0.4255, 0.0, 0.4282, 0.6424, 0.703] +2026-04-13 08:38:40.411647: Epoch time: 99.98 s +2026-04-13 08:38:41.579029: +2026-04-13 08:38:41.581031: Epoch 2380 +2026-04-13 08:38:41.583422: Current learning rate: 0.00443 +2026-04-13 08:40:21.698467: train_loss -0.3944 +2026-04-13 08:40:21.705074: val_loss -0.3637 +2026-04-13 08:40:21.707602: Pseudo dice [0.6675, 0.0, 0.6462, 0.0, 0.4006, 0.3184, 0.7654] +2026-04-13 08:40:21.710067: Epoch time: 100.12 s +2026-04-13 08:40:22.924868: +2026-04-13 08:40:22.926903: Epoch 2381 +2026-04-13 08:40:22.929292: Current learning rate: 0.00443 +2026-04-13 08:42:03.556507: train_loss -0.375 +2026-04-13 08:42:03.563083: val_loss -0.3617 +2026-04-13 08:42:03.565012: Pseudo dice [0.1853, 0.0, 0.5948, 0.0, 0.5117, 0.7224, 0.4131] +2026-04-13 08:42:03.567541: Epoch time: 100.63 s +2026-04-13 08:42:04.779043: +2026-04-13 08:42:04.781321: Epoch 2382 +2026-04-13 08:42:04.783604: Current learning rate: 0.00443 +2026-04-13 08:43:44.813329: train_loss -0.3877 +2026-04-13 08:43:44.821075: val_loss -0.4018 +2026-04-13 08:43:44.823284: Pseudo dice [0.5224, 0.0, 0.7253, 0.0579, 0.497, 0.8388, 0.6618] +2026-04-13 08:43:44.825766: Epoch time: 100.04 s +2026-04-13 08:43:46.012141: +2026-04-13 08:43:46.014945: Epoch 2383 +2026-04-13 08:43:46.017297: Current learning rate: 0.00443 +2026-04-13 08:45:26.080593: train_loss -0.382 +2026-04-13 08:45:26.089476: val_loss -0.3697 +2026-04-13 08:45:26.092233: Pseudo dice [0.4767, 0.0, 0.7691, 0.6491, 0.3854, 0.7703, 0.5977] +2026-04-13 08:45:26.094648: Epoch time: 100.07 s +2026-04-13 08:45:27.295297: +2026-04-13 08:45:27.298096: Epoch 2384 +2026-04-13 08:45:27.300199: Current learning rate: 0.00442 +2026-04-13 08:47:07.655055: train_loss -0.3758 +2026-04-13 08:47:07.661505: val_loss -0.3297 +2026-04-13 08:47:07.664025: Pseudo dice [0.6047, 0.0, 0.6206, 0.0, 0.1396, 0.2549, 0.6621] +2026-04-13 08:47:07.666687: Epoch time: 100.36 s +2026-04-13 08:47:08.892702: +2026-04-13 08:47:08.895517: Epoch 2385 +2026-04-13 08:47:08.897999: Current learning rate: 0.00442 +2026-04-13 08:48:49.227306: train_loss -0.3644 +2026-04-13 08:48:49.234468: val_loss -0.3777 +2026-04-13 08:48:49.236598: Pseudo dice [0.4542, 0.0, 0.7971, 0.201, 0.0, 0.7478, 0.5942] +2026-04-13 08:48:49.239294: Epoch time: 100.34 s +2026-04-13 08:48:50.441064: +2026-04-13 08:48:50.443595: Epoch 2386 +2026-04-13 08:48:50.445921: Current learning rate: 0.00442 +2026-04-13 08:50:30.568081: train_loss -0.3617 +2026-04-13 08:50:30.573948: val_loss -0.3949 +2026-04-13 08:50:30.575819: Pseudo dice [0.0, 0.0, 0.5133, 0.0, 0.0, 0.7381, 0.5988] +2026-04-13 08:50:30.578112: Epoch time: 100.13 s +2026-04-13 08:50:31.783680: +2026-04-13 08:50:31.785715: Epoch 2387 +2026-04-13 08:50:31.787924: Current learning rate: 0.00442 +2026-04-13 08:52:11.943528: train_loss -0.3461 +2026-04-13 08:52:11.950589: val_loss -0.3918 +2026-04-13 08:52:11.952786: Pseudo dice [0.0, 0.0, 0.6459, 0.0, 0.0, 0.7358, 0.6572] +2026-04-13 08:52:11.955490: Epoch time: 100.16 s +2026-04-13 08:52:13.152115: +2026-04-13 08:52:13.155049: Epoch 2388 +2026-04-13 08:52:13.157256: Current learning rate: 0.00441 +2026-04-13 08:53:53.362086: train_loss -0.3761 +2026-04-13 08:53:53.370986: val_loss -0.3541 +2026-04-13 08:53:53.372908: Pseudo dice [0.0, 0.0, 0.7018, 0.0025, 0.0421, 0.5238, 0.8133] +2026-04-13 08:53:53.375456: Epoch time: 100.21 s +2026-04-13 08:53:54.604280: +2026-04-13 08:53:54.606244: Epoch 2389 +2026-04-13 08:53:54.608215: Current learning rate: 0.00441 +2026-04-13 08:55:34.759143: train_loss -0.376 +2026-04-13 08:55:34.765136: val_loss -0.3734 +2026-04-13 08:55:34.766987: Pseudo dice [0.0, 0.0, 0.7441, 0.2203, 0.3647, 0.7338, 0.8013] +2026-04-13 08:55:34.769612: Epoch time: 100.16 s +2026-04-13 08:55:36.031490: +2026-04-13 08:55:36.034057: Epoch 2390 +2026-04-13 08:55:36.036561: Current learning rate: 0.00441 +2026-04-13 08:57:16.193356: train_loss -0.3879 +2026-04-13 08:57:16.203274: val_loss -0.3559 +2026-04-13 08:57:16.205702: Pseudo dice [0.0633, 0.0, 0.7045, 0.0, 0.3436, 0.5598, 0.7133] +2026-04-13 08:57:16.208697: Epoch time: 100.16 s +2026-04-13 08:57:17.692379: +2026-04-13 08:57:17.694398: Epoch 2391 +2026-04-13 08:57:17.696589: Current learning rate: 0.00441 +2026-04-13 08:58:57.767523: train_loss -0.3838 +2026-04-13 08:58:57.774889: val_loss -0.3216 +2026-04-13 08:58:57.778213: Pseudo dice [0.4925, 0.0, 0.7379, 0.0351, 0.3023, 0.4715, 0.4434] +2026-04-13 08:58:57.781598: Epoch time: 100.08 s +2026-04-13 08:58:58.963952: +2026-04-13 08:58:58.966427: Epoch 2392 +2026-04-13 08:58:58.969272: Current learning rate: 0.0044 +2026-04-13 09:00:39.345004: train_loss -0.4127 +2026-04-13 09:00:39.351573: val_loss -0.3708 +2026-04-13 09:00:39.354711: Pseudo dice [0.469, 0.0, 0.79, 0.2384, 0.3598, 0.7716, 0.6713] +2026-04-13 09:00:39.357023: Epoch time: 100.38 s +2026-04-13 09:00:40.564766: +2026-04-13 09:00:40.566587: Epoch 2393 +2026-04-13 09:00:40.568466: Current learning rate: 0.0044 +2026-04-13 09:02:21.669806: train_loss -0.4226 +2026-04-13 09:02:21.676262: val_loss -0.374 +2026-04-13 09:02:21.678473: Pseudo dice [0.5234, 0.0, 0.68, 0.0079, 0.2705, 0.8303, 0.8158] +2026-04-13 09:02:21.681096: Epoch time: 101.11 s +2026-04-13 09:02:22.892567: +2026-04-13 09:02:22.894409: Epoch 2394 +2026-04-13 09:02:22.896509: Current learning rate: 0.0044 +2026-04-13 09:04:03.351859: train_loss -0.4327 +2026-04-13 09:04:03.358589: val_loss -0.4223 +2026-04-13 09:04:03.360728: Pseudo dice [0.5184, 0.0, 0.8826, 0.8447, 0.2334, 0.8386, 0.8772] +2026-04-13 09:04:03.363737: Epoch time: 100.46 s +2026-04-13 09:04:04.605559: +2026-04-13 09:04:04.608132: Epoch 2395 +2026-04-13 09:04:04.611280: Current learning rate: 0.0044 +2026-04-13 09:05:45.055295: train_loss -0.4028 +2026-04-13 09:05:45.063828: val_loss -0.3105 +2026-04-13 09:05:45.066015: Pseudo dice [0.5412, 0.0, 0.5679, 0.0, 0.3027, 0.7965, 0.409] +2026-04-13 09:05:45.068890: Epoch time: 100.45 s +2026-04-13 09:05:46.292700: +2026-04-13 09:05:46.294568: Epoch 2396 +2026-04-13 09:05:46.296579: Current learning rate: 0.00439 +2026-04-13 09:07:26.202470: train_loss -0.4103 +2026-04-13 09:07:26.211142: val_loss -0.4111 +2026-04-13 09:07:26.213328: Pseudo dice [0.5947, 0.0, 0.5666, 0.0, 0.5461, 0.8047, 0.6976] +2026-04-13 09:07:26.215755: Epoch time: 99.91 s +2026-04-13 09:07:27.435851: +2026-04-13 09:07:27.438738: Epoch 2397 +2026-04-13 09:07:27.441187: Current learning rate: 0.00439 +2026-04-13 09:09:07.555903: train_loss -0.4111 +2026-04-13 09:09:07.562231: val_loss -0.3594 +2026-04-13 09:09:07.565450: Pseudo dice [0.5659, 0.0, 0.6499, 0.0665, 0.452, 0.7671, 0.7735] +2026-04-13 09:09:07.568046: Epoch time: 100.12 s +2026-04-13 09:09:08.768775: +2026-04-13 09:09:08.771306: Epoch 2398 +2026-04-13 09:09:08.774510: Current learning rate: 0.00439 +2026-04-13 09:10:49.212512: train_loss -0.3694 +2026-04-13 09:10:49.218816: val_loss -0.3313 +2026-04-13 09:10:49.221036: Pseudo dice [0.6566, 0.0, 0.7461, 0.0001, 0.3422, 0.8014, 0.7176] +2026-04-13 09:10:49.223598: Epoch time: 100.45 s +2026-04-13 09:10:50.447924: +2026-04-13 09:10:50.450480: Epoch 2399 +2026-04-13 09:10:50.454667: Current learning rate: 0.00439 +2026-04-13 09:12:30.979173: train_loss -0.4169 +2026-04-13 09:12:30.985957: val_loss -0.3317 +2026-04-13 09:12:30.988327: Pseudo dice [0.2313, 0.0, 0.7005, 0.0311, 0.3819, 0.857, 0.6522] +2026-04-13 09:12:30.991374: Epoch time: 100.53 s +2026-04-13 09:12:33.896368: +2026-04-13 09:12:33.898562: Epoch 2400 +2026-04-13 09:12:33.901174: Current learning rate: 0.00438 +2026-04-13 09:14:14.866893: train_loss -0.4076 +2026-04-13 09:14:14.874908: val_loss -0.3826 +2026-04-13 09:14:14.878193: Pseudo dice [0.5983, 0.0, 0.7257, 0.5488, 0.449, 0.5666, 0.6036] +2026-04-13 09:14:14.881423: Epoch time: 100.97 s +2026-04-13 09:14:16.105841: +2026-04-13 09:14:16.107919: Epoch 2401 +2026-04-13 09:14:16.109811: Current learning rate: 0.00438 +2026-04-13 09:15:56.310543: train_loss -0.4045 +2026-04-13 09:15:56.317077: val_loss -0.2828 +2026-04-13 09:15:56.319816: Pseudo dice [0.6776, 0.0, 0.7082, 0.0, 0.3699, 0.5359, 0.7395] +2026-04-13 09:15:56.322269: Epoch time: 100.21 s +2026-04-13 09:15:57.554436: +2026-04-13 09:15:57.556201: Epoch 2402 +2026-04-13 09:15:57.558441: Current learning rate: 0.00438 +2026-04-13 09:17:38.143778: train_loss -0.3702 +2026-04-13 09:17:38.153723: val_loss -0.3637 +2026-04-13 09:17:38.156050: Pseudo dice [0.5773, 0.0, 0.7485, 0.5349, 0.1728, 0.6059, 0.8648] +2026-04-13 09:17:38.161178: Epoch time: 100.59 s +2026-04-13 09:17:39.374802: +2026-04-13 09:17:39.378231: Epoch 2403 +2026-04-13 09:17:39.382107: Current learning rate: 0.00438 +2026-04-13 09:19:20.111100: train_loss -0.4014 +2026-04-13 09:19:20.117668: val_loss -0.3508 +2026-04-13 09:19:20.120428: Pseudo dice [0.1854, 0.0, 0.6522, 0.0, 0.0812, 0.7102, 0.833] +2026-04-13 09:19:20.122907: Epoch time: 100.74 s +2026-04-13 09:19:21.312084: +2026-04-13 09:19:21.313837: Epoch 2404 +2026-04-13 09:19:21.316398: Current learning rate: 0.00437 +2026-04-13 09:21:01.947483: train_loss -0.379 +2026-04-13 09:21:01.954729: val_loss -0.357 +2026-04-13 09:21:01.958035: Pseudo dice [0.4205, 0.0, 0.5957, 0.0, 0.3058, 0.447, 0.51] +2026-04-13 09:21:01.960956: Epoch time: 100.64 s +2026-04-13 09:21:03.189553: +2026-04-13 09:21:03.191736: Epoch 2405 +2026-04-13 09:21:03.193958: Current learning rate: 0.00437 +2026-04-13 09:22:43.593154: train_loss -0.4001 +2026-04-13 09:22:43.600393: val_loss -0.3363 +2026-04-13 09:22:43.602853: Pseudo dice [0.0594, 0.0, 0.6486, 0.3663, 0.2618, 0.5875, 0.5751] +2026-04-13 09:22:43.605247: Epoch time: 100.41 s +2026-04-13 09:22:44.810904: +2026-04-13 09:22:44.813415: Epoch 2406 +2026-04-13 09:22:44.815586: Current learning rate: 0.00437 +2026-04-13 09:24:25.544562: train_loss -0.397 +2026-04-13 09:24:25.554037: val_loss -0.3552 +2026-04-13 09:24:25.558122: Pseudo dice [0.6126, 0.0, 0.6852, 0.0502, 0.2078, 0.8394, 0.8305] +2026-04-13 09:24:25.563428: Epoch time: 100.74 s +2026-04-13 09:24:26.773103: +2026-04-13 09:24:26.775143: Epoch 2407 +2026-04-13 09:24:26.777425: Current learning rate: 0.00437 +2026-04-13 09:26:06.784578: train_loss -0.4108 +2026-04-13 09:26:06.791747: val_loss -0.341 +2026-04-13 09:26:06.794394: Pseudo dice [0.5605, 0.0, 0.4337, 0.0, 0.4242, 0.7681, 0.859] +2026-04-13 09:26:06.797346: Epoch time: 100.01 s +2026-04-13 09:26:08.010522: +2026-04-13 09:26:08.012430: Epoch 2408 +2026-04-13 09:26:08.014705: Current learning rate: 0.00436 +2026-04-13 09:27:48.541506: train_loss -0.4004 +2026-04-13 09:27:48.548693: val_loss -0.3453 +2026-04-13 09:27:48.551210: Pseudo dice [0.554, 0.0, 0.7629, 0.0219, 0.4352, 0.8116, 0.7919] +2026-04-13 09:27:48.553927: Epoch time: 100.53 s +2026-04-13 09:27:49.742718: +2026-04-13 09:27:49.744677: Epoch 2409 +2026-04-13 09:27:49.747190: Current learning rate: 0.00436 +2026-04-13 09:29:29.824784: train_loss -0.3928 +2026-04-13 09:29:29.830201: val_loss -0.3034 +2026-04-13 09:29:29.832772: Pseudo dice [0.4759, 0.0, 0.613, 0.0, 0.4364, 0.7758, 0.7374] +2026-04-13 09:29:29.835609: Epoch time: 100.09 s +2026-04-13 09:29:31.032445: +2026-04-13 09:29:31.034388: Epoch 2410 +2026-04-13 09:29:31.036614: Current learning rate: 0.00436 +2026-04-13 09:31:11.101334: train_loss -0.3851 +2026-04-13 09:31:11.108219: val_loss -0.3187 +2026-04-13 09:31:11.111396: Pseudo dice [0.6615, 0.0, 0.6765, 0.0829, 0.1539, 0.4461, 0.7017] +2026-04-13 09:31:11.117260: Epoch time: 100.07 s +2026-04-13 09:31:12.334462: +2026-04-13 09:31:12.336938: Epoch 2411 +2026-04-13 09:31:12.339581: Current learning rate: 0.00436 +2026-04-13 09:32:52.590555: train_loss -0.3701 +2026-04-13 09:32:52.604811: val_loss -0.3542 +2026-04-13 09:32:52.608821: Pseudo dice [0.3752, 0.0, 0.5016, 0.7022, 0.1888, 0.5222, 0.2417] +2026-04-13 09:32:52.612323: Epoch time: 100.26 s +2026-04-13 09:32:53.859828: +2026-04-13 09:32:53.862170: Epoch 2412 +2026-04-13 09:32:53.864968: Current learning rate: 0.00435 +2026-04-13 09:34:34.293489: train_loss -0.3977 +2026-04-13 09:34:34.299139: val_loss -0.383 +2026-04-13 09:34:34.301114: Pseudo dice [0.4653, 0.0, 0.7567, 0.107, 0.2427, 0.8002, 0.6683] +2026-04-13 09:34:34.303558: Epoch time: 100.44 s +2026-04-13 09:34:35.512835: +2026-04-13 09:34:35.516216: Epoch 2413 +2026-04-13 09:34:35.518579: Current learning rate: 0.00435 +2026-04-13 09:36:17.017018: train_loss -0.3916 +2026-04-13 09:36:17.026575: val_loss -0.3225 +2026-04-13 09:36:17.029235: Pseudo dice [0.3072, 0.0, 0.6616, 0.0752, 0.2875, 0.713, 0.4436] +2026-04-13 09:36:17.033174: Epoch time: 101.51 s +2026-04-13 09:36:18.246201: +2026-04-13 09:36:18.248190: Epoch 2414 +2026-04-13 09:36:18.250283: Current learning rate: 0.00435 +2026-04-13 09:37:58.376280: train_loss -0.3971 +2026-04-13 09:37:58.384138: val_loss -0.3731 +2026-04-13 09:37:58.386355: Pseudo dice [0.6448, 0.0, 0.4151, 0.0, 0.4904, 0.7347, 0.7484] +2026-04-13 09:37:58.389350: Epoch time: 100.13 s +2026-04-13 09:37:59.627484: +2026-04-13 09:37:59.632402: Epoch 2415 +2026-04-13 09:37:59.634599: Current learning rate: 0.00435 +2026-04-13 09:39:39.703277: train_loss -0.3524 +2026-04-13 09:39:39.709997: val_loss -0.3765 +2026-04-13 09:39:39.713329: Pseudo dice [0.0, 0.0, 0.6331, 0.8133, 0.0, 0.5735, 0.7762] +2026-04-13 09:39:39.715933: Epoch time: 100.08 s +2026-04-13 09:39:40.939213: +2026-04-13 09:39:40.941208: Epoch 2416 +2026-04-13 09:39:40.943357: Current learning rate: 0.00434 +2026-04-13 09:41:21.150269: train_loss -0.3981 +2026-04-13 09:41:21.158371: val_loss -0.3763 +2026-04-13 09:41:21.161599: Pseudo dice [0.5153, 0.0, 0.7243, 0.0, 0.3864, 0.8008, 0.8184] +2026-04-13 09:41:21.165071: Epoch time: 100.21 s +2026-04-13 09:41:22.393450: +2026-04-13 09:41:22.395570: Epoch 2417 +2026-04-13 09:41:22.397668: Current learning rate: 0.00434 +2026-04-13 09:43:02.338914: train_loss -0.4022 +2026-04-13 09:43:02.345394: val_loss -0.4155 +2026-04-13 09:43:02.351632: Pseudo dice [0.773, 0.0, 0.7614, 0.0, 0.372, 0.6834, 0.8359] +2026-04-13 09:43:02.354415: Epoch time: 99.95 s +2026-04-13 09:43:03.566411: +2026-04-13 09:43:03.568972: Epoch 2418 +2026-04-13 09:43:03.572047: Current learning rate: 0.00434 +2026-04-13 09:44:44.555291: train_loss -0.4217 +2026-04-13 09:44:44.563687: val_loss -0.4102 +2026-04-13 09:44:44.566746: Pseudo dice [0.0, 0.0, 0.8294, 0.7056, 0.4508, 0.4887, 0.7827] +2026-04-13 09:44:44.569432: Epoch time: 100.99 s +2026-04-13 09:44:45.776419: +2026-04-13 09:44:45.778791: Epoch 2419 +2026-04-13 09:44:45.780796: Current learning rate: 0.00434 +2026-04-13 09:46:26.341017: train_loss -0.3967 +2026-04-13 09:46:26.347234: val_loss -0.4105 +2026-04-13 09:46:26.349740: Pseudo dice [0.2222, 0.0, 0.7544, 0.0, 0.5516, 0.7533, 0.7447] +2026-04-13 09:46:26.352486: Epoch time: 100.57 s +2026-04-13 09:46:27.626602: +2026-04-13 09:46:27.628569: Epoch 2420 +2026-04-13 09:46:27.630897: Current learning rate: 0.00433 +2026-04-13 09:48:07.933398: train_loss -0.4097 +2026-04-13 09:48:07.939424: val_loss -0.4163 +2026-04-13 09:48:07.941844: Pseudo dice [0.0197, 0.0, 0.6094, 0.1944, 0.2901, 0.7938, 0.8248] +2026-04-13 09:48:07.944683: Epoch time: 100.31 s +2026-04-13 09:48:09.156571: +2026-04-13 09:48:09.158765: Epoch 2421 +2026-04-13 09:48:09.162078: Current learning rate: 0.00433 +2026-04-13 09:49:49.603697: train_loss -0.3954 +2026-04-13 09:49:49.610091: val_loss -0.4105 +2026-04-13 09:49:49.612293: Pseudo dice [0.2525, 0.0, 0.7136, 0.0, 0.3095, 0.5913, 0.8257] +2026-04-13 09:49:49.614787: Epoch time: 100.45 s +2026-04-13 09:49:50.821697: +2026-04-13 09:49:50.823656: Epoch 2422 +2026-04-13 09:49:50.825678: Current learning rate: 0.00433 +2026-04-13 09:51:31.074208: train_loss -0.4184 +2026-04-13 09:51:31.079528: val_loss -0.321 +2026-04-13 09:51:31.081544: Pseudo dice [0.4492, 0.0, 0.663, 0.02, 0.5393, 0.751, 0.6665] +2026-04-13 09:51:31.083608: Epoch time: 100.26 s +2026-04-13 09:51:32.261614: +2026-04-13 09:51:32.263425: Epoch 2423 +2026-04-13 09:51:32.265371: Current learning rate: 0.00433 +2026-04-13 09:53:12.311538: train_loss -0.4438 +2026-04-13 09:53:12.317158: val_loss -0.4281 +2026-04-13 09:53:12.318868: Pseudo dice [0.0, 0.0, 0.7718, 0.4731, 0.478, 0.8128, 0.8343] +2026-04-13 09:53:12.321228: Epoch time: 100.05 s +2026-04-13 09:53:13.515414: +2026-04-13 09:53:13.517865: Epoch 2424 +2026-04-13 09:53:13.520284: Current learning rate: 0.00432 +2026-04-13 09:54:54.010232: train_loss -0.4072 +2026-04-13 09:54:54.017381: val_loss -0.2552 +2026-04-13 09:54:54.019561: Pseudo dice [0.5328, 0.0, 0.7563, 0.0463, 0.3321, 0.1069, 0.6437] +2026-04-13 09:54:54.022142: Epoch time: 100.5 s +2026-04-13 09:54:55.222361: +2026-04-13 09:54:55.224802: Epoch 2425 +2026-04-13 09:54:55.226911: Current learning rate: 0.00432 +2026-04-13 09:56:35.735652: train_loss -0.4057 +2026-04-13 09:56:35.742204: val_loss -0.3782 +2026-04-13 09:56:35.744478: Pseudo dice [0.0462, 0.0, 0.66, 0.204, 0.3487, 0.8712, 0.6627] +2026-04-13 09:56:35.748759: Epoch time: 100.52 s +2026-04-13 09:56:36.968762: +2026-04-13 09:56:36.971061: Epoch 2426 +2026-04-13 09:56:36.978466: Current learning rate: 0.00432 +2026-04-13 09:58:18.092869: train_loss -0.4191 +2026-04-13 09:58:18.102879: val_loss -0.3046 +2026-04-13 09:58:18.105684: Pseudo dice [0.2125, 0.0, 0.7427, 0.0609, 0.2927, 0.8542, 0.8077] +2026-04-13 09:58:18.110190: Epoch time: 101.13 s +2026-04-13 09:58:19.335774: +2026-04-13 09:58:19.338051: Epoch 2427 +2026-04-13 09:58:19.340676: Current learning rate: 0.00432 +2026-04-13 09:59:59.762764: train_loss -0.3992 +2026-04-13 09:59:59.771637: val_loss -0.2241 +2026-04-13 09:59:59.774664: Pseudo dice [0.644, 0.0, 0.449, 0.0, 0.0, 0.8138, 0.5989] +2026-04-13 09:59:59.776999: Epoch time: 100.43 s +2026-04-13 10:00:00.994025: +2026-04-13 10:00:00.996880: Epoch 2428 +2026-04-13 10:00:00.999192: Current learning rate: 0.00431 +2026-04-13 10:01:41.534747: train_loss -0.3803 +2026-04-13 10:01:41.540916: val_loss -0.373 +2026-04-13 10:01:41.543905: Pseudo dice [0.6183, 0.0, 0.7144, 0.7275, 0.0, 0.3791, 0.6688] +2026-04-13 10:01:41.547046: Epoch time: 100.54 s +2026-04-13 10:01:42.775279: +2026-04-13 10:01:42.778672: Epoch 2429 +2026-04-13 10:01:42.781608: Current learning rate: 0.00431 +2026-04-13 10:03:22.734301: train_loss -0.3955 +2026-04-13 10:03:22.741715: val_loss -0.3139 +2026-04-13 10:03:22.744063: Pseudo dice [0.1541, 0.0, 0.6818, 0.0, 0.4355, 0.7186, 0.7128] +2026-04-13 10:03:22.751549: Epoch time: 99.96 s +2026-04-13 10:03:23.981387: +2026-04-13 10:03:23.983597: Epoch 2430 +2026-04-13 10:03:23.986186: Current learning rate: 0.00431 +2026-04-13 10:05:04.506776: train_loss -0.4087 +2026-04-13 10:05:04.514877: val_loss -0.3355 +2026-04-13 10:05:04.516912: Pseudo dice [0.3897, 0.0, 0.721, 0.1142, 0.1134, 0.7673, 0.7022] +2026-04-13 10:05:04.520110: Epoch time: 100.53 s +2026-04-13 10:05:05.741729: +2026-04-13 10:05:05.745195: Epoch 2431 +2026-04-13 10:05:05.748363: Current learning rate: 0.00431 +2026-04-13 10:06:45.737920: train_loss -0.3558 +2026-04-13 10:06:45.748734: val_loss -0.346 +2026-04-13 10:06:45.751415: Pseudo dice [0.0685, 0.0, 0.6431, 0.6538, 0.189, 0.8263, 0.8439] +2026-04-13 10:06:45.754210: Epoch time: 100.0 s +2026-04-13 10:06:47.000464: +2026-04-13 10:06:47.002544: Epoch 2432 +2026-04-13 10:06:47.004854: Current learning rate: 0.0043 +2026-04-13 10:08:28.417961: train_loss -0.4029 +2026-04-13 10:08:28.431194: val_loss -0.3926 +2026-04-13 10:08:28.434337: Pseudo dice [0.5597, 0.0, 0.7197, 0.6175, 0.2988, 0.8433, 0.7313] +2026-04-13 10:08:28.437979: Epoch time: 101.42 s +2026-04-13 10:08:29.656488: +2026-04-13 10:08:29.660692: Epoch 2433 +2026-04-13 10:08:29.664725: Current learning rate: 0.0043 +2026-04-13 10:10:10.078635: train_loss -0.4232 +2026-04-13 10:10:10.084207: val_loss -0.3755 +2026-04-13 10:10:10.086426: Pseudo dice [0.69, 0.0, 0.6959, 0.014, 0.1286, 0.5561, 0.8443] +2026-04-13 10:10:10.090067: Epoch time: 100.43 s +2026-04-13 10:10:12.413901: +2026-04-13 10:10:12.416982: Epoch 2434 +2026-04-13 10:10:12.419544: Current learning rate: 0.0043 +2026-04-13 10:11:53.492541: train_loss -0.4162 +2026-04-13 10:11:53.501179: val_loss -0.332 +2026-04-13 10:11:53.503564: Pseudo dice [0.0, 0.0, 0.7602, 0.0, 0.3776, 0.8066, 0.6349] +2026-04-13 10:11:53.506304: Epoch time: 101.08 s +2026-04-13 10:11:54.707164: +2026-04-13 10:11:54.709684: Epoch 2435 +2026-04-13 10:11:54.712019: Current learning rate: 0.0043 +2026-04-13 10:13:35.440670: train_loss -0.4101 +2026-04-13 10:13:35.448051: val_loss -0.4062 +2026-04-13 10:13:35.450312: Pseudo dice [0.1214, 0.0, 0.6764, 0.2407, 0.5182, 0.7552, 0.6712] +2026-04-13 10:13:35.454921: Epoch time: 100.74 s +2026-04-13 10:13:36.693183: +2026-04-13 10:13:36.695742: Epoch 2436 +2026-04-13 10:13:36.699236: Current learning rate: 0.00429 +2026-04-13 10:15:16.867976: train_loss -0.4008 +2026-04-13 10:15:16.877547: val_loss -0.4035 +2026-04-13 10:15:16.880009: Pseudo dice [0.0, 0.0, 0.6772, 0.0, 0.4619, 0.8255, 0.8331] +2026-04-13 10:15:16.883766: Epoch time: 100.18 s +2026-04-13 10:15:18.098107: +2026-04-13 10:15:18.100800: Epoch 2437 +2026-04-13 10:15:18.104043: Current learning rate: 0.00429 +2026-04-13 10:16:58.918126: train_loss -0.3958 +2026-04-13 10:16:58.925499: val_loss -0.3763 +2026-04-13 10:16:58.928020: Pseudo dice [0.0, 0.0, 0.682, 0.0, 0.4965, 0.6796, 0.7709] +2026-04-13 10:16:58.931548: Epoch time: 100.82 s +2026-04-13 10:17:00.167255: +2026-04-13 10:17:00.169912: Epoch 2438 +2026-04-13 10:17:00.183370: Current learning rate: 0.00429 +2026-04-13 10:18:41.263976: train_loss -0.4203 +2026-04-13 10:18:41.273514: val_loss -0.3601 +2026-04-13 10:18:41.276228: Pseudo dice [0.0, 0.0, 0.6642, 0.1533, 0.209, 0.7516, 0.8328] +2026-04-13 10:18:41.279157: Epoch time: 101.1 s +2026-04-13 10:18:42.705749: +2026-04-13 10:18:42.708159: Epoch 2439 +2026-04-13 10:18:42.710571: Current learning rate: 0.00429 +2026-04-13 10:20:22.867924: train_loss -0.4003 +2026-04-13 10:20:22.876174: val_loss -0.3013 +2026-04-13 10:20:22.879085: Pseudo dice [0.0, 0.0, 0.3846, 0.0295, 0.2219, 0.8593, 0.814] +2026-04-13 10:20:22.882169: Epoch time: 100.17 s +2026-04-13 10:20:24.096371: +2026-04-13 10:20:24.098878: Epoch 2440 +2026-04-13 10:20:24.101843: Current learning rate: 0.00429 +2026-04-13 10:22:04.285600: train_loss -0.4135 +2026-04-13 10:22:04.292981: val_loss -0.3934 +2026-04-13 10:22:04.295539: Pseudo dice [0.0002, 0.0, 0.7809, 0.0883, 0.3629, 0.7263, 0.8188] +2026-04-13 10:22:04.297933: Epoch time: 100.19 s +2026-04-13 10:22:05.495455: +2026-04-13 10:22:05.498006: Epoch 2441 +2026-04-13 10:22:05.500357: Current learning rate: 0.00428 +2026-04-13 10:23:46.430763: train_loss -0.409 +2026-04-13 10:23:46.438238: val_loss -0.4257 +2026-04-13 10:23:46.440277: Pseudo dice [0.3933, 0.0, 0.664, 0.0, 0.3656, 0.6366, 0.8781] +2026-04-13 10:23:46.443112: Epoch time: 100.94 s +2026-04-13 10:23:47.670762: +2026-04-13 10:23:47.673906: Epoch 2442 +2026-04-13 10:23:47.676148: Current learning rate: 0.00428 +2026-04-13 10:25:29.263238: train_loss -0.4273 +2026-04-13 10:25:29.271543: val_loss -0.3888 +2026-04-13 10:25:29.274036: Pseudo dice [0.6255, 0.0, 0.7483, 0.0, 0.332, 0.8225, 0.7581] +2026-04-13 10:25:29.277363: Epoch time: 101.6 s +2026-04-13 10:25:30.499252: +2026-04-13 10:25:30.501181: Epoch 2443 +2026-04-13 10:25:30.503552: Current learning rate: 0.00428 +2026-04-13 10:27:11.036725: train_loss -0.402 +2026-04-13 10:27:11.045451: val_loss -0.4131 +2026-04-13 10:27:11.048212: Pseudo dice [0.4938, 0.0, 0.7272, 0.5803, 0.4714, 0.782, 0.6919] +2026-04-13 10:27:11.051241: Epoch time: 100.54 s +2026-04-13 10:27:12.284636: +2026-04-13 10:27:12.287117: Epoch 2444 +2026-04-13 10:27:12.289574: Current learning rate: 0.00428 +2026-04-13 10:28:53.011202: train_loss -0.4092 +2026-04-13 10:28:53.017140: val_loss -0.2986 +2026-04-13 10:28:53.019366: Pseudo dice [0.5727, 0.0, 0.7118, 0.0389, 0.259, 0.817, 0.7094] +2026-04-13 10:28:53.021899: Epoch time: 100.73 s +2026-04-13 10:28:54.224703: +2026-04-13 10:28:54.227108: Epoch 2445 +2026-04-13 10:28:54.229356: Current learning rate: 0.00427 +2026-04-13 10:30:34.564407: train_loss -0.4124 +2026-04-13 10:30:34.571651: val_loss -0.3899 +2026-04-13 10:30:34.574343: Pseudo dice [0.4386, 0.0, 0.6908, 0.0, 0.1305, 0.6777, 0.7512] +2026-04-13 10:30:34.577160: Epoch time: 100.34 s +2026-04-13 10:30:35.801400: +2026-04-13 10:30:35.804354: Epoch 2446 +2026-04-13 10:30:35.806904: Current learning rate: 0.00427 +2026-04-13 10:32:15.947600: train_loss -0.3994 +2026-04-13 10:32:15.953883: val_loss -0.3276 +2026-04-13 10:32:15.956906: Pseudo dice [0.0683, 0.0, 0.6532, 0.2653, 0.4496, 0.5417, 0.6022] +2026-04-13 10:32:15.959786: Epoch time: 100.15 s +2026-04-13 10:32:17.159304: +2026-04-13 10:32:17.161159: Epoch 2447 +2026-04-13 10:32:17.163098: Current learning rate: 0.00427 +2026-04-13 10:33:57.582985: train_loss -0.3588 +2026-04-13 10:33:57.589352: val_loss -0.3686 +2026-04-13 10:33:57.591645: Pseudo dice [0.4469, 0.0, 0.6505, 0.7618, 0.2479, 0.619, 0.531] +2026-04-13 10:33:57.593900: Epoch time: 100.43 s +2026-04-13 10:33:58.774887: +2026-04-13 10:33:58.777153: Epoch 2448 +2026-04-13 10:33:58.779547: Current learning rate: 0.00427 +2026-04-13 10:35:39.052821: train_loss -0.3948 +2026-04-13 10:35:39.059425: val_loss -0.3978 +2026-04-13 10:35:39.062764: Pseudo dice [0.6794, 0.0, 0.7371, 0.0, 0.4227, 0.577, 0.7465] +2026-04-13 10:35:39.065970: Epoch time: 100.28 s +2026-04-13 10:35:40.274084: +2026-04-13 10:35:40.276119: Epoch 2449 +2026-04-13 10:35:40.278719: Current learning rate: 0.00426 +2026-04-13 10:37:20.731961: train_loss -0.3916 +2026-04-13 10:37:20.738307: val_loss -0.3485 +2026-04-13 10:37:20.740719: Pseudo dice [0.0907, 0.0, 0.7313, 0.0, 0.0276, 0.1231, 0.0396] +2026-04-13 10:37:20.743058: Epoch time: 100.46 s +2026-04-13 10:37:23.748243: +2026-04-13 10:37:23.751136: Epoch 2450 +2026-04-13 10:37:23.754127: Current learning rate: 0.00426 +2026-04-13 10:39:04.021682: train_loss -0.3507 +2026-04-13 10:39:04.029697: val_loss -0.3913 +2026-04-13 10:39:04.033146: Pseudo dice [0.4465, 0.0, 0.6855, 0.1917, 0.2176, 0.6894, 0.7919] +2026-04-13 10:39:04.035702: Epoch time: 100.28 s +2026-04-13 10:39:05.262008: +2026-04-13 10:39:05.264241: Epoch 2451 +2026-04-13 10:39:05.267523: Current learning rate: 0.00426 +2026-04-13 10:40:46.768825: train_loss -0.3679 +2026-04-13 10:40:46.777936: val_loss -0.3689 +2026-04-13 10:40:46.780405: Pseudo dice [0.0, 0.0, 0.6775, 0.0, 0.001, 0.4688, 0.8778] +2026-04-13 10:40:46.783236: Epoch time: 101.51 s +2026-04-13 10:40:47.967361: +2026-04-13 10:40:47.971503: Epoch 2452 +2026-04-13 10:40:47.973934: Current learning rate: 0.00426 +2026-04-13 10:42:28.404982: train_loss -0.362 +2026-04-13 10:42:28.413020: val_loss -0.3406 +2026-04-13 10:42:28.415813: Pseudo dice [0.1357, 0.0, 0.4996, 0.0, 0.2637, 0.6776, 0.7554] +2026-04-13 10:42:28.421372: Epoch time: 100.44 s +2026-04-13 10:42:29.639086: +2026-04-13 10:42:29.641937: Epoch 2453 +2026-04-13 10:42:29.644255: Current learning rate: 0.00425 +2026-04-13 10:44:12.047512: train_loss -0.4038 +2026-04-13 10:44:12.054713: val_loss -0.3906 +2026-04-13 10:44:12.058101: Pseudo dice [0.5391, 0.0, 0.6794, 0.0, 0.4463, 0.823, 0.8181] +2026-04-13 10:44:12.076350: Epoch time: 102.41 s +2026-04-13 10:44:13.285452: +2026-04-13 10:44:13.288747: Epoch 2454 +2026-04-13 10:44:13.292213: Current learning rate: 0.00425 +2026-04-13 10:45:54.497201: train_loss -0.409 +2026-04-13 10:45:54.505280: val_loss -0.4199 +2026-04-13 10:45:54.508749: Pseudo dice [0.6395, 0.0, 0.8041, 0.1218, 0.367, 0.7125, 0.8303] +2026-04-13 10:45:54.511176: Epoch time: 101.21 s +2026-04-13 10:45:55.745041: +2026-04-13 10:45:55.747096: Epoch 2455 +2026-04-13 10:45:55.749214: Current learning rate: 0.00425 +2026-04-13 10:47:36.818651: train_loss -0.4254 +2026-04-13 10:47:36.824966: val_loss -0.4252 +2026-04-13 10:47:36.827050: Pseudo dice [0.6259, 0.0, 0.7092, 0.8201, 0.5266, 0.8647, 0.8118] +2026-04-13 10:47:36.828945: Epoch time: 101.08 s +2026-04-13 10:47:38.032682: +2026-04-13 10:47:38.034277: Epoch 2456 +2026-04-13 10:47:38.036581: Current learning rate: 0.00425 +2026-04-13 10:49:18.414644: train_loss -0.4085 +2026-04-13 10:49:18.424886: val_loss -0.4079 +2026-04-13 10:49:18.427199: Pseudo dice [0.5274, 0.0, 0.6978, 0.3781, 0.396, 0.7848, 0.6729] +2026-04-13 10:49:18.429731: Epoch time: 100.38 s +2026-04-13 10:49:19.656723: +2026-04-13 10:49:19.658572: Epoch 2457 +2026-04-13 10:49:19.662401: Current learning rate: 0.00424 +2026-04-13 10:51:00.554026: train_loss -0.389 +2026-04-13 10:51:00.570419: val_loss -0.3413 +2026-04-13 10:51:00.573098: Pseudo dice [0.0906, 0.0, 0.72, 0.0, 0.1761, 0.7056, 0.6673] +2026-04-13 10:51:00.575600: Epoch time: 100.9 s +2026-04-13 10:51:01.799483: +2026-04-13 10:51:01.801585: Epoch 2458 +2026-04-13 10:51:01.804183: Current learning rate: 0.00424 +2026-04-13 10:52:42.019985: train_loss -0.3974 +2026-04-13 10:52:42.034018: val_loss -0.3899 +2026-04-13 10:52:42.037788: Pseudo dice [0.4924, 0.0, 0.7541, 0.0, 0.0, 0.5314, 0.5701] +2026-04-13 10:52:42.041355: Epoch time: 100.22 s +2026-04-13 10:52:43.244575: +2026-04-13 10:52:43.246963: Epoch 2459 +2026-04-13 10:52:43.249966: Current learning rate: 0.00424 +2026-04-13 10:54:24.121185: train_loss -0.383 +2026-04-13 10:54:24.127350: val_loss -0.3453 +2026-04-13 10:54:24.129992: Pseudo dice [0.3267, 0.0, 0.6486, 0.0, 0.39, 0.8012, 0.4837] +2026-04-13 10:54:24.133702: Epoch time: 100.88 s +2026-04-13 10:54:25.328189: +2026-04-13 10:54:25.330615: Epoch 2460 +2026-04-13 10:54:25.332803: Current learning rate: 0.00424 +2026-04-13 10:56:05.851701: train_loss -0.3807 +2026-04-13 10:56:05.860085: val_loss -0.3861 +2026-04-13 10:56:05.862735: Pseudo dice [0.5599, 0.0, 0.3471, 0.4358, 0.0738, 0.8047, 0.5828] +2026-04-13 10:56:05.869289: Epoch time: 100.53 s +2026-04-13 10:56:07.078285: +2026-04-13 10:56:07.080433: Epoch 2461 +2026-04-13 10:56:07.082904: Current learning rate: 0.00423 +2026-04-13 10:57:47.893040: train_loss -0.4204 +2026-04-13 10:57:47.904319: val_loss -0.3943 +2026-04-13 10:57:47.907033: Pseudo dice [0.6729, 0.0, 0.8078, 0.0, 0.4639, 0.6331, 0.6227] +2026-04-13 10:57:47.909721: Epoch time: 100.82 s +2026-04-13 10:57:49.174840: +2026-04-13 10:57:49.177242: Epoch 2462 +2026-04-13 10:57:49.179561: Current learning rate: 0.00423 +2026-04-13 10:59:30.380888: train_loss -0.4151 +2026-04-13 10:59:30.388247: val_loss -0.3837 +2026-04-13 10:59:30.390605: Pseudo dice [0.6753, 0.0, 0.5519, 0.4175, 0.5798, 0.7691, 0.7027] +2026-04-13 10:59:30.393809: Epoch time: 101.21 s +2026-04-13 10:59:31.625719: +2026-04-13 10:59:31.629198: Epoch 2463 +2026-04-13 10:59:31.632258: Current learning rate: 0.00423 +2026-04-13 11:01:11.525888: train_loss -0.4144 +2026-04-13 11:01:11.532091: val_loss -0.3745 +2026-04-13 11:01:11.535181: Pseudo dice [0.4571, 0.0, 0.6578, 0.0, 0.0, 0.7956, 0.6532] +2026-04-13 11:01:11.538743: Epoch time: 99.9 s +2026-04-13 11:01:12.730832: +2026-04-13 11:01:12.732739: Epoch 2464 +2026-04-13 11:01:12.734963: Current learning rate: 0.00423 +2026-04-13 11:02:53.209357: train_loss -0.3921 +2026-04-13 11:02:53.236762: val_loss -0.3461 +2026-04-13 11:02:53.239223: Pseudo dice [0.0015, 0.0, 0.5385, 0.0, 0.0, 0.7618, 0.8414] +2026-04-13 11:02:53.242017: Epoch time: 100.48 s +2026-04-13 11:02:54.434131: +2026-04-13 11:02:54.435797: Epoch 2465 +2026-04-13 11:02:54.437950: Current learning rate: 0.00422 +2026-04-13 11:04:35.543178: train_loss -0.351 +2026-04-13 11:04:35.550492: val_loss -0.2544 +2026-04-13 11:04:35.554900: Pseudo dice [0.0, 0.0, 0.4082, 0.0, 0.3472, 0.461, 0.2205] +2026-04-13 11:04:35.557480: Epoch time: 101.11 s +2026-04-13 11:04:36.776042: +2026-04-13 11:04:36.781745: Epoch 2466 +2026-04-13 11:04:36.786605: Current learning rate: 0.00422 +2026-04-13 11:06:17.530815: train_loss -0.3681 +2026-04-13 11:06:17.541965: val_loss -0.3712 +2026-04-13 11:06:17.544962: Pseudo dice [0.0, 0.0, 0.6216, 0.0, 0.0, 0.7653, 0.1293] +2026-04-13 11:06:17.547667: Epoch time: 100.76 s +2026-04-13 11:06:18.742323: +2026-04-13 11:06:18.744728: Epoch 2467 +2026-04-13 11:06:18.746776: Current learning rate: 0.00422 +2026-04-13 11:07:59.308162: train_loss -0.3645 +2026-04-13 11:07:59.315163: val_loss -0.3131 +2026-04-13 11:07:59.317775: Pseudo dice [0.0, 0.0, 0.6703, 0.0068, 0.0, 0.7073, 0.809] +2026-04-13 11:07:59.319928: Epoch time: 100.57 s +2026-04-13 11:08:00.516412: +2026-04-13 11:08:00.519499: Epoch 2468 +2026-04-13 11:08:00.522150: Current learning rate: 0.00422 +2026-04-13 11:09:40.986129: train_loss -0.3789 +2026-04-13 11:09:40.994836: val_loss -0.3911 +2026-04-13 11:09:40.997494: Pseudo dice [0.0, 0.0, 0.7325, 0.4523, 0.3289, 0.7751, 0.8599] +2026-04-13 11:09:41.002301: Epoch time: 100.47 s +2026-04-13 11:09:42.215162: +2026-04-13 11:09:42.217205: Epoch 2469 +2026-04-13 11:09:42.219686: Current learning rate: 0.00421 +2026-04-13 11:11:22.804524: train_loss -0.4102 +2026-04-13 11:11:22.811629: val_loss -0.4014 +2026-04-13 11:11:22.814475: Pseudo dice [0.0892, 0.0, 0.7411, 0.4215, 0.4129, 0.8676, 0.8219] +2026-04-13 11:11:22.817606: Epoch time: 100.59 s +2026-04-13 11:11:24.070212: +2026-04-13 11:11:24.073061: Epoch 2470 +2026-04-13 11:11:24.076057: Current learning rate: 0.00421 +2026-04-13 11:13:05.525266: train_loss -0.4052 +2026-04-13 11:13:05.535595: val_loss -0.3871 +2026-04-13 11:13:05.537987: Pseudo dice [0.7886, 0.0, 0.7513, 0.4386, 0.3532, 0.7509, 0.8416] +2026-04-13 11:13:05.540914: Epoch time: 101.46 s +2026-04-13 11:13:06.732687: +2026-04-13 11:13:06.735043: Epoch 2471 +2026-04-13 11:13:06.737306: Current learning rate: 0.00421 +2026-04-13 11:14:47.064799: train_loss -0.3886 +2026-04-13 11:14:47.072961: val_loss -0.3926 +2026-04-13 11:14:47.075807: Pseudo dice [0.3936, 0.0, 0.5624, 0.0, 0.4006, 0.2831, 0.668] +2026-04-13 11:14:47.078662: Epoch time: 100.34 s +2026-04-13 11:14:48.293215: +2026-04-13 11:14:48.295433: Epoch 2472 +2026-04-13 11:14:48.300730: Current learning rate: 0.00421 +2026-04-13 11:16:29.941586: train_loss -0.3866 +2026-04-13 11:16:29.949526: val_loss -0.2684 +2026-04-13 11:16:29.952218: Pseudo dice [0.0621, 0.0, 0.6816, 0.0, 0.2744, 0.6193, 0.3189] +2026-04-13 11:16:29.955014: Epoch time: 101.65 s +2026-04-13 11:16:31.156218: +2026-04-13 11:16:31.158131: Epoch 2473 +2026-04-13 11:16:31.160108: Current learning rate: 0.0042 +2026-04-13 11:18:11.286140: train_loss -0.3896 +2026-04-13 11:18:11.293309: val_loss -0.3726 +2026-04-13 11:18:11.295666: Pseudo dice [0.3469, 0.0, 0.5329, 0.0127, 0.3555, 0.6083, 0.6421] +2026-04-13 11:18:11.298555: Epoch time: 100.13 s +2026-04-13 11:18:13.575785: +2026-04-13 11:18:13.578190: Epoch 2474 +2026-04-13 11:18:13.580653: Current learning rate: 0.0042 +2026-04-13 11:19:54.808469: train_loss -0.3958 +2026-04-13 11:19:54.815732: val_loss -0.2386 +2026-04-13 11:19:54.818332: Pseudo dice [0.4567, 0.0, 0.6999, 0.022, 0.3706, 0.7336, 0.7056] +2026-04-13 11:19:54.820716: Epoch time: 101.24 s +2026-04-13 11:19:56.037758: +2026-04-13 11:19:56.039806: Epoch 2475 +2026-04-13 11:19:56.041991: Current learning rate: 0.0042 +2026-04-13 11:21:36.771355: train_loss -0.3905 +2026-04-13 11:21:36.778408: val_loss -0.3147 +2026-04-13 11:21:36.781681: Pseudo dice [0.1231, 0.0, 0.5002, 0.0929, 0.0253, 0.6502, 0.8643] +2026-04-13 11:21:36.784738: Epoch time: 100.74 s +2026-04-13 11:21:38.015922: +2026-04-13 11:21:38.018184: Epoch 2476 +2026-04-13 11:21:38.020444: Current learning rate: 0.0042 +2026-04-13 11:23:19.580403: train_loss -0.3798 +2026-04-13 11:23:19.591138: val_loss -0.3692 +2026-04-13 11:23:19.598250: Pseudo dice [0.2189, 0.0, 0.698, 0.2388, 0.2949, 0.5446, 0.6006] +2026-04-13 11:23:19.609902: Epoch time: 101.57 s +2026-04-13 11:23:20.835247: +2026-04-13 11:23:20.837759: Epoch 2477 +2026-04-13 11:23:20.840567: Current learning rate: 0.00419 +2026-04-13 11:25:03.043463: train_loss -0.3603 +2026-04-13 11:25:03.051377: val_loss -0.3913 +2026-04-13 11:25:03.055983: Pseudo dice [0.6269, 0.0, 0.7226, 0.0, 0.0, 0.5705, 0.691] +2026-04-13 11:25:03.067407: Epoch time: 102.21 s +2026-04-13 11:25:04.280097: +2026-04-13 11:25:04.284966: Epoch 2478 +2026-04-13 11:25:04.287965: Current learning rate: 0.00419 +2026-04-13 11:26:45.408754: train_loss -0.4076 +2026-04-13 11:26:45.424622: val_loss -0.4048 +2026-04-13 11:26:45.427799: Pseudo dice [0.3748, 0.0, 0.7452, 0.5704, 0.2564, 0.8143, 0.7721] +2026-04-13 11:26:45.430942: Epoch time: 101.13 s +2026-04-13 11:26:46.666741: +2026-04-13 11:26:46.668882: Epoch 2479 +2026-04-13 11:26:46.671946: Current learning rate: 0.00419 +2026-04-13 11:28:27.452291: train_loss -0.3769 +2026-04-13 11:28:27.462593: val_loss -0.3062 +2026-04-13 11:28:27.465986: Pseudo dice [0.284, 0.0, 0.6427, 0.0, 0.3111, 0.6034, 0.6801] +2026-04-13 11:28:27.468831: Epoch time: 100.79 s +2026-04-13 11:28:28.688525: +2026-04-13 11:28:28.690925: Epoch 2480 +2026-04-13 11:28:28.693994: Current learning rate: 0.00419 +2026-04-13 11:30:09.602393: train_loss -0.3492 +2026-04-13 11:30:09.611937: val_loss -0.258 +2026-04-13 11:30:09.614485: Pseudo dice [0.0, 0.0, 0.639, 0.0645, 0.2558, 0.2987, 0.5453] +2026-04-13 11:30:09.617375: Epoch time: 100.92 s +2026-04-13 11:30:10.850300: +2026-04-13 11:30:10.852211: Epoch 2481 +2026-04-13 11:30:10.854385: Current learning rate: 0.00418 +2026-04-13 11:31:51.155579: train_loss -0.3671 +2026-04-13 11:31:51.164113: val_loss -0.3892 +2026-04-13 11:31:51.167612: Pseudo dice [0.0, 0.0, 0.4752, 0.0, 0.2582, 0.6937, 0.765] +2026-04-13 11:31:51.170613: Epoch time: 100.31 s +2026-04-13 11:31:52.380875: +2026-04-13 11:31:52.383413: Epoch 2482 +2026-04-13 11:31:52.386419: Current learning rate: 0.00418 +2026-04-13 11:33:33.455163: train_loss -0.3876 +2026-04-13 11:33:33.461995: val_loss -0.3793 +2026-04-13 11:33:33.465141: Pseudo dice [0.0801, 0.0, 0.6848, 0.8541, 0.3864, 0.7311, 0.6844] +2026-04-13 11:33:33.468988: Epoch time: 101.08 s +2026-04-13 11:33:34.691390: +2026-04-13 11:33:34.693352: Epoch 2483 +2026-04-13 11:33:34.695345: Current learning rate: 0.00418 +2026-04-13 11:35:14.996650: train_loss -0.3839 +2026-04-13 11:35:15.006309: val_loss -0.4041 +2026-04-13 11:35:15.008937: Pseudo dice [0.2524, 0.0, 0.6868, 0.0, 0.3296, 0.768, 0.8144] +2026-04-13 11:35:15.014108: Epoch time: 100.31 s +2026-04-13 11:35:16.220004: +2026-04-13 11:35:16.221993: Epoch 2484 +2026-04-13 11:35:16.224367: Current learning rate: 0.00418 +2026-04-13 11:36:57.468524: train_loss -0.4013 +2026-04-13 11:36:57.477262: val_loss -0.3521 +2026-04-13 11:36:57.481258: Pseudo dice [0.673, 0.0, 0.7469, 0.0018, 0.1228, 0.7706, 0.8498] +2026-04-13 11:36:57.485593: Epoch time: 101.25 s +2026-04-13 11:36:58.705012: +2026-04-13 11:36:58.715562: Epoch 2485 +2026-04-13 11:36:58.719522: Current learning rate: 0.00417 +2026-04-13 11:38:39.430631: train_loss -0.3929 +2026-04-13 11:38:39.437075: val_loss -0.308 +2026-04-13 11:38:39.441480: Pseudo dice [0.4505, 0.0, 0.5823, 0.0724, 0.238, 0.8403, 0.7339] +2026-04-13 11:38:39.446298: Epoch time: 100.73 s +2026-04-13 11:38:40.669523: +2026-04-13 11:38:40.671859: Epoch 2486 +2026-04-13 11:38:40.674172: Current learning rate: 0.00417 +2026-04-13 11:40:21.475633: train_loss -0.4088 +2026-04-13 11:40:21.486605: val_loss -0.3659 +2026-04-13 11:40:21.489906: Pseudo dice [0.6624, 0.0, 0.7028, 0.7335, 0.3924, 0.6961, 0.5402] +2026-04-13 11:40:21.492857: Epoch time: 100.81 s +2026-04-13 11:40:22.687556: +2026-04-13 11:40:22.689824: Epoch 2487 +2026-04-13 11:40:22.692409: Current learning rate: 0.00417 +2026-04-13 11:42:03.769612: train_loss -0.3846 +2026-04-13 11:42:03.783349: val_loss -0.3787 +2026-04-13 11:42:03.788033: Pseudo dice [0.0, 0.0, 0.771, 0.0, 0.4243, 0.586, 0.6077] +2026-04-13 11:42:03.791338: Epoch time: 101.09 s +2026-04-13 11:42:05.024106: +2026-04-13 11:42:05.027283: Epoch 2488 +2026-04-13 11:42:05.030792: Current learning rate: 0.00417 +2026-04-13 11:43:46.456718: train_loss -0.4193 +2026-04-13 11:43:46.465242: val_loss -0.4019 +2026-04-13 11:43:46.468505: Pseudo dice [0.4578, 0.0, 0.618, 0.3456, 0.5377, 0.384, 0.5717] +2026-04-13 11:43:46.472028: Epoch time: 101.44 s +2026-04-13 11:43:47.672827: +2026-04-13 11:43:47.690991: Epoch 2489 +2026-04-13 11:43:47.695676: Current learning rate: 0.00416 +2026-04-13 11:45:28.586445: train_loss -0.4103 +2026-04-13 11:45:28.593705: val_loss -0.3211 +2026-04-13 11:45:28.597371: Pseudo dice [0.0, 0.0, 0.7146, 0.0, 0.2817, 0.7482, 0.5471] +2026-04-13 11:45:28.601318: Epoch time: 100.92 s +2026-04-13 11:45:29.821784: +2026-04-13 11:45:29.824387: Epoch 2490 +2026-04-13 11:45:29.826832: Current learning rate: 0.00416 +2026-04-13 11:47:11.237389: train_loss -0.3901 +2026-04-13 11:47:11.245499: val_loss -0.3579 +2026-04-13 11:47:11.248274: Pseudo dice [0.2629, 0.0, 0.6298, 0.0, 0.3575, 0.7265, 0.7836] +2026-04-13 11:47:11.251473: Epoch time: 101.42 s +2026-04-13 11:47:12.471351: +2026-04-13 11:47:12.473534: Epoch 2491 +2026-04-13 11:47:12.476454: Current learning rate: 0.00416 +2026-04-13 11:48:53.753196: train_loss -0.4089 +2026-04-13 11:48:53.764335: val_loss -0.3219 +2026-04-13 11:48:53.770154: Pseudo dice [0.6504, 0.0, 0.697, 0.0, 0.3054, 0.6998, 0.6529] +2026-04-13 11:48:53.776704: Epoch time: 101.28 s +2026-04-13 11:48:54.979434: +2026-04-13 11:48:54.981848: Epoch 2492 +2026-04-13 11:48:54.984509: Current learning rate: 0.00416 +2026-04-13 11:50:36.082940: train_loss -0.4121 +2026-04-13 11:50:36.090253: val_loss -0.4025 +2026-04-13 11:50:36.093976: Pseudo dice [0.0343, 0.0, 0.7612, 0.1491, 0.2794, 0.7789, 0.7796] +2026-04-13 11:50:36.097537: Epoch time: 101.11 s +2026-04-13 11:50:37.331157: +2026-04-13 11:50:37.335244: Epoch 2493 +2026-04-13 11:50:37.342481: Current learning rate: 0.00415 +2026-04-13 11:52:18.013495: train_loss -0.4081 +2026-04-13 11:52:18.020965: val_loss -0.4105 +2026-04-13 11:52:18.023943: Pseudo dice [0.4095, 0.0, 0.6448, 0.2552, 0.4672, 0.7616, 0.6766] +2026-04-13 11:52:18.026951: Epoch time: 100.69 s +2026-04-13 11:52:19.213808: +2026-04-13 11:52:19.216898: Epoch 2494 +2026-04-13 11:52:19.220198: Current learning rate: 0.00415 +2026-04-13 11:54:02.366075: train_loss -0.4123 +2026-04-13 11:54:02.376738: val_loss -0.3999 +2026-04-13 11:54:02.379498: Pseudo dice [0.1176, 0.0, 0.6348, 0.0, 0.3388, 0.6118, 0.66] +2026-04-13 11:54:02.382920: Epoch time: 103.16 s +2026-04-13 11:54:03.594299: +2026-04-13 11:54:03.599525: Epoch 2495 +2026-04-13 11:54:03.609136: Current learning rate: 0.00415 +2026-04-13 11:55:44.939561: train_loss -0.4256 +2026-04-13 11:55:44.946689: val_loss -0.3912 +2026-04-13 11:55:44.949977: Pseudo dice [0.6542, 0.0, 0.62, 0.0, 0.3813, 0.8072, 0.8259] +2026-04-13 11:55:44.953265: Epoch time: 101.35 s +2026-04-13 11:55:46.184004: +2026-04-13 11:55:46.186702: Epoch 2496 +2026-04-13 11:55:46.190697: Current learning rate: 0.00415 +2026-04-13 11:57:27.554721: train_loss -0.4136 +2026-04-13 11:57:27.563028: val_loss -0.3203 +2026-04-13 11:57:27.566904: Pseudo dice [0.0869, 0.0, 0.6697, 0.0, 0.357, 0.7865, 0.6786] +2026-04-13 11:57:27.569832: Epoch time: 101.37 s +2026-04-13 11:57:28.781471: +2026-04-13 11:57:28.787441: Epoch 2497 +2026-04-13 11:57:28.789358: Current learning rate: 0.00414 +2026-04-13 11:59:09.315443: train_loss -0.4155 +2026-04-13 11:59:09.322517: val_loss -0.3411 +2026-04-13 11:59:09.324874: Pseudo dice [0.6078, 0.0, 0.6535, 0.189, 0.4117, 0.6156, 0.4561] +2026-04-13 11:59:09.327368: Epoch time: 100.54 s +2026-04-13 11:59:10.546939: +2026-04-13 11:59:10.550486: Epoch 2498 +2026-04-13 11:59:10.553792: Current learning rate: 0.00414 +2026-04-13 12:00:51.271192: train_loss -0.4111 +2026-04-13 12:00:51.280543: val_loss -0.3755 +2026-04-13 12:00:51.283454: Pseudo dice [0.6403, 0.0, 0.643, 0.1705, 0.5435, 0.6908, 0.7117] +2026-04-13 12:00:51.286656: Epoch time: 100.73 s +2026-04-13 12:00:52.496732: +2026-04-13 12:00:52.501554: Epoch 2499 +2026-04-13 12:00:52.505733: Current learning rate: 0.00414 +2026-04-13 12:03:48.691223: train_loss -0.4268 +2026-04-13 12:03:48.697247: val_loss -0.4321 +2026-04-13 12:03:48.699402: Pseudo dice [0.6483, 0.0, 0.7804, 0.8765, 0.057, 0.9031, 0.8707] +2026-04-13 12:03:48.702038: Epoch time: 176.2 s +2026-04-13 12:04:00.731473: +2026-04-13 12:04:00.735384: Epoch 2500 +2026-04-13 12:04:00.739122: Current learning rate: 0.00414 +2026-04-13 12:13:55.982513: train_loss -0.4276 +2026-04-13 12:13:56.021806: val_loss -0.4022 +2026-04-13 12:13:56.046431: Pseudo dice [0.0698, 0.0, 0.6946, 0.0, 0.446, 0.859, 0.5158] +2026-04-13 12:13:56.091346: Epoch time: 595.25 s +2026-04-13 12:14:02.253425: +2026-04-13 12:14:02.256622: Epoch 2501 +2026-04-13 12:14:02.260877: Current learning rate: 0.00413 +2026-04-13 12:16:55.944079: train_loss -0.4338 +2026-04-13 12:16:55.952388: val_loss -0.399 +2026-04-13 12:16:55.955296: Pseudo dice [0.0009, 0.0, 0.5924, 0.4346, 0.542, 0.8088, 0.7217] +2026-04-13 12:16:55.958310: Epoch time: 173.71 s +2026-04-13 12:16:57.190415: +2026-04-13 12:16:57.195639: Epoch 2502 +2026-04-13 12:16:57.198852: Current learning rate: 0.00413 +2026-04-13 12:20:09.290612: train_loss -0.3997 +2026-04-13 12:20:09.297755: val_loss -0.3992 +2026-04-13 12:20:09.301720: Pseudo dice [0.5095, 0.0, 0.7895, 0.5437, 0.2648, 0.7838, 0.8555] +2026-04-13 12:20:09.305359: Epoch time: 192.1 s +2026-04-13 12:20:10.516017: +2026-04-13 12:20:10.520257: Epoch 2503 +2026-04-13 12:20:10.523566: Current learning rate: 0.00413 +2026-04-13 12:21:51.118093: train_loss -0.4141 +2026-04-13 12:21:51.128212: val_loss -0.398 +2026-04-13 12:21:51.130709: Pseudo dice [0.3403, 0.0, 0.5898, 0.0, 0.2869, 0.8086, 0.7103] +2026-04-13 12:21:51.136969: Epoch time: 100.61 s +2026-04-13 12:21:52.354856: +2026-04-13 12:21:52.357894: Epoch 2504 +2026-04-13 12:21:52.360131: Current learning rate: 0.00413 +2026-04-13 12:24:38.027838: train_loss -0.4037 +2026-04-13 12:24:38.035536: val_loss -0.3902 +2026-04-13 12:24:38.037894: Pseudo dice [0.2647, 0.0, 0.6082, 0.0, 0.3777, 0.6318, 0.6229] +2026-04-13 12:24:38.040475: Epoch time: 165.68 s +2026-04-13 12:24:43.770597: +2026-04-13 12:24:43.774396: Epoch 2505 +2026-04-13 12:24:43.777708: Current learning rate: 0.00412 +2026-04-13 12:28:00.657291: train_loss -0.4267 +2026-04-13 12:28:00.671066: val_loss -0.4017 +2026-04-13 12:28:00.676904: Pseudo dice [0.0, 0.0, 0.6806, 0.5436, 0.538, 0.6522, 0.6975] +2026-04-13 12:28:00.679930: Epoch time: 196.95 s +2026-04-13 12:28:01.897618: +2026-04-13 12:28:01.902172: Epoch 2506 +2026-04-13 12:28:01.905116: Current learning rate: 0.00412 +2026-04-13 12:34:15.542578: train_loss -0.4197 +2026-04-13 12:34:15.552053: val_loss -0.3699 +2026-04-13 12:34:15.555202: Pseudo dice [0.7662, 0.0, 0.7198, 0.2313, 0.1208, 0.7787, 0.4536] +2026-04-13 12:34:15.558546: Epoch time: 373.65 s +2026-04-13 12:34:23.962771: +2026-04-13 12:34:23.967547: Epoch 2507 +2026-04-13 12:34:23.969932: Current learning rate: 0.00412 +2026-04-13 12:43:27.569333: train_loss -0.4142 +2026-04-13 12:43:27.577837: val_loss -0.3126 +2026-04-13 12:43:27.580220: Pseudo dice [0.0399, 0.0, 0.69, 0.0, 0.0, 0.8175, 0.6143] +2026-04-13 12:43:27.583162: Epoch time: 543.61 s +2026-04-13 12:43:28.792254: +2026-04-13 12:43:28.794948: Epoch 2508 +2026-04-13 12:43:28.797535: Current learning rate: 0.00412 +2026-04-13 12:45:09.155938: train_loss -0.4235 +2026-04-13 12:45:09.163862: val_loss -0.3768 +2026-04-13 12:45:09.168022: Pseudo dice [0.4091, 0.0, 0.8089, 0.3475, 0.3669, 0.8871, 0.4778] +2026-04-13 12:45:09.171395: Epoch time: 100.37 s +2026-04-13 12:45:10.376884: +2026-04-13 12:45:10.382245: Epoch 2509 +2026-04-13 12:45:10.385097: Current learning rate: 0.00411 +2026-04-13 12:46:52.284619: train_loss -0.3982 +2026-04-13 12:46:52.291352: val_loss -0.3174 +2026-04-13 12:46:52.294366: Pseudo dice [0.3187, 0.0, 0.5245, 0.0682, 0.3293, 0.8021, 0.7309] +2026-04-13 12:46:52.297801: Epoch time: 101.91 s +2026-04-13 12:46:53.539316: +2026-04-13 12:46:53.541724: Epoch 2510 +2026-04-13 12:46:53.543911: Current learning rate: 0.00411 +2026-04-13 12:48:34.815633: train_loss -0.4113 +2026-04-13 12:48:34.824514: val_loss -0.417 +2026-04-13 12:48:34.828262: Pseudo dice [0.7253, 0.0, 0.7283, 0.0, 0.3181, 0.7652, 0.8181] +2026-04-13 12:48:34.831830: Epoch time: 101.28 s +2026-04-13 12:48:36.110095: +2026-04-13 12:48:36.112280: Epoch 2511 +2026-04-13 12:48:36.114597: Current learning rate: 0.00411 +2026-04-13 12:50:18.073459: train_loss -0.409 +2026-04-13 12:50:18.080562: val_loss -0.3589 +2026-04-13 12:50:18.084098: Pseudo dice [0.2107, 0.0, 0.5996, 0.0501, 0.5128, 0.6415, 0.8607] +2026-04-13 12:50:18.087332: Epoch time: 101.97 s +2026-04-13 12:50:19.300758: +2026-04-13 12:50:19.303001: Epoch 2512 +2026-04-13 12:50:19.305225: Current learning rate: 0.00411 +2026-04-13 12:52:00.627257: train_loss -0.3808 +2026-04-13 12:52:00.637138: val_loss -0.3443 +2026-04-13 12:52:00.640606: Pseudo dice [0.4749, 0.0, 0.517, 0.0, 0.0, 0.7621, 0.7605] +2026-04-13 12:52:00.643425: Epoch time: 101.33 s +2026-04-13 12:52:01.905310: +2026-04-13 12:52:01.908614: Epoch 2513 +2026-04-13 12:52:01.911742: Current learning rate: 0.0041 +2026-04-13 12:53:42.468388: train_loss -0.3543 +2026-04-13 12:53:42.476568: val_loss -0.3627 +2026-04-13 12:53:42.478790: Pseudo dice [0.6017, 0.0, 0.7072, 0.1989, 0.1088, 0.0213, 0.5946] +2026-04-13 12:53:42.481518: Epoch time: 100.57 s +2026-04-13 12:53:44.739051: +2026-04-13 12:53:44.742079: Epoch 2514 +2026-04-13 12:53:44.744283: Current learning rate: 0.0041 +2026-04-13 12:55:25.489430: train_loss -0.3736 +2026-04-13 12:55:25.500776: val_loss -0.2187 +2026-04-13 12:55:25.503595: Pseudo dice [0.6453, 0.0, 0.6139, 0.0496, 0.0, 0.284, 0.4894] +2026-04-13 12:55:25.506929: Epoch time: 100.75 s +2026-04-13 12:55:26.696320: +2026-04-13 12:55:26.699911: Epoch 2515 +2026-04-13 12:55:26.702737: Current learning rate: 0.0041 +2026-04-13 12:57:07.207034: train_loss -0.3921 +2026-04-13 12:57:07.214967: val_loss -0.2734 +2026-04-13 12:57:07.218186: Pseudo dice [0.2245, 0.0, 0.5107, 0.0, 0.1373, 0.3125, 0.4226] +2026-04-13 12:57:07.220376: Epoch time: 100.51 s +2026-04-13 12:57:08.421885: +2026-04-13 12:57:08.423987: Epoch 2516 +2026-04-13 12:57:08.426710: Current learning rate: 0.0041 +2026-04-13 12:58:49.623559: train_loss -0.4061 +2026-04-13 12:58:49.636582: val_loss -0.3894 +2026-04-13 12:58:49.641726: Pseudo dice [0.5366, 0.0, 0.6029, 0.616, 0.5422, 0.7634, 0.3846] +2026-04-13 12:58:49.647681: Epoch time: 101.2 s +2026-04-13 12:58:50.885174: +2026-04-13 12:58:50.888493: Epoch 2517 +2026-04-13 12:58:50.891369: Current learning rate: 0.00409 +2026-04-13 13:00:31.327869: train_loss -0.388 +2026-04-13 13:00:31.337409: val_loss -0.3997 +2026-04-13 13:00:31.340827: Pseudo dice [0.5809, 0.0, 0.5805, 0.0233, 0.3096, 0.4266, 0.6805] +2026-04-13 13:00:31.345269: Epoch time: 100.45 s +2026-04-13 13:00:32.529492: +2026-04-13 13:00:32.531409: Epoch 2518 +2026-04-13 13:00:32.533814: Current learning rate: 0.00409 +2026-04-13 13:02:12.928321: train_loss -0.3534 +2026-04-13 13:02:12.935247: val_loss -0.3802 +2026-04-13 13:02:12.938644: Pseudo dice [0.4528, 0.0, 0.7146, 0.0, 0.4634, 0.0719, 0.6685] +2026-04-13 13:02:12.941402: Epoch time: 100.4 s +2026-04-13 13:02:14.179128: +2026-04-13 13:02:14.181314: Epoch 2519 +2026-04-13 13:02:14.184123: Current learning rate: 0.00409 +2026-04-13 13:03:55.612987: train_loss -0.3897 +2026-04-13 13:03:55.620109: val_loss -0.3949 +2026-04-13 13:03:55.623050: Pseudo dice [0.1572, 0.0, 0.7239, 0.6372, 0.3902, 0.3459, 0.7877] +2026-04-13 13:03:55.625956: Epoch time: 101.44 s +2026-04-13 13:03:56.832921: +2026-04-13 13:03:56.835777: Epoch 2520 +2026-04-13 13:03:56.838733: Current learning rate: 0.00409 +2026-04-13 13:05:38.099341: train_loss -0.4278 +2026-04-13 13:05:38.106499: val_loss -0.3197 +2026-04-13 13:05:38.109432: Pseudo dice [0.1703, 0.0, 0.6358, 0.0514, 0.431, 0.7008, 0.7241] +2026-04-13 13:05:38.112720: Epoch time: 101.27 s +2026-04-13 13:05:39.314189: +2026-04-13 13:05:39.316170: Epoch 2521 +2026-04-13 13:05:39.318558: Current learning rate: 0.00408 +2026-04-13 13:07:19.946260: train_loss -0.4031 +2026-04-13 13:07:19.956534: val_loss -0.3126 +2026-04-13 13:07:19.962626: Pseudo dice [0.2158, 0.0, 0.5332, 0.0807, 0.4468, 0.5139, 0.0224] +2026-04-13 13:07:19.965337: Epoch time: 100.64 s +2026-04-13 13:07:21.174704: +2026-04-13 13:07:21.177794: Epoch 2522 +2026-04-13 13:07:21.180167: Current learning rate: 0.00408 +2026-04-13 13:09:02.005021: train_loss -0.3871 +2026-04-13 13:09:02.017488: val_loss -0.368 +2026-04-13 13:09:02.020791: Pseudo dice [0.0, 0.0, 0.7259, 0.0649, 0.0474, 0.7039, 0.6701] +2026-04-13 13:09:02.027909: Epoch time: 100.83 s +2026-04-13 13:09:03.262749: +2026-04-13 13:09:03.265372: Epoch 2523 +2026-04-13 13:09:03.267846: Current learning rate: 0.00408 +2026-04-13 13:10:44.009402: train_loss -0.3993 +2026-04-13 13:10:44.020430: val_loss -0.3598 +2026-04-13 13:10:44.025163: Pseudo dice [0.5849, 0.0, 0.7904, 0.0, 0.2624, 0.6013, 0.724] +2026-04-13 13:10:44.028245: Epoch time: 100.75 s +2026-04-13 13:10:45.236045: +2026-04-13 13:10:45.237964: Epoch 2524 +2026-04-13 13:10:45.240313: Current learning rate: 0.00408 +2026-04-13 13:12:26.749404: train_loss -0.4036 +2026-04-13 13:12:26.759527: val_loss -0.3254 +2026-04-13 13:12:26.764769: Pseudo dice [0.729, 0.0, 0.7394, 0.0, 0.2379, 0.3138, 0.6946] +2026-04-13 13:12:26.769727: Epoch time: 101.52 s +2026-04-13 13:12:27.974804: +2026-04-13 13:12:27.980567: Epoch 2525 +2026-04-13 13:12:27.985134: Current learning rate: 0.00407 +2026-04-13 13:14:08.939560: train_loss -0.4147 +2026-04-13 13:14:08.946706: val_loss -0.3596 +2026-04-13 13:14:08.948843: Pseudo dice [0.4326, 0.0, 0.6641, 0.2876, 0.4898, 0.7811, 0.799] +2026-04-13 13:14:08.950717: Epoch time: 100.97 s +2026-04-13 13:14:10.388123: +2026-04-13 13:14:10.390289: Epoch 2526 +2026-04-13 13:14:10.392627: Current learning rate: 0.00407 +2026-04-13 13:15:50.903915: train_loss -0.3985 +2026-04-13 13:15:50.911032: val_loss -0.3839 +2026-04-13 13:15:50.915152: Pseudo dice [0.6288, 0.0, 0.6891, 0.3227, 0.1856, 0.7867, 0.7874] +2026-04-13 13:15:50.918842: Epoch time: 100.52 s +2026-04-13 13:15:52.131268: +2026-04-13 13:15:52.135537: Epoch 2527 +2026-04-13 13:15:52.138865: Current learning rate: 0.00407 +2026-04-13 13:17:32.633383: train_loss -0.3934 +2026-04-13 13:17:32.640070: val_loss -0.3355 +2026-04-13 13:17:32.642309: Pseudo dice [0.3374, 0.0, 0.7153, 0.0, 0.4252, 0.5793, 0.6933] +2026-04-13 13:17:32.645032: Epoch time: 100.51 s +2026-04-13 13:17:33.847137: +2026-04-13 13:17:33.849244: Epoch 2528 +2026-04-13 13:17:33.851510: Current learning rate: 0.00407 +2026-04-13 13:19:14.874342: train_loss -0.4004 +2026-04-13 13:19:14.883918: val_loss -0.3659 +2026-04-13 13:19:14.887656: Pseudo dice [0.0451, 0.0, 0.686, 0.0, 0.4095, 0.4429, 0.6777] +2026-04-13 13:19:14.892526: Epoch time: 101.03 s +2026-04-13 13:19:16.122211: +2026-04-13 13:19:16.126289: Epoch 2529 +2026-04-13 13:19:16.132154: Current learning rate: 0.00406 +2026-04-13 13:20:57.083035: train_loss -0.4072 +2026-04-13 13:20:57.090039: val_loss -0.4201 +2026-04-13 13:20:57.092221: Pseudo dice [0.5188, 0.0, 0.7701, 0.4579, 0.5024, 0.8401, 0.7755] +2026-04-13 13:20:57.095074: Epoch time: 100.96 s +2026-04-13 13:20:58.318146: +2026-04-13 13:20:58.320382: Epoch 2530 +2026-04-13 13:20:58.322796: Current learning rate: 0.00406 +2026-04-13 13:22:40.039080: train_loss -0.4077 +2026-04-13 13:22:40.044536: val_loss -0.3647 +2026-04-13 13:22:40.046624: Pseudo dice [0.5273, 0.0, 0.8215, 0.0618, 0.3399, 0.2824, 0.5565] +2026-04-13 13:22:40.049002: Epoch time: 101.72 s +2026-04-13 13:22:41.271497: +2026-04-13 13:22:41.273531: Epoch 2531 +2026-04-13 13:22:41.275803: Current learning rate: 0.00406 +2026-04-13 13:24:22.196689: train_loss -0.3863 +2026-04-13 13:24:22.205673: val_loss -0.3829 +2026-04-13 13:24:22.209175: Pseudo dice [0.5787, 0.0, 0.8359, 0.6263, 0.3222, 0.7145, 0.7348] +2026-04-13 13:24:22.211846: Epoch time: 100.93 s +2026-04-13 13:24:23.429318: +2026-04-13 13:24:23.432396: Epoch 2532 +2026-04-13 13:24:23.435084: Current learning rate: 0.00406 +2026-04-13 13:26:04.802868: train_loss -0.401 +2026-04-13 13:26:04.813588: val_loss -0.4028 +2026-04-13 13:26:04.816526: Pseudo dice [0.6188, 0.0, 0.7403, 0.7584, 0.3226, 0.7475, 0.6678] +2026-04-13 13:26:04.820108: Epoch time: 101.38 s +2026-04-13 13:26:06.027859: +2026-04-13 13:26:06.030446: Epoch 2533 +2026-04-13 13:26:06.033774: Current learning rate: 0.00405 +2026-04-13 13:27:46.868571: train_loss -0.4306 +2026-04-13 13:27:46.877121: val_loss -0.3958 +2026-04-13 13:27:46.879796: Pseudo dice [0.5336, 0.0, 0.5044, 0.7429, 0.4029, 0.797, 0.4886] +2026-04-13 13:27:46.882663: Epoch time: 100.84 s +2026-04-13 13:27:48.090930: +2026-04-13 13:27:48.093224: Epoch 2534 +2026-04-13 13:27:48.096840: Current learning rate: 0.00405 +2026-04-13 13:29:29.799130: train_loss -0.4119 +2026-04-13 13:29:29.806805: val_loss -0.3963 +2026-04-13 13:29:29.808963: Pseudo dice [0.6844, 0.0, 0.7267, 0.0, 0.1991, 0.7727, 0.5328] +2026-04-13 13:29:29.811661: Epoch time: 101.71 s +2026-04-13 13:29:31.012511: +2026-04-13 13:29:31.014834: Epoch 2535 +2026-04-13 13:29:31.017468: Current learning rate: 0.00405 +2026-04-13 13:31:11.744950: train_loss -0.4212 +2026-04-13 13:31:11.753982: val_loss -0.3257 +2026-04-13 13:31:11.756057: Pseudo dice [0.4238, 0.0, 0.6802, 0.0, 0.2565, 0.8636, 0.7471] +2026-04-13 13:31:11.759814: Epoch time: 100.74 s +2026-04-13 13:31:12.960009: +2026-04-13 13:31:12.962549: Epoch 2536 +2026-04-13 13:31:12.967463: Current learning rate: 0.00405 +2026-04-13 13:32:53.876024: train_loss -0.4236 +2026-04-13 13:32:53.887768: val_loss -0.3174 +2026-04-13 13:32:53.898780: Pseudo dice [0.7068, 0.0, 0.4783, 0.0, 0.2864, 0.2838, 0.7924] +2026-04-13 13:32:53.907521: Epoch time: 100.92 s +2026-04-13 13:32:55.122551: +2026-04-13 13:32:55.127691: Epoch 2537 +2026-04-13 13:32:55.132279: Current learning rate: 0.00404 +2026-04-13 13:34:35.638432: train_loss -0.431 +2026-04-13 13:34:35.645954: val_loss -0.3515 +2026-04-13 13:34:35.649355: Pseudo dice [0.6416, 0.0, 0.5121, 0.0056, 0.4947, 0.7576, 0.4397] +2026-04-13 13:34:35.652401: Epoch time: 100.52 s +2026-04-13 13:34:36.861250: +2026-04-13 13:34:36.863559: Epoch 2538 +2026-04-13 13:34:36.867453: Current learning rate: 0.00404 +2026-04-13 13:36:18.750338: train_loss -0.4188 +2026-04-13 13:36:18.757681: val_loss -0.3411 +2026-04-13 13:36:18.760878: Pseudo dice [0.7412, 0.0, 0.652, 0.108, 0.5814, 0.7443, 0.6829] +2026-04-13 13:36:18.765290: Epoch time: 101.89 s +2026-04-13 13:36:19.957837: +2026-04-13 13:36:19.960007: Epoch 2539 +2026-04-13 13:36:19.962239: Current learning rate: 0.00404 +2026-04-13 13:38:01.002316: train_loss -0.3955 +2026-04-13 13:38:01.010442: val_loss -0.3515 +2026-04-13 13:38:01.013046: Pseudo dice [0.0, 0.0, 0.5351, 0.6448, 0.0888, 0.2868, 0.6237] +2026-04-13 13:38:01.015724: Epoch time: 101.05 s +2026-04-13 13:38:02.224882: +2026-04-13 13:38:02.227402: Epoch 2540 +2026-04-13 13:38:02.230323: Current learning rate: 0.00404 +2026-04-13 13:39:43.837903: train_loss -0.3745 +2026-04-13 13:39:43.844457: val_loss -0.3692 +2026-04-13 13:39:43.847339: Pseudo dice [0.0, 0.0, 0.6583, 0.3114, 0.1658, 0.6042, 0.7158] +2026-04-13 13:39:43.850490: Epoch time: 101.62 s +2026-04-13 13:39:45.060369: +2026-04-13 13:39:45.062672: Epoch 2541 +2026-04-13 13:39:45.064955: Current learning rate: 0.00403 +2026-04-13 13:41:27.013433: train_loss -0.4058 +2026-04-13 13:41:27.022250: val_loss -0.3735 +2026-04-13 13:41:27.025979: Pseudo dice [0.0002, 0.0, 0.7644, 0.0067, 0.3298, 0.5635, 0.7925] +2026-04-13 13:41:27.029042: Epoch time: 101.96 s +2026-04-13 13:41:28.227702: +2026-04-13 13:41:28.230228: Epoch 2542 +2026-04-13 13:41:28.233798: Current learning rate: 0.00403 +2026-04-13 13:43:09.223015: train_loss -0.3543 +2026-04-13 13:43:09.228989: val_loss -0.3323 +2026-04-13 13:43:09.230834: Pseudo dice [0.1666, 0.0, 0.6626, 0.0, 0.1607, 0.4033, 0.4205] +2026-04-13 13:43:09.233211: Epoch time: 101.0 s +2026-04-13 13:43:10.430392: +2026-04-13 13:43:10.432368: Epoch 2543 +2026-04-13 13:43:10.434390: Current learning rate: 0.00403 +2026-04-13 13:44:51.190842: train_loss -0.3776 +2026-04-13 13:44:51.201342: val_loss -0.3731 +2026-04-13 13:44:51.204877: Pseudo dice [0.587, 0.0, 0.658, 0.82, 0.1696, 0.6335, 0.7163] +2026-04-13 13:44:51.211874: Epoch time: 100.76 s +2026-04-13 13:44:52.446917: +2026-04-13 13:44:52.450295: Epoch 2544 +2026-04-13 13:44:52.453298: Current learning rate: 0.00403 +2026-04-13 13:46:33.987871: train_loss -0.4096 +2026-04-13 13:46:33.995750: val_loss -0.4241 +2026-04-13 13:46:34.000266: Pseudo dice [0.6556, 0.0, 0.815, 0.7585, 0.5453, 0.8461, 0.6881] +2026-04-13 13:46:34.003875: Epoch time: 101.54 s +2026-04-13 13:46:35.202899: +2026-04-13 13:46:35.205760: Epoch 2545 +2026-04-13 13:46:35.208742: Current learning rate: 0.00402 +2026-04-13 13:48:16.066016: train_loss -0.3919 +2026-04-13 13:48:16.073155: val_loss -0.3813 +2026-04-13 13:48:16.075899: Pseudo dice [0.0, 0.0, 0.4332, 0.0, 0.2326, 0.7176, 0.778] +2026-04-13 13:48:16.079419: Epoch time: 100.87 s +2026-04-13 13:48:17.270055: +2026-04-13 13:48:17.272688: Epoch 2546 +2026-04-13 13:48:17.275676: Current learning rate: 0.00402 +2026-04-13 13:49:59.057350: train_loss -0.3874 +2026-04-13 13:49:59.063975: val_loss -0.4031 +2026-04-13 13:49:59.068773: Pseudo dice [0.0, 0.0, 0.7535, 0.6626, 0.2567, 0.4787, 0.7821] +2026-04-13 13:49:59.072348: Epoch time: 101.79 s +2026-04-13 13:50:00.290192: +2026-04-13 13:50:00.293531: Epoch 2547 +2026-04-13 13:50:00.295983: Current learning rate: 0.00402 +2026-04-13 13:51:41.566229: train_loss -0.3988 +2026-04-13 13:51:41.574709: val_loss -0.3418 +2026-04-13 13:51:41.578066: Pseudo dice [0.0, 0.0, 0.6389, 0.0948, 0.2435, 0.7573, 0.7843] +2026-04-13 13:51:41.582367: Epoch time: 101.28 s +2026-04-13 13:51:42.787406: +2026-04-13 13:51:42.789356: Epoch 2548 +2026-04-13 13:51:42.797083: Current learning rate: 0.00402 +2026-04-13 13:53:23.953616: train_loss -0.4046 +2026-04-13 13:53:23.959868: val_loss -0.4196 +2026-04-13 13:53:23.963007: Pseudo dice [0.0, 0.0, 0.7796, 0.7419, 0.4864, 0.7901, 0.906] +2026-04-13 13:53:23.965892: Epoch time: 101.17 s +2026-04-13 13:53:25.151555: +2026-04-13 13:53:25.154571: Epoch 2549 +2026-04-13 13:53:25.157180: Current learning rate: 0.00401 +2026-04-13 13:55:06.087711: train_loss -0.3825 +2026-04-13 13:55:06.097857: val_loss -0.3528 +2026-04-13 13:55:06.101091: Pseudo dice [0.0, 0.0, 0.5677, 0.0, 0.0826, 0.68, 0.6238] +2026-04-13 13:55:06.103817: Epoch time: 100.94 s +2026-04-13 13:55:08.771106: +2026-04-13 13:55:08.774257: Epoch 2550 +2026-04-13 13:55:08.776597: Current learning rate: 0.00401 +2026-04-13 13:56:49.446495: train_loss -0.4109 +2026-04-13 13:56:49.453485: val_loss -0.3923 +2026-04-13 13:56:49.456714: Pseudo dice [0.0, 0.0, 0.6311, 0.0, 0.3477, 0.5329, 0.586] +2026-04-13 13:56:49.463187: Epoch time: 100.68 s +2026-04-13 13:56:50.669301: +2026-04-13 13:56:50.673147: Epoch 2551 +2026-04-13 13:56:50.675912: Current learning rate: 0.00401 +2026-04-13 13:58:32.338433: train_loss -0.4177 +2026-04-13 13:58:32.345620: val_loss -0.4238 +2026-04-13 13:58:32.348439: Pseudo dice [0.0, 0.0, 0.7293, 0.5725, 0.3274, 0.7248, 0.8101] +2026-04-13 13:58:32.352730: Epoch time: 101.67 s +2026-04-13 13:58:33.551924: +2026-04-13 13:58:33.553923: Epoch 2552 +2026-04-13 13:58:33.556085: Current learning rate: 0.00401 +2026-04-13 14:00:13.666547: train_loss -0.4082 +2026-04-13 14:00:13.672852: val_loss -0.386 +2026-04-13 14:00:13.674924: Pseudo dice [0.0, 0.0, 0.5268, 0.7408, 0.0002, 0.7545, 0.7754] +2026-04-13 14:00:13.677896: Epoch time: 100.12 s +2026-04-13 14:00:14.872273: +2026-04-13 14:00:14.874756: Epoch 2553 +2026-04-13 14:00:14.876983: Current learning rate: 0.004 +2026-04-13 14:01:56.168631: train_loss -0.3773 +2026-04-13 14:01:56.175576: val_loss -0.3263 +2026-04-13 14:01:56.178536: Pseudo dice [0.0, 0.0, 0.6025, 0.0, 0.0, 0.4973, 0.6996] +2026-04-13 14:01:56.181522: Epoch time: 101.3 s +2026-04-13 14:01:58.395336: +2026-04-13 14:01:58.397640: Epoch 2554 +2026-04-13 14:01:58.400109: Current learning rate: 0.004 +2026-04-13 14:03:39.042999: train_loss -0.3582 +2026-04-13 14:03:39.071745: val_loss -0.3441 +2026-04-13 14:03:39.074524: Pseudo dice [0.0122, 0.0, 0.4897, 0.1199, 0.0, 0.7668, 0.6693] +2026-04-13 14:03:39.077889: Epoch time: 100.65 s +2026-04-13 14:03:40.295285: +2026-04-13 14:03:40.297560: Epoch 2555 +2026-04-13 14:03:40.299832: Current learning rate: 0.004 +2026-04-13 14:05:22.107575: train_loss -0.3927 +2026-04-13 14:05:22.117666: val_loss -0.3669 +2026-04-13 14:05:22.125718: Pseudo dice [0.333, 0.0, 0.7193, 0.0, 0.0, 0.8199, 0.7231] +2026-04-13 14:05:22.129344: Epoch time: 101.82 s +2026-04-13 14:05:23.324996: +2026-04-13 14:05:23.328825: Epoch 2556 +2026-04-13 14:05:23.332116: Current learning rate: 0.004 +2026-04-13 14:07:03.484603: train_loss -0.4203 +2026-04-13 14:07:03.493109: val_loss -0.3886 +2026-04-13 14:07:03.495860: Pseudo dice [0.4343, 0.0, 0.7748, 0.0, 0.449, 0.6594, 0.6352] +2026-04-13 14:07:03.500068: Epoch time: 100.16 s +2026-04-13 14:07:04.691176: +2026-04-13 14:07:04.693877: Epoch 2557 +2026-04-13 14:07:04.698310: Current learning rate: 0.00399 +2026-04-13 14:08:45.524562: train_loss -0.402 +2026-04-13 14:08:45.535251: val_loss -0.4089 +2026-04-13 14:08:45.538487: Pseudo dice [0.6687, 0.0, 0.7708, 0.0, 0.0, 0.5573, 0.7377] +2026-04-13 14:08:45.541784: Epoch time: 100.84 s +2026-04-13 14:08:46.753672: +2026-04-13 14:08:46.756530: Epoch 2558 +2026-04-13 14:08:46.759452: Current learning rate: 0.00399 +2026-04-13 14:10:29.489891: train_loss -0.385 +2026-04-13 14:10:29.505512: val_loss -0.3392 +2026-04-13 14:10:29.509455: Pseudo dice [0.5941, 0.0, 0.6433, 0.0602, 0.0928, 0.6358, 0.7091] +2026-04-13 14:10:29.514179: Epoch time: 102.74 s +2026-04-13 14:10:30.738328: +2026-04-13 14:10:30.741406: Epoch 2559 +2026-04-13 14:10:30.745718: Current learning rate: 0.00399 +2026-04-13 14:12:13.591358: train_loss -0.4084 +2026-04-13 14:12:13.599290: val_loss -0.4426 +2026-04-13 14:12:13.601365: Pseudo dice [0.6704, 0.0, 0.8091, 0.7859, 0.4342, 0.6084, 0.894] +2026-04-13 14:12:13.604029: Epoch time: 102.86 s +2026-04-13 14:12:14.808131: +2026-04-13 14:12:14.810307: Epoch 2560 +2026-04-13 14:12:14.812680: Current learning rate: 0.00399 +2026-04-13 14:13:55.929310: train_loss -0.4247 +2026-04-13 14:13:55.936135: val_loss -0.3325 +2026-04-13 14:13:55.939372: Pseudo dice [0.0, 0.0, 0.696, 0.0, 0.3696, 0.4676, 0.7569] +2026-04-13 14:13:55.941931: Epoch time: 101.12 s +2026-04-13 14:13:57.175251: +2026-04-13 14:13:57.178990: Epoch 2561 +2026-04-13 14:13:57.182268: Current learning rate: 0.00398 +2026-04-13 14:15:38.054501: train_loss -0.3981 +2026-04-13 14:15:38.064893: val_loss -0.3628 +2026-04-13 14:15:38.067475: Pseudo dice [0.5813, 0.0, 0.5711, 0.0, 0.2848, 0.2609, 0.5868] +2026-04-13 14:15:38.070356: Epoch time: 100.88 s +2026-04-13 14:15:39.272224: +2026-04-13 14:15:39.274714: Epoch 2562 +2026-04-13 14:15:39.277258: Current learning rate: 0.00398 +2026-04-13 14:17:21.359163: train_loss -0.4193 +2026-04-13 14:17:21.368462: val_loss -0.269 +2026-04-13 14:17:21.372783: Pseudo dice [0.0558, 0.0, 0.635, 0.0, 0.2977, 0.5035, 0.7909] +2026-04-13 14:17:21.377860: Epoch time: 102.09 s +2026-04-13 14:17:22.590649: +2026-04-13 14:17:22.594083: Epoch 2563 +2026-04-13 14:17:22.598989: Current learning rate: 0.00398 +2026-04-13 14:19:03.722760: train_loss -0.4181 +2026-04-13 14:19:03.729543: val_loss -0.4217 +2026-04-13 14:19:03.731507: Pseudo dice [0.0, 0.0, 0.7556, 0.0, 0.5009, 0.8099, 0.8694] +2026-04-13 14:19:03.735522: Epoch time: 101.14 s +2026-04-13 14:19:04.945675: +2026-04-13 14:19:04.947987: Epoch 2564 +2026-04-13 14:19:04.949992: Current learning rate: 0.00398 +2026-04-13 14:20:46.845857: train_loss -0.3917 +2026-04-13 14:20:46.854679: val_loss -0.3525 +2026-04-13 14:20:46.857071: Pseudo dice [0.4369, 0.0, 0.7727, 0.0, 0.3673, 0.3734, 0.2625] +2026-04-13 14:20:46.859828: Epoch time: 101.9 s +2026-04-13 14:20:48.061281: +2026-04-13 14:20:48.065999: Epoch 2565 +2026-04-13 14:20:48.069057: Current learning rate: 0.00397 +2026-04-13 14:22:29.774340: train_loss -0.4054 +2026-04-13 14:22:29.786331: val_loss -0.367 +2026-04-13 14:22:29.789204: Pseudo dice [0.0, 0.0, 0.6863, 0.3337, 0.2696, 0.806, 0.5737] +2026-04-13 14:22:29.792082: Epoch time: 101.72 s +2026-04-13 14:22:30.994477: +2026-04-13 14:22:30.997000: Epoch 2566 +2026-04-13 14:22:31.000538: Current learning rate: 0.00397 +2026-04-13 14:24:12.637203: train_loss -0.3781 +2026-04-13 14:24:12.645227: val_loss -0.3094 +2026-04-13 14:24:12.648433: Pseudo dice [0.4757, 0.0, 0.6147, 0.0, 0.4402, 0.3776, 0.7346] +2026-04-13 14:24:12.653017: Epoch time: 101.65 s +2026-04-13 14:24:13.879267: +2026-04-13 14:24:13.881434: Epoch 2567 +2026-04-13 14:24:13.883480: Current learning rate: 0.00397 +2026-04-13 14:25:55.696901: train_loss -0.3929 +2026-04-13 14:25:55.704888: val_loss -0.3825 +2026-04-13 14:25:55.707721: Pseudo dice [0.0, 0.0, 0.7084, 0.0, 0.0, 0.7922, 0.6915] +2026-04-13 14:25:55.711173: Epoch time: 101.82 s +2026-04-13 14:25:56.925606: +2026-04-13 14:25:56.928351: Epoch 2568 +2026-04-13 14:25:56.930351: Current learning rate: 0.00397 +2026-04-13 14:27:38.405261: train_loss -0.3856 +2026-04-13 14:27:38.416859: val_loss -0.3934 +2026-04-13 14:27:38.420358: Pseudo dice [0.3717, 0.0, 0.6505, 0.0, 0.4385, 0.5844, 0.8187] +2026-04-13 14:27:38.427425: Epoch time: 101.48 s +2026-04-13 14:27:39.648321: +2026-04-13 14:27:39.651376: Epoch 2569 +2026-04-13 14:27:39.653895: Current learning rate: 0.00396 +2026-04-13 14:29:20.566612: train_loss -0.3927 +2026-04-13 14:29:20.578246: val_loss -0.359 +2026-04-13 14:29:20.580867: Pseudo dice [0.6161, 0.0, 0.4496, 0.0, 0.0, 0.4219, 0.539] +2026-04-13 14:29:20.584515: Epoch time: 100.92 s +2026-04-13 14:29:21.823230: +2026-04-13 14:29:21.825713: Epoch 2570 +2026-04-13 14:29:21.828144: Current learning rate: 0.00396 +2026-04-13 14:31:02.666094: train_loss -0.3513 +2026-04-13 14:31:02.675318: val_loss -0.3337 +2026-04-13 14:31:02.678873: Pseudo dice [0.0573, 0.0, 0.4707, 0.0, 0.0, 0.622, 0.4109] +2026-04-13 14:31:02.683297: Epoch time: 100.85 s +2026-04-13 14:31:03.945695: +2026-04-13 14:31:03.948253: Epoch 2571 +2026-04-13 14:31:03.951808: Current learning rate: 0.00396 +2026-04-13 14:32:45.366405: train_loss -0.3706 +2026-04-13 14:32:45.383367: val_loss -0.3984 +2026-04-13 14:32:45.386675: Pseudo dice [0.1305, 0.0, 0.6555, 0.0, 0.0, 0.7365, 0.6541] +2026-04-13 14:32:45.389900: Epoch time: 101.42 s +2026-04-13 14:32:46.598230: +2026-04-13 14:32:46.601572: Epoch 2572 +2026-04-13 14:32:46.604988: Current learning rate: 0.00396 +2026-04-13 14:34:27.256186: train_loss -0.3883 +2026-04-13 14:34:27.266036: val_loss -0.3251 +2026-04-13 14:34:27.269309: Pseudo dice [0.2879, 0.0, 0.6222, 0.0, 0.0, 0.6422, 0.5434] +2026-04-13 14:34:27.272860: Epoch time: 100.66 s +2026-04-13 14:34:28.528004: +2026-04-13 14:34:28.530238: Epoch 2573 +2026-04-13 14:34:28.532943: Current learning rate: 0.00395 +2026-04-13 14:36:08.614195: train_loss -0.381 +2026-04-13 14:36:08.622083: val_loss -0.3542 +2026-04-13 14:36:08.624343: Pseudo dice [0.186, 0.0, 0.6029, 0.0, 0.0, 0.4077, 0.5981] +2026-04-13 14:36:08.628092: Epoch time: 100.09 s +2026-04-13 14:36:09.916555: +2026-04-13 14:36:09.918650: Epoch 2574 +2026-04-13 14:36:09.920801: Current learning rate: 0.00395 +2026-04-13 14:37:53.437870: train_loss -0.4001 +2026-04-13 14:37:53.447296: val_loss -0.4162 +2026-04-13 14:37:53.451512: Pseudo dice [0.4517, 0.0, 0.7931, 0.3314, 0.0, 0.6169, 0.8268] +2026-04-13 14:37:53.454490: Epoch time: 103.52 s +2026-04-13 14:37:54.720245: +2026-04-13 14:37:54.722741: Epoch 2575 +2026-04-13 14:37:54.727852: Current learning rate: 0.00395 +2026-04-13 14:39:35.331115: train_loss -0.4164 +2026-04-13 14:39:35.338641: val_loss -0.412 +2026-04-13 14:39:35.342616: Pseudo dice [0.554, 0.0, 0.833, 0.8522, 0.0, 0.8413, 0.7163] +2026-04-13 14:39:35.346164: Epoch time: 100.61 s +2026-04-13 14:39:36.557349: +2026-04-13 14:39:36.560078: Epoch 2576 +2026-04-13 14:39:36.562433: Current learning rate: 0.00395 +2026-04-13 14:41:18.398169: train_loss -0.4097 +2026-04-13 14:41:18.405023: val_loss -0.3556 +2026-04-13 14:41:18.406944: Pseudo dice [0.5481, 0.0, 0.528, 0.0, 0.0, 0.8765, 0.7067] +2026-04-13 14:41:18.409446: Epoch time: 101.84 s +2026-04-13 14:41:19.750483: +2026-04-13 14:41:19.753441: Epoch 2577 +2026-04-13 14:41:19.755786: Current learning rate: 0.00394 +2026-04-13 14:43:00.564098: train_loss -0.4103 +2026-04-13 14:43:00.581434: val_loss -0.3713 +2026-04-13 14:43:00.585323: Pseudo dice [0.6205, 0.0, 0.58, 0.0, 0.2013, 0.8183, 0.618] +2026-04-13 14:43:00.596620: Epoch time: 100.82 s +2026-04-13 14:43:01.814595: +2026-04-13 14:43:01.817790: Epoch 2578 +2026-04-13 14:43:01.823145: Current learning rate: 0.00394 +2026-04-13 14:44:43.159485: train_loss -0.401 +2026-04-13 14:44:43.169806: val_loss -0.2444 +2026-04-13 14:44:43.173949: Pseudo dice [0.3119, 0.0, 0.64, 0.0, 0.2213, 0.6904, 0.5502] +2026-04-13 14:44:43.177169: Epoch time: 101.35 s +2026-04-13 14:44:44.401135: +2026-04-13 14:44:44.403594: Epoch 2579 +2026-04-13 14:44:44.406555: Current learning rate: 0.00394 +2026-04-13 14:46:25.686835: train_loss -0.4048 +2026-04-13 14:46:25.693026: val_loss -0.3115 +2026-04-13 14:46:25.695804: Pseudo dice [0.3867, 0.0, 0.7276, 0.071, 0.3327, 0.8001, 0.5798] +2026-04-13 14:46:25.698443: Epoch time: 101.29 s +2026-04-13 14:46:26.936865: +2026-04-13 14:46:26.939483: Epoch 2580 +2026-04-13 14:46:26.941839: Current learning rate: 0.00394 +2026-04-13 14:48:07.395585: train_loss -0.4149 +2026-04-13 14:48:07.402609: val_loss -0.3971 +2026-04-13 14:48:07.405697: Pseudo dice [0.4625, 0.0, 0.6663, 0.7296, 0.2817, 0.7422, 0.7016] +2026-04-13 14:48:07.409158: Epoch time: 100.46 s +2026-04-13 14:48:08.657994: +2026-04-13 14:48:08.660300: Epoch 2581 +2026-04-13 14:48:08.662608: Current learning rate: 0.00393 +2026-04-13 14:49:49.527601: train_loss -0.4219 +2026-04-13 14:49:49.537073: val_loss -0.3147 +2026-04-13 14:49:49.539421: Pseudo dice [0.5145, 0.0, 0.6912, 0.0, 0.2307, 0.8432, 0.6182] +2026-04-13 14:49:49.541881: Epoch time: 100.87 s +2026-04-13 14:49:50.797651: +2026-04-13 14:49:50.799774: Epoch 2582 +2026-04-13 14:49:50.802620: Current learning rate: 0.00393 +2026-04-13 14:51:32.824616: train_loss -0.401 +2026-04-13 14:51:32.833275: val_loss -0.3728 +2026-04-13 14:51:32.835906: Pseudo dice [0.4875, 0.0, 0.6364, 0.6307, 0.5574, 0.231, 0.6885] +2026-04-13 14:51:32.840217: Epoch time: 102.03 s +2026-04-13 14:51:34.073174: +2026-04-13 14:51:34.078936: Epoch 2583 +2026-04-13 14:51:34.082617: Current learning rate: 0.00393 +2026-04-13 14:53:15.496143: train_loss -0.3985 +2026-04-13 14:53:15.504241: val_loss -0.3178 +2026-04-13 14:53:15.506795: Pseudo dice [0.1464, 0.0, 0.697, 0.0918, 0.4153, 0.7402, 0.6724] +2026-04-13 14:53:15.510648: Epoch time: 101.43 s +2026-04-13 14:53:16.730163: +2026-04-13 14:53:16.732472: Epoch 2584 +2026-04-13 14:53:16.734990: Current learning rate: 0.00393 +2026-04-13 14:54:57.311979: train_loss -0.4016 +2026-04-13 14:54:57.318259: val_loss -0.4233 +2026-04-13 14:54:57.320649: Pseudo dice [0.2704, 0.0, 0.7588, 0.9204, 0.6461, 0.2829, 0.7534] +2026-04-13 14:54:57.323197: Epoch time: 100.58 s +2026-04-13 14:54:58.584711: +2026-04-13 14:54:58.586648: Epoch 2585 +2026-04-13 14:54:58.588902: Current learning rate: 0.00392 +2026-04-13 14:56:39.823547: train_loss -0.4076 +2026-04-13 14:56:39.830276: val_loss -0.4052 +2026-04-13 14:56:39.835143: Pseudo dice [0.295, 0.0, 0.6765, 0.8652, 0.4351, 0.8749, 0.7945] +2026-04-13 14:56:39.837417: Epoch time: 101.24 s +2026-04-13 14:56:41.058184: +2026-04-13 14:56:41.061708: Epoch 2586 +2026-04-13 14:56:41.065591: Current learning rate: 0.00392 +2026-04-13 14:58:21.569161: train_loss -0.3866 +2026-04-13 14:58:21.575678: val_loss -0.3695 +2026-04-13 14:58:21.577893: Pseudo dice [0.0, 0.0, 0.7601, 0.1659, 0.271, 0.7359, 0.5907] +2026-04-13 14:58:21.582324: Epoch time: 100.51 s +2026-04-13 14:58:22.783127: +2026-04-13 14:58:22.785151: Epoch 2587 +2026-04-13 14:58:22.787569: Current learning rate: 0.00392 +2026-04-13 15:00:03.696854: train_loss -0.3787 +2026-04-13 15:00:03.705610: val_loss -0.3169 +2026-04-13 15:00:03.708568: Pseudo dice [0.7089, 0.0, 0.4514, 0.0207, 0.5068, 0.4946, 0.756] +2026-04-13 15:00:03.712507: Epoch time: 100.92 s +2026-04-13 15:00:04.962806: +2026-04-13 15:00:04.965481: Epoch 2588 +2026-04-13 15:00:04.968555: Current learning rate: 0.00392 +2026-04-13 15:01:46.691330: train_loss -0.4006 +2026-04-13 15:01:46.702791: val_loss -0.3805 +2026-04-13 15:01:46.705771: Pseudo dice [0.3738, 0.0, 0.6277, 0.0, 0.4418, 0.6472, 0.6809] +2026-04-13 15:01:46.709188: Epoch time: 101.73 s +2026-04-13 15:01:47.946672: +2026-04-13 15:01:47.949013: Epoch 2589 +2026-04-13 15:01:47.951482: Current learning rate: 0.00391 +2026-04-13 15:03:28.502874: train_loss -0.416 +2026-04-13 15:03:28.512234: val_loss -0.3675 +2026-04-13 15:03:28.515336: Pseudo dice [0.4587, 0.0, 0.5977, 0.0791, 0.2994, 0.6226, 0.7227] +2026-04-13 15:03:28.517942: Epoch time: 100.56 s +2026-04-13 15:03:29.763468: +2026-04-13 15:03:29.766651: Epoch 2590 +2026-04-13 15:03:29.771376: Current learning rate: 0.00391 +2026-04-13 15:05:10.833177: train_loss -0.3953 +2026-04-13 15:05:10.839846: val_loss -0.3317 +2026-04-13 15:05:10.843235: Pseudo dice [0.1391, 0.0, 0.6154, 0.0, 0.2861, 0.791, 0.91] +2026-04-13 15:05:10.845568: Epoch time: 101.07 s +2026-04-13 15:05:12.098885: +2026-04-13 15:05:12.101336: Epoch 2591 +2026-04-13 15:05:12.103558: Current learning rate: 0.00391 +2026-04-13 15:06:52.841241: train_loss -0.3879 +2026-04-13 15:06:52.847141: val_loss -0.3506 +2026-04-13 15:06:52.849458: Pseudo dice [0.1446, 0.0, 0.5855, 0.0, 0.0032, 0.7443, 0.5037] +2026-04-13 15:06:52.851952: Epoch time: 100.75 s +2026-04-13 15:06:54.140144: +2026-04-13 15:06:54.142693: Epoch 2592 +2026-04-13 15:06:54.145147: Current learning rate: 0.00391 +2026-04-13 15:08:35.058821: train_loss -0.4053 +2026-04-13 15:08:35.067459: val_loss -0.416 +2026-04-13 15:08:35.069833: Pseudo dice [0.1092, 0.0, 0.7613, 0.0, 0.1422, 0.4665, 0.8299] +2026-04-13 15:08:35.072382: Epoch time: 100.92 s +2026-04-13 15:08:36.321545: +2026-04-13 15:08:36.323507: Epoch 2593 +2026-04-13 15:08:36.325595: Current learning rate: 0.0039 +2026-04-13 15:10:16.952031: train_loss -0.4213 +2026-04-13 15:10:16.958694: val_loss -0.4443 +2026-04-13 15:10:16.961334: Pseudo dice [0.7073, 0.0, 0.8239, 0.0, 0.3535, 0.6562, 0.8755] +2026-04-13 15:10:16.966677: Epoch time: 100.63 s +2026-04-13 15:10:18.242549: +2026-04-13 15:10:18.244638: Epoch 2594 +2026-04-13 15:10:18.246882: Current learning rate: 0.0039 +2026-04-13 15:11:58.918390: train_loss -0.4259 +2026-04-13 15:11:58.926188: val_loss -0.3756 +2026-04-13 15:11:58.930042: Pseudo dice [0.5919, 0.0, 0.767, 0.0, 0.2289, 0.6344, 0.6243] +2026-04-13 15:11:58.932606: Epoch time: 100.68 s +2026-04-13 15:12:01.284981: +2026-04-13 15:12:01.287658: Epoch 2595 +2026-04-13 15:12:01.290000: Current learning rate: 0.0039 +2026-04-13 15:13:42.965250: train_loss -0.3782 +2026-04-13 15:13:42.973778: val_loss -0.3783 +2026-04-13 15:13:42.977382: Pseudo dice [0.6411, 0.0, 0.7818, 0.0, 0.2904, 0.5235, 0.4675] +2026-04-13 15:13:42.982287: Epoch time: 101.68 s +2026-04-13 15:13:44.271197: +2026-04-13 15:13:44.273147: Epoch 2596 +2026-04-13 15:13:44.275595: Current learning rate: 0.0039 +2026-04-13 15:15:25.251053: train_loss -0.4033 +2026-04-13 15:15:25.258945: val_loss -0.3645 +2026-04-13 15:15:25.262157: Pseudo dice [0.7348, 0.0, 0.6999, 0.0, 0.0913, 0.6327, 0.8367] +2026-04-13 15:15:25.268372: Epoch time: 100.98 s +2026-04-13 15:15:26.520769: +2026-04-13 15:15:26.522767: Epoch 2597 +2026-04-13 15:15:26.524816: Current learning rate: 0.00389 +2026-04-13 15:17:07.444039: train_loss -0.4074 +2026-04-13 15:17:07.452547: val_loss -0.3459 +2026-04-13 15:17:07.456527: Pseudo dice [0.0, 0.0, 0.7401, 0.0819, 0.1085, 0.6571, 0.7313] +2026-04-13 15:17:07.460390: Epoch time: 100.93 s +2026-04-13 15:17:08.728521: +2026-04-13 15:17:08.732421: Epoch 2598 +2026-04-13 15:17:08.736080: Current learning rate: 0.00389 +2026-04-13 15:18:48.989636: train_loss -0.4096 +2026-04-13 15:18:48.997140: val_loss -0.3648 +2026-04-13 15:18:49.000830: Pseudo dice [0.4306, 0.0, 0.5781, 0.0, 0.3237, 0.8049, 0.8717] +2026-04-13 15:18:49.004758: Epoch time: 100.26 s +2026-04-13 15:18:50.243788: +2026-04-13 15:18:50.245864: Epoch 2599 +2026-04-13 15:18:50.248103: Current learning rate: 0.00389 +2026-04-13 15:20:30.924561: train_loss -0.3842 +2026-04-13 15:20:30.932029: val_loss -0.3895 +2026-04-13 15:20:30.934228: Pseudo dice [0.3919, 0.0, 0.5696, 0.0, 0.0, 0.5162, 0.2282] +2026-04-13 15:20:30.937122: Epoch time: 100.68 s +2026-04-13 15:20:33.856125: +2026-04-13 15:20:33.858989: Epoch 2600 +2026-04-13 15:20:33.861580: Current learning rate: 0.00389 +2026-04-13 15:22:14.763797: train_loss -0.3818 +2026-04-13 15:22:14.770758: val_loss -0.3996 +2026-04-13 15:22:14.773509: Pseudo dice [0.5004, 0.0, 0.5433, 0.2287, 0.104, 0.8047, 0.8938] +2026-04-13 15:22:14.776628: Epoch time: 100.91 s +2026-04-13 15:22:16.035591: +2026-04-13 15:22:16.037300: Epoch 2601 +2026-04-13 15:22:16.039413: Current learning rate: 0.00388 +2026-04-13 15:23:56.344752: train_loss -0.4059 +2026-04-13 15:23:56.351091: val_loss -0.3945 +2026-04-13 15:23:56.353414: Pseudo dice [0.5163, 0.0, 0.6271, 0.4015, 0.4408, 0.7353, 0.5622] +2026-04-13 15:23:56.356509: Epoch time: 100.31 s +2026-04-13 15:23:57.573535: +2026-04-13 15:23:57.577501: Epoch 2602 +2026-04-13 15:23:57.580169: Current learning rate: 0.00388 +2026-04-13 15:25:39.073005: train_loss -0.3953 +2026-04-13 15:25:39.080143: val_loss -0.3766 +2026-04-13 15:25:39.083239: Pseudo dice [0.5721, 0.0, 0.6607, 0.2019, 0.2061, 0.7972, 0.7232] +2026-04-13 15:25:39.085574: Epoch time: 101.5 s +2026-04-13 15:25:40.313245: +2026-04-13 15:25:40.315050: Epoch 2603 +2026-04-13 15:25:40.317174: Current learning rate: 0.00388 +2026-04-13 15:27:20.671592: train_loss -0.3926 +2026-04-13 15:27:20.677553: val_loss -0.3647 +2026-04-13 15:27:20.680087: Pseudo dice [0.5669, 0.0, 0.6353, 0.361, 0.4681, 0.7523, 0.6267] +2026-04-13 15:27:20.683163: Epoch time: 100.36 s +2026-04-13 15:27:21.922295: +2026-04-13 15:27:21.924837: Epoch 2604 +2026-04-13 15:27:21.927410: Current learning rate: 0.00388 +2026-04-13 15:29:02.798121: train_loss -0.3922 +2026-04-13 15:29:02.804925: val_loss -0.2546 +2026-04-13 15:29:02.807418: Pseudo dice [0.2253, 0.0, 0.5323, 0.0, 0.0, 0.7491, 0.6288] +2026-04-13 15:29:02.809560: Epoch time: 100.88 s +2026-04-13 15:29:04.031883: +2026-04-13 15:29:04.033888: Epoch 2605 +2026-04-13 15:29:04.036152: Current learning rate: 0.00387 +2026-04-13 15:30:45.056500: train_loss -0.3914 +2026-04-13 15:30:45.064272: val_loss -0.4129 +2026-04-13 15:30:45.066457: Pseudo dice [0.7185, 0.0, 0.5778, 0.0001, 0.37, 0.7089, 0.8586] +2026-04-13 15:30:45.069304: Epoch time: 101.03 s +2026-04-13 15:30:46.328681: +2026-04-13 15:30:46.330956: Epoch 2606 +2026-04-13 15:30:46.334156: Current learning rate: 0.00387 +2026-04-13 15:32:26.904175: train_loss -0.3944 +2026-04-13 15:32:26.910596: val_loss -0.4125 +2026-04-13 15:32:26.913085: Pseudo dice [0.4127, 0.0, 0.7835, 0.0, 0.4381, 0.7368, 0.7596] +2026-04-13 15:32:26.916236: Epoch time: 100.58 s +2026-04-13 15:32:28.144835: +2026-04-13 15:32:28.146902: Epoch 2607 +2026-04-13 15:32:28.149415: Current learning rate: 0.00387 +2026-04-13 15:34:08.534358: train_loss -0.4057 +2026-04-13 15:34:08.544215: val_loss -0.3594 +2026-04-13 15:34:08.548214: Pseudo dice [0.0253, 0.0, 0.73, 0.0, 0.4806, 0.5946, 0.8266] +2026-04-13 15:34:08.553877: Epoch time: 100.39 s +2026-04-13 15:34:09.778430: +2026-04-13 15:34:09.780740: Epoch 2608 +2026-04-13 15:34:09.783246: Current learning rate: 0.00387 +2026-04-13 15:35:50.233145: train_loss -0.3967 +2026-04-13 15:35:50.240350: val_loss -0.3876 +2026-04-13 15:35:50.242325: Pseudo dice [0.5426, 0.0, 0.6221, 0.0472, 0.1303, 0.6169, 0.5976] +2026-04-13 15:35:50.244433: Epoch time: 100.46 s +2026-04-13 15:35:51.635910: +2026-04-13 15:35:51.638075: Epoch 2609 +2026-04-13 15:35:51.641174: Current learning rate: 0.00386 +2026-04-13 15:37:32.831850: train_loss -0.3922 +2026-04-13 15:37:32.838100: val_loss -0.3899 +2026-04-13 15:37:32.841266: Pseudo dice [0.5361, 0.0, 0.77, 0.0, 0.0, 0.8436, 0.8422] +2026-04-13 15:37:32.843807: Epoch time: 101.2 s +2026-04-13 15:37:34.063828: +2026-04-13 15:37:34.065979: Epoch 2610 +2026-04-13 15:37:34.068776: Current learning rate: 0.00386 +2026-04-13 15:39:14.629396: train_loss -0.4057 +2026-04-13 15:39:14.636863: val_loss -0.4163 +2026-04-13 15:39:14.639490: Pseudo dice [0.4236, 0.0, 0.7721, 0.0, 0.0476, 0.8078, 0.8377] +2026-04-13 15:39:14.642395: Epoch time: 100.57 s +2026-04-13 15:39:15.843392: +2026-04-13 15:39:15.845381: Epoch 2611 +2026-04-13 15:39:15.848629: Current learning rate: 0.00386 +2026-04-13 15:40:55.976629: train_loss -0.3876 +2026-04-13 15:40:55.984452: val_loss -0.3896 +2026-04-13 15:40:55.988392: Pseudo dice [0.0, 0.0, 0.6921, 0.5658, 0.2027, 0.5228, 0.6366] +2026-04-13 15:40:55.991756: Epoch time: 100.14 s +2026-04-13 15:40:57.211155: +2026-04-13 15:40:57.213158: Epoch 2612 +2026-04-13 15:40:57.215086: Current learning rate: 0.00386 +2026-04-13 15:42:38.086341: train_loss -0.3866 +2026-04-13 15:42:38.094380: val_loss -0.3956 +2026-04-13 15:42:38.098226: Pseudo dice [0.582, 0.0, 0.792, 0.0, 0.0, 0.5451, 0.8376] +2026-04-13 15:42:38.101515: Epoch time: 100.88 s +2026-04-13 15:42:39.349712: +2026-04-13 15:42:39.351401: Epoch 2613 +2026-04-13 15:42:39.353559: Current learning rate: 0.00385 +2026-04-13 15:44:20.055799: train_loss -0.4105 +2026-04-13 15:44:20.062442: val_loss -0.3821 +2026-04-13 15:44:20.064738: Pseudo dice [0.6047, 0.0, 0.4538, 0.3027, 0.3322, 0.8311, 0.6305] +2026-04-13 15:44:20.067258: Epoch time: 100.71 s +2026-04-13 15:44:21.270057: +2026-04-13 15:44:21.272194: Epoch 2614 +2026-04-13 15:44:21.275195: Current learning rate: 0.00385 +2026-04-13 15:46:02.480972: train_loss -0.4318 +2026-04-13 15:46:02.488326: val_loss -0.4261 +2026-04-13 15:46:02.491366: Pseudo dice [0.5332, 0.0, 0.8264, 0.0557, 0.4938, 0.6824, 0.7463] +2026-04-13 15:46:02.495117: Epoch time: 101.21 s +2026-04-13 15:46:03.716419: +2026-04-13 15:46:03.720121: Epoch 2615 +2026-04-13 15:46:03.722564: Current learning rate: 0.00385 +2026-04-13 15:47:44.147521: train_loss -0.4408 +2026-04-13 15:47:44.153547: val_loss -0.3255 +2026-04-13 15:47:44.156005: Pseudo dice [0.5557, 0.0, 0.4096, 0.0447, 0.4436, 0.7077, 0.687] +2026-04-13 15:47:44.158191: Epoch time: 100.43 s +2026-04-13 15:47:45.379737: +2026-04-13 15:47:45.381738: Epoch 2616 +2026-04-13 15:47:45.384078: Current learning rate: 0.00385 +2026-04-13 15:49:25.906254: train_loss -0.4324 +2026-04-13 15:49:25.923609: val_loss -0.3135 +2026-04-13 15:49:25.925808: Pseudo dice [0.7146, 0.0, 0.6172, 0.0, 0.3299, 0.7666, 0.6523] +2026-04-13 15:49:25.928367: Epoch time: 100.53 s +2026-04-13 15:49:27.133459: +2026-04-13 15:49:27.136085: Epoch 2617 +2026-04-13 15:49:27.138149: Current learning rate: 0.00384 +2026-04-13 15:51:07.242207: train_loss -0.419 +2026-04-13 15:51:07.248804: val_loss -0.3871 +2026-04-13 15:51:07.253114: Pseudo dice [0.2896, 0.0, 0.6298, 0.7397, 0.2257, 0.6604, 0.8255] +2026-04-13 15:51:07.257685: Epoch time: 100.11 s +2026-04-13 15:51:08.450845: +2026-04-13 15:51:08.453086: Epoch 2618 +2026-04-13 15:51:08.455212: Current learning rate: 0.00384 +2026-04-13 15:52:49.003001: train_loss -0.427 +2026-04-13 15:52:49.009742: val_loss -0.4042 +2026-04-13 15:52:49.012712: Pseudo dice [0.6075, 0.0, 0.756, 0.747, 0.2282, 0.6108, 0.6733] +2026-04-13 15:52:49.015712: Epoch time: 100.56 s +2026-04-13 15:52:50.227334: +2026-04-13 15:52:50.229569: Epoch 2619 +2026-04-13 15:52:50.232215: Current learning rate: 0.00384 +2026-04-13 15:54:31.313567: train_loss -0.4207 +2026-04-13 15:54:31.321801: val_loss -0.3843 +2026-04-13 15:54:31.324457: Pseudo dice [0.7123, 0.0, 0.7147, 0.8287, 0.363, 0.7137, 0.7687] +2026-04-13 15:54:31.327543: Epoch time: 101.09 s +2026-04-13 15:54:32.586116: +2026-04-13 15:54:32.588421: Epoch 2620 +2026-04-13 15:54:32.592421: Current learning rate: 0.00384 +2026-04-13 15:56:13.820298: train_loss -0.4242 +2026-04-13 15:56:13.827273: val_loss -0.3896 +2026-04-13 15:56:13.831075: Pseudo dice [0.6915, 0.0, 0.7671, 0.0, 0.5411, 0.7082, 0.7682] +2026-04-13 15:56:13.833638: Epoch time: 101.24 s +2026-04-13 15:56:15.049768: +2026-04-13 15:56:15.052088: Epoch 2621 +2026-04-13 15:56:15.054768: Current learning rate: 0.00383 +2026-04-13 15:57:55.272199: train_loss -0.405 +2026-04-13 15:57:55.282063: val_loss -0.3622 +2026-04-13 15:57:55.284758: Pseudo dice [0.5108, 0.0, 0.5842, 0.0, 0.3837, 0.752, 0.7403] +2026-04-13 15:57:55.288385: Epoch time: 100.23 s +2026-04-13 15:57:56.519284: +2026-04-13 15:57:56.521649: Epoch 2622 +2026-04-13 15:57:56.536835: Current learning rate: 0.00383 +2026-04-13 15:59:37.045154: train_loss -0.4278 +2026-04-13 15:59:37.053182: val_loss -0.3836 +2026-04-13 15:59:37.056004: Pseudo dice [0.4171, 0.0, 0.8468, 0.0907, 0.6168, 0.7387, 0.8269] +2026-04-13 15:59:37.058546: Epoch time: 100.53 s +2026-04-13 15:59:38.293846: +2026-04-13 15:59:38.296346: Epoch 2623 +2026-04-13 15:59:38.299438: Current learning rate: 0.00383 +2026-04-13 16:01:18.465310: train_loss -0.43 +2026-04-13 16:01:18.479531: val_loss -0.3956 +2026-04-13 16:01:18.482843: Pseudo dice [0.4818, 0.0, 0.7382, 0.0881, 0.2674, 0.6745, 0.7124] +2026-04-13 16:01:18.489448: Epoch time: 100.17 s +2026-04-13 16:01:19.699707: +2026-04-13 16:01:19.701983: Epoch 2624 +2026-04-13 16:01:19.704359: Current learning rate: 0.00383 +2026-04-13 16:03:00.186030: train_loss -0.4126 +2026-04-13 16:03:00.192531: val_loss -0.4074 +2026-04-13 16:03:00.196023: Pseudo dice [0.0, 0.0, 0.7856, 0.3494, 0.2262, 0.7034, 0.7363] +2026-04-13 16:03:00.198298: Epoch time: 100.49 s +2026-04-13 16:03:01.443502: +2026-04-13 16:03:01.446779: Epoch 2625 +2026-04-13 16:03:01.449047: Current learning rate: 0.00382 +2026-04-13 16:04:41.898347: train_loss -0.4019 +2026-04-13 16:04:41.910906: val_loss -0.3696 +2026-04-13 16:04:41.913870: Pseudo dice [0.0, 0.0, 0.6677, 0.0, 0.2857, 0.2247, 0.849] +2026-04-13 16:04:41.916705: Epoch time: 100.46 s +2026-04-13 16:04:43.135079: +2026-04-13 16:04:43.137007: Epoch 2626 +2026-04-13 16:04:43.139298: Current learning rate: 0.00382 +2026-04-13 16:06:23.309273: train_loss -0.4037 +2026-04-13 16:06:23.318491: val_loss -0.4069 +2026-04-13 16:06:23.321317: Pseudo dice [0.6015, 0.0, 0.6918, 0.4384, 0.5245, 0.2323, 0.7861] +2026-04-13 16:06:23.324539: Epoch time: 100.18 s +2026-04-13 16:06:24.568038: +2026-04-13 16:06:24.570282: Epoch 2627 +2026-04-13 16:06:24.572334: Current learning rate: 0.00382 +2026-04-13 16:08:05.046054: train_loss -0.4102 +2026-04-13 16:08:05.052810: val_loss -0.3346 +2026-04-13 16:08:05.054919: Pseudo dice [0.4158, 0.0, 0.6886, 0.0548, 0.5518, 0.8678, 0.6734] +2026-04-13 16:08:05.058241: Epoch time: 100.48 s +2026-04-13 16:08:06.264356: +2026-04-13 16:08:06.267622: Epoch 2628 +2026-04-13 16:08:06.269943: Current learning rate: 0.00382 +2026-04-13 16:09:46.739149: train_loss -0.413 +2026-04-13 16:09:46.745556: val_loss -0.3495 +2026-04-13 16:09:46.747968: Pseudo dice [0.6572, 0.0, 0.5485, 0.0723, 0.3161, 0.8243, 0.6683] +2026-04-13 16:09:46.750362: Epoch time: 100.48 s +2026-04-13 16:09:47.977186: +2026-04-13 16:09:47.978971: Epoch 2629 +2026-04-13 16:09:47.981489: Current learning rate: 0.00381 +2026-04-13 16:11:29.223492: train_loss -0.4232 +2026-04-13 16:11:29.229961: val_loss -0.3301 +2026-04-13 16:11:29.232301: Pseudo dice [0.4249, 0.0, 0.7251, 0.0, 0.3728, 0.7851, 0.6405] +2026-04-13 16:11:29.234837: Epoch time: 101.25 s +2026-04-13 16:11:30.468299: +2026-04-13 16:11:30.476936: Epoch 2630 +2026-04-13 16:11:30.480244: Current learning rate: 0.00381 +2026-04-13 16:13:10.937184: train_loss -0.424 +2026-04-13 16:13:10.946568: val_loss -0.3468 +2026-04-13 16:13:10.949590: Pseudo dice [0.7496, 0.0, 0.6679, 0.0, 0.3833, 0.7089, 0.7704] +2026-04-13 16:13:10.952672: Epoch time: 100.47 s +2026-04-13 16:13:12.184552: +2026-04-13 16:13:12.186958: Epoch 2631 +2026-04-13 16:13:12.190299: Current learning rate: 0.00381 +2026-04-13 16:14:52.702523: train_loss -0.4236 +2026-04-13 16:14:52.710760: val_loss -0.3727 +2026-04-13 16:14:52.713027: Pseudo dice [0.7064, 0.0, 0.5991, 0.0745, 0.2047, 0.8013, 0.5517] +2026-04-13 16:14:52.716090: Epoch time: 100.52 s +2026-04-13 16:14:53.911079: +2026-04-13 16:14:53.914680: Epoch 2632 +2026-04-13 16:14:53.920805: Current learning rate: 0.00381 +2026-04-13 16:16:33.998669: train_loss -0.4067 +2026-04-13 16:16:34.006200: val_loss -0.3421 +2026-04-13 16:16:34.008926: Pseudo dice [0.1068, 0.0, 0.6774, 0.4685, 0.3384, 0.4653, 0.2105] +2026-04-13 16:16:34.012246: Epoch time: 100.09 s +2026-04-13 16:16:35.233673: +2026-04-13 16:16:35.235939: Epoch 2633 +2026-04-13 16:16:35.238856: Current learning rate: 0.0038 +2026-04-13 16:18:15.742603: train_loss -0.4044 +2026-04-13 16:18:15.751167: val_loss -0.3154 +2026-04-13 16:18:15.755552: Pseudo dice [0.0009, 0.0, 0.6693, 0.0, 0.1641, 0.6774, 0.6048] +2026-04-13 16:18:15.758948: Epoch time: 100.51 s +2026-04-13 16:18:16.998223: +2026-04-13 16:18:17.000748: Epoch 2634 +2026-04-13 16:18:17.004447: Current learning rate: 0.0038 +2026-04-13 16:19:58.652856: train_loss -0.3905 +2026-04-13 16:19:58.660372: val_loss -0.3732 +2026-04-13 16:19:58.662629: Pseudo dice [0.4428, 0.0, 0.5746, 0.0, 0.2286, 0.7107, 0.7] +2026-04-13 16:19:58.665709: Epoch time: 101.66 s +2026-04-13 16:19:59.893482: +2026-04-13 16:19:59.898727: Epoch 2635 +2026-04-13 16:19:59.904072: Current learning rate: 0.0038 +2026-04-13 16:21:41.220095: train_loss -0.4073 +2026-04-13 16:21:41.228753: val_loss -0.405 +2026-04-13 16:21:41.231873: Pseudo dice [0.471, 0.0, 0.7773, 0.7433, 0.4092, 0.6689, 0.6807] +2026-04-13 16:21:41.234941: Epoch time: 101.33 s +2026-04-13 16:21:42.448269: +2026-04-13 16:21:42.453311: Epoch 2636 +2026-04-13 16:21:42.458068: Current learning rate: 0.0038 +2026-04-13 16:23:23.688896: train_loss -0.4076 +2026-04-13 16:23:23.696239: val_loss -0.3733 +2026-04-13 16:23:23.699014: Pseudo dice [0.0, 0.0, 0.7689, 0.6529, 0.3598, 0.7039, 0.5936] +2026-04-13 16:23:23.702933: Epoch time: 101.24 s +2026-04-13 16:23:24.926037: +2026-04-13 16:23:24.929151: Epoch 2637 +2026-04-13 16:23:24.931559: Current learning rate: 0.00379 +2026-04-13 16:25:05.011204: train_loss -0.3988 +2026-04-13 16:25:05.017885: val_loss -0.3943 +2026-04-13 16:25:05.020602: Pseudo dice [0.332, 0.0, 0.6482, 0.0, 0.0, 0.7212, 0.6602] +2026-04-13 16:25:05.023680: Epoch time: 100.09 s +2026-04-13 16:25:06.208417: +2026-04-13 16:25:06.212194: Epoch 2638 +2026-04-13 16:25:06.214488: Current learning rate: 0.00379 +2026-04-13 16:26:46.823338: train_loss -0.3508 +2026-04-13 16:26:46.838886: val_loss -0.3423 +2026-04-13 16:26:46.841506: Pseudo dice [0.2581, 0.0, 0.4302, 0.0, 0.0779, 0.4035, 0.354] +2026-04-13 16:26:46.844297: Epoch time: 100.62 s +2026-04-13 16:26:48.073930: +2026-04-13 16:26:48.075962: Epoch 2639 +2026-04-13 16:26:48.078135: Current learning rate: 0.00379 +2026-04-13 16:28:28.583036: train_loss -0.3676 +2026-04-13 16:28:28.597838: val_loss -0.3693 +2026-04-13 16:28:28.601875: Pseudo dice [0.4499, 0.0, 0.6919, 0.0, 0.347, 0.7683, 0.5814] +2026-04-13 16:28:28.604331: Epoch time: 100.51 s +2026-04-13 16:28:29.827019: +2026-04-13 16:28:29.829222: Epoch 2640 +2026-04-13 16:28:29.831363: Current learning rate: 0.00379 +2026-04-13 16:30:10.070146: train_loss -0.4202 +2026-04-13 16:30:10.075618: val_loss -0.3745 +2026-04-13 16:30:10.077581: Pseudo dice [0.3916, 0.0, 0.7314, 0.0, 0.3412, 0.818, 0.7237] +2026-04-13 16:30:10.079623: Epoch time: 100.25 s +2026-04-13 16:30:11.312357: +2026-04-13 16:30:11.314250: Epoch 2641 +2026-04-13 16:30:11.317919: Current learning rate: 0.00378 +2026-04-13 16:31:51.426168: train_loss -0.4385 +2026-04-13 16:31:51.432370: val_loss -0.4207 +2026-04-13 16:31:51.434951: Pseudo dice [0.7695, 0.0, 0.5132, 0.5337, 0.4727, 0.7503, 0.8086] +2026-04-13 16:31:51.437517: Epoch time: 100.12 s +2026-04-13 16:31:52.640024: +2026-04-13 16:31:52.642088: Epoch 2642 +2026-04-13 16:31:52.643926: Current learning rate: 0.00378 +2026-04-13 16:33:32.844096: train_loss -0.4432 +2026-04-13 16:33:32.851329: val_loss -0.3724 +2026-04-13 16:33:32.857111: Pseudo dice [0.628, 0.0, 0.6909, 0.0496, 0.5172, 0.8241, 0.4488] +2026-04-13 16:33:32.860908: Epoch time: 100.21 s +2026-04-13 16:33:34.079466: +2026-04-13 16:33:34.081836: Epoch 2643 +2026-04-13 16:33:34.084268: Current learning rate: 0.00378 +2026-04-13 16:35:14.049538: train_loss -0.4401 +2026-04-13 16:35:14.056139: val_loss -0.4085 +2026-04-13 16:35:14.058810: Pseudo dice [0.661, 0.0, 0.7281, 0.2159, 0.5559, 0.7406, 0.7674] +2026-04-13 16:35:14.061203: Epoch time: 99.97 s +2026-04-13 16:35:15.290487: +2026-04-13 16:35:15.293088: Epoch 2644 +2026-04-13 16:35:15.294679: Current learning rate: 0.00378 +2026-04-13 16:36:55.408390: train_loss -0.425 +2026-04-13 16:36:55.415414: val_loss -0.4112 +2026-04-13 16:36:55.417643: Pseudo dice [0.0, 0.0, 0.7666, 0.674, 0.342, 0.7665, 0.7713] +2026-04-13 16:36:55.420254: Epoch time: 100.12 s +2026-04-13 16:36:56.642737: +2026-04-13 16:36:56.644559: Epoch 2645 +2026-04-13 16:36:56.646338: Current learning rate: 0.00377 +2026-04-13 16:38:36.910886: train_loss -0.4332 +2026-04-13 16:38:36.916220: val_loss -0.3256 +2026-04-13 16:38:36.918066: Pseudo dice [0.3817, 0.0, 0.7432, 0.0, 0.348, 0.8098, 0.1319] +2026-04-13 16:38:36.920715: Epoch time: 100.27 s +2026-04-13 16:38:38.141366: +2026-04-13 16:38:38.143385: Epoch 2646 +2026-04-13 16:38:38.145192: Current learning rate: 0.00377 +2026-04-13 16:40:18.095606: train_loss -0.4094 +2026-04-13 16:40:18.104728: val_loss -0.3844 +2026-04-13 16:40:18.109956: Pseudo dice [0.5124, 0.0, 0.6174, 0.0, 0.0, 0.7221, 0.6436] +2026-04-13 16:40:18.113965: Epoch time: 99.96 s +2026-04-13 16:40:19.319707: +2026-04-13 16:40:19.322602: Epoch 2647 +2026-04-13 16:40:19.324732: Current learning rate: 0.00377 +2026-04-13 16:41:59.725185: train_loss -0.4043 +2026-04-13 16:41:59.737462: val_loss -0.3715 +2026-04-13 16:41:59.739837: Pseudo dice [0.7521, 0.0, 0.6002, 0.1134, 0.3125, 0.8202, 0.6573] +2026-04-13 16:41:59.742013: Epoch time: 100.41 s +2026-04-13 16:42:00.954195: +2026-04-13 16:42:00.956435: Epoch 2648 +2026-04-13 16:42:00.958135: Current learning rate: 0.00377 +2026-04-13 16:43:41.534463: train_loss -0.3904 +2026-04-13 16:43:41.541353: val_loss -0.405 +2026-04-13 16:43:41.543243: Pseudo dice [0.5542, 0.0, 0.6668, 0.2429, 0.4063, 0.8325, 0.6098] +2026-04-13 16:43:41.545700: Epoch time: 100.58 s +2026-04-13 16:43:42.776233: +2026-04-13 16:43:42.778594: Epoch 2649 +2026-04-13 16:43:42.780654: Current learning rate: 0.00376 +2026-04-13 16:45:23.290500: train_loss -0.4269 +2026-04-13 16:45:23.298735: val_loss -0.4196 +2026-04-13 16:45:23.300802: Pseudo dice [0.6586, 0.0, 0.7328, 0.0, 0.4669, 0.7303, 0.736] +2026-04-13 16:45:23.304971: Epoch time: 100.52 s +2026-04-13 16:45:26.256909: +2026-04-13 16:45:26.259142: Epoch 2650 +2026-04-13 16:45:26.261189: Current learning rate: 0.00376 +2026-04-13 16:47:06.436016: train_loss -0.434 +2026-04-13 16:47:06.442580: val_loss -0.3345 +2026-04-13 16:47:06.444696: Pseudo dice [0.6246, 0.0, 0.6464, 0.0531, 0.2864, 0.7267, 0.5627] +2026-04-13 16:47:06.447395: Epoch time: 100.18 s +2026-04-13 16:47:07.666023: +2026-04-13 16:47:07.668226: Epoch 2651 +2026-04-13 16:47:07.670208: Current learning rate: 0.00376 +2026-04-13 16:48:48.040867: train_loss -0.4274 +2026-04-13 16:48:48.049032: val_loss -0.363 +2026-04-13 16:48:48.052193: Pseudo dice [0.7514, 0.0, 0.7497, 0.0613, 0.4675, 0.8402, 0.8535] +2026-04-13 16:48:48.056138: Epoch time: 100.38 s +2026-04-13 16:48:49.255613: +2026-04-13 16:48:49.259425: Epoch 2652 +2026-04-13 16:48:49.262511: Current learning rate: 0.00376 +2026-04-13 16:50:29.522557: train_loss -0.4456 +2026-04-13 16:50:29.530081: val_loss -0.4339 +2026-04-13 16:50:29.533969: Pseudo dice [0.4728, 0.0, 0.8073, 0.5438, 0.5491, 0.7168, 0.8091] +2026-04-13 16:50:29.536530: Epoch time: 100.27 s +2026-04-13 16:50:30.736334: +2026-04-13 16:50:30.738084: Epoch 2653 +2026-04-13 16:50:30.739815: Current learning rate: 0.00375 +2026-04-13 16:52:11.349732: train_loss -0.4101 +2026-04-13 16:52:11.357920: val_loss -0.3321 +2026-04-13 16:52:11.360250: Pseudo dice [0.0, 0.0, 0.3642, 0.0671, 0.4391, 0.3167, 0.5275] +2026-04-13 16:52:11.363014: Epoch time: 100.62 s +2026-04-13 16:52:13.745561: +2026-04-13 16:52:13.748624: Epoch 2654 +2026-04-13 16:52:13.751207: Current learning rate: 0.00375 +2026-04-13 16:53:54.045291: train_loss -0.4151 +2026-04-13 16:53:54.052070: val_loss -0.3677 +2026-04-13 16:53:54.054357: Pseudo dice [0.7387, 0.0, 0.7238, 0.0616, 0.4536, 0.7763, 0.8332] +2026-04-13 16:53:54.057184: Epoch time: 100.3 s +2026-04-13 16:53:55.294979: +2026-04-13 16:53:55.299115: Epoch 2655 +2026-04-13 16:53:55.301603: Current learning rate: 0.00375 +2026-04-13 16:55:36.113070: train_loss -0.4259 +2026-04-13 16:55:36.120260: val_loss -0.3976 +2026-04-13 16:55:36.122522: Pseudo dice [0.7493, 0.0, 0.7613, 0.7622, 0.3753, 0.7151, 0.6321] +2026-04-13 16:55:36.125017: Epoch time: 100.82 s +2026-04-13 16:55:37.369100: +2026-04-13 16:55:37.371290: Epoch 2656 +2026-04-13 16:55:37.373205: Current learning rate: 0.00375 +2026-04-13 16:57:18.491063: train_loss -0.424 +2026-04-13 16:57:18.498937: val_loss -0.385 +2026-04-13 16:57:18.501597: Pseudo dice [0.4495, 0.0, 0.5628, 0.0, 0.3155, 0.7778, 0.7947] +2026-04-13 16:57:18.504869: Epoch time: 101.12 s +2026-04-13 16:57:19.772518: +2026-04-13 16:57:19.775088: Epoch 2657 +2026-04-13 16:57:19.777017: Current learning rate: 0.00374 +2026-04-13 16:59:00.234448: train_loss -0.3883 +2026-04-13 16:59:00.244084: val_loss -0.374 +2026-04-13 16:59:00.246694: Pseudo dice [0.5808, 0.0, 0.6545, 0.0, 0.4868, 0.8159, 0.4713] +2026-04-13 16:59:00.248929: Epoch time: 100.46 s +2026-04-13 16:59:01.483588: +2026-04-13 16:59:01.485722: Epoch 2658 +2026-04-13 16:59:01.488111: Current learning rate: 0.00374 +2026-04-13 17:00:41.837221: train_loss -0.3935 +2026-04-13 17:00:41.851176: val_loss -0.4254 +2026-04-13 17:00:41.853440: Pseudo dice [0.2395, 0.0, 0.8014, 0.4019, 0.3876, 0.8962, 0.8183] +2026-04-13 17:00:41.856533: Epoch time: 100.36 s +2026-04-13 17:00:43.126361: +2026-04-13 17:00:43.128984: Epoch 2659 +2026-04-13 17:00:43.131102: Current learning rate: 0.00374 +2026-04-13 17:02:24.308941: train_loss -0.4239 +2026-04-13 17:02:24.325972: val_loss -0.4075 +2026-04-13 17:02:24.333257: Pseudo dice [0.3426, 0.0, 0.5503, 0.6671, 0.4952, 0.7281, 0.7426] +2026-04-13 17:02:24.336859: Epoch time: 101.19 s +2026-04-13 17:02:25.613329: +2026-04-13 17:02:25.619343: Epoch 2660 +2026-04-13 17:02:25.623448: Current learning rate: 0.00374 +2026-04-13 17:04:05.854154: train_loss -0.4282 +2026-04-13 17:04:05.864089: val_loss -0.3802 +2026-04-13 17:04:05.868011: Pseudo dice [0.6898, 0.0, 0.6262, 0.5608, 0.2298, 0.8552, 0.7535] +2026-04-13 17:04:05.870840: Epoch time: 100.24 s +2026-04-13 17:04:07.088821: +2026-04-13 17:04:07.090545: Epoch 2661 +2026-04-13 17:04:07.093923: Current learning rate: 0.00373 +2026-04-13 17:05:47.703036: train_loss -0.4237 +2026-04-13 17:05:47.710015: val_loss -0.4079 +2026-04-13 17:05:47.712266: Pseudo dice [0.1426, 0.0, 0.7915, 0.0, 0.2926, 0.8167, 0.7506] +2026-04-13 17:05:47.714949: Epoch time: 100.62 s +2026-04-13 17:05:48.933730: +2026-04-13 17:05:48.935823: Epoch 2662 +2026-04-13 17:05:48.937719: Current learning rate: 0.00373 +2026-04-13 17:07:28.964902: train_loss -0.4274 +2026-04-13 17:07:28.972729: val_loss -0.3422 +2026-04-13 17:07:28.975158: Pseudo dice [0.0, 0.0, 0.674, 0.043, 0.4705, 0.68, 0.7481] +2026-04-13 17:07:28.979156: Epoch time: 100.03 s +2026-04-13 17:07:30.223880: +2026-04-13 17:07:30.227499: Epoch 2663 +2026-04-13 17:07:30.230194: Current learning rate: 0.00373 +2026-04-13 17:09:11.655513: train_loss -0.4276 +2026-04-13 17:09:11.663840: val_loss -0.3693 +2026-04-13 17:09:11.667596: Pseudo dice [0.4981, 0.0, 0.7585, 0.2422, 0.525, 0.836, 0.7624] +2026-04-13 17:09:11.672905: Epoch time: 101.43 s +2026-04-13 17:09:12.912199: +2026-04-13 17:09:12.914305: Epoch 2664 +2026-04-13 17:09:12.916589: Current learning rate: 0.00373 +2026-04-13 17:10:53.162266: train_loss -0.4319 +2026-04-13 17:10:53.169268: val_loss -0.4146 +2026-04-13 17:10:53.171961: Pseudo dice [0.7335, 0.0, 0.6199, 0.0952, 0.4309, 0.8384, 0.7568] +2026-04-13 17:10:53.174298: Epoch time: 100.25 s +2026-04-13 17:10:54.415514: +2026-04-13 17:10:54.417479: Epoch 2665 +2026-04-13 17:10:54.419346: Current learning rate: 0.00372 +2026-04-13 17:12:34.393256: train_loss -0.413 +2026-04-13 17:12:34.401093: val_loss -0.4236 +2026-04-13 17:12:34.403151: Pseudo dice [0.0, 0.0, 0.6996, 0.5116, 0.439, 0.8423, 0.834] +2026-04-13 17:12:34.405576: Epoch time: 99.98 s +2026-04-13 17:12:35.622280: +2026-04-13 17:12:35.624019: Epoch 2666 +2026-04-13 17:12:35.625903: Current learning rate: 0.00372 +2026-04-13 17:14:16.410513: train_loss -0.4177 +2026-04-13 17:14:16.416904: val_loss -0.3755 +2026-04-13 17:14:16.419368: Pseudo dice [0.4525, 0.0, 0.4285, 0.0, 0.3784, 0.5304, 0.7733] +2026-04-13 17:14:16.423174: Epoch time: 100.79 s +2026-04-13 17:14:17.644173: +2026-04-13 17:14:17.646403: Epoch 2667 +2026-04-13 17:14:17.648643: Current learning rate: 0.00372 +2026-04-13 17:15:58.370180: train_loss -0.3993 +2026-04-13 17:15:58.377613: val_loss -0.3679 +2026-04-13 17:15:58.379905: Pseudo dice [0.5199, 0.0, 0.5925, 0.1294, 0.4207, 0.8511, 0.7439] +2026-04-13 17:15:58.382634: Epoch time: 100.73 s +2026-04-13 17:15:59.586300: +2026-04-13 17:15:59.588309: Epoch 2668 +2026-04-13 17:15:59.591849: Current learning rate: 0.00372 +2026-04-13 17:17:39.788353: train_loss -0.4129 +2026-04-13 17:17:39.796740: val_loss -0.3225 +2026-04-13 17:17:39.799922: Pseudo dice [0.1158, 0.0, 0.5168, 0.0118, 0.1348, 0.63, 0.7094] +2026-04-13 17:17:39.803983: Epoch time: 100.21 s +2026-04-13 17:17:41.019101: +2026-04-13 17:17:41.021292: Epoch 2669 +2026-04-13 17:17:41.023436: Current learning rate: 0.00371 +2026-04-13 17:19:21.753481: train_loss -0.4095 +2026-04-13 17:19:21.759965: val_loss -0.4389 +2026-04-13 17:19:21.762464: Pseudo dice [0.7257, 0.0, 0.6524, 0.3158, 0.3708, 0.8419, 0.6726] +2026-04-13 17:19:21.765510: Epoch time: 100.74 s +2026-04-13 17:19:23.056868: +2026-04-13 17:19:23.060328: Epoch 2670 +2026-04-13 17:19:23.062487: Current learning rate: 0.00371 +2026-04-13 17:21:03.118460: train_loss -0.4257 +2026-04-13 17:21:03.124489: val_loss -0.4194 +2026-04-13 17:21:03.126966: Pseudo dice [0.5905, 0.0, 0.8279, 0.6531, 0.4007, 0.508, 0.8007] +2026-04-13 17:21:03.129511: Epoch time: 100.06 s +2026-04-13 17:21:04.401519: +2026-04-13 17:21:04.404747: Epoch 2671 +2026-04-13 17:21:04.407962: Current learning rate: 0.00371 +2026-04-13 17:22:44.930375: train_loss -0.4426 +2026-04-13 17:22:44.937393: val_loss -0.4019 +2026-04-13 17:22:44.939666: Pseudo dice [0.7214, 0.0, 0.4281, 0.5688, 0.6376, 0.8103, 0.6815] +2026-04-13 17:22:44.941776: Epoch time: 100.53 s +2026-04-13 17:22:46.197593: +2026-04-13 17:22:46.199903: Epoch 2672 +2026-04-13 17:22:46.201947: Current learning rate: 0.00371 +2026-04-13 17:24:27.370272: train_loss -0.4256 +2026-04-13 17:24:27.377076: val_loss -0.3142 +2026-04-13 17:24:27.380345: Pseudo dice [0.7165, 0.0, 0.5575, 0.0, 0.4469, 0.2469, 0.5901] +2026-04-13 17:24:27.386242: Epoch time: 101.18 s +2026-04-13 17:24:28.619610: +2026-04-13 17:24:28.621946: Epoch 2673 +2026-04-13 17:24:28.624182: Current learning rate: 0.0037 +2026-04-13 17:26:10.509068: train_loss -0.4255 +2026-04-13 17:26:10.514373: val_loss -0.4244 +2026-04-13 17:26:10.516325: Pseudo dice [0.3092, 0.0, 0.8176, 0.8131, 0.3306, 0.8385, 0.7752] +2026-04-13 17:26:10.518966: Epoch time: 101.89 s +2026-04-13 17:26:11.743245: +2026-04-13 17:26:11.745313: Epoch 2674 +2026-04-13 17:26:11.746913: Current learning rate: 0.0037 +2026-04-13 17:27:52.936727: train_loss -0.4192 +2026-04-13 17:27:52.953149: val_loss -0.3493 +2026-04-13 17:27:52.955441: Pseudo dice [0.4812, 0.0, 0.6401, 0.1037, 0.2858, 0.7802, 0.5614] +2026-04-13 17:27:52.957895: Epoch time: 101.2 s +2026-04-13 17:27:54.174780: +2026-04-13 17:27:54.176902: Epoch 2675 +2026-04-13 17:27:54.179098: Current learning rate: 0.0037 +2026-04-13 17:29:34.386039: train_loss -0.3711 +2026-04-13 17:29:34.391253: val_loss -0.3669 +2026-04-13 17:29:34.393216: Pseudo dice [0.5142, 0.0, 0.6691, 0.0, 0.1941, 0.0961, 0.6212] +2026-04-13 17:29:34.395754: Epoch time: 100.21 s +2026-04-13 17:29:35.597815: +2026-04-13 17:29:35.600361: Epoch 2676 +2026-04-13 17:29:35.602467: Current learning rate: 0.0037 +2026-04-13 17:31:15.785263: train_loss -0.4073 +2026-04-13 17:31:15.790805: val_loss -0.4191 +2026-04-13 17:31:15.793110: Pseudo dice [0.6717, 0.0, 0.7201, 0.4576, 0.3188, 0.5614, 0.9032] +2026-04-13 17:31:15.795500: Epoch time: 100.19 s +2026-04-13 17:31:17.018958: +2026-04-13 17:31:17.021856: Epoch 2677 +2026-04-13 17:31:17.025488: Current learning rate: 0.00369 +2026-04-13 17:32:57.808179: train_loss -0.4202 +2026-04-13 17:32:57.814189: val_loss -0.4209 +2026-04-13 17:32:57.816888: Pseudo dice [0.5223, 0.0, 0.7625, 0.8113, 0.3833, 0.7908, 0.6686] +2026-04-13 17:32:57.819274: Epoch time: 100.79 s +2026-04-13 17:32:59.034377: +2026-04-13 17:32:59.036595: Epoch 2678 +2026-04-13 17:32:59.039080: Current learning rate: 0.00369 +2026-04-13 17:34:39.209544: train_loss -0.4319 +2026-04-13 17:34:39.217724: val_loss -0.3772 +2026-04-13 17:34:39.220032: Pseudo dice [0.179, 0.0, 0.6882, 0.1362, 0.1753, 0.74, 0.6413] +2026-04-13 17:34:39.223067: Epoch time: 100.18 s +2026-04-13 17:34:40.426923: +2026-04-13 17:34:40.431474: Epoch 2679 +2026-04-13 17:34:40.433626: Current learning rate: 0.00369 +2026-04-13 17:36:20.578040: train_loss -0.4299 +2026-04-13 17:36:20.584999: val_loss -0.4229 +2026-04-13 17:36:20.587929: Pseudo dice [0.402, 0.0, 0.5603, 0.8833, 0.4287, 0.6899, 0.8429] +2026-04-13 17:36:20.591562: Epoch time: 100.15 s +2026-04-13 17:36:21.786071: +2026-04-13 17:36:21.788363: Epoch 2680 +2026-04-13 17:36:21.790005: Current learning rate: 0.00369 +2026-04-13 17:38:02.024181: train_loss -0.4055 +2026-04-13 17:38:02.030394: val_loss -0.3379 +2026-04-13 17:38:02.032623: Pseudo dice [0.3839, 0.0, 0.4433, 0.1106, 0.2198, 0.61, 0.4686] +2026-04-13 17:38:02.035085: Epoch time: 100.24 s +2026-04-13 17:38:03.231708: +2026-04-13 17:38:03.233834: Epoch 2681 +2026-04-13 17:38:03.236208: Current learning rate: 0.00368 +2026-04-13 17:39:43.697796: train_loss -0.4294 +2026-04-13 17:39:43.705119: val_loss -0.3146 +2026-04-13 17:39:43.707731: Pseudo dice [0.5885, 0.0, 0.5564, 0.0005, 0.3816, 0.748, 0.2373] +2026-04-13 17:39:43.710559: Epoch time: 100.47 s +2026-04-13 17:39:44.931067: +2026-04-13 17:39:44.932970: Epoch 2682 +2026-04-13 17:39:44.934978: Current learning rate: 0.00368 +2026-04-13 17:41:25.751135: train_loss -0.4107 +2026-04-13 17:41:25.759453: val_loss -0.4176 +2026-04-13 17:41:25.761742: Pseudo dice [0.4995, 0.0, 0.5825, 0.3468, 0.5134, 0.7585, 0.8083] +2026-04-13 17:41:25.765158: Epoch time: 100.82 s +2026-04-13 17:41:27.039394: +2026-04-13 17:41:27.041703: Epoch 2683 +2026-04-13 17:41:27.043578: Current learning rate: 0.00368 +2026-04-13 17:43:07.087687: train_loss -0.4291 +2026-04-13 17:43:07.093622: val_loss -0.3735 +2026-04-13 17:43:07.095324: Pseudo dice [0.0038, 0.0, 0.7269, 0.0238, 0.5149, 0.3621, 0.7421] +2026-04-13 17:43:07.097347: Epoch time: 100.05 s +2026-04-13 17:43:08.297610: +2026-04-13 17:43:08.299696: Epoch 2684 +2026-04-13 17:43:08.301528: Current learning rate: 0.00368 +2026-04-13 17:44:48.583228: train_loss -0.4349 +2026-04-13 17:44:48.588585: val_loss -0.4085 +2026-04-13 17:44:48.590518: Pseudo dice [0.6178, 0.0, 0.8055, 0.7443, 0.2605, 0.856, 0.6467] +2026-04-13 17:44:48.592757: Epoch time: 100.29 s +2026-04-13 17:44:49.813464: +2026-04-13 17:44:49.815243: Epoch 2685 +2026-04-13 17:44:49.816949: Current learning rate: 0.00367 +2026-04-13 17:46:31.377409: train_loss -0.4175 +2026-04-13 17:46:31.383591: val_loss -0.3747 +2026-04-13 17:46:31.385771: Pseudo dice [0.2098, 0.0, 0.6133, 0.1156, 0.3087, 0.6432, 0.6158] +2026-04-13 17:46:31.388699: Epoch time: 101.57 s +2026-04-13 17:46:32.634178: +2026-04-13 17:46:32.636235: Epoch 2686 +2026-04-13 17:46:32.638124: Current learning rate: 0.00367 +2026-04-13 17:48:12.878189: train_loss -0.4263 +2026-04-13 17:48:12.885538: val_loss -0.3559 +2026-04-13 17:48:12.888212: Pseudo dice [0.4307, 0.0, 0.6194, 0.0, 0.4019, 0.7937, 0.6814] +2026-04-13 17:48:12.891015: Epoch time: 100.25 s +2026-04-13 17:48:14.103064: +2026-04-13 17:48:14.104998: Epoch 2687 +2026-04-13 17:48:14.106745: Current learning rate: 0.00367 +2026-04-13 17:49:54.143045: train_loss -0.4284 +2026-04-13 17:49:54.150381: val_loss -0.3234 +2026-04-13 17:49:54.152888: Pseudo dice [0.6775, 0.0, 0.5844, 0.0567, 0.1112, 0.5785, 0.6943] +2026-04-13 17:49:54.155333: Epoch time: 100.04 s +2026-04-13 17:49:55.453790: +2026-04-13 17:49:55.457207: Epoch 2688 +2026-04-13 17:49:55.459063: Current learning rate: 0.00367 +2026-04-13 17:51:36.051504: train_loss -0.422 +2026-04-13 17:51:36.057756: val_loss -0.3038 +2026-04-13 17:51:36.061425: Pseudo dice [0.6217, 0.0, 0.6772, 0.0425, 0.4751, 0.8427, 0.6681] +2026-04-13 17:51:36.064409: Epoch time: 100.6 s +2026-04-13 17:51:37.284696: +2026-04-13 17:51:37.286909: Epoch 2689 +2026-04-13 17:51:37.288613: Current learning rate: 0.00366 +2026-04-13 17:53:17.387973: train_loss -0.4077 +2026-04-13 17:53:17.395520: val_loss -0.2909 +2026-04-13 17:53:17.398704: Pseudo dice [0.5546, 0.0, 0.4962, 0.001, 0.4226, 0.5363, 0.7733] +2026-04-13 17:53:17.401349: Epoch time: 100.11 s +2026-04-13 17:53:18.604093: +2026-04-13 17:53:18.606295: Epoch 2690 +2026-04-13 17:53:18.608325: Current learning rate: 0.00366 +2026-04-13 17:54:58.800982: train_loss -0.428 +2026-04-13 17:54:58.806905: val_loss -0.3648 +2026-04-13 17:54:58.808889: Pseudo dice [0.7205, 0.0, 0.6515, 0.4119, 0.4563, 0.3242, 0.6015] +2026-04-13 17:54:58.811069: Epoch time: 100.2 s +2026-04-13 17:55:00.047229: +2026-04-13 17:55:00.049012: Epoch 2691 +2026-04-13 17:55:00.050648: Current learning rate: 0.00366 +2026-04-13 17:56:40.229078: train_loss -0.4051 +2026-04-13 17:56:40.237961: val_loss -0.3314 +2026-04-13 17:56:40.240486: Pseudo dice [0.0, 0.0, 0.6652, 0.0572, 0.1991, 0.7848, 0.7261] +2026-04-13 17:56:40.244957: Epoch time: 100.18 s +2026-04-13 17:56:41.461391: +2026-04-13 17:56:41.464103: Epoch 2692 +2026-04-13 17:56:41.466268: Current learning rate: 0.00366 +2026-04-13 17:58:21.998494: train_loss -0.4261 +2026-04-13 17:58:22.005061: val_loss -0.3655 +2026-04-13 17:58:22.007466: Pseudo dice [0.5836, 0.0, 0.6742, 0.0047, 0.0119, 0.7576, 0.7714] +2026-04-13 17:58:22.009864: Epoch time: 100.54 s +2026-04-13 17:58:23.249302: +2026-04-13 17:58:23.251622: Epoch 2693 +2026-04-13 17:58:23.253391: Current learning rate: 0.00365 +2026-04-13 18:00:03.799934: train_loss -0.4347 +2026-04-13 18:00:03.808364: val_loss -0.3853 +2026-04-13 18:00:03.810646: Pseudo dice [0.6825, 0.0, 0.6551, 0.0, 0.1271, 0.8107, 0.7454] +2026-04-13 18:00:03.812957: Epoch time: 100.55 s +2026-04-13 18:00:05.020624: +2026-04-13 18:00:05.024159: Epoch 2694 +2026-04-13 18:00:05.028416: Current learning rate: 0.00365 +2026-04-13 18:01:45.545400: train_loss -0.4071 +2026-04-13 18:01:45.550768: val_loss -0.3237 +2026-04-13 18:01:45.553578: Pseudo dice [0.6255, 0.0, 0.5908, 0.0665, 0.2078, 0.3591, 0.5739] +2026-04-13 18:01:45.555795: Epoch time: 100.53 s +2026-04-13 18:01:47.846334: +2026-04-13 18:01:47.848326: Epoch 2695 +2026-04-13 18:01:47.850078: Current learning rate: 0.00365 +2026-04-13 18:03:28.079145: train_loss -0.4269 +2026-04-13 18:03:28.085063: val_loss -0.3823 +2026-04-13 18:03:28.087155: Pseudo dice [0.7168, 0.0, 0.6854, 0.0, 0.3473, 0.5925, 0.5495] +2026-04-13 18:03:28.089261: Epoch time: 100.24 s +2026-04-13 18:03:29.325635: +2026-04-13 18:03:29.327632: Epoch 2696 +2026-04-13 18:03:29.329501: Current learning rate: 0.00365 +2026-04-13 18:05:10.283265: train_loss -0.4199 +2026-04-13 18:05:10.290718: val_loss -0.3893 +2026-04-13 18:05:10.293049: Pseudo dice [0.0743, 0.0, 0.6579, 0.599, 0.4708, 0.76, 0.6953] +2026-04-13 18:05:10.296597: Epoch time: 100.96 s +2026-04-13 18:05:11.512464: +2026-04-13 18:05:11.514834: Epoch 2697 +2026-04-13 18:05:11.516668: Current learning rate: 0.00364 +2026-04-13 18:06:52.066539: train_loss -0.403 +2026-04-13 18:06:52.073962: val_loss -0.3562 +2026-04-13 18:06:52.077004: Pseudo dice [0.2358, 0.0, 0.7281, 0.1864, 0.339, 0.7004, 0.5781] +2026-04-13 18:06:52.079423: Epoch time: 100.56 s +2026-04-13 18:06:53.333966: +2026-04-13 18:06:53.336350: Epoch 2698 +2026-04-13 18:06:53.339856: Current learning rate: 0.00364 +2026-04-13 18:08:33.657589: train_loss -0.4163 +2026-04-13 18:08:33.664842: val_loss -0.3814 +2026-04-13 18:08:33.668844: Pseudo dice [0.0, 0.0, 0.6279, 0.1862, 0.5259, 0.6612, 0.7759] +2026-04-13 18:08:33.672853: Epoch time: 100.33 s +2026-04-13 18:08:34.950703: +2026-04-13 18:08:34.952933: Epoch 2699 +2026-04-13 18:08:34.954825: Current learning rate: 0.00364 +2026-04-13 18:10:15.355929: train_loss -0.43 +2026-04-13 18:10:15.362246: val_loss -0.4119 +2026-04-13 18:10:15.364335: Pseudo dice [0.5944, 0.0, 0.8346, 0.0, 0.4756, 0.7574, 0.5367] +2026-04-13 18:10:15.366614: Epoch time: 100.41 s +2026-04-13 18:10:18.314555: +2026-04-13 18:10:18.316857: Epoch 2700 +2026-04-13 18:10:18.318968: Current learning rate: 0.00364 +2026-04-13 18:11:59.543855: train_loss -0.4302 +2026-04-13 18:11:59.554987: val_loss -0.4138 +2026-04-13 18:11:59.559398: Pseudo dice [0.3783, 0.0, 0.5089, 0.5373, 0.4081, 0.6738, 0.6339] +2026-04-13 18:11:59.562890: Epoch time: 101.23 s +2026-04-13 18:12:00.773552: +2026-04-13 18:12:00.775664: Epoch 2701 +2026-04-13 18:12:00.777683: Current learning rate: 0.00363 +2026-04-13 18:13:42.220049: train_loss -0.3947 +2026-04-13 18:13:42.229432: val_loss -0.4027 +2026-04-13 18:13:42.231982: Pseudo dice [0.045, 0.0, 0.7673, 0.7046, 0.0, 0.7695, 0.5537] +2026-04-13 18:13:42.236189: Epoch time: 101.45 s +2026-04-13 18:13:43.466743: +2026-04-13 18:13:43.469019: Epoch 2702 +2026-04-13 18:13:43.472279: Current learning rate: 0.00363 +2026-04-13 18:15:23.807839: train_loss -0.3859 +2026-04-13 18:15:23.813955: val_loss -0.3649 +2026-04-13 18:15:23.817324: Pseudo dice [0.5277, 0.0, 0.6138, 0.0, 0.1936, 0.1956, 0.7314] +2026-04-13 18:15:23.820657: Epoch time: 100.34 s +2026-04-13 18:15:25.039883: +2026-04-13 18:15:25.042926: Epoch 2703 +2026-04-13 18:15:25.045683: Current learning rate: 0.00363 +2026-04-13 18:17:05.128372: train_loss -0.3935 +2026-04-13 18:17:05.134341: val_loss -0.3686 +2026-04-13 18:17:05.136852: Pseudo dice [0.3872, 0.0, 0.5002, 0.0, 0.3764, 0.6726, 0.3251] +2026-04-13 18:17:05.139045: Epoch time: 100.09 s +2026-04-13 18:17:06.420344: +2026-04-13 18:17:06.423163: Epoch 2704 +2026-04-13 18:17:06.425071: Current learning rate: 0.00363 +2026-04-13 18:18:46.569786: train_loss -0.3806 +2026-04-13 18:18:46.575471: val_loss -0.3653 +2026-04-13 18:18:46.577852: Pseudo dice [0.2385, 0.0, 0.656, 0.3103, 0.0203, 0.5483, 0.6973] +2026-04-13 18:18:46.580334: Epoch time: 100.15 s +2026-04-13 18:18:47.763717: +2026-04-13 18:18:47.765545: Epoch 2705 +2026-04-13 18:18:47.767432: Current learning rate: 0.00362 +2026-04-13 18:20:28.069272: train_loss -0.3966 +2026-04-13 18:20:28.076558: val_loss -0.3528 +2026-04-13 18:20:28.079739: Pseudo dice [0.6082, 0.0, 0.7761, 0.0, 0.6151, 0.8249, 0.7677] +2026-04-13 18:20:28.082159: Epoch time: 100.31 s +2026-04-13 18:20:29.326366: +2026-04-13 18:20:29.330345: Epoch 2706 +2026-04-13 18:20:29.333230: Current learning rate: 0.00362 +2026-04-13 18:22:09.348088: train_loss -0.4223 +2026-04-13 18:22:09.355818: val_loss -0.3655 +2026-04-13 18:22:09.358133: Pseudo dice [0.2701, 0.0, 0.7156, 0.2427, 0.1451, 0.8572, 0.7587] +2026-04-13 18:22:09.361417: Epoch time: 100.02 s +2026-04-13 18:22:10.593027: +2026-04-13 18:22:10.595027: Epoch 2707 +2026-04-13 18:22:10.596544: Current learning rate: 0.00362 +2026-04-13 18:23:51.446955: train_loss -0.4174 +2026-04-13 18:23:51.454942: val_loss -0.3791 +2026-04-13 18:23:51.458111: Pseudo dice [0.0601, 0.0, 0.6988, 0.0964, 0.383, 0.7129, 0.6786] +2026-04-13 18:23:51.461108: Epoch time: 100.86 s +2026-04-13 18:23:52.657387: +2026-04-13 18:23:52.659620: Epoch 2708 +2026-04-13 18:23:52.661252: Current learning rate: 0.00362 +2026-04-13 18:25:33.182032: train_loss -0.3991 +2026-04-13 18:25:33.190523: val_loss -0.3908 +2026-04-13 18:25:33.192862: Pseudo dice [0.3665, 0.0, 0.671, 0.2631, 0.4642, 0.6485, 0.8687] +2026-04-13 18:25:33.197402: Epoch time: 100.53 s +2026-04-13 18:25:34.472551: +2026-04-13 18:25:34.474353: Epoch 2709 +2026-04-13 18:25:34.476155: Current learning rate: 0.00361 +2026-04-13 18:27:14.623915: train_loss -0.4064 +2026-04-13 18:27:14.629991: val_loss -0.3438 +2026-04-13 18:27:14.632396: Pseudo dice [0.0, 0.0, 0.8311, 0.0863, 0.2631, 0.7024, 0.5957] +2026-04-13 18:27:14.635026: Epoch time: 100.15 s +2026-04-13 18:27:15.825315: +2026-04-13 18:27:15.827863: Epoch 2710 +2026-04-13 18:27:15.829746: Current learning rate: 0.00361 +2026-04-13 18:28:56.190505: train_loss -0.3892 +2026-04-13 18:28:56.196075: val_loss -0.4056 +2026-04-13 18:28:56.198274: Pseudo dice [0.0, 0.0, 0.5518, 0.7257, 0.4812, 0.7447, 0.6977] +2026-04-13 18:28:56.200738: Epoch time: 100.37 s +2026-04-13 18:28:57.440562: +2026-04-13 18:28:57.443438: Epoch 2711 +2026-04-13 18:28:57.447581: Current learning rate: 0.00361 +2026-04-13 18:30:38.440465: train_loss -0.3948 +2026-04-13 18:30:38.450135: val_loss -0.3936 +2026-04-13 18:30:38.458834: Pseudo dice [0.1017, 0.0, 0.5935, 0.4447, 0.3645, 0.7081, 0.3529] +2026-04-13 18:30:38.461302: Epoch time: 101.0 s +2026-04-13 18:30:39.694747: +2026-04-13 18:30:39.696701: Epoch 2712 +2026-04-13 18:30:39.698856: Current learning rate: 0.00361 +2026-04-13 18:32:19.993461: train_loss -0.4055 +2026-04-13 18:32:19.998269: val_loss -0.3937 +2026-04-13 18:32:20.000418: Pseudo dice [0.3801, 0.0, 0.7366, 0.4009, 0.3581, 0.6124, 0.8502] +2026-04-13 18:32:20.002829: Epoch time: 100.3 s +2026-04-13 18:32:21.285483: +2026-04-13 18:32:21.287675: Epoch 2713 +2026-04-13 18:32:21.289418: Current learning rate: 0.0036 +2026-04-13 18:34:01.337484: train_loss -0.4133 +2026-04-13 18:34:01.344926: val_loss -0.3111 +2026-04-13 18:34:01.347553: Pseudo dice [0.4061, 0.0, 0.4462, 0.1132, 0.3268, 0.5799, 0.8171] +2026-04-13 18:34:01.350064: Epoch time: 100.06 s +2026-04-13 18:34:02.545452: +2026-04-13 18:34:02.547513: Epoch 2714 +2026-04-13 18:34:02.549313: Current learning rate: 0.0036 +2026-04-13 18:35:43.790218: train_loss -0.4248 +2026-04-13 18:35:43.801983: val_loss -0.3365 +2026-04-13 18:35:43.803937: Pseudo dice [0.7206, 0.0, 0.4671, 0.0, 0.4791, 0.8318, 0.8023] +2026-04-13 18:35:43.805817: Epoch time: 101.25 s +2026-04-13 18:35:45.028183: +2026-04-13 18:35:45.030266: Epoch 2715 +2026-04-13 18:35:45.031862: Current learning rate: 0.0036 +2026-04-13 18:37:25.284221: train_loss -0.4388 +2026-04-13 18:37:25.289616: val_loss -0.429 +2026-04-13 18:37:25.291434: Pseudo dice [0.2492, 0.0, 0.3894, 0.7369, 0.4958, 0.8506, 0.7907] +2026-04-13 18:37:25.295436: Epoch time: 100.26 s +2026-04-13 18:37:26.522071: +2026-04-13 18:37:26.524066: Epoch 2716 +2026-04-13 18:37:26.525656: Current learning rate: 0.0036 +2026-04-13 18:39:07.135475: train_loss -0.4145 +2026-04-13 18:39:07.143264: val_loss -0.4033 +2026-04-13 18:39:07.145307: Pseudo dice [0.6225, 0.0, 0.6825, 0.7763, 0.2364, 0.7465, 0.7076] +2026-04-13 18:39:07.149475: Epoch time: 100.62 s +2026-04-13 18:39:08.364235: +2026-04-13 18:39:08.367064: Epoch 2717 +2026-04-13 18:39:08.370132: Current learning rate: 0.00359 +2026-04-13 18:40:49.133243: train_loss -0.4064 +2026-04-13 18:40:49.141066: val_loss -0.351 +2026-04-13 18:40:49.144976: Pseudo dice [0.6328, 0.0, 0.715, 0.0, 0.4253, 0.3939, 0.6976] +2026-04-13 18:40:49.150485: Epoch time: 100.77 s +2026-04-13 18:40:50.382050: +2026-04-13 18:40:50.384521: Epoch 2718 +2026-04-13 18:40:50.387770: Current learning rate: 0.00359 +2026-04-13 18:42:30.366229: train_loss -0.4245 +2026-04-13 18:42:30.372543: val_loss -0.3445 +2026-04-13 18:42:30.374836: Pseudo dice [0.0391, 0.0, 0.7648, 0.09, 0.3111, 0.7788, 0.8809] +2026-04-13 18:42:30.377888: Epoch time: 99.99 s +2026-04-13 18:42:31.584989: +2026-04-13 18:42:31.587005: Epoch 2719 +2026-04-13 18:42:31.588674: Current learning rate: 0.00359 +2026-04-13 18:44:12.106488: train_loss -0.4222 +2026-04-13 18:44:12.113188: val_loss -0.3904 +2026-04-13 18:44:12.115460: Pseudo dice [0.3164, 0.0, 0.6958, 0.3063, 0.5469, 0.8302, 0.6095] +2026-04-13 18:44:12.117699: Epoch time: 100.52 s +2026-04-13 18:44:13.339210: +2026-04-13 18:44:13.341305: Epoch 2720 +2026-04-13 18:44:13.343223: Current learning rate: 0.00359 +2026-04-13 18:45:53.937891: train_loss -0.4297 +2026-04-13 18:45:53.946162: val_loss -0.4046 +2026-04-13 18:45:53.949849: Pseudo dice [0.0411, 0.0, 0.7121, 0.0, 0.4249, 0.7772, 0.6233] +2026-04-13 18:45:53.952557: Epoch time: 100.6 s +2026-04-13 18:45:55.188054: +2026-04-13 18:45:55.190102: Epoch 2721 +2026-04-13 18:45:55.191679: Current learning rate: 0.00358 +2026-04-13 18:47:36.484184: train_loss -0.4299 +2026-04-13 18:47:36.490718: val_loss -0.3833 +2026-04-13 18:47:36.493051: Pseudo dice [0.5091, 0.0, 0.6645, 0.0, 0.1343, 0.7634, 0.8859] +2026-04-13 18:47:36.496736: Epoch time: 101.3 s +2026-04-13 18:47:37.732704: +2026-04-13 18:47:37.735275: Epoch 2722 +2026-04-13 18:47:37.737731: Current learning rate: 0.00358 +2026-04-13 18:49:18.259249: train_loss -0.4247 +2026-04-13 18:49:18.265583: val_loss -0.3844 +2026-04-13 18:49:18.268493: Pseudo dice [0.1279, 0.0, 0.6279, 0.0, 0.0068, 0.8337, 0.6979] +2026-04-13 18:49:18.271460: Epoch time: 100.53 s +2026-04-13 18:49:19.485723: +2026-04-13 18:49:19.488109: Epoch 2723 +2026-04-13 18:49:19.490142: Current learning rate: 0.00358 +2026-04-13 18:51:00.667322: train_loss -0.4064 +2026-04-13 18:51:00.673727: val_loss -0.2746 +2026-04-13 18:51:00.676650: Pseudo dice [0.1861, 0.0, 0.4458, 0.038, 0.4264, 0.778, 0.4622] +2026-04-13 18:51:00.679165: Epoch time: 101.18 s +2026-04-13 18:51:01.910165: +2026-04-13 18:51:01.912289: Epoch 2724 +2026-04-13 18:51:01.913836: Current learning rate: 0.00358 +2026-04-13 18:52:42.180746: train_loss -0.3839 +2026-04-13 18:52:42.188418: val_loss -0.3787 +2026-04-13 18:52:42.191144: Pseudo dice [0.4393, 0.0, 0.3049, 0.0, 0.4852, 0.1708, 0.7501] +2026-04-13 18:52:42.194098: Epoch time: 100.27 s +2026-04-13 18:52:43.457178: +2026-04-13 18:52:43.459170: Epoch 2725 +2026-04-13 18:52:43.461128: Current learning rate: 0.00357 +2026-04-13 18:54:23.737189: train_loss -0.4005 +2026-04-13 18:54:23.742766: val_loss -0.422 +2026-04-13 18:54:23.745313: Pseudo dice [0.5951, 0.0, 0.758, 0.0, 0.3906, 0.6702, 0.8061] +2026-04-13 18:54:23.747522: Epoch time: 100.28 s +2026-04-13 18:54:24.968489: +2026-04-13 18:54:24.970937: Epoch 2726 +2026-04-13 18:54:24.973284: Current learning rate: 0.00357 +2026-04-13 18:56:05.050856: train_loss -0.4389 +2026-04-13 18:56:05.062192: val_loss -0.4529 +2026-04-13 18:56:05.065078: Pseudo dice [0.5993, 0.0, 0.6898, 0.8598, 0.5443, 0.758, 0.8687] +2026-04-13 18:56:05.068757: Epoch time: 100.09 s +2026-04-13 18:56:06.271025: +2026-04-13 18:56:06.272816: Epoch 2727 +2026-04-13 18:56:06.274599: Current learning rate: 0.00357 +2026-04-13 18:57:47.050980: train_loss -0.4314 +2026-04-13 18:57:47.057541: val_loss -0.3871 +2026-04-13 18:57:47.059964: Pseudo dice [0.6323, 0.0, 0.7157, 0.0, 0.4666, 0.7613, 0.4281] +2026-04-13 18:57:47.062724: Epoch time: 100.78 s +2026-04-13 18:57:48.285421: +2026-04-13 18:57:48.288005: Epoch 2728 +2026-04-13 18:57:48.290087: Current learning rate: 0.00357 +2026-04-13 18:59:28.486192: train_loss -0.4315 +2026-04-13 18:59:28.494093: val_loss -0.4248 +2026-04-13 18:59:28.504594: Pseudo dice [0.1589, 0.0, 0.6164, 0.3883, 0.2805, 0.7405, 0.8476] +2026-04-13 18:59:28.507861: Epoch time: 100.2 s +2026-04-13 18:59:29.713706: +2026-04-13 18:59:29.718969: Epoch 2729 +2026-04-13 18:59:29.721671: Current learning rate: 0.00356 +2026-04-13 19:01:10.754516: train_loss -0.4134 +2026-04-13 19:01:10.762886: val_loss -0.3953 +2026-04-13 19:01:10.765896: Pseudo dice [0.3041, 0.0, 0.6696, 0.8598, 0.2019, 0.7624, 0.707] +2026-04-13 19:01:10.768433: Epoch time: 101.04 s +2026-04-13 19:01:11.964753: +2026-04-13 19:01:11.966966: Epoch 2730 +2026-04-13 19:01:11.968958: Current learning rate: 0.00356 +2026-04-13 19:02:52.540951: train_loss -0.4258 +2026-04-13 19:02:52.545706: val_loss -0.3389 +2026-04-13 19:02:52.547764: Pseudo dice [0.1662, 0.0, 0.6407, 0.0935, 0.1221, 0.7737, 0.847] +2026-04-13 19:02:52.549692: Epoch time: 100.58 s +2026-04-13 19:02:53.747255: +2026-04-13 19:02:53.753075: Epoch 2731 +2026-04-13 19:02:53.756431: Current learning rate: 0.00356 +2026-04-13 19:04:33.887460: train_loss -0.4339 +2026-04-13 19:04:33.894117: val_loss -0.2801 +2026-04-13 19:04:33.896212: Pseudo dice [0.771, 0.0, 0.7126, 0.0, 0.5043, 0.6271, 0.7647] +2026-04-13 19:04:33.898205: Epoch time: 100.14 s +2026-04-13 19:04:35.122828: +2026-04-13 19:04:35.125566: Epoch 2732 +2026-04-13 19:04:35.127777: Current learning rate: 0.00356 +2026-04-13 19:06:15.848312: train_loss -0.4299 +2026-04-13 19:06:15.854524: val_loss -0.3629 +2026-04-13 19:06:15.857592: Pseudo dice [0.6555, 0.0, 0.6425, 0.5432, 0.4177, 0.6763, 0.6622] +2026-04-13 19:06:15.860235: Epoch time: 100.73 s +2026-04-13 19:06:17.068214: +2026-04-13 19:06:17.070464: Epoch 2733 +2026-04-13 19:06:17.072436: Current learning rate: 0.00355 +2026-04-13 19:07:57.035077: train_loss -0.4293 +2026-04-13 19:07:57.041452: val_loss -0.3643 +2026-04-13 19:07:57.044647: Pseudo dice [0.0, 0.0, 0.7525, 0.1084, 0.4979, 0.6115, 0.8648] +2026-04-13 19:07:57.046835: Epoch time: 99.97 s +2026-04-13 19:07:58.258804: +2026-04-13 19:07:58.261127: Epoch 2734 +2026-04-13 19:07:58.262900: Current learning rate: 0.00355 +2026-04-13 19:09:39.040035: train_loss -0.3938 +2026-04-13 19:09:39.045786: val_loss -0.3601 +2026-04-13 19:09:39.047865: Pseudo dice [0.0, 0.0, 0.6001, 0.1411, 0.5324, 0.7969, 0.6497] +2026-04-13 19:09:39.050095: Epoch time: 100.78 s +2026-04-13 19:09:40.250485: +2026-04-13 19:09:40.252468: Epoch 2735 +2026-04-13 19:09:40.254143: Current learning rate: 0.00355 +2026-04-13 19:11:20.358267: train_loss -0.417 +2026-04-13 19:11:20.368001: val_loss -0.4003 +2026-04-13 19:11:20.371515: Pseudo dice [0.6324, 0.0, 0.6218, 0.0, 0.2161, 0.7935, 0.8037] +2026-04-13 19:11:20.374468: Epoch time: 100.11 s +2026-04-13 19:11:21.592438: +2026-04-13 19:11:21.594686: Epoch 2736 +2026-04-13 19:11:21.596757: Current learning rate: 0.00355 +2026-04-13 19:13:02.521643: train_loss -0.3936 +2026-04-13 19:13:02.531404: val_loss -0.3495 +2026-04-13 19:13:02.534175: Pseudo dice [0.2168, 0.0, 0.5523, 0.0, 0.0, 0.7424, 0.6471] +2026-04-13 19:13:02.544743: Epoch time: 100.93 s +2026-04-13 19:13:03.771159: +2026-04-13 19:13:03.773371: Epoch 2737 +2026-04-13 19:13:03.775161: Current learning rate: 0.00354 +2026-04-13 19:14:43.862454: train_loss -0.3782 +2026-04-13 19:14:43.868378: val_loss -0.3584 +2026-04-13 19:14:43.870677: Pseudo dice [0.0, 0.0, 0.6991, 0.0, 0.0, 0.8401, 0.596] +2026-04-13 19:14:43.873350: Epoch time: 100.09 s +2026-04-13 19:14:45.103393: +2026-04-13 19:14:45.105464: Epoch 2738 +2026-04-13 19:14:45.107131: Current learning rate: 0.00354 +2026-04-13 19:16:25.303182: train_loss -0.3841 +2026-04-13 19:16:25.309907: val_loss -0.3755 +2026-04-13 19:16:25.313187: Pseudo dice [0.5794, 0.0, 0.7352, 0.0, 0.0, 0.6839, 0.7221] +2026-04-13 19:16:25.315851: Epoch time: 100.2 s +2026-04-13 19:16:26.527970: +2026-04-13 19:16:26.531243: Epoch 2739 +2026-04-13 19:16:26.533313: Current learning rate: 0.00354 +2026-04-13 19:18:06.619986: train_loss -0.406 +2026-04-13 19:18:06.627800: val_loss -0.3627 +2026-04-13 19:18:06.630285: Pseudo dice [0.5644, 0.0, 0.6956, 0.0874, 0.0001, 0.7371, 0.7176] +2026-04-13 19:18:06.633528: Epoch time: 100.1 s +2026-04-13 19:18:07.845271: +2026-04-13 19:18:07.847156: Epoch 2740 +2026-04-13 19:18:07.848836: Current learning rate: 0.00354 +2026-04-13 19:19:48.493846: train_loss -0.4206 +2026-04-13 19:19:48.499291: val_loss -0.4193 +2026-04-13 19:19:48.504090: Pseudo dice [0.6786, 0.0, 0.7808, 0.8644, 0.3259, 0.8198, 0.7875] +2026-04-13 19:19:48.506387: Epoch time: 100.65 s +2026-04-13 19:19:49.733645: +2026-04-13 19:19:49.735889: Epoch 2741 +2026-04-13 19:19:49.737682: Current learning rate: 0.00353 +2026-04-13 19:21:30.140329: train_loss -0.4047 +2026-04-13 19:21:30.146874: val_loss -0.3898 +2026-04-13 19:21:30.148764: Pseudo dice [0.13, 0.0, 0.7027, 0.1442, 0.3392, 0.6533, 0.7857] +2026-04-13 19:21:30.151393: Epoch time: 100.41 s +2026-04-13 19:21:31.371157: +2026-04-13 19:21:31.373075: Epoch 2742 +2026-04-13 19:21:31.374794: Current learning rate: 0.00353 +2026-04-13 19:23:12.191469: train_loss -0.4184 +2026-04-13 19:23:12.197456: val_loss -0.2849 +2026-04-13 19:23:12.199757: Pseudo dice [0.0, 0.0, 0.667, 0.0, 0.5108, 0.8299, 0.7923] +2026-04-13 19:23:12.201970: Epoch time: 100.82 s +2026-04-13 19:23:13.417210: +2026-04-13 19:23:13.419039: Epoch 2743 +2026-04-13 19:23:13.420717: Current learning rate: 0.00353 +2026-04-13 19:24:53.916879: train_loss -0.3981 +2026-04-13 19:24:53.923429: val_loss -0.3206 +2026-04-13 19:24:53.925912: Pseudo dice [0.6455, 0.0, 0.4651, 0.0467, 0.0, 0.0194, 0.8462] +2026-04-13 19:24:53.928804: Epoch time: 100.5 s +2026-04-13 19:24:55.123580: +2026-04-13 19:24:55.125406: Epoch 2744 +2026-04-13 19:24:55.127093: Current learning rate: 0.00353 +2026-04-13 19:26:35.284734: train_loss -0.3932 +2026-04-13 19:26:35.292351: val_loss -0.3578 +2026-04-13 19:26:35.296128: Pseudo dice [0.0, 0.0, 0.7271, 0.0, 0.3077, 0.6467, 0.4794] +2026-04-13 19:26:35.300378: Epoch time: 100.16 s +2026-04-13 19:26:36.507894: +2026-04-13 19:26:36.510053: Epoch 2745 +2026-04-13 19:26:36.512496: Current learning rate: 0.00352 +2026-04-13 19:28:17.166939: train_loss -0.413 +2026-04-13 19:28:17.174134: val_loss -0.3988 +2026-04-13 19:28:17.176252: Pseudo dice [0.3159, 0.0, 0.7635, 0.8091, 0.5132, 0.6609, 0.7059] +2026-04-13 19:28:17.178277: Epoch time: 100.66 s +2026-04-13 19:28:18.367953: +2026-04-13 19:28:18.371065: Epoch 2746 +2026-04-13 19:28:18.373213: Current learning rate: 0.00352 +2026-04-13 19:29:58.612931: train_loss -0.4217 +2026-04-13 19:29:58.619141: val_loss -0.4163 +2026-04-13 19:29:58.622774: Pseudo dice [0.2947, 0.0, 0.7369, 0.0, 0.4475, 0.8116, 0.7532] +2026-04-13 19:29:58.625576: Epoch time: 100.25 s +2026-04-13 19:29:59.835585: +2026-04-13 19:29:59.837345: Epoch 2747 +2026-04-13 19:29:59.839831: Current learning rate: 0.00352 +2026-04-13 19:31:40.020556: train_loss -0.4127 +2026-04-13 19:31:40.028122: val_loss -0.4007 +2026-04-13 19:31:40.030877: Pseudo dice [0.2096, 0.0, 0.6533, 0.0, 0.319, 0.8582, 0.8064] +2026-04-13 19:31:40.034857: Epoch time: 100.19 s +2026-04-13 19:31:41.259425: +2026-04-13 19:31:41.261594: Epoch 2748 +2026-04-13 19:31:41.263707: Current learning rate: 0.00352 +2026-04-13 19:33:21.446821: train_loss -0.4194 +2026-04-13 19:33:21.453471: val_loss -0.429 +2026-04-13 19:33:21.455720: Pseudo dice [0.1739, 0.0, 0.5648, 0.0, 0.6873, 0.6446, 0.8437] +2026-04-13 19:33:21.457886: Epoch time: 100.19 s +2026-04-13 19:33:22.683212: +2026-04-13 19:33:22.684914: Epoch 2749 +2026-04-13 19:33:22.686426: Current learning rate: 0.00351 +2026-04-13 19:35:02.730806: train_loss -0.3988 +2026-04-13 19:35:02.736658: val_loss -0.331 +2026-04-13 19:35:02.739374: Pseudo dice [0.7342, 0.0, 0.6133, 0.0, 0.1907, 0.7304, 0.261] +2026-04-13 19:35:02.742378: Epoch time: 100.05 s +2026-04-13 19:35:05.945662: +2026-04-13 19:35:05.948151: Epoch 2750 +2026-04-13 19:35:05.950453: Current learning rate: 0.00351 +2026-04-13 19:36:46.345938: train_loss -0.3948 +2026-04-13 19:36:46.352406: val_loss -0.3984 +2026-04-13 19:36:46.354874: Pseudo dice [0.8042, 0.0, 0.5809, 0.0, 0.0, 0.7631, 0.7609] +2026-04-13 19:36:46.358041: Epoch time: 100.4 s +2026-04-13 19:36:47.575758: +2026-04-13 19:36:47.578104: Epoch 2751 +2026-04-13 19:36:47.580241: Current learning rate: 0.00351 +2026-04-13 19:38:27.543581: train_loss -0.4102 +2026-04-13 19:38:27.548555: val_loss -0.3087 +2026-04-13 19:38:27.550549: Pseudo dice [0.7044, 0.0, 0.7577, 0.0376, 0.3377, 0.603, 0.6086] +2026-04-13 19:38:27.555421: Epoch time: 99.97 s +2026-04-13 19:38:28.763508: +2026-04-13 19:38:28.765389: Epoch 2752 +2026-04-13 19:38:28.767087: Current learning rate: 0.00351 +2026-04-13 19:40:08.871543: train_loss -0.4069 +2026-04-13 19:40:08.876779: val_loss -0.3705 +2026-04-13 19:40:08.878904: Pseudo dice [0.4085, 0.0, 0.6615, 0.3669, 0.2514, 0.7114, 0.512] +2026-04-13 19:40:08.881476: Epoch time: 100.11 s +2026-04-13 19:40:10.111416: +2026-04-13 19:40:10.113275: Epoch 2753 +2026-04-13 19:40:10.115111: Current learning rate: 0.0035 +2026-04-13 19:41:50.090025: train_loss -0.4277 +2026-04-13 19:41:50.096115: val_loss -0.3553 +2026-04-13 19:41:50.098312: Pseudo dice [0.6891, 0.0, 0.7104, 0.0, 0.3654, 0.749, 0.6361] +2026-04-13 19:41:50.101055: Epoch time: 99.98 s +2026-04-13 19:41:51.298353: +2026-04-13 19:41:51.300801: Epoch 2754 +2026-04-13 19:41:51.302605: Current learning rate: 0.0035 +2026-04-13 19:43:33.205833: train_loss -0.4067 +2026-04-13 19:43:33.213486: val_loss -0.3678 +2026-04-13 19:43:33.215458: Pseudo dice [0.3812, 0.0, 0.622, 0.0, 0.2338, 0.7296, 0.6574] +2026-04-13 19:43:33.218301: Epoch time: 101.91 s +2026-04-13 19:43:34.439416: +2026-04-13 19:43:34.441217: Epoch 2755 +2026-04-13 19:43:34.443902: Current learning rate: 0.0035 +2026-04-13 19:45:14.704332: train_loss -0.4013 +2026-04-13 19:45:14.711156: val_loss -0.3845 +2026-04-13 19:45:14.713498: Pseudo dice [0.7624, 0.0, 0.5365, 0.5876, 0.3845, 0.6741, 0.6917] +2026-04-13 19:45:14.716578: Epoch time: 100.27 s +2026-04-13 19:45:15.948337: +2026-04-13 19:45:15.950394: Epoch 2756 +2026-04-13 19:45:15.952292: Current learning rate: 0.0035 +2026-04-13 19:46:56.593837: train_loss -0.3759 +2026-04-13 19:46:56.600884: val_loss -0.4038 +2026-04-13 19:46:56.603311: Pseudo dice [0.0868, 0.0, 0.7228, 0.7588, 0.1456, 0.0661, 0.7853] +2026-04-13 19:46:56.606168: Epoch time: 100.65 s +2026-04-13 19:46:57.829630: +2026-04-13 19:46:57.837064: Epoch 2757 +2026-04-13 19:46:57.839042: Current learning rate: 0.00349 +2026-04-13 19:48:37.837800: train_loss -0.3477 +2026-04-13 19:48:37.845008: val_loss -0.3159 +2026-04-13 19:48:37.847912: Pseudo dice [0.4985, 0.0, 0.6349, 0.0, 0.3579, 0.0, 0.3047] +2026-04-13 19:48:37.850604: Epoch time: 100.01 s +2026-04-13 19:48:39.071257: +2026-04-13 19:48:39.072910: Epoch 2758 +2026-04-13 19:48:39.074812: Current learning rate: 0.00349 +2026-04-13 19:50:19.101894: train_loss -0.3886 +2026-04-13 19:50:19.107466: val_loss -0.3568 +2026-04-13 19:50:19.109737: Pseudo dice [0.6612, 0.0, 0.721, 0.2737, 0.1864, 0.2158, 0.6017] +2026-04-13 19:50:19.112290: Epoch time: 100.03 s +2026-04-13 19:50:20.324120: +2026-04-13 19:50:20.325863: Epoch 2759 +2026-04-13 19:50:20.327415: Current learning rate: 0.00349 +2026-04-13 19:52:00.262459: train_loss -0.4228 +2026-04-13 19:52:00.272043: val_loss -0.4223 +2026-04-13 19:52:00.275275: Pseudo dice [0.2323, 0.0, 0.7529, 0.8298, 0.3567, 0.7603, 0.6845] +2026-04-13 19:52:00.278964: Epoch time: 99.94 s +2026-04-13 19:52:01.501494: +2026-04-13 19:52:01.503658: Epoch 2760 +2026-04-13 19:52:01.505936: Current learning rate: 0.00349 +2026-04-13 19:53:41.564062: train_loss -0.405 +2026-04-13 19:53:41.570010: val_loss -0.3479 +2026-04-13 19:53:41.572255: Pseudo dice [0.6743, 0.0, 0.732, 0.5129, 0.4157, 0.1682, 0.9061] +2026-04-13 19:53:41.575010: Epoch time: 100.07 s +2026-04-13 19:53:42.771558: +2026-04-13 19:53:42.773638: Epoch 2761 +2026-04-13 19:53:42.775978: Current learning rate: 0.00348 +2026-04-13 19:55:23.048589: train_loss -0.4107 +2026-04-13 19:55:23.054497: val_loss -0.4352 +2026-04-13 19:55:23.057031: Pseudo dice [0.3861, 0.0, 0.8091, 0.0, 0.4423, 0.7206, 0.7998] +2026-04-13 19:55:23.059556: Epoch time: 100.28 s +2026-04-13 19:55:24.283195: +2026-04-13 19:55:24.284888: Epoch 2762 +2026-04-13 19:55:24.287146: Current learning rate: 0.00348 +2026-04-13 19:57:04.643592: train_loss -0.4255 +2026-04-13 19:57:04.660344: val_loss -0.3209 +2026-04-13 19:57:04.662969: Pseudo dice [0.0, 0.0, 0.5921, 0.154, 0.4244, 0.6741, 0.6103] +2026-04-13 19:57:04.665755: Epoch time: 100.36 s +2026-04-13 19:57:05.857789: +2026-04-13 19:57:05.860521: Epoch 2763 +2026-04-13 19:57:05.862496: Current learning rate: 0.00348 +2026-04-13 19:58:45.776526: train_loss -0.4082 +2026-04-13 19:58:45.788380: val_loss -0.3744 +2026-04-13 19:58:45.791290: Pseudo dice [0.4804, 0.0, 0.6686, 0.0, 0.2709, 0.6611, 0.83] +2026-04-13 19:58:45.795093: Epoch time: 99.92 s +2026-04-13 19:58:47.034714: +2026-04-13 19:58:47.036778: Epoch 2764 +2026-04-13 19:58:47.038834: Current learning rate: 0.00348 +2026-04-13 20:00:27.679443: train_loss -0.4347 +2026-04-13 20:00:27.685478: val_loss -0.4174 +2026-04-13 20:00:27.687423: Pseudo dice [0.4942, 0.0, 0.7363, 0.0, 0.3743, 0.7983, 0.874] +2026-04-13 20:00:27.690114: Epoch time: 100.65 s +2026-04-13 20:00:28.886312: +2026-04-13 20:00:28.889097: Epoch 2765 +2026-04-13 20:00:28.890934: Current learning rate: 0.00347 +2026-04-13 20:02:08.943784: train_loss -0.4272 +2026-04-13 20:02:08.950028: val_loss -0.3892 +2026-04-13 20:02:08.952620: Pseudo dice [0.5918, 0.0, 0.4871, 0.8666, 0.4433, 0.6752, 0.7272] +2026-04-13 20:02:08.955015: Epoch time: 100.06 s +2026-04-13 20:02:10.152369: +2026-04-13 20:02:10.154353: Epoch 2766 +2026-04-13 20:02:10.156010: Current learning rate: 0.00347 +2026-04-13 20:03:50.262116: train_loss -0.4056 +2026-04-13 20:03:50.269931: val_loss -0.3947 +2026-04-13 20:03:50.272258: Pseudo dice [0.0, 0.0, 0.574, 0.886, 0.3438, 0.6103, 0.8239] +2026-04-13 20:03:50.275446: Epoch time: 100.11 s +2026-04-13 20:03:51.488158: +2026-04-13 20:03:51.490752: Epoch 2767 +2026-04-13 20:03:51.492819: Current learning rate: 0.00347 +2026-04-13 20:05:32.467845: train_loss -0.4182 +2026-04-13 20:05:32.472932: val_loss -0.3765 +2026-04-13 20:05:32.475543: Pseudo dice [0.0443, 0.0, 0.497, 0.8179, 0.4161, 0.6567, 0.7539] +2026-04-13 20:05:32.477759: Epoch time: 100.98 s +2026-04-13 20:05:33.689272: +2026-04-13 20:05:33.691093: Epoch 2768 +2026-04-13 20:05:33.692681: Current learning rate: 0.00346 +2026-04-13 20:07:13.749839: train_loss -0.4097 +2026-04-13 20:07:13.761476: val_loss -0.3648 +2026-04-13 20:07:13.765515: Pseudo dice [0.609, 0.0, 0.5921, 0.0, 0.3451, 0.8466, 0.6221] +2026-04-13 20:07:13.770294: Epoch time: 100.06 s +2026-04-13 20:07:14.966445: +2026-04-13 20:07:14.968260: Epoch 2769 +2026-04-13 20:07:14.970307: Current learning rate: 0.00346 +2026-04-13 20:08:55.294369: train_loss -0.3968 +2026-04-13 20:08:55.300925: val_loss -0.384 +2026-04-13 20:08:55.303271: Pseudo dice [0.0, 0.0, 0.7497, 0.3379, 0.3918, 0.8304, 0.8387] +2026-04-13 20:08:55.305679: Epoch time: 100.33 s +2026-04-13 20:08:56.550759: +2026-04-13 20:08:56.552781: Epoch 2770 +2026-04-13 20:08:56.554913: Current learning rate: 0.00346 +2026-04-13 20:10:36.961471: train_loss -0.4097 +2026-04-13 20:10:36.967376: val_loss -0.396 +2026-04-13 20:10:36.969297: Pseudo dice [0.0, 0.0, 0.6492, 0.1256, 0.5401, 0.5058, 0.6379] +2026-04-13 20:10:36.971482: Epoch time: 100.41 s +2026-04-13 20:10:38.173210: +2026-04-13 20:10:38.175401: Epoch 2771 +2026-04-13 20:10:38.177301: Current learning rate: 0.00346 +2026-04-13 20:12:18.567201: train_loss -0.4236 +2026-04-13 20:12:18.573067: val_loss -0.4137 +2026-04-13 20:12:18.574831: Pseudo dice [0.3292, 0.0, 0.597, 0.3061, 0.5128, 0.816, 0.8959] +2026-04-13 20:12:18.577225: Epoch time: 100.4 s +2026-04-13 20:12:19.803596: +2026-04-13 20:12:19.805377: Epoch 2772 +2026-04-13 20:12:19.807117: Current learning rate: 0.00345 +2026-04-13 20:13:59.933089: train_loss -0.4362 +2026-04-13 20:13:59.939807: val_loss -0.4241 +2026-04-13 20:13:59.942197: Pseudo dice [0.8025, 0.0, 0.6294, 0.8411, 0.4433, 0.8285, 0.4382] +2026-04-13 20:13:59.945915: Epoch time: 100.13 s +2026-04-13 20:14:01.163125: +2026-04-13 20:14:01.165111: Epoch 2773 +2026-04-13 20:14:01.167735: Current learning rate: 0.00345 +2026-04-13 20:15:41.418030: train_loss -0.4378 +2026-04-13 20:15:41.431205: val_loss -0.3618 +2026-04-13 20:15:41.447797: Pseudo dice [0.6406, 0.0, 0.576, 0.1486, 0.5836, 0.6796, 0.6191] +2026-04-13 20:15:41.451363: Epoch time: 100.26 s +2026-04-13 20:15:42.681973: +2026-04-13 20:15:42.684620: Epoch 2774 +2026-04-13 20:15:42.686594: Current learning rate: 0.00345 +2026-04-13 20:17:24.173484: train_loss -0.4282 +2026-04-13 20:17:24.180607: val_loss -0.3508 +2026-04-13 20:17:24.182798: Pseudo dice [0.7084, 0.0, 0.5302, 0.0474, 0.5193, 0.7675, 0.8114] +2026-04-13 20:17:24.185435: Epoch time: 101.49 s +2026-04-13 20:17:25.403937: +2026-04-13 20:17:25.406423: Epoch 2775 +2026-04-13 20:17:25.408775: Current learning rate: 0.00345 +2026-04-13 20:19:05.437601: train_loss -0.4119 +2026-04-13 20:19:05.444971: val_loss -0.3842 +2026-04-13 20:19:05.447460: Pseudo dice [0.7861, 0.0, 0.6499, 0.5658, 0.1944, 0.5283, 0.6751] +2026-04-13 20:19:05.449577: Epoch time: 100.04 s +2026-04-13 20:19:06.672384: +2026-04-13 20:19:06.674465: Epoch 2776 +2026-04-13 20:19:06.676384: Current learning rate: 0.00344 +2026-04-13 20:20:48.244297: train_loss -0.426 +2026-04-13 20:20:48.251479: val_loss -0.3468 +2026-04-13 20:20:48.254326: Pseudo dice [0.5411, 0.0, 0.6796, 0.0635, 0.319, 0.739, 0.6441] +2026-04-13 20:20:48.257022: Epoch time: 101.57 s +2026-04-13 20:20:49.497623: +2026-04-13 20:20:49.500620: Epoch 2777 +2026-04-13 20:20:49.502635: Current learning rate: 0.00344 +2026-04-13 20:22:30.489550: train_loss -0.4198 +2026-04-13 20:22:30.495058: val_loss -0.3895 +2026-04-13 20:22:30.497366: Pseudo dice [0.0, 0.0, 0.7324, 0.4789, 0.4216, 0.8414, 0.4864] +2026-04-13 20:22:30.500486: Epoch time: 101.0 s +2026-04-13 20:22:31.717078: +2026-04-13 20:22:31.718832: Epoch 2778 +2026-04-13 20:22:31.720744: Current learning rate: 0.00344 +2026-04-13 20:24:11.986410: train_loss -0.4151 +2026-04-13 20:24:11.993032: val_loss -0.2796 +2026-04-13 20:24:11.995753: Pseudo dice [0.4742, 0.0, 0.6016, 0.0939, 0.5008, 0.5437, 0.4453] +2026-04-13 20:24:11.999263: Epoch time: 100.27 s +2026-04-13 20:24:13.213392: +2026-04-13 20:24:13.215571: Epoch 2779 +2026-04-13 20:24:13.217989: Current learning rate: 0.00344 +2026-04-13 20:25:53.236894: train_loss -0.4101 +2026-04-13 20:25:53.246280: val_loss -0.4014 +2026-04-13 20:25:53.248698: Pseudo dice [0.6179, 0.0, 0.6417, 0.245, 0.275, 0.6572, 0.8394] +2026-04-13 20:25:53.251311: Epoch time: 100.03 s +2026-04-13 20:25:54.481323: +2026-04-13 20:25:54.485344: Epoch 2780 +2026-04-13 20:25:54.487810: Current learning rate: 0.00343 +2026-04-13 20:27:34.650297: train_loss -0.3819 +2026-04-13 20:27:34.655498: val_loss -0.3643 +2026-04-13 20:27:34.657689: Pseudo dice [0.3179, 0.0, 0.6563, 0.498, 0.2463, 0.7476, 0.6136] +2026-04-13 20:27:34.659881: Epoch time: 100.17 s +2026-04-13 20:27:35.866688: +2026-04-13 20:27:35.869057: Epoch 2781 +2026-04-13 20:27:35.871418: Current learning rate: 0.00343 +2026-04-13 20:29:16.018733: train_loss -0.3969 +2026-04-13 20:29:16.025624: val_loss -0.3937 +2026-04-13 20:29:16.028042: Pseudo dice [0.3021, 0.0, 0.6629, 0.7018, 0.4406, 0.8563, 0.6534] +2026-04-13 20:29:16.031368: Epoch time: 100.16 s +2026-04-13 20:29:17.251022: +2026-04-13 20:29:17.253014: Epoch 2782 +2026-04-13 20:29:17.254974: Current learning rate: 0.00343 +2026-04-13 20:30:57.169506: train_loss -0.4203 +2026-04-13 20:30:57.176529: val_loss -0.34 +2026-04-13 20:30:57.179484: Pseudo dice [0.7049, 0.0, 0.627, 0.0, 0.0873, 0.7429, 0.6074] +2026-04-13 20:30:57.183018: Epoch time: 99.92 s +2026-04-13 20:30:58.389674: +2026-04-13 20:30:58.391678: Epoch 2783 +2026-04-13 20:30:58.393346: Current learning rate: 0.00343 +2026-04-13 20:32:38.445223: train_loss -0.4143 +2026-04-13 20:32:38.451825: val_loss -0.3769 +2026-04-13 20:32:38.454244: Pseudo dice [0.6331, 0.0, 0.6091, 0.068, 0.1831, 0.5756, 0.7309] +2026-04-13 20:32:38.456597: Epoch time: 100.06 s +2026-04-13 20:32:39.655499: +2026-04-13 20:32:39.672766: Epoch 2784 +2026-04-13 20:32:39.674656: Current learning rate: 0.00342 +2026-04-13 20:34:19.950272: train_loss -0.4196 +2026-04-13 20:34:19.957633: val_loss -0.4013 +2026-04-13 20:34:19.960775: Pseudo dice [0.4654, 0.0, 0.7798, 0.0, 0.223, 0.6851, 0.6524] +2026-04-13 20:34:19.963240: Epoch time: 100.3 s +2026-04-13 20:34:21.193750: +2026-04-13 20:34:21.196129: Epoch 2785 +2026-04-13 20:34:21.197973: Current learning rate: 0.00342 +2026-04-13 20:36:01.500383: train_loss -0.4263 +2026-04-13 20:36:01.506322: val_loss -0.3758 +2026-04-13 20:36:01.508073: Pseudo dice [0.751, 0.0, 0.6462, 0.0765, 0.4294, 0.771, 0.6955] +2026-04-13 20:36:01.511118: Epoch time: 100.31 s +2026-04-13 20:36:02.705746: +2026-04-13 20:36:02.721671: Epoch 2786 +2026-04-13 20:36:02.723572: Current learning rate: 0.00342 +2026-04-13 20:37:42.745539: train_loss -0.4201 +2026-04-13 20:37:42.751224: val_loss -0.4179 +2026-04-13 20:37:42.753632: Pseudo dice [0.0, 0.0, 0.6632, 0.6212, 0.1814, 0.7224, 0.6437] +2026-04-13 20:37:42.757421: Epoch time: 100.04 s +2026-04-13 20:37:44.008628: +2026-04-13 20:37:44.011961: Epoch 2787 +2026-04-13 20:37:44.015484: Current learning rate: 0.00342 +2026-04-13 20:39:24.480842: train_loss -0.4079 +2026-04-13 20:39:24.487069: val_loss -0.3626 +2026-04-13 20:39:24.489980: Pseudo dice [0.1942, 0.0, 0.7081, 0.8423, 0.366, 0.7514, 0.4208] +2026-04-13 20:39:24.492834: Epoch time: 100.48 s +2026-04-13 20:39:25.713231: +2026-04-13 20:39:25.716142: Epoch 2788 +2026-04-13 20:39:25.718668: Current learning rate: 0.00341 +2026-04-13 20:41:05.982079: train_loss -0.4185 +2026-04-13 20:41:05.987798: val_loss -0.3507 +2026-04-13 20:41:05.990005: Pseudo dice [0.8279, 0.0, 0.5455, 0.0, 0.5349, 0.8164, 0.7472] +2026-04-13 20:41:05.992925: Epoch time: 100.27 s +2026-04-13 20:41:07.211562: +2026-04-13 20:41:07.215088: Epoch 2789 +2026-04-13 20:41:07.217068: Current learning rate: 0.00341 +2026-04-13 20:42:47.822797: train_loss -0.4222 +2026-04-13 20:42:47.833461: val_loss -0.3509 +2026-04-13 20:42:47.842928: Pseudo dice [0.3672, 0.0, 0.5181, 0.0, 0.3644, 0.7958, 0.818] +2026-04-13 20:42:47.846575: Epoch time: 100.61 s +2026-04-13 20:42:49.087724: +2026-04-13 20:42:49.090665: Epoch 2790 +2026-04-13 20:42:49.092968: Current learning rate: 0.00341 +2026-04-13 20:44:29.113203: train_loss -0.4186 +2026-04-13 20:44:29.122021: val_loss -0.3547 +2026-04-13 20:44:29.126623: Pseudo dice [0.0, 0.0, 0.5732, 0.0, 0.0, 0.4973, 0.6692] +2026-04-13 20:44:29.129482: Epoch time: 100.03 s +2026-04-13 20:44:30.347223: +2026-04-13 20:44:30.349241: Epoch 2791 +2026-04-13 20:44:30.351689: Current learning rate: 0.00341 +2026-04-13 20:46:11.785380: train_loss -0.3649 +2026-04-13 20:46:11.791485: val_loss -0.3537 +2026-04-13 20:46:11.793644: Pseudo dice [0.0, 0.0, 0.7096, 0.0, 0.3014, 0.7105, 0.81] +2026-04-13 20:46:11.799479: Epoch time: 101.44 s +2026-04-13 20:46:13.032642: +2026-04-13 20:46:13.035183: Epoch 2792 +2026-04-13 20:46:13.037742: Current learning rate: 0.0034 +2026-04-13 20:47:54.446726: train_loss -0.4057 +2026-04-13 20:47:54.454159: val_loss -0.4089 +2026-04-13 20:47:54.456620: Pseudo dice [0.4788, 0.0, 0.7444, 0.4561, 0.3887, 0.4529, 0.821] +2026-04-13 20:47:54.459083: Epoch time: 101.42 s +2026-04-13 20:47:55.690631: +2026-04-13 20:47:55.693253: Epoch 2793 +2026-04-13 20:47:55.695832: Current learning rate: 0.0034 +2026-04-13 20:49:36.305256: train_loss -0.4185 +2026-04-13 20:49:36.311285: val_loss -0.408 +2026-04-13 20:49:36.313642: Pseudo dice [0.7099, 0.0, 0.6696, 0.759, 0.3744, 0.494, 0.7594] +2026-04-13 20:49:36.316333: Epoch time: 100.62 s +2026-04-13 20:49:37.526076: +2026-04-13 20:49:37.528059: Epoch 2794 +2026-04-13 20:49:37.530089: Current learning rate: 0.0034 +2026-04-13 20:51:18.598357: train_loss -0.4114 +2026-04-13 20:51:18.605316: val_loss -0.2946 +2026-04-13 20:51:18.607545: Pseudo dice [0.7409, 0.0, 0.5328, 0.066, 0.4102, 0.7031, 0.5628] +2026-04-13 20:51:18.610043: Epoch time: 101.08 s +2026-04-13 20:51:19.800694: +2026-04-13 20:51:19.802453: Epoch 2795 +2026-04-13 20:51:19.804164: Current learning rate: 0.0034 +2026-04-13 20:53:00.087049: train_loss -0.4133 +2026-04-13 20:53:00.093689: val_loss -0.4192 +2026-04-13 20:53:00.096159: Pseudo dice [0.7809, 0.0, 0.6913, 0.8956, 0.3962, 0.8131, 0.7906] +2026-04-13 20:53:00.098604: Epoch time: 100.29 s +2026-04-13 20:53:01.328880: +2026-04-13 20:53:01.331018: Epoch 2796 +2026-04-13 20:53:01.334382: Current learning rate: 0.00339 +2026-04-13 20:54:41.436991: train_loss -0.4103 +2026-04-13 20:54:41.442036: val_loss -0.3252 +2026-04-13 20:54:41.443912: Pseudo dice [0.0, 0.0, 0.4673, 0.0, 0.3268, 0.0, 0.8684] +2026-04-13 20:54:41.449777: Epoch time: 100.11 s +2026-04-13 20:54:42.662922: +2026-04-13 20:54:42.664787: Epoch 2797 +2026-04-13 20:54:42.666698: Current learning rate: 0.00339 +2026-04-13 20:56:22.747288: train_loss -0.3848 +2026-04-13 20:56:22.756703: val_loss -0.4105 +2026-04-13 20:56:22.758740: Pseudo dice [0.8241, 0.0, 0.72, 0.7028, 0.3898, 0.0281, 0.8338] +2026-04-13 20:56:22.761037: Epoch time: 100.09 s +2026-04-13 20:56:23.969395: +2026-04-13 20:56:23.971524: Epoch 2798 +2026-04-13 20:56:23.973391: Current learning rate: 0.00339 +2026-04-13 20:58:04.153791: train_loss -0.3952 +2026-04-13 20:58:04.159130: val_loss -0.3882 +2026-04-13 20:58:04.161781: Pseudo dice [0.0, 0.0, 0.7018, 0.7329, 0.1645, 0.6323, 0.8435] +2026-04-13 20:58:04.163955: Epoch time: 100.19 s +2026-04-13 20:58:05.366863: +2026-04-13 20:58:05.368880: Epoch 2799 +2026-04-13 20:58:05.370451: Current learning rate: 0.00339 +2026-04-13 20:59:45.673639: train_loss -0.4013 +2026-04-13 20:59:45.681657: val_loss -0.3883 +2026-04-13 20:59:45.684963: Pseudo dice [0.4242, 0.0, 0.4001, 0.9108, 0.2365, 0.8224, 0.4199] +2026-04-13 20:59:45.688194: Epoch time: 100.31 s +2026-04-13 20:59:48.652433: +2026-04-13 20:59:48.654482: Epoch 2800 +2026-04-13 20:59:48.656346: Current learning rate: 0.00338 +2026-04-13 21:01:29.016096: train_loss -0.4215 +2026-04-13 21:01:29.022348: val_loss -0.3841 +2026-04-13 21:01:29.024545: Pseudo dice [0.1453, 0.0, 0.6791, 0.6606, 0.3118, 0.3044, 0.8464] +2026-04-13 21:01:29.026724: Epoch time: 100.37 s +2026-04-13 21:01:30.210210: +2026-04-13 21:01:30.212628: Epoch 2801 +2026-04-13 21:01:30.214657: Current learning rate: 0.00338 +2026-04-13 21:03:11.707378: train_loss -0.4033 +2026-04-13 21:03:11.714711: val_loss -0.3959 +2026-04-13 21:03:11.718307: Pseudo dice [0.6492, 0.0, 0.6506, 0.8494, 0.5392, 0.4357, 0.6687] +2026-04-13 21:03:11.721194: Epoch time: 101.5 s +2026-04-13 21:03:12.937079: +2026-04-13 21:03:12.939387: Epoch 2802 +2026-04-13 21:03:12.944212: Current learning rate: 0.00338 +2026-04-13 21:04:53.070018: train_loss -0.4143 +2026-04-13 21:04:53.077912: val_loss -0.4149 +2026-04-13 21:04:53.080189: Pseudo dice [0.7406, 0.0, 0.7704, 0.0, 0.3769, 0.4827, 0.6194] +2026-04-13 21:04:53.083059: Epoch time: 100.14 s +2026-04-13 21:04:54.290097: +2026-04-13 21:04:54.292384: Epoch 2803 +2026-04-13 21:04:54.294286: Current learning rate: 0.00338 +2026-04-13 21:06:34.546458: train_loss -0.4328 +2026-04-13 21:06:34.553230: val_loss -0.4004 +2026-04-13 21:06:34.555473: Pseudo dice [0.6959, 0.0, 0.7435, 0.0, 0.4152, 0.7384, 0.2656] +2026-04-13 21:06:34.557811: Epoch time: 100.26 s +2026-04-13 21:06:35.756609: +2026-04-13 21:06:35.758549: Epoch 2804 +2026-04-13 21:06:35.760665: Current learning rate: 0.00337 +2026-04-13 21:08:17.233619: train_loss -0.4018 +2026-04-13 21:08:17.239831: val_loss -0.3627 +2026-04-13 21:08:17.242343: Pseudo dice [0.0, 0.0, 0.6745, 0.8575, 0.2659, 0.0, 0.7232] +2026-04-13 21:08:17.245196: Epoch time: 101.48 s +2026-04-13 21:08:18.486629: +2026-04-13 21:08:18.489357: Epoch 2805 +2026-04-13 21:08:18.491797: Current learning rate: 0.00337 +2026-04-13 21:09:59.573371: train_loss -0.3744 +2026-04-13 21:09:59.579456: val_loss -0.3301 +2026-04-13 21:09:59.581268: Pseudo dice [0.0, 0.0, 0.4348, 0.0, 0.5324, 0.0, 0.3396] +2026-04-13 21:09:59.583512: Epoch time: 101.09 s +2026-04-13 21:10:00.791336: +2026-04-13 21:10:00.793627: Epoch 2806 +2026-04-13 21:10:00.795776: Current learning rate: 0.00337 +2026-04-13 21:11:40.954714: train_loss -0.3917 +2026-04-13 21:11:40.963314: val_loss -0.4052 +2026-04-13 21:11:40.967101: Pseudo dice [0.7066, 0.0, 0.6979, 0.0812, 0.4158, 0.7363, 0.7604] +2026-04-13 21:11:40.970361: Epoch time: 100.17 s +2026-04-13 21:11:42.255194: +2026-04-13 21:11:42.257265: Epoch 2807 +2026-04-13 21:11:42.259122: Current learning rate: 0.00337 +2026-04-13 21:13:22.342280: train_loss -0.4213 +2026-04-13 21:13:22.350177: val_loss -0.3082 +2026-04-13 21:13:22.353504: Pseudo dice [0.4017, 0.0, 0.6725, 0.0, 0.1571, 0.7285, 0.499] +2026-04-13 21:13:22.356611: Epoch time: 100.09 s +2026-04-13 21:13:23.570536: +2026-04-13 21:13:23.572926: Epoch 2808 +2026-04-13 21:13:23.575071: Current learning rate: 0.00336 +2026-04-13 21:15:04.129649: train_loss -0.3989 +2026-04-13 21:15:04.140028: val_loss -0.3793 +2026-04-13 21:15:04.142342: Pseudo dice [0.6016, 0.0, 0.7704, 0.0, 0.5503, 0.7953, 0.4723] +2026-04-13 21:15:04.148088: Epoch time: 100.56 s +2026-04-13 21:15:05.348077: +2026-04-13 21:15:05.350650: Epoch 2809 +2026-04-13 21:15:05.352999: Current learning rate: 0.00336 +2026-04-13 21:16:45.861576: train_loss -0.415 +2026-04-13 21:16:45.874911: val_loss -0.326 +2026-04-13 21:16:45.877223: Pseudo dice [0.421, 0.0, 0.6443, 0.0, 0.438, 0.771, 0.848] +2026-04-13 21:16:45.879591: Epoch time: 100.52 s +2026-04-13 21:16:47.126491: +2026-04-13 21:16:47.129150: Epoch 2810 +2026-04-13 21:16:47.131342: Current learning rate: 0.00336 +2026-04-13 21:18:27.871020: train_loss -0.4309 +2026-04-13 21:18:27.877967: val_loss -0.3661 +2026-04-13 21:18:27.880195: Pseudo dice [0.6021, 0.0, 0.5762, 0.4432, 0.4387, 0.5527, 0.5807] +2026-04-13 21:18:27.882549: Epoch time: 100.75 s +2026-04-13 21:18:29.096005: +2026-04-13 21:18:29.098223: Epoch 2811 +2026-04-13 21:18:29.099855: Current learning rate: 0.00336 +2026-04-13 21:20:10.485839: train_loss -0.4366 +2026-04-13 21:20:10.498388: val_loss -0.405 +2026-04-13 21:20:10.502454: Pseudo dice [0.3976, 0.0, 0.6685, 0.7645, 0.3549, 0.8701, 0.5215] +2026-04-13 21:20:10.505601: Epoch time: 101.39 s +2026-04-13 21:20:11.727522: +2026-04-13 21:20:11.729689: Epoch 2812 +2026-04-13 21:20:11.731912: Current learning rate: 0.00335 +2026-04-13 21:21:52.423956: train_loss -0.4268 +2026-04-13 21:21:52.440947: val_loss -0.4072 +2026-04-13 21:21:52.443479: Pseudo dice [0.4481, 0.0, 0.7148, 0.0, 0.3499, 0.7526, 0.7602] +2026-04-13 21:21:52.446841: Epoch time: 100.7 s +2026-04-13 21:21:53.662057: +2026-04-13 21:21:53.664493: Epoch 2813 +2026-04-13 21:21:53.667541: Current learning rate: 0.00335 +2026-04-13 21:23:33.933114: train_loss -0.4223 +2026-04-13 21:23:33.943581: val_loss -0.256 +2026-04-13 21:23:33.946310: Pseudo dice [0.1913, 0.0, 0.5187, 0.0, 0.4216, 0.7878, 0.7696] +2026-04-13 21:23:33.948738: Epoch time: 100.27 s +2026-04-13 21:23:36.251015: +2026-04-13 21:23:36.253846: Epoch 2814 +2026-04-13 21:23:36.255553: Current learning rate: 0.00335 +2026-04-13 21:25:16.456286: train_loss -0.4348 +2026-04-13 21:25:16.464381: val_loss -0.4181 +2026-04-13 21:25:16.468609: Pseudo dice [0.6761, 0.0, 0.8125, 0.0, 0.4369, 0.763, 0.8245] +2026-04-13 21:25:16.471005: Epoch time: 100.21 s +2026-04-13 21:25:17.682301: +2026-04-13 21:25:17.684289: Epoch 2815 +2026-04-13 21:25:17.685912: Current learning rate: 0.00335 +2026-04-13 21:26:58.762227: train_loss -0.4471 +2026-04-13 21:26:58.769482: val_loss -0.4101 +2026-04-13 21:26:58.772980: Pseudo dice [0.479, 0.0, 0.6254, 0.434, 0.4754, 0.8172, 0.5895] +2026-04-13 21:26:58.776484: Epoch time: 101.08 s +2026-04-13 21:26:59.998907: +2026-04-13 21:27:00.000933: Epoch 2816 +2026-04-13 21:27:00.004163: Current learning rate: 0.00334 +2026-04-13 21:28:41.812605: train_loss -0.4244 +2026-04-13 21:28:41.819669: val_loss -0.4105 +2026-04-13 21:28:41.822273: Pseudo dice [0.0402, 0.0, 0.7756, 0.3359, 0.31, 0.8129, 0.8623] +2026-04-13 21:28:41.828240: Epoch time: 101.82 s +2026-04-13 21:28:43.051334: +2026-04-13 21:28:43.053806: Epoch 2817 +2026-04-13 21:28:43.055760: Current learning rate: 0.00334 +2026-04-13 21:30:23.940172: train_loss -0.4261 +2026-04-13 21:30:23.949587: val_loss -0.4104 +2026-04-13 21:30:23.952140: Pseudo dice [0.1996, 0.0, 0.7098, 0.6529, 0.5125, 0.6816, 0.853] +2026-04-13 21:30:23.955361: Epoch time: 100.89 s +2026-04-13 21:30:25.161685: +2026-04-13 21:30:25.164744: Epoch 2818 +2026-04-13 21:30:25.167200: Current learning rate: 0.00334 +2026-04-13 21:32:05.343467: train_loss -0.4277 +2026-04-13 21:32:05.348954: val_loss -0.3898 +2026-04-13 21:32:05.351414: Pseudo dice [0.4537, 0.0, 0.7534, 0.0, 0.233, 0.7484, 0.5075] +2026-04-13 21:32:05.355778: Epoch time: 100.18 s +2026-04-13 21:32:06.556422: +2026-04-13 21:32:06.558481: Epoch 2819 +2026-04-13 21:32:06.560283: Current learning rate: 0.00334 +2026-04-13 21:33:47.050737: train_loss -0.4279 +2026-04-13 21:33:47.059973: val_loss -0.3109 +2026-04-13 21:33:47.062385: Pseudo dice [0.2343, 0.0, 0.5063, 0.0923, 0.3061, 0.5571, 0.7415] +2026-04-13 21:33:47.066064: Epoch time: 100.5 s +2026-04-13 21:33:48.277909: +2026-04-13 21:33:48.280330: Epoch 2820 +2026-04-13 21:33:48.282076: Current learning rate: 0.00333 +2026-04-13 21:35:29.175858: train_loss -0.4333 +2026-04-13 21:35:29.183021: val_loss -0.4102 +2026-04-13 21:35:29.186644: Pseudo dice [0.7002, 0.0, 0.667, 0.0, 0.4361, 0.7613, 0.8032] +2026-04-13 21:35:29.189349: Epoch time: 100.9 s +2026-04-13 21:35:30.406851: +2026-04-13 21:35:30.408901: Epoch 2821 +2026-04-13 21:35:30.411322: Current learning rate: 0.00333 +2026-04-13 21:37:11.326958: train_loss -0.4332 +2026-04-13 21:37:11.333335: val_loss -0.4224 +2026-04-13 21:37:11.336646: Pseudo dice [0.5734, 0.0, 0.7373, 0.0, 0.2924, 0.805, 0.8893] +2026-04-13 21:37:11.340041: Epoch time: 100.92 s +2026-04-13 21:37:12.569637: +2026-04-13 21:37:12.574077: Epoch 2822 +2026-04-13 21:37:12.577501: Current learning rate: 0.00333 +2026-04-13 21:38:52.927361: train_loss -0.4273 +2026-04-13 21:38:52.933225: val_loss -0.4143 +2026-04-13 21:38:52.936413: Pseudo dice [0.6206, 0.0, 0.7526, 0.0, 0.3725, 0.8048, 0.7652] +2026-04-13 21:38:52.939053: Epoch time: 100.36 s +2026-04-13 21:38:54.175283: +2026-04-13 21:38:54.177734: Epoch 2823 +2026-04-13 21:38:54.181938: Current learning rate: 0.00333 +2026-04-13 21:40:35.279765: train_loss -0.4325 +2026-04-13 21:40:35.285405: val_loss -0.4215 +2026-04-13 21:40:35.287527: Pseudo dice [0.449, 0.0, 0.7198, 0.5143, 0.334, 0.6696, 0.8593] +2026-04-13 21:40:35.290094: Epoch time: 101.11 s +2026-04-13 21:40:36.552040: +2026-04-13 21:40:36.553933: Epoch 2824 +2026-04-13 21:40:36.555718: Current learning rate: 0.00332 +2026-04-13 21:42:16.554606: train_loss -0.4392 +2026-04-13 21:42:16.560746: val_loss -0.406 +2026-04-13 21:42:16.563012: Pseudo dice [0.6378, 0.0, 0.7307, 0.6447, 0.5527, 0.7648, 0.5184] +2026-04-13 21:42:16.566069: Epoch time: 100.01 s +2026-04-13 21:42:17.801534: +2026-04-13 21:42:17.804027: Epoch 2825 +2026-04-13 21:42:17.805695: Current learning rate: 0.00332 +2026-04-13 21:43:58.219155: train_loss -0.447 +2026-04-13 21:43:58.224743: val_loss -0.3159 +2026-04-13 21:43:58.226493: Pseudo dice [0.5102, 0.0, 0.7833, 0.0, 0.3045, 0.1429, 0.6606] +2026-04-13 21:43:58.228936: Epoch time: 100.42 s +2026-04-13 21:43:59.431536: +2026-04-13 21:43:59.435021: Epoch 2826 +2026-04-13 21:43:59.436844: Current learning rate: 0.00332 +2026-04-13 21:45:40.061323: train_loss -0.4457 +2026-04-13 21:45:40.068033: val_loss -0.3966 +2026-04-13 21:45:40.070394: Pseudo dice [0.743, 0.0, 0.7144, 0.2983, 0.4634, 0.9108, 0.6917] +2026-04-13 21:45:40.073789: Epoch time: 100.63 s +2026-04-13 21:45:41.305974: +2026-04-13 21:45:41.311585: Epoch 2827 +2026-04-13 21:45:41.314337: Current learning rate: 0.00332 +2026-04-13 21:47:21.583959: train_loss -0.4356 +2026-04-13 21:47:21.590291: val_loss -0.3914 +2026-04-13 21:47:21.593238: Pseudo dice [0.1955, 0.0, 0.8019, 0.8347, 0.5443, 0.7987, 0.6854] +2026-04-13 21:47:21.595706: Epoch time: 100.28 s +2026-04-13 21:47:22.828366: +2026-04-13 21:47:22.830339: Epoch 2828 +2026-04-13 21:47:22.832084: Current learning rate: 0.00331 +2026-04-13 21:49:04.410719: train_loss -0.4277 +2026-04-13 21:49:04.421289: val_loss -0.3726 +2026-04-13 21:49:04.425767: Pseudo dice [0.3973, 0.0, 0.7545, 0.0824, 0.1055, 0.6933, 0.5361] +2026-04-13 21:49:04.429554: Epoch time: 101.59 s +2026-04-13 21:49:05.626029: +2026-04-13 21:49:05.628683: Epoch 2829 +2026-04-13 21:49:05.630752: Current learning rate: 0.00331 +2026-04-13 21:50:46.839403: train_loss -0.424 +2026-04-13 21:50:46.847361: val_loss -0.3786 +2026-04-13 21:50:46.850605: Pseudo dice [0.1666, 0.0, 0.7368, 0.0, 0.2017, 0.4391, 0.7298] +2026-04-13 21:50:46.853405: Epoch time: 101.22 s +2026-04-13 21:50:48.077812: +2026-04-13 21:50:48.080994: Epoch 2830 +2026-04-13 21:50:48.082973: Current learning rate: 0.00331 +2026-04-13 21:52:28.150486: train_loss -0.4048 +2026-04-13 21:52:28.155598: val_loss -0.3551 +2026-04-13 21:52:28.157512: Pseudo dice [0.6595, 0.0, 0.4712, 0.1784, 0.242, 0.7326, 0.7674] +2026-04-13 21:52:28.159741: Epoch time: 100.08 s +2026-04-13 21:52:29.367404: +2026-04-13 21:52:29.369548: Epoch 2831 +2026-04-13 21:52:29.372371: Current learning rate: 0.00331 +2026-04-13 21:54:10.027995: train_loss -0.4173 +2026-04-13 21:54:10.036650: val_loss -0.4138 +2026-04-13 21:54:10.039668: Pseudo dice [0.6858, 0.0, 0.7081, 0.6873, 0.2827, 0.7342, 0.7929] +2026-04-13 21:54:10.042253: Epoch time: 100.66 s +2026-04-13 21:54:11.261567: +2026-04-13 21:54:11.263730: Epoch 2832 +2026-04-13 21:54:11.265625: Current learning rate: 0.0033 +2026-04-13 21:55:51.793391: train_loss -0.4257 +2026-04-13 21:55:51.799865: val_loss -0.2796 +2026-04-13 21:55:51.801521: Pseudo dice [0.6307, 0.0, 0.6015, 0.0, 0.1534, 0.7764, 0.5732] +2026-04-13 21:55:51.806528: Epoch time: 100.54 s +2026-04-13 21:55:53.028270: +2026-04-13 21:55:53.030160: Epoch 2833 +2026-04-13 21:55:53.032043: Current learning rate: 0.0033 +2026-04-13 21:57:33.281142: train_loss -0.4432 +2026-04-13 21:57:33.290336: val_loss -0.3598 +2026-04-13 21:57:33.292797: Pseudo dice [0.5604, 0.0, 0.6745, 0.0, 0.6362, 0.7975, 0.4578] +2026-04-13 21:57:33.296010: Epoch time: 100.26 s +2026-04-13 21:57:34.514045: +2026-04-13 21:57:34.516297: Epoch 2834 +2026-04-13 21:57:34.518253: Current learning rate: 0.0033 +2026-04-13 21:59:16.395441: train_loss -0.4423 +2026-04-13 21:59:16.401498: val_loss -0.4109 +2026-04-13 21:59:16.406236: Pseudo dice [0.6761, 0.0, 0.6737, 0.0, 0.3418, 0.6808, 0.8326] +2026-04-13 21:59:16.408927: Epoch time: 101.88 s +2026-04-13 21:59:17.650728: +2026-04-13 21:59:17.652927: Epoch 2835 +2026-04-13 21:59:17.655279: Current learning rate: 0.00329 +2026-04-13 22:00:58.454160: train_loss -0.4207 +2026-04-13 22:00:58.461851: val_loss -0.3755 +2026-04-13 22:00:58.464386: Pseudo dice [0.2133, 0.0, 0.6243, 0.0, 0.4191, 0.7747, 0.8807] +2026-04-13 22:00:58.467225: Epoch time: 100.81 s +2026-04-13 22:00:59.679693: +2026-04-13 22:00:59.681945: Epoch 2836 +2026-04-13 22:00:59.683630: Current learning rate: 0.00329 +2026-04-13 22:02:39.951337: train_loss -0.4115 +2026-04-13 22:02:39.958244: val_loss -0.3845 +2026-04-13 22:02:39.960324: Pseudo dice [0.34, 0.0, 0.5708, 0.2025, 0.3645, 0.6423, 0.5617] +2026-04-13 22:02:39.963285: Epoch time: 100.27 s +2026-04-13 22:02:41.169149: +2026-04-13 22:02:41.171378: Epoch 2837 +2026-04-13 22:02:41.173614: Current learning rate: 0.00329 +2026-04-13 22:04:22.275942: train_loss -0.4068 +2026-04-13 22:04:22.284409: val_loss -0.3975 +2026-04-13 22:04:22.288696: Pseudo dice [0.2578, 0.0, 0.7243, 0.2839, 0.305, 0.39, 0.7867] +2026-04-13 22:04:22.291179: Epoch time: 101.11 s +2026-04-13 22:04:23.511561: +2026-04-13 22:04:23.513475: Epoch 2838 +2026-04-13 22:04:23.516104: Current learning rate: 0.00329 +2026-04-13 22:06:03.851505: train_loss -0.4078 +2026-04-13 22:06:03.858150: val_loss -0.315 +2026-04-13 22:06:03.860644: Pseudo dice [0.7414, 0.0, 0.6293, 0.0703, 0.0128, 0.5387, 0.8422] +2026-04-13 22:06:03.863805: Epoch time: 100.34 s +2026-04-13 22:06:05.072147: +2026-04-13 22:06:05.074081: Epoch 2839 +2026-04-13 22:06:05.075801: Current learning rate: 0.00328 +2026-04-13 22:07:44.948587: train_loss -0.4248 +2026-04-13 22:07:44.954621: val_loss -0.4161 +2026-04-13 22:07:44.956537: Pseudo dice [0.0963, 0.0, 0.7799, 0.8212, 0.2752, 0.7473, 0.7225] +2026-04-13 22:07:44.959615: Epoch time: 99.88 s +2026-04-13 22:07:46.165979: +2026-04-13 22:07:46.167780: Epoch 2840 +2026-04-13 22:07:46.169511: Current learning rate: 0.00328 +2026-04-13 22:09:26.198587: train_loss -0.4234 +2026-04-13 22:09:26.206069: val_loss -0.3724 +2026-04-13 22:09:26.208792: Pseudo dice [0.6428, 0.0, 0.599, 0.095, 0.1907, 0.5991, 0.7822] +2026-04-13 22:09:26.211352: Epoch time: 100.04 s +2026-04-13 22:09:27.428174: +2026-04-13 22:09:27.430288: Epoch 2841 +2026-04-13 22:09:27.432194: Current learning rate: 0.00328 +2026-04-13 22:11:07.431165: train_loss -0.4192 +2026-04-13 22:11:07.438158: val_loss -0.408 +2026-04-13 22:11:07.440163: Pseudo dice [0.5414, 0.0, 0.7532, 0.3378, 0.3566, 0.6877, 0.6402] +2026-04-13 22:11:07.442834: Epoch time: 100.01 s +2026-04-13 22:11:08.681024: +2026-04-13 22:11:08.683082: Epoch 2842 +2026-04-13 22:11:08.685124: Current learning rate: 0.00328 +2026-04-13 22:12:49.247070: train_loss -0.4107 +2026-04-13 22:12:49.255120: val_loss -0.4051 +2026-04-13 22:12:49.258559: Pseudo dice [0.7626, 0.0, 0.7953, 0.5913, 0.2965, 0.6512, 0.73] +2026-04-13 22:12:49.263713: Epoch time: 100.57 s +2026-04-13 22:12:50.469161: +2026-04-13 22:12:50.471027: Epoch 2843 +2026-04-13 22:12:50.474877: Current learning rate: 0.00327 +2026-04-13 22:14:31.296734: train_loss -0.4072 +2026-04-13 22:14:31.302392: val_loss -0.325 +2026-04-13 22:14:31.304972: Pseudo dice [0.274, 0.0, 0.3391, 0.002, 0.3646, 0.7478, 0.682] +2026-04-13 22:14:31.307358: Epoch time: 100.83 s +2026-04-13 22:14:32.518044: +2026-04-13 22:14:32.522029: Epoch 2844 +2026-04-13 22:14:32.524537: Current learning rate: 0.00327 +2026-04-13 22:16:13.256221: train_loss -0.4131 +2026-04-13 22:16:13.262393: val_loss -0.4223 +2026-04-13 22:16:13.265054: Pseudo dice [0.1063, 0.0, 0.7903, 0.8453, 0.3665, 0.6437, 0.6993] +2026-04-13 22:16:13.267723: Epoch time: 100.74 s +2026-04-13 22:16:14.515450: +2026-04-13 22:16:14.517494: Epoch 2845 +2026-04-13 22:16:14.519683: Current learning rate: 0.00327 +2026-04-13 22:17:54.449044: train_loss -0.4052 +2026-04-13 22:17:54.456067: val_loss -0.4114 +2026-04-13 22:17:54.458307: Pseudo dice [0.326, 0.0, 0.6891, 0.0, 0.4552, 0.6802, 0.7636] +2026-04-13 22:17:54.462921: Epoch time: 99.94 s +2026-04-13 22:17:55.676409: +2026-04-13 22:17:55.680026: Epoch 2846 +2026-04-13 22:17:55.681850: Current learning rate: 0.00327 +2026-04-13 22:19:36.786479: train_loss -0.4217 +2026-04-13 22:19:36.798540: val_loss -0.3646 +2026-04-13 22:19:36.802475: Pseudo dice [0.6845, 0.0, 0.6249, 0.0362, 0.2628, 0.3675, 0.9088] +2026-04-13 22:19:36.807055: Epoch time: 101.11 s +2026-04-13 22:19:38.049097: +2026-04-13 22:19:38.051398: Epoch 2847 +2026-04-13 22:19:38.053568: Current learning rate: 0.00326 +2026-04-13 22:21:18.181126: train_loss -0.4395 +2026-04-13 22:21:18.188072: val_loss -0.3976 +2026-04-13 22:21:18.191161: Pseudo dice [0.6033, 0.0, 0.5098, 0.7238, 0.4682, 0.4783, 0.6925] +2026-04-13 22:21:18.193752: Epoch time: 100.14 s +2026-04-13 22:21:19.440759: +2026-04-13 22:21:19.444756: Epoch 2848 +2026-04-13 22:21:19.447548: Current learning rate: 0.00326 +2026-04-13 22:22:59.887674: train_loss -0.4347 +2026-04-13 22:22:59.894635: val_loss -0.433 +2026-04-13 22:22:59.899325: Pseudo dice [0.5883, 0.0, 0.7845, 0.5583, 0.3461, 0.709, 0.9034] +2026-04-13 22:22:59.902803: Epoch time: 100.45 s +2026-04-13 22:23:01.119943: +2026-04-13 22:23:01.123558: Epoch 2849 +2026-04-13 22:23:01.126548: Current learning rate: 0.00326 +2026-04-13 22:24:41.105933: train_loss -0.4352 +2026-04-13 22:24:41.115615: val_loss -0.3374 +2026-04-13 22:24:41.118985: Pseudo dice [0.5067, 0.0, 0.7183, 0.0762, 0.23, 0.4713, 0.7749] +2026-04-13 22:24:41.121708: Epoch time: 99.99 s +2026-04-13 22:24:44.002194: +2026-04-13 22:24:44.004465: Epoch 2850 +2026-04-13 22:24:44.006404: Current learning rate: 0.00326 +2026-04-13 22:26:24.148535: train_loss -0.4231 +2026-04-13 22:26:24.154506: val_loss -0.3977 +2026-04-13 22:26:24.156630: Pseudo dice [0.5295, 0.0, 0.7701, 0.7838, 0.3437, 0.468, 0.3661] +2026-04-13 22:26:24.160593: Epoch time: 100.15 s +2026-04-13 22:26:25.375236: +2026-04-13 22:26:25.377217: Epoch 2851 +2026-04-13 22:26:25.379140: Current learning rate: 0.00325 +2026-04-13 22:28:05.257678: train_loss -0.4147 +2026-04-13 22:28:05.265026: val_loss -0.2755 +2026-04-13 22:28:05.270798: Pseudo dice [0.5635, 0.0, 0.4928, 0.0564, 0.3269, 0.4422, 0.7762] +2026-04-13 22:28:05.273744: Epoch time: 99.89 s +2026-04-13 22:28:06.481377: +2026-04-13 22:28:06.483681: Epoch 2852 +2026-04-13 22:28:06.486098: Current learning rate: 0.00325 +2026-04-13 22:29:46.499991: train_loss -0.3967 +2026-04-13 22:29:46.506088: val_loss -0.3334 +2026-04-13 22:29:46.508230: Pseudo dice [0.3798, 0.0, 0.7138, 0.0055, 0.3818, 0.5649, 0.7764] +2026-04-13 22:29:46.510567: Epoch time: 100.02 s +2026-04-13 22:29:47.744939: +2026-04-13 22:29:47.747011: Epoch 2853 +2026-04-13 22:29:47.748834: Current learning rate: 0.00325 +2026-04-13 22:31:28.040257: train_loss -0.4175 +2026-04-13 22:31:28.046184: val_loss -0.3936 +2026-04-13 22:31:28.048907: Pseudo dice [0.7336, 0.0, 0.7607, 0.3261, 0.3541, 0.5775, 0.3727] +2026-04-13 22:31:28.051976: Epoch time: 100.3 s +2026-04-13 22:31:30.397455: +2026-04-13 22:31:30.399775: Epoch 2854 +2026-04-13 22:31:30.401655: Current learning rate: 0.00325 +2026-04-13 22:33:10.597684: train_loss -0.4128 +2026-04-13 22:33:10.606142: val_loss -0.4136 +2026-04-13 22:33:10.609529: Pseudo dice [0.4869, 0.0, 0.8321, 0.8105, 0.3684, 0.6719, 0.6645] +2026-04-13 22:33:10.613030: Epoch time: 100.2 s +2026-04-13 22:33:11.830823: +2026-04-13 22:33:11.832841: Epoch 2855 +2026-04-13 22:33:11.834495: Current learning rate: 0.00324 +2026-04-13 22:34:52.336017: train_loss -0.4373 +2026-04-13 22:34:52.342209: val_loss -0.4192 +2026-04-13 22:34:52.344071: Pseudo dice [0.7681, 0.0, 0.6582, 0.1711, 0.2725, 0.7207, 0.8254] +2026-04-13 22:34:52.346435: Epoch time: 100.51 s +2026-04-13 22:34:53.592553: +2026-04-13 22:34:53.594400: Epoch 2856 +2026-04-13 22:34:53.596412: Current learning rate: 0.00324 +2026-04-13 22:36:34.080408: train_loss -0.4108 +2026-04-13 22:36:34.088488: val_loss -0.3843 +2026-04-13 22:36:34.092069: Pseudo dice [0.6057, 0.0, 0.7115, 0.0724, 0.3889, 0.6523, 0.5841] +2026-04-13 22:36:34.095082: Epoch time: 100.49 s +2026-04-13 22:36:35.350852: +2026-04-13 22:36:35.353008: Epoch 2857 +2026-04-13 22:36:35.355379: Current learning rate: 0.00324 +2026-04-13 22:38:15.952904: train_loss -0.4138 +2026-04-13 22:38:15.959364: val_loss -0.3751 +2026-04-13 22:38:15.961754: Pseudo dice [0.6946, 0.0, 0.791, 0.1624, 0.4279, 0.65, 0.6245] +2026-04-13 22:38:15.964159: Epoch time: 100.61 s +2026-04-13 22:38:17.189351: +2026-04-13 22:38:17.191329: Epoch 2858 +2026-04-13 22:38:17.193205: Current learning rate: 0.00324 +2026-04-13 22:39:57.475422: train_loss -0.4107 +2026-04-13 22:39:57.481631: val_loss -0.319 +2026-04-13 22:39:57.484515: Pseudo dice [0.1103, 0.0, 0.5067, 0.0, 0.109, 0.5985, 0.7296] +2026-04-13 22:39:57.486930: Epoch time: 100.29 s +2026-04-13 22:39:58.729195: +2026-04-13 22:39:58.731594: Epoch 2859 +2026-04-13 22:39:58.733306: Current learning rate: 0.00323 +2026-04-13 22:41:39.246201: train_loss -0.4253 +2026-04-13 22:41:39.251392: val_loss -0.3995 +2026-04-13 22:41:39.253058: Pseudo dice [0.6216, 0.0, 0.695, 0.0, 0.4117, 0.3877, 0.6697] +2026-04-13 22:41:39.254845: Epoch time: 100.52 s +2026-04-13 22:41:40.512724: +2026-04-13 22:41:40.514557: Epoch 2860 +2026-04-13 22:41:40.516181: Current learning rate: 0.00323 +2026-04-13 22:43:21.197788: train_loss -0.4114 +2026-04-13 22:43:21.203424: val_loss -0.3562 +2026-04-13 22:43:21.205738: Pseudo dice [0.3711, 0.0, 0.6627, 0.0602, 0.392, 0.7016, 0.7699] +2026-04-13 22:43:21.208094: Epoch time: 100.69 s +2026-04-13 22:43:22.443834: +2026-04-13 22:43:22.445886: Epoch 2861 +2026-04-13 22:43:22.447761: Current learning rate: 0.00323 +2026-04-13 22:45:03.051070: train_loss -0.4174 +2026-04-13 22:45:03.056419: val_loss -0.3316 +2026-04-13 22:45:03.058598: Pseudo dice [0.5882, 0.0, 0.6452, 0.0994, 0.2261, 0.195, 0.6153] +2026-04-13 22:45:03.060733: Epoch time: 100.61 s +2026-04-13 22:45:04.312193: +2026-04-13 22:45:04.313902: Epoch 2862 +2026-04-13 22:45:04.315679: Current learning rate: 0.00323 +2026-04-13 22:46:44.981996: train_loss -0.3992 +2026-04-13 22:46:44.987309: val_loss -0.3963 +2026-04-13 22:46:44.989506: Pseudo dice [0.7227, 0.0, 0.7763, 0.876, 0.3891, 0.5844, 0.7527] +2026-04-13 22:46:44.992583: Epoch time: 100.67 s +2026-04-13 22:46:46.228980: +2026-04-13 22:46:46.231845: Epoch 2863 +2026-04-13 22:46:46.233877: Current learning rate: 0.00322 +2026-04-13 22:48:27.106698: train_loss -0.4366 +2026-04-13 22:48:27.112674: val_loss -0.3382 +2026-04-13 22:48:27.115197: Pseudo dice [0.0, 0.0, 0.7614, 0.0658, 0.3093, 0.5528, 0.6367] +2026-04-13 22:48:27.117958: Epoch time: 100.88 s +2026-04-13 22:48:28.336504: +2026-04-13 22:48:28.338643: Epoch 2864 +2026-04-13 22:48:28.340630: Current learning rate: 0.00322 +2026-04-13 22:50:09.389149: train_loss -0.426 +2026-04-13 22:50:09.395242: val_loss -0.434 +2026-04-13 22:50:09.397677: Pseudo dice [0.2053, 0.0, 0.7652, 0.7633, 0.2851, 0.7522, 0.8738] +2026-04-13 22:50:09.400147: Epoch time: 101.06 s +2026-04-13 22:50:10.643933: +2026-04-13 22:50:10.645662: Epoch 2865 +2026-04-13 22:50:10.647260: Current learning rate: 0.00322 +2026-04-13 22:51:52.918612: train_loss -0.4312 +2026-04-13 22:51:52.926030: val_loss -0.403 +2026-04-13 22:51:52.928933: Pseudo dice [0.7102, 0.0, 0.6856, 0.1482, 0.3478, 0.7433, 0.8059] +2026-04-13 22:51:52.932349: Epoch time: 102.28 s +2026-04-13 22:51:54.163265: +2026-04-13 22:51:54.165221: Epoch 2866 +2026-04-13 22:51:54.166970: Current learning rate: 0.00322 +2026-04-13 22:53:34.272936: train_loss -0.432 +2026-04-13 22:53:34.279482: val_loss -0.3903 +2026-04-13 22:53:34.281735: Pseudo dice [0.2936, 0.0, 0.7776, 0.6332, 0.2279, 0.5854, 0.7587] +2026-04-13 22:53:34.284528: Epoch time: 100.11 s +2026-04-13 22:53:35.561507: +2026-04-13 22:53:35.565161: Epoch 2867 +2026-04-13 22:53:35.568434: Current learning rate: 0.00321 +2026-04-13 22:55:15.616602: train_loss -0.4336 +2026-04-13 22:55:15.622681: val_loss -0.3962 +2026-04-13 22:55:15.624439: Pseudo dice [0.6347, 0.0, 0.7911, 0.7435, 0.4202, 0.7146, 0.7522] +2026-04-13 22:55:15.626914: Epoch time: 100.06 s +2026-04-13 22:55:16.840467: +2026-04-13 22:55:16.843775: Epoch 2868 +2026-04-13 22:55:16.846650: Current learning rate: 0.00321 +2026-04-13 22:56:56.950286: train_loss -0.4333 +2026-04-13 22:56:56.957433: val_loss -0.4301 +2026-04-13 22:56:56.960464: Pseudo dice [0.726, 0.0, 0.6525, 0.6897, 0.5067, 0.6758, 0.8918] +2026-04-13 22:56:56.966366: Epoch time: 100.11 s +2026-04-13 22:56:58.187543: +2026-04-13 22:56:58.190303: Epoch 2869 +2026-04-13 22:56:58.192519: Current learning rate: 0.00321 +2026-04-13 22:58:38.275812: train_loss -0.4095 +2026-04-13 22:58:38.282208: val_loss -0.3588 +2026-04-13 22:58:38.285078: Pseudo dice [0.3854, 0.0, 0.8321, 0.0, 0.345, 0.5192, 0.5302] +2026-04-13 22:58:38.287979: Epoch time: 100.09 s +2026-04-13 22:58:39.527981: +2026-04-13 22:58:39.530216: Epoch 2870 +2026-04-13 22:58:39.532747: Current learning rate: 0.00321 +2026-04-13 23:00:19.981341: train_loss -0.4152 +2026-04-13 23:00:19.988555: val_loss -0.3759 +2026-04-13 23:00:19.991777: Pseudo dice [0.0, 0.0, 0.7427, 0.001, 0.5859, 0.7232, 0.7916] +2026-04-13 23:00:19.996354: Epoch time: 100.46 s +2026-04-13 23:00:21.222841: +2026-04-13 23:00:21.228663: Epoch 2871 +2026-04-13 23:00:21.230881: Current learning rate: 0.0032 +2026-04-13 23:02:01.310012: train_loss -0.4225 +2026-04-13 23:02:01.315870: val_loss -0.391 +2026-04-13 23:02:01.317865: Pseudo dice [0.0038, 0.0, 0.6978, 0.0, 0.6119, 0.7532, 0.4096] +2026-04-13 23:02:01.320045: Epoch time: 100.09 s +2026-04-13 23:02:02.533431: +2026-04-13 23:02:02.540052: Epoch 2872 +2026-04-13 23:02:02.541893: Current learning rate: 0.0032 +2026-04-13 23:03:42.750371: train_loss -0.4347 +2026-04-13 23:03:42.758564: val_loss -0.3319 +2026-04-13 23:03:42.760888: Pseudo dice [0.0, 0.0, 0.736, 0.1437, 0.5002, 0.7689, 0.7417] +2026-04-13 23:03:42.763681: Epoch time: 100.22 s +2026-04-13 23:03:44.048319: +2026-04-13 23:03:44.050395: Epoch 2873 +2026-04-13 23:03:44.052724: Current learning rate: 0.0032 +2026-04-13 23:05:24.380778: train_loss -0.4295 +2026-04-13 23:05:24.389192: val_loss -0.4111 +2026-04-13 23:05:24.392544: Pseudo dice [0.6446, 0.0, 0.8041, 0.3934, 0.2738, 0.8532, 0.6123] +2026-04-13 23:05:24.395228: Epoch time: 100.34 s +2026-04-13 23:05:25.616391: +2026-04-13 23:05:25.618824: Epoch 2874 +2026-04-13 23:05:25.621240: Current learning rate: 0.0032 +2026-04-13 23:07:07.208052: train_loss -0.4396 +2026-04-13 23:07:07.214862: val_loss -0.3113 +2026-04-13 23:07:07.218309: Pseudo dice [0.0, 0.0, 0.4844, 0.1564, 0.3735, 0.7696, 0.4571] +2026-04-13 23:07:07.220977: Epoch time: 101.59 s +2026-04-13 23:07:08.448901: +2026-04-13 23:07:08.452199: Epoch 2875 +2026-04-13 23:07:08.454331: Current learning rate: 0.00319 +2026-04-13 23:08:49.382335: train_loss -0.4065 +2026-04-13 23:08:49.390181: val_loss -0.4448 +2026-04-13 23:08:49.392623: Pseudo dice [0.4055, 0.0, 0.8056, 0.0, 0.4223, 0.7972, 0.8011] +2026-04-13 23:08:49.396308: Epoch time: 100.94 s +2026-04-13 23:08:50.624237: +2026-04-13 23:08:50.627816: Epoch 2876 +2026-04-13 23:08:50.630615: Current learning rate: 0.00319 +2026-04-13 23:10:31.459564: train_loss -0.419 +2026-04-13 23:10:31.465817: val_loss -0.3372 +2026-04-13 23:10:31.468076: Pseudo dice [0.6768, 0.0, 0.6859, 0.1066, 0.3744, 0.6386, 0.7831] +2026-04-13 23:10:31.470420: Epoch time: 100.84 s +2026-04-13 23:10:32.741225: +2026-04-13 23:10:32.743147: Epoch 2877 +2026-04-13 23:10:32.745115: Current learning rate: 0.00319 +2026-04-13 23:12:13.527299: train_loss -0.4013 +2026-04-13 23:12:13.533297: val_loss -0.3675 +2026-04-13 23:12:13.536222: Pseudo dice [0.6451, 0.0, 0.6754, 0.0806, 0.4624, 0.3903, 0.8614] +2026-04-13 23:12:13.539049: Epoch time: 100.79 s +2026-04-13 23:12:14.777663: +2026-04-13 23:12:14.779674: Epoch 2878 +2026-04-13 23:12:14.781293: Current learning rate: 0.00319 +2026-04-13 23:13:55.622907: train_loss -0.3943 +2026-04-13 23:13:55.630137: val_loss -0.388 +2026-04-13 23:13:55.632382: Pseudo dice [0.0988, 0.0, 0.7359, 0.4359, 0.2619, 0.4657, 0.7694] +2026-04-13 23:13:55.636004: Epoch time: 100.85 s +2026-04-13 23:13:56.917992: +2026-04-13 23:13:56.919968: Epoch 2879 +2026-04-13 23:13:56.922408: Current learning rate: 0.00318 +2026-04-13 23:15:37.287817: train_loss -0.4153 +2026-04-13 23:15:37.294232: val_loss -0.4143 +2026-04-13 23:15:37.297134: Pseudo dice [0.3455, 0.0, 0.6469, 0.0, 0.4168, 0.748, 0.8292] +2026-04-13 23:15:37.300620: Epoch time: 100.37 s +2026-04-13 23:15:38.555374: +2026-04-13 23:15:38.559386: Epoch 2880 +2026-04-13 23:15:38.563672: Current learning rate: 0.00318 +2026-04-13 23:17:19.701631: train_loss -0.4149 +2026-04-13 23:17:19.708072: val_loss -0.4233 +2026-04-13 23:17:19.711680: Pseudo dice [0.7199, 0.0, 0.6995, 0.0, 0.3308, 0.6816, 0.7088] +2026-04-13 23:17:19.715321: Epoch time: 101.15 s +2026-04-13 23:17:20.963844: +2026-04-13 23:17:20.966009: Epoch 2881 +2026-04-13 23:17:20.968012: Current learning rate: 0.00318 +2026-04-13 23:19:02.235269: train_loss -0.4235 +2026-04-13 23:19:02.242084: val_loss -0.4016 +2026-04-13 23:19:02.245020: Pseudo dice [0.6192, 0.0, 0.4905, 0.6978, 0.4773, 0.7888, 0.815] +2026-04-13 23:19:02.248571: Epoch time: 101.27 s +2026-04-13 23:19:03.493580: +2026-04-13 23:19:03.495888: Epoch 2882 +2026-04-13 23:19:03.497533: Current learning rate: 0.00317 +2026-04-13 23:20:44.424463: train_loss -0.4117 +2026-04-13 23:20:44.431093: val_loss -0.3975 +2026-04-13 23:20:44.433154: Pseudo dice [0.3144, 0.0, 0.5303, 0.0, 0.4475, 0.5386, 0.7536] +2026-04-13 23:20:44.435671: Epoch time: 100.93 s +2026-04-13 23:20:45.701045: +2026-04-13 23:20:45.703464: Epoch 2883 +2026-04-13 23:20:45.705301: Current learning rate: 0.00317 +2026-04-13 23:22:25.713565: train_loss -0.4215 +2026-04-13 23:22:25.727523: val_loss -0.3392 +2026-04-13 23:22:25.729475: Pseudo dice [0.2742, 0.0, 0.6038, 0.0, 0.4558, 0.5968, 0.8083] +2026-04-13 23:22:25.732641: Epoch time: 100.02 s +2026-04-13 23:22:26.951013: +2026-04-13 23:22:26.952722: Epoch 2884 +2026-04-13 23:22:26.954235: Current learning rate: 0.00317 +2026-04-13 23:24:07.284828: train_loss -0.4267 +2026-04-13 23:24:07.291199: val_loss -0.4087 +2026-04-13 23:24:07.293614: Pseudo dice [0.343, 0.0, 0.583, 0.6999, 0.4041, 0.3357, 0.8482] +2026-04-13 23:24:07.297839: Epoch time: 100.34 s +2026-04-13 23:24:08.546749: +2026-04-13 23:24:08.549950: Epoch 2885 +2026-04-13 23:24:08.552011: Current learning rate: 0.00317 +2026-04-13 23:25:48.743339: train_loss -0.4045 +2026-04-13 23:25:48.748857: val_loss -0.3949 +2026-04-13 23:25:48.751682: Pseudo dice [0.7206, 0.0, 0.454, 0.0, 0.2484, 0.5786, 0.8544] +2026-04-13 23:25:48.754104: Epoch time: 100.2 s +2026-04-13 23:25:49.982759: +2026-04-13 23:25:49.985404: Epoch 2886 +2026-04-13 23:25:49.987343: Current learning rate: 0.00316 +2026-04-13 23:27:31.709550: train_loss -0.3939 +2026-04-13 23:27:31.716114: val_loss -0.333 +2026-04-13 23:27:31.719744: Pseudo dice [0.0116, 0.0, 0.6445, 0.358, 0.2996, 0.7609, 0.2387] +2026-04-13 23:27:31.723356: Epoch time: 101.73 s +2026-04-13 23:27:32.976054: +2026-04-13 23:27:32.978902: Epoch 2887 +2026-04-13 23:27:32.983175: Current learning rate: 0.00316 +2026-04-13 23:29:13.345343: train_loss -0.4217 +2026-04-13 23:29:13.355372: val_loss -0.3663 +2026-04-13 23:29:13.357970: Pseudo dice [0.6811, 0.0, 0.7808, 0.0281, 0.4554, 0.8066, 0.8099] +2026-04-13 23:29:13.361573: Epoch time: 100.37 s +2026-04-13 23:29:14.615920: +2026-04-13 23:29:14.618207: Epoch 2888 +2026-04-13 23:29:14.619876: Current learning rate: 0.00316 +2026-04-13 23:30:54.621626: train_loss -0.4162 +2026-04-13 23:30:54.637463: val_loss -0.3239 +2026-04-13 23:30:54.640944: Pseudo dice [0.3394, 0.0, 0.7077, 0.0, 0.271, 0.3433, 0.7244] +2026-04-13 23:30:54.643553: Epoch time: 100.01 s +2026-04-13 23:30:55.889940: +2026-04-13 23:30:55.893034: Epoch 2889 +2026-04-13 23:30:55.895155: Current learning rate: 0.00316 +2026-04-13 23:32:36.144998: train_loss -0.4322 +2026-04-13 23:32:36.152435: val_loss -0.3758 +2026-04-13 23:32:36.154962: Pseudo dice [0.2785, 0.0, 0.8214, 0.0846, 0.3176, 0.7887, 0.6989] +2026-04-13 23:32:36.157766: Epoch time: 100.26 s +2026-04-13 23:32:37.462198: +2026-04-13 23:32:37.464556: Epoch 2890 +2026-04-13 23:32:37.466542: Current learning rate: 0.00315 +2026-04-13 23:34:18.051801: train_loss -0.4295 +2026-04-13 23:34:18.058125: val_loss -0.3822 +2026-04-13 23:34:18.060818: Pseudo dice [0.6287, 0.0, 0.7818, 0.1975, 0.3739, 0.6124, 0.8009] +2026-04-13 23:34:18.063851: Epoch time: 100.59 s +2026-04-13 23:34:19.278470: +2026-04-13 23:34:19.281155: Epoch 2891 +2026-04-13 23:34:19.284572: Current learning rate: 0.00315 +2026-04-13 23:35:59.419393: train_loss -0.4432 +2026-04-13 23:35:59.425740: val_loss -0.3716 +2026-04-13 23:35:59.427907: Pseudo dice [0.6868, 0.0, 0.2677, 0.3504, 0.5697, 0.4965, 0.5797] +2026-04-13 23:35:59.430873: Epoch time: 100.14 s +2026-04-13 23:36:00.679186: +2026-04-13 23:36:00.681161: Epoch 2892 +2026-04-13 23:36:00.683164: Current learning rate: 0.00315 +2026-04-13 23:37:41.155135: train_loss -0.4398 +2026-04-13 23:37:41.168940: val_loss -0.3557 +2026-04-13 23:37:41.172231: Pseudo dice [0.7081, 0.0, 0.6946, 0.0, 0.419, 0.6983, 0.8415] +2026-04-13 23:37:41.178738: Epoch time: 100.48 s +2026-04-13 23:37:42.408431: +2026-04-13 23:37:42.410821: Epoch 2893 +2026-04-13 23:37:42.413575: Current learning rate: 0.00315 +2026-04-13 23:39:22.674637: train_loss -0.4231 +2026-04-13 23:39:22.684796: val_loss -0.3998 +2026-04-13 23:39:22.688656: Pseudo dice [0.651, 0.0, 0.8224, 0.0, 0.165, 0.7571, 0.6906] +2026-04-13 23:39:22.692943: Epoch time: 100.27 s +2026-04-13 23:39:24.989227: +2026-04-13 23:39:24.994709: Epoch 2894 +2026-04-13 23:39:24.998336: Current learning rate: 0.00314 +2026-04-13 23:41:05.859724: train_loss -0.444 +2026-04-13 23:41:05.866767: val_loss -0.3322 +2026-04-13 23:41:05.872193: Pseudo dice [0.7348, 0.0, 0.6503, 0.1148, 0.3737, 0.5252, 0.7196] +2026-04-13 23:41:05.874791: Epoch time: 100.87 s +2026-04-13 23:41:07.138301: +2026-04-13 23:41:07.140373: Epoch 2895 +2026-04-13 23:41:07.142498: Current learning rate: 0.00314 +2026-04-13 23:42:47.485079: train_loss -0.4519 +2026-04-13 23:42:47.495755: val_loss -0.4538 +2026-04-13 23:42:47.497783: Pseudo dice [0.7345, 0.0, 0.7321, 0.0, 0.4026, 0.8688, 0.8879] +2026-04-13 23:42:47.500077: Epoch time: 100.35 s +2026-04-13 23:42:48.774359: +2026-04-13 23:42:48.776609: Epoch 2896 +2026-04-13 23:42:48.778363: Current learning rate: 0.00314 +2026-04-13 23:44:29.239361: train_loss -0.431 +2026-04-13 23:44:29.245472: val_loss -0.2985 +2026-04-13 23:44:29.248221: Pseudo dice [0.4089, 0.0, 0.6069, 0.058, 0.2184, 0.8139, 0.5385] +2026-04-13 23:44:29.250699: Epoch time: 100.47 s +2026-04-13 23:44:30.500427: +2026-04-13 23:44:30.502689: Epoch 2897 +2026-04-13 23:44:30.504898: Current learning rate: 0.00314 +2026-04-13 23:46:11.618317: train_loss -0.431 +2026-04-13 23:46:11.625473: val_loss -0.4127 +2026-04-13 23:46:11.627635: Pseudo dice [0.4319, 0.0, 0.4971, 0.9175, 0.5609, 0.8725, 0.7051] +2026-04-13 23:46:11.631603: Epoch time: 101.12 s +2026-04-13 23:46:12.861751: +2026-04-13 23:46:12.863808: Epoch 2898 +2026-04-13 23:46:12.865756: Current learning rate: 0.00313 +2026-04-13 23:47:53.793448: train_loss -0.4332 +2026-04-13 23:47:53.801577: val_loss -0.3563 +2026-04-13 23:47:53.803960: Pseudo dice [0.608, 0.0, 0.679, 0.0661, 0.4836, 0.6855, 0.7023] +2026-04-13 23:47:53.806572: Epoch time: 100.93 s +2026-04-13 23:47:55.051938: +2026-04-13 23:47:55.054357: Epoch 2899 +2026-04-13 23:47:55.056907: Current learning rate: 0.00313 +2026-04-13 23:49:35.206877: train_loss -0.4313 +2026-04-13 23:49:35.212005: val_loss -0.4163 +2026-04-13 23:49:35.213728: Pseudo dice [0.5027, 0.0, 0.782, 0.0, 0.2772, 0.7791, 0.771] +2026-04-13 23:49:35.216114: Epoch time: 100.16 s +2026-04-13 23:49:38.158020: +2026-04-13 23:49:38.160476: Epoch 2900 +2026-04-13 23:49:38.162855: Current learning rate: 0.00313 +2026-04-13 23:51:18.325226: train_loss -0.4302 +2026-04-13 23:51:18.331838: val_loss -0.3705 +2026-04-13 23:51:18.333971: Pseudo dice [0.8056, 0.0, 0.7842, 0.0, 0.2715, 0.6494, 0.7828] +2026-04-13 23:51:18.336516: Epoch time: 100.17 s +2026-04-13 23:51:19.583042: +2026-04-13 23:51:19.585937: Epoch 2901 +2026-04-13 23:51:19.588424: Current learning rate: 0.00313 +2026-04-13 23:53:00.402094: train_loss -0.4162 +2026-04-13 23:53:00.414916: val_loss -0.3661 +2026-04-13 23:53:00.417615: Pseudo dice [0.1103, 0.0, 0.6633, 0.0, 0.4556, 0.2496, 0.5813] +2026-04-13 23:53:00.420917: Epoch time: 100.82 s +2026-04-13 23:53:01.660987: +2026-04-13 23:53:01.667814: Epoch 2902 +2026-04-13 23:53:01.670923: Current learning rate: 0.00312 +2026-04-13 23:54:41.800461: train_loss -0.4189 +2026-04-13 23:54:41.807331: val_loss -0.3384 +2026-04-13 23:54:41.810058: Pseudo dice [0.8132, 0.0, 0.592, 0.0591, 0.5084, 0.8625, 0.4909] +2026-04-13 23:54:41.812585: Epoch time: 100.14 s +2026-04-13 23:54:43.094543: +2026-04-13 23:54:43.097266: Epoch 2903 +2026-04-13 23:54:43.098978: Current learning rate: 0.00312 +2026-04-13 23:56:23.518605: train_loss -0.4413 +2026-04-13 23:56:23.525037: val_loss -0.3273 +2026-04-13 23:56:23.526981: Pseudo dice [0.7534, 0.0, 0.5817, 0.0443, 0.1504, 0.7067, 0.7519] +2026-04-13 23:56:23.529618: Epoch time: 100.43 s +2026-04-13 23:56:24.779718: +2026-04-13 23:56:24.781408: Epoch 2904 +2026-04-13 23:56:24.783365: Current learning rate: 0.00312 +2026-04-13 23:58:05.880058: train_loss -0.4297 +2026-04-13 23:58:05.886570: val_loss -0.3863 +2026-04-13 23:58:05.889089: Pseudo dice [0.455, 0.0, 0.7302, 0.0, 0.2194, 0.6234, 0.7898] +2026-04-13 23:58:05.892121: Epoch time: 101.1 s +2026-04-13 23:58:07.143100: +2026-04-13 23:58:07.144786: Epoch 2905 +2026-04-13 23:58:07.146326: Current learning rate: 0.00312 +2026-04-13 23:59:47.063506: train_loss -0.4483 +2026-04-13 23:59:47.069635: val_loss -0.3915 +2026-04-13 23:59:47.071752: Pseudo dice [0.5219, 0.0, 0.7279, 0.0, 0.3597, 0.7679, 0.7634] +2026-04-13 23:59:47.074025: Epoch time: 99.92 s +2026-04-13 23:59:48.289685: +2026-04-13 23:59:48.291789: Epoch 2906 +2026-04-13 23:59:48.293431: Current learning rate: 0.00311 +2026-04-14 00:01:29.924403: train_loss -0.4394 +2026-04-14 00:01:29.934453: val_loss -0.3812 +2026-04-14 00:01:29.938633: Pseudo dice [0.8328, 0.0, 0.7808, 0.0535, 0.3985, 0.2679, 0.7631] +2026-04-14 00:01:29.944513: Epoch time: 101.64 s +2026-04-14 00:01:31.214385: +2026-04-14 00:01:31.218261: Epoch 2907 +2026-04-14 00:01:31.221402: Current learning rate: 0.00311 +2026-04-14 00:03:11.544957: train_loss -0.4197 +2026-04-14 00:03:11.550452: val_loss -0.4345 +2026-04-14 00:03:11.552556: Pseudo dice [0.695, 0.0, 0.6426, 0.7741, 0.1245, 0.7641, 0.8256] +2026-04-14 00:03:11.555258: Epoch time: 100.33 s +2026-04-14 00:03:12.811216: +2026-04-14 00:03:12.814075: Epoch 2908 +2026-04-14 00:03:12.816158: Current learning rate: 0.00311 +2026-04-14 00:04:53.307456: train_loss -0.4367 +2026-04-14 00:04:53.314309: val_loss -0.4147 +2026-04-14 00:04:53.316715: Pseudo dice [0.5756, 0.0, 0.6498, 0.0, 0.4793, 0.8429, 0.6397] +2026-04-14 00:04:53.320131: Epoch time: 100.5 s +2026-04-14 00:04:54.548286: +2026-04-14 00:04:54.550320: Epoch 2909 +2026-04-14 00:04:54.552182: Current learning rate: 0.00311 +2026-04-14 00:06:34.688438: train_loss -0.4226 +2026-04-14 00:06:34.694037: val_loss -0.4323 +2026-04-14 00:06:34.696247: Pseudo dice [0.7179, 0.0, 0.5528, 0.5078, 0.4992, 0.8207, 0.8497] +2026-04-14 00:06:34.698492: Epoch time: 100.14 s +2026-04-14 00:06:36.260049: +2026-04-14 00:06:36.262694: Epoch 2910 +2026-04-14 00:06:36.264464: Current learning rate: 0.0031 +2026-04-14 00:08:16.294992: train_loss -0.4286 +2026-04-14 00:08:16.300754: val_loss -0.316 +2026-04-14 00:08:16.304950: Pseudo dice [0.7238, 0.0, 0.4223, 0.0526, 0.2327, 0.8211, 0.7015] +2026-04-14 00:08:16.307289: Epoch time: 100.04 s +2026-04-14 00:08:17.576070: +2026-04-14 00:08:17.578666: Epoch 2911 +2026-04-14 00:08:17.580927: Current learning rate: 0.0031 +2026-04-14 00:09:57.479077: train_loss -0.4108 +2026-04-14 00:09:57.484609: val_loss -0.3508 +2026-04-14 00:09:57.486812: Pseudo dice [0.5907, 0.0, 0.7593, 0.0854, 0.3775, 0.679, 0.7162] +2026-04-14 00:09:57.488817: Epoch time: 99.91 s +2026-04-14 00:09:58.744825: +2026-04-14 00:09:58.746971: Epoch 2912 +2026-04-14 00:09:58.750489: Current learning rate: 0.0031 +2026-04-14 00:11:39.451980: train_loss -0.4365 +2026-04-14 00:11:39.456752: val_loss -0.3979 +2026-04-14 00:11:39.458851: Pseudo dice [0.6181, 0.0, 0.7604, 0.0, 0.5056, 0.4047, 0.8158] +2026-04-14 00:11:39.463527: Epoch time: 100.71 s +2026-04-14 00:11:40.693132: +2026-04-14 00:11:40.694943: Epoch 2913 +2026-04-14 00:11:40.696449: Current learning rate: 0.0031 +2026-04-14 00:13:20.661504: train_loss -0.4336 +2026-04-14 00:13:20.669355: val_loss -0.4175 +2026-04-14 00:13:20.672230: Pseudo dice [0.0, 0.0, 0.759, 0.8346, 0.2913, 0.6342, 0.8237] +2026-04-14 00:13:20.675692: Epoch time: 99.97 s +2026-04-14 00:13:22.903160: +2026-04-14 00:13:22.905431: Epoch 2914 +2026-04-14 00:13:22.907206: Current learning rate: 0.00309 +2026-04-14 00:15:04.831129: train_loss -0.4169 +2026-04-14 00:15:04.838296: val_loss -0.3974 +2026-04-14 00:15:04.840692: Pseudo dice [0.0371, 0.0, 0.6287, 0.5136, 0.4989, 0.7427, 0.7014] +2026-04-14 00:15:04.843338: Epoch time: 101.93 s +2026-04-14 00:15:06.094572: +2026-04-14 00:15:06.096893: Epoch 2915 +2026-04-14 00:15:06.099082: Current learning rate: 0.00309 +2026-04-14 00:16:46.534337: train_loss -0.3848 +2026-04-14 00:16:46.539980: val_loss -0.1967 +2026-04-14 00:16:46.541996: Pseudo dice [0.0376, 0.0, 0.4856, 0.0, 0.2628, 0.6207, 0.5205] +2026-04-14 00:16:46.544553: Epoch time: 100.44 s +2026-04-14 00:16:47.796699: +2026-04-14 00:16:47.798881: Epoch 2916 +2026-04-14 00:16:47.801718: Current learning rate: 0.00309 +2026-04-14 00:18:27.654280: train_loss -0.3748 +2026-04-14 00:18:27.660164: val_loss -0.4035 +2026-04-14 00:18:27.662135: Pseudo dice [0.5004, 0.0, 0.7589, 0.2642, 0.0006, 0.6737, 0.8041] +2026-04-14 00:18:27.664226: Epoch time: 99.86 s +2026-04-14 00:18:28.898026: +2026-04-14 00:18:28.899932: Epoch 2917 +2026-04-14 00:18:28.901908: Current learning rate: 0.00309 +2026-04-14 00:20:09.115435: train_loss -0.4043 +2026-04-14 00:20:09.149274: val_loss -0.4121 +2026-04-14 00:20:09.152001: Pseudo dice [0.3342, 0.0, 0.7456, 0.0, 0.3508, 0.7858, 0.7897] +2026-04-14 00:20:09.154553: Epoch time: 100.22 s +2026-04-14 00:20:10.373275: +2026-04-14 00:20:10.377218: Epoch 2918 +2026-04-14 00:20:10.382324: Current learning rate: 0.00308 +2026-04-14 00:21:51.183196: train_loss -0.3978 +2026-04-14 00:21:51.189715: val_loss -0.336 +2026-04-14 00:21:51.192806: Pseudo dice [0.2828, 0.0, 0.6259, 0.0062, 0.1861, 0.7346, 0.5783] +2026-04-14 00:21:51.195571: Epoch time: 100.81 s +2026-04-14 00:21:52.464900: +2026-04-14 00:21:52.469411: Epoch 2919 +2026-04-14 00:21:52.471898: Current learning rate: 0.00308 +2026-04-14 00:23:32.900720: train_loss -0.4076 +2026-04-14 00:23:32.913707: val_loss -0.3296 +2026-04-14 00:23:32.919112: Pseudo dice [0.6984, 0.0, 0.7396, 0.0922, 0.3216, 0.6649, 0.7785] +2026-04-14 00:23:32.923696: Epoch time: 100.44 s +2026-04-14 00:23:34.170186: +2026-04-14 00:23:34.171935: Epoch 2920 +2026-04-14 00:23:34.173589: Current learning rate: 0.00308 +2026-04-14 00:25:14.327120: train_loss -0.4193 +2026-04-14 00:25:14.332570: val_loss -0.4042 +2026-04-14 00:25:14.334571: Pseudo dice [0.5805, 0.0, 0.4906, 0.0, 0.4086, 0.7115, 0.6063] +2026-04-14 00:25:14.337442: Epoch time: 100.16 s +2026-04-14 00:25:15.570855: +2026-04-14 00:25:15.573140: Epoch 2921 +2026-04-14 00:25:15.574947: Current learning rate: 0.00308 +2026-04-14 00:26:55.731704: train_loss -0.4373 +2026-04-14 00:26:55.737891: val_loss -0.406 +2026-04-14 00:26:55.740062: Pseudo dice [0.5871, 0.0, 0.7247, 0.9101, 0.4369, 0.7587, 0.6276] +2026-04-14 00:26:55.742496: Epoch time: 100.16 s +2026-04-14 00:26:56.972894: +2026-04-14 00:26:56.974797: Epoch 2922 +2026-04-14 00:26:56.976628: Current learning rate: 0.00307 +2026-04-14 00:28:37.071918: train_loss -0.4401 +2026-04-14 00:28:37.077063: val_loss -0.3963 +2026-04-14 00:28:37.079083: Pseudo dice [0.6233, 0.0, 0.678, 0.0, 0.1591, 0.6449, 0.7802] +2026-04-14 00:28:37.081206: Epoch time: 100.1 s +2026-04-14 00:28:38.308160: +2026-04-14 00:28:38.310128: Epoch 2923 +2026-04-14 00:28:38.311757: Current learning rate: 0.00307 +2026-04-14 00:30:18.446910: train_loss -0.4106 +2026-04-14 00:30:18.452668: val_loss -0.3394 +2026-04-14 00:30:18.455259: Pseudo dice [0.423, 0.0, 0.5833, 0.0063, 0.3673, 0.8297, 0.7305] +2026-04-14 00:30:18.457632: Epoch time: 100.14 s +2026-04-14 00:30:19.695282: +2026-04-14 00:30:19.697177: Epoch 2924 +2026-04-14 00:30:19.699153: Current learning rate: 0.00307 +2026-04-14 00:31:59.850606: train_loss -0.4341 +2026-04-14 00:31:59.856028: val_loss -0.4078 +2026-04-14 00:31:59.858583: Pseudo dice [0.6854, 0.0, 0.8894, 0.3568, 0.4672, 0.5569, 0.7359] +2026-04-14 00:31:59.861082: Epoch time: 100.16 s +2026-04-14 00:32:01.119812: +2026-04-14 00:32:01.121688: Epoch 2925 +2026-04-14 00:32:01.123341: Current learning rate: 0.00306 +2026-04-14 00:33:41.288555: train_loss -0.4387 +2026-04-14 00:33:41.293855: val_loss -0.3221 +2026-04-14 00:33:41.296002: Pseudo dice [0.6504, 0.0, 0.7164, 0.0878, 0.323, 0.7855, 0.8231] +2026-04-14 00:33:41.298086: Epoch time: 100.17 s +2026-04-14 00:33:42.530668: +2026-04-14 00:33:42.533133: Epoch 2926 +2026-04-14 00:33:42.535291: Current learning rate: 0.00306 +2026-04-14 00:35:22.459086: train_loss -0.4334 +2026-04-14 00:35:22.464753: val_loss -0.3309 +2026-04-14 00:35:22.466771: Pseudo dice [0.7086, 0.0, 0.6263, 0.0098, 0.346, 0.6027, 0.8108] +2026-04-14 00:35:22.468996: Epoch time: 99.93 s +2026-04-14 00:35:23.680565: +2026-04-14 00:35:23.682352: Epoch 2927 +2026-04-14 00:35:23.683933: Current learning rate: 0.00306 +2026-04-14 00:37:03.714954: train_loss -0.4315 +2026-04-14 00:37:03.722189: val_loss -0.3862 +2026-04-14 00:37:03.724561: Pseudo dice [0.3587, 0.0, 0.5929, 0.2868, 0.3128, 0.6341, 0.7005] +2026-04-14 00:37:03.727270: Epoch time: 100.04 s +2026-04-14 00:37:04.947622: +2026-04-14 00:37:04.949875: Epoch 2928 +2026-04-14 00:37:04.952082: Current learning rate: 0.00306 +2026-04-14 00:38:44.822692: train_loss -0.4332 +2026-04-14 00:38:44.831023: val_loss -0.3131 +2026-04-14 00:38:44.833749: Pseudo dice [0.4445, 0.0, 0.5994, 0.0389, 0.2207, 0.7581, 0.5131] +2026-04-14 00:38:44.835896: Epoch time: 99.88 s +2026-04-14 00:38:46.088309: +2026-04-14 00:38:46.090855: Epoch 2929 +2026-04-14 00:38:46.092740: Current learning rate: 0.00305 +2026-04-14 00:40:26.152259: train_loss -0.4242 +2026-04-14 00:40:26.157873: val_loss -0.3933 +2026-04-14 00:40:26.160035: Pseudo dice [0.5693, 0.0, 0.688, 0.0, 0.3626, 0.618, 0.6859] +2026-04-14 00:40:26.162821: Epoch time: 100.07 s +2026-04-14 00:40:27.454712: +2026-04-14 00:40:27.456728: Epoch 2930 +2026-04-14 00:40:27.458404: Current learning rate: 0.00305 +2026-04-14 00:42:07.537094: train_loss -0.4107 +2026-04-14 00:42:07.542252: val_loss -0.3733 +2026-04-14 00:42:07.544463: Pseudo dice [0.2607, 0.0, 0.5396, 0.0, 0.5086, 0.2634, 0.3263] +2026-04-14 00:42:07.546855: Epoch time: 100.09 s +2026-04-14 00:42:08.772797: +2026-04-14 00:42:08.774410: Epoch 2931 +2026-04-14 00:42:08.776323: Current learning rate: 0.00305 +2026-04-14 00:43:48.938226: train_loss -0.4012 +2026-04-14 00:43:48.944607: val_loss -0.4114 +2026-04-14 00:43:48.946604: Pseudo dice [0.509, 0.0, 0.7373, 0.0, 0.3533, 0.7358, 0.8657] +2026-04-14 00:43:48.948668: Epoch time: 100.17 s +2026-04-14 00:43:50.188118: +2026-04-14 00:43:50.190737: Epoch 2932 +2026-04-14 00:43:50.192762: Current learning rate: 0.00305 +2026-04-14 00:45:30.649975: train_loss -0.418 +2026-04-14 00:45:30.655520: val_loss -0.3937 +2026-04-14 00:45:30.657593: Pseudo dice [0.0, 0.0, 0.6287, 0.5813, 0.3517, 0.4682, 0.6825] +2026-04-14 00:45:30.659868: Epoch time: 100.46 s +2026-04-14 00:45:31.929775: +2026-04-14 00:45:31.931648: Epoch 2933 +2026-04-14 00:45:31.933199: Current learning rate: 0.00304 +2026-04-14 00:47:12.928526: train_loss -0.4366 +2026-04-14 00:47:12.934832: val_loss -0.3974 +2026-04-14 00:47:12.937022: Pseudo dice [0.6063, 0.0, 0.6911, 0.5664, 0.2507, 0.6557, 0.7546] +2026-04-14 00:47:12.939078: Epoch time: 101.0 s +2026-04-14 00:47:14.211304: +2026-04-14 00:47:14.213321: Epoch 2934 +2026-04-14 00:47:14.214914: Current learning rate: 0.00304 +2026-04-14 00:48:54.126638: train_loss -0.427 +2026-04-14 00:48:54.135113: val_loss -0.3514 +2026-04-14 00:48:54.137247: Pseudo dice [0.4948, 0.0, 0.6205, 0.0, 0.1462, 0.3782, 0.6143] +2026-04-14 00:48:54.139475: Epoch time: 99.92 s +2026-04-14 00:48:55.383632: +2026-04-14 00:48:55.385578: Epoch 2935 +2026-04-14 00:48:55.387233: Current learning rate: 0.00304 +2026-04-14 00:50:35.516299: train_loss -0.4194 +2026-04-14 00:50:35.521622: val_loss -0.4076 +2026-04-14 00:50:35.523606: Pseudo dice [0.4868, 0.0, 0.7094, 0.0, 0.5399, 0.6954, 0.8538] +2026-04-14 00:50:35.525779: Epoch time: 100.14 s +2026-04-14 00:50:36.775996: +2026-04-14 00:50:36.778540: Epoch 2936 +2026-04-14 00:50:36.782210: Current learning rate: 0.00304 +2026-04-14 00:52:16.899085: train_loss -0.437 +2026-04-14 00:52:16.906137: val_loss -0.4317 +2026-04-14 00:52:16.908165: Pseudo dice [0.8144, 0.0, 0.6773, 0.0, 0.4682, 0.8214, 0.8793] +2026-04-14 00:52:16.910353: Epoch time: 100.13 s +2026-04-14 00:52:18.133903: +2026-04-14 00:52:18.135631: Epoch 2937 +2026-04-14 00:52:18.137773: Current learning rate: 0.00303 +2026-04-14 00:53:58.230342: train_loss -0.4276 +2026-04-14 00:53:58.236871: val_loss -0.3699 +2026-04-14 00:53:58.239541: Pseudo dice [0.0567, 0.0, 0.6533, 0.0929, 0.0682, 0.7826, 0.8792] +2026-04-14 00:53:58.242899: Epoch time: 100.1 s +2026-04-14 00:53:59.487134: +2026-04-14 00:53:59.489557: Epoch 2938 +2026-04-14 00:53:59.491573: Current learning rate: 0.00303 +2026-04-14 00:55:39.777869: train_loss -0.4023 +2026-04-14 00:55:39.783796: val_loss -0.3877 +2026-04-14 00:55:39.786761: Pseudo dice [0.4142, 0.0, 0.8128, 0.0, 0.3303, 0.5606, 0.6269] +2026-04-14 00:55:39.788918: Epoch time: 100.29 s +2026-04-14 00:55:41.066052: +2026-04-14 00:55:41.067809: Epoch 2939 +2026-04-14 00:55:41.069634: Current learning rate: 0.00303 +2026-04-14 00:57:21.176818: train_loss -0.3907 +2026-04-14 00:57:21.187004: val_loss -0.2957 +2026-04-14 00:57:21.189294: Pseudo dice [0.3934, 0.0, 0.5674, 0.0, 0.2973, 0.6324, 0.5796] +2026-04-14 00:57:21.192720: Epoch time: 100.11 s +2026-04-14 00:57:22.427618: +2026-04-14 00:57:22.429499: Epoch 2940 +2026-04-14 00:57:22.431166: Current learning rate: 0.00303 +2026-04-14 00:59:02.362494: train_loss -0.4278 +2026-04-14 00:59:02.367154: val_loss -0.4192 +2026-04-14 00:59:02.370891: Pseudo dice [0.3969, 0.0, 0.2916, 0.8166, 0.3224, 0.8604, 0.8366] +2026-04-14 00:59:02.373382: Epoch time: 99.94 s +2026-04-14 00:59:03.592610: +2026-04-14 00:59:03.595125: Epoch 2941 +2026-04-14 00:59:03.596789: Current learning rate: 0.00302 +2026-04-14 01:00:43.694391: train_loss -0.4085 +2026-04-14 01:00:43.701220: val_loss -0.4161 +2026-04-14 01:00:43.703222: Pseudo dice [0.6288, 0.0, 0.6372, 0.0, 0.2318, 0.8811, 0.6867] +2026-04-14 01:00:43.707225: Epoch time: 100.1 s +2026-04-14 01:00:44.936360: +2026-04-14 01:00:44.938241: Epoch 2942 +2026-04-14 01:00:44.940123: Current learning rate: 0.00302 +2026-04-14 01:02:25.295527: train_loss -0.4184 +2026-04-14 01:02:25.301533: val_loss -0.3013 +2026-04-14 01:02:25.304978: Pseudo dice [0.4698, 0.0, 0.5144, 0.0, 0.4931, 0.7305, 0.8295] +2026-04-14 01:02:25.308905: Epoch time: 100.36 s +2026-04-14 01:02:26.559757: +2026-04-14 01:02:26.562489: Epoch 2943 +2026-04-14 01:02:26.564443: Current learning rate: 0.00302 +2026-04-14 01:04:06.843488: train_loss -0.4004 +2026-04-14 01:04:06.850142: val_loss -0.412 +2026-04-14 01:04:06.852168: Pseudo dice [0.7034, 0.0, 0.7934, 0.8481, 0.4265, 0.6145, 0.7544] +2026-04-14 01:04:06.854472: Epoch time: 100.29 s +2026-04-14 01:04:08.121414: +2026-04-14 01:04:08.123259: Epoch 2944 +2026-04-14 01:04:08.124926: Current learning rate: 0.00302 +2026-04-14 01:05:48.077156: train_loss -0.4408 +2026-04-14 01:05:48.089360: val_loss -0.428 +2026-04-14 01:05:48.092294: Pseudo dice [0.7228, 0.0, 0.759, 0.8218, 0.2777, 0.7886, 0.7988] +2026-04-14 01:05:48.094589: Epoch time: 99.96 s +2026-04-14 01:05:49.330614: +2026-04-14 01:05:49.332427: Epoch 2945 +2026-04-14 01:05:49.333909: Current learning rate: 0.00301 +2026-04-14 01:07:29.566825: train_loss -0.4335 +2026-04-14 01:07:29.572349: val_loss -0.3462 +2026-04-14 01:07:29.575324: Pseudo dice [0.093, 0.0, 0.5846, 0.1746, 0.3072, 0.5948, 0.5877] +2026-04-14 01:07:29.577647: Epoch time: 100.24 s +2026-04-14 01:07:30.825921: +2026-04-14 01:07:30.827817: Epoch 2946 +2026-04-14 01:07:30.829513: Current learning rate: 0.00301 +2026-04-14 01:09:11.002329: train_loss -0.407 +2026-04-14 01:09:11.007970: val_loss -0.381 +2026-04-14 01:09:11.010186: Pseudo dice [0.6486, 0.0, 0.7028, 0.0, 0.4913, 0.8093, 0.6769] +2026-04-14 01:09:11.012610: Epoch time: 100.18 s +2026-04-14 01:09:12.248202: +2026-04-14 01:09:12.250666: Epoch 2947 +2026-04-14 01:09:12.252573: Current learning rate: 0.00301 +2026-04-14 01:10:52.343281: train_loss -0.4389 +2026-04-14 01:10:52.348648: val_loss -0.4225 +2026-04-14 01:10:52.350908: Pseudo dice [0.6478, 0.0, 0.7194, 0.0, 0.3985, 0.8067, 0.867] +2026-04-14 01:10:52.353495: Epoch time: 100.1 s +2026-04-14 01:10:53.590357: +2026-04-14 01:10:53.593139: Epoch 2948 +2026-04-14 01:10:53.595458: Current learning rate: 0.00301 +2026-04-14 01:12:33.564299: train_loss -0.431 +2026-04-14 01:12:33.573325: val_loss -0.426 +2026-04-14 01:12:33.575685: Pseudo dice [0.7319, 0.0, 0.7611, 0.7412, 0.4408, 0.809, 0.8432] +2026-04-14 01:12:33.578395: Epoch time: 99.98 s +2026-04-14 01:12:34.815836: +2026-04-14 01:12:34.818221: Epoch 2949 +2026-04-14 01:12:34.821820: Current learning rate: 0.003 +2026-04-14 01:14:14.704582: train_loss -0.4412 +2026-04-14 01:14:14.711203: val_loss -0.4157 +2026-04-14 01:14:14.713301: Pseudo dice [0.0, 0.0, 0.8072, 0.0, 0.2252, 0.8136, 0.7994] +2026-04-14 01:14:14.716002: Epoch time: 99.89 s +2026-04-14 01:14:17.622332: +2026-04-14 01:14:17.624375: Epoch 2950 +2026-04-14 01:14:17.626435: Current learning rate: 0.003 +2026-04-14 01:15:57.684873: train_loss -0.4201 +2026-04-14 01:15:57.691411: val_loss -0.3736 +2026-04-14 01:15:57.694636: Pseudo dice [0.777, 0.0, 0.7628, 0.0, 0.2437, 0.7292, 0.7025] +2026-04-14 01:15:57.697312: Epoch time: 100.07 s +2026-04-14 01:15:58.944738: +2026-04-14 01:15:58.946361: Epoch 2951 +2026-04-14 01:15:58.948081: Current learning rate: 0.003 +2026-04-14 01:17:38.905164: train_loss -0.4527 +2026-04-14 01:17:38.911217: val_loss -0.343 +2026-04-14 01:17:38.913647: Pseudo dice [0.6062, 0.0, 0.6227, 0.0, 0.4045, 0.8518, 0.5986] +2026-04-14 01:17:38.916265: Epoch time: 99.96 s +2026-04-14 01:17:40.160297: +2026-04-14 01:17:40.162764: Epoch 2952 +2026-04-14 01:17:40.164757: Current learning rate: 0.003 +2026-04-14 01:19:20.234012: train_loss -0.4484 +2026-04-14 01:19:20.239610: val_loss -0.4357 +2026-04-14 01:19:20.242386: Pseudo dice [0.5962, 0.0, 0.8018, 0.5966, 0.5291, 0.7429, 0.6731] +2026-04-14 01:19:20.245179: Epoch time: 100.08 s +2026-04-14 01:19:22.505311: +2026-04-14 01:19:22.507604: Epoch 2953 +2026-04-14 01:19:22.509418: Current learning rate: 0.00299 +2026-04-14 01:21:02.644236: train_loss -0.4413 +2026-04-14 01:21:02.649358: val_loss -0.429 +2026-04-14 01:21:02.651229: Pseudo dice [0.7022, 0.0, 0.735, 0.8042, 0.5108, 0.7671, 0.8433] +2026-04-14 01:21:02.653305: Epoch time: 100.14 s +2026-04-14 01:21:03.891789: +2026-04-14 01:21:03.894121: Epoch 2954 +2026-04-14 01:21:03.895857: Current learning rate: 0.00299 +2026-04-14 01:22:43.898463: train_loss -0.4372 +2026-04-14 01:22:43.904214: val_loss -0.434 +2026-04-14 01:22:43.908061: Pseudo dice [0.0, 0.0, 0.7872, 0.0, 0.4277, 0.6849, 0.883] +2026-04-14 01:22:43.911478: Epoch time: 100.01 s +2026-04-14 01:22:45.158132: +2026-04-14 01:22:45.162137: Epoch 2955 +2026-04-14 01:22:45.164277: Current learning rate: 0.00299 +2026-04-14 01:24:25.860022: train_loss -0.4455 +2026-04-14 01:24:25.865746: val_loss -0.4019 +2026-04-14 01:24:25.867415: Pseudo dice [0.7446, 0.0, 0.4868, 0.5369, 0.3599, 0.8574, 0.7877] +2026-04-14 01:24:25.870306: Epoch time: 100.7 s +2026-04-14 01:24:27.124361: +2026-04-14 01:24:27.126180: Epoch 2956 +2026-04-14 01:24:27.127852: Current learning rate: 0.00299 +2026-04-14 01:26:07.509619: train_loss -0.4228 +2026-04-14 01:26:07.517102: val_loss -0.2317 +2026-04-14 01:26:07.520586: Pseudo dice [0.0, 0.0, 0.6688, 0.0387, 0.3073, 0.7421, 0.4064] +2026-04-14 01:26:07.523315: Epoch time: 100.39 s +2026-04-14 01:26:08.779512: +2026-04-14 01:26:08.781276: Epoch 2957 +2026-04-14 01:26:08.782832: Current learning rate: 0.00298 +2026-04-14 01:27:48.947045: train_loss -0.4355 +2026-04-14 01:27:48.952621: val_loss -0.3145 +2026-04-14 01:27:48.954628: Pseudo dice [0.1189, 0.0, 0.6671, 0.022, 0.4061, 0.8195, 0.7941] +2026-04-14 01:27:48.956930: Epoch time: 100.17 s +2026-04-14 01:27:50.162718: +2026-04-14 01:27:50.164526: Epoch 2958 +2026-04-14 01:27:50.166260: Current learning rate: 0.00298 +2026-04-14 01:29:30.185556: train_loss -0.4374 +2026-04-14 01:29:30.190652: val_loss -0.4132 +2026-04-14 01:29:30.192442: Pseudo dice [0.4889, 0.0, 0.7773, 0.7121, 0.4715, 0.8193, 0.8109] +2026-04-14 01:29:30.194590: Epoch time: 100.03 s +2026-04-14 01:29:31.424047: +2026-04-14 01:29:31.426167: Epoch 2959 +2026-04-14 01:29:31.427890: Current learning rate: 0.00298 +2026-04-14 01:31:11.408086: train_loss -0.4337 +2026-04-14 01:31:11.414004: val_loss -0.2627 +2026-04-14 01:31:11.415902: Pseudo dice [0.7434, 0.0, 0.4638, 0.031, 0.3745, 0.8562, 0.8266] +2026-04-14 01:31:11.418251: Epoch time: 99.99 s +2026-04-14 01:31:12.656625: +2026-04-14 01:31:12.658515: Epoch 2960 +2026-04-14 01:31:12.660190: Current learning rate: 0.00297 +2026-04-14 01:32:52.664232: train_loss -0.43 +2026-04-14 01:32:52.671997: val_loss -0.3194 +2026-04-14 01:32:52.674265: Pseudo dice [0.3238, 0.0, 0.535, 0.1017, 0.4024, 0.717, 0.8636] +2026-04-14 01:32:52.676406: Epoch time: 100.01 s +2026-04-14 01:32:53.913416: +2026-04-14 01:32:53.915251: Epoch 2961 +2026-04-14 01:32:53.916989: Current learning rate: 0.00297 +2026-04-14 01:34:33.982488: train_loss -0.439 +2026-04-14 01:34:33.987352: val_loss -0.416 +2026-04-14 01:34:33.989294: Pseudo dice [0.8124, 0.0, 0.6458, 0.0, 0.4371, 0.6216, 0.776] +2026-04-14 01:34:33.991448: Epoch time: 100.07 s +2026-04-14 01:34:35.265452: +2026-04-14 01:34:35.267631: Epoch 2962 +2026-04-14 01:34:35.269585: Current learning rate: 0.00297 +2026-04-14 01:36:15.258055: train_loss -0.4545 +2026-04-14 01:36:15.264519: val_loss -0.3728 +2026-04-14 01:36:15.267043: Pseudo dice [0.7703, 0.0, 0.807, 0.7789, 0.0722, 0.3289, 0.5279] +2026-04-14 01:36:15.269362: Epoch time: 100.0 s +2026-04-14 01:36:16.485970: +2026-04-14 01:36:16.487985: Epoch 2963 +2026-04-14 01:36:16.490008: Current learning rate: 0.00297 +2026-04-14 01:37:56.503869: train_loss -0.4359 +2026-04-14 01:37:56.509734: val_loss -0.3946 +2026-04-14 01:37:56.514166: Pseudo dice [0.7674, 0.0, 0.5722, 0.2038, 0.2094, 0.7121, 0.8134] +2026-04-14 01:37:56.517451: Epoch time: 100.02 s +2026-04-14 01:37:57.754013: +2026-04-14 01:37:57.757173: Epoch 2964 +2026-04-14 01:37:57.759510: Current learning rate: 0.00296 +2026-04-14 01:39:37.906936: train_loss -0.4527 +2026-04-14 01:39:37.912841: val_loss -0.4093 +2026-04-14 01:39:37.915156: Pseudo dice [0.5616, 0.0, 0.7132, 0.9253, 0.4277, 0.7067, 0.4949] +2026-04-14 01:39:37.918028: Epoch time: 100.16 s +2026-04-14 01:39:39.164561: +2026-04-14 01:39:39.167060: Epoch 2965 +2026-04-14 01:39:39.170475: Current learning rate: 0.00296 +2026-04-14 01:41:19.431217: train_loss -0.444 +2026-04-14 01:41:19.438789: val_loss -0.4172 +2026-04-14 01:41:19.440732: Pseudo dice [0.4857, 0.0, 0.7043, 0.0, 0.3529, 0.7065, 0.8699] +2026-04-14 01:41:19.443311: Epoch time: 100.27 s +2026-04-14 01:41:20.689026: +2026-04-14 01:41:20.692153: Epoch 2966 +2026-04-14 01:41:20.693990: Current learning rate: 0.00296 +2026-04-14 01:43:00.743011: train_loss -0.4533 +2026-04-14 01:43:00.753195: val_loss -0.2496 +2026-04-14 01:43:00.757987: Pseudo dice [0.6025, 0.0, 0.6031, 0.0298, 0.3378, 0.7961, 0.8456] +2026-04-14 01:43:00.760562: Epoch time: 100.06 s +2026-04-14 01:43:02.011897: +2026-04-14 01:43:02.014273: Epoch 2967 +2026-04-14 01:43:02.016438: Current learning rate: 0.00296 +2026-04-14 01:44:42.031909: train_loss -0.441 +2026-04-14 01:44:42.038507: val_loss -0.4041 +2026-04-14 01:44:42.040651: Pseudo dice [0.7346, 0.0, 0.6167, 0.0, 0.3631, 0.7216, 0.7717] +2026-04-14 01:44:42.044627: Epoch time: 100.02 s +2026-04-14 01:44:43.266476: +2026-04-14 01:44:43.268181: Epoch 2968 +2026-04-14 01:44:43.269788: Current learning rate: 0.00295 +2026-04-14 01:46:23.460472: train_loss -0.4325 +2026-04-14 01:46:23.468313: val_loss -0.4252 +2026-04-14 01:46:23.470763: Pseudo dice [0.1751, 0.0, 0.7269, 0.0886, 0.3881, 0.8656, 0.7974] +2026-04-14 01:46:23.473620: Epoch time: 100.2 s +2026-04-14 01:46:24.719515: +2026-04-14 01:46:24.721314: Epoch 2969 +2026-04-14 01:46:24.723528: Current learning rate: 0.00295 +2026-04-14 01:48:04.990741: train_loss -0.4234 +2026-04-14 01:48:04.997696: val_loss -0.4177 +2026-04-14 01:48:04.999788: Pseudo dice [0.6692, 0.0, 0.7367, 0.8103, 0.1334, 0.6542, 0.6115] +2026-04-14 01:48:05.002201: Epoch time: 100.27 s +2026-04-14 01:48:06.260466: +2026-04-14 01:48:06.262172: Epoch 2970 +2026-04-14 01:48:06.263653: Current learning rate: 0.00295 +2026-04-14 01:49:46.423510: train_loss -0.403 +2026-04-14 01:49:46.428632: val_loss -0.2589 +2026-04-14 01:49:46.430308: Pseudo dice [0.0, 0.0, 0.5624, 0.0387, 0.3697, 0.3597, 0.5154] +2026-04-14 01:49:46.433690: Epoch time: 100.17 s +2026-04-14 01:49:47.683185: +2026-04-14 01:49:47.684916: Epoch 2971 +2026-04-14 01:49:47.686423: Current learning rate: 0.00295 +2026-04-14 01:51:27.675180: train_loss -0.4063 +2026-04-14 01:51:27.683441: val_loss -0.2636 +2026-04-14 01:51:27.686746: Pseudo dice [0.6367, 0.0, 0.6099, 0.0, 0.3725, 0.7554, 0.7895] +2026-04-14 01:51:27.689610: Epoch time: 100.0 s +2026-04-14 01:51:28.949213: +2026-04-14 01:51:28.951313: Epoch 2972 +2026-04-14 01:51:28.952964: Current learning rate: 0.00294 +2026-04-14 01:53:09.119344: train_loss -0.4261 +2026-04-14 01:53:09.125882: val_loss -0.4137 +2026-04-14 01:53:09.127723: Pseudo dice [0.779, 0.0, 0.8205, 0.6238, 0.3754, 0.7117, 0.7467] +2026-04-14 01:53:09.129899: Epoch time: 100.17 s +2026-04-14 01:53:11.374001: +2026-04-14 01:53:11.375746: Epoch 2973 +2026-04-14 01:53:11.377499: Current learning rate: 0.00294 +2026-04-14 01:54:51.427284: train_loss -0.4216 +2026-04-14 01:54:51.432965: val_loss -0.3718 +2026-04-14 01:54:51.435415: Pseudo dice [0.0, 0.0, 0.6351, 0.6563, 0.0, 0.6689, 0.4592] +2026-04-14 01:54:51.437400: Epoch time: 100.06 s +2026-04-14 01:54:52.686819: +2026-04-14 01:54:52.688589: Epoch 2974 +2026-04-14 01:54:52.691120: Current learning rate: 0.00294 +2026-04-14 01:56:33.005171: train_loss -0.4027 +2026-04-14 01:56:33.010800: val_loss -0.4296 +2026-04-14 01:56:33.013256: Pseudo dice [0.037, 0.0, 0.75, 0.0, 0.2561, 0.6652, 0.8459] +2026-04-14 01:56:33.015400: Epoch time: 100.32 s +2026-04-14 01:56:34.296425: +2026-04-14 01:56:34.299020: Epoch 2975 +2026-04-14 01:56:34.301084: Current learning rate: 0.00294 +2026-04-14 01:58:14.429541: train_loss -0.436 +2026-04-14 01:58:14.434538: val_loss -0.3422 +2026-04-14 01:58:14.437103: Pseudo dice [0.1421, 0.0, 0.5365, 0.4362, 0.2103, 0.6188, 0.3777] +2026-04-14 01:58:14.439821: Epoch time: 100.14 s +2026-04-14 01:58:15.688775: +2026-04-14 01:58:15.690609: Epoch 2976 +2026-04-14 01:58:15.692363: Current learning rate: 0.00293 +2026-04-14 01:59:55.696249: train_loss -0.4245 +2026-04-14 01:59:55.702044: val_loss -0.3116 +2026-04-14 01:59:55.703789: Pseudo dice [0.7437, 0.0, 0.7793, 0.0576, 0.4206, 0.696, 0.8057] +2026-04-14 01:59:55.706134: Epoch time: 100.01 s +2026-04-14 01:59:56.966909: +2026-04-14 01:59:56.968855: Epoch 2977 +2026-04-14 01:59:56.970780: Current learning rate: 0.00293 +2026-04-14 02:01:37.145881: train_loss -0.4172 +2026-04-14 02:01:37.152942: val_loss -0.3616 +2026-04-14 02:01:37.155135: Pseudo dice [0.6166, 0.0, 0.7005, 0.0377, 0.3934, 0.7443, 0.8378] +2026-04-14 02:01:37.158193: Epoch time: 100.18 s +2026-04-14 02:01:38.480717: +2026-04-14 02:01:38.483438: Epoch 2978 +2026-04-14 02:01:38.485555: Current learning rate: 0.00293 +2026-04-14 02:03:18.376891: train_loss -0.4234 +2026-04-14 02:03:18.384070: val_loss -0.3803 +2026-04-14 02:03:18.386201: Pseudo dice [0.0, 0.0, 0.5788, 0.4985, 0.2199, 0.7743, 0.7453] +2026-04-14 02:03:18.390065: Epoch time: 99.9 s +2026-04-14 02:03:19.631838: +2026-04-14 02:03:19.633851: Epoch 2979 +2026-04-14 02:03:19.636458: Current learning rate: 0.00293 +2026-04-14 02:04:59.985651: train_loss -0.4262 +2026-04-14 02:04:59.993710: val_loss -0.4299 +2026-04-14 02:04:59.996830: Pseudo dice [0.357, 0.0, 0.8378, 0.0, 0.4912, 0.6248, 0.8383] +2026-04-14 02:04:59.999161: Epoch time: 100.36 s +2026-04-14 02:05:01.247439: +2026-04-14 02:05:01.249744: Epoch 2980 +2026-04-14 02:05:01.252449: Current learning rate: 0.00292 +2026-04-14 02:06:41.351964: train_loss -0.4296 +2026-04-14 02:06:41.359286: val_loss -0.4351 +2026-04-14 02:06:41.361947: Pseudo dice [0.7706, 0.0, 0.6538, 0.7783, 0.0539, 0.7978, 0.7899] +2026-04-14 02:06:41.364618: Epoch time: 100.11 s +2026-04-14 02:06:42.612632: +2026-04-14 02:06:42.614957: Epoch 2981 +2026-04-14 02:06:42.616818: Current learning rate: 0.00292 +2026-04-14 02:08:23.112738: train_loss -0.4146 +2026-04-14 02:08:23.121035: val_loss -0.3933 +2026-04-14 02:08:23.123272: Pseudo dice [0.3724, 0.0, 0.7504, 0.5539, 0.29, 0.8283, 0.8262] +2026-04-14 02:08:23.126051: Epoch time: 100.5 s +2026-04-14 02:08:24.376490: +2026-04-14 02:08:24.378846: Epoch 2982 +2026-04-14 02:08:24.380906: Current learning rate: 0.00292 +2026-04-14 02:10:04.620148: train_loss -0.4023 +2026-04-14 02:10:04.627716: val_loss -0.3821 +2026-04-14 02:10:04.630174: Pseudo dice [0.5494, 0.0, 0.7116, 0.7859, 0.4093, 0.8399, 0.39] +2026-04-14 02:10:04.632430: Epoch time: 100.25 s +2026-04-14 02:10:05.916820: +2026-04-14 02:10:05.919799: Epoch 2983 +2026-04-14 02:10:05.924293: Current learning rate: 0.00292 +2026-04-14 02:11:46.025358: train_loss -0.4039 +2026-04-14 02:11:46.032172: val_loss -0.3277 +2026-04-14 02:11:46.034081: Pseudo dice [0.6574, 0.0, 0.5814, 0.0509, 0.2856, 0.5751, 0.8434] +2026-04-14 02:11:46.037137: Epoch time: 100.11 s +2026-04-14 02:11:47.278397: +2026-04-14 02:11:47.280795: Epoch 2984 +2026-04-14 02:11:47.283163: Current learning rate: 0.00291 +2026-04-14 02:13:27.734729: train_loss -0.4143 +2026-04-14 02:13:27.742375: val_loss -0.3452 +2026-04-14 02:13:27.744533: Pseudo dice [0.5736, 0.0, 0.5565, 0.0, 0.5799, 0.7707, 0.8111] +2026-04-14 02:13:27.747415: Epoch time: 100.46 s +2026-04-14 02:13:28.988395: +2026-04-14 02:13:28.990330: Epoch 2985 +2026-04-14 02:13:28.992864: Current learning rate: 0.00291 +2026-04-14 02:15:09.201355: train_loss -0.4008 +2026-04-14 02:15:09.209733: val_loss -0.4134 +2026-04-14 02:15:09.213306: Pseudo dice [0.4926, 0.0, 0.6697, 0.7411, 0.0, 0.8081, 0.8274] +2026-04-14 02:15:09.216317: Epoch time: 100.22 s +2026-04-14 02:15:10.480818: +2026-04-14 02:15:10.482654: Epoch 2986 +2026-04-14 02:15:10.484738: Current learning rate: 0.00291 +2026-04-14 02:16:51.029684: train_loss -0.424 +2026-04-14 02:16:51.038114: val_loss -0.3739 +2026-04-14 02:16:51.041301: Pseudo dice [0.4526, 0.0, 0.7493, 0.4918, 0.4323, 0.7308, 0.6204] +2026-04-14 02:16:51.044423: Epoch time: 100.55 s +2026-04-14 02:16:52.295819: +2026-04-14 02:16:52.298391: Epoch 2987 +2026-04-14 02:16:52.302700: Current learning rate: 0.00291 +2026-04-14 02:18:33.094644: train_loss -0.4225 +2026-04-14 02:18:33.102459: val_loss -0.4064 +2026-04-14 02:18:33.105911: Pseudo dice [0.6675, 0.0, 0.8288, 0.0654, 0.4423, 0.5801, 0.7406] +2026-04-14 02:18:33.109140: Epoch time: 100.8 s +2026-04-14 02:18:34.329610: +2026-04-14 02:18:34.331687: Epoch 2988 +2026-04-14 02:18:34.333892: Current learning rate: 0.0029 +2026-04-14 02:20:16.272067: train_loss -0.4412 +2026-04-14 02:20:16.278583: val_loss -0.3406 +2026-04-14 02:20:16.280725: Pseudo dice [0.0, 0.0, 0.5679, 0.0494, 0.5363, 0.8756, 0.7454] +2026-04-14 02:20:16.283067: Epoch time: 101.95 s +2026-04-14 02:20:17.550305: +2026-04-14 02:20:17.553216: Epoch 2989 +2026-04-14 02:20:17.556297: Current learning rate: 0.0029 +2026-04-14 02:21:58.140716: train_loss -0.4596 +2026-04-14 02:21:58.153228: val_loss -0.3873 +2026-04-14 02:21:58.165903: Pseudo dice [0.0, 0.0, 0.6414, 0.1135, 0.3054, 0.5343, 0.7637] +2026-04-14 02:21:58.168459: Epoch time: 100.59 s +2026-04-14 02:21:59.475856: +2026-04-14 02:21:59.477945: Epoch 2990 +2026-04-14 02:21:59.480075: Current learning rate: 0.0029 +2026-04-14 02:23:40.543231: train_loss -0.4241 +2026-04-14 02:23:40.550381: val_loss -0.3807 +2026-04-14 02:23:40.552880: Pseudo dice [0.7057, 0.0, 0.7821, 0.0984, 0.4646, 0.4547, 0.514] +2026-04-14 02:23:40.555711: Epoch time: 101.07 s +2026-04-14 02:23:41.808852: +2026-04-14 02:23:41.810981: Epoch 2991 +2026-04-14 02:23:41.814091: Current learning rate: 0.00289 +2026-04-14 02:25:22.258733: train_loss -0.3885 +2026-04-14 02:25:22.265595: val_loss -0.3461 +2026-04-14 02:25:22.268864: Pseudo dice [0.5906, 0.0, 0.5551, 0.3459, 0.4063, 0.4394, 0.6989] +2026-04-14 02:25:22.271769: Epoch time: 100.45 s +2026-04-14 02:25:23.514797: +2026-04-14 02:25:23.517158: Epoch 2992 +2026-04-14 02:25:23.519744: Current learning rate: 0.00289 +2026-04-14 02:27:05.387856: train_loss -0.4354 +2026-04-14 02:27:05.394364: val_loss -0.3724 +2026-04-14 02:27:05.396553: Pseudo dice [0.2873, 0.0, 0.5393, 0.0, 0.1969, 0.8582, 0.7868] +2026-04-14 02:27:05.399623: Epoch time: 101.88 s +2026-04-14 02:27:06.644741: +2026-04-14 02:27:06.646681: Epoch 2993 +2026-04-14 02:27:06.649003: Current learning rate: 0.00289 +2026-04-14 02:28:46.913539: train_loss -0.4349 +2026-04-14 02:28:46.922406: val_loss -0.4168 +2026-04-14 02:28:46.924628: Pseudo dice [0.817, 0.0, 0.8223, 0.0, 0.3835, 0.7118, 0.6897] +2026-04-14 02:28:46.927351: Epoch time: 100.27 s +2026-04-14 02:28:48.172392: +2026-04-14 02:28:48.174691: Epoch 2994 +2026-04-14 02:28:48.176797: Current learning rate: 0.00289 +2026-04-14 02:30:28.849946: train_loss -0.4365 +2026-04-14 02:30:28.859536: val_loss -0.3759 +2026-04-14 02:30:28.861935: Pseudo dice [0.6197, 0.0, 0.7275, 0.6644, 0.4387, 0.7988, 0.5834] +2026-04-14 02:30:28.865078: Epoch time: 100.68 s +2026-04-14 02:30:30.096714: +2026-04-14 02:30:30.098785: Epoch 2995 +2026-04-14 02:30:30.101049: Current learning rate: 0.00288 +2026-04-14 02:32:10.779150: train_loss -0.4383 +2026-04-14 02:32:10.786964: val_loss -0.363 +2026-04-14 02:32:10.789384: Pseudo dice [0.4083, 0.0, 0.384, 0.1379, 0.4681, 0.7695, 0.5725] +2026-04-14 02:32:10.791838: Epoch time: 100.69 s +2026-04-14 02:32:12.059230: +2026-04-14 02:32:12.061161: Epoch 2996 +2026-04-14 02:32:12.063446: Current learning rate: 0.00288 +2026-04-14 02:33:52.356515: train_loss -0.4275 +2026-04-14 02:33:52.363164: val_loss -0.374 +2026-04-14 02:33:52.365149: Pseudo dice [0.7013, 0.0, 0.6827, 0.0, 0.6845, 0.8234, 0.6623] +2026-04-14 02:33:52.367551: Epoch time: 100.3 s +2026-04-14 02:33:53.687715: +2026-04-14 02:33:53.689794: Epoch 2997 +2026-04-14 02:33:53.692168: Current learning rate: 0.00288 +2026-04-14 02:35:35.207129: train_loss -0.4447 +2026-04-14 02:35:35.213889: val_loss -0.3914 +2026-04-14 02:35:35.216415: Pseudo dice [0.6774, 0.0, 0.7933, 0.1292, 0.2495, 0.8156, 0.6962] +2026-04-14 02:35:35.220025: Epoch time: 101.52 s +2026-04-14 02:35:36.482295: +2026-04-14 02:35:36.484271: Epoch 2998 +2026-04-14 02:35:36.486579: Current learning rate: 0.00288 +2026-04-14 02:37:16.847353: train_loss -0.4436 +2026-04-14 02:37:16.854138: val_loss -0.4222 +2026-04-14 02:37:16.856512: Pseudo dice [0.784, 0.0, 0.7395, 0.5646, 0.4157, 0.7658, 0.78] +2026-04-14 02:37:16.859337: Epoch time: 100.37 s +2026-04-14 02:37:18.109138: +2026-04-14 02:37:18.114835: Epoch 2999 +2026-04-14 02:37:18.118133: Current learning rate: 0.00287 +2026-04-14 02:38:58.750330: train_loss -0.4144 +2026-04-14 02:38:58.756819: val_loss -0.3203 +2026-04-14 02:38:58.759926: Pseudo dice [0.5914, 0.0, 0.5607, 0.06, 0.1518, 0.735, 0.8702] +2026-04-14 02:38:58.763171: Epoch time: 100.64 s +2026-04-14 02:39:01.715843: +2026-04-14 02:39:01.718359: Epoch 3000 +2026-04-14 02:39:01.720631: Current learning rate: 0.00287 +2026-04-14 02:40:42.301501: train_loss -0.4093 +2026-04-14 02:40:42.307322: val_loss -0.4205 +2026-04-14 02:40:42.309613: Pseudo dice [0.7853, 0.0, 0.8075, 0.3448, 0.421, 0.784, 0.8171] +2026-04-14 02:40:42.311955: Epoch time: 100.59 s +2026-04-14 02:40:43.568157: +2026-04-14 02:40:43.569935: Epoch 3001 +2026-04-14 02:40:43.571970: Current learning rate: 0.00287 +2026-04-14 02:42:23.905190: train_loss -0.4141 +2026-04-14 02:42:23.911889: val_loss -0.3371 +2026-04-14 02:42:23.914015: Pseudo dice [0.5866, 0.0, 0.6125, 0.0, 0.2159, 0.4165, 0.774] +2026-04-14 02:42:23.916595: Epoch time: 100.34 s +2026-04-14 02:42:25.155208: +2026-04-14 02:42:25.157197: Epoch 3002 +2026-04-14 02:42:25.159389: Current learning rate: 0.00287 +2026-04-14 02:44:06.388738: train_loss -0.4233 +2026-04-14 02:44:06.395390: val_loss -0.3422 +2026-04-14 02:44:06.398768: Pseudo dice [0.7287, 0.0, 0.77, 0.0, 0.1982, 0.8634, 0.6567] +2026-04-14 02:44:06.401711: Epoch time: 101.24 s +2026-04-14 02:44:07.674380: +2026-04-14 02:44:07.676452: Epoch 3003 +2026-04-14 02:44:07.680710: Current learning rate: 0.00286 +2026-04-14 02:45:48.547195: train_loss -0.4203 +2026-04-14 02:45:48.554737: val_loss -0.4193 +2026-04-14 02:45:48.557172: Pseudo dice [0.4467, 0.0, 0.7537, 0.4826, 0.1743, 0.76, 0.7832] +2026-04-14 02:45:48.560153: Epoch time: 100.88 s +2026-04-14 02:45:49.846453: +2026-04-14 02:45:49.848873: Epoch 3004 +2026-04-14 02:45:49.851050: Current learning rate: 0.00286 +2026-04-14 02:47:30.627434: train_loss -0.4247 +2026-04-14 02:47:30.633495: val_loss -0.3531 +2026-04-14 02:47:30.635665: Pseudo dice [0.2026, 0.0, 0.7476, 0.0558, 0.4678, 0.7503, 0.5815] +2026-04-14 02:47:30.638474: Epoch time: 100.78 s +2026-04-14 02:47:31.943048: +2026-04-14 02:47:31.945021: Epoch 3005 +2026-04-14 02:47:31.947341: Current learning rate: 0.00286 +2026-04-14 02:49:12.355472: train_loss -0.4376 +2026-04-14 02:49:12.362194: val_loss -0.3885 +2026-04-14 02:49:12.366283: Pseudo dice [0.1588, 0.0, 0.7417, 0.7654, 0.4778, 0.7472, 0.6808] +2026-04-14 02:49:12.368576: Epoch time: 100.42 s +2026-04-14 02:49:13.628409: +2026-04-14 02:49:13.631085: Epoch 3006 +2026-04-14 02:49:13.635305: Current learning rate: 0.00286 +2026-04-14 02:50:54.286677: train_loss -0.4088 +2026-04-14 02:50:54.293136: val_loss -0.3997 +2026-04-14 02:50:54.295787: Pseudo dice [0.5269, 0.0, 0.761, 0.8234, 0.2787, 0.7818, 0.856] +2026-04-14 02:50:54.298317: Epoch time: 100.66 s +2026-04-14 02:50:55.591038: +2026-04-14 02:50:55.594568: Epoch 3007 +2026-04-14 02:50:55.597631: Current learning rate: 0.00285 +2026-04-14 02:52:36.607355: train_loss -0.4436 +2026-04-14 02:52:36.616289: val_loss -0.2657 +2026-04-14 02:52:36.619934: Pseudo dice [0.6438, 0.0, 0.6936, 0.0306, 0.029, 0.8474, 0.7495] +2026-04-14 02:52:36.623457: Epoch time: 101.02 s +2026-04-14 02:52:37.907588: +2026-04-14 02:52:37.909770: Epoch 3008 +2026-04-14 02:52:37.913320: Current learning rate: 0.00285 +2026-04-14 02:54:18.885508: train_loss -0.4389 +2026-04-14 02:54:18.898479: val_loss -0.4389 +2026-04-14 02:54:18.901601: Pseudo dice [0.5599, 0.0, 0.7166, 0.2005, 0.5304, 0.819, 0.6865] +2026-04-14 02:54:18.904141: Epoch time: 100.98 s +2026-04-14 02:54:20.157599: +2026-04-14 02:54:20.159954: Epoch 3009 +2026-04-14 02:54:20.162990: Current learning rate: 0.00285 +2026-04-14 02:56:00.430836: train_loss -0.4313 +2026-04-14 02:56:00.437301: val_loss -0.3499 +2026-04-14 02:56:00.440195: Pseudo dice [0.6738, 0.0, 0.6247, 0.0, 0.3112, 0.6579, 0.7857] +2026-04-14 02:56:00.443305: Epoch time: 100.28 s +2026-04-14 02:56:01.682485: +2026-04-14 02:56:01.685153: Epoch 3010 +2026-04-14 02:56:01.687870: Current learning rate: 0.00285 +2026-04-14 02:57:42.542805: train_loss -0.4459 +2026-04-14 02:57:42.553461: val_loss -0.3863 +2026-04-14 02:57:42.555918: Pseudo dice [0.6925, 0.0, 0.7026, 0.5287, 0.4864, 0.8226, 0.7185] +2026-04-14 02:57:42.558646: Epoch time: 100.86 s +2026-04-14 02:57:43.812011: +2026-04-14 02:57:43.815289: Epoch 3011 +2026-04-14 02:57:43.819003: Current learning rate: 0.00284 +2026-04-14 02:59:23.909848: train_loss -0.4375 +2026-04-14 02:59:23.919000: val_loss -0.3952 +2026-04-14 02:59:23.921865: Pseudo dice [0.4965, 0.0, 0.7152, 0.0979, 0.1325, 0.7808, 0.7737] +2026-04-14 02:59:23.924285: Epoch time: 100.1 s +2026-04-14 02:59:26.254633: +2026-04-14 02:59:26.256856: Epoch 3012 +2026-04-14 02:59:26.259157: Current learning rate: 0.00284 +2026-04-14 03:01:06.403750: train_loss -0.4367 +2026-04-14 03:01:06.410888: val_loss -0.383 +2026-04-14 03:01:06.414035: Pseudo dice [0.3772, 0.0, 0.6341, 0.0467, 0.4468, 0.8192, 0.7668] +2026-04-14 03:01:06.416668: Epoch time: 100.15 s +2026-04-14 03:01:07.671766: +2026-04-14 03:01:07.673863: Epoch 3013 +2026-04-14 03:01:07.675997: Current learning rate: 0.00284 +2026-04-14 03:02:49.148642: train_loss -0.4145 +2026-04-14 03:02:49.160440: val_loss -0.3751 +2026-04-14 03:02:49.162950: Pseudo dice [0.7374, 0.0, 0.7393, 0.0, 0.3389, 0.7886, 0.6546] +2026-04-14 03:02:49.165873: Epoch time: 101.48 s +2026-04-14 03:02:50.421530: +2026-04-14 03:02:50.424140: Epoch 3014 +2026-04-14 03:02:50.426676: Current learning rate: 0.00284 +2026-04-14 03:04:30.521013: train_loss -0.4215 +2026-04-14 03:04:30.527455: val_loss -0.4267 +2026-04-14 03:04:30.529563: Pseudo dice [0.5532, 0.0, 0.6887, 0.9095, 0.4784, 0.7738, 0.6823] +2026-04-14 03:04:30.533298: Epoch time: 100.1 s +2026-04-14 03:04:31.767255: +2026-04-14 03:04:31.769323: Epoch 3015 +2026-04-14 03:04:31.771571: Current learning rate: 0.00283 +2026-04-14 03:06:11.958624: train_loss -0.4039 +2026-04-14 03:06:11.972005: val_loss -0.3731 +2026-04-14 03:06:11.975123: Pseudo dice [0.591, 0.0, 0.5245, 0.0, 0.3028, 0.801, 0.7905] +2026-04-14 03:06:11.977911: Epoch time: 100.19 s +2026-04-14 03:06:13.218723: +2026-04-14 03:06:13.221926: Epoch 3016 +2026-04-14 03:06:13.224580: Current learning rate: 0.00283 +2026-04-14 03:07:54.427435: train_loss -0.422 +2026-04-14 03:07:54.435529: val_loss -0.3761 +2026-04-14 03:07:54.437778: Pseudo dice [0.5788, 0.0, 0.8069, 0.0831, 0.4718, 0.7404, 0.8105] +2026-04-14 03:07:54.441883: Epoch time: 101.21 s +2026-04-14 03:07:55.717363: +2026-04-14 03:07:55.719702: Epoch 3017 +2026-04-14 03:07:55.723069: Current learning rate: 0.00283 +2026-04-14 03:09:36.836841: train_loss -0.4483 +2026-04-14 03:09:36.842748: val_loss -0.3998 +2026-04-14 03:09:36.844884: Pseudo dice [0.3381, 0.0, 0.8273, 0.0005, 0.1118, 0.7677, 0.8182] +2026-04-14 03:09:36.848706: Epoch time: 101.12 s +2026-04-14 03:09:38.081124: +2026-04-14 03:09:38.083361: Epoch 3018 +2026-04-14 03:09:38.085941: Current learning rate: 0.00283 +2026-04-14 03:11:18.818122: train_loss -0.4075 +2026-04-14 03:11:18.824303: val_loss -0.3365 +2026-04-14 03:11:18.828442: Pseudo dice [0.3858, 0.0, 0.5906, 0.7789, 0.2377, 0.5484, 0.5111] +2026-04-14 03:11:18.833477: Epoch time: 100.74 s +2026-04-14 03:11:20.081148: +2026-04-14 03:11:20.084315: Epoch 3019 +2026-04-14 03:11:20.086591: Current learning rate: 0.00282 +2026-04-14 03:12:59.944506: train_loss -0.3824 +2026-04-14 03:12:59.950842: val_loss -0.3754 +2026-04-14 03:12:59.953886: Pseudo dice [0.0067, 0.0, 0.7648, 0.0146, 0.437, 0.7674, 0.8028] +2026-04-14 03:12:59.957062: Epoch time: 99.87 s +2026-04-14 03:13:01.190606: +2026-04-14 03:13:01.194644: Epoch 3020 +2026-04-14 03:13:01.198885: Current learning rate: 0.00282 +2026-04-14 03:14:41.871119: train_loss -0.3977 +2026-04-14 03:14:41.877740: val_loss -0.3855 +2026-04-14 03:14:41.879695: Pseudo dice [0.5603, 0.0, 0.7583, 0.5052, 0.48, 0.6354, 0.5745] +2026-04-14 03:14:41.882160: Epoch time: 100.68 s +2026-04-14 03:14:43.119068: +2026-04-14 03:14:43.121085: Epoch 3021 +2026-04-14 03:14:43.123601: Current learning rate: 0.00282 +2026-04-14 03:16:23.148340: train_loss -0.4272 +2026-04-14 03:16:23.156766: val_loss -0.4047 +2026-04-14 03:16:23.159450: Pseudo dice [0.6422, 0.0, 0.7204, 0.0, 0.4268, 0.6965, 0.7841] +2026-04-14 03:16:23.163567: Epoch time: 100.03 s +2026-04-14 03:16:24.402363: +2026-04-14 03:16:24.404608: Epoch 3022 +2026-04-14 03:16:24.408161: Current learning rate: 0.00281 +2026-04-14 03:18:04.623521: train_loss -0.4328 +2026-04-14 03:18:04.632493: val_loss -0.4357 +2026-04-14 03:18:04.635400: Pseudo dice [0.7159, 0.0, 0.8628, 0.4226, 0.3165, 0.8379, 0.8478] +2026-04-14 03:18:04.638819: Epoch time: 100.22 s +2026-04-14 03:18:05.880265: +2026-04-14 03:18:05.882556: Epoch 3023 +2026-04-14 03:18:05.885103: Current learning rate: 0.00281 +2026-04-14 03:19:46.496654: train_loss -0.4457 +2026-04-14 03:19:46.503864: val_loss -0.3961 +2026-04-14 03:19:46.507476: Pseudo dice [0.6564, 0.0, 0.6922, 0.8266, 0.5768, 0.8531, 0.6775] +2026-04-14 03:19:46.510339: Epoch time: 100.62 s +2026-04-14 03:19:47.770279: +2026-04-14 03:19:47.772559: Epoch 3024 +2026-04-14 03:19:47.774911: Current learning rate: 0.00281 +2026-04-14 03:21:28.787727: train_loss -0.4512 +2026-04-14 03:21:28.794790: val_loss -0.3966 +2026-04-14 03:21:28.797582: Pseudo dice [0.6207, 0.0, 0.7454, 0.116, 0.3251, 0.8231, 0.8629] +2026-04-14 03:21:28.800181: Epoch time: 101.02 s +2026-04-14 03:21:30.041184: +2026-04-14 03:21:30.043173: Epoch 3025 +2026-04-14 03:21:30.045397: Current learning rate: 0.00281 +2026-04-14 03:23:11.087457: train_loss -0.4476 +2026-04-14 03:23:11.096566: val_loss -0.4171 +2026-04-14 03:23:11.100004: Pseudo dice [0.825, 0.0, 0.8788, 0.0, 0.3094, 0.8204, 0.6498] +2026-04-14 03:23:11.102859: Epoch time: 101.05 s +2026-04-14 03:23:12.332857: +2026-04-14 03:23:12.334932: Epoch 3026 +2026-04-14 03:23:12.336905: Current learning rate: 0.0028 +2026-04-14 03:24:52.706398: train_loss -0.4413 +2026-04-14 03:24:52.714859: val_loss -0.4071 +2026-04-14 03:24:52.718560: Pseudo dice [0.6415, 0.0, 0.746, 0.0, 0.2345, 0.5833, 0.7825] +2026-04-14 03:24:52.721627: Epoch time: 100.38 s +2026-04-14 03:24:53.959575: +2026-04-14 03:24:53.961892: Epoch 3027 +2026-04-14 03:24:53.964189: Current learning rate: 0.0028 +2026-04-14 03:26:35.144847: train_loss -0.4459 +2026-04-14 03:26:35.153976: val_loss -0.3373 +2026-04-14 03:26:35.156609: Pseudo dice [0.73, 0.0, 0.7333, 0.1355, 0.4567, 0.8021, 0.5594] +2026-04-14 03:26:35.160968: Epoch time: 101.19 s +2026-04-14 03:26:36.406407: +2026-04-14 03:26:36.408906: Epoch 3028 +2026-04-14 03:26:36.411362: Current learning rate: 0.0028 +2026-04-14 03:28:17.403020: train_loss -0.432 +2026-04-14 03:28:17.412126: val_loss -0.3521 +2026-04-14 03:28:17.416259: Pseudo dice [0.7725, 0.0, 0.7074, 0.0615, 0.0056, 0.8628, 0.7736] +2026-04-14 03:28:17.419276: Epoch time: 101.0 s +2026-04-14 03:28:18.698697: +2026-04-14 03:28:18.700942: Epoch 3029 +2026-04-14 03:28:18.703671: Current learning rate: 0.0028 +2026-04-14 03:30:00.036381: train_loss -0.4415 +2026-04-14 03:30:00.043602: val_loss -0.4154 +2026-04-14 03:30:00.046402: Pseudo dice [0.5933, 0.0, 0.8358, 0.0, 0.5475, 0.759, 0.7111] +2026-04-14 03:30:00.049425: Epoch time: 101.34 s +2026-04-14 03:30:01.442814: +2026-04-14 03:30:01.446852: Epoch 3030 +2026-04-14 03:30:01.454483: Current learning rate: 0.00279 +2026-04-14 03:31:41.610597: train_loss -0.4348 +2026-04-14 03:31:41.619228: val_loss -0.3697 +2026-04-14 03:31:41.621878: Pseudo dice [0.6837, 0.0, 0.7106, 0.4138, 0.2593, 0.4845, 0.8745] +2026-04-14 03:31:41.624799: Epoch time: 100.17 s +2026-04-14 03:31:42.860381: +2026-04-14 03:31:42.862793: Epoch 3031 +2026-04-14 03:31:42.865769: Current learning rate: 0.00279 +2026-04-14 03:33:23.724685: train_loss -0.4268 +2026-04-14 03:33:23.732919: val_loss -0.3286 +2026-04-14 03:33:23.735816: Pseudo dice [0.7704, 0.0, 0.6404, 0.0, 0.365, 0.3232, 0.8295] +2026-04-14 03:33:23.738707: Epoch time: 100.87 s +2026-04-14 03:33:26.045857: +2026-04-14 03:33:26.048259: Epoch 3032 +2026-04-14 03:33:26.051527: Current learning rate: 0.00279 +2026-04-14 03:35:06.377547: train_loss -0.4247 +2026-04-14 03:35:06.384859: val_loss -0.3726 +2026-04-14 03:35:06.388890: Pseudo dice [0.3086, 0.0, 0.4616, 0.0, 0.2282, 0.5326, 0.8455] +2026-04-14 03:35:06.392441: Epoch time: 100.33 s +2026-04-14 03:35:07.661789: +2026-04-14 03:35:07.663684: Epoch 3033 +2026-04-14 03:35:07.666353: Current learning rate: 0.00279 +2026-04-14 03:36:48.346585: train_loss -0.429 +2026-04-14 03:36:48.354146: val_loss -0.3559 +2026-04-14 03:36:48.357630: Pseudo dice [0.6927, 0.0, 0.7046, 0.0, 0.3852, 0.8226, 0.3491] +2026-04-14 03:36:48.360474: Epoch time: 100.69 s +2026-04-14 03:36:49.604103: +2026-04-14 03:36:49.606336: Epoch 3034 +2026-04-14 03:36:49.608538: Current learning rate: 0.00278 +2026-04-14 03:38:30.766494: train_loss -0.4176 +2026-04-14 03:38:30.775286: val_loss -0.39 +2026-04-14 03:38:30.778231: Pseudo dice [0.0, 0.0, 0.7153, 0.0, 0.4587, 0.2062, 0.7482] +2026-04-14 03:38:30.781188: Epoch time: 101.17 s +2026-04-14 03:38:31.998949: +2026-04-14 03:38:32.003074: Epoch 3035 +2026-04-14 03:38:32.006840: Current learning rate: 0.00278 +2026-04-14 03:40:11.943432: train_loss -0.427 +2026-04-14 03:40:11.949711: val_loss -0.3791 +2026-04-14 03:40:11.952487: Pseudo dice [0.3907, 0.0, 0.8014, 0.1177, 0.4397, 0.8031, 0.6649] +2026-04-14 03:40:11.955300: Epoch time: 99.95 s +2026-04-14 03:40:13.207987: +2026-04-14 03:40:13.210655: Epoch 3036 +2026-04-14 03:40:13.213308: Current learning rate: 0.00278 +2026-04-14 03:41:54.588693: train_loss -0.4348 +2026-04-14 03:41:54.596006: val_loss -0.3979 +2026-04-14 03:41:54.598635: Pseudo dice [0.6141, 0.0, 0.6916, 0.0, 0.3035, 0.6356, 0.8938] +2026-04-14 03:41:54.609402: Epoch time: 101.38 s +2026-04-14 03:41:55.887740: +2026-04-14 03:41:55.889526: Epoch 3037 +2026-04-14 03:41:55.891677: Current learning rate: 0.00278 +2026-04-14 03:43:37.487188: train_loss -0.4164 +2026-04-14 03:43:37.495381: val_loss -0.3418 +2026-04-14 03:43:37.500719: Pseudo dice [0.1468, 0.0, 0.6506, 0.0, 0.2959, 0.8619, 0.7214] +2026-04-14 03:43:37.504037: Epoch time: 101.6 s +2026-04-14 03:43:38.762816: +2026-04-14 03:43:38.765014: Epoch 3038 +2026-04-14 03:43:38.767747: Current learning rate: 0.00277 +2026-04-14 03:45:19.519100: train_loss -0.447 +2026-04-14 03:45:19.528821: val_loss -0.3985 +2026-04-14 03:45:19.531303: Pseudo dice [0.4314, 0.0, 0.7164, 0.3215, 0.4808, 0.8337, 0.6994] +2026-04-14 03:45:19.534599: Epoch time: 100.76 s +2026-04-14 03:45:20.811573: +2026-04-14 03:45:20.815283: Epoch 3039 +2026-04-14 03:45:20.818076: Current learning rate: 0.00277 +2026-04-14 03:47:02.349401: train_loss -0.4351 +2026-04-14 03:47:02.358393: val_loss -0.3277 +2026-04-14 03:47:02.362173: Pseudo dice [0.7061, 0.0, 0.734, 0.0, 0.3814, 0.7522, 0.8429] +2026-04-14 03:47:02.365358: Epoch time: 101.54 s +2026-04-14 03:47:03.605581: +2026-04-14 03:47:03.607653: Epoch 3040 +2026-04-14 03:47:03.609713: Current learning rate: 0.00277 +2026-04-14 03:48:46.499212: train_loss -0.4508 +2026-04-14 03:48:46.506938: val_loss -0.3579 +2026-04-14 03:48:46.511739: Pseudo dice [0.7283, 0.0, 0.6726, 0.0, 0.4166, 0.8437, 0.5644] +2026-04-14 03:48:46.515447: Epoch time: 102.9 s +2026-04-14 03:48:47.799869: +2026-04-14 03:48:47.802063: Epoch 3041 +2026-04-14 03:48:47.805104: Current learning rate: 0.00277 +2026-04-14 03:50:28.745774: train_loss -0.4418 +2026-04-14 03:50:28.755173: val_loss -0.42 +2026-04-14 03:50:28.758157: Pseudo dice [0.5658, 0.0, 0.7702, 0.925, 0.4998, 0.6433, 0.4273] +2026-04-14 03:50:28.764778: Epoch time: 100.95 s +2026-04-14 03:50:30.015087: +2026-04-14 03:50:30.017350: Epoch 3042 +2026-04-14 03:50:30.020476: Current learning rate: 0.00276 +2026-04-14 03:52:10.829619: train_loss -0.4405 +2026-04-14 03:52:10.837365: val_loss -0.3377 +2026-04-14 03:52:10.839787: Pseudo dice [0.713, 0.0, 0.7446, 0.0537, 0.3525, 0.7342, 0.5794] +2026-04-14 03:52:10.842494: Epoch time: 100.82 s +2026-04-14 03:52:12.088387: +2026-04-14 03:52:12.090491: Epoch 3043 +2026-04-14 03:52:12.092730: Current learning rate: 0.00276 +2026-04-14 03:53:53.412308: train_loss -0.4412 +2026-04-14 03:53:53.420840: val_loss -0.3944 +2026-04-14 03:53:53.423147: Pseudo dice [0.7883, 0.0, 0.6886, 0.0, 0.3964, 0.7986, 0.7087] +2026-04-14 03:53:53.426218: Epoch time: 101.33 s +2026-04-14 03:53:54.686759: +2026-04-14 03:53:54.689782: Epoch 3044 +2026-04-14 03:53:54.692429: Current learning rate: 0.00276 +2026-04-14 03:55:35.746330: train_loss -0.4424 +2026-04-14 03:55:35.755258: val_loss -0.3702 +2026-04-14 03:55:35.757739: Pseudo dice [0.7659, 0.0, 0.5983, 0.039, 0.3782, 0.7757, 0.8241] +2026-04-14 03:55:35.760572: Epoch time: 101.06 s +2026-04-14 03:55:37.010815: +2026-04-14 03:55:37.014312: Epoch 3045 +2026-04-14 03:55:37.017237: Current learning rate: 0.00276 +2026-04-14 03:57:17.611836: train_loss -0.4581 +2026-04-14 03:57:17.620522: val_loss -0.4066 +2026-04-14 03:57:17.622794: Pseudo dice [0.7906, 0.0, 0.6876, 0.4407, 0.3494, 0.6936, 0.8006] +2026-04-14 03:57:17.625090: Epoch time: 100.6 s +2026-04-14 03:57:18.890726: +2026-04-14 03:57:18.892629: Epoch 3046 +2026-04-14 03:57:18.894878: Current learning rate: 0.00275 +2026-04-14 03:59:00.323612: train_loss -0.447 +2026-04-14 03:59:00.332896: val_loss -0.404 +2026-04-14 03:59:00.335437: Pseudo dice [0.2738, 0.0, 0.7748, 0.0, 0.1498, 0.7281, 0.7049] +2026-04-14 03:59:00.337998: Epoch time: 101.44 s +2026-04-14 03:59:01.576771: +2026-04-14 03:59:01.578803: Epoch 3047 +2026-04-14 03:59:01.581351: Current learning rate: 0.00275 +2026-04-14 04:00:42.728107: train_loss -0.4022 +2026-04-14 04:00:42.736645: val_loss -0.2654 +2026-04-14 04:00:42.739932: Pseudo dice [0.0, 0.0, 0.7489, 0.0445, 0.3177, 0.3536, 0.4803] +2026-04-14 04:00:42.744195: Epoch time: 101.15 s +2026-04-14 04:00:43.978531: +2026-04-14 04:00:43.981135: Epoch 3048 +2026-04-14 04:00:43.984748: Current learning rate: 0.00275 +2026-04-14 04:02:24.835614: train_loss -0.4088 +2026-04-14 04:02:24.850070: val_loss -0.3917 +2026-04-14 04:02:24.853428: Pseudo dice [0.6436, 0.0, 0.727, 0.6015, 0.3176, 0.879, 0.4371] +2026-04-14 04:02:24.856314: Epoch time: 100.86 s +2026-04-14 04:02:26.116863: +2026-04-14 04:02:26.119202: Epoch 3049 +2026-04-14 04:02:26.121279: Current learning rate: 0.00274 +2026-04-14 04:04:07.179836: train_loss -0.4229 +2026-04-14 04:04:07.188910: val_loss -0.3583 +2026-04-14 04:04:07.193406: Pseudo dice [0.0, 0.0, 0.5519, 0.0, 0.4096, 0.7896, 0.8007] +2026-04-14 04:04:07.197079: Epoch time: 101.07 s +2026-04-14 04:04:10.269356: +2026-04-14 04:04:10.273679: Epoch 3050 +2026-04-14 04:04:10.276751: Current learning rate: 0.00274 +2026-04-14 04:05:50.894830: train_loss -0.4327 +2026-04-14 04:05:50.901436: val_loss -0.3888 +2026-04-14 04:05:50.904070: Pseudo dice [0.786, 0.0, 0.675, 0.5931, 0.3655, 0.7489, 0.741] +2026-04-14 04:05:50.908614: Epoch time: 100.63 s +2026-04-14 04:05:52.147500: +2026-04-14 04:05:52.150242: Epoch 3051 +2026-04-14 04:05:52.154827: Current learning rate: 0.00274 +2026-04-14 04:07:33.796068: train_loss -0.4465 +2026-04-14 04:07:33.804235: val_loss -0.4198 +2026-04-14 04:07:33.807266: Pseudo dice [0.4708, 0.0, 0.6107, 0.0, 0.4013, 0.8275, 0.8392] +2026-04-14 04:07:33.811199: Epoch time: 101.65 s +2026-04-14 04:07:35.054432: +2026-04-14 04:07:35.056751: Epoch 3052 +2026-04-14 04:07:35.059002: Current learning rate: 0.00274 +2026-04-14 04:09:15.496573: train_loss -0.4344 +2026-04-14 04:09:15.504303: val_loss -0.413 +2026-04-14 04:09:15.506599: Pseudo dice [0.3479, 0.0, 0.4487, 0.5426, 0.5091, 0.7893, 0.6778] +2026-04-14 04:09:15.509340: Epoch time: 100.45 s +2026-04-14 04:09:16.789121: +2026-04-14 04:09:16.791033: Epoch 3053 +2026-04-14 04:09:16.793389: Current learning rate: 0.00273 +2026-04-14 04:10:57.842579: train_loss -0.4354 +2026-04-14 04:10:57.850965: val_loss -0.3834 +2026-04-14 04:10:57.853340: Pseudo dice [0.6533, 0.0, 0.7883, 0.0, 0.405, 0.8155, 0.8104] +2026-04-14 04:10:57.856033: Epoch time: 101.06 s +2026-04-14 04:10:59.126296: +2026-04-14 04:10:59.130180: Epoch 3054 +2026-04-14 04:10:59.132968: Current learning rate: 0.00273 +2026-04-14 04:12:40.532682: train_loss -0.4405 +2026-04-14 04:12:40.548082: val_loss -0.4071 +2026-04-14 04:12:40.553932: Pseudo dice [0.7012, 0.0, 0.5465, 0.0, 0.4235, 0.8339, 0.7731] +2026-04-14 04:12:40.558666: Epoch time: 101.41 s +2026-04-14 04:12:41.795270: +2026-04-14 04:12:41.797834: Epoch 3055 +2026-04-14 04:12:41.800155: Current learning rate: 0.00273 +2026-04-14 04:14:22.239090: train_loss -0.4397 +2026-04-14 04:14:22.250373: val_loss -0.4068 +2026-04-14 04:14:22.253349: Pseudo dice [0.4233, 0.0, 0.7419, 0.069, 0.3839, 0.8549, 0.6835] +2026-04-14 04:14:22.256203: Epoch time: 100.45 s +2026-04-14 04:14:23.499323: +2026-04-14 04:14:23.501588: Epoch 3056 +2026-04-14 04:14:23.504075: Current learning rate: 0.00273 +2026-04-14 04:16:04.022300: train_loss -0.4212 +2026-04-14 04:16:04.028143: val_loss -0.3258 +2026-04-14 04:16:04.031632: Pseudo dice [0.6354, 0.0, 0.6595, 0.0012, 0.4165, 0.8458, 0.5364] +2026-04-14 04:16:04.034805: Epoch time: 100.53 s +2026-04-14 04:16:05.304890: +2026-04-14 04:16:05.307406: Epoch 3057 +2026-04-14 04:16:05.309639: Current learning rate: 0.00272 +2026-04-14 04:17:46.414566: train_loss -0.4168 +2026-04-14 04:17:46.422293: val_loss -0.3811 +2026-04-14 04:17:46.425278: Pseudo dice [0.7203, 0.0, 0.369, 0.6655, 0.3193, 0.7769, 0.5856] +2026-04-14 04:17:46.427904: Epoch time: 101.11 s +2026-04-14 04:17:47.674942: +2026-04-14 04:17:47.678328: Epoch 3058 +2026-04-14 04:17:47.682398: Current learning rate: 0.00272 +2026-04-14 04:19:29.587457: train_loss -0.4184 +2026-04-14 04:19:29.596277: val_loss -0.373 +2026-04-14 04:19:29.598671: Pseudo dice [0.0, 0.0, 0.7059, 0.0, 0.5417, 0.6676, 0.4062] +2026-04-14 04:19:29.603822: Epoch time: 101.92 s +2026-04-14 04:19:30.878673: +2026-04-14 04:19:30.881644: Epoch 3059 +2026-04-14 04:19:30.884396: Current learning rate: 0.00272 +2026-04-14 04:21:11.649476: train_loss -0.4225 +2026-04-14 04:21:11.656038: val_loss -0.2769 +2026-04-14 04:21:11.657972: Pseudo dice [0.3698, 0.0, 0.5263, 0.0, 0.574, 0.8181, 0.7426] +2026-04-14 04:21:11.660582: Epoch time: 100.77 s +2026-04-14 04:21:12.917636: +2026-04-14 04:21:12.920895: Epoch 3060 +2026-04-14 04:21:12.924214: Current learning rate: 0.00272 +2026-04-14 04:22:54.052327: train_loss -0.439 +2026-04-14 04:22:54.060676: val_loss -0.4103 +2026-04-14 04:22:54.067523: Pseudo dice [0.591, 0.0, 0.7778, 0.5201, 0.1735, 0.6693, 0.7188] +2026-04-14 04:22:54.070383: Epoch time: 101.14 s +2026-04-14 04:22:55.332474: +2026-04-14 04:22:55.334872: Epoch 3061 +2026-04-14 04:22:55.337357: Current learning rate: 0.00271 +2026-04-14 04:24:35.670930: train_loss -0.4399 +2026-04-14 04:24:35.678241: val_loss -0.4097 +2026-04-14 04:24:35.681629: Pseudo dice [0.5844, 0.0, 0.8071, 0.2064, 0.2806, 0.8529, 0.7787] +2026-04-14 04:24:35.685291: Epoch time: 100.34 s +2026-04-14 04:24:36.927976: +2026-04-14 04:24:36.931936: Epoch 3062 +2026-04-14 04:24:36.937047: Current learning rate: 0.00271 +2026-04-14 04:26:16.980310: train_loss -0.4483 +2026-04-14 04:26:16.986873: val_loss -0.392 +2026-04-14 04:26:16.989059: Pseudo dice [0.1821, 0.0, 0.6302, 0.0, 0.5276, 0.6975, 0.7344] +2026-04-14 04:26:16.991392: Epoch time: 100.06 s +2026-04-14 04:26:18.255982: +2026-04-14 04:26:18.258098: Epoch 3063 +2026-04-14 04:26:18.260194: Current learning rate: 0.00271 +2026-04-14 04:27:58.274812: train_loss -0.4432 +2026-04-14 04:27:58.292334: val_loss -0.3419 +2026-04-14 04:27:58.308856: Pseudo dice [0.0, 0.0, 0.5733, 0.0989, 0.4169, 0.6095, 0.7894] +2026-04-14 04:27:58.312757: Epoch time: 100.02 s +2026-04-14 04:27:59.541538: +2026-04-14 04:27:59.543499: Epoch 3064 +2026-04-14 04:27:59.545460: Current learning rate: 0.00271 +2026-04-14 04:29:40.951947: train_loss -0.4366 +2026-04-14 04:29:40.960763: val_loss -0.3351 +2026-04-14 04:29:40.963182: Pseudo dice [0.065, 0.0, 0.6483, 0.0, 0.4661, 0.8522, 0.7677] +2026-04-14 04:29:40.967665: Epoch time: 101.41 s +2026-04-14 04:29:42.208595: +2026-04-14 04:29:42.210749: Epoch 3065 +2026-04-14 04:29:42.213032: Current learning rate: 0.0027 +2026-04-14 04:31:22.751032: train_loss -0.4656 +2026-04-14 04:31:22.758841: val_loss -0.3636 +2026-04-14 04:31:22.763226: Pseudo dice [0.5549, 0.0, 0.8373, 0.0208, 0.5307, 0.7039, 0.8025] +2026-04-14 04:31:22.766320: Epoch time: 100.55 s +2026-04-14 04:31:24.029468: +2026-04-14 04:31:24.031910: Epoch 3066 +2026-04-14 04:31:24.034144: Current learning rate: 0.0027 +2026-04-14 04:33:05.307084: train_loss -0.4361 +2026-04-14 04:33:05.316996: val_loss -0.4179 +2026-04-14 04:33:05.323812: Pseudo dice [0.6485, 0.0, 0.6895, 0.6877, 0.3557, 0.7942, 0.7942] +2026-04-14 04:33:05.327394: Epoch time: 101.28 s +2026-04-14 04:33:06.574161: +2026-04-14 04:33:06.576226: Epoch 3067 +2026-04-14 04:33:06.579300: Current learning rate: 0.0027 +2026-04-14 04:34:47.351020: train_loss -0.4382 +2026-04-14 04:34:47.359879: val_loss -0.4057 +2026-04-14 04:34:47.364425: Pseudo dice [0.7857, 0.0, 0.7291, 0.1864, 0.277, 0.7421, 0.7382] +2026-04-14 04:34:47.368073: Epoch time: 100.78 s +2026-04-14 04:34:48.603028: +2026-04-14 04:34:48.604753: Epoch 3068 +2026-04-14 04:34:48.606990: Current learning rate: 0.0027 +2026-04-14 04:36:28.804757: train_loss -0.428 +2026-04-14 04:36:28.811067: val_loss -0.3686 +2026-04-14 04:36:28.813988: Pseudo dice [0.71, 0.0, 0.7065, 0.0, 0.0535, 0.5282, 0.6781] +2026-04-14 04:36:28.816564: Epoch time: 100.2 s +2026-04-14 04:36:30.061650: +2026-04-14 04:36:30.063802: Epoch 3069 +2026-04-14 04:36:30.066182: Current learning rate: 0.00269 +2026-04-14 04:38:11.085289: train_loss -0.4255 +2026-04-14 04:38:11.096186: val_loss -0.3953 +2026-04-14 04:38:11.098619: Pseudo dice [0.649, 0.0, 0.7584, 0.6074, 0.4929, 0.4847, 0.5348] +2026-04-14 04:38:11.104580: Epoch time: 101.03 s +2026-04-14 04:38:12.352820: +2026-04-14 04:38:12.355297: Epoch 3070 +2026-04-14 04:38:12.357826: Current learning rate: 0.00269 +2026-04-14 04:39:52.398832: train_loss -0.4153 +2026-04-14 04:39:52.409521: val_loss -0.4191 +2026-04-14 04:39:52.420288: Pseudo dice [0.3828, 0.0, 0.7591, 0.0, 0.4528, 0.8824, 0.7078] +2026-04-14 04:39:52.423739: Epoch time: 100.05 s +2026-04-14 04:39:54.766857: +2026-04-14 04:39:54.769106: Epoch 3071 +2026-04-14 04:39:54.771749: Current learning rate: 0.00269 +2026-04-14 04:41:35.603870: train_loss -0.426 +2026-04-14 04:41:35.610563: val_loss -0.4065 +2026-04-14 04:41:35.613557: Pseudo dice [0.2102, 0.0, 0.6392, 0.8504, 0.3962, 0.8423, 0.6667] +2026-04-14 04:41:35.617036: Epoch time: 100.84 s +2026-04-14 04:41:36.880762: +2026-04-14 04:41:36.882849: Epoch 3072 +2026-04-14 04:41:36.885639: Current learning rate: 0.00268 +2026-04-14 04:43:18.527959: train_loss -0.4498 +2026-04-14 04:43:18.537110: val_loss -0.418 +2026-04-14 04:43:18.540620: Pseudo dice [0.4743, 0.0, 0.7421, 0.8011, 0.2499, 0.839, 0.7114] +2026-04-14 04:43:18.543623: Epoch time: 101.65 s +2026-04-14 04:43:19.796903: +2026-04-14 04:43:19.799173: Epoch 3073 +2026-04-14 04:43:19.806179: Current learning rate: 0.00268 +2026-04-14 04:45:01.279482: train_loss -0.4303 +2026-04-14 04:45:01.286098: val_loss -0.3973 +2026-04-14 04:45:01.288511: Pseudo dice [0.1663, 0.0, 0.6826, 0.7619, 0.2702, 0.8164, 0.4998] +2026-04-14 04:45:01.291245: Epoch time: 101.49 s +2026-04-14 04:45:02.556396: +2026-04-14 04:45:02.558770: Epoch 3074 +2026-04-14 04:45:02.563002: Current learning rate: 0.00268 +2026-04-14 04:46:44.189317: train_loss -0.4549 +2026-04-14 04:46:44.198251: val_loss -0.3962 +2026-04-14 04:46:44.201566: Pseudo dice [0.0, 0.0, 0.8284, 0.7143, 0.2307, 0.779, 0.7493] +2026-04-14 04:46:44.204735: Epoch time: 101.64 s +2026-04-14 04:46:45.519138: +2026-04-14 04:46:45.521268: Epoch 3075 +2026-04-14 04:46:45.524156: Current learning rate: 0.00268 +2026-04-14 04:48:25.993200: train_loss -0.4383 +2026-04-14 04:48:26.000881: val_loss -0.3983 +2026-04-14 04:48:26.003412: Pseudo dice [0.0, 0.0, 0.7019, 0.2508, 0.4842, 0.6776, 0.8384] +2026-04-14 04:48:26.006580: Epoch time: 100.48 s +2026-04-14 04:48:27.270724: +2026-04-14 04:48:27.273082: Epoch 3076 +2026-04-14 04:48:27.276152: Current learning rate: 0.00267 +2026-04-14 04:50:07.592029: train_loss -0.445 +2026-04-14 04:50:07.598989: val_loss -0.4215 +2026-04-14 04:50:07.601726: Pseudo dice [0.5177, 0.0, 0.4263, 0.543, 0.3887, 0.7466, 0.863] +2026-04-14 04:50:07.604927: Epoch time: 100.32 s +2026-04-14 04:50:08.872066: +2026-04-14 04:50:08.874065: Epoch 3077 +2026-04-14 04:50:08.876464: Current learning rate: 0.00267 +2026-04-14 04:51:49.230354: train_loss -0.4352 +2026-04-14 04:51:49.237748: val_loss -0.3375 +2026-04-14 04:51:49.241044: Pseudo dice [0.2202, 0.0, 0.7653, 0.0, 0.2848, 0.8467, 0.769] +2026-04-14 04:51:49.244998: Epoch time: 100.36 s +2026-04-14 04:51:50.477565: +2026-04-14 04:51:50.479738: Epoch 3078 +2026-04-14 04:51:50.482295: Current learning rate: 0.00267 +2026-04-14 04:53:31.557877: train_loss -0.439 +2026-04-14 04:53:31.569413: val_loss -0.4094 +2026-04-14 04:53:31.572456: Pseudo dice [0.305, 0.0, 0.8456, 0.6255, 0.2831, 0.7935, 0.8118] +2026-04-14 04:53:31.576132: Epoch time: 101.08 s +2026-04-14 04:53:32.857541: +2026-04-14 04:53:32.860577: Epoch 3079 +2026-04-14 04:53:32.863097: Current learning rate: 0.00267 +2026-04-14 04:55:13.813776: train_loss -0.4248 +2026-04-14 04:55:13.828456: val_loss -0.3895 +2026-04-14 04:55:13.832012: Pseudo dice [0.675, 0.0, 0.6434, 0.7718, 0.4191, 0.717, 0.7171] +2026-04-14 04:55:13.834690: Epoch time: 100.96 s +2026-04-14 04:55:15.087523: +2026-04-14 04:55:15.090227: Epoch 3080 +2026-04-14 04:55:15.092671: Current learning rate: 0.00266 +2026-04-14 04:56:55.603801: train_loss -0.4366 +2026-04-14 04:56:55.612944: val_loss -0.4175 +2026-04-14 04:56:55.615575: Pseudo dice [0.4184, 0.0, 0.7538, 0.0057, 0.2522, 0.7567, 0.8462] +2026-04-14 04:56:55.618898: Epoch time: 100.52 s +2026-04-14 04:56:56.895017: +2026-04-14 04:56:56.897292: Epoch 3081 +2026-04-14 04:56:56.899645: Current learning rate: 0.00266 +2026-04-14 04:58:38.345964: train_loss -0.4547 +2026-04-14 04:58:38.353218: val_loss -0.388 +2026-04-14 04:58:38.356613: Pseudo dice [0.7404, 0.0, 0.6921, 0.0023, 0.3504, 0.7072, 0.695] +2026-04-14 04:58:38.360033: Epoch time: 101.45 s +2026-04-14 04:58:39.607017: +2026-04-14 04:58:39.608962: Epoch 3082 +2026-04-14 04:58:39.611509: Current learning rate: 0.00266 +2026-04-14 05:00:20.449507: train_loss -0.4455 +2026-04-14 05:00:20.456482: val_loss -0.3814 +2026-04-14 05:00:20.458890: Pseudo dice [0.4017, 0.0, 0.71, 0.0, 0.3314, 0.7709, 0.5365] +2026-04-14 05:00:20.461743: Epoch time: 100.85 s +2026-04-14 05:00:21.708279: +2026-04-14 05:00:21.710509: Epoch 3083 +2026-04-14 05:00:21.712582: Current learning rate: 0.00266 +2026-04-14 05:02:02.120379: train_loss -0.4452 +2026-04-14 05:02:02.126850: val_loss -0.3799 +2026-04-14 05:02:02.129741: Pseudo dice [0.48, 0.0, 0.7604, 0.2094, 0.3871, 0.8591, 0.6929] +2026-04-14 05:02:02.132470: Epoch time: 100.42 s +2026-04-14 05:02:03.380815: +2026-04-14 05:02:03.383030: Epoch 3084 +2026-04-14 05:02:03.387041: Current learning rate: 0.00265 +2026-04-14 05:03:44.096687: train_loss -0.4318 +2026-04-14 05:03:44.102601: val_loss -0.3986 +2026-04-14 05:03:44.104684: Pseudo dice [0.7241, 0.0, 0.746, 0.2823, 0.2915, 0.788, 0.4765] +2026-04-14 05:03:44.107134: Epoch time: 100.72 s +2026-04-14 05:03:45.344274: +2026-04-14 05:03:45.347952: Epoch 3085 +2026-04-14 05:03:45.350624: Current learning rate: 0.00265 +2026-04-14 05:05:25.524981: train_loss -0.4485 +2026-04-14 05:05:25.532137: val_loss -0.374 +2026-04-14 05:05:25.534621: Pseudo dice [0.7948, 0.0, 0.4958, 0.0946, 0.2549, 0.5827, 0.8377] +2026-04-14 05:05:25.537204: Epoch time: 100.18 s +2026-04-14 05:05:26.767494: +2026-04-14 05:05:26.770785: Epoch 3086 +2026-04-14 05:05:26.773081: Current learning rate: 0.00265 +2026-04-14 05:07:06.808386: train_loss -0.4325 +2026-04-14 05:07:06.815934: val_loss -0.3952 +2026-04-14 05:07:06.819199: Pseudo dice [0.7586, 0.0, 0.8203, 0.216, 0.1136, 0.7416, 0.8131] +2026-04-14 05:07:06.823097: Epoch time: 100.04 s +2026-04-14 05:07:08.084493: +2026-04-14 05:07:08.086537: Epoch 3087 +2026-04-14 05:07:08.088874: Current learning rate: 0.00265 +2026-04-14 05:08:49.126470: train_loss -0.4431 +2026-04-14 05:08:49.140058: val_loss -0.2859 +2026-04-14 05:08:49.144511: Pseudo dice [0.0, 0.0, 0.6415, 0.0167, 0.4799, 0.4652, 0.8264] +2026-04-14 05:08:49.147424: Epoch time: 101.04 s +2026-04-14 05:08:50.398343: +2026-04-14 05:08:50.401258: Epoch 3088 +2026-04-14 05:08:50.406259: Current learning rate: 0.00264 +2026-04-14 05:10:31.643960: train_loss -0.4196 +2026-04-14 05:10:31.670645: val_loss -0.3723 +2026-04-14 05:10:31.673430: Pseudo dice [0.6366, 0.0, 0.8042, 0.1158, 0.3079, 0.6654, 0.6954] +2026-04-14 05:10:31.676102: Epoch time: 101.25 s +2026-04-14 05:10:32.917942: +2026-04-14 05:10:32.920109: Epoch 3089 +2026-04-14 05:10:32.922919: Current learning rate: 0.00264 +2026-04-14 05:12:13.798976: train_loss -0.4173 +2026-04-14 05:12:13.806145: val_loss -0.3581 +2026-04-14 05:12:13.808830: Pseudo dice [0.2108, 0.0, 0.6879, 0.0898, 0.0, 0.3758, 0.8683] +2026-04-14 05:12:13.811879: Epoch time: 100.88 s +2026-04-14 05:12:15.053146: +2026-04-14 05:12:15.055754: Epoch 3090 +2026-04-14 05:12:15.058316: Current learning rate: 0.00264 +2026-04-14 05:13:56.149482: train_loss -0.4107 +2026-04-14 05:13:56.157103: val_loss -0.3997 +2026-04-14 05:13:56.159441: Pseudo dice [0.6792, 0.0, 0.7559, 0.0, 0.0, 0.6887, 0.7997] +2026-04-14 05:13:56.161833: Epoch time: 101.1 s +2026-04-14 05:13:57.418673: +2026-04-14 05:13:57.420789: Epoch 3091 +2026-04-14 05:13:57.422854: Current learning rate: 0.00264 +2026-04-14 05:15:38.776526: train_loss -0.4455 +2026-04-14 05:15:38.783360: val_loss -0.4005 +2026-04-14 05:15:38.785352: Pseudo dice [0.6635, 0.0, 0.6772, 0.0, 0.2468, 0.8331, 0.8115] +2026-04-14 05:15:38.787838: Epoch time: 101.36 s +2026-04-14 05:15:40.038225: +2026-04-14 05:15:40.040599: Epoch 3092 +2026-04-14 05:15:40.043195: Current learning rate: 0.00263 +2026-04-14 05:17:21.311482: train_loss -0.4208 +2026-04-14 05:17:21.318015: val_loss -0.4027 +2026-04-14 05:17:21.320425: Pseudo dice [0.5169, 0.0, 0.542, 0.0, 0.4033, 0.6591, 0.7885] +2026-04-14 05:17:21.323053: Epoch time: 101.28 s +2026-04-14 05:17:22.572432: +2026-04-14 05:17:22.575342: Epoch 3093 +2026-04-14 05:17:22.577408: Current learning rate: 0.00263 +2026-04-14 05:19:03.525211: train_loss -0.4219 +2026-04-14 05:19:03.534016: val_loss -0.4209 +2026-04-14 05:19:03.536364: Pseudo dice [0.7121, 0.0, 0.7288, 0.0, 0.4243, 0.7822, 0.884] +2026-04-14 05:19:03.539063: Epoch time: 100.96 s +2026-04-14 05:19:04.832598: +2026-04-14 05:19:04.835167: Epoch 3094 +2026-04-14 05:19:04.838030: Current learning rate: 0.00263 +2026-04-14 05:20:45.533348: train_loss -0.4349 +2026-04-14 05:20:45.540093: val_loss -0.3741 +2026-04-14 05:20:45.542768: Pseudo dice [0.7232, 0.0, 0.6153, 0.0579, 0.3765, 0.7746, 0.7135] +2026-04-14 05:20:45.545567: Epoch time: 100.7 s +2026-04-14 05:20:46.795620: +2026-04-14 05:20:46.797872: Epoch 3095 +2026-04-14 05:20:46.800098: Current learning rate: 0.00263 +2026-04-14 05:22:28.170235: train_loss -0.4554 +2026-04-14 05:22:28.177026: val_loss -0.4286 +2026-04-14 05:22:28.179784: Pseudo dice [0.7068, 0.0, 0.8161, 0.0622, 0.5257, 0.6685, 0.8628] +2026-04-14 05:22:28.182239: Epoch time: 101.38 s +2026-04-14 05:22:29.425330: +2026-04-14 05:22:29.429917: Epoch 3096 +2026-04-14 05:22:29.432115: Current learning rate: 0.00262 +2026-04-14 05:24:10.591843: train_loss -0.4566 +2026-04-14 05:24:10.597406: val_loss -0.4339 +2026-04-14 05:24:10.600454: Pseudo dice [0.0536, 0.0, 0.8522, 0.8563, 0.6346, 0.7617, 0.7691] +2026-04-14 05:24:10.602488: Epoch time: 101.17 s +2026-04-14 05:24:11.852087: +2026-04-14 05:24:11.864188: Epoch 3097 +2026-04-14 05:24:11.866860: Current learning rate: 0.00262 +2026-04-14 05:25:52.336406: train_loss -0.4347 +2026-04-14 05:25:52.342736: val_loss -0.3715 +2026-04-14 05:25:52.344666: Pseudo dice [0.0, 0.0, 0.7026, 0.1123, 0.3303, 0.7511, 0.8332] +2026-04-14 05:25:52.346983: Epoch time: 100.49 s +2026-04-14 05:25:53.629744: +2026-04-14 05:25:53.632237: Epoch 3098 +2026-04-14 05:25:53.634545: Current learning rate: 0.00262 +2026-04-14 05:27:33.974207: train_loss -0.4389 +2026-04-14 05:27:33.981530: val_loss -0.302 +2026-04-14 05:27:33.985011: Pseudo dice [0.7688, 0.0, 0.6111, 0.003, 0.0762, 0.8114, 0.7292] +2026-04-14 05:27:33.987767: Epoch time: 100.35 s +2026-04-14 05:27:35.209868: +2026-04-14 05:27:35.212155: Epoch 3099 +2026-04-14 05:27:35.214550: Current learning rate: 0.00261 +2026-04-14 05:29:16.670675: train_loss -0.4336 +2026-04-14 05:29:16.678334: val_loss -0.4061 +2026-04-14 05:29:16.682000: Pseudo dice [0.0, 0.0, 0.6197, 0.1011, 0.3912, 0.6547, 0.8641] +2026-04-14 05:29:16.684973: Epoch time: 101.46 s +2026-04-14 05:29:19.679154: +2026-04-14 05:29:19.681950: Epoch 3100 +2026-04-14 05:29:19.685095: Current learning rate: 0.00261 +2026-04-14 05:31:00.720771: train_loss -0.4398 +2026-04-14 05:31:00.727623: val_loss -0.4124 +2026-04-14 05:31:00.730305: Pseudo dice [0.4385, 0.0, 0.655, 0.8395, 0.52, 0.7953, 0.4651] +2026-04-14 05:31:00.734401: Epoch time: 101.04 s +2026-04-14 05:31:01.978401: +2026-04-14 05:31:01.981727: Epoch 3101 +2026-04-14 05:31:01.985372: Current learning rate: 0.00261 +2026-04-14 05:32:42.939880: train_loss -0.4467 +2026-04-14 05:32:42.947127: val_loss -0.4323 +2026-04-14 05:32:42.950243: Pseudo dice [0.6856, 0.0, 0.6499, 0.2375, 0.6155, 0.8146, 0.6893] +2026-04-14 05:32:42.952475: Epoch time: 100.96 s +2026-04-14 05:32:44.169410: +2026-04-14 05:32:44.171263: Epoch 3102 +2026-04-14 05:32:44.173430: Current learning rate: 0.00261 +2026-04-14 05:34:24.398312: train_loss -0.461 +2026-04-14 05:34:24.406340: val_loss -0.4136 +2026-04-14 05:34:24.408739: Pseudo dice [0.6417, 0.0, 0.7386, 0.44, 0.5026, 0.6503, 0.7597] +2026-04-14 05:34:24.411558: Epoch time: 100.23 s +2026-04-14 05:34:25.683941: +2026-04-14 05:34:25.686046: Epoch 3103 +2026-04-14 05:34:25.688611: Current learning rate: 0.0026 +2026-04-14 05:36:06.017501: train_loss -0.4472 +2026-04-14 05:36:06.024980: val_loss -0.4375 +2026-04-14 05:36:06.027231: Pseudo dice [0.2338, 0.0, 0.6756, 0.2422, 0.425, 0.7083, 0.8656] +2026-04-14 05:36:06.030412: Epoch time: 100.34 s +2026-04-14 05:36:07.302561: +2026-04-14 05:36:07.304802: Epoch 3104 +2026-04-14 05:36:07.307019: Current learning rate: 0.0026 +2026-04-14 05:37:48.588782: train_loss -0.4583 +2026-04-14 05:37:48.594893: val_loss -0.3725 +2026-04-14 05:37:48.596971: Pseudo dice [0.7349, 0.0, 0.7154, 0.1268, 0.5147, 0.7805, 0.7251] +2026-04-14 05:37:48.599499: Epoch time: 101.29 s +2026-04-14 05:37:49.825158: +2026-04-14 05:37:49.827517: Epoch 3105 +2026-04-14 05:37:49.830102: Current learning rate: 0.0026 +2026-04-14 05:39:30.828567: train_loss -0.4457 +2026-04-14 05:39:30.839019: val_loss -0.4474 +2026-04-14 05:39:30.844067: Pseudo dice [0.7203, 0.0, 0.7426, 0.0, 0.4002, 0.8296, 0.7809] +2026-04-14 05:39:30.849370: Epoch time: 101.01 s +2026-04-14 05:39:32.095852: +2026-04-14 05:39:32.097667: Epoch 3106 +2026-04-14 05:39:32.102135: Current learning rate: 0.0026 +2026-04-14 05:41:12.686314: train_loss -0.4431 +2026-04-14 05:41:12.693941: val_loss -0.4004 +2026-04-14 05:41:12.709496: Pseudo dice [0.6839, 0.0, 0.7413, 0.5052, 0.3303, 0.7477, 0.7196] +2026-04-14 05:41:12.715359: Epoch time: 100.59 s +2026-04-14 05:41:13.978016: +2026-04-14 05:41:13.980335: Epoch 3107 +2026-04-14 05:41:13.982449: Current learning rate: 0.00259 +2026-04-14 05:42:54.848813: train_loss -0.4403 +2026-04-14 05:42:54.856080: val_loss -0.3844 +2026-04-14 05:42:54.858972: Pseudo dice [0.2281, 0.0, 0.5416, 0.0, 0.4856, 0.7853, 0.7002] +2026-04-14 05:42:54.861987: Epoch time: 100.87 s +2026-04-14 05:42:56.098276: +2026-04-14 05:42:56.100474: Epoch 3108 +2026-04-14 05:42:56.102826: Current learning rate: 0.00259 +2026-04-14 05:44:37.006227: train_loss -0.4445 +2026-04-14 05:44:37.014197: val_loss -0.3526 +2026-04-14 05:44:37.017426: Pseudo dice [0.5402, 0.0, 0.729, 0.0, 0.2845, 0.6871, 0.6511] +2026-04-14 05:44:37.020182: Epoch time: 100.91 s +2026-04-14 05:44:38.266529: +2026-04-14 05:44:38.268467: Epoch 3109 +2026-04-14 05:44:38.271054: Current learning rate: 0.00259 +2026-04-14 05:46:18.731210: train_loss -0.4345 +2026-04-14 05:46:18.739427: val_loss -0.4188 +2026-04-14 05:46:18.741616: Pseudo dice [0.4121, 0.0, 0.6914, 0.9182, 0.1123, 0.8368, 0.6718] +2026-04-14 05:46:18.744347: Epoch time: 100.47 s +2026-04-14 05:46:19.995974: +2026-04-14 05:46:19.997777: Epoch 3110 +2026-04-14 05:46:20.000042: Current learning rate: 0.00259 +2026-04-14 05:48:01.138054: train_loss -0.4456 +2026-04-14 05:48:01.146346: val_loss -0.3633 +2026-04-14 05:48:01.149191: Pseudo dice [0.6524, 0.0, 0.7109, 0.0681, 0.3849, 0.3189, 0.783] +2026-04-14 05:48:01.152623: Epoch time: 101.15 s +2026-04-14 05:48:03.539241: +2026-04-14 05:48:03.541158: Epoch 3111 +2026-04-14 05:48:03.543519: Current learning rate: 0.00258 +2026-04-14 05:49:44.531702: train_loss -0.4471 +2026-04-14 05:49:44.540426: val_loss -0.4221 +2026-04-14 05:49:44.542654: Pseudo dice [0.7598, 0.0, 0.7462, 0.2824, 0.446, 0.801, 0.6057] +2026-04-14 05:49:44.545790: Epoch time: 101.0 s +2026-04-14 05:49:45.822451: +2026-04-14 05:49:45.825047: Epoch 3112 +2026-04-14 05:49:45.828825: Current learning rate: 0.00258 +2026-04-14 05:51:26.377283: train_loss -0.4485 +2026-04-14 05:51:26.384196: val_loss -0.4082 +2026-04-14 05:51:26.386602: Pseudo dice [0.6936, 0.0, 0.8502, 0.0, 0.5116, 0.7928, 0.8548] +2026-04-14 05:51:26.389135: Epoch time: 100.56 s +2026-04-14 05:51:27.658951: +2026-04-14 05:51:27.661052: Epoch 3113 +2026-04-14 05:51:27.663420: Current learning rate: 0.00258 +2026-04-14 05:53:09.670725: train_loss -0.4318 +2026-04-14 05:53:09.678219: val_loss -0.4041 +2026-04-14 05:53:09.680201: Pseudo dice [0.6891, 0.0, 0.705, 0.5835, 0.293, 0.7655, 0.8715] +2026-04-14 05:53:09.683640: Epoch time: 102.01 s +2026-04-14 05:53:10.962746: +2026-04-14 05:53:10.964993: Epoch 3114 +2026-04-14 05:53:10.969736: Current learning rate: 0.00258 +2026-04-14 05:54:51.930009: train_loss -0.4426 +2026-04-14 05:54:51.938801: val_loss -0.3688 +2026-04-14 05:54:51.943568: Pseudo dice [0.6566, 0.0, 0.7659, 0.1475, 0.3426, 0.8027, 0.4619] +2026-04-14 05:54:51.946999: Epoch time: 100.97 s +2026-04-14 05:54:53.212199: +2026-04-14 05:54:53.213947: Epoch 3115 +2026-04-14 05:54:53.215939: Current learning rate: 0.00257 +2026-04-14 05:56:33.558581: train_loss -0.4269 +2026-04-14 05:56:33.567435: val_loss -0.4147 +2026-04-14 05:56:33.569663: Pseudo dice [0.8003, 0.0, 0.8188, 0.4587, 0.2786, 0.5631, 0.7666] +2026-04-14 05:56:33.573411: Epoch time: 100.35 s +2026-04-14 05:56:34.828079: +2026-04-14 05:56:34.830498: Epoch 3116 +2026-04-14 05:56:34.832475: Current learning rate: 0.00257 +2026-04-14 05:58:15.187414: train_loss -0.4322 +2026-04-14 05:58:15.193029: val_loss -0.2966 +2026-04-14 05:58:15.195139: Pseudo dice [0.6016, 0.0, 0.6602, 0.0, 0.1964, 0.7137, 0.2829] +2026-04-14 05:58:15.197222: Epoch time: 100.36 s +2026-04-14 05:58:16.444375: +2026-04-14 05:58:16.446886: Epoch 3117 +2026-04-14 05:58:16.449336: Current learning rate: 0.00257 +2026-04-14 05:59:57.968357: train_loss -0.4486 +2026-04-14 05:59:57.977459: val_loss -0.3327 +2026-04-14 05:59:57.980149: Pseudo dice [0.581, 0.0, 0.6481, 0.0384, 0.2065, 0.7639, 0.7542] +2026-04-14 05:59:57.983109: Epoch time: 101.53 s +2026-04-14 05:59:59.251182: +2026-04-14 05:59:59.253605: Epoch 3118 +2026-04-14 05:59:59.256366: Current learning rate: 0.00256 +2026-04-14 06:01:40.610856: train_loss -0.4494 +2026-04-14 06:01:40.618654: val_loss -0.3711 +2026-04-14 06:01:40.621181: Pseudo dice [0.6531, 0.0, 0.6476, 0.0, 0.322, 0.6526, 0.6846] +2026-04-14 06:01:40.624296: Epoch time: 101.36 s +2026-04-14 06:01:41.911207: +2026-04-14 06:01:41.914551: Epoch 3119 +2026-04-14 06:01:41.917574: Current learning rate: 0.00256 +2026-04-14 06:03:22.512272: train_loss -0.4428 +2026-04-14 06:03:22.521479: val_loss -0.4172 +2026-04-14 06:03:22.525523: Pseudo dice [0.365, 0.0, 0.8367, 0.3536, 0.4535, 0.75, 0.8869] +2026-04-14 06:03:22.529908: Epoch time: 100.6 s +2026-04-14 06:03:23.803740: +2026-04-14 06:03:23.809109: Epoch 3120 +2026-04-14 06:03:23.813543: Current learning rate: 0.00256 +2026-04-14 06:05:04.968593: train_loss -0.4155 +2026-04-14 06:05:04.974964: val_loss -0.3677 +2026-04-14 06:05:04.976781: Pseudo dice [0.2802, 0.0, 0.7013, 0.1215, 0.4243, 0.1378, 0.8678] +2026-04-14 06:05:04.979323: Epoch time: 101.17 s +2026-04-14 06:05:06.229516: +2026-04-14 06:05:06.233671: Epoch 3121 +2026-04-14 06:05:06.236170: Current learning rate: 0.00256 +2026-04-14 06:06:46.544249: train_loss -0.4292 +2026-04-14 06:06:46.551466: val_loss -0.3442 +2026-04-14 06:06:46.553455: Pseudo dice [0.5204, 0.0, 0.7993, 0.0964, 0.2072, 0.5963, 0.7197] +2026-04-14 06:06:46.556114: Epoch time: 100.32 s +2026-04-14 06:06:47.841731: +2026-04-14 06:06:47.844754: Epoch 3122 +2026-04-14 06:06:47.848777: Current learning rate: 0.00255 +2026-04-14 06:08:28.629614: train_loss -0.4387 +2026-04-14 06:08:28.636500: val_loss -0.4118 +2026-04-14 06:08:28.638816: Pseudo dice [0.5818, 0.0, 0.7612, 0.7815, 0.5108, 0.6918, 0.8602] +2026-04-14 06:08:28.641932: Epoch time: 100.79 s +2026-04-14 06:08:29.885318: +2026-04-14 06:08:29.887433: Epoch 3123 +2026-04-14 06:08:29.889895: Current learning rate: 0.00255 +2026-04-14 06:10:10.140734: train_loss -0.4491 +2026-04-14 06:10:10.148746: val_loss -0.3972 +2026-04-14 06:10:10.151512: Pseudo dice [0.4608, 0.0, 0.5962, 0.0, 0.3904, 0.738, 0.8779] +2026-04-14 06:10:10.155192: Epoch time: 100.26 s +2026-04-14 06:10:11.422416: +2026-04-14 06:10:11.424174: Epoch 3124 +2026-04-14 06:10:11.426253: Current learning rate: 0.00255 +2026-04-14 06:11:51.723184: train_loss -0.438 +2026-04-14 06:11:51.729966: val_loss -0.429 +2026-04-14 06:11:51.732400: Pseudo dice [0.7415, 0.0, 0.7036, 0.8489, 0.4139, 0.7503, 0.751] +2026-04-14 06:11:51.735597: Epoch time: 100.3 s +2026-04-14 06:11:52.968275: +2026-04-14 06:11:52.974342: Epoch 3125 +2026-04-14 06:11:52.977695: Current learning rate: 0.00255 +2026-04-14 06:13:33.154017: train_loss -0.4445 +2026-04-14 06:13:33.163740: val_loss -0.4006 +2026-04-14 06:13:33.166332: Pseudo dice [0.5512, 0.0, 0.8046, 0.1775, 0.3882, 0.706, 0.6864] +2026-04-14 06:13:33.170142: Epoch time: 100.19 s +2026-04-14 06:13:34.407736: +2026-04-14 06:13:34.410272: Epoch 3126 +2026-04-14 06:13:34.413261: Current learning rate: 0.00254 +2026-04-14 06:15:15.071671: train_loss -0.4449 +2026-04-14 06:15:15.078734: val_loss -0.3472 +2026-04-14 06:15:15.081342: Pseudo dice [0.7724, 0.0, 0.7108, 0.0439, 0.4768, 0.7976, 0.9021] +2026-04-14 06:15:15.083986: Epoch time: 100.67 s +2026-04-14 06:15:16.380388: +2026-04-14 06:15:16.382441: Epoch 3127 +2026-04-14 06:15:16.384547: Current learning rate: 0.00254 +2026-04-14 06:16:57.311463: train_loss -0.4514 +2026-04-14 06:16:57.323207: val_loss -0.4142 +2026-04-14 06:16:57.325603: Pseudo dice [0.7879, 0.0, 0.818, 0.5716, 0.4227, 0.6595, 0.6826] +2026-04-14 06:16:57.328144: Epoch time: 100.93 s +2026-04-14 06:16:58.566614: +2026-04-14 06:16:58.568452: Epoch 3128 +2026-04-14 06:16:58.571035: Current learning rate: 0.00254 +2026-04-14 06:18:39.180868: train_loss -0.447 +2026-04-14 06:18:39.188980: val_loss -0.4406 +2026-04-14 06:18:39.191708: Pseudo dice [0.3686, 0.0, 0.7556, 0.7364, 0.3192, 0.8557, 0.8994] +2026-04-14 06:18:39.194421: Epoch time: 100.62 s +2026-04-14 06:18:40.458363: +2026-04-14 06:18:40.460915: Epoch 3129 +2026-04-14 06:18:40.463728: Current learning rate: 0.00254 +2026-04-14 06:20:21.404217: train_loss -0.4395 +2026-04-14 06:20:21.411274: val_loss -0.4053 +2026-04-14 06:20:21.414722: Pseudo dice [0.3509, 0.0, 0.7002, 0.1713, 0.4893, 0.7698, 0.7007] +2026-04-14 06:20:21.417576: Epoch time: 100.95 s +2026-04-14 06:20:22.678542: +2026-04-14 06:20:22.681803: Epoch 3130 +2026-04-14 06:20:22.685658: Current learning rate: 0.00253 +2026-04-14 06:22:04.158574: train_loss -0.4469 +2026-04-14 06:22:04.169094: val_loss -0.3985 +2026-04-14 06:22:04.171936: Pseudo dice [0.6394, 0.0, 0.7793, 0.7208, 0.46, 0.6333, 0.746] +2026-04-14 06:22:04.176373: Epoch time: 101.48 s +2026-04-14 06:22:05.422101: +2026-04-14 06:22:05.424345: Epoch 3131 +2026-04-14 06:22:05.426393: Current learning rate: 0.00253 +2026-04-14 06:23:46.063412: train_loss -0.4499 +2026-04-14 06:23:46.070923: val_loss -0.4264 +2026-04-14 06:23:46.073490: Pseudo dice [0.6021, 0.0, 0.7976, 0.048, 0.348, 0.6466, 0.8052] +2026-04-14 06:23:46.077370: Epoch time: 100.64 s +2026-04-14 06:23:47.336356: +2026-04-14 06:23:47.338881: Epoch 3132 +2026-04-14 06:23:47.341169: Current learning rate: 0.00253 +2026-04-14 06:25:28.266763: train_loss -0.44 +2026-04-14 06:25:28.275239: val_loss -0.3968 +2026-04-14 06:25:28.278090: Pseudo dice [0.2643, 0.0, 0.7241, 0.1314, 0.2742, 0.6062, 0.5599] +2026-04-14 06:25:28.281205: Epoch time: 100.93 s +2026-04-14 06:25:29.514327: +2026-04-14 06:25:29.516684: Epoch 3133 +2026-04-14 06:25:29.519209: Current learning rate: 0.00253 +2026-04-14 06:27:10.452535: train_loss -0.4395 +2026-04-14 06:27:10.460017: val_loss -0.3937 +2026-04-14 06:27:10.462816: Pseudo dice [0.3612, 0.0, 0.7489, 0.2574, 0.5406, 0.4446, 0.6021] +2026-04-14 06:27:10.466269: Epoch time: 100.94 s +2026-04-14 06:27:11.719329: +2026-04-14 06:27:11.721597: Epoch 3134 +2026-04-14 06:27:11.724905: Current learning rate: 0.00252 +2026-04-14 06:28:52.408850: train_loss -0.4375 +2026-04-14 06:28:52.417817: val_loss -0.3242 +2026-04-14 06:28:52.420329: Pseudo dice [0.7724, 0.0, 0.6024, 0.1019, 0.3809, 0.6656, 0.7624] +2026-04-14 06:28:52.423154: Epoch time: 100.69 s +2026-04-14 06:28:53.674812: +2026-04-14 06:28:53.676642: Epoch 3135 +2026-04-14 06:28:53.678836: Current learning rate: 0.00252 +2026-04-14 06:30:34.699760: train_loss -0.4349 +2026-04-14 06:30:34.708713: val_loss -0.3892 +2026-04-14 06:30:34.711102: Pseudo dice [0.7755, 0.0, 0.7242, 0.0, 0.4147, 0.5354, 0.8538] +2026-04-14 06:30:34.714045: Epoch time: 101.03 s +2026-04-14 06:30:35.973223: +2026-04-14 06:30:35.975542: Epoch 3136 +2026-04-14 06:30:35.977833: Current learning rate: 0.00252 +2026-04-14 06:32:16.406238: train_loss -0.4609 +2026-04-14 06:32:16.414131: val_loss -0.4035 +2026-04-14 06:32:16.417100: Pseudo dice [0.516, 0.0, 0.7567, 0.0, 0.321, 0.7506, 0.6207] +2026-04-14 06:32:16.422465: Epoch time: 100.44 s +2026-04-14 06:32:17.669766: +2026-04-14 06:32:17.672318: Epoch 3137 +2026-04-14 06:32:17.675117: Current learning rate: 0.00252 +2026-04-14 06:33:57.962926: train_loss -0.4615 +2026-04-14 06:33:57.970945: val_loss -0.4391 +2026-04-14 06:33:57.973572: Pseudo dice [0.5336, 0.0, 0.8292, 0.8444, 0.2724, 0.6882, 0.7784] +2026-04-14 06:33:57.976266: Epoch time: 100.3 s +2026-04-14 06:33:59.244790: +2026-04-14 06:33:59.247910: Epoch 3138 +2026-04-14 06:33:59.251165: Current learning rate: 0.00251 +2026-04-14 06:35:39.523550: train_loss -0.4589 +2026-04-14 06:35:39.530483: val_loss -0.4094 +2026-04-14 06:35:39.533169: Pseudo dice [0.7131, 0.0, 0.6609, 0.827, 0.389, 0.7889, 0.595] +2026-04-14 06:35:39.535671: Epoch time: 100.28 s +2026-04-14 06:35:40.782552: +2026-04-14 06:35:40.784627: Epoch 3139 +2026-04-14 06:35:40.787501: Current learning rate: 0.00251 +2026-04-14 06:37:20.964217: train_loss -0.4491 +2026-04-14 06:37:20.970334: val_loss -0.4182 +2026-04-14 06:37:20.972511: Pseudo dice [0.2937, 0.0, 0.576, 0.8566, 0.3678, 0.7549, 0.8334] +2026-04-14 06:37:20.975008: Epoch time: 100.18 s +2026-04-14 06:37:22.219354: +2026-04-14 06:37:22.221850: Epoch 3140 +2026-04-14 06:37:22.227513: Current learning rate: 0.00251 +2026-04-14 06:39:03.253228: train_loss -0.4628 +2026-04-14 06:39:03.259533: val_loss -0.4348 +2026-04-14 06:39:03.262434: Pseudo dice [0.7872, 0.0, 0.7592, 0.0, 0.5785, 0.7817, 0.7824] +2026-04-14 06:39:03.264791: Epoch time: 101.04 s +2026-04-14 06:39:04.513800: +2026-04-14 06:39:04.515650: Epoch 3141 +2026-04-14 06:39:04.517510: Current learning rate: 0.0025 +2026-04-14 06:40:45.392881: train_loss -0.4638 +2026-04-14 06:40:45.398932: val_loss -0.3493 +2026-04-14 06:40:45.401050: Pseudo dice [0.7393, 0.0, 0.7255, 0.0507, 0.3792, 0.7624, 0.7794] +2026-04-14 06:40:45.403689: Epoch time: 100.88 s +2026-04-14 06:40:46.698529: +2026-04-14 06:40:46.700752: Epoch 3142 +2026-04-14 06:40:46.703065: Current learning rate: 0.0025 +2026-04-14 06:42:27.123135: train_loss -0.4526 +2026-04-14 06:42:27.128269: val_loss -0.4056 +2026-04-14 06:42:27.130585: Pseudo dice [0.808, 0.0, 0.8096, 0.1628, 0.4279, 0.6989, 0.7637] +2026-04-14 06:42:27.133080: Epoch time: 100.43 s +2026-04-14 06:42:28.399178: +2026-04-14 06:42:28.401349: Epoch 3143 +2026-04-14 06:42:28.404077: Current learning rate: 0.0025 +2026-04-14 06:44:09.167823: train_loss -0.4509 +2026-04-14 06:44:09.180004: val_loss -0.3454 +2026-04-14 06:44:09.181917: Pseudo dice [0.3456, 0.0, 0.8722, 0.0447, 0.2677, 0.8168, 0.6991] +2026-04-14 06:44:09.184947: Epoch time: 100.77 s +2026-04-14 06:44:10.454626: +2026-04-14 06:44:10.456344: Epoch 3144 +2026-04-14 06:44:10.458591: Current learning rate: 0.0025 +2026-04-14 06:45:51.906879: train_loss -0.4589 +2026-04-14 06:45:51.912966: val_loss -0.4457 +2026-04-14 06:45:51.915682: Pseudo dice [0.7608, 0.0, 0.6207, 0.2795, 0.3961, 0.7327, 0.7425] +2026-04-14 06:45:51.918291: Epoch time: 101.46 s +2026-04-14 06:45:53.148190: +2026-04-14 06:45:53.150168: Epoch 3145 +2026-04-14 06:45:53.152412: Current learning rate: 0.00249 +2026-04-14 06:47:33.862657: train_loss -0.4335 +2026-04-14 06:47:33.868887: val_loss -0.2448 +2026-04-14 06:47:33.870949: Pseudo dice [0.796, 0.0, 0.6577, 0.0, 0.4194, 0.8507, 0.6312] +2026-04-14 06:47:33.873251: Epoch time: 100.72 s +2026-04-14 06:47:35.134544: +2026-04-14 06:47:35.136627: Epoch 3146 +2026-04-14 06:47:35.138753: Current learning rate: 0.00249 +2026-04-14 06:49:15.388790: train_loss -0.438 +2026-04-14 06:49:15.396809: val_loss -0.3457 +2026-04-14 06:49:15.399667: Pseudo dice [0.5509, 0.0, 0.7775, 0.0876, 0.3913, 0.6733, 0.56] +2026-04-14 06:49:15.402764: Epoch time: 100.26 s +2026-04-14 06:49:16.672356: +2026-04-14 06:49:16.674441: Epoch 3147 +2026-04-14 06:49:16.676704: Current learning rate: 0.00249 +2026-04-14 06:50:56.900085: train_loss -0.434 +2026-04-14 06:50:56.907553: val_loss -0.3698 +2026-04-14 06:50:56.910495: Pseudo dice [0.3879, 0.0, 0.6496, 0.0644, 0.3508, 0.6276, 0.8704] +2026-04-14 06:50:56.916534: Epoch time: 100.23 s +2026-04-14 06:50:58.147824: +2026-04-14 06:50:58.149989: Epoch 3148 +2026-04-14 06:50:58.152377: Current learning rate: 0.00249 +2026-04-14 06:52:38.528169: train_loss -0.448 +2026-04-14 06:52:38.535942: val_loss -0.4094 +2026-04-14 06:52:38.538247: Pseudo dice [0.6609, 0.0, 0.7575, 0.2904, 0.2208, 0.8381, 0.846] +2026-04-14 06:52:38.541336: Epoch time: 100.38 s +2026-04-14 06:52:39.797504: +2026-04-14 06:52:39.799446: Epoch 3149 +2026-04-14 06:52:39.801971: Current learning rate: 0.00248 +2026-04-14 06:54:19.909332: train_loss -0.4587 +2026-04-14 06:54:19.915734: val_loss -0.4104 +2026-04-14 06:54:19.919047: Pseudo dice [0.3986, 0.0, 0.5796, 0.2566, 0.0, 0.8679, 0.6709] +2026-04-14 06:54:19.921937: Epoch time: 100.11 s +2026-04-14 06:54:23.913540: +2026-04-14 06:54:23.915703: Epoch 3150 +2026-04-14 06:54:23.917847: Current learning rate: 0.00248 +2026-04-14 06:56:04.666981: train_loss -0.4525 +2026-04-14 06:56:04.674745: val_loss -0.3836 +2026-04-14 06:56:04.677278: Pseudo dice [0.7955, 0.0, 0.6427, 0.0031, 0.3734, 0.7694, 0.6245] +2026-04-14 06:56:04.679985: Epoch time: 100.76 s +2026-04-14 06:56:05.927107: +2026-04-14 06:56:05.929670: Epoch 3151 +2026-04-14 06:56:05.932516: Current learning rate: 0.00248 +2026-04-14 06:57:46.760911: train_loss -0.4548 +2026-04-14 06:57:46.766938: val_loss -0.3539 +2026-04-14 06:57:46.770182: Pseudo dice [0.565, 0.0, 0.5477, 0.0313, 0.3391, 0.5896, 0.8873] +2026-04-14 06:57:46.773391: Epoch time: 100.84 s +2026-04-14 06:57:48.184902: +2026-04-14 06:57:48.186797: Epoch 3152 +2026-04-14 06:57:48.189215: Current learning rate: 0.00248 +2026-04-14 06:59:28.717526: train_loss -0.4327 +2026-04-14 06:59:28.723308: val_loss -0.3958 +2026-04-14 06:59:28.725456: Pseudo dice [0.4211, 0.0, 0.7068, 0.5537, 0.2779, 0.7539, 0.8582] +2026-04-14 06:59:28.728232: Epoch time: 100.54 s +2026-04-14 06:59:30.035594: +2026-04-14 06:59:30.038131: Epoch 3153 +2026-04-14 06:59:30.040534: Current learning rate: 0.00247 +2026-04-14 07:01:10.402585: train_loss -0.4462 +2026-04-14 07:01:10.411602: val_loss -0.4001 +2026-04-14 07:01:10.414985: Pseudo dice [0.7553, 0.0, 0.5093, 0.1191, 0.3931, 0.5244, 0.8853] +2026-04-14 07:01:10.417587: Epoch time: 100.37 s +2026-04-14 07:01:11.685838: +2026-04-14 07:01:11.687928: Epoch 3154 +2026-04-14 07:01:11.690041: Current learning rate: 0.00247 +2026-04-14 07:02:52.136710: train_loss -0.4301 +2026-04-14 07:02:52.142352: val_loss -0.3316 +2026-04-14 07:02:52.144222: Pseudo dice [0.4785, 0.0, 0.6122, 0.0242, 0.3125, 0.4135, 0.8739] +2026-04-14 07:02:52.146337: Epoch time: 100.45 s +2026-04-14 07:02:53.386489: +2026-04-14 07:02:53.388505: Epoch 3155 +2026-04-14 07:02:53.390407: Current learning rate: 0.00247 +2026-04-14 07:04:33.754274: train_loss -0.4426 +2026-04-14 07:04:33.763033: val_loss -0.3698 +2026-04-14 07:04:33.765163: Pseudo dice [0.4843, 0.0, 0.761, 0.0, 0.2778, 0.8025, 0.7364] +2026-04-14 07:04:33.767429: Epoch time: 100.37 s +2026-04-14 07:04:35.033040: +2026-04-14 07:04:35.034729: Epoch 3156 +2026-04-14 07:04:35.036627: Current learning rate: 0.00247 +2026-04-14 07:06:15.165547: train_loss -0.4228 +2026-04-14 07:06:15.174531: val_loss -0.4195 +2026-04-14 07:06:15.176894: Pseudo dice [0.7886, 0.0, 0.6623, 0.0, 0.3344, 0.8407, 0.7566] +2026-04-14 07:06:15.179254: Epoch time: 100.14 s +2026-04-14 07:06:16.416355: +2026-04-14 07:06:16.418251: Epoch 3157 +2026-04-14 07:06:16.420299: Current learning rate: 0.00246 +2026-04-14 07:07:56.949717: train_loss -0.4364 +2026-04-14 07:07:56.956346: val_loss -0.4 +2026-04-14 07:07:56.958879: Pseudo dice [0.2862, 0.0, 0.7235, 0.0, 0.3114, 0.845, 0.7104] +2026-04-14 07:07:56.963574: Epoch time: 100.54 s +2026-04-14 07:07:58.236101: +2026-04-14 07:07:58.237976: Epoch 3158 +2026-04-14 07:07:58.240363: Current learning rate: 0.00246 +2026-04-14 07:09:38.508135: train_loss -0.436 +2026-04-14 07:09:38.514616: val_loss -0.3443 +2026-04-14 07:09:38.516535: Pseudo dice [0.7661, 0.0, 0.3636, 0.0688, 0.3296, 0.6897, 0.8146] +2026-04-14 07:09:38.519516: Epoch time: 100.28 s +2026-04-14 07:09:39.770544: +2026-04-14 07:09:39.772880: Epoch 3159 +2026-04-14 07:09:39.775368: Current learning rate: 0.00246 +2026-04-14 07:11:20.909217: train_loss -0.454 +2026-04-14 07:11:20.936592: val_loss -0.4425 +2026-04-14 07:11:20.938434: Pseudo dice [0.6318, 0.0, 0.7951, 0.8724, 0.3485, 0.69, 0.8515] +2026-04-14 07:11:20.940849: Epoch time: 101.14 s +2026-04-14 07:11:22.224295: +2026-04-14 07:11:22.227319: Epoch 3160 +2026-04-14 07:11:22.230506: Current learning rate: 0.00245 +2026-04-14 07:13:02.521545: train_loss -0.4363 +2026-04-14 07:13:02.528409: val_loss -0.4333 +2026-04-14 07:13:02.530382: Pseudo dice [0.4991, 0.0, 0.6881, 0.0, 0.6415, 0.7086, 0.7031] +2026-04-14 07:13:02.532971: Epoch time: 100.3 s +2026-04-14 07:13:03.802620: +2026-04-14 07:13:03.804718: Epoch 3161 +2026-04-14 07:13:03.807019: Current learning rate: 0.00245 +2026-04-14 07:14:44.128401: train_loss -0.4398 +2026-04-14 07:14:44.134372: val_loss -0.3107 +2026-04-14 07:14:44.136613: Pseudo dice [0.7222, 0.0, 0.4725, 0.0307, 0.2811, 0.8181, 0.854] +2026-04-14 07:14:44.139812: Epoch time: 100.33 s +2026-04-14 07:14:45.387944: +2026-04-14 07:14:45.390301: Epoch 3162 +2026-04-14 07:14:45.392498: Current learning rate: 0.00245 +2026-04-14 07:16:25.590012: train_loss -0.4542 +2026-04-14 07:16:25.598054: val_loss -0.4132 +2026-04-14 07:16:25.600258: Pseudo dice [0.7149, 0.0, 0.7382, 0.5817, 0.3515, 0.7738, 0.6471] +2026-04-14 07:16:25.603432: Epoch time: 100.21 s +2026-04-14 07:16:26.869344: +2026-04-14 07:16:26.871222: Epoch 3163 +2026-04-14 07:16:26.873254: Current learning rate: 0.00245 +2026-04-14 07:18:07.534378: train_loss -0.4522 +2026-04-14 07:18:07.541216: val_loss -0.3951 +2026-04-14 07:18:07.543401: Pseudo dice [0.6596, 0.0, 0.6736, 0.1844, 0.2442, 0.5438, 0.7791] +2026-04-14 07:18:07.547140: Epoch time: 100.67 s +2026-04-14 07:18:08.806530: +2026-04-14 07:18:08.808844: Epoch 3164 +2026-04-14 07:18:08.811298: Current learning rate: 0.00244 +2026-04-14 07:19:49.131493: train_loss -0.4375 +2026-04-14 07:19:49.137266: val_loss -0.3174 +2026-04-14 07:19:49.139143: Pseudo dice [0.7602, 0.0, 0.6118, 0.0263, 0.3703, 0.6723, 0.7451] +2026-04-14 07:19:49.141384: Epoch time: 100.33 s +2026-04-14 07:19:50.376068: +2026-04-14 07:19:50.377930: Epoch 3165 +2026-04-14 07:19:50.380446: Current learning rate: 0.00244 +2026-04-14 07:21:30.685450: train_loss -0.4322 +2026-04-14 07:21:30.693533: val_loss -0.3348 +2026-04-14 07:21:30.696225: Pseudo dice [0.4328, 0.0, 0.6765, 0.0062, 0.3117, 0.7622, 0.6595] +2026-04-14 07:21:30.698955: Epoch time: 100.31 s +2026-04-14 07:21:31.928672: +2026-04-14 07:21:31.930988: Epoch 3166 +2026-04-14 07:21:31.933422: Current learning rate: 0.00244 +2026-04-14 07:23:12.203189: train_loss -0.432 +2026-04-14 07:23:12.210988: val_loss -0.3901 +2026-04-14 07:23:12.213411: Pseudo dice [0.4458, 0.0, 0.638, 0.0051, 0.4075, 0.505, 0.5938] +2026-04-14 07:23:12.215931: Epoch time: 100.28 s +2026-04-14 07:23:13.465554: +2026-04-14 07:23:13.469847: Epoch 3167 +2026-04-14 07:23:13.472421: Current learning rate: 0.00244 +2026-04-14 07:24:54.371119: train_loss -0.436 +2026-04-14 07:24:54.377987: val_loss -0.3179 +2026-04-14 07:24:54.380289: Pseudo dice [0.3865, 0.0, 0.6472, 0.0, 0.4545, 0.705, 0.2184] +2026-04-14 07:24:54.383147: Epoch time: 100.91 s +2026-04-14 07:24:55.604987: +2026-04-14 07:24:55.607261: Epoch 3168 +2026-04-14 07:24:55.609674: Current learning rate: 0.00243 +2026-04-14 07:26:36.050441: train_loss -0.4251 +2026-04-14 07:26:36.063406: val_loss -0.434 +2026-04-14 07:26:36.066037: Pseudo dice [0.6127, 0.0, 0.8059, 0.3914, 0.4212, 0.7402, 0.8476] +2026-04-14 07:26:36.069368: Epoch time: 100.45 s +2026-04-14 07:26:37.319068: +2026-04-14 07:26:37.321240: Epoch 3169 +2026-04-14 07:26:37.323425: Current learning rate: 0.00243 +2026-04-14 07:28:17.800582: train_loss -0.4228 +2026-04-14 07:28:17.807128: val_loss -0.3927 +2026-04-14 07:28:17.810439: Pseudo dice [0.1536, 0.0, 0.5568, 0.3176, 0.4367, 0.6186, 0.7101] +2026-04-14 07:28:17.813255: Epoch time: 100.48 s +2026-04-14 07:28:20.138737: +2026-04-14 07:28:20.141077: Epoch 3170 +2026-04-14 07:28:20.143455: Current learning rate: 0.00243 +2026-04-14 07:30:00.371464: train_loss -0.4354 +2026-04-14 07:30:00.377818: val_loss -0.4045 +2026-04-14 07:30:00.380350: Pseudo dice [0.723, 0.0, 0.6756, 0.8527, 0.3896, 0.6797, 0.7539] +2026-04-14 07:30:00.382859: Epoch time: 100.24 s +2026-04-14 07:30:01.633381: +2026-04-14 07:30:01.635264: Epoch 3171 +2026-04-14 07:30:01.637243: Current learning rate: 0.00243 +2026-04-14 07:31:42.200996: train_loss -0.4313 +2026-04-14 07:31:42.207735: val_loss -0.3764 +2026-04-14 07:31:42.209925: Pseudo dice [0.0, 0.0, 0.7476, 0.7439, 0.2, 0.8538, 0.7797] +2026-04-14 07:31:42.212551: Epoch time: 100.57 s +2026-04-14 07:31:43.446108: +2026-04-14 07:31:43.448206: Epoch 3172 +2026-04-14 07:31:43.450401: Current learning rate: 0.00242 +2026-04-14 07:33:24.320972: train_loss -0.4296 +2026-04-14 07:33:24.326333: val_loss -0.3239 +2026-04-14 07:33:24.328811: Pseudo dice [0.5822, 0.0, 0.5325, 0.0415, 0.4932, 0.775, 0.8443] +2026-04-14 07:33:24.331573: Epoch time: 100.88 s +2026-04-14 07:33:25.782543: +2026-04-14 07:33:25.784748: Epoch 3173 +2026-04-14 07:33:25.786862: Current learning rate: 0.00242 +2026-04-14 07:35:06.732761: train_loss -0.4322 +2026-04-14 07:35:06.740236: val_loss -0.3368 +2026-04-14 07:35:06.742431: Pseudo dice [0.7412, 0.0, 0.7474, 0.125, 0.4898, 0.7856, 0.7892] +2026-04-14 07:35:06.744826: Epoch time: 100.95 s +2026-04-14 07:35:07.989686: +2026-04-14 07:35:07.991728: Epoch 3174 +2026-04-14 07:35:07.993982: Current learning rate: 0.00242 +2026-04-14 07:36:48.565609: train_loss -0.4471 +2026-04-14 07:36:48.582520: val_loss -0.3783 +2026-04-14 07:36:48.585743: Pseudo dice [0.7536, 0.0, 0.762, 0.0, 0.4413, 0.8789, 0.8678] +2026-04-14 07:36:48.588195: Epoch time: 100.58 s +2026-04-14 07:36:49.839614: +2026-04-14 07:36:49.841544: Epoch 3175 +2026-04-14 07:36:49.843696: Current learning rate: 0.00242 +2026-04-14 07:38:30.246811: train_loss -0.4397 +2026-04-14 07:38:30.254622: val_loss -0.3808 +2026-04-14 07:38:30.261586: Pseudo dice [0.4449, 0.0, 0.7445, 0.3728, 0.3281, 0.8153, 0.7209] +2026-04-14 07:38:30.264212: Epoch time: 100.41 s +2026-04-14 07:38:31.509305: +2026-04-14 07:38:31.511175: Epoch 3176 +2026-04-14 07:38:31.513227: Current learning rate: 0.00241 +2026-04-14 07:40:12.311925: train_loss -0.4507 +2026-04-14 07:40:12.318414: val_loss -0.4001 +2026-04-14 07:40:12.321260: Pseudo dice [0.8085, 0.0, 0.568, 0.3099, 0.4187, 0.6922, 0.8538] +2026-04-14 07:40:12.323888: Epoch time: 100.81 s +2026-04-14 07:40:13.560256: +2026-04-14 07:40:13.562613: Epoch 3177 +2026-04-14 07:40:13.565182: Current learning rate: 0.00241 +2026-04-14 07:41:53.792637: train_loss -0.4505 +2026-04-14 07:41:53.798584: val_loss -0.3665 +2026-04-14 07:41:53.800859: Pseudo dice [0.4079, 0.0, 0.5021, 0.0, 0.3808, 0.7957, 0.726] +2026-04-14 07:41:53.803764: Epoch time: 100.24 s +2026-04-14 07:41:55.071782: +2026-04-14 07:41:55.073679: Epoch 3178 +2026-04-14 07:41:55.075580: Current learning rate: 0.00241 +2026-04-14 07:43:35.995346: train_loss -0.4428 +2026-04-14 07:43:36.001750: val_loss -0.3537 +2026-04-14 07:43:36.004330: Pseudo dice [0.706, 0.0, 0.4945, 0.0762, 0.2955, 0.7408, 0.5855] +2026-04-14 07:43:36.006908: Epoch time: 100.93 s +2026-04-14 07:43:37.250795: +2026-04-14 07:43:37.253562: Epoch 3179 +2026-04-14 07:43:37.255625: Current learning rate: 0.0024 +2026-04-14 07:45:17.617945: train_loss -0.4556 +2026-04-14 07:45:17.623915: val_loss -0.4261 +2026-04-14 07:45:17.625844: Pseudo dice [0.6682, 0.0, 0.7911, 0.0, 0.5524, 0.7167, 0.8413] +2026-04-14 07:45:17.628315: Epoch time: 100.37 s +2026-04-14 07:45:18.886107: +2026-04-14 07:45:18.887974: Epoch 3180 +2026-04-14 07:45:18.890167: Current learning rate: 0.0024 +2026-04-14 07:46:59.265873: train_loss -0.4364 +2026-04-14 07:46:59.273591: val_loss -0.2746 +2026-04-14 07:46:59.276170: Pseudo dice [0.709, 0.0, 0.603, 0.0, 0.5049, 0.6272, 0.7052] +2026-04-14 07:46:59.280658: Epoch time: 100.38 s +2026-04-14 07:47:00.525776: +2026-04-14 07:47:00.527738: Epoch 3181 +2026-04-14 07:47:00.530854: Current learning rate: 0.0024 +2026-04-14 07:48:41.448203: train_loss -0.4559 +2026-04-14 07:48:41.455823: val_loss -0.4257 +2026-04-14 07:48:41.458593: Pseudo dice [0.7305, 0.0, 0.7273, 0.147, 0.5144, 0.7984, 0.8549] +2026-04-14 07:48:41.462518: Epoch time: 100.93 s +2026-04-14 07:48:42.739748: +2026-04-14 07:48:42.741863: Epoch 3182 +2026-04-14 07:48:42.744083: Current learning rate: 0.0024 +2026-04-14 07:50:23.004107: train_loss -0.4647 +2026-04-14 07:50:23.011910: val_loss -0.3971 +2026-04-14 07:50:23.014486: Pseudo dice [0.6732, 0.0, 0.7739, 0.0, 0.5018, 0.8384, 0.8866] +2026-04-14 07:50:23.017483: Epoch time: 100.27 s +2026-04-14 07:50:24.271482: +2026-04-14 07:50:24.274048: Epoch 3183 +2026-04-14 07:50:24.276086: Current learning rate: 0.00239 +2026-04-14 07:52:04.580703: train_loss -0.4582 +2026-04-14 07:52:04.586610: val_loss -0.2268 +2026-04-14 07:52:04.590109: Pseudo dice [0.7583, 0.0, 0.5303, 0.0, 0.4155, 0.7566, 0.6638] +2026-04-14 07:52:04.595516: Epoch time: 100.31 s +2026-04-14 07:52:05.836133: +2026-04-14 07:52:05.838390: Epoch 3184 +2026-04-14 07:52:05.840471: Current learning rate: 0.00239 +2026-04-14 07:53:46.674407: train_loss -0.4493 +2026-04-14 07:53:46.680245: val_loss -0.4503 +2026-04-14 07:53:46.683281: Pseudo dice [0.3085, 0.0, 0.7658, 0.8416, 0.3894, 0.6431, 0.901] +2026-04-14 07:53:46.685459: Epoch time: 100.84 s +2026-04-14 07:53:47.918636: +2026-04-14 07:53:47.920572: Epoch 3185 +2026-04-14 07:53:47.922820: Current learning rate: 0.00239 +2026-04-14 07:55:28.143542: train_loss -0.4526 +2026-04-14 07:55:28.149809: val_loss -0.4174 +2026-04-14 07:55:28.151967: Pseudo dice [0.7733, 0.0, 0.7639, 0.095, 0.4667, 0.8582, 0.5837] +2026-04-14 07:55:28.154494: Epoch time: 100.23 s +2026-04-14 07:55:29.416932: +2026-04-14 07:55:29.418926: Epoch 3186 +2026-04-14 07:55:29.421246: Current learning rate: 0.00239 +2026-04-14 07:57:09.608631: train_loss -0.4564 +2026-04-14 07:57:09.617902: val_loss -0.4121 +2026-04-14 07:57:09.621081: Pseudo dice [0.4368, 0.0, 0.7009, 0.7589, 0.3922, 0.7975, 0.7674] +2026-04-14 07:57:09.623917: Epoch time: 100.19 s +2026-04-14 07:57:10.863795: +2026-04-14 07:57:10.866068: Epoch 3187 +2026-04-14 07:57:10.868164: Current learning rate: 0.00238 +2026-04-14 07:58:51.554855: train_loss -0.4312 +2026-04-14 07:58:51.560875: val_loss -0.3698 +2026-04-14 07:58:51.563430: Pseudo dice [0.3763, 0.0, 0.61, 0.0, 0.1377, 0.8378, 0.7227] +2026-04-14 07:58:51.565643: Epoch time: 100.69 s +2026-04-14 07:58:52.791384: +2026-04-14 07:58:52.793270: Epoch 3188 +2026-04-14 07:58:52.795596: Current learning rate: 0.00238 +2026-04-14 08:00:33.673819: train_loss -0.4256 +2026-04-14 08:00:33.681313: val_loss -0.3918 +2026-04-14 08:00:33.684385: Pseudo dice [0.7764, 0.0, 0.7271, 0.5629, 0.5074, 0.7402, 0.5533] +2026-04-14 08:00:33.687220: Epoch time: 100.89 s +2026-04-14 08:00:34.913341: +2026-04-14 08:00:34.915416: Epoch 3189 +2026-04-14 08:00:34.917695: Current learning rate: 0.00238 +2026-04-14 08:02:16.676865: train_loss -0.4456 +2026-04-14 08:02:16.684415: val_loss -0.4143 +2026-04-14 08:02:16.686919: Pseudo dice [0.7169, 0.0, 0.8133, 0.4012, 0.5272, 0.8398, 0.6201] +2026-04-14 08:02:16.690166: Epoch time: 101.77 s +2026-04-14 08:02:17.936956: +2026-04-14 08:02:17.939556: Epoch 3190 +2026-04-14 08:02:17.942755: Current learning rate: 0.00238 +2026-04-14 08:03:58.299721: train_loss -0.4605 +2026-04-14 08:03:58.308755: val_loss -0.3539 +2026-04-14 08:03:58.311255: Pseudo dice [0.6135, 0.0, 0.8057, 0.0, 0.4869, 0.5404, 0.8695] +2026-04-14 08:03:58.314052: Epoch time: 100.37 s +2026-04-14 08:03:59.567726: +2026-04-14 08:03:59.569731: Epoch 3191 +2026-04-14 08:03:59.572590: Current learning rate: 0.00237 +2026-04-14 08:05:40.215595: train_loss -0.4176 +2026-04-14 08:05:40.222610: val_loss -0.3755 +2026-04-14 08:05:40.226529: Pseudo dice [0.0383, 0.0, 0.7603, 0.3024, 0.3961, 0.7321, 0.8241] +2026-04-14 08:05:40.229038: Epoch time: 100.65 s +2026-04-14 08:05:41.494017: +2026-04-14 08:05:41.496228: Epoch 3192 +2026-04-14 08:05:41.499061: Current learning rate: 0.00237 +2026-04-14 08:07:22.132213: train_loss -0.4401 +2026-04-14 08:07:22.139075: val_loss -0.3618 +2026-04-14 08:07:22.141450: Pseudo dice [0.437, 0.0, 0.6632, 0.0, 0.5208, 0.8827, 0.8239] +2026-04-14 08:07:22.144658: Epoch time: 100.64 s +2026-04-14 08:07:23.380012: +2026-04-14 08:07:23.381855: Epoch 3193 +2026-04-14 08:07:23.383976: Current learning rate: 0.00237 +2026-04-14 08:09:03.513721: train_loss -0.4316 +2026-04-14 08:09:03.520995: val_loss -0.3109 +2026-04-14 08:09:03.523343: Pseudo dice [0.7251, 0.0, 0.6866, 0.0982, 0.1154, 0.5601, 0.6893] +2026-04-14 08:09:03.526116: Epoch time: 100.14 s +2026-04-14 08:09:04.736624: +2026-04-14 08:09:04.738570: Epoch 3194 +2026-04-14 08:09:04.740478: Current learning rate: 0.00237 +2026-04-14 08:10:45.651947: train_loss -0.4363 +2026-04-14 08:10:45.659505: val_loss -0.3565 +2026-04-14 08:10:45.662050: Pseudo dice [0.6893, 0.0, 0.8031, 0.0, 0.3562, 0.4243, 0.6836] +2026-04-14 08:10:45.665133: Epoch time: 100.92 s +2026-04-14 08:10:46.938760: +2026-04-14 08:10:46.941046: Epoch 3195 +2026-04-14 08:10:46.943378: Current learning rate: 0.00236 +2026-04-14 08:12:27.417130: train_loss -0.4316 +2026-04-14 08:12:27.424341: val_loss -0.3717 +2026-04-14 08:12:27.426843: Pseudo dice [0.6767, 0.0, 0.6689, 0.1672, 0.4811, 0.6323, 0.8233] +2026-04-14 08:12:27.431071: Epoch time: 100.48 s +2026-04-14 08:12:28.684481: +2026-04-14 08:12:28.686558: Epoch 3196 +2026-04-14 08:12:28.688600: Current learning rate: 0.00236 +2026-04-14 08:14:09.000114: train_loss -0.4276 +2026-04-14 08:14:09.007767: val_loss -0.3545 +2026-04-14 08:14:09.009980: Pseudo dice [0.6829, 0.0, 0.6601, 0.0, 0.5143, 0.7914, 0.8387] +2026-04-14 08:14:09.012511: Epoch time: 100.32 s +2026-04-14 08:14:10.267937: +2026-04-14 08:14:10.269805: Epoch 3197 +2026-04-14 08:14:10.272619: Current learning rate: 0.00236 +2026-04-14 08:15:51.074110: train_loss -0.4552 +2026-04-14 08:15:51.085247: val_loss -0.3109 +2026-04-14 08:15:51.087855: Pseudo dice [0.6893, 0.0, 0.7506, 0.0312, 0.2811, 0.836, 0.6707] +2026-04-14 08:15:51.090687: Epoch time: 100.81 s +2026-04-14 08:15:52.359013: +2026-04-14 08:15:52.361246: Epoch 3198 +2026-04-14 08:15:52.363315: Current learning rate: 0.00235 +2026-04-14 08:17:33.114122: train_loss -0.4539 +2026-04-14 08:17:33.122464: val_loss -0.3939 +2026-04-14 08:17:33.126304: Pseudo dice [0.8654, 0.0, 0.7913, 0.1369, 0.3211, 0.7006, 0.7307] +2026-04-14 08:17:33.129055: Epoch time: 100.76 s +2026-04-14 08:17:34.385856: +2026-04-14 08:17:34.388617: Epoch 3199 +2026-04-14 08:17:34.390972: Current learning rate: 0.00235 +2026-04-14 08:19:15.435925: train_loss -0.4478 +2026-04-14 08:19:15.443361: val_loss -0.4175 +2026-04-14 08:19:15.445900: Pseudo dice [0.766, 0.0, 0.8198, 0.5469, 0.3483, 0.6421, 0.4727] +2026-04-14 08:19:15.449667: Epoch time: 101.05 s +2026-04-14 08:19:18.438416: +2026-04-14 08:19:18.441051: Epoch 3200 +2026-04-14 08:19:18.443220: Current learning rate: 0.00235 +2026-04-14 08:20:58.939803: train_loss -0.4365 +2026-04-14 08:20:58.950109: val_loss -0.4084 +2026-04-14 08:20:58.953168: Pseudo dice [0.4679, 0.0, 0.7536, 0.5227, 0.1475, 0.668, 0.7004] +2026-04-14 08:20:58.955855: Epoch time: 100.5 s +2026-04-14 08:21:00.183419: +2026-04-14 08:21:00.185231: Epoch 3201 +2026-04-14 08:21:00.187618: Current learning rate: 0.00235 +2026-04-14 08:22:40.354500: train_loss -0.4385 +2026-04-14 08:22:40.362036: val_loss -0.425 +2026-04-14 08:22:40.364952: Pseudo dice [0.6899, 0.0, 0.73, 0.0, 0.4841, 0.7737, 0.7996] +2026-04-14 08:22:40.367726: Epoch time: 100.17 s +2026-04-14 08:22:41.610237: +2026-04-14 08:22:41.612368: Epoch 3202 +2026-04-14 08:22:41.614936: Current learning rate: 0.00234 +2026-04-14 08:24:22.321004: train_loss -0.4523 +2026-04-14 08:24:22.327420: val_loss -0.346 +2026-04-14 08:24:22.329683: Pseudo dice [0.7209, 0.0, 0.5458, 0.0, 0.4017, 0.6235, 0.8064] +2026-04-14 08:24:22.332186: Epoch time: 100.71 s +2026-04-14 08:24:23.568971: +2026-04-14 08:24:23.571109: Epoch 3203 +2026-04-14 08:24:23.573303: Current learning rate: 0.00234 +2026-04-14 08:26:03.662341: train_loss -0.45 +2026-04-14 08:26:03.669228: val_loss -0.3158 +2026-04-14 08:26:03.671986: Pseudo dice [0.8535, 0.0, 0.4348, 0.0, 0.3912, 0.794, 0.7872] +2026-04-14 08:26:03.674805: Epoch time: 100.1 s +2026-04-14 08:26:04.905718: +2026-04-14 08:26:04.908525: Epoch 3204 +2026-04-14 08:26:04.910952: Current learning rate: 0.00234 +2026-04-14 08:27:46.187575: train_loss -0.4458 +2026-04-14 08:27:46.195748: val_loss -0.4114 +2026-04-14 08:27:46.200644: Pseudo dice [0.1537, 0.0, 0.7837, 0.3117, 0.3943, 0.5553, 0.8088] +2026-04-14 08:27:46.204856: Epoch time: 101.29 s +2026-04-14 08:27:47.465029: +2026-04-14 08:27:47.467507: Epoch 3205 +2026-04-14 08:27:47.469915: Current learning rate: 0.00234 +2026-04-14 08:29:28.870466: train_loss -0.4402 +2026-04-14 08:29:28.880897: val_loss -0.4026 +2026-04-14 08:29:28.883243: Pseudo dice [0.7534, 0.0, 0.5793, 0.0, 0.1807, 0.7595, 0.7692] +2026-04-14 08:29:28.886491: Epoch time: 101.41 s +2026-04-14 08:29:30.126917: +2026-04-14 08:29:30.129514: Epoch 3206 +2026-04-14 08:29:30.132471: Current learning rate: 0.00233 +2026-04-14 08:31:10.805795: train_loss -0.4545 +2026-04-14 08:31:10.811377: val_loss -0.3847 +2026-04-14 08:31:10.818322: Pseudo dice [0.7503, 0.0, 0.6344, 0.0, 0.1407, 0.7926, 0.7813] +2026-04-14 08:31:10.821687: Epoch time: 100.68 s +2026-04-14 08:31:12.086198: +2026-04-14 08:31:12.088625: Epoch 3207 +2026-04-14 08:31:12.090911: Current learning rate: 0.00233 +2026-04-14 08:32:52.630500: train_loss -0.438 +2026-04-14 08:32:52.638999: val_loss -0.3961 +2026-04-14 08:32:52.641128: Pseudo dice [0.3249, 0.0, 0.6992, 0.609, 0.4077, 0.7157, 0.6382] +2026-04-14 08:32:52.643515: Epoch time: 100.55 s +2026-04-14 08:32:53.942273: +2026-04-14 08:32:53.944583: Epoch 3208 +2026-04-14 08:32:53.947303: Current learning rate: 0.00233 +2026-04-14 08:34:34.449002: train_loss -0.4489 +2026-04-14 08:34:34.456170: val_loss -0.3384 +2026-04-14 08:34:34.458131: Pseudo dice [0.233, 0.0, 0.6481, 0.0617, 0.3815, 0.4512, 0.7774] +2026-04-14 08:34:34.460557: Epoch time: 100.51 s +2026-04-14 08:34:36.848811: +2026-04-14 08:34:36.851016: Epoch 3209 +2026-04-14 08:34:36.853642: Current learning rate: 0.00233 +2026-04-14 08:36:17.129499: train_loss -0.4665 +2026-04-14 08:36:17.134680: val_loss -0.3993 +2026-04-14 08:36:17.137033: Pseudo dice [0.828, 0.0, 0.746, 0.0, 0.4178, 0.7545, 0.5119] +2026-04-14 08:36:17.139954: Epoch time: 100.28 s +2026-04-14 08:36:18.418221: +2026-04-14 08:36:18.420525: Epoch 3210 +2026-04-14 08:36:18.423055: Current learning rate: 0.00232 +2026-04-14 08:37:59.049631: train_loss -0.4558 +2026-04-14 08:37:59.055791: val_loss -0.3021 +2026-04-14 08:37:59.058665: Pseudo dice [0.0, 0.0, 0.6155, 0.075, 0.4587, 0.2012, 0.878] +2026-04-14 08:37:59.063665: Epoch time: 100.63 s +2026-04-14 08:38:00.350045: +2026-04-14 08:38:00.352989: Epoch 3211 +2026-04-14 08:38:00.354947: Current learning rate: 0.00232 +2026-04-14 08:39:41.002858: train_loss -0.4318 +2026-04-14 08:39:41.008845: val_loss -0.4102 +2026-04-14 08:39:41.010919: Pseudo dice [0.7916, 0.0, 0.7924, 0.8598, 0.2004, 0.8763, 0.8294] +2026-04-14 08:39:41.013164: Epoch time: 100.66 s +2026-04-14 08:39:42.248285: +2026-04-14 08:39:42.250128: Epoch 3212 +2026-04-14 08:39:42.252238: Current learning rate: 0.00232 +2026-04-14 08:41:22.874074: train_loss -0.4461 +2026-04-14 08:41:22.881840: val_loss -0.4159 +2026-04-14 08:41:22.884029: Pseudo dice [0.2878, 0.0, 0.7178, 0.4605, 0.6244, 0.602, 0.7218] +2026-04-14 08:41:22.887242: Epoch time: 100.63 s +2026-04-14 08:41:24.129976: +2026-04-14 08:41:24.131936: Epoch 3213 +2026-04-14 08:41:24.134690: Current learning rate: 0.00231 +2026-04-14 08:43:04.687327: train_loss -0.4561 +2026-04-14 08:43:04.692899: val_loss -0.3426 +2026-04-14 08:43:04.695376: Pseudo dice [0.8127, 0.0, 0.7137, 0.0519, 0.348, 0.8684, 0.9201] +2026-04-14 08:43:04.698239: Epoch time: 100.56 s +2026-04-14 08:43:05.928605: +2026-04-14 08:43:05.931031: Epoch 3214 +2026-04-14 08:43:05.933113: Current learning rate: 0.00231 +2026-04-14 08:44:46.176298: train_loss -0.4558 +2026-04-14 08:44:46.183168: val_loss -0.4139 +2026-04-14 08:44:46.185271: Pseudo dice [0.6464, 0.0, 0.778, 0.0, 0.4053, 0.7445, 0.6323] +2026-04-14 08:44:46.187353: Epoch time: 100.25 s +2026-04-14 08:44:47.608389: +2026-04-14 08:44:47.610861: Epoch 3215 +2026-04-14 08:44:47.613353: Current learning rate: 0.00231 +2026-04-14 08:46:27.972738: train_loss -0.4573 +2026-04-14 08:46:27.979692: val_loss -0.4066 +2026-04-14 08:46:27.982649: Pseudo dice [0.6552, 0.0, 0.5962, 0.3698, 0.2989, 0.6975, 0.6566] +2026-04-14 08:46:27.986242: Epoch time: 100.37 s +2026-04-14 08:46:29.220632: +2026-04-14 08:46:29.222696: Epoch 3216 +2026-04-14 08:46:29.225365: Current learning rate: 0.00231 +2026-04-14 08:48:09.652148: train_loss -0.4529 +2026-04-14 08:48:09.660087: val_loss -0.4243 +2026-04-14 08:48:09.663006: Pseudo dice [0.7365, 0.0, 0.7794, 0.4736, 0.3006, 0.8303, 0.7248] +2026-04-14 08:48:09.666811: Epoch time: 100.43 s +2026-04-14 08:48:10.907631: +2026-04-14 08:48:10.909838: Epoch 3217 +2026-04-14 08:48:10.912323: Current learning rate: 0.0023 +2026-04-14 08:49:51.389189: train_loss -0.4362 +2026-04-14 08:49:51.394570: val_loss -0.4316 +2026-04-14 08:49:51.397220: Pseudo dice [0.8383, 0.0, 0.7659, 0.0, 0.3478, 0.6051, 0.8131] +2026-04-14 08:49:51.399486: Epoch time: 100.48 s +2026-04-14 08:49:52.680910: +2026-04-14 08:49:52.683159: Epoch 3218 +2026-04-14 08:49:52.686014: Current learning rate: 0.0023 +2026-04-14 08:51:33.132482: train_loss -0.4393 +2026-04-14 08:51:33.138960: val_loss -0.4289 +2026-04-14 08:51:33.141109: Pseudo dice [0.8389, 0.0, 0.7875, 0.4691, 0.5364, 0.8454, 0.7969] +2026-04-14 08:51:33.143712: Epoch time: 100.45 s +2026-04-14 08:51:34.393782: +2026-04-14 08:51:34.395565: Epoch 3219 +2026-04-14 08:51:34.398102: Current learning rate: 0.0023 +2026-04-14 08:53:15.040563: train_loss -0.4363 +2026-04-14 08:53:15.046755: val_loss -0.3302 +2026-04-14 08:53:15.048901: Pseudo dice [0.7036, 0.0, 0.5579, 0.1112, 0.3158, 0.5053, 0.8525] +2026-04-14 08:53:15.051492: Epoch time: 100.65 s +2026-04-14 08:53:16.309570: +2026-04-14 08:53:16.312419: Epoch 3220 +2026-04-14 08:53:16.314921: Current learning rate: 0.0023 +2026-04-14 08:54:57.085760: train_loss -0.4465 +2026-04-14 08:54:57.092153: val_loss -0.3963 +2026-04-14 08:54:57.094638: Pseudo dice [0.3905, 0.0, 0.7467, 0.669, 0.3614, 0.8047, 0.5255] +2026-04-14 08:54:57.097582: Epoch time: 100.78 s +2026-04-14 08:54:58.343276: +2026-04-14 08:54:58.345759: Epoch 3221 +2026-04-14 08:54:58.348151: Current learning rate: 0.00229 +2026-04-14 08:56:39.276023: train_loss -0.4455 +2026-04-14 08:56:39.282429: val_loss -0.4215 +2026-04-14 08:56:39.284622: Pseudo dice [0.6834, 0.0, 0.6981, 0.5907, 0.3155, 0.8215, 0.7819] +2026-04-14 08:56:39.286833: Epoch time: 100.94 s +2026-04-14 08:56:40.525736: +2026-04-14 08:56:40.527508: Epoch 3222 +2026-04-14 08:56:40.529642: Current learning rate: 0.00229 +2026-04-14 08:58:20.847327: train_loss -0.4567 +2026-04-14 08:58:20.854100: val_loss -0.3431 +2026-04-14 08:58:20.856031: Pseudo dice [0.3499, 0.0, 0.74, 0.0949, 0.225, 0.8334, 0.8979] +2026-04-14 08:58:20.859615: Epoch time: 100.32 s +2026-04-14 08:58:22.121623: +2026-04-14 08:58:22.123977: Epoch 3223 +2026-04-14 08:58:22.129752: Current learning rate: 0.00229 +2026-04-14 09:00:03.032154: train_loss -0.4447 +2026-04-14 09:00:03.038112: val_loss -0.3839 +2026-04-14 09:00:03.040207: Pseudo dice [0.7014, 0.0, 0.7208, 0.0, 0.5205, 0.4824, 0.5773] +2026-04-14 09:00:03.042894: Epoch time: 100.91 s +2026-04-14 09:00:04.334860: +2026-04-14 09:00:04.336901: Epoch 3224 +2026-04-14 09:00:04.339123: Current learning rate: 0.00229 +2026-04-14 09:01:45.790241: train_loss -0.4522 +2026-04-14 09:01:45.795405: val_loss -0.4269 +2026-04-14 09:01:45.798256: Pseudo dice [0.2428, 0.0, 0.7401, 0.0, 0.4829, 0.689, 0.8897] +2026-04-14 09:01:45.800767: Epoch time: 101.46 s +2026-04-14 09:01:47.054132: +2026-04-14 09:01:47.056090: Epoch 3225 +2026-04-14 09:01:47.058736: Current learning rate: 0.00228 +2026-04-14 09:03:27.742224: train_loss -0.4603 +2026-04-14 09:03:27.747853: val_loss -0.4322 +2026-04-14 09:03:27.749901: Pseudo dice [0.6655, 0.0, 0.5966, 0.0, 0.342, 0.8483, 0.6559] +2026-04-14 09:03:27.752495: Epoch time: 100.69 s +2026-04-14 09:03:28.995495: +2026-04-14 09:03:28.997671: Epoch 3226 +2026-04-14 09:03:28.999525: Current learning rate: 0.00228 +2026-04-14 09:05:09.548805: train_loss -0.4689 +2026-04-14 09:05:09.555156: val_loss -0.4205 +2026-04-14 09:05:09.556863: Pseudo dice [0.6219, 0.0, 0.7629, 0.7168, 0.4299, 0.7911, 0.8448] +2026-04-14 09:05:09.559015: Epoch time: 100.56 s +2026-04-14 09:05:10.786777: +2026-04-14 09:05:10.789061: Epoch 3227 +2026-04-14 09:05:10.791082: Current learning rate: 0.00228 +2026-04-14 09:06:51.066427: train_loss -0.4443 +2026-04-14 09:06:51.073095: val_loss -0.3425 +2026-04-14 09:06:51.075426: Pseudo dice [0.7473, 0.0, 0.727, 0.2019, 0.2454, 0.5729, 0.7813] +2026-04-14 09:06:51.077904: Epoch time: 100.28 s +2026-04-14 09:06:52.336395: +2026-04-14 09:06:52.338787: Epoch 3228 +2026-04-14 09:06:52.341018: Current learning rate: 0.00228 +2026-04-14 09:08:33.186618: train_loss -0.4446 +2026-04-14 09:08:33.193537: val_loss -0.4059 +2026-04-14 09:08:33.196813: Pseudo dice [0.0, 0.0, 0.8153, 0.4545, 0.2686, 0.8229, 0.8611] +2026-04-14 09:08:33.200036: Epoch time: 100.85 s +2026-04-14 09:08:35.547734: +2026-04-14 09:08:35.549597: Epoch 3229 +2026-04-14 09:08:35.551763: Current learning rate: 0.00227 +2026-04-14 09:10:16.204504: train_loss -0.451 +2026-04-14 09:10:16.211489: val_loss -0.3554 +2026-04-14 09:10:16.213607: Pseudo dice [0.7803, 0.0, 0.624, 0.0, 0.5194, 0.8924, 0.62] +2026-04-14 09:10:16.216310: Epoch time: 100.66 s +2026-04-14 09:10:17.453918: +2026-04-14 09:10:17.456213: Epoch 3230 +2026-04-14 09:10:17.458702: Current learning rate: 0.00227 +2026-04-14 09:11:57.860872: train_loss -0.4451 +2026-04-14 09:11:57.885764: val_loss -0.2467 +2026-04-14 09:11:57.887702: Pseudo dice [0.0, 0.0, 0.5626, 0.0, 0.2379, 0.7831, 0.7952] +2026-04-14 09:11:57.889827: Epoch time: 100.41 s +2026-04-14 09:11:59.115059: +2026-04-14 09:11:59.116969: Epoch 3231 +2026-04-14 09:11:59.118731: Current learning rate: 0.00227 +2026-04-14 09:13:39.853579: train_loss -0.4517 +2026-04-14 09:13:39.861242: val_loss -0.419 +2026-04-14 09:13:39.864071: Pseudo dice [0.6918, 0.0, 0.7618, 0.0, 0.4249, 0.7942, 0.7881] +2026-04-14 09:13:39.866836: Epoch time: 100.74 s +2026-04-14 09:13:41.162082: +2026-04-14 09:13:41.164395: Epoch 3232 +2026-04-14 09:13:41.166370: Current learning rate: 0.00226 +2026-04-14 09:15:21.594253: train_loss -0.4583 +2026-04-14 09:15:21.601001: val_loss -0.4514 +2026-04-14 09:15:21.603511: Pseudo dice [0.7701, 0.0, 0.7956, 0.0, 0.5867, 0.8539, 0.556] +2026-04-14 09:15:21.606110: Epoch time: 100.44 s +2026-04-14 09:15:22.903494: +2026-04-14 09:15:22.905750: Epoch 3233 +2026-04-14 09:15:22.907989: Current learning rate: 0.00226 +2026-04-14 09:17:03.287050: train_loss -0.4572 +2026-04-14 09:17:03.292906: val_loss -0.3694 +2026-04-14 09:17:03.295089: Pseudo dice [0.7641, 0.0, 0.5659, 0.1518, 0.3462, 0.908, 0.5581] +2026-04-14 09:17:03.297073: Epoch time: 100.39 s +2026-04-14 09:17:04.531158: +2026-04-14 09:17:04.533348: Epoch 3234 +2026-04-14 09:17:04.535045: Current learning rate: 0.00226 +2026-04-14 09:18:45.038085: train_loss -0.4633 +2026-04-14 09:18:45.044694: val_loss -0.4257 +2026-04-14 09:18:45.048647: Pseudo dice [0.6774, 0.0, 0.7412, 0.0539, 0.4271, 0.7067, 0.8744] +2026-04-14 09:18:45.052787: Epoch time: 100.51 s +2026-04-14 09:18:46.329757: +2026-04-14 09:18:46.333507: Epoch 3235 +2026-04-14 09:18:46.337036: Current learning rate: 0.00226 +2026-04-14 09:20:27.015266: train_loss -0.454 +2026-04-14 09:20:27.024114: val_loss -0.4196 +2026-04-14 09:20:27.026390: Pseudo dice [0.7949, 0.0, 0.4792, 0.5037, 0.6061, 0.8075, 0.827] +2026-04-14 09:20:27.028869: Epoch time: 100.69 s +2026-04-14 09:20:28.303521: +2026-04-14 09:20:28.306922: Epoch 3236 +2026-04-14 09:20:28.309176: Current learning rate: 0.00225 +2026-04-14 09:22:09.035288: train_loss -0.4656 +2026-04-14 09:22:09.040061: val_loss -0.4226 +2026-04-14 09:22:09.041970: Pseudo dice [0.6923, 0.0, 0.7317, 0.0, 0.5347, 0.8005, 0.7584] +2026-04-14 09:22:09.043984: Epoch time: 100.73 s +2026-04-14 09:22:10.325735: +2026-04-14 09:22:10.328230: Epoch 3237 +2026-04-14 09:22:10.330325: Current learning rate: 0.00225 +2026-04-14 09:23:50.624448: train_loss -0.442 +2026-04-14 09:23:50.631295: val_loss -0.3621 +2026-04-14 09:23:50.634190: Pseudo dice [0.6277, 0.0, 0.2643, 0.1428, 0.3104, 0.6895, 0.7148] +2026-04-14 09:23:50.637392: Epoch time: 100.3 s +2026-04-14 09:23:51.903618: +2026-04-14 09:23:51.905873: Epoch 3238 +2026-04-14 09:23:51.908252: Current learning rate: 0.00225 +2026-04-14 09:25:32.626028: train_loss -0.4395 +2026-04-14 09:25:32.631482: val_loss -0.392 +2026-04-14 09:25:32.633507: Pseudo dice [0.4935, 0.0, 0.6738, 0.236, 0.2703, 0.7701, 0.7961] +2026-04-14 09:25:32.635954: Epoch time: 100.73 s +2026-04-14 09:25:33.948540: +2026-04-14 09:25:33.950613: Epoch 3239 +2026-04-14 09:25:33.953005: Current learning rate: 0.00225 +2026-04-14 09:27:14.330942: train_loss -0.446 +2026-04-14 09:27:14.336371: val_loss -0.4205 +2026-04-14 09:27:14.338949: Pseudo dice [0.655, 0.0, 0.7961, 0.0322, 0.2858, 0.7811, 0.743] +2026-04-14 09:27:14.341275: Epoch time: 100.39 s +2026-04-14 09:27:15.580438: +2026-04-14 09:27:15.582417: Epoch 3240 +2026-04-14 09:27:15.584188: Current learning rate: 0.00224 +2026-04-14 09:28:56.082357: train_loss -0.4441 +2026-04-14 09:28:56.088084: val_loss -0.4097 +2026-04-14 09:28:56.089867: Pseudo dice [0.7041, 0.0, 0.7628, 0.7876, 0.2093, 0.786, 0.8912] +2026-04-14 09:28:56.094297: Epoch time: 100.5 s +2026-04-14 09:28:57.330719: +2026-04-14 09:28:57.332636: Epoch 3241 +2026-04-14 09:28:57.334329: Current learning rate: 0.00224 +2026-04-14 09:30:37.694545: train_loss -0.4434 +2026-04-14 09:30:37.700663: val_loss -0.3795 +2026-04-14 09:30:37.702993: Pseudo dice [0.1602, 0.0, 0.6801, 0.6627, 0.3391, 0.6698, 0.5295] +2026-04-14 09:30:37.705348: Epoch time: 100.37 s +2026-04-14 09:30:38.956147: +2026-04-14 09:30:38.959167: Epoch 3242 +2026-04-14 09:30:38.962079: Current learning rate: 0.00224 +2026-04-14 09:32:19.184708: train_loss -0.4365 +2026-04-14 09:32:19.191354: val_loss -0.4445 +2026-04-14 09:32:19.193474: Pseudo dice [0.4511, 0.0, 0.7604, 0.0, 0.6481, 0.8509, 0.86] +2026-04-14 09:32:19.196490: Epoch time: 100.23 s +2026-04-14 09:32:20.445627: +2026-04-14 09:32:20.448404: Epoch 3243 +2026-04-14 09:32:20.450990: Current learning rate: 0.00224 +2026-04-14 09:34:00.776386: train_loss -0.4496 +2026-04-14 09:34:00.783223: val_loss -0.4324 +2026-04-14 09:34:00.785842: Pseudo dice [0.7272, 0.0, 0.7747, 0.8509, 0.5972, 0.7132, 0.8793] +2026-04-14 09:34:00.789381: Epoch time: 100.33 s +2026-04-14 09:34:02.040359: +2026-04-14 09:34:02.042799: Epoch 3244 +2026-04-14 09:34:02.045103: Current learning rate: 0.00223 +2026-04-14 09:35:42.637933: train_loss -0.451 +2026-04-14 09:35:42.645120: val_loss -0.3577 +2026-04-14 09:35:42.647722: Pseudo dice [0.4935, 0.0, 0.6978, 0.0, 0.4311, 0.7315, 0.8643] +2026-04-14 09:35:42.651058: Epoch time: 100.6 s +2026-04-14 09:35:43.977416: +2026-04-14 09:35:43.979638: Epoch 3245 +2026-04-14 09:35:43.981617: Current learning rate: 0.00223 +2026-04-14 09:37:24.738693: train_loss -0.4592 +2026-04-14 09:37:24.747613: val_loss -0.4498 +2026-04-14 09:37:24.751518: Pseudo dice [0.2638, 0.0, 0.7539, 0.8112, 0.5622, 0.7865, 0.8747] +2026-04-14 09:37:24.754418: Epoch time: 100.76 s +2026-04-14 09:37:25.996491: +2026-04-14 09:37:25.998410: Epoch 3246 +2026-04-14 09:37:26.000173: Current learning rate: 0.00223 +2026-04-14 09:39:06.561553: train_loss -0.4472 +2026-04-14 09:39:06.569213: val_loss -0.3133 +2026-04-14 09:39:06.571505: Pseudo dice [0.6597, 0.0, 0.8456, 0.003, 0.6566, 0.8758, 0.8526] +2026-04-14 09:39:06.574049: Epoch time: 100.57 s +2026-04-14 09:39:07.850811: +2026-04-14 09:39:07.853242: Epoch 3247 +2026-04-14 09:39:07.855669: Current learning rate: 0.00222 +2026-04-14 09:40:48.119254: train_loss -0.4555 +2026-04-14 09:40:48.124781: val_loss -0.4029 +2026-04-14 09:40:48.126787: Pseudo dice [0.7843, 0.0, 0.6752, 0.5169, 0.3701, 0.8684, 0.8398] +2026-04-14 09:40:48.129121: Epoch time: 100.27 s +2026-04-14 09:40:49.344866: +2026-04-14 09:40:49.346977: Epoch 3248 +2026-04-14 09:40:49.348890: Current learning rate: 0.00222 +2026-04-14 09:42:31.570821: train_loss -0.4595 +2026-04-14 09:42:31.577182: val_loss -0.3932 +2026-04-14 09:42:31.579124: Pseudo dice [0.7511, 0.0, 0.8064, 0.1192, 0.1896, 0.8303, 0.8279] +2026-04-14 09:42:31.582361: Epoch time: 102.23 s +2026-04-14 09:42:32.850483: +2026-04-14 09:42:32.852819: Epoch 3249 +2026-04-14 09:42:32.854985: Current learning rate: 0.00222 +2026-04-14 09:44:13.103446: train_loss -0.4603 +2026-04-14 09:44:13.108773: val_loss -0.3469 +2026-04-14 09:44:13.110676: Pseudo dice [0.0, 0.0, 0.7595, 0.022, 0.5643, 0.7637, 0.7724] +2026-04-14 09:44:13.113301: Epoch time: 100.26 s +2026-04-14 09:44:16.113471: +2026-04-14 09:44:16.116256: Epoch 3250 +2026-04-14 09:44:16.118286: Current learning rate: 0.00222 +2026-04-14 09:45:56.429905: train_loss -0.4646 +2026-04-14 09:45:56.435979: val_loss -0.353 +2026-04-14 09:45:56.438355: Pseudo dice [0.5493, 0.0, 0.7388, 0.1583, 0.3095, 0.8847, 0.761] +2026-04-14 09:45:56.442132: Epoch time: 100.32 s +2026-04-14 09:45:57.709877: +2026-04-14 09:45:57.713836: Epoch 3251 +2026-04-14 09:45:57.715784: Current learning rate: 0.00221 +2026-04-14 09:47:38.253033: train_loss -0.4685 +2026-04-14 09:47:38.262042: val_loss -0.4235 +2026-04-14 09:47:38.265701: Pseudo dice [0.8068, 0.0, 0.7607, 0.7311, 0.3894, 0.7496, 0.8352] +2026-04-14 09:47:38.269311: Epoch time: 100.55 s +2026-04-14 09:47:39.551324: +2026-04-14 09:47:39.553356: Epoch 3252 +2026-04-14 09:47:39.555256: Current learning rate: 0.00221 +2026-04-14 09:49:20.024589: train_loss -0.4564 +2026-04-14 09:49:20.030663: val_loss -0.3259 +2026-04-14 09:49:20.032786: Pseudo dice [0.6581, 0.0, 0.7112, 0.0674, 0.5033, 0.8009, 0.857] +2026-04-14 09:49:20.035374: Epoch time: 100.48 s +2026-04-14 09:49:21.282131: +2026-04-14 09:49:21.284533: Epoch 3253 +2026-04-14 09:49:21.286516: Current learning rate: 0.00221 +2026-04-14 09:51:01.570560: train_loss -0.4599 +2026-04-14 09:51:01.577137: val_loss -0.4184 +2026-04-14 09:51:01.579457: Pseudo dice [0.7383, 0.0, 0.7233, 0.1084, 0.5563, 0.858, 0.8471] +2026-04-14 09:51:01.581513: Epoch time: 100.29 s +2026-04-14 09:51:02.805685: +2026-04-14 09:51:02.807817: Epoch 3254 +2026-04-14 09:51:02.809838: Current learning rate: 0.00221 +2026-04-14 09:52:43.383398: train_loss -0.4633 +2026-04-14 09:52:43.389765: val_loss -0.2376 +2026-04-14 09:52:43.392425: Pseudo dice [0.6986, 0.0, 0.6635, 0.0111, 0.3582, 0.7434, 0.8719] +2026-04-14 09:52:43.395131: Epoch time: 100.58 s +2026-04-14 09:52:44.655738: +2026-04-14 09:52:44.658303: Epoch 3255 +2026-04-14 09:52:44.661047: Current learning rate: 0.0022 +2026-04-14 09:54:25.323345: train_loss -0.4568 +2026-04-14 09:54:25.331913: val_loss -0.4138 +2026-04-14 09:54:25.334149: Pseudo dice [0.7269, 0.0, 0.6182, 0.5517, 0.5469, 0.8372, 0.7173] +2026-04-14 09:54:25.336225: Epoch time: 100.67 s +2026-04-14 09:54:26.594090: +2026-04-14 09:54:26.596045: Epoch 3256 +2026-04-14 09:54:26.597642: Current learning rate: 0.0022 +2026-04-14 09:56:07.014588: train_loss -0.4584 +2026-04-14 09:56:07.019719: val_loss -0.3957 +2026-04-14 09:56:07.021433: Pseudo dice [0.1277, 0.0, 0.6919, 0.0, 0.4202, 0.7237, 0.6626] +2026-04-14 09:56:07.025064: Epoch time: 100.42 s +2026-04-14 09:56:08.275002: +2026-04-14 09:56:08.277223: Epoch 3257 +2026-04-14 09:56:08.279189: Current learning rate: 0.0022 +2026-04-14 09:57:48.645420: train_loss -0.4535 +2026-04-14 09:57:48.651171: val_loss -0.3889 +2026-04-14 09:57:48.653007: Pseudo dice [0.5135, 0.0, 0.675, 0.0064, 0.6067, 0.8199, 0.8893] +2026-04-14 09:57:48.655323: Epoch time: 100.37 s +2026-04-14 09:57:49.909959: +2026-04-14 09:57:49.911865: Epoch 3258 +2026-04-14 09:57:49.913666: Current learning rate: 0.0022 +2026-04-14 09:59:30.340453: train_loss -0.4584 +2026-04-14 09:59:30.346710: val_loss -0.3799 +2026-04-14 09:59:30.349649: Pseudo dice [0.7869, 0.0, 0.6076, 0.3854, 0.3983, 0.7633, 0.535] +2026-04-14 09:59:30.352731: Epoch time: 100.43 s +2026-04-14 09:59:31.597351: +2026-04-14 09:59:31.599377: Epoch 3259 +2026-04-14 09:59:31.601426: Current learning rate: 0.00219 +2026-04-14 10:01:12.021286: train_loss -0.4538 +2026-04-14 10:01:12.028684: val_loss -0.4513 +2026-04-14 10:01:12.031265: Pseudo dice [0.5337, 0.0, 0.6893, 0.8038, 0.6008, 0.7713, 0.8759] +2026-04-14 10:01:12.033761: Epoch time: 100.43 s +2026-04-14 10:01:13.275179: +2026-04-14 10:01:13.277367: Epoch 3260 +2026-04-14 10:01:13.279388: Current learning rate: 0.00219 +2026-04-14 10:02:53.548602: train_loss -0.4591 +2026-04-14 10:02:53.555089: val_loss -0.2193 +2026-04-14 10:02:53.556905: Pseudo dice [0.3669, 0.0, 0.5557, 0.0203, 0.2532, 0.8522, 0.6517] +2026-04-14 10:02:53.559931: Epoch time: 100.28 s +2026-04-14 10:02:54.803176: +2026-04-14 10:02:54.805153: Epoch 3261 +2026-04-14 10:02:54.806926: Current learning rate: 0.00219 +2026-04-14 10:04:35.170462: train_loss -0.4451 +2026-04-14 10:04:35.176842: val_loss -0.3948 +2026-04-14 10:04:35.178703: Pseudo dice [0.615, 0.0, 0.7002, 0.2671, 0.4144, 0.7988, 0.5946] +2026-04-14 10:04:35.181003: Epoch time: 100.37 s +2026-04-14 10:04:36.440618: +2026-04-14 10:04:36.443118: Epoch 3262 +2026-04-14 10:04:36.445079: Current learning rate: 0.00218 +2026-04-14 10:06:16.752832: train_loss -0.4325 +2026-04-14 10:06:16.759579: val_loss -0.4263 +2026-04-14 10:06:16.761958: Pseudo dice [0.1939, 0.0, 0.5783, 0.3228, 0.6167, 0.772, 0.6159] +2026-04-14 10:06:16.764349: Epoch time: 100.32 s +2026-04-14 10:06:18.022221: +2026-04-14 10:06:18.028525: Epoch 3263 +2026-04-14 10:06:18.030629: Current learning rate: 0.00218 +2026-04-14 10:07:58.326849: train_loss -0.4505 +2026-04-14 10:07:58.331916: val_loss -0.4279 +2026-04-14 10:07:58.333922: Pseudo dice [0.0342, 0.0, 0.8332, 0.0, 0.4641, 0.8195, 0.7164] +2026-04-14 10:07:58.338101: Epoch time: 100.31 s +2026-04-14 10:07:59.590404: +2026-04-14 10:07:59.592413: Epoch 3264 +2026-04-14 10:07:59.594190: Current learning rate: 0.00218 +2026-04-14 10:09:39.808547: train_loss -0.453 +2026-04-14 10:09:39.815077: val_loss -0.4228 +2026-04-14 10:09:39.817848: Pseudo dice [0.3389, 0.0, 0.6953, 0.8644, 0.4236, 0.7897, 0.792] +2026-04-14 10:09:39.821175: Epoch time: 100.22 s +2026-04-14 10:09:41.067606: +2026-04-14 10:09:41.071969: Epoch 3265 +2026-04-14 10:09:41.074028: Current learning rate: 0.00218 +2026-04-14 10:11:21.408424: train_loss -0.4546 +2026-04-14 10:11:21.416812: val_loss -0.3658 +2026-04-14 10:11:21.421387: Pseudo dice [0.6512, 0.0, 0.7219, 0.2071, 0.4231, 0.8729, 0.7176] +2026-04-14 10:11:21.425812: Epoch time: 100.34 s +2026-04-14 10:11:22.659801: +2026-04-14 10:11:22.661621: Epoch 3266 +2026-04-14 10:11:22.663487: Current learning rate: 0.00217 +2026-04-14 10:13:03.012346: train_loss -0.4421 +2026-04-14 10:13:03.022416: val_loss -0.3988 +2026-04-14 10:13:03.024739: Pseudo dice [0.2604, 0.0, 0.74, 0.0, 0.2579, 0.7474, 0.8819] +2026-04-14 10:13:03.027328: Epoch time: 100.36 s +2026-04-14 10:13:04.258156: +2026-04-14 10:13:04.260136: Epoch 3267 +2026-04-14 10:13:04.262029: Current learning rate: 0.00217 +2026-04-14 10:14:44.556980: train_loss -0.4482 +2026-04-14 10:14:44.563581: val_loss -0.3827 +2026-04-14 10:14:44.566034: Pseudo dice [0.408, 0.0, 0.7724, 0.0791, 0.3211, 0.7956, 0.8186] +2026-04-14 10:14:44.568255: Epoch time: 100.3 s +2026-04-14 10:14:46.861950: +2026-04-14 10:14:46.865140: Epoch 3268 +2026-04-14 10:14:46.866817: Current learning rate: 0.00217 +2026-04-14 10:16:27.206853: train_loss -0.4529 +2026-04-14 10:16:27.220288: val_loss -0.4387 +2026-04-14 10:16:27.224383: Pseudo dice [0.7741, 0.0, 0.7967, 0.0, 0.283, 0.7633, 0.8559] +2026-04-14 10:16:27.227291: Epoch time: 100.35 s +2026-04-14 10:16:28.461759: +2026-04-14 10:16:28.463868: Epoch 3269 +2026-04-14 10:16:28.465702: Current learning rate: 0.00217 +2026-04-14 10:18:09.130238: train_loss -0.4528 +2026-04-14 10:18:09.136886: val_loss -0.3342 +2026-04-14 10:18:09.139215: Pseudo dice [0.7037, 0.0, 0.4542, 0.0134, 0.5812, 0.9181, 0.5031] +2026-04-14 10:18:09.141227: Epoch time: 100.67 s +2026-04-14 10:18:10.416796: +2026-04-14 10:18:10.418675: Epoch 3270 +2026-04-14 10:18:10.420367: Current learning rate: 0.00216 +2026-04-14 10:22:58.382025: train_loss -0.4547 +2026-04-14 10:22:58.486943: val_loss -0.3927 +2026-04-14 10:22:58.551079: Pseudo dice [0.7866, 0.0, 0.525, 0.452, 0.5355, 0.6698, 0.7507] +2026-04-14 10:22:58.648340: Epoch time: 287.95 s +2026-04-14 10:23:13.684335: +2026-04-14 10:23:13.689234: Epoch 3271 +2026-04-14 10:23:13.691443: Current learning rate: 0.00216 +2026-04-14 10:27:03.868552: train_loss -0.4612 +2026-04-14 10:27:03.877857: val_loss -0.3441 +2026-04-14 10:27:03.880052: Pseudo dice [0.7129, 0.0, 0.7604, 0.0945, 0.2636, 0.7511, 0.8573] +2026-04-14 10:27:03.882746: Epoch time: 230.21 s +2026-04-14 10:27:05.130750: +2026-04-14 10:27:05.132607: Epoch 3272 +2026-04-14 10:27:05.134212: Current learning rate: 0.00216 +2026-04-14 10:28:46.514615: train_loss -0.4597 +2026-04-14 10:28:46.521430: val_loss -0.4099 +2026-04-14 10:28:46.523442: Pseudo dice [0.1772, 0.0, 0.6626, 0.0, 0.3493, 0.7285, 0.6609] +2026-04-14 10:28:46.526751: Epoch time: 101.39 s +2026-04-14 10:28:47.784821: +2026-04-14 10:28:47.788336: Epoch 3273 +2026-04-14 10:28:47.791164: Current learning rate: 0.00216 +2026-04-14 10:30:28.267922: train_loss -0.4524 +2026-04-14 10:30:28.275031: val_loss -0.3846 +2026-04-14 10:30:28.277170: Pseudo dice [0.7115, 0.0, 0.5025, 0.1727, 0.6662, 0.7585, 0.6855] +2026-04-14 10:30:28.279333: Epoch time: 100.49 s +2026-04-14 10:30:29.549140: +2026-04-14 10:30:29.550942: Epoch 3274 +2026-04-14 10:30:29.552504: Current learning rate: 0.00215 +2026-04-14 10:32:09.938570: train_loss -0.4501 +2026-04-14 10:32:09.946333: val_loss -0.3097 +2026-04-14 10:32:09.948674: Pseudo dice [0.0, 0.0, 0.6151, 0.0238, 0.365, 0.5818, 0.8039] +2026-04-14 10:32:09.951207: Epoch time: 100.39 s +2026-04-14 10:32:11.237364: +2026-04-14 10:32:11.239197: Epoch 3275 +2026-04-14 10:32:11.240771: Current learning rate: 0.00215 +2026-04-14 10:33:51.539635: train_loss -0.4558 +2026-04-14 10:33:51.548314: val_loss -0.4008 +2026-04-14 10:33:51.550697: Pseudo dice [0.6557, 0.0, 0.4152, 0.0, 0.4687, 0.8705, 0.7951] +2026-04-14 10:33:51.552904: Epoch time: 100.31 s +2026-04-14 10:33:52.798212: +2026-04-14 10:33:52.800446: Epoch 3276 +2026-04-14 10:33:52.802218: Current learning rate: 0.00215 +2026-04-14 10:35:33.174412: train_loss -0.4584 +2026-04-14 10:35:33.182923: val_loss -0.4288 +2026-04-14 10:35:33.186094: Pseudo dice [0.7713, 0.0, 0.8896, 0.4726, 0.5086, 0.815, 0.7446] +2026-04-14 10:35:33.189514: Epoch time: 100.38 s +2026-04-14 10:35:34.478585: +2026-04-14 10:35:34.483986: Epoch 3277 +2026-04-14 10:35:34.486657: Current learning rate: 0.00214 +2026-04-14 10:43:38.036704: train_loss -0.4618 +2026-04-14 10:43:38.044496: val_loss -0.4072 +2026-04-14 10:43:38.046648: Pseudo dice [0.7723, 0.0, 0.5436, 0.0, 0.4109, 0.8565, 0.8841] +2026-04-14 10:43:38.049407: Epoch time: 483.56 s +2026-04-14 10:43:47.102810: +2026-04-14 10:43:47.105890: Epoch 3278 +2026-04-14 10:43:47.109502: Current learning rate: 0.00214 +2026-04-14 10:55:44.282657: train_loss -0.4496 +2026-04-14 10:55:44.288257: val_loss -0.3672 +2026-04-14 10:55:44.290403: Pseudo dice [0.8106, 0.0, 0.5905, 0.1576, 0.4285, 0.6422, 0.5181] +2026-04-14 10:55:44.293076: Epoch time: 717.23 s +2026-04-14 10:55:45.579860: +2026-04-14 10:55:45.581614: Epoch 3279 +2026-04-14 10:55:45.583843: Current learning rate: 0.00214 +2026-04-14 10:57:25.597923: train_loss -0.4712 +2026-04-14 10:57:25.603123: val_loss -0.4361 +2026-04-14 10:57:25.605052: Pseudo dice [0.616, 0.0, 0.8411, 0.4944, 0.4104, 0.7172, 0.8218] +2026-04-14 10:57:25.607296: Epoch time: 100.02 s +2026-04-14 10:57:26.859572: +2026-04-14 10:57:26.861495: Epoch 3280 +2026-04-14 10:57:26.863493: Current learning rate: 0.00214 +2026-04-14 10:59:07.240721: train_loss -0.4612 +2026-04-14 10:59:07.248462: val_loss -0.4254 +2026-04-14 10:59:07.251016: Pseudo dice [0.8386, 0.0, 0.7145, 0.0, 0.3641, 0.5741, 0.6216] +2026-04-14 10:59:07.253976: Epoch time: 100.38 s +2026-04-14 10:59:08.517020: +2026-04-14 10:59:08.520201: Epoch 3281 +2026-04-14 10:59:08.522211: Current learning rate: 0.00213 +2026-04-14 11:01:23.372014: train_loss -0.4563 +2026-04-14 11:01:23.379413: val_loss -0.3183 +2026-04-14 11:01:23.381695: Pseudo dice [0.5409, 0.0, 0.7039, 0.0547, 0.307, 0.7263, 0.817] +2026-04-14 11:01:23.384709: Epoch time: 134.86 s +2026-04-14 11:01:31.058125: +2026-04-14 11:01:31.061856: Epoch 3282 +2026-04-14 11:01:31.064271: Current learning rate: 0.00213 +2026-04-14 11:12:30.332228: train_loss -0.4651 +2026-04-14 11:12:30.338741: val_loss -0.4006 +2026-04-14 11:12:30.340812: Pseudo dice [0.4999, 0.0, 0.7792, 0.1935, 0.6479, 0.8189, 0.6601] +2026-04-14 11:12:30.342699: Epoch time: 659.29 s +2026-04-14 11:12:31.593874: +2026-04-14 11:12:31.595985: Epoch 3283 +2026-04-14 11:12:31.598588: Current learning rate: 0.00213 +2026-04-14 11:14:12.160897: train_loss -0.4639 +2026-04-14 11:14:12.169175: val_loss -0.3969 +2026-04-14 11:14:12.171401: Pseudo dice [0.7558, 0.0, 0.7342, 0.0, 0.5614, 0.6734, 0.8796] +2026-04-14 11:14:12.174332: Epoch time: 100.57 s +2026-04-14 11:14:13.479111: +2026-04-14 11:14:13.481902: Epoch 3284 +2026-04-14 11:14:13.485350: Current learning rate: 0.00213 +2026-04-14 11:15:53.861573: train_loss -0.4429 +2026-04-14 11:15:53.866837: val_loss -0.421 +2026-04-14 11:15:53.868814: Pseudo dice [0.178, 0.0, 0.7713, 0.4375, 0.3798, 0.6076, 0.8562] +2026-04-14 11:15:53.871412: Epoch time: 100.39 s +2026-04-14 11:15:55.143433: +2026-04-14 11:15:55.145416: Epoch 3285 +2026-04-14 11:15:55.147365: Current learning rate: 0.00212 +2026-04-14 11:17:35.513990: train_loss -0.456 +2026-04-14 11:17:35.520677: val_loss -0.3953 +2026-04-14 11:17:35.523861: Pseudo dice [0.3267, 0.0, 0.7402, 0.0, 0.1354, 0.8804, 0.6192] +2026-04-14 11:17:35.526536: Epoch time: 100.37 s +2026-04-14 11:17:36.767375: +2026-04-14 11:17:36.769370: Epoch 3286 +2026-04-14 11:17:36.771055: Current learning rate: 0.00212 +2026-04-14 11:19:17.240722: train_loss -0.4522 +2026-04-14 11:19:17.246393: val_loss -0.3973 +2026-04-14 11:19:17.248645: Pseudo dice [0.8233, 0.0, 0.6805, 0.0975, 0.3309, 0.8272, 0.8114] +2026-04-14 11:19:17.250886: Epoch time: 100.48 s +2026-04-14 11:19:19.547608: +2026-04-14 11:19:19.549611: Epoch 3287 +2026-04-14 11:19:19.551250: Current learning rate: 0.00212 +2026-04-14 11:21:00.015298: train_loss -0.4533 +2026-04-14 11:21:00.020629: val_loss -0.4254 +2026-04-14 11:21:00.023169: Pseudo dice [0.5286, 0.0, 0.7124, 0.0, 0.2907, 0.6169, 0.7337] +2026-04-14 11:21:00.026835: Epoch time: 100.47 s +2026-04-14 11:21:01.282403: +2026-04-14 11:21:01.284425: Epoch 3288 +2026-04-14 11:21:01.286408: Current learning rate: 0.00212 +2026-04-14 11:22:42.392728: train_loss -0.4291 +2026-04-14 11:22:42.400385: val_loss -0.398 +2026-04-14 11:22:42.402637: Pseudo dice [0.2827, 0.0, 0.7704, 0.4065, 0.3566, 0.6182, 0.6525] +2026-04-14 11:22:42.406005: Epoch time: 101.11 s +2026-04-14 11:22:43.649328: +2026-04-14 11:22:43.651426: Epoch 3289 +2026-04-14 11:22:43.653377: Current learning rate: 0.00211 +2026-04-14 11:24:23.982020: train_loss -0.4529 +2026-04-14 11:24:23.987908: val_loss -0.4419 +2026-04-14 11:24:23.989974: Pseudo dice [0.8697, 0.0, 0.825, 0.0, 0.4157, 0.8586, 0.8374] +2026-04-14 11:24:23.992405: Epoch time: 100.34 s +2026-04-14 11:24:25.233797: +2026-04-14 11:24:25.235506: Epoch 3290 +2026-04-14 11:24:25.237171: Current learning rate: 0.00211 +2026-04-14 11:26:05.571682: train_loss -0.4694 +2026-04-14 11:26:05.577319: val_loss -0.4187 +2026-04-14 11:26:05.579800: Pseudo dice [0.805, 0.0, 0.6666, 0.2263, 0.3719, 0.8251, 0.8476] +2026-04-14 11:26:05.582580: Epoch time: 100.34 s +2026-04-14 11:26:06.866022: +2026-04-14 11:26:06.868577: Epoch 3291 +2026-04-14 11:26:06.870467: Current learning rate: 0.00211 +2026-04-14 11:27:47.221142: train_loss -0.4576 +2026-04-14 11:27:47.226459: val_loss -0.3989 +2026-04-14 11:27:47.228643: Pseudo dice [0.6681, 0.0, 0.8485, 0.0, 0.4831, 0.758, 0.2226] +2026-04-14 11:27:47.231285: Epoch time: 100.36 s +2026-04-14 11:27:48.481487: +2026-04-14 11:27:48.483674: Epoch 3292 +2026-04-14 11:27:48.485664: Current learning rate: 0.0021 +2026-04-14 11:29:28.900373: train_loss -0.453 +2026-04-14 11:29:28.908047: val_loss -0.3766 +2026-04-14 11:29:28.911384: Pseudo dice [0.7528, 0.0, 0.842, 0.2482, 0.7069, 0.6508, 0.5787] +2026-04-14 11:29:28.915540: Epoch time: 100.42 s +2026-04-14 11:29:30.170291: +2026-04-14 11:29:30.172355: Epoch 3293 +2026-04-14 11:29:30.174196: Current learning rate: 0.0021 +2026-04-14 11:31:10.422835: train_loss -0.4582 +2026-04-14 11:31:10.430015: val_loss -0.3788 +2026-04-14 11:31:10.433114: Pseudo dice [0.8324, 0.0, 0.4852, 0.1039, 0.1247, 0.8189, 0.7281] +2026-04-14 11:31:10.435520: Epoch time: 100.26 s +2026-04-14 11:31:11.675620: +2026-04-14 11:31:11.677461: Epoch 3294 +2026-04-14 11:31:11.679009: Current learning rate: 0.0021 +2026-04-14 11:32:52.429798: train_loss -0.4562 +2026-04-14 11:32:52.434914: val_loss -0.394 +2026-04-14 11:32:52.436808: Pseudo dice [0.7416, 0.0, 0.6791, 0.3294, 0.5078, 0.5742, 0.8046] +2026-04-14 11:32:52.439017: Epoch time: 100.76 s +2026-04-14 11:32:53.714156: +2026-04-14 11:32:53.715980: Epoch 3295 +2026-04-14 11:32:53.717566: Current learning rate: 0.0021 +2026-04-14 11:34:34.022129: train_loss -0.4363 +2026-04-14 11:34:34.027655: val_loss -0.4055 +2026-04-14 11:34:34.029693: Pseudo dice [0.7863, 0.0, 0.6406, 0.6674, 0.4607, 0.6101, 0.7053] +2026-04-14 11:34:34.031904: Epoch time: 100.31 s +2026-04-14 11:34:35.305589: +2026-04-14 11:34:35.308184: Epoch 3296 +2026-04-14 11:34:35.311510: Current learning rate: 0.00209 +2026-04-14 11:36:15.671937: train_loss -0.4333 +2026-04-14 11:36:15.677766: val_loss -0.4038 +2026-04-14 11:36:15.679648: Pseudo dice [0.7161, 0.0, 0.8408, 0.1997, 0.1086, 0.4494, 0.8907] +2026-04-14 11:36:15.682393: Epoch time: 100.37 s +2026-04-14 11:36:16.929101: +2026-04-14 11:36:16.931069: Epoch 3297 +2026-04-14 11:36:16.932912: Current learning rate: 0.00209 +2026-04-14 11:37:57.217961: train_loss -0.458 +2026-04-14 11:37:57.222616: val_loss -0.3948 +2026-04-14 11:37:57.224691: Pseudo dice [0.0421, 0.0, 0.816, 0.2156, 0.3833, 0.8465, 0.7523] +2026-04-14 11:37:57.227868: Epoch time: 100.29 s +2026-04-14 11:37:58.477376: +2026-04-14 11:37:58.479097: Epoch 3298 +2026-04-14 11:37:58.480806: Current learning rate: 0.00209 +2026-04-14 11:39:38.862754: train_loss -0.4512 +2026-04-14 11:39:38.868099: val_loss -0.4242 +2026-04-14 11:39:38.869890: Pseudo dice [0.4862, 0.0, 0.8285, 0.8474, 0.4936, 0.8457, 0.7301] +2026-04-14 11:39:38.872166: Epoch time: 100.39 s +2026-04-14 11:39:40.116003: +2026-04-14 11:39:40.118749: Epoch 3299 +2026-04-14 11:39:40.121283: Current learning rate: 0.00209 +2026-04-14 11:45:38.424622: train_loss -0.4548 +2026-04-14 11:45:38.434295: val_loss -0.4143 +2026-04-14 11:45:38.437706: Pseudo dice [0.463, 0.0, 0.713, 0.4477, 0.411, 0.847, 0.8491] +2026-04-14 11:45:38.441258: Epoch time: 358.31 s +2026-04-14 11:45:51.855118: +2026-04-14 11:45:51.859574: Epoch 3300 +2026-04-14 11:45:51.862993: Current learning rate: 0.00208 +2026-04-14 11:48:42.059087: train_loss -0.4506 +2026-04-14 11:48:42.064977: val_loss -0.4361 +2026-04-14 11:48:42.067475: Pseudo dice [0.7621, 0.0, 0.7827, 0.6732, 0.3442, 0.7751, 0.828] +2026-04-14 11:48:42.072180: Epoch time: 170.24 s +2026-04-14 11:48:43.321513: +2026-04-14 11:48:43.323331: Epoch 3301 +2026-04-14 11:48:43.325171: Current learning rate: 0.00208 +2026-04-14 11:50:23.601194: train_loss -0.4422 +2026-04-14 11:50:23.608215: val_loss -0.4249 +2026-04-14 11:50:23.610459: Pseudo dice [0.6323, 0.0, 0.6509, 0.5123, 0.0582, 0.772, 0.8161] +2026-04-14 11:50:23.613696: Epoch time: 100.28 s +2026-04-14 11:50:24.899152: +2026-04-14 11:50:24.900991: Epoch 3302 +2026-04-14 11:50:24.902701: Current learning rate: 0.00208 +2026-04-14 11:52:05.229214: train_loss -0.4446 +2026-04-14 11:52:05.238665: val_loss -0.42 +2026-04-14 11:52:05.240844: Pseudo dice [0.1765, 0.0, 0.806, 0.5815, 0.3453, 0.7169, 0.7488] +2026-04-14 11:52:05.243715: Epoch time: 100.33 s +2026-04-14 11:52:06.523466: +2026-04-14 11:52:06.526471: Epoch 3303 +2026-04-14 11:52:06.528368: Current learning rate: 0.00208 +2026-04-14 11:53:49.723736: train_loss -0.4656 +2026-04-14 11:53:49.738608: val_loss -0.2797 +2026-04-14 11:53:49.742066: Pseudo dice [0.6998, 0.0, 0.7098, 0.0451, 0.4609, 0.8228, 0.6724] +2026-04-14 11:53:49.745048: Epoch time: 103.2 s +2026-04-14 11:53:51.037256: +2026-04-14 11:53:51.039119: Epoch 3304 +2026-04-14 11:53:51.040977: Current learning rate: 0.00207 +2026-04-14 11:55:31.438755: train_loss -0.4564 +2026-04-14 11:55:31.446597: val_loss -0.4058 +2026-04-14 11:55:31.449733: Pseudo dice [0.7881, 0.0, 0.7308, 0.3284, 0.4901, 0.7805, 0.68] +2026-04-14 11:55:31.452919: Epoch time: 100.4 s +2026-04-14 11:55:32.720596: +2026-04-14 11:55:32.722892: Epoch 3305 +2026-04-14 11:55:32.725259: Current learning rate: 0.00207 +2026-04-14 11:57:13.056392: train_loss -0.4682 +2026-04-14 11:57:13.064273: val_loss -0.3967 +2026-04-14 11:57:13.066868: Pseudo dice [0.7908, 0.0, 0.754, 0.0, 0.2821, 0.7565, 0.7221] +2026-04-14 11:57:13.069448: Epoch time: 100.34 s +2026-04-14 11:57:14.337095: +2026-04-14 11:57:14.339645: Epoch 3306 +2026-04-14 11:57:14.342316: Current learning rate: 0.00207 +2026-04-14 11:58:56.286907: train_loss -0.4639 +2026-04-14 11:58:56.299499: val_loss -0.3333 +2026-04-14 11:58:56.302229: Pseudo dice [0.7959, 0.0, 0.7604, 0.0, 0.3589, 0.6762, 0.4138] +2026-04-14 11:58:56.304456: Epoch time: 101.95 s +2026-04-14 11:58:57.577322: +2026-04-14 11:58:57.579210: Epoch 3307 +2026-04-14 11:58:57.581458: Current learning rate: 0.00206 +2026-04-14 12:00:40.377694: train_loss -0.4393 +2026-04-14 12:00:40.389778: val_loss -0.4177 +2026-04-14 12:00:40.392349: Pseudo dice [0.7246, 0.0, 0.6209, 0.4887, 0.3594, 0.718, 0.8284] +2026-04-14 12:00:40.394857: Epoch time: 102.8 s +2026-04-14 12:00:41.671391: +2026-04-14 12:00:41.673304: Epoch 3308 +2026-04-14 12:00:41.675329: Current learning rate: 0.00206 +2026-04-14 12:02:22.203108: train_loss -0.4532 +2026-04-14 12:02:22.210461: val_loss -0.3981 +2026-04-14 12:02:22.213068: Pseudo dice [0.5777, 0.0, 0.6858, 0.0, 0.2907, 0.8366, 0.7523] +2026-04-14 12:02:22.215881: Epoch time: 100.53 s +2026-04-14 12:02:23.484611: +2026-04-14 12:02:23.486520: Epoch 3309 +2026-04-14 12:02:23.488419: Current learning rate: 0.00206 +2026-04-14 12:04:04.082390: train_loss -0.4301 +2026-04-14 12:04:04.090982: val_loss -0.3935 +2026-04-14 12:04:04.093916: Pseudo dice [0.6881, 0.0, 0.6255, 0.0, 0.4738, 0.8059, 0.8441] +2026-04-14 12:04:04.096519: Epoch time: 100.6 s +2026-04-14 12:04:05.406823: +2026-04-14 12:04:05.409250: Epoch 3310 +2026-04-14 12:04:05.410817: Current learning rate: 0.00206 +2026-04-14 12:05:45.892846: train_loss -0.4466 +2026-04-14 12:05:45.901167: val_loss -0.3928 +2026-04-14 12:05:45.903562: Pseudo dice [0.6781, 0.0, 0.7271, 0.6029, 0.4288, 0.6888, 0.6259] +2026-04-14 12:05:45.906137: Epoch time: 100.49 s +2026-04-14 12:05:47.167148: +2026-04-14 12:05:47.169010: Epoch 3311 +2026-04-14 12:05:47.170888: Current learning rate: 0.00205 +2026-04-14 12:07:27.635870: train_loss -0.4618 +2026-04-14 12:07:27.643115: val_loss -0.385 +2026-04-14 12:07:27.645502: Pseudo dice [0.1414, 0.0, 0.715, 0.2488, 0.3487, 0.8157, 0.7079] +2026-04-14 12:07:27.647581: Epoch time: 100.47 s +2026-04-14 12:07:28.927645: +2026-04-14 12:07:28.930006: Epoch 3312 +2026-04-14 12:07:28.931878: Current learning rate: 0.00205 +2026-04-14 12:09:09.251196: train_loss -0.4487 +2026-04-14 12:09:09.267789: val_loss -0.4302 +2026-04-14 12:09:09.269831: Pseudo dice [0.0, 0.0, 0.7898, 0.0, 0.4292, 0.63, 0.8429] +2026-04-14 12:09:09.272564: Epoch time: 100.33 s +2026-04-14 12:09:10.560037: +2026-04-14 12:09:10.562320: Epoch 3313 +2026-04-14 12:09:10.566914: Current learning rate: 0.00205 +2026-04-14 12:10:51.100461: train_loss -0.4569 +2026-04-14 12:10:51.110416: val_loss -0.4015 +2026-04-14 12:10:51.112412: Pseudo dice [0.7804, 0.0, 0.7086, 0.1262, 0.5539, 0.8123, 0.8642] +2026-04-14 12:10:51.115275: Epoch time: 100.54 s +2026-04-14 12:10:52.389654: +2026-04-14 12:10:52.391879: Epoch 3314 +2026-04-14 12:10:52.393637: Current learning rate: 0.00205 +2026-04-14 12:12:32.318431: train_loss -0.4639 +2026-04-14 12:12:32.326874: val_loss -0.4204 +2026-04-14 12:12:32.329658: Pseudo dice [0.1168, 0.0, 0.7254, 0.4755, 0.5978, 0.7378, 0.782] +2026-04-14 12:12:32.332484: Epoch time: 99.93 s +2026-04-14 12:12:33.584779: +2026-04-14 12:12:33.586883: Epoch 3315 +2026-04-14 12:12:33.588843: Current learning rate: 0.00204 +2026-04-14 12:14:13.581448: train_loss -0.4468 +2026-04-14 12:14:13.607899: val_loss -0.3408 +2026-04-14 12:14:13.609768: Pseudo dice [0.8351, 0.0, 0.639, 0.0481, 0.3399, 0.1428, 0.8365] +2026-04-14 12:14:13.611742: Epoch time: 100.0 s +2026-04-14 12:14:14.857729: +2026-04-14 12:14:14.860128: Epoch 3316 +2026-04-14 12:14:14.862719: Current learning rate: 0.00204 +2026-04-14 12:15:55.281286: train_loss -0.4546 +2026-04-14 12:15:55.290355: val_loss -0.4118 +2026-04-14 12:15:55.293253: Pseudo dice [0.4451, 0.0, 0.7833, 0.0, 0.4688, 0.7674, 0.7819] +2026-04-14 12:15:55.295539: Epoch time: 100.43 s +2026-04-14 12:15:56.573522: +2026-04-14 12:15:56.575651: Epoch 3317 +2026-04-14 12:15:56.577523: Current learning rate: 0.00204 +2026-04-14 12:17:36.684263: train_loss -0.4668 +2026-04-14 12:17:36.690921: val_loss -0.3969 +2026-04-14 12:17:36.692736: Pseudo dice [0.5285, 0.0, 0.782, 0.7293, 0.4173, 0.8234, 0.5742] +2026-04-14 12:17:36.695391: Epoch time: 100.11 s +2026-04-14 12:17:37.932221: +2026-04-14 12:17:37.934415: Epoch 3318 +2026-04-14 12:17:37.936227: Current learning rate: 0.00203 +2026-04-14 12:19:18.158350: train_loss -0.441 +2026-04-14 12:19:18.174811: val_loss -0.3709 +2026-04-14 12:19:18.177747: Pseudo dice [0.4828, 0.0, 0.6449, 0.0478, 0.2355, 0.7858, 0.8462] +2026-04-14 12:19:18.180758: Epoch time: 100.23 s +2026-04-14 12:19:19.470870: +2026-04-14 12:19:19.473234: Epoch 3319 +2026-04-14 12:19:19.475046: Current learning rate: 0.00203 +2026-04-14 12:20:59.569173: train_loss -0.454 +2026-04-14 12:20:59.592504: val_loss -0.4038 +2026-04-14 12:20:59.594988: Pseudo dice [0.6901, 0.0, 0.6794, 0.534, 0.2609, 0.8221, 0.8005] +2026-04-14 12:20:59.597078: Epoch time: 100.1 s +2026-04-14 12:21:00.836682: +2026-04-14 12:21:00.838822: Epoch 3320 +2026-04-14 12:21:00.840973: Current learning rate: 0.00203 +2026-04-14 12:22:41.011637: train_loss -0.4445 +2026-04-14 12:22:41.019850: val_loss -0.4186 +2026-04-14 12:22:41.021944: Pseudo dice [0.6451, 0.0, 0.6458, 0.5099, 0.4137, 0.7562, 0.7858] +2026-04-14 12:22:41.024349: Epoch time: 100.18 s +2026-04-14 12:22:42.276685: +2026-04-14 12:22:42.278633: Epoch 3321 +2026-04-14 12:22:42.280184: Current learning rate: 0.00203 +2026-04-14 12:24:22.588836: train_loss -0.4526 +2026-04-14 12:24:22.596872: val_loss -0.4165 +2026-04-14 12:24:22.599103: Pseudo dice [0.8167, 0.0, 0.7744, 0.121, 0.3138, 0.8439, 0.7384] +2026-04-14 12:24:22.601856: Epoch time: 100.32 s +2026-04-14 12:24:23.856373: +2026-04-14 12:24:23.859062: Epoch 3322 +2026-04-14 12:24:23.860734: Current learning rate: 0.00202 +2026-04-14 12:26:03.987816: train_loss -0.4534 +2026-04-14 12:26:03.995562: val_loss -0.4241 +2026-04-14 12:26:03.997993: Pseudo dice [0.5956, 0.0, 0.7178, 0.6363, 0.3444, 0.7063, 0.8162] +2026-04-14 12:26:04.000359: Epoch time: 100.13 s +2026-04-14 12:26:05.258810: +2026-04-14 12:26:05.260917: Epoch 3323 +2026-04-14 12:26:05.262730: Current learning rate: 0.00202 +2026-04-14 12:27:45.594470: train_loss -0.4489 +2026-04-14 12:27:45.601517: val_loss -0.3466 +2026-04-14 12:27:45.603613: Pseudo dice [0.6071, 0.0, 0.6826, 0.0778, 0.3467, 0.8388, 0.7714] +2026-04-14 12:27:45.605840: Epoch time: 100.34 s +2026-04-14 12:27:46.854287: +2026-04-14 12:27:46.856543: Epoch 3324 +2026-04-14 12:27:46.858214: Current learning rate: 0.00202 +2026-04-14 12:29:27.035894: train_loss -0.4498 +2026-04-14 12:29:27.041607: val_loss -0.4407 +2026-04-14 12:29:27.043898: Pseudo dice [0.5778, 0.0, 0.7936, 0.9188, 0.5252, 0.5318, 0.9138] +2026-04-14 12:29:27.046458: Epoch time: 100.18 s +2026-04-14 12:29:28.313270: +2026-04-14 12:29:28.315974: Epoch 3325 +2026-04-14 12:29:28.318165: Current learning rate: 0.00202 +2026-04-14 12:31:08.541512: train_loss -0.4643 +2026-04-14 12:31:08.546961: val_loss -0.4395 +2026-04-14 12:31:08.548977: Pseudo dice [0.7699, 0.0, 0.7592, 0.8112, 0.3231, 0.7829, 0.9058] +2026-04-14 12:31:08.551098: Epoch time: 100.23 s +2026-04-14 12:31:10.789583: +2026-04-14 12:31:10.792289: Epoch 3326 +2026-04-14 12:31:10.794557: Current learning rate: 0.00201 +2026-04-14 12:32:50.798701: train_loss -0.4466 +2026-04-14 12:32:50.805074: val_loss -0.4186 +2026-04-14 12:32:50.807430: Pseudo dice [0.3495, 0.0, 0.693, 0.0, 0.4518, 0.6237, 0.7974] +2026-04-14 12:32:50.810185: Epoch time: 100.01 s +2026-04-14 12:32:52.061377: +2026-04-14 12:32:52.064122: Epoch 3327 +2026-04-14 12:32:52.066071: Current learning rate: 0.00201 +2026-04-14 12:34:32.193026: train_loss -0.4535 +2026-04-14 12:34:32.198431: val_loss -0.4239 +2026-04-14 12:34:32.200730: Pseudo dice [0.6173, 0.0, 0.7881, 0.6645, 0.4894, 0.8005, 0.8319] +2026-04-14 12:34:32.203027: Epoch time: 100.13 s +2026-04-14 12:34:33.474370: +2026-04-14 12:34:33.476754: Epoch 3328 +2026-04-14 12:34:33.478492: Current learning rate: 0.00201 +2026-04-14 12:36:13.687445: train_loss -0.4672 +2026-04-14 12:36:13.693154: val_loss -0.4132 +2026-04-14 12:36:13.695150: Pseudo dice [0.7969, 0.0, 0.7786, 0.6982, 0.393, 0.7166, 0.8033] +2026-04-14 12:36:13.697173: Epoch time: 100.22 s +2026-04-14 12:36:14.954304: +2026-04-14 12:36:14.957084: Epoch 3329 +2026-04-14 12:36:14.959719: Current learning rate: 0.00201 +2026-04-14 12:37:55.315301: train_loss -0.4414 +2026-04-14 12:37:55.323841: val_loss -0.342 +2026-04-14 12:37:55.326479: Pseudo dice [0.3671, 0.0, 0.8088, 0.1146, 0.2377, 0.4203, 0.6509] +2026-04-14 12:37:55.328666: Epoch time: 100.36 s +2026-04-14 12:37:56.584064: +2026-04-14 12:37:56.586013: Epoch 3330 +2026-04-14 12:37:56.588167: Current learning rate: 0.002 +2026-04-14 12:39:36.955829: train_loss -0.4557 +2026-04-14 12:39:36.962496: val_loss -0.3764 +2026-04-14 12:39:36.965143: Pseudo dice [0.718, 0.0, 0.6233, 0.0, 0.3616, 0.8057, 0.5929] +2026-04-14 12:39:36.968048: Epoch time: 100.37 s +2026-04-14 12:39:38.198526: +2026-04-14 12:39:38.200758: Epoch 3331 +2026-04-14 12:39:38.202680: Current learning rate: 0.002 +2026-04-14 12:41:18.514964: train_loss -0.4617 +2026-04-14 12:41:18.522099: val_loss -0.4374 +2026-04-14 12:41:18.524633: Pseudo dice [0.7618, 0.0, 0.6489, 0.6232, 0.4329, 0.7649, 0.7243] +2026-04-14 12:41:18.527891: Epoch time: 100.32 s +2026-04-14 12:41:19.801378: +2026-04-14 12:41:19.803238: Epoch 3332 +2026-04-14 12:41:19.804993: Current learning rate: 0.002 +2026-04-14 12:43:00.085602: train_loss -0.4607 +2026-04-14 12:43:00.093541: val_loss -0.4188 +2026-04-14 12:43:00.096520: Pseudo dice [0.7456, 0.0, 0.6003, 0.9198, 0.3341, 0.6555, 0.8283] +2026-04-14 12:43:00.099476: Epoch time: 100.29 s +2026-04-14 12:43:01.357423: +2026-04-14 12:43:01.359514: Epoch 3333 +2026-04-14 12:43:01.361471: Current learning rate: 0.00199 +2026-04-14 12:44:41.605222: train_loss -0.4634 +2026-04-14 12:44:41.612160: val_loss -0.4394 +2026-04-14 12:44:41.616942: Pseudo dice [0.6842, 0.0, 0.7672, 0.878, 0.3323, 0.8374, 0.7799] +2026-04-14 12:44:41.620222: Epoch time: 100.25 s +2026-04-14 12:44:42.889121: +2026-04-14 12:44:42.891130: Epoch 3334 +2026-04-14 12:44:42.893074: Current learning rate: 0.00199 +2026-04-14 12:46:23.067372: train_loss -0.4653 +2026-04-14 12:46:23.073690: val_loss -0.3163 +2026-04-14 12:46:23.075682: Pseudo dice [0.2902, 0.0, 0.5841, 0.0386, 0.4845, 0.8554, 0.755] +2026-04-14 12:46:23.077807: Epoch time: 100.18 s +2026-04-14 12:46:24.361238: +2026-04-14 12:46:24.363350: Epoch 3335 +2026-04-14 12:46:24.365055: Current learning rate: 0.00199 +2026-04-14 12:48:04.595311: train_loss -0.4711 +2026-04-14 12:48:04.601415: val_loss -0.4276 +2026-04-14 12:48:04.603456: Pseudo dice [0.7387, 0.0, 0.7306, 0.0, 0.4197, 0.7807, 0.7213] +2026-04-14 12:48:04.605718: Epoch time: 100.24 s +2026-04-14 12:48:05.899733: +2026-04-14 12:48:05.901490: Epoch 3336 +2026-04-14 12:48:05.903179: Current learning rate: 0.00199 +2026-04-14 12:49:46.184570: train_loss -0.4587 +2026-04-14 12:49:46.191812: val_loss -0.4054 +2026-04-14 12:49:46.194532: Pseudo dice [0.3013, 0.0, 0.7759, 0.3064, 0.4673, 0.7661, 0.75] +2026-04-14 12:49:46.197123: Epoch time: 100.29 s +2026-04-14 12:49:47.456008: +2026-04-14 12:49:47.458060: Epoch 3337 +2026-04-14 12:49:47.459939: Current learning rate: 0.00198 +2026-04-14 12:51:27.991594: train_loss -0.4672 +2026-04-14 12:51:27.999135: val_loss -0.381 +2026-04-14 12:51:28.001507: Pseudo dice [0.3375, 0.0, 0.7348, 0.021, 0.2678, 0.7539, 0.885] +2026-04-14 12:51:28.003773: Epoch time: 100.54 s +2026-04-14 12:51:29.270543: +2026-04-14 12:51:29.273186: Epoch 3338 +2026-04-14 12:51:29.275074: Current learning rate: 0.00198 +2026-04-14 12:53:09.482917: train_loss -0.4619 +2026-04-14 12:53:09.492825: val_loss -0.436 +2026-04-14 12:53:09.494964: Pseudo dice [0.5194, 0.0, 0.7421, 0.86, 0.5509, 0.8543, 0.8132] +2026-04-14 12:53:09.497238: Epoch time: 100.22 s +2026-04-14 12:53:10.764935: +2026-04-14 12:53:10.767069: Epoch 3339 +2026-04-14 12:53:10.768920: Current learning rate: 0.00198 +2026-04-14 12:54:51.032140: train_loss -0.4557 +2026-04-14 12:54:51.038222: val_loss -0.4451 +2026-04-14 12:54:51.041739: Pseudo dice [0.5629, 0.0, 0.7566, 0.7946, 0.4516, 0.8586, 0.7882] +2026-04-14 12:54:51.044374: Epoch time: 100.27 s +2026-04-14 12:54:52.323343: +2026-04-14 12:54:52.325226: Epoch 3340 +2026-04-14 12:54:52.327255: Current learning rate: 0.00198 +2026-04-14 12:56:32.705712: train_loss -0.4595 +2026-04-14 12:56:32.712671: val_loss -0.4215 +2026-04-14 12:56:32.716731: Pseudo dice [0.7599, 0.0, 0.7191, 0.3777, 0.5676, 0.8522, 0.4992] +2026-04-14 12:56:32.719059: Epoch time: 100.39 s +2026-04-14 12:56:34.014168: +2026-04-14 12:56:34.016102: Epoch 3341 +2026-04-14 12:56:34.017902: Current learning rate: 0.00197 +2026-04-14 12:58:14.343804: train_loss -0.4467 +2026-04-14 12:58:14.351685: val_loss -0.4127 +2026-04-14 12:58:14.354060: Pseudo dice [0.5828, 0.0, 0.7553, 0.2843, 0.3011, 0.8275, 0.8503] +2026-04-14 12:58:14.356883: Epoch time: 100.33 s +2026-04-14 12:58:15.651673: +2026-04-14 12:58:15.654328: Epoch 3342 +2026-04-14 12:58:15.656265: Current learning rate: 0.00197 +2026-04-14 12:59:56.007436: train_loss -0.4601 +2026-04-14 12:59:56.013074: val_loss -0.4038 +2026-04-14 12:59:56.016865: Pseudo dice [0.7067, 0.0, 0.7284, 0.1538, 0.4771, 0.7659, 0.7738] +2026-04-14 12:59:56.019629: Epoch time: 100.36 s +2026-04-14 12:59:57.270441: +2026-04-14 12:59:57.272194: Epoch 3343 +2026-04-14 12:59:57.273821: Current learning rate: 0.00197 +2026-04-14 13:01:37.491555: train_loss -0.4295 +2026-04-14 13:01:37.498334: val_loss -0.4105 +2026-04-14 13:01:37.500978: Pseudo dice [0.4595, 0.0, 0.6988, 0.4937, 0.3717, 0.709, 0.7374] +2026-04-14 13:01:37.503857: Epoch time: 100.22 s +2026-04-14 13:01:38.784532: +2026-04-14 13:01:38.786848: Epoch 3344 +2026-04-14 13:01:38.788736: Current learning rate: 0.00196 +2026-04-14 13:03:19.053694: train_loss -0.4492 +2026-04-14 13:03:19.059957: val_loss -0.4013 +2026-04-14 13:03:19.062150: Pseudo dice [0.4758, 0.0, 0.6556, 0.0, 0.4235, 0.8328, 0.5672] +2026-04-14 13:03:19.064465: Epoch time: 100.27 s +2026-04-14 13:03:20.338350: +2026-04-14 13:03:20.340711: Epoch 3345 +2026-04-14 13:03:20.342512: Current learning rate: 0.00196 +2026-04-14 13:05:00.324688: train_loss -0.4534 +2026-04-14 13:05:00.330746: val_loss -0.423 +2026-04-14 13:05:00.332710: Pseudo dice [0.805, 0.0, 0.6883, 0.4606, 0.4589, 0.6912, 0.6511] +2026-04-14 13:05:00.334835: Epoch time: 99.99 s +2026-04-14 13:05:02.681704: +2026-04-14 13:05:02.683922: Epoch 3346 +2026-04-14 13:05:02.685726: Current learning rate: 0.00196 +2026-04-14 13:06:43.212043: train_loss -0.429 +2026-04-14 13:06:43.219135: val_loss -0.3779 +2026-04-14 13:06:43.221542: Pseudo dice [0.8034, 0.0, 0.7161, 0.1341, 0.3831, 0.7511, 0.7751] +2026-04-14 13:06:43.224385: Epoch time: 100.53 s +2026-04-14 13:06:44.522039: +2026-04-14 13:06:44.523911: Epoch 3347 +2026-04-14 13:06:44.525824: Current learning rate: 0.00196 +2026-04-14 13:08:25.000887: train_loss -0.4409 +2026-04-14 13:08:25.010785: val_loss -0.3391 +2026-04-14 13:08:25.014278: Pseudo dice [0.519, 0.0, 0.6342, 0.108, 0.2099, 0.594, 0.7384] +2026-04-14 13:08:25.017373: Epoch time: 100.48 s +2026-04-14 13:08:26.286013: +2026-04-14 13:08:26.288023: Epoch 3348 +2026-04-14 13:08:26.290058: Current learning rate: 0.00195 +2026-04-14 13:10:06.588953: train_loss -0.4354 +2026-04-14 13:10:06.594808: val_loss -0.337 +2026-04-14 13:10:06.596985: Pseudo dice [0.418, 0.0, 0.5248, 0.0, 0.173, 0.8382, 0.866] +2026-04-14 13:10:06.599241: Epoch time: 100.31 s +2026-04-14 13:10:07.883259: +2026-04-14 13:10:07.885520: Epoch 3349 +2026-04-14 13:10:07.887614: Current learning rate: 0.00195 +2026-04-14 13:11:48.344835: train_loss -0.439 +2026-04-14 13:11:48.350675: val_loss -0.3284 +2026-04-14 13:11:48.352991: Pseudo dice [0.6582, 0.0, 0.8027, 0.0552, 0.4379, 0.5863, 0.6474] +2026-04-14 13:11:48.355316: Epoch time: 100.46 s +2026-04-14 13:11:51.344833: +2026-04-14 13:11:51.346998: Epoch 3350 +2026-04-14 13:11:51.348953: Current learning rate: 0.00195 +2026-04-14 13:13:31.599936: train_loss -0.4624 +2026-04-14 13:13:31.607326: val_loss -0.4138 +2026-04-14 13:13:31.610677: Pseudo dice [0.7697, 0.0, 0.6615, 0.2512, 0.3384, 0.8473, 0.8697] +2026-04-14 13:13:31.612969: Epoch time: 100.26 s +2026-04-14 13:13:32.895144: +2026-04-14 13:13:32.897606: Epoch 3351 +2026-04-14 13:13:32.899616: Current learning rate: 0.00195 +2026-04-14 13:15:13.048608: train_loss -0.4493 +2026-04-14 13:15:13.054319: val_loss -0.4155 +2026-04-14 13:15:13.057347: Pseudo dice [0.4557, 0.0, 0.7844, 0.8149, 0.3784, 0.5494, 0.7816] +2026-04-14 13:15:13.059436: Epoch time: 100.16 s +2026-04-14 13:15:14.318681: +2026-04-14 13:15:14.321176: Epoch 3352 +2026-04-14 13:15:14.323421: Current learning rate: 0.00194 +2026-04-14 13:16:54.674942: train_loss -0.4636 +2026-04-14 13:16:54.681881: val_loss -0.4455 +2026-04-14 13:16:54.684119: Pseudo dice [0.7489, 0.0, 0.7015, 0.3447, 0.6114, 0.7924, 0.8305] +2026-04-14 13:16:54.686383: Epoch time: 100.36 s +2026-04-14 13:16:55.944117: +2026-04-14 13:16:55.946615: Epoch 3353 +2026-04-14 13:16:55.948463: Current learning rate: 0.00194 +2026-04-14 13:18:36.081822: train_loss -0.4581 +2026-04-14 13:18:36.088482: val_loss -0.4051 +2026-04-14 13:18:36.091550: Pseudo dice [0.7852, 0.0, 0.7366, 0.0, 0.4387, 0.6505, 0.8129] +2026-04-14 13:18:36.094144: Epoch time: 100.14 s +2026-04-14 13:18:37.381040: +2026-04-14 13:18:37.383728: Epoch 3354 +2026-04-14 13:18:37.385543: Current learning rate: 0.00194 +2026-04-14 13:20:17.669868: train_loss -0.4326 +2026-04-14 13:20:17.676939: val_loss -0.3924 +2026-04-14 13:20:17.678817: Pseudo dice [0.1715, 0.0, 0.8128, 0.0, 0.4732, 0.279, 0.7076] +2026-04-14 13:20:17.680820: Epoch time: 100.29 s +2026-04-14 13:20:18.958970: +2026-04-14 13:20:18.961230: Epoch 3355 +2026-04-14 13:20:18.963081: Current learning rate: 0.00194 +2026-04-14 13:21:59.313014: train_loss -0.4633 +2026-04-14 13:21:59.329540: val_loss -0.3995 +2026-04-14 13:21:59.332157: Pseudo dice [0.2897, 0.0, 0.717, 0.8001, 0.4406, 0.7531, 0.7373] +2026-04-14 13:21:59.335412: Epoch time: 100.36 s +2026-04-14 13:22:00.601555: +2026-04-14 13:22:00.612358: Epoch 3356 +2026-04-14 13:22:00.614418: Current learning rate: 0.00193 +2026-04-14 13:23:40.930638: train_loss -0.4704 +2026-04-14 13:23:40.941690: val_loss -0.3231 +2026-04-14 13:23:40.944288: Pseudo dice [0.3322, 0.0, 0.6911, 0.065, 0.4795, 0.7092, 0.9279] +2026-04-14 13:23:40.946502: Epoch time: 100.33 s +2026-04-14 13:23:42.234063: +2026-04-14 13:23:42.236154: Epoch 3357 +2026-04-14 13:23:42.237939: Current learning rate: 0.00193 +2026-04-14 13:25:22.479487: train_loss -0.4659 +2026-04-14 13:25:22.487600: val_loss -0.3845 +2026-04-14 13:25:22.491609: Pseudo dice [0.4593, 0.0, 0.6567, 0.1149, 0.2571, 0.7814, 0.8988] +2026-04-14 13:25:22.494936: Epoch time: 100.25 s +2026-04-14 13:25:23.754762: +2026-04-14 13:25:23.757305: Epoch 3358 +2026-04-14 13:25:23.758847: Current learning rate: 0.00193 +2026-04-14 13:27:03.827065: train_loss -0.4707 +2026-04-14 13:27:03.833941: val_loss -0.4009 +2026-04-14 13:27:03.836448: Pseudo dice [0.442, 0.0, 0.5136, 0.0697, 0.4722, 0.7548, 0.7775] +2026-04-14 13:27:03.839958: Epoch time: 100.08 s +2026-04-14 13:27:05.136879: +2026-04-14 13:27:05.138726: Epoch 3359 +2026-04-14 13:27:05.140299: Current learning rate: 0.00192 +2026-04-14 13:28:45.223420: train_loss -0.4561 +2026-04-14 13:28:45.230127: val_loss -0.3785 +2026-04-14 13:28:45.232223: Pseudo dice [0.498, 0.0, 0.7741, 0.0, 0.5312, 0.7024, 0.5945] +2026-04-14 13:28:45.234849: Epoch time: 100.09 s +2026-04-14 13:28:46.497138: +2026-04-14 13:28:46.498887: Epoch 3360 +2026-04-14 13:28:46.500712: Current learning rate: 0.00192 +2026-04-14 13:30:26.827023: train_loss -0.4589 +2026-04-14 13:30:26.835430: val_loss -0.4206 +2026-04-14 13:30:26.837911: Pseudo dice [0.653, 0.0, 0.8028, 0.2214, 0.5931, 0.603, 0.8645] +2026-04-14 13:30:26.840499: Epoch time: 100.33 s +2026-04-14 13:30:28.190868: +2026-04-14 13:30:28.193053: Epoch 3361 +2026-04-14 13:30:28.195098: Current learning rate: 0.00192 +2026-04-14 13:32:08.474603: train_loss -0.4571 +2026-04-14 13:32:08.480448: val_loss -0.4393 +2026-04-14 13:32:08.482736: Pseudo dice [0.8179, 0.0, 0.7866, 0.8478, 0.566, 0.6394, 0.7659] +2026-04-14 13:32:08.485954: Epoch time: 100.29 s +2026-04-14 13:32:09.761901: +2026-04-14 13:32:09.764167: Epoch 3362 +2026-04-14 13:32:09.766599: Current learning rate: 0.00192 +2026-04-14 13:33:50.014437: train_loss -0.4658 +2026-04-14 13:33:50.020279: val_loss -0.3798 +2026-04-14 13:33:50.021833: Pseudo dice [0.6664, 0.0, 0.6395, 0.0, 0.5457, 0.5474, 0.699] +2026-04-14 13:33:50.024137: Epoch time: 100.26 s +2026-04-14 13:33:51.286812: +2026-04-14 13:33:51.288846: Epoch 3363 +2026-04-14 13:33:51.290589: Current learning rate: 0.00191 +2026-04-14 13:35:31.589959: train_loss -0.4631 +2026-04-14 13:35:31.595961: val_loss -0.3682 +2026-04-14 13:35:31.597974: Pseudo dice [0.6393, 0.0, 0.4472, 0.0, 0.6978, 0.8512, 0.6013] +2026-04-14 13:35:31.600158: Epoch time: 100.31 s +2026-04-14 13:35:32.867803: +2026-04-14 13:35:32.870119: Epoch 3364 +2026-04-14 13:35:32.871815: Current learning rate: 0.00191 +2026-04-14 13:37:13.820948: train_loss -0.4674 +2026-04-14 13:37:13.827273: val_loss -0.392 +2026-04-14 13:37:13.830109: Pseudo dice [0.6926, 0.0, 0.7313, 0.0868, 0.2502, 0.8222, 0.8062] +2026-04-14 13:37:13.833390: Epoch time: 100.96 s +2026-04-14 13:37:16.236156: +2026-04-14 13:37:16.238029: Epoch 3365 +2026-04-14 13:37:16.239846: Current learning rate: 0.00191 +2026-04-14 13:38:57.045650: train_loss -0.4699 +2026-04-14 13:38:57.052255: val_loss -0.4266 +2026-04-14 13:38:57.054197: Pseudo dice [0.388, 0.0, 0.7779, 0.9057, 0.5939, 0.7978, 0.7217] +2026-04-14 13:38:57.057667: Epoch time: 100.81 s +2026-04-14 13:38:58.332108: +2026-04-14 13:38:58.334163: Epoch 3366 +2026-04-14 13:38:58.336089: Current learning rate: 0.00191 +2026-04-14 13:40:38.562473: train_loss -0.4553 +2026-04-14 13:40:38.569340: val_loss -0.4317 +2026-04-14 13:40:38.571604: Pseudo dice [0.6409, 0.0, 0.8401, 0.0, 0.3319, 0.7918, 0.8356] +2026-04-14 13:40:38.573844: Epoch time: 100.23 s +2026-04-14 13:40:39.858819: +2026-04-14 13:40:39.860767: Epoch 3367 +2026-04-14 13:40:39.862248: Current learning rate: 0.0019 +2026-04-14 13:42:20.484195: train_loss -0.4569 +2026-04-14 13:42:20.490874: val_loss -0.386 +2026-04-14 13:42:20.493216: Pseudo dice [0.6873, 0.0, 0.8419, 0.0377, 0.4533, 0.578, 0.7241] +2026-04-14 13:42:20.496284: Epoch time: 100.63 s +2026-04-14 13:42:21.778929: +2026-04-14 13:42:21.781195: Epoch 3368 +2026-04-14 13:42:21.782953: Current learning rate: 0.0019 +2026-04-14 13:44:02.033471: train_loss -0.448 +2026-04-14 13:44:02.039696: val_loss -0.3779 +2026-04-14 13:44:02.041344: Pseudo dice [0.7008, 0.0, 0.5439, 0.507, 0.3879, 0.679, 0.825] +2026-04-14 13:44:02.043892: Epoch time: 100.26 s +2026-04-14 13:44:03.336131: +2026-04-14 13:44:03.339084: Epoch 3369 +2026-04-14 13:44:03.342088: Current learning rate: 0.0019 +2026-04-14 13:45:43.668960: train_loss -0.4593 +2026-04-14 13:45:43.675417: val_loss -0.4414 +2026-04-14 13:45:43.677408: Pseudo dice [0.0452, 0.0, 0.7811, 0.7784, 0.5546, 0.7263, 0.7739] +2026-04-14 13:45:43.679688: Epoch time: 100.34 s +2026-04-14 13:45:44.957740: +2026-04-14 13:45:44.959495: Epoch 3370 +2026-04-14 13:45:44.961092: Current learning rate: 0.00189 +2026-04-14 13:47:25.160643: train_loss -0.4727 +2026-04-14 13:47:25.169663: val_loss -0.3734 +2026-04-14 13:47:25.172806: Pseudo dice [0.6537, 0.0, 0.7578, 0.1376, 0.4701, 0.8042, 0.4534] +2026-04-14 13:47:25.180318: Epoch time: 100.21 s +2026-04-14 13:47:26.465378: +2026-04-14 13:47:26.471835: Epoch 3371 +2026-04-14 13:47:26.475131: Current learning rate: 0.00189 +2026-04-14 13:49:06.540813: train_loss -0.4708 +2026-04-14 13:49:06.548015: val_loss -0.257 +2026-04-14 13:49:06.551095: Pseudo dice [0.5738, 0.0, 0.5941, 0.0127, 0.4132, 0.7375, 0.8104] +2026-04-14 13:49:06.554070: Epoch time: 100.08 s +2026-04-14 13:49:07.889586: +2026-04-14 13:49:07.891445: Epoch 3372 +2026-04-14 13:49:07.893023: Current learning rate: 0.00189 +2026-04-14 13:50:48.141126: train_loss -0.463 +2026-04-14 13:50:48.147938: val_loss -0.4121 +2026-04-14 13:50:48.149759: Pseudo dice [0.7971, 0.0, 0.6008, 0.343, 0.3835, 0.834, 0.6166] +2026-04-14 13:50:48.151982: Epoch time: 100.25 s +2026-04-14 13:50:49.434053: +2026-04-14 13:50:49.435755: Epoch 3373 +2026-04-14 13:50:49.437198: Current learning rate: 0.00189 +2026-04-14 13:52:29.795594: train_loss -0.4593 +2026-04-14 13:52:29.803831: val_loss -0.4123 +2026-04-14 13:52:29.806746: Pseudo dice [0.5075, 0.0, 0.8026, 0.6616, 0.5388, 0.7554, 0.7578] +2026-04-14 13:52:29.810282: Epoch time: 100.36 s +2026-04-14 13:52:31.084562: +2026-04-14 13:52:31.086631: Epoch 3374 +2026-04-14 13:52:31.088461: Current learning rate: 0.00188 +2026-04-14 13:54:11.679757: train_loss -0.4564 +2026-04-14 13:54:11.685883: val_loss -0.2628 +2026-04-14 13:54:11.687989: Pseudo dice [0.0238, 0.0, 0.5425, 0.0324, 0.3695, 0.8186, 0.8662] +2026-04-14 13:54:11.693928: Epoch time: 100.6 s +2026-04-14 13:54:12.997194: +2026-04-14 13:54:13.000697: Epoch 3375 +2026-04-14 13:54:13.003323: Current learning rate: 0.00188 +2026-04-14 13:55:53.256386: train_loss -0.4751 +2026-04-14 13:55:53.262512: val_loss -0.4248 +2026-04-14 13:55:53.264865: Pseudo dice [0.6488, 0.0, 0.8217, 0.0, 0.3231, 0.8841, 0.876] +2026-04-14 13:55:53.268070: Epoch time: 100.26 s +2026-04-14 13:55:54.559495: +2026-04-14 13:55:54.562920: Epoch 3376 +2026-04-14 13:55:54.564815: Current learning rate: 0.00188 +2026-04-14 13:57:34.727726: train_loss -0.4679 +2026-04-14 13:57:34.733390: val_loss -0.4393 +2026-04-14 13:57:34.735341: Pseudo dice [0.6892, 0.0, 0.7866, 0.1761, 0.3641, 0.7432, 0.7814] +2026-04-14 13:57:34.738365: Epoch time: 100.17 s +2026-04-14 13:57:36.204119: +2026-04-14 13:57:36.208082: Epoch 3377 +2026-04-14 13:57:36.210657: Current learning rate: 0.00188 +2026-04-14 13:59:16.364363: train_loss -0.4685 +2026-04-14 13:59:16.372529: val_loss -0.4215 +2026-04-14 13:59:16.375456: Pseudo dice [0.7415, 0.0, 0.7298, 0.7378, 0.4242, 0.8134, 0.7107] +2026-04-14 13:59:16.377922: Epoch time: 100.16 s +2026-04-14 13:59:17.649008: +2026-04-14 13:59:17.650937: Epoch 3378 +2026-04-14 13:59:17.652741: Current learning rate: 0.00187 +2026-04-14 14:00:57.759711: train_loss -0.4636 +2026-04-14 14:00:57.766809: val_loss -0.399 +2026-04-14 14:00:57.769881: Pseudo dice [0.6404, 0.0, 0.824, 0.2105, 0.08, 0.8244, 0.7912] +2026-04-14 14:00:57.772595: Epoch time: 100.11 s +2026-04-14 14:00:59.060912: +2026-04-14 14:00:59.062923: Epoch 3379 +2026-04-14 14:00:59.064794: Current learning rate: 0.00187 +2026-04-14 14:02:39.108880: train_loss -0.4615 +2026-04-14 14:02:39.114988: val_loss -0.3776 +2026-04-14 14:02:39.119018: Pseudo dice [0.7932, 0.0, 0.7812, 0.0, 0.1642, 0.7742, 0.8686] +2026-04-14 14:02:39.121969: Epoch time: 100.05 s +2026-04-14 14:02:40.396046: +2026-04-14 14:02:40.397800: Epoch 3380 +2026-04-14 14:02:40.399631: Current learning rate: 0.00187 +2026-04-14 14:04:20.462369: train_loss -0.4441 +2026-04-14 14:04:20.469216: val_loss -0.4373 +2026-04-14 14:04:20.471933: Pseudo dice [0.8341, 0.0, 0.7988, 0.5312, 0.3515, 0.8, 0.7235] +2026-04-14 14:04:20.476348: Epoch time: 100.07 s +2026-04-14 14:04:21.774990: +2026-04-14 14:04:21.778044: Epoch 3381 +2026-04-14 14:04:21.779977: Current learning rate: 0.00186 +2026-04-14 14:06:02.469378: train_loss -0.4756 +2026-04-14 14:06:02.476948: val_loss -0.4006 +2026-04-14 14:06:02.479092: Pseudo dice [0.7384, 0.0, 0.7316, 0.0977, 0.453, 0.8335, 0.6949] +2026-04-14 14:06:02.481771: Epoch time: 100.7 s +2026-04-14 14:06:03.768170: +2026-04-14 14:06:03.769830: Epoch 3382 +2026-04-14 14:06:03.772218: Current learning rate: 0.00186 +2026-04-14 14:07:44.591377: train_loss -0.4575 +2026-04-14 14:07:44.599440: val_loss -0.4214 +2026-04-14 14:07:44.602296: Pseudo dice [0.8168, 0.0, 0.823, 0.2474, 0.284, 0.6136, 0.6237] +2026-04-14 14:07:44.605193: Epoch time: 100.83 s +2026-04-14 14:07:45.903782: +2026-04-14 14:07:45.905777: Epoch 3383 +2026-04-14 14:07:45.907222: Current learning rate: 0.00186 +2026-04-14 14:09:26.141957: train_loss -0.4598 +2026-04-14 14:09:26.148205: val_loss -0.3943 +2026-04-14 14:09:26.149995: Pseudo dice [0.4772, 0.0, 0.8256, 0.8595, 0.4812, 0.6947, 0.7932] +2026-04-14 14:09:26.152620: Epoch time: 100.24 s +2026-04-14 14:09:27.415206: +2026-04-14 14:09:27.417388: Epoch 3384 +2026-04-14 14:09:27.419025: Current learning rate: 0.00186 +2026-04-14 14:11:08.515833: train_loss -0.453 +2026-04-14 14:11:08.523066: val_loss -0.3878 +2026-04-14 14:11:08.525634: Pseudo dice [0.7028, 0.0, 0.7277, 0.0, 0.2984, 0.7604, 0.6109] +2026-04-14 14:11:08.528637: Epoch time: 101.1 s +2026-04-14 14:11:09.807511: +2026-04-14 14:11:09.809800: Epoch 3385 +2026-04-14 14:11:09.811748: Current learning rate: 0.00185 +2026-04-14 14:12:50.669474: train_loss -0.4479 +2026-04-14 14:12:50.675190: val_loss -0.4087 +2026-04-14 14:12:50.677240: Pseudo dice [0.476, 0.0, 0.6911, 0.803, 0.2009, 0.4848, 0.7632] +2026-04-14 14:12:50.679954: Epoch time: 100.86 s +2026-04-14 14:12:52.007324: +2026-04-14 14:12:52.009561: Epoch 3386 +2026-04-14 14:12:52.011821: Current learning rate: 0.00185 +2026-04-14 14:14:33.144847: train_loss -0.4569 +2026-04-14 14:14:33.153116: val_loss -0.3393 +2026-04-14 14:14:33.155254: Pseudo dice [0.3164, 0.0, 0.5853, 0.049, 0.1371, 0.8549, 0.7746] +2026-04-14 14:14:33.158185: Epoch time: 101.14 s +2026-04-14 14:14:34.449283: +2026-04-14 14:14:34.451530: Epoch 3387 +2026-04-14 14:14:34.453501: Current learning rate: 0.00185 +2026-04-14 14:16:14.839170: train_loss -0.4444 +2026-04-14 14:16:14.846536: val_loss -0.3518 +2026-04-14 14:16:14.849548: Pseudo dice [0.6625, 0.0, 0.7903, 0.0739, 0.4649, 0.5796, 0.7731] +2026-04-14 14:16:14.868123: Epoch time: 100.39 s +2026-04-14 14:16:16.126908: +2026-04-14 14:16:16.129184: Epoch 3388 +2026-04-14 14:16:16.131371: Current learning rate: 0.00185 +2026-04-14 14:17:56.629104: train_loss -0.4534 +2026-04-14 14:17:56.634838: val_loss -0.4094 +2026-04-14 14:17:56.636960: Pseudo dice [0.711, 0.0, 0.749, 0.7609, 0.1655, 0.8338, 0.8488] +2026-04-14 14:17:56.639128: Epoch time: 100.51 s +2026-04-14 14:17:57.924388: +2026-04-14 14:17:57.926076: Epoch 3389 +2026-04-14 14:17:57.927915: Current learning rate: 0.00184 +2026-04-14 14:19:38.241619: train_loss -0.4689 +2026-04-14 14:19:38.248505: val_loss -0.3139 +2026-04-14 14:19:38.250625: Pseudo dice [0.8135, 0.0, 0.605, 0.0387, 0.4793, 0.7944, 0.6886] +2026-04-14 14:19:38.253355: Epoch time: 100.32 s +2026-04-14 14:19:39.541024: +2026-04-14 14:19:39.543107: Epoch 3390 +2026-04-14 14:19:39.544694: Current learning rate: 0.00184 +2026-04-14 14:21:20.717199: train_loss -0.4219 +2026-04-14 14:21:20.723501: val_loss -0.4178 +2026-04-14 14:21:20.728115: Pseudo dice [0.7661, 0.0, 0.6804, 0.4677, 0.4558, 0.8104, 0.885] +2026-04-14 14:21:20.731309: Epoch time: 101.18 s +2026-04-14 14:21:22.008439: +2026-04-14 14:21:22.010448: Epoch 3391 +2026-04-14 14:21:22.012706: Current learning rate: 0.00184 +2026-04-14 14:23:02.444028: train_loss -0.4434 +2026-04-14 14:23:02.450275: val_loss -0.4185 +2026-04-14 14:23:02.452337: Pseudo dice [0.7861, 0.0, 0.7224, 0.9162, 0.3185, 0.7774, 0.7862] +2026-04-14 14:23:02.454650: Epoch time: 100.44 s +2026-04-14 14:23:03.734180: +2026-04-14 14:23:03.736156: Epoch 3392 +2026-04-14 14:23:03.737769: Current learning rate: 0.00184 +2026-04-14 14:24:44.041066: train_loss -0.465 +2026-04-14 14:24:44.048246: val_loss -0.407 +2026-04-14 14:24:44.050670: Pseudo dice [0.6719, 0.0, 0.6307, 0.4363, 0.3291, 0.5225, 0.775] +2026-04-14 14:24:44.053978: Epoch time: 100.31 s +2026-04-14 14:24:45.343577: +2026-04-14 14:24:45.345824: Epoch 3393 +2026-04-14 14:24:45.347756: Current learning rate: 0.00183 +2026-04-14 14:26:25.638295: train_loss -0.4587 +2026-04-14 14:26:25.647469: val_loss -0.3933 +2026-04-14 14:26:25.650486: Pseudo dice [0.7702, 0.0, 0.6637, 0.0835, 0.5456, 0.7237, 0.7839] +2026-04-14 14:26:25.653802: Epoch time: 100.3 s +2026-04-14 14:26:26.918141: +2026-04-14 14:26:26.920644: Epoch 3394 +2026-04-14 14:26:26.923014: Current learning rate: 0.00183 +2026-04-14 14:28:07.397579: train_loss -0.4509 +2026-04-14 14:28:07.403824: val_loss -0.4361 +2026-04-14 14:28:07.405958: Pseudo dice [0.6348, 0.0, 0.6201, 0.0295, 0.5817, 0.8853, 0.9059] +2026-04-14 14:28:07.408177: Epoch time: 100.48 s +2026-04-14 14:28:08.699642: +2026-04-14 14:28:08.701954: Epoch 3395 +2026-04-14 14:28:08.703841: Current learning rate: 0.00183 +2026-04-14 14:29:49.189238: train_loss -0.4721 +2026-04-14 14:29:49.197438: val_loss -0.3446 +2026-04-14 14:29:49.200248: Pseudo dice [0.7167, 0.0, 0.742, 0.0433, 0.3309, 0.7611, 0.5978] +2026-04-14 14:29:49.203191: Epoch time: 100.49 s +2026-04-14 14:29:50.492064: +2026-04-14 14:29:50.494192: Epoch 3396 +2026-04-14 14:29:50.496808: Current learning rate: 0.00182 +2026-04-14 14:31:30.837358: train_loss -0.4815 +2026-04-14 14:31:30.844607: val_loss -0.433 +2026-04-14 14:31:30.847082: Pseudo dice [0.756, 0.0, 0.7973, 0.4402, 0.5428, 0.8268, 0.823] +2026-04-14 14:31:30.849696: Epoch time: 100.35 s +2026-04-14 14:31:32.119343: +2026-04-14 14:31:32.121243: Epoch 3397 +2026-04-14 14:31:32.123127: Current learning rate: 0.00182 +2026-04-14 14:33:12.567899: train_loss -0.4815 +2026-04-14 14:33:12.574093: val_loss -0.4419 +2026-04-14 14:33:12.576962: Pseudo dice [0.8413, 0.0, 0.7514, 0.226, 0.4754, 0.8063, 0.8736] +2026-04-14 14:33:12.579834: Epoch time: 100.45 s +2026-04-14 14:33:13.853897: +2026-04-14 14:33:13.855553: Epoch 3398 +2026-04-14 14:33:13.857164: Current learning rate: 0.00182 +2026-04-14 14:34:54.297304: train_loss -0.4789 +2026-04-14 14:34:54.303498: val_loss -0.3297 +2026-04-14 14:34:54.307695: Pseudo dice [0.457, 0.0, 0.7067, 0.0, 0.3546, 0.8306, 0.486] +2026-04-14 14:34:54.311149: Epoch time: 100.45 s +2026-04-14 14:34:55.576810: +2026-04-14 14:34:55.578769: Epoch 3399 +2026-04-14 14:34:55.580385: Current learning rate: 0.00182 +2026-04-14 14:36:36.326245: train_loss -0.4808 +2026-04-14 14:36:36.332366: val_loss -0.3295 +2026-04-14 14:36:36.336875: Pseudo dice [0.8118, 0.0, 0.7044, 0.0797, 0.3375, 0.6015, 0.8534] +2026-04-14 14:36:36.340315: Epoch time: 100.75 s +2026-04-14 14:36:39.367424: +2026-04-14 14:36:39.369763: Epoch 3400 +2026-04-14 14:36:39.371363: Current learning rate: 0.00181 +2026-04-14 14:38:19.776549: train_loss -0.4847 +2026-04-14 14:38:19.783353: val_loss -0.3667 +2026-04-14 14:38:19.787040: Pseudo dice [0.8048, 0.0, 0.6109, 0.1088, 0.4742, 0.8206, 0.7339] +2026-04-14 14:38:19.790880: Epoch time: 100.41 s +2026-04-14 14:38:21.063650: +2026-04-14 14:38:21.065682: Epoch 3401 +2026-04-14 14:38:21.068140: Current learning rate: 0.00181 +2026-04-14 14:40:01.673260: train_loss -0.4776 +2026-04-14 14:40:01.680228: val_loss -0.4191 +2026-04-14 14:40:01.682462: Pseudo dice [0.8569, 0.0, 0.7483, 0.0922, 0.5332, 0.8847, 0.7668] +2026-04-14 14:40:01.685765: Epoch time: 100.61 s +2026-04-14 14:40:02.936208: +2026-04-14 14:40:02.938237: Epoch 3402 +2026-04-14 14:40:02.941099: Current learning rate: 0.00181 +2026-04-14 14:41:43.355329: train_loss -0.4924 +2026-04-14 14:41:43.361543: val_loss -0.4237 +2026-04-14 14:41:43.365345: Pseudo dice [0.8883, 0.0, 0.8249, 0.0008, 0.6313, 0.8469, 0.7861] +2026-04-14 14:41:43.367920: Epoch time: 100.42 s +2026-04-14 14:41:44.629844: +2026-04-14 14:41:44.631749: Epoch 3403 +2026-04-14 14:41:44.633481: Current learning rate: 0.00181 +2026-04-14 14:43:26.015323: train_loss -0.4689 +2026-04-14 14:43:26.021472: val_loss -0.4249 +2026-04-14 14:43:26.023287: Pseudo dice [0.6711, 0.0, 0.7682, 0.0, 0.1856, 0.8236, 0.6503] +2026-04-14 14:43:26.025599: Epoch time: 101.39 s +2026-04-14 14:43:27.263155: +2026-04-14 14:43:27.269641: Epoch 3404 +2026-04-14 14:43:27.272598: Current learning rate: 0.0018 +2026-04-14 14:45:07.562200: train_loss -0.476 +2026-04-14 14:45:07.569029: val_loss -0.4146 +2026-04-14 14:45:07.571160: Pseudo dice [0.7784, 0.0, 0.7352, 0.4848, 0.3742, 0.827, 0.7311] +2026-04-14 14:45:07.573796: Epoch time: 100.3 s +2026-04-14 14:45:08.838998: +2026-04-14 14:45:08.841453: Epoch 3405 +2026-04-14 14:45:08.843182: Current learning rate: 0.0018 +2026-04-14 14:46:49.116373: train_loss -0.4659 +2026-04-14 14:46:49.122308: val_loss -0.4227 +2026-04-14 14:46:49.124708: Pseudo dice [0.6354, 0.0, 0.8054, 0.3163, 0.61, 0.7846, 0.8423] +2026-04-14 14:46:49.126908: Epoch time: 100.28 s +2026-04-14 14:46:50.388794: +2026-04-14 14:46:50.391158: Epoch 3406 +2026-04-14 14:46:50.392825: Current learning rate: 0.0018 +2026-04-14 14:48:30.802645: train_loss -0.4666 +2026-04-14 14:48:30.810930: val_loss -0.3924 +2026-04-14 14:48:30.813049: Pseudo dice [0.5894, 0.0, 0.5831, 0.0, 0.3659, 0.8896, 0.9184] +2026-04-14 14:48:30.815563: Epoch time: 100.42 s +2026-04-14 14:48:32.095307: +2026-04-14 14:48:32.097680: Epoch 3407 +2026-04-14 14:48:32.099704: Current learning rate: 0.00179 +2026-04-14 14:50:12.493505: train_loss -0.4667 +2026-04-14 14:50:12.500647: val_loss -0.4373 +2026-04-14 14:50:12.504241: Pseudo dice [0.6184, 0.0, 0.845, 0.0, 0.4762, 0.8216, 0.8452] +2026-04-14 14:50:12.507766: Epoch time: 100.4 s +2026-04-14 14:50:13.802859: +2026-04-14 14:50:13.804832: Epoch 3408 +2026-04-14 14:50:13.807231: Current learning rate: 0.00179 +2026-04-14 14:51:54.125181: train_loss -0.4592 +2026-04-14 14:51:54.133422: val_loss -0.3683 +2026-04-14 14:51:54.135999: Pseudo dice [0.4531, 0.0, 0.7508, 0.0, 0.4023, 0.8394, 0.7451] +2026-04-14 14:51:54.139711: Epoch time: 100.33 s +2026-04-14 14:51:55.466514: +2026-04-14 14:51:55.468168: Epoch 3409 +2026-04-14 14:51:55.469819: Current learning rate: 0.00179 +2026-04-14 14:53:35.891968: train_loss -0.4598 +2026-04-14 14:53:35.898767: val_loss -0.3803 +2026-04-14 14:53:35.901136: Pseudo dice [0.4899, 0.0, 0.6613, 0.0, 0.3787, 0.4813, 0.8081] +2026-04-14 14:53:35.903764: Epoch time: 100.43 s +2026-04-14 14:53:37.155316: +2026-04-14 14:53:37.157386: Epoch 3410 +2026-04-14 14:53:37.159535: Current learning rate: 0.00179 +2026-04-14 14:55:18.209596: train_loss -0.4679 +2026-04-14 14:55:18.215818: val_loss -0.4013 +2026-04-14 14:55:18.217928: Pseudo dice [0.7636, 0.0, 0.6637, 0.1685, 0.4833, 0.839, 0.761] +2026-04-14 14:55:18.220507: Epoch time: 101.06 s +2026-04-14 14:55:19.482615: +2026-04-14 14:55:19.484472: Epoch 3411 +2026-04-14 14:55:19.486587: Current learning rate: 0.00178 +2026-04-14 14:57:00.285652: train_loss -0.457 +2026-04-14 14:57:00.292105: val_loss -0.4117 +2026-04-14 14:57:00.294397: Pseudo dice [0.7686, 0.0, 0.6366, 0.0444, 0.4883, 0.812, 0.9014] +2026-04-14 14:57:00.296969: Epoch time: 100.81 s +2026-04-14 14:57:01.582355: +2026-04-14 14:57:01.584723: Epoch 3412 +2026-04-14 14:57:01.586385: Current learning rate: 0.00178 +2026-04-14 14:58:42.058836: train_loss -0.464 +2026-04-14 14:58:42.065007: val_loss -0.4169 +2026-04-14 14:58:42.066988: Pseudo dice [0.8133, 0.0, 0.5303, 0.0, 0.3377, 0.8176, 0.6303] +2026-04-14 14:58:42.069036: Epoch time: 100.48 s +2026-04-14 14:58:43.314221: +2026-04-14 14:58:43.316024: Epoch 3413 +2026-04-14 14:58:43.317584: Current learning rate: 0.00178 +2026-04-14 15:00:23.641412: train_loss -0.4608 +2026-04-14 15:00:23.647797: val_loss -0.3838 +2026-04-14 15:00:23.650080: Pseudo dice [0.1976, 0.0, 0.6841, 0.0417, 0.3048, 0.5548, 0.8677] +2026-04-14 15:00:23.652403: Epoch time: 100.33 s +2026-04-14 15:00:24.900603: +2026-04-14 15:00:24.902251: Epoch 3414 +2026-04-14 15:00:24.903760: Current learning rate: 0.00178 +2026-04-14 15:02:05.092021: train_loss -0.4649 +2026-04-14 15:02:05.098332: val_loss -0.3619 +2026-04-14 15:02:05.100963: Pseudo dice [0.8193, 0.0, 0.6763, 0.0, 0.4437, 0.7789, 0.6374] +2026-04-14 15:02:05.104495: Epoch time: 100.19 s +2026-04-14 15:02:06.376769: +2026-04-14 15:02:06.378638: Epoch 3415 +2026-04-14 15:02:06.380388: Current learning rate: 0.00177 +2026-04-14 15:03:46.761170: train_loss -0.4643 +2026-04-14 15:03:46.767262: val_loss -0.4119 +2026-04-14 15:03:46.770826: Pseudo dice [0.6684, 0.0, 0.8223, 0.2863, 0.252, 0.6675, 0.6027] +2026-04-14 15:03:46.773509: Epoch time: 100.39 s +2026-04-14 15:03:48.022393: +2026-04-14 15:03:48.025016: Epoch 3416 +2026-04-14 15:03:48.027185: Current learning rate: 0.00177 +2026-04-14 15:05:28.616721: train_loss -0.4624 +2026-04-14 15:05:28.623230: val_loss -0.4464 +2026-04-14 15:05:28.625386: Pseudo dice [0.6597, 0.0, 0.734, 0.5641, 0.6207, 0.8059, 0.7933] +2026-04-14 15:05:28.627475: Epoch time: 100.6 s +2026-04-14 15:05:30.188890: +2026-04-14 15:05:30.190798: Epoch 3417 +2026-04-14 15:05:30.192551: Current learning rate: 0.00177 +2026-04-14 15:07:10.881238: train_loss -0.4623 +2026-04-14 15:07:10.888933: val_loss -0.3534 +2026-04-14 15:07:10.891742: Pseudo dice [0.7533, 0.0, 0.6991, 0.1257, 0.476, 0.4916, 0.6396] +2026-04-14 15:07:10.897235: Epoch time: 100.7 s +2026-04-14 15:07:12.189305: +2026-04-14 15:07:12.191241: Epoch 3418 +2026-04-14 15:07:12.192940: Current learning rate: 0.00176 +2026-04-14 15:08:52.429441: train_loss -0.4493 +2026-04-14 15:08:52.436193: val_loss -0.3011 +2026-04-14 15:08:52.438664: Pseudo dice [0.5924, 0.0, 0.6978, 0.0, 0.6173, 0.8097, 0.6617] +2026-04-14 15:08:52.441587: Epoch time: 100.24 s +2026-04-14 15:08:53.732578: +2026-04-14 15:08:53.734808: Epoch 3419 +2026-04-14 15:08:53.737431: Current learning rate: 0.00176 +2026-04-14 15:10:33.944375: train_loss -0.4634 +2026-04-14 15:10:33.951143: val_loss -0.4144 +2026-04-14 15:10:33.957575: Pseudo dice [0.7827, 0.0, 0.6423, 0.0002, 0.4917, 0.5816, 0.8012] +2026-04-14 15:10:33.960486: Epoch time: 100.21 s +2026-04-14 15:10:35.232402: +2026-04-14 15:10:35.234421: Epoch 3420 +2026-04-14 15:10:35.236248: Current learning rate: 0.00176 +2026-04-14 15:12:15.512017: train_loss -0.458 +2026-04-14 15:12:15.518600: val_loss -0.4198 +2026-04-14 15:12:15.521619: Pseudo dice [0.691, 0.0, 0.6814, 0.5197, 0.526, 0.7639, 0.885] +2026-04-14 15:12:15.524833: Epoch time: 100.28 s +2026-04-14 15:12:16.792982: +2026-04-14 15:12:16.794873: Epoch 3421 +2026-04-14 15:12:16.796683: Current learning rate: 0.00176 +2026-04-14 15:13:57.095928: train_loss -0.4745 +2026-04-14 15:13:57.102319: val_loss -0.2093 +2026-04-14 15:13:57.104702: Pseudo dice [0.8231, 0.0, 0.1562, 0.0349, 0.209, 0.6159, 0.6258] +2026-04-14 15:13:57.107378: Epoch time: 100.31 s +2026-04-14 15:13:58.383922: +2026-04-14 15:13:58.386408: Epoch 3422 +2026-04-14 15:13:58.388730: Current learning rate: 0.00175 +2026-04-14 15:15:38.767689: train_loss -0.4611 +2026-04-14 15:15:38.794969: val_loss -0.4006 +2026-04-14 15:15:38.797206: Pseudo dice [0.6573, 0.0, 0.7429, 0.8819, 0.5169, 0.8029, 0.5306] +2026-04-14 15:15:38.799769: Epoch time: 100.39 s +2026-04-14 15:15:41.133061: +2026-04-14 15:15:41.135799: Epoch 3423 +2026-04-14 15:15:41.137665: Current learning rate: 0.00175 +2026-04-14 15:17:21.988847: train_loss -0.4728 +2026-04-14 15:17:22.004600: val_loss -0.3303 +2026-04-14 15:17:22.008888: Pseudo dice [0.7687, 0.0, 0.8296, 0.0185, 0.3958, 0.674, 0.7245] +2026-04-14 15:17:22.016161: Epoch time: 100.86 s +2026-04-14 15:17:23.292742: +2026-04-14 15:17:23.294989: Epoch 3424 +2026-04-14 15:17:23.297998: Current learning rate: 0.00175 +2026-04-14 15:19:04.390329: train_loss -0.4875 +2026-04-14 15:19:04.397180: val_loss -0.3681 +2026-04-14 15:19:04.400445: Pseudo dice [0.5701, 0.0, 0.7596, 0.007, 0.4236, 0.6535, 0.7085] +2026-04-14 15:19:04.402793: Epoch time: 101.1 s +2026-04-14 15:19:05.671333: +2026-04-14 15:19:05.673263: Epoch 3425 +2026-04-14 15:19:05.674975: Current learning rate: 0.00175 +2026-04-14 15:20:46.014685: train_loss -0.4707 +2026-04-14 15:20:46.022148: val_loss -0.4357 +2026-04-14 15:20:46.024440: Pseudo dice [0.7145, 0.0, 0.7631, 0.8957, 0.3633, 0.7663, 0.8322] +2026-04-14 15:20:46.027984: Epoch time: 100.35 s +2026-04-14 15:20:47.316213: +2026-04-14 15:20:47.318500: Epoch 3426 +2026-04-14 15:20:47.320716: Current learning rate: 0.00174 +2026-04-14 15:22:27.810243: train_loss -0.4745 +2026-04-14 15:22:27.816688: val_loss -0.3226 +2026-04-14 15:22:27.818657: Pseudo dice [0.761, 0.0, 0.6564, 0.0432, 0.3069, 0.7831, 0.6533] +2026-04-14 15:22:27.823045: Epoch time: 100.5 s +2026-04-14 15:22:29.116881: +2026-04-14 15:22:29.119108: Epoch 3427 +2026-04-14 15:22:29.120915: Current learning rate: 0.00174 +2026-04-14 15:24:09.359642: train_loss -0.4717 +2026-04-14 15:24:09.365363: val_loss -0.3965 +2026-04-14 15:24:09.367117: Pseudo dice [0.4444, 0.0, 0.6557, 0.0, 0.0001, 0.7047, 0.8385] +2026-04-14 15:24:09.369515: Epoch time: 100.25 s +2026-04-14 15:24:10.616792: +2026-04-14 15:24:10.618680: Epoch 3428 +2026-04-14 15:24:10.620642: Current learning rate: 0.00174 +2026-04-14 15:25:50.902187: train_loss -0.4476 +2026-04-14 15:25:50.909823: val_loss -0.4228 +2026-04-14 15:25:50.911832: Pseudo dice [0.6888, 0.0, 0.6603, 0.5321, 0.3078, 0.8387, 0.8002] +2026-04-14 15:25:50.914263: Epoch time: 100.29 s +2026-04-14 15:25:52.198407: +2026-04-14 15:25:52.200556: Epoch 3429 +2026-04-14 15:25:52.202399: Current learning rate: 0.00173 +2026-04-14 15:27:32.745185: train_loss -0.4489 +2026-04-14 15:27:32.754673: val_loss -0.4078 +2026-04-14 15:27:32.760775: Pseudo dice [0.7711, 0.0, 0.6503, 0.0, 0.4533, 0.8278, 0.8116] +2026-04-14 15:27:32.763299: Epoch time: 100.55 s +2026-04-14 15:27:34.060288: +2026-04-14 15:27:34.062507: Epoch 3430 +2026-04-14 15:27:34.064920: Current learning rate: 0.00173 +2026-04-14 15:29:14.600698: train_loss -0.4723 +2026-04-14 15:29:14.607373: val_loss -0.3651 +2026-04-14 15:29:14.610352: Pseudo dice [0.0006, 0.0, 0.7495, 0.0, 0.4975, 0.6061, 0.8704] +2026-04-14 15:29:14.613802: Epoch time: 100.54 s +2026-04-14 15:29:15.887679: +2026-04-14 15:29:15.890432: Epoch 3431 +2026-04-14 15:29:15.892400: Current learning rate: 0.00173 +2026-04-14 15:30:56.186431: train_loss -0.4687 +2026-04-14 15:30:56.192384: val_loss -0.403 +2026-04-14 15:30:56.194283: Pseudo dice [0.8425, 0.0, 0.4546, 0.3035, 0.5186, 0.4179, 0.7566] +2026-04-14 15:30:56.196179: Epoch time: 100.3 s +2026-04-14 15:30:57.455146: +2026-04-14 15:30:57.456920: Epoch 3432 +2026-04-14 15:30:57.458713: Current learning rate: 0.00173 +2026-04-14 15:32:38.180186: train_loss -0.4464 +2026-04-14 15:32:38.189163: val_loss -0.3242 +2026-04-14 15:32:38.191084: Pseudo dice [0.4816, 0.0, 0.4692, 0.0, 0.3214, 0.5281, 0.6095] +2026-04-14 15:32:38.193623: Epoch time: 100.73 s +2026-04-14 15:32:39.500192: +2026-04-14 15:32:39.502364: Epoch 3433 +2026-04-14 15:32:39.504119: Current learning rate: 0.00172 +2026-04-14 15:34:20.176044: train_loss -0.4562 +2026-04-14 15:34:20.183242: val_loss -0.2713 +2026-04-14 15:34:20.187186: Pseudo dice [0.7547, 0.0, 0.7239, 0.0101, 0.3467, 0.8444, 0.8634] +2026-04-14 15:34:20.189686: Epoch time: 100.68 s +2026-04-14 15:34:21.471851: +2026-04-14 15:34:21.474009: Epoch 3434 +2026-04-14 15:34:21.475981: Current learning rate: 0.00172 +2026-04-14 15:36:01.831225: train_loss -0.4488 +2026-04-14 15:36:01.836835: val_loss -0.3712 +2026-04-14 15:36:01.838883: Pseudo dice [0.779, 0.0, 0.7811, 0.0, 0.5761, 0.6525, 0.7452] +2026-04-14 15:36:01.841130: Epoch time: 100.36 s +2026-04-14 15:36:03.109336: +2026-04-14 15:36:03.112128: Epoch 3435 +2026-04-14 15:36:03.114272: Current learning rate: 0.00172 +2026-04-14 15:37:43.369432: train_loss -0.4659 +2026-04-14 15:37:43.376423: val_loss -0.4472 +2026-04-14 15:37:43.381492: Pseudo dice [0.8006, 0.0, 0.7972, 0.1329, 0.5084, 0.7621, 0.8325] +2026-04-14 15:37:43.384705: Epoch time: 100.26 s +2026-04-14 15:37:44.646902: +2026-04-14 15:37:44.649068: Epoch 3436 +2026-04-14 15:37:44.650909: Current learning rate: 0.00172 +2026-04-14 15:39:25.088093: train_loss -0.4656 +2026-04-14 15:39:25.095667: val_loss -0.4173 +2026-04-14 15:39:25.097897: Pseudo dice [0.829, 0.0, 0.6706, 0.0948, 0.439, 0.753, 0.8202] +2026-04-14 15:39:25.100103: Epoch time: 100.44 s +2026-04-14 15:39:26.342354: +2026-04-14 15:39:26.344764: Epoch 3437 +2026-04-14 15:39:26.346475: Current learning rate: 0.00171 +2026-04-14 15:41:06.883506: train_loss -0.4644 +2026-04-14 15:41:06.890862: val_loss -0.4059 +2026-04-14 15:41:06.895189: Pseudo dice [0.7998, 0.0, 0.8013, 0.0777, 0.4563, 0.8255, 0.8597] +2026-04-14 15:41:06.897783: Epoch time: 100.54 s +2026-04-14 15:41:08.181851: +2026-04-14 15:41:08.183884: Epoch 3438 +2026-04-14 15:41:08.186222: Current learning rate: 0.00171 +2026-04-14 15:42:48.853050: train_loss -0.4651 +2026-04-14 15:42:48.860267: val_loss -0.3541 +2026-04-14 15:42:48.863516: Pseudo dice [0.5021, 0.0, 0.8156, 0.1131, 0.5219, 0.5929, 0.7312] +2026-04-14 15:42:48.865853: Epoch time: 100.67 s +2026-04-14 15:42:50.163278: +2026-04-14 15:42:50.165348: Epoch 3439 +2026-04-14 15:42:50.167331: Current learning rate: 0.00171 +2026-04-14 15:44:30.917507: train_loss -0.4873 +2026-04-14 15:44:30.936070: val_loss -0.3716 +2026-04-14 15:44:30.943867: Pseudo dice [0.7792, 0.0, 0.7525, 0.0, 0.4242, 0.6883, 0.9027] +2026-04-14 15:44:30.946410: Epoch time: 100.76 s +2026-04-14 15:44:32.258932: +2026-04-14 15:44:32.261011: Epoch 3440 +2026-04-14 15:44:32.263126: Current learning rate: 0.0017 +2026-04-14 15:46:13.195719: train_loss -0.4833 +2026-04-14 15:46:13.203375: val_loss -0.3771 +2026-04-14 15:46:13.206073: Pseudo dice [0.7792, 0.0, 0.787, 0.0, 0.6508, 0.7686, 0.757] +2026-04-14 15:46:13.208675: Epoch time: 100.94 s +2026-04-14 15:46:14.452655: +2026-04-14 15:46:14.455053: Epoch 3441 +2026-04-14 15:46:14.456802: Current learning rate: 0.0017 +2026-04-14 15:47:54.889461: train_loss -0.4681 +2026-04-14 15:47:54.896626: val_loss -0.4308 +2026-04-14 15:47:54.898964: Pseudo dice [0.5848, 0.0, 0.8014, 0.0, 0.4383, 0.8502, 0.7972] +2026-04-14 15:47:54.901426: Epoch time: 100.44 s +2026-04-14 15:47:56.218194: +2026-04-14 15:47:56.220701: Epoch 3442 +2026-04-14 15:47:56.223100: Current learning rate: 0.0017 +2026-04-14 15:49:36.474500: train_loss -0.4703 +2026-04-14 15:49:36.486140: val_loss -0.4237 +2026-04-14 15:49:36.490400: Pseudo dice [0.5027, 0.0, 0.6945, 0.5576, 0.463, 0.7631, 0.7401] +2026-04-14 15:49:36.494301: Epoch time: 100.26 s +2026-04-14 15:49:38.870747: +2026-04-14 15:49:38.872834: Epoch 3443 +2026-04-14 15:49:38.874869: Current learning rate: 0.0017 +2026-04-14 15:51:19.310961: train_loss -0.4706 +2026-04-14 15:51:19.317454: val_loss -0.3199 +2026-04-14 15:51:19.319829: Pseudo dice [0.6087, 0.0, 0.5254, 0.0401, 0.3153, 0.8943, 0.8908] +2026-04-14 15:51:19.329034: Epoch time: 100.44 s +2026-04-14 15:51:20.603846: +2026-04-14 15:51:20.606171: Epoch 3444 +2026-04-14 15:51:20.607913: Current learning rate: 0.00169 +2026-04-14 15:53:00.855042: train_loss -0.4746 +2026-04-14 15:53:00.861808: val_loss -0.3321 +2026-04-14 15:53:00.864214: Pseudo dice [0.7045, 0.0, 0.6761, 0.0, 0.3764, 0.8588, 0.6742] +2026-04-14 15:53:00.868671: Epoch time: 100.25 s +2026-04-14 15:53:02.131294: +2026-04-14 15:53:02.133188: Epoch 3445 +2026-04-14 15:53:02.134726: Current learning rate: 0.00169 +2026-04-14 15:54:42.341305: train_loss -0.4755 +2026-04-14 15:54:42.350355: val_loss -0.4268 +2026-04-14 15:54:42.352834: Pseudo dice [0.7916, 0.0, 0.7461, 0.8032, 0.3291, 0.7338, 0.9125] +2026-04-14 15:54:42.355441: Epoch time: 100.21 s +2026-04-14 15:54:43.644732: +2026-04-14 15:54:43.647108: Epoch 3446 +2026-04-14 15:54:43.648939: Current learning rate: 0.00169 +2026-04-14 15:56:23.961819: train_loss -0.4684 +2026-04-14 15:56:23.974709: val_loss -0.4133 +2026-04-14 15:56:23.977015: Pseudo dice [0.6135, 0.0, 0.6349, 0.6174, 0.3758, 0.7749, 0.7855] +2026-04-14 15:56:23.982710: Epoch time: 100.32 s +2026-04-14 15:56:25.265988: +2026-04-14 15:56:25.268141: Epoch 3447 +2026-04-14 15:56:25.273797: Current learning rate: 0.00168 +2026-04-14 15:58:05.666404: train_loss -0.4606 +2026-04-14 15:58:05.673173: val_loss -0.3979 +2026-04-14 15:58:05.675251: Pseudo dice [0.7935, 0.0, 0.7311, 0.0942, 0.452, 0.7364, 0.803] +2026-04-14 15:58:05.678316: Epoch time: 100.4 s +2026-04-14 15:58:06.996954: +2026-04-14 15:58:06.999221: Epoch 3448 +2026-04-14 15:58:07.000749: Current learning rate: 0.00168 +2026-04-14 15:59:47.460613: train_loss -0.4645 +2026-04-14 15:59:47.466424: val_loss -0.4037 +2026-04-14 15:59:47.468363: Pseudo dice [0.6144, 0.0, 0.6073, 0.467, 0.0437, 0.8075, 0.6832] +2026-04-14 15:59:47.470469: Epoch time: 100.47 s +2026-04-14 15:59:48.736341: +2026-04-14 15:59:48.738305: Epoch 3449 +2026-04-14 15:59:48.740185: Current learning rate: 0.00168 +2026-04-14 16:01:29.164189: train_loss -0.4613 +2026-04-14 16:01:29.169998: val_loss -0.3925 +2026-04-14 16:01:29.172506: Pseudo dice [0.6878, 0.0, 0.8573, 0.3618, 0.3127, 0.7623, 0.7702] +2026-04-14 16:01:29.174941: Epoch time: 100.43 s +2026-04-14 16:01:32.287383: +2026-04-14 16:01:32.290066: Epoch 3450 +2026-04-14 16:01:32.292076: Current learning rate: 0.00168 +2026-04-14 16:03:12.683150: train_loss -0.4656 +2026-04-14 16:03:12.692961: val_loss -0.4249 +2026-04-14 16:03:12.696282: Pseudo dice [0.7387, 0.0, 0.7136, 0.3431, 0.121, 0.8342, 0.8644] +2026-04-14 16:03:12.699199: Epoch time: 100.4 s +2026-04-14 16:03:13.995275: +2026-04-14 16:03:13.997677: Epoch 3451 +2026-04-14 16:03:13.999752: Current learning rate: 0.00167 +2026-04-14 16:04:54.340516: train_loss -0.469 +2026-04-14 16:04:54.348408: val_loss -0.3425 +2026-04-14 16:04:54.351424: Pseudo dice [0.7286, 0.0, 0.6724, 0.1075, 0.19, 0.6895, 0.8641] +2026-04-14 16:04:54.354322: Epoch time: 100.35 s +2026-04-14 16:04:55.621334: +2026-04-14 16:04:55.623958: Epoch 3452 +2026-04-14 16:04:55.626622: Current learning rate: 0.00167 +2026-04-14 16:06:35.901622: train_loss -0.463 +2026-04-14 16:06:35.908428: val_loss -0.3278 +2026-04-14 16:06:35.910695: Pseudo dice [0.7992, 0.0, 0.7989, 0.0554, 0.3809, 0.6485, 0.567] +2026-04-14 16:06:35.914217: Epoch time: 100.28 s +2026-04-14 16:06:37.209958: +2026-04-14 16:06:37.212525: Epoch 3453 +2026-04-14 16:06:37.215130: Current learning rate: 0.00167 +2026-04-14 16:08:17.426256: train_loss -0.4718 +2026-04-14 16:08:17.441105: val_loss -0.3801 +2026-04-14 16:08:17.443147: Pseudo dice [0.588, 0.0, 0.6657, 0.0634, 0.4745, 0.6659, 0.8576] +2026-04-14 16:08:17.445324: Epoch time: 100.22 s +2026-04-14 16:08:18.748214: +2026-04-14 16:08:18.750760: Epoch 3454 +2026-04-14 16:08:18.752988: Current learning rate: 0.00167 +2026-04-14 16:09:59.031003: train_loss -0.485 +2026-04-14 16:09:59.038476: val_loss -0.4111 +2026-04-14 16:09:59.042591: Pseudo dice [0.7613, 0.0, 0.7728, 0.0, 0.3949, 0.5287, 0.8953] +2026-04-14 16:09:59.045002: Epoch time: 100.29 s +2026-04-14 16:10:00.362934: +2026-04-14 16:10:00.365162: Epoch 3455 +2026-04-14 16:10:00.367428: Current learning rate: 0.00166 +2026-04-14 16:11:40.910623: train_loss -0.4656 +2026-04-14 16:11:40.915744: val_loss -0.4303 +2026-04-14 16:11:40.917700: Pseudo dice [0.4242, 0.0, 0.7381, 0.0, 0.5891, 0.8189, 0.7106] +2026-04-14 16:11:40.919939: Epoch time: 100.55 s +2026-04-14 16:11:42.206799: +2026-04-14 16:11:42.208753: Epoch 3456 +2026-04-14 16:11:42.210458: Current learning rate: 0.00166 +2026-04-14 16:13:22.564669: train_loss -0.4767 +2026-04-14 16:13:22.571908: val_loss -0.4425 +2026-04-14 16:13:22.574110: Pseudo dice [0.7656, 0.0, 0.4975, 0.3591, 0.4753, 0.867, 0.7505] +2026-04-14 16:13:22.576804: Epoch time: 100.36 s +2026-04-14 16:13:23.836995: +2026-04-14 16:13:23.839600: Epoch 3457 +2026-04-14 16:13:23.842152: Current learning rate: 0.00166 +2026-04-14 16:15:04.937171: train_loss -0.4756 +2026-04-14 16:15:04.946779: val_loss -0.3943 +2026-04-14 16:15:04.949208: Pseudo dice [0.8128, 0.0, 0.7324, 0.0, 0.4716, 0.8598, 0.7365] +2026-04-14 16:15:04.952225: Epoch time: 101.1 s +2026-04-14 16:15:06.248197: +2026-04-14 16:15:06.250036: Epoch 3458 +2026-04-14 16:15:06.252014: Current learning rate: 0.00165 +2026-04-14 16:16:47.860806: train_loss -0.4529 +2026-04-14 16:16:47.868133: val_loss -0.4326 +2026-04-14 16:16:47.870188: Pseudo dice [0.5384, 0.0, 0.6799, 0.7992, 0.3652, 0.8133, 0.763] +2026-04-14 16:16:47.872422: Epoch time: 101.62 s +2026-04-14 16:16:49.192546: +2026-04-14 16:16:49.195159: Epoch 3459 +2026-04-14 16:16:49.197553: Current learning rate: 0.00165 +2026-04-14 16:18:29.472715: train_loss -0.4725 +2026-04-14 16:18:29.481200: val_loss -0.3428 +2026-04-14 16:18:29.484320: Pseudo dice [0.5148, 0.0, 0.6291, 0.0, 0.2557, 0.8254, 0.8428] +2026-04-14 16:18:29.487368: Epoch time: 100.28 s +2026-04-14 16:18:30.768332: +2026-04-14 16:18:30.770209: Epoch 3460 +2026-04-14 16:18:30.772320: Current learning rate: 0.00165 +2026-04-14 16:20:11.122784: train_loss -0.4593 +2026-04-14 16:20:11.130511: val_loss -0.3559 +2026-04-14 16:20:11.132682: Pseudo dice [0.7157, 0.0, 0.5571, 0.0185, 0.4584, 0.6052, 0.9055] +2026-04-14 16:20:11.134961: Epoch time: 100.36 s +2026-04-14 16:20:12.396242: +2026-04-14 16:20:12.398236: Epoch 3461 +2026-04-14 16:20:12.400048: Current learning rate: 0.00165 +2026-04-14 16:21:52.883007: train_loss -0.4606 +2026-04-14 16:21:52.888308: val_loss -0.3905 +2026-04-14 16:21:52.890440: Pseudo dice [0.4129, 0.0, 0.6942, 0.3245, 0.354, 0.8, 0.4303] +2026-04-14 16:21:52.892365: Epoch time: 100.49 s +2026-04-14 16:21:54.167536: +2026-04-14 16:21:54.169726: Epoch 3462 +2026-04-14 16:21:54.171519: Current learning rate: 0.00164 +2026-04-14 16:23:34.853440: train_loss -0.4555 +2026-04-14 16:23:34.859578: val_loss -0.3652 +2026-04-14 16:23:34.862228: Pseudo dice [0.6129, 0.0, 0.6397, 0.0228, 0.4769, 0.7888, 0.7631] +2026-04-14 16:23:34.865216: Epoch time: 100.69 s +2026-04-14 16:23:37.254773: +2026-04-14 16:23:37.256843: Epoch 3463 +2026-04-14 16:23:37.258468: Current learning rate: 0.00164 +2026-04-14 16:25:17.711351: train_loss -0.4668 +2026-04-14 16:25:17.718433: val_loss -0.4403 +2026-04-14 16:25:17.720917: Pseudo dice [0.5107, 0.0, 0.7307, 0.0, 0.5929, 0.7935, 0.6795] +2026-04-14 16:25:17.724626: Epoch time: 100.46 s +2026-04-14 16:25:18.991650: +2026-04-14 16:25:18.994060: Epoch 3464 +2026-04-14 16:25:18.995948: Current learning rate: 0.00164 +2026-04-14 16:26:59.427386: train_loss -0.4591 +2026-04-14 16:26:59.434331: val_loss -0.4286 +2026-04-14 16:26:59.437475: Pseudo dice [0.7879, 0.0, 0.7923, 0.4938, 0.4044, 0.8718, 0.7697] +2026-04-14 16:26:59.440463: Epoch time: 100.44 s +2026-04-14 16:27:00.687521: +2026-04-14 16:27:00.689605: Epoch 3465 +2026-04-14 16:27:00.691286: Current learning rate: 0.00164 +2026-04-14 16:28:41.697311: train_loss -0.4632 +2026-04-14 16:28:41.704818: val_loss -0.4253 +2026-04-14 16:28:41.707187: Pseudo dice [0.7939, 0.0, 0.8418, 0.0, 0.3903, 0.7808, 0.8234] +2026-04-14 16:28:41.710665: Epoch time: 101.01 s +2026-04-14 16:28:43.430375: +2026-04-14 16:28:43.432530: Epoch 3466 +2026-04-14 16:28:43.434369: Current learning rate: 0.00163 +2026-04-14 16:30:23.889812: train_loss -0.4808 +2026-04-14 16:30:23.902030: val_loss -0.426 +2026-04-14 16:30:23.904437: Pseudo dice [0.708, 0.0, 0.7872, 0.6497, 0.3138, 0.8083, 0.8421] +2026-04-14 16:30:23.907335: Epoch time: 100.46 s +2026-04-14 16:30:25.187152: +2026-04-14 16:30:25.188919: Epoch 3467 +2026-04-14 16:30:25.190603: Current learning rate: 0.00163 +2026-04-14 16:32:05.658813: train_loss -0.4712 +2026-04-14 16:32:05.664633: val_loss -0.4033 +2026-04-14 16:32:05.668002: Pseudo dice [0.7098, 0.0, 0.6786, 0.1335, 0.4813, 0.6967, 0.8357] +2026-04-14 16:32:05.670554: Epoch time: 100.47 s +2026-04-14 16:32:06.928784: +2026-04-14 16:32:06.934806: Epoch 3468 +2026-04-14 16:32:06.936564: Current learning rate: 0.00163 +2026-04-14 16:33:47.391840: train_loss -0.4647 +2026-04-14 16:33:47.399846: val_loss -0.3628 +2026-04-14 16:33:47.401493: Pseudo dice [0.3143, 0.0, 0.7792, 0.0885, 0.2523, 0.8822, 0.7884] +2026-04-14 16:33:47.404307: Epoch time: 100.47 s +2026-04-14 16:33:48.662333: +2026-04-14 16:33:48.668930: Epoch 3469 +2026-04-14 16:33:48.670753: Current learning rate: 0.00162 +2026-04-14 16:35:29.451924: train_loss -0.4671 +2026-04-14 16:35:29.460812: val_loss -0.414 +2026-04-14 16:35:29.462913: Pseudo dice [0.7738, 0.0, 0.8491, 0.1913, 0.2764, 0.7888, 0.7594] +2026-04-14 16:35:29.465287: Epoch time: 100.79 s +2026-04-14 16:35:30.735379: +2026-04-14 16:35:30.738361: Epoch 3470 +2026-04-14 16:35:30.740424: Current learning rate: 0.00162 +2026-04-14 16:37:11.137504: train_loss -0.4748 +2026-04-14 16:37:11.144051: val_loss -0.2707 +2026-04-14 16:37:11.146503: Pseudo dice [0.4253, 0.0, 0.6586, 0.0, 0.4466, 0.7358, 0.7559] +2026-04-14 16:37:11.148779: Epoch time: 100.41 s +2026-04-14 16:37:12.413906: +2026-04-14 16:37:12.415918: Epoch 3471 +2026-04-14 16:37:12.417674: Current learning rate: 0.00162 +2026-04-14 16:38:53.307045: train_loss -0.4662 +2026-04-14 16:38:53.312977: val_loss -0.4032 +2026-04-14 16:38:53.315349: Pseudo dice [0.6799, 0.0, 0.7335, 0.0012, 0.3585, 0.8432, 0.8051] +2026-04-14 16:38:53.318044: Epoch time: 100.9 s +2026-04-14 16:38:54.654013: +2026-04-14 16:38:54.656196: Epoch 3472 +2026-04-14 16:38:54.657956: Current learning rate: 0.00162 +2026-04-14 16:40:35.322858: train_loss -0.4638 +2026-04-14 16:40:35.330127: val_loss -0.4095 +2026-04-14 16:40:35.332167: Pseudo dice [0.7775, 0.0, 0.7573, 0.0, 0.3653, 0.8379, 0.849] +2026-04-14 16:40:35.334873: Epoch time: 100.67 s +2026-04-14 16:40:36.643244: +2026-04-14 16:40:36.644906: Epoch 3473 +2026-04-14 16:40:36.647017: Current learning rate: 0.00161 +2026-04-14 16:42:17.103988: train_loss -0.4685 +2026-04-14 16:42:17.112689: val_loss -0.4527 +2026-04-14 16:42:17.115715: Pseudo dice [0.6325, 0.0, 0.8436, 0.1499, 0.6143, 0.738, 0.9149] +2026-04-14 16:42:17.118038: Epoch time: 100.46 s +2026-04-14 16:42:18.373821: +2026-04-14 16:42:18.376388: Epoch 3474 +2026-04-14 16:42:18.378932: Current learning rate: 0.00161 +2026-04-14 16:43:58.729084: train_loss -0.4484 +2026-04-14 16:43:58.735228: val_loss -0.421 +2026-04-14 16:43:58.737540: Pseudo dice [0.726, 0.0, 0.8246, 0.7909, 0.3526, 0.8225, 0.8135] +2026-04-14 16:43:58.740103: Epoch time: 100.36 s +2026-04-14 16:43:59.988168: +2026-04-14 16:43:59.990544: Epoch 3475 +2026-04-14 16:43:59.993477: Current learning rate: 0.00161 +2026-04-14 16:45:40.373564: train_loss -0.4651 +2026-04-14 16:45:40.382306: val_loss -0.4181 +2026-04-14 16:45:40.384571: Pseudo dice [0.2314, 0.0, 0.8074, 0.0, 0.2731, 0.8459, 0.7652] +2026-04-14 16:45:40.387600: Epoch time: 100.39 s +2026-04-14 16:45:41.646369: +2026-04-14 16:45:41.648242: Epoch 3476 +2026-04-14 16:45:41.649988: Current learning rate: 0.00161 +2026-04-14 16:47:21.849367: train_loss -0.4621 +2026-04-14 16:47:21.855315: val_loss -0.3771 +2026-04-14 16:47:21.857433: Pseudo dice [0.0, 0.0, 0.7539, 0.1381, 0.39, 0.732, 0.6837] +2026-04-14 16:47:21.860008: Epoch time: 100.21 s +2026-04-14 16:47:23.116137: +2026-04-14 16:47:23.118263: Epoch 3477 +2026-04-14 16:47:23.119913: Current learning rate: 0.0016 +2026-04-14 16:49:03.792817: train_loss -0.4785 +2026-04-14 16:49:03.799682: val_loss -0.4163 +2026-04-14 16:49:03.802163: Pseudo dice [0.5836, 0.0, 0.4769, 0.0, 0.4354, 0.7395, 0.7655] +2026-04-14 16:49:03.804741: Epoch time: 100.68 s +2026-04-14 16:49:05.072774: +2026-04-14 16:49:05.077892: Epoch 3478 +2026-04-14 16:49:05.079952: Current learning rate: 0.0016 +2026-04-14 16:50:45.866055: train_loss -0.4678 +2026-04-14 16:50:45.875567: val_loss -0.4544 +2026-04-14 16:50:45.881222: Pseudo dice [0.6571, 0.0, 0.6413, 0.692, 0.607, 0.8099, 0.743] +2026-04-14 16:50:45.886233: Epoch time: 100.8 s +2026-04-14 16:50:47.146407: +2026-04-14 16:50:47.148629: Epoch 3479 +2026-04-14 16:50:47.150532: Current learning rate: 0.0016 +2026-04-14 16:52:27.417993: train_loss -0.4576 +2026-04-14 16:52:27.424307: val_loss -0.4241 +2026-04-14 16:52:27.427087: Pseudo dice [0.6045, 0.0, 0.6698, 0.0, 0.432, 0.6838, 0.877] +2026-04-14 16:52:27.430109: Epoch time: 100.27 s +2026-04-14 16:52:28.695162: +2026-04-14 16:52:28.697155: Epoch 3480 +2026-04-14 16:52:28.699453: Current learning rate: 0.00159 +2026-04-14 16:54:09.246320: train_loss -0.4634 +2026-04-14 16:54:09.253371: val_loss -0.3707 +2026-04-14 16:54:09.255672: Pseudo dice [0.7329, 0.0, 0.3219, 0.0649, 0.5426, 0.7887, 0.6555] +2026-04-14 16:54:09.258470: Epoch time: 100.55 s +2026-04-14 16:54:10.545916: +2026-04-14 16:54:10.547932: Epoch 3481 +2026-04-14 16:54:10.549505: Current learning rate: 0.00159 +2026-04-14 16:55:50.969168: train_loss -0.4759 +2026-04-14 16:55:50.975904: val_loss -0.4386 +2026-04-14 16:55:50.978416: Pseudo dice [0.6855, 0.0, 0.824, 0.6611, 0.4438, 0.863, 0.9103] +2026-04-14 16:55:50.980955: Epoch time: 100.43 s +2026-04-14 16:55:52.254997: +2026-04-14 16:55:52.257275: Epoch 3482 +2026-04-14 16:55:52.259862: Current learning rate: 0.00159 +2026-04-14 16:57:34.121475: train_loss -0.4703 +2026-04-14 16:57:34.127331: val_loss -0.439 +2026-04-14 16:57:34.130128: Pseudo dice [0.5184, 0.0, 0.7992, 0.8696, 0.5218, 0.8178, 0.5302] +2026-04-14 16:57:34.132953: Epoch time: 101.87 s +2026-04-14 16:57:35.480273: +2026-04-14 16:57:35.497890: Epoch 3483 +2026-04-14 16:57:35.501042: Current learning rate: 0.00159 +2026-04-14 16:59:16.322854: train_loss -0.4747 +2026-04-14 16:59:16.328608: val_loss -0.3638 +2026-04-14 16:59:16.330858: Pseudo dice [0.7051, 0.0, 0.822, 0.1984, 0.2473, 0.9042, 0.6734] +2026-04-14 16:59:16.333104: Epoch time: 100.85 s +2026-04-14 16:59:17.598857: +2026-04-14 16:59:17.605952: Epoch 3484 +2026-04-14 16:59:17.612476: Current learning rate: 0.00158 +2026-04-14 17:00:58.089214: train_loss -0.4712 +2026-04-14 17:00:58.104409: val_loss -0.385 +2026-04-14 17:00:58.106917: Pseudo dice [0.7495, 0.0, 0.5159, 0.0009, 0.2591, 0.8933, 0.7849] +2026-04-14 17:00:58.110261: Epoch time: 100.49 s +2026-04-14 17:00:59.366398: +2026-04-14 17:00:59.368614: Epoch 3485 +2026-04-14 17:00:59.370339: Current learning rate: 0.00158 +2026-04-14 17:02:39.796662: train_loss -0.4837 +2026-04-14 17:02:39.805428: val_loss -0.4319 +2026-04-14 17:02:39.807551: Pseudo dice [0.7383, 0.0, 0.7489, 0.0, 0.5369, 0.8383, 0.7555] +2026-04-14 17:02:39.810120: Epoch time: 100.43 s +2026-04-14 17:02:41.072262: +2026-04-14 17:02:41.075625: Epoch 3486 +2026-04-14 17:02:41.077213: Current learning rate: 0.00158 +2026-04-14 17:04:21.567436: train_loss -0.4829 +2026-04-14 17:04:21.578273: val_loss -0.4583 +2026-04-14 17:04:21.580254: Pseudo dice [0.6527, 0.0, 0.7545, 0.0364, 0.365, 0.8364, 0.8418] +2026-04-14 17:04:21.582983: Epoch time: 100.5 s +2026-04-14 17:04:22.839666: +2026-04-14 17:04:22.841678: Epoch 3487 +2026-04-14 17:04:22.843539: Current learning rate: 0.00157 +2026-04-14 17:06:03.238982: train_loss -0.4662 +2026-04-14 17:06:03.245191: val_loss -0.4134 +2026-04-14 17:06:03.247297: Pseudo dice [0.6001, 0.0, 0.8437, 0.4113, 0.3346, 0.7598, 0.7986] +2026-04-14 17:06:03.250457: Epoch time: 100.4 s +2026-04-14 17:06:04.532738: +2026-04-14 17:06:04.534691: Epoch 3488 +2026-04-14 17:06:04.536447: Current learning rate: 0.00157 +2026-04-14 17:07:44.967467: train_loss -0.474 +2026-04-14 17:07:44.974583: val_loss -0.3701 +2026-04-14 17:07:44.976818: Pseudo dice [0.8269, 0.0, 0.6035, 0.0618, 0.3983, 0.8418, 0.5901] +2026-04-14 17:07:44.979092: Epoch time: 100.44 s +2026-04-14 17:07:46.243881: +2026-04-14 17:07:46.245722: Epoch 3489 +2026-04-14 17:07:46.247751: Current learning rate: 0.00157 +2026-04-14 17:09:26.531736: train_loss -0.4606 +2026-04-14 17:09:26.540746: val_loss -0.3553 +2026-04-14 17:09:26.544045: Pseudo dice [0.6498, 0.0, 0.7634, 0.0, 0.548, 0.7672, 0.567] +2026-04-14 17:09:26.546951: Epoch time: 100.29 s +2026-04-14 17:09:27.805322: +2026-04-14 17:09:27.807402: Epoch 3490 +2026-04-14 17:09:27.809306: Current learning rate: 0.00157 +2026-04-14 17:11:08.275776: train_loss -0.4698 +2026-04-14 17:11:08.282485: val_loss -0.4086 +2026-04-14 17:11:08.284503: Pseudo dice [0.7311, 0.0, 0.769, 0.6768, 0.4696, 0.696, 0.8824] +2026-04-14 17:11:08.287216: Epoch time: 100.47 s +2026-04-14 17:11:09.583515: +2026-04-14 17:11:09.586258: Epoch 3491 +2026-04-14 17:11:09.588424: Current learning rate: 0.00156 +2026-04-14 17:12:50.009984: train_loss -0.4658 +2026-04-14 17:12:50.016860: val_loss -0.3829 +2026-04-14 17:12:50.018950: Pseudo dice [0.5043, 0.0, 0.7631, 0.0, 0.4514, 0.8706, 0.538] +2026-04-14 17:12:50.021353: Epoch time: 100.43 s +2026-04-14 17:12:51.293818: +2026-04-14 17:12:51.298133: Epoch 3492 +2026-04-14 17:12:51.301027: Current learning rate: 0.00156 +2026-04-14 17:14:31.607301: train_loss -0.4814 +2026-04-14 17:14:31.615039: val_loss -0.4223 +2026-04-14 17:14:31.618358: Pseudo dice [0.5875, 0.0, 0.7934, 0.2164, 0.426, 0.8318, 0.8389] +2026-04-14 17:14:31.621115: Epoch time: 100.32 s +2026-04-14 17:14:32.907205: +2026-04-14 17:14:32.909362: Epoch 3493 +2026-04-14 17:14:32.911005: Current learning rate: 0.00156 +2026-04-14 17:16:13.193243: train_loss -0.474 +2026-04-14 17:16:13.199922: val_loss -0.319 +2026-04-14 17:16:13.202522: Pseudo dice [0.7648, 0.0, 0.4058, 0.0001, 0.3024, 0.7982, 0.9065] +2026-04-14 17:16:13.205761: Epoch time: 100.29 s +2026-04-14 17:16:14.463846: +2026-04-14 17:16:14.465795: Epoch 3494 +2026-04-14 17:16:14.467613: Current learning rate: 0.00156 +2026-04-14 17:17:54.923289: train_loss -0.4751 +2026-04-14 17:17:54.928782: val_loss -0.4567 +2026-04-14 17:17:54.931144: Pseudo dice [0.7877, 0.0, 0.7891, 0.0029, 0.5104, 0.7907, 0.8961] +2026-04-14 17:17:54.933697: Epoch time: 100.46 s +2026-04-14 17:17:56.243255: +2026-04-14 17:17:56.246015: Epoch 3495 +2026-04-14 17:17:56.247810: Current learning rate: 0.00155 +2026-04-14 17:19:36.549303: train_loss -0.4872 +2026-04-14 17:19:36.556378: val_loss -0.4324 +2026-04-14 17:19:36.558761: Pseudo dice [0.7269, 0.0, 0.7003, 0.0, 0.4019, 0.8652, 0.7275] +2026-04-14 17:19:36.562253: Epoch time: 100.31 s +2026-04-14 17:19:37.828524: +2026-04-14 17:19:37.831136: Epoch 3496 +2026-04-14 17:19:37.833204: Current learning rate: 0.00155 +2026-04-14 17:21:18.465304: train_loss -0.4814 +2026-04-14 17:21:18.480602: val_loss -0.4632 +2026-04-14 17:21:18.482992: Pseudo dice [0.6236, 0.0, 0.7942, 0.6873, 0.5561, 0.8574, 0.8629] +2026-04-14 17:21:18.485357: Epoch time: 100.64 s +2026-04-14 17:21:19.755073: +2026-04-14 17:21:19.757107: Epoch 3497 +2026-04-14 17:21:19.759006: Current learning rate: 0.00155 +2026-04-14 17:23:00.238643: train_loss -0.4733 +2026-04-14 17:23:00.244499: val_loss -0.4614 +2026-04-14 17:23:00.247024: Pseudo dice [0.8036, 0.0, 0.7912, 0.8163, 0.7312, 0.8772, 0.8728] +2026-04-14 17:23:00.249544: Epoch time: 100.49 s +2026-04-14 17:23:01.508194: +2026-04-14 17:23:01.510033: Epoch 3498 +2026-04-14 17:23:01.511783: Current learning rate: 0.00154 +2026-04-14 17:24:41.853558: train_loss -0.4799 +2026-04-14 17:24:41.859801: val_loss -0.4265 +2026-04-14 17:24:41.862313: Pseudo dice [0.7003, 0.0, 0.6659, 0.0, 0.3578, 0.6509, 0.7474] +2026-04-14 17:24:41.865906: Epoch time: 100.35 s +2026-04-14 17:24:43.142946: +2026-04-14 17:24:43.145022: Epoch 3499 +2026-04-14 17:24:43.146652: Current learning rate: 0.00154 +2026-04-14 17:26:23.470565: train_loss -0.4798 +2026-04-14 17:26:23.476362: val_loss -0.4457 +2026-04-14 17:26:23.478578: Pseudo dice [0.715, 0.0, 0.7436, 0.6668, 0.4712, 0.8361, 0.6918] +2026-04-14 17:26:23.481410: Epoch time: 100.33 s +2026-04-14 17:26:26.574722: +2026-04-14 17:26:26.576522: Epoch 3500 +2026-04-14 17:26:26.578614: Current learning rate: 0.00154 +2026-04-14 17:28:06.783463: train_loss -0.4828 +2026-04-14 17:28:06.790680: val_loss -0.4446 +2026-04-14 17:28:06.793810: Pseudo dice [0.8253, 0.0, 0.8432, 0.2477, 0.4807, 0.8928, 0.8654] +2026-04-14 17:28:06.796455: Epoch time: 100.21 s +2026-04-14 17:28:08.073379: +2026-04-14 17:28:08.075603: Epoch 3501 +2026-04-14 17:28:08.077424: Current learning rate: 0.00154 +2026-04-14 17:29:48.417525: train_loss -0.4765 +2026-04-14 17:29:48.425122: val_loss -0.4587 +2026-04-14 17:29:48.428829: Pseudo dice [0.4298, 0.0, 0.8258, 0.8085, 0.5679, 0.8458, 0.9126] +2026-04-14 17:29:48.431453: Epoch time: 100.35 s +2026-04-14 17:29:48.437531: Yayy! New best EMA pseudo Dice: 0.5471 +2026-04-14 17:29:52.421006: +2026-04-14 17:29:52.423226: Epoch 3502 +2026-04-14 17:29:52.425023: Current learning rate: 0.00153 +2026-04-14 17:31:32.774074: train_loss -0.4856 +2026-04-14 17:31:32.779524: val_loss -0.4108 +2026-04-14 17:31:32.781502: Pseudo dice [0.5332, 0.0, 0.675, 0.0, 0.3654, 0.8499, 0.8872] +2026-04-14 17:31:32.783539: Epoch time: 100.36 s +2026-04-14 17:31:34.054958: +2026-04-14 17:31:34.059792: Epoch 3503 +2026-04-14 17:31:34.061720: Current learning rate: 0.00153 +2026-04-14 17:33:15.204594: train_loss -0.4748 +2026-04-14 17:33:15.212890: val_loss -0.4442 +2026-04-14 17:33:15.214938: Pseudo dice [0.6698, 0.0, 0.826, 0.0, 0.5163, 0.8768, 0.8622] +2026-04-14 17:33:15.217067: Epoch time: 101.15 s +2026-04-14 17:33:16.503778: +2026-04-14 17:33:16.505967: Epoch 3504 +2026-04-14 17:33:16.507452: Current learning rate: 0.00153 +2026-04-14 17:34:57.107763: train_loss -0.4746 +2026-04-14 17:34:57.119346: val_loss -0.4276 +2026-04-14 17:34:57.121502: Pseudo dice [0.5971, 0.0, 0.684, 0.0441, 0.5131, 0.6144, 0.7758] +2026-04-14 17:34:57.124252: Epoch time: 100.61 s +2026-04-14 17:34:58.383486: +2026-04-14 17:34:58.385827: Epoch 3505 +2026-04-14 17:34:58.388098: Current learning rate: 0.00153 +2026-04-14 17:36:39.564596: train_loss -0.4819 +2026-04-14 17:36:39.570996: val_loss -0.4213 +2026-04-14 17:36:39.573064: Pseudo dice [0.4661, 0.0, 0.8178, 0.0, 0.5374, 0.6921, 0.6959] +2026-04-14 17:36:39.576152: Epoch time: 101.18 s +2026-04-14 17:36:40.885224: +2026-04-14 17:36:40.887590: Epoch 3506 +2026-04-14 17:36:40.889446: Current learning rate: 0.00152 +2026-04-14 17:38:21.450483: train_loss -0.4648 +2026-04-14 17:38:21.456381: val_loss -0.3734 +2026-04-14 17:38:21.458591: Pseudo dice [0.6633, 0.0, 0.6392, 0.0195, 0.5719, 0.6134, 0.7714] +2026-04-14 17:38:21.460824: Epoch time: 100.57 s +2026-04-14 17:38:22.728071: +2026-04-14 17:38:22.730141: Epoch 3507 +2026-04-14 17:38:22.732148: Current learning rate: 0.00152 +2026-04-14 17:40:03.080419: train_loss -0.4723 +2026-04-14 17:40:03.088706: val_loss -0.4132 +2026-04-14 17:40:03.091443: Pseudo dice [0.4133, 0.0, 0.7601, 0.0477, 0.6816, 0.6178, 0.9145] +2026-04-14 17:40:03.094946: Epoch time: 100.36 s +2026-04-14 17:40:04.354635: +2026-04-14 17:40:04.356788: Epoch 3508 +2026-04-14 17:40:04.359066: Current learning rate: 0.00152 +2026-04-14 17:41:45.579108: train_loss -0.4884 +2026-04-14 17:41:45.588025: val_loss -0.4163 +2026-04-14 17:41:45.590824: Pseudo dice [0.7047, 0.0, 0.6429, 0.4739, 0.3953, 0.826, 0.4709] +2026-04-14 17:41:45.594336: Epoch time: 101.23 s +2026-04-14 17:41:46.857189: +2026-04-14 17:41:46.859195: Epoch 3509 +2026-04-14 17:41:46.861497: Current learning rate: 0.00151 +2026-04-14 17:43:27.419425: train_loss -0.4652 +2026-04-14 17:43:27.427411: val_loss -0.3586 +2026-04-14 17:43:27.430089: Pseudo dice [0.6722, 0.0, 0.5065, 0.0432, 0.5005, 0.2861, 0.8052] +2026-04-14 17:43:27.432553: Epoch time: 100.57 s +2026-04-14 17:43:28.708696: +2026-04-14 17:43:28.711687: Epoch 3510 +2026-04-14 17:43:28.713298: Current learning rate: 0.00151 +2026-04-14 17:45:09.054820: train_loss -0.4746 +2026-04-14 17:45:09.064941: val_loss -0.3578 +2026-04-14 17:45:09.075013: Pseudo dice [0.6082, 0.0, 0.7228, 0.0656, 0.3567, 0.7248, 0.6891] +2026-04-14 17:45:09.086219: Epoch time: 100.35 s +2026-04-14 17:45:10.357177: +2026-04-14 17:45:10.360084: Epoch 3511 +2026-04-14 17:45:10.364902: Current learning rate: 0.00151 +2026-04-14 17:46:50.848575: train_loss -0.4586 +2026-04-14 17:46:50.855615: val_loss -0.428 +2026-04-14 17:46:50.857716: Pseudo dice [0.639, 0.0, 0.852, 0.8307, 0.3544, 0.5998, 0.8899] +2026-04-14 17:46:50.860656: Epoch time: 100.49 s +2026-04-14 17:46:52.153613: +2026-04-14 17:46:52.157976: Epoch 3512 +2026-04-14 17:46:52.159847: Current learning rate: 0.00151 +2026-04-14 17:48:33.737165: train_loss -0.4635 +2026-04-14 17:48:33.743753: val_loss -0.4361 +2026-04-14 17:48:33.746142: Pseudo dice [0.574, 0.0, 0.8158, 0.1674, 0.4153, 0.5846, 0.8615] +2026-04-14 17:48:33.748664: Epoch time: 101.59 s +2026-04-14 17:48:35.020576: +2026-04-14 17:48:35.022544: Epoch 3513 +2026-04-14 17:48:35.024405: Current learning rate: 0.0015 +2026-04-14 17:50:15.508890: train_loss -0.4719 +2026-04-14 17:50:15.515678: val_loss -0.3502 +2026-04-14 17:50:15.518174: Pseudo dice [0.7856, 0.0, 0.7576, 0.0, 0.4694, 0.8253, 0.8173] +2026-04-14 17:50:15.520158: Epoch time: 100.49 s +2026-04-14 17:50:16.779418: +2026-04-14 17:50:16.781395: Epoch 3514 +2026-04-14 17:50:16.783299: Current learning rate: 0.0015 +2026-04-14 17:51:56.909499: train_loss -0.4826 +2026-04-14 17:51:56.916930: val_loss -0.4188 +2026-04-14 17:51:56.919971: Pseudo dice [0.305, 0.0, 0.6339, 0.0416, 0.2991, 0.7959, 0.9019] +2026-04-14 17:51:56.922996: Epoch time: 100.13 s +2026-04-14 17:51:58.173330: +2026-04-14 17:51:58.175407: Epoch 3515 +2026-04-14 17:51:58.177334: Current learning rate: 0.0015 +2026-04-14 17:53:39.130771: train_loss -0.4782 +2026-04-14 17:53:39.137438: val_loss -0.3004 +2026-04-14 17:53:39.140671: Pseudo dice [0.5456, 0.0, 0.4115, 0.0204, 0.4141, 0.7932, 0.7067] +2026-04-14 17:53:39.143869: Epoch time: 100.96 s +2026-04-14 17:53:40.383530: +2026-04-14 17:53:40.385874: Epoch 3516 +2026-04-14 17:53:40.387875: Current learning rate: 0.00149 +2026-04-14 17:55:21.947173: train_loss -0.47 +2026-04-14 17:55:21.953592: val_loss -0.4461 +2026-04-14 17:55:21.956547: Pseudo dice [0.8722, 0.0, 0.78, 0.6563, 0.4675, 0.6936, 0.8472] +2026-04-14 17:55:21.959601: Epoch time: 101.57 s +2026-04-14 17:55:23.243714: +2026-04-14 17:55:23.247204: Epoch 3517 +2026-04-14 17:55:23.250725: Current learning rate: 0.00149 +2026-04-14 17:57:03.706341: train_loss -0.4675 +2026-04-14 17:57:03.714302: val_loss -0.4418 +2026-04-14 17:57:03.722792: Pseudo dice [0.8631, 0.0, 0.8501, 0.4615, 0.6681, 0.6968, 0.4645] +2026-04-14 17:57:03.732607: Epoch time: 100.47 s +2026-04-14 17:57:04.994870: +2026-04-14 17:57:04.996798: Epoch 3518 +2026-04-14 17:57:04.998864: Current learning rate: 0.00149 +2026-04-14 17:58:45.600720: train_loss -0.4773 +2026-04-14 17:58:45.609166: val_loss -0.421 +2026-04-14 17:58:45.611480: Pseudo dice [0.7867, 0.0, 0.8248, 0.7443, 0.364, 0.6576, 0.6582] +2026-04-14 17:58:45.614292: Epoch time: 100.61 s +2026-04-14 17:58:46.904062: +2026-04-14 17:58:46.906510: Epoch 3519 +2026-04-14 17:58:46.908566: Current learning rate: 0.00149 +2026-04-14 18:00:27.670277: train_loss -0.4631 +2026-04-14 18:00:27.676556: val_loss -0.382 +2026-04-14 18:00:27.678495: Pseudo dice [0.7954, 0.0, 0.6991, 0.0, 0.4982, 0.6678, 0.893] +2026-04-14 18:00:27.682400: Epoch time: 100.77 s +2026-04-14 18:00:28.957771: +2026-04-14 18:00:28.960510: Epoch 3520 +2026-04-14 18:00:28.962593: Current learning rate: 0.00148 +2026-04-14 18:02:09.371234: train_loss -0.4634 +2026-04-14 18:02:09.376977: val_loss -0.395 +2026-04-14 18:02:09.379023: Pseudo dice [0.471, 0.0, 0.6162, 0.151, 0.4424, 0.8074, 0.7155] +2026-04-14 18:02:09.381591: Epoch time: 100.42 s +2026-04-14 18:02:10.650489: +2026-04-14 18:02:10.652495: Epoch 3521 +2026-04-14 18:02:10.654313: Current learning rate: 0.00148 +2026-04-14 18:03:52.376131: train_loss -0.4731 +2026-04-14 18:03:52.382695: val_loss -0.4077 +2026-04-14 18:03:52.386053: Pseudo dice [0.5189, 0.0, 0.6276, 0.5448, 0.4287, 0.8357, 0.6508] +2026-04-14 18:03:52.388893: Epoch time: 101.73 s +2026-04-14 18:03:53.669163: +2026-04-14 18:03:53.670994: Epoch 3522 +2026-04-14 18:03:53.672873: Current learning rate: 0.00148 +2026-04-14 18:05:34.110461: train_loss -0.466 +2026-04-14 18:05:34.118258: val_loss -0.3851 +2026-04-14 18:05:34.120779: Pseudo dice [0.7431, 0.0, 0.3945, 0.0458, 0.3709, 0.8213, 0.878] +2026-04-14 18:05:34.123907: Epoch time: 100.44 s +2026-04-14 18:05:35.408319: +2026-04-14 18:05:35.410683: Epoch 3523 +2026-04-14 18:05:35.412757: Current learning rate: 0.00148 +2026-04-14 18:07:15.942108: train_loss -0.47 +2026-04-14 18:07:15.949000: val_loss -0.4291 +2026-04-14 18:07:15.951726: Pseudo dice [0.8178, 0.0, 0.6979, 0.8186, 0.4579, 0.7126, 0.6037] +2026-04-14 18:07:15.954119: Epoch time: 100.54 s +2026-04-14 18:07:17.215884: +2026-04-14 18:07:17.217803: Epoch 3524 +2026-04-14 18:07:17.219644: Current learning rate: 0.00147 +2026-04-14 18:08:57.624890: train_loss -0.4666 +2026-04-14 18:08:57.636701: val_loss -0.4288 +2026-04-14 18:08:57.639489: Pseudo dice [0.6984, 0.0, 0.7207, 0.9071, 0.6111, 0.7758, 0.8101] +2026-04-14 18:08:57.641932: Epoch time: 100.41 s +2026-04-14 18:08:58.911298: +2026-04-14 18:08:58.913316: Epoch 3525 +2026-04-14 18:08:58.915197: Current learning rate: 0.00147 +2026-04-14 18:10:39.409792: train_loss -0.4702 +2026-04-14 18:10:39.417259: val_loss -0.4086 +2026-04-14 18:10:39.419404: Pseudo dice [0.7114, 0.0, 0.7105, 0.5815, 0.5936, 0.7289, 0.7305] +2026-04-14 18:10:39.424000: Epoch time: 100.5 s +2026-04-14 18:10:40.746369: +2026-04-14 18:10:40.748510: Epoch 3526 +2026-04-14 18:10:40.750100: Current learning rate: 0.00147 +2026-04-14 18:12:20.921833: train_loss -0.4902 +2026-04-14 18:12:20.928238: val_loss -0.4465 +2026-04-14 18:12:20.929877: Pseudo dice [0.8426, 0.0, 0.7966, 0.006, 0.4635, 0.626, 0.9153] +2026-04-14 18:12:20.932313: Epoch time: 100.18 s +2026-04-14 18:12:22.194286: +2026-04-14 18:12:22.196460: Epoch 3527 +2026-04-14 18:12:22.198290: Current learning rate: 0.00146 +2026-04-14 18:14:02.500653: train_loss -0.4819 +2026-04-14 18:14:02.508978: val_loss -0.4287 +2026-04-14 18:14:02.512023: Pseudo dice [0.1871, 0.0, 0.7541, 0.4236, 0.4722, 0.7514, 0.6044] +2026-04-14 18:14:02.515883: Epoch time: 100.31 s +2026-04-14 18:14:03.766435: +2026-04-14 18:14:03.768688: Epoch 3528 +2026-04-14 18:14:03.770648: Current learning rate: 0.00146 +2026-04-14 18:15:43.841280: train_loss -0.4677 +2026-04-14 18:15:43.846933: val_loss -0.4291 +2026-04-14 18:15:43.848829: Pseudo dice [0.159, 0.0, 0.8219, 0.8653, 0.318, 0.7239, 0.9022] +2026-04-14 18:15:43.851151: Epoch time: 100.08 s +2026-04-14 18:15:45.116091: +2026-04-14 18:15:45.118237: Epoch 3529 +2026-04-14 18:15:45.119908: Current learning rate: 0.00146 +2026-04-14 18:17:25.665727: train_loss -0.4779 +2026-04-14 18:17:25.671593: val_loss -0.4219 +2026-04-14 18:17:25.673469: Pseudo dice [0.7206, 0.0, 0.7704, 0.192, 0.5465, 0.8324, 0.6519] +2026-04-14 18:17:25.675929: Epoch time: 100.55 s +2026-04-14 18:17:26.928539: +2026-04-14 18:17:26.937383: Epoch 3530 +2026-04-14 18:17:26.942490: Current learning rate: 0.00146 +2026-04-14 18:19:06.932992: train_loss -0.4741 +2026-04-14 18:19:06.948056: val_loss -0.4506 +2026-04-14 18:19:06.950823: Pseudo dice [0.7732, 0.0, 0.8103, 0.8943, 0.4658, 0.8337, 0.7929] +2026-04-14 18:19:06.953829: Epoch time: 100.01 s +2026-04-14 18:19:08.197392: +2026-04-14 18:19:08.199452: Epoch 3531 +2026-04-14 18:19:08.201110: Current learning rate: 0.00145 +2026-04-14 18:20:48.286282: train_loss -0.4639 +2026-04-14 18:20:48.292418: val_loss -0.4343 +2026-04-14 18:20:48.294100: Pseudo dice [0.6912, 0.0, 0.8066, 0.3995, 0.4244, 0.8163, 0.9002] +2026-04-14 18:20:48.296562: Epoch time: 100.09 s +2026-04-14 18:20:49.540211: +2026-04-14 18:20:49.542370: Epoch 3532 +2026-04-14 18:20:49.544057: Current learning rate: 0.00145 +2026-04-14 18:22:29.516943: train_loss -0.4598 +2026-04-14 18:22:29.522787: val_loss -0.4247 +2026-04-14 18:22:29.525161: Pseudo dice [0.8009, 0.0, 0.7603, 0.312, 0.286, 0.8071, 0.8435] +2026-04-14 18:22:29.528122: Epoch time: 99.98 s +2026-04-14 18:22:30.788081: +2026-04-14 18:22:30.790254: Epoch 3533 +2026-04-14 18:22:30.792022: Current learning rate: 0.00145 +2026-04-14 18:24:10.408691: train_loss -0.4833 +2026-04-14 18:24:10.414383: val_loss -0.4185 +2026-04-14 18:24:10.416904: Pseudo dice [0.7965, 0.0, 0.7611, 0.2441, 0.2536, 0.7728, 0.8969] +2026-04-14 18:24:10.419113: Epoch time: 99.62 s +2026-04-14 18:24:11.686244: +2026-04-14 18:24:11.688795: Epoch 3534 +2026-04-14 18:24:11.690243: Current learning rate: 0.00144 +2026-04-14 18:25:52.629477: train_loss -0.4644 +2026-04-14 18:25:52.637968: val_loss -0.4538 +2026-04-14 18:25:52.640763: Pseudo dice [0.622, 0.0, 0.8268, 0.783, 0.2727, 0.8181, 0.8401] +2026-04-14 18:25:52.644075: Epoch time: 100.95 s +2026-04-14 18:25:53.955814: +2026-04-14 18:25:53.958968: Epoch 3535 +2026-04-14 18:25:53.961216: Current learning rate: 0.00144 +2026-04-14 18:27:33.680421: train_loss -0.4787 +2026-04-14 18:27:33.692281: val_loss -0.4449 +2026-04-14 18:27:33.694676: Pseudo dice [0.7898, 0.0, 0.7374, 0.8699, 0.4046, 0.7758, 0.877] +2026-04-14 18:27:33.697743: Epoch time: 99.73 s +2026-04-14 18:27:33.700473: Yayy! New best EMA pseudo Dice: 0.5559 +2026-04-14 18:27:36.680695: +2026-04-14 18:27:36.683517: Epoch 3536 +2026-04-14 18:27:36.685470: Current learning rate: 0.00144 +2026-04-14 18:29:16.420230: train_loss -0.4748 +2026-04-14 18:29:16.429561: val_loss -0.4312 +2026-04-14 18:29:16.431619: Pseudo dice [0.7479, 0.0, 0.847, 0.7744, 0.3978, 0.5368, 0.8517] +2026-04-14 18:29:16.434188: Epoch time: 99.74 s +2026-04-14 18:29:16.436511: Yayy! New best EMA pseudo Dice: 0.5597 +2026-04-14 18:29:19.289455: +2026-04-14 18:29:19.292876: Epoch 3537 +2026-04-14 18:29:19.295078: Current learning rate: 0.00144 +2026-04-14 18:30:59.065742: train_loss -0.4807 +2026-04-14 18:30:59.071341: val_loss -0.3597 +2026-04-14 18:30:59.073396: Pseudo dice [0.5791, 0.0, 0.7702, 0.1274, 0.3897, 0.8554, 0.6822] +2026-04-14 18:30:59.075677: Epoch time: 99.78 s +2026-04-14 18:31:00.307683: +2026-04-14 18:31:00.309710: Epoch 3538 +2026-04-14 18:31:00.311952: Current learning rate: 0.00143 +2026-04-14 18:32:40.344745: train_loss -0.4702 +2026-04-14 18:32:40.351985: val_loss -0.2342 +2026-04-14 18:32:40.355327: Pseudo dice [0.7357, 0.0, 0.6282, 0.0463, 0.3787, 0.748, 0.73] +2026-04-14 18:32:40.357759: Epoch time: 100.04 s +2026-04-14 18:32:41.628256: +2026-04-14 18:32:41.630157: Epoch 3539 +2026-04-14 18:32:41.632410: Current learning rate: 0.00143 +2026-04-14 18:34:21.873891: train_loss -0.4835 +2026-04-14 18:34:21.884288: val_loss -0.4161 +2026-04-14 18:34:21.887525: Pseudo dice [0.5645, 0.0, 0.7448, 0.4683, 0.3452, 0.8183, 0.7373] +2026-04-14 18:34:21.890499: Epoch time: 100.25 s +2026-04-14 18:34:24.200018: +2026-04-14 18:34:24.202413: Epoch 3540 +2026-04-14 18:34:24.204170: Current learning rate: 0.00143 +2026-04-14 18:36:04.334693: train_loss -0.4804 +2026-04-14 18:36:04.343274: val_loss -0.4398 +2026-04-14 18:36:04.345792: Pseudo dice [0.6323, 0.0, 0.7972, 0.0, 0.3151, 0.7135, 0.8297] +2026-04-14 18:36:04.349394: Epoch time: 100.14 s +2026-04-14 18:36:05.621317: +2026-04-14 18:36:05.623563: Epoch 3541 +2026-04-14 18:36:05.625455: Current learning rate: 0.00142 +2026-04-14 18:37:46.104139: train_loss -0.4721 +2026-04-14 18:37:46.113274: val_loss -0.428 +2026-04-14 18:37:46.116154: Pseudo dice [0.5005, 0.0, 0.6789, 0.482, 0.3245, 0.7313, 0.8041] +2026-04-14 18:37:46.120009: Epoch time: 100.48 s +2026-04-14 18:37:47.389545: +2026-04-14 18:37:47.391624: Epoch 3542 +2026-04-14 18:37:47.393973: Current learning rate: 0.00142 +2026-04-14 18:39:27.889272: train_loss -0.4779 +2026-04-14 18:39:27.897489: val_loss -0.3922 +2026-04-14 18:39:27.899730: Pseudo dice [0.3714, 0.0, 0.6276, 0.1619, 0.4275, 0.8518, 0.7344] +2026-04-14 18:39:27.902539: Epoch time: 100.5 s +2026-04-14 18:39:29.161008: +2026-04-14 18:39:29.162966: Epoch 3543 +2026-04-14 18:39:29.164595: Current learning rate: 0.00142 +2026-04-14 18:41:09.340236: train_loss -0.4822 +2026-04-14 18:41:09.349906: val_loss -0.4368 +2026-04-14 18:41:09.352205: Pseudo dice [0.5168, 0.0, 0.8528, 0.9304, 0.1861, 0.8236, 0.6959] +2026-04-14 18:41:09.355626: Epoch time: 100.18 s +2026-04-14 18:41:10.619596: +2026-04-14 18:41:10.621911: Epoch 3544 +2026-04-14 18:41:10.623832: Current learning rate: 0.00142 +2026-04-14 18:42:50.520964: train_loss -0.4713 +2026-04-14 18:42:50.529373: val_loss -0.4547 +2026-04-14 18:42:50.532106: Pseudo dice [0.8213, 0.0, 0.7829, 0.8896, 0.5215, 0.827, 0.8209] +2026-04-14 18:42:50.534788: Epoch time: 99.9 s +2026-04-14 18:42:51.797354: +2026-04-14 18:42:51.799094: Epoch 3545 +2026-04-14 18:42:51.800864: Current learning rate: 0.00141 +2026-04-14 18:44:31.503280: train_loss -0.4843 +2026-04-14 18:44:31.510026: val_loss -0.4255 +2026-04-14 18:44:31.512082: Pseudo dice [0.6049, 0.0, 0.64, 0.4391, 0.4255, 0.8411, 0.8767] +2026-04-14 18:44:31.514625: Epoch time: 99.71 s +2026-04-14 18:44:32.785714: +2026-04-14 18:44:32.787572: Epoch 3546 +2026-04-14 18:44:32.789473: Current learning rate: 0.00141 +2026-04-14 18:46:12.684414: train_loss -0.4836 +2026-04-14 18:46:12.691647: val_loss -0.3617 +2026-04-14 18:46:12.694009: Pseudo dice [0.7201, 0.0, 0.7963, 0.0633, 0.4494, 0.3053, 0.7581] +2026-04-14 18:46:12.697351: Epoch time: 99.9 s +2026-04-14 18:46:13.943225: +2026-04-14 18:46:13.945316: Epoch 3547 +2026-04-14 18:46:13.947237: Current learning rate: 0.00141 +2026-04-14 18:47:53.989204: train_loss -0.473 +2026-04-14 18:47:53.996645: val_loss -0.4113 +2026-04-14 18:47:54.001164: Pseudo dice [0.7349, 0.0, 0.7269, 0.0, 0.415, 0.7931, 0.8919] +2026-04-14 18:47:54.003707: Epoch time: 100.05 s +2026-04-14 18:47:55.260071: +2026-04-14 18:47:55.262031: Epoch 3548 +2026-04-14 18:47:55.264166: Current learning rate: 0.00141 +2026-04-14 18:49:35.025881: train_loss -0.4812 +2026-04-14 18:49:35.033948: val_loss -0.4473 +2026-04-14 18:49:35.036199: Pseudo dice [0.6972, 0.0, 0.8114, 0.8694, 0.3571, 0.6721, 0.8771] +2026-04-14 18:49:35.039122: Epoch time: 99.77 s +2026-04-14 18:49:36.307618: +2026-04-14 18:49:36.309783: Epoch 3549 +2026-04-14 18:49:36.311984: Current learning rate: 0.0014 +2026-04-14 18:51:16.355064: train_loss -0.4744 +2026-04-14 18:51:16.362529: val_loss -0.4255 +2026-04-14 18:51:16.365423: Pseudo dice [0.802, 0.0, 0.7856, 0.0034, 0.3848, 0.8926, 0.8529] +2026-04-14 18:51:16.368547: Epoch time: 100.05 s +2026-04-14 18:51:19.496931: +2026-04-14 18:51:19.501060: Epoch 3550 +2026-04-14 18:51:19.503643: Current learning rate: 0.0014 +2026-04-14 18:52:59.359425: train_loss -0.4759 +2026-04-14 18:52:59.372669: val_loss -0.3662 +2026-04-14 18:52:59.374678: Pseudo dice [0.8032, 0.0, 0.6985, 0.164, 0.285, 0.7535, 0.8117] +2026-04-14 18:52:59.378885: Epoch time: 99.86 s +2026-04-14 18:53:00.624739: +2026-04-14 18:53:00.627770: Epoch 3551 +2026-04-14 18:53:00.629907: Current learning rate: 0.0014 +2026-04-14 18:54:41.057757: train_loss -0.4783 +2026-04-14 18:54:41.067515: val_loss -0.4156 +2026-04-14 18:54:41.073750: Pseudo dice [0.3112, 0.0, 0.6876, 0.8625, 0.348, 0.6319, 0.8709] +2026-04-14 18:54:41.077754: Epoch time: 100.43 s +2026-04-14 18:54:42.344978: +2026-04-14 18:54:42.346958: Epoch 3552 +2026-04-14 18:54:42.349530: Current learning rate: 0.00139 +2026-04-14 18:56:22.589289: train_loss -0.4777 +2026-04-14 18:56:22.597328: val_loss -0.4437 +2026-04-14 18:56:22.601220: Pseudo dice [0.8099, 0.0, 0.7767, 0.7105, 0.3946, 0.8898, 0.8072] +2026-04-14 18:56:22.604895: Epoch time: 100.25 s +2026-04-14 18:56:23.880838: +2026-04-14 18:56:23.883301: Epoch 3553 +2026-04-14 18:56:23.885469: Current learning rate: 0.00139 +2026-04-14 18:58:04.273583: train_loss -0.4837 +2026-04-14 18:58:04.282391: val_loss -0.4134 +2026-04-14 18:58:04.285121: Pseudo dice [0.6106, 0.0, 0.8181, 0.3903, 0.3431, 0.7672, 0.8084] +2026-04-14 18:58:04.289071: Epoch time: 100.39 s +2026-04-14 18:58:05.548455: +2026-04-14 18:58:05.551302: Epoch 3554 +2026-04-14 18:58:05.553626: Current learning rate: 0.00139 +2026-04-14 18:59:45.773380: train_loss -0.4813 +2026-04-14 18:59:45.790895: val_loss -0.3997 +2026-04-14 18:59:45.794842: Pseudo dice [0.0008, 0.0, 0.7199, 0.3483, 0.4168, 0.7345, 0.6464] +2026-04-14 18:59:45.798622: Epoch time: 100.23 s +2026-04-14 18:59:47.146813: +2026-04-14 18:59:47.149240: Epoch 3555 +2026-04-14 18:59:47.151796: Current learning rate: 0.00139 +2026-04-14 19:01:27.429183: train_loss -0.4751 +2026-04-14 19:01:27.435692: val_loss -0.4305 +2026-04-14 19:01:27.437915: Pseudo dice [0.7264, 0.0, 0.8604, 0.5319, 0.4437, 0.5614, 0.6811] +2026-04-14 19:01:27.440060: Epoch time: 100.29 s +2026-04-14 19:01:28.697498: +2026-04-14 19:01:28.699341: Epoch 3556 +2026-04-14 19:01:28.702136: Current learning rate: 0.00138 +2026-04-14 19:03:09.259065: train_loss -0.4782 +2026-04-14 19:03:09.269273: val_loss -0.3352 +2026-04-14 19:03:09.271935: Pseudo dice [0.1336, 0.0, 0.7508, 0.0549, 0.4551, 0.8126, 0.8577] +2026-04-14 19:03:09.274949: Epoch time: 100.56 s +2026-04-14 19:03:10.585964: +2026-04-14 19:03:10.588315: Epoch 3557 +2026-04-14 19:03:10.591187: Current learning rate: 0.00138 +2026-04-14 19:04:50.539179: train_loss -0.4775 +2026-04-14 19:04:50.544667: val_loss -0.4341 +2026-04-14 19:04:50.547594: Pseudo dice [0.7688, 0.0, 0.8512, 0.0008, 0.4421, 0.8429, 0.8929] +2026-04-14 19:04:50.550173: Epoch time: 99.96 s +2026-04-14 19:04:51.791173: +2026-04-14 19:04:51.794627: Epoch 3558 +2026-04-14 19:04:51.796930: Current learning rate: 0.00138 +2026-04-14 19:06:32.756876: train_loss -0.4768 +2026-04-14 19:06:32.771094: val_loss -0.3775 +2026-04-14 19:06:32.773942: Pseudo dice [0.5572, 0.0, 0.7978, 0.1388, 0.5199, 0.8787, 0.6901] +2026-04-14 19:06:32.777458: Epoch time: 100.97 s +2026-04-14 19:06:34.060388: +2026-04-14 19:06:34.062674: Epoch 3559 +2026-04-14 19:06:34.066046: Current learning rate: 0.00137 +2026-04-14 19:08:15.171496: train_loss -0.4763 +2026-04-14 19:08:15.179245: val_loss -0.3978 +2026-04-14 19:08:15.182056: Pseudo dice [0.4873, 0.0, 0.7658, 0.0, 0.2826, 0.8144, 0.7445] +2026-04-14 19:08:15.185254: Epoch time: 101.11 s +2026-04-14 19:08:16.450100: +2026-04-14 19:08:16.452314: Epoch 3560 +2026-04-14 19:08:16.455214: Current learning rate: 0.00137 +2026-04-14 19:09:56.186051: train_loss -0.4867 +2026-04-14 19:09:56.192688: val_loss -0.4465 +2026-04-14 19:09:56.195458: Pseudo dice [0.7399, 0.0, 0.8046, 0.344, 0.6034, 0.745, 0.9007] +2026-04-14 19:09:56.197413: Epoch time: 99.74 s +2026-04-14 19:09:57.443735: +2026-04-14 19:09:57.447359: Epoch 3561 +2026-04-14 19:09:57.449524: Current learning rate: 0.00137 +2026-04-14 19:11:37.736927: train_loss -0.4689 +2026-04-14 19:11:37.746770: val_loss -0.3759 +2026-04-14 19:11:37.749231: Pseudo dice [0.7096, 0.0, 0.6634, 0.1215, 0.4952, 0.857, 0.6999] +2026-04-14 19:11:37.751618: Epoch time: 100.3 s +2026-04-14 19:11:39.014878: +2026-04-14 19:11:39.020307: Epoch 3562 +2026-04-14 19:11:39.024363: Current learning rate: 0.00137 +2026-04-14 19:13:19.487079: train_loss -0.4926 +2026-04-14 19:13:19.494845: val_loss -0.4632 +2026-04-14 19:13:19.497296: Pseudo dice [0.7819, 0.0, 0.7654, 0.7206, 0.3853, 0.7347, 0.8718] +2026-04-14 19:13:19.500594: Epoch time: 100.48 s +2026-04-14 19:13:20.770499: +2026-04-14 19:13:20.772619: Epoch 3563 +2026-04-14 19:13:20.774854: Current learning rate: 0.00136 +2026-04-14 19:15:01.180824: train_loss -0.4868 +2026-04-14 19:15:01.188828: val_loss -0.3491 +2026-04-14 19:15:01.191004: Pseudo dice [0.8062, 0.0, 0.6734, 0.0597, 0.3616, 0.6279, 0.8321] +2026-04-14 19:15:01.193829: Epoch time: 100.41 s +2026-04-14 19:15:02.456434: +2026-04-14 19:15:02.458492: Epoch 3564 +2026-04-14 19:15:02.461916: Current learning rate: 0.00136 +2026-04-14 19:16:42.599541: train_loss -0.4738 +2026-04-14 19:16:42.605228: val_loss -0.3471 +2026-04-14 19:16:42.607976: Pseudo dice [0.594, 0.0, 0.6999, 0.1034, 0.2107, 0.7428, 0.5565] +2026-04-14 19:16:42.610371: Epoch time: 100.15 s +2026-04-14 19:16:43.869663: +2026-04-14 19:16:43.874116: Epoch 3565 +2026-04-14 19:16:43.876405: Current learning rate: 0.00136 +2026-04-14 19:18:24.824141: train_loss -0.4856 +2026-04-14 19:18:24.831769: val_loss -0.4172 +2026-04-14 19:18:24.834826: Pseudo dice [0.6833, 0.0, 0.8082, 0.025, 0.3839, 0.777, 0.6672] +2026-04-14 19:18:24.837758: Epoch time: 100.96 s +2026-04-14 19:18:26.116379: +2026-04-14 19:18:26.119044: Epoch 3566 +2026-04-14 19:18:26.121591: Current learning rate: 0.00135 +2026-04-14 19:20:07.504860: train_loss -0.4856 +2026-04-14 19:20:07.514483: val_loss -0.4172 +2026-04-14 19:20:07.516889: Pseudo dice [0.76, 0.0, 0.7397, 0.0, 0.4462, 0.8166, 0.7614] +2026-04-14 19:20:07.519532: Epoch time: 101.39 s +2026-04-14 19:20:08.782332: +2026-04-14 19:20:08.786228: Epoch 3567 +2026-04-14 19:20:08.789128: Current learning rate: 0.00135 +2026-04-14 19:21:48.985674: train_loss -0.4813 +2026-04-14 19:21:48.993474: val_loss -0.398 +2026-04-14 19:21:48.996905: Pseudo dice [0.7793, 0.0, 0.7294, 0.0001, 0.5223, 0.8368, 0.8344] +2026-04-14 19:21:49.001761: Epoch time: 100.21 s +2026-04-14 19:21:50.283219: +2026-04-14 19:21:50.285163: Epoch 3568 +2026-04-14 19:21:50.287964: Current learning rate: 0.00135 +2026-04-14 19:23:31.190209: train_loss -0.4883 +2026-04-14 19:23:31.197153: val_loss -0.4322 +2026-04-14 19:23:31.199570: Pseudo dice [0.7616, 0.0, 0.7848, 0.5811, 0.5491, 0.8713, 0.8421] +2026-04-14 19:23:31.203186: Epoch time: 100.91 s +2026-04-14 19:23:32.474561: +2026-04-14 19:23:32.477486: Epoch 3569 +2026-04-14 19:23:32.480548: Current learning rate: 0.00135 +2026-04-14 19:25:13.024884: train_loss -0.4783 +2026-04-14 19:25:13.031833: val_loss -0.3592 +2026-04-14 19:25:13.034048: Pseudo dice [0.7525, 0.0, 0.6379, 0.0627, 0.4374, 0.8538, 0.7086] +2026-04-14 19:25:13.036685: Epoch time: 100.55 s +2026-04-14 19:25:14.316915: +2026-04-14 19:25:14.319337: Epoch 3570 +2026-04-14 19:25:14.322205: Current learning rate: 0.00134 +2026-04-14 19:26:54.484243: train_loss -0.4808 +2026-04-14 19:26:54.491463: val_loss -0.4254 +2026-04-14 19:26:54.494314: Pseudo dice [0.3499, 0.0, 0.7645, 0.6927, 0.5108, 0.7563, 0.7191] +2026-04-14 19:26:54.496689: Epoch time: 100.17 s +2026-04-14 19:26:55.789617: +2026-04-14 19:26:55.792277: Epoch 3571 +2026-04-14 19:26:55.795293: Current learning rate: 0.00134 +2026-04-14 19:28:35.469805: train_loss -0.4774 +2026-04-14 19:28:35.477925: val_loss -0.4273 +2026-04-14 19:28:35.481206: Pseudo dice [0.6292, 0.0, 0.7828, 0.5417, 0.4311, 0.6865, 0.8902] +2026-04-14 19:28:35.484300: Epoch time: 99.68 s +2026-04-14 19:28:36.748079: +2026-04-14 19:28:36.750671: Epoch 3572 +2026-04-14 19:28:36.753407: Current learning rate: 0.00134 +2026-04-14 19:30:16.770359: train_loss -0.4793 +2026-04-14 19:30:16.778462: val_loss -0.4449 +2026-04-14 19:30:16.781411: Pseudo dice [0.5604, 0.0, 0.8236, 0.7976, 0.4972, 0.9013, 0.8212] +2026-04-14 19:30:16.784120: Epoch time: 100.03 s +2026-04-14 19:30:18.058663: +2026-04-14 19:30:18.061477: Epoch 3573 +2026-04-14 19:30:18.063916: Current learning rate: 0.00134 +2026-04-14 19:31:59.051647: train_loss -0.477 +2026-04-14 19:31:59.058536: val_loss -0.3904 +2026-04-14 19:31:59.060919: Pseudo dice [0.4693, 0.0, 0.7921, 0.0315, 0.5269, 0.8059, 0.8587] +2026-04-14 19:31:59.064192: Epoch time: 101.0 s +2026-04-14 19:32:00.346146: +2026-04-14 19:32:00.348902: Epoch 3574 +2026-04-14 19:32:00.351556: Current learning rate: 0.00133 +2026-04-14 19:33:40.463333: train_loss -0.4766 +2026-04-14 19:33:40.470561: val_loss -0.3961 +2026-04-14 19:33:40.474364: Pseudo dice [0.6486, 0.0, 0.7995, 0.2378, 0.5695, 0.8135, 0.7843] +2026-04-14 19:33:40.476701: Epoch time: 100.12 s +2026-04-14 19:33:41.740666: +2026-04-14 19:33:41.742865: Epoch 3575 +2026-04-14 19:33:41.745336: Current learning rate: 0.00133 +2026-04-14 19:35:23.063721: train_loss -0.4771 +2026-04-14 19:35:23.073110: val_loss -0.4421 +2026-04-14 19:35:23.076011: Pseudo dice [0.531, 0.0, 0.8418, 0.0007, 0.4454, 0.7995, 0.8783] +2026-04-14 19:35:23.080888: Epoch time: 101.33 s +2026-04-14 19:35:24.341191: +2026-04-14 19:35:24.345734: Epoch 3576 +2026-04-14 19:35:24.348366: Current learning rate: 0.00133 +2026-04-14 19:37:06.209012: train_loss -0.4783 +2026-04-14 19:37:06.216278: val_loss -0.4487 +2026-04-14 19:37:06.218702: Pseudo dice [0.7517, 0.0, 0.802, 0.7613, 0.2689, 0.8872, 0.9179] +2026-04-14 19:37:06.223358: Epoch time: 101.87 s +2026-04-14 19:37:07.488982: +2026-04-14 19:37:07.491009: Epoch 3577 +2026-04-14 19:37:07.493321: Current learning rate: 0.00132 +2026-04-14 19:38:49.135487: train_loss -0.4785 +2026-04-14 19:38:49.142929: val_loss -0.427 +2026-04-14 19:38:49.146568: Pseudo dice [0.6494, 0.0, 0.7786, 0.7647, 0.5719, 0.7964, 0.8324] +2026-04-14 19:38:49.150690: Epoch time: 101.65 s +2026-04-14 19:38:50.408990: +2026-04-14 19:38:50.413288: Epoch 3578 +2026-04-14 19:38:50.417349: Current learning rate: 0.00132 +2026-04-14 19:40:31.058315: train_loss -0.48 +2026-04-14 19:40:31.066226: val_loss -0.4231 +2026-04-14 19:40:31.068907: Pseudo dice [0.6793, 0.0, 0.6498, 0.0106, 0.4563, 0.8052, 0.6082] +2026-04-14 19:40:31.072085: Epoch time: 100.65 s +2026-04-14 19:40:33.450516: +2026-04-14 19:40:33.453741: Epoch 3579 +2026-04-14 19:40:33.456720: Current learning rate: 0.00132 +2026-04-14 19:42:14.268579: train_loss -0.4855 +2026-04-14 19:42:14.275657: val_loss -0.3993 +2026-04-14 19:42:14.278076: Pseudo dice [0.5071, 0.0, 0.6492, 0.1977, 0.5435, 0.881, 0.7184] +2026-04-14 19:42:14.280916: Epoch time: 100.82 s +2026-04-14 19:42:15.570005: +2026-04-14 19:42:15.572343: Epoch 3580 +2026-04-14 19:42:15.576709: Current learning rate: 0.00132 +2026-04-14 19:43:55.871988: train_loss -0.4767 +2026-04-14 19:43:55.881391: val_loss -0.3316 +2026-04-14 19:43:55.886380: Pseudo dice [0.4593, 0.0, 0.4344, 0.048, 0.6034, 0.7617, 0.8009] +2026-04-14 19:43:55.889882: Epoch time: 100.31 s +2026-04-14 19:43:57.159750: +2026-04-14 19:43:57.162117: Epoch 3581 +2026-04-14 19:43:57.164350: Current learning rate: 0.00131 +2026-04-14 19:45:38.189096: train_loss -0.4874 +2026-04-14 19:45:38.196394: val_loss -0.4116 +2026-04-14 19:45:38.199574: Pseudo dice [0.836, 0.0, 0.7652, 0.0, 0.4193, 0.9023, 0.6112] +2026-04-14 19:45:38.202688: Epoch time: 101.03 s +2026-04-14 19:45:39.490587: +2026-04-14 19:45:39.493588: Epoch 3582 +2026-04-14 19:45:39.495549: Current learning rate: 0.00131 +2026-04-14 19:47:19.867844: train_loss -0.4784 +2026-04-14 19:47:19.876120: val_loss -0.4375 +2026-04-14 19:47:19.879145: Pseudo dice [0.8126, 0.0, 0.813, 0.8292, 0.3842, 0.8009, 0.8882] +2026-04-14 19:47:19.881506: Epoch time: 100.38 s +2026-04-14 19:47:21.144150: +2026-04-14 19:47:21.146143: Epoch 3583 +2026-04-14 19:47:21.148108: Current learning rate: 0.00131 +2026-04-14 19:49:02.103002: train_loss -0.4836 +2026-04-14 19:49:02.111405: val_loss -0.4574 +2026-04-14 19:49:02.115269: Pseudo dice [0.6084, 0.0, 0.7709, 0.6763, 0.5466, 0.7791, 0.9157] +2026-04-14 19:49:02.118910: Epoch time: 100.96 s +2026-04-14 19:49:03.382488: +2026-04-14 19:49:03.384624: Epoch 3584 +2026-04-14 19:49:03.387418: Current learning rate: 0.0013 +2026-04-14 19:50:43.985219: train_loss -0.4802 +2026-04-14 19:50:43.993518: val_loss -0.4483 +2026-04-14 19:50:43.996263: Pseudo dice [0.8252, 0.0, 0.7289, 0.0, 0.6199, 0.8249, 0.8658] +2026-04-14 19:50:43.999045: Epoch time: 100.61 s +2026-04-14 19:50:45.275851: +2026-04-14 19:50:45.277813: Epoch 3585 +2026-04-14 19:50:45.279836: Current learning rate: 0.0013 +2026-04-14 19:52:26.051532: train_loss -0.4775 +2026-04-14 19:52:26.061180: val_loss -0.4034 +2026-04-14 19:52:26.063992: Pseudo dice [0.5624, 0.0, 0.6524, 0.1523, 0.2376, 0.8824, 0.6773] +2026-04-14 19:52:26.066574: Epoch time: 100.78 s +2026-04-14 19:52:27.348162: +2026-04-14 19:52:27.350637: Epoch 3586 +2026-04-14 19:52:27.353296: Current learning rate: 0.0013 +2026-04-14 19:54:08.289081: train_loss -0.478 +2026-04-14 19:54:08.296832: val_loss -0.3832 +2026-04-14 19:54:08.298890: Pseudo dice [0.4767, 0.0, 0.5649, 0.0623, 0.5595, 0.859, 0.5797] +2026-04-14 19:54:08.301594: Epoch time: 100.94 s +2026-04-14 19:54:09.547993: +2026-04-14 19:54:09.550522: Epoch 3587 +2026-04-14 19:54:09.552975: Current learning rate: 0.0013 +2026-04-14 19:55:50.141823: train_loss -0.4811 +2026-04-14 19:55:50.150207: val_loss -0.3757 +2026-04-14 19:55:50.153304: Pseudo dice [0.7481, 0.0, 0.7821, 0.1472, 0.477, 0.687, 0.6141] +2026-04-14 19:55:50.157745: Epoch time: 100.6 s +2026-04-14 19:55:51.447031: +2026-04-14 19:55:51.448947: Epoch 3588 +2026-04-14 19:55:51.451449: Current learning rate: 0.00129 +2026-04-14 19:57:32.789297: train_loss -0.4838 +2026-04-14 19:57:32.797831: val_loss -0.4287 +2026-04-14 19:57:32.801409: Pseudo dice [0.7937, 0.0, 0.6155, 0.1758, 0.476, 0.8366, 0.7556] +2026-04-14 19:57:32.804180: Epoch time: 101.35 s +2026-04-14 19:57:34.084334: +2026-04-14 19:57:34.086598: Epoch 3589 +2026-04-14 19:57:34.089087: Current learning rate: 0.00129 +2026-04-14 19:59:16.081197: train_loss -0.4797 +2026-04-14 19:59:16.100386: val_loss -0.4397 +2026-04-14 19:59:16.104891: Pseudo dice [0.681, 0.0, 0.7406, 0.7397, 0.4076, 0.746, 0.8391] +2026-04-14 19:59:16.108709: Epoch time: 102.0 s +2026-04-14 19:59:17.389282: +2026-04-14 19:59:17.393130: Epoch 3590 +2026-04-14 19:59:17.397559: Current learning rate: 0.00129 +2026-04-14 20:00:58.936528: train_loss -0.4843 +2026-04-14 20:00:58.945123: val_loss -0.4506 +2026-04-14 20:00:58.948239: Pseudo dice [0.6723, 0.0, 0.818, 0.3864, 0.389, 0.8365, 0.7324] +2026-04-14 20:00:58.951059: Epoch time: 101.55 s +2026-04-14 20:01:00.212139: +2026-04-14 20:01:00.215078: Epoch 3591 +2026-04-14 20:01:00.217524: Current learning rate: 0.00128 +2026-04-14 20:02:40.297153: train_loss -0.4816 +2026-04-14 20:02:40.305767: val_loss -0.4084 +2026-04-14 20:02:40.308204: Pseudo dice [0.7377, 0.0, 0.8478, 0.1295, 0.4228, 0.8639, 0.8803] +2026-04-14 20:02:40.312650: Epoch time: 100.09 s +2026-04-14 20:02:41.570962: +2026-04-14 20:02:41.575927: Epoch 3592 +2026-04-14 20:02:41.583269: Current learning rate: 0.00128 +2026-04-14 20:04:22.637333: train_loss -0.4836 +2026-04-14 20:04:22.646751: val_loss -0.4452 +2026-04-14 20:04:22.649395: Pseudo dice [0.4797, 0.0, 0.704, 0.4609, 0.4667, 0.8338, 0.7401] +2026-04-14 20:04:22.652401: Epoch time: 101.07 s +2026-04-14 20:04:23.913120: +2026-04-14 20:04:23.915271: Epoch 3593 +2026-04-14 20:04:23.917967: Current learning rate: 0.00128 +2026-04-14 20:06:03.720492: train_loss -0.4809 +2026-04-14 20:06:03.730068: val_loss -0.36 +2026-04-14 20:06:03.732739: Pseudo dice [0.7909, 0.0, 0.7082, 0.0, 0.5126, 0.8861, 0.8101] +2026-04-14 20:06:03.736606: Epoch time: 99.81 s +2026-04-14 20:06:04.989097: +2026-04-14 20:06:04.991488: Epoch 3594 +2026-04-14 20:06:04.993876: Current learning rate: 0.00128 +2026-04-14 20:07:46.058267: train_loss -0.4905 +2026-04-14 20:07:46.063893: val_loss -0.4498 +2026-04-14 20:07:46.065771: Pseudo dice [0.0352, 0.0, 0.7198, 0.6632, 0.3718, 0.8874, 0.8794] +2026-04-14 20:07:46.068002: Epoch time: 101.07 s +2026-04-14 20:07:47.304745: +2026-04-14 20:07:47.306611: Epoch 3595 +2026-04-14 20:07:47.308892: Current learning rate: 0.00127 +2026-04-14 20:09:27.317055: train_loss -0.4868 +2026-04-14 20:09:27.326823: val_loss -0.4438 +2026-04-14 20:09:27.329378: Pseudo dice [0.6814, 0.0, 0.7967, 0.9134, 0.4419, 0.8793, 0.8604] +2026-04-14 20:09:27.331522: Epoch time: 100.02 s +2026-04-14 20:09:28.602499: +2026-04-14 20:09:28.604263: Epoch 3596 +2026-04-14 20:09:28.606758: Current learning rate: 0.00127 +2026-04-14 20:11:09.833279: train_loss -0.4922 +2026-04-14 20:11:09.841581: val_loss -0.3979 +2026-04-14 20:11:09.844105: Pseudo dice [0.8036, 0.0, 0.76, 0.0571, 0.4157, 0.8901, 0.7574] +2026-04-14 20:11:09.846536: Epoch time: 101.23 s +2026-04-14 20:11:11.109922: +2026-04-14 20:11:11.112513: Epoch 3597 +2026-04-14 20:11:11.115025: Current learning rate: 0.00127 +2026-04-14 20:12:50.937191: train_loss -0.4646 +2026-04-14 20:12:50.944671: val_loss -0.4121 +2026-04-14 20:12:50.946540: Pseudo dice [0.6864, 0.0, 0.7521, 0.6674, 0.4152, 0.3778, 0.909] +2026-04-14 20:12:50.949981: Epoch time: 99.83 s +2026-04-14 20:12:52.208427: +2026-04-14 20:12:52.210513: Epoch 3598 +2026-04-14 20:12:52.212903: Current learning rate: 0.00126 +2026-04-14 20:14:33.781590: train_loss -0.4764 +2026-04-14 20:14:33.790277: val_loss -0.4518 +2026-04-14 20:14:33.793000: Pseudo dice [0.7567, 0.0, 0.7713, 0.8481, 0.5913, 0.6073, 0.8642] +2026-04-14 20:14:33.796015: Epoch time: 101.58 s +2026-04-14 20:14:35.050375: +2026-04-14 20:14:35.052988: Epoch 3599 +2026-04-14 20:14:35.055390: Current learning rate: 0.00126 +2026-04-14 20:16:15.969590: train_loss -0.4806 +2026-04-14 20:16:15.976568: val_loss -0.4479 +2026-04-14 20:16:15.978913: Pseudo dice [0.5504, 0.0, 0.7943, 0.8915, 0.4371, 0.9008, 0.8245] +2026-04-14 20:16:15.982517: Epoch time: 100.92 s +2026-04-14 20:16:18.909936: +2026-04-14 20:16:18.913233: Epoch 3600 +2026-04-14 20:16:18.915600: Current learning rate: 0.00126 +2026-04-14 20:17:58.921261: train_loss -0.4807 +2026-04-14 20:17:58.927491: val_loss -0.3181 +2026-04-14 20:17:58.929552: Pseudo dice [0.7045, 0.0, 0.6211, 0.0292, 0.2704, 0.7928, 0.7085] +2026-04-14 20:17:58.932176: Epoch time: 100.01 s +2026-04-14 20:18:00.172573: +2026-04-14 20:18:00.174861: Epoch 3601 +2026-04-14 20:18:00.177297: Current learning rate: 0.00126 +2026-04-14 20:19:40.224799: train_loss -0.4722 +2026-04-14 20:19:40.233994: val_loss -0.3802 +2026-04-14 20:19:40.236545: Pseudo dice [0.5951, 0.0, 0.6735, 0.0253, 0.4153, 0.8489, 0.8842] +2026-04-14 20:19:40.239392: Epoch time: 100.06 s +2026-04-14 20:19:41.520370: +2026-04-14 20:19:41.522757: Epoch 3602 +2026-04-14 20:19:41.525003: Current learning rate: 0.00125 +2026-04-14 20:21:21.813550: train_loss -0.4805 +2026-04-14 20:21:21.820805: val_loss -0.2775 +2026-04-14 20:21:21.823205: Pseudo dice [0.6393, 0.0, 0.6918, 0.0146, 0.2579, 0.6474, 0.5066] +2026-04-14 20:21:21.826598: Epoch time: 100.3 s +2026-04-14 20:21:23.101857: +2026-04-14 20:21:23.105243: Epoch 3603 +2026-04-14 20:21:23.108577: Current learning rate: 0.00125 +2026-04-14 20:23:03.344809: train_loss -0.4752 +2026-04-14 20:23:03.351106: val_loss -0.4283 +2026-04-14 20:23:03.353693: Pseudo dice [0.3939, 0.0, 0.8239, 0.6377, 0.3092, 0.858, 0.8401] +2026-04-14 20:23:03.356846: Epoch time: 100.25 s +2026-04-14 20:23:04.611701: +2026-04-14 20:23:04.613721: Epoch 3604 +2026-04-14 20:23:04.615999: Current learning rate: 0.00125 +2026-04-14 20:24:44.514942: train_loss -0.486 +2026-04-14 20:24:44.524009: val_loss -0.4143 +2026-04-14 20:24:44.526694: Pseudo dice [0.7414, 0.0, 0.7188, 0.0, 0.3418, 0.9018, 0.6541] +2026-04-14 20:24:44.530833: Epoch time: 99.91 s +2026-04-14 20:24:45.784383: +2026-04-14 20:24:45.786684: Epoch 3605 +2026-04-14 20:24:45.788950: Current learning rate: 0.00124 +2026-04-14 20:26:26.013578: train_loss -0.4705 +2026-04-14 20:26:26.020693: val_loss -0.4373 +2026-04-14 20:26:26.023282: Pseudo dice [0.7727, 0.0, 0.6495, 0.8529, 0.572, 0.8247, 0.7889] +2026-04-14 20:26:26.025674: Epoch time: 100.23 s +2026-04-14 20:26:27.282556: +2026-04-14 20:26:27.284632: Epoch 3606 +2026-04-14 20:26:27.286958: Current learning rate: 0.00124 +2026-04-14 20:28:07.156254: train_loss -0.4817 +2026-04-14 20:28:07.167223: val_loss -0.4474 +2026-04-14 20:28:07.169759: Pseudo dice [0.7757, 0.0, 0.7441, 0.6851, 0.3904, 0.8558, 0.8118] +2026-04-14 20:28:07.173856: Epoch time: 99.88 s +2026-04-14 20:28:08.415500: +2026-04-14 20:28:08.420856: Epoch 3607 +2026-04-14 20:28:08.424297: Current learning rate: 0.00124 +2026-04-14 20:29:48.954313: train_loss -0.4722 +2026-04-14 20:29:48.964031: val_loss -0.338 +2026-04-14 20:29:48.966758: Pseudo dice [0.7766, 0.0, 0.3778, 0.0487, 0.4178, 0.8699, 0.8648] +2026-04-14 20:29:48.969822: Epoch time: 100.54 s +2026-04-14 20:29:50.242682: +2026-04-14 20:29:50.245022: Epoch 3608 +2026-04-14 20:29:50.247472: Current learning rate: 0.00124 +2026-04-14 20:31:30.193560: train_loss -0.4839 +2026-04-14 20:31:30.206617: val_loss -0.4195 +2026-04-14 20:31:30.210438: Pseudo dice [0.8065, 0.0, 0.7601, 0.0, 0.4285, 0.8514, 0.8164] +2026-04-14 20:31:30.214849: Epoch time: 99.95 s +2026-04-14 20:31:31.456108: +2026-04-14 20:31:31.458396: Epoch 3609 +2026-04-14 20:31:31.460916: Current learning rate: 0.00123 +2026-04-14 20:33:10.972822: train_loss -0.4801 +2026-04-14 20:33:10.981459: val_loss -0.426 +2026-04-14 20:33:10.984706: Pseudo dice [0.6516, 0.0, 0.6913, 0.5958, 0.5594, 0.7805, 0.7513] +2026-04-14 20:33:10.987132: Epoch time: 99.52 s +2026-04-14 20:33:12.257364: +2026-04-14 20:33:12.259631: Epoch 3610 +2026-04-14 20:33:12.261517: Current learning rate: 0.00123 +2026-04-14 20:34:51.814811: train_loss -0.4831 +2026-04-14 20:34:51.821052: val_loss -0.4327 +2026-04-14 20:34:51.823366: Pseudo dice [0.5598, 0.0, 0.6734, 0.0, 0.587, 0.8341, 0.6908] +2026-04-14 20:34:51.825588: Epoch time: 99.56 s +2026-04-14 20:34:53.071250: +2026-04-14 20:34:53.073415: Epoch 3611 +2026-04-14 20:34:53.075748: Current learning rate: 0.00123 +2026-04-14 20:36:32.859658: train_loss -0.4915 +2026-04-14 20:36:32.866445: val_loss -0.3997 +2026-04-14 20:36:32.868559: Pseudo dice [0.4121, 0.0, 0.8594, 0.0872, 0.5149, 0.779, 0.6922] +2026-04-14 20:36:32.871117: Epoch time: 99.79 s +2026-04-14 20:36:34.132888: +2026-04-14 20:36:34.134935: Epoch 3612 +2026-04-14 20:36:34.136922: Current learning rate: 0.00122 +2026-04-14 20:38:14.360408: train_loss -0.4914 +2026-04-14 20:38:14.368799: val_loss -0.4519 +2026-04-14 20:38:14.371704: Pseudo dice [0.6958, 0.0, 0.68, 0.7097, 0.4459, 0.8913, 0.6551] +2026-04-14 20:38:14.374002: Epoch time: 100.23 s +2026-04-14 20:38:15.657089: +2026-04-14 20:38:15.659467: Epoch 3613 +2026-04-14 20:38:15.661820: Current learning rate: 0.00122 +2026-04-14 20:39:56.061961: train_loss -0.4793 +2026-04-14 20:39:56.068879: val_loss -0.4351 +2026-04-14 20:39:56.072984: Pseudo dice [0.7927, 0.0, 0.7295, 0.816, 0.3315, 0.8331, 0.9007] +2026-04-14 20:39:56.076452: Epoch time: 100.41 s +2026-04-14 20:39:57.328075: +2026-04-14 20:39:57.330585: Epoch 3614 +2026-04-14 20:39:57.333392: Current learning rate: 0.00122 +2026-04-14 20:41:37.914827: train_loss -0.484 +2026-04-14 20:41:37.922921: val_loss -0.3789 +2026-04-14 20:41:37.925727: Pseudo dice [0.267, 0.0, 0.8009, 0.194, 0.1586, 0.8648, 0.8269] +2026-04-14 20:41:37.929206: Epoch time: 100.59 s +2026-04-14 20:41:39.187587: +2026-04-14 20:41:39.189488: Epoch 3615 +2026-04-14 20:41:39.191583: Current learning rate: 0.00122 +2026-04-14 20:43:18.690082: train_loss -0.4928 +2026-04-14 20:43:18.696247: val_loss -0.4487 +2026-04-14 20:43:18.699050: Pseudo dice [0.8427, 0.0, 0.8095, 0.2716, 0.3362, 0.8237, 0.9139] +2026-04-14 20:43:18.702483: Epoch time: 99.51 s +2026-04-14 20:43:19.943706: +2026-04-14 20:43:19.947333: Epoch 3616 +2026-04-14 20:43:19.949828: Current learning rate: 0.00121 +2026-04-14 20:45:00.063894: train_loss -0.4726 +2026-04-14 20:45:00.072569: val_loss -0.415 +2026-04-14 20:45:00.074952: Pseudo dice [0.6183, 0.0, 0.7381, 0.9067, 0.3394, 0.8493, 0.5554] +2026-04-14 20:45:00.077838: Epoch time: 100.12 s +2026-04-14 20:45:01.346464: +2026-04-14 20:45:01.348531: Epoch 3617 +2026-04-14 20:45:01.351002: Current learning rate: 0.00121 +2026-04-14 20:46:41.677983: train_loss -0.4789 +2026-04-14 20:46:41.687005: val_loss -0.4148 +2026-04-14 20:46:41.690752: Pseudo dice [0.7388, 0.0, 0.672, 0.0003, 0.4222, 0.7778, 0.7458] +2026-04-14 20:46:41.695954: Epoch time: 100.33 s +2026-04-14 20:46:43.992554: +2026-04-14 20:46:43.994752: Epoch 3618 +2026-04-14 20:46:43.997009: Current learning rate: 0.00121 +2026-04-14 20:48:24.551744: train_loss -0.4901 +2026-04-14 20:48:24.557681: val_loss -0.4626 +2026-04-14 20:48:24.559283: Pseudo dice [0.3982, 0.0, 0.8773, 0.5189, 0.5472, 0.8083, 0.8126] +2026-04-14 20:48:24.561702: Epoch time: 100.56 s +2026-04-14 20:48:25.868447: +2026-04-14 20:48:25.870433: Epoch 3619 +2026-04-14 20:48:25.872541: Current learning rate: 0.0012 +2026-04-14 20:50:06.315053: train_loss -0.483 +2026-04-14 20:50:06.322384: val_loss -0.4352 +2026-04-14 20:50:06.324844: Pseudo dice [0.8181, 0.0, 0.5514, 0.6395, 0.4753, 0.8036, 0.8192] +2026-04-14 20:50:06.328185: Epoch time: 100.45 s +2026-04-14 20:50:07.591427: +2026-04-14 20:50:07.594750: Epoch 3620 +2026-04-14 20:50:07.597647: Current learning rate: 0.0012 +2026-04-14 20:51:48.176836: train_loss -0.4876 +2026-04-14 20:51:48.185462: val_loss -0.4028 +2026-04-14 20:51:48.187575: Pseudo dice [0.105, 0.0, 0.6478, 0.6666, 0.2896, 0.7476, 0.7713] +2026-04-14 20:51:48.190498: Epoch time: 100.59 s +2026-04-14 20:51:49.451072: +2026-04-14 20:51:49.453333: Epoch 3621 +2026-04-14 20:51:49.455688: Current learning rate: 0.0012 +2026-04-14 20:53:29.480322: train_loss -0.4886 +2026-04-14 20:53:29.489657: val_loss -0.4443 +2026-04-14 20:53:29.492213: Pseudo dice [0.818, 0.0, 0.684, 0.3164, 0.3686, 0.8584, 0.6387] +2026-04-14 20:53:29.495042: Epoch time: 100.03 s +2026-04-14 20:53:30.751814: +2026-04-14 20:53:30.757155: Epoch 3622 +2026-04-14 20:53:30.759573: Current learning rate: 0.0012 +2026-04-14 20:55:10.861889: train_loss -0.4811 +2026-04-14 20:55:10.869804: val_loss -0.3997 +2026-04-14 20:55:10.872217: Pseudo dice [0.8252, 0.0, 0.6492, 0.0, 0.4786, 0.7833, 0.8691] +2026-04-14 20:55:10.874995: Epoch time: 100.11 s +2026-04-14 20:55:12.151700: +2026-04-14 20:55:12.155273: Epoch 3623 +2026-04-14 20:55:12.158384: Current learning rate: 0.00119 +2026-04-14 20:56:52.865330: train_loss -0.4865 +2026-04-14 20:56:52.872274: val_loss -0.3065 +2026-04-14 20:56:52.874778: Pseudo dice [0.8374, 0.0, 0.7722, 0.0307, 0.5946, 0.8644, 0.8157] +2026-04-14 20:56:52.877270: Epoch time: 100.72 s +2026-04-14 20:56:54.131865: +2026-04-14 20:56:54.133931: Epoch 3624 +2026-04-14 20:56:54.136266: Current learning rate: 0.00119 +2026-04-14 20:58:34.710551: train_loss -0.4792 +2026-04-14 20:58:34.716230: val_loss -0.4521 +2026-04-14 20:58:34.718472: Pseudo dice [0.8217, 0.0, 0.7836, 0.5927, 0.4229, 0.8714, 0.7769] +2026-04-14 20:58:34.720807: Epoch time: 100.58 s +2026-04-14 20:58:35.952518: +2026-04-14 20:58:35.954599: Epoch 3625 +2026-04-14 20:58:35.956985: Current learning rate: 0.00119 +2026-04-14 21:00:16.694966: train_loss -0.4818 +2026-04-14 21:00:16.699909: val_loss -0.4183 +2026-04-14 21:00:16.702177: Pseudo dice [0.87, 0.0, 0.6826, 0.1545, 0.5982, 0.846, 0.6954] +2026-04-14 21:00:16.705487: Epoch time: 100.75 s +2026-04-14 21:00:17.956944: +2026-04-14 21:00:17.958935: Epoch 3626 +2026-04-14 21:00:17.961875: Current learning rate: 0.00119 +2026-04-14 21:01:58.327155: train_loss -0.4907 +2026-04-14 21:01:58.335921: val_loss -0.391 +2026-04-14 21:01:58.339227: Pseudo dice [0.8184, 0.0, 0.768, 0.0613, 0.5327, 0.889, 0.8451] +2026-04-14 21:01:58.342511: Epoch time: 100.37 s +2026-04-14 21:01:59.597344: +2026-04-14 21:01:59.599151: Epoch 3627 +2026-04-14 21:01:59.601675: Current learning rate: 0.00118 +2026-04-14 21:03:39.180648: train_loss -0.4903 +2026-04-14 21:03:39.189103: val_loss -0.3918 +2026-04-14 21:03:39.192369: Pseudo dice [0.8721, 0.0, 0.7971, 0.2275, 0.4558, 0.7673, 0.8779] +2026-04-14 21:03:39.195679: Epoch time: 99.59 s +2026-04-14 21:03:40.464798: +2026-04-14 21:03:40.475434: Epoch 3628 +2026-04-14 21:03:40.478532: Current learning rate: 0.00118 +2026-04-14 21:05:20.555676: train_loss -0.4855 +2026-04-14 21:05:20.564433: val_loss -0.2856 +2026-04-14 21:05:20.568759: Pseudo dice [0.8583, 0.0, 0.3996, 0.0337, 0.3107, 0.895, 0.8471] +2026-04-14 21:05:20.572534: Epoch time: 100.09 s +2026-04-14 21:05:21.829661: +2026-04-14 21:05:21.831778: Epoch 3629 +2026-04-14 21:05:21.834611: Current learning rate: 0.00118 +2026-04-14 21:07:01.687672: train_loss -0.4848 +2026-04-14 21:07:01.695132: val_loss -0.4434 +2026-04-14 21:07:01.697994: Pseudo dice [0.569, 0.0, 0.7911, 0.7114, 0.4672, 0.8687, 0.6332] +2026-04-14 21:07:01.701784: Epoch time: 99.86 s +2026-04-14 21:07:02.970342: +2026-04-14 21:07:02.972340: Epoch 3630 +2026-04-14 21:07:02.974488: Current learning rate: 0.00117 +2026-04-14 21:08:44.089103: train_loss -0.493 +2026-04-14 21:08:44.098258: val_loss -0.4156 +2026-04-14 21:08:44.101301: Pseudo dice [0.8237, 0.0, 0.8119, 0.5538, 0.4327, 0.7446, 0.7094] +2026-04-14 21:08:44.104485: Epoch time: 101.12 s +2026-04-14 21:08:45.372341: +2026-04-14 21:08:45.375846: Epoch 3631 +2026-04-14 21:08:45.378357: Current learning rate: 0.00117 +2026-04-14 21:10:25.100143: train_loss -0.494 +2026-04-14 21:10:25.111791: val_loss -0.4278 +2026-04-14 21:10:25.115316: Pseudo dice [0.4691, 0.0, 0.6884, 0.0147, 0.3419, 0.8993, 0.8458] +2026-04-14 21:10:25.118243: Epoch time: 99.73 s +2026-04-14 21:10:26.371648: +2026-04-14 21:10:26.373823: Epoch 3632 +2026-04-14 21:10:26.375969: Current learning rate: 0.00117 +2026-04-14 21:12:06.747322: train_loss -0.4968 +2026-04-14 21:12:06.758190: val_loss -0.4082 +2026-04-14 21:12:06.760894: Pseudo dice [0.4663, 0.0, 0.7063, 0.4554, 0.1152, 0.9049, 0.8801] +2026-04-14 21:12:06.763687: Epoch time: 100.38 s +2026-04-14 21:12:08.046269: +2026-04-14 21:12:08.049821: Epoch 3633 +2026-04-14 21:12:08.052645: Current learning rate: 0.00117 +2026-04-14 21:13:49.524431: train_loss -0.4755 +2026-04-14 21:13:49.532949: val_loss -0.286 +2026-04-14 21:13:49.536702: Pseudo dice [0.7623, 0.0, 0.6475, 0.0736, 0.3791, 0.7385, 0.8798] +2026-04-14 21:13:49.539482: Epoch time: 101.48 s +2026-04-14 21:13:50.804820: +2026-04-14 21:13:50.808560: Epoch 3634 +2026-04-14 21:13:50.811025: Current learning rate: 0.00116 +2026-04-14 21:15:31.099031: train_loss -0.4761 +2026-04-14 21:15:31.111211: val_loss -0.3392 +2026-04-14 21:15:31.114021: Pseudo dice [0.6288, 0.0, 0.7206, 0.0226, 0.2764, 0.8322, 0.8707] +2026-04-14 21:15:31.117714: Epoch time: 100.3 s +2026-04-14 21:15:32.383668: +2026-04-14 21:15:32.386239: Epoch 3635 +2026-04-14 21:15:32.390983: Current learning rate: 0.00116 +2026-04-14 21:17:12.690017: train_loss -0.4862 +2026-04-14 21:17:12.698097: val_loss -0.4064 +2026-04-14 21:17:12.701164: Pseudo dice [0.5845, 0.0, 0.714, 0.7103, 0.4274, 0.6852, 0.8511] +2026-04-14 21:17:12.703144: Epoch time: 100.31 s +2026-04-14 21:17:13.950279: +2026-04-14 21:17:13.952570: Epoch 3636 +2026-04-14 21:17:13.954438: Current learning rate: 0.00116 +2026-04-14 21:18:53.781434: train_loss -0.4764 +2026-04-14 21:18:53.788532: val_loss -0.3926 +2026-04-14 21:18:53.791257: Pseudo dice [0.817, 0.0, 0.7176, 0.0401, 0.6086, 0.8339, 0.666] +2026-04-14 21:18:53.795202: Epoch time: 99.83 s +2026-04-14 21:18:55.070106: +2026-04-14 21:18:55.073598: Epoch 3637 +2026-04-14 21:18:55.076450: Current learning rate: 0.00115 +2026-04-14 21:20:37.266501: train_loss -0.4839 +2026-04-14 21:20:37.273516: val_loss -0.3622 +2026-04-14 21:20:37.276331: Pseudo dice [0.543, 0.0, 0.5592, 0.0, 0.4302, 0.843, 0.8236] +2026-04-14 21:20:37.280365: Epoch time: 102.2 s +2026-04-14 21:20:38.542772: +2026-04-14 21:20:38.548402: Epoch 3638 +2026-04-14 21:20:38.550814: Current learning rate: 0.00115 +2026-04-14 21:22:18.399265: train_loss -0.4839 +2026-04-14 21:22:18.407134: val_loss -0.4068 +2026-04-14 21:22:18.410214: Pseudo dice [0.1249, 0.0, 0.8165, 0.0006, 0.4723, 0.7131, 0.759] +2026-04-14 21:22:18.412485: Epoch time: 99.86 s +2026-04-14 21:22:19.680259: +2026-04-14 21:22:19.682446: Epoch 3639 +2026-04-14 21:22:19.685198: Current learning rate: 0.00115 +2026-04-14 21:24:00.223340: train_loss -0.48 +2026-04-14 21:24:00.233197: val_loss -0.4173 +2026-04-14 21:24:00.237566: Pseudo dice [0.7793, 0.0, 0.7276, 0.3182, 0.5414, 0.7804, 0.75] +2026-04-14 21:24:00.242029: Epoch time: 100.55 s +2026-04-14 21:24:01.503960: +2026-04-14 21:24:01.507485: Epoch 3640 +2026-04-14 21:24:01.510041: Current learning rate: 0.00115 +2026-04-14 21:25:41.399693: train_loss -0.4893 +2026-04-14 21:25:41.406548: val_loss -0.4389 +2026-04-14 21:25:41.410466: Pseudo dice [0.5828, 0.0, 0.785, 0.3551, 0.4892, 0.7291, 0.9027] +2026-04-14 21:25:41.417810: Epoch time: 99.9 s +2026-04-14 21:25:42.676600: +2026-04-14 21:25:42.678704: Epoch 3641 +2026-04-14 21:25:42.681216: Current learning rate: 0.00114 +2026-04-14 21:27:22.971429: train_loss -0.4909 +2026-04-14 21:27:22.978200: val_loss -0.4018 +2026-04-14 21:27:22.982318: Pseudo dice [0.6919, 0.0, 0.7012, 0.0006, 0.4871, 0.8091, 0.84] +2026-04-14 21:27:22.985054: Epoch time: 100.3 s +2026-04-14 21:27:24.246778: +2026-04-14 21:27:24.250365: Epoch 3642 +2026-04-14 21:27:24.252987: Current learning rate: 0.00114 +2026-04-14 21:29:04.764017: train_loss -0.4817 +2026-04-14 21:29:04.776866: val_loss -0.455 +2026-04-14 21:29:04.780011: Pseudo dice [0.8164, 0.0, 0.7296, 0.3554, 0.4016, 0.8055, 0.8835] +2026-04-14 21:29:04.782903: Epoch time: 100.52 s +2026-04-14 21:29:06.080790: +2026-04-14 21:29:06.082925: Epoch 3643 +2026-04-14 21:29:06.085672: Current learning rate: 0.00114 +2026-04-14 21:30:47.434624: train_loss -0.4765 +2026-04-14 21:30:47.443809: val_loss -0.4334 +2026-04-14 21:30:47.447697: Pseudo dice [0.7867, 0.0, 0.7852, 0.6117, 0.4488, 0.8159, 0.7381] +2026-04-14 21:30:47.450800: Epoch time: 101.36 s +2026-04-14 21:30:48.724484: +2026-04-14 21:30:48.726848: Epoch 3644 +2026-04-14 21:30:48.729740: Current learning rate: 0.00113 +2026-04-14 21:32:30.453934: train_loss -0.4769 +2026-04-14 21:32:30.463572: val_loss -0.4648 +2026-04-14 21:32:30.466546: Pseudo dice [0.7919, 0.0, 0.8331, 0.6603, 0.273, 0.849, 0.8969] +2026-04-14 21:32:30.469426: Epoch time: 101.73 s +2026-04-14 21:32:31.728501: +2026-04-14 21:32:31.730647: Epoch 3645 +2026-04-14 21:32:31.732896: Current learning rate: 0.00113 +2026-04-14 21:34:12.064457: train_loss -0.4797 +2026-04-14 21:34:12.072963: val_loss -0.4004 +2026-04-14 21:34:12.075333: Pseudo dice [0.8478, 0.0, 0.7429, 0.2128, 0.4466, 0.7887, 0.683] +2026-04-14 21:34:12.078053: Epoch time: 100.34 s +2026-04-14 21:34:13.373708: +2026-04-14 21:34:13.376367: Epoch 3646 +2026-04-14 21:34:13.379168: Current learning rate: 0.00113 +2026-04-14 21:35:54.312469: train_loss -0.4902 +2026-04-14 21:35:54.319406: val_loss -0.46 +2026-04-14 21:35:54.322024: Pseudo dice [0.7242, 0.0, 0.8166, 0.6698, 0.5321, 0.8295, 0.8961] +2026-04-14 21:35:54.324144: Epoch time: 100.94 s +2026-04-14 21:35:55.604430: +2026-04-14 21:35:55.607138: Epoch 3647 +2026-04-14 21:35:55.609439: Current learning rate: 0.00112 +2026-04-14 21:37:36.703062: train_loss -0.4893 +2026-04-14 21:37:36.712030: val_loss -0.4549 +2026-04-14 21:37:36.714537: Pseudo dice [0.4635, 0.0, 0.7959, 0.4399, 0.6358, 0.8615, 0.8655] +2026-04-14 21:37:36.717845: Epoch time: 101.1 s +2026-04-14 21:37:37.990739: +2026-04-14 21:37:37.994827: Epoch 3648 +2026-04-14 21:37:37.997381: Current learning rate: 0.00112 +2026-04-14 21:39:18.376148: train_loss -0.4853 +2026-04-14 21:39:18.385235: val_loss -0.3714 +2026-04-14 21:39:18.387819: Pseudo dice [0.4099, 0.0, 0.7144, 0.0701, 0.5015, 0.7371, 0.827] +2026-04-14 21:39:18.390970: Epoch time: 100.39 s +2026-04-14 21:39:19.682987: +2026-04-14 21:39:19.685456: Epoch 3649 +2026-04-14 21:39:19.687982: Current learning rate: 0.00112 +2026-04-14 21:41:00.468389: train_loss -0.4817 +2026-04-14 21:41:00.478022: val_loss -0.4041 +2026-04-14 21:41:00.481189: Pseudo dice [0.7957, 0.0, 0.7016, 0.0, 0.4349, 0.6603, 0.7105] +2026-04-14 21:41:00.486580: Epoch time: 100.79 s +2026-04-14 21:41:03.367423: +2026-04-14 21:41:03.371531: Epoch 3650 +2026-04-14 21:41:03.373818: Current learning rate: 0.00112 +2026-04-14 21:42:43.985604: train_loss -0.471 +2026-04-14 21:42:43.993875: val_loss -0.4246 +2026-04-14 21:42:43.997756: Pseudo dice [0.7266, 0.0, 0.7082, 0.0848, 0.7583, 0.8563, 0.8502] +2026-04-14 21:42:44.001343: Epoch time: 100.62 s +2026-04-14 21:42:45.259595: +2026-04-14 21:42:45.262002: Epoch 3651 +2026-04-14 21:42:45.264628: Current learning rate: 0.00111 +2026-04-14 21:44:26.121324: train_loss -0.4911 +2026-04-14 21:44:26.130749: val_loss -0.4497 +2026-04-14 21:44:26.133038: Pseudo dice [0.8247, 0.0, 0.8485, 0.9175, 0.4533, 0.7705, 0.726] +2026-04-14 21:44:26.136231: Epoch time: 100.86 s +2026-04-14 21:44:27.408846: +2026-04-14 21:44:27.411072: Epoch 3652 +2026-04-14 21:44:27.413207: Current learning rate: 0.00111 +2026-04-14 21:46:07.444446: train_loss -0.4816 +2026-04-14 21:46:07.451936: val_loss -0.45 +2026-04-14 21:46:07.454715: Pseudo dice [0.7684, 0.0, 0.7564, 0.7243, 0.5068, 0.6328, 0.844] +2026-04-14 21:46:07.457344: Epoch time: 100.04 s +2026-04-14 21:46:08.709372: +2026-04-14 21:46:08.711918: Epoch 3653 +2026-04-14 21:46:08.714379: Current learning rate: 0.00111 +2026-04-14 21:47:49.095588: train_loss -0.4873 +2026-04-14 21:47:49.102741: val_loss -0.4687 +2026-04-14 21:47:49.107130: Pseudo dice [0.8332, 0.0, 0.8071, 0.0006, 0.6524, 0.7863, 0.9009] +2026-04-14 21:47:49.113473: Epoch time: 100.39 s +2026-04-14 21:47:50.357840: +2026-04-14 21:47:50.360276: Epoch 3654 +2026-04-14 21:47:50.363036: Current learning rate: 0.0011 +2026-04-14 21:49:31.039955: train_loss -0.4738 +2026-04-14 21:49:31.046643: val_loss -0.3542 +2026-04-14 21:49:31.048957: Pseudo dice [0.6646, 0.0, 0.7749, 0.0002, 0.3997, 0.326, 0.7989] +2026-04-14 21:49:31.051508: Epoch time: 100.69 s +2026-04-14 21:49:32.299599: +2026-04-14 21:49:32.301394: Epoch 3655 +2026-04-14 21:49:32.303420: Current learning rate: 0.0011 +2026-04-14 21:51:13.264319: train_loss -0.4811 +2026-04-14 21:51:13.271514: val_loss -0.3947 +2026-04-14 21:51:13.274804: Pseudo dice [0.8566, 0.0, 0.8219, 0.0621, 0.4344, 0.6572, 0.8685] +2026-04-14 21:51:13.277218: Epoch time: 100.97 s +2026-04-14 21:51:14.536628: +2026-04-14 21:51:14.541141: Epoch 3656 +2026-04-14 21:51:14.543256: Current learning rate: 0.0011 +2026-04-14 21:52:55.115978: train_loss -0.4892 +2026-04-14 21:52:55.126076: val_loss -0.2924 +2026-04-14 21:52:55.128818: Pseudo dice [0.8526, 0.0, 0.7038, 0.0, 0.4357, 0.5305, 0.7738] +2026-04-14 21:52:55.131491: Epoch time: 100.58 s +2026-04-14 21:52:57.424763: +2026-04-14 21:52:57.426768: Epoch 3657 +2026-04-14 21:52:57.428849: Current learning rate: 0.0011 +2026-04-14 21:54:38.235934: train_loss -0.4901 +2026-04-14 21:54:38.243386: val_loss -0.4497 +2026-04-14 21:54:38.245879: Pseudo dice [0.7501, 0.0, 0.7367, 0.5031, 0.5433, 0.8043, 0.8879] +2026-04-14 21:54:38.248950: Epoch time: 100.81 s +2026-04-14 21:54:39.511875: +2026-04-14 21:54:39.513781: Epoch 3658 +2026-04-14 21:54:39.516120: Current learning rate: 0.00109 +2026-04-14 21:56:20.153341: train_loss -0.4838 +2026-04-14 21:56:20.160061: val_loss -0.3904 +2026-04-14 21:56:20.162362: Pseudo dice [0.8709, 0.0, 0.6537, 0.0283, 0.5346, 0.16, 0.8873] +2026-04-14 21:56:20.165708: Epoch time: 100.64 s +2026-04-14 21:56:21.430129: +2026-04-14 21:56:21.432379: Epoch 3659 +2026-04-14 21:56:21.435577: Current learning rate: 0.00109 +2026-04-14 21:58:02.380884: train_loss -0.4938 +2026-04-14 21:58:02.392851: val_loss -0.4619 +2026-04-14 21:58:02.395805: Pseudo dice [0.7331, 0.0, 0.8286, 0.8066, 0.5624, 0.7076, 0.8425] +2026-04-14 21:58:02.399779: Epoch time: 100.95 s +2026-04-14 21:58:03.662647: +2026-04-14 21:58:03.665759: Epoch 3660 +2026-04-14 21:58:03.668266: Current learning rate: 0.00109 +2026-04-14 21:59:44.423949: train_loss -0.4891 +2026-04-14 21:59:44.429920: val_loss -0.4373 +2026-04-14 21:59:44.431974: Pseudo dice [0.8041, 0.0, 0.7811, 0.5956, 0.4249, 0.8595, 0.8813] +2026-04-14 21:59:44.434406: Epoch time: 100.76 s +2026-04-14 21:59:45.687905: +2026-04-14 21:59:45.689897: Epoch 3661 +2026-04-14 21:59:45.691932: Current learning rate: 0.00108 +2026-04-14 22:01:25.892632: train_loss -0.4906 +2026-04-14 22:01:25.899866: val_loss -0.4308 +2026-04-14 22:01:25.902267: Pseudo dice [0.6636, 0.0, 0.8101, 0.3514, 0.5208, 0.9253, 0.854] +2026-04-14 22:01:25.905109: Epoch time: 100.21 s +2026-04-14 22:01:27.179303: +2026-04-14 22:01:27.181476: Epoch 3662 +2026-04-14 22:01:27.183570: Current learning rate: 0.00108 +2026-04-14 22:03:08.755404: train_loss -0.5083 +2026-04-14 22:03:08.763884: val_loss -0.4548 +2026-04-14 22:03:08.766658: Pseudo dice [0.7704, 0.0, 0.7621, 0.0594, 0.3511, 0.886, 0.867] +2026-04-14 22:03:08.769270: Epoch time: 101.58 s +2026-04-14 22:03:10.035848: +2026-04-14 22:03:10.038030: Epoch 3663 +2026-04-14 22:03:10.040800: Current learning rate: 0.00108 +2026-04-14 22:04:51.153553: train_loss -0.4929 +2026-04-14 22:04:51.162192: val_loss -0.203 +2026-04-14 22:04:51.166015: Pseudo dice [0.7829, 0.0, 0.483, 0.0264, 0.416, 0.7396, 0.7776] +2026-04-14 22:04:51.169362: Epoch time: 101.12 s +2026-04-14 22:04:52.439157: +2026-04-14 22:04:52.441294: Epoch 3664 +2026-04-14 22:04:52.443879: Current learning rate: 0.00108 +2026-04-14 22:06:32.373485: train_loss -0.4958 +2026-04-14 22:06:32.381833: val_loss -0.409 +2026-04-14 22:06:32.384014: Pseudo dice [0.8195, 0.0, 0.6964, 0.2298, 0.5428, 0.8201, 0.6067] +2026-04-14 22:06:32.387811: Epoch time: 99.94 s +2026-04-14 22:06:33.657401: +2026-04-14 22:06:33.666588: Epoch 3665 +2026-04-14 22:06:33.671169: Current learning rate: 0.00107 +2026-04-14 22:08:13.580850: train_loss -0.4973 +2026-04-14 22:08:13.587505: val_loss -0.4257 +2026-04-14 22:08:13.589481: Pseudo dice [0.8309, 0.0, 0.726, 0.1108, 0.4836, 0.5991, 0.76] +2026-04-14 22:08:13.592282: Epoch time: 99.93 s +2026-04-14 22:08:14.866819: +2026-04-14 22:08:14.869476: Epoch 3666 +2026-04-14 22:08:14.871714: Current learning rate: 0.00107 +2026-04-14 22:09:54.596576: train_loss -0.4903 +2026-04-14 22:09:54.603806: val_loss -0.3716 +2026-04-14 22:09:54.606189: Pseudo dice [0.7939, 0.0, 0.7993, 0.0073, 0.4255, 0.9192, 0.7327] +2026-04-14 22:09:54.611496: Epoch time: 99.73 s +2026-04-14 22:09:55.884187: +2026-04-14 22:09:55.886697: Epoch 3667 +2026-04-14 22:09:55.888977: Current learning rate: 0.00107 +2026-04-14 22:11:35.759667: train_loss -0.4855 +2026-04-14 22:11:35.766437: val_loss -0.4397 +2026-04-14 22:11:35.769148: Pseudo dice [0.5711, 0.0, 0.8408, 0.2733, 0.5788, 0.7731, 0.8279] +2026-04-14 22:11:35.772269: Epoch time: 99.88 s +2026-04-14 22:11:37.037038: +2026-04-14 22:11:37.040191: Epoch 3668 +2026-04-14 22:11:37.042836: Current learning rate: 0.00106 +2026-04-14 22:13:17.376611: train_loss -0.4873 +2026-04-14 22:13:17.384875: val_loss -0.4141 +2026-04-14 22:13:17.388035: Pseudo dice [0.8131, 0.0, 0.5396, 0.0218, 0.6562, 0.8887, 0.8921] +2026-04-14 22:13:17.391305: Epoch time: 100.34 s +2026-04-14 22:13:18.688612: +2026-04-14 22:13:18.690754: Epoch 3669 +2026-04-14 22:13:18.693183: Current learning rate: 0.00106 +2026-04-14 22:14:58.650725: train_loss -0.4778 +2026-04-14 22:14:58.657450: val_loss -0.4459 +2026-04-14 22:14:58.659718: Pseudo dice [0.7396, 0.0, 0.8276, 0.0, 0.4572, 0.787, 0.8175] +2026-04-14 22:14:58.661841: Epoch time: 99.97 s +2026-04-14 22:14:59.923529: +2026-04-14 22:14:59.932618: Epoch 3670 +2026-04-14 22:14:59.935069: Current learning rate: 0.00106 +2026-04-14 22:16:40.208378: train_loss -0.492 +2026-04-14 22:16:40.216651: val_loss -0.4489 +2026-04-14 22:16:40.220634: Pseudo dice [0.8503, 0.0, 0.5279, 0.9232, 0.6057, 0.7842, 0.8308] +2026-04-14 22:16:40.224007: Epoch time: 100.29 s +2026-04-14 22:16:41.487734: +2026-04-14 22:16:41.490742: Epoch 3671 +2026-04-14 22:16:41.493191: Current learning rate: 0.00106 +2026-04-14 22:18:21.432325: train_loss -0.4795 +2026-04-14 22:18:21.439556: val_loss -0.4628 +2026-04-14 22:18:21.442412: Pseudo dice [0.8212, 0.0, 0.7952, 0.3062, 0.4438, 0.8621, 0.8794] +2026-04-14 22:18:21.446297: Epoch time: 99.95 s +2026-04-14 22:18:22.717157: +2026-04-14 22:18:22.720099: Epoch 3672 +2026-04-14 22:18:22.722521: Current learning rate: 0.00105 +2026-04-14 22:20:02.779072: train_loss -0.4881 +2026-04-14 22:20:02.785547: val_loss -0.4173 +2026-04-14 22:20:02.788048: Pseudo dice [0.8334, 0.0, 0.7894, 0.0953, 0.2867, 0.8604, 0.8551] +2026-04-14 22:20:02.790974: Epoch time: 100.07 s +2026-04-14 22:20:04.056642: +2026-04-14 22:20:04.058525: Epoch 3673 +2026-04-14 22:20:04.061041: Current learning rate: 0.00105 +2026-04-14 22:21:44.159342: train_loss -0.495 +2026-04-14 22:21:44.167244: val_loss -0.3039 +2026-04-14 22:21:44.170938: Pseudo dice [0.8161, 0.0, 0.7676, 0.0363, 0.3494, 0.845, 0.8018] +2026-04-14 22:21:44.173862: Epoch time: 100.11 s +2026-04-14 22:21:45.460773: +2026-04-14 22:21:45.463624: Epoch 3674 +2026-04-14 22:21:45.466021: Current learning rate: 0.00105 +2026-04-14 22:23:26.120486: train_loss -0.4938 +2026-04-14 22:23:26.130222: val_loss -0.462 +2026-04-14 22:23:26.133208: Pseudo dice [0.7822, 0.0, 0.7595, 0.9177, 0.463, 0.9001, 0.7429] +2026-04-14 22:23:26.138075: Epoch time: 100.66 s +2026-04-14 22:23:27.439954: +2026-04-14 22:23:27.442983: Epoch 3675 +2026-04-14 22:23:27.445109: Current learning rate: 0.00104 +2026-04-14 22:25:08.677585: train_loss -0.4954 +2026-04-14 22:25:08.686701: val_loss -0.4434 +2026-04-14 22:25:08.690982: Pseudo dice [0.6541, 0.0, 0.7525, 0.6168, 0.5122, 0.8247, 0.9197] +2026-04-14 22:25:08.693676: Epoch time: 101.24 s +2026-04-14 22:25:08.696677: Yayy! New best EMA pseudo Dice: 0.5623 +2026-04-14 22:25:11.772252: +2026-04-14 22:25:11.776334: Epoch 3676 +2026-04-14 22:25:11.779089: Current learning rate: 0.00104 +2026-04-14 22:26:53.208838: train_loss -0.5026 +2026-04-14 22:26:53.219102: val_loss -0.3837 +2026-04-14 22:26:53.222683: Pseudo dice [0.315, 0.0, 0.7878, 0.4747, 0.3565, 0.6523, 0.3824] +2026-04-14 22:26:53.239815: Epoch time: 101.44 s +2026-04-14 22:26:54.509384: +2026-04-14 22:26:54.511658: Epoch 3677 +2026-04-14 22:26:54.514094: Current learning rate: 0.00104 +2026-04-14 22:28:35.357290: train_loss -0.495 +2026-04-14 22:28:35.364802: val_loss -0.4201 +2026-04-14 22:28:35.367560: Pseudo dice [0.7912, 0.0, 0.8287, 0.217, 0.547, 0.8233, 0.8677] +2026-04-14 22:28:35.370318: Epoch time: 100.85 s +2026-04-14 22:28:36.675346: +2026-04-14 22:28:36.678333: Epoch 3678 +2026-04-14 22:28:36.681378: Current learning rate: 0.00104 +2026-04-14 22:30:17.913827: train_loss -0.4847 +2026-04-14 22:30:17.921434: val_loss -0.4223 +2026-04-14 22:30:17.925165: Pseudo dice [0.8296, 0.0, 0.6279, 0.0, 0.5158, 0.866, 0.8517] +2026-04-14 22:30:17.928060: Epoch time: 101.24 s +2026-04-14 22:30:19.193568: +2026-04-14 22:30:19.197058: Epoch 3679 +2026-04-14 22:30:19.199262: Current learning rate: 0.00103 +2026-04-14 22:31:59.305624: train_loss -0.4802 +2026-04-14 22:31:59.314104: val_loss -0.4282 +2026-04-14 22:31:59.317578: Pseudo dice [0.8353, 0.0, 0.6878, 0.7405, 0.4796, 0.8619, 0.7563] +2026-04-14 22:31:59.320229: Epoch time: 100.12 s +2026-04-14 22:32:00.569243: +2026-04-14 22:32:00.571842: Epoch 3680 +2026-04-14 22:32:00.574030: Current learning rate: 0.00103 +2026-04-14 22:33:41.715802: train_loss -0.4723 +2026-04-14 22:33:41.726535: val_loss -0.4005 +2026-04-14 22:33:41.729579: Pseudo dice [0.5304, 0.0, 0.7833, 0.0067, 0.2286, 0.7257, 0.6957] +2026-04-14 22:33:41.733599: Epoch time: 101.15 s +2026-04-14 22:33:43.018334: +2026-04-14 22:33:43.020818: Epoch 3681 +2026-04-14 22:33:43.024827: Current learning rate: 0.00103 +2026-04-14 22:35:23.204449: train_loss -0.4849 +2026-04-14 22:35:23.214347: val_loss -0.4541 +2026-04-14 22:35:23.216654: Pseudo dice [0.8214, 0.0, 0.8611, 0.9008, 0.4969, 0.9022, 0.803] +2026-04-14 22:35:23.219834: Epoch time: 100.19 s +2026-04-14 22:35:24.479066: +2026-04-14 22:35:24.481355: Epoch 3682 +2026-04-14 22:35:24.483531: Current learning rate: 0.00102 +2026-04-14 22:37:04.752286: train_loss -0.4874 +2026-04-14 22:37:04.764028: val_loss -0.4561 +2026-04-14 22:37:04.766211: Pseudo dice [0.8446, 0.0, 0.7388, 0.8872, 0.1892, 0.8766, 0.8884] +2026-04-14 22:37:04.770450: Epoch time: 100.28 s +2026-04-14 22:37:04.772916: Yayy! New best EMA pseudo Dice: 0.565 +2026-04-14 22:37:07.679611: +2026-04-14 22:37:07.683992: Epoch 3683 +2026-04-14 22:37:07.686159: Current learning rate: 0.00102 +2026-04-14 22:38:48.491031: train_loss -0.4982 +2026-04-14 22:38:48.500796: val_loss -0.3584 +2026-04-14 22:38:48.503440: Pseudo dice [0.4847, 0.0, 0.6749, 0.0692, 0.3547, 0.8994, 0.5482] +2026-04-14 22:38:48.510509: Epoch time: 100.81 s +2026-04-14 22:38:49.793698: +2026-04-14 22:38:49.797116: Epoch 3684 +2026-04-14 22:38:49.799750: Current learning rate: 0.00102 +2026-04-14 22:40:30.766460: train_loss -0.4956 +2026-04-14 22:40:30.773797: val_loss -0.4423 +2026-04-14 22:40:30.775954: Pseudo dice [0.7929, 0.0, 0.7994, 0.0007, 0.5108, 0.8553, 0.7885] +2026-04-14 22:40:30.778886: Epoch time: 100.98 s +2026-04-14 22:40:32.035527: +2026-04-14 22:40:32.039091: Epoch 3685 +2026-04-14 22:40:32.041603: Current learning rate: 0.00102 +2026-04-14 22:42:12.688901: train_loss -0.494 +2026-04-14 22:42:12.697865: val_loss -0.4656 +2026-04-14 22:42:12.700016: Pseudo dice [0.8304, 0.0, 0.731, 0.1496, 0.3261, 0.8645, 0.8581] +2026-04-14 22:42:12.702658: Epoch time: 100.66 s +2026-04-14 22:42:13.973956: +2026-04-14 22:42:13.977038: Epoch 3686 +2026-04-14 22:42:13.979831: Current learning rate: 0.00101 +2026-04-14 22:43:53.996384: train_loss -0.4919 +2026-04-14 22:43:54.009381: val_loss -0.4535 +2026-04-14 22:43:54.012445: Pseudo dice [0.8729, 0.0, 0.8039, 0.7133, 0.3297, 0.7758, 0.8808] +2026-04-14 22:43:54.015187: Epoch time: 100.03 s +2026-04-14 22:43:55.292728: +2026-04-14 22:43:55.294792: Epoch 3687 +2026-04-14 22:43:55.296759: Current learning rate: 0.00101 +2026-04-14 22:45:35.840033: train_loss -0.4759 +2026-04-14 22:45:35.847021: val_loss -0.4241 +2026-04-14 22:45:35.850251: Pseudo dice [0.5006, 0.0, 0.8232, 0.0, 0.2936, 0.7183, 0.6773] +2026-04-14 22:45:35.853093: Epoch time: 100.55 s +2026-04-14 22:45:37.114142: +2026-04-14 22:45:37.117627: Epoch 3688 +2026-04-14 22:45:37.120940: Current learning rate: 0.00101 +2026-04-14 22:47:17.333508: train_loss -0.4919 +2026-04-14 22:47:17.341007: val_loss -0.4497 +2026-04-14 22:47:17.345155: Pseudo dice [0.8553, 0.0, 0.836, 0.027, 0.5103, 0.8309, 0.8376] +2026-04-14 22:47:17.348623: Epoch time: 100.22 s +2026-04-14 22:47:18.634564: +2026-04-14 22:47:18.638217: Epoch 3689 +2026-04-14 22:47:18.642596: Current learning rate: 0.001 +2026-04-14 22:48:59.456822: train_loss -0.4892 +2026-04-14 22:48:59.463703: val_loss -0.3684 +2026-04-14 22:48:59.465874: Pseudo dice [0.7758, 0.0, 0.7632, 0.0375, 0.3837, 0.8645, 0.8538] +2026-04-14 22:48:59.469176: Epoch time: 100.83 s +2026-04-14 22:49:00.723520: +2026-04-14 22:49:00.725252: Epoch 3690 +2026-04-14 22:49:00.727361: Current learning rate: 0.001 +2026-04-14 22:50:41.377281: train_loss -0.4888 +2026-04-14 22:50:41.385689: val_loss -0.4279 +2026-04-14 22:50:41.388443: Pseudo dice [0.7353, 0.0, 0.8553, 0.218, 0.4507, 0.8068, 0.8603] +2026-04-14 22:50:41.393918: Epoch time: 100.66 s +2026-04-14 22:50:42.668765: +2026-04-14 22:50:42.673218: Epoch 3691 +2026-04-14 22:50:42.676870: Current learning rate: 0.001 +2026-04-14 22:52:23.681310: train_loss -0.49 +2026-04-14 22:52:23.687705: val_loss -0.4416 +2026-04-14 22:52:23.689854: Pseudo dice [0.7901, 0.0, 0.5385, 0.8968, 0.376, 0.7949, 0.6795] +2026-04-14 22:52:23.693106: Epoch time: 101.02 s +2026-04-14 22:52:24.953341: +2026-04-14 22:52:24.956097: Epoch 3692 +2026-04-14 22:52:24.959781: Current learning rate: 0.001 +2026-04-14 22:54:04.658562: train_loss -0.4959 +2026-04-14 22:54:04.668091: val_loss -0.3515 +2026-04-14 22:54:04.670146: Pseudo dice [0.765, 0.0, 0.6818, 0.0001, 0.4642, 0.8592, 0.8278] +2026-04-14 22:54:04.672734: Epoch time: 99.71 s +2026-04-14 22:54:05.945557: +2026-04-14 22:54:05.947864: Epoch 3693 +2026-04-14 22:54:05.950783: Current learning rate: 0.00099 +2026-04-14 22:55:46.299587: train_loss -0.4899 +2026-04-14 22:55:46.307565: val_loss -0.4286 +2026-04-14 22:55:46.310106: Pseudo dice [0.7343, 0.0, 0.7084, 0.5971, 0.4614, 0.8092, 0.6404] +2026-04-14 22:55:46.312487: Epoch time: 100.36 s +2026-04-14 22:55:47.565629: +2026-04-14 22:55:47.567809: Epoch 3694 +2026-04-14 22:55:47.571429: Current learning rate: 0.00099 +2026-04-14 22:57:27.313368: train_loss -0.5024 +2026-04-14 22:57:27.321551: val_loss -0.363 +2026-04-14 22:57:27.324680: Pseudo dice [0.481, 0.0, 0.7622, 0.0796, 0.3294, 0.8498, 0.6899] +2026-04-14 22:57:27.327893: Epoch time: 99.75 s +2026-04-14 22:57:28.609109: +2026-04-14 22:57:28.611388: Epoch 3695 +2026-04-14 22:57:28.613949: Current learning rate: 0.00099 +2026-04-14 22:59:09.608957: train_loss -0.4866 +2026-04-14 22:59:09.615825: val_loss -0.4227 +2026-04-14 22:59:09.617841: Pseudo dice [0.8729, 0.0, 0.7655, 0.2708, 0.2954, 0.7194, 0.6375] +2026-04-14 22:59:09.620401: Epoch time: 101.0 s +2026-04-14 22:59:10.902862: +2026-04-14 22:59:10.906754: Epoch 3696 +2026-04-14 22:59:10.909955: Current learning rate: 0.00098 +2026-04-14 23:00:50.227983: train_loss -0.4979 +2026-04-14 23:00:50.236960: val_loss -0.3939 +2026-04-14 23:00:50.240248: Pseudo dice [0.7629, 0.0, 0.7211, 0.1233, 0.3227, 0.8584, 0.8819] +2026-04-14 23:00:50.242919: Epoch time: 99.33 s +2026-04-14 23:00:51.604221: +2026-04-14 23:00:51.606715: Epoch 3697 +2026-04-14 23:00:51.609116: Current learning rate: 0.00098 +2026-04-14 23:02:31.222643: train_loss -0.4914 +2026-04-14 23:02:31.229149: val_loss -0.4285 +2026-04-14 23:02:31.231786: Pseudo dice [0.0, 0.0, 0.9162, 0.1093, 0.4631, 0.8256, 0.8243] +2026-04-14 23:02:31.234241: Epoch time: 99.62 s +2026-04-14 23:02:32.483239: +2026-04-14 23:02:32.485758: Epoch 3698 +2026-04-14 23:02:32.488749: Current learning rate: 0.00098 +2026-04-14 23:04:12.916962: train_loss -0.4937 +2026-04-14 23:04:12.925591: val_loss -0.3778 +2026-04-14 23:04:12.928345: Pseudo dice [0.7432, 0.0, 0.7303, 0.0092, 0.5757, 0.7671, 0.8332] +2026-04-14 23:04:12.932173: Epoch time: 100.44 s +2026-04-14 23:04:14.204163: +2026-04-14 23:04:14.207756: Epoch 3699 +2026-04-14 23:04:14.212366: Current learning rate: 0.00097 +2026-04-14 23:05:54.815438: train_loss -0.4981 +2026-04-14 23:05:54.822383: val_loss -0.3839 +2026-04-14 23:05:54.824766: Pseudo dice [0.7695, 0.0, 0.7066, 0.1684, 0.479, 0.719, 0.7395] +2026-04-14 23:05:54.827511: Epoch time: 100.61 s +2026-04-14 23:05:57.540033: +2026-04-14 23:05:57.542679: Epoch 3700 +2026-04-14 23:05:57.544892: Current learning rate: 0.00097 +2026-04-14 23:07:38.154374: train_loss -0.491 +2026-04-14 23:07:38.161625: val_loss -0.4489 +2026-04-14 23:07:38.165975: Pseudo dice [0.6424, 0.0, 0.6915, 0.5366, 0.5041, 0.7473, 0.8174] +2026-04-14 23:07:38.170918: Epoch time: 100.62 s +2026-04-14 23:07:39.458108: +2026-04-14 23:07:39.460793: Epoch 3701 +2026-04-14 23:07:39.463526: Current learning rate: 0.00097 +2026-04-14 23:09:20.069524: train_loss -0.4933 +2026-04-14 23:09:20.076530: val_loss -0.4665 +2026-04-14 23:09:20.080017: Pseudo dice [0.6384, 0.0, 0.8639, 0.4833, 0.4043, 0.821, 0.879] +2026-04-14 23:09:20.083545: Epoch time: 100.61 s +2026-04-14 23:09:21.342394: +2026-04-14 23:09:21.345113: Epoch 3702 +2026-04-14 23:09:21.348637: Current learning rate: 0.00097 +2026-04-14 23:11:01.910610: train_loss -0.5025 +2026-04-14 23:11:01.918379: val_loss -0.3869 +2026-04-14 23:11:01.920832: Pseudo dice [0.8452, 0.0, 0.7707, 0.0614, 0.3819, 0.905, 0.6183] +2026-04-14 23:11:01.924568: Epoch time: 100.57 s +2026-04-14 23:11:03.207499: +2026-04-14 23:11:03.209500: Epoch 3703 +2026-04-14 23:11:03.211861: Current learning rate: 0.00096 +2026-04-14 23:12:43.376684: train_loss -0.4958 +2026-04-14 23:12:43.384720: val_loss -0.4546 +2026-04-14 23:12:43.387393: Pseudo dice [0.8392, 0.0, 0.7054, 0.8634, 0.453, 0.8454, 0.8557] +2026-04-14 23:12:43.391198: Epoch time: 100.17 s +2026-04-14 23:12:44.659622: +2026-04-14 23:12:44.661490: Epoch 3704 +2026-04-14 23:12:44.663539: Current learning rate: 0.00096 +2026-04-14 23:14:24.794859: train_loss -0.4982 +2026-04-14 23:14:24.804596: val_loss -0.4213 +2026-04-14 23:14:24.806958: Pseudo dice [0.8273, 0.0, 0.7453, 0.2574, 0.4838, 0.8046, 0.8126] +2026-04-14 23:14:24.810283: Epoch time: 100.14 s +2026-04-14 23:14:26.092891: +2026-04-14 23:14:26.094890: Epoch 3705 +2026-04-14 23:14:26.097441: Current learning rate: 0.00096 +2026-04-14 23:16:06.475296: train_loss -0.4898 +2026-04-14 23:16:06.482216: val_loss -0.4607 +2026-04-14 23:16:06.484288: Pseudo dice [0.4124, 0.0, 0.828, 0.0501, 0.4813, 0.8452, 0.8496] +2026-04-14 23:16:06.487738: Epoch time: 100.39 s +2026-04-14 23:16:07.748643: +2026-04-14 23:16:07.751116: Epoch 3706 +2026-04-14 23:16:07.754057: Current learning rate: 0.00095 +2026-04-14 23:17:47.851757: train_loss -0.4866 +2026-04-14 23:17:47.858927: val_loss -0.4633 +2026-04-14 23:17:47.861198: Pseudo dice [0.6354, 0.0, 0.7777, 0.6858, 0.4875, 0.8095, 0.8818] +2026-04-14 23:17:47.863716: Epoch time: 100.11 s +2026-04-14 23:17:49.145388: +2026-04-14 23:17:49.147487: Epoch 3707 +2026-04-14 23:17:49.150010: Current learning rate: 0.00095 +2026-04-14 23:19:31.194570: train_loss -0.4873 +2026-04-14 23:19:31.216499: val_loss -0.4207 +2026-04-14 23:19:31.228345: Pseudo dice [0.7745, 0.0, 0.7272, 0.0015, 0.6237, 0.8803, 0.9259] +2026-04-14 23:19:31.235839: Epoch time: 102.05 s +2026-04-14 23:19:32.515807: +2026-04-14 23:19:32.517995: Epoch 3708 +2026-04-14 23:19:32.520816: Current learning rate: 0.00095 +2026-04-14 23:21:14.021864: train_loss -0.4906 +2026-04-14 23:21:14.038340: val_loss -0.4621 +2026-04-14 23:21:14.043155: Pseudo dice [0.1784, 0.0, 0.8209, 0.5054, 0.3248, 0.6056, 0.6427] +2026-04-14 23:21:14.055768: Epoch time: 101.51 s +2026-04-14 23:21:15.336300: +2026-04-14 23:21:15.338940: Epoch 3709 +2026-04-14 23:21:15.340977: Current learning rate: 0.00095 +2026-04-14 23:22:55.783156: train_loss -0.498 +2026-04-14 23:22:55.791584: val_loss -0.4631 +2026-04-14 23:22:55.794320: Pseudo dice [0.8071, 0.0, 0.6653, 0.869, 0.5753, 0.8143, 0.8996] +2026-04-14 23:22:55.796996: Epoch time: 100.45 s +2026-04-14 23:22:57.090594: +2026-04-14 23:22:57.092736: Epoch 3710 +2026-04-14 23:22:57.095167: Current learning rate: 0.00094 +2026-04-14 23:24:38.076541: train_loss -0.4925 +2026-04-14 23:24:38.083757: val_loss -0.3928 +2026-04-14 23:24:38.086461: Pseudo dice [0.8483, 0.0, 0.7089, 0.0, 0.5589, 0.8829, 0.9073] +2026-04-14 23:24:38.088932: Epoch time: 100.99 s +2026-04-14 23:24:39.348947: +2026-04-14 23:24:39.351045: Epoch 3711 +2026-04-14 23:24:39.354284: Current learning rate: 0.00094 +2026-04-14 23:26:19.731480: train_loss -0.5085 +2026-04-14 23:26:19.742449: val_loss -0.4675 +2026-04-14 23:26:19.746042: Pseudo dice [0.509, 0.0, 0.866, 0.5001, 0.6096, 0.8075, 0.8969] +2026-04-14 23:26:19.750675: Epoch time: 100.39 s +2026-04-14 23:26:21.016652: +2026-04-14 23:26:21.019869: Epoch 3712 +2026-04-14 23:26:21.022632: Current learning rate: 0.00094 +2026-04-14 23:28:01.119721: train_loss -0.4943 +2026-04-14 23:28:01.127502: val_loss -0.372 +2026-04-14 23:28:01.131254: Pseudo dice [0.4636, 0.0, 0.8062, 0.1103, 0.4402, 0.8847, 0.7228] +2026-04-14 23:28:01.134812: Epoch time: 100.11 s +2026-04-14 23:28:02.386227: +2026-04-14 23:28:02.388110: Epoch 3713 +2026-04-14 23:28:02.390756: Current learning rate: 0.00093 +2026-04-14 23:29:42.580576: train_loss -0.4896 +2026-04-14 23:29:42.588274: val_loss -0.4671 +2026-04-14 23:29:42.590579: Pseudo dice [0.8186, 0.0, 0.7734, 0.527, 0.5682, 0.8764, 0.8863] +2026-04-14 23:29:42.592893: Epoch time: 100.2 s +2026-04-14 23:29:43.904064: +2026-04-14 23:29:43.919499: Epoch 3714 +2026-04-14 23:29:43.923389: Current learning rate: 0.00093 +2026-04-14 23:31:24.362396: train_loss -0.4908 +2026-04-14 23:31:24.370890: val_loss -0.4513 +2026-04-14 23:31:24.373305: Pseudo dice [0.7505, 0.0, 0.7682, 0.2897, 0.3788, 0.7614, 0.7128] +2026-04-14 23:31:24.375826: Epoch time: 100.46 s +2026-04-14 23:31:26.701349: +2026-04-14 23:31:26.703761: Epoch 3715 +2026-04-14 23:31:26.706239: Current learning rate: 0.00093 +2026-04-14 23:33:07.493119: train_loss -0.4938 +2026-04-14 23:33:07.499708: val_loss -0.3125 +2026-04-14 23:33:07.502272: Pseudo dice [0.8591, 0.0, 0.7221, 0.0093, 0.4329, 0.7557, 0.6586] +2026-04-14 23:33:07.505071: Epoch time: 100.79 s +2026-04-14 23:33:08.777101: +2026-04-14 23:33:08.778945: Epoch 3716 +2026-04-14 23:33:08.781138: Current learning rate: 0.00092 +2026-04-14 23:34:49.509071: train_loss -0.4897 +2026-04-14 23:34:49.516523: val_loss -0.3547 +2026-04-14 23:34:49.519831: Pseudo dice [0.7951, 0.0, 0.746, 0.0533, 0.4204, 0.7147, 0.8819] +2026-04-14 23:34:49.522960: Epoch time: 100.73 s +2026-04-14 23:34:50.777374: +2026-04-14 23:34:50.779549: Epoch 3717 +2026-04-14 23:34:50.783536: Current learning rate: 0.00092 +2026-04-14 23:36:31.853483: train_loss -0.4905 +2026-04-14 23:36:31.861230: val_loss -0.4128 +2026-04-14 23:36:31.864164: Pseudo dice [0.7832, 0.0, 0.832, 0.6264, 0.3869, 0.7976, 0.6525] +2026-04-14 23:36:31.866795: Epoch time: 101.08 s +2026-04-14 23:36:33.128393: +2026-04-14 23:36:33.131215: Epoch 3718 +2026-04-14 23:36:33.133752: Current learning rate: 0.00092 +2026-04-14 23:38:14.079988: train_loss -0.4893 +2026-04-14 23:38:14.085794: val_loss -0.3863 +2026-04-14 23:38:14.088316: Pseudo dice [0.7808, 0.0, 0.6889, 0.0036, 0.4067, 0.8974, 0.6972] +2026-04-14 23:38:14.091218: Epoch time: 100.95 s +2026-04-14 23:38:15.362161: +2026-04-14 23:38:15.364272: Epoch 3719 +2026-04-14 23:38:15.366581: Current learning rate: 0.00092 +2026-04-14 23:39:56.357393: train_loss -0.4655 +2026-04-14 23:39:56.365439: val_loss -0.4383 +2026-04-14 23:39:56.368296: Pseudo dice [0.8208, 0.0, 0.7042, 0.2938, 0.4186, 0.894, 0.7906] +2026-04-14 23:39:56.371950: Epoch time: 101.0 s +2026-04-14 23:39:57.642835: +2026-04-14 23:39:57.644847: Epoch 3720 +2026-04-14 23:39:57.647458: Current learning rate: 0.00091 +2026-04-14 23:41:39.851572: train_loss -0.4864 +2026-04-14 23:41:39.859103: val_loss -0.4312 +2026-04-14 23:41:39.861977: Pseudo dice [0.6006, 0.0, 0.6966, 0.895, 0.2721, 0.8202, 0.852] +2026-04-14 23:41:39.864983: Epoch time: 102.21 s +2026-04-14 23:41:41.149343: +2026-04-14 23:41:41.152471: Epoch 3721 +2026-04-14 23:41:41.155081: Current learning rate: 0.00091 +2026-04-14 23:43:23.798540: train_loss -0.4748 +2026-04-14 23:43:23.807151: val_loss -0.3664 +2026-04-14 23:43:23.809423: Pseudo dice [0.8232, 0.0, 0.6321, 0.0689, 0.5745, 0.8355, 0.7921] +2026-04-14 23:43:23.812151: Epoch time: 102.65 s +2026-04-14 23:43:25.072757: +2026-04-14 23:43:25.075736: Epoch 3722 +2026-04-14 23:43:25.078653: Current learning rate: 0.00091 +2026-04-14 23:45:05.669077: train_loss -0.4911 +2026-04-14 23:45:05.676781: val_loss -0.4439 +2026-04-14 23:45:05.681541: Pseudo dice [0.6403, 0.0, 0.6478, 0.0014, 0.417, 0.8477, 0.8088] +2026-04-14 23:45:05.685575: Epoch time: 100.6 s +2026-04-14 23:45:06.979215: +2026-04-14 23:45:06.982025: Epoch 3723 +2026-04-14 23:45:06.985052: Current learning rate: 0.0009 +2026-04-14 23:46:47.519827: train_loss -0.4961 +2026-04-14 23:46:47.524618: val_loss -0.4465 +2026-04-14 23:46:47.527215: Pseudo dice [0.8593, 0.0, 0.8463, 0.5104, 0.2962, 0.8033, 0.6087] +2026-04-14 23:46:47.529498: Epoch time: 100.54 s +2026-04-14 23:46:48.766197: +2026-04-14 23:46:48.768230: Epoch 3724 +2026-04-14 23:46:48.770846: Current learning rate: 0.0009 +2026-04-14 23:48:29.086978: train_loss -0.4958 +2026-04-14 23:48:29.094673: val_loss -0.4071 +2026-04-14 23:48:29.097127: Pseudo dice [0.8166, 0.0, 0.6076, 0.1394, 0.3061, 0.8948, 0.683] +2026-04-14 23:48:29.099495: Epoch time: 100.32 s +2026-04-14 23:48:30.397159: +2026-04-14 23:48:30.399431: Epoch 3725 +2026-04-14 23:48:30.401743: Current learning rate: 0.0009 +2026-04-14 23:50:10.733697: train_loss -0.4863 +2026-04-14 23:50:10.742454: val_loss -0.3936 +2026-04-14 23:50:10.746457: Pseudo dice [0.6656, 0.0, 0.7617, 0.0935, 0.5431, 0.7933, 0.8116] +2026-04-14 23:50:10.749544: Epoch time: 100.34 s +2026-04-14 23:50:12.015090: +2026-04-14 23:50:12.017417: Epoch 3726 +2026-04-14 23:50:12.019734: Current learning rate: 0.0009 +2026-04-14 23:51:52.781159: train_loss -0.4967 +2026-04-14 23:51:52.789050: val_loss -0.4116 +2026-04-14 23:51:52.791330: Pseudo dice [0.4333, 0.0, 0.7692, 0.0003, 0.5197, 0.9178, 0.7735] +2026-04-14 23:51:52.794248: Epoch time: 100.77 s +2026-04-14 23:51:54.086607: +2026-04-14 23:51:54.090074: Epoch 3727 +2026-04-14 23:51:54.094492: Current learning rate: 0.00089 +2026-04-14 23:53:35.402207: train_loss -0.501 +2026-04-14 23:53:35.409376: val_loss -0.4495 +2026-04-14 23:53:35.411381: Pseudo dice [0.8768, 0.0, 0.7241, 0.8955, 0.2214, 0.853, 0.6966] +2026-04-14 23:53:35.414355: Epoch time: 101.32 s +2026-04-14 23:53:36.710165: +2026-04-14 23:53:36.711983: Epoch 3728 +2026-04-14 23:53:36.714401: Current learning rate: 0.00089 +2026-04-14 23:55:17.432072: train_loss -0.4872 +2026-04-14 23:55:17.438825: val_loss -0.4383 +2026-04-14 23:55:17.441059: Pseudo dice [0.5969, 0.0, 0.797, 0.8575, 0.3882, 0.7493, 0.7692] +2026-04-14 23:55:17.444757: Epoch time: 100.72 s +2026-04-14 23:55:18.713011: +2026-04-14 23:55:18.714953: Epoch 3729 +2026-04-14 23:55:18.717214: Current learning rate: 0.00089 +2026-04-14 23:56:58.551519: train_loss -0.4916 +2026-04-14 23:56:58.561682: val_loss -0.3914 +2026-04-14 23:56:58.564148: Pseudo dice [0.6227, 0.0, 0.6549, 0.0851, 0.4711, 0.7014, 0.8809] +2026-04-14 23:56:58.567157: Epoch time: 99.84 s +2026-04-14 23:56:59.871479: +2026-04-14 23:56:59.874954: Epoch 3730 +2026-04-14 23:56:59.877634: Current learning rate: 0.00088 +2026-04-14 23:58:39.479729: train_loss -0.4951 +2026-04-14 23:58:39.486934: val_loss -0.4032 +2026-04-14 23:58:39.489787: Pseudo dice [0.6751, 0.0, 0.7451, 0.1367, 0.2287, 0.8921, 0.7755] +2026-04-14 23:58:39.492672: Epoch time: 99.61 s +2026-04-14 23:58:40.745809: +2026-04-14 23:58:40.747986: Epoch 3731 +2026-04-14 23:58:40.750303: Current learning rate: 0.00088 +2026-04-15 00:00:21.962408: train_loss -0.4939 +2026-04-15 00:00:21.971352: val_loss -0.4131 +2026-04-15 00:00:21.975300: Pseudo dice [0.6951, 0.0, 0.7831, 0.0286, 0.3028, 0.8677, 0.7122] +2026-04-15 00:00:21.978729: Epoch time: 101.22 s +2026-04-15 00:00:23.244394: +2026-04-15 00:00:23.246878: Epoch 3732 +2026-04-15 00:00:23.249538: Current learning rate: 0.00088 +2026-04-15 00:02:04.538950: train_loss -0.4867 +2026-04-15 00:02:04.548434: val_loss -0.414 +2026-04-15 00:02:04.551153: Pseudo dice [0.7641, 0.0, 0.7572, 0.0871, 0.3571, 0.8173, 0.7107] +2026-04-15 00:02:04.554806: Epoch time: 101.3 s +2026-04-15 00:02:05.826643: +2026-04-15 00:02:05.829212: Epoch 3733 +2026-04-15 00:02:05.831716: Current learning rate: 0.00087 +2026-04-15 00:03:47.117473: train_loss -0.4963 +2026-04-15 00:03:47.124626: val_loss -0.3714 +2026-04-15 00:03:47.127100: Pseudo dice [0.8554, 0.0, 0.7086, 0.0004, 0.5784, 0.6805, 0.8187] +2026-04-15 00:03:47.130244: Epoch time: 101.29 s +2026-04-15 00:03:48.386966: +2026-04-15 00:03:48.388792: Epoch 3734 +2026-04-15 00:03:48.391119: Current learning rate: 0.00087 +2026-04-15 00:05:29.826391: train_loss -0.4841 +2026-04-15 00:05:29.834860: val_loss -0.4597 +2026-04-15 00:05:29.837523: Pseudo dice [0.7811, 0.0, 0.7799, 0.0024, 0.5064, 0.8222, 0.7999] +2026-04-15 00:05:29.841946: Epoch time: 101.44 s +2026-04-15 00:05:31.122797: +2026-04-15 00:05:31.124723: Epoch 3735 +2026-04-15 00:05:31.127681: Current learning rate: 0.00087 +2026-04-15 00:07:11.127310: train_loss -0.4959 +2026-04-15 00:07:11.132647: val_loss -0.4082 +2026-04-15 00:07:11.134844: Pseudo dice [0.8014, 0.0, 0.7898, 0.6847, 0.3283, 0.8485, 0.4478] +2026-04-15 00:07:11.137030: Epoch time: 100.01 s +2026-04-15 00:07:12.387279: +2026-04-15 00:07:12.399729: Epoch 3736 +2026-04-15 00:07:12.402775: Current learning rate: 0.00087 +2026-04-15 00:08:53.393655: train_loss -0.4949 +2026-04-15 00:08:53.401189: val_loss -0.3431 +2026-04-15 00:08:53.404314: Pseudo dice [0.8823, 0.0, 0.6499, 0.043, 0.4234, 0.7346, 0.891] +2026-04-15 00:08:53.408520: Epoch time: 101.01 s +2026-04-15 00:08:54.681227: +2026-04-15 00:08:54.683641: Epoch 3737 +2026-04-15 00:08:54.685773: Current learning rate: 0.00086 +2026-04-15 00:10:35.022344: train_loss -0.4944 +2026-04-15 00:10:35.029318: val_loss -0.4438 +2026-04-15 00:10:35.032006: Pseudo dice [0.3067, 0.0, 0.7772, 0.1082, 0.5036, 0.8723, 0.9109] +2026-04-15 00:10:35.034195: Epoch time: 100.34 s +2026-04-15 00:10:36.318589: +2026-04-15 00:10:36.321351: Epoch 3738 +2026-04-15 00:10:36.323978: Current learning rate: 0.00086 +2026-04-15 00:12:17.084163: train_loss -0.5066 +2026-04-15 00:12:17.092038: val_loss -0.4016 +2026-04-15 00:12:17.095399: Pseudo dice [0.3867, 0.0, 0.7457, 0.099, 0.2468, 0.9072, 0.9018] +2026-04-15 00:12:17.098425: Epoch time: 100.77 s +2026-04-15 00:12:18.391323: +2026-04-15 00:12:18.394494: Epoch 3739 +2026-04-15 00:12:18.397007: Current learning rate: 0.00086 +2026-04-15 00:13:59.839148: train_loss -0.4889 +2026-04-15 00:13:59.845547: val_loss -0.4289 +2026-04-15 00:13:59.847905: Pseudo dice [0.6158, 0.0, 0.8276, 0.3524, 0.4255, 0.8646, 0.6279] +2026-04-15 00:13:59.851013: Epoch time: 101.45 s +2026-04-15 00:14:01.112732: +2026-04-15 00:14:01.115318: Epoch 3740 +2026-04-15 00:14:01.118852: Current learning rate: 0.00085 +2026-04-15 00:15:41.220599: train_loss -0.4931 +2026-04-15 00:15:41.227269: val_loss -0.367 +2026-04-15 00:15:41.229707: Pseudo dice [0.8087, 0.0, 0.516, 0.0273, 0.5099, 0.8194, 0.9117] +2026-04-15 00:15:41.232861: Epoch time: 100.11 s +2026-04-15 00:15:42.486197: +2026-04-15 00:15:42.489851: Epoch 3741 +2026-04-15 00:15:42.492927: Current learning rate: 0.00085 +2026-04-15 00:17:23.328118: train_loss -0.5087 +2026-04-15 00:17:23.336363: val_loss -0.4255 +2026-04-15 00:17:23.339030: Pseudo dice [0.3957, 0.0, 0.8376, 0.065, 0.5792, 0.7692, 0.8998] +2026-04-15 00:17:23.341398: Epoch time: 100.84 s +2026-04-15 00:17:24.623672: +2026-04-15 00:17:24.625703: Epoch 3742 +2026-04-15 00:17:24.628158: Current learning rate: 0.00085 +2026-04-15 00:19:04.565134: train_loss -0.4986 +2026-04-15 00:19:04.572146: val_loss -0.4323 +2026-04-15 00:19:04.574731: Pseudo dice [0.7114, 0.0, 0.8447, 0.0, 0.5441, 0.7364, 0.7895] +2026-04-15 00:19:04.577742: Epoch time: 99.94 s +2026-04-15 00:19:05.817293: +2026-04-15 00:19:05.819658: Epoch 3743 +2026-04-15 00:19:05.822782: Current learning rate: 0.00085 +2026-04-15 00:20:46.560558: train_loss -0.493 +2026-04-15 00:20:46.567607: val_loss -0.4209 +2026-04-15 00:20:46.570009: Pseudo dice [0.7086, 0.0, 0.8244, 0.319, 0.2494, 0.8464, 0.8994] +2026-04-15 00:20:46.572949: Epoch time: 100.75 s +2026-04-15 00:20:47.846997: +2026-04-15 00:20:47.848940: Epoch 3744 +2026-04-15 00:20:47.851091: Current learning rate: 0.00084 +2026-04-15 00:22:28.575292: train_loss -0.5043 +2026-04-15 00:22:28.584755: val_loss -0.4392 +2026-04-15 00:22:28.589515: Pseudo dice [0.6209, 0.0, 0.8273, 0.7258, 0.478, 0.6288, 0.884] +2026-04-15 00:22:28.592510: Epoch time: 100.73 s +2026-04-15 00:22:29.854756: +2026-04-15 00:22:29.856886: Epoch 3745 +2026-04-15 00:22:29.858923: Current learning rate: 0.00084 +2026-04-15 00:24:09.791543: train_loss -0.489 +2026-04-15 00:24:09.798836: val_loss -0.3789 +2026-04-15 00:24:09.801017: Pseudo dice [0.7741, 0.0, 0.6084, 0.0439, 0.4217, 0.8491, 0.5601] +2026-04-15 00:24:09.803898: Epoch time: 99.94 s +2026-04-15 00:24:11.070937: +2026-04-15 00:24:11.073344: Epoch 3746 +2026-04-15 00:24:11.075493: Current learning rate: 0.00084 +2026-04-15 00:25:52.199824: train_loss -0.4988 +2026-04-15 00:25:52.214722: val_loss -0.4628 +2026-04-15 00:25:52.217329: Pseudo dice [0.6195, 0.0, 0.7005, 0.8014, 0.1306, 0.8739, 0.8694] +2026-04-15 00:25:52.219602: Epoch time: 101.13 s +2026-04-15 00:25:53.485743: +2026-04-15 00:25:53.488117: Epoch 3747 +2026-04-15 00:25:53.490204: Current learning rate: 0.00083 +2026-04-15 00:27:33.327545: train_loss -0.5178 +2026-04-15 00:27:33.334952: val_loss -0.4647 +2026-04-15 00:27:33.336988: Pseudo dice [0.7665, 0.0, 0.6841, 0.877, 0.4662, 0.7489, 0.6521] +2026-04-15 00:27:33.339439: Epoch time: 99.85 s +2026-04-15 00:27:34.582264: +2026-04-15 00:27:34.584147: Epoch 3748 +2026-04-15 00:27:34.586589: Current learning rate: 0.00083 +2026-04-15 00:29:14.474063: train_loss -0.5607 +2026-04-15 00:29:14.479611: val_loss -0.5406 +2026-04-15 00:29:14.481481: Pseudo dice [0.7586, 0.0, 0.8729, 0.1793, 0.4137, 0.7415, 0.7972] +2026-04-15 00:29:14.483158: Epoch time: 99.89 s +2026-04-15 00:29:15.719093: +2026-04-15 00:29:15.721558: Epoch 3749 +2026-04-15 00:29:15.723895: Current learning rate: 0.00083 +2026-04-15 00:30:55.500849: train_loss -0.613 +2026-04-15 00:30:55.507215: val_loss -0.5569 +2026-04-15 00:30:55.509268: Pseudo dice [0.7651, 0.0, 0.8381, 0.7609, 0.4366, 0.6411, 0.7924] +2026-04-15 00:30:55.511710: Epoch time: 99.78 s +2026-04-15 00:30:58.510650: +2026-04-15 00:30:58.516840: Epoch 3750 +2026-04-15 00:30:58.518940: Current learning rate: 0.00082 +2026-04-15 00:32:38.859886: train_loss -0.6247 +2026-04-15 00:32:38.866202: val_loss -0.5336 +2026-04-15 00:32:38.869592: Pseudo dice [0.8108, 0.0, 0.7161, 0.074, 0.4884, 0.5917, 0.873] +2026-04-15 00:32:38.872365: Epoch time: 100.35 s +2026-04-15 00:32:40.118379: +2026-04-15 00:32:40.120197: Epoch 3751 +2026-04-15 00:32:40.122434: Current learning rate: 0.00082 +2026-04-15 00:34:20.081027: train_loss -0.6528 +2026-04-15 00:34:20.089079: val_loss -0.5729 +2026-04-15 00:34:20.091420: Pseudo dice [0.78, 0.0, 0.6631, 0.6519, 0.3502, 0.8484, 0.6126] +2026-04-15 00:34:20.094496: Epoch time: 99.97 s +2026-04-15 00:34:21.323485: +2026-04-15 00:34:21.325630: Epoch 3752 +2026-04-15 00:34:21.328043: Current learning rate: 0.00082 +2026-04-15 00:36:01.855712: train_loss -0.6422 +2026-04-15 00:36:01.862586: val_loss -0.5488 +2026-04-15 00:36:01.865183: Pseudo dice [0.8244, 0.0, 0.7323, 0.3412, 0.3561, 0.8031, 0.7655] +2026-04-15 00:36:01.868915: Epoch time: 100.54 s +2026-04-15 00:36:03.103399: +2026-04-15 00:36:03.105935: Epoch 3753 +2026-04-15 00:36:03.107948: Current learning rate: 0.00082 +2026-04-15 00:37:44.257195: train_loss -0.6515 +2026-04-15 00:37:44.268109: val_loss -0.498 +2026-04-15 00:37:44.272195: Pseudo dice [0.5329, 0.0, 0.3785, 0.0768, 0.5437, 0.7105, 0.777] +2026-04-15 00:37:44.275048: Epoch time: 101.16 s +2026-04-15 00:37:45.531306: +2026-04-15 00:37:45.533993: Epoch 3754 +2026-04-15 00:37:45.537170: Current learning rate: 0.00081 +2026-04-15 00:39:25.532831: train_loss -0.6463 +2026-04-15 00:39:25.539945: val_loss -0.5108 +2026-04-15 00:39:25.542395: Pseudo dice [0.8162, 0.0, 0.7496, 0.0194, 0.2436, 0.8174, 0.7492] +2026-04-15 00:39:25.546275: Epoch time: 100.0 s +2026-04-15 00:39:26.789258: +2026-04-15 00:39:26.791505: Epoch 3755 +2026-04-15 00:39:26.793687: Current learning rate: 0.00081 +2026-04-15 00:41:06.933770: train_loss -0.6405 +2026-04-15 00:41:06.939928: val_loss -0.573 +2026-04-15 00:41:06.942870: Pseudo dice [0.7735, 0.0, 0.8351, 0.1093, 0.4543, 0.7616, 0.8384] +2026-04-15 00:41:06.945260: Epoch time: 100.15 s +2026-04-15 00:41:08.192120: +2026-04-15 00:41:08.194252: Epoch 3756 +2026-04-15 00:41:08.196527: Current learning rate: 0.00081 +2026-04-15 00:42:48.840240: train_loss -0.6439 +2026-04-15 00:42:48.848847: val_loss -0.5671 +2026-04-15 00:42:48.851771: Pseudo dice [0.2875, 0.0, 0.8131, 0.5667, 0.4918, 0.7915, 0.4283] +2026-04-15 00:42:48.854165: Epoch time: 100.65 s +2026-04-15 00:42:50.093228: +2026-04-15 00:42:50.095287: Epoch 3757 +2026-04-15 00:42:50.097319: Current learning rate: 0.0008 +2026-04-15 00:44:30.061715: train_loss -0.6553 +2026-04-15 00:44:30.070325: val_loss -0.5486 +2026-04-15 00:44:30.072495: Pseudo dice [0.7662, 0.0, 0.8734, 0.4294, 0.3187, 0.8507, 0.5905] +2026-04-15 00:44:30.074386: Epoch time: 99.97 s +2026-04-15 00:44:31.315104: +2026-04-15 00:44:31.317200: Epoch 3758 +2026-04-15 00:44:31.319263: Current learning rate: 0.0008 +2026-04-15 00:46:11.322804: train_loss -0.6556 +2026-04-15 00:46:11.330459: val_loss -0.5671 +2026-04-15 00:46:11.334187: Pseudo dice [0.6675, 0.0, 0.761, 0.6435, 0.3401, 0.886, 0.593] +2026-04-15 00:46:11.337816: Epoch time: 100.01 s +2026-04-15 00:46:12.589020: +2026-04-15 00:46:12.591461: Epoch 3759 +2026-04-15 00:46:12.593910: Current learning rate: 0.0008 +2026-04-15 00:47:53.248842: train_loss -0.6479 +2026-04-15 00:47:53.256943: val_loss -0.5826 +2026-04-15 00:47:53.259913: Pseudo dice [0.7501, 0.0, 0.7232, 0.0027, 0.3582, 0.7715, 0.8047] +2026-04-15 00:47:53.263144: Epoch time: 100.66 s +2026-04-15 00:47:54.537433: +2026-04-15 00:47:54.539558: Epoch 3760 +2026-04-15 00:47:54.541921: Current learning rate: 0.00079 +2026-04-15 00:49:34.619236: train_loss -0.6369 +2026-04-15 00:49:34.626948: val_loss -0.5852 +2026-04-15 00:49:34.629541: Pseudo dice [0.7666, 0.0, 0.7109, 0.0, 0.4822, 0.8212, 0.8562] +2026-04-15 00:49:34.633595: Epoch time: 100.08 s +2026-04-15 00:49:35.881263: +2026-04-15 00:49:35.883655: Epoch 3761 +2026-04-15 00:49:35.885546: Current learning rate: 0.00079 +2026-04-15 00:51:16.723253: train_loss -0.6437 +2026-04-15 00:51:16.730835: val_loss -0.5647 +2026-04-15 00:51:16.733140: Pseudo dice [0.4768, 0.0, 0.726, 0.6024, 0.6232, 0.8821, 0.5809] +2026-04-15 00:51:16.736022: Epoch time: 100.85 s +2026-04-15 00:51:18.069264: +2026-04-15 00:51:18.071430: Epoch 3762 +2026-04-15 00:51:18.074277: Current learning rate: 0.00079 +2026-04-15 00:52:59.074049: train_loss -0.6352 +2026-04-15 00:52:59.080055: val_loss -0.6006 +2026-04-15 00:52:59.081829: Pseudo dice [0.8635, 0.0, 0.7547, 0.9273, 0.7246, 0.8067, 0.8235] +2026-04-15 00:52:59.084293: Epoch time: 101.01 s +2026-04-15 00:53:00.323415: +2026-04-15 00:53:00.331191: Epoch 3763 +2026-04-15 00:53:00.334503: Current learning rate: 0.00079 +2026-04-15 00:54:40.873193: train_loss -0.6519 +2026-04-15 00:54:40.880172: val_loss -0.4996 +2026-04-15 00:54:40.882505: Pseudo dice [0.733, 0.0, 0.7756, 0.0878, 0.3499, 0.8587, 0.7628] +2026-04-15 00:54:40.885199: Epoch time: 100.55 s +2026-04-15 00:54:42.191102: +2026-04-15 00:54:42.193342: Epoch 3764 +2026-04-15 00:54:42.195977: Current learning rate: 0.00078 +2026-04-15 00:56:22.608423: train_loss -0.6556 +2026-04-15 00:56:22.616010: val_loss -0.4691 +2026-04-15 00:56:22.618081: Pseudo dice [0.3374, 0.0, 0.5701, 0.016, 0.5426, 0.843, 0.8877] +2026-04-15 00:56:22.620907: Epoch time: 100.42 s +2026-04-15 00:56:23.888438: +2026-04-15 00:56:23.891412: Epoch 3765 +2026-04-15 00:56:23.894154: Current learning rate: 0.00078 +2026-04-15 00:58:04.243762: train_loss -0.659 +2026-04-15 00:58:04.252822: val_loss -0.5786 +2026-04-15 00:58:04.255793: Pseudo dice [0.7612, 0.0, 0.654, 0.0024, 0.5272, 0.7752, 0.6679] +2026-04-15 00:58:04.259203: Epoch time: 100.36 s +2026-04-15 00:58:05.501489: +2026-04-15 00:58:05.503822: Epoch 3766 +2026-04-15 00:58:05.506137: Current learning rate: 0.00078 +2026-04-15 00:59:46.125946: train_loss -0.6539 +2026-04-15 00:59:46.134165: val_loss -0.417 +2026-04-15 00:59:46.136861: Pseudo dice [0.629, 0.0, 0.495, 0.0002, 0.4537, 0.8328, 0.6412] +2026-04-15 00:59:46.140554: Epoch time: 100.63 s +2026-04-15 00:59:47.407474: +2026-04-15 00:59:47.409813: Epoch 3767 +2026-04-15 00:59:47.412482: Current learning rate: 0.00077 +2026-04-15 01:01:28.051425: train_loss -0.636 +2026-04-15 01:01:28.059056: val_loss -0.5095 +2026-04-15 01:01:28.062257: Pseudo dice [0.7446, 0.0, 0.636, 0.0806, 0.4051, 0.8664, 0.7577] +2026-04-15 01:01:28.064799: Epoch time: 100.65 s +2026-04-15 01:01:29.330169: +2026-04-15 01:01:29.332296: Epoch 3768 +2026-04-15 01:01:29.334534: Current learning rate: 0.00077 +2026-04-15 01:03:09.997987: train_loss -0.6523 +2026-04-15 01:03:10.006180: val_loss -0.5892 +2026-04-15 01:03:10.009769: Pseudo dice [0.6586, 0.0, 0.8486, 0.2733, 0.4939, 0.7549, 0.914] +2026-04-15 01:03:10.013279: Epoch time: 100.67 s +2026-04-15 01:03:11.277796: +2026-04-15 01:03:11.280313: Epoch 3769 +2026-04-15 01:03:11.283086: Current learning rate: 0.00077 +2026-04-15 01:04:51.317000: train_loss -0.6582 +2026-04-15 01:04:51.325320: val_loss -0.5971 +2026-04-15 01:04:51.330561: Pseudo dice [0.7334, 0.0, 0.7754, 0.4456, 0.4893, 0.693, 0.8976] +2026-04-15 01:04:51.334039: Epoch time: 100.04 s +2026-04-15 01:04:52.587569: +2026-04-15 01:04:52.589642: Epoch 3770 +2026-04-15 01:04:52.591978: Current learning rate: 0.00077 +2026-04-15 01:06:33.012206: train_loss -0.6393 +2026-04-15 01:06:33.018780: val_loss -0.538 +2026-04-15 01:06:33.028540: Pseudo dice [0.7325, 0.0, 0.5191, 0.173, 0.2104, 0.6792, 0.8568] +2026-04-15 01:06:33.031157: Epoch time: 100.43 s +2026-04-15 01:06:34.290174: +2026-04-15 01:06:34.293482: Epoch 3771 +2026-04-15 01:06:34.296254: Current learning rate: 0.00076 +2026-04-15 01:08:14.811976: train_loss -0.6434 +2026-04-15 01:08:14.818934: val_loss -0.6055 +2026-04-15 01:08:14.821601: Pseudo dice [0.6508, 0.0, 0.7457, 0.0, 0.5651, 0.7554, 0.7645] +2026-04-15 01:08:14.824343: Epoch time: 100.53 s +2026-04-15 01:08:16.062023: +2026-04-15 01:08:16.066064: Epoch 3772 +2026-04-15 01:08:16.068470: Current learning rate: 0.00076 +2026-04-15 01:09:56.545518: train_loss -0.6544 +2026-04-15 01:09:56.553823: val_loss -0.5708 +2026-04-15 01:09:56.556091: Pseudo dice [0.6237, 0.0, 0.8222, 0.3649, 0.4264, 0.8482, 0.6012] +2026-04-15 01:09:56.559225: Epoch time: 100.49 s +2026-04-15 01:09:57.839566: +2026-04-15 01:09:57.841830: Epoch 3773 +2026-04-15 01:09:57.844502: Current learning rate: 0.00076 +2026-04-15 01:11:38.804157: train_loss -0.6436 +2026-04-15 01:11:38.811391: val_loss -0.5155 +2026-04-15 01:11:38.814682: Pseudo dice [0.7172, 0.0, 0.6236, 0.0, 0.3421, 0.8416, 0.8221] +2026-04-15 01:11:38.817307: Epoch time: 100.97 s +2026-04-15 01:11:40.054597: +2026-04-15 01:11:40.056789: Epoch 3774 +2026-04-15 01:11:40.058958: Current learning rate: 0.00075 +2026-04-15 01:13:20.538323: train_loss -0.6681 +2026-04-15 01:13:20.544665: val_loss -0.5844 +2026-04-15 01:13:20.547244: Pseudo dice [0.743, 0.0, 0.6308, 0.1249, 0.3835, 0.6526, 0.9366] +2026-04-15 01:13:20.549911: Epoch time: 100.49 s +2026-04-15 01:13:21.794959: +2026-04-15 01:13:21.796929: Epoch 3775 +2026-04-15 01:13:21.799219: Current learning rate: 0.00075 +2026-04-15 01:15:02.210800: train_loss -0.6589 +2026-04-15 01:15:02.218371: val_loss -0.497 +2026-04-15 01:15:02.220952: Pseudo dice [0.7852, 0.0, 0.6749, 0.0673, 0.4324, 0.8922, 0.7142] +2026-04-15 01:15:02.223417: Epoch time: 100.42 s +2026-04-15 01:15:03.495031: +2026-04-15 01:15:03.498188: Epoch 3776 +2026-04-15 01:15:03.500322: Current learning rate: 0.00075 +2026-04-15 01:16:43.272262: train_loss -0.6496 +2026-04-15 01:16:43.278958: val_loss -0.6111 +2026-04-15 01:16:43.282421: Pseudo dice [0.2819, 0.0, 0.7363, 0.8919, 0.6021, 0.534, 0.8495] +2026-04-15 01:16:43.285473: Epoch time: 99.78 s +2026-04-15 01:16:44.511261: +2026-04-15 01:16:44.513395: Epoch 3777 +2026-04-15 01:16:44.516155: Current learning rate: 0.00074 +2026-04-15 01:18:24.365104: train_loss -0.6504 +2026-04-15 01:18:24.374937: val_loss -0.5306 +2026-04-15 01:18:24.378300: Pseudo dice [0.5165, 0.0, 0.7461, 0.1367, 0.5261, 0.8686, 0.8137] +2026-04-15 01:18:24.381030: Epoch time: 99.86 s +2026-04-15 01:18:25.630349: +2026-04-15 01:18:25.632302: Epoch 3778 +2026-04-15 01:18:25.634333: Current learning rate: 0.00074 +2026-04-15 01:20:05.795112: train_loss -0.6563 +2026-04-15 01:20:05.801481: val_loss -0.6015 +2026-04-15 01:20:05.803822: Pseudo dice [0.8054, 0.0, 0.7663, 0.309, 0.4426, 0.7826, 0.7063] +2026-04-15 01:20:05.806869: Epoch time: 100.17 s +2026-04-15 01:20:07.054168: +2026-04-15 01:20:07.056016: Epoch 3779 +2026-04-15 01:20:07.058054: Current learning rate: 0.00074 +2026-04-15 01:21:47.575233: train_loss -0.6525 +2026-04-15 01:21:47.584284: val_loss -0.6047 +2026-04-15 01:21:47.587064: Pseudo dice [0.6977, 0.0, 0.7734, 0.3147, 0.5892, 0.8363, 0.7695] +2026-04-15 01:21:47.590838: Epoch time: 100.52 s +2026-04-15 01:21:48.829041: +2026-04-15 01:21:48.830863: Epoch 3780 +2026-04-15 01:21:48.832838: Current learning rate: 0.00074 +2026-04-15 01:23:29.075598: train_loss -0.6549 +2026-04-15 01:23:29.082547: val_loss -0.6222 +2026-04-15 01:23:29.084730: Pseudo dice [0.8076, 0.0, 0.8625, 0.6974, 0.4079, 0.8864, 0.861] +2026-04-15 01:23:29.087190: Epoch time: 100.25 s +2026-04-15 01:23:30.329922: +2026-04-15 01:23:30.332197: Epoch 3781 +2026-04-15 01:23:30.334438: Current learning rate: 0.00073 +2026-04-15 01:25:11.050023: train_loss -0.6531 +2026-04-15 01:25:11.056652: val_loss -0.5272 +2026-04-15 01:25:11.059068: Pseudo dice [0.861, 0.0, 0.7754, 0.0738, 0.4678, 0.8653, 0.8475] +2026-04-15 01:25:11.062114: Epoch time: 100.72 s +2026-04-15 01:25:12.320243: +2026-04-15 01:25:12.323299: Epoch 3782 +2026-04-15 01:25:12.325652: Current learning rate: 0.00073 +2026-04-15 01:26:53.082656: train_loss -0.663 +2026-04-15 01:26:53.091201: val_loss -0.5653 +2026-04-15 01:26:53.094787: Pseudo dice [0.6945, 0.0, 0.8202, 0.4741, 0.2996, 0.7037, 0.8116] +2026-04-15 01:26:53.097562: Epoch time: 100.77 s +2026-04-15 01:26:54.354785: +2026-04-15 01:26:54.360759: Epoch 3783 +2026-04-15 01:26:54.367763: Current learning rate: 0.00073 +2026-04-15 01:28:34.699745: train_loss -0.6623 +2026-04-15 01:28:34.707262: val_loss -0.535 +2026-04-15 01:28:34.715224: Pseudo dice [0.8369, 0.0, 0.5754, 0.2423, 0.4413, 0.8944, 0.9069] +2026-04-15 01:28:34.718210: Epoch time: 100.35 s +2026-04-15 01:28:36.020142: +2026-04-15 01:28:36.023934: Epoch 3784 +2026-04-15 01:28:36.026402: Current learning rate: 0.00072 +2026-04-15 01:30:16.030214: train_loss -0.6612 +2026-04-15 01:30:16.037587: val_loss -0.5732 +2026-04-15 01:30:16.039575: Pseudo dice [0.7393, 0.0, 0.7374, 0.8541, 0.3151, 0.8416, 0.7655] +2026-04-15 01:30:16.041754: Epoch time: 100.01 s +2026-04-15 01:30:17.285693: +2026-04-15 01:30:17.288097: Epoch 3785 +2026-04-15 01:30:17.290242: Current learning rate: 0.00072 +2026-04-15 01:31:58.286564: train_loss -0.6605 +2026-04-15 01:31:58.294716: val_loss -0.5709 +2026-04-15 01:31:58.298234: Pseudo dice [0.1746, 0.0, 0.804, 0.8774, 0.4228, 0.7825, 0.8954] +2026-04-15 01:31:58.301116: Epoch time: 101.0 s +2026-04-15 01:31:59.564354: +2026-04-15 01:31:59.566411: Epoch 3786 +2026-04-15 01:31:59.568584: Current learning rate: 0.00072 +2026-04-15 01:33:40.142277: train_loss -0.6559 +2026-04-15 01:33:40.150406: val_loss -0.573 +2026-04-15 01:33:40.153519: Pseudo dice [0.8377, 0.0, 0.7222, 0.9059, 0.5125, 0.777, 0.8354] +2026-04-15 01:33:40.156311: Epoch time: 100.58 s +2026-04-15 01:33:41.412103: +2026-04-15 01:33:41.414941: Epoch 3787 +2026-04-15 01:33:41.417770: Current learning rate: 0.00071 +2026-04-15 01:35:21.443805: train_loss -0.6559 +2026-04-15 01:35:21.451936: val_loss -0.5837 +2026-04-15 01:35:21.454449: Pseudo dice [0.8115, 0.0, 0.8214, 0.8734, 0.5087, 0.8757, 0.8007] +2026-04-15 01:35:21.457628: Epoch time: 100.03 s +2026-04-15 01:35:21.459836: Yayy! New best EMA pseudo Dice: 0.5708 +2026-04-15 01:35:24.614706: +2026-04-15 01:35:24.617652: Epoch 3788 +2026-04-15 01:35:24.620347: Current learning rate: 0.00071 +2026-04-15 01:37:05.044119: train_loss -0.6545 +2026-04-15 01:37:05.051180: val_loss -0.505 +2026-04-15 01:37:05.053456: Pseudo dice [0.6711, 0.0, 0.6141, 0.1077, 0.3311, 0.8524, 0.9264] +2026-04-15 01:37:05.056061: Epoch time: 100.43 s +2026-04-15 01:37:06.300420: +2026-04-15 01:37:06.303339: Epoch 3789 +2026-04-15 01:37:06.305663: Current learning rate: 0.00071 +2026-04-15 01:38:46.717969: train_loss -0.6615 +2026-04-15 01:38:46.727080: val_loss -0.5338 +2026-04-15 01:38:46.729438: Pseudo dice [0.5718, 0.0, 0.7593, 0.0756, 0.5866, 0.7928, 0.8971] +2026-04-15 01:38:46.732906: Epoch time: 100.42 s +2026-04-15 01:38:48.034766: +2026-04-15 01:38:48.036863: Epoch 3790 +2026-04-15 01:38:48.039079: Current learning rate: 0.0007 +2026-04-15 01:40:28.493799: train_loss -0.6565 +2026-04-15 01:40:28.501458: val_loss -0.5617 +2026-04-15 01:40:28.503846: Pseudo dice [0.8254, 0.0, 0.7131, 0.0, 0.3696, 0.8742, 0.7259] +2026-04-15 01:40:28.506435: Epoch time: 100.46 s +2026-04-15 01:40:29.766141: +2026-04-15 01:40:29.768234: Epoch 3791 +2026-04-15 01:40:29.770344: Current learning rate: 0.0007 +2026-04-15 01:42:10.560481: train_loss -0.6569 +2026-04-15 01:42:10.567877: val_loss -0.5213 +2026-04-15 01:42:10.569524: Pseudo dice [0.5876, 0.0, 0.675, 0.1198, 0.2344, 0.7629, 0.7859] +2026-04-15 01:42:10.573602: Epoch time: 100.8 s +2026-04-15 01:42:11.843367: +2026-04-15 01:42:11.846506: Epoch 3792 +2026-04-15 01:42:11.848393: Current learning rate: 0.0007 +2026-04-15 01:43:52.208604: train_loss -0.6584 +2026-04-15 01:43:52.214519: val_loss -0.5394 +2026-04-15 01:43:52.216600: Pseudo dice [0.8175, 0.0, 0.7725, 0.3618, 0.3377, 0.6433, 0.5905] +2026-04-15 01:43:52.220443: Epoch time: 100.37 s +2026-04-15 01:43:54.547326: +2026-04-15 01:43:54.550159: Epoch 3793 +2026-04-15 01:43:54.552700: Current learning rate: 0.0007 +2026-04-15 01:45:35.468057: train_loss -0.6548 +2026-04-15 01:45:35.477890: val_loss -0.5057 +2026-04-15 01:45:35.480990: Pseudo dice [0.7352, 0.0, 0.714, 0.0, 0.4714, 0.608, 0.8914] +2026-04-15 01:45:35.484657: Epoch time: 100.92 s +2026-04-15 01:45:36.801684: +2026-04-15 01:45:36.803761: Epoch 3794 +2026-04-15 01:45:36.806165: Current learning rate: 0.00069 +2026-04-15 01:47:17.918492: train_loss -0.6509 +2026-04-15 01:47:17.932722: val_loss -0.5645 +2026-04-15 01:47:17.935651: Pseudo dice [0.743, 0.0, 0.8086, 0.1674, 0.278, 0.8815, 0.7315] +2026-04-15 01:47:17.938665: Epoch time: 101.12 s +2026-04-15 01:47:19.195686: +2026-04-15 01:47:19.197778: Epoch 3795 +2026-04-15 01:47:19.200638: Current learning rate: 0.00069 +2026-04-15 01:48:59.914911: train_loss -0.6644 +2026-04-15 01:48:59.922319: val_loss -0.5792 +2026-04-15 01:48:59.926238: Pseudo dice [0.6594, 0.0, 0.8004, 0.0014, 0.5457, 0.8643, 0.916] +2026-04-15 01:48:59.928936: Epoch time: 100.72 s +2026-04-15 01:49:01.222275: +2026-04-15 01:49:01.224672: Epoch 3796 +2026-04-15 01:49:01.226908: Current learning rate: 0.00069 +2026-04-15 01:50:41.890139: train_loss -0.6598 +2026-04-15 01:50:41.897514: val_loss -0.6201 +2026-04-15 01:50:41.899451: Pseudo dice [0.8187, 0.0, 0.8442, 0.7409, 0.641, 0.88, 0.7571] +2026-04-15 01:50:41.901903: Epoch time: 100.67 s +2026-04-15 01:50:43.161710: +2026-04-15 01:50:43.163666: Epoch 3797 +2026-04-15 01:50:43.165785: Current learning rate: 0.00068 +2026-04-15 01:52:23.553058: train_loss -0.6532 +2026-04-15 01:52:23.562954: val_loss -0.5722 +2026-04-15 01:52:23.565421: Pseudo dice [0.8392, 0.0, 0.7463, 0.2222, 0.2703, 0.8457, 0.7492] +2026-04-15 01:52:23.569479: Epoch time: 100.39 s +2026-04-15 01:52:24.843794: +2026-04-15 01:52:24.846352: Epoch 3798 +2026-04-15 01:52:24.848656: Current learning rate: 0.00068 +2026-04-15 01:54:05.983254: train_loss -0.6602 +2026-04-15 01:54:05.990307: val_loss -0.5764 +2026-04-15 01:54:05.992512: Pseudo dice [0.6315, 0.0, 0.8117, 0.8731, 0.4974, 0.8974, 0.7864] +2026-04-15 01:54:05.995518: Epoch time: 101.14 s +2026-04-15 01:54:07.238902: +2026-04-15 01:54:07.241735: Epoch 3799 +2026-04-15 01:54:07.243749: Current learning rate: 0.00068 +2026-04-15 01:55:47.528450: train_loss -0.6542 +2026-04-15 01:55:47.535592: val_loss -0.5754 +2026-04-15 01:55:47.539132: Pseudo dice [0.7974, 0.0, 0.7979, 0.6183, 0.4694, 0.8586, 0.7067] +2026-04-15 01:55:47.541904: Epoch time: 100.29 s +2026-04-15 01:55:50.647627: +2026-04-15 01:55:50.650238: Epoch 3800 +2026-04-15 01:55:50.653132: Current learning rate: 0.00067 +2026-04-15 01:57:30.785401: train_loss -0.6621 +2026-04-15 01:57:30.793144: val_loss -0.5353 +2026-04-15 01:57:30.795413: Pseudo dice [0.8382, 0.0, 0.8124, 0.0013, 0.4138, 0.8959, 0.8397] +2026-04-15 01:57:30.798040: Epoch time: 100.14 s +2026-04-15 01:57:32.062601: +2026-04-15 01:57:32.064481: Epoch 3801 +2026-04-15 01:57:32.066672: Current learning rate: 0.00067 +2026-04-15 01:59:12.446420: train_loss -0.6597 +2026-04-15 01:59:12.456148: val_loss -0.6122 +2026-04-15 01:59:12.458340: Pseudo dice [0.314, 0.0, 0.8389, 0.7527, 0.3454, 0.8043, 0.8563] +2026-04-15 01:59:12.460779: Epoch time: 100.39 s +2026-04-15 01:59:13.731854: +2026-04-15 01:59:13.734684: Epoch 3802 +2026-04-15 01:59:13.737008: Current learning rate: 0.00067 +2026-04-15 02:00:54.735494: train_loss -0.655 +2026-04-15 02:00:54.741850: val_loss -0.4142 +2026-04-15 02:00:54.744019: Pseudo dice [0.8173, 0.0, 0.7136, 0.0, 0.5579, 0.8249, 0.8133] +2026-04-15 02:00:54.747356: Epoch time: 101.01 s +2026-04-15 02:00:56.006544: +2026-04-15 02:00:56.008425: Epoch 3803 +2026-04-15 02:00:56.010919: Current learning rate: 0.00067 +2026-04-15 02:02:36.600227: train_loss -0.6617 +2026-04-15 02:02:36.607793: val_loss -0.5772 +2026-04-15 02:02:36.610594: Pseudo dice [0.0979, 0.0, 0.7036, 0.0011, 0.344, 0.8867, 0.7265] +2026-04-15 02:02:36.612904: Epoch time: 100.6 s +2026-04-15 02:02:37.882694: +2026-04-15 02:02:37.885034: Epoch 3804 +2026-04-15 02:02:37.887472: Current learning rate: 0.00066 +2026-04-15 02:04:18.556563: train_loss -0.6644 +2026-04-15 02:04:18.563040: val_loss -0.5422 +2026-04-15 02:04:18.565057: Pseudo dice [0.6639, 0.0, 0.6768, 0.0003, 0.4896, 0.8567, 0.7496] +2026-04-15 02:04:18.567900: Epoch time: 100.68 s +2026-04-15 02:04:19.822620: +2026-04-15 02:04:19.824373: Epoch 3805 +2026-04-15 02:04:19.826479: Current learning rate: 0.00066 +2026-04-15 02:06:00.266644: train_loss -0.6521 +2026-04-15 02:06:00.274133: val_loss -0.4959 +2026-04-15 02:06:00.276331: Pseudo dice [0.7582, 0.0, 0.6972, 0.0162, 0.4522, 0.91, 0.9186] +2026-04-15 02:06:00.278655: Epoch time: 100.45 s +2026-04-15 02:06:01.583790: +2026-04-15 02:06:01.586041: Epoch 3806 +2026-04-15 02:06:01.588634: Current learning rate: 0.00066 +2026-04-15 02:07:42.116842: train_loss -0.6581 +2026-04-15 02:07:42.123424: val_loss -0.6278 +2026-04-15 02:07:42.126491: Pseudo dice [0.8325, 0.0, 0.8649, 0.841, 0.5908, 0.8713, 0.8538] +2026-04-15 02:07:42.129345: Epoch time: 100.54 s +2026-04-15 02:07:43.430676: +2026-04-15 02:07:43.432608: Epoch 3807 +2026-04-15 02:07:43.434763: Current learning rate: 0.00065 +2026-04-15 02:09:24.400929: train_loss -0.6688 +2026-04-15 02:09:24.408271: val_loss -0.5873 +2026-04-15 02:09:24.412759: Pseudo dice [0.8087, 0.0, 0.7634, 0.7672, 0.3233, 0.7947, 0.7188] +2026-04-15 02:09:24.416914: Epoch time: 100.97 s +2026-04-15 02:09:25.677175: +2026-04-15 02:09:25.679355: Epoch 3808 +2026-04-15 02:09:25.681488: Current learning rate: 0.00065 +2026-04-15 02:11:05.864031: train_loss -0.6671 +2026-04-15 02:11:05.871025: val_loss -0.5619 +2026-04-15 02:11:05.873334: Pseudo dice [0.7398, 0.0, 0.7721, 0.8496, 0.5661, 0.8484, 0.761] +2026-04-15 02:11:05.875842: Epoch time: 100.19 s +2026-04-15 02:11:07.141276: +2026-04-15 02:11:07.143648: Epoch 3809 +2026-04-15 02:11:07.145818: Current learning rate: 0.00065 +2026-04-15 02:12:47.566336: train_loss -0.6677 +2026-04-15 02:12:47.572878: val_loss -0.545 +2026-04-15 02:12:47.574861: Pseudo dice [0.5224, 0.0, 0.6324, 0.0726, 0.449, 0.8623, 0.6458] +2026-04-15 02:12:47.577249: Epoch time: 100.43 s +2026-04-15 02:12:48.862222: +2026-04-15 02:12:48.864299: Epoch 3810 +2026-04-15 02:12:48.866333: Current learning rate: 0.00064 +2026-04-15 02:14:30.425344: train_loss -0.6531 +2026-04-15 02:14:30.433618: val_loss -0.5666 +2026-04-15 02:14:30.435697: Pseudo dice [0.4072, 0.0, 0.7843, 0.2879, 0.2032, 0.8363, 0.742] +2026-04-15 02:14:30.438558: Epoch time: 101.57 s +2026-04-15 02:14:31.793526: +2026-04-15 02:14:31.795606: Epoch 3811 +2026-04-15 02:14:31.798102: Current learning rate: 0.00064 +2026-04-15 02:16:11.963860: train_loss -0.6659 +2026-04-15 02:16:11.970456: val_loss -0.6137 +2026-04-15 02:16:11.972723: Pseudo dice [0.8654, 0.0, 0.8394, 0.6821, 0.2201, 0.8411, 0.8547] +2026-04-15 02:16:11.975599: Epoch time: 100.17 s +2026-04-15 02:16:14.377004: +2026-04-15 02:16:14.379967: Epoch 3812 +2026-04-15 02:16:14.382313: Current learning rate: 0.00064 +2026-04-15 02:17:54.539827: train_loss -0.6554 +2026-04-15 02:17:54.546446: val_loss -0.5113 +2026-04-15 02:17:54.549083: Pseudo dice [0.7229, 0.0, 0.7886, 0.1615, 0.4814, 0.8397, 0.7135] +2026-04-15 02:17:54.551692: Epoch time: 100.17 s +2026-04-15 02:17:55.864941: +2026-04-15 02:17:55.867265: Epoch 3813 +2026-04-15 02:17:55.869882: Current learning rate: 0.00064 +2026-04-15 02:19:36.331846: train_loss -0.6551 +2026-04-15 02:19:36.338527: val_loss -0.4974 +2026-04-15 02:19:36.341620: Pseudo dice [0.6422, 0.0, 0.495, 0.0346, 0.4821, 0.8257, 0.8947] +2026-04-15 02:19:36.344522: Epoch time: 100.47 s +2026-04-15 02:19:37.633828: +2026-04-15 02:19:37.645587: Epoch 3814 +2026-04-15 02:19:37.647933: Current learning rate: 0.00063 +2026-04-15 02:21:18.057100: train_loss -0.6592 +2026-04-15 02:21:18.084394: val_loss -0.5039 +2026-04-15 02:21:18.086862: Pseudo dice [0.8637, 0.0, 0.7662, 0.1217, 0.3694, 0.8671, 0.8709] +2026-04-15 02:21:18.089414: Epoch time: 100.43 s +2026-04-15 02:21:19.376775: +2026-04-15 02:21:19.378858: Epoch 3815 +2026-04-15 02:21:19.381353: Current learning rate: 0.00063 +2026-04-15 02:23:00.232473: train_loss -0.6422 +2026-04-15 02:23:00.241012: val_loss -0.5179 +2026-04-15 02:23:00.244753: Pseudo dice [0.8442, 0.0, 0.8645, 0.1681, 0.4256, 0.8447, 0.7848] +2026-04-15 02:23:00.249170: Epoch time: 100.86 s +2026-04-15 02:23:01.560000: +2026-04-15 02:23:01.562374: Epoch 3816 +2026-04-15 02:23:01.565188: Current learning rate: 0.00063 +2026-04-15 02:24:42.828760: train_loss -0.6652 +2026-04-15 02:24:42.835561: val_loss -0.5963 +2026-04-15 02:24:42.838245: Pseudo dice [0.8469, 0.0, 0.8157, 0.7786, 0.3746, 0.7983, 0.8152] +2026-04-15 02:24:42.841041: Epoch time: 101.27 s +2026-04-15 02:24:44.117840: +2026-04-15 02:24:44.119873: Epoch 3817 +2026-04-15 02:24:44.122139: Current learning rate: 0.00062 +2026-04-15 02:26:24.589890: train_loss -0.6713 +2026-04-15 02:26:24.601969: val_loss -0.6258 +2026-04-15 02:26:24.604188: Pseudo dice [0.8232, 0.0, 0.8802, 0.9279, 0.3222, 0.8883, 0.8525] +2026-04-15 02:26:24.606960: Epoch time: 100.48 s +2026-04-15 02:26:25.881302: +2026-04-15 02:26:25.883347: Epoch 3818 +2026-04-15 02:26:25.885705: Current learning rate: 0.00062 +2026-04-15 02:28:06.182816: train_loss -0.6663 +2026-04-15 02:28:06.190800: val_loss -0.6008 +2026-04-15 02:28:06.193378: Pseudo dice [0.7512, 0.0, 0.7489, 0.0, 0.5987, 0.8608, 0.8887] +2026-04-15 02:28:06.195703: Epoch time: 100.3 s +2026-04-15 02:28:07.446983: +2026-04-15 02:28:07.448786: Epoch 3819 +2026-04-15 02:28:07.450644: Current learning rate: 0.00062 +2026-04-15 02:29:47.826025: train_loss -0.6501 +2026-04-15 02:29:47.832656: val_loss -0.6268 +2026-04-15 02:29:47.834751: Pseudo dice [0.8734, 0.0, 0.8129, 0.0017, 0.4194, 0.8606, 0.9001] +2026-04-15 02:29:47.837363: Epoch time: 100.38 s +2026-04-15 02:29:49.097774: +2026-04-15 02:29:49.099699: Epoch 3820 +2026-04-15 02:29:49.101799: Current learning rate: 0.00061 +2026-04-15 02:31:29.821724: train_loss -0.6513 +2026-04-15 02:31:29.829163: val_loss -0.4473 +2026-04-15 02:31:29.831331: Pseudo dice [0.5413, 0.0, 0.7658, 0.0182, 0.3991, 0.6373, 0.8453] +2026-04-15 02:31:29.834577: Epoch time: 100.73 s +2026-04-15 02:31:31.117617: +2026-04-15 02:31:31.119976: Epoch 3821 +2026-04-15 02:31:31.122327: Current learning rate: 0.00061 +2026-04-15 02:33:11.463221: train_loss -0.6565 +2026-04-15 02:33:11.473289: val_loss -0.634 +2026-04-15 02:33:11.476490: Pseudo dice [0.7523, 0.0, 0.8582, 0.8758, 0.4595, 0.7552, 0.9008] +2026-04-15 02:33:11.479303: Epoch time: 100.35 s +2026-04-15 02:33:12.755038: +2026-04-15 02:33:12.759056: Epoch 3822 +2026-04-15 02:33:12.761734: Current learning rate: 0.00061 +2026-04-15 02:34:53.726304: train_loss -0.653 +2026-04-15 02:34:53.733019: val_loss -0.4921 +2026-04-15 02:34:53.735997: Pseudo dice [0.8612, 0.0, 0.5376, 0.0874, 0.3831, 0.8949, 0.6807] +2026-04-15 02:34:53.739492: Epoch time: 100.97 s +2026-04-15 02:34:55.030558: +2026-04-15 02:34:55.032960: Epoch 3823 +2026-04-15 02:34:55.035196: Current learning rate: 0.0006 +2026-04-15 02:36:35.501296: train_loss -0.6509 +2026-04-15 02:36:35.508862: val_loss -0.6036 +2026-04-15 02:36:35.511233: Pseudo dice [0.8705, 0.0, 0.7113, 0.0348, 0.4359, 0.847, 0.8199] +2026-04-15 02:36:35.514060: Epoch time: 100.47 s +2026-04-15 02:36:36.839652: +2026-04-15 02:36:36.841407: Epoch 3824 +2026-04-15 02:36:36.844097: Current learning rate: 0.0006 +2026-04-15 02:38:17.259298: train_loss -0.6687 +2026-04-15 02:38:17.268656: val_loss -0.5507 +2026-04-15 02:38:17.271105: Pseudo dice [0.6754, 0.0, 0.8274, 0.1672, 0.354, 0.8231, 0.6027] +2026-04-15 02:38:17.274035: Epoch time: 100.42 s +2026-04-15 02:38:18.605753: +2026-04-15 02:38:18.608118: Epoch 3825 +2026-04-15 02:38:18.610616: Current learning rate: 0.0006 +2026-04-15 02:39:59.391893: train_loss -0.6542 +2026-04-15 02:39:59.408288: val_loss -0.6068 +2026-04-15 02:39:59.413258: Pseudo dice [0.6401, 0.0, 0.8679, 0.8553, 0.4761, 0.7618, 0.5665] +2026-04-15 02:39:59.415475: Epoch time: 100.79 s +2026-04-15 02:40:00.668983: +2026-04-15 02:40:00.670819: Epoch 3826 +2026-04-15 02:40:00.672940: Current learning rate: 0.0006 +2026-04-15 02:41:40.972522: train_loss -0.6509 +2026-04-15 02:41:40.979417: val_loss -0.5962 +2026-04-15 02:41:40.982572: Pseudo dice [0.8246, 0.0, 0.797, 0.4539, 0.3735, 0.7955, 0.9155] +2026-04-15 02:41:40.985706: Epoch time: 100.31 s +2026-04-15 02:41:42.259627: +2026-04-15 02:41:42.261759: Epoch 3827 +2026-04-15 02:41:42.263977: Current learning rate: 0.00059 +2026-04-15 02:43:22.738080: train_loss -0.6562 +2026-04-15 02:43:22.744766: val_loss -0.5931 +2026-04-15 02:43:22.747226: Pseudo dice [0.6494, 0.0, 0.8234, 0.3539, 0.4266, 0.8369, 0.6814] +2026-04-15 02:43:22.749603: Epoch time: 100.48 s +2026-04-15 02:43:24.033044: +2026-04-15 02:43:24.034981: Epoch 3828 +2026-04-15 02:43:24.036892: Current learning rate: 0.00059 +2026-04-15 02:45:04.209851: train_loss -0.6687 +2026-04-15 02:45:04.216094: val_loss -0.6024 +2026-04-15 02:45:04.218265: Pseudo dice [0.8646, 0.0, 0.818, 0.8906, 0.4935, 0.8431, 0.7957] +2026-04-15 02:45:04.220720: Epoch time: 100.18 s +2026-04-15 02:45:05.492084: +2026-04-15 02:45:05.494297: Epoch 3829 +2026-04-15 02:45:05.496516: Current learning rate: 0.00059 +2026-04-15 02:46:46.080545: train_loss -0.6496 +2026-04-15 02:46:46.086737: val_loss -0.5886 +2026-04-15 02:46:46.089083: Pseudo dice [0.8238, 0.0, 0.8371, 0.4143, 0.3917, 0.8223, 0.7255] +2026-04-15 02:46:46.091513: Epoch time: 100.59 s +2026-04-15 02:46:47.356383: +2026-04-15 02:46:47.358784: Epoch 3830 +2026-04-15 02:46:47.360882: Current learning rate: 0.00058 +2026-04-15 02:48:27.745427: train_loss -0.6528 +2026-04-15 02:48:27.751559: val_loss -0.5606 +2026-04-15 02:48:27.755682: Pseudo dice [0.8507, 0.0, 0.6137, 0.4377, 0.3542, 0.7959, 0.8072] +2026-04-15 02:48:27.759417: Epoch time: 100.39 s +2026-04-15 02:48:29.028803: +2026-04-15 02:48:29.031400: Epoch 3831 +2026-04-15 02:48:29.033481: Current learning rate: 0.00058 +2026-04-15 02:50:10.016601: train_loss -0.6483 +2026-04-15 02:50:10.023147: val_loss -0.6085 +2026-04-15 02:50:10.025012: Pseudo dice [0.7285, 0.0, 0.7749, 0.362, 0.527, 0.8342, 0.8615] +2026-04-15 02:50:10.027516: Epoch time: 100.99 s +2026-04-15 02:50:12.544393: +2026-04-15 02:50:12.548322: Epoch 3832 +2026-04-15 02:50:12.550864: Current learning rate: 0.00058 +2026-04-15 02:51:53.141843: train_loss -0.6412 +2026-04-15 02:51:53.149980: val_loss -0.5438 +2026-04-15 02:51:53.152887: Pseudo dice [0.4912, 0.0, 0.7788, 0.0, 0.4607, 0.8414, 0.7748] +2026-04-15 02:51:53.155603: Epoch time: 100.6 s +2026-04-15 02:51:54.445453: +2026-04-15 02:51:54.447595: Epoch 3833 +2026-04-15 02:51:54.449654: Current learning rate: 0.00057 +2026-04-15 02:53:34.627883: train_loss -0.6642 +2026-04-15 02:53:34.635268: val_loss -0.6193 +2026-04-15 02:53:34.637802: Pseudo dice [0.8316, 0.0, 0.8477, 0.8664, 0.2783, 0.8709, 0.8688] +2026-04-15 02:53:34.640444: Epoch time: 100.19 s +2026-04-15 02:53:35.918740: +2026-04-15 02:53:35.920521: Epoch 3834 +2026-04-15 02:53:35.922678: Current learning rate: 0.00057 +2026-04-15 02:55:16.148545: train_loss -0.6601 +2026-04-15 02:55:16.154300: val_loss -0.5543 +2026-04-15 02:55:16.156245: Pseudo dice [0.4517, 0.0, 0.7772, 0.8677, 0.3618, 0.8355, 0.5409] +2026-04-15 02:55:16.159127: Epoch time: 100.23 s +2026-04-15 02:55:17.431273: +2026-04-15 02:55:17.433139: Epoch 3835 +2026-04-15 02:55:17.435213: Current learning rate: 0.00057 +2026-04-15 02:56:58.471946: train_loss -0.6575 +2026-04-15 02:56:58.478845: val_loss -0.5914 +2026-04-15 02:56:58.481561: Pseudo dice [0.8679, 0.0, 0.7828, 0.9232, 0.3318, 0.7994, 0.7305] +2026-04-15 02:56:58.484108: Epoch time: 101.04 s +2026-04-15 02:56:58.486844: Yayy! New best EMA pseudo Dice: 0.5726 +2026-04-15 02:57:01.566148: +2026-04-15 02:57:01.568705: Epoch 3836 +2026-04-15 02:57:01.570816: Current learning rate: 0.00056 +2026-04-15 02:58:41.919629: train_loss -0.661 +2026-04-15 02:58:41.926939: val_loss -0.6163 +2026-04-15 02:58:41.929134: Pseudo dice [0.7058, 0.0, 0.7412, 0.5335, 0.4867, 0.8507, 0.8424] +2026-04-15 02:58:41.932096: Epoch time: 100.36 s +2026-04-15 02:58:41.934460: Yayy! New best EMA pseudo Dice: 0.5748 +2026-04-15 02:58:45.028694: +2026-04-15 02:58:45.031191: Epoch 3837 +2026-04-15 02:58:45.033388: Current learning rate: 0.00056 +2026-04-15 03:00:25.273357: train_loss -0.667 +2026-04-15 03:00:25.279259: val_loss -0.5981 +2026-04-15 03:00:25.281584: Pseudo dice [0.749, 0.0, 0.7037, 0.8885, 0.3106, 0.8086, 0.8044] +2026-04-15 03:00:25.284399: Epoch time: 100.25 s +2026-04-15 03:00:25.286493: Yayy! New best EMA pseudo Dice: 0.5782 +2026-04-15 03:00:28.325814: +2026-04-15 03:00:28.328346: Epoch 3838 +2026-04-15 03:00:28.330432: Current learning rate: 0.00056 +2026-04-15 03:02:08.684139: train_loss -0.6578 +2026-04-15 03:02:08.690328: val_loss -0.5889 +2026-04-15 03:02:08.692626: Pseudo dice [0.5737, 0.0, 0.8049, 0.577, 0.2332, 0.8355, 0.7461] +2026-04-15 03:02:08.695068: Epoch time: 100.36 s +2026-04-15 03:02:09.946900: +2026-04-15 03:02:09.949082: Epoch 3839 +2026-04-15 03:02:09.950935: Current learning rate: 0.00055 +2026-04-15 03:03:50.041875: train_loss -0.6758 +2026-04-15 03:03:50.048658: val_loss -0.5033 +2026-04-15 03:03:50.051570: Pseudo dice [0.8652, 0.0, 0.702, 0.0882, 0.4496, 0.7929, 0.8083] +2026-04-15 03:03:50.053869: Epoch time: 100.1 s +2026-04-15 03:03:51.296233: +2026-04-15 03:03:51.298469: Epoch 3840 +2026-04-15 03:03:51.300521: Current learning rate: 0.00055 +2026-04-15 03:05:31.433991: train_loss -0.6674 +2026-04-15 03:05:31.440799: val_loss -0.584 +2026-04-15 03:05:31.443065: Pseudo dice [0.6992, 0.0, 0.82, 0.7647, 0.3381, 0.8435, 0.8483] +2026-04-15 03:05:31.446395: Epoch time: 100.14 s +2026-04-15 03:05:32.727283: +2026-04-15 03:05:32.729606: Epoch 3841 +2026-04-15 03:05:32.732026: Current learning rate: 0.00055 +2026-04-15 03:07:13.186716: train_loss -0.6656 +2026-04-15 03:07:13.192776: val_loss -0.6216 +2026-04-15 03:07:13.195158: Pseudo dice [0.3507, 0.0, 0.8311, 0.0008, 0.4296, 0.8407, 0.9125] +2026-04-15 03:07:13.197141: Epoch time: 100.46 s +2026-04-15 03:07:14.465225: +2026-04-15 03:07:14.467648: Epoch 3842 +2026-04-15 03:07:14.469424: Current learning rate: 0.00055 +2026-04-15 03:08:54.744165: train_loss -0.6687 +2026-04-15 03:08:54.749992: val_loss -0.5982 +2026-04-15 03:08:54.751961: Pseudo dice [0.7777, 0.0, 0.7904, 0.1237, 0.2212, 0.7541, 0.8648] +2026-04-15 03:08:54.754411: Epoch time: 100.28 s +2026-04-15 03:08:56.011713: +2026-04-15 03:08:56.013681: Epoch 3843 +2026-04-15 03:08:56.015365: Current learning rate: 0.00054 +2026-04-15 03:10:36.140817: train_loss -0.6528 +2026-04-15 03:10:36.147708: val_loss -0.5636 +2026-04-15 03:10:36.150606: Pseudo dice [0.7793, 0.0, 0.7615, 0.6184, 0.5258, 0.8474, 0.7977] +2026-04-15 03:10:36.153193: Epoch time: 100.13 s +2026-04-15 03:10:37.410728: +2026-04-15 03:10:37.412611: Epoch 3844 +2026-04-15 03:10:37.414260: Current learning rate: 0.00054 +2026-04-15 03:12:18.069710: train_loss -0.6724 +2026-04-15 03:12:18.077225: val_loss -0.4582 +2026-04-15 03:12:18.080150: Pseudo dice [0.857, 0.0, 0.7697, 0.0001, 0.6521, 0.8337, 0.8044] +2026-04-15 03:12:18.083283: Epoch time: 100.66 s +2026-04-15 03:12:19.359412: +2026-04-15 03:12:19.361242: Epoch 3845 +2026-04-15 03:12:19.362963: Current learning rate: 0.00054 +2026-04-15 03:13:59.955077: train_loss -0.6625 +2026-04-15 03:13:59.962215: val_loss -0.5957 +2026-04-15 03:13:59.964665: Pseudo dice [0.8766, 0.0, 0.8145, 0.6063, 0.5652, 0.7401, 0.8307] +2026-04-15 03:13:59.967244: Epoch time: 100.6 s +2026-04-15 03:14:01.275642: +2026-04-15 03:14:01.277602: Epoch 3846 +2026-04-15 03:14:01.279368: Current learning rate: 0.00053 +2026-04-15 03:15:41.417688: train_loss -0.6692 +2026-04-15 03:15:41.425252: val_loss -0.5487 +2026-04-15 03:15:41.427664: Pseudo dice [0.508, 0.0, 0.7426, 0.2259, 0.4062, 0.7687, 0.768] +2026-04-15 03:15:41.429964: Epoch time: 100.15 s +2026-04-15 03:15:42.709285: +2026-04-15 03:15:42.711288: Epoch 3847 +2026-04-15 03:15:42.712988: Current learning rate: 0.00053 +2026-04-15 03:17:22.810969: train_loss -0.6561 +2026-04-15 03:17:22.818395: val_loss -0.5842 +2026-04-15 03:17:22.820584: Pseudo dice [0.7953, 0.0, 0.8248, 0.2117, 0.383, 0.8694, 0.9091] +2026-04-15 03:17:22.823375: Epoch time: 100.1 s +2026-04-15 03:17:24.121329: +2026-04-15 03:17:24.123267: Epoch 3848 +2026-04-15 03:17:24.124908: Current learning rate: 0.00053 +2026-04-15 03:19:04.395450: train_loss -0.6762 +2026-04-15 03:19:04.405317: val_loss -0.564 +2026-04-15 03:19:04.407974: Pseudo dice [0.705, 0.0, 0.7676, 0.0006, 0.444, 0.8651, 0.9004] +2026-04-15 03:19:04.411430: Epoch time: 100.28 s +2026-04-15 03:19:05.703602: +2026-04-15 03:19:05.705760: Epoch 3849 +2026-04-15 03:19:05.707649: Current learning rate: 0.00052 +2026-04-15 03:20:45.812779: train_loss -0.6665 +2026-04-15 03:20:45.819680: val_loss -0.6177 +2026-04-15 03:20:45.822437: Pseudo dice [0.6164, 0.0, 0.8061, 0.7448, 0.5027, 0.8556, 0.8516] +2026-04-15 03:20:45.824459: Epoch time: 100.11 s +2026-04-15 03:20:48.922191: +2026-04-15 03:20:48.925014: Epoch 3850 +2026-04-15 03:20:48.926967: Current learning rate: 0.00052 +2026-04-15 03:22:30.212808: train_loss -0.6766 +2026-04-15 03:22:30.219534: val_loss -0.4907 +2026-04-15 03:22:30.221586: Pseudo dice [0.8494, 0.0, 0.8059, 0.0707, 0.0453, 0.4373, 0.8738] +2026-04-15 03:22:30.223991: Epoch time: 101.29 s +2026-04-15 03:22:31.517617: +2026-04-15 03:22:31.519487: Epoch 3851 +2026-04-15 03:22:31.522034: Current learning rate: 0.00052 +2026-04-15 03:24:11.811461: train_loss -0.6579 +2026-04-15 03:24:11.817896: val_loss -0.5653 +2026-04-15 03:24:11.820299: Pseudo dice [0.8471, 0.0, 0.8215, 0.0006, 0.4763, 0.7674, 0.7597] +2026-04-15 03:24:11.822577: Epoch time: 100.3 s +2026-04-15 03:24:13.212692: +2026-04-15 03:24:13.214705: Epoch 3852 +2026-04-15 03:24:13.216352: Current learning rate: 0.00051 +2026-04-15 03:25:53.374774: train_loss -0.6613 +2026-04-15 03:25:53.383869: val_loss -0.6129 +2026-04-15 03:25:53.387801: Pseudo dice [0.8561, 0.0, 0.7504, 0.2172, 0.5938, 0.8792, 0.8229] +2026-04-15 03:25:53.390475: Epoch time: 100.17 s +2026-04-15 03:25:54.675147: +2026-04-15 03:25:54.677345: Epoch 3853 +2026-04-15 03:25:54.679209: Current learning rate: 0.00051 +2026-04-15 03:27:35.071281: train_loss -0.6765 +2026-04-15 03:27:35.078150: val_loss -0.5949 +2026-04-15 03:27:35.080106: Pseudo dice [0.6044, 0.0, 0.8164, 0.0015, 0.4434, 0.8994, 0.7921] +2026-04-15 03:27:35.082325: Epoch time: 100.4 s +2026-04-15 03:27:36.369052: +2026-04-15 03:27:36.372900: Epoch 3854 +2026-04-15 03:27:36.374887: Current learning rate: 0.00051 +2026-04-15 03:29:16.591214: train_loss -0.6764 +2026-04-15 03:29:16.597498: val_loss -0.5988 +2026-04-15 03:29:16.600327: Pseudo dice [0.5729, 0.0, 0.7694, 0.0077, 0.486, 0.8105, 0.9205] +2026-04-15 03:29:16.603166: Epoch time: 100.23 s +2026-04-15 03:29:17.871310: +2026-04-15 03:29:17.873279: Epoch 3855 +2026-04-15 03:29:17.875072: Current learning rate: 0.00051 +2026-04-15 03:30:58.184867: train_loss -0.6617 +2026-04-15 03:30:58.191909: val_loss -0.5226 +2026-04-15 03:30:58.193916: Pseudo dice [0.6234, 0.0, 0.7883, 0.0326, 0.5905, 0.884, 0.6306] +2026-04-15 03:30:58.199466: Epoch time: 100.32 s +2026-04-15 03:30:59.462388: +2026-04-15 03:30:59.464475: Epoch 3856 +2026-04-15 03:30:59.466301: Current learning rate: 0.0005 +2026-04-15 03:32:40.193843: train_loss -0.6614 +2026-04-15 03:32:40.202270: val_loss -0.6365 +2026-04-15 03:32:40.205291: Pseudo dice [0.8769, 0.0, 0.8097, 0.8761, 0.6105, 0.8318, 0.7825] +2026-04-15 03:32:40.211291: Epoch time: 100.73 s +2026-04-15 03:32:41.506507: +2026-04-15 03:32:41.509048: Epoch 3857 +2026-04-15 03:32:41.511528: Current learning rate: 0.0005 +2026-04-15 03:34:21.939597: train_loss -0.6622 +2026-04-15 03:34:21.948182: val_loss -0.5493 +2026-04-15 03:34:21.950810: Pseudo dice [0.846, 0.0, 0.8219, 0.3804, 0.5935, 0.8453, 0.7303] +2026-04-15 03:34:21.953595: Epoch time: 100.44 s +2026-04-15 03:34:23.291983: +2026-04-15 03:34:23.293833: Epoch 3858 +2026-04-15 03:34:23.295927: Current learning rate: 0.0005 +2026-04-15 03:36:03.559832: train_loss -0.6658 +2026-04-15 03:36:03.567324: val_loss -0.5693 +2026-04-15 03:36:03.569422: Pseudo dice [0.7042, 0.0, 0.7911, 0.6125, 0.5864, 0.8037, 0.6547] +2026-04-15 03:36:03.571940: Epoch time: 100.27 s +2026-04-15 03:36:04.866657: +2026-04-15 03:36:04.869076: Epoch 3859 +2026-04-15 03:36:04.871199: Current learning rate: 0.00049 +2026-04-15 03:37:45.075710: train_loss -0.6566 +2026-04-15 03:37:45.083751: val_loss -0.6165 +2026-04-15 03:37:45.086611: Pseudo dice [0.8209, 0.0, 0.818, 0.9051, 0.3372, 0.8595, 0.8461] +2026-04-15 03:37:45.089678: Epoch time: 100.21 s +2026-04-15 03:37:46.421327: +2026-04-15 03:37:46.423722: Epoch 3860 +2026-04-15 03:37:46.425441: Current learning rate: 0.00049 +2026-04-15 03:39:27.146279: train_loss -0.6569 +2026-04-15 03:39:27.154283: val_loss -0.5587 +2026-04-15 03:39:27.157462: Pseudo dice [0.0685, 0.0, 0.737, 0.0426, 0.5568, 0.8431, 0.8752] +2026-04-15 03:39:27.161450: Epoch time: 100.73 s +2026-04-15 03:39:28.439475: +2026-04-15 03:39:28.441621: Epoch 3861 +2026-04-15 03:39:28.444479: Current learning rate: 0.00049 +2026-04-15 03:41:08.982966: train_loss -0.6697 +2026-04-15 03:41:08.990361: val_loss -0.5281 +2026-04-15 03:41:08.992217: Pseudo dice [0.8435, 0.0, 0.6981, 0.1665, 0.6272, 0.8544, 0.875] +2026-04-15 03:41:08.994833: Epoch time: 100.55 s +2026-04-15 03:41:10.272625: +2026-04-15 03:41:10.274884: Epoch 3862 +2026-04-15 03:41:10.276735: Current learning rate: 0.00048 +2026-04-15 03:42:50.454836: train_loss -0.6678 +2026-04-15 03:42:50.462391: val_loss -0.569 +2026-04-15 03:42:50.465013: Pseudo dice [0.7581, 0.0, 0.8362, 0.1981, 0.2856, 0.8663, 0.7211] +2026-04-15 03:42:50.468020: Epoch time: 100.19 s +2026-04-15 03:42:51.740239: +2026-04-15 03:42:51.742088: Epoch 3863 +2026-04-15 03:42:51.743993: Current learning rate: 0.00048 +2026-04-15 03:44:32.574735: train_loss -0.6687 +2026-04-15 03:44:32.583169: val_loss -0.5928 +2026-04-15 03:44:32.585439: Pseudo dice [0.5786, 0.0, 0.8051, 0.0006, 0.6686, 0.9136, 0.8235] +2026-04-15 03:44:32.588207: Epoch time: 100.84 s +2026-04-15 03:44:33.893436: +2026-04-15 03:44:33.902437: Epoch 3864 +2026-04-15 03:44:33.904446: Current learning rate: 0.00048 +2026-04-15 03:46:14.295923: train_loss -0.6754 +2026-04-15 03:46:14.303119: val_loss -0.6145 +2026-04-15 03:46:14.305789: Pseudo dice [0.8681, 0.0, 0.8921, 0.8915, 0.3891, 0.8896, 0.9111] +2026-04-15 03:46:14.308275: Epoch time: 100.41 s +2026-04-15 03:46:15.610432: +2026-04-15 03:46:15.612995: Epoch 3865 +2026-04-15 03:46:15.614894: Current learning rate: 0.00047 +2026-04-15 03:47:56.076433: train_loss -0.6745 +2026-04-15 03:47:56.083848: val_loss -0.6358 +2026-04-15 03:47:56.086326: Pseudo dice [0.7716, 0.0, 0.875, 0.3332, 0.6865, 0.7934, 0.8678] +2026-04-15 03:47:56.089712: Epoch time: 100.47 s +2026-04-15 03:47:57.404646: +2026-04-15 03:47:57.406813: Epoch 3866 +2026-04-15 03:47:57.408626: Current learning rate: 0.00047 +2026-04-15 03:49:37.606268: train_loss -0.6664 +2026-04-15 03:49:37.612675: val_loss -0.5935 +2026-04-15 03:49:37.614519: Pseudo dice [0.3538, 0.0, 0.8375, 0.6184, 0.4256, 0.7746, 0.7507] +2026-04-15 03:49:37.617322: Epoch time: 100.2 s +2026-04-15 03:49:38.882975: +2026-04-15 03:49:38.884675: Epoch 3867 +2026-04-15 03:49:38.886336: Current learning rate: 0.00047 +2026-04-15 03:51:19.768082: train_loss -0.677 +2026-04-15 03:51:19.775587: val_loss -0.5915 +2026-04-15 03:51:19.778311: Pseudo dice [0.8608, 0.0, 0.8241, 0.2569, 0.366, 0.787, 0.7404] +2026-04-15 03:51:19.782156: Epoch time: 100.89 s +2026-04-15 03:51:21.097481: +2026-04-15 03:51:21.099584: Epoch 3868 +2026-04-15 03:51:21.101388: Current learning rate: 0.00046 +2026-04-15 03:53:01.838152: train_loss -0.6711 +2026-04-15 03:53:01.846524: val_loss -0.5999 +2026-04-15 03:53:01.849005: Pseudo dice [0.7916, 0.0, 0.8044, 0.6553, 0.3741, 0.7969, 0.82] +2026-04-15 03:53:01.853886: Epoch time: 100.74 s +2026-04-15 03:53:03.137936: +2026-04-15 03:53:03.140727: Epoch 3869 +2026-04-15 03:53:03.143588: Current learning rate: 0.00046 +2026-04-15 03:54:43.595759: train_loss -0.6608 +2026-04-15 03:54:43.602850: val_loss -0.593 +2026-04-15 03:54:43.605540: Pseudo dice [0.8511, 0.0, 0.8005, 0.3885, 0.3685, 0.8338, 0.7878] +2026-04-15 03:54:43.607886: Epoch time: 100.46 s +2026-04-15 03:54:45.997533: +2026-04-15 03:54:46.010669: Epoch 3870 +2026-04-15 03:54:46.017834: Current learning rate: 0.00046 +2026-04-15 03:56:26.211477: train_loss -0.6777 +2026-04-15 03:56:26.218892: val_loss -0.6201 +2026-04-15 03:56:26.221348: Pseudo dice [0.8825, 0.0, 0.7881, 0.8358, 0.61, 0.8104, 0.8293] +2026-04-15 03:56:26.224519: Epoch time: 100.22 s +2026-04-15 03:56:26.226528: Yayy! New best EMA pseudo Dice: 0.5837 +2026-04-15 03:56:29.295612: +2026-04-15 03:56:29.298707: Epoch 3871 +2026-04-15 03:56:29.300604: Current learning rate: 0.00045 +2026-04-15 03:58:09.428512: train_loss -0.6746 +2026-04-15 03:58:09.435446: val_loss -0.5701 +2026-04-15 03:58:09.437903: Pseudo dice [0.6379, 0.0, 0.822, 0.0527, 0.3073, 0.867, 0.7916] +2026-04-15 03:58:09.440293: Epoch time: 100.14 s +2026-04-15 03:58:10.709332: +2026-04-15 03:58:10.711385: Epoch 3872 +2026-04-15 03:58:10.712998: Current learning rate: 0.00045 +2026-04-15 03:59:51.141639: train_loss -0.6706 +2026-04-15 03:59:51.148572: val_loss -0.6035 +2026-04-15 03:59:51.151972: Pseudo dice [0.8587, 0.0, 0.7548, 0.8618, 0.4658, 0.7892, 0.7792] +2026-04-15 03:59:51.156390: Epoch time: 100.44 s +2026-04-15 03:59:52.442731: +2026-04-15 03:59:52.444875: Epoch 3873 +2026-04-15 03:59:52.446895: Current learning rate: 0.00045 +2026-04-15 04:01:33.079558: train_loss -0.6757 +2026-04-15 04:01:33.086237: val_loss -0.5524 +2026-04-15 04:01:33.090034: Pseudo dice [0.7136, 0.0, 0.7681, 0.1029, 0.4952, 0.7994, 0.914] +2026-04-15 04:01:33.093241: Epoch time: 100.64 s +2026-04-15 04:01:34.404125: +2026-04-15 04:01:34.406302: Epoch 3874 +2026-04-15 04:01:34.408141: Current learning rate: 0.00045 +2026-04-15 04:03:14.670057: train_loss -0.6613 +2026-04-15 04:03:14.675281: val_loss -0.5958 +2026-04-15 04:03:14.677276: Pseudo dice [0.5758, 0.0, 0.8288, 0.5881, 0.418, 0.7122, 0.928] +2026-04-15 04:03:14.679922: Epoch time: 100.27 s +2026-04-15 04:03:15.970878: +2026-04-15 04:03:15.973062: Epoch 3875 +2026-04-15 04:03:15.974965: Current learning rate: 0.00044 +2026-04-15 04:04:56.297389: train_loss -0.6686 +2026-04-15 04:04:56.304878: val_loss -0.5288 +2026-04-15 04:04:56.307150: Pseudo dice [0.8514, 0.0, 0.8303, 0.1062, 0.4428, 0.8617, 0.8785] +2026-04-15 04:04:56.309689: Epoch time: 100.33 s +2026-04-15 04:04:57.591682: +2026-04-15 04:04:57.593892: Epoch 3876 +2026-04-15 04:04:57.595464: Current learning rate: 0.00044 +2026-04-15 04:06:37.844335: train_loss -0.6751 +2026-04-15 04:06:37.850215: val_loss -0.5558 +2026-04-15 04:06:37.851991: Pseudo dice [0.785, 0.0, 0.77, 0.0387, 0.5196, 0.8965, 0.8957] +2026-04-15 04:06:37.854364: Epoch time: 100.26 s +2026-04-15 04:06:39.117305: +2026-04-15 04:06:39.119233: Epoch 3877 +2026-04-15 04:06:39.121058: Current learning rate: 0.00044 +2026-04-15 04:08:19.285251: train_loss -0.6733 +2026-04-15 04:08:19.292056: val_loss -0.5804 +2026-04-15 04:08:19.294562: Pseudo dice [0.8469, 0.0, 0.6762, 0.5228, 0.1775, 0.7627, 0.8381] +2026-04-15 04:08:19.297442: Epoch time: 100.17 s +2026-04-15 04:08:20.544827: +2026-04-15 04:08:20.546751: Epoch 3878 +2026-04-15 04:08:20.548604: Current learning rate: 0.00043 +2026-04-15 04:10:01.000945: train_loss -0.6799 +2026-04-15 04:10:01.008110: val_loss -0.5882 +2026-04-15 04:10:01.010100: Pseudo dice [0.8639, 0.0, 0.8369, 0.6678, 0.475, 0.7397, 0.7196] +2026-04-15 04:10:01.012560: Epoch time: 100.46 s +2026-04-15 04:10:02.287740: +2026-04-15 04:10:02.289793: Epoch 3879 +2026-04-15 04:10:02.291411: Current learning rate: 0.00043 +2026-04-15 04:11:42.547264: train_loss -0.6676 +2026-04-15 04:11:42.556186: val_loss -0.613 +2026-04-15 04:11:42.558270: Pseudo dice [0.7841, 0.0, 0.854, 0.5695, 0.4709, 0.8395, 0.885] +2026-04-15 04:11:42.561623: Epoch time: 100.26 s +2026-04-15 04:11:43.865415: +2026-04-15 04:11:43.867537: Epoch 3880 +2026-04-15 04:11:43.869638: Current learning rate: 0.00043 +2026-04-15 04:13:24.082325: train_loss -0.6799 +2026-04-15 04:13:24.089503: val_loss -0.5259 +2026-04-15 04:13:24.091774: Pseudo dice [0.4538, 0.0, 0.7499, 0.0886, 0.532, 0.7805, 0.7745] +2026-04-15 04:13:24.094584: Epoch time: 100.22 s +2026-04-15 04:13:25.401515: +2026-04-15 04:13:25.404274: Epoch 3881 +2026-04-15 04:13:25.406183: Current learning rate: 0.00042 +2026-04-15 04:15:06.160676: train_loss -0.6751 +2026-04-15 04:15:06.166384: val_loss -0.6214 +2026-04-15 04:15:06.168442: Pseudo dice [0.356, 0.0, 0.7728, 0.0007, 0.4883, 0.8369, 0.8843] +2026-04-15 04:15:06.170939: Epoch time: 100.76 s +2026-04-15 04:15:07.451150: +2026-04-15 04:15:07.458194: Epoch 3882 +2026-04-15 04:15:07.460502: Current learning rate: 0.00042 +2026-04-15 04:16:47.642066: train_loss -0.6782 +2026-04-15 04:16:47.649735: val_loss -0.6254 +2026-04-15 04:16:47.652666: Pseudo dice [0.8487, 0.0, 0.8306, 0.8886, 0.3776, 0.8957, 0.9041] +2026-04-15 04:16:47.655681: Epoch time: 100.19 s +2026-04-15 04:16:48.967568: +2026-04-15 04:16:48.969561: Epoch 3883 +2026-04-15 04:16:48.971325: Current learning rate: 0.00042 +2026-04-15 04:18:29.407190: train_loss -0.6705 +2026-04-15 04:18:29.415072: val_loss -0.4739 +2026-04-15 04:18:29.419040: Pseudo dice [0.6701, 0.0, 0.741, 0.0612, 0.1652, 0.8478, 0.9215] +2026-04-15 04:18:29.421867: Epoch time: 100.44 s +2026-04-15 04:18:30.724529: +2026-04-15 04:18:30.726834: Epoch 3884 +2026-04-15 04:18:30.728945: Current learning rate: 0.00041 +2026-04-15 04:20:11.020534: train_loss -0.6784 +2026-04-15 04:20:11.026184: val_loss -0.3464 +2026-04-15 04:20:11.027903: Pseudo dice [0.6693, 0.0, 0.5336, 0.0317, 0.5128, 0.8235, 0.8804] +2026-04-15 04:20:11.029778: Epoch time: 100.3 s +2026-04-15 04:20:12.299276: +2026-04-15 04:20:12.301229: Epoch 3885 +2026-04-15 04:20:12.303009: Current learning rate: 0.00041 +2026-04-15 04:21:52.603364: train_loss -0.68 +2026-04-15 04:21:52.609960: val_loss -0.6207 +2026-04-15 04:21:52.612285: Pseudo dice [0.5896, 0.0, 0.7288, 0.9327, 0.4107, 0.8703, 0.8939] +2026-04-15 04:21:52.614399: Epoch time: 100.31 s +2026-04-15 04:21:53.886722: +2026-04-15 04:21:53.888721: Epoch 3886 +2026-04-15 04:21:53.890368: Current learning rate: 0.00041 +2026-04-15 04:23:34.290191: train_loss -0.6728 +2026-04-15 04:23:34.296779: val_loss -0.4987 +2026-04-15 04:23:34.299003: Pseudo dice [0.7572, 0.0, 0.6874, 0.0, 0.2296, 0.8699, 0.8987] +2026-04-15 04:23:34.301068: Epoch time: 100.41 s +2026-04-15 04:23:35.561283: +2026-04-15 04:23:35.563428: Epoch 3887 +2026-04-15 04:23:35.565348: Current learning rate: 0.0004 +2026-04-15 04:25:15.737266: train_loss -0.6696 +2026-04-15 04:25:15.744673: val_loss -0.6044 +2026-04-15 04:25:15.747427: Pseudo dice [0.5505, 0.0, 0.7868, 0.5379, 0.2739, 0.8854, 0.763] +2026-04-15 04:25:15.750260: Epoch time: 100.18 s +2026-04-15 04:25:17.044161: +2026-04-15 04:25:17.046713: Epoch 3888 +2026-04-15 04:25:17.048528: Current learning rate: 0.0004 +2026-04-15 04:26:57.778458: train_loss -0.66 +2026-04-15 04:26:57.786578: val_loss -0.5849 +2026-04-15 04:26:57.805176: Pseudo dice [0.8093, 0.0, 0.7564, 0.3307, 0.4133, 0.8004, 0.9277] +2026-04-15 04:26:57.808127: Epoch time: 100.74 s +2026-04-15 04:26:59.989772: +2026-04-15 04:26:59.991921: Epoch 3889 +2026-04-15 04:26:59.993845: Current learning rate: 0.0004 +2026-04-15 04:28:40.433412: train_loss -0.6751 +2026-04-15 04:28:40.441050: val_loss -0.5353 +2026-04-15 04:28:40.443743: Pseudo dice [0.6779, 0.0, 0.6, 0.1187, 0.3847, 0.72, 0.6807] +2026-04-15 04:28:40.446468: Epoch time: 100.45 s +2026-04-15 04:28:41.757565: +2026-04-15 04:28:41.761400: Epoch 3890 +2026-04-15 04:28:41.763635: Current learning rate: 0.00039 +2026-04-15 04:30:21.957999: train_loss -0.6611 +2026-04-15 04:30:21.965813: val_loss -0.6198 +2026-04-15 04:30:21.968299: Pseudo dice [0.8737, 0.0, 0.8305, 0.7144, 0.3216, 0.8454, 0.9074] +2026-04-15 04:30:21.970813: Epoch time: 100.2 s +2026-04-15 04:30:23.288337: +2026-04-15 04:30:23.291244: Epoch 3891 +2026-04-15 04:30:23.293482: Current learning rate: 0.00039 +2026-04-15 04:32:03.377824: train_loss -0.6714 +2026-04-15 04:32:03.385354: val_loss -0.5678 +2026-04-15 04:32:03.387670: Pseudo dice [0.8565, 0.0, 0.8166, 0.7219, 0.294, 0.7727, 0.7405] +2026-04-15 04:32:03.390623: Epoch time: 100.09 s +2026-04-15 04:32:04.691442: +2026-04-15 04:32:04.693549: Epoch 3892 +2026-04-15 04:32:04.695381: Current learning rate: 0.00039 +2026-04-15 04:33:44.882552: train_loss -0.6605 +2026-04-15 04:33:44.890427: val_loss -0.5755 +2026-04-15 04:33:44.892986: Pseudo dice [0.8065, 0.0, 0.7478, 0.9248, 0.3441, 0.8053, 0.7417] +2026-04-15 04:33:44.895624: Epoch time: 100.19 s +2026-04-15 04:33:46.186768: +2026-04-15 04:33:46.190197: Epoch 3893 +2026-04-15 04:33:46.193315: Current learning rate: 0.00038 +2026-04-15 04:35:26.462488: train_loss -0.6734 +2026-04-15 04:35:26.469804: val_loss -0.6366 +2026-04-15 04:35:26.472613: Pseudo dice [0.5116, 0.0, 0.8432, 0.0, 0.6478, 0.8876, 0.9269] +2026-04-15 04:35:26.476557: Epoch time: 100.28 s +2026-04-15 04:35:27.867469: +2026-04-15 04:35:27.869473: Epoch 3894 +2026-04-15 04:35:27.871365: Current learning rate: 0.00038 +2026-04-15 04:37:08.175162: train_loss -0.6674 +2026-04-15 04:37:08.182408: val_loss -0.6177 +2026-04-15 04:37:08.184533: Pseudo dice [0.796, 0.0, 0.8312, 0.8323, 0.3273, 0.872, 0.9287] +2026-04-15 04:37:08.187088: Epoch time: 100.31 s +2026-04-15 04:37:09.477409: +2026-04-15 04:37:09.479241: Epoch 3895 +2026-04-15 04:37:09.480908: Current learning rate: 0.00038 +2026-04-15 04:38:50.083379: train_loss -0.6732 +2026-04-15 04:38:50.088571: val_loss -0.6122 +2026-04-15 04:38:50.090935: Pseudo dice [0.6287, 0.0, 0.8307, 0.5635, 0.4482, 0.738, 0.9168] +2026-04-15 04:38:50.092917: Epoch time: 100.61 s +2026-04-15 04:38:51.409317: +2026-04-15 04:38:51.411537: Epoch 3896 +2026-04-15 04:38:51.413417: Current learning rate: 0.00037 +2026-04-15 04:40:31.814572: train_loss -0.665 +2026-04-15 04:40:31.822198: val_loss -0.5893 +2026-04-15 04:40:31.824569: Pseudo dice [0.87, 0.0, 0.7782, 0.7525, 0.3666, 0.7817, 0.7657] +2026-04-15 04:40:31.826983: Epoch time: 100.41 s +2026-04-15 04:40:33.133522: +2026-04-15 04:40:33.136330: Epoch 3897 +2026-04-15 04:40:33.138124: Current learning rate: 0.00037 +2026-04-15 04:42:13.381576: train_loss -0.6776 +2026-04-15 04:42:13.387922: val_loss -0.5715 +2026-04-15 04:42:13.390247: Pseudo dice [0.8132, 0.0, 0.8375, 0.0566, 0.2913, 0.7711, 0.7393] +2026-04-15 04:42:13.393181: Epoch time: 100.25 s +2026-04-15 04:42:14.677718: +2026-04-15 04:42:14.679917: Epoch 3898 +2026-04-15 04:42:14.682027: Current learning rate: 0.00037 +2026-04-15 04:43:54.843821: train_loss -0.6718 +2026-04-15 04:43:54.849280: val_loss -0.5085 +2026-04-15 04:43:54.851411: Pseudo dice [0.8599, 0.0, 0.7455, 0.0642, 0.4192, 0.8878, 0.6241] +2026-04-15 04:43:54.854524: Epoch time: 100.17 s +2026-04-15 04:43:56.114563: +2026-04-15 04:43:56.117182: Epoch 3899 +2026-04-15 04:43:56.119550: Current learning rate: 0.00036 +2026-04-15 04:45:36.696299: train_loss -0.6774 +2026-04-15 04:45:36.705256: val_loss -0.3738 +2026-04-15 04:45:36.707552: Pseudo dice [0.7176, 0.0, 0.7499, 0.0354, 0.1776, 0.9103, 0.7819] +2026-04-15 04:45:36.710944: Epoch time: 100.58 s +2026-04-15 04:45:39.716689: +2026-04-15 04:45:39.719001: Epoch 3900 +2026-04-15 04:45:39.721038: Current learning rate: 0.00036 +2026-04-15 04:47:20.050000: train_loss -0.6788 +2026-04-15 04:47:20.056454: val_loss -0.6106 +2026-04-15 04:47:20.058500: Pseudo dice [0.8018, 0.0, 0.7945, 0.9053, 0.5558, 0.8157, 0.846] +2026-04-15 04:47:20.061545: Epoch time: 100.34 s +2026-04-15 04:47:21.333191: +2026-04-15 04:47:21.335602: Epoch 3901 +2026-04-15 04:47:21.337851: Current learning rate: 0.00036 +2026-04-15 04:49:01.604649: train_loss -0.671 +2026-04-15 04:49:01.612712: val_loss -0.4207 +2026-04-15 04:49:01.615309: Pseudo dice [0.8683, 0.0, 0.5416, 0.0001, 0.5042, 0.8157, 0.5639] +2026-04-15 04:49:01.617961: Epoch time: 100.27 s +2026-04-15 04:49:02.908898: +2026-04-15 04:49:02.911030: Epoch 3902 +2026-04-15 04:49:02.913267: Current learning rate: 0.00036 +2026-04-15 04:50:43.031125: train_loss -0.6698 +2026-04-15 04:50:43.037438: val_loss -0.6191 +2026-04-15 04:50:43.039352: Pseudo dice [0.863, 0.0, 0.8375, 0.7112, 0.3865, 0.8681, 0.9188] +2026-04-15 04:50:43.041440: Epoch time: 100.13 s +2026-04-15 04:50:44.335836: +2026-04-15 04:50:44.337961: Epoch 3903 +2026-04-15 04:50:44.339618: Current learning rate: 0.00035 +2026-04-15 04:52:24.460838: train_loss -0.6741 +2026-04-15 04:52:24.468960: val_loss -0.5888 +2026-04-15 04:52:24.471716: Pseudo dice [0.6575, 0.0, 0.8213, 0.2493, 0.3302, 0.8548, 0.3758] +2026-04-15 04:52:24.474623: Epoch time: 100.13 s +2026-04-15 04:52:25.790681: +2026-04-15 04:52:25.792955: Epoch 3904 +2026-04-15 04:52:25.794882: Current learning rate: 0.00035 +2026-04-15 04:54:06.030070: train_loss -0.67 +2026-04-15 04:54:06.035462: val_loss -0.4837 +2026-04-15 04:54:06.037283: Pseudo dice [0.5908, 0.0, 0.7134, 0.0859, 0.3293, 0.8404, 0.9207] +2026-04-15 04:54:06.039589: Epoch time: 100.24 s +2026-04-15 04:54:07.314822: +2026-04-15 04:54:07.316527: Epoch 3905 +2026-04-15 04:54:07.318031: Current learning rate: 0.00035 +2026-04-15 04:55:47.741412: train_loss -0.6763 +2026-04-15 04:55:47.750059: val_loss -0.617 +2026-04-15 04:55:47.752180: Pseudo dice [0.6325, 0.0, 0.7534, 0.86, 0.2873, 0.8937, 0.8743] +2026-04-15 04:55:47.755307: Epoch time: 100.43 s +2026-04-15 04:55:49.024223: +2026-04-15 04:55:49.026298: Epoch 3906 +2026-04-15 04:55:49.028039: Current learning rate: 0.00034 +2026-04-15 04:57:29.433573: train_loss -0.6699 +2026-04-15 04:57:29.452461: val_loss -0.6136 +2026-04-15 04:57:29.454845: Pseudo dice [0.7873, 0.0, 0.8514, 0.0215, 0.2295, 0.8986, 0.8881] +2026-04-15 04:57:29.457404: Epoch time: 100.41 s +2026-04-15 04:57:30.740458: +2026-04-15 04:57:30.749112: Epoch 3907 +2026-04-15 04:57:30.751789: Current learning rate: 0.00034 +2026-04-15 04:59:11.064584: train_loss -0.6782 +2026-04-15 04:59:11.071341: val_loss -0.5479 +2026-04-15 04:59:11.074093: Pseudo dice [0.6213, 0.0, 0.804, 0.0005, 0.6407, 0.8292, 0.6951] +2026-04-15 04:59:11.076761: Epoch time: 100.33 s +2026-04-15 04:59:12.332970: +2026-04-15 04:59:12.334778: Epoch 3908 +2026-04-15 04:59:12.336403: Current learning rate: 0.00034 +2026-04-15 05:00:53.515435: train_loss -0.6726 +2026-04-15 05:00:53.522603: val_loss -0.5953 +2026-04-15 05:00:53.525422: Pseudo dice [0.8033, 0.0, 0.8007, 0.0018, 0.559, 0.8455, 0.8648] +2026-04-15 05:00:53.528806: Epoch time: 101.19 s +2026-04-15 05:00:54.809690: +2026-04-15 05:00:54.811580: Epoch 3909 +2026-04-15 05:00:54.813484: Current learning rate: 0.00033 +2026-04-15 05:02:35.375047: train_loss -0.6704 +2026-04-15 05:02:35.381353: val_loss -0.6114 +2026-04-15 05:02:35.383767: Pseudo dice [0.8901, 0.0, 0.718, 0.8748, 0.2872, 0.8738, 0.8511] +2026-04-15 05:02:35.385716: Epoch time: 100.57 s +2026-04-15 05:02:36.679344: +2026-04-15 05:02:36.681155: Epoch 3910 +2026-04-15 05:02:36.682930: Current learning rate: 0.00033 +2026-04-15 05:04:16.904734: train_loss -0.6753 +2026-04-15 05:04:16.912426: val_loss -0.6165 +2026-04-15 05:04:16.914465: Pseudo dice [0.7616, 0.0, 0.8254, 0.822, 0.4725, 0.8829, 0.7789] +2026-04-15 05:04:16.917092: Epoch time: 100.23 s +2026-04-15 05:04:18.251069: +2026-04-15 05:04:18.253752: Epoch 3911 +2026-04-15 05:04:18.255908: Current learning rate: 0.00033 +2026-04-15 05:05:58.555048: train_loss -0.6793 +2026-04-15 05:05:58.561808: val_loss -0.5877 +2026-04-15 05:05:58.563759: Pseudo dice [0.8514, 0.0, 0.7631, 0.8176, 0.5385, 0.7188, 0.9141] +2026-04-15 05:05:58.566050: Epoch time: 100.31 s +2026-04-15 05:05:59.894936: +2026-04-15 05:05:59.897465: Epoch 3912 +2026-04-15 05:05:59.899498: Current learning rate: 0.00032 +2026-04-15 05:07:40.206150: train_loss -0.6692 +2026-04-15 05:07:40.212629: val_loss -0.4753 +2026-04-15 05:07:40.216155: Pseudo dice [0.672, 0.0, 0.7367, 0.0003, 0.4468, 0.873, 0.8669] +2026-04-15 05:07:40.219759: Epoch time: 100.31 s +2026-04-15 05:07:41.525385: +2026-04-15 05:07:41.527367: Epoch 3913 +2026-04-15 05:07:41.528948: Current learning rate: 0.00032 +2026-04-15 05:09:21.816791: train_loss -0.6678 +2026-04-15 05:09:21.824587: val_loss -0.5038 +2026-04-15 05:09:21.827155: Pseudo dice [0.8629, 0.0, 0.7272, 0.0, 0.276, 0.7785, 0.9118] +2026-04-15 05:09:21.830325: Epoch time: 100.29 s +2026-04-15 05:09:23.094260: +2026-04-15 05:09:23.096663: Epoch 3914 +2026-04-15 05:09:23.098371: Current learning rate: 0.00032 +2026-04-15 05:11:04.070306: train_loss -0.6657 +2026-04-15 05:11:04.075737: val_loss -0.6186 +2026-04-15 05:11:04.078008: Pseudo dice [0.4967, 0.0, 0.7728, 0.2772, 0.4874, 0.8379, 0.7399] +2026-04-15 05:11:04.080510: Epoch time: 100.98 s +2026-04-15 05:11:05.360401: +2026-04-15 05:11:05.362710: Epoch 3915 +2026-04-15 05:11:05.365379: Current learning rate: 0.00031 +2026-04-15 05:12:45.450044: train_loss -0.6758 +2026-04-15 05:12:45.457088: val_loss -0.492 +2026-04-15 05:12:45.459485: Pseudo dice [0.847, 0.0, 0.5998, 0.0952, 0.5523, 0.7919, 0.5174] +2026-04-15 05:12:45.464108: Epoch time: 100.09 s +2026-04-15 05:12:46.748601: +2026-04-15 05:12:46.750520: Epoch 3916 +2026-04-15 05:12:46.752825: Current learning rate: 0.00031 +2026-04-15 05:14:26.932509: train_loss -0.6747 +2026-04-15 05:14:26.940196: val_loss -0.6182 +2026-04-15 05:14:26.942831: Pseudo dice [0.7903, 0.0, 0.8697, 0.1755, 0.56, 0.8391, 0.9026] +2026-04-15 05:14:26.946657: Epoch time: 100.19 s +2026-04-15 05:14:28.232071: +2026-04-15 05:14:28.235398: Epoch 3917 +2026-04-15 05:14:28.237789: Current learning rate: 0.00031 +2026-04-15 05:16:08.638999: train_loss -0.6842 +2026-04-15 05:16:08.646580: val_loss -0.6155 +2026-04-15 05:16:08.648535: Pseudo dice [0.8215, 0.0, 0.8419, 0.1957, 0.5513, 0.9029, 0.9038] +2026-04-15 05:16:08.651203: Epoch time: 100.41 s +2026-04-15 05:16:09.941748: +2026-04-15 05:16:09.943918: Epoch 3918 +2026-04-15 05:16:09.945902: Current learning rate: 0.0003 +2026-04-15 05:17:50.118418: train_loss -0.6765 +2026-04-15 05:17:50.130303: val_loss -0.4971 +2026-04-15 05:17:50.132913: Pseudo dice [0.8765, 0.0, 0.7533, 0.0527, 0.5424, 0.6222, 0.9237] +2026-04-15 05:17:50.135725: Epoch time: 100.18 s +2026-04-15 05:17:51.433103: +2026-04-15 05:17:51.435166: Epoch 3919 +2026-04-15 05:17:51.437758: Current learning rate: 0.0003 +2026-04-15 05:19:31.741221: train_loss -0.6768 +2026-04-15 05:19:31.747652: val_loss -0.6108 +2026-04-15 05:19:31.749505: Pseudo dice [0.8535, 0.0, 0.7729, 0.1602, 0.4395, 0.862, 0.917] +2026-04-15 05:19:31.752064: Epoch time: 100.31 s +2026-04-15 05:19:33.036931: +2026-04-15 05:19:33.039567: Epoch 3920 +2026-04-15 05:19:33.041567: Current learning rate: 0.0003 +2026-04-15 05:21:13.188406: train_loss -0.6747 +2026-04-15 05:21:13.193755: val_loss -0.6222 +2026-04-15 05:21:13.195768: Pseudo dice [0.3165, 0.0, 0.7991, 0.9171, 0.5492, 0.8274, 0.8972] +2026-04-15 05:21:13.197993: Epoch time: 100.15 s +2026-04-15 05:21:14.466083: +2026-04-15 05:21:14.468088: Epoch 3921 +2026-04-15 05:21:14.469784: Current learning rate: 0.00029 +2026-04-15 05:22:54.833302: train_loss -0.6785 +2026-04-15 05:22:54.842699: val_loss -0.6099 +2026-04-15 05:22:54.851811: Pseudo dice [0.8307, 0.0, 0.8562, 0.9043, 0.5722, 0.897, 0.9238] +2026-04-15 05:22:54.858476: Epoch time: 100.37 s +2026-04-15 05:22:56.139278: +2026-04-15 05:22:56.141275: Epoch 3922 +2026-04-15 05:22:56.142963: Current learning rate: 0.00029 +2026-04-15 05:24:36.631530: train_loss -0.6767 +2026-04-15 05:24:36.640343: val_loss -0.5748 +2026-04-15 05:24:36.643039: Pseudo dice [0.8531, 0.0, 0.5737, 0.482, 0.4274, 0.8768, 0.7366] +2026-04-15 05:24:36.648353: Epoch time: 100.5 s +2026-04-15 05:24:37.932203: +2026-04-15 05:24:37.936735: Epoch 3923 +2026-04-15 05:24:37.938751: Current learning rate: 0.00029 +2026-04-15 05:26:18.237511: train_loss -0.673 +2026-04-15 05:26:18.243688: val_loss -0.5758 +2026-04-15 05:26:18.245525: Pseudo dice [0.5442, 0.0, 0.8108, 0.2764, 0.1491, 0.8308, 0.8179] +2026-04-15 05:26:18.247668: Epoch time: 100.31 s +2026-04-15 05:26:19.528947: +2026-04-15 05:26:19.531174: Epoch 3924 +2026-04-15 05:26:19.533223: Current learning rate: 0.00028 +2026-04-15 05:28:00.145319: train_loss -0.6819 +2026-04-15 05:28:00.152351: val_loss -0.5497 +2026-04-15 05:28:00.154266: Pseudo dice [0.8497, 0.0, 0.8233, 0.2129, 0.3345, 0.7817, 0.726] +2026-04-15 05:28:00.156354: Epoch time: 100.62 s +2026-04-15 05:28:01.450354: +2026-04-15 05:28:01.452307: Epoch 3925 +2026-04-15 05:28:01.453948: Current learning rate: 0.00028 +2026-04-15 05:29:42.076642: train_loss -0.6762 +2026-04-15 05:29:42.082842: val_loss -0.4519 +2026-04-15 05:29:42.084856: Pseudo dice [0.8611, 0.0, 0.666, 0.0001, 0.3281, 0.8835, 0.8466] +2026-04-15 05:29:42.088202: Epoch time: 100.63 s +2026-04-15 05:29:43.404637: +2026-04-15 05:29:43.406997: Epoch 3926 +2026-04-15 05:29:43.409188: Current learning rate: 0.00028 +2026-04-15 05:31:24.033856: train_loss -0.6793 +2026-04-15 05:31:24.039891: val_loss -0.5617 +2026-04-15 05:31:24.041846: Pseudo dice [0.8829, 0.0, 0.751, 0.0039, 0.5131, 0.8225, 0.8355] +2026-04-15 05:31:24.044957: Epoch time: 100.63 s +2026-04-15 05:31:25.320642: +2026-04-15 05:31:25.322856: Epoch 3927 +2026-04-15 05:31:25.324765: Current learning rate: 0.00027 +2026-04-15 05:33:05.406067: train_loss -0.6777 +2026-04-15 05:33:05.413736: val_loss -0.5835 +2026-04-15 05:33:05.416252: Pseudo dice [0.7054, 0.0, 0.7957, 0.1188, 0.472, 0.6668, 0.9101] +2026-04-15 05:33:05.418990: Epoch time: 100.09 s +2026-04-15 05:33:07.916732: +2026-04-15 05:33:07.919107: Epoch 3928 +2026-04-15 05:33:07.921148: Current learning rate: 0.00027 +2026-04-15 05:34:48.196178: train_loss -0.6999 +2026-04-15 05:34:48.201990: val_loss -0.6003 +2026-04-15 05:34:48.204609: Pseudo dice [0.5833, 0.0, 0.6638, 0.0, 0.5708, 0.7474, 0.6831] +2026-04-15 05:34:48.206882: Epoch time: 100.28 s +2026-04-15 05:34:49.581748: +2026-04-15 05:34:49.583780: Epoch 3929 +2026-04-15 05:34:49.586621: Current learning rate: 0.00027 +2026-04-15 05:36:29.812136: train_loss -0.6883 +2026-04-15 05:36:29.819870: val_loss -0.6206 +2026-04-15 05:36:29.822251: Pseudo dice [0.2937, 0.0, 0.8245, 0.5661, 0.4082, 0.8801, 0.8954] +2026-04-15 05:36:29.825807: Epoch time: 100.23 s +2026-04-15 05:36:31.108169: +2026-04-15 05:36:31.110283: Epoch 3930 +2026-04-15 05:36:31.111921: Current learning rate: 0.00026 +2026-04-15 05:38:11.423949: train_loss -0.69 +2026-04-15 05:38:11.429529: val_loss -0.5335 +2026-04-15 05:38:11.432014: Pseudo dice [0.8639, 0.0, 0.7335, 0.0379, 0.3507, 0.9031, 0.7419] +2026-04-15 05:38:11.434405: Epoch time: 100.32 s +2026-04-15 05:38:12.710810: +2026-04-15 05:38:12.713278: Epoch 3931 +2026-04-15 05:38:12.715047: Current learning rate: 0.00026 +2026-04-15 05:39:52.869492: train_loss -0.6948 +2026-04-15 05:39:52.877089: val_loss -0.6454 +2026-04-15 05:39:52.879780: Pseudo dice [0.8012, 0.0, 0.7401, 0.8017, 0.4518, 0.8174, 0.7968] +2026-04-15 05:39:52.882749: Epoch time: 100.16 s +2026-04-15 05:39:54.168847: +2026-04-15 05:39:54.170865: Epoch 3932 +2026-04-15 05:39:54.172412: Current learning rate: 0.00026 +2026-04-15 05:41:34.386713: train_loss -0.7157 +2026-04-15 05:41:34.395444: val_loss -0.6527 +2026-04-15 05:41:34.398336: Pseudo dice [0.7644, 0.0, 0.7386, 0.0016, 0.2861, 0.892, 0.8878] +2026-04-15 05:41:34.400831: Epoch time: 100.22 s +2026-04-15 05:41:35.718346: +2026-04-15 05:41:35.720450: Epoch 3933 +2026-04-15 05:41:35.723007: Current learning rate: 0.00025 +2026-04-15 05:43:15.895548: train_loss -0.7348 +2026-04-15 05:43:15.901580: val_loss -0.5257 +2026-04-15 05:43:15.903862: Pseudo dice [0.8403, 0.0, 0.557, 0.0957, 0.4418, 0.6761, 0.6643] +2026-04-15 05:43:15.906727: Epoch time: 100.18 s +2026-04-15 05:43:17.201345: +2026-04-15 05:43:17.203148: Epoch 3934 +2026-04-15 05:43:17.205461: Current learning rate: 0.00025 +2026-04-15 05:44:57.685639: train_loss -0.7241 +2026-04-15 05:44:57.691206: val_loss -0.6596 +2026-04-15 05:44:57.693554: Pseudo dice [0.7193, 0.0, 0.7037, 0.3985, 0.5187, 0.7537, 0.909] +2026-04-15 05:44:57.696383: Epoch time: 100.49 s +2026-04-15 05:44:58.965904: +2026-04-15 05:44:58.967996: Epoch 3935 +2026-04-15 05:44:58.970767: Current learning rate: 0.00025 +2026-04-15 05:46:39.224125: train_loss -0.732 +2026-04-15 05:46:39.233599: val_loss -0.5784 +2026-04-15 05:46:39.242887: Pseudo dice [0.6, 0.0, 0.8153, 0.0563, 0.4886, 0.8456, 0.7841] +2026-04-15 05:46:39.246525: Epoch time: 100.26 s +2026-04-15 05:46:40.515848: +2026-04-15 05:46:40.518446: Epoch 3936 +2026-04-15 05:46:40.520709: Current learning rate: 0.00024 +2026-04-15 05:48:20.815762: train_loss -0.7393 +2026-04-15 05:48:20.822852: val_loss -0.6147 +2026-04-15 05:48:20.824755: Pseudo dice [0.5809, 0.0, 0.6958, 0.1001, 0.3631, 0.8962, 0.8953] +2026-04-15 05:48:20.827399: Epoch time: 100.3 s +2026-04-15 05:48:22.108303: +2026-04-15 05:48:22.110297: Epoch 3937 +2026-04-15 05:48:22.111961: Current learning rate: 0.00024 +2026-04-15 05:50:02.612966: train_loss -0.7442 +2026-04-15 05:50:02.627454: val_loss -0.6704 +2026-04-15 05:50:02.629389: Pseudo dice [0.7385, 0.0, 0.8468, 0.7243, 0.4202, 0.8689, 0.7254] +2026-04-15 05:50:02.631466: Epoch time: 100.51 s +2026-04-15 05:50:03.902480: +2026-04-15 05:50:03.904643: Epoch 3938 +2026-04-15 05:50:03.906569: Current learning rate: 0.00024 +2026-04-15 05:51:44.332508: train_loss -0.7505 +2026-04-15 05:51:44.338749: val_loss -0.614 +2026-04-15 05:51:44.342002: Pseudo dice [0.3586, 0.0, 0.6245, 0.5861, 0.3391, 0.88, 0.5477] +2026-04-15 05:51:44.345431: Epoch time: 100.43 s +2026-04-15 05:51:45.615358: +2026-04-15 05:51:45.617600: Epoch 3939 +2026-04-15 05:51:45.619406: Current learning rate: 0.00023 +2026-04-15 05:53:25.974634: train_loss -0.7378 +2026-04-15 05:53:25.982279: val_loss -0.714 +2026-04-15 05:53:25.984605: Pseudo dice [0.7543, 0.0, 0.679, 0.0, 0.6757, 0.92, 0.9063] +2026-04-15 05:53:25.987401: Epoch time: 100.36 s +2026-04-15 05:53:27.320680: +2026-04-15 05:53:27.322881: Epoch 3940 +2026-04-15 05:53:27.324968: Current learning rate: 0.00023 +2026-04-15 05:55:07.529532: train_loss -0.7561 +2026-04-15 05:55:07.535649: val_loss -0.6828 +2026-04-15 05:55:07.538233: Pseudo dice [0.8014, 0.0, 0.7383, 0.0, 0.5401, 0.8683, 0.6518] +2026-04-15 05:55:07.541036: Epoch time: 100.21 s +2026-04-15 05:55:08.845557: +2026-04-15 05:55:08.847707: Epoch 3941 +2026-04-15 05:55:08.849699: Current learning rate: 0.00022 +2026-04-15 05:56:49.150496: train_loss -0.7471 +2026-04-15 05:56:49.155836: val_loss -0.6469 +2026-04-15 05:56:49.158472: Pseudo dice [0.7728, 0.0, 0.8135, 0.9026, 0.4771, 0.8686, 0.7238] +2026-04-15 05:56:49.160701: Epoch time: 100.31 s +2026-04-15 05:56:50.430260: +2026-04-15 05:56:50.432254: Epoch 3942 +2026-04-15 05:56:50.433881: Current learning rate: 0.00022 +2026-04-15 05:58:30.847804: train_loss -0.7546 +2026-04-15 05:58:30.854555: val_loss -0.6833 +2026-04-15 05:58:30.856282: Pseudo dice [0.5917, 0.0, 0.8328, 0.024, 0.4847, 0.7704, 0.8869] +2026-04-15 05:58:30.859739: Epoch time: 100.42 s +2026-04-15 05:58:32.115614: +2026-04-15 05:58:32.117904: Epoch 3943 +2026-04-15 05:58:32.119799: Current learning rate: 0.00022 +2026-04-15 06:00:12.543468: train_loss -0.7434 +2026-04-15 06:00:12.557070: val_loss -0.6806 +2026-04-15 06:00:12.559000: Pseudo dice [0.7717, 0.0, 0.8277, 0.8288, 0.4854, 0.8614, 0.8306] +2026-04-15 06:00:12.562950: Epoch time: 100.43 s +2026-04-15 06:00:13.846973: +2026-04-15 06:00:13.848758: Epoch 3944 +2026-04-15 06:00:13.850414: Current learning rate: 0.00021 +2026-04-15 06:01:54.283267: train_loss -0.7415 +2026-04-15 06:01:54.290318: val_loss -0.4993 +2026-04-15 06:01:54.293431: Pseudo dice [0.8602, 0.0, 0.708, 0.0875, 0.3938, 0.8215, 0.897] +2026-04-15 06:01:54.296025: Epoch time: 100.44 s +2026-04-15 06:01:55.563337: +2026-04-15 06:01:55.565269: Epoch 3945 +2026-04-15 06:01:55.567029: Current learning rate: 0.00021 +2026-04-15 06:03:36.005646: train_loss -0.7531 +2026-04-15 06:03:36.014352: val_loss -0.5983 +2026-04-15 06:03:36.016519: Pseudo dice [0.635, 0.0, 0.4937, 0.1059, 0.503, 0.8126, 0.8644] +2026-04-15 06:03:36.019503: Epoch time: 100.45 s +2026-04-15 06:03:37.321305: +2026-04-15 06:03:37.323390: Epoch 3946 +2026-04-15 06:03:37.325010: Current learning rate: 0.00021 +2026-04-15 06:05:18.326963: train_loss -0.7372 +2026-04-15 06:05:18.334152: val_loss -0.5824 +2026-04-15 06:05:18.336581: Pseudo dice [0.8369, 0.0, 0.726, 0.0576, 0.6183, 0.8213, 0.8445] +2026-04-15 06:05:18.339791: Epoch time: 101.01 s +2026-04-15 06:05:19.654945: +2026-04-15 06:05:19.657077: Epoch 3947 +2026-04-15 06:05:19.658858: Current learning rate: 0.0002 +2026-04-15 06:07:00.885630: train_loss -0.7417 +2026-04-15 06:07:00.892727: val_loss -0.5714 +2026-04-15 06:07:00.894890: Pseudo dice [0.8269, 0.0, 0.8735, 0.0, 0.3929, 0.7847, 0.8612] +2026-04-15 06:07:00.897847: Epoch time: 101.23 s +2026-04-15 06:07:02.375117: +2026-04-15 06:07:02.378007: Epoch 3948 +2026-04-15 06:07:02.379974: Current learning rate: 0.0002 +2026-04-15 06:08:42.743232: train_loss -0.7513 +2026-04-15 06:08:42.752732: val_loss -0.6402 +2026-04-15 06:08:42.755428: Pseudo dice [0.7059, 0.0, 0.6609, 0.2113, 0.5574, 0.8895, 0.9211] +2026-04-15 06:08:42.758793: Epoch time: 100.37 s +2026-04-15 06:08:44.047746: +2026-04-15 06:08:44.050043: Epoch 3949 +2026-04-15 06:08:44.051893: Current learning rate: 0.0002 +2026-04-15 06:10:24.346960: train_loss -0.7478 +2026-04-15 06:10:24.353668: val_loss -0.6301 +2026-04-15 06:10:24.356677: Pseudo dice [0.8435, 0.0, 0.7697, 0.6266, 0.4314, 0.8717, 0.4632] +2026-04-15 06:10:24.359502: Epoch time: 100.3 s +2026-04-15 06:10:27.446190: +2026-04-15 06:10:27.448373: Epoch 3950 +2026-04-15 06:10:27.450234: Current learning rate: 0.00019 +2026-04-15 06:12:08.025434: train_loss -0.7572 +2026-04-15 06:12:08.031255: val_loss -0.6483 +2026-04-15 06:12:08.033398: Pseudo dice [0.8322, 0.0, 0.8138, 0.6089, 0.4139, 0.8486, 0.9014] +2026-04-15 06:12:08.035732: Epoch time: 100.58 s +2026-04-15 06:12:09.335567: +2026-04-15 06:12:09.337923: Epoch 3951 +2026-04-15 06:12:09.339831: Current learning rate: 0.00019 +2026-04-15 06:13:49.505973: train_loss -0.7495 +2026-04-15 06:13:49.513982: val_loss -0.6192 +2026-04-15 06:13:49.517359: Pseudo dice [0.808, 0.0, 0.7226, 0.0713, 0.6125, 0.8305, 0.9031] +2026-04-15 06:13:49.520344: Epoch time: 100.17 s +2026-04-15 06:13:50.799716: +2026-04-15 06:13:50.801567: Epoch 3952 +2026-04-15 06:13:50.803402: Current learning rate: 0.00019 +2026-04-15 06:15:31.132083: train_loss -0.7441 +2026-04-15 06:15:31.138109: val_loss -0.5894 +2026-04-15 06:15:31.140341: Pseudo dice [0.8625, 0.0, 0.7937, 0.1293, 0.3857, 0.8578, 0.8039] +2026-04-15 06:15:31.142406: Epoch time: 100.34 s +2026-04-15 06:15:32.444895: +2026-04-15 06:15:32.447454: Epoch 3953 +2026-04-15 06:15:32.449595: Current learning rate: 0.00018 +2026-04-15 06:17:12.823912: train_loss -0.7561 +2026-04-15 06:17:12.830131: val_loss -0.6628 +2026-04-15 06:17:12.832979: Pseudo dice [0.5481, 0.0, 0.7997, 0.521, 0.5642, 0.9103, 0.9177] +2026-04-15 06:17:12.835712: Epoch time: 100.38 s +2026-04-15 06:17:14.169381: +2026-04-15 06:17:14.171975: Epoch 3954 +2026-04-15 06:17:14.173800: Current learning rate: 0.00018 +2026-04-15 06:18:54.358646: train_loss -0.7531 +2026-04-15 06:18:54.365421: val_loss -0.6803 +2026-04-15 06:18:54.367607: Pseudo dice [0.8268, 0.0, 0.801, 0.8287, 0.5328, 0.8214, 0.7451] +2026-04-15 06:18:54.369916: Epoch time: 100.19 s +2026-04-15 06:18:55.646822: +2026-04-15 06:18:55.648900: Epoch 3955 +2026-04-15 06:18:55.650473: Current learning rate: 0.00018 +2026-04-15 06:20:35.736781: train_loss -0.7501 +2026-04-15 06:20:35.744548: val_loss -0.5581 +2026-04-15 06:20:35.746950: Pseudo dice [0.7907, 0.0, 0.6812, 0.0596, 0.3116, 0.8296, 0.804] +2026-04-15 06:20:35.750782: Epoch time: 100.09 s +2026-04-15 06:20:37.102303: +2026-04-15 06:20:37.104465: Epoch 3956 +2026-04-15 06:20:37.108136: Current learning rate: 0.00017 +2026-04-15 06:22:17.667431: train_loss -0.7464 +2026-04-15 06:22:17.673331: val_loss -0.7152 +2026-04-15 06:22:17.675570: Pseudo dice [0.7902, 0.0, 0.737, 0.1967, 0.618, 0.7166, 0.9288] +2026-04-15 06:22:17.678141: Epoch time: 100.57 s +2026-04-15 06:22:18.973930: +2026-04-15 06:22:18.975895: Epoch 3957 +2026-04-15 06:22:18.978747: Current learning rate: 0.00017 +2026-04-15 06:23:59.144842: train_loss -0.7221 +2026-04-15 06:23:59.174532: val_loss -0.678 +2026-04-15 06:23:59.176688: Pseudo dice [0.8434, 0.0, 0.8428, 0.663, 0.4575, 0.92, 0.6866] +2026-04-15 06:23:59.180018: Epoch time: 100.17 s +2026-04-15 06:24:00.467843: +2026-04-15 06:24:00.469600: Epoch 3958 +2026-04-15 06:24:00.471140: Current learning rate: 0.00017 +2026-04-15 06:25:40.646643: train_loss -0.7563 +2026-04-15 06:25:40.653680: val_loss -0.6363 +2026-04-15 06:25:40.655759: Pseudo dice [0.5234, 0.0, 0.7649, 0.0016, 0.6605, 0.7803, 0.7338] +2026-04-15 06:25:40.658597: Epoch time: 100.18 s +2026-04-15 06:25:41.951493: +2026-04-15 06:25:41.954953: Epoch 3959 +2026-04-15 06:25:41.956809: Current learning rate: 0.00016 +2026-04-15 06:27:22.228418: train_loss -0.7607 +2026-04-15 06:27:22.236032: val_loss -0.6656 +2026-04-15 06:27:22.239775: Pseudo dice [0.5495, 0.0, 0.7236, 0.2632, 0.3407, 0.8959, 0.8346] +2026-04-15 06:27:22.242498: Epoch time: 100.28 s +2026-04-15 06:27:23.533369: +2026-04-15 06:27:23.536664: Epoch 3960 +2026-04-15 06:27:23.538649: Current learning rate: 0.00016 +2026-04-15 06:29:04.127163: train_loss -0.7542 +2026-04-15 06:29:04.133648: val_loss -0.5868 +2026-04-15 06:29:04.135696: Pseudo dice [0.7413, 0.0, 0.6564, 0.023, 0.4293, 0.8888, 0.9193] +2026-04-15 06:29:04.137913: Epoch time: 100.6 s +2026-04-15 06:29:05.441268: +2026-04-15 06:29:05.443688: Epoch 3961 +2026-04-15 06:29:05.446059: Current learning rate: 0.00015 +2026-04-15 06:30:45.704291: train_loss -0.7639 +2026-04-15 06:30:45.711355: val_loss -0.6908 +2026-04-15 06:30:45.713283: Pseudo dice [0.8675, 0.0, 0.8613, 0.4634, 0.5162, 0.7612, 0.7902] +2026-04-15 06:30:45.715685: Epoch time: 100.27 s +2026-04-15 06:30:47.000996: +2026-04-15 06:30:47.003806: Epoch 3962 +2026-04-15 06:30:47.005614: Current learning rate: 0.00015 +2026-04-15 06:32:27.288288: train_loss -0.7535 +2026-04-15 06:32:27.294101: val_loss -0.6614 +2026-04-15 06:32:27.296182: Pseudo dice [0.8252, 0.0, 0.8579, 0.859, 0.5787, 0.8506, 0.7798] +2026-04-15 06:32:27.298189: Epoch time: 100.29 s +2026-04-15 06:32:28.605712: +2026-04-15 06:32:28.607937: Epoch 3963 +2026-04-15 06:32:28.610325: Current learning rate: 0.00015 +2026-04-15 06:34:08.849116: train_loss -0.758 +2026-04-15 06:34:08.861863: val_loss -0.6365 +2026-04-15 06:34:08.865935: Pseudo dice [0.4203, 0.0, 0.8355, 0.0758, 0.3498, 0.9281, 0.6507] +2026-04-15 06:34:08.869005: Epoch time: 100.25 s +2026-04-15 06:34:10.206841: +2026-04-15 06:34:10.209099: Epoch 3964 +2026-04-15 06:34:10.210881: Current learning rate: 0.00014 +2026-04-15 06:35:50.475812: train_loss -0.7617 +2026-04-15 06:35:50.481674: val_loss -0.674 +2026-04-15 06:35:50.484062: Pseudo dice [0.5787, 0.0, 0.8215, 0.901, 0.5531, 0.8194, 0.7485] +2026-04-15 06:35:50.486777: Epoch time: 100.27 s +2026-04-15 06:35:51.758217: +2026-04-15 06:35:51.760249: Epoch 3965 +2026-04-15 06:35:51.761871: Current learning rate: 0.00014 +2026-04-15 06:37:32.436859: train_loss -0.7602 +2026-04-15 06:37:32.444152: val_loss -0.676 +2026-04-15 06:37:32.446704: Pseudo dice [0.8759, 0.0, 0.7784, 0.0574, 0.4342, 0.7697, 0.8167] +2026-04-15 06:37:32.449102: Epoch time: 100.68 s +2026-04-15 06:37:33.789116: +2026-04-15 06:37:33.791192: Epoch 3966 +2026-04-15 06:37:33.793249: Current learning rate: 0.00014 +2026-04-15 06:39:14.866129: train_loss -0.7554 +2026-04-15 06:39:14.871531: val_loss -0.6597 +2026-04-15 06:39:14.873504: Pseudo dice [0.6869, 0.0, 0.873, 0.0012, 0.6235, 0.8099, 0.7563] +2026-04-15 06:39:14.876234: Epoch time: 101.08 s +2026-04-15 06:39:16.153486: +2026-04-15 06:39:16.155833: Epoch 3967 +2026-04-15 06:39:16.157986: Current learning rate: 0.00013 +2026-04-15 06:40:56.859049: train_loss -0.7607 +2026-04-15 06:40:56.865372: val_loss -0.7165 +2026-04-15 06:40:56.867783: Pseudo dice [0.8759, 0.0, 0.8263, 0.9153, 0.7827, 0.8437, 0.8677] +2026-04-15 06:40:56.870019: Epoch time: 100.71 s +2026-04-15 06:40:58.157336: +2026-04-15 06:40:58.159359: Epoch 3968 +2026-04-15 06:40:58.161693: Current learning rate: 0.00013 +2026-04-15 06:42:39.899625: train_loss -0.7489 +2026-04-15 06:42:39.911033: val_loss -0.4725 +2026-04-15 06:42:39.913901: Pseudo dice [0.4741, 0.0, 0.5001, 0.012, 0.6568, 0.7437, 0.7145] +2026-04-15 06:42:39.916705: Epoch time: 101.75 s +2026-04-15 06:42:41.223705: +2026-04-15 06:42:41.226445: Epoch 3969 +2026-04-15 06:42:41.228568: Current learning rate: 0.00013 +2026-04-15 06:44:22.000032: train_loss -0.7569 +2026-04-15 06:44:22.007512: val_loss -0.6501 +2026-04-15 06:44:22.009376: Pseudo dice [0.8437, 0.0, 0.8178, 0.8413, 0.4715, 0.789, 0.5691] +2026-04-15 06:44:22.011322: Epoch time: 100.78 s +2026-04-15 06:44:23.301409: +2026-04-15 06:44:23.303522: Epoch 3970 +2026-04-15 06:44:23.306046: Current learning rate: 0.00012 +2026-04-15 06:46:03.775126: train_loss -0.75 +2026-04-15 06:46:03.781827: val_loss -0.695 +2026-04-15 06:46:03.784040: Pseudo dice [0.7128, 0.0, 0.8181, 0.8914, 0.5668, 0.9005, 0.9068] +2026-04-15 06:46:03.786849: Epoch time: 100.48 s +2026-04-15 06:46:05.056989: +2026-04-15 06:46:05.059304: Epoch 3971 +2026-04-15 06:46:05.061073: Current learning rate: 0.00012 +2026-04-15 06:47:45.326825: train_loss -0.7611 +2026-04-15 06:47:45.333582: val_loss -0.6943 +2026-04-15 06:47:45.336896: Pseudo dice [0.503, 0.0, 0.7727, 0.1581, 0.5966, 0.8879, 0.848] +2026-04-15 06:47:45.339275: Epoch time: 100.27 s +2026-04-15 06:47:46.617950: +2026-04-15 06:47:46.619898: Epoch 3972 +2026-04-15 06:47:46.621752: Current learning rate: 0.00011 +2026-04-15 06:49:26.862324: train_loss -0.7457 +2026-04-15 06:49:26.869277: val_loss -0.7029 +2026-04-15 06:49:26.871550: Pseudo dice [0.8631, 0.0, 0.8253, 0.8922, 0.785, 0.8267, 0.9317] +2026-04-15 06:49:26.874715: Epoch time: 100.25 s +2026-04-15 06:49:26.880254: Yayy! New best EMA pseudo Dice: 0.5933 +2026-04-15 06:49:30.012605: +2026-04-15 06:49:30.014605: Epoch 3973 +2026-04-15 06:49:30.016313: Current learning rate: 0.00011 +2026-04-15 06:51:10.315962: train_loss -0.7557 +2026-04-15 06:51:10.321621: val_loss -0.5225 +2026-04-15 06:51:10.323878: Pseudo dice [0.7854, 0.0, 0.5331, 0.0721, 0.5314, 0.917, 0.764] +2026-04-15 06:51:10.325773: Epoch time: 100.31 s +2026-04-15 06:51:11.578403: +2026-04-15 06:51:11.580434: Epoch 3974 +2026-04-15 06:51:11.582069: Current learning rate: 0.00011 +2026-04-15 06:52:52.336272: train_loss -0.7546 +2026-04-15 06:52:52.344110: val_loss -0.7084 +2026-04-15 06:52:52.346160: Pseudo dice [0.8166, 0.0, 0.7615, 0.501, 0.5611, 0.8459, 0.9262] +2026-04-15 06:52:52.348565: Epoch time: 100.76 s +2026-04-15 06:52:53.609888: +2026-04-15 06:52:53.612120: Epoch 3975 +2026-04-15 06:52:53.614406: Current learning rate: 0.0001 +2026-04-15 06:54:34.490752: train_loss -0.7539 +2026-04-15 06:54:34.498794: val_loss -0.6866 +2026-04-15 06:54:34.501712: Pseudo dice [0.7833, 0.0, 0.817, 0.8946, 0.3301, 0.8873, 0.8347] +2026-04-15 06:54:34.504359: Epoch time: 100.88 s +2026-04-15 06:54:34.506881: Yayy! New best EMA pseudo Dice: 0.5959 +2026-04-15 06:54:37.505467: +2026-04-15 06:54:37.507441: Epoch 3976 +2026-04-15 06:54:37.509641: Current learning rate: 0.0001 +2026-04-15 06:56:18.233404: train_loss -0.7611 +2026-04-15 06:56:18.242155: val_loss -0.6592 +2026-04-15 06:56:18.244698: Pseudo dice [0.5238, 0.0, 0.7306, 0.8754, 0.5245, 0.915, 0.7357] +2026-04-15 06:56:18.247402: Epoch time: 100.73 s +2026-04-15 06:56:18.249810: Yayy! New best EMA pseudo Dice: 0.5978 +2026-04-15 06:56:21.466175: +2026-04-15 06:56:21.469243: Epoch 3977 +2026-04-15 06:56:21.471928: Current learning rate: 0.0001 +2026-04-15 06:58:01.578861: train_loss -0.7506 +2026-04-15 06:58:01.586810: val_loss -0.7264 +2026-04-15 06:58:01.589937: Pseudo dice [0.8733, 0.0, 0.7978, 0.2657, 0.5418, 0.7815, 0.9134] +2026-04-15 06:58:01.593163: Epoch time: 100.12 s +2026-04-15 06:58:02.885943: +2026-04-15 06:58:02.888357: Epoch 3978 +2026-04-15 06:58:02.889938: Current learning rate: 9e-05 +2026-04-15 06:59:43.017553: train_loss -0.7534 +2026-04-15 06:59:43.024463: val_loss -0.6391 +2026-04-15 06:59:43.027349: Pseudo dice [0.8229, 0.0, 0.6643, 0.2319, 0.3554, 0.9034, 0.5894] +2026-04-15 06:59:43.029403: Epoch time: 100.13 s +2026-04-15 06:59:44.271708: +2026-04-15 06:59:44.273569: Epoch 3979 +2026-04-15 06:59:44.275224: Current learning rate: 9e-05 +2026-04-15 07:01:24.961268: train_loss -0.76 +2026-04-15 07:01:24.968934: val_loss -0.6074 +2026-04-15 07:01:24.971694: Pseudo dice [0.7731, 0.0, 0.7287, 0.0, 0.5113, 0.8407, 0.9295] +2026-04-15 07:01:24.974590: Epoch time: 100.69 s +2026-04-15 07:01:26.261360: +2026-04-15 07:01:26.263525: Epoch 3980 +2026-04-15 07:01:26.265419: Current learning rate: 8e-05 +2026-04-15 07:03:06.585940: train_loss -0.7648 +2026-04-15 07:03:06.592087: val_loss -0.6403 +2026-04-15 07:03:06.593979: Pseudo dice [0.8114, 0.0, 0.8086, 0.0301, 0.6686, 0.8589, 0.7866] +2026-04-15 07:03:06.596059: Epoch time: 100.33 s +2026-04-15 07:03:07.869042: +2026-04-15 07:03:07.872959: Epoch 3981 +2026-04-15 07:03:07.874795: Current learning rate: 8e-05 +2026-04-15 07:04:47.989452: train_loss -0.7584 +2026-04-15 07:04:47.995344: val_loss -0.6679 +2026-04-15 07:04:47.997725: Pseudo dice [0.8762, 0.0, 0.6956, 0.1895, 0.5534, 0.8419, 0.8855] +2026-04-15 07:04:48.000091: Epoch time: 100.12 s +2026-04-15 07:04:49.261851: +2026-04-15 07:04:49.263828: Epoch 3982 +2026-04-15 07:04:49.265935: Current learning rate: 8e-05 +2026-04-15 07:06:29.598382: train_loss -0.7529 +2026-04-15 07:06:29.606590: val_loss -0.615 +2026-04-15 07:06:29.609083: Pseudo dice [0.8697, 0.0, 0.7007, 0.0878, 0.4346, 0.8595, 0.8543] +2026-04-15 07:06:29.612000: Epoch time: 100.34 s +2026-04-15 07:06:31.074575: +2026-04-15 07:06:31.076611: Epoch 3983 +2026-04-15 07:06:31.078314: Current learning rate: 7e-05 +2026-04-15 07:08:11.923039: train_loss -0.7649 +2026-04-15 07:08:11.933688: val_loss -0.7139 +2026-04-15 07:08:11.936592: Pseudo dice [0.4617, 0.0, 0.7652, 0.7207, 0.5123, 0.8235, 0.8906] +2026-04-15 07:08:11.939261: Epoch time: 100.85 s +2026-04-15 07:08:13.227627: +2026-04-15 07:08:13.230816: Epoch 3984 +2026-04-15 07:08:13.233006: Current learning rate: 7e-05 +2026-04-15 07:09:53.450168: train_loss -0.7573 +2026-04-15 07:09:53.456490: val_loss -0.5922 +2026-04-15 07:09:53.458569: Pseudo dice [0.3747, 0.0, 0.5925, 0.1021, 0.6294, 0.8485, 0.5775] +2026-04-15 07:09:53.460898: Epoch time: 100.23 s +2026-04-15 07:09:55.725014: +2026-04-15 07:09:55.727412: Epoch 3985 +2026-04-15 07:09:55.729120: Current learning rate: 7e-05 +2026-04-15 07:11:35.821521: train_loss -0.755 +2026-04-15 07:11:35.827736: val_loss -0.6253 +2026-04-15 07:11:35.829854: Pseudo dice [0.7974, 0.0, 0.7714, 0.0001, 0.4489, 0.8957, 0.7539] +2026-04-15 07:11:35.832304: Epoch time: 100.1 s +2026-04-15 07:11:37.113247: +2026-04-15 07:11:37.115433: Epoch 3986 +2026-04-15 07:11:37.117156: Current learning rate: 6e-05 +2026-04-15 07:13:17.982497: train_loss -0.7613 +2026-04-15 07:13:17.988924: val_loss -0.6042 +2026-04-15 07:13:17.991737: Pseudo dice [0.3077, 0.0, 0.6827, 0.0884, 0.4957, 0.7852, 0.8529] +2026-04-15 07:13:17.995437: Epoch time: 100.87 s +2026-04-15 07:13:19.300877: +2026-04-15 07:13:19.302803: Epoch 3987 +2026-04-15 07:13:19.304832: Current learning rate: 6e-05 +2026-04-15 07:14:59.904305: train_loss -0.7515 +2026-04-15 07:14:59.911922: val_loss -0.6523 +2026-04-15 07:14:59.914366: Pseudo dice [0.7793, 0.0, 0.7915, 0.9002, 0.4326, 0.8339, 0.6408] +2026-04-15 07:14:59.917235: Epoch time: 100.61 s +2026-04-15 07:15:01.192393: +2026-04-15 07:15:01.195289: Epoch 3988 +2026-04-15 07:15:01.198121: Current learning rate: 5e-05 +2026-04-15 07:16:41.635928: train_loss -0.7625 +2026-04-15 07:16:41.642852: val_loss -0.7047 +2026-04-15 07:16:41.645369: Pseudo dice [0.8364, 0.0, 0.8289, 0.8509, 0.5947, 0.8414, 0.9305] +2026-04-15 07:16:41.649685: Epoch time: 100.45 s +2026-04-15 07:16:42.899874: +2026-04-15 07:16:42.901992: Epoch 3989 +2026-04-15 07:16:42.903875: Current learning rate: 5e-05 +2026-04-15 07:18:23.088901: train_loss -0.7553 +2026-04-15 07:18:23.094534: val_loss -0.5797 +2026-04-15 07:18:23.096722: Pseudo dice [0.354, 0.0, 0.6521, 0.0002, 0.3625, 0.7041, 0.7316] +2026-04-15 07:18:23.099034: Epoch time: 100.19 s +2026-04-15 07:18:24.362893: +2026-04-15 07:18:24.364893: Epoch 3990 +2026-04-15 07:18:24.366821: Current learning rate: 5e-05 +2026-04-15 07:20:04.513626: train_loss -0.7631 +2026-04-15 07:20:04.520643: val_loss -0.6276 +2026-04-15 07:20:04.522530: Pseudo dice [0.7776, 0.0, 0.5086, 0.7688, 0.4418, 0.8781, 0.6217] +2026-04-15 07:20:04.525239: Epoch time: 100.15 s +2026-04-15 07:20:05.783281: +2026-04-15 07:20:05.785710: Epoch 3991 +2026-04-15 07:20:05.787539: Current learning rate: 4e-05 +2026-04-15 07:21:46.047897: train_loss -0.7687 +2026-04-15 07:21:46.053927: val_loss -0.6812 +2026-04-15 07:21:46.055612: Pseudo dice [0.6318, 0.0, 0.8059, 0.8924, 0.5006, 0.8942, 0.8428] +2026-04-15 07:21:46.058228: Epoch time: 100.27 s +2026-04-15 07:21:47.321332: +2026-04-15 07:21:47.323173: Epoch 3992 +2026-04-15 07:21:47.324939: Current learning rate: 4e-05 +2026-04-15 07:23:27.971307: train_loss -0.7578 +2026-04-15 07:23:27.977755: val_loss -0.7144 +2026-04-15 07:23:27.980294: Pseudo dice [0.8349, 0.0, 0.7211, 0.8773, 0.5733, 0.9164, 0.8613] +2026-04-15 07:23:27.983333: Epoch time: 100.65 s +2026-04-15 07:23:29.272244: +2026-04-15 07:23:29.274562: Epoch 3993 +2026-04-15 07:23:29.276811: Current learning rate: 3e-05 +2026-04-15 07:25:09.649402: train_loss -0.7459 +2026-04-15 07:25:09.657687: val_loss -0.7156 +2026-04-15 07:25:09.660493: Pseudo dice [0.6059, 0.0, 0.7763, 0.7659, 0.351, 0.8632, 0.8537] +2026-04-15 07:25:09.663822: Epoch time: 100.38 s +2026-04-15 07:25:10.935932: +2026-04-15 07:25:10.938248: Epoch 3994 +2026-04-15 07:25:10.940120: Current learning rate: 3e-05 +2026-04-15 07:26:51.792685: train_loss -0.7579 +2026-04-15 07:26:51.800547: val_loss -0.6921 +2026-04-15 07:26:51.803748: Pseudo dice [0.4972, 0.0, 0.8367, 0.9237, 0.5694, 0.8733, 0.8778] +2026-04-15 07:26:51.806280: Epoch time: 100.86 s +2026-04-15 07:26:53.094209: +2026-04-15 07:26:53.096805: Epoch 3995 +2026-04-15 07:26:53.099076: Current learning rate: 2e-05 +2026-04-15 07:28:33.633534: train_loss -0.7589 +2026-04-15 07:28:33.642286: val_loss -0.6585 +2026-04-15 07:28:33.652884: Pseudo dice [0.8653, 0.0, 0.7015, 0.0144, 0.5883, 0.7022, 0.7627] +2026-04-15 07:28:33.664011: Epoch time: 100.54 s +2026-04-15 07:28:34.911774: +2026-04-15 07:28:34.913595: Epoch 3996 +2026-04-15 07:28:34.915273: Current learning rate: 2e-05 +2026-04-15 07:30:15.062636: train_loss -0.755 +2026-04-15 07:30:15.070438: val_loss -0.6661 +2026-04-15 07:30:15.073184: Pseudo dice [0.8643, 0.0, 0.8173, 0.2004, 0.3578, 0.8592, 0.9102] +2026-04-15 07:30:15.075991: Epoch time: 100.15 s +2026-04-15 07:30:16.389965: +2026-04-15 07:30:16.392717: Epoch 3997 +2026-04-15 07:30:16.395385: Current learning rate: 2e-05 +2026-04-15 07:31:57.117308: train_loss -0.7654 +2026-04-15 07:31:57.128827: val_loss -0.5864 +2026-04-15 07:31:57.132892: Pseudo dice [0.8589, 0.0, 0.6854, 0.0162, 0.4865, 0.8855, 0.8725] +2026-04-15 07:31:57.136047: Epoch time: 100.73 s +2026-04-15 07:31:58.484999: +2026-04-15 07:31:58.487406: Epoch 3998 +2026-04-15 07:31:58.490160: Current learning rate: 1e-05 +2026-04-15 07:33:38.688253: train_loss -0.7557 +2026-04-15 07:33:38.696489: val_loss -0.5677 +2026-04-15 07:33:38.699734: Pseudo dice [0.5492, 0.0, 0.4095, 0.0515, 0.4244, 0.7789, 0.7706] +2026-04-15 07:33:38.702556: Epoch time: 100.21 s +2026-04-15 07:33:39.960771: +2026-04-15 07:33:39.963049: Epoch 3999 +2026-04-15 07:33:39.964683: Current learning rate: 1e-05 +2026-04-15 07:35:20.411184: train_loss -0.7501 +2026-04-15 07:35:20.419895: val_loss -0.6176 +2026-04-15 07:35:20.422399: Pseudo dice [0.7697, 0.0, 0.71, 0.1153, 0.5391, 0.875, 0.7366] +2026-04-15 07:35:20.427901: Epoch time: 100.45 s +2026-04-15 07:35:23.490120: Training done. +2026-04-15 07:35:23.803085: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json +2026-04-15 07:35:23.813794: The split file contains 5 splits. +2026-04-15 07:35:23.816369: Desired fold for training: 0 +2026-04-15 07:35:23.818935: This split has 387 training and 97 validation cases. +2026-04-15 07:35:23.822811: predicting MSWAL_0017 +2026-04-15 07:35:23.835046: MSWAL_0017, shape torch.Size([1, 177, 532, 532]), rank 0 +2026-04-15 07:36:24.592407: predicting MSWAL_0018 +2026-04-15 07:36:24.611927: MSWAL_0018, shape torch.Size([1, 285, 507, 507]), rank 0 +2026-04-15 07:36:45.463845: predicting MSWAL_0020 +2026-04-15 07:36:45.480616: MSWAL_0020, shape torch.Size([1, 433, 595, 595]), rank 0 +2026-04-15 07:37:36.757395: predicting MSWAL_0028 +2026-04-15 07:37:36.782743: MSWAL_0028, shape torch.Size([1, 137, 507, 507]), rank 0 +2026-04-15 07:37:45.479953: predicting MSWAL_0031 +2026-04-15 07:37:45.492110: MSWAL_0031, shape torch.Size([1, 217, 507, 507]), rank 0 +2026-04-15 07:37:58.642540: predicting MSWAL_0040 +2026-04-15 07:37:58.674508: MSWAL_0040, shape torch.Size([1, 189, 551, 551]), rank 0 +2026-04-15 07:38:20.700170: predicting MSWAL_0041 +2026-04-15 07:38:20.726282: MSWAL_0041, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 07:38:29.248718: predicting MSWAL_0046 +2026-04-15 07:38:29.263382: MSWAL_0046, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:38:41.890417: predicting MSWAL_0050 +2026-04-15 07:38:41.910300: MSWAL_0050, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 07:38:50.597979: predicting MSWAL_0059 +2026-04-15 07:38:50.609164: MSWAL_0059, shape torch.Size([1, 189, 565, 565]), rank 0 +2026-04-15 07:39:12.767999: predicting MSWAL_0060 +2026-04-15 07:39:12.784862: MSWAL_0060, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:39:25.253792: predicting MSWAL_0066 +2026-04-15 07:39:25.270913: MSWAL_0066, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 07:39:33.746918: predicting MSWAL_0069 +2026-04-15 07:39:33.761158: MSWAL_0069, shape torch.Size([1, 177, 569, 569]), rank 0 +2026-04-15 07:39:56.375513: predicting MSWAL_0080 +2026-04-15 07:39:56.393617: MSWAL_0080, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:40:08.953249: predicting MSWAL_0084 +2026-04-15 07:40:08.967986: MSWAL_0084, shape torch.Size([1, 145, 507, 507]), rank 0 +2026-04-15 07:40:17.665359: predicting MSWAL_0085 +2026-04-15 07:40:17.676786: MSWAL_0085, shape torch.Size([1, 177, 563, 563]), rank 0 +2026-04-15 07:40:40.486195: predicting MSWAL_0099 +2026-04-15 07:40:40.504534: MSWAL_0099, shape torch.Size([1, 193, 489, 489]), rank 0 +2026-04-15 07:40:52.992505: predicting MSWAL_0102 +2026-04-15 07:40:53.012349: MSWAL_0102, shape torch.Size([1, 194, 463, 463]), rank 0 +2026-04-15 07:41:05.404243: predicting MSWAL_0124 +2026-04-15 07:41:05.425756: MSWAL_0124, shape torch.Size([1, 317, 599, 599]), rank 0 +2026-04-15 07:41:41.869154: predicting MSWAL_0125 +2026-04-15 07:41:41.900934: MSWAL_0125, shape torch.Size([1, 169, 507, 507]), rank 0 +2026-04-15 07:41:54.680636: predicting MSWAL_0127 +2026-04-15 07:41:54.693429: MSWAL_0127, shape torch.Size([1, 169, 507, 507]), rank 0 +2026-04-15 07:42:07.344615: predicting MSWAL_0130 +2026-04-15 07:42:07.360036: MSWAL_0130, shape torch.Size([1, 205, 507, 507]), rank 0 +2026-04-15 07:42:20.057326: predicting MSWAL_0140 +2026-04-15 07:42:20.070582: MSWAL_0140, shape torch.Size([1, 458, 573, 573]), rank 0 +2026-04-15 07:43:21.037231: predicting MSWAL_0142 +2026-04-15 07:43:21.067079: MSWAL_0142, shape torch.Size([1, 274, 480, 480]), rank 0 +2026-04-15 07:43:37.750352: predicting MSWAL_0143 +2026-04-15 07:43:37.776410: MSWAL_0143, shape torch.Size([1, 466, 615, 615]), rank 0 +2026-04-15 07:44:36.493196: predicting MSWAL_0145 +2026-04-15 07:44:36.520648: MSWAL_0145, shape torch.Size([1, 373, 631, 631]), rank 0 +2026-04-15 07:45:20.466163: predicting MSWAL_0148 +2026-04-15 07:45:20.491379: MSWAL_0148, shape torch.Size([1, 270, 480, 480]), rank 0 +2026-04-15 07:45:37.416236: predicting MSWAL_0162 +2026-04-15 07:45:37.434010: MSWAL_0162, shape torch.Size([1, 326, 533, 533]), rank 0 +2026-04-15 07:46:14.132616: predicting MSWAL_0168 +2026-04-15 07:46:14.157990: MSWAL_0168, shape torch.Size([1, 134, 529, 529]), rank 0 +2026-04-15 07:46:28.936375: predicting MSWAL_0188 +2026-04-15 07:46:28.954154: MSWAL_0188, shape torch.Size([1, 478, 623, 623]), rank 0 +2026-04-15 07:47:27.646035: predicting MSWAL_0189 +2026-04-15 07:47:27.670950: MSWAL_0189, shape torch.Size([1, 313, 507, 507]), rank 0 +2026-04-15 07:47:48.679714: predicting MSWAL_0194 +2026-04-15 07:47:48.706408: MSWAL_0194, shape torch.Size([1, 330, 529, 529]), rank 0 +2026-04-15 07:48:25.317497: predicting MSWAL_0207 +2026-04-15 07:48:25.343099: MSWAL_0207, shape torch.Size([1, 298, 480, 480]), rank 0 +2026-04-15 07:48:46.221394: predicting MSWAL_0217 +2026-04-15 07:48:46.242573: MSWAL_0217, shape torch.Size([1, 185, 507, 507]), rank 0 +2026-04-15 07:48:58.947557: predicting MSWAL_0223 +2026-04-15 07:48:58.964051: MSWAL_0223, shape torch.Size([1, 201, 580, 580]), rank 0 +2026-04-15 07:49:21.277110: predicting MSWAL_0225 +2026-04-15 07:49:21.292543: MSWAL_0225, shape torch.Size([1, 193, 507, 507]), rank 0 +2026-04-15 07:49:34.098014: predicting MSWAL_0226 +2026-04-15 07:49:34.121307: MSWAL_0226, shape torch.Size([1, 485, 548, 548]), rank 0 +2026-04-15 07:50:32.252689: predicting MSWAL_0227 +2026-04-15 07:50:32.289172: MSWAL_0227, shape torch.Size([1, 309, 507, 507]), rank 0 +2026-04-15 07:50:53.290279: predicting MSWAL_0233 +2026-04-15 07:50:53.320353: MSWAL_0233, shape torch.Size([1, 296, 556, 556]), rank 0 +2026-04-15 07:51:29.673636: predicting MSWAL_0234 +2026-04-15 07:51:29.698582: MSWAL_0234, shape torch.Size([1, 553, 572, 572]), rank 0 +2026-04-15 07:52:35.525903: predicting MSWAL_0245 +2026-04-15 07:52:35.556958: MSWAL_0245, shape torch.Size([1, 286, 452, 452]), rank 0 +2026-04-15 07:52:56.189466: predicting MSWAL_0254 +2026-04-15 07:52:56.207632: MSWAL_0254, shape torch.Size([1, 322, 496, 496]), rank 0 +2026-04-15 07:53:17.050472: predicting MSWAL_0259 +2026-04-15 07:53:17.066494: MSWAL_0259, shape torch.Size([1, 305, 507, 507]), rank 0 +2026-04-15 07:53:37.818222: predicting MSWAL_0261 +2026-04-15 07:53:37.841271: MSWAL_0261, shape torch.Size([1, 176, 555, 555]), rank 0 +2026-04-15 07:53:59.978887: predicting MSWAL_0262 +2026-04-15 07:53:59.994842: MSWAL_0262, shape torch.Size([1, 355, 515, 515]), rank 0 +2026-04-15 07:54:43.704383: predicting MSWAL_0265 +2026-04-15 07:54:43.729691: MSWAL_0265, shape torch.Size([1, 182, 480, 480]), rank 0 +2026-04-15 07:54:56.388798: predicting MSWAL_0273 +2026-04-15 07:54:56.417084: MSWAL_0273, shape torch.Size([1, 367, 507, 507]), rank 0 +2026-04-15 07:55:21.530396: predicting MSWAL_0279 +2026-04-15 07:55:21.559489: MSWAL_0279, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:55:34.143447: predicting MSWAL_0293 +2026-04-15 07:55:34.168090: MSWAL_0293, shape torch.Size([1, 174, 507, 507]), rank 0 +2026-04-15 07:55:46.770806: predicting MSWAL_0296 +2026-04-15 07:55:46.798715: MSWAL_0296, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:55:59.454576: predicting MSWAL_0313 +2026-04-15 07:55:59.471241: MSWAL_0313, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 07:56:08.204273: predicting MSWAL_0316 +2026-04-15 07:56:08.224140: MSWAL_0316, shape torch.Size([1, 177, 432, 432]), rank 0 +2026-04-15 07:56:20.722211: predicting MSWAL_0323 +2026-04-15 07:56:20.736390: MSWAL_0323, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:56:33.623361: predicting MSWAL_0331 +2026-04-15 07:56:33.651972: MSWAL_0331, shape torch.Size([1, 189, 480, 480]), rank 0 +2026-04-15 07:56:46.186430: predicting MSWAL_0391 +2026-04-15 07:56:46.209504: MSWAL_0391, shape torch.Size([1, 369, 537, 537]), rank 0 +2026-04-15 07:57:29.872668: predicting MSWAL_0410 +2026-04-15 07:57:29.894334: MSWAL_0410, shape torch.Size([1, 177, 512, 512]), rank 0 +2026-04-15 07:57:42.542440: predicting MSWAL_0412 +2026-04-15 07:57:42.566933: MSWAL_0412, shape torch.Size([1, 197, 507, 507]), rank 0 +2026-04-15 07:57:55.461181: predicting MSWAL_0416 +2026-04-15 07:57:55.481647: MSWAL_0416, shape torch.Size([1, 197, 564, 564]), rank 0 +2026-04-15 07:58:18.060429: predicting MSWAL_0419 +2026-04-15 07:58:18.087985: MSWAL_0419, shape torch.Size([1, 295, 552, 552]), rank 0 +2026-04-15 07:58:54.560330: predicting MSWAL_0420 +2026-04-15 07:58:54.584254: MSWAL_0420, shape torch.Size([1, 165, 579, 579]), rank 0 +2026-04-15 07:59:09.536379: predicting MSWAL_0421 +2026-04-15 07:59:09.559222: MSWAL_0421, shape torch.Size([1, 377, 556, 556]), rank 0 +2026-04-15 07:59:53.575423: predicting MSWAL_0422 +2026-04-15 07:59:53.594727: MSWAL_0422, shape torch.Size([1, 379, 583, 583]), rank 0 +2026-04-15 08:00:37.864267: predicting MSWAL_0430 +2026-04-15 08:00:37.890750: MSWAL_0430, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 08:00:50.344984: predicting MSWAL_0436 +2026-04-15 08:00:50.357575: MSWAL_0436, shape torch.Size([1, 349, 536, 536]), rank 0 +2026-04-15 08:01:34.144624: predicting MSWAL_0446 +2026-04-15 08:01:34.190121: MSWAL_0446, shape torch.Size([1, 333, 531, 531]), rank 0 +2026-04-15 08:02:10.682434: predicting MSWAL_0453 +2026-04-15 08:02:10.704896: MSWAL_0453, shape torch.Size([1, 164, 444, 444]), rank 0 +2026-04-15 08:02:19.407626: predicting MSWAL_0465 +2026-04-15 08:02:19.420493: MSWAL_0465, shape torch.Size([1, 301, 543, 543]), rank 0 +2026-04-15 08:02:55.902423: predicting MSWAL_0475 +2026-04-15 08:02:55.937324: MSWAL_0475, shape torch.Size([1, 184, 507, 507]), rank 0 +2026-04-15 08:03:08.566701: predicting MSWAL_0476 +2026-04-15 08:03:08.582752: MSWAL_0476, shape torch.Size([1, 185, 507, 507]), rank 0 +2026-04-15 08:03:21.204560: predicting MSWAL_0484 +2026-04-15 08:03:21.230943: MSWAL_0484, shape torch.Size([1, 197, 507, 507]), rank 0 +2026-04-15 08:03:33.887141: predicting MSWAL_0492 +2026-04-15 08:03:33.925180: MSWAL_0492, shape torch.Size([1, 237, 507, 507]), rank 0 +2026-04-15 08:03:50.755463: predicting MSWAL_0504 +2026-04-15 08:03:50.775221: MSWAL_0504, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 08:04:03.499467: predicting MSWAL_0505 +2026-04-15 08:04:03.511340: MSWAL_0505, shape torch.Size([1, 277, 531, 531]), rank 0 +2026-04-15 08:04:32.935874: predicting MSWAL_0510 +2026-04-15 08:04:32.952793: MSWAL_0510, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 08:04:46.059350: predicting MSWAL_0527 +2026-04-15 08:04:46.086635: MSWAL_0527, shape torch.Size([1, 189, 524, 524]), rank 0 +2026-04-15 08:05:08.188630: predicting MSWAL_0530 +2026-04-15 08:05:08.206717: MSWAL_0530, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 08:05:20.817195: predicting MSWAL_0540 +2026-04-15 08:05:20.831264: MSWAL_0540, shape torch.Size([1, 329, 615, 615]), rank 0 +2026-04-15 08:05:57.632907: predicting MSWAL_0544 +2026-04-15 08:05:57.654271: MSWAL_0544, shape torch.Size([1, 264, 605, 605]), rank 0 +2026-04-15 08:06:27.411675: predicting MSWAL_0545 +2026-04-15 08:06:27.430143: MSWAL_0545, shape torch.Size([1, 185, 539, 539]), rank 0 +2026-04-15 08:06:49.651106: predicting MSWAL_0546 +2026-04-15 08:06:49.664268: MSWAL_0546, shape torch.Size([1, 273, 543, 543]), rank 0 +2026-04-15 08:07:19.011008: predicting MSWAL_0547 +2026-04-15 08:07:19.032996: MSWAL_0547, shape torch.Size([1, 285, 537, 537]), rank 0 +2026-04-15 08:07:56.443757: predicting MSWAL_0552 +2026-04-15 08:07:56.472975: MSWAL_0552, shape torch.Size([1, 297, 617, 617]), rank 0 +2026-04-15 08:08:33.023812: predicting MSWAL_0554 +2026-04-15 08:08:33.052361: MSWAL_0554, shape torch.Size([1, 177, 555, 555]), rank 0 +2026-04-15 08:08:55.161964: predicting MSWAL_0563 +2026-04-15 08:08:55.177785: MSWAL_0563, shape torch.Size([1, 284, 561, 561]), rank 0 +2026-04-15 08:09:31.619192: predicting MSWAL_0568 +2026-04-15 08:09:31.634836: MSWAL_0568, shape torch.Size([1, 323, 529, 529]), rank 0 +2026-04-15 08:10:09.520557: predicting MSWAL_0573 +2026-04-15 08:10:09.541566: MSWAL_0573, shape torch.Size([1, 189, 507, 507]), rank 0 +2026-04-15 08:10:22.259023: predicting MSWAL_0621 +2026-04-15 08:10:22.273736: MSWAL_0621, shape torch.Size([1, 208, 351, 351]), rank 0 +2026-04-15 08:10:27.972724: predicting MSWAL_0628 +2026-04-15 08:10:27.994904: MSWAL_0628, shape torch.Size([1, 258, 543, 543]), rank 0 +2026-04-15 08:10:57.133295: predicting MSWAL_0641 +2026-04-15 08:10:57.156874: MSWAL_0641, shape torch.Size([1, 328, 559, 559]), rank 0 +2026-04-15 08:11:33.928456: predicting MSWAL_0643 +2026-04-15 08:11:33.955645: MSWAL_0643, shape torch.Size([1, 402, 536, 536]), rank 0 +2026-04-15 08:12:24.983799: predicting MSWAL_0649 +2026-04-15 08:12:25.001138: MSWAL_0649, shape torch.Size([1, 225, 507, 507]), rank 0 +2026-04-15 08:12:41.696314: predicting MSWAL_0651 +2026-04-15 08:12:41.717562: MSWAL_0651, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 08:12:54.414831: predicting MSWAL_0662 +2026-04-15 08:12:54.444941: MSWAL_0662, shape torch.Size([1, 338, 496, 496]), rank 0 +2026-04-15 08:13:19.279570: predicting MSWAL_0675 +2026-04-15 08:13:19.295046: MSWAL_0675, shape torch.Size([1, 340, 597, 597]), rank 0 +2026-04-15 08:14:03.418799: predicting MSWAL_0677 +2026-04-15 08:14:03.452704: MSWAL_0677, shape torch.Size([1, 280, 593, 593]), rank 0 +2026-04-15 08:14:32.869159: predicting MSWAL_0679 +2026-04-15 08:14:32.884473: MSWAL_0679, shape torch.Size([1, 502, 480, 480]), rank 0 +2026-04-15 08:15:06.132204: predicting MSWAL_0692 +2026-04-15 08:15:06.171589: MSWAL_0692, shape torch.Size([1, 293, 540, 540]), rank 0 +2026-04-15 08:17:06.141905: Validation complete +2026-04-15 08:17:06.144916: Mean Validation Dice: 0.383443692718922 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_1/checkpoint_best.pth b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_1/checkpoint_best.pth new file mode 100644 index 0000000000000000000000000000000000000000..dc390bc9b494ca59b8803afc84ef0809302850ec --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_1/checkpoint_best.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a2c549291e2464beb3b4299c121838156add3207d77f514701e1205e903eb3db +size 1132086610 diff --git 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b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_1/debug.json @@ -0,0 +1,53 @@ +{ + "_best_ema": "None", + "batch_size": "2", + "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}", + "configuration_name": "3d_fullres", + "cudnn_version": 90100, + "current_epoch": "0", + "dataloader_train": "", + "dataloader_train.generator": "", + "dataloader_train.num_processes": "12", + "dataloader_train.transform": "None", + "dataloader_val": "", + "dataloader_val.generator": "", + "dataloader_val.num_processes": "6", + "dataloader_val.transform": "None", + "dataset_json": "{'name': 'MSWAL', 'description': ' 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset', 'licence': 'CC BY-NC 4.0', 'relase': 'July 8, 2025', 'tensorImageSize': '3D', 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'gallstone': 1, 'kidney stone': 2, 'liver tumor': 3, 'kidney tumor': 4, 'pancreatic cancer': 5, 'liver cyst': 6, 'kidney cyst': 7}, 'numTraining': 484, 'numTest': 210, 'training': [{'image': './imagesTr/MSWAL_0001_0000.nii.gz', 'label': './labelsTr/MSWAL_0001.nii.gz'}, {'image': './imagesTr/MSWAL_0002_0000.nii.gz', 'label': './labelsTr/MSWAL_0002.nii.gz'}, {'image': './imagesTr/MSWAL_0003_0000.nii.gz', 'label': './labelsTr/MSWAL_0003.nii.gz'}, {'image': './imagesTr/MSWAL_0008_0000.nii.gz', 'label': './labelsTr/MSWAL_0008.nii.gz'}, {'image': './imagesTr/MSWAL_0009_0000.nii.gz', 'label': './labelsTr/MSWAL_0009.nii.gz'}, {'image': './imagesTr/MSWAL_0011_0000.nii.gz', 'label': './labelsTr/MSWAL_0011.nii.gz'}, {'image': './imagesTr/MSWAL_0013_0000.nii.gz', 'label': './labelsTr/MSWAL_0013.nii.gz'}, {'image': './imagesTr/MSWAL_0014_0000.nii.gz', 'label': './labelsTr/MSWAL_0014.nii.gz'}, {'image': './imagesTr/MSWAL_0015_0000.nii.gz', 'label': './labelsTr/MSWAL_0015.nii.gz'}, {'image': './imagesTr/MSWAL_0017_0000.nii.gz', 'label': './labelsTr/MSWAL_0017.nii.gz'}, {'image': './imagesTr/MSWAL_0018_0000.nii.gz', 'label': './labelsTr/MSWAL_0018.nii.gz'}, {'image': './imagesTr/MSWAL_0020_0000.nii.gz', 'label': './labelsTr/MSWAL_0020.nii.gz'}, {'image': './imagesTr/MSWAL_0021_0000.nii.gz', 'label': './labelsTr/MSWAL_0021.nii.gz'}, {'image': './imagesTr/MSWAL_0022_0000.nii.gz', 'label': './labelsTr/MSWAL_0022.nii.gz'}, {'image': './imagesTr/MSWAL_0024_0000.nii.gz', 'label': './labelsTr/MSWAL_0024.nii.gz'}, {'image': './imagesTr/MSWAL_0026_0000.nii.gz', 'label': './labelsTr/MSWAL_0026.nii.gz'}, {'image': './imagesTr/MSWAL_0027_0000.nii.gz', 'label': './labelsTr/MSWAL_0027.nii.gz'}, {'image': './imagesTr/MSWAL_0028_0000.nii.gz', 'label': './labelsTr/MSWAL_0028.nii.gz'}, {'image': './imagesTr/MSWAL_0029_0000.nii.gz', 'label': './labelsTr/MSWAL_0029.nii.gz'}, {'image': './imagesTr/MSWAL_0031_0000.nii.gz', 'label': './labelsTr/MSWAL_0031.nii.gz'}, {'image': './imagesTr/MSWAL_0032_0000.nii.gz', 'label': './labelsTr/MSWAL_0032.nii.gz'}, {'image': './imagesTr/MSWAL_0033_0000.nii.gz', 'label': './labelsTr/MSWAL_0033.nii.gz'}, {'image': './imagesTr/MSWAL_0034_0000.nii.gz', 'label': './labelsTr/MSWAL_0034.nii.gz'}, {'image': './imagesTr/MSWAL_0035_0000.nii.gz', 'label': './labelsTr/MSWAL_0035.nii.gz'}, {'image': './imagesTr/MSWAL_0037_0000.nii.gz', 'label': './labelsTr/MSWAL_0037.nii.gz'}, {'image': './imagesTr/MSWAL_0038_0000.nii.gz', 'label': './labelsTr/MSWAL_0038.nii.gz'}, {'image': './imagesTr/MSWAL_0039_0000.nii.gz', 'label': './labelsTr/MSWAL_0039.nii.gz'}, {'image': './imagesTr/MSWAL_0040_0000.nii.gz', 'label': './labelsTr/MSWAL_0040.nii.gz'}, {'image': './imagesTr/MSWAL_0041_0000.nii.gz', 'label': './labelsTr/MSWAL_0041.nii.gz'}, {'image': './imagesTr/MSWAL_0042_0000.nii.gz', 'label': './labelsTr/MSWAL_0042.nii.gz'}, {'image': './imagesTr/MSWAL_0045_0000.nii.gz', 'label': './labelsTr/MSWAL_0045.nii.gz'}, {'image': './imagesTr/MSWAL_0046_0000.nii.gz', 'label': './labelsTr/MSWAL_0046.nii.gz'}, {'image': './imagesTr/MSWAL_0049_0000.nii.gz', 'label': './labelsTr/MSWAL_0049.nii.gz'}, {'image': './imagesTr/MSWAL_0050_0000.nii.gz', 'label': './labelsTr/MSWAL_0050.nii.gz'}, {'image': './imagesTr/MSWAL_0051_0000.nii.gz', 'label': './labelsTr/MSWAL_0051.nii.gz'}, {'image': './imagesTr/MSWAL_0052_0000.nii.gz', 'label': './labelsTr/MSWAL_0052.nii.gz'}, {'image': './imagesTr/MSWAL_0054_0000.nii.gz', 'label': 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{'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 35, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [190, 381, 381], 'spacing': [1.6798954741801528, 1.0079372845080916, 1.0079372845080916], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 71.96339416503906, 'median': 45.0, 'min': -932.0, 'percentile_00_5': -93.0, 'percentile_99_5': 1052.0, 'std': 141.6230926513672}}}", + "preprocessed_dataset_folder": "/data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/nnUNetPlans_3d_fullres", + "preprocessed_dataset_folder_base": "/data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL", + "save_every": "50", + "torch_version": "2.5.0+cu121", + "unpack_dataset": "True", + "was_initialized": "True", + "weight_decay": "3e-05" +} \ No newline at end of file diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_1/progress.png b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_1/progress.png new file mode 100644 index 0000000000000000000000000000000000000000..7a7865c2f6fad31ac4288a9ddc1a4492b8cc49d2 --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_1/progress.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a9f8655745d8d3dd59a17bd2b98e86387b4464f6eb5ac63cee7178437c2b4ed +size 1661341 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_1/training_log_2026_4_10_10_09_52.txt b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_1/training_log_2026_4_10_10_09_52.txt new file mode 100644 index 0000000000000000000000000000000000000000..46ea3487462576c193f4ff39b7eb63ee97660feb --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_1/training_log_2026_4_10_10_09_52.txt @@ -0,0 +1,28386 @@ + +####################################################################### +Please cite the following paper when using nnU-Net: +Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. +####################################################################### + +2026-04-10 10:09:52.094360: do_dummy_2d_data_aug: False +2026-04-10 10:09:52.149707: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json +2026-04-10 10:09:52.153424: The split file contains 5 splits. +2026-04-10 10:09:52.154908: Desired fold for training: 1 +2026-04-10 10:09:52.156154: This split has 387 training and 97 validation cases. +2026-04-10 10:10:00.712683: Using torch.compile... + +This is the configuration used by this training: +Configuration name: 3d_fullres + {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True} + +These are the global plan.json settings: + {'dataset_name': 'Dataset201_MSWAL', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.25, 0.75, 0.75], 'original_median_shape_after_transp': [261, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 71.96339416503906, 'median': 45.0, 'min': -932.0, 'percentile_00_5': -93.0, 'percentile_99_5': 1052.0, 'std': 141.6230926513672}}} + +2026-04-10 10:10:02.164617: unpacking dataset... +2026-04-10 10:10:09.260720: unpacking done... +2026-04-10 10:10:09.284341: Unable to plot network architecture: nnUNet_compile is enabled! +2026-04-10 10:10:09.340915: +2026-04-10 10:10:09.342430: Epoch 0 +2026-04-10 10:10:09.344067: Current learning rate: 0.01 +2026-04-10 10:14:38.894948: train_loss 0.2056 +2026-04-10 10:14:38.900762: val_loss 0.0775 +2026-04-10 10:14:38.902420: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:14:38.904009: Epoch time: 269.56 s +2026-04-10 10:14:38.905407: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 10:14:41.798955: +2026-04-10 10:14:41.801142: Epoch 1 +2026-04-10 10:14:41.803657: Current learning rate: 0.01 +2026-04-10 10:16:23.529362: train_loss 0.0717 +2026-04-10 10:16:23.536141: val_loss 0.0549 +2026-04-10 10:16:23.538465: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:16:23.540540: Epoch time: 101.73 s +2026-04-10 10:16:24.805605: +2026-04-10 10:16:24.807033: Epoch 2 +2026-04-10 10:16:24.808580: Current learning rate: 0.01 +2026-04-10 10:18:07.070944: train_loss 0.0674 +2026-04-10 10:18:07.094800: val_loss 0.0567 +2026-04-10 10:18:07.099042: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:18:07.105544: Epoch time: 102.27 s +2026-04-10 10:18:08.471705: +2026-04-10 10:18:08.474625: Epoch 3 +2026-04-10 10:18:08.477539: Current learning rate: 0.00999 +2026-04-10 10:19:50.730834: train_loss 0.0624 +2026-04-10 10:19:50.739673: val_loss 0.0434 +2026-04-10 10:19:50.742757: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:19:50.746510: Epoch time: 102.26 s +2026-04-10 10:19:52.076884: +2026-04-10 10:19:52.080630: Epoch 4 +2026-04-10 10:19:52.083153: Current learning rate: 0.00999 +2026-04-10 10:21:34.742225: train_loss 0.0502 +2026-04-10 10:21:34.748140: val_loss 0.0401 +2026-04-10 10:21:34.751509: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:21:34.754161: Epoch time: 102.67 s +2026-04-10 10:21:36.084290: +2026-04-10 10:21:36.086199: Epoch 5 +2026-04-10 10:21:36.087988: Current learning rate: 0.00999 +2026-04-10 10:23:18.103901: train_loss 0.0558 +2026-04-10 10:23:18.114694: val_loss 0.0531 +2026-04-10 10:23:18.116703: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:23:18.119200: Epoch time: 102.02 s +2026-04-10 10:23:19.384581: +2026-04-10 10:23:19.386241: Epoch 6 +2026-04-10 10:23:19.387733: Current learning rate: 0.00999 +2026-04-10 10:25:01.844327: train_loss 0.0479 +2026-04-10 10:25:01.851366: val_loss 0.0534 +2026-04-10 10:25:01.853395: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:25:01.855597: Epoch time: 102.46 s +2026-04-10 10:25:03.158790: +2026-04-10 10:25:03.160997: Epoch 7 +2026-04-10 10:25:03.162563: Current learning rate: 0.00998 +2026-04-10 10:26:45.347379: train_loss 0.0514 +2026-04-10 10:26:45.355191: val_loss 0.0455 +2026-04-10 10:26:45.357025: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:26:45.361759: Epoch time: 102.19 s +2026-04-10 10:26:46.688041: +2026-04-10 10:26:46.689770: Epoch 8 +2026-04-10 10:26:46.691667: Current learning rate: 0.00998 +2026-04-10 10:28:29.204863: train_loss 0.0577 +2026-04-10 10:28:29.209882: val_loss 0.0497 +2026-04-10 10:28:29.211969: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:28:29.213793: Epoch time: 102.52 s +2026-04-10 10:28:30.527569: +2026-04-10 10:28:30.529409: Epoch 9 +2026-04-10 10:28:30.530962: Current learning rate: 0.00998 +2026-04-10 10:30:13.010175: train_loss 0.0494 +2026-04-10 10:30:13.016119: val_loss 0.0405 +2026-04-10 10:30:13.018167: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:30:13.020491: Epoch time: 102.49 s +2026-04-10 10:30:14.247347: +2026-04-10 10:30:14.248974: Epoch 10 +2026-04-10 10:30:14.250711: Current learning rate: 0.00998 +2026-04-10 10:31:56.479291: train_loss 0.0565 +2026-04-10 10:31:56.486056: val_loss 0.0473 +2026-04-10 10:31:56.488321: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:31:56.491446: Epoch time: 102.24 s +2026-04-10 10:31:57.736119: +2026-04-10 10:31:57.738040: Epoch 11 +2026-04-10 10:31:57.739890: Current learning rate: 0.00998 +2026-04-10 10:33:53.369372: train_loss 0.0522 +2026-04-10 10:33:53.375992: val_loss 0.0792 +2026-04-10 10:33:53.378014: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:33:53.379596: Epoch time: 115.64 s +2026-04-10 10:33:54.628258: +2026-04-10 10:33:54.630065: Epoch 12 +2026-04-10 10:33:54.631593: Current learning rate: 0.00997 +2026-04-10 10:35:50.951274: train_loss 0.0404 +2026-04-10 10:35:50.956204: val_loss 0.0221 +2026-04-10 10:35:50.958118: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:35:50.960339: Epoch time: 116.33 s +2026-04-10 10:35:52.274470: +2026-04-10 10:35:52.276319: Epoch 13 +2026-04-10 10:35:52.278056: Current learning rate: 0.00997 +2026-04-10 10:37:35.580962: train_loss 0.0623 +2026-04-10 10:37:35.592474: val_loss 0.0517 +2026-04-10 10:37:35.595707: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:37:35.598707: Epoch time: 103.31 s +2026-04-10 10:37:36.891773: +2026-04-10 10:37:36.894489: Epoch 14 +2026-04-10 10:37:36.896245: Current learning rate: 0.00997 +2026-04-10 10:39:19.170080: train_loss 0.0506 +2026-04-10 10:39:19.176627: val_loss 0.0659 +2026-04-10 10:39:19.178542: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:39:19.181742: Epoch time: 102.28 s +2026-04-10 10:39:20.487288: +2026-04-10 10:39:20.488968: Epoch 15 +2026-04-10 10:39:20.491463: Current learning rate: 0.00997 +2026-04-10 10:41:02.729664: train_loss 0.0554 +2026-04-10 10:41:02.735214: val_loss 0.0581 +2026-04-10 10:41:02.736776: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:41:02.738913: Epoch time: 102.25 s +2026-04-10 10:41:04.069086: +2026-04-10 10:41:04.070679: Epoch 16 +2026-04-10 10:41:04.072068: Current learning rate: 0.00996 +2026-04-10 10:42:47.000222: train_loss 0.0533 +2026-04-10 10:42:47.005329: val_loss 0.0398 +2026-04-10 10:42:47.007244: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:42:47.009808: Epoch time: 102.94 s +2026-04-10 10:42:48.364299: +2026-04-10 10:42:48.366560: Epoch 17 +2026-04-10 10:42:48.368392: Current learning rate: 0.00996 +2026-04-10 10:44:31.175980: train_loss 0.047 +2026-04-10 10:44:31.183221: val_loss 0.0321 +2026-04-10 10:44:31.185496: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:44:31.188374: Epoch time: 102.82 s +2026-04-10 10:44:32.503663: +2026-04-10 10:44:32.506087: Epoch 18 +2026-04-10 10:44:32.507759: Current learning rate: 0.00996 +2026-04-10 10:46:15.299649: train_loss 0.0446 +2026-04-10 10:46:15.306886: val_loss 0.0514 +2026-04-10 10:46:15.309301: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:46:15.311394: Epoch time: 102.8 s +2026-04-10 10:46:16.624007: +2026-04-10 10:46:16.625516: Epoch 19 +2026-04-10 10:46:16.626778: Current learning rate: 0.00996 +2026-04-10 10:48:01.651936: train_loss 0.0436 +2026-04-10 10:48:01.663063: val_loss 0.0329 +2026-04-10 10:48:01.675108: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:48:01.685375: Epoch time: 105.03 s +2026-04-10 10:48:03.037306: +2026-04-10 10:48:03.039657: Epoch 20 +2026-04-10 10:48:03.042291: Current learning rate: 0.00995 +2026-04-10 10:49:47.641025: train_loss 0.046 +2026-04-10 10:49:47.659148: val_loss 0.0483 +2026-04-10 10:49:47.667241: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:49:47.679588: Epoch time: 104.61 s +2026-04-10 10:49:49.049579: +2026-04-10 10:49:49.051505: Epoch 21 +2026-04-10 10:49:49.054618: Current learning rate: 0.00995 +2026-04-10 10:51:32.788555: train_loss 0.0421 +2026-04-10 10:51:32.796478: val_loss 0.0472 +2026-04-10 10:51:32.798573: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:51:32.801303: Epoch time: 103.74 s +2026-04-10 10:51:34.028406: +2026-04-10 10:51:34.031012: Epoch 22 +2026-04-10 10:51:34.035355: Current learning rate: 0.00995 +2026-04-10 10:53:16.969995: train_loss 0.0397 +2026-04-10 10:53:16.976528: val_loss 0.066 +2026-04-10 10:53:16.978992: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:53:16.981314: Epoch time: 102.95 s +2026-04-10 10:53:18.235809: +2026-04-10 10:53:18.237461: Epoch 23 +2026-04-10 10:53:18.238877: Current learning rate: 0.00995 +2026-04-10 10:55:00.951864: train_loss 0.037 +2026-04-10 10:55:00.959457: val_loss 0.0305 +2026-04-10 10:55:00.963652: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:55:00.968168: Epoch time: 102.72 s +2026-04-10 10:55:02.268898: +2026-04-10 10:55:02.270595: Epoch 24 +2026-04-10 10:55:02.272237: Current learning rate: 0.00995 +2026-04-10 10:56:50.192226: train_loss 0.0438 +2026-04-10 10:56:50.197780: val_loss 0.0523 +2026-04-10 10:56:50.201243: Pseudo dice [0.0, 0.0, 0.0001, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:56:50.203456: Epoch time: 107.93 s +2026-04-10 10:56:50.205263: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 10:56:53.816358: +2026-04-10 10:56:53.822308: Epoch 25 +2026-04-10 10:56:53.826313: Current learning rate: 0.00994 +2026-04-10 10:58:44.270219: train_loss 0.0413 +2026-04-10 10:58:44.306536: val_loss 0.0576 +2026-04-10 10:58:44.309075: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:58:44.319127: Epoch time: 110.46 s +2026-04-10 10:58:45.594683: +2026-04-10 10:58:45.596755: Epoch 26 +2026-04-10 10:58:45.599511: Current learning rate: 0.00994 +2026-04-10 11:00:28.693134: train_loss 0.0441 +2026-04-10 11:00:28.724655: val_loss 0.0492 +2026-04-10 11:00:28.728342: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:00:28.733846: Epoch time: 103.1 s +2026-04-10 11:00:30.070409: +2026-04-10 11:00:30.076094: Epoch 27 +2026-04-10 11:00:30.080006: Current learning rate: 0.00994 +2026-04-10 11:02:12.845718: train_loss 0.0444 +2026-04-10 11:02:12.853073: val_loss 0.0749 +2026-04-10 11:02:12.855194: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:02:12.859373: Epoch time: 102.78 s +2026-04-10 11:02:14.156121: +2026-04-10 11:02:14.157716: Epoch 28 +2026-04-10 11:02:14.159236: Current learning rate: 0.00994 +2026-04-10 11:03:56.864331: train_loss 0.0477 +2026-04-10 11:03:56.872169: val_loss 0.0275 +2026-04-10 11:03:56.874111: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:03:56.875860: Epoch time: 102.71 s +2026-04-10 11:03:58.194379: +2026-04-10 11:03:58.196164: Epoch 29 +2026-04-10 11:03:58.198018: Current learning rate: 0.00993 +2026-04-10 11:05:40.732898: train_loss 0.0371 +2026-04-10 11:05:40.738194: val_loss 0.0459 +2026-04-10 11:05:40.740347: Pseudo dice [0.0, 0.0, 0.02, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:05:40.742955: Epoch time: 102.54 s +2026-04-10 11:05:40.744774: Yayy! New best EMA pseudo Dice: 0.0003 +2026-04-10 11:05:43.862884: +2026-04-10 11:05:43.865265: Epoch 30 +2026-04-10 11:05:43.866840: Current learning rate: 0.00993 +2026-04-10 11:07:26.446858: train_loss 0.0384 +2026-04-10 11:07:26.451675: val_loss 0.0457 +2026-04-10 11:07:26.453484: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:07:26.455515: Epoch time: 102.59 s +2026-04-10 11:07:27.760189: +2026-04-10 11:07:27.762057: Epoch 31 +2026-04-10 11:07:27.763473: Current learning rate: 0.00993 +2026-04-10 11:09:10.264336: train_loss 0.0441 +2026-04-10 11:09:10.270207: val_loss 0.067 +2026-04-10 11:09:10.272217: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:09:10.274363: Epoch time: 102.51 s +2026-04-10 11:09:11.582678: +2026-04-10 11:09:11.584327: Epoch 32 +2026-04-10 11:09:11.585762: Current learning rate: 0.00993 +2026-04-10 11:10:54.613669: train_loss 0.035 +2026-04-10 11:10:54.621904: val_loss 0.0347 +2026-04-10 11:10:54.623485: Pseudo dice [0.0, 0.0, 0.014, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:10:54.626945: Epoch time: 103.03 s +2026-04-10 11:10:54.628656: Yayy! New best EMA pseudo Dice: 0.0004 +2026-04-10 11:10:57.775826: +2026-04-10 11:10:57.777922: Epoch 33 +2026-04-10 11:10:57.779845: Current learning rate: 0.00993 +2026-04-10 11:12:40.882707: train_loss 0.0493 +2026-04-10 11:12:40.887506: val_loss 0.0325 +2026-04-10 11:12:40.889026: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:12:40.891056: Epoch time: 103.11 s +2026-04-10 11:12:42.180041: +2026-04-10 11:12:42.182821: Epoch 34 +2026-04-10 11:12:42.184431: Current learning rate: 0.00992 +2026-04-10 11:14:25.412012: train_loss 0.0374 +2026-04-10 11:14:25.426890: val_loss 0.0273 +2026-04-10 11:14:25.429743: Pseudo dice [0.0, 0.0, 0.0042, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:14:25.432074: Epoch time: 103.24 s +2026-04-10 11:14:26.772350: +2026-04-10 11:14:26.773934: Epoch 35 +2026-04-10 11:14:26.775695: Current learning rate: 0.00992 +2026-04-10 11:16:10.582295: train_loss 0.0407 +2026-04-10 11:16:10.601099: val_loss 0.0476 +2026-04-10 11:16:10.604483: Pseudo dice [0.0, 0.0, 0.0023, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:16:10.618706: Epoch time: 103.81 s +2026-04-10 11:16:11.997913: +2026-04-10 11:16:11.999475: Epoch 36 +2026-04-10 11:16:12.001141: Current learning rate: 0.00992 +2026-04-10 11:17:54.886980: train_loss 0.0342 +2026-04-10 11:17:54.894190: val_loss 0.0553 +2026-04-10 11:17:54.897758: Pseudo dice [0.0, 0.0, 0.0611, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:17:54.900530: Epoch time: 102.89 s +2026-04-10 11:17:54.902426: Yayy! New best EMA pseudo Dice: 0.0012 +2026-04-10 11:17:58.046498: +2026-04-10 11:17:58.048058: Epoch 37 +2026-04-10 11:17:58.049549: Current learning rate: 0.00992 +2026-04-10 11:19:40.994764: train_loss 0.0336 +2026-04-10 11:19:40.999930: val_loss 0.0227 +2026-04-10 11:19:41.002370: Pseudo dice [0.0, 0.0, 0.0381, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:19:41.004464: Epoch time: 102.95 s +2026-04-10 11:19:41.006175: Yayy! New best EMA pseudo Dice: 0.0016 +2026-04-10 11:19:45.239083: +2026-04-10 11:19:45.241027: Epoch 38 +2026-04-10 11:19:45.242899: Current learning rate: 0.00991 +2026-04-10 11:21:28.494956: train_loss 0.0342 +2026-04-10 11:21:28.500754: val_loss 0.037 +2026-04-10 11:21:28.502547: Pseudo dice [0.0, 0.0, 0.3154, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:21:28.504596: Epoch time: 103.26 s +2026-04-10 11:21:28.506593: Yayy! New best EMA pseudo Dice: 0.006 +2026-04-10 11:21:31.525305: +2026-04-10 11:21:31.527016: Epoch 39 +2026-04-10 11:21:31.528374: Current learning rate: 0.00991 +2026-04-10 11:23:14.611639: train_loss 0.0367 +2026-04-10 11:23:14.618652: val_loss 0.0317 +2026-04-10 11:23:14.620195: Pseudo dice [0.0, 0.0, 0.002, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:23:14.622874: Epoch time: 103.09 s +2026-04-10 11:23:15.943960: +2026-04-10 11:23:15.947652: Epoch 40 +2026-04-10 11:23:15.949898: Current learning rate: 0.00991 +2026-04-10 11:25:00.300600: train_loss 0.0233 +2026-04-10 11:25:00.306405: val_loss 0.031 +2026-04-10 11:25:00.308308: Pseudo dice [0.0, 0.0, 0.0324, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:25:00.310579: Epoch time: 104.36 s +2026-04-10 11:25:01.627296: +2026-04-10 11:25:01.628998: Epoch 41 +2026-04-10 11:25:01.631163: Current learning rate: 0.00991 +2026-04-10 11:26:44.963670: train_loss 0.0309 +2026-04-10 11:26:44.969141: val_loss 0.041 +2026-04-10 11:26:44.971340: Pseudo dice [0.0, 0.0, 0.0676, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:26:44.974089: Epoch time: 103.34 s +2026-04-10 11:26:46.263093: +2026-04-10 11:26:46.265364: Epoch 42 +2026-04-10 11:26:46.267024: Current learning rate: 0.00991 +2026-04-10 11:28:28.838871: train_loss 0.0323 +2026-04-10 11:28:28.845719: val_loss 0.0324 +2026-04-10 11:28:28.847832: Pseudo dice [0.0, 0.0, 0.2065, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:28:28.851353: Epoch time: 102.58 s +2026-04-10 11:28:28.853279: Yayy! New best EMA pseudo Dice: 0.0081 +2026-04-10 11:28:31.896236: +2026-04-10 11:28:31.897862: Epoch 43 +2026-04-10 11:28:31.899322: Current learning rate: 0.0099 +2026-04-10 11:30:14.645622: train_loss 0.0211 +2026-04-10 11:30:14.652583: val_loss 0.039 +2026-04-10 11:30:14.654794: Pseudo dice [0.0, 0.0, 0.0383, 0.0, 0.0, 0.0, 0.3554] +2026-04-10 11:30:14.657955: Epoch time: 102.75 s +2026-04-10 11:30:14.660127: Yayy! New best EMA pseudo Dice: 0.013 +2026-04-10 11:30:17.725291: +2026-04-10 11:30:17.739006: Epoch 44 +2026-04-10 11:30:17.741060: Current learning rate: 0.0099 +2026-04-10 11:32:00.669795: train_loss 0.0198 +2026-04-10 11:32:00.674178: val_loss 0.0391 +2026-04-10 11:32:00.676079: Pseudo dice [0.0, 0.0, 0.0327, 0.0, 0.0, 0.0, 0.0985] +2026-04-10 11:32:00.678159: Epoch time: 102.95 s +2026-04-10 11:32:00.679776: Yayy! New best EMA pseudo Dice: 0.0135 +2026-04-10 11:32:03.689578: +2026-04-10 11:32:03.691904: Epoch 45 +2026-04-10 11:32:03.693392: Current learning rate: 0.0099 +2026-04-10 11:33:46.862314: train_loss 0.0251 +2026-04-10 11:33:46.867733: val_loss 0.0363 +2026-04-10 11:33:46.870256: Pseudo dice [0.0, 0.0, 0.1287, 0.0, 0.0, 0.0, 0.2332] +2026-04-10 11:33:46.873462: Epoch time: 103.18 s +2026-04-10 11:33:46.876646: Yayy! New best EMA pseudo Dice: 0.0173 +2026-04-10 11:33:49.937385: +2026-04-10 11:33:49.939814: Epoch 46 +2026-04-10 11:33:49.941693: Current learning rate: 0.0099 +2026-04-10 11:35:33.362550: train_loss 0.0244 +2026-04-10 11:35:33.369396: val_loss 0.0168 +2026-04-10 11:35:33.372432: Pseudo dice [0.0, 0.0, 0.0374, 0.0, 0.0, 0.0, 0.4366] +2026-04-10 11:35:33.374993: Epoch time: 103.43 s +2026-04-10 11:35:33.377101: Yayy! New best EMA pseudo Dice: 0.0224 +2026-04-10 11:35:36.471984: +2026-04-10 11:35:36.473699: Epoch 47 +2026-04-10 11:35:36.475329: Current learning rate: 0.00989 +2026-04-10 11:37:21.025350: train_loss 0.0182 +2026-04-10 11:37:21.029991: val_loss 0.0475 +2026-04-10 11:37:21.032031: Pseudo dice [0.0, 0.0, 0.1205, 0.0, 0.0, 0.0, 0.1955] +2026-04-10 11:37:21.034414: Epoch time: 104.56 s +2026-04-10 11:37:21.036160: Yayy! New best EMA pseudo Dice: 0.0247 +2026-04-10 11:37:24.083434: +2026-04-10 11:37:24.085869: Epoch 48 +2026-04-10 11:37:24.087581: Current learning rate: 0.00989 +2026-04-10 11:39:06.864712: train_loss 0.0237 +2026-04-10 11:39:06.872594: val_loss 0.012 +2026-04-10 11:39:06.874325: Pseudo dice [0.0, 0.0, 0.2045, 0.0, 0.0, 0.0, 0.4045] +2026-04-10 11:39:06.876498: Epoch time: 102.78 s +2026-04-10 11:39:06.878386: Yayy! New best EMA pseudo Dice: 0.0309 +2026-04-10 11:39:09.990478: +2026-04-10 11:39:09.992308: Epoch 49 +2026-04-10 11:39:09.993901: Current learning rate: 0.00989 +2026-04-10 11:40:53.121468: train_loss 0.022 +2026-04-10 11:40:53.128477: val_loss -0.0038 +2026-04-10 11:40:53.131991: Pseudo dice [0.0, 0.0, 0.3911, 0.0, 0.0, 0.0, 0.2873] +2026-04-10 11:40:53.134555: Epoch time: 103.13 s +2026-04-10 11:40:54.959835: Yayy! New best EMA pseudo Dice: 0.0375 +2026-04-10 11:40:57.925063: +2026-04-10 11:40:57.927404: Epoch 50 +2026-04-10 11:40:57.929499: Current learning rate: 0.00989 +2026-04-10 11:42:42.632527: train_loss 0.0082 +2026-04-10 11:42:42.640398: val_loss 0.006 +2026-04-10 11:42:42.642577: Pseudo dice [0.0, 0.0, 0.3728, 0.0, 0.0, 0.0, 0.4067] +2026-04-10 11:42:42.644289: Epoch time: 104.71 s +2026-04-10 11:42:42.645684: Yayy! New best EMA pseudo Dice: 0.0449 +2026-04-10 11:42:45.693887: +2026-04-10 11:42:45.697126: Epoch 51 +2026-04-10 11:42:45.698773: Current learning rate: 0.00989 +2026-04-10 11:44:29.032032: train_loss 0.0149 +2026-04-10 11:44:29.038770: val_loss 0.0295 +2026-04-10 11:44:29.040728: Pseudo dice [0.0, 0.0, 0.1162, 0.0, 0.0, 0.0, 0.3207] +2026-04-10 11:44:29.042913: Epoch time: 103.34 s +2026-04-10 11:44:29.045147: Yayy! New best EMA pseudo Dice: 0.0466 +2026-04-10 11:44:32.152224: +2026-04-10 11:44:32.154825: Epoch 52 +2026-04-10 11:44:32.157337: Current learning rate: 0.00988 +2026-04-10 11:46:15.882214: train_loss 0.0121 +2026-04-10 11:46:15.894227: val_loss -0.0015 +2026-04-10 11:46:15.897998: Pseudo dice [0.0, 0.0, 0.2401, 0.0, 0.0, 0.0, 0.3172] +2026-04-10 11:46:15.901414: Epoch time: 103.73 s +2026-04-10 11:46:15.904657: Yayy! New best EMA pseudo Dice: 0.0499 +2026-04-10 11:46:19.063179: +2026-04-10 11:46:19.066443: Epoch 53 +2026-04-10 11:46:19.068504: Current learning rate: 0.00988 +2026-04-10 11:48:02.038426: train_loss 0.008 +2026-04-10 11:48:02.043387: val_loss 0.0019 +2026-04-10 11:48:02.045134: Pseudo dice [0.0, 0.0, 0.2206, 0.0, 0.0, 0.0249, 0.3086] +2026-04-10 11:48:02.047716: Epoch time: 102.98 s +2026-04-10 11:48:02.049396: Yayy! New best EMA pseudo Dice: 0.0529 +2026-04-10 11:48:05.218671: +2026-04-10 11:48:05.220515: Epoch 54 +2026-04-10 11:48:05.222265: Current learning rate: 0.00988 +2026-04-10 11:49:48.483902: train_loss 0.0103 +2026-04-10 11:49:48.491977: val_loss 0.0237 +2026-04-10 11:49:48.496340: Pseudo dice [0.0, 0.0, 0.4139, 0.0, 0.0, 0.0044, 0.2966] +2026-04-10 11:49:48.501326: Epoch time: 103.27 s +2026-04-10 11:49:48.504842: Yayy! New best EMA pseudo Dice: 0.0578 +2026-04-10 11:49:52.834440: +2026-04-10 11:49:52.835931: Epoch 55 +2026-04-10 11:49:52.837358: Current learning rate: 0.00988 +2026-04-10 11:51:36.268417: train_loss 0.0168 +2026-04-10 11:51:36.274724: val_loss 0.0157 +2026-04-10 11:51:36.276757: Pseudo dice [0.0, 0.0, 0.1198, 0.0, 0.0, 0.001, 0.5229] +2026-04-10 11:51:36.278942: Epoch time: 103.44 s +2026-04-10 11:51:36.280890: Yayy! New best EMA pseudo Dice: 0.0612 +2026-04-10 11:51:39.353872: +2026-04-10 11:51:39.355808: Epoch 56 +2026-04-10 11:51:39.357583: Current learning rate: 0.00987 +2026-04-10 11:53:22.386713: train_loss 0.0124 +2026-04-10 11:53:22.392113: val_loss -0.0122 +2026-04-10 11:53:22.394449: Pseudo dice [0.0, 0.0, 0.1927, 0.0, 0.0, 0.0923, 0.3769] +2026-04-10 11:53:22.396528: Epoch time: 103.04 s +2026-04-10 11:53:22.398328: Yayy! New best EMA pseudo Dice: 0.0645 +2026-04-10 11:53:25.458874: +2026-04-10 11:53:25.460850: Epoch 57 +2026-04-10 11:53:25.462456: Current learning rate: 0.00987 +2026-04-10 11:55:08.609871: train_loss 0.0134 +2026-04-10 11:55:08.616997: val_loss 0.0607 +2026-04-10 11:55:08.620018: Pseudo dice [0.0, 0.0, 0.1203, 0.0, 0.0, 0.002, 0.4454] +2026-04-10 11:55:08.623432: Epoch time: 103.15 s +2026-04-10 11:55:08.625354: Yayy! New best EMA pseudo Dice: 0.0662 +2026-04-10 11:55:11.753875: +2026-04-10 11:55:11.755603: Epoch 58 +2026-04-10 11:55:11.758090: Current learning rate: 0.00987 +2026-04-10 11:56:54.642169: train_loss 0.013 +2026-04-10 11:56:54.647477: val_loss 0.0023 +2026-04-10 11:56:54.649276: Pseudo dice [0.0, 0.0, 0.0819, 0.0, 0.0, 0.2631, 0.4003] +2026-04-10 11:56:54.651469: Epoch time: 102.89 s +2026-04-10 11:56:54.653189: Yayy! New best EMA pseudo Dice: 0.0702 +2026-04-10 11:56:57.762594: +2026-04-10 11:56:57.767878: Epoch 59 +2026-04-10 11:56:57.769492: Current learning rate: 0.00987 +2026-04-10 11:58:40.860981: train_loss -0.0002 +2026-04-10 11:58:40.867288: val_loss -0.0053 +2026-04-10 11:58:40.869903: Pseudo dice [0.0, 0.0, 0.2847, 0.0, 0.0, 0.0372, 0.3886] +2026-04-10 11:58:40.872191: Epoch time: 103.1 s +2026-04-10 11:58:40.873916: Yayy! New best EMA pseudo Dice: 0.0733 +2026-04-10 11:58:43.998084: +2026-04-10 11:58:43.999777: Epoch 60 +2026-04-10 11:58:44.001157: Current learning rate: 0.00986 +2026-04-10 12:00:27.020129: train_loss 0.0072 +2026-04-10 12:00:27.025799: val_loss 0.0128 +2026-04-10 12:00:27.027567: Pseudo dice [0.0, 0.0, 0.256, 0.0, 0.0, 0.0506, 0.4835] +2026-04-10 12:00:27.030219: Epoch time: 103.03 s +2026-04-10 12:00:27.032213: Yayy! New best EMA pseudo Dice: 0.0773 +2026-04-10 12:00:30.175069: +2026-04-10 12:00:30.177140: Epoch 61 +2026-04-10 12:00:30.178442: Current learning rate: 0.00986 +2026-04-10 12:02:13.880823: train_loss -0.0124 +2026-04-10 12:02:13.886706: val_loss 0.0034 +2026-04-10 12:02:13.888707: Pseudo dice [0.0, 0.0, 0.3014, 0.0, 0.0, 0.0518, 0.137] +2026-04-10 12:02:13.891057: Epoch time: 103.71 s +2026-04-10 12:02:15.204747: +2026-04-10 12:02:15.206503: Epoch 62 +2026-04-10 12:02:15.208053: Current learning rate: 0.00986 +2026-04-10 12:03:58.129632: train_loss -0.0032 +2026-04-10 12:03:58.135323: val_loss 0.0139 +2026-04-10 12:03:58.137068: Pseudo dice [0.0, 0.0, 0.4245, 0.0, 0.0, 0.0402, 0.355] +2026-04-10 12:03:58.139850: Epoch time: 102.93 s +2026-04-10 12:03:58.141455: Yayy! New best EMA pseudo Dice: 0.0806 +2026-04-10 12:04:01.406125: +2026-04-10 12:04:01.408236: Epoch 63 +2026-04-10 12:04:01.409738: Current learning rate: 0.00986 +2026-04-10 12:05:45.674867: train_loss 0.0011 +2026-04-10 12:05:45.683622: val_loss 0.0072 +2026-04-10 12:05:45.686157: Pseudo dice [0.0, 0.0, 0.0199, 0.0, 0.0, 0.2888, 0.0888] +2026-04-10 12:05:45.690081: Epoch time: 104.27 s +2026-04-10 12:05:47.013490: +2026-04-10 12:05:47.015507: Epoch 64 +2026-04-10 12:05:47.017514: Current learning rate: 0.00986 +2026-04-10 12:07:29.925565: train_loss -0.003 +2026-04-10 12:07:29.931473: val_loss -0.025 +2026-04-10 12:07:29.933121: Pseudo dice [0.0, 0.0, 0.071, 0.0, 0.0, 0.4496, 0.3859] +2026-04-10 12:07:29.935546: Epoch time: 102.92 s +2026-04-10 12:07:29.937872: Yayy! New best EMA pseudo Dice: 0.0834 +2026-04-10 12:07:33.057876: +2026-04-10 12:07:33.059613: Epoch 65 +2026-04-10 12:07:33.061144: Current learning rate: 0.00985 +2026-04-10 12:09:16.158185: train_loss -0.0149 +2026-04-10 12:09:16.162713: val_loss -0.018 +2026-04-10 12:09:16.164388: Pseudo dice [0.0, 0.0, 0.4228, 0.0, 0.0, 0.1723, 0.401] +2026-04-10 12:09:16.166465: Epoch time: 103.1 s +2026-04-10 12:09:16.168086: Yayy! New best EMA pseudo Dice: 0.0893 +2026-04-10 12:09:19.256240: +2026-04-10 12:09:19.258029: Epoch 66 +2026-04-10 12:09:19.259449: Current learning rate: 0.00985 +2026-04-10 12:11:02.132297: train_loss -0.0158 +2026-04-10 12:11:02.139106: val_loss -0.0175 +2026-04-10 12:11:02.141188: Pseudo dice [0.0, 0.0, 0.3915, 0.0, 0.0, 0.2253, 0.4251] +2026-04-10 12:11:02.144079: Epoch time: 102.88 s +2026-04-10 12:11:02.145908: Yayy! New best EMA pseudo Dice: 0.0952 +2026-04-10 12:11:05.292773: +2026-04-10 12:11:05.294802: Epoch 67 +2026-04-10 12:11:05.296308: Current learning rate: 0.00985 +2026-04-10 12:12:48.296893: train_loss -0.0227 +2026-04-10 12:12:48.302920: val_loss 0.0034 +2026-04-10 12:12:48.305631: Pseudo dice [0.0, 0.0, 0.1324, 0.0, 0.0, 0.1393, 0.2959] +2026-04-10 12:12:48.308455: Epoch time: 103.01 s +2026-04-10 12:12:49.641488: +2026-04-10 12:12:49.649692: Epoch 68 +2026-04-10 12:12:49.652113: Current learning rate: 0.00985 +2026-04-10 12:14:32.597344: train_loss -0.0089 +2026-04-10 12:14:32.602081: val_loss -0.0202 +2026-04-10 12:14:32.603919: Pseudo dice [0.0, 0.0, 0.5424, 0.0, 0.0, 0.3963, 0.4472] +2026-04-10 12:14:32.605853: Epoch time: 102.96 s +2026-04-10 12:14:32.607344: Yayy! New best EMA pseudo Dice: 0.1042 +2026-04-10 12:14:35.806468: +2026-04-10 12:14:35.808379: Epoch 69 +2026-04-10 12:14:35.810161: Current learning rate: 0.00984 +2026-04-10 12:16:19.193304: train_loss -0.0117 +2026-04-10 12:16:19.201504: val_loss -0.0239 +2026-04-10 12:16:19.204515: Pseudo dice [0.0, 0.0, 0.3337, 0.0, 0.0, 0.333, 0.5107] +2026-04-10 12:16:19.207194: Epoch time: 103.39 s +2026-04-10 12:16:19.209273: Yayy! New best EMA pseudo Dice: 0.1106 +2026-04-10 12:16:22.414820: +2026-04-10 12:16:22.417351: Epoch 70 +2026-04-10 12:16:22.419529: Current learning rate: 0.00984 +2026-04-10 12:18:05.289611: train_loss -0.0102 +2026-04-10 12:18:05.295455: val_loss 0.0288 +2026-04-10 12:18:05.297302: Pseudo dice [0.0, 0.0, 0.2975, 0.0, 0.0, 0.0477, 0.4848] +2026-04-10 12:18:05.299271: Epoch time: 102.88 s +2026-04-10 12:18:05.301245: Yayy! New best EMA pseudo Dice: 0.1114 +2026-04-10 12:18:09.596801: +2026-04-10 12:18:09.598859: Epoch 71 +2026-04-10 12:18:09.600921: Current learning rate: 0.00984 +2026-04-10 12:19:52.661166: train_loss -0.0225 +2026-04-10 12:19:52.666998: val_loss -0.0024 +2026-04-10 12:19:52.668741: Pseudo dice [0.0, 0.0, 0.3938, 0.0, 0.0, 0.0336, 0.4611] +2026-04-10 12:19:52.671023: Epoch time: 103.07 s +2026-04-10 12:19:52.674050: Yayy! New best EMA pseudo Dice: 0.113 +2026-04-10 12:19:55.828764: +2026-04-10 12:19:55.831797: Epoch 72 +2026-04-10 12:19:55.833652: Current learning rate: 0.00984 +2026-04-10 12:21:38.853093: train_loss -0.0208 +2026-04-10 12:21:38.858069: val_loss -0.0196 +2026-04-10 12:21:38.860277: Pseudo dice [0.0, 0.0, 0.2926, 0.0, 0.0, 0.0524, 0.6522] +2026-04-10 12:21:38.862342: Epoch time: 103.03 s +2026-04-10 12:21:38.863956: Yayy! New best EMA pseudo Dice: 0.1159 +2026-04-10 12:21:41.973472: +2026-04-10 12:21:41.976305: Epoch 73 +2026-04-10 12:21:41.980235: Current learning rate: 0.00984 +2026-04-10 12:23:24.665235: train_loss -0.0263 +2026-04-10 12:23:24.670207: val_loss -0.0099 +2026-04-10 12:23:24.672101: Pseudo dice [0.0, 0.0, 0.4273, 0.0, 0.0, 0.053, 0.4866] +2026-04-10 12:23:24.674290: Epoch time: 102.7 s +2026-04-10 12:23:24.676549: Yayy! New best EMA pseudo Dice: 0.1181 +2026-04-10 12:23:27.889222: +2026-04-10 12:23:27.891224: Epoch 74 +2026-04-10 12:23:27.892733: Current learning rate: 0.00983 +2026-04-10 12:25:10.565540: train_loss -0.0296 +2026-04-10 12:25:10.571400: val_loss -0.0172 +2026-04-10 12:25:10.573516: Pseudo dice [0.0, 0.0, 0.3699, 0.0, 0.0, 0.2756, 0.4364] +2026-04-10 12:25:10.577188: Epoch time: 102.68 s +2026-04-10 12:25:10.579282: Yayy! New best EMA pseudo Dice: 0.1218 +2026-04-10 12:25:13.742919: +2026-04-10 12:25:13.745050: Epoch 75 +2026-04-10 12:25:13.746752: Current learning rate: 0.00983 +2026-04-10 12:26:56.902832: train_loss -0.0152 +2026-04-10 12:26:56.907451: val_loss -0.026 +2026-04-10 12:26:56.909034: Pseudo dice [0.0, 0.0, 0.4415, 0.0, 0.0, 0.2673, 0.1848] +2026-04-10 12:26:56.910837: Epoch time: 103.16 s +2026-04-10 12:26:56.912318: Yayy! New best EMA pseudo Dice: 0.1224 +2026-04-10 12:27:00.091619: +2026-04-10 12:27:00.093335: Epoch 76 +2026-04-10 12:27:00.094917: Current learning rate: 0.00983 +2026-04-10 12:28:42.952444: train_loss -0.0303 +2026-04-10 12:28:42.958502: val_loss 0.0169 +2026-04-10 12:28:42.960413: Pseudo dice [0.0, 0.0, 0.4483, 0.0, 0.0, 0.0735, 0.3832] +2026-04-10 12:28:42.962833: Epoch time: 102.86 s +2026-04-10 12:28:42.964643: Yayy! New best EMA pseudo Dice: 0.1231 +2026-04-10 12:28:46.154562: +2026-04-10 12:28:46.158190: Epoch 77 +2026-04-10 12:28:46.160435: Current learning rate: 0.00983 +2026-04-10 12:30:29.130387: train_loss -0.041 +2026-04-10 12:30:29.137609: val_loss 0.0183 +2026-04-10 12:30:29.139966: Pseudo dice [0.0, 0.0, 0.2444, 0.0, 0.0, 0.0219, 0.5481] +2026-04-10 12:30:29.143462: Epoch time: 102.98 s +2026-04-10 12:30:30.546839: +2026-04-10 12:30:30.549556: Epoch 78 +2026-04-10 12:30:30.553229: Current learning rate: 0.00982 +2026-04-10 12:32:13.918228: train_loss -0.0313 +2026-04-10 12:32:13.926458: val_loss 0.007 +2026-04-10 12:32:13.928553: Pseudo dice [0.0, 0.0, 0.4333, 0.0, 0.0, 0.0308, 0.5198] +2026-04-10 12:32:13.933423: Epoch time: 103.37 s +2026-04-10 12:32:13.935459: Yayy! New best EMA pseudo Dice: 0.1242 +2026-04-10 12:32:17.151139: +2026-04-10 12:32:17.153134: Epoch 79 +2026-04-10 12:32:17.154950: Current learning rate: 0.00982 +2026-04-10 12:34:00.562065: train_loss -0.0386 +2026-04-10 12:34:00.568439: val_loss -0.0029 +2026-04-10 12:34:00.570133: Pseudo dice [0.0, 0.0, 0.1103, 0.0, 0.0, 0.032, 0.3526] +2026-04-10 12:34:00.572346: Epoch time: 103.41 s +2026-04-10 12:34:01.936401: +2026-04-10 12:34:01.938488: Epoch 80 +2026-04-10 12:34:01.939945: Current learning rate: 0.00982 +2026-04-10 12:35:45.270800: train_loss -0.0379 +2026-04-10 12:35:45.275711: val_loss -0.0249 +2026-04-10 12:35:45.277435: Pseudo dice [0.0, 0.0, 0.3719, 0.0, 0.0, 0.0773, 0.4944] +2026-04-10 12:35:45.279630: Epoch time: 103.34 s +2026-04-10 12:35:46.646879: +2026-04-10 12:35:46.648452: Epoch 81 +2026-04-10 12:35:46.649854: Current learning rate: 0.00982 +2026-04-10 12:37:29.749354: train_loss -0.0469 +2026-04-10 12:37:29.754790: val_loss -0.0563 +2026-04-10 12:37:29.756911: Pseudo dice [0.0, 0.0, 0.3861, 0.0, 0.0, 0.3489, 0.3748] +2026-04-10 12:37:29.760654: Epoch time: 103.11 s +2026-04-10 12:37:29.762713: Yayy! New best EMA pseudo Dice: 0.1243 +2026-04-10 12:37:32.997061: +2026-04-10 12:37:32.999795: Epoch 82 +2026-04-10 12:37:33.006692: Current learning rate: 0.00982 +2026-04-10 12:39:16.000389: train_loss -0.021 +2026-04-10 12:39:16.005033: val_loss -0.0379 +2026-04-10 12:39:16.007161: Pseudo dice [0.0, 0.0, 0.212, 0.0, 0.0, 0.2659, 0.6167] +2026-04-10 12:39:16.008927: Epoch time: 103.01 s +2026-04-10 12:39:16.010616: Yayy! New best EMA pseudo Dice: 0.1275 +2026-04-10 12:39:19.095273: +2026-04-10 12:39:19.096958: Epoch 83 +2026-04-10 12:39:19.098391: Current learning rate: 0.00981 +2026-04-10 12:41:01.878232: train_loss -0.0295 +2026-04-10 12:41:01.885519: val_loss 0.0137 +2026-04-10 12:41:01.888132: Pseudo dice [0.001, 0.0, 0.1464, 0.0, 0.0, 0.0354, 0.4773] +2026-04-10 12:41:01.890659: Epoch time: 102.79 s +2026-04-10 12:41:03.139186: +2026-04-10 12:41:03.140723: Epoch 84 +2026-04-10 12:41:03.142283: Current learning rate: 0.00981 +2026-04-10 12:42:45.845825: train_loss -0.0303 +2026-04-10 12:42:45.871165: val_loss 0.1121 +2026-04-10 12:42:45.873458: Pseudo dice [0.0091, 0.0, 0.0357, 0.0, 0.0, 0.0081, 0.3863] +2026-04-10 12:42:45.876113: Epoch time: 102.71 s +2026-04-10 12:42:47.157080: +2026-04-10 12:42:47.160442: Epoch 85 +2026-04-10 12:42:47.163684: Current learning rate: 0.00981 +2026-04-10 12:44:29.893019: train_loss -0.0363 +2026-04-10 12:44:29.899429: val_loss -0.0068 +2026-04-10 12:44:29.901940: Pseudo dice [0.0655, 0.0, 0.4825, 0.0, 0.0, 0.0606, 0.5198] +2026-04-10 12:44:29.904438: Epoch time: 102.74 s +2026-04-10 12:44:31.178272: +2026-04-10 12:44:31.180450: Epoch 86 +2026-04-10 12:44:31.181964: Current learning rate: 0.00981 +2026-04-10 12:46:13.708184: train_loss -0.0471 +2026-04-10 12:46:13.716920: val_loss -0.0367 +2026-04-10 12:46:13.719355: Pseudo dice [0.2797, 0.0, 0.4203, 0.0, 0.0, 0.2413, 0.5212] +2026-04-10 12:46:13.722379: Epoch time: 102.53 s +2026-04-10 12:46:13.724748: Yayy! New best EMA pseudo Dice: 0.131 +2026-04-10 12:46:17.046229: +2026-04-10 12:46:17.048298: Epoch 87 +2026-04-10 12:46:17.051118: Current learning rate: 0.0098 +2026-04-10 12:48:00.082873: train_loss -0.0417 +2026-04-10 12:48:00.087813: val_loss -0.0197 +2026-04-10 12:48:00.089849: Pseudo dice [0.4451, 0.0, 0.4652, 0.0, 0.0, 0.0675, 0.3119] +2026-04-10 12:48:00.091984: Epoch time: 103.04 s +2026-04-10 12:48:00.094482: Yayy! New best EMA pseudo Dice: 0.1363 +2026-04-10 12:48:04.417711: +2026-04-10 12:48:04.419598: Epoch 88 +2026-04-10 12:48:04.421177: Current learning rate: 0.0098 +2026-04-10 12:49:47.271770: train_loss -0.0435 +2026-04-10 12:49:47.278963: val_loss -0.0157 +2026-04-10 12:49:47.281673: Pseudo dice [0.0611, 0.0, 0.4279, 0.0, 0.0, 0.0495, 0.63] +2026-04-10 12:49:47.285435: Epoch time: 102.86 s +2026-04-10 12:49:47.287665: Yayy! New best EMA pseudo Dice: 0.1394 +2026-04-10 12:49:50.414627: +2026-04-10 12:49:50.417003: Epoch 89 +2026-04-10 12:49:50.418566: Current learning rate: 0.0098 +2026-04-10 12:51:33.520246: train_loss -0.0397 +2026-04-10 12:51:33.531659: val_loss -0.0049 +2026-04-10 12:51:33.533953: Pseudo dice [0.1025, 0.0, 0.0293, 0.0, 0.0, 0.057, 0.5468] +2026-04-10 12:51:33.546947: Epoch time: 103.11 s +2026-04-10 12:51:35.046543: +2026-04-10 12:51:35.048331: Epoch 90 +2026-04-10 12:51:35.049700: Current learning rate: 0.0098 +2026-04-10 12:53:17.934943: train_loss -0.0458 +2026-04-10 12:53:17.940942: val_loss -0.0179 +2026-04-10 12:53:17.942579: Pseudo dice [0.2543, 0.0, 0.3637, 0.0, 0.0, 0.0258, 0.7167] +2026-04-10 12:53:17.945149: Epoch time: 102.89 s +2026-04-10 12:53:17.947679: Yayy! New best EMA pseudo Dice: 0.1418 +2026-04-10 12:53:21.153210: +2026-04-10 12:53:21.154998: Epoch 91 +2026-04-10 12:53:21.156526: Current learning rate: 0.0098 +2026-04-10 12:55:04.575943: train_loss -0.0318 +2026-04-10 12:55:04.583670: val_loss 0.0406 +2026-04-10 12:55:04.585556: Pseudo dice [0.1803, 0.0, 0.4431, 0.0, 0.0, 0.0257, 0.3978] +2026-04-10 12:55:04.588672: Epoch time: 103.43 s +2026-04-10 12:55:04.590793: Yayy! New best EMA pseudo Dice: 0.1426 +2026-04-10 12:55:07.701373: +2026-04-10 12:55:07.705289: Epoch 92 +2026-04-10 12:55:07.706906: Current learning rate: 0.00979 +2026-04-10 12:56:50.861559: train_loss -0.0616 +2026-04-10 12:56:50.866168: val_loss -0.014 +2026-04-10 12:56:50.867787: Pseudo dice [0.231, 0.0, 0.354, 0.0, 0.0, 0.0685, 0.5569] +2026-04-10 12:56:50.869333: Epoch time: 103.16 s +2026-04-10 12:56:50.870913: Yayy! New best EMA pseudo Dice: 0.1456 +2026-04-10 12:56:53.953326: +2026-04-10 12:56:53.958521: Epoch 93 +2026-04-10 12:56:53.962354: Current learning rate: 0.00979 +2026-04-10 12:58:36.997797: train_loss -0.0502 +2026-04-10 12:58:37.003985: val_loss -0.0296 +2026-04-10 12:58:37.006534: Pseudo dice [0.2978, 0.0, 0.2352, 0.0, 0.0, 0.0583, 0.5136] +2026-04-10 12:58:37.008938: Epoch time: 103.05 s +2026-04-10 12:58:37.010601: Yayy! New best EMA pseudo Dice: 0.1468 +2026-04-10 12:58:40.131623: +2026-04-10 12:58:40.133720: Epoch 94 +2026-04-10 12:58:40.135394: Current learning rate: 0.00979 +2026-04-10 13:00:23.423051: train_loss -0.047 +2026-04-10 13:00:23.427665: val_loss -0.0505 +2026-04-10 13:00:23.429781: Pseudo dice [0.3731, 0.0, 0.5451, 0.0, 0.0, 0.1861, 0.501] +2026-04-10 13:00:23.432863: Epoch time: 103.3 s +2026-04-10 13:00:23.434429: Yayy! New best EMA pseudo Dice: 0.1551 +2026-04-10 13:00:26.420701: +2026-04-10 13:00:26.422807: Epoch 95 +2026-04-10 13:00:26.424424: Current learning rate: 0.00979 +2026-04-10 13:02:09.227974: train_loss -0.0551 +2026-04-10 13:02:09.232961: val_loss -0.0464 +2026-04-10 13:02:09.234767: Pseudo dice [0.5424, 0.0, 0.4302, 0.0, 0.0, 0.3153, 0.5827] +2026-04-10 13:02:09.236635: Epoch time: 102.81 s +2026-04-10 13:02:09.238017: Yayy! New best EMA pseudo Dice: 0.1663 +2026-04-10 13:02:12.266371: +2026-04-10 13:02:12.268731: Epoch 96 +2026-04-10 13:02:12.270315: Current learning rate: 0.00978 +2026-04-10 13:03:55.498878: train_loss -0.0508 +2026-04-10 13:03:55.508059: val_loss -0.034 +2026-04-10 13:03:55.510702: Pseudo dice [0.2858, 0.0, 0.2799, 0.0, 0.0, 0.0309, 0.5394] +2026-04-10 13:03:55.517611: Epoch time: 103.24 s +2026-04-10 13:03:56.804940: +2026-04-10 13:03:56.806546: Epoch 97 +2026-04-10 13:03:56.808574: Current learning rate: 0.00978 +2026-04-10 13:05:39.756965: train_loss -0.0511 +2026-04-10 13:05:39.765497: val_loss -0.0619 +2026-04-10 13:05:39.768454: Pseudo dice [0.1443, 0.0, 0.4222, 0.0, 0.0, 0.5378, 0.5725] +2026-04-10 13:05:39.770586: Epoch time: 102.95 s +2026-04-10 13:05:39.772944: Yayy! New best EMA pseudo Dice: 0.1733 +2026-04-10 13:05:42.866872: +2026-04-10 13:05:42.868640: Epoch 98 +2026-04-10 13:05:42.870232: Current learning rate: 0.00978 +2026-04-10 13:07:26.701883: train_loss -0.0561 +2026-04-10 13:07:26.716681: val_loss 0.0091 +2026-04-10 13:07:26.721489: Pseudo dice [0.1777, 0.0, 0.2222, 0.0, 0.0, 0.0328, 0.5215] +2026-04-10 13:07:26.726288: Epoch time: 103.84 s +2026-04-10 13:07:28.044740: +2026-04-10 13:07:28.056978: Epoch 99 +2026-04-10 13:07:28.065845: Current learning rate: 0.00978 +2026-04-10 13:09:10.773433: train_loss -0.0512 +2026-04-10 13:09:10.788020: val_loss -0.0097 +2026-04-10 13:09:10.790453: Pseudo dice [0.0735, 0.0, 0.1903, 0.0, 0.0, 0.0776, 0.4432] +2026-04-10 13:09:10.792877: Epoch time: 102.73 s +2026-04-10 13:09:13.851095: +2026-04-10 13:09:13.852798: Epoch 100 +2026-04-10 13:09:13.854304: Current learning rate: 0.00977 +2026-04-10 13:10:56.473169: train_loss -0.0654 +2026-04-10 13:10:56.478506: val_loss 0.0071 +2026-04-10 13:10:56.481540: Pseudo dice [0.1368, 0.0, 0.4596, 0.0, 0.0, 0.0455, 0.4355] +2026-04-10 13:10:56.483896: Epoch time: 102.63 s +2026-04-10 13:10:57.775344: +2026-04-10 13:10:57.776934: Epoch 101 +2026-04-10 13:10:57.778502: Current learning rate: 0.00977 +2026-04-10 13:12:40.214190: train_loss -0.0479 +2026-04-10 13:12:40.219900: val_loss 0.0384 +2026-04-10 13:12:40.221949: Pseudo dice [0.1032, 0.0, 0.1386, 0.0, 0.0, 0.0262, 0.4035] +2026-04-10 13:12:40.224073: Epoch time: 102.44 s +2026-04-10 13:12:41.490214: +2026-04-10 13:12:41.491982: Epoch 102 +2026-04-10 13:12:41.493514: Current learning rate: 0.00977 +2026-04-10 13:14:24.378890: train_loss -0.0585 +2026-04-10 13:14:24.389803: val_loss -0.0534 +2026-04-10 13:14:24.402729: Pseudo dice [0.3666, 0.0, 0.3216, 0.0001, 0.0, 0.1746, 0.7348] +2026-04-10 13:14:24.419148: Epoch time: 102.89 s +2026-04-10 13:14:25.703494: +2026-04-10 13:14:25.705194: Epoch 103 +2026-04-10 13:14:25.707016: Current learning rate: 0.00977 +2026-04-10 13:16:08.479559: train_loss -0.0598 +2026-04-10 13:16:08.484680: val_loss 0.04 +2026-04-10 13:16:08.486858: Pseudo dice [0.2086, 0.0, 0.2839, 0.0, 0.0, 0.0288, 0.4394] +2026-04-10 13:16:08.489098: Epoch time: 102.78 s +2026-04-10 13:16:09.763653: +2026-04-10 13:16:09.765296: Epoch 104 +2026-04-10 13:16:09.766988: Current learning rate: 0.00977 +2026-04-10 13:17:52.190480: train_loss -0.0645 +2026-04-10 13:17:52.199358: val_loss -0.0644 +2026-04-10 13:17:52.201639: Pseudo dice [0.6343, 0.0, 0.1263, 0.0, 0.0, 0.5404, 0.748] +2026-04-10 13:17:52.204142: Epoch time: 102.43 s +2026-04-10 13:17:52.218534: Yayy! New best EMA pseudo Dice: 0.1739 +2026-04-10 13:17:55.377018: +2026-04-10 13:17:55.379183: Epoch 105 +2026-04-10 13:17:55.380702: Current learning rate: 0.00976 +2026-04-10 13:19:38.307478: train_loss -0.0535 +2026-04-10 13:19:38.311882: val_loss -0.0102 +2026-04-10 13:19:38.313661: Pseudo dice [0.0914, 0.0, 0.3198, 0.0, 0.0, 0.0553, 0.6332] +2026-04-10 13:19:38.315384: Epoch time: 102.93 s +2026-04-10 13:19:40.718282: +2026-04-10 13:19:40.720059: Epoch 106 +2026-04-10 13:19:40.721594: Current learning rate: 0.00976 +2026-04-10 13:21:23.348803: train_loss -0.063 +2026-04-10 13:21:23.354954: val_loss -0.0279 +2026-04-10 13:21:23.356579: Pseudo dice [0.1699, 0.0, 0.3429, 0.0, 0.0, 0.0628, 0.547] +2026-04-10 13:21:23.359107: Epoch time: 102.63 s +2026-04-10 13:21:24.674783: +2026-04-10 13:21:24.676378: Epoch 107 +2026-04-10 13:21:24.677854: Current learning rate: 0.00976 +2026-04-10 13:23:07.499835: train_loss -0.0576 +2026-04-10 13:23:07.504635: val_loss -0.0074 +2026-04-10 13:23:07.506398: Pseudo dice [0.3371, 0.0, 0.1762, 0.0, 0.0, 0.0617, 0.7225] +2026-04-10 13:23:07.508373: Epoch time: 102.83 s +2026-04-10 13:23:08.801685: +2026-04-10 13:23:08.803460: Epoch 108 +2026-04-10 13:23:08.804945: Current learning rate: 0.00976 +2026-04-10 13:24:51.733759: train_loss -0.0614 +2026-04-10 13:24:51.741660: val_loss -0.0475 +2026-04-10 13:24:51.744285: Pseudo dice [0.193, 0.0, 0.5268, 0.0, 0.0, 0.0822, 0.6078] +2026-04-10 13:24:51.749319: Epoch time: 102.94 s +2026-04-10 13:24:51.751339: Yayy! New best EMA pseudo Dice: 0.1754 +2026-04-10 13:24:54.915415: +2026-04-10 13:24:54.917475: Epoch 109 +2026-04-10 13:24:54.918932: Current learning rate: 0.00975 +2026-04-10 13:26:38.540348: train_loss -0.0564 +2026-04-10 13:26:38.546054: val_loss 0.0025 +2026-04-10 13:26:38.547952: Pseudo dice [0.0931, 0.0, 0.2845, 0.0, 0.0, 0.0309, 0.5656] +2026-04-10 13:26:38.549549: Epoch time: 103.63 s +2026-04-10 13:26:39.842762: +2026-04-10 13:26:39.844618: Epoch 110 +2026-04-10 13:26:39.846125: Current learning rate: 0.00975 +2026-04-10 13:28:22.749509: train_loss -0.0674 +2026-04-10 13:28:22.756824: val_loss 0.0013 +2026-04-10 13:28:22.759054: Pseudo dice [0.1809, 0.0, 0.1665, 0.0, 0.0, 0.0612, 0.567] +2026-04-10 13:28:22.766188: Epoch time: 102.91 s +2026-04-10 13:28:24.062427: +2026-04-10 13:28:24.064098: Epoch 111 +2026-04-10 13:28:24.065534: Current learning rate: 0.00975 +2026-04-10 13:30:07.505540: train_loss -0.0664 +2026-04-10 13:30:07.511657: val_loss -0.0493 +2026-04-10 13:30:07.513588: Pseudo dice [0.6271, 0.0, 0.5833, 0.0, 0.0, 0.0747, 0.59] +2026-04-10 13:30:07.516869: Epoch time: 103.45 s +2026-04-10 13:30:07.519530: Yayy! New best EMA pseudo Dice: 0.1785 +2026-04-10 13:30:10.720475: +2026-04-10 13:30:10.722650: Epoch 112 +2026-04-10 13:30:10.724784: Current learning rate: 0.00975 +2026-04-10 13:31:53.449719: train_loss -0.0686 +2026-04-10 13:31:53.454984: val_loss -0.0902 +2026-04-10 13:31:53.456703: Pseudo dice [0.611, 0.0, 0.7028, 0.0, 0.0, 0.5861, 0.8592] +2026-04-10 13:31:53.458169: Epoch time: 102.73 s +2026-04-10 13:31:53.459539: Yayy! New best EMA pseudo Dice: 0.2 +2026-04-10 13:31:56.663076: +2026-04-10 13:31:56.665114: Epoch 113 +2026-04-10 13:31:56.666708: Current learning rate: 0.00975 +2026-04-10 13:33:39.575346: train_loss -0.0597 +2026-04-10 13:33:39.579995: val_loss -0.0554 +2026-04-10 13:33:39.582300: Pseudo dice [0.4543, 0.0, 0.6425, 0.0, 0.0, 0.1033, 0.4722] +2026-04-10 13:33:39.584856: Epoch time: 102.92 s +2026-04-10 13:33:39.586632: Yayy! New best EMA pseudo Dice: 0.2039 +2026-04-10 13:33:42.747550: +2026-04-10 13:33:42.749521: Epoch 114 +2026-04-10 13:33:42.751156: Current learning rate: 0.00974 +2026-04-10 13:35:26.486773: train_loss -0.0646 +2026-04-10 13:35:26.495147: val_loss -0.0771 +2026-04-10 13:35:26.497345: Pseudo dice [0.1422, 0.0, 0.5125, 0.0, 0.0, 0.2046, 0.5194] +2026-04-10 13:35:26.499862: Epoch time: 103.74 s +2026-04-10 13:35:27.782522: +2026-04-10 13:35:27.784410: Epoch 115 +2026-04-10 13:35:27.785828: Current learning rate: 0.00974 +2026-04-10 13:37:11.528343: train_loss -0.0729 +2026-04-10 13:37:11.534608: val_loss -0.055 +2026-04-10 13:37:11.536671: Pseudo dice [0.4744, 0.0, 0.4667, 0.0, 0.0, 0.2033, 0.8197] +2026-04-10 13:37:11.540320: Epoch time: 103.75 s +2026-04-10 13:37:11.542017: Yayy! New best EMA pseudo Dice: 0.211 +2026-04-10 13:37:14.745564: +2026-04-10 13:37:14.747496: Epoch 116 +2026-04-10 13:37:14.749609: Current learning rate: 0.00974 +2026-04-10 13:38:57.717913: train_loss -0.0731 +2026-04-10 13:38:57.724260: val_loss -0.0422 +2026-04-10 13:38:57.725994: Pseudo dice [0.2196, 0.0, 0.5095, 0.0, 0.0, 0.0296, 0.7224] +2026-04-10 13:38:57.728289: Epoch time: 102.98 s +2026-04-10 13:38:57.730116: Yayy! New best EMA pseudo Dice: 0.211 +2026-04-10 13:39:00.888938: +2026-04-10 13:39:00.897812: Epoch 117 +2026-04-10 13:39:00.901340: Current learning rate: 0.00974 +2026-04-10 13:40:43.719057: train_loss -0.0692 +2026-04-10 13:40:43.724698: val_loss -0.04 +2026-04-10 13:40:43.726868: Pseudo dice [0.0975, 0.0, 0.6386, 0.0, 0.0, 0.0964, 0.5597] +2026-04-10 13:40:43.728913: Epoch time: 102.83 s +2026-04-10 13:40:45.049211: +2026-04-10 13:40:45.050907: Epoch 118 +2026-04-10 13:40:45.052288: Current learning rate: 0.00973 +2026-04-10 13:42:28.044092: train_loss -0.0518 +2026-04-10 13:42:28.048942: val_loss -0.0563 +2026-04-10 13:42:28.050762: Pseudo dice [0.1929, 0.0, 0.4163, 0.0026, 0.0, 0.2276, 0.6587] +2026-04-10 13:42:28.052895: Epoch time: 103.0 s +2026-04-10 13:42:29.379385: +2026-04-10 13:42:29.382392: Epoch 119 +2026-04-10 13:42:29.383870: Current learning rate: 0.00973 +2026-04-10 13:44:12.839323: train_loss -0.072 +2026-04-10 13:44:12.844080: val_loss -0.0155 +2026-04-10 13:44:12.845914: Pseudo dice [0.2098, 0.0, 0.2277, 0.0017, 0.0, 0.0972, 0.2844] +2026-04-10 13:44:12.847939: Epoch time: 103.46 s +2026-04-10 13:44:14.191814: +2026-04-10 13:44:14.193973: Epoch 120 +2026-04-10 13:44:14.195849: Current learning rate: 0.00973 +2026-04-10 13:45:57.256520: train_loss -0.0641 +2026-04-10 13:45:57.261732: val_loss -0.0711 +2026-04-10 13:45:57.264482: Pseudo dice [0.3734, 0.0, 0.563, 0.0, 0.0, 0.5502, 0.4644] +2026-04-10 13:45:57.267512: Epoch time: 103.07 s +2026-04-10 13:45:58.586612: +2026-04-10 13:45:58.593115: Epoch 121 +2026-04-10 13:45:58.596683: Current learning rate: 0.00973 +2026-04-10 13:47:42.481832: train_loss -0.0757 +2026-04-10 13:47:42.487336: val_loss -0.0508 +2026-04-10 13:47:42.489400: Pseudo dice [0.6263, 0.0, 0.425, 0.0, 0.0, 0.28, 0.4733] +2026-04-10 13:47:42.491294: Epoch time: 103.9 s +2026-04-10 13:47:42.493977: Yayy! New best EMA pseudo Dice: 0.2136 +2026-04-10 13:47:45.673435: +2026-04-10 13:47:45.675250: Epoch 122 +2026-04-10 13:47:45.676647: Current learning rate: 0.00973 +2026-04-10 13:49:28.351556: train_loss -0.0745 +2026-04-10 13:49:28.358993: val_loss -0.0676 +2026-04-10 13:49:28.361985: Pseudo dice [0.5433, 0.0, 0.4044, 0.0, 0.0, 0.6952, 0.6618] +2026-04-10 13:49:28.364558: Epoch time: 102.68 s +2026-04-10 13:49:28.367599: Yayy! New best EMA pseudo Dice: 0.2252 +2026-04-10 13:49:31.560276: +2026-04-10 13:49:31.561921: Epoch 123 +2026-04-10 13:49:31.563430: Current learning rate: 0.00972 +2026-04-10 13:51:14.516651: train_loss -0.066 +2026-04-10 13:51:14.528332: val_loss -0.048 +2026-04-10 13:51:14.531487: Pseudo dice [0.1901, 0.0, 0.4947, 0.0005, 0.0, 0.2088, 0.6944] +2026-04-10 13:51:14.534455: Epoch time: 102.96 s +2026-04-10 13:51:14.537665: Yayy! New best EMA pseudo Dice: 0.2254 +2026-04-10 13:51:18.715618: +2026-04-10 13:51:18.717443: Epoch 124 +2026-04-10 13:51:18.719288: Current learning rate: 0.00972 +2026-04-10 13:53:01.821671: train_loss -0.0723 +2026-04-10 13:53:01.828197: val_loss -0.0508 +2026-04-10 13:53:01.830158: Pseudo dice [0.4405, 0.0, 0.4329, 0.0, 0.0, 0.0685, 0.6427] +2026-04-10 13:53:01.832904: Epoch time: 103.11 s +2026-04-10 13:53:01.835245: Yayy! New best EMA pseudo Dice: 0.2255 +2026-04-10 13:53:05.043884: +2026-04-10 13:53:05.045929: Epoch 125 +2026-04-10 13:53:05.047561: Current learning rate: 0.00972 +2026-04-10 13:54:48.115605: train_loss -0.0706 +2026-04-10 13:54:48.120597: val_loss -0.0863 +2026-04-10 13:54:48.122437: Pseudo dice [0.2645, 0.0, 0.6438, 0.0, 0.0, 0.5288, 0.5807] +2026-04-10 13:54:48.124429: Epoch time: 103.08 s +2026-04-10 13:54:48.126250: Yayy! New best EMA pseudo Dice: 0.2317 +2026-04-10 13:54:51.252904: +2026-04-10 13:54:51.254875: Epoch 126 +2026-04-10 13:54:51.256470: Current learning rate: 0.00972 +2026-04-10 13:56:35.427696: train_loss -0.0702 +2026-04-10 13:56:35.432958: val_loss -0.0747 +2026-04-10 13:56:35.435051: Pseudo dice [0.1718, 0.0, 0.5675, 0.0, 0.0, 0.4647, 0.5702] +2026-04-10 13:56:35.437423: Epoch time: 104.18 s +2026-04-10 13:56:35.439178: Yayy! New best EMA pseudo Dice: 0.2339 +2026-04-10 13:56:38.582572: +2026-04-10 13:56:38.584411: Epoch 127 +2026-04-10 13:56:38.585834: Current learning rate: 0.00971 +2026-04-10 13:58:21.478689: train_loss -0.0664 +2026-04-10 13:58:21.484289: val_loss -0.0748 +2026-04-10 13:58:21.486010: Pseudo dice [0.2882, 0.0, 0.5988, 0.0, 0.0, 0.3615, 0.5652] +2026-04-10 13:58:21.488203: Epoch time: 102.9 s +2026-04-10 13:58:21.489694: Yayy! New best EMA pseudo Dice: 0.2364 +2026-04-10 13:58:24.678106: +2026-04-10 13:58:24.679704: Epoch 128 +2026-04-10 13:58:24.681493: Current learning rate: 0.00971 +2026-04-10 14:00:07.498191: train_loss -0.0572 +2026-04-10 14:00:07.508673: val_loss -0.0341 +2026-04-10 14:00:07.510731: Pseudo dice [0.325, 0.0001, 0.5257, 0.0, 0.0, 0.0749, 0.6162] +2026-04-10 14:00:07.513577: Epoch time: 102.82 s +2026-04-10 14:00:08.891250: +2026-04-10 14:00:08.893423: Epoch 129 +2026-04-10 14:00:08.894854: Current learning rate: 0.00971 +2026-04-10 14:01:51.335644: train_loss -0.0713 +2026-04-10 14:01:51.339971: val_loss -0.0583 +2026-04-10 14:01:51.343928: Pseudo dice [0.1272, 0.0483, 0.6192, 0.0, 0.0, 0.3297, 0.666] +2026-04-10 14:01:51.346264: Epoch time: 102.45 s +2026-04-10 14:01:51.347695: Yayy! New best EMA pseudo Dice: 0.2369 +2026-04-10 14:01:54.533444: +2026-04-10 14:01:54.535152: Epoch 130 +2026-04-10 14:01:54.536753: Current learning rate: 0.00971 +2026-04-10 14:03:38.184910: train_loss -0.0675 +2026-04-10 14:03:38.190638: val_loss -0.0734 +2026-04-10 14:03:38.194570: Pseudo dice [0.6926, 0.114, 0.4718, 0.0, 0.0, 0.3208, 0.6354] +2026-04-10 14:03:38.197716: Epoch time: 103.66 s +2026-04-10 14:03:38.199968: Yayy! New best EMA pseudo Dice: 0.2451 +2026-04-10 14:03:41.325001: +2026-04-10 14:03:41.331032: Epoch 131 +2026-04-10 14:03:41.333008: Current learning rate: 0.0097 +2026-04-10 14:05:24.142360: train_loss -0.0768 +2026-04-10 14:05:24.147335: val_loss -0.0621 +2026-04-10 14:05:24.149130: Pseudo dice [0.1931, 0.192, 0.4955, 0.0097, 0.0, 0.485, 0.3883] +2026-04-10 14:05:24.151067: Epoch time: 102.82 s +2026-04-10 14:05:24.152742: Yayy! New best EMA pseudo Dice: 0.2458 +2026-04-10 14:05:27.307771: +2026-04-10 14:05:27.309792: Epoch 132 +2026-04-10 14:05:27.311292: Current learning rate: 0.0097 +2026-04-10 14:07:10.530154: train_loss -0.0812 +2026-04-10 14:07:10.535846: val_loss -0.0748 +2026-04-10 14:07:10.538608: Pseudo dice [0.2685, 0.5064, 0.4428, 0.0, 0.0, 0.052, 0.6797] +2026-04-10 14:07:10.541430: Epoch time: 103.23 s +2026-04-10 14:07:10.543176: Yayy! New best EMA pseudo Dice: 0.2491 +2026-04-10 14:07:13.750204: +2026-04-10 14:07:13.752162: Epoch 133 +2026-04-10 14:07:13.753816: Current learning rate: 0.0097 +2026-04-10 14:08:56.525800: train_loss -0.0885 +2026-04-10 14:08:56.531399: val_loss -0.0565 +2026-04-10 14:08:56.533226: Pseudo dice [0.28, 0.3479, 0.3064, 0.0, 0.0, 0.3085, 0.5016] +2026-04-10 14:08:56.535241: Epoch time: 102.78 s +2026-04-10 14:08:56.536984: Yayy! New best EMA pseudo Dice: 0.2491 +2026-04-10 14:08:59.658271: +2026-04-10 14:08:59.660156: Epoch 134 +2026-04-10 14:08:59.662131: Current learning rate: 0.0097 +2026-04-10 14:10:42.592216: train_loss -0.0652 +2026-04-10 14:10:42.597394: val_loss 0.0668 +2026-04-10 14:10:42.599270: Pseudo dice [0.2212, 0.5181, 0.1983, 0.0041, 0.0002, 0.007, 0.6423] +2026-04-10 14:10:42.601711: Epoch time: 102.94 s +2026-04-10 14:10:43.973385: +2026-04-10 14:10:43.975383: Epoch 135 +2026-04-10 14:10:43.976924: Current learning rate: 0.0097 +2026-04-10 14:12:26.873598: train_loss -0.1018 +2026-04-10 14:12:26.879995: val_loss -0.0906 +2026-04-10 14:12:26.881775: Pseudo dice [0.5459, 0.465, 0.6305, 0.0, 0.0059, 0.5863, 0.4712] +2026-04-10 14:12:26.884070: Epoch time: 102.9 s +2026-04-10 14:12:26.886330: Yayy! New best EMA pseudo Dice: 0.2609 +2026-04-10 14:12:30.114054: +2026-04-10 14:12:30.115895: Epoch 136 +2026-04-10 14:12:30.117416: Current learning rate: 0.00969 +2026-04-10 14:14:13.157022: train_loss -0.0761 +2026-04-10 14:14:13.164372: val_loss -0.0609 +2026-04-10 14:14:13.167333: Pseudo dice [0.7285, 0.2122, 0.4978, 0.0829, 0.0067, 0.0996, 0.7238] +2026-04-10 14:14:13.170477: Epoch time: 103.05 s +2026-04-10 14:14:13.173256: Yayy! New best EMA pseudo Dice: 0.2684 +2026-04-10 14:14:16.281280: +2026-04-10 14:14:16.283004: Epoch 137 +2026-04-10 14:14:16.284486: Current learning rate: 0.00969 +2026-04-10 14:16:01.140761: train_loss -0.0898 +2026-04-10 14:16:01.145271: val_loss -0.0515 +2026-04-10 14:16:01.147265: Pseudo dice [0.162, 0.5234, 0.4499, 0.0974, 0.0953, 0.0695, 0.6652] +2026-04-10 14:16:01.149217: Epoch time: 104.86 s +2026-04-10 14:16:01.151109: Yayy! New best EMA pseudo Dice: 0.271 +2026-04-10 14:16:04.355333: +2026-04-10 14:16:04.357385: Epoch 138 +2026-04-10 14:16:04.358765: Current learning rate: 0.00969 +2026-04-10 14:17:47.413183: train_loss -0.0799 +2026-04-10 14:17:47.421455: val_loss -0.092 +2026-04-10 14:17:47.423531: Pseudo dice [0.2817, 0.5305, 0.5124, 0.2248, 0.159, 0.2678, 0.6616] +2026-04-10 14:17:47.426178: Epoch time: 103.06 s +2026-04-10 14:17:47.427937: Yayy! New best EMA pseudo Dice: 0.2816 +2026-04-10 14:17:50.592470: +2026-04-10 14:17:50.594340: Epoch 139 +2026-04-10 14:17:50.596805: Current learning rate: 0.00969 +2026-04-10 14:19:33.324294: train_loss -0.0869 +2026-04-10 14:19:33.331840: val_loss -0.0335 +2026-04-10 14:19:33.334280: Pseudo dice [0.3587, 0.258, 0.6866, 0.5311, 0.0145, 0.0804, 0.5609] +2026-04-10 14:19:33.338403: Epoch time: 102.74 s +2026-04-10 14:19:33.341329: Yayy! New best EMA pseudo Dice: 0.289 +2026-04-10 14:19:37.548480: +2026-04-10 14:19:37.550697: Epoch 140 +2026-04-10 14:19:37.552584: Current learning rate: 0.00968 +2026-04-10 14:21:20.778999: train_loss -0.0817 +2026-04-10 14:21:20.784875: val_loss -0.1002 +2026-04-10 14:21:20.786681: Pseudo dice [0.2823, 0.4517, 0.6257, 0.0774, 0.0836, 0.7029, 0.363] +2026-04-10 14:21:20.790772: Epoch time: 103.23 s +2026-04-10 14:21:20.792732: Yayy! New best EMA pseudo Dice: 0.2971 +2026-04-10 14:21:23.999809: +2026-04-10 14:21:24.001777: Epoch 141 +2026-04-10 14:21:24.003288: Current learning rate: 0.00968 +2026-04-10 14:23:07.154709: train_loss -0.0912 +2026-04-10 14:23:07.159612: val_loss -0.1127 +2026-04-10 14:23:07.161366: Pseudo dice [0.2942, 0.4547, 0.5202, 0.1035, 0.1573, 0.6264, 0.7893] +2026-04-10 14:23:07.165164: Epoch time: 103.16 s +2026-04-10 14:23:07.173933: Yayy! New best EMA pseudo Dice: 0.3094 +2026-04-10 14:23:10.335697: +2026-04-10 14:23:10.337403: Epoch 142 +2026-04-10 14:23:10.339006: Current learning rate: 0.00968 +2026-04-10 14:24:53.160822: train_loss -0.0879 +2026-04-10 14:24:53.169335: val_loss -0.0713 +2026-04-10 14:24:53.171896: Pseudo dice [0.3042, 0.1925, 0.2757, 0.0277, 0.2482, 0.1394, 0.6217] +2026-04-10 14:24:53.175076: Epoch time: 102.83 s +2026-04-10 14:24:54.512346: +2026-04-10 14:24:54.513832: Epoch 143 +2026-04-10 14:24:54.515353: Current learning rate: 0.00968 +2026-04-10 14:26:37.229753: train_loss -0.0913 +2026-04-10 14:26:37.234627: val_loss -0.1082 +2026-04-10 14:26:37.236281: Pseudo dice [0.5863, 0.6379, 0.6495, 0.1647, 0.1722, 0.6409, 0.8245] +2026-04-10 14:26:37.240002: Epoch time: 102.72 s +2026-04-10 14:26:37.242265: Yayy! New best EMA pseudo Dice: 0.3264 +2026-04-10 14:26:40.514164: +2026-04-10 14:26:40.517317: Epoch 144 +2026-04-10 14:26:40.519497: Current learning rate: 0.00968 +2026-04-10 14:28:23.186355: train_loss -0.0971 +2026-04-10 14:28:23.191446: val_loss -0.1242 +2026-04-10 14:28:23.193666: Pseudo dice [0.3016, 0.7334, 0.6016, 0.096, 0.3263, 0.6451, 0.6825] +2026-04-10 14:28:23.197180: Epoch time: 102.68 s +2026-04-10 14:28:23.199429: Yayy! New best EMA pseudo Dice: 0.3422 +2026-04-10 14:28:26.453907: +2026-04-10 14:28:26.455699: Epoch 145 +2026-04-10 14:28:26.457754: Current learning rate: 0.00967 +2026-04-10 14:30:09.409776: train_loss -0.0969 +2026-04-10 14:30:09.415469: val_loss -0.0069 +2026-04-10 14:30:09.417316: Pseudo dice [0.4165, 0.3088, 0.4424, 0.5126, 0.1165, 0.1063, 0.7083] +2026-04-10 14:30:09.419286: Epoch time: 102.96 s +2026-04-10 14:30:09.420977: Yayy! New best EMA pseudo Dice: 0.3452 +2026-04-10 14:30:12.537239: +2026-04-10 14:30:12.539454: Epoch 146 +2026-04-10 14:30:12.541373: Current learning rate: 0.00967 +2026-04-10 14:31:55.703903: train_loss -0.0879 +2026-04-10 14:31:55.710027: val_loss -0.0301 +2026-04-10 14:31:55.711890: Pseudo dice [0.4387, 0.7107, 0.572, 0.2061, 0.3084, 0.0278, 0.6898] +2026-04-10 14:31:55.714631: Epoch time: 103.17 s +2026-04-10 14:31:55.716616: Yayy! New best EMA pseudo Dice: 0.3529 +2026-04-10 14:31:58.908014: +2026-04-10 14:31:58.910497: Epoch 147 +2026-04-10 14:31:58.912972: Current learning rate: 0.00967 +2026-04-10 14:33:42.047909: train_loss -0.0974 +2026-04-10 14:33:42.054255: val_loss -0.0878 +2026-04-10 14:33:42.056043: Pseudo dice [0.5538, 0.595, 0.4881, 0.4131, 0.1761, 0.3467, 0.763] +2026-04-10 14:33:42.058888: Epoch time: 103.14 s +2026-04-10 14:33:42.060946: Yayy! New best EMA pseudo Dice: 0.3653 +2026-04-10 14:33:45.283477: +2026-04-10 14:33:45.285357: Epoch 148 +2026-04-10 14:33:45.287196: Current learning rate: 0.00967 +2026-04-10 14:35:28.296185: train_loss -0.0965 +2026-04-10 14:35:28.300971: val_loss -0.0598 +2026-04-10 14:35:28.302741: Pseudo dice [0.1954, 0.6571, 0.589, 0.3607, 0.2025, 0.0699, 0.5966] +2026-04-10 14:35:28.304529: Epoch time: 103.02 s +2026-04-10 14:35:28.306521: Yayy! New best EMA pseudo Dice: 0.3669 +2026-04-10 14:35:31.505979: +2026-04-10 14:35:31.509104: Epoch 149 +2026-04-10 14:35:31.512141: Current learning rate: 0.00966 +2026-04-10 14:37:15.838979: train_loss -0.0857 +2026-04-10 14:37:15.861932: val_loss -0.0433 +2026-04-10 14:37:15.880246: Pseudo dice [0.1528, 0.2693, 0.4889, 0.1786, 0.2237, 0.199, 0.3181] +2026-04-10 14:37:15.885215: Epoch time: 104.34 s +2026-04-10 14:37:19.054799: +2026-04-10 14:37:19.056644: Epoch 150 +2026-04-10 14:37:19.058656: Current learning rate: 0.00966 +2026-04-10 14:39:02.140150: train_loss -0.0784 +2026-04-10 14:39:02.144485: val_loss -0.0738 +2026-04-10 14:39:02.146291: Pseudo dice [0.3286, 0.5568, 0.5347, 0.0236, 0.0463, 0.3956, 0.3997] +2026-04-10 14:39:02.148319: Epoch time: 103.09 s +2026-04-10 14:39:03.498263: +2026-04-10 14:39:03.499999: Epoch 151 +2026-04-10 14:39:03.501612: Current learning rate: 0.00966 +2026-04-10 14:40:46.515825: train_loss -0.0816 +2026-04-10 14:40:46.520847: val_loss -0.0716 +2026-04-10 14:40:46.522451: Pseudo dice [0.6451, 0.6665, 0.5181, 0.0632, 0.5478, 0.0555, 0.7181] +2026-04-10 14:40:46.525153: Epoch time: 103.02 s +2026-04-10 14:40:47.866583: +2026-04-10 14:40:47.868427: Epoch 152 +2026-04-10 14:40:47.869773: Current learning rate: 0.00966 +2026-04-10 14:42:30.763296: train_loss -0.1031 +2026-04-10 14:42:30.781093: val_loss -0.0826 +2026-04-10 14:42:30.786671: Pseudo dice [0.0927, 0.7161, 0.6459, 0.3353, 0.1538, 0.1567, 0.5402] +2026-04-10 14:42:30.792387: Epoch time: 102.9 s +2026-04-10 14:42:32.149551: +2026-04-10 14:42:32.154578: Epoch 153 +2026-04-10 14:42:32.159803: Current learning rate: 0.00966 +2026-04-10 14:44:14.912148: train_loss -0.1064 +2026-04-10 14:44:14.917329: val_loss -0.083 +2026-04-10 14:44:14.919053: Pseudo dice [0.4125, 0.5223, 0.5463, 0.3032, 0.2086, 0.1564, 0.4891] +2026-04-10 14:44:14.921303: Epoch time: 102.77 s +2026-04-10 14:44:16.268102: +2026-04-10 14:44:16.270016: Epoch 154 +2026-04-10 14:44:16.271679: Current learning rate: 0.00965 +2026-04-10 14:45:59.995598: train_loss -0.1116 +2026-04-10 14:46:00.000846: val_loss -0.0743 +2026-04-10 14:46:00.002776: Pseudo dice [0.5488, 0.5341, 0.4784, 0.469, 0.3706, 0.059, 0.6107] +2026-04-10 14:46:00.004768: Epoch time: 103.73 s +2026-04-10 14:46:00.006738: Yayy! New best EMA pseudo Dice: 0.3737 +2026-04-10 14:46:03.216775: +2026-04-10 14:46:03.219064: Epoch 155 +2026-04-10 14:46:03.221197: Current learning rate: 0.00965 +2026-04-10 14:47:47.746625: train_loss -0.0978 +2026-04-10 14:47:47.754956: val_loss -0.0476 +2026-04-10 14:47:47.759460: Pseudo dice [0.5218, 0.1661, 0.5308, 0.0, 0.1224, 0.1759, 0.6214] +2026-04-10 14:47:47.762583: Epoch time: 104.53 s +2026-04-10 14:47:49.139000: +2026-04-10 14:47:49.148338: Epoch 156 +2026-04-10 14:47:49.150151: Current learning rate: 0.00965 +2026-04-10 14:49:33.181658: train_loss -0.1236 +2026-04-10 14:49:33.186604: val_loss -0.0896 +2026-04-10 14:49:33.188607: Pseudo dice [0.6429, 0.4309, 0.5724, 0.2457, 0.1041, 0.1933, 0.6693] +2026-04-10 14:49:33.190674: Epoch time: 104.05 s +2026-04-10 14:49:34.550422: +2026-04-10 14:49:34.552444: Epoch 157 +2026-04-10 14:49:34.555295: Current learning rate: 0.00965 +2026-04-10 14:51:17.206941: train_loss -0.1218 +2026-04-10 14:51:17.212889: val_loss -0.074 +2026-04-10 14:51:17.214993: Pseudo dice [0.0764, 0.4988, 0.4549, 0.1616, 0.3289, 0.3905, 0.6321] +2026-04-10 14:51:17.217801: Epoch time: 102.66 s +2026-04-10 14:51:18.582342: +2026-04-10 14:51:18.583907: Epoch 158 +2026-04-10 14:51:18.585361: Current learning rate: 0.00964 +2026-04-10 14:53:01.130083: train_loss -0.1023 +2026-04-10 14:53:01.136133: val_loss -0.0992 +2026-04-10 14:53:01.143309: Pseudo dice [0.3621, 0.6091, 0.4318, 0.4705, 0.4326, 0.7343, 0.6091] +2026-04-10 14:53:01.146282: Epoch time: 102.55 s +2026-04-10 14:53:01.148354: Yayy! New best EMA pseudo Dice: 0.3853 +2026-04-10 14:53:04.501664: +2026-04-10 14:53:04.504569: Epoch 159 +2026-04-10 14:53:04.506418: Current learning rate: 0.00964 +2026-04-10 14:54:47.350041: train_loss -0.1189 +2026-04-10 14:54:47.355364: val_loss -0.1019 +2026-04-10 14:54:47.357225: Pseudo dice [0.5145, 0.6965, 0.6292, 0.0083, 0.2247, 0.3746, 0.7153] +2026-04-10 14:54:47.359374: Epoch time: 102.85 s +2026-04-10 14:54:47.362644: Yayy! New best EMA pseudo Dice: 0.392 +2026-04-10 14:54:50.600263: +2026-04-10 14:54:50.602668: Epoch 160 +2026-04-10 14:54:50.604444: Current learning rate: 0.00964 +2026-04-10 14:56:33.693123: train_loss -0.1156 +2026-04-10 14:56:33.698241: val_loss -0.0796 +2026-04-10 14:56:33.699896: Pseudo dice [0.6023, 0.5899, 0.5598, 0.0485, 0.4862, 0.074, 0.5697] +2026-04-10 14:56:33.703456: Epoch time: 103.1 s +2026-04-10 14:56:33.705472: Yayy! New best EMA pseudo Dice: 0.3947 +2026-04-10 14:56:36.888610: +2026-04-10 14:56:36.890389: Epoch 161 +2026-04-10 14:56:36.892204: Current learning rate: 0.00964 +2026-04-10 14:58:20.072029: train_loss -0.1084 +2026-04-10 14:58:20.077969: val_loss -0.0537 +2026-04-10 14:58:20.079894: Pseudo dice [0.4202, 0.419, 0.5757, 0.2768, 0.2804, 0.0675, 0.2515] +2026-04-10 14:58:20.082004: Epoch time: 103.19 s +2026-04-10 14:58:21.479243: +2026-04-10 14:58:21.481628: Epoch 162 +2026-04-10 14:58:21.484038: Current learning rate: 0.00963 +2026-04-10 15:00:04.515031: train_loss -0.1162 +2026-04-10 15:00:04.519294: val_loss -0.0552 +2026-04-10 15:00:04.520844: Pseudo dice [0.6429, 0.401, 0.509, 0.0383, 0.3084, 0.0262, 0.5527] +2026-04-10 15:00:04.522645: Epoch time: 103.04 s +2026-04-10 15:00:05.898376: +2026-04-10 15:00:05.899998: Epoch 163 +2026-04-10 15:00:05.901674: Current learning rate: 0.00963 +2026-04-10 15:01:49.028482: train_loss -0.1259 +2026-04-10 15:01:49.034225: val_loss -0.0609 +2026-04-10 15:01:49.036300: Pseudo dice [0.3843, 0.7353, 0.6124, 0.2875, 0.2528, 0.0605, 0.3796] +2026-04-10 15:01:49.038935: Epoch time: 103.13 s +2026-04-10 15:01:50.403057: +2026-04-10 15:01:50.405075: Epoch 164 +2026-04-10 15:01:50.406501: Current learning rate: 0.00963 +2026-04-10 15:03:33.388388: train_loss -0.1024 +2026-04-10 15:03:33.395311: val_loss -0.1131 +2026-04-10 15:03:33.397116: Pseudo dice [0.7024, 0.2725, 0.6315, 0.2541, 0.495, 0.4544, 0.7186] +2026-04-10 15:03:33.400794: Epoch time: 102.99 s +2026-04-10 15:03:33.403227: Yayy! New best EMA pseudo Dice: 0.3968 +2026-04-10 15:03:36.622426: +2026-04-10 15:03:36.624435: Epoch 165 +2026-04-10 15:03:36.626054: Current learning rate: 0.00963 +2026-04-10 15:05:19.603320: train_loss -0.1129 +2026-04-10 15:05:19.609762: val_loss -0.1092 +2026-04-10 15:05:19.612003: Pseudo dice [0.3128, 0.7595, 0.5749, 0.0, 0.3135, 0.1654, 0.7732] +2026-04-10 15:05:19.614350: Epoch time: 102.98 s +2026-04-10 15:05:19.618348: Yayy! New best EMA pseudo Dice: 0.3985 +2026-04-10 15:05:22.818743: +2026-04-10 15:05:22.820342: Epoch 166 +2026-04-10 15:05:22.822093: Current learning rate: 0.00963 +2026-04-10 15:07:05.747338: train_loss -0.1179 +2026-04-10 15:07:05.751859: val_loss -0.1241 +2026-04-10 15:07:05.753412: Pseudo dice [0.3255, 0.7651, 0.5191, 0.4012, 0.4787, 0.1766, 0.5382] +2026-04-10 15:07:05.755923: Epoch time: 102.93 s +2026-04-10 15:07:05.759235: Yayy! New best EMA pseudo Dice: 0.4044 +2026-04-10 15:07:08.902204: +2026-04-10 15:07:08.903993: Epoch 167 +2026-04-10 15:07:08.905474: Current learning rate: 0.00962 +2026-04-10 15:08:51.701540: train_loss -0.1221 +2026-04-10 15:08:51.706559: val_loss -0.0848 +2026-04-10 15:08:51.708663: Pseudo dice [0.4156, 0.5768, 0.5184, 0.1274, 0.3453, 0.0743, 0.8384] +2026-04-10 15:08:51.710938: Epoch time: 102.8 s +2026-04-10 15:08:51.712766: Yayy! New best EMA pseudo Dice: 0.4054 +2026-04-10 15:08:55.046230: +2026-04-10 15:08:55.048071: Epoch 168 +2026-04-10 15:08:55.049752: Current learning rate: 0.00962 +2026-04-10 15:10:38.133276: train_loss -0.1152 +2026-04-10 15:10:38.139531: val_loss -0.1229 +2026-04-10 15:10:38.141139: Pseudo dice [0.6289, 0.6242, 0.6628, 0.4729, 0.5031, 0.5764, 0.5855] +2026-04-10 15:10:38.143009: Epoch time: 103.09 s +2026-04-10 15:10:38.144459: Yayy! New best EMA pseudo Dice: 0.4227 +2026-04-10 15:10:41.319664: +2026-04-10 15:10:41.323800: Epoch 169 +2026-04-10 15:10:41.325301: Current learning rate: 0.00962 +2026-04-10 15:12:24.186988: train_loss -0.1201 +2026-04-10 15:12:24.193757: val_loss -0.0913 +2026-04-10 15:12:24.196439: Pseudo dice [0.3682, 0.2127, 0.5244, 0.0, 0.2304, 0.7493, 0.6928] +2026-04-10 15:12:24.200723: Epoch time: 102.87 s +2026-04-10 15:12:25.571419: +2026-04-10 15:12:25.573155: Epoch 170 +2026-04-10 15:12:25.574606: Current learning rate: 0.00962 +2026-04-10 15:14:09.000327: train_loss -0.1145 +2026-04-10 15:14:09.005919: val_loss -0.0799 +2026-04-10 15:14:09.008012: Pseudo dice [0.7459, 0.6234, 0.6311, 0.3031, 0.1871, 0.033, 0.4033] +2026-04-10 15:14:09.011035: Epoch time: 103.43 s +2026-04-10 15:14:10.379407: +2026-04-10 15:14:10.381213: Epoch 171 +2026-04-10 15:14:10.382925: Current learning rate: 0.00961 +2026-04-10 15:15:53.467044: train_loss -0.1298 +2026-04-10 15:15:53.472237: val_loss -0.0786 +2026-04-10 15:15:53.473973: Pseudo dice [0.5293, 0.7656, 0.4135, 0.3124, 0.2485, 0.0908, 0.2358] +2026-04-10 15:15:53.476159: Epoch time: 103.09 s +2026-04-10 15:15:54.852858: +2026-04-10 15:15:54.855146: Epoch 172 +2026-04-10 15:15:54.856758: Current learning rate: 0.00961 +2026-04-10 15:17:45.721862: train_loss -0.1257 +2026-04-10 15:17:45.727145: val_loss -0.0302 +2026-04-10 15:17:45.729906: Pseudo dice [0.5235, 0.7628, 0.3984, 0.0711, 0.2571, 0.0197, 0.7602] +2026-04-10 15:17:45.732373: Epoch time: 110.87 s +2026-04-10 15:17:47.093628: +2026-04-10 15:17:47.095382: Epoch 173 +2026-04-10 15:17:47.097363: Current learning rate: 0.00961 +2026-04-10 15:19:31.723680: train_loss -0.1207 +2026-04-10 15:19:31.730197: val_loss -0.1292 +2026-04-10 15:19:31.732998: Pseudo dice [0.6696, 0.1301, 0.4969, 0.2992, 0.3813, 0.2562, 0.7703] +2026-04-10 15:19:31.735357: Epoch time: 104.63 s +2026-04-10 15:19:33.107719: +2026-04-10 15:19:33.110182: Epoch 174 +2026-04-10 15:19:33.112022: Current learning rate: 0.00961 +2026-04-10 15:21:16.638147: train_loss -0.1218 +2026-04-10 15:21:16.650596: val_loss -0.0965 +2026-04-10 15:21:16.652830: Pseudo dice [0.6711, 0.5379, 0.3977, 0.2199, 0.2373, 0.4253, 0.5333] +2026-04-10 15:21:16.656044: Epoch time: 103.53 s +2026-04-10 15:21:18.018270: +2026-04-10 15:21:18.020004: Epoch 175 +2026-04-10 15:21:18.022204: Current learning rate: 0.00961 +2026-04-10 15:23:00.795417: train_loss -0.1165 +2026-04-10 15:23:00.800865: val_loss -0.1001 +2026-04-10 15:23:00.803314: Pseudo dice [0.422, 0.7809, 0.3322, 0.0034, 0.1114, 0.791, 0.5137] +2026-04-10 15:23:00.859862: Epoch time: 102.78 s +2026-04-10 15:23:02.243032: +2026-04-10 15:23:02.244848: Epoch 176 +2026-04-10 15:23:02.246649: Current learning rate: 0.0096 +2026-04-10 15:24:45.252864: train_loss -0.1116 +2026-04-10 15:24:45.259542: val_loss -0.1193 +2026-04-10 15:24:45.261616: Pseudo dice [0.4949, 0.5473, 0.581, 0.6557, 0.2802, 0.0896, 0.8301] +2026-04-10 15:24:45.263923: Epoch time: 103.01 s +2026-04-10 15:24:45.266016: Yayy! New best EMA pseudo Dice: 0.4252 +2026-04-10 15:24:48.533342: +2026-04-10 15:24:48.535414: Epoch 177 +2026-04-10 15:24:48.537534: Current learning rate: 0.0096 +2026-04-10 15:26:31.464141: train_loss -0.1227 +2026-04-10 15:26:31.469637: val_loss -0.1013 +2026-04-10 15:26:31.472000: Pseudo dice [0.5057, 0.465, 0.4569, 0.482, 0.3803, 0.4145, 0.4255] +2026-04-10 15:26:31.474998: Epoch time: 102.93 s +2026-04-10 15:26:31.476805: Yayy! New best EMA pseudo Dice: 0.4274 +2026-04-10 15:26:34.700720: +2026-04-10 15:26:34.702820: Epoch 178 +2026-04-10 15:26:34.704477: Current learning rate: 0.0096 +2026-04-10 15:28:18.360088: train_loss -0.1299 +2026-04-10 15:28:18.365740: val_loss -0.105 +2026-04-10 15:28:18.368196: Pseudo dice [0.606, 0.6083, 0.5644, 0.2452, 0.3283, 0.3954, 0.4096] +2026-04-10 15:28:18.370670: Epoch time: 103.66 s +2026-04-10 15:28:18.373087: Yayy! New best EMA pseudo Dice: 0.4297 +2026-04-10 15:28:21.652938: +2026-04-10 15:28:21.654847: Epoch 179 +2026-04-10 15:28:21.656486: Current learning rate: 0.0096 +2026-04-10 15:30:05.543263: train_loss -0.1224 +2026-04-10 15:30:05.548702: val_loss -0.0661 +2026-04-10 15:30:05.551224: Pseudo dice [0.6735, 0.4052, 0.6705, 0.2024, 0.4708, 0.0481, 0.8082] +2026-04-10 15:30:05.553700: Epoch time: 103.89 s +2026-04-10 15:30:05.557056: Yayy! New best EMA pseudo Dice: 0.4336 +2026-04-10 15:30:08.808023: +2026-04-10 15:30:08.809834: Epoch 180 +2026-04-10 15:30:08.812531: Current learning rate: 0.00959 +2026-04-10 15:31:51.744006: train_loss -0.1276 +2026-04-10 15:31:51.750438: val_loss -0.1022 +2026-04-10 15:31:51.752438: Pseudo dice [0.4902, 0.7112, 0.6004, 0.1627, 0.2987, 0.2399, 0.6618] +2026-04-10 15:31:51.754925: Epoch time: 102.94 s +2026-04-10 15:31:51.757246: Yayy! New best EMA pseudo Dice: 0.4355 +2026-04-10 15:31:54.957047: +2026-04-10 15:31:54.960021: Epoch 181 +2026-04-10 15:31:54.962064: Current learning rate: 0.00959 +2026-04-10 15:33:37.533700: train_loss -0.1116 +2026-04-10 15:33:37.540082: val_loss -0.1096 +2026-04-10 15:33:37.542812: Pseudo dice [0.3714, 0.4554, 0.7766, 0.6233, 0.4082, 0.0972, 0.6139] +2026-04-10 15:33:37.545663: Epoch time: 102.58 s +2026-04-10 15:33:37.547523: Yayy! New best EMA pseudo Dice: 0.4397 +2026-04-10 15:33:40.937106: +2026-04-10 15:33:40.938913: Epoch 182 +2026-04-10 15:33:40.940912: Current learning rate: 0.00959 +2026-04-10 15:35:26.357239: train_loss -0.127 +2026-04-10 15:35:26.365030: val_loss -0.0872 +2026-04-10 15:35:26.368651: Pseudo dice [0.4472, 0.7452, 0.5073, 0.2183, 0.3913, 0.0518, 0.6613] +2026-04-10 15:35:26.371143: Epoch time: 105.42 s +2026-04-10 15:35:27.763754: +2026-04-10 15:35:27.765382: Epoch 183 +2026-04-10 15:35:27.767038: Current learning rate: 0.00959 +2026-04-10 15:37:11.172108: train_loss -0.1153 +2026-04-10 15:37:11.177684: val_loss -0.0694 +2026-04-10 15:37:11.179632: Pseudo dice [0.6253, 0.7734, 0.5791, 0.1647, 0.2645, 0.0938, 0.3484] +2026-04-10 15:37:11.181894: Epoch time: 103.41 s +2026-04-10 15:37:12.524190: +2026-04-10 15:37:12.526702: Epoch 184 +2026-04-10 15:37:12.528839: Current learning rate: 0.00959 +2026-04-10 15:38:55.216187: train_loss -0.1323 +2026-04-10 15:38:55.221495: val_loss -0.0968 +2026-04-10 15:38:55.223377: Pseudo dice [0.7786, 0.1178, 0.7886, 0.0003, 0.3591, 0.0632, 0.7963] +2026-04-10 15:38:55.226704: Epoch time: 102.7 s +2026-04-10 15:38:56.564436: +2026-04-10 15:38:56.565988: Epoch 185 +2026-04-10 15:38:56.567658: Current learning rate: 0.00958 +2026-04-10 15:40:39.281490: train_loss -0.1246 +2026-04-10 15:40:39.287319: val_loss -0.0166 +2026-04-10 15:40:39.291557: Pseudo dice [0.5055, 0.3901, 0.54, 0.4986, 0.3721, 0.0454, 0.4962] +2026-04-10 15:40:39.293953: Epoch time: 102.72 s +2026-04-10 15:40:40.630251: +2026-04-10 15:40:40.632031: Epoch 186 +2026-04-10 15:40:40.633864: Current learning rate: 0.00958 +2026-04-10 15:42:23.056209: train_loss -0.1111 +2026-04-10 15:42:23.062797: val_loss -0.1143 +2026-04-10 15:42:23.064745: Pseudo dice [0.4688, 0.6214, 0.5455, 0.0507, 0.3839, 0.7315, 0.4746] +2026-04-10 15:42:23.066964: Epoch time: 102.43 s +2026-04-10 15:42:24.429347: +2026-04-10 15:42:24.431934: Epoch 187 +2026-04-10 15:42:24.433481: Current learning rate: 0.00958 +2026-04-10 15:44:06.927631: train_loss -0.1171 +2026-04-10 15:44:06.933478: val_loss -0.1168 +2026-04-10 15:44:06.935304: Pseudo dice [0.6375, 0.4031, 0.5596, 0.4479, 0.34, 0.4869, 0.7436] +2026-04-10 15:44:06.938505: Epoch time: 102.5 s +2026-04-10 15:44:06.940341: Yayy! New best EMA pseudo Dice: 0.4429 +2026-04-10 15:44:10.210317: +2026-04-10 15:44:10.212396: Epoch 188 +2026-04-10 15:44:10.214092: Current learning rate: 0.00958 +2026-04-10 15:45:53.252396: train_loss -0.1222 +2026-04-10 15:45:53.261392: val_loss -0.0547 +2026-04-10 15:45:53.263699: Pseudo dice [0.6189, 0.4701, 0.4454, 0.3947, 0.343, 0.0446, 0.5753] +2026-04-10 15:45:53.266207: Epoch time: 103.05 s +2026-04-10 15:45:54.609649: +2026-04-10 15:45:54.611791: Epoch 189 +2026-04-10 15:45:54.613707: Current learning rate: 0.00957 +2026-04-10 15:47:38.196933: train_loss -0.1293 +2026-04-10 15:47:38.204806: val_loss -0.1258 +2026-04-10 15:47:38.206930: Pseudo dice [0.5797, 0.3688, 0.5854, 0.5775, 0.3911, 0.6206, 0.6683] +2026-04-10 15:47:38.209242: Epoch time: 103.59 s +2026-04-10 15:47:38.211151: Yayy! New best EMA pseudo Dice: 0.4501 +2026-04-10 15:47:41.524959: +2026-04-10 15:47:41.527187: Epoch 190 +2026-04-10 15:47:41.529613: Current learning rate: 0.00957 +2026-04-10 15:49:25.622935: train_loss -0.1223 +2026-04-10 15:49:25.628202: val_loss -0.1134 +2026-04-10 15:49:25.630274: Pseudo dice [0.7672, 0.6683, 0.5388, 0.3813, 0.2788, 0.2809, 0.6668] +2026-04-10 15:49:25.633994: Epoch time: 104.1 s +2026-04-10 15:49:25.636086: Yayy! New best EMA pseudo Dice: 0.4563 +2026-04-10 15:49:28.804566: +2026-04-10 15:49:28.806512: Epoch 191 +2026-04-10 15:49:28.808240: Current learning rate: 0.00957 +2026-04-10 15:51:11.659846: train_loss -0.1203 +2026-04-10 15:51:11.666597: val_loss -0.0697 +2026-04-10 15:51:11.669184: Pseudo dice [0.3743, 0.1706, 0.5288, 0.4621, 0.183, 0.0433, 0.6947] +2026-04-10 15:51:11.673512: Epoch time: 102.86 s +2026-04-10 15:51:13.032875: +2026-04-10 15:51:13.035420: Epoch 192 +2026-04-10 15:51:13.037098: Current learning rate: 0.00957 +2026-04-10 15:52:55.933562: train_loss -0.1156 +2026-04-10 15:52:55.945485: val_loss -0.0401 +2026-04-10 15:52:55.951009: Pseudo dice [0.3411, 0.505, 0.3625, 0.0013, 0.2141, 0.0205, 0.5893] +2026-04-10 15:52:55.953995: Epoch time: 102.9 s +2026-04-10 15:52:57.312409: +2026-04-10 15:52:57.317366: Epoch 193 +2026-04-10 15:52:57.322048: Current learning rate: 0.00956 +2026-04-10 15:54:41.235827: train_loss -0.1156 +2026-04-10 15:54:41.243540: val_loss -0.1041 +2026-04-10 15:54:41.246434: Pseudo dice [0.4395, 0.4508, 0.5352, 0.0068, 0.442, 0.1085, 0.5591] +2026-04-10 15:54:41.248877: Epoch time: 103.93 s +2026-04-10 15:54:42.612895: +2026-04-10 15:54:42.614693: Epoch 194 +2026-04-10 15:54:42.616830: Current learning rate: 0.00956 +2026-04-10 15:56:25.829566: train_loss -0.1149 +2026-04-10 15:56:25.835690: val_loss -0.1248 +2026-04-10 15:56:25.837934: Pseudo dice [0.3611, 0.7122, 0.7175, 0.008, 0.4517, 0.5427, 0.7978] +2026-04-10 15:56:25.840642: Epoch time: 103.22 s +2026-04-10 15:56:27.197400: +2026-04-10 15:56:27.199578: Epoch 195 +2026-04-10 15:56:27.201355: Current learning rate: 0.00956 +2026-04-10 15:58:10.310201: train_loss -0.1238 +2026-04-10 15:58:10.315051: val_loss 0.0342 +2026-04-10 15:58:10.317263: Pseudo dice [0.4393, 0.7102, 0.1544, 0.0094, 0.3307, 0.0122, 0.6486] +2026-04-10 15:58:10.319923: Epoch time: 103.12 s +2026-04-10 15:58:11.726078: +2026-04-10 15:58:11.728868: Epoch 196 +2026-04-10 15:58:11.730766: Current learning rate: 0.00956 +2026-04-10 15:59:55.128311: train_loss -0.1281 +2026-04-10 15:59:55.135428: val_loss -0.0923 +2026-04-10 15:59:55.138020: Pseudo dice [0.4866, 0.6172, 0.4321, 0.4027, 0.4784, 0.0313, 0.793] +2026-04-10 15:59:55.140770: Epoch time: 103.4 s +2026-04-10 15:59:56.532960: +2026-04-10 15:59:56.535691: Epoch 197 +2026-04-10 15:59:56.537215: Current learning rate: 0.00956 +2026-04-10 16:01:40.093349: train_loss -0.1373 +2026-04-10 16:01:40.098345: val_loss -0.1155 +2026-04-10 16:01:40.100285: Pseudo dice [0.3614, 0.4154, 0.684, 0.0505, 0.4822, 0.7028, 0.5354] +2026-04-10 16:01:40.102319: Epoch time: 103.56 s +2026-04-10 16:01:41.474598: +2026-04-10 16:01:41.476799: Epoch 198 +2026-04-10 16:01:41.478557: Current learning rate: 0.00955 +2026-04-10 16:03:24.591423: train_loss -0.1388 +2026-04-10 16:03:24.597457: val_loss -0.1368 +2026-04-10 16:03:24.599364: Pseudo dice [0.1838, 0.5798, 0.6754, 0.5116, 0.3619, 0.6301, 0.7778] +2026-04-10 16:03:24.601433: Epoch time: 103.12 s +2026-04-10 16:03:25.958354: +2026-04-10 16:03:25.960004: Epoch 199 +2026-04-10 16:03:25.961493: Current learning rate: 0.00955 +2026-04-10 16:05:11.113293: train_loss -0.1335 +2026-04-10 16:05:11.121843: val_loss -0.1166 +2026-04-10 16:05:11.126254: Pseudo dice [0.4147, 0.3928, 0.5861, 0.2298, 0.4011, 0.2613, 0.76] +2026-04-10 16:05:11.130985: Epoch time: 105.16 s +2026-04-10 16:05:14.319697: +2026-04-10 16:05:14.321740: Epoch 200 +2026-04-10 16:05:14.323304: Current learning rate: 0.00955 +2026-04-10 16:06:58.998934: train_loss -0.1371 +2026-04-10 16:06:59.005381: val_loss -0.0157 +2026-04-10 16:06:59.007584: Pseudo dice [0.477, 0.218, 0.3049, 0.464, 0.3708, 0.0336, 0.7424] +2026-04-10 16:06:59.011026: Epoch time: 104.68 s +2026-04-10 16:07:00.396258: +2026-04-10 16:07:00.399142: Epoch 201 +2026-04-10 16:07:00.402705: Current learning rate: 0.00955 +2026-04-10 16:08:44.542548: train_loss -0.1406 +2026-04-10 16:08:44.549208: val_loss -0.0904 +2026-04-10 16:08:44.552644: Pseudo dice [0.7364, 0.1115, 0.2486, 0.0292, 0.2855, 0.0777, 0.6052] +2026-04-10 16:08:44.555471: Epoch time: 104.15 s +2026-04-10 16:08:45.938848: +2026-04-10 16:08:45.940847: Epoch 202 +2026-04-10 16:08:45.943578: Current learning rate: 0.00954 +2026-04-10 16:10:29.743209: train_loss -0.1348 +2026-04-10 16:10:29.749988: val_loss -0.1123 +2026-04-10 16:10:29.753527: Pseudo dice [0.5652, 0.7413, 0.717, 0.3802, 0.4213, 0.4766, 0.3226] +2026-04-10 16:10:29.757132: Epoch time: 103.81 s +2026-04-10 16:10:31.129231: +2026-04-10 16:10:31.131417: Epoch 203 +2026-04-10 16:10:31.133156: Current learning rate: 0.00954 +2026-04-10 16:12:14.183779: train_loss -0.1266 +2026-04-10 16:12:14.190768: val_loss -0.0601 +2026-04-10 16:12:14.192455: Pseudo dice [0.3948, 0.7276, 0.5778, 0.5912, 0.309, 0.0723, 0.6612] +2026-04-10 16:12:14.194555: Epoch time: 103.06 s +2026-04-10 16:12:15.546852: +2026-04-10 16:12:15.548538: Epoch 204 +2026-04-10 16:12:15.550349: Current learning rate: 0.00954 +2026-04-10 16:13:59.207974: train_loss -0.1348 +2026-04-10 16:13:59.216469: val_loss -0.1195 +2026-04-10 16:13:59.218286: Pseudo dice [0.2169, 0.2289, 0.5953, 0.1299, 0.3401, 0.6395, 0.7917] +2026-04-10 16:13:59.221146: Epoch time: 103.66 s +2026-04-10 16:14:00.607191: +2026-04-10 16:14:00.609043: Epoch 205 +2026-04-10 16:14:00.610534: Current learning rate: 0.00954 +2026-04-10 16:15:43.560068: train_loss -0.1165 +2026-04-10 16:15:43.565420: val_loss -0.1254 +2026-04-10 16:15:43.567272: Pseudo dice [0.491, 0.2844, 0.5019, 0.3429, 0.4048, 0.218, 0.6344] +2026-04-10 16:15:43.569443: Epoch time: 102.96 s +2026-04-10 16:15:44.870525: +2026-04-10 16:15:44.872050: Epoch 206 +2026-04-10 16:15:44.873677: Current learning rate: 0.00954 +2026-04-10 16:17:28.237720: train_loss -0.1217 +2026-04-10 16:17:28.242948: val_loss -0.1318 +2026-04-10 16:17:28.246109: Pseudo dice [0.4275, 0.5351, 0.479, 0.0429, 0.4577, 0.4008, 0.662] +2026-04-10 16:17:28.248367: Epoch time: 103.37 s +2026-04-10 16:17:29.538386: +2026-04-10 16:17:29.540659: Epoch 207 +2026-04-10 16:17:29.543360: Current learning rate: 0.00953 +2026-04-10 16:19:12.696907: train_loss -0.1228 +2026-04-10 16:19:12.702565: val_loss -0.1162 +2026-04-10 16:19:12.704723: Pseudo dice [0.6177, 0.6365, 0.619, 0.1272, 0.5094, 0.3996, 0.5713] +2026-04-10 16:19:12.707595: Epoch time: 103.16 s +2026-04-10 16:19:13.985249: +2026-04-10 16:19:13.986758: Epoch 208 +2026-04-10 16:19:13.988169: Current learning rate: 0.00953 +2026-04-10 16:20:57.444022: train_loss -0.1321 +2026-04-10 16:20:57.449789: val_loss -0.1557 +2026-04-10 16:20:57.451767: Pseudo dice [0.4422, 0.5145, 0.5976, 0.4869, 0.5396, 0.5173, 0.7305] +2026-04-10 16:20:57.455705: Epoch time: 103.46 s +2026-04-10 16:20:58.754422: +2026-04-10 16:20:58.756473: Epoch 209 +2026-04-10 16:20:58.758039: Current learning rate: 0.00953 +2026-04-10 16:22:41.416181: train_loss -0.1447 +2026-04-10 16:22:41.421816: val_loss -0.1317 +2026-04-10 16:22:41.423562: Pseudo dice [0.4571, 0.6038, 0.6551, 0.1011, 0.5162, 0.8173, 0.6966] +2026-04-10 16:22:41.425612: Epoch time: 102.67 s +2026-04-10 16:22:41.427430: Yayy! New best EMA pseudo Dice: 0.4582 +2026-04-10 16:22:45.597128: +2026-04-10 16:22:45.599328: Epoch 210 +2026-04-10 16:22:45.601593: Current learning rate: 0.00953 +2026-04-10 16:24:28.437270: train_loss -0.1409 +2026-04-10 16:24:28.443304: val_loss -0.0921 +2026-04-10 16:24:28.445647: Pseudo dice [0.832, 0.7861, 0.8427, 0.0135, 0.5483, 0.1869, 0.6544] +2026-04-10 16:24:28.448471: Epoch time: 102.84 s +2026-04-10 16:24:28.450382: Yayy! New best EMA pseudo Dice: 0.4676 +2026-04-10 16:24:31.572533: +2026-04-10 16:24:31.574485: Epoch 211 +2026-04-10 16:24:31.576569: Current learning rate: 0.00952 +2026-04-10 16:26:14.229011: train_loss -0.1383 +2026-04-10 16:26:14.234266: val_loss -0.134 +2026-04-10 16:26:14.237095: Pseudo dice [0.5156, 0.7127, 0.589, 0.4082, 0.4221, 0.6367, 0.8639] +2026-04-10 16:26:14.239445: Epoch time: 102.66 s +2026-04-10 16:26:14.241960: Yayy! New best EMA pseudo Dice: 0.4801 +2026-04-10 16:26:17.358857: +2026-04-10 16:26:17.361132: Epoch 212 +2026-04-10 16:26:17.362916: Current learning rate: 0.00952 +2026-04-10 16:28:00.672652: train_loss -0.1294 +2026-04-10 16:28:00.693771: val_loss -0.0642 +2026-04-10 16:28:00.702240: Pseudo dice [0.3127, 0.5175, 0.4281, 0.022, 0.4802, 0.0976, 0.6555] +2026-04-10 16:28:00.709127: Epoch time: 103.32 s +2026-04-10 16:28:01.995842: +2026-04-10 16:28:01.997521: Epoch 213 +2026-04-10 16:28:01.999071: Current learning rate: 0.00952 +2026-04-10 16:29:44.636962: train_loss -0.1572 +2026-04-10 16:29:44.643608: val_loss -0.0993 +2026-04-10 16:29:44.645375: Pseudo dice [0.5259, 0.4636, 0.5503, 0.3545, 0.588, 0.0889, 0.7448] +2026-04-10 16:29:44.648044: Epoch time: 102.64 s +2026-04-10 16:29:45.962235: +2026-04-10 16:29:45.964500: Epoch 214 +2026-04-10 16:29:45.966568: Current learning rate: 0.00952 +2026-04-10 16:31:29.387258: train_loss -0.152 +2026-04-10 16:31:29.393328: val_loss -0.1466 +2026-04-10 16:31:29.395719: Pseudo dice [0.5171, 0.2565, 0.6441, 0.653, 0.5885, 0.794, 0.6848] +2026-04-10 16:31:29.398167: Epoch time: 103.43 s +2026-04-10 16:31:29.400841: Yayy! New best EMA pseudo Dice: 0.4808 +2026-04-10 16:31:32.581457: +2026-04-10 16:31:32.583276: Epoch 215 +2026-04-10 16:31:32.585151: Current learning rate: 0.00951 +2026-04-10 16:33:15.334906: train_loss -0.1398 +2026-04-10 16:33:15.341950: val_loss -0.0791 +2026-04-10 16:33:15.344122: Pseudo dice [0.6266, 0.3951, 0.5072, 0.2283, 0.3022, 0.0329, 0.5954] +2026-04-10 16:33:15.346489: Epoch time: 102.76 s +2026-04-10 16:33:16.666525: +2026-04-10 16:33:16.668299: Epoch 216 +2026-04-10 16:33:16.670130: Current learning rate: 0.00951 +2026-04-10 16:34:59.539293: train_loss -0.1122 +2026-04-10 16:34:59.548514: val_loss -0.0728 +2026-04-10 16:34:59.550917: Pseudo dice [0.5228, 0.4099, 0.5148, 0.172, 0.2981, 0.0678, 0.6829] +2026-04-10 16:34:59.553974: Epoch time: 102.88 s +2026-04-10 16:35:00.862787: +2026-04-10 16:35:00.864359: Epoch 217 +2026-04-10 16:35:00.865913: Current learning rate: 0.00951 +2026-04-10 16:36:43.970581: train_loss -0.1202 +2026-04-10 16:36:43.976757: val_loss -0.1093 +2026-04-10 16:36:43.978853: Pseudo dice [0.5592, 0.5841, 0.7336, 0.2631, 0.4067, 0.0787, 0.7331] +2026-04-10 16:36:43.980843: Epoch time: 103.11 s +2026-04-10 16:36:45.278358: +2026-04-10 16:36:45.280413: Epoch 218 +2026-04-10 16:36:45.282471: Current learning rate: 0.00951 +2026-04-10 16:38:28.945254: train_loss -0.1343 +2026-04-10 16:38:28.952013: val_loss -0.0755 +2026-04-10 16:38:28.954710: Pseudo dice [0.6586, 0.7349, 0.5778, 0.316, 0.3578, 0.0646, 0.7934] +2026-04-10 16:38:28.958807: Epoch time: 103.67 s +2026-04-10 16:38:30.287695: +2026-04-10 16:38:30.289404: Epoch 219 +2026-04-10 16:38:30.291233: Current learning rate: 0.00951 +2026-04-10 16:40:13.238347: train_loss -0.124 +2026-04-10 16:40:13.244970: val_loss -0.122 +2026-04-10 16:40:13.247041: Pseudo dice [0.5022, 0.5109, 0.5128, 0.1475, 0.5744, 0.2202, 0.7793] +2026-04-10 16:40:13.249503: Epoch time: 102.95 s +2026-04-10 16:40:14.548841: +2026-04-10 16:40:14.550781: Epoch 220 +2026-04-10 16:40:14.553888: Current learning rate: 0.0095 +2026-04-10 16:41:58.914790: train_loss -0.127 +2026-04-10 16:41:58.920943: val_loss -0.1501 +2026-04-10 16:41:58.922778: Pseudo dice [0.5332, 0.8121, 0.6952, 0.4049, 0.581, 0.5609, 0.7145] +2026-04-10 16:41:58.925381: Epoch time: 104.37 s +2026-04-10 16:41:58.927076: Yayy! New best EMA pseudo Dice: 0.4819 +2026-04-10 16:42:02.131718: +2026-04-10 16:42:02.133708: Epoch 221 +2026-04-10 16:42:02.135318: Current learning rate: 0.0095 +2026-04-10 16:43:45.177811: train_loss -0.1406 +2026-04-10 16:43:45.188062: val_loss -0.1331 +2026-04-10 16:43:45.190159: Pseudo dice [0.4664, 0.7549, 0.6329, 0.0, 0.6006, 0.6443, 0.2172] +2026-04-10 16:43:45.193333: Epoch time: 103.05 s +2026-04-10 16:43:46.489515: +2026-04-10 16:43:46.491372: Epoch 222 +2026-04-10 16:43:46.492873: Current learning rate: 0.0095 +2026-04-10 16:45:30.602741: train_loss -0.1459 +2026-04-10 16:45:30.610061: val_loss -0.1227 +2026-04-10 16:45:30.612449: Pseudo dice [0.3908, 0.517, 0.6317, 0.0921, 0.3913, 0.1791, 0.4817] +2026-04-10 16:45:30.615354: Epoch time: 104.12 s +2026-04-10 16:45:31.904680: +2026-04-10 16:45:31.906731: Epoch 223 +2026-04-10 16:45:31.908521: Current learning rate: 0.0095 +2026-04-10 16:47:16.159043: train_loss -0.1553 +2026-04-10 16:47:16.165229: val_loss -0.1176 +2026-04-10 16:47:16.167172: Pseudo dice [0.2061, 0.6217, 0.5068, 0.6577, 0.3953, 0.8001, 0.8199] +2026-04-10 16:47:16.170441: Epoch time: 104.26 s +2026-04-10 16:47:17.466660: +2026-04-10 16:47:17.468956: Epoch 224 +2026-04-10 16:47:17.470746: Current learning rate: 0.00949 +2026-04-10 16:49:00.434106: train_loss -0.1363 +2026-04-10 16:49:00.440833: val_loss -0.0933 +2026-04-10 16:49:00.442508: Pseudo dice [0.6411, 0.6389, 0.4278, 0.0915, 0.4956, 0.05, 0.5867] +2026-04-10 16:49:00.445316: Epoch time: 102.97 s +2026-04-10 16:49:01.740437: +2026-04-10 16:49:01.742248: Epoch 225 +2026-04-10 16:49:01.745016: Current learning rate: 0.00949 +2026-04-10 16:50:45.550351: train_loss -0.122 +2026-04-10 16:50:45.557405: val_loss -0.1168 +2026-04-10 16:50:45.560719: Pseudo dice [0.3777, 0.6756, 0.5306, 0.4867, 0.2322, 0.7278, 0.6465] +2026-04-10 16:50:45.563068: Epoch time: 103.81 s +2026-04-10 16:50:46.862990: +2026-04-10 16:50:46.865171: Epoch 226 +2026-04-10 16:50:46.866927: Current learning rate: 0.00949 +2026-04-10 16:52:29.639315: train_loss -0.1414 +2026-04-10 16:52:29.644261: val_loss -0.1231 +2026-04-10 16:52:29.645842: Pseudo dice [0.351, 0.2698, 0.4729, 0.6569, 0.2874, 0.7584, 0.4653] +2026-04-10 16:52:29.648092: Epoch time: 102.78 s +2026-04-10 16:52:30.935424: +2026-04-10 16:52:30.948439: Epoch 227 +2026-04-10 16:52:30.950866: Current learning rate: 0.00949 +2026-04-10 16:54:13.519973: train_loss -0.1243 +2026-04-10 16:54:13.527881: val_loss -0.1283 +2026-04-10 16:54:13.529686: Pseudo dice [0.8046, 0.5842, 0.6869, 0.0922, 0.4408, 0.5853, 0.2939] +2026-04-10 16:54:13.532062: Epoch time: 102.59 s +2026-04-10 16:54:14.820547: +2026-04-10 16:54:14.822557: Epoch 228 +2026-04-10 16:54:14.824732: Current learning rate: 0.00949 +2026-04-10 16:55:57.380392: train_loss -0.1415 +2026-04-10 16:55:57.388563: val_loss -0.0862 +2026-04-10 16:55:57.391214: Pseudo dice [0.4104, 0.338, 0.5599, 0.7203, 0.4118, 0.0978, 0.7895] +2026-04-10 16:55:57.393600: Epoch time: 102.56 s +2026-04-10 16:55:59.857043: +2026-04-10 16:55:59.859293: Epoch 229 +2026-04-10 16:55:59.861299: Current learning rate: 0.00948 +2026-04-10 16:57:43.256294: train_loss -0.1268 +2026-04-10 16:57:43.261551: val_loss -0.1259 +2026-04-10 16:57:43.263555: Pseudo dice [0.6921, 0.6383, 0.6444, 0.2116, 0.4932, 0.6458, 0.7411] +2026-04-10 16:57:43.266590: Epoch time: 103.4 s +2026-04-10 16:57:43.269279: Yayy! New best EMA pseudo Dice: 0.4903 +2026-04-10 16:57:46.518592: +2026-04-10 16:57:46.520794: Epoch 230 +2026-04-10 16:57:46.522538: Current learning rate: 0.00948 +2026-04-10 16:59:29.907172: train_loss -0.1498 +2026-04-10 16:59:29.913228: val_loss -0.1074 +2026-04-10 16:59:29.915686: Pseudo dice [0.4267, 0.8437, 0.5259, 0.5821, 0.122, 0.288, 0.7278] +2026-04-10 16:59:29.917735: Epoch time: 103.39 s +2026-04-10 16:59:29.920661: Yayy! New best EMA pseudo Dice: 0.4915 +2026-04-10 16:59:33.194094: +2026-04-10 16:59:33.195697: Epoch 231 +2026-04-10 16:59:33.197086: Current learning rate: 0.00948 +2026-04-10 17:01:15.844076: train_loss -0.1386 +2026-04-10 17:01:15.850211: val_loss -0.1376 +2026-04-10 17:01:15.852343: Pseudo dice [0.6984, 0.5348, 0.6415, 0.0008, 0.604, 0.3808, 0.8168] +2026-04-10 17:01:15.855165: Epoch time: 102.65 s +2026-04-10 17:01:15.857092: Yayy! New best EMA pseudo Dice: 0.4949 +2026-04-10 17:01:19.117722: +2026-04-10 17:01:19.119534: Epoch 232 +2026-04-10 17:01:19.121422: Current learning rate: 0.00948 +2026-04-10 17:03:01.901906: train_loss -0.1318 +2026-04-10 17:03:01.907958: val_loss -0.0247 +2026-04-10 17:03:01.910110: Pseudo dice [0.5729, 0.4714, 0.4739, 0.0006, 0.2981, 0.0207, 0.5806] +2026-04-10 17:03:01.912675: Epoch time: 102.79 s +2026-04-10 17:03:03.234588: +2026-04-10 17:03:03.236384: Epoch 233 +2026-04-10 17:03:03.238079: Current learning rate: 0.00947 +2026-04-10 17:04:46.278994: train_loss -0.1379 +2026-04-10 17:04:46.290260: val_loss -0.1427 +2026-04-10 17:04:46.293443: Pseudo dice [0.7895, 0.6219, 0.6438, 0.381, 0.4879, 0.6073, 0.6626] +2026-04-10 17:04:46.296000: Epoch time: 103.05 s +2026-04-10 17:04:47.593254: +2026-04-10 17:04:47.594971: Epoch 234 +2026-04-10 17:04:47.596962: Current learning rate: 0.00947 +2026-04-10 17:06:30.023505: train_loss -0.1362 +2026-04-10 17:06:30.029458: val_loss -0.1399 +2026-04-10 17:06:30.032673: Pseudo dice [0.4635, 0.6319, 0.7151, 0.5068, 0.4702, 0.6688, 0.717] +2026-04-10 17:06:30.034906: Epoch time: 102.43 s +2026-04-10 17:06:30.037086: Yayy! New best EMA pseudo Dice: 0.5023 +2026-04-10 17:06:33.179904: +2026-04-10 17:06:33.181891: Epoch 235 +2026-04-10 17:06:33.183355: Current learning rate: 0.00947 +2026-04-10 17:08:15.683579: train_loss -0.1523 +2026-04-10 17:08:15.689302: val_loss -0.0813 +2026-04-10 17:08:15.690851: Pseudo dice [0.4617, 0.6338, 0.6467, 0.0064, 0.3903, 0.1845, 0.6501] +2026-04-10 17:08:15.693380: Epoch time: 102.51 s +2026-04-10 17:08:16.979838: +2026-04-10 17:08:16.982393: Epoch 236 +2026-04-10 17:08:16.984138: Current learning rate: 0.00947 +2026-04-10 17:10:00.077945: train_loss -0.1421 +2026-04-10 17:10:00.083939: val_loss -0.047 +2026-04-10 17:10:00.085727: Pseudo dice [0.588, 0.5082, 0.5789, 0.5031, 0.2806, 0.0253, 0.7439] +2026-04-10 17:10:00.087676: Epoch time: 103.1 s +2026-04-10 17:10:01.390425: +2026-04-10 17:10:01.393171: Epoch 237 +2026-04-10 17:10:01.395130: Current learning rate: 0.00947 +2026-04-10 17:11:44.517804: train_loss -0.1506 +2026-04-10 17:11:44.523219: val_loss -0.1007 +2026-04-10 17:11:44.524920: Pseudo dice [0.494, 0.3717, 0.5637, 0.8284, 0.4563, 0.2606, 0.611] +2026-04-10 17:11:44.527384: Epoch time: 103.13 s +2026-04-10 17:11:45.815876: +2026-04-10 17:11:45.817674: Epoch 238 +2026-04-10 17:11:45.819350: Current learning rate: 0.00946 +2026-04-10 17:13:28.601843: train_loss -0.1413 +2026-04-10 17:13:28.608166: val_loss -0.132 +2026-04-10 17:13:28.610174: Pseudo dice [0.6807, 0.3471, 0.7051, 0.2988, 0.3465, 0.5818, 0.6277] +2026-04-10 17:13:28.612344: Epoch time: 102.79 s +2026-04-10 17:13:29.928778: +2026-04-10 17:13:29.931078: Epoch 239 +2026-04-10 17:13:29.933181: Current learning rate: 0.00946 +2026-04-10 17:15:13.000068: train_loss -0.1391 +2026-04-10 17:15:13.007145: val_loss -0.1448 +2026-04-10 17:15:13.009838: Pseudo dice [0.5383, 0.5449, 0.5458, 0.3387, 0.5164, 0.663, 0.753] +2026-04-10 17:15:13.012376: Epoch time: 103.08 s +2026-04-10 17:15:14.317006: +2026-04-10 17:15:14.320283: Epoch 240 +2026-04-10 17:15:14.322553: Current learning rate: 0.00946 +2026-04-10 17:16:56.985700: train_loss -0.1435 +2026-04-10 17:16:56.992752: val_loss -0.1066 +2026-04-10 17:16:56.994705: Pseudo dice [0.4127, 0.8621, 0.5482, 0.6008, 0.4179, 0.1439, 0.7098] +2026-04-10 17:16:56.997196: Epoch time: 102.67 s +2026-04-10 17:16:56.999569: Yayy! New best EMA pseudo Dice: 0.5041 +2026-04-10 17:17:00.173854: +2026-04-10 17:17:00.176672: Epoch 241 +2026-04-10 17:17:00.178673: Current learning rate: 0.00946 +2026-04-10 17:18:43.418174: train_loss -0.1477 +2026-04-10 17:18:43.425105: val_loss -0.1492 +2026-04-10 17:18:43.428361: Pseudo dice [0.3777, 0.5927, 0.5275, 0.6028, 0.5604, 0.6473, 0.5073] +2026-04-10 17:18:43.431031: Epoch time: 103.25 s +2026-04-10 17:18:43.433293: Yayy! New best EMA pseudo Dice: 0.5082 +2026-04-10 17:18:46.669760: +2026-04-10 17:18:46.671813: Epoch 242 +2026-04-10 17:18:46.673652: Current learning rate: 0.00945 +2026-04-10 17:20:30.481438: train_loss -0.1237 +2026-04-10 17:20:30.489838: val_loss -0.086 +2026-04-10 17:20:30.492660: Pseudo dice [0.2831, 0.7073, 0.4759, 0.1214, 0.4135, 0.1251, 0.5968] +2026-04-10 17:20:30.495179: Epoch time: 103.82 s +2026-04-10 17:20:31.804919: +2026-04-10 17:20:31.807513: Epoch 243 +2026-04-10 17:20:31.809522: Current learning rate: 0.00945 +2026-04-10 17:22:15.721163: train_loss -0.1408 +2026-04-10 17:22:15.734877: val_loss -0.0918 +2026-04-10 17:22:15.737331: Pseudo dice [0.3776, 0.6124, 0.654, 0.4061, 0.5347, 0.0467, 0.7396] +2026-04-10 17:22:15.739386: Epoch time: 103.92 s +2026-04-10 17:22:17.081966: +2026-04-10 17:22:17.083942: Epoch 244 +2026-04-10 17:22:17.085876: Current learning rate: 0.00945 +2026-04-10 17:24:00.386530: train_loss -0.1444 +2026-04-10 17:24:00.392543: val_loss -0.1071 +2026-04-10 17:24:00.394838: Pseudo dice [0.5051, 0.5239, 0.407, 0.1711, 0.4418, 0.8035, 0.6582] +2026-04-10 17:24:00.398109: Epoch time: 103.31 s +2026-04-10 17:24:01.707985: +2026-04-10 17:24:01.709846: Epoch 245 +2026-04-10 17:24:01.711682: Current learning rate: 0.00945 +2026-04-10 17:25:45.089204: train_loss -0.1467 +2026-04-10 17:25:45.094836: val_loss -0.0923 +2026-04-10 17:25:45.097259: Pseudo dice [0.2444, 0.3823, 0.3407, 0.0167, 0.3947, 0.3725, 0.5797] +2026-04-10 17:25:45.101331: Epoch time: 103.39 s +2026-04-10 17:25:46.432258: +2026-04-10 17:25:46.433990: Epoch 246 +2026-04-10 17:25:46.435947: Current learning rate: 0.00944 +2026-04-10 17:27:29.891010: train_loss -0.1235 +2026-04-10 17:27:29.897079: val_loss -0.0746 +2026-04-10 17:27:29.899083: Pseudo dice [0.5137, 0.2682, 0.2341, 0.3257, 0.0241, 0.8161, 0.3835] +2026-04-10 17:27:29.901566: Epoch time: 103.46 s +2026-04-10 17:27:31.220464: +2026-04-10 17:27:31.222255: Epoch 247 +2026-04-10 17:27:31.224080: Current learning rate: 0.00944 +2026-04-10 17:29:14.979306: train_loss -0.1437 +2026-04-10 17:29:14.984132: val_loss -0.1083 +2026-04-10 17:29:14.986212: Pseudo dice [0.5129, 0.6344, 0.5832, 0.4483, 0.5588, 0.0882, 0.7887] +2026-04-10 17:29:14.988277: Epoch time: 103.76 s +2026-04-10 17:29:17.507789: +2026-04-10 17:29:17.513542: Epoch 248 +2026-04-10 17:29:17.517213: Current learning rate: 0.00944 +2026-04-10 17:31:00.955041: train_loss -0.1361 +2026-04-10 17:31:00.960057: val_loss -0.1363 +2026-04-10 17:31:00.961716: Pseudo dice [0.6476, 0.7456, 0.5957, 0.3599, 0.292, 0.6378, 0.7884] +2026-04-10 17:31:00.963613: Epoch time: 103.45 s +2026-04-10 17:31:02.285620: +2026-04-10 17:31:02.287613: Epoch 249 +2026-04-10 17:31:02.289305: Current learning rate: 0.00944 +2026-04-10 17:32:45.980510: train_loss -0.1453 +2026-04-10 17:32:45.986434: val_loss -0.1312 +2026-04-10 17:32:45.988881: Pseudo dice [0.3065, 0.6357, 0.6074, 0.0551, 0.5418, 0.7205, 0.5866] +2026-04-10 17:32:45.992302: Epoch time: 103.7 s +2026-04-10 17:32:49.261405: +2026-04-10 17:32:49.263704: Epoch 250 +2026-04-10 17:32:49.265682: Current learning rate: 0.00944 +2026-04-10 17:34:33.951797: train_loss -0.1507 +2026-04-10 17:34:33.957538: val_loss -0.1262 +2026-04-10 17:34:33.960141: Pseudo dice [0.4904, 0.6204, 0.5809, 0.7291, 0.3015, 0.7418, 0.7434] +2026-04-10 17:34:33.962888: Epoch time: 104.69 s +2026-04-10 17:34:35.295428: +2026-04-10 17:34:35.298034: Epoch 251 +2026-04-10 17:34:35.300787: Current learning rate: 0.00943 +2026-04-10 17:36:19.559518: train_loss -0.1461 +2026-04-10 17:36:19.568789: val_loss -0.1019 +2026-04-10 17:36:19.573917: Pseudo dice [0.4843, 0.3464, 0.6575, 0.449, 0.3153, 0.0851, 0.4277] +2026-04-10 17:36:19.580265: Epoch time: 104.27 s +2026-04-10 17:36:20.902480: +2026-04-10 17:36:20.904737: Epoch 252 +2026-04-10 17:36:20.907150: Current learning rate: 0.00943 +2026-04-10 17:38:04.233337: train_loss -0.1406 +2026-04-10 17:38:04.240146: val_loss -0.1243 +2026-04-10 17:38:04.242194: Pseudo dice [0.5157, 0.4535, 0.6279, 0.4424, 0.6518, 0.6257, 0.5979] +2026-04-10 17:38:04.244367: Epoch time: 103.33 s +2026-04-10 17:38:05.621244: +2026-04-10 17:38:05.623595: Epoch 253 +2026-04-10 17:38:05.625219: Current learning rate: 0.00943 +2026-04-10 17:39:50.139701: train_loss -0.1444 +2026-04-10 17:39:50.147893: val_loss -0.1138 +2026-04-10 17:39:50.150286: Pseudo dice [0.099, 0.3759, 0.4675, 0.773, 0.4908, 0.826, 0.6186] +2026-04-10 17:39:50.153700: Epoch time: 104.52 s +2026-04-10 17:39:51.485857: +2026-04-10 17:39:51.488558: Epoch 254 +2026-04-10 17:39:51.490924: Current learning rate: 0.00943 +2026-04-10 17:41:34.209596: train_loss -0.1503 +2026-04-10 17:41:34.217934: val_loss -0.1487 +2026-04-10 17:41:34.220959: Pseudo dice [0.4401, 0.5287, 0.6435, 0.5527, 0.5062, 0.5556, 0.6708] +2026-04-10 17:41:34.224535: Epoch time: 102.73 s +2026-04-10 17:41:35.566272: +2026-04-10 17:41:35.568976: Epoch 255 +2026-04-10 17:41:35.572602: Current learning rate: 0.00942 +2026-04-10 17:43:19.509901: train_loss -0.1563 +2026-04-10 17:43:19.517871: val_loss -0.1212 +2026-04-10 17:43:19.521204: Pseudo dice [0.8358, 0.6869, 0.6851, 0.2873, 0.5395, 0.0754, 0.7792] +2026-04-10 17:43:19.523874: Epoch time: 103.95 s +2026-04-10 17:43:20.865750: +2026-04-10 17:43:20.868305: Epoch 256 +2026-04-10 17:43:20.871132: Current learning rate: 0.00942 +2026-04-10 17:45:05.304137: train_loss -0.1477 +2026-04-10 17:45:05.313747: val_loss -0.102 +2026-04-10 17:45:05.316053: Pseudo dice [0.168, 0.7345, 0.3694, 0.5616, 0.4002, 0.5208, 0.6077] +2026-04-10 17:45:05.318349: Epoch time: 104.44 s +2026-04-10 17:45:06.642234: +2026-04-10 17:45:06.644043: Epoch 257 +2026-04-10 17:45:06.647750: Current learning rate: 0.00942 +2026-04-10 17:46:50.572248: train_loss -0.1471 +2026-04-10 17:46:50.582942: val_loss -0.1003 +2026-04-10 17:46:50.586005: Pseudo dice [0.2884, 0.1967, 0.6198, 0.5932, 0.4751, 0.0893, 0.5429] +2026-04-10 17:46:50.589758: Epoch time: 103.93 s +2026-04-10 17:46:51.933662: +2026-04-10 17:46:51.937164: Epoch 258 +2026-04-10 17:46:51.939178: Current learning rate: 0.00942 +2026-04-10 17:48:36.684291: train_loss -0.1383 +2026-04-10 17:48:36.692613: val_loss -0.1417 +2026-04-10 17:48:36.695620: Pseudo dice [0.4674, 0.677, 0.7699, 0.0, 0.5185, 0.4416, 0.6247] +2026-04-10 17:48:36.698495: Epoch time: 104.75 s +2026-04-10 17:48:38.030279: +2026-04-10 17:48:38.033091: Epoch 259 +2026-04-10 17:48:38.035017: Current learning rate: 0.00942 +2026-04-10 17:50:22.448849: train_loss -0.1433 +2026-04-10 17:50:22.458248: val_loss -0.1028 +2026-04-10 17:50:22.461874: Pseudo dice [0.6108, 0.7153, 0.5625, 0.4685, 0.4658, 0.1921, 0.4805] +2026-04-10 17:50:22.464565: Epoch time: 104.42 s +2026-04-10 17:50:23.813123: +2026-04-10 17:50:23.815687: Epoch 260 +2026-04-10 17:50:23.818422: Current learning rate: 0.00941 +2026-04-10 17:52:07.642168: train_loss -0.1425 +2026-04-10 17:52:07.648497: val_loss -0.1238 +2026-04-10 17:52:07.650843: Pseudo dice [0.3528, 0.5483, 0.5793, 0.4314, 0.4356, 0.1706, 0.5746] +2026-04-10 17:52:07.654238: Epoch time: 103.83 s +2026-04-10 17:52:09.012224: +2026-04-10 17:52:09.014270: Epoch 261 +2026-04-10 17:52:09.017276: Current learning rate: 0.00941 +2026-04-10 17:53:52.428391: train_loss -0.158 +2026-04-10 17:53:52.436963: val_loss -0.0625 +2026-04-10 17:53:52.439969: Pseudo dice [0.572, 0.2852, 0.5106, 0.539, 0.5468, 0.071, 0.7171] +2026-04-10 17:53:52.442707: Epoch time: 103.42 s +2026-04-10 17:53:53.756229: +2026-04-10 17:53:53.769638: Epoch 262 +2026-04-10 17:53:53.771599: Current learning rate: 0.00941 +2026-04-10 17:55:37.223225: train_loss -0.1549 +2026-04-10 17:55:37.230387: val_loss -0.1409 +2026-04-10 17:55:37.233705: Pseudo dice [0.68, 0.6983, 0.5357, 0.6408, 0.4239, 0.2181, 0.4184] +2026-04-10 17:55:37.236441: Epoch time: 103.47 s +2026-04-10 17:55:38.548501: +2026-04-10 17:55:38.550931: Epoch 263 +2026-04-10 17:55:38.552643: Current learning rate: 0.00941 +2026-04-10 17:57:24.880515: train_loss -0.1389 +2026-04-10 17:57:24.891408: val_loss -0.0393 +2026-04-10 17:57:24.894866: Pseudo dice [0.6771, 0.5776, 0.3249, 0.0534, 0.2834, 0.0363, 0.605] +2026-04-10 17:57:24.898481: Epoch time: 106.33 s +2026-04-10 17:57:26.223872: +2026-04-10 17:57:26.226917: Epoch 264 +2026-04-10 17:57:26.228944: Current learning rate: 0.0094 +2026-04-10 17:59:11.553339: train_loss -0.1523 +2026-04-10 17:59:11.560469: val_loss -0.1506 +2026-04-10 17:59:11.563800: Pseudo dice [0.6135, 0.475, 0.6414, 0.8224, 0.3427, 0.7349, 0.8106] +2026-04-10 17:59:11.567040: Epoch time: 105.33 s +2026-04-10 17:59:12.898010: +2026-04-10 17:59:12.901447: Epoch 265 +2026-04-10 17:59:12.903522: Current learning rate: 0.0094 +2026-04-10 18:01:01.317261: train_loss -0.161 +2026-04-10 18:01:01.327898: val_loss -0.1258 +2026-04-10 18:01:01.331441: Pseudo dice [0.3897, 0.5724, 0.5376, 0.4428, 0.2499, 0.163, 0.7677] +2026-04-10 18:01:01.335668: Epoch time: 108.42 s +2026-04-10 18:01:02.677747: +2026-04-10 18:01:02.682435: Epoch 266 +2026-04-10 18:01:02.688360: Current learning rate: 0.0094 +2026-04-10 18:02:46.026748: train_loss -0.1372 +2026-04-10 18:02:46.046395: val_loss -0.1347 +2026-04-10 18:02:46.056375: Pseudo dice [0.5379, 0.7619, 0.6359, 0.4733, 0.5116, 0.6432, 0.8394] +2026-04-10 18:02:46.060229: Epoch time: 103.35 s +2026-04-10 18:02:47.446054: +2026-04-10 18:02:47.451066: Epoch 267 +2026-04-10 18:02:47.453530: Current learning rate: 0.0094 +2026-04-10 18:04:31.801566: train_loss -0.1565 +2026-04-10 18:04:31.811473: val_loss -0.0647 +2026-04-10 18:04:31.814176: Pseudo dice [0.5825, 0.5887, 0.448, 0.4543, 0.3021, 0.1524, 0.5099] +2026-04-10 18:04:31.816644: Epoch time: 104.36 s +2026-04-10 18:04:34.439746: +2026-04-10 18:04:34.443025: Epoch 268 +2026-04-10 18:04:34.445567: Current learning rate: 0.00939 +2026-04-10 18:06:18.491865: train_loss -0.1603 +2026-04-10 18:06:18.497311: val_loss -0.1744 +2026-04-10 18:06:18.500152: Pseudo dice [0.5024, 0.7284, 0.6807, 0.5722, 0.5816, 0.4402, 0.7807] +2026-04-10 18:06:18.503669: Epoch time: 104.06 s +2026-04-10 18:06:19.856090: +2026-04-10 18:06:19.868143: Epoch 269 +2026-04-10 18:06:19.870553: Current learning rate: 0.00939 +2026-04-10 18:08:04.244444: train_loss -0.1549 +2026-04-10 18:08:04.252854: val_loss -0.0634 +2026-04-10 18:08:04.256873: Pseudo dice [0.3538, 0.6802, 0.7142, 0.3712, 0.577, 0.0533, 0.7969] +2026-04-10 18:08:04.260191: Epoch time: 104.39 s +2026-04-10 18:08:05.613941: +2026-04-10 18:08:05.619854: Epoch 270 +2026-04-10 18:08:05.631335: Current learning rate: 0.00939 +2026-04-10 18:09:49.846772: train_loss -0.1313 +2026-04-10 18:09:49.854831: val_loss -0.1104 +2026-04-10 18:09:49.858137: Pseudo dice [0.6365, 0.2633, 0.633, 0.7283, 0.1404, 0.3589, 0.4515] +2026-04-10 18:09:49.864266: Epoch time: 104.24 s +2026-04-10 18:09:51.209200: +2026-04-10 18:09:51.213403: Epoch 271 +2026-04-10 18:09:51.216962: Current learning rate: 0.00939 +2026-04-10 18:11:35.133475: train_loss -0.1456 +2026-04-10 18:11:35.140211: val_loss -0.1317 +2026-04-10 18:11:35.143030: Pseudo dice [0.5608, 0.3317, 0.6435, 0.0661, 0.5797, 0.6404, 0.8055] +2026-04-10 18:11:35.145698: Epoch time: 103.93 s +2026-04-10 18:11:36.502682: +2026-04-10 18:11:36.505148: Epoch 272 +2026-04-10 18:11:36.507935: Current learning rate: 0.00939 +2026-04-10 18:13:19.745946: train_loss -0.1573 +2026-04-10 18:13:19.753552: val_loss -0.1139 +2026-04-10 18:13:19.755397: Pseudo dice [0.3537, 0.3978, 0.4158, 0.6167, 0.3953, 0.3833, 0.6257] +2026-04-10 18:13:19.758048: Epoch time: 103.25 s +2026-04-10 18:13:21.081919: +2026-04-10 18:13:21.083763: Epoch 273 +2026-04-10 18:13:21.086011: Current learning rate: 0.00938 +2026-04-10 18:15:04.447250: train_loss -0.1509 +2026-04-10 18:15:04.454536: val_loss -0.1525 +2026-04-10 18:15:04.456704: Pseudo dice [0.7978, 0.487, 0.6017, 0.11, 0.564, 0.3302, 0.7563] +2026-04-10 18:15:04.459822: Epoch time: 103.37 s +2026-04-10 18:15:05.807613: +2026-04-10 18:15:05.809812: Epoch 274 +2026-04-10 18:15:05.812916: Current learning rate: 0.00938 +2026-04-10 18:16:50.522404: train_loss -0.1414 +2026-04-10 18:16:50.533687: val_loss -0.098 +2026-04-10 18:16:50.537118: Pseudo dice [0.514, 0.4603, 0.7589, 0.2997, 0.5842, 0.0532, 0.5606] +2026-04-10 18:16:50.539752: Epoch time: 104.72 s +2026-04-10 18:16:52.109439: +2026-04-10 18:16:52.111355: Epoch 275 +2026-04-10 18:16:52.113060: Current learning rate: 0.00938 +2026-04-10 18:18:37.964300: train_loss -0.1536 +2026-04-10 18:18:37.971388: val_loss -0.1052 +2026-04-10 18:18:37.974406: Pseudo dice [0.7762, 0.2848, 0.5166, 0.349, 0.436, 0.1974, 0.6327] +2026-04-10 18:18:37.977141: Epoch time: 105.86 s +2026-04-10 18:18:39.305733: +2026-04-10 18:18:39.308533: Epoch 276 +2026-04-10 18:18:39.310511: Current learning rate: 0.00938 +2026-04-10 18:20:23.250179: train_loss -0.1533 +2026-04-10 18:20:23.258286: val_loss -0.0966 +2026-04-10 18:20:23.261095: Pseudo dice [0.604, 0.7345, 0.6323, 0.3745, 0.3098, 0.2645, 0.5156] +2026-04-10 18:20:23.264094: Epoch time: 103.95 s +2026-04-10 18:20:24.585432: +2026-04-10 18:20:24.587800: Epoch 277 +2026-04-10 18:20:24.589432: Current learning rate: 0.00937 +2026-04-10 18:22:09.269793: train_loss -0.1437 +2026-04-10 18:22:09.278205: val_loss -0.0809 +2026-04-10 18:22:09.281132: Pseudo dice [0.2968, 0.5543, 0.5492, 0.3871, 0.5301, 0.0792, 0.6876] +2026-04-10 18:22:09.284271: Epoch time: 104.69 s +2026-04-10 18:22:10.625191: +2026-04-10 18:22:10.628163: Epoch 278 +2026-04-10 18:22:10.630092: Current learning rate: 0.00937 +2026-04-10 18:23:54.144698: train_loss -0.1253 +2026-04-10 18:23:54.152381: val_loss -0.1395 +2026-04-10 18:23:54.154977: Pseudo dice [0.8017, 0.6668, 0.6062, 0.5324, 0.436, 0.6232, 0.7029] +2026-04-10 18:23:54.157964: Epoch time: 103.52 s +2026-04-10 18:23:55.495685: +2026-04-10 18:23:55.498815: Epoch 279 +2026-04-10 18:23:55.502362: Current learning rate: 0.00937 +2026-04-10 18:25:39.834958: train_loss -0.1434 +2026-04-10 18:25:39.842046: val_loss -0.1307 +2026-04-10 18:25:39.844401: Pseudo dice [0.4549, 0.6378, 0.5896, 0.4049, 0.2285, 0.4563, 0.7291] +2026-04-10 18:25:39.847309: Epoch time: 104.34 s +2026-04-10 18:25:41.197824: +2026-04-10 18:25:41.201340: Epoch 280 +2026-04-10 18:25:41.203765: Current learning rate: 0.00937 +2026-04-10 18:27:26.078241: train_loss -0.1466 +2026-04-10 18:27:26.084904: val_loss -0.1251 +2026-04-10 18:27:26.087367: Pseudo dice [0.6792, 0.6876, 0.6544, 0.403, 0.5785, 0.1914, 0.8598] +2026-04-10 18:27:26.089729: Epoch time: 104.88 s +2026-04-10 18:27:26.093408: Yayy! New best EMA pseudo Dice: 0.5091 +2026-04-10 18:27:29.387053: +2026-04-10 18:27:29.389839: Epoch 281 +2026-04-10 18:27:29.392002: Current learning rate: 0.00937 +2026-04-10 18:29:13.086782: train_loss -0.1408 +2026-04-10 18:29:13.091665: val_loss -0.101 +2026-04-10 18:29:13.093968: Pseudo dice [0.4599, 0.5934, 0.6763, 0.6662, 0.3672, 0.0641, 0.8562] +2026-04-10 18:29:13.096282: Epoch time: 103.7 s +2026-04-10 18:29:13.098389: Yayy! New best EMA pseudo Dice: 0.5108 +2026-04-10 18:29:16.333147: +2026-04-10 18:29:16.335841: Epoch 282 +2026-04-10 18:29:16.337920: Current learning rate: 0.00936 +2026-04-10 18:31:00.559936: train_loss -0.1537 +2026-04-10 18:31:00.568189: val_loss -0.0949 +2026-04-10 18:31:00.572294: Pseudo dice [0.53, 0.78, 0.5993, 0.285, 0.1946, 0.0554, 0.6912] +2026-04-10 18:31:00.575725: Epoch time: 104.23 s +2026-04-10 18:31:01.919202: +2026-04-10 18:31:01.923052: Epoch 283 +2026-04-10 18:31:01.927426: Current learning rate: 0.00936 +2026-04-10 18:32:46.484338: train_loss -0.1601 +2026-04-10 18:32:46.493801: val_loss -0.1054 +2026-04-10 18:32:46.496734: Pseudo dice [0.4467, 0.366, 0.5029, 0.0216, 0.3358, 0.4716, 0.7116] +2026-04-10 18:32:46.501950: Epoch time: 104.57 s +2026-04-10 18:32:47.877733: +2026-04-10 18:32:47.879891: Epoch 284 +2026-04-10 18:32:47.883236: Current learning rate: 0.00936 +2026-04-10 18:34:31.088708: train_loss -0.1555 +2026-04-10 18:34:31.095391: val_loss -0.105 +2026-04-10 18:34:31.099832: Pseudo dice [0.0906, 0.7247, 0.6731, 0.0831, 0.3967, 0.1427, 0.5982] +2026-04-10 18:34:31.104087: Epoch time: 103.21 s +2026-04-10 18:34:32.447857: +2026-04-10 18:34:32.450217: Epoch 285 +2026-04-10 18:34:32.453026: Current learning rate: 0.00936 +2026-04-10 18:36:15.965307: train_loss -0.1509 +2026-04-10 18:36:15.971325: val_loss -0.0678 +2026-04-10 18:36:15.974034: Pseudo dice [0.5514, 0.6521, 0.423, 0.6169, 0.2981, 0.0757, 0.2559] +2026-04-10 18:36:15.977055: Epoch time: 103.52 s +2026-04-10 18:36:17.315404: +2026-04-10 18:36:17.317616: Epoch 286 +2026-04-10 18:36:17.320019: Current learning rate: 0.00935 +2026-04-10 18:38:01.059948: train_loss -0.1669 +2026-04-10 18:38:01.065659: val_loss -0.0863 +2026-04-10 18:38:01.067698: Pseudo dice [0.5841, 0.7353, 0.4632, 0.1256, 0.5253, 0.1086, 0.8542] +2026-04-10 18:38:01.070176: Epoch time: 103.75 s +2026-04-10 18:38:02.438882: +2026-04-10 18:38:02.441085: Epoch 287 +2026-04-10 18:38:02.442873: Current learning rate: 0.00935 +2026-04-10 18:39:48.127739: train_loss -0.1567 +2026-04-10 18:39:48.136861: val_loss -0.1328 +2026-04-10 18:39:48.139449: Pseudo dice [0.5567, 0.365, 0.7715, 0.731, 0.3945, 0.7022, 0.4837] +2026-04-10 18:39:48.142249: Epoch time: 105.69 s +2026-04-10 18:39:49.501142: +2026-04-10 18:39:49.502951: Epoch 288 +2026-04-10 18:39:49.504759: Current learning rate: 0.00935 +2026-04-10 18:41:34.621541: train_loss -0.1568 +2026-04-10 18:41:34.629100: val_loss -0.1218 +2026-04-10 18:41:34.632006: Pseudo dice [0.419, 0.557, 0.5397, 0.0091, 0.615, 0.234, 0.6973] +2026-04-10 18:41:34.635321: Epoch time: 105.12 s +2026-04-10 18:41:35.987308: +2026-04-10 18:41:35.989797: Epoch 289 +2026-04-10 18:41:35.991931: Current learning rate: 0.00935 +2026-04-10 18:43:20.247683: train_loss -0.1597 +2026-04-10 18:43:20.259018: val_loss -0.1165 +2026-04-10 18:43:20.261653: Pseudo dice [0.5099, 0.8285, 0.5803, 0.2293, 0.6695, 0.1826, 0.6564] +2026-04-10 18:43:20.265316: Epoch time: 104.26 s +2026-04-10 18:43:21.622150: +2026-04-10 18:43:21.624762: Epoch 290 +2026-04-10 18:43:21.628152: Current learning rate: 0.00935 +2026-04-10 18:45:05.957837: train_loss -0.1493 +2026-04-10 18:45:05.965334: val_loss -0.1329 +2026-04-10 18:45:05.968680: Pseudo dice [0.6292, 0.6072, 0.6558, 0.5276, 0.3494, 0.2022, 0.8147] +2026-04-10 18:45:05.972291: Epoch time: 104.34 s +2026-04-10 18:45:07.324977: +2026-04-10 18:45:07.326834: Epoch 291 +2026-04-10 18:45:07.328811: Current learning rate: 0.00934 +2026-04-10 18:46:52.641523: train_loss -0.1488 +2026-04-10 18:46:52.650984: val_loss -0.0933 +2026-04-10 18:46:52.656606: Pseudo dice [0.31, 0.301, 0.5244, 0.3373, 0.572, 0.1816, 0.701] +2026-04-10 18:46:52.659082: Epoch time: 105.32 s +2026-04-10 18:46:54.023940: +2026-04-10 18:46:54.026595: Epoch 292 +2026-04-10 18:46:54.028796: Current learning rate: 0.00934 +2026-04-10 18:48:37.591989: train_loss -0.1341 +2026-04-10 18:48:37.597363: val_loss -0.0615 +2026-04-10 18:48:37.600577: Pseudo dice [0.3857, 0.7082, 0.5548, 0.0179, 0.5473, 0.0533, 0.7376] +2026-04-10 18:48:37.602730: Epoch time: 103.57 s +2026-04-10 18:48:38.959378: +2026-04-10 18:48:38.961299: Epoch 293 +2026-04-10 18:48:38.963297: Current learning rate: 0.00934 +2026-04-10 18:50:21.904569: train_loss -0.1553 +2026-04-10 18:50:21.911625: val_loss -0.1063 +2026-04-10 18:50:21.914327: Pseudo dice [0.3928, 0.5153, 0.4856, 0.2697, 0.4125, 0.0996, 0.6685] +2026-04-10 18:50:21.917734: Epoch time: 102.95 s +2026-04-10 18:50:23.269086: +2026-04-10 18:50:23.271086: Epoch 294 +2026-04-10 18:50:23.272953: Current learning rate: 0.00934 +2026-04-10 18:52:06.251356: train_loss -0.1464 +2026-04-10 18:52:06.260074: val_loss -0.1241 +2026-04-10 18:52:06.264335: Pseudo dice [0.3779, 0.458, 0.5097, 0.6837, 0.408, 0.2288, 0.749] +2026-04-10 18:52:06.266992: Epoch time: 102.99 s +2026-04-10 18:52:07.619975: +2026-04-10 18:52:07.622587: Epoch 295 +2026-04-10 18:52:07.624410: Current learning rate: 0.00933 +2026-04-10 18:53:50.860702: train_loss -0.1347 +2026-04-10 18:53:50.869070: val_loss -0.0959 +2026-04-10 18:53:50.871514: Pseudo dice [0.4898, 0.4168, 0.552, 0.52, 0.4765, 0.3453, 0.3047] +2026-04-10 18:53:50.877684: Epoch time: 103.24 s +2026-04-10 18:53:52.239628: +2026-04-10 18:53:52.242799: Epoch 296 +2026-04-10 18:53:52.245019: Current learning rate: 0.00933 +2026-04-10 18:55:36.846403: train_loss -0.1419 +2026-04-10 18:55:36.853236: val_loss -0.1228 +2026-04-10 18:55:36.855649: Pseudo dice [0.7851, 0.318, 0.6268, 0.3906, 0.3407, 0.3448, 0.5794] +2026-04-10 18:55:36.859031: Epoch time: 104.61 s +2026-04-10 18:55:38.216478: +2026-04-10 18:55:38.219293: Epoch 297 +2026-04-10 18:55:38.222657: Current learning rate: 0.00933 +2026-04-10 18:57:22.545772: train_loss -0.1541 +2026-04-10 18:57:22.551762: val_loss -0.066 +2026-04-10 18:57:22.553679: Pseudo dice [0.3748, 0.6484, 0.5432, 0.7078, 0.5232, 0.0873, 0.8797] +2026-04-10 18:57:22.557261: Epoch time: 104.33 s +2026-04-10 18:57:23.943449: +2026-04-10 18:57:23.946014: Epoch 298 +2026-04-10 18:57:23.948196: Current learning rate: 0.00933 +2026-04-10 18:59:08.361564: train_loss -0.1585 +2026-04-10 18:59:08.371041: val_loss -0.1111 +2026-04-10 18:59:08.374904: Pseudo dice [0.8725, 0.7049, 0.5829, 0.4639, 0.5981, 0.0734, 0.6778] +2026-04-10 18:59:08.378822: Epoch time: 104.42 s +2026-04-10 18:59:09.736320: +2026-04-10 18:59:09.738834: Epoch 299 +2026-04-10 18:59:09.742842: Current learning rate: 0.00932 +2026-04-10 19:00:54.052469: train_loss -0.1504 +2026-04-10 19:00:54.059987: val_loss -0.1262 +2026-04-10 19:00:54.062945: Pseudo dice [0.5566, 0.2473, 0.7471, 0.6716, 0.5387, 0.0871, 0.6529] +2026-04-10 19:00:54.065774: Epoch time: 104.32 s +2026-04-10 19:00:57.347034: +2026-04-10 19:00:57.349625: Epoch 300 +2026-04-10 19:00:57.351702: Current learning rate: 0.00932 +2026-04-10 19:02:44.011814: train_loss -0.159 +2026-04-10 19:02:44.020981: val_loss -0.1306 +2026-04-10 19:02:44.038905: Pseudo dice [0.6985, 0.4666, 0.7176, 0.3686, 0.5072, 0.0676, 0.7559] +2026-04-10 19:02:44.042548: Epoch time: 106.67 s +2026-04-10 19:02:45.403386: +2026-04-10 19:02:45.407930: Epoch 301 +2026-04-10 19:02:45.412108: Current learning rate: 0.00932 +2026-04-10 19:04:29.900513: train_loss -0.1585 +2026-04-10 19:04:29.909096: val_loss -0.1056 +2026-04-10 19:04:29.912332: Pseudo dice [0.3113, 0.4535, 0.5814, 0.4876, 0.4796, 0.0777, 0.5198] +2026-04-10 19:04:29.917487: Epoch time: 104.5 s +2026-04-10 19:04:31.296406: +2026-04-10 19:04:31.299412: Epoch 302 +2026-04-10 19:04:31.303014: Current learning rate: 0.00932 +2026-04-10 19:06:14.184416: train_loss -0.1532 +2026-04-10 19:06:14.191016: val_loss -0.0815 +2026-04-10 19:06:14.193538: Pseudo dice [0.391, 0.7189, 0.6848, 0.4077, 0.2017, 0.0874, 0.5478] +2026-04-10 19:06:14.196068: Epoch time: 102.89 s +2026-04-10 19:06:15.572948: +2026-04-10 19:06:15.576798: Epoch 303 +2026-04-10 19:06:15.580807: Current learning rate: 0.00932 +2026-04-10 19:07:59.413385: train_loss -0.1512 +2026-04-10 19:07:59.420991: val_loss -0.1097 +2026-04-10 19:07:59.423172: Pseudo dice [0.1329, 0.4042, 0.5349, 0.2667, 0.5131, 0.0755, 0.5873] +2026-04-10 19:07:59.426136: Epoch time: 103.84 s +2026-04-10 19:08:00.803591: +2026-04-10 19:08:00.805662: Epoch 304 +2026-04-10 19:08:00.807761: Current learning rate: 0.00931 +2026-04-10 19:09:45.260439: train_loss -0.1699 +2026-04-10 19:09:45.267804: val_loss -0.0599 +2026-04-10 19:09:45.270744: Pseudo dice [0.663, 0.3357, 0.5421, 0.6782, 0.2506, 0.0647, 0.4845] +2026-04-10 19:09:45.273368: Epoch time: 104.46 s +2026-04-10 19:09:46.630384: +2026-04-10 19:09:46.633162: Epoch 305 +2026-04-10 19:09:46.635133: Current learning rate: 0.00931 +2026-04-10 19:11:30.925528: train_loss -0.1592 +2026-04-10 19:11:30.932980: val_loss -0.1183 +2026-04-10 19:11:30.936681: Pseudo dice [0.4017, 0.792, 0.4799, 0.0294, 0.3041, 0.7061, 0.7998] +2026-04-10 19:11:30.939284: Epoch time: 104.3 s +2026-04-10 19:11:32.302945: +2026-04-10 19:11:32.305162: Epoch 306 +2026-04-10 19:11:32.308998: Current learning rate: 0.00931 +2026-04-10 19:13:17.045146: train_loss -0.1435 +2026-04-10 19:13:17.053048: val_loss -0.1101 +2026-04-10 19:13:17.055708: Pseudo dice [0.3997, 0.4376, 0.6369, 0.6136, 0.4039, 0.4768, 0.4557] +2026-04-10 19:13:17.058733: Epoch time: 104.74 s +2026-04-10 19:13:19.572069: +2026-04-10 19:13:19.573773: Epoch 307 +2026-04-10 19:13:19.575536: Current learning rate: 0.00931 +2026-04-10 19:15:04.087723: train_loss -0.1382 +2026-04-10 19:15:04.095156: val_loss -0.0824 +2026-04-10 19:15:04.098952: Pseudo dice [0.147, 0.7555, 0.4614, 0.1962, 0.4868, 0.0837, 0.4153] +2026-04-10 19:15:04.102709: Epoch time: 104.52 s +2026-04-10 19:15:05.455845: +2026-04-10 19:15:05.457973: Epoch 308 +2026-04-10 19:15:05.459991: Current learning rate: 0.0093 +2026-04-10 19:16:49.327974: train_loss -0.1237 +2026-04-10 19:16:49.336425: val_loss -0.0927 +2026-04-10 19:16:49.339738: Pseudo dice [0.2775, 0.4602, 0.1794, 0.6558, 0.3489, 0.0582, 0.6198] +2026-04-10 19:16:49.343101: Epoch time: 103.87 s +2026-04-10 19:16:50.712468: +2026-04-10 19:16:50.714529: Epoch 309 +2026-04-10 19:16:50.716452: Current learning rate: 0.0093 +2026-04-10 19:18:34.353015: train_loss -0.1389 +2026-04-10 19:18:34.361586: val_loss -0.0729 +2026-04-10 19:18:34.363628: Pseudo dice [0.3131, 0.5484, 0.4747, 0.5411, 0.5618, 0.0642, 0.5766] +2026-04-10 19:18:34.368034: Epoch time: 103.64 s +2026-04-10 19:18:35.727498: +2026-04-10 19:18:35.730507: Epoch 310 +2026-04-10 19:18:35.733751: Current learning rate: 0.0093 +2026-04-10 19:20:19.215496: train_loss -0.1457 +2026-04-10 19:20:19.228349: val_loss -0.1622 +2026-04-10 19:20:19.231616: Pseudo dice [0.552, 0.5897, 0.6817, 0.5915, 0.5169, 0.7904, 0.848] +2026-04-10 19:20:19.234507: Epoch time: 103.49 s +2026-04-10 19:20:20.591593: +2026-04-10 19:20:20.594582: Epoch 311 +2026-04-10 19:20:20.597212: Current learning rate: 0.0093 +2026-04-10 19:22:04.595690: train_loss -0.1422 +2026-04-10 19:22:04.605014: val_loss -0.0626 +2026-04-10 19:22:04.608233: Pseudo dice [0.263, 0.3489, 0.3986, 0.0602, 0.5375, 0.093, 0.7615] +2026-04-10 19:22:04.612225: Epoch time: 104.01 s +2026-04-10 19:22:06.157230: +2026-04-10 19:22:06.158921: Epoch 312 +2026-04-10 19:22:06.161101: Current learning rate: 0.0093 +2026-04-10 19:23:49.764336: train_loss -0.1586 +2026-04-10 19:23:49.773622: val_loss -0.1093 +2026-04-10 19:23:49.776715: Pseudo dice [0.2706, 0.3886, 0.6752, 0.6087, 0.6776, 0.1564, 0.8092] +2026-04-10 19:23:49.780347: Epoch time: 103.61 s +2026-04-10 19:23:51.118233: +2026-04-10 19:23:51.122183: Epoch 313 +2026-04-10 19:23:51.125316: Current learning rate: 0.00929 +2026-04-10 19:25:34.232229: train_loss -0.1612 +2026-04-10 19:25:34.241002: val_loss -0.151 +2026-04-10 19:25:34.243369: Pseudo dice [0.282, 0.7875, 0.6635, 0.655, 0.5979, 0.6573, 0.7951] +2026-04-10 19:25:34.245657: Epoch time: 103.12 s +2026-04-10 19:25:35.620529: +2026-04-10 19:25:35.622463: Epoch 314 +2026-04-10 19:25:35.624959: Current learning rate: 0.00929 +2026-04-10 19:27:18.096702: train_loss -0.1591 +2026-04-10 19:27:18.103884: val_loss -0.0041 +2026-04-10 19:27:18.106450: Pseudo dice [0.6306, 0.7524, 0.5302, 0.5798, 0.487, 0.0333, 0.6975] +2026-04-10 19:27:18.108992: Epoch time: 102.48 s +2026-04-10 19:27:19.474517: +2026-04-10 19:27:19.477521: Epoch 315 +2026-04-10 19:27:19.480330: Current learning rate: 0.00929 +2026-04-10 19:29:03.897893: train_loss -0.1484 +2026-04-10 19:29:03.907734: val_loss -0.1448 +2026-04-10 19:29:03.910147: Pseudo dice [0.4055, 0.5904, 0.7036, 0.5341, 0.455, 0.7113, 0.7729] +2026-04-10 19:29:03.913151: Epoch time: 104.43 s +2026-04-10 19:29:05.273556: +2026-04-10 19:29:05.276038: Epoch 316 +2026-04-10 19:29:05.278066: Current learning rate: 0.00929 +2026-04-10 19:30:50.223608: train_loss -0.1565 +2026-04-10 19:30:50.232148: val_loss -0.1345 +2026-04-10 19:30:50.235365: Pseudo dice [0.7242, 0.5204, 0.6854, 0.4317, 0.5186, 0.0556, 0.806] +2026-04-10 19:30:50.238403: Epoch time: 104.95 s +2026-04-10 19:30:51.608736: +2026-04-10 19:30:51.611192: Epoch 317 +2026-04-10 19:30:51.615269: Current learning rate: 0.00928 +2026-04-10 19:32:37.076888: train_loss -0.1443 +2026-04-10 19:32:37.088643: val_loss -0.0988 +2026-04-10 19:32:37.092904: Pseudo dice [0.5361, 0.1625, 0.5317, 0.3366, 0.4606, 0.0739, 0.6412] +2026-04-10 19:32:37.096537: Epoch time: 105.47 s +2026-04-10 19:32:38.455784: +2026-04-10 19:32:38.458605: Epoch 318 +2026-04-10 19:32:38.460486: Current learning rate: 0.00928 +2026-04-10 19:34:21.998154: train_loss -0.1607 +2026-04-10 19:34:22.007560: val_loss -0.1056 +2026-04-10 19:34:22.010383: Pseudo dice [0.3984, 0.2313, 0.6198, 0.5824, 0.5661, 0.1336, 0.8435] +2026-04-10 19:34:22.013417: Epoch time: 103.54 s +2026-04-10 19:34:23.386398: +2026-04-10 19:34:23.389067: Epoch 319 +2026-04-10 19:34:23.392104: Current learning rate: 0.00928 +2026-04-10 19:36:07.205077: train_loss -0.1609 +2026-04-10 19:36:07.212080: val_loss -0.1343 +2026-04-10 19:36:07.214495: Pseudo dice [0.5173, 0.4968, 0.742, 0.5861, 0.3177, 0.2205, 0.8368] +2026-04-10 19:36:07.217418: Epoch time: 103.82 s +2026-04-10 19:36:08.575530: +2026-04-10 19:36:08.578081: Epoch 320 +2026-04-10 19:36:08.579860: Current learning rate: 0.00928 +2026-04-10 19:37:58.465235: train_loss -0.1575 +2026-04-10 19:37:58.483523: val_loss -0.1132 +2026-04-10 19:37:58.488253: Pseudo dice [0.2911, 0.4585, 0.762, 0.5011, 0.4336, 0.2323, 0.5129] +2026-04-10 19:37:58.493950: Epoch time: 109.89 s +2026-04-10 19:37:59.864695: +2026-04-10 19:37:59.867964: Epoch 321 +2026-04-10 19:37:59.870870: Current learning rate: 0.00927 +2026-04-10 19:39:51.957727: train_loss -0.1545 +2026-04-10 19:39:51.967960: val_loss -0.0986 +2026-04-10 19:39:51.970898: Pseudo dice [0.6316, 0.7328, 0.4268, 0.6546, 0.3621, 0.0856, 0.771] +2026-04-10 19:39:51.974879: Epoch time: 112.1 s +2026-04-10 19:39:53.345200: +2026-04-10 19:39:53.348380: Epoch 322 +2026-04-10 19:39:53.351882: Current learning rate: 0.00927 +2026-04-10 19:41:38.742239: train_loss -0.1437 +2026-04-10 19:41:38.751698: val_loss -0.1401 +2026-04-10 19:41:38.756494: Pseudo dice [0.1394, 0.8243, 0.5551, 0.728, 0.3927, 0.0777, 0.8287] +2026-04-10 19:41:38.761280: Epoch time: 105.4 s +2026-04-10 19:41:40.130718: +2026-04-10 19:41:40.133041: Epoch 323 +2026-04-10 19:41:40.137109: Current learning rate: 0.00927 +2026-04-10 19:43:25.316278: train_loss -0.1582 +2026-04-10 19:43:25.327102: val_loss -0.1368 +2026-04-10 19:43:25.332291: Pseudo dice [0.4549, 0.8312, 0.6774, 0.6477, 0.4948, 0.2198, 0.7865] +2026-04-10 19:43:25.336572: Epoch time: 105.19 s +2026-04-10 19:43:26.688862: +2026-04-10 19:43:26.691444: Epoch 324 +2026-04-10 19:43:26.694141: Current learning rate: 0.00927 +2026-04-10 19:45:12.312401: train_loss -0.1494 +2026-04-10 19:45:12.322322: val_loss -0.1408 +2026-04-10 19:45:12.326636: Pseudo dice [0.7333, 0.6762, 0.4943, 0.4296, 0.6066, 0.2159, 0.6868] +2026-04-10 19:45:12.330830: Epoch time: 105.63 s +2026-04-10 19:45:13.725785: +2026-04-10 19:45:13.729341: Epoch 325 +2026-04-10 19:45:13.734479: Current learning rate: 0.00927 +2026-04-10 19:46:58.406637: train_loss -0.1489 +2026-04-10 19:46:58.412598: val_loss -0.1521 +2026-04-10 19:46:58.414621: Pseudo dice [0.7617, 0.5421, 0.7368, 0.5644, 0.3926, 0.3144, 0.5739] +2026-04-10 19:46:58.417359: Epoch time: 104.68 s +2026-04-10 19:46:58.419501: Yayy! New best EMA pseudo Dice: 0.5126 +2026-04-10 19:47:01.788539: +2026-04-10 19:47:01.791446: Epoch 326 +2026-04-10 19:47:01.794992: Current learning rate: 0.00926 +2026-04-10 19:48:47.341616: train_loss -0.164 +2026-04-10 19:48:47.351296: val_loss -0.0783 +2026-04-10 19:48:47.354534: Pseudo dice [0.4505, 0.587, 0.7261, 0.5158, 0.3282, 0.043, 0.672] +2026-04-10 19:48:47.357758: Epoch time: 105.56 s +2026-04-10 19:48:48.760162: +2026-04-10 19:48:48.762820: Epoch 327 +2026-04-10 19:48:48.764812: Current learning rate: 0.00926 +2026-04-10 19:50:33.055150: train_loss -0.1578 +2026-04-10 19:50:33.062109: val_loss -0.0484 +2026-04-10 19:50:33.064539: Pseudo dice [0.6706, 0.6534, 0.4996, 0.486, 0.2979, 0.0524, 0.899] +2026-04-10 19:50:33.067609: Epoch time: 104.3 s +2026-04-10 19:50:34.439447: +2026-04-10 19:50:34.442418: Epoch 328 +2026-04-10 19:50:34.445666: Current learning rate: 0.00926 +2026-04-10 19:52:18.491981: train_loss -0.1774 +2026-04-10 19:52:18.499774: val_loss -0.1436 +2026-04-10 19:52:18.503015: Pseudo dice [0.8483, 0.5578, 0.5753, 0.2596, 0.5268, 0.3296, 0.7328] +2026-04-10 19:52:18.505885: Epoch time: 104.06 s +2026-04-10 19:52:18.508177: Yayy! New best EMA pseudo Dice: 0.5126 +2026-04-10 19:52:21.864229: +2026-04-10 19:52:21.866645: Epoch 329 +2026-04-10 19:52:21.868968: Current learning rate: 0.00926 +2026-04-10 19:54:06.332506: train_loss -0.1662 +2026-04-10 19:54:06.338921: val_loss -0.1029 +2026-04-10 19:54:06.340996: Pseudo dice [0.7446, 0.7548, 0.5997, 0.4108, 0.4819, 0.0515, 0.6919] +2026-04-10 19:54:06.343772: Epoch time: 104.47 s +2026-04-10 19:54:06.346264: Yayy! New best EMA pseudo Dice: 0.5147 +2026-04-10 19:54:09.625311: +2026-04-10 19:54:09.628253: Epoch 330 +2026-04-10 19:54:09.630081: Current learning rate: 0.00925 +2026-04-10 19:55:56.090982: train_loss -0.1624 +2026-04-10 19:55:56.097409: val_loss -0.1111 +2026-04-10 19:55:56.100154: Pseudo dice [0.5606, 0.314, 0.4538, 0.6452, 0.359, 0.0849, 0.5798] +2026-04-10 19:55:56.103514: Epoch time: 106.47 s +2026-04-10 19:55:57.467343: +2026-04-10 19:55:57.470021: Epoch 331 +2026-04-10 19:55:57.472041: Current learning rate: 0.00925 +2026-04-10 19:57:41.094001: train_loss -0.1672 +2026-04-10 19:57:41.101325: val_loss -0.1525 +2026-04-10 19:57:41.104168: Pseudo dice [0.5436, 0.5573, 0.5731, 0.253, 0.5092, 0.6998, 0.6839] +2026-04-10 19:57:41.108067: Epoch time: 103.63 s +2026-04-10 19:57:42.461051: +2026-04-10 19:57:42.465430: Epoch 332 +2026-04-10 19:57:42.467530: Current learning rate: 0.00925 +2026-04-10 19:59:25.841435: train_loss -0.1693 +2026-04-10 19:59:25.849673: val_loss -0.0995 +2026-04-10 19:59:25.852104: Pseudo dice [0.8024, 0.3243, 0.7813, 0.5356, 0.2775, 0.151, 0.4411] +2026-04-10 19:59:25.854848: Epoch time: 103.38 s +2026-04-10 19:59:27.217300: +2026-04-10 19:59:27.219740: Epoch 333 +2026-04-10 19:59:27.221849: Current learning rate: 0.00925 +2026-04-10 20:01:12.347595: train_loss -0.1394 +2026-04-10 20:01:12.355341: val_loss -0.042 +2026-04-10 20:01:12.359359: Pseudo dice [0.5575, 0.7006, 0.571, 0.5843, 0.3912, 0.068, 0.7067] +2026-04-10 20:01:12.362168: Epoch time: 105.13 s +2026-04-10 20:01:13.725392: +2026-04-10 20:01:13.731288: Epoch 334 +2026-04-10 20:01:13.734096: Current learning rate: 0.00925 +2026-04-10 20:03:02.445643: train_loss -0.1555 +2026-04-10 20:03:02.452621: val_loss -0.1037 +2026-04-10 20:03:02.455781: Pseudo dice [0.5606, 0.4782, 0.4806, 0.362, 0.3296, 0.0945, 0.6004] +2026-04-10 20:03:02.458759: Epoch time: 108.72 s +2026-04-10 20:03:03.858603: +2026-04-10 20:03:03.861401: Epoch 335 +2026-04-10 20:03:03.864045: Current learning rate: 0.00924 +2026-04-10 20:04:49.010096: train_loss -0.1512 +2026-04-10 20:04:49.017990: val_loss -0.1154 +2026-04-10 20:04:49.021741: Pseudo dice [0.5757, 0.3271, 0.6335, 0.7603, 0.4538, 0.1267, 0.6058] +2026-04-10 20:04:49.026045: Epoch time: 105.15 s +2026-04-10 20:04:50.438116: +2026-04-10 20:04:50.440204: Epoch 336 +2026-04-10 20:04:50.443955: Current learning rate: 0.00924 +2026-04-10 20:06:34.490801: train_loss -0.1637 +2026-04-10 20:06:34.499112: val_loss -0.07 +2026-04-10 20:06:34.501846: Pseudo dice [0.7328, 0.6961, 0.4899, 0.0121, 0.6405, 0.0185, 0.565] +2026-04-10 20:06:34.504811: Epoch time: 104.06 s +2026-04-10 20:06:35.917588: +2026-04-10 20:06:35.920673: Epoch 337 +2026-04-10 20:06:35.922666: Current learning rate: 0.00924 +2026-04-10 20:08:19.981338: train_loss -0.1481 +2026-04-10 20:08:19.987395: val_loss -0.14 +2026-04-10 20:08:19.990082: Pseudo dice [0.3416, 0.5742, 0.6145, 0.4946, 0.4007, 0.552, 0.7239] +2026-04-10 20:08:19.993069: Epoch time: 104.07 s +2026-04-10 20:08:21.373347: +2026-04-10 20:08:21.376092: Epoch 338 +2026-04-10 20:08:21.378536: Current learning rate: 0.00924 +2026-04-10 20:10:06.454521: train_loss -0.1665 +2026-04-10 20:10:06.461666: val_loss -0.1093 +2026-04-10 20:10:06.465066: Pseudo dice [0.428, 0.4016, 0.3808, 0.4686, 0.4018, 0.1649, 0.8251] +2026-04-10 20:10:06.467982: Epoch time: 105.08 s +2026-04-10 20:10:07.861682: +2026-04-10 20:10:07.864604: Epoch 339 +2026-04-10 20:10:07.867836: Current learning rate: 0.00923 +2026-04-10 20:11:52.822008: train_loss -0.1769 +2026-04-10 20:11:52.831909: val_loss -0.1411 +2026-04-10 20:11:52.835320: Pseudo dice [0.2402, 0.7977, 0.5542, 0.557, 0.3, 0.2322, 0.5364] +2026-04-10 20:11:52.839040: Epoch time: 104.96 s +2026-04-10 20:11:54.246677: +2026-04-10 20:11:54.250480: Epoch 340 +2026-04-10 20:11:54.256235: Current learning rate: 0.00923 +2026-04-10 20:13:38.509085: train_loss -0.1676 +2026-04-10 20:13:38.518051: val_loss -0.1439 +2026-04-10 20:13:38.521519: Pseudo dice [0.3575, 0.5487, 0.7149, 0.5529, 0.3175, 0.3867, 0.6537] +2026-04-10 20:13:38.524720: Epoch time: 104.27 s +2026-04-10 20:13:39.928751: +2026-04-10 20:13:39.931888: Epoch 341 +2026-04-10 20:13:39.934824: Current learning rate: 0.00923 +2026-04-10 20:15:24.469656: train_loss -0.164 +2026-04-10 20:15:24.480285: val_loss -0.1343 +2026-04-10 20:15:24.484491: Pseudo dice [0.7382, 0.8416, 0.7329, 0.7062, 0.6562, 0.1167, 0.7975] +2026-04-10 20:15:24.488342: Epoch time: 104.54 s +2026-04-10 20:15:25.873665: +2026-04-10 20:15:25.875965: Epoch 342 +2026-04-10 20:15:25.878054: Current learning rate: 0.00923 +2026-04-10 20:17:09.334982: train_loss -0.1633 +2026-04-10 20:17:09.343637: val_loss -0.1525 +2026-04-10 20:17:09.347023: Pseudo dice [0.6578, 0.7608, 0.3935, 0.8083, 0.5931, 0.7779, 0.8565] +2026-04-10 20:17:09.350390: Epoch time: 103.47 s +2026-04-10 20:17:09.353747: Yayy! New best EMA pseudo Dice: 0.5246 +2026-04-10 20:17:12.658680: +2026-04-10 20:17:12.661188: Epoch 343 +2026-04-10 20:17:12.662925: Current learning rate: 0.00922 +2026-04-10 20:18:56.760508: train_loss -0.1483 +2026-04-10 20:18:56.766679: val_loss -0.1319 +2026-04-10 20:18:56.769313: Pseudo dice [0.3943, 0.6748, 0.5965, 0.3906, 0.2128, 0.1741, 0.3769] +2026-04-10 20:18:56.772031: Epoch time: 104.11 s +2026-04-10 20:18:58.180110: +2026-04-10 20:18:58.183226: Epoch 344 +2026-04-10 20:18:58.186087: Current learning rate: 0.00922 +2026-04-10 20:20:43.457155: train_loss -0.1562 +2026-04-10 20:20:43.469469: val_loss -0.136 +2026-04-10 20:20:43.471802: Pseudo dice [0.5739, 0.575, 0.6511, 0.1523, 0.4846, 0.5142, 0.3363] +2026-04-10 20:20:43.477015: Epoch time: 105.28 s +2026-04-10 20:20:44.886972: +2026-04-10 20:20:44.889274: Epoch 345 +2026-04-10 20:20:44.891035: Current learning rate: 0.00922 +2026-04-10 20:22:28.807566: train_loss -0.163 +2026-04-10 20:22:28.815429: val_loss -0.1765 +2026-04-10 20:22:28.818418: Pseudo dice [0.3272, 0.7033, 0.6065, 0.3567, 0.587, 0.5702, 0.8416] +2026-04-10 20:22:28.821663: Epoch time: 103.92 s +2026-04-10 20:22:30.218480: +2026-04-10 20:22:30.220737: Epoch 346 +2026-04-10 20:22:30.223008: Current learning rate: 0.00922 +2026-04-10 20:24:15.106396: train_loss -0.1556 +2026-04-10 20:24:15.112774: val_loss -0.1152 +2026-04-10 20:24:15.115291: Pseudo dice [0.1824, 0.6818, 0.4864, 0.1041, 0.4296, 0.3113, 0.6374] +2026-04-10 20:24:15.117975: Epoch time: 104.89 s +2026-04-10 20:24:16.536762: +2026-04-10 20:24:16.538878: Epoch 347 +2026-04-10 20:24:16.541018: Current learning rate: 0.00922 +2026-04-10 20:26:00.881387: train_loss -0.159 +2026-04-10 20:26:00.887698: val_loss -0.1107 +2026-04-10 20:26:00.890223: Pseudo dice [0.5805, 0.5864, 0.6613, 0.5832, 0.5484, 0.081, 0.8722] +2026-04-10 20:26:00.893645: Epoch time: 104.35 s +2026-04-10 20:26:02.291271: +2026-04-10 20:26:02.293682: Epoch 348 +2026-04-10 20:26:02.297337: Current learning rate: 0.00921 +2026-04-10 20:27:46.567122: train_loss -0.1722 +2026-04-10 20:27:46.576931: val_loss -0.1047 +2026-04-10 20:27:46.584420: Pseudo dice [0.6167, 0.4105, 0.4875, 0.5374, 0.5101, 0.0775, 0.7535] +2026-04-10 20:27:46.587926: Epoch time: 104.28 s +2026-04-10 20:27:47.968868: +2026-04-10 20:27:47.972062: Epoch 349 +2026-04-10 20:27:47.975495: Current learning rate: 0.00921 +2026-04-10 20:29:34.335650: train_loss -0.1574 +2026-04-10 20:29:34.341610: val_loss -0.1324 +2026-04-10 20:29:34.343832: Pseudo dice [0.5275, 0.4506, 0.6501, 0.6225, 0.3348, 0.4222, 0.6959] +2026-04-10 20:29:34.346604: Epoch time: 106.37 s +2026-04-10 20:29:37.747630: +2026-04-10 20:29:37.749630: Epoch 350 +2026-04-10 20:29:37.751486: Current learning rate: 0.00921 +2026-04-10 20:31:22.646778: train_loss -0.1651 +2026-04-10 20:31:22.652926: val_loss -0.0805 +2026-04-10 20:31:22.656380: Pseudo dice [0.5269, 0.5157, 0.576, 0.4516, 0.198, 0.1047, 0.222] +2026-04-10 20:31:22.660459: Epoch time: 104.9 s +2026-04-10 20:31:24.061080: +2026-04-10 20:31:24.064879: Epoch 351 +2026-04-10 20:31:24.067841: Current learning rate: 0.00921 +2026-04-10 20:33:09.193453: train_loss -0.1723 +2026-04-10 20:33:09.201890: val_loss -0.0735 +2026-04-10 20:33:09.204473: Pseudo dice [0.6806, 0.4959, 0.6886, 0.3429, 0.4314, 0.0753, 0.6918] +2026-04-10 20:33:09.208576: Epoch time: 105.13 s +2026-04-10 20:33:10.643259: +2026-04-10 20:33:10.646001: Epoch 352 +2026-04-10 20:33:10.648975: Current learning rate: 0.0092 +2026-04-10 20:34:54.492131: train_loss -0.1606 +2026-04-10 20:34:54.502628: val_loss -0.1308 +2026-04-10 20:34:54.505762: Pseudo dice [0.2612, 0.4985, 0.5563, 0.5221, 0.3803, 0.3098, 0.8533] +2026-04-10 20:34:54.509482: Epoch time: 103.85 s +2026-04-10 20:34:55.902203: +2026-04-10 20:34:55.905424: Epoch 353 +2026-04-10 20:34:55.907943: Current learning rate: 0.0092 +2026-04-10 20:36:40.036735: train_loss -0.1589 +2026-04-10 20:36:40.043191: val_loss -0.1261 +2026-04-10 20:36:40.046188: Pseudo dice [0.5538, 0.3876, 0.5258, 0.7368, 0.5968, 0.0808, 0.6794] +2026-04-10 20:36:40.049559: Epoch time: 104.14 s +2026-04-10 20:36:41.452656: +2026-04-10 20:36:41.455354: Epoch 354 +2026-04-10 20:36:41.459135: Current learning rate: 0.0092 +2026-04-10 20:38:26.147975: train_loss -0.1613 +2026-04-10 20:38:26.155609: val_loss -0.0961 +2026-04-10 20:38:26.159335: Pseudo dice [0.2208, 0.1651, 0.5579, 0.3586, 0.5327, 0.5855, 0.6051] +2026-04-10 20:38:26.165944: Epoch time: 104.7 s +2026-04-10 20:38:27.564936: +2026-04-10 20:38:27.568817: Epoch 355 +2026-04-10 20:38:27.571166: Current learning rate: 0.0092 +2026-04-10 20:40:12.070842: train_loss -0.1514 +2026-04-10 20:40:12.078673: val_loss -0.1524 +2026-04-10 20:40:12.080911: Pseudo dice [0.1845, 0.4317, 0.5532, 0.5703, 0.5359, 0.4132, 0.885] +2026-04-10 20:40:12.083627: Epoch time: 104.51 s +2026-04-10 20:40:13.479122: +2026-04-10 20:40:13.481791: Epoch 356 +2026-04-10 20:40:13.483759: Current learning rate: 0.0092 +2026-04-10 20:42:02.014823: train_loss -0.1584 +2026-04-10 20:42:02.022569: val_loss -0.0515 +2026-04-10 20:42:02.027145: Pseudo dice [0.5572, 0.6799, 0.5563, 0.2477, 0.57, 0.1144, 0.4989] +2026-04-10 20:42:02.031275: Epoch time: 108.53 s +2026-04-10 20:42:03.460765: +2026-04-10 20:42:03.464685: Epoch 357 +2026-04-10 20:42:03.467618: Current learning rate: 0.00919 +2026-04-10 20:43:47.862570: train_loss -0.1499 +2026-04-10 20:43:47.870622: val_loss -0.1292 +2026-04-10 20:43:47.873745: Pseudo dice [0.558, 0.4149, 0.6298, 0.0817, 0.6343, 0.1147, 0.7197] +2026-04-10 20:43:47.878023: Epoch time: 104.41 s +2026-04-10 20:43:49.279580: +2026-04-10 20:43:49.282150: Epoch 358 +2026-04-10 20:43:49.285533: Current learning rate: 0.00919 +2026-04-10 20:45:32.961278: train_loss -0.1611 +2026-04-10 20:45:32.968086: val_loss -0.1553 +2026-04-10 20:45:32.970491: Pseudo dice [0.754, 0.7046, 0.8176, 0.4842, 0.638, 0.2729, 0.7091] +2026-04-10 20:45:32.973684: Epoch time: 103.69 s +2026-04-10 20:45:34.595905: +2026-04-10 20:45:34.598249: Epoch 359 +2026-04-10 20:45:34.600085: Current learning rate: 0.00919 +2026-04-10 20:47:19.265561: train_loss -0.1585 +2026-04-10 20:47:19.276483: val_loss -0.1018 +2026-04-10 20:47:19.279694: Pseudo dice [0.6259, 0.6457, 0.7058, 0.4976, 0.6316, 0.3048, 0.8003] +2026-04-10 20:47:19.281943: Epoch time: 104.68 s +2026-04-10 20:47:20.676799: +2026-04-10 20:47:20.679466: Epoch 360 +2026-04-10 20:47:20.682003: Current learning rate: 0.00919 +2026-04-10 20:49:04.852154: train_loss -0.1671 +2026-04-10 20:49:04.862312: val_loss -0.0908 +2026-04-10 20:49:04.865411: Pseudo dice [0.2772, 0.3464, 0.664, 0.1385, 0.5558, 0.4527, 0.5549] +2026-04-10 20:49:04.868271: Epoch time: 104.18 s +2026-04-10 20:49:06.278385: +2026-04-10 20:49:06.282016: Epoch 361 +2026-04-10 20:49:06.283917: Current learning rate: 0.00918 +2026-04-10 20:50:50.497976: train_loss -0.1659 +2026-04-10 20:50:50.505884: val_loss -0.1107 +2026-04-10 20:50:50.515743: Pseudo dice [0.2232, 0.1498, 0.2845, 0.2951, 0.3011, 0.7627, 0.748] +2026-04-10 20:50:50.520048: Epoch time: 104.22 s +2026-04-10 20:50:51.921275: +2026-04-10 20:50:51.924079: Epoch 362 +2026-04-10 20:50:51.926309: Current learning rate: 0.00918 +2026-04-10 20:52:36.041070: train_loss -0.1482 +2026-04-10 20:52:36.048099: val_loss -0.0286 +2026-04-10 20:52:36.050276: Pseudo dice [0.6035, 0.682, 0.4714, 0.1402, 0.292, 0.0345, 0.159] +2026-04-10 20:52:36.052667: Epoch time: 104.12 s +2026-04-10 20:52:37.456408: +2026-04-10 20:52:37.459899: Epoch 363 +2026-04-10 20:52:37.463162: Current learning rate: 0.00918 +2026-04-10 20:54:23.879554: train_loss -0.176 +2026-04-10 20:54:23.887989: val_loss -0.143 +2026-04-10 20:54:23.890731: Pseudo dice [0.3766, 0.4217, 0.7898, 0.5885, 0.3431, 0.7016, 0.3879] +2026-04-10 20:54:23.895086: Epoch time: 106.43 s +2026-04-10 20:54:25.306272: +2026-04-10 20:54:25.310592: Epoch 364 +2026-04-10 20:54:25.313120: Current learning rate: 0.00918 +2026-04-10 20:56:09.489560: train_loss -0.1471 +2026-04-10 20:56:09.495623: val_loss -0.0597 +2026-04-10 20:56:09.497669: Pseudo dice [0.5828, 0.5722, 0.7455, 0.5372, 0.3089, 0.1648, 0.7972] +2026-04-10 20:56:09.500102: Epoch time: 104.19 s +2026-04-10 20:56:10.890366: +2026-04-10 20:56:10.894010: Epoch 365 +2026-04-10 20:56:10.896320: Current learning rate: 0.00917 +2026-04-10 20:57:54.659942: train_loss -0.1493 +2026-04-10 20:57:54.668480: val_loss -0.1547 +2026-04-10 20:57:54.670958: Pseudo dice [0.7051, 0.6876, 0.7608, 0.5662, 0.4692, 0.3258, 0.7389] +2026-04-10 20:57:54.674917: Epoch time: 103.77 s +2026-04-10 20:57:56.030343: +2026-04-10 20:57:56.032058: Epoch 366 +2026-04-10 20:57:56.033670: Current learning rate: 0.00917 +2026-04-10 20:59:42.908914: train_loss -0.1586 +2026-04-10 20:59:42.916569: val_loss -0.1166 +2026-04-10 20:59:42.919107: Pseudo dice [0.6699, 0.7396, 0.7017, 0.6553, 0.439, 0.3026, 0.5398] +2026-04-10 20:59:42.922613: Epoch time: 106.88 s +2026-04-10 20:59:44.316781: +2026-04-10 20:59:44.319292: Epoch 367 +2026-04-10 20:59:44.320802: Current learning rate: 0.00917 +2026-04-10 21:01:28.201894: train_loss -0.1586 +2026-04-10 21:01:28.209603: val_loss -0.0817 +2026-04-10 21:01:28.213135: Pseudo dice [0.6543, 0.6777, 0.5995, 0.5336, 0.2259, 0.1336, 0.6579] +2026-04-10 21:01:28.216540: Epoch time: 103.89 s +2026-04-10 21:01:29.610479: +2026-04-10 21:01:29.613053: Epoch 368 +2026-04-10 21:01:29.614699: Current learning rate: 0.00917 +2026-04-10 21:03:14.203378: train_loss -0.1528 +2026-04-10 21:03:14.212957: val_loss -0.064 +2026-04-10 21:03:14.215928: Pseudo dice [0.4971, 0.3157, 0.4934, 0.505, 0.4681, 0.0654, 0.6676] +2026-04-10 21:03:14.218802: Epoch time: 104.6 s +2026-04-10 21:03:15.632330: +2026-04-10 21:03:15.635074: Epoch 369 +2026-04-10 21:03:15.637558: Current learning rate: 0.00917 +2026-04-10 21:05:00.976957: train_loss -0.1661 +2026-04-10 21:05:00.991403: val_loss -0.0799 +2026-04-10 21:05:00.996394: Pseudo dice [0.5577, 0.6012, 0.42, 0.0634, 0.5989, 0.0342, 0.7706] +2026-04-10 21:05:01.001588: Epoch time: 105.35 s +2026-04-10 21:05:02.401849: +2026-04-10 21:05:02.406156: Epoch 370 +2026-04-10 21:05:02.408691: Current learning rate: 0.00916 +2026-04-10 21:06:47.675329: train_loss -0.1671 +2026-04-10 21:06:47.683570: val_loss -0.1212 +2026-04-10 21:06:47.686603: Pseudo dice [0.5616, 0.1401, 0.7926, 0.6106, 0.3557, 0.3989, 0.6863] +2026-04-10 21:06:47.689484: Epoch time: 105.28 s +2026-04-10 21:06:49.100787: +2026-04-10 21:06:49.103910: Epoch 371 +2026-04-10 21:06:49.107322: Current learning rate: 0.00916 +2026-04-10 21:08:35.301394: train_loss -0.1466 +2026-04-10 21:08:35.307574: val_loss -0.1286 +2026-04-10 21:08:35.309679: Pseudo dice [0.6032, 0.3442, 0.3858, 0.6934, 0.3037, 0.5798, 0.5723] +2026-04-10 21:08:35.312824: Epoch time: 106.2 s +2026-04-10 21:08:36.752642: +2026-04-10 21:08:36.755478: Epoch 372 +2026-04-10 21:08:36.757648: Current learning rate: 0.00916 +2026-04-10 21:10:21.949916: train_loss -0.1393 +2026-04-10 21:10:21.957643: val_loss -0.1482 +2026-04-10 21:10:21.961750: Pseudo dice [0.6622, 0.4728, 0.692, 0.357, 0.4719, 0.4974, 0.7142] +2026-04-10 21:10:21.965933: Epoch time: 105.2 s +2026-04-10 21:10:23.375738: +2026-04-10 21:10:23.379912: Epoch 373 +2026-04-10 21:10:23.384580: Current learning rate: 0.00916 +2026-04-10 21:12:06.926584: train_loss -0.1653 +2026-04-10 21:12:06.932642: val_loss -0.1417 +2026-04-10 21:12:06.935385: Pseudo dice [0.5976, 0.5273, 0.7036, 0.3194, 0.376, 0.6821, 0.4012] +2026-04-10 21:12:06.938570: Epoch time: 103.55 s +2026-04-10 21:12:08.339893: +2026-04-10 21:12:08.343614: Epoch 374 +2026-04-10 21:12:08.347508: Current learning rate: 0.00915 +2026-04-10 21:13:53.134432: train_loss -0.1683 +2026-04-10 21:13:53.147507: val_loss -0.0632 +2026-04-10 21:13:53.152190: Pseudo dice [0.5292, 0.4666, 0.4308, 0.7852, 0.2602, 0.121, 0.9146] +2026-04-10 21:13:53.158401: Epoch time: 104.8 s +2026-04-10 21:13:54.563482: +2026-04-10 21:13:54.565780: Epoch 375 +2026-04-10 21:13:54.567965: Current learning rate: 0.00915 +2026-04-10 21:15:38.808937: train_loss -0.1645 +2026-04-10 21:15:38.815973: val_loss -0.1417 +2026-04-10 21:15:38.818679: Pseudo dice [0.7052, 0.4962, 0.4472, 0.5132, 0.5502, 0.1339, 0.8245] +2026-04-10 21:15:38.821415: Epoch time: 104.25 s +2026-04-10 21:15:40.192730: +2026-04-10 21:15:40.196467: Epoch 376 +2026-04-10 21:15:40.198938: Current learning rate: 0.00915 +2026-04-10 21:17:24.433396: train_loss -0.1618 +2026-04-10 21:17:24.442391: val_loss -0.0765 +2026-04-10 21:17:24.444591: Pseudo dice [0.3798, 0.7135, 0.3981, 0.5328, 0.4813, 0.032, 0.5207] +2026-04-10 21:17:24.450600: Epoch time: 104.24 s +2026-04-10 21:17:25.840082: +2026-04-10 21:17:25.843924: Epoch 377 +2026-04-10 21:17:25.846539: Current learning rate: 0.00915 +2026-04-10 21:19:10.354125: train_loss -0.1668 +2026-04-10 21:19:10.365717: val_loss -0.1377 +2026-04-10 21:19:10.370457: Pseudo dice [0.7635, 0.3856, 0.608, 0.3829, 0.3621, 0.6237, 0.3576] +2026-04-10 21:19:10.373842: Epoch time: 104.52 s +2026-04-10 21:19:11.793109: +2026-04-10 21:19:11.795946: Epoch 378 +2026-04-10 21:19:11.798372: Current learning rate: 0.00915 +2026-04-10 21:20:56.572011: train_loss -0.1391 +2026-04-10 21:20:56.579415: val_loss -0.0892 +2026-04-10 21:20:56.582271: Pseudo dice [0.368, 0.3373, 0.6604, 0.7643, 0.431, 0.0522, 0.7165] +2026-04-10 21:20:56.585921: Epoch time: 104.78 s +2026-04-10 21:20:57.959613: +2026-04-10 21:20:57.962530: Epoch 379 +2026-04-10 21:20:57.964780: Current learning rate: 0.00914 +2026-04-10 21:22:42.994807: train_loss -0.1617 +2026-04-10 21:22:43.001614: val_loss -0.0444 +2026-04-10 21:22:43.004412: Pseudo dice [0.7001, 0.2378, 0.6243, 0.6752, 0.3007, 0.0343, 0.8433] +2026-04-10 21:22:43.008873: Epoch time: 105.04 s +2026-04-10 21:22:44.406580: +2026-04-10 21:22:44.409134: Epoch 380 +2026-04-10 21:22:44.411892: Current learning rate: 0.00914 +2026-04-10 21:24:31.365910: train_loss -0.1666 +2026-04-10 21:24:31.375370: val_loss -0.1412 +2026-04-10 21:24:31.378633: Pseudo dice [0.7555, 0.5581, 0.66, 0.6095, 0.6051, 0.2008, 0.6834] +2026-04-10 21:24:31.382589: Epoch time: 106.96 s +2026-04-10 21:24:32.772026: +2026-04-10 21:24:32.775597: Epoch 381 +2026-04-10 21:24:32.778469: Current learning rate: 0.00914 +2026-04-10 21:26:17.867427: train_loss -0.1718 +2026-04-10 21:26:17.880480: val_loss -0.1335 +2026-04-10 21:26:17.885032: Pseudo dice [0.4847, 0.7855, 0.7039, 0.5245, 0.2175, 0.6881, 0.6126] +2026-04-10 21:26:17.889341: Epoch time: 105.1 s +2026-04-10 21:26:19.317367: +2026-04-10 21:26:19.321453: Epoch 382 +2026-04-10 21:26:19.323900: Current learning rate: 0.00914 +2026-04-10 21:28:05.265252: train_loss -0.1572 +2026-04-10 21:28:05.279491: val_loss -0.1169 +2026-04-10 21:28:05.282974: Pseudo dice [0.2687, 0.5124, 0.6312, 0.5767, 0.5677, 0.075, 0.7355] +2026-04-10 21:28:05.294362: Epoch time: 105.95 s +2026-04-10 21:28:07.747627: +2026-04-10 21:28:07.754736: Epoch 383 +2026-04-10 21:28:07.757882: Current learning rate: 0.00913 +2026-04-10 21:29:52.416312: train_loss -0.1547 +2026-04-10 21:29:52.428092: val_loss -0.1231 +2026-04-10 21:29:52.432805: Pseudo dice [0.5859, 0.4749, 0.4334, 0.1135, 0.5636, 0.3045, 0.7998] +2026-04-10 21:29:52.437006: Epoch time: 104.67 s +2026-04-10 21:29:53.853006: +2026-04-10 21:29:53.856763: Epoch 384 +2026-04-10 21:29:53.861359: Current learning rate: 0.00913 +2026-04-10 21:31:39.292961: train_loss -0.14 +2026-04-10 21:31:39.299124: val_loss -0.124 +2026-04-10 21:31:39.301938: Pseudo dice [0.6904, 0.6204, 0.3324, 0.624, 0.2265, 0.5416, 0.384] +2026-04-10 21:31:39.304723: Epoch time: 105.44 s +2026-04-10 21:31:40.728745: +2026-04-10 21:31:40.731231: Epoch 385 +2026-04-10 21:31:40.733868: Current learning rate: 0.00913 +2026-04-10 21:33:25.980630: train_loss -0.1494 +2026-04-10 21:33:25.987454: val_loss -0.1323 +2026-04-10 21:33:25.989934: Pseudo dice [0.6639, 0.6201, 0.5672, 0.3341, 0.611, 0.0381, 0.6602] +2026-04-10 21:33:25.992541: Epoch time: 105.26 s +2026-04-10 21:33:27.416088: +2026-04-10 21:33:27.420777: Epoch 386 +2026-04-10 21:33:27.423243: Current learning rate: 0.00913 +2026-04-10 21:35:12.996327: train_loss -0.1461 +2026-04-10 21:35:13.007309: val_loss -0.1379 +2026-04-10 21:35:13.011742: Pseudo dice [0.7378, 0.7198, 0.6792, 0.3173, 0.41, 0.353, 0.7623] +2026-04-10 21:35:13.015454: Epoch time: 105.58 s +2026-04-10 21:35:14.459730: +2026-04-10 21:35:14.464822: Epoch 387 +2026-04-10 21:35:14.471232: Current learning rate: 0.00912 +2026-04-10 21:37:00.248504: train_loss -0.1449 +2026-04-10 21:37:00.267112: val_loss -0.0991 +2026-04-10 21:37:00.274991: Pseudo dice [0.6584, 0.5769, 0.3445, 0.0541, 0.1585, 0.1872, 0.5762] +2026-04-10 21:37:00.281416: Epoch time: 105.79 s +2026-04-10 21:37:01.717008: +2026-04-10 21:37:01.720053: Epoch 388 +2026-04-10 21:37:01.723904: Current learning rate: 0.00912 +2026-04-10 21:38:46.691836: train_loss -0.1401 +2026-04-10 21:38:46.699855: val_loss -0.1388 +2026-04-10 21:38:46.702308: Pseudo dice [0.4396, 0.4427, 0.7791, 0.4446, 0.3304, 0.7073, 0.5374] +2026-04-10 21:38:46.707031: Epoch time: 104.98 s +2026-04-10 21:38:48.115513: +2026-04-10 21:38:48.118654: Epoch 389 +2026-04-10 21:38:48.122941: Current learning rate: 0.00912 +2026-04-10 21:40:33.142710: train_loss -0.1537 +2026-04-10 21:40:33.151724: val_loss -0.1352 +2026-04-10 21:40:33.154642: Pseudo dice [0.7679, 0.5072, 0.718, 0.4269, 0.4571, 0.0743, 0.8414] +2026-04-10 21:40:33.158705: Epoch time: 105.03 s +2026-04-10 21:40:34.582152: +2026-04-10 21:40:34.584538: Epoch 390 +2026-04-10 21:40:34.587001: Current learning rate: 0.00912 +2026-04-10 21:42:19.569550: train_loss -0.1685 +2026-04-10 21:42:19.576863: val_loss -0.1169 +2026-04-10 21:42:19.581324: Pseudo dice [0.4058, 0.3413, 0.6358, 0.6218, 0.3855, 0.3412, 0.6443] +2026-04-10 21:42:19.584629: Epoch time: 104.99 s +2026-04-10 21:42:21.017409: +2026-04-10 21:42:21.019313: Epoch 391 +2026-04-10 21:42:21.022015: Current learning rate: 0.00912 +2026-04-10 21:44:06.720554: train_loss -0.1596 +2026-04-10 21:44:06.729887: val_loss -0.1182 +2026-04-10 21:44:06.733572: Pseudo dice [0.3653, 0.677, 0.6535, 0.5336, 0.3646, 0.2015, 0.3809] +2026-04-10 21:44:06.740135: Epoch time: 105.71 s +2026-04-10 21:44:08.160349: +2026-04-10 21:44:08.162975: Epoch 392 +2026-04-10 21:44:08.165924: Current learning rate: 0.00911 +2026-04-10 21:45:53.797305: train_loss -0.1555 +2026-04-10 21:45:53.805769: val_loss -0.1158 +2026-04-10 21:45:53.808713: Pseudo dice [0.1921, 0.6871, 0.6659, 0.6683, 0.4651, 0.3193, 0.8281] +2026-04-10 21:45:53.811778: Epoch time: 105.64 s +2026-04-10 21:45:55.227904: +2026-04-10 21:45:55.230693: Epoch 393 +2026-04-10 21:45:55.233358: Current learning rate: 0.00911 +2026-04-10 21:47:40.849498: train_loss -0.1552 +2026-04-10 21:47:40.862221: val_loss -0.1191 +2026-04-10 21:47:40.865368: Pseudo dice [0.3645, 0.6831, 0.7029, 0.6299, 0.4282, 0.097, 0.7674] +2026-04-10 21:47:40.870051: Epoch time: 105.63 s +2026-04-10 21:47:42.278677: +2026-04-10 21:47:42.280990: Epoch 394 +2026-04-10 21:47:42.283310: Current learning rate: 0.00911 +2026-04-10 21:49:27.412199: train_loss -0.1799 +2026-04-10 21:49:27.420845: val_loss -0.1286 +2026-04-10 21:49:27.423577: Pseudo dice [0.7142, 0.513, 0.7106, 0.3777, 0.5704, 0.7737, 0.5996] +2026-04-10 21:49:27.426781: Epoch time: 105.14 s +2026-04-10 21:49:28.867050: +2026-04-10 21:49:28.869685: Epoch 395 +2026-04-10 21:49:28.872893: Current learning rate: 0.00911 +2026-04-10 21:51:15.010042: train_loss -0.1531 +2026-04-10 21:51:15.024226: val_loss -0.1672 +2026-04-10 21:51:15.026911: Pseudo dice [0.29, 0.711, 0.6655, 0.6759, 0.6335, 0.7193, 0.8261] +2026-04-10 21:51:15.030473: Epoch time: 106.15 s +2026-04-10 21:51:15.033640: Yayy! New best EMA pseudo Dice: 0.5264 +2026-04-10 21:51:18.503955: +2026-04-10 21:51:18.508359: Epoch 396 +2026-04-10 21:51:18.512247: Current learning rate: 0.0091 +2026-04-10 21:53:04.595393: train_loss -0.1718 +2026-04-10 21:53:04.603666: val_loss -0.1655 +2026-04-10 21:53:04.607390: Pseudo dice [0.664, 0.466, 0.6595, 0.6516, 0.6158, 0.8042, 0.6868] +2026-04-10 21:53:04.611193: Epoch time: 106.1 s +2026-04-10 21:53:04.613940: Yayy! New best EMA pseudo Dice: 0.5387 +2026-04-10 21:53:08.037028: +2026-04-10 21:53:08.039793: Epoch 397 +2026-04-10 21:53:08.041837: Current learning rate: 0.0091 +2026-04-10 21:54:55.510267: train_loss -0.1789 +2026-04-10 21:54:55.523737: val_loss -0.145 +2026-04-10 21:54:55.528692: Pseudo dice [0.7735, 0.5214, 0.6245, 0.6247, 0.5249, 0.1658, 0.7696] +2026-04-10 21:54:55.533387: Epoch time: 107.48 s +2026-04-10 21:54:55.538555: Yayy! New best EMA pseudo Dice: 0.542 +2026-04-10 21:54:58.972718: +2026-04-10 21:54:58.974888: Epoch 398 +2026-04-10 21:54:58.977017: Current learning rate: 0.0091 +2026-04-10 21:56:44.713140: train_loss -0.1753 +2026-04-10 21:56:44.724900: val_loss -0.1534 +2026-04-10 21:56:44.728034: Pseudo dice [0.6934, 0.7569, 0.6878, 0.3639, 0.3501, 0.7488, 0.5654] +2026-04-10 21:56:44.731927: Epoch time: 105.74 s +2026-04-10 21:56:44.734550: Yayy! New best EMA pseudo Dice: 0.5474 +2026-04-10 21:56:48.022810: +2026-04-10 21:56:48.024861: Epoch 399 +2026-04-10 21:56:48.027204: Current learning rate: 0.0091 +2026-04-10 21:58:34.014077: train_loss -0.1744 +2026-04-10 21:58:34.022069: val_loss -0.0429 +2026-04-10 21:58:34.024722: Pseudo dice [0.6051, 0.5502, 0.2474, 0.2263, 0.6483, 0.0591, 0.7659] +2026-04-10 21:58:34.031415: Epoch time: 106.0 s +2026-04-10 21:58:37.473495: +2026-04-10 21:58:37.476170: Epoch 400 +2026-04-10 21:58:37.478909: Current learning rate: 0.0091 +2026-04-10 22:00:26.322542: train_loss -0.1549 +2026-04-10 22:00:26.332605: val_loss -0.1387 +2026-04-10 22:00:26.340419: Pseudo dice [0.7725, 0.8266, 0.6259, 0.1793, 0.4253, 0.1184, 0.8163] +2026-04-10 22:00:26.345437: Epoch time: 108.85 s +2026-04-10 22:00:27.764491: +2026-04-10 22:00:27.767980: Epoch 401 +2026-04-10 22:00:27.770700: Current learning rate: 0.00909 +2026-04-10 22:02:12.717846: train_loss -0.1681 +2026-04-10 22:02:12.727273: val_loss -0.1801 +2026-04-10 22:02:12.731572: Pseudo dice [0.4574, 0.641, 0.7438, 0.8291, 0.5841, 0.832, 0.8109] +2026-04-10 22:02:12.737207: Epoch time: 104.96 s +2026-04-10 22:02:12.740791: Yayy! New best EMA pseudo Dice: 0.5533 +2026-04-10 22:02:16.370606: +2026-04-10 22:02:16.373151: Epoch 402 +2026-04-10 22:02:16.376152: Current learning rate: 0.00909 +2026-04-10 22:04:01.308928: train_loss -0.1746 +2026-04-10 22:04:01.318125: val_loss -0.092 +2026-04-10 22:04:01.321497: Pseudo dice [0.6344, 0.7437, 0.6211, 0.083, 0.4019, 0.2895, 0.5392] +2026-04-10 22:04:01.325252: Epoch time: 104.94 s +2026-04-10 22:04:02.738214: +2026-04-10 22:04:02.742779: Epoch 403 +2026-04-10 22:04:02.745249: Current learning rate: 0.00909 +2026-04-10 22:05:47.656479: train_loss -0.161 +2026-04-10 22:05:47.665484: val_loss -0.1434 +2026-04-10 22:05:47.670196: Pseudo dice [0.4817, 0.4744, 0.4726, 0.7613, 0.3589, 0.2649, 0.5825] +2026-04-10 22:05:47.674503: Epoch time: 104.92 s +2026-04-10 22:05:49.107414: +2026-04-10 22:05:49.110657: Epoch 404 +2026-04-10 22:05:49.113227: Current learning rate: 0.00909 +2026-04-10 22:07:34.237097: train_loss -0.1642 +2026-04-10 22:07:34.245096: val_loss -0.1379 +2026-04-10 22:07:34.248237: Pseudo dice [0.5753, 0.7007, 0.5651, 0.1761, 0.4702, 0.2916, 0.5522] +2026-04-10 22:07:34.252621: Epoch time: 105.13 s +2026-04-10 22:07:35.672146: +2026-04-10 22:07:35.675555: Epoch 405 +2026-04-10 22:07:35.679498: Current learning rate: 0.00908 +2026-04-10 22:09:19.568058: train_loss -0.1609 +2026-04-10 22:09:19.577720: val_loss -0.1501 +2026-04-10 22:09:19.580645: Pseudo dice [0.6127, 0.7563, 0.6905, 0.141, 0.4604, 0.5437, 0.7535] +2026-04-10 22:09:19.584979: Epoch time: 103.9 s +2026-04-10 22:09:20.992258: +2026-04-10 22:09:20.994286: Epoch 406 +2026-04-10 22:09:20.996495: Current learning rate: 0.00908 +2026-04-10 22:11:07.033302: train_loss -0.1676 +2026-04-10 22:11:07.040611: val_loss -0.1526 +2026-04-10 22:11:07.043904: Pseudo dice [0.5996, 0.4858, 0.5648, 0.004, 0.5475, 0.3972, 0.6328] +2026-04-10 22:11:07.047580: Epoch time: 106.04 s +2026-04-10 22:11:08.452944: +2026-04-10 22:11:08.456072: Epoch 407 +2026-04-10 22:11:08.459077: Current learning rate: 0.00908 +2026-04-10 22:12:54.206151: train_loss -0.1493 +2026-04-10 22:12:54.214188: val_loss -0.132 +2026-04-10 22:12:54.216554: Pseudo dice [0.4619, 0.3936, 0.5186, 0.0432, 0.6474, 0.0959, 0.6327] +2026-04-10 22:12:54.221247: Epoch time: 105.76 s +2026-04-10 22:12:55.725521: +2026-04-10 22:12:55.728130: Epoch 408 +2026-04-10 22:12:55.730782: Current learning rate: 0.00908 +2026-04-10 22:14:42.165798: train_loss -0.177 +2026-04-10 22:14:42.173502: val_loss -0.1302 +2026-04-10 22:14:42.176227: Pseudo dice [0.7312, 0.538, 0.7023, 0.6812, 0.6593, 0.21, 0.8547] +2026-04-10 22:14:42.179050: Epoch time: 106.44 s +2026-04-10 22:14:43.605455: +2026-04-10 22:14:43.607821: Epoch 409 +2026-04-10 22:14:43.611372: Current learning rate: 0.00907 +2026-04-10 22:16:31.817813: train_loss -0.1594 +2026-04-10 22:16:31.825272: val_loss -0.1516 +2026-04-10 22:16:31.828602: Pseudo dice [0.6814, 0.5274, 0.7235, 0.6422, 0.4245, 0.4891, 0.4551] +2026-04-10 22:16:31.831615: Epoch time: 108.22 s +2026-04-10 22:16:33.352030: +2026-04-10 22:16:33.356728: Epoch 410 +2026-04-10 22:16:33.359273: Current learning rate: 0.00907 +2026-04-10 22:18:18.256735: train_loss -0.1569 +2026-04-10 22:18:18.265508: val_loss -0.1154 +2026-04-10 22:18:18.269103: Pseudo dice [0.2525, 0.6698, 0.7308, 0.5507, 0.5241, 0.3924, 0.3219] +2026-04-10 22:18:18.272433: Epoch time: 104.91 s +2026-04-10 22:18:19.586639: +2026-04-10 22:18:19.589450: Epoch 411 +2026-04-10 22:18:19.592167: Current learning rate: 0.00907 +2026-04-10 22:20:04.422575: train_loss -0.149 +2026-04-10 22:20:04.434992: val_loss -0.1712 +2026-04-10 22:20:04.437471: Pseudo dice [0.1587, 0.783, 0.7069, 0.3723, 0.5039, 0.4354, 0.7494] +2026-04-10 22:20:04.440517: Epoch time: 104.84 s +2026-04-10 22:20:05.792702: +2026-04-10 22:20:05.795289: Epoch 412 +2026-04-10 22:20:05.797718: Current learning rate: 0.00907 +2026-04-10 22:21:51.322489: train_loss -0.1693 +2026-04-10 22:21:51.332067: val_loss -0.1143 +2026-04-10 22:21:51.335473: Pseudo dice [0.6466, 0.4172, 0.6753, 0.4378, 0.6272, 0.1086, 0.4955] +2026-04-10 22:21:51.338270: Epoch time: 105.53 s +2026-04-10 22:21:52.691857: +2026-04-10 22:21:52.694719: Epoch 413 +2026-04-10 22:21:52.698412: Current learning rate: 0.00907 +2026-04-10 22:23:37.352613: train_loss -0.1776 +2026-04-10 22:23:37.362744: val_loss -0.0123 +2026-04-10 22:23:37.366843: Pseudo dice [0.3402, 0.4455, 0.6874, 0.7605, 0.2958, 0.0215, 0.3576] +2026-04-10 22:23:37.371352: Epoch time: 104.66 s +2026-04-10 22:23:38.703710: +2026-04-10 22:23:38.707717: Epoch 414 +2026-04-10 22:23:38.710154: Current learning rate: 0.00906 +2026-04-10 22:25:23.212102: train_loss -0.1717 +2026-04-10 22:25:23.223506: val_loss -0.165 +2026-04-10 22:25:23.226507: Pseudo dice [0.6461, 0.5374, 0.6557, 0.2842, 0.4748, 0.498, 0.6838] +2026-04-10 22:25:23.233439: Epoch time: 104.51 s +2026-04-10 22:25:24.559401: +2026-04-10 22:25:24.562054: Epoch 415 +2026-04-10 22:25:24.564588: Current learning rate: 0.00906 +2026-04-10 22:27:12.121237: train_loss -0.1703 +2026-04-10 22:27:12.139060: val_loss -0.1497 +2026-04-10 22:27:12.147460: Pseudo dice [0.7274, 0.6344, 0.6143, 0.0195, 0.365, 0.4129, 0.8274] +2026-04-10 22:27:12.155509: Epoch time: 107.57 s +2026-04-10 22:27:13.521653: +2026-04-10 22:27:13.525235: Epoch 416 +2026-04-10 22:27:13.528690: Current learning rate: 0.00906 +2026-04-10 22:28:58.704916: train_loss -0.1564 +2026-04-10 22:28:58.713081: val_loss -0.0657 +2026-04-10 22:28:58.717094: Pseudo dice [0.5822, 0.3993, 0.5407, 0.734, 0.4992, 0.0751, 0.6622] +2026-04-10 22:28:58.722356: Epoch time: 105.19 s +2026-04-10 22:29:00.047707: +2026-04-10 22:29:00.051225: Epoch 417 +2026-04-10 22:29:00.057766: Current learning rate: 0.00906 +2026-04-10 22:30:45.424826: train_loss -0.164 +2026-04-10 22:30:45.432493: val_loss -0.119 +2026-04-10 22:30:45.435859: Pseudo dice [0.5269, 0.7544, 0.6626, 0.6327, 0.3994, 0.3122, 0.6229] +2026-04-10 22:30:45.442190: Epoch time: 105.38 s +2026-04-10 22:30:46.793453: +2026-04-10 22:30:46.797105: Epoch 418 +2026-04-10 22:30:46.801272: Current learning rate: 0.00905 +2026-04-10 22:32:33.180872: train_loss -0.1659 +2026-04-10 22:32:33.189047: val_loss -0.1131 +2026-04-10 22:32:33.191957: Pseudo dice [0.5281, 0.4012, 0.5484, 0.7011, 0.4785, 0.0554, 0.6743] +2026-04-10 22:32:33.194618: Epoch time: 106.39 s +2026-04-10 22:32:34.520863: +2026-04-10 22:32:34.523856: Epoch 419 +2026-04-10 22:32:34.528501: Current learning rate: 0.00905 +2026-04-10 22:34:35.753131: train_loss -0.1672 +2026-04-10 22:34:35.762899: val_loss -0.1165 +2026-04-10 22:34:35.765536: Pseudo dice [0.6135, 0.4938, 0.5469, 0.795, 0.4997, 0.1202, 0.7497] +2026-04-10 22:34:35.769306: Epoch time: 121.23 s +2026-04-10 22:34:38.509388: +2026-04-10 22:34:38.511727: Epoch 420 +2026-04-10 22:34:38.513607: Current learning rate: 0.00905 +2026-04-10 22:36:28.123002: train_loss -0.1681 +2026-04-10 22:36:28.132930: val_loss -0.1427 +2026-04-10 22:36:28.136261: Pseudo dice [0.5334, 0.2766, 0.7512, 0.501, 0.428, 0.3674, 0.8516] +2026-04-10 22:36:28.140032: Epoch time: 109.62 s +2026-04-10 22:36:29.478248: +2026-04-10 22:36:29.481257: Epoch 421 +2026-04-10 22:36:29.483191: Current learning rate: 0.00905 +2026-04-10 22:38:15.438519: train_loss -0.1739 +2026-04-10 22:38:15.452184: val_loss -0.1361 +2026-04-10 22:38:15.454947: Pseudo dice [0.4442, 0.5826, 0.6826, 0.2105, 0.4709, 0.382, 0.6836] +2026-04-10 22:38:15.458535: Epoch time: 105.96 s +2026-04-10 22:38:16.817252: +2026-04-10 22:38:16.819703: Epoch 422 +2026-04-10 22:38:16.822574: Current learning rate: 0.00905 +2026-04-10 22:40:05.185121: train_loss -0.1663 +2026-04-10 22:40:05.201679: val_loss -0.1208 +2026-04-10 22:40:05.205409: Pseudo dice [0.685, 0.7215, 0.4178, 0.5952, 0.4936, 0.055, 0.7626] +2026-04-10 22:40:05.212284: Epoch time: 108.37 s +2026-04-10 22:40:06.573319: +2026-04-10 22:40:06.577138: Epoch 423 +2026-04-10 22:40:06.581402: Current learning rate: 0.00904 +2026-04-10 22:42:03.589404: train_loss -0.1706 +2026-04-10 22:42:03.597634: val_loss -0.143 +2026-04-10 22:42:03.601741: Pseudo dice [0.49, 0.7613, 0.6845, 0.6101, 0.5134, 0.6145, 0.794] +2026-04-10 22:42:03.606013: Epoch time: 117.02 s +2026-04-10 22:42:04.962824: +2026-04-10 22:42:04.967782: Epoch 424 +2026-04-10 22:42:04.971092: Current learning rate: 0.00904 +2026-04-10 22:43:51.343694: train_loss -0.1679 +2026-04-10 22:43:51.352654: val_loss -0.0315 +2026-04-10 22:43:51.357419: Pseudo dice [0.2808, 0.5874, 0.5987, 0.6349, 0.2617, 0.0413, 0.795] +2026-04-10 22:43:51.361172: Epoch time: 106.38 s +2026-04-10 22:43:52.717239: +2026-04-10 22:43:52.720202: Epoch 425 +2026-04-10 22:43:52.723274: Current learning rate: 0.00904 +2026-04-10 22:45:37.086804: train_loss -0.1571 +2026-04-10 22:45:37.099116: val_loss -0.1416 +2026-04-10 22:45:37.104074: Pseudo dice [0.5247, 0.4673, 0.4535, 0.4269, 0.4055, 0.2384, 0.5834] +2026-04-10 22:45:37.109203: Epoch time: 104.37 s +2026-04-10 22:45:38.454012: +2026-04-10 22:45:38.458153: Epoch 426 +2026-04-10 22:45:38.460249: Current learning rate: 0.00904 +2026-04-10 22:47:28.376124: train_loss -0.1614 +2026-04-10 22:47:28.383392: val_loss -0.1141 +2026-04-10 22:47:28.386094: Pseudo dice [0.1763, 0.5458, 0.348, 0.7329, 0.4152, 0.5732, 0.6438] +2026-04-10 22:47:28.388943: Epoch time: 109.93 s +2026-04-10 22:47:29.742296: +2026-04-10 22:47:29.744743: Epoch 427 +2026-04-10 22:47:29.746924: Current learning rate: 0.00903 +2026-04-10 22:49:15.877326: train_loss -0.1605 +2026-04-10 22:49:15.888841: val_loss -0.1514 +2026-04-10 22:49:15.891843: Pseudo dice [0.3698, 0.7358, 0.6387, 0.463, 0.5768, 0.3067, 0.8171] +2026-04-10 22:49:15.895522: Epoch time: 106.14 s +2026-04-10 22:49:17.254580: +2026-04-10 22:49:17.257831: Epoch 428 +2026-04-10 22:49:17.261419: Current learning rate: 0.00903 +2026-04-10 22:51:03.221588: train_loss -0.1607 +2026-04-10 22:51:03.229605: val_loss -0.1388 +2026-04-10 22:51:03.232356: Pseudo dice [0.7601, 0.2912, 0.6484, 0.4287, 0.2494, 0.7504, 0.8625] +2026-04-10 22:51:03.234742: Epoch time: 105.97 s +2026-04-10 22:51:04.569867: +2026-04-10 22:51:04.575518: Epoch 429 +2026-04-10 22:51:04.581557: Current learning rate: 0.00903 +2026-04-10 22:52:51.152930: train_loss -0.175 +2026-04-10 22:52:51.162136: val_loss -0.0501 +2026-04-10 22:52:51.165690: Pseudo dice [0.8458, 0.7896, 0.4271, 0.2935, 0.4729, 0.0172, 0.8542] +2026-04-10 22:52:51.169601: Epoch time: 106.59 s +2026-04-10 22:52:52.520702: +2026-04-10 22:52:52.523721: Epoch 430 +2026-04-10 22:52:52.526424: Current learning rate: 0.00903 +2026-04-10 22:54:37.519615: train_loss -0.1693 +2026-04-10 22:54:37.527051: val_loss -0.152 +2026-04-10 22:54:37.529638: Pseudo dice [0.5592, 0.4813, 0.53, 0.6314, 0.4437, 0.5424, 0.786] +2026-04-10 22:54:37.532539: Epoch time: 105.0 s +2026-04-10 22:54:39.130235: +2026-04-10 22:54:39.133459: Epoch 431 +2026-04-10 22:54:39.136148: Current learning rate: 0.00902 +2026-04-10 22:56:23.809612: train_loss -0.1652 +2026-04-10 22:56:23.818022: val_loss -0.096 +2026-04-10 22:56:23.821487: Pseudo dice [0.3399, 0.4469, 0.5962, 0.2601, 0.3764, 0.1341, 0.5533] +2026-04-10 22:56:23.824497: Epoch time: 104.68 s +2026-04-10 22:56:25.214266: +2026-04-10 22:56:25.217213: Epoch 432 +2026-04-10 22:56:25.233292: Current learning rate: 0.00902 +2026-04-10 22:58:09.397655: train_loss -0.1824 +2026-04-10 22:58:09.404039: val_loss -0.1503 +2026-04-10 22:58:09.407529: Pseudo dice [0.5446, 0.5476, 0.7125, 0.8007, 0.4991, 0.7666, 0.5479] +2026-04-10 22:58:09.411226: Epoch time: 104.19 s +2026-04-10 22:58:10.766579: +2026-04-10 22:58:10.786116: Epoch 433 +2026-04-10 22:58:10.796442: Current learning rate: 0.00902 +2026-04-10 22:59:55.805453: train_loss -0.1851 +2026-04-10 22:59:55.815073: val_loss -0.1491 +2026-04-10 22:59:55.819483: Pseudo dice [0.4532, 0.5195, 0.6677, 0.7119, 0.4386, 0.2295, 0.6707] +2026-04-10 22:59:55.822404: Epoch time: 105.04 s +2026-04-10 22:59:57.188677: +2026-04-10 22:59:57.191933: Epoch 434 +2026-04-10 22:59:57.194880: Current learning rate: 0.00902 +2026-04-10 23:01:42.862776: train_loss -0.1737 +2026-04-10 23:01:42.871978: val_loss -0.0744 +2026-04-10 23:01:42.874624: Pseudo dice [0.534, 0.5854, 0.6451, 0.4383, 0.5272, 0.0121, 0.8831] +2026-04-10 23:01:42.878409: Epoch time: 105.68 s +2026-04-10 23:01:44.221655: +2026-04-10 23:01:44.225327: Epoch 435 +2026-04-10 23:01:44.227417: Current learning rate: 0.00902 +2026-04-10 23:03:29.299586: train_loss -0.1781 +2026-04-10 23:03:29.307580: val_loss -0.1327 +2026-04-10 23:03:29.310265: Pseudo dice [0.5897, 0.5831, 0.596, 0.0196, 0.2504, 0.7542, 0.6704] +2026-04-10 23:03:29.314198: Epoch time: 105.08 s +2026-04-10 23:03:30.654666: +2026-04-10 23:03:30.657015: Epoch 436 +2026-04-10 23:03:30.659953: Current learning rate: 0.00901 +2026-04-10 23:05:16.974993: train_loss -0.1776 +2026-04-10 23:05:16.983402: val_loss -0.1347 +2026-04-10 23:05:16.985870: Pseudo dice [0.5109, 0.7348, 0.6682, 0.0595, 0.6319, 0.2527, 0.6222] +2026-04-10 23:05:16.988793: Epoch time: 106.32 s +2026-04-10 23:05:18.343755: +2026-04-10 23:05:18.346233: Epoch 437 +2026-04-10 23:05:18.348673: Current learning rate: 0.00901 +2026-04-10 23:07:03.003771: train_loss -0.1307 +2026-04-10 23:07:03.013273: val_loss -0.0932 +2026-04-10 23:07:03.016872: Pseudo dice [0.1541, 0.4796, 0.5259, 0.1009, 0.3777, 0.117, 0.8082] +2026-04-10 23:07:03.021322: Epoch time: 104.66 s +2026-04-10 23:07:04.373959: +2026-04-10 23:07:04.377413: Epoch 438 +2026-04-10 23:07:04.380056: Current learning rate: 0.00901 +2026-04-10 23:08:49.097005: train_loss -0.1671 +2026-04-10 23:08:49.108353: val_loss -0.1026 +2026-04-10 23:08:49.111041: Pseudo dice [0.6872, 0.6774, 0.3982, 0.5491, 0.2654, 0.2464, 0.8924] +2026-04-10 23:08:49.114643: Epoch time: 104.73 s +2026-04-10 23:08:50.488014: +2026-04-10 23:08:50.492318: Epoch 439 +2026-04-10 23:08:50.495868: Current learning rate: 0.00901 +2026-04-10 23:10:35.280863: train_loss -0.1609 +2026-04-10 23:10:35.291056: val_loss -0.1283 +2026-04-10 23:10:35.306919: Pseudo dice [0.4263, 0.6559, 0.689, 0.4759, 0.4641, 0.1364, 0.903] +2026-04-10 23:10:35.311321: Epoch time: 104.8 s +2026-04-10 23:10:36.655456: +2026-04-10 23:10:36.658506: Epoch 440 +2026-04-10 23:10:36.660753: Current learning rate: 0.009 +2026-04-10 23:12:22.451218: train_loss -0.1684 +2026-04-10 23:12:22.459889: val_loss -0.1552 +2026-04-10 23:12:22.462750: Pseudo dice [0.8075, 0.5775, 0.6981, 0.5005, 0.456, 0.6296, 0.571] +2026-04-10 23:12:22.467035: Epoch time: 105.8 s +2026-04-10 23:12:23.803771: +2026-04-10 23:12:23.806216: Epoch 441 +2026-04-10 23:12:23.809644: Current learning rate: 0.009 +2026-04-10 23:14:09.265491: train_loss -0.1537 +2026-04-10 23:14:09.276168: val_loss -0.1289 +2026-04-10 23:14:09.280799: Pseudo dice [0.6164, 0.5925, 0.6237, 0.4452, 0.3387, 0.6342, 0.7866] +2026-04-10 23:14:09.285035: Epoch time: 105.47 s +2026-04-10 23:14:10.648270: +2026-04-10 23:14:10.651033: Epoch 442 +2026-04-10 23:14:10.653775: Current learning rate: 0.009 +2026-04-10 23:15:56.819581: train_loss -0.1538 +2026-04-10 23:15:56.830802: val_loss -0.1115 +2026-04-10 23:15:56.833564: Pseudo dice [0.4025, 0.5522, 0.6229, 0.5254, 0.3389, 0.2237, 0.5026] +2026-04-10 23:15:56.837744: Epoch time: 106.17 s +2026-04-10 23:15:58.191396: +2026-04-10 23:15:58.195695: Epoch 443 +2026-04-10 23:15:58.200198: Current learning rate: 0.009 +2026-04-10 23:17:43.563018: train_loss -0.1596 +2026-04-10 23:17:43.577978: val_loss -0.1211 +2026-04-10 23:17:43.583210: Pseudo dice [0.7372, 0.5537, 0.725, 0.2661, 0.2818, 0.641, 0.7745] +2026-04-10 23:17:43.587876: Epoch time: 105.38 s +2026-04-10 23:17:44.928641: +2026-04-10 23:17:44.931705: Epoch 444 +2026-04-10 23:17:44.934649: Current learning rate: 0.009 +2026-04-10 23:19:31.073824: train_loss -0.1615 +2026-04-10 23:19:31.082622: val_loss -0.1453 +2026-04-10 23:19:31.085459: Pseudo dice [0.5712, 0.7888, 0.5337, 0.6733, 0.5577, 0.7082, 0.6342] +2026-04-10 23:19:31.090751: Epoch time: 106.15 s +2026-04-10 23:19:32.418717: +2026-04-10 23:19:32.422220: Epoch 445 +2026-04-10 23:19:32.428036: Current learning rate: 0.00899 +2026-04-10 23:21:17.469569: train_loss -0.1711 +2026-04-10 23:21:17.480937: val_loss -0.0807 +2026-04-10 23:21:17.484010: Pseudo dice [0.3238, 0.6918, 0.5171, 0.5326, 0.498, 0.07, 0.61] +2026-04-10 23:21:17.487441: Epoch time: 105.05 s +2026-04-10 23:21:18.838577: +2026-04-10 23:21:18.841894: Epoch 446 +2026-04-10 23:21:18.846627: Current learning rate: 0.00899 +2026-04-10 23:23:04.797135: train_loss -0.1707 +2026-04-10 23:23:04.806827: val_loss -0.1507 +2026-04-10 23:23:04.809398: Pseudo dice [0.7752, 0.4504, 0.6009, 0.6613, 0.5068, 0.7856, 0.731] +2026-04-10 23:23:04.811939: Epoch time: 105.96 s +2026-04-10 23:23:06.159372: +2026-04-10 23:23:06.162291: Epoch 447 +2026-04-10 23:23:06.165075: Current learning rate: 0.00899 +2026-04-10 23:24:51.624610: train_loss -0.1658 +2026-04-10 23:24:51.635768: val_loss -0.1054 +2026-04-10 23:24:51.640482: Pseudo dice [0.4566, 0.4914, 0.6695, 0.519, 0.4912, 0.22, 0.5441] +2026-04-10 23:24:51.644559: Epoch time: 105.47 s +2026-04-10 23:24:53.025175: +2026-04-10 23:24:53.028175: Epoch 448 +2026-04-10 23:24:53.030980: Current learning rate: 0.00899 +2026-04-10 23:26:38.281862: train_loss -0.1657 +2026-04-10 23:26:38.288342: val_loss -0.1426 +2026-04-10 23:26:38.291069: Pseudo dice [0.6333, 0.4034, 0.6322, 0.5898, 0.6055, 0.3208, 0.7703] +2026-04-10 23:26:38.293837: Epoch time: 105.26 s +2026-04-10 23:26:39.632627: +2026-04-10 23:26:39.636843: Epoch 449 +2026-04-10 23:26:39.639907: Current learning rate: 0.00898 +2026-04-10 23:28:25.979425: train_loss -0.1866 +2026-04-10 23:28:25.991439: val_loss -0.1727 +2026-04-10 23:28:25.999989: Pseudo dice [0.7477, 0.6269, 0.7535, 0.7153, 0.6115, 0.8286, 0.8715] +2026-04-10 23:28:26.009343: Epoch time: 106.35 s +2026-04-10 23:28:28.113001: Yayy! New best EMA pseudo Dice: 0.5566 +2026-04-10 23:28:31.461767: +2026-04-10 23:28:31.465191: Epoch 450 +2026-04-10 23:28:31.467103: Current learning rate: 0.00898 +2026-04-10 23:30:17.581209: train_loss -0.1761 +2026-04-10 23:30:17.587488: val_loss -0.1555 +2026-04-10 23:30:17.590341: Pseudo dice [0.2358, 0.7183, 0.6349, 0.7335, 0.5227, 0.4185, 0.6872] +2026-04-10 23:30:17.592853: Epoch time: 106.12 s +2026-04-10 23:30:17.595920: Yayy! New best EMA pseudo Dice: 0.5574 +2026-04-10 23:30:20.863921: +2026-04-10 23:30:20.866627: Epoch 451 +2026-04-10 23:30:20.868661: Current learning rate: 0.00898 +2026-04-10 23:32:05.732153: train_loss -0.1744 +2026-04-10 23:32:05.743968: val_loss -0.1274 +2026-04-10 23:32:05.747287: Pseudo dice [0.7315, 0.7802, 0.5405, 0.0134, 0.5284, 0.3437, 0.9003] +2026-04-10 23:32:05.751057: Epoch time: 104.87 s +2026-04-10 23:32:07.120483: +2026-04-10 23:32:07.125726: Epoch 452 +2026-04-10 23:32:07.132652: Current learning rate: 0.00898 +2026-04-10 23:33:54.760232: train_loss -0.1652 +2026-04-10 23:33:54.767452: val_loss -0.1524 +2026-04-10 23:33:54.770330: Pseudo dice [0.6061, 0.4573, 0.7271, 0.4816, 0.2506, 0.7175, 0.8382] +2026-04-10 23:33:54.774417: Epoch time: 107.64 s +2026-04-10 23:33:54.777429: Yayy! New best EMA pseudo Dice: 0.5591 +2026-04-10 23:33:58.104135: +2026-04-10 23:33:58.106164: Epoch 453 +2026-04-10 23:33:58.108597: Current learning rate: 0.00897 +2026-04-10 23:35:42.333521: train_loss -0.1623 +2026-04-10 23:35:42.346196: val_loss -0.1201 +2026-04-10 23:35:42.348606: Pseudo dice [0.4394, 0.7225, 0.5657, 0.6892, 0.2023, 0.1292, 0.4993] +2026-04-10 23:35:42.350985: Epoch time: 104.23 s +2026-04-10 23:35:43.717553: +2026-04-10 23:35:43.720380: Epoch 454 +2026-04-10 23:35:43.723022: Current learning rate: 0.00897 +2026-04-10 23:37:29.377685: train_loss -0.1744 +2026-04-10 23:37:29.383596: val_loss -0.1839 +2026-04-10 23:37:29.386812: Pseudo dice [0.5749, 0.6022, 0.6461, 0.4168, 0.6564, 0.8195, 0.6652] +2026-04-10 23:37:29.389147: Epoch time: 105.66 s +2026-04-10 23:37:30.732691: +2026-04-10 23:37:30.734988: Epoch 455 +2026-04-10 23:37:30.736841: Current learning rate: 0.00897 +2026-04-10 23:39:15.226552: train_loss -0.163 +2026-04-10 23:39:15.235417: val_loss -0.1275 +2026-04-10 23:39:15.239107: Pseudo dice [0.7794, 0.6283, 0.3736, 0.5348, 0.3601, 0.0999, 0.8011] +2026-04-10 23:39:15.241836: Epoch time: 104.5 s +2026-04-10 23:39:16.613993: +2026-04-10 23:39:16.616953: Epoch 456 +2026-04-10 23:39:16.620316: Current learning rate: 0.00897 +2026-04-10 23:41:05.059859: train_loss -0.1693 +2026-04-10 23:41:05.067610: val_loss -0.1287 +2026-04-10 23:41:05.071392: Pseudo dice [0.7105, 0.3331, 0.6064, 0.0556, 0.5819, 0.3337, 0.7079] +2026-04-10 23:41:05.075358: Epoch time: 108.45 s +2026-04-10 23:41:06.447352: +2026-04-10 23:41:06.450932: Epoch 457 +2026-04-10 23:41:06.454625: Current learning rate: 0.00897 +2026-04-10 23:42:52.138443: train_loss -0.1845 +2026-04-10 23:42:52.153944: val_loss -0.1127 +2026-04-10 23:42:52.157242: Pseudo dice [0.357, 0.8233, 0.3859, 0.4072, 0.4357, 0.1081, 0.8149] +2026-04-10 23:42:52.160990: Epoch time: 105.69 s +2026-04-10 23:42:53.524299: +2026-04-10 23:42:53.528851: Epoch 458 +2026-04-10 23:42:53.533013: Current learning rate: 0.00896 +2026-04-10 23:44:37.857250: train_loss -0.1594 +2026-04-10 23:44:37.863537: val_loss -0.0473 +2026-04-10 23:44:37.866408: Pseudo dice [0.6837, 0.1867, 0.4344, 0.7009, 0.5781, 0.0677, 0.793] +2026-04-10 23:44:37.869715: Epoch time: 104.34 s +2026-04-10 23:44:40.370436: +2026-04-10 23:44:40.374689: Epoch 459 +2026-04-10 23:44:40.376886: Current learning rate: 0.00896 +2026-04-10 23:46:28.704272: train_loss -0.1808 +2026-04-10 23:46:28.714680: val_loss -0.0994 +2026-04-10 23:46:28.719184: Pseudo dice [0.5676, 0.4875, 0.6051, 0.7739, 0.5452, 0.1872, 0.5906] +2026-04-10 23:46:28.725202: Epoch time: 108.34 s +2026-04-10 23:46:30.071232: +2026-04-10 23:46:30.074808: Epoch 460 +2026-04-10 23:46:30.078589: Current learning rate: 0.00896 +2026-04-10 23:48:16.401745: train_loss -0.1534 +2026-04-10 23:48:16.409320: val_loss -0.111 +2026-04-10 23:48:16.412151: Pseudo dice [0.4998, 0.3814, 0.5628, 0.7544, 0.538, 0.1163, 0.5597] +2026-04-10 23:48:16.414793: Epoch time: 106.33 s +2026-04-10 23:48:17.788877: +2026-04-10 23:48:17.791218: Epoch 461 +2026-04-10 23:48:17.792991: Current learning rate: 0.00896 +2026-04-10 23:50:01.931293: train_loss -0.161 +2026-04-10 23:50:01.939546: val_loss -0.0435 +2026-04-10 23:50:01.942040: Pseudo dice [0.4037, 0.733, 0.635, 0.5632, 0.3215, 0.0215, 0.2914] +2026-04-10 23:50:01.945655: Epoch time: 104.15 s +2026-04-10 23:50:03.272673: +2026-04-10 23:50:03.275789: Epoch 462 +2026-04-10 23:50:03.278615: Current learning rate: 0.00895 +2026-04-10 23:51:51.851371: train_loss -0.156 +2026-04-10 23:51:51.862998: val_loss -0.1337 +2026-04-10 23:51:51.865941: Pseudo dice [0.4613, 0.5848, 0.6001, 0.7824, 0.565, 0.6955, 0.8371] +2026-04-10 23:51:51.869979: Epoch time: 108.58 s +2026-04-10 23:51:53.218196: +2026-04-10 23:51:53.222216: Epoch 463 +2026-04-10 23:51:53.226485: Current learning rate: 0.00895 +2026-04-10 23:53:38.930550: train_loss -0.1646 +2026-04-10 23:53:38.940011: val_loss -0.1729 +2026-04-10 23:53:38.949086: Pseudo dice [0.2143, 0.8314, 0.5626, 0.6949, 0.5534, 0.7777, 0.7113] +2026-04-10 23:53:38.953543: Epoch time: 105.72 s +2026-04-10 23:53:40.292338: +2026-04-10 23:53:40.294853: Epoch 464 +2026-04-10 23:53:40.296757: Current learning rate: 0.00895 +2026-04-10 23:55:26.755153: train_loss -0.1696 +2026-04-10 23:55:26.764676: val_loss -0.1101 +2026-04-10 23:55:26.769050: Pseudo dice [0.7579, 0.6451, 0.6588, 0.1667, 0.5047, 0.0232, 0.713] +2026-04-10 23:55:26.773518: Epoch time: 106.47 s +2026-04-10 23:55:28.118490: +2026-04-10 23:55:28.120208: Epoch 465 +2026-04-10 23:55:28.122396: Current learning rate: 0.00895 +2026-04-10 23:57:13.241242: train_loss -0.164 +2026-04-10 23:57:13.247454: val_loss -0.1379 +2026-04-10 23:57:13.250345: Pseudo dice [0.2302, 0.437, 0.7135, 0.7032, 0.5939, 0.493, 0.7264] +2026-04-10 23:57:13.252959: Epoch time: 105.13 s +2026-04-10 23:57:14.592631: +2026-04-10 23:57:14.594848: Epoch 466 +2026-04-10 23:57:14.597285: Current learning rate: 0.00895 +2026-04-10 23:59:00.711189: train_loss -0.1661 +2026-04-10 23:59:00.718890: val_loss -0.1468 +2026-04-10 23:59:00.721743: Pseudo dice [0.4039, 0.574, 0.627, 0.3398, 0.5999, 0.6805, 0.8382] +2026-04-10 23:59:00.726423: Epoch time: 106.12 s +2026-04-10 23:59:02.138040: +2026-04-10 23:59:02.144991: Epoch 467 +2026-04-10 23:59:02.150898: Current learning rate: 0.00894 +2026-04-11 00:01:05.724532: train_loss -0.1599 +2026-04-11 00:01:05.751433: val_loss -0.096 +2026-04-11 00:01:05.761854: Pseudo dice [0.5434, 0.571, 0.4357, 0.2933, 0.3823, 0.2499, 0.5787] +2026-04-11 00:01:05.773187: Epoch time: 123.59 s +2026-04-11 00:01:07.181060: +2026-04-11 00:01:07.197405: Epoch 468 +2026-04-11 00:01:07.205480: Current learning rate: 0.00894 +2026-04-11 00:03:49.584446: train_loss -0.1781 +2026-04-11 00:03:49.608987: val_loss -0.1213 +2026-04-11 00:03:49.621985: Pseudo dice [0.3604, 0.4681, 0.6299, 0.5712, 0.4297, 0.2668, 0.7936] +2026-04-11 00:03:49.629735: Epoch time: 162.41 s +2026-04-11 00:03:51.020964: +2026-04-11 00:03:51.027307: Epoch 469 +2026-04-11 00:03:51.034441: Current learning rate: 0.00894 +2026-04-11 00:06:15.328063: train_loss -0.1679 +2026-04-11 00:06:15.336893: val_loss -0.1077 +2026-04-11 00:06:15.341260: Pseudo dice [0.5845, 0.3754, 0.5964, 0.5403, 0.5353, 0.1601, 0.8998] +2026-04-11 00:06:15.345562: Epoch time: 144.31 s +2026-04-11 00:06:16.708779: +2026-04-11 00:06:16.715592: Epoch 470 +2026-04-11 00:06:16.722721: Current learning rate: 0.00894 +2026-04-11 00:08:07.341654: train_loss -0.1717 +2026-04-11 00:08:07.350371: val_loss -0.1483 +2026-04-11 00:08:07.354129: Pseudo dice [0.3178, 0.8269, 0.6202, 0.7927, 0.1658, 0.739, 0.6256] +2026-04-11 00:08:07.359723: Epoch time: 110.64 s +2026-04-11 00:08:08.762964: +2026-04-11 00:08:08.765499: Epoch 471 +2026-04-11 00:08:08.768026: Current learning rate: 0.00893 +2026-04-11 00:09:54.988421: train_loss -0.1749 +2026-04-11 00:09:54.996927: val_loss -0.156 +2026-04-11 00:09:55.000046: Pseudo dice [0.5907, 0.1986, 0.6062, 0.7157, 0.5183, 0.2554, 0.8059] +2026-04-11 00:09:55.002726: Epoch time: 106.23 s +2026-04-11 00:09:56.386531: +2026-04-11 00:09:56.388825: Epoch 472 +2026-04-11 00:09:56.391223: Current learning rate: 0.00893 +2026-04-11 00:11:43.589087: train_loss -0.1863 +2026-04-11 00:11:43.599977: val_loss -0.1502 +2026-04-11 00:11:43.603063: Pseudo dice [0.6071, 0.2669, 0.7893, 0.6807, 0.4477, 0.3489, 0.6922] +2026-04-11 00:11:43.609060: Epoch time: 107.21 s +2026-04-11 00:11:44.970267: +2026-04-11 00:11:44.972744: Epoch 473 +2026-04-11 00:11:44.976192: Current learning rate: 0.00893 +2026-04-11 00:13:31.713198: train_loss -0.166 +2026-04-11 00:13:31.724726: val_loss -0.1216 +2026-04-11 00:13:31.726735: Pseudo dice [0.4741, 0.7097, 0.6565, 0.5758, 0.3616, 0.148, 0.751] +2026-04-11 00:13:31.728822: Epoch time: 106.75 s +2026-04-11 00:13:33.094741: +2026-04-11 00:13:33.098468: Epoch 474 +2026-04-11 00:13:33.101117: Current learning rate: 0.00893 +2026-04-11 00:15:20.203082: train_loss -0.177 +2026-04-11 00:15:20.212633: val_loss -0.1537 +2026-04-11 00:15:20.216825: Pseudo dice [0.7855, 0.4091, 0.725, 0.6799, 0.4705, 0.4497, 0.7151] +2026-04-11 00:15:20.219733: Epoch time: 107.11 s +2026-04-11 00:15:21.565869: +2026-04-11 00:15:21.571091: Epoch 475 +2026-04-11 00:15:21.575505: Current learning rate: 0.00892 +2026-04-11 00:17:06.289143: train_loss -0.1798 +2026-04-11 00:17:06.297868: val_loss -0.1597 +2026-04-11 00:17:06.301733: Pseudo dice [0.4763, 0.792, 0.8007, 0.5322, 0.4582, 0.7683, 0.8241] +2026-04-11 00:17:06.305803: Epoch time: 104.73 s +2026-04-11 00:17:07.924445: +2026-04-11 00:17:07.926403: Epoch 476 +2026-04-11 00:17:07.929409: Current learning rate: 0.00892 +2026-04-11 00:18:54.667578: train_loss -0.1731 +2026-04-11 00:18:54.676585: val_loss -0.1543 +2026-04-11 00:18:54.679693: Pseudo dice [0.4168, 0.4453, 0.5995, 0.0177, 0.5465, 0.4774, 0.8843] +2026-04-11 00:18:54.684016: Epoch time: 106.75 s +2026-04-11 00:18:56.052886: +2026-04-11 00:18:56.054883: Epoch 477 +2026-04-11 00:18:56.058472: Current learning rate: 0.00892 +2026-04-11 00:20:42.528403: train_loss -0.1629 +2026-04-11 00:20:42.536066: val_loss -0.0727 +2026-04-11 00:20:42.540039: Pseudo dice [0.7154, 0.8387, 0.6977, 0.5659, 0.1787, 0.0385, 0.7523] +2026-04-11 00:20:42.542716: Epoch time: 106.48 s +2026-04-11 00:20:43.902880: +2026-04-11 00:20:43.905599: Epoch 478 +2026-04-11 00:20:43.908024: Current learning rate: 0.00892 +2026-04-11 00:22:28.664921: train_loss -0.1734 +2026-04-11 00:22:28.674538: val_loss -0.1032 +2026-04-11 00:22:28.678862: Pseudo dice [0.6539, 0.4768, 0.3653, 0.3782, 0.5194, 0.0267, 0.8597] +2026-04-11 00:22:28.686228: Epoch time: 104.77 s +2026-04-11 00:22:31.348439: +2026-04-11 00:22:31.350693: Epoch 479 +2026-04-11 00:22:31.352554: Current learning rate: 0.00892 +2026-04-11 00:24:18.377186: train_loss -0.1692 +2026-04-11 00:24:18.386876: val_loss -0.1504 +2026-04-11 00:24:18.389448: Pseudo dice [0.5753, 0.5775, 0.725, 0.4705, 0.4677, 0.7682, 0.6537] +2026-04-11 00:24:18.392958: Epoch time: 107.03 s +2026-04-11 00:24:19.781048: +2026-04-11 00:24:19.783238: Epoch 480 +2026-04-11 00:24:19.785671: Current learning rate: 0.00891 +2026-04-11 00:26:05.581009: train_loss -0.1801 +2026-04-11 00:26:05.592298: val_loss -0.1352 +2026-04-11 00:26:05.595412: Pseudo dice [0.2677, 0.6742, 0.6881, 0.1675, 0.5956, 0.3919, 0.873] +2026-04-11 00:26:05.598863: Epoch time: 105.8 s +2026-04-11 00:26:06.961347: +2026-04-11 00:26:06.965079: Epoch 481 +2026-04-11 00:26:06.969762: Current learning rate: 0.00891 +2026-04-11 00:27:56.040972: train_loss -0.1691 +2026-04-11 00:27:56.047965: val_loss -0.1461 +2026-04-11 00:27:56.050020: Pseudo dice [0.4774, 0.6438, 0.6343, 0.4833, 0.4936, 0.5553, 0.8892] +2026-04-11 00:27:56.053206: Epoch time: 109.08 s +2026-04-11 00:27:57.438917: +2026-04-11 00:27:57.441887: Epoch 482 +2026-04-11 00:27:57.443770: Current learning rate: 0.00891 +2026-04-11 00:29:43.842957: train_loss -0.1612 +2026-04-11 00:29:43.852230: val_loss -0.1451 +2026-04-11 00:29:43.854795: Pseudo dice [0.601, 0.1565, 0.7538, 0.6434, 0.4239, 0.3822, 0.7815] +2026-04-11 00:29:43.857162: Epoch time: 106.41 s +2026-04-11 00:29:45.246366: +2026-04-11 00:29:45.249718: Epoch 483 +2026-04-11 00:29:45.252643: Current learning rate: 0.00891 +2026-04-11 00:31:41.183466: train_loss -0.1583 +2026-04-11 00:31:41.188759: val_loss -0.1309 +2026-04-11 00:31:41.190735: Pseudo dice [0.3628, 0.7851, 0.395, 0.7749, 0.5564, 0.178, 0.6997] +2026-04-11 00:31:41.195466: Epoch time: 115.94 s +2026-04-11 00:31:42.571999: +2026-04-11 00:31:42.574841: Epoch 484 +2026-04-11 00:31:42.577861: Current learning rate: 0.0089 +2026-04-11 00:33:37.836547: train_loss -0.1762 +2026-04-11 00:33:37.852735: val_loss -0.1262 +2026-04-11 00:33:37.857799: Pseudo dice [0.4351, 0.3589, 0.5251, 0.6441, 0.6472, 0.1465, 0.5727] +2026-04-11 00:33:37.862998: Epoch time: 115.26 s +2026-04-11 00:33:39.251945: +2026-04-11 00:33:39.255140: Epoch 485 +2026-04-11 00:33:39.257557: Current learning rate: 0.0089 +2026-04-11 00:35:23.834617: train_loss -0.172 +2026-04-11 00:35:23.849557: val_loss -0.0488 +2026-04-11 00:35:23.854302: Pseudo dice [0.3621, 0.3991, 0.4932, 0.2599, 0.3526, 0.0335, 0.4474] +2026-04-11 00:35:23.859868: Epoch time: 104.59 s +2026-04-11 00:35:25.254720: +2026-04-11 00:35:25.259024: Epoch 486 +2026-04-11 00:35:25.261696: Current learning rate: 0.0089 +2026-04-11 00:37:11.754363: train_loss -0.1721 +2026-04-11 00:37:11.759830: val_loss -0.1007 +2026-04-11 00:37:11.762375: Pseudo dice [0.6026, 0.6462, 0.544, 0.5855, 0.2658, 0.475, 0.3227] +2026-04-11 00:37:11.764537: Epoch time: 106.5 s +2026-04-11 00:37:13.139060: +2026-04-11 00:37:13.141399: Epoch 487 +2026-04-11 00:37:13.143301: Current learning rate: 0.0089 +2026-04-11 00:38:57.736713: train_loss -0.1716 +2026-04-11 00:38:57.749926: val_loss -0.0876 +2026-04-11 00:38:57.753725: Pseudo dice [0.785, 0.742, 0.349, 0.2063, 0.3991, 0.4104, 0.4635] +2026-04-11 00:38:57.757313: Epoch time: 104.6 s +2026-04-11 00:38:59.130869: +2026-04-11 00:38:59.133137: Epoch 488 +2026-04-11 00:38:59.135594: Current learning rate: 0.00889 +2026-04-11 00:40:48.244439: train_loss -0.1617 +2026-04-11 00:40:48.253073: val_loss -0.1499 +2026-04-11 00:40:48.255644: Pseudo dice [0.5237, 0.7407, 0.6375, 0.1792, 0.5888, 0.3084, 0.8691] +2026-04-11 00:40:48.258515: Epoch time: 109.12 s +2026-04-11 00:40:49.609928: +2026-04-11 00:40:49.611955: Epoch 489 +2026-04-11 00:40:49.613789: Current learning rate: 0.00889 +2026-04-11 00:42:41.848325: train_loss -0.164 +2026-04-11 00:42:41.856663: val_loss -0.166 +2026-04-11 00:42:41.859900: Pseudo dice [0.5624, 0.587, 0.804, 0.6027, 0.3413, 0.5596, 0.7427] +2026-04-11 00:42:41.866674: Epoch time: 112.24 s +2026-04-11 00:42:43.249388: +2026-04-11 00:42:43.254514: Epoch 490 +2026-04-11 00:42:43.257028: Current learning rate: 0.00889 +2026-04-11 00:44:28.814071: train_loss -0.1595 +2026-04-11 00:44:28.828526: val_loss -0.11 +2026-04-11 00:44:28.831550: Pseudo dice [0.222, 0.6992, 0.4117, 0.4895, 0.4444, 0.1438, 0.8666] +2026-04-11 00:44:28.835079: Epoch time: 105.56 s +2026-04-11 00:44:30.217365: +2026-04-11 00:44:30.220629: Epoch 491 +2026-04-11 00:44:30.222675: Current learning rate: 0.00889 +2026-04-11 00:46:16.679009: train_loss -0.1661 +2026-04-11 00:46:16.689329: val_loss -0.1316 +2026-04-11 00:46:16.692529: Pseudo dice [0.7209, 0.5178, 0.5595, 0.6305, 0.6011, 0.5181, 0.8282] +2026-04-11 00:46:16.697290: Epoch time: 106.47 s +2026-04-11 00:46:18.082518: +2026-04-11 00:46:18.086437: Epoch 492 +2026-04-11 00:46:18.089248: Current learning rate: 0.00889 +2026-04-11 00:48:07.069867: train_loss -0.1765 +2026-04-11 00:48:07.078734: val_loss -0.1339 +2026-04-11 00:48:07.081961: Pseudo dice [0.5636, 0.316, 0.6586, 0.6053, 0.4983, 0.3808, 0.7289] +2026-04-11 00:48:07.084990: Epoch time: 108.99 s +2026-04-11 00:48:08.464844: +2026-04-11 00:48:08.467351: Epoch 493 +2026-04-11 00:48:08.469908: Current learning rate: 0.00888 +2026-04-11 00:49:56.671131: train_loss -0.1529 +2026-04-11 00:49:56.683135: val_loss -0.1257 +2026-04-11 00:49:56.692505: Pseudo dice [0.1428, 0.7558, 0.4834, 0.6307, 0.2367, 0.3456, 0.7289] +2026-04-11 00:49:56.695764: Epoch time: 108.21 s +2026-04-11 00:49:58.065290: +2026-04-11 00:49:58.068097: Epoch 494 +2026-04-11 00:49:58.070055: Current learning rate: 0.00888 +2026-04-11 00:51:43.759015: train_loss -0.1527 +2026-04-11 00:51:43.767593: val_loss -0.152 +2026-04-11 00:51:43.772020: Pseudo dice [0.4264, 0.455, 0.7384, 0.6537, 0.4515, 0.6177, 0.7873] +2026-04-11 00:51:43.774848: Epoch time: 105.7 s +2026-04-11 00:51:45.130953: +2026-04-11 00:51:45.132666: Epoch 495 +2026-04-11 00:51:45.134301: Current learning rate: 0.00888 +2026-04-11 00:53:29.302177: train_loss -0.1646 +2026-04-11 00:53:29.307374: val_loss -0.0999 +2026-04-11 00:53:29.309682: Pseudo dice [0.3226, 0.5285, 0.5723, 0.5011, 0.4016, 0.247, 0.7969] +2026-04-11 00:53:29.312788: Epoch time: 104.18 s +2026-04-11 00:53:30.676236: +2026-04-11 00:53:30.678188: Epoch 496 +2026-04-11 00:53:30.680394: Current learning rate: 0.00888 +2026-04-11 00:55:15.360888: train_loss -0.1589 +2026-04-11 00:55:15.371400: val_loss -0.1498 +2026-04-11 00:55:15.376064: Pseudo dice [0.5301, 0.455, 0.5358, 0.6087, 0.3881, 0.5405, 0.781] +2026-04-11 00:55:15.392216: Epoch time: 104.69 s +2026-04-11 00:55:16.831760: +2026-04-11 00:55:16.833863: Epoch 497 +2026-04-11 00:55:16.835586: Current learning rate: 0.00887 +2026-04-11 00:57:02.053473: train_loss -0.1555 +2026-04-11 00:57:02.061256: val_loss -0.1561 +2026-04-11 00:57:02.063671: Pseudo dice [0.5554, 0.6009, 0.6869, 0.6108, 0.6135, 0.7828, 0.8049] +2026-04-11 00:57:02.065952: Epoch time: 105.23 s +2026-04-11 00:57:03.428408: +2026-04-11 00:57:03.430659: Epoch 498 +2026-04-11 00:57:03.432385: Current learning rate: 0.00887 +2026-04-11 00:58:48.254741: train_loss -0.1559 +2026-04-11 00:58:48.287762: val_loss -0.1531 +2026-04-11 00:58:48.303339: Pseudo dice [0.3393, 0.666, 0.5957, 0.0766, 0.4316, 0.6766, 0.6977] +2026-04-11 00:58:48.309949: Epoch time: 104.83 s +2026-04-11 00:58:49.699791: +2026-04-11 00:58:49.702248: Epoch 499 +2026-04-11 00:58:49.704897: Current learning rate: 0.00887 +2026-04-11 01:00:33.490713: train_loss -0.1681 +2026-04-11 01:00:33.496405: val_loss -0.1322 +2026-04-11 01:00:33.498517: Pseudo dice [0.3752, 0.5286, 0.6053, 0.7918, 0.4764, 0.8003, 0.7781] +2026-04-11 01:00:33.501504: Epoch time: 103.79 s +2026-04-11 01:00:36.905296: +2026-04-11 01:00:36.908036: Epoch 500 +2026-04-11 01:00:36.910535: Current learning rate: 0.00887 +2026-04-11 01:02:20.564836: train_loss -0.1669 +2026-04-11 01:02:20.573904: val_loss -0.1301 +2026-04-11 01:02:20.576729: Pseudo dice [0.7588, 0.2345, 0.5176, 0.7649, 0.5282, 0.0853, 0.8169] +2026-04-11 01:02:20.579050: Epoch time: 103.66 s +2026-04-11 01:02:21.957983: +2026-04-11 01:02:21.961297: Epoch 501 +2026-04-11 01:02:21.963110: Current learning rate: 0.00887 +2026-04-11 01:04:05.461688: train_loss -0.166 +2026-04-11 01:04:05.488555: val_loss -0.1136 +2026-04-11 01:04:05.490830: Pseudo dice [0.5197, 0.6152, 0.5705, 0.222, 0.342, 0.5545, 0.7479] +2026-04-11 01:04:05.493944: Epoch time: 103.51 s +2026-04-11 01:04:06.851963: +2026-04-11 01:04:06.854031: Epoch 502 +2026-04-11 01:04:06.857183: Current learning rate: 0.00886 +2026-04-11 01:05:51.797978: train_loss -0.1784 +2026-04-11 01:05:51.805556: val_loss -0.1584 +2026-04-11 01:05:51.807664: Pseudo dice [0.6647, 0.5835, 0.7421, 0.6712, 0.4379, 0.5877, 0.859] +2026-04-11 01:05:51.811525: Epoch time: 104.95 s +2026-04-11 01:05:53.188548: +2026-04-11 01:05:53.190337: Epoch 503 +2026-04-11 01:05:53.192019: Current learning rate: 0.00886 +2026-04-11 01:07:39.790946: train_loss -0.1818 +2026-04-11 01:07:39.796896: val_loss -0.1689 +2026-04-11 01:07:39.799106: Pseudo dice [0.8173, 0.5569, 0.8097, 0.6662, 0.5544, 0.2887, 0.7989] +2026-04-11 01:07:39.801888: Epoch time: 106.61 s +2026-04-11 01:07:39.803878: Yayy! New best EMA pseudo Dice: 0.5608 +2026-04-11 01:07:43.040547: +2026-04-11 01:07:43.042961: Epoch 504 +2026-04-11 01:07:43.044550: Current learning rate: 0.00886 +2026-04-11 01:09:27.502256: train_loss -0.1697 +2026-04-11 01:09:27.509785: val_loss -0.1349 +2026-04-11 01:09:27.512939: Pseudo dice [0.8194, 0.3828, 0.4874, 0.1515, 0.5202, 0.5999, 0.5462] +2026-04-11 01:09:27.517190: Epoch time: 104.47 s +2026-04-11 01:09:28.871814: +2026-04-11 01:09:28.873842: Epoch 505 +2026-04-11 01:09:28.875355: Current learning rate: 0.00886 +2026-04-11 01:11:12.503683: train_loss -0.1633 +2026-04-11 01:11:12.508834: val_loss -0.0664 +2026-04-11 01:11:12.510704: Pseudo dice [0.5195, 0.7589, 0.456, 0.3804, 0.3407, 0.0773, 0.5785] +2026-04-11 01:11:12.512741: Epoch time: 103.64 s +2026-04-11 01:11:13.901056: +2026-04-11 01:11:13.905336: Epoch 506 +2026-04-11 01:11:13.906944: Current learning rate: 0.00885 +2026-04-11 01:12:57.062654: train_loss -0.165 +2026-04-11 01:12:57.068192: val_loss -0.1395 +2026-04-11 01:12:57.071122: Pseudo dice [0.3957, 0.6299, 0.7719, 0.0744, 0.3231, 0.2227, 0.8313] +2026-04-11 01:12:57.073496: Epoch time: 103.17 s +2026-04-11 01:12:58.452112: +2026-04-11 01:12:58.454023: Epoch 507 +2026-04-11 01:12:58.456185: Current learning rate: 0.00885 +2026-04-11 01:14:41.806964: train_loss -0.1675 +2026-04-11 01:14:41.812592: val_loss -0.1252 +2026-04-11 01:14:41.814745: Pseudo dice [0.4691, 0.7211, 0.6565, 0.5023, 0.6613, 0.0818, 0.8402] +2026-04-11 01:14:41.817021: Epoch time: 103.36 s +2026-04-11 01:14:43.212847: +2026-04-11 01:14:43.214877: Epoch 508 +2026-04-11 01:14:43.216345: Current learning rate: 0.00885 +2026-04-11 01:16:27.225873: train_loss -0.1589 +2026-04-11 01:16:27.233308: val_loss -0.1219 +2026-04-11 01:16:27.235914: Pseudo dice [0.4145, 0.7517, 0.5374, 0.0657, 0.4408, 0.0881, 0.7446] +2026-04-11 01:16:27.239612: Epoch time: 104.01 s +2026-04-11 01:16:28.625584: +2026-04-11 01:16:28.627558: Epoch 509 +2026-04-11 01:16:28.629553: Current learning rate: 0.00885 +2026-04-11 01:18:12.167269: train_loss -0.1784 +2026-04-11 01:18:12.173378: val_loss -0.0425 +2026-04-11 01:18:12.175321: Pseudo dice [0.6129, 0.4429, 0.7125, 0.5808, 0.3038, 0.0239, 0.6857] +2026-04-11 01:18:12.177660: Epoch time: 103.55 s +2026-04-11 01:18:13.634694: +2026-04-11 01:18:13.636667: Epoch 510 +2026-04-11 01:18:13.638763: Current learning rate: 0.00884 +2026-04-11 01:19:56.977185: train_loss -0.1711 +2026-04-11 01:19:56.983226: val_loss -0.1531 +2026-04-11 01:19:56.986491: Pseudo dice [0.2884, 0.5345, 0.5473, 0.4836, 0.4091, 0.7767, 0.8022] +2026-04-11 01:19:56.989173: Epoch time: 103.35 s +2026-04-11 01:19:58.384427: +2026-04-11 01:19:58.386269: Epoch 511 +2026-04-11 01:19:58.388010: Current learning rate: 0.00884 +2026-04-11 01:21:42.111423: train_loss -0.1482 +2026-04-11 01:21:42.117901: val_loss -0.026 +2026-04-11 01:21:42.120121: Pseudo dice [0.319, 0.4107, 0.4996, 0.5485, 0.5738, 0.0297, 0.5518] +2026-04-11 01:21:42.122531: Epoch time: 103.73 s +2026-04-11 01:21:43.511198: +2026-04-11 01:21:43.512844: Epoch 512 +2026-04-11 01:21:43.514514: Current learning rate: 0.00884 +2026-04-11 01:23:26.175282: train_loss -0.1637 +2026-04-11 01:23:26.181631: val_loss -0.1528 +2026-04-11 01:23:26.183836: Pseudo dice [0.6437, 0.812, 0.7255, 0.1119, 0.4622, 0.4802, 0.8037] +2026-04-11 01:23:26.186432: Epoch time: 102.67 s +2026-04-11 01:23:27.575895: +2026-04-11 01:23:27.577848: Epoch 513 +2026-04-11 01:23:27.579456: Current learning rate: 0.00884 +2026-04-11 01:25:11.073849: train_loss -0.1694 +2026-04-11 01:25:11.079905: val_loss -0.1063 +2026-04-11 01:25:11.082419: Pseudo dice [0.8278, 0.3464, 0.417, 0.5076, 0.5598, 0.1603, 0.6955] +2026-04-11 01:25:11.085990: Epoch time: 103.5 s +2026-04-11 01:25:12.447950: +2026-04-11 01:25:12.449730: Epoch 514 +2026-04-11 01:25:12.452631: Current learning rate: 0.00884 +2026-04-11 01:26:55.563395: train_loss -0.1556 +2026-04-11 01:26:55.569588: val_loss -0.1434 +2026-04-11 01:26:55.572225: Pseudo dice [0.5608, 0.5375, 0.3213, 0.5186, 0.3693, 0.2375, 0.8346] +2026-04-11 01:26:55.574769: Epoch time: 103.12 s +2026-04-11 01:26:56.931148: +2026-04-11 01:26:56.933522: Epoch 515 +2026-04-11 01:26:56.935926: Current learning rate: 0.00883 +2026-04-11 01:28:40.776904: train_loss -0.1741 +2026-04-11 01:28:40.783958: val_loss -0.0825 +2026-04-11 01:28:40.786486: Pseudo dice [0.4333, 0.5491, 0.3454, 0.5838, 0.5627, 0.0763, 0.8843] +2026-04-11 01:28:40.788941: Epoch time: 103.85 s +2026-04-11 01:28:42.142772: +2026-04-11 01:28:42.144947: Epoch 516 +2026-04-11 01:28:42.146602: Current learning rate: 0.00883 +2026-04-11 01:30:27.574802: train_loss -0.1555 +2026-04-11 01:30:27.580054: val_loss -0.0934 +2026-04-11 01:30:27.582696: Pseudo dice [0.2827, 0.6954, 0.6798, 0.6419, 0.6076, 0.1004, 0.8571] +2026-04-11 01:30:27.585133: Epoch time: 105.44 s +2026-04-11 01:30:28.929528: +2026-04-11 01:30:28.931228: Epoch 517 +2026-04-11 01:30:28.932760: Current learning rate: 0.00883 +2026-04-11 01:32:12.560641: train_loss -0.1761 +2026-04-11 01:32:12.566707: val_loss -0.1374 +2026-04-11 01:32:12.569187: Pseudo dice [0.3058, 0.703, 0.4634, 0.6314, 0.5115, 0.1242, 0.7015] +2026-04-11 01:32:12.572102: Epoch time: 103.63 s +2026-04-11 01:32:15.128944: +2026-04-11 01:32:15.130794: Epoch 518 +2026-04-11 01:32:15.132208: Current learning rate: 0.00883 +2026-04-11 01:34:00.451262: train_loss -0.1544 +2026-04-11 01:34:00.456669: val_loss -0.135 +2026-04-11 01:34:00.461187: Pseudo dice [0.508, 0.7277, 0.752, 0.5912, 0.2727, 0.7636, 0.7244] +2026-04-11 01:34:00.463995: Epoch time: 105.33 s +2026-04-11 01:34:01.833999: +2026-04-11 01:34:01.836657: Epoch 519 +2026-04-11 01:34:01.838700: Current learning rate: 0.00882 +2026-04-11 01:35:45.531537: train_loss -0.1709 +2026-04-11 01:35:45.537439: val_loss -0.0569 +2026-04-11 01:35:45.539909: Pseudo dice [0.3467, 0.5342, 0.6568, 0.1125, 0.3372, 0.0398, 0.7893] +2026-04-11 01:35:45.542545: Epoch time: 103.7 s +2026-04-11 01:35:46.900309: +2026-04-11 01:35:46.903092: Epoch 520 +2026-04-11 01:35:46.904985: Current learning rate: 0.00882 +2026-04-11 01:37:30.007226: train_loss -0.1597 +2026-04-11 01:37:30.013282: val_loss -0.1382 +2026-04-11 01:37:30.015765: Pseudo dice [0.7138, 0.6404, 0.5579, 0.4446, 0.564, 0.7965, 0.7414] +2026-04-11 01:37:30.018466: Epoch time: 103.11 s +2026-04-11 01:37:31.401411: +2026-04-11 01:37:31.403570: Epoch 521 +2026-04-11 01:37:31.405453: Current learning rate: 0.00882 +2026-04-11 01:39:13.805558: train_loss -0.1685 +2026-04-11 01:39:13.811393: val_loss -0.0852 +2026-04-11 01:39:13.814629: Pseudo dice [0.7668, 0.6789, 0.6299, 0.6593, 0.3958, 0.1559, 0.7857] +2026-04-11 01:39:13.818374: Epoch time: 102.41 s +2026-04-11 01:39:15.185575: +2026-04-11 01:39:15.187288: Epoch 522 +2026-04-11 01:39:15.188751: Current learning rate: 0.00882 +2026-04-11 01:40:57.891702: train_loss -0.1697 +2026-04-11 01:40:57.915101: val_loss -0.1665 +2026-04-11 01:40:57.923915: Pseudo dice [0.5788, 0.7445, 0.7056, 0.1667, 0.6245, 0.5107, 0.5378] +2026-04-11 01:40:57.926398: Epoch time: 102.71 s +2026-04-11 01:40:59.269683: +2026-04-11 01:40:59.271547: Epoch 523 +2026-04-11 01:40:59.273890: Current learning rate: 0.00882 +2026-04-11 01:42:42.169652: train_loss -0.1691 +2026-04-11 01:42:42.176651: val_loss -0.1112 +2026-04-11 01:42:42.178742: Pseudo dice [0.6599, 0.77, 0.6856, 0.7162, 0.652, 0.0966, 0.5473] +2026-04-11 01:42:42.181730: Epoch time: 102.9 s +2026-04-11 01:42:43.566147: +2026-04-11 01:42:43.567904: Epoch 524 +2026-04-11 01:42:43.569744: Current learning rate: 0.00881 +2026-04-11 01:44:26.252865: train_loss -0.1838 +2026-04-11 01:44:26.258876: val_loss -0.1141 +2026-04-11 01:44:26.261737: Pseudo dice [0.1815, 0.7878, 0.6541, 0.1161, 0.2961, 0.1811, 0.6539] +2026-04-11 01:44:26.264364: Epoch time: 102.69 s +2026-04-11 01:44:27.628042: +2026-04-11 01:44:27.630310: Epoch 525 +2026-04-11 01:44:27.632061: Current learning rate: 0.00881 +2026-04-11 01:46:10.289303: train_loss -0.1752 +2026-04-11 01:46:10.294942: val_loss -0.1526 +2026-04-11 01:46:10.297393: Pseudo dice [0.4108, 0.514, 0.7521, 0.8159, 0.2193, 0.8113, 0.4546] +2026-04-11 01:46:10.299889: Epoch time: 102.67 s +2026-04-11 01:46:11.676806: +2026-04-11 01:46:11.678703: Epoch 526 +2026-04-11 01:46:11.680612: Current learning rate: 0.00881 +2026-04-11 01:47:55.050265: train_loss -0.1687 +2026-04-11 01:47:55.058342: val_loss -0.1486 +2026-04-11 01:47:55.060629: Pseudo dice [0.5732, 0.6925, 0.4048, 0.7477, 0.2796, 0.4142, 0.7496] +2026-04-11 01:47:55.063301: Epoch time: 103.38 s +2026-04-11 01:47:56.519284: +2026-04-11 01:47:56.521268: Epoch 527 +2026-04-11 01:47:56.523992: Current learning rate: 0.00881 +2026-04-11 01:49:39.844080: train_loss -0.1673 +2026-04-11 01:49:39.850346: val_loss -0.1647 +2026-04-11 01:49:39.853450: Pseudo dice [0.6126, 0.4241, 0.4776, 0.396, 0.5542, 0.2667, 0.7466] +2026-04-11 01:49:39.855897: Epoch time: 103.33 s +2026-04-11 01:49:41.231380: +2026-04-11 01:49:41.233455: Epoch 528 +2026-04-11 01:49:41.235075: Current learning rate: 0.0088 +2026-04-11 01:51:24.131768: train_loss -0.1653 +2026-04-11 01:51:24.137812: val_loss -0.1334 +2026-04-11 01:51:24.139745: Pseudo dice [0.1585, 0.4259, 0.5225, 0.6354, 0.5744, 0.3544, 0.8117] +2026-04-11 01:51:24.144582: Epoch time: 102.9 s +2026-04-11 01:51:25.543797: +2026-04-11 01:51:25.545864: Epoch 529 +2026-04-11 01:51:25.547632: Current learning rate: 0.0088 +2026-04-11 01:53:08.092256: train_loss -0.1735 +2026-04-11 01:53:08.098332: val_loss -0.1369 +2026-04-11 01:53:08.100336: Pseudo dice [0.7201, 0.5575, 0.7353, 0.6488, 0.5187, 0.076, 0.8069] +2026-04-11 01:53:08.102665: Epoch time: 102.55 s +2026-04-11 01:53:09.498710: +2026-04-11 01:53:09.501399: Epoch 530 +2026-04-11 01:53:09.504104: Current learning rate: 0.0088 +2026-04-11 01:54:53.398426: train_loss -0.1693 +2026-04-11 01:54:53.404549: val_loss -0.18 +2026-04-11 01:54:53.406425: Pseudo dice [0.4604, 0.6109, 0.7342, 0.789, 0.4406, 0.543, 0.827] +2026-04-11 01:54:53.409139: Epoch time: 103.9 s +2026-04-11 01:54:54.820746: +2026-04-11 01:54:54.822607: Epoch 531 +2026-04-11 01:54:54.824239: Current learning rate: 0.0088 +2026-04-11 01:56:37.706111: train_loss -0.1929 +2026-04-11 01:56:37.711300: val_loss -0.1414 +2026-04-11 01:56:37.713531: Pseudo dice [0.4916, 0.5879, 0.6046, 0.7527, 0.4871, 0.1694, 0.8615] +2026-04-11 01:56:37.716286: Epoch time: 102.89 s +2026-04-11 01:56:39.082663: +2026-04-11 01:56:39.085390: Epoch 532 +2026-04-11 01:56:39.087010: Current learning rate: 0.00879 +2026-04-11 01:58:21.926739: train_loss -0.1882 +2026-04-11 01:58:21.932455: val_loss -0.1158 +2026-04-11 01:58:21.934464: Pseudo dice [0.7428, 0.6453, 0.5323, 0.6552, 0.384, 0.1214, 0.5608] +2026-04-11 01:58:21.936748: Epoch time: 102.85 s +2026-04-11 01:58:23.320885: +2026-04-11 01:58:23.324116: Epoch 533 +2026-04-11 01:58:23.326010: Current learning rate: 0.00879 +2026-04-11 02:00:06.168980: train_loss -0.1668 +2026-04-11 02:00:06.174959: val_loss -0.0627 +2026-04-11 02:00:06.176795: Pseudo dice [0.8181, 0.7322, 0.7785, 0.7184, 0.3106, 0.0201, 0.7651] +2026-04-11 02:00:06.179621: Epoch time: 102.85 s +2026-04-11 02:00:07.557940: +2026-04-11 02:00:07.561631: Epoch 534 +2026-04-11 02:00:07.563496: Current learning rate: 0.00879 +2026-04-11 02:01:50.404192: train_loss -0.1743 +2026-04-11 02:01:50.410111: val_loss -0.1286 +2026-04-11 02:01:50.412341: Pseudo dice [0.7183, 0.5935, 0.4904, 0.866, 0.4809, 0.0427, 0.9129] +2026-04-11 02:01:50.415133: Epoch time: 102.85 s +2026-04-11 02:01:51.768246: +2026-04-11 02:01:51.769971: Epoch 535 +2026-04-11 02:01:51.771536: Current learning rate: 0.00879 +2026-04-11 02:03:34.447672: train_loss -0.1785 +2026-04-11 02:03:34.453649: val_loss -0.1484 +2026-04-11 02:03:34.455643: Pseudo dice [0.4816, 0.6102, 0.5122, 0.2243, 0.3845, 0.801, 0.8582] +2026-04-11 02:03:34.457875: Epoch time: 102.68 s +2026-04-11 02:03:35.829387: +2026-04-11 02:03:35.831844: Epoch 536 +2026-04-11 02:03:35.833519: Current learning rate: 0.00879 +2026-04-11 02:05:19.228189: train_loss -0.1915 +2026-04-11 02:05:19.233559: val_loss -0.1455 +2026-04-11 02:05:19.235277: Pseudo dice [0.5272, 0.4426, 0.6634, 0.7169, 0.3936, 0.8399, 0.7389] +2026-04-11 02:05:19.238043: Epoch time: 103.4 s +2026-04-11 02:05:20.598313: +2026-04-11 02:05:20.599967: Epoch 537 +2026-04-11 02:05:20.601593: Current learning rate: 0.00878 +2026-04-11 02:07:03.463225: train_loss -0.1904 +2026-04-11 02:07:03.469309: val_loss -0.1281 +2026-04-11 02:07:03.471681: Pseudo dice [0.441, 0.5838, 0.4108, 0.2766, 0.3254, 0.5206, 0.7679] +2026-04-11 02:07:03.474360: Epoch time: 102.87 s +2026-04-11 02:07:04.856286: +2026-04-11 02:07:04.859162: Epoch 538 +2026-04-11 02:07:04.861399: Current learning rate: 0.00878 +2026-04-11 02:08:49.140365: train_loss -0.1813 +2026-04-11 02:08:49.146147: val_loss -0.1188 +2026-04-11 02:08:49.149424: Pseudo dice [0.6338, 0.3643, 0.5046, 0.6549, 0.5777, 0.0521, 0.7429] +2026-04-11 02:08:49.151783: Epoch time: 104.29 s +2026-04-11 02:08:50.554448: +2026-04-11 02:08:50.556844: Epoch 539 +2026-04-11 02:08:50.558697: Current learning rate: 0.00878 +2026-04-11 02:10:33.050443: train_loss -0.1695 +2026-04-11 02:10:33.055382: val_loss -0.0903 +2026-04-11 02:10:33.057259: Pseudo dice [0.569, 0.5189, 0.489, 0.6145, 0.5534, 0.1059, 0.3861] +2026-04-11 02:10:33.059158: Epoch time: 102.5 s +2026-04-11 02:10:34.403565: +2026-04-11 02:10:34.405349: Epoch 540 +2026-04-11 02:10:34.406840: Current learning rate: 0.00878 +2026-04-11 02:12:17.310678: train_loss -0.1633 +2026-04-11 02:12:17.315444: val_loss -0.0697 +2026-04-11 02:12:17.318018: Pseudo dice [0.5304, 0.2581, 0.6928, 0.372, 0.4312, 0.1276, 0.8433] +2026-04-11 02:12:17.320421: Epoch time: 102.91 s +2026-04-11 02:12:18.682178: +2026-04-11 02:12:18.684052: Epoch 541 +2026-04-11 02:12:18.686038: Current learning rate: 0.00877 +2026-04-11 02:14:01.747771: train_loss -0.1817 +2026-04-11 02:14:01.754468: val_loss -0.0443 +2026-04-11 02:14:01.756876: Pseudo dice [0.4603, 0.5015, 0.5947, 0.284, 0.4228, 0.0801, 0.6226] +2026-04-11 02:14:01.759538: Epoch time: 103.07 s +2026-04-11 02:14:03.113694: +2026-04-11 02:14:03.115883: Epoch 542 +2026-04-11 02:14:03.118536: Current learning rate: 0.00877 +2026-04-11 02:15:45.915873: train_loss -0.1622 +2026-04-11 02:15:45.921429: val_loss -0.0628 +2026-04-11 02:15:45.923369: Pseudo dice [0.5489, 0.4086, 0.3056, 0.6857, 0.6036, 0.1658, 0.7234] +2026-04-11 02:15:45.925456: Epoch time: 102.81 s +2026-04-11 02:15:47.289026: +2026-04-11 02:15:47.291095: Epoch 543 +2026-04-11 02:15:47.292654: Current learning rate: 0.00877 +2026-04-11 02:17:30.048991: train_loss -0.1771 +2026-04-11 02:17:30.055255: val_loss -0.0841 +2026-04-11 02:17:30.057823: Pseudo dice [0.73, 0.428, 0.406, 0.5333, 0.371, 0.1195, 0.8545] +2026-04-11 02:17:30.060318: Epoch time: 102.76 s +2026-04-11 02:17:31.516993: +2026-04-11 02:17:31.519210: Epoch 544 +2026-04-11 02:17:31.521115: Current learning rate: 0.00877 +2026-04-11 02:19:14.628891: train_loss -0.1618 +2026-04-11 02:19:14.635198: val_loss -0.127 +2026-04-11 02:19:14.637409: Pseudo dice [0.4295, 0.7824, 0.6855, 0.624, 0.5704, 0.0575, 0.7507] +2026-04-11 02:19:14.643114: Epoch time: 103.12 s +2026-04-11 02:19:16.027245: +2026-04-11 02:19:16.029165: Epoch 545 +2026-04-11 02:19:16.030972: Current learning rate: 0.00876 +2026-04-11 02:20:59.120685: train_loss -0.1794 +2026-04-11 02:20:59.127049: val_loss -0.1408 +2026-04-11 02:20:59.130608: Pseudo dice [0.3547, 0.4783, 0.7095, 0.6505, 0.4153, 0.1831, 0.6343] +2026-04-11 02:20:59.134313: Epoch time: 103.1 s +2026-04-11 02:21:00.496767: +2026-04-11 02:21:00.499964: Epoch 546 +2026-04-11 02:21:00.501872: Current learning rate: 0.00876 +2026-04-11 02:22:44.059846: train_loss -0.171 +2026-04-11 02:22:44.065442: val_loss -0.1143 +2026-04-11 02:22:44.067703: Pseudo dice [0.2906, 0.4202, 0.5576, 0.3972, 0.6026, 0.1733, 0.8782] +2026-04-11 02:22:44.070189: Epoch time: 103.57 s +2026-04-11 02:22:45.444082: +2026-04-11 02:22:45.446410: Epoch 547 +2026-04-11 02:22:45.448386: Current learning rate: 0.00876 +2026-04-11 02:24:29.121691: train_loss -0.1652 +2026-04-11 02:24:29.127477: val_loss -0.1267 +2026-04-11 02:24:29.129658: Pseudo dice [0.2125, 0.2718, 0.6236, 0.2006, 0.5116, 0.448, 0.703] +2026-04-11 02:24:29.132462: Epoch time: 103.68 s +2026-04-11 02:24:30.528591: +2026-04-11 02:24:30.530360: Epoch 548 +2026-04-11 02:24:30.531985: Current learning rate: 0.00876 +2026-04-11 02:26:13.327760: train_loss -0.1787 +2026-04-11 02:26:13.334214: val_loss -0.1187 +2026-04-11 02:26:13.336076: Pseudo dice [0.4845, 0.545, 0.5454, 0.4745, 0.6109, 0.1633, 0.7841] +2026-04-11 02:26:13.338540: Epoch time: 102.8 s +2026-04-11 02:26:14.709197: +2026-04-11 02:26:14.712430: Epoch 549 +2026-04-11 02:26:14.715756: Current learning rate: 0.00876 +2026-04-11 02:27:57.708112: train_loss -0.1739 +2026-04-11 02:27:57.723115: val_loss -0.1522 +2026-04-11 02:27:57.726740: Pseudo dice [0.6983, 0.6348, 0.511, 0.6624, 0.5604, 0.1903, 0.8124] +2026-04-11 02:27:57.730633: Epoch time: 103.0 s +2026-04-11 02:28:01.094717: +2026-04-11 02:28:01.097801: Epoch 550 +2026-04-11 02:28:01.100029: Current learning rate: 0.00875 +2026-04-11 02:29:44.894313: train_loss -0.1785 +2026-04-11 02:29:44.905490: val_loss -0.0803 +2026-04-11 02:29:44.909621: Pseudo dice [0.4799, 0.6784, 0.6081, 0.7686, 0.6617, 0.1787, 0.861] +2026-04-11 02:29:44.915279: Epoch time: 103.8 s +2026-04-11 02:29:46.299478: +2026-04-11 02:29:46.301829: Epoch 551 +2026-04-11 02:29:46.303508: Current learning rate: 0.00875 +2026-04-11 02:31:31.150450: train_loss -0.1723 +2026-04-11 02:31:31.157871: val_loss -0.1425 +2026-04-11 02:31:31.160011: Pseudo dice [0.3498, 0.5283, 0.746, 0.5688, 0.5667, 0.1846, 0.8862] +2026-04-11 02:31:31.162883: Epoch time: 104.85 s +2026-04-11 02:31:32.524786: +2026-04-11 02:31:32.526788: Epoch 552 +2026-04-11 02:31:32.528557: Current learning rate: 0.00875 +2026-04-11 02:33:15.882709: train_loss -0.1836 +2026-04-11 02:33:15.888627: val_loss -0.117 +2026-04-11 02:33:15.891095: Pseudo dice [0.7407, 0.3325, 0.6578, 0.7502, 0.4351, 0.1669, 0.7695] +2026-04-11 02:33:15.894420: Epoch time: 103.36 s +2026-04-11 02:33:17.299740: +2026-04-11 02:33:17.304393: Epoch 553 +2026-04-11 02:33:17.306851: Current learning rate: 0.00875 +2026-04-11 02:35:00.537264: train_loss -0.1624 +2026-04-11 02:35:00.545784: val_loss -0.1164 +2026-04-11 02:35:00.548216: Pseudo dice [0.5718, 0.4156, 0.4445, 0.7354, 0.4363, 0.4694, 0.6927] +2026-04-11 02:35:00.551064: Epoch time: 103.24 s +2026-04-11 02:35:01.909304: +2026-04-11 02:35:01.911399: Epoch 554 +2026-04-11 02:35:01.912935: Current learning rate: 0.00874 +2026-04-11 02:36:44.961892: train_loss -0.1618 +2026-04-11 02:36:44.969273: val_loss -0.1355 +2026-04-11 02:36:44.972150: Pseudo dice [0.4371, 0.4969, 0.6011, 0.2083, 0.4286, 0.702, 0.8255] +2026-04-11 02:36:44.975235: Epoch time: 103.06 s +2026-04-11 02:36:46.353988: +2026-04-11 02:36:46.356649: Epoch 555 +2026-04-11 02:36:46.358611: Current learning rate: 0.00874 +2026-04-11 02:38:29.672725: train_loss -0.1691 +2026-04-11 02:38:29.677812: val_loss -0.1132 +2026-04-11 02:38:29.679972: Pseudo dice [0.5414, 0.6107, 0.6005, 0.5978, 0.1433, 0.2814, 0.5866] +2026-04-11 02:38:29.682429: Epoch time: 103.32 s +2026-04-11 02:38:31.057667: +2026-04-11 02:38:31.059506: Epoch 556 +2026-04-11 02:38:31.061488: Current learning rate: 0.00874 +2026-04-11 02:40:14.831857: train_loss -0.1708 +2026-04-11 02:40:14.838464: val_loss -0.1451 +2026-04-11 02:40:14.839980: Pseudo dice [0.5201, 0.5602, 0.6548, 0.6559, 0.3879, 0.6769, 0.5861] +2026-04-11 02:40:14.842845: Epoch time: 103.78 s +2026-04-11 02:40:16.223955: +2026-04-11 02:40:16.225770: Epoch 557 +2026-04-11 02:40:16.227189: Current learning rate: 0.00874 +2026-04-11 02:41:59.017331: train_loss -0.1639 +2026-04-11 02:41:59.024270: val_loss -0.166 +2026-04-11 02:41:59.026992: Pseudo dice [0.7227, 0.5719, 0.547, 0.8238, 0.5662, 0.6871, 0.7667] +2026-04-11 02:41:59.029193: Epoch time: 102.8 s +2026-04-11 02:42:01.590634: +2026-04-11 02:42:01.597996: Epoch 558 +2026-04-11 02:42:01.599606: Current learning rate: 0.00874 +2026-04-11 02:43:45.679025: train_loss -0.1761 +2026-04-11 02:43:45.693244: val_loss -0.0942 +2026-04-11 02:43:45.698260: Pseudo dice [0.8082, 0.3732, 0.6482, 0.7656, 0.5553, 0.091, 0.7708] +2026-04-11 02:43:45.702935: Epoch time: 104.09 s +2026-04-11 02:43:47.111573: +2026-04-11 02:43:47.113820: Epoch 559 +2026-04-11 02:43:47.115763: Current learning rate: 0.00873 +2026-04-11 02:45:29.952184: train_loss -0.1585 +2026-04-11 02:45:29.959115: val_loss -0.11 +2026-04-11 02:45:29.961511: Pseudo dice [0.5652, 0.5015, 0.5408, 0.105, 0.5386, 0.0406, 0.5132] +2026-04-11 02:45:29.963970: Epoch time: 102.84 s +2026-04-11 02:45:31.434442: +2026-04-11 02:45:31.436912: Epoch 560 +2026-04-11 02:45:31.438869: Current learning rate: 0.00873 +2026-04-11 02:47:14.772298: train_loss -0.1682 +2026-04-11 02:47:14.777765: val_loss -0.1566 +2026-04-11 02:47:14.779883: Pseudo dice [0.5337, 0.2502, 0.6963, 0.4461, 0.555, 0.6559, 0.8407] +2026-04-11 02:47:14.782250: Epoch time: 103.34 s +2026-04-11 02:47:16.153101: +2026-04-11 02:47:16.155107: Epoch 561 +2026-04-11 02:47:16.156727: Current learning rate: 0.00873 +2026-04-11 02:48:59.167848: train_loss -0.1728 +2026-04-11 02:48:59.172959: val_loss -0.0752 +2026-04-11 02:48:59.174886: Pseudo dice [0.3251, 0.557, 0.4096, 0.38, 0.5131, 0.0252, 0.486] +2026-04-11 02:48:59.177743: Epoch time: 103.02 s +2026-04-11 02:49:00.571658: +2026-04-11 02:49:00.573326: Epoch 562 +2026-04-11 02:49:00.574824: Current learning rate: 0.00873 +2026-04-11 02:50:43.603529: train_loss -0.1764 +2026-04-11 02:50:43.609900: val_loss -0.0609 +2026-04-11 02:50:43.612036: Pseudo dice [0.3266, 0.3582, 0.4443, 0.7099, 0.2739, 0.0354, 0.2592] +2026-04-11 02:50:43.615049: Epoch time: 103.04 s +2026-04-11 02:50:44.992444: +2026-04-11 02:50:44.994436: Epoch 563 +2026-04-11 02:50:44.995901: Current learning rate: 0.00872 +2026-04-11 02:52:27.658749: train_loss -0.1572 +2026-04-11 02:52:27.663522: val_loss -0.0592 +2026-04-11 02:52:27.665709: Pseudo dice [0.3713, 0.5306, 0.4025, 0.5924, 0.6353, 0.0491, 0.7963] +2026-04-11 02:52:27.667656: Epoch time: 102.67 s +2026-04-11 02:52:29.016315: +2026-04-11 02:52:29.017971: Epoch 564 +2026-04-11 02:52:29.019392: Current learning rate: 0.00872 +2026-04-11 02:54:12.371255: train_loss -0.1711 +2026-04-11 02:54:12.378825: val_loss -0.1002 +2026-04-11 02:54:12.381117: Pseudo dice [0.738, 0.2979, 0.643, 0.0513, 0.417, 0.0642, 0.6414] +2026-04-11 02:54:12.383899: Epoch time: 103.36 s +2026-04-11 02:54:13.809074: +2026-04-11 02:54:13.812132: Epoch 565 +2026-04-11 02:54:13.813794: Current learning rate: 0.00872 +2026-04-11 02:55:56.561404: train_loss -0.1775 +2026-04-11 02:55:56.567064: val_loss -0.1273 +2026-04-11 02:55:56.569955: Pseudo dice [0.4215, 0.6074, 0.8082, 0.7357, 0.5113, 0.3679, 0.7709] +2026-04-11 02:55:56.572321: Epoch time: 102.76 s +2026-04-11 02:55:57.934943: +2026-04-11 02:55:57.936853: Epoch 566 +2026-04-11 02:55:57.939074: Current learning rate: 0.00872 +2026-04-11 02:57:41.380925: train_loss -0.1732 +2026-04-11 02:57:41.387763: val_loss -0.1148 +2026-04-11 02:57:41.397630: Pseudo dice [0.3761, 0.4047, 0.6415, 0.3967, 0.4743, 0.2873, 0.6836] +2026-04-11 02:57:41.400457: Epoch time: 103.45 s +2026-04-11 02:57:42.765296: +2026-04-11 02:57:42.767638: Epoch 567 +2026-04-11 02:57:42.769727: Current learning rate: 0.00871 +2026-04-11 02:59:25.876478: train_loss -0.1785 +2026-04-11 02:59:25.881544: val_loss -0.0916 +2026-04-11 02:59:25.883880: Pseudo dice [0.6561, 0.1855, 0.7723, 0.4861, 0.5844, 0.0366, 0.8001] +2026-04-11 02:59:25.886831: Epoch time: 103.12 s +2026-04-11 02:59:27.255271: +2026-04-11 02:59:27.257086: Epoch 568 +2026-04-11 02:59:27.259777: Current learning rate: 0.00871 +2026-04-11 03:01:10.098023: train_loss -0.1704 +2026-04-11 03:01:10.104077: val_loss -0.1439 +2026-04-11 03:01:10.106240: Pseudo dice [0.5973, 0.4578, 0.626, 0.3713, 0.531, 0.6652, 0.6997] +2026-04-11 03:01:10.109564: Epoch time: 102.85 s +2026-04-11 03:01:11.488078: +2026-04-11 03:01:11.491005: Epoch 569 +2026-04-11 03:01:11.492934: Current learning rate: 0.00871 +2026-04-11 03:02:54.773349: train_loss -0.1615 +2026-04-11 03:02:54.778596: val_loss -0.117 +2026-04-11 03:02:54.780594: Pseudo dice [0.6239, 0.4832, 0.5444, 0.3866, 0.4306, 0.064, 0.5187] +2026-04-11 03:02:54.783389: Epoch time: 103.29 s +2026-04-11 03:02:56.173112: +2026-04-11 03:02:56.174913: Epoch 570 +2026-04-11 03:02:56.176911: Current learning rate: 0.00871 +2026-04-11 03:04:38.799882: train_loss -0.1709 +2026-04-11 03:04:38.807481: val_loss -0.0936 +2026-04-11 03:04:38.809302: Pseudo dice [0.7945, 0.412, 0.6008, 0.6005, 0.6181, 0.0996, 0.5722] +2026-04-11 03:04:38.811638: Epoch time: 102.63 s +2026-04-11 03:04:40.287181: +2026-04-11 03:04:40.288883: Epoch 571 +2026-04-11 03:04:40.290382: Current learning rate: 0.00871 +2026-04-11 03:06:23.398568: train_loss -0.1681 +2026-04-11 03:06:23.403723: val_loss -0.1063 +2026-04-11 03:06:23.405988: Pseudo dice [0.5576, 0.6209, 0.4596, 0.7928, 0.4762, 0.0298, 0.699] +2026-04-11 03:06:23.408090: Epoch time: 103.12 s +2026-04-11 03:06:24.766917: +2026-04-11 03:06:24.769666: Epoch 572 +2026-04-11 03:06:24.771746: Current learning rate: 0.0087 +2026-04-11 03:08:08.131601: train_loss -0.1837 +2026-04-11 03:08:08.138069: val_loss -0.1128 +2026-04-11 03:08:08.140446: Pseudo dice [0.4969, 0.4745, 0.5235, 0.0221, 0.5378, 0.2514, 0.6847] +2026-04-11 03:08:08.143086: Epoch time: 103.37 s +2026-04-11 03:08:09.547624: +2026-04-11 03:08:09.549616: Epoch 573 +2026-04-11 03:08:09.551081: Current learning rate: 0.0087 +2026-04-11 03:09:53.191641: train_loss -0.1737 +2026-04-11 03:09:53.200205: val_loss -0.1354 +2026-04-11 03:09:53.202239: Pseudo dice [0.5538, 0.4867, 0.5504, 0.6529, 0.5417, 0.8002, 0.7805] +2026-04-11 03:09:53.205329: Epoch time: 103.65 s +2026-04-11 03:09:54.635396: +2026-04-11 03:09:54.638660: Epoch 574 +2026-04-11 03:09:54.640569: Current learning rate: 0.0087 +2026-04-11 03:11:38.229506: train_loss -0.1718 +2026-04-11 03:11:38.235894: val_loss -0.1496 +2026-04-11 03:11:38.238103: Pseudo dice [0.5765, 0.5162, 0.6287, 0.6921, 0.2422, 0.5246, 0.4452] +2026-04-11 03:11:38.240583: Epoch time: 103.6 s +2026-04-11 03:11:39.652998: +2026-04-11 03:11:39.655169: Epoch 575 +2026-04-11 03:11:39.657664: Current learning rate: 0.0087 +2026-04-11 03:13:22.729472: train_loss -0.1654 +2026-04-11 03:13:22.739258: val_loss -0.0894 +2026-04-11 03:13:22.741543: Pseudo dice [0.3614, 0.6028, 0.6653, 0.2302, 0.4261, 0.1512, 0.543] +2026-04-11 03:13:22.744453: Epoch time: 103.08 s +2026-04-11 03:13:24.173557: +2026-04-11 03:13:24.175454: Epoch 576 +2026-04-11 03:13:24.177959: Current learning rate: 0.00869 +2026-04-11 03:15:07.610681: train_loss -0.1683 +2026-04-11 03:15:07.617076: val_loss -0.0742 +2026-04-11 03:15:07.618705: Pseudo dice [0.8486, 0.3218, 0.5578, 0.496, 0.5335, 0.0252, 0.7596] +2026-04-11 03:15:07.620803: Epoch time: 103.44 s +2026-04-11 03:15:09.114419: +2026-04-11 03:15:09.116704: Epoch 577 +2026-04-11 03:15:09.118842: Current learning rate: 0.00869 +2026-04-11 03:16:53.192901: train_loss -0.1702 +2026-04-11 03:16:53.200068: val_loss -0.1215 +2026-04-11 03:16:53.202064: Pseudo dice [0.5926, 0.3607, 0.6411, 0.8022, 0.4664, 0.1607, 0.6547] +2026-04-11 03:16:53.204147: Epoch time: 104.08 s +2026-04-11 03:16:55.823558: +2026-04-11 03:16:55.825581: Epoch 578 +2026-04-11 03:16:55.828370: Current learning rate: 0.00869 +2026-04-11 03:18:38.655353: train_loss -0.1755 +2026-04-11 03:18:38.660902: val_loss -0.1384 +2026-04-11 03:18:38.662619: Pseudo dice [0.5214, 0.5211, 0.6622, 0.2479, 0.4375, 0.2865, 0.5915] +2026-04-11 03:18:38.664763: Epoch time: 102.84 s +2026-04-11 03:18:40.057649: +2026-04-11 03:18:40.059752: Epoch 579 +2026-04-11 03:18:40.061338: Current learning rate: 0.00869 +2026-04-11 03:20:22.966753: train_loss -0.1859 +2026-04-11 03:20:22.971473: val_loss -0.1329 +2026-04-11 03:20:22.972960: Pseudo dice [0.4781, 0.3614, 0.6502, 0.2189, 0.3547, 0.1832, 0.5721] +2026-04-11 03:20:22.975080: Epoch time: 102.91 s +2026-04-11 03:20:24.364522: +2026-04-11 03:20:24.366167: Epoch 580 +2026-04-11 03:20:24.368055: Current learning rate: 0.00868 +2026-04-11 03:22:07.714861: train_loss -0.1722 +2026-04-11 03:22:07.722202: val_loss -0.1052 +2026-04-11 03:22:07.724602: Pseudo dice [0.4935, 0.658, 0.5794, 0.0215, 0.5175, 0.0407, 0.2609] +2026-04-11 03:22:07.727116: Epoch time: 103.35 s +2026-04-11 03:22:09.128757: +2026-04-11 03:22:09.130798: Epoch 581 +2026-04-11 03:22:09.132998: Current learning rate: 0.00868 +2026-04-11 03:23:53.265745: train_loss -0.1734 +2026-04-11 03:23:53.271319: val_loss -0.1216 +2026-04-11 03:23:53.273660: Pseudo dice [0.4931, 0.5525, 0.5672, 0.7459, 0.362, 0.5418, 0.6507] +2026-04-11 03:23:53.275782: Epoch time: 104.14 s +2026-04-11 03:23:54.677631: +2026-04-11 03:23:54.679431: Epoch 582 +2026-04-11 03:23:54.680912: Current learning rate: 0.00868 +2026-04-11 03:25:37.370833: train_loss -0.1696 +2026-04-11 03:25:37.378654: val_loss -0.1297 +2026-04-11 03:25:37.380776: Pseudo dice [0.7068, 0.6434, 0.651, 0.7386, 0.5045, 0.0605, 0.7968] +2026-04-11 03:25:37.383950: Epoch time: 102.69 s +2026-04-11 03:25:38.808069: +2026-04-11 03:25:38.810243: Epoch 583 +2026-04-11 03:25:38.812153: Current learning rate: 0.00868 +2026-04-11 03:27:21.778419: train_loss -0.1741 +2026-04-11 03:27:21.784758: val_loss -0.1067 +2026-04-11 03:27:21.786714: Pseudo dice [0.3247, 0.7135, 0.5625, 0.2138, 0.5747, 0.0617, 0.5797] +2026-04-11 03:27:21.789197: Epoch time: 102.97 s +2026-04-11 03:27:23.201675: +2026-04-11 03:27:23.203827: Epoch 584 +2026-04-11 03:27:23.205473: Current learning rate: 0.00868 +2026-04-11 03:29:05.989001: train_loss -0.1744 +2026-04-11 03:29:06.000261: val_loss -0.1315 +2026-04-11 03:29:06.002744: Pseudo dice [0.4077, 0.7585, 0.5705, 0.6092, 0.3816, 0.0598, 0.804] +2026-04-11 03:29:06.005635: Epoch time: 102.79 s +2026-04-11 03:29:07.519529: +2026-04-11 03:29:07.521395: Epoch 585 +2026-04-11 03:29:07.523131: Current learning rate: 0.00867 +2026-04-11 03:30:50.296878: train_loss -0.1775 +2026-04-11 03:30:50.302376: val_loss -0.0825 +2026-04-11 03:30:50.304173: Pseudo dice [0.6832, 0.7091, 0.5168, 0.1364, 0.6927, 0.108, 0.8125] +2026-04-11 03:30:50.305963: Epoch time: 102.78 s +2026-04-11 03:30:51.699232: +2026-04-11 03:30:51.701244: Epoch 586 +2026-04-11 03:30:51.703013: Current learning rate: 0.00867 +2026-04-11 03:32:34.809375: train_loss -0.1769 +2026-04-11 03:32:34.814658: val_loss -0.086 +2026-04-11 03:32:34.816620: Pseudo dice [0.3502, 0.6137, 0.6992, 0.2553, 0.4521, 0.2065, 0.5426] +2026-04-11 03:32:34.819342: Epoch time: 103.11 s +2026-04-11 03:32:36.210964: +2026-04-11 03:32:36.213015: Epoch 587 +2026-04-11 03:32:36.214669: Current learning rate: 0.00867 +2026-04-11 03:34:18.861702: train_loss -0.1785 +2026-04-11 03:34:18.872086: val_loss -0.1031 +2026-04-11 03:34:18.874156: Pseudo dice [0.767, 0.4203, 0.5896, 0.4619, 0.5432, 0.2482, 0.7152] +2026-04-11 03:34:18.880136: Epoch time: 102.65 s +2026-04-11 03:34:20.265873: +2026-04-11 03:34:20.267806: Epoch 588 +2026-04-11 03:34:20.269564: Current learning rate: 0.00867 +2026-04-11 03:36:04.190384: train_loss -0.1632 +2026-04-11 03:36:04.198517: val_loss -0.1135 +2026-04-11 03:36:04.200295: Pseudo dice [0.5785, 0.8575, 0.5976, 0.03, 0.3981, 0.0629, 0.5722] +2026-04-11 03:36:04.202803: Epoch time: 103.93 s +2026-04-11 03:36:05.616075: +2026-04-11 03:36:05.618313: Epoch 589 +2026-04-11 03:36:05.620172: Current learning rate: 0.00866 +2026-04-11 03:37:48.396784: train_loss -0.1871 +2026-04-11 03:37:48.405816: val_loss -0.1122 +2026-04-11 03:37:48.409549: Pseudo dice [0.5464, 0.7073, 0.6033, 0.0651, 0.4599, 0.3967, 0.8071] +2026-04-11 03:37:48.412538: Epoch time: 102.78 s +2026-04-11 03:37:49.836936: +2026-04-11 03:37:49.839000: Epoch 590 +2026-04-11 03:37:49.842405: Current learning rate: 0.00866 +2026-04-11 03:39:34.775199: train_loss -0.1578 +2026-04-11 03:39:34.782578: val_loss -0.1523 +2026-04-11 03:39:34.784616: Pseudo dice [0.4851, 0.7347, 0.7718, 0.0788, 0.4948, 0.4291, 0.6549] +2026-04-11 03:39:34.787188: Epoch time: 104.94 s +2026-04-11 03:39:36.206318: +2026-04-11 03:39:36.208246: Epoch 591 +2026-04-11 03:39:36.209970: Current learning rate: 0.00866 +2026-04-11 03:41:19.030023: train_loss -0.1655 +2026-04-11 03:41:19.037205: val_loss -0.0987 +2026-04-11 03:41:19.039580: Pseudo dice [0.4839, 0.2175, 0.4166, 0.0005, 0.5591, 0.0714, 0.5084] +2026-04-11 03:41:19.042307: Epoch time: 102.83 s +2026-04-11 03:41:20.449669: +2026-04-11 03:41:20.451797: Epoch 592 +2026-04-11 03:41:20.454415: Current learning rate: 0.00866 +2026-04-11 03:43:03.498443: train_loss -0.1821 +2026-04-11 03:43:03.506030: val_loss -0.048 +2026-04-11 03:43:03.508165: Pseudo dice [0.7836, 0.5559, 0.4764, 0.0993, 0.5964, 0.1291, 0.5861] +2026-04-11 03:43:03.510476: Epoch time: 103.05 s +2026-04-11 03:43:04.952849: +2026-04-11 03:43:04.954460: Epoch 593 +2026-04-11 03:43:04.955913: Current learning rate: 0.00866 +2026-04-11 03:44:48.387019: train_loss -0.1735 +2026-04-11 03:44:48.393988: val_loss -0.1458 +2026-04-11 03:44:48.395839: Pseudo dice [0.7796, 0.2371, 0.5556, 0.4875, 0.5814, 0.1474, 0.7393] +2026-04-11 03:44:48.398254: Epoch time: 103.44 s +2026-04-11 03:44:49.796816: +2026-04-11 03:44:49.798419: Epoch 594 +2026-04-11 03:44:49.799939: Current learning rate: 0.00865 +2026-04-11 03:46:32.600186: train_loss -0.1701 +2026-04-11 03:46:32.604961: val_loss -0.1492 +2026-04-11 03:46:32.606576: Pseudo dice [0.3382, 0.5659, 0.6156, 0.7783, 0.5309, 0.6191, 0.7341] +2026-04-11 03:46:32.608627: Epoch time: 102.81 s +2026-04-11 03:46:34.030820: +2026-04-11 03:46:34.032797: Epoch 595 +2026-04-11 03:46:34.034357: Current learning rate: 0.00865 +2026-04-11 03:48:16.785902: train_loss -0.1689 +2026-04-11 03:48:16.791052: val_loss -0.0633 +2026-04-11 03:48:16.793063: Pseudo dice [0.3984, 0.3258, 0.4183, 0.6235, 0.5009, 0.029, 0.7587] +2026-04-11 03:48:16.795665: Epoch time: 102.76 s +2026-04-11 03:48:18.192377: +2026-04-11 03:48:18.194495: Epoch 596 +2026-04-11 03:48:18.195901: Current learning rate: 0.00865 +2026-04-11 03:50:00.935528: train_loss -0.1823 +2026-04-11 03:50:00.941221: val_loss -0.1344 +2026-04-11 03:50:00.943050: Pseudo dice [0.4927, 0.6433, 0.5994, 0.5508, 0.4756, 0.2341, 0.7153] +2026-04-11 03:50:00.945345: Epoch time: 102.75 s +2026-04-11 03:50:02.342157: +2026-04-11 03:50:02.343701: Epoch 597 +2026-04-11 03:50:02.345610: Current learning rate: 0.00865 +2026-04-11 03:51:45.076522: train_loss -0.1682 +2026-04-11 03:51:45.082850: val_loss -0.1387 +2026-04-11 03:51:45.086225: Pseudo dice [0.5049, 0.603, 0.6839, 0.0687, 0.4641, 0.2303, 0.647] +2026-04-11 03:51:45.088875: Epoch time: 102.74 s +2026-04-11 03:51:47.691250: +2026-04-11 03:51:47.694049: Epoch 598 +2026-04-11 03:51:47.695608: Current learning rate: 0.00864 +2026-04-11 03:53:30.517793: train_loss -0.1856 +2026-04-11 03:53:30.522929: val_loss -0.1679 +2026-04-11 03:53:30.524719: Pseudo dice [0.8301, 0.8655, 0.7257, 0.8743, 0.5853, 0.7302, 0.8916] +2026-04-11 03:53:30.527481: Epoch time: 102.83 s +2026-04-11 03:53:31.907410: +2026-04-11 03:53:31.909204: Epoch 599 +2026-04-11 03:53:31.910792: Current learning rate: 0.00864 +2026-04-11 03:55:15.383948: train_loss -0.1799 +2026-04-11 03:55:15.389886: val_loss -0.1773 +2026-04-11 03:55:15.395869: Pseudo dice [0.761, 0.7491, 0.693, 0.523, 0.49, 0.7742, 0.7583] +2026-04-11 03:55:15.399558: Epoch time: 103.48 s +2026-04-11 03:55:18.628661: +2026-04-11 03:55:18.631479: Epoch 600 +2026-04-11 03:55:18.633537: Current learning rate: 0.00864 +2026-04-11 03:57:01.681102: train_loss -0.1886 +2026-04-11 03:57:01.686318: val_loss -0.1429 +2026-04-11 03:57:01.688084: Pseudo dice [0.5468, 0.818, 0.6929, 0.7673, 0.4483, 0.6961, 0.778] +2026-04-11 03:57:01.690825: Epoch time: 103.06 s +2026-04-11 03:57:03.136896: +2026-04-11 03:57:03.138530: Epoch 601 +2026-04-11 03:57:03.139922: Current learning rate: 0.00864 +2026-04-11 03:58:46.516295: train_loss -0.1727 +2026-04-11 03:58:46.521230: val_loss -0.1402 +2026-04-11 03:58:46.522807: Pseudo dice [0.4373, 0.6471, 0.8098, 0.396, 0.5702, 0.2395, 0.8109] +2026-04-11 03:58:46.524910: Epoch time: 103.38 s +2026-04-11 03:58:47.928060: +2026-04-11 03:58:47.930288: Epoch 602 +2026-04-11 03:58:47.931731: Current learning rate: 0.00863 +2026-04-11 04:00:31.365682: train_loss -0.1746 +2026-04-11 04:00:31.370903: val_loss -0.0387 +2026-04-11 04:00:31.372857: Pseudo dice [0.5216, 0.4608, 0.4219, 0.294, 0.4263, 0.1684, 0.5019] +2026-04-11 04:00:31.375193: Epoch time: 103.44 s +2026-04-11 04:00:32.796010: +2026-04-11 04:00:32.797933: Epoch 603 +2026-04-11 04:00:32.799747: Current learning rate: 0.00863 +2026-04-11 04:02:15.684566: train_loss -0.1752 +2026-04-11 04:02:15.691954: val_loss -0.083 +2026-04-11 04:02:15.694665: Pseudo dice [0.4693, 0.4857, 0.3947, 0.2879, 0.5939, 0.1373, 0.5928] +2026-04-11 04:02:15.697100: Epoch time: 102.89 s +2026-04-11 04:02:17.129651: +2026-04-11 04:02:17.132581: Epoch 604 +2026-04-11 04:02:17.134617: Current learning rate: 0.00863 +2026-04-11 04:04:00.944643: train_loss -0.1819 +2026-04-11 04:04:00.949991: val_loss -0.1474 +2026-04-11 04:04:00.951962: Pseudo dice [0.7827, 0.6704, 0.6034, 0.6811, 0.5454, 0.0131, 0.8867] +2026-04-11 04:04:00.954145: Epoch time: 103.82 s +2026-04-11 04:04:02.376266: +2026-04-11 04:04:02.378387: Epoch 605 +2026-04-11 04:04:02.380090: Current learning rate: 0.00863 +2026-04-11 04:05:46.235413: train_loss -0.2241 +2026-04-11 04:05:46.241032: val_loss -0.1442 +2026-04-11 04:05:46.244415: Pseudo dice [0.3455, 0.5112, 0.2039, 0.7094, 0.6457, 0.1905, 0.8658] +2026-04-11 04:05:46.247679: Epoch time: 103.86 s +2026-04-11 04:05:47.640026: +2026-04-11 04:05:47.642066: Epoch 606 +2026-04-11 04:05:47.643820: Current learning rate: 0.00863 +2026-04-11 04:07:30.514147: train_loss -0.2689 +2026-04-11 04:07:30.520042: val_loss -0.2299 +2026-04-11 04:07:30.522426: Pseudo dice [0.7203, 0.5869, 0.415, 0.2166, 0.6169, 0.3611, 0.414] +2026-04-11 04:07:30.524895: Epoch time: 102.88 s +2026-04-11 04:07:31.881968: +2026-04-11 04:07:31.883762: Epoch 607 +2026-04-11 04:07:31.888174: Current learning rate: 0.00862 +2026-04-11 04:09:14.928913: train_loss -0.286 +2026-04-11 04:09:14.936093: val_loss -0.2028 +2026-04-11 04:09:14.939000: Pseudo dice [0.3783, 0.805, 0.6385, 0.0505, 0.5177, 0.103, 0.8339] +2026-04-11 04:09:14.941302: Epoch time: 103.05 s +2026-04-11 04:09:16.340856: +2026-04-11 04:09:16.342847: Epoch 608 +2026-04-11 04:09:16.344441: Current learning rate: 0.00862 +2026-04-11 04:10:59.314569: train_loss -0.2547 +2026-04-11 04:10:59.319663: val_loss -0.1265 +2026-04-11 04:10:59.321518: Pseudo dice [0.0908, 0.3975, 0.2077, 0.5955, 0.3872, 0.1998, 0.4725] +2026-04-11 04:10:59.323799: Epoch time: 102.98 s +2026-04-11 04:11:00.719497: +2026-04-11 04:11:00.721429: Epoch 609 +2026-04-11 04:11:00.723277: Current learning rate: 0.00862 +2026-04-11 04:12:43.610470: train_loss -0.2578 +2026-04-11 04:12:43.615955: val_loss -0.2083 +2026-04-11 04:12:43.618194: Pseudo dice [0.368, 0.5352, 0.6948, 0.1862, 0.3532, 0.3598, 0.6831] +2026-04-11 04:12:43.620620: Epoch time: 102.89 s +2026-04-11 04:12:45.015355: +2026-04-11 04:12:45.017317: Epoch 610 +2026-04-11 04:12:45.018785: Current learning rate: 0.00862 +2026-04-11 04:14:27.758044: train_loss -0.2653 +2026-04-11 04:14:27.764120: val_loss -0.2632 +2026-04-11 04:14:27.766471: Pseudo dice [0.2408, 0.8006, 0.7401, 0.3206, 0.5483, 0.8275, 0.7267] +2026-04-11 04:14:27.768631: Epoch time: 102.75 s +2026-04-11 04:14:29.152561: +2026-04-11 04:14:29.154758: Epoch 611 +2026-04-11 04:14:29.156965: Current learning rate: 0.00861 +2026-04-11 04:16:12.367306: train_loss -0.264 +2026-04-11 04:16:12.373158: val_loss -0.2294 +2026-04-11 04:16:12.374981: Pseudo dice [0.5965, 0.3933, 0.6829, 0.5538, 0.251, 0.1969, 0.7283] +2026-04-11 04:16:12.377792: Epoch time: 103.22 s +2026-04-11 04:16:13.744752: +2026-04-11 04:16:13.747123: Epoch 612 +2026-04-11 04:16:13.748656: Current learning rate: 0.00861 +2026-04-11 04:17:57.729974: train_loss -0.2562 +2026-04-11 04:17:57.738637: val_loss -0.2314 +2026-04-11 04:17:57.741142: Pseudo dice [0.4425, 0.6159, 0.4658, 0.3561, 0.4596, 0.7264, 0.7863] +2026-04-11 04:17:57.746222: Epoch time: 103.99 s +2026-04-11 04:17:59.183897: +2026-04-11 04:17:59.186009: Epoch 613 +2026-04-11 04:17:59.187824: Current learning rate: 0.00861 +2026-04-11 04:19:41.972892: train_loss -0.2814 +2026-04-11 04:19:41.978225: val_loss -0.2085 +2026-04-11 04:19:41.980294: Pseudo dice [0.1452, 0.3037, 0.6277, 0.1539, 0.4122, 0.1046, 0.8464] +2026-04-11 04:19:41.982432: Epoch time: 102.79 s +2026-04-11 04:19:43.364222: +2026-04-11 04:19:43.367598: Epoch 614 +2026-04-11 04:19:43.369367: Current learning rate: 0.00861 +2026-04-11 04:21:26.584781: train_loss -0.2753 +2026-04-11 04:21:26.589927: val_loss -0.263 +2026-04-11 04:21:26.592253: Pseudo dice [0.5498, 0.7032, 0.7002, 0.6707, 0.5644, 0.7059, 0.7693] +2026-04-11 04:21:26.594656: Epoch time: 103.22 s +2026-04-11 04:21:28.206195: +2026-04-11 04:21:28.207863: Epoch 615 +2026-04-11 04:21:28.209881: Current learning rate: 0.0086 +2026-04-11 04:23:11.916553: train_loss -0.2862 +2026-04-11 04:23:11.923340: val_loss -0.2759 +2026-04-11 04:23:11.927071: Pseudo dice [0.3165, 0.6569, 0.7354, 0.6329, 0.5985, 0.644, 0.7649] +2026-04-11 04:23:11.930357: Epoch time: 103.71 s +2026-04-11 04:23:13.334109: +2026-04-11 04:23:13.336058: Epoch 616 +2026-04-11 04:23:13.337712: Current learning rate: 0.0086 +2026-04-11 04:24:56.407135: train_loss -0.26 +2026-04-11 04:24:56.413458: val_loss -0.2085 +2026-04-11 04:24:56.415541: Pseudo dice [0.6295, 0.6574, 0.4386, 0.2793, 0.3644, 0.2955, 0.7388] +2026-04-11 04:24:56.418995: Epoch time: 103.08 s +2026-04-11 04:24:57.791898: +2026-04-11 04:24:57.793681: Epoch 617 +2026-04-11 04:24:57.795463: Current learning rate: 0.0086 +2026-04-11 04:26:41.439956: train_loss -0.2739 +2026-04-11 04:26:41.448002: val_loss -0.2308 +2026-04-11 04:26:41.451143: Pseudo dice [0.7101, 0.8955, 0.6176, 0.01, 0.6152, 0.1721, 0.543] +2026-04-11 04:26:41.453394: Epoch time: 103.65 s +2026-04-11 04:26:44.085289: +2026-04-11 04:26:44.088506: Epoch 618 +2026-04-11 04:26:44.090442: Current learning rate: 0.0086 +2026-04-11 04:28:27.688334: train_loss -0.2711 +2026-04-11 04:28:27.694149: val_loss -0.2094 +2026-04-11 04:28:27.696742: Pseudo dice [0.6053, 0.8127, 0.699, 0.0315, 0.3484, 0.1265, 0.5902] +2026-04-11 04:28:27.698759: Epoch time: 103.61 s +2026-04-11 04:28:29.086052: +2026-04-11 04:28:29.088203: Epoch 619 +2026-04-11 04:28:29.089822: Current learning rate: 0.0086 +2026-04-11 04:30:11.579864: train_loss -0.2839 +2026-04-11 04:30:11.596504: val_loss -0.2174 +2026-04-11 04:30:11.598970: Pseudo dice [0.8577, 0.5402, 0.5794, 0.7198, 0.6162, 0.0233, 0.799] +2026-04-11 04:30:11.602627: Epoch time: 102.5 s +2026-04-11 04:30:12.976709: +2026-04-11 04:30:12.978917: Epoch 620 +2026-04-11 04:30:12.980642: Current learning rate: 0.00859 +2026-04-11 04:31:55.959251: train_loss -0.2751 +2026-04-11 04:31:55.965865: val_loss -0.1736 +2026-04-11 04:31:55.968439: Pseudo dice [0.8283, 0.7807, 0.3859, 0.2279, 0.5187, 0.0149, 0.7586] +2026-04-11 04:31:55.972078: Epoch time: 102.99 s +2026-04-11 04:31:57.385929: +2026-04-11 04:31:57.388075: Epoch 621 +2026-04-11 04:31:57.389612: Current learning rate: 0.00859 +2026-04-11 04:33:40.150725: train_loss -0.2657 +2026-04-11 04:33:40.156862: val_loss -0.2534 +2026-04-11 04:33:40.158403: Pseudo dice [0.7076, 0.6162, 0.6687, 0.5539, 0.4039, 0.2398, 0.8397] +2026-04-11 04:33:40.160419: Epoch time: 102.77 s +2026-04-11 04:33:41.542217: +2026-04-11 04:33:41.544047: Epoch 622 +2026-04-11 04:33:41.545491: Current learning rate: 0.00859 +2026-04-11 04:35:24.619318: train_loss -0.2845 +2026-04-11 04:35:24.625170: val_loss -0.2448 +2026-04-11 04:35:24.627187: Pseudo dice [0.5445, 0.7823, 0.7328, 0.1501, 0.4497, 0.1289, 0.5651] +2026-04-11 04:35:24.630952: Epoch time: 103.08 s +2026-04-11 04:35:26.026227: +2026-04-11 04:35:26.028470: Epoch 623 +2026-04-11 04:35:26.029860: Current learning rate: 0.00859 +2026-04-11 04:37:08.896292: train_loss -0.2771 +2026-04-11 04:37:08.902091: val_loss -0.2313 +2026-04-11 04:37:08.905000: Pseudo dice [0.2082, 0.6629, 0.4004, 0.0051, 0.4736, 0.4376, 0.8018] +2026-04-11 04:37:08.907649: Epoch time: 102.87 s +2026-04-11 04:37:10.310074: +2026-04-11 04:37:10.311902: Epoch 624 +2026-04-11 04:37:10.313576: Current learning rate: 0.00858 +2026-04-11 04:38:54.277199: train_loss -0.2708 +2026-04-11 04:38:54.283810: val_loss -0.0963 +2026-04-11 04:38:54.286812: Pseudo dice [0.5169, 0.5634, 0.4934, 0.6719, 0.2907, 0.151, 0.3527] +2026-04-11 04:38:54.289661: Epoch time: 103.97 s +2026-04-11 04:38:55.685144: +2026-04-11 04:38:55.686801: Epoch 625 +2026-04-11 04:38:55.688169: Current learning rate: 0.00858 +2026-04-11 04:40:38.427898: train_loss -0.2691 +2026-04-11 04:40:38.433216: val_loss -0.2584 +2026-04-11 04:40:38.435220: Pseudo dice [0.5546, 0.5499, 0.5949, 0.4016, 0.6092, 0.4107, 0.7981] +2026-04-11 04:40:38.437629: Epoch time: 102.75 s +2026-04-11 04:40:39.844931: +2026-04-11 04:40:39.848430: Epoch 626 +2026-04-11 04:40:39.851326: Current learning rate: 0.00858 +2026-04-11 04:42:22.578950: train_loss -0.2704 +2026-04-11 04:42:22.584158: val_loss -0.2314 +2026-04-11 04:42:22.586424: Pseudo dice [0.3453, 0.3266, 0.5661, 0.8375, 0.3666, 0.2286, 0.2656] +2026-04-11 04:42:22.588950: Epoch time: 102.74 s +2026-04-11 04:42:23.973012: +2026-04-11 04:42:23.975169: Epoch 627 +2026-04-11 04:42:23.977607: Current learning rate: 0.00858 +2026-04-11 04:44:07.385265: train_loss -0.2849 +2026-04-11 04:44:07.390757: val_loss -0.0498 +2026-04-11 04:44:07.392533: Pseudo dice [0.3237, 0.7134, 0.5611, 0.6986, 0.5362, 0.0739, 0.8188] +2026-04-11 04:44:07.395036: Epoch time: 103.42 s +2026-04-11 04:44:08.795747: +2026-04-11 04:44:08.797382: Epoch 628 +2026-04-11 04:44:08.798892: Current learning rate: 0.00858 +2026-04-11 04:45:52.104380: train_loss -0.2784 +2026-04-11 04:45:52.112519: val_loss -0.1983 +2026-04-11 04:45:52.114635: Pseudo dice [0.2362, 0.7845, 0.6279, 0.3564, 0.4874, 0.1469, 0.707] +2026-04-11 04:45:52.117584: Epoch time: 103.31 s +2026-04-11 04:45:53.764008: +2026-04-11 04:45:53.766545: Epoch 629 +2026-04-11 04:45:53.768256: Current learning rate: 0.00857 +2026-04-11 04:47:36.908997: train_loss -0.2675 +2026-04-11 04:47:36.914610: val_loss -0.233 +2026-04-11 04:47:36.917325: Pseudo dice [0.3556, 0.462, 0.632, 0.7232, 0.3967, 0.3496, 0.8504] +2026-04-11 04:47:36.920416: Epoch time: 103.15 s +2026-04-11 04:47:38.332663: +2026-04-11 04:47:38.334347: Epoch 630 +2026-04-11 04:47:38.335815: Current learning rate: 0.00857 +2026-04-11 04:49:22.059630: train_loss -0.2775 +2026-04-11 04:49:22.068889: val_loss -0.2541 +2026-04-11 04:49:22.072850: Pseudo dice [0.4147, 0.3285, 0.7479, 0.5018, 0.4425, 0.7535, 0.701] +2026-04-11 04:49:22.075801: Epoch time: 103.73 s +2026-04-11 04:49:23.474262: +2026-04-11 04:49:23.476929: Epoch 631 +2026-04-11 04:49:23.478521: Current learning rate: 0.00857 +2026-04-11 04:51:06.254777: train_loss -0.2777 +2026-04-11 04:51:06.261229: val_loss -0.2425 +2026-04-11 04:51:06.263552: Pseudo dice [0.4845, 0.5989, 0.7883, 0.4, 0.5992, 0.504, 0.4474] +2026-04-11 04:51:06.266043: Epoch time: 102.78 s +2026-04-11 04:51:07.651073: +2026-04-11 04:51:07.652648: Epoch 632 +2026-04-11 04:51:07.654616: Current learning rate: 0.00857 +2026-04-11 04:52:51.352552: train_loss -0.2679 +2026-04-11 04:52:51.369587: val_loss -0.1594 +2026-04-11 04:52:51.375054: Pseudo dice [0.4417, 0.1872, 0.5543, 0.3812, 0.1566, 0.0684, 0.8076] +2026-04-11 04:52:51.383056: Epoch time: 103.7 s +2026-04-11 04:52:52.826278: +2026-04-11 04:52:52.828080: Epoch 633 +2026-04-11 04:52:52.829607: Current learning rate: 0.00856 +2026-04-11 04:54:35.683369: train_loss -0.2758 +2026-04-11 04:54:35.691771: val_loss -0.2637 +2026-04-11 04:54:35.694839: Pseudo dice [0.4057, 0.6383, 0.6934, 0.2765, 0.5962, 0.5796, 0.7183] +2026-04-11 04:54:35.697979: Epoch time: 102.86 s +2026-04-11 04:54:37.074805: +2026-04-11 04:54:37.076737: Epoch 634 +2026-04-11 04:54:37.079187: Current learning rate: 0.00856 +2026-04-11 04:56:19.840487: train_loss -0.2663 +2026-04-11 04:56:19.845794: val_loss -0.2443 +2026-04-11 04:56:19.847651: Pseudo dice [0.2113, 0.7859, 0.4764, 0.6197, 0.5058, 0.4125, 0.7791] +2026-04-11 04:56:19.849598: Epoch time: 102.77 s +2026-04-11 04:56:21.228541: +2026-04-11 04:56:21.230047: Epoch 635 +2026-04-11 04:56:21.232114: Current learning rate: 0.00856 +2026-04-11 04:58:04.235377: train_loss -0.2946 +2026-04-11 04:58:04.242250: val_loss -0.2892 +2026-04-11 04:58:04.244196: Pseudo dice [0.5669, 0.8778, 0.657, 0.6561, 0.6426, 0.6328, 0.8668] +2026-04-11 04:58:04.246964: Epoch time: 103.01 s +2026-04-11 04:58:05.645502: +2026-04-11 04:58:05.647357: Epoch 636 +2026-04-11 04:58:05.648714: Current learning rate: 0.00856 +2026-04-11 04:59:50.330954: train_loss -0.3013 +2026-04-11 04:59:50.338485: val_loss -0.1798 +2026-04-11 04:59:50.340861: Pseudo dice [0.469, 0.6322, 0.5515, 0.6142, 0.5899, 0.0209, 0.6768] +2026-04-11 04:59:50.344378: Epoch time: 104.69 s +2026-04-11 04:59:51.725975: +2026-04-11 04:59:51.727842: Epoch 637 +2026-04-11 04:59:51.729485: Current learning rate: 0.00855 +2026-04-11 05:01:35.597651: train_loss -0.2834 +2026-04-11 05:01:35.603624: val_loss -0.277 +2026-04-11 05:01:35.605757: Pseudo dice [0.5139, 0.6439, 0.6643, 0.0994, 0.5642, 0.7054, 0.6367] +2026-04-11 05:01:35.608566: Epoch time: 103.87 s +2026-04-11 05:01:38.196751: +2026-04-11 05:01:38.198325: Epoch 638 +2026-04-11 05:01:38.199758: Current learning rate: 0.00855 +2026-04-11 05:03:22.368111: train_loss -0.2865 +2026-04-11 05:03:22.374264: val_loss -0.2589 +2026-04-11 05:03:22.376186: Pseudo dice [0.8312, 0.5255, 0.5156, 0.6438, 0.6059, 0.3331, 0.6992] +2026-04-11 05:03:22.378701: Epoch time: 104.18 s +2026-04-11 05:03:24.015667: +2026-04-11 05:03:24.018181: Epoch 639 +2026-04-11 05:03:24.020413: Current learning rate: 0.00855 +2026-04-11 05:05:06.798438: train_loss -0.2968 +2026-04-11 05:05:06.804750: val_loss -0.2631 +2026-04-11 05:05:06.806825: Pseudo dice [0.7667, 0.5778, 0.6299, 0.5236, 0.5611, 0.7801, 0.5881] +2026-04-11 05:05:06.808862: Epoch time: 102.79 s +2026-04-11 05:05:08.189021: +2026-04-11 05:05:08.190828: Epoch 640 +2026-04-11 05:05:08.192583: Current learning rate: 0.00855 +2026-04-11 05:06:51.797168: train_loss -0.2757 +2026-04-11 05:06:51.811738: val_loss -0.2514 +2026-04-11 05:06:51.817379: Pseudo dice [0.586, 0.5799, 0.5642, 0.5323, 0.3778, 0.7175, 0.5479] +2026-04-11 05:06:51.823420: Epoch time: 103.61 s +2026-04-11 05:06:53.218085: +2026-04-11 05:06:53.219984: Epoch 641 +2026-04-11 05:06:53.221514: Current learning rate: 0.00855 +2026-04-11 05:08:36.644684: train_loss -0.2742 +2026-04-11 05:08:36.650722: val_loss -0.2311 +2026-04-11 05:08:36.653366: Pseudo dice [0.6807, 0.4181, 0.6543, 0.0184, 0.4921, 0.0471, 0.7396] +2026-04-11 05:08:36.655652: Epoch time: 103.43 s +2026-04-11 05:08:38.051098: +2026-04-11 05:08:38.053053: Epoch 642 +2026-04-11 05:08:38.054806: Current learning rate: 0.00854 +2026-04-11 05:10:21.664697: train_loss -0.2819 +2026-04-11 05:10:21.673908: val_loss -0.1201 +2026-04-11 05:10:21.676600: Pseudo dice [0.3903, 0.8138, 0.4054, 0.4642, 0.1335, 0.1538, 0.6706] +2026-04-11 05:10:21.679384: Epoch time: 103.62 s +2026-04-11 05:10:23.058043: +2026-04-11 05:10:23.060190: Epoch 643 +2026-04-11 05:10:23.062244: Current learning rate: 0.00854 +2026-04-11 05:12:05.839445: train_loss -0.2911 +2026-04-11 05:12:05.845586: val_loss -0.2142 +2026-04-11 05:12:05.848040: Pseudo dice [0.5178, 0.6401, 0.6009, 0.7272, 0.4092, 0.1698, 0.3528] +2026-04-11 05:12:05.850509: Epoch time: 102.79 s +2026-04-11 05:12:07.337882: +2026-04-11 05:12:07.340110: Epoch 644 +2026-04-11 05:12:07.341650: Current learning rate: 0.00854 +2026-04-11 05:13:50.765435: train_loss -0.2815 +2026-04-11 05:13:50.771783: val_loss -0.2505 +2026-04-11 05:13:50.774035: Pseudo dice [0.8085, 0.4373, 0.7401, 0.854, 0.4073, 0.3682, 0.8145] +2026-04-11 05:13:50.776095: Epoch time: 103.43 s +2026-04-11 05:13:52.165349: +2026-04-11 05:13:52.167286: Epoch 645 +2026-04-11 05:13:52.170514: Current learning rate: 0.00854 +2026-04-11 05:15:35.250669: train_loss -0.2853 +2026-04-11 05:15:35.255316: val_loss -0.1685 +2026-04-11 05:15:35.257518: Pseudo dice [0.4247, 0.2032, 0.5365, 0.7561, 0.5612, 0.1575, 0.8791] +2026-04-11 05:15:35.260020: Epoch time: 103.09 s +2026-04-11 05:15:36.650986: +2026-04-11 05:15:36.653593: Epoch 646 +2026-04-11 05:15:36.655138: Current learning rate: 0.00853 +2026-04-11 05:17:19.701216: train_loss -0.2726 +2026-04-11 05:17:19.710455: val_loss -0.1966 +2026-04-11 05:17:19.712631: Pseudo dice [0.1676, 0.2642, 0.592, 0.5286, 0.133, 0.3641, 0.3488] +2026-04-11 05:17:19.715120: Epoch time: 103.05 s +2026-04-11 05:17:21.113650: +2026-04-11 05:17:21.126279: Epoch 647 +2026-04-11 05:17:21.134672: Current learning rate: 0.00853 +2026-04-11 05:19:03.960860: train_loss -0.2674 +2026-04-11 05:19:03.965778: val_loss -0.1958 +2026-04-11 05:19:03.967645: Pseudo dice [0.3517, 0.4648, 0.389, 0.3578, 0.0779, 0.1584, 0.7205] +2026-04-11 05:19:03.969897: Epoch time: 102.85 s +2026-04-11 05:19:05.355203: +2026-04-11 05:19:05.356866: Epoch 648 +2026-04-11 05:19:05.358589: Current learning rate: 0.00853 +2026-04-11 05:20:48.557666: train_loss -0.2641 +2026-04-11 05:20:48.562913: val_loss -0.1899 +2026-04-11 05:20:48.564597: Pseudo dice [0.3648, 0.7944, 0.461, 0.6436, 0.3326, 0.181, 0.2532] +2026-04-11 05:20:48.566888: Epoch time: 103.21 s +2026-04-11 05:20:49.978323: +2026-04-11 05:20:49.980604: Epoch 649 +2026-04-11 05:20:49.982145: Current learning rate: 0.00853 +2026-04-11 05:22:32.695544: train_loss -0.2723 +2026-04-11 05:22:32.702675: val_loss -0.2307 +2026-04-11 05:22:32.705150: Pseudo dice [0.0918, 0.3839, 0.5024, 0.0388, 0.6058, 0.4128, 0.7882] +2026-04-11 05:22:32.707821: Epoch time: 102.72 s +2026-04-11 05:22:36.003589: +2026-04-11 05:22:36.005628: Epoch 650 +2026-04-11 05:22:36.007281: Current learning rate: 0.00852 +2026-04-11 05:24:18.759776: train_loss -0.2578 +2026-04-11 05:24:18.764883: val_loss -0.2381 +2026-04-11 05:24:18.766471: Pseudo dice [0.7473, 0.5426, 0.6781, 0.3374, 0.4489, 0.2418, 0.6569] +2026-04-11 05:24:18.768778: Epoch time: 102.76 s +2026-04-11 05:24:20.217440: +2026-04-11 05:24:20.221195: Epoch 651 +2026-04-11 05:24:20.224409: Current learning rate: 0.00852 +2026-04-11 05:26:03.017844: train_loss -0.2753 +2026-04-11 05:26:03.027908: val_loss -0.2675 +2026-04-11 05:26:03.030014: Pseudo dice [0.7479, 0.4496, 0.7185, 0.7399, 0.2804, 0.8115, 0.7605] +2026-04-11 05:26:03.032072: Epoch time: 102.8 s +2026-04-11 05:26:04.742336: +2026-04-11 05:26:04.744529: Epoch 652 +2026-04-11 05:26:04.746708: Current learning rate: 0.00852 +2026-04-11 05:27:47.271023: train_loss -0.268 +2026-04-11 05:27:47.276173: val_loss -0.2276 +2026-04-11 05:27:47.277983: Pseudo dice [0.2362, 0.4632, 0.6842, 0.7217, 0.2744, 0.3846, 0.151] +2026-04-11 05:27:47.279897: Epoch time: 102.53 s +2026-04-11 05:27:48.655120: +2026-04-11 05:27:48.657119: Epoch 653 +2026-04-11 05:27:48.658772: Current learning rate: 0.00852 +2026-04-11 05:29:31.485450: train_loss -0.257 +2026-04-11 05:29:31.496256: val_loss -0.2705 +2026-04-11 05:29:31.500545: Pseudo dice [0.24, 0.7239, 0.7595, 0.7233, 0.4239, 0.7027, 0.7348] +2026-04-11 05:29:31.506441: Epoch time: 102.83 s +2026-04-11 05:29:32.980609: +2026-04-11 05:29:32.982055: Epoch 654 +2026-04-11 05:29:32.984855: Current learning rate: 0.00852 +2026-04-11 05:31:15.711259: train_loss -0.2573 +2026-04-11 05:31:15.717655: val_loss -0.2391 +2026-04-11 05:31:15.719613: Pseudo dice [0.679, 0.8487, 0.6573, 0.4019, 0.4706, 0.1963, 0.8398] +2026-04-11 05:31:15.721830: Epoch time: 102.73 s +2026-04-11 05:31:17.118491: +2026-04-11 05:31:17.120232: Epoch 655 +2026-04-11 05:31:17.121775: Current learning rate: 0.00851 +2026-04-11 05:32:59.895658: train_loss -0.2647 +2026-04-11 05:32:59.900017: val_loss -0.2448 +2026-04-11 05:32:59.901481: Pseudo dice [0.5927, 0.7704, 0.581, 0.638, 0.4533, 0.3356, 0.5903] +2026-04-11 05:32:59.903610: Epoch time: 102.78 s +2026-04-11 05:33:01.300788: +2026-04-11 05:33:01.302586: Epoch 656 +2026-04-11 05:33:01.304226: Current learning rate: 0.00851 +2026-04-11 05:34:44.036234: train_loss -0.2821 +2026-04-11 05:34:44.042776: val_loss -0.2304 +2026-04-11 05:34:44.045396: Pseudo dice [0.6302, 0.7304, 0.4483, 0.3653, 0.4256, 0.4385, 0.561] +2026-04-11 05:34:44.047401: Epoch time: 102.74 s +2026-04-11 05:34:45.454859: +2026-04-11 05:34:45.457144: Epoch 657 +2026-04-11 05:34:45.458792: Current learning rate: 0.00851 +2026-04-11 05:36:28.879798: train_loss -0.2713 +2026-04-11 05:36:28.885564: val_loss -0.1478 +2026-04-11 05:36:28.888098: Pseudo dice [0.6514, 0.5553, 0.6995, 0.2147, 0.5975, 0.0845, 0.5842] +2026-04-11 05:36:28.890674: Epoch time: 103.43 s +2026-04-11 05:36:31.463428: +2026-04-11 05:36:31.465556: Epoch 658 +2026-04-11 05:36:31.467655: Current learning rate: 0.00851 +2026-04-11 05:38:15.011218: train_loss -0.26 +2026-04-11 05:38:15.017917: val_loss -0.1908 +2026-04-11 05:38:15.020698: Pseudo dice [0.4304, 0.619, 0.6276, 0.0815, 0.4954, 0.1751, 0.7258] +2026-04-11 05:38:15.024006: Epoch time: 103.55 s +2026-04-11 05:38:16.410083: +2026-04-11 05:38:16.412394: Epoch 659 +2026-04-11 05:38:16.413893: Current learning rate: 0.0085 +2026-04-11 05:39:59.268289: train_loss -0.2421 +2026-04-11 05:39:59.276229: val_loss -0.2644 +2026-04-11 05:39:59.278094: Pseudo dice [0.5099, 0.4924, 0.6216, 0.7954, 0.5286, 0.6542, 0.6605] +2026-04-11 05:39:59.281680: Epoch time: 102.86 s +2026-04-11 05:40:00.662359: +2026-04-11 05:40:00.664170: Epoch 660 +2026-04-11 05:40:00.665932: Current learning rate: 0.0085 +2026-04-11 05:41:44.179669: train_loss -0.2487 +2026-04-11 05:41:44.184640: val_loss -0.1797 +2026-04-11 05:41:44.187733: Pseudo dice [0.5028, 0.3776, 0.6122, 0.604, 0.3514, 0.0446, 0.7002] +2026-04-11 05:41:44.190576: Epoch time: 103.52 s +2026-04-11 05:41:45.584348: +2026-04-11 05:41:45.587269: Epoch 661 +2026-04-11 05:41:45.590415: Current learning rate: 0.0085 +2026-04-11 05:43:28.635223: train_loss -0.2728 +2026-04-11 05:43:28.643104: val_loss -0.1846 +2026-04-11 05:43:28.645128: Pseudo dice [0.4042, 0.515, 0.5774, 0.128, 0.5588, 0.0319, 0.6968] +2026-04-11 05:43:28.647539: Epoch time: 103.05 s +2026-04-11 05:43:30.043578: +2026-04-11 05:43:30.045400: Epoch 662 +2026-04-11 05:43:30.047604: Current learning rate: 0.0085 +2026-04-11 05:45:12.787482: train_loss -0.2725 +2026-04-11 05:45:12.795698: val_loss -0.1654 +2026-04-11 05:45:12.798145: Pseudo dice [0.5477, 0.4659, 0.5457, 0.2187, 0.5435, 0.3641, 0.4378] +2026-04-11 05:45:12.801093: Epoch time: 102.75 s +2026-04-11 05:45:14.199841: +2026-04-11 05:45:14.202771: Epoch 663 +2026-04-11 05:45:14.205207: Current learning rate: 0.0085 +2026-04-11 05:46:58.690738: train_loss -0.283 +2026-04-11 05:46:58.695609: val_loss -0.2786 +2026-04-11 05:46:58.697522: Pseudo dice [0.8457, 0.3887, 0.7557, 0.6806, 0.3964, 0.5485, 0.501] +2026-04-11 05:46:58.699552: Epoch time: 104.49 s +2026-04-11 05:47:00.101736: +2026-04-11 05:47:00.104183: Epoch 664 +2026-04-11 05:47:00.106897: Current learning rate: 0.00849 +2026-04-11 05:48:42.821199: train_loss -0.2601 +2026-04-11 05:48:42.826088: val_loss -0.249 +2026-04-11 05:48:42.827830: Pseudo dice [0.7398, 0.2594, 0.6855, 0.2132, 0.5824, 0.2231, 0.8263] +2026-04-11 05:48:42.829722: Epoch time: 102.72 s +2026-04-11 05:48:44.239814: +2026-04-11 05:48:44.241802: Epoch 665 +2026-04-11 05:48:44.243459: Current learning rate: 0.00849 +2026-04-11 05:50:27.038351: train_loss -0.2662 +2026-04-11 05:50:27.046402: val_loss -0.0944 +2026-04-11 05:50:27.048619: Pseudo dice [0.6163, 0.4661, 0.4662, 0.3256, 0.54, 0.0314, 0.7562] +2026-04-11 05:50:27.050531: Epoch time: 102.8 s +2026-04-11 05:50:28.449451: +2026-04-11 05:50:28.451745: Epoch 666 +2026-04-11 05:50:28.453280: Current learning rate: 0.00849 +2026-04-11 05:52:11.692322: train_loss -0.2644 +2026-04-11 05:52:11.697462: val_loss -0.2123 +2026-04-11 05:52:11.699351: Pseudo dice [0.256, 0.2632, 0.6645, 0.3209, 0.4752, 0.0951, 0.8493] +2026-04-11 05:52:11.701533: Epoch time: 103.25 s +2026-04-11 05:52:13.165633: +2026-04-11 05:52:13.167909: Epoch 667 +2026-04-11 05:52:13.169551: Current learning rate: 0.00849 +2026-04-11 05:53:56.380045: train_loss -0.2186 +2026-04-11 05:53:56.385504: val_loss -0.2257 +2026-04-11 05:53:56.387949: Pseudo dice [0.4479, 0.6964, 0.7886, 0.0426, 0.3369, 0.2625, 0.3982] +2026-04-11 05:53:56.390553: Epoch time: 103.22 s +2026-04-11 05:53:57.805426: +2026-04-11 05:53:57.807197: Epoch 668 +2026-04-11 05:53:57.808758: Current learning rate: 0.00848 +2026-04-11 05:55:40.564079: train_loss -0.2722 +2026-04-11 05:55:40.571626: val_loss -0.1902 +2026-04-11 05:55:40.574046: Pseudo dice [0.6562, 0.4798, 0.726, 0.4191, 0.3437, 0.1029, 0.8118] +2026-04-11 05:55:40.576390: Epoch time: 102.76 s +2026-04-11 05:55:41.983781: +2026-04-11 05:55:41.985667: Epoch 669 +2026-04-11 05:55:41.987288: Current learning rate: 0.00848 +2026-04-11 05:57:24.826080: train_loss -0.2711 +2026-04-11 05:57:24.830729: val_loss -0.2361 +2026-04-11 05:57:24.832762: Pseudo dice [0.5305, 0.4708, 0.6291, 0.4198, 0.5664, 0.0743, 0.78] +2026-04-11 05:57:24.834529: Epoch time: 102.85 s +2026-04-11 05:57:26.268967: +2026-04-11 05:57:26.270774: Epoch 670 +2026-04-11 05:57:26.273031: Current learning rate: 0.00848 +2026-04-11 05:59:09.528923: train_loss -0.287 +2026-04-11 05:59:09.534737: val_loss -0.2579 +2026-04-11 05:59:09.536836: Pseudo dice [0.654, 0.4943, 0.6831, 0.5573, 0.5377, 0.3921, 0.7931] +2026-04-11 05:59:09.539801: Epoch time: 103.26 s +2026-04-11 05:59:10.947671: +2026-04-11 05:59:10.949467: Epoch 671 +2026-04-11 05:59:10.951448: Current learning rate: 0.00848 +2026-04-11 06:00:54.393452: train_loss -0.2962 +2026-04-11 06:00:54.403850: val_loss -0.282 +2026-04-11 06:00:54.409859: Pseudo dice [0.685, 0.6525, 0.8017, 0.591, 0.5323, 0.726, 0.8211] +2026-04-11 06:00:54.414833: Epoch time: 103.45 s +2026-04-11 06:00:55.845965: +2026-04-11 06:00:55.848337: Epoch 672 +2026-04-11 06:00:55.850009: Current learning rate: 0.00847 +2026-04-11 06:02:39.051854: train_loss -0.2941 +2026-04-11 06:02:39.065682: val_loss -0.2447 +2026-04-11 06:02:39.069766: Pseudo dice [0.7645, 0.6982, 0.6516, 0.3347, 0.3628, 0.5977, 0.6352] +2026-04-11 06:02:39.074174: Epoch time: 103.21 s +2026-04-11 06:02:40.493520: +2026-04-11 06:02:40.495449: Epoch 673 +2026-04-11 06:02:40.497931: Current learning rate: 0.00847 +2026-04-11 06:04:23.553905: train_loss -0.2924 +2026-04-11 06:04:23.559726: val_loss -0.2336 +2026-04-11 06:04:23.561620: Pseudo dice [0.7838, 0.514, 0.558, 0.1, 0.684, 0.1131, 0.7687] +2026-04-11 06:04:23.563749: Epoch time: 103.06 s +2026-04-11 06:04:24.982478: +2026-04-11 06:04:24.984637: Epoch 674 +2026-04-11 06:04:24.986055: Current learning rate: 0.00847 +2026-04-11 06:06:07.748271: train_loss -0.2919 +2026-04-11 06:06:07.753484: val_loss -0.2288 +2026-04-11 06:06:07.755158: Pseudo dice [0.6924, 0.3063, 0.5394, 0.5863, 0.4966, 0.8622, 0.6193] +2026-04-11 06:06:07.757467: Epoch time: 102.77 s +2026-04-11 06:06:09.169645: +2026-04-11 06:06:09.171467: Epoch 675 +2026-04-11 06:06:09.172872: Current learning rate: 0.00847 +2026-04-11 06:07:52.403269: train_loss -0.2813 +2026-04-11 06:07:52.408194: val_loss -0.2075 +2026-04-11 06:07:52.410156: Pseudo dice [0.1454, 0.3436, 0.6203, 0.0382, 0.451, 0.2351, 0.7703] +2026-04-11 06:07:52.412339: Epoch time: 103.24 s +2026-04-11 06:07:53.852795: +2026-04-11 06:07:53.854151: Epoch 676 +2026-04-11 06:07:53.855560: Current learning rate: 0.00847 +2026-04-11 06:09:38.983830: train_loss -0.284 +2026-04-11 06:09:38.991724: val_loss -0.2341 +2026-04-11 06:09:38.993658: Pseudo dice [0.8029, 0.3843, 0.6941, 0.4505, 0.5977, 0.0402, 0.8029] +2026-04-11 06:09:38.995632: Epoch time: 105.13 s +2026-04-11 06:09:40.409857: +2026-04-11 06:09:40.411822: Epoch 677 +2026-04-11 06:09:40.413493: Current learning rate: 0.00846 +2026-04-11 06:11:24.993763: train_loss -0.2699 +2026-04-11 06:11:24.999628: val_loss -0.2579 +2026-04-11 06:11:25.001453: Pseudo dice [0.6336, 0.3284, 0.6287, 0.5452, 0.591, 0.6803, 0.7352] +2026-04-11 06:11:25.004475: Epoch time: 104.59 s +2026-04-11 06:11:26.406235: +2026-04-11 06:11:26.408014: Epoch 678 +2026-04-11 06:11:26.410350: Current learning rate: 0.00846 +2026-04-11 06:13:09.171260: train_loss -0.2769 +2026-04-11 06:13:09.177035: val_loss -0.2365 +2026-04-11 06:13:09.178908: Pseudo dice [0.5864, 0.8386, 0.6181, 0.5899, 0.4045, 0.3504, 0.677] +2026-04-11 06:13:09.181409: Epoch time: 102.77 s +2026-04-11 06:13:10.610148: +2026-04-11 06:13:10.612188: Epoch 679 +2026-04-11 06:13:10.613830: Current learning rate: 0.00846 +2026-04-11 06:14:53.463374: train_loss -0.2869 +2026-04-11 06:14:53.468635: val_loss -0.1953 +2026-04-11 06:14:53.470631: Pseudo dice [0.6809, 0.3229, 0.5246, 0.5643, 0.4923, 0.0807, 0.602] +2026-04-11 06:14:53.473015: Epoch time: 102.86 s +2026-04-11 06:14:54.893701: +2026-04-11 06:14:54.895264: Epoch 680 +2026-04-11 06:14:54.896713: Current learning rate: 0.00846 +2026-04-11 06:16:37.984086: train_loss -0.3012 +2026-04-11 06:16:37.990213: val_loss -0.251 +2026-04-11 06:16:37.991947: Pseudo dice [0.4629, 0.4585, 0.6302, 0.0732, 0.3154, 0.2199, 0.5314] +2026-04-11 06:16:37.995221: Epoch time: 103.09 s +2026-04-11 06:16:39.410141: +2026-04-11 06:16:39.411906: Epoch 681 +2026-04-11 06:16:39.413481: Current learning rate: 0.00845 +2026-04-11 06:18:23.314979: train_loss -0.2637 +2026-04-11 06:18:23.320910: val_loss -0.2556 +2026-04-11 06:18:23.323004: Pseudo dice [0.8103, 0.7982, 0.6765, 0.4575, 0.3624, 0.6655, 0.6246] +2026-04-11 06:18:23.325164: Epoch time: 103.91 s +2026-04-11 06:18:24.739791: +2026-04-11 06:18:24.741508: Epoch 682 +2026-04-11 06:18:24.743169: Current learning rate: 0.00845 +2026-04-11 06:20:07.544475: train_loss -0.2999 +2026-04-11 06:20:07.554105: val_loss -0.2751 +2026-04-11 06:20:07.556591: Pseudo dice [0.8037, 0.7176, 0.7368, 0.5288, 0.3805, 0.4784, 0.4065] +2026-04-11 06:20:07.559808: Epoch time: 102.81 s +2026-04-11 06:20:08.966418: +2026-04-11 06:20:08.968043: Epoch 683 +2026-04-11 06:20:08.969786: Current learning rate: 0.00845 +2026-04-11 06:21:52.264335: train_loss -0.2925 +2026-04-11 06:21:52.270077: val_loss -0.256 +2026-04-11 06:21:52.275861: Pseudo dice [0.2649, 0.5907, 0.6149, 0.5565, 0.6093, 0.4381, 0.6296] +2026-04-11 06:21:52.278925: Epoch time: 103.3 s +2026-04-11 06:21:53.808237: +2026-04-11 06:21:53.810286: Epoch 684 +2026-04-11 06:21:53.812228: Current learning rate: 0.00845 +2026-04-11 06:23:37.152784: train_loss -0.2571 +2026-04-11 06:23:37.165887: val_loss -0.2147 +2026-04-11 06:23:37.167655: Pseudo dice [0.3464, 0.2636, 0.6968, 0.3636, 0.5501, 0.2265, 0.673] +2026-04-11 06:23:37.169742: Epoch time: 103.35 s +2026-04-11 06:23:38.601946: +2026-04-11 06:23:38.603689: Epoch 685 +2026-04-11 06:23:38.605168: Current learning rate: 0.00844 +2026-04-11 06:25:22.292531: train_loss -0.2755 +2026-04-11 06:25:22.309322: val_loss -0.1865 +2026-04-11 06:25:22.314580: Pseudo dice [0.5865, 0.3789, 0.7212, 0.0046, 0.5332, 0.1061, 0.6154] +2026-04-11 06:25:22.320382: Epoch time: 103.69 s +2026-04-11 06:25:23.788280: +2026-04-11 06:25:23.790755: Epoch 686 +2026-04-11 06:25:23.792733: Current learning rate: 0.00844 +2026-04-11 06:27:07.549186: train_loss -0.2791 +2026-04-11 06:27:07.558245: val_loss -0.2666 +2026-04-11 06:27:07.561021: Pseudo dice [0.3507, 0.7749, 0.6196, 0.3681, 0.529, 0.5656, 0.7426] +2026-04-11 06:27:07.564855: Epoch time: 103.76 s +2026-04-11 06:27:09.042912: +2026-04-11 06:27:09.045730: Epoch 687 +2026-04-11 06:27:09.047835: Current learning rate: 0.00844 +2026-04-11 06:28:52.301448: train_loss -0.2753 +2026-04-11 06:28:52.307310: val_loss -0.2166 +2026-04-11 06:28:52.309339: Pseudo dice [0.1699, 0.6416, 0.6812, 0.4506, 0.4809, 0.1436, 0.6928] +2026-04-11 06:28:52.311918: Epoch time: 103.26 s +2026-04-11 06:28:53.789399: +2026-04-11 06:28:53.790879: Epoch 688 +2026-04-11 06:28:53.792326: Current learning rate: 0.00844 +2026-04-11 06:30:37.161906: train_loss -0.2836 +2026-04-11 06:30:37.174904: val_loss -0.2631 +2026-04-11 06:30:37.195902: Pseudo dice [0.4008, 0.4181, 0.5913, 0.6093, 0.5212, 0.6927, 0.8781] +2026-04-11 06:30:37.198003: Epoch time: 103.38 s +2026-04-11 06:30:38.634209: +2026-04-11 06:30:38.636478: Epoch 689 +2026-04-11 06:30:38.638423: Current learning rate: 0.00844 +2026-04-11 06:32:22.210054: train_loss -0.2849 +2026-04-11 06:32:22.215503: val_loss -0.0876 +2026-04-11 06:32:22.217991: Pseudo dice [0.3988, 0.4957, 0.5513, 0.279, 0.539, 0.0244, 0.8121] +2026-04-11 06:32:22.220500: Epoch time: 103.58 s +2026-04-11 06:32:23.664777: +2026-04-11 06:32:23.666436: Epoch 690 +2026-04-11 06:32:23.668191: Current learning rate: 0.00843 +2026-04-11 06:34:07.099231: train_loss -0.2781 +2026-04-11 06:34:07.105640: val_loss -0.1809 +2026-04-11 06:34:07.107774: Pseudo dice [0.7515, 0.5274, 0.591, 0.7136, 0.4611, 0.0335, 0.7378] +2026-04-11 06:34:07.110072: Epoch time: 103.44 s +2026-04-11 06:34:08.536090: +2026-04-11 06:34:08.538018: Epoch 691 +2026-04-11 06:34:08.539822: Current learning rate: 0.00843 +2026-04-11 06:35:51.927152: train_loss -0.2838 +2026-04-11 06:35:51.935213: val_loss -0.2936 +2026-04-11 06:35:51.937453: Pseudo dice [0.5695, 0.2132, 0.5752, 0.815, 0.5517, 0.7244, 0.8076] +2026-04-11 06:35:51.940920: Epoch time: 103.39 s +2026-04-11 06:35:53.403745: +2026-04-11 06:35:53.405740: Epoch 692 +2026-04-11 06:35:53.407775: Current learning rate: 0.00843 +2026-04-11 06:37:36.702943: train_loss -0.2887 +2026-04-11 06:37:36.708122: val_loss -0.2332 +2026-04-11 06:37:36.710630: Pseudo dice [0.4221, 0.2953, 0.5551, 0.0979, 0.4979, 0.1351, 0.7178] +2026-04-11 06:37:36.713101: Epoch time: 103.3 s +2026-04-11 06:37:38.172521: +2026-04-11 06:37:38.174414: Epoch 693 +2026-04-11 06:37:38.176409: Current learning rate: 0.00843 +2026-04-11 06:39:21.818703: train_loss -0.3108 +2026-04-11 06:39:21.823327: val_loss -0.2763 +2026-04-11 06:39:21.825459: Pseudo dice [0.6473, 0.6476, 0.7712, 0.4074, 0.4097, 0.6572, 0.5708] +2026-04-11 06:39:21.827674: Epoch time: 103.65 s +2026-04-11 06:39:23.328082: +2026-04-11 06:39:23.329963: Epoch 694 +2026-04-11 06:39:23.331337: Current learning rate: 0.00842 +2026-04-11 06:41:06.886363: train_loss -0.3061 +2026-04-11 06:41:06.891299: val_loss -0.2858 +2026-04-11 06:41:06.893398: Pseudo dice [0.3065, 0.8718, 0.6339, 0.8124, 0.5488, 0.223, 0.8217] +2026-04-11 06:41:06.896044: Epoch time: 103.56 s +2026-04-11 06:41:08.339523: +2026-04-11 06:41:08.341032: Epoch 695 +2026-04-11 06:41:08.342636: Current learning rate: 0.00842 +2026-04-11 06:42:51.895949: train_loss -0.3005 +2026-04-11 06:42:51.902037: val_loss -0.1504 +2026-04-11 06:42:51.904006: Pseudo dice [0.4442, 0.6141, 0.6696, 0.523, 0.555, 0.372, 0.7172] +2026-04-11 06:42:51.906447: Epoch time: 103.56 s +2026-04-11 06:42:53.392010: +2026-04-11 06:42:53.393403: Epoch 696 +2026-04-11 06:42:53.394875: Current learning rate: 0.00842 +2026-04-11 06:44:37.533772: train_loss -0.2892 +2026-04-11 06:44:37.540836: val_loss -0.1848 +2026-04-11 06:44:37.544888: Pseudo dice [0.7582, 0.6818, 0.3813, 0.3761, 0.4434, 0.0409, 0.467] +2026-04-11 06:44:37.548005: Epoch time: 104.15 s +2026-04-11 06:44:40.288565: +2026-04-11 06:44:40.290466: Epoch 697 +2026-04-11 06:44:40.292187: Current learning rate: 0.00842 +2026-04-11 06:46:23.822542: train_loss -0.2881 +2026-04-11 06:46:23.828531: val_loss -0.2085 +2026-04-11 06:46:23.830557: Pseudo dice [0.2196, 0.3412, 0.552, 0.3326, 0.3187, 0.0368, 0.7298] +2026-04-11 06:46:23.833185: Epoch time: 103.54 s +2026-04-11 06:46:25.271308: +2026-04-11 06:46:25.273026: Epoch 698 +2026-04-11 06:46:25.274552: Current learning rate: 0.00841 +2026-04-11 06:48:10.689131: train_loss -0.2867 +2026-04-11 06:48:10.694948: val_loss -0.2689 +2026-04-11 06:48:10.696559: Pseudo dice [0.3109, 0.808, 0.7112, 0.5986, 0.4005, 0.2438, 0.6295] +2026-04-11 06:48:10.699086: Epoch time: 105.42 s +2026-04-11 06:48:12.165130: +2026-04-11 06:48:12.170351: Epoch 699 +2026-04-11 06:48:12.175030: Current learning rate: 0.00841 +2026-04-11 06:49:55.596584: train_loss -0.2956 +2026-04-11 06:49:55.602726: val_loss -0.251 +2026-04-11 06:49:55.604471: Pseudo dice [0.4174, 0.5232, 0.6483, 0.5674, 0.4324, 0.6046, 0.4717] +2026-04-11 06:49:55.606316: Epoch time: 103.44 s +2026-04-11 06:49:58.879837: +2026-04-11 06:49:58.881502: Epoch 700 +2026-04-11 06:49:58.882913: Current learning rate: 0.00841 +2026-04-11 06:51:42.477173: train_loss -0.2863 +2026-04-11 06:51:42.482755: val_loss -0.1222 +2026-04-11 06:51:42.485158: Pseudo dice [0.4723, 0.7018, 0.7183, 0.5262, 0.3663, 0.1065, 0.8399] +2026-04-11 06:51:42.487881: Epoch time: 103.6 s +2026-04-11 06:51:43.970316: +2026-04-11 06:51:43.971923: Epoch 701 +2026-04-11 06:51:43.973432: Current learning rate: 0.00841 +2026-04-11 06:53:28.690096: train_loss -0.3083 +2026-04-11 06:53:28.696197: val_loss -0.2262 +2026-04-11 06:53:28.700158: Pseudo dice [0.6716, 0.4831, 0.5933, 0.7544, 0.1971, 0.1453, 0.4249] +2026-04-11 06:53:28.702993: Epoch time: 104.72 s +2026-04-11 06:53:30.165435: +2026-04-11 06:53:30.167251: Epoch 702 +2026-04-11 06:53:30.170228: Current learning rate: 0.00841 +2026-04-11 06:55:14.088310: train_loss -0.2833 +2026-04-11 06:55:14.093185: val_loss -0.2219 +2026-04-11 06:55:14.095614: Pseudo dice [0.464, 0.6304, 0.5287, 0.0012, 0.4243, 0.1164, 0.2872] +2026-04-11 06:55:14.098813: Epoch time: 103.93 s +2026-04-11 06:55:15.548917: +2026-04-11 06:55:15.550538: Epoch 703 +2026-04-11 06:55:15.552003: Current learning rate: 0.0084 +2026-04-11 06:57:00.109744: train_loss -0.2768 +2026-04-11 06:57:00.114793: val_loss -0.2512 +2026-04-11 06:57:00.116969: Pseudo dice [0.4909, 0.4884, 0.5988, 0.715, 0.35, 0.7664, 0.7818] +2026-04-11 06:57:00.119693: Epoch time: 104.56 s +2026-04-11 06:57:01.627989: +2026-04-11 06:57:01.632968: Epoch 704 +2026-04-11 06:57:01.646961: Current learning rate: 0.0084 +2026-04-11 06:58:45.412981: train_loss -0.2687 +2026-04-11 06:58:45.420764: val_loss -0.1775 +2026-04-11 06:58:45.423245: Pseudo dice [0.5697, 0.8227, 0.3236, 0.3191, 0.1401, 0.1038, 0.369] +2026-04-11 06:58:45.425356: Epoch time: 103.79 s +2026-04-11 06:58:46.845789: +2026-04-11 06:58:46.847667: Epoch 705 +2026-04-11 06:58:46.849406: Current learning rate: 0.0084 +2026-04-11 07:00:30.011779: train_loss -0.2996 +2026-04-11 07:00:30.019161: val_loss -0.2669 +2026-04-11 07:00:30.021408: Pseudo dice [0.7106, 0.5091, 0.4686, 0.4472, 0.4752, 0.4778, 0.7905] +2026-04-11 07:00:30.025318: Epoch time: 103.17 s +2026-04-11 07:00:31.472477: +2026-04-11 07:00:31.474673: Epoch 706 +2026-04-11 07:00:31.476411: Current learning rate: 0.0084 +2026-04-11 07:02:14.557921: train_loss -0.2968 +2026-04-11 07:02:14.564259: val_loss -0.2766 +2026-04-11 07:02:14.566334: Pseudo dice [0.7216, 0.5348, 0.67, 0.7495, 0.3786, 0.7052, 0.8131] +2026-04-11 07:02:14.568972: Epoch time: 103.09 s +2026-04-11 07:02:15.994929: +2026-04-11 07:02:15.997055: Epoch 707 +2026-04-11 07:02:15.998657: Current learning rate: 0.00839 +2026-04-11 07:03:59.218586: train_loss -0.284 +2026-04-11 07:03:59.224257: val_loss -0.2562 +2026-04-11 07:03:59.226248: Pseudo dice [0.7294, 0.6411, 0.5664, 0.5414, 0.3132, 0.5779, 0.6278] +2026-04-11 07:03:59.228245: Epoch time: 103.23 s +2026-04-11 07:04:00.742017: +2026-04-11 07:04:00.743977: Epoch 708 +2026-04-11 07:04:00.745588: Current learning rate: 0.00839 +2026-04-11 07:05:43.851686: train_loss -0.2941 +2026-04-11 07:05:43.857978: val_loss -0.1875 +2026-04-11 07:05:43.859627: Pseudo dice [0.6749, 0.732, 0.3615, 0.5106, 0.3559, 0.1457, 0.6777] +2026-04-11 07:05:43.861864: Epoch time: 103.11 s +2026-04-11 07:05:45.309739: +2026-04-11 07:05:45.311329: Epoch 709 +2026-04-11 07:05:45.312883: Current learning rate: 0.00839 +2026-04-11 07:07:28.371531: train_loss -0.2861 +2026-04-11 07:07:28.377190: val_loss -0.2696 +2026-04-11 07:07:28.379625: Pseudo dice [0.5671, 0.239, 0.58, 0.1563, 0.599, 0.6208, 0.707] +2026-04-11 07:07:28.382724: Epoch time: 103.07 s +2026-04-11 07:07:29.853983: +2026-04-11 07:07:29.856078: Epoch 710 +2026-04-11 07:07:29.857719: Current learning rate: 0.00839 +2026-04-11 07:09:12.792846: train_loss -0.278 +2026-04-11 07:09:12.799130: val_loss -0.2226 +2026-04-11 07:09:12.801080: Pseudo dice [0.5754, 0.7206, 0.5326, 0.5805, 0.6284, 0.0877, 0.7958] +2026-04-11 07:09:12.803561: Epoch time: 102.94 s +2026-04-11 07:09:14.277006: +2026-04-11 07:09:14.278630: Epoch 711 +2026-04-11 07:09:14.280258: Current learning rate: 0.00839 +2026-04-11 07:10:57.512131: train_loss -0.2884 +2026-04-11 07:10:57.516901: val_loss -0.1961 +2026-04-11 07:10:57.518907: Pseudo dice [0.5783, 0.407, 0.6593, 0.0249, 0.3767, 0.0491, 0.6789] +2026-04-11 07:10:57.521005: Epoch time: 103.24 s +2026-04-11 07:10:59.046744: +2026-04-11 07:10:59.048966: Epoch 712 +2026-04-11 07:10:59.050571: Current learning rate: 0.00838 +2026-04-11 07:12:42.818869: train_loss -0.2622 +2026-04-11 07:12:42.824623: val_loss -0.2518 +2026-04-11 07:12:42.826602: Pseudo dice [0.4867, 0.4024, 0.7785, 0.5222, 0.5496, 0.6857, 0.7918] +2026-04-11 07:12:42.828926: Epoch time: 103.78 s +2026-04-11 07:12:44.291137: +2026-04-11 07:12:44.293106: Epoch 713 +2026-04-11 07:12:44.295458: Current learning rate: 0.00838 +2026-04-11 07:14:27.271081: train_loss -0.2811 +2026-04-11 07:14:27.278075: val_loss -0.2869 +2026-04-11 07:14:27.280086: Pseudo dice [0.6516, 0.3376, 0.5794, 0.7121, 0.5429, 0.7202, 0.8573] +2026-04-11 07:14:27.282969: Epoch time: 102.98 s +2026-04-11 07:14:29.540322: +2026-04-11 07:14:29.542195: Epoch 714 +2026-04-11 07:14:29.544078: Current learning rate: 0.00838 +2026-04-11 07:16:13.087650: train_loss -0.2938 +2026-04-11 07:16:13.094793: val_loss -0.237 +2026-04-11 07:16:13.097757: Pseudo dice [0.5112, 0.4002, 0.6131, 0.5837, 0.6589, 0.2551, 0.7866] +2026-04-11 07:16:13.100427: Epoch time: 103.55 s +2026-04-11 07:16:14.529407: +2026-04-11 07:16:14.531359: Epoch 715 +2026-04-11 07:16:14.533040: Current learning rate: 0.00838 +2026-04-11 07:17:57.515677: train_loss -0.2817 +2026-04-11 07:17:57.520788: val_loss -0.2544 +2026-04-11 07:17:57.522918: Pseudo dice [0.3113, 0.6187, 0.4298, 0.0542, 0.5579, 0.5068, 0.7672] +2026-04-11 07:17:57.526533: Epoch time: 102.99 s +2026-04-11 07:18:00.185283: +2026-04-11 07:18:00.187153: Epoch 716 +2026-04-11 07:18:00.188614: Current learning rate: 0.00837 +2026-04-11 07:19:43.621206: train_loss -0.2878 +2026-04-11 07:19:43.626259: val_loss -0.1849 +2026-04-11 07:19:43.629728: Pseudo dice [0.4275, 0.6902, 0.6129, 0.4751, 0.3568, 0.1468, 0.4563] +2026-04-11 07:19:43.633431: Epoch time: 103.44 s +2026-04-11 07:19:45.050187: +2026-04-11 07:19:45.051901: Epoch 717 +2026-04-11 07:19:45.053349: Current learning rate: 0.00837 +2026-04-11 07:21:28.233404: train_loss -0.2928 +2026-04-11 07:21:28.238641: val_loss -0.2398 +2026-04-11 07:21:28.240332: Pseudo dice [0.8062, 0.8854, 0.568, 0.2656, 0.4821, 0.264, 0.8405] +2026-04-11 07:21:28.242792: Epoch time: 103.19 s +2026-04-11 07:21:29.720581: +2026-04-11 07:21:29.722057: Epoch 718 +2026-04-11 07:21:29.723403: Current learning rate: 0.00837 +2026-04-11 07:23:12.766896: train_loss -0.3022 +2026-04-11 07:23:12.771711: val_loss -0.2632 +2026-04-11 07:23:12.774069: Pseudo dice [0.3723, 0.7272, 0.6455, 0.2848, 0.4474, 0.526, 0.8173] +2026-04-11 07:23:12.776435: Epoch time: 103.05 s +2026-04-11 07:23:14.269525: +2026-04-11 07:23:14.271234: Epoch 719 +2026-04-11 07:23:14.272782: Current learning rate: 0.00837 +2026-04-11 07:24:57.512710: train_loss -0.2913 +2026-04-11 07:24:57.518558: val_loss -0.2264 +2026-04-11 07:24:57.520806: Pseudo dice [0.1126, 0.3212, 0.6105, 0.7231, 0.5342, 0.1602, 0.548] +2026-04-11 07:24:57.523559: Epoch time: 103.25 s +2026-04-11 07:24:58.962757: +2026-04-11 07:24:58.964362: Epoch 720 +2026-04-11 07:24:58.965773: Current learning rate: 0.00836 +2026-04-11 07:26:41.910205: train_loss -0.2818 +2026-04-11 07:26:41.915919: val_loss -0.1581 +2026-04-11 07:26:41.917663: Pseudo dice [0.1367, 0.4267, 0.7423, 0.8088, 0.3691, 0.0446, 0.5716] +2026-04-11 07:26:41.919632: Epoch time: 102.95 s +2026-04-11 07:26:43.382597: +2026-04-11 07:26:43.384234: Epoch 721 +2026-04-11 07:26:43.385785: Current learning rate: 0.00836 +2026-04-11 07:28:26.580701: train_loss -0.2936 +2026-04-11 07:28:26.591636: val_loss -0.2731 +2026-04-11 07:28:26.605815: Pseudo dice [0.7568, 0.4981, 0.7015, 0.2364, 0.3671, 0.437, 0.7794] +2026-04-11 07:28:26.611169: Epoch time: 103.2 s +2026-04-11 07:28:28.075333: +2026-04-11 07:28:28.077186: Epoch 722 +2026-04-11 07:28:28.078644: Current learning rate: 0.00836 +2026-04-11 07:30:11.083841: train_loss -0.3028 +2026-04-11 07:30:11.088882: val_loss -0.1953 +2026-04-11 07:30:11.090647: Pseudo dice [0.5941, 0.6138, 0.6038, 0.5722, 0.4742, 0.2701, 0.5071] +2026-04-11 07:30:11.093192: Epoch time: 103.01 s +2026-04-11 07:30:12.545687: +2026-04-11 07:30:12.547560: Epoch 723 +2026-04-11 07:30:12.559790: Current learning rate: 0.00836 +2026-04-11 07:31:56.190470: train_loss -0.2459 +2026-04-11 07:31:56.196368: val_loss -0.2659 +2026-04-11 07:31:56.200129: Pseudo dice [0.4903, 0.7039, 0.7268, 0.0104, 0.4495, 0.779, 0.6547] +2026-04-11 07:31:56.203006: Epoch time: 103.65 s +2026-04-11 07:31:57.630171: +2026-04-11 07:31:57.631933: Epoch 724 +2026-04-11 07:31:57.633765: Current learning rate: 0.00836 +2026-04-11 07:33:40.492533: train_loss -0.2813 +2026-04-11 07:33:40.499609: val_loss -0.2386 +2026-04-11 07:33:40.502052: Pseudo dice [0.4875, 0.3206, 0.7899, 0.1599, 0.5037, 0.3496, 0.7687] +2026-04-11 07:33:40.504591: Epoch time: 102.87 s +2026-04-11 07:33:41.924522: +2026-04-11 07:33:41.926197: Epoch 725 +2026-04-11 07:33:41.927752: Current learning rate: 0.00835 +2026-04-11 07:35:25.442243: train_loss -0.2913 +2026-04-11 07:35:25.448399: val_loss -0.2031 +2026-04-11 07:35:25.450992: Pseudo dice [0.5037, 0.6529, 0.5538, 0.0155, 0.5882, 0.1401, 0.7755] +2026-04-11 07:35:25.457677: Epoch time: 103.52 s +2026-04-11 07:35:26.902032: +2026-04-11 07:35:26.903591: Epoch 726 +2026-04-11 07:35:26.905550: Current learning rate: 0.00835 +2026-04-11 07:37:09.835729: train_loss -0.3001 +2026-04-11 07:37:09.840964: val_loss -0.1229 +2026-04-11 07:37:09.842790: Pseudo dice [0.2914, 0.7837, 0.7048, 0.1823, 0.5458, 0.0264, 0.8044] +2026-04-11 07:37:09.844908: Epoch time: 102.94 s +2026-04-11 07:37:11.268122: +2026-04-11 07:37:11.269623: Epoch 727 +2026-04-11 07:37:11.271549: Current learning rate: 0.00835 +2026-04-11 07:38:53.736512: train_loss -0.2768 +2026-04-11 07:38:53.741242: val_loss -0.218 +2026-04-11 07:38:53.743139: Pseudo dice [0.4978, 0.7583, 0.4986, 0.1007, 0.5381, 0.1225, 0.7488] +2026-04-11 07:38:53.745405: Epoch time: 102.47 s +2026-04-11 07:38:55.160933: +2026-04-11 07:38:55.163280: Epoch 728 +2026-04-11 07:38:55.165799: Current learning rate: 0.00835 +2026-04-11 07:40:37.966783: train_loss -0.274 +2026-04-11 07:40:37.975116: val_loss -0.2839 +2026-04-11 07:40:37.977463: Pseudo dice [0.5264, 0.315, 0.5619, 0.4832, 0.5087, 0.7317, 0.7063] +2026-04-11 07:40:37.980837: Epoch time: 102.81 s +2026-04-11 07:40:39.410463: +2026-04-11 07:40:39.412637: Epoch 729 +2026-04-11 07:40:39.414186: Current learning rate: 0.00834 +2026-04-11 07:42:22.843360: train_loss -0.2822 +2026-04-11 07:42:22.853901: val_loss -0.1848 +2026-04-11 07:42:22.856018: Pseudo dice [0.4381, 0.8279, 0.52, 0.3486, 0.6169, 0.019, 0.4127] +2026-04-11 07:42:22.858982: Epoch time: 103.44 s +2026-04-11 07:42:24.291659: +2026-04-11 07:42:24.293642: Epoch 730 +2026-04-11 07:42:24.295728: Current learning rate: 0.00834 +2026-04-11 07:44:07.609991: train_loss -0.3111 +2026-04-11 07:44:07.614844: val_loss -0.2267 +2026-04-11 07:44:07.616767: Pseudo dice [0.691, 0.7092, 0.7385, 0.3193, 0.523, 0.1948, 0.8214] +2026-04-11 07:44:07.618998: Epoch time: 103.32 s +2026-04-11 07:44:09.034381: +2026-04-11 07:44:09.035939: Epoch 731 +2026-04-11 07:44:09.037526: Current learning rate: 0.00834 +2026-04-11 07:45:51.562584: train_loss -0.2863 +2026-04-11 07:45:51.571976: val_loss -0.1719 +2026-04-11 07:45:51.576015: Pseudo dice [0.3467, 0.5099, 0.6672, 0.618, 0.567, 0.1797, 0.5547] +2026-04-11 07:45:51.578750: Epoch time: 102.53 s +2026-04-11 07:45:52.992589: +2026-04-11 07:45:52.994654: Epoch 732 +2026-04-11 07:45:52.996496: Current learning rate: 0.00834 +2026-04-11 07:47:36.378804: train_loss -0.2973 +2026-04-11 07:47:36.384927: val_loss -0.2528 +2026-04-11 07:47:36.386833: Pseudo dice [0.4515, 0.4121, 0.5583, 0.8391, 0.5416, 0.7056, 0.6988] +2026-04-11 07:47:36.389825: Epoch time: 103.39 s +2026-04-11 07:47:37.830622: +2026-04-11 07:47:37.832368: Epoch 733 +2026-04-11 07:47:37.834466: Current learning rate: 0.00833 +2026-04-11 07:49:21.146135: train_loss -0.2926 +2026-04-11 07:49:21.153692: val_loss -0.2333 +2026-04-11 07:49:21.155663: Pseudo dice [0.5582, 0.4942, 0.6978, 0.7688, 0.393, 0.2549, 0.7278] +2026-04-11 07:49:21.158481: Epoch time: 103.32 s +2026-04-11 07:49:22.653645: +2026-04-11 07:49:22.655835: Epoch 734 +2026-04-11 07:49:22.657502: Current learning rate: 0.00833 +2026-04-11 07:51:07.528235: train_loss -0.2619 +2026-04-11 07:51:07.533926: val_loss -0.239 +2026-04-11 07:51:07.536152: Pseudo dice [0.5999, 0.4199, 0.657, 0.5661, 0.4689, 0.4622, 0.6243] +2026-04-11 07:51:07.538574: Epoch time: 104.88 s +2026-04-11 07:51:08.999956: +2026-04-11 07:51:09.002342: Epoch 735 +2026-04-11 07:51:09.004096: Current learning rate: 0.00833 +2026-04-11 07:52:53.153666: train_loss -0.2694 +2026-04-11 07:52:53.161029: val_loss -0.2002 +2026-04-11 07:52:53.163930: Pseudo dice [0.5327, 0.4639, 0.7065, 0.7557, 0.3136, 0.0603, 0.519] +2026-04-11 07:52:53.166800: Epoch time: 104.16 s +2026-04-11 07:52:56.046630: +2026-04-11 07:52:56.050489: Epoch 736 +2026-04-11 07:52:56.052517: Current learning rate: 0.00833 +2026-04-11 07:54:39.951453: train_loss -0.2763 +2026-04-11 07:54:39.958631: val_loss -0.2216 +2026-04-11 07:54:39.961441: Pseudo dice [0.6141, 0.5591, 0.5041, 0.0217, 0.4026, 0.452, 0.4042] +2026-04-11 07:54:39.966951: Epoch time: 103.91 s +2026-04-11 07:54:41.437222: +2026-04-11 07:54:41.439907: Epoch 737 +2026-04-11 07:54:41.442659: Current learning rate: 0.00833 +2026-04-11 07:56:25.390501: train_loss -0.287 +2026-04-11 07:56:25.398078: val_loss -0.1654 +2026-04-11 07:56:25.400797: Pseudo dice [0.5397, 0.7536, 0.4749, 0.3349, 0.5113, 0.2326, 0.4445] +2026-04-11 07:56:25.404156: Epoch time: 103.96 s +2026-04-11 07:56:26.833555: +2026-04-11 07:56:26.835907: Epoch 738 +2026-04-11 07:56:26.838498: Current learning rate: 0.00832 +2026-04-11 07:58:10.868717: train_loss -0.2865 +2026-04-11 07:58:10.876148: val_loss -0.1789 +2026-04-11 07:58:10.878144: Pseudo dice [0.5148, 0.3182, 0.4449, 0.5426, 0.3392, 0.0719, 0.2842] +2026-04-11 07:58:10.880499: Epoch time: 104.04 s +2026-04-11 07:58:12.321030: +2026-04-11 07:58:12.323043: Epoch 739 +2026-04-11 07:58:12.325246: Current learning rate: 0.00832 +2026-04-11 07:59:56.558477: train_loss -0.2765 +2026-04-11 07:59:56.564578: val_loss -0.1998 +2026-04-11 07:59:56.567257: Pseudo dice [0.7576, 0.0932, 0.5987, 0.5512, 0.5355, 0.0994, 0.8314] +2026-04-11 07:59:56.570800: Epoch time: 104.24 s +2026-04-11 07:59:58.067472: +2026-04-11 07:59:58.069985: Epoch 740 +2026-04-11 07:59:58.071900: Current learning rate: 0.00832 +2026-04-11 08:01:41.028965: train_loss -0.2707 +2026-04-11 08:01:41.037595: val_loss -0.2451 +2026-04-11 08:01:41.042430: Pseudo dice [0.222, 0.6252, 0.5563, 0.1629, 0.5848, 0.2581, 0.6309] +2026-04-11 08:01:41.044846: Epoch time: 102.97 s +2026-04-11 08:01:42.537782: +2026-04-11 08:01:42.540529: Epoch 741 +2026-04-11 08:01:42.543175: Current learning rate: 0.00832 +2026-04-11 08:03:27.054257: train_loss -0.2686 +2026-04-11 08:03:27.060479: val_loss -0.2415 +2026-04-11 08:03:27.063063: Pseudo dice [0.2546, 0.553, 0.5576, 0.4413, 0.4514, 0.4909, 0.6125] +2026-04-11 08:03:27.065772: Epoch time: 104.52 s +2026-04-11 08:03:28.501625: +2026-04-11 08:03:28.503885: Epoch 742 +2026-04-11 08:03:28.506035: Current learning rate: 0.00831 +2026-04-11 08:05:11.916888: train_loss -0.2795 +2026-04-11 08:05:11.922722: val_loss -0.2601 +2026-04-11 08:05:11.925467: Pseudo dice [0.5473, 0.3943, 0.6789, 0.4624, 0.3608, 0.732, 0.8563] +2026-04-11 08:05:11.928273: Epoch time: 103.42 s +2026-04-11 08:05:13.457763: +2026-04-11 08:05:13.459664: Epoch 743 +2026-04-11 08:05:13.461902: Current learning rate: 0.00831 +2026-04-11 08:06:56.373254: train_loss -0.2768 +2026-04-11 08:06:56.379891: val_loss -0.2177 +2026-04-11 08:06:56.381679: Pseudo dice [0.5586, 0.382, 0.4937, 0.1542, 0.4902, 0.09, 0.8015] +2026-04-11 08:06:56.384860: Epoch time: 102.92 s +2026-04-11 08:06:57.842628: +2026-04-11 08:06:57.844614: Epoch 744 +2026-04-11 08:06:57.847221: Current learning rate: 0.00831 +2026-04-11 08:08:41.341766: train_loss -0.2809 +2026-04-11 08:08:41.348154: val_loss -0.2169 +2026-04-11 08:08:41.350338: Pseudo dice [0.6949, 0.6244, 0.4467, 0.6071, 0.505, 0.1685, 0.8416] +2026-04-11 08:08:41.353264: Epoch time: 103.5 s +2026-04-11 08:08:42.810797: +2026-04-11 08:08:42.812753: Epoch 745 +2026-04-11 08:08:42.814719: Current learning rate: 0.00831 +2026-04-11 08:10:26.825572: train_loss -0.2667 +2026-04-11 08:10:26.831311: val_loss -0.1979 +2026-04-11 08:10:26.833608: Pseudo dice [0.603, 0.7795, 0.5041, 0.3712, 0.3788, 0.0688, 0.7265] +2026-04-11 08:10:26.836444: Epoch time: 104.02 s +2026-04-11 08:10:28.319273: +2026-04-11 08:10:28.324567: Epoch 746 +2026-04-11 08:10:28.329908: Current learning rate: 0.0083 +2026-04-11 08:12:12.748673: train_loss -0.2924 +2026-04-11 08:12:12.757674: val_loss -0.2283 +2026-04-11 08:12:12.760261: Pseudo dice [0.6024, 0.5279, 0.668, 0.3079, 0.5416, 0.1119, 0.8426] +2026-04-11 08:12:12.763406: Epoch time: 104.43 s +2026-04-11 08:12:14.257878: +2026-04-11 08:12:14.259701: Epoch 747 +2026-04-11 08:12:14.261670: Current learning rate: 0.0083 +2026-04-11 08:13:58.297474: train_loss -0.2845 +2026-04-11 08:13:58.306677: val_loss -0.2592 +2026-04-11 08:13:58.310121: Pseudo dice [0.4328, 0.7918, 0.6282, 0.7709, 0.6323, 0.202, 0.8297] +2026-04-11 08:13:58.313347: Epoch time: 104.04 s +2026-04-11 08:13:59.813281: +2026-04-11 08:13:59.815262: Epoch 748 +2026-04-11 08:13:59.817470: Current learning rate: 0.0083 +2026-04-11 08:15:43.169567: train_loss -0.2939 +2026-04-11 08:15:43.183671: val_loss -0.1213 +2026-04-11 08:15:43.185806: Pseudo dice [0.2678, 0.8516, 0.523, 0.5757, 0.4304, 0.0654, 0.5258] +2026-04-11 08:15:43.188124: Epoch time: 103.36 s +2026-04-11 08:15:44.626518: +2026-04-11 08:15:44.628425: Epoch 749 +2026-04-11 08:15:44.631365: Current learning rate: 0.0083 +2026-04-11 08:17:28.434013: train_loss -0.2457 +2026-04-11 08:17:28.443907: val_loss -0.1893 +2026-04-11 08:17:28.449038: Pseudo dice [0.6456, 0.664, 0.5614, 0.2912, 0.2378, 0.2781, 0.5176] +2026-04-11 08:17:28.453915: Epoch time: 103.81 s +2026-04-11 08:17:31.932185: +2026-04-11 08:17:31.935983: Epoch 750 +2026-04-11 08:17:31.939671: Current learning rate: 0.0083 +2026-04-11 08:19:15.331621: train_loss -0.2667 +2026-04-11 08:19:15.340024: val_loss -0.1822 +2026-04-11 08:19:15.342322: Pseudo dice [0.4859, 0.7295, 0.3224, 0.4313, 0.3605, 0.0767, 0.8421] +2026-04-11 08:19:15.345035: Epoch time: 103.4 s +2026-04-11 08:19:16.821134: +2026-04-11 08:19:16.823622: Epoch 751 +2026-04-11 08:19:16.825933: Current learning rate: 0.00829 +2026-04-11 08:20:59.643468: train_loss -0.2968 +2026-04-11 08:20:59.650034: val_loss -0.1833 +2026-04-11 08:20:59.652895: Pseudo dice [0.5506, 0.533, 0.661, 0.4447, 0.4898, 0.0273, 0.7272] +2026-04-11 08:20:59.657009: Epoch time: 102.83 s +2026-04-11 08:21:01.136745: +2026-04-11 08:21:01.138827: Epoch 752 +2026-04-11 08:21:01.142254: Current learning rate: 0.00829 +2026-04-11 08:22:44.129712: train_loss -0.288 +2026-04-11 08:22:44.135408: val_loss -0.2347 +2026-04-11 08:22:44.138456: Pseudo dice [0.5629, 0.4678, 0.6161, 0.57, 0.122, 0.3877, 0.5432] +2026-04-11 08:22:44.140960: Epoch time: 103.0 s +2026-04-11 08:22:45.623669: +2026-04-11 08:22:45.625296: Epoch 753 +2026-04-11 08:22:45.627661: Current learning rate: 0.00829 +2026-04-11 08:24:29.316903: train_loss -0.2877 +2026-04-11 08:24:29.322757: val_loss -0.2683 +2026-04-11 08:24:29.324966: Pseudo dice [0.2189, 0.6198, 0.7313, 0.6962, 0.52, 0.7194, 0.6964] +2026-04-11 08:24:29.327599: Epoch time: 103.7 s +2026-04-11 08:24:30.790985: +2026-04-11 08:24:30.792755: Epoch 754 +2026-04-11 08:24:30.795010: Current learning rate: 0.00829 +2026-04-11 08:26:14.425478: train_loss -0.2965 +2026-04-11 08:26:14.432338: val_loss -0.2316 +2026-04-11 08:26:14.434391: Pseudo dice [0.4849, 0.482, 0.6392, 0.0311, 0.5104, 0.116, 0.8268] +2026-04-11 08:26:14.437069: Epoch time: 103.64 s +2026-04-11 08:26:17.076912: +2026-04-11 08:26:17.079250: Epoch 755 +2026-04-11 08:26:17.081263: Current learning rate: 0.00828 +2026-04-11 08:28:00.787590: train_loss -0.305 +2026-04-11 08:28:00.793921: val_loss -0.2159 +2026-04-11 08:28:00.796024: Pseudo dice [0.3449, 0.6412, 0.5986, 0.6617, 0.6276, 0.0704, 0.6787] +2026-04-11 08:28:00.798712: Epoch time: 103.71 s +2026-04-11 08:28:02.273137: +2026-04-11 08:28:02.274878: Epoch 756 +2026-04-11 08:28:02.277287: Current learning rate: 0.00828 +2026-04-11 08:29:46.640459: train_loss -0.271 +2026-04-11 08:29:46.648398: val_loss -0.2456 +2026-04-11 08:29:46.650808: Pseudo dice [0.8487, 0.5675, 0.7322, 0.713, 0.4184, 0.1161, 0.5595] +2026-04-11 08:29:46.653346: Epoch time: 104.37 s +2026-04-11 08:29:48.104294: +2026-04-11 08:29:48.107365: Epoch 757 +2026-04-11 08:29:48.109596: Current learning rate: 0.00828 +2026-04-11 08:31:32.670218: train_loss -0.3185 +2026-04-11 08:31:32.677866: val_loss -0.2685 +2026-04-11 08:31:32.680944: Pseudo dice [0.2141, 0.5126, 0.6798, 0.5765, 0.5546, 0.8403, 0.7857] +2026-04-11 08:31:32.684153: Epoch time: 104.57 s +2026-04-11 08:31:34.160708: +2026-04-11 08:31:34.162650: Epoch 758 +2026-04-11 08:31:34.165298: Current learning rate: 0.00828 +2026-04-11 08:33:17.488199: train_loss -0.3135 +2026-04-11 08:33:17.497215: val_loss -0.2171 +2026-04-11 08:33:17.500608: Pseudo dice [0.5901, 0.2188, 0.5395, 0.4403, 0.6291, 0.333, 0.7719] +2026-04-11 08:33:17.504128: Epoch time: 103.33 s +2026-04-11 08:33:18.944521: +2026-04-11 08:33:18.947101: Epoch 759 +2026-04-11 08:33:18.950682: Current learning rate: 0.00827 +2026-04-11 08:35:02.133074: train_loss -0.2925 +2026-04-11 08:35:02.141237: val_loss -0.2555 +2026-04-11 08:35:02.143914: Pseudo dice [0.2681, 0.8173, 0.6573, 0.6071, 0.268, 0.7722, 0.8238] +2026-04-11 08:35:02.147313: Epoch time: 103.19 s +2026-04-11 08:35:03.608359: +2026-04-11 08:35:03.611475: Epoch 760 +2026-04-11 08:35:03.613929: Current learning rate: 0.00827 +2026-04-11 08:36:47.007747: train_loss -0.2801 +2026-04-11 08:36:47.015003: val_loss -0.2084 +2026-04-11 08:36:47.017800: Pseudo dice [0.5424, 0.3732, 0.5966, 0.0206, 0.5456, 0.0468, 0.6652] +2026-04-11 08:36:47.020101: Epoch time: 103.4 s +2026-04-11 08:36:48.551383: +2026-04-11 08:36:48.553265: Epoch 761 +2026-04-11 08:36:48.556108: Current learning rate: 0.00827 +2026-04-11 08:38:32.366936: train_loss -0.2941 +2026-04-11 08:38:32.372888: val_loss -0.2902 +2026-04-11 08:38:32.374828: Pseudo dice [0.6813, 0.4645, 0.6473, 0.7035, 0.6543, 0.6818, 0.7569] +2026-04-11 08:38:32.377458: Epoch time: 103.82 s +2026-04-11 08:38:33.872313: +2026-04-11 08:38:33.875202: Epoch 762 +2026-04-11 08:38:33.877435: Current learning rate: 0.00827 +2026-04-11 08:40:16.953662: train_loss -0.2912 +2026-04-11 08:40:16.960182: val_loss -0.2395 +2026-04-11 08:40:16.962429: Pseudo dice [0.6343, 0.539, 0.3977, 0.2951, 0.3364, 0.7347, 0.5696] +2026-04-11 08:40:16.964921: Epoch time: 103.09 s +2026-04-11 08:40:18.429718: +2026-04-11 08:40:18.431924: Epoch 763 +2026-04-11 08:40:18.434052: Current learning rate: 0.00827 +2026-04-11 08:42:01.296152: train_loss -0.2817 +2026-04-11 08:42:01.302977: val_loss -0.2175 +2026-04-11 08:42:01.305580: Pseudo dice [0.4676, 0.5394, 0.4291, 0.6596, 0.4611, 0.0587, 0.5845] +2026-04-11 08:42:01.308114: Epoch time: 102.87 s +2026-04-11 08:42:02.818967: +2026-04-11 08:42:02.820919: Epoch 764 +2026-04-11 08:42:02.822816: Current learning rate: 0.00826 +2026-04-11 08:43:46.377161: train_loss -0.2874 +2026-04-11 08:43:46.383646: val_loss -0.2138 +2026-04-11 08:43:46.385874: Pseudo dice [0.428, 0.3197, 0.8843, 0.2566, 0.5245, 0.0852, 0.8467] +2026-04-11 08:43:46.388558: Epoch time: 103.56 s +2026-04-11 08:43:47.932715: +2026-04-11 08:43:47.934686: Epoch 765 +2026-04-11 08:43:47.937213: Current learning rate: 0.00826 +2026-04-11 08:45:31.632530: train_loss -0.2924 +2026-04-11 08:45:31.638662: val_loss -0.2707 +2026-04-11 08:45:31.640680: Pseudo dice [0.7387, 0.7776, 0.6104, 0.2846, 0.625, 0.4373, 0.7569] +2026-04-11 08:45:31.643115: Epoch time: 103.7 s +2026-04-11 08:45:33.109932: +2026-04-11 08:45:33.112599: Epoch 766 +2026-04-11 08:45:33.114951: Current learning rate: 0.00826 +2026-04-11 08:47:16.799245: train_loss -0.2987 +2026-04-11 08:47:16.805668: val_loss -0.2934 +2026-04-11 08:47:16.807970: Pseudo dice [0.5697, 0.3713, 0.71, 0.6232, 0.6078, 0.7061, 0.8581] +2026-04-11 08:47:16.811852: Epoch time: 103.69 s +2026-04-11 08:47:18.344784: +2026-04-11 08:47:18.346874: Epoch 767 +2026-04-11 08:47:18.349168: Current learning rate: 0.00826 +2026-04-11 08:49:02.473400: train_loss -0.2989 +2026-04-11 08:49:02.482774: val_loss -0.24 +2026-04-11 08:49:02.484729: Pseudo dice [0.3337, 0.6824, 0.514, 0.4984, 0.3153, 0.2965, 0.7824] +2026-04-11 08:49:02.487177: Epoch time: 104.13 s +2026-04-11 08:49:03.948732: +2026-04-11 08:49:03.950509: Epoch 768 +2026-04-11 08:49:03.952343: Current learning rate: 0.00825 +2026-04-11 08:50:48.720873: train_loss -0.2789 +2026-04-11 08:50:48.726654: val_loss -0.1663 +2026-04-11 08:50:48.729281: Pseudo dice [0.533, 0.5815, 0.5942, 0.2475, 0.2362, 0.036, 0.4232] +2026-04-11 08:50:48.731493: Epoch time: 104.78 s +2026-04-11 08:50:50.192508: +2026-04-11 08:50:50.194326: Epoch 769 +2026-04-11 08:50:50.196689: Current learning rate: 0.00825 +2026-04-11 08:52:34.048647: train_loss -0.3005 +2026-04-11 08:52:34.053947: val_loss -0.24 +2026-04-11 08:52:34.056248: Pseudo dice [0.7616, 0.8349, 0.4364, 0.3402, 0.4198, 0.2018, 0.4162] +2026-04-11 08:52:34.059323: Epoch time: 103.86 s +2026-04-11 08:52:35.625782: +2026-04-11 08:52:35.627722: Epoch 770 +2026-04-11 08:52:35.629840: Current learning rate: 0.00825 +2026-04-11 08:54:19.196942: train_loss -0.3029 +2026-04-11 08:54:19.205007: val_loss -0.2509 +2026-04-11 08:54:19.207688: Pseudo dice [0.5309, 0.6454, 0.6729, 0.3626, 0.5153, 0.13, 0.8363] +2026-04-11 08:54:19.210238: Epoch time: 103.57 s +2026-04-11 08:54:20.690989: +2026-04-11 08:54:20.692822: Epoch 771 +2026-04-11 08:54:20.694930: Current learning rate: 0.00825 +2026-04-11 08:56:04.471949: train_loss -0.287 +2026-04-11 08:56:04.478157: val_loss -0.2366 +2026-04-11 08:56:04.480204: Pseudo dice [0.3335, 0.4819, 0.5494, 0.018, 0.5027, 0.0477, 0.8695] +2026-04-11 08:56:04.483638: Epoch time: 103.78 s +2026-04-11 08:56:06.010515: +2026-04-11 08:56:06.012148: Epoch 772 +2026-04-11 08:56:06.014276: Current learning rate: 0.00824 +2026-04-11 08:57:50.987582: train_loss -0.2775 +2026-04-11 08:57:50.993388: val_loss -0.2606 +2026-04-11 08:57:50.995459: Pseudo dice [0.5614, 0.7818, 0.7247, 0.0117, 0.6073, 0.3936, 0.6013] +2026-04-11 08:57:50.998189: Epoch time: 104.98 s +2026-04-11 08:57:52.463266: +2026-04-11 08:57:52.465334: Epoch 773 +2026-04-11 08:57:52.467825: Current learning rate: 0.00824 +2026-04-11 08:59:36.730239: train_loss -0.2741 +2026-04-11 08:59:36.737679: val_loss -0.2213 +2026-04-11 08:59:36.740474: Pseudo dice [0.4616, 0.7936, 0.6554, 0.8432, 0.5096, 0.0928, 0.8822] +2026-04-11 08:59:36.743187: Epoch time: 104.27 s +2026-04-11 08:59:38.239481: +2026-04-11 08:59:38.241296: Epoch 774 +2026-04-11 08:59:38.243559: Current learning rate: 0.00824 +2026-04-11 09:01:21.977940: train_loss -0.3 +2026-04-11 09:01:21.984765: val_loss -0.2514 +2026-04-11 09:01:21.986833: Pseudo dice [0.3552, 0.4184, 0.5113, 0.5675, 0.6354, 0.7809, 0.7047] +2026-04-11 09:01:21.989434: Epoch time: 103.74 s +2026-04-11 09:01:25.201656: +2026-04-11 09:01:25.203422: Epoch 775 +2026-04-11 09:01:25.205501: Current learning rate: 0.00824 +2026-04-11 09:03:08.564215: train_loss -0.2951 +2026-04-11 09:03:08.571333: val_loss -0.2578 +2026-04-11 09:03:08.573467: Pseudo dice [0.6633, 0.7796, 0.5858, 0.6863, 0.3313, 0.6972, 0.1964] +2026-04-11 09:03:08.575962: Epoch time: 103.37 s +2026-04-11 09:03:10.052469: +2026-04-11 09:03:10.054712: Epoch 776 +2026-04-11 09:03:10.057710: Current learning rate: 0.00824 +2026-04-11 09:04:53.582462: train_loss -0.294 +2026-04-11 09:04:53.589438: val_loss -0.2413 +2026-04-11 09:04:53.591407: Pseudo dice [0.6895, 0.4231, 0.6555, 0.4719, 0.4116, 0.1129, 0.7878] +2026-04-11 09:04:53.594994: Epoch time: 103.53 s +2026-04-11 09:04:55.052154: +2026-04-11 09:04:55.054985: Epoch 777 +2026-04-11 09:04:55.057070: Current learning rate: 0.00823 +2026-04-11 09:06:38.878076: train_loss -0.2903 +2026-04-11 09:06:38.884475: val_loss -0.2772 +2026-04-11 09:06:38.886287: Pseudo dice [0.7103, 0.836, 0.624, 0.6287, 0.5855, 0.2687, 0.8048] +2026-04-11 09:06:38.888925: Epoch time: 103.83 s +2026-04-11 09:06:40.375986: +2026-04-11 09:06:40.378677: Epoch 778 +2026-04-11 09:06:40.381252: Current learning rate: 0.00823 +2026-04-11 09:08:24.327939: train_loss -0.2931 +2026-04-11 09:08:24.334462: val_loss -0.2296 +2026-04-11 09:08:24.337434: Pseudo dice [0.5111, 0.1619, 0.5207, 0.5741, 0.3007, 0.2437, 0.3915] +2026-04-11 09:08:24.340328: Epoch time: 103.96 s +2026-04-11 09:08:25.857515: +2026-04-11 09:08:25.861720: Epoch 779 +2026-04-11 09:08:25.863778: Current learning rate: 0.00823 +2026-04-11 09:10:09.997178: train_loss -0.289 +2026-04-11 09:10:10.003673: val_loss -0.1438 +2026-04-11 09:10:10.006074: Pseudo dice [0.7259, 0.2965, 0.489, 0.3952, 0.5655, 0.1759, 0.3772] +2026-04-11 09:10:10.009036: Epoch time: 104.14 s +2026-04-11 09:10:11.491705: +2026-04-11 09:10:11.493823: Epoch 780 +2026-04-11 09:10:11.496321: Current learning rate: 0.00823 +2026-04-11 09:11:54.755923: train_loss -0.2808 +2026-04-11 09:11:54.763093: val_loss -0.2463 +2026-04-11 09:11:54.766642: Pseudo dice [0.3879, 0.3903, 0.6105, 0.5289, 0.0247, 0.6914, 0.707] +2026-04-11 09:11:54.769853: Epoch time: 103.27 s +2026-04-11 09:11:56.231217: +2026-04-11 09:11:56.244870: Epoch 781 +2026-04-11 09:11:56.248089: Current learning rate: 0.00822 +2026-04-11 09:13:39.342180: train_loss -0.2774 +2026-04-11 09:13:39.347445: val_loss -0.2005 +2026-04-11 09:13:39.349690: Pseudo dice [0.5014, 0.4656, 0.4158, 0.6163, 0.056, 0.0609, 0.8032] +2026-04-11 09:13:39.352116: Epoch time: 103.11 s +2026-04-11 09:13:40.837925: +2026-04-11 09:13:40.840195: Epoch 782 +2026-04-11 09:13:40.845692: Current learning rate: 0.00822 +2026-04-11 09:15:24.984746: train_loss -0.2739 +2026-04-11 09:15:24.991950: val_loss -0.2407 +2026-04-11 09:15:24.993922: Pseudo dice [0.6406, 0.5082, 0.6378, 0.3447, 0.4917, 0.1081, 0.6925] +2026-04-11 09:15:24.996217: Epoch time: 104.15 s +2026-04-11 09:15:26.533494: +2026-04-11 09:15:26.535434: Epoch 783 +2026-04-11 09:15:26.537647: Current learning rate: 0.00822 +2026-04-11 09:17:10.034161: train_loss -0.2905 +2026-04-11 09:17:10.040174: val_loss -0.2548 +2026-04-11 09:17:10.042540: Pseudo dice [0.5195, 0.6883, 0.4817, 0.6793, 0.4556, 0.5037, 0.7465] +2026-04-11 09:17:10.045521: Epoch time: 103.5 s +2026-04-11 09:17:11.550627: +2026-04-11 09:17:11.552887: Epoch 784 +2026-04-11 09:17:11.555079: Current learning rate: 0.00822 +2026-04-11 09:18:54.559482: train_loss -0.2922 +2026-04-11 09:18:54.564967: val_loss -0.1894 +2026-04-11 09:18:54.568238: Pseudo dice [0.4521, 0.5378, 0.7023, 0.3383, 0.3258, 0.0887, 0.7178] +2026-04-11 09:18:54.570340: Epoch time: 103.01 s +2026-04-11 09:18:56.030620: +2026-04-11 09:18:56.032480: Epoch 785 +2026-04-11 09:18:56.034559: Current learning rate: 0.00822 +2026-04-11 09:20:39.348706: train_loss -0.3002 +2026-04-11 09:20:39.354643: val_loss -0.2414 +2026-04-11 09:20:39.356972: Pseudo dice [0.3225, 0.308, 0.6392, 0.5539, 0.4307, 0.3218, 0.7949] +2026-04-11 09:20:39.359312: Epoch time: 103.32 s +2026-04-11 09:20:40.856752: +2026-04-11 09:20:40.858665: Epoch 786 +2026-04-11 09:20:40.861797: Current learning rate: 0.00821 +2026-04-11 09:22:25.340112: train_loss -0.2926 +2026-04-11 09:22:25.347920: val_loss -0.1751 +2026-04-11 09:22:25.350434: Pseudo dice [0.3301, 0.4244, 0.4194, 0.3852, 0.4381, 0.0617, 0.8226] +2026-04-11 09:22:25.353995: Epoch time: 104.49 s +2026-04-11 09:22:26.943409: +2026-04-11 09:22:26.945629: Epoch 787 +2026-04-11 09:22:26.948160: Current learning rate: 0.00821 +2026-04-11 09:24:10.429309: train_loss -0.2845 +2026-04-11 09:24:10.435369: val_loss -0.1849 +2026-04-11 09:24:10.437699: Pseudo dice [0.2875, 0.2715, 0.4829, 0.4153, 0.4058, 0.0764, 0.632] +2026-04-11 09:24:10.440053: Epoch time: 103.49 s +2026-04-11 09:24:11.942427: +2026-04-11 09:24:11.944355: Epoch 788 +2026-04-11 09:24:11.946348: Current learning rate: 0.00821 +2026-04-11 09:25:55.484294: train_loss -0.2776 +2026-04-11 09:25:55.491291: val_loss -0.0856 +2026-04-11 09:25:55.493483: Pseudo dice [0.3768, 0.3638, 0.6361, 0.6741, 0.2735, 0.0114, 0.6805] +2026-04-11 09:25:55.496261: Epoch time: 103.55 s +2026-04-11 09:25:57.059872: +2026-04-11 09:25:57.061507: Epoch 789 +2026-04-11 09:25:57.063640: Current learning rate: 0.00821 +2026-04-11 09:27:40.548047: train_loss -0.2563 +2026-04-11 09:27:40.554833: val_loss -0.2839 +2026-04-11 09:27:40.556907: Pseudo dice [0.0692, 0.5224, 0.6566, 0.5439, 0.5985, 0.739, 0.9136] +2026-04-11 09:27:40.558993: Epoch time: 103.49 s +2026-04-11 09:27:42.095680: +2026-04-11 09:27:42.097734: Epoch 790 +2026-04-11 09:27:42.100299: Current learning rate: 0.0082 +2026-04-11 09:29:25.120989: train_loss -0.2828 +2026-04-11 09:29:25.126393: val_loss -0.2047 +2026-04-11 09:29:25.128904: Pseudo dice [0.6643, 0.6998, 0.4138, 0.0274, 0.489, 0.0303, 0.7769] +2026-04-11 09:29:25.132612: Epoch time: 103.03 s +2026-04-11 09:29:26.645433: +2026-04-11 09:29:26.647501: Epoch 791 +2026-04-11 09:29:26.649878: Current learning rate: 0.0082 +2026-04-11 09:31:09.682493: train_loss -0.2733 +2026-04-11 09:31:09.690291: val_loss -0.2416 +2026-04-11 09:31:09.693672: Pseudo dice [0.4897, 0.3968, 0.5354, 0.6698, 0.4865, 0.2388, 0.3408] +2026-04-11 09:31:09.696485: Epoch time: 103.04 s +2026-04-11 09:31:11.185493: +2026-04-11 09:31:11.187892: Epoch 792 +2026-04-11 09:31:11.190303: Current learning rate: 0.0082 +2026-04-11 09:32:54.033673: train_loss -0.268 +2026-04-11 09:32:54.040752: val_loss -0.2679 +2026-04-11 09:32:54.045149: Pseudo dice [0.5259, 0.6839, 0.5503, 0.8363, 0.4887, 0.2539, 0.4599] +2026-04-11 09:32:54.047519: Epoch time: 102.85 s +2026-04-11 09:32:55.547814: +2026-04-11 09:32:55.550461: Epoch 793 +2026-04-11 09:32:55.552474: Current learning rate: 0.0082 +2026-04-11 09:34:39.194293: train_loss -0.2896 +2026-04-11 09:34:39.199811: val_loss -0.2312 +2026-04-11 09:34:39.202449: Pseudo dice [0.2739, 0.7059, 0.4216, 0.7624, 0.3549, 0.0867, 0.5769] +2026-04-11 09:34:39.204989: Epoch time: 103.65 s +2026-04-11 09:34:40.660221: +2026-04-11 09:34:40.663678: Epoch 794 +2026-04-11 09:34:40.666130: Current learning rate: 0.00819 +2026-04-11 09:36:25.636108: train_loss -0.2736 +2026-04-11 09:36:25.642015: val_loss -0.2286 +2026-04-11 09:36:25.644919: Pseudo dice [0.8313, 0.5137, 0.5378, 0.0659, 0.3253, 0.3411, 0.3512] +2026-04-11 09:36:25.648172: Epoch time: 104.98 s +2026-04-11 09:36:27.152220: +2026-04-11 09:36:27.154842: Epoch 795 +2026-04-11 09:36:27.157320: Current learning rate: 0.00819 +2026-04-11 09:38:10.477301: train_loss -0.2732 +2026-04-11 09:38:10.483938: val_loss -0.1668 +2026-04-11 09:38:10.486038: Pseudo dice [0.7502, 0.801, 0.6204, 0.2505, 0.5018, 0.0431, 0.6227] +2026-04-11 09:38:10.488332: Epoch time: 103.33 s +2026-04-11 09:38:12.052803: +2026-04-11 09:38:12.054467: Epoch 796 +2026-04-11 09:38:12.056647: Current learning rate: 0.00819 +2026-04-11 09:39:55.496876: train_loss -0.2738 +2026-04-11 09:39:55.504050: val_loss -0.2568 +2026-04-11 09:39:55.507044: Pseudo dice [0.6652, 0.7819, 0.6509, 0.5232, 0.6904, 0.1053, 0.6611] +2026-04-11 09:39:55.509852: Epoch time: 103.45 s +2026-04-11 09:39:56.979126: +2026-04-11 09:39:56.981710: Epoch 797 +2026-04-11 09:39:56.985024: Current learning rate: 0.00819 +2026-04-11 09:41:40.942572: train_loss -0.2878 +2026-04-11 09:41:40.953703: val_loss -0.26 +2026-04-11 09:41:40.956730: Pseudo dice [0.8052, 0.5302, 0.5211, 0.6099, 0.2979, 0.7363, 0.2631] +2026-04-11 09:41:40.958984: Epoch time: 103.97 s +2026-04-11 09:41:42.411801: +2026-04-11 09:41:42.413675: Epoch 798 +2026-04-11 09:41:42.416248: Current learning rate: 0.00819 +2026-04-11 09:43:26.046717: train_loss -0.2953 +2026-04-11 09:43:26.054193: val_loss -0.2626 +2026-04-11 09:43:26.056214: Pseudo dice [0.3654, 0.2164, 0.5767, 0.4795, 0.338, 0.8053, 0.6209] +2026-04-11 09:43:26.059354: Epoch time: 103.64 s +2026-04-11 09:43:27.593427: +2026-04-11 09:43:27.595206: Epoch 799 +2026-04-11 09:43:27.598712: Current learning rate: 0.00818 +2026-04-11 09:45:11.175090: train_loss -0.2705 +2026-04-11 09:45:11.184035: val_loss -0.1395 +2026-04-11 09:45:11.188031: Pseudo dice [0.7888, 0.4878, 0.4063, 0.6823, 0.3042, 0.0234, 0.681] +2026-04-11 09:45:11.193553: Epoch time: 103.59 s +2026-04-11 09:45:14.645887: +2026-04-11 09:45:14.647869: Epoch 800 +2026-04-11 09:45:14.650378: Current learning rate: 0.00818 +2026-04-11 09:46:59.476871: train_loss -0.2657 +2026-04-11 09:46:59.484493: val_loss -0.2031 +2026-04-11 09:46:59.486778: Pseudo dice [0.5955, 0.8739, 0.6395, 0.6399, 0.4098, 0.2151, 0.5466] +2026-04-11 09:46:59.489849: Epoch time: 104.83 s +2026-04-11 09:47:01.011944: +2026-04-11 09:47:01.014590: Epoch 801 +2026-04-11 09:47:01.016619: Current learning rate: 0.00818 +2026-04-11 09:48:45.753500: train_loss -0.2929 +2026-04-11 09:48:45.763399: val_loss -0.2518 +2026-04-11 09:48:45.766923: Pseudo dice [0.3212, 0.6846, 0.8082, 0.1769, 0.42, 0.3155, 0.5922] +2026-04-11 09:48:45.770626: Epoch time: 104.75 s +2026-04-11 09:48:47.284153: +2026-04-11 09:48:47.289258: Epoch 802 +2026-04-11 09:48:47.292724: Current learning rate: 0.00818 +2026-04-11 09:50:30.896619: train_loss -0.3015 +2026-04-11 09:50:30.907490: val_loss -0.1996 +2026-04-11 09:50:30.910713: Pseudo dice [0.4923, 0.6836, 0.567, 0.25, 0.3726, 0.0378, 0.7213] +2026-04-11 09:50:30.914225: Epoch time: 103.62 s +2026-04-11 09:50:32.398425: +2026-04-11 09:50:32.400285: Epoch 803 +2026-04-11 09:50:32.402664: Current learning rate: 0.00817 +2026-04-11 09:52:16.579991: train_loss -0.2975 +2026-04-11 09:52:16.587719: val_loss -0.259 +2026-04-11 09:52:16.589909: Pseudo dice [0.7045, 0.4408, 0.6871, 0.6414, 0.4996, 0.6878, 0.6911] +2026-04-11 09:52:16.592954: Epoch time: 104.19 s +2026-04-11 09:52:18.117012: +2026-04-11 09:52:18.118902: Epoch 804 +2026-04-11 09:52:18.120940: Current learning rate: 0.00817 +2026-04-11 09:54:03.923614: train_loss -0.2802 +2026-04-11 09:54:03.934086: val_loss -0.2731 +2026-04-11 09:54:03.936887: Pseudo dice [0.4044, 0.3157, 0.7976, 0.6659, 0.6146, 0.7093, 0.731] +2026-04-11 09:54:03.939783: Epoch time: 105.81 s +2026-04-11 09:54:05.449056: +2026-04-11 09:54:05.450896: Epoch 805 +2026-04-11 09:54:05.452841: Current learning rate: 0.00817 +2026-04-11 09:55:49.476897: train_loss -0.3005 +2026-04-11 09:55:49.483724: val_loss -0.2481 +2026-04-11 09:55:49.485890: Pseudo dice [0.6722, 0.8233, 0.6122, 0.0205, 0.5249, 0.1932, 0.6524] +2026-04-11 09:55:49.488148: Epoch time: 104.03 s +2026-04-11 09:55:50.963674: +2026-04-11 09:55:50.969658: Epoch 806 +2026-04-11 09:55:50.972557: Current learning rate: 0.00817 +2026-04-11 09:57:34.545200: train_loss -0.302 +2026-04-11 09:57:34.552913: val_loss -0.2304 +2026-04-11 09:57:34.555021: Pseudo dice [0.6735, 0.1006, 0.7126, 0.7694, 0.5679, 0.162, 0.4293] +2026-04-11 09:57:34.557426: Epoch time: 103.59 s +2026-04-11 09:57:36.034461: +2026-04-11 09:57:36.036240: Epoch 807 +2026-04-11 09:57:36.038015: Current learning rate: 0.00816 +2026-04-11 09:59:19.752035: train_loss -0.2787 +2026-04-11 09:59:19.758523: val_loss -0.2539 +2026-04-11 09:59:19.760734: Pseudo dice [0.2944, 0.5198, 0.5682, 0.5816, 0.3522, 0.6489, 0.6278] +2026-04-11 09:59:19.763159: Epoch time: 103.72 s +2026-04-11 09:59:21.294553: +2026-04-11 09:59:21.297181: Epoch 808 +2026-04-11 09:59:21.299792: Current learning rate: 0.00816 +2026-04-11 10:01:05.306737: train_loss -0.3056 +2026-04-11 10:01:05.312966: val_loss -0.2584 +2026-04-11 10:01:05.315039: Pseudo dice [0.5441, 0.4796, 0.718, 0.6488, 0.3252, 0.4637, 0.6705] +2026-04-11 10:01:05.318161: Epoch time: 104.02 s +2026-04-11 10:01:06.801866: +2026-04-11 10:01:06.803494: Epoch 809 +2026-04-11 10:01:06.806131: Current learning rate: 0.00816 +2026-04-11 10:02:49.949041: train_loss -0.2929 +2026-04-11 10:02:49.954808: val_loss -0.2161 +2026-04-11 10:02:49.957018: Pseudo dice [0.1787, 0.4245, 0.6006, 0.4506, 0.2887, 0.5401, 0.5338] +2026-04-11 10:02:49.959404: Epoch time: 103.15 s +2026-04-11 10:02:51.455109: +2026-04-11 10:02:51.456839: Epoch 810 +2026-04-11 10:02:51.459160: Current learning rate: 0.00816 +2026-04-11 10:04:34.759736: train_loss -0.2922 +2026-04-11 10:04:34.767019: val_loss -0.1348 +2026-04-11 10:04:34.769202: Pseudo dice [0.3885, 0.4313, 0.4791, 0.2013, 0.442, 0.018, 0.4983] +2026-04-11 10:04:34.772180: Epoch time: 103.31 s +2026-04-11 10:04:36.254584: +2026-04-11 10:04:36.256313: Epoch 811 +2026-04-11 10:04:36.258172: Current learning rate: 0.00816 +2026-04-11 10:06:19.604844: train_loss -0.2848 +2026-04-11 10:06:19.632011: val_loss -0.2653 +2026-04-11 10:06:19.634100: Pseudo dice [0.4667, 0.6886, 0.5714, 0.7073, 0.4782, 0.4276, 0.7746] +2026-04-11 10:06:19.636756: Epoch time: 103.35 s +2026-04-11 10:06:21.092148: +2026-04-11 10:06:21.094085: Epoch 812 +2026-04-11 10:06:21.096549: Current learning rate: 0.00815 +2026-04-11 10:08:05.583150: train_loss -0.2784 +2026-04-11 10:08:05.590756: val_loss -0.2587 +2026-04-11 10:08:05.592941: Pseudo dice [0.4899, 0.4355, 0.5166, 0.3785, 0.5116, 0.5664, 0.4736] +2026-04-11 10:08:05.595255: Epoch time: 104.49 s +2026-04-11 10:08:08.374433: +2026-04-11 10:08:08.376308: Epoch 813 +2026-04-11 10:08:08.378464: Current learning rate: 0.00815 +2026-04-11 10:09:52.031779: train_loss -0.2859 +2026-04-11 10:09:52.037931: val_loss -0.2071 +2026-04-11 10:09:52.040221: Pseudo dice [0.3638, 0.4737, 0.6285, 0.7572, 0.3894, 0.3322, 0.2647] +2026-04-11 10:09:52.042797: Epoch time: 103.66 s +2026-04-11 10:09:53.974537: +2026-04-11 10:09:53.977412: Epoch 814 +2026-04-11 10:09:53.979613: Current learning rate: 0.00815 +2026-04-11 10:11:38.315587: train_loss -0.2908 +2026-04-11 10:11:38.321444: val_loss -0.2113 +2026-04-11 10:11:38.324418: Pseudo dice [0.7509, 0.5381, 0.5245, 0.5229, 0.5163, 0.0466, 0.5638] +2026-04-11 10:11:38.326811: Epoch time: 104.34 s +2026-04-11 10:11:39.882238: +2026-04-11 10:11:39.886844: Epoch 815 +2026-04-11 10:11:39.888909: Current learning rate: 0.00815 +2026-04-11 10:13:24.433203: train_loss -0.2907 +2026-04-11 10:13:24.439601: val_loss -0.2935 +2026-04-11 10:13:24.441590: Pseudo dice [0.6219, 0.6792, 0.6507, 0.7059, 0.6082, 0.6249, 0.8265] +2026-04-11 10:13:24.443922: Epoch time: 104.55 s +2026-04-11 10:13:25.978111: +2026-04-11 10:13:25.980373: Epoch 816 +2026-04-11 10:13:25.982508: Current learning rate: 0.00814 +2026-04-11 10:15:10.562766: train_loss -0.3201 +2026-04-11 10:15:10.569783: val_loss -0.2834 +2026-04-11 10:15:10.572734: Pseudo dice [0.5057, 0.5015, 0.7039, 0.5203, 0.5807, 0.5991, 0.7325] +2026-04-11 10:15:10.575598: Epoch time: 104.59 s +2026-04-11 10:15:12.081463: +2026-04-11 10:15:12.088073: Epoch 817 +2026-04-11 10:15:12.091047: Current learning rate: 0.00814 +2026-04-11 10:16:55.345114: train_loss -0.2988 +2026-04-11 10:16:55.351848: val_loss -0.1711 +2026-04-11 10:16:55.354866: Pseudo dice [0.5233, 0.427, 0.4603, 0.1995, 0.2352, 0.0373, 0.7918] +2026-04-11 10:16:55.357352: Epoch time: 103.27 s +2026-04-11 10:16:56.847006: +2026-04-11 10:16:56.849126: Epoch 818 +2026-04-11 10:16:56.851823: Current learning rate: 0.00814 +2026-04-11 10:18:40.782100: train_loss -0.2877 +2026-04-11 10:18:40.788196: val_loss -0.1724 +2026-04-11 10:18:40.790202: Pseudo dice [0.4879, 0.6233, 0.4628, 0.7345, 0.3323, 0.0914, 0.6714] +2026-04-11 10:18:40.793320: Epoch time: 103.94 s +2026-04-11 10:18:42.294064: +2026-04-11 10:18:42.296328: Epoch 819 +2026-04-11 10:18:42.298870: Current learning rate: 0.00814 +2026-04-11 10:20:25.364385: train_loss -0.2939 +2026-04-11 10:20:25.370988: val_loss -0.2313 +2026-04-11 10:20:25.373060: Pseudo dice [0.2299, 0.3976, 0.2307, 0.7605, 0.5754, 0.4713, 0.7556] +2026-04-11 10:20:25.375507: Epoch time: 103.07 s +2026-04-11 10:20:26.791425: +2026-04-11 10:20:26.793253: Epoch 820 +2026-04-11 10:20:26.795106: Current learning rate: 0.00813 +2026-04-11 10:22:09.804505: train_loss -0.2908 +2026-04-11 10:22:09.810272: val_loss -0.2164 +2026-04-11 10:22:09.812011: Pseudo dice [0.3458, 0.6377, 0.5039, 0.3761, 0.1118, 0.1755, 0.565] +2026-04-11 10:22:09.814239: Epoch time: 103.02 s +2026-04-11 10:22:11.233164: +2026-04-11 10:22:11.235263: Epoch 821 +2026-04-11 10:22:11.237457: Current learning rate: 0.00813 +2026-04-11 10:23:54.159424: train_loss -0.2639 +2026-04-11 10:23:54.165049: val_loss -0.1312 +2026-04-11 10:23:54.167149: Pseudo dice [0.3619, 0.5975, 0.4098, 0.7077, 0.3793, 0.049, 0.6096] +2026-04-11 10:23:54.169256: Epoch time: 102.93 s +2026-04-11 10:23:55.557035: +2026-04-11 10:23:55.558702: Epoch 822 +2026-04-11 10:23:55.561450: Current learning rate: 0.00813 +2026-04-11 10:25:39.392631: train_loss -0.2939 +2026-04-11 10:25:39.397526: val_loss -0.2332 +2026-04-11 10:25:39.400394: Pseudo dice [0.3435, 0.4336, 0.5917, 0.2653, 0.5666, 0.1477, 0.6823] +2026-04-11 10:25:39.402784: Epoch time: 103.84 s +2026-04-11 10:25:40.788522: +2026-04-11 10:25:40.790528: Epoch 823 +2026-04-11 10:25:40.793595: Current learning rate: 0.00813 +2026-04-11 10:27:24.228316: train_loss -0.3075 +2026-04-11 10:27:24.236224: val_loss -0.2697 +2026-04-11 10:27:24.240269: Pseudo dice [0.0817, 0.6918, 0.837, 0.9001, 0.4052, 0.6832, 0.5228] +2026-04-11 10:27:24.242705: Epoch time: 103.44 s +2026-04-11 10:27:25.646934: +2026-04-11 10:27:25.648773: Epoch 824 +2026-04-11 10:27:25.650973: Current learning rate: 0.00813 +2026-04-11 10:29:09.016917: train_loss -0.2938 +2026-04-11 10:29:09.023373: val_loss -0.2928 +2026-04-11 10:29:09.025401: Pseudo dice [0.06, 0.375, 0.7092, 0.7384, 0.598, 0.8584, 0.8779] +2026-04-11 10:29:09.027859: Epoch time: 103.37 s +2026-04-11 10:29:10.408305: +2026-04-11 10:29:10.410387: Epoch 825 +2026-04-11 10:29:10.412471: Current learning rate: 0.00812 +2026-04-11 10:30:54.266735: train_loss -0.2888 +2026-04-11 10:30:54.272605: val_loss -0.114 +2026-04-11 10:30:54.275496: Pseudo dice [0.2214, 0.2414, 0.6462, 0.0197, 0.333, 0.0189, 0.6461] +2026-04-11 10:30:54.277989: Epoch time: 103.86 s +2026-04-11 10:30:55.663579: +2026-04-11 10:30:55.665504: Epoch 826 +2026-04-11 10:30:55.667475: Current learning rate: 0.00812 +2026-04-11 10:32:38.694900: train_loss -0.3024 +2026-04-11 10:32:38.702554: val_loss -0.2397 +2026-04-11 10:32:38.704892: Pseudo dice [0.5828, 0.2141, 0.6921, 0.7735, 0.4774, 0.1628, 0.9115] +2026-04-11 10:32:38.707458: Epoch time: 103.04 s +2026-04-11 10:32:40.103467: +2026-04-11 10:32:40.105512: Epoch 827 +2026-04-11 10:32:40.107607: Current learning rate: 0.00812 +2026-04-11 10:34:23.442393: train_loss -0.2986 +2026-04-11 10:34:23.452199: val_loss -0.2825 +2026-04-11 10:34:23.454157: Pseudo dice [0.3522, 0.8571, 0.5936, 0.5253, 0.5648, 0.8106, 0.5885] +2026-04-11 10:34:23.456733: Epoch time: 103.34 s +2026-04-11 10:34:24.879847: +2026-04-11 10:34:24.881785: Epoch 828 +2026-04-11 10:34:24.884288: Current learning rate: 0.00812 +2026-04-11 10:36:08.232758: train_loss -0.2833 +2026-04-11 10:36:08.250974: val_loss -0.2065 +2026-04-11 10:36:08.256035: Pseudo dice [0.405, 0.4131, 0.564, 0.8374, 0.3443, 0.0618, 0.7137] +2026-04-11 10:36:08.261999: Epoch time: 103.36 s +2026-04-11 10:36:09.652515: +2026-04-11 10:36:09.655712: Epoch 829 +2026-04-11 10:36:09.659102: Current learning rate: 0.00811 +2026-04-11 10:37:54.535966: train_loss -0.2749 +2026-04-11 10:37:54.542966: val_loss -0.2861 +2026-04-11 10:37:54.545149: Pseudo dice [0.2122, 0.3246, 0.5508, 0.5926, 0.513, 0.7966, 0.8254] +2026-04-11 10:37:54.547591: Epoch time: 104.89 s +2026-04-11 10:37:55.942625: +2026-04-11 10:37:55.944758: Epoch 830 +2026-04-11 10:37:55.947747: Current learning rate: 0.00811 +2026-04-11 10:39:39.464372: train_loss -0.2952 +2026-04-11 10:39:39.472433: val_loss -0.2952 +2026-04-11 10:39:39.475334: Pseudo dice [0.7852, 0.4494, 0.7854, 0.1472, 0.6248, 0.7711, 0.754] +2026-04-11 10:39:39.478624: Epoch time: 103.53 s +2026-04-11 10:39:40.858776: +2026-04-11 10:39:40.860640: Epoch 831 +2026-04-11 10:39:40.862790: Current learning rate: 0.00811 +2026-04-11 10:41:24.684190: train_loss -0.3071 +2026-04-11 10:41:24.688835: val_loss -0.2014 +2026-04-11 10:41:24.690870: Pseudo dice [0.7246, 0.2923, 0.4727, 0.6222, 0.4674, 0.0228, 0.7099] +2026-04-11 10:41:24.693006: Epoch time: 103.83 s +2026-04-11 10:41:26.069946: +2026-04-11 10:41:26.071593: Epoch 832 +2026-04-11 10:41:26.073837: Current learning rate: 0.00811 +2026-04-11 10:43:09.683139: train_loss -0.2938 +2026-04-11 10:43:09.691516: val_loss -0.2823 +2026-04-11 10:43:09.693889: Pseudo dice [0.7446, 0.4453, 0.647, 0.5635, 0.4607, 0.3549, 0.7945] +2026-04-11 10:43:09.696772: Epoch time: 103.62 s +2026-04-11 10:43:11.145813: +2026-04-11 10:43:11.147752: Epoch 833 +2026-04-11 10:43:11.149731: Current learning rate: 0.0081 +2026-04-11 10:44:56.188712: train_loss -0.2989 +2026-04-11 10:44:56.205308: val_loss -0.258 +2026-04-11 10:44:56.207475: Pseudo dice [0.5182, 0.6686, 0.6537, 0.0214, 0.349, 0.3094, 0.7186] +2026-04-11 10:44:56.209753: Epoch time: 105.05 s +2026-04-11 10:44:57.650883: +2026-04-11 10:44:57.653942: Epoch 834 +2026-04-11 10:44:57.656772: Current learning rate: 0.0081 +2026-04-11 10:46:40.613564: train_loss -0.2828 +2026-04-11 10:46:40.620224: val_loss -0.2403 +2026-04-11 10:46:40.623065: Pseudo dice [0.3774, 0.4209, 0.7326, 0.2943, 0.3245, 0.4849, 0.7026] +2026-04-11 10:46:40.626520: Epoch time: 102.97 s +2026-04-11 10:46:42.070615: +2026-04-11 10:46:42.072917: Epoch 835 +2026-04-11 10:46:42.075462: Current learning rate: 0.0081 +2026-04-11 10:48:25.616041: train_loss -0.2918 +2026-04-11 10:48:25.623056: val_loss -0.2563 +2026-04-11 10:48:25.626126: Pseudo dice [0.5863, 0.3483, 0.7106, 0.685, 0.4673, 0.7215, 0.6773] +2026-04-11 10:48:25.629413: Epoch time: 103.55 s +2026-04-11 10:48:27.038089: +2026-04-11 10:48:27.040409: Epoch 836 +2026-04-11 10:48:27.043486: Current learning rate: 0.0081 +2026-04-11 10:50:10.700296: train_loss -0.2755 +2026-04-11 10:50:10.706226: val_loss -0.2534 +2026-04-11 10:50:10.708948: Pseudo dice [0.4638, 0.7765, 0.3555, 0.7918, 0.3804, 0.6591, 0.8246] +2026-04-11 10:50:10.712236: Epoch time: 103.67 s +2026-04-11 10:50:12.125534: +2026-04-11 10:50:12.127643: Epoch 837 +2026-04-11 10:50:12.130162: Current learning rate: 0.0081 +2026-04-11 10:51:55.179444: train_loss -0.2557 +2026-04-11 10:51:55.185644: val_loss -0.2299 +2026-04-11 10:51:55.188259: Pseudo dice [0.1869, 0.2626, 0.5462, 0.6637, 0.3652, 0.2132, 0.7719] +2026-04-11 10:51:55.190948: Epoch time: 103.06 s +2026-04-11 10:51:56.636602: +2026-04-11 10:51:56.638426: Epoch 838 +2026-04-11 10:51:56.640568: Current learning rate: 0.00809 +2026-04-11 10:53:40.249150: train_loss -0.2481 +2026-04-11 10:53:40.256117: val_loss -0.2054 +2026-04-11 10:53:40.258908: Pseudo dice [0.4181, 0.6024, 0.6464, 0.4196, 0.2981, 0.1977, 0.4356] +2026-04-11 10:53:40.261856: Epoch time: 103.62 s +2026-04-11 10:53:41.693105: +2026-04-11 10:53:41.695102: Epoch 839 +2026-04-11 10:53:41.698050: Current learning rate: 0.00809 +2026-04-11 10:55:24.841043: train_loss -0.2773 +2026-04-11 10:55:24.848078: val_loss -0.2557 +2026-04-11 10:55:24.850240: Pseudo dice [0.2515, 0.7917, 0.6437, 0.0, 0.5895, 0.1132, 0.8225] +2026-04-11 10:55:24.854190: Epoch time: 103.15 s +2026-04-11 10:55:26.278415: +2026-04-11 10:55:26.281051: Epoch 840 +2026-04-11 10:55:26.283191: Current learning rate: 0.00809 +2026-04-11 10:57:10.588342: train_loss -0.2913 +2026-04-11 10:57:10.596279: val_loss -0.2641 +2026-04-11 10:57:10.599441: Pseudo dice [0.6315, 0.7239, 0.531, 0.3048, 0.462, 0.6033, 0.7561] +2026-04-11 10:57:10.602603: Epoch time: 104.31 s +2026-04-11 10:57:11.984347: +2026-04-11 10:57:11.986512: Epoch 841 +2026-04-11 10:57:11.989861: Current learning rate: 0.00809 +2026-04-11 10:58:54.843478: train_loss -0.2917 +2026-04-11 10:58:54.849232: val_loss -0.2196 +2026-04-11 10:58:54.851763: Pseudo dice [0.6113, 0.5313, 0.5487, 0.093, 0.1389, 0.2789, 0.4238] +2026-04-11 10:58:54.854243: Epoch time: 102.86 s +2026-04-11 10:58:56.251439: +2026-04-11 10:58:56.253349: Epoch 842 +2026-04-11 10:58:56.255255: Current learning rate: 0.00808 +2026-04-11 11:00:40.094037: train_loss -0.2646 +2026-04-11 11:00:40.100965: val_loss -0.2155 +2026-04-11 11:00:40.103338: Pseudo dice [0.4739, 0.6547, 0.6844, 0.4681, 0.3876, 0.1395, 0.6644] +2026-04-11 11:00:40.107262: Epoch time: 103.85 s +2026-04-11 11:00:41.528446: +2026-04-11 11:00:41.530662: Epoch 843 +2026-04-11 11:00:41.533465: Current learning rate: 0.00808 +2026-04-11 11:02:25.101187: train_loss -0.2883 +2026-04-11 11:02:25.110051: val_loss -0.2316 +2026-04-11 11:02:25.114074: Pseudo dice [0.3558, 0.63, 0.3975, 0.4965, 0.3542, 0.0449, 0.8177] +2026-04-11 11:02:25.117022: Epoch time: 103.58 s +2026-04-11 11:02:26.510056: +2026-04-11 11:02:26.512250: Epoch 844 +2026-04-11 11:02:26.514362: Current learning rate: 0.00808 +2026-04-11 11:04:10.434895: train_loss -0.2999 +2026-04-11 11:04:10.442621: val_loss -0.2228 +2026-04-11 11:04:10.445405: Pseudo dice [0.4001, 0.7255, 0.7371, 0.4246, 0.3525, 0.088, 0.7167] +2026-04-11 11:04:10.448101: Epoch time: 103.93 s +2026-04-11 11:04:11.866063: +2026-04-11 11:04:11.875619: Epoch 845 +2026-04-11 11:04:11.878550: Current learning rate: 0.00808 +2026-04-11 11:05:54.947455: train_loss -0.2656 +2026-04-11 11:05:54.954464: val_loss -0.2056 +2026-04-11 11:05:54.957070: Pseudo dice [0.4679, 0.3457, 0.5551, 0.5992, 0.3875, 0.0877, 0.4528] +2026-04-11 11:05:54.959558: Epoch time: 103.09 s +2026-04-11 11:05:56.349253: +2026-04-11 11:05:56.351621: Epoch 846 +2026-04-11 11:05:56.354160: Current learning rate: 0.00807 +2026-04-11 11:07:40.706199: train_loss -0.2813 +2026-04-11 11:07:40.711440: val_loss -0.2285 +2026-04-11 11:07:40.713577: Pseudo dice [0.2512, 0.6455, 0.6076, 0.7658, 0.3722, 0.0837, 0.727] +2026-04-11 11:07:40.716868: Epoch time: 104.36 s +2026-04-11 11:07:42.145769: +2026-04-11 11:07:42.147790: Epoch 847 +2026-04-11 11:07:42.149931: Current learning rate: 0.00807 +2026-04-11 11:09:24.808624: train_loss -0.2855 +2026-04-11 11:09:24.814352: val_loss -0.2144 +2026-04-11 11:09:24.816370: Pseudo dice [0.7522, 0.4686, 0.5993, 0.4781, 0.4771, 0.0637, 0.7232] +2026-04-11 11:09:24.819590: Epoch time: 102.67 s +2026-04-11 11:09:26.229251: +2026-04-11 11:09:26.231245: Epoch 848 +2026-04-11 11:09:26.233189: Current learning rate: 0.00807 +2026-04-11 11:11:09.231890: train_loss -0.3097 +2026-04-11 11:11:09.242100: val_loss -0.298 +2026-04-11 11:11:09.244498: Pseudo dice [0.4806, 0.7059, 0.6131, 0.7633, 0.416, 0.7645, 0.8158] +2026-04-11 11:11:09.247362: Epoch time: 103.01 s +2026-04-11 11:11:10.583669: +2026-04-11 11:11:10.586157: Epoch 849 +2026-04-11 11:11:10.588616: Current learning rate: 0.00807 +2026-04-11 11:12:54.315570: train_loss -0.3092 +2026-04-11 11:12:54.323751: val_loss -0.2795 +2026-04-11 11:12:54.327511: Pseudo dice [0.4825, 0.3834, 0.6923, 0.6682, 0.4066, 0.477, 0.8244] +2026-04-11 11:12:54.329847: Epoch time: 103.74 s +2026-04-11 11:12:57.680870: +2026-04-11 11:12:57.682531: Epoch 850 +2026-04-11 11:12:57.685317: Current learning rate: 0.00807 +2026-04-11 11:14:40.592848: train_loss -0.2925 +2026-04-11 11:14:40.599554: val_loss -0.2635 +2026-04-11 11:14:40.601724: Pseudo dice [0.7208, 0.7877, 0.6905, 0.8486, 0.5995, 0.0938, 0.8268] +2026-04-11 11:14:40.605096: Epoch time: 102.92 s +2026-04-11 11:14:42.057551: +2026-04-11 11:14:42.059852: Epoch 851 +2026-04-11 11:14:42.062301: Current learning rate: 0.00806 +2026-04-11 11:16:26.089652: train_loss -0.2962 +2026-04-11 11:16:26.097612: val_loss -0.2612 +2026-04-11 11:16:26.100355: Pseudo dice [0.3485, 0.5594, 0.6567, 0.654, 0.4381, 0.7766, 0.8784] +2026-04-11 11:16:26.103223: Epoch time: 104.04 s +2026-04-11 11:16:27.559009: +2026-04-11 11:16:27.561253: Epoch 852 +2026-04-11 11:16:27.563473: Current learning rate: 0.00806 +2026-04-11 11:18:10.506075: train_loss -0.2918 +2026-04-11 11:18:10.512686: val_loss -0.2295 +2026-04-11 11:18:10.515614: Pseudo dice [0.468, 0.4806, 0.6734, 0.5672, 0.4805, 0.0342, 0.8285] +2026-04-11 11:18:10.517865: Epoch time: 102.95 s +2026-04-11 11:18:11.951417: +2026-04-11 11:18:11.952996: Epoch 853 +2026-04-11 11:18:11.955012: Current learning rate: 0.00806 +2026-04-11 11:19:56.761207: train_loss -0.2771 +2026-04-11 11:19:56.767277: val_loss -0.1463 +2026-04-11 11:19:56.770015: Pseudo dice [0.5194, 0.5287, 0.5232, 0.5816, 0.2719, 0.0943, 0.7611] +2026-04-11 11:19:56.772337: Epoch time: 104.81 s +2026-04-11 11:19:58.587808: +2026-04-11 11:19:58.589511: Epoch 854 +2026-04-11 11:19:58.591464: Current learning rate: 0.00806 +2026-04-11 11:21:42.095603: train_loss -0.2874 +2026-04-11 11:21:42.101657: val_loss -0.2011 +2026-04-11 11:21:42.103932: Pseudo dice [0.1185, 0.8011, 0.6102, 0.4958, 0.2806, 0.0615, 0.3445] +2026-04-11 11:21:42.107018: Epoch time: 103.51 s +2026-04-11 11:21:43.491780: +2026-04-11 11:21:43.493681: Epoch 855 +2026-04-11 11:21:43.495679: Current learning rate: 0.00805 +2026-04-11 11:23:26.758737: train_loss -0.2977 +2026-04-11 11:23:26.766138: val_loss -0.2912 +2026-04-11 11:23:26.769562: Pseudo dice [0.1908, 0.2847, 0.7496, 0.7896, 0.5971, 0.6866, 0.6972] +2026-04-11 11:23:26.771963: Epoch time: 103.27 s +2026-04-11 11:23:28.251442: +2026-04-11 11:23:28.253705: Epoch 856 +2026-04-11 11:23:28.255713: Current learning rate: 0.00805 +2026-04-11 11:25:11.452859: train_loss -0.3208 +2026-04-11 11:25:11.459867: val_loss -0.1896 +2026-04-11 11:25:11.462135: Pseudo dice [0.8115, 0.3312, 0.7353, 0.679, 0.6228, 0.0674, 0.7067] +2026-04-11 11:25:11.465102: Epoch time: 103.21 s +2026-04-11 11:25:12.854923: +2026-04-11 11:25:12.856654: Epoch 857 +2026-04-11 11:25:12.858690: Current learning rate: 0.00805 +2026-04-11 11:26:56.691164: train_loss -0.2802 +2026-04-11 11:26:56.697477: val_loss -0.2729 +2026-04-11 11:26:56.700843: Pseudo dice [0.2897, 0.511, 0.7624, 0.011, 0.4717, 0.1917, 0.7072] +2026-04-11 11:26:56.703277: Epoch time: 103.84 s +2026-04-11 11:26:58.116922: +2026-04-11 11:26:58.119686: Epoch 858 +2026-04-11 11:26:58.122923: Current learning rate: 0.00805 +2026-04-11 11:28:41.990000: train_loss -0.2929 +2026-04-11 11:28:41.996341: val_loss -0.2542 +2026-04-11 11:28:41.998570: Pseudo dice [0.553, 0.7689, 0.6248, 0.8097, 0.553, 0.1276, 0.4515] +2026-04-11 11:28:42.001149: Epoch time: 103.88 s +2026-04-11 11:28:43.397235: +2026-04-11 11:28:43.399231: Epoch 859 +2026-04-11 11:28:43.401441: Current learning rate: 0.00804 +2026-04-11 11:30:26.071752: train_loss -0.2858 +2026-04-11 11:30:26.077735: val_loss -0.2491 +2026-04-11 11:30:26.079833: Pseudo dice [0.5385, 0.6149, 0.5776, 0.3815, 0.2905, 0.2871, 0.7683] +2026-04-11 11:30:26.081839: Epoch time: 102.68 s +2026-04-11 11:30:27.485849: +2026-04-11 11:30:27.487783: Epoch 860 +2026-04-11 11:30:27.489803: Current learning rate: 0.00804 +2026-04-11 11:32:11.064741: train_loss -0.2573 +2026-04-11 11:32:11.070357: val_loss -0.2749 +2026-04-11 11:32:11.072352: Pseudo dice [0.4715, 0.7306, 0.6156, 0.7852, 0.504, 0.6218, 0.5703] +2026-04-11 11:32:11.074402: Epoch time: 103.58 s +2026-04-11 11:32:12.442531: +2026-04-11 11:32:12.444913: Epoch 861 +2026-04-11 11:32:12.446875: Current learning rate: 0.00804 +2026-04-11 11:33:55.686280: train_loss -0.3053 +2026-04-11 11:33:55.692465: val_loss -0.2587 +2026-04-11 11:33:55.694524: Pseudo dice [0.5539, 0.66, 0.5971, 0.0174, 0.449, 0.7597, 0.8393] +2026-04-11 11:33:55.697510: Epoch time: 103.25 s +2026-04-11 11:33:57.089160: +2026-04-11 11:33:57.091371: Epoch 862 +2026-04-11 11:33:57.093495: Current learning rate: 0.00804 +2026-04-11 11:35:39.682639: train_loss -0.2964 +2026-04-11 11:35:39.688410: val_loss -0.2746 +2026-04-11 11:35:39.690101: Pseudo dice [0.6379, 0.4375, 0.4835, 0.1328, 0.4898, 0.7697, 0.8144] +2026-04-11 11:35:39.693273: Epoch time: 102.6 s +2026-04-11 11:35:41.072051: +2026-04-11 11:35:41.073761: Epoch 863 +2026-04-11 11:35:41.075569: Current learning rate: 0.00804 +2026-04-11 11:37:24.523064: train_loss -0.2877 +2026-04-11 11:37:24.530877: val_loss -0.2361 +2026-04-11 11:37:24.533745: Pseudo dice [0.568, 0.4409, 0.6516, 0.5966, 0.4734, 0.581, 0.7841] +2026-04-11 11:37:24.536627: Epoch time: 103.45 s +2026-04-11 11:37:25.927238: +2026-04-11 11:37:25.929748: Epoch 864 +2026-04-11 11:37:25.932409: Current learning rate: 0.00803 +2026-04-11 11:39:08.998583: train_loss -0.2964 +2026-04-11 11:39:09.005246: val_loss -0.2044 +2026-04-11 11:39:09.007553: Pseudo dice [0.7359, 0.666, 0.6969, 0.7439, 0.4636, 0.0784, 0.615] +2026-04-11 11:39:09.010404: Epoch time: 103.08 s +2026-04-11 11:39:10.370524: +2026-04-11 11:39:10.372520: Epoch 865 +2026-04-11 11:39:10.374600: Current learning rate: 0.00803 +2026-04-11 11:40:53.283613: train_loss -0.3057 +2026-04-11 11:40:53.289827: val_loss -0.274 +2026-04-11 11:40:53.292376: Pseudo dice [0.78, 0.6202, 0.6087, 0.6086, 0.5629, 0.4357, 0.7916] +2026-04-11 11:40:53.295009: Epoch time: 102.92 s +2026-04-11 11:40:54.694987: +2026-04-11 11:40:54.697272: Epoch 866 +2026-04-11 11:40:54.699491: Current learning rate: 0.00803 +2026-04-11 11:42:38.189264: train_loss -0.2819 +2026-04-11 11:42:38.194853: val_loss -0.2687 +2026-04-11 11:42:38.197014: Pseudo dice [0.4073, 0.4638, 0.4912, 0.0598, 0.3977, 0.81, 0.6682] +2026-04-11 11:42:38.201096: Epoch time: 103.5 s +2026-04-11 11:42:39.596052: +2026-04-11 11:42:39.598912: Epoch 867 +2026-04-11 11:42:39.605626: Current learning rate: 0.00803 +2026-04-11 11:44:22.906911: train_loss -0.2951 +2026-04-11 11:44:22.914912: val_loss -0.2918 +2026-04-11 11:44:22.917740: Pseudo dice [0.4242, 0.5732, 0.79, 0.5258, 0.5766, 0.7709, 0.7726] +2026-04-11 11:44:22.920418: Epoch time: 103.31 s +2026-04-11 11:44:24.301048: +2026-04-11 11:44:24.303734: Epoch 868 +2026-04-11 11:44:24.306023: Current learning rate: 0.00802 +2026-04-11 11:46:08.914941: train_loss -0.2888 +2026-04-11 11:46:08.922451: val_loss -0.2622 +2026-04-11 11:46:08.925423: Pseudo dice [0.7174, 0.5314, 0.6563, 0.1362, 0.172, 0.7112, 0.855] +2026-04-11 11:46:08.928967: Epoch time: 104.62 s +2026-04-11 11:46:10.355048: +2026-04-11 11:46:10.356801: Epoch 869 +2026-04-11 11:46:10.358759: Current learning rate: 0.00802 +2026-04-11 11:47:54.230968: train_loss -0.2906 +2026-04-11 11:47:54.236576: val_loss -0.2455 +2026-04-11 11:47:54.239094: Pseudo dice [0.5549, 0.6003, 0.6906, 0.4731, 0.2923, 0.2367, 0.3794] +2026-04-11 11:47:54.241612: Epoch time: 103.88 s +2026-04-11 11:47:55.631086: +2026-04-11 11:47:55.633068: Epoch 870 +2026-04-11 11:47:55.635117: Current learning rate: 0.00802 +2026-04-11 11:49:38.960471: train_loss -0.2821 +2026-04-11 11:49:38.965888: val_loss -0.2787 +2026-04-11 11:49:38.968085: Pseudo dice [0.7244, 0.5539, 0.6482, 0.8296, 0.4935, 0.7887, 0.8945] +2026-04-11 11:49:38.970802: Epoch time: 103.33 s +2026-04-11 11:49:40.351706: +2026-04-11 11:49:40.354534: Epoch 871 +2026-04-11 11:49:40.357374: Current learning rate: 0.00802 +2026-04-11 11:51:23.668575: train_loss -0.2837 +2026-04-11 11:51:23.675389: val_loss -0.2485 +2026-04-11 11:51:23.677745: Pseudo dice [0.583, 0.457, 0.5812, 0.4469, 0.2046, 0.3342, 0.8127] +2026-04-11 11:51:23.680717: Epoch time: 103.32 s +2026-04-11 11:51:25.063008: +2026-04-11 11:51:25.065033: Epoch 872 +2026-04-11 11:51:25.067487: Current learning rate: 0.00801 +2026-04-11 11:53:08.034349: train_loss -0.2998 +2026-04-11 11:53:08.040409: val_loss -0.2079 +2026-04-11 11:53:08.042701: Pseudo dice [0.2873, 0.4019, 0.5961, 0.7941, 0.487, 0.2227, 0.85] +2026-04-11 11:53:08.045196: Epoch time: 102.98 s +2026-04-11 11:53:09.446075: +2026-04-11 11:53:09.448013: Epoch 873 +2026-04-11 11:53:09.450471: Current learning rate: 0.00801 +2026-04-11 11:54:52.227212: train_loss -0.3077 +2026-04-11 11:54:52.233069: val_loss -0.2666 +2026-04-11 11:54:52.235641: Pseudo dice [0.7286, 0.6273, 0.7608, 0.1322, 0.2427, 0.5873, 0.588] +2026-04-11 11:54:52.239631: Epoch time: 102.78 s +2026-04-11 11:54:54.870535: +2026-04-11 11:54:54.872363: Epoch 874 +2026-04-11 11:54:54.874439: Current learning rate: 0.00801 +2026-04-11 11:56:37.570782: train_loss -0.2937 +2026-04-11 11:56:37.591884: val_loss -0.2648 +2026-04-11 11:56:37.595606: Pseudo dice [0.4828, 0.2756, 0.6477, 0.7099, 0.4643, 0.6397, 0.8162] +2026-04-11 11:56:37.598425: Epoch time: 102.7 s +2026-04-11 11:56:39.003888: +2026-04-11 11:56:39.005870: Epoch 875 +2026-04-11 11:56:39.008907: Current learning rate: 0.00801 +2026-04-11 11:58:22.635562: train_loss -0.2713 +2026-04-11 11:58:22.642600: val_loss -0.2397 +2026-04-11 11:58:22.644904: Pseudo dice [0.2767, 0.7553, 0.6765, 0.7813, 0.4801, 0.1803, 0.7284] +2026-04-11 11:58:22.648124: Epoch time: 103.64 s +2026-04-11 11:58:24.039375: +2026-04-11 11:58:24.041741: Epoch 876 +2026-04-11 11:58:24.044568: Current learning rate: 0.00801 +2026-04-11 12:00:07.185553: train_loss -0.3007 +2026-04-11 12:00:07.192715: val_loss -0.2575 +2026-04-11 12:00:07.194930: Pseudo dice [0.7766, 0.7728, 0.589, 0.0636, 0.5273, 0.5705, 0.8193] +2026-04-11 12:00:07.197574: Epoch time: 103.15 s +2026-04-11 12:00:08.578280: +2026-04-11 12:00:08.586921: Epoch 877 +2026-04-11 12:00:08.589492: Current learning rate: 0.008 +2026-04-11 12:01:51.397744: train_loss -0.2859 +2026-04-11 12:01:51.404206: val_loss -0.2019 +2026-04-11 12:01:51.406611: Pseudo dice [0.7459, 0.3734, 0.6956, 0.3662, 0.392, 0.1732, 0.7592] +2026-04-11 12:01:51.409205: Epoch time: 102.82 s +2026-04-11 12:01:52.811321: +2026-04-11 12:01:52.813177: Epoch 878 +2026-04-11 12:01:52.815305: Current learning rate: 0.008 +2026-04-11 12:03:35.971683: train_loss -0.2932 +2026-04-11 12:03:35.979781: val_loss -0.1924 +2026-04-11 12:03:35.982128: Pseudo dice [0.6252, 0.4259, 0.5906, 0.7058, 0.3543, 0.2439, 0.7315] +2026-04-11 12:03:35.985049: Epoch time: 103.16 s +2026-04-11 12:03:37.382388: +2026-04-11 12:03:37.384585: Epoch 879 +2026-04-11 12:03:37.387277: Current learning rate: 0.008 +2026-04-11 12:05:19.849836: train_loss -0.3012 +2026-04-11 12:05:19.855946: val_loss -0.2633 +2026-04-11 12:05:19.857913: Pseudo dice [0.5962, 0.4364, 0.7194, 0.4861, 0.5208, 0.5978, 0.6515] +2026-04-11 12:05:19.860344: Epoch time: 102.47 s +2026-04-11 12:05:21.241049: +2026-04-11 12:05:21.243577: Epoch 880 +2026-04-11 12:05:21.246606: Current learning rate: 0.008 +2026-04-11 12:07:04.473524: train_loss -0.2992 +2026-04-11 12:07:04.481965: val_loss -0.158 +2026-04-11 12:07:04.486169: Pseudo dice [0.6157, 0.5182, 0.7052, 0.814, 0.2591, 0.0656, 0.3195] +2026-04-11 12:07:04.495061: Epoch time: 103.24 s +2026-04-11 12:07:05.903391: +2026-04-11 12:07:05.905175: Epoch 881 +2026-04-11 12:07:05.907098: Current learning rate: 0.00799 +2026-04-11 12:08:48.904011: train_loss -0.3077 +2026-04-11 12:08:48.909269: val_loss -0.22 +2026-04-11 12:08:48.911292: Pseudo dice [0.6688, 0.6439, 0.6952, 0.4615, 0.396, 0.1233, 0.8] +2026-04-11 12:08:48.914290: Epoch time: 103.0 s +2026-04-11 12:08:50.323673: +2026-04-11 12:08:50.326576: Epoch 882 +2026-04-11 12:08:50.328837: Current learning rate: 0.00799 +2026-04-11 12:10:32.975359: train_loss -0.3077 +2026-04-11 12:10:32.981435: val_loss -0.1258 +2026-04-11 12:10:32.984156: Pseudo dice [0.5118, 0.3061, 0.6033, 0.5313, 0.3603, 0.0858, 0.4434] +2026-04-11 12:10:32.986830: Epoch time: 102.66 s +2026-04-11 12:10:34.369089: +2026-04-11 12:10:34.371125: Epoch 883 +2026-04-11 12:10:34.372988: Current learning rate: 0.00799 +2026-04-11 12:12:17.807965: train_loss -0.3083 +2026-04-11 12:12:17.814354: val_loss -0.2746 +2026-04-11 12:12:17.816594: Pseudo dice [0.7218, 0.3223, 0.7953, 0.3056, 0.421, 0.4878, 0.5916] +2026-04-11 12:12:17.818872: Epoch time: 103.44 s +2026-04-11 12:12:19.202326: +2026-04-11 12:12:19.204852: Epoch 884 +2026-04-11 12:12:19.207133: Current learning rate: 0.00799 +2026-04-11 12:14:02.255212: train_loss -0.306 +2026-04-11 12:14:02.265637: val_loss -0.2256 +2026-04-11 12:14:02.270143: Pseudo dice [0.6335, 0.6488, 0.7682, 0.0244, 0.5107, 0.0863, 0.4527] +2026-04-11 12:14:02.274208: Epoch time: 103.06 s +2026-04-11 12:14:03.636388: +2026-04-11 12:14:03.638223: Epoch 885 +2026-04-11 12:14:03.640048: Current learning rate: 0.00798 +2026-04-11 12:15:46.646353: train_loss -0.2916 +2026-04-11 12:15:46.652958: val_loss -0.2514 +2026-04-11 12:15:46.657052: Pseudo dice [0.5065, 0.6592, 0.609, 0.7953, 0.634, 0.2316, 0.774] +2026-04-11 12:15:46.659474: Epoch time: 103.01 s +2026-04-11 12:15:48.055914: +2026-04-11 12:15:48.057721: Epoch 886 +2026-04-11 12:15:48.059951: Current learning rate: 0.00798 +2026-04-11 12:17:30.548131: train_loss -0.2924 +2026-04-11 12:17:30.553676: val_loss -0.1898 +2026-04-11 12:17:30.555657: Pseudo dice [0.4783, 0.5426, 0.5936, 0.6788, 0.0855, 0.0671, 0.5831] +2026-04-11 12:17:30.557729: Epoch time: 102.5 s +2026-04-11 12:17:31.947030: +2026-04-11 12:17:31.950092: Epoch 887 +2026-04-11 12:17:31.958007: Current learning rate: 0.00798 +2026-04-11 12:19:14.541212: train_loss -0.2971 +2026-04-11 12:19:14.547862: val_loss -0.1533 +2026-04-11 12:19:14.550945: Pseudo dice [0.5559, 0.5084, 0.6499, 0.7572, 0.5183, 0.0634, 0.7665] +2026-04-11 12:19:14.554182: Epoch time: 102.6 s +2026-04-11 12:19:15.963114: +2026-04-11 12:19:15.965155: Epoch 888 +2026-04-11 12:19:15.967248: Current learning rate: 0.00798 +2026-04-11 12:20:58.559467: train_loss -0.2957 +2026-04-11 12:20:58.565598: val_loss -0.2225 +2026-04-11 12:20:58.567572: Pseudo dice [0.44, 0.3042, 0.4991, 0.2515, 0.5177, 0.0503, 0.8351] +2026-04-11 12:20:58.570250: Epoch time: 102.6 s +2026-04-11 12:20:59.937832: +2026-04-11 12:20:59.940202: Epoch 889 +2026-04-11 12:20:59.942653: Current learning rate: 0.00798 +2026-04-11 12:22:43.234282: train_loss -0.2875 +2026-04-11 12:22:43.241038: val_loss -0.2585 +2026-04-11 12:22:43.243006: Pseudo dice [0.3748, 0.1672, 0.5126, 0.6837, 0.6015, 0.5778, 0.7981] +2026-04-11 12:22:43.245546: Epoch time: 103.3 s +2026-04-11 12:22:44.682809: +2026-04-11 12:22:44.685291: Epoch 890 +2026-04-11 12:22:44.687764: Current learning rate: 0.00797 +2026-04-11 12:24:28.089181: train_loss -0.2982 +2026-04-11 12:24:28.095684: val_loss -0.2211 +2026-04-11 12:24:28.098110: Pseudo dice [0.4034, 0.5899, 0.5148, 0.7322, 0.4293, 0.0348, 0.6426] +2026-04-11 12:24:28.100612: Epoch time: 103.41 s +2026-04-11 12:24:29.461575: +2026-04-11 12:24:29.463822: Epoch 891 +2026-04-11 12:24:29.465849: Current learning rate: 0.00797 +2026-04-11 12:26:12.343785: train_loss -0.3008 +2026-04-11 12:26:12.356392: val_loss -0.2813 +2026-04-11 12:26:12.360951: Pseudo dice [0.4449, 0.3653, 0.7465, 0.5637, 0.7269, 0.7919, 0.6329] +2026-04-11 12:26:12.366574: Epoch time: 102.89 s +2026-04-11 12:26:13.761891: +2026-04-11 12:26:13.765257: Epoch 892 +2026-04-11 12:26:13.780829: Current learning rate: 0.00797 +2026-04-11 12:27:57.404606: train_loss -0.2718 +2026-04-11 12:27:57.414175: val_loss -0.229 +2026-04-11 12:27:57.417674: Pseudo dice [0.4762, 0.8211, 0.6695, 0.7441, 0.2784, 0.0532, 0.8638] +2026-04-11 12:27:57.420705: Epoch time: 103.65 s +2026-04-11 12:27:58.821917: +2026-04-11 12:27:58.823793: Epoch 893 +2026-04-11 12:27:58.826978: Current learning rate: 0.00797 +2026-04-11 12:29:42.245702: train_loss -0.3022 +2026-04-11 12:29:42.252545: val_loss -0.2536 +2026-04-11 12:29:42.255370: Pseudo dice [0.7947, 0.426, 0.6625, 0.6879, 0.1818, 0.1557, 0.4185] +2026-04-11 12:29:42.258136: Epoch time: 103.43 s +2026-04-11 12:29:43.645613: +2026-04-11 12:29:43.648033: Epoch 894 +2026-04-11 12:29:43.650140: Current learning rate: 0.00796 +2026-04-11 12:31:26.941113: train_loss -0.3064 +2026-04-11 12:31:26.948270: val_loss -0.102 +2026-04-11 12:31:26.950330: Pseudo dice [0.7114, 0.8032, 0.3022, 0.657, 0.4, 0.1042, 0.601] +2026-04-11 12:31:26.952843: Epoch time: 103.3 s +2026-04-11 12:31:29.734704: +2026-04-11 12:31:29.736788: Epoch 895 +2026-04-11 12:31:29.739212: Current learning rate: 0.00796 +2026-04-11 12:33:13.094679: train_loss -0.2871 +2026-04-11 12:33:13.101947: val_loss -0.2537 +2026-04-11 12:33:13.104397: Pseudo dice [0.4801, 0.8316, 0.5912, 0.5411, 0.6072, 0.1546, 0.6384] +2026-04-11 12:33:13.107474: Epoch time: 103.36 s +2026-04-11 12:33:14.509048: +2026-04-11 12:33:14.511997: Epoch 896 +2026-04-11 12:33:14.514469: Current learning rate: 0.00796 +2026-04-11 12:34:58.168709: train_loss -0.3213 +2026-04-11 12:34:58.177642: val_loss -0.2064 +2026-04-11 12:34:58.180083: Pseudo dice [0.4233, 0.697, 0.6425, 0.5864, 0.4155, 0.3391, 0.7141] +2026-04-11 12:34:58.183074: Epoch time: 103.66 s +2026-04-11 12:34:59.707195: +2026-04-11 12:34:59.708962: Epoch 897 +2026-04-11 12:34:59.711623: Current learning rate: 0.00796 +2026-04-11 12:36:43.484693: train_loss -0.2901 +2026-04-11 12:36:43.492205: val_loss -0.1945 +2026-04-11 12:36:43.494318: Pseudo dice [0.5388, 0.6994, 0.5163, 0.6464, 0.5855, 0.0453, 0.3728] +2026-04-11 12:36:43.496896: Epoch time: 103.78 s +2026-04-11 12:36:44.946244: +2026-04-11 12:36:44.948833: Epoch 898 +2026-04-11 12:36:44.951100: Current learning rate: 0.00795 +2026-04-11 12:38:28.937686: train_loss -0.2877 +2026-04-11 12:38:28.954745: val_loss -0.1775 +2026-04-11 12:38:28.956955: Pseudo dice [0.5139, 0.4556, 0.5998, 0.6403, 0.5716, 0.0609, 0.8038] +2026-04-11 12:38:28.960027: Epoch time: 104.0 s +2026-04-11 12:38:30.398868: +2026-04-11 12:38:30.401037: Epoch 899 +2026-04-11 12:38:30.403441: Current learning rate: 0.00795 +2026-04-11 12:40:13.388288: train_loss -0.3006 +2026-04-11 12:40:13.395473: val_loss -0.1391 +2026-04-11 12:40:13.398022: Pseudo dice [0.471, 0.7729, 0.5544, 0.3007, 0.5084, 0.0651, 0.6712] +2026-04-11 12:40:13.400350: Epoch time: 102.99 s +2026-04-11 12:40:16.656410: +2026-04-11 12:40:16.658369: Epoch 900 +2026-04-11 12:40:16.661007: Current learning rate: 0.00795 +2026-04-11 12:42:00.328916: train_loss -0.2815 +2026-04-11 12:42:00.336118: val_loss -0.1233 +2026-04-11 12:42:00.338058: Pseudo dice [0.648, 0.4758, 0.4426, 0.3963, 0.0751, 0.0157, 0.4054] +2026-04-11 12:42:00.340462: Epoch time: 103.68 s +2026-04-11 12:42:01.719216: +2026-04-11 12:42:01.722726: Epoch 901 +2026-04-11 12:42:01.724785: Current learning rate: 0.00795 +2026-04-11 12:43:45.290028: train_loss -0.2781 +2026-04-11 12:43:45.297585: val_loss -0.2 +2026-04-11 12:43:45.299799: Pseudo dice [0.428, 0.2543, 0.5905, 0.0325, 0.3475, 0.0409, 0.7116] +2026-04-11 12:43:45.302784: Epoch time: 103.57 s +2026-04-11 12:43:46.724391: +2026-04-11 12:43:46.726990: Epoch 902 +2026-04-11 12:43:46.729035: Current learning rate: 0.00795 +2026-04-11 12:45:31.261521: train_loss -0.2823 +2026-04-11 12:45:31.268447: val_loss -0.2547 +2026-04-11 12:45:31.270397: Pseudo dice [0.545, 0.199, 0.5614, 0.5599, 0.5454, 0.6183, 0.669] +2026-04-11 12:45:31.273744: Epoch time: 104.54 s +2026-04-11 12:45:32.671722: +2026-04-11 12:45:32.673544: Epoch 903 +2026-04-11 12:45:32.675594: Current learning rate: 0.00794 +2026-04-11 12:47:15.630800: train_loss -0.2944 +2026-04-11 12:47:15.638055: val_loss -0.2679 +2026-04-11 12:47:15.640725: Pseudo dice [0.7284, 0.7728, 0.6796, 0.1115, 0.5239, 0.3287, 0.7245] +2026-04-11 12:47:15.644693: Epoch time: 102.96 s +2026-04-11 12:47:17.029595: +2026-04-11 12:47:17.032042: Epoch 904 +2026-04-11 12:47:17.035095: Current learning rate: 0.00794 +2026-04-11 12:48:59.924168: train_loss -0.295 +2026-04-11 12:48:59.930446: val_loss -0.273 +2026-04-11 12:48:59.932936: Pseudo dice [0.6964, 0.844, 0.7333, 0.4855, 0.5656, 0.3644, 0.5768] +2026-04-11 12:48:59.935453: Epoch time: 102.9 s +2026-04-11 12:49:01.320216: +2026-04-11 12:49:01.322401: Epoch 905 +2026-04-11 12:49:01.324693: Current learning rate: 0.00794 +2026-04-11 12:50:44.283267: train_loss -0.3088 +2026-04-11 12:50:44.290026: val_loss -0.2273 +2026-04-11 12:50:44.291999: Pseudo dice [0.2391, 0.6453, 0.7376, 0.3226, 0.3695, 0.2888, 0.4488] +2026-04-11 12:50:44.294565: Epoch time: 102.97 s +2026-04-11 12:50:45.677783: +2026-04-11 12:50:45.679650: Epoch 906 +2026-04-11 12:50:45.682314: Current learning rate: 0.00794 +2026-04-11 12:52:29.199925: train_loss -0.2986 +2026-04-11 12:52:29.206893: val_loss -0.2098 +2026-04-11 12:52:29.209857: Pseudo dice [0.2348, 0.8269, 0.4078, 0.1412, 0.1132, 0.28, 0.4064] +2026-04-11 12:52:29.213223: Epoch time: 103.53 s +2026-04-11 12:52:30.625639: +2026-04-11 12:52:30.631450: Epoch 907 +2026-04-11 12:52:30.635530: Current learning rate: 0.00793 +2026-04-11 12:54:13.496826: train_loss -0.2895 +2026-04-11 12:54:13.507429: val_loss -0.2747 +2026-04-11 12:54:13.509880: Pseudo dice [0.3178, 0.1456, 0.6871, 0.2916, 0.6098, 0.3691, 0.685] +2026-04-11 12:54:13.512646: Epoch time: 102.88 s +2026-04-11 12:54:14.898672: +2026-04-11 12:54:14.905639: Epoch 908 +2026-04-11 12:54:14.907870: Current learning rate: 0.00793 +2026-04-11 12:55:58.516284: train_loss -0.2934 +2026-04-11 12:55:58.521780: val_loss -0.2116 +2026-04-11 12:55:58.523825: Pseudo dice [0.7893, 0.5232, 0.6454, 0.7671, 0.5775, 0.0873, 0.4892] +2026-04-11 12:55:58.526551: Epoch time: 103.62 s +2026-04-11 12:55:59.900661: +2026-04-11 12:55:59.902405: Epoch 909 +2026-04-11 12:55:59.904706: Current learning rate: 0.00793 +2026-04-11 12:57:43.417506: train_loss -0.3021 +2026-04-11 12:57:43.423093: val_loss -0.2333 +2026-04-11 12:57:43.425686: Pseudo dice [0.3558, 0.5965, 0.6463, 0.2942, 0.2599, 0.3557, 0.6409] +2026-04-11 12:57:43.429512: Epoch time: 103.52 s +2026-04-11 12:57:44.823362: +2026-04-11 12:57:44.826035: Epoch 910 +2026-04-11 12:57:44.828320: Current learning rate: 0.00793 +2026-04-11 12:59:28.372528: train_loss -0.2753 +2026-04-11 12:59:28.379045: val_loss -0.2352 +2026-04-11 12:59:28.381154: Pseudo dice [0.5284, 0.6222, 0.4156, 0.4434, 0.2452, 0.2708, 0.7921] +2026-04-11 12:59:28.383595: Epoch time: 103.55 s +2026-04-11 12:59:29.748112: +2026-04-11 12:59:29.750311: Epoch 911 +2026-04-11 12:59:29.752568: Current learning rate: 0.00792 +2026-04-11 13:01:13.431345: train_loss -0.2949 +2026-04-11 13:01:13.437728: val_loss -0.2767 +2026-04-11 13:01:13.439986: Pseudo dice [0.5346, 0.4911, 0.6548, 0.6536, 0.5705, 0.3946, 0.81] +2026-04-11 13:01:13.442468: Epoch time: 103.69 s +2026-04-11 13:01:14.820417: +2026-04-11 13:01:14.822674: Epoch 912 +2026-04-11 13:01:14.824754: Current learning rate: 0.00792 +2026-04-11 13:02:57.917490: train_loss -0.2826 +2026-04-11 13:02:57.927666: val_loss -0.2348 +2026-04-11 13:02:57.932267: Pseudo dice [0.8165, 0.4661, 0.4385, 0.1223, 0.5412, 0.1242, 0.7303] +2026-04-11 13:02:57.936077: Epoch time: 103.1 s +2026-04-11 13:02:59.325412: +2026-04-11 13:02:59.328712: Epoch 913 +2026-04-11 13:02:59.332229: Current learning rate: 0.00792 +2026-04-11 13:04:42.848428: train_loss -0.2847 +2026-04-11 13:04:42.855422: val_loss -0.1584 +2026-04-11 13:04:42.858335: Pseudo dice [0.7797, 0.6903, 0.1662, 0.6088, 0.5076, 0.0538, 0.8366] +2026-04-11 13:04:42.861253: Epoch time: 103.53 s +2026-04-11 13:04:44.246660: +2026-04-11 13:04:44.250261: Epoch 914 +2026-04-11 13:04:44.253365: Current learning rate: 0.00792 +2026-04-11 13:06:27.583752: train_loss -0.2964 +2026-04-11 13:06:27.591437: val_loss -0.2331 +2026-04-11 13:06:27.594377: Pseudo dice [0.4349, 0.2731, 0.6627, 0.1284, 0.5095, 0.3201, 0.8564] +2026-04-11 13:06:27.598505: Epoch time: 103.34 s +2026-04-11 13:06:30.246602: +2026-04-11 13:06:30.248593: Epoch 915 +2026-04-11 13:06:30.250901: Current learning rate: 0.00792 +2026-04-11 13:08:13.699032: train_loss -0.2664 +2026-04-11 13:08:13.708155: val_loss -0.207 +2026-04-11 13:08:13.710443: Pseudo dice [0.2852, 0.3462, 0.5322, 0.4001, 0.2585, 0.1289, 0.5711] +2026-04-11 13:08:13.713255: Epoch time: 103.46 s +2026-04-11 13:08:15.168994: +2026-04-11 13:08:15.171069: Epoch 916 +2026-04-11 13:08:15.173795: Current learning rate: 0.00791 +2026-04-11 13:09:58.096807: train_loss -0.2996 +2026-04-11 13:09:58.104121: val_loss -0.2545 +2026-04-11 13:09:58.106234: Pseudo dice [0.313, 0.7511, 0.6697, 0.3398, 0.3554, 0.7569, 0.5032] +2026-04-11 13:09:58.110784: Epoch time: 102.93 s +2026-04-11 13:09:59.473941: +2026-04-11 13:09:59.475855: Epoch 917 +2026-04-11 13:09:59.478311: Current learning rate: 0.00791 +2026-04-11 13:11:42.968396: train_loss -0.3009 +2026-04-11 13:11:42.975737: val_loss -0.3038 +2026-04-11 13:11:42.978854: Pseudo dice [0.4922, 0.7172, 0.5466, 0.7923, 0.6206, 0.8142, 0.6913] +2026-04-11 13:11:42.981643: Epoch time: 103.5 s +2026-04-11 13:11:44.390421: +2026-04-11 13:11:44.392221: Epoch 918 +2026-04-11 13:11:44.394304: Current learning rate: 0.00791 +2026-04-11 13:13:28.083831: train_loss -0.3082 +2026-04-11 13:13:28.089692: val_loss -0.2441 +2026-04-11 13:13:28.092096: Pseudo dice [0.3581, 0.6573, 0.7503, 0.4736, 0.3912, 0.1496, 0.5433] +2026-04-11 13:13:28.094857: Epoch time: 103.7 s +2026-04-11 13:13:29.477628: +2026-04-11 13:13:29.479591: Epoch 919 +2026-04-11 13:13:29.481787: Current learning rate: 0.00791 +2026-04-11 13:15:12.632399: train_loss -0.2898 +2026-04-11 13:15:12.639014: val_loss -0.2821 +2026-04-11 13:15:12.640983: Pseudo dice [0.6225, 0.6743, 0.4613, 0.1421, 0.236, 0.7051, 0.7265] +2026-04-11 13:15:12.644111: Epoch time: 103.16 s +2026-04-11 13:15:14.071757: +2026-04-11 13:15:14.073993: Epoch 920 +2026-04-11 13:15:14.077319: Current learning rate: 0.0079 +2026-04-11 13:16:57.689825: train_loss -0.2749 +2026-04-11 13:16:57.696559: val_loss -0.1682 +2026-04-11 13:16:57.698537: Pseudo dice [0.6126, 0.3774, 0.6224, 0.4731, 0.6061, 0.1429, 0.6755] +2026-04-11 13:16:57.700851: Epoch time: 103.62 s +2026-04-11 13:16:59.074297: +2026-04-11 13:16:59.076565: Epoch 921 +2026-04-11 13:16:59.079124: Current learning rate: 0.0079 +2026-04-11 13:18:41.762451: train_loss -0.2985 +2026-04-11 13:18:41.767985: val_loss -0.1798 +2026-04-11 13:18:41.770103: Pseudo dice [0.7395, 0.8502, 0.2685, 0.0069, 0.4431, 0.034, 0.6377] +2026-04-11 13:18:41.772557: Epoch time: 102.69 s +2026-04-11 13:18:43.125133: +2026-04-11 13:18:43.128630: Epoch 922 +2026-04-11 13:18:43.131096: Current learning rate: 0.0079 +2026-04-11 13:20:26.912571: train_loss -0.2812 +2026-04-11 13:20:26.918158: val_loss -0.2206 +2026-04-11 13:20:26.919986: Pseudo dice [0.5455, 0.594, 0.6207, 0.5065, 0.4841, 0.3297, 0.9019] +2026-04-11 13:20:26.922359: Epoch time: 103.79 s +2026-04-11 13:20:28.315751: +2026-04-11 13:20:28.318511: Epoch 923 +2026-04-11 13:20:28.322101: Current learning rate: 0.0079 +2026-04-11 13:22:11.335560: train_loss -0.3115 +2026-04-11 13:22:11.344102: val_loss -0.2504 +2026-04-11 13:22:11.346126: Pseudo dice [0.6894, 0.7664, 0.6415, 0.2755, 0.493, 0.2722, 0.5836] +2026-04-11 13:22:11.348515: Epoch time: 103.02 s +2026-04-11 13:22:12.744753: +2026-04-11 13:22:12.746524: Epoch 924 +2026-04-11 13:22:12.748644: Current learning rate: 0.00789 +2026-04-11 13:23:55.550887: train_loss -0.3138 +2026-04-11 13:23:55.557196: val_loss -0.2824 +2026-04-11 13:23:55.559222: Pseudo dice [0.4108, 0.4657, 0.7905, 0.1047, 0.5589, 0.7501, 0.7399] +2026-04-11 13:23:55.561434: Epoch time: 102.81 s +2026-04-11 13:23:56.943619: +2026-04-11 13:23:56.945947: Epoch 925 +2026-04-11 13:23:56.949299: Current learning rate: 0.00789 +2026-04-11 13:25:39.452960: train_loss -0.2996 +2026-04-11 13:25:39.458225: val_loss -0.2687 +2026-04-11 13:25:39.460372: Pseudo dice [0.647, 0.7917, 0.6886, 0.3352, 0.43, 0.7453, 0.7498] +2026-04-11 13:25:39.462669: Epoch time: 102.51 s +2026-04-11 13:25:40.825052: +2026-04-11 13:25:40.826948: Epoch 926 +2026-04-11 13:25:40.829628: Current learning rate: 0.00789 +2026-04-11 13:27:24.078930: train_loss -0.2869 +2026-04-11 13:27:24.085107: val_loss -0.2252 +2026-04-11 13:27:24.087323: Pseudo dice [0.4031, 0.5813, 0.7243, 0.6709, 0.0198, 0.4126, 0.4107] +2026-04-11 13:27:24.089928: Epoch time: 103.26 s +2026-04-11 13:27:25.501456: +2026-04-11 13:27:25.503334: Epoch 927 +2026-04-11 13:27:25.505167: Current learning rate: 0.00789 +2026-04-11 13:29:08.953096: train_loss -0.2983 +2026-04-11 13:29:08.959623: val_loss -0.2166 +2026-04-11 13:29:08.961882: Pseudo dice [0.5047, 0.4417, 0.5888, 0.1442, 0.2359, 0.2259, 0.8209] +2026-04-11 13:29:08.964525: Epoch time: 103.46 s +2026-04-11 13:29:10.345359: +2026-04-11 13:29:10.347840: Epoch 928 +2026-04-11 13:29:10.350451: Current learning rate: 0.00789 +2026-04-11 13:30:52.832379: train_loss -0.3031 +2026-04-11 13:30:52.839524: val_loss -0.2676 +2026-04-11 13:30:52.842066: Pseudo dice [0.5119, 0.8099, 0.6066, 0.6403, 0.4694, 0.5554, 0.7231] +2026-04-11 13:30:52.844316: Epoch time: 102.49 s +2026-04-11 13:30:54.227988: +2026-04-11 13:30:54.229874: Epoch 929 +2026-04-11 13:30:54.232001: Current learning rate: 0.00788 +2026-04-11 13:32:38.348799: train_loss -0.3188 +2026-04-11 13:32:38.359620: val_loss -0.2182 +2026-04-11 13:32:38.362877: Pseudo dice [0.4912, 0.7678, 0.7705, 0.3669, 0.6938, 0.1586, 0.7257] +2026-04-11 13:32:38.365648: Epoch time: 104.12 s +2026-04-11 13:32:39.810951: +2026-04-11 13:32:39.813315: Epoch 930 +2026-04-11 13:32:39.816540: Current learning rate: 0.00788 +2026-04-11 13:34:22.916713: train_loss -0.3231 +2026-04-11 13:34:22.925070: val_loss -0.2549 +2026-04-11 13:34:22.928277: Pseudo dice [0.3151, 0.4152, 0.6791, 0.7444, 0.5043, 0.3191, 0.5112] +2026-04-11 13:34:22.933167: Epoch time: 103.11 s +2026-04-11 13:34:24.325891: +2026-04-11 13:34:24.328501: Epoch 931 +2026-04-11 13:34:24.331062: Current learning rate: 0.00788 +2026-04-11 13:36:08.720192: train_loss -0.319 +2026-04-11 13:36:08.726018: val_loss -0.1547 +2026-04-11 13:36:08.728064: Pseudo dice [0.7848, 0.7187, 0.619, 0.6225, 0.3529, 0.0388, 0.8115] +2026-04-11 13:36:08.730574: Epoch time: 104.4 s +2026-04-11 13:36:10.096897: +2026-04-11 13:36:10.098855: Epoch 932 +2026-04-11 13:36:10.101176: Current learning rate: 0.00788 +2026-04-11 13:37:54.522189: train_loss -0.3114 +2026-04-11 13:37:54.528731: val_loss -0.3067 +2026-04-11 13:37:54.531183: Pseudo dice [0.496, 0.4109, 0.7721, 0.7462, 0.5944, 0.8115, 0.7587] +2026-04-11 13:37:54.534405: Epoch time: 104.43 s +2026-04-11 13:37:55.969880: +2026-04-11 13:37:55.973343: Epoch 933 +2026-04-11 13:37:55.975894: Current learning rate: 0.00787 +2026-04-11 13:39:39.082495: train_loss -0.3098 +2026-04-11 13:39:39.089511: val_loss -0.1928 +2026-04-11 13:39:39.091673: Pseudo dice [0.4498, 0.3032, 0.7463, 0.2736, 0.5428, 0.0788, 0.4696] +2026-04-11 13:39:39.094582: Epoch time: 103.12 s +2026-04-11 13:39:40.520988: +2026-04-11 13:39:40.522798: Epoch 934 +2026-04-11 13:39:40.526055: Current learning rate: 0.00787 +2026-04-11 13:41:24.056096: train_loss -0.2984 +2026-04-11 13:41:24.062418: val_loss -0.2048 +2026-04-11 13:41:24.065451: Pseudo dice [0.7596, 0.509, 0.6373, 0.4491, 0.3631, 0.0174, 0.2403] +2026-04-11 13:41:24.069858: Epoch time: 103.54 s +2026-04-11 13:41:25.459462: +2026-04-11 13:41:25.463427: Epoch 935 +2026-04-11 13:41:25.469291: Current learning rate: 0.00787 +2026-04-11 13:43:08.283467: train_loss -0.3001 +2026-04-11 13:43:08.292192: val_loss -0.272 +2026-04-11 13:43:08.294919: Pseudo dice [0.707, 0.6527, 0.791, 0.5436, 0.274, 0.7349, 0.7155] +2026-04-11 13:43:08.298401: Epoch time: 102.83 s +2026-04-11 13:43:11.005324: +2026-04-11 13:43:11.007494: Epoch 936 +2026-04-11 13:43:11.009937: Current learning rate: 0.00787 +2026-04-11 13:44:54.848747: train_loss -0.2981 +2026-04-11 13:44:54.854888: val_loss -0.2761 +2026-04-11 13:44:54.857081: Pseudo dice [0.3146, 0.6213, 0.7344, 0.5353, 0.3809, 0.6472, 0.379] +2026-04-11 13:44:54.862469: Epoch time: 103.85 s +2026-04-11 13:44:56.285855: +2026-04-11 13:44:56.289453: Epoch 937 +2026-04-11 13:44:56.291635: Current learning rate: 0.00786 +2026-04-11 13:46:40.932155: train_loss -0.2956 +2026-04-11 13:46:40.938110: val_loss -0.2805 +2026-04-11 13:46:40.940744: Pseudo dice [0.7193, 0.5054, 0.6388, 0.5767, 0.3951, 0.6947, 0.5424] +2026-04-11 13:46:40.943266: Epoch time: 104.65 s +2026-04-11 13:46:42.338808: +2026-04-11 13:46:42.341058: Epoch 938 +2026-04-11 13:46:42.343535: Current learning rate: 0.00786 +2026-04-11 13:48:25.147349: train_loss -0.2949 +2026-04-11 13:48:25.156904: val_loss -0.153 +2026-04-11 13:48:25.160749: Pseudo dice [0.6626, 0.7292, 0.5782, 0.2648, 0.6048, 0.1055, 0.5841] +2026-04-11 13:48:25.164522: Epoch time: 102.81 s +2026-04-11 13:48:26.572006: +2026-04-11 13:48:26.574219: Epoch 939 +2026-04-11 13:48:26.577050: Current learning rate: 0.00786 +2026-04-11 13:50:09.936893: train_loss -0.2794 +2026-04-11 13:50:09.943500: val_loss -0.2579 +2026-04-11 13:50:09.945590: Pseudo dice [0.488, 0.7301, 0.71, 0.0052, 0.4004, 0.6892, 0.2691] +2026-04-11 13:50:09.948280: Epoch time: 103.37 s +2026-04-11 13:50:11.333064: +2026-04-11 13:50:11.335380: Epoch 940 +2026-04-11 13:50:11.338049: Current learning rate: 0.00786 +2026-04-11 13:51:55.409429: train_loss -0.2758 +2026-04-11 13:51:55.417207: val_loss -0.213 +2026-04-11 13:51:55.420377: Pseudo dice [0.4812, 0.6541, 0.6094, 0.0278, 0.5819, 0.0681, 0.7843] +2026-04-11 13:51:55.423566: Epoch time: 104.08 s +2026-04-11 13:51:56.837450: +2026-04-11 13:51:56.840125: Epoch 941 +2026-04-11 13:51:56.842522: Current learning rate: 0.00786 +2026-04-11 13:53:40.157322: train_loss -0.2935 +2026-04-11 13:53:40.165442: val_loss -0.2231 +2026-04-11 13:53:40.167801: Pseudo dice [0.4262, 0.3402, 0.4795, 0.0292, 0.4215, 0.1819, 0.8719] +2026-04-11 13:53:40.170883: Epoch time: 103.32 s +2026-04-11 13:53:41.610326: +2026-04-11 13:53:41.612153: Epoch 942 +2026-04-11 13:53:41.613964: Current learning rate: 0.00785 +2026-04-11 13:55:25.602251: train_loss -0.2841 +2026-04-11 13:55:25.609033: val_loss -0.2499 +2026-04-11 13:55:25.611169: Pseudo dice [0.7791, 0.3243, 0.667, 0.1951, 0.5746, 0.581, 0.8322] +2026-04-11 13:55:25.613398: Epoch time: 104.0 s +2026-04-11 13:55:27.025189: +2026-04-11 13:55:27.027158: Epoch 943 +2026-04-11 13:55:27.029329: Current learning rate: 0.00785 +2026-04-11 13:57:10.296267: train_loss -0.2785 +2026-04-11 13:57:10.302876: val_loss -0.2142 +2026-04-11 13:57:10.305231: Pseudo dice [0.5178, 0.6616, 0.2447, 0.0658, 0.342, 0.568, 0.4216] +2026-04-11 13:57:10.307842: Epoch time: 103.28 s +2026-04-11 13:57:11.684973: +2026-04-11 13:57:11.687171: Epoch 944 +2026-04-11 13:57:11.689815: Current learning rate: 0.00785 +2026-04-11 13:58:55.073877: train_loss -0.2791 +2026-04-11 13:58:55.080307: val_loss -0.2463 +2026-04-11 13:58:55.082968: Pseudo dice [0.748, 0.6628, 0.5337, 0.2666, 0.3144, 0.2352, 0.6091] +2026-04-11 13:58:55.087013: Epoch time: 103.39 s +2026-04-11 13:58:56.577793: +2026-04-11 13:58:56.579924: Epoch 945 +2026-04-11 13:58:56.582868: Current learning rate: 0.00785 +2026-04-11 14:00:40.531687: train_loss -0.2787 +2026-04-11 14:00:40.538938: val_loss -0.1936 +2026-04-11 14:00:40.541354: Pseudo dice [0.3415, 0.5508, 0.5957, 0.642, 0.3631, 0.0825, 0.3802] +2026-04-11 14:00:40.543824: Epoch time: 103.96 s +2026-04-11 14:00:41.981180: +2026-04-11 14:00:41.982915: Epoch 946 +2026-04-11 14:00:41.985520: Current learning rate: 0.00784 +2026-04-11 14:02:25.117978: train_loss -0.3034 +2026-04-11 14:02:25.124887: val_loss -0.2767 +2026-04-11 14:02:25.127890: Pseudo dice [0.76, 0.6718, 0.671, 0.5635, 0.2915, 0.7743, 0.529] +2026-04-11 14:02:25.130482: Epoch time: 103.14 s +2026-04-11 14:02:26.505396: +2026-04-11 14:02:26.508038: Epoch 947 +2026-04-11 14:02:26.510921: Current learning rate: 0.00784 +2026-04-11 14:04:09.321907: train_loss -0.3048 +2026-04-11 14:04:09.328582: val_loss -0.2658 +2026-04-11 14:04:09.330461: Pseudo dice [0.3772, 0.559, 0.3427, 0.3427, 0.4621, 0.7274, 0.8586] +2026-04-11 14:04:09.333421: Epoch time: 102.82 s +2026-04-11 14:04:10.714551: +2026-04-11 14:04:10.716731: Epoch 948 +2026-04-11 14:04:10.719142: Current learning rate: 0.00784 +2026-04-11 14:05:54.850515: train_loss -0.314 +2026-04-11 14:05:54.857800: val_loss -0.2133 +2026-04-11 14:05:54.860407: Pseudo dice [0.1885, 0.347, 0.4569, 0.6247, 0.3453, 0.1733, 0.5138] +2026-04-11 14:05:54.863761: Epoch time: 104.14 s +2026-04-11 14:05:56.275144: +2026-04-11 14:05:56.277169: Epoch 949 +2026-04-11 14:05:56.279491: Current learning rate: 0.00784 +2026-04-11 14:07:39.330065: train_loss -0.3066 +2026-04-11 14:07:39.336948: val_loss -0.2673 +2026-04-11 14:07:39.339036: Pseudo dice [0.737, 0.6384, 0.7462, 0.0805, 0.4475, 0.6962, 0.6414] +2026-04-11 14:07:39.342106: Epoch time: 103.06 s +2026-04-11 14:07:42.738622: +2026-04-11 14:07:42.742779: Epoch 950 +2026-04-11 14:07:42.746872: Current learning rate: 0.00783 +2026-04-11 14:09:25.968578: train_loss -0.2965 +2026-04-11 14:09:25.978158: val_loss -0.235 +2026-04-11 14:09:25.982163: Pseudo dice [0.5927, 0.4557, 0.6429, 0.8437, 0.4337, 0.0592, 0.8228] +2026-04-11 14:09:25.984537: Epoch time: 103.23 s +2026-04-11 14:09:27.423502: +2026-04-11 14:09:27.426018: Epoch 951 +2026-04-11 14:09:27.428074: Current learning rate: 0.00783 +2026-04-11 14:11:11.266051: train_loss -0.2894 +2026-04-11 14:11:11.271554: val_loss -0.1642 +2026-04-11 14:11:11.273723: Pseudo dice [0.3392, 0.6286, 0.546, 0.1904, 0.4292, 0.1279, 0.8433] +2026-04-11 14:11:11.276548: Epoch time: 103.85 s +2026-04-11 14:11:12.701154: +2026-04-11 14:11:12.703382: Epoch 952 +2026-04-11 14:11:12.705421: Current learning rate: 0.00783 +2026-04-11 14:12:55.895876: train_loss -0.2897 +2026-04-11 14:12:55.902268: val_loss -0.1764 +2026-04-11 14:12:55.904997: Pseudo dice [0.3376, 0.6868, 0.4464, 0.0135, 0.1125, 0.0482, 0.6872] +2026-04-11 14:12:55.909766: Epoch time: 103.2 s +2026-04-11 14:12:57.270025: +2026-04-11 14:12:57.273169: Epoch 953 +2026-04-11 14:12:57.275594: Current learning rate: 0.00783 +2026-04-11 14:14:40.352710: train_loss -0.2931 +2026-04-11 14:14:40.359761: val_loss -0.1816 +2026-04-11 14:14:40.361853: Pseudo dice [0.4166, 0.672, 0.5107, 0.2609, 0.2482, 0.2623, 0.8088] +2026-04-11 14:14:40.364149: Epoch time: 103.09 s +2026-04-11 14:14:41.756795: +2026-04-11 14:14:41.758707: Epoch 954 +2026-04-11 14:14:41.760646: Current learning rate: 0.00783 +2026-04-11 14:16:24.928575: train_loss -0.2923 +2026-04-11 14:16:24.933722: val_loss -0.2499 +2026-04-11 14:16:24.935606: Pseudo dice [0.7398, 0.4288, 0.4443, 0.7814, 0.4977, 0.3692, 0.6021] +2026-04-11 14:16:24.938065: Epoch time: 103.18 s +2026-04-11 14:16:26.324484: +2026-04-11 14:16:26.326224: Epoch 955 +2026-04-11 14:16:26.328408: Current learning rate: 0.00782 +2026-04-11 14:18:10.745579: train_loss -0.2753 +2026-04-11 14:18:10.753979: val_loss -0.2733 +2026-04-11 14:18:10.755776: Pseudo dice [0.6843, 0.6359, 0.5409, 0.5917, 0.5554, 0.464, 0.3441] +2026-04-11 14:18:10.758871: Epoch time: 104.42 s +2026-04-11 14:18:12.230411: +2026-04-11 14:18:12.232129: Epoch 956 +2026-04-11 14:18:12.234272: Current learning rate: 0.00782 +2026-04-11 14:19:56.731050: train_loss -0.2975 +2026-04-11 14:19:56.740125: val_loss -0.248 +2026-04-11 14:19:56.742400: Pseudo dice [0.6036, 0.6421, 0.3369, 0.7035, 0.6132, 0.4765, 0.8097] +2026-04-11 14:19:56.745259: Epoch time: 104.5 s +2026-04-11 14:19:58.137238: +2026-04-11 14:19:58.139240: Epoch 957 +2026-04-11 14:19:58.141268: Current learning rate: 0.00782 +2026-04-11 14:21:41.371178: train_loss -0.2874 +2026-04-11 14:21:41.376915: val_loss -0.1517 +2026-04-11 14:21:41.380701: Pseudo dice [0.3459, 0.4987, 0.5265, 0.6583, 0.32, 0.02, 0.5607] +2026-04-11 14:21:41.382973: Epoch time: 103.24 s +2026-04-11 14:21:42.810787: +2026-04-11 14:21:42.812998: Epoch 958 +2026-04-11 14:21:42.815228: Current learning rate: 0.00782 +2026-04-11 14:23:26.576141: train_loss -0.269 +2026-04-11 14:23:26.583727: val_loss -0.2647 +2026-04-11 14:23:26.593055: Pseudo dice [0.2962, 0.4289, 0.559, 0.572, 0.5763, 0.4619, 0.5759] +2026-04-11 14:23:26.595948: Epoch time: 103.77 s +2026-04-11 14:23:28.015287: +2026-04-11 14:23:28.017395: Epoch 959 +2026-04-11 14:23:28.019480: Current learning rate: 0.00781 +2026-04-11 14:25:11.290468: train_loss -0.2795 +2026-04-11 14:25:11.299971: val_loss -0.2549 +2026-04-11 14:25:11.302934: Pseudo dice [0.4918, 0.6129, 0.625, 0.6004, 0.6113, 0.1586, 0.4684] +2026-04-11 14:25:11.305565: Epoch time: 103.28 s +2026-04-11 14:25:12.792617: +2026-04-11 14:25:12.794739: Epoch 960 +2026-04-11 14:25:12.796690: Current learning rate: 0.00781 +2026-04-11 14:26:56.164722: train_loss -0.2797 +2026-04-11 14:26:56.171954: val_loss -0.2499 +2026-04-11 14:26:56.174338: Pseudo dice [0.2014, 0.7603, 0.6367, 0.5674, 0.3453, 0.7711, 0.4411] +2026-04-11 14:26:56.178362: Epoch time: 103.38 s +2026-04-11 14:26:57.588853: +2026-04-11 14:26:57.590624: Epoch 961 +2026-04-11 14:26:57.592739: Current learning rate: 0.00781 +2026-04-11 14:28:40.993556: train_loss -0.3052 +2026-04-11 14:28:41.000987: val_loss -0.2991 +2026-04-11 14:28:41.003474: Pseudo dice [0.1771, 0.7923, 0.8033, 0.7953, 0.6484, 0.7554, 0.7189] +2026-04-11 14:28:41.006492: Epoch time: 103.41 s +2026-04-11 14:28:42.403383: +2026-04-11 14:28:42.405737: Epoch 962 +2026-04-11 14:28:42.407839: Current learning rate: 0.00781 +2026-04-11 14:30:25.827276: train_loss -0.3147 +2026-04-11 14:30:25.835028: val_loss -0.2829 +2026-04-11 14:30:25.837120: Pseudo dice [0.7196, 0.6104, 0.6454, 0.6203, 0.6263, 0.7647, 0.8337] +2026-04-11 14:30:25.839640: Epoch time: 103.43 s +2026-04-11 14:30:27.291375: +2026-04-11 14:30:27.293540: Epoch 963 +2026-04-11 14:30:27.296611: Current learning rate: 0.0078 +2026-04-11 14:32:10.009463: train_loss -0.3184 +2026-04-11 14:32:10.019007: val_loss -0.2255 +2026-04-11 14:32:10.022037: Pseudo dice [0.4446, 0.4293, 0.575, 0.5359, 0.605, 0.1263, 0.8281] +2026-04-11 14:32:10.024091: Epoch time: 102.72 s +2026-04-11 14:32:11.448940: +2026-04-11 14:32:11.451313: Epoch 964 +2026-04-11 14:32:11.453464: Current learning rate: 0.0078 +2026-04-11 14:33:55.041793: train_loss -0.2954 +2026-04-11 14:33:55.047541: val_loss -0.2474 +2026-04-11 14:33:55.050627: Pseudo dice [0.2232, 0.783, 0.8278, 0.617, 0.2349, 0.347, 0.7682] +2026-04-11 14:33:55.052869: Epoch time: 103.6 s +2026-04-11 14:33:56.514992: +2026-04-11 14:33:56.516814: Epoch 965 +2026-04-11 14:33:56.518803: Current learning rate: 0.0078 +2026-04-11 14:35:39.382323: train_loss -0.3025 +2026-04-11 14:35:39.389441: val_loss -0.2836 +2026-04-11 14:35:39.391633: Pseudo dice [0.664, 0.8684, 0.6775, 0.6663, 0.594, 0.6298, 0.8475] +2026-04-11 14:35:39.394293: Epoch time: 102.87 s +2026-04-11 14:35:40.865359: +2026-04-11 14:35:40.867709: Epoch 966 +2026-04-11 14:35:40.869972: Current learning rate: 0.0078 +2026-04-11 14:37:24.003138: train_loss -0.2989 +2026-04-11 14:37:24.008510: val_loss -0.1835 +2026-04-11 14:37:24.010952: Pseudo dice [0.5469, 0.7575, 0.6367, 0.0661, 0.4464, 0.182, 0.6564] +2026-04-11 14:37:24.013318: Epoch time: 103.14 s +2026-04-11 14:37:25.420828: +2026-04-11 14:37:25.422900: Epoch 967 +2026-04-11 14:37:25.424988: Current learning rate: 0.0078 +2026-04-11 14:39:08.118964: train_loss -0.2806 +2026-04-11 14:39:08.125525: val_loss -0.2374 +2026-04-11 14:39:08.127736: Pseudo dice [0.7951, 0.3183, 0.6185, 0.3031, 0.1448, 0.535, 0.342] +2026-04-11 14:39:08.131902: Epoch time: 102.7 s +2026-04-11 14:39:09.587774: +2026-04-11 14:39:09.590041: Epoch 968 +2026-04-11 14:39:09.593138: Current learning rate: 0.00779 +2026-04-11 14:40:52.929360: train_loss -0.252 +2026-04-11 14:40:52.934918: val_loss -0.1988 +2026-04-11 14:40:52.938915: Pseudo dice [0.26, 0.6759, 0.5531, 0.6072, 0.235, 0.0837, 0.2417] +2026-04-11 14:40:52.941483: Epoch time: 103.35 s +2026-04-11 14:40:54.370242: +2026-04-11 14:40:54.393415: Epoch 969 +2026-04-11 14:40:54.395513: Current learning rate: 0.00779 +2026-04-11 14:42:36.887612: train_loss -0.2963 +2026-04-11 14:42:36.894430: val_loss -0.2256 +2026-04-11 14:42:36.896723: Pseudo dice [0.791, 0.5, 0.5727, 0.0425, 0.3029, 0.1371, 0.7054] +2026-04-11 14:42:36.899374: Epoch time: 102.52 s +2026-04-11 14:42:38.292831: +2026-04-11 14:42:38.294770: Epoch 970 +2026-04-11 14:42:38.296620: Current learning rate: 0.00779 +2026-04-11 14:44:20.688493: train_loss -0.2863 +2026-04-11 14:44:20.695255: val_loss -0.2469 +2026-04-11 14:44:20.697426: Pseudo dice [0.4517, 0.6026, 0.7243, 0.2972, 0.5167, 0.1115, 0.6931] +2026-04-11 14:44:20.699890: Epoch time: 102.4 s +2026-04-11 14:44:22.085978: +2026-04-11 14:44:22.087705: Epoch 971 +2026-04-11 14:44:22.090483: Current learning rate: 0.00779 +2026-04-11 14:46:05.078945: train_loss -0.2965 +2026-04-11 14:46:05.085947: val_loss -0.1799 +2026-04-11 14:46:05.088215: Pseudo dice [0.624, 0.4865, 0.4611, 0.3085, 0.3295, 0.0585, 0.5074] +2026-04-11 14:46:05.091321: Epoch time: 103.0 s +2026-04-11 14:46:06.494437: +2026-04-11 14:46:06.496724: Epoch 972 +2026-04-11 14:46:06.498799: Current learning rate: 0.00778 +2026-04-11 14:47:49.301555: train_loss -0.3141 +2026-04-11 14:47:49.307626: val_loss -0.2678 +2026-04-11 14:47:49.309892: Pseudo dice [0.801, 0.6202, 0.7664, 0.4705, 0.5229, 0.5064, 0.8097] +2026-04-11 14:47:49.313299: Epoch time: 102.81 s +2026-04-11 14:47:50.733678: +2026-04-11 14:47:50.735676: Epoch 973 +2026-04-11 14:47:50.738457: Current learning rate: 0.00778 +2026-04-11 14:49:33.143280: train_loss -0.3049 +2026-04-11 14:49:33.150096: val_loss -0.1858 +2026-04-11 14:49:33.152208: Pseudo dice [0.3568, 0.2212, 0.7793, 0.1769, 0.506, 0.0617, 0.8238] +2026-04-11 14:49:33.154443: Epoch time: 102.41 s +2026-04-11 14:49:34.557745: +2026-04-11 14:49:34.559470: Epoch 974 +2026-04-11 14:49:34.561623: Current learning rate: 0.00778 +2026-04-11 14:51:17.698048: train_loss -0.3045 +2026-04-11 14:51:17.704938: val_loss -0.2593 +2026-04-11 14:51:17.707340: Pseudo dice [0.8332, 0.66, 0.7161, 0.2625, 0.2923, 0.2627, 0.6888] +2026-04-11 14:51:17.709805: Epoch time: 103.14 s +2026-04-11 14:51:19.124057: +2026-04-11 14:51:19.125694: Epoch 975 +2026-04-11 14:51:19.127946: Current learning rate: 0.00778 +2026-04-11 14:53:01.881626: train_loss -0.2941 +2026-04-11 14:53:01.888496: val_loss -0.2219 +2026-04-11 14:53:01.890746: Pseudo dice [0.3319, 0.4085, 0.6928, 0.2177, 0.5215, 0.1664, 0.8273] +2026-04-11 14:53:01.893368: Epoch time: 102.76 s +2026-04-11 14:53:03.313723: +2026-04-11 14:53:03.319200: Epoch 976 +2026-04-11 14:53:03.323148: Current learning rate: 0.00777 +2026-04-11 14:54:46.041057: train_loss -0.2779 +2026-04-11 14:54:46.050093: val_loss -0.1927 +2026-04-11 14:54:46.052779: Pseudo dice [0.6461, 0.624, 0.4851, 0.1775, 0.3528, 0.091, 0.7941] +2026-04-11 14:54:46.056045: Epoch time: 102.73 s +2026-04-11 14:54:48.791284: +2026-04-11 14:54:48.793217: Epoch 977 +2026-04-11 14:54:48.795125: Current learning rate: 0.00777 +2026-04-11 14:56:32.119970: train_loss -0.2726 +2026-04-11 14:56:32.126400: val_loss -0.2607 +2026-04-11 14:56:32.128479: Pseudo dice [0.7367, 0.2216, 0.7214, 0.7787, 0.4772, 0.2779, 0.5518] +2026-04-11 14:56:32.131385: Epoch time: 103.33 s +2026-04-11 14:56:33.545136: +2026-04-11 14:56:33.547027: Epoch 978 +2026-04-11 14:56:33.549170: Current learning rate: 0.00777 +2026-04-11 14:58:16.385615: train_loss -0.283 +2026-04-11 14:58:16.400233: val_loss -0.2664 +2026-04-11 14:58:16.406157: Pseudo dice [0.2795, 0.4366, 0.7018, 0.026, 0.6152, 0.1796, 0.7028] +2026-04-11 14:58:16.410728: Epoch time: 102.84 s +2026-04-11 14:58:17.810798: +2026-04-11 14:58:17.815223: Epoch 979 +2026-04-11 14:58:17.817100: Current learning rate: 0.00777 +2026-04-11 15:00:00.569419: train_loss -0.2991 +2026-04-11 15:00:00.575123: val_loss -0.2014 +2026-04-11 15:00:00.577012: Pseudo dice [0.4415, 0.5768, 0.437, 0.5409, 0.3659, 0.0722, 0.6036] +2026-04-11 15:00:00.579693: Epoch time: 102.76 s +2026-04-11 15:00:01.987070: +2026-04-11 15:00:01.989698: Epoch 980 +2026-04-11 15:00:01.991989: Current learning rate: 0.00777 +2026-04-11 15:01:44.557972: train_loss -0.2961 +2026-04-11 15:01:44.563905: val_loss -0.2341 +2026-04-11 15:01:44.565737: Pseudo dice [0.3989, 0.5392, 0.6749, 0.5274, 0.449, 0.1001, 0.8627] +2026-04-11 15:01:44.568802: Epoch time: 102.58 s +2026-04-11 15:01:45.967257: +2026-04-11 15:01:45.969329: Epoch 981 +2026-04-11 15:01:45.971746: Current learning rate: 0.00776 +2026-04-11 15:03:28.981691: train_loss -0.2955 +2026-04-11 15:03:28.986488: val_loss -0.208 +2026-04-11 15:03:28.988445: Pseudo dice [0.7711, 0.1929, 0.6789, 0.4047, 0.6336, 0.1122, 0.7853] +2026-04-11 15:03:28.991003: Epoch time: 103.02 s +2026-04-11 15:03:30.398630: +2026-04-11 15:03:30.400367: Epoch 982 +2026-04-11 15:03:30.402234: Current learning rate: 0.00776 +2026-04-11 15:05:12.986898: train_loss -0.286 +2026-04-11 15:05:12.993181: val_loss -0.236 +2026-04-11 15:05:12.995317: Pseudo dice [0.6292, 0.6773, 0.6995, 0.0782, 0.4774, 0.6659, 0.7308] +2026-04-11 15:05:12.999451: Epoch time: 102.59 s +2026-04-11 15:05:14.446754: +2026-04-11 15:05:14.448436: Epoch 983 +2026-04-11 15:05:14.450856: Current learning rate: 0.00776 +2026-04-11 15:06:57.177154: train_loss -0.2769 +2026-04-11 15:06:57.191154: val_loss -0.2533 +2026-04-11 15:06:57.195613: Pseudo dice [0.627, 0.7021, 0.6947, 0.7661, 0.1208, 0.6264, 0.8365] +2026-04-11 15:06:57.200275: Epoch time: 102.73 s +2026-04-11 15:06:58.721442: +2026-04-11 15:06:58.723263: Epoch 984 +2026-04-11 15:06:58.725106: Current learning rate: 0.00776 +2026-04-11 15:08:41.150868: train_loss -0.2809 +2026-04-11 15:08:41.156512: val_loss -0.2341 +2026-04-11 15:08:41.159171: Pseudo dice [0.6806, 0.5454, 0.4585, 0.5043, 0.5095, 0.705, 0.5466] +2026-04-11 15:08:41.161673: Epoch time: 102.43 s +2026-04-11 15:08:42.588093: +2026-04-11 15:08:42.590008: Epoch 985 +2026-04-11 15:08:42.592304: Current learning rate: 0.00775 +2026-04-11 15:10:25.232197: train_loss -0.2724 +2026-04-11 15:10:25.238647: val_loss -0.2376 +2026-04-11 15:10:25.240746: Pseudo dice [0.5555, 0.7949, 0.6255, 0.0572, 0.6457, 0.1876, 0.7519] +2026-04-11 15:10:25.243433: Epoch time: 102.65 s +2026-04-11 15:10:26.680557: +2026-04-11 15:10:26.682375: Epoch 986 +2026-04-11 15:10:26.685302: Current learning rate: 0.00775 +2026-04-11 15:12:09.190541: train_loss -0.3041 +2026-04-11 15:12:09.199599: val_loss -0.2283 +2026-04-11 15:12:09.202452: Pseudo dice [0.7965, 0.4422, 0.5755, 0.0665, 0.3597, 0.2904, 0.7097] +2026-04-11 15:12:09.205938: Epoch time: 102.51 s +2026-04-11 15:12:10.623030: +2026-04-11 15:12:10.624974: Epoch 987 +2026-04-11 15:12:10.627797: Current learning rate: 0.00775 +2026-04-11 15:13:52.959638: train_loss -0.3099 +2026-04-11 15:13:52.965527: val_loss -0.322 +2026-04-11 15:13:52.967913: Pseudo dice [0.769, 0.6153, 0.7898, 0.7149, 0.6755, 0.7658, 0.6406] +2026-04-11 15:13:52.970255: Epoch time: 102.34 s +2026-04-11 15:13:54.373199: +2026-04-11 15:13:54.376024: Epoch 988 +2026-04-11 15:13:54.378620: Current learning rate: 0.00775 +2026-04-11 15:15:36.903104: train_loss -0.2961 +2026-04-11 15:15:36.909524: val_loss -0.2196 +2026-04-11 15:15:36.911666: Pseudo dice [0.675, 0.6577, 0.4367, 0.6973, 0.3277, 0.2956, 0.694] +2026-04-11 15:15:36.913728: Epoch time: 102.53 s +2026-04-11 15:15:38.325933: +2026-04-11 15:15:38.328146: Epoch 989 +2026-04-11 15:15:38.330333: Current learning rate: 0.00774 +2026-04-11 15:17:20.771102: train_loss -0.2759 +2026-04-11 15:17:20.777191: val_loss -0.2672 +2026-04-11 15:17:20.779288: Pseudo dice [0.1536, 0.5274, 0.4335, 0.5109, 0.3822, 0.7619, 0.6466] +2026-04-11 15:17:20.781703: Epoch time: 102.45 s +2026-04-11 15:17:22.175277: +2026-04-11 15:17:22.177312: Epoch 990 +2026-04-11 15:17:22.179103: Current learning rate: 0.00774 +2026-04-11 15:19:04.808595: train_loss -0.2851 +2026-04-11 15:19:04.814837: val_loss -0.2201 +2026-04-11 15:19:04.817046: Pseudo dice [0.8478, 0.6507, 0.4628, 0.5576, 0.3892, 0.2179, 0.2291] +2026-04-11 15:19:04.821721: Epoch time: 102.64 s +2026-04-11 15:19:06.221355: +2026-04-11 15:19:06.224067: Epoch 991 +2026-04-11 15:19:06.226871: Current learning rate: 0.00774 +2026-04-11 15:20:48.592593: train_loss -0.2901 +2026-04-11 15:20:48.600234: val_loss -0.2753 +2026-04-11 15:20:48.603117: Pseudo dice [0.4455, 0.8736, 0.5044, 0.0528, 0.4881, 0.674, 0.5882] +2026-04-11 15:20:48.606421: Epoch time: 102.38 s +2026-04-11 15:20:50.011754: +2026-04-11 15:20:50.013477: Epoch 992 +2026-04-11 15:20:50.015400: Current learning rate: 0.00774 +2026-04-11 15:22:32.659619: train_loss -0.3083 +2026-04-11 15:22:32.666167: val_loss -0.2844 +2026-04-11 15:22:32.668120: Pseudo dice [0.5137, 0.5679, 0.5585, 0.5836, 0.6109, 0.2111, 0.5975] +2026-04-11 15:22:32.670560: Epoch time: 102.65 s +2026-04-11 15:22:34.072660: +2026-04-11 15:22:34.074869: Epoch 993 +2026-04-11 15:22:34.076736: Current learning rate: 0.00774 +2026-04-11 15:24:16.385448: train_loss -0.2955 +2026-04-11 15:24:16.390505: val_loss -0.2213 +2026-04-11 15:24:16.392179: Pseudo dice [0.558, 0.5477, 0.3657, 0.0411, 0.3208, 0.3478, 0.5667] +2026-04-11 15:24:16.394202: Epoch time: 102.32 s +2026-04-11 15:24:17.780524: +2026-04-11 15:24:17.782161: Epoch 994 +2026-04-11 15:24:17.784553: Current learning rate: 0.00773 +2026-04-11 15:26:00.376729: train_loss -0.2976 +2026-04-11 15:26:00.382900: val_loss -0.2601 +2026-04-11 15:26:00.384790: Pseudo dice [0.483, 0.7509, 0.6269, 0.8537, 0.4028, 0.4237, 0.7727] +2026-04-11 15:26:00.386993: Epoch time: 102.6 s +2026-04-11 15:26:01.815792: +2026-04-11 15:26:01.817604: Epoch 995 +2026-04-11 15:26:01.820201: Current learning rate: 0.00773 +2026-04-11 15:27:44.207006: train_loss -0.3023 +2026-04-11 15:27:44.213004: val_loss -0.1909 +2026-04-11 15:27:44.215164: Pseudo dice [0.4187, 0.174, 0.4289, 0.5775, 0.4573, 0.2227, 0.8814] +2026-04-11 15:27:44.218002: Epoch time: 102.39 s +2026-04-11 15:27:45.624601: +2026-04-11 15:27:45.626579: Epoch 996 +2026-04-11 15:27:45.628630: Current learning rate: 0.00773 +2026-04-11 15:29:27.917248: train_loss -0.2878 +2026-04-11 15:29:27.933732: val_loss -0.2174 +2026-04-11 15:29:27.936185: Pseudo dice [0.2426, 0.3935, 0.6264, 0.5189, 0.4544, 0.1183, 0.5993] +2026-04-11 15:29:27.939392: Epoch time: 102.3 s +2026-04-11 15:29:29.332984: +2026-04-11 15:29:29.334864: Epoch 997 +2026-04-11 15:29:29.337321: Current learning rate: 0.00773 +2026-04-11 15:31:13.694691: train_loss -0.2969 +2026-04-11 15:31:13.700805: val_loss -0.1872 +2026-04-11 15:31:13.703064: Pseudo dice [0.6101, 0.0799, 0.5904, 0.4617, 0.3661, 0.1192, 0.4071] +2026-04-11 15:31:13.705746: Epoch time: 104.37 s +2026-04-11 15:31:15.083360: +2026-04-11 15:31:15.085801: Epoch 998 +2026-04-11 15:31:15.087974: Current learning rate: 0.00772 +2026-04-11 15:32:57.528713: train_loss -0.2747 +2026-04-11 15:32:57.533682: val_loss -0.239 +2026-04-11 15:32:57.535780: Pseudo dice [0.3494, 0.8094, 0.7231, 0.3425, 0.4964, 0.2462, 0.6033] +2026-04-11 15:32:57.538320: Epoch time: 102.45 s +2026-04-11 15:32:58.915298: +2026-04-11 15:32:58.918095: Epoch 999 +2026-04-11 15:32:58.920946: Current learning rate: 0.00772 +2026-04-11 15:34:41.405741: train_loss -0.2852 +2026-04-11 15:34:41.411972: val_loss -0.2379 +2026-04-11 15:34:41.414627: Pseudo dice [0.2378, 0.245, 0.6593, 0.1972, 0.599, 0.2743, 0.2987] +2026-04-11 15:34:41.417646: Epoch time: 102.49 s +2026-04-11 15:34:44.722128: +2026-04-11 15:34:44.724027: Epoch 1000 +2026-04-11 15:34:44.726026: Current learning rate: 0.00772 +2026-04-11 15:36:27.337516: train_loss -0.2994 +2026-04-11 15:36:27.344795: val_loss -0.261 +2026-04-11 15:36:27.346460: Pseudo dice [0.3669, 0.2862, 0.4947, 0.141, 0.5705, 0.3061, 0.935] +2026-04-11 15:36:27.349205: Epoch time: 102.62 s +2026-04-11 15:36:28.761052: +2026-04-11 15:36:28.762948: Epoch 1001 +2026-04-11 15:36:28.764922: Current learning rate: 0.00772 +2026-04-11 15:38:11.204535: train_loss -0.283 +2026-04-11 15:38:11.210680: val_loss -0.2298 +2026-04-11 15:38:11.213125: Pseudo dice [0.5395, 0.353, 0.7466, 0.742, 0.2956, 0.1596, 0.6659] +2026-04-11 15:38:11.215240: Epoch time: 102.45 s +2026-04-11 15:38:12.596343: +2026-04-11 15:38:12.598928: Epoch 1002 +2026-04-11 15:38:12.601862: Current learning rate: 0.00771 +2026-04-11 15:39:55.030628: train_loss -0.3032 +2026-04-11 15:39:55.036945: val_loss -0.2889 +2026-04-11 15:39:55.038996: Pseudo dice [0.817, 0.8814, 0.7183, 0.3705, 0.6686, 0.7896, 0.8684] +2026-04-11 15:39:55.041735: Epoch time: 102.44 s +2026-04-11 15:39:56.452901: +2026-04-11 15:39:56.454650: Epoch 1003 +2026-04-11 15:39:56.457284: Current learning rate: 0.00771 +2026-04-11 15:41:38.873060: train_loss -0.2859 +2026-04-11 15:41:38.879605: val_loss -0.1765 +2026-04-11 15:41:38.882414: Pseudo dice [0.222, 0.302, 0.5361, 0.5162, 0.5492, 0.1178, 0.8479] +2026-04-11 15:41:38.885072: Epoch time: 102.42 s +2026-04-11 15:41:40.262208: +2026-04-11 15:41:40.264042: Epoch 1004 +2026-04-11 15:41:40.266187: Current learning rate: 0.00771 +2026-04-11 15:43:22.644947: train_loss -0.2848 +2026-04-11 15:43:22.657202: val_loss -0.2813 +2026-04-11 15:43:22.659395: Pseudo dice [0.5413, 0.384, 0.6164, 0.6206, 0.369, 0.7249, 0.8626] +2026-04-11 15:43:22.662547: Epoch time: 102.39 s +2026-04-11 15:43:24.082414: +2026-04-11 15:43:24.084717: Epoch 1005 +2026-04-11 15:43:24.087004: Current learning rate: 0.00771 +2026-04-11 15:45:06.322477: train_loss -0.2916 +2026-04-11 15:45:06.328146: val_loss -0.2549 +2026-04-11 15:45:06.330225: Pseudo dice [0.666, 0.3792, 0.6556, 0.621, 0.4206, 0.1263, 0.8777] +2026-04-11 15:45:06.333531: Epoch time: 102.24 s +2026-04-11 15:45:07.719638: +2026-04-11 15:45:07.721432: Epoch 1006 +2026-04-11 15:45:07.723296: Current learning rate: 0.0077 +2026-04-11 15:46:49.927131: train_loss -0.3058 +2026-04-11 15:46:49.931920: val_loss -0.2212 +2026-04-11 15:46:49.933540: Pseudo dice [0.3587, 0.6573, 0.5989, 0.6631, 0.5072, 0.1637, 0.8554] +2026-04-11 15:46:49.935889: Epoch time: 102.21 s +2026-04-11 15:46:51.317336: +2026-04-11 15:46:51.319132: Epoch 1007 +2026-04-11 15:46:51.320784: Current learning rate: 0.0077 +2026-04-11 15:48:33.746941: train_loss -0.3025 +2026-04-11 15:48:33.752509: val_loss -0.1705 +2026-04-11 15:48:33.754367: Pseudo dice [0.6507, 0.3308, 0.3946, 0.5921, 0.5431, 0.0552, 0.7752] +2026-04-11 15:48:33.756722: Epoch time: 102.43 s +2026-04-11 15:48:35.175501: +2026-04-11 15:48:35.177431: Epoch 1008 +2026-04-11 15:48:35.179883: Current learning rate: 0.0077 +2026-04-11 15:50:17.920005: train_loss -0.2714 +2026-04-11 15:50:17.927598: val_loss -0.227 +2026-04-11 15:50:17.929630: Pseudo dice [0.3801, 0.8623, 0.6776, 0.4566, 0.1261, 0.4829, 0.4789] +2026-04-11 15:50:17.932588: Epoch time: 102.75 s +2026-04-11 15:50:19.328808: +2026-04-11 15:50:19.330725: Epoch 1009 +2026-04-11 15:50:19.333415: Current learning rate: 0.0077 +2026-04-11 15:52:03.124694: train_loss -0.2617 +2026-04-11 15:52:03.130801: val_loss -0.2686 +2026-04-11 15:52:03.133246: Pseudo dice [0.4598, 0.8646, 0.6282, 0.4753, 0.5735, 0.6129, 0.6921] +2026-04-11 15:52:03.136142: Epoch time: 103.8 s +2026-04-11 15:52:04.551933: +2026-04-11 15:52:04.555257: Epoch 1010 +2026-04-11 15:52:04.557213: Current learning rate: 0.0077 +2026-04-11 15:53:47.779753: train_loss -0.3098 +2026-04-11 15:53:47.786329: val_loss -0.24 +2026-04-11 15:53:47.787965: Pseudo dice [0.541, 0.5919, 0.6043, 0.6057, 0.4909, 0.1833, 0.8409] +2026-04-11 15:53:47.790884: Epoch time: 103.23 s +2026-04-11 15:53:49.226212: +2026-04-11 15:53:49.228102: Epoch 1011 +2026-04-11 15:53:49.230134: Current learning rate: 0.00769 +2026-04-11 15:55:32.262268: train_loss -0.301 +2026-04-11 15:55:32.269119: val_loss -0.1471 +2026-04-11 15:55:32.271831: Pseudo dice [0.7682, 0.6574, 0.5422, 0.7167, 0.5407, 0.0759, 0.7347] +2026-04-11 15:55:32.274002: Epoch time: 103.04 s +2026-04-11 15:55:33.682003: +2026-04-11 15:55:33.683946: Epoch 1012 +2026-04-11 15:55:33.686049: Current learning rate: 0.00769 +2026-04-11 15:57:16.922741: train_loss -0.2929 +2026-04-11 15:57:16.929942: val_loss -0.1962 +2026-04-11 15:57:16.932364: Pseudo dice [0.3122, 0.262, 0.6678, 0.2583, 0.5468, 0.1323, 0.8356] +2026-04-11 15:57:16.935749: Epoch time: 103.24 s +2026-04-11 15:57:18.328517: +2026-04-11 15:57:18.330364: Epoch 1013 +2026-04-11 15:57:18.332421: Current learning rate: 0.00769 +2026-04-11 15:59:00.678924: train_loss -0.3135 +2026-04-11 15:59:00.685323: val_loss -0.2077 +2026-04-11 15:59:00.687608: Pseudo dice [0.3759, 0.6577, 0.5949, 0.7621, 0.526, 0.1515, 0.7843] +2026-04-11 15:59:00.689849: Epoch time: 102.35 s +2026-04-11 15:59:02.059950: +2026-04-11 15:59:02.061578: Epoch 1014 +2026-04-11 15:59:02.063103: Current learning rate: 0.00769 +2026-04-11 16:00:44.639857: train_loss -0.3153 +2026-04-11 16:00:44.648568: val_loss -0.114 +2026-04-11 16:00:44.650603: Pseudo dice [0.6308, 0.7109, 0.5772, 0.6928, 0.6567, 0.0599, 0.4631] +2026-04-11 16:00:44.653373: Epoch time: 102.58 s +2026-04-11 16:00:46.045666: +2026-04-11 16:00:46.048373: Epoch 1015 +2026-04-11 16:00:46.050481: Current learning rate: 0.00768 +2026-04-11 16:02:28.880702: train_loss -0.2977 +2026-04-11 16:02:28.886213: val_loss -0.2199 +2026-04-11 16:02:28.889349: Pseudo dice [0.7338, 0.7364, 0.6243, 0.4786, 0.4481, 0.0483, 0.5264] +2026-04-11 16:02:28.892998: Epoch time: 102.84 s +2026-04-11 16:02:30.365973: +2026-04-11 16:02:30.367935: Epoch 1016 +2026-04-11 16:02:30.369846: Current learning rate: 0.00768 +2026-04-11 16:04:13.359003: train_loss -0.2991 +2026-04-11 16:04:13.364171: val_loss -0.2587 +2026-04-11 16:04:13.366330: Pseudo dice [0.7887, 0.7884, 0.7362, 0.6512, 0.3321, 0.6254, 0.8299] +2026-04-11 16:04:13.369366: Epoch time: 103.0 s +2026-04-11 16:04:14.750235: +2026-04-11 16:04:14.752054: Epoch 1017 +2026-04-11 16:04:14.753643: Current learning rate: 0.00768 +2026-04-11 16:05:57.101670: train_loss -0.2908 +2026-04-11 16:05:57.107222: val_loss -0.1982 +2026-04-11 16:05:57.109073: Pseudo dice [0.8405, 0.4443, 0.5484, 0.6261, 0.35, 0.189, 0.5275] +2026-04-11 16:05:57.111295: Epoch time: 102.36 s +2026-04-11 16:05:59.632729: +2026-04-11 16:05:59.634915: Epoch 1018 +2026-04-11 16:05:59.636872: Current learning rate: 0.00768 +2026-04-11 16:07:42.499095: train_loss -0.3004 +2026-04-11 16:07:42.505384: val_loss -0.2324 +2026-04-11 16:07:42.507241: Pseudo dice [0.5338, 0.5858, 0.6073, 0.6879, 0.266, 0.3413, 0.6091] +2026-04-11 16:07:42.509570: Epoch time: 102.87 s +2026-04-11 16:07:43.911101: +2026-04-11 16:07:43.912753: Epoch 1019 +2026-04-11 16:07:43.914912: Current learning rate: 0.00767 +2026-04-11 16:09:26.827565: train_loss -0.3101 +2026-04-11 16:09:26.833234: val_loss -0.2825 +2026-04-11 16:09:26.835247: Pseudo dice [0.8095, 0.7683, 0.7656, 0.4374, 0.371, 0.7307, 0.3073] +2026-04-11 16:09:26.837944: Epoch time: 102.92 s +2026-04-11 16:09:28.231478: +2026-04-11 16:09:28.233307: Epoch 1020 +2026-04-11 16:09:28.235377: Current learning rate: 0.00767 +2026-04-11 16:11:10.693128: train_loss -0.2907 +2026-04-11 16:11:10.698406: val_loss -0.2278 +2026-04-11 16:11:10.700559: Pseudo dice [0.3811, 0.8087, 0.5757, 0.0302, 0.4684, 0.5662, 0.4359] +2026-04-11 16:11:10.702685: Epoch time: 102.47 s +2026-04-11 16:11:12.123395: +2026-04-11 16:11:12.125287: Epoch 1021 +2026-04-11 16:11:12.127263: Current learning rate: 0.00767 +2026-04-11 16:12:54.707604: train_loss -0.2748 +2026-04-11 16:12:54.712930: val_loss -0.1812 +2026-04-11 16:12:54.715049: Pseudo dice [0.8095, 0.4201, 0.5349, 0.159, 0.4453, 0.2691, 0.6513] +2026-04-11 16:12:54.717474: Epoch time: 102.59 s +2026-04-11 16:12:56.122613: +2026-04-11 16:12:56.124509: Epoch 1022 +2026-04-11 16:12:56.127208: Current learning rate: 0.00767 +2026-04-11 16:14:38.497704: train_loss -0.2946 +2026-04-11 16:14:38.503180: val_loss -0.2878 +2026-04-11 16:14:38.505080: Pseudo dice [0.6775, 0.6654, 0.7641, 0.5042, 0.5534, 0.7507, 0.4159] +2026-04-11 16:14:38.507338: Epoch time: 102.38 s +2026-04-11 16:14:39.892563: +2026-04-11 16:14:39.894291: Epoch 1023 +2026-04-11 16:14:39.896096: Current learning rate: 0.00767 +2026-04-11 16:16:22.252070: train_loss -0.2918 +2026-04-11 16:16:22.257018: val_loss -0.2813 +2026-04-11 16:16:22.259544: Pseudo dice [0.6207, 0.219, 0.6567, 0.381, 0.3841, 0.7262, 0.8757] +2026-04-11 16:16:22.261783: Epoch time: 102.36 s +2026-04-11 16:16:23.681257: +2026-04-11 16:16:23.683643: Epoch 1024 +2026-04-11 16:16:23.686789: Current learning rate: 0.00766 +2026-04-11 16:18:06.174958: train_loss -0.2778 +2026-04-11 16:18:06.181932: val_loss -0.2475 +2026-04-11 16:18:06.184310: Pseudo dice [0.4457, 0.6387, 0.7282, 0.0757, 0.4273, 0.6959, 0.6087] +2026-04-11 16:18:06.186842: Epoch time: 102.5 s +2026-04-11 16:18:07.574717: +2026-04-11 16:18:07.578816: Epoch 1025 +2026-04-11 16:18:07.581095: Current learning rate: 0.00766 +2026-04-11 16:19:49.996795: train_loss -0.2912 +2026-04-11 16:19:50.002830: val_loss -0.254 +2026-04-11 16:19:50.004948: Pseudo dice [0.7173, 0.4223, 0.5986, 0.4754, 0.4989, 0.5214, 0.6362] +2026-04-11 16:19:50.007437: Epoch time: 102.43 s +2026-04-11 16:19:51.423331: +2026-04-11 16:19:51.425114: Epoch 1026 +2026-04-11 16:19:51.427129: Current learning rate: 0.00766 +2026-04-11 16:21:33.857046: train_loss -0.2883 +2026-04-11 16:21:33.864088: val_loss -0.2493 +2026-04-11 16:21:33.865959: Pseudo dice [0.322, 0.3878, 0.6635, 0.1448, 0.5501, 0.7189, 0.7627] +2026-04-11 16:21:33.868170: Epoch time: 102.44 s +2026-04-11 16:21:35.240118: +2026-04-11 16:21:35.241957: Epoch 1027 +2026-04-11 16:21:35.243594: Current learning rate: 0.00766 +2026-04-11 16:23:17.622582: train_loss -0.2891 +2026-04-11 16:23:17.627913: val_loss -0.2552 +2026-04-11 16:23:17.629950: Pseudo dice [0.4304, 0.6552, 0.5196, 0.6642, 0.5693, 0.5138, 0.892] +2026-04-11 16:23:17.632390: Epoch time: 102.39 s +2026-04-11 16:23:19.038096: +2026-04-11 16:23:19.040037: Epoch 1028 +2026-04-11 16:23:19.042142: Current learning rate: 0.00765 +2026-04-11 16:25:01.239715: train_loss -0.2797 +2026-04-11 16:25:01.246226: val_loss -0.2544 +2026-04-11 16:25:01.249193: Pseudo dice [0.323, 0.798, 0.5478, 0.5199, 0.5388, 0.6475, 0.698] +2026-04-11 16:25:01.251676: Epoch time: 102.21 s +2026-04-11 16:25:02.656360: +2026-04-11 16:25:02.659656: Epoch 1029 +2026-04-11 16:25:02.661433: Current learning rate: 0.00765 +2026-04-11 16:26:45.091618: train_loss -0.2554 +2026-04-11 16:26:45.097272: val_loss -0.1654 +2026-04-11 16:26:45.099490: Pseudo dice [0.5265, 0.8593, 0.4824, 0.5882, 0.1036, 0.1565, 0.6194] +2026-04-11 16:26:45.102026: Epoch time: 102.44 s +2026-04-11 16:26:46.485829: +2026-04-11 16:26:46.488102: Epoch 1030 +2026-04-11 16:26:46.491572: Current learning rate: 0.00765 +2026-04-11 16:28:29.009472: train_loss -0.2844 +2026-04-11 16:28:29.014873: val_loss -0.2204 +2026-04-11 16:28:29.017078: Pseudo dice [0.8352, 0.5042, 0.223, 0.0551, 0.4756, 0.4279, 0.855] +2026-04-11 16:28:29.019318: Epoch time: 102.53 s +2026-04-11 16:28:30.425361: +2026-04-11 16:28:30.427677: Epoch 1031 +2026-04-11 16:28:30.429833: Current learning rate: 0.00765 +2026-04-11 16:30:12.787738: train_loss -0.3008 +2026-04-11 16:30:12.792806: val_loss -0.2773 +2026-04-11 16:30:12.794937: Pseudo dice [0.8101, 0.3764, 0.6331, 0.7417, 0.5724, 0.1709, 0.8437] +2026-04-11 16:30:12.797434: Epoch time: 102.37 s +2026-04-11 16:30:14.200151: +2026-04-11 16:30:14.201902: Epoch 1032 +2026-04-11 16:30:14.203950: Current learning rate: 0.00764 +2026-04-11 16:31:56.791357: train_loss -0.3089 +2026-04-11 16:31:56.797456: val_loss -0.2824 +2026-04-11 16:31:56.799830: Pseudo dice [0.3807, 0.3692, 0.7454, 0.0278, 0.5876, 0.7714, 0.82] +2026-04-11 16:31:56.802207: Epoch time: 102.59 s +2026-04-11 16:31:58.214462: +2026-04-11 16:31:58.219105: Epoch 1033 +2026-04-11 16:31:58.221236: Current learning rate: 0.00764 +2026-04-11 16:33:40.667237: train_loss -0.3121 +2026-04-11 16:33:40.673137: val_loss -0.273 +2026-04-11 16:33:40.675578: Pseudo dice [0.6294, 0.6889, 0.5874, 0.6913, 0.6549, 0.5729, 0.7315] +2026-04-11 16:33:40.678009: Epoch time: 102.46 s +2026-04-11 16:33:42.073331: +2026-04-11 16:33:42.075008: Epoch 1034 +2026-04-11 16:33:42.076870: Current learning rate: 0.00764 +2026-04-11 16:35:24.573803: train_loss -0.3062 +2026-04-11 16:35:24.578466: val_loss -0.2684 +2026-04-11 16:35:24.580158: Pseudo dice [0.3748, 0.8298, 0.5134, 0.766, 0.4371, 0.2128, 0.8721] +2026-04-11 16:35:24.582504: Epoch time: 102.5 s +2026-04-11 16:35:25.978713: +2026-04-11 16:35:25.981137: Epoch 1035 +2026-04-11 16:35:25.982746: Current learning rate: 0.00764 +2026-04-11 16:37:08.471625: train_loss -0.2985 +2026-04-11 16:37:08.478800: val_loss -0.2774 +2026-04-11 16:37:08.481128: Pseudo dice [0.5437, 0.6083, 0.5078, 0.2308, 0.5706, 0.2774, 0.7482] +2026-04-11 16:37:08.483628: Epoch time: 102.5 s +2026-04-11 16:37:09.856629: +2026-04-11 16:37:09.858842: Epoch 1036 +2026-04-11 16:37:09.860643: Current learning rate: 0.00764 +2026-04-11 16:38:52.289340: train_loss -0.2803 +2026-04-11 16:38:52.294966: val_loss -0.2448 +2026-04-11 16:38:52.297517: Pseudo dice [0.6239, 0.7079, 0.7172, 0.3284, 0.2246, 0.1977, 0.5983] +2026-04-11 16:38:52.300147: Epoch time: 102.44 s +2026-04-11 16:38:53.704282: +2026-04-11 16:38:53.705878: Epoch 1037 +2026-04-11 16:38:53.707467: Current learning rate: 0.00763 +2026-04-11 16:40:36.377438: train_loss -0.3057 +2026-04-11 16:40:36.382359: val_loss -0.2501 +2026-04-11 16:40:36.384293: Pseudo dice [0.3279, 0.8413, 0.4934, 0.8045, 0.4748, 0.0626, 0.7] +2026-04-11 16:40:36.386876: Epoch time: 102.68 s +2026-04-11 16:40:37.784441: +2026-04-11 16:40:37.786825: Epoch 1038 +2026-04-11 16:40:37.788735: Current learning rate: 0.00763 +2026-04-11 16:42:21.589895: train_loss -0.3089 +2026-04-11 16:42:21.599147: val_loss -0.2893 +2026-04-11 16:42:21.601409: Pseudo dice [0.5964, 0.591, 0.7043, 0.6919, 0.5211, 0.6706, 0.8749] +2026-04-11 16:42:21.604479: Epoch time: 103.81 s +2026-04-11 16:42:22.983948: +2026-04-11 16:42:22.985784: Epoch 1039 +2026-04-11 16:42:22.987882: Current learning rate: 0.00763 +2026-04-11 16:44:05.418076: train_loss -0.2948 +2026-04-11 16:44:05.423271: val_loss -0.176 +2026-04-11 16:44:05.425947: Pseudo dice [0.4668, 0.8194, 0.444, 0.519, 0.5316, 0.0819, 0.7541] +2026-04-11 16:44:05.428084: Epoch time: 102.44 s +2026-04-11 16:44:06.816813: +2026-04-11 16:44:06.819021: Epoch 1040 +2026-04-11 16:44:06.821140: Current learning rate: 0.00763 +2026-04-11 16:45:49.489940: train_loss -0.3019 +2026-04-11 16:45:49.496189: val_loss -0.2497 +2026-04-11 16:45:49.497885: Pseudo dice [0.57, 0.5439, 0.4817, 0.6117, 0.5581, 0.424, 0.6941] +2026-04-11 16:45:49.500387: Epoch time: 102.68 s +2026-04-11 16:45:50.934535: +2026-04-11 16:45:50.936839: Epoch 1041 +2026-04-11 16:45:50.938531: Current learning rate: 0.00762 +2026-04-11 16:47:33.341566: train_loss -0.2551 +2026-04-11 16:47:33.348255: val_loss -0.2162 +2026-04-11 16:47:33.350213: Pseudo dice [0.386, 0.3842, 0.6647, 0.6367, 0.4753, 0.1177, 0.7073] +2026-04-11 16:47:33.352640: Epoch time: 102.41 s +2026-04-11 16:47:34.758284: +2026-04-11 16:47:34.760515: Epoch 1042 +2026-04-11 16:47:34.762279: Current learning rate: 0.00762 +2026-04-11 16:49:17.349154: train_loss -0.2773 +2026-04-11 16:49:17.355639: val_loss -0.2753 +2026-04-11 16:49:17.357735: Pseudo dice [0.6742, 0.6325, 0.557, 0.2573, 0.519, 0.7845, 0.79] +2026-04-11 16:49:17.359813: Epoch time: 102.59 s +2026-04-11 16:49:18.752215: +2026-04-11 16:49:18.754304: Epoch 1043 +2026-04-11 16:49:18.755870: Current learning rate: 0.00762 +2026-04-11 16:51:01.465075: train_loss -0.2988 +2026-04-11 16:51:01.471616: val_loss -0.195 +2026-04-11 16:51:01.473894: Pseudo dice [0.2933, 0.1274, 0.4612, 0.1229, 0.4873, 0.0546, 0.8192] +2026-04-11 16:51:01.476546: Epoch time: 102.72 s +2026-04-11 16:51:02.871129: +2026-04-11 16:51:02.872990: Epoch 1044 +2026-04-11 16:51:02.874936: Current learning rate: 0.00762 +2026-04-11 16:52:45.076929: train_loss -0.2854 +2026-04-11 16:52:45.083957: val_loss -0.2189 +2026-04-11 16:52:45.086032: Pseudo dice [0.5161, 0.8931, 0.3934, 0.0367, 0.5043, 0.0998, 0.687] +2026-04-11 16:52:45.088940: Epoch time: 102.21 s +2026-04-11 16:52:46.482466: +2026-04-11 16:52:46.484165: Epoch 1045 +2026-04-11 16:52:46.485848: Current learning rate: 0.00761 +2026-04-11 16:54:29.062036: train_loss -0.2825 +2026-04-11 16:54:29.068327: val_loss -0.1802 +2026-04-11 16:54:29.070433: Pseudo dice [0.6652, 0.7666, 0.6674, 0.395, 0.3784, 0.0707, 0.5926] +2026-04-11 16:54:29.073608: Epoch time: 102.58 s +2026-04-11 16:54:30.470323: +2026-04-11 16:54:30.472155: Epoch 1046 +2026-04-11 16:54:30.473696: Current learning rate: 0.00761 +2026-04-11 16:56:12.693036: train_loss -0.2879 +2026-04-11 16:56:12.697998: val_loss -0.2994 +2026-04-11 16:56:12.699888: Pseudo dice [0.5925, 0.4491, 0.6697, 0.7047, 0.444, 0.6991, 0.6996] +2026-04-11 16:56:12.702371: Epoch time: 102.23 s +2026-04-11 16:56:14.120429: +2026-04-11 16:56:14.122369: Epoch 1047 +2026-04-11 16:56:14.124074: Current learning rate: 0.00761 +2026-04-11 16:57:56.564411: train_loss -0.2973 +2026-04-11 16:57:56.570279: val_loss -0.2424 +2026-04-11 16:57:56.572339: Pseudo dice [0.7659, 0.577, 0.5942, 0.6286, 0.5676, 0.0343, 0.7537] +2026-04-11 16:57:56.574951: Epoch time: 102.45 s +2026-04-11 16:57:58.004864: +2026-04-11 16:57:58.007026: Epoch 1048 +2026-04-11 16:57:58.008698: Current learning rate: 0.00761 +2026-04-11 16:59:40.320697: train_loss -0.2918 +2026-04-11 16:59:40.326257: val_loss -0.2135 +2026-04-11 16:59:40.329278: Pseudo dice [0.6277, 0.3451, 0.485, 0.5237, 0.4542, 0.1284, 0.7888] +2026-04-11 16:59:40.332528: Epoch time: 102.32 s +2026-04-11 16:59:41.723757: +2026-04-11 16:59:41.725603: Epoch 1049 +2026-04-11 16:59:41.727099: Current learning rate: 0.00761 +2026-04-11 17:01:24.112021: train_loss -0.2701 +2026-04-11 17:01:24.118103: val_loss -0.283 +2026-04-11 17:01:24.120107: Pseudo dice [0.5609, 0.5071, 0.5782, 0.6176, 0.6539, 0.7776, 0.5573] +2026-04-11 17:01:24.122286: Epoch time: 102.39 s +2026-04-11 17:01:27.422099: +2026-04-11 17:01:27.424119: Epoch 1050 +2026-04-11 17:01:27.425919: Current learning rate: 0.0076 +2026-04-11 17:03:09.783355: train_loss -0.2705 +2026-04-11 17:03:09.789222: val_loss -0.2304 +2026-04-11 17:03:09.791925: Pseudo dice [0.6473, 0.4018, 0.5054, 0.1866, 0.1569, 0.202, 0.3918] +2026-04-11 17:03:09.794121: Epoch time: 102.37 s +2026-04-11 17:03:11.199333: +2026-04-11 17:03:11.202120: Epoch 1051 +2026-04-11 17:03:11.203853: Current learning rate: 0.0076 +2026-04-11 17:04:53.706000: train_loss -0.2549 +2026-04-11 17:04:53.715087: val_loss -0.1384 +2026-04-11 17:04:53.717386: Pseudo dice [0.7982, 0.6945, 0.3856, 0.5826, 0.4869, 0.037, 0.8123] +2026-04-11 17:04:53.720004: Epoch time: 102.51 s +2026-04-11 17:04:55.238061: +2026-04-11 17:04:55.239795: Epoch 1052 +2026-04-11 17:04:55.241743: Current learning rate: 0.0076 +2026-04-11 17:06:37.814615: train_loss -0.2955 +2026-04-11 17:06:37.820765: val_loss -0.2504 +2026-04-11 17:06:37.822877: Pseudo dice [0.5235, 0.814, 0.5953, 0.1744, 0.5863, 0.3453, 0.7952] +2026-04-11 17:06:37.824849: Epoch time: 102.58 s +2026-04-11 17:06:39.232249: +2026-04-11 17:06:39.234313: Epoch 1053 +2026-04-11 17:06:39.236156: Current learning rate: 0.0076 +2026-04-11 17:08:21.719263: train_loss -0.3065 +2026-04-11 17:08:21.724788: val_loss -0.278 +2026-04-11 17:08:21.727197: Pseudo dice [0.8168, 0.2983, 0.6846, 0.1276, 0.6165, 0.7616, 0.6775] +2026-04-11 17:08:21.730374: Epoch time: 102.49 s +2026-04-11 17:08:23.149176: +2026-04-11 17:08:23.150949: Epoch 1054 +2026-04-11 17:08:23.153344: Current learning rate: 0.00759 +2026-04-11 17:10:05.890792: train_loss -0.3015 +2026-04-11 17:10:05.897333: val_loss -0.2693 +2026-04-11 17:10:05.899410: Pseudo dice [0.7688, 0.8148, 0.7144, 0.667, 0.3682, 0.5065, 0.6772] +2026-04-11 17:10:05.901721: Epoch time: 102.75 s +2026-04-11 17:10:07.302074: +2026-04-11 17:10:07.304226: Epoch 1055 +2026-04-11 17:10:07.305881: Current learning rate: 0.00759 +2026-04-11 17:11:49.849023: train_loss -0.3106 +2026-04-11 17:11:49.854422: val_loss -0.219 +2026-04-11 17:11:49.856364: Pseudo dice [0.6916, 0.438, 0.6569, 0.5474, 0.5704, 0.0595, 0.8606] +2026-04-11 17:11:49.858731: Epoch time: 102.55 s +2026-04-11 17:11:51.253448: +2026-04-11 17:11:51.255679: Epoch 1056 +2026-04-11 17:11:51.257702: Current learning rate: 0.00759 +2026-04-11 17:13:33.903040: train_loss -0.307 +2026-04-11 17:13:33.908850: val_loss -0.2474 +2026-04-11 17:13:33.911132: Pseudo dice [0.7787, 0.7577, 0.3753, 0.4675, 0.499, 0.351, 0.6178] +2026-04-11 17:13:33.913882: Epoch time: 102.65 s +2026-04-11 17:13:35.317674: +2026-04-11 17:13:35.320752: Epoch 1057 +2026-04-11 17:13:35.322697: Current learning rate: 0.00759 +2026-04-11 17:15:18.140376: train_loss -0.314 +2026-04-11 17:15:18.146593: val_loss -0.2679 +2026-04-11 17:15:18.148768: Pseudo dice [0.4401, 0.1907, 0.5728, 0.6719, 0.6122, 0.4871, 0.59] +2026-04-11 17:15:18.151363: Epoch time: 102.83 s +2026-04-11 17:15:19.561555: +2026-04-11 17:15:19.563621: Epoch 1058 +2026-04-11 17:15:19.565385: Current learning rate: 0.00758 +2026-04-11 17:17:03.247720: train_loss -0.3092 +2026-04-11 17:17:03.253110: val_loss -0.2678 +2026-04-11 17:17:03.255147: Pseudo dice [0.5847, 0.2252, 0.4733, 0.0799, 0.3977, 0.7503, 0.5112] +2026-04-11 17:17:03.257460: Epoch time: 103.69 s +2026-04-11 17:17:04.664188: +2026-04-11 17:17:04.666083: Epoch 1059 +2026-04-11 17:17:04.667867: Current learning rate: 0.00758 +2026-04-11 17:18:47.619220: train_loss -0.3042 +2026-04-11 17:18:47.626119: val_loss -0.2794 +2026-04-11 17:18:47.628027: Pseudo dice [0.8033, 0.62, 0.7662, 0.7072, 0.4227, 0.7629, 0.3308] +2026-04-11 17:18:47.630513: Epoch time: 102.96 s +2026-04-11 17:18:49.055749: +2026-04-11 17:18:49.057417: Epoch 1060 +2026-04-11 17:18:49.059222: Current learning rate: 0.00758 +2026-04-11 17:20:31.906506: train_loss -0.3015 +2026-04-11 17:20:31.913649: val_loss -0.2306 +2026-04-11 17:20:31.915872: Pseudo dice [0.3224, 0.58, 0.633, 0.5906, 0.4037, 0.2873, 0.5668] +2026-04-11 17:20:31.918347: Epoch time: 102.85 s +2026-04-11 17:20:33.361110: +2026-04-11 17:20:33.363181: Epoch 1061 +2026-04-11 17:20:33.364861: Current learning rate: 0.00758 +2026-04-11 17:22:16.583343: train_loss -0.3238 +2026-04-11 17:22:16.588840: val_loss -0.261 +2026-04-11 17:22:16.590554: Pseudo dice [0.7872, 0.5772, 0.6647, 0.814, 0.3542, 0.0936, 0.9035] +2026-04-11 17:22:16.593042: Epoch time: 103.23 s +2026-04-11 17:22:18.065464: +2026-04-11 17:22:18.067336: Epoch 1062 +2026-04-11 17:22:18.069944: Current learning rate: 0.00758 +2026-04-11 17:24:01.007106: train_loss -0.2847 +2026-04-11 17:24:01.013002: val_loss -0.2453 +2026-04-11 17:24:01.015535: Pseudo dice [0.5186, 0.3275, 0.7289, 0.547, 0.3515, 0.2635, 0.7478] +2026-04-11 17:24:01.018235: Epoch time: 102.95 s +2026-04-11 17:24:02.424201: +2026-04-11 17:24:02.427299: Epoch 1063 +2026-04-11 17:24:02.429479: Current learning rate: 0.00757 +2026-04-11 17:25:45.356454: train_loss -0.2867 +2026-04-11 17:25:45.361926: val_loss -0.2335 +2026-04-11 17:25:45.363841: Pseudo dice [0.6065, 0.5073, 0.6524, 0.7468, 0.4347, 0.2106, 0.7418] +2026-04-11 17:25:45.366198: Epoch time: 102.94 s +2026-04-11 17:25:46.787609: +2026-04-11 17:25:46.789228: Epoch 1064 +2026-04-11 17:25:46.790743: Current learning rate: 0.00757 +2026-04-11 17:27:29.899510: train_loss -0.3133 +2026-04-11 17:27:29.906830: val_loss -0.3017 +2026-04-11 17:27:29.908739: Pseudo dice [0.7321, 0.7177, 0.6085, 0.6832, 0.5696, 0.7652, 0.6976] +2026-04-11 17:27:29.911445: Epoch time: 103.12 s +2026-04-11 17:27:31.342988: +2026-04-11 17:27:31.344803: Epoch 1065 +2026-04-11 17:27:31.346428: Current learning rate: 0.00757 +2026-04-11 17:29:14.783460: train_loss -0.3127 +2026-04-11 17:29:14.792096: val_loss -0.2597 +2026-04-11 17:29:14.796147: Pseudo dice [0.8071, 0.4487, 0.7723, 0.8035, 0.5233, 0.1198, 0.7956] +2026-04-11 17:29:14.799002: Epoch time: 103.44 s +2026-04-11 17:29:16.282985: +2026-04-11 17:29:16.284724: Epoch 1066 +2026-04-11 17:29:16.286303: Current learning rate: 0.00757 +2026-04-11 17:30:59.179940: train_loss -0.3154 +2026-04-11 17:30:59.186579: val_loss -0.2866 +2026-04-11 17:30:59.188286: Pseudo dice [0.6148, 0.7676, 0.6064, 0.8505, 0.6425, 0.5795, 0.8029] +2026-04-11 17:30:59.190923: Epoch time: 102.9 s +2026-04-11 17:30:59.192869: Yayy! New best EMA pseudo Dice: 0.5707 +2026-04-11 17:31:02.582152: +2026-04-11 17:31:02.584217: Epoch 1067 +2026-04-11 17:31:02.585913: Current learning rate: 0.00756 +2026-04-11 17:32:45.213053: train_loss -0.2993 +2026-04-11 17:32:45.218808: val_loss -0.1159 +2026-04-11 17:32:45.220549: Pseudo dice [0.6498, 0.8074, 0.3267, 0.6549, 0.638, 0.0402, 0.7052] +2026-04-11 17:32:45.223792: Epoch time: 102.63 s +2026-04-11 17:32:46.634139: +2026-04-11 17:32:46.636311: Epoch 1068 +2026-04-11 17:32:46.637809: Current learning rate: 0.00756 +2026-04-11 17:34:29.575249: train_loss -0.2885 +2026-04-11 17:34:29.581232: val_loss -0.1072 +2026-04-11 17:34:29.583453: Pseudo dice [0.6319, 0.33, 0.4751, 0.6786, 0.5616, 0.0443, 0.7663] +2026-04-11 17:34:29.585840: Epoch time: 102.94 s +2026-04-11 17:34:31.004533: +2026-04-11 17:34:31.006814: Epoch 1069 +2026-04-11 17:34:31.008519: Current learning rate: 0.00756 +2026-04-11 17:36:13.872793: train_loss -0.3035 +2026-04-11 17:36:13.879293: val_loss -0.2973 +2026-04-11 17:36:13.881495: Pseudo dice [0.7528, 0.5757, 0.6951, 0.797, 0.6761, 0.7887, 0.6324] +2026-04-11 17:36:13.883646: Epoch time: 102.87 s +2026-04-11 17:36:13.885512: Yayy! New best EMA pseudo Dice: 0.5753 +2026-04-11 17:36:17.165504: +2026-04-11 17:36:17.168602: Epoch 1070 +2026-04-11 17:36:17.170158: Current learning rate: 0.00756 +2026-04-11 17:37:59.889961: train_loss -0.3006 +2026-04-11 17:37:59.895362: val_loss -0.2349 +2026-04-11 17:37:59.897105: Pseudo dice [0.6489, 0.5625, 0.7627, 0.0902, 0.3572, 0.4643, 0.5277] +2026-04-11 17:37:59.900134: Epoch time: 102.73 s +2026-04-11 17:38:01.376266: +2026-04-11 17:38:01.378110: Epoch 1071 +2026-04-11 17:38:01.379816: Current learning rate: 0.00755 +2026-04-11 17:39:44.087204: train_loss -0.2855 +2026-04-11 17:39:44.093383: val_loss -0.217 +2026-04-11 17:39:44.095960: Pseudo dice [0.7799, 0.4193, 0.5374, 0.0615, 0.4868, 0.1794, 0.5341] +2026-04-11 17:39:44.098552: Epoch time: 102.71 s +2026-04-11 17:39:45.486235: +2026-04-11 17:39:45.488338: Epoch 1072 +2026-04-11 17:39:45.490927: Current learning rate: 0.00755 +2026-04-11 17:41:28.199011: train_loss -0.2863 +2026-04-11 17:41:28.205628: val_loss -0.2938 +2026-04-11 17:41:28.208209: Pseudo dice [0.7148, 0.1467, 0.6681, 0.4486, 0.5811, 0.4054, 0.7607] +2026-04-11 17:41:28.210260: Epoch time: 102.72 s +2026-04-11 17:41:29.625579: +2026-04-11 17:41:29.627404: Epoch 1073 +2026-04-11 17:41:29.629326: Current learning rate: 0.00755 +2026-04-11 17:43:12.448663: train_loss -0.3056 +2026-04-11 17:43:12.455944: val_loss -0.2939 +2026-04-11 17:43:12.457874: Pseudo dice [0.4011, 0.6085, 0.6661, 0.4229, 0.5899, 0.6835, 0.8205] +2026-04-11 17:43:12.460702: Epoch time: 102.83 s +2026-04-11 17:43:13.907134: +2026-04-11 17:43:13.908833: Epoch 1074 +2026-04-11 17:43:13.910522: Current learning rate: 0.00755 +2026-04-11 17:44:56.417854: train_loss -0.2939 +2026-04-11 17:44:56.424305: val_loss -0.2724 +2026-04-11 17:44:56.426239: Pseudo dice [0.6796, 0.6207, 0.7928, 0.0417, 0.6302, 0.1857, 0.7405] +2026-04-11 17:44:56.428525: Epoch time: 102.52 s +2026-04-11 17:44:57.823025: +2026-04-11 17:44:57.824668: Epoch 1075 +2026-04-11 17:44:57.826519: Current learning rate: 0.00755 +2026-04-11 17:46:40.285172: train_loss -0.277 +2026-04-11 17:46:40.290353: val_loss -0.2422 +2026-04-11 17:46:40.292021: Pseudo dice [0.7165, 0.5891, 0.6042, 0.7602, 0.4746, 0.1207, 0.8465] +2026-04-11 17:46:40.294154: Epoch time: 102.47 s +2026-04-11 17:46:41.706394: +2026-04-11 17:46:41.709117: Epoch 1076 +2026-04-11 17:46:41.711155: Current learning rate: 0.00754 +2026-04-11 17:48:24.107939: train_loss -0.285 +2026-04-11 17:48:24.113793: val_loss -0.2624 +2026-04-11 17:48:24.116026: Pseudo dice [0.3417, 0.6205, 0.5324, 0.4542, 0.5871, 0.7239, 0.5571] +2026-04-11 17:48:24.119103: Epoch time: 102.41 s +2026-04-11 17:48:25.514405: +2026-04-11 17:48:25.516110: Epoch 1077 +2026-04-11 17:48:25.517741: Current learning rate: 0.00754 +2026-04-11 17:50:08.131278: train_loss -0.2841 +2026-04-11 17:50:08.140919: val_loss -0.2539 +2026-04-11 17:50:08.143074: Pseudo dice [0.4414, 0.5911, 0.609, 0.4253, 0.6186, 0.6632, 0.6745] +2026-04-11 17:50:08.145312: Epoch time: 102.62 s +2026-04-11 17:50:10.736051: +2026-04-11 17:50:10.738020: Epoch 1078 +2026-04-11 17:50:10.739779: Current learning rate: 0.00754 +2026-04-11 17:51:53.618876: train_loss -0.28 +2026-04-11 17:51:53.623693: val_loss -0.245 +2026-04-11 17:51:53.625564: Pseudo dice [0.5173, 0.8312, 0.7574, 0.6262, 0.4948, 0.4747, 0.388] +2026-04-11 17:51:53.627936: Epoch time: 102.89 s +2026-04-11 17:51:55.068504: +2026-04-11 17:51:55.070541: Epoch 1079 +2026-04-11 17:51:55.072130: Current learning rate: 0.00754 +2026-04-11 17:53:37.994641: train_loss -0.3093 +2026-04-11 17:53:38.000362: val_loss -0.1808 +2026-04-11 17:53:38.002673: Pseudo dice [0.818, 0.5304, 0.2762, 0.8183, 0.4553, 0.019, 0.7079] +2026-04-11 17:53:38.005236: Epoch time: 102.93 s +2026-04-11 17:53:39.440301: +2026-04-11 17:53:39.442439: Epoch 1080 +2026-04-11 17:53:39.444383: Current learning rate: 0.00753 +2026-04-11 17:55:22.622237: train_loss -0.3027 +2026-04-11 17:55:22.627519: val_loss -0.2483 +2026-04-11 17:55:22.629059: Pseudo dice [0.4514, 0.6603, 0.6955, 0.0397, 0.4633, 0.1307, 0.797] +2026-04-11 17:55:22.631402: Epoch time: 103.19 s +2026-04-11 17:55:24.052691: +2026-04-11 17:55:24.054354: Epoch 1081 +2026-04-11 17:55:24.055867: Current learning rate: 0.00753 +2026-04-11 17:57:06.789910: train_loss -0.2943 +2026-04-11 17:57:06.794962: val_loss -0.2081 +2026-04-11 17:57:06.796774: Pseudo dice [0.3525, 0.6731, 0.6283, 0.664, 0.4274, 0.2941, 0.8563] +2026-04-11 17:57:06.798710: Epoch time: 102.74 s +2026-04-11 17:57:08.203252: +2026-04-11 17:57:08.205220: Epoch 1082 +2026-04-11 17:57:08.206764: Current learning rate: 0.00753 +2026-04-11 17:58:51.119596: train_loss -0.2926 +2026-04-11 17:58:51.125168: val_loss -0.151 +2026-04-11 17:58:51.127092: Pseudo dice [0.3475, 0.2178, 0.6934, 0.7324, 0.5231, 0.0896, 0.7484] +2026-04-11 17:58:51.129359: Epoch time: 102.92 s +2026-04-11 17:58:52.534127: +2026-04-11 17:58:52.535996: Epoch 1083 +2026-04-11 17:58:52.537884: Current learning rate: 0.00753 +2026-04-11 18:00:35.595380: train_loss -0.2739 +2026-04-11 18:00:35.602469: val_loss -0.1888 +2026-04-11 18:00:35.605006: Pseudo dice [0.2159, 0.2737, 0.4744, 0.6713, 0.468, 0.1105, 0.6283] +2026-04-11 18:00:35.607247: Epoch time: 103.07 s +2026-04-11 18:00:37.015758: +2026-04-11 18:00:37.017903: Epoch 1084 +2026-04-11 18:00:37.019547: Current learning rate: 0.00752 +2026-04-11 18:02:19.616734: train_loss -0.2894 +2026-04-11 18:02:19.622822: val_loss -0.1541 +2026-04-11 18:02:19.625056: Pseudo dice [0.4103, 0.6742, 0.5105, 0.5717, 0.5935, 0.06, 0.8348] +2026-04-11 18:02:19.627197: Epoch time: 102.6 s +2026-04-11 18:02:21.022564: +2026-04-11 18:02:21.024703: Epoch 1085 +2026-04-11 18:02:21.026241: Current learning rate: 0.00752 +2026-04-11 18:04:03.541982: train_loss -0.3057 +2026-04-11 18:04:03.547720: val_loss -0.236 +2026-04-11 18:04:03.549914: Pseudo dice [0.6962, 0.4678, 0.5395, 0.3261, 0.3919, 0.1193, 0.5702] +2026-04-11 18:04:03.552309: Epoch time: 102.52 s +2026-04-11 18:04:04.973826: +2026-04-11 18:04:04.975609: Epoch 1086 +2026-04-11 18:04:04.977276: Current learning rate: 0.00752 +2026-04-11 18:05:47.473806: train_loss -0.3041 +2026-04-11 18:05:47.478892: val_loss -0.2434 +2026-04-11 18:05:47.481150: Pseudo dice [0.7045, 0.513, 0.5565, 0.2118, 0.5269, 0.1469, 0.8659] +2026-04-11 18:05:47.484095: Epoch time: 102.5 s +2026-04-11 18:05:48.953596: +2026-04-11 18:05:48.956295: Epoch 1087 +2026-04-11 18:05:48.957857: Current learning rate: 0.00752 +2026-04-11 18:07:31.615850: train_loss -0.2794 +2026-04-11 18:07:31.621727: val_loss -0.2433 +2026-04-11 18:07:31.623939: Pseudo dice [0.6933, 0.4929, 0.8204, 0.282, 0.4865, 0.1, 0.7702] +2026-04-11 18:07:31.626114: Epoch time: 102.67 s +2026-04-11 18:07:33.033072: +2026-04-11 18:07:33.034895: Epoch 1088 +2026-04-11 18:07:33.036868: Current learning rate: 0.00751 +2026-04-11 18:09:15.584879: train_loss -0.2939 +2026-04-11 18:09:15.591007: val_loss -0.2307 +2026-04-11 18:09:15.593036: Pseudo dice [0.5864, 0.6083, 0.6566, 0.7639, 0.4387, 0.052, 0.8052] +2026-04-11 18:09:15.595502: Epoch time: 102.56 s +2026-04-11 18:09:16.996631: +2026-04-11 18:09:16.998851: Epoch 1089 +2026-04-11 18:09:17.000589: Current learning rate: 0.00751 +2026-04-11 18:10:59.423506: train_loss -0.2747 +2026-04-11 18:10:59.428900: val_loss -0.2229 +2026-04-11 18:10:59.430582: Pseudo dice [0.5458, 0.2951, 0.5829, 0.0232, 0.4406, 0.1197, 0.7542] +2026-04-11 18:10:59.432994: Epoch time: 102.43 s +2026-04-11 18:11:00.839315: +2026-04-11 18:11:00.841245: Epoch 1090 +2026-04-11 18:11:00.843139: Current learning rate: 0.00751 +2026-04-11 18:12:43.315121: train_loss -0.3031 +2026-04-11 18:12:43.320929: val_loss -0.2764 +2026-04-11 18:12:43.323539: Pseudo dice [0.8273, 0.515, 0.5957, 0.7233, 0.4836, 0.3075, 0.6933] +2026-04-11 18:12:43.326121: Epoch time: 102.48 s +2026-04-11 18:12:44.723694: +2026-04-11 18:12:44.725225: Epoch 1091 +2026-04-11 18:12:44.726822: Current learning rate: 0.00751 +2026-04-11 18:14:27.342534: train_loss -0.2923 +2026-04-11 18:14:27.347481: val_loss -0.2167 +2026-04-11 18:14:27.349649: Pseudo dice [0.3262, 0.6834, 0.4477, 0.3263, 0.4574, 0.1992, 0.6472] +2026-04-11 18:14:27.351773: Epoch time: 102.62 s +2026-04-11 18:14:28.786379: +2026-04-11 18:14:28.788148: Epoch 1092 +2026-04-11 18:14:28.789710: Current learning rate: 0.00751 +2026-04-11 18:16:11.472458: train_loss -0.3035 +2026-04-11 18:16:11.481392: val_loss -0.2102 +2026-04-11 18:16:11.484447: Pseudo dice [0.5752, 0.2857, 0.5807, 0.1464, 0.5599, 0.0825, 0.7459] +2026-04-11 18:16:11.486937: Epoch time: 102.69 s +2026-04-11 18:16:12.916053: +2026-04-11 18:16:12.918095: Epoch 1093 +2026-04-11 18:16:12.919721: Current learning rate: 0.0075 +2026-04-11 18:17:55.684556: train_loss -0.3087 +2026-04-11 18:17:55.689923: val_loss -0.2782 +2026-04-11 18:17:55.691714: Pseudo dice [0.6353, 0.7197, 0.5399, 0.6253, 0.6079, 0.7997, 0.7923] +2026-04-11 18:17:55.693894: Epoch time: 102.77 s +2026-04-11 18:17:57.093294: +2026-04-11 18:17:57.095170: Epoch 1094 +2026-04-11 18:17:57.097001: Current learning rate: 0.0075 +2026-04-11 18:19:39.597399: train_loss -0.3067 +2026-04-11 18:19:39.602982: val_loss -0.2569 +2026-04-11 18:19:39.604630: Pseudo dice [0.7521, 0.7754, 0.6038, 0.7792, 0.5367, 0.1965, 0.7802] +2026-04-11 18:19:39.606894: Epoch time: 102.51 s +2026-04-11 18:19:41.011620: +2026-04-11 18:19:41.013362: Epoch 1095 +2026-04-11 18:19:41.015028: Current learning rate: 0.0075 +2026-04-11 18:21:23.460233: train_loss -0.3059 +2026-04-11 18:21:23.465111: val_loss -0.275 +2026-04-11 18:21:23.466878: Pseudo dice [0.5884, 0.8557, 0.6534, 0.7176, 0.5387, 0.4583, 0.5554] +2026-04-11 18:21:23.468929: Epoch time: 102.45 s +2026-04-11 18:21:24.881051: +2026-04-11 18:21:24.883099: Epoch 1096 +2026-04-11 18:21:24.884838: Current learning rate: 0.0075 +2026-04-11 18:23:07.165177: train_loss -0.2986 +2026-04-11 18:23:07.170794: val_loss -0.1792 +2026-04-11 18:23:07.172500: Pseudo dice [0.2605, 0.4855, 0.5368, 0.2413, 0.5156, 0.2558, 0.709] +2026-04-11 18:23:07.174576: Epoch time: 102.29 s +2026-04-11 18:23:08.556402: +2026-04-11 18:23:08.558379: Epoch 1097 +2026-04-11 18:23:08.560014: Current learning rate: 0.00749 +2026-04-11 18:24:50.963732: train_loss -0.3053 +2026-04-11 18:24:50.969727: val_loss -0.2147 +2026-04-11 18:24:50.971578: Pseudo dice [0.4477, 0.658, 0.6821, 0.0154, 0.3403, 0.0679, 0.8751] +2026-04-11 18:24:50.973745: Epoch time: 102.41 s +2026-04-11 18:24:52.363444: +2026-04-11 18:24:52.365314: Epoch 1098 +2026-04-11 18:24:52.366966: Current learning rate: 0.00749 +2026-04-11 18:26:35.740780: train_loss -0.2773 +2026-04-11 18:26:35.746585: val_loss -0.2947 +2026-04-11 18:26:35.749180: Pseudo dice [0.5385, 0.3452, 0.7484, 0.5768, 0.5141, 0.7376, 0.3841] +2026-04-11 18:26:35.751307: Epoch time: 103.38 s +2026-04-11 18:26:37.205095: +2026-04-11 18:26:37.208159: Epoch 1099 +2026-04-11 18:26:37.210268: Current learning rate: 0.00749 +2026-04-11 18:28:19.742778: train_loss -0.2887 +2026-04-11 18:28:19.747503: val_loss -0.2307 +2026-04-11 18:28:19.749599: Pseudo dice [0.6627, 0.5785, 0.5874, 0.0896, 0.2491, 0.2785, 0.7968] +2026-04-11 18:28:19.752146: Epoch time: 102.54 s +2026-04-11 18:28:23.049180: +2026-04-11 18:28:23.051018: Epoch 1100 +2026-04-11 18:28:23.052498: Current learning rate: 0.00749 +2026-04-11 18:30:05.547611: train_loss -0.292 +2026-04-11 18:30:05.552687: val_loss -0.2644 +2026-04-11 18:30:05.554779: Pseudo dice [0.7862, 0.7102, 0.7141, 0.6777, 0.6465, 0.502, 0.7481] +2026-04-11 18:30:05.557627: Epoch time: 102.5 s +2026-04-11 18:30:06.972836: +2026-04-11 18:30:06.975590: Epoch 1101 +2026-04-11 18:30:06.977505: Current learning rate: 0.00748 +2026-04-11 18:31:49.385038: train_loss -0.3013 +2026-04-11 18:31:49.390920: val_loss -0.2376 +2026-04-11 18:31:49.393190: Pseudo dice [0.4448, 0.4236, 0.2653, 0.7297, 0.5757, 0.1214, 0.6353] +2026-04-11 18:31:49.396104: Epoch time: 102.42 s +2026-04-11 18:31:50.804905: +2026-04-11 18:31:50.806542: Epoch 1102 +2026-04-11 18:31:50.808103: Current learning rate: 0.00748 +2026-04-11 18:33:33.268155: train_loss -0.3004 +2026-04-11 18:33:33.272972: val_loss -0.2301 +2026-04-11 18:33:33.275907: Pseudo dice [0.6599, 0.3715, 0.5253, 0.7694, 0.5274, 0.0595, 0.8095] +2026-04-11 18:33:33.278226: Epoch time: 102.47 s +2026-04-11 18:33:34.684159: +2026-04-11 18:33:34.686000: Epoch 1103 +2026-04-11 18:33:34.687697: Current learning rate: 0.00748 +2026-04-11 18:35:17.008435: train_loss -0.3067 +2026-04-11 18:35:17.013980: val_loss -0.1796 +2026-04-11 18:35:17.015940: Pseudo dice [0.261, 0.5812, 0.3975, 0.7786, 0.436, 0.0643, 0.7667] +2026-04-11 18:35:17.018869: Epoch time: 102.33 s +2026-04-11 18:35:18.430896: +2026-04-11 18:35:18.432712: Epoch 1104 +2026-04-11 18:35:18.434302: Current learning rate: 0.00748 +2026-04-11 18:37:00.708746: train_loss -0.2984 +2026-04-11 18:37:00.714371: val_loss -0.2925 +2026-04-11 18:37:00.716454: Pseudo dice [0.4034, 0.8078, 0.7194, 0.6392, 0.5791, 0.4933, 0.7743] +2026-04-11 18:37:00.718921: Epoch time: 102.28 s +2026-04-11 18:37:02.115196: +2026-04-11 18:37:02.116922: Epoch 1105 +2026-04-11 18:37:02.118890: Current learning rate: 0.00748 +2026-04-11 18:38:44.404679: train_loss -0.3023 +2026-04-11 18:38:44.409490: val_loss -0.2421 +2026-04-11 18:38:44.411340: Pseudo dice [0.6418, 0.7206, 0.689, 0.0037, 0.2781, 0.0203, 0.8741] +2026-04-11 18:38:44.413247: Epoch time: 102.29 s +2026-04-11 18:38:45.847398: +2026-04-11 18:38:45.849375: Epoch 1106 +2026-04-11 18:38:45.851032: Current learning rate: 0.00747 +2026-04-11 18:40:28.210961: train_loss -0.3041 +2026-04-11 18:40:28.217505: val_loss -0.2128 +2026-04-11 18:40:28.219472: Pseudo dice [0.658, 0.3137, 0.4698, 0.7024, 0.4432, 0.0292, 0.7092] +2026-04-11 18:40:28.222345: Epoch time: 102.37 s +2026-04-11 18:40:29.633375: +2026-04-11 18:40:29.635517: Epoch 1107 +2026-04-11 18:40:29.637345: Current learning rate: 0.00747 +2026-04-11 18:42:11.983305: train_loss -0.3065 +2026-04-11 18:42:11.991126: val_loss -0.1929 +2026-04-11 18:42:11.993358: Pseudo dice [0.7582, 0.5323, 0.293, 0.8973, 0.4881, 0.0574, 0.7648] +2026-04-11 18:42:11.996096: Epoch time: 102.35 s +2026-04-11 18:42:13.480039: +2026-04-11 18:42:13.481853: Epoch 1108 +2026-04-11 18:42:13.483521: Current learning rate: 0.00747 +2026-04-11 18:43:55.961772: train_loss -0.2984 +2026-04-11 18:43:55.966881: val_loss -0.2241 +2026-04-11 18:43:55.968643: Pseudo dice [0.367, 0.622, 0.7905, 0.002, 0.4273, 0.3168, 0.5269] +2026-04-11 18:43:55.970648: Epoch time: 102.49 s +2026-04-11 18:43:57.382942: +2026-04-11 18:43:57.384677: Epoch 1109 +2026-04-11 18:43:57.386216: Current learning rate: 0.00747 +2026-04-11 18:45:39.772877: train_loss -0.2961 +2026-04-11 18:45:39.777838: val_loss -0.2124 +2026-04-11 18:45:39.779685: Pseudo dice [0.4819, 0.4553, 0.3064, 0.593, 0.4838, 0.0651, 0.888] +2026-04-11 18:45:39.781652: Epoch time: 102.39 s +2026-04-11 18:45:41.183089: +2026-04-11 18:45:41.185000: Epoch 1110 +2026-04-11 18:45:41.186809: Current learning rate: 0.00746 +2026-04-11 18:47:23.695709: train_loss -0.276 +2026-04-11 18:47:23.701048: val_loss -0.1375 +2026-04-11 18:47:23.703285: Pseudo dice [0.7086, 0.5277, 0.5667, 0.1005, 0.1977, 0.0313, 0.5116] +2026-04-11 18:47:23.705914: Epoch time: 102.52 s +2026-04-11 18:47:25.116204: +2026-04-11 18:47:25.118524: Epoch 1111 +2026-04-11 18:47:25.120206: Current learning rate: 0.00746 +2026-04-11 18:49:07.261006: train_loss -0.2932 +2026-04-11 18:49:07.266418: val_loss -0.221 +2026-04-11 18:49:07.268757: Pseudo dice [0.7991, 0.637, 0.5986, 0.6461, 0.6339, 0.1003, 0.8137] +2026-04-11 18:49:07.271537: Epoch time: 102.15 s +2026-04-11 18:49:08.679441: +2026-04-11 18:49:08.681878: Epoch 1112 +2026-04-11 18:49:08.684036: Current learning rate: 0.00746 +2026-04-11 18:50:51.326886: train_loss -0.2656 +2026-04-11 18:50:51.331893: val_loss -0.2543 +2026-04-11 18:50:51.333696: Pseudo dice [0.6008, 0.529, 0.5777, 0.5598, 0.4793, 0.318, 0.5895] +2026-04-11 18:50:51.336389: Epoch time: 102.65 s +2026-04-11 18:50:52.763224: +2026-04-11 18:50:52.765014: Epoch 1113 +2026-04-11 18:50:52.766634: Current learning rate: 0.00746 +2026-04-11 18:52:34.854536: train_loss -0.2973 +2026-04-11 18:52:34.860172: val_loss -0.2775 +2026-04-11 18:52:34.861904: Pseudo dice [0.2412, 0.6739, 0.7154, 0.6289, 0.653, 0.2881, 0.8457] +2026-04-11 18:52:34.863844: Epoch time: 102.1 s +2026-04-11 18:52:36.269077: +2026-04-11 18:52:36.270826: Epoch 1114 +2026-04-11 18:52:36.272344: Current learning rate: 0.00745 +2026-04-11 18:54:18.480075: train_loss -0.3096 +2026-04-11 18:54:18.485256: val_loss -0.183 +2026-04-11 18:54:18.487377: Pseudo dice [0.686, 0.7587, 0.6316, 0.8167, 0.4696, 0.0413, 0.6454] +2026-04-11 18:54:18.490042: Epoch time: 102.21 s +2026-04-11 18:54:19.902540: +2026-04-11 18:54:19.904899: Epoch 1115 +2026-04-11 18:54:19.906821: Current learning rate: 0.00745 +2026-04-11 18:56:02.293250: train_loss -0.3007 +2026-04-11 18:56:02.300370: val_loss -0.1865 +2026-04-11 18:56:02.302378: Pseudo dice [0.3176, 0.5463, 0.5792, 0.6117, 0.5342, 0.0698, 0.8067] +2026-04-11 18:56:02.304794: Epoch time: 102.39 s +2026-04-11 18:56:03.685718: +2026-04-11 18:56:03.687783: Epoch 1116 +2026-04-11 18:56:03.689380: Current learning rate: 0.00745 +2026-04-11 18:57:45.885802: train_loss -0.2922 +2026-04-11 18:57:45.892292: val_loss -0.189 +2026-04-11 18:57:45.894757: Pseudo dice [0.2921, 0.4964, 0.3862, 0.0424, 0.1699, 0.0702, 0.4497] +2026-04-11 18:57:45.897123: Epoch time: 102.2 s +2026-04-11 18:57:47.303607: +2026-04-11 18:57:47.305571: Epoch 1117 +2026-04-11 18:57:47.311543: Current learning rate: 0.00745 +2026-04-11 18:59:29.441040: train_loss -0.312 +2026-04-11 18:59:29.446049: val_loss -0.2303 +2026-04-11 18:59:29.447936: Pseudo dice [0.3683, 0.5074, 0.626, 0.1505, 0.3517, 0.2155, 0.6367] +2026-04-11 18:59:29.450325: Epoch time: 102.14 s +2026-04-11 18:59:30.859250: +2026-04-11 18:59:30.861561: Epoch 1118 +2026-04-11 18:59:30.863438: Current learning rate: 0.00745 +2026-04-11 19:01:13.033054: train_loss -0.2918 +2026-04-11 19:01:13.064010: val_loss -0.2766 +2026-04-11 19:01:13.065801: Pseudo dice [0.4607, 0.3663, 0.6695, 0.2827, 0.6345, 0.759, 0.7069] +2026-04-11 19:01:13.068111: Epoch time: 102.18 s +2026-04-11 19:01:15.438386: +2026-04-11 19:01:15.440050: Epoch 1119 +2026-04-11 19:01:15.441698: Current learning rate: 0.00744 +2026-04-11 19:02:57.805746: train_loss -0.2991 +2026-04-11 19:02:57.810833: val_loss -0.2786 +2026-04-11 19:02:57.813056: Pseudo dice [0.7147, 0.2781, 0.5681, 0.6656, 0.6191, 0.7137, 0.8252] +2026-04-11 19:02:57.815796: Epoch time: 102.37 s +2026-04-11 19:02:59.225019: +2026-04-11 19:02:59.240267: Epoch 1120 +2026-04-11 19:02:59.242791: Current learning rate: 0.00744 +2026-04-11 19:04:41.635562: train_loss -0.283 +2026-04-11 19:04:41.641155: val_loss -0.2624 +2026-04-11 19:04:41.643595: Pseudo dice [0.1998, 0.5465, 0.5572, 0.6369, 0.5634, 0.8115, 0.8946] +2026-04-11 19:04:41.646076: Epoch time: 102.41 s +2026-04-11 19:04:43.082937: +2026-04-11 19:04:43.084728: Epoch 1121 +2026-04-11 19:04:43.087163: Current learning rate: 0.00744 +2026-04-11 19:06:25.235909: train_loss -0.2952 +2026-04-11 19:06:25.241409: val_loss -0.2585 +2026-04-11 19:06:25.243783: Pseudo dice [0.7708, 0.8566, 0.8319, 0.7182, 0.6344, 0.1643, 0.7352] +2026-04-11 19:06:25.246328: Epoch time: 102.16 s +2026-04-11 19:06:26.659125: +2026-04-11 19:06:26.662671: Epoch 1122 +2026-04-11 19:06:26.664430: Current learning rate: 0.00744 +2026-04-11 19:08:08.882353: train_loss -0.3087 +2026-04-11 19:08:08.887433: val_loss -0.2741 +2026-04-11 19:08:08.889598: Pseudo dice [0.7737, 0.3453, 0.7628, 0.8232, 0.6616, 0.1532, 0.8029] +2026-04-11 19:08:08.892146: Epoch time: 102.23 s +2026-04-11 19:08:10.287549: +2026-04-11 19:08:10.289528: Epoch 1123 +2026-04-11 19:08:10.291685: Current learning rate: 0.00743 +2026-04-11 19:09:52.628559: train_loss -0.3145 +2026-04-11 19:09:52.633958: val_loss -0.2401 +2026-04-11 19:09:52.636391: Pseudo dice [0.7576, 0.607, 0.7917, 0.7593, 0.5351, 0.0365, 0.5748] +2026-04-11 19:09:52.639446: Epoch time: 102.34 s +2026-04-11 19:09:54.036456: +2026-04-11 19:09:54.038460: Epoch 1124 +2026-04-11 19:09:54.040137: Current learning rate: 0.00743 +2026-04-11 19:11:36.506450: train_loss -0.3031 +2026-04-11 19:11:36.511342: val_loss -0.3075 +2026-04-11 19:11:36.513071: Pseudo dice [0.6497, 0.4084, 0.6468, 0.4023, 0.5904, 0.8481, 0.7676] +2026-04-11 19:11:36.515855: Epoch time: 102.47 s +2026-04-11 19:11:37.923258: +2026-04-11 19:11:37.925084: Epoch 1125 +2026-04-11 19:11:37.927321: Current learning rate: 0.00743 +2026-04-11 19:13:20.108590: train_loss -0.287 +2026-04-11 19:13:20.115260: val_loss -0.211 +2026-04-11 19:13:20.118048: Pseudo dice [0.4069, 0.5583, 0.6948, 0.4191, 0.4937, 0.0592, 0.5599] +2026-04-11 19:13:20.120689: Epoch time: 102.19 s +2026-04-11 19:13:21.518785: +2026-04-11 19:13:21.520998: Epoch 1126 +2026-04-11 19:13:21.523084: Current learning rate: 0.00743 +2026-04-11 19:15:03.742667: train_loss -0.2856 +2026-04-11 19:15:03.748391: val_loss -0.2488 +2026-04-11 19:15:03.750151: Pseudo dice [0.6976, 0.2552, 0.7022, 0.5624, 0.4985, 0.268, 0.7731] +2026-04-11 19:15:03.752065: Epoch time: 102.23 s +2026-04-11 19:15:05.173759: +2026-04-11 19:15:05.175769: Epoch 1127 +2026-04-11 19:15:05.177617: Current learning rate: 0.00742 +2026-04-11 19:16:47.508607: train_loss -0.3079 +2026-04-11 19:16:47.513776: val_loss -0.2699 +2026-04-11 19:16:47.515741: Pseudo dice [0.4635, 0.5479, 0.5267, 0.6726, 0.4252, 0.5931, 0.3275] +2026-04-11 19:16:47.517872: Epoch time: 102.34 s +2026-04-11 19:16:48.932276: +2026-04-11 19:16:48.936452: Epoch 1128 +2026-04-11 19:16:48.940353: Current learning rate: 0.00742 +2026-04-11 19:18:31.323087: train_loss -0.2964 +2026-04-11 19:18:31.328395: val_loss -0.2096 +2026-04-11 19:18:31.330332: Pseudo dice [0.656, 0.5807, 0.4749, 0.3597, 0.622, 0.0528, 0.6913] +2026-04-11 19:18:31.332752: Epoch time: 102.39 s +2026-04-11 19:18:32.735372: +2026-04-11 19:18:32.737120: Epoch 1129 +2026-04-11 19:18:32.738860: Current learning rate: 0.00742 +2026-04-11 19:20:14.989107: train_loss -0.2864 +2026-04-11 19:20:14.996539: val_loss -0.2665 +2026-04-11 19:20:14.998508: Pseudo dice [0.3439, 0.7386, 0.5492, 0.4953, 0.5793, 0.7438, 0.8367] +2026-04-11 19:20:15.000875: Epoch time: 102.26 s +2026-04-11 19:20:16.392676: +2026-04-11 19:20:16.394839: Epoch 1130 +2026-04-11 19:20:16.396263: Current learning rate: 0.00742 +2026-04-11 19:21:58.618158: train_loss -0.3142 +2026-04-11 19:21:58.623356: val_loss -0.2224 +2026-04-11 19:21:58.625633: Pseudo dice [0.7883, 0.7474, 0.7177, 0.5306, 0.4789, 0.0545, 0.7808] +2026-04-11 19:21:58.628211: Epoch time: 102.23 s +2026-04-11 19:22:00.034722: +2026-04-11 19:22:00.036593: Epoch 1131 +2026-04-11 19:22:00.038277: Current learning rate: 0.00741 +2026-04-11 19:23:42.173916: train_loss -0.2988 +2026-04-11 19:23:42.179174: val_loss -0.2013 +2026-04-11 19:23:42.181329: Pseudo dice [0.4147, 0.481, 0.5568, 0.8274, 0.5379, 0.0114, 0.8172] +2026-04-11 19:23:42.183846: Epoch time: 102.14 s +2026-04-11 19:23:43.594691: +2026-04-11 19:23:43.596639: Epoch 1132 +2026-04-11 19:23:43.598471: Current learning rate: 0.00741 +2026-04-11 19:25:25.839517: train_loss -0.3077 +2026-04-11 19:25:25.844561: val_loss -0.1787 +2026-04-11 19:25:25.846891: Pseudo dice [0.3765, 0.7111, 0.7081, 0.6581, 0.3743, 0.0563, 0.6055] +2026-04-11 19:25:25.849957: Epoch time: 102.25 s +2026-04-11 19:25:27.262429: +2026-04-11 19:25:27.264063: Epoch 1133 +2026-04-11 19:25:27.265735: Current learning rate: 0.00741 +2026-04-11 19:27:09.589753: train_loss -0.3023 +2026-04-11 19:27:09.595994: val_loss -0.2724 +2026-04-11 19:27:09.598243: Pseudo dice [0.7255, 0.3067, 0.6057, 0.5552, 0.342, 0.8388, 0.599] +2026-04-11 19:27:09.600597: Epoch time: 102.33 s +2026-04-11 19:27:11.005256: +2026-04-11 19:27:11.006871: Epoch 1134 +2026-04-11 19:27:11.008529: Current learning rate: 0.00741 +2026-04-11 19:28:53.168970: train_loss -0.2998 +2026-04-11 19:28:53.175970: val_loss -0.2562 +2026-04-11 19:28:53.178519: Pseudo dice [0.532, 0.6272, 0.5918, 0.7479, 0.6408, 0.1833, 0.4009] +2026-04-11 19:28:53.181595: Epoch time: 102.17 s +2026-04-11 19:28:54.580725: +2026-04-11 19:28:54.583158: Epoch 1135 +2026-04-11 19:28:54.584934: Current learning rate: 0.00741 +2026-04-11 19:30:36.964885: train_loss -0.2936 +2026-04-11 19:30:36.992674: val_loss -0.2624 +2026-04-11 19:30:36.995913: Pseudo dice [0.7983, 0.6046, 0.5524, 0.6763, 0.6937, 0.2196, 0.6746] +2026-04-11 19:30:36.998249: Epoch time: 102.39 s +2026-04-11 19:30:38.402984: +2026-04-11 19:30:38.404802: Epoch 1136 +2026-04-11 19:30:38.406477: Current learning rate: 0.0074 +2026-04-11 19:32:20.593724: train_loss -0.3037 +2026-04-11 19:32:20.600387: val_loss -0.2763 +2026-04-11 19:32:20.602631: Pseudo dice [0.5062, 0.6143, 0.8104, 0.1497, 0.2951, 0.7452, 0.6022] +2026-04-11 19:32:20.604780: Epoch time: 102.19 s +2026-04-11 19:32:22.016955: +2026-04-11 19:32:22.018796: Epoch 1137 +2026-04-11 19:32:22.020276: Current learning rate: 0.0074 +2026-04-11 19:34:04.306268: train_loss -0.2843 +2026-04-11 19:34:04.311386: val_loss -0.2904 +2026-04-11 19:34:04.313066: Pseudo dice [0.6966, 0.7126, 0.71, 0.5847, 0.4609, 0.5971, 0.798] +2026-04-11 19:34:04.315861: Epoch time: 102.29 s +2026-04-11 19:34:05.728433: +2026-04-11 19:34:05.730074: Epoch 1138 +2026-04-11 19:34:05.731884: Current learning rate: 0.0074 +2026-04-11 19:35:48.012224: train_loss -0.307 +2026-04-11 19:35:48.017179: val_loss -0.2678 +2026-04-11 19:35:48.019213: Pseudo dice [0.7139, 0.3712, 0.6764, 0.0633, 0.5285, 0.3839, 0.4785] +2026-04-11 19:35:48.021511: Epoch time: 102.29 s +2026-04-11 19:35:49.451970: +2026-04-11 19:35:49.454104: Epoch 1139 +2026-04-11 19:35:49.455806: Current learning rate: 0.0074 +2026-04-11 19:37:32.825372: train_loss -0.2913 +2026-04-11 19:37:32.830535: val_loss -0.2622 +2026-04-11 19:37:32.832498: Pseudo dice [0.7071, 0.8717, 0.4991, 0.7935, 0.4129, 0.2107, 0.6143] +2026-04-11 19:37:32.834584: Epoch time: 103.38 s +2026-04-11 19:37:34.248511: +2026-04-11 19:37:34.250849: Epoch 1140 +2026-04-11 19:37:34.253165: Current learning rate: 0.00739 +2026-04-11 19:39:16.560049: train_loss -0.3094 +2026-04-11 19:39:16.564759: val_loss -0.2545 +2026-04-11 19:39:16.567648: Pseudo dice [0.5863, 0.4354, 0.5602, 0.6273, 0.6071, 0.1724, 0.7312] +2026-04-11 19:39:16.570060: Epoch time: 102.32 s +2026-04-11 19:39:17.964359: +2026-04-11 19:39:17.966016: Epoch 1141 +2026-04-11 19:39:17.967555: Current learning rate: 0.00739 +2026-04-11 19:41:00.325828: train_loss -0.3022 +2026-04-11 19:41:00.332961: val_loss -0.2488 +2026-04-11 19:41:00.335928: Pseudo dice [0.4125, 0.4799, 0.6953, 0.5672, 0.4062, 0.7486, 0.7916] +2026-04-11 19:41:00.339132: Epoch time: 102.37 s +2026-04-11 19:41:01.749338: +2026-04-11 19:41:01.751322: Epoch 1142 +2026-04-11 19:41:01.752932: Current learning rate: 0.00739 +2026-04-11 19:42:44.044310: train_loss -0.2954 +2026-04-11 19:42:44.050250: val_loss -0.2815 +2026-04-11 19:42:44.052829: Pseudo dice [0.6083, 0.5526, 0.6822, 0.6261, 0.4943, 0.3773, 0.7924] +2026-04-11 19:42:44.055259: Epoch time: 102.3 s +2026-04-11 19:42:45.456292: +2026-04-11 19:42:45.459097: Epoch 1143 +2026-04-11 19:42:45.460814: Current learning rate: 0.00739 +2026-04-11 19:44:27.749826: train_loss -0.3053 +2026-04-11 19:44:27.755022: val_loss -0.2067 +2026-04-11 19:44:27.756730: Pseudo dice [0.3251, 0.1854, 0.5488, 0.2798, 0.4047, 0.0551, 0.7611] +2026-04-11 19:44:27.758596: Epoch time: 102.3 s +2026-04-11 19:44:29.199675: +2026-04-11 19:44:29.201317: Epoch 1144 +2026-04-11 19:44:29.203008: Current learning rate: 0.00738 +2026-04-11 19:46:11.826396: train_loss -0.2868 +2026-04-11 19:46:11.831211: val_loss -0.2588 +2026-04-11 19:46:11.832860: Pseudo dice [0.6095, 0.7321, 0.4981, 0.6384, 0.5953, 0.0901, 0.8699] +2026-04-11 19:46:11.835238: Epoch time: 102.63 s +2026-04-11 19:46:13.262131: +2026-04-11 19:46:13.264566: Epoch 1145 +2026-04-11 19:46:13.266245: Current learning rate: 0.00738 +2026-04-11 19:47:55.917244: train_loss -0.264 +2026-04-11 19:47:55.923540: val_loss -0.2045 +2026-04-11 19:47:55.925887: Pseudo dice [0.5086, 0.3464, 0.5603, 0.1446, 0.3931, 0.148, 0.699] +2026-04-11 19:47:55.928211: Epoch time: 102.66 s +2026-04-11 19:47:57.363036: +2026-04-11 19:47:57.366412: Epoch 1146 +2026-04-11 19:47:57.369002: Current learning rate: 0.00738 +2026-04-11 19:49:40.466989: train_loss -0.272 +2026-04-11 19:49:40.472780: val_loss -0.2484 +2026-04-11 19:49:40.474890: Pseudo dice [0.402, 0.3493, 0.6577, 0.5751, 0.4417, 0.2833, 0.6208] +2026-04-11 19:49:40.477327: Epoch time: 103.11 s +2026-04-11 19:49:41.933071: +2026-04-11 19:49:41.935237: Epoch 1147 +2026-04-11 19:49:41.937067: Current learning rate: 0.00738 +2026-04-11 19:51:24.749009: train_loss -0.2742 +2026-04-11 19:51:24.753983: val_loss -0.1513 +2026-04-11 19:51:24.755960: Pseudo dice [0.7594, 0.7461, 0.4295, 0.7017, 0.4207, 0.0024, 0.5866] +2026-04-11 19:51:24.758849: Epoch time: 102.82 s +2026-04-11 19:51:26.198061: +2026-04-11 19:51:26.199644: Epoch 1148 +2026-04-11 19:51:26.201225: Current learning rate: 0.00738 +2026-04-11 19:53:08.470881: train_loss -0.2846 +2026-04-11 19:53:08.476475: val_loss -0.3087 +2026-04-11 19:53:08.479745: Pseudo dice [0.358, 0.7071, 0.7074, 0.7786, 0.6393, 0.7676, 0.8724] +2026-04-11 19:53:08.482137: Epoch time: 102.28 s +2026-04-11 19:53:09.904809: +2026-04-11 19:53:09.907402: Epoch 1149 +2026-04-11 19:53:09.909263: Current learning rate: 0.00737 +2026-04-11 19:54:52.206086: train_loss -0.2964 +2026-04-11 19:54:52.213703: val_loss -0.2353 +2026-04-11 19:54:52.215748: Pseudo dice [0.5841, 0.525, 0.7582, 0.3027, 0.4258, 0.1045, 0.6038] +2026-04-11 19:54:52.220891: Epoch time: 102.31 s +2026-04-11 19:54:55.431338: +2026-04-11 19:54:55.433138: Epoch 1150 +2026-04-11 19:54:55.434773: Current learning rate: 0.00737 +2026-04-11 19:56:37.687675: train_loss -0.263 +2026-04-11 19:56:37.692978: val_loss -0.1925 +2026-04-11 19:56:37.694978: Pseudo dice [0.5807, 0.5039, 0.3986, 0.6283, 0.5504, 0.0247, 0.6761] +2026-04-11 19:56:37.697102: Epoch time: 102.26 s +2026-04-11 19:56:39.124193: +2026-04-11 19:56:39.125935: Epoch 1151 +2026-04-11 19:56:39.127600: Current learning rate: 0.00737 +2026-04-11 19:58:21.443166: train_loss -0.3001 +2026-04-11 19:58:21.450582: val_loss -0.2663 +2026-04-11 19:58:21.453827: Pseudo dice [0.5681, 0.455, 0.7116, 0.2146, 0.4582, 0.6534, 0.589] +2026-04-11 19:58:21.456955: Epoch time: 102.32 s +2026-04-11 19:58:22.896973: +2026-04-11 19:58:22.898572: Epoch 1152 +2026-04-11 19:58:22.900159: Current learning rate: 0.00737 +2026-04-11 20:00:05.383896: train_loss -0.2882 +2026-04-11 20:00:05.391633: val_loss -0.2799 +2026-04-11 20:00:05.393755: Pseudo dice [0.3371, 0.4829, 0.7408, 0.7888, 0.5542, 0.813, 0.8776] +2026-04-11 20:00:05.397681: Epoch time: 102.49 s +2026-04-11 20:00:06.862858: +2026-04-11 20:00:06.864953: Epoch 1153 +2026-04-11 20:00:06.866839: Current learning rate: 0.00736 +2026-04-11 20:01:49.091753: train_loss -0.2652 +2026-04-11 20:01:49.099550: val_loss -0.1737 +2026-04-11 20:01:49.101786: Pseudo dice [0.5031, 0.0653, 0.6337, 0.0295, 0.2553, 0.144, 0.4943] +2026-04-11 20:01:49.104966: Epoch time: 102.23 s +2026-04-11 20:01:50.527958: +2026-04-11 20:01:50.529790: Epoch 1154 +2026-04-11 20:01:50.532001: Current learning rate: 0.00736 +2026-04-11 20:03:32.823586: train_loss -0.2719 +2026-04-11 20:03:32.831736: val_loss -0.2402 +2026-04-11 20:03:32.834185: Pseudo dice [0.5324, 0.4034, 0.6119, 0.4571, 0.5323, 0.3003, 0.6754] +2026-04-11 20:03:32.837099: Epoch time: 102.3 s +2026-04-11 20:03:34.284901: +2026-04-11 20:03:34.287215: Epoch 1155 +2026-04-11 20:03:34.288938: Current learning rate: 0.00736 +2026-04-11 20:05:16.547193: train_loss -0.2928 +2026-04-11 20:05:16.552941: val_loss -0.2159 +2026-04-11 20:05:16.554738: Pseudo dice [0.6947, 0.6968, 0.6338, 0.0115, 0.3608, 0.0294, 0.7481] +2026-04-11 20:05:16.557324: Epoch time: 102.27 s +2026-04-11 20:05:18.010296: +2026-04-11 20:05:18.012351: Epoch 1156 +2026-04-11 20:05:18.015082: Current learning rate: 0.00736 +2026-04-11 20:07:00.233196: train_loss -0.3028 +2026-04-11 20:07:00.239295: val_loss -0.1466 +2026-04-11 20:07:00.242261: Pseudo dice [0.0745, 0.2965, 0.5768, 0.7714, 0.4705, 0.0237, 0.7251] +2026-04-11 20:07:00.245253: Epoch time: 102.23 s +2026-04-11 20:07:01.669004: +2026-04-11 20:07:01.670837: Epoch 1157 +2026-04-11 20:07:01.672467: Current learning rate: 0.00735 +2026-04-11 20:08:43.927199: train_loss -0.2878 +2026-04-11 20:08:43.932396: val_loss -0.2244 +2026-04-11 20:08:43.934487: Pseudo dice [0.7192, 0.3439, 0.6726, 0.5126, 0.3876, 0.1064, 0.5767] +2026-04-11 20:08:43.937242: Epoch time: 102.26 s +2026-04-11 20:08:45.384213: +2026-04-11 20:08:45.386351: Epoch 1158 +2026-04-11 20:08:45.387985: Current learning rate: 0.00735 +2026-04-11 20:10:27.845898: train_loss -0.2923 +2026-04-11 20:10:27.851394: val_loss -0.212 +2026-04-11 20:10:27.853644: Pseudo dice [0.6051, 0.6406, 0.5184, 0.3963, 0.5733, 0.0517, 0.6722] +2026-04-11 20:10:27.855854: Epoch time: 102.47 s +2026-04-11 20:10:29.299741: +2026-04-11 20:10:29.302175: Epoch 1159 +2026-04-11 20:10:29.303977: Current learning rate: 0.00735 +2026-04-11 20:12:12.695961: train_loss -0.2924 +2026-04-11 20:12:12.701939: val_loss -0.277 +2026-04-11 20:12:12.703665: Pseudo dice [0.7321, 0.643, 0.5461, 0.7826, 0.3163, 0.7774, 0.8758] +2026-04-11 20:12:12.705909: Epoch time: 103.4 s +2026-04-11 20:12:14.131548: +2026-04-11 20:12:14.133561: Epoch 1160 +2026-04-11 20:12:14.135513: Current learning rate: 0.00735 +2026-04-11 20:13:56.569340: train_loss -0.3151 +2026-04-11 20:13:56.575984: val_loss -0.1199 +2026-04-11 20:13:56.577875: Pseudo dice [0.3038, 0.7338, 0.7012, 0.6975, 0.0328, 0.0462, 0.4987] +2026-04-11 20:13:56.580523: Epoch time: 102.44 s +2026-04-11 20:13:58.016081: +2026-04-11 20:13:58.018384: Epoch 1161 +2026-04-11 20:13:58.020324: Current learning rate: 0.00735 +2026-04-11 20:15:40.920131: train_loss -0.2829 +2026-04-11 20:15:40.926449: val_loss -0.2134 +2026-04-11 20:15:40.928537: Pseudo dice [0.5195, 0.3526, 0.5519, 0.6254, 0.3438, 0.0685, 0.6747] +2026-04-11 20:15:40.930710: Epoch time: 102.91 s +2026-04-11 20:15:42.381689: +2026-04-11 20:15:42.384327: Epoch 1162 +2026-04-11 20:15:42.387051: Current learning rate: 0.00734 +2026-04-11 20:17:24.654247: train_loss -0.2969 +2026-04-11 20:17:24.660017: val_loss -0.1962 +2026-04-11 20:17:24.661713: Pseudo dice [0.7291, 0.7397, 0.4935, 0.0, 0.4246, 0.0188, 0.8391] +2026-04-11 20:17:24.664038: Epoch time: 102.28 s +2026-04-11 20:17:26.106380: +2026-04-11 20:17:26.108082: Epoch 1163 +2026-04-11 20:17:26.109639: Current learning rate: 0.00734 +2026-04-11 20:19:08.600277: train_loss -0.2947 +2026-04-11 20:19:08.605973: val_loss -0.2608 +2026-04-11 20:19:08.608682: Pseudo dice [0.1217, 0.5594, 0.7437, 0.2905, 0.3537, 0.7181, 0.7046] +2026-04-11 20:19:08.611777: Epoch time: 102.5 s +2026-04-11 20:19:10.069606: +2026-04-11 20:19:10.071618: Epoch 1164 +2026-04-11 20:19:10.073838: Current learning rate: 0.00734 +2026-04-11 20:20:52.221591: train_loss -0.2779 +2026-04-11 20:20:52.229203: val_loss -0.2 +2026-04-11 20:20:52.232700: Pseudo dice [0.6276, 0.4249, 0.6997, 0.5447, 0.4132, 0.1008, 0.7365] +2026-04-11 20:20:52.235507: Epoch time: 102.16 s +2026-04-11 20:20:53.688955: +2026-04-11 20:20:53.690821: Epoch 1165 +2026-04-11 20:20:53.692406: Current learning rate: 0.00734 +2026-04-11 20:22:36.116437: train_loss -0.2909 +2026-04-11 20:22:36.121132: val_loss -0.2785 +2026-04-11 20:22:36.123129: Pseudo dice [0.3259, 0.6842, 0.6898, 0.3079, 0.4123, 0.7158, 0.4958] +2026-04-11 20:22:36.125092: Epoch time: 102.43 s +2026-04-11 20:22:37.567044: +2026-04-11 20:22:37.568921: Epoch 1166 +2026-04-11 20:22:37.570807: Current learning rate: 0.00733 +2026-04-11 20:24:20.010998: train_loss -0.2996 +2026-04-11 20:24:20.016437: val_loss -0.2915 +2026-04-11 20:24:20.018379: Pseudo dice [0.661, 0.7313, 0.6514, 0.8404, 0.6922, 0.4003, 0.8996] +2026-04-11 20:24:20.021003: Epoch time: 102.45 s +2026-04-11 20:24:21.478153: +2026-04-11 20:24:21.480279: Epoch 1167 +2026-04-11 20:24:21.482478: Current learning rate: 0.00733 +2026-04-11 20:26:03.799996: train_loss -0.2969 +2026-04-11 20:26:03.805485: val_loss -0.2435 +2026-04-11 20:26:03.808947: Pseudo dice [0.2189, 0.4286, 0.6196, 0.0089, 0.3718, 0.6701, 0.4449] +2026-04-11 20:26:03.811697: Epoch time: 102.33 s +2026-04-11 20:26:05.266902: +2026-04-11 20:26:05.269104: Epoch 1168 +2026-04-11 20:26:05.271124: Current learning rate: 0.00733 +2026-04-11 20:27:47.701338: train_loss -0.3102 +2026-04-11 20:27:47.706343: val_loss -0.2909 +2026-04-11 20:27:47.708181: Pseudo dice [0.5649, 0.6494, 0.6618, 0.4756, 0.5868, 0.5065, 0.869] +2026-04-11 20:27:47.710724: Epoch time: 102.44 s +2026-04-11 20:27:49.113235: +2026-04-11 20:27:49.114961: Epoch 1169 +2026-04-11 20:27:49.116693: Current learning rate: 0.00733 +2026-04-11 20:29:31.723760: train_loss -0.3026 +2026-04-11 20:29:31.729152: val_loss -0.2661 +2026-04-11 20:29:31.731328: Pseudo dice [0.5887, 0.8172, 0.751, 0.0253, 0.5694, 0.2822, 0.731] +2026-04-11 20:29:31.733592: Epoch time: 102.61 s +2026-04-11 20:29:33.187877: +2026-04-11 20:29:33.190193: Epoch 1170 +2026-04-11 20:29:33.191972: Current learning rate: 0.00732 +2026-04-11 20:31:15.817904: train_loss -0.3227 +2026-04-11 20:31:15.822980: val_loss -0.2714 +2026-04-11 20:31:15.824885: Pseudo dice [0.7364, 0.8026, 0.7374, 0.6109, 0.3745, 0.4262, 0.7887] +2026-04-11 20:31:15.826915: Epoch time: 102.63 s +2026-04-11 20:31:17.272830: +2026-04-11 20:31:17.274601: Epoch 1171 +2026-04-11 20:31:17.278052: Current learning rate: 0.00732 +2026-04-11 20:32:59.756947: train_loss -0.2955 +2026-04-11 20:32:59.762681: val_loss -0.2785 +2026-04-11 20:32:59.764558: Pseudo dice [0.856, 0.7537, 0.5824, 0.5173, 0.5042, 0.6393, 0.7562] +2026-04-11 20:32:59.766692: Epoch time: 102.49 s +2026-04-11 20:33:01.206427: +2026-04-11 20:33:01.208401: Epoch 1172 +2026-04-11 20:33:01.209931: Current learning rate: 0.00732 +2026-04-11 20:34:43.751236: train_loss -0.3003 +2026-04-11 20:34:43.755856: val_loss -0.2851 +2026-04-11 20:34:43.757883: Pseudo dice [0.8494, 0.7258, 0.729, 0.6206, 0.5764, 0.6554, 0.766] +2026-04-11 20:34:43.760118: Epoch time: 102.55 s +2026-04-11 20:34:45.187382: +2026-04-11 20:34:45.189550: Epoch 1173 +2026-04-11 20:34:45.191392: Current learning rate: 0.00732 +2026-04-11 20:36:27.412412: train_loss -0.2906 +2026-04-11 20:36:27.416746: val_loss -0.1896 +2026-04-11 20:36:27.418941: Pseudo dice [0.2614, 0.6491, 0.4623, 0.456, 0.1086, 0.1121, 0.799] +2026-04-11 20:36:27.420802: Epoch time: 102.23 s +2026-04-11 20:36:28.844937: +2026-04-11 20:36:28.847038: Epoch 1174 +2026-04-11 20:36:28.848669: Current learning rate: 0.00731 +2026-04-11 20:38:12.634401: train_loss -0.2926 +2026-04-11 20:38:12.639953: val_loss -0.2703 +2026-04-11 20:38:12.641954: Pseudo dice [0.3191, 0.7705, 0.6904, 0.7882, 0.598, 0.1239, 0.8987] +2026-04-11 20:38:12.644737: Epoch time: 103.79 s +2026-04-11 20:38:14.086416: +2026-04-11 20:38:14.088516: Epoch 1175 +2026-04-11 20:38:14.090513: Current learning rate: 0.00731 +2026-04-11 20:39:56.526263: train_loss -0.3043 +2026-04-11 20:39:56.531386: val_loss -0.2242 +2026-04-11 20:39:56.534226: Pseudo dice [0.6319, 0.3662, 0.4258, 0.5915, 0.6318, 0.1951, 0.6296] +2026-04-11 20:39:56.536813: Epoch time: 102.44 s +2026-04-11 20:39:57.952952: +2026-04-11 20:39:57.955130: Epoch 1176 +2026-04-11 20:39:57.957076: Current learning rate: 0.00731 +2026-04-11 20:41:40.504414: train_loss -0.3024 +2026-04-11 20:41:40.516667: val_loss -0.1993 +2026-04-11 20:41:40.519279: Pseudo dice [0.5667, 0.6235, 0.7845, 0.297, 0.4131, 0.0894, 0.6782] +2026-04-11 20:41:40.522125: Epoch time: 102.56 s +2026-04-11 20:41:41.974277: +2026-04-11 20:41:41.979631: Epoch 1177 +2026-04-11 20:41:41.984850: Current learning rate: 0.00731 +2026-04-11 20:43:24.275921: train_loss -0.3133 +2026-04-11 20:43:24.280718: val_loss -0.1952 +2026-04-11 20:43:24.282728: Pseudo dice [0.7128, 0.6904, 0.7045, 0.2022, 0.5589, 0.0601, 0.8436] +2026-04-11 20:43:24.285225: Epoch time: 102.31 s +2026-04-11 20:43:25.731984: +2026-04-11 20:43:25.735070: Epoch 1178 +2026-04-11 20:43:25.736740: Current learning rate: 0.00731 +2026-04-11 20:45:08.131883: train_loss -0.2387 +2026-04-11 20:45:08.136742: val_loss -0.2112 +2026-04-11 20:45:08.138699: Pseudo dice [0.8169, 0.6822, 0.3139, 0.0198, 0.3885, 0.0392, 0.5894] +2026-04-11 20:45:08.140987: Epoch time: 102.4 s +2026-04-11 20:45:09.578522: +2026-04-11 20:45:09.580477: Epoch 1179 +2026-04-11 20:45:09.582104: Current learning rate: 0.0073 +2026-04-11 20:46:53.009071: train_loss -0.285 +2026-04-11 20:46:53.013730: val_loss -0.2408 +2026-04-11 20:46:53.015985: Pseudo dice [0.6591, 0.6092, 0.5052, 0.7224, 0.5282, 0.0354, 0.6668] +2026-04-11 20:46:53.018363: Epoch time: 103.43 s +2026-04-11 20:46:54.445297: +2026-04-11 20:46:54.447074: Epoch 1180 +2026-04-11 20:46:54.448638: Current learning rate: 0.0073 +2026-04-11 20:48:36.741683: train_loss -0.3128 +2026-04-11 20:48:36.747147: val_loss -0.2952 +2026-04-11 20:48:36.748969: Pseudo dice [0.7174, 0.7821, 0.6924, 0.1043, 0.4646, 0.5729, 0.751] +2026-04-11 20:48:36.751433: Epoch time: 102.3 s +2026-04-11 20:48:38.162725: +2026-04-11 20:48:38.165021: Epoch 1181 +2026-04-11 20:48:38.166683: Current learning rate: 0.0073 +2026-04-11 20:50:20.396976: train_loss -0.3049 +2026-04-11 20:50:20.401970: val_loss -0.2168 +2026-04-11 20:50:20.404029: Pseudo dice [0.6372, 0.7702, 0.4393, 0.6328, 0.3977, 0.268, 0.7387] +2026-04-11 20:50:20.406414: Epoch time: 102.24 s +2026-04-11 20:50:21.849046: +2026-04-11 20:50:21.850667: Epoch 1182 +2026-04-11 20:50:21.852177: Current learning rate: 0.0073 +2026-04-11 20:52:03.977709: train_loss -0.2993 +2026-04-11 20:52:03.982826: val_loss -0.2544 +2026-04-11 20:52:03.985409: Pseudo dice [0.7024, 0.7083, 0.4855, 0.0272, 0.3246, 0.1987, 0.8633] +2026-04-11 20:52:03.987596: Epoch time: 102.13 s +2026-04-11 20:52:05.451332: +2026-04-11 20:52:05.453393: Epoch 1183 +2026-04-11 20:52:05.454965: Current learning rate: 0.00729 +2026-04-11 20:53:48.247300: train_loss -0.2693 +2026-04-11 20:53:48.254704: val_loss -0.1983 +2026-04-11 20:53:48.256793: Pseudo dice [0.3681, 0.4135, 0.5908, 0.663, 0.3852, 0.1261, 0.7148] +2026-04-11 20:53:48.259838: Epoch time: 102.8 s +2026-04-11 20:53:49.717392: +2026-04-11 20:53:49.719664: Epoch 1184 +2026-04-11 20:53:49.721792: Current learning rate: 0.00729 +2026-04-11 20:55:31.975781: train_loss -0.307 +2026-04-11 20:55:31.980187: val_loss -0.2847 +2026-04-11 20:55:31.982146: Pseudo dice [0.4921, 0.573, 0.3516, 0.7774, 0.505, 0.7805, 0.8247] +2026-04-11 20:55:31.984163: Epoch time: 102.26 s +2026-04-11 20:55:33.427824: +2026-04-11 20:55:33.429518: Epoch 1185 +2026-04-11 20:55:33.430954: Current learning rate: 0.00729 +2026-04-11 20:57:15.697752: train_loss -0.3096 +2026-04-11 20:57:15.702652: val_loss -0.2138 +2026-04-11 20:57:15.704371: Pseudo dice [0.3233, 0.3381, 0.6782, 0.8061, 0.4839, 0.0254, 0.8831] +2026-04-11 20:57:15.706695: Epoch time: 102.27 s +2026-04-11 20:57:17.169148: +2026-04-11 20:57:17.171757: Epoch 1186 +2026-04-11 20:57:17.173445: Current learning rate: 0.00729 +2026-04-11 20:58:59.437631: train_loss -0.279 +2026-04-11 20:58:59.442497: val_loss -0.243 +2026-04-11 20:58:59.445226: Pseudo dice [0.3205, 0.7986, 0.3759, 0.0121, 0.4319, 0.5043, 0.6452] +2026-04-11 20:58:59.448329: Epoch time: 102.27 s +2026-04-11 20:59:00.855046: +2026-04-11 20:59:00.857115: Epoch 1187 +2026-04-11 20:59:00.859141: Current learning rate: 0.00728 +2026-04-11 21:00:43.210999: train_loss -0.2945 +2026-04-11 21:00:43.215553: val_loss -0.1896 +2026-04-11 21:00:43.217393: Pseudo dice [0.848, 0.7937, 0.5727, 0.6697, 0.6298, 0.0641, 0.7658] +2026-04-11 21:00:43.219603: Epoch time: 102.36 s +2026-04-11 21:00:44.655163: +2026-04-11 21:00:44.657076: Epoch 1188 +2026-04-11 21:00:44.658926: Current learning rate: 0.00728 +2026-04-11 21:02:27.088504: train_loss -0.3061 +2026-04-11 21:02:27.094345: val_loss -0.2232 +2026-04-11 21:02:27.096735: Pseudo dice [0.4425, 0.8077, 0.6894, 0.3566, 0.3595, 0.2218, 0.6405] +2026-04-11 21:02:27.099470: Epoch time: 102.44 s +2026-04-11 21:02:28.526228: +2026-04-11 21:02:28.528192: Epoch 1189 +2026-04-11 21:02:28.530166: Current learning rate: 0.00728 +2026-04-11 21:04:11.550446: train_loss -0.2812 +2026-04-11 21:04:11.555086: val_loss -0.1891 +2026-04-11 21:04:11.557035: Pseudo dice [0.3767, 0.7969, 0.5144, 0.7744, 0.5941, 0.0313, 0.7753] +2026-04-11 21:04:11.559474: Epoch time: 103.03 s +2026-04-11 21:04:13.044795: +2026-04-11 21:04:13.046410: Epoch 1190 +2026-04-11 21:04:13.048860: Current learning rate: 0.00728 +2026-04-11 21:05:55.537757: train_loss -0.2959 +2026-04-11 21:05:55.542374: val_loss -0.2702 +2026-04-11 21:05:55.543989: Pseudo dice [0.5212, 0.607, 0.6756, 0.6221, 0.4096, 0.4513, 0.8214] +2026-04-11 21:05:55.546935: Epoch time: 102.5 s +2026-04-11 21:05:56.979740: +2026-04-11 21:05:56.981354: Epoch 1191 +2026-04-11 21:05:56.982861: Current learning rate: 0.00728 +2026-04-11 21:07:39.495441: train_loss -0.2924 +2026-04-11 21:07:39.502047: val_loss -0.2359 +2026-04-11 21:07:39.503890: Pseudo dice [0.8032, 0.6181, 0.6214, 0.7425, 0.5139, 0.2553, 0.7615] +2026-04-11 21:07:39.506338: Epoch time: 102.52 s +2026-04-11 21:07:40.971154: +2026-04-11 21:07:40.973558: Epoch 1192 +2026-04-11 21:07:40.975219: Current learning rate: 0.00727 +2026-04-11 21:09:23.430104: train_loss -0.2916 +2026-04-11 21:09:23.454897: val_loss -0.269 +2026-04-11 21:09:23.456450: Pseudo dice [0.4571, 0.2738, 0.7917, 0.7108, 0.3945, 0.5345, 0.6838] +2026-04-11 21:09:23.458697: Epoch time: 102.46 s +2026-04-11 21:09:24.893480: +2026-04-11 21:09:24.895447: Epoch 1193 +2026-04-11 21:09:24.897051: Current learning rate: 0.00727 +2026-04-11 21:11:07.351624: train_loss -0.2926 +2026-04-11 21:11:07.357089: val_loss -0.256 +2026-04-11 21:11:07.358935: Pseudo dice [0.2852, 0.6316, 0.6076, 0.3535, 0.5168, 0.3368, 0.662] +2026-04-11 21:11:07.361079: Epoch time: 102.46 s +2026-04-11 21:11:08.782430: +2026-04-11 21:11:08.784186: Epoch 1194 +2026-04-11 21:11:08.785705: Current learning rate: 0.00727 +2026-04-11 21:12:51.564315: train_loss -0.2881 +2026-04-11 21:12:51.569562: val_loss -0.1935 +2026-04-11 21:12:51.572414: Pseudo dice [0.6952, 0.5989, 0.4727, 0.6747, 0.4619, 0.1625, 0.6064] +2026-04-11 21:12:51.574584: Epoch time: 102.79 s +2026-04-11 21:12:53.022282: +2026-04-11 21:12:53.024491: Epoch 1195 +2026-04-11 21:12:53.026904: Current learning rate: 0.00727 +2026-04-11 21:14:35.323307: train_loss -0.2525 +2026-04-11 21:14:35.331915: val_loss -0.1562 +2026-04-11 21:14:35.346479: Pseudo dice [0.3654, 0.5785, 0.4915, 0.4569, 0.6017, 0.1477, 0.78] +2026-04-11 21:14:35.349178: Epoch time: 102.3 s +2026-04-11 21:14:36.794270: +2026-04-11 21:14:36.796586: Epoch 1196 +2026-04-11 21:14:36.798444: Current learning rate: 0.00726 +2026-04-11 21:16:19.170920: train_loss -0.3051 +2026-04-11 21:16:19.175529: val_loss -0.263 +2026-04-11 21:16:19.177108: Pseudo dice [0.7854, 0.5097, 0.7085, 0.5129, 0.6375, 0.2749, 0.3971] +2026-04-11 21:16:19.179390: Epoch time: 102.38 s +2026-04-11 21:16:20.615508: +2026-04-11 21:16:20.617138: Epoch 1197 +2026-04-11 21:16:20.619098: Current learning rate: 0.00726 +2026-04-11 21:18:02.895986: train_loss -0.3028 +2026-04-11 21:18:02.901075: val_loss -0.1458 +2026-04-11 21:18:02.903070: Pseudo dice [0.6978, 0.5511, 0.5751, 0.0818, 0.3815, 0.124, 0.4346] +2026-04-11 21:18:02.905362: Epoch time: 102.28 s +2026-04-11 21:18:04.340801: +2026-04-11 21:18:04.342459: Epoch 1198 +2026-04-11 21:18:04.344130: Current learning rate: 0.00726 +2026-04-11 21:19:46.507324: train_loss -0.2879 +2026-04-11 21:19:46.521388: val_loss -0.2625 +2026-04-11 21:19:46.525252: Pseudo dice [0.5921, 0.3772, 0.6324, 0.7808, 0.5735, 0.2902, 0.8144] +2026-04-11 21:19:46.527423: Epoch time: 102.17 s +2026-04-11 21:19:47.941067: +2026-04-11 21:19:47.942650: Epoch 1199 +2026-04-11 21:19:47.944461: Current learning rate: 0.00726 +2026-04-11 21:21:31.421188: train_loss -0.302 +2026-04-11 21:21:31.427253: val_loss -0.2096 +2026-04-11 21:21:31.429285: Pseudo dice [0.6611, 0.7377, 0.6979, 0.6205, 0.6477, 0.2497, 0.5787] +2026-04-11 21:21:31.431713: Epoch time: 103.48 s +2026-04-11 21:21:34.734665: +2026-04-11 21:21:34.736538: Epoch 1200 +2026-04-11 21:21:34.738376: Current learning rate: 0.00725 +2026-04-11 21:23:17.018292: train_loss -0.2915 +2026-04-11 21:23:17.023893: val_loss -0.181 +2026-04-11 21:23:17.026205: Pseudo dice [0.7099, 0.375, 0.4378, 0.6031, 0.6162, 0.1401, 0.6697] +2026-04-11 21:23:17.028741: Epoch time: 102.29 s +2026-04-11 21:23:18.478249: +2026-04-11 21:23:18.481233: Epoch 1201 +2026-04-11 21:23:18.483277: Current learning rate: 0.00725 +2026-04-11 21:25:00.686969: train_loss -0.2949 +2026-04-11 21:25:00.692001: val_loss -0.2844 +2026-04-11 21:25:00.696252: Pseudo dice [0.6223, 0.4233, 0.7188, 0.6395, 0.4551, 0.5876, 0.5158] +2026-04-11 21:25:00.698390: Epoch time: 102.21 s +2026-04-11 21:25:02.123204: +2026-04-11 21:25:02.125774: Epoch 1202 +2026-04-11 21:25:02.127345: Current learning rate: 0.00725 +2026-04-11 21:26:44.406337: train_loss -0.3096 +2026-04-11 21:26:44.412637: val_loss -0.2284 +2026-04-11 21:26:44.414583: Pseudo dice [0.4407, 0.2524, 0.6626, 0.4755, 0.5664, 0.2131, 0.837] +2026-04-11 21:26:44.416637: Epoch time: 102.29 s +2026-04-11 21:26:45.840714: +2026-04-11 21:26:45.842885: Epoch 1203 +2026-04-11 21:26:45.844697: Current learning rate: 0.00725 +2026-04-11 21:28:28.195709: train_loss -0.28 +2026-04-11 21:28:28.201129: val_loss -0.1528 +2026-04-11 21:28:28.203065: Pseudo dice [0.7213, 0.7881, 0.614, 0.2677, 0.5032, 0.1499, 0.6534] +2026-04-11 21:28:28.205556: Epoch time: 102.36 s +2026-04-11 21:28:29.647546: +2026-04-11 21:28:29.649214: Epoch 1204 +2026-04-11 21:28:29.650719: Current learning rate: 0.00724 +2026-04-11 21:30:11.977052: train_loss -0.2982 +2026-04-11 21:30:11.984199: val_loss -0.2217 +2026-04-11 21:30:11.987300: Pseudo dice [0.46, 0.6092, 0.6021, 0.6802, 0.4113, 0.1871, 0.3495] +2026-04-11 21:30:11.990302: Epoch time: 102.33 s +2026-04-11 21:30:13.415044: +2026-04-11 21:30:13.417135: Epoch 1205 +2026-04-11 21:30:13.418762: Current learning rate: 0.00724 +2026-04-11 21:31:56.011472: train_loss -0.3052 +2026-04-11 21:31:56.016906: val_loss -0.1704 +2026-04-11 21:31:56.018713: Pseudo dice [0.5027, 0.1579, 0.7633, 0.621, 0.5028, 0.2663, 0.787] +2026-04-11 21:31:56.020855: Epoch time: 102.6 s +2026-04-11 21:31:57.488176: +2026-04-11 21:31:57.489830: Epoch 1206 +2026-04-11 21:31:57.491567: Current learning rate: 0.00724 +2026-04-11 21:33:40.063444: train_loss -0.3028 +2026-04-11 21:33:40.070323: val_loss -0.2035 +2026-04-11 21:33:40.072646: Pseudo dice [0.3057, 0.3994, 0.6728, 0.7464, 0.5428, 0.1102, 0.71] +2026-04-11 21:33:40.076283: Epoch time: 102.58 s +2026-04-11 21:33:41.520209: +2026-04-11 21:33:41.522187: Epoch 1207 +2026-04-11 21:33:41.523752: Current learning rate: 0.00724 +2026-04-11 21:35:24.047947: train_loss -0.312 +2026-04-11 21:35:24.053356: val_loss -0.229 +2026-04-11 21:35:24.055408: Pseudo dice [0.7948, 0.4111, 0.5785, 0.5748, 0.2521, 0.2273, 0.1576] +2026-04-11 21:35:24.057912: Epoch time: 102.53 s +2026-04-11 21:35:25.482853: +2026-04-11 21:35:25.484627: Epoch 1208 +2026-04-11 21:35:25.486252: Current learning rate: 0.00724 +2026-04-11 21:37:07.687304: train_loss -0.2923 +2026-04-11 21:37:07.692719: val_loss -0.2355 +2026-04-11 21:37:07.695625: Pseudo dice [0.8001, 0.5208, 0.6736, 0.2839, 0.6152, 0.1119, 0.1183] +2026-04-11 21:37:07.698346: Epoch time: 102.21 s +2026-04-11 21:37:09.144475: +2026-04-11 21:37:09.146448: Epoch 1209 +2026-04-11 21:37:09.148107: Current learning rate: 0.00723 +2026-04-11 21:38:51.242836: train_loss -0.2725 +2026-04-11 21:38:51.258097: val_loss -0.2105 +2026-04-11 21:38:51.263608: Pseudo dice [0.2947, 0.6419, 0.4633, 0.5751, 0.4521, 0.0924, 0.6477] +2026-04-11 21:38:51.269403: Epoch time: 102.1 s +2026-04-11 21:38:52.713108: +2026-04-11 21:38:52.714717: Epoch 1210 +2026-04-11 21:38:52.716230: Current learning rate: 0.00723 +2026-04-11 21:40:34.777223: train_loss -0.2617 +2026-04-11 21:40:34.783198: val_loss -0.2677 +2026-04-11 21:40:34.785285: Pseudo dice [0.5076, 0.6132, 0.6985, 0.3964, 0.1154, 0.7021, 0.6238] +2026-04-11 21:40:34.787683: Epoch time: 102.07 s +2026-04-11 21:40:36.226055: +2026-04-11 21:40:36.228772: Epoch 1211 +2026-04-11 21:40:36.230543: Current learning rate: 0.00723 +2026-04-11 21:42:18.297884: train_loss -0.2929 +2026-04-11 21:42:18.304036: val_loss -0.1773 +2026-04-11 21:42:18.306080: Pseudo dice [0.7336, 0.0904, 0.7048, 0.3453, 0.2207, 0.2841, 0.4993] +2026-04-11 21:42:18.308376: Epoch time: 102.08 s +2026-04-11 21:42:19.733358: +2026-04-11 21:42:19.735197: Epoch 1212 +2026-04-11 21:42:19.736970: Current learning rate: 0.00723 +2026-04-11 21:44:01.994274: train_loss -0.2853 +2026-04-11 21:44:01.999872: val_loss -0.2025 +2026-04-11 21:44:02.001663: Pseudo dice [0.5669, 0.7082, 0.2556, 0.7087, 0.3879, 0.0668, 0.8972] +2026-04-11 21:44:02.003961: Epoch time: 102.26 s +2026-04-11 21:44:03.472828: +2026-04-11 21:44:03.476012: Epoch 1213 +2026-04-11 21:44:03.477572: Current learning rate: 0.00722 +2026-04-11 21:45:45.819148: train_loss -0.2806 +2026-04-11 21:45:45.824312: val_loss -0.1991 +2026-04-11 21:45:45.826596: Pseudo dice [0.6198, 0.3182, 0.5852, 0.8726, 0.2839, 0.1535, 0.8033] +2026-04-11 21:45:45.828919: Epoch time: 102.35 s +2026-04-11 21:45:47.272328: +2026-04-11 21:45:47.275012: Epoch 1214 +2026-04-11 21:45:47.276952: Current learning rate: 0.00722 +2026-04-11 21:47:29.912294: train_loss -0.2899 +2026-04-11 21:47:29.918727: val_loss -0.2731 +2026-04-11 21:47:29.921432: Pseudo dice [0.6489, 0.3066, 0.7924, 0.0035, 0.3659, 0.7391, 0.6616] +2026-04-11 21:47:29.924803: Epoch time: 102.64 s +2026-04-11 21:47:31.372347: +2026-04-11 21:47:31.374207: Epoch 1215 +2026-04-11 21:47:31.376493: Current learning rate: 0.00722 +2026-04-11 21:49:14.368918: train_loss -0.3085 +2026-04-11 21:49:14.375122: val_loss -0.2532 +2026-04-11 21:49:14.377347: Pseudo dice [0.7364, 0.6916, 0.4437, 0.5217, 0.3385, 0.7597, 0.8726] +2026-04-11 21:49:14.379627: Epoch time: 103.0 s +2026-04-11 21:49:15.809727: +2026-04-11 21:49:15.812005: Epoch 1216 +2026-04-11 21:49:15.814478: Current learning rate: 0.00722 +2026-04-11 21:50:58.993729: train_loss -0.2853 +2026-04-11 21:50:59.000257: val_loss -0.2283 +2026-04-11 21:50:59.003065: Pseudo dice [0.663, 0.6611, 0.5192, 0.5049, 0.2006, 0.1711, 0.6198] +2026-04-11 21:50:59.006051: Epoch time: 103.19 s +2026-04-11 21:51:00.431133: +2026-04-11 21:51:00.433259: Epoch 1217 +2026-04-11 21:51:00.435253: Current learning rate: 0.00721 +2026-04-11 21:52:42.847935: train_loss -0.3032 +2026-04-11 21:52:42.853889: val_loss -0.2022 +2026-04-11 21:52:42.856639: Pseudo dice [0.7035, 0.5244, 0.3473, 0.6827, 0.5319, 0.0112, 0.7691] +2026-04-11 21:52:42.859211: Epoch time: 102.42 s +2026-04-11 21:52:44.301811: +2026-04-11 21:52:44.303513: Epoch 1218 +2026-04-11 21:52:44.305664: Current learning rate: 0.00721 +2026-04-11 21:54:27.440025: train_loss -0.299 +2026-04-11 21:54:27.446332: val_loss -0.2713 +2026-04-11 21:54:27.448750: Pseudo dice [0.3978, 0.4006, 0.5218, 0.3642, 0.6034, 0.7445, 0.7046] +2026-04-11 21:54:27.452735: Epoch time: 103.14 s +2026-04-11 21:54:28.896860: +2026-04-11 21:54:28.898947: Epoch 1219 +2026-04-11 21:54:28.901039: Current learning rate: 0.00721 +2026-04-11 21:56:13.835886: train_loss -0.3066 +2026-04-11 21:56:13.845799: val_loss -0.223 +2026-04-11 21:56:13.848043: Pseudo dice [0.677, 0.6363, 0.523, 0.6876, 0.5876, 0.0252, 0.5487] +2026-04-11 21:56:13.850503: Epoch time: 104.94 s +2026-04-11 21:56:15.270150: +2026-04-11 21:56:15.271997: Epoch 1220 +2026-04-11 21:56:15.274676: Current learning rate: 0.00721 +2026-04-11 21:57:58.266398: train_loss -0.3237 +2026-04-11 21:57:58.273571: val_loss -0.1563 +2026-04-11 21:57:58.276306: Pseudo dice [0.4539, 0.836, 0.384, 0.6376, 0.4935, 0.1274, 0.8162] +2026-04-11 21:57:58.279115: Epoch time: 103.0 s +2026-04-11 21:57:59.727597: +2026-04-11 21:57:59.729691: Epoch 1221 +2026-04-11 21:57:59.731564: Current learning rate: 0.00721 +2026-04-11 21:59:43.312348: train_loss -0.3073 +2026-04-11 21:59:43.317225: val_loss -0.2104 +2026-04-11 21:59:43.319450: Pseudo dice [0.622, 0.4882, 0.4628, 0.5295, 0.4338, 0.1428, 0.7694] +2026-04-11 21:59:43.321589: Epoch time: 103.59 s +2026-04-11 21:59:44.760460: +2026-04-11 21:59:44.762454: Epoch 1222 +2026-04-11 21:59:44.764900: Current learning rate: 0.0072 +2026-04-11 22:01:27.862366: train_loss -0.3127 +2026-04-11 22:01:27.868511: val_loss -0.2281 +2026-04-11 22:01:27.870681: Pseudo dice [0.4185, 0.2988, 0.6415, 0.0275, 0.5235, 0.1137, 0.7185] +2026-04-11 22:01:27.873328: Epoch time: 103.11 s +2026-04-11 22:01:29.331493: +2026-04-11 22:01:29.333462: Epoch 1223 +2026-04-11 22:01:29.335541: Current learning rate: 0.0072 +2026-04-11 22:03:12.164408: train_loss -0.2985 +2026-04-11 22:03:12.169995: val_loss -0.2135 +2026-04-11 22:03:12.171701: Pseudo dice [0.5204, 0.5364, 0.7371, 0.3177, 0.432, 0.0597, 0.7956] +2026-04-11 22:03:12.175193: Epoch time: 102.84 s +2026-04-11 22:03:13.618853: +2026-04-11 22:03:13.620881: Epoch 1224 +2026-04-11 22:03:13.623301: Current learning rate: 0.0072 +2026-04-11 22:04:56.107185: train_loss -0.3013 +2026-04-11 22:04:56.112636: val_loss -0.3017 +2026-04-11 22:04:56.114547: Pseudo dice [0.6948, 0.7937, 0.7805, 0.6113, 0.412, 0.7825, 0.788] +2026-04-11 22:04:56.116977: Epoch time: 102.49 s +2026-04-11 22:04:57.562063: +2026-04-11 22:04:57.563979: Epoch 1225 +2026-04-11 22:04:57.566337: Current learning rate: 0.0072 +2026-04-11 22:06:40.344688: train_loss -0.3095 +2026-04-11 22:06:40.351344: val_loss -0.1848 +2026-04-11 22:06:40.354170: Pseudo dice [0.613, 0.7917, 0.5385, 0.7161, 0.3086, 0.0784, 0.5549] +2026-04-11 22:06:40.356502: Epoch time: 102.79 s +2026-04-11 22:06:41.823501: +2026-04-11 22:06:41.825455: Epoch 1226 +2026-04-11 22:06:41.827644: Current learning rate: 0.00719 +2026-04-11 22:08:24.208951: train_loss -0.3043 +2026-04-11 22:08:24.215149: val_loss -0.2122 +2026-04-11 22:08:24.218032: Pseudo dice [0.7878, 0.5373, 0.4412, 0.1455, 0.4101, 0.3447, 0.8306] +2026-04-11 22:08:24.221188: Epoch time: 102.39 s +2026-04-11 22:08:25.682177: +2026-04-11 22:08:25.683888: Epoch 1227 +2026-04-11 22:08:25.685692: Current learning rate: 0.00719 +2026-04-11 22:10:07.862910: train_loss -0.3003 +2026-04-11 22:10:07.871469: val_loss -0.2548 +2026-04-11 22:10:07.875463: Pseudo dice [0.88, 0.7351, 0.7098, 0.2208, 0.4779, 0.2509, 0.8775] +2026-04-11 22:10:07.879760: Epoch time: 102.18 s +2026-04-11 22:10:09.330639: +2026-04-11 22:10:09.332623: Epoch 1228 +2026-04-11 22:10:09.335672: Current learning rate: 0.00719 +2026-04-11 22:11:51.404173: train_loss -0.3049 +2026-04-11 22:11:51.412823: val_loss -0.2174 +2026-04-11 22:11:51.415769: Pseudo dice [0.322, 0.6527, 0.6141, 0.4133, 0.5517, 0.2017, 0.8587] +2026-04-11 22:11:51.418343: Epoch time: 102.08 s +2026-04-11 22:11:52.871128: +2026-04-11 22:11:52.873037: Epoch 1229 +2026-04-11 22:11:52.875222: Current learning rate: 0.00719 +2026-04-11 22:13:35.400669: train_loss -0.2817 +2026-04-11 22:13:35.408966: val_loss -0.2229 +2026-04-11 22:13:35.411638: Pseudo dice [0.2156, 0.8256, 0.5483, 0.6709, 0.3732, 0.0929, 0.6531] +2026-04-11 22:13:35.413970: Epoch time: 102.53 s +2026-04-11 22:13:36.883953: +2026-04-11 22:13:36.888589: Epoch 1230 +2026-04-11 22:13:36.891547: Current learning rate: 0.00718 +2026-04-11 22:15:19.566149: train_loss -0.3083 +2026-04-11 22:15:19.571441: val_loss -0.2581 +2026-04-11 22:15:19.573326: Pseudo dice [0.8136, 0.3003, 0.6816, 0.6305, 0.3248, 0.1951, 0.8436] +2026-04-11 22:15:19.575876: Epoch time: 102.69 s +2026-04-11 22:15:21.029746: +2026-04-11 22:15:21.032316: Epoch 1231 +2026-04-11 22:15:21.034382: Current learning rate: 0.00718 +2026-04-11 22:17:03.476394: train_loss -0.3222 +2026-04-11 22:17:03.483087: val_loss -0.2719 +2026-04-11 22:17:03.484951: Pseudo dice [0.3241, 0.6849, 0.7453, 0.8365, 0.5531, 0.3621, 0.9198] +2026-04-11 22:17:03.487634: Epoch time: 102.45 s +2026-04-11 22:17:04.953842: +2026-04-11 22:17:04.962674: Epoch 1232 +2026-04-11 22:17:04.965335: Current learning rate: 0.00718 +2026-04-11 22:18:47.676702: train_loss -0.286 +2026-04-11 22:18:47.686322: val_loss -0.2293 +2026-04-11 22:18:47.688176: Pseudo dice [0.4469, 0.5367, 0.6175, 0.0017, 0.631, 0.1481, 0.8343] +2026-04-11 22:18:47.692895: Epoch time: 102.73 s +2026-04-11 22:18:49.159079: +2026-04-11 22:18:49.161291: Epoch 1233 +2026-04-11 22:18:49.163480: Current learning rate: 0.00718 +2026-04-11 22:20:31.369392: train_loss -0.2925 +2026-04-11 22:20:31.374661: val_loss -0.2078 +2026-04-11 22:20:31.378383: Pseudo dice [0.54, 0.7461, 0.5019, 0.0283, 0.5304, 0.0108, 0.751] +2026-04-11 22:20:31.381607: Epoch time: 102.21 s +2026-04-11 22:20:32.853456: +2026-04-11 22:20:32.855691: Epoch 1234 +2026-04-11 22:20:32.858916: Current learning rate: 0.00717 +2026-04-11 22:22:15.319674: train_loss -0.3004 +2026-04-11 22:22:15.328162: val_loss -0.2875 +2026-04-11 22:22:15.330076: Pseudo dice [0.4996, 0.4712, 0.6868, 0.6511, 0.5277, 0.8029, 0.8077] +2026-04-11 22:22:15.332199: Epoch time: 102.47 s +2026-04-11 22:22:16.773210: +2026-04-11 22:22:16.775045: Epoch 1235 +2026-04-11 22:22:16.777980: Current learning rate: 0.00717 +2026-04-11 22:23:59.160416: train_loss -0.3006 +2026-04-11 22:23:59.168574: val_loss -0.2046 +2026-04-11 22:23:59.170548: Pseudo dice [0.5104, 0.226, 0.5151, 0.6354, 0.2154, 0.1956, 0.3626] +2026-04-11 22:23:59.173124: Epoch time: 102.39 s +2026-04-11 22:24:00.621853: +2026-04-11 22:24:00.623994: Epoch 1236 +2026-04-11 22:24:00.626003: Current learning rate: 0.00717 +2026-04-11 22:25:43.095483: train_loss -0.3026 +2026-04-11 22:25:43.101209: val_loss -0.2222 +2026-04-11 22:25:43.103532: Pseudo dice [0.4621, 0.4726, 0.5794, 0.0544, 0.2983, 0.0604, 0.8139] +2026-04-11 22:25:43.105928: Epoch time: 102.48 s +2026-04-11 22:25:44.802196: +2026-04-11 22:25:44.804467: Epoch 1237 +2026-04-11 22:25:44.806484: Current learning rate: 0.00717 +2026-04-11 22:27:27.741072: train_loss -0.3077 +2026-04-11 22:27:27.747673: val_loss -0.2235 +2026-04-11 22:27:27.749489: Pseudo dice [0.207, 0.4455, 0.669, 0.7269, 0.6342, 0.1284, 0.8156] +2026-04-11 22:27:27.751890: Epoch time: 102.94 s +2026-04-11 22:27:29.214016: +2026-04-11 22:27:29.216739: Epoch 1238 +2026-04-11 22:27:29.218772: Current learning rate: 0.00717 +2026-04-11 22:29:11.955571: train_loss -0.2888 +2026-04-11 22:29:11.962812: val_loss -0.2247 +2026-04-11 22:29:11.965003: Pseudo dice [0.1942, 0.4913, 0.5756, 0.4825, 0.1813, 0.0933, 0.7883] +2026-04-11 22:29:11.967686: Epoch time: 102.75 s +2026-04-11 22:29:13.411344: +2026-04-11 22:29:13.413432: Epoch 1239 +2026-04-11 22:29:13.415716: Current learning rate: 0.00716 +2026-04-11 22:30:56.700520: train_loss -0.2912 +2026-04-11 22:30:56.707394: val_loss -0.2424 +2026-04-11 22:30:56.709455: Pseudo dice [0.4945, 0.5955, 0.5902, 0.4163, 0.537, 0.1646, 0.8574] +2026-04-11 22:30:56.711766: Epoch time: 103.29 s +2026-04-11 22:30:58.158168: +2026-04-11 22:30:58.159910: Epoch 1240 +2026-04-11 22:30:58.161928: Current learning rate: 0.00716 +2026-04-11 22:32:40.683236: train_loss -0.2894 +2026-04-11 22:32:40.689296: val_loss -0.2233 +2026-04-11 22:32:40.691931: Pseudo dice [0.6952, 0.7042, 0.5032, 0.5793, 0.5471, 0.2003, 0.6646] +2026-04-11 22:32:40.695251: Epoch time: 102.53 s +2026-04-11 22:32:42.141076: +2026-04-11 22:32:42.143375: Epoch 1241 +2026-04-11 22:32:42.147878: Current learning rate: 0.00716 +2026-04-11 22:34:25.161494: train_loss -0.2956 +2026-04-11 22:34:25.167230: val_loss -0.269 +2026-04-11 22:34:25.169618: Pseudo dice [0.4986, 0.5751, 0.7286, 0.4196, 0.5804, 0.1665, 0.8351] +2026-04-11 22:34:25.172372: Epoch time: 103.02 s +2026-04-11 22:34:26.627050: +2026-04-11 22:34:26.628816: Epoch 1242 +2026-04-11 22:34:26.630832: Current learning rate: 0.00716 +2026-04-11 22:36:11.265576: train_loss -0.2919 +2026-04-11 22:36:11.272412: val_loss -0.2096 +2026-04-11 22:36:11.274666: Pseudo dice [0.2348, 0.6775, 0.5829, 0.2858, 0.3774, 0.1366, 0.8542] +2026-04-11 22:36:11.278435: Epoch time: 104.64 s +2026-04-11 22:36:12.726021: +2026-04-11 22:36:12.728875: Epoch 1243 +2026-04-11 22:36:12.730760: Current learning rate: 0.00715 +2026-04-11 22:37:55.683679: train_loss -0.2961 +2026-04-11 22:37:55.690295: val_loss -0.2051 +2026-04-11 22:37:55.692425: Pseudo dice [0.4462, 0.8071, 0.5943, 0.5685, 0.5943, 0.0558, 0.8339] +2026-04-11 22:37:55.695626: Epoch time: 102.96 s +2026-04-11 22:37:57.132396: +2026-04-11 22:37:57.134533: Epoch 1244 +2026-04-11 22:37:57.136607: Current learning rate: 0.00715 +2026-04-11 22:39:40.368403: train_loss -0.276 +2026-04-11 22:39:40.374867: val_loss -0.2815 +2026-04-11 22:39:40.377418: Pseudo dice [0.3447, 0.4617, 0.5887, 0.5098, 0.552, 0.5553, 0.7699] +2026-04-11 22:39:40.379548: Epoch time: 103.24 s +2026-04-11 22:39:41.838899: +2026-04-11 22:39:41.854707: Epoch 1245 +2026-04-11 22:39:41.856785: Current learning rate: 0.00715 +2026-04-11 22:41:24.316612: train_loss -0.2928 +2026-04-11 22:41:24.322411: val_loss -0.2594 +2026-04-11 22:41:24.324764: Pseudo dice [0.5879, 0.4819, 0.7435, 0.8227, 0.521, 0.2693, 0.7843] +2026-04-11 22:41:24.327386: Epoch time: 102.48 s +2026-04-11 22:41:25.766242: +2026-04-11 22:41:25.768128: Epoch 1246 +2026-04-11 22:41:25.770279: Current learning rate: 0.00715 +2026-04-11 22:43:08.328327: train_loss -0.3086 +2026-04-11 22:43:08.336492: val_loss -0.2085 +2026-04-11 22:43:08.340835: Pseudo dice [0.5051, 0.735, 0.589, 0.5818, 0.5085, 0.2416, 0.7453] +2026-04-11 22:43:08.345747: Epoch time: 102.57 s +2026-04-11 22:43:09.817196: +2026-04-11 22:43:09.819596: Epoch 1247 +2026-04-11 22:43:09.821590: Current learning rate: 0.00714 +2026-04-11 22:44:52.295165: train_loss -0.2872 +2026-04-11 22:44:52.302000: val_loss -0.2673 +2026-04-11 22:44:52.304318: Pseudo dice [0.464, 0.6319, 0.713, 0.4933, 0.6213, 0.6171, 0.7114] +2026-04-11 22:44:52.306765: Epoch time: 102.48 s +2026-04-11 22:44:53.757475: +2026-04-11 22:44:53.759768: Epoch 1248 +2026-04-11 22:44:53.762965: Current learning rate: 0.00714 +2026-04-11 22:46:36.017568: train_loss -0.3001 +2026-04-11 22:46:36.022537: val_loss -0.2385 +2026-04-11 22:46:36.024175: Pseudo dice [0.6007, 0.4845, 0.6513, 0.7071, 0.5426, 0.0957, 0.7989] +2026-04-11 22:46:36.026237: Epoch time: 102.26 s +2026-04-11 22:46:37.465863: +2026-04-11 22:46:37.467643: Epoch 1249 +2026-04-11 22:46:37.469563: Current learning rate: 0.00714 +2026-04-11 22:48:20.457856: train_loss -0.3089 +2026-04-11 22:48:20.465636: val_loss -0.183 +2026-04-11 22:48:20.468016: Pseudo dice [0.2646, 0.4736, 0.6976, 0.4149, 0.3187, 0.0884, 0.5075] +2026-04-11 22:48:20.470542: Epoch time: 103.0 s +2026-04-11 22:48:23.834118: +2026-04-11 22:48:23.836245: Epoch 1250 +2026-04-11 22:48:23.838619: Current learning rate: 0.00714 +2026-04-11 22:50:06.060128: train_loss -0.3212 +2026-04-11 22:50:06.066998: val_loss -0.2739 +2026-04-11 22:50:06.069914: Pseudo dice [0.7242, 0.6309, 0.6973, 0.8585, 0.3737, 0.2343, 0.78] +2026-04-11 22:50:06.072618: Epoch time: 102.23 s +2026-04-11 22:50:07.528814: +2026-04-11 22:50:07.530980: Epoch 1251 +2026-04-11 22:50:07.533761: Current learning rate: 0.00714 +2026-04-11 22:51:50.138430: train_loss -0.2984 +2026-04-11 22:51:50.144414: val_loss -0.2716 +2026-04-11 22:51:50.146371: Pseudo dice [0.5659, 0.3892, 0.6896, 0.478, 0.4312, 0.5785, 0.8017] +2026-04-11 22:51:50.148701: Epoch time: 102.61 s +2026-04-11 22:51:51.620971: +2026-04-11 22:51:51.622898: Epoch 1252 +2026-04-11 22:51:51.625011: Current learning rate: 0.00713 +2026-04-11 22:53:35.747521: train_loss -0.2999 +2026-04-11 22:53:35.753626: val_loss -0.2749 +2026-04-11 22:53:35.755893: Pseudo dice [0.6351, 0.4292, 0.6989, 0.8562, 0.6425, 0.1983, 0.5889] +2026-04-11 22:53:35.758150: Epoch time: 104.13 s +2026-04-11 22:53:37.208463: +2026-04-11 22:53:37.211828: Epoch 1253 +2026-04-11 22:53:37.214728: Current learning rate: 0.00713 +2026-04-11 22:55:19.990812: train_loss -0.3117 +2026-04-11 22:55:19.998111: val_loss -0.234 +2026-04-11 22:55:20.000376: Pseudo dice [0.313, 0.4474, 0.6481, 0.8155, 0.575, 0.26, 0.8012] +2026-04-11 22:55:20.003697: Epoch time: 102.79 s +2026-04-11 22:55:21.464153: +2026-04-11 22:55:21.467452: Epoch 1254 +2026-04-11 22:55:21.469707: Current learning rate: 0.00713 +2026-04-11 22:57:04.560502: train_loss -0.3196 +2026-04-11 22:57:04.567361: val_loss -0.2771 +2026-04-11 22:57:04.569911: Pseudo dice [0.3455, 0.4445, 0.6498, 0.7528, 0.5007, 0.8053, 0.7738] +2026-04-11 22:57:04.571987: Epoch time: 103.1 s +2026-04-11 22:57:06.025518: +2026-04-11 22:57:06.027599: Epoch 1255 +2026-04-11 22:57:06.029565: Current learning rate: 0.00713 +2026-04-11 22:58:48.708777: train_loss -0.3175 +2026-04-11 22:58:48.715028: val_loss -0.2297 +2026-04-11 22:58:48.718421: Pseudo dice [0.3006, 0.3966, 0.671, 0.0013, 0.624, 0.0427, 0.8002] +2026-04-11 22:58:48.721741: Epoch time: 102.69 s +2026-04-11 22:58:50.162196: +2026-04-11 22:58:50.163975: Epoch 1256 +2026-04-11 22:58:50.165926: Current learning rate: 0.00712 +2026-04-11 23:00:32.792160: train_loss -0.3164 +2026-04-11 23:00:32.799159: val_loss -0.298 +2026-04-11 23:00:32.801536: Pseudo dice [0.2663, 0.6026, 0.8406, 0.8856, 0.3523, 0.7383, 0.4778] +2026-04-11 23:00:32.804219: Epoch time: 102.63 s +2026-04-11 23:00:34.255225: +2026-04-11 23:00:34.257204: Epoch 1257 +2026-04-11 23:00:34.260043: Current learning rate: 0.00712 +2026-04-11 23:02:16.443371: train_loss -0.326 +2026-04-11 23:02:16.450689: val_loss -0.2094 +2026-04-11 23:02:16.452488: Pseudo dice [0.1925, 0.6251, 0.742, 0.5906, 0.3166, 0.1378, 0.18] +2026-04-11 23:02:16.454600: Epoch time: 102.19 s +2026-04-11 23:02:17.881292: +2026-04-11 23:02:17.883147: Epoch 1258 +2026-04-11 23:02:17.885303: Current learning rate: 0.00712 +2026-04-11 23:04:00.171441: train_loss -0.2938 +2026-04-11 23:04:00.179231: val_loss -0.2103 +2026-04-11 23:04:00.181073: Pseudo dice [0.5553, 0.5023, 0.6639, 0.5786, 0.4986, 0.0631, 0.6367] +2026-04-11 23:04:00.183089: Epoch time: 102.29 s +2026-04-11 23:04:01.640924: +2026-04-11 23:04:01.644498: Epoch 1259 +2026-04-11 23:04:01.648088: Current learning rate: 0.00712 +2026-04-11 23:05:45.704153: train_loss -0.314 +2026-04-11 23:05:45.711731: val_loss -0.284 +2026-04-11 23:05:45.714499: Pseudo dice [0.7453, 0.4025, 0.6887, 0.8402, 0.4477, 0.4535, 0.5261] +2026-04-11 23:05:45.717616: Epoch time: 104.07 s +2026-04-11 23:05:47.161531: +2026-04-11 23:05:47.164394: Epoch 1260 +2026-04-11 23:05:47.166687: Current learning rate: 0.00711 +2026-04-11 23:07:29.416229: train_loss -0.3114 +2026-04-11 23:07:29.424004: val_loss -0.2775 +2026-04-11 23:07:29.426127: Pseudo dice [0.6452, 0.6655, 0.6734, 0.6978, 0.6614, 0.7685, 0.4253] +2026-04-11 23:07:29.428548: Epoch time: 102.26 s +2026-04-11 23:07:30.858972: +2026-04-11 23:07:30.860626: Epoch 1261 +2026-04-11 23:07:30.862619: Current learning rate: 0.00711 +2026-04-11 23:09:13.464508: train_loss -0.2887 +2026-04-11 23:09:13.471661: val_loss -0.2418 +2026-04-11 23:09:13.473946: Pseudo dice [0.4012, 0.4094, 0.5356, 0.7095, 0.5407, 0.0533, 0.5475] +2026-04-11 23:09:13.476722: Epoch time: 102.61 s +2026-04-11 23:09:14.943389: +2026-04-11 23:09:14.945065: Epoch 1262 +2026-04-11 23:09:14.948000: Current learning rate: 0.00711 +2026-04-11 23:10:57.468206: train_loss -0.2946 +2026-04-11 23:10:57.475295: val_loss -0.2563 +2026-04-11 23:10:57.477144: Pseudo dice [0.5674, 0.7491, 0.5928, 0.0064, 0.5583, 0.4536, 0.5611] +2026-04-11 23:10:57.479357: Epoch time: 102.53 s +2026-04-11 23:10:58.903001: +2026-04-11 23:10:58.905985: Epoch 1263 +2026-04-11 23:10:58.909532: Current learning rate: 0.00711 +2026-04-11 23:12:41.416908: train_loss -0.2824 +2026-04-11 23:12:41.423450: val_loss -0.271 +2026-04-11 23:12:41.425390: Pseudo dice [0.6829, 0.3612, 0.6442, 0.827, 0.4348, 0.6638, 0.628] +2026-04-11 23:12:41.427888: Epoch time: 102.52 s +2026-04-11 23:12:42.889241: +2026-04-11 23:12:42.891187: Epoch 1264 +2026-04-11 23:12:42.893451: Current learning rate: 0.0071 +2026-04-11 23:14:25.758956: train_loss -0.3092 +2026-04-11 23:14:25.764235: val_loss -0.2496 +2026-04-11 23:14:25.766362: Pseudo dice [0.7258, 0.819, 0.6448, 0.6927, 0.5733, 0.431, 0.5421] +2026-04-11 23:14:25.768808: Epoch time: 102.87 s +2026-04-11 23:14:27.218964: +2026-04-11 23:14:27.221279: Epoch 1265 +2026-04-11 23:14:27.223244: Current learning rate: 0.0071 +2026-04-11 23:16:10.129262: train_loss -0.311 +2026-04-11 23:16:10.134329: val_loss -0.2232 +2026-04-11 23:16:10.137244: Pseudo dice [0.4359, 0.8196, 0.4788, 0.6978, 0.5325, 0.0912, 0.851] +2026-04-11 23:16:10.139892: Epoch time: 102.91 s +2026-04-11 23:16:11.578444: +2026-04-11 23:16:11.580250: Epoch 1266 +2026-04-11 23:16:11.582242: Current learning rate: 0.0071 +2026-04-11 23:17:53.956315: train_loss -0.3103 +2026-04-11 23:17:53.968024: val_loss -0.2201 +2026-04-11 23:17:53.970442: Pseudo dice [0.2467, 0.6091, 0.6276, 0.4539, 0.6452, 0.0319, 0.2984] +2026-04-11 23:17:53.974865: Epoch time: 102.38 s +2026-04-11 23:17:55.448395: +2026-04-11 23:17:55.452579: Epoch 1267 +2026-04-11 23:17:55.455957: Current learning rate: 0.0071 +2026-04-11 23:19:37.766684: train_loss -0.2912 +2026-04-11 23:19:37.773614: val_loss -0.2577 +2026-04-11 23:19:37.775930: Pseudo dice [0.7442, 0.7516, 0.6414, 0.5873, 0.7009, 0.2807, 0.5707] +2026-04-11 23:19:37.778859: Epoch time: 102.32 s +2026-04-11 23:19:39.212594: +2026-04-11 23:19:39.214321: Epoch 1268 +2026-04-11 23:19:39.216304: Current learning rate: 0.0071 +2026-04-11 23:21:21.621408: train_loss -0.2963 +2026-04-11 23:21:21.627696: val_loss -0.2457 +2026-04-11 23:21:21.629691: Pseudo dice [0.4702, 0.5024, 0.5763, 0.6553, 0.6061, 0.1087, 0.5967] +2026-04-11 23:21:21.632202: Epoch time: 102.41 s +2026-04-11 23:21:23.073150: +2026-04-11 23:21:23.074850: Epoch 1269 +2026-04-11 23:21:23.077003: Current learning rate: 0.00709 +2026-04-11 23:23:05.593264: train_loss -0.3001 +2026-04-11 23:23:05.599463: val_loss -0.179 +2026-04-11 23:23:05.601401: Pseudo dice [0.1045, 0.2992, 0.6104, 0.0864, 0.4246, 0.0739, 0.7737] +2026-04-11 23:23:05.603921: Epoch time: 102.52 s +2026-04-11 23:23:07.045838: +2026-04-11 23:23:07.047766: Epoch 1270 +2026-04-11 23:23:07.050903: Current learning rate: 0.00709 +2026-04-11 23:24:49.714960: train_loss -0.2926 +2026-04-11 23:24:49.721198: val_loss -0.2314 +2026-04-11 23:24:49.723036: Pseudo dice [0.4841, 0.1504, 0.6555, 0.4087, 0.2969, 0.3537, 0.582] +2026-04-11 23:24:49.725472: Epoch time: 102.67 s +2026-04-11 23:24:51.181826: +2026-04-11 23:24:51.184178: Epoch 1271 +2026-04-11 23:24:51.186536: Current learning rate: 0.00709 +2026-04-11 23:26:33.351264: train_loss -0.2947 +2026-04-11 23:26:33.357254: val_loss -0.2841 +2026-04-11 23:26:33.360160: Pseudo dice [0.6411, 0.2589, 0.5592, 0.7176, 0.4888, 0.8103, 0.7858] +2026-04-11 23:26:33.362580: Epoch time: 102.17 s +2026-04-11 23:26:34.812727: +2026-04-11 23:26:34.814435: Epoch 1272 +2026-04-11 23:26:34.817005: Current learning rate: 0.00709 +2026-04-11 23:28:17.250428: train_loss -0.3057 +2026-04-11 23:28:17.260008: val_loss -0.1332 +2026-04-11 23:28:17.262651: Pseudo dice [0.6114, 0.3656, 0.7159, 0.1199, 0.3136, 0.0258, 0.8777] +2026-04-11 23:28:17.265164: Epoch time: 102.44 s +2026-04-11 23:28:18.717603: +2026-04-11 23:28:18.719288: Epoch 1273 +2026-04-11 23:28:18.721336: Current learning rate: 0.00708 +2026-04-11 23:30:00.914159: train_loss -0.3137 +2026-04-11 23:30:00.920000: val_loss -0.2845 +2026-04-11 23:30:00.921781: Pseudo dice [0.2767, 0.6554, 0.7368, 0.7937, 0.6216, 0.2074, 0.7453] +2026-04-11 23:30:00.924163: Epoch time: 102.2 s +2026-04-11 23:30:02.366052: +2026-04-11 23:30:02.368367: Epoch 1274 +2026-04-11 23:30:02.370423: Current learning rate: 0.00708 +2026-04-11 23:31:44.427907: train_loss -0.3029 +2026-04-11 23:31:44.433687: val_loss -0.27 +2026-04-11 23:31:44.436324: Pseudo dice [0.4547, 0.4151, 0.7484, 0.631, 0.6017, 0.1382, 0.6474] +2026-04-11 23:31:44.438597: Epoch time: 102.07 s +2026-04-11 23:31:45.882656: +2026-04-11 23:31:45.885224: Epoch 1275 +2026-04-11 23:31:45.887640: Current learning rate: 0.00708 +2026-04-11 23:33:28.457540: train_loss -0.2821 +2026-04-11 23:33:28.462843: val_loss -0.1398 +2026-04-11 23:33:28.464653: Pseudo dice [0.4485, 0.7224, 0.5908, 0.1596, 0.2931, 0.1285, 0.7979] +2026-04-11 23:33:28.466769: Epoch time: 102.58 s +2026-04-11 23:33:29.886615: +2026-04-11 23:33:29.888465: Epoch 1276 +2026-04-11 23:33:29.890328: Current learning rate: 0.00708 +2026-04-11 23:35:12.103433: train_loss -0.3097 +2026-04-11 23:35:12.109478: val_loss -0.2239 +2026-04-11 23:35:12.112612: Pseudo dice [0.7154, 0.3395, 0.6387, 0.086, 0.2673, 0.0556, 0.5816] +2026-04-11 23:35:12.115205: Epoch time: 102.22 s +2026-04-11 23:35:13.600346: +2026-04-11 23:35:13.602706: Epoch 1277 +2026-04-11 23:35:13.604998: Current learning rate: 0.00707 +2026-04-11 23:36:56.658573: train_loss -0.321 +2026-04-11 23:36:56.667005: val_loss -0.2776 +2026-04-11 23:36:56.668949: Pseudo dice [0.498, 0.7278, 0.6688, 0.5868, 0.5052, 0.3413, 0.7495] +2026-04-11 23:36:56.671387: Epoch time: 103.06 s +2026-04-11 23:36:58.154046: +2026-04-11 23:36:58.155998: Epoch 1278 +2026-04-11 23:36:58.159011: Current learning rate: 0.00707 +2026-04-11 23:38:40.838204: train_loss -0.3044 +2026-04-11 23:38:40.845748: val_loss -0.2514 +2026-04-11 23:38:40.848106: Pseudo dice [0.4971, 0.5126, 0.4539, 0.6597, 0.4675, 0.0742, 0.8797] +2026-04-11 23:38:40.850932: Epoch time: 102.69 s +2026-04-11 23:38:43.467996: +2026-04-11 23:38:43.469793: Epoch 1279 +2026-04-11 23:38:43.471796: Current learning rate: 0.00707 +2026-04-11 23:40:25.949291: train_loss -0.3251 +2026-04-11 23:40:25.957120: val_loss -0.2678 +2026-04-11 23:40:25.960270: Pseudo dice [0.7329, 0.8839, 0.5903, 0.2845, 0.1886, 0.5195, 0.3806] +2026-04-11 23:40:25.963305: Epoch time: 102.49 s +2026-04-11 23:40:27.403974: +2026-04-11 23:40:27.406750: Epoch 1280 +2026-04-11 23:40:27.409536: Current learning rate: 0.00707 +2026-04-11 23:42:09.802603: train_loss -0.2961 +2026-04-11 23:42:09.809170: val_loss -0.2774 +2026-04-11 23:42:09.811684: Pseudo dice [0.5722, 0.5859, 0.6186, 0.0849, 0.5542, 0.2458, 0.605] +2026-04-11 23:42:09.814168: Epoch time: 102.4 s +2026-04-11 23:42:11.288557: +2026-04-11 23:42:11.290614: Epoch 1281 +2026-04-11 23:42:11.292493: Current learning rate: 0.00707 +2026-04-11 23:43:53.818912: train_loss -0.3146 +2026-04-11 23:43:53.826471: val_loss -0.2152 +2026-04-11 23:43:53.828851: Pseudo dice [0.8252, 0.4674, 0.5418, 0.6742, 0.5554, 0.0914, 0.3423] +2026-04-11 23:43:53.831389: Epoch time: 102.53 s +2026-04-11 23:43:55.274868: +2026-04-11 23:43:55.276901: Epoch 1282 +2026-04-11 23:43:55.279308: Current learning rate: 0.00706 +2026-04-11 23:45:37.456738: train_loss -0.3068 +2026-04-11 23:45:37.464771: val_loss -0.2415 +2026-04-11 23:45:37.467399: Pseudo dice [0.4864, 0.4589, 0.6819, 0.6341, 0.3815, 0.5104, 0.7296] +2026-04-11 23:45:37.469718: Epoch time: 102.19 s +2026-04-11 23:45:38.928361: +2026-04-11 23:45:38.930609: Epoch 1283 +2026-04-11 23:45:38.932614: Current learning rate: 0.00706 +2026-04-11 23:47:21.789983: train_loss -0.3015 +2026-04-11 23:47:21.796921: val_loss -0.2559 +2026-04-11 23:47:21.799094: Pseudo dice [0.3965, 0.3731, 0.6579, 0.6896, 0.3961, 0.7645, 0.6995] +2026-04-11 23:47:21.801791: Epoch time: 102.87 s +2026-04-11 23:47:23.248709: +2026-04-11 23:47:23.250695: Epoch 1284 +2026-04-11 23:47:23.253028: Current learning rate: 0.00706 +2026-04-11 23:49:06.289829: train_loss -0.3174 +2026-04-11 23:49:06.297630: val_loss -0.3179 +2026-04-11 23:49:06.300139: Pseudo dice [0.7034, 0.9018, 0.6451, 0.6954, 0.652, 0.6514, 0.8145] +2026-04-11 23:49:06.302822: Epoch time: 103.04 s +2026-04-11 23:49:07.794578: +2026-04-11 23:49:07.796361: Epoch 1285 +2026-04-11 23:49:07.798367: Current learning rate: 0.00706 +2026-04-11 23:50:50.081069: train_loss -0.3189 +2026-04-11 23:50:50.086359: val_loss -0.2679 +2026-04-11 23:50:50.088372: Pseudo dice [0.6722, 0.5328, 0.6731, 0.6632, 0.2842, 0.403, 0.664] +2026-04-11 23:50:50.090466: Epoch time: 102.29 s +2026-04-11 23:50:51.538377: +2026-04-11 23:50:51.540238: Epoch 1286 +2026-04-11 23:50:51.542752: Current learning rate: 0.00705 +2026-04-11 23:52:33.769867: train_loss -0.3243 +2026-04-11 23:52:33.777422: val_loss -0.2237 +2026-04-11 23:52:33.779927: Pseudo dice [0.6768, 0.7828, 0.6503, 0.7485, 0.5022, 0.1628, 0.54] +2026-04-11 23:52:33.782419: Epoch time: 102.24 s +2026-04-11 23:52:35.251879: +2026-04-11 23:52:35.254071: Epoch 1287 +2026-04-11 23:52:35.256073: Current learning rate: 0.00705 +2026-04-11 23:54:17.811430: train_loss -0.3074 +2026-04-11 23:54:17.816953: val_loss -0.2387 +2026-04-11 23:54:17.818976: Pseudo dice [0.6768, 0.5557, 0.7557, 0.206, 0.3005, 0.8908, 0.8139] +2026-04-11 23:54:17.821333: Epoch time: 102.56 s +2026-04-11 23:54:19.265501: +2026-04-11 23:54:19.267772: Epoch 1288 +2026-04-11 23:54:19.269849: Current learning rate: 0.00705 +2026-04-11 23:56:01.449270: train_loss -0.2946 +2026-04-11 23:56:01.455492: val_loss -0.2783 +2026-04-11 23:56:01.458322: Pseudo dice [0.3244, 0.4726, 0.6304, 0.4564, 0.5928, 0.786, 0.7784] +2026-04-11 23:56:01.463944: Epoch time: 102.19 s +2026-04-11 23:56:02.920220: +2026-04-11 23:56:02.922472: Epoch 1289 +2026-04-11 23:56:02.924806: Current learning rate: 0.00705 +2026-04-11 23:57:45.144910: train_loss -0.2862 +2026-04-11 23:57:45.150720: val_loss -0.1971 +2026-04-11 23:57:45.152721: Pseudo dice [0.5837, 0.578, 0.417, 0.392, 0.4127, 0.0835, 0.6989] +2026-04-11 23:57:45.155155: Epoch time: 102.23 s +2026-04-11 23:57:46.573244: +2026-04-11 23:57:46.575041: Epoch 1290 +2026-04-11 23:57:46.578527: Current learning rate: 0.00704 +2026-04-11 23:59:29.476165: train_loss -0.3062 +2026-04-11 23:59:29.483192: val_loss -0.2371 +2026-04-11 23:59:29.485978: Pseudo dice [0.5935, 0.1235, 0.5856, 0.6862, 0.5748, 0.053, 0.6651] +2026-04-11 23:59:29.491147: Epoch time: 102.91 s +2026-04-11 23:59:30.935285: +2026-04-11 23:59:30.937316: Epoch 1291 +2026-04-11 23:59:30.939325: Current learning rate: 0.00704 +2026-04-12 00:01:34.485789: train_loss -0.2916 +2026-04-12 00:01:34.504738: val_loss -0.1701 +2026-04-12 00:01:34.508507: Pseudo dice [0.6096, 0.689, 0.559, 0.65, 0.2038, 0.0512, 0.2321] +2026-04-12 00:01:34.513552: Epoch time: 123.55 s +2026-04-12 00:01:36.009247: +2026-04-12 00:01:36.012683: Epoch 1292 +2026-04-12 00:01:36.019342: Current learning rate: 0.00704 +2026-04-12 00:03:57.341832: train_loss -0.2887 +2026-04-12 00:03:57.356002: val_loss -0.2572 +2026-04-12 00:03:57.359107: Pseudo dice [0.423, 0.5563, 0.742, 0.4452, 0.4657, 0.7705, 0.635] +2026-04-12 00:03:57.363991: Epoch time: 141.34 s +2026-04-12 00:03:58.849463: +2026-04-12 00:03:58.852622: Epoch 1293 +2026-04-12 00:03:58.858366: Current learning rate: 0.00704 +2026-04-12 00:05:51.383673: train_loss -0.3227 +2026-04-12 00:05:51.394093: val_loss -0.2123 +2026-04-12 00:05:51.397554: Pseudo dice [0.575, 0.3595, 0.4957, 0.8296, 0.4809, 0.0821, 0.853] +2026-04-12 00:05:51.401807: Epoch time: 112.54 s +2026-04-12 00:05:52.881011: +2026-04-12 00:05:52.883353: Epoch 1294 +2026-04-12 00:05:52.886808: Current learning rate: 0.00703 +2026-04-12 00:07:35.757930: train_loss -0.3128 +2026-04-12 00:07:35.765085: val_loss -0.2239 +2026-04-12 00:07:35.768158: Pseudo dice [0.5054, 0.2431, 0.5968, 0.3161, 0.4374, 0.3276, 0.8056] +2026-04-12 00:07:35.771362: Epoch time: 102.88 s +2026-04-12 00:07:37.213217: +2026-04-12 00:07:37.216063: Epoch 1295 +2026-04-12 00:07:37.218929: Current learning rate: 0.00703 +2026-04-12 00:09:21.344858: train_loss -0.3137 +2026-04-12 00:09:21.351745: val_loss -0.23 +2026-04-12 00:09:21.353896: Pseudo dice [0.4094, 0.6542, 0.7266, 0.475, 0.3105, 0.4457, 0.567] +2026-04-12 00:09:21.355964: Epoch time: 104.14 s +2026-04-12 00:09:22.827463: +2026-04-12 00:09:22.829576: Epoch 1296 +2026-04-12 00:09:22.832279: Current learning rate: 0.00703 +2026-04-12 00:11:05.922026: train_loss -0.3239 +2026-04-12 00:11:05.929532: val_loss -0.2373 +2026-04-12 00:11:05.931856: Pseudo dice [0.2249, 0.7224, 0.6062, 0.8, 0.2595, 0.2451, 0.204] +2026-04-12 00:11:05.935214: Epoch time: 103.1 s +2026-04-12 00:11:07.402103: +2026-04-12 00:11:07.404322: Epoch 1297 +2026-04-12 00:11:07.406454: Current learning rate: 0.00703 +2026-04-12 00:12:50.240640: train_loss -0.2882 +2026-04-12 00:12:50.247352: val_loss -0.2136 +2026-04-12 00:12:50.249545: Pseudo dice [0.703, 0.6749, 0.7967, 0.7238, 0.2805, 0.1059, 0.492] +2026-04-12 00:12:50.252394: Epoch time: 102.84 s +2026-04-12 00:12:51.719421: +2026-04-12 00:12:51.721323: Epoch 1298 +2026-04-12 00:12:51.723259: Current learning rate: 0.00703 +2026-04-12 00:14:33.833264: train_loss -0.301 +2026-04-12 00:14:33.843258: val_loss -0.2586 +2026-04-12 00:14:33.846481: Pseudo dice [0.6116, 0.6182, 0.679, 0.625, 0.4367, 0.2436, 0.5757] +2026-04-12 00:14:33.849671: Epoch time: 102.12 s +2026-04-12 00:14:35.316709: +2026-04-12 00:14:35.319013: Epoch 1299 +2026-04-12 00:14:35.321005: Current learning rate: 0.00702 +2026-04-12 00:16:19.144334: train_loss -0.3004 +2026-04-12 00:16:19.150947: val_loss -0.2291 +2026-04-12 00:16:19.153616: Pseudo dice [0.6654, 0.572, 0.6458, 0.3866, 0.1574, 0.1114, 0.3034] +2026-04-12 00:16:19.156389: Epoch time: 103.83 s +2026-04-12 00:16:22.561902: +2026-04-12 00:16:22.563630: Epoch 1300 +2026-04-12 00:16:22.565813: Current learning rate: 0.00702 +2026-04-12 00:18:05.335434: train_loss -0.3102 +2026-04-12 00:18:05.342334: val_loss -0.2098 +2026-04-12 00:18:05.344518: Pseudo dice [0.7232, 0.7974, 0.2967, 0.7847, 0.6344, 0.0615, 0.8713] +2026-04-12 00:18:05.347063: Epoch time: 102.78 s +2026-04-12 00:18:06.794913: +2026-04-12 00:18:06.796973: Epoch 1301 +2026-04-12 00:18:06.799278: Current learning rate: 0.00702 +2026-04-12 00:19:49.546325: train_loss -0.2941 +2026-04-12 00:19:49.556730: val_loss -0.2629 +2026-04-12 00:19:49.560025: Pseudo dice [0.2117, 0.8221, 0.5992, 0.1962, 0.3247, 0.7537, 0.6731] +2026-04-12 00:19:49.563239: Epoch time: 102.76 s +2026-04-12 00:19:51.038921: +2026-04-12 00:19:51.041659: Epoch 1302 +2026-04-12 00:19:51.044366: Current learning rate: 0.00702 +2026-04-12 00:21:33.810447: train_loss -0.2997 +2026-04-12 00:21:33.820618: val_loss -0.2789 +2026-04-12 00:21:33.822963: Pseudo dice [0.3211, 0.5175, 0.644, 0.5706, 0.4789, 0.7631, 0.752] +2026-04-12 00:21:33.826241: Epoch time: 102.78 s +2026-04-12 00:21:35.339109: +2026-04-12 00:21:35.342415: Epoch 1303 +2026-04-12 00:21:35.344672: Current learning rate: 0.00701 +2026-04-12 00:23:17.787159: train_loss -0.3013 +2026-04-12 00:23:17.794012: val_loss -0.2821 +2026-04-12 00:23:17.795920: Pseudo dice [0.7509, 0.6363, 0.6619, 0.8036, 0.4144, 0.8022, 0.4959] +2026-04-12 00:23:17.798324: Epoch time: 102.45 s +2026-04-12 00:23:19.264142: +2026-04-12 00:23:19.265908: Epoch 1304 +2026-04-12 00:23:19.268050: Current learning rate: 0.00701 +2026-04-12 00:25:02.089873: train_loss -0.3264 +2026-04-12 00:25:02.097317: val_loss -0.2055 +2026-04-12 00:25:02.101711: Pseudo dice [0.4906, 0.2015, 0.6499, 0.3263, 0.4701, 0.1089, 0.7023] +2026-04-12 00:25:02.104275: Epoch time: 102.83 s +2026-04-12 00:25:03.570353: +2026-04-12 00:25:03.572400: Epoch 1305 +2026-04-12 00:25:03.574999: Current learning rate: 0.00701 +2026-04-12 00:26:46.402657: train_loss -0.3184 +2026-04-12 00:26:46.409549: val_loss -0.1802 +2026-04-12 00:26:46.411837: Pseudo dice [0.5129, 0.6378, 0.6167, 0.8456, 0.2347, 0.0333, 0.7105] +2026-04-12 00:26:46.432889: Epoch time: 102.84 s +2026-04-12 00:26:47.884033: +2026-04-12 00:26:47.886244: Epoch 1306 +2026-04-12 00:26:47.888664: Current learning rate: 0.00701 +2026-04-12 00:28:30.838849: train_loss -0.2879 +2026-04-12 00:28:30.846023: val_loss -0.1859 +2026-04-12 00:28:30.848702: Pseudo dice [0.5073, 0.2556, 0.406, 0.3419, 0.4138, 0.1631, 0.6173] +2026-04-12 00:28:30.851685: Epoch time: 102.96 s +2026-04-12 00:28:32.298136: +2026-04-12 00:28:32.300339: Epoch 1307 +2026-04-12 00:28:32.303159: Current learning rate: 0.007 +2026-04-12 00:30:16.119290: train_loss -0.2924 +2026-04-12 00:30:16.125537: val_loss -0.2548 +2026-04-12 00:30:16.127355: Pseudo dice [0.4645, 0.5127, 0.6037, 0.7064, 0.308, 0.1616, 0.5854] +2026-04-12 00:30:16.129501: Epoch time: 103.83 s +2026-04-12 00:30:17.571146: +2026-04-12 00:30:17.573952: Epoch 1308 +2026-04-12 00:30:17.576344: Current learning rate: 0.007 +2026-04-12 00:32:00.095064: train_loss -0.2975 +2026-04-12 00:32:00.105661: val_loss -0.2937 +2026-04-12 00:32:00.109017: Pseudo dice [0.6618, 0.4546, 0.696, 0.0267, 0.3792, 0.8164, 0.4801] +2026-04-12 00:32:00.114551: Epoch time: 102.53 s +2026-04-12 00:32:01.575234: +2026-04-12 00:32:01.577887: Epoch 1309 +2026-04-12 00:32:01.580445: Current learning rate: 0.007 +2026-04-12 00:33:43.791269: train_loss -0.2758 +2026-04-12 00:33:43.796488: val_loss -0.2534 +2026-04-12 00:33:43.798238: Pseudo dice [0.262, 0.8159, 0.6666, 0.7834, 0.5888, 0.4902, 0.4045] +2026-04-12 00:33:43.800262: Epoch time: 102.22 s +2026-04-12 00:33:45.283393: +2026-04-12 00:33:45.285114: Epoch 1310 +2026-04-12 00:33:45.287166: Current learning rate: 0.007 +2026-04-12 00:35:28.238422: train_loss -0.3061 +2026-04-12 00:35:28.243721: val_loss -0.1715 +2026-04-12 00:35:28.247627: Pseudo dice [0.8259, 0.7053, 0.6992, 0.6108, 0.5976, 0.0417, 0.67] +2026-04-12 00:35:28.249931: Epoch time: 102.96 s +2026-04-12 00:35:29.689071: +2026-04-12 00:35:29.691308: Epoch 1311 +2026-04-12 00:35:29.693468: Current learning rate: 0.00699 +2026-04-12 00:37:12.388093: train_loss -0.2915 +2026-04-12 00:37:12.397117: val_loss -0.172 +2026-04-12 00:37:12.399613: Pseudo dice [0.4074, 0.6957, 0.5369, 0.7569, 0.4502, 0.122, 0.4318] +2026-04-12 00:37:12.402679: Epoch time: 102.7 s +2026-04-12 00:37:13.835229: +2026-04-12 00:37:13.837471: Epoch 1312 +2026-04-12 00:37:13.839652: Current learning rate: 0.00699 +2026-04-12 00:38:56.782409: train_loss -0.2912 +2026-04-12 00:38:56.790353: val_loss -0.2942 +2026-04-12 00:38:56.792691: Pseudo dice [0.5949, 0.3074, 0.7288, 0.7643, 0.4796, 0.5985, 0.8358] +2026-04-12 00:38:56.795204: Epoch time: 102.95 s +2026-04-12 00:38:58.264292: +2026-04-12 00:38:58.266124: Epoch 1313 +2026-04-12 00:38:58.269488: Current learning rate: 0.00699 +2026-04-12 00:40:41.211744: train_loss -0.3073 +2026-04-12 00:40:41.218171: val_loss -0.1844 +2026-04-12 00:40:41.220078: Pseudo dice [0.1809, 0.6895, 0.6525, 0.0549, 0.53, 0.0621, 0.8355] +2026-04-12 00:40:41.222448: Epoch time: 102.95 s +2026-04-12 00:40:42.700734: +2026-04-12 00:40:42.702758: Epoch 1314 +2026-04-12 00:40:42.705353: Current learning rate: 0.00699 +2026-04-12 00:42:25.407009: train_loss -0.2929 +2026-04-12 00:42:25.415062: val_loss -0.2635 +2026-04-12 00:42:25.421391: Pseudo dice [0.8138, 0.4006, 0.7999, 0.2567, 0.4009, 0.3326, 0.4826] +2026-04-12 00:42:25.433915: Epoch time: 102.71 s +2026-04-12 00:42:26.891124: +2026-04-12 00:42:26.892960: Epoch 1315 +2026-04-12 00:42:26.894884: Current learning rate: 0.00699 +2026-04-12 00:44:09.063455: train_loss -0.2894 +2026-04-12 00:44:09.069908: val_loss -0.2056 +2026-04-12 00:44:09.072102: Pseudo dice [0.0645, 0.122, 0.6286, 0.4606, 0.4507, 0.4111, 0.7863] +2026-04-12 00:44:09.074171: Epoch time: 102.18 s +2026-04-12 00:44:10.531051: +2026-04-12 00:44:10.532945: Epoch 1316 +2026-04-12 00:44:10.534948: Current learning rate: 0.00698 +2026-04-12 00:45:52.806176: train_loss -0.3078 +2026-04-12 00:45:52.812935: val_loss -0.2915 +2026-04-12 00:45:52.814952: Pseudo dice [0.3701, 0.6486, 0.5185, 0.6506, 0.637, 0.797, 0.7753] +2026-04-12 00:45:52.817199: Epoch time: 102.28 s +2026-04-12 00:45:54.254046: +2026-04-12 00:45:54.256271: Epoch 1317 +2026-04-12 00:45:54.258596: Current learning rate: 0.00698 +2026-04-12 00:47:37.088932: train_loss -0.3003 +2026-04-12 00:47:37.094743: val_loss -0.2765 +2026-04-12 00:47:37.096748: Pseudo dice [0.3527, 0.631, 0.5409, 0.4948, 0.5147, 0.7927, 0.7703] +2026-04-12 00:47:37.099959: Epoch time: 102.84 s +2026-04-12 00:47:38.544869: +2026-04-12 00:47:38.546447: Epoch 1318 +2026-04-12 00:47:38.548497: Current learning rate: 0.00698 +2026-04-12 00:49:21.096624: train_loss -0.3043 +2026-04-12 00:49:21.102491: val_loss -0.2754 +2026-04-12 00:49:21.104666: Pseudo dice [0.8652, 0.4437, 0.7134, 0.6098, 0.5794, 0.5777, 0.618] +2026-04-12 00:49:21.108265: Epoch time: 102.56 s +2026-04-12 00:49:23.699372: +2026-04-12 00:49:23.701274: Epoch 1319 +2026-04-12 00:49:23.703336: Current learning rate: 0.00698 +2026-04-12 00:51:06.157856: train_loss -0.2996 +2026-04-12 00:51:06.164494: val_loss -0.2602 +2026-04-12 00:51:06.166979: Pseudo dice [0.4587, 0.452, 0.6689, 0.0276, 0.6898, 0.2569, 0.8525] +2026-04-12 00:51:06.169280: Epoch time: 102.46 s +2026-04-12 00:51:07.631958: +2026-04-12 00:51:07.634000: Epoch 1320 +2026-04-12 00:51:07.635924: Current learning rate: 0.00697 +2026-04-12 00:52:50.479977: train_loss -0.3041 +2026-04-12 00:52:50.487918: val_loss -0.2416 +2026-04-12 00:52:50.490789: Pseudo dice [0.6937, 0.8337, 0.651, 0.1645, 0.3734, 0.1632, 0.4079] +2026-04-12 00:52:50.493699: Epoch time: 102.85 s +2026-04-12 00:52:51.936309: +2026-04-12 00:52:51.938397: Epoch 1321 +2026-04-12 00:52:51.940564: Current learning rate: 0.00697 +2026-04-12 00:54:35.331997: train_loss -0.3209 +2026-04-12 00:54:35.337837: val_loss -0.2854 +2026-04-12 00:54:35.348596: Pseudo dice [0.5255, 0.7038, 0.6754, 0.2866, 0.3562, 0.8009, 0.726] +2026-04-12 00:54:35.351017: Epoch time: 103.4 s +2026-04-12 00:54:36.812453: +2026-04-12 00:54:36.815506: Epoch 1322 +2026-04-12 00:54:36.818671: Current learning rate: 0.00697 +2026-04-12 00:56:20.332223: train_loss -0.301 +2026-04-12 00:56:20.338864: val_loss -0.1555 +2026-04-12 00:56:20.340984: Pseudo dice [0.2903, 0.594, 0.4452, 0.7661, 0.2386, 0.0432, 0.7057] +2026-04-12 00:56:20.343707: Epoch time: 103.52 s +2026-04-12 00:56:21.810427: +2026-04-12 00:56:21.818648: Epoch 1323 +2026-04-12 00:56:21.831529: Current learning rate: 0.00697 +2026-04-12 00:58:05.806209: train_loss -0.3091 +2026-04-12 00:58:05.813667: val_loss -0.2856 +2026-04-12 00:58:05.816092: Pseudo dice [0.6127, 0.4869, 0.4529, 0.7576, 0.613, 0.7267, 0.7623] +2026-04-12 00:58:05.818916: Epoch time: 104.0 s +2026-04-12 00:58:07.276587: +2026-04-12 00:58:07.278560: Epoch 1324 +2026-04-12 00:58:07.281355: Current learning rate: 0.00696 +2026-04-12 00:59:51.308786: train_loss -0.3114 +2026-04-12 00:59:51.314930: val_loss -0.2586 +2026-04-12 00:59:51.318368: Pseudo dice [0.3559, 0.6913, 0.7689, 0.6972, 0.3899, 0.6721, 0.3207] +2026-04-12 00:59:51.321952: Epoch time: 104.04 s +2026-04-12 00:59:52.786871: +2026-04-12 00:59:52.789008: Epoch 1325 +2026-04-12 00:59:52.791255: Current learning rate: 0.00696 +2026-04-12 01:01:58.644636: train_loss -0.2911 +2026-04-12 01:01:58.653881: val_loss -0.2333 +2026-04-12 01:01:58.657372: Pseudo dice [0.586, 0.2997, 0.5718, 0.665, 0.4803, 0.0322, 0.8089] +2026-04-12 01:01:58.660802: Epoch time: 125.86 s +2026-04-12 01:02:00.118694: +2026-04-12 01:02:00.121075: Epoch 1326 +2026-04-12 01:02:00.124637: Current learning rate: 0.00696 +2026-04-12 01:04:21.242593: train_loss -0.2974 +2026-04-12 01:04:21.258764: val_loss -0.2336 +2026-04-12 01:04:21.264155: Pseudo dice [0.7251, 0.1389, 0.7119, 0.7318, 0.3814, 0.1378, 0.6392] +2026-04-12 01:04:21.269777: Epoch time: 141.13 s +2026-04-12 01:04:22.761104: +2026-04-12 01:04:22.764557: Epoch 1327 +2026-04-12 01:04:22.768703: Current learning rate: 0.00696 +2026-04-12 01:06:55.143887: train_loss -0.3033 +2026-04-12 01:06:55.158066: val_loss -0.2884 +2026-04-12 01:06:55.163006: Pseudo dice [0.4397, 0.4696, 0.7408, 0.6768, 0.3769, 0.7114, 0.7554] +2026-04-12 01:06:55.168626: Epoch time: 152.39 s +2026-04-12 01:06:56.640445: +2026-04-12 01:06:56.644244: Epoch 1328 +2026-04-12 01:06:56.649059: Current learning rate: 0.00696 +2026-04-12 01:09:31.542315: train_loss -0.3135 +2026-04-12 01:09:31.559565: val_loss -0.3174 +2026-04-12 01:09:31.565003: Pseudo dice [0.7217, 0.76, 0.7138, 0.7379, 0.5907, 0.8152, 0.8557] +2026-04-12 01:09:31.570243: Epoch time: 154.91 s +2026-04-12 01:09:33.087160: +2026-04-12 01:09:33.093414: Epoch 1329 +2026-04-12 01:09:33.098560: Current learning rate: 0.00695 +2026-04-12 01:12:19.969825: train_loss -0.3104 +2026-04-12 01:12:19.988054: val_loss -0.148 +2026-04-12 01:12:19.993707: Pseudo dice [0.4336, 0.6967, 0.5102, 0.6977, 0.4072, 0.1371, 0.7015] +2026-04-12 01:12:20.000297: Epoch time: 166.89 s +2026-04-12 01:12:21.551192: +2026-04-12 01:12:21.560008: Epoch 1330 +2026-04-12 01:12:21.566291: Current learning rate: 0.00695 +2026-04-12 01:14:56.808367: train_loss -0.2847 +2026-04-12 01:14:56.817244: val_loss -0.218 +2026-04-12 01:14:56.820283: Pseudo dice [0.8246, 0.4273, 0.4911, 0.604, 0.531, 0.1584, 0.67] +2026-04-12 01:14:56.824097: Epoch time: 155.26 s +2026-04-12 01:14:58.288010: +2026-04-12 01:14:58.290082: Epoch 1331 +2026-04-12 01:14:58.292837: Current learning rate: 0.00695 +2026-04-12 01:17:22.559335: train_loss -0.2939 +2026-04-12 01:17:22.578028: val_loss -0.2668 +2026-04-12 01:17:22.582799: Pseudo dice [0.5919, 0.5612, 0.7455, 0.6759, 0.5315, 0.5759, 0.6824] +2026-04-12 01:17:22.588454: Epoch time: 144.28 s +2026-04-12 01:17:24.064126: +2026-04-12 01:17:24.081411: Epoch 1332 +2026-04-12 01:17:24.085805: Current learning rate: 0.00695 +2026-04-12 01:19:57.064979: train_loss -0.3019 +2026-04-12 01:19:57.083053: val_loss -0.2711 +2026-04-12 01:19:57.094489: Pseudo dice [0.4599, 0.7105, 0.6405, 0.2986, 0.6168, 0.3631, 0.7495] +2026-04-12 01:19:57.101547: Epoch time: 153.0 s +2026-04-12 01:19:58.586107: +2026-04-12 01:19:58.592043: Epoch 1333 +2026-04-12 01:19:58.598309: Current learning rate: 0.00694 +2026-04-12 01:22:13.495639: train_loss -0.3054 +2026-04-12 01:22:13.503287: val_loss -0.2939 +2026-04-12 01:22:13.505784: Pseudo dice [0.7356, 0.3592, 0.6207, 0.3806, 0.6026, 0.7099, 0.9019] +2026-04-12 01:22:13.511632: Epoch time: 134.91 s +2026-04-12 01:22:14.991310: +2026-04-12 01:22:14.994614: Epoch 1334 +2026-04-12 01:22:14.997712: Current learning rate: 0.00694 +2026-04-12 01:24:02.653535: train_loss -0.3208 +2026-04-12 01:24:02.661860: val_loss -0.2475 +2026-04-12 01:24:02.664486: Pseudo dice [0.4743, 0.4594, 0.6718, 0.6683, 0.5692, 0.3026, 0.813] +2026-04-12 01:24:02.667137: Epoch time: 107.67 s +2026-04-12 01:24:04.161274: +2026-04-12 01:24:04.164396: Epoch 1335 +2026-04-12 01:24:04.167566: Current learning rate: 0.00694 +2026-04-12 01:26:28.026051: train_loss -0.3056 +2026-04-12 01:26:28.035593: val_loss -0.2874 +2026-04-12 01:26:28.040082: Pseudo dice [0.6784, 0.8634, 0.7141, 0.7953, 0.5305, 0.4988, 0.851] +2026-04-12 01:26:28.044763: Epoch time: 143.87 s +2026-04-12 01:26:28.049157: Yayy! New best EMA pseudo Dice: 0.5757 +2026-04-12 01:26:31.771681: +2026-04-12 01:26:31.774676: Epoch 1336 +2026-04-12 01:26:31.778075: Current learning rate: 0.00694 +2026-04-12 01:28:51.743792: train_loss -0.3032 +2026-04-12 01:28:51.756242: val_loss -0.2351 +2026-04-12 01:28:51.760953: Pseudo dice [0.8332, 0.7963, 0.631, 0.1682, 0.4376, 0.0916, 0.3596] +2026-04-12 01:28:51.765647: Epoch time: 139.98 s +2026-04-12 01:28:53.276920: +2026-04-12 01:28:53.280924: Epoch 1337 +2026-04-12 01:28:53.291192: Current learning rate: 0.00693 +2026-04-12 01:31:32.976815: train_loss -0.3042 +2026-04-12 01:31:32.992499: val_loss -0.1312 +2026-04-12 01:31:32.998538: Pseudo dice [0.4514, 0.4988, 0.4562, 0.5121, 0.475, 0.0441, 0.5589] +2026-04-12 01:31:33.004473: Epoch time: 159.7 s +2026-04-12 01:31:35.768650: +2026-04-12 01:31:35.772414: Epoch 1338 +2026-04-12 01:31:35.777745: Current learning rate: 0.00693 +2026-04-12 01:33:59.319628: train_loss -0.3174 +2026-04-12 01:33:59.336572: val_loss -0.2924 +2026-04-12 01:33:59.343019: Pseudo dice [0.2771, 0.485, 0.6651, 0.8675, 0.7355, 0.8074, 0.8208] +2026-04-12 01:33:59.347915: Epoch time: 143.55 s +2026-04-12 01:34:00.834588: +2026-04-12 01:34:00.839283: Epoch 1339 +2026-04-12 01:34:00.844221: Current learning rate: 0.00693 +2026-04-12 01:36:35.244308: train_loss -0.3093 +2026-04-12 01:36:35.254233: val_loss -0.1625 +2026-04-12 01:36:35.259544: Pseudo dice [0.5383, 0.4976, 0.3206, 0.5041, 0.4454, 0.0477, 0.6712] +2026-04-12 01:36:35.264409: Epoch time: 154.41 s +2026-04-12 01:36:36.754461: +2026-04-12 01:36:36.760826: Epoch 1340 +2026-04-12 01:36:36.768987: Current learning rate: 0.00693 +2026-04-12 01:38:45.487763: train_loss -0.2907 +2026-04-12 01:38:45.498079: val_loss -0.2472 +2026-04-12 01:38:45.501028: Pseudo dice [0.5302, 0.6262, 0.7219, 0.2778, 0.3945, 0.0476, 0.8174] +2026-04-12 01:38:45.504122: Epoch time: 128.74 s +2026-04-12 01:38:46.978157: +2026-04-12 01:38:46.983920: Epoch 1341 +2026-04-12 01:38:46.988698: Current learning rate: 0.00692 +2026-04-12 01:41:11.435941: train_loss -0.2957 +2026-04-12 01:41:11.444002: val_loss -0.2186 +2026-04-12 01:41:11.446929: Pseudo dice [0.3889, 0.621, 0.566, 0.5774, 0.5453, 0.2773, 0.5761] +2026-04-12 01:41:11.454578: Epoch time: 144.46 s +2026-04-12 01:41:12.946227: +2026-04-12 01:41:12.949694: Epoch 1342 +2026-04-12 01:41:12.952340: Current learning rate: 0.00692 +2026-04-12 01:43:40.130555: train_loss -0.3207 +2026-04-12 01:43:40.138690: val_loss -0.2721 +2026-04-12 01:43:40.141504: Pseudo dice [0.5232, 0.4991, 0.5944, 0.5898, 0.3508, 0.7633, 0.6608] +2026-04-12 01:43:40.144813: Epoch time: 147.19 s +2026-04-12 01:43:41.648082: +2026-04-12 01:43:41.652475: Epoch 1343 +2026-04-12 01:43:41.657136: Current learning rate: 0.00692 +2026-04-12 01:46:13.083946: train_loss -0.3088 +2026-04-12 01:46:13.098344: val_loss -0.2294 +2026-04-12 01:46:13.101541: Pseudo dice [0.7554, 0.7566, 0.5833, 0.3071, 0.4656, 0.273, 0.4132] +2026-04-12 01:46:13.105754: Epoch time: 151.44 s +2026-04-12 01:46:14.618751: +2026-04-12 01:46:14.625106: Epoch 1344 +2026-04-12 01:46:14.631871: Current learning rate: 0.00692 +2026-04-12 01:48:35.234632: train_loss -0.3135 +2026-04-12 01:48:35.254502: val_loss -0.2164 +2026-04-12 01:48:35.260257: Pseudo dice [0.2312, 0.2273, 0.6581, 0.2516, 0.1969, 0.1971, 0.7336] +2026-04-12 01:48:35.266637: Epoch time: 140.62 s +2026-04-12 01:48:36.765223: +2026-04-12 01:48:36.768580: Epoch 1345 +2026-04-12 01:48:36.773398: Current learning rate: 0.00692 +2026-04-12 01:51:09.674073: train_loss -0.2998 +2026-04-12 01:51:09.684883: val_loss -0.2524 +2026-04-12 01:51:09.688119: Pseudo dice [0.6287, 0.6377, 0.7035, 0.1762, 0.2857, 0.1189, 0.8348] +2026-04-12 01:51:09.692660: Epoch time: 152.91 s +2026-04-12 01:51:11.211087: +2026-04-12 01:51:11.215222: Epoch 1346 +2026-04-12 01:51:11.218724: Current learning rate: 0.00691 +2026-04-12 01:53:38.836431: train_loss -0.311 +2026-04-12 01:53:38.863532: val_loss -0.2998 +2026-04-12 01:53:38.867174: Pseudo dice [0.5826, 0.4934, 0.7121, 0.7459, 0.4696, 0.8332, 0.6242] +2026-04-12 01:53:38.870394: Epoch time: 147.63 s +2026-04-12 01:53:40.383336: +2026-04-12 01:53:40.388764: Epoch 1347 +2026-04-12 01:53:40.392308: Current learning rate: 0.00691 +2026-04-12 01:56:16.127345: train_loss -0.3045 +2026-04-12 01:56:16.144988: val_loss -0.2681 +2026-04-12 01:56:16.151892: Pseudo dice [0.506, 0.7702, 0.7804, 0.0436, 0.6667, 0.0986, 0.8702] +2026-04-12 01:56:16.158422: Epoch time: 155.75 s +2026-04-12 01:56:17.652711: +2026-04-12 01:56:17.658973: Epoch 1348 +2026-04-12 01:56:17.665478: Current learning rate: 0.00691 +2026-04-12 01:58:29.978502: train_loss -0.3155 +2026-04-12 01:58:29.990164: val_loss -0.2824 +2026-04-12 01:58:29.994045: Pseudo dice [0.3407, 0.6068, 0.6729, 0.0276, 0.5491, 0.3453, 0.9041] +2026-04-12 01:58:29.997800: Epoch time: 132.33 s +2026-04-12 01:58:31.484960: +2026-04-12 01:58:31.488186: Epoch 1349 +2026-04-12 01:58:31.491923: Current learning rate: 0.00691 +2026-04-12 02:00:58.360086: train_loss -0.3193 +2026-04-12 02:00:58.373864: val_loss -0.3005 +2026-04-12 02:00:58.377178: Pseudo dice [0.6669, 0.6964, 0.782, 0.0156, 0.5461, 0.6768, 0.815] +2026-04-12 02:00:58.383534: Epoch time: 146.88 s +2026-04-12 02:01:03.300957: +2026-04-12 02:01:03.303313: Epoch 1350 +2026-04-12 02:01:03.305935: Current learning rate: 0.0069 +2026-04-12 02:03:28.400999: train_loss -0.2997 +2026-04-12 02:03:28.412237: val_loss -0.2343 +2026-04-12 02:03:28.416352: Pseudo dice [0.346, 0.4199, 0.5366, 0.4071, 0.4621, 0.2485, 0.5801] +2026-04-12 02:03:28.421256: Epoch time: 145.1 s +2026-04-12 02:03:29.951690: +2026-04-12 02:03:29.957134: Epoch 1351 +2026-04-12 02:03:29.961058: Current learning rate: 0.0069 +2026-04-12 02:05:38.783453: train_loss -0.2922 +2026-04-12 02:05:38.801201: val_loss -0.09 +2026-04-12 02:05:38.808943: Pseudo dice [0.3514, 0.4234, 0.4961, 0.3911, 0.3618, 0.0447, 0.6231] +2026-04-12 02:05:38.816076: Epoch time: 128.84 s +2026-04-12 02:05:40.323517: +2026-04-12 02:05:40.329002: Epoch 1352 +2026-04-12 02:05:40.335471: Current learning rate: 0.0069 +2026-04-12 02:08:11.677032: train_loss -0.3088 +2026-04-12 02:08:11.695495: val_loss -0.2607 +2026-04-12 02:08:11.702153: Pseudo dice [0.5417, 0.5744, 0.5244, 0.359, 0.6711, 0.516, 0.8018] +2026-04-12 02:08:11.712598: Epoch time: 151.36 s +2026-04-12 02:08:13.216853: +2026-04-12 02:08:13.223514: Epoch 1353 +2026-04-12 02:08:13.232077: Current learning rate: 0.0069 +2026-04-12 02:10:39.822302: train_loss -0.3179 +2026-04-12 02:10:39.833656: val_loss -0.2237 +2026-04-12 02:10:39.837206: Pseudo dice [0.5256, 0.5901, 0.7065, 0.6189, 0.2156, 0.0906, 0.89] +2026-04-12 02:10:39.841731: Epoch time: 146.61 s +2026-04-12 02:10:41.329439: +2026-04-12 02:10:41.335699: Epoch 1354 +2026-04-12 02:10:41.340741: Current learning rate: 0.00689 +2026-04-12 02:13:11.554060: train_loss -0.3018 +2026-04-12 02:13:11.562351: val_loss -0.1957 +2026-04-12 02:13:11.565581: Pseudo dice [0.1469, 0.4693, 0.4228, 0.2171, 0.5179, 0.1346, 0.5623] +2026-04-12 02:13:11.568976: Epoch time: 150.23 s +2026-04-12 02:13:13.097197: +2026-04-12 02:13:13.104028: Epoch 1355 +2026-04-12 02:13:13.110482: Current learning rate: 0.00689 +2026-04-12 02:15:40.374330: train_loss -0.278 +2026-04-12 02:15:40.392670: val_loss -0.14 +2026-04-12 02:15:40.400311: Pseudo dice [0.1832, 0.4466, 0.5479, 0.6174, 0.4622, 0.1766, 0.6548] +2026-04-12 02:15:40.409088: Epoch time: 147.28 s +2026-04-12 02:15:41.932274: +2026-04-12 02:15:41.937095: Epoch 1356 +2026-04-12 02:15:41.942398: Current learning rate: 0.00689 +2026-04-12 02:18:07.412035: train_loss -0.2955 +2026-04-12 02:18:07.422679: val_loss -0.2903 +2026-04-12 02:18:07.426015: Pseudo dice [0.6093, 0.5637, 0.7567, 0.7139, 0.6757, 0.2652, 0.8539] +2026-04-12 02:18:07.430266: Epoch time: 145.48 s +2026-04-12 02:18:08.938560: +2026-04-12 02:18:08.941308: Epoch 1357 +2026-04-12 02:18:08.944733: Current learning rate: 0.00689 +2026-04-12 02:20:27.317337: train_loss -0.2961 +2026-04-12 02:20:27.333953: val_loss -0.2592 +2026-04-12 02:20:27.339946: Pseudo dice [0.5548, 0.5123, 0.5039, 0.7651, 0.5836, 0.1239, 0.9315] +2026-04-12 02:20:27.346142: Epoch time: 138.38 s +2026-04-12 02:20:28.882439: +2026-04-12 02:20:28.886906: Epoch 1358 +2026-04-12 02:20:28.892523: Current learning rate: 0.00688 +2026-04-12 02:22:41.726878: train_loss -0.2998 +2026-04-12 02:22:41.734678: val_loss -0.2868 +2026-04-12 02:22:41.737230: Pseudo dice [0.5937, 0.6906, 0.7747, 0.7407, 0.4444, 0.7121, 0.8865] +2026-04-12 02:22:41.740451: Epoch time: 132.85 s +2026-04-12 02:22:43.255578: +2026-04-12 02:22:43.258778: Epoch 1359 +2026-04-12 02:22:43.261927: Current learning rate: 0.00688 +2026-04-12 02:24:54.216721: train_loss -0.298 +2026-04-12 02:24:54.226096: val_loss -0.2674 +2026-04-12 02:24:54.229048: Pseudo dice [0.7528, 0.4844, 0.6695, 0.7799, 0.5775, 0.1195, 0.7572] +2026-04-12 02:24:54.232694: Epoch time: 130.96 s +2026-04-12 02:24:55.716152: +2026-04-12 02:24:55.718917: Epoch 1360 +2026-04-12 02:24:55.741837: Current learning rate: 0.00688 +2026-04-12 02:27:01.688642: train_loss -0.3159 +2026-04-12 02:27:01.698892: val_loss -0.2547 +2026-04-12 02:27:01.702606: Pseudo dice [0.4812, 0.5646, 0.6879, 0.7737, 0.1955, 0.6759, 0.6958] +2026-04-12 02:27:01.706649: Epoch time: 125.98 s +2026-04-12 02:27:03.196886: +2026-04-12 02:27:03.200365: Epoch 1361 +2026-04-12 02:27:03.203694: Current learning rate: 0.00688 +2026-04-12 02:28:59.850816: train_loss -0.2965 +2026-04-12 02:28:59.861220: val_loss -0.2913 +2026-04-12 02:28:59.864732: Pseudo dice [0.5982, 0.8878, 0.6311, 0.7257, 0.5649, 0.554, 0.7653] +2026-04-12 02:28:59.878525: Epoch time: 116.66 s +2026-04-12 02:29:01.361763: +2026-04-12 02:29:01.364084: Epoch 1362 +2026-04-12 02:29:01.366741: Current learning rate: 0.00688 +2026-04-12 02:31:02.119438: train_loss -0.2984 +2026-04-12 02:31:02.129895: val_loss -0.2674 +2026-04-12 02:31:02.132622: Pseudo dice [0.3934, 0.762, 0.5677, 0.3261, 0.3766, 0.4132, 0.5668] +2026-04-12 02:31:02.135998: Epoch time: 120.76 s +2026-04-12 02:31:03.631141: +2026-04-12 02:31:03.633846: Epoch 1363 +2026-04-12 02:31:03.636933: Current learning rate: 0.00687 +2026-04-12 02:33:10.549497: train_loss -0.2986 +2026-04-12 02:33:10.559880: val_loss -0.261 +2026-04-12 02:33:10.563472: Pseudo dice [0.665, 0.504, 0.6916, 0.8485, 0.4778, 0.6338, 0.9195] +2026-04-12 02:33:10.567033: Epoch time: 126.92 s +2026-04-12 02:33:12.041539: +2026-04-12 02:33:12.044787: Epoch 1364 +2026-04-12 02:33:12.048072: Current learning rate: 0.00687 +2026-04-12 02:35:13.159657: train_loss -0.3004 +2026-04-12 02:35:13.172052: val_loss -0.1969 +2026-04-12 02:35:13.175410: Pseudo dice [0.7037, 0.8175, 0.4667, 0.6792, 0.6235, 0.3113, 0.8636] +2026-04-12 02:35:13.178741: Epoch time: 121.12 s +2026-04-12 02:35:14.662323: +2026-04-12 02:35:14.667679: Epoch 1365 +2026-04-12 02:35:14.672426: Current learning rate: 0.00687 +2026-04-12 02:37:16.132737: train_loss -0.3012 +2026-04-12 02:37:16.141545: val_loss -0.2278 +2026-04-12 02:37:16.145386: Pseudo dice [0.7887, 0.2661, 0.5606, 0.6664, 0.4595, 0.0497, 0.2912] +2026-04-12 02:37:16.149929: Epoch time: 121.47 s +2026-04-12 02:37:17.635711: +2026-04-12 02:37:17.638723: Epoch 1366 +2026-04-12 02:37:17.641774: Current learning rate: 0.00687 +2026-04-12 02:39:16.661806: train_loss -0.3126 +2026-04-12 02:39:16.675201: val_loss -0.2465 +2026-04-12 02:39:16.679458: Pseudo dice [0.7431, 0.7537, 0.6168, 0.4303, 0.5154, 0.0943, 0.7513] +2026-04-12 02:39:16.683537: Epoch time: 119.03 s +2026-04-12 02:39:18.160930: +2026-04-12 02:39:18.163150: Epoch 1367 +2026-04-12 02:39:18.165870: Current learning rate: 0.00686 +2026-04-12 02:41:14.516149: train_loss -0.3064 +2026-04-12 02:41:14.527531: val_loss -0.2955 +2026-04-12 02:41:14.529843: Pseudo dice [0.7696, 0.6228, 0.592, 0.8287, 0.216, 0.8446, 0.8089] +2026-04-12 02:41:14.533348: Epoch time: 116.36 s +2026-04-12 02:41:16.011317: +2026-04-12 02:41:16.013245: Epoch 1368 +2026-04-12 02:41:16.015887: Current learning rate: 0.00686 +2026-04-12 02:43:16.803411: train_loss -0.3117 +2026-04-12 02:43:16.812247: val_loss -0.1806 +2026-04-12 02:43:16.815186: Pseudo dice [0.6503, 0.7209, 0.4845, 0.8231, 0.1825, 0.0303, 0.6305] +2026-04-12 02:43:16.819224: Epoch time: 120.8 s +2026-04-12 02:43:18.268504: +2026-04-12 02:43:18.270765: Epoch 1369 +2026-04-12 02:43:18.272736: Current learning rate: 0.00686 +2026-04-12 02:45:19.825904: train_loss -0.2971 +2026-04-12 02:45:19.832207: val_loss -0.2639 +2026-04-12 02:45:19.834447: Pseudo dice [0.6052, 0.7739, 0.5306, 0.5313, 0.4249, 0.2701, 0.6591] +2026-04-12 02:45:19.837531: Epoch time: 121.56 s +2026-04-12 02:45:21.331023: +2026-04-12 02:45:21.335601: Epoch 1370 +2026-04-12 02:45:21.340865: Current learning rate: 0.00686 +2026-04-12 02:47:05.572109: train_loss -0.3063 +2026-04-12 02:47:05.587396: val_loss -0.292 +2026-04-12 02:47:05.590440: Pseudo dice [0.4869, 0.5071, 0.6278, 0.7987, 0.4418, 0.6614, 0.7456] +2026-04-12 02:47:05.593473: Epoch time: 104.24 s +2026-04-12 02:47:07.081927: +2026-04-12 02:47:07.086190: Epoch 1371 +2026-04-12 02:47:07.092472: Current learning rate: 0.00685 +2026-04-12 02:48:54.214502: train_loss -0.322 +2026-04-12 02:48:54.222461: val_loss -0.2384 +2026-04-12 02:48:54.224961: Pseudo dice [0.738, 0.6391, 0.6672, 0.6088, 0.5791, 0.117, 0.8805] +2026-04-12 02:48:54.227660: Epoch time: 107.14 s +2026-04-12 02:48:55.791692: +2026-04-12 02:48:55.794436: Epoch 1372 +2026-04-12 02:48:55.797103: Current learning rate: 0.00685 +2026-04-12 02:50:39.367210: train_loss -0.3273 +2026-04-12 02:50:39.375600: val_loss -0.2017 +2026-04-12 02:50:39.378705: Pseudo dice [0.4968, 0.3742, 0.6776, 0.5279, 0.6288, 0.0634, 0.6445] +2026-04-12 02:50:39.381805: Epoch time: 103.58 s +2026-04-12 02:50:40.862196: +2026-04-12 02:50:40.864719: Epoch 1373 +2026-04-12 02:50:40.866775: Current learning rate: 0.00685 +2026-04-12 02:52:29.766158: train_loss -0.3228 +2026-04-12 02:52:29.776254: val_loss -0.2665 +2026-04-12 02:52:29.780408: Pseudo dice [0.6506, 0.5312, 0.7436, 0.5276, 0.3138, 0.853, 0.3391] +2026-04-12 02:52:29.792758: Epoch time: 108.91 s +2026-04-12 02:52:31.316493: +2026-04-12 02:52:31.320882: Epoch 1374 +2026-04-12 02:52:31.326520: Current learning rate: 0.00685 +2026-04-12 02:54:16.282560: train_loss -0.2866 +2026-04-12 02:54:16.304669: val_loss -0.1886 +2026-04-12 02:54:16.307367: Pseudo dice [0.6106, 0.7165, 0.3871, 0.5657, 0.4998, 0.0445, 0.615] +2026-04-12 02:54:16.310138: Epoch time: 104.97 s +2026-04-12 02:54:18.085856: +2026-04-12 02:54:18.087800: Epoch 1375 +2026-04-12 02:54:18.090192: Current learning rate: 0.00684 +2026-04-12 02:56:06.572364: train_loss -0.3062 +2026-04-12 02:56:06.584999: val_loss -0.2303 +2026-04-12 02:56:06.588933: Pseudo dice [0.3677, 0.7396, 0.7269, 0.3858, 0.483, 0.0936, 0.5956] +2026-04-12 02:56:06.592383: Epoch time: 108.49 s +2026-04-12 02:56:08.397327: +2026-04-12 02:56:08.402205: Epoch 1376 +2026-04-12 02:56:08.408228: Current learning rate: 0.00684 +2026-04-12 02:57:52.523162: train_loss -0.3019 +2026-04-12 02:57:52.530884: val_loss -0.2792 +2026-04-12 02:57:52.533938: Pseudo dice [0.3166, 0.5193, 0.7037, 0.4803, 0.4066, 0.5305, 0.6465] +2026-04-12 02:57:52.537052: Epoch time: 104.13 s +2026-04-12 02:57:54.056686: +2026-04-12 02:57:54.058735: Epoch 1377 +2026-04-12 02:57:54.061605: Current learning rate: 0.00684 +2026-04-12 02:59:38.214927: train_loss -0.3153 +2026-04-12 02:59:38.223434: val_loss -0.3046 +2026-04-12 02:59:38.229605: Pseudo dice [0.6804, 0.8305, 0.8147, 0.5839, 0.3591, 0.8155, 0.8566] +2026-04-12 02:59:38.232822: Epoch time: 104.16 s +2026-04-12 02:59:39.731469: +2026-04-12 02:59:39.735003: Epoch 1378 +2026-04-12 02:59:39.737840: Current learning rate: 0.00684 +2026-04-12 03:01:25.741895: train_loss -0.3071 +2026-04-12 03:01:25.758393: val_loss -0.2106 +2026-04-12 03:01:25.769969: Pseudo dice [0.2844, 0.705, 0.4458, 0.4459, 0.5427, 0.225, 0.2022] +2026-04-12 03:01:25.773468: Epoch time: 106.01 s +2026-04-12 03:01:27.290833: +2026-04-12 03:01:27.293310: Epoch 1379 +2026-04-12 03:01:27.295767: Current learning rate: 0.00684 +2026-04-12 03:03:11.516451: train_loss -0.2913 +2026-04-12 03:03:11.525019: val_loss -0.2369 +2026-04-12 03:03:11.528606: Pseudo dice [0.7889, 0.6184, 0.5051, 0.649, 0.3218, 0.2201, 0.8306] +2026-04-12 03:03:11.531555: Epoch time: 104.23 s +2026-04-12 03:03:13.030526: +2026-04-12 03:03:13.032884: Epoch 1380 +2026-04-12 03:03:13.035696: Current learning rate: 0.00683 +2026-04-12 03:04:58.949446: train_loss -0.3096 +2026-04-12 03:04:58.957283: val_loss -0.2425 +2026-04-12 03:04:58.959710: Pseudo dice [0.6773, 0.5541, 0.5041, 0.5266, 0.4348, 0.1266, 0.6799] +2026-04-12 03:04:58.962769: Epoch time: 105.92 s +2026-04-12 03:05:00.444055: +2026-04-12 03:05:00.451400: Epoch 1381 +2026-04-12 03:05:00.457824: Current learning rate: 0.00683 +2026-04-12 03:06:48.636306: train_loss -0.2993 +2026-04-12 03:06:48.643814: val_loss -0.2781 +2026-04-12 03:06:48.646358: Pseudo dice [0.6684, 0.632, 0.6338, 0.795, 0.3812, 0.788, 0.542] +2026-04-12 03:06:48.650288: Epoch time: 108.2 s +2026-04-12 03:06:50.144359: +2026-04-12 03:06:50.148082: Epoch 1382 +2026-04-12 03:06:50.151940: Current learning rate: 0.00683 +2026-04-12 03:08:39.752828: train_loss -0.2999 +2026-04-12 03:08:39.761240: val_loss -0.2465 +2026-04-12 03:08:39.764472: Pseudo dice [0.7375, 0.2724, 0.6461, 0.1302, 0.2722, 0.2485, 0.2996] +2026-04-12 03:08:39.767685: Epoch time: 109.61 s +2026-04-12 03:08:41.486922: +2026-04-12 03:08:41.489441: Epoch 1383 +2026-04-12 03:08:41.494024: Current learning rate: 0.00683 +2026-04-12 03:10:29.351688: train_loss -0.2972 +2026-04-12 03:10:29.358914: val_loss -0.2187 +2026-04-12 03:10:29.361635: Pseudo dice [0.1108, 0.7238, 0.4228, 0.5458, 0.5843, 0.0928, 0.3143] +2026-04-12 03:10:29.364647: Epoch time: 107.87 s +2026-04-12 03:10:30.861782: +2026-04-12 03:10:30.863516: Epoch 1384 +2026-04-12 03:10:30.866313: Current learning rate: 0.00682 +2026-04-12 03:12:16.880872: train_loss -0.3048 +2026-04-12 03:12:16.889481: val_loss -0.2392 +2026-04-12 03:12:16.892141: Pseudo dice [0.5759, 0.3255, 0.663, 0.0336, 0.5655, 0.1184, 0.7568] +2026-04-12 03:12:16.896526: Epoch time: 106.02 s +2026-04-12 03:12:18.403728: +2026-04-12 03:12:18.406659: Epoch 1385 +2026-04-12 03:12:18.411957: Current learning rate: 0.00682 +2026-04-12 03:14:03.888023: train_loss -0.297 +2026-04-12 03:14:03.896236: val_loss -0.2194 +2026-04-12 03:14:03.898848: Pseudo dice [0.7034, 0.6712, 0.6418, 0.7121, 0.6069, 0.0606, 0.8483] +2026-04-12 03:14:03.901664: Epoch time: 105.49 s +2026-04-12 03:14:05.461563: +2026-04-12 03:14:05.464971: Epoch 1386 +2026-04-12 03:14:05.469965: Current learning rate: 0.00682 +2026-04-12 03:15:50.157500: train_loss -0.2995 +2026-04-12 03:15:50.165465: val_loss -0.2037 +2026-04-12 03:15:50.171416: Pseudo dice [0.8152, 0.4483, 0.5788, 0.7178, 0.5534, 0.0254, 0.5649] +2026-04-12 03:15:50.178673: Epoch time: 104.7 s +2026-04-12 03:15:51.655004: +2026-04-12 03:15:51.658668: Epoch 1387 +2026-04-12 03:15:51.662313: Current learning rate: 0.00682 +2026-04-12 03:17:38.819649: train_loss -0.2963 +2026-04-12 03:17:38.831705: val_loss -0.2582 +2026-04-12 03:17:38.835600: Pseudo dice [0.5938, 0.6943, 0.7176, 0.7738, 0.5431, 0.1037, 0.6205] +2026-04-12 03:17:38.840138: Epoch time: 107.17 s +2026-04-12 03:17:40.342610: +2026-04-12 03:17:40.345871: Epoch 1388 +2026-04-12 03:17:40.349122: Current learning rate: 0.00681 +2026-04-12 03:19:27.472141: train_loss -0.3008 +2026-04-12 03:19:27.480059: val_loss -0.2485 +2026-04-12 03:19:27.482991: Pseudo dice [0.5014, 0.5682, 0.4952, 0.1395, 0.6944, 0.1499, 0.9136] +2026-04-12 03:19:27.488011: Epoch time: 107.13 s +2026-04-12 03:19:28.997162: +2026-04-12 03:19:28.999801: Epoch 1389 +2026-04-12 03:19:29.002508: Current learning rate: 0.00681 +2026-04-12 03:21:19.571417: train_loss -0.3107 +2026-04-12 03:21:19.580179: val_loss -0.2708 +2026-04-12 03:21:19.583956: Pseudo dice [0.7534, 0.5231, 0.5943, 0.4093, 0.3727, 0.8403, 0.3858] +2026-04-12 03:21:19.587912: Epoch time: 110.58 s +2026-04-12 03:21:21.115761: +2026-04-12 03:21:21.118294: Epoch 1390 +2026-04-12 03:21:21.121924: Current learning rate: 0.00681 +2026-04-12 03:23:07.292863: train_loss -0.3155 +2026-04-12 03:23:07.304551: val_loss -0.2146 +2026-04-12 03:23:07.307356: Pseudo dice [0.4111, 0.4816, 0.4994, 0.0321, 0.5243, 0.3468, 0.7485] +2026-04-12 03:23:07.310266: Epoch time: 106.18 s +2026-04-12 03:23:08.797109: +2026-04-12 03:23:08.799917: Epoch 1391 +2026-04-12 03:23:08.803548: Current learning rate: 0.00681 +2026-04-12 03:24:56.538529: train_loss -0.2938 +2026-04-12 03:24:56.552468: val_loss -0.1976 +2026-04-12 03:24:56.556376: Pseudo dice [0.7505, 0.7065, 0.3815, 0.2376, 0.6153, 0.0498, 0.885] +2026-04-12 03:24:56.563121: Epoch time: 107.75 s +2026-04-12 03:24:58.047295: +2026-04-12 03:24:58.052757: Epoch 1392 +2026-04-12 03:24:58.057912: Current learning rate: 0.0068 +2026-04-12 03:26:46.483269: train_loss -0.2964 +2026-04-12 03:26:46.493191: val_loss -0.2835 +2026-04-12 03:26:46.495503: Pseudo dice [0.86, 0.5945, 0.7608, 0.5267, 0.6062, 0.314, 0.6204] +2026-04-12 03:26:46.499027: Epoch time: 108.44 s +2026-04-12 03:26:48.002089: +2026-04-12 03:26:48.005020: Epoch 1393 +2026-04-12 03:26:48.007121: Current learning rate: 0.0068 +2026-04-12 03:28:35.126967: train_loss -0.3058 +2026-04-12 03:28:35.135773: val_loss -0.2583 +2026-04-12 03:28:35.139400: Pseudo dice [0.5391, 0.6881, 0.6044, 0.3365, 0.2435, 0.4185, 0.7525] +2026-04-12 03:28:35.142699: Epoch time: 107.13 s +2026-04-12 03:28:36.653559: +2026-04-12 03:28:36.656258: Epoch 1394 +2026-04-12 03:28:36.661292: Current learning rate: 0.0068 +2026-04-12 03:30:22.988016: train_loss -0.3011 +2026-04-12 03:30:22.996135: val_loss -0.2827 +2026-04-12 03:30:22.999067: Pseudo dice [0.8466, 0.6545, 0.423, 0.5191, 0.4445, 0.3819, 0.7824] +2026-04-12 03:30:23.002844: Epoch time: 106.34 s +2026-04-12 03:30:24.504258: +2026-04-12 03:30:24.507518: Epoch 1395 +2026-04-12 03:30:24.511188: Current learning rate: 0.0068 +2026-04-12 03:32:11.421566: train_loss -0.3104 +2026-04-12 03:32:11.431056: val_loss -0.2743 +2026-04-12 03:32:11.434148: Pseudo dice [0.5748, 0.6482, 0.5491, 0.6971, 0.5187, 0.2161, 0.8671] +2026-04-12 03:32:11.436619: Epoch time: 106.92 s +2026-04-12 03:32:12.940861: +2026-04-12 03:32:12.946121: Epoch 1396 +2026-04-12 03:32:12.949027: Current learning rate: 0.0068 +2026-04-12 03:34:01.652220: train_loss -0.3097 +2026-04-12 03:34:01.661183: val_loss -0.2769 +2026-04-12 03:34:01.663944: Pseudo dice [0.6222, 0.8385, 0.7061, 0.4051, 0.4758, 0.8062, 0.7683] +2026-04-12 03:34:01.667663: Epoch time: 108.72 s +2026-04-12 03:34:03.186379: +2026-04-12 03:34:03.188625: Epoch 1397 +2026-04-12 03:34:03.191656: Current learning rate: 0.00679 +2026-04-12 03:35:48.475712: train_loss -0.2793 +2026-04-12 03:35:48.486846: val_loss -0.2062 +2026-04-12 03:35:48.490574: Pseudo dice [0.2965, 0.4259, 0.4397, 0.3369, 0.1927, 0.2467, 0.787] +2026-04-12 03:35:48.493565: Epoch time: 105.29 s +2026-04-12 03:35:49.974036: +2026-04-12 03:35:49.977098: Epoch 1398 +2026-04-12 03:35:49.980391: Current learning rate: 0.00679 +2026-04-12 03:37:39.124676: train_loss -0.3024 +2026-04-12 03:37:39.135630: val_loss -0.278 +2026-04-12 03:37:39.138172: Pseudo dice [0.7096, 0.7326, 0.486, 0.7191, 0.516, 0.3524, 0.8921] +2026-04-12 03:37:39.142064: Epoch time: 109.15 s +2026-04-12 03:37:40.700051: +2026-04-12 03:37:40.702916: Epoch 1399 +2026-04-12 03:37:40.705441: Current learning rate: 0.00679 +2026-04-12 03:39:24.480257: train_loss -0.3103 +2026-04-12 03:39:24.489701: val_loss -0.2296 +2026-04-12 03:39:24.492790: Pseudo dice [0.2336, 0.5086, 0.5783, 0.4993, 0.4823, 0.1133, 0.6729] +2026-04-12 03:39:24.496330: Epoch time: 103.78 s +2026-04-12 03:39:27.995154: +2026-04-12 03:39:27.998310: Epoch 1400 +2026-04-12 03:39:28.001762: Current learning rate: 0.00679 +2026-04-12 03:41:16.787368: train_loss -0.3152 +2026-04-12 03:41:16.797574: val_loss -0.2149 +2026-04-12 03:41:16.799973: Pseudo dice [0.1493, 0.5956, 0.584, 0.3586, 0.3948, 0.1742, 0.7553] +2026-04-12 03:41:16.802700: Epoch time: 108.8 s +2026-04-12 03:41:18.322738: +2026-04-12 03:41:18.327537: Epoch 1401 +2026-04-12 03:41:18.331428: Current learning rate: 0.00678 +2026-04-12 03:43:02.653443: train_loss -0.3131 +2026-04-12 03:43:02.662757: val_loss -0.2751 +2026-04-12 03:43:02.664850: Pseudo dice [0.6976, 0.3037, 0.6628, 0.0612, 0.4914, 0.6971, 0.854] +2026-04-12 03:43:02.667137: Epoch time: 104.33 s +2026-04-12 03:43:04.148895: +2026-04-12 03:43:04.150973: Epoch 1402 +2026-04-12 03:43:04.153214: Current learning rate: 0.00678 +2026-04-12 03:44:47.444217: train_loss -0.3127 +2026-04-12 03:44:47.460560: val_loss -0.2152 +2026-04-12 03:44:47.465581: Pseudo dice [0.6307, 0.5991, 0.5016, 0.4304, 0.3908, 0.2185, 0.8574] +2026-04-12 03:44:47.468408: Epoch time: 103.3 s +2026-04-12 03:44:48.960883: +2026-04-12 03:44:48.963846: Epoch 1403 +2026-04-12 03:44:48.970914: Current learning rate: 0.00678 +2026-04-12 03:46:33.448107: train_loss -0.2983 +2026-04-12 03:46:33.459652: val_loss -0.2449 +2026-04-12 03:46:33.462002: Pseudo dice [0.4595, 0.5595, 0.5954, 0.6798, 0.5356, 0.1077, 0.7347] +2026-04-12 03:46:33.464597: Epoch time: 104.49 s +2026-04-12 03:46:34.988266: +2026-04-12 03:46:34.992472: Epoch 1404 +2026-04-12 03:46:34.995077: Current learning rate: 0.00678 +2026-04-12 03:48:19.046050: train_loss -0.3226 +2026-04-12 03:48:19.053785: val_loss -0.2775 +2026-04-12 03:48:19.056239: Pseudo dice [0.5743, 0.8104, 0.6451, 0.8296, 0.394, 0.6747, 0.8521] +2026-04-12 03:48:19.068948: Epoch time: 104.06 s +2026-04-12 03:48:20.554174: +2026-04-12 03:48:20.563068: Epoch 1405 +2026-04-12 03:48:20.568362: Current learning rate: 0.00677 +2026-04-12 03:50:04.221194: train_loss -0.3249 +2026-04-12 03:50:04.227358: val_loss -0.1961 +2026-04-12 03:50:04.230164: Pseudo dice [0.3937, 0.6552, 0.6493, 0.0139, 0.5329, 0.0484, 0.8036] +2026-04-12 03:50:04.234090: Epoch time: 103.67 s +2026-04-12 03:50:05.711910: +2026-04-12 03:50:05.715539: Epoch 1406 +2026-04-12 03:50:05.718855: Current learning rate: 0.00677 +2026-04-12 03:51:50.180736: train_loss -0.3171 +2026-04-12 03:51:50.190451: val_loss -0.1907 +2026-04-12 03:51:50.193829: Pseudo dice [0.6913, 0.5106, 0.6432, 0.662, 0.2603, 0.4502, 0.7549] +2026-04-12 03:51:50.197612: Epoch time: 104.47 s +2026-04-12 03:51:51.709714: +2026-04-12 03:51:51.712913: Epoch 1407 +2026-04-12 03:51:51.725401: Current learning rate: 0.00677 +2026-04-12 03:53:35.692874: train_loss -0.307 +2026-04-12 03:53:35.699435: val_loss -0.2695 +2026-04-12 03:53:35.701636: Pseudo dice [0.6165, 0.453, 0.6889, 0.0355, 0.5118, 0.2018, 0.7098] +2026-04-12 03:53:35.704757: Epoch time: 103.99 s +2026-04-12 03:53:37.177859: +2026-04-12 03:53:37.179631: Epoch 1408 +2026-04-12 03:53:37.181892: Current learning rate: 0.00677 +2026-04-12 03:55:23.157725: train_loss -0.3 +2026-04-12 03:55:23.165937: val_loss -0.2198 +2026-04-12 03:55:23.168267: Pseudo dice [0.5489, 0.3206, 0.5733, 0.6181, 0.399, 0.0513, 0.699] +2026-04-12 03:55:23.170677: Epoch time: 105.98 s +2026-04-12 03:55:24.945820: +2026-04-12 03:55:24.947854: Epoch 1409 +2026-04-12 03:55:24.950422: Current learning rate: 0.00676 +2026-04-12 03:57:08.350513: train_loss -0.3086 +2026-04-12 03:57:08.361112: val_loss -0.2616 +2026-04-12 03:57:08.364985: Pseudo dice [0.792, 0.7577, 0.6557, 0.4593, 0.5084, 0.3876, 0.711] +2026-04-12 03:57:08.368055: Epoch time: 103.41 s +2026-04-12 03:57:09.881699: +2026-04-12 03:57:09.884877: Epoch 1410 +2026-04-12 03:57:09.889283: Current learning rate: 0.00676 +2026-04-12 03:58:53.438655: train_loss -0.3107 +2026-04-12 03:58:53.445585: val_loss -0.2561 +2026-04-12 03:58:53.448211: Pseudo dice [0.4007, 0.5548, 0.6282, 0.4666, 0.5748, 0.4227, 0.7105] +2026-04-12 03:58:53.450870: Epoch time: 103.56 s +2026-04-12 03:58:54.975413: +2026-04-12 03:58:54.977219: Epoch 1411 +2026-04-12 03:58:54.979231: Current learning rate: 0.00676 +2026-04-12 04:00:39.164369: train_loss -0.3216 +2026-04-12 04:00:39.173986: val_loss -0.252 +2026-04-12 04:00:39.178422: Pseudo dice [0.577, 0.5433, 0.623, 0.3858, 0.3113, 0.241, 0.6828] +2026-04-12 04:00:39.183418: Epoch time: 104.19 s +2026-04-12 04:00:40.696071: +2026-04-12 04:00:40.699718: Epoch 1412 +2026-04-12 04:00:40.702540: Current learning rate: 0.00676 +2026-04-12 04:02:27.284655: train_loss -0.309 +2026-04-12 04:02:27.295476: val_loss -0.2477 +2026-04-12 04:02:27.299589: Pseudo dice [0.4539, 0.477, 0.6873, 0.7334, 0.2281, 0.0972, 0.7873] +2026-04-12 04:02:27.302950: Epoch time: 106.59 s +2026-04-12 04:02:28.777570: +2026-04-12 04:02:28.792372: Epoch 1413 +2026-04-12 04:02:28.808851: Current learning rate: 0.00676 +2026-04-12 04:04:15.616157: train_loss -0.3071 +2026-04-12 04:04:15.623579: val_loss -0.3054 +2026-04-12 04:04:15.626768: Pseudo dice [0.2977, 0.8264, 0.6433, 0.8454, 0.6385, 0.689, 0.617] +2026-04-12 04:04:15.630593: Epoch time: 106.84 s +2026-04-12 04:04:17.113898: +2026-04-12 04:04:17.116020: Epoch 1414 +2026-04-12 04:04:17.118276: Current learning rate: 0.00675 +2026-04-12 04:06:00.609518: train_loss -0.2947 +2026-04-12 04:06:00.617238: val_loss -0.2884 +2026-04-12 04:06:00.619731: Pseudo dice [0.3216, 0.5091, 0.6257, 0.8189, 0.6639, 0.788, 0.4845] +2026-04-12 04:06:00.626504: Epoch time: 103.5 s +2026-04-12 04:06:02.136185: +2026-04-12 04:06:02.139280: Epoch 1415 +2026-04-12 04:06:02.142459: Current learning rate: 0.00675 +2026-04-12 04:07:45.177414: train_loss -0.3037 +2026-04-12 04:07:45.185661: val_loss -0.252 +2026-04-12 04:07:45.190336: Pseudo dice [0.4322, 0.5092, 0.3153, 0.3742, 0.3719, 0.3071, 0.7182] +2026-04-12 04:07:45.193454: Epoch time: 103.05 s +2026-04-12 04:07:46.699898: +2026-04-12 04:07:46.702291: Epoch 1416 +2026-04-12 04:07:46.704930: Current learning rate: 0.00675 +2026-04-12 04:09:29.895061: train_loss -0.2988 +2026-04-12 04:09:29.904830: val_loss -0.1887 +2026-04-12 04:09:29.907732: Pseudo dice [0.5037, 0.6514, 0.6067, 0.6698, 0.6559, 0.2002, 0.7604] +2026-04-12 04:09:29.914151: Epoch time: 103.2 s +2026-04-12 04:09:31.418947: +2026-04-12 04:09:31.421669: Epoch 1417 +2026-04-12 04:09:31.424360: Current learning rate: 0.00675 +2026-04-12 04:11:16.182332: train_loss -0.299 +2026-04-12 04:11:16.189926: val_loss -0.2384 +2026-04-12 04:11:16.192617: Pseudo dice [0.5137, 0.6364, 0.3211, 0.2839, 0.4355, 0.4927, 0.6556] +2026-04-12 04:11:16.195412: Epoch time: 104.77 s +2026-04-12 04:11:17.684943: +2026-04-12 04:11:17.687380: Epoch 1418 +2026-04-12 04:11:17.689555: Current learning rate: 0.00674 +2026-04-12 04:13:03.410301: train_loss -0.2972 +2026-04-12 04:13:03.420882: val_loss -0.2687 +2026-04-12 04:13:03.423470: Pseudo dice [0.3955, 0.5362, 0.7756, 0.0009, 0.4463, 0.5791, 0.6301] +2026-04-12 04:13:03.425441: Epoch time: 105.73 s +2026-04-12 04:13:04.914061: +2026-04-12 04:13:04.915756: Epoch 1419 +2026-04-12 04:13:04.917716: Current learning rate: 0.00674 +2026-04-12 04:14:49.415720: train_loss -0.2855 +2026-04-12 04:14:49.423679: val_loss -0.2497 +2026-04-12 04:14:49.426369: Pseudo dice [0.6744, 0.5085, 0.5729, 0.2118, 0.1856, 0.4205, 0.4532] +2026-04-12 04:14:49.429582: Epoch time: 104.51 s +2026-04-12 04:14:50.951339: +2026-04-12 04:14:50.954738: Epoch 1420 +2026-04-12 04:14:50.958811: Current learning rate: 0.00674 +2026-04-12 04:16:36.344656: train_loss -0.2949 +2026-04-12 04:16:36.351312: val_loss -0.2168 +2026-04-12 04:16:36.353584: Pseudo dice [0.6114, 0.4215, 0.4311, 0.8009, 0.3511, 0.1469, 0.5511] +2026-04-12 04:16:36.356137: Epoch time: 105.4 s +2026-04-12 04:16:37.868036: +2026-04-12 04:16:37.869796: Epoch 1421 +2026-04-12 04:16:37.871833: Current learning rate: 0.00674 +2026-04-12 04:18:21.371505: train_loss -0.3071 +2026-04-12 04:18:21.378971: val_loss -0.2421 +2026-04-12 04:18:21.381653: Pseudo dice [0.5278, 0.3964, 0.4862, 0.7783, 0.453, 0.0376, 0.8887] +2026-04-12 04:18:21.385459: Epoch time: 103.51 s +2026-04-12 04:18:22.863042: +2026-04-12 04:18:22.865122: Epoch 1422 +2026-04-12 04:18:22.867652: Current learning rate: 0.00673 +2026-04-12 04:20:07.542682: train_loss -0.3018 +2026-04-12 04:20:07.548845: val_loss -0.2738 +2026-04-12 04:20:07.551054: Pseudo dice [0.2011, 0.0922, 0.6626, 0.444, 0.5569, 0.5387, 0.8158] +2026-04-12 04:20:07.554391: Epoch time: 104.68 s +2026-04-12 04:20:09.040426: +2026-04-12 04:20:09.042576: Epoch 1423 +2026-04-12 04:20:09.044881: Current learning rate: 0.00673 +2026-04-12 04:21:52.236114: train_loss -0.2963 +2026-04-12 04:21:52.244084: val_loss -0.2124 +2026-04-12 04:21:52.246982: Pseudo dice [0.5262, 0.6054, 0.6043, 0.0034, 0.3423, 0.0713, 0.651] +2026-04-12 04:21:52.250607: Epoch time: 103.2 s +2026-04-12 04:21:53.760161: +2026-04-12 04:21:53.762027: Epoch 1424 +2026-04-12 04:21:53.764313: Current learning rate: 0.00673 +2026-04-12 04:23:36.548262: train_loss -0.2933 +2026-04-12 04:23:36.555893: val_loss -0.3113 +2026-04-12 04:23:36.558914: Pseudo dice [0.3701, 0.7745, 0.71, 0.2575, 0.6208, 0.8519, 0.887] +2026-04-12 04:23:36.561841: Epoch time: 102.79 s +2026-04-12 04:23:38.061795: +2026-04-12 04:23:38.063540: Epoch 1425 +2026-04-12 04:23:38.065603: Current learning rate: 0.00673 +2026-04-12 04:25:21.055277: train_loss -0.3033 +2026-04-12 04:25:21.062346: val_loss -0.2673 +2026-04-12 04:25:21.065470: Pseudo dice [0.7119, 0.7995, 0.6211, 0.454, 0.5619, 0.5021, 0.8012] +2026-04-12 04:25:21.069227: Epoch time: 103.0 s +2026-04-12 04:25:22.550927: +2026-04-12 04:25:22.552962: Epoch 1426 +2026-04-12 04:25:22.555086: Current learning rate: 0.00673 +2026-04-12 04:27:05.949574: train_loss -0.3185 +2026-04-12 04:27:05.955949: val_loss -0.2489 +2026-04-12 04:27:05.958226: Pseudo dice [0.7271, 0.4403, 0.6024, 0.3622, 0.5905, 0.1573, 0.711] +2026-04-12 04:27:05.961091: Epoch time: 103.4 s +2026-04-12 04:27:07.515854: +2026-04-12 04:27:07.518049: Epoch 1427 +2026-04-12 04:27:07.521494: Current learning rate: 0.00672 +2026-04-12 04:28:51.665300: train_loss -0.3172 +2026-04-12 04:28:51.672807: val_loss -0.2236 +2026-04-12 04:28:51.675330: Pseudo dice [0.541, 0.7742, 0.6473, 0.7363, 0.6703, 0.0238, 0.8909] +2026-04-12 04:28:51.679054: Epoch time: 104.15 s +2026-04-12 04:28:53.171441: +2026-04-12 04:28:53.173947: Epoch 1428 +2026-04-12 04:28:53.176652: Current learning rate: 0.00672 +2026-04-12 04:30:37.362171: train_loss -0.301 +2026-04-12 04:30:37.368861: val_loss -0.1349 +2026-04-12 04:30:37.371128: Pseudo dice [0.6621, 0.8622, 0.5419, 0.0578, 0.2291, 0.0683, 0.605] +2026-04-12 04:30:37.373900: Epoch time: 104.19 s +2026-04-12 04:30:38.923214: +2026-04-12 04:30:38.925493: Epoch 1429 +2026-04-12 04:30:38.927850: Current learning rate: 0.00672 +2026-04-12 04:32:21.696792: train_loss -0.3223 +2026-04-12 04:32:21.703755: val_loss -0.234 +2026-04-12 04:32:21.706712: Pseudo dice [0.4143, 0.6949, 0.5555, 0.5805, 0.3952, 0.1257, 0.7441] +2026-04-12 04:32:21.709185: Epoch time: 102.78 s +2026-04-12 04:32:23.253709: +2026-04-12 04:32:23.255888: Epoch 1430 +2026-04-12 04:32:23.258827: Current learning rate: 0.00672 +2026-04-12 04:34:07.249072: train_loss -0.3109 +2026-04-12 04:34:07.255264: val_loss -0.2545 +2026-04-12 04:34:07.258027: Pseudo dice [0.5575, 0.8251, 0.7193, 0.6896, 0.4287, 0.0675, 0.846] +2026-04-12 04:34:07.260490: Epoch time: 104.0 s +2026-04-12 04:34:08.787109: +2026-04-12 04:34:08.789339: Epoch 1431 +2026-04-12 04:34:08.791536: Current learning rate: 0.00671 +2026-04-12 04:35:53.401743: train_loss -0.3132 +2026-04-12 04:35:53.407626: val_loss -0.2902 +2026-04-12 04:35:53.409587: Pseudo dice [0.6654, 0.6968, 0.6957, 0.1778, 0.4033, 0.7565, 0.8008] +2026-04-12 04:35:53.412224: Epoch time: 104.62 s +2026-04-12 04:35:54.921726: +2026-04-12 04:35:54.925202: Epoch 1432 +2026-04-12 04:35:54.928778: Current learning rate: 0.00671 +2026-04-12 04:37:39.517425: train_loss -0.2862 +2026-04-12 04:37:39.523464: val_loss -0.2682 +2026-04-12 04:37:39.525618: Pseudo dice [0.5118, 0.8497, 0.6736, 0.5789, 0.5063, 0.7223, 0.5083] +2026-04-12 04:37:39.529022: Epoch time: 104.6 s +2026-04-12 04:37:42.147147: +2026-04-12 04:37:42.149139: Epoch 1433 +2026-04-12 04:37:42.151299: Current learning rate: 0.00671 +2026-04-12 04:39:26.635869: train_loss -0.2904 +2026-04-12 04:39:26.643205: val_loss -0.2357 +2026-04-12 04:39:26.646384: Pseudo dice [0.5477, 0.3078, 0.556, 0.8669, 0.6224, 0.23, 0.6763] +2026-04-12 04:39:26.649083: Epoch time: 104.49 s +2026-04-12 04:39:28.140533: +2026-04-12 04:39:28.143270: Epoch 1434 +2026-04-12 04:39:28.146252: Current learning rate: 0.00671 +2026-04-12 04:41:12.496822: train_loss -0.2765 +2026-04-12 04:41:12.509418: val_loss -0.266 +2026-04-12 04:41:12.511545: Pseudo dice [0.7037, 0.5248, 0.5808, 0.6279, 0.1842, 0.7238, 0.6383] +2026-04-12 04:41:12.513824: Epoch time: 104.36 s +2026-04-12 04:41:14.018217: +2026-04-12 04:41:14.020316: Epoch 1435 +2026-04-12 04:41:14.022426: Current learning rate: 0.0067 +2026-04-12 04:42:59.840512: train_loss -0.2884 +2026-04-12 04:42:59.847167: val_loss -0.22 +2026-04-12 04:42:59.850249: Pseudo dice [0.4038, 0.4718, 0.639, 0.6769, 0.4065, 0.1017, 0.7454] +2026-04-12 04:42:59.852730: Epoch time: 105.83 s +2026-04-12 04:43:01.342766: +2026-04-12 04:43:01.344886: Epoch 1436 +2026-04-12 04:43:01.348839: Current learning rate: 0.0067 +2026-04-12 04:44:43.923027: train_loss -0.2997 +2026-04-12 04:44:43.929404: val_loss -0.2868 +2026-04-12 04:44:43.932127: Pseudo dice [0.5732, 0.7712, 0.7583, 0.6424, 0.6412, 0.6818, 0.896] +2026-04-12 04:44:43.934875: Epoch time: 102.58 s +2026-04-12 04:44:45.431482: +2026-04-12 04:44:45.434126: Epoch 1437 +2026-04-12 04:44:45.436179: Current learning rate: 0.0067 +2026-04-12 04:46:28.147270: train_loss -0.3011 +2026-04-12 04:46:28.155907: val_loss -0.2177 +2026-04-12 04:46:28.159075: Pseudo dice [0.7591, 0.736, 0.4002, 0.4724, 0.1933, 0.1606, 0.4166] +2026-04-12 04:46:28.161830: Epoch time: 102.72 s +2026-04-12 04:46:29.677609: +2026-04-12 04:46:29.680485: Epoch 1438 +2026-04-12 04:46:29.683411: Current learning rate: 0.0067 +2026-04-12 04:48:13.166138: train_loss -0.3013 +2026-04-12 04:48:13.173282: val_loss -0.2795 +2026-04-12 04:48:13.175375: Pseudo dice [0.7241, 0.435, 0.7324, 0.8331, 0.4764, 0.6912, 0.7571] +2026-04-12 04:48:13.178176: Epoch time: 103.49 s +2026-04-12 04:48:14.670473: +2026-04-12 04:48:14.672548: Epoch 1439 +2026-04-12 04:48:14.674953: Current learning rate: 0.00669 +2026-04-12 04:49:58.327548: train_loss -0.3079 +2026-04-12 04:49:58.333931: val_loss -0.2824 +2026-04-12 04:49:58.336130: Pseudo dice [0.5574, 0.5927, 0.6202, 0.4125, 0.6593, 0.8567, 0.7722] +2026-04-12 04:49:58.338866: Epoch time: 103.66 s +2026-04-12 04:49:59.854080: +2026-04-12 04:49:59.855976: Epoch 1440 +2026-04-12 04:49:59.858464: Current learning rate: 0.00669 +2026-04-12 04:51:43.987056: train_loss -0.2957 +2026-04-12 04:51:43.994356: val_loss -0.214 +2026-04-12 04:51:43.997296: Pseudo dice [0.6474, 0.8037, 0.6112, 0.5832, 0.4046, 0.1293, 0.7064] +2026-04-12 04:51:44.000090: Epoch time: 104.14 s +2026-04-12 04:51:45.486075: +2026-04-12 04:51:45.488544: Epoch 1441 +2026-04-12 04:51:45.491462: Current learning rate: 0.00669 +2026-04-12 04:53:30.526592: train_loss -0.3106 +2026-04-12 04:53:30.533201: val_loss -0.2925 +2026-04-12 04:53:30.535830: Pseudo dice [0.4567, 0.4624, 0.7508, 0.1664, 0.6543, 0.8124, 0.5923] +2026-04-12 04:53:30.538798: Epoch time: 105.04 s +2026-04-12 04:53:32.030139: +2026-04-12 04:53:32.044175: Epoch 1442 +2026-04-12 04:53:32.064396: Current learning rate: 0.00669 +2026-04-12 04:55:15.200692: train_loss -0.3187 +2026-04-12 04:55:15.208228: val_loss -0.2935 +2026-04-12 04:55:15.210562: Pseudo dice [0.5639, 0.7127, 0.6534, 0.7695, 0.638, 0.5053, 0.7889] +2026-04-12 04:55:15.212675: Epoch time: 103.17 s +2026-04-12 04:55:16.704345: +2026-04-12 04:55:16.707033: Epoch 1443 +2026-04-12 04:55:16.709611: Current learning rate: 0.00669 +2026-04-12 04:57:00.171088: train_loss -0.3227 +2026-04-12 04:57:00.183659: val_loss -0.2004 +2026-04-12 04:57:00.187316: Pseudo dice [0.5703, 0.6153, 0.5908, 0.652, 0.5308, 0.0453, 0.6585] +2026-04-12 04:57:00.193099: Epoch time: 103.47 s +2026-04-12 04:57:01.720153: +2026-04-12 04:57:01.726515: Epoch 1444 +2026-04-12 04:57:01.733802: Current learning rate: 0.00668 +2026-04-12 04:58:45.467683: train_loss -0.3091 +2026-04-12 04:58:45.474610: val_loss -0.2944 +2026-04-12 04:58:45.476725: Pseudo dice [0.6442, 0.6392, 0.6982, 0.8023, 0.4607, 0.7019, 0.8302] +2026-04-12 04:58:45.479362: Epoch time: 103.75 s +2026-04-12 04:58:45.482740: Yayy! New best EMA pseudo Dice: 0.5801 +2026-04-12 04:58:49.000859: +2026-04-12 04:58:49.005273: Epoch 1445 +2026-04-12 04:58:49.007467: Current learning rate: 0.00668 +2026-04-12 05:00:31.713694: train_loss -0.306 +2026-04-12 05:00:31.719692: val_loss -0.2043 +2026-04-12 05:00:31.721877: Pseudo dice [0.737, 0.6044, 0.6285, 0.4389, 0.5355, 0.0197, 0.7744] +2026-04-12 05:00:31.724760: Epoch time: 102.72 s +2026-04-12 05:00:33.227857: +2026-04-12 05:00:33.229935: Epoch 1446 +2026-04-12 05:00:33.231997: Current learning rate: 0.00668 +2026-04-12 05:02:17.279140: train_loss -0.2989 +2026-04-12 05:02:17.286814: val_loss -0.1804 +2026-04-12 05:02:17.289300: Pseudo dice [0.6912, 0.5692, 0.6894, 0.0063, 0.6208, 0.0255, 0.5179] +2026-04-12 05:02:17.292679: Epoch time: 104.06 s +2026-04-12 05:02:18.820642: +2026-04-12 05:02:18.843595: Epoch 1447 +2026-04-12 05:02:18.847433: Current learning rate: 0.00668 +2026-04-12 05:04:02.545062: train_loss -0.2804 +2026-04-12 05:04:02.555664: val_loss -0.255 +2026-04-12 05:04:02.559963: Pseudo dice [0.6179, 0.4711, 0.6236, 0.4161, 0.1456, 0.7602, 0.2753] +2026-04-12 05:04:02.563689: Epoch time: 103.73 s +2026-04-12 05:04:04.040799: +2026-04-12 05:04:04.044326: Epoch 1448 +2026-04-12 05:04:04.048384: Current learning rate: 0.00667 +2026-04-12 05:05:48.165510: train_loss -0.2787 +2026-04-12 05:05:48.174700: val_loss -0.1802 +2026-04-12 05:05:48.177351: Pseudo dice [0.811, 0.8396, 0.5138, 0.0239, 0.1791, 0.0477, 0.5383] +2026-04-12 05:05:48.180628: Epoch time: 104.13 s +2026-04-12 05:05:49.719085: +2026-04-12 05:05:49.721740: Epoch 1449 +2026-04-12 05:05:49.723784: Current learning rate: 0.00667 +2026-04-12 05:07:32.645224: train_loss -0.3113 +2026-04-12 05:07:32.651282: val_loss -0.2787 +2026-04-12 05:07:32.655783: Pseudo dice [0.4293, 0.6302, 0.6284, 0.6028, 0.6351, 0.1407, 0.6981] +2026-04-12 05:07:32.658483: Epoch time: 102.93 s +2026-04-12 05:07:36.106368: +2026-04-12 05:07:36.109773: Epoch 1450 +2026-04-12 05:07:36.112127: Current learning rate: 0.00667 +2026-04-12 05:09:19.508684: train_loss -0.3025 +2026-04-12 05:09:19.514028: val_loss -0.2418 +2026-04-12 05:09:19.516949: Pseudo dice [0.5948, 0.8396, 0.6601, 0.6729, 0.2447, 0.0484, 0.5574] +2026-04-12 05:09:19.519621: Epoch time: 103.41 s +2026-04-12 05:09:20.984759: +2026-04-12 05:09:20.986423: Epoch 1451 +2026-04-12 05:09:20.988370: Current learning rate: 0.00667 +2026-04-12 05:11:03.662815: train_loss -0.2891 +2026-04-12 05:11:03.668837: val_loss -0.2509 +2026-04-12 05:11:03.670824: Pseudo dice [0.5374, 0.2489, 0.7267, 0.6125, 0.3982, 0.0984, 0.7383] +2026-04-12 05:11:03.673150: Epoch time: 102.68 s +2026-04-12 05:11:06.285593: +2026-04-12 05:11:06.287778: Epoch 1452 +2026-04-12 05:11:06.290127: Current learning rate: 0.00666 +2026-04-12 05:12:50.217440: train_loss -0.3095 +2026-04-12 05:12:50.242883: val_loss -0.2111 +2026-04-12 05:12:50.245006: Pseudo dice [0.6864, 0.6678, 0.6391, 0.0414, 0.5766, 0.0696, 0.7526] +2026-04-12 05:12:50.247097: Epoch time: 103.94 s +2026-04-12 05:12:51.736670: +2026-04-12 05:12:51.739035: Epoch 1453 +2026-04-12 05:12:51.741328: Current learning rate: 0.00666 +2026-04-12 05:14:34.207680: train_loss -0.3007 +2026-04-12 05:14:34.213987: val_loss -0.2388 +2026-04-12 05:14:34.217105: Pseudo dice [0.6822, 0.5971, 0.4473, 0.766, 0.5623, 0.0241, 0.6706] +2026-04-12 05:14:34.221548: Epoch time: 102.47 s +2026-04-12 05:14:35.706768: +2026-04-12 05:14:35.708965: Epoch 1454 +2026-04-12 05:14:35.711220: Current learning rate: 0.00666 +2026-04-12 05:16:20.056212: train_loss -0.312 +2026-04-12 05:16:20.067050: val_loss -0.2922 +2026-04-12 05:16:20.071024: Pseudo dice [0.7403, 0.6959, 0.7316, 0.6508, 0.5818, 0.6582, 0.8752] +2026-04-12 05:16:20.074934: Epoch time: 104.35 s +2026-04-12 05:16:21.570052: +2026-04-12 05:16:21.571887: Epoch 1455 +2026-04-12 05:16:21.574492: Current learning rate: 0.00666 +2026-04-12 05:18:04.233241: train_loss -0.3037 +2026-04-12 05:18:04.240273: val_loss -0.2353 +2026-04-12 05:18:04.242459: Pseudo dice [0.509, 0.5409, 0.7098, 0.7562, 0.4998, 0.221, 0.6913] +2026-04-12 05:18:04.244880: Epoch time: 102.67 s +2026-04-12 05:18:05.732944: +2026-04-12 05:18:05.735160: Epoch 1456 +2026-04-12 05:18:05.737282: Current learning rate: 0.00665 +2026-04-12 05:19:49.526265: train_loss -0.2785 +2026-04-12 05:19:49.531968: val_loss -0.2221 +2026-04-12 05:19:49.534293: Pseudo dice [0.1683, 0.5546, 0.7665, 0.6012, 0.4134, 0.057, 0.6286] +2026-04-12 05:19:49.536789: Epoch time: 103.8 s +2026-04-12 05:19:51.029433: +2026-04-12 05:19:51.031693: Epoch 1457 +2026-04-12 05:19:51.033787: Current learning rate: 0.00665 +2026-04-12 05:21:33.421885: train_loss -0.319 +2026-04-12 05:21:33.427692: val_loss -0.1765 +2026-04-12 05:21:33.430260: Pseudo dice [0.6884, 0.8248, 0.5073, 0.5406, 0.4524, 0.1732, 0.687] +2026-04-12 05:21:33.432753: Epoch time: 102.4 s +2026-04-12 05:21:34.924570: +2026-04-12 05:21:34.926446: Epoch 1458 +2026-04-12 05:21:34.928555: Current learning rate: 0.00665 +2026-04-12 05:23:18.128131: train_loss -0.3073 +2026-04-12 05:23:18.135375: val_loss -0.2272 +2026-04-12 05:23:18.139407: Pseudo dice [0.3862, 0.4972, 0.5759, 0.4834, 0.5484, 0.1757, 0.7947] +2026-04-12 05:23:18.142574: Epoch time: 103.21 s +2026-04-12 05:23:19.654845: +2026-04-12 05:23:19.656638: Epoch 1459 +2026-04-12 05:23:19.658556: Current learning rate: 0.00665 +2026-04-12 05:25:03.407500: train_loss -0.3106 +2026-04-12 05:25:03.415086: val_loss -0.2706 +2026-04-12 05:25:03.417459: Pseudo dice [0.3684, 0.4917, 0.6793, 0.862, 0.262, 0.4628, 0.7429] +2026-04-12 05:25:03.420111: Epoch time: 103.76 s +2026-04-12 05:25:04.896342: +2026-04-12 05:25:04.900636: Epoch 1460 +2026-04-12 05:25:04.902832: Current learning rate: 0.00665 +2026-04-12 05:26:48.232394: train_loss -0.3004 +2026-04-12 05:26:48.238274: val_loss -0.2609 +2026-04-12 05:26:48.241590: Pseudo dice [0.4795, 0.4919, 0.634, 0.7004, 0.4309, 0.6466, 0.4102] +2026-04-12 05:26:48.244638: Epoch time: 103.34 s +2026-04-12 05:26:49.742721: +2026-04-12 05:26:49.745144: Epoch 1461 +2026-04-12 05:26:49.747422: Current learning rate: 0.00664 +2026-04-12 05:28:32.963232: train_loss -0.2931 +2026-04-12 05:28:32.972049: val_loss -0.2759 +2026-04-12 05:28:32.975006: Pseudo dice [0.3641, 0.6442, 0.5381, 0.235, 0.4382, 0.6371, 0.6737] +2026-04-12 05:28:32.978375: Epoch time: 103.22 s +2026-04-12 05:28:34.473336: +2026-04-12 05:28:34.475540: Epoch 1462 +2026-04-12 05:28:34.478939: Current learning rate: 0.00664 +2026-04-12 05:30:19.014113: train_loss -0.2777 +2026-04-12 05:30:19.020907: val_loss -0.2619 +2026-04-12 05:30:19.023956: Pseudo dice [0.7736, 0.8148, 0.5195, 0.4404, 0.4803, 0.1852, 0.7275] +2026-04-12 05:30:19.027874: Epoch time: 104.54 s +2026-04-12 05:30:20.519569: +2026-04-12 05:30:20.521637: Epoch 1463 +2026-04-12 05:30:20.524573: Current learning rate: 0.00664 +2026-04-12 05:32:03.172636: train_loss -0.3048 +2026-04-12 05:32:03.181880: val_loss -0.3227 +2026-04-12 05:32:03.185632: Pseudo dice [0.5992, 0.7, 0.7477, 0.2987, 0.6152, 0.7542, 0.8833] +2026-04-12 05:32:03.188537: Epoch time: 102.66 s +2026-04-12 05:32:04.686644: +2026-04-12 05:32:04.688665: Epoch 1464 +2026-04-12 05:32:04.690758: Current learning rate: 0.00664 +2026-04-12 05:33:48.844857: train_loss -0.3116 +2026-04-12 05:33:48.859259: val_loss -0.2703 +2026-04-12 05:33:48.864978: Pseudo dice [0.6652, 0.5524, 0.7051, 0.5722, 0.4718, 0.3872, 0.8322] +2026-04-12 05:33:48.869291: Epoch time: 104.16 s +2026-04-12 05:33:50.379891: +2026-04-12 05:33:50.383166: Epoch 1465 +2026-04-12 05:33:50.385834: Current learning rate: 0.00663 +2026-04-12 05:35:34.794142: train_loss -0.2995 +2026-04-12 05:35:34.801721: val_loss -0.1984 +2026-04-12 05:35:34.804219: Pseudo dice [0.5634, 0.5068, 0.6334, 0.6168, 0.2493, 0.0416, 0.507] +2026-04-12 05:35:34.808091: Epoch time: 104.42 s +2026-04-12 05:35:36.292770: +2026-04-12 05:35:36.294661: Epoch 1466 +2026-04-12 05:35:36.297326: Current learning rate: 0.00663 +2026-04-12 05:37:19.143684: train_loss -0.3031 +2026-04-12 05:37:19.155397: val_loss -0.2208 +2026-04-12 05:37:19.160169: Pseudo dice [0.3769, 0.7456, 0.5173, 0.5152, 0.1397, 0.0474, 0.7728] +2026-04-12 05:37:19.163368: Epoch time: 102.85 s +2026-04-12 05:37:20.674947: +2026-04-12 05:37:20.677577: Epoch 1467 +2026-04-12 05:37:20.680291: Current learning rate: 0.00663 +2026-04-12 05:39:05.260081: train_loss -0.3056 +2026-04-12 05:39:05.266991: val_loss -0.2342 +2026-04-12 05:39:05.269012: Pseudo dice [0.4527, 0.7044, 0.6458, 0.4782, 0.4736, 0.2486, 0.5339] +2026-04-12 05:39:05.271315: Epoch time: 104.59 s +2026-04-12 05:39:06.772659: +2026-04-12 05:39:06.774965: Epoch 1468 +2026-04-12 05:39:06.777749: Current learning rate: 0.00663 +2026-04-12 05:40:52.312354: train_loss -0.2886 +2026-04-12 05:40:52.322176: val_loss -0.2889 +2026-04-12 05:40:52.325348: Pseudo dice [0.3982, 0.6207, 0.6334, 0.6178, 0.3792, 0.8066, 0.8784] +2026-04-12 05:40:52.328163: Epoch time: 105.54 s +2026-04-12 05:40:53.829954: +2026-04-12 05:40:53.836310: Epoch 1469 +2026-04-12 05:40:53.840580: Current learning rate: 0.00662 +2026-04-12 05:42:36.803917: train_loss -0.3102 +2026-04-12 05:42:36.811397: val_loss -0.2859 +2026-04-12 05:42:36.813687: Pseudo dice [0.7539, 0.4577, 0.7253, 0.4299, 0.5423, 0.5551, 0.5354] +2026-04-12 05:42:36.816560: Epoch time: 102.98 s +2026-04-12 05:42:38.315372: +2026-04-12 05:42:38.317941: Epoch 1470 +2026-04-12 05:42:38.320117: Current learning rate: 0.00662 +2026-04-12 05:44:24.010555: train_loss -0.2967 +2026-04-12 05:44:24.016897: val_loss -0.2086 +2026-04-12 05:44:24.019705: Pseudo dice [0.7366, 0.3484, 0.411, 0.4299, 0.4403, 0.053, 0.5841] +2026-04-12 05:44:24.022630: Epoch time: 105.7 s +2026-04-12 05:44:25.525566: +2026-04-12 05:44:25.527442: Epoch 1471 +2026-04-12 05:44:25.529734: Current learning rate: 0.00662 +2026-04-12 05:46:09.007385: train_loss -0.3068 +2026-04-12 05:46:09.012725: val_loss -0.2265 +2026-04-12 05:46:09.014915: Pseudo dice [0.7553, 0.4847, 0.6102, 0.7416, 0.127, 0.0907, 0.3902] +2026-04-12 05:46:09.017057: Epoch time: 103.49 s +2026-04-12 05:46:11.654629: +2026-04-12 05:46:11.657075: Epoch 1472 +2026-04-12 05:46:11.659396: Current learning rate: 0.00662 +2026-04-12 05:47:54.446705: train_loss -0.2894 +2026-04-12 05:47:54.453747: val_loss -0.2707 +2026-04-12 05:47:54.455712: Pseudo dice [0.3984, 0.2218, 0.6497, 0.6427, 0.3345, 0.6117, 0.6409] +2026-04-12 05:47:54.457920: Epoch time: 102.8 s +2026-04-12 05:47:55.951647: +2026-04-12 05:47:55.953616: Epoch 1473 +2026-04-12 05:47:55.955794: Current learning rate: 0.00661 +2026-04-12 05:49:39.807305: train_loss -0.302 +2026-04-12 05:49:39.825280: val_loss -0.144 +2026-04-12 05:49:39.833735: Pseudo dice [0.764, 0.4461, 0.6512, 0.4837, 0.4511, 0.198, 0.7457] +2026-04-12 05:49:39.839809: Epoch time: 103.86 s +2026-04-12 05:49:41.370314: +2026-04-12 05:49:41.372306: Epoch 1474 +2026-04-12 05:49:41.376363: Current learning rate: 0.00661 +2026-04-12 05:51:24.740709: train_loss -0.3113 +2026-04-12 05:51:24.749573: val_loss -0.2716 +2026-04-12 05:51:24.752519: Pseudo dice [0.4656, 0.8379, 0.7519, 0.5895, 0.5279, 0.6954, 0.2439] +2026-04-12 05:51:24.755993: Epoch time: 103.37 s +2026-04-12 05:51:26.260190: +2026-04-12 05:51:26.263176: Epoch 1475 +2026-04-12 05:51:26.265705: Current learning rate: 0.00661 +2026-04-12 05:53:09.238071: train_loss -0.3107 +2026-04-12 05:53:09.243735: val_loss -0.2238 +2026-04-12 05:53:09.246318: Pseudo dice [0.7265, 0.687, 0.574, 0.6844, 0.3353, 0.1278, 0.6691] +2026-04-12 05:53:09.248415: Epoch time: 102.98 s +2026-04-12 05:53:10.763113: +2026-04-12 05:53:10.765204: Epoch 1476 +2026-04-12 05:53:10.767298: Current learning rate: 0.00661 +2026-04-12 05:54:54.882415: train_loss -0.3101 +2026-04-12 05:54:54.890184: val_loss -0.1895 +2026-04-12 05:54:54.892604: Pseudo dice [0.7269, 0.4569, 0.6929, 0.3245, 0.5235, 0.2919, 0.7609] +2026-04-12 05:54:54.895690: Epoch time: 104.12 s +2026-04-12 05:54:56.404102: +2026-04-12 05:54:56.409744: Epoch 1477 +2026-04-12 05:54:56.416583: Current learning rate: 0.0066 +2026-04-12 05:56:39.824417: train_loss -0.3231 +2026-04-12 05:56:39.830367: val_loss -0.317 +2026-04-12 05:56:39.833081: Pseudo dice [0.5337, 0.5747, 0.7627, 0.5145, 0.6431, 0.7936, 0.7501] +2026-04-12 05:56:39.835379: Epoch time: 103.42 s +2026-04-12 05:56:41.327651: +2026-04-12 05:56:41.330099: Epoch 1478 +2026-04-12 05:56:41.332281: Current learning rate: 0.0066 +2026-04-12 05:58:26.648785: train_loss -0.3207 +2026-04-12 05:58:26.660031: val_loss -0.2537 +2026-04-12 05:58:26.663397: Pseudo dice [0.8291, 0.334, 0.6105, 0.4407, 0.5798, 0.1463, 0.827] +2026-04-12 05:58:26.666376: Epoch time: 105.32 s +2026-04-12 05:58:28.185941: +2026-04-12 05:58:28.188568: Epoch 1479 +2026-04-12 05:58:28.191495: Current learning rate: 0.0066 +2026-04-12 06:00:12.765062: train_loss -0.3157 +2026-04-12 06:00:12.773449: val_loss -0.3002 +2026-04-12 06:00:12.776885: Pseudo dice [0.3763, 0.694, 0.7154, 0.8103, 0.5681, 0.6501, 0.7861] +2026-04-12 06:00:12.779444: Epoch time: 104.58 s +2026-04-12 06:00:14.288684: +2026-04-12 06:00:14.296157: Epoch 1480 +2026-04-12 06:00:14.298364: Current learning rate: 0.0066 +2026-04-12 06:01:59.062700: train_loss -0.2956 +2026-04-12 06:01:59.070053: val_loss -0.1923 +2026-04-12 06:01:59.072388: Pseudo dice [0.5005, 0.3931, 0.5734, 0.4213, 0.3982, 0.0621, 0.3451] +2026-04-12 06:01:59.074646: Epoch time: 104.78 s +2026-04-12 06:02:00.585889: +2026-04-12 06:02:00.587686: Epoch 1481 +2026-04-12 06:02:00.589545: Current learning rate: 0.0066 +2026-04-12 06:03:43.964811: train_loss -0.2974 +2026-04-12 06:03:43.973259: val_loss -0.2621 +2026-04-12 06:03:43.975369: Pseudo dice [0.3819, 0.5047, 0.5672, 0.6647, 0.4316, 0.5579, 0.8324] +2026-04-12 06:03:43.978781: Epoch time: 103.38 s +2026-04-12 06:03:45.474605: +2026-04-12 06:03:45.476595: Epoch 1482 +2026-04-12 06:03:45.478533: Current learning rate: 0.00659 +2026-04-12 06:05:32.781026: train_loss -0.3056 +2026-04-12 06:05:32.789198: val_loss -0.2203 +2026-04-12 06:05:32.791191: Pseudo dice [0.5114, 0.5871, 0.508, 0.6096, 0.4663, 0.1557, 0.8931] +2026-04-12 06:05:32.794989: Epoch time: 107.31 s +2026-04-12 06:05:34.312687: +2026-04-12 06:05:34.314858: Epoch 1483 +2026-04-12 06:05:34.316984: Current learning rate: 0.00659 +2026-04-12 06:07:19.085243: train_loss -0.2949 +2026-04-12 06:07:19.091353: val_loss -0.2617 +2026-04-12 06:07:19.093807: Pseudo dice [0.5097, 0.2877, 0.656, 0.7899, 0.4856, 0.7074, 0.4055] +2026-04-12 06:07:19.096078: Epoch time: 104.78 s +2026-04-12 06:07:20.652593: +2026-04-12 06:07:20.654557: Epoch 1484 +2026-04-12 06:07:20.657228: Current learning rate: 0.00659 +2026-04-12 06:09:05.199912: train_loss -0.2615 +2026-04-12 06:09:05.205642: val_loss -0.2399 +2026-04-12 06:09:05.207505: Pseudo dice [0.5789, 0.739, 0.6047, 0.303, 0.2823, 0.2126, 0.4719] +2026-04-12 06:09:05.210274: Epoch time: 104.55 s +2026-04-12 06:09:06.720988: +2026-04-12 06:09:06.722769: Epoch 1485 +2026-04-12 06:09:06.725225: Current learning rate: 0.00659 +2026-04-12 06:10:49.438941: train_loss -0.3032 +2026-04-12 06:10:49.444923: val_loss -0.2667 +2026-04-12 06:10:49.447797: Pseudo dice [0.406, 0.4898, 0.6725, 0.7358, 0.4411, 0.3025, 0.6312] +2026-04-12 06:10:49.451110: Epoch time: 102.72 s +2026-04-12 06:10:50.968814: +2026-04-12 06:10:50.971192: Epoch 1486 +2026-04-12 06:10:50.974058: Current learning rate: 0.00658 +2026-04-12 06:12:33.995473: train_loss -0.3098 +2026-04-12 06:12:34.002153: val_loss -0.2356 +2026-04-12 06:12:34.004231: Pseudo dice [0.664, 0.271, 0.5775, 0.0016, 0.4222, 0.0557, 0.6916] +2026-04-12 06:12:34.007716: Epoch time: 103.03 s +2026-04-12 06:12:35.510499: +2026-04-12 06:12:35.512806: Epoch 1487 +2026-04-12 06:12:35.515516: Current learning rate: 0.00658 +2026-04-12 06:14:21.555567: train_loss -0.3106 +2026-04-12 06:14:21.562800: val_loss -0.2751 +2026-04-12 06:14:21.565032: Pseudo dice [0.6979, 0.8289, 0.6834, 0.15, 0.5568, 0.2335, 0.7925] +2026-04-12 06:14:21.568893: Epoch time: 106.05 s +2026-04-12 06:14:23.094805: +2026-04-12 06:14:23.097291: Epoch 1488 +2026-04-12 06:14:23.100302: Current learning rate: 0.00658 +2026-04-12 06:16:08.158824: train_loss -0.3213 +2026-04-12 06:16:08.165458: val_loss -0.2303 +2026-04-12 06:16:08.168299: Pseudo dice [0.6628, 0.5224, 0.819, 0.1516, 0.4929, 0.1614, 0.7941] +2026-04-12 06:16:08.171491: Epoch time: 105.07 s +2026-04-12 06:16:09.670512: +2026-04-12 06:16:09.672672: Epoch 1489 +2026-04-12 06:16:09.675391: Current learning rate: 0.00658 +2026-04-12 06:17:53.230893: train_loss -0.3166 +2026-04-12 06:17:53.238931: val_loss -0.2949 +2026-04-12 06:17:53.241341: Pseudo dice [0.4861, 0.7981, 0.4166, 0.8388, 0.5553, 0.6937, 0.8681] +2026-04-12 06:17:53.243935: Epoch time: 103.56 s +2026-04-12 06:17:54.761960: +2026-04-12 06:17:54.764614: Epoch 1490 +2026-04-12 06:17:54.767228: Current learning rate: 0.00657 +2026-04-12 06:19:37.825541: train_loss -0.3215 +2026-04-12 06:19:37.833044: val_loss -0.2818 +2026-04-12 06:19:37.835761: Pseudo dice [0.6971, 0.6522, 0.7442, 0.5169, 0.5382, 0.3126, 0.6433] +2026-04-12 06:19:37.838544: Epoch time: 103.07 s +2026-04-12 06:19:39.329695: +2026-04-12 06:19:39.332577: Epoch 1491 +2026-04-12 06:19:39.335696: Current learning rate: 0.00657 +2026-04-12 06:21:25.644215: train_loss -0.3103 +2026-04-12 06:21:25.649291: val_loss -0.2788 +2026-04-12 06:21:25.651421: Pseudo dice [0.771, 0.3689, 0.5707, 0.7181, 0.404, 0.5389, 0.439] +2026-04-12 06:21:25.654110: Epoch time: 106.32 s +2026-04-12 06:21:27.160968: +2026-04-12 06:21:27.165501: Epoch 1492 +2026-04-12 06:21:27.168024: Current learning rate: 0.00657 +2026-04-12 06:23:10.760887: train_loss -0.3173 +2026-04-12 06:23:10.767957: val_loss -0.1785 +2026-04-12 06:23:10.770275: Pseudo dice [0.8039, 0.5941, 0.624, 0.7112, 0.3293, 0.0174, 0.662] +2026-04-12 06:23:10.774014: Epoch time: 103.6 s +2026-04-12 06:23:12.287656: +2026-04-12 06:23:12.290360: Epoch 1493 +2026-04-12 06:23:12.293710: Current learning rate: 0.00657 +2026-04-12 06:25:01.620905: train_loss -0.3153 +2026-04-12 06:25:01.627682: val_loss -0.2161 +2026-04-12 06:25:01.633991: Pseudo dice [0.5681, 0.6643, 0.6405, 0.7444, 0.5614, 0.0291, 0.8024] +2026-04-12 06:25:01.638793: Epoch time: 109.34 s +2026-04-12 06:25:03.162356: +2026-04-12 06:25:03.164983: Epoch 1494 +2026-04-12 06:25:03.167822: Current learning rate: 0.00656 +2026-04-12 06:26:47.651222: train_loss -0.3118 +2026-04-12 06:26:47.659517: val_loss -0.2437 +2026-04-12 06:26:47.662245: Pseudo dice [0.7539, 0.6419, 0.5239, 0.7434, 0.3543, 0.1742, 0.4719] +2026-04-12 06:26:47.665566: Epoch time: 104.49 s +2026-04-12 06:26:49.189103: +2026-04-12 06:26:49.191498: Epoch 1495 +2026-04-12 06:26:49.194433: Current learning rate: 0.00656 +2026-04-12 06:28:32.841028: train_loss -0.3073 +2026-04-12 06:28:32.847755: val_loss -0.2749 +2026-04-12 06:28:32.850320: Pseudo dice [0.7707, 0.7544, 0.6224, 0.6937, 0.5973, 0.1819, 0.737] +2026-04-12 06:28:32.852661: Epoch time: 103.66 s +2026-04-12 06:28:34.361184: +2026-04-12 06:28:34.363281: Epoch 1496 +2026-04-12 06:28:34.365897: Current learning rate: 0.00656 +2026-04-12 06:30:18.826091: train_loss -0.305 +2026-04-12 06:30:18.834905: val_loss -0.2709 +2026-04-12 06:30:18.837968: Pseudo dice [0.549, 0.5636, 0.8028, 0.6886, 0.4372, 0.7564, 0.5674] +2026-04-12 06:30:18.840858: Epoch time: 104.47 s +2026-04-12 06:30:20.341533: +2026-04-12 06:30:20.343269: Epoch 1497 +2026-04-12 06:30:20.346017: Current learning rate: 0.00656 +2026-04-12 06:32:05.676057: train_loss -0.2912 +2026-04-12 06:32:05.682464: val_loss -0.2268 +2026-04-12 06:32:05.684687: Pseudo dice [0.4879, 0.7959, 0.5369, 0.7218, 0.5689, 0.0868, 0.8062] +2026-04-12 06:32:05.687530: Epoch time: 105.34 s +2026-04-12 06:32:07.209252: +2026-04-12 06:32:07.211831: Epoch 1498 +2026-04-12 06:32:07.215849: Current learning rate: 0.00656 +2026-04-12 06:33:52.061019: train_loss -0.3017 +2026-04-12 06:33:52.067371: val_loss -0.262 +2026-04-12 06:33:52.069475: Pseudo dice [0.8472, 0.6167, 0.6566, 0.6671, 0.3616, 0.1735, 0.6813] +2026-04-12 06:33:52.071795: Epoch time: 104.86 s +2026-04-12 06:33:53.587389: +2026-04-12 06:33:53.589878: Epoch 1499 +2026-04-12 06:33:53.592152: Current learning rate: 0.00655 +2026-04-12 06:35:39.109149: train_loss -0.3194 +2026-04-12 06:35:39.116111: val_loss -0.1901 +2026-04-12 06:35:39.118417: Pseudo dice [0.3617, 0.8488, 0.5848, 0.6925, 0.1115, 0.3633, 0.5749] +2026-04-12 06:35:39.120856: Epoch time: 105.53 s +2026-04-12 06:35:42.535214: +2026-04-12 06:35:42.536984: Epoch 1500 +2026-04-12 06:35:42.539275: Current learning rate: 0.00655 +2026-04-12 06:37:32.718604: train_loss -0.298 +2026-04-12 06:37:32.728926: val_loss -0.2483 +2026-04-12 06:37:32.732161: Pseudo dice [0.738, 0.4148, 0.5724, 0.1452, 0.3484, 0.5149, 0.8191] +2026-04-12 06:37:32.735229: Epoch time: 110.19 s +2026-04-12 06:37:34.244748: +2026-04-12 06:37:34.247966: Epoch 1501 +2026-04-12 06:37:34.251990: Current learning rate: 0.00655 +2026-04-12 06:39:18.343685: train_loss -0.2854 +2026-04-12 06:39:18.352204: val_loss -0.2235 +2026-04-12 06:39:18.355598: Pseudo dice [0.4039, 0.5465, 0.6743, 0.0502, 0.2648, 0.3697, 0.805] +2026-04-12 06:39:18.358355: Epoch time: 104.1 s +2026-04-12 06:39:20.126579: +2026-04-12 06:39:20.128315: Epoch 1502 +2026-04-12 06:39:20.131094: Current learning rate: 0.00655 +2026-04-12 06:41:07.500071: train_loss -0.3058 +2026-04-12 06:41:07.508468: val_loss -0.2671 +2026-04-12 06:41:07.513467: Pseudo dice [0.4276, 0.3868, 0.6402, 0.0154, 0.6081, 0.1973, 0.8254] +2026-04-12 06:41:07.516693: Epoch time: 107.38 s +2026-04-12 06:41:09.032156: +2026-04-12 06:41:09.034311: Epoch 1503 +2026-04-12 06:41:09.036512: Current learning rate: 0.00654 +2026-04-12 06:42:53.640309: train_loss -0.316 +2026-04-12 06:42:53.653986: val_loss -0.2423 +2026-04-12 06:42:53.657967: Pseudo dice [0.3687, 0.7352, 0.6955, 0.0363, 0.5535, 0.4217, 0.7528] +2026-04-12 06:42:53.660636: Epoch time: 104.61 s +2026-04-12 06:42:55.143390: +2026-04-12 06:42:55.145194: Epoch 1504 +2026-04-12 06:42:55.147296: Current learning rate: 0.00654 +2026-04-12 06:44:38.387026: train_loss -0.2903 +2026-04-12 06:44:38.392012: val_loss -0.2574 +2026-04-12 06:44:38.394878: Pseudo dice [0.5076, 0.2787, 0.4286, 0.074, 0.5847, 0.4221, 0.6957] +2026-04-12 06:44:38.397403: Epoch time: 103.25 s +2026-04-12 06:44:39.907480: +2026-04-12 06:44:39.909312: Epoch 1505 +2026-04-12 06:44:39.911300: Current learning rate: 0.00654 +2026-04-12 06:46:24.381192: train_loss -0.2952 +2026-04-12 06:46:24.395635: val_loss -0.2771 +2026-04-12 06:46:24.400979: Pseudo dice [0.4826, 0.792, 0.367, 0.9114, 0.5789, 0.7147, 0.5889] +2026-04-12 06:46:24.408719: Epoch time: 104.48 s +2026-04-12 06:46:25.921232: +2026-04-12 06:46:25.924409: Epoch 1506 +2026-04-12 06:46:25.932039: Current learning rate: 0.00654 +2026-04-12 06:48:09.147467: train_loss -0.3042 +2026-04-12 06:48:09.154372: val_loss -0.2457 +2026-04-12 06:48:09.156536: Pseudo dice [0.5857, 0.8001, 0.3834, 0.0126, 0.4897, 0.6982, 0.8525] +2026-04-12 06:48:09.159250: Epoch time: 103.23 s +2026-04-12 06:48:10.660193: +2026-04-12 06:48:10.662927: Epoch 1507 +2026-04-12 06:48:10.665521: Current learning rate: 0.00653 +2026-04-12 06:49:55.855866: train_loss -0.2823 +2026-04-12 06:49:55.866244: val_loss -0.2644 +2026-04-12 06:49:55.868754: Pseudo dice [0.243, 0.7554, 0.7482, 0.4864, 0.5427, 0.6949, 0.7388] +2026-04-12 06:49:55.871403: Epoch time: 105.2 s +2026-04-12 06:49:57.368286: +2026-04-12 06:49:57.370576: Epoch 1508 +2026-04-12 06:49:57.372661: Current learning rate: 0.00653 +2026-04-12 06:51:44.414918: train_loss -0.2814 +2026-04-12 06:51:44.421717: val_loss -0.2572 +2026-04-12 06:51:44.425133: Pseudo dice [0.5617, 0.7757, 0.3048, 0.021, 0.5332, 0.6019, 0.7746] +2026-04-12 06:51:44.430295: Epoch time: 107.05 s +2026-04-12 06:51:45.939020: +2026-04-12 06:51:45.941748: Epoch 1509 +2026-04-12 06:51:45.943879: Current learning rate: 0.00653 +2026-04-12 06:53:29.397883: train_loss -0.2846 +2026-04-12 06:53:29.407151: val_loss -0.275 +2026-04-12 06:53:29.416888: Pseudo dice [0.6577, 0.6953, 0.6487, 0.7965, 0.6451, 0.2883, 0.8936] +2026-04-12 06:53:29.424240: Epoch time: 103.46 s +2026-04-12 06:53:30.959896: +2026-04-12 06:53:30.961977: Epoch 1510 +2026-04-12 06:53:30.964234: Current learning rate: 0.00653 +2026-04-12 06:55:14.353292: train_loss -0.3033 +2026-04-12 06:55:14.359587: val_loss -0.29 +2026-04-12 06:55:14.361842: Pseudo dice [0.2002, 0.7701, 0.4593, 0.8194, 0.6285, 0.8308, 0.7254] +2026-04-12 06:55:14.364174: Epoch time: 103.4 s +2026-04-12 06:55:15.872255: +2026-04-12 06:55:15.874640: Epoch 1511 +2026-04-12 06:55:15.876901: Current learning rate: 0.00652 +2026-04-12 06:56:59.866768: train_loss -0.3099 +2026-04-12 06:56:59.872285: val_loss -0.2462 +2026-04-12 06:56:59.874407: Pseudo dice [0.2278, 0.4069, 0.6153, 0.6589, 0.6446, 0.1016, 0.7877] +2026-04-12 06:56:59.876655: Epoch time: 104.0 s +2026-04-12 06:57:01.378794: +2026-04-12 06:57:01.380941: Epoch 1512 +2026-04-12 06:57:01.382813: Current learning rate: 0.00652 +2026-04-12 06:58:44.484180: train_loss -0.3057 +2026-04-12 06:58:44.490570: val_loss -0.1848 +2026-04-12 06:58:44.492866: Pseudo dice [0.6022, 0.3792, 0.3917, 0.7929, 0.5941, 0.2142, 0.8663] +2026-04-12 06:58:44.495718: Epoch time: 103.11 s +2026-04-12 06:58:46.000337: +2026-04-12 06:58:46.002467: Epoch 1513 +2026-04-12 06:58:46.004547: Current learning rate: 0.00652 +2026-04-12 07:00:32.076323: train_loss -0.3127 +2026-04-12 07:00:32.085667: val_loss -0.3037 +2026-04-12 07:00:32.089827: Pseudo dice [0.4638, 0.7, 0.7198, 0.7367, 0.5523, 0.5266, 0.8743] +2026-04-12 07:00:32.092959: Epoch time: 106.08 s +2026-04-12 07:00:33.599499: +2026-04-12 07:00:33.601800: Epoch 1514 +2026-04-12 07:00:33.605018: Current learning rate: 0.00652 +2026-04-12 07:02:16.543584: train_loss -0.3071 +2026-04-12 07:02:16.555370: val_loss -0.2382 +2026-04-12 07:02:16.557353: Pseudo dice [0.674, 0.4192, 0.5204, 0.4415, 0.5474, 0.1777, 0.7809] +2026-04-12 07:02:16.560606: Epoch time: 102.95 s +2026-04-12 07:02:18.274588: +2026-04-12 07:02:18.278246: Epoch 1515 +2026-04-12 07:02:18.280385: Current learning rate: 0.00652 +2026-04-12 07:04:01.196500: train_loss -0.2995 +2026-04-12 07:04:01.202538: val_loss -0.2772 +2026-04-12 07:04:01.204674: Pseudo dice [0.484, 0.4122, 0.628, 0.7803, 0.3575, 0.6393, 0.8651] +2026-04-12 07:04:01.207743: Epoch time: 102.93 s +2026-04-12 07:04:02.729340: +2026-04-12 07:04:02.731654: Epoch 1516 +2026-04-12 07:04:02.733702: Current learning rate: 0.00651 +2026-04-12 07:05:46.765792: train_loss -0.3131 +2026-04-12 07:05:46.772400: val_loss -0.2546 +2026-04-12 07:05:46.774389: Pseudo dice [0.6302, 0.3649, 0.6057, 0.741, 0.4549, 0.2937, 0.7639] +2026-04-12 07:05:46.778189: Epoch time: 104.04 s +2026-04-12 07:05:48.293956: +2026-04-12 07:05:48.296243: Epoch 1517 +2026-04-12 07:05:48.298632: Current learning rate: 0.00651 +2026-04-12 07:07:32.414399: train_loss -0.3047 +2026-04-12 07:07:32.420472: val_loss -0.2205 +2026-04-12 07:07:32.422621: Pseudo dice [0.4129, 0.5864, 0.6475, 0.21, 0.6207, 0.2055, 0.8795] +2026-04-12 07:07:32.427130: Epoch time: 104.12 s +2026-04-12 07:07:33.923793: +2026-04-12 07:07:33.925827: Epoch 1518 +2026-04-12 07:07:33.928209: Current learning rate: 0.00651 +2026-04-12 07:09:16.812109: train_loss -0.3164 +2026-04-12 07:09:16.817436: val_loss -0.3104 +2026-04-12 07:09:16.819408: Pseudo dice [0.6386, 0.611, 0.8247, 0.4845, 0.4944, 0.7743, 0.691] +2026-04-12 07:09:16.822700: Epoch time: 102.89 s +2026-04-12 07:09:18.326735: +2026-04-12 07:09:18.330545: Epoch 1519 +2026-04-12 07:09:18.334547: Current learning rate: 0.00651 +2026-04-12 07:11:00.568806: train_loss -0.3278 +2026-04-12 07:11:00.587620: val_loss -0.2343 +2026-04-12 07:11:00.591631: Pseudo dice [0.7526, 0.3866, 0.6676, 0.67, 0.5273, 0.068, 0.884] +2026-04-12 07:11:00.596913: Epoch time: 102.25 s +2026-04-12 07:11:02.107303: +2026-04-12 07:11:02.109312: Epoch 1520 +2026-04-12 07:11:02.111622: Current learning rate: 0.0065 +2026-04-12 07:12:44.912997: train_loss -0.2994 +2026-04-12 07:12:44.921210: val_loss -0.2288 +2026-04-12 07:12:44.923290: Pseudo dice [0.5966, 0.754, 0.6381, 0.0441, 0.6233, 0.4814, 0.7251] +2026-04-12 07:12:44.926164: Epoch time: 102.81 s +2026-04-12 07:12:46.430346: +2026-04-12 07:12:46.432699: Epoch 1521 +2026-04-12 07:12:46.435498: Current learning rate: 0.0065 +2026-04-12 07:14:29.557100: train_loss -0.3125 +2026-04-12 07:14:29.563152: val_loss -0.2558 +2026-04-12 07:14:29.566317: Pseudo dice [0.6454, 0.7772, 0.3543, 0.7012, 0.4379, 0.5261, 0.4552] +2026-04-12 07:14:29.569518: Epoch time: 103.13 s +2026-04-12 07:14:31.066977: +2026-04-12 07:14:31.068731: Epoch 1522 +2026-04-12 07:14:31.070619: Current learning rate: 0.0065 +2026-04-12 07:16:13.735223: train_loss -0.2844 +2026-04-12 07:16:13.740938: val_loss -0.2044 +2026-04-12 07:16:13.742826: Pseudo dice [0.5675, 0.3585, 0.5304, 0.0061, 0.3915, 0.0229, 0.5582] +2026-04-12 07:16:13.745238: Epoch time: 102.67 s +2026-04-12 07:16:15.237499: +2026-04-12 07:16:15.239661: Epoch 1523 +2026-04-12 07:16:15.241967: Current learning rate: 0.0065 +2026-04-12 07:17:58.225784: train_loss -0.3009 +2026-04-12 07:17:58.233227: val_loss -0.2361 +2026-04-12 07:17:58.236095: Pseudo dice [0.1914, 0.6328, 0.4917, 0.7167, 0.5803, 0.3913, 0.6193] +2026-04-12 07:17:58.241014: Epoch time: 102.99 s +2026-04-12 07:17:59.758500: +2026-04-12 07:17:59.761268: Epoch 1524 +2026-04-12 07:17:59.763449: Current learning rate: 0.00649 +2026-04-12 07:19:42.756519: train_loss -0.3017 +2026-04-12 07:19:42.766015: val_loss -0.1872 +2026-04-12 07:19:42.768552: Pseudo dice [0.5454, 0.5493, 0.6656, 0.0, 0.2565, 0.3337, 0.3095] +2026-04-12 07:19:42.771348: Epoch time: 103.0 s +2026-04-12 07:19:44.297024: +2026-04-12 07:19:44.299061: Epoch 1525 +2026-04-12 07:19:44.301145: Current learning rate: 0.00649 +2026-04-12 07:21:26.666605: train_loss -0.3144 +2026-04-12 07:21:26.672376: val_loss -0.282 +2026-04-12 07:21:26.674977: Pseudo dice [0.4197, 0.6869, 0.6586, 0.1643, 0.6708, 0.669, 0.8761] +2026-04-12 07:21:26.677253: Epoch time: 102.37 s +2026-04-12 07:21:28.211051: +2026-04-12 07:21:28.213115: Epoch 1526 +2026-04-12 07:21:28.215066: Current learning rate: 0.00649 +2026-04-12 07:23:11.555137: train_loss -0.3103 +2026-04-12 07:23:11.560244: val_loss -0.2562 +2026-04-12 07:23:11.562285: Pseudo dice [0.6426, 0.7205, 0.7322, 0.6082, 0.3741, 0.3234, 0.8235] +2026-04-12 07:23:11.564505: Epoch time: 103.35 s +2026-04-12 07:23:13.096054: +2026-04-12 07:23:13.098513: Epoch 1527 +2026-04-12 07:23:13.100517: Current learning rate: 0.00649 +2026-04-12 07:24:57.281856: train_loss -0.3128 +2026-04-12 07:24:57.287708: val_loss -0.2505 +2026-04-12 07:24:57.295184: Pseudo dice [0.7268, 0.4283, 0.6241, 0.6915, 0.5242, 0.0787, 0.8595] +2026-04-12 07:24:57.299971: Epoch time: 104.19 s +2026-04-12 07:24:58.827771: +2026-04-12 07:24:58.830666: Epoch 1528 +2026-04-12 07:24:58.832614: Current learning rate: 0.00648 +2026-04-12 07:26:43.060934: train_loss -0.3076 +2026-04-12 07:26:43.069593: val_loss -0.2615 +2026-04-12 07:26:43.071676: Pseudo dice [0.3233, 0.5139, 0.7189, 0.3487, 0.4707, 0.1877, 0.7774] +2026-04-12 07:26:43.074106: Epoch time: 104.24 s +2026-04-12 07:26:44.611942: +2026-04-12 07:26:44.614192: Epoch 1529 +2026-04-12 07:26:44.616513: Current learning rate: 0.00648 +2026-04-12 07:28:27.474376: train_loss -0.2991 +2026-04-12 07:28:27.481752: val_loss -0.2025 +2026-04-12 07:28:27.484102: Pseudo dice [0.165, 0.3116, 0.5741, 0.6502, 0.5007, 0.0349, 0.8471] +2026-04-12 07:28:27.487270: Epoch time: 102.87 s +2026-04-12 07:28:30.169713: +2026-04-12 07:28:30.172017: Epoch 1530 +2026-04-12 07:28:30.173884: Current learning rate: 0.00648 +2026-04-12 07:30:12.963161: train_loss -0.3103 +2026-04-12 07:30:12.970180: val_loss -0.1731 +2026-04-12 07:30:12.976597: Pseudo dice [0.4713, 0.6479, 0.386, 0.6848, 0.5571, 0.0182, 0.7843] +2026-04-12 07:30:12.979427: Epoch time: 102.8 s +2026-04-12 07:30:14.506150: +2026-04-12 07:30:14.508671: Epoch 1531 +2026-04-12 07:30:14.511119: Current learning rate: 0.00648 +2026-04-12 07:31:58.153722: train_loss -0.2869 +2026-04-12 07:31:58.160321: val_loss -0.2174 +2026-04-12 07:31:58.163040: Pseudo dice [0.3923, 0.777, 0.724, 0.7822, 0.5996, 0.0621, 0.8981] +2026-04-12 07:31:58.167379: Epoch time: 103.65 s +2026-04-12 07:31:59.695477: +2026-04-12 07:31:59.697401: Epoch 1532 +2026-04-12 07:31:59.699971: Current learning rate: 0.00648 +2026-04-12 07:33:42.695664: train_loss -0.3001 +2026-04-12 07:33:42.702784: val_loss -0.24 +2026-04-12 07:33:42.705135: Pseudo dice [0.5961, 0.3523, 0.5344, 0.5687, 0.5472, 0.2115, 0.7964] +2026-04-12 07:33:42.707510: Epoch time: 103.0 s +2026-04-12 07:33:44.481468: +2026-04-12 07:33:44.483642: Epoch 1533 +2026-04-12 07:33:44.485976: Current learning rate: 0.00647 +2026-04-12 07:35:27.237092: train_loss -0.3064 +2026-04-12 07:35:27.244301: val_loss -0.2242 +2026-04-12 07:35:27.246932: Pseudo dice [0.7557, 0.5276, 0.6126, 0.0064, 0.3922, 0.1346, 0.7731] +2026-04-12 07:35:27.249259: Epoch time: 102.76 s +2026-04-12 07:35:28.825765: +2026-04-12 07:35:28.827840: Epoch 1534 +2026-04-12 07:35:28.830015: Current learning rate: 0.00647 +2026-04-12 07:37:12.421175: train_loss -0.3115 +2026-04-12 07:37:12.428446: val_loss -0.1961 +2026-04-12 07:37:12.430828: Pseudo dice [0.4709, 0.5966, 0.6844, 0.0104, 0.6315, 0.1009, 0.6948] +2026-04-12 07:37:12.434105: Epoch time: 103.6 s +2026-04-12 07:37:13.965129: +2026-04-12 07:37:13.970316: Epoch 1535 +2026-04-12 07:37:13.975026: Current learning rate: 0.00647 +2026-04-12 07:38:56.583491: train_loss -0.2822 +2026-04-12 07:38:56.589023: val_loss -0.2772 +2026-04-12 07:38:56.591046: Pseudo dice [0.5042, 0.4244, 0.4937, 0.4768, 0.6067, 0.7221, 0.7645] +2026-04-12 07:38:56.593864: Epoch time: 102.62 s +2026-04-12 07:38:58.117728: +2026-04-12 07:38:58.119688: Epoch 1536 +2026-04-12 07:38:58.121975: Current learning rate: 0.00647 +2026-04-12 07:40:41.663752: train_loss -0.2969 +2026-04-12 07:40:41.671589: val_loss -0.2089 +2026-04-12 07:40:41.673879: Pseudo dice [0.7656, 0.6488, 0.7276, 0.7203, 0.4239, 0.0683, 0.4338] +2026-04-12 07:40:41.676590: Epoch time: 103.55 s +2026-04-12 07:40:43.202546: +2026-04-12 07:40:43.204782: Epoch 1537 +2026-04-12 07:40:43.206946: Current learning rate: 0.00646 +2026-04-12 07:42:25.622213: train_loss -0.3131 +2026-04-12 07:42:25.629391: val_loss -0.2736 +2026-04-12 07:42:25.631438: Pseudo dice [0.666, 0.7525, 0.6957, 0.2815, 0.4218, 0.6815, 0.6465] +2026-04-12 07:42:25.633856: Epoch time: 102.42 s +2026-04-12 07:42:27.127198: +2026-04-12 07:42:27.129372: Epoch 1538 +2026-04-12 07:42:27.137459: Current learning rate: 0.00646 +2026-04-12 07:44:12.673217: train_loss -0.2999 +2026-04-12 07:44:12.679987: val_loss -0.2539 +2026-04-12 07:44:12.682997: Pseudo dice [0.7587, 0.2757, 0.7162, 0.7246, 0.6124, 0.1744, 0.7604] +2026-04-12 07:44:12.685705: Epoch time: 105.55 s +2026-04-12 07:44:14.218795: +2026-04-12 07:44:14.221219: Epoch 1539 +2026-04-12 07:44:14.223363: Current learning rate: 0.00646 +2026-04-12 07:45:57.625139: train_loss -0.31 +2026-04-12 07:45:57.630815: val_loss -0.2154 +2026-04-12 07:45:57.633438: Pseudo dice [0.7107, 0.2626, 0.7095, 0.0676, 0.6758, 0.0904, 0.1651] +2026-04-12 07:45:57.635857: Epoch time: 103.41 s +2026-04-12 07:45:59.217334: +2026-04-12 07:45:59.219210: Epoch 1540 +2026-04-12 07:45:59.221269: Current learning rate: 0.00646 +2026-04-12 07:47:42.537504: train_loss -0.3076 +2026-04-12 07:47:42.545153: val_loss -0.2517 +2026-04-12 07:47:42.547904: Pseudo dice [0.4212, 0.6881, 0.7475, 0.5018, 0.6403, 0.1036, 0.7578] +2026-04-12 07:47:42.551194: Epoch time: 103.32 s +2026-04-12 07:47:44.079450: +2026-04-12 07:47:44.082183: Epoch 1541 +2026-04-12 07:47:44.084304: Current learning rate: 0.00645 +2026-04-12 07:49:27.908420: train_loss -0.295 +2026-04-12 07:49:27.916610: val_loss -0.1953 +2026-04-12 07:49:27.919678: Pseudo dice [0.4333, 0.5982, 0.6459, 0.0441, 0.3861, 0.1847, 0.7094] +2026-04-12 07:49:27.922987: Epoch time: 103.83 s +2026-04-12 07:49:29.449614: +2026-04-12 07:49:29.452324: Epoch 1542 +2026-04-12 07:49:29.454903: Current learning rate: 0.00645 +2026-04-12 07:51:11.994138: train_loss -0.268 +2026-04-12 07:51:12.000647: val_loss -0.2512 +2026-04-12 07:51:12.003839: Pseudo dice [0.605, 0.4971, 0.6585, 0.0354, 0.5034, 0.5849, 0.8302] +2026-04-12 07:51:12.006917: Epoch time: 102.55 s +2026-04-12 07:51:13.518610: +2026-04-12 07:51:13.520622: Epoch 1543 +2026-04-12 07:51:13.522731: Current learning rate: 0.00645 +2026-04-12 07:52:56.229237: train_loss -0.2727 +2026-04-12 07:52:56.235774: val_loss -0.2815 +2026-04-12 07:52:56.238215: Pseudo dice [0.6023, 0.5782, 0.6482, 0.6714, 0.3385, 0.8163, 0.4987] +2026-04-12 07:52:56.240811: Epoch time: 102.71 s +2026-04-12 07:52:57.752939: +2026-04-12 07:52:57.754842: Epoch 1544 +2026-04-12 07:52:57.756808: Current learning rate: 0.00645 +2026-04-12 07:54:42.590126: train_loss -0.2886 +2026-04-12 07:54:42.596900: val_loss -0.2441 +2026-04-12 07:54:42.599230: Pseudo dice [0.3589, 0.5297, 0.5067, 0.7135, 0.3692, 0.792, 0.7017] +2026-04-12 07:54:42.602197: Epoch time: 104.84 s +2026-04-12 07:54:44.115340: +2026-04-12 07:54:44.117362: Epoch 1545 +2026-04-12 07:54:44.120183: Current learning rate: 0.00644 +2026-04-12 07:56:28.766419: train_loss -0.2944 +2026-04-12 07:56:28.772993: val_loss -0.23 +2026-04-12 07:56:28.775613: Pseudo dice [0.6488, 0.6135, 0.5331, 0.7385, 0.6062, 0.1304, 0.675] +2026-04-12 07:56:28.778475: Epoch time: 104.65 s +2026-04-12 07:56:30.303687: +2026-04-12 07:56:30.305581: Epoch 1546 +2026-04-12 07:56:30.307452: Current learning rate: 0.00644 +2026-04-12 07:58:13.392033: train_loss -0.3238 +2026-04-12 07:58:13.398087: val_loss -0.2854 +2026-04-12 07:58:13.400733: Pseudo dice [0.4315, 0.7072, 0.6896, 0.1311, 0.6019, 0.6241, 0.6638] +2026-04-12 07:58:13.404399: Epoch time: 103.09 s +2026-04-12 07:58:14.951368: +2026-04-12 07:58:14.953612: Epoch 1547 +2026-04-12 07:58:14.958343: Current learning rate: 0.00644 +2026-04-12 07:59:58.771796: train_loss -0.2982 +2026-04-12 07:59:58.778208: val_loss -0.2847 +2026-04-12 07:59:58.779961: Pseudo dice [0.3412, 0.7254, 0.5344, 0.2223, 0.4709, 0.798, 0.7745] +2026-04-12 07:59:58.782489: Epoch time: 103.82 s +2026-04-12 08:00:00.282003: +2026-04-12 08:00:00.284708: Epoch 1548 +2026-04-12 08:00:00.287131: Current learning rate: 0.00644 +2026-04-12 08:01:44.148534: train_loss -0.3083 +2026-04-12 08:01:44.155963: val_loss -0.2988 +2026-04-12 08:01:44.158576: Pseudo dice [0.6454, 0.2018, 0.7862, 0.5579, 0.4427, 0.842, 0.6166] +2026-04-12 08:01:44.161876: Epoch time: 103.87 s +2026-04-12 08:01:46.861855: +2026-04-12 08:01:46.864285: Epoch 1549 +2026-04-12 08:01:46.866768: Current learning rate: 0.00644 +2026-04-12 08:03:30.231951: train_loss -0.3032 +2026-04-12 08:03:30.237991: val_loss -0.1886 +2026-04-12 08:03:30.240421: Pseudo dice [0.4085, 0.4368, 0.3938, 0.3091, 0.2648, 0.0355, 0.6358] +2026-04-12 08:03:30.243528: Epoch time: 103.37 s +2026-04-12 08:03:33.759438: +2026-04-12 08:03:33.761445: Epoch 1550 +2026-04-12 08:03:33.763281: Current learning rate: 0.00643 +2026-04-12 08:05:16.985427: train_loss -0.3067 +2026-04-12 08:05:16.990856: val_loss -0.1906 +2026-04-12 08:05:16.992826: Pseudo dice [0.3836, 0.4828, 0.479, 0.8795, 0.5628, 0.1201, 0.6661] +2026-04-12 08:05:16.994988: Epoch time: 103.23 s +2026-04-12 08:05:18.512716: +2026-04-12 08:05:18.514685: Epoch 1551 +2026-04-12 08:05:18.516638: Current learning rate: 0.00643 +2026-04-12 08:07:01.984166: train_loss -0.2954 +2026-04-12 08:07:01.992190: val_loss -0.1992 +2026-04-12 08:07:01.994363: Pseudo dice [0.2537, 0.4331, 0.5681, 0.7167, 0.3847, 0.0768, 0.3277] +2026-04-12 08:07:01.996645: Epoch time: 103.48 s +2026-04-12 08:07:03.504149: +2026-04-12 08:07:03.506776: Epoch 1552 +2026-04-12 08:07:03.508452: Current learning rate: 0.00643 +2026-04-12 08:08:47.565636: train_loss -0.2868 +2026-04-12 08:08:47.573078: val_loss -0.1948 +2026-04-12 08:08:47.575256: Pseudo dice [0.5606, 0.4779, 0.5767, 0.5675, 0.5, 0.1269, 0.7741] +2026-04-12 08:08:47.577971: Epoch time: 104.07 s +2026-04-12 08:08:49.106164: +2026-04-12 08:08:49.107846: Epoch 1553 +2026-04-12 08:08:49.109473: Current learning rate: 0.00643 +2026-04-12 08:10:31.922261: train_loss -0.3136 +2026-04-12 08:10:31.929924: val_loss -0.2501 +2026-04-12 08:10:31.934931: Pseudo dice [0.3921, 0.5911, 0.7012, 0.1545, 0.3691, 0.6055, 0.385] +2026-04-12 08:10:31.938226: Epoch time: 102.82 s +2026-04-12 08:10:33.461364: +2026-04-12 08:10:33.463242: Epoch 1554 +2026-04-12 08:10:33.465264: Current learning rate: 0.00642 +2026-04-12 08:12:16.127984: train_loss -0.3073 +2026-04-12 08:12:16.133835: val_loss -0.2448 +2026-04-12 08:12:16.135762: Pseudo dice [0.4955, 0.4736, 0.4443, 0.3975, 0.4515, 0.4533, 0.7035] +2026-04-12 08:12:16.138727: Epoch time: 102.67 s +2026-04-12 08:12:17.654772: +2026-04-12 08:12:17.656450: Epoch 1555 +2026-04-12 08:12:17.658196: Current learning rate: 0.00642 +2026-04-12 08:14:05.000444: train_loss -0.3027 +2026-04-12 08:14:05.007020: val_loss -0.2587 +2026-04-12 08:14:05.009355: Pseudo dice [0.5337, 0.4138, 0.675, 0.6258, 0.6213, 0.1112, 0.8177] +2026-04-12 08:14:05.013124: Epoch time: 107.35 s +2026-04-12 08:14:06.529389: +2026-04-12 08:14:06.531176: Epoch 1556 +2026-04-12 08:14:06.533098: Current learning rate: 0.00642 +2026-04-12 08:15:49.466519: train_loss -0.3117 +2026-04-12 08:15:49.473669: val_loss -0.2754 +2026-04-12 08:15:49.476152: Pseudo dice [0.441, 0.5879, 0.5418, 0.5677, 0.6301, 0.7698, 0.7198] +2026-04-12 08:15:49.478948: Epoch time: 102.94 s +2026-04-12 08:15:50.997207: +2026-04-12 08:15:50.999923: Epoch 1557 +2026-04-12 08:15:51.001701: Current learning rate: 0.00642 +2026-04-12 08:17:33.519908: train_loss -0.3025 +2026-04-12 08:17:33.533293: val_loss -0.2887 +2026-04-12 08:17:33.535946: Pseudo dice [0.6448, 0.5218, 0.4999, 0.3082, 0.6354, 0.6386, 0.8215] +2026-04-12 08:17:33.539041: Epoch time: 102.53 s +2026-04-12 08:17:35.065963: +2026-04-12 08:17:35.067585: Epoch 1558 +2026-04-12 08:17:35.069922: Current learning rate: 0.00641 +2026-04-12 08:19:18.560791: train_loss -0.3353 +2026-04-12 08:19:18.566786: val_loss -0.3089 +2026-04-12 08:19:18.568723: Pseudo dice [0.447, 0.4313, 0.4509, 0.7361, 0.6475, 0.5242, 0.8323] +2026-04-12 08:19:18.571862: Epoch time: 103.5 s +2026-04-12 08:19:20.093690: +2026-04-12 08:19:20.095630: Epoch 1559 +2026-04-12 08:19:20.097743: Current learning rate: 0.00641 +2026-04-12 08:21:03.606257: train_loss -0.2891 +2026-04-12 08:21:03.612499: val_loss -0.1563 +2026-04-12 08:21:03.614535: Pseudo dice [0.3526, 0.4539, 0.3731, 0.3765, 0.244, 0.1604, 0.6261] +2026-04-12 08:21:03.617179: Epoch time: 103.52 s +2026-04-12 08:21:05.125775: +2026-04-12 08:21:05.128430: Epoch 1560 +2026-04-12 08:21:05.130563: Current learning rate: 0.00641 +2026-04-12 08:22:48.458941: train_loss -0.2982 +2026-04-12 08:22:48.466048: val_loss -0.2732 +2026-04-12 08:22:48.468279: Pseudo dice [0.7375, 0.4087, 0.7098, 0.6754, 0.508, 0.6376, 0.6579] +2026-04-12 08:22:48.471150: Epoch time: 103.34 s +2026-04-12 08:22:49.997122: +2026-04-12 08:22:49.998808: Epoch 1561 +2026-04-12 08:22:50.000485: Current learning rate: 0.00641 +2026-04-12 08:24:32.418822: train_loss -0.2978 +2026-04-12 08:24:32.426471: val_loss -0.207 +2026-04-12 08:24:32.428465: Pseudo dice [0.4571, 0.8356, 0.5581, 0.0481, 0.5161, 0.0474, 0.5902] +2026-04-12 08:24:32.431758: Epoch time: 102.43 s +2026-04-12 08:24:33.966611: +2026-04-12 08:24:33.968586: Epoch 1562 +2026-04-12 08:24:33.970646: Current learning rate: 0.0064 +2026-04-12 08:26:16.432543: train_loss -0.2943 +2026-04-12 08:26:16.438390: val_loss -0.237 +2026-04-12 08:26:16.440308: Pseudo dice [0.3504, 0.7145, 0.5079, 0.2662, 0.5263, 0.1018, 0.5801] +2026-04-12 08:26:16.442598: Epoch time: 102.47 s +2026-04-12 08:26:18.229981: +2026-04-12 08:26:18.231663: Epoch 1563 +2026-04-12 08:26:18.233366: Current learning rate: 0.0064 +2026-04-12 08:28:00.949203: train_loss -0.2999 +2026-04-12 08:28:00.960274: val_loss -0.2239 +2026-04-12 08:28:00.963041: Pseudo dice [0.6214, 0.676, 0.6183, 0.3286, 0.568, 0.0441, 0.6511] +2026-04-12 08:28:00.966403: Epoch time: 102.72 s +2026-04-12 08:28:02.521325: +2026-04-12 08:28:02.523558: Epoch 1564 +2026-04-12 08:28:02.525991: Current learning rate: 0.0064 +2026-04-12 08:29:44.835334: train_loss -0.3218 +2026-04-12 08:29:44.841486: val_loss -0.1691 +2026-04-12 08:29:44.844569: Pseudo dice [0.7769, 0.5535, 0.5378, 0.4359, 0.5897, 0.0316, 0.6677] +2026-04-12 08:29:44.847248: Epoch time: 102.32 s +2026-04-12 08:29:46.372684: +2026-04-12 08:29:46.375315: Epoch 1565 +2026-04-12 08:29:46.377484: Current learning rate: 0.0064 +2026-04-12 08:31:29.250117: train_loss -0.3164 +2026-04-12 08:31:29.255650: val_loss -0.2306 +2026-04-12 08:31:29.257650: Pseudo dice [0.5061, 0.3627, 0.7083, 0.7705, 0.5965, 0.0412, 0.8321] +2026-04-12 08:31:29.259620: Epoch time: 102.88 s +2026-04-12 08:31:30.787759: +2026-04-12 08:31:30.790050: Epoch 1566 +2026-04-12 08:31:30.791618: Current learning rate: 0.00639 +2026-04-12 08:33:14.130833: train_loss -0.3281 +2026-04-12 08:33:14.141448: val_loss -0.1957 +2026-04-12 08:33:14.144303: Pseudo dice [0.4909, 0.8296, 0.7657, 0.7583, 0.5215, 0.0892, 0.9041] +2026-04-12 08:33:14.147499: Epoch time: 103.35 s +2026-04-12 08:33:15.695146: +2026-04-12 08:33:15.697128: Epoch 1567 +2026-04-12 08:33:15.698910: Current learning rate: 0.00639 +2026-04-12 08:34:59.182628: train_loss -0.3175 +2026-04-12 08:34:59.193011: val_loss -0.3059 +2026-04-12 08:34:59.197096: Pseudo dice [0.3702, 0.156, 0.689, 0.9034, 0.6497, 0.721, 0.8765] +2026-04-12 08:34:59.201585: Epoch time: 103.49 s +2026-04-12 08:35:00.748039: +2026-04-12 08:35:00.750441: Epoch 1568 +2026-04-12 08:35:00.753364: Current learning rate: 0.00639 +2026-04-12 08:36:45.646984: train_loss -0.3168 +2026-04-12 08:36:45.657192: val_loss -0.2403 +2026-04-12 08:36:45.658930: Pseudo dice [0.3059, 0.5784, 0.6645, 0.6013, 0.612, 0.1862, 0.6578] +2026-04-12 08:36:45.661086: Epoch time: 104.9 s +2026-04-12 08:36:47.182884: +2026-04-12 08:36:47.184798: Epoch 1569 +2026-04-12 08:36:47.186329: Current learning rate: 0.00639 +2026-04-12 08:38:30.235582: train_loss -0.284 +2026-04-12 08:38:30.243303: val_loss -0.1331 +2026-04-12 08:38:30.245641: Pseudo dice [0.7759, 0.7354, 0.7119, 0.5063, 0.3334, 0.3004, 0.7187] +2026-04-12 08:38:30.248744: Epoch time: 103.06 s +2026-04-12 08:38:31.791938: +2026-04-12 08:38:31.793700: Epoch 1570 +2026-04-12 08:38:31.795572: Current learning rate: 0.00639 +2026-04-12 08:40:19.710768: train_loss -0.3014 +2026-04-12 08:40:19.717061: val_loss -0.1983 +2026-04-12 08:40:19.719198: Pseudo dice [0.6364, 0.7458, 0.5778, 0.5061, 0.5683, 0.0694, 0.7532] +2026-04-12 08:40:19.721791: Epoch time: 107.92 s +2026-04-12 08:40:21.246977: +2026-04-12 08:40:21.248949: Epoch 1571 +2026-04-12 08:40:21.250794: Current learning rate: 0.00638 +2026-04-12 08:42:04.812750: train_loss -0.2956 +2026-04-12 08:42:04.829103: val_loss -0.2514 +2026-04-12 08:42:04.831579: Pseudo dice [0.5362, 0.5991, 0.677, 0.5089, 0.6558, 0.0885, 0.8355] +2026-04-12 08:42:04.833677: Epoch time: 103.57 s +2026-04-12 08:42:06.357218: +2026-04-12 08:42:06.359152: Epoch 1572 +2026-04-12 08:42:06.360991: Current learning rate: 0.00638 +2026-04-12 08:43:49.140645: train_loss -0.3126 +2026-04-12 08:43:49.146285: val_loss -0.2573 +2026-04-12 08:43:49.148809: Pseudo dice [0.2302, 0.261, 0.6854, 0.2049, 0.7286, 0.3655, 0.6728] +2026-04-12 08:43:49.151077: Epoch time: 102.79 s +2026-04-12 08:43:50.664227: +2026-04-12 08:43:50.666003: Epoch 1573 +2026-04-12 08:43:50.667590: Current learning rate: 0.00638 +2026-04-12 08:45:34.757535: train_loss -0.2969 +2026-04-12 08:45:34.763858: val_loss -0.2052 +2026-04-12 08:45:34.766432: Pseudo dice [0.48, 0.4673, 0.5514, 0.7639, 0.6727, 0.1052, 0.7961] +2026-04-12 08:45:34.768655: Epoch time: 104.1 s +2026-04-12 08:45:36.323246: +2026-04-12 08:45:36.325172: Epoch 1574 +2026-04-12 08:45:36.326939: Current learning rate: 0.00638 +2026-04-12 08:47:19.726118: train_loss -0.2922 +2026-04-12 08:47:19.732456: val_loss -0.2339 +2026-04-12 08:47:19.734341: Pseudo dice [0.5436, 0.4906, 0.5266, 0.6164, 0.6272, 0.4412, 0.8357] +2026-04-12 08:47:19.736404: Epoch time: 103.41 s +2026-04-12 08:47:21.255269: +2026-04-12 08:47:21.260535: Epoch 1575 +2026-04-12 08:47:21.262240: Current learning rate: 0.00637 +2026-04-12 08:49:04.747203: train_loss -0.3052 +2026-04-12 08:49:04.754228: val_loss -0.2792 +2026-04-12 08:49:04.756805: Pseudo dice [0.6554, 0.3018, 0.6116, 0.6806, 0.5388, 0.7622, 0.7978] +2026-04-12 08:49:04.760065: Epoch time: 103.5 s +2026-04-12 08:49:06.299182: +2026-04-12 08:49:06.301159: Epoch 1576 +2026-04-12 08:49:06.304539: Current learning rate: 0.00637 +2026-04-12 08:50:50.547056: train_loss -0.3193 +2026-04-12 08:50:50.553753: val_loss -0.2736 +2026-04-12 08:50:50.555895: Pseudo dice [0.2981, 0.6743, 0.6067, 0.6784, 0.5466, 0.2943, 0.7905] +2026-04-12 08:50:50.558717: Epoch time: 104.25 s +2026-04-12 08:50:52.076113: +2026-04-12 08:50:52.078124: Epoch 1577 +2026-04-12 08:50:52.079904: Current learning rate: 0.00637 +2026-04-12 08:52:35.673334: train_loss -0.3136 +2026-04-12 08:52:35.681282: val_loss -0.1819 +2026-04-12 08:52:35.684593: Pseudo dice [0.7713, 0.7195, 0.2335, 0.5388, 0.2991, 0.0877, 0.8691] +2026-04-12 08:52:35.700016: Epoch time: 103.6 s +2026-04-12 08:52:37.243982: +2026-04-12 08:52:37.246071: Epoch 1578 +2026-04-12 08:52:37.248305: Current learning rate: 0.00637 +2026-04-12 08:54:20.532948: train_loss -0.3195 +2026-04-12 08:54:20.547440: val_loss -0.1974 +2026-04-12 08:54:20.550743: Pseudo dice [0.4744, 0.6252, 0.4724, 0.6862, 0.461, 0.0415, 0.7618] +2026-04-12 08:54:20.555165: Epoch time: 103.29 s +2026-04-12 08:54:22.090099: +2026-04-12 08:54:22.094124: Epoch 1579 +2026-04-12 08:54:22.096804: Current learning rate: 0.00636 +2026-04-12 08:56:08.281885: train_loss -0.318 +2026-04-12 08:56:08.291420: val_loss -0.2222 +2026-04-12 08:56:08.294430: Pseudo dice [0.6033, 0.4556, 0.6073, 0.3811, 0.5885, 0.0561, 0.8725] +2026-04-12 08:56:08.301658: Epoch time: 106.2 s +2026-04-12 08:56:09.830437: +2026-04-12 08:56:09.832844: Epoch 1580 +2026-04-12 08:56:09.835021: Current learning rate: 0.00636 +2026-04-12 08:57:54.163748: train_loss -0.3185 +2026-04-12 08:57:54.172628: val_loss -0.2203 +2026-04-12 08:57:54.175081: Pseudo dice [0.4256, 0.5915, 0.5614, 0.8074, 0.2185, 0.0809, 0.3416] +2026-04-12 08:57:54.179617: Epoch time: 104.34 s +2026-04-12 08:57:55.704905: +2026-04-12 08:57:55.707856: Epoch 1581 +2026-04-12 08:57:55.711140: Current learning rate: 0.00636 +2026-04-12 08:59:43.541826: train_loss -0.3281 +2026-04-12 08:59:43.547987: val_loss -0.2765 +2026-04-12 08:59:43.550256: Pseudo dice [0.6801, 0.2594, 0.738, 0.0593, 0.5253, 0.3163, 0.8328] +2026-04-12 08:59:43.552577: Epoch time: 107.84 s +2026-04-12 08:59:45.084547: +2026-04-12 08:59:45.086412: Epoch 1582 +2026-04-12 08:59:45.088776: Current learning rate: 0.00636 +2026-04-12 09:01:28.849303: train_loss -0.3185 +2026-04-12 09:01:28.856487: val_loss -0.294 +2026-04-12 09:01:28.860950: Pseudo dice [0.5892, 0.6382, 0.6968, 0.7236, 0.5421, 0.8221, 0.6947] +2026-04-12 09:01:28.863605: Epoch time: 103.77 s +2026-04-12 09:01:30.392484: +2026-04-12 09:01:30.394446: Epoch 1583 +2026-04-12 09:01:30.396190: Current learning rate: 0.00635 +2026-04-12 09:03:14.220512: train_loss -0.3175 +2026-04-12 09:03:14.227762: val_loss -0.2948 +2026-04-12 09:03:14.230041: Pseudo dice [0.6754, 0.77, 0.6626, 0.7004, 0.5937, 0.7081, 0.774] +2026-04-12 09:03:14.232872: Epoch time: 103.83 s +2026-04-12 09:03:15.764796: +2026-04-12 09:03:15.767561: Epoch 1584 +2026-04-12 09:03:15.770396: Current learning rate: 0.00635 +2026-04-12 09:04:59.869542: train_loss -0.3091 +2026-04-12 09:04:59.887678: val_loss -0.2604 +2026-04-12 09:04:59.889802: Pseudo dice [0.5737, 0.3307, 0.6304, 0.7602, 0.3851, 0.6294, 0.6728] +2026-04-12 09:04:59.893040: Epoch time: 104.11 s +2026-04-12 09:05:01.404628: +2026-04-12 09:05:01.407732: Epoch 1585 +2026-04-12 09:05:01.411006: Current learning rate: 0.00635 +2026-04-12 09:06:44.854554: train_loss -0.3111 +2026-04-12 09:06:44.861897: val_loss -0.2572 +2026-04-12 09:06:44.864205: Pseudo dice [0.7089, 0.7246, 0.6034, 0.4242, 0.3024, 0.6488, 0.5026] +2026-04-12 09:06:44.867182: Epoch time: 103.45 s +2026-04-12 09:06:46.383397: +2026-04-12 09:06:46.385479: Epoch 1586 +2026-04-12 09:06:46.387488: Current learning rate: 0.00635 +2026-04-12 09:08:30.800621: train_loss -0.2975 +2026-04-12 09:08:30.808561: val_loss -0.2157 +2026-04-12 09:08:30.812733: Pseudo dice [0.7728, 0.4497, 0.3219, 0.2417, 0.5682, 0.0732, 0.6166] +2026-04-12 09:08:30.815776: Epoch time: 104.42 s +2026-04-12 09:08:32.352367: +2026-04-12 09:08:32.357830: Epoch 1587 +2026-04-12 09:08:32.365160: Current learning rate: 0.00635 +2026-04-12 09:10:15.903574: train_loss -0.3121 +2026-04-12 09:10:15.911588: val_loss -0.2515 +2026-04-12 09:10:15.914675: Pseudo dice [0.4208, 0.6345, 0.6896, 0.7076, 0.6771, 0.3123, 0.7711] +2026-04-12 09:10:15.917672: Epoch time: 103.55 s +2026-04-12 09:10:17.428502: +2026-04-12 09:10:17.430353: Epoch 1588 +2026-04-12 09:10:17.433159: Current learning rate: 0.00634 +2026-04-12 09:12:00.507471: train_loss -0.331 +2026-04-12 09:12:00.512886: val_loss -0.2721 +2026-04-12 09:12:00.514925: Pseudo dice [0.5709, 0.8911, 0.5968, 0.0767, 0.4108, 0.4898, 0.8226] +2026-04-12 09:12:00.517226: Epoch time: 103.08 s +2026-04-12 09:12:02.171546: +2026-04-12 09:12:02.173430: Epoch 1589 +2026-04-12 09:12:02.175054: Current learning rate: 0.00634 +2026-04-12 09:13:48.499177: train_loss -0.31 +2026-04-12 09:13:48.506144: val_loss -0.2199 +2026-04-12 09:13:48.509062: Pseudo dice [0.6948, 0.7246, 0.6348, 0.3502, 0.5732, 0.0732, 0.6168] +2026-04-12 09:13:48.512154: Epoch time: 106.33 s +2026-04-12 09:13:50.042597: +2026-04-12 09:13:50.044545: Epoch 1590 +2026-04-12 09:13:50.046882: Current learning rate: 0.00634 +2026-04-12 09:15:35.570283: train_loss -0.2814 +2026-04-12 09:15:35.576555: val_loss -0.2851 +2026-04-12 09:15:35.579077: Pseudo dice [0.5903, 0.7439, 0.545, 0.5089, 0.5826, 0.5079, 0.6664] +2026-04-12 09:15:35.581615: Epoch time: 105.53 s +2026-04-12 09:15:37.088730: +2026-04-12 09:15:37.090444: Epoch 1591 +2026-04-12 09:15:37.092595: Current learning rate: 0.00634 +2026-04-12 09:17:21.171091: train_loss -0.3192 +2026-04-12 09:17:21.178432: val_loss -0.2962 +2026-04-12 09:17:21.181375: Pseudo dice [0.5873, 0.5897, 0.6406, 0.7127, 0.4763, 0.8008, 0.8818] +2026-04-12 09:17:21.184375: Epoch time: 104.09 s +2026-04-12 09:17:22.724417: +2026-04-12 09:17:22.726193: Epoch 1592 +2026-04-12 09:17:22.728039: Current learning rate: 0.00633 +2026-04-12 09:19:06.587061: train_loss -0.3101 +2026-04-12 09:19:06.592652: val_loss -0.2553 +2026-04-12 09:19:06.594519: Pseudo dice [0.2838, 0.5862, 0.7074, 0.2849, 0.5268, 0.179, 0.7103] +2026-04-12 09:19:06.598366: Epoch time: 103.87 s +2026-04-12 09:19:08.127494: +2026-04-12 09:19:08.129907: Epoch 1593 +2026-04-12 09:19:08.131824: Current learning rate: 0.00633 +2026-04-12 09:20:53.311561: train_loss -0.2931 +2026-04-12 09:20:53.318603: val_loss -0.1889 +2026-04-12 09:20:53.321808: Pseudo dice [0.6466, 0.6307, 0.6444, 0.2474, 0.5496, 0.0878, 0.7852] +2026-04-12 09:20:53.324243: Epoch time: 105.19 s +2026-04-12 09:20:54.836594: +2026-04-12 09:20:54.838652: Epoch 1594 +2026-04-12 09:20:54.840501: Current learning rate: 0.00633 +2026-04-12 09:22:37.328686: train_loss -0.2923 +2026-04-12 09:22:37.335656: val_loss -0.1602 +2026-04-12 09:22:37.338343: Pseudo dice [0.6096, 0.3845, 0.745, 0.4534, 0.3182, 0.0274, 0.4487] +2026-04-12 09:22:37.340864: Epoch time: 102.5 s +2026-04-12 09:22:38.883533: +2026-04-12 09:22:38.885917: Epoch 1595 +2026-04-12 09:22:38.888045: Current learning rate: 0.00633 +2026-04-12 09:24:23.645467: train_loss -0.2937 +2026-04-12 09:24:23.655074: val_loss -0.2737 +2026-04-12 09:24:23.657577: Pseudo dice [0.63, 0.5627, 0.6606, 0.0527, 0.476, 0.1652, 0.7764] +2026-04-12 09:24:23.660271: Epoch time: 104.77 s +2026-04-12 09:24:25.174895: +2026-04-12 09:24:25.176794: Epoch 1596 +2026-04-12 09:24:25.178827: Current learning rate: 0.00632 +2026-04-12 09:26:07.601742: train_loss -0.3077 +2026-04-12 09:26:07.607617: val_loss -0.2788 +2026-04-12 09:26:07.610014: Pseudo dice [0.134, 0.1892, 0.7036, 0.6433, 0.578, 0.5897, 0.7225] +2026-04-12 09:26:07.613281: Epoch time: 102.43 s +2026-04-12 09:26:09.141191: +2026-04-12 09:26:09.144260: Epoch 1597 +2026-04-12 09:26:09.147230: Current learning rate: 0.00632 +2026-04-12 09:27:53.572362: train_loss -0.311 +2026-04-12 09:27:53.583064: val_loss -0.2773 +2026-04-12 09:27:53.585998: Pseudo dice [0.7262, 0.3921, 0.7934, 0.269, 0.503, 0.7383, 0.4397] +2026-04-12 09:27:53.589428: Epoch time: 104.43 s +2026-04-12 09:27:55.128032: +2026-04-12 09:27:55.130191: Epoch 1598 +2026-04-12 09:27:55.132163: Current learning rate: 0.00632 +2026-04-12 09:29:40.218032: train_loss -0.3139 +2026-04-12 09:29:40.223386: val_loss -0.1946 +2026-04-12 09:29:40.225570: Pseudo dice [0.4357, 0.0951, 0.6235, 0.8132, 0.4599, 0.0421, 0.783] +2026-04-12 09:29:40.228631: Epoch time: 105.09 s +2026-04-12 09:29:41.729715: +2026-04-12 09:29:41.731468: Epoch 1599 +2026-04-12 09:29:41.733042: Current learning rate: 0.00632 +2026-04-12 09:31:24.933411: train_loss -0.3064 +2026-04-12 09:31:24.941906: val_loss -0.228 +2026-04-12 09:31:24.944379: Pseudo dice [0.7187, 0.7889, 0.4121, 0.693, 0.6585, 0.0854, 0.5651] +2026-04-12 09:31:24.946929: Epoch time: 103.21 s +2026-04-12 09:31:28.448791: +2026-04-12 09:31:28.450808: Epoch 1600 +2026-04-12 09:31:28.452827: Current learning rate: 0.00631 +2026-04-12 09:33:11.597755: train_loss -0.2838 +2026-04-12 09:33:11.605002: val_loss -0.2447 +2026-04-12 09:33:11.608363: Pseudo dice [0.5002, 0.6395, 0.6501, 0.5144, 0.3971, 0.085, 0.8836] +2026-04-12 09:33:11.611004: Epoch time: 103.15 s +2026-04-12 09:33:13.154974: +2026-04-12 09:33:13.156996: Epoch 1601 +2026-04-12 09:33:13.158692: Current learning rate: 0.00631 +2026-04-12 09:34:57.526291: train_loss -0.2771 +2026-04-12 09:34:57.534989: val_loss -0.2025 +2026-04-12 09:34:57.537987: Pseudo dice [0.6978, 0.7103, 0.5972, 0.1352, 0.2813, 0.408, 0.5863] +2026-04-12 09:34:57.540832: Epoch time: 104.38 s +2026-04-12 09:34:59.091443: +2026-04-12 09:34:59.094195: Epoch 1602 +2026-04-12 09:34:59.096680: Current learning rate: 0.00631 +2026-04-12 09:36:43.342062: train_loss -0.3143 +2026-04-12 09:36:43.348870: val_loss -0.3093 +2026-04-12 09:36:43.350880: Pseudo dice [0.5082, 0.8054, 0.6672, 0.8624, 0.5963, 0.7876, 0.8775] +2026-04-12 09:36:43.353406: Epoch time: 104.25 s +2026-04-12 09:36:44.886902: +2026-04-12 09:36:44.888754: Epoch 1603 +2026-04-12 09:36:44.890595: Current learning rate: 0.00631 +2026-04-12 09:38:27.291291: train_loss -0.3044 +2026-04-12 09:38:27.297208: val_loss -0.2799 +2026-04-12 09:38:27.299358: Pseudo dice [0.6543, 0.6372, 0.4469, 0.665, 0.5651, 0.6223, 0.8057] +2026-04-12 09:38:27.302091: Epoch time: 102.41 s +2026-04-12 09:38:28.966937: +2026-04-12 09:38:28.969829: Epoch 1604 +2026-04-12 09:38:28.972098: Current learning rate: 0.0063 +2026-04-12 09:40:12.830026: train_loss -0.3155 +2026-04-12 09:40:12.838539: val_loss -0.1929 +2026-04-12 09:40:12.842892: Pseudo dice [0.7466, 0.4729, 0.6206, 0.6462, 0.5962, 0.0327, 0.833] +2026-04-12 09:40:12.845699: Epoch time: 103.87 s +2026-04-12 09:40:14.378485: +2026-04-12 09:40:14.387715: Epoch 1605 +2026-04-12 09:40:14.401487: Current learning rate: 0.0063 +2026-04-12 09:41:56.962712: train_loss -0.3164 +2026-04-12 09:41:56.968534: val_loss -0.206 +2026-04-12 09:41:56.971142: Pseudo dice [0.2118, 0.8922, 0.6993, 0.821, 0.5788, 0.06, 0.8415] +2026-04-12 09:41:56.974078: Epoch time: 102.59 s +2026-04-12 09:41:58.497165: +2026-04-12 09:41:58.499193: Epoch 1606 +2026-04-12 09:41:58.501028: Current learning rate: 0.0063 +2026-04-12 09:43:43.297623: train_loss -0.2974 +2026-04-12 09:43:43.303645: val_loss -0.1922 +2026-04-12 09:43:43.305720: Pseudo dice [0.7168, 0.7356, 0.5001, 0.009, 0.6013, 0.0401, 0.8294] +2026-04-12 09:43:43.308571: Epoch time: 104.8 s +2026-04-12 09:43:44.826331: +2026-04-12 09:43:44.828226: Epoch 1607 +2026-04-12 09:43:44.830191: Current learning rate: 0.0063 +2026-04-12 09:45:28.611888: train_loss -0.2719 +2026-04-12 09:45:28.618438: val_loss -0.2764 +2026-04-12 09:45:28.621446: Pseudo dice [0.7422, 0.6695, 0.7486, 0.0963, 0.2955, 0.394, 0.8711] +2026-04-12 09:45:28.623898: Epoch time: 103.79 s +2026-04-12 09:45:30.184383: +2026-04-12 09:45:30.186872: Epoch 1608 +2026-04-12 09:45:30.189228: Current learning rate: 0.0063 +2026-04-12 09:47:14.932273: train_loss -0.3085 +2026-04-12 09:47:14.938867: val_loss -0.2649 +2026-04-12 09:47:14.942630: Pseudo dice [0.5843, 0.3345, 0.594, 0.629, 0.4743, 0.584, 0.8158] +2026-04-12 09:47:14.946011: Epoch time: 104.75 s +2026-04-12 09:47:16.483474: +2026-04-12 09:47:16.485668: Epoch 1609 +2026-04-12 09:47:16.487490: Current learning rate: 0.00629 +2026-04-12 09:49:01.364421: train_loss -0.3043 +2026-04-12 09:49:01.370887: val_loss -0.2783 +2026-04-12 09:49:01.374028: Pseudo dice [0.473, 0.4027, 0.384, 0.7053, 0.6403, 0.5945, 0.807] +2026-04-12 09:49:01.377390: Epoch time: 104.88 s +2026-04-12 09:49:02.890434: +2026-04-12 09:49:02.892660: Epoch 1610 +2026-04-12 09:49:02.894773: Current learning rate: 0.00629 +2026-04-12 09:50:51.726572: train_loss -0.2794 +2026-04-12 09:50:51.732203: val_loss -0.233 +2026-04-12 09:50:51.734575: Pseudo dice [0.1732, 0.5268, 0.5062, 0.6569, 0.311, 0.1714, 0.7874] +2026-04-12 09:50:51.737098: Epoch time: 108.84 s +2026-04-12 09:50:53.249803: +2026-04-12 09:50:53.251968: Epoch 1611 +2026-04-12 09:50:53.253733: Current learning rate: 0.00629 +2026-04-12 09:52:36.434617: train_loss -0.2927 +2026-04-12 09:52:36.441212: val_loss -0.2494 +2026-04-12 09:52:36.443673: Pseudo dice [0.7405, 0.7233, 0.6734, 0.752, 0.6462, 0.0276, 0.8921] +2026-04-12 09:52:36.445968: Epoch time: 103.19 s +2026-04-12 09:52:37.956014: +2026-04-12 09:52:37.957965: Epoch 1612 +2026-04-12 09:52:37.959616: Current learning rate: 0.00629 +2026-04-12 09:54:23.091477: train_loss -0.2871 +2026-04-12 09:54:23.098127: val_loss -0.204 +2026-04-12 09:54:23.100387: Pseudo dice [0.7342, 0.8098, 0.2153, 0.6488, 0.6288, 0.0145, 0.6159] +2026-04-12 09:54:23.103018: Epoch time: 105.14 s +2026-04-12 09:54:24.633061: +2026-04-12 09:54:24.635116: Epoch 1613 +2026-04-12 09:54:24.636737: Current learning rate: 0.00628 +2026-04-12 09:56:08.589962: train_loss -0.3107 +2026-04-12 09:56:08.600688: val_loss -0.2692 +2026-04-12 09:56:08.603167: Pseudo dice [0.5047, 0.6412, 0.6943, 0.6081, 0.2927, 0.5391, 0.7143] +2026-04-12 09:56:08.606729: Epoch time: 103.96 s +2026-04-12 09:56:10.116825: +2026-04-12 09:56:10.118827: Epoch 1614 +2026-04-12 09:56:10.120710: Current learning rate: 0.00628 +2026-04-12 09:57:52.752990: train_loss -0.307 +2026-04-12 09:57:52.759343: val_loss -0.2768 +2026-04-12 09:57:52.762352: Pseudo dice [0.6505, 0.5098, 0.5545, 0.7724, 0.7141, 0.2699, 0.6188] +2026-04-12 09:57:52.764967: Epoch time: 102.64 s +2026-04-12 09:57:54.301326: +2026-04-12 09:57:54.303520: Epoch 1615 +2026-04-12 09:57:54.305480: Current learning rate: 0.00628 +2026-04-12 09:59:38.020444: train_loss -0.3089 +2026-04-12 09:59:38.025919: val_loss -0.2744 +2026-04-12 09:59:38.028021: Pseudo dice [0.7742, 0.4968, 0.6569, 0.258, 0.6222, 0.5998, 0.8052] +2026-04-12 09:59:38.031237: Epoch time: 103.72 s +2026-04-12 09:59:39.552899: +2026-04-12 09:59:39.555525: Epoch 1616 +2026-04-12 09:59:39.557149: Current learning rate: 0.00628 +2026-04-12 10:01:22.806404: train_loss -0.3036 +2026-04-12 10:01:22.814991: val_loss -0.3105 +2026-04-12 10:01:22.817179: Pseudo dice [0.5913, 0.2786, 0.7324, 0.1391, 0.51, 0.7521, 0.7218] +2026-04-12 10:01:22.820677: Epoch time: 103.26 s +2026-04-12 10:01:24.360504: +2026-04-12 10:01:24.362315: Epoch 1617 +2026-04-12 10:01:24.364077: Current learning rate: 0.00627 +2026-04-12 10:03:11.083272: train_loss -0.2997 +2026-04-12 10:03:11.089216: val_loss -0.2751 +2026-04-12 10:03:11.091388: Pseudo dice [0.6617, 0.4628, 0.7934, 0.4203, 0.2241, 0.7909, 0.8625] +2026-04-12 10:03:11.093863: Epoch time: 106.73 s +2026-04-12 10:03:12.620966: +2026-04-12 10:03:12.623226: Epoch 1618 +2026-04-12 10:03:12.625068: Current learning rate: 0.00627 +2026-04-12 10:04:57.313240: train_loss -0.3137 +2026-04-12 10:04:57.318240: val_loss -0.234 +2026-04-12 10:04:57.320223: Pseudo dice [0.5856, 0.5689, 0.6454, 0.1787, 0.676, 0.4592, 0.4248] +2026-04-12 10:04:57.322836: Epoch time: 104.7 s +2026-04-12 10:04:58.829935: +2026-04-12 10:04:58.831597: Epoch 1619 +2026-04-12 10:04:58.833268: Current learning rate: 0.00627 +2026-04-12 10:06:42.227569: train_loss -0.3191 +2026-04-12 10:06:42.235350: val_loss -0.2627 +2026-04-12 10:06:42.239262: Pseudo dice [0.8504, 0.5953, 0.6884, 0.2998, 0.6266, 0.6192, 0.4304] +2026-04-12 10:06:42.247763: Epoch time: 103.4 s +2026-04-12 10:06:43.799510: +2026-04-12 10:06:43.803393: Epoch 1620 +2026-04-12 10:06:43.806713: Current learning rate: 0.00627 +2026-04-12 10:08:29.545899: train_loss -0.3082 +2026-04-12 10:08:29.552937: val_loss -0.2041 +2026-04-12 10:08:29.555024: Pseudo dice [0.7678, 0.5436, 0.657, 0.6503, 0.5664, 0.0512, 0.8214] +2026-04-12 10:08:29.558079: Epoch time: 105.75 s +2026-04-12 10:08:31.180398: +2026-04-12 10:08:31.182530: Epoch 1621 +2026-04-12 10:08:31.184954: Current learning rate: 0.00626 +2026-04-12 10:10:15.415147: train_loss -0.3089 +2026-04-12 10:10:15.421016: val_loss -0.2395 +2026-04-12 10:10:15.423125: Pseudo dice [0.8184, 0.226, 0.6788, 0.7631, 0.3586, 0.1467, 0.8107] +2026-04-12 10:10:15.425727: Epoch time: 104.24 s +2026-04-12 10:10:16.965069: +2026-04-12 10:10:16.971647: Epoch 1622 +2026-04-12 10:10:16.977085: Current learning rate: 0.00626 +2026-04-12 10:11:59.600500: train_loss -0.3089 +2026-04-12 10:11:59.617252: val_loss -0.2463 +2026-04-12 10:11:59.622466: Pseudo dice [0.5707, 0.5872, 0.4977, 0.5364, 0.5207, 0.3975, 0.7139] +2026-04-12 10:11:59.627161: Epoch time: 102.64 s +2026-04-12 10:12:01.170375: +2026-04-12 10:12:01.174358: Epoch 1623 +2026-04-12 10:12:01.178364: Current learning rate: 0.00626 +2026-04-12 10:13:45.097919: train_loss -0.2792 +2026-04-12 10:13:45.105007: val_loss -0.2482 +2026-04-12 10:13:45.107872: Pseudo dice [0.2455, 0.2311, 0.6909, 0.1916, 0.5298, 0.1705, 0.8653] +2026-04-12 10:13:45.110705: Epoch time: 103.93 s +2026-04-12 10:13:46.618185: +2026-04-12 10:13:46.619870: Epoch 1624 +2026-04-12 10:13:46.621570: Current learning rate: 0.00626 +2026-04-12 10:15:31.760745: train_loss -0.3071 +2026-04-12 10:15:31.779158: val_loss -0.2395 +2026-04-12 10:15:31.784463: Pseudo dice [0.3091, 0.5728, 0.6101, 0.2382, 0.2579, 0.1973, 0.6047] +2026-04-12 10:15:31.791808: Epoch time: 105.15 s +2026-04-12 10:15:33.341882: +2026-04-12 10:15:33.346066: Epoch 1625 +2026-04-12 10:15:33.350834: Current learning rate: 0.00626 +2026-04-12 10:17:17.721231: train_loss -0.3187 +2026-04-12 10:17:17.727211: val_loss -0.2906 +2026-04-12 10:17:17.729159: Pseudo dice [0.6552, 0.1927, 0.5964, 0.8103, 0.5783, 0.7052, 0.8232] +2026-04-12 10:17:17.732134: Epoch time: 104.38 s +2026-04-12 10:17:19.287616: +2026-04-12 10:17:19.289803: Epoch 1626 +2026-04-12 10:17:19.291624: Current learning rate: 0.00625 +2026-04-12 10:19:03.069906: train_loss -0.3136 +2026-04-12 10:19:03.078825: val_loss -0.2318 +2026-04-12 10:19:03.081560: Pseudo dice [0.7241, 0.7325, 0.6521, 0.2587, 0.3148, 0.4128, 0.4192] +2026-04-12 10:19:03.083950: Epoch time: 103.79 s +2026-04-12 10:19:04.621771: +2026-04-12 10:19:04.624153: Epoch 1627 +2026-04-12 10:19:04.626333: Current learning rate: 0.00625 +2026-04-12 10:20:47.602175: train_loss -0.3168 +2026-04-12 10:20:47.609786: val_loss -0.1807 +2026-04-12 10:20:47.612459: Pseudo dice [0.6689, 0.706, 0.5187, 0.6718, 0.4834, 0.053, 0.8287] +2026-04-12 10:20:47.616026: Epoch time: 102.98 s +2026-04-12 10:20:49.160901: +2026-04-12 10:20:49.165313: Epoch 1628 +2026-04-12 10:20:49.170785: Current learning rate: 0.00625 +2026-04-12 10:22:32.633405: train_loss -0.3108 +2026-04-12 10:22:32.638718: val_loss -0.2525 +2026-04-12 10:22:32.640813: Pseudo dice [0.6342, 0.3609, 0.7017, 0.3747, 0.5207, 0.6277, 0.3655] +2026-04-12 10:22:32.643342: Epoch time: 103.48 s +2026-04-12 10:22:34.169825: +2026-04-12 10:22:34.171712: Epoch 1629 +2026-04-12 10:22:34.173610: Current learning rate: 0.00625 +2026-04-12 10:24:17.409567: train_loss -0.3038 +2026-04-12 10:24:17.415977: val_loss -0.2345 +2026-04-12 10:24:17.418549: Pseudo dice [0.4271, 0.7721, 0.7895, 0.8434, 0.4384, 0.1709, 0.7594] +2026-04-12 10:24:17.421336: Epoch time: 103.24 s +2026-04-12 10:24:18.951825: +2026-04-12 10:24:18.955035: Epoch 1630 +2026-04-12 10:24:18.956845: Current learning rate: 0.00624 +2026-04-12 10:26:04.635005: train_loss -0.3082 +2026-04-12 10:26:04.641263: val_loss -0.1431 +2026-04-12 10:26:04.643770: Pseudo dice [0.7215, 0.6752, 0.4776, 0.8172, 0.5075, 0.1546, 0.3158] +2026-04-12 10:26:04.646633: Epoch time: 105.69 s +2026-04-12 10:26:06.182432: +2026-04-12 10:26:06.184417: Epoch 1631 +2026-04-12 10:26:06.186354: Current learning rate: 0.00624 +2026-04-12 10:27:48.902571: train_loss -0.3102 +2026-04-12 10:27:48.909020: val_loss -0.2488 +2026-04-12 10:27:48.912189: Pseudo dice [0.4442, 0.3686, 0.6316, 0.7386, 0.4067, 0.4737, 0.4476] +2026-04-12 10:27:48.915100: Epoch time: 102.72 s +2026-04-12 10:27:50.444536: +2026-04-12 10:27:50.446257: Epoch 1632 +2026-04-12 10:27:50.447853: Current learning rate: 0.00624 +2026-04-12 10:29:33.049950: train_loss -0.3178 +2026-04-12 10:29:33.055875: val_loss -0.1576 +2026-04-12 10:29:33.057601: Pseudo dice [0.4426, 0.2939, 0.7154, 0.2701, 0.2754, 0.0585, 0.5491] +2026-04-12 10:29:33.060057: Epoch time: 102.61 s +2026-04-12 10:29:34.613301: +2026-04-12 10:29:34.617664: Epoch 1633 +2026-04-12 10:29:34.620643: Current learning rate: 0.00624 +2026-04-12 10:31:20.817866: train_loss -0.2951 +2026-04-12 10:31:20.824279: val_loss -0.2077 +2026-04-12 10:31:20.825831: Pseudo dice [0.5793, 0.4101, 0.6052, 0.8442, 0.6749, 0.0517, 0.8608] +2026-04-12 10:31:20.828039: Epoch time: 106.21 s +2026-04-12 10:31:22.354502: +2026-04-12 10:31:22.356353: Epoch 1634 +2026-04-12 10:31:22.358035: Current learning rate: 0.00623 +2026-04-12 10:33:05.815148: train_loss -0.3031 +2026-04-12 10:33:05.821842: val_loss -0.2648 +2026-04-12 10:33:05.823867: Pseudo dice [0.6076, 0.4914, 0.6514, 0.6816, 0.4188, 0.2084, 0.5706] +2026-04-12 10:33:05.826581: Epoch time: 103.46 s +2026-04-12 10:33:07.338632: +2026-04-12 10:33:07.340286: Epoch 1635 +2026-04-12 10:33:07.341971: Current learning rate: 0.00623 +2026-04-12 10:34:49.803632: train_loss -0.3162 +2026-04-12 10:34:49.809841: val_loss -0.245 +2026-04-12 10:34:49.812438: Pseudo dice [0.6463, 0.5345, 0.6312, 0.3372, 0.4392, 0.0691, 0.774] +2026-04-12 10:34:49.814886: Epoch time: 102.47 s +2026-04-12 10:34:51.337585: +2026-04-12 10:34:51.339595: Epoch 1636 +2026-04-12 10:34:51.341388: Current learning rate: 0.00623 +2026-04-12 10:36:33.552632: train_loss -0.323 +2026-04-12 10:36:33.558056: val_loss -0.3009 +2026-04-12 10:36:33.560357: Pseudo dice [0.5619, 0.8616, 0.7467, 0.6276, 0.6436, 0.3248, 0.7684] +2026-04-12 10:36:33.563519: Epoch time: 102.22 s +2026-04-12 10:36:35.092493: +2026-04-12 10:36:35.094872: Epoch 1637 +2026-04-12 10:36:35.096618: Current learning rate: 0.00623 +2026-04-12 10:38:18.689092: train_loss -0.2946 +2026-04-12 10:38:18.694842: val_loss -0.2186 +2026-04-12 10:38:18.697011: Pseudo dice [0.8736, 0.7312, 0.0945, 0.0313, 0.3896, 0.6602, 0.782] +2026-04-12 10:38:18.699587: Epoch time: 103.6 s +2026-04-12 10:38:20.172831: +2026-04-12 10:38:20.174625: Epoch 1638 +2026-04-12 10:38:20.176343: Current learning rate: 0.00622 +2026-04-12 10:40:02.439141: train_loss -0.2959 +2026-04-12 10:40:02.444754: val_loss -0.2066 +2026-04-12 10:40:02.446785: Pseudo dice [0.4244, 0.4353, 0.5394, 0.0451, 0.3053, 0.0576, 0.7829] +2026-04-12 10:40:02.449259: Epoch time: 102.27 s +2026-04-12 10:40:03.949233: +2026-04-12 10:40:03.951415: Epoch 1639 +2026-04-12 10:40:03.953634: Current learning rate: 0.00622 +2026-04-12 10:41:47.434602: train_loss -0.2932 +2026-04-12 10:41:47.445869: val_loss -0.2667 +2026-04-12 10:41:47.448343: Pseudo dice [0.7948, 0.3127, 0.6221, 0.0295, 0.4145, 0.7872, 0.5957] +2026-04-12 10:41:47.451298: Epoch time: 103.49 s +2026-04-12 10:41:48.945735: +2026-04-12 10:41:48.947877: Epoch 1640 +2026-04-12 10:41:48.950309: Current learning rate: 0.00622 +2026-04-12 10:43:33.743165: train_loss -0.2932 +2026-04-12 10:43:33.748873: val_loss -0.2736 +2026-04-12 10:43:33.750730: Pseudo dice [0.5502, 0.7102, 0.7416, 0.121, 0.4326, 0.3949, 0.6874] +2026-04-12 10:43:33.753034: Epoch time: 104.8 s +2026-04-12 10:43:35.225870: +2026-04-12 10:43:35.227864: Epoch 1641 +2026-04-12 10:43:35.229769: Current learning rate: 0.00622 +2026-04-12 10:45:17.830436: train_loss -0.2867 +2026-04-12 10:45:17.838495: val_loss -0.2495 +2026-04-12 10:45:17.841903: Pseudo dice [0.3942, 0.5613, 0.6721, 0.407, 0.5347, 0.1534, 0.5617] +2026-04-12 10:45:17.845793: Epoch time: 102.61 s +2026-04-12 10:45:19.316878: +2026-04-12 10:45:19.319203: Epoch 1642 +2026-04-12 10:45:19.320884: Current learning rate: 0.00621 +2026-04-12 10:47:02.011290: train_loss -0.3212 +2026-04-12 10:47:02.021009: val_loss -0.2421 +2026-04-12 10:47:02.023823: Pseudo dice [0.3651, 0.8834, 0.7115, 0.6785, 0.554, 0.0755, 0.9076] +2026-04-12 10:47:02.026417: Epoch time: 102.7 s +2026-04-12 10:47:03.513316: +2026-04-12 10:47:03.515921: Epoch 1643 +2026-04-12 10:47:03.517692: Current learning rate: 0.00621 +2026-04-12 10:48:48.215404: train_loss -0.3137 +2026-04-12 10:48:48.223593: val_loss -0.2926 +2026-04-12 10:48:48.226277: Pseudo dice [0.8656, 0.8128, 0.7379, 0.4922, 0.6735, 0.7566, 0.7122] +2026-04-12 10:48:48.229173: Epoch time: 104.71 s +2026-04-12 10:48:49.725430: +2026-04-12 10:48:49.727472: Epoch 1644 +2026-04-12 10:48:49.729185: Current learning rate: 0.00621 +2026-04-12 10:50:32.247519: train_loss -0.2752 +2026-04-12 10:50:32.257417: val_loss -0.2587 +2026-04-12 10:50:32.259995: Pseudo dice [0.7696, 0.6432, 0.7607, 0.1596, 0.4822, 0.6393, 0.5557] +2026-04-12 10:50:32.264132: Epoch time: 102.53 s +2026-04-12 10:50:35.027536: +2026-04-12 10:50:35.029221: Epoch 1645 +2026-04-12 10:50:35.031349: Current learning rate: 0.00621 +2026-04-12 10:52:17.959246: train_loss -0.3094 +2026-04-12 10:52:17.964436: val_loss -0.2617 +2026-04-12 10:52:17.966239: Pseudo dice [0.4292, 0.3919, 0.5398, 0.8331, 0.4579, 0.187, 0.7591] +2026-04-12 10:52:17.970277: Epoch time: 102.94 s +2026-04-12 10:52:19.450752: +2026-04-12 10:52:19.452877: Epoch 1646 +2026-04-12 10:52:19.455110: Current learning rate: 0.00621 +2026-04-12 10:54:02.471370: train_loss -0.3012 +2026-04-12 10:54:02.477597: val_loss -0.2778 +2026-04-12 10:54:02.479246: Pseudo dice [0.4234, 0.2968, 0.7237, 0.8778, 0.3183, 0.7419, 0.3884] +2026-04-12 10:54:02.481449: Epoch time: 103.02 s +2026-04-12 10:54:03.945296: +2026-04-12 10:54:03.947302: Epoch 1647 +2026-04-12 10:54:03.949137: Current learning rate: 0.0062 +2026-04-12 10:55:46.448980: train_loss -0.3125 +2026-04-12 10:55:46.454207: val_loss -0.2922 +2026-04-12 10:55:46.456436: Pseudo dice [0.6781, 0.6439, 0.7673, 0.6519, 0.6247, 0.6953, 0.8507] +2026-04-12 10:55:46.459267: Epoch time: 102.51 s +2026-04-12 10:55:47.948066: +2026-04-12 10:55:47.949787: Epoch 1648 +2026-04-12 10:55:47.951354: Current learning rate: 0.0062 +2026-04-12 10:57:31.985197: train_loss -0.3108 +2026-04-12 10:57:31.990577: val_loss -0.2494 +2026-04-12 10:57:31.992508: Pseudo dice [0.521, 0.7939, 0.5749, 0.1088, 0.5498, 0.1554, 0.8603] +2026-04-12 10:57:31.995013: Epoch time: 104.04 s +2026-04-12 10:57:33.496278: +2026-04-12 10:57:33.498126: Epoch 1649 +2026-04-12 10:57:33.500566: Current learning rate: 0.0062 +2026-04-12 10:59:17.072192: train_loss -0.3169 +2026-04-12 10:59:17.077144: val_loss -0.1459 +2026-04-12 10:59:17.079097: Pseudo dice [0.7282, 0.6157, 0.4828, 0.6023, 0.5832, 0.1543, 0.5668] +2026-04-12 10:59:17.081332: Epoch time: 103.58 s +2026-04-12 10:59:20.463316: +2026-04-12 10:59:20.465064: Epoch 1650 +2026-04-12 10:59:20.466871: Current learning rate: 0.0062 +2026-04-12 11:01:03.204851: train_loss -0.3197 +2026-04-12 11:01:03.212970: val_loss -0.297 +2026-04-12 11:01:03.215349: Pseudo dice [0.8477, 0.3875, 0.6796, 0.7723, 0.6337, 0.3124, 0.8496] +2026-04-12 11:01:03.217835: Epoch time: 102.75 s +2026-04-12 11:01:04.700320: +2026-04-12 11:01:04.701991: Epoch 1651 +2026-04-12 11:01:04.703701: Current learning rate: 0.00619 +2026-04-12 11:02:47.516622: train_loss -0.3274 +2026-04-12 11:02:47.522072: val_loss -0.1844 +2026-04-12 11:02:47.523847: Pseudo dice [0.7165, 0.8439, 0.6143, 0.7978, 0.2842, 0.0775, 0.7352] +2026-04-12 11:02:47.526081: Epoch time: 102.82 s +2026-04-12 11:02:49.037098: +2026-04-12 11:02:49.039034: Epoch 1652 +2026-04-12 11:02:49.040672: Current learning rate: 0.00619 +2026-04-12 11:04:32.825633: train_loss -0.3246 +2026-04-12 11:04:32.833881: val_loss -0.2887 +2026-04-12 11:04:32.836215: Pseudo dice [0.5207, 0.6596, 0.7187, 0.9009, 0.375, 0.6415, 0.8491] +2026-04-12 11:04:32.839683: Epoch time: 103.79 s +2026-04-12 11:04:34.551229: +2026-04-12 11:04:34.553542: Epoch 1653 +2026-04-12 11:04:34.555509: Current learning rate: 0.00619 +2026-04-12 11:06:17.383909: train_loss -0.3274 +2026-04-12 11:06:17.391121: val_loss -0.2734 +2026-04-12 11:06:17.393263: Pseudo dice [0.707, 0.6276, 0.8255, 0.7115, 0.6262, 0.2433, 0.8997] +2026-04-12 11:06:17.395450: Epoch time: 102.84 s +2026-04-12 11:06:17.397297: Yayy! New best EMA pseudo Dice: 0.5805 +2026-04-12 11:06:20.824577: +2026-04-12 11:06:20.826568: Epoch 1654 +2026-04-12 11:06:20.828168: Current learning rate: 0.00619 +2026-04-12 11:08:03.940540: train_loss -0.3173 +2026-04-12 11:08:03.949020: val_loss -0.2536 +2026-04-12 11:08:03.951199: Pseudo dice [0.5266, 0.6501, 0.6186, 0.6475, 0.5769, 0.1717, 0.6261] +2026-04-12 11:08:03.953760: Epoch time: 103.12 s +2026-04-12 11:08:05.451615: +2026-04-12 11:08:05.453411: Epoch 1655 +2026-04-12 11:08:05.455208: Current learning rate: 0.00618 +2026-04-12 11:09:49.278912: train_loss -0.3097 +2026-04-12 11:09:49.285915: val_loss -0.2459 +2026-04-12 11:09:49.288182: Pseudo dice [0.6701, 0.674, 0.5727, 0.5864, 0.5445, 0.1698, 0.7539] +2026-04-12 11:09:49.290522: Epoch time: 103.83 s +2026-04-12 11:09:50.768858: +2026-04-12 11:09:50.770741: Epoch 1656 +2026-04-12 11:09:50.772622: Current learning rate: 0.00618 +2026-04-12 11:11:34.086429: train_loss -0.3128 +2026-04-12 11:11:34.093737: val_loss -0.292 +2026-04-12 11:11:34.095999: Pseudo dice [0.3716, 0.7218, 0.7136, 0.2551, 0.5512, 0.7823, 0.8331] +2026-04-12 11:11:34.098638: Epoch time: 103.32 s +2026-04-12 11:11:35.592934: +2026-04-12 11:11:35.597780: Epoch 1657 +2026-04-12 11:11:35.602922: Current learning rate: 0.00618 +2026-04-12 11:13:18.618143: train_loss -0.3148 +2026-04-12 11:13:18.623955: val_loss -0.2662 +2026-04-12 11:13:18.625837: Pseudo dice [0.8382, 0.8176, 0.6333, 0.7794, 0.4311, 0.088, 0.8888] +2026-04-12 11:13:18.628448: Epoch time: 103.03 s +2026-04-12 11:13:18.630540: Yayy! New best EMA pseudo Dice: 0.5849 +2026-04-12 11:13:22.171967: +2026-04-12 11:13:22.173879: Epoch 1658 +2026-04-12 11:13:22.175723: Current learning rate: 0.00618 +2026-04-12 11:15:05.041712: train_loss -0.3108 +2026-04-12 11:15:05.066557: val_loss -0.2633 +2026-04-12 11:15:05.068387: Pseudo dice [0.4127, 0.4521, 0.6563, 0.4904, 0.3816, 0.4954, 0.7229] +2026-04-12 11:15:05.070869: Epoch time: 102.87 s +2026-04-12 11:15:06.591583: +2026-04-12 11:15:06.593389: Epoch 1659 +2026-04-12 11:15:06.594898: Current learning rate: 0.00617 +2026-04-12 11:16:50.144182: train_loss -0.3155 +2026-04-12 11:16:50.150475: val_loss -0.2772 +2026-04-12 11:16:50.153167: Pseudo dice [0.3445, 0.3788, 0.7268, 0.2175, 0.5338, 0.4954, 0.6307] +2026-04-12 11:16:50.155110: Epoch time: 103.56 s +2026-04-12 11:16:51.624258: +2026-04-12 11:16:51.626115: Epoch 1660 +2026-04-12 11:16:51.627738: Current learning rate: 0.00617 +2026-04-12 11:18:34.199204: train_loss -0.3076 +2026-04-12 11:18:34.205184: val_loss -0.2087 +2026-04-12 11:18:34.207767: Pseudo dice [0.7057, 0.136, 0.6653, 0.5412, 0.2988, 0.1367, 0.5829] +2026-04-12 11:18:34.212508: Epoch time: 102.58 s +2026-04-12 11:18:35.721496: +2026-04-12 11:18:35.723274: Epoch 1661 +2026-04-12 11:18:35.725151: Current learning rate: 0.00617 +2026-04-12 11:20:19.564175: train_loss -0.3076 +2026-04-12 11:20:19.569782: val_loss -0.2841 +2026-04-12 11:20:19.571639: Pseudo dice [0.4526, 0.6886, 0.6745, 0.701, 0.5973, 0.2912, 0.8663] +2026-04-12 11:20:19.574745: Epoch time: 103.85 s +2026-04-12 11:20:21.050998: +2026-04-12 11:20:21.052990: Epoch 1662 +2026-04-12 11:20:21.054573: Current learning rate: 0.00617 +2026-04-12 11:22:03.395635: train_loss -0.3365 +2026-04-12 11:22:03.400454: val_loss -0.2533 +2026-04-12 11:22:03.402423: Pseudo dice [0.7616, 0.646, 0.7939, 0.5943, 0.5698, 0.0622, 0.582] +2026-04-12 11:22:03.404911: Epoch time: 102.35 s +2026-04-12 11:22:04.882951: +2026-04-12 11:22:04.884598: Epoch 1663 +2026-04-12 11:22:04.886307: Current learning rate: 0.00617 +2026-04-12 11:23:48.782700: train_loss -0.3247 +2026-04-12 11:23:48.787803: val_loss -0.2833 +2026-04-12 11:23:48.789826: Pseudo dice [0.5615, 0.693, 0.6036, 0.5664, 0.302, 0.731, 0.7693] +2026-04-12 11:23:48.792077: Epoch time: 103.9 s +2026-04-12 11:23:50.259196: +2026-04-12 11:23:50.261723: Epoch 1664 +2026-04-12 11:23:50.263649: Current learning rate: 0.00616 +2026-04-12 11:25:32.788206: train_loss -0.3198 +2026-04-12 11:25:32.795073: val_loss -0.2797 +2026-04-12 11:25:32.798025: Pseudo dice [0.4158, 0.4747, 0.6157, 0.7269, 0.3951, 0.6056, 0.9] +2026-04-12 11:25:32.800948: Epoch time: 102.53 s +2026-04-12 11:25:34.294696: +2026-04-12 11:25:34.297070: Epoch 1665 +2026-04-12 11:25:34.298788: Current learning rate: 0.00616 +2026-04-12 11:27:16.757246: train_loss -0.2903 +2026-04-12 11:27:16.762663: val_loss -0.2038 +2026-04-12 11:27:16.764412: Pseudo dice [0.5846, 0.8032, 0.6968, 0.1668, 0.5225, 0.4443, 0.8127] +2026-04-12 11:27:16.767868: Epoch time: 102.47 s +2026-04-12 11:27:18.274521: +2026-04-12 11:27:18.276779: Epoch 1666 +2026-04-12 11:27:18.278617: Current learning rate: 0.00616 +2026-04-12 11:29:03.739987: train_loss -0.2864 +2026-04-12 11:29:03.745336: val_loss -0.2425 +2026-04-12 11:29:03.747431: Pseudo dice [0.3218, 0.763, 0.7327, 0.2747, 0.3096, 0.3128, 0.2569] +2026-04-12 11:29:03.749512: Epoch time: 105.47 s +2026-04-12 11:29:05.255878: +2026-04-12 11:29:05.258132: Epoch 1667 +2026-04-12 11:29:05.260070: Current learning rate: 0.00616 +2026-04-12 11:30:48.234027: train_loss -0.3171 +2026-04-12 11:30:48.253028: val_loss -0.2335 +2026-04-12 11:30:48.255671: Pseudo dice [0.5884, 0.572, 0.7684, 0.4853, 0.7224, 0.0445, 0.8163] +2026-04-12 11:30:48.258150: Epoch time: 102.98 s +2026-04-12 11:30:49.774421: +2026-04-12 11:30:49.776602: Epoch 1668 +2026-04-12 11:30:49.778172: Current learning rate: 0.00615 +2026-04-12 11:32:32.282108: train_loss -0.3056 +2026-04-12 11:32:32.291045: val_loss -0.2211 +2026-04-12 11:32:32.295004: Pseudo dice [0.8063, 0.6236, 0.5595, 0.821, 0.5923, 0.0238, 0.8513] +2026-04-12 11:32:32.297339: Epoch time: 102.51 s +2026-04-12 11:32:33.792904: +2026-04-12 11:32:33.795829: Epoch 1669 +2026-04-12 11:32:33.797845: Current learning rate: 0.00615 +2026-04-12 11:34:16.601316: train_loss -0.3121 +2026-04-12 11:34:16.608104: val_loss -0.2911 +2026-04-12 11:34:16.610151: Pseudo dice [0.7397, 0.4183, 0.7213, 0.7065, 0.4134, 0.6618, 0.4121] +2026-04-12 11:34:16.613276: Epoch time: 102.81 s +2026-04-12 11:34:18.122822: +2026-04-12 11:34:18.125382: Epoch 1670 +2026-04-12 11:34:18.127542: Current learning rate: 0.00615 +2026-04-12 11:36:00.478649: train_loss -0.3215 +2026-04-12 11:36:00.483537: val_loss -0.2536 +2026-04-12 11:36:00.485282: Pseudo dice [0.603, 0.7504, 0.5073, 0.8805, 0.4862, 0.0583, 0.8556] +2026-04-12 11:36:00.487610: Epoch time: 102.36 s +2026-04-12 11:36:02.001392: +2026-04-12 11:36:02.004250: Epoch 1671 +2026-04-12 11:36:02.006173: Current learning rate: 0.00615 +2026-04-12 11:37:45.083925: train_loss -0.3213 +2026-04-12 11:37:45.089340: val_loss -0.1477 +2026-04-12 11:37:45.091187: Pseudo dice [0.5437, 0.5778, 0.4269, 0.5452, 0.5445, 0.0807, 0.8074] +2026-04-12 11:37:45.094215: Epoch time: 103.09 s +2026-04-12 11:37:46.608335: +2026-04-12 11:37:46.611729: Epoch 1672 +2026-04-12 11:37:46.613601: Current learning rate: 0.00614 +2026-04-12 11:39:29.851699: train_loss -0.3073 +2026-04-12 11:39:29.860478: val_loss -0.284 +2026-04-12 11:39:29.863353: Pseudo dice [0.472, 0.5601, 0.6551, 0.2979, 0.3953, 0.7179, 0.5782] +2026-04-12 11:39:29.866909: Epoch time: 103.25 s +2026-04-12 11:39:31.384651: +2026-04-12 11:39:31.388205: Epoch 1673 +2026-04-12 11:39:31.390113: Current learning rate: 0.00614 +2026-04-12 11:41:13.634716: train_loss -0.3241 +2026-04-12 11:41:13.640352: val_loss -0.1692 +2026-04-12 11:41:13.642081: Pseudo dice [0.4922, 0.2956, 0.7364, 0.386, 0.3775, 0.0579, 0.4549] +2026-04-12 11:41:13.646719: Epoch time: 102.25 s +2026-04-12 11:41:15.142150: +2026-04-12 11:41:15.144034: Epoch 1674 +2026-04-12 11:41:15.145658: Current learning rate: 0.00614 +2026-04-12 11:42:58.397972: train_loss -0.3031 +2026-04-12 11:42:58.404485: val_loss -0.2613 +2026-04-12 11:42:58.406800: Pseudo dice [0.6096, 0.5769, 0.7908, 0.7233, 0.278, 0.8446, 0.4228] +2026-04-12 11:42:58.409859: Epoch time: 103.26 s +2026-04-12 11:42:59.927364: +2026-04-12 11:42:59.929268: Epoch 1675 +2026-04-12 11:42:59.931140: Current learning rate: 0.00614 +2026-04-12 11:44:42.192900: train_loss -0.3077 +2026-04-12 11:44:42.199123: val_loss -0.2157 +2026-04-12 11:44:42.201481: Pseudo dice [0.1917, 0.2702, 0.5117, 0.1646, 0.3182, 0.2202, 0.6893] +2026-04-12 11:44:42.204071: Epoch time: 102.27 s +2026-04-12 11:44:43.753550: +2026-04-12 11:44:43.758135: Epoch 1676 +2026-04-12 11:44:43.762613: Current learning rate: 0.00613 +2026-04-12 11:46:26.462199: train_loss -0.299 +2026-04-12 11:46:26.467130: val_loss -0.2795 +2026-04-12 11:46:26.468859: Pseudo dice [0.5676, 0.8231, 0.6665, 0.4023, 0.5691, 0.4347, 0.817] +2026-04-12 11:46:26.471047: Epoch time: 102.71 s +2026-04-12 11:46:27.976403: +2026-04-12 11:46:27.978339: Epoch 1677 +2026-04-12 11:46:27.980157: Current learning rate: 0.00613 +2026-04-12 11:48:10.900810: train_loss -0.3177 +2026-04-12 11:48:10.906058: val_loss -0.1977 +2026-04-12 11:48:10.908467: Pseudo dice [0.4923, 0.709, 0.5673, 0.6443, 0.3706, 0.1178, 0.6877] +2026-04-12 11:48:10.910483: Epoch time: 102.93 s +2026-04-12 11:48:12.414634: +2026-04-12 11:48:12.416228: Epoch 1678 +2026-04-12 11:48:12.418252: Current learning rate: 0.00613 +2026-04-12 11:49:55.047011: train_loss -0.331 +2026-04-12 11:49:55.054775: val_loss -0.2905 +2026-04-12 11:49:55.058165: Pseudo dice [0.5483, 0.453, 0.7414, 0.835, 0.58, 0.8754, 0.7539] +2026-04-12 11:49:55.061266: Epoch time: 102.64 s +2026-04-12 11:49:56.614349: +2026-04-12 11:49:56.616154: Epoch 1679 +2026-04-12 11:49:56.619536: Current learning rate: 0.00613 +2026-04-12 11:51:39.172930: train_loss -0.2929 +2026-04-12 11:51:39.178221: val_loss -0.1847 +2026-04-12 11:51:39.181142: Pseudo dice [0.4308, 0.5399, 0.6298, 0.0148, 0.4962, 0.1309, 0.6298] +2026-04-12 11:51:39.183701: Epoch time: 102.56 s +2026-04-12 11:51:40.690389: +2026-04-12 11:51:40.692718: Epoch 1680 +2026-04-12 11:51:40.695656: Current learning rate: 0.00612 +2026-04-12 11:53:23.212307: train_loss -0.2895 +2026-04-12 11:53:23.219924: val_loss -0.3016 +2026-04-12 11:53:23.221660: Pseudo dice [0.5972, 0.489, 0.8257, 0.1016, 0.596, 0.8, 0.8003] +2026-04-12 11:53:23.224375: Epoch time: 102.53 s +2026-04-12 11:53:24.750390: +2026-04-12 11:53:24.752102: Epoch 1681 +2026-04-12 11:53:24.753623: Current learning rate: 0.00612 +2026-04-12 11:55:08.984034: train_loss -0.2935 +2026-04-12 11:55:08.990415: val_loss -0.2033 +2026-04-12 11:55:08.992365: Pseudo dice [0.5607, 0.7874, 0.6377, 0.6045, 0.5304, 0.0271, 0.7551] +2026-04-12 11:55:08.996889: Epoch time: 104.24 s +2026-04-12 11:55:10.499584: +2026-04-12 11:55:10.501668: Epoch 1682 +2026-04-12 11:55:10.503895: Current learning rate: 0.00612 +2026-04-12 11:56:53.477368: train_loss -0.2848 +2026-04-12 11:56:53.482626: val_loss -0.2444 +2026-04-12 11:56:53.485535: Pseudo dice [0.474, 0.1018, 0.5478, 0.7274, 0.561, 0.133, 0.8293] +2026-04-12 11:56:53.488145: Epoch time: 102.98 s +2026-04-12 11:56:56.141824: +2026-04-12 11:56:56.143819: Epoch 1683 +2026-04-12 11:56:56.145402: Current learning rate: 0.00612 +2026-04-12 11:58:39.517503: train_loss -0.3047 +2026-04-12 11:58:39.522545: val_loss -0.2651 +2026-04-12 11:58:39.525100: Pseudo dice [0.7748, 0.6875, 0.5842, 0.3858, 0.4024, 0.345, 0.7091] +2026-04-12 11:58:39.527240: Epoch time: 103.38 s +2026-04-12 11:58:41.042116: +2026-04-12 11:58:41.044632: Epoch 1684 +2026-04-12 11:58:41.047227: Current learning rate: 0.00612 +2026-04-12 12:00:23.799605: train_loss -0.262 +2026-04-12 12:00:23.807536: val_loss -0.2102 +2026-04-12 12:00:23.811498: Pseudo dice [0.399, 0.573, 0.6347, 0.7251, 0.2897, 0.0713, 0.6249] +2026-04-12 12:00:23.815573: Epoch time: 102.76 s +2026-04-12 12:00:25.336959: +2026-04-12 12:00:25.338997: Epoch 1685 +2026-04-12 12:00:25.340645: Current learning rate: 0.00611 +2026-04-12 12:02:08.290191: train_loss -0.293 +2026-04-12 12:02:08.300169: val_loss -0.2748 +2026-04-12 12:02:08.302460: Pseudo dice [0.8576, 0.6144, 0.5741, 0.7047, 0.4172, 0.1434, 0.767] +2026-04-12 12:02:08.304715: Epoch time: 102.96 s +2026-04-12 12:02:09.801462: +2026-04-12 12:02:09.803476: Epoch 1686 +2026-04-12 12:02:09.805352: Current learning rate: 0.00611 +2026-04-12 12:03:52.367539: train_loss -0.3133 +2026-04-12 12:03:52.374042: val_loss -0.2972 +2026-04-12 12:03:52.378218: Pseudo dice [0.7422, 0.4441, 0.7, 0.3858, 0.4288, 0.6468, 0.6359] +2026-04-12 12:03:52.381284: Epoch time: 102.57 s +2026-04-12 12:03:53.881698: +2026-04-12 12:03:53.884066: Epoch 1687 +2026-04-12 12:03:53.886015: Current learning rate: 0.00611 +2026-04-12 12:05:36.582116: train_loss -0.3027 +2026-04-12 12:05:36.588958: val_loss -0.2951 +2026-04-12 12:05:36.591350: Pseudo dice [0.3063, 0.4074, 0.7741, 0.0394, 0.4347, 0.3152, 0.6814] +2026-04-12 12:05:36.593351: Epoch time: 102.7 s +2026-04-12 12:05:38.106334: +2026-04-12 12:05:38.108312: Epoch 1688 +2026-04-12 12:05:38.109904: Current learning rate: 0.00611 +2026-04-12 12:07:22.228443: train_loss -0.3094 +2026-04-12 12:07:22.234986: val_loss -0.2856 +2026-04-12 12:07:22.237924: Pseudo dice [0.7195, 0.2363, 0.8116, 0.6618, 0.5346, 0.3934, 0.8018] +2026-04-12 12:07:22.241616: Epoch time: 104.13 s +2026-04-12 12:07:23.765220: +2026-04-12 12:07:23.767191: Epoch 1689 +2026-04-12 12:07:23.768976: Current learning rate: 0.0061 +2026-04-12 12:09:06.493697: train_loss -0.3186 +2026-04-12 12:09:06.499112: val_loss -0.3041 +2026-04-12 12:09:06.501660: Pseudo dice [0.5497, 0.6592, 0.679, 0.841, 0.6146, 0.5044, 0.8559] +2026-04-12 12:09:06.503799: Epoch time: 102.73 s +2026-04-12 12:09:07.995154: +2026-04-12 12:09:07.998653: Epoch 1690 +2026-04-12 12:09:08.000895: Current learning rate: 0.0061 +2026-04-12 12:10:50.977252: train_loss -0.3088 +2026-04-12 12:10:50.986704: val_loss -0.2928 +2026-04-12 12:10:50.989621: Pseudo dice [0.4468, 0.8141, 0.5766, 0.6527, 0.587, 0.7103, 0.7512] +2026-04-12 12:10:50.992431: Epoch time: 102.99 s +2026-04-12 12:10:52.530140: +2026-04-12 12:10:52.532128: Epoch 1691 +2026-04-12 12:10:52.533756: Current learning rate: 0.0061 +2026-04-12 12:12:37.014167: train_loss -0.3138 +2026-04-12 12:12:37.020590: val_loss -0.281 +2026-04-12 12:12:37.022694: Pseudo dice [0.5295, 0.5548, 0.8104, 0.7314, 0.44, 0.2951, 0.9071] +2026-04-12 12:12:37.025461: Epoch time: 104.49 s +2026-04-12 12:12:38.567528: +2026-04-12 12:12:38.569528: Epoch 1692 +2026-04-12 12:12:38.571736: Current learning rate: 0.0061 +2026-04-12 12:14:21.786680: train_loss -0.306 +2026-04-12 12:14:21.793532: val_loss -0.2977 +2026-04-12 12:14:21.795969: Pseudo dice [0.6851, 0.8303, 0.7097, 0.3626, 0.6126, 0.6051, 0.7592] +2026-04-12 12:14:21.798767: Epoch time: 103.22 s +2026-04-12 12:14:23.295369: +2026-04-12 12:14:23.297895: Epoch 1693 +2026-04-12 12:14:23.299424: Current learning rate: 0.00609 +2026-04-12 12:16:05.647911: train_loss -0.3143 +2026-04-12 12:16:05.653774: val_loss -0.1896 +2026-04-12 12:16:05.655789: Pseudo dice [0.2312, 0.8233, 0.6753, 0.848, 0.3185, 0.0653, 0.8129] +2026-04-12 12:16:05.658054: Epoch time: 102.36 s +2026-04-12 12:16:07.167050: +2026-04-12 12:16:07.169887: Epoch 1694 +2026-04-12 12:16:07.171509: Current learning rate: 0.00609 +2026-04-12 12:17:49.761683: train_loss -0.2954 +2026-04-12 12:17:49.767772: val_loss -0.2107 +2026-04-12 12:17:49.770946: Pseudo dice [0.2935, 0.623, 0.7137, 0.5388, 0.3963, 0.1271, 0.7279] +2026-04-12 12:17:49.774351: Epoch time: 102.6 s +2026-04-12 12:17:51.301899: +2026-04-12 12:17:51.305021: Epoch 1695 +2026-04-12 12:17:51.307146: Current learning rate: 0.00609 +2026-04-12 12:19:34.607587: train_loss -0.3023 +2026-04-12 12:19:34.614018: val_loss -0.2928 +2026-04-12 12:19:34.616331: Pseudo dice [0.6989, 0.2585, 0.7838, 0.4274, 0.4255, 0.8562, 0.8772] +2026-04-12 12:19:34.618735: Epoch time: 103.31 s +2026-04-12 12:19:36.167685: +2026-04-12 12:19:36.169644: Epoch 1696 +2026-04-12 12:19:36.172482: Current learning rate: 0.00609 +2026-04-12 12:21:19.268929: train_loss -0.3178 +2026-04-12 12:21:19.274723: val_loss -0.1717 +2026-04-12 12:21:19.276663: Pseudo dice [0.6468, 0.5731, 0.4875, 0.6989, 0.4055, 0.0492, 0.6795] +2026-04-12 12:21:19.278929: Epoch time: 103.11 s +2026-04-12 12:21:20.791161: +2026-04-12 12:21:20.796251: Epoch 1697 +2026-04-12 12:21:20.799400: Current learning rate: 0.00608 +2026-04-12 12:23:03.027206: train_loss -0.3114 +2026-04-12 12:23:03.036323: val_loss -0.2504 +2026-04-12 12:23:03.038492: Pseudo dice [0.4897, 0.5881, 0.6933, 0.8099, 0.4818, 0.192, 0.6918] +2026-04-12 12:23:03.041557: Epoch time: 102.24 s +2026-04-12 12:23:04.548393: +2026-04-12 12:23:04.550367: Epoch 1698 +2026-04-12 12:23:04.552427: Current learning rate: 0.00608 +2026-04-12 12:24:46.736939: train_loss -0.3108 +2026-04-12 12:24:46.742758: val_loss -0.2433 +2026-04-12 12:24:46.744708: Pseudo dice [0.5625, 0.5435, 0.644, 0.7003, 0.5559, 0.0506, 0.8706] +2026-04-12 12:24:46.747032: Epoch time: 102.19 s +2026-04-12 12:24:48.279300: +2026-04-12 12:24:48.281601: Epoch 1699 +2026-04-12 12:24:48.283757: Current learning rate: 0.00608 +2026-04-12 12:26:30.495481: train_loss -0.3157 +2026-04-12 12:26:30.502110: val_loss -0.2316 +2026-04-12 12:26:30.504182: Pseudo dice [0.238, 0.5713, 0.3687, 0.0158, 0.5584, 0.1977, 0.6912] +2026-04-12 12:26:30.506580: Epoch time: 102.22 s +2026-04-12 12:26:33.945653: +2026-04-12 12:26:33.947778: Epoch 1700 +2026-04-12 12:26:33.950047: Current learning rate: 0.00608 +2026-04-12 12:28:17.185840: train_loss -0.2891 +2026-04-12 12:28:17.201845: val_loss -0.2238 +2026-04-12 12:28:17.207652: Pseudo dice [0.1853, 0.5003, 0.411, 0.5434, 0.368, 0.2045, 0.4937] +2026-04-12 12:28:17.214210: Epoch time: 103.24 s +2026-04-12 12:28:18.726629: +2026-04-12 12:28:18.728431: Epoch 1701 +2026-04-12 12:28:18.730082: Current learning rate: 0.00607 +2026-04-12 12:30:00.998770: train_loss -0.312 +2026-04-12 12:30:01.005345: val_loss -0.1735 +2026-04-12 12:30:01.007130: Pseudo dice [0.8634, 0.8403, 0.4371, 0.2437, 0.0478, 0.107, 0.2733] +2026-04-12 12:30:01.009513: Epoch time: 102.28 s +2026-04-12 12:30:02.531754: +2026-04-12 12:30:02.533601: Epoch 1702 +2026-04-12 12:30:02.535322: Current learning rate: 0.00607 +2026-04-12 12:31:46.195096: train_loss -0.3089 +2026-04-12 12:31:46.199886: val_loss -0.2566 +2026-04-12 12:31:46.201606: Pseudo dice [0.6936, 0.4816, 0.6807, 0.4515, 0.3143, 0.6977, 0.6296] +2026-04-12 12:31:46.203494: Epoch time: 103.67 s +2026-04-12 12:31:47.717556: +2026-04-12 12:31:47.719285: Epoch 1703 +2026-04-12 12:31:47.721043: Current learning rate: 0.00607 +2026-04-12 12:33:30.002750: train_loss -0.308 +2026-04-12 12:33:30.008953: val_loss -0.2226 +2026-04-12 12:33:30.010647: Pseudo dice [0.5407, 0.4368, 0.6683, 0.4748, 0.5094, 0.1117, 0.8601] +2026-04-12 12:33:30.012565: Epoch time: 102.29 s +2026-04-12 12:33:31.511880: +2026-04-12 12:33:31.513612: Epoch 1704 +2026-04-12 12:33:31.515299: Current learning rate: 0.00607 +2026-04-12 12:35:13.814734: train_loss -0.3087 +2026-04-12 12:35:13.821769: val_loss -0.204 +2026-04-12 12:35:13.823720: Pseudo dice [0.7701, 0.4273, 0.4306, 0.7806, 0.3728, 0.1884, 0.5182] +2026-04-12 12:35:13.826188: Epoch time: 102.31 s +2026-04-12 12:35:15.333141: +2026-04-12 12:35:15.335315: Epoch 1705 +2026-04-12 12:35:15.336922: Current learning rate: 0.00607 +2026-04-12 12:36:58.090293: train_loss -0.313 +2026-04-12 12:36:58.103538: val_loss -0.2656 +2026-04-12 12:36:58.108975: Pseudo dice [0.3293, 0.2092, 0.7427, 0.028, 0.5754, 0.3061, 0.6365] +2026-04-12 12:36:58.114612: Epoch time: 102.76 s +2026-04-12 12:36:59.638651: +2026-04-12 12:36:59.645249: Epoch 1706 +2026-04-12 12:36:59.653157: Current learning rate: 0.00606 +2026-04-12 12:38:42.009925: train_loss -0.2917 +2026-04-12 12:38:42.015969: val_loss -0.2681 +2026-04-12 12:38:42.018040: Pseudo dice [0.8314, 0.7352, 0.6483, 0.6274, 0.5826, 0.1671, 0.8386] +2026-04-12 12:38:42.020672: Epoch time: 102.38 s +2026-04-12 12:38:43.545130: +2026-04-12 12:38:43.547168: Epoch 1707 +2026-04-12 12:38:43.548947: Current learning rate: 0.00606 +2026-04-12 12:40:26.651173: train_loss -0.2998 +2026-04-12 12:40:26.658464: val_loss -0.2844 +2026-04-12 12:40:26.663116: Pseudo dice [0.8284, 0.594, 0.751, 0.618, 0.5173, 0.6416, 0.913] +2026-04-12 12:40:26.667663: Epoch time: 103.11 s +2026-04-12 12:40:28.183081: +2026-04-12 12:40:28.185826: Epoch 1708 +2026-04-12 12:40:28.187403: Current learning rate: 0.00606 +2026-04-12 12:42:10.370094: train_loss -0.3101 +2026-04-12 12:42:10.375544: val_loss -0.239 +2026-04-12 12:42:10.377384: Pseudo dice [0.5076, 0.4494, 0.6123, 0.5791, 0.4894, 0.1662, 0.4468] +2026-04-12 12:42:10.379602: Epoch time: 102.19 s +2026-04-12 12:42:11.887980: +2026-04-12 12:42:11.890264: Epoch 1709 +2026-04-12 12:42:11.892186: Current learning rate: 0.00606 +2026-04-12 12:43:54.171806: train_loss -0.3076 +2026-04-12 12:43:54.177827: val_loss -0.2265 +2026-04-12 12:43:54.179868: Pseudo dice [0.5464, 0.4874, 0.5412, 0.3594, 0.5565, 0.1861, 0.5516] +2026-04-12 12:43:54.182424: Epoch time: 102.29 s +2026-04-12 12:43:55.701344: +2026-04-12 12:43:55.703426: Epoch 1710 +2026-04-12 12:43:55.704970: Current learning rate: 0.00605 +2026-04-12 12:45:38.992201: train_loss -0.3211 +2026-04-12 12:45:38.999291: val_loss -0.2983 +2026-04-12 12:45:39.001970: Pseudo dice [0.3922, 0.7487, 0.5906, 0.7325, 0.6633, 0.787, 0.7428] +2026-04-12 12:45:39.004386: Epoch time: 103.29 s +2026-04-12 12:45:40.524894: +2026-04-12 12:45:40.531139: Epoch 1711 +2026-04-12 12:45:40.535902: Current learning rate: 0.00605 +2026-04-12 12:47:22.607132: train_loss -0.3093 +2026-04-12 12:47:22.612538: val_loss -0.2879 +2026-04-12 12:47:22.614944: Pseudo dice [0.3598, 0.6622, 0.7303, 0.4346, 0.5516, 0.4924, 0.8942] +2026-04-12 12:47:22.618065: Epoch time: 102.09 s +2026-04-12 12:47:24.125127: +2026-04-12 12:47:24.127016: Epoch 1712 +2026-04-12 12:47:24.128890: Current learning rate: 0.00605 +2026-04-12 12:49:06.706721: train_loss -0.3274 +2026-04-12 12:49:06.711834: val_loss -0.1941 +2026-04-12 12:49:06.713924: Pseudo dice [0.5865, 0.6051, 0.7162, 0.5222, 0.5286, 0.0871, 0.8609] +2026-04-12 12:49:06.716294: Epoch time: 102.59 s +2026-04-12 12:49:08.217444: +2026-04-12 12:49:08.219667: Epoch 1713 +2026-04-12 12:49:08.221437: Current learning rate: 0.00605 +2026-04-12 12:50:50.399208: train_loss -0.325 +2026-04-12 12:50:50.404583: val_loss -0.3087 +2026-04-12 12:50:50.406647: Pseudo dice [0.5988, 0.5478, 0.795, 0.8502, 0.4992, 0.6062, 0.8699] +2026-04-12 12:50:50.409564: Epoch time: 102.19 s +2026-04-12 12:50:51.937477: +2026-04-12 12:50:51.939548: Epoch 1714 +2026-04-12 12:50:51.941581: Current learning rate: 0.00604 +2026-04-12 12:52:34.107233: train_loss -0.3171 +2026-04-12 12:52:34.113206: val_loss -0.2744 +2026-04-12 12:52:34.115018: Pseudo dice [0.6885, 0.6725, 0.6415, 0.692, 0.5412, 0.3443, 0.8456] +2026-04-12 12:52:34.117051: Epoch time: 102.17 s +2026-04-12 12:52:35.617815: +2026-04-12 12:52:35.619737: Epoch 1715 +2026-04-12 12:52:35.621450: Current learning rate: 0.00604 +2026-04-12 12:54:18.552806: train_loss -0.3143 +2026-04-12 12:54:18.560717: val_loss -0.2458 +2026-04-12 12:54:18.564342: Pseudo dice [0.3883, 0.4294, 0.5601, 0.0082, 0.6413, 0.0888, 0.814] +2026-04-12 12:54:18.568254: Epoch time: 102.94 s +2026-04-12 12:54:20.096501: +2026-04-12 12:54:20.098524: Epoch 1716 +2026-04-12 12:54:20.100242: Current learning rate: 0.00604 +2026-04-12 12:56:02.460867: train_loss -0.3036 +2026-04-12 12:56:02.466608: val_loss -0.2469 +2026-04-12 12:56:02.469147: Pseudo dice [0.5567, 0.7298, 0.6648, 0.4099, 0.5901, 0.3655, 0.797] +2026-04-12 12:56:02.471519: Epoch time: 102.37 s +2026-04-12 12:56:03.979421: +2026-04-12 12:56:03.981167: Epoch 1717 +2026-04-12 12:56:03.983825: Current learning rate: 0.00604 +2026-04-12 12:57:47.071450: train_loss -0.3149 +2026-04-12 12:57:47.079098: val_loss -0.2787 +2026-04-12 12:57:47.081744: Pseudo dice [0.6639, 0.6572, 0.7074, 0.7402, 0.526, 0.7041, 0.8486] +2026-04-12 12:57:47.086167: Epoch time: 103.1 s +2026-04-12 12:57:48.612858: +2026-04-12 12:57:48.615492: Epoch 1718 +2026-04-12 12:57:48.617673: Current learning rate: 0.00603 +2026-04-12 12:59:31.545897: train_loss -0.3248 +2026-04-12 12:59:31.550905: val_loss -0.203 +2026-04-12 12:59:31.552697: Pseudo dice [0.5326, 0.6529, 0.6679, 0.5172, 0.3458, 0.0606, 0.7052] +2026-04-12 12:59:31.555747: Epoch time: 102.94 s +2026-04-12 12:59:33.070338: +2026-04-12 12:59:33.072932: Epoch 1719 +2026-04-12 12:59:33.075238: Current learning rate: 0.00603 +2026-04-12 13:01:16.701593: train_loss -0.3057 +2026-04-12 13:01:16.711775: val_loss -0.2316 +2026-04-12 13:01:16.714156: Pseudo dice [0.3726, 0.535, 0.5403, 0.4195, 0.3715, 0.1368, 0.6786] +2026-04-12 13:01:16.717495: Epoch time: 103.64 s +2026-04-12 13:01:18.241398: +2026-04-12 13:01:18.243198: Epoch 1720 +2026-04-12 13:01:18.245248: Current learning rate: 0.00603 +2026-04-12 13:03:01.819881: train_loss -0.3184 +2026-04-12 13:03:01.827276: val_loss -0.2594 +2026-04-12 13:03:01.829899: Pseudo dice [0.582, 0.6926, 0.6413, 0.7219, 0.1432, 0.2978, 0.6475] +2026-04-12 13:03:01.832688: Epoch time: 103.58 s +2026-04-12 13:03:03.340309: +2026-04-12 13:03:03.342647: Epoch 1721 +2026-04-12 13:03:03.344409: Current learning rate: 0.00603 +2026-04-12 13:04:46.095695: train_loss -0.3153 +2026-04-12 13:04:46.100465: val_loss -0.1523 +2026-04-12 13:04:46.102916: Pseudo dice [0.1854, 0.529, 0.6032, 0.214, 0.4937, 0.0497, 0.6709] +2026-04-12 13:04:46.106848: Epoch time: 102.76 s +2026-04-12 13:04:48.713823: +2026-04-12 13:04:48.715434: Epoch 1722 +2026-04-12 13:04:48.717170: Current learning rate: 0.00602 +2026-04-12 13:06:31.513061: train_loss -0.306 +2026-04-12 13:06:31.518839: val_loss -0.1626 +2026-04-12 13:06:31.521171: Pseudo dice [0.6496, 0.1875, 0.4059, 0.0543, 0.5495, 0.1941, 0.6198] +2026-04-12 13:06:31.523521: Epoch time: 102.8 s +2026-04-12 13:06:33.030309: +2026-04-12 13:06:33.033268: Epoch 1723 +2026-04-12 13:06:33.035123: Current learning rate: 0.00602 +2026-04-12 13:08:15.964295: train_loss -0.2812 +2026-04-12 13:08:15.971713: val_loss -0.2533 +2026-04-12 13:08:15.974064: Pseudo dice [0.5675, 0.6195, 0.5285, 0.3673, 0.578, 0.2578, 0.7562] +2026-04-12 13:08:15.976702: Epoch time: 102.94 s +2026-04-12 13:08:17.508871: +2026-04-12 13:08:17.510889: Epoch 1724 +2026-04-12 13:08:17.512692: Current learning rate: 0.00602 +2026-04-12 13:10:00.167787: train_loss -0.2997 +2026-04-12 13:10:00.173274: val_loss -0.3044 +2026-04-12 13:10:00.175284: Pseudo dice [0.7173, 0.3553, 0.773, 0.7469, 0.6173, 0.6043, 0.8178] +2026-04-12 13:10:00.177520: Epoch time: 102.66 s +2026-04-12 13:10:01.712925: +2026-04-12 13:10:01.714731: Epoch 1725 +2026-04-12 13:10:01.717206: Current learning rate: 0.00602 +2026-04-12 13:11:44.702834: train_loss -0.3106 +2026-04-12 13:11:44.708900: val_loss -0.2433 +2026-04-12 13:11:44.711143: Pseudo dice [0.6974, 0.8219, 0.6065, 0.5448, 0.3882, 0.2101, 0.6485] +2026-04-12 13:11:44.713288: Epoch time: 102.99 s +2026-04-12 13:11:46.225006: +2026-04-12 13:11:46.226999: Epoch 1726 +2026-04-12 13:11:46.228717: Current learning rate: 0.00602 +2026-04-12 13:13:28.941105: train_loss -0.3193 +2026-04-12 13:13:28.947135: val_loss -0.2008 +2026-04-12 13:13:28.949690: Pseudo dice [0.7073, 0.7808, 0.6254, 0.7672, 0.5712, 0.0578, 0.8027] +2026-04-12 13:13:28.952421: Epoch time: 102.72 s +2026-04-12 13:13:30.470068: +2026-04-12 13:13:30.472090: Epoch 1727 +2026-04-12 13:13:30.473732: Current learning rate: 0.00601 +2026-04-12 13:15:14.234367: train_loss -0.3058 +2026-04-12 13:15:14.239348: val_loss -0.2401 +2026-04-12 13:15:14.241708: Pseudo dice [0.5953, 0.5024, 0.7141, 0.7627, 0.4801, 0.091, 0.8882] +2026-04-12 13:15:14.244772: Epoch time: 103.77 s +2026-04-12 13:15:15.768813: +2026-04-12 13:15:15.770515: Epoch 1728 +2026-04-12 13:15:15.772205: Current learning rate: 0.00601 +2026-04-12 13:16:58.435100: train_loss -0.3252 +2026-04-12 13:16:58.443044: val_loss -0.2688 +2026-04-12 13:16:58.446131: Pseudo dice [0.5215, 0.5142, 0.7825, 0.0562, 0.3544, 0.0822, 0.8124] +2026-04-12 13:16:58.448476: Epoch time: 102.67 s +2026-04-12 13:16:59.979077: +2026-04-12 13:16:59.981588: Epoch 1729 +2026-04-12 13:16:59.983471: Current learning rate: 0.00601 +2026-04-12 13:18:43.312108: train_loss -0.2991 +2026-04-12 13:18:43.317345: val_loss -0.2158 +2026-04-12 13:18:43.319192: Pseudo dice [0.2192, 0.4769, 0.5992, 0.5065, 0.3921, 0.0745, 0.652] +2026-04-12 13:18:43.321609: Epoch time: 103.34 s +2026-04-12 13:18:44.839638: +2026-04-12 13:18:44.842166: Epoch 1730 +2026-04-12 13:18:44.844068: Current learning rate: 0.00601 +2026-04-12 13:20:27.175238: train_loss -0.2834 +2026-04-12 13:20:27.191127: val_loss -0.2963 +2026-04-12 13:20:27.195448: Pseudo dice [0.4029, 0.6065, 0.5512, 0.757, 0.6705, 0.7331, 0.6546] +2026-04-12 13:20:27.199487: Epoch time: 102.34 s +2026-04-12 13:20:28.736942: +2026-04-12 13:20:28.739041: Epoch 1731 +2026-04-12 13:20:28.742981: Current learning rate: 0.006 +2026-04-12 13:22:11.086442: train_loss -0.2999 +2026-04-12 13:22:11.094226: val_loss -0.2144 +2026-04-12 13:22:11.098669: Pseudo dice [0.8605, 0.5761, 0.6032, 0.4286, 0.4856, 0.0325, 0.8724] +2026-04-12 13:22:11.101749: Epoch time: 102.35 s +2026-04-12 13:22:12.620593: +2026-04-12 13:22:12.622405: Epoch 1732 +2026-04-12 13:22:12.624250: Current learning rate: 0.006 +2026-04-12 13:23:54.958109: train_loss -0.2947 +2026-04-12 13:23:54.963700: val_loss -0.2265 +2026-04-12 13:23:54.965835: Pseudo dice [0.5187, 0.3451, 0.7496, 0.1674, 0.5213, 0.6822, 0.7574] +2026-04-12 13:23:54.968471: Epoch time: 102.34 s +2026-04-12 13:23:56.509773: +2026-04-12 13:23:56.512118: Epoch 1733 +2026-04-12 13:23:56.513894: Current learning rate: 0.006 +2026-04-12 13:25:39.113231: train_loss -0.282 +2026-04-12 13:25:39.118076: val_loss -0.1389 +2026-04-12 13:25:39.119758: Pseudo dice [0.8075, 0.3686, 0.4692, 0.567, 0.3976, 0.023, 0.7847] +2026-04-12 13:25:39.121876: Epoch time: 102.61 s +2026-04-12 13:25:40.606704: +2026-04-12 13:25:40.610730: Epoch 1734 +2026-04-12 13:25:40.615478: Current learning rate: 0.006 +2026-04-12 13:27:23.169261: train_loss -0.297 +2026-04-12 13:27:23.174951: val_loss -0.3035 +2026-04-12 13:27:23.177241: Pseudo dice [0.5444, 0.5839, 0.6506, 0.7507, 0.4427, 0.7382, 0.5911] +2026-04-12 13:27:23.179883: Epoch time: 102.57 s +2026-04-12 13:27:24.683171: +2026-04-12 13:27:24.684645: Epoch 1735 +2026-04-12 13:27:24.686358: Current learning rate: 0.00599 +2026-04-12 13:29:08.151443: train_loss -0.313 +2026-04-12 13:29:08.156202: val_loss -0.2231 +2026-04-12 13:29:08.158075: Pseudo dice [0.2003, 0.634, 0.6161, 0.2597, 0.02, 0.3135, 0.5894] +2026-04-12 13:29:08.160049: Epoch time: 103.47 s +2026-04-12 13:29:09.679394: +2026-04-12 13:29:09.681223: Epoch 1736 +2026-04-12 13:29:09.682762: Current learning rate: 0.00599 +2026-04-12 13:30:52.470671: train_loss -0.2727 +2026-04-12 13:30:52.476063: val_loss -0.2747 +2026-04-12 13:30:52.479786: Pseudo dice [0.5694, 0.4804, 0.7136, 0.8187, 0.4574, 0.4286, 0.8555] +2026-04-12 13:30:52.482455: Epoch time: 102.79 s +2026-04-12 13:30:54.008646: +2026-04-12 13:30:54.010451: Epoch 1737 +2026-04-12 13:30:54.011937: Current learning rate: 0.00599 +2026-04-12 13:32:36.052525: train_loss -0.3123 +2026-04-12 13:32:36.059410: val_loss -0.2314 +2026-04-12 13:32:36.061858: Pseudo dice [0.0209, 0.3325, 0.697, 0.0865, 0.5162, 0.2337, 0.5333] +2026-04-12 13:32:36.064130: Epoch time: 102.05 s +2026-04-12 13:32:37.583529: +2026-04-12 13:32:37.585335: Epoch 1738 +2026-04-12 13:32:37.586863: Current learning rate: 0.00599 +2026-04-12 13:34:20.302947: train_loss -0.3122 +2026-04-12 13:34:20.308163: val_loss -0.2366 +2026-04-12 13:34:20.310061: Pseudo dice [0.7146, 0.7343, 0.466, 0.6801, 0.326, 0.3845, 0.5982] +2026-04-12 13:34:20.312232: Epoch time: 102.72 s +2026-04-12 13:34:21.841691: +2026-04-12 13:34:21.843869: Epoch 1739 +2026-04-12 13:34:21.845645: Current learning rate: 0.00598 +2026-04-12 13:36:04.193616: train_loss -0.3039 +2026-04-12 13:36:04.198646: val_loss -0.1787 +2026-04-12 13:36:04.200974: Pseudo dice [0.3939, 0.7877, 0.7035, 0.1527, 0.5008, 0.0657, 0.7996] +2026-04-12 13:36:04.203092: Epoch time: 102.36 s +2026-04-12 13:36:05.711210: +2026-04-12 13:36:05.713535: Epoch 1740 +2026-04-12 13:36:05.715460: Current learning rate: 0.00598 +2026-04-12 13:37:48.089673: train_loss -0.3193 +2026-04-12 13:37:48.095489: val_loss -0.2048 +2026-04-12 13:37:48.098776: Pseudo dice [0.7375, 0.4574, 0.6367, 0.7911, 0.3624, 0.0467, 0.7944] +2026-04-12 13:37:48.100862: Epoch time: 102.38 s +2026-04-12 13:37:49.619578: +2026-04-12 13:37:49.621467: Epoch 1741 +2026-04-12 13:37:49.623132: Current learning rate: 0.00598 +2026-04-12 13:39:32.252729: train_loss -0.3061 +2026-04-12 13:39:32.257827: val_loss -0.2083 +2026-04-12 13:39:32.260390: Pseudo dice [0.7616, 0.4134, 0.6741, 0.8823, 0.513, 0.0206, 0.5187] +2026-04-12 13:39:32.263353: Epoch time: 102.64 s +2026-04-12 13:39:34.925540: +2026-04-12 13:39:34.927485: Epoch 1742 +2026-04-12 13:39:34.928942: Current learning rate: 0.00598 +2026-04-12 13:41:17.180026: train_loss -0.2997 +2026-04-12 13:41:17.184987: val_loss -0.2114 +2026-04-12 13:41:17.187252: Pseudo dice [0.5874, 0.6104, 0.6791, 0.305, 0.4889, 0.0569, 0.7929] +2026-04-12 13:41:17.189542: Epoch time: 102.26 s +2026-04-12 13:41:18.694942: +2026-04-12 13:41:18.696762: Epoch 1743 +2026-04-12 13:41:18.698651: Current learning rate: 0.00597 +2026-04-12 13:43:00.988842: train_loss -0.3194 +2026-04-12 13:43:00.994723: val_loss -0.2224 +2026-04-12 13:43:00.996602: Pseudo dice [0.535, 0.4524, 0.6187, 0.6074, 0.5206, 0.0797, 0.6843] +2026-04-12 13:43:00.998484: Epoch time: 102.3 s +2026-04-12 13:43:02.498073: +2026-04-12 13:43:02.499758: Epoch 1744 +2026-04-12 13:43:02.501464: Current learning rate: 0.00597 +2026-04-12 13:44:44.723112: train_loss -0.3127 +2026-04-12 13:44:44.730538: val_loss -0.2784 +2026-04-12 13:44:44.733099: Pseudo dice [0.5931, 0.3033, 0.3563, 0.6336, 0.5749, 0.4984, 0.7447] +2026-04-12 13:44:44.736138: Epoch time: 102.23 s +2026-04-12 13:44:46.230077: +2026-04-12 13:44:46.232151: Epoch 1745 +2026-04-12 13:44:46.234143: Current learning rate: 0.00597 +2026-04-12 13:46:29.099456: train_loss -0.3108 +2026-04-12 13:46:29.107529: val_loss -0.2103 +2026-04-12 13:46:29.111251: Pseudo dice [0.6362, 0.5614, 0.4969, 0.7013, 0.2583, 0.0565, 0.7173] +2026-04-12 13:46:29.113101: Epoch time: 102.87 s +2026-04-12 13:46:30.631657: +2026-04-12 13:46:30.640163: Epoch 1746 +2026-04-12 13:46:30.644121: Current learning rate: 0.00597 +2026-04-12 13:48:13.287480: train_loss -0.3156 +2026-04-12 13:48:13.293364: val_loss -0.3082 +2026-04-12 13:48:13.296174: Pseudo dice [0.8018, 0.8034, 0.7916, 0.5079, 0.5648, 0.5489, 0.7905] +2026-04-12 13:48:13.298660: Epoch time: 102.66 s +2026-04-12 13:48:14.816781: +2026-04-12 13:48:14.818851: Epoch 1747 +2026-04-12 13:48:14.820533: Current learning rate: 0.00597 +2026-04-12 13:50:06.219695: train_loss -0.3165 +2026-04-12 13:50:06.229738: val_loss -0.2483 +2026-04-12 13:50:06.232601: Pseudo dice [0.587, 0.5527, 0.698, 0.7887, 0.2311, 0.5851, 0.4338] +2026-04-12 13:50:06.236357: Epoch time: 111.41 s +2026-04-12 13:50:07.771425: +2026-04-12 13:50:07.773180: Epoch 1748 +2026-04-12 13:50:07.776123: Current learning rate: 0.00596 +2026-04-12 13:51:50.213586: train_loss -0.3143 +2026-04-12 13:51:50.220971: val_loss -0.2122 +2026-04-12 13:51:50.223173: Pseudo dice [0.4425, 0.7086, 0.6415, 0.7291, 0.5764, 0.1654, 0.5253] +2026-04-12 13:51:50.225384: Epoch time: 102.45 s +2026-04-12 13:51:51.766052: +2026-04-12 13:51:51.767788: Epoch 1749 +2026-04-12 13:51:51.769501: Current learning rate: 0.00596 +2026-04-12 13:53:34.822592: train_loss -0.3014 +2026-04-12 13:53:34.830266: val_loss -0.2341 +2026-04-12 13:53:34.832145: Pseudo dice [0.3979, 0.3212, 0.4395, 0.6474, 0.393, 0.2164, 0.6182] +2026-04-12 13:53:34.835009: Epoch time: 103.06 s +2026-04-12 13:53:38.354249: +2026-04-12 13:53:38.356349: Epoch 1750 +2026-04-12 13:53:38.358084: Current learning rate: 0.00596 +2026-04-12 13:55:21.439273: train_loss -0.3098 +2026-04-12 13:55:21.444613: val_loss -0.2548 +2026-04-12 13:55:21.446549: Pseudo dice [0.5228, 0.2828, 0.623, 0.5982, 0.2375, 0.8306, 0.6065] +2026-04-12 13:55:21.449171: Epoch time: 103.09 s +2026-04-12 13:55:22.982139: +2026-04-12 13:55:22.984630: Epoch 1751 +2026-04-12 13:55:22.987933: Current learning rate: 0.00596 +2026-04-12 13:57:05.234445: train_loss -0.3116 +2026-04-12 13:57:05.240959: val_loss -0.2314 +2026-04-12 13:57:05.243298: Pseudo dice [0.6062, 0.4772, 0.518, 0.7, 0.4964, 0.0905, 0.6964] +2026-04-12 13:57:05.246089: Epoch time: 102.26 s +2026-04-12 13:57:06.745759: +2026-04-12 13:57:06.747593: Epoch 1752 +2026-04-12 13:57:06.749164: Current learning rate: 0.00595 +2026-04-12 13:58:49.045349: train_loss -0.3205 +2026-04-12 13:58:49.051153: val_loss -0.249 +2026-04-12 13:58:49.053038: Pseudo dice [0.4902, 0.6542, 0.7227, 0.4933, 0.4907, 0.1491, 0.8718] +2026-04-12 13:58:49.055192: Epoch time: 102.3 s +2026-04-12 13:58:50.553972: +2026-04-12 13:58:50.555618: Epoch 1753 +2026-04-12 13:58:50.557567: Current learning rate: 0.00595 +2026-04-12 14:00:33.937760: train_loss -0.3046 +2026-04-12 14:00:33.942904: val_loss -0.1987 +2026-04-12 14:00:33.944823: Pseudo dice [0.3682, 0.6571, 0.5587, 0.1381, 0.3769, 0.4728, 0.735] +2026-04-12 14:00:33.947014: Epoch time: 103.39 s +2026-04-12 14:00:35.469297: +2026-04-12 14:00:35.471492: Epoch 1754 +2026-04-12 14:00:35.473244: Current learning rate: 0.00595 +2026-04-12 14:02:18.870422: train_loss -0.3144 +2026-04-12 14:02:18.876690: val_loss -0.2217 +2026-04-12 14:02:18.878526: Pseudo dice [0.6157, 0.8015, 0.6838, 0.1664, 0.5424, 0.0357, 0.5444] +2026-04-12 14:02:18.880509: Epoch time: 103.4 s +2026-04-12 14:02:20.375353: +2026-04-12 14:02:20.377720: Epoch 1755 +2026-04-12 14:02:20.379383: Current learning rate: 0.00595 +2026-04-12 14:04:03.300973: train_loss -0.3097 +2026-04-12 14:04:03.306890: val_loss -0.296 +2026-04-12 14:04:03.309156: Pseudo dice [0.8114, 0.7441, 0.6156, 0.8272, 0.6187, 0.6687, 0.7607] +2026-04-12 14:04:03.312005: Epoch time: 102.93 s +2026-04-12 14:04:04.843739: +2026-04-12 14:04:04.848329: Epoch 1756 +2026-04-12 14:04:04.863247: Current learning rate: 0.00594 +2026-04-12 14:05:47.321584: train_loss -0.31 +2026-04-12 14:05:47.329050: val_loss -0.2392 +2026-04-12 14:05:47.332090: Pseudo dice [0.5575, 0.6229, 0.2594, 0.5537, 0.2533, 0.7093, 0.5806] +2026-04-12 14:05:47.335519: Epoch time: 102.48 s +2026-04-12 14:05:48.872236: +2026-04-12 14:05:48.875329: Epoch 1757 +2026-04-12 14:05:48.877452: Current learning rate: 0.00594 +2026-04-12 14:07:32.268266: train_loss -0.3026 +2026-04-12 14:07:32.274875: val_loss -0.2318 +2026-04-12 14:07:32.276736: Pseudo dice [0.4786, 0.7485, 0.4915, 0.7985, 0.4828, 0.0272, 0.7924] +2026-04-12 14:07:32.278862: Epoch time: 103.4 s +2026-04-12 14:07:33.776410: +2026-04-12 14:07:33.778198: Epoch 1758 +2026-04-12 14:07:33.779918: Current learning rate: 0.00594 +2026-04-12 14:09:16.349859: train_loss -0.3287 +2026-04-12 14:09:16.355349: val_loss -0.1986 +2026-04-12 14:09:16.357014: Pseudo dice [0.6864, 0.169, 0.6256, 0.6161, 0.429, 0.2254, 0.5056] +2026-04-12 14:09:16.358938: Epoch time: 102.58 s +2026-04-12 14:09:17.855925: +2026-04-12 14:09:17.857578: Epoch 1759 +2026-04-12 14:09:17.859388: Current learning rate: 0.00594 +2026-04-12 14:11:00.531296: train_loss -0.3181 +2026-04-12 14:11:00.536731: val_loss -0.2892 +2026-04-12 14:11:00.538651: Pseudo dice [0.3715, 0.8219, 0.8285, 0.6912, 0.5503, 0.7946, 0.898] +2026-04-12 14:11:00.540777: Epoch time: 102.68 s +2026-04-12 14:11:02.076657: +2026-04-12 14:11:02.079072: Epoch 1760 +2026-04-12 14:11:02.080885: Current learning rate: 0.00593 +2026-04-12 14:12:44.490217: train_loss -0.3248 +2026-04-12 14:12:44.495752: val_loss -0.2373 +2026-04-12 14:12:44.498714: Pseudo dice [0.5611, 0.52, 0.7358, 0.8414, 0.6245, 0.08, 0.8952] +2026-04-12 14:12:44.501860: Epoch time: 102.42 s +2026-04-12 14:12:47.150852: +2026-04-12 14:12:47.152882: Epoch 1761 +2026-04-12 14:12:47.154717: Current learning rate: 0.00593 +2026-04-12 14:14:30.221683: train_loss -0.3153 +2026-04-12 14:14:30.236379: val_loss -0.2609 +2026-04-12 14:14:30.240407: Pseudo dice [0.5337, 0.3713, 0.4696, 0.7265, 0.4841, 0.6914, 0.8174] +2026-04-12 14:14:30.244410: Epoch time: 103.07 s +2026-04-12 14:14:31.771535: +2026-04-12 14:14:31.773413: Epoch 1762 +2026-04-12 14:14:31.775717: Current learning rate: 0.00593 +2026-04-12 14:16:15.590796: train_loss -0.3325 +2026-04-12 14:16:15.596771: val_loss -0.2446 +2026-04-12 14:16:15.599216: Pseudo dice [0.6609, 0.1229, 0.5657, 0.6697, 0.5404, 0.4705, 0.8116] +2026-04-12 14:16:15.601925: Epoch time: 103.82 s +2026-04-12 14:16:17.117489: +2026-04-12 14:16:17.119941: Epoch 1763 +2026-04-12 14:16:17.121835: Current learning rate: 0.00593 +2026-04-12 14:17:59.998924: train_loss -0.3328 +2026-04-12 14:18:00.004128: val_loss -0.2889 +2026-04-12 14:18:00.005908: Pseudo dice [0.7862, 0.6699, 0.6366, 0.6631, 0.5936, 0.3164, 0.7694] +2026-04-12 14:18:00.007926: Epoch time: 102.89 s +2026-04-12 14:18:01.517119: +2026-04-12 14:18:01.518969: Epoch 1764 +2026-04-12 14:18:01.520396: Current learning rate: 0.00592 +2026-04-12 14:19:43.796822: train_loss -0.2963 +2026-04-12 14:19:43.802920: val_loss -0.0609 +2026-04-12 14:19:43.805937: Pseudo dice [0.5703, 0.6623, 0.2366, 0.7002, 0.6461, 0.0178, 0.8414] +2026-04-12 14:19:43.809525: Epoch time: 102.28 s +2026-04-12 14:19:45.318529: +2026-04-12 14:19:45.320441: Epoch 1765 +2026-04-12 14:19:45.322148: Current learning rate: 0.00592 +2026-04-12 14:21:28.772905: train_loss -0.3012 +2026-04-12 14:21:28.777553: val_loss -0.2773 +2026-04-12 14:21:28.781429: Pseudo dice [0.8126, 0.4732, 0.6684, 0.6065, 0.5268, 0.2584, 0.7439] +2026-04-12 14:21:28.784093: Epoch time: 103.46 s +2026-04-12 14:21:30.302214: +2026-04-12 14:21:30.304334: Epoch 1766 +2026-04-12 14:21:30.306333: Current learning rate: 0.00592 +2026-04-12 14:23:13.191918: train_loss -0.318 +2026-04-12 14:23:13.197967: val_loss -0.2897 +2026-04-12 14:23:13.199894: Pseudo dice [0.5397, 0.6656, 0.6871, 0.7904, 0.6286, 0.7357, 0.6906] +2026-04-12 14:23:13.202204: Epoch time: 102.89 s +2026-04-12 14:23:14.728059: +2026-04-12 14:23:14.731039: Epoch 1767 +2026-04-12 14:23:14.733414: Current learning rate: 0.00592 +2026-04-12 14:24:56.846528: train_loss -0.3129 +2026-04-12 14:24:56.851780: val_loss -0.2528 +2026-04-12 14:24:56.853614: Pseudo dice [0.7738, 0.5243, 0.7417, 0.7967, 0.6028, 0.0944, 0.8917] +2026-04-12 14:24:56.855961: Epoch time: 102.12 s +2026-04-12 14:24:58.369253: +2026-04-12 14:24:58.371593: Epoch 1768 +2026-04-12 14:24:58.373454: Current learning rate: 0.00592 +2026-04-12 14:26:41.561885: train_loss -0.303 +2026-04-12 14:26:41.567835: val_loss -0.1654 +2026-04-12 14:26:41.569902: Pseudo dice [0.8227, 0.3892, 0.2575, 0.3293, 0.5458, 0.0776, 0.6709] +2026-04-12 14:26:41.572961: Epoch time: 103.2 s +2026-04-12 14:26:43.135524: +2026-04-12 14:26:43.137555: Epoch 1769 +2026-04-12 14:26:43.139503: Current learning rate: 0.00591 +2026-04-12 14:28:25.869570: train_loss -0.3052 +2026-04-12 14:28:25.875032: val_loss -0.218 +2026-04-12 14:28:25.876823: Pseudo dice [0.6482, 0.7933, 0.6581, 0.1741, 0.6877, 0.1827, 0.6192] +2026-04-12 14:28:25.878981: Epoch time: 102.74 s +2026-04-12 14:28:27.399274: +2026-04-12 14:28:27.400927: Epoch 1770 +2026-04-12 14:28:27.402565: Current learning rate: 0.00591 +2026-04-12 14:30:09.663737: train_loss -0.2749 +2026-04-12 14:30:09.668977: val_loss -0.274 +2026-04-12 14:30:09.670895: Pseudo dice [0.409, 0.6215, 0.7045, 0.2859, 0.2533, 0.7265, 0.8586] +2026-04-12 14:30:09.672999: Epoch time: 102.27 s +2026-04-12 14:30:11.185566: +2026-04-12 14:30:11.187783: Epoch 1771 +2026-04-12 14:30:11.189612: Current learning rate: 0.00591 +2026-04-12 14:31:53.410642: train_loss -0.295 +2026-04-12 14:31:53.439323: val_loss -0.2912 +2026-04-12 14:31:53.443149: Pseudo dice [0.2981, 0.6134, 0.7569, 0.3713, 0.2844, 0.605, 0.6728] +2026-04-12 14:31:53.445740: Epoch time: 102.23 s +2026-04-12 14:31:54.933273: +2026-04-12 14:31:54.935932: Epoch 1772 +2026-04-12 14:31:54.937605: Current learning rate: 0.00591 +2026-04-12 14:33:38.123061: train_loss -0.3155 +2026-04-12 14:33:38.128449: val_loss -0.2463 +2026-04-12 14:33:38.130560: Pseudo dice [0.7029, 0.7651, 0.6744, 0.3806, 0.4556, 0.1975, 0.7949] +2026-04-12 14:33:38.132913: Epoch time: 103.19 s +2026-04-12 14:33:39.632411: +2026-04-12 14:33:39.633940: Epoch 1773 +2026-04-12 14:33:39.635552: Current learning rate: 0.0059 +2026-04-12 14:35:22.029809: train_loss -0.2718 +2026-04-12 14:35:22.035563: val_loss -0.2669 +2026-04-12 14:35:22.037412: Pseudo dice [0.1316, 0.1542, 0.4915, 0.2131, 0.3671, 0.6215, 0.6912] +2026-04-12 14:35:22.039964: Epoch time: 102.4 s +2026-04-12 14:35:23.529488: +2026-04-12 14:35:23.531440: Epoch 1774 +2026-04-12 14:35:23.533041: Current learning rate: 0.0059 +2026-04-12 14:37:06.052694: train_loss -0.3022 +2026-04-12 14:37:06.058736: val_loss -0.2079 +2026-04-12 14:37:06.061448: Pseudo dice [0.3326, 0.8604, 0.5496, 0.5466, 0.2657, 0.0619, 0.7637] +2026-04-12 14:37:06.063985: Epoch time: 102.53 s +2026-04-12 14:37:07.569297: +2026-04-12 14:37:07.571029: Epoch 1775 +2026-04-12 14:37:07.572999: Current learning rate: 0.0059 +2026-04-12 14:38:49.959039: train_loss -0.3101 +2026-04-12 14:38:49.963846: val_loss -0.1966 +2026-04-12 14:38:49.965638: Pseudo dice [0.8183, 0.4525, 0.5403, 0.6076, 0.4886, 0.0207, 0.8423] +2026-04-12 14:38:49.968243: Epoch time: 102.39 s +2026-04-12 14:38:51.480427: +2026-04-12 14:38:51.482193: Epoch 1776 +2026-04-12 14:38:51.483713: Current learning rate: 0.0059 +2026-04-12 14:40:33.820945: train_loss -0.306 +2026-04-12 14:40:33.832582: val_loss -0.2481 +2026-04-12 14:40:33.836968: Pseudo dice [0.1397, 0.667, 0.8639, 0.7797, 0.4732, 0.076, 0.582] +2026-04-12 14:40:33.842463: Epoch time: 102.34 s +2026-04-12 14:40:35.369159: +2026-04-12 14:40:35.371781: Epoch 1777 +2026-04-12 14:40:35.373479: Current learning rate: 0.00589 +2026-04-12 14:42:18.040416: train_loss -0.322 +2026-04-12 14:42:18.045552: val_loss -0.2969 +2026-04-12 14:42:18.047449: Pseudo dice [0.767, 0.5571, 0.6781, 0.0306, 0.6661, 0.5075, 0.7726] +2026-04-12 14:42:18.050014: Epoch time: 102.68 s +2026-04-12 14:42:19.548174: +2026-04-12 14:42:19.549882: Epoch 1778 +2026-04-12 14:42:19.551904: Current learning rate: 0.00589 +2026-04-12 14:44:01.893915: train_loss -0.304 +2026-04-12 14:44:01.899368: val_loss -0.2581 +2026-04-12 14:44:01.901295: Pseudo dice [0.6422, 0.7356, 0.5905, 0.7713, 0.3553, 0.303, 0.6448] +2026-04-12 14:44:01.904275: Epoch time: 102.35 s +2026-04-12 14:44:03.437043: +2026-04-12 14:44:03.439285: Epoch 1779 +2026-04-12 14:44:03.441064: Current learning rate: 0.00589 +2026-04-12 14:45:45.873740: train_loss -0.3091 +2026-04-12 14:45:45.880044: val_loss -0.2095 +2026-04-12 14:45:45.886442: Pseudo dice [0.7233, 0.6052, 0.5669, 0.1326, 0.5218, 0.1314, 0.8009] +2026-04-12 14:45:45.888453: Epoch time: 102.44 s +2026-04-12 14:45:47.401109: +2026-04-12 14:45:47.403117: Epoch 1780 +2026-04-12 14:45:47.405400: Current learning rate: 0.00589 +2026-04-12 14:47:30.985059: train_loss -0.3214 +2026-04-12 14:47:30.990825: val_loss -0.1878 +2026-04-12 14:47:30.992676: Pseudo dice [0.2931, 0.8582, 0.5127, 0.2037, 0.5697, 0.4036, 0.7048] +2026-04-12 14:47:30.995195: Epoch time: 103.59 s +2026-04-12 14:47:33.699174: +2026-04-12 14:47:33.701722: Epoch 1781 +2026-04-12 14:47:33.703433: Current learning rate: 0.00588 +2026-04-12 14:49:16.827693: train_loss -0.3137 +2026-04-12 14:49:16.833542: val_loss -0.2569 +2026-04-12 14:49:16.836165: Pseudo dice [0.3244, 0.6503, 0.529, 0.5783, 0.4239, 0.4521, 0.8045] +2026-04-12 14:49:16.839628: Epoch time: 103.13 s +2026-04-12 14:49:18.439152: +2026-04-12 14:49:18.440909: Epoch 1782 +2026-04-12 14:49:18.442520: Current learning rate: 0.00588 +2026-04-12 14:51:01.581707: train_loss -0.3182 +2026-04-12 14:51:01.586469: val_loss -0.1388 +2026-04-12 14:51:01.588597: Pseudo dice [0.7904, 0.7325, 0.3879, 0.1232, 0.4007, 0.0418, 0.7354] +2026-04-12 14:51:01.592476: Epoch time: 103.15 s +2026-04-12 14:51:03.083483: +2026-04-12 14:51:03.085485: Epoch 1783 +2026-04-12 14:51:03.087421: Current learning rate: 0.00588 +2026-04-12 14:52:46.110913: train_loss -0.3218 +2026-04-12 14:52:46.117685: val_loss -0.2744 +2026-04-12 14:52:46.121005: Pseudo dice [0.6926, 0.6488, 0.7655, 0.7213, 0.1927, 0.8109, 0.7581] +2026-04-12 14:52:46.124107: Epoch time: 103.03 s +2026-04-12 14:52:47.659363: +2026-04-12 14:52:47.661860: Epoch 1784 +2026-04-12 14:52:47.663637: Current learning rate: 0.00588 +2026-04-12 14:54:31.762786: train_loss -0.2949 +2026-04-12 14:54:31.767907: val_loss -0.2916 +2026-04-12 14:54:31.770212: Pseudo dice [0.5979, 0.612, 0.5346, 0.1146, 0.6629, 0.7639, 0.849] +2026-04-12 14:54:31.772532: Epoch time: 104.11 s +2026-04-12 14:54:33.278229: +2026-04-12 14:54:33.280699: Epoch 1785 +2026-04-12 14:54:33.282457: Current learning rate: 0.00587 +2026-04-12 14:56:15.892221: train_loss -0.3113 +2026-04-12 14:56:15.897127: val_loss -0.2204 +2026-04-12 14:56:15.899327: Pseudo dice [0.8374, 0.5538, 0.6111, 0.4703, 0.4874, 0.0798, 0.56] +2026-04-12 14:56:15.901350: Epoch time: 102.62 s +2026-04-12 14:56:17.420306: +2026-04-12 14:56:17.422369: Epoch 1786 +2026-04-12 14:56:17.425513: Current learning rate: 0.00587 +2026-04-12 14:57:59.931141: train_loss -0.3241 +2026-04-12 14:57:59.937084: val_loss -0.2425 +2026-04-12 14:57:59.939560: Pseudo dice [0.6696, 0.2089, 0.7051, 0.5322, 0.6294, 0.259, 0.7923] +2026-04-12 14:57:59.942384: Epoch time: 102.51 s +2026-04-12 14:58:01.457650: +2026-04-12 14:58:01.459453: Epoch 1787 +2026-04-12 14:58:01.461088: Current learning rate: 0.00587 +2026-04-12 14:59:44.841246: train_loss -0.3126 +2026-04-12 14:59:44.847755: val_loss -0.2076 +2026-04-12 14:59:44.849766: Pseudo dice [0.6428, 0.3874, 0.497, 0.0442, 0.4806, 0.0327, 0.445] +2026-04-12 14:59:44.852839: Epoch time: 103.39 s +2026-04-12 14:59:46.420927: +2026-04-12 14:59:46.424007: Epoch 1788 +2026-04-12 14:59:46.426299: Current learning rate: 0.00587 +2026-04-12 15:01:29.309041: train_loss -0.2864 +2026-04-12 15:01:29.314989: val_loss -0.2066 +2026-04-12 15:01:29.317270: Pseudo dice [0.5708, 0.6761, 0.5943, 0.8153, 0.5459, 0.0427, 0.7818] +2026-04-12 15:01:29.319461: Epoch time: 102.89 s +2026-04-12 15:01:30.829726: +2026-04-12 15:01:30.832040: Epoch 1789 +2026-04-12 15:01:30.834511: Current learning rate: 0.00587 +2026-04-12 15:03:13.645647: train_loss -0.3128 +2026-04-12 15:03:13.656138: val_loss -0.3029 +2026-04-12 15:03:13.659421: Pseudo dice [0.7217, 0.239, 0.7557, 0.8704, 0.5823, 0.7641, 0.8615] +2026-04-12 15:03:13.662435: Epoch time: 102.82 s +2026-04-12 15:03:15.244557: +2026-04-12 15:03:15.247820: Epoch 1790 +2026-04-12 15:03:15.249987: Current learning rate: 0.00586 +2026-04-12 15:04:57.862005: train_loss -0.322 +2026-04-12 15:04:57.867362: val_loss -0.3065 +2026-04-12 15:04:57.869396: Pseudo dice [0.7247, 0.4048, 0.7022, 0.6318, 0.6142, 0.7016, 0.8192] +2026-04-12 15:04:57.872161: Epoch time: 102.62 s +2026-04-12 15:04:59.365403: +2026-04-12 15:04:59.367858: Epoch 1791 +2026-04-12 15:04:59.369561: Current learning rate: 0.00586 +2026-04-12 15:06:42.129393: train_loss -0.3155 +2026-04-12 15:06:42.136939: val_loss -0.2639 +2026-04-12 15:06:42.139338: Pseudo dice [0.6122, 0.7765, 0.5938, 0.8204, 0.4518, 0.0654, 0.7827] +2026-04-12 15:06:42.141738: Epoch time: 102.77 s +2026-04-12 15:06:43.683299: +2026-04-12 15:06:43.684954: Epoch 1792 +2026-04-12 15:06:43.686564: Current learning rate: 0.00586 +2026-04-12 15:08:26.306390: train_loss -0.3155 +2026-04-12 15:08:26.312065: val_loss -0.2425 +2026-04-12 15:08:26.314471: Pseudo dice [0.8426, 0.7877, 0.5719, 0.3519, 0.3903, 0.0516, 0.7265] +2026-04-12 15:08:26.316809: Epoch time: 102.63 s +2026-04-12 15:08:27.837535: +2026-04-12 15:08:27.839137: Epoch 1793 +2026-04-12 15:08:27.840681: Current learning rate: 0.00586 +2026-04-12 15:10:10.383296: train_loss -0.3417 +2026-04-12 15:10:10.388239: val_loss -0.3038 +2026-04-12 15:10:10.389861: Pseudo dice [0.7214, 0.687, 0.7299, 0.8037, 0.4374, 0.7333, 0.8425] +2026-04-12 15:10:10.391915: Epoch time: 102.55 s +2026-04-12 15:10:11.889409: +2026-04-12 15:10:11.891484: Epoch 1794 +2026-04-12 15:10:11.893029: Current learning rate: 0.00585 +2026-04-12 15:11:54.546223: train_loss -0.3081 +2026-04-12 15:11:54.552603: val_loss -0.2774 +2026-04-12 15:11:54.555974: Pseudo dice [0.8509, 0.8284, 0.6236, 0.0233, 0.3082, 0.775, 0.6783] +2026-04-12 15:11:54.558657: Epoch time: 102.66 s +2026-04-12 15:11:56.045933: +2026-04-12 15:11:56.048496: Epoch 1795 +2026-04-12 15:11:56.050212: Current learning rate: 0.00585 +2026-04-12 15:13:39.426007: train_loss -0.2893 +2026-04-12 15:13:39.433981: val_loss -0.232 +2026-04-12 15:13:39.436486: Pseudo dice [0.8824, 0.6072, 0.5546, 0.0175, 0.4838, 0.1118, 0.6956] +2026-04-12 15:13:39.438951: Epoch time: 103.38 s +2026-04-12 15:13:40.955433: +2026-04-12 15:13:40.958355: Epoch 1796 +2026-04-12 15:13:40.961671: Current learning rate: 0.00585 +2026-04-12 15:15:23.656627: train_loss -0.2743 +2026-04-12 15:15:23.664102: val_loss -0.2341 +2026-04-12 15:15:23.667736: Pseudo dice [0.5496, 0.4867, 0.359, 0.6242, 0.5132, 0.2805, 0.7118] +2026-04-12 15:15:23.670128: Epoch time: 102.7 s +2026-04-12 15:15:25.213806: +2026-04-12 15:15:25.216604: Epoch 1797 +2026-04-12 15:15:25.219612: Current learning rate: 0.00585 +2026-04-12 15:17:08.137182: train_loss -0.29 +2026-04-12 15:17:08.146884: val_loss -0.1458 +2026-04-12 15:17:08.148891: Pseudo dice [0.7328, 0.2163, 0.4133, 0.0031, 0.1959, 0.0453, 0.6939] +2026-04-12 15:17:08.151680: Epoch time: 102.93 s +2026-04-12 15:17:09.694971: +2026-04-12 15:17:09.705727: Epoch 1798 +2026-04-12 15:17:09.713274: Current learning rate: 0.00584 +2026-04-12 15:18:52.968001: train_loss -0.2822 +2026-04-12 15:18:52.976970: val_loss -0.1978 +2026-04-12 15:18:52.982041: Pseudo dice [0.3679, 0.4996, 0.6255, 0.2075, 0.2461, 0.0744, 0.389] +2026-04-12 15:18:52.984983: Epoch time: 103.28 s +2026-04-12 15:18:54.559317: +2026-04-12 15:18:54.561156: Epoch 1799 +2026-04-12 15:18:54.562928: Current learning rate: 0.00584 +2026-04-12 15:20:37.401341: train_loss -0.3055 +2026-04-12 15:20:37.407117: val_loss -0.2458 +2026-04-12 15:20:37.409659: Pseudo dice [0.5667, 0.8465, 0.7136, 0.5971, 0.3437, 0.2309, 0.4344] +2026-04-12 15:20:37.411950: Epoch time: 102.85 s +2026-04-12 15:20:41.053051: +2026-04-12 15:20:41.055099: Epoch 1800 +2026-04-12 15:20:41.057185: Current learning rate: 0.00584 +2026-04-12 15:22:25.167468: train_loss -0.3163 +2026-04-12 15:22:25.172430: val_loss -0.2703 +2026-04-12 15:22:25.174506: Pseudo dice [0.7068, 0.1734, 0.6307, 0.3863, 0.4683, 0.3073, 0.5161] +2026-04-12 15:22:25.176797: Epoch time: 104.12 s +2026-04-12 15:22:26.740992: +2026-04-12 15:22:26.743623: Epoch 1801 +2026-04-12 15:22:26.745805: Current learning rate: 0.00584 +2026-04-12 15:24:11.411259: train_loss -0.3252 +2026-04-12 15:24:11.416902: val_loss -0.3046 +2026-04-12 15:24:11.418593: Pseudo dice [0.1827, 0.6018, 0.7191, 0.8052, 0.4361, 0.6412, 0.8348] +2026-04-12 15:24:11.420861: Epoch time: 104.67 s +2026-04-12 15:24:12.962527: +2026-04-12 15:24:12.964415: Epoch 1802 +2026-04-12 15:24:12.966560: Current learning rate: 0.00583 +2026-04-12 15:25:56.018932: train_loss -0.3233 +2026-04-12 15:25:56.025979: val_loss -0.2664 +2026-04-12 15:25:56.032431: Pseudo dice [0.5139, 0.7349, 0.642, 0.0624, 0.403, 0.4988, 0.7203] +2026-04-12 15:25:56.035144: Epoch time: 103.06 s +2026-04-12 15:25:57.601713: +2026-04-12 15:25:57.615995: Epoch 1803 +2026-04-12 15:25:57.624480: Current learning rate: 0.00583 +2026-04-12 15:27:41.057520: train_loss -0.3129 +2026-04-12 15:27:41.064107: val_loss -0.1904 +2026-04-12 15:27:41.066037: Pseudo dice [0.4468, 0.8049, 0.615, 0.7622, 0.3907, 0.0903, 0.5989] +2026-04-12 15:27:41.068731: Epoch time: 103.46 s +2026-04-12 15:27:42.675123: +2026-04-12 15:27:42.677667: Epoch 1804 +2026-04-12 15:27:42.679738: Current learning rate: 0.00583 +2026-04-12 15:29:25.367920: train_loss -0.3359 +2026-04-12 15:29:25.373525: val_loss -0.0827 +2026-04-12 15:29:25.375824: Pseudo dice [0.2574, 0.862, 0.5129, 0.3491, 0.1963, 0.0932, 0.5528] +2026-04-12 15:29:25.378412: Epoch time: 102.7 s +2026-04-12 15:29:26.893523: +2026-04-12 15:29:26.895288: Epoch 1805 +2026-04-12 15:29:26.897089: Current learning rate: 0.00583 +2026-04-12 15:31:09.348855: train_loss -0.3206 +2026-04-12 15:31:09.354111: val_loss -0.2104 +2026-04-12 15:31:09.356282: Pseudo dice [0.3249, 0.5059, 0.4019, 0.6122, 0.1892, 0.0401, 0.8682] +2026-04-12 15:31:09.358927: Epoch time: 102.46 s +2026-04-12 15:31:10.896584: +2026-04-12 15:31:10.898597: Epoch 1806 +2026-04-12 15:31:10.900778: Current learning rate: 0.00582 +2026-04-12 15:32:54.369002: train_loss -0.3169 +2026-04-12 15:32:54.379354: val_loss -0.2322 +2026-04-12 15:32:54.381771: Pseudo dice [0.7554, 0.579, 0.7495, 0.78, 0.2856, 0.2558, 0.7449] +2026-04-12 15:32:54.384429: Epoch time: 103.48 s +2026-04-12 15:32:55.918155: +2026-04-12 15:32:55.920179: Epoch 1807 +2026-04-12 15:32:55.922494: Current learning rate: 0.00582 +2026-04-12 15:34:38.758580: train_loss -0.3262 +2026-04-12 15:34:38.765057: val_loss -0.1701 +2026-04-12 15:34:38.767294: Pseudo dice [0.8086, 0.4245, 0.7097, 0.7396, 0.6528, 0.0999, 0.665] +2026-04-12 15:34:38.769571: Epoch time: 102.84 s +2026-04-12 15:34:40.309783: +2026-04-12 15:34:40.316061: Epoch 1808 +2026-04-12 15:34:40.322388: Current learning rate: 0.00582 +2026-04-12 15:36:24.738168: train_loss -0.3098 +2026-04-12 15:36:24.744692: val_loss -0.272 +2026-04-12 15:36:24.747353: Pseudo dice [0.3623, 0.7306, 0.7119, 0.2395, 0.5371, 0.8071, 0.8063] +2026-04-12 15:36:24.751103: Epoch time: 104.43 s +2026-04-12 15:36:26.383510: +2026-04-12 15:36:26.386097: Epoch 1809 +2026-04-12 15:36:26.388788: Current learning rate: 0.00582 +2026-04-12 15:38:09.076782: train_loss -0.3021 +2026-04-12 15:38:09.084040: val_loss -0.2773 +2026-04-12 15:38:09.086110: Pseudo dice [0.7454, 0.6159, 0.5287, 0.4525, 0.6394, 0.7449, 0.3165] +2026-04-12 15:38:09.089334: Epoch time: 102.7 s +2026-04-12 15:38:10.613525: +2026-04-12 15:38:10.616951: Epoch 1810 +2026-04-12 15:38:10.620442: Current learning rate: 0.00581 +2026-04-12 15:39:54.087032: train_loss -0.3078 +2026-04-12 15:39:54.094009: val_loss -0.179 +2026-04-12 15:39:54.095956: Pseudo dice [0.7337, 0.2702, 0.4785, 0.6049, 0.3221, 0.0917, 0.5519] +2026-04-12 15:39:54.098531: Epoch time: 103.48 s +2026-04-12 15:39:55.617787: +2026-04-12 15:39:55.619867: Epoch 1811 +2026-04-12 15:39:55.622242: Current learning rate: 0.00581 +2026-04-12 15:41:39.901312: train_loss -0.3042 +2026-04-12 15:41:39.907274: val_loss -0.2507 +2026-04-12 15:41:39.910127: Pseudo dice [0.8504, 0.4786, 0.5571, 0.6304, 0.6548, 0.1425, 0.3551] +2026-04-12 15:41:39.912452: Epoch time: 104.29 s +2026-04-12 15:41:41.463256: +2026-04-12 15:41:41.464852: Epoch 1812 +2026-04-12 15:41:41.466977: Current learning rate: 0.00581 +2026-04-12 15:43:24.512473: train_loss -0.2881 +2026-04-12 15:43:24.518113: val_loss -0.2482 +2026-04-12 15:43:24.520775: Pseudo dice [0.5744, 0.3662, 0.5374, 0.7513, 0.5592, 0.0936, 0.7813] +2026-04-12 15:43:24.523044: Epoch time: 103.05 s +2026-04-12 15:43:26.098491: +2026-04-12 15:43:26.100410: Epoch 1813 +2026-04-12 15:43:26.102936: Current learning rate: 0.00581 +2026-04-12 15:45:08.784715: train_loss -0.3101 +2026-04-12 15:45:08.795111: val_loss -0.2944 +2026-04-12 15:45:08.798344: Pseudo dice [0.5126, 0.3483, 0.6459, 0.5289, 0.6236, 0.7764, 0.7798] +2026-04-12 15:45:08.801506: Epoch time: 102.69 s +2026-04-12 15:45:10.328758: +2026-04-12 15:45:10.335764: Epoch 1814 +2026-04-12 15:45:10.339928: Current learning rate: 0.00581 +2026-04-12 15:46:54.169499: train_loss -0.3229 +2026-04-12 15:46:54.175759: val_loss -0.2662 +2026-04-12 15:46:54.177843: Pseudo dice [0.68, 0.6703, 0.8325, 0.6076, 0.3173, 0.5925, 0.8159] +2026-04-12 15:46:54.180533: Epoch time: 103.84 s +2026-04-12 15:46:55.767456: +2026-04-12 15:46:55.769162: Epoch 1815 +2026-04-12 15:46:55.771029: Current learning rate: 0.0058 +2026-04-12 15:48:38.369766: train_loss -0.3107 +2026-04-12 15:48:38.375339: val_loss -0.2662 +2026-04-12 15:48:38.377062: Pseudo dice [0.6342, 0.7241, 0.68, 0.099, 0.555, 0.5037, 0.3488] +2026-04-12 15:48:38.379336: Epoch time: 102.61 s +2026-04-12 15:48:39.956036: +2026-04-12 15:48:39.957667: Epoch 1816 +2026-04-12 15:48:39.959526: Current learning rate: 0.0058 +2026-04-12 15:50:22.617371: train_loss -0.3116 +2026-04-12 15:50:22.624575: val_loss -0.2245 +2026-04-12 15:50:22.626564: Pseudo dice [0.6969, 0.7105, 0.6569, 0.6629, 0.3955, 0.1239, 0.652] +2026-04-12 15:50:22.628730: Epoch time: 102.67 s +2026-04-12 15:50:24.240141: +2026-04-12 15:50:24.243345: Epoch 1817 +2026-04-12 15:50:24.246135: Current learning rate: 0.0058 +2026-04-12 15:52:07.061204: train_loss -0.3118 +2026-04-12 15:52:07.067827: val_loss -0.1777 +2026-04-12 15:52:07.069972: Pseudo dice [0.637, 0.5625, 0.6558, 0.7978, 0.5065, 0.1627, 0.7028] +2026-04-12 15:52:07.073462: Epoch time: 102.82 s +2026-04-12 15:52:08.683493: +2026-04-12 15:52:08.687986: Epoch 1818 +2026-04-12 15:52:08.692446: Current learning rate: 0.0058 +2026-04-12 15:53:52.207663: train_loss -0.314 +2026-04-12 15:53:52.212409: val_loss -0.2299 +2026-04-12 15:53:52.214473: Pseudo dice [0.6585, 0.4098, 0.4699, 0.7767, 0.4012, 0.2474, 0.6955] +2026-04-12 15:53:52.216436: Epoch time: 103.53 s +2026-04-12 15:53:53.897020: +2026-04-12 15:53:53.898619: Epoch 1819 +2026-04-12 15:53:53.901331: Current learning rate: 0.00579 +2026-04-12 15:55:36.665920: train_loss -0.3028 +2026-04-12 15:55:36.673467: val_loss -0.2608 +2026-04-12 15:55:36.675259: Pseudo dice [0.7265, 0.7138, 0.6627, 0.5785, 0.3619, 0.1262, 0.7497] +2026-04-12 15:55:36.678812: Epoch time: 102.77 s +2026-04-12 15:55:38.324196: +2026-04-12 15:55:38.326511: Epoch 1820 +2026-04-12 15:55:38.328500: Current learning rate: 0.00579 +2026-04-12 15:57:23.154579: train_loss -0.3114 +2026-04-12 15:57:23.161263: val_loss -0.162 +2026-04-12 15:57:23.163328: Pseudo dice [0.6456, 0.3883, 0.4198, 0.6142, 0.3439, 0.0515, 0.7613] +2026-04-12 15:57:23.166105: Epoch time: 104.83 s +2026-04-12 15:57:24.704477: +2026-04-12 15:57:24.706378: Epoch 1821 +2026-04-12 15:57:24.708324: Current learning rate: 0.00579 +2026-04-12 15:59:08.000187: train_loss -0.2866 +2026-04-12 15:59:08.008298: val_loss -0.2203 +2026-04-12 15:59:08.010113: Pseudo dice [0.6878, 0.554, 0.5295, 0.3258, 0.1951, 0.3696, 0.56] +2026-04-12 15:59:08.013648: Epoch time: 103.3 s +2026-04-12 15:59:09.600127: +2026-04-12 15:59:09.602748: Epoch 1822 +2026-04-12 15:59:09.605316: Current learning rate: 0.00579 +2026-04-12 16:00:52.989710: train_loss -0.3054 +2026-04-12 16:00:52.999403: val_loss -0.2451 +2026-04-12 16:00:53.002505: Pseudo dice [0.4783, 0.2923, 0.7819, 0.7263, 0.1631, 0.5287, 0.5384] +2026-04-12 16:00:53.007397: Epoch time: 103.39 s +2026-04-12 16:00:54.534994: +2026-04-12 16:00:54.537918: Epoch 1823 +2026-04-12 16:00:54.540143: Current learning rate: 0.00578 +2026-04-12 16:02:37.852391: train_loss -0.3044 +2026-04-12 16:02:37.863474: val_loss -0.2343 +2026-04-12 16:02:37.865638: Pseudo dice [0.5785, 0.6482, 0.487, 0.4617, 0.3985, 0.0663, 0.6527] +2026-04-12 16:02:37.867898: Epoch time: 103.32 s +2026-04-12 16:02:39.400348: +2026-04-12 16:02:39.402363: Epoch 1824 +2026-04-12 16:02:39.404899: Current learning rate: 0.00578 +2026-04-12 16:04:22.054484: train_loss -0.307 +2026-04-12 16:04:22.066989: val_loss -0.2322 +2026-04-12 16:04:22.071264: Pseudo dice [0.2898, 0.7146, 0.6366, 0.6571, 0.4681, 0.1102, 0.797] +2026-04-12 16:04:22.075047: Epoch time: 102.66 s +2026-04-12 16:04:23.620075: +2026-04-12 16:04:23.623249: Epoch 1825 +2026-04-12 16:04:23.626116: Current learning rate: 0.00578 +2026-04-12 16:06:06.441468: train_loss -0.3008 +2026-04-12 16:06:06.447370: val_loss -0.2403 +2026-04-12 16:06:06.449971: Pseudo dice [0.4811, 0.3149, 0.3747, 0.7427, 0.4595, 0.2622, 0.7879] +2026-04-12 16:06:06.453257: Epoch time: 102.83 s +2026-04-12 16:06:07.965068: +2026-04-12 16:06:07.966937: Epoch 1826 +2026-04-12 16:06:07.969449: Current learning rate: 0.00578 +2026-04-12 16:07:51.372415: train_loss -0.3095 +2026-04-12 16:07:51.378003: val_loss -0.3028 +2026-04-12 16:07:51.381025: Pseudo dice [0.4587, 0.8192, 0.7009, 0.7939, 0.5175, 0.7881, 0.8536] +2026-04-12 16:07:51.383737: Epoch time: 103.41 s +2026-04-12 16:07:52.917077: +2026-04-12 16:07:52.919725: Epoch 1827 +2026-04-12 16:07:52.923007: Current learning rate: 0.00577 +2026-04-12 16:09:36.125352: train_loss -0.3133 +2026-04-12 16:09:36.132282: val_loss -0.2761 +2026-04-12 16:09:36.134483: Pseudo dice [0.7006, 0.6394, 0.669, 0.7265, 0.6159, 0.2669, 0.8487] +2026-04-12 16:09:36.136995: Epoch time: 103.21 s +2026-04-12 16:09:37.677238: +2026-04-12 16:09:37.679034: Epoch 1828 +2026-04-12 16:09:37.681612: Current learning rate: 0.00577 +2026-04-12 16:11:20.308309: train_loss -0.3159 +2026-04-12 16:11:20.315601: val_loss -0.2943 +2026-04-12 16:11:20.318321: Pseudo dice [0.7521, 0.4036, 0.701, 0.8764, 0.7002, 0.7503, 0.8223] +2026-04-12 16:11:20.320682: Epoch time: 102.63 s +2026-04-12 16:11:21.875170: +2026-04-12 16:11:21.877235: Epoch 1829 +2026-04-12 16:11:21.879102: Current learning rate: 0.00577 +2026-04-12 16:13:05.503134: train_loss -0.3139 +2026-04-12 16:13:05.509560: val_loss -0.3014 +2026-04-12 16:13:05.511358: Pseudo dice [0.5929, 0.6538, 0.7302, 0.6384, 0.5479, 0.7485, 0.808] +2026-04-12 16:13:05.513704: Epoch time: 103.63 s +2026-04-12 16:13:07.088496: +2026-04-12 16:13:07.090418: Epoch 1830 +2026-04-12 16:13:07.092078: Current learning rate: 0.00577 +2026-04-12 16:14:50.352513: train_loss -0.3352 +2026-04-12 16:14:50.358105: val_loss -0.288 +2026-04-12 16:14:50.360846: Pseudo dice [0.3768, 0.6233, 0.5775, 0.5748, 0.6299, 0.4462, 0.7585] +2026-04-12 16:14:50.363368: Epoch time: 103.27 s +2026-04-12 16:14:51.878407: +2026-04-12 16:14:51.880285: Epoch 1831 +2026-04-12 16:14:51.881834: Current learning rate: 0.00576 +2026-04-12 16:16:36.088171: train_loss -0.3306 +2026-04-12 16:16:36.093200: val_loss -0.2754 +2026-04-12 16:16:36.095289: Pseudo dice [0.7419, 0.4166, 0.6562, 0.5621, 0.5149, 0.4722, 0.7159] +2026-04-12 16:16:36.097796: Epoch time: 104.21 s +2026-04-12 16:16:37.724463: +2026-04-12 16:16:37.726399: Epoch 1832 +2026-04-12 16:16:37.727955: Current learning rate: 0.00576 +2026-04-12 16:18:20.911686: train_loss -0.3171 +2026-04-12 16:18:20.918911: val_loss -0.2259 +2026-04-12 16:18:20.921111: Pseudo dice [0.4568, 0.364, 0.4644, 0.6236, 0.4772, 0.3231, 0.4661] +2026-04-12 16:18:20.924221: Epoch time: 103.19 s +2026-04-12 16:18:22.474173: +2026-04-12 16:18:22.477102: Epoch 1833 +2026-04-12 16:18:22.479294: Current learning rate: 0.00576 +2026-04-12 16:20:05.519113: train_loss -0.325 +2026-04-12 16:20:05.525506: val_loss -0.3014 +2026-04-12 16:20:05.527835: Pseudo dice [0.326, 0.6211, 0.7604, 0.6435, 0.6685, 0.745, 0.7699] +2026-04-12 16:20:05.530799: Epoch time: 103.05 s +2026-04-12 16:20:07.088917: +2026-04-12 16:20:07.092370: Epoch 1834 +2026-04-12 16:20:07.094900: Current learning rate: 0.00576 +2026-04-12 16:21:50.165421: train_loss -0.2945 +2026-04-12 16:21:50.173308: val_loss -0.2471 +2026-04-12 16:21:50.175400: Pseudo dice [0.4308, 0.3756, 0.5207, 0.7844, 0.2764, 0.3131, 0.2354] +2026-04-12 16:21:50.178307: Epoch time: 103.08 s +2026-04-12 16:21:51.760033: +2026-04-12 16:21:51.761974: Epoch 1835 +2026-04-12 16:21:51.763846: Current learning rate: 0.00576 +2026-04-12 16:23:35.216243: train_loss -0.3064 +2026-04-12 16:23:35.221390: val_loss -0.2682 +2026-04-12 16:23:35.223439: Pseudo dice [0.1621, 0.5503, 0.5356, 0.779, 0.507, 0.3235, 0.7258] +2026-04-12 16:23:35.225811: Epoch time: 103.46 s +2026-04-12 16:23:36.752374: +2026-04-12 16:23:36.754112: Epoch 1836 +2026-04-12 16:23:36.756624: Current learning rate: 0.00575 +2026-04-12 16:25:19.517915: train_loss -0.3169 +2026-04-12 16:25:19.523148: val_loss -0.2813 +2026-04-12 16:25:19.525086: Pseudo dice [0.749, 0.6041, 0.6539, 0.7246, 0.576, 0.2457, 0.8528] +2026-04-12 16:25:19.527295: Epoch time: 102.77 s +2026-04-12 16:25:21.064003: +2026-04-12 16:25:21.066550: Epoch 1837 +2026-04-12 16:25:21.068145: Current learning rate: 0.00575 +2026-04-12 16:27:03.821836: train_loss -0.3027 +2026-04-12 16:27:03.829360: val_loss -0.2559 +2026-04-12 16:27:03.832019: Pseudo dice [0.4847, 0.7139, 0.6307, 0.5577, 0.1761, 0.3991, 0.5962] +2026-04-12 16:27:03.838955: Epoch time: 102.76 s +2026-04-12 16:27:05.407539: +2026-04-12 16:27:05.409725: Epoch 1838 +2026-04-12 16:27:05.411492: Current learning rate: 0.00575 +2026-04-12 16:28:48.674168: train_loss -0.2964 +2026-04-12 16:28:48.679746: val_loss -0.2186 +2026-04-12 16:28:48.682060: Pseudo dice [0.3999, 0.4308, 0.5129, 0.639, 0.6131, 0.0713, 0.6875] +2026-04-12 16:28:48.684415: Epoch time: 103.27 s +2026-04-12 16:28:50.189345: +2026-04-12 16:28:50.191193: Epoch 1839 +2026-04-12 16:28:50.193053: Current learning rate: 0.00575 +2026-04-12 16:30:33.012170: train_loss -0.3192 +2026-04-12 16:30:33.019198: val_loss -0.2665 +2026-04-12 16:30:33.022728: Pseudo dice [0.8438, 0.7695, 0.6552, 0.7743, 0.5779, 0.7301, 0.1997] +2026-04-12 16:30:33.027216: Epoch time: 102.83 s +2026-04-12 16:30:34.568020: +2026-04-12 16:30:34.569927: Epoch 1840 +2026-04-12 16:30:34.571928: Current learning rate: 0.00574 +2026-04-12 16:32:18.502906: train_loss -0.3198 +2026-04-12 16:32:18.508958: val_loss -0.2608 +2026-04-12 16:32:18.511199: Pseudo dice [0.5909, 0.5665, 0.7594, 0.628, 0.6792, 0.0367, 0.8832] +2026-04-12 16:32:18.513912: Epoch time: 103.94 s +2026-04-12 16:32:20.022576: +2026-04-12 16:32:20.025343: Epoch 1841 +2026-04-12 16:32:20.027406: Current learning rate: 0.00574 +2026-04-12 16:34:02.698570: train_loss -0.3124 +2026-04-12 16:34:02.703945: val_loss -0.1791 +2026-04-12 16:34:02.705544: Pseudo dice [0.757, 0.6304, 0.7408, 0.7629, 0.394, 0.0406, 0.8305] +2026-04-12 16:34:02.708100: Epoch time: 102.68 s +2026-04-12 16:34:04.259025: +2026-04-12 16:34:04.261577: Epoch 1842 +2026-04-12 16:34:04.263658: Current learning rate: 0.00574 +2026-04-12 16:35:47.656277: train_loss -0.3043 +2026-04-12 16:35:47.663960: val_loss -0.2288 +2026-04-12 16:35:47.666183: Pseudo dice [0.4722, 0.7016, 0.5927, 0.3746, 0.4579, 0.1767, 0.8425] +2026-04-12 16:35:47.669299: Epoch time: 103.4 s +2026-04-12 16:35:49.231031: +2026-04-12 16:35:49.233590: Epoch 1843 +2026-04-12 16:35:49.236619: Current learning rate: 0.00574 +2026-04-12 16:37:32.169520: train_loss -0.3264 +2026-04-12 16:37:32.174903: val_loss -0.3194 +2026-04-12 16:37:32.177106: Pseudo dice [0.6764, 0.8337, 0.8221, 0.4293, 0.5248, 0.7419, 0.8701] +2026-04-12 16:37:32.180094: Epoch time: 102.94 s +2026-04-12 16:37:33.668951: +2026-04-12 16:37:33.670992: Epoch 1844 +2026-04-12 16:37:33.672523: Current learning rate: 0.00573 +2026-04-12 16:39:17.084463: train_loss -0.3175 +2026-04-12 16:39:17.090441: val_loss -0.2951 +2026-04-12 16:39:17.092372: Pseudo dice [0.4494, 0.754, 0.6103, 0.7452, 0.448, 0.6712, 0.8604] +2026-04-12 16:39:17.094896: Epoch time: 103.42 s +2026-04-12 16:39:18.628509: +2026-04-12 16:39:18.630136: Epoch 1845 +2026-04-12 16:39:18.631700: Current learning rate: 0.00573 +2026-04-12 16:41:01.341162: train_loss -0.3274 +2026-04-12 16:41:01.346706: val_loss -0.2143 +2026-04-12 16:41:01.348721: Pseudo dice [0.29, 0.7723, 0.6172, 0.3464, 0.5486, 0.2422, 0.8576] +2026-04-12 16:41:01.351371: Epoch time: 102.72 s +2026-04-12 16:41:02.975410: +2026-04-12 16:41:02.977572: Epoch 1846 +2026-04-12 16:41:02.979276: Current learning rate: 0.00573 +2026-04-12 16:42:46.422794: train_loss -0.3279 +2026-04-12 16:42:46.429916: val_loss -0.2534 +2026-04-12 16:42:46.432173: Pseudo dice [0.5324, 0.6248, 0.6765, 0.6065, 0.3649, 0.3444, 0.6201] +2026-04-12 16:42:46.435690: Epoch time: 103.45 s +2026-04-12 16:42:47.976156: +2026-04-12 16:42:47.980526: Epoch 1847 +2026-04-12 16:42:47.982483: Current learning rate: 0.00573 +2026-04-12 16:44:32.058655: train_loss -0.3228 +2026-04-12 16:44:32.063514: val_loss -0.2953 +2026-04-12 16:44:32.065556: Pseudo dice [0.6492, 0.6045, 0.742, 0.8806, 0.4768, 0.6395, 0.4819] +2026-04-12 16:44:32.067533: Epoch time: 104.09 s +2026-04-12 16:44:33.638993: +2026-04-12 16:44:33.640728: Epoch 1848 +2026-04-12 16:44:33.642452: Current learning rate: 0.00572 +2026-04-12 16:46:17.339767: train_loss -0.3312 +2026-04-12 16:46:17.344738: val_loss -0.2284 +2026-04-12 16:46:17.346579: Pseudo dice [0.5212, 0.8193, 0.6016, 0.737, 0.6063, 0.2764, 0.8367] +2026-04-12 16:46:17.349225: Epoch time: 103.7 s +2026-04-12 16:46:18.876171: +2026-04-12 16:46:18.877797: Epoch 1849 +2026-04-12 16:46:18.879455: Current learning rate: 0.00572 +2026-04-12 16:48:02.235607: train_loss -0.321 +2026-04-12 16:48:02.244485: val_loss -0.2841 +2026-04-12 16:48:02.247465: Pseudo dice [0.5464, 0.7328, 0.8611, 0.6936, 0.2692, 0.7492, 0.4649] +2026-04-12 16:48:02.251357: Epoch time: 103.36 s +2026-04-12 16:48:04.274451: Yayy! New best EMA pseudo Dice: 0.5866 +2026-04-12 16:48:07.655078: +2026-04-12 16:48:07.656757: Epoch 1850 +2026-04-12 16:48:07.658801: Current learning rate: 0.00572 +2026-04-12 16:49:51.675474: train_loss -0.3094 +2026-04-12 16:49:51.681059: val_loss -0.2196 +2026-04-12 16:49:51.683106: Pseudo dice [0.833, 0.5903, 0.6224, 0.2456, 0.5337, 0.0316, 0.2194] +2026-04-12 16:49:51.685320: Epoch time: 104.02 s +2026-04-12 16:49:53.232489: +2026-04-12 16:49:53.234678: Epoch 1851 +2026-04-12 16:49:53.237675: Current learning rate: 0.00572 +2026-04-12 16:51:37.437321: train_loss -0.2933 +2026-04-12 16:51:37.445173: val_loss -0.1993 +2026-04-12 16:51:37.447489: Pseudo dice [0.4808, 0.2974, 0.7104, 0.3233, 0.4469, 0.0736, 0.8043] +2026-04-12 16:51:37.450828: Epoch time: 104.21 s +2026-04-12 16:51:39.000734: +2026-04-12 16:51:39.002690: Epoch 1852 +2026-04-12 16:51:39.004391: Current learning rate: 0.00571 +2026-04-12 16:53:22.162695: train_loss -0.3141 +2026-04-12 16:53:22.167408: val_loss -0.2771 +2026-04-12 16:53:22.169263: Pseudo dice [0.5782, 0.6554, 0.705, 0.5728, 0.4744, 0.5907, 0.6031] +2026-04-12 16:53:22.171726: Epoch time: 103.17 s +2026-04-12 16:53:23.800976: +2026-04-12 16:53:23.803104: Epoch 1853 +2026-04-12 16:53:23.805382: Current learning rate: 0.00571 +2026-04-12 16:55:07.535129: train_loss -0.3025 +2026-04-12 16:55:07.540047: val_loss -0.2411 +2026-04-12 16:55:07.543003: Pseudo dice [0.5982, 0.5303, 0.5695, 0.8462, 0.5532, 0.0328, 0.5632] +2026-04-12 16:55:07.545265: Epoch time: 103.74 s +2026-04-12 16:55:09.116242: +2026-04-12 16:55:09.118713: Epoch 1854 +2026-04-12 16:55:09.120723: Current learning rate: 0.00571 +2026-04-12 16:56:52.364300: train_loss -0.3066 +2026-04-12 16:56:52.372399: val_loss -0.2849 +2026-04-12 16:56:52.374700: Pseudo dice [0.618, 0.6217, 0.6155, 0.6165, 0.5094, 0.5966, 0.9275] +2026-04-12 16:56:52.377702: Epoch time: 103.25 s +2026-04-12 16:56:53.990665: +2026-04-12 16:56:53.992916: Epoch 1855 +2026-04-12 16:56:53.995001: Current learning rate: 0.00571 +2026-04-12 16:58:37.918115: train_loss -0.2975 +2026-04-12 16:58:37.927895: val_loss -0.2878 +2026-04-12 16:58:37.930629: Pseudo dice [0.1336, 0.4585, 0.7252, 0.8702, 0.6201, 0.778, 0.817] +2026-04-12 16:58:37.934545: Epoch time: 103.93 s +2026-04-12 16:58:39.561524: +2026-04-12 16:58:39.564950: Epoch 1856 +2026-04-12 16:58:39.568105: Current learning rate: 0.0057 +2026-04-12 17:00:24.314796: train_loss -0.3096 +2026-04-12 17:00:24.323428: val_loss -0.2714 +2026-04-12 17:00:24.325518: Pseudo dice [0.4443, 0.821, 0.7508, 0.6008, 0.609, 0.2142, 0.8328] +2026-04-12 17:00:24.328533: Epoch time: 104.76 s +2026-04-12 17:00:25.970909: +2026-04-12 17:00:25.973298: Epoch 1857 +2026-04-12 17:00:25.975477: Current learning rate: 0.0057 +2026-04-12 17:02:09.316967: train_loss -0.305 +2026-04-12 17:02:09.324747: val_loss -0.2429 +2026-04-12 17:02:09.327878: Pseudo dice [0.4872, 0.7732, 0.5871, 0.6305, 0.5223, 0.3705, 0.7356] +2026-04-12 17:02:09.330423: Epoch time: 103.35 s +2026-04-12 17:02:10.930544: +2026-04-12 17:02:10.934430: Epoch 1858 +2026-04-12 17:02:10.937579: Current learning rate: 0.0057 +2026-04-12 17:03:54.238234: train_loss -0.304 +2026-04-12 17:03:54.245382: val_loss -0.2214 +2026-04-12 17:03:54.248551: Pseudo dice [0.6063, 0.7596, 0.289, 0.4215, 0.2947, 0.1115, 0.6126] +2026-04-12 17:03:54.251535: Epoch time: 103.31 s +2026-04-12 17:03:55.769824: +2026-04-12 17:03:55.771625: Epoch 1859 +2026-04-12 17:03:55.774031: Current learning rate: 0.0057 +2026-04-12 17:05:41.842083: train_loss -0.2894 +2026-04-12 17:05:41.853464: val_loss -0.1913 +2026-04-12 17:05:41.857532: Pseudo dice [0.5335, 0.6194, 0.446, 0.7889, 0.4321, 0.0292, 0.6051] +2026-04-12 17:05:41.861842: Epoch time: 106.08 s +2026-04-12 17:05:43.417394: +2026-04-12 17:05:43.419894: Epoch 1860 +2026-04-12 17:05:43.422228: Current learning rate: 0.0057 +2026-04-12 17:07:26.584241: train_loss -0.2943 +2026-04-12 17:07:26.589806: val_loss -0.267 +2026-04-12 17:07:26.591974: Pseudo dice [0.6943, 0.6987, 0.5668, 0.8956, 0.467, 0.648, 0.4585] +2026-04-12 17:07:26.597196: Epoch time: 103.17 s +2026-04-12 17:07:28.142623: +2026-04-12 17:07:28.146380: Epoch 1861 +2026-04-12 17:07:28.150270: Current learning rate: 0.00569 +2026-04-12 17:09:13.298042: train_loss -0.3042 +2026-04-12 17:09:13.314350: val_loss -0.2381 +2026-04-12 17:09:13.316930: Pseudo dice [0.7694, 0.7565, 0.6152, 0.172, 0.4754, 0.1455, 0.7805] +2026-04-12 17:09:13.330313: Epoch time: 105.16 s +2026-04-12 17:09:14.926499: +2026-04-12 17:09:14.930157: Epoch 1862 +2026-04-12 17:09:14.934229: Current learning rate: 0.00569 +2026-04-12 17:10:57.912867: train_loss -0.3121 +2026-04-12 17:10:57.924174: val_loss -0.2436 +2026-04-12 17:10:57.926727: Pseudo dice [0.8031, 0.7104, 0.4159, 0.4207, 0.618, 0.1344, 0.8259] +2026-04-12 17:10:57.930176: Epoch time: 102.99 s +2026-04-12 17:10:59.453294: +2026-04-12 17:10:59.455299: Epoch 1863 +2026-04-12 17:10:59.457627: Current learning rate: 0.00569 +2026-04-12 17:12:42.709899: train_loss -0.3137 +2026-04-12 17:12:42.716840: val_loss -0.2576 +2026-04-12 17:12:42.719036: Pseudo dice [0.5436, 0.4566, 0.5622, 0.5115, 0.3165, 0.5744, 0.767] +2026-04-12 17:12:42.721550: Epoch time: 103.26 s +2026-04-12 17:12:44.350083: +2026-04-12 17:12:44.352360: Epoch 1864 +2026-04-12 17:12:44.354723: Current learning rate: 0.00569 +2026-04-12 17:14:30.407699: train_loss -0.3231 +2026-04-12 17:14:30.414302: val_loss -0.2827 +2026-04-12 17:14:30.416769: Pseudo dice [0.7633, 0.5349, 0.6215, 0.8396, 0.571, 0.622, 0.8834] +2026-04-12 17:14:30.418877: Epoch time: 106.06 s +2026-04-12 17:14:31.986781: +2026-04-12 17:14:31.988963: Epoch 1865 +2026-04-12 17:14:31.990878: Current learning rate: 0.00568 +2026-04-12 17:16:14.920632: train_loss -0.3113 +2026-04-12 17:16:14.927192: val_loss -0.2427 +2026-04-12 17:16:14.928955: Pseudo dice [0.6671, 0.6829, 0.6362, 0.8154, 0.3874, 0.1148, 0.7887] +2026-04-12 17:16:14.932077: Epoch time: 102.94 s +2026-04-12 17:16:16.496701: +2026-04-12 17:16:16.499130: Epoch 1866 +2026-04-12 17:16:16.501659: Current learning rate: 0.00568 +2026-04-12 17:17:59.242833: train_loss -0.3183 +2026-04-12 17:17:59.249995: val_loss -0.2734 +2026-04-12 17:17:59.252101: Pseudo dice [0.4016, 0.4394, 0.6911, 0.7139, 0.5004, 0.7661, 0.7751] +2026-04-12 17:17:59.254514: Epoch time: 102.75 s +2026-04-12 17:18:00.806067: +2026-04-12 17:18:00.807793: Epoch 1867 +2026-04-12 17:18:00.809945: Current learning rate: 0.00568 +2026-04-12 17:19:46.190493: train_loss -0.3013 +2026-04-12 17:19:46.197411: val_loss -0.2517 +2026-04-12 17:19:46.199545: Pseudo dice [0.3267, 0.7254, 0.737, 0.6305, 0.3538, 0.4312, 0.5746] +2026-04-12 17:19:46.202043: Epoch time: 105.39 s +2026-04-12 17:19:47.838402: +2026-04-12 17:19:47.840423: Epoch 1868 +2026-04-12 17:19:47.842277: Current learning rate: 0.00568 +2026-04-12 17:21:31.598103: train_loss -0.3182 +2026-04-12 17:21:31.607390: val_loss -0.2603 +2026-04-12 17:21:31.609996: Pseudo dice [0.5479, 0.5349, 0.5607, 0.6295, 0.5883, 0.0934, 0.8427] +2026-04-12 17:21:31.612431: Epoch time: 103.76 s +2026-04-12 17:21:33.218244: +2026-04-12 17:21:33.221220: Epoch 1869 +2026-04-12 17:21:33.223638: Current learning rate: 0.00567 +2026-04-12 17:23:16.489524: train_loss -0.3134 +2026-04-12 17:23:16.505014: val_loss -0.2847 +2026-04-12 17:23:16.510124: Pseudo dice [0.3951, 0.6965, 0.6515, 0.6604, 0.6143, 0.33, 0.711] +2026-04-12 17:23:16.516640: Epoch time: 103.28 s +2026-04-12 17:23:18.118299: +2026-04-12 17:23:18.120534: Epoch 1870 +2026-04-12 17:23:18.123840: Current learning rate: 0.00567 +2026-04-12 17:25:02.454250: train_loss -0.3227 +2026-04-12 17:25:02.461443: val_loss -0.273 +2026-04-12 17:25:02.464168: Pseudo dice [0.2519, 0.6699, 0.6498, 0.2747, 0.4801, 0.4209, 0.5685] +2026-04-12 17:25:02.466867: Epoch time: 104.34 s +2026-04-12 17:25:03.979607: +2026-04-12 17:25:03.981834: Epoch 1871 +2026-04-12 17:25:03.984041: Current learning rate: 0.00567 +2026-04-12 17:26:47.669589: train_loss -0.3224 +2026-04-12 17:26:47.681530: val_loss -0.1763 +2026-04-12 17:26:47.692710: Pseudo dice [0.8047, 0.6114, 0.3271, 0.7959, 0.3623, 0.018, 0.473] +2026-04-12 17:26:47.698632: Epoch time: 103.69 s +2026-04-12 17:26:49.270720: +2026-04-12 17:26:49.272908: Epoch 1872 +2026-04-12 17:26:49.274880: Current learning rate: 0.00567 +2026-04-12 17:28:32.274792: train_loss -0.3276 +2026-04-12 17:28:32.285083: val_loss -0.2948 +2026-04-12 17:28:32.289937: Pseudo dice [0.4437, 0.7064, 0.6552, 0.6133, 0.612, 0.5122, 0.9007] +2026-04-12 17:28:32.295921: Epoch time: 103.01 s +2026-04-12 17:28:33.843292: +2026-04-12 17:28:33.846403: Epoch 1873 +2026-04-12 17:28:33.850277: Current learning rate: 0.00566 +2026-04-12 17:30:17.872391: train_loss -0.3259 +2026-04-12 17:30:17.877950: val_loss -0.1962 +2026-04-12 17:30:17.879994: Pseudo dice [0.3813, 0.649, 0.7621, 0.8717, 0.4634, 0.1003, 0.7631] +2026-04-12 17:30:17.882498: Epoch time: 104.03 s +2026-04-12 17:30:19.615321: +2026-04-12 17:30:19.617363: Epoch 1874 +2026-04-12 17:30:19.619655: Current learning rate: 0.00566 +2026-04-12 17:32:02.422994: train_loss -0.3199 +2026-04-12 17:32:02.432524: val_loss -0.2919 +2026-04-12 17:32:02.435048: Pseudo dice [0.6249, 0.467, 0.7131, 0.835, 0.4175, 0.7422, 0.7239] +2026-04-12 17:32:02.438518: Epoch time: 102.81 s +2026-04-12 17:32:03.984943: +2026-04-12 17:32:03.986770: Epoch 1875 +2026-04-12 17:32:03.988938: Current learning rate: 0.00566 +2026-04-12 17:33:46.753484: train_loss -0.3182 +2026-04-12 17:33:46.761020: val_loss -0.27 +2026-04-12 17:33:46.764237: Pseudo dice [0.4127, 0.6636, 0.5557, 0.6159, 0.4611, 0.7683, 0.6701] +2026-04-12 17:33:46.770393: Epoch time: 102.77 s +2026-04-12 17:33:48.339113: +2026-04-12 17:33:48.342007: Epoch 1876 +2026-04-12 17:33:48.344260: Current learning rate: 0.00566 +2026-04-12 17:35:33.798180: train_loss -0.3118 +2026-04-12 17:35:33.805422: val_loss -0.2392 +2026-04-12 17:35:33.808118: Pseudo dice [0.4443, 0.1627, 0.6471, 0.7027, 0.4313, 0.0847, 0.7156] +2026-04-12 17:35:33.811931: Epoch time: 105.46 s +2026-04-12 17:35:35.363085: +2026-04-12 17:35:35.365660: Epoch 1877 +2026-04-12 17:35:35.367907: Current learning rate: 0.00565 +2026-04-12 17:37:18.621892: train_loss -0.3044 +2026-04-12 17:37:18.627733: val_loss -0.1989 +2026-04-12 17:37:18.630174: Pseudo dice [0.2978, 0.3947, 0.567, 0.6988, 0.5393, 0.0801, 0.8705] +2026-04-12 17:37:18.632852: Epoch time: 103.26 s +2026-04-12 17:37:20.204070: +2026-04-12 17:37:20.205840: Epoch 1878 +2026-04-12 17:37:20.208202: Current learning rate: 0.00565 +2026-04-12 17:39:02.692303: train_loss -0.303 +2026-04-12 17:39:02.698148: val_loss -0.1377 +2026-04-12 17:39:02.702546: Pseudo dice [0.3679, 0.5947, 0.644, 0.5737, 0.5031, 0.0735, 0.7733] +2026-04-12 17:39:02.705327: Epoch time: 102.49 s +2026-04-12 17:39:05.413569: +2026-04-12 17:39:05.415580: Epoch 1879 +2026-04-12 17:39:05.417857: Current learning rate: 0.00565 +2026-04-12 17:40:48.208195: train_loss -0.3038 +2026-04-12 17:40:48.216993: val_loss -0.1855 +2026-04-12 17:40:48.219238: Pseudo dice [0.3769, 0.5593, 0.6199, 0.78, 0.4651, 0.2221, 0.7724] +2026-04-12 17:40:48.221869: Epoch time: 102.8 s +2026-04-12 17:40:49.761667: +2026-04-12 17:40:49.763715: Epoch 1880 +2026-04-12 17:40:49.766024: Current learning rate: 0.00565 +2026-04-12 17:42:32.601309: train_loss -0.3102 +2026-04-12 17:42:32.607462: val_loss -0.2625 +2026-04-12 17:42:32.609749: Pseudo dice [0.4005, 0.5545, 0.6591, 0.6435, 0.5012, 0.4971, 0.423] +2026-04-12 17:42:32.614044: Epoch time: 102.84 s +2026-04-12 17:42:34.184488: +2026-04-12 17:42:34.186360: Epoch 1881 +2026-04-12 17:42:34.188371: Current learning rate: 0.00564 +2026-04-12 17:44:19.002701: train_loss -0.3021 +2026-04-12 17:44:19.010500: val_loss -0.2804 +2026-04-12 17:44:19.012977: Pseudo dice [0.5057, 0.305, 0.5355, 0.8717, 0.5617, 0.1563, 0.8023] +2026-04-12 17:44:19.017321: Epoch time: 104.82 s +2026-04-12 17:44:20.556483: +2026-04-12 17:44:20.559161: Epoch 1882 +2026-04-12 17:44:20.561778: Current learning rate: 0.00564 +2026-04-12 17:46:03.612729: train_loss -0.3191 +2026-04-12 17:46:03.620457: val_loss -0.2199 +2026-04-12 17:46:03.622380: Pseudo dice [0.5708, 0.5247, 0.4408, 0.8183, 0.4395, 0.2933, 0.8239] +2026-04-12 17:46:03.624916: Epoch time: 103.06 s +2026-04-12 17:46:05.227860: +2026-04-12 17:46:05.229668: Epoch 1883 +2026-04-12 17:46:05.232006: Current learning rate: 0.00564 +2026-04-12 17:47:48.721190: train_loss -0.3314 +2026-04-12 17:47:48.728176: val_loss -0.2592 +2026-04-12 17:47:48.729974: Pseudo dice [0.759, 0.4148, 0.5786, 0.4526, 0.6641, 0.0914, 0.8861] +2026-04-12 17:47:48.733226: Epoch time: 103.5 s +2026-04-12 17:47:50.368376: +2026-04-12 17:47:50.370169: Epoch 1884 +2026-04-12 17:47:50.372170: Current learning rate: 0.00564 +2026-04-12 17:49:34.243536: train_loss -0.3171 +2026-04-12 17:49:34.253307: val_loss -0.113 +2026-04-12 17:49:34.257072: Pseudo dice [0.8045, 0.8319, 0.5304, 0.4382, 0.2772, 0.0773, 0.7308] +2026-04-12 17:49:34.260227: Epoch time: 103.88 s +2026-04-12 17:49:35.778830: +2026-04-12 17:49:35.781016: Epoch 1885 +2026-04-12 17:49:35.783354: Current learning rate: 0.00564 +2026-04-12 17:51:18.545078: train_loss -0.3174 +2026-04-12 17:51:18.552997: val_loss -0.3085 +2026-04-12 17:51:18.556133: Pseudo dice [0.6162, 0.3644, 0.7679, 0.8228, 0.5379, 0.7688, 0.8527] +2026-04-12 17:51:18.559167: Epoch time: 102.77 s +2026-04-12 17:51:20.190528: +2026-04-12 17:51:20.192384: Epoch 1886 +2026-04-12 17:51:20.194694: Current learning rate: 0.00563 +2026-04-12 17:53:03.525687: train_loss -0.3254 +2026-04-12 17:53:03.532969: val_loss -0.1566 +2026-04-12 17:53:03.535346: Pseudo dice [0.4751, 0.5861, 0.5563, 0.4913, 0.428, 0.0903, 0.8235] +2026-04-12 17:53:03.538338: Epoch time: 103.34 s +2026-04-12 17:53:05.101669: +2026-04-12 17:53:05.103665: Epoch 1887 +2026-04-12 17:53:05.105911: Current learning rate: 0.00563 +2026-04-12 17:54:50.804516: train_loss -0.3207 +2026-04-12 17:54:50.810965: val_loss -0.2535 +2026-04-12 17:54:50.813534: Pseudo dice [0.7339, 0.5279, 0.7218, 0.594, 0.45, 0.415, 0.6319] +2026-04-12 17:54:50.816122: Epoch time: 105.71 s +2026-04-12 17:54:52.421731: +2026-04-12 17:54:52.423955: Epoch 1888 +2026-04-12 17:54:52.426204: Current learning rate: 0.00563 +2026-04-12 17:56:35.153675: train_loss -0.3304 +2026-04-12 17:56:35.159402: val_loss -0.2973 +2026-04-12 17:56:35.162321: Pseudo dice [0.3465, 0.6499, 0.6378, 0.1405, 0.6794, 0.6935, 0.8988] +2026-04-12 17:56:35.165210: Epoch time: 102.74 s +2026-04-12 17:56:36.698047: +2026-04-12 17:56:36.702140: Epoch 1889 +2026-04-12 17:56:36.704701: Current learning rate: 0.00563 +2026-04-12 17:58:19.802242: train_loss -0.3356 +2026-04-12 17:58:19.810529: val_loss -0.2706 +2026-04-12 17:58:19.813651: Pseudo dice [0.7501, 0.3593, 0.5806, 0.9185, 0.6329, 0.0945, 0.8411] +2026-04-12 17:58:19.817885: Epoch time: 103.11 s +2026-04-12 17:58:21.379071: +2026-04-12 17:58:21.381288: Epoch 1890 +2026-04-12 17:58:21.383965: Current learning rate: 0.00562 +2026-04-12 18:00:05.894140: train_loss -0.3351 +2026-04-12 18:00:05.900491: val_loss -0.3082 +2026-04-12 18:00:05.902501: Pseudo dice [0.7755, 0.8185, 0.6736, 0.8611, 0.6077, 0.8122, 0.8436] +2026-04-12 18:00:05.904464: Epoch time: 104.52 s +2026-04-12 18:00:07.424130: +2026-04-12 18:00:07.426164: Epoch 1891 +2026-04-12 18:00:07.428118: Current learning rate: 0.00562 +2026-04-12 18:01:51.268154: train_loss -0.3218 +2026-04-12 18:01:51.274071: val_loss -0.2561 +2026-04-12 18:01:51.278016: Pseudo dice [0.7633, 0.2725, 0.5654, 0.515, 0.4807, 0.1018, 0.8771] +2026-04-12 18:01:51.283600: Epoch time: 103.85 s +2026-04-12 18:01:52.832029: +2026-04-12 18:01:52.834078: Epoch 1892 +2026-04-12 18:01:52.836573: Current learning rate: 0.00562 +2026-04-12 18:03:36.424809: train_loss -0.3225 +2026-04-12 18:03:36.433377: val_loss -0.2736 +2026-04-12 18:03:36.436668: Pseudo dice [0.5353, 0.5333, 0.6736, 0.5045, 0.2663, 0.7422, 0.7976] +2026-04-12 18:03:36.440964: Epoch time: 103.6 s +2026-04-12 18:03:37.989030: +2026-04-12 18:03:37.994488: Epoch 1893 +2026-04-12 18:03:37.996980: Current learning rate: 0.00562 +2026-04-12 18:05:22.243780: train_loss -0.3378 +2026-04-12 18:05:22.250410: val_loss -0.2253 +2026-04-12 18:05:22.253090: Pseudo dice [0.7979, 0.2376, 0.7096, 0.3538, 0.3504, 0.0529, 0.838] +2026-04-12 18:05:22.256007: Epoch time: 104.26 s +2026-04-12 18:05:23.795960: +2026-04-12 18:05:23.798143: Epoch 1894 +2026-04-12 18:05:23.800404: Current learning rate: 0.00561 +2026-04-12 18:07:06.803527: train_loss -0.3303 +2026-04-12 18:07:06.809599: val_loss -0.2487 +2026-04-12 18:07:06.811913: Pseudo dice [0.5372, 0.7087, 0.8006, 0.157, 0.5077, 0.2297, 0.7219] +2026-04-12 18:07:06.814598: Epoch time: 103.01 s +2026-04-12 18:07:08.376539: +2026-04-12 18:07:08.378519: Epoch 1895 +2026-04-12 18:07:08.380792: Current learning rate: 0.00561 +2026-04-12 18:08:52.152544: train_loss -0.3117 +2026-04-12 18:08:52.171354: val_loss -0.198 +2026-04-12 18:08:52.178362: Pseudo dice [0.3862, 0.6544, 0.5024, 0.7467, 0.5372, 0.2904, 0.5807] +2026-04-12 18:08:52.185574: Epoch time: 103.78 s +2026-04-12 18:08:53.776291: +2026-04-12 18:08:53.779632: Epoch 1896 +2026-04-12 18:08:53.784352: Current learning rate: 0.00561 +2026-04-12 18:10:36.738810: train_loss -0.312 +2026-04-12 18:10:36.745807: val_loss -0.2247 +2026-04-12 18:10:36.747998: Pseudo dice [0.4818, 0.2366, 0.3578, 0.6915, 0.3286, 0.1962, 0.4811] +2026-04-12 18:10:36.750919: Epoch time: 102.97 s +2026-04-12 18:10:38.317116: +2026-04-12 18:10:38.321117: Epoch 1897 +2026-04-12 18:10:38.324275: Current learning rate: 0.00561 +2026-04-12 18:12:21.422580: train_loss -0.2957 +2026-04-12 18:12:21.429416: val_loss -0.2531 +2026-04-12 18:12:21.431770: Pseudo dice [0.515, 0.5696, 0.7882, 0.8508, 0.4934, 0.1871, 0.7808] +2026-04-12 18:12:21.434961: Epoch time: 103.11 s +2026-04-12 18:12:22.982501: +2026-04-12 18:12:22.985195: Epoch 1898 +2026-04-12 18:12:22.989964: Current learning rate: 0.0056 +2026-04-12 18:14:07.029212: train_loss -0.3174 +2026-04-12 18:14:07.041673: val_loss -0.2816 +2026-04-12 18:14:07.046307: Pseudo dice [0.4611, 0.3501, 0.7771, 0.0307, 0.514, 0.7987, 0.8957] +2026-04-12 18:14:07.052356: Epoch time: 104.05 s +2026-04-12 18:14:09.768870: +2026-04-12 18:14:09.770987: Epoch 1899 +2026-04-12 18:14:09.773491: Current learning rate: 0.0056 +2026-04-12 18:15:54.725432: train_loss -0.2944 +2026-04-12 18:15:54.733337: val_loss -0.2356 +2026-04-12 18:15:54.736102: Pseudo dice [0.4336, 0.5805, 0.5237, 0.6039, 0.467, 0.0866, 0.7475] +2026-04-12 18:15:54.740388: Epoch time: 104.96 s +2026-04-12 18:15:58.381322: +2026-04-12 18:15:58.383652: Epoch 1900 +2026-04-12 18:15:58.385999: Current learning rate: 0.0056 +2026-04-12 18:17:41.499916: train_loss -0.2907 +2026-04-12 18:17:41.505718: val_loss -0.2303 +2026-04-12 18:17:41.508168: Pseudo dice [0.2863, 0.4656, 0.5188, 0.1387, 0.5215, 0.517, 0.8439] +2026-04-12 18:17:41.510580: Epoch time: 103.12 s +2026-04-12 18:17:43.023384: +2026-04-12 18:17:43.025543: Epoch 1901 +2026-04-12 18:17:43.027911: Current learning rate: 0.0056 +2026-04-12 18:19:26.773589: train_loss -0.3307 +2026-04-12 18:19:26.779246: val_loss -0.2595 +2026-04-12 18:19:26.781812: Pseudo dice [0.6641, 0.4638, 0.6055, 0.3613, 0.1341, 0.6668, 0.8091] +2026-04-12 18:19:26.784318: Epoch time: 103.75 s +2026-04-12 18:19:28.314562: +2026-04-12 18:19:28.316927: Epoch 1902 +2026-04-12 18:19:28.318992: Current learning rate: 0.00559 +2026-04-12 18:21:11.849642: train_loss -0.323 +2026-04-12 18:21:11.858091: val_loss -0.2657 +2026-04-12 18:21:11.860364: Pseudo dice [0.6142, 0.6047, 0.6959, 0.8655, 0.4383, 0.7452, 0.3817] +2026-04-12 18:21:11.863883: Epoch time: 103.54 s +2026-04-12 18:21:13.448154: +2026-04-12 18:21:13.449926: Epoch 1903 +2026-04-12 18:21:13.451807: Current learning rate: 0.00559 +2026-04-12 18:22:56.394185: train_loss -0.3286 +2026-04-12 18:22:56.400081: val_loss -0.2986 +2026-04-12 18:22:56.401957: Pseudo dice [0.8028, 0.4336, 0.6611, 0.7299, 0.6508, 0.1675, 0.8416] +2026-04-12 18:22:56.404174: Epoch time: 102.95 s +2026-04-12 18:22:58.026152: +2026-04-12 18:22:58.028486: Epoch 1904 +2026-04-12 18:22:58.030855: Current learning rate: 0.00559 +2026-04-12 18:24:44.248278: train_loss -0.2902 +2026-04-12 18:24:44.256856: val_loss -0.2578 +2026-04-12 18:24:44.259359: Pseudo dice [0.6402, 0.6443, 0.7887, 0.5455, 0.5407, 0.1355, 0.6612] +2026-04-12 18:24:44.262266: Epoch time: 106.23 s +2026-04-12 18:24:45.833799: +2026-04-12 18:24:45.836123: Epoch 1905 +2026-04-12 18:24:45.838930: Current learning rate: 0.00559 +2026-04-12 18:26:28.760437: train_loss -0.3144 +2026-04-12 18:26:28.765940: val_loss -0.2181 +2026-04-12 18:26:28.768195: Pseudo dice [0.5115, 0.725, 0.5211, 0.0067, 0.4601, 0.2375, 0.6893] +2026-04-12 18:26:28.771035: Epoch time: 102.93 s +2026-04-12 18:26:30.322920: +2026-04-12 18:26:30.325457: Epoch 1906 +2026-04-12 18:26:30.327655: Current learning rate: 0.00559 +2026-04-12 18:28:13.361626: train_loss -0.3263 +2026-04-12 18:28:13.367158: val_loss -0.1767 +2026-04-12 18:28:13.370007: Pseudo dice [0.6848, 0.3721, 0.6052, 0.1463, 0.2519, 0.1696, 0.609] +2026-04-12 18:28:13.374442: Epoch time: 103.04 s +2026-04-12 18:28:15.048003: +2026-04-12 18:28:15.049861: Epoch 1907 +2026-04-12 18:28:15.051806: Current learning rate: 0.00558 +2026-04-12 18:29:59.845032: train_loss -0.3252 +2026-04-12 18:29:59.853683: val_loss -0.2658 +2026-04-12 18:29:59.856169: Pseudo dice [0.3752, 0.6196, 0.6043, 0.3085, 0.4348, 0.6301, 0.5177] +2026-04-12 18:29:59.859100: Epoch time: 104.8 s +2026-04-12 18:30:01.420039: +2026-04-12 18:30:01.422374: Epoch 1908 +2026-04-12 18:30:01.424485: Current learning rate: 0.00558 +2026-04-12 18:31:44.508440: train_loss -0.33 +2026-04-12 18:31:44.516108: val_loss -0.2948 +2026-04-12 18:31:44.518514: Pseudo dice [0.1979, 0.7441, 0.6666, 0.6045, 0.4871, 0.7029, 0.8877] +2026-04-12 18:31:44.521147: Epoch time: 103.09 s +2026-04-12 18:31:46.150963: +2026-04-12 18:31:46.153721: Epoch 1909 +2026-04-12 18:31:46.156068: Current learning rate: 0.00558 +2026-04-12 18:33:29.484349: train_loss -0.346 +2026-04-12 18:33:29.491090: val_loss -0.3039 +2026-04-12 18:33:29.493442: Pseudo dice [0.4612, 0.3736, 0.4728, 0.523, 0.5435, 0.5043, 0.8078] +2026-04-12 18:33:29.495792: Epoch time: 103.34 s +2026-04-12 18:33:31.067060: +2026-04-12 18:33:31.069195: Epoch 1910 +2026-04-12 18:33:31.072154: Current learning rate: 0.00558 +2026-04-12 18:35:15.425515: train_loss -0.3533 +2026-04-12 18:35:15.431918: val_loss -0.255 +2026-04-12 18:35:15.434217: Pseudo dice [0.3124, 0.4086, 0.6048, 0.734, 0.6255, 0.0891, 0.7662] +2026-04-12 18:35:15.436641: Epoch time: 104.36 s +2026-04-12 18:35:17.048034: +2026-04-12 18:35:17.050262: Epoch 1911 +2026-04-12 18:35:17.053678: Current learning rate: 0.00557 +2026-04-12 18:36:59.970696: train_loss -0.3451 +2026-04-12 18:36:59.977404: val_loss -0.2744 +2026-04-12 18:36:59.979831: Pseudo dice [0.1607, 0.3794, 0.6578, 0.2774, 0.3979, 0.1503, 0.7541] +2026-04-12 18:36:59.982802: Epoch time: 102.93 s +2026-04-12 18:37:01.504774: +2026-04-12 18:37:01.506642: Epoch 1912 +2026-04-12 18:37:01.508850: Current learning rate: 0.00557 +2026-04-12 18:38:44.223181: train_loss -0.3624 +2026-04-12 18:38:44.228689: val_loss -0.3058 +2026-04-12 18:38:44.230666: Pseudo dice [0.7481, 0.1666, 0.6664, 0.8624, 0.4082, 0.1303, 0.8308] +2026-04-12 18:38:44.233385: Epoch time: 102.72 s +2026-04-12 18:38:45.841406: +2026-04-12 18:38:45.843332: Epoch 1913 +2026-04-12 18:38:45.845403: Current learning rate: 0.00557 +2026-04-12 18:40:29.286105: train_loss -0.3807 +2026-04-12 18:40:29.292932: val_loss -0.2507 +2026-04-12 18:40:29.295168: Pseudo dice [0.654, 0.6355, 0.7766, 0.7947, 0.6088, 0.1323, 0.8741] +2026-04-12 18:40:29.297943: Epoch time: 103.45 s +2026-04-12 18:40:30.835180: +2026-04-12 18:40:30.837276: Epoch 1914 +2026-04-12 18:40:30.839460: Current learning rate: 0.00557 +2026-04-12 18:42:14.201263: train_loss -0.3745 +2026-04-12 18:42:14.207530: val_loss -0.3005 +2026-04-12 18:42:14.209703: Pseudo dice [0.8054, 0.2609, 0.5655, 0.7657, 0.1367, 0.5585, 0.6517] +2026-04-12 18:42:14.213935: Epoch time: 103.37 s +2026-04-12 18:42:15.844002: +2026-04-12 18:42:15.846429: Epoch 1915 +2026-04-12 18:42:15.849414: Current learning rate: 0.00556 +2026-04-12 18:43:58.395016: train_loss -0.3753 +2026-04-12 18:43:58.407476: val_loss -0.3066 +2026-04-12 18:43:58.412732: Pseudo dice [0.7629, 0.2261, 0.6598, 0.1118, 0.5462, 0.121, 0.7239] +2026-04-12 18:43:58.414992: Epoch time: 102.55 s +2026-04-12 18:43:59.972447: +2026-04-12 18:43:59.976319: Epoch 1916 +2026-04-12 18:43:59.980960: Current learning rate: 0.00556 +2026-04-12 18:45:43.151481: train_loss -0.3722 +2026-04-12 18:45:43.159800: val_loss -0.3407 +2026-04-12 18:45:43.163913: Pseudo dice [0.6012, 0.6486, 0.6013, 0.8135, 0.3965, 0.6176, 0.8791] +2026-04-12 18:45:43.166262: Epoch time: 103.18 s +2026-04-12 18:45:44.732590: +2026-04-12 18:45:44.734507: Epoch 1917 +2026-04-12 18:45:44.736662: Current learning rate: 0.00556 +2026-04-12 18:47:28.110439: train_loss -0.3705 +2026-04-12 18:47:28.116832: val_loss -0.2959 +2026-04-12 18:47:28.119267: Pseudo dice [0.7059, 0.143, 0.5637, 0.7972, 0.4897, 0.2391, 0.6113] +2026-04-12 18:47:28.121703: Epoch time: 103.38 s +2026-04-12 18:47:29.809355: +2026-04-12 18:47:29.811497: Epoch 1918 +2026-04-12 18:47:29.813769: Current learning rate: 0.00556 +2026-04-12 18:49:14.078894: train_loss -0.3677 +2026-04-12 18:49:14.087660: val_loss -0.3379 +2026-04-12 18:49:14.089753: Pseudo dice [0.8013, 0.5998, 0.5913, 0.4142, 0.5642, 0.419, 0.9152] +2026-04-12 18:49:14.092970: Epoch time: 104.27 s +2026-04-12 18:49:15.715717: +2026-04-12 18:49:15.720675: Epoch 1919 +2026-04-12 18:49:15.723502: Current learning rate: 0.00555 +2026-04-12 18:50:59.362875: train_loss -0.3791 +2026-04-12 18:50:59.368554: val_loss -0.3266 +2026-04-12 18:50:59.370692: Pseudo dice [0.7392, 0.066, 0.6363, 0.4972, 0.5857, 0.6299, 0.8305] +2026-04-12 18:50:59.373309: Epoch time: 103.65 s +2026-04-12 18:51:00.993063: +2026-04-12 18:51:00.995424: Epoch 1920 +2026-04-12 18:51:00.997961: Current learning rate: 0.00555 +2026-04-12 18:52:43.738033: train_loss -0.3769 +2026-04-12 18:52:43.754634: val_loss -0.2924 +2026-04-12 18:52:43.758471: Pseudo dice [0.3842, 0.2804, 0.6006, 0.5026, 0.4775, 0.0571, 0.713] +2026-04-12 18:52:43.760700: Epoch time: 102.75 s +2026-04-12 18:52:45.387849: +2026-04-12 18:52:45.390006: Epoch 1921 +2026-04-12 18:52:45.392036: Current learning rate: 0.00555 +2026-04-12 18:54:28.979591: train_loss -0.3587 +2026-04-12 18:54:28.989066: val_loss -0.2382 +2026-04-12 18:54:28.993780: Pseudo dice [0.2638, 0.1545, 0.6401, 0.113, 0.3291, 0.1607, 0.6028] +2026-04-12 18:54:28.996941: Epoch time: 103.6 s +2026-04-12 18:54:30.593097: +2026-04-12 18:54:30.596788: Epoch 1922 +2026-04-12 18:54:30.600866: Current learning rate: 0.00555 +2026-04-12 18:56:13.482183: train_loss -0.3679 +2026-04-12 18:56:13.487413: val_loss -0.2946 +2026-04-12 18:56:13.490655: Pseudo dice [0.485, 0.7075, 0.5293, 0.3165, 0.6826, 0.132, 0.8123] +2026-04-12 18:56:13.493982: Epoch time: 102.89 s +2026-04-12 18:56:15.035319: +2026-04-12 18:56:15.037411: Epoch 1923 +2026-04-12 18:56:15.041040: Current learning rate: 0.00554 +2026-04-12 18:57:58.040059: train_loss -0.3633 +2026-04-12 18:57:58.047322: val_loss -0.3436 +2026-04-12 18:57:58.049681: Pseudo dice [0.6155, 0.1477, 0.7761, 0.8814, 0.6045, 0.583, 0.8228] +2026-04-12 18:57:58.052271: Epoch time: 103.01 s +2026-04-12 18:57:59.701456: +2026-04-12 18:57:59.703497: Epoch 1924 +2026-04-12 18:57:59.705565: Current learning rate: 0.00554 +2026-04-12 18:59:44.963798: train_loss -0.3737 +2026-04-12 18:59:44.973838: val_loss -0.322 +2026-04-12 18:59:44.978119: Pseudo dice [0.4476, 0.0091, 0.5802, 0.4695, 0.6364, 0.3414, 0.8543] +2026-04-12 18:59:44.980489: Epoch time: 105.27 s +2026-04-12 18:59:46.578565: +2026-04-12 18:59:46.582018: Epoch 1925 +2026-04-12 18:59:46.584946: Current learning rate: 0.00554 +2026-04-12 19:01:30.171553: train_loss -0.3489 +2026-04-12 19:01:30.178643: val_loss -0.1469 +2026-04-12 19:01:30.181437: Pseudo dice [0.2657, 0.3721, 0.2771, 0.7709, 0.3317, 0.0406, 0.5423] +2026-04-12 19:01:30.183925: Epoch time: 103.6 s +2026-04-12 19:01:31.784480: +2026-04-12 19:01:31.786225: Epoch 1926 +2026-04-12 19:01:31.788889: Current learning rate: 0.00554 +2026-04-12 19:03:14.533344: train_loss -0.3442 +2026-04-12 19:03:14.538532: val_loss -0.3286 +2026-04-12 19:03:14.540361: Pseudo dice [0.6187, 0.3483, 0.6487, 0.3365, 0.5238, 0.8109, 0.5011] +2026-04-12 19:03:14.542711: Epoch time: 102.75 s +2026-04-12 19:03:16.170727: +2026-04-12 19:03:16.172562: Epoch 1927 +2026-04-12 19:03:16.175064: Current learning rate: 0.00553 +2026-04-12 19:04:58.918708: train_loss -0.3622 +2026-04-12 19:04:58.924731: val_loss -0.3204 +2026-04-12 19:04:58.926662: Pseudo dice [0.4502, 0.4892, 0.615, 0.825, 0.6133, 0.3954, 0.8416] +2026-04-12 19:04:58.928677: Epoch time: 102.75 s +2026-04-12 19:05:00.527666: +2026-04-12 19:05:00.529535: Epoch 1928 +2026-04-12 19:05:00.531446: Current learning rate: 0.00553 +2026-04-12 19:06:43.535728: train_loss -0.35 +2026-04-12 19:06:43.546517: val_loss -0.165 +2026-04-12 19:06:43.549790: Pseudo dice [0.5085, 0.5191, 0.518, 0.4289, 0.4122, 0.1669, 0.6065] +2026-04-12 19:06:43.554127: Epoch time: 103.01 s +2026-04-12 19:06:45.153812: +2026-04-12 19:06:45.156879: Epoch 1929 +2026-04-12 19:06:45.159835: Current learning rate: 0.00553 +2026-04-12 19:08:28.234992: train_loss -0.3782 +2026-04-12 19:08:28.243102: val_loss -0.2545 +2026-04-12 19:08:28.245552: Pseudo dice [0.6534, 0.3164, 0.6615, 0.8274, 0.2452, 0.1042, 0.5684] +2026-04-12 19:08:28.248260: Epoch time: 103.08 s +2026-04-12 19:08:29.787283: +2026-04-12 19:08:29.790061: Epoch 1930 +2026-04-12 19:08:29.792562: Current learning rate: 0.00553 +2026-04-12 19:10:12.534467: train_loss -0.3549 +2026-04-12 19:10:12.541292: val_loss -0.3534 +2026-04-12 19:10:12.543568: Pseudo dice [0.7894, 0.7246, 0.6049, 0.7116, 0.5887, 0.634, 0.8162] +2026-04-12 19:10:12.546055: Epoch time: 102.75 s +2026-04-12 19:10:14.152114: +2026-04-12 19:10:14.154849: Epoch 1931 +2026-04-12 19:10:14.157786: Current learning rate: 0.00552 +2026-04-12 19:11:58.792727: train_loss -0.3687 +2026-04-12 19:11:58.799710: val_loss -0.1462 +2026-04-12 19:11:58.802625: Pseudo dice [0.6124, 0.351, 0.2838, 0.7027, 0.211, 0.0812, 0.6446] +2026-04-12 19:11:58.805506: Epoch time: 104.64 s +2026-04-12 19:12:00.414766: +2026-04-12 19:12:00.417087: Epoch 1932 +2026-04-12 19:12:00.419825: Current learning rate: 0.00552 +2026-04-12 19:13:43.607721: train_loss -0.3381 +2026-04-12 19:13:43.613905: val_loss -0.2448 +2026-04-12 19:13:43.616534: Pseudo dice [0.7955, 0.762, 0.5894, 0.5368, 0.4679, 0.0442, 0.8144] +2026-04-12 19:13:43.619081: Epoch time: 103.2 s +2026-04-12 19:13:45.265720: +2026-04-12 19:13:45.268671: Epoch 1933 +2026-04-12 19:13:45.270527: Current learning rate: 0.00552 +2026-04-12 19:15:28.239000: train_loss -0.3523 +2026-04-12 19:15:28.244739: val_loss -0.281 +2026-04-12 19:15:28.246851: Pseudo dice [0.3637, 0.3389, 0.6474, 0.5444, 0.2843, 0.1784, 0.7439] +2026-04-12 19:15:28.249233: Epoch time: 102.98 s +2026-04-12 19:15:29.791338: +2026-04-12 19:15:29.793070: Epoch 1934 +2026-04-12 19:15:29.795252: Current learning rate: 0.00552 +2026-04-12 19:17:12.740740: train_loss -0.3677 +2026-04-12 19:17:12.746278: val_loss -0.3166 +2026-04-12 19:17:12.748852: Pseudo dice [0.7892, 0.4932, 0.6705, 0.1425, 0.2491, 0.8286, 0.4468] +2026-04-12 19:17:12.751904: Epoch time: 102.95 s +2026-04-12 19:17:14.327986: +2026-04-12 19:17:14.329945: Epoch 1935 +2026-04-12 19:17:14.331972: Current learning rate: 0.00552 +2026-04-12 19:18:57.842173: train_loss -0.3479 +2026-04-12 19:18:57.873133: val_loss -0.2273 +2026-04-12 19:18:57.876211: Pseudo dice [0.4607, 0.1302, 0.474, 0.6391, 0.5022, 0.0261, 0.7266] +2026-04-12 19:18:57.879044: Epoch time: 103.52 s +2026-04-12 19:18:59.461742: +2026-04-12 19:18:59.463989: Epoch 1936 +2026-04-12 19:18:59.467448: Current learning rate: 0.00551 +2026-04-12 19:20:42.839503: train_loss -0.348 +2026-04-12 19:20:42.847608: val_loss -0.2574 +2026-04-12 19:20:42.849920: Pseudo dice [0.6536, 0.5377, 0.5268, 0.8261, 0.6705, 0.1115, 0.9166] +2026-04-12 19:20:42.852795: Epoch time: 103.38 s +2026-04-12 19:20:44.420425: +2026-04-12 19:20:44.422643: Epoch 1937 +2026-04-12 19:20:44.425182: Current learning rate: 0.00551 +2026-04-12 19:22:27.217667: train_loss -0.374 +2026-04-12 19:22:27.224499: val_loss -0.3649 +2026-04-12 19:22:27.227001: Pseudo dice [0.3444, 0.8221, 0.5992, 0.6451, 0.5752, 0.7902, 0.7978] +2026-04-12 19:22:27.230510: Epoch time: 102.8 s +2026-04-12 19:22:29.948590: +2026-04-12 19:22:29.951085: Epoch 1938 +2026-04-12 19:22:29.953210: Current learning rate: 0.00551 +2026-04-12 19:24:12.634955: train_loss -0.3768 +2026-04-12 19:24:12.645650: val_loss -0.2925 +2026-04-12 19:24:12.649286: Pseudo dice [0.6558, 0.0991, 0.7132, 0.8026, 0.4906, 0.3634, 0.6933] +2026-04-12 19:24:12.653899: Epoch time: 102.69 s +2026-04-12 19:24:14.195024: +2026-04-12 19:24:14.197062: Epoch 1939 +2026-04-12 19:24:14.199689: Current learning rate: 0.00551 +2026-04-12 19:25:56.737513: train_loss -0.3675 +2026-04-12 19:25:56.743279: val_loss -0.2064 +2026-04-12 19:25:56.745620: Pseudo dice [0.5926, 0.275, 0.3347, 0.3959, 0.6449, 0.146, 0.6713] +2026-04-12 19:25:56.748274: Epoch time: 102.55 s +2026-04-12 19:25:58.319309: +2026-04-12 19:25:58.321074: Epoch 1940 +2026-04-12 19:25:58.323374: Current learning rate: 0.0055 +2026-04-12 19:27:40.825100: train_loss -0.3656 +2026-04-12 19:27:40.831492: val_loss -0.3064 +2026-04-12 19:27:40.833812: Pseudo dice [0.82, 0.0573, 0.6831, 0.8858, 0.5093, 0.2925, 0.5561] +2026-04-12 19:27:40.835857: Epoch time: 102.51 s +2026-04-12 19:27:42.389118: +2026-04-12 19:27:42.391340: Epoch 1941 +2026-04-12 19:27:42.393475: Current learning rate: 0.0055 +2026-04-12 19:29:25.362986: train_loss -0.3844 +2026-04-12 19:29:25.373205: val_loss -0.3487 +2026-04-12 19:29:25.375624: Pseudo dice [0.424, 0.6658, 0.8797, 0.7987, 0.5188, 0.6669, 0.7699] +2026-04-12 19:29:25.378312: Epoch time: 102.98 s +2026-04-12 19:29:27.039996: +2026-04-12 19:29:27.043539: Epoch 1942 +2026-04-12 19:29:27.046290: Current learning rate: 0.0055 +2026-04-12 19:31:10.055178: train_loss -0.3856 +2026-04-12 19:31:10.061113: val_loss -0.2613 +2026-04-12 19:31:10.062955: Pseudo dice [0.3399, 0.5225, 0.4269, 0.5078, 0.2469, 0.1206, 0.6022] +2026-04-12 19:31:10.065231: Epoch time: 103.02 s +2026-04-12 19:31:11.637529: +2026-04-12 19:31:11.639194: Epoch 1943 +2026-04-12 19:31:11.641928: Current learning rate: 0.0055 +2026-04-12 19:32:54.641825: train_loss -0.3665 +2026-04-12 19:32:54.649260: val_loss -0.2773 +2026-04-12 19:32:54.652013: Pseudo dice [0.4772, 0.2109, 0.7772, 0.0134, 0.342, 0.1727, 0.6659] +2026-04-12 19:32:54.655167: Epoch time: 103.01 s +2026-04-12 19:32:56.246643: +2026-04-12 19:32:56.249442: Epoch 1944 +2026-04-12 19:32:56.251801: Current learning rate: 0.00549 +2026-04-12 19:34:39.892330: train_loss -0.3561 +2026-04-12 19:34:39.898851: val_loss -0.2334 +2026-04-12 19:34:39.901708: Pseudo dice [0.3334, 0.3993, 0.609, 0.5427, 0.4089, 0.0723, 0.1872] +2026-04-12 19:34:39.906653: Epoch time: 103.65 s +2026-04-12 19:34:41.489506: +2026-04-12 19:34:41.491417: Epoch 1945 +2026-04-12 19:34:41.493796: Current learning rate: 0.00549 +2026-04-12 19:36:24.446517: train_loss -0.3734 +2026-04-12 19:36:24.454585: val_loss -0.3175 +2026-04-12 19:36:24.457074: Pseudo dice [0.6298, 0.554, 0.6256, 0.2478, 0.5966, 0.7534, 0.6745] +2026-04-12 19:36:24.460762: Epoch time: 102.96 s +2026-04-12 19:36:26.059616: +2026-04-12 19:36:26.061842: Epoch 1946 +2026-04-12 19:36:26.064193: Current learning rate: 0.00549 +2026-04-12 19:38:08.663050: train_loss -0.3443 +2026-04-12 19:38:08.669803: val_loss -0.3213 +2026-04-12 19:38:08.671955: Pseudo dice [0.5109, 0.0641, 0.6253, 0.0256, 0.4155, 0.6628, 0.4852] +2026-04-12 19:38:08.674290: Epoch time: 102.61 s +2026-04-12 19:38:10.241125: +2026-04-12 19:38:10.246353: Epoch 1947 +2026-04-12 19:38:10.250869: Current learning rate: 0.00549 +2026-04-12 19:39:53.181958: train_loss -0.3472 +2026-04-12 19:39:53.189920: val_loss -0.3118 +2026-04-12 19:39:53.193282: Pseudo dice [0.2457, 0.1794, 0.7953, 0.3614, 0.5262, 0.19, 0.8175] +2026-04-12 19:39:53.195888: Epoch time: 102.94 s +2026-04-12 19:39:54.776128: +2026-04-12 19:39:54.778013: Epoch 1948 +2026-04-12 19:39:54.780935: Current learning rate: 0.00548 +2026-04-12 19:41:39.923012: train_loss -0.3465 +2026-04-12 19:41:39.930941: val_loss -0.3297 +2026-04-12 19:41:39.933983: Pseudo dice [0.5042, 0.3933, 0.662, 0.3408, 0.485, 0.6105, 0.815] +2026-04-12 19:41:39.936458: Epoch time: 105.15 s +2026-04-12 19:41:41.491846: +2026-04-12 19:41:41.493451: Epoch 1949 +2026-04-12 19:41:41.495899: Current learning rate: 0.00548 +2026-04-12 19:43:24.428809: train_loss -0.3533 +2026-04-12 19:43:24.435401: val_loss -0.2856 +2026-04-12 19:43:24.437489: Pseudo dice [0.3769, 0.6159, 0.7744, 0.8548, 0.4586, 0.2602, 0.6589] +2026-04-12 19:43:24.440612: Epoch time: 102.94 s +2026-04-12 19:43:28.028928: +2026-04-12 19:43:28.032032: Epoch 1950 +2026-04-12 19:43:28.035299: Current learning rate: 0.00548 +2026-04-12 19:45:10.682941: train_loss -0.3464 +2026-04-12 19:45:10.696281: val_loss -0.3345 +2026-04-12 19:45:10.701255: Pseudo dice [0.2423, 0.5491, 0.6717, 0.8241, 0.5753, 0.3765, 0.8343] +2026-04-12 19:45:10.705918: Epoch time: 102.66 s +2026-04-12 19:45:12.275745: +2026-04-12 19:45:12.277677: Epoch 1951 +2026-04-12 19:45:12.280078: Current learning rate: 0.00548 +2026-04-12 19:46:54.840438: train_loss -0.3787 +2026-04-12 19:46:54.846158: val_loss -0.1591 +2026-04-12 19:46:54.850235: Pseudo dice [0.7303, 0.865, 0.4935, 0.4742, 0.2132, 0.0343, 0.2872] +2026-04-12 19:46:54.852709: Epoch time: 102.57 s +2026-04-12 19:46:56.509245: +2026-04-12 19:46:56.512003: Epoch 1952 +2026-04-12 19:46:56.514307: Current learning rate: 0.00547 +2026-04-12 19:48:40.088456: train_loss -0.3625 +2026-04-12 19:48:40.093964: val_loss -0.2876 +2026-04-12 19:48:40.096743: Pseudo dice [0.6178, 0.2911, 0.6489, 0.474, 0.2988, 0.7156, 0.756] +2026-04-12 19:48:40.100245: Epoch time: 103.58 s +2026-04-12 19:48:41.728843: +2026-04-12 19:48:41.730593: Epoch 1953 +2026-04-12 19:48:41.732732: Current learning rate: 0.00547 +2026-04-12 19:50:24.853441: train_loss -0.3709 +2026-04-12 19:50:24.860442: val_loss -0.2875 +2026-04-12 19:50:24.862942: Pseudo dice [0.4399, 0.0978, 0.5547, 0.8563, 0.6819, 0.1161, 0.8113] +2026-04-12 19:50:24.866076: Epoch time: 103.13 s +2026-04-12 19:50:26.431700: +2026-04-12 19:50:26.434286: Epoch 1954 +2026-04-12 19:50:26.436865: Current learning rate: 0.00547 +2026-04-12 19:52:10.104321: train_loss -0.3737 +2026-04-12 19:52:10.110925: val_loss -0.2648 +2026-04-12 19:52:10.113383: Pseudo dice [0.4615, 0.1547, 0.5031, 0.8678, 0.4442, 0.0978, 0.8821] +2026-04-12 19:52:10.115842: Epoch time: 103.68 s +2026-04-12 19:52:11.685183: +2026-04-12 19:52:11.687076: Epoch 1955 +2026-04-12 19:52:11.689558: Current learning rate: 0.00547 +2026-04-12 19:53:54.862619: train_loss -0.3776 +2026-04-12 19:53:54.872146: val_loss -0.3442 +2026-04-12 19:53:54.875221: Pseudo dice [0.6675, 0.1539, 0.6565, 0.7556, 0.6106, 0.5696, 0.7303] +2026-04-12 19:53:54.878187: Epoch time: 103.18 s +2026-04-12 19:53:56.469180: +2026-04-12 19:53:56.472065: Epoch 1956 +2026-04-12 19:53:56.474246: Current learning rate: 0.00546 +2026-04-12 19:55:39.179080: train_loss -0.3772 +2026-04-12 19:55:39.190388: val_loss -0.2004 +2026-04-12 19:55:39.201083: Pseudo dice [0.3587, 0.2718, 0.6096, 0.5703, 0.6119, 0.2052, 0.807] +2026-04-12 19:55:39.203199: Epoch time: 102.71 s +2026-04-12 19:55:40.784852: +2026-04-12 19:55:40.786785: Epoch 1957 +2026-04-12 19:55:40.789793: Current learning rate: 0.00546 +2026-04-12 19:57:25.143570: train_loss -0.3802 +2026-04-12 19:57:25.149199: val_loss -0.2991 +2026-04-12 19:57:25.150887: Pseudo dice [0.709, 0.2056, 0.6093, 0.8444, 0.3989, 0.1124, 0.6571] +2026-04-12 19:57:25.152951: Epoch time: 104.36 s +2026-04-12 19:57:26.748710: +2026-04-12 19:57:26.750780: Epoch 1958 +2026-04-12 19:57:26.753123: Current learning rate: 0.00546 +2026-04-12 19:59:09.404092: train_loss -0.3779 +2026-04-12 19:59:09.411839: val_loss -0.2984 +2026-04-12 19:59:09.414004: Pseudo dice [0.6703, 0.3687, 0.5591, 0.8246, 0.6274, 0.0443, 0.6247] +2026-04-12 19:59:09.417011: Epoch time: 102.66 s +2026-04-12 19:59:10.986561: +2026-04-12 19:59:10.988664: Epoch 1959 +2026-04-12 19:59:10.992004: Current learning rate: 0.00546 +2026-04-12 20:00:53.838071: train_loss -0.3788 +2026-04-12 20:00:53.850581: val_loss -0.326 +2026-04-12 20:00:53.853332: Pseudo dice [0.6888, 0.2131, 0.7035, 0.7621, 0.5668, 0.4067, 0.595] +2026-04-12 20:00:53.857754: Epoch time: 102.86 s +2026-04-12 20:00:55.414414: +2026-04-12 20:00:55.417565: Epoch 1960 +2026-04-12 20:00:55.419565: Current learning rate: 0.00546 +2026-04-12 20:02:39.278149: train_loss -0.3677 +2026-04-12 20:02:39.284056: val_loss -0.2874 +2026-04-12 20:02:39.288678: Pseudo dice [0.6505, 0.2999, 0.4449, 0.378, 0.5963, 0.1314, 0.8219] +2026-04-12 20:02:39.291860: Epoch time: 103.87 s +2026-04-12 20:02:40.861219: +2026-04-12 20:02:40.863013: Epoch 1961 +2026-04-12 20:02:40.864951: Current learning rate: 0.00545 +2026-04-12 20:04:23.170894: train_loss -0.3753 +2026-04-12 20:04:23.178127: val_loss -0.3657 +2026-04-12 20:04:23.180616: Pseudo dice [0.5834, 0.62, 0.7483, 0.363, 0.5946, 0.808, 0.8893] +2026-04-12 20:04:23.183619: Epoch time: 102.31 s +2026-04-12 20:04:24.773077: +2026-04-12 20:04:24.775442: Epoch 1962 +2026-04-12 20:04:24.777961: Current learning rate: 0.00545 +2026-04-12 20:06:08.131376: train_loss -0.3942 +2026-04-12 20:06:08.137846: val_loss -0.3131 +2026-04-12 20:06:08.139847: Pseudo dice [0.4293, 0.5558, 0.7005, 0.804, 0.4435, 0.316, 0.9013] +2026-04-12 20:06:08.143898: Epoch time: 103.36 s +2026-04-12 20:06:09.713402: +2026-04-12 20:06:09.715133: Epoch 1963 +2026-04-12 20:06:09.716955: Current learning rate: 0.00545 +2026-04-12 20:07:53.213841: train_loss -0.3825 +2026-04-12 20:07:53.219563: val_loss -0.2051 +2026-04-12 20:07:53.221580: Pseudo dice [0.4127, 0.2817, 0.6573, 0.1373, 0.502, 0.1347, 0.823] +2026-04-12 20:07:53.223981: Epoch time: 103.5 s +2026-04-12 20:07:54.779105: +2026-04-12 20:07:54.780902: Epoch 1964 +2026-04-12 20:07:54.783089: Current learning rate: 0.00545 +2026-04-12 20:09:37.456497: train_loss -0.3613 +2026-04-12 20:09:37.469989: val_loss -0.3372 +2026-04-12 20:09:37.472114: Pseudo dice [0.5987, 0.8169, 0.7467, 0.0167, 0.5429, 0.7224, 0.7337] +2026-04-12 20:09:37.482410: Epoch time: 102.68 s +2026-04-12 20:09:39.027131: +2026-04-12 20:09:39.029459: Epoch 1965 +2026-04-12 20:09:39.031871: Current learning rate: 0.00544 +2026-04-12 20:11:21.874928: train_loss -0.371 +2026-04-12 20:11:21.879549: val_loss -0.3041 +2026-04-12 20:11:21.881371: Pseudo dice [0.6566, 0.4274, 0.6425, 0.624, 0.5637, 0.1314, 0.737] +2026-04-12 20:11:21.883584: Epoch time: 102.85 s +2026-04-12 20:11:23.429558: +2026-04-12 20:11:23.431404: Epoch 1966 +2026-04-12 20:11:23.433963: Current learning rate: 0.00544 +2026-04-12 20:13:07.715160: train_loss -0.3735 +2026-04-12 20:13:07.720789: val_loss -0.3123 +2026-04-12 20:13:07.723079: Pseudo dice [0.3405, 0.2727, 0.6438, 0.5547, 0.4388, 0.3008, 0.8267] +2026-04-12 20:13:07.725564: Epoch time: 104.29 s +2026-04-12 20:13:09.338719: +2026-04-12 20:13:09.340877: Epoch 1967 +2026-04-12 20:13:09.343184: Current learning rate: 0.00544 +2026-04-12 20:14:52.065133: train_loss -0.3766 +2026-04-12 20:14:52.071075: val_loss -0.3468 +2026-04-12 20:14:52.073170: Pseudo dice [0.2675, 0.5867, 0.6852, 0.81, 0.6137, 0.722, 0.8217] +2026-04-12 20:14:52.076605: Epoch time: 102.73 s +2026-04-12 20:14:53.665325: +2026-04-12 20:14:53.667572: Epoch 1968 +2026-04-12 20:14:53.670178: Current learning rate: 0.00544 +2026-04-12 20:16:36.232080: train_loss -0.3801 +2026-04-12 20:16:36.238068: val_loss -0.3226 +2026-04-12 20:16:36.240507: Pseudo dice [0.455, 0.3572, 0.5856, 0.6965, 0.6051, 0.8147, 0.845] +2026-04-12 20:16:36.243227: Epoch time: 102.57 s +2026-04-12 20:16:37.833076: +2026-04-12 20:16:37.835446: Epoch 1969 +2026-04-12 20:16:37.837961: Current learning rate: 0.00543 +2026-04-12 20:18:22.928957: train_loss -0.3574 +2026-04-12 20:18:22.936666: val_loss -0.3006 +2026-04-12 20:18:22.939023: Pseudo dice [0.4944, 0.0, 0.7296, 0.6948, 0.4884, 0.1192, 0.8216] +2026-04-12 20:18:22.941936: Epoch time: 105.1 s +2026-04-12 20:18:24.596963: +2026-04-12 20:18:24.599030: Epoch 1970 +2026-04-12 20:18:24.601582: Current learning rate: 0.00543 +2026-04-12 20:20:07.532876: train_loss -0.3713 +2026-04-12 20:20:07.540292: val_loss -0.2145 +2026-04-12 20:20:07.542356: Pseudo dice [0.7414, 0.0, 0.6539, 0.5852, 0.5756, 0.0591, 0.6678] +2026-04-12 20:20:07.546529: Epoch time: 102.94 s +2026-04-12 20:20:09.192881: +2026-04-12 20:20:09.194873: Epoch 1971 +2026-04-12 20:20:09.197510: Current learning rate: 0.00543 +2026-04-12 20:21:51.819966: train_loss -0.3571 +2026-04-12 20:21:51.827774: val_loss -0.3496 +2026-04-12 20:21:51.830871: Pseudo dice [0.7117, 0.1319, 0.8373, 0.0049, 0.5367, 0.6564, 0.3564] +2026-04-12 20:21:51.833172: Epoch time: 102.63 s +2026-04-12 20:21:53.395612: +2026-04-12 20:21:53.397370: Epoch 1972 +2026-04-12 20:21:53.399950: Current learning rate: 0.00543 +2026-04-12 20:23:36.741421: train_loss -0.3786 +2026-04-12 20:23:36.747234: val_loss -0.3419 +2026-04-12 20:23:36.749649: Pseudo dice [0.2729, 0.6135, 0.6852, 0.7141, 0.3454, 0.7684, 0.7612] +2026-04-12 20:23:36.752098: Epoch time: 103.35 s +2026-04-12 20:23:38.506628: +2026-04-12 20:23:38.509020: Epoch 1973 +2026-04-12 20:23:38.511049: Current learning rate: 0.00542 +2026-04-12 20:25:22.226521: train_loss -0.3514 +2026-04-12 20:25:22.238937: val_loss -0.2284 +2026-04-12 20:25:22.242731: Pseudo dice [0.6438, 0.7021, 0.2667, 0.471, 0.2805, 0.0684, 0.8526] +2026-04-12 20:25:22.247985: Epoch time: 103.72 s +2026-04-12 20:25:23.887263: +2026-04-12 20:25:23.889163: Epoch 1974 +2026-04-12 20:25:23.892974: Current learning rate: 0.00542 +2026-04-12 20:27:06.361653: train_loss -0.3469 +2026-04-12 20:27:06.368409: val_loss -0.2341 +2026-04-12 20:27:06.371159: Pseudo dice [0.5479, 0.6279, 0.5079, 0.5348, 0.4072, 0.0282, 0.3542] +2026-04-12 20:27:06.373577: Epoch time: 102.48 s +2026-04-12 20:27:08.018028: +2026-04-12 20:27:08.019810: Epoch 1975 +2026-04-12 20:27:08.021666: Current learning rate: 0.00542 +2026-04-12 20:28:51.555215: train_loss -0.3477 +2026-04-12 20:28:51.565272: val_loss -0.3263 +2026-04-12 20:28:51.567327: Pseudo dice [0.6443, 0.3893, 0.7043, 0.7889, 0.6511, 0.2543, 0.7085] +2026-04-12 20:28:51.569804: Epoch time: 103.54 s +2026-04-12 20:28:53.137511: +2026-04-12 20:28:53.140025: Epoch 1976 +2026-04-12 20:28:53.142661: Current learning rate: 0.00542 +2026-04-12 20:30:36.527788: train_loss -0.3678 +2026-04-12 20:30:36.535444: val_loss -0.3036 +2026-04-12 20:30:36.537952: Pseudo dice [0.2583, 0.4692, 0.8025, 0.8292, 0.505, 0.2235, 0.8488] +2026-04-12 20:30:36.540695: Epoch time: 103.4 s +2026-04-12 20:30:39.291511: +2026-04-12 20:30:39.293411: Epoch 1977 +2026-04-12 20:30:39.295674: Current learning rate: 0.00541 +2026-04-12 20:32:21.798039: train_loss -0.3698 +2026-04-12 20:32:21.804263: val_loss -0.3173 +2026-04-12 20:32:21.806532: Pseudo dice [0.6912, 0.221, 0.431, 0.5372, 0.604, 0.071, 0.8835] +2026-04-12 20:32:21.808692: Epoch time: 102.51 s +2026-04-12 20:32:23.353512: +2026-04-12 20:32:23.355252: Epoch 1978 +2026-04-12 20:32:23.357373: Current learning rate: 0.00541 +2026-04-12 20:34:06.640612: train_loss -0.3792 +2026-04-12 20:34:06.654149: val_loss -0.2641 +2026-04-12 20:34:06.657900: Pseudo dice [0.6267, 0.4464, 0.609, 0.446, 0.2709, 0.0454, 0.4566] +2026-04-12 20:34:06.660197: Epoch time: 103.29 s +2026-04-12 20:34:08.224839: +2026-04-12 20:34:08.227020: Epoch 1979 +2026-04-12 20:34:08.229048: Current learning rate: 0.00541 +2026-04-12 20:35:51.531123: train_loss -0.3398 +2026-04-12 20:35:51.538552: val_loss -0.2801 +2026-04-12 20:35:51.540751: Pseudo dice [0.4122, 0.1912, 0.6043, 0.4493, 0.4624, 0.3435, 0.5098] +2026-04-12 20:35:51.543499: Epoch time: 103.31 s +2026-04-12 20:35:53.088289: +2026-04-12 20:35:53.091657: Epoch 1980 +2026-04-12 20:35:53.094191: Current learning rate: 0.00541 +2026-04-12 20:37:35.677907: train_loss -0.3665 +2026-04-12 20:37:35.683459: val_loss -0.3077 +2026-04-12 20:37:35.685247: Pseudo dice [0.2039, 0.2676, 0.6972, 0.8574, 0.2327, 0.402, 0.5606] +2026-04-12 20:37:35.687209: Epoch time: 102.59 s +2026-04-12 20:37:37.221313: +2026-04-12 20:37:37.223238: Epoch 1981 +2026-04-12 20:37:37.225333: Current learning rate: 0.0054 +2026-04-12 20:39:20.172533: train_loss -0.3787 +2026-04-12 20:39:20.178980: val_loss -0.361 +2026-04-12 20:39:20.181118: Pseudo dice [0.7468, 0.6163, 0.7696, 0.7739, 0.5907, 0.7162, 0.7182] +2026-04-12 20:39:20.183262: Epoch time: 102.96 s +2026-04-12 20:39:21.751753: +2026-04-12 20:39:21.753657: Epoch 1982 +2026-04-12 20:39:21.755515: Current learning rate: 0.0054 +2026-04-12 20:41:04.504355: train_loss -0.3878 +2026-04-12 20:41:04.521246: val_loss -0.2134 +2026-04-12 20:41:04.532602: Pseudo dice [0.135, 0.7354, 0.6385, 0.0314, 0.6544, 0.174, 0.7504] +2026-04-12 20:41:04.539024: Epoch time: 102.76 s +2026-04-12 20:41:06.140362: +2026-04-12 20:41:06.143882: Epoch 1983 +2026-04-12 20:41:06.146961: Current learning rate: 0.0054 +2026-04-12 20:42:49.453133: train_loss -0.3963 +2026-04-12 20:42:49.460994: val_loss -0.2727 +2026-04-12 20:42:49.463947: Pseudo dice [0.7879, 0.847, 0.2675, 0.5493, 0.6537, 0.2383, 0.8246] +2026-04-12 20:42:49.468215: Epoch time: 103.32 s +2026-04-12 20:42:51.031915: +2026-04-12 20:42:51.034945: Epoch 1984 +2026-04-12 20:42:51.037243: Current learning rate: 0.0054 +2026-04-12 20:44:34.932599: train_loss -0.3721 +2026-04-12 20:44:34.938514: val_loss -0.2667 +2026-04-12 20:44:34.940694: Pseudo dice [0.3504, 0.4665, 0.4955, 0.3757, 0.2269, 0.1508, 0.5382] +2026-04-12 20:44:34.942703: Epoch time: 103.9 s +2026-04-12 20:44:36.605721: +2026-04-12 20:44:36.607538: Epoch 1985 +2026-04-12 20:44:36.609497: Current learning rate: 0.0054 +2026-04-12 20:46:20.042624: train_loss -0.3625 +2026-04-12 20:46:20.049382: val_loss -0.2626 +2026-04-12 20:46:20.053241: Pseudo dice [0.4468, 0.4588, 0.429, 0.0506, 0.5995, 0.3981, 0.5793] +2026-04-12 20:46:20.056170: Epoch time: 103.44 s +2026-04-12 20:46:21.658775: +2026-04-12 20:46:21.660538: Epoch 1986 +2026-04-12 20:46:21.663224: Current learning rate: 0.00539 +2026-04-12 20:48:05.030288: train_loss -0.3496 +2026-04-12 20:48:05.044337: val_loss -0.2855 +2026-04-12 20:48:05.046850: Pseudo dice [0.4787, 0.5403, 0.371, 0.3426, 0.477, 0.1777, 0.7342] +2026-04-12 20:48:05.054071: Epoch time: 103.38 s +2026-04-12 20:48:06.661924: +2026-04-12 20:48:06.665157: Epoch 1987 +2026-04-12 20:48:06.667930: Current learning rate: 0.00539 +2026-04-12 20:49:50.023832: train_loss -0.3657 +2026-04-12 20:49:50.030251: val_loss -0.3048 +2026-04-12 20:49:50.032649: Pseudo dice [0.7041, 0.8363, 0.7123, 0.5796, 0.3659, 0.1656, 0.7735] +2026-04-12 20:49:50.035071: Epoch time: 103.37 s +2026-04-12 20:49:51.603839: +2026-04-12 20:49:51.607120: Epoch 1988 +2026-04-12 20:49:51.609803: Current learning rate: 0.00539 +2026-04-12 20:51:34.028099: train_loss -0.37 +2026-04-12 20:51:34.035060: val_loss -0.2758 +2026-04-12 20:51:34.037802: Pseudo dice [0.5562, 0.5577, 0.6192, 0.6938, 0.4853, 0.1228, 0.8371] +2026-04-12 20:51:34.041058: Epoch time: 102.43 s +2026-04-12 20:51:35.638633: +2026-04-12 20:51:35.641803: Epoch 1989 +2026-04-12 20:51:35.644061: Current learning rate: 0.00539 +2026-04-12 20:53:18.972623: train_loss -0.3725 +2026-04-12 20:53:18.979323: val_loss -0.2701 +2026-04-12 20:53:18.982313: Pseudo dice [0.4023, 0.618, 0.6591, 0.6696, 0.3797, 0.0776, 0.2498] +2026-04-12 20:53:18.985192: Epoch time: 103.34 s +2026-04-12 20:53:20.585637: +2026-04-12 20:53:20.587644: Epoch 1990 +2026-04-12 20:53:20.589574: Current learning rate: 0.00538 +2026-04-12 20:55:03.530303: train_loss -0.3737 +2026-04-12 20:55:03.538617: val_loss -0.3261 +2026-04-12 20:55:03.541385: Pseudo dice [0.358, 0.5939, 0.584, 0.3183, 0.6245, 0.3479, 0.7171] +2026-04-12 20:55:03.544473: Epoch time: 102.95 s +2026-04-12 20:55:05.132766: +2026-04-12 20:55:05.134907: Epoch 1991 +2026-04-12 20:55:05.137089: Current learning rate: 0.00538 +2026-04-12 20:56:47.915710: train_loss -0.3809 +2026-04-12 20:56:47.929646: val_loss -0.2383 +2026-04-12 20:56:47.932369: Pseudo dice [0.4676, 0.0, 0.5216, 0.6338, 0.362, 0.0699, 0.7489] +2026-04-12 20:56:47.934983: Epoch time: 102.79 s +2026-04-12 20:56:49.533787: +2026-04-12 20:56:49.536256: Epoch 1992 +2026-04-12 20:56:49.538828: Current learning rate: 0.00538 +2026-04-12 20:58:32.200519: train_loss -0.3845 +2026-04-12 20:58:32.215367: val_loss -0.2775 +2026-04-12 20:58:32.220669: Pseudo dice [0.7703, 0.0, 0.6427, 0.8183, 0.677, 0.1655, 0.4402] +2026-04-12 20:58:32.227290: Epoch time: 102.67 s +2026-04-12 20:58:33.773496: +2026-04-12 20:58:33.775335: Epoch 1993 +2026-04-12 20:58:33.777660: Current learning rate: 0.00538 +2026-04-12 21:00:16.947460: train_loss -0.3856 +2026-04-12 21:00:16.953435: val_loss -0.2975 +2026-04-12 21:00:16.955637: Pseudo dice [0.7569, 0.0658, 0.6916, 0.3223, 0.5182, 0.4042, 0.8353] +2026-04-12 21:00:16.957846: Epoch time: 103.18 s +2026-04-12 21:00:18.501802: +2026-04-12 21:00:18.503639: Epoch 1994 +2026-04-12 21:00:18.505448: Current learning rate: 0.00537 +2026-04-12 21:02:01.716726: train_loss -0.3914 +2026-04-12 21:02:01.722322: val_loss -0.3171 +2026-04-12 21:02:01.724207: Pseudo dice [0.6668, 0.5745, 0.4682, 0.6442, 0.4721, 0.4675, 0.6176] +2026-04-12 21:02:01.727103: Epoch time: 103.22 s +2026-04-12 21:02:03.241021: +2026-04-12 21:02:03.243209: Epoch 1995 +2026-04-12 21:02:03.245324: Current learning rate: 0.00537 +2026-04-12 21:03:47.752737: train_loss -0.369 +2026-04-12 21:03:47.760881: val_loss -0.3246 +2026-04-12 21:03:47.763020: Pseudo dice [0.5675, 0.3384, 0.6489, 0.7456, 0.5295, 0.6291, 0.7852] +2026-04-12 21:03:47.766020: Epoch time: 104.52 s +2026-04-12 21:03:49.410588: +2026-04-12 21:03:49.412214: Epoch 1996 +2026-04-12 21:03:49.414696: Current learning rate: 0.00537 +2026-04-12 21:05:31.950981: train_loss -0.3612 +2026-04-12 21:05:31.957907: val_loss -0.2786 +2026-04-12 21:05:31.960404: Pseudo dice [0.5212, 0.2627, 0.3767, 0.8743, 0.5661, 0.1218, 0.484] +2026-04-12 21:05:31.962683: Epoch time: 102.54 s +2026-04-12 21:05:34.698839: +2026-04-12 21:05:34.700840: Epoch 1997 +2026-04-12 21:05:34.702780: Current learning rate: 0.00537 +2026-04-12 21:07:17.766943: train_loss -0.3229 +2026-04-12 21:07:17.773648: val_loss -0.2853 +2026-04-12 21:07:17.776080: Pseudo dice [0.4434, 0.8487, 0.6717, 0.4534, 0.5163, 0.2278, 0.3592] +2026-04-12 21:07:17.778656: Epoch time: 103.07 s +2026-04-12 21:07:19.397421: +2026-04-12 21:07:19.399454: Epoch 1998 +2026-04-12 21:07:19.401955: Current learning rate: 0.00536 +2026-04-12 21:09:02.127047: train_loss -0.366 +2026-04-12 21:09:02.133798: val_loss -0.2655 +2026-04-12 21:09:02.136369: Pseudo dice [0.5814, 0.6141, 0.7253, 0.6353, 0.3311, 0.158, 0.6129] +2026-04-12 21:09:02.138540: Epoch time: 102.73 s +2026-04-12 21:09:03.664934: +2026-04-12 21:09:03.666773: Epoch 1999 +2026-04-12 21:09:03.668778: Current learning rate: 0.00536 +2026-04-12 21:10:46.091641: train_loss -0.3773 +2026-04-12 21:10:46.102786: val_loss -0.3109 +2026-04-12 21:10:46.106065: Pseudo dice [0.7031, 0.7405, 0.7771, 0.7948, 0.5976, 0.0977, 0.7671] +2026-04-12 21:10:46.109596: Epoch time: 102.43 s +2026-04-12 21:10:49.633506: +2026-04-12 21:10:49.636600: Epoch 2000 +2026-04-12 21:10:49.638968: Current learning rate: 0.00536 +2026-04-12 21:12:32.652203: train_loss -0.3802 +2026-04-12 21:12:32.665239: val_loss -0.2802 +2026-04-12 21:12:32.667512: Pseudo dice [0.7314, 0.5101, 0.6099, 0.7654, 0.6686, 0.0546, 0.6355] +2026-04-12 21:12:32.670342: Epoch time: 103.02 s +2026-04-12 21:12:34.253012: +2026-04-12 21:12:34.255093: Epoch 2001 +2026-04-12 21:12:34.257542: Current learning rate: 0.00536 +2026-04-12 21:14:17.193443: train_loss -0.363 +2026-04-12 21:14:17.199725: val_loss -0.3395 +2026-04-12 21:14:17.201789: Pseudo dice [0.2358, 0.4114, 0.6005, 0.7715, 0.5436, 0.7806, 0.8946] +2026-04-12 21:14:17.204052: Epoch time: 102.94 s +2026-04-12 21:14:18.822062: +2026-04-12 21:14:18.824617: Epoch 2002 +2026-04-12 21:14:18.826867: Current learning rate: 0.00535 +2026-04-12 21:16:01.572488: train_loss -0.3592 +2026-04-12 21:16:01.579922: val_loss -0.2277 +2026-04-12 21:16:01.582244: Pseudo dice [0.7587, 0.0009, 0.303, 0.1649, 0.535, 0.0663, 0.7345] +2026-04-12 21:16:01.584700: Epoch time: 102.75 s +2026-04-12 21:16:03.170359: +2026-04-12 21:16:03.172554: Epoch 2003 +2026-04-12 21:16:03.174916: Current learning rate: 0.00535 +2026-04-12 21:17:45.762069: train_loss -0.3721 +2026-04-12 21:17:45.767678: val_loss -0.3046 +2026-04-12 21:17:45.770394: Pseudo dice [0.7771, 0.0, 0.6866, 0.5525, 0.4594, 0.1007, 0.8974] +2026-04-12 21:17:45.773222: Epoch time: 102.6 s +2026-04-12 21:17:47.346546: +2026-04-12 21:17:47.349914: Epoch 2004 +2026-04-12 21:17:47.352041: Current learning rate: 0.00535 +2026-04-12 21:19:30.163322: train_loss -0.3774 +2026-04-12 21:19:30.170740: val_loss -0.2521 +2026-04-12 21:19:30.172663: Pseudo dice [0.7604, 0.3049, 0.4252, 0.1281, 0.5797, 0.0346, 0.8403] +2026-04-12 21:19:30.175728: Epoch time: 102.82 s +2026-04-12 21:19:31.836587: +2026-04-12 21:19:31.838968: Epoch 2005 +2026-04-12 21:19:31.843804: Current learning rate: 0.00535 +2026-04-12 21:21:14.566749: train_loss -0.3852 +2026-04-12 21:21:14.573252: val_loss -0.3166 +2026-04-12 21:21:14.575778: Pseudo dice [0.7444, 0.2382, 0.7375, 0.6252, 0.4309, 0.6738, 0.8555] +2026-04-12 21:21:14.578478: Epoch time: 102.73 s +2026-04-12 21:21:16.117562: +2026-04-12 21:21:16.119686: Epoch 2006 +2026-04-12 21:21:16.121789: Current learning rate: 0.00534 +2026-04-12 21:22:59.597101: train_loss -0.3824 +2026-04-12 21:22:59.603324: val_loss -0.264 +2026-04-12 21:22:59.606601: Pseudo dice [0.5335, 0.1395, 0.6221, 0.5926, 0.5644, 0.1367, 0.8228] +2026-04-12 21:22:59.609443: Epoch time: 103.48 s +2026-04-12 21:23:01.191612: +2026-04-12 21:23:01.193553: Epoch 2007 +2026-04-12 21:23:01.196283: Current learning rate: 0.00534 +2026-04-12 21:24:44.795369: train_loss -0.3802 +2026-04-12 21:24:44.801538: val_loss -0.2709 +2026-04-12 21:24:44.803241: Pseudo dice [0.6963, 0.3229, 0.6997, 0.4977, 0.2165, 0.1228, 0.8563] +2026-04-12 21:24:44.805836: Epoch time: 103.61 s +2026-04-12 21:24:46.944142: +2026-04-12 21:24:46.947325: Epoch 2008 +2026-04-12 21:24:46.949597: Current learning rate: 0.00534 +2026-04-12 21:26:29.983359: train_loss -0.3691 +2026-04-12 21:26:29.988229: val_loss -0.2814 +2026-04-12 21:26:29.991456: Pseudo dice [0.3443, 0.1061, 0.6533, 0.6513, 0.6192, 0.248, 0.75] +2026-04-12 21:26:29.994542: Epoch time: 103.04 s +2026-04-12 21:26:31.546981: +2026-04-12 21:26:31.548813: Epoch 2009 +2026-04-12 21:26:31.550960: Current learning rate: 0.00534 +2026-04-12 21:28:13.903377: train_loss -0.3681 +2026-04-12 21:28:13.908501: val_loss -0.3058 +2026-04-12 21:28:13.910931: Pseudo dice [0.4395, 0.1946, 0.5006, 0.747, 0.3786, 0.3135, 0.6758] +2026-04-12 21:28:13.913259: Epoch time: 102.36 s +2026-04-12 21:28:15.445833: +2026-04-12 21:28:15.447646: Epoch 2010 +2026-04-12 21:28:15.449866: Current learning rate: 0.00533 +2026-04-12 21:29:58.526218: train_loss -0.3733 +2026-04-12 21:29:58.535409: val_loss -0.319 +2026-04-12 21:29:58.537814: Pseudo dice [0.4843, 0.4922, 0.6085, 0.6452, 0.6001, 0.6054, 0.5424] +2026-04-12 21:29:58.541155: Epoch time: 103.08 s +2026-04-12 21:30:00.159350: +2026-04-12 21:30:00.162055: Epoch 2011 +2026-04-12 21:30:00.164676: Current learning rate: 0.00533 +2026-04-12 21:31:42.615079: train_loss -0.3752 +2026-04-12 21:31:42.623809: val_loss -0.3276 +2026-04-12 21:31:42.626964: Pseudo dice [0.7168, 0.3105, 0.6165, 0.1974, 0.6009, 0.6572, 0.8915] +2026-04-12 21:31:42.629713: Epoch time: 102.46 s +2026-04-12 21:31:44.199826: +2026-04-12 21:31:44.201966: Epoch 2012 +2026-04-12 21:31:44.204388: Current learning rate: 0.00533 +2026-04-12 21:33:26.728165: train_loss -0.363 +2026-04-12 21:33:26.733187: val_loss -0.3208 +2026-04-12 21:33:26.735155: Pseudo dice [0.6949, 0.6758, 0.5286, 0.1825, 0.2389, 0.4148, 0.8723] +2026-04-12 21:33:26.737283: Epoch time: 102.53 s +2026-04-12 21:33:28.295718: +2026-04-12 21:33:28.297433: Epoch 2013 +2026-04-12 21:33:28.299976: Current learning rate: 0.00533 +2026-04-12 21:35:11.477180: train_loss -0.3496 +2026-04-12 21:35:11.488345: val_loss -0.2696 +2026-04-12 21:35:11.490109: Pseudo dice [0.6628, 0.3412, 0.5057, 0.5225, 0.4841, 0.127, 0.7117] +2026-04-12 21:35:11.492537: Epoch time: 103.19 s +2026-04-12 21:35:13.084538: +2026-04-12 21:35:13.092167: Epoch 2014 +2026-04-12 21:35:13.097776: Current learning rate: 0.00533 +2026-04-12 21:36:55.584512: train_loss -0.3652 +2026-04-12 21:36:55.590462: val_loss -0.1755 +2026-04-12 21:36:55.592411: Pseudo dice [0.7956, 0.2182, 0.6565, 0.5736, 0.4039, 0.0424, 0.4435] +2026-04-12 21:36:55.595323: Epoch time: 102.5 s +2026-04-12 21:36:57.125867: +2026-04-12 21:36:57.128197: Epoch 2015 +2026-04-12 21:36:57.130293: Current learning rate: 0.00532 +2026-04-12 21:38:39.775783: train_loss -0.364 +2026-04-12 21:38:39.781835: val_loss -0.2304 +2026-04-12 21:38:39.784751: Pseudo dice [0.5338, 0.422, 0.4873, 0.4169, 0.4716, 0.0142, 0.7433] +2026-04-12 21:38:39.787184: Epoch time: 102.65 s +2026-04-12 21:38:41.332546: +2026-04-12 21:38:41.334540: Epoch 2016 +2026-04-12 21:38:41.336679: Current learning rate: 0.00532 +2026-04-12 21:40:24.893319: train_loss -0.3738 +2026-04-12 21:40:24.899755: val_loss -0.293 +2026-04-12 21:40:24.903147: Pseudo dice [0.649, 0.3125, 0.6789, 0.6596, 0.4718, 0.5859, 0.5671] +2026-04-12 21:40:24.905479: Epoch time: 103.56 s +2026-04-12 21:40:26.441667: +2026-04-12 21:40:26.445694: Epoch 2017 +2026-04-12 21:40:26.447886: Current learning rate: 0.00532 +2026-04-12 21:42:10.655437: train_loss -0.3808 +2026-04-12 21:42:10.667569: val_loss -0.3049 +2026-04-12 21:42:10.672607: Pseudo dice [0.6796, 0.1488, 0.5525, 0.2834, 0.3297, 0.2054, 0.7043] +2026-04-12 21:42:10.677229: Epoch time: 104.22 s +2026-04-12 21:42:12.271326: +2026-04-12 21:42:12.273096: Epoch 2018 +2026-04-12 21:42:12.275279: Current learning rate: 0.00532 +2026-04-12 21:43:55.415878: train_loss -0.3817 +2026-04-12 21:43:55.422751: val_loss -0.294 +2026-04-12 21:43:55.425147: Pseudo dice [0.4968, 0.5785, 0.5709, 0.4886, 0.6777, 0.0875, 0.8455] +2026-04-12 21:43:55.427640: Epoch time: 103.15 s +2026-04-12 21:43:56.997230: +2026-04-12 21:43:56.999131: Epoch 2019 +2026-04-12 21:43:57.001601: Current learning rate: 0.00531 +2026-04-12 21:45:39.974669: train_loss -0.3768 +2026-04-12 21:45:39.983619: val_loss -0.1917 +2026-04-12 21:45:39.986685: Pseudo dice [0.4868, 0.0, 0.5052, 0.1444, 0.4691, 0.0478, 0.7158] +2026-04-12 21:45:39.989673: Epoch time: 102.98 s +2026-04-12 21:45:41.546688: +2026-04-12 21:45:41.551270: Epoch 2020 +2026-04-12 21:45:41.553374: Current learning rate: 0.00531 +2026-04-12 21:47:24.957221: train_loss -0.3532 +2026-04-12 21:47:24.965201: val_loss -0.2352 +2026-04-12 21:47:24.968428: Pseudo dice [0.6439, 0.0, 0.6034, 0.106, 0.3172, 0.1538, 0.6285] +2026-04-12 21:47:24.974293: Epoch time: 103.41 s +2026-04-12 21:47:26.584801: +2026-04-12 21:47:26.588951: Epoch 2021 +2026-04-12 21:47:26.591720: Current learning rate: 0.00531 +2026-04-12 21:49:09.148375: train_loss -0.3437 +2026-04-12 21:49:09.159467: val_loss -0.3094 +2026-04-12 21:49:09.161364: Pseudo dice [0.5782, 0.0, 0.7164, 0.7902, 0.4734, 0.4896, 0.8459] +2026-04-12 21:49:09.164790: Epoch time: 102.57 s +2026-04-12 21:49:10.745710: +2026-04-12 21:49:10.748790: Epoch 2022 +2026-04-12 21:49:10.751439: Current learning rate: 0.00531 +2026-04-12 21:50:53.317150: train_loss -0.3875 +2026-04-12 21:50:53.322685: val_loss -0.3002 +2026-04-12 21:50:53.324598: Pseudo dice [0.5063, 0.0, 0.6968, 0.7286, 0.2874, 0.5017, 0.7735] +2026-04-12 21:50:53.326749: Epoch time: 102.58 s +2026-04-12 21:50:54.888273: +2026-04-12 21:50:54.890219: Epoch 2023 +2026-04-12 21:50:54.892125: Current learning rate: 0.0053 +2026-04-12 21:52:37.457936: train_loss -0.3649 +2026-04-12 21:52:37.464608: val_loss -0.3388 +2026-04-12 21:52:37.470965: Pseudo dice [0.6674, 0.1506, 0.7538, 0.7, 0.5585, 0.8312, 0.7306] +2026-04-12 21:52:37.474793: Epoch time: 102.57 s +2026-04-12 21:52:39.073172: +2026-04-12 21:52:39.076708: Epoch 2024 +2026-04-12 21:52:39.081772: Current learning rate: 0.0053 +2026-04-12 21:54:21.696876: train_loss -0.3737 +2026-04-12 21:54:21.705926: val_loss -0.3274 +2026-04-12 21:54:21.708697: Pseudo dice [0.3755, 0.044, 0.709, 0.6991, 0.3684, 0.6678, 0.5753] +2026-04-12 21:54:21.711852: Epoch time: 102.63 s +2026-04-12 21:54:23.286144: +2026-04-12 21:54:23.289828: Epoch 2025 +2026-04-12 21:54:23.292177: Current learning rate: 0.0053 +2026-04-12 21:56:06.508840: train_loss -0.378 +2026-04-12 21:56:06.516003: val_loss -0.3333 +2026-04-12 21:56:06.518552: Pseudo dice [0.2345, 0.1867, 0.6133, 0.621, 0.6258, 0.3374, 0.7765] +2026-04-12 21:56:06.521674: Epoch time: 103.23 s +2026-04-12 21:56:08.072443: +2026-04-12 21:56:08.074421: Epoch 2026 +2026-04-12 21:56:08.077063: Current learning rate: 0.0053 +2026-04-12 21:57:50.811344: train_loss -0.3685 +2026-04-12 21:57:50.819449: val_loss -0.2465 +2026-04-12 21:57:50.823191: Pseudo dice [0.699, 0.009, 0.642, 0.7746, 0.5137, 0.0528, 0.9257] +2026-04-12 21:57:50.826009: Epoch time: 102.74 s +2026-04-12 21:57:52.434307: +2026-04-12 21:57:52.436483: Epoch 2027 +2026-04-12 21:57:52.438565: Current learning rate: 0.00529 +2026-04-12 21:59:34.981923: train_loss -0.3548 +2026-04-12 21:59:34.990079: val_loss -0.2755 +2026-04-12 21:59:34.992787: Pseudo dice [0.485, 0.296, 0.6606, 0.634, 0.5857, 0.0336, 0.8116] +2026-04-12 21:59:34.995326: Epoch time: 102.55 s +2026-04-12 21:59:36.540036: +2026-04-12 21:59:36.542896: Epoch 2028 +2026-04-12 21:59:36.545217: Current learning rate: 0.00529 +2026-04-12 22:01:20.371239: train_loss -0.3677 +2026-04-12 22:01:20.377373: val_loss -0.338 +2026-04-12 22:01:20.379815: Pseudo dice [0.5964, 0.2093, 0.7117, 0.0068, 0.5464, 0.8156, 0.7884] +2026-04-12 22:01:20.382317: Epoch time: 103.84 s +2026-04-12 22:01:21.984453: +2026-04-12 22:01:21.986541: Epoch 2029 +2026-04-12 22:01:21.988557: Current learning rate: 0.00529 +2026-04-12 22:03:04.582686: train_loss -0.393 +2026-04-12 22:03:04.588116: val_loss -0.308 +2026-04-12 22:03:04.590122: Pseudo dice [0.3225, 0.1145, 0.6797, 0.5251, 0.5589, 0.2557, 0.8318] +2026-04-12 22:03:04.592923: Epoch time: 102.6 s +2026-04-12 22:03:06.132534: +2026-04-12 22:03:06.134495: Epoch 2030 +2026-04-12 22:03:06.136555: Current learning rate: 0.00529 +2026-04-12 22:04:49.488554: train_loss -0.3623 +2026-04-12 22:04:49.494320: val_loss -0.318 +2026-04-12 22:04:49.496344: Pseudo dice [0.5025, 0.2997, 0.6447, 0.7506, 0.5198, 0.2549, 0.8773] +2026-04-12 22:04:49.498963: Epoch time: 103.36 s +2026-04-12 22:04:51.058959: +2026-04-12 22:04:51.061305: Epoch 2031 +2026-04-12 22:04:51.063282: Current learning rate: 0.00528 +2026-04-12 22:06:35.089468: train_loss -0.3666 +2026-04-12 22:06:35.095471: val_loss -0.3142 +2026-04-12 22:06:35.097760: Pseudo dice [0.4995, 0.009, 0.6787, 0.5092, 0.5231, 0.1176, 0.7668] +2026-04-12 22:06:35.100240: Epoch time: 104.03 s +2026-04-12 22:06:36.666900: +2026-04-12 22:06:36.669627: Epoch 2032 +2026-04-12 22:06:36.671830: Current learning rate: 0.00528 +2026-04-12 22:08:19.458101: train_loss -0.3647 +2026-04-12 22:08:19.466988: val_loss -0.257 +2026-04-12 22:08:19.469589: Pseudo dice [0.6344, 0.201, 0.6967, 0.6205, 0.5168, 0.1162, 0.7815] +2026-04-12 22:08:19.472553: Epoch time: 102.79 s +2026-04-12 22:08:21.064510: +2026-04-12 22:08:21.066433: Epoch 2033 +2026-04-12 22:08:21.068962: Current learning rate: 0.00528 +2026-04-12 22:10:04.261967: train_loss -0.3646 +2026-04-12 22:10:04.268436: val_loss -0.256 +2026-04-12 22:10:04.270938: Pseudo dice [0.7865, 0.155, 0.6917, 0.147, 0.672, 0.1069, 0.6804] +2026-04-12 22:10:04.273346: Epoch time: 103.2 s +2026-04-12 22:10:05.821111: +2026-04-12 22:10:05.823009: Epoch 2034 +2026-04-12 22:10:05.825130: Current learning rate: 0.00528 +2026-04-12 22:11:49.299403: train_loss -0.3667 +2026-04-12 22:11:49.306767: val_loss -0.2069 +2026-04-12 22:11:49.309295: Pseudo dice [0.3875, 0.5007, 0.5018, 0.2948, 0.5518, 0.0565, 0.8815] +2026-04-12 22:11:49.312269: Epoch time: 103.48 s +2026-04-12 22:11:50.864830: +2026-04-12 22:11:50.866709: Epoch 2035 +2026-04-12 22:11:50.869070: Current learning rate: 0.00527 +2026-04-12 22:13:33.391953: train_loss -0.3664 +2026-04-12 22:13:33.397928: val_loss -0.3313 +2026-04-12 22:13:33.400931: Pseudo dice [0.7271, 0.1852, 0.7036, 0.3021, 0.4579, 0.7871, 0.7578] +2026-04-12 22:13:33.404263: Epoch time: 102.53 s +2026-04-12 22:13:36.078389: +2026-04-12 22:13:36.081209: Epoch 2036 +2026-04-12 22:13:36.084983: Current learning rate: 0.00527 +2026-04-12 22:15:18.630092: train_loss -0.3754 +2026-04-12 22:15:18.637582: val_loss -0.1816 +2026-04-12 22:15:18.640203: Pseudo dice [0.5534, 0.4016, 0.3136, 0.68, 0.6466, 0.1579, 0.7329] +2026-04-12 22:15:18.643124: Epoch time: 102.56 s +2026-04-12 22:15:20.235665: +2026-04-12 22:15:20.237824: Epoch 2037 +2026-04-12 22:15:20.239899: Current learning rate: 0.00527 +2026-04-12 22:17:02.835001: train_loss -0.3537 +2026-04-12 22:17:02.844836: val_loss -0.3525 +2026-04-12 22:17:02.848177: Pseudo dice [0.5261, 0.4836, 0.7355, 0.8437, 0.3561, 0.6702, 0.6342] +2026-04-12 22:17:02.850891: Epoch time: 102.6 s +2026-04-12 22:17:04.422089: +2026-04-12 22:17:04.425063: Epoch 2038 +2026-04-12 22:17:04.427830: Current learning rate: 0.00527 +2026-04-12 22:18:46.789865: train_loss -0.3693 +2026-04-12 22:18:46.795937: val_loss -0.2335 +2026-04-12 22:18:46.798592: Pseudo dice [0.1549, 0.1037, 0.6086, 0.3062, 0.3114, 0.1287, 0.5327] +2026-04-12 22:18:46.801103: Epoch time: 102.37 s +2026-04-12 22:18:48.319956: +2026-04-12 22:18:48.321776: Epoch 2039 +2026-04-12 22:18:48.323922: Current learning rate: 0.00526 +2026-04-12 22:20:31.882869: train_loss -0.3721 +2026-04-12 22:20:31.890856: val_loss -0.3494 +2026-04-12 22:20:31.893811: Pseudo dice [0.3928, 0.519, 0.6986, 0.652, 0.5234, 0.7645, 0.6579] +2026-04-12 22:20:31.896700: Epoch time: 103.57 s +2026-04-12 22:20:33.436022: +2026-04-12 22:20:33.438143: Epoch 2040 +2026-04-12 22:20:33.440317: Current learning rate: 0.00526 +2026-04-12 22:22:17.057362: train_loss -0.3794 +2026-04-12 22:22:17.062285: val_loss -0.3368 +2026-04-12 22:22:17.064659: Pseudo dice [0.6479, 0.6326, 0.73, 0.6679, 0.4663, 0.8642, 0.7775] +2026-04-12 22:22:17.070187: Epoch time: 103.63 s +2026-04-12 22:22:18.630837: +2026-04-12 22:22:18.632719: Epoch 2041 +2026-04-12 22:22:18.634856: Current learning rate: 0.00526 +2026-04-12 22:24:01.023955: train_loss -0.3827 +2026-04-12 22:24:01.029792: val_loss -0.265 +2026-04-12 22:24:01.031754: Pseudo dice [0.4547, 0.1022, 0.37, 0.4109, 0.4935, 0.244, 0.8277] +2026-04-12 22:24:01.034200: Epoch time: 102.4 s +2026-04-12 22:24:02.618411: +2026-04-12 22:24:02.620869: Epoch 2042 +2026-04-12 22:24:02.623078: Current learning rate: 0.00526 +2026-04-12 22:25:46.541145: train_loss -0.3725 +2026-04-12 22:25:46.550522: val_loss -0.2697 +2026-04-12 22:25:46.558338: Pseudo dice [0.5476, 0.0, 0.6204, 0.8935, 0.502, 0.0563, 0.9116] +2026-04-12 22:25:46.561902: Epoch time: 103.93 s +2026-04-12 22:25:48.154902: +2026-04-12 22:25:48.158525: Epoch 2043 +2026-04-12 22:25:48.161379: Current learning rate: 0.00526 +2026-04-12 22:27:30.925270: train_loss -0.3691 +2026-04-12 22:27:30.931540: val_loss -0.2685 +2026-04-12 22:27:30.938136: Pseudo dice [0.1627, 0.5234, 0.6405, 0.4353, 0.4486, 0.0249, 0.7015] +2026-04-12 22:27:30.940871: Epoch time: 102.77 s +2026-04-12 22:27:32.557194: +2026-04-12 22:27:32.560014: Epoch 2044 +2026-04-12 22:27:32.561930: Current learning rate: 0.00525 +2026-04-12 22:29:14.782164: train_loss -0.3591 +2026-04-12 22:29:14.787987: val_loss -0.2553 +2026-04-12 22:29:14.790001: Pseudo dice [0.7152, 0.6154, 0.4481, 0.6337, 0.3379, 0.0284, 0.7119] +2026-04-12 22:29:14.793265: Epoch time: 102.23 s +2026-04-12 22:29:16.357707: +2026-04-12 22:29:16.359532: Epoch 2045 +2026-04-12 22:29:16.361650: Current learning rate: 0.00525 +2026-04-12 22:30:58.778395: train_loss -0.3645 +2026-04-12 22:30:58.786201: val_loss -0.3356 +2026-04-12 22:30:58.789060: Pseudo dice [0.6277, 0.7171, 0.7168, 0.5478, 0.5863, 0.2415, 0.8359] +2026-04-12 22:30:58.793146: Epoch time: 102.42 s +2026-04-12 22:31:00.344385: +2026-04-12 22:31:00.346091: Epoch 2046 +2026-04-12 22:31:00.348245: Current learning rate: 0.00525 +2026-04-12 22:32:42.952332: train_loss -0.3738 +2026-04-12 22:32:42.959883: val_loss -0.3531 +2026-04-12 22:32:42.961774: Pseudo dice [0.5969, 0.4711, 0.5513, 0.6747, 0.6438, 0.6384, 0.8784] +2026-04-12 22:32:42.964143: Epoch time: 102.61 s +2026-04-12 22:32:44.494897: +2026-04-12 22:32:44.496744: Epoch 2047 +2026-04-12 22:32:44.498886: Current learning rate: 0.00525 +2026-04-12 22:34:27.234695: train_loss -0.3661 +2026-04-12 22:34:27.244284: val_loss -0.2226 +2026-04-12 22:34:27.247260: Pseudo dice [0.318, 0.1215, 0.3064, 0.0276, 0.7329, 0.1078, 0.5636] +2026-04-12 22:34:27.249731: Epoch time: 102.74 s +2026-04-12 22:34:28.705719: +2026-04-12 22:34:28.707897: Epoch 2048 +2026-04-12 22:34:28.710093: Current learning rate: 0.00524 +2026-04-12 22:36:11.488245: train_loss -0.3443 +2026-04-12 22:36:11.496626: val_loss -0.2735 +2026-04-12 22:36:11.498873: Pseudo dice [0.3655, 0.0947, 0.6916, 0.1426, 0.5557, 0.1121, 0.8466] +2026-04-12 22:36:11.502812: Epoch time: 102.79 s +2026-04-12 22:36:12.974831: +2026-04-12 22:36:12.976758: Epoch 2049 +2026-04-12 22:36:12.979224: Current learning rate: 0.00524 +2026-04-12 22:37:56.211452: train_loss -0.3582 +2026-04-12 22:37:56.220611: val_loss -0.3065 +2026-04-12 22:37:56.223478: Pseudo dice [0.6132, 0.6374, 0.5324, 0.552, 0.6491, 0.4122, 0.8774] +2026-04-12 22:37:56.227370: Epoch time: 103.24 s +2026-04-12 22:37:59.858932: +2026-04-12 22:37:59.861123: Epoch 2050 +2026-04-12 22:37:59.865226: Current learning rate: 0.00524 +2026-04-12 22:39:42.785334: train_loss -0.3874 +2026-04-12 22:39:42.791276: val_loss -0.3133 +2026-04-12 22:39:42.793506: Pseudo dice [0.5293, 0.281, 0.6489, 0.791, 0.594, 0.1645, 0.8537] +2026-04-12 22:39:42.795786: Epoch time: 102.93 s +2026-04-12 22:39:44.269783: +2026-04-12 22:39:44.271651: Epoch 2051 +2026-04-12 22:39:44.273718: Current learning rate: 0.00524 +2026-04-12 22:41:27.269116: train_loss -0.3853 +2026-04-12 22:41:27.276831: val_loss -0.3459 +2026-04-12 22:41:27.279223: Pseudo dice [0.6771, 0.2457, 0.7268, 0.7282, 0.4971, 0.7308, 0.8146] +2026-04-12 22:41:27.284953: Epoch time: 103.0 s +2026-04-12 22:41:28.840975: +2026-04-12 22:41:28.842895: Epoch 2052 +2026-04-12 22:41:28.845925: Current learning rate: 0.00523 +2026-04-12 22:43:13.547488: train_loss -0.3826 +2026-04-12 22:43:13.564746: val_loss -0.2632 +2026-04-12 22:43:13.567269: Pseudo dice [0.8375, 0.6342, 0.6294, 0.8858, 0.282, 0.2412, 0.6307] +2026-04-12 22:43:13.570967: Epoch time: 104.71 s +2026-04-12 22:43:15.132120: +2026-04-12 22:43:15.133850: Epoch 2053 +2026-04-12 22:43:15.136617: Current learning rate: 0.00523 +2026-04-12 22:44:58.143344: train_loss -0.3727 +2026-04-12 22:44:58.149130: val_loss -0.3447 +2026-04-12 22:44:58.151401: Pseudo dice [0.5279, 0.4493, 0.687, 0.3298, 0.53, 0.7674, 0.9097] +2026-04-12 22:44:58.154105: Epoch time: 103.01 s +2026-04-12 22:44:59.701019: +2026-04-12 22:44:59.702983: Epoch 2054 +2026-04-12 22:44:59.705604: Current learning rate: 0.00523 +2026-04-12 22:46:43.334089: train_loss -0.3803 +2026-04-12 22:46:43.342666: val_loss -0.3467 +2026-04-12 22:46:43.344637: Pseudo dice [0.255, 0.2136, 0.7067, 0.7973, 0.4924, 0.8168, 0.8077] +2026-04-12 22:46:43.347157: Epoch time: 103.64 s +2026-04-12 22:46:44.846008: +2026-04-12 22:46:44.848361: Epoch 2055 +2026-04-12 22:46:44.850757: Current learning rate: 0.00523 +2026-04-12 22:48:29.596710: train_loss -0.3786 +2026-04-12 22:48:29.604427: val_loss -0.2716 +2026-04-12 22:48:29.607783: Pseudo dice [0.7267, 0.5354, 0.6664, 0.7101, 0.5743, 0.1816, 0.6953] +2026-04-12 22:48:29.610364: Epoch time: 104.75 s +2026-04-12 22:48:31.079715: +2026-04-12 22:48:31.085899: Epoch 2056 +2026-04-12 22:48:31.088313: Current learning rate: 0.00522 +2026-04-12 22:50:14.468770: train_loss -0.3767 +2026-04-12 22:50:14.477564: val_loss -0.1535 +2026-04-12 22:50:14.480690: Pseudo dice [0.6916, 0.2185, 0.4426, 0.6173, 0.4242, 0.0576, 0.4287] +2026-04-12 22:50:14.485399: Epoch time: 103.39 s +2026-04-12 22:50:16.000952: +2026-04-12 22:50:16.006672: Epoch 2057 +2026-04-12 22:50:16.010823: Current learning rate: 0.00522 +2026-04-12 22:51:59.242385: train_loss -0.3651 +2026-04-12 22:51:59.249797: val_loss -0.2804 +2026-04-12 22:51:59.252300: Pseudo dice [0.6547, 0.8066, 0.6885, 0.6767, 0.3479, 0.4008, 0.6162] +2026-04-12 22:51:59.255472: Epoch time: 103.25 s +2026-04-12 22:52:00.701328: +2026-04-12 22:52:00.704668: Epoch 2058 +2026-04-12 22:52:00.708045: Current learning rate: 0.00522 +2026-04-12 22:53:43.781842: train_loss -0.3839 +2026-04-12 22:53:43.788025: val_loss -0.2973 +2026-04-12 22:53:43.790275: Pseudo dice [0.854, 0.5044, 0.4683, 0.2803, 0.6098, 0.329, 0.6492] +2026-04-12 22:53:43.794033: Epoch time: 103.08 s +2026-04-12 22:53:45.314118: +2026-04-12 22:53:45.316624: Epoch 2059 +2026-04-12 22:53:45.320571: Current learning rate: 0.00522 +2026-04-12 22:55:28.257860: train_loss -0.3661 +2026-04-12 22:55:28.264054: val_loss -0.3367 +2026-04-12 22:55:28.267585: Pseudo dice [0.569, 0.6236, 0.5939, 0.5794, 0.5718, 0.305, 0.739] +2026-04-12 22:55:28.270445: Epoch time: 102.95 s +2026-04-12 22:55:29.813517: +2026-04-12 22:55:29.815694: Epoch 2060 +2026-04-12 22:55:29.818542: Current learning rate: 0.00521 +2026-04-12 22:57:13.163875: train_loss -0.3745 +2026-04-12 22:57:13.170952: val_loss -0.3086 +2026-04-12 22:57:13.173954: Pseudo dice [0.8653, 0.2665, 0.7687, 0.0085, 0.6295, 0.1436, 0.8568] +2026-04-12 22:57:13.176754: Epoch time: 103.35 s +2026-04-12 22:57:14.732792: +2026-04-12 22:57:14.735584: Epoch 2061 +2026-04-12 22:57:14.738497: Current learning rate: 0.00521 +2026-04-12 22:58:57.855468: train_loss -0.3808 +2026-04-12 22:58:57.862020: val_loss -0.2775 +2026-04-12 22:58:57.864136: Pseudo dice [0.7772, 0.7975, 0.5615, 0.6704, 0.4455, 0.1155, 0.5747] +2026-04-12 22:58:57.866293: Epoch time: 103.13 s +2026-04-12 22:58:59.363079: +2026-04-12 22:58:59.365029: Epoch 2062 +2026-04-12 22:58:59.366995: Current learning rate: 0.00521 +2026-04-12 23:00:42.970597: train_loss -0.3809 +2026-04-12 23:00:42.977433: val_loss -0.273 +2026-04-12 23:00:42.979892: Pseudo dice [0.6824, 0.4212, 0.7025, 0.8597, 0.6793, 0.174, 0.8592] +2026-04-12 23:00:42.982686: Epoch time: 103.61 s +2026-04-12 23:00:44.461142: +2026-04-12 23:00:44.465478: Epoch 2063 +2026-04-12 23:00:44.468628: Current learning rate: 0.00521 +2026-04-12 23:02:27.684628: train_loss -0.3687 +2026-04-12 23:02:27.690531: val_loss -0.3165 +2026-04-12 23:02:27.692423: Pseudo dice [0.8348, 0.1967, 0.6122, 0.7021, 0.612, 0.288, 0.7006] +2026-04-12 23:02:27.695676: Epoch time: 103.23 s +2026-04-12 23:02:29.158352: +2026-04-12 23:02:29.160493: Epoch 2064 +2026-04-12 23:02:29.162526: Current learning rate: 0.0052 +2026-04-12 23:04:11.806168: train_loss -0.3738 +2026-04-12 23:04:11.815754: val_loss -0.327 +2026-04-12 23:04:11.818165: Pseudo dice [0.8585, 0.4868, 0.7175, 0.4202, 0.5082, 0.7432, 0.2249] +2026-04-12 23:04:11.821253: Epoch time: 102.65 s +2026-04-12 23:04:13.375576: +2026-04-12 23:04:13.394696: Epoch 2065 +2026-04-12 23:04:13.396667: Current learning rate: 0.0052 +2026-04-12 23:05:57.812762: train_loss -0.388 +2026-04-12 23:05:57.819327: val_loss -0.3476 +2026-04-12 23:05:57.821376: Pseudo dice [0.6087, 0.2966, 0.6285, 0.7346, 0.502, 0.7081, 0.8659] +2026-04-12 23:05:57.823652: Epoch time: 104.44 s +2026-04-12 23:05:59.307718: +2026-04-12 23:05:59.309913: Epoch 2066 +2026-04-12 23:05:59.312102: Current learning rate: 0.0052 +2026-04-12 23:07:42.375693: train_loss -0.378 +2026-04-12 23:07:42.383512: val_loss -0.3455 +2026-04-12 23:07:42.385644: Pseudo dice [0.7809, 0.3299, 0.6913, 0.8273, 0.4191, 0.599, 0.8628] +2026-04-12 23:07:42.388795: Epoch time: 103.07 s +2026-04-12 23:07:43.895927: +2026-04-12 23:07:43.897830: Epoch 2067 +2026-04-12 23:07:43.899793: Current learning rate: 0.0052 +2026-04-12 23:09:27.398020: train_loss -0.3635 +2026-04-12 23:09:27.403639: val_loss -0.2925 +2026-04-12 23:09:27.406026: Pseudo dice [0.7495, 0.8827, 0.622, 0.7855, 0.5694, 0.1854, 0.7959] +2026-04-12 23:09:27.409232: Epoch time: 103.51 s +2026-04-12 23:09:28.913958: +2026-04-12 23:09:28.916141: Epoch 2068 +2026-04-12 23:09:28.920546: Current learning rate: 0.00519 +2026-04-12 23:11:13.134640: train_loss -0.3665 +2026-04-12 23:11:13.146649: val_loss -0.3339 +2026-04-12 23:11:13.150085: Pseudo dice [0.5558, 0.17, 0.7365, 0.6289, 0.5512, 0.5445, 0.4129] +2026-04-12 23:11:13.154113: Epoch time: 104.22 s +2026-04-12 23:11:14.677144: +2026-04-12 23:11:14.679362: Epoch 2069 +2026-04-12 23:11:14.681408: Current learning rate: 0.00519 +2026-04-12 23:12:58.089033: train_loss -0.3702 +2026-04-12 23:12:58.097950: val_loss -0.2542 +2026-04-12 23:12:58.102195: Pseudo dice [0.2509, 0.0506, 0.6006, 0.6716, 0.6857, 0.0928, 0.4105] +2026-04-12 23:12:58.104698: Epoch time: 103.42 s +2026-04-12 23:12:59.607551: +2026-04-12 23:12:59.610074: Epoch 2070 +2026-04-12 23:12:59.611957: Current learning rate: 0.00519 +2026-04-12 23:14:43.049300: train_loss -0.3725 +2026-04-12 23:14:43.056181: val_loss -0.2749 +2026-04-12 23:14:43.058674: Pseudo dice [0.5411, 0.0356, 0.6341, 0.7505, 0.4083, 0.2071, 0.6094] +2026-04-12 23:14:43.061496: Epoch time: 103.44 s +2026-04-12 23:14:44.566769: +2026-04-12 23:14:44.568696: Epoch 2071 +2026-04-12 23:14:44.571158: Current learning rate: 0.00519 +2026-04-12 23:16:28.227913: train_loss -0.3646 +2026-04-12 23:16:28.235728: val_loss -0.3249 +2026-04-12 23:16:28.238484: Pseudo dice [0.7676, 0.2251, 0.6472, 0.7841, 0.4251, 0.1769, 0.8137] +2026-04-12 23:16:28.241261: Epoch time: 103.67 s +2026-04-12 23:16:29.774769: +2026-04-12 23:16:29.776871: Epoch 2072 +2026-04-12 23:16:29.779058: Current learning rate: 0.00518 +2026-04-12 23:18:12.194282: train_loss -0.375 +2026-04-12 23:18:12.202241: val_loss -0.3405 +2026-04-12 23:18:12.204710: Pseudo dice [0.7579, 0.7552, 0.5007, 0.8044, 0.2013, 0.6909, 0.7973] +2026-04-12 23:18:12.208066: Epoch time: 102.42 s +2026-04-12 23:18:13.767588: +2026-04-12 23:18:13.770063: Epoch 2073 +2026-04-12 23:18:13.772689: Current learning rate: 0.00518 +2026-04-12 23:19:56.711543: train_loss -0.3834 +2026-04-12 23:19:56.718144: val_loss -0.199 +2026-04-12 23:19:56.720943: Pseudo dice [0.7142, 0.6115, 0.7029, 0.6438, 0.5667, 0.0898, 0.5602] +2026-04-12 23:19:56.723173: Epoch time: 102.95 s +2026-04-12 23:19:58.197967: +2026-04-12 23:19:58.200918: Epoch 2074 +2026-04-12 23:19:58.203028: Current learning rate: 0.00518 +2026-04-12 23:21:41.076972: train_loss -0.383 +2026-04-12 23:21:41.083964: val_loss -0.3121 +2026-04-12 23:21:41.086203: Pseudo dice [0.4077, 0.1996, 0.5334, 0.7822, 0.3668, 0.1782, 0.7897] +2026-04-12 23:21:41.088737: Epoch time: 102.88 s +2026-04-12 23:21:42.597325: +2026-04-12 23:21:42.599544: Epoch 2075 +2026-04-12 23:21:42.601544: Current learning rate: 0.00518 +2026-04-12 23:23:25.677242: train_loss -0.3777 +2026-04-12 23:23:25.684795: val_loss -0.3041 +2026-04-12 23:23:25.687984: Pseudo dice [0.3734, 0.443, 0.6084, 0.6872, 0.4309, 0.8005, 0.6591] +2026-04-12 23:23:25.690744: Epoch time: 103.08 s +2026-04-12 23:23:28.266404: +2026-04-12 23:23:28.268329: Epoch 2076 +2026-04-12 23:23:28.270317: Current learning rate: 0.00518 +2026-04-12 23:25:11.140180: train_loss -0.3774 +2026-04-12 23:25:11.147123: val_loss -0.3321 +2026-04-12 23:25:11.149291: Pseudo dice [0.4305, 0.5873, 0.7638, 0.1799, 0.2563, 0.7938, 0.5683] +2026-04-12 23:25:11.151971: Epoch time: 102.88 s +2026-04-12 23:25:12.615539: +2026-04-12 23:25:12.617789: Epoch 2077 +2026-04-12 23:25:12.619986: Current learning rate: 0.00517 +2026-04-12 23:26:56.123448: train_loss -0.3782 +2026-04-12 23:26:56.129940: val_loss -0.323 +2026-04-12 23:26:56.133248: Pseudo dice [0.5327, 0.0308, 0.7759, 0.0767, 0.3212, 0.72, 0.8867] +2026-04-12 23:26:56.135735: Epoch time: 103.51 s +2026-04-12 23:26:57.644068: +2026-04-12 23:26:57.647496: Epoch 2078 +2026-04-12 23:26:57.651502: Current learning rate: 0.00517 +2026-04-12 23:28:41.118405: train_loss -0.3737 +2026-04-12 23:28:41.125935: val_loss -0.3078 +2026-04-12 23:28:41.128531: Pseudo dice [0.8244, 0.1371, 0.692, 0.5052, 0.2914, 0.5164, 0.8105] +2026-04-12 23:28:41.130968: Epoch time: 103.48 s +2026-04-12 23:28:42.680527: +2026-04-12 23:28:42.683328: Epoch 2079 +2026-04-12 23:28:42.685529: Current learning rate: 0.00517 +2026-04-12 23:30:25.977161: train_loss -0.3576 +2026-04-12 23:30:25.986282: val_loss -0.3183 +2026-04-12 23:30:25.988245: Pseudo dice [0.8062, 0.1222, 0.5295, 0.3056, 0.4934, 0.6117, 0.7444] +2026-04-12 23:30:25.990552: Epoch time: 103.3 s +2026-04-12 23:30:27.470745: +2026-04-12 23:30:27.473255: Epoch 2080 +2026-04-12 23:30:27.476172: Current learning rate: 0.00517 +2026-04-12 23:32:12.419401: train_loss -0.3842 +2026-04-12 23:32:12.427180: val_loss -0.314 +2026-04-12 23:32:12.429712: Pseudo dice [0.3684, 0.4625, 0.6437, 0.5779, 0.3746, 0.2694, 0.7772] +2026-04-12 23:32:12.431913: Epoch time: 104.95 s +2026-04-12 23:32:13.941682: +2026-04-12 23:32:13.943613: Epoch 2081 +2026-04-12 23:32:13.945721: Current learning rate: 0.00516 +2026-04-12 23:33:56.976532: train_loss -0.3785 +2026-04-12 23:33:56.983263: val_loss -0.3208 +2026-04-12 23:33:56.985974: Pseudo dice [0.7169, 0.2532, 0.7638, 0.7722, 0.3083, 0.397, 0.5703] +2026-04-12 23:33:56.988571: Epoch time: 103.04 s +2026-04-12 23:33:58.558084: +2026-04-12 23:33:58.559938: Epoch 2082 +2026-04-12 23:33:58.562126: Current learning rate: 0.00516 +2026-04-12 23:35:42.070026: train_loss -0.3671 +2026-04-12 23:35:42.076039: val_loss -0.2934 +2026-04-12 23:35:42.079614: Pseudo dice [0.8315, 0.5472, 0.7332, 0.641, 0.523, 0.1116, 0.5131] +2026-04-12 23:35:42.099421: Epoch time: 103.52 s +2026-04-12 23:35:43.579280: +2026-04-12 23:35:43.581350: Epoch 2083 +2026-04-12 23:35:43.584164: Current learning rate: 0.00516 +2026-04-12 23:37:27.458338: train_loss -0.3552 +2026-04-12 23:37:27.473601: val_loss -0.3197 +2026-04-12 23:37:27.479393: Pseudo dice [0.5738, 0.2414, 0.6341, 0.0469, 0.2347, 0.7263, 0.708] +2026-04-12 23:37:27.484141: Epoch time: 103.88 s +2026-04-12 23:37:28.984614: +2026-04-12 23:37:28.986420: Epoch 2084 +2026-04-12 23:37:28.988812: Current learning rate: 0.00516 +2026-04-12 23:39:11.370283: train_loss -0.3625 +2026-04-12 23:39:11.377126: val_loss -0.2712 +2026-04-12 23:39:11.379154: Pseudo dice [0.7218, 0.6388, 0.4746, 0.825, 0.4158, 0.1915, 0.862] +2026-04-12 23:39:11.383569: Epoch time: 102.39 s +2026-04-12 23:39:12.921951: +2026-04-12 23:39:12.923718: Epoch 2085 +2026-04-12 23:39:12.925704: Current learning rate: 0.00515 +2026-04-12 23:40:55.586179: train_loss -0.3507 +2026-04-12 23:40:55.592229: val_loss -0.3058 +2026-04-12 23:40:55.594346: Pseudo dice [0.1995, 0.128, 0.7994, 0.0044, 0.3568, 0.808, 0.3593] +2026-04-12 23:40:55.597251: Epoch time: 102.67 s +2026-04-12 23:40:57.095254: +2026-04-12 23:40:57.098045: Epoch 2086 +2026-04-12 23:40:57.100155: Current learning rate: 0.00515 +2026-04-12 23:42:41.347642: train_loss -0.3544 +2026-04-12 23:42:41.353598: val_loss -0.288 +2026-04-12 23:42:41.356337: Pseudo dice [0.4221, 0.1029, 0.3327, 0.3242, 0.5697, 0.3182, 0.5061] +2026-04-12 23:42:41.358536: Epoch time: 104.26 s +2026-04-12 23:42:42.851655: +2026-04-12 23:42:42.854462: Epoch 2087 +2026-04-12 23:42:42.857904: Current learning rate: 0.00515 +2026-04-12 23:44:26.081405: train_loss -0.3628 +2026-04-12 23:44:26.088164: val_loss -0.2783 +2026-04-12 23:44:26.090450: Pseudo dice [0.5197, 0.2945, 0.3785, 0.2115, 0.3011, 0.4168, 0.7955] +2026-04-12 23:44:26.092770: Epoch time: 103.23 s +2026-04-12 23:44:27.604711: +2026-04-12 23:44:27.607149: Epoch 2088 +2026-04-12 23:44:27.609264: Current learning rate: 0.00515 +2026-04-12 23:46:10.201633: train_loss -0.3754 +2026-04-12 23:46:10.207284: val_loss -0.3481 +2026-04-12 23:46:10.209477: Pseudo dice [0.7157, 0.657, 0.5503, 0.299, 0.5573, 0.8141, 0.6093] +2026-04-12 23:46:10.211896: Epoch time: 102.6 s +2026-04-12 23:46:11.681670: +2026-04-12 23:46:11.683508: Epoch 2089 +2026-04-12 23:46:11.685838: Current learning rate: 0.00514 +2026-04-12 23:47:54.386262: train_loss -0.3719 +2026-04-12 23:47:54.394175: val_loss -0.3108 +2026-04-12 23:47:54.396769: Pseudo dice [0.4309, 0.2661, 0.7287, 0.7404, 0.638, 0.166, 0.8492] +2026-04-12 23:47:54.399492: Epoch time: 102.71 s +2026-04-12 23:47:55.896102: +2026-04-12 23:47:55.897995: Epoch 2090 +2026-04-12 23:47:55.900389: Current learning rate: 0.00514 +2026-04-12 23:49:38.870485: train_loss -0.3716 +2026-04-12 23:49:38.876446: val_loss -0.3171 +2026-04-12 23:49:38.879632: Pseudo dice [0.6747, 0.206, 0.691, 0.6205, 0.6373, 0.3954, 0.5391] +2026-04-12 23:49:38.881998: Epoch time: 102.98 s +2026-04-12 23:49:40.354100: +2026-04-12 23:49:40.355780: Epoch 2091 +2026-04-12 23:49:40.358624: Current learning rate: 0.00514 +2026-04-12 23:51:23.032859: train_loss -0.3629 +2026-04-12 23:51:23.043518: val_loss -0.2457 +2026-04-12 23:51:23.045911: Pseudo dice [0.505, 0.1192, 0.7045, 0.7435, 0.3895, 0.1391, 0.6235] +2026-04-12 23:51:23.048216: Epoch time: 102.68 s +2026-04-12 23:51:24.538136: +2026-04-12 23:51:24.539972: Epoch 2092 +2026-04-12 23:51:24.542462: Current learning rate: 0.00514 +2026-04-12 23:53:07.026108: train_loss -0.358 +2026-04-12 23:53:07.031074: val_loss -0.2601 +2026-04-12 23:53:07.033174: Pseudo dice [0.8481, 0.2113, 0.6606, 0.5806, 0.388, 0.2773, 0.7147] +2026-04-12 23:53:07.035288: Epoch time: 102.49 s +2026-04-12 23:53:08.470193: +2026-04-12 23:53:08.474216: Epoch 2093 +2026-04-12 23:53:08.476179: Current learning rate: 0.00513 +2026-04-12 23:54:51.981075: train_loss -0.3775 +2026-04-12 23:54:51.995744: val_loss -0.3196 +2026-04-12 23:54:51.998449: Pseudo dice [0.6889, 0.2743, 0.7497, 0.7121, 0.6178, 0.1599, 0.7663] +2026-04-12 23:54:52.002211: Epoch time: 103.51 s +2026-04-12 23:54:53.487704: +2026-04-12 23:54:53.489973: Epoch 2094 +2026-04-12 23:54:53.492544: Current learning rate: 0.00513 +2026-04-12 23:56:35.951556: train_loss -0.3468 +2026-04-12 23:56:35.957473: val_loss -0.2825 +2026-04-12 23:56:35.959960: Pseudo dice [0.7601, 0.6239, 0.5244, 0.3662, 0.6137, 0.1199, 0.799] +2026-04-12 23:56:35.961842: Epoch time: 102.47 s +2026-04-12 23:56:37.410311: +2026-04-12 23:56:37.413495: Epoch 2095 +2026-04-12 23:56:37.416242: Current learning rate: 0.00513 +2026-04-12 23:58:20.271982: train_loss -0.3651 +2026-04-12 23:58:20.277723: val_loss -0.2455 +2026-04-12 23:58:20.279567: Pseudo dice [0.3071, 0.1355, 0.5533, 0.3976, 0.5536, 0.0589, 0.2947] +2026-04-12 23:58:20.282073: Epoch time: 102.87 s +2026-04-12 23:58:21.768711: +2026-04-12 23:58:21.770508: Epoch 2096 +2026-04-12 23:58:21.772667: Current learning rate: 0.00513 +2026-04-13 00:00:04.742847: train_loss -0.3586 +2026-04-13 00:00:04.748803: val_loss -0.145 +2026-04-13 00:00:04.750697: Pseudo dice [0.5848, 0.1712, 0.4773, 0.544, 0.4471, 0.0226, 0.5681] +2026-04-13 00:00:04.753446: Epoch time: 102.98 s +2026-04-13 00:00:07.365921: +2026-04-13 00:00:07.368249: Epoch 2097 +2026-04-13 00:00:07.370505: Current learning rate: 0.00512 +2026-04-13 00:02:03.252601: train_loss -0.3737 +2026-04-13 00:02:03.260438: val_loss -0.2638 +2026-04-13 00:02:03.262684: Pseudo dice [0.7554, 0.3777, 0.6901, 0.6187, 0.5514, 0.1257, 0.7528] +2026-04-13 00:02:03.266280: Epoch time: 115.89 s +2026-04-13 00:02:04.787515: +2026-04-13 00:02:04.790009: Epoch 2098 +2026-04-13 00:02:04.792421: Current learning rate: 0.00512 +2026-04-13 00:03:48.156689: train_loss -0.3821 +2026-04-13 00:03:48.163197: val_loss -0.2017 +2026-04-13 00:03:48.166063: Pseudo dice [0.7333, 0.4906, 0.6875, 0.7712, 0.4028, 0.1107, 0.8729] +2026-04-13 00:03:48.168989: Epoch time: 103.37 s +2026-04-13 00:03:49.653107: +2026-04-13 00:03:49.655185: Epoch 2099 +2026-04-13 00:03:49.658941: Current learning rate: 0.00512 +2026-04-13 00:05:34.269677: train_loss -0.3886 +2026-04-13 00:05:34.280388: val_loss -0.2315 +2026-04-13 00:05:34.283378: Pseudo dice [0.4061, 0.3063, 0.5547, 0.5117, 0.4281, 0.026, 0.7809] +2026-04-13 00:05:34.289325: Epoch time: 104.62 s +2026-04-13 00:05:37.871398: +2026-04-13 00:05:37.874164: Epoch 2100 +2026-04-13 00:05:37.876311: Current learning rate: 0.00512 +2026-04-13 00:07:20.914863: train_loss -0.3782 +2026-04-13 00:07:20.921081: val_loss -0.2822 +2026-04-13 00:07:20.923036: Pseudo dice [0.4795, 0.0468, 0.6505, 0.2361, 0.4158, 0.2963, 0.8812] +2026-04-13 00:07:20.926113: Epoch time: 103.05 s +2026-04-13 00:07:22.378265: +2026-04-13 00:07:22.380210: Epoch 2101 +2026-04-13 00:07:22.383548: Current learning rate: 0.00511 +2026-04-13 00:09:05.550498: train_loss -0.3671 +2026-04-13 00:09:05.558439: val_loss -0.3645 +2026-04-13 00:09:05.560401: Pseudo dice [0.5832, 0.6146, 0.7344, 0.7727, 0.4469, 0.8776, 0.8933] +2026-04-13 00:09:05.562791: Epoch time: 103.18 s +2026-04-13 00:09:07.068398: +2026-04-13 00:09:07.071314: Epoch 2102 +2026-04-13 00:09:07.073493: Current learning rate: 0.00511 +2026-04-13 00:10:50.551234: train_loss -0.3869 +2026-04-13 00:10:50.557517: val_loss -0.2583 +2026-04-13 00:10:50.559969: Pseudo dice [0.2152, 0.6306, 0.5725, 0.8442, 0.4907, 0.0506, 0.8125] +2026-04-13 00:10:50.562416: Epoch time: 103.49 s +2026-04-13 00:10:52.138061: +2026-04-13 00:10:52.140164: Epoch 2103 +2026-04-13 00:10:52.142451: Current learning rate: 0.00511 +2026-04-13 00:12:34.930166: train_loss -0.3837 +2026-04-13 00:12:34.937901: val_loss -0.2784 +2026-04-13 00:12:34.940724: Pseudo dice [0.2792, 0.0732, 0.6007, 0.3786, 0.3, 0.193, 0.5251] +2026-04-13 00:12:34.943296: Epoch time: 102.8 s +2026-04-13 00:12:36.465471: +2026-04-13 00:12:36.467123: Epoch 2104 +2026-04-13 00:12:36.469186: Current learning rate: 0.00511 +2026-04-13 00:14:19.792521: train_loss -0.369 +2026-04-13 00:14:19.799407: val_loss -0.2989 +2026-04-13 00:14:19.802963: Pseudo dice [0.4404, 0.5435, 0.5999, 0.8, 0.4957, 0.0943, 0.6869] +2026-04-13 00:14:19.805514: Epoch time: 103.33 s +2026-04-13 00:14:21.331895: +2026-04-13 00:14:21.333787: Epoch 2105 +2026-04-13 00:14:21.335757: Current learning rate: 0.0051 +2026-04-13 00:16:05.408688: train_loss -0.3895 +2026-04-13 00:16:05.416749: val_loss -0.2677 +2026-04-13 00:16:05.419171: Pseudo dice [0.574, 0.5244, 0.5703, 0.3354, 0.4122, 0.0837, 0.6075] +2026-04-13 00:16:05.422506: Epoch time: 104.08 s +2026-04-13 00:16:06.939497: +2026-04-13 00:16:06.941462: Epoch 2106 +2026-04-13 00:16:06.943891: Current learning rate: 0.0051 +2026-04-13 00:17:49.534478: train_loss -0.3598 +2026-04-13 00:17:49.545117: val_loss -0.3159 +2026-04-13 00:17:49.547945: Pseudo dice [0.2082, 0.4945, 0.726, 0.8299, 0.2234, 0.326, 0.5154] +2026-04-13 00:17:49.551115: Epoch time: 102.6 s +2026-04-13 00:17:51.052076: +2026-04-13 00:17:51.054070: Epoch 2107 +2026-04-13 00:17:51.056034: Current learning rate: 0.0051 +2026-04-13 00:19:33.459246: train_loss -0.3674 +2026-04-13 00:19:33.467397: val_loss -0.3516 +2026-04-13 00:19:33.470729: Pseudo dice [0.7881, 0.2024, 0.7288, 0.6479, 0.6614, 0.7016, 0.9104] +2026-04-13 00:19:33.474434: Epoch time: 102.41 s +2026-04-13 00:19:34.948026: +2026-04-13 00:19:34.949892: Epoch 2108 +2026-04-13 00:19:34.951992: Current learning rate: 0.0051 +2026-04-13 00:21:17.366765: train_loss -0.3878 +2026-04-13 00:21:17.392857: val_loss -0.3103 +2026-04-13 00:21:17.394944: Pseudo dice [0.4709, 0.3987, 0.6295, 0.6322, 0.4705, 0.264, 0.3332] +2026-04-13 00:21:17.398088: Epoch time: 102.42 s +2026-04-13 00:21:18.897578: +2026-04-13 00:21:18.901175: Epoch 2109 +2026-04-13 00:21:18.903949: Current learning rate: 0.0051 +2026-04-13 00:23:01.876029: train_loss -0.3829 +2026-04-13 00:23:01.883992: val_loss -0.2901 +2026-04-13 00:23:01.885793: Pseudo dice [0.8173, 0.3163, 0.6325, 0.8141, 0.5279, 0.2423, 0.8426] +2026-04-13 00:23:01.887907: Epoch time: 102.98 s +2026-04-13 00:23:03.356705: +2026-04-13 00:23:03.358765: Epoch 2110 +2026-04-13 00:23:03.360917: Current learning rate: 0.00509 +2026-04-13 00:24:45.672535: train_loss -0.3903 +2026-04-13 00:24:45.677984: val_loss -0.3537 +2026-04-13 00:24:45.679976: Pseudo dice [0.4452, 0.323, 0.8148, 0.8458, 0.6168, 0.7908, 0.7402] +2026-04-13 00:24:45.683123: Epoch time: 102.32 s +2026-04-13 00:24:47.158447: +2026-04-13 00:24:47.160192: Epoch 2111 +2026-04-13 00:24:47.162036: Current learning rate: 0.00509 +2026-04-13 00:26:31.867879: train_loss -0.3791 +2026-04-13 00:26:31.877117: val_loss -0.2759 +2026-04-13 00:26:31.880475: Pseudo dice [0.2913, 0.5015, 0.366, 0.6365, 0.445, 0.1666, 0.7217] +2026-04-13 00:26:31.884741: Epoch time: 104.71 s +2026-04-13 00:26:33.380422: +2026-04-13 00:26:33.383392: Epoch 2112 +2026-04-13 00:26:33.386701: Current learning rate: 0.00509 +2026-04-13 00:28:16.297980: train_loss -0.3741 +2026-04-13 00:28:16.305910: val_loss -0.3339 +2026-04-13 00:28:16.308835: Pseudo dice [0.6716, 0.488, 0.6199, 0.7092, 0.4559, 0.2408, 0.7161] +2026-04-13 00:28:16.311422: Epoch time: 102.92 s +2026-04-13 00:28:17.781676: +2026-04-13 00:28:17.783429: Epoch 2113 +2026-04-13 00:28:17.785655: Current learning rate: 0.00509 +2026-04-13 00:30:00.709715: train_loss -0.3795 +2026-04-13 00:30:00.716304: val_loss -0.3391 +2026-04-13 00:30:00.719064: Pseudo dice [0.3922, 0.481, 0.7055, 0.7345, 0.4776, 0.853, 0.5669] +2026-04-13 00:30:00.721565: Epoch time: 102.93 s +2026-04-13 00:30:02.218088: +2026-04-13 00:30:02.220399: Epoch 2114 +2026-04-13 00:30:02.223364: Current learning rate: 0.00508 +2026-04-13 00:31:44.862274: train_loss -0.3511 +2026-04-13 00:31:44.867358: val_loss -0.2504 +2026-04-13 00:31:44.869543: Pseudo dice [0.1827, 0.3585, 0.6801, 0.6405, 0.5638, 0.137, 0.4579] +2026-04-13 00:31:44.871551: Epoch time: 102.65 s +2026-04-13 00:31:46.345309: +2026-04-13 00:31:46.347097: Epoch 2115 +2026-04-13 00:31:46.349049: Current learning rate: 0.00508 +2026-04-13 00:33:29.105875: train_loss -0.3866 +2026-04-13 00:33:29.114430: val_loss -0.3181 +2026-04-13 00:33:29.116481: Pseudo dice [0.4297, 0.07, 0.5484, 0.626, 0.4528, 0.1463, 0.3847] +2026-04-13 00:33:29.118658: Epoch time: 102.76 s +2026-04-13 00:33:30.619462: +2026-04-13 00:33:30.621335: Epoch 2116 +2026-04-13 00:33:30.623650: Current learning rate: 0.00508 +2026-04-13 00:35:13.384678: train_loss -0.3646 +2026-04-13 00:35:13.391557: val_loss -0.2578 +2026-04-13 00:35:13.393270: Pseudo dice [0.7059, 0.2921, 0.666, 0.6207, 0.5462, 0.1631, 0.6514] +2026-04-13 00:35:13.395613: Epoch time: 102.77 s +2026-04-13 00:35:14.833682: +2026-04-13 00:35:14.835537: Epoch 2117 +2026-04-13 00:35:14.837498: Current learning rate: 0.00508 +2026-04-13 00:36:58.295361: train_loss -0.3613 +2026-04-13 00:36:58.302549: val_loss -0.2774 +2026-04-13 00:36:58.304841: Pseudo dice [0.7614, 0.1174, 0.5509, 0.8143, 0.2478, 0.0822, 0.8981] +2026-04-13 00:36:58.307663: Epoch time: 103.47 s +2026-04-13 00:36:59.793828: +2026-04-13 00:36:59.795706: Epoch 2118 +2026-04-13 00:36:59.797624: Current learning rate: 0.00507 +2026-04-13 00:38:43.468475: train_loss -0.3641 +2026-04-13 00:38:43.478599: val_loss -0.3315 +2026-04-13 00:38:43.480997: Pseudo dice [0.7346, 0.0284, 0.6581, 0.6057, 0.5634, 0.7453, 0.7049] +2026-04-13 00:38:43.483295: Epoch time: 103.68 s +2026-04-13 00:38:44.975789: +2026-04-13 00:38:44.977961: Epoch 2119 +2026-04-13 00:38:44.980438: Current learning rate: 0.00507 +2026-04-13 00:40:28.017154: train_loss -0.3716 +2026-04-13 00:40:28.023668: val_loss -0.2973 +2026-04-13 00:40:28.026197: Pseudo dice [0.6736, 0.3556, 0.6983, 0.7276, 0.3362, 0.3748, 0.2376] +2026-04-13 00:40:28.028489: Epoch time: 103.05 s +2026-04-13 00:40:29.562933: +2026-04-13 00:40:29.564750: Epoch 2120 +2026-04-13 00:40:29.566835: Current learning rate: 0.00507 +2026-04-13 00:42:12.041233: train_loss -0.3562 +2026-04-13 00:42:12.046843: val_loss -0.3138 +2026-04-13 00:42:12.048566: Pseudo dice [0.5732, 0.4516, 0.6589, 0.6428, 0.3703, 0.6778, 0.773] +2026-04-13 00:42:12.051764: Epoch time: 102.48 s +2026-04-13 00:42:13.510384: +2026-04-13 00:42:13.514351: Epoch 2121 +2026-04-13 00:42:13.516312: Current learning rate: 0.00507 +2026-04-13 00:43:56.154685: train_loss -0.3751 +2026-04-13 00:43:56.161574: val_loss -0.3356 +2026-04-13 00:43:56.164110: Pseudo dice [0.549, 0.2945, 0.6556, 0.7903, 0.2693, 0.3669, 0.7116] +2026-04-13 00:43:56.166825: Epoch time: 102.65 s +2026-04-13 00:43:57.640222: +2026-04-13 00:43:57.641882: Epoch 2122 +2026-04-13 00:43:57.643824: Current learning rate: 0.00506 +2026-04-13 00:45:41.246202: train_loss -0.3646 +2026-04-13 00:45:41.254542: val_loss -0.2287 +2026-04-13 00:45:41.258990: Pseudo dice [0.6025, 0.0533, 0.6305, 0.4174, 0.3764, 0.1585, 0.876] +2026-04-13 00:45:41.262101: Epoch time: 103.61 s +2026-04-13 00:45:42.790986: +2026-04-13 00:45:42.792885: Epoch 2123 +2026-04-13 00:45:42.794885: Current learning rate: 0.00506 +2026-04-13 00:47:25.557185: train_loss -0.3805 +2026-04-13 00:47:25.563078: val_loss -0.2337 +2026-04-13 00:47:25.564713: Pseudo dice [0.5458, 0.3304, 0.6964, 0.6256, 0.131, 0.1505, 0.5126] +2026-04-13 00:47:25.567234: Epoch time: 102.77 s +2026-04-13 00:47:27.080759: +2026-04-13 00:47:27.082576: Epoch 2124 +2026-04-13 00:47:27.084452: Current learning rate: 0.00506 +2026-04-13 00:49:09.626416: train_loss -0.358 +2026-04-13 00:49:09.631793: val_loss -0.2759 +2026-04-13 00:49:09.633780: Pseudo dice [0.3743, 0.3661, 0.4893, 0.7992, 0.4743, 0.1016, 0.8249] +2026-04-13 00:49:09.636111: Epoch time: 102.55 s +2026-04-13 00:49:11.144005: +2026-04-13 00:49:11.145870: Epoch 2125 +2026-04-13 00:49:11.147954: Current learning rate: 0.00506 +2026-04-13 00:50:53.900251: train_loss -0.3537 +2026-04-13 00:50:53.906805: val_loss -0.2848 +2026-04-13 00:50:53.908548: Pseudo dice [0.4448, 0.294, 0.4624, 0.4504, 0.6411, 0.1618, 0.7411] +2026-04-13 00:50:53.911118: Epoch time: 102.76 s +2026-04-13 00:50:55.398639: +2026-04-13 00:50:55.401010: Epoch 2126 +2026-04-13 00:50:55.403189: Current learning rate: 0.00505 +2026-04-13 00:52:37.937254: train_loss -0.3818 +2026-04-13 00:52:37.943288: val_loss -0.3276 +2026-04-13 00:52:37.945213: Pseudo dice [0.7804, 0.1466, 0.8354, 0.7332, 0.596, 0.2453, 0.5749] +2026-04-13 00:52:37.947663: Epoch time: 102.54 s +2026-04-13 00:52:39.426238: +2026-04-13 00:52:39.428307: Epoch 2127 +2026-04-13 00:52:39.430597: Current learning rate: 0.00505 +2026-04-13 00:54:22.094376: train_loss -0.3741 +2026-04-13 00:54:22.100077: val_loss -0.3148 +2026-04-13 00:54:22.102111: Pseudo dice [0.4044, 0.5071, 0.54, 0.378, 0.573, 0.5719, 0.7689] +2026-04-13 00:54:22.104630: Epoch time: 102.67 s +2026-04-13 00:54:23.588221: +2026-04-13 00:54:23.592782: Epoch 2128 +2026-04-13 00:54:23.594943: Current learning rate: 0.00505 +2026-04-13 00:56:06.088966: train_loss -0.3796 +2026-04-13 00:56:06.095587: val_loss -0.324 +2026-04-13 00:56:06.098146: Pseudo dice [0.7569, 0.6487, 0.7137, 0.6022, 0.3253, 0.3198, 0.577] +2026-04-13 00:56:06.100318: Epoch time: 102.5 s +2026-04-13 00:56:07.605424: +2026-04-13 00:56:07.607154: Epoch 2129 +2026-04-13 00:56:07.609576: Current learning rate: 0.00505 +2026-04-13 00:57:50.421920: train_loss -0.3725 +2026-04-13 00:57:50.427907: val_loss -0.2539 +2026-04-13 00:57:50.437974: Pseudo dice [0.34, 0.5732, 0.5031, 0.3759, 0.2254, 0.0586, 0.2957] +2026-04-13 00:57:50.442360: Epoch time: 102.82 s +2026-04-13 00:57:52.015980: +2026-04-13 00:57:52.018049: Epoch 2130 +2026-04-13 00:57:52.020045: Current learning rate: 0.00504 +2026-04-13 00:59:34.568469: train_loss -0.3826 +2026-04-13 00:59:34.575220: val_loss -0.3678 +2026-04-13 00:59:34.577574: Pseudo dice [0.725, 0.3721, 0.5483, 0.7591, 0.4087, 0.6575, 0.8732] +2026-04-13 00:59:34.580189: Epoch time: 102.56 s +2026-04-13 00:59:36.304015: +2026-04-13 00:59:36.305939: Epoch 2131 +2026-04-13 00:59:36.311019: Current learning rate: 0.00504 +2026-04-13 01:01:19.094159: train_loss -0.3839 +2026-04-13 01:01:19.100629: val_loss -0.303 +2026-04-13 01:01:19.104426: Pseudo dice [0.7826, 0.4115, 0.6083, 0.7871, 0.4196, 0.1106, 0.7543] +2026-04-13 01:01:19.108277: Epoch time: 102.79 s +2026-04-13 01:01:20.614832: +2026-04-13 01:01:20.617625: Epoch 2132 +2026-04-13 01:01:20.620471: Current learning rate: 0.00504 +2026-04-13 01:03:03.410401: train_loss -0.387 +2026-04-13 01:03:03.419110: val_loss -0.2784 +2026-04-13 01:03:03.422642: Pseudo dice [0.3315, 0.3716, 0.6586, 0.6762, 0.4798, 0.1397, 0.7404] +2026-04-13 01:03:03.425470: Epoch time: 102.8 s +2026-04-13 01:03:04.911507: +2026-04-13 01:03:04.913610: Epoch 2133 +2026-04-13 01:03:04.916256: Current learning rate: 0.00504 +2026-04-13 01:04:47.788931: train_loss -0.4047 +2026-04-13 01:04:47.798378: val_loss -0.3522 +2026-04-13 01:04:47.801799: Pseudo dice [0.3862, 0.7662, 0.8258, 0.5735, 0.4583, 0.8675, 0.6797] +2026-04-13 01:04:47.804454: Epoch time: 102.88 s +2026-04-13 01:04:49.295264: +2026-04-13 01:04:49.297788: Epoch 2134 +2026-04-13 01:04:49.300937: Current learning rate: 0.00503 +2026-04-13 01:06:31.782186: train_loss -0.399 +2026-04-13 01:06:31.788091: val_loss -0.3436 +2026-04-13 01:06:31.790372: Pseudo dice [0.7605, 0.386, 0.5721, 0.0272, 0.7189, 0.8007, 0.827] +2026-04-13 01:06:31.793576: Epoch time: 102.49 s +2026-04-13 01:06:33.325544: +2026-04-13 01:06:33.327398: Epoch 2135 +2026-04-13 01:06:33.329632: Current learning rate: 0.00503 +2026-04-13 01:08:15.747101: train_loss -0.3839 +2026-04-13 01:08:15.752999: val_loss -0.2666 +2026-04-13 01:08:15.755039: Pseudo dice [0.6061, 0.8829, 0.7063, 0.5605, 0.5568, 0.0829, 0.8264] +2026-04-13 01:08:15.757222: Epoch time: 102.43 s +2026-04-13 01:08:17.213802: +2026-04-13 01:08:17.215551: Epoch 2136 +2026-04-13 01:08:17.217821: Current learning rate: 0.00503 +2026-04-13 01:10:00.759017: train_loss -0.3725 +2026-04-13 01:10:00.766911: val_loss -0.241 +2026-04-13 01:10:00.770567: Pseudo dice [0.7549, 0.3277, 0.5908, 0.547, 0.1764, 0.0319, 0.9005] +2026-04-13 01:10:00.774165: Epoch time: 103.55 s +2026-04-13 01:10:02.261447: +2026-04-13 01:10:02.265397: Epoch 2137 +2026-04-13 01:10:02.271028: Current learning rate: 0.00503 +2026-04-13 01:11:44.822981: train_loss -0.3723 +2026-04-13 01:11:44.830200: val_loss -0.3081 +2026-04-13 01:11:44.832521: Pseudo dice [0.5924, 0.3824, 0.6413, 0.6385, 0.3188, 0.5081, 0.2795] +2026-04-13 01:11:44.835151: Epoch time: 102.57 s +2026-04-13 01:11:47.464293: +2026-04-13 01:11:47.466375: Epoch 2138 +2026-04-13 01:11:47.468323: Current learning rate: 0.00502 +2026-04-13 01:13:30.105023: train_loss -0.3691 +2026-04-13 01:13:30.111724: val_loss -0.3061 +2026-04-13 01:13:30.114251: Pseudo dice [0.5488, 0.0452, 0.7563, 0.7077, 0.2796, 0.664, 0.4157] +2026-04-13 01:13:30.117016: Epoch time: 102.64 s +2026-04-13 01:13:31.640671: +2026-04-13 01:13:31.642484: Epoch 2139 +2026-04-13 01:13:31.644430: Current learning rate: 0.00502 +2026-04-13 01:15:14.050083: train_loss -0.3724 +2026-04-13 01:15:14.057925: val_loss -0.2444 +2026-04-13 01:15:14.060358: Pseudo dice [0.1409, 0.1247, 0.6503, 0.5104, 0.3445, 0.1544, 0.8105] +2026-04-13 01:15:14.062950: Epoch time: 102.41 s +2026-04-13 01:15:15.556483: +2026-04-13 01:15:15.558978: Epoch 2140 +2026-04-13 01:15:15.561377: Current learning rate: 0.00502 +2026-04-13 01:16:58.246590: train_loss -0.37 +2026-04-13 01:16:58.252408: val_loss -0.2887 +2026-04-13 01:16:58.255430: Pseudo dice [0.6391, 0.8313, 0.3942, 0.6838, 0.134, 0.1984, 0.4193] +2026-04-13 01:16:58.257869: Epoch time: 102.69 s +2026-04-13 01:16:59.748945: +2026-04-13 01:16:59.751153: Epoch 2141 +2026-04-13 01:16:59.753358: Current learning rate: 0.00502 +2026-04-13 01:18:42.516661: train_loss -0.3641 +2026-04-13 01:18:42.523511: val_loss -0.3182 +2026-04-13 01:18:42.525487: Pseudo dice [0.349, 0.1739, 0.7184, 0.408, 0.3337, 0.7773, 0.7832] +2026-04-13 01:18:42.528217: Epoch time: 102.77 s +2026-04-13 01:18:44.079230: +2026-04-13 01:18:44.081091: Epoch 2142 +2026-04-13 01:18:44.082958: Current learning rate: 0.00502 +2026-04-13 01:20:26.695925: train_loss -0.3873 +2026-04-13 01:20:26.702669: val_loss -0.3574 +2026-04-13 01:20:26.704657: Pseudo dice [0.5204, 0.6626, 0.7716, 0.4225, 0.5986, 0.6648, 0.6883] +2026-04-13 01:20:26.707470: Epoch time: 102.62 s +2026-04-13 01:20:28.153789: +2026-04-13 01:20:28.155597: Epoch 2143 +2026-04-13 01:20:28.157435: Current learning rate: 0.00501 +2026-04-13 01:22:11.679186: train_loss -0.387 +2026-04-13 01:22:11.685555: val_loss -0.267 +2026-04-13 01:22:11.690708: Pseudo dice [0.2392, 0.3744, 0.733, 0.0173, 0.2419, 0.1018, 0.5952] +2026-04-13 01:22:11.693975: Epoch time: 103.53 s +2026-04-13 01:22:13.166246: +2026-04-13 01:22:13.168934: Epoch 2144 +2026-04-13 01:22:13.171013: Current learning rate: 0.00501 +2026-04-13 01:23:56.045145: train_loss -0.3766 +2026-04-13 01:23:56.052934: val_loss -0.3159 +2026-04-13 01:23:56.055113: Pseudo dice [0.3227, 0.3076, 0.6865, 0.8435, 0.3907, 0.7641, 0.3915] +2026-04-13 01:23:56.057625: Epoch time: 102.88 s +2026-04-13 01:23:57.523713: +2026-04-13 01:23:57.525740: Epoch 2145 +2026-04-13 01:23:57.527929: Current learning rate: 0.00501 +2026-04-13 01:25:40.005051: train_loss -0.3709 +2026-04-13 01:25:40.012350: val_loss -0.2936 +2026-04-13 01:25:40.014188: Pseudo dice [0.4835, 0.3715, 0.589, 0.2023, 0.7412, 0.2079, 0.7211] +2026-04-13 01:25:40.016375: Epoch time: 102.49 s +2026-04-13 01:25:41.453858: +2026-04-13 01:25:41.455816: Epoch 2146 +2026-04-13 01:25:41.457659: Current learning rate: 0.00501 +2026-04-13 01:27:24.341692: train_loss -0.3822 +2026-04-13 01:27:24.348363: val_loss -0.3168 +2026-04-13 01:27:24.350652: Pseudo dice [0.357, 0.1326, 0.6085, 0.8845, 0.433, 0.2227, 0.7129] +2026-04-13 01:27:24.353077: Epoch time: 102.89 s +2026-04-13 01:27:25.859878: +2026-04-13 01:27:25.861929: Epoch 2147 +2026-04-13 01:27:25.864110: Current learning rate: 0.005 +2026-04-13 01:29:08.715699: train_loss -0.3774 +2026-04-13 01:29:08.722439: val_loss -0.2973 +2026-04-13 01:29:08.724420: Pseudo dice [0.5646, 0.0392, 0.6274, 0.661, 0.5013, 0.1281, 0.84] +2026-04-13 01:29:08.726792: Epoch time: 102.86 s +2026-04-13 01:29:10.211975: +2026-04-13 01:29:10.213686: Epoch 2148 +2026-04-13 01:29:10.215895: Current learning rate: 0.005 +2026-04-13 01:30:52.459598: train_loss -0.3857 +2026-04-13 01:30:52.466312: val_loss -0.3008 +2026-04-13 01:30:52.468635: Pseudo dice [0.4119, 0.2926, 0.6153, 0.6412, 0.4026, 0.213, 0.8838] +2026-04-13 01:30:52.471855: Epoch time: 102.25 s +2026-04-13 01:30:53.903013: +2026-04-13 01:30:53.905346: Epoch 2149 +2026-04-13 01:30:53.907245: Current learning rate: 0.005 +2026-04-13 01:32:36.529330: train_loss -0.3756 +2026-04-13 01:32:36.537505: val_loss -0.2821 +2026-04-13 01:32:36.539892: Pseudo dice [0.0938, 0.6864, 0.526, 0.0301, 0.5488, 0.1152, 0.6508] +2026-04-13 01:32:36.543169: Epoch time: 102.63 s +2026-04-13 01:32:39.959596: +2026-04-13 01:32:39.961759: Epoch 2150 +2026-04-13 01:32:39.963665: Current learning rate: 0.005 +2026-04-13 01:34:22.244987: train_loss -0.3818 +2026-04-13 01:34:22.250025: val_loss -0.3214 +2026-04-13 01:34:22.251895: Pseudo dice [0.5486, 0.3645, 0.6657, 0.6088, 0.3828, 0.6572, 0.6257] +2026-04-13 01:34:22.254646: Epoch time: 102.29 s +2026-04-13 01:34:23.708979: +2026-04-13 01:34:23.710762: Epoch 2151 +2026-04-13 01:34:23.712838: Current learning rate: 0.00499 +2026-04-13 01:36:06.667728: train_loss -0.3647 +2026-04-13 01:36:06.673430: val_loss -0.3021 +2026-04-13 01:36:06.675171: Pseudo dice [0.8362, 0.7423, 0.763, 0.4844, 0.3982, 0.4779, 0.6229] +2026-04-13 01:36:06.677345: Epoch time: 102.96 s +2026-04-13 01:36:08.191413: +2026-04-13 01:36:08.194617: Epoch 2152 +2026-04-13 01:36:08.197013: Current learning rate: 0.00499 +2026-04-13 01:37:50.487020: train_loss -0.3672 +2026-04-13 01:37:50.492707: val_loss -0.1786 +2026-04-13 01:37:50.494959: Pseudo dice [0.4449, 0.2837, 0.5623, 0.0472, 0.4833, 0.1724, 0.7569] +2026-04-13 01:37:50.497417: Epoch time: 102.3 s +2026-04-13 01:37:52.006825: +2026-04-13 01:37:52.008935: Epoch 2153 +2026-04-13 01:37:52.012349: Current learning rate: 0.00499 +2026-04-13 01:39:34.403856: train_loss -0.3683 +2026-04-13 01:39:34.409944: val_loss -0.3374 +2026-04-13 01:39:34.411936: Pseudo dice [0.4682, 0.0671, 0.7225, 0.7005, 0.2765, 0.7963, 0.8403] +2026-04-13 01:39:34.413974: Epoch time: 102.4 s +2026-04-13 01:39:35.901568: +2026-04-13 01:39:35.903448: Epoch 2154 +2026-04-13 01:39:35.905369: Current learning rate: 0.00499 +2026-04-13 01:41:18.176662: train_loss -0.3771 +2026-04-13 01:41:18.183536: val_loss -0.3575 +2026-04-13 01:41:18.185575: Pseudo dice [0.3424, 0.3247, 0.7385, 0.7827, 0.5847, 0.7378, 0.6453] +2026-04-13 01:41:18.187898: Epoch time: 102.28 s +2026-04-13 01:41:19.698834: +2026-04-13 01:41:19.700546: Epoch 2155 +2026-04-13 01:41:19.702448: Current learning rate: 0.00498 +2026-04-13 01:43:02.111858: train_loss -0.3775 +2026-04-13 01:43:02.117939: val_loss -0.2274 +2026-04-13 01:43:02.121520: Pseudo dice [0.1876, 0.3184, 0.5365, 0.5751, 0.6723, 0.1116, 0.8259] +2026-04-13 01:43:02.124482: Epoch time: 102.42 s +2026-04-13 01:43:03.618967: +2026-04-13 01:43:03.622512: Epoch 2156 +2026-04-13 01:43:03.624579: Current learning rate: 0.00498 +2026-04-13 01:44:46.139094: train_loss -0.373 +2026-04-13 01:44:46.144431: val_loss -0.3443 +2026-04-13 01:44:46.146975: Pseudo dice [0.0982, 0.484, 0.6345, 0.3633, 0.4555, 0.7981, 0.8124] +2026-04-13 01:44:46.149296: Epoch time: 102.52 s +2026-04-13 01:44:47.594064: +2026-04-13 01:44:47.595789: Epoch 2157 +2026-04-13 01:44:47.597949: Current learning rate: 0.00498 +2026-04-13 01:46:29.963610: train_loss -0.3732 +2026-04-13 01:46:29.970644: val_loss -0.3548 +2026-04-13 01:46:29.972883: Pseudo dice [0.1831, 0.5677, 0.7102, 0.6506, 0.4383, 0.5606, 0.6348] +2026-04-13 01:46:29.974984: Epoch time: 102.37 s +2026-04-13 01:46:31.437901: +2026-04-13 01:46:31.439671: Epoch 2158 +2026-04-13 01:46:31.441675: Current learning rate: 0.00498 +2026-04-13 01:48:14.820020: train_loss -0.3777 +2026-04-13 01:48:14.827447: val_loss -0.2836 +2026-04-13 01:48:14.830757: Pseudo dice [0.5069, 0.7596, 0.5597, 0.8483, 0.4136, 0.1088, 0.6625] +2026-04-13 01:48:14.833730: Epoch time: 103.39 s +2026-04-13 01:48:16.330626: +2026-04-13 01:48:16.333337: Epoch 2159 +2026-04-13 01:48:16.336244: Current learning rate: 0.00497 +2026-04-13 01:49:59.130216: train_loss -0.3684 +2026-04-13 01:49:59.136002: val_loss -0.3754 +2026-04-13 01:49:59.139380: Pseudo dice [0.76, 0.4715, 0.7953, 0.8684, 0.6953, 0.4296, 0.7258] +2026-04-13 01:49:59.142925: Epoch time: 102.8 s +2026-04-13 01:50:00.699612: +2026-04-13 01:50:00.701357: Epoch 2160 +2026-04-13 01:50:00.703344: Current learning rate: 0.00497 +2026-04-13 01:51:43.252841: train_loss -0.3852 +2026-04-13 01:51:43.259453: val_loss -0.3319 +2026-04-13 01:51:43.261705: Pseudo dice [0.2698, 0.106, 0.8048, 0.3451, 0.3555, 0.3463, 0.8438] +2026-04-13 01:51:43.264526: Epoch time: 102.56 s +2026-04-13 01:51:44.741020: +2026-04-13 01:51:44.745899: Epoch 2161 +2026-04-13 01:51:44.750132: Current learning rate: 0.00497 +2026-04-13 01:53:27.359643: train_loss -0.35 +2026-04-13 01:53:27.367471: val_loss -0.3379 +2026-04-13 01:53:27.369506: Pseudo dice [0.3519, 0.181, 0.6571, 0.8339, 0.6309, 0.178, 0.7629] +2026-04-13 01:53:27.372066: Epoch time: 102.62 s +2026-04-13 01:53:28.912493: +2026-04-13 01:53:28.914810: Epoch 2162 +2026-04-13 01:53:28.916706: Current learning rate: 0.00497 +2026-04-13 01:55:12.173155: train_loss -0.3652 +2026-04-13 01:55:12.178971: val_loss -0.2403 +2026-04-13 01:55:12.181268: Pseudo dice [0.5078, 0.5475, 0.4051, 0.2594, 0.5509, 0.1927, 0.5724] +2026-04-13 01:55:12.183730: Epoch time: 103.26 s +2026-04-13 01:55:13.633957: +2026-04-13 01:55:13.636229: Epoch 2163 +2026-04-13 01:55:13.639572: Current learning rate: 0.00496 +2026-04-13 01:56:56.022065: train_loss -0.3786 +2026-04-13 01:56:56.026876: val_loss -0.3137 +2026-04-13 01:56:56.028552: Pseudo dice [0.6269, 0.6576, 0.6874, 0.6834, 0.4105, 0.1659, 0.6978] +2026-04-13 01:56:56.031060: Epoch time: 102.39 s +2026-04-13 01:56:57.542095: +2026-04-13 01:56:57.544230: Epoch 2164 +2026-04-13 01:56:57.546190: Current learning rate: 0.00496 +2026-04-13 01:58:40.064145: train_loss -0.3991 +2026-04-13 01:58:40.070137: val_loss -0.2631 +2026-04-13 01:58:40.072480: Pseudo dice [0.3361, 0.3664, 0.6427, 0.7169, 0.4239, 0.1446, 0.4762] +2026-04-13 01:58:40.075997: Epoch time: 102.53 s +2026-04-13 01:58:41.560618: +2026-04-13 01:58:41.562422: Epoch 2165 +2026-04-13 01:58:41.564173: Current learning rate: 0.00496 +2026-04-13 02:00:25.782784: train_loss -0.3864 +2026-04-13 02:00:25.790252: val_loss -0.3206 +2026-04-13 02:00:25.793482: Pseudo dice [0.36, 0.6336, 0.6013, 0.8377, 0.3315, 0.638, 0.6236] +2026-04-13 02:00:25.795899: Epoch time: 104.23 s +2026-04-13 02:00:27.296443: +2026-04-13 02:00:27.298442: Epoch 2166 +2026-04-13 02:00:27.300555: Current learning rate: 0.00496 +2026-04-13 02:02:09.760786: train_loss -0.3894 +2026-04-13 02:02:09.766511: val_loss -0.3415 +2026-04-13 02:02:09.768355: Pseudo dice [0.1338, 0.6883, 0.6081, 0.8108, 0.391, 0.8216, 0.5765] +2026-04-13 02:02:09.770356: Epoch time: 102.47 s +2026-04-13 02:02:11.253134: +2026-04-13 02:02:11.258476: Epoch 2167 +2026-04-13 02:02:11.263696: Current learning rate: 0.00495 +2026-04-13 02:03:53.991082: train_loss -0.3748 +2026-04-13 02:03:53.999639: val_loss -0.3356 +2026-04-13 02:03:54.002082: Pseudo dice [0.2681, 0.6761, 0.7009, 0.4668, 0.2252, 0.8022, 0.6295] +2026-04-13 02:03:54.005979: Epoch time: 102.74 s +2026-04-13 02:03:55.549022: +2026-04-13 02:03:55.551485: Epoch 2168 +2026-04-13 02:03:55.554179: Current learning rate: 0.00495 +2026-04-13 02:05:37.751332: train_loss -0.3852 +2026-04-13 02:05:37.757325: val_loss -0.3228 +2026-04-13 02:05:37.761107: Pseudo dice [0.509, 0.3505, 0.7644, 0.8677, 0.2966, 0.7895, 0.5092] +2026-04-13 02:05:37.763782: Epoch time: 102.21 s +2026-04-13 02:05:39.289180: +2026-04-13 02:05:39.291067: Epoch 2169 +2026-04-13 02:05:39.293044: Current learning rate: 0.00495 +2026-04-13 02:07:21.769128: train_loss -0.3677 +2026-04-13 02:07:21.775602: val_loss -0.3119 +2026-04-13 02:07:21.777779: Pseudo dice [0.6824, 0.0611, 0.7175, 0.5704, 0.5309, 0.332, 0.6815] +2026-04-13 02:07:21.780696: Epoch time: 102.48 s +2026-04-13 02:07:23.280483: +2026-04-13 02:07:23.282923: Epoch 2170 +2026-04-13 02:07:23.285216: Current learning rate: 0.00495 +2026-04-13 02:09:05.748761: train_loss -0.3528 +2026-04-13 02:09:05.757046: val_loss -0.3375 +2026-04-13 02:09:05.760070: Pseudo dice [0.6835, 0.2316, 0.6742, 0.6978, 0.4627, 0.7214, 0.8344] +2026-04-13 02:09:05.762943: Epoch time: 102.47 s +2026-04-13 02:09:07.243762: +2026-04-13 02:09:07.246026: Epoch 2171 +2026-04-13 02:09:07.248032: Current learning rate: 0.00494 +2026-04-13 02:10:49.797559: train_loss -0.3707 +2026-04-13 02:10:49.807332: val_loss -0.3387 +2026-04-13 02:10:49.811153: Pseudo dice [0.3183, 0.5312, 0.8006, 0.8105, 0.4558, 0.5022, 0.7382] +2026-04-13 02:10:49.816004: Epoch time: 102.56 s +2026-04-13 02:10:51.276422: +2026-04-13 02:10:51.278774: Epoch 2172 +2026-04-13 02:10:51.280837: Current learning rate: 0.00494 +2026-04-13 02:12:34.302675: train_loss -0.353 +2026-04-13 02:12:34.309127: val_loss -0.3118 +2026-04-13 02:12:34.311623: Pseudo dice [0.5534, 0.0, 0.6506, 0.0617, 0.4873, 0.7462, 0.7515] +2026-04-13 02:12:34.313954: Epoch time: 103.03 s +2026-04-13 02:12:35.798010: +2026-04-13 02:12:35.799922: Epoch 2173 +2026-04-13 02:12:35.802085: Current learning rate: 0.00494 +2026-04-13 02:14:18.732070: train_loss -0.352 +2026-04-13 02:14:18.738941: val_loss -0.3225 +2026-04-13 02:14:18.741316: Pseudo dice [0.5089, 0.218, 0.6704, 0.5269, 0.4333, 0.2093, 0.8135] +2026-04-13 02:14:18.744198: Epoch time: 102.94 s +2026-04-13 02:14:20.235725: +2026-04-13 02:14:20.238175: Epoch 2174 +2026-04-13 02:14:20.240247: Current learning rate: 0.00494 +2026-04-13 02:16:02.560924: train_loss -0.377 +2026-04-13 02:16:02.569505: val_loss -0.283 +2026-04-13 02:16:02.572553: Pseudo dice [0.2338, 0.8004, 0.6314, 0.7981, 0.2577, 0.3086, 0.772] +2026-04-13 02:16:02.574904: Epoch time: 102.33 s +2026-04-13 02:16:04.039873: +2026-04-13 02:16:04.041789: Epoch 2175 +2026-04-13 02:16:04.044260: Current learning rate: 0.00493 +2026-04-13 02:17:46.188743: train_loss -0.3751 +2026-04-13 02:17:46.194745: val_loss -0.3076 +2026-04-13 02:17:46.196697: Pseudo dice [0.2837, 0.1317, 0.5935, 0.2176, 0.5614, 0.1317, 0.7876] +2026-04-13 02:17:46.201479: Epoch time: 102.15 s +2026-04-13 02:17:47.659794: +2026-04-13 02:17:47.661812: Epoch 2176 +2026-04-13 02:17:47.663912: Current learning rate: 0.00493 +2026-04-13 02:19:31.074132: train_loss -0.3763 +2026-04-13 02:19:31.084224: val_loss -0.2263 +2026-04-13 02:19:31.086448: Pseudo dice [0.0967, 0.4246, 0.6362, 0.825, 0.5079, 0.0394, 0.7422] +2026-04-13 02:19:31.091531: Epoch time: 103.42 s +2026-04-13 02:19:32.571238: +2026-04-13 02:19:32.573935: Epoch 2177 +2026-04-13 02:19:32.577101: Current learning rate: 0.00493 +2026-04-13 02:21:14.494055: train_loss -0.3712 +2026-04-13 02:21:14.500112: val_loss -0.3096 +2026-04-13 02:21:14.502009: Pseudo dice [0.4681, 0.3764, 0.4824, 0.4955, 0.418, 0.1811, 0.6822] +2026-04-13 02:21:14.504324: Epoch time: 101.93 s +2026-04-13 02:21:15.984313: +2026-04-13 02:21:15.986057: Epoch 2178 +2026-04-13 02:21:15.988270: Current learning rate: 0.00493 +2026-04-13 02:22:57.995500: train_loss -0.3913 +2026-04-13 02:22:58.001077: val_loss -0.2761 +2026-04-13 02:22:58.003010: Pseudo dice [0.2024, 0.6394, 0.7174, 0.0593, 0.2239, 0.2288, 0.8555] +2026-04-13 02:22:58.005122: Epoch time: 102.02 s +2026-04-13 02:23:00.683455: +2026-04-13 02:23:00.685335: Epoch 2179 +2026-04-13 02:23:00.687326: Current learning rate: 0.00493 +2026-04-13 02:24:43.504992: train_loss -0.3711 +2026-04-13 02:24:43.512356: val_loss -0.2804 +2026-04-13 02:24:43.514657: Pseudo dice [0.7339, 0.5955, 0.628, 0.6319, 0.5419, 0.1257, 0.607] +2026-04-13 02:24:43.518129: Epoch time: 102.83 s +2026-04-13 02:24:45.004194: +2026-04-13 02:24:45.015713: Epoch 2180 +2026-04-13 02:24:45.018193: Current learning rate: 0.00492 +2026-04-13 02:26:27.505535: train_loss -0.376 +2026-04-13 02:26:27.511415: val_loss -0.3246 +2026-04-13 02:26:27.513888: Pseudo dice [0.4557, 0.1274, 0.6425, 0.7314, 0.3064, 0.5244, 0.6866] +2026-04-13 02:26:27.516479: Epoch time: 102.51 s +2026-04-13 02:26:29.013276: +2026-04-13 02:26:29.015563: Epoch 2181 +2026-04-13 02:26:29.017524: Current learning rate: 0.00492 +2026-04-13 02:28:11.804436: train_loss -0.3894 +2026-04-13 02:28:11.809685: val_loss -0.282 +2026-04-13 02:28:11.812007: Pseudo dice [0.6708, 0.3463, 0.7628, 0.4652, 0.5739, 0.0655, 0.878] +2026-04-13 02:28:11.814709: Epoch time: 102.8 s +2026-04-13 02:28:13.278225: +2026-04-13 02:28:13.279917: Epoch 2182 +2026-04-13 02:28:13.281651: Current learning rate: 0.00492 +2026-04-13 02:29:55.670073: train_loss -0.3692 +2026-04-13 02:29:55.681665: val_loss -0.3296 +2026-04-13 02:29:55.683662: Pseudo dice [0.6847, 0.2882, 0.6107, 0.8251, 0.5319, 0.6785, 0.6789] +2026-04-13 02:29:55.686118: Epoch time: 102.4 s +2026-04-13 02:29:57.177900: +2026-04-13 02:29:57.180557: Epoch 2183 +2026-04-13 02:29:57.183260: Current learning rate: 0.00492 +2026-04-13 02:31:41.056490: train_loss -0.3854 +2026-04-13 02:31:41.064448: val_loss -0.3473 +2026-04-13 02:31:41.068352: Pseudo dice [0.4645, 0.1774, 0.7123, 0.7367, 0.4113, 0.7523, 0.7546] +2026-04-13 02:31:41.071303: Epoch time: 103.88 s +2026-04-13 02:31:42.563838: +2026-04-13 02:31:42.565591: Epoch 2184 +2026-04-13 02:31:42.567116: Current learning rate: 0.00491 +2026-04-13 02:33:25.007111: train_loss -0.3823 +2026-04-13 02:33:25.012691: val_loss -0.3262 +2026-04-13 02:33:25.014642: Pseudo dice [0.792, 0.1731, 0.7647, 0.3719, 0.3658, 0.5066, 0.6526] +2026-04-13 02:33:25.016652: Epoch time: 102.45 s +2026-04-13 02:33:26.495982: +2026-04-13 02:33:26.497635: Epoch 2185 +2026-04-13 02:33:26.499187: Current learning rate: 0.00491 +2026-04-13 02:35:08.570761: train_loss -0.3872 +2026-04-13 02:35:08.575929: val_loss -0.3073 +2026-04-13 02:35:08.578648: Pseudo dice [0.7533, 0.6974, 0.519, 0.7766, 0.6069, 0.18, 0.7606] +2026-04-13 02:35:08.581735: Epoch time: 102.08 s +2026-04-13 02:35:10.042876: +2026-04-13 02:35:10.046160: Epoch 2186 +2026-04-13 02:35:10.048976: Current learning rate: 0.00491 +2026-04-13 02:36:53.385484: train_loss -0.3655 +2026-04-13 02:36:53.397093: val_loss -0.3168 +2026-04-13 02:36:53.400121: Pseudo dice [0.6867, 0.7285, 0.6603, 0.3406, 0.5121, 0.081, 0.6699] +2026-04-13 02:36:53.404031: Epoch time: 103.35 s +2026-04-13 02:36:54.858891: +2026-04-13 02:36:54.861288: Epoch 2187 +2026-04-13 02:36:54.863396: Current learning rate: 0.00491 +2026-04-13 02:38:37.239143: train_loss -0.3932 +2026-04-13 02:38:37.247006: val_loss -0.3321 +2026-04-13 02:38:37.248970: Pseudo dice [0.459, 0.8236, 0.6162, 0.3673, 0.4924, 0.8139, 0.8249] +2026-04-13 02:38:37.252006: Epoch time: 102.38 s +2026-04-13 02:38:38.715580: +2026-04-13 02:38:38.717546: Epoch 2188 +2026-04-13 02:38:38.719400: Current learning rate: 0.0049 +2026-04-13 02:40:20.884679: train_loss -0.398 +2026-04-13 02:40:20.889377: val_loss -0.346 +2026-04-13 02:40:20.891440: Pseudo dice [0.4571, 0.7029, 0.63, 0.4857, 0.6191, 0.7535, 0.5116] +2026-04-13 02:40:20.893490: Epoch time: 102.17 s +2026-04-13 02:40:22.354549: +2026-04-13 02:40:22.356579: Epoch 2189 +2026-04-13 02:40:22.358138: Current learning rate: 0.0049 +2026-04-13 02:42:04.624949: train_loss -0.3726 +2026-04-13 02:42:04.631357: val_loss -0.3313 +2026-04-13 02:42:04.633361: Pseudo dice [0.5839, 0.1125, 0.6278, 0.7875, 0.6835, 0.3671, 0.6215] +2026-04-13 02:42:04.635917: Epoch time: 102.27 s +2026-04-13 02:42:06.100875: +2026-04-13 02:42:06.102735: Epoch 2190 +2026-04-13 02:42:06.105170: Current learning rate: 0.0049 +2026-04-13 02:43:48.803553: train_loss -0.3594 +2026-04-13 02:43:48.808860: val_loss -0.3075 +2026-04-13 02:43:48.810888: Pseudo dice [0.5796, 0.3296, 0.6516, 0.8403, 0.3482, 0.1805, 0.8312] +2026-04-13 02:43:48.813098: Epoch time: 102.71 s +2026-04-13 02:43:50.280947: +2026-04-13 02:43:50.282684: Epoch 2191 +2026-04-13 02:43:50.284619: Current learning rate: 0.0049 +2026-04-13 02:45:32.752640: train_loss -0.3528 +2026-04-13 02:45:32.758499: val_loss -0.3187 +2026-04-13 02:45:32.760565: Pseudo dice [0.5754, 0.7104, 0.5096, 0.6638, 0.2285, 0.7312, 0.4516] +2026-04-13 02:45:32.763050: Epoch time: 102.48 s +2026-04-13 02:45:34.227386: +2026-04-13 02:45:34.229138: Epoch 2192 +2026-04-13 02:45:34.230732: Current learning rate: 0.00489 +2026-04-13 02:47:16.521478: train_loss -0.3721 +2026-04-13 02:47:16.527724: val_loss -0.255 +2026-04-13 02:47:16.529838: Pseudo dice [0.7757, 0.6086, 0.6123, 0.2078, 0.4579, 0.1828, 0.0529] +2026-04-13 02:47:16.532102: Epoch time: 102.3 s +2026-04-13 02:47:18.013381: +2026-04-13 02:47:18.015050: Epoch 2193 +2026-04-13 02:47:18.016717: Current learning rate: 0.00489 +2026-04-13 02:49:00.220961: train_loss -0.3709 +2026-04-13 02:49:00.226597: val_loss -0.3304 +2026-04-13 02:49:00.228393: Pseudo dice [0.4908, 0.8841, 0.6287, 0.7949, 0.6479, 0.1209, 0.6933] +2026-04-13 02:49:00.230485: Epoch time: 102.21 s +2026-04-13 02:49:01.690487: +2026-04-13 02:49:01.692948: Epoch 2194 +2026-04-13 02:49:01.695356: Current learning rate: 0.00489 +2026-04-13 02:50:43.789632: train_loss -0.3651 +2026-04-13 02:50:43.795391: val_loss -0.2859 +2026-04-13 02:50:43.799290: Pseudo dice [0.3771, 0.5224, 0.5233, 0.7626, 0.5773, 0.1016, 0.8068] +2026-04-13 02:50:43.804096: Epoch time: 102.1 s +2026-04-13 02:50:45.254848: +2026-04-13 02:50:45.256433: Epoch 2195 +2026-04-13 02:50:45.257963: Current learning rate: 0.00489 +2026-04-13 02:52:27.474397: train_loss -0.3735 +2026-04-13 02:52:27.479598: val_loss -0.1879 +2026-04-13 02:52:27.481555: Pseudo dice [0.2905, 0.3327, 0.6115, 0.1775, 0.521, 0.0273, 0.7682] +2026-04-13 02:52:27.483882: Epoch time: 102.22 s +2026-04-13 02:52:28.935159: +2026-04-13 02:52:28.936774: Epoch 2196 +2026-04-13 02:52:28.938398: Current learning rate: 0.00488 +2026-04-13 02:54:11.604744: train_loss -0.3781 +2026-04-13 02:54:11.611808: val_loss -0.309 +2026-04-13 02:54:11.614675: Pseudo dice [0.6236, 0.3107, 0.5641, 0.0, 0.4918, 0.4171, 0.6725] +2026-04-13 02:54:11.616995: Epoch time: 102.67 s +2026-04-13 02:54:13.087872: +2026-04-13 02:54:13.089621: Epoch 2197 +2026-04-13 02:54:13.091641: Current learning rate: 0.00488 +2026-04-13 02:55:57.770782: train_loss -0.3971 +2026-04-13 02:55:57.776988: val_loss -0.3264 +2026-04-13 02:55:57.779323: Pseudo dice [0.6154, 0.1757, 0.8048, 0.0056, 0.406, 0.6608, 0.502] +2026-04-13 02:55:57.781505: Epoch time: 104.69 s +2026-04-13 02:55:59.254109: +2026-04-13 02:55:59.258777: Epoch 2198 +2026-04-13 02:55:59.260512: Current learning rate: 0.00488 +2026-04-13 02:57:41.406113: train_loss -0.386 +2026-04-13 02:57:41.411697: val_loss -0.3394 +2026-04-13 02:57:41.413753: Pseudo dice [0.4548, 0.2707, 0.7132, 0.7597, 0.5739, 0.7482, 0.7447] +2026-04-13 02:57:41.415968: Epoch time: 102.16 s +2026-04-13 02:57:42.872543: +2026-04-13 02:57:42.874335: Epoch 2199 +2026-04-13 02:57:42.875879: Current learning rate: 0.00488 +2026-04-13 02:59:24.947278: train_loss -0.3846 +2026-04-13 02:59:24.953724: val_loss -0.3025 +2026-04-13 02:59:24.957136: Pseudo dice [0.3433, 0.4274, 0.5409, 0.4921, 0.4316, 0.636, 0.5667] +2026-04-13 02:59:24.959510: Epoch time: 102.08 s +2026-04-13 02:59:29.317811: +2026-04-13 02:59:29.319805: Epoch 2200 +2026-04-13 02:59:29.321433: Current learning rate: 0.00487 +2026-04-13 03:01:11.495224: train_loss -0.3757 +2026-04-13 03:01:11.502017: val_loss -0.3076 +2026-04-13 03:01:11.504978: Pseudo dice [0.6675, 0.4684, 0.6346, 0.589, 0.6282, 0.2348, 0.7172] +2026-04-13 03:01:11.507548: Epoch time: 102.18 s +2026-04-13 03:01:12.983094: +2026-04-13 03:01:12.984823: Epoch 2201 +2026-04-13 03:01:12.986359: Current learning rate: 0.00487 +2026-04-13 03:02:55.461030: train_loss -0.3766 +2026-04-13 03:02:55.466391: val_loss -0.2927 +2026-04-13 03:02:55.468572: Pseudo dice [0.3055, 0.3845, 0.5736, 0.7853, 0.5909, 0.1401, 0.639] +2026-04-13 03:02:55.471176: Epoch time: 102.48 s +2026-04-13 03:02:56.908931: +2026-04-13 03:02:56.910699: Epoch 2202 +2026-04-13 03:02:56.912730: Current learning rate: 0.00487 +2026-04-13 03:04:39.034038: train_loss -0.3831 +2026-04-13 03:04:39.039735: val_loss -0.2113 +2026-04-13 03:04:39.041867: Pseudo dice [0.5793, 0.6786, 0.5452, 0.4098, 0.3954, 0.0864, 0.4976] +2026-04-13 03:04:39.044626: Epoch time: 102.13 s +2026-04-13 03:04:40.511536: +2026-04-13 03:04:40.513466: Epoch 2203 +2026-04-13 03:04:40.515243: Current learning rate: 0.00487 +2026-04-13 03:06:22.705672: train_loss -0.3585 +2026-04-13 03:06:22.711571: val_loss -0.279 +2026-04-13 03:06:22.713563: Pseudo dice [0.4836, 0.5859, 0.7869, 0.0024, 0.5829, 0.0849, 0.7354] +2026-04-13 03:06:22.716327: Epoch time: 102.2 s +2026-04-13 03:06:24.175732: +2026-04-13 03:06:24.177743: Epoch 2204 +2026-04-13 03:06:24.179183: Current learning rate: 0.00486 +2026-04-13 03:08:06.414720: train_loss -0.3494 +2026-04-13 03:08:06.420206: val_loss -0.2895 +2026-04-13 03:08:06.422125: Pseudo dice [0.3525, 0.0763, 0.438, 0.4205, 0.5258, 0.158, 0.6864] +2026-04-13 03:08:06.424866: Epoch time: 102.24 s +2026-04-13 03:08:07.883012: +2026-04-13 03:08:07.884996: Epoch 2205 +2026-04-13 03:08:07.886852: Current learning rate: 0.00486 +2026-04-13 03:09:52.013703: train_loss -0.3678 +2026-04-13 03:09:52.024061: val_loss -0.2381 +2026-04-13 03:09:52.026129: Pseudo dice [0.3593, 0.3099, 0.6947, 0.7582, 0.6231, 0.0883, 0.8331] +2026-04-13 03:09:52.028768: Epoch time: 104.13 s +2026-04-13 03:09:53.489292: +2026-04-13 03:09:53.491292: Epoch 2206 +2026-04-13 03:09:53.493841: Current learning rate: 0.00486 +2026-04-13 03:11:35.580325: train_loss -0.357 +2026-04-13 03:11:35.588441: val_loss -0.3072 +2026-04-13 03:11:35.590629: Pseudo dice [0.2906, 0.0668, 0.6812, 0.5987, 0.6196, 0.1292, 0.7803] +2026-04-13 03:11:35.592895: Epoch time: 102.09 s +2026-04-13 03:11:37.041188: +2026-04-13 03:11:37.042724: Epoch 2207 +2026-04-13 03:11:37.044497: Current learning rate: 0.00486 +2026-04-13 03:13:19.302142: train_loss -0.3759 +2026-04-13 03:13:19.309282: val_loss -0.3405 +2026-04-13 03:13:19.311887: Pseudo dice [0.6236, 0.4808, 0.6504, 0.7691, 0.6024, 0.4339, 0.5339] +2026-04-13 03:13:19.314043: Epoch time: 102.26 s +2026-04-13 03:13:20.766578: +2026-04-13 03:13:20.768308: Epoch 2208 +2026-04-13 03:13:20.769953: Current learning rate: 0.00485 +2026-04-13 03:15:03.579164: train_loss -0.3836 +2026-04-13 03:15:03.585854: val_loss -0.2896 +2026-04-13 03:15:03.588408: Pseudo dice [0.4757, 0.3463, 0.637, 0.6368, 0.5358, 0.2501, 0.8499] +2026-04-13 03:15:03.590702: Epoch time: 102.82 s +2026-04-13 03:15:05.064369: +2026-04-13 03:15:05.066689: Epoch 2209 +2026-04-13 03:15:05.069491: Current learning rate: 0.00485 +2026-04-13 03:16:47.351669: train_loss -0.3717 +2026-04-13 03:16:47.359337: val_loss -0.1262 +2026-04-13 03:16:47.361107: Pseudo dice [0.3172, 0.0556, 0.5022, 0.7294, 0.563, 0.0454, 0.8238] +2026-04-13 03:16:47.365159: Epoch time: 102.29 s +2026-04-13 03:16:48.836082: +2026-04-13 03:16:48.838441: Epoch 2210 +2026-04-13 03:16:48.840032: Current learning rate: 0.00485 +2026-04-13 03:18:30.861685: train_loss -0.3707 +2026-04-13 03:18:30.871743: val_loss -0.3759 +2026-04-13 03:18:30.873781: Pseudo dice [0.3708, 0.278, 0.8059, 0.7324, 0.4508, 0.7952, 0.8043] +2026-04-13 03:18:30.876016: Epoch time: 102.03 s +2026-04-13 03:18:32.318668: +2026-04-13 03:18:32.320553: Epoch 2211 +2026-04-13 03:18:32.322316: Current learning rate: 0.00485 +2026-04-13 03:20:14.418939: train_loss -0.3783 +2026-04-13 03:20:14.425787: val_loss -0.3443 +2026-04-13 03:20:14.427716: Pseudo dice [0.6831, 0.7073, 0.7497, 0.7516, 0.5632, 0.6426, 0.8334] +2026-04-13 03:20:14.429935: Epoch time: 102.1 s +2026-04-13 03:20:15.854124: +2026-04-13 03:20:15.856056: Epoch 2212 +2026-04-13 03:20:15.857671: Current learning rate: 0.00484 +2026-04-13 03:21:58.169649: train_loss -0.3801 +2026-04-13 03:21:58.197529: val_loss -0.3155 +2026-04-13 03:21:58.199586: Pseudo dice [0.4561, 0.3298, 0.4478, 0.0552, 0.4392, 0.2522, 0.6605] +2026-04-13 03:21:58.203365: Epoch time: 102.32 s +2026-04-13 03:21:59.711936: +2026-04-13 03:21:59.714277: Epoch 2213 +2026-04-13 03:21:59.716335: Current learning rate: 0.00484 +2026-04-13 03:23:42.374189: train_loss -0.3795 +2026-04-13 03:23:42.383360: val_loss -0.2965 +2026-04-13 03:23:42.385847: Pseudo dice [0.1809, 0.611, 0.6883, 0.2192, 0.3839, 0.4328, 0.5513] +2026-04-13 03:23:42.388336: Epoch time: 102.67 s +2026-04-13 03:23:43.910857: +2026-04-13 03:23:43.912520: Epoch 2214 +2026-04-13 03:23:43.914150: Current learning rate: 0.00484 +2026-04-13 03:25:26.183303: train_loss -0.3469 +2026-04-13 03:25:26.190043: val_loss -0.242 +2026-04-13 03:25:26.191793: Pseudo dice [0.7745, 0.1284, 0.6236, 0.0963, 0.3632, 0.0612, 0.712] +2026-04-13 03:25:26.194178: Epoch time: 102.28 s +2026-04-13 03:25:27.674871: +2026-04-13 03:25:27.676950: Epoch 2215 +2026-04-13 03:25:27.678622: Current learning rate: 0.00484 +2026-04-13 03:27:10.219621: train_loss -0.3655 +2026-04-13 03:27:10.225740: val_loss -0.286 +2026-04-13 03:27:10.227602: Pseudo dice [0.4663, 0.4197, 0.5859, 0.6896, 0.544, 0.2667, 0.736] +2026-04-13 03:27:10.229637: Epoch time: 102.55 s +2026-04-13 03:27:11.706747: +2026-04-13 03:27:11.708472: Epoch 2216 +2026-04-13 03:27:11.710068: Current learning rate: 0.00484 +2026-04-13 03:28:54.169302: train_loss -0.3591 +2026-04-13 03:28:54.177930: val_loss -0.3164 +2026-04-13 03:28:54.180442: Pseudo dice [0.5345, 0.0, 0.723, 0.8248, 0.4992, 0.2784, 0.7672] +2026-04-13 03:28:54.183060: Epoch time: 102.47 s +2026-04-13 03:28:55.660293: +2026-04-13 03:28:55.661931: Epoch 2217 +2026-04-13 03:28:55.663580: Current learning rate: 0.00483 +2026-04-13 03:30:39.192214: train_loss -0.3589 +2026-04-13 03:30:39.208966: val_loss -0.3024 +2026-04-13 03:30:39.214336: Pseudo dice [0.4949, 0.0, 0.7093, 0.0343, 0.5019, 0.5919, 0.8979] +2026-04-13 03:30:39.220126: Epoch time: 103.54 s +2026-04-13 03:30:40.767859: +2026-04-13 03:30:40.771681: Epoch 2218 +2026-04-13 03:30:40.774339: Current learning rate: 0.00483 +2026-04-13 03:32:23.196663: train_loss -0.3669 +2026-04-13 03:32:23.204007: val_loss -0.23 +2026-04-13 03:32:23.205966: Pseudo dice [0.7022, 0.0, 0.6042, 0.6323, 0.4699, 0.0451, 0.2931] +2026-04-13 03:32:23.208027: Epoch time: 102.43 s +2026-04-13 03:32:24.710880: +2026-04-13 03:32:24.712569: Epoch 2219 +2026-04-13 03:32:24.714141: Current learning rate: 0.00483 +2026-04-13 03:34:07.355179: train_loss -0.3708 +2026-04-13 03:34:07.362486: val_loss -0.2963 +2026-04-13 03:34:07.364660: Pseudo dice [0.3533, 0.0131, 0.6706, 0.3538, 0.3193, 0.7507, 0.5122] +2026-04-13 03:34:07.366848: Epoch time: 102.65 s +2026-04-13 03:34:09.984450: +2026-04-13 03:34:09.986491: Epoch 2220 +2026-04-13 03:34:09.988271: Current learning rate: 0.00483 +2026-04-13 03:35:52.260923: train_loss -0.3712 +2026-04-13 03:35:52.268760: val_loss -0.358 +2026-04-13 03:35:52.271042: Pseudo dice [0.4883, 0.3554, 0.7429, 0.7905, 0.4665, 0.581, 0.4022] +2026-04-13 03:35:52.273045: Epoch time: 102.28 s +2026-04-13 03:35:53.770236: +2026-04-13 03:35:53.772894: Epoch 2221 +2026-04-13 03:35:53.775100: Current learning rate: 0.00482 +2026-04-13 03:37:36.023304: train_loss -0.3815 +2026-04-13 03:37:36.030683: val_loss -0.3366 +2026-04-13 03:37:36.032417: Pseudo dice [0.5771, 0.6149, 0.7625, 0.7894, 0.632, 0.7583, 0.8508] +2026-04-13 03:37:36.034401: Epoch time: 102.26 s +2026-04-13 03:37:37.500993: +2026-04-13 03:37:37.502773: Epoch 2222 +2026-04-13 03:37:37.504265: Current learning rate: 0.00482 +2026-04-13 03:39:19.679032: train_loss -0.3712 +2026-04-13 03:39:19.685577: val_loss -0.2396 +2026-04-13 03:39:19.687500: Pseudo dice [0.4232, 0.7985, 0.5682, 0.5995, 0.6375, 0.0972, 0.8828] +2026-04-13 03:39:19.690216: Epoch time: 102.18 s +2026-04-13 03:39:21.136410: +2026-04-13 03:39:21.138102: Epoch 2223 +2026-04-13 03:39:21.139682: Current learning rate: 0.00482 +2026-04-13 03:41:03.643877: train_loss -0.3708 +2026-04-13 03:41:03.652954: val_loss -0.3427 +2026-04-13 03:41:03.655306: Pseudo dice [0.641, 0.5447, 0.7461, 0.2139, 0.3012, 0.8428, 0.788] +2026-04-13 03:41:03.658350: Epoch time: 102.51 s +2026-04-13 03:41:05.139110: +2026-04-13 03:41:05.140874: Epoch 2224 +2026-04-13 03:41:05.142559: Current learning rate: 0.00482 +2026-04-13 03:42:48.759329: train_loss -0.3687 +2026-04-13 03:42:48.767135: val_loss -0.2781 +2026-04-13 03:42:48.769167: Pseudo dice [0.7939, 0.0569, 0.7039, 0.7556, 0.5554, 0.0353, 0.8685] +2026-04-13 03:42:48.771714: Epoch time: 103.62 s +2026-04-13 03:42:50.274464: +2026-04-13 03:42:50.299212: Epoch 2225 +2026-04-13 03:42:50.311316: Current learning rate: 0.00481 +2026-04-13 03:44:32.863034: train_loss -0.3775 +2026-04-13 03:44:32.869992: val_loss -0.3142 +2026-04-13 03:44:32.872906: Pseudo dice [0.4543, 0.0848, 0.6751, 0.7675, 0.3417, 0.5396, 0.9059] +2026-04-13 03:44:32.876981: Epoch time: 102.59 s +2026-04-13 03:44:34.401193: +2026-04-13 03:44:34.403185: Epoch 2226 +2026-04-13 03:44:34.404894: Current learning rate: 0.00481 +2026-04-13 03:46:17.224808: train_loss -0.3909 +2026-04-13 03:46:17.231759: val_loss -0.2833 +2026-04-13 03:46:17.235420: Pseudo dice [0.5192, 0.4494, 0.6801, 0.1832, 0.5279, 0.1079, 0.751] +2026-04-13 03:46:17.239080: Epoch time: 102.83 s +2026-04-13 03:46:18.713839: +2026-04-13 03:46:18.716768: Epoch 2227 +2026-04-13 03:46:18.719635: Current learning rate: 0.00481 +2026-04-13 03:48:01.020543: train_loss -0.3798 +2026-04-13 03:48:01.032943: val_loss -0.2745 +2026-04-13 03:48:01.035253: Pseudo dice [0.7995, 0.0918, 0.7075, 0.7806, 0.583, 0.2742, 0.8123] +2026-04-13 03:48:01.037641: Epoch time: 102.31 s +2026-04-13 03:48:02.524678: +2026-04-13 03:48:02.526503: Epoch 2228 +2026-04-13 03:48:02.528274: Current learning rate: 0.00481 +2026-04-13 03:49:44.957258: train_loss -0.3761 +2026-04-13 03:49:44.962643: val_loss -0.2889 +2026-04-13 03:49:44.964536: Pseudo dice [0.7003, 0.232, 0.7519, 0.0071, 0.4334, 0.2583, 0.6744] +2026-04-13 03:49:44.967148: Epoch time: 102.44 s +2026-04-13 03:49:46.477369: +2026-04-13 03:49:46.479016: Epoch 2229 +2026-04-13 03:49:46.480714: Current learning rate: 0.0048 +2026-04-13 03:51:29.206769: train_loss -0.3851 +2026-04-13 03:51:29.213344: val_loss -0.2424 +2026-04-13 03:51:29.215895: Pseudo dice [0.631, 0.2135, 0.7011, 0.3842, 0.5335, 0.1915, 0.8238] +2026-04-13 03:51:29.218486: Epoch time: 102.73 s +2026-04-13 03:51:30.676945: +2026-04-13 03:51:30.678667: Epoch 2230 +2026-04-13 03:51:30.680511: Current learning rate: 0.0048 +2026-04-13 03:53:13.310186: train_loss -0.3913 +2026-04-13 03:53:13.314772: val_loss -0.321 +2026-04-13 03:53:13.316627: Pseudo dice [0.4174, 0.6478, 0.7583, 0.33, 0.4355, 0.205, 0.9181] +2026-04-13 03:53:13.318667: Epoch time: 102.64 s +2026-04-13 03:53:14.779447: +2026-04-13 03:53:14.781181: Epoch 2231 +2026-04-13 03:53:14.782699: Current learning rate: 0.0048 +2026-04-13 03:54:57.158037: train_loss -0.3802 +2026-04-13 03:54:57.164100: val_loss -0.3353 +2026-04-13 03:54:57.167680: Pseudo dice [0.7123, 0.7506, 0.6682, 0.8146, 0.6364, 0.3442, 0.7682] +2026-04-13 03:54:57.172381: Epoch time: 102.38 s +2026-04-13 03:54:58.710921: +2026-04-13 03:54:58.712871: Epoch 2232 +2026-04-13 03:54:58.714650: Current learning rate: 0.0048 +2026-04-13 03:56:41.266849: train_loss -0.3832 +2026-04-13 03:56:41.271265: val_loss -0.3106 +2026-04-13 03:56:41.272832: Pseudo dice [0.6816, 0.4827, 0.6192, 0.8217, 0.5247, 0.1782, 0.6382] +2026-04-13 03:56:41.274778: Epoch time: 102.56 s +2026-04-13 03:56:42.824031: +2026-04-13 03:56:42.825888: Epoch 2233 +2026-04-13 03:56:42.827512: Current learning rate: 0.00479 +2026-04-13 03:58:25.594338: train_loss -0.3853 +2026-04-13 03:58:25.600767: val_loss -0.3551 +2026-04-13 03:58:25.606053: Pseudo dice [0.7435, 0.5156, 0.7402, 0.352, 0.4432, 0.7327, 0.7058] +2026-04-13 03:58:25.608937: Epoch time: 102.77 s +2026-04-13 03:58:27.077122: +2026-04-13 03:58:27.079059: Epoch 2234 +2026-04-13 03:58:27.080784: Current learning rate: 0.00479 +2026-04-13 04:00:09.391182: train_loss -0.3861 +2026-04-13 04:00:09.396079: val_loss -0.3149 +2026-04-13 04:00:09.397915: Pseudo dice [0.4442, 0.367, 0.6387, 0.1275, 0.4884, 0.7098, 0.8326] +2026-04-13 04:00:09.400754: Epoch time: 102.32 s +2026-04-13 04:00:10.907284: +2026-04-13 04:00:10.909072: Epoch 2235 +2026-04-13 04:00:10.911210: Current learning rate: 0.00479 +2026-04-13 04:01:53.345755: train_loss -0.3631 +2026-04-13 04:01:53.355867: val_loss -0.2994 +2026-04-13 04:01:53.359772: Pseudo dice [0.3295, 0.474, 0.6831, 0.8314, 0.5291, 0.1405, 0.8205] +2026-04-13 04:01:53.363668: Epoch time: 102.44 s +2026-04-13 04:01:54.877804: +2026-04-13 04:01:54.879578: Epoch 2236 +2026-04-13 04:01:54.881432: Current learning rate: 0.00479 +2026-04-13 04:03:37.342946: train_loss -0.3715 +2026-04-13 04:03:37.349378: val_loss -0.3133 +2026-04-13 04:03:37.353624: Pseudo dice [0.5388, 0.5786, 0.6195, 0.8291, 0.4111, 0.2411, 0.6981] +2026-04-13 04:03:37.355704: Epoch time: 102.47 s +2026-04-13 04:03:38.849609: +2026-04-13 04:03:38.852188: Epoch 2237 +2026-04-13 04:03:38.853745: Current learning rate: 0.00478 +2026-04-13 04:05:22.390563: train_loss -0.3699 +2026-04-13 04:05:22.396376: val_loss -0.2257 +2026-04-13 04:05:22.399161: Pseudo dice [0.3145, 0.1497, 0.4864, 0.5646, 0.5497, 0.1054, 0.7903] +2026-04-13 04:05:22.402695: Epoch time: 103.54 s +2026-04-13 04:05:23.947291: +2026-04-13 04:05:23.949274: Epoch 2238 +2026-04-13 04:05:23.950958: Current learning rate: 0.00478 +2026-04-13 04:07:07.185777: train_loss -0.3678 +2026-04-13 04:07:07.193602: val_loss -0.2647 +2026-04-13 04:07:07.196135: Pseudo dice [0.6891, 0.1275, 0.4847, 0.0665, 0.651, 0.1977, 0.7721] +2026-04-13 04:07:07.198893: Epoch time: 103.24 s +2026-04-13 04:07:08.736680: +2026-04-13 04:07:08.739406: Epoch 2239 +2026-04-13 04:07:08.741915: Current learning rate: 0.00478 +2026-04-13 04:08:51.402895: train_loss -0.393 +2026-04-13 04:08:51.408873: val_loss -0.3289 +2026-04-13 04:08:51.411248: Pseudo dice [0.7978, 0.4375, 0.7968, 0.2205, 0.6592, 0.1395, 0.877] +2026-04-13 04:08:51.413604: Epoch time: 102.67 s +2026-04-13 04:08:52.911386: +2026-04-13 04:08:52.913334: Epoch 2240 +2026-04-13 04:08:52.915518: Current learning rate: 0.00478 +2026-04-13 04:10:35.459542: train_loss -0.395 +2026-04-13 04:10:35.467199: val_loss -0.2751 +2026-04-13 04:10:35.469596: Pseudo dice [0.3535, 0.575, 0.6952, 0.0244, 0.6633, 0.0725, 0.6873] +2026-04-13 04:10:35.472722: Epoch time: 102.55 s +2026-04-13 04:10:38.230578: +2026-04-13 04:10:38.232985: Epoch 2241 +2026-04-13 04:10:38.234879: Current learning rate: 0.00477 +2026-04-13 04:12:20.714983: train_loss -0.3823 +2026-04-13 04:12:20.720367: val_loss -0.2929 +2026-04-13 04:12:20.722995: Pseudo dice [0.4738, 0.6252, 0.6965, 0.6942, 0.644, 0.1181, 0.8453] +2026-04-13 04:12:20.725731: Epoch time: 102.49 s +2026-04-13 04:12:22.211518: +2026-04-13 04:12:22.215542: Epoch 2242 +2026-04-13 04:12:22.217734: Current learning rate: 0.00477 +2026-04-13 04:14:04.625329: train_loss -0.3445 +2026-04-13 04:14:04.630428: val_loss -0.2812 +2026-04-13 04:14:04.632037: Pseudo dice [0.615, 0.1098, 0.461, 0.685, 0.4022, 0.0533, 0.6304] +2026-04-13 04:14:04.634757: Epoch time: 102.42 s +2026-04-13 04:14:06.123623: +2026-04-13 04:14:06.125507: Epoch 2243 +2026-04-13 04:14:06.127135: Current learning rate: 0.00477 +2026-04-13 04:15:48.830561: train_loss -0.3641 +2026-04-13 04:15:48.836340: val_loss -0.2669 +2026-04-13 04:15:48.838613: Pseudo dice [0.6125, 0.3259, 0.603, 0.3402, 0.5684, 0.1098, 0.848] +2026-04-13 04:15:48.841186: Epoch time: 102.71 s +2026-04-13 04:15:50.305902: +2026-04-13 04:15:50.307810: Epoch 2244 +2026-04-13 04:15:50.309459: Current learning rate: 0.00477 +2026-04-13 04:17:32.660373: train_loss -0.377 +2026-04-13 04:17:32.665337: val_loss -0.2886 +2026-04-13 04:17:32.667599: Pseudo dice [0.7213, 0.3254, 0.7482, 0.6829, 0.3549, 0.2079, 0.2062] +2026-04-13 04:17:32.669772: Epoch time: 102.36 s +2026-04-13 04:17:34.140670: +2026-04-13 04:17:34.142604: Epoch 2245 +2026-04-13 04:17:34.144428: Current learning rate: 0.00476 +2026-04-13 04:19:17.325527: train_loss -0.3361 +2026-04-13 04:19:17.332563: val_loss -0.2627 +2026-04-13 04:19:17.334496: Pseudo dice [0.6723, 0.6953, 0.3416, 0.4306, 0.6058, 0.0432, 0.891] +2026-04-13 04:19:17.337298: Epoch time: 103.19 s +2026-04-13 04:19:18.815271: +2026-04-13 04:19:18.818261: Epoch 2246 +2026-04-13 04:19:18.819955: Current learning rate: 0.00476 +2026-04-13 04:21:01.239208: train_loss -0.3676 +2026-04-13 04:21:01.245745: val_loss -0.325 +2026-04-13 04:21:01.248867: Pseudo dice [0.689, 0.4841, 0.7309, 0.0137, 0.4877, 0.4904, 0.6983] +2026-04-13 04:21:01.251563: Epoch time: 102.43 s +2026-04-13 04:21:02.833560: +2026-04-13 04:21:02.835764: Epoch 2247 +2026-04-13 04:21:02.837400: Current learning rate: 0.00476 +2026-04-13 04:22:45.139662: train_loss -0.3693 +2026-04-13 04:22:45.148100: val_loss -0.3466 +2026-04-13 04:22:45.150449: Pseudo dice [0.6548, 0.4388, 0.7878, 0.8481, 0.4661, 0.3003, 0.7293] +2026-04-13 04:22:45.153185: Epoch time: 102.31 s +2026-04-13 04:22:46.625121: +2026-04-13 04:22:46.627152: Epoch 2248 +2026-04-13 04:22:46.628852: Current learning rate: 0.00476 +2026-04-13 04:24:29.065290: train_loss -0.3738 +2026-04-13 04:24:29.070714: val_loss -0.2499 +2026-04-13 04:24:29.072664: Pseudo dice [0.3822, 0.0416, 0.4704, 0.816, 0.5548, 0.0567, 0.7954] +2026-04-13 04:24:29.074977: Epoch time: 102.44 s +2026-04-13 04:24:30.533862: +2026-04-13 04:24:30.536055: Epoch 2249 +2026-04-13 04:24:30.537893: Current learning rate: 0.00475 +2026-04-13 04:26:12.928982: train_loss -0.3538 +2026-04-13 04:26:12.935672: val_loss -0.2922 +2026-04-13 04:26:12.937957: Pseudo dice [0.3625, 0.0243, 0.5221, 0.2796, 0.6661, 0.1011, 0.7698] +2026-04-13 04:26:12.940753: Epoch time: 102.4 s +2026-04-13 04:26:16.415280: +2026-04-13 04:26:16.417353: Epoch 2250 +2026-04-13 04:26:16.419235: Current learning rate: 0.00475 +2026-04-13 04:27:58.810272: train_loss -0.3694 +2026-04-13 04:27:58.816436: val_loss -0.2936 +2026-04-13 04:27:58.818562: Pseudo dice [0.6608, 0.2654, 0.3658, 0.9141, 0.6371, 0.1092, 0.6589] +2026-04-13 04:27:58.820809: Epoch time: 102.4 s +2026-04-13 04:28:00.300482: +2026-04-13 04:28:00.302282: Epoch 2251 +2026-04-13 04:28:00.304226: Current learning rate: 0.00475 +2026-04-13 04:29:42.796171: train_loss -0.369 +2026-04-13 04:29:42.802316: val_loss -0.3216 +2026-04-13 04:29:42.804742: Pseudo dice [0.2086, 0.3973, 0.7144, 0.7676, 0.5221, 0.1952, 0.852] +2026-04-13 04:29:42.807481: Epoch time: 102.5 s +2026-04-13 04:29:44.355177: +2026-04-13 04:29:44.356891: Epoch 2252 +2026-04-13 04:29:44.359224: Current learning rate: 0.00475 +2026-04-13 04:31:26.955947: train_loss -0.3943 +2026-04-13 04:31:26.961489: val_loss -0.2912 +2026-04-13 04:31:26.963578: Pseudo dice [0.6928, 0.4556, 0.6326, 0.1339, 0.5788, 0.0705, 0.5457] +2026-04-13 04:31:26.965768: Epoch time: 102.6 s +2026-04-13 04:31:28.457781: +2026-04-13 04:31:28.459939: Epoch 2253 +2026-04-13 04:31:28.462561: Current learning rate: 0.00474 +2026-04-13 04:33:10.976906: train_loss -0.3861 +2026-04-13 04:33:10.982235: val_loss -0.3345 +2026-04-13 04:33:10.984027: Pseudo dice [0.8663, 0.5015, 0.5242, 0.751, 0.577, 0.3791, 0.7223] +2026-04-13 04:33:10.987083: Epoch time: 102.52 s +2026-04-13 04:33:12.472628: +2026-04-13 04:33:12.475147: Epoch 2254 +2026-04-13 04:33:12.476938: Current learning rate: 0.00474 +2026-04-13 04:34:55.158698: train_loss -0.3827 +2026-04-13 04:34:55.167381: val_loss -0.2825 +2026-04-13 04:34:55.169447: Pseudo dice [0.7659, 0.7961, 0.7464, 0.6925, 0.4705, 0.3725, 0.6619] +2026-04-13 04:34:55.172229: Epoch time: 102.69 s +2026-04-13 04:34:56.670896: +2026-04-13 04:34:56.672878: Epoch 2255 +2026-04-13 04:34:56.674480: Current learning rate: 0.00474 +2026-04-13 04:36:39.211378: train_loss -0.3754 +2026-04-13 04:36:39.221379: val_loss -0.2834 +2026-04-13 04:36:39.224506: Pseudo dice [0.2177, 0.5838, 0.3815, 0.1319, 0.4217, 0.1672, 0.8496] +2026-04-13 04:36:39.227086: Epoch time: 102.54 s +2026-04-13 04:36:40.731597: +2026-04-13 04:36:40.734259: Epoch 2256 +2026-04-13 04:36:40.736633: Current learning rate: 0.00474 +2026-04-13 04:38:23.520114: train_loss -0.3792 +2026-04-13 04:38:23.526518: val_loss -0.2648 +2026-04-13 04:38:23.528845: Pseudo dice [0.4215, 0.3591, 0.6615, 0.4941, 0.4403, 0.0508, 0.565] +2026-04-13 04:38:23.531859: Epoch time: 102.79 s +2026-04-13 04:38:25.024190: +2026-04-13 04:38:25.026046: Epoch 2257 +2026-04-13 04:38:25.027802: Current learning rate: 0.00473 +2026-04-13 04:40:07.251675: train_loss -0.3835 +2026-04-13 04:40:07.264450: val_loss -0.3472 +2026-04-13 04:40:07.266794: Pseudo dice [0.7995, 0.6828, 0.7396, 0.5691, 0.6286, 0.3472, 0.818] +2026-04-13 04:40:07.269119: Epoch time: 102.23 s +2026-04-13 04:40:08.753717: +2026-04-13 04:40:08.755769: Epoch 2258 +2026-04-13 04:40:08.757642: Current learning rate: 0.00473 +2026-04-13 04:41:51.218287: train_loss -0.3929 +2026-04-13 04:41:51.223154: val_loss -0.3488 +2026-04-13 04:41:51.224888: Pseudo dice [0.6938, 0.4256, 0.786, 0.8522, 0.649, 0.5798, 0.6492] +2026-04-13 04:41:51.226960: Epoch time: 102.47 s +2026-04-13 04:41:52.747416: +2026-04-13 04:41:52.749186: Epoch 2259 +2026-04-13 04:41:52.750723: Current learning rate: 0.00473 +2026-04-13 04:43:35.235957: train_loss -0.3957 +2026-04-13 04:43:35.242856: val_loss -0.2692 +2026-04-13 04:43:35.244915: Pseudo dice [0.2086, 0.1756, 0.6241, 0.7965, 0.4553, 0.1493, 0.6495] +2026-04-13 04:43:35.247754: Epoch time: 102.49 s +2026-04-13 04:43:36.720401: +2026-04-13 04:43:36.722493: Epoch 2260 +2026-04-13 04:43:36.724110: Current learning rate: 0.00473 +2026-04-13 04:45:19.122854: train_loss -0.3937 +2026-04-13 04:45:19.128087: val_loss -0.2551 +2026-04-13 04:45:19.130076: Pseudo dice [0.7069, 0.6355, 0.4458, 0.8728, 0.381, 0.0562, 0.7135] +2026-04-13 04:45:19.132252: Epoch time: 102.41 s +2026-04-13 04:45:20.607340: +2026-04-13 04:45:20.609308: Epoch 2261 +2026-04-13 04:45:20.611067: Current learning rate: 0.00473 +2026-04-13 04:47:04.713444: train_loss -0.3972 +2026-04-13 04:47:04.718616: val_loss -0.3628 +2026-04-13 04:47:04.720956: Pseudo dice [0.7345, 0.6748, 0.6795, 0.1469, 0.4559, 0.6603, 0.8751] +2026-04-13 04:47:04.723051: Epoch time: 104.11 s +2026-04-13 04:47:06.207456: +2026-04-13 04:47:06.209458: Epoch 2262 +2026-04-13 04:47:06.211315: Current learning rate: 0.00472 +2026-04-13 04:48:48.790756: train_loss -0.3872 +2026-04-13 04:48:48.796687: val_loss -0.3036 +2026-04-13 04:48:48.798604: Pseudo dice [0.3292, 0.2324, 0.5173, 0.1884, 0.3075, 0.755, 0.7495] +2026-04-13 04:48:48.800665: Epoch time: 102.59 s +2026-04-13 04:48:50.278528: +2026-04-13 04:48:50.280290: Epoch 2263 +2026-04-13 04:48:50.281990: Current learning rate: 0.00472 +2026-04-13 04:50:32.790535: train_loss -0.3944 +2026-04-13 04:50:32.806324: val_loss -0.3376 +2026-04-13 04:50:32.814875: Pseudo dice [0.6082, 0.3991, 0.6663, 0.3164, 0.5571, 0.2289, 0.6644] +2026-04-13 04:50:32.819363: Epoch time: 102.52 s +2026-04-13 04:50:34.306163: +2026-04-13 04:50:34.309586: Epoch 2264 +2026-04-13 04:50:34.311713: Current learning rate: 0.00472 +2026-04-13 04:52:17.052417: train_loss -0.395 +2026-04-13 04:52:17.058605: val_loss -0.3014 +2026-04-13 04:52:17.061515: Pseudo dice [0.8333, 0.6395, 0.6589, 0.6834, 0.4337, 0.2617, 0.3083] +2026-04-13 04:52:17.064321: Epoch time: 102.75 s +2026-04-13 04:52:18.617273: +2026-04-13 04:52:18.619358: Epoch 2265 +2026-04-13 04:52:18.621184: Current learning rate: 0.00472 +2026-04-13 04:54:01.203658: train_loss -0.3825 +2026-04-13 04:54:01.208780: val_loss -0.2723 +2026-04-13 04:54:01.210688: Pseudo dice [0.1208, 0.1279, 0.5694, 0.7045, 0.4993, 0.5129, 0.8295] +2026-04-13 04:54:01.213009: Epoch time: 102.59 s +2026-04-13 04:54:02.672017: +2026-04-13 04:54:02.674807: Epoch 2266 +2026-04-13 04:54:02.676350: Current learning rate: 0.00471 +2026-04-13 04:55:45.150664: train_loss -0.3826 +2026-04-13 04:55:45.155717: val_loss -0.3567 +2026-04-13 04:55:45.157578: Pseudo dice [0.1166, 0.4022, 0.6733, 0.7288, 0.4504, 0.7151, 0.6427] +2026-04-13 04:55:45.159525: Epoch time: 102.48 s +2026-04-13 04:55:46.655856: +2026-04-13 04:55:46.658565: Epoch 2267 +2026-04-13 04:55:46.661916: Current learning rate: 0.00471 +2026-04-13 04:57:29.219769: train_loss -0.3867 +2026-04-13 04:57:29.225902: val_loss -0.2863 +2026-04-13 04:57:29.228893: Pseudo dice [0.8412, 0.0, 0.6466, 0.619, 0.5137, 0.0597, 0.813] +2026-04-13 04:57:29.231983: Epoch time: 102.57 s +2026-04-13 04:57:30.761408: +2026-04-13 04:57:30.763657: Epoch 2268 +2026-04-13 04:57:30.765267: Current learning rate: 0.00471 +2026-04-13 04:59:13.331569: train_loss -0.3752 +2026-04-13 04:59:13.340651: val_loss -0.3114 +2026-04-13 04:59:13.343087: Pseudo dice [0.4897, 0.1271, 0.6712, 0.8062, 0.413, 0.6575, 0.7801] +2026-04-13 04:59:13.345573: Epoch time: 102.57 s +2026-04-13 04:59:14.913190: +2026-04-13 04:59:14.914860: Epoch 2269 +2026-04-13 04:59:14.916455: Current learning rate: 0.00471 +2026-04-13 05:00:57.290913: train_loss -0.3782 +2026-04-13 05:00:57.298061: val_loss -0.2278 +2026-04-13 05:00:57.300768: Pseudo dice [0.462, 0.6854, 0.7709, 0.3907, 0.6388, 0.0585, 0.2833] +2026-04-13 05:00:57.305075: Epoch time: 102.38 s +2026-04-13 05:00:58.757596: +2026-04-13 05:00:58.760156: Epoch 2270 +2026-04-13 05:00:58.762100: Current learning rate: 0.0047 +2026-04-13 05:02:41.604997: train_loss -0.3754 +2026-04-13 05:02:41.615199: val_loss -0.2397 +2026-04-13 05:02:41.618951: Pseudo dice [0.5039, 0.7023, 0.4754, 0.3834, 0.2626, 0.0419, 0.7081] +2026-04-13 05:02:41.622306: Epoch time: 102.85 s +2026-04-13 05:02:43.124823: +2026-04-13 05:02:43.126630: Epoch 2271 +2026-04-13 05:02:43.128551: Current learning rate: 0.0047 +2026-04-13 05:04:25.491037: train_loss -0.3643 +2026-04-13 05:04:25.498103: val_loss -0.2163 +2026-04-13 05:04:25.500774: Pseudo dice [0.6887, 0.0829, 0.6487, 0.8003, 0.3648, 0.1011, 0.7006] +2026-04-13 05:04:25.503828: Epoch time: 102.37 s +2026-04-13 05:04:27.004390: +2026-04-13 05:04:27.006800: Epoch 2272 +2026-04-13 05:04:27.008848: Current learning rate: 0.0047 +2026-04-13 05:06:09.421241: train_loss -0.366 +2026-04-13 05:06:09.425427: val_loss -0.2965 +2026-04-13 05:06:09.427085: Pseudo dice [0.6895, 0.0323, 0.5816, 0.7183, 0.6771, 0.1664, 0.7109] +2026-04-13 05:06:09.429210: Epoch time: 102.42 s +2026-04-13 05:06:10.905944: +2026-04-13 05:06:10.907785: Epoch 2273 +2026-04-13 05:06:10.909485: Current learning rate: 0.0047 +2026-04-13 05:07:53.206840: train_loss -0.3647 +2026-04-13 05:07:53.212383: val_loss -0.3231 +2026-04-13 05:07:53.214492: Pseudo dice [0.5002, 0.3881, 0.5534, 0.6617, 0.4316, 0.4198, 0.7621] +2026-04-13 05:07:53.217032: Epoch time: 102.3 s +2026-04-13 05:07:54.709313: +2026-04-13 05:07:54.711167: Epoch 2274 +2026-04-13 05:07:54.713393: Current learning rate: 0.00469 +2026-04-13 05:09:37.227692: train_loss -0.38 +2026-04-13 05:09:37.232748: val_loss -0.2957 +2026-04-13 05:09:37.234909: Pseudo dice [0.8266, 0.1522, 0.6888, 0.3298, 0.56, 0.0402, 0.735] +2026-04-13 05:09:37.237957: Epoch time: 102.52 s +2026-04-13 05:09:38.722270: +2026-04-13 05:09:38.724766: Epoch 2275 +2026-04-13 05:09:38.726885: Current learning rate: 0.00469 +2026-04-13 05:11:21.543936: train_loss -0.3878 +2026-04-13 05:11:21.550476: val_loss -0.3277 +2026-04-13 05:11:21.552562: Pseudo dice [0.686, 0.5898, 0.5427, 0.4412, 0.5242, 0.6151, 0.784] +2026-04-13 05:11:21.554852: Epoch time: 102.83 s +2026-04-13 05:11:23.104667: +2026-04-13 05:11:23.107587: Epoch 2276 +2026-04-13 05:11:23.109864: Current learning rate: 0.00469 +2026-04-13 05:13:05.264356: train_loss -0.4018 +2026-04-13 05:13:05.270945: val_loss -0.3202 +2026-04-13 05:13:05.273288: Pseudo dice [0.788, 0.2268, 0.5809, 0.7594, 0.6091, 0.6153, 0.5002] +2026-04-13 05:13:05.275398: Epoch time: 102.16 s +2026-04-13 05:13:06.769065: +2026-04-13 05:13:06.771007: Epoch 2277 +2026-04-13 05:13:06.773340: Current learning rate: 0.00469 +2026-04-13 05:14:49.791979: train_loss -0.374 +2026-04-13 05:14:49.797443: val_loss -0.286 +2026-04-13 05:14:49.800057: Pseudo dice [0.4916, 0.1446, 0.3277, 0.8081, 0.52, 0.4384, 0.4721] +2026-04-13 05:14:49.802438: Epoch time: 103.03 s +2026-04-13 05:14:51.277862: +2026-04-13 05:14:51.279848: Epoch 2278 +2026-04-13 05:14:51.281626: Current learning rate: 0.00468 +2026-04-13 05:16:33.668083: train_loss -0.3574 +2026-04-13 05:16:33.673156: val_loss -0.334 +2026-04-13 05:16:33.675378: Pseudo dice [0.4283, 0.4622, 0.4411, 0.5713, 0.443, 0.6958, 0.6827] +2026-04-13 05:16:33.677453: Epoch time: 102.39 s +2026-04-13 05:16:35.113447: +2026-04-13 05:16:35.115218: Epoch 2279 +2026-04-13 05:16:35.117159: Current learning rate: 0.00468 +2026-04-13 05:18:17.352916: train_loss -0.3701 +2026-04-13 05:18:17.358311: val_loss -0.2924 +2026-04-13 05:18:17.360171: Pseudo dice [0.808, 0.3173, 0.452, 0.7813, 0.5867, 0.0503, 0.6926] +2026-04-13 05:18:17.362434: Epoch time: 102.24 s +2026-04-13 05:18:18.848280: +2026-04-13 05:18:18.850047: Epoch 2280 +2026-04-13 05:18:18.852530: Current learning rate: 0.00468 +2026-04-13 05:20:01.766080: train_loss -0.3723 +2026-04-13 05:20:01.773440: val_loss -0.3373 +2026-04-13 05:20:01.775790: Pseudo dice [0.3119, 0.2156, 0.5957, 0.2774, 0.4137, 0.7212, 0.8544] +2026-04-13 05:20:01.777910: Epoch time: 102.92 s +2026-04-13 05:20:03.291372: +2026-04-13 05:20:03.294437: Epoch 2281 +2026-04-13 05:20:03.297253: Current learning rate: 0.00468 +2026-04-13 05:21:45.629221: train_loss -0.3505 +2026-04-13 05:21:45.634498: val_loss -0.2689 +2026-04-13 05:21:45.636301: Pseudo dice [0.7224, 0.2693, 0.5082, 0.2549, 0.5718, 0.1153, 0.7178] +2026-04-13 05:21:45.638380: Epoch time: 102.34 s +2026-04-13 05:21:48.219350: +2026-04-13 05:21:48.221304: Epoch 2282 +2026-04-13 05:21:48.223226: Current learning rate: 0.00467 +2026-04-13 05:23:30.788093: train_loss -0.3567 +2026-04-13 05:23:30.793651: val_loss -0.2434 +2026-04-13 05:23:30.795427: Pseudo dice [0.6288, 0.4831, 0.4933, 0.4369, 0.508, 0.0504, 0.4823] +2026-04-13 05:23:30.797451: Epoch time: 102.57 s +2026-04-13 05:23:32.283334: +2026-04-13 05:23:32.285009: Epoch 2283 +2026-04-13 05:23:32.286760: Current learning rate: 0.00467 +2026-04-13 05:25:14.767656: train_loss -0.3748 +2026-04-13 05:25:14.771786: val_loss -0.2633 +2026-04-13 05:25:14.774222: Pseudo dice [0.667, 0.6049, 0.5763, 0.5788, 0.463, 0.0434, 0.6592] +2026-04-13 05:25:14.776527: Epoch time: 102.49 s +2026-04-13 05:25:16.260345: +2026-04-13 05:25:16.262368: Epoch 2284 +2026-04-13 05:25:16.264057: Current learning rate: 0.00467 +2026-04-13 05:26:59.171595: train_loss -0.3713 +2026-04-13 05:26:59.178699: val_loss -0.3196 +2026-04-13 05:26:59.180802: Pseudo dice [0.4438, 0.0882, 0.623, 0.4972, 0.5614, 0.3482, 0.8113] +2026-04-13 05:26:59.183119: Epoch time: 102.92 s +2026-04-13 05:27:00.671241: +2026-04-13 05:27:00.673938: Epoch 2285 +2026-04-13 05:27:00.675877: Current learning rate: 0.00467 +2026-04-13 05:28:43.180559: train_loss -0.357 +2026-04-13 05:28:43.185271: val_loss -0.3289 +2026-04-13 05:28:43.187066: Pseudo dice [0.852, 0.681, 0.6183, 0.54, 0.288, 0.3871, 0.528] +2026-04-13 05:28:43.189032: Epoch time: 102.51 s +2026-04-13 05:28:44.715034: +2026-04-13 05:28:44.716784: Epoch 2286 +2026-04-13 05:28:44.718328: Current learning rate: 0.00466 +2026-04-13 05:30:27.340452: train_loss -0.3624 +2026-04-13 05:30:27.351917: val_loss -0.3116 +2026-04-13 05:30:27.356600: Pseudo dice [0.7216, 0.4405, 0.5648, 0.4624, 0.5471, 0.446, 0.7208] +2026-04-13 05:30:27.361619: Epoch time: 102.63 s +2026-04-13 05:30:28.910334: +2026-04-13 05:30:28.911981: Epoch 2287 +2026-04-13 05:30:28.913512: Current learning rate: 0.00466 +2026-04-13 05:32:11.771589: train_loss -0.3726 +2026-04-13 05:32:11.778731: val_loss -0.3389 +2026-04-13 05:32:11.780705: Pseudo dice [0.447, 0.2002, 0.5454, 0.0198, 0.54, 0.7136, 0.8186] +2026-04-13 05:32:11.783274: Epoch time: 102.87 s +2026-04-13 05:32:13.260112: +2026-04-13 05:32:13.261964: Epoch 2288 +2026-04-13 05:32:13.264088: Current learning rate: 0.00466 +2026-04-13 05:33:56.101263: train_loss -0.3766 +2026-04-13 05:33:56.107224: val_loss -0.3178 +2026-04-13 05:33:56.109628: Pseudo dice [0.446, 0.265, 0.5078, 0.5372, 0.571, 0.4582, 0.7732] +2026-04-13 05:33:56.112063: Epoch time: 102.84 s +2026-04-13 05:33:57.593891: +2026-04-13 05:33:57.596353: Epoch 2289 +2026-04-13 05:33:57.598010: Current learning rate: 0.00466 +2026-04-13 05:35:40.326474: train_loss -0.3563 +2026-04-13 05:35:40.332562: val_loss -0.2937 +2026-04-13 05:35:40.334565: Pseudo dice [0.6076, 0.0643, 0.496, 0.009, 0.3773, 0.6414, 0.7718] +2026-04-13 05:35:40.336823: Epoch time: 102.74 s +2026-04-13 05:35:42.036156: +2026-04-13 05:35:42.037924: Epoch 2290 +2026-04-13 05:35:42.039861: Current learning rate: 0.00465 +2026-04-13 05:37:24.428430: train_loss -0.3421 +2026-04-13 05:37:24.434394: val_loss -0.2903 +2026-04-13 05:37:24.436446: Pseudo dice [0.6191, 0.686, 0.6134, 0.6204, 0.3648, 0.0972, 0.644] +2026-04-13 05:37:24.438602: Epoch time: 102.4 s +2026-04-13 05:37:25.946498: +2026-04-13 05:37:25.948204: Epoch 2291 +2026-04-13 05:37:25.949801: Current learning rate: 0.00465 +2026-04-13 05:39:08.298183: train_loss -0.3703 +2026-04-13 05:39:08.305060: val_loss -0.2654 +2026-04-13 05:39:08.307606: Pseudo dice [0.4241, 0.2926, 0.6207, 0.4597, 0.4982, 0.1646, 0.9009] +2026-04-13 05:39:08.310035: Epoch time: 102.36 s +2026-04-13 05:39:09.806885: +2026-04-13 05:39:09.809645: Epoch 2292 +2026-04-13 05:39:09.811730: Current learning rate: 0.00465 +2026-04-13 05:40:52.424588: train_loss -0.3703 +2026-04-13 05:40:52.433248: val_loss -0.2976 +2026-04-13 05:40:52.435522: Pseudo dice [0.3642, 0.4331, 0.4918, 0.8403, 0.5113, 0.1693, 0.8007] +2026-04-13 05:40:52.437986: Epoch time: 102.62 s +2026-04-13 05:40:53.930291: +2026-04-13 05:40:53.932036: Epoch 2293 +2026-04-13 05:40:53.934357: Current learning rate: 0.00465 +2026-04-13 05:42:36.379877: train_loss -0.3731 +2026-04-13 05:42:36.386044: val_loss -0.261 +2026-04-13 05:42:36.388655: Pseudo dice [0.732, 0.7924, 0.5617, 0.8007, 0.5041, 0.0617, 0.7357] +2026-04-13 05:42:36.391049: Epoch time: 102.45 s +2026-04-13 05:42:37.853275: +2026-04-13 05:42:37.855148: Epoch 2294 +2026-04-13 05:42:37.856670: Current learning rate: 0.00464 +2026-04-13 05:44:20.251801: train_loss -0.3816 +2026-04-13 05:44:20.258294: val_loss -0.2793 +2026-04-13 05:44:20.260142: Pseudo dice [0.7505, 0.7091, 0.6329, 0.7622, 0.5272, 0.1435, 0.8821] +2026-04-13 05:44:20.263132: Epoch time: 102.4 s +2026-04-13 05:44:21.969048: +2026-04-13 05:44:21.970889: Epoch 2295 +2026-04-13 05:44:21.972447: Current learning rate: 0.00464 +2026-04-13 05:46:04.507242: train_loss -0.35 +2026-04-13 05:46:04.513304: val_loss -0.3027 +2026-04-13 05:46:04.516108: Pseudo dice [0.3602, 0.5964, 0.6388, 0.45, 0.3309, 0.0943, 0.6287] +2026-04-13 05:46:04.518716: Epoch time: 102.54 s +2026-04-13 05:46:06.008871: +2026-04-13 05:46:06.010560: Epoch 2296 +2026-04-13 05:46:06.012218: Current learning rate: 0.00464 +2026-04-13 05:47:48.388495: train_loss -0.3724 +2026-04-13 05:47:48.395755: val_loss -0.3286 +2026-04-13 05:47:48.397505: Pseudo dice [0.3606, 0.2471, 0.7296, 0.6429, 0.5981, 0.7287, 0.6156] +2026-04-13 05:47:48.400004: Epoch time: 102.38 s +2026-04-13 05:47:49.912917: +2026-04-13 05:47:49.914576: Epoch 2297 +2026-04-13 05:47:49.916645: Current learning rate: 0.00464 +2026-04-13 05:49:32.309185: train_loss -0.3947 +2026-04-13 05:49:32.315232: val_loss -0.2742 +2026-04-13 05:49:32.317394: Pseudo dice [0.1328, 0.3548, 0.6273, 0.3162, 0.4867, 0.126, 0.5098] +2026-04-13 05:49:32.320344: Epoch time: 102.4 s +2026-04-13 05:49:33.803863: +2026-04-13 05:49:33.805728: Epoch 2298 +2026-04-13 05:49:33.807637: Current learning rate: 0.00463 +2026-04-13 05:51:16.193338: train_loss -0.3954 +2026-04-13 05:51:16.198994: val_loss -0.3215 +2026-04-13 05:51:16.201156: Pseudo dice [0.1674, 0.3695, 0.6604, 0.5022, 0.6411, 0.7538, 0.8719] +2026-04-13 05:51:16.204169: Epoch time: 102.39 s +2026-04-13 05:51:17.700881: +2026-04-13 05:51:17.702624: Epoch 2299 +2026-04-13 05:51:17.704329: Current learning rate: 0.00463 +2026-04-13 05:53:00.049368: train_loss -0.3835 +2026-04-13 05:53:00.055713: val_loss -0.317 +2026-04-13 05:53:00.058286: Pseudo dice [0.7076, 0.1658, 0.7677, 0.8485, 0.4168, 0.2116, 0.5837] +2026-04-13 05:53:00.061474: Epoch time: 102.35 s +2026-04-13 05:53:03.543214: +2026-04-13 05:53:03.546387: Epoch 2300 +2026-04-13 05:53:03.548973: Current learning rate: 0.00463 +2026-04-13 05:54:46.166409: train_loss -0.4132 +2026-04-13 05:54:46.177255: val_loss -0.3359 +2026-04-13 05:54:46.179783: Pseudo dice [0.2272, 0.4813, 0.7387, 0.7878, 0.6105, 0.6701, 0.748] +2026-04-13 05:54:46.182167: Epoch time: 102.63 s +2026-04-13 05:54:47.700162: +2026-04-13 05:54:47.702333: Epoch 2301 +2026-04-13 05:54:47.704973: Current learning rate: 0.00463 +2026-04-13 05:56:31.503652: train_loss -0.3562 +2026-04-13 05:56:31.512702: val_loss -0.2832 +2026-04-13 05:56:31.515613: Pseudo dice [0.2021, 0.8156, 0.3631, 0.1922, 0.4903, 0.4986, 0.612] +2026-04-13 05:56:31.519125: Epoch time: 103.81 s +2026-04-13 05:56:33.023916: +2026-04-13 05:56:33.026945: Epoch 2302 +2026-04-13 05:56:33.029467: Current learning rate: 0.00462 +2026-04-13 05:58:16.635558: train_loss -0.3645 +2026-04-13 05:58:16.645404: val_loss -0.287 +2026-04-13 05:58:16.647512: Pseudo dice [0.506, 0.2233, 0.6083, 0.735, 0.505, 0.1795, 0.9013] +2026-04-13 05:58:16.650134: Epoch time: 103.62 s +2026-04-13 05:58:18.162734: +2026-04-13 05:58:18.164705: Epoch 2303 +2026-04-13 05:58:18.166409: Current learning rate: 0.00462 +2026-04-13 06:00:00.824509: train_loss -0.3412 +2026-04-13 06:00:00.828987: val_loss -0.2759 +2026-04-13 06:00:00.831066: Pseudo dice [0.7333, 0.5947, 0.3597, 0.2744, 0.4374, 0.3756, 0.6562] +2026-04-13 06:00:00.833131: Epoch time: 102.67 s +2026-04-13 06:00:02.320932: +2026-04-13 06:00:02.322822: Epoch 2304 +2026-04-13 06:00:02.324846: Current learning rate: 0.00462 +2026-04-13 06:01:46.348602: train_loss -0.3613 +2026-04-13 06:01:46.363858: val_loss -0.3219 +2026-04-13 06:01:46.369232: Pseudo dice [0.5894, 0.15, 0.5197, 0.654, 0.3422, 0.4896, 0.504] +2026-04-13 06:01:46.375916: Epoch time: 104.03 s +2026-04-13 06:01:47.864464: +2026-04-13 06:01:47.866347: Epoch 2305 +2026-04-13 06:01:47.868340: Current learning rate: 0.00462 +2026-04-13 06:03:30.500349: train_loss -0.3824 +2026-04-13 06:03:30.505238: val_loss -0.2985 +2026-04-13 06:03:30.507050: Pseudo dice [0.4808, 0.5217, 0.6877, 0.6066, 0.5004, 0.1758, 0.8635] +2026-04-13 06:03:30.508989: Epoch time: 102.64 s +2026-04-13 06:03:31.960940: +2026-04-13 06:03:31.962992: Epoch 2306 +2026-04-13 06:03:31.964586: Current learning rate: 0.00461 +2026-04-13 06:05:14.631436: train_loss -0.3802 +2026-04-13 06:05:14.641391: val_loss -0.3363 +2026-04-13 06:05:14.646397: Pseudo dice [0.3055, 0.7812, 0.719, 0.6912, 0.4632, 0.2401, 0.7864] +2026-04-13 06:05:14.653143: Epoch time: 102.67 s +2026-04-13 06:05:16.149642: +2026-04-13 06:05:16.151452: Epoch 2307 +2026-04-13 06:05:16.153014: Current learning rate: 0.00461 +2026-04-13 06:06:58.625951: train_loss -0.3882 +2026-04-13 06:06:58.633641: val_loss -0.1672 +2026-04-13 06:06:58.636361: Pseudo dice [0.6635, 0.4542, 0.4332, 0.8055, 0.2764, 0.0337, 0.4399] +2026-04-13 06:06:58.639826: Epoch time: 102.48 s +2026-04-13 06:07:00.090301: +2026-04-13 06:07:00.091887: Epoch 2308 +2026-04-13 06:07:00.093850: Current learning rate: 0.00461 +2026-04-13 06:08:42.438969: train_loss -0.3948 +2026-04-13 06:08:42.444574: val_loss -0.3331 +2026-04-13 06:08:42.446525: Pseudo dice [0.4425, 0.5425, 0.6929, 0.5053, 0.3998, 0.7186, 0.8776] +2026-04-13 06:08:42.449023: Epoch time: 102.35 s +2026-04-13 06:08:43.908773: +2026-04-13 06:08:43.911984: Epoch 2309 +2026-04-13 06:08:43.914437: Current learning rate: 0.00461 +2026-04-13 06:10:26.566411: train_loss -0.3912 +2026-04-13 06:10:26.571267: val_loss -0.3277 +2026-04-13 06:10:26.573655: Pseudo dice [0.1193, 0.4715, 0.7434, 0.6981, 0.4415, 0.6346, 0.4859] +2026-04-13 06:10:26.575894: Epoch time: 102.66 s +2026-04-13 06:10:28.128006: +2026-04-13 06:10:28.129722: Epoch 2310 +2026-04-13 06:10:28.131380: Current learning rate: 0.00461 +2026-04-13 06:12:10.558690: train_loss -0.3836 +2026-04-13 06:12:10.564129: val_loss -0.3278 +2026-04-13 06:12:10.565782: Pseudo dice [0.7355, 0.1864, 0.6064, 0.7517, 0.5581, 0.4767, 0.7518] +2026-04-13 06:12:10.568161: Epoch time: 102.43 s +2026-04-13 06:12:12.054111: +2026-04-13 06:12:12.055794: Epoch 2311 +2026-04-13 06:12:12.057511: Current learning rate: 0.0046 +2026-04-13 06:13:54.581653: train_loss -0.369 +2026-04-13 06:13:54.591930: val_loss -0.3218 +2026-04-13 06:13:54.595433: Pseudo dice [0.2005, 0.318, 0.7236, 0.4856, 0.5945, 0.7923, 0.6028] +2026-04-13 06:13:54.604609: Epoch time: 102.53 s +2026-04-13 06:13:56.139239: +2026-04-13 06:13:56.142432: Epoch 2312 +2026-04-13 06:13:56.144228: Current learning rate: 0.0046 +2026-04-13 06:15:39.092150: train_loss -0.3571 +2026-04-13 06:15:39.100553: val_loss -0.2649 +2026-04-13 06:15:39.103331: Pseudo dice [0.4235, 0.0, 0.7099, 0.5039, 0.2397, 0.2195, 0.3855] +2026-04-13 06:15:39.105853: Epoch time: 102.96 s +2026-04-13 06:15:40.598311: +2026-04-13 06:15:40.599974: Epoch 2313 +2026-04-13 06:15:40.601932: Current learning rate: 0.0046 +2026-04-13 06:17:22.911551: train_loss -0.3824 +2026-04-13 06:17:22.917660: val_loss -0.3161 +2026-04-13 06:17:22.920273: Pseudo dice [0.7144, 0.0022, 0.8367, 0.1821, 0.6233, 0.8281, 0.8103] +2026-04-13 06:17:22.922948: Epoch time: 102.32 s +2026-04-13 06:17:24.404174: +2026-04-13 06:17:24.406056: Epoch 2314 +2026-04-13 06:17:24.407803: Current learning rate: 0.0046 +2026-04-13 06:19:06.845217: train_loss -0.3605 +2026-04-13 06:19:06.851575: val_loss -0.264 +2026-04-13 06:19:06.853471: Pseudo dice [0.3338, 0.0019, 0.6947, 0.0041, 0.3619, 0.1737, 0.4463] +2026-04-13 06:19:06.855413: Epoch time: 102.44 s +2026-04-13 06:19:08.319120: +2026-04-13 06:19:08.320906: Epoch 2315 +2026-04-13 06:19:08.322789: Current learning rate: 0.00459 +2026-04-13 06:20:50.933987: train_loss -0.3883 +2026-04-13 06:20:50.939790: val_loss -0.3397 +2026-04-13 06:20:50.942213: Pseudo dice [0.6155, 0.4705, 0.7272, 0.6672, 0.5348, 0.1572, 0.71] +2026-04-13 06:20:50.944705: Epoch time: 102.62 s +2026-04-13 06:20:52.460910: +2026-04-13 06:20:52.462909: Epoch 2316 +2026-04-13 06:20:52.464887: Current learning rate: 0.00459 +2026-04-13 06:22:34.825311: train_loss -0.3718 +2026-04-13 06:22:34.830681: val_loss -0.3032 +2026-04-13 06:22:34.832526: Pseudo dice [0.8086, 0.1926, 0.7059, 0.2494, 0.5201, 0.07, 0.8364] +2026-04-13 06:22:34.834663: Epoch time: 102.37 s +2026-04-13 06:22:36.315484: +2026-04-13 06:22:36.317296: Epoch 2317 +2026-04-13 06:22:36.319182: Current learning rate: 0.00459 +2026-04-13 06:24:19.115758: train_loss -0.3621 +2026-04-13 06:24:19.120365: val_loss -0.3776 +2026-04-13 06:24:19.122178: Pseudo dice [0.5823, 0.5833, 0.7224, 0.8161, 0.4507, 0.5303, 0.909] +2026-04-13 06:24:19.124339: Epoch time: 102.8 s +2026-04-13 06:24:20.627858: +2026-04-13 06:24:20.629715: Epoch 2318 +2026-04-13 06:24:20.631494: Current learning rate: 0.00459 +2026-04-13 06:26:03.137889: train_loss -0.3881 +2026-04-13 06:26:03.143753: val_loss -0.3106 +2026-04-13 06:26:03.146121: Pseudo dice [0.8298, 0.5427, 0.7379, 0.3308, 0.421, 0.3506, 0.5826] +2026-04-13 06:26:03.149035: Epoch time: 102.51 s +2026-04-13 06:26:04.633421: +2026-04-13 06:26:04.636324: Epoch 2319 +2026-04-13 06:26:04.638204: Current learning rate: 0.00458 +2026-04-13 06:27:47.081663: train_loss -0.3876 +2026-04-13 06:27:47.086564: val_loss -0.2984 +2026-04-13 06:27:47.088162: Pseudo dice [0.4013, 0.6204, 0.7303, 0.5508, 0.5177, 0.3026, 0.579] +2026-04-13 06:27:47.090100: Epoch time: 102.45 s +2026-04-13 06:27:48.544976: +2026-04-13 06:27:48.547076: Epoch 2320 +2026-04-13 06:27:48.549057: Current learning rate: 0.00458 +2026-04-13 06:29:30.872584: train_loss -0.3972 +2026-04-13 06:29:30.877378: val_loss -0.3443 +2026-04-13 06:29:30.878978: Pseudo dice [0.5133, 0.4869, 0.8259, 0.8403, 0.5458, 0.5528, 0.7719] +2026-04-13 06:29:30.881864: Epoch time: 102.33 s +2026-04-13 06:29:32.381308: +2026-04-13 06:29:32.383026: Epoch 2321 +2026-04-13 06:29:32.384788: Current learning rate: 0.00458 +2026-04-13 06:31:14.727827: train_loss -0.3995 +2026-04-13 06:31:14.734227: val_loss -0.3273 +2026-04-13 06:31:14.737144: Pseudo dice [0.7893, 0.3413, 0.7161, 0.4714, 0.5141, 0.3444, 0.4973] +2026-04-13 06:31:14.739435: Epoch time: 102.35 s +2026-04-13 06:31:16.250996: +2026-04-13 06:31:16.252655: Epoch 2322 +2026-04-13 06:31:16.254462: Current learning rate: 0.00458 +2026-04-13 06:32:58.692981: train_loss -0.3804 +2026-04-13 06:32:58.698828: val_loss -0.3332 +2026-04-13 06:32:58.701272: Pseudo dice [0.2828, 0.0, 0.849, 0.8019, 0.5434, 0.5678, 0.8855] +2026-04-13 06:32:58.703790: Epoch time: 102.45 s +2026-04-13 06:33:01.261067: +2026-04-13 06:33:01.263154: Epoch 2323 +2026-04-13 06:33:01.265588: Current learning rate: 0.00457 +2026-04-13 06:34:43.999449: train_loss -0.3569 +2026-04-13 06:34:44.006880: val_loss -0.3103 +2026-04-13 06:34:44.008548: Pseudo dice [0.3784, 0.0, 0.5207, 0.6328, 0.5217, 0.548, 0.7665] +2026-04-13 06:34:44.011010: Epoch time: 102.74 s +2026-04-13 06:34:45.582101: +2026-04-13 06:34:45.584133: Epoch 2324 +2026-04-13 06:34:45.586160: Current learning rate: 0.00457 +2026-04-13 06:36:28.218654: train_loss -0.3664 +2026-04-13 06:36:28.223476: val_loss -0.3216 +2026-04-13 06:36:28.225568: Pseudo dice [0.5921, 0.0, 0.785, 0.774, 0.4522, 0.6605, 0.5386] +2026-04-13 06:36:28.228155: Epoch time: 102.64 s +2026-04-13 06:36:29.699514: +2026-04-13 06:36:29.701681: Epoch 2325 +2026-04-13 06:36:29.703724: Current learning rate: 0.00457 +2026-04-13 06:38:12.393145: train_loss -0.3761 +2026-04-13 06:38:12.398087: val_loss -0.2191 +2026-04-13 06:38:12.399934: Pseudo dice [0.4587, 0.0, 0.5778, 0.7421, 0.5966, 0.0827, 0.8433] +2026-04-13 06:38:12.402169: Epoch time: 102.7 s +2026-04-13 06:38:13.892510: +2026-04-13 06:38:13.894428: Epoch 2326 +2026-04-13 06:38:13.896488: Current learning rate: 0.00457 +2026-04-13 06:39:57.001892: train_loss -0.3599 +2026-04-13 06:39:57.018868: val_loss -0.3103 +2026-04-13 06:39:57.025612: Pseudo dice [0.6749, 0.0, 0.6014, 0.7192, 0.6156, 0.7435, 0.4615] +2026-04-13 06:39:57.032760: Epoch time: 103.11 s +2026-04-13 06:39:58.560129: +2026-04-13 06:39:58.562637: Epoch 2327 +2026-04-13 06:39:58.564302: Current learning rate: 0.00456 +2026-04-13 06:41:42.310035: train_loss -0.3866 +2026-04-13 06:41:42.316029: val_loss -0.3163 +2026-04-13 06:41:42.318034: Pseudo dice [0.3993, 0.0, 0.6912, 0.8708, 0.2539, 0.5666, 0.5827] +2026-04-13 06:41:42.321604: Epoch time: 103.75 s +2026-04-13 06:41:43.813896: +2026-04-13 06:41:43.815850: Epoch 2328 +2026-04-13 06:41:43.817796: Current learning rate: 0.00456 +2026-04-13 06:43:26.176087: train_loss -0.3825 +2026-04-13 06:43:26.181542: val_loss -0.2105 +2026-04-13 06:43:26.183653: Pseudo dice [0.6084, 0.0, 0.3366, 0.3264, 0.5067, 0.0654, 0.7735] +2026-04-13 06:43:26.186090: Epoch time: 102.37 s +2026-04-13 06:43:27.623880: +2026-04-13 06:43:27.625609: Epoch 2329 +2026-04-13 06:43:27.627052: Current learning rate: 0.00456 +2026-04-13 06:45:09.983213: train_loss -0.3809 +2026-04-13 06:45:09.988092: val_loss -0.3341 +2026-04-13 06:45:09.989968: Pseudo dice [0.8716, 0.0001, 0.6642, 0.7826, 0.513, 0.2836, 0.9146] +2026-04-13 06:45:09.991806: Epoch time: 102.36 s +2026-04-13 06:45:11.456050: +2026-04-13 06:45:11.458050: Epoch 2330 +2026-04-13 06:45:11.459687: Current learning rate: 0.00456 +2026-04-13 06:46:54.544473: train_loss -0.376 +2026-04-13 06:46:54.549086: val_loss -0.3074 +2026-04-13 06:46:54.551839: Pseudo dice [0.56, 0.0004, 0.7562, 0.7916, 0.3587, 0.2047, 0.6531] +2026-04-13 06:46:54.553922: Epoch time: 103.09 s +2026-04-13 06:46:56.056225: +2026-04-13 06:46:56.058096: Epoch 2331 +2026-04-13 06:46:56.059773: Current learning rate: 0.00455 +2026-04-13 06:48:38.392702: train_loss -0.3615 +2026-04-13 06:48:38.398322: val_loss -0.3332 +2026-04-13 06:48:38.400099: Pseudo dice [0.8233, 0.031, 0.673, 0.3692, 0.564, 0.7618, 0.6107] +2026-04-13 06:48:38.402152: Epoch time: 102.34 s +2026-04-13 06:48:39.866241: +2026-04-13 06:48:39.867924: Epoch 2332 +2026-04-13 06:48:39.869510: Current learning rate: 0.00455 +2026-04-13 06:50:22.277154: train_loss -0.374 +2026-04-13 06:50:22.285762: val_loss -0.2945 +2026-04-13 06:50:22.287961: Pseudo dice [0.7599, 0.3522, 0.7577, 0.7161, 0.6012, 0.0781, 0.5299] +2026-04-13 06:50:22.290833: Epoch time: 102.41 s +2026-04-13 06:50:23.762159: +2026-04-13 06:50:23.764062: Epoch 2333 +2026-04-13 06:50:23.766232: Current learning rate: 0.00455 +2026-04-13 06:52:07.406912: train_loss -0.3887 +2026-04-13 06:52:07.414479: val_loss -0.3235 +2026-04-13 06:52:07.417015: Pseudo dice [0.6281, 0.1526, 0.5774, 0.4743, 0.618, 0.1451, 0.7169] +2026-04-13 06:52:07.420730: Epoch time: 103.65 s +2026-04-13 06:52:08.966884: +2026-04-13 06:52:08.969096: Epoch 2334 +2026-04-13 06:52:08.970881: Current learning rate: 0.00455 +2026-04-13 06:53:51.568192: train_loss -0.4095 +2026-04-13 06:53:51.573007: val_loss -0.3431 +2026-04-13 06:53:51.575015: Pseudo dice [0.1085, 0.6421, 0.703, 0.6311, 0.5806, 0.7803, 0.7624] +2026-04-13 06:53:51.577089: Epoch time: 102.61 s +2026-04-13 06:53:53.050834: +2026-04-13 06:53:53.053822: Epoch 2335 +2026-04-13 06:53:53.055536: Current learning rate: 0.00454 +2026-04-13 06:55:35.370827: train_loss -0.3689 +2026-04-13 06:55:35.375803: val_loss -0.3246 +2026-04-13 06:55:35.378243: Pseudo dice [0.6824, 0.3045, 0.5091, 0.595, 0.6196, 0.6241, 0.3448] +2026-04-13 06:55:35.380107: Epoch time: 102.32 s +2026-04-13 06:55:36.842347: +2026-04-13 06:55:36.844126: Epoch 2336 +2026-04-13 06:55:36.846000: Current learning rate: 0.00454 +2026-04-13 06:57:20.123780: train_loss -0.3724 +2026-04-13 06:57:20.129535: val_loss -0.3029 +2026-04-13 06:57:20.132431: Pseudo dice [0.74, 0.6633, 0.6271, 0.0355, 0.587, 0.068, 0.6662] +2026-04-13 06:57:20.134449: Epoch time: 103.29 s +2026-04-13 06:57:21.609801: +2026-04-13 06:57:21.611936: Epoch 2337 +2026-04-13 06:57:21.613506: Current learning rate: 0.00454 +2026-04-13 06:59:04.039845: train_loss -0.3923 +2026-04-13 06:59:04.046038: val_loss -0.2763 +2026-04-13 06:59:04.048075: Pseudo dice [0.6688, 0.002, 0.5674, 0.5979, 0.4953, 0.0163, 0.7515] +2026-04-13 06:59:04.050807: Epoch time: 102.43 s +2026-04-13 06:59:05.535463: +2026-04-13 06:59:05.537301: Epoch 2338 +2026-04-13 06:59:05.542383: Current learning rate: 0.00454 +2026-04-13 07:00:48.033109: train_loss -0.3642 +2026-04-13 07:00:48.043376: val_loss -0.2539 +2026-04-13 07:00:48.048788: Pseudo dice [0.5622, 0.1525, 0.6206, 0.2714, 0.3114, 0.0418, 0.2786] +2026-04-13 07:00:48.050969: Epoch time: 102.5 s +2026-04-13 07:00:49.541542: +2026-04-13 07:00:49.543327: Epoch 2339 +2026-04-13 07:00:49.544745: Current learning rate: 0.00453 +2026-04-13 07:02:32.315448: train_loss -0.3713 +2026-04-13 07:02:32.321970: val_loss -0.332 +2026-04-13 07:02:32.324374: Pseudo dice [0.4478, 0.0605, 0.6789, 0.7835, 0.6937, 0.2476, 0.7609] +2026-04-13 07:02:32.327098: Epoch time: 102.78 s +2026-04-13 07:02:33.789320: +2026-04-13 07:02:33.791186: Epoch 2340 +2026-04-13 07:02:33.792768: Current learning rate: 0.00453 +2026-04-13 07:04:16.398396: train_loss -0.389 +2026-04-13 07:04:16.403896: val_loss -0.2903 +2026-04-13 07:04:16.406384: Pseudo dice [0.3952, 0.0183, 0.5635, 0.0735, 0.5568, 0.7378, 0.724] +2026-04-13 07:04:16.408681: Epoch time: 102.61 s +2026-04-13 07:04:17.921991: +2026-04-13 07:04:17.923992: Epoch 2341 +2026-04-13 07:04:17.925611: Current learning rate: 0.00453 +2026-04-13 07:06:00.445760: train_loss -0.3619 +2026-04-13 07:06:00.452739: val_loss -0.313 +2026-04-13 07:06:00.455039: Pseudo dice [0.3211, 0.4554, 0.6982, 0.0558, 0.2353, 0.8207, 0.4141] +2026-04-13 07:06:00.459363: Epoch time: 102.53 s +2026-04-13 07:06:01.969608: +2026-04-13 07:06:01.971452: Epoch 2342 +2026-04-13 07:06:01.973085: Current learning rate: 0.00453 +2026-04-13 07:07:44.528553: train_loss -0.3733 +2026-04-13 07:07:44.535557: val_loss -0.2261 +2026-04-13 07:07:44.537776: Pseudo dice [0.6413, 0.5612, 0.7469, 0.654, 0.282, 0.2303, 0.4903] +2026-04-13 07:07:44.539847: Epoch time: 102.56 s +2026-04-13 07:07:46.257639: +2026-04-13 07:07:46.259452: Epoch 2343 +2026-04-13 07:07:46.260920: Current learning rate: 0.00452 +2026-04-13 07:09:28.584748: train_loss -0.3923 +2026-04-13 07:09:28.595325: val_loss -0.3534 +2026-04-13 07:09:28.598679: Pseudo dice [0.7735, 0.4634, 0.6311, 0.774, 0.4792, 0.7033, 0.8082] +2026-04-13 07:09:28.601797: Epoch time: 102.33 s +2026-04-13 07:09:31.171172: +2026-04-13 07:09:31.173038: Epoch 2344 +2026-04-13 07:09:31.174733: Current learning rate: 0.00452 +2026-04-13 07:11:13.573575: train_loss -0.3763 +2026-04-13 07:11:13.579461: val_loss -0.3366 +2026-04-13 07:11:13.581696: Pseudo dice [0.6918, 0.2652, 0.6835, 0.7788, 0.6022, 0.3523, 0.8114] +2026-04-13 07:11:13.584078: Epoch time: 102.41 s +2026-04-13 07:11:15.081883: +2026-04-13 07:11:15.083866: Epoch 2345 +2026-04-13 07:11:15.085440: Current learning rate: 0.00452 +2026-04-13 07:12:57.363594: train_loss -0.3824 +2026-04-13 07:12:57.369356: val_loss -0.3077 +2026-04-13 07:12:57.372091: Pseudo dice [0.7405, 0.3404, 0.6961, 0.4983, 0.5198, 0.1476, 0.7049] +2026-04-13 07:12:57.374733: Epoch time: 102.29 s +2026-04-13 07:12:58.854726: +2026-04-13 07:12:58.856486: Epoch 2346 +2026-04-13 07:12:58.858403: Current learning rate: 0.00452 +2026-04-13 07:14:41.336646: train_loss -0.3641 +2026-04-13 07:14:41.341065: val_loss -0.3267 +2026-04-13 07:14:41.344326: Pseudo dice [0.6399, 0.407, 0.5789, 0.774, 0.4642, 0.7284, 0.6043] +2026-04-13 07:14:41.347002: Epoch time: 102.49 s +2026-04-13 07:14:42.799511: +2026-04-13 07:14:42.803986: Epoch 2347 +2026-04-13 07:14:42.813406: Current learning rate: 0.00451 +2026-04-13 07:16:25.458101: train_loss -0.3636 +2026-04-13 07:16:25.463709: val_loss -0.3106 +2026-04-13 07:16:25.465932: Pseudo dice [0.292, 0.3665, 0.5382, 0.6588, 0.5503, 0.1215, 0.6858] +2026-04-13 07:16:25.468511: Epoch time: 102.66 s +2026-04-13 07:16:26.906126: +2026-04-13 07:16:26.907862: Epoch 2348 +2026-04-13 07:16:26.909799: Current learning rate: 0.00451 +2026-04-13 07:18:09.612225: train_loss -0.3772 +2026-04-13 07:18:09.618508: val_loss -0.2905 +2026-04-13 07:18:09.621479: Pseudo dice [0.2707, 0.5358, 0.5977, 0.1838, 0.4724, 0.0697, 0.7908] +2026-04-13 07:18:09.624413: Epoch time: 102.71 s +2026-04-13 07:18:11.083069: +2026-04-13 07:18:11.086347: Epoch 2349 +2026-04-13 07:18:11.089008: Current learning rate: 0.00451 +2026-04-13 07:19:53.600740: train_loss -0.3629 +2026-04-13 07:19:53.606102: val_loss -0.3642 +2026-04-13 07:19:53.608265: Pseudo dice [0.2082, 0.2614, 0.6097, 0.8397, 0.6237, 0.786, 0.8909] +2026-04-13 07:19:53.610145: Epoch time: 102.52 s +2026-04-13 07:19:57.036116: +2026-04-13 07:19:57.038391: Epoch 2350 +2026-04-13 07:19:57.040137: Current learning rate: 0.00451 +2026-04-13 07:21:39.644499: train_loss -0.3897 +2026-04-13 07:21:39.651952: val_loss -0.2736 +2026-04-13 07:21:39.654272: Pseudo dice [0.6752, 0.0677, 0.5912, 0.5635, 0.5908, 0.0336, 0.4468] +2026-04-13 07:21:39.657897: Epoch time: 102.61 s +2026-04-13 07:21:41.142904: +2026-04-13 07:21:41.144638: Epoch 2351 +2026-04-13 07:21:41.146330: Current learning rate: 0.0045 +2026-04-13 07:23:23.906648: train_loss -0.3743 +2026-04-13 07:23:23.918351: val_loss -0.3623 +2026-04-13 07:23:23.922066: Pseudo dice [0.3815, 0.7706, 0.764, 0.7937, 0.4112, 0.594, 0.6191] +2026-04-13 07:23:23.927494: Epoch time: 102.77 s +2026-04-13 07:23:25.419278: +2026-04-13 07:23:25.421391: Epoch 2352 +2026-04-13 07:23:25.423181: Current learning rate: 0.0045 +2026-04-13 07:25:07.802591: train_loss -0.3785 +2026-04-13 07:25:07.807975: val_loss -0.3334 +2026-04-13 07:25:07.811601: Pseudo dice [0.4934, 0.6253, 0.6841, 0.5474, 0.6507, 0.6285, 0.5623] +2026-04-13 07:25:07.814355: Epoch time: 102.39 s +2026-04-13 07:25:09.273551: +2026-04-13 07:25:09.275418: Epoch 2353 +2026-04-13 07:25:09.277430: Current learning rate: 0.0045 +2026-04-13 07:26:51.858014: train_loss -0.3829 +2026-04-13 07:26:51.863498: val_loss -0.1984 +2026-04-13 07:26:51.865244: Pseudo dice [0.473, 0.4312, 0.692, 0.1819, 0.4418, 0.0711, 0.6125] +2026-04-13 07:26:51.867343: Epoch time: 102.59 s +2026-04-13 07:26:53.328846: +2026-04-13 07:26:53.330460: Epoch 2354 +2026-04-13 07:26:53.332065: Current learning rate: 0.0045 +2026-04-13 07:28:35.906154: train_loss -0.3889 +2026-04-13 07:28:35.919980: val_loss -0.2965 +2026-04-13 07:28:35.922330: Pseudo dice [0.5932, 0.6893, 0.7475, 0.3057, 0.3407, 0.1368, 0.8143] +2026-04-13 07:28:35.925070: Epoch time: 102.58 s +2026-04-13 07:28:37.415887: +2026-04-13 07:28:37.419080: Epoch 2355 +2026-04-13 07:28:37.421223: Current learning rate: 0.00449 +2026-04-13 07:30:20.455472: train_loss -0.3814 +2026-04-13 07:30:20.460703: val_loss -0.2014 +2026-04-13 07:30:20.462728: Pseudo dice [0.5262, 0.225, 0.6807, 0.7781, 0.3275, 0.0299, 0.7447] +2026-04-13 07:30:20.465109: Epoch time: 103.04 s +2026-04-13 07:30:21.995953: +2026-04-13 07:30:21.997864: Epoch 2356 +2026-04-13 07:30:21.999908: Current learning rate: 0.00449 +2026-04-13 07:32:04.380341: train_loss -0.3612 +2026-04-13 07:32:04.385268: val_loss -0.2882 +2026-04-13 07:32:04.387365: Pseudo dice [0.4891, 0.1656, 0.6072, 0.7492, 0.3665, 0.1433, 0.4888] +2026-04-13 07:32:04.390235: Epoch time: 102.39 s +2026-04-13 07:32:05.831805: +2026-04-13 07:32:05.833808: Epoch 2357 +2026-04-13 07:32:05.835309: Current learning rate: 0.00449 +2026-04-13 07:33:48.152658: train_loss -0.3735 +2026-04-13 07:33:48.160125: val_loss -0.3346 +2026-04-13 07:33:48.162914: Pseudo dice [0.4625, 0.2179, 0.6012, 0.4802, 0.5803, 0.6779, 0.7936] +2026-04-13 07:33:48.165088: Epoch time: 102.32 s +2026-04-13 07:33:49.691607: +2026-04-13 07:33:49.693883: Epoch 2358 +2026-04-13 07:33:49.696376: Current learning rate: 0.00449 +2026-04-13 07:35:33.414611: train_loss -0.3645 +2026-04-13 07:35:33.421552: val_loss -0.2439 +2026-04-13 07:35:33.423545: Pseudo dice [0.6593, 0.4994, 0.5907, 0.7769, 0.6006, 0.011, 0.7897] +2026-04-13 07:35:33.426148: Epoch time: 103.73 s +2026-04-13 07:35:34.904082: +2026-04-13 07:35:34.906094: Epoch 2359 +2026-04-13 07:35:34.907764: Current learning rate: 0.00448 +2026-04-13 07:37:17.286214: train_loss -0.3868 +2026-04-13 07:37:17.292069: val_loss -0.3516 +2026-04-13 07:37:17.293987: Pseudo dice [0.5665, 0.3306, 0.7148, 0.0675, 0.5514, 0.8747, 0.7527] +2026-04-13 07:37:17.296220: Epoch time: 102.39 s +2026-04-13 07:37:18.769309: +2026-04-13 07:37:18.771373: Epoch 2360 +2026-04-13 07:37:18.773536: Current learning rate: 0.00448 +2026-04-13 07:39:01.219540: train_loss -0.3772 +2026-04-13 07:39:01.225580: val_loss -0.3321 +2026-04-13 07:39:01.227945: Pseudo dice [0.3297, 0.6831, 0.7282, 0.6117, 0.6269, 0.3163, 0.8616] +2026-04-13 07:39:01.230770: Epoch time: 102.45 s +2026-04-13 07:39:02.695203: +2026-04-13 07:39:02.696849: Epoch 2361 +2026-04-13 07:39:02.698622: Current learning rate: 0.00448 +2026-04-13 07:40:45.603306: train_loss -0.3869 +2026-04-13 07:40:45.609329: val_loss -0.3392 +2026-04-13 07:40:45.611360: Pseudo dice [0.3134, 0.6438, 0.7041, 0.7378, 0.6385, 0.4559, 0.7153] +2026-04-13 07:40:45.613526: Epoch time: 102.91 s +2026-04-13 07:40:47.104773: +2026-04-13 07:40:47.106670: Epoch 2362 +2026-04-13 07:40:47.108620: Current learning rate: 0.00448 +2026-04-13 07:42:29.963052: train_loss -0.3787 +2026-04-13 07:42:29.968711: val_loss -0.3207 +2026-04-13 07:42:29.970835: Pseudo dice [0.3911, 0.5857, 0.6796, 0.3609, 0.496, 0.7893, 0.5995] +2026-04-13 07:42:29.973100: Epoch time: 102.86 s +2026-04-13 07:42:31.449992: +2026-04-13 07:42:31.451974: Epoch 2363 +2026-04-13 07:42:31.453553: Current learning rate: 0.00447 +2026-04-13 07:44:13.824602: train_loss -0.3754 +2026-04-13 07:44:13.829215: val_loss -0.2365 +2026-04-13 07:44:13.831142: Pseudo dice [0.2409, 0.1406, 0.537, 0.7507, 0.4759, 0.0963, 0.8406] +2026-04-13 07:44:13.833006: Epoch time: 102.38 s +2026-04-13 07:44:16.347450: +2026-04-13 07:44:16.349244: Epoch 2364 +2026-04-13 07:44:16.350904: Current learning rate: 0.00447 +2026-04-13 07:45:59.220356: train_loss -0.3744 +2026-04-13 07:45:59.226704: val_loss -0.3055 +2026-04-13 07:45:59.228904: Pseudo dice [0.2539, 0.5737, 0.6574, 0.4601, 0.5054, 0.4158, 0.801] +2026-04-13 07:45:59.231223: Epoch time: 102.88 s +2026-04-13 07:46:00.700420: +2026-04-13 07:46:00.702441: Epoch 2365 +2026-04-13 07:46:00.704226: Current learning rate: 0.00447 +2026-04-13 07:47:42.944427: train_loss -0.37 +2026-04-13 07:47:42.950394: val_loss -0.3214 +2026-04-13 07:47:42.952825: Pseudo dice [0.7327, 0.2829, 0.7356, 0.3823, 0.5122, 0.6949, 0.7667] +2026-04-13 07:47:42.955027: Epoch time: 102.25 s +2026-04-13 07:47:44.515628: +2026-04-13 07:47:44.517257: Epoch 2366 +2026-04-13 07:47:44.518859: Current learning rate: 0.00447 +2026-04-13 07:49:27.026203: train_loss -0.3514 +2026-04-13 07:49:27.031407: val_loss -0.3094 +2026-04-13 07:49:27.033673: Pseudo dice [0.6162, 0.0793, 0.4307, 0.7967, 0.5711, 0.563, 0.777] +2026-04-13 07:49:27.036117: Epoch time: 102.51 s +2026-04-13 07:49:28.495532: +2026-04-13 07:49:28.497177: Epoch 2367 +2026-04-13 07:49:28.498861: Current learning rate: 0.00447 +2026-04-13 07:51:11.052790: train_loss -0.3658 +2026-04-13 07:51:11.059177: val_loss -0.2621 +2026-04-13 07:51:11.062284: Pseudo dice [0.5186, 0.1324, 0.7147, 0.4194, 0.2913, 0.3347, 0.6406] +2026-04-13 07:51:11.065113: Epoch time: 102.56 s +2026-04-13 07:51:12.563890: +2026-04-13 07:51:12.566268: Epoch 2368 +2026-04-13 07:51:12.567811: Current learning rate: 0.00446 +2026-04-13 07:52:54.942642: train_loss -0.3478 +2026-04-13 07:52:54.947887: val_loss -0.2846 +2026-04-13 07:52:54.950019: Pseudo dice [0.7258, 0.0027, 0.6755, 0.3249, 0.2744, 0.27, 0.7153] +2026-04-13 07:52:54.952579: Epoch time: 102.38 s +2026-04-13 07:52:56.461676: +2026-04-13 07:52:56.463923: Epoch 2369 +2026-04-13 07:52:56.465636: Current learning rate: 0.00446 +2026-04-13 07:54:38.888103: train_loss -0.3653 +2026-04-13 07:54:38.893755: val_loss -0.2194 +2026-04-13 07:54:38.895734: Pseudo dice [0.453, 0.241, 0.385, 0.0087, 0.5472, 0.0596, 0.7054] +2026-04-13 07:54:38.898013: Epoch time: 102.43 s +2026-04-13 07:54:40.389792: +2026-04-13 07:54:40.391813: Epoch 2370 +2026-04-13 07:54:40.393547: Current learning rate: 0.00446 +2026-04-13 07:56:22.841800: train_loss -0.3833 +2026-04-13 07:56:22.850329: val_loss -0.3253 +2026-04-13 07:56:22.852931: Pseudo dice [0.6097, 0.4068, 0.7717, 0.5511, 0.4029, 0.6944, 0.4575] +2026-04-13 07:56:22.855358: Epoch time: 102.46 s +2026-04-13 07:56:24.339130: +2026-04-13 07:56:24.341194: Epoch 2371 +2026-04-13 07:56:24.343113: Current learning rate: 0.00446 +2026-04-13 07:58:07.427110: train_loss -0.3777 +2026-04-13 07:58:07.432884: val_loss -0.2837 +2026-04-13 07:58:07.436437: Pseudo dice [0.3353, 0.6574, 0.7166, 0.7184, 0.378, 0.1743, 0.684] +2026-04-13 07:58:07.439329: Epoch time: 103.09 s +2026-04-13 07:58:08.929194: +2026-04-13 07:58:08.931559: Epoch 2372 +2026-04-13 07:58:08.933789: Current learning rate: 0.00445 +2026-04-13 07:59:51.771676: train_loss -0.3853 +2026-04-13 07:59:51.777261: val_loss -0.309 +2026-04-13 07:59:51.779332: Pseudo dice [0.5045, 0.453, 0.671, 0.7015, 0.3743, 0.2394, 0.5631] +2026-04-13 07:59:51.782635: Epoch time: 102.85 s +2026-04-13 07:59:53.320802: +2026-04-13 07:59:53.322553: Epoch 2373 +2026-04-13 07:59:53.324537: Current learning rate: 0.00445 +2026-04-13 08:01:36.179188: train_loss -0.3696 +2026-04-13 08:01:36.186853: val_loss -0.2929 +2026-04-13 08:01:36.189038: Pseudo dice [0.5292, 0.5652, 0.7201, 0.7933, 0.3409, 0.1133, 0.7892] +2026-04-13 08:01:36.195792: Epoch time: 102.86 s +2026-04-13 08:01:37.672268: +2026-04-13 08:01:37.677284: Epoch 2374 +2026-04-13 08:01:37.679383: Current learning rate: 0.00445 +2026-04-13 08:03:20.483222: train_loss -0.3774 +2026-04-13 08:03:20.488727: val_loss -0.2572 +2026-04-13 08:03:20.490581: Pseudo dice [0.5713, 0.5538, 0.4484, 0.7922, 0.53, 0.0901, 0.7959] +2026-04-13 08:03:20.492669: Epoch time: 102.81 s +2026-04-13 08:03:21.985276: +2026-04-13 08:03:21.987222: Epoch 2375 +2026-04-13 08:03:21.989297: Current learning rate: 0.00445 +2026-04-13 08:05:04.730936: train_loss -0.3955 +2026-04-13 08:05:04.738143: val_loss -0.381 +2026-04-13 08:05:04.740487: Pseudo dice [0.7997, 0.1322, 0.719, 0.6076, 0.6555, 0.7831, 0.8787] +2026-04-13 08:05:04.743634: Epoch time: 102.75 s +2026-04-13 08:05:06.220688: +2026-04-13 08:05:06.222551: Epoch 2376 +2026-04-13 08:05:06.224636: Current learning rate: 0.00444 +2026-04-13 08:06:49.163617: train_loss -0.386 +2026-04-13 08:06:49.171185: val_loss -0.2318 +2026-04-13 08:06:49.173406: Pseudo dice [0.2161, 0.1719, 0.6436, 0.7456, 0.644, 0.1513, 0.6222] +2026-04-13 08:06:49.177182: Epoch time: 102.95 s +2026-04-13 08:06:50.632447: +2026-04-13 08:06:50.636788: Epoch 2377 +2026-04-13 08:06:50.640510: Current learning rate: 0.00444 +2026-04-13 08:08:34.423625: train_loss -0.3837 +2026-04-13 08:08:34.430829: val_loss -0.2718 +2026-04-13 08:08:34.433444: Pseudo dice [0.0634, 0.0601, 0.6137, 0.5923, 0.3099, 0.0652, 0.4501] +2026-04-13 08:08:34.435844: Epoch time: 103.79 s +2026-04-13 08:08:35.973421: +2026-04-13 08:08:35.976023: Epoch 2378 +2026-04-13 08:08:35.978275: Current learning rate: 0.00444 +2026-04-13 08:10:18.809589: train_loss -0.3833 +2026-04-13 08:10:18.815712: val_loss -0.2455 +2026-04-13 08:10:18.818830: Pseudo dice [0.8574, 0.2281, 0.6716, 0.3747, 0.3054, 0.202, 0.3434] +2026-04-13 08:10:18.822315: Epoch time: 102.84 s +2026-04-13 08:10:20.324476: +2026-04-13 08:10:20.326450: Epoch 2379 +2026-04-13 08:10:20.329280: Current learning rate: 0.00444 +2026-04-13 08:12:03.530993: train_loss -0.3879 +2026-04-13 08:12:03.539715: val_loss -0.3368 +2026-04-13 08:12:03.542103: Pseudo dice [0.573, 0.1583, 0.6386, 0.637, 0.5123, 0.8345, 0.8304] +2026-04-13 08:12:03.544814: Epoch time: 103.21 s +2026-04-13 08:12:05.040840: +2026-04-13 08:12:05.043513: Epoch 2380 +2026-04-13 08:12:05.045769: Current learning rate: 0.00443 +2026-04-13 08:13:48.839258: train_loss -0.3615 +2026-04-13 08:13:48.846777: val_loss -0.3488 +2026-04-13 08:13:48.849304: Pseudo dice [0.4985, 0.4733, 0.6454, 0.7166, 0.5929, 0.7915, 0.777] +2026-04-13 08:13:48.852210: Epoch time: 103.8 s +2026-04-13 08:13:50.387435: +2026-04-13 08:13:50.390105: Epoch 2381 +2026-04-13 08:13:50.392412: Current learning rate: 0.00443 +2026-04-13 08:15:33.259313: train_loss -0.3943 +2026-04-13 08:15:33.264884: val_loss -0.3247 +2026-04-13 08:15:33.266753: Pseudo dice [0.3097, 0.3124, 0.6707, 0.6308, 0.4906, 0.1121, 0.8179] +2026-04-13 08:15:33.269484: Epoch time: 102.88 s +2026-04-13 08:15:34.796433: +2026-04-13 08:15:34.798545: Epoch 2382 +2026-04-13 08:15:34.801682: Current learning rate: 0.00443 +2026-04-13 08:17:17.737938: train_loss -0.3837 +2026-04-13 08:17:17.743607: val_loss -0.2775 +2026-04-13 08:17:17.746361: Pseudo dice [0.5592, 0.5362, 0.6265, 0.3504, 0.4839, 0.0731, 0.8508] +2026-04-13 08:17:17.750235: Epoch time: 102.95 s +2026-04-13 08:17:19.309700: +2026-04-13 08:17:19.313442: Epoch 2383 +2026-04-13 08:17:19.315912: Current learning rate: 0.00443 +2026-04-13 08:19:02.820337: train_loss -0.3746 +2026-04-13 08:19:02.826123: val_loss -0.3344 +2026-04-13 08:19:02.827984: Pseudo dice [0.6447, 0.2358, 0.6885, 0.4602, 0.5029, 0.4246, 0.7057] +2026-04-13 08:19:02.830479: Epoch time: 103.51 s +2026-04-13 08:19:04.353150: +2026-04-13 08:19:04.354835: Epoch 2384 +2026-04-13 08:19:04.357008: Current learning rate: 0.00442 +2026-04-13 08:20:47.480171: train_loss -0.3924 +2026-04-13 08:20:47.486753: val_loss -0.1871 +2026-04-13 08:20:47.489028: Pseudo dice [0.455, 0.736, 0.4913, 0.6801, 0.4469, 0.1217, 0.838] +2026-04-13 08:20:47.491592: Epoch time: 103.13 s +2026-04-13 08:20:50.148444: +2026-04-13 08:20:50.150167: Epoch 2385 +2026-04-13 08:20:50.152177: Current learning rate: 0.00442 +2026-04-13 08:22:33.093934: train_loss -0.3906 +2026-04-13 08:22:33.102672: val_loss -0.3586 +2026-04-13 08:22:33.105935: Pseudo dice [0.3929, 0.2451, 0.7916, 0.7697, 0.6011, 0.8196, 0.6436] +2026-04-13 08:22:33.108945: Epoch time: 102.95 s +2026-04-13 08:22:34.639112: +2026-04-13 08:22:34.644104: Epoch 2386 +2026-04-13 08:22:34.647765: Current learning rate: 0.00442 +2026-04-13 08:24:17.740165: train_loss -0.3691 +2026-04-13 08:24:17.745220: val_loss -0.3304 +2026-04-13 08:24:17.747093: Pseudo dice [0.7146, 0.4635, 0.6858, 0.7776, 0.5792, 0.0792, 0.8503] +2026-04-13 08:24:17.749121: Epoch time: 103.1 s +2026-04-13 08:24:19.238792: +2026-04-13 08:24:19.240950: Epoch 2387 +2026-04-13 08:24:19.242993: Current learning rate: 0.00442 +2026-04-13 08:26:02.961903: train_loss -0.3765 +2026-04-13 08:26:02.967638: val_loss -0.2007 +2026-04-13 08:26:02.969956: Pseudo dice [0.4797, 0.5493, 0.5448, 0.8284, 0.585, 0.1982, 0.8759] +2026-04-13 08:26:02.972419: Epoch time: 103.73 s +2026-04-13 08:26:04.530843: +2026-04-13 08:26:04.532689: Epoch 2388 +2026-04-13 08:26:04.536341: Current learning rate: 0.00441 +2026-04-13 08:27:47.696598: train_loss -0.3874 +2026-04-13 08:27:47.701717: val_loss -0.3452 +2026-04-13 08:27:47.703676: Pseudo dice [0.694, 0.246, 0.7239, 0.7448, 0.567, 0.3551, 0.8307] +2026-04-13 08:27:47.705901: Epoch time: 103.17 s +2026-04-13 08:27:49.255878: +2026-04-13 08:27:49.257850: Epoch 2389 +2026-04-13 08:27:49.259939: Current learning rate: 0.00441 +2026-04-13 08:29:33.082486: train_loss -0.3758 +2026-04-13 08:29:33.092925: val_loss -0.1647 +2026-04-13 08:29:33.095479: Pseudo dice [0.7915, 0.2698, 0.6449, 0.2319, 0.1832, 0.1116, 0.8549] +2026-04-13 08:29:33.097753: Epoch time: 103.83 s +2026-04-13 08:29:34.618729: +2026-04-13 08:29:34.621016: Epoch 2390 +2026-04-13 08:29:34.623056: Current learning rate: 0.00441 +2026-04-13 08:31:17.727554: train_loss -0.3725 +2026-04-13 08:31:17.737397: val_loss -0.327 +2026-04-13 08:31:17.740802: Pseudo dice [0.2633, 0.559, 0.5767, 0.8075, 0.456, 0.7549, 0.8868] +2026-04-13 08:31:17.743200: Epoch time: 103.11 s +2026-04-13 08:31:19.273264: +2026-04-13 08:31:19.275328: Epoch 2391 +2026-04-13 08:31:19.277999: Current learning rate: 0.00441 +2026-04-13 08:33:01.992586: train_loss -0.3793 +2026-04-13 08:33:01.999060: val_loss -0.2896 +2026-04-13 08:33:02.002005: Pseudo dice [0.2911, 0.5765, 0.6036, 0.6238, 0.5439, 0.1101, 0.8237] +2026-04-13 08:33:02.005419: Epoch time: 102.72 s +2026-04-13 08:33:03.545184: +2026-04-13 08:33:03.548364: Epoch 2392 +2026-04-13 08:33:03.552382: Current learning rate: 0.0044 +2026-04-13 08:34:46.599334: train_loss -0.3824 +2026-04-13 08:34:46.604398: val_loss -0.3298 +2026-04-13 08:34:46.606622: Pseudo dice [0.2414, 0.2517, 0.7264, 0.5746, 0.5107, 0.8104, 0.7843] +2026-04-13 08:34:46.609445: Epoch time: 103.06 s +2026-04-13 08:34:48.092737: +2026-04-13 08:34:48.094591: Epoch 2393 +2026-04-13 08:34:48.096940: Current learning rate: 0.0044 +2026-04-13 08:36:31.269966: train_loss -0.3911 +2026-04-13 08:36:31.276100: val_loss -0.3496 +2026-04-13 08:36:31.278047: Pseudo dice [0.6328, 0.3295, 0.6926, 0.7269, 0.5902, 0.8146, 0.8669] +2026-04-13 08:36:31.280652: Epoch time: 103.18 s +2026-04-13 08:36:32.818070: +2026-04-13 08:36:32.820238: Epoch 2394 +2026-04-13 08:36:32.822148: Current learning rate: 0.0044 +2026-04-13 08:38:15.180114: train_loss -0.378 +2026-04-13 08:38:15.188552: val_loss -0.2495 +2026-04-13 08:38:15.190975: Pseudo dice [0.5395, 0.6814, 0.6477, 0.0075, 0.476, 0.1829, 0.7526] +2026-04-13 08:38:15.193417: Epoch time: 102.37 s +2026-04-13 08:38:16.754349: +2026-04-13 08:38:16.757994: Epoch 2395 +2026-04-13 08:38:16.760670: Current learning rate: 0.0044 +2026-04-13 08:40:00.385643: train_loss -0.3657 +2026-04-13 08:40:00.392454: val_loss -0.2256 +2026-04-13 08:40:00.395015: Pseudo dice [0.4534, 0.0572, 0.625, 0.6457, 0.239, 0.0821, 0.7015] +2026-04-13 08:40:00.398215: Epoch time: 103.64 s +2026-04-13 08:40:01.973071: +2026-04-13 08:40:01.975286: Epoch 2396 +2026-04-13 08:40:01.977887: Current learning rate: 0.00439 +2026-04-13 08:41:44.790065: train_loss -0.3785 +2026-04-13 08:41:44.795614: val_loss -0.3117 +2026-04-13 08:41:44.797788: Pseudo dice [0.6981, 0.368, 0.5822, 0.3186, 0.295, 0.22, 0.2937] +2026-04-13 08:41:44.800492: Epoch time: 102.82 s +2026-04-13 08:41:46.359510: +2026-04-13 08:41:46.361583: Epoch 2397 +2026-04-13 08:41:46.363777: Current learning rate: 0.00439 +2026-04-13 08:43:28.733788: train_loss -0.3839 +2026-04-13 08:43:28.739362: val_loss -0.298 +2026-04-13 08:43:28.741750: Pseudo dice [0.2615, 0.0711, 0.7441, 0.761, 0.1439, 0.8039, 0.6911] +2026-04-13 08:43:28.744039: Epoch time: 102.38 s +2026-04-13 08:43:30.290047: +2026-04-13 08:43:30.292998: Epoch 2398 +2026-04-13 08:43:30.295518: Current learning rate: 0.00439 +2026-04-13 08:45:15.072118: train_loss -0.3831 +2026-04-13 08:45:15.079822: val_loss -0.3155 +2026-04-13 08:45:15.082108: Pseudo dice [0.1335, 0.3287, 0.5252, 0.8667, 0.5471, 0.4937, 0.8277] +2026-04-13 08:45:15.084449: Epoch time: 104.79 s +2026-04-13 08:45:16.673549: +2026-04-13 08:45:16.675489: Epoch 2399 +2026-04-13 08:45:16.678100: Current learning rate: 0.00439 +2026-04-13 08:47:00.427209: train_loss -0.3676 +2026-04-13 08:47:00.433747: val_loss -0.3419 +2026-04-13 08:47:00.436357: Pseudo dice [0.7754, 0.5212, 0.6861, 0.9104, 0.2546, 0.7558, 0.4605] +2026-04-13 08:47:00.438417: Epoch time: 103.76 s +2026-04-13 08:47:03.969707: +2026-04-13 08:47:03.971640: Epoch 2400 +2026-04-13 08:47:03.974471: Current learning rate: 0.00438 +2026-04-13 08:48:46.934706: train_loss -0.3867 +2026-04-13 08:48:46.939888: val_loss -0.3272 +2026-04-13 08:48:46.941756: Pseudo dice [0.6187, 0.495, 0.6557, 0.7586, 0.4526, 0.8058, 0.4202] +2026-04-13 08:48:46.943881: Epoch time: 102.97 s +2026-04-13 08:48:48.434614: +2026-04-13 08:48:48.436384: Epoch 2401 +2026-04-13 08:48:48.438955: Current learning rate: 0.00438 +2026-04-13 08:50:31.586703: train_loss -0.3792 +2026-04-13 08:50:31.592939: val_loss -0.3336 +2026-04-13 08:50:31.595324: Pseudo dice [0.4813, 0.6056, 0.7151, 0.8112, 0.3374, 0.3608, 0.811] +2026-04-13 08:50:31.597824: Epoch time: 103.16 s +2026-04-13 08:50:33.127545: +2026-04-13 08:50:33.129689: Epoch 2402 +2026-04-13 08:50:33.132056: Current learning rate: 0.00438 +2026-04-13 08:52:15.694189: train_loss -0.3792 +2026-04-13 08:52:15.702568: val_loss -0.3185 +2026-04-13 08:52:15.704663: Pseudo dice [0.6325, 0.2949, 0.7098, 0.6713, 0.5196, 0.1118, 0.7533] +2026-04-13 08:52:15.706909: Epoch time: 102.57 s +2026-04-13 08:52:17.228942: +2026-04-13 08:52:17.230886: Epoch 2403 +2026-04-13 08:52:17.233130: Current learning rate: 0.00438 +2026-04-13 08:54:00.463955: train_loss -0.3893 +2026-04-13 08:54:00.469296: val_loss -0.3161 +2026-04-13 08:54:00.471172: Pseudo dice [0.1975, 0.279, 0.7416, 0.7228, 0.4461, 0.0931, 0.8372] +2026-04-13 08:54:00.473995: Epoch time: 103.24 s +2026-04-13 08:54:01.994127: +2026-04-13 08:54:01.996031: Epoch 2404 +2026-04-13 08:54:01.998008: Current learning rate: 0.00437 +2026-04-13 08:55:45.466033: train_loss -0.3885 +2026-04-13 08:55:45.472000: val_loss -0.3357 +2026-04-13 08:55:45.474627: Pseudo dice [0.6858, 0.3105, 0.7946, 0.6453, 0.303, 0.4684, 0.7043] +2026-04-13 08:55:45.477660: Epoch time: 103.48 s +2026-04-13 08:55:48.072223: +2026-04-13 08:55:48.074281: Epoch 2405 +2026-04-13 08:55:48.077085: Current learning rate: 0.00437 +2026-04-13 08:57:31.438827: train_loss -0.3794 +2026-04-13 08:57:31.446434: val_loss -0.3295 +2026-04-13 08:57:31.449541: Pseudo dice [0.293, 0.1856, 0.6403, 0.6465, 0.4386, 0.5974, 0.6093] +2026-04-13 08:57:31.451752: Epoch time: 103.37 s +2026-04-13 08:57:32.958839: +2026-04-13 08:57:32.962487: Epoch 2406 +2026-04-13 08:57:32.965535: Current learning rate: 0.00437 +2026-04-13 08:59:16.351865: train_loss -0.3812 +2026-04-13 08:59:16.359809: val_loss -0.3175 +2026-04-13 08:59:16.362156: Pseudo dice [0.7, 0.6054, 0.4863, 0.849, 0.5241, 0.0898, 0.8234] +2026-04-13 08:59:16.366538: Epoch time: 103.4 s +2026-04-13 08:59:17.853961: +2026-04-13 08:59:17.857005: Epoch 2407 +2026-04-13 08:59:17.859848: Current learning rate: 0.00437 +2026-04-13 09:01:00.364779: train_loss -0.3944 +2026-04-13 09:01:00.373844: val_loss -0.2782 +2026-04-13 09:01:00.376869: Pseudo dice [0.3413, 0.5362, 0.6831, 0.7173, 0.6462, 0.1804, 0.8353] +2026-04-13 09:01:00.379716: Epoch time: 102.51 s +2026-04-13 09:01:01.863956: +2026-04-13 09:01:01.866067: Epoch 2408 +2026-04-13 09:01:01.868220: Current learning rate: 0.00436 +2026-04-13 09:02:44.382426: train_loss -0.3951 +2026-04-13 09:02:44.388240: val_loss -0.3031 +2026-04-13 09:02:44.390777: Pseudo dice [0.6321, 0.2004, 0.6458, 0.8454, 0.612, 0.2539, 0.8654] +2026-04-13 09:02:44.393294: Epoch time: 102.52 s +2026-04-13 09:02:45.889632: +2026-04-13 09:02:45.891254: Epoch 2409 +2026-04-13 09:02:45.893184: Current learning rate: 0.00436 +2026-04-13 09:04:31.082030: train_loss -0.3955 +2026-04-13 09:04:31.091588: val_loss -0.3616 +2026-04-13 09:04:31.095711: Pseudo dice [0.5173, 0.838, 0.7993, 0.7401, 0.5605, 0.5894, 0.8487] +2026-04-13 09:04:31.100192: Epoch time: 105.2 s +2026-04-13 09:04:32.576034: +2026-04-13 09:04:32.578326: Epoch 2410 +2026-04-13 09:04:32.580722: Current learning rate: 0.00436 +2026-04-13 09:06:15.706512: train_loss -0.4001 +2026-04-13 09:06:15.711402: val_loss -0.3749 +2026-04-13 09:06:15.713851: Pseudo dice [0.5634, 0.3003, 0.7569, 0.8725, 0.4203, 0.8673, 0.7808] +2026-04-13 09:06:15.716306: Epoch time: 103.13 s +2026-04-13 09:06:17.281069: +2026-04-13 09:06:17.282999: Epoch 2411 +2026-04-13 09:06:17.285363: Current learning rate: 0.00436 +2026-04-13 09:07:59.861959: train_loss -0.4038 +2026-04-13 09:07:59.869139: val_loss -0.3494 +2026-04-13 09:07:59.872023: Pseudo dice [0.823, 0.6028, 0.6899, 0.8271, 0.6142, 0.1441, 0.8127] +2026-04-13 09:07:59.874643: Epoch time: 102.58 s +2026-04-13 09:08:01.492247: +2026-04-13 09:08:01.494250: Epoch 2412 +2026-04-13 09:08:01.496382: Current learning rate: 0.00435 +2026-04-13 09:09:44.166857: train_loss -0.395 +2026-04-13 09:09:44.172459: val_loss -0.2404 +2026-04-13 09:09:44.176551: Pseudo dice [0.4351, 0.8157, 0.5699, 0.6579, 0.5055, 0.1249, 0.5496] +2026-04-13 09:09:44.178772: Epoch time: 102.68 s +2026-04-13 09:09:45.707734: +2026-04-13 09:09:45.709852: Epoch 2413 +2026-04-13 09:09:45.711931: Current learning rate: 0.00435 +2026-04-13 09:11:28.507570: train_loss -0.3921 +2026-04-13 09:11:28.514093: val_loss -0.2941 +2026-04-13 09:11:28.517514: Pseudo dice [0.3033, 0.3472, 0.6968, 0.8267, 0.5442, 0.3149, 0.7654] +2026-04-13 09:11:28.520963: Epoch time: 102.8 s +2026-04-13 09:11:30.049070: +2026-04-13 09:11:30.051127: Epoch 2414 +2026-04-13 09:11:30.053406: Current learning rate: 0.00435 +2026-04-13 09:13:13.252154: train_loss -0.3789 +2026-04-13 09:13:13.259978: val_loss -0.3346 +2026-04-13 09:13:13.262602: Pseudo dice [0.5453, 0.3238, 0.5649, 0.2635, 0.497, 0.5753, 0.6881] +2026-04-13 09:13:13.264775: Epoch time: 103.21 s +2026-04-13 09:13:14.835555: +2026-04-13 09:13:14.838112: Epoch 2415 +2026-04-13 09:13:14.841024: Current learning rate: 0.00435 +2026-04-13 09:14:57.750185: train_loss -0.3714 +2026-04-13 09:14:57.755218: val_loss -0.289 +2026-04-13 09:14:57.757596: Pseudo dice [0.3512, 0.5483, 0.6754, 0.789, 0.6394, 0.3471, 0.8635] +2026-04-13 09:14:57.760858: Epoch time: 102.92 s +2026-04-13 09:14:59.322972: +2026-04-13 09:14:59.324690: Epoch 2416 +2026-04-13 09:14:59.326546: Current learning rate: 0.00434 +2026-04-13 09:16:41.956853: train_loss -0.3653 +2026-04-13 09:16:41.967122: val_loss -0.329 +2026-04-13 09:16:41.971388: Pseudo dice [0.7824, 0.288, 0.684, 0.3068, 0.4104, 0.258, 0.8029] +2026-04-13 09:16:41.977635: Epoch time: 102.64 s +2026-04-13 09:16:43.492220: +2026-04-13 09:16:43.496182: Epoch 2417 +2026-04-13 09:16:43.499125: Current learning rate: 0.00434 +2026-04-13 09:18:26.542003: train_loss -0.3654 +2026-04-13 09:18:26.549042: val_loss -0.3293 +2026-04-13 09:18:26.551149: Pseudo dice [0.715, 0.0657, 0.6772, 0.0275, 0.6237, 0.6917, 0.6792] +2026-04-13 09:18:26.553417: Epoch time: 103.05 s +2026-04-13 09:18:28.055760: +2026-04-13 09:18:28.058560: Epoch 2418 +2026-04-13 09:18:28.060612: Current learning rate: 0.00434 +2026-04-13 09:20:12.142762: train_loss -0.3792 +2026-04-13 09:20:12.147858: val_loss -0.261 +2026-04-13 09:20:12.149989: Pseudo dice [0.7031, 0.3893, 0.4998, 0.3214, 0.3671, 0.0251, 0.8632] +2026-04-13 09:20:12.152217: Epoch time: 104.09 s +2026-04-13 09:20:13.684724: +2026-04-13 09:20:13.686937: Epoch 2419 +2026-04-13 09:20:13.690702: Current learning rate: 0.00434 +2026-04-13 09:21:57.573789: train_loss -0.3816 +2026-04-13 09:21:57.579192: val_loss -0.3436 +2026-04-13 09:21:57.582951: Pseudo dice [0.666, 0.5726, 0.5814, 0.7665, 0.6338, 0.669, 0.8267] +2026-04-13 09:21:57.585397: Epoch time: 103.89 s +2026-04-13 09:21:59.116127: +2026-04-13 09:21:59.118714: Epoch 2420 +2026-04-13 09:21:59.120904: Current learning rate: 0.00433 +2026-04-13 09:23:43.180391: train_loss -0.3847 +2026-04-13 09:23:43.189453: val_loss -0.2922 +2026-04-13 09:23:43.192470: Pseudo dice [0.7218, 0.5314, 0.5563, 0.3995, 0.6566, 0.2666, 0.4263] +2026-04-13 09:23:43.195726: Epoch time: 104.07 s +2026-04-13 09:23:44.743486: +2026-04-13 09:23:44.745848: Epoch 2421 +2026-04-13 09:23:44.747957: Current learning rate: 0.00433 +2026-04-13 09:25:27.309909: train_loss -0.3788 +2026-04-13 09:25:27.316725: val_loss -0.317 +2026-04-13 09:25:27.327894: Pseudo dice [0.4319, 0.3146, 0.5185, 0.5738, 0.3582, 0.4125, 0.9] +2026-04-13 09:25:27.330712: Epoch time: 102.57 s +2026-04-13 09:25:28.921320: +2026-04-13 09:25:28.923857: Epoch 2422 +2026-04-13 09:25:28.927802: Current learning rate: 0.00433 +2026-04-13 09:27:11.600605: train_loss -0.3751 +2026-04-13 09:27:11.606882: val_loss -0.2614 +2026-04-13 09:27:11.610123: Pseudo dice [0.7299, 0.3083, 0.7419, 0.4957, 0.2533, 0.1585, 0.6535] +2026-04-13 09:27:11.612789: Epoch time: 102.68 s +2026-04-13 09:27:13.167888: +2026-04-13 09:27:13.170499: Epoch 2423 +2026-04-13 09:27:13.172509: Current learning rate: 0.00433 +2026-04-13 09:28:57.450285: train_loss -0.3802 +2026-04-13 09:28:57.456480: val_loss -0.3522 +2026-04-13 09:28:57.460175: Pseudo dice [0.7617, 0.25, 0.6747, 0.7966, 0.549, 0.5454, 0.7211] +2026-04-13 09:28:57.462966: Epoch time: 104.29 s +2026-04-13 09:28:59.034527: +2026-04-13 09:28:59.036728: Epoch 2424 +2026-04-13 09:28:59.039066: Current learning rate: 0.00432 +2026-04-13 09:30:41.605221: train_loss -0.3911 +2026-04-13 09:30:41.610888: val_loss -0.2353 +2026-04-13 09:30:41.613427: Pseudo dice [0.48, 0.4504, 0.2581, 0.7779, 0.352, 0.2892, 0.8202] +2026-04-13 09:30:41.617958: Epoch time: 102.57 s +2026-04-13 09:30:43.102806: +2026-04-13 09:30:43.107852: Epoch 2425 +2026-04-13 09:30:43.112268: Current learning rate: 0.00432 +2026-04-13 09:32:25.587448: train_loss -0.3895 +2026-04-13 09:32:25.597981: val_loss -0.2862 +2026-04-13 09:32:25.600729: Pseudo dice [0.6555, 0.4943, 0.4, 0.77, 0.5591, 0.263, 0.742] +2026-04-13 09:32:25.603745: Epoch time: 102.49 s +2026-04-13 09:32:28.179626: +2026-04-13 09:32:28.182513: Epoch 2426 +2026-04-13 09:32:28.185403: Current learning rate: 0.00432 +2026-04-13 09:34:10.529500: train_loss -0.4006 +2026-04-13 09:34:10.536705: val_loss -0.3421 +2026-04-13 09:34:10.540495: Pseudo dice [0.6, 0.3321, 0.642, 0.7238, 0.6199, 0.2695, 0.9097] +2026-04-13 09:34:10.543504: Epoch time: 102.35 s +2026-04-13 09:34:12.065649: +2026-04-13 09:34:12.068025: Epoch 2427 +2026-04-13 09:34:12.070326: Current learning rate: 0.00432 +2026-04-13 09:35:54.355446: train_loss -0.3766 +2026-04-13 09:35:54.360850: val_loss -0.2778 +2026-04-13 09:35:54.363241: Pseudo dice [0.7449, 0.4367, 0.5873, 0.0176, 0.5419, 0.0925, 0.3627] +2026-04-13 09:35:54.365746: Epoch time: 102.29 s +2026-04-13 09:35:55.906744: +2026-04-13 09:35:55.909218: Epoch 2428 +2026-04-13 09:35:55.911934: Current learning rate: 0.00431 +2026-04-13 09:37:38.549859: train_loss -0.3662 +2026-04-13 09:37:38.556347: val_loss -0.3067 +2026-04-13 09:37:38.558558: Pseudo dice [0.5477, 0.0894, 0.6836, 0.8524, 0.4665, 0.118, 0.9019] +2026-04-13 09:37:38.560986: Epoch time: 102.65 s +2026-04-13 09:37:40.049666: +2026-04-13 09:37:40.051948: Epoch 2429 +2026-04-13 09:37:40.054622: Current learning rate: 0.00431 +2026-04-13 09:39:22.549077: train_loss -0.3771 +2026-04-13 09:39:22.562516: val_loss -0.3334 +2026-04-13 09:39:22.568585: Pseudo dice [0.6715, 0.3282, 0.7601, 0.3484, 0.6079, 0.1468, 0.7421] +2026-04-13 09:39:22.575275: Epoch time: 102.5 s +2026-04-13 09:39:24.130481: +2026-04-13 09:39:24.132916: Epoch 2430 +2026-04-13 09:39:24.135550: Current learning rate: 0.00431 +2026-04-13 09:41:07.621002: train_loss -0.3586 +2026-04-13 09:41:07.630283: val_loss -0.2893 +2026-04-13 09:41:07.634729: Pseudo dice [0.6795, 0.2567, 0.5796, 0.5261, 0.3275, 0.1486, 0.4195] +2026-04-13 09:41:07.638453: Epoch time: 103.49 s +2026-04-13 09:41:09.162015: +2026-04-13 09:41:09.164087: Epoch 2431 +2026-04-13 09:41:09.166257: Current learning rate: 0.00431 +2026-04-13 09:42:51.612303: train_loss -0.3744 +2026-04-13 09:42:51.620296: val_loss -0.2941 +2026-04-13 09:42:51.625324: Pseudo dice [0.1867, 0.148, 0.5546, 0.1818, 0.4905, 0.7492, 0.7707] +2026-04-13 09:42:51.628757: Epoch time: 102.45 s +2026-04-13 09:42:53.169658: +2026-04-13 09:42:53.171579: Epoch 2432 +2026-04-13 09:42:53.173783: Current learning rate: 0.0043 +2026-04-13 09:44:36.111187: train_loss -0.372 +2026-04-13 09:44:36.120120: val_loss -0.3019 +2026-04-13 09:44:36.122744: Pseudo dice [0.5897, 0.0017, 0.6746, 0.6885, 0.3125, 0.156, 0.6993] +2026-04-13 09:44:36.125149: Epoch time: 102.95 s +2026-04-13 09:44:37.723653: +2026-04-13 09:44:37.725954: Epoch 2433 +2026-04-13 09:44:37.728275: Current learning rate: 0.0043 +2026-04-13 09:46:21.582709: train_loss -0.3453 +2026-04-13 09:46:21.596825: val_loss -0.3429 +2026-04-13 09:46:21.601658: Pseudo dice [0.7359, 0.4655, 0.637, 0.8338, 0.5186, 0.579, 0.8831] +2026-04-13 09:46:21.605613: Epoch time: 103.86 s +2026-04-13 09:46:23.110293: +2026-04-13 09:46:23.112445: Epoch 2434 +2026-04-13 09:46:23.114593: Current learning rate: 0.0043 +2026-04-13 09:48:05.865164: train_loss -0.3602 +2026-04-13 09:48:05.871148: val_loss -0.3665 +2026-04-13 09:48:05.873283: Pseudo dice [0.4798, 0.1338, 0.7927, 0.5008, 0.5904, 0.7518, 0.9196] +2026-04-13 09:48:05.875861: Epoch time: 102.76 s +2026-04-13 09:48:07.389958: +2026-04-13 09:48:07.391735: Epoch 2435 +2026-04-13 09:48:07.393876: Current learning rate: 0.0043 +2026-04-13 09:49:50.192124: train_loss -0.3737 +2026-04-13 09:49:50.198823: val_loss -0.2456 +2026-04-13 09:49:50.201170: Pseudo dice [0.5641, 0.414, 0.3306, 0.1191, 0.4952, 0.0731, 0.8203] +2026-04-13 09:49:50.203739: Epoch time: 102.81 s +2026-04-13 09:49:51.721726: +2026-04-13 09:49:51.725914: Epoch 2436 +2026-04-13 09:49:51.728180: Current learning rate: 0.00429 +2026-04-13 09:51:35.502193: train_loss -0.3852 +2026-04-13 09:51:35.509749: val_loss -0.3544 +2026-04-13 09:51:35.512541: Pseudo dice [0.719, 0.5153, 0.7261, 0.612, 0.6121, 0.5403, 0.7374] +2026-04-13 09:51:35.516011: Epoch time: 103.78 s +2026-04-13 09:51:37.042578: +2026-04-13 09:51:37.045455: Epoch 2437 +2026-04-13 09:51:37.048117: Current learning rate: 0.00429 +2026-04-13 09:53:20.226665: train_loss -0.3931 +2026-04-13 09:53:20.234199: val_loss -0.2986 +2026-04-13 09:53:20.239391: Pseudo dice [0.4355, 0.4807, 0.5941, 0.6685, 0.5763, 0.2053, 0.6548] +2026-04-13 09:53:20.242293: Epoch time: 103.19 s +2026-04-13 09:53:21.815691: +2026-04-13 09:53:21.817385: Epoch 2438 +2026-04-13 09:53:21.820880: Current learning rate: 0.00429 +2026-04-13 09:55:04.967088: train_loss -0.3824 +2026-04-13 09:55:04.974766: val_loss -0.1812 +2026-04-13 09:55:04.976759: Pseudo dice [0.7733, 0.402, 0.621, 0.2539, 0.4704, 0.0348, 0.7481] +2026-04-13 09:55:04.978913: Epoch time: 103.16 s +2026-04-13 09:55:06.555366: +2026-04-13 09:55:06.559655: Epoch 2439 +2026-04-13 09:55:06.562711: Current learning rate: 0.00429 +2026-04-13 09:56:50.198302: train_loss -0.3924 +2026-04-13 09:56:50.204601: val_loss -0.2625 +2026-04-13 09:56:50.207062: Pseudo dice [0.5998, 0.3382, 0.6856, 0.5368, 0.2979, 0.0408, 0.839] +2026-04-13 09:56:50.209304: Epoch time: 103.65 s +2026-04-13 09:56:51.741519: +2026-04-13 09:56:51.745610: Epoch 2440 +2026-04-13 09:56:51.748430: Current learning rate: 0.00429 +2026-04-13 09:58:34.372094: train_loss -0.3817 +2026-04-13 09:58:34.377674: val_loss -0.2668 +2026-04-13 09:58:34.380071: Pseudo dice [0.7114, 0.3879, 0.6255, 0.853, 0.3175, 0.2063, 0.4296] +2026-04-13 09:58:34.383095: Epoch time: 102.63 s +2026-04-13 09:58:35.968934: +2026-04-13 09:58:35.971249: Epoch 2441 +2026-04-13 09:58:35.973640: Current learning rate: 0.00428 +2026-04-13 10:00:18.694350: train_loss -0.3888 +2026-04-13 10:00:18.701299: val_loss -0.3335 +2026-04-13 10:00:18.704356: Pseudo dice [0.7484, 0.2991, 0.6423, 0.7589, 0.4702, 0.8125, 0.8407] +2026-04-13 10:00:18.706889: Epoch time: 102.73 s +2026-04-13 10:00:20.266949: +2026-04-13 10:00:20.268930: Epoch 2442 +2026-04-13 10:00:20.270831: Current learning rate: 0.00428 +2026-04-13 10:02:04.206148: train_loss -0.3895 +2026-04-13 10:02:04.215245: val_loss -0.2673 +2026-04-13 10:02:04.219323: Pseudo dice [0.6287, 0.5195, 0.5964, 0.8092, 0.4019, 0.2299, 0.7426] +2026-04-13 10:02:04.222198: Epoch time: 103.94 s +2026-04-13 10:02:05.732901: +2026-04-13 10:02:05.736522: Epoch 2443 +2026-04-13 10:02:05.739202: Current learning rate: 0.00428 +2026-04-13 10:03:48.548906: train_loss -0.3872 +2026-04-13 10:03:48.556559: val_loss -0.3417 +2026-04-13 10:03:48.558929: Pseudo dice [0.582, 0.2262, 0.7722, 0.6132, 0.5811, 0.4543, 0.8039] +2026-04-13 10:03:48.563093: Epoch time: 102.82 s +2026-04-13 10:03:50.121546: +2026-04-13 10:03:50.124268: Epoch 2444 +2026-04-13 10:03:50.126836: Current learning rate: 0.00428 +2026-04-13 10:05:33.857497: train_loss -0.4017 +2026-04-13 10:05:33.864372: val_loss -0.2699 +2026-04-13 10:05:33.867669: Pseudo dice [0.2903, 0.2018, 0.6705, 0.7421, 0.3273, 0.0844, 0.796] +2026-04-13 10:05:33.870221: Epoch time: 103.74 s +2026-04-13 10:05:35.392088: +2026-04-13 10:05:35.393956: Epoch 2445 +2026-04-13 10:05:35.395992: Current learning rate: 0.00427 +2026-04-13 10:07:19.479057: train_loss -0.387 +2026-04-13 10:07:19.485827: val_loss -0.2764 +2026-04-13 10:07:19.487980: Pseudo dice [0.7697, 0.3685, 0.4923, 0.5899, 0.3236, 0.3706, 0.7743] +2026-04-13 10:07:19.490901: Epoch time: 104.09 s +2026-04-13 10:07:22.168691: +2026-04-13 10:07:22.170856: Epoch 2446 +2026-04-13 10:07:22.175146: Current learning rate: 0.00427 +2026-04-13 10:09:04.991794: train_loss -0.3738 +2026-04-13 10:09:04.998960: val_loss -0.2645 +2026-04-13 10:09:05.001700: Pseudo dice [0.4894, 0.5955, 0.7285, 0.696, 0.2264, 0.1459, 0.8267] +2026-04-13 10:09:05.005159: Epoch time: 102.83 s +2026-04-13 10:09:06.548886: +2026-04-13 10:09:06.550925: Epoch 2447 +2026-04-13 10:09:06.553354: Current learning rate: 0.00427 +2026-04-13 10:10:49.675041: train_loss -0.4016 +2026-04-13 10:10:49.682705: val_loss -0.3091 +2026-04-13 10:10:49.685007: Pseudo dice [0.7922, 0.5773, 0.4266, 0.7701, 0.4854, 0.1691, 0.4856] +2026-04-13 10:10:49.688375: Epoch time: 103.13 s +2026-04-13 10:10:51.186802: +2026-04-13 10:10:51.189076: Epoch 2448 +2026-04-13 10:10:51.191547: Current learning rate: 0.00427 +2026-04-13 10:12:35.419984: train_loss -0.3851 +2026-04-13 10:12:35.426313: val_loss -0.3609 +2026-04-13 10:12:35.429306: Pseudo dice [0.7791, 0.7805, 0.654, 0.7546, 0.5954, 0.5139, 0.7701] +2026-04-13 10:12:35.431981: Epoch time: 104.24 s +2026-04-13 10:12:37.016629: +2026-04-13 10:12:37.018396: Epoch 2449 +2026-04-13 10:12:37.020272: Current learning rate: 0.00426 +2026-04-13 10:14:20.070863: train_loss -0.3989 +2026-04-13 10:14:20.076999: val_loss -0.3354 +2026-04-13 10:14:20.079429: Pseudo dice [0.3245, 0.5796, 0.79, 0.0454, 0.4288, 0.1614, 0.7423] +2026-04-13 10:14:20.082249: Epoch time: 103.06 s +2026-04-13 10:14:23.641774: +2026-04-13 10:14:23.645299: Epoch 2450 +2026-04-13 10:14:23.648204: Current learning rate: 0.00426 +2026-04-13 10:16:07.717283: train_loss -0.3907 +2026-04-13 10:16:07.727422: val_loss -0.2842 +2026-04-13 10:16:07.729824: Pseudo dice [0.3676, 0.4442, 0.5292, 0.1258, 0.3323, 0.1565, 0.69] +2026-04-13 10:16:07.732942: Epoch time: 104.08 s +2026-04-13 10:16:09.239601: +2026-04-13 10:16:09.243061: Epoch 2451 +2026-04-13 10:16:09.245255: Current learning rate: 0.00426 +2026-04-13 10:17:51.856269: train_loss -0.3639 +2026-04-13 10:17:51.863043: val_loss -0.2621 +2026-04-13 10:17:51.864921: Pseudo dice [0.7571, 0.6343, 0.749, 0.3942, 0.3987, 0.0403, 0.4813] +2026-04-13 10:17:51.867087: Epoch time: 102.62 s +2026-04-13 10:17:53.401425: +2026-04-13 10:17:53.403175: Epoch 2452 +2026-04-13 10:17:53.405808: Current learning rate: 0.00426 +2026-04-13 10:19:36.238328: train_loss -0.3755 +2026-04-13 10:19:36.244264: val_loss -0.3636 +2026-04-13 10:19:36.246704: Pseudo dice [0.6112, 0.5044, 0.6752, 0.1416, 0.613, 0.7524, 0.8064] +2026-04-13 10:19:36.249345: Epoch time: 102.84 s +2026-04-13 10:19:37.815538: +2026-04-13 10:19:37.817523: Epoch 2453 +2026-04-13 10:19:37.819525: Current learning rate: 0.00425 +2026-04-13 10:21:21.042125: train_loss -0.3693 +2026-04-13 10:21:21.049201: val_loss -0.326 +2026-04-13 10:21:21.051392: Pseudo dice [0.4462, 0.6217, 0.6672, 0.7606, 0.6643, 0.4296, 0.6674] +2026-04-13 10:21:21.055680: Epoch time: 103.23 s +2026-04-13 10:21:22.593175: +2026-04-13 10:21:22.595389: Epoch 2454 +2026-04-13 10:21:22.597940: Current learning rate: 0.00425 +2026-04-13 10:23:07.631687: train_loss -0.3829 +2026-04-13 10:23:07.637565: val_loss -0.2564 +2026-04-13 10:23:07.639966: Pseudo dice [0.2936, 0.3839, 0.5705, 0.0058, 0.1099, 0.0388, 0.5951] +2026-04-13 10:23:07.642376: Epoch time: 105.04 s +2026-04-13 10:23:09.156661: +2026-04-13 10:23:09.158443: Epoch 2455 +2026-04-13 10:23:09.160431: Current learning rate: 0.00425 +2026-04-13 10:24:52.749224: train_loss -0.383 +2026-04-13 10:24:52.760753: val_loss -0.2849 +2026-04-13 10:24:52.764512: Pseudo dice [0.2799, 0.2079, 0.6457, 0.7458, 0.2982, 0.3185, 0.7905] +2026-04-13 10:24:52.767304: Epoch time: 103.6 s +2026-04-13 10:24:54.290064: +2026-04-13 10:24:54.292758: Epoch 2456 +2026-04-13 10:24:54.294759: Current learning rate: 0.00425 +2026-04-13 10:26:37.139954: train_loss -0.384 +2026-04-13 10:26:37.146612: val_loss -0.3584 +2026-04-13 10:26:37.149237: Pseudo dice [0.7546, 0.8474, 0.6179, 0.7265, 0.552, 0.6835, 0.8451] +2026-04-13 10:26:37.152304: Epoch time: 102.85 s +2026-04-13 10:26:38.691815: +2026-04-13 10:26:38.693724: Epoch 2457 +2026-04-13 10:26:38.695723: Current learning rate: 0.00424 +2026-04-13 10:28:21.392684: train_loss -0.3868 +2026-04-13 10:28:21.398136: val_loss -0.3579 +2026-04-13 10:28:21.399987: Pseudo dice [0.5375, 0.6821, 0.638, 0.7713, 0.6591, 0.7098, 0.8285] +2026-04-13 10:28:21.402526: Epoch time: 102.7 s +2026-04-13 10:28:22.919294: +2026-04-13 10:28:22.921425: Epoch 2458 +2026-04-13 10:28:22.924040: Current learning rate: 0.00424 +2026-04-13 10:30:06.092627: train_loss -0.3834 +2026-04-13 10:30:06.098164: val_loss -0.28 +2026-04-13 10:30:06.100480: Pseudo dice [0.4582, 0.3117, 0.6974, 0.5756, 0.4577, 0.2386, 0.5357] +2026-04-13 10:30:06.103110: Epoch time: 103.18 s +2026-04-13 10:30:07.662673: +2026-04-13 10:30:07.664882: Epoch 2459 +2026-04-13 10:30:07.666982: Current learning rate: 0.00424 +2026-04-13 10:31:51.345957: train_loss -0.3844 +2026-04-13 10:31:51.363106: val_loss -0.3482 +2026-04-13 10:31:51.365183: Pseudo dice [0.7286, 0.7988, 0.6224, 0.7702, 0.5317, 0.5114, 0.5509] +2026-04-13 10:31:51.367551: Epoch time: 103.69 s +2026-04-13 10:31:52.901378: +2026-04-13 10:31:52.903649: Epoch 2460 +2026-04-13 10:31:52.906399: Current learning rate: 0.00424 +2026-04-13 10:33:35.768304: train_loss -0.3865 +2026-04-13 10:33:35.773916: val_loss -0.2823 +2026-04-13 10:33:35.775979: Pseudo dice [0.2842, 0.1631, 0.5922, 0.6602, 0.4301, 0.0269, 0.7863] +2026-04-13 10:33:35.779774: Epoch time: 102.87 s +2026-04-13 10:33:37.289863: +2026-04-13 10:33:37.292166: Epoch 2461 +2026-04-13 10:33:37.294357: Current learning rate: 0.00423 +2026-04-13 10:35:22.296106: train_loss -0.3778 +2026-04-13 10:35:22.303713: val_loss -0.2978 +2026-04-13 10:35:22.308055: Pseudo dice [0.5675, 0.3793, 0.5756, 0.23, 0.5128, 0.5175, 0.7211] +2026-04-13 10:35:22.311160: Epoch time: 105.01 s +2026-04-13 10:35:23.829352: +2026-04-13 10:35:23.831972: Epoch 2462 +2026-04-13 10:35:23.834007: Current learning rate: 0.00423 +2026-04-13 10:37:07.670812: train_loss -0.3881 +2026-04-13 10:37:07.676383: val_loss -0.3481 +2026-04-13 10:37:07.678850: Pseudo dice [0.2902, 0.1383, 0.635, 0.822, 0.6344, 0.8864, 0.8422] +2026-04-13 10:37:07.681191: Epoch time: 103.85 s +2026-04-13 10:37:09.206361: +2026-04-13 10:37:09.208417: Epoch 2463 +2026-04-13 10:37:09.210371: Current learning rate: 0.00423 +2026-04-13 10:38:52.408096: train_loss -0.3877 +2026-04-13 10:38:52.419476: val_loss -0.369 +2026-04-13 10:38:52.423938: Pseudo dice [0.4188, 0.7234, 0.7248, 0.7174, 0.6253, 0.5363, 0.8575] +2026-04-13 10:38:52.427349: Epoch time: 103.21 s +2026-04-13 10:38:53.930109: +2026-04-13 10:38:53.931896: Epoch 2464 +2026-04-13 10:38:53.934098: Current learning rate: 0.00423 +2026-04-13 10:40:36.546880: train_loss -0.3938 +2026-04-13 10:40:36.555652: val_loss -0.3311 +2026-04-13 10:40:36.558280: Pseudo dice [0.2023, 0.3454, 0.7483, 0.3778, 0.5616, 0.1689, 0.7005] +2026-04-13 10:40:36.560647: Epoch time: 102.62 s +2026-04-13 10:40:38.079333: +2026-04-13 10:40:38.081177: Epoch 2465 +2026-04-13 10:40:38.083449: Current learning rate: 0.00422 +2026-04-13 10:42:20.671158: train_loss -0.379 +2026-04-13 10:42:20.689185: val_loss -0.2951 +2026-04-13 10:42:20.694480: Pseudo dice [0.2922, 0.4176, 0.5854, 0.1119, 0.5204, 0.6496, 0.7791] +2026-04-13 10:42:20.699362: Epoch time: 102.6 s +2026-04-13 10:42:22.254872: +2026-04-13 10:42:22.259852: Epoch 2466 +2026-04-13 10:42:22.265167: Current learning rate: 0.00422 +2026-04-13 10:44:07.076699: train_loss -0.4013 +2026-04-13 10:44:07.082697: val_loss -0.3288 +2026-04-13 10:44:07.085322: Pseudo dice [0.3805, 0.537, 0.7766, 0.0788, 0.5697, 0.2141, 0.9169] +2026-04-13 10:44:07.087865: Epoch time: 104.83 s +2026-04-13 10:44:08.588491: +2026-04-13 10:44:08.590572: Epoch 2467 +2026-04-13 10:44:08.593492: Current learning rate: 0.00422 +2026-04-13 10:45:52.401526: train_loss -0.3828 +2026-04-13 10:45:52.411538: val_loss -0.2522 +2026-04-13 10:45:52.414607: Pseudo dice [0.773, 0.6258, 0.6437, 0.1929, 0.5622, 0.1275, 0.1684] +2026-04-13 10:45:52.417689: Epoch time: 103.82 s +2026-04-13 10:45:53.960864: +2026-04-13 10:45:53.963380: Epoch 2468 +2026-04-13 10:45:53.965939: Current learning rate: 0.00422 +2026-04-13 10:47:37.763345: train_loss -0.379 +2026-04-13 10:47:37.769179: val_loss -0.2825 +2026-04-13 10:47:37.771341: Pseudo dice [0.245, 0.0736, 0.5423, 0.7434, 0.6314, 0.6411, 0.3807] +2026-04-13 10:47:37.773646: Epoch time: 103.81 s +2026-04-13 10:47:39.279293: +2026-04-13 10:47:39.281200: Epoch 2469 +2026-04-13 10:47:39.283205: Current learning rate: 0.00421 +2026-04-13 10:49:24.065358: train_loss -0.3742 +2026-04-13 10:49:24.077855: val_loss -0.2597 +2026-04-13 10:49:24.083018: Pseudo dice [0.35, 0.7233, 0.4603, 0.6382, 0.5497, 0.1771, 0.8174] +2026-04-13 10:49:24.088825: Epoch time: 104.79 s +2026-04-13 10:49:25.595329: +2026-04-13 10:49:25.597460: Epoch 2470 +2026-04-13 10:49:25.599906: Current learning rate: 0.00421 +2026-04-13 10:51:09.021281: train_loss -0.3731 +2026-04-13 10:51:09.029452: val_loss -0.2856 +2026-04-13 10:51:09.033630: Pseudo dice [0.5751, 0.5593, 0.6329, 0.5382, 0.5031, 0.0897, 0.8033] +2026-04-13 10:51:09.036282: Epoch time: 103.43 s +2026-04-13 10:51:10.592824: +2026-04-13 10:51:10.594596: Epoch 2471 +2026-04-13 10:51:10.596707: Current learning rate: 0.00421 +2026-04-13 10:52:54.568102: train_loss -0.376 +2026-04-13 10:52:54.577678: val_loss -0.1868 +2026-04-13 10:52:54.582009: Pseudo dice [0.5595, 0.3072, 0.6735, 0.3052, 0.5119, 0.056, 0.6713] +2026-04-13 10:52:54.585171: Epoch time: 103.98 s +2026-04-13 10:52:56.195924: +2026-04-13 10:52:56.199203: Epoch 2472 +2026-04-13 10:52:56.202891: Current learning rate: 0.00421 +2026-04-13 10:54:40.053721: train_loss -0.3649 +2026-04-13 10:54:40.059610: val_loss -0.3142 +2026-04-13 10:54:40.062411: Pseudo dice [0.552, 0.1718, 0.5952, 0.7189, 0.1637, 0.3131, 0.5762] +2026-04-13 10:54:40.068522: Epoch time: 103.86 s +2026-04-13 10:54:41.587554: +2026-04-13 10:54:41.589853: Epoch 2473 +2026-04-13 10:54:41.592251: Current learning rate: 0.0042 +2026-04-13 10:56:25.740721: train_loss -0.3893 +2026-04-13 10:56:25.748121: val_loss -0.3276 +2026-04-13 10:56:25.750485: Pseudo dice [0.6899, 0.8323, 0.655, 0.6604, 0.4489, 0.7656, 0.5691] +2026-04-13 10:56:25.752931: Epoch time: 104.16 s +2026-04-13 10:56:27.299585: +2026-04-13 10:56:27.302083: Epoch 2474 +2026-04-13 10:56:27.304487: Current learning rate: 0.0042 +2026-04-13 10:58:11.016934: train_loss -0.3771 +2026-04-13 10:58:11.023097: val_loss -0.3307 +2026-04-13 10:58:11.025305: Pseudo dice [0.4479, 0.3404, 0.7268, 0.7628, 0.6837, 0.3137, 0.8345] +2026-04-13 10:58:11.027956: Epoch time: 103.72 s +2026-04-13 10:58:12.568702: +2026-04-13 10:58:12.570914: Epoch 2475 +2026-04-13 10:58:12.575770: Current learning rate: 0.0042 +2026-04-13 10:59:56.415485: train_loss -0.3857 +2026-04-13 10:59:56.426910: val_loss -0.2722 +2026-04-13 10:59:56.429701: Pseudo dice [0.5159, 0.3185, 0.6067, 0.5916, 0.2538, 0.1379, 0.7037] +2026-04-13 10:59:56.432572: Epoch time: 103.85 s +2026-04-13 10:59:57.947424: +2026-04-13 10:59:57.949645: Epoch 2476 +2026-04-13 10:59:57.951729: Current learning rate: 0.0042 +2026-04-13 11:01:41.431270: train_loss -0.3769 +2026-04-13 11:01:41.438830: val_loss -0.3266 +2026-04-13 11:01:41.441659: Pseudo dice [0.4013, 0.2876, 0.5557, 0.4739, 0.4397, 0.8683, 0.5868] +2026-04-13 11:01:41.446339: Epoch time: 103.49 s +2026-04-13 11:01:42.977734: +2026-04-13 11:01:42.982209: Epoch 2477 +2026-04-13 11:01:42.984904: Current learning rate: 0.00419 +2026-04-13 11:03:26.996233: train_loss -0.3851 +2026-04-13 11:03:27.002304: val_loss -0.3574 +2026-04-13 11:03:27.004634: Pseudo dice [0.4028, 0.1862, 0.6675, 0.668, 0.4304, 0.8196, 0.8044] +2026-04-13 11:03:27.008144: Epoch time: 104.02 s +2026-04-13 11:03:28.578470: +2026-04-13 11:03:28.580874: Epoch 2478 +2026-04-13 11:03:28.583256: Current learning rate: 0.00419 +2026-04-13 11:05:11.154483: train_loss -0.3913 +2026-04-13 11:05:11.160560: val_loss -0.3039 +2026-04-13 11:05:11.165015: Pseudo dice [0.68, 0.5491, 0.6346, 0.4676, 0.5682, 0.0819, 0.7306] +2026-04-13 11:05:11.167907: Epoch time: 102.58 s +2026-04-13 11:05:12.686327: +2026-04-13 11:05:12.690122: Epoch 2479 +2026-04-13 11:05:12.692451: Current learning rate: 0.00419 +2026-04-13 11:06:57.578902: train_loss -0.3719 +2026-04-13 11:06:57.586199: val_loss -0.3103 +2026-04-13 11:06:57.588856: Pseudo dice [0.6579, 0.6014, 0.8083, 0.0223, 0.4711, 0.3791, 0.6921] +2026-04-13 11:06:57.592484: Epoch time: 104.9 s +2026-04-13 11:06:59.117617: +2026-04-13 11:06:59.120206: Epoch 2480 +2026-04-13 11:06:59.124473: Current learning rate: 0.00419 +2026-04-13 11:08:43.116670: train_loss -0.356 +2026-04-13 11:08:43.122894: val_loss -0.2399 +2026-04-13 11:08:43.125045: Pseudo dice [0.2913, 0.0002, 0.5825, 0.0209, 0.0373, 0.2051, 0.6777] +2026-04-13 11:08:43.127441: Epoch time: 104.0 s +2026-04-13 11:08:44.641657: +2026-04-13 11:08:44.643892: Epoch 2481 +2026-04-13 11:08:44.645954: Current learning rate: 0.00418 +2026-04-13 11:10:28.544467: train_loss -0.3671 +2026-04-13 11:10:28.560779: val_loss -0.3165 +2026-04-13 11:10:28.565342: Pseudo dice [0.5276, 0.2156, 0.682, 0.6702, 0.5353, 0.2026, 0.74] +2026-04-13 11:10:28.572253: Epoch time: 103.91 s +2026-04-13 11:10:30.095811: +2026-04-13 11:10:30.098735: Epoch 2482 +2026-04-13 11:10:30.101572: Current learning rate: 0.00418 +2026-04-13 11:12:13.922525: train_loss -0.3825 +2026-04-13 11:12:13.939681: val_loss -0.3518 +2026-04-13 11:12:13.942257: Pseudo dice [0.2441, 0.6021, 0.6903, 0.7452, 0.6255, 0.8045, 0.8833] +2026-04-13 11:12:13.949823: Epoch time: 103.83 s +2026-04-13 11:12:15.503053: +2026-04-13 11:12:15.505723: Epoch 2483 +2026-04-13 11:12:15.508902: Current learning rate: 0.00418 +2026-04-13 11:13:59.401608: train_loss -0.3925 +2026-04-13 11:13:59.408580: val_loss -0.3294 +2026-04-13 11:13:59.410724: Pseudo dice [0.1979, 0.45, 0.67, 0.7158, 0.4937, 0.402, 0.6328] +2026-04-13 11:13:59.412999: Epoch time: 103.9 s +2026-04-13 11:14:00.924935: +2026-04-13 11:14:00.926973: Epoch 2484 +2026-04-13 11:14:00.929561: Current learning rate: 0.00418 +2026-04-13 11:15:43.947790: train_loss -0.3749 +2026-04-13 11:15:43.954808: val_loss -0.3043 +2026-04-13 11:15:43.957137: Pseudo dice [0.3713, 0.1937, 0.7041, 0.6165, 0.5364, 0.0471, 0.8178] +2026-04-13 11:15:43.969357: Epoch time: 103.03 s +2026-04-13 11:15:45.497014: +2026-04-13 11:15:45.503290: Epoch 2485 +2026-04-13 11:15:45.505366: Current learning rate: 0.00417 +2026-04-13 11:17:30.811669: train_loss -0.3896 +2026-04-13 11:17:30.820242: val_loss -0.2872 +2026-04-13 11:17:30.823444: Pseudo dice [0.6288, 0.437, 0.5502, 0.2665, 0.3223, 0.1155, 0.6474] +2026-04-13 11:17:30.827127: Epoch time: 105.32 s +2026-04-13 11:17:32.357477: +2026-04-13 11:17:32.360587: Epoch 2486 +2026-04-13 11:17:32.362754: Current learning rate: 0.00417 +2026-04-13 11:19:15.242865: train_loss -0.3889 +2026-04-13 11:19:15.249151: val_loss -0.3424 +2026-04-13 11:19:15.252895: Pseudo dice [0.8415, 0.1931, 0.7341, 0.7597, 0.5926, 0.7673, 0.8434] +2026-04-13 11:19:15.255319: Epoch time: 102.89 s +2026-04-13 11:19:17.933483: +2026-04-13 11:19:17.935538: Epoch 2487 +2026-04-13 11:19:17.937789: Current learning rate: 0.00417 +2026-04-13 11:21:03.733476: train_loss -0.4036 +2026-04-13 11:21:03.741405: val_loss -0.3599 +2026-04-13 11:21:03.743309: Pseudo dice [0.2311, 0.6535, 0.755, 0.9217, 0.6613, 0.8198, 0.9083] +2026-04-13 11:21:03.746121: Epoch time: 105.8 s +2026-04-13 11:21:05.275571: +2026-04-13 11:21:05.278313: Epoch 2488 +2026-04-13 11:21:05.280900: Current learning rate: 0.00417 +2026-04-13 11:22:53.036729: train_loss -0.3714 +2026-04-13 11:22:53.042948: val_loss -0.2455 +2026-04-13 11:22:53.044918: Pseudo dice [0.2122, 0.2495, 0.4277, 0.8781, 0.525, 0.0695, 0.8158] +2026-04-13 11:22:53.047141: Epoch time: 107.77 s +2026-04-13 11:22:54.644450: +2026-04-13 11:22:54.649341: Epoch 2489 +2026-04-13 11:22:54.652136: Current learning rate: 0.00416 +2026-04-13 11:24:41.757156: train_loss -0.3937 +2026-04-13 11:24:41.769951: val_loss -0.3706 +2026-04-13 11:24:41.773486: Pseudo dice [0.673, 0.5952, 0.6722, 0.792, 0.6598, 0.8368, 0.7526] +2026-04-13 11:24:41.776523: Epoch time: 107.12 s +2026-04-13 11:24:43.309435: +2026-04-13 11:24:43.312927: Epoch 2490 +2026-04-13 11:24:43.315730: Current learning rate: 0.00416 +2026-04-13 11:26:30.746968: train_loss -0.3641 +2026-04-13 11:26:30.765104: val_loss -0.3218 +2026-04-13 11:26:30.767670: Pseudo dice [0.8178, 0.7473, 0.5879, 0.5309, 0.5325, 0.7167, 0.5248] +2026-04-13 11:26:30.771506: Epoch time: 107.44 s +2026-04-13 11:26:32.297824: +2026-04-13 11:26:32.299527: Epoch 2491 +2026-04-13 11:26:32.301676: Current learning rate: 0.00416 +2026-04-13 11:28:22.003128: train_loss -0.3865 +2026-04-13 11:28:22.026880: val_loss -0.2923 +2026-04-13 11:28:22.034481: Pseudo dice [0.7858, 0.1458, 0.6673, 0.4859, 0.2303, 0.2305, 0.6878] +2026-04-13 11:28:22.040429: Epoch time: 109.71 s +2026-04-13 11:28:23.578268: +2026-04-13 11:28:23.581696: Epoch 2492 +2026-04-13 11:28:23.585478: Current learning rate: 0.00416 +2026-04-13 11:30:07.789075: train_loss -0.3956 +2026-04-13 11:30:07.801652: val_loss -0.2363 +2026-04-13 11:30:07.806091: Pseudo dice [0.7532, 0.4533, 0.638, 0.4045, 0.5949, 0.0865, 0.7329] +2026-04-13 11:30:07.810480: Epoch time: 104.21 s +2026-04-13 11:30:09.331990: +2026-04-13 11:30:09.334033: Epoch 2493 +2026-04-13 11:30:09.340899: Current learning rate: 0.00415 +2026-04-13 11:31:53.584577: train_loss -0.3777 +2026-04-13 11:31:53.590635: val_loss -0.2919 +2026-04-13 11:31:53.593122: Pseudo dice [0.7329, 0.0562, 0.6997, 0.0589, 0.5533, 0.2738, 0.4457] +2026-04-13 11:31:53.595332: Epoch time: 104.26 s +2026-04-13 11:31:55.120719: +2026-04-13 11:31:55.122905: Epoch 2494 +2026-04-13 11:31:55.125551: Current learning rate: 0.00415 +2026-04-13 11:33:39.622396: train_loss -0.3872 +2026-04-13 11:33:39.629228: val_loss -0.3155 +2026-04-13 11:33:39.631856: Pseudo dice [0.4519, 0.3375, 0.7184, 0.7394, 0.5933, 0.0902, 0.8828] +2026-04-13 11:33:39.634722: Epoch time: 104.51 s +2026-04-13 11:33:41.184985: +2026-04-13 11:33:41.189145: Epoch 2495 +2026-04-13 11:33:41.194362: Current learning rate: 0.00415 +2026-04-13 11:35:24.826432: train_loss -0.3965 +2026-04-13 11:35:24.832399: val_loss -0.3357 +2026-04-13 11:35:24.835041: Pseudo dice [0.5305, 0.3514, 0.6097, 0.4774, 0.359, 0.8111, 0.3692] +2026-04-13 11:35:24.837371: Epoch time: 103.65 s +2026-04-13 11:35:26.372580: +2026-04-13 11:35:26.375314: Epoch 2496 +2026-04-13 11:35:26.377400: Current learning rate: 0.00415 +2026-04-13 11:37:09.642598: train_loss -0.3921 +2026-04-13 11:37:09.650556: val_loss -0.302 +2026-04-13 11:37:09.655893: Pseudo dice [0.5387, 0.0733, 0.4102, 0.751, 0.6089, 0.2407, 0.9061] +2026-04-13 11:37:09.661869: Epoch time: 103.27 s +2026-04-13 11:37:11.233869: +2026-04-13 11:37:11.237035: Epoch 2497 +2026-04-13 11:37:11.239822: Current learning rate: 0.00414 +2026-04-13 11:38:57.694789: train_loss -0.3833 +2026-04-13 11:38:57.702625: val_loss -0.3227 +2026-04-13 11:38:57.706018: Pseudo dice [0.7025, 0.6338, 0.7023, 0.6473, 0.499, 0.1454, 0.3063] +2026-04-13 11:38:57.709163: Epoch time: 106.46 s +2026-04-13 11:38:59.243826: +2026-04-13 11:38:59.247472: Epoch 2498 +2026-04-13 11:38:59.249740: Current learning rate: 0.00414 +2026-04-13 11:40:42.152942: train_loss -0.3822 +2026-04-13 11:40:42.161918: val_loss -0.2557 +2026-04-13 11:40:42.165131: Pseudo dice [0.6662, 0.1537, 0.7481, 0.5555, 0.3261, 0.1771, 0.7693] +2026-04-13 11:40:42.169106: Epoch time: 102.91 s +2026-04-13 11:40:43.675835: +2026-04-13 11:40:43.679594: Epoch 2499 +2026-04-13 11:40:43.682414: Current learning rate: 0.00414 +2026-04-13 11:42:30.176654: train_loss -0.3851 +2026-04-13 11:42:30.193200: val_loss -0.3166 +2026-04-13 11:42:30.201258: Pseudo dice [0.476, 0.7524, 0.6931, 0.6628, 0.6101, 0.3753, 0.7807] +2026-04-13 11:42:30.205157: Epoch time: 106.5 s +2026-04-13 11:42:34.027411: +2026-04-13 11:42:34.029281: Epoch 2500 +2026-04-13 11:42:34.031348: Current learning rate: 0.00414 +2026-04-13 11:44:18.581910: train_loss -0.4048 +2026-04-13 11:44:18.590994: val_loss -0.2897 +2026-04-13 11:44:18.593028: Pseudo dice [0.8606, 0.3779, 0.6307, 0.7104, 0.5189, 0.1441, 0.6771] +2026-04-13 11:44:18.597012: Epoch time: 104.56 s +2026-04-13 11:44:20.110516: +2026-04-13 11:44:20.112213: Epoch 2501 +2026-04-13 11:44:20.116797: Current learning rate: 0.00413 +2026-04-13 11:46:03.665222: train_loss -0.3942 +2026-04-13 11:46:03.674923: val_loss -0.2906 +2026-04-13 11:46:03.678129: Pseudo dice [0.5719, 0.7002, 0.708, 0.6677, 0.5977, 0.1042, 0.746] +2026-04-13 11:46:03.682969: Epoch time: 103.56 s +2026-04-13 11:46:05.279358: +2026-04-13 11:46:05.284633: Epoch 2502 +2026-04-13 11:46:05.287991: Current learning rate: 0.00413 +2026-04-13 11:47:53.244405: train_loss -0.4057 +2026-04-13 11:47:53.253027: val_loss -0.3266 +2026-04-13 11:47:53.255541: Pseudo dice [0.6185, 0.7489, 0.6167, 0.8849, 0.6838, 0.7667, 0.7941] +2026-04-13 11:47:53.258521: Epoch time: 107.97 s +2026-04-13 11:47:54.803381: +2026-04-13 11:47:54.805284: Epoch 2503 +2026-04-13 11:47:54.809432: Current learning rate: 0.00413 +2026-04-13 11:49:37.822536: train_loss -0.3965 +2026-04-13 11:49:37.830455: val_loss -0.353 +2026-04-13 11:49:37.833409: Pseudo dice [0.509, 0.8342, 0.802, 0.737, 0.6816, 0.3507, 0.8603] +2026-04-13 11:49:37.836710: Epoch time: 103.02 s +2026-04-13 11:49:39.362476: +2026-04-13 11:49:39.364603: Epoch 2504 +2026-04-13 11:49:39.366835: Current learning rate: 0.00413 +2026-04-13 11:51:24.663942: train_loss -0.4007 +2026-04-13 11:51:24.676593: val_loss -0.2749 +2026-04-13 11:51:24.678929: Pseudo dice [0.6866, 0.6728, 0.5829, 0.4042, 0.6171, 0.3003, 0.3808] +2026-04-13 11:51:24.684633: Epoch time: 105.31 s +2026-04-13 11:51:26.191670: +2026-04-13 11:51:26.199896: Epoch 2505 +2026-04-13 11:51:26.208076: Current learning rate: 0.00412 +2026-04-13 11:53:10.300737: train_loss -0.3892 +2026-04-13 11:53:10.306918: val_loss -0.2723 +2026-04-13 11:53:10.309762: Pseudo dice [0.5567, 0.4572, 0.58, 0.435, 0.5571, 0.115, 0.4367] +2026-04-13 11:53:10.313184: Epoch time: 104.11 s +2026-04-13 11:53:11.830597: +2026-04-13 11:53:11.832916: Epoch 2506 +2026-04-13 11:53:11.835685: Current learning rate: 0.00412 +2026-04-13 11:54:56.946630: train_loss -0.3986 +2026-04-13 11:54:56.959492: val_loss -0.2165 +2026-04-13 11:54:56.962599: Pseudo dice [0.392, 0.2117, 0.5289, 0.6937, 0.6074, 0.0301, 0.5008] +2026-04-13 11:54:56.964764: Epoch time: 105.12 s +2026-04-13 11:54:58.522923: +2026-04-13 11:54:58.524902: Epoch 2507 +2026-04-13 11:54:58.527289: Current learning rate: 0.00412 +2026-04-13 11:56:47.060463: train_loss -0.3906 +2026-04-13 11:56:47.070676: val_loss -0.266 +2026-04-13 11:56:47.076567: Pseudo dice [0.4578, 0.502, 0.7454, 0.4007, 0.5417, 0.1386, 0.3176] +2026-04-13 11:56:47.081223: Epoch time: 108.54 s +2026-04-13 11:56:48.585568: +2026-04-13 11:56:48.588659: Epoch 2508 +2026-04-13 11:56:48.592926: Current learning rate: 0.00412 +2026-04-13 11:58:30.992830: train_loss -0.3861 +2026-04-13 11:58:31.001717: val_loss -0.232 +2026-04-13 11:58:31.004766: Pseudo dice [0.6439, 0.5896, 0.383, 0.4006, 0.3844, 0.2095, 0.5474] +2026-04-13 11:58:31.007723: Epoch time: 102.41 s +2026-04-13 11:58:32.535685: +2026-04-13 11:58:32.537593: Epoch 2509 +2026-04-13 11:58:32.539608: Current learning rate: 0.00411 +2026-04-13 12:00:15.018530: train_loss -0.3795 +2026-04-13 12:00:15.027562: val_loss -0.3295 +2026-04-13 12:00:15.030865: Pseudo dice [0.6981, 0.5401, 0.5803, 0.3899, 0.5259, 0.6976, 0.6163] +2026-04-13 12:00:15.033685: Epoch time: 102.49 s +2026-04-13 12:00:16.538324: +2026-04-13 12:00:16.540555: Epoch 2510 +2026-04-13 12:00:16.542833: Current learning rate: 0.00411 +2026-04-13 12:02:00.860232: train_loss -0.3875 +2026-04-13 12:02:00.869543: val_loss -0.3499 +2026-04-13 12:02:00.874124: Pseudo dice [0.3432, 0.3482, 0.6182, 0.6287, 0.5987, 0.6983, 0.7619] +2026-04-13 12:02:00.878008: Epoch time: 104.33 s +2026-04-13 12:02:02.409345: +2026-04-13 12:02:02.411469: Epoch 2511 +2026-04-13 12:02:02.416316: Current learning rate: 0.00411 +2026-04-13 12:03:44.727769: train_loss -0.3859 +2026-04-13 12:03:44.735215: val_loss -0.2889 +2026-04-13 12:03:44.737769: Pseudo dice [0.2279, 0.4039, 0.6643, 0.5533, 0.5041, 0.2358, 0.7451] +2026-04-13 12:03:44.740788: Epoch time: 102.32 s +2026-04-13 12:03:46.266324: +2026-04-13 12:03:46.268402: Epoch 2512 +2026-04-13 12:03:46.270839: Current learning rate: 0.00411 +2026-04-13 12:05:30.227316: train_loss -0.3905 +2026-04-13 12:05:30.234386: val_loss -0.3381 +2026-04-13 12:05:30.238150: Pseudo dice [0.7208, 0.3305, 0.6002, 0.0641, 0.4732, 0.7251, 0.8636] +2026-04-13 12:05:30.241189: Epoch time: 103.96 s +2026-04-13 12:05:31.738504: +2026-04-13 12:05:31.740441: Epoch 2513 +2026-04-13 12:05:31.742622: Current learning rate: 0.0041 +2026-04-13 12:07:16.598464: train_loss -0.3959 +2026-04-13 12:07:16.606652: val_loss -0.3353 +2026-04-13 12:07:16.609046: Pseudo dice [0.7977, 0.4165, 0.7094, 0.6696, 0.486, 0.3422, 0.898] +2026-04-13 12:07:16.612288: Epoch time: 104.86 s +2026-04-13 12:07:18.172900: +2026-04-13 12:07:18.175791: Epoch 2514 +2026-04-13 12:07:18.178036: Current learning rate: 0.0041 +2026-04-13 12:09:03.300150: train_loss -0.3726 +2026-04-13 12:09:03.318353: val_loss -0.3104 +2026-04-13 12:09:03.321347: Pseudo dice [0.3833, 0.8284, 0.5729, 0.6098, 0.3827, 0.1648, 0.8323] +2026-04-13 12:09:03.326184: Epoch time: 105.13 s +2026-04-13 12:09:04.914528: +2026-04-13 12:09:04.921687: Epoch 2515 +2026-04-13 12:09:04.928045: Current learning rate: 0.0041 +2026-04-13 12:10:50.678630: train_loss -0.3693 +2026-04-13 12:10:50.686122: val_loss -0.2628 +2026-04-13 12:10:50.692679: Pseudo dice [0.3534, 0.754, 0.6106, 0.6629, 0.2109, 0.1543, 0.5932] +2026-04-13 12:10:50.696046: Epoch time: 105.77 s +2026-04-13 12:10:52.244155: +2026-04-13 12:10:52.247116: Epoch 2516 +2026-04-13 12:10:52.250876: Current learning rate: 0.0041 +2026-04-13 12:12:37.266767: train_loss -0.3781 +2026-04-13 12:12:37.272419: val_loss -0.2566 +2026-04-13 12:12:37.274760: Pseudo dice [0.2746, 0.0577, 0.6222, 0.4832, 0.5094, 0.1372, 0.7765] +2026-04-13 12:12:37.277735: Epoch time: 105.03 s +2026-04-13 12:12:38.799113: +2026-04-13 12:12:38.801088: Epoch 2517 +2026-04-13 12:12:38.803225: Current learning rate: 0.00409 +2026-04-13 12:14:22.844349: train_loss -0.3806 +2026-04-13 12:14:22.852587: val_loss -0.2807 +2026-04-13 12:14:22.854884: Pseudo dice [0.2779, 0.1512, 0.4823, 0.8023, 0.6458, 0.096, 0.9104] +2026-04-13 12:14:22.858869: Epoch time: 104.05 s +2026-04-13 12:14:24.430387: +2026-04-13 12:14:24.439006: Epoch 2518 +2026-04-13 12:14:24.441549: Current learning rate: 0.00409 +2026-04-13 12:16:11.450946: train_loss -0.3946 +2026-04-13 12:16:11.457096: val_loss -0.324 +2026-04-13 12:16:11.461194: Pseudo dice [0.6159, 0.4056, 0.7514, 0.5144, 0.4666, 0.3844, 0.7649] +2026-04-13 12:16:11.464081: Epoch time: 107.02 s +2026-04-13 12:16:12.973439: +2026-04-13 12:16:12.975696: Epoch 2519 +2026-04-13 12:16:12.978083: Current learning rate: 0.00409 +2026-04-13 12:17:58.348249: train_loss -0.3814 +2026-04-13 12:17:58.355857: val_loss -0.2618 +2026-04-13 12:17:58.358098: Pseudo dice [0.6224, 0.5531, 0.7126, 0.7274, 0.4302, 0.049, 0.8588] +2026-04-13 12:17:58.361542: Epoch time: 105.38 s +2026-04-13 12:17:59.884555: +2026-04-13 12:17:59.886591: Epoch 2520 +2026-04-13 12:17:59.891083: Current learning rate: 0.00409 +2026-04-13 12:19:44.033873: train_loss -0.3665 +2026-04-13 12:19:44.046415: val_loss -0.3415 +2026-04-13 12:19:44.056119: Pseudo dice [0.5568, 0.2872, 0.5746, 0.7291, 0.6776, 0.4608, 0.7829] +2026-04-13 12:19:44.079913: Epoch time: 104.15 s +2026-04-13 12:19:45.598418: +2026-04-13 12:19:45.600366: Epoch 2521 +2026-04-13 12:19:45.603099: Current learning rate: 0.00408 +2026-04-13 12:21:29.804416: train_loss -0.375 +2026-04-13 12:21:29.811219: val_loss -0.2553 +2026-04-13 12:21:29.814112: Pseudo dice [0.4958, 0.217, 0.7403, 0.423, 0.6059, 0.0891, 0.8734] +2026-04-13 12:21:29.816676: Epoch time: 104.21 s +2026-04-13 12:21:31.343523: +2026-04-13 12:21:31.346969: Epoch 2522 +2026-04-13 12:21:31.351577: Current learning rate: 0.00408 +2026-04-13 12:23:18.850541: train_loss -0.3983 +2026-04-13 12:23:18.862109: val_loss -0.3327 +2026-04-13 12:23:18.865034: Pseudo dice [0.673, 0.2975, 0.7208, 0.7408, 0.6343, 0.5342, 0.6607] +2026-04-13 12:23:18.867858: Epoch time: 107.51 s +2026-04-13 12:23:20.390715: +2026-04-13 12:23:20.395069: Epoch 2523 +2026-04-13 12:23:20.399377: Current learning rate: 0.00408 +2026-04-13 12:25:04.866290: train_loss -0.3801 +2026-04-13 12:25:04.874205: val_loss -0.3049 +2026-04-13 12:25:04.877468: Pseudo dice [0.4763, 0.4851, 0.6585, 0.0082, 0.5034, 0.4272, 0.7478] +2026-04-13 12:25:04.880567: Epoch time: 104.48 s +2026-04-13 12:25:06.391160: +2026-04-13 12:25:06.392888: Epoch 2524 +2026-04-13 12:25:06.394830: Current learning rate: 0.00408 +2026-04-13 12:26:50.122769: train_loss -0.3886 +2026-04-13 12:26:50.128528: val_loss -0.3541 +2026-04-13 12:26:50.136463: Pseudo dice [0.2215, 0.773, 0.6843, 0.4227, 0.4843, 0.8418, 0.8866] +2026-04-13 12:26:50.138891: Epoch time: 103.74 s +2026-04-13 12:26:51.638899: +2026-04-13 12:26:51.641629: Epoch 2525 +2026-04-13 12:26:51.644149: Current learning rate: 0.00407 +2026-04-13 12:28:35.000206: train_loss -0.3869 +2026-04-13 12:28:35.007502: val_loss -0.2554 +2026-04-13 12:28:35.010005: Pseudo dice [0.6982, 0.6749, 0.4903, 0.5117, 0.5994, 0.0389, 0.8734] +2026-04-13 12:28:35.013375: Epoch time: 103.37 s +2026-04-13 12:28:36.528122: +2026-04-13 12:28:36.530544: Epoch 2526 +2026-04-13 12:28:36.532962: Current learning rate: 0.00407 +2026-04-13 12:30:22.336786: train_loss -0.3861 +2026-04-13 12:30:22.348652: val_loss -0.2843 +2026-04-13 12:30:22.351535: Pseudo dice [0.6444, 0.795, 0.4518, 0.7046, 0.4343, 0.04, 0.736] +2026-04-13 12:30:22.354704: Epoch time: 105.81 s +2026-04-13 12:30:23.879798: +2026-04-13 12:30:23.884923: Epoch 2527 +2026-04-13 12:30:23.889462: Current learning rate: 0.00407 +2026-04-13 12:32:10.635767: train_loss -0.3962 +2026-04-13 12:32:10.643169: val_loss -0.338 +2026-04-13 12:32:10.646768: Pseudo dice [0.2085, 0.3986, 0.7091, 0.8654, 0.5887, 0.6581, 0.6072] +2026-04-13 12:32:10.652978: Epoch time: 106.76 s +2026-04-13 12:32:12.223811: +2026-04-13 12:32:12.226851: Epoch 2528 +2026-04-13 12:32:12.230535: Current learning rate: 0.00407 +2026-04-13 12:33:58.794317: train_loss -0.3996 +2026-04-13 12:33:58.805660: val_loss -0.3119 +2026-04-13 12:33:58.809659: Pseudo dice [0.4433, 0.3625, 0.7111, 0.3334, 0.3463, 0.8919, 0.6827] +2026-04-13 12:33:58.815160: Epoch time: 106.57 s +2026-04-13 12:34:00.329129: +2026-04-13 12:34:00.331505: Epoch 2529 +2026-04-13 12:34:00.333935: Current learning rate: 0.00406 +2026-04-13 12:35:45.032436: train_loss -0.3919 +2026-04-13 12:35:45.042553: val_loss -0.3469 +2026-04-13 12:35:45.045494: Pseudo dice [0.6465, 0.8359, 0.6952, 0.5329, 0.6202, 0.8505, 0.7205] +2026-04-13 12:35:45.048383: Epoch time: 104.71 s +2026-04-13 12:35:46.637052: +2026-04-13 12:35:46.640445: Epoch 2530 +2026-04-13 12:35:46.645618: Current learning rate: 0.00406 +2026-04-13 12:37:31.248061: train_loss -0.3802 +2026-04-13 12:37:31.255363: val_loss -0.1604 +2026-04-13 12:37:31.258191: Pseudo dice [0.561, 0.1255, 0.7541, 0.805, 0.6344, 0.0352, 0.8787] +2026-04-13 12:37:31.262598: Epoch time: 104.62 s +2026-04-13 12:37:32.788085: +2026-04-13 12:37:32.793432: Epoch 2531 +2026-04-13 12:37:32.797330: Current learning rate: 0.00406 +2026-04-13 12:39:17.167193: train_loss -0.3758 +2026-04-13 12:39:17.173238: val_loss -0.3504 +2026-04-13 12:39:17.176465: Pseudo dice [0.6116, 0.262, 0.523, 0.7918, 0.5903, 0.6327, 0.8572] +2026-04-13 12:39:17.178957: Epoch time: 104.38 s +2026-04-13 12:39:18.675038: +2026-04-13 12:39:18.678666: Epoch 2532 +2026-04-13 12:39:18.680700: Current learning rate: 0.00406 +2026-04-13 12:41:02.879066: train_loss -0.3755 +2026-04-13 12:41:02.894828: val_loss -0.3125 +2026-04-13 12:41:02.900400: Pseudo dice [0.6446, 0.3839, 0.5829, 0.4645, 0.5852, 0.6328, 0.6428] +2026-04-13 12:41:02.905333: Epoch time: 104.21 s +2026-04-13 12:41:04.456984: +2026-04-13 12:41:04.459539: Epoch 2533 +2026-04-13 12:41:04.462956: Current learning rate: 0.00405 +2026-04-13 12:42:47.905933: train_loss -0.3586 +2026-04-13 12:42:47.918253: val_loss -0.285 +2026-04-13 12:42:47.920677: Pseudo dice [0.3629, 0.36, 0.6545, 0.745, 0.6023, 0.2062, 0.8353] +2026-04-13 12:42:47.923559: Epoch time: 103.45 s +2026-04-13 12:42:49.449539: +2026-04-13 12:42:49.454552: Epoch 2534 +2026-04-13 12:42:49.459956: Current learning rate: 0.00405 +2026-04-13 12:44:33.459666: train_loss -0.3896 +2026-04-13 12:44:33.466980: val_loss -0.3163 +2026-04-13 12:44:33.469253: Pseudo dice [0.5065, 0.7017, 0.651, 0.658, 0.2851, 0.7806, 0.5972] +2026-04-13 12:44:33.472794: Epoch time: 104.01 s +2026-04-13 12:44:34.971094: +2026-04-13 12:44:34.972980: Epoch 2535 +2026-04-13 12:44:34.975173: Current learning rate: 0.00405 +2026-04-13 12:46:19.272775: train_loss -0.388 +2026-04-13 12:46:19.307237: val_loss -0.313 +2026-04-13 12:46:19.312985: Pseudo dice [0.7011, 0.5994, 0.62, 0.7695, 0.3942, 0.7795, 0.521] +2026-04-13 12:46:19.319148: Epoch time: 104.3 s +2026-04-13 12:46:20.873587: +2026-04-13 12:46:20.881085: Epoch 2536 +2026-04-13 12:46:20.886708: Current learning rate: 0.00405 +2026-04-13 12:48:06.341655: train_loss -0.3842 +2026-04-13 12:48:06.348709: val_loss -0.3628 +2026-04-13 12:48:06.351312: Pseudo dice [0.624, 0.5692, 0.7208, 0.8519, 0.5525, 0.5617, 0.8515] +2026-04-13 12:48:06.355281: Epoch time: 105.47 s +2026-04-13 12:48:07.871857: +2026-04-13 12:48:07.875109: Epoch 2537 +2026-04-13 12:48:07.878095: Current learning rate: 0.00404 +2026-04-13 12:49:51.905746: train_loss -0.3838 +2026-04-13 12:49:51.921441: val_loss -0.2187 +2026-04-13 12:49:51.927019: Pseudo dice [0.5162, 0.5513, 0.7014, 0.7213, 0.6104, 0.1072, 0.8096] +2026-04-13 12:49:51.931375: Epoch time: 104.04 s +2026-04-13 12:49:53.419655: +2026-04-13 12:49:53.423829: Epoch 2538 +2026-04-13 12:49:53.427677: Current learning rate: 0.00404 +2026-04-13 12:51:38.012978: train_loss -0.3997 +2026-04-13 12:51:38.020562: val_loss -0.3706 +2026-04-13 12:51:38.023196: Pseudo dice [0.7461, 0.6687, 0.674, 0.8301, 0.5742, 0.1695, 0.9037] +2026-04-13 12:51:38.026066: Epoch time: 104.6 s +2026-04-13 12:51:38.028211: Yayy! New best EMA pseudo Dice: 0.587 +2026-04-13 12:51:41.565858: +2026-04-13 12:51:41.569164: Epoch 2539 +2026-04-13 12:51:41.571093: Current learning rate: 0.00404 +2026-04-13 12:53:27.919187: train_loss -0.3891 +2026-04-13 12:53:27.924833: val_loss -0.3555 +2026-04-13 12:53:27.926974: Pseudo dice [0.5898, 0.2373, 0.7956, 0.8597, 0.5508, 0.5866, 0.8147] +2026-04-13 12:53:27.929261: Epoch time: 106.36 s +2026-04-13 12:53:27.931003: Yayy! New best EMA pseudo Dice: 0.5916 +2026-04-13 12:53:31.403705: +2026-04-13 12:53:31.407130: Epoch 2540 +2026-04-13 12:53:31.409153: Current learning rate: 0.00404 +2026-04-13 12:55:18.178024: train_loss -0.3874 +2026-04-13 12:55:18.184997: val_loss -0.252 +2026-04-13 12:55:18.186933: Pseudo dice [0.5552, 0.5941, 0.7763, 0.6298, 0.5166, 0.0566, 0.6663] +2026-04-13 12:55:18.189919: Epoch time: 106.78 s +2026-04-13 12:55:19.681004: +2026-04-13 12:55:19.682847: Epoch 2541 +2026-04-13 12:55:19.685424: Current learning rate: 0.00403 +2026-04-13 12:57:03.687735: train_loss -0.4023 +2026-04-13 12:57:03.693215: val_loss -0.3606 +2026-04-13 12:57:03.699015: Pseudo dice [0.5525, 0.6669, 0.832, 0.748, 0.4016, 0.8216, 0.3568] +2026-04-13 12:57:03.703028: Epoch time: 104.01 s +2026-04-13 12:57:05.216376: +2026-04-13 12:57:05.218842: Epoch 2542 +2026-04-13 12:57:05.233685: Current learning rate: 0.00403 +2026-04-13 12:58:52.028063: train_loss -0.4025 +2026-04-13 12:58:52.036825: val_loss -0.3582 +2026-04-13 12:58:52.040439: Pseudo dice [0.8398, 0.4502, 0.717, 0.8243, 0.3791, 0.8164, 0.7991] +2026-04-13 12:58:52.045519: Epoch time: 106.82 s +2026-04-13 12:58:52.049156: Yayy! New best EMA pseudo Dice: 0.6004 +2026-04-13 12:58:55.661702: +2026-04-13 12:58:55.663957: Epoch 2543 +2026-04-13 12:58:55.666235: Current learning rate: 0.00403 +2026-04-13 13:00:38.238623: train_loss -0.3885 +2026-04-13 13:00:38.246140: val_loss -0.284 +2026-04-13 13:00:38.250017: Pseudo dice [0.7841, 0.8796, 0.709, 0.546, 0.3669, 0.073, 0.7089] +2026-04-13 13:00:38.252816: Epoch time: 102.58 s +2026-04-13 13:00:39.756342: +2026-04-13 13:00:39.758430: Epoch 2544 +2026-04-13 13:00:39.760474: Current learning rate: 0.00403 +2026-04-13 13:02:24.935616: train_loss -0.3889 +2026-04-13 13:02:24.942531: val_loss -0.3165 +2026-04-13 13:02:24.945048: Pseudo dice [0.5645, 0.4464, 0.6015, 0.5183, 0.405, 0.0975, 0.7341] +2026-04-13 13:02:24.947943: Epoch time: 105.18 s +2026-04-13 13:02:27.517401: +2026-04-13 13:02:27.519949: Epoch 2545 +2026-04-13 13:02:27.522032: Current learning rate: 0.00402 +2026-04-13 13:04:11.289928: train_loss -0.3848 +2026-04-13 13:04:11.296475: val_loss -0.2685 +2026-04-13 13:04:11.300220: Pseudo dice [0.7496, 0.1445, 0.5148, 0.3836, 0.4635, 0.0298, 0.7934] +2026-04-13 13:04:11.302982: Epoch time: 103.78 s +2026-04-13 13:04:12.794249: +2026-04-13 13:04:12.796745: Epoch 2546 +2026-04-13 13:04:12.799492: Current learning rate: 0.00402 +2026-04-13 13:05:59.938946: train_loss -0.4051 +2026-04-13 13:05:59.947149: val_loss -0.3144 +2026-04-13 13:05:59.958035: Pseudo dice [0.8314, 0.3435, 0.6441, 0.4337, 0.4755, 0.4559, 0.816] +2026-04-13 13:05:59.970182: Epoch time: 107.15 s +2026-04-13 13:06:01.468227: +2026-04-13 13:06:01.470404: Epoch 2547 +2026-04-13 13:06:01.472618: Current learning rate: 0.00402 +2026-04-13 13:07:45.646080: train_loss -0.399 +2026-04-13 13:07:45.656134: val_loss -0.3457 +2026-04-13 13:07:45.660078: Pseudo dice [0.7927, 0.0381, 0.8027, 0.8801, 0.4979, 0.499, 0.633] +2026-04-13 13:07:45.663087: Epoch time: 104.18 s +2026-04-13 13:07:47.187619: +2026-04-13 13:07:47.190633: Epoch 2548 +2026-04-13 13:07:47.193692: Current learning rate: 0.00402 +2026-04-13 13:09:32.895411: train_loss -0.402 +2026-04-13 13:09:32.902841: val_loss -0.3068 +2026-04-13 13:09:32.906027: Pseudo dice [0.7028, 0.5351, 0.7521, 0.7601, 0.4482, 0.1716, 0.7918] +2026-04-13 13:09:32.914687: Epoch time: 105.71 s +2026-04-13 13:09:34.398585: +2026-04-13 13:09:34.400458: Epoch 2549 +2026-04-13 13:09:34.403297: Current learning rate: 0.00401 +2026-04-13 13:11:18.124279: train_loss -0.3842 +2026-04-13 13:11:18.135108: val_loss -0.2969 +2026-04-13 13:11:18.138892: Pseudo dice [0.8701, 0.4175, 0.6234, 0.3741, 0.4324, 0.1444, 0.4467] +2026-04-13 13:11:18.142038: Epoch time: 103.73 s +2026-04-13 13:11:21.864997: +2026-04-13 13:11:21.870139: Epoch 2550 +2026-04-13 13:11:21.875520: Current learning rate: 0.00401 +2026-04-13 13:13:05.245793: train_loss -0.367 +2026-04-13 13:13:05.251609: val_loss -0.2344 +2026-04-13 13:13:05.253562: Pseudo dice [0.497, 0.0897, 0.4302, 0.6828, 0.4987, 0.0973, 0.6579] +2026-04-13 13:13:05.255713: Epoch time: 103.38 s +2026-04-13 13:13:06.765118: +2026-04-13 13:13:06.767358: Epoch 2551 +2026-04-13 13:13:06.770976: Current learning rate: 0.00401 +2026-04-13 13:14:52.598171: train_loss -0.3411 +2026-04-13 13:14:52.605377: val_loss -0.285 +2026-04-13 13:14:52.607414: Pseudo dice [0.4982, 0.0215, 0.462, 0.5306, 0.5847, 0.0831, 0.7605] +2026-04-13 13:14:52.611043: Epoch time: 105.84 s +2026-04-13 13:14:54.096340: +2026-04-13 13:14:54.098601: Epoch 2552 +2026-04-13 13:14:54.100885: Current learning rate: 0.00401 +2026-04-13 13:16:38.408178: train_loss -0.3653 +2026-04-13 13:16:38.415781: val_loss -0.2443 +2026-04-13 13:16:38.419416: Pseudo dice [0.7993, 0.6535, 0.5665, 0.0293, 0.3743, 0.0583, 0.452] +2026-04-13 13:16:38.422526: Epoch time: 104.32 s +2026-04-13 13:16:39.967240: +2026-04-13 13:16:39.973624: Epoch 2553 +2026-04-13 13:16:39.980278: Current learning rate: 0.004 +2026-04-13 13:18:24.203588: train_loss -0.3968 +2026-04-13 13:18:24.211145: val_loss -0.2452 +2026-04-13 13:18:24.213419: Pseudo dice [0.5744, 0.3634, 0.3541, 0.6425, 0.2828, 0.2021, 0.7267] +2026-04-13 13:18:24.217962: Epoch time: 104.24 s +2026-04-13 13:18:25.728430: +2026-04-13 13:18:25.730275: Epoch 2554 +2026-04-13 13:18:25.732352: Current learning rate: 0.004 +2026-04-13 13:20:10.808016: train_loss -0.3729 +2026-04-13 13:20:10.814561: val_loss -0.335 +2026-04-13 13:20:10.816953: Pseudo dice [0.7719, 0.7992, 0.7049, 0.509, 0.5488, 0.0968, 0.7961] +2026-04-13 13:20:10.820822: Epoch time: 105.08 s +2026-04-13 13:20:12.331266: +2026-04-13 13:20:12.333974: Epoch 2555 +2026-04-13 13:20:12.337221: Current learning rate: 0.004 +2026-04-13 13:21:56.669935: train_loss -0.3747 +2026-04-13 13:21:56.682341: val_loss -0.2927 +2026-04-13 13:21:56.688133: Pseudo dice [0.5877, 0.3764, 0.7253, 0.3983, 0.5553, 0.203, 0.8403] +2026-04-13 13:21:56.690489: Epoch time: 104.34 s +2026-04-13 13:21:58.190882: +2026-04-13 13:21:58.193638: Epoch 2556 +2026-04-13 13:21:58.196955: Current learning rate: 0.004 +2026-04-13 13:23:45.068490: train_loss -0.3823 +2026-04-13 13:23:45.074985: val_loss -0.3299 +2026-04-13 13:23:45.077436: Pseudo dice [0.8657, 0.3598, 0.5954, 0.0791, 0.6641, 0.5728, 0.7874] +2026-04-13 13:23:45.080338: Epoch time: 106.88 s +2026-04-13 13:23:46.578235: +2026-04-13 13:23:46.581061: Epoch 2557 +2026-04-13 13:23:46.584027: Current learning rate: 0.00399 +2026-04-13 13:25:33.202515: train_loss -0.3817 +2026-04-13 13:25:33.209807: val_loss -0.3523 +2026-04-13 13:25:33.213169: Pseudo dice [0.6743, 0.1241, 0.7179, 0.8613, 0.5317, 0.2605, 0.8019] +2026-04-13 13:25:33.215975: Epoch time: 106.63 s +2026-04-13 13:25:34.796775: +2026-04-13 13:25:34.798803: Epoch 2558 +2026-04-13 13:25:34.800880: Current learning rate: 0.00399 +2026-04-13 13:27:19.833658: train_loss -0.386 +2026-04-13 13:27:19.843402: val_loss -0.3444 +2026-04-13 13:27:19.846509: Pseudo dice [0.638, 0.4684, 0.6585, 0.6736, 0.5202, 0.5274, 0.7921] +2026-04-13 13:27:19.850069: Epoch time: 105.04 s +2026-04-13 13:27:21.357846: +2026-04-13 13:27:21.359739: Epoch 2559 +2026-04-13 13:27:21.362013: Current learning rate: 0.00399 +2026-04-13 13:29:06.702785: train_loss -0.3836 +2026-04-13 13:29:06.711748: val_loss -0.3518 +2026-04-13 13:29:06.714664: Pseudo dice [0.8111, 0.1277, 0.5744, 0.6913, 0.5778, 0.7964, 0.6341] +2026-04-13 13:29:06.717977: Epoch time: 105.35 s +2026-04-13 13:29:08.224164: +2026-04-13 13:29:08.226999: Epoch 2560 +2026-04-13 13:29:08.229317: Current learning rate: 0.00399 +2026-04-13 13:30:55.575586: train_loss -0.3714 +2026-04-13 13:30:55.582711: val_loss -0.3114 +2026-04-13 13:30:55.585502: Pseudo dice [0.7698, 0.4443, 0.7003, 0.264, 0.4575, 0.055, 0.5611] +2026-04-13 13:30:55.589437: Epoch time: 107.36 s +2026-04-13 13:30:57.103982: +2026-04-13 13:30:57.107300: Epoch 2561 +2026-04-13 13:30:57.110357: Current learning rate: 0.00398 +2026-04-13 13:32:40.885875: train_loss -0.3851 +2026-04-13 13:32:40.891437: val_loss -0.3496 +2026-04-13 13:32:40.893632: Pseudo dice [0.4144, 0.4964, 0.6714, 0.6907, 0.6152, 0.4503, 0.779] +2026-04-13 13:32:40.896155: Epoch time: 103.79 s +2026-04-13 13:32:42.373896: +2026-04-13 13:32:42.376055: Epoch 2562 +2026-04-13 13:32:42.378034: Current learning rate: 0.00398 +2026-04-13 13:34:26.806517: train_loss -0.3781 +2026-04-13 13:34:26.813929: val_loss -0.2435 +2026-04-13 13:34:26.816585: Pseudo dice [0.6546, 0.5565, 0.4257, 0.7011, 0.5642, 0.067, 0.4821] +2026-04-13 13:34:26.820915: Epoch time: 104.44 s +2026-04-13 13:34:28.331208: +2026-04-13 13:34:28.333111: Epoch 2563 +2026-04-13 13:34:28.335693: Current learning rate: 0.00398 +2026-04-13 13:36:14.042237: train_loss -0.3999 +2026-04-13 13:36:14.050785: val_loss -0.3473 +2026-04-13 13:36:14.053440: Pseudo dice [0.7636, 0.4541, 0.8488, 0.0308, 0.694, 0.4223, 0.6834] +2026-04-13 13:36:14.055944: Epoch time: 105.71 s +2026-04-13 13:36:15.567776: +2026-04-13 13:36:15.571420: Epoch 2564 +2026-04-13 13:36:15.574942: Current learning rate: 0.00398 +2026-04-13 13:38:01.145012: train_loss -0.3944 +2026-04-13 13:38:01.155199: val_loss -0.3127 +2026-04-13 13:38:01.160441: Pseudo dice [0.4842, 0.4865, 0.4366, 0.6768, 0.6472, 0.1035, 0.8425] +2026-04-13 13:38:01.163328: Epoch time: 105.58 s +2026-04-13 13:38:03.766248: +2026-04-13 13:38:03.768570: Epoch 2565 +2026-04-13 13:38:03.772033: Current learning rate: 0.00397 +2026-04-13 13:39:47.726761: train_loss -0.3899 +2026-04-13 13:39:47.738085: val_loss -0.3188 +2026-04-13 13:39:47.741238: Pseudo dice [0.7181, 0.3581, 0.6143, 0.2541, 0.6348, 0.3658, 0.835] +2026-04-13 13:39:47.745178: Epoch time: 103.96 s +2026-04-13 13:39:49.287800: +2026-04-13 13:39:49.290229: Epoch 2566 +2026-04-13 13:39:49.292363: Current learning rate: 0.00397 +2026-04-13 13:41:33.490216: train_loss -0.3746 +2026-04-13 13:41:33.495996: val_loss -0.3435 +2026-04-13 13:41:33.497809: Pseudo dice [0.5938, 0.672, 0.7164, 0.7701, 0.3405, 0.6575, 0.7235] +2026-04-13 13:41:33.500477: Epoch time: 104.21 s +2026-04-13 13:41:35.025987: +2026-04-13 13:41:35.027798: Epoch 2567 +2026-04-13 13:41:35.030750: Current learning rate: 0.00397 +2026-04-13 13:43:17.636741: train_loss -0.368 +2026-04-13 13:43:17.644283: val_loss -0.3508 +2026-04-13 13:43:17.646577: Pseudo dice [0.6786, 0.2996, 0.6221, 0.6682, 0.6226, 0.7057, 0.8453] +2026-04-13 13:43:17.649765: Epoch time: 102.61 s +2026-04-13 13:43:19.137751: +2026-04-13 13:43:19.140057: Epoch 2568 +2026-04-13 13:43:19.143437: Current learning rate: 0.00397 +2026-04-13 13:45:02.945519: train_loss -0.3835 +2026-04-13 13:45:02.952605: val_loss -0.3569 +2026-04-13 13:45:02.955049: Pseudo dice [0.7653, 0.232, 0.7413, 0.8756, 0.6456, 0.6936, 0.7555] +2026-04-13 13:45:02.960721: Epoch time: 103.81 s +2026-04-13 13:45:04.456244: +2026-04-13 13:45:04.458996: Epoch 2569 +2026-04-13 13:45:04.462086: Current learning rate: 0.00396 +2026-04-13 13:46:50.494515: train_loss -0.4108 +2026-04-13 13:46:50.503579: val_loss -0.3381 +2026-04-13 13:46:50.518476: Pseudo dice [0.8258, 0.7514, 0.6779, 0.7515, 0.4722, 0.6609, 0.5688] +2026-04-13 13:46:50.521389: Epoch time: 106.04 s +2026-04-13 13:46:52.039928: +2026-04-13 13:46:52.042652: Epoch 2570 +2026-04-13 13:46:52.045184: Current learning rate: 0.00396 +2026-04-13 13:48:35.430150: train_loss -0.3949 +2026-04-13 13:48:35.439392: val_loss -0.2729 +2026-04-13 13:48:35.447696: Pseudo dice [0.2003, 0.7046, 0.658, 0.3565, 0.633, 0.2035, 0.8033] +2026-04-13 13:48:35.450860: Epoch time: 103.39 s +2026-04-13 13:48:36.973132: +2026-04-13 13:48:36.976086: Epoch 2571 +2026-04-13 13:48:36.978184: Current learning rate: 0.00396 +2026-04-13 13:50:21.693850: train_loss -0.3813 +2026-04-13 13:50:21.704894: val_loss -0.2698 +2026-04-13 13:50:21.708329: Pseudo dice [0.7148, 0.6301, 0.7111, 0.0033, 0.3886, 0.0831, 0.5297] +2026-04-13 13:50:21.712874: Epoch time: 104.72 s +2026-04-13 13:50:23.207924: +2026-04-13 13:50:23.211566: Epoch 2572 +2026-04-13 13:50:23.216318: Current learning rate: 0.00396 +2026-04-13 13:52:06.081197: train_loss -0.3914 +2026-04-13 13:52:06.089711: val_loss -0.3433 +2026-04-13 13:52:06.092715: Pseudo dice [0.7169, 0.7807, 0.6617, 0.4494, 0.5223, 0.4727, 0.9296] +2026-04-13 13:52:06.096161: Epoch time: 102.88 s +2026-04-13 13:52:07.565413: +2026-04-13 13:52:07.569789: Epoch 2573 +2026-04-13 13:52:07.572690: Current learning rate: 0.00395 +2026-04-13 13:53:52.297792: train_loss -0.4037 +2026-04-13 13:53:52.305083: val_loss -0.3387 +2026-04-13 13:53:52.313570: Pseudo dice [0.7294, 0.7766, 0.7285, 0.2915, 0.6357, 0.5486, 0.8238] +2026-04-13 13:53:52.323917: Epoch time: 104.74 s +2026-04-13 13:53:53.831225: +2026-04-13 13:53:53.833511: Epoch 2574 +2026-04-13 13:53:53.836834: Current learning rate: 0.00395 +2026-04-13 13:55:38.931685: train_loss -0.4029 +2026-04-13 13:55:38.938732: val_loss -0.3557 +2026-04-13 13:55:38.941613: Pseudo dice [0.7005, 0.4119, 0.6836, 0.2775, 0.4168, 0.7883, 0.8946] +2026-04-13 13:55:38.944273: Epoch time: 105.1 s +2026-04-13 13:55:40.458749: +2026-04-13 13:55:40.463946: Epoch 2575 +2026-04-13 13:55:40.469800: Current learning rate: 0.00395 +2026-04-13 13:57:23.637808: train_loss -0.4036 +2026-04-13 13:57:23.677701: val_loss -0.3286 +2026-04-13 13:57:23.683750: Pseudo dice [0.8286, 0.5415, 0.7297, 0.5837, 0.4003, 0.6766, 0.4916] +2026-04-13 13:57:23.686105: Epoch time: 103.18 s +2026-04-13 13:57:25.181794: +2026-04-13 13:57:25.183897: Epoch 2576 +2026-04-13 13:57:25.187409: Current learning rate: 0.00395 +2026-04-13 13:59:10.070653: train_loss -0.3948 +2026-04-13 13:59:10.077749: val_loss -0.3106 +2026-04-13 13:59:10.081053: Pseudo dice [0.5788, 0.7052, 0.7096, 0.5926, 0.6303, 0.0482, 0.85] +2026-04-13 13:59:10.083473: Epoch time: 104.89 s +2026-04-13 13:59:11.583522: +2026-04-13 13:59:11.585448: Epoch 2577 +2026-04-13 13:59:11.588063: Current learning rate: 0.00394 +2026-04-13 14:00:56.662364: train_loss -0.4019 +2026-04-13 14:00:56.671339: val_loss -0.2891 +2026-04-13 14:00:56.674178: Pseudo dice [0.633, 0.5864, 0.6962, 0.9012, 0.1269, 0.1142, 0.5352] +2026-04-13 14:00:56.678185: Epoch time: 105.08 s +2026-04-13 14:00:58.171712: +2026-04-13 14:00:58.176499: Epoch 2578 +2026-04-13 14:00:58.179467: Current learning rate: 0.00394 +2026-04-13 14:02:42.178354: train_loss -0.4119 +2026-04-13 14:02:42.186324: val_loss -0.3502 +2026-04-13 14:02:42.188844: Pseudo dice [0.7965, 0.8057, 0.7078, 0.8632, 0.5141, 0.7068, 0.7195] +2026-04-13 14:02:42.191502: Epoch time: 104.01 s +2026-04-13 14:02:43.693721: +2026-04-13 14:02:43.695611: Epoch 2579 +2026-04-13 14:02:43.697899: Current learning rate: 0.00394 +2026-04-13 14:04:26.697426: train_loss -0.3698 +2026-04-13 14:04:26.704111: val_loss -0.3169 +2026-04-13 14:04:26.706531: Pseudo dice [0.5472, 0.442, 0.6874, 0.6053, 0.3502, 0.6132, 0.5197] +2026-04-13 14:04:26.708867: Epoch time: 103.01 s +2026-04-13 14:04:28.207870: +2026-04-13 14:04:28.210021: Epoch 2580 +2026-04-13 14:04:28.213329: Current learning rate: 0.00394 +2026-04-13 14:06:11.883354: train_loss -0.381 +2026-04-13 14:06:11.900011: val_loss -0.3057 +2026-04-13 14:06:11.902453: Pseudo dice [0.7065, 0.2035, 0.5403, 0.3555, 0.3474, 0.7059, 0.4278] +2026-04-13 14:06:11.905832: Epoch time: 103.68 s +2026-04-13 14:06:13.405192: +2026-04-13 14:06:13.407986: Epoch 2581 +2026-04-13 14:06:13.411948: Current learning rate: 0.00393 +2026-04-13 14:07:58.105893: train_loss -0.3808 +2026-04-13 14:07:58.112540: val_loss -0.3474 +2026-04-13 14:07:58.114581: Pseudo dice [0.7557, 0.792, 0.7242, 0.6084, 0.5186, 0.6165, 0.874] +2026-04-13 14:07:58.116754: Epoch time: 104.7 s +2026-04-13 14:07:59.636173: +2026-04-13 14:07:59.639123: Epoch 2582 +2026-04-13 14:07:59.641776: Current learning rate: 0.00393 +2026-04-13 14:09:43.899381: train_loss -0.3882 +2026-04-13 14:09:43.909936: val_loss -0.3755 +2026-04-13 14:09:43.913884: Pseudo dice [0.7795, 0.3595, 0.6846, 0.6332, 0.5964, 0.7811, 0.8638] +2026-04-13 14:09:43.917760: Epoch time: 104.27 s +2026-04-13 14:09:45.440158: +2026-04-13 14:09:45.442800: Epoch 2583 +2026-04-13 14:09:45.446240: Current learning rate: 0.00393 +2026-04-13 14:11:28.162472: train_loss -0.3862 +2026-04-13 14:11:28.169148: val_loss -0.2487 +2026-04-13 14:11:28.171207: Pseudo dice [0.493, 0.1677, 0.7079, 0.482, 0.293, 0.1918, 0.3609] +2026-04-13 14:11:28.173783: Epoch time: 102.73 s +2026-04-13 14:11:29.657536: +2026-04-13 14:11:29.659794: Epoch 2584 +2026-04-13 14:11:29.661927: Current learning rate: 0.00393 +2026-04-13 14:13:14.209861: train_loss -0.3985 +2026-04-13 14:13:14.216202: val_loss -0.2806 +2026-04-13 14:13:14.218579: Pseudo dice [0.6146, 0.3718, 0.6714, 0.2286, 0.3967, 0.3204, 0.7733] +2026-04-13 14:13:14.220782: Epoch time: 104.56 s +2026-04-13 14:13:15.746001: +2026-04-13 14:13:15.748898: Epoch 2585 +2026-04-13 14:13:15.752135: Current learning rate: 0.00392 +2026-04-13 14:15:00.962519: train_loss -0.398 +2026-04-13 14:15:00.977249: val_loss -0.3148 +2026-04-13 14:15:00.981239: Pseudo dice [0.8412, 0.3267, 0.6858, 0.3879, 0.3835, 0.5183, 0.5052] +2026-04-13 14:15:00.985444: Epoch time: 105.22 s +2026-04-13 14:15:02.538229: +2026-04-13 14:15:02.542030: Epoch 2586 +2026-04-13 14:15:02.545784: Current learning rate: 0.00392 +2026-04-13 14:16:45.389882: train_loss -0.3734 +2026-04-13 14:16:45.395457: val_loss -0.2578 +2026-04-13 14:16:45.398153: Pseudo dice [0.629, 0.336, 0.2984, 0.4495, 0.5706, 0.0461, 0.6727] +2026-04-13 14:16:45.400908: Epoch time: 102.86 s +2026-04-13 14:16:46.993478: +2026-04-13 14:16:46.995968: Epoch 2587 +2026-04-13 14:16:46.998130: Current learning rate: 0.00392 +2026-04-13 14:18:30.201865: train_loss -0.3907 +2026-04-13 14:18:30.208379: val_loss -0.2724 +2026-04-13 14:18:30.210584: Pseudo dice [0.5219, 0.3774, 0.6629, 0.6948, 0.6256, 0.1382, 0.9091] +2026-04-13 14:18:30.212886: Epoch time: 103.21 s +2026-04-13 14:18:31.721268: +2026-04-13 14:18:31.723002: Epoch 2588 +2026-04-13 14:18:31.724937: Current learning rate: 0.00392 +2026-04-13 14:20:15.398940: train_loss -0.3731 +2026-04-13 14:20:15.409668: val_loss -0.2991 +2026-04-13 14:20:15.426049: Pseudo dice [0.3444, 0.6122, 0.6075, 0.8003, 0.617, 0.2798, 0.7997] +2026-04-13 14:20:15.429252: Epoch time: 103.68 s +2026-04-13 14:20:16.952420: +2026-04-13 14:20:16.954864: Epoch 2589 +2026-04-13 14:20:16.958723: Current learning rate: 0.00391 +2026-04-13 14:22:01.266409: train_loss -0.3865 +2026-04-13 14:22:01.286651: val_loss -0.3254 +2026-04-13 14:22:01.295081: Pseudo dice [0.7351, 0.2141, 0.582, 0.1502, 0.5549, 0.4511, 0.7485] +2026-04-13 14:22:01.299554: Epoch time: 104.32 s +2026-04-13 14:22:02.799609: +2026-04-13 14:22:02.802314: Epoch 2590 +2026-04-13 14:22:02.804757: Current learning rate: 0.00391 +2026-04-13 14:23:48.290495: train_loss -0.3811 +2026-04-13 14:23:48.299536: val_loss -0.2901 +2026-04-13 14:23:48.303852: Pseudo dice [0.7714, 0.6878, 0.3445, 0.8451, 0.586, 0.186, 0.7601] +2026-04-13 14:23:48.307197: Epoch time: 105.49 s +2026-04-13 14:23:49.822558: +2026-04-13 14:23:49.833588: Epoch 2591 +2026-04-13 14:23:49.851906: Current learning rate: 0.00391 +2026-04-13 14:25:35.063487: train_loss -0.39 +2026-04-13 14:25:35.069971: val_loss -0.3303 +2026-04-13 14:25:35.071858: Pseudo dice [0.4177, 0.1244, 0.6394, 0.7034, 0.6498, 0.5809, 0.7987] +2026-04-13 14:25:35.075856: Epoch time: 105.24 s +2026-04-13 14:25:36.579293: +2026-04-13 14:25:36.582175: Epoch 2592 +2026-04-13 14:25:36.584605: Current learning rate: 0.00391 +2026-04-13 14:27:22.695572: train_loss -0.3925 +2026-04-13 14:27:22.723656: val_loss -0.2674 +2026-04-13 14:27:22.726360: Pseudo dice [0.5953, 0.7413, 0.7489, 0.7036, 0.5137, 0.1159, 0.7478] +2026-04-13 14:27:22.729727: Epoch time: 106.12 s +2026-04-13 14:27:24.214451: +2026-04-13 14:27:24.224765: Epoch 2593 +2026-04-13 14:27:24.228547: Current learning rate: 0.0039 +2026-04-13 14:29:08.974499: train_loss -0.3871 +2026-04-13 14:29:08.984975: val_loss -0.2227 +2026-04-13 14:29:08.990678: Pseudo dice [0.7066, 0.3123, 0.6894, 0.7874, 0.5788, 0.165, 0.626] +2026-04-13 14:29:08.993619: Epoch time: 104.76 s +2026-04-13 14:29:10.516470: +2026-04-13 14:29:10.529398: Epoch 2594 +2026-04-13 14:29:10.537936: Current learning rate: 0.0039 +2026-04-13 14:30:54.527494: train_loss -0.3915 +2026-04-13 14:30:54.535119: val_loss -0.265 +2026-04-13 14:30:54.537392: Pseudo dice [0.7834, 0.4323, 0.576, 0.0078, 0.3276, 0.1108, 0.532] +2026-04-13 14:30:54.540585: Epoch time: 104.01 s +2026-04-13 14:30:56.045859: +2026-04-13 14:30:56.049067: Epoch 2595 +2026-04-13 14:30:56.051358: Current learning rate: 0.0039 +2026-04-13 14:32:39.869587: train_loss -0.3911 +2026-04-13 14:32:39.879112: val_loss -0.3289 +2026-04-13 14:32:39.881063: Pseudo dice [0.4417, 0.127, 0.6449, 0.2511, 0.3817, 0.2217, 0.8281] +2026-04-13 14:32:39.883610: Epoch time: 103.83 s +2026-04-13 14:32:41.386405: +2026-04-13 14:32:41.389468: Epoch 2596 +2026-04-13 14:32:41.391802: Current learning rate: 0.0039 +2026-04-13 14:34:24.282066: train_loss -0.3884 +2026-04-13 14:34:24.290205: val_loss -0.3324 +2026-04-13 14:34:24.295546: Pseudo dice [0.5218, 0.554, 0.6069, 0.5975, 0.4195, 0.3233, 0.6998] +2026-04-13 14:34:24.299557: Epoch time: 102.9 s +2026-04-13 14:34:25.781651: +2026-04-13 14:34:25.784116: Epoch 2597 +2026-04-13 14:34:25.788790: Current learning rate: 0.00389 +2026-04-13 14:36:08.538374: train_loss -0.382 +2026-04-13 14:36:08.544056: val_loss -0.2597 +2026-04-13 14:36:08.546389: Pseudo dice [0.747, 0.4013, 0.6052, 0.4319, 0.4742, 0.2203, 0.7345] +2026-04-13 14:36:08.548796: Epoch time: 102.76 s +2026-04-13 14:36:10.060920: +2026-04-13 14:36:10.068049: Epoch 2598 +2026-04-13 14:36:10.074183: Current learning rate: 0.00389 +2026-04-13 14:37:54.464303: train_loss -0.3815 +2026-04-13 14:37:54.471143: val_loss -0.2877 +2026-04-13 14:37:54.473880: Pseudo dice [0.5539, 0.0593, 0.5786, 0.733, 0.3965, 0.095, 0.8881] +2026-04-13 14:37:54.476592: Epoch time: 104.41 s +2026-04-13 14:37:55.954943: +2026-04-13 14:37:55.956995: Epoch 2599 +2026-04-13 14:37:55.959407: Current learning rate: 0.00389 +2026-04-13 14:39:38.725884: train_loss -0.3854 +2026-04-13 14:39:38.740252: val_loss -0.3165 +2026-04-13 14:39:38.745490: Pseudo dice [0.7522, 0.6179, 0.7082, 0.5295, 0.6719, 0.1931, 0.6042] +2026-04-13 14:39:38.751388: Epoch time: 102.77 s +2026-04-13 14:39:42.457376: +2026-04-13 14:39:42.462180: Epoch 2600 +2026-04-13 14:39:42.466828: Current learning rate: 0.00389 +2026-04-13 14:41:24.942890: train_loss -0.3853 +2026-04-13 14:41:24.948644: val_loss -0.3364 +2026-04-13 14:41:24.951660: Pseudo dice [0.6189, 0.376, 0.396, 0.7637, 0.3574, 0.7446, 0.8564] +2026-04-13 14:41:24.954520: Epoch time: 102.49 s +2026-04-13 14:41:26.459582: +2026-04-13 14:41:26.461692: Epoch 2601 +2026-04-13 14:41:26.463848: Current learning rate: 0.00388 +2026-04-13 14:43:10.804627: train_loss -0.3791 +2026-04-13 14:43:10.812025: val_loss -0.3443 +2026-04-13 14:43:10.815143: Pseudo dice [0.4433, 0.3565, 0.6046, 0.7117, 0.5378, 0.6374, 0.8225] +2026-04-13 14:43:10.819205: Epoch time: 104.35 s +2026-04-13 14:43:12.347751: +2026-04-13 14:43:12.350335: Epoch 2602 +2026-04-13 14:43:12.352378: Current learning rate: 0.00388 +2026-04-13 14:44:57.685292: train_loss -0.3878 +2026-04-13 14:44:57.695954: val_loss -0.3069 +2026-04-13 14:44:57.698498: Pseudo dice [0.4744, 0.2999, 0.6725, 0.4202, 0.5578, 0.5754, 0.749] +2026-04-13 14:44:57.701598: Epoch time: 105.34 s +2026-04-13 14:44:59.220595: +2026-04-13 14:44:59.228900: Epoch 2603 +2026-04-13 14:44:59.235509: Current learning rate: 0.00388 +2026-04-13 14:46:41.970933: train_loss -0.3867 +2026-04-13 14:46:41.979505: val_loss -0.2243 +2026-04-13 14:46:41.981874: Pseudo dice [0.2373, 0.1158, 0.6281, 0.3922, 0.2564, 0.09, 0.647] +2026-04-13 14:46:41.985392: Epoch time: 102.75 s +2026-04-13 14:46:43.484644: +2026-04-13 14:46:43.488497: Epoch 2604 +2026-04-13 14:46:43.496206: Current learning rate: 0.00388 +2026-04-13 14:48:27.430017: train_loss -0.3969 +2026-04-13 14:48:27.438207: val_loss -0.3 +2026-04-13 14:48:27.442160: Pseudo dice [0.8647, 0.0212, 0.498, 0.7383, 0.4464, 0.1878, 0.432] +2026-04-13 14:48:27.445352: Epoch time: 103.95 s +2026-04-13 14:48:30.109476: +2026-04-13 14:48:30.112648: Epoch 2605 +2026-04-13 14:48:30.117003: Current learning rate: 0.00387 +2026-04-13 14:50:19.696193: train_loss -0.3767 +2026-04-13 14:50:19.733452: val_loss -0.2989 +2026-04-13 14:50:19.799951: Pseudo dice [0.2236, 0.4199, 0.5002, 0.133, 0.6853, 0.3656, 0.7618] +2026-04-13 14:50:19.818893: Epoch time: 109.59 s +2026-04-13 14:50:21.355275: +2026-04-13 14:50:21.357287: Epoch 2606 +2026-04-13 14:50:21.360574: Current learning rate: 0.00387 +2026-04-13 14:52:06.694685: train_loss -0.3847 +2026-04-13 14:52:06.703608: val_loss -0.3485 +2026-04-13 14:52:06.705873: Pseudo dice [0.455, 0.3292, 0.7833, 0.7843, 0.5086, 0.8297, 0.7715] +2026-04-13 14:52:06.719754: Epoch time: 105.34 s +2026-04-13 14:52:08.207787: +2026-04-13 14:52:08.210175: Epoch 2607 +2026-04-13 14:52:08.212204: Current learning rate: 0.00387 +2026-04-13 14:53:51.973424: train_loss -0.3944 +2026-04-13 14:53:51.980831: val_loss -0.3006 +2026-04-13 14:53:51.983530: Pseudo dice [0.6035, 0.6086, 0.5797, 0.6685, 0.6208, 0.1421, 0.7793] +2026-04-13 14:53:51.988249: Epoch time: 103.77 s +2026-04-13 14:53:53.490027: +2026-04-13 14:53:53.493721: Epoch 2608 +2026-04-13 14:53:53.499485: Current learning rate: 0.00387 +2026-04-13 14:55:38.462094: train_loss -0.3882 +2026-04-13 14:55:38.470555: val_loss -0.3212 +2026-04-13 14:55:38.473306: Pseudo dice [0.1744, 0.1936, 0.6511, 0.8904, 0.6222, 0.4551, 0.4988] +2026-04-13 14:55:38.478284: Epoch time: 104.98 s +2026-04-13 14:55:40.028540: +2026-04-13 14:55:40.030614: Epoch 2609 +2026-04-13 14:55:40.033226: Current learning rate: 0.00386 +2026-04-13 14:57:22.570052: train_loss -0.3957 +2026-04-13 14:57:22.577406: val_loss -0.2991 +2026-04-13 14:57:22.580376: Pseudo dice [0.7337, 0.8174, 0.6235, 0.7121, 0.6522, 0.079, 0.7199] +2026-04-13 14:57:22.582612: Epoch time: 102.55 s +2026-04-13 14:57:24.091200: +2026-04-13 14:57:24.093658: Epoch 2610 +2026-04-13 14:57:24.096973: Current learning rate: 0.00386 +2026-04-13 14:59:06.825182: train_loss -0.3928 +2026-04-13 14:59:06.831239: val_loss -0.3224 +2026-04-13 14:59:06.833051: Pseudo dice [0.5838, 0.0, 0.6264, 0.3727, 0.681, 0.2224, 0.8739] +2026-04-13 14:59:06.835682: Epoch time: 102.74 s +2026-04-13 14:59:08.316870: +2026-04-13 14:59:08.318796: Epoch 2611 +2026-04-13 14:59:08.321933: Current learning rate: 0.00386 +2026-04-13 15:00:52.660856: train_loss -0.3966 +2026-04-13 15:00:52.670041: val_loss -0.2591 +2026-04-13 15:00:52.680122: Pseudo dice [0.8017, 0.0086, 0.7456, 0.1207, 0.6341, 0.1544, 0.5913] +2026-04-13 15:00:52.684056: Epoch time: 104.35 s +2026-04-13 15:00:54.192899: +2026-04-13 15:00:54.195044: Epoch 2612 +2026-04-13 15:00:54.197447: Current learning rate: 0.00386 +2026-04-13 15:02:36.807809: train_loss -0.3997 +2026-04-13 15:02:36.815728: val_loss -0.3614 +2026-04-13 15:02:36.818533: Pseudo dice [0.381, 0.2115, 0.6755, 0.842, 0.5973, 0.5342, 0.6615] +2026-04-13 15:02:36.821216: Epoch time: 102.62 s +2026-04-13 15:02:38.345613: +2026-04-13 15:02:38.351044: Epoch 2613 +2026-04-13 15:02:38.353702: Current learning rate: 0.00385 +2026-04-13 15:04:21.617776: train_loss -0.4108 +2026-04-13 15:04:21.631269: val_loss -0.3293 +2026-04-13 15:04:21.633868: Pseudo dice [0.6805, 0.6418, 0.6504, 0.5961, 0.3951, 0.3628, 0.584] +2026-04-13 15:04:21.637079: Epoch time: 103.28 s +2026-04-13 15:04:23.138950: +2026-04-13 15:04:23.141807: Epoch 2614 +2026-04-13 15:04:23.145615: Current learning rate: 0.00385 +2026-04-13 15:06:06.603573: train_loss -0.4001 +2026-04-13 15:06:06.614799: val_loss -0.2405 +2026-04-13 15:06:06.618341: Pseudo dice [0.4342, 0.7934, 0.5165, 0.6043, 0.5823, 0.1658, 0.7993] +2026-04-13 15:06:06.621462: Epoch time: 103.47 s +2026-04-13 15:06:08.135617: +2026-04-13 15:06:08.138660: Epoch 2615 +2026-04-13 15:06:08.140641: Current learning rate: 0.00385 +2026-04-13 15:07:51.862826: train_loss -0.3925 +2026-04-13 15:07:51.868889: val_loss -0.3539 +2026-04-13 15:07:51.872011: Pseudo dice [0.351, 0.2295, 0.6107, 0.5066, 0.435, 0.776, 0.8806] +2026-04-13 15:07:51.875544: Epoch time: 103.73 s +2026-04-13 15:07:53.375689: +2026-04-13 15:07:53.377598: Epoch 2616 +2026-04-13 15:07:53.379510: Current learning rate: 0.00385 +2026-04-13 15:09:36.532635: train_loss -0.3837 +2026-04-13 15:09:36.540750: val_loss -0.3602 +2026-04-13 15:09:36.544796: Pseudo dice [0.6343, 0.3754, 0.5546, 0.6429, 0.592, 0.8517, 0.7706] +2026-04-13 15:09:36.546670: Epoch time: 103.16 s +2026-04-13 15:09:38.040429: +2026-04-13 15:09:38.043355: Epoch 2617 +2026-04-13 15:09:38.046061: Current learning rate: 0.00384 +2026-04-13 15:11:21.773883: train_loss -0.3969 +2026-04-13 15:11:21.783746: val_loss -0.2909 +2026-04-13 15:11:21.786773: Pseudo dice [0.7682, 0.8111, 0.5042, 0.6329, 0.5746, 0.0694, 0.9088] +2026-04-13 15:11:21.790070: Epoch time: 103.74 s +2026-04-13 15:11:23.321784: +2026-04-13 15:11:23.323859: Epoch 2618 +2026-04-13 15:11:23.326165: Current learning rate: 0.00384 +2026-04-13 15:13:06.195164: train_loss -0.4014 +2026-04-13 15:13:06.201611: val_loss -0.3091 +2026-04-13 15:13:06.203322: Pseudo dice [0.5243, 0.1877, 0.5999, 0.8542, 0.4883, 0.2511, 0.8218] +2026-04-13 15:13:06.205524: Epoch time: 102.88 s +2026-04-13 15:13:07.698411: +2026-04-13 15:13:07.700148: Epoch 2619 +2026-04-13 15:13:07.702151: Current learning rate: 0.00384 +2026-04-13 15:14:50.073123: train_loss -0.4004 +2026-04-13 15:14:50.082337: val_loss -0.3182 +2026-04-13 15:14:50.085809: Pseudo dice [0.4195, 0.6358, 0.4391, 0.8064, 0.4313, 0.7438, 0.5366] +2026-04-13 15:14:50.089093: Epoch time: 102.38 s +2026-04-13 15:14:51.599217: +2026-04-13 15:14:51.601758: Epoch 2620 +2026-04-13 15:14:51.605505: Current learning rate: 0.00384 +2026-04-13 15:16:37.201768: train_loss -0.3936 +2026-04-13 15:16:37.212974: val_loss -0.3698 +2026-04-13 15:16:37.215472: Pseudo dice [0.3204, 0.7249, 0.7763, 0.7016, 0.5531, 0.8177, 0.8557] +2026-04-13 15:16:37.218676: Epoch time: 105.6 s +2026-04-13 15:16:38.726495: +2026-04-13 15:16:38.731706: Epoch 2621 +2026-04-13 15:16:38.734471: Current learning rate: 0.00383 +2026-04-13 15:18:21.555799: train_loss -0.3974 +2026-04-13 15:18:21.561680: val_loss -0.2269 +2026-04-13 15:18:21.565786: Pseudo dice [0.7416, 0.4916, 0.4089, 0.0314, 0.3828, 0.0366, 0.3486] +2026-04-13 15:18:21.568523: Epoch time: 102.83 s +2026-04-13 15:18:23.080479: +2026-04-13 15:18:23.084195: Epoch 2622 +2026-04-13 15:18:23.087943: Current learning rate: 0.00383 +2026-04-13 15:20:05.193009: train_loss -0.385 +2026-04-13 15:20:05.202911: val_loss -0.1854 +2026-04-13 15:20:05.205775: Pseudo dice [0.3797, 0.2388, 0.536, 0.4328, 0.3986, 0.0869, 0.8093] +2026-04-13 15:20:05.208534: Epoch time: 102.12 s +2026-04-13 15:20:06.703558: +2026-04-13 15:20:06.705450: Epoch 2623 +2026-04-13 15:20:06.707596: Current learning rate: 0.00383 +2026-04-13 15:21:49.872601: train_loss -0.3685 +2026-04-13 15:21:49.882269: val_loss -0.2837 +2026-04-13 15:21:49.884624: Pseudo dice [0.3659, 0.1092, 0.3892, 0.7668, 0.6618, 0.1504, 0.7688] +2026-04-13 15:21:49.888556: Epoch time: 103.17 s +2026-04-13 15:21:51.390649: +2026-04-13 15:21:51.392780: Epoch 2624 +2026-04-13 15:21:51.394841: Current learning rate: 0.00383 +2026-04-13 15:23:34.903075: train_loss -0.3964 +2026-04-13 15:23:34.910860: val_loss -0.3049 +2026-04-13 15:23:34.913304: Pseudo dice [0.5069, 0.4978, 0.6174, 0.5985, 0.4399, 0.1803, 0.7919] +2026-04-13 15:23:34.916566: Epoch time: 103.52 s +2026-04-13 15:23:36.434101: +2026-04-13 15:23:36.436309: Epoch 2625 +2026-04-13 15:23:36.438964: Current learning rate: 0.00382 +2026-04-13 15:25:21.112785: train_loss -0.3857 +2026-04-13 15:25:21.120344: val_loss -0.3345 +2026-04-13 15:25:21.123490: Pseudo dice [0.5776, 0.6652, 0.7815, 0.7703, 0.3542, 0.6444, 0.5639] +2026-04-13 15:25:21.126478: Epoch time: 104.68 s +2026-04-13 15:25:22.627932: +2026-04-13 15:25:22.630980: Epoch 2626 +2026-04-13 15:25:22.634957: Current learning rate: 0.00382 +2026-04-13 15:27:05.561447: train_loss -0.3904 +2026-04-13 15:27:05.572120: val_loss -0.2522 +2026-04-13 15:27:05.576170: Pseudo dice [0.3992, 0.5132, 0.56, 0.301, 0.3213, 0.0499, 0.2964] +2026-04-13 15:27:05.579012: Epoch time: 102.94 s +2026-04-13 15:27:07.091639: +2026-04-13 15:27:07.093689: Epoch 2627 +2026-04-13 15:27:07.096979: Current learning rate: 0.00382 +2026-04-13 15:28:49.168618: train_loss -0.3933 +2026-04-13 15:28:49.174406: val_loss -0.2565 +2026-04-13 15:28:49.176675: Pseudo dice [0.4658, 0.5529, 0.3404, 0.588, 0.4437, 0.1616, 0.7113] +2026-04-13 15:28:49.179860: Epoch time: 102.08 s +2026-04-13 15:28:50.691468: +2026-04-13 15:28:50.693565: Epoch 2628 +2026-04-13 15:28:50.695641: Current learning rate: 0.00382 +2026-04-13 15:30:33.075890: train_loss -0.3795 +2026-04-13 15:30:33.083204: val_loss -0.3143 +2026-04-13 15:30:33.086675: Pseudo dice [0.6186, 0.7063, 0.7045, 0.8341, 0.5759, 0.0914, 0.7872] +2026-04-13 15:30:33.089873: Epoch time: 102.39 s +2026-04-13 15:30:34.591434: +2026-04-13 15:30:34.593147: Epoch 2629 +2026-04-13 15:30:34.595205: Current learning rate: 0.00381 +2026-04-13 15:32:17.557930: train_loss -0.3777 +2026-04-13 15:32:17.565836: val_loss -0.3158 +2026-04-13 15:32:17.568093: Pseudo dice [0.6903, 0.4237, 0.7109, 0.4583, 0.6482, 0.074, 0.7893] +2026-04-13 15:32:17.570812: Epoch time: 102.97 s +2026-04-13 15:32:19.064847: +2026-04-13 15:32:19.066814: Epoch 2630 +2026-04-13 15:32:19.068997: Current learning rate: 0.00381 +2026-04-13 15:34:02.096406: train_loss -0.3869 +2026-04-13 15:34:02.102383: val_loss -0.2907 +2026-04-13 15:34:02.104388: Pseudo dice [0.7251, 0.2662, 0.6449, 0.7233, 0.5096, 0.2158, 0.4598] +2026-04-13 15:34:02.107158: Epoch time: 103.04 s +2026-04-13 15:34:03.616741: +2026-04-13 15:34:03.619028: Epoch 2631 +2026-04-13 15:34:03.621711: Current learning rate: 0.00381 +2026-04-13 15:35:47.734218: train_loss -0.3666 +2026-04-13 15:35:47.740024: val_loss -0.2879 +2026-04-13 15:35:47.742123: Pseudo dice [0.6663, 0.2421, 0.5811, 0.2603, 0.5562, 0.0988, 0.758] +2026-04-13 15:35:47.746009: Epoch time: 104.12 s +2026-04-13 15:35:49.243249: +2026-04-13 15:35:49.245836: Epoch 2632 +2026-04-13 15:35:49.248160: Current learning rate: 0.00381 +2026-04-13 15:37:32.328742: train_loss -0.368 +2026-04-13 15:37:32.335248: val_loss -0.3256 +2026-04-13 15:37:32.338002: Pseudo dice [0.7406, 0.6525, 0.4735, 0.769, 0.5622, 0.2342, 0.7628] +2026-04-13 15:37:32.341460: Epoch time: 103.09 s +2026-04-13 15:37:33.849870: +2026-04-13 15:37:33.852054: Epoch 2633 +2026-04-13 15:37:33.854425: Current learning rate: 0.0038 +2026-04-13 15:39:16.629565: train_loss -0.3736 +2026-04-13 15:39:16.636789: val_loss -0.1793 +2026-04-13 15:39:16.639558: Pseudo dice [0.7818, 0.5473, 0.4188, 0.324, 0.5219, 0.0156, 0.2744] +2026-04-13 15:39:16.642052: Epoch time: 102.78 s +2026-04-13 15:39:18.161268: +2026-04-13 15:39:18.163337: Epoch 2634 +2026-04-13 15:39:18.165716: Current learning rate: 0.0038 +2026-04-13 15:41:01.201536: train_loss -0.3727 +2026-04-13 15:41:01.209618: val_loss -0.2468 +2026-04-13 15:41:01.211874: Pseudo dice [0.4729, 0.1008, 0.4583, 0.0, 0.1973, 0.1668, 0.7173] +2026-04-13 15:41:01.214551: Epoch time: 103.04 s +2026-04-13 15:41:02.712268: +2026-04-13 15:41:02.714283: Epoch 2635 +2026-04-13 15:41:02.716760: Current learning rate: 0.0038 +2026-04-13 15:42:46.482063: train_loss -0.3633 +2026-04-13 15:42:46.496809: val_loss -0.3239 +2026-04-13 15:42:46.501741: Pseudo dice [0.7816, 0.2358, 0.7671, 0.8092, 0.5044, 0.7106, 0.7159] +2026-04-13 15:42:46.507130: Epoch time: 103.77 s +2026-04-13 15:42:48.014955: +2026-04-13 15:42:48.016892: Epoch 2636 +2026-04-13 15:42:48.019163: Current learning rate: 0.0038 +2026-04-13 15:44:31.123892: train_loss -0.3882 +2026-04-13 15:44:31.131498: val_loss -0.2611 +2026-04-13 15:44:31.133273: Pseudo dice [0.6197, 0.5928, 0.579, 0.7064, 0.579, 0.0436, 0.6834] +2026-04-13 15:44:31.136475: Epoch time: 103.11 s +2026-04-13 15:44:32.615741: +2026-04-13 15:44:32.617864: Epoch 2637 +2026-04-13 15:44:32.620041: Current learning rate: 0.00379 +2026-04-13 15:46:15.117631: train_loss -0.3682 +2026-04-13 15:46:15.125444: val_loss -0.3214 +2026-04-13 15:46:15.131243: Pseudo dice [0.3516, 0.257, 0.5049, 0.5188, 0.5277, 0.7728, 0.7042] +2026-04-13 15:46:15.134752: Epoch time: 102.51 s +2026-04-13 15:46:16.664837: +2026-04-13 15:46:16.667218: Epoch 2638 +2026-04-13 15:46:16.670036: Current learning rate: 0.00379 +2026-04-13 15:48:03.236909: train_loss -0.3531 +2026-04-13 15:48:03.243800: val_loss -0.344 +2026-04-13 15:48:03.245852: Pseudo dice [0.2925, 0.0724, 0.5989, 0.594, 0.5137, 0.8192, 0.613] +2026-04-13 15:48:03.248457: Epoch time: 106.58 s +2026-04-13 15:48:04.746311: +2026-04-13 15:48:04.748120: Epoch 2639 +2026-04-13 15:48:04.750116: Current learning rate: 0.00379 +2026-04-13 15:49:47.403578: train_loss -0.3735 +2026-04-13 15:49:47.409614: val_loss -0.2641 +2026-04-13 15:49:47.411459: Pseudo dice [0.5652, 0.1181, 0.5895, 0.1579, 0.2096, 0.2998, 0.8169] +2026-04-13 15:49:47.413518: Epoch time: 102.66 s +2026-04-13 15:49:48.932339: +2026-04-13 15:49:48.934039: Epoch 2640 +2026-04-13 15:49:48.935980: Current learning rate: 0.00379 +2026-04-13 15:51:31.829324: train_loss -0.3728 +2026-04-13 15:51:31.835717: val_loss -0.3332 +2026-04-13 15:51:31.838810: Pseudo dice [0.6905, 0.7022, 0.703, 0.2203, 0.5146, 0.8051, 0.5648] +2026-04-13 15:51:31.841324: Epoch time: 102.9 s +2026-04-13 15:51:33.326898: +2026-04-13 15:51:33.328975: Epoch 2641 +2026-04-13 15:51:33.330971: Current learning rate: 0.00378 +2026-04-13 15:53:17.549299: train_loss -0.3811 +2026-04-13 15:53:17.557311: val_loss -0.332 +2026-04-13 15:53:17.559641: Pseudo dice [0.5052, 0.1596, 0.4862, 0.67, 0.4682, 0.8263, 0.5432] +2026-04-13 15:53:17.561953: Epoch time: 104.23 s +2026-04-13 15:53:19.089967: +2026-04-13 15:53:19.092815: Epoch 2642 +2026-04-13 15:53:19.095958: Current learning rate: 0.00378 +2026-04-13 15:55:01.335210: train_loss -0.3874 +2026-04-13 15:55:01.341318: val_loss -0.3211 +2026-04-13 15:55:01.343528: Pseudo dice [0.7295, 0.1842, 0.585, 0.6646, 0.556, 0.6721, 0.7314] +2026-04-13 15:55:01.345891: Epoch time: 102.25 s +2026-04-13 15:55:03.081175: +2026-04-13 15:55:03.082777: Epoch 2643 +2026-04-13 15:55:03.085209: Current learning rate: 0.00378 +2026-04-13 15:56:45.259973: train_loss -0.3825 +2026-04-13 15:56:45.265405: val_loss -0.3108 +2026-04-13 15:56:45.267373: Pseudo dice [0.6725, 0.1738, 0.5427, 0.6809, 0.6071, 0.2756, 0.5853] +2026-04-13 15:56:45.269855: Epoch time: 102.18 s +2026-04-13 15:56:46.772367: +2026-04-13 15:56:46.774082: Epoch 2644 +2026-04-13 15:56:46.776053: Current learning rate: 0.00378 +2026-04-13 15:58:29.921385: train_loss -0.3755 +2026-04-13 15:58:29.928674: val_loss -0.286 +2026-04-13 15:58:29.931714: Pseudo dice [0.5317, 0.3109, 0.5094, 0.5196, 0.4651, 0.0962, 0.7993] +2026-04-13 15:58:29.934686: Epoch time: 103.15 s +2026-04-13 15:58:31.432029: +2026-04-13 15:58:31.434204: Epoch 2645 +2026-04-13 15:58:31.436621: Current learning rate: 0.00377 +2026-04-13 16:00:15.381490: train_loss -0.3772 +2026-04-13 16:00:15.387612: val_loss -0.3172 +2026-04-13 16:00:15.389709: Pseudo dice [0.579, 0.6222, 0.6869, 0.7085, 0.2997, 0.7287, 0.4091] +2026-04-13 16:00:15.392177: Epoch time: 103.95 s +2026-04-13 16:00:16.877614: +2026-04-13 16:00:16.879542: Epoch 2646 +2026-04-13 16:00:16.881536: Current learning rate: 0.00377 +2026-04-13 16:02:00.005014: train_loss -0.3768 +2026-04-13 16:02:00.012168: val_loss -0.3313 +2026-04-13 16:02:00.014500: Pseudo dice [0.6428, 0.4684, 0.8157, 0.5653, 0.5178, 0.7644, 0.8676] +2026-04-13 16:02:00.018614: Epoch time: 103.13 s +2026-04-13 16:02:01.531488: +2026-04-13 16:02:01.534424: Epoch 2647 +2026-04-13 16:02:01.536772: Current learning rate: 0.00377 +2026-04-13 16:03:45.713460: train_loss -0.3885 +2026-04-13 16:03:45.719577: val_loss -0.3313 +2026-04-13 16:03:45.721869: Pseudo dice [0.3048, 0.2989, 0.7264, 0.702, 0.3449, 0.7938, 0.3753] +2026-04-13 16:03:45.724148: Epoch time: 104.18 s +2026-04-13 16:03:47.237455: +2026-04-13 16:03:47.240346: Epoch 2648 +2026-04-13 16:03:47.242639: Current learning rate: 0.00377 +2026-04-13 16:05:29.803690: train_loss -0.3827 +2026-04-13 16:05:29.811337: val_loss -0.3302 +2026-04-13 16:05:29.813341: Pseudo dice [0.7399, 0.0533, 0.611, 0.1529, 0.5332, 0.1587, 0.6643] +2026-04-13 16:05:29.816522: Epoch time: 102.57 s +2026-04-13 16:05:31.318267: +2026-04-13 16:05:31.320194: Epoch 2649 +2026-04-13 16:05:31.322206: Current learning rate: 0.00376 +2026-04-13 16:07:13.800390: train_loss -0.378 +2026-04-13 16:07:13.810602: val_loss -0.3096 +2026-04-13 16:07:13.813032: Pseudo dice [0.6052, 0.4122, 0.729, 0.5401, 0.3797, 0.7757, 0.5024] +2026-04-13 16:07:13.815773: Epoch time: 102.49 s +2026-04-13 16:07:17.338208: +2026-04-13 16:07:17.341192: Epoch 2650 +2026-04-13 16:07:17.344689: Current learning rate: 0.00376 +2026-04-13 16:09:01.608741: train_loss -0.3709 +2026-04-13 16:09:01.616611: val_loss -0.2692 +2026-04-13 16:09:01.618896: Pseudo dice [0.1804, 0.5198, 0.5704, 0.9086, 0.533, 0.1999, 0.8618] +2026-04-13 16:09:01.623417: Epoch time: 104.27 s +2026-04-13 16:09:03.129196: +2026-04-13 16:09:03.134366: Epoch 2651 +2026-04-13 16:09:03.136791: Current learning rate: 0.00376 +2026-04-13 16:10:45.474929: train_loss -0.3855 +2026-04-13 16:10:45.481512: val_loss -0.2589 +2026-04-13 16:10:45.483952: Pseudo dice [0.7591, 0.8141, 0.5997, 0.2067, 0.6205, 0.1345, 0.7872] +2026-04-13 16:10:45.486201: Epoch time: 102.35 s +2026-04-13 16:10:46.978170: +2026-04-13 16:10:46.984546: Epoch 2652 +2026-04-13 16:10:46.990148: Current learning rate: 0.00376 +2026-04-13 16:12:30.653329: train_loss -0.3968 +2026-04-13 16:12:30.660615: val_loss -0.3124 +2026-04-13 16:12:30.663149: Pseudo dice [0.5645, 0.6848, 0.5675, 0.8606, 0.4485, 0.0884, 0.8781] +2026-04-13 16:12:30.665882: Epoch time: 103.68 s +2026-04-13 16:12:32.181257: +2026-04-13 16:12:32.184061: Epoch 2653 +2026-04-13 16:12:32.189110: Current learning rate: 0.00375 +2026-04-13 16:14:15.676307: train_loss -0.3947 +2026-04-13 16:14:15.683902: val_loss -0.3129 +2026-04-13 16:14:15.686559: Pseudo dice [0.8168, 0.3113, 0.7921, 0.788, 0.453, 0.1082, 0.6696] +2026-04-13 16:14:15.689334: Epoch time: 103.5 s +2026-04-13 16:14:17.214346: +2026-04-13 16:14:17.217969: Epoch 2654 +2026-04-13 16:14:17.220477: Current learning rate: 0.00375 +2026-04-13 16:16:00.605824: train_loss -0.3884 +2026-04-13 16:16:00.612006: val_loss -0.3109 +2026-04-13 16:16:00.614228: Pseudo dice [0.4763, 0.4859, 0.7408, 0.4676, 0.5259, 0.192, 0.7174] +2026-04-13 16:16:00.616643: Epoch time: 103.4 s +2026-04-13 16:16:02.117475: +2026-04-13 16:16:02.120077: Epoch 2655 +2026-04-13 16:16:02.122793: Current learning rate: 0.00375 +2026-04-13 16:17:47.100558: train_loss -0.3865 +2026-04-13 16:17:47.125000: val_loss -0.3032 +2026-04-13 16:17:47.129620: Pseudo dice [0.593, 0.5149, 0.5967, 0.7236, 0.5675, 0.08, 0.8088] +2026-04-13 16:17:47.137480: Epoch time: 104.99 s +2026-04-13 16:17:48.700979: +2026-04-13 16:17:48.703645: Epoch 2656 +2026-04-13 16:17:48.706606: Current learning rate: 0.00375 +2026-04-13 16:19:32.100929: train_loss -0.3934 +2026-04-13 16:19:32.107382: val_loss -0.3564 +2026-04-13 16:19:32.110856: Pseudo dice [0.4644, 0.6496, 0.6019, 0.7642, 0.4646, 0.8421, 0.597] +2026-04-13 16:19:32.113594: Epoch time: 103.4 s +2026-04-13 16:19:33.645363: +2026-04-13 16:19:33.647323: Epoch 2657 +2026-04-13 16:19:33.649962: Current learning rate: 0.00374 +2026-04-13 16:21:16.241988: train_loss -0.3859 +2026-04-13 16:21:16.248779: val_loss -0.2496 +2026-04-13 16:21:16.251802: Pseudo dice [0.6098, 0.5641, 0.484, 0.7483, 0.5341, 0.1908, 0.4956] +2026-04-13 16:21:16.254890: Epoch time: 102.6 s +2026-04-13 16:21:17.768847: +2026-04-13 16:21:17.770681: Epoch 2658 +2026-04-13 16:21:17.772979: Current learning rate: 0.00374 +2026-04-13 16:23:01.223899: train_loss -0.3888 +2026-04-13 16:23:01.231595: val_loss -0.3099 +2026-04-13 16:23:01.233411: Pseudo dice [0.3852, 0.697, 0.5994, 0.7995, 0.5963, 0.04, 0.7429] +2026-04-13 16:23:01.236402: Epoch time: 103.46 s +2026-04-13 16:23:02.757064: +2026-04-13 16:23:02.759840: Epoch 2659 +2026-04-13 16:23:02.764617: Current learning rate: 0.00374 +2026-04-13 16:24:46.727051: train_loss -0.379 +2026-04-13 16:24:46.736292: val_loss -0.2829 +2026-04-13 16:24:46.738742: Pseudo dice [0.3476, 0.0709, 0.5976, 0.3198, 0.6504, 0.1083, 0.7824] +2026-04-13 16:24:46.740978: Epoch time: 103.97 s +2026-04-13 16:24:48.246790: +2026-04-13 16:24:48.248744: Epoch 2660 +2026-04-13 16:24:48.252714: Current learning rate: 0.00374 +2026-04-13 16:26:30.801341: train_loss -0.3954 +2026-04-13 16:26:30.824614: val_loss -0.2974 +2026-04-13 16:26:30.826588: Pseudo dice [0.8034, 0.3984, 0.5571, 0.5994, 0.592, 0.1701, 0.8169] +2026-04-13 16:26:30.840630: Epoch time: 102.56 s +2026-04-13 16:26:32.362650: +2026-04-13 16:26:32.366647: Epoch 2661 +2026-04-13 16:26:32.369156: Current learning rate: 0.00373 +2026-04-13 16:28:15.330589: train_loss -0.3853 +2026-04-13 16:28:15.336951: val_loss -0.3171 +2026-04-13 16:28:15.339512: Pseudo dice [0.6331, 0.079, 0.7779, 0.4906, 0.4224, 0.3561, 0.8194] +2026-04-13 16:28:15.342024: Epoch time: 102.97 s +2026-04-13 16:28:16.859075: +2026-04-13 16:28:16.860700: Epoch 2662 +2026-04-13 16:28:16.862930: Current learning rate: 0.00373 +2026-04-13 16:30:00.936907: train_loss -0.3718 +2026-04-13 16:30:00.943316: val_loss -0.264 +2026-04-13 16:30:00.946084: Pseudo dice [0.4825, 0.3533, 0.5925, 0.3861, 0.4874, 0.0751, 0.5026] +2026-04-13 16:30:00.952016: Epoch time: 104.08 s +2026-04-13 16:30:02.450668: +2026-04-13 16:30:02.452960: Epoch 2663 +2026-04-13 16:30:02.455564: Current learning rate: 0.00373 +2026-04-13 16:31:45.624061: train_loss -0.3952 +2026-04-13 16:31:45.631162: val_loss -0.3594 +2026-04-13 16:31:45.633397: Pseudo dice [0.4329, 0.7284, 0.7915, 0.3567, 0.4384, 0.8467, 0.7604] +2026-04-13 16:31:45.635891: Epoch time: 103.18 s +2026-04-13 16:31:47.144833: +2026-04-13 16:31:47.146904: Epoch 2664 +2026-04-13 16:31:47.149027: Current learning rate: 0.00373 +2026-04-13 16:33:31.278999: train_loss -0.3962 +2026-04-13 16:33:31.285282: val_loss -0.2897 +2026-04-13 16:33:31.287624: Pseudo dice [0.5877, 0.7835, 0.7063, 0.5718, 0.4806, 0.3305, 0.6911] +2026-04-13 16:33:31.297605: Epoch time: 104.14 s +2026-04-13 16:33:34.056239: +2026-04-13 16:33:34.058604: Epoch 2665 +2026-04-13 16:33:34.061002: Current learning rate: 0.00372 +2026-04-13 16:35:16.872200: train_loss -0.3954 +2026-04-13 16:35:16.878707: val_loss -0.3315 +2026-04-13 16:35:16.880960: Pseudo dice [0.6105, 0.2756, 0.8364, 0.0458, 0.5001, 0.8185, 0.6494] +2026-04-13 16:35:16.883527: Epoch time: 102.82 s +2026-04-13 16:35:18.388353: +2026-04-13 16:35:18.390308: Epoch 2666 +2026-04-13 16:35:18.392132: Current learning rate: 0.00372 +2026-04-13 16:37:01.339083: train_loss -0.3805 +2026-04-13 16:37:01.357575: val_loss -0.2876 +2026-04-13 16:37:01.362478: Pseudo dice [0.3073, 0.0495, 0.737, 0.2047, 0.7525, 0.1957, 0.849] +2026-04-13 16:37:01.367532: Epoch time: 102.95 s +2026-04-13 16:37:02.882504: +2026-04-13 16:37:02.885527: Epoch 2667 +2026-04-13 16:37:02.891180: Current learning rate: 0.00372 +2026-04-13 16:38:48.814208: train_loss -0.4032 +2026-04-13 16:38:48.822527: val_loss -0.294 +2026-04-13 16:38:48.825245: Pseudo dice [0.5726, 0.3133, 0.5897, 0.0779, 0.3721, 0.4774, 0.8813] +2026-04-13 16:38:48.830571: Epoch time: 105.94 s +2026-04-13 16:38:50.358873: +2026-04-13 16:38:50.361865: Epoch 2668 +2026-04-13 16:38:50.364778: Current learning rate: 0.00372 +2026-04-13 16:40:33.670903: train_loss -0.4055 +2026-04-13 16:40:33.677111: val_loss -0.2609 +2026-04-13 16:40:33.679262: Pseudo dice [0.6416, 0.4126, 0.6639, 0.6105, 0.5044, 0.2502, 0.8976] +2026-04-13 16:40:33.681924: Epoch time: 103.32 s +2026-04-13 16:40:35.218611: +2026-04-13 16:40:35.220651: Epoch 2669 +2026-04-13 16:40:35.222996: Current learning rate: 0.00371 +2026-04-13 16:42:18.912372: train_loss -0.3861 +2026-04-13 16:42:18.924307: val_loss -0.296 +2026-04-13 16:42:18.929717: Pseudo dice [0.3773, 0.1434, 0.6845, 0.599, 0.6423, 0.2042, 0.5451] +2026-04-13 16:42:18.934580: Epoch time: 103.7 s +2026-04-13 16:42:20.469813: +2026-04-13 16:42:20.473282: Epoch 2670 +2026-04-13 16:42:20.476439: Current learning rate: 0.00371 +2026-04-13 16:44:03.689009: train_loss -0.3818 +2026-04-13 16:44:03.695009: val_loss -0.3473 +2026-04-13 16:44:03.697015: Pseudo dice [0.639, 0.5681, 0.7443, 0.8603, 0.4858, 0.2577, 0.7259] +2026-04-13 16:44:03.699119: Epoch time: 103.22 s +2026-04-13 16:44:05.228262: +2026-04-13 16:44:05.230290: Epoch 2671 +2026-04-13 16:44:05.232154: Current learning rate: 0.00371 +2026-04-13 16:45:48.050139: train_loss -0.3879 +2026-04-13 16:45:48.056593: val_loss -0.2297 +2026-04-13 16:45:48.058992: Pseudo dice [0.7117, 0.4093, 0.4152, 0.8657, 0.1977, 0.0752, 0.5211] +2026-04-13 16:45:48.061918: Epoch time: 102.83 s +2026-04-13 16:45:49.579027: +2026-04-13 16:45:49.580661: Epoch 2672 +2026-04-13 16:45:49.582828: Current learning rate: 0.00371 +2026-04-13 16:47:32.060035: train_loss -0.3748 +2026-04-13 16:47:32.068665: val_loss -0.3383 +2026-04-13 16:47:32.070904: Pseudo dice [0.8328, 0.1269, 0.6095, 0.4451, 0.4486, 0.4353, 0.2515] +2026-04-13 16:47:32.073892: Epoch time: 102.48 s +2026-04-13 16:47:33.595928: +2026-04-13 16:47:33.598116: Epoch 2673 +2026-04-13 16:47:33.600662: Current learning rate: 0.0037 +2026-04-13 16:49:16.481006: train_loss -0.3841 +2026-04-13 16:49:16.491504: val_loss -0.3124 +2026-04-13 16:49:16.502134: Pseudo dice [0.5487, 0.6294, 0.6392, 0.7164, 0.5066, 0.3267, 0.8039] +2026-04-13 16:49:16.511965: Epoch time: 102.89 s +2026-04-13 16:49:18.017722: +2026-04-13 16:49:18.022213: Epoch 2674 +2026-04-13 16:49:18.027317: Current learning rate: 0.0037 +2026-04-13 16:51:01.595680: train_loss -0.4011 +2026-04-13 16:51:01.601509: val_loss -0.1859 +2026-04-13 16:51:01.603731: Pseudo dice [0.1605, 0.6472, 0.5116, 0.6762, 0.4213, 0.1102, 0.6423] +2026-04-13 16:51:01.605941: Epoch time: 103.58 s +2026-04-13 16:51:03.118462: +2026-04-13 16:51:03.120189: Epoch 2675 +2026-04-13 16:51:03.123155: Current learning rate: 0.0037 +2026-04-13 16:52:46.565670: train_loss -0.3888 +2026-04-13 16:52:46.577712: val_loss -0.3506 +2026-04-13 16:52:46.582155: Pseudo dice [0.706, 0.4826, 0.6467, 0.6444, 0.4723, 0.6747, 0.8661] +2026-04-13 16:52:46.586477: Epoch time: 103.45 s +2026-04-13 16:52:48.108560: +2026-04-13 16:52:48.111028: Epoch 2676 +2026-04-13 16:52:48.113483: Current learning rate: 0.0037 +2026-04-13 16:54:30.810767: train_loss -0.3718 +2026-04-13 16:54:30.817097: val_loss -0.3495 +2026-04-13 16:54:30.819503: Pseudo dice [0.7398, 0.5767, 0.6868, 0.838, 0.5113, 0.5228, 0.8036] +2026-04-13 16:54:30.822682: Epoch time: 102.71 s +2026-04-13 16:54:32.324472: +2026-04-13 16:54:32.327696: Epoch 2677 +2026-04-13 16:54:32.330015: Current learning rate: 0.00369 +2026-04-13 16:56:16.103482: train_loss -0.3565 +2026-04-13 16:56:16.109832: val_loss -0.2001 +2026-04-13 16:56:16.112233: Pseudo dice [0.2428, 0.4906, 0.7456, 0.6387, 0.1294, 0.0875, 0.3981] +2026-04-13 16:56:16.114721: Epoch time: 103.78 s +2026-04-13 16:56:17.620360: +2026-04-13 16:56:17.622085: Epoch 2678 +2026-04-13 16:56:17.624109: Current learning rate: 0.00369 +2026-04-13 16:57:59.974598: train_loss -0.377 +2026-04-13 16:57:59.983016: val_loss -0.2925 +2026-04-13 16:57:59.985719: Pseudo dice [0.4651, 0.4731, 0.6553, 0.2416, 0.6153, 0.1542, 0.6781] +2026-04-13 16:57:59.988662: Epoch time: 102.36 s +2026-04-13 16:58:01.514962: +2026-04-13 16:58:01.518114: Epoch 2679 +2026-04-13 16:58:01.521124: Current learning rate: 0.00369 +2026-04-13 16:59:44.842999: train_loss -0.3879 +2026-04-13 16:59:44.851988: val_loss -0.2856 +2026-04-13 16:59:44.854378: Pseudo dice [0.7044, 0.3737, 0.6492, 0.7851, 0.5869, 0.1114, 0.8901] +2026-04-13 16:59:44.857126: Epoch time: 103.33 s +2026-04-13 16:59:46.408278: +2026-04-13 16:59:46.410643: Epoch 2680 +2026-04-13 16:59:46.415172: Current learning rate: 0.00369 +2026-04-13 17:01:30.208420: train_loss -0.3908 +2026-04-13 17:01:30.215004: val_loss -0.3386 +2026-04-13 17:01:30.216957: Pseudo dice [0.8313, 0.8252, 0.4007, 0.7993, 0.6374, 0.1336, 0.8423] +2026-04-13 17:01:30.219644: Epoch time: 103.8 s +2026-04-13 17:01:31.732924: +2026-04-13 17:01:31.735595: Epoch 2681 +2026-04-13 17:01:31.737936: Current learning rate: 0.00368 +2026-04-13 17:03:18.289742: train_loss -0.3967 +2026-04-13 17:03:18.297488: val_loss -0.3432 +2026-04-13 17:03:18.300551: Pseudo dice [0.4272, 0.7182, 0.7631, 0.7151, 0.5003, 0.7159, 0.6001] +2026-04-13 17:03:18.303403: Epoch time: 106.56 s +2026-04-13 17:03:19.841699: +2026-04-13 17:03:19.843710: Epoch 2682 +2026-04-13 17:03:19.846234: Current learning rate: 0.00368 +2026-04-13 17:05:03.149908: train_loss -0.3806 +2026-04-13 17:05:03.157620: val_loss -0.3493 +2026-04-13 17:05:03.160551: Pseudo dice [0.5125, 0.8374, 0.73, 0.1214, 0.6013, 0.4403, 0.8167] +2026-04-13 17:05:03.163696: Epoch time: 103.31 s +2026-04-13 17:05:04.672401: +2026-04-13 17:05:04.674376: Epoch 2683 +2026-04-13 17:05:04.676588: Current learning rate: 0.00368 +2026-04-13 17:06:46.761165: train_loss -0.3895 +2026-04-13 17:06:46.767344: val_loss -0.3014 +2026-04-13 17:06:46.769593: Pseudo dice [0.3089, 0.7694, 0.4857, 0.6452, 0.6128, 0.2037, 0.6401] +2026-04-13 17:06:46.772440: Epoch time: 102.09 s +2026-04-13 17:06:48.286401: +2026-04-13 17:06:48.289366: Epoch 2684 +2026-04-13 17:06:48.293209: Current learning rate: 0.00368 +2026-04-13 17:08:34.785087: train_loss -0.3909 +2026-04-13 17:08:34.795658: val_loss -0.3716 +2026-04-13 17:08:34.798285: Pseudo dice [0.615, 0.2635, 0.7077, 0.6973, 0.7203, 0.8127, 0.9329] +2026-04-13 17:08:34.801659: Epoch time: 106.5 s +2026-04-13 17:08:36.332874: +2026-04-13 17:08:36.335786: Epoch 2685 +2026-04-13 17:08:36.338153: Current learning rate: 0.00367 +2026-04-13 17:10:20.760773: train_loss -0.3812 +2026-04-13 17:10:20.767008: val_loss -0.3479 +2026-04-13 17:10:20.769228: Pseudo dice [0.4548, 0.4909, 0.6508, 0.7352, 0.5463, 0.7469, 0.8631] +2026-04-13 17:10:20.771487: Epoch time: 104.43 s +2026-04-13 17:10:22.262426: +2026-04-13 17:10:22.265298: Epoch 2686 +2026-04-13 17:10:22.267421: Current learning rate: 0.00367 +2026-04-13 17:12:04.507956: train_loss -0.3892 +2026-04-13 17:12:04.513958: val_loss -0.2818 +2026-04-13 17:12:04.516149: Pseudo dice [0.6845, 0.1663, 0.5817, 0.804, 0.6324, 0.1406, 0.7044] +2026-04-13 17:12:04.518569: Epoch time: 102.25 s +2026-04-13 17:12:06.017936: +2026-04-13 17:12:06.020035: Epoch 2687 +2026-04-13 17:12:06.022193: Current learning rate: 0.00367 +2026-04-13 17:13:51.887795: train_loss -0.3966 +2026-04-13 17:13:51.894356: val_loss -0.3118 +2026-04-13 17:13:51.897372: Pseudo dice [0.7137, 0.4984, 0.6792, 0.3502, 0.1958, 0.4413, 0.5087] +2026-04-13 17:13:51.900337: Epoch time: 105.87 s +2026-04-13 17:13:53.442219: +2026-04-13 17:13:53.445566: Epoch 2688 +2026-04-13 17:13:53.448239: Current learning rate: 0.00367 +2026-04-13 17:15:36.941486: train_loss -0.4009 +2026-04-13 17:15:36.955775: val_loss -0.3684 +2026-04-13 17:15:36.959354: Pseudo dice [0.1658, 0.8147, 0.6212, 0.3743, 0.6644, 0.876, 0.9023] +2026-04-13 17:15:36.965425: Epoch time: 103.5 s +2026-04-13 17:15:38.469244: +2026-04-13 17:15:38.472421: Epoch 2689 +2026-04-13 17:15:38.474614: Current learning rate: 0.00366 +2026-04-13 17:17:22.432240: train_loss -0.4068 +2026-04-13 17:17:22.451191: val_loss -0.2676 +2026-04-13 17:17:22.458000: Pseudo dice [0.3315, 0.6631, 0.3247, 0.4204, 0.4584, 0.0599, 0.5706] +2026-04-13 17:17:22.468232: Epoch time: 103.96 s +2026-04-13 17:17:24.006827: +2026-04-13 17:17:24.013370: Epoch 2690 +2026-04-13 17:17:24.018945: Current learning rate: 0.00366 +2026-04-13 17:19:08.604614: train_loss -0.3993 +2026-04-13 17:19:08.610588: val_loss -0.3584 +2026-04-13 17:19:08.613348: Pseudo dice [0.6344, 0.4478, 0.7211, 0.8796, 0.5491, 0.8212, 0.9026] +2026-04-13 17:19:08.616267: Epoch time: 104.6 s +2026-04-13 17:19:10.138786: +2026-04-13 17:19:10.142626: Epoch 2691 +2026-04-13 17:19:10.149295: Current learning rate: 0.00366 +2026-04-13 17:20:53.055543: train_loss -0.3851 +2026-04-13 17:20:53.064204: val_loss -0.2961 +2026-04-13 17:20:53.066785: Pseudo dice [0.5489, 0.8002, 0.5184, 0.4973, 0.4989, 0.1765, 0.7284] +2026-04-13 17:20:53.069745: Epoch time: 102.92 s +2026-04-13 17:20:54.582772: +2026-04-13 17:20:54.584965: Epoch 2692 +2026-04-13 17:20:54.587081: Current learning rate: 0.00366 +2026-04-13 17:22:40.029928: train_loss -0.378 +2026-04-13 17:22:40.046624: val_loss -0.2782 +2026-04-13 17:22:40.049557: Pseudo dice [0.7956, 0.7516, 0.5441, 0.0847, 0.5003, 0.1516, 0.8066] +2026-04-13 17:22:40.053742: Epoch time: 105.45 s +2026-04-13 17:22:41.572156: +2026-04-13 17:22:41.576020: Epoch 2693 +2026-04-13 17:22:41.586188: Current learning rate: 0.00365 +2026-04-13 17:24:27.348336: train_loss -0.3832 +2026-04-13 17:24:27.355443: val_loss -0.3391 +2026-04-13 17:24:27.358714: Pseudo dice [0.5298, 0.3747, 0.6899, 0.9184, 0.4856, 0.7379, 0.7959] +2026-04-13 17:24:27.361804: Epoch time: 105.78 s +2026-04-13 17:24:28.871031: +2026-04-13 17:24:28.873528: Epoch 2694 +2026-04-13 17:24:28.876020: Current learning rate: 0.00365 +2026-04-13 17:26:11.374811: train_loss -0.3792 +2026-04-13 17:26:11.383219: val_loss -0.1349 +2026-04-13 17:26:11.386280: Pseudo dice [0.5924, 0.6018, 0.5746, 0.2521, 0.508, 0.0569, 0.598] +2026-04-13 17:26:11.389827: Epoch time: 102.51 s +2026-04-13 17:26:12.900603: +2026-04-13 17:26:12.902844: Epoch 2695 +2026-04-13 17:26:12.905827: Current learning rate: 0.00365 +2026-04-13 17:27:56.445854: train_loss -0.3846 +2026-04-13 17:27:56.471369: val_loss -0.2289 +2026-04-13 17:27:56.473795: Pseudo dice [0.3822, 0.5513, 0.522, 0.1877, 0.6646, 0.0957, 0.8711] +2026-04-13 17:27:56.476807: Epoch time: 103.55 s +2026-04-13 17:27:57.981674: +2026-04-13 17:27:57.983950: Epoch 2696 +2026-04-13 17:27:57.988506: Current learning rate: 0.00365 +2026-04-13 17:29:40.682481: train_loss -0.39 +2026-04-13 17:29:40.690158: val_loss -0.3562 +2026-04-13 17:29:40.692339: Pseudo dice [0.6704, 0.6044, 0.6543, 0.5574, 0.5823, 0.8026, 0.7809] +2026-04-13 17:29:40.694834: Epoch time: 102.7 s +2026-04-13 17:29:42.201231: +2026-04-13 17:29:42.204565: Epoch 2697 +2026-04-13 17:29:42.206707: Current learning rate: 0.00364 +2026-04-13 17:31:25.210564: train_loss -0.3828 +2026-04-13 17:31:25.216503: val_loss -0.299 +2026-04-13 17:31:25.218577: Pseudo dice [0.4631, 0.4297, 0.6533, 0.3399, 0.6197, 0.3072, 0.6578] +2026-04-13 17:31:25.221005: Epoch time: 103.01 s +2026-04-13 17:31:26.733527: +2026-04-13 17:31:26.735806: Epoch 2698 +2026-04-13 17:31:26.738079: Current learning rate: 0.00364 +2026-04-13 17:33:10.142410: train_loss -0.3883 +2026-04-13 17:33:10.149344: val_loss -0.3137 +2026-04-13 17:33:10.151716: Pseudo dice [0.5903, 0.4654, 0.6027, 0.1512, 0.3024, 0.3586, 0.5232] +2026-04-13 17:33:10.154068: Epoch time: 103.41 s +2026-04-13 17:33:11.655269: +2026-04-13 17:33:11.657861: Epoch 2699 +2026-04-13 17:33:11.660062: Current learning rate: 0.00364 +2026-04-13 17:34:54.268662: train_loss -0.3912 +2026-04-13 17:34:54.275814: val_loss -0.3032 +2026-04-13 17:34:54.278315: Pseudo dice [0.2635, 0.6051, 0.6615, 0.5438, 0.3587, 0.7612, 0.7207] +2026-04-13 17:34:54.281100: Epoch time: 102.62 s +2026-04-13 17:34:57.820978: +2026-04-13 17:34:57.823709: Epoch 2700 +2026-04-13 17:34:57.826188: Current learning rate: 0.00364 +2026-04-13 17:36:40.100199: train_loss -0.3922 +2026-04-13 17:36:40.107409: val_loss -0.333 +2026-04-13 17:36:40.109424: Pseudo dice [0.7436, 0.0546, 0.7117, 0.6182, 0.4729, 0.5729, 0.5915] +2026-04-13 17:36:40.112344: Epoch time: 102.28 s +2026-04-13 17:36:41.611592: +2026-04-13 17:36:41.616149: Epoch 2701 +2026-04-13 17:36:41.622008: Current learning rate: 0.00363 +2026-04-13 17:38:24.879136: train_loss -0.3944 +2026-04-13 17:38:24.886524: val_loss -0.3482 +2026-04-13 17:38:24.890385: Pseudo dice [0.6495, 0.0907, 0.7383, 0.7167, 0.4014, 0.8112, 0.5207] +2026-04-13 17:38:24.894032: Epoch time: 103.27 s +2026-04-13 17:38:26.431041: +2026-04-13 17:38:26.432981: Epoch 2702 +2026-04-13 17:38:26.435919: Current learning rate: 0.00363 +2026-04-13 17:40:11.839676: train_loss -0.3896 +2026-04-13 17:40:11.848872: val_loss -0.3379 +2026-04-13 17:40:11.852367: Pseudo dice [0.3748, 0.3365, 0.6875, 0.6699, 0.4766, 0.2789, 0.9025] +2026-04-13 17:40:11.855629: Epoch time: 105.41 s +2026-04-13 17:40:13.375138: +2026-04-13 17:40:13.377366: Epoch 2703 +2026-04-13 17:40:13.379687: Current learning rate: 0.00363 +2026-04-13 17:41:56.078062: train_loss -0.3904 +2026-04-13 17:41:56.083627: val_loss -0.34 +2026-04-13 17:41:56.086912: Pseudo dice [0.678, 0.8243, 0.7148, 0.7991, 0.6628, 0.4658, 0.7584] +2026-04-13 17:41:56.089190: Epoch time: 102.71 s +2026-04-13 17:41:57.620733: +2026-04-13 17:41:57.622951: Epoch 2704 +2026-04-13 17:41:57.625615: Current learning rate: 0.00363 +2026-04-13 17:43:39.766576: train_loss -0.3989 +2026-04-13 17:43:39.772502: val_loss -0.3646 +2026-04-13 17:43:39.774467: Pseudo dice [0.5856, 0.6891, 0.7423, 0.8399, 0.4047, 0.7291, 0.7905] +2026-04-13 17:43:39.776583: Epoch time: 102.15 s +2026-04-13 17:43:42.406049: +2026-04-13 17:43:42.408320: Epoch 2705 +2026-04-13 17:43:42.410506: Current learning rate: 0.00362 +2026-04-13 17:45:24.617336: train_loss -0.4049 +2026-04-13 17:45:24.627036: val_loss -0.2745 +2026-04-13 17:45:24.629318: Pseudo dice [0.5364, 0.511, 0.6031, 0.6474, 0.1609, 0.2435, 0.4995] +2026-04-13 17:45:24.632324: Epoch time: 102.21 s +2026-04-13 17:45:26.132861: +2026-04-13 17:45:26.136037: Epoch 2706 +2026-04-13 17:45:26.138405: Current learning rate: 0.00362 +2026-04-13 17:47:08.458656: train_loss -0.3996 +2026-04-13 17:47:08.464268: val_loss -0.2461 +2026-04-13 17:47:08.466189: Pseudo dice [0.7055, 0.4367, 0.7625, 0.7852, 0.5006, 0.0671, 0.7103] +2026-04-13 17:47:08.469682: Epoch time: 102.33 s +2026-04-13 17:47:09.973441: +2026-04-13 17:47:09.975335: Epoch 2707 +2026-04-13 17:47:09.977382: Current learning rate: 0.00362 +2026-04-13 17:48:53.077864: train_loss -0.3961 +2026-04-13 17:48:53.083803: val_loss -0.3088 +2026-04-13 17:48:53.086159: Pseudo dice [0.3667, 0.1892, 0.4673, 0.6973, 0.5944, 0.0879, 0.8417] +2026-04-13 17:48:53.088919: Epoch time: 103.11 s +2026-04-13 17:48:54.582027: +2026-04-13 17:48:54.583747: Epoch 2708 +2026-04-13 17:48:54.585756: Current learning rate: 0.00362 +2026-04-13 17:50:36.661698: train_loss -0.3931 +2026-04-13 17:50:36.667347: val_loss -0.2872 +2026-04-13 17:50:36.669221: Pseudo dice [0.6548, 0.51, 0.5503, 0.2985, 0.567, 0.1719, 0.7701] +2026-04-13 17:50:36.672297: Epoch time: 102.08 s +2026-04-13 17:50:38.167908: +2026-04-13 17:50:38.169467: Epoch 2709 +2026-04-13 17:50:38.171262: Current learning rate: 0.00361 +2026-04-13 17:52:21.273414: train_loss -0.3895 +2026-04-13 17:52:21.281701: val_loss -0.3013 +2026-04-13 17:52:21.290636: Pseudo dice [0.3829, 0.6958, 0.6207, 0.4415, 0.5155, 0.2217, 0.8296] +2026-04-13 17:52:21.293766: Epoch time: 103.11 s +2026-04-13 17:52:22.812941: +2026-04-13 17:52:22.815216: Epoch 2710 +2026-04-13 17:52:22.817397: Current learning rate: 0.00361 +2026-04-13 17:54:06.640953: train_loss -0.3746 +2026-04-13 17:54:06.649768: val_loss -0.273 +2026-04-13 17:54:06.651800: Pseudo dice [0.1512, 0.3132, 0.5697, 0.7905, 0.5429, 0.2005, 0.8026] +2026-04-13 17:54:06.655212: Epoch time: 103.83 s +2026-04-13 17:54:08.159959: +2026-04-13 17:54:08.162234: Epoch 2711 +2026-04-13 17:54:08.164255: Current learning rate: 0.00361 +2026-04-13 17:55:50.783923: train_loss -0.376 +2026-04-13 17:55:50.790271: val_loss -0.301 +2026-04-13 17:55:50.792035: Pseudo dice [0.6905, 0.5545, 0.5854, 0.5549, 0.35, 0.3169, 0.7913] +2026-04-13 17:55:50.794775: Epoch time: 102.63 s +2026-04-13 17:55:52.300578: +2026-04-13 17:55:52.302755: Epoch 2712 +2026-04-13 17:55:52.304961: Current learning rate: 0.00361 +2026-04-13 17:57:34.583742: train_loss -0.371 +2026-04-13 17:57:34.589630: val_loss -0.3528 +2026-04-13 17:57:34.591243: Pseudo dice [0.8015, 0.7378, 0.7187, 0.1724, 0.4773, 0.8019, 0.7295] +2026-04-13 17:57:34.593344: Epoch time: 102.29 s +2026-04-13 17:57:36.114224: +2026-04-13 17:57:36.116789: Epoch 2713 +2026-04-13 17:57:36.119009: Current learning rate: 0.0036 +2026-04-13 17:59:19.773506: train_loss -0.383 +2026-04-13 17:59:19.780520: val_loss -0.2816 +2026-04-13 17:59:19.782458: Pseudo dice [0.5387, 0.5781, 0.6338, 0.5636, 0.6644, 0.2295, 0.8563] +2026-04-13 17:59:19.784939: Epoch time: 103.66 s +2026-04-13 17:59:21.301529: +2026-04-13 17:59:21.303273: Epoch 2714 +2026-04-13 17:59:21.305558: Current learning rate: 0.0036 +2026-04-13 18:01:03.793574: train_loss -0.3979 +2026-04-13 18:01:03.798980: val_loss -0.3383 +2026-04-13 18:01:03.800909: Pseudo dice [0.5877, 0.2452, 0.6842, 0.1921, 0.6012, 0.764, 0.8635] +2026-04-13 18:01:03.803149: Epoch time: 102.5 s +2026-04-13 18:01:05.334546: +2026-04-13 18:01:05.336308: Epoch 2715 +2026-04-13 18:01:05.339537: Current learning rate: 0.0036 +2026-04-13 18:02:53.103952: train_loss -0.3991 +2026-04-13 18:02:53.111306: val_loss -0.3379 +2026-04-13 18:02:53.114654: Pseudo dice [0.6573, 0.2648, 0.6134, 0.7011, 0.6023, 0.6585, 0.7633] +2026-04-13 18:02:53.119305: Epoch time: 107.77 s +2026-04-13 18:02:54.635239: +2026-04-13 18:02:54.636949: Epoch 2716 +2026-04-13 18:02:54.639179: Current learning rate: 0.0036 +2026-04-13 18:04:37.992452: train_loss -0.389 +2026-04-13 18:04:38.008206: val_loss -0.3731 +2026-04-13 18:04:38.013892: Pseudo dice [0.6158, 0.0803, 0.7566, 0.8238, 0.5654, 0.525, 0.8396] +2026-04-13 18:04:38.016865: Epoch time: 103.36 s +2026-04-13 18:04:39.519533: +2026-04-13 18:04:39.522378: Epoch 2717 +2026-04-13 18:04:39.524891: Current learning rate: 0.00359 +2026-04-13 18:06:24.200961: train_loss -0.383 +2026-04-13 18:06:24.207786: val_loss -0.286 +2026-04-13 18:06:24.211622: Pseudo dice [0.6178, 0.2004, 0.5449, 0.5039, 0.5481, 0.1689, 0.7378] +2026-04-13 18:06:24.214641: Epoch time: 104.69 s +2026-04-13 18:06:25.719737: +2026-04-13 18:06:25.722551: Epoch 2718 +2026-04-13 18:06:25.727024: Current learning rate: 0.00359 +2026-04-13 18:08:13.095044: train_loss -0.398 +2026-04-13 18:08:13.102492: val_loss -0.3358 +2026-04-13 18:08:13.105341: Pseudo dice [0.4673, 0.4938, 0.7412, 0.0742, 0.4246, 0.8432, 0.7514] +2026-04-13 18:08:13.108209: Epoch time: 107.38 s +2026-04-13 18:08:14.632985: +2026-04-13 18:08:14.635269: Epoch 2719 +2026-04-13 18:08:14.637325: Current learning rate: 0.00359 +2026-04-13 18:09:58.413760: train_loss -0.3862 +2026-04-13 18:09:58.420957: val_loss -0.3356 +2026-04-13 18:09:58.423033: Pseudo dice [0.4196, 0.4609, 0.7085, 0.7823, 0.6123, 0.0581, 0.8762] +2026-04-13 18:09:58.425369: Epoch time: 103.78 s +2026-04-13 18:10:00.011146: +2026-04-13 18:10:00.013082: Epoch 2720 +2026-04-13 18:10:00.015709: Current learning rate: 0.00359 +2026-04-13 18:11:46.669621: train_loss -0.3834 +2026-04-13 18:11:46.676332: val_loss -0.3258 +2026-04-13 18:11:46.678456: Pseudo dice [0.7327, 0.0101, 0.7218, 0.5752, 0.5477, 0.6406, 0.8651] +2026-04-13 18:11:46.680881: Epoch time: 106.66 s +2026-04-13 18:11:48.208325: +2026-04-13 18:11:48.212185: Epoch 2721 +2026-04-13 18:11:48.214257: Current learning rate: 0.00358 +2026-04-13 18:13:31.939833: train_loss -0.3748 +2026-04-13 18:13:31.947440: val_loss -0.3037 +2026-04-13 18:13:31.949791: Pseudo dice [0.409, 0.1333, 0.7469, 0.7722, 0.5601, 0.5441, 0.7494] +2026-04-13 18:13:31.952613: Epoch time: 103.74 s +2026-04-13 18:13:33.442150: +2026-04-13 18:13:33.444031: Epoch 2722 +2026-04-13 18:13:33.447847: Current learning rate: 0.00358 +2026-04-13 18:15:16.944503: train_loss -0.3804 +2026-04-13 18:15:16.952101: val_loss -0.2551 +2026-04-13 18:15:16.954367: Pseudo dice [0.7677, 0.3825, 0.5124, 0.5577, 0.5465, 0.1558, 0.8111] +2026-04-13 18:15:16.958382: Epoch time: 103.51 s +2026-04-13 18:15:18.474992: +2026-04-13 18:15:18.477920: Epoch 2723 +2026-04-13 18:15:18.480425: Current learning rate: 0.00358 +2026-04-13 18:17:02.212564: train_loss -0.3903 +2026-04-13 18:17:02.218230: val_loss -0.3259 +2026-04-13 18:17:02.220520: Pseudo dice [0.2956, 0.2693, 0.6362, 0.8719, 0.5547, 0.412, 0.7416] +2026-04-13 18:17:02.223170: Epoch time: 103.74 s +2026-04-13 18:17:03.730157: +2026-04-13 18:17:03.731784: Epoch 2724 +2026-04-13 18:17:03.733843: Current learning rate: 0.00358 +2026-04-13 18:18:46.943747: train_loss -0.4021 +2026-04-13 18:18:46.953485: val_loss -0.345 +2026-04-13 18:18:46.956205: Pseudo dice [0.6055, 0.7581, 0.668, 0.5915, 0.2324, 0.7937, 0.4976] +2026-04-13 18:18:46.959802: Epoch time: 103.22 s +2026-04-13 18:18:48.474721: +2026-04-13 18:18:48.476999: Epoch 2725 +2026-04-13 18:18:48.479282: Current learning rate: 0.00357 +2026-04-13 18:20:31.891198: train_loss -0.3916 +2026-04-13 18:20:31.896769: val_loss -0.2316 +2026-04-13 18:20:31.898802: Pseudo dice [0.7052, 0.408, 0.4801, 0.3245, 0.5611, 0.1102, 0.6435] +2026-04-13 18:20:31.901793: Epoch time: 103.42 s +2026-04-13 18:20:33.405382: +2026-04-13 18:20:33.409024: Epoch 2726 +2026-04-13 18:20:33.411373: Current learning rate: 0.00357 +2026-04-13 18:22:16.290009: train_loss -0.4018 +2026-04-13 18:22:16.295130: val_loss -0.3095 +2026-04-13 18:22:16.297279: Pseudo dice [0.5456, 0.6973, 0.7081, 0.844, 0.5716, 0.2811, 0.7784] +2026-04-13 18:22:16.300714: Epoch time: 102.89 s +2026-04-13 18:22:17.796868: +2026-04-13 18:22:17.799264: Epoch 2727 +2026-04-13 18:22:17.801290: Current learning rate: 0.00357 +2026-04-13 18:24:00.264087: train_loss -0.3982 +2026-04-13 18:24:00.270369: val_loss -0.3614 +2026-04-13 18:24:00.272356: Pseudo dice [0.796, 0.5756, 0.7479, 0.5296, 0.662, 0.7547, 0.7819] +2026-04-13 18:24:00.274768: Epoch time: 102.47 s +2026-04-13 18:24:01.795964: +2026-04-13 18:24:01.797972: Epoch 2728 +2026-04-13 18:24:01.799836: Current learning rate: 0.00357 +2026-04-13 18:25:43.930310: train_loss -0.3896 +2026-04-13 18:25:43.939566: val_loss -0.3247 +2026-04-13 18:25:43.942085: Pseudo dice [0.2084, 0.4293, 0.7642, 0.7139, 0.4511, 0.2339, 0.8319] +2026-04-13 18:25:43.944699: Epoch time: 102.14 s +2026-04-13 18:25:45.435514: +2026-04-13 18:25:45.438020: Epoch 2729 +2026-04-13 18:25:45.440089: Current learning rate: 0.00356 +2026-04-13 18:27:28.454009: train_loss -0.4035 +2026-04-13 18:27:28.463013: val_loss -0.2294 +2026-04-13 18:27:28.471201: Pseudo dice [0.6246, 0.8047, 0.7368, 0.899, 0.4795, 0.0659, 0.7377] +2026-04-13 18:27:28.474572: Epoch time: 103.02 s +2026-04-13 18:27:29.998584: +2026-04-13 18:27:30.001938: Epoch 2730 +2026-04-13 18:27:30.006487: Current learning rate: 0.00356 +2026-04-13 18:29:13.242636: train_loss -0.4035 +2026-04-13 18:29:13.249093: val_loss -0.3153 +2026-04-13 18:29:13.251297: Pseudo dice [0.2233, 0.1255, 0.7183, 0.8754, 0.6994, 0.2346, 0.8286] +2026-04-13 18:29:13.255515: Epoch time: 103.25 s +2026-04-13 18:29:14.794786: +2026-04-13 18:29:14.798099: Epoch 2731 +2026-04-13 18:29:14.801886: Current learning rate: 0.00356 +2026-04-13 18:30:57.956069: train_loss -0.3966 +2026-04-13 18:30:57.964541: val_loss -0.3029 +2026-04-13 18:30:57.967286: Pseudo dice [0.7481, 0.2071, 0.6456, 0.8166, 0.5955, 0.0495, 0.8982] +2026-04-13 18:30:57.970695: Epoch time: 103.17 s +2026-04-13 18:30:59.501150: +2026-04-13 18:30:59.504838: Epoch 2732 +2026-04-13 18:30:59.507206: Current learning rate: 0.00356 +2026-04-13 18:32:42.193637: train_loss -0.3834 +2026-04-13 18:32:42.198951: val_loss -0.3067 +2026-04-13 18:32:42.201251: Pseudo dice [0.3207, 0.4557, 0.6703, 0.2048, 0.2312, 0.2969, 0.7523] +2026-04-13 18:32:42.203798: Epoch time: 102.7 s +2026-04-13 18:32:43.682162: +2026-04-13 18:32:43.684054: Epoch 2733 +2026-04-13 18:32:43.686170: Current learning rate: 0.00355 +2026-04-13 18:34:26.156520: train_loss -0.3747 +2026-04-13 18:34:26.162229: val_loss -0.3503 +2026-04-13 18:34:26.166201: Pseudo dice [0.5206, 0.6852, 0.7478, 0.8252, 0.6582, 0.7147, 0.6577] +2026-04-13 18:34:26.169930: Epoch time: 102.48 s +2026-04-13 18:34:27.673814: +2026-04-13 18:34:27.676108: Epoch 2734 +2026-04-13 18:34:27.678398: Current learning rate: 0.00355 +2026-04-13 18:36:10.399940: train_loss -0.3775 +2026-04-13 18:36:10.405681: val_loss -0.3621 +2026-04-13 18:36:10.407878: Pseudo dice [0.8117, 0.8143, 0.7287, 0.8023, 0.6515, 0.3361, 0.7976] +2026-04-13 18:36:10.410246: Epoch time: 102.73 s +2026-04-13 18:36:11.900573: +2026-04-13 18:36:11.902565: Epoch 2735 +2026-04-13 18:36:11.905125: Current learning rate: 0.00355 +2026-04-13 18:37:53.945612: train_loss -0.3912 +2026-04-13 18:37:53.950760: val_loss -0.3597 +2026-04-13 18:37:53.953088: Pseudo dice [0.6395, 0.6653, 0.7833, 0.697, 0.6337, 0.8339, 0.87] +2026-04-13 18:37:53.956151: Epoch time: 102.05 s +2026-04-13 18:37:55.459261: +2026-04-13 18:37:55.461133: Epoch 2736 +2026-04-13 18:37:55.464097: Current learning rate: 0.00355 +2026-04-13 18:39:38.481739: train_loss -0.4001 +2026-04-13 18:39:38.488931: val_loss -0.3371 +2026-04-13 18:39:38.491131: Pseudo dice [0.5438, 0.746, 0.6674, 0.0149, 0.5651, 0.706, 0.5476] +2026-04-13 18:39:38.493842: Epoch time: 103.03 s +2026-04-13 18:39:39.991585: +2026-04-13 18:39:39.993825: Epoch 2737 +2026-04-13 18:39:39.996730: Current learning rate: 0.00354 +2026-04-13 18:41:26.529963: train_loss -0.3885 +2026-04-13 18:41:26.539502: val_loss -0.2867 +2026-04-13 18:41:26.543052: Pseudo dice [0.4655, 0.4665, 0.6145, 0.8161, 0.5063, 0.096, 0.7814] +2026-04-13 18:41:26.548162: Epoch time: 106.54 s +2026-04-13 18:41:28.060861: +2026-04-13 18:41:28.063068: Epoch 2738 +2026-04-13 18:41:28.066285: Current learning rate: 0.00354 +2026-04-13 18:43:10.464563: train_loss -0.3718 +2026-04-13 18:43:10.471786: val_loss -0.3014 +2026-04-13 18:43:10.474163: Pseudo dice [0.7926, 0.556, 0.6633, 0.8266, 0.4996, 0.3199, 0.846] +2026-04-13 18:43:10.476652: Epoch time: 102.41 s +2026-04-13 18:43:12.003738: +2026-04-13 18:43:12.005887: Epoch 2739 +2026-04-13 18:43:12.007897: Current learning rate: 0.00354 +2026-04-13 18:44:57.501395: train_loss -0.3771 +2026-04-13 18:44:57.511500: val_loss -0.2618 +2026-04-13 18:44:57.515526: Pseudo dice [0.4058, 0.6061, 0.6312, 0.2019, 0.4673, 0.0609, 0.6622] +2026-04-13 18:44:57.521677: Epoch time: 105.5 s +2026-04-13 18:44:59.049701: +2026-04-13 18:44:59.052394: Epoch 2740 +2026-04-13 18:44:59.057360: Current learning rate: 0.00354 +2026-04-13 18:46:42.103213: train_loss -0.3924 +2026-04-13 18:46:42.109327: val_loss -0.307 +2026-04-13 18:46:42.112552: Pseudo dice [0.8308, 0.5717, 0.681, 0.7659, 0.6017, 0.2624, 0.8009] +2026-04-13 18:46:42.115202: Epoch time: 103.06 s +2026-04-13 18:46:43.648611: +2026-04-13 18:46:43.650522: Epoch 2741 +2026-04-13 18:46:43.652390: Current learning rate: 0.00353 +2026-04-13 18:48:25.565729: train_loss -0.4014 +2026-04-13 18:48:25.572674: val_loss -0.3137 +2026-04-13 18:48:25.574606: Pseudo dice [0.7011, 0.7585, 0.6986, 0.8006, 0.4891, 0.1292, 0.4139] +2026-04-13 18:48:25.577033: Epoch time: 101.92 s +2026-04-13 18:48:27.075964: +2026-04-13 18:48:27.078592: Epoch 2742 +2026-04-13 18:48:27.080762: Current learning rate: 0.00353 +2026-04-13 18:50:12.690366: train_loss -0.389 +2026-04-13 18:50:12.696365: val_loss -0.3531 +2026-04-13 18:50:12.698515: Pseudo dice [0.5302, 0.845, 0.7355, 0.5063, 0.6647, 0.6999, 0.8152] +2026-04-13 18:50:12.701093: Epoch time: 105.62 s +2026-04-13 18:50:14.204861: +2026-04-13 18:50:14.207223: Epoch 2743 +2026-04-13 18:50:14.211680: Current learning rate: 0.00353 +2026-04-13 18:51:56.454997: train_loss -0.3898 +2026-04-13 18:51:56.460331: val_loss -0.3401 +2026-04-13 18:51:56.469754: Pseudo dice [0.6464, 0.7271, 0.6195, 0.8027, 0.5652, 0.3279, 0.888] +2026-04-13 18:51:56.472726: Epoch time: 102.25 s +2026-04-13 18:51:58.005366: +2026-04-13 18:51:58.010518: Epoch 2744 +2026-04-13 18:51:58.015843: Current learning rate: 0.00353 +2026-04-13 18:53:40.353565: train_loss -0.4012 +2026-04-13 18:53:40.361842: val_loss -0.3418 +2026-04-13 18:53:40.364347: Pseudo dice [0.5374, 0.244, 0.7737, 0.709, 0.1351, 0.8625, 0.8507] +2026-04-13 18:53:40.368531: Epoch time: 102.35 s +2026-04-13 18:53:41.867605: +2026-04-13 18:53:41.873538: Epoch 2745 +2026-04-13 18:53:41.879948: Current learning rate: 0.00352 +2026-04-13 18:55:27.372325: train_loss -0.3734 +2026-04-13 18:55:27.378472: val_loss -0.2928 +2026-04-13 18:55:27.381094: Pseudo dice [0.7311, 0.2659, 0.4425, 0.6663, 0.4125, 0.1191, 0.8753] +2026-04-13 18:55:27.384008: Epoch time: 105.51 s +2026-04-13 18:55:28.919286: +2026-04-13 18:55:28.921914: Epoch 2746 +2026-04-13 18:55:28.929427: Current learning rate: 0.00352 +2026-04-13 18:57:11.075049: train_loss -0.3678 +2026-04-13 18:57:11.081290: val_loss -0.3233 +2026-04-13 18:57:11.083810: Pseudo dice [0.772, 0.3234, 0.6945, 0.7192, 0.5921, 0.4721, 0.8313] +2026-04-13 18:57:11.086776: Epoch time: 102.16 s +2026-04-13 18:57:12.610952: +2026-04-13 18:57:12.613142: Epoch 2747 +2026-04-13 18:57:12.615530: Current learning rate: 0.00352 +2026-04-13 18:58:58.809337: train_loss -0.372 +2026-04-13 18:58:58.816000: val_loss -0.3497 +2026-04-13 18:58:58.818489: Pseudo dice [0.6888, 0.4297, 0.6535, 0.9299, 0.535, 0.7833, 0.7477] +2026-04-13 18:58:58.822316: Epoch time: 106.2 s +2026-04-13 18:59:00.331402: +2026-04-13 18:59:00.333164: Epoch 2748 +2026-04-13 18:59:00.334702: Current learning rate: 0.00352 +2026-04-13 19:00:42.723957: train_loss -0.3955 +2026-04-13 19:00:42.730586: val_loss -0.3159 +2026-04-13 19:00:42.733481: Pseudo dice [0.8736, 0.1779, 0.714, 0.0599, 0.5308, 0.0834, 0.8191] +2026-04-13 19:00:42.736356: Epoch time: 102.4 s +2026-04-13 19:00:44.260895: +2026-04-13 19:00:44.265216: Epoch 2749 +2026-04-13 19:00:44.268960: Current learning rate: 0.00351 +2026-04-13 19:02:26.614787: train_loss -0.3892 +2026-04-13 19:02:26.622565: val_loss -0.2983 +2026-04-13 19:02:26.625233: Pseudo dice [0.7209, 0.0, 0.5116, 0.8793, 0.3561, 0.1556, 0.6186] +2026-04-13 19:02:26.627567: Epoch time: 102.36 s +2026-04-13 19:02:30.160610: +2026-04-13 19:02:30.164380: Epoch 2750 +2026-04-13 19:02:30.166589: Current learning rate: 0.00351 +2026-04-13 19:04:13.455753: train_loss -0.39 +2026-04-13 19:04:13.461971: val_loss -0.3292 +2026-04-13 19:04:13.464035: Pseudo dice [0.6312, 0.0, 0.7943, 0.361, 0.3223, 0.7976, 0.5428] +2026-04-13 19:04:13.467157: Epoch time: 103.3 s +2026-04-13 19:04:14.979448: +2026-04-13 19:04:14.982680: Epoch 2751 +2026-04-13 19:04:14.985640: Current learning rate: 0.00351 +2026-04-13 19:05:57.600978: train_loss -0.3963 +2026-04-13 19:05:57.607021: val_loss -0.2463 +2026-04-13 19:05:57.608782: Pseudo dice [0.7976, 0.0, 0.5988, 0.6956, 0.3258, 0.1095, 0.839] +2026-04-13 19:05:57.611268: Epoch time: 102.62 s +2026-04-13 19:05:59.123524: +2026-04-13 19:05:59.128466: Epoch 2752 +2026-04-13 19:05:59.132479: Current learning rate: 0.00351 +2026-04-13 19:07:42.344766: train_loss -0.4002 +2026-04-13 19:07:42.352906: val_loss -0.3087 +2026-04-13 19:07:42.355732: Pseudo dice [0.7096, 0.0, 0.6319, 0.7552, 0.5912, 0.5454, 0.8056] +2026-04-13 19:07:42.358310: Epoch time: 103.22 s +2026-04-13 19:07:43.890595: +2026-04-13 19:07:43.893222: Epoch 2753 +2026-04-13 19:07:43.896359: Current learning rate: 0.0035 +2026-04-13 19:09:26.058393: train_loss -0.3846 +2026-04-13 19:09:26.064071: val_loss -0.3115 +2026-04-13 19:09:26.066099: Pseudo dice [0.1775, 0.0, 0.7465, 0.3725, 0.4966, 0.1617, 0.8191] +2026-04-13 19:09:26.068464: Epoch time: 102.17 s +2026-04-13 19:09:27.570337: +2026-04-13 19:09:27.572251: Epoch 2754 +2026-04-13 19:09:27.574471: Current learning rate: 0.0035 +2026-04-13 19:11:09.486148: train_loss -0.3671 +2026-04-13 19:11:09.492779: val_loss -0.3071 +2026-04-13 19:11:09.495073: Pseudo dice [0.6638, 0.0223, 0.6811, 0.1706, 0.3166, 0.5363, 0.383] +2026-04-13 19:11:09.497466: Epoch time: 101.92 s +2026-04-13 19:11:11.046513: +2026-04-13 19:11:11.049565: Epoch 2755 +2026-04-13 19:11:11.051979: Current learning rate: 0.0035 +2026-04-13 19:12:55.404719: train_loss -0.3601 +2026-04-13 19:12:55.411901: val_loss -0.3092 +2026-04-13 19:12:55.416646: Pseudo dice [0.7838, 0.5751, 0.7668, 0.7593, 0.5383, 0.2214, 0.7405] +2026-04-13 19:12:55.419289: Epoch time: 104.36 s +2026-04-13 19:12:56.923533: +2026-04-13 19:12:56.925362: Epoch 2756 +2026-04-13 19:12:56.927133: Current learning rate: 0.0035 +2026-04-13 19:14:39.570539: train_loss -0.3821 +2026-04-13 19:14:39.576844: val_loss -0.297 +2026-04-13 19:14:39.578984: Pseudo dice [0.8295, 0.2019, 0.5629, 0.5199, 0.2098, 0.1954, 0.8138] +2026-04-13 19:14:39.581370: Epoch time: 102.65 s +2026-04-13 19:14:41.100583: +2026-04-13 19:14:41.102291: Epoch 2757 +2026-04-13 19:14:41.104063: Current learning rate: 0.00349 +2026-04-13 19:16:24.396834: train_loss -0.3835 +2026-04-13 19:16:24.403571: val_loss -0.291 +2026-04-13 19:16:24.405785: Pseudo dice [0.8337, 0.6336, 0.6955, 0.8661, 0.5438, 0.0525, 0.8006] +2026-04-13 19:16:24.408552: Epoch time: 103.3 s +2026-04-13 19:16:25.930278: +2026-04-13 19:16:25.932831: Epoch 2758 +2026-04-13 19:16:25.934468: Current learning rate: 0.00349 +2026-04-13 19:18:09.226185: train_loss -0.3763 +2026-04-13 19:18:09.232111: val_loss -0.2564 +2026-04-13 19:18:09.234448: Pseudo dice [0.5108, 0.3872, 0.7418, 0.3011, 0.2523, 0.1656, 0.138] +2026-04-13 19:18:09.237715: Epoch time: 103.3 s +2026-04-13 19:18:10.741815: +2026-04-13 19:18:10.743824: Epoch 2759 +2026-04-13 19:18:10.745701: Current learning rate: 0.00349 +2026-04-13 19:19:52.858298: train_loss -0.3813 +2026-04-13 19:19:52.865497: val_loss -0.2855 +2026-04-13 19:19:52.868130: Pseudo dice [0.7352, 0.3114, 0.607, 0.032, 0.4572, 0.333, 0.4409] +2026-04-13 19:19:52.870544: Epoch time: 102.12 s +2026-04-13 19:19:54.367591: +2026-04-13 19:19:54.369709: Epoch 2760 +2026-04-13 19:19:54.371816: Current learning rate: 0.00349 +2026-04-13 19:21:38.901430: train_loss -0.3912 +2026-04-13 19:21:38.910493: val_loss -0.2934 +2026-04-13 19:21:38.912950: Pseudo dice [0.8025, 0.6324, 0.7267, 0.0062, 0.6457, 0.1157, 0.4926] +2026-04-13 19:21:38.914968: Epoch time: 104.54 s +2026-04-13 19:21:40.418157: +2026-04-13 19:21:40.420121: Epoch 2761 +2026-04-13 19:21:40.421999: Current learning rate: 0.00348 +2026-04-13 19:23:22.717437: train_loss -0.3927 +2026-04-13 19:23:22.724786: val_loss -0.3081 +2026-04-13 19:23:22.727846: Pseudo dice [0.3603, 0.5866, 0.5306, 0.6618, 0.5693, 0.3156, 0.6638] +2026-04-13 19:23:22.731369: Epoch time: 102.3 s +2026-04-13 19:23:24.241865: +2026-04-13 19:23:24.243507: Epoch 2762 +2026-04-13 19:23:24.245159: Current learning rate: 0.00348 +2026-04-13 19:25:07.148487: train_loss -0.3778 +2026-04-13 19:25:07.160835: val_loss -0.3513 +2026-04-13 19:25:07.162561: Pseudo dice [0.6444, 0.5121, 0.6969, 0.6901, 0.6709, 0.6802, 0.8772] +2026-04-13 19:25:07.164786: Epoch time: 102.91 s +2026-04-13 19:25:08.672401: +2026-04-13 19:25:08.674299: Epoch 2763 +2026-04-13 19:25:08.676224: Current learning rate: 0.00348 +2026-04-13 19:26:52.112077: train_loss -0.362 +2026-04-13 19:26:52.118484: val_loss -0.3259 +2026-04-13 19:26:52.121930: Pseudo dice [0.6767, 0.2931, 0.7077, 0.2961, 0.439, 0.4691, 0.6713] +2026-04-13 19:26:52.129314: Epoch time: 103.44 s +2026-04-13 19:26:53.629899: +2026-04-13 19:26:53.632087: Epoch 2764 +2026-04-13 19:26:53.634120: Current learning rate: 0.00348 +2026-04-13 19:28:35.907580: train_loss -0.3687 +2026-04-13 19:28:35.924391: val_loss -0.3195 +2026-04-13 19:28:35.929939: Pseudo dice [0.4451, 0.4886, 0.5143, 0.8686, 0.5168, 0.2794, 0.8751] +2026-04-13 19:28:35.935746: Epoch time: 102.28 s +2026-04-13 19:28:38.648569: +2026-04-13 19:28:38.650947: Epoch 2765 +2026-04-13 19:28:38.652843: Current learning rate: 0.00347 +2026-04-13 19:30:21.198747: train_loss -0.3813 +2026-04-13 19:30:21.205679: val_loss -0.3225 +2026-04-13 19:30:21.207821: Pseudo dice [0.401, 0.1439, 0.5457, 0.0028, 0.5153, 0.6971, 0.5674] +2026-04-13 19:30:21.213932: Epoch time: 102.55 s +2026-04-13 19:30:22.725877: +2026-04-13 19:30:22.730038: Epoch 2766 +2026-04-13 19:30:22.734319: Current learning rate: 0.00347 +2026-04-13 19:32:05.072396: train_loss -0.3843 +2026-04-13 19:32:05.078878: val_loss -0.2685 +2026-04-13 19:32:05.080630: Pseudo dice [0.4842, 0.6222, 0.5674, 0.3908, 0.487, 0.036, 0.8306] +2026-04-13 19:32:05.083554: Epoch time: 102.35 s +2026-04-13 19:32:06.592653: +2026-04-13 19:32:06.594502: Epoch 2767 +2026-04-13 19:32:06.596092: Current learning rate: 0.00347 +2026-04-13 19:33:48.931659: train_loss -0.39 +2026-04-13 19:33:48.937005: val_loss -0.3404 +2026-04-13 19:33:48.939264: Pseudo dice [0.7525, 0.7861, 0.5123, 0.4611, 0.6631, 0.1462, 0.6771] +2026-04-13 19:33:48.942032: Epoch time: 102.34 s +2026-04-13 19:33:50.457293: +2026-04-13 19:33:50.458887: Epoch 2768 +2026-04-13 19:33:50.460438: Current learning rate: 0.00346 +2026-04-13 19:35:33.691538: train_loss -0.397 +2026-04-13 19:35:33.710255: val_loss -0.3562 +2026-04-13 19:35:33.716345: Pseudo dice [0.7083, 0.7497, 0.6678, 0.19, 0.5847, 0.8754, 0.8011] +2026-04-13 19:35:33.721923: Epoch time: 103.24 s +2026-04-13 19:35:35.250713: +2026-04-13 19:35:35.252964: Epoch 2769 +2026-04-13 19:35:35.255514: Current learning rate: 0.00346 +2026-04-13 19:37:18.001269: train_loss -0.3997 +2026-04-13 19:37:18.006799: val_loss -0.3091 +2026-04-13 19:37:18.009198: Pseudo dice [0.6932, 0.6342, 0.6893, 0.0784, 0.5934, 0.0551, 0.8565] +2026-04-13 19:37:18.011612: Epoch time: 102.75 s +2026-04-13 19:37:19.509391: +2026-04-13 19:37:19.511034: Epoch 2770 +2026-04-13 19:37:19.512826: Current learning rate: 0.00346 +2026-04-13 19:39:01.369185: train_loss -0.409 +2026-04-13 19:39:01.377674: val_loss -0.2989 +2026-04-13 19:39:01.379972: Pseudo dice [0.5812, 0.7659, 0.6535, 0.8708, 0.5033, 0.2643, 0.9177] +2026-04-13 19:39:01.385877: Epoch time: 101.86 s +2026-04-13 19:39:02.912788: +2026-04-13 19:39:02.916995: Epoch 2771 +2026-04-13 19:39:02.918768: Current learning rate: 0.00346 +2026-04-13 19:40:45.974648: train_loss -0.4111 +2026-04-13 19:40:45.983335: val_loss -0.3288 +2026-04-13 19:40:45.985704: Pseudo dice [0.7632, 0.5334, 0.738, 0.5105, 0.344, 0.1614, 0.6886] +2026-04-13 19:40:45.989195: Epoch time: 103.07 s +2026-04-13 19:40:47.503523: +2026-04-13 19:40:47.505277: Epoch 2772 +2026-04-13 19:40:47.506938: Current learning rate: 0.00345 +2026-04-13 19:42:31.087918: train_loss -0.4021 +2026-04-13 19:42:31.095627: val_loss -0.3662 +2026-04-13 19:42:31.097715: Pseudo dice [0.6283, 0.7765, 0.7054, 0.0724, 0.4698, 0.7797, 0.7107] +2026-04-13 19:42:31.099684: Epoch time: 103.59 s +2026-04-13 19:42:32.607748: +2026-04-13 19:42:32.609476: Epoch 2773 +2026-04-13 19:42:32.611747: Current learning rate: 0.00345 +2026-04-13 19:44:15.574795: train_loss -0.3939 +2026-04-13 19:44:15.579687: val_loss -0.307 +2026-04-13 19:44:15.581609: Pseudo dice [0.3099, 0.0306, 0.6067, 0.6304, 0.6503, 0.1233, 0.7008] +2026-04-13 19:44:15.583906: Epoch time: 102.97 s +2026-04-13 19:44:17.125498: +2026-04-13 19:44:17.127521: Epoch 2774 +2026-04-13 19:44:17.129088: Current learning rate: 0.00345 +2026-04-13 19:45:59.883047: train_loss -0.3862 +2026-04-13 19:45:59.890080: val_loss -0.3224 +2026-04-13 19:45:59.894398: Pseudo dice [0.724, 0.4504, 0.7513, 0.4141, 0.4688, 0.0703, 0.7123] +2026-04-13 19:45:59.897631: Epoch time: 102.76 s +2026-04-13 19:46:01.397389: +2026-04-13 19:46:01.399484: Epoch 2775 +2026-04-13 19:46:01.401453: Current learning rate: 0.00345 +2026-04-13 19:47:43.498180: train_loss -0.3929 +2026-04-13 19:47:43.506082: val_loss -0.2741 +2026-04-13 19:47:43.508881: Pseudo dice [0.4864, 0.4408, 0.5543, 0.587, 0.6608, 0.0766, 0.7929] +2026-04-13 19:47:43.511600: Epoch time: 102.1 s +2026-04-13 19:47:45.008415: +2026-04-13 19:47:45.011187: Epoch 2776 +2026-04-13 19:47:45.012913: Current learning rate: 0.00344 +2026-04-13 19:49:27.612648: train_loss -0.3919 +2026-04-13 19:49:27.618037: val_loss -0.2881 +2026-04-13 19:49:27.619923: Pseudo dice [0.8488, 0.6827, 0.7259, 0.782, 0.4772, 0.2119, 0.6652] +2026-04-13 19:49:27.623134: Epoch time: 102.61 s +2026-04-13 19:49:29.135301: +2026-04-13 19:49:29.137676: Epoch 2777 +2026-04-13 19:49:29.140572: Current learning rate: 0.00344 +2026-04-13 19:51:11.256515: train_loss -0.3974 +2026-04-13 19:51:11.264977: val_loss -0.3562 +2026-04-13 19:51:11.267024: Pseudo dice [0.2224, 0.6295, 0.7499, 0.641, 0.5528, 0.864, 0.9159] +2026-04-13 19:51:11.269443: Epoch time: 102.12 s +2026-04-13 19:51:12.774862: +2026-04-13 19:51:12.777428: Epoch 2778 +2026-04-13 19:51:12.780829: Current learning rate: 0.00344 +2026-04-13 19:52:54.805866: train_loss -0.3985 +2026-04-13 19:52:54.813376: val_loss -0.329 +2026-04-13 19:52:54.815362: Pseudo dice [0.3535, 0.6464, 0.7195, 0.4356, 0.3861, 0.7853, 0.4443] +2026-04-13 19:52:54.817605: Epoch time: 102.03 s +2026-04-13 19:52:56.316824: +2026-04-13 19:52:56.318613: Epoch 2779 +2026-04-13 19:52:56.320426: Current learning rate: 0.00344 +2026-04-13 19:54:38.356481: train_loss -0.3988 +2026-04-13 19:54:38.363595: val_loss -0.3325 +2026-04-13 19:54:38.366120: Pseudo dice [0.1812, 0.1212, 0.6946, 0.7797, 0.6318, 0.837, 0.8192] +2026-04-13 19:54:38.369334: Epoch time: 102.04 s +2026-04-13 19:54:39.976802: +2026-04-13 19:54:39.979355: Epoch 2780 +2026-04-13 19:54:39.980842: Current learning rate: 0.00343 +2026-04-13 19:56:22.261268: train_loss -0.3926 +2026-04-13 19:56:22.269355: val_loss -0.2648 +2026-04-13 19:56:22.271949: Pseudo dice [0.3925, 0.696, 0.6155, 0.0693, 0.2116, 0.1082, 0.5592] +2026-04-13 19:56:22.274885: Epoch time: 102.29 s +2026-04-13 19:56:23.783151: +2026-04-13 19:56:23.785731: Epoch 2781 +2026-04-13 19:56:23.787547: Current learning rate: 0.00343 +2026-04-13 19:58:06.194072: train_loss -0.3919 +2026-04-13 19:58:06.202390: val_loss -0.274 +2026-04-13 19:58:06.204534: Pseudo dice [0.0954, 0.2531, 0.6346, 0.2017, 0.4821, 0.4065, 0.9086] +2026-04-13 19:58:06.209134: Epoch time: 102.41 s +2026-04-13 19:58:07.717206: +2026-04-13 19:58:07.722298: Epoch 2782 +2026-04-13 19:58:07.724440: Current learning rate: 0.00343 +2026-04-13 19:59:49.945277: train_loss -0.3981 +2026-04-13 19:59:49.952317: val_loss -0.3551 +2026-04-13 19:59:49.954310: Pseudo dice [0.4305, 0.3661, 0.7283, 0.8786, 0.376, 0.814, 0.6884] +2026-04-13 19:59:49.957362: Epoch time: 102.23 s +2026-04-13 19:59:51.475816: +2026-04-13 19:59:51.477753: Epoch 2783 +2026-04-13 19:59:51.479855: Current learning rate: 0.00343 +2026-04-13 20:01:33.526772: train_loss -0.3871 +2026-04-13 20:01:33.538129: val_loss -0.3534 +2026-04-13 20:01:33.540780: Pseudo dice [0.7072, 0.8576, 0.7829, 0.7535, 0.4585, 0.7848, 0.8562] +2026-04-13 20:01:33.543087: Epoch time: 102.05 s +2026-04-13 20:01:35.029685: +2026-04-13 20:01:35.032243: Epoch 2784 +2026-04-13 20:01:35.035117: Current learning rate: 0.00342 +2026-04-13 20:03:17.221497: train_loss -0.3862 +2026-04-13 20:03:17.228858: val_loss -0.3004 +2026-04-13 20:03:17.230809: Pseudo dice [0.4846, 0.7042, 0.6503, 0.0164, 0.3276, 0.3577, 0.7712] +2026-04-13 20:03:17.232920: Epoch time: 102.2 s +2026-04-13 20:03:18.733753: +2026-04-13 20:03:18.736470: Epoch 2785 +2026-04-13 20:03:18.738059: Current learning rate: 0.00342 +2026-04-13 20:05:02.258884: train_loss -0.3869 +2026-04-13 20:05:02.268269: val_loss -0.2431 +2026-04-13 20:05:02.270256: Pseudo dice [0.5322, 0.821, 0.471, 0.3346, 0.6351, 0.0447, 0.6783] +2026-04-13 20:05:02.272708: Epoch time: 103.53 s +2026-04-13 20:05:03.760522: +2026-04-13 20:05:03.762310: Epoch 2786 +2026-04-13 20:05:03.764786: Current learning rate: 0.00342 +2026-04-13 20:06:47.340520: train_loss -0.3823 +2026-04-13 20:06:47.347339: val_loss -0.3553 +2026-04-13 20:06:47.349260: Pseudo dice [0.7053, 0.7075, 0.6556, 0.5474, 0.5701, 0.8655, 0.8143] +2026-04-13 20:06:47.351191: Epoch time: 103.58 s +2026-04-13 20:06:49.126065: +2026-04-13 20:06:49.127982: Epoch 2787 +2026-04-13 20:06:49.129647: Current learning rate: 0.00342 +2026-04-13 20:08:31.374177: train_loss -0.4049 +2026-04-13 20:08:31.383520: val_loss -0.2431 +2026-04-13 20:08:31.385321: Pseudo dice [0.4796, 0.7884, 0.6248, 0.4388, 0.5629, 0.1542, 0.8732] +2026-04-13 20:08:31.387872: Epoch time: 102.25 s +2026-04-13 20:08:32.883169: +2026-04-13 20:08:32.885531: Epoch 2788 +2026-04-13 20:08:32.887127: Current learning rate: 0.00341 +2026-04-13 20:10:15.106443: train_loss -0.3816 +2026-04-13 20:10:15.115480: val_loss -0.3071 +2026-04-13 20:10:15.117923: Pseudo dice [0.6946, 0.1942, 0.6832, 0.849, 0.3754, 0.0388, 0.8552] +2026-04-13 20:10:15.122346: Epoch time: 102.23 s +2026-04-13 20:10:16.643747: +2026-04-13 20:10:16.649079: Epoch 2789 +2026-04-13 20:10:16.652544: Current learning rate: 0.00341 +2026-04-13 20:11:59.601506: train_loss -0.3773 +2026-04-13 20:11:59.608515: val_loss -0.3132 +2026-04-13 20:11:59.610168: Pseudo dice [0.488, 0.8227, 0.6636, 0.3796, 0.539, 0.1207, 0.6494] +2026-04-13 20:11:59.612476: Epoch time: 102.96 s +2026-04-13 20:12:01.372702: +2026-04-13 20:12:01.374490: Epoch 2790 +2026-04-13 20:12:01.376318: Current learning rate: 0.00341 +2026-04-13 20:13:43.446276: train_loss -0.383 +2026-04-13 20:13:43.452917: val_loss -0.3497 +2026-04-13 20:13:43.454547: Pseudo dice [0.105, 0.7488, 0.7054, 0.6873, 0.6118, 0.5379, 0.7682] +2026-04-13 20:13:43.456560: Epoch time: 102.08 s +2026-04-13 20:13:44.969878: +2026-04-13 20:13:44.971966: Epoch 2791 +2026-04-13 20:13:44.973575: Current learning rate: 0.00341 +2026-04-13 20:15:27.098617: train_loss -0.3823 +2026-04-13 20:15:27.105528: val_loss -0.2722 +2026-04-13 20:15:27.107199: Pseudo dice [0.5309, 0.0677, 0.548, 0.5982, 0.3798, 0.12, 0.8273] +2026-04-13 20:15:27.109342: Epoch time: 102.13 s +2026-04-13 20:15:28.595742: +2026-04-13 20:15:28.597848: Epoch 2792 +2026-04-13 20:15:28.599977: Current learning rate: 0.0034 +2026-04-13 20:17:11.325459: train_loss -0.4044 +2026-04-13 20:17:11.332026: val_loss -0.3487 +2026-04-13 20:17:11.334214: Pseudo dice [0.6154, 0.2459, 0.7383, 0.7845, 0.6243, 0.7274, 0.8691] +2026-04-13 20:17:11.336608: Epoch time: 102.73 s +2026-04-13 20:17:12.854842: +2026-04-13 20:17:12.856957: Epoch 2793 +2026-04-13 20:17:12.859555: Current learning rate: 0.0034 +2026-04-13 20:18:54.903645: train_loss -0.3995 +2026-04-13 20:18:54.912748: val_loss -0.319 +2026-04-13 20:18:54.915446: Pseudo dice [0.4713, 0.1276, 0.6732, 0.7945, 0.5065, 0.1352, 0.7779] +2026-04-13 20:18:54.917813: Epoch time: 102.05 s +2026-04-13 20:18:56.440340: +2026-04-13 20:18:56.442632: Epoch 2794 +2026-04-13 20:18:56.447567: Current learning rate: 0.0034 +2026-04-13 20:20:40.857194: train_loss -0.3742 +2026-04-13 20:20:40.865914: val_loss -0.2849 +2026-04-13 20:20:40.868281: Pseudo dice [0.406, 0.4832, 0.4405, 0.4717, 0.5597, 0.0751, 0.7267] +2026-04-13 20:20:40.872581: Epoch time: 104.42 s +2026-04-13 20:20:42.391455: +2026-04-13 20:20:42.393527: Epoch 2795 +2026-04-13 20:20:42.395188: Current learning rate: 0.0034 +2026-04-13 20:22:24.458002: train_loss -0.3688 +2026-04-13 20:22:24.468067: val_loss -0.2721 +2026-04-13 20:22:24.472552: Pseudo dice [0.6351, 0.5485, 0.5437, 0.5583, 0.5561, 0.1313, 0.8558] +2026-04-13 20:22:24.475228: Epoch time: 102.07 s +2026-04-13 20:22:25.960660: +2026-04-13 20:22:25.962449: Epoch 2796 +2026-04-13 20:22:25.964501: Current learning rate: 0.00339 +2026-04-13 20:24:08.392405: train_loss -0.3751 +2026-04-13 20:24:08.400995: val_loss -0.3043 +2026-04-13 20:24:08.405588: Pseudo dice [0.3284, 0.291, 0.486, 0.8526, 0.3838, 0.8226, 0.8986] +2026-04-13 20:24:08.408098: Epoch time: 102.44 s +2026-04-13 20:24:09.905980: +2026-04-13 20:24:09.907683: Epoch 2797 +2026-04-13 20:24:09.909363: Current learning rate: 0.00339 +2026-04-13 20:25:51.801312: train_loss -0.383 +2026-04-13 20:25:51.808440: val_loss -0.3734 +2026-04-13 20:25:51.810785: Pseudo dice [0.8328, 0.5131, 0.8111, 0.8212, 0.6002, 0.8657, 0.7928] +2026-04-13 20:25:51.812826: Epoch time: 101.9 s +2026-04-13 20:25:53.342604: +2026-04-13 20:25:53.344279: Epoch 2798 +2026-04-13 20:25:53.345959: Current learning rate: 0.00339 +2026-04-13 20:27:35.615973: train_loss -0.3852 +2026-04-13 20:27:35.620894: val_loss -0.2505 +2026-04-13 20:27:35.622757: Pseudo dice [0.5415, 0.3384, 0.41, 0.7316, 0.3778, 0.0754, 0.7247] +2026-04-13 20:27:35.625002: Epoch time: 102.28 s +2026-04-13 20:27:37.123162: +2026-04-13 20:27:37.125109: Epoch 2799 +2026-04-13 20:27:37.126776: Current learning rate: 0.00339 +2026-04-13 20:29:19.713268: train_loss -0.4088 +2026-04-13 20:29:19.737604: val_loss -0.3155 +2026-04-13 20:29:19.740338: Pseudo dice [0.775, 0.5747, 0.6287, 0.8243, 0.5956, 0.0483, 0.7891] +2026-04-13 20:29:19.742489: Epoch time: 102.59 s +2026-04-13 20:29:23.236508: +2026-04-13 20:29:23.239122: Epoch 2800 +2026-04-13 20:29:23.241146: Current learning rate: 0.00338 +2026-04-13 20:31:05.559933: train_loss -0.3974 +2026-04-13 20:31:05.566529: val_loss -0.3578 +2026-04-13 20:31:05.568967: Pseudo dice [0.6868, 0.5014, 0.6837, 0.619, 0.7029, 0.86, 0.7831] +2026-04-13 20:31:05.571406: Epoch time: 102.33 s +2026-04-13 20:31:07.089983: +2026-04-13 20:31:07.092046: Epoch 2801 +2026-04-13 20:31:07.093678: Current learning rate: 0.00338 +2026-04-13 20:32:49.262779: train_loss -0.4065 +2026-04-13 20:32:49.268853: val_loss -0.3651 +2026-04-13 20:32:49.271492: Pseudo dice [0.6579, 0.6239, 0.8303, 0.2412, 0.6999, 0.8111, 0.9086] +2026-04-13 20:32:49.273767: Epoch time: 102.18 s +2026-04-13 20:32:50.771755: +2026-04-13 20:32:50.773952: Epoch 2802 +2026-04-13 20:32:50.775598: Current learning rate: 0.00338 +2026-04-13 20:34:32.804578: train_loss -0.4084 +2026-04-13 20:34:32.812767: val_loss -0.3569 +2026-04-13 20:34:32.816537: Pseudo dice [0.6323, 0.3863, 0.7634, 0.1197, 0.4815, 0.7836, 0.7626] +2026-04-13 20:34:32.818885: Epoch time: 102.03 s +2026-04-13 20:34:34.345980: +2026-04-13 20:34:34.348105: Epoch 2803 +2026-04-13 20:34:34.350129: Current learning rate: 0.00338 +2026-04-13 20:36:19.038765: train_loss -0.3994 +2026-04-13 20:36:19.044279: val_loss -0.2831 +2026-04-13 20:36:19.046372: Pseudo dice [0.7021, 0.4882, 0.6365, 0.8343, 0.7151, 0.0669, 0.7822] +2026-04-13 20:36:19.050488: Epoch time: 104.7 s +2026-04-13 20:36:20.561198: +2026-04-13 20:36:20.563440: Epoch 2804 +2026-04-13 20:36:20.565353: Current learning rate: 0.00337 +2026-04-13 20:38:02.733735: train_loss -0.3846 +2026-04-13 20:38:02.740517: val_loss -0.2858 +2026-04-13 20:38:02.744104: Pseudo dice [0.3752, 0.1519, 0.4725, 0.3661, 0.5089, 0.3393, 0.7343] +2026-04-13 20:38:02.747437: Epoch time: 102.18 s +2026-04-13 20:38:04.268062: +2026-04-13 20:38:04.270604: Epoch 2805 +2026-04-13 20:38:04.272421: Current learning rate: 0.00337 +2026-04-13 20:39:49.054600: train_loss -0.395 +2026-04-13 20:39:49.059992: val_loss -0.3532 +2026-04-13 20:39:49.061648: Pseudo dice [0.7515, 0.6177, 0.8389, 0.8417, 0.3786, 0.8332, 0.6597] +2026-04-13 20:39:49.064175: Epoch time: 104.79 s +2026-04-13 20:39:50.557173: +2026-04-13 20:39:50.559166: Epoch 2806 +2026-04-13 20:39:50.560766: Current learning rate: 0.00337 +2026-04-13 20:41:38.275713: train_loss -0.3845 +2026-04-13 20:41:38.283446: val_loss -0.2124 +2026-04-13 20:41:38.285970: Pseudo dice [0.7472, 0.4405, 0.4605, 0.8327, 0.3988, 0.082, 0.891] +2026-04-13 20:41:38.291324: Epoch time: 107.72 s +2026-04-13 20:41:39.807076: +2026-04-13 20:41:39.808744: Epoch 2807 +2026-04-13 20:41:39.810438: Current learning rate: 0.00337 +2026-04-13 20:43:25.849072: train_loss -0.3719 +2026-04-13 20:43:25.867654: val_loss -0.3081 +2026-04-13 20:43:25.873447: Pseudo dice [0.47, 0.7982, 0.5843, 0.6165, 0.5319, 0.4509, 0.8098] +2026-04-13 20:43:25.879696: Epoch time: 106.05 s +2026-04-13 20:43:27.408142: +2026-04-13 20:43:27.410766: Epoch 2808 +2026-04-13 20:43:27.413371: Current learning rate: 0.00336 +2026-04-13 20:45:10.103397: train_loss -0.3674 +2026-04-13 20:45:10.110623: val_loss -0.3272 +2026-04-13 20:45:10.112954: Pseudo dice [0.5671, 0.7921, 0.615, 0.7318, 0.6333, 0.4918, 0.7186] +2026-04-13 20:45:10.115875: Epoch time: 102.7 s +2026-04-13 20:45:11.631572: +2026-04-13 20:45:11.633511: Epoch 2809 +2026-04-13 20:45:11.635729: Current learning rate: 0.00336 +2026-04-13 20:46:54.456875: train_loss -0.3897 +2026-04-13 20:46:54.465889: val_loss -0.3328 +2026-04-13 20:46:54.469817: Pseudo dice [0.2412, 0.752, 0.7647, 0.7292, 0.5753, 0.2625, 0.6248] +2026-04-13 20:46:54.472718: Epoch time: 102.83 s +2026-04-13 20:46:55.990481: +2026-04-13 20:46:55.993430: Epoch 2810 +2026-04-13 20:46:55.995296: Current learning rate: 0.00336 +2026-04-13 20:48:39.481872: train_loss -0.4104 +2026-04-13 20:48:39.496338: val_loss -0.2823 +2026-04-13 20:48:39.501252: Pseudo dice [0.6901, 0.7606, 0.7326, 0.786, 0.5142, 0.082, 0.8657] +2026-04-13 20:48:39.504628: Epoch time: 103.5 s +2026-04-13 20:48:41.025331: +2026-04-13 20:48:41.027744: Epoch 2811 +2026-04-13 20:48:41.029411: Current learning rate: 0.00336 +2026-04-13 20:50:24.657961: train_loss -0.4026 +2026-04-13 20:50:24.665641: val_loss -0.3333 +2026-04-13 20:50:24.667928: Pseudo dice [0.4288, 0.7245, 0.6119, 0.7723, 0.5022, 0.8204, 0.6591] +2026-04-13 20:50:24.672106: Epoch time: 103.64 s +2026-04-13 20:50:26.171101: +2026-04-13 20:50:26.173118: Epoch 2812 +2026-04-13 20:50:26.175282: Current learning rate: 0.00335 +2026-04-13 20:52:08.498875: train_loss -0.3944 +2026-04-13 20:52:08.504127: val_loss -0.2212 +2026-04-13 20:52:08.506220: Pseudo dice [0.6042, 0.4895, 0.5715, 0.1566, 0.6047, 0.0926, 0.4891] +2026-04-13 20:52:08.508513: Epoch time: 102.33 s +2026-04-13 20:52:10.028404: +2026-04-13 20:52:10.030740: Epoch 2813 +2026-04-13 20:52:10.033561: Current learning rate: 0.00335 +2026-04-13 20:53:52.708467: train_loss -0.3894 +2026-04-13 20:53:52.714382: val_loss -0.3319 +2026-04-13 20:53:52.716302: Pseudo dice [0.7912, 0.5139, 0.7141, 0.6735, 0.424, 0.6656, 0.737] +2026-04-13 20:53:52.719022: Epoch time: 102.68 s +2026-04-13 20:53:54.226431: +2026-04-13 20:53:54.228207: Epoch 2814 +2026-04-13 20:53:54.229905: Current learning rate: 0.00335 +2026-04-13 20:55:36.525728: train_loss -0.3817 +2026-04-13 20:55:36.532033: val_loss -0.3232 +2026-04-13 20:55:36.533912: Pseudo dice [0.4446, 0.2789, 0.6244, 0.7865, 0.6222, 0.1928, 0.8395] +2026-04-13 20:55:36.536795: Epoch time: 102.3 s +2026-04-13 20:55:38.045105: +2026-04-13 20:55:38.047292: Epoch 2815 +2026-04-13 20:55:38.048842: Current learning rate: 0.00335 +2026-04-13 20:57:20.244461: train_loss -0.3945 +2026-04-13 20:57:20.250940: val_loss -0.269 +2026-04-13 20:57:20.253266: Pseudo dice [0.4678, 0.7919, 0.2294, 0.6236, 0.5901, 0.1376, 0.581] +2026-04-13 20:57:20.255581: Epoch time: 102.2 s +2026-04-13 20:57:21.762953: +2026-04-13 20:57:21.764889: Epoch 2816 +2026-04-13 20:57:21.766494: Current learning rate: 0.00334 +2026-04-13 20:59:03.882791: train_loss -0.3765 +2026-04-13 20:59:03.887800: val_loss -0.2367 +2026-04-13 20:59:03.890193: Pseudo dice [0.2314, 0.2775, 0.2107, 0.3399, 0.5889, 0.0627, 0.566] +2026-04-13 20:59:03.892102: Epoch time: 102.12 s +2026-04-13 20:59:05.400959: +2026-04-13 20:59:05.404314: Epoch 2817 +2026-04-13 20:59:05.405976: Current learning rate: 0.00334 +2026-04-13 21:00:48.112711: train_loss -0.3796 +2026-04-13 21:00:48.119230: val_loss -0.2436 +2026-04-13 21:00:48.121308: Pseudo dice [0.5784, 0.6563, 0.6234, 0.642, 0.5636, 0.1892, 0.758] +2026-04-13 21:00:48.123830: Epoch time: 102.72 s +2026-04-13 21:00:49.621984: +2026-04-13 21:00:49.624538: Epoch 2818 +2026-04-13 21:00:49.626346: Current learning rate: 0.00334 +2026-04-13 21:02:31.840575: train_loss -0.3962 +2026-04-13 21:02:31.845530: val_loss -0.3358 +2026-04-13 21:02:31.847333: Pseudo dice [0.2586, 0.2488, 0.7008, 0.6901, 0.4957, 0.8013, 0.7244] +2026-04-13 21:02:31.849452: Epoch time: 102.22 s +2026-04-13 21:02:33.369877: +2026-04-13 21:02:33.371625: Epoch 2819 +2026-04-13 21:02:33.373214: Current learning rate: 0.00334 +2026-04-13 21:04:15.827223: train_loss -0.3896 +2026-04-13 21:04:15.831777: val_loss -0.3338 +2026-04-13 21:04:15.834102: Pseudo dice [0.7262, 0.3545, 0.7216, 0.7474, 0.4155, 0.4085, 0.678] +2026-04-13 21:04:15.836300: Epoch time: 102.46 s +2026-04-13 21:04:17.334635: +2026-04-13 21:04:17.336984: Epoch 2820 +2026-04-13 21:04:17.339068: Current learning rate: 0.00333 +2026-04-13 21:05:59.678891: train_loss -0.3767 +2026-04-13 21:05:59.685263: val_loss -0.3472 +2026-04-13 21:05:59.687450: Pseudo dice [0.253, 0.4655, 0.6531, 0.1739, 0.3892, 0.832, 0.919] +2026-04-13 21:05:59.690998: Epoch time: 102.35 s +2026-04-13 21:06:01.185766: +2026-04-13 21:06:01.187949: Epoch 2821 +2026-04-13 21:06:01.189677: Current learning rate: 0.00333 +2026-04-13 21:07:43.575624: train_loss -0.3813 +2026-04-13 21:07:43.581033: val_loss -0.345 +2026-04-13 21:07:43.584976: Pseudo dice [0.7495, 0.518, 0.6445, 0.61, 0.3991, 0.7942, 0.7775] +2026-04-13 21:07:43.587528: Epoch time: 102.39 s +2026-04-13 21:07:45.104405: +2026-04-13 21:07:45.106413: Epoch 2822 +2026-04-13 21:07:45.109656: Current learning rate: 0.00333 +2026-04-13 21:09:28.499080: train_loss -0.3833 +2026-04-13 21:09:28.508380: val_loss -0.2356 +2026-04-13 21:09:28.511121: Pseudo dice [0.2617, 0.5212, 0.5239, 0.4332, 0.5393, 0.0762, 0.7844] +2026-04-13 21:09:28.513530: Epoch time: 103.4 s +2026-04-13 21:09:30.044529: +2026-04-13 21:09:30.047418: Epoch 2823 +2026-04-13 21:09:30.049464: Current learning rate: 0.00333 +2026-04-13 21:11:12.720918: train_loss -0.389 +2026-04-13 21:11:12.726957: val_loss -0.2688 +2026-04-13 21:11:12.732659: Pseudo dice [0.7775, 0.4818, 0.3841, 0.3625, 0.2759, 0.1326, 0.7767] +2026-04-13 21:11:12.735195: Epoch time: 102.68 s +2026-04-13 21:11:14.250751: +2026-04-13 21:11:14.252753: Epoch 2824 +2026-04-13 21:11:14.255669: Current learning rate: 0.00332 +2026-04-13 21:12:56.639009: train_loss -0.3825 +2026-04-13 21:12:56.644713: val_loss -0.2845 +2026-04-13 21:12:56.646682: Pseudo dice [0.8168, 0.3804, 0.4581, 0.6688, 0.5457, 0.0886, 0.8463] +2026-04-13 21:12:56.649118: Epoch time: 102.39 s +2026-04-13 21:12:58.167114: +2026-04-13 21:12:58.168816: Epoch 2825 +2026-04-13 21:12:58.170913: Current learning rate: 0.00332 +2026-04-13 21:14:43.303551: train_loss -0.3827 +2026-04-13 21:14:43.308067: val_loss -0.3428 +2026-04-13 21:14:43.309731: Pseudo dice [0.4438, 0.2877, 0.88, 0.847, 0.352, 0.7202, 0.6119] +2026-04-13 21:14:43.312187: Epoch time: 105.14 s +2026-04-13 21:14:44.840081: +2026-04-13 21:14:44.842216: Epoch 2826 +2026-04-13 21:14:44.844334: Current learning rate: 0.00332 +2026-04-13 21:16:26.901117: train_loss -0.3683 +2026-04-13 21:16:26.908054: val_loss -0.2867 +2026-04-13 21:16:26.910228: Pseudo dice [0.3546, 0.7072, 0.6413, 0.8242, 0.6278, 0.3929, 0.7066] +2026-04-13 21:16:26.914756: Epoch time: 102.06 s +2026-04-13 21:16:28.435581: +2026-04-13 21:16:28.437804: Epoch 2827 +2026-04-13 21:16:28.439524: Current learning rate: 0.00332 +2026-04-13 21:18:12.375980: train_loss -0.3912 +2026-04-13 21:18:12.383143: val_loss -0.3363 +2026-04-13 21:18:12.385281: Pseudo dice [0.5455, 0.583, 0.6809, 0.847, 0.4718, 0.6747, 0.6645] +2026-04-13 21:18:12.387316: Epoch time: 103.94 s +2026-04-13 21:18:14.024257: +2026-04-13 21:18:14.028610: Epoch 2828 +2026-04-13 21:18:14.030895: Current learning rate: 0.00331 +2026-04-13 21:19:56.095248: train_loss -0.3956 +2026-04-13 21:19:56.100909: val_loss -0.3515 +2026-04-13 21:19:56.103081: Pseudo dice [0.4524, 0.3285, 0.6761, 0.8176, 0.4966, 0.8059, 0.7352] +2026-04-13 21:19:56.105427: Epoch time: 102.07 s +2026-04-13 21:19:57.631740: +2026-04-13 21:19:57.634413: Epoch 2829 +2026-04-13 21:19:57.636091: Current learning rate: 0.00331 +2026-04-13 21:21:42.473454: train_loss -0.4071 +2026-04-13 21:21:42.490914: val_loss -0.2916 +2026-04-13 21:21:42.496719: Pseudo dice [0.35, 0.0, 0.67, 0.7345, 0.1951, 0.281, 0.6376] +2026-04-13 21:21:42.501236: Epoch time: 104.85 s +2026-04-13 21:21:44.043374: +2026-04-13 21:21:44.047213: Epoch 2830 +2026-04-13 21:21:44.049500: Current learning rate: 0.00331 +2026-04-13 21:23:26.241053: train_loss -0.4062 +2026-04-13 21:23:26.247799: val_loss -0.266 +2026-04-13 21:23:26.251919: Pseudo dice [0.8759, 0.1657, 0.608, 0.7876, 0.5096, 0.1476, 0.5945] +2026-04-13 21:23:26.254191: Epoch time: 102.2 s +2026-04-13 21:23:27.770152: +2026-04-13 21:23:27.772006: Epoch 2831 +2026-04-13 21:23:27.773523: Current learning rate: 0.00331 +2026-04-13 21:25:10.435032: train_loss -0.3955 +2026-04-13 21:25:10.443344: val_loss -0.3469 +2026-04-13 21:25:10.445510: Pseudo dice [0.8024, 0.5917, 0.8067, 0.2341, 0.4241, 0.7725, 0.7087] +2026-04-13 21:25:10.447726: Epoch time: 102.67 s +2026-04-13 21:25:11.971695: +2026-04-13 21:25:11.974162: Epoch 2832 +2026-04-13 21:25:11.975849: Current learning rate: 0.0033 +2026-04-13 21:26:56.088103: train_loss -0.4032 +2026-04-13 21:26:56.095065: val_loss -0.3502 +2026-04-13 21:26:56.097985: Pseudo dice [0.7225, 0.1417, 0.7269, 0.8178, 0.62, 0.142, 0.8802] +2026-04-13 21:26:56.101050: Epoch time: 104.12 s +2026-04-13 21:26:57.631370: +2026-04-13 21:26:57.633574: Epoch 2833 +2026-04-13 21:26:57.635651: Current learning rate: 0.0033 +2026-04-13 21:28:40.833407: train_loss -0.3931 +2026-04-13 21:28:40.839157: val_loss -0.343 +2026-04-13 21:28:40.841957: Pseudo dice [0.7144, 0.729, 0.7925, 0.4279, 0.361, 0.768, 0.6647] +2026-04-13 21:28:40.845096: Epoch time: 103.21 s +2026-04-13 21:28:42.373415: +2026-04-13 21:28:42.376206: Epoch 2834 +2026-04-13 21:28:42.378597: Current learning rate: 0.0033 +2026-04-13 21:30:26.284125: train_loss -0.4089 +2026-04-13 21:30:26.291723: val_loss -0.2826 +2026-04-13 21:30:26.296457: Pseudo dice [0.4692, 0.4425, 0.652, 0.701, 0.4539, 0.199, 0.7418] +2026-04-13 21:30:26.298908: Epoch time: 103.91 s +2026-04-13 21:30:27.827545: +2026-04-13 21:30:27.829390: Epoch 2835 +2026-04-13 21:30:27.831507: Current learning rate: 0.00329 +2026-04-13 21:32:10.432508: train_loss -0.3918 +2026-04-13 21:32:10.449135: val_loss -0.3499 +2026-04-13 21:32:10.451788: Pseudo dice [0.8648, 0.7194, 0.5256, 0.4118, 0.6594, 0.7157, 0.6607] +2026-04-13 21:32:10.454249: Epoch time: 102.61 s +2026-04-13 21:32:11.968025: +2026-04-13 21:32:11.969806: Epoch 2836 +2026-04-13 21:32:11.972127: Current learning rate: 0.00329 +2026-04-13 21:33:56.082026: train_loss -0.3808 +2026-04-13 21:33:56.095556: val_loss -0.2874 +2026-04-13 21:33:56.106306: Pseudo dice [0.2167, 0.8029, 0.4872, 0.0388, 0.5889, 0.1855, 0.6947] +2026-04-13 21:33:56.112253: Epoch time: 104.12 s +2026-04-13 21:33:57.648528: +2026-04-13 21:33:57.654775: Epoch 2837 +2026-04-13 21:33:57.661047: Current learning rate: 0.00329 +2026-04-13 21:35:40.636723: train_loss -0.4059 +2026-04-13 21:35:40.642119: val_loss -0.3339 +2026-04-13 21:35:40.644831: Pseudo dice [0.3916, 0.4697, 0.6197, 0.3967, 0.3535, 0.7954, 0.8129] +2026-04-13 21:35:40.647178: Epoch time: 102.99 s +2026-04-13 21:35:42.190114: +2026-04-13 21:35:42.192271: Epoch 2838 +2026-04-13 21:35:42.194012: Current learning rate: 0.00329 +2026-04-13 21:37:24.531622: train_loss -0.4032 +2026-04-13 21:37:24.538411: val_loss -0.2757 +2026-04-13 21:37:24.541032: Pseudo dice [0.2521, 0.5844, 0.5754, 0.63, 0.6168, 0.0704, 0.8137] +2026-04-13 21:37:24.543743: Epoch time: 102.35 s +2026-04-13 21:37:26.080482: +2026-04-13 21:37:26.083875: Epoch 2839 +2026-04-13 21:37:26.086298: Current learning rate: 0.00328 +2026-04-13 21:39:09.869313: train_loss -0.3955 +2026-04-13 21:39:09.876209: val_loss -0.3432 +2026-04-13 21:39:09.879525: Pseudo dice [0.84, 0.6407, 0.673, 0.2527, 0.598, 0.5976, 0.8508] +2026-04-13 21:39:09.882847: Epoch time: 103.79 s +2026-04-13 21:39:11.493179: +2026-04-13 21:39:11.495514: Epoch 2840 +2026-04-13 21:39:11.497026: Current learning rate: 0.00328 +2026-04-13 21:40:53.845268: train_loss -0.3989 +2026-04-13 21:40:53.850128: val_loss -0.3576 +2026-04-13 21:40:53.853794: Pseudo dice [0.7102, 0.633, 0.7439, 0.7567, 0.6745, 0.8133, 0.8288] +2026-04-13 21:40:53.856210: Epoch time: 102.36 s +2026-04-13 21:40:55.390198: +2026-04-13 21:40:55.392722: Epoch 2841 +2026-04-13 21:40:55.394464: Current learning rate: 0.00328 +2026-04-13 21:42:39.059041: train_loss -0.408 +2026-04-13 21:42:39.064993: val_loss -0.3338 +2026-04-13 21:42:39.067299: Pseudo dice [0.4501, 0.8117, 0.622, 0.5586, 0.3569, 0.2022, 0.6107] +2026-04-13 21:42:39.070347: Epoch time: 103.67 s +2026-04-13 21:42:40.588387: +2026-04-13 21:42:40.590277: Epoch 2842 +2026-04-13 21:42:40.592144: Current learning rate: 0.00328 +2026-04-13 21:44:24.764467: train_loss -0.3972 +2026-04-13 21:44:24.770489: val_loss -0.295 +2026-04-13 21:44:24.774826: Pseudo dice [0.4714, 0.523, 0.7032, 0.5405, 0.6108, 0.1949, 0.636] +2026-04-13 21:44:24.777939: Epoch time: 104.18 s +2026-04-13 21:44:26.317323: +2026-04-13 21:44:26.319366: Epoch 2843 +2026-04-13 21:44:26.321256: Current learning rate: 0.00327 +2026-04-13 21:46:12.257908: train_loss -0.3995 +2026-04-13 21:46:12.265472: val_loss -0.3097 +2026-04-13 21:46:12.268515: Pseudo dice [0.3787, 0.1814, 0.4717, 0.4765, 0.2461, 0.5759, 0.5616] +2026-04-13 21:46:12.272133: Epoch time: 105.94 s +2026-04-13 21:46:13.802562: +2026-04-13 21:46:13.804651: Epoch 2844 +2026-04-13 21:46:13.808765: Current learning rate: 0.00327 +2026-04-13 21:47:56.368356: train_loss -0.4002 +2026-04-13 21:47:56.378083: val_loss -0.3559 +2026-04-13 21:47:56.380014: Pseudo dice [0.5919, 0.3386, 0.7405, 0.2494, 0.592, 0.7682, 0.7054] +2026-04-13 21:47:56.384383: Epoch time: 102.57 s +2026-04-13 21:47:57.928118: +2026-04-13 21:47:57.930113: Epoch 2845 +2026-04-13 21:47:57.932061: Current learning rate: 0.00327 +2026-04-13 21:49:45.503581: train_loss -0.4029 +2026-04-13 21:49:45.512141: val_loss -0.324 +2026-04-13 21:49:45.514329: Pseudo dice [0.7314, 0.6957, 0.6921, 0.3462, 0.5438, 0.3194, 0.6621] +2026-04-13 21:49:45.517097: Epoch time: 107.58 s +2026-04-13 21:49:47.033328: +2026-04-13 21:49:47.041603: Epoch 2846 +2026-04-13 21:49:47.043267: Current learning rate: 0.00327 +2026-04-13 21:51:29.211426: train_loss -0.4059 +2026-04-13 21:51:29.218862: val_loss -0.3757 +2026-04-13 21:51:29.223195: Pseudo dice [0.5519, 0.6474, 0.7788, 0.0933, 0.6707, 0.6342, 0.8167] +2026-04-13 21:51:29.226449: Epoch time: 102.18 s +2026-04-13 21:51:30.740777: +2026-04-13 21:51:30.742758: Epoch 2847 +2026-04-13 21:51:30.744518: Current learning rate: 0.00326 +2026-04-13 21:53:16.468868: train_loss -0.4057 +2026-04-13 21:53:16.473976: val_loss -0.3465 +2026-04-13 21:53:16.476011: Pseudo dice [0.5769, 0.3646, 0.7383, 0.4754, 0.523, 0.7827, 0.8154] +2026-04-13 21:53:16.477994: Epoch time: 105.73 s +2026-04-13 21:53:17.993353: +2026-04-13 21:53:17.996321: Epoch 2848 +2026-04-13 21:53:17.998048: Current learning rate: 0.00326 +2026-04-13 21:55:00.611973: train_loss -0.4031 +2026-04-13 21:55:00.618011: val_loss -0.3686 +2026-04-13 21:55:00.623531: Pseudo dice [0.33, 0.3194, 0.7368, 0.4258, 0.3997, 0.7865, 0.8727] +2026-04-13 21:55:00.627438: Epoch time: 102.62 s +2026-04-13 21:55:02.152756: +2026-04-13 21:55:02.154648: Epoch 2849 +2026-04-13 21:55:02.156510: Current learning rate: 0.00326 +2026-04-13 21:56:47.780628: train_loss -0.4023 +2026-04-13 21:56:47.787020: val_loss -0.2635 +2026-04-13 21:56:47.789602: Pseudo dice [0.8546, 0.3717, 0.7089, 0.6227, 0.6611, 0.1099, 0.5246] +2026-04-13 21:56:47.791924: Epoch time: 105.63 s +2026-04-13 21:56:51.390096: +2026-04-13 21:56:51.392472: Epoch 2850 +2026-04-13 21:56:51.394338: Current learning rate: 0.00326 +2026-04-13 21:58:34.300659: train_loss -0.4076 +2026-04-13 21:58:34.308457: val_loss -0.3539 +2026-04-13 21:58:34.311121: Pseudo dice [0.6484, 0.3439, 0.6805, 0.5436, 0.6636, 0.7974, 0.8203] +2026-04-13 21:58:34.313802: Epoch time: 102.91 s +2026-04-13 21:58:35.843583: +2026-04-13 21:58:35.847776: Epoch 2851 +2026-04-13 21:58:35.849761: Current learning rate: 0.00325 +2026-04-13 22:00:22.636278: train_loss -0.3943 +2026-04-13 22:00:22.643553: val_loss -0.2837 +2026-04-13 22:00:22.648275: Pseudo dice [0.8781, 0.3233, 0.5567, 0.7591, 0.4684, 0.0919, 0.703] +2026-04-13 22:00:22.650648: Epoch time: 106.8 s +2026-04-13 22:00:24.174697: +2026-04-13 22:00:24.176892: Epoch 2852 +2026-04-13 22:00:24.178832: Current learning rate: 0.00325 +2026-04-13 22:02:06.328695: train_loss -0.4086 +2026-04-13 22:02:06.334291: val_loss -0.3468 +2026-04-13 22:02:06.338421: Pseudo dice [0.8246, 0.4561, 0.7461, 0.7464, 0.4708, 0.4212, 0.6559] +2026-04-13 22:02:06.340944: Epoch time: 102.16 s +2026-04-13 22:02:07.845952: +2026-04-13 22:02:07.847963: Epoch 2853 +2026-04-13 22:02:07.850893: Current learning rate: 0.00325 +2026-04-13 22:03:51.081420: train_loss -0.4027 +2026-04-13 22:03:51.086915: val_loss -0.2611 +2026-04-13 22:03:51.089973: Pseudo dice [0.4913, 0.5602, 0.5143, 0.2405, 0.3759, 0.0722, 0.546] +2026-04-13 22:03:51.093715: Epoch time: 103.24 s +2026-04-13 22:03:52.629056: +2026-04-13 22:03:52.630915: Epoch 2854 +2026-04-13 22:03:52.632544: Current learning rate: 0.00325 +2026-04-13 22:05:35.062913: train_loss -0.3844 +2026-04-13 22:05:35.069354: val_loss -0.2607 +2026-04-13 22:05:35.071195: Pseudo dice [0.6546, 0.4862, 0.6098, 0.8365, 0.6508, 0.1339, 0.7968] +2026-04-13 22:05:35.073764: Epoch time: 102.44 s +2026-04-13 22:05:36.586212: +2026-04-13 22:05:36.588257: Epoch 2855 +2026-04-13 22:05:36.590228: Current learning rate: 0.00324 +2026-04-13 22:07:19.302232: train_loss -0.3779 +2026-04-13 22:07:19.308811: val_loss -0.3272 +2026-04-13 22:07:19.316786: Pseudo dice [0.8075, 0.1802, 0.7789, 0.7336, 0.2769, 0.4025, 0.8057] +2026-04-13 22:07:19.320864: Epoch time: 102.72 s +2026-04-13 22:07:20.844307: +2026-04-13 22:07:20.846534: Epoch 2856 +2026-04-13 22:07:20.848489: Current learning rate: 0.00324 +2026-04-13 22:09:03.814471: train_loss -0.4043 +2026-04-13 22:09:03.819738: val_loss -0.3409 +2026-04-13 22:09:03.821502: Pseudo dice [0.8162, 0.4509, 0.8069, 0.8037, 0.288, 0.7956, 0.9176] +2026-04-13 22:09:03.823663: Epoch time: 102.97 s +2026-04-13 22:09:05.330087: +2026-04-13 22:09:05.332670: Epoch 2857 +2026-04-13 22:09:05.337360: Current learning rate: 0.00324 +2026-04-13 22:10:48.566687: train_loss -0.3878 +2026-04-13 22:10:48.572045: val_loss -0.2993 +2026-04-13 22:10:48.574041: Pseudo dice [0.7172, 0.7241, 0.627, 0.5407, 0.5386, 0.1311, 0.8314] +2026-04-13 22:10:48.576427: Epoch time: 103.24 s +2026-04-13 22:10:50.119944: +2026-04-13 22:10:50.121715: Epoch 2858 +2026-04-13 22:10:50.124102: Current learning rate: 0.00324 +2026-04-13 22:12:34.706260: train_loss -0.4092 +2026-04-13 22:12:34.711349: val_loss -0.2833 +2026-04-13 22:12:34.713285: Pseudo dice [0.4962, 0.5819, 0.6662, 0.8048, 0.5526, 0.2312, 0.8044] +2026-04-13 22:12:34.715499: Epoch time: 104.59 s +2026-04-13 22:12:36.237558: +2026-04-13 22:12:36.240551: Epoch 2859 +2026-04-13 22:12:36.243808: Current learning rate: 0.00323 +2026-04-13 22:14:19.210349: train_loss -0.3936 +2026-04-13 22:14:19.218475: val_loss -0.3475 +2026-04-13 22:14:19.221413: Pseudo dice [0.6912, 0.76, 0.7309, 0.6691, 0.2886, 0.7353, 0.8923] +2026-04-13 22:14:19.225095: Epoch time: 102.98 s +2026-04-13 22:14:20.771138: +2026-04-13 22:14:20.773249: Epoch 2860 +2026-04-13 22:14:20.775567: Current learning rate: 0.00323 +2026-04-13 22:16:05.380294: train_loss -0.4123 +2026-04-13 22:16:05.387927: val_loss -0.1722 +2026-04-13 22:16:05.390914: Pseudo dice [0.5757, 0.6316, 0.7435, 0.0506, 0.3432, 0.0486, 0.8694] +2026-04-13 22:16:05.393756: Epoch time: 104.61 s +2026-04-13 22:16:06.966226: +2026-04-13 22:16:06.967947: Epoch 2861 +2026-04-13 22:16:06.969409: Current learning rate: 0.00323 +2026-04-13 22:17:49.791074: train_loss -0.3916 +2026-04-13 22:17:49.799136: val_loss -0.2865 +2026-04-13 22:17:49.802727: Pseudo dice [0.7302, 0.247, 0.7396, 0.2326, 0.5784, 0.1602, 0.4905] +2026-04-13 22:17:49.806182: Epoch time: 102.83 s +2026-04-13 22:17:51.344071: +2026-04-13 22:17:51.345975: Epoch 2862 +2026-04-13 22:17:51.348448: Current learning rate: 0.00323 +2026-04-13 22:19:36.911497: train_loss -0.4042 +2026-04-13 22:19:36.917834: val_loss -0.3269 +2026-04-13 22:19:36.920187: Pseudo dice [0.6527, 0.2875, 0.8407, 0.5592, 0.4568, 0.2614, 0.882] +2026-04-13 22:19:36.924872: Epoch time: 105.57 s +2026-04-13 22:19:38.457399: +2026-04-13 22:19:38.464242: Epoch 2863 +2026-04-13 22:19:38.467601: Current learning rate: 0.00322 +2026-04-13 22:21:20.601897: train_loss -0.3973 +2026-04-13 22:21:20.608724: val_loss -0.3019 +2026-04-13 22:21:20.611642: Pseudo dice [0.6865, 0.5761, 0.5226, 0.821, 0.5469, 0.3176, 0.7501] +2026-04-13 22:21:20.613786: Epoch time: 102.15 s +2026-04-13 22:21:22.145001: +2026-04-13 22:21:22.146785: Epoch 2864 +2026-04-13 22:21:22.148984: Current learning rate: 0.00322 +2026-04-13 22:23:04.712893: train_loss -0.3996 +2026-04-13 22:23:04.719266: val_loss -0.3451 +2026-04-13 22:23:04.721226: Pseudo dice [0.8114, 0.5138, 0.5927, 0.0233, 0.3693, 0.8374, 0.6016] +2026-04-13 22:23:04.723930: Epoch time: 102.57 s +2026-04-13 22:23:07.476139: +2026-04-13 22:23:07.481474: Epoch 2865 +2026-04-13 22:23:07.484287: Current learning rate: 0.00322 +2026-04-13 22:24:50.114148: train_loss -0.3885 +2026-04-13 22:24:50.120300: val_loss -0.3583 +2026-04-13 22:24:50.122441: Pseudo dice [0.7388, 0.2458, 0.6094, 0.3409, 0.4998, 0.8062, 0.9099] +2026-04-13 22:24:50.125223: Epoch time: 102.64 s +2026-04-13 22:24:51.684150: +2026-04-13 22:24:51.686515: Epoch 2866 +2026-04-13 22:24:51.689129: Current learning rate: 0.00322 +2026-04-13 22:26:34.809084: train_loss -0.3979 +2026-04-13 22:26:34.818684: val_loss -0.2618 +2026-04-13 22:26:34.821162: Pseudo dice [0.3568, 0.4092, 0.6236, 0.8513, 0.1056, 0.0971, 0.7867] +2026-04-13 22:26:34.825984: Epoch time: 103.13 s +2026-04-13 22:26:36.375182: +2026-04-13 22:26:36.378480: Epoch 2867 +2026-04-13 22:26:36.382500: Current learning rate: 0.00321 +2026-04-13 22:28:19.528613: train_loss -0.3869 +2026-04-13 22:28:19.535039: val_loss -0.2414 +2026-04-13 22:28:19.537622: Pseudo dice [0.8319, 0.7287, 0.6172, 0.7965, 0.6059, 0.3109, 0.5225] +2026-04-13 22:28:19.540888: Epoch time: 103.16 s +2026-04-13 22:28:21.082430: +2026-04-13 22:28:21.084675: Epoch 2868 +2026-04-13 22:28:21.087029: Current learning rate: 0.00321 +2026-04-13 22:30:04.480011: train_loss -0.3973 +2026-04-13 22:30:04.484945: val_loss -0.3077 +2026-04-13 22:30:04.486909: Pseudo dice [0.4693, 0.4179, 0.5908, 0.2949, 0.4684, 0.4256, 0.7148] +2026-04-13 22:30:04.490115: Epoch time: 103.4 s +2026-04-13 22:30:06.054317: +2026-04-13 22:30:06.056659: Epoch 2869 +2026-04-13 22:30:06.061514: Current learning rate: 0.00321 +2026-04-13 22:31:48.293914: train_loss -0.4007 +2026-04-13 22:31:48.301106: val_loss -0.3449 +2026-04-13 22:31:48.303429: Pseudo dice [0.6603, 0.6022, 0.7634, 0.0909, 0.4135, 0.781, 0.6025] +2026-04-13 22:31:48.306064: Epoch time: 102.24 s +2026-04-13 22:31:50.066355: +2026-04-13 22:31:50.068223: Epoch 2870 +2026-04-13 22:31:50.069808: Current learning rate: 0.00321 +2026-04-13 22:33:33.072155: train_loss -0.4043 +2026-04-13 22:33:33.079826: val_loss -0.2731 +2026-04-13 22:33:33.082079: Pseudo dice [0.4766, 0.7328, 0.6918, 0.372, 0.571, 0.1889, 0.7696] +2026-04-13 22:33:33.084721: Epoch time: 103.01 s +2026-04-13 22:33:34.626835: +2026-04-13 22:33:34.632493: Epoch 2871 +2026-04-13 22:33:34.635087: Current learning rate: 0.0032 +2026-04-13 22:35:17.074031: train_loss -0.405 +2026-04-13 22:35:17.080111: val_loss -0.3352 +2026-04-13 22:35:17.082371: Pseudo dice [0.5526, 0.1004, 0.6202, 0.1042, 0.3529, 0.6863, 0.8622] +2026-04-13 22:35:17.085400: Epoch time: 102.45 s +2026-04-13 22:35:18.613755: +2026-04-13 22:35:18.617260: Epoch 2872 +2026-04-13 22:35:18.619406: Current learning rate: 0.0032 +2026-04-13 22:37:02.387018: train_loss -0.398 +2026-04-13 22:37:02.394139: val_loss -0.3662 +2026-04-13 22:37:02.396688: Pseudo dice [0.7635, 0.7598, 0.7689, 0.2504, 0.6414, 0.8931, 0.7279] +2026-04-13 22:37:02.401501: Epoch time: 103.78 s +2026-04-13 22:37:03.951866: +2026-04-13 22:37:03.954365: Epoch 2873 +2026-04-13 22:37:03.956912: Current learning rate: 0.0032 +2026-04-13 22:38:47.757050: train_loss -0.3922 +2026-04-13 22:38:47.765386: val_loss -0.2877 +2026-04-13 22:38:47.768500: Pseudo dice [0.7785, 0.0, 0.6187, 0.4855, 0.5034, 0.2021, 0.7985] +2026-04-13 22:38:47.771382: Epoch time: 103.81 s +2026-04-13 22:38:49.329213: +2026-04-13 22:38:49.331717: Epoch 2874 +2026-04-13 22:38:49.335123: Current learning rate: 0.0032 +2026-04-13 22:40:33.956360: train_loss -0.3822 +2026-04-13 22:40:33.967443: val_loss -0.2689 +2026-04-13 22:40:33.971439: Pseudo dice [0.2041, 0.0, 0.7251, 0.5132, 0.6197, 0.3158, 0.6392] +2026-04-13 22:40:33.974744: Epoch time: 104.63 s +2026-04-13 22:40:35.510565: +2026-04-13 22:40:35.512329: Epoch 2875 +2026-04-13 22:40:35.514048: Current learning rate: 0.00319 +2026-04-13 22:42:17.726486: train_loss -0.3866 +2026-04-13 22:42:17.738069: val_loss -0.3461 +2026-04-13 22:42:17.748633: Pseudo dice [0.5028, 0.0, 0.7303, 0.4479, 0.6204, 0.9113, 0.819] +2026-04-13 22:42:17.752562: Epoch time: 102.22 s +2026-04-13 22:42:19.315821: +2026-04-13 22:42:19.319587: Epoch 2876 +2026-04-13 22:42:19.322361: Current learning rate: 0.00319 +2026-04-13 22:44:04.109793: train_loss -0.3877 +2026-04-13 22:44:04.118953: val_loss -0.3289 +2026-04-13 22:44:04.121445: Pseudo dice [0.5802, 0.0, 0.6776, 0.2485, 0.6272, 0.7766, 0.803] +2026-04-13 22:44:04.123637: Epoch time: 104.8 s +2026-04-13 22:44:05.662280: +2026-04-13 22:44:05.664406: Epoch 2877 +2026-04-13 22:44:05.666927: Current learning rate: 0.00319 +2026-04-13 22:45:48.270051: train_loss -0.3867 +2026-04-13 22:45:48.277730: val_loss -0.3229 +2026-04-13 22:45:48.280334: Pseudo dice [0.244, 0.0703, 0.6793, 0.2214, 0.1938, 0.8245, 0.794] +2026-04-13 22:45:48.282870: Epoch time: 102.61 s +2026-04-13 22:45:49.808860: +2026-04-13 22:45:49.813307: Epoch 2878 +2026-04-13 22:45:49.815391: Current learning rate: 0.00319 +2026-04-13 22:47:33.005872: train_loss -0.3709 +2026-04-13 22:47:33.011794: val_loss -0.359 +2026-04-13 22:47:33.015575: Pseudo dice [0.5945, 0.45, 0.7282, 0.8006, 0.5628, 0.7194, 0.8823] +2026-04-13 22:47:33.017829: Epoch time: 103.2 s +2026-04-13 22:47:34.571371: +2026-04-13 22:47:34.573954: Epoch 2879 +2026-04-13 22:47:34.575811: Current learning rate: 0.00318 +2026-04-13 22:49:19.325565: train_loss -0.3613 +2026-04-13 22:49:19.334185: val_loss -0.3384 +2026-04-13 22:49:19.336687: Pseudo dice [0.7898, 0.7232, 0.7162, 0.7763, 0.5607, 0.5135, 0.8681] +2026-04-13 22:49:19.339972: Epoch time: 104.76 s +2026-04-13 22:49:20.900884: +2026-04-13 22:49:20.905410: Epoch 2880 +2026-04-13 22:49:20.909496: Current learning rate: 0.00318 +2026-04-13 22:51:04.931177: train_loss -0.3772 +2026-04-13 22:51:04.936963: val_loss -0.3038 +2026-04-13 22:51:04.942073: Pseudo dice [0.6783, 0.491, 0.549, 0.3693, 0.3994, 0.8155, 0.4821] +2026-04-13 22:51:04.945100: Epoch time: 104.03 s +2026-04-13 22:51:06.509961: +2026-04-13 22:51:06.513560: Epoch 2881 +2026-04-13 22:51:06.515505: Current learning rate: 0.00318 +2026-04-13 22:52:56.764497: train_loss -0.4031 +2026-04-13 22:52:56.770487: val_loss -0.3523 +2026-04-13 22:52:56.772489: Pseudo dice [0.5559, 0.6082, 0.7017, 0.1429, 0.4241, 0.7708, 0.6383] +2026-04-13 22:52:56.774856: Epoch time: 110.26 s +2026-04-13 22:52:58.360161: +2026-04-13 22:52:58.361881: Epoch 2882 +2026-04-13 22:52:58.363648: Current learning rate: 0.00317 +2026-04-13 22:54:41.132757: train_loss -0.3985 +2026-04-13 22:54:41.137359: val_loss -0.2956 +2026-04-13 22:54:41.140237: Pseudo dice [0.7038, 0.6805, 0.5167, 0.6351, 0.4943, 0.3398, 0.8977] +2026-04-13 22:54:41.142230: Epoch time: 102.78 s +2026-04-13 22:54:42.758602: +2026-04-13 22:54:42.760363: Epoch 2883 +2026-04-13 22:54:42.762060: Current learning rate: 0.00317 +2026-04-13 22:56:25.201635: train_loss -0.4024 +2026-04-13 22:56:25.208037: val_loss -0.311 +2026-04-13 22:56:25.212447: Pseudo dice [0.5323, 0.8313, 0.6961, 0.5452, 0.3068, 0.154, 0.7293] +2026-04-13 22:56:25.215546: Epoch time: 102.45 s +2026-04-13 22:56:26.774665: +2026-04-13 22:56:26.776513: Epoch 2884 +2026-04-13 22:56:26.778436: Current learning rate: 0.00317 +2026-04-13 22:58:13.228774: train_loss -0.4078 +2026-04-13 22:58:13.236613: val_loss -0.3474 +2026-04-13 22:58:13.239444: Pseudo dice [0.6291, 0.5255, 0.6925, 0.4336, 0.405, 0.2362, 0.5807] +2026-04-13 22:58:13.242752: Epoch time: 106.46 s +2026-04-13 22:58:16.109699: +2026-04-13 22:58:16.112886: Epoch 2885 +2026-04-13 22:58:16.115229: Current learning rate: 0.00317 +2026-04-13 22:59:59.417561: train_loss -0.4121 +2026-04-13 22:59:59.424824: val_loss -0.2782 +2026-04-13 22:59:59.428564: Pseudo dice [0.4257, 0.8782, 0.6607, 0.8393, 0.3427, 0.365, 0.5284] +2026-04-13 22:59:59.431950: Epoch time: 103.31 s +2026-04-13 23:00:01.045297: +2026-04-13 23:00:01.047166: Epoch 2886 +2026-04-13 23:00:01.049151: Current learning rate: 0.00316 +2026-04-13 23:01:44.472371: train_loss -0.4068 +2026-04-13 23:01:44.477618: val_loss -0.3206 +2026-04-13 23:01:44.480006: Pseudo dice [0.4975, 0.6883, 0.6766, 0.5805, 0.5025, 0.15, 0.6718] +2026-04-13 23:01:44.482300: Epoch time: 103.43 s +2026-04-13 23:01:46.012672: +2026-04-13 23:01:46.014627: Epoch 2887 +2026-04-13 23:01:46.016909: Current learning rate: 0.00316 +2026-04-13 23:03:28.982923: train_loss -0.4076 +2026-04-13 23:03:28.989696: val_loss -0.3301 +2026-04-13 23:03:28.992383: Pseudo dice [0.5412, 0.0904, 0.6522, 0.6409, 0.6494, 0.3293, 0.8127] +2026-04-13 23:03:28.994809: Epoch time: 102.97 s +2026-04-13 23:03:30.575268: +2026-04-13 23:03:30.578116: Epoch 2888 +2026-04-13 23:03:30.581309: Current learning rate: 0.00316 +2026-04-13 23:05:13.525166: train_loss -0.4073 +2026-04-13 23:05:13.530682: val_loss -0.3348 +2026-04-13 23:05:13.532575: Pseudo dice [0.1382, 0.5119, 0.6721, 0.5706, 0.3358, 0.7139, 0.475] +2026-04-13 23:05:13.536642: Epoch time: 102.95 s +2026-04-13 23:05:15.115044: +2026-04-13 23:05:15.116960: Epoch 2889 +2026-04-13 23:05:15.118749: Current learning rate: 0.00316 +2026-04-13 23:06:57.810349: train_loss -0.4071 +2026-04-13 23:06:57.820014: val_loss -0.2713 +2026-04-13 23:06:57.822513: Pseudo dice [0.637, 0.7117, 0.5458, 0.7222, 0.5069, 0.0571, 0.5426] +2026-04-13 23:06:57.825877: Epoch time: 102.7 s +2026-04-13 23:06:59.429410: +2026-04-13 23:06:59.432100: Epoch 2890 +2026-04-13 23:06:59.435364: Current learning rate: 0.00315 +2026-04-13 23:08:41.987715: train_loss -0.3921 +2026-04-13 23:08:41.993923: val_loss -0.2722 +2026-04-13 23:08:41.995849: Pseudo dice [0.5565, 0.4872, 0.5497, 0.6664, 0.2676, 0.1469, 0.8145] +2026-04-13 23:08:41.997886: Epoch time: 102.56 s +2026-04-13 23:08:43.522330: +2026-04-13 23:08:43.524346: Epoch 2891 +2026-04-13 23:08:43.527395: Current learning rate: 0.00315 +2026-04-13 23:10:26.180371: train_loss -0.3824 +2026-04-13 23:10:26.187761: val_loss -0.3463 +2026-04-13 23:10:26.190363: Pseudo dice [0.5327, 0.2819, 0.7204, 0.2165, 0.6041, 0.5183, 0.8294] +2026-04-13 23:10:26.193084: Epoch time: 102.66 s +2026-04-13 23:10:27.744768: +2026-04-13 23:10:27.746869: Epoch 2892 +2026-04-13 23:10:27.748922: Current learning rate: 0.00315 +2026-04-13 23:12:13.861896: train_loss -0.3939 +2026-04-13 23:12:13.868480: val_loss -0.3406 +2026-04-13 23:12:13.870684: Pseudo dice [0.3696, 0.1892, 0.6401, 0.5975, 0.4997, 0.6037, 0.8379] +2026-04-13 23:12:13.872902: Epoch time: 106.12 s +2026-04-13 23:12:15.526545: +2026-04-13 23:12:15.532621: Epoch 2893 +2026-04-13 23:12:15.534594: Current learning rate: 0.00315 +2026-04-13 23:13:58.175968: train_loss -0.3966 +2026-04-13 23:13:58.182834: val_loss -0.2414 +2026-04-13 23:13:58.185103: Pseudo dice [0.6178, 0.4825, 0.7553, 0.7273, 0.4467, 0.0247, 0.7734] +2026-04-13 23:13:58.187469: Epoch time: 102.65 s +2026-04-13 23:13:59.748264: +2026-04-13 23:13:59.750415: Epoch 2894 +2026-04-13 23:13:59.752578: Current learning rate: 0.00314 +2026-04-13 23:15:44.247638: train_loss -0.3989 +2026-04-13 23:15:44.256063: val_loss -0.3211 +2026-04-13 23:15:44.258618: Pseudo dice [0.4189, 0.6067, 0.6846, 0.3891, 0.2107, 0.8317, 0.5365] +2026-04-13 23:15:44.264178: Epoch time: 104.5 s +2026-04-13 23:15:45.855590: +2026-04-13 23:15:45.859147: Epoch 2895 +2026-04-13 23:15:45.861881: Current learning rate: 0.00314 +2026-04-13 23:17:29.798241: train_loss -0.3993 +2026-04-13 23:17:29.811151: val_loss -0.3554 +2026-04-13 23:17:29.819172: Pseudo dice [0.8464, 0.4682, 0.7394, 0.6195, 0.4343, 0.2709, 0.811] +2026-04-13 23:17:29.822895: Epoch time: 103.95 s +2026-04-13 23:17:31.428698: +2026-04-13 23:17:31.432456: Epoch 2896 +2026-04-13 23:17:31.435777: Current learning rate: 0.00314 +2026-04-13 23:19:14.802271: train_loss -0.4085 +2026-04-13 23:19:14.807717: val_loss -0.2495 +2026-04-13 23:19:14.810805: Pseudo dice [0.1374, 0.1685, 0.658, 0.3405, 0.5396, 0.0698, 0.5036] +2026-04-13 23:19:14.813184: Epoch time: 103.38 s +2026-04-13 23:19:16.432822: +2026-04-13 23:19:16.435215: Epoch 2897 +2026-04-13 23:19:16.437160: Current learning rate: 0.00314 +2026-04-13 23:20:59.911931: train_loss -0.3985 +2026-04-13 23:20:59.920390: val_loss -0.3031 +2026-04-13 23:20:59.923149: Pseudo dice [0.877, 0.341, 0.5053, 0.6845, 0.329, 0.4004, 0.4287] +2026-04-13 23:20:59.925988: Epoch time: 103.48 s +2026-04-13 23:21:01.524807: +2026-04-13 23:21:01.526891: Epoch 2898 +2026-04-13 23:21:01.528955: Current learning rate: 0.00313 +2026-04-13 23:22:45.565755: train_loss -0.3892 +2026-04-13 23:22:45.572925: val_loss -0.308 +2026-04-13 23:22:45.575349: Pseudo dice [0.7802, 0.8225, 0.6005, 0.5632, 0.3901, 0.31, 0.6268] +2026-04-13 23:22:45.578325: Epoch time: 104.04 s +2026-04-13 23:22:47.338850: +2026-04-13 23:22:47.340970: Epoch 2899 +2026-04-13 23:22:47.343366: Current learning rate: 0.00313 +2026-04-13 23:24:31.836823: train_loss -0.3921 +2026-04-13 23:24:31.845422: val_loss -0.255 +2026-04-13 23:24:31.848339: Pseudo dice [0.5473, 0.5303, 0.7547, 0.0909, 0.5534, 0.1383, 0.6974] +2026-04-13 23:24:31.852695: Epoch time: 104.5 s +2026-04-13 23:24:35.541316: +2026-04-13 23:24:35.544686: Epoch 2900 +2026-04-13 23:24:35.546395: Current learning rate: 0.00313 +2026-04-13 23:26:20.063770: train_loss -0.3834 +2026-04-13 23:26:20.070319: val_loss -0.3011 +2026-04-13 23:26:20.072679: Pseudo dice [0.4837, 0.7094, 0.5974, 0.5786, 0.3139, 0.434, 0.3272] +2026-04-13 23:26:20.075480: Epoch time: 104.53 s +2026-04-13 23:26:21.642603: +2026-04-13 23:26:21.645897: Epoch 2901 +2026-04-13 23:26:21.650059: Current learning rate: 0.00313 +2026-04-13 23:28:05.329988: train_loss -0.3837 +2026-04-13 23:28:05.338656: val_loss -0.234 +2026-04-13 23:28:05.341212: Pseudo dice [0.5724, 0.7364, 0.5524, 0.7959, 0.4889, 0.0427, 0.7036] +2026-04-13 23:28:05.343525: Epoch time: 103.69 s +2026-04-13 23:28:06.984122: +2026-04-13 23:28:06.987815: Epoch 2902 +2026-04-13 23:28:06.990626: Current learning rate: 0.00312 +2026-04-13 23:29:50.295042: train_loss -0.3757 +2026-04-13 23:29:50.304548: val_loss -0.312 +2026-04-13 23:29:50.306893: Pseudo dice [0.4265, 0.3322, 0.7866, 0.5975, 0.6295, 0.6714, 0.3412] +2026-04-13 23:29:50.309995: Epoch time: 103.31 s +2026-04-13 23:29:51.937998: +2026-04-13 23:29:51.940408: Epoch 2903 +2026-04-13 23:29:51.943462: Current learning rate: 0.00312 +2026-04-13 23:31:34.436577: train_loss -0.3968 +2026-04-13 23:31:34.441505: val_loss -0.3485 +2026-04-13 23:31:34.443384: Pseudo dice [0.227, 0.7009, 0.7043, 0.7389, 0.6217, 0.2815, 0.8598] +2026-04-13 23:31:34.445556: Epoch time: 102.5 s +2026-04-13 23:31:35.985226: +2026-04-13 23:31:35.986942: Epoch 2904 +2026-04-13 23:31:35.988609: Current learning rate: 0.00312 +2026-04-13 23:33:19.165365: train_loss -0.4001 +2026-04-13 23:33:19.171124: val_loss -0.2798 +2026-04-13 23:33:19.173020: Pseudo dice [0.6652, 0.1256, 0.4767, 0.5433, 0.4169, 0.0731, 0.7783] +2026-04-13 23:33:19.175207: Epoch time: 103.18 s +2026-04-13 23:33:21.973144: +2026-04-13 23:33:21.974833: Epoch 2905 +2026-04-13 23:33:21.976547: Current learning rate: 0.00312 +2026-04-13 23:35:04.962487: train_loss -0.3888 +2026-04-13 23:35:04.969135: val_loss -0.3415 +2026-04-13 23:35:04.971226: Pseudo dice [0.7772, 0.1094, 0.6568, 0.3242, 0.5603, 0.8193, 0.7147] +2026-04-13 23:35:04.973893: Epoch time: 102.99 s +2026-04-13 23:35:06.638843: +2026-04-13 23:35:06.641942: Epoch 2906 +2026-04-13 23:35:06.644253: Current learning rate: 0.00311 +2026-04-13 23:36:50.591233: train_loss -0.394 +2026-04-13 23:36:50.598052: val_loss -0.2896 +2026-04-13 23:36:50.599811: Pseudo dice [0.7416, 0.5524, 0.7674, 0.6493, 0.3353, 0.0691, 0.9012] +2026-04-13 23:36:50.602607: Epoch time: 103.96 s +2026-04-13 23:36:52.152017: +2026-04-13 23:36:52.153605: Epoch 2907 +2026-04-13 23:36:52.155621: Current learning rate: 0.00311 +2026-04-13 23:38:38.059120: train_loss -0.3975 +2026-04-13 23:38:38.065111: val_loss -0.3064 +2026-04-13 23:38:38.067214: Pseudo dice [0.7042, 0.6338, 0.7206, 0.468, 0.6649, 0.0884, 0.7413] +2026-04-13 23:38:38.069770: Epoch time: 105.91 s +2026-04-13 23:38:39.624509: +2026-04-13 23:38:39.626098: Epoch 2908 +2026-04-13 23:38:39.627659: Current learning rate: 0.00311 +2026-04-13 23:40:23.403768: train_loss -0.3998 +2026-04-13 23:40:23.413262: val_loss -0.292 +2026-04-13 23:40:23.416649: Pseudo dice [0.582, 0.1105, 0.6734, 0.0649, 0.4166, 0.1703, 0.6311] +2026-04-13 23:40:23.420376: Epoch time: 103.78 s +2026-04-13 23:40:25.054446: +2026-04-13 23:40:25.056233: Epoch 2909 +2026-04-13 23:40:25.058041: Current learning rate: 0.00311 +2026-04-13 23:42:08.830438: train_loss -0.3972 +2026-04-13 23:42:08.837526: val_loss -0.3095 +2026-04-13 23:42:08.839391: Pseudo dice [0.748, 0.5935, 0.6022, 0.1701, 0.431, 0.7945, 0.577] +2026-04-13 23:42:08.842714: Epoch time: 103.78 s +2026-04-13 23:42:10.412479: +2026-04-13 23:42:10.415772: Epoch 2910 +2026-04-13 23:42:10.419636: Current learning rate: 0.0031 +2026-04-13 23:43:54.776875: train_loss -0.3887 +2026-04-13 23:43:54.782060: val_loss -0.3034 +2026-04-13 23:43:54.784003: Pseudo dice [0.799, 0.3133, 0.7198, 0.5489, 0.4469, 0.2408, 0.8628] +2026-04-13 23:43:54.786595: Epoch time: 104.37 s +2026-04-13 23:43:56.331926: +2026-04-13 23:43:56.334538: Epoch 2911 +2026-04-13 23:43:56.336414: Current learning rate: 0.0031 +2026-04-13 23:45:42.600336: train_loss -0.3869 +2026-04-13 23:45:42.606713: val_loss -0.2928 +2026-04-13 23:45:42.609045: Pseudo dice [0.7055, 0.346, 0.587, 0.8846, 0.585, 0.0536, 0.6933] +2026-04-13 23:45:42.611282: Epoch time: 106.27 s +2026-04-13 23:45:44.228210: +2026-04-13 23:45:44.230104: Epoch 2912 +2026-04-13 23:45:44.231973: Current learning rate: 0.0031 +2026-04-13 23:47:29.537669: train_loss -0.4001 +2026-04-13 23:47:29.542807: val_loss -0.2936 +2026-04-13 23:47:29.545157: Pseudo dice [0.6859, 0.1641, 0.5608, 0.8765, 0.5888, 0.0583, 0.8444] +2026-04-13 23:47:29.547328: Epoch time: 105.31 s +2026-04-13 23:47:31.143252: +2026-04-13 23:47:31.144951: Epoch 2913 +2026-04-13 23:47:31.146969: Current learning rate: 0.0031 +2026-04-13 23:49:18.378061: train_loss -0.3913 +2026-04-13 23:49:18.383630: val_loss -0.2608 +2026-04-13 23:49:18.386524: Pseudo dice [0.504, 0.2424, 0.6801, 0.7685, 0.212, 0.0851, 0.7805] +2026-04-13 23:49:18.389114: Epoch time: 107.24 s +2026-04-13 23:49:19.980609: +2026-04-13 23:49:19.982487: Epoch 2914 +2026-04-13 23:49:19.984681: Current learning rate: 0.00309 +2026-04-13 23:51:04.209960: train_loss -0.4063 +2026-04-13 23:51:04.222305: val_loss -0.3236 +2026-04-13 23:51:04.226601: Pseudo dice [0.7251, 0.8061, 0.6928, 0.891, 0.4455, 0.1216, 0.5649] +2026-04-13 23:51:04.230179: Epoch time: 104.23 s +2026-04-13 23:51:05.790324: +2026-04-13 23:51:05.792434: Epoch 2915 +2026-04-13 23:51:05.794248: Current learning rate: 0.00309 +2026-04-13 23:52:51.560724: train_loss -0.4085 +2026-04-13 23:52:51.565484: val_loss -0.3326 +2026-04-13 23:52:51.567608: Pseudo dice [0.585, 0.4967, 0.5871, 0.7964, 0.5898, 0.7287, 0.7444] +2026-04-13 23:52:51.570184: Epoch time: 105.77 s +2026-04-13 23:52:53.185634: +2026-04-13 23:52:53.187443: Epoch 2916 +2026-04-13 23:52:53.189263: Current learning rate: 0.00309 +2026-04-13 23:54:36.512814: train_loss -0.4068 +2026-04-13 23:54:36.518482: val_loss -0.3248 +2026-04-13 23:54:36.520403: Pseudo dice [0.3401, 0.7231, 0.3501, 0.4439, 0.5307, 0.2332, 0.8007] +2026-04-13 23:54:36.523298: Epoch time: 103.33 s +2026-04-13 23:54:38.058999: +2026-04-13 23:54:38.060700: Epoch 2917 +2026-04-13 23:54:38.062291: Current learning rate: 0.00309 +2026-04-13 23:56:22.446012: train_loss -0.4 +2026-04-13 23:56:22.451313: val_loss -0.3468 +2026-04-13 23:56:22.453227: Pseudo dice [0.7442, 0.5035, 0.6517, 0.7703, 0.3313, 0.8544, 0.9028] +2026-04-13 23:56:22.455406: Epoch time: 104.39 s +2026-04-13 23:56:24.069830: +2026-04-13 23:56:24.071560: Epoch 2918 +2026-04-13 23:56:24.073261: Current learning rate: 0.00308 +2026-04-13 23:58:07.827543: train_loss -0.4072 +2026-04-13 23:58:07.835046: val_loss -0.3447 +2026-04-13 23:58:07.839137: Pseudo dice [0.4883, 0.4534, 0.3298, 0.8826, 0.4579, 0.2141, 0.8864] +2026-04-13 23:58:07.841561: Epoch time: 103.76 s +2026-04-13 23:58:09.400865: +2026-04-13 23:58:09.403789: Epoch 2919 +2026-04-13 23:58:09.405937: Current learning rate: 0.00308 +2026-04-13 23:59:52.971225: train_loss -0.4211 +2026-04-13 23:59:52.978051: val_loss -0.2521 +2026-04-13 23:59:52.981079: Pseudo dice [0.6866, 0.2368, 0.7902, 0.8889, 0.473, 0.0701, 0.7922] +2026-04-13 23:59:52.984181: Epoch time: 103.57 s +2026-04-13 23:59:54.606122: +2026-04-13 23:59:54.608192: Epoch 2920 +2026-04-13 23:59:54.610410: Current learning rate: 0.00308 +2026-04-14 00:02:17.486836: train_loss -0.3965 +2026-04-14 00:02:17.503377: val_loss -0.2659 +2026-04-14 00:02:17.510322: Pseudo dice [0.7708, 0.1103, 0.658, 0.3853, 0.363, 0.1945, 0.2839] +2026-04-14 00:02:17.515518: Epoch time: 142.88 s +2026-04-14 00:02:19.094222: +2026-04-14 00:02:19.097788: Epoch 2921 +2026-04-14 00:02:19.101441: Current learning rate: 0.00308 +2026-04-14 00:04:27.817864: train_loss -0.3913 +2026-04-14 00:04:27.828095: val_loss -0.3224 +2026-04-14 00:04:27.830953: Pseudo dice [0.4069, 0.7311, 0.6506, 0.0117, 0.44, 0.4887, 0.5206] +2026-04-14 00:04:27.833915: Epoch time: 128.73 s +2026-04-14 00:04:29.356674: +2026-04-14 00:04:29.358662: Epoch 2922 +2026-04-14 00:04:29.360620: Current learning rate: 0.00307 +2026-04-14 00:06:11.868560: train_loss -0.4003 +2026-04-14 00:06:11.874780: val_loss -0.2689 +2026-04-14 00:06:11.877305: Pseudo dice [0.6651, 0.8214, 0.7579, 0.8264, 0.1914, 0.2169, 0.5579] +2026-04-14 00:06:11.880360: Epoch time: 102.52 s +2026-04-14 00:06:13.446309: +2026-04-14 00:06:13.448922: Epoch 2923 +2026-04-14 00:06:13.451232: Current learning rate: 0.00307 +2026-04-14 00:07:56.932197: train_loss -0.4055 +2026-04-14 00:07:56.937663: val_loss -0.363 +2026-04-14 00:07:56.939715: Pseudo dice [0.7563, 0.3617, 0.5776, 0.8978, 0.5975, 0.7928, 0.8993] +2026-04-14 00:07:56.942537: Epoch time: 103.49 s +2026-04-14 00:07:59.626661: +2026-04-14 00:07:59.628713: Epoch 2924 +2026-04-14 00:07:59.630275: Current learning rate: 0.00307 +2026-04-14 00:09:42.805933: train_loss -0.3915 +2026-04-14 00:09:42.812840: val_loss -0.3345 +2026-04-14 00:09:42.814782: Pseudo dice [0.3542, 0.497, 0.6559, 0.1377, 0.4451, 0.7014, 0.8849] +2026-04-14 00:09:42.817434: Epoch time: 103.18 s +2026-04-14 00:09:44.467470: +2026-04-14 00:09:44.469225: Epoch 2925 +2026-04-14 00:09:44.471330: Current learning rate: 0.00306 +2026-04-14 00:11:27.097716: train_loss -0.4045 +2026-04-14 00:11:27.103206: val_loss -0.3374 +2026-04-14 00:11:27.105073: Pseudo dice [0.7012, 0.3167, 0.7395, 0.5082, 0.3059, 0.4139, 0.6276] +2026-04-14 00:11:27.107204: Epoch time: 102.63 s +2026-04-14 00:11:28.706024: +2026-04-14 00:11:28.707829: Epoch 2926 +2026-04-14 00:11:28.709742: Current learning rate: 0.00306 +2026-04-14 00:13:11.971714: train_loss -0.412 +2026-04-14 00:13:11.986482: val_loss -0.3209 +2026-04-14 00:13:11.991003: Pseudo dice [0.5795, 0.5132, 0.7761, 0.5771, 0.6122, 0.1158, 0.8847] +2026-04-14 00:13:11.994791: Epoch time: 103.27 s +2026-04-14 00:13:13.564685: +2026-04-14 00:13:13.566473: Epoch 2927 +2026-04-14 00:13:13.568137: Current learning rate: 0.00306 +2026-04-14 00:14:56.170089: train_loss -0.3981 +2026-04-14 00:14:56.175138: val_loss -0.3039 +2026-04-14 00:14:56.177035: Pseudo dice [0.5007, 0.5194, 0.7882, 0.6982, 0.3815, 0.3669, 0.5228] +2026-04-14 00:14:56.179276: Epoch time: 102.61 s +2026-04-14 00:14:57.717970: +2026-04-14 00:14:57.719824: Epoch 2928 +2026-04-14 00:14:57.721701: Current learning rate: 0.00306 +2026-04-14 00:16:41.112314: train_loss -0.4097 +2026-04-14 00:16:41.123442: val_loss -0.3615 +2026-04-14 00:16:41.125618: Pseudo dice [0.4386, 0.5044, 0.6935, 0.1295, 0.5783, 0.7805, 0.8778] +2026-04-14 00:16:41.128695: Epoch time: 103.4 s +2026-04-14 00:16:42.695397: +2026-04-14 00:16:42.697498: Epoch 2929 +2026-04-14 00:16:42.699407: Current learning rate: 0.00305 +2026-04-14 00:18:27.091571: train_loss -0.4069 +2026-04-14 00:18:27.097825: val_loss -0.2712 +2026-04-14 00:18:27.100172: Pseudo dice [0.5725, 0.0654, 0.653, 0.4357, 0.4798, 0.0511, 0.778] +2026-04-14 00:18:27.102973: Epoch time: 104.4 s +2026-04-14 00:18:28.683461: +2026-04-14 00:18:28.685914: Epoch 2930 +2026-04-14 00:18:28.687979: Current learning rate: 0.00305 +2026-04-14 00:20:12.239167: train_loss -0.4016 +2026-04-14 00:20:12.244920: val_loss -0.3491 +2026-04-14 00:20:12.247230: Pseudo dice [0.748, 0.2197, 0.7564, 0.4115, 0.6478, 0.5263, 0.7995] +2026-04-14 00:20:12.249721: Epoch time: 103.56 s +2026-04-14 00:20:13.824935: +2026-04-14 00:20:13.826828: Epoch 2931 +2026-04-14 00:20:13.828434: Current learning rate: 0.00305 +2026-04-14 00:21:57.038323: train_loss -0.4048 +2026-04-14 00:21:57.045168: val_loss -0.33 +2026-04-14 00:21:57.047179: Pseudo dice [0.5812, 0.3875, 0.5925, 0.8705, 0.606, 0.2923, 0.8133] +2026-04-14 00:21:57.049690: Epoch time: 103.22 s +2026-04-14 00:21:58.621792: +2026-04-14 00:21:58.623720: Epoch 2932 +2026-04-14 00:21:58.625401: Current learning rate: 0.00305 +2026-04-14 00:23:43.461981: train_loss -0.4015 +2026-04-14 00:23:43.467515: val_loss -0.2217 +2026-04-14 00:23:43.469593: Pseudo dice [0.643, 0.765, 0.5039, 0.416, 0.6534, 0.2313, 0.7409] +2026-04-14 00:23:43.472177: Epoch time: 104.84 s +2026-04-14 00:23:45.115528: +2026-04-14 00:23:45.117755: Epoch 2933 +2026-04-14 00:23:45.119334: Current learning rate: 0.00304 +2026-04-14 00:25:27.520928: train_loss -0.4132 +2026-04-14 00:25:27.526973: val_loss -0.3175 +2026-04-14 00:25:27.529665: Pseudo dice [0.4597, 0.5153, 0.6374, 0.7384, 0.27, 0.8175, 0.2852] +2026-04-14 00:25:27.532877: Epoch time: 102.41 s +2026-04-14 00:25:29.130168: +2026-04-14 00:25:29.132064: Epoch 2934 +2026-04-14 00:25:29.133902: Current learning rate: 0.00304 +2026-04-14 00:27:11.467139: train_loss -0.4 +2026-04-14 00:27:11.473543: val_loss -0.3604 +2026-04-14 00:27:11.476391: Pseudo dice [0.6849, 0.6181, 0.6911, 0.2618, 0.5555, 0.8735, 0.7278] +2026-04-14 00:27:11.478997: Epoch time: 102.34 s +2026-04-14 00:27:13.066483: +2026-04-14 00:27:13.068612: Epoch 2935 +2026-04-14 00:27:13.070512: Current learning rate: 0.00304 +2026-04-14 00:28:55.701904: train_loss -0.3854 +2026-04-14 00:28:55.709180: val_loss -0.3613 +2026-04-14 00:28:55.713187: Pseudo dice [0.487, 0.7862, 0.7052, 0.5982, 0.5818, 0.7977, 0.8095] +2026-04-14 00:28:55.716246: Epoch time: 102.64 s +2026-04-14 00:28:57.282415: +2026-04-14 00:28:57.285195: Epoch 2936 +2026-04-14 00:28:57.287235: Current learning rate: 0.00304 +2026-04-14 00:30:39.889690: train_loss -0.3771 +2026-04-14 00:30:39.895910: val_loss -0.2673 +2026-04-14 00:30:39.898112: Pseudo dice [0.6501, 0.5606, 0.5398, 0.6621, 0.2971, 0.2649, 0.4736] +2026-04-14 00:30:39.900734: Epoch time: 102.61 s +2026-04-14 00:30:41.529648: +2026-04-14 00:30:41.532092: Epoch 2937 +2026-04-14 00:30:41.533762: Current learning rate: 0.00303 +2026-04-14 00:32:24.283014: train_loss -0.4055 +2026-04-14 00:32:24.288711: val_loss -0.3054 +2026-04-14 00:32:24.290552: Pseudo dice [0.7571, 0.5158, 0.6836, 0.0224, 0.3302, 0.2433, 0.4729] +2026-04-14 00:32:24.293317: Epoch time: 102.76 s +2026-04-14 00:32:25.952394: +2026-04-14 00:32:25.954440: Epoch 2938 +2026-04-14 00:32:25.956590: Current learning rate: 0.00303 +2026-04-14 00:34:08.594246: train_loss -0.3983 +2026-04-14 00:34:08.599982: val_loss -0.2935 +2026-04-14 00:34:08.601614: Pseudo dice [0.8144, 0.2498, 0.7574, 0.6675, 0.3855, 0.406, 0.8774] +2026-04-14 00:34:08.606243: Epoch time: 102.65 s +2026-04-14 00:34:10.163797: +2026-04-14 00:34:10.165702: Epoch 2939 +2026-04-14 00:34:10.167358: Current learning rate: 0.00303 +2026-04-14 00:35:53.845060: train_loss -0.4069 +2026-04-14 00:35:53.849768: val_loss -0.246 +2026-04-14 00:35:53.851455: Pseudo dice [0.74, 0.19, 0.4745, 0.2951, 0.3765, 0.1022, 0.483] +2026-04-14 00:35:53.853607: Epoch time: 103.68 s +2026-04-14 00:35:55.392126: +2026-04-14 00:35:55.394295: Epoch 2940 +2026-04-14 00:35:55.396526: Current learning rate: 0.00303 +2026-04-14 00:37:39.054452: train_loss -0.4157 +2026-04-14 00:37:39.061930: val_loss -0.3289 +2026-04-14 00:37:39.064333: Pseudo dice [0.6881, 0.6556, 0.7185, 0.8538, 0.2067, 0.6875, 0.8618] +2026-04-14 00:37:39.066961: Epoch time: 103.67 s +2026-04-14 00:37:40.698047: +2026-04-14 00:37:40.699917: Epoch 2941 +2026-04-14 00:37:40.701706: Current learning rate: 0.00302 +2026-04-14 00:39:23.780867: train_loss -0.3952 +2026-04-14 00:39:23.789277: val_loss -0.3336 +2026-04-14 00:39:23.792081: Pseudo dice [0.7098, 0.4048, 0.6217, 0.483, 0.1988, 0.8396, 0.5042] +2026-04-14 00:39:23.794042: Epoch time: 103.09 s +2026-04-14 00:39:25.376758: +2026-04-14 00:39:25.379374: Epoch 2942 +2026-04-14 00:39:25.382031: Current learning rate: 0.00302 +2026-04-14 00:41:08.446088: train_loss -0.3923 +2026-04-14 00:41:08.453353: val_loss -0.3261 +2026-04-14 00:41:08.455225: Pseudo dice [0.8081, 0.6802, 0.5728, 0.518, 0.5619, 0.2449, 0.7749] +2026-04-14 00:41:08.457490: Epoch time: 103.07 s +2026-04-14 00:41:10.011872: +2026-04-14 00:41:10.013722: Epoch 2943 +2026-04-14 00:41:10.015695: Current learning rate: 0.00302 +2026-04-14 00:42:53.137142: train_loss -0.3958 +2026-04-14 00:42:53.144587: val_loss -0.2985 +2026-04-14 00:42:53.146241: Pseudo dice [0.4046, 0.5467, 0.5355, 0.7327, 0.6245, 0.2445, 0.9063] +2026-04-14 00:42:53.148255: Epoch time: 103.13 s +2026-04-14 00:42:55.872077: +2026-04-14 00:42:55.873793: Epoch 2944 +2026-04-14 00:42:55.875445: Current learning rate: 0.00302 +2026-04-14 00:44:38.959317: train_loss -0.3926 +2026-04-14 00:44:38.969853: val_loss -0.3167 +2026-04-14 00:44:38.979657: Pseudo dice [0.6336, 0.2592, 0.6534, 0.7549, 0.3623, 0.1519, 0.7216] +2026-04-14 00:44:38.983497: Epoch time: 103.09 s +2026-04-14 00:44:40.551900: +2026-04-14 00:44:40.553897: Epoch 2945 +2026-04-14 00:44:40.555750: Current learning rate: 0.00301 +2026-04-14 00:46:23.842767: train_loss -0.3803 +2026-04-14 00:46:23.856368: val_loss -0.2417 +2026-04-14 00:46:23.858562: Pseudo dice [0.313, 0.6217, 0.3709, 0.0661, 0.4551, 0.0576, 0.7115] +2026-04-14 00:46:23.861338: Epoch time: 103.29 s +2026-04-14 00:46:25.475069: +2026-04-14 00:46:25.476874: Epoch 2946 +2026-04-14 00:46:25.479415: Current learning rate: 0.00301 +2026-04-14 00:48:08.081273: train_loss -0.3892 +2026-04-14 00:48:08.088922: val_loss -0.3016 +2026-04-14 00:48:08.090771: Pseudo dice [0.6815, 0.5389, 0.6586, 0.1521, 0.4732, 0.1601, 0.9137] +2026-04-14 00:48:08.094412: Epoch time: 102.61 s +2026-04-14 00:48:09.872310: +2026-04-14 00:48:09.876302: Epoch 2947 +2026-04-14 00:48:09.878326: Current learning rate: 0.00301 +2026-04-14 00:49:52.875868: train_loss -0.4086 +2026-04-14 00:49:52.883535: val_loss -0.325 +2026-04-14 00:49:52.887217: Pseudo dice [0.7907, 0.7861, 0.7281, 0.3394, 0.5591, 0.1845, 0.8112] +2026-04-14 00:49:52.890532: Epoch time: 103.01 s +2026-04-14 00:49:54.491923: +2026-04-14 00:49:54.493862: Epoch 2948 +2026-04-14 00:49:54.495748: Current learning rate: 0.00301 +2026-04-14 00:51:37.102854: train_loss -0.4094 +2026-04-14 00:51:37.109098: val_loss -0.3485 +2026-04-14 00:51:37.110962: Pseudo dice [0.3751, 0.7354, 0.3933, 0.334, 0.5087, 0.6954, 0.8545] +2026-04-14 00:51:37.113062: Epoch time: 102.61 s +2026-04-14 00:51:38.662582: +2026-04-14 00:51:38.664500: Epoch 2949 +2026-04-14 00:51:38.666142: Current learning rate: 0.003 +2026-04-14 00:53:21.119627: train_loss -0.3964 +2026-04-14 00:53:21.128895: val_loss -0.3692 +2026-04-14 00:53:21.130766: Pseudo dice [0.7443, 0.6033, 0.6492, 0.0681, 0.5596, 0.8262, 0.6474] +2026-04-14 00:53:21.133038: Epoch time: 102.46 s +2026-04-14 00:53:24.703403: +2026-04-14 00:53:24.708449: Epoch 2950 +2026-04-14 00:53:24.710792: Current learning rate: 0.003 +2026-04-14 00:55:07.366683: train_loss -0.4066 +2026-04-14 00:55:07.374482: val_loss -0.2708 +2026-04-14 00:55:07.376748: Pseudo dice [0.4355, 0.2433, 0.5893, 0.7776, 0.403, 0.4205, 0.3843] +2026-04-14 00:55:07.379444: Epoch time: 102.67 s +2026-04-14 00:55:08.946078: +2026-04-14 00:55:08.947931: Epoch 2951 +2026-04-14 00:55:08.950404: Current learning rate: 0.003 +2026-04-14 00:56:52.102389: train_loss -0.4209 +2026-04-14 00:56:52.112598: val_loss -0.3095 +2026-04-14 00:56:52.114861: Pseudo dice [0.2635, 0.2796, 0.5482, 0.751, 0.4798, 0.2022, 0.7927] +2026-04-14 00:56:52.117034: Epoch time: 103.16 s +2026-04-14 00:56:53.681934: +2026-04-14 00:56:53.683801: Epoch 2952 +2026-04-14 00:56:53.685606: Current learning rate: 0.003 +2026-04-14 00:58:36.268791: train_loss -0.4117 +2026-04-14 00:58:36.277073: val_loss -0.1496 +2026-04-14 00:58:36.278799: Pseudo dice [0.4697, 0.6582, 0.5743, 0.5115, 0.2928, 0.1155, 0.7742] +2026-04-14 00:58:36.281038: Epoch time: 102.59 s +2026-04-14 00:58:37.858674: +2026-04-14 00:58:37.861574: Epoch 2953 +2026-04-14 00:58:37.863381: Current learning rate: 0.00299 +2026-04-14 01:00:20.750807: train_loss -0.4041 +2026-04-14 01:00:20.762824: val_loss -0.2649 +2026-04-14 01:00:20.766374: Pseudo dice [0.1504, 0.8189, 0.5713, 0.8936, 0.4649, 0.0399, 0.8565] +2026-04-14 01:00:20.768639: Epoch time: 102.9 s +2026-04-14 01:00:22.373617: +2026-04-14 01:00:22.375690: Epoch 2954 +2026-04-14 01:00:22.377417: Current learning rate: 0.00299 +2026-04-14 01:02:04.822237: train_loss -0.4093 +2026-04-14 01:02:04.830008: val_loss -0.3312 +2026-04-14 01:02:04.833213: Pseudo dice [0.3546, 0.1987, 0.6803, 0.3351, 0.4141, 0.8073, 0.9275] +2026-04-14 01:02:04.835300: Epoch time: 102.45 s +2026-04-14 01:02:06.435787: +2026-04-14 01:02:06.437428: Epoch 2955 +2026-04-14 01:02:06.439600: Current learning rate: 0.00299 +2026-04-14 01:03:50.023969: train_loss -0.3994 +2026-04-14 01:03:50.032829: val_loss -0.2816 +2026-04-14 01:03:50.035639: Pseudo dice [0.3655, 0.3716, 0.6087, 0.7329, 0.6091, 0.2397, 0.7183] +2026-04-14 01:03:50.039511: Epoch time: 103.59 s +2026-04-14 01:03:51.662304: +2026-04-14 01:03:51.665552: Epoch 2956 +2026-04-14 01:03:51.668270: Current learning rate: 0.00299 +2026-04-14 01:05:34.832638: train_loss -0.3968 +2026-04-14 01:05:34.843747: val_loss -0.1977 +2026-04-14 01:05:34.846865: Pseudo dice [0.3616, 0.8201, 0.636, 0.4956, 0.2403, 0.1111, 0.8025] +2026-04-14 01:05:34.849555: Epoch time: 103.17 s +2026-04-14 01:05:36.505158: +2026-04-14 01:05:36.506852: Epoch 2957 +2026-04-14 01:05:36.508722: Current learning rate: 0.00298 +2026-04-14 01:07:19.050403: train_loss -0.3939 +2026-04-14 01:07:19.059341: val_loss -0.2925 +2026-04-14 01:07:19.061562: Pseudo dice [0.6955, 0.1737, 0.7248, 0.6964, 0.4905, 0.1596, 0.501] +2026-04-14 01:07:19.064918: Epoch time: 102.55 s +2026-04-14 01:07:20.601974: +2026-04-14 01:07:20.603580: Epoch 2958 +2026-04-14 01:07:20.605325: Current learning rate: 0.00298 +2026-04-14 01:09:03.196235: train_loss -0.3928 +2026-04-14 01:09:03.207341: val_loss -0.3454 +2026-04-14 01:09:03.209675: Pseudo dice [0.4276, 0.4827, 0.7819, 0.8822, 0.402, 0.7732, 0.6435] +2026-04-14 01:09:03.212920: Epoch time: 102.6 s +2026-04-14 01:09:04.767860: +2026-04-14 01:09:04.770167: Epoch 2959 +2026-04-14 01:09:04.771717: Current learning rate: 0.00298 +2026-04-14 01:10:47.954944: train_loss -0.3909 +2026-04-14 01:10:47.963398: val_loss -0.2668 +2026-04-14 01:10:47.966062: Pseudo dice [0.8599, 0.7468, 0.512, 0.3852, 0.505, 0.1261, 0.8175] +2026-04-14 01:10:47.967922: Epoch time: 103.19 s +2026-04-14 01:10:49.529564: +2026-04-14 01:10:49.534579: Epoch 2960 +2026-04-14 01:10:49.538756: Current learning rate: 0.00297 +2026-04-14 01:12:32.498360: train_loss -0.4092 +2026-04-14 01:12:32.507001: val_loss -0.3684 +2026-04-14 01:12:32.508892: Pseudo dice [0.3026, 0.6432, 0.7235, 0.8032, 0.4043, 0.7696, 0.8272] +2026-04-14 01:12:32.511044: Epoch time: 102.97 s +2026-04-14 01:12:34.089890: +2026-04-14 01:12:34.091640: Epoch 2961 +2026-04-14 01:12:34.093127: Current learning rate: 0.00297 +2026-04-14 01:14:19.683450: train_loss -0.3972 +2026-04-14 01:14:19.688545: val_loss -0.3005 +2026-04-14 01:14:19.690248: Pseudo dice [0.3241, 0.2156, 0.666, 0.1534, 0.4568, 0.3672, 0.7244] +2026-04-14 01:14:19.692236: Epoch time: 105.6 s +2026-04-14 01:14:21.266472: +2026-04-14 01:14:21.268440: Epoch 2962 +2026-04-14 01:14:21.270068: Current learning rate: 0.00297 +2026-04-14 01:16:03.974005: train_loss -0.3939 +2026-04-14 01:16:03.979155: val_loss -0.2804 +2026-04-14 01:16:03.981007: Pseudo dice [0.5769, 0.2197, 0.4653, 0.5589, 0.3104, 0.1827, 0.8593] +2026-04-14 01:16:03.983552: Epoch time: 102.71 s +2026-04-14 01:16:05.559391: +2026-04-14 01:16:05.561176: Epoch 2963 +2026-04-14 01:16:05.562742: Current learning rate: 0.00297 +2026-04-14 01:17:48.361775: train_loss -0.3849 +2026-04-14 01:17:48.367572: val_loss -0.3273 +2026-04-14 01:17:48.369328: Pseudo dice [0.6517, 0.724, 0.7321, 0.721, 0.3447, 0.8513, 0.6901] +2026-04-14 01:17:48.371726: Epoch time: 102.81 s +2026-04-14 01:17:51.047237: +2026-04-14 01:17:51.049619: Epoch 2964 +2026-04-14 01:17:51.051223: Current learning rate: 0.00296 +2026-04-14 01:19:33.973916: train_loss -0.3738 +2026-04-14 01:19:33.980336: val_loss -0.3615 +2026-04-14 01:19:33.985488: Pseudo dice [0.8634, 0.6127, 0.558, 0.1367, 0.3233, 0.6646, 0.7592] +2026-04-14 01:19:33.987909: Epoch time: 102.93 s +2026-04-14 01:19:35.552316: +2026-04-14 01:19:35.554213: Epoch 2965 +2026-04-14 01:19:35.556277: Current learning rate: 0.00296 +2026-04-14 01:21:18.754912: train_loss -0.3798 +2026-04-14 01:21:18.773096: val_loss -0.3683 +2026-04-14 01:21:18.779572: Pseudo dice [0.5477, 0.4956, 0.7831, 0.3069, 0.6752, 0.8039, 0.9014] +2026-04-14 01:21:18.785185: Epoch time: 103.21 s +2026-04-14 01:21:20.388158: +2026-04-14 01:21:20.389978: Epoch 2966 +2026-04-14 01:21:20.392002: Current learning rate: 0.00296 +2026-04-14 01:23:02.940510: train_loss -0.4069 +2026-04-14 01:23:02.947904: val_loss -0.3616 +2026-04-14 01:23:02.950316: Pseudo dice [0.3431, 0.3371, 0.7853, 0.801, 0.5483, 0.8449, 0.7874] +2026-04-14 01:23:02.952867: Epoch time: 102.56 s +2026-04-14 01:23:04.545849: +2026-04-14 01:23:04.547466: Epoch 2967 +2026-04-14 01:23:04.549210: Current learning rate: 0.00296 +2026-04-14 01:24:47.910289: train_loss -0.3933 +2026-04-14 01:24:47.918566: val_loss -0.3506 +2026-04-14 01:24:47.921855: Pseudo dice [0.4062, 0.3207, 0.7246, 0.7017, 0.6227, 0.3738, 0.7999] +2026-04-14 01:24:47.926095: Epoch time: 103.37 s +2026-04-14 01:24:49.516947: +2026-04-14 01:24:49.518541: Epoch 2968 +2026-04-14 01:24:49.520150: Current learning rate: 0.00295 +2026-04-14 01:26:32.530106: train_loss -0.3966 +2026-04-14 01:26:32.535344: val_loss -0.3276 +2026-04-14 01:26:32.537340: Pseudo dice [0.7896, 0.749, 0.7053, 0.5703, 0.3114, 0.7103, 0.3366] +2026-04-14 01:26:32.539415: Epoch time: 103.02 s +2026-04-14 01:26:34.160548: +2026-04-14 01:26:34.162632: Epoch 2969 +2026-04-14 01:26:34.164166: Current learning rate: 0.00295 +2026-04-14 01:28:17.082978: train_loss -0.3957 +2026-04-14 01:28:17.090435: val_loss -0.3481 +2026-04-14 01:28:17.092890: Pseudo dice [0.5573, 0.4517, 0.7179, 0.381, 0.715, 0.3852, 0.6447] +2026-04-14 01:28:17.096006: Epoch time: 102.93 s +2026-04-14 01:28:18.655020: +2026-04-14 01:28:18.657193: Epoch 2970 +2026-04-14 01:28:18.659112: Current learning rate: 0.00295 +2026-04-14 01:30:01.546704: train_loss -0.4038 +2026-04-14 01:30:01.552599: val_loss -0.3231 +2026-04-14 01:30:01.554525: Pseudo dice [0.4765, 0.4399, 0.4429, 0.5549, 0.671, 0.1139, 0.85] +2026-04-14 01:30:01.556779: Epoch time: 102.9 s +2026-04-14 01:30:03.102183: +2026-04-14 01:30:03.103861: Epoch 2971 +2026-04-14 01:30:03.105561: Current learning rate: 0.00295 +2026-04-14 01:31:45.837246: train_loss -0.4122 +2026-04-14 01:31:45.842602: val_loss -0.2759 +2026-04-14 01:31:45.844301: Pseudo dice [0.4909, 0.5744, 0.8045, 0.1309, 0.3228, 0.1887, 0.6279] +2026-04-14 01:31:45.846703: Epoch time: 102.74 s +2026-04-14 01:31:47.391363: +2026-04-14 01:31:47.393428: Epoch 2972 +2026-04-14 01:31:47.394950: Current learning rate: 0.00294 +2026-04-14 01:33:30.117293: train_loss -0.4048 +2026-04-14 01:33:30.123803: val_loss -0.2589 +2026-04-14 01:33:30.125727: Pseudo dice [0.6109, 0.2816, 0.6338, 0.3602, 0.5704, 0.1686, 0.6412] +2026-04-14 01:33:30.128933: Epoch time: 102.73 s +2026-04-14 01:33:31.713126: +2026-04-14 01:33:31.715709: Epoch 2973 +2026-04-14 01:33:31.717387: Current learning rate: 0.00294 +2026-04-14 01:35:14.051369: train_loss -0.4115 +2026-04-14 01:35:14.056301: val_loss -0.3656 +2026-04-14 01:35:14.058484: Pseudo dice [0.6583, 0.6795, 0.7219, 0.8802, 0.5968, 0.4999, 0.826] +2026-04-14 01:35:14.060644: Epoch time: 102.34 s +2026-04-14 01:35:15.606386: +2026-04-14 01:35:15.608325: Epoch 2974 +2026-04-14 01:35:15.609984: Current learning rate: 0.00294 +2026-04-14 01:36:59.546803: train_loss -0.3966 +2026-04-14 01:36:59.553116: val_loss -0.3361 +2026-04-14 01:36:59.556421: Pseudo dice [0.8025, 0.484, 0.7468, 0.2761, 0.4725, 0.4957, 0.6364] +2026-04-14 01:36:59.561250: Epoch time: 103.94 s +2026-04-14 01:37:01.134016: +2026-04-14 01:37:01.135721: Epoch 2975 +2026-04-14 01:37:01.137787: Current learning rate: 0.00294 +2026-04-14 01:38:43.740204: train_loss -0.4049 +2026-04-14 01:38:43.754675: val_loss -0.3545 +2026-04-14 01:38:43.756543: Pseudo dice [0.8268, 0.4189, 0.7859, 0.2028, 0.3384, 0.7758, 0.441] +2026-04-14 01:38:43.759404: Epoch time: 102.61 s +2026-04-14 01:38:45.313856: +2026-04-14 01:38:45.315448: Epoch 2976 +2026-04-14 01:38:45.316980: Current learning rate: 0.00293 +2026-04-14 01:40:28.954147: train_loss -0.4009 +2026-04-14 01:40:28.963652: val_loss -0.3695 +2026-04-14 01:40:28.967417: Pseudo dice [0.8653, 0.8273, 0.7175, 0.75, 0.6219, 0.8062, 0.8248] +2026-04-14 01:40:28.971596: Epoch time: 103.64 s +2026-04-14 01:40:30.564446: +2026-04-14 01:40:30.566875: Epoch 2977 +2026-04-14 01:40:30.569705: Current learning rate: 0.00293 +2026-04-14 01:42:13.354034: train_loss -0.4163 +2026-04-14 01:42:13.360047: val_loss -0.3598 +2026-04-14 01:42:13.363773: Pseudo dice [0.6235, 0.7865, 0.7367, 0.8555, 0.642, 0.689, 0.8712] +2026-04-14 01:42:13.367284: Epoch time: 102.79 s +2026-04-14 01:42:14.931030: +2026-04-14 01:42:14.932943: Epoch 2978 +2026-04-14 01:42:14.936308: Current learning rate: 0.00293 +2026-04-14 01:43:57.893744: train_loss -0.4114 +2026-04-14 01:43:57.901430: val_loss -0.266 +2026-04-14 01:43:57.903706: Pseudo dice [0.6049, 0.1202, 0.6983, 0.4837, 0.6261, 0.2854, 0.8213] +2026-04-14 01:43:57.907041: Epoch time: 102.97 s +2026-04-14 01:43:59.439136: +2026-04-14 01:43:59.441410: Epoch 2979 +2026-04-14 01:43:59.442994: Current learning rate: 0.00293 +2026-04-14 01:45:42.303913: train_loss -0.3967 +2026-04-14 01:45:42.311308: val_loss -0.2666 +2026-04-14 01:45:42.315919: Pseudo dice [0.6726, 0.8516, 0.7607, 0.2104, 0.6606, 0.1859, 0.79] +2026-04-14 01:45:42.318932: Epoch time: 102.87 s +2026-04-14 01:45:43.897085: +2026-04-14 01:45:43.898855: Epoch 2980 +2026-04-14 01:45:43.900486: Current learning rate: 0.00292 +2026-04-14 01:47:26.814881: train_loss -0.3982 +2026-04-14 01:47:26.821424: val_loss -0.3198 +2026-04-14 01:47:26.824702: Pseudo dice [0.3474, 0.2066, 0.5527, 0.7012, 0.2744, 0.2282, 0.7531] +2026-04-14 01:47:26.827267: Epoch time: 102.92 s +2026-04-14 01:47:28.457363: +2026-04-14 01:47:28.459341: Epoch 2981 +2026-04-14 01:47:28.461137: Current learning rate: 0.00292 +2026-04-14 01:49:11.412544: train_loss -0.3987 +2026-04-14 01:49:11.418316: val_loss -0.2846 +2026-04-14 01:49:11.420492: Pseudo dice [0.5756, 0.2397, 0.6664, 0.4695, 0.5988, 0.0889, 0.9055] +2026-04-14 01:49:11.422513: Epoch time: 102.96 s +2026-04-14 01:49:12.957359: +2026-04-14 01:49:12.959319: Epoch 2982 +2026-04-14 01:49:12.961125: Current learning rate: 0.00292 +2026-04-14 01:50:56.008057: train_loss -0.4077 +2026-04-14 01:50:56.014915: val_loss -0.3412 +2026-04-14 01:50:56.020059: Pseudo dice [0.0631, 0.2311, 0.539, 0.0368, 0.3741, 0.8103, 0.8948] +2026-04-14 01:50:56.022640: Epoch time: 103.05 s +2026-04-14 01:50:57.599131: +2026-04-14 01:50:57.601075: Epoch 2983 +2026-04-14 01:50:57.602964: Current learning rate: 0.00292 +2026-04-14 01:52:42.048445: train_loss -0.3906 +2026-04-14 01:52:42.054212: val_loss -0.3232 +2026-04-14 01:52:42.056275: Pseudo dice [0.1707, 0.3387, 0.6992, 0.0269, 0.6261, 0.7165, 0.5274] +2026-04-14 01:52:42.058425: Epoch time: 104.45 s +2026-04-14 01:52:43.635455: +2026-04-14 01:52:43.637430: Epoch 2984 +2026-04-14 01:52:43.639551: Current learning rate: 0.00291 +2026-04-14 01:54:26.504676: train_loss -0.403 +2026-04-14 01:54:26.511028: val_loss -0.3174 +2026-04-14 01:54:26.513555: Pseudo dice [0.6904, 0.7715, 0.5836, 0.4564, 0.5809, 0.1493, 0.6726] +2026-04-14 01:54:26.516459: Epoch time: 102.87 s +2026-04-14 01:54:28.102554: +2026-04-14 01:54:28.105515: Epoch 2985 +2026-04-14 01:54:28.107597: Current learning rate: 0.00291 +2026-04-14 01:56:11.048030: train_loss -0.3932 +2026-04-14 01:56:11.052584: val_loss -0.2897 +2026-04-14 01:56:11.054295: Pseudo dice [0.5773, 0.6874, 0.7463, 0.7984, 0.2433, 0.2185, 0.6318] +2026-04-14 01:56:11.056212: Epoch time: 102.95 s +2026-04-14 01:56:12.604174: +2026-04-14 01:56:12.606421: Epoch 2986 +2026-04-14 01:56:12.608363: Current learning rate: 0.00291 +2026-04-14 01:57:55.816396: train_loss -0.3821 +2026-04-14 01:57:55.822290: val_loss -0.3417 +2026-04-14 01:57:55.824553: Pseudo dice [0.6663, 0.5821, 0.7078, 0.8597, 0.619, 0.3506, 0.7469] +2026-04-14 01:57:55.827387: Epoch time: 103.22 s +2026-04-14 01:57:57.396090: +2026-04-14 01:57:57.397704: Epoch 2987 +2026-04-14 01:57:57.399383: Current learning rate: 0.00291 +2026-04-14 01:59:40.337632: train_loss -0.391 +2026-04-14 01:59:40.342800: val_loss -0.3294 +2026-04-14 01:59:40.344429: Pseudo dice [0.5591, 0.4939, 0.705, 0.0414, 0.5285, 0.6158, 0.6803] +2026-04-14 01:59:40.346678: Epoch time: 102.95 s +2026-04-14 01:59:41.917491: +2026-04-14 01:59:41.919974: Epoch 2988 +2026-04-14 01:59:41.921886: Current learning rate: 0.0029 +2026-04-14 02:01:25.248765: train_loss -0.3942 +2026-04-14 02:01:25.254637: val_loss -0.3308 +2026-04-14 02:01:25.256854: Pseudo dice [0.5697, 0.1467, 0.7773, 0.5196, 0.6009, 0.7737, 0.739] +2026-04-14 02:01:25.259237: Epoch time: 103.33 s +2026-04-14 02:01:26.858680: +2026-04-14 02:01:26.861001: Epoch 2989 +2026-04-14 02:01:26.862649: Current learning rate: 0.0029 +2026-04-14 02:03:10.479874: train_loss -0.3931 +2026-04-14 02:03:10.485857: val_loss -0.2935 +2026-04-14 02:03:10.488347: Pseudo dice [0.7038, 0.688, 0.6686, 0.5012, 0.3063, 0.1785, 0.5432] +2026-04-14 02:03:10.491040: Epoch time: 103.62 s +2026-04-14 02:03:12.092492: +2026-04-14 02:03:12.094523: Epoch 2990 +2026-04-14 02:03:12.096934: Current learning rate: 0.0029 +2026-04-14 02:04:55.572948: train_loss -0.4064 +2026-04-14 02:04:55.580709: val_loss -0.3073 +2026-04-14 02:04:55.583057: Pseudo dice [0.5405, 0.0, 0.693, 0.7495, 0.4316, 0.2697, 0.681] +2026-04-14 02:04:55.585303: Epoch time: 103.48 s +2026-04-14 02:04:57.241499: +2026-04-14 02:04:57.243533: Epoch 2991 +2026-04-14 02:04:57.245646: Current learning rate: 0.00289 +2026-04-14 02:06:40.270087: train_loss -0.3996 +2026-04-14 02:06:40.276707: val_loss -0.3433 +2026-04-14 02:06:40.278816: Pseudo dice [0.7789, 0.4671, 0.7399, 0.1993, 0.6349, 0.5483, 0.4494] +2026-04-14 02:06:40.282135: Epoch time: 103.03 s +2026-04-14 02:06:41.861268: +2026-04-14 02:06:41.864524: Epoch 2992 +2026-04-14 02:06:41.866632: Current learning rate: 0.00289 +2026-04-14 02:08:25.257091: train_loss -0.3902 +2026-04-14 02:08:25.263211: val_loss -0.3112 +2026-04-14 02:08:25.265648: Pseudo dice [0.4385, 0.2425, 0.7, 0.359, 0.5183, 0.087, 0.5633] +2026-04-14 02:08:25.268687: Epoch time: 103.4 s +2026-04-14 02:08:26.812310: +2026-04-14 02:08:26.815069: Epoch 2993 +2026-04-14 02:08:26.817932: Current learning rate: 0.00289 +2026-04-14 02:10:10.127919: train_loss -0.4078 +2026-04-14 02:10:10.133013: val_loss -0.3222 +2026-04-14 02:10:10.135053: Pseudo dice [0.8186, 0.6213, 0.7413, 0.1074, 0.3975, 0.3309, 0.6647] +2026-04-14 02:10:10.137017: Epoch time: 103.32 s +2026-04-14 02:10:11.712873: +2026-04-14 02:10:11.715936: Epoch 2994 +2026-04-14 02:10:11.718583: Current learning rate: 0.00289 +2026-04-14 02:11:55.248405: train_loss -0.4005 +2026-04-14 02:11:55.254836: val_loss -0.3158 +2026-04-14 02:11:55.257139: Pseudo dice [0.8521, 0.4745, 0.7168, 0.904, 0.5452, 0.1335, 0.9501] +2026-04-14 02:11:55.259508: Epoch time: 103.54 s +2026-04-14 02:11:56.846863: +2026-04-14 02:11:56.848901: Epoch 2995 +2026-04-14 02:11:56.851087: Current learning rate: 0.00288 +2026-04-14 02:13:39.761979: train_loss -0.402 +2026-04-14 02:13:39.769757: val_loss -0.3003 +2026-04-14 02:13:39.774058: Pseudo dice [0.6553, 0.7708, 0.6218, 0.1283, 0.599, 0.1373, 0.8166] +2026-04-14 02:13:39.778571: Epoch time: 102.92 s +2026-04-14 02:13:41.311654: +2026-04-14 02:13:41.313594: Epoch 2996 +2026-04-14 02:13:41.315814: Current learning rate: 0.00288 +2026-04-14 02:15:24.936949: train_loss -0.4226 +2026-04-14 02:15:24.942960: val_loss -0.3255 +2026-04-14 02:15:24.944907: Pseudo dice [0.7884, 0.4771, 0.6444, 0.746, 0.5249, 0.2803, 0.6351] +2026-04-14 02:15:24.947316: Epoch time: 103.63 s +2026-04-14 02:15:26.474877: +2026-04-14 02:15:26.477654: Epoch 2997 +2026-04-14 02:15:26.480298: Current learning rate: 0.00288 +2026-04-14 02:17:11.467086: train_loss -0.4121 +2026-04-14 02:17:11.475269: val_loss -0.2893 +2026-04-14 02:17:11.477359: Pseudo dice [0.6151, 0.4553, 0.7392, 0.9091, 0.2819, 0.3965, 0.3949] +2026-04-14 02:17:11.480153: Epoch time: 105.0 s +2026-04-14 02:17:13.001335: +2026-04-14 02:17:13.003202: Epoch 2998 +2026-04-14 02:17:13.005087: Current learning rate: 0.00288 +2026-04-14 02:18:56.096947: train_loss -0.3964 +2026-04-14 02:18:56.104248: val_loss -0.3684 +2026-04-14 02:18:56.106453: Pseudo dice [0.5547, 0.592, 0.6801, 0.8422, 0.5304, 0.8026, 0.8646] +2026-04-14 02:18:56.110117: Epoch time: 103.1 s +2026-04-14 02:18:57.722939: +2026-04-14 02:18:57.724976: Epoch 2999 +2026-04-14 02:18:57.727090: Current learning rate: 0.00287 +2026-04-14 02:20:40.997346: train_loss -0.4081 +2026-04-14 02:20:41.006383: val_loss -0.2493 +2026-04-14 02:20:41.008779: Pseudo dice [0.8909, 0.5727, 0.6627, 0.8181, 0.4584, 0.0179, 0.8252] +2026-04-14 02:20:41.011407: Epoch time: 103.28 s +2026-04-14 02:20:44.664393: +2026-04-14 02:20:44.668954: Epoch 3000 +2026-04-14 02:20:44.675126: Current learning rate: 0.00287 +2026-04-14 02:22:27.993456: train_loss -0.4062 +2026-04-14 02:22:27.999544: val_loss -0.2992 +2026-04-14 02:22:28.001529: Pseudo dice [0.5081, 0.4399, 0.7756, 0.8319, 0.4475, 0.0865, 0.6042] +2026-04-14 02:22:28.004148: Epoch time: 103.33 s +2026-04-14 02:22:29.677952: +2026-04-14 02:22:29.680553: Epoch 3001 +2026-04-14 02:22:29.682539: Current learning rate: 0.00287 +2026-04-14 02:24:12.889937: train_loss -0.3961 +2026-04-14 02:24:12.895958: val_loss -0.3448 +2026-04-14 02:24:12.898404: Pseudo dice [0.6733, 0.4129, 0.5348, 0.833, 0.5814, 0.4575, 0.7345] +2026-04-14 02:24:12.901005: Epoch time: 103.22 s +2026-04-14 02:24:14.557816: +2026-04-14 02:24:14.561098: Epoch 3002 +2026-04-14 02:24:14.563998: Current learning rate: 0.00287 +2026-04-14 02:25:58.199757: train_loss -0.4171 +2026-04-14 02:25:58.207080: val_loss -0.3425 +2026-04-14 02:25:58.209990: Pseudo dice [0.4521, 0.4309, 0.7747, 0.4092, 0.5372, 0.5286, 0.8431] +2026-04-14 02:25:58.214323: Epoch time: 103.65 s +2026-04-14 02:26:00.936858: +2026-04-14 02:26:00.938899: Epoch 3003 +2026-04-14 02:26:00.941724: Current learning rate: 0.00286 +2026-04-14 02:27:44.984026: train_loss -0.4035 +2026-04-14 02:27:44.990813: val_loss -0.2648 +2026-04-14 02:27:44.992984: Pseudo dice [0.8535, 0.3124, 0.5877, 0.8788, 0.5679, 0.1467, 0.8823] +2026-04-14 02:27:44.996180: Epoch time: 104.05 s +2026-04-14 02:27:46.628627: +2026-04-14 02:27:46.632134: Epoch 3004 +2026-04-14 02:27:46.636047: Current learning rate: 0.00286 +2026-04-14 02:29:31.399350: train_loss -0.4028 +2026-04-14 02:29:31.405118: val_loss -0.2751 +2026-04-14 02:29:31.407015: Pseudo dice [0.5875, 0.2297, 0.6705, 0.6977, 0.3658, 0.1593, 0.4097] +2026-04-14 02:29:31.409497: Epoch time: 104.77 s +2026-04-14 02:29:32.963797: +2026-04-14 02:29:32.966277: Epoch 3005 +2026-04-14 02:29:32.969190: Current learning rate: 0.00286 +2026-04-14 02:31:16.664562: train_loss -0.4054 +2026-04-14 02:31:16.671522: val_loss -0.3523 +2026-04-14 02:31:16.674106: Pseudo dice [0.7701, 0.3293, 0.7028, 0.799, 0.58, 0.8421, 0.7166] +2026-04-14 02:31:16.677698: Epoch time: 103.7 s +2026-04-14 02:31:18.300531: +2026-04-14 02:31:18.304226: Epoch 3006 +2026-04-14 02:31:18.307276: Current learning rate: 0.00286 +2026-04-14 02:33:02.016006: train_loss -0.4153 +2026-04-14 02:33:02.023006: val_loss -0.3137 +2026-04-14 02:33:02.025496: Pseudo dice [0.376, 0.8007, 0.7276, 0.799, 0.4615, 0.3297, 0.7099] +2026-04-14 02:33:02.028582: Epoch time: 103.72 s +2026-04-14 02:33:03.631761: +2026-04-14 02:33:03.633983: Epoch 3007 +2026-04-14 02:33:03.636268: Current learning rate: 0.00285 +2026-04-14 02:34:46.645852: train_loss -0.3993 +2026-04-14 02:34:46.655422: val_loss -0.3607 +2026-04-14 02:34:46.659482: Pseudo dice [0.6551, 0.1607, 0.7419, 0.8676, 0.6678, 0.2667, 0.525] +2026-04-14 02:34:46.661979: Epoch time: 103.02 s +2026-04-14 02:34:48.223225: +2026-04-14 02:34:48.225071: Epoch 3008 +2026-04-14 02:34:48.229090: Current learning rate: 0.00285 +2026-04-14 02:36:31.969094: train_loss -0.4028 +2026-04-14 02:36:31.977287: val_loss -0.275 +2026-04-14 02:36:31.979512: Pseudo dice [0.1578, 0.8024, 0.5708, 0.6608, 0.4966, 0.1081, 0.7607] +2026-04-14 02:36:31.982048: Epoch time: 103.75 s +2026-04-14 02:36:33.515657: +2026-04-14 02:36:33.517515: Epoch 3009 +2026-04-14 02:36:33.520286: Current learning rate: 0.00285 +2026-04-14 02:38:20.267174: train_loss -0.4016 +2026-04-14 02:38:20.276539: val_loss -0.2896 +2026-04-14 02:38:20.279013: Pseudo dice [0.2281, 0.3824, 0.7847, 0.3364, 0.5806, 0.138, 0.8002] +2026-04-14 02:38:20.281600: Epoch time: 106.75 s +2026-04-14 02:38:21.876701: +2026-04-14 02:38:21.878464: Epoch 3010 +2026-04-14 02:38:21.880655: Current learning rate: 0.00285 +2026-04-14 02:40:05.560330: train_loss -0.3978 +2026-04-14 02:40:05.565978: val_loss -0.2519 +2026-04-14 02:40:05.568241: Pseudo dice [0.7616, 0.6246, 0.5467, 0.3521, 0.4786, 0.2335, 0.7322] +2026-04-14 02:40:05.570800: Epoch time: 103.69 s +2026-04-14 02:40:07.236568: +2026-04-14 02:40:07.238710: Epoch 3011 +2026-04-14 02:40:07.241060: Current learning rate: 0.00284 +2026-04-14 02:41:50.863519: train_loss -0.3958 +2026-04-14 02:41:50.870909: val_loss -0.2827 +2026-04-14 02:41:50.873471: Pseudo dice [0.8182, 0.4213, 0.685, 0.3414, 0.3692, 0.1293, 0.3723] +2026-04-14 02:41:50.876604: Epoch time: 103.63 s +2026-04-14 02:41:52.444181: +2026-04-14 02:41:52.446189: Epoch 3012 +2026-04-14 02:41:52.447903: Current learning rate: 0.00284 +2026-04-14 02:43:36.030939: train_loss -0.4034 +2026-04-14 02:43:36.038832: val_loss -0.2363 +2026-04-14 02:43:36.041932: Pseudo dice [0.6765, 0.7114, 0.6924, 0.6353, 0.5383, 0.1007, 0.8058] +2026-04-14 02:43:36.044864: Epoch time: 103.59 s +2026-04-14 02:43:37.656643: +2026-04-14 02:43:37.659419: Epoch 3013 +2026-04-14 02:43:37.662310: Current learning rate: 0.00284 +2026-04-14 02:45:21.342186: train_loss -0.4039 +2026-04-14 02:45:21.351912: val_loss -0.3791 +2026-04-14 02:45:21.355188: Pseudo dice [0.2321, 0.6766, 0.7689, 0.7604, 0.6231, 0.7603, 0.7459] +2026-04-14 02:45:21.359836: Epoch time: 103.69 s +2026-04-14 02:45:22.997816: +2026-04-14 02:45:23.000332: Epoch 3014 +2026-04-14 02:45:23.002662: Current learning rate: 0.00284 +2026-04-14 02:47:06.098407: train_loss -0.3988 +2026-04-14 02:47:06.104837: val_loss -0.2456 +2026-04-14 02:47:06.106888: Pseudo dice [0.1542, 0.3067, 0.5772, 0.8333, 0.6862, 0.0979, 0.9372] +2026-04-14 02:47:06.109825: Epoch time: 103.1 s +2026-04-14 02:47:07.751724: +2026-04-14 02:47:07.753816: Epoch 3015 +2026-04-14 02:47:07.756319: Current learning rate: 0.00283 +2026-04-14 02:48:52.590242: train_loss -0.4046 +2026-04-14 02:48:52.595531: val_loss -0.3624 +2026-04-14 02:48:52.597508: Pseudo dice [0.7168, 0.3291, 0.7148, 0.8599, 0.5532, 0.8315, 0.8595] +2026-04-14 02:48:52.599694: Epoch time: 104.84 s +2026-04-14 02:48:54.238584: +2026-04-14 02:48:54.241037: Epoch 3016 +2026-04-14 02:48:54.243095: Current learning rate: 0.00283 +2026-04-14 02:50:38.042663: train_loss -0.4096 +2026-04-14 02:50:38.049764: val_loss -0.3125 +2026-04-14 02:50:38.051625: Pseudo dice [0.8763, 0.3816, 0.7408, 0.8382, 0.5648, 0.2042, 0.7659] +2026-04-14 02:50:38.054067: Epoch time: 103.81 s +2026-04-14 02:50:39.627248: +2026-04-14 02:50:39.629390: Epoch 3017 +2026-04-14 02:50:39.631645: Current learning rate: 0.00283 +2026-04-14 02:52:22.982473: train_loss -0.3964 +2026-04-14 02:52:22.989623: val_loss -0.2636 +2026-04-14 02:52:22.992012: Pseudo dice [0.1716, 0.7384, 0.615, 0.0063, 0.6881, 0.0577, 0.8115] +2026-04-14 02:52:22.994537: Epoch time: 103.36 s +2026-04-14 02:52:24.555229: +2026-04-14 02:52:24.557497: Epoch 3018 +2026-04-14 02:52:24.559448: Current learning rate: 0.00283 +2026-04-14 02:54:08.161106: train_loss -0.3995 +2026-04-14 02:54:08.167554: val_loss -0.2758 +2026-04-14 02:54:08.170758: Pseudo dice [0.7099, 0.6122, 0.6838, 0.6155, 0.2678, 0.0915, 0.4407] +2026-04-14 02:54:08.173377: Epoch time: 103.61 s +2026-04-14 02:54:09.728414: +2026-04-14 02:54:09.730479: Epoch 3019 +2026-04-14 02:54:09.732676: Current learning rate: 0.00282 +2026-04-14 02:55:53.559536: train_loss -0.4078 +2026-04-14 02:55:53.574868: val_loss -0.304 +2026-04-14 02:55:53.580278: Pseudo dice [0.27, 0.7036, 0.4273, 0.4583, 0.419, 0.7051, 0.478] +2026-04-14 02:55:53.584296: Epoch time: 103.83 s +2026-04-14 02:55:55.156590: +2026-04-14 02:55:55.158465: Epoch 3020 +2026-04-14 02:55:55.161453: Current learning rate: 0.00282 +2026-04-14 02:57:38.492004: train_loss -0.4067 +2026-04-14 02:57:38.497697: val_loss -0.2822 +2026-04-14 02:57:38.500069: Pseudo dice [0.7823, 0.7325, 0.7439, 0.5809, 0.2371, 0.4025, 0.5529] +2026-04-14 02:57:38.502830: Epoch time: 103.34 s +2026-04-14 02:57:40.087827: +2026-04-14 02:57:40.089651: Epoch 3021 +2026-04-14 02:57:40.091917: Current learning rate: 0.00282 +2026-04-14 02:59:23.097113: train_loss -0.412 +2026-04-14 02:59:23.104130: val_loss -0.2765 +2026-04-14 02:59:23.107008: Pseudo dice [0.8174, 0.8263, 0.5588, 0.6771, 0.3707, 0.0338, 0.8396] +2026-04-14 02:59:23.110323: Epoch time: 103.01 s +2026-04-14 02:59:24.665571: +2026-04-14 02:59:24.667445: Epoch 3022 +2026-04-14 02:59:24.669694: Current learning rate: 0.00281 +2026-04-14 03:01:08.375985: train_loss -0.3967 +2026-04-14 03:01:08.382055: val_loss -0.3136 +2026-04-14 03:01:08.384553: Pseudo dice [0.6599, 0.6679, 0.696, 0.1159, 0.5895, 0.0892, 0.8327] +2026-04-14 03:01:08.389135: Epoch time: 103.71 s +2026-04-14 03:01:11.083793: +2026-04-14 03:01:11.086065: Epoch 3023 +2026-04-14 03:01:11.087945: Current learning rate: 0.00281 +2026-04-14 03:02:56.684115: train_loss -0.4035 +2026-04-14 03:02:56.691597: val_loss -0.3293 +2026-04-14 03:02:56.694693: Pseudo dice [0.4634, 0.711, 0.5735, 0.5878, 0.616, 0.3502, 0.851] +2026-04-14 03:02:56.697454: Epoch time: 105.6 s +2026-04-14 03:02:58.251891: +2026-04-14 03:02:58.255445: Epoch 3024 +2026-04-14 03:02:58.258549: Current learning rate: 0.00281 +2026-04-14 03:04:41.473232: train_loss -0.4232 +2026-04-14 03:04:41.481632: val_loss -0.372 +2026-04-14 03:04:41.484225: Pseudo dice [0.4987, 0.7242, 0.6618, 0.801, 0.221, 0.7, 0.7991] +2026-04-14 03:04:41.487525: Epoch time: 103.22 s +2026-04-14 03:04:43.105265: +2026-04-14 03:04:43.109155: Epoch 3025 +2026-04-14 03:04:43.111958: Current learning rate: 0.00281 +2026-04-14 03:06:29.523359: train_loss -0.4114 +2026-04-14 03:06:29.534606: val_loss -0.2847 +2026-04-14 03:06:29.537896: Pseudo dice [0.4696, 0.8035, 0.7257, 0.3296, 0.4128, 0.1457, 0.5923] +2026-04-14 03:06:29.541489: Epoch time: 106.42 s +2026-04-14 03:06:31.189862: +2026-04-14 03:06:31.192953: Epoch 3026 +2026-04-14 03:06:31.195308: Current learning rate: 0.0028 +2026-04-14 03:08:14.927221: train_loss -0.3977 +2026-04-14 03:08:14.934894: val_loss -0.3857 +2026-04-14 03:08:14.937929: Pseudo dice [0.6513, 0.446, 0.6804, 0.1531, 0.6688, 0.7862, 0.6092] +2026-04-14 03:08:14.940932: Epoch time: 103.74 s +2026-04-14 03:08:16.536773: +2026-04-14 03:08:16.539194: Epoch 3027 +2026-04-14 03:08:16.541185: Current learning rate: 0.0028 +2026-04-14 03:10:01.406810: train_loss -0.4079 +2026-04-14 03:10:01.413475: val_loss -0.3428 +2026-04-14 03:10:01.415956: Pseudo dice [0.7017, 0.5514, 0.6509, 0.7376, 0.46, 0.1364, 0.8655] +2026-04-14 03:10:01.419106: Epoch time: 104.87 s +2026-04-14 03:10:03.068946: +2026-04-14 03:10:03.070984: Epoch 3028 +2026-04-14 03:10:03.073394: Current learning rate: 0.0028 +2026-04-14 03:11:47.245823: train_loss -0.4233 +2026-04-14 03:11:47.262625: val_loss -0.3351 +2026-04-14 03:11:47.266897: Pseudo dice [0.6516, 0.8006, 0.7106, 0.8709, 0.0859, 0.7607, 0.5847] +2026-04-14 03:11:47.271544: Epoch time: 104.18 s +2026-04-14 03:11:48.808617: +2026-04-14 03:11:48.810440: Epoch 3029 +2026-04-14 03:11:48.812387: Current learning rate: 0.0028 +2026-04-14 03:13:33.586687: train_loss -0.4136 +2026-04-14 03:13:33.592901: val_loss -0.369 +2026-04-14 03:13:33.594800: Pseudo dice [0.8705, 0.6635, 0.5442, 0.8409, 0.5706, 0.7729, 0.8355] +2026-04-14 03:13:33.597071: Epoch time: 104.78 s +2026-04-14 03:13:35.143858: +2026-04-14 03:13:35.145665: Epoch 3030 +2026-04-14 03:13:35.148200: Current learning rate: 0.00279 +2026-04-14 03:15:19.046870: train_loss -0.4107 +2026-04-14 03:15:19.055208: val_loss -0.3029 +2026-04-14 03:15:19.058164: Pseudo dice [0.2017, 0.4231, 0.5823, 0.4768, 0.3285, 0.1242, 0.7762] +2026-04-14 03:15:19.061430: Epoch time: 103.9 s +2026-04-14 03:15:20.696840: +2026-04-14 03:15:20.699187: Epoch 3031 +2026-04-14 03:15:20.701216: Current learning rate: 0.00279 +2026-04-14 03:17:04.037958: train_loss -0.4034 +2026-04-14 03:17:04.047611: val_loss -0.3533 +2026-04-14 03:17:04.050421: Pseudo dice [0.828, 0.7129, 0.5801, 0.8496, 0.3825, 0.6799, 0.8044] +2026-04-14 03:17:04.052754: Epoch time: 103.34 s +2026-04-14 03:17:05.694258: +2026-04-14 03:17:05.696429: Epoch 3032 +2026-04-14 03:17:05.698832: Current learning rate: 0.00279 +2026-04-14 03:18:50.149135: train_loss -0.41 +2026-04-14 03:18:50.155709: val_loss -0.3572 +2026-04-14 03:18:50.158044: Pseudo dice [0.6095, 0.3292, 0.695, 0.4614, 0.5121, 0.8313, 0.7653] +2026-04-14 03:18:50.160271: Epoch time: 104.46 s +2026-04-14 03:18:51.743141: +2026-04-14 03:18:51.745287: Epoch 3033 +2026-04-14 03:18:51.748571: Current learning rate: 0.00279 +2026-04-14 03:20:36.071317: train_loss -0.4044 +2026-04-14 03:20:36.079535: val_loss -0.3579 +2026-04-14 03:20:36.081895: Pseudo dice [0.671, 0.3102, 0.8023, 0.3884, 0.4496, 0.7533, 0.7903] +2026-04-14 03:20:36.084604: Epoch time: 104.33 s +2026-04-14 03:20:37.651081: +2026-04-14 03:20:37.654360: Epoch 3034 +2026-04-14 03:20:37.658869: Current learning rate: 0.00278 +2026-04-14 03:22:21.786967: train_loss -0.4097 +2026-04-14 03:22:21.801920: val_loss -0.3244 +2026-04-14 03:22:21.807071: Pseudo dice [0.4541, 0.2549, 0.8059, 0.9033, 0.5939, 0.5745, 0.3836] +2026-04-14 03:22:21.810422: Epoch time: 104.14 s +2026-04-14 03:22:23.371023: +2026-04-14 03:22:23.378782: Epoch 3035 +2026-04-14 03:22:23.381737: Current learning rate: 0.00278 +2026-04-14 03:24:07.097829: train_loss -0.4183 +2026-04-14 03:24:07.110106: val_loss -0.3154 +2026-04-14 03:24:07.113172: Pseudo dice [0.4708, 0.7657, 0.6873, 0.364, 0.6047, 0.1355, 0.8836] +2026-04-14 03:24:07.115663: Epoch time: 103.73 s +2026-04-14 03:24:08.764246: +2026-04-14 03:24:08.767936: Epoch 3036 +2026-04-14 03:24:08.771324: Current learning rate: 0.00278 +2026-04-14 03:25:53.382508: train_loss -0.4126 +2026-04-14 03:25:53.390290: val_loss -0.2989 +2026-04-14 03:25:53.393749: Pseudo dice [0.48, 0.2908, 0.7317, 0.815, 0.6906, 0.1186, 0.8045] +2026-04-14 03:25:53.397303: Epoch time: 104.62 s +2026-04-14 03:25:54.979637: +2026-04-14 03:25:54.982116: Epoch 3037 +2026-04-14 03:25:54.985338: Current learning rate: 0.00278 +2026-04-14 03:27:40.141344: train_loss -0.4028 +2026-04-14 03:27:40.149341: val_loss -0.3267 +2026-04-14 03:27:40.153043: Pseudo dice [0.6436, 0.4452, 0.6779, 0.4691, 0.3337, 0.78, 0.8119] +2026-04-14 03:27:40.157824: Epoch time: 105.17 s +2026-04-14 03:27:41.779674: +2026-04-14 03:27:41.782387: Epoch 3038 +2026-04-14 03:27:41.784789: Current learning rate: 0.00277 +2026-04-14 03:29:25.892997: train_loss -0.3899 +2026-04-14 03:29:25.908232: val_loss -0.2482 +2026-04-14 03:29:25.913651: Pseudo dice [0.545, 0.554, 0.5696, 0.804, 0.3208, 0.1403, 0.9185] +2026-04-14 03:29:25.924657: Epoch time: 104.12 s +2026-04-14 03:29:27.464740: +2026-04-14 03:29:27.470521: Epoch 3039 +2026-04-14 03:29:27.475751: Current learning rate: 0.00277 +2026-04-14 03:31:11.384434: train_loss -0.3952 +2026-04-14 03:31:11.392056: val_loss -0.3262 +2026-04-14 03:31:11.394208: Pseudo dice [0.7406, 0.4329, 0.5909, 0.4942, 0.3121, 0.2053, 0.8243] +2026-04-14 03:31:11.396624: Epoch time: 103.92 s +2026-04-14 03:31:12.993757: +2026-04-14 03:31:12.995861: Epoch 3040 +2026-04-14 03:31:12.998220: Current learning rate: 0.00277 +2026-04-14 03:32:57.083918: train_loss -0.4039 +2026-04-14 03:32:57.093274: val_loss -0.234 +2026-04-14 03:32:57.097961: Pseudo dice [0.2722, 0.6012, 0.6223, 0.8348, 0.3951, 0.0669, 0.7589] +2026-04-14 03:32:57.103016: Epoch time: 104.09 s +2026-04-14 03:32:58.637839: +2026-04-14 03:32:58.641302: Epoch 3041 +2026-04-14 03:32:58.647497: Current learning rate: 0.00277 +2026-04-14 03:34:42.520101: train_loss -0.4102 +2026-04-14 03:34:42.537684: val_loss -0.3147 +2026-04-14 03:34:42.544074: Pseudo dice [0.8172, 0.6142, 0.7281, 0.8262, 0.6657, 0.0453, 0.8086] +2026-04-14 03:34:42.549672: Epoch time: 103.89 s +2026-04-14 03:34:44.174226: +2026-04-14 03:34:44.176800: Epoch 3042 +2026-04-14 03:34:44.179363: Current learning rate: 0.00276 +2026-04-14 03:36:28.447688: train_loss -0.4108 +2026-04-14 03:36:28.455409: val_loss -0.358 +2026-04-14 03:36:28.459128: Pseudo dice [0.7102, 0.2489, 0.7262, 0.5918, 0.5185, 0.7475, 0.5171] +2026-04-14 03:36:28.464282: Epoch time: 104.28 s +2026-04-14 03:36:31.120429: +2026-04-14 03:36:31.123202: Epoch 3043 +2026-04-14 03:36:31.126521: Current learning rate: 0.00276 +2026-04-14 03:38:16.775657: train_loss -0.3942 +2026-04-14 03:38:16.783551: val_loss -0.3543 +2026-04-14 03:38:16.786485: Pseudo dice [0.6704, 0.3131, 0.8017, 0.7134, 0.4833, 0.6851, 0.5301] +2026-04-14 03:38:16.788801: Epoch time: 105.66 s +2026-04-14 03:38:18.340838: +2026-04-14 03:38:18.343619: Epoch 3044 +2026-04-14 03:38:18.346166: Current learning rate: 0.00276 +2026-04-14 03:40:03.627190: train_loss -0.3926 +2026-04-14 03:40:03.633496: val_loss -0.3198 +2026-04-14 03:40:03.636103: Pseudo dice [0.5423, 0.705, 0.6379, 0.8346, 0.2605, 0.672, 0.6108] +2026-04-14 03:40:03.638733: Epoch time: 105.29 s +2026-04-14 03:40:05.205528: +2026-04-14 03:40:05.208129: Epoch 3045 +2026-04-14 03:40:05.211015: Current learning rate: 0.00276 +2026-04-14 03:41:49.282262: train_loss -0.3978 +2026-04-14 03:41:49.297802: val_loss -0.2892 +2026-04-14 03:41:49.303579: Pseudo dice [0.3201, 0.3857, 0.7306, 0.0736, 0.2466, 0.1532, 0.6554] +2026-04-14 03:41:49.309264: Epoch time: 104.08 s +2026-04-14 03:41:50.883947: +2026-04-14 03:41:50.886364: Epoch 3046 +2026-04-14 03:41:50.889103: Current learning rate: 0.00275 +2026-04-14 03:43:34.844891: train_loss -0.3999 +2026-04-14 03:43:34.855692: val_loss -0.3388 +2026-04-14 03:43:34.859107: Pseudo dice [0.8102, 0.1201, 0.7444, 0.6724, 0.5418, 0.7814, 0.5821] +2026-04-14 03:43:34.864104: Epoch time: 103.96 s +2026-04-14 03:43:36.505118: +2026-04-14 03:43:36.507111: Epoch 3047 +2026-04-14 03:43:36.509372: Current learning rate: 0.00275 +2026-04-14 03:45:20.733725: train_loss -0.3981 +2026-04-14 03:45:20.740107: val_loss -0.3706 +2026-04-14 03:45:20.742679: Pseudo dice [0.6073, 0.6474, 0.7268, 0.784, 0.6213, 0.4246, 0.8791] +2026-04-14 03:45:20.745069: Epoch time: 104.23 s +2026-04-14 03:45:22.340440: +2026-04-14 03:45:22.342438: Epoch 3048 +2026-04-14 03:45:22.344553: Current learning rate: 0.00275 +2026-04-14 03:47:06.704166: train_loss -0.3867 +2026-04-14 03:47:06.710911: val_loss -0.3168 +2026-04-14 03:47:06.713072: Pseudo dice [0.6009, 0.7552, 0.6069, 0.8691, 0.5805, 0.0653, 0.8502] +2026-04-14 03:47:06.715615: Epoch time: 104.37 s +2026-04-14 03:47:08.360419: +2026-04-14 03:47:08.362480: Epoch 3049 +2026-04-14 03:47:08.364660: Current learning rate: 0.00274 +2026-04-14 03:48:53.236495: train_loss -0.3847 +2026-04-14 03:48:53.244027: val_loss -0.3785 +2026-04-14 03:48:53.246512: Pseudo dice [0.5189, 0.6809, 0.6403, 0.7904, 0.5286, 0.876, 0.8438] +2026-04-14 03:48:53.249937: Epoch time: 104.88 s +2026-04-14 03:48:57.131045: +2026-04-14 03:48:57.134685: Epoch 3050 +2026-04-14 03:48:57.138234: Current learning rate: 0.00274 +2026-04-14 03:50:40.666961: train_loss -0.419 +2026-04-14 03:50:40.676629: val_loss -0.2503 +2026-04-14 03:50:40.680672: Pseudo dice [0.5937, 0.2183, 0.521, 0.7372, 0.6346, 0.0598, 0.8321] +2026-04-14 03:50:40.683496: Epoch time: 103.54 s +2026-04-14 03:50:42.412652: +2026-04-14 03:50:42.416419: Epoch 3051 +2026-04-14 03:50:42.420415: Current learning rate: 0.00274 +2026-04-14 03:52:28.349097: train_loss -0.4138 +2026-04-14 03:52:28.358021: val_loss -0.3219 +2026-04-14 03:52:28.361571: Pseudo dice [0.758, 0.8028, 0.6941, 0.5315, 0.5763, 0.1285, 0.8588] +2026-04-14 03:52:28.364225: Epoch time: 105.94 s +2026-04-14 03:52:30.003541: +2026-04-14 03:52:30.005793: Epoch 3052 +2026-04-14 03:52:30.009679: Current learning rate: 0.00274 +2026-04-14 03:54:14.830957: train_loss -0.4098 +2026-04-14 03:54:14.837123: val_loss -0.3362 +2026-04-14 03:54:14.839280: Pseudo dice [0.6137, 0.5728, 0.6998, 0.7867, 0.4485, 0.7135, 0.8141] +2026-04-14 03:54:14.842162: Epoch time: 104.83 s +2026-04-14 03:54:16.453120: +2026-04-14 03:54:16.455114: Epoch 3053 +2026-04-14 03:54:16.457519: Current learning rate: 0.00273 +2026-04-14 03:55:59.524554: train_loss -0.4146 +2026-04-14 03:55:59.531072: val_loss -0.2451 +2026-04-14 03:55:59.533223: Pseudo dice [0.7166, 0.5589, 0.7552, 0.8525, 0.5873, 0.0612, 0.6077] +2026-04-14 03:55:59.535622: Epoch time: 103.08 s +2026-04-14 03:56:01.129081: +2026-04-14 03:56:01.130903: Epoch 3054 +2026-04-14 03:56:01.132881: Current learning rate: 0.00273 +2026-04-14 03:57:45.284867: train_loss -0.402 +2026-04-14 03:57:45.290398: val_loss -0.2919 +2026-04-14 03:57:45.293203: Pseudo dice [0.778, 0.2001, 0.7354, 0.6299, 0.44, 0.1811, 0.4818] +2026-04-14 03:57:45.295629: Epoch time: 104.16 s +2026-04-14 03:57:46.873573: +2026-04-14 03:57:46.875559: Epoch 3055 +2026-04-14 03:57:46.877923: Current learning rate: 0.00273 +2026-04-14 03:59:32.317739: train_loss -0.4027 +2026-04-14 03:59:32.330400: val_loss -0.2411 +2026-04-14 03:59:32.334410: Pseudo dice [0.7217, 0.6644, 0.6551, 0.8083, 0.4833, 0.0543, 0.7441] +2026-04-14 03:59:32.338153: Epoch time: 105.45 s +2026-04-14 03:59:33.963971: +2026-04-14 03:59:33.966555: Epoch 3056 +2026-04-14 03:59:33.969956: Current learning rate: 0.00273 +2026-04-14 04:01:18.001067: train_loss -0.3617 +2026-04-14 04:01:18.007335: val_loss -0.3095 +2026-04-14 04:01:18.009419: Pseudo dice [0.7021, 0.5897, 0.6208, 0.6718, 0.5029, 0.3775, 0.7759] +2026-04-14 04:01:18.011703: Epoch time: 104.04 s +2026-04-14 04:01:19.858093: +2026-04-14 04:01:19.860584: Epoch 3057 +2026-04-14 04:01:19.863162: Current learning rate: 0.00272 +2026-04-14 04:03:03.605984: train_loss -0.3988 +2026-04-14 04:03:03.611871: val_loss -0.3286 +2026-04-14 04:03:03.616597: Pseudo dice [0.2945, 0.1366, 0.6984, 0.3365, 0.5787, 0.3863, 0.8058] +2026-04-14 04:03:03.619291: Epoch time: 103.75 s +2026-04-14 04:03:05.206194: +2026-04-14 04:03:05.208323: Epoch 3058 +2026-04-14 04:03:05.210359: Current learning rate: 0.00272 +2026-04-14 04:04:49.304591: train_loss -0.3978 +2026-04-14 04:04:49.313391: val_loss -0.3533 +2026-04-14 04:04:49.317210: Pseudo dice [0.6185, 0.5596, 0.6381, 0.6319, 0.676, 0.7813, 0.8682] +2026-04-14 04:04:49.320063: Epoch time: 104.1 s +2026-04-14 04:04:50.906019: +2026-04-14 04:04:50.909396: Epoch 3059 +2026-04-14 04:04:50.916195: Current learning rate: 0.00272 +2026-04-14 04:06:34.309795: train_loss -0.4005 +2026-04-14 04:06:34.317135: val_loss -0.3231 +2026-04-14 04:06:34.319603: Pseudo dice [0.4353, 0.3293, 0.6965, 0.6936, 0.4185, 0.4993, 0.6706] +2026-04-14 04:06:34.322709: Epoch time: 103.41 s +2026-04-14 04:06:35.888052: +2026-04-14 04:06:35.890580: Epoch 3060 +2026-04-14 04:06:35.893380: Current learning rate: 0.00272 +2026-04-14 04:08:20.133745: train_loss -0.4029 +2026-04-14 04:08:20.141551: val_loss -0.3459 +2026-04-14 04:08:20.144320: Pseudo dice [0.421, 0.7693, 0.749, 0.0033, 0.6425, 0.8059, 0.6317] +2026-04-14 04:08:20.148089: Epoch time: 104.25 s +2026-04-14 04:08:21.733377: +2026-04-14 04:08:21.735117: Epoch 3061 +2026-04-14 04:08:21.737175: Current learning rate: 0.00271 +2026-04-14 04:10:06.234776: train_loss -0.3906 +2026-04-14 04:10:06.252780: val_loss -0.2762 +2026-04-14 04:10:06.255570: Pseudo dice [0.6736, 0.086, 0.5564, 0.6594, 0.3672, 0.6155, 0.413] +2026-04-14 04:10:06.259233: Epoch time: 104.51 s +2026-04-14 04:10:07.810566: +2026-04-14 04:10:07.814335: Epoch 3062 +2026-04-14 04:10:07.817005: Current learning rate: 0.00271 +2026-04-14 04:11:52.927782: train_loss -0.3884 +2026-04-14 04:11:52.939121: val_loss -0.3346 +2026-04-14 04:11:52.947014: Pseudo dice [0.5942, 0.592, 0.7707, 0.4708, 0.2652, 0.5849, 0.8695] +2026-04-14 04:11:52.955637: Epoch time: 105.12 s +2026-04-14 04:11:54.532674: +2026-04-14 04:11:54.534606: Epoch 3063 +2026-04-14 04:11:54.536884: Current learning rate: 0.00271 +2026-04-14 04:13:38.275569: train_loss -0.4043 +2026-04-14 04:13:38.282131: val_loss -0.2741 +2026-04-14 04:13:38.284929: Pseudo dice [0.235, 0.1794, 0.5686, 0.6281, 0.5212, 0.1931, 0.744] +2026-04-14 04:13:38.288233: Epoch time: 103.75 s +2026-04-14 04:13:40.074091: +2026-04-14 04:13:40.076268: Epoch 3064 +2026-04-14 04:13:40.078174: Current learning rate: 0.00271 +2026-04-14 04:15:26.638298: train_loss -0.4065 +2026-04-14 04:15:26.650089: val_loss -0.2939 +2026-04-14 04:15:26.655610: Pseudo dice [0.6597, 0.4055, 0.6375, 0.7764, 0.2011, 0.3598, 0.8132] +2026-04-14 04:15:26.661402: Epoch time: 106.57 s +2026-04-14 04:15:28.240503: +2026-04-14 04:15:28.242891: Epoch 3065 +2026-04-14 04:15:28.245021: Current learning rate: 0.0027 +2026-04-14 04:17:12.299718: train_loss -0.3955 +2026-04-14 04:17:12.306302: val_loss -0.3443 +2026-04-14 04:17:12.308177: Pseudo dice [0.5219, 0.1657, 0.6732, 0.8291, 0.4846, 0.4158, 0.595] +2026-04-14 04:17:12.310782: Epoch time: 104.06 s +2026-04-14 04:17:13.952715: +2026-04-14 04:17:13.956531: Epoch 3066 +2026-04-14 04:17:13.962086: Current learning rate: 0.0027 +2026-04-14 04:19:00.607859: train_loss -0.3971 +2026-04-14 04:19:00.615793: val_loss -0.33 +2026-04-14 04:19:00.619311: Pseudo dice [0.797, 0.2284, 0.7369, 0.8841, 0.5042, 0.4863, 0.8814] +2026-04-14 04:19:00.623958: Epoch time: 106.66 s +2026-04-14 04:19:02.237125: +2026-04-14 04:19:02.239165: Epoch 3067 +2026-04-14 04:19:02.241596: Current learning rate: 0.0027 +2026-04-14 04:20:46.196406: train_loss -0.3887 +2026-04-14 04:20:46.204091: val_loss -0.3518 +2026-04-14 04:20:46.206343: Pseudo dice [0.7112, 0.2291, 0.753, 0.5628, 0.6428, 0.7578, 0.821] +2026-04-14 04:20:46.209047: Epoch time: 103.96 s +2026-04-14 04:20:47.860808: +2026-04-14 04:20:47.863026: Epoch 3068 +2026-04-14 04:20:47.865742: Current learning rate: 0.0027 +2026-04-14 04:22:32.850100: train_loss -0.3971 +2026-04-14 04:22:32.858103: val_loss -0.3576 +2026-04-14 04:22:32.860598: Pseudo dice [0.5411, 0.1801, 0.8077, 0.8767, 0.6244, 0.7702, 0.8383] +2026-04-14 04:22:32.863096: Epoch time: 104.99 s +2026-04-14 04:22:34.444555: +2026-04-14 04:22:34.446460: Epoch 3069 +2026-04-14 04:22:34.448717: Current learning rate: 0.00269 +2026-04-14 04:24:18.130020: train_loss -0.4065 +2026-04-14 04:24:18.135891: val_loss -0.3578 +2026-04-14 04:24:18.137982: Pseudo dice [0.7027, 0.7624, 0.8065, 0.7882, 0.536, 0.5667, 0.876] +2026-04-14 04:24:18.142456: Epoch time: 103.69 s +2026-04-14 04:24:19.944805: +2026-04-14 04:24:19.947096: Epoch 3070 +2026-04-14 04:24:19.949293: Current learning rate: 0.00269 +2026-04-14 04:26:03.341484: train_loss -0.3889 +2026-04-14 04:26:03.349418: val_loss -0.3381 +2026-04-14 04:26:03.354896: Pseudo dice [0.2581, 0.2215, 0.737, 0.9185, 0.6034, 0.7476, 0.475] +2026-04-14 04:26:03.359451: Epoch time: 103.4 s +2026-04-14 04:26:04.952099: +2026-04-14 04:26:04.955229: Epoch 3071 +2026-04-14 04:26:04.958666: Current learning rate: 0.00269 +2026-04-14 04:27:51.137861: train_loss -0.4082 +2026-04-14 04:27:51.143721: val_loss -0.3189 +2026-04-14 04:27:51.145917: Pseudo dice [0.1491, 0.5035, 0.6786, 0.8342, 0.4596, 0.4696, 0.7327] +2026-04-14 04:27:51.148696: Epoch time: 106.19 s +2026-04-14 04:27:52.749049: +2026-04-14 04:27:52.751345: Epoch 3072 +2026-04-14 04:27:52.753915: Current learning rate: 0.00268 +2026-04-14 04:29:36.297860: train_loss -0.4002 +2026-04-14 04:29:36.307217: val_loss -0.3004 +2026-04-14 04:29:36.309482: Pseudo dice [0.4106, 0.544, 0.6869, 0.6124, 0.3429, 0.5319, 0.7256] +2026-04-14 04:29:36.312260: Epoch time: 103.55 s +2026-04-14 04:29:37.998394: +2026-04-14 04:29:38.000254: Epoch 3073 +2026-04-14 04:29:38.002506: Current learning rate: 0.00268 +2026-04-14 04:31:22.171831: train_loss -0.4149 +2026-04-14 04:31:22.178657: val_loss -0.3123 +2026-04-14 04:31:22.185633: Pseudo dice [0.4895, 0.6656, 0.7419, 0.2921, 0.6537, 0.1839, 0.8336] +2026-04-14 04:31:22.188747: Epoch time: 104.18 s +2026-04-14 04:31:23.715664: +2026-04-14 04:31:23.718660: Epoch 3074 +2026-04-14 04:31:23.720726: Current learning rate: 0.00268 +2026-04-14 04:33:07.684854: train_loss -0.4028 +2026-04-14 04:33:07.690881: val_loss -0.2945 +2026-04-14 04:33:07.692735: Pseudo dice [0.5013, 0.2756, 0.6978, 0.4073, 0.5611, 0.1708, 0.7404] +2026-04-14 04:33:07.696035: Epoch time: 103.97 s +2026-04-14 04:33:09.282885: +2026-04-14 04:33:09.285484: Epoch 3075 +2026-04-14 04:33:09.287502: Current learning rate: 0.00268 +2026-04-14 04:34:52.683410: train_loss -0.396 +2026-04-14 04:34:52.689459: val_loss -0.3271 +2026-04-14 04:34:52.691509: Pseudo dice [0.7354, 0.3358, 0.6779, 0.1994, 0.5272, 0.1283, 0.4315] +2026-04-14 04:34:52.699930: Epoch time: 103.4 s +2026-04-14 04:34:54.276045: +2026-04-14 04:34:54.278637: Epoch 3076 +2026-04-14 04:34:54.282155: Current learning rate: 0.00267 +2026-04-14 04:36:37.941494: train_loss -0.4178 +2026-04-14 04:36:37.948875: val_loss -0.2958 +2026-04-14 04:36:37.951938: Pseudo dice [0.6451, 0.2336, 0.6975, 0.3073, 0.6371, 0.1556, 0.7977] +2026-04-14 04:36:37.955087: Epoch time: 103.67 s +2026-04-14 04:36:39.545068: +2026-04-14 04:36:39.547329: Epoch 3077 +2026-04-14 04:36:39.551831: Current learning rate: 0.00267 +2026-04-14 04:38:24.652692: train_loss -0.4132 +2026-04-14 04:38:24.661890: val_loss -0.3094 +2026-04-14 04:38:24.664287: Pseudo dice [0.7096, 0.4304, 0.7484, 0.1324, 0.4745, 0.0648, 0.891] +2026-04-14 04:38:24.667012: Epoch time: 105.11 s +2026-04-14 04:38:26.321531: +2026-04-14 04:38:26.324637: Epoch 3078 +2026-04-14 04:38:26.328136: Current learning rate: 0.00267 +2026-04-14 04:40:11.230867: train_loss -0.414 +2026-04-14 04:40:11.235990: val_loss -0.2029 +2026-04-14 04:40:11.238124: Pseudo dice [0.4318, 0.3409, 0.4094, 0.6988, 0.5388, 0.0827, 0.586] +2026-04-14 04:40:11.240389: Epoch time: 104.91 s +2026-04-14 04:40:12.820687: +2026-04-14 04:40:12.822743: Epoch 3079 +2026-04-14 04:40:12.825617: Current learning rate: 0.00267 +2026-04-14 04:41:57.996320: train_loss -0.4145 +2026-04-14 04:41:58.003803: val_loss -0.3439 +2026-04-14 04:41:58.006339: Pseudo dice [0.6749, 0.2263, 0.5865, 0.824, 0.664, 0.1638, 0.8977] +2026-04-14 04:41:58.008947: Epoch time: 105.18 s +2026-04-14 04:41:59.617792: +2026-04-14 04:41:59.620216: Epoch 3080 +2026-04-14 04:41:59.622588: Current learning rate: 0.00266 +2026-04-14 04:43:44.520341: train_loss -0.4149 +2026-04-14 04:43:44.533606: val_loss -0.3262 +2026-04-14 04:43:44.536163: Pseudo dice [0.5568, 0.6467, 0.7041, 0.3581, 0.4286, 0.3146, 0.4377] +2026-04-14 04:43:44.539098: Epoch time: 104.91 s +2026-04-14 04:43:46.084370: +2026-04-14 04:43:46.086377: Epoch 3081 +2026-04-14 04:43:46.088986: Current learning rate: 0.00266 +2026-04-14 04:45:30.126182: train_loss -0.4088 +2026-04-14 04:45:30.139775: val_loss -0.2552 +2026-04-14 04:45:30.142618: Pseudo dice [0.5669, 0.4298, 0.5601, 0.6881, 0.6027, 0.1131, 0.8131] +2026-04-14 04:45:30.146468: Epoch time: 104.05 s +2026-04-14 04:45:32.967187: +2026-04-14 04:45:32.969054: Epoch 3082 +2026-04-14 04:45:32.971228: Current learning rate: 0.00266 +2026-04-14 04:47:16.260638: train_loss -0.41 +2026-04-14 04:47:16.266953: val_loss -0.3264 +2026-04-14 04:47:16.269263: Pseudo dice [0.7246, 0.2673, 0.7636, 0.8174, 0.5478, 0.4363, 0.7086] +2026-04-14 04:47:16.273480: Epoch time: 103.3 s +2026-04-14 04:47:17.852674: +2026-04-14 04:47:17.855165: Epoch 3083 +2026-04-14 04:47:17.857477: Current learning rate: 0.00266 +2026-04-14 04:49:01.035691: train_loss -0.4221 +2026-04-14 04:49:01.044704: val_loss -0.2941 +2026-04-14 04:49:01.047084: Pseudo dice [0.7175, 0.058, 0.7334, 0.7207, 0.385, 0.0802, 0.4836] +2026-04-14 04:49:01.059670: Epoch time: 103.19 s +2026-04-14 04:49:02.642001: +2026-04-14 04:49:02.644000: Epoch 3084 +2026-04-14 04:49:02.646031: Current learning rate: 0.00265 +2026-04-14 04:50:46.229007: train_loss -0.3987 +2026-04-14 04:50:46.234938: val_loss -0.3422 +2026-04-14 04:50:46.238316: Pseudo dice [0.5483, 0.346, 0.7383, 0.8282, 0.4249, 0.4781, 0.7257] +2026-04-14 04:50:46.241289: Epoch time: 103.59 s +2026-04-14 04:50:47.812612: +2026-04-14 04:50:47.814705: Epoch 3085 +2026-04-14 04:50:47.816784: Current learning rate: 0.00265 +2026-04-14 04:52:32.625687: train_loss -0.4002 +2026-04-14 04:52:32.632275: val_loss -0.1646 +2026-04-14 04:52:32.634478: Pseudo dice [0.3285, 0.5278, 0.5414, 0.7816, 0.4364, 0.0322, 0.6773] +2026-04-14 04:52:32.636728: Epoch time: 104.82 s +2026-04-14 04:52:34.212646: +2026-04-14 04:52:34.214955: Epoch 3086 +2026-04-14 04:52:34.217419: Current learning rate: 0.00265 +2026-04-14 04:54:17.513003: train_loss -0.412 +2026-04-14 04:54:17.519896: val_loss -0.2349 +2026-04-14 04:54:17.522442: Pseudo dice [0.6423, 0.3292, 0.8259, 0.2789, 0.2494, 0.0216, 0.7007] +2026-04-14 04:54:17.525153: Epoch time: 103.3 s +2026-04-14 04:54:19.092187: +2026-04-14 04:54:19.094304: Epoch 3087 +2026-04-14 04:54:19.096504: Current learning rate: 0.00265 +2026-04-14 04:56:03.598539: train_loss -0.402 +2026-04-14 04:56:03.605441: val_loss -0.3658 +2026-04-14 04:56:03.608750: Pseudo dice [0.4668, 0.4319, 0.8071, 0.764, 0.3419, 0.789, 0.5212] +2026-04-14 04:56:03.612172: Epoch time: 104.51 s +2026-04-14 04:56:05.198078: +2026-04-14 04:56:05.201618: Epoch 3088 +2026-04-14 04:56:05.203867: Current learning rate: 0.00264 +2026-04-14 04:57:49.363971: train_loss -0.4153 +2026-04-14 04:57:49.370658: val_loss -0.3567 +2026-04-14 04:57:49.372793: Pseudo dice [0.4917, 0.4815, 0.7869, 0.8548, 0.5089, 0.346, 0.8346] +2026-04-14 04:57:49.376578: Epoch time: 104.17 s +2026-04-14 04:57:51.030238: +2026-04-14 04:57:51.036108: Epoch 3089 +2026-04-14 04:57:51.039889: Current learning rate: 0.00264 +2026-04-14 04:59:34.283173: train_loss -0.4181 +2026-04-14 04:59:34.290554: val_loss -0.357 +2026-04-14 04:59:34.293015: Pseudo dice [0.8002, 0.7389, 0.6789, 0.8765, 0.4429, 0.4706, 0.8633] +2026-04-14 04:59:34.295330: Epoch time: 103.26 s +2026-04-14 04:59:35.893619: +2026-04-14 04:59:35.895776: Epoch 3090 +2026-04-14 04:59:35.898150: Current learning rate: 0.00264 +2026-04-14 05:01:20.138648: train_loss -0.4086 +2026-04-14 05:01:20.145890: val_loss -0.2977 +2026-04-14 05:01:20.148913: Pseudo dice [0.4657, 0.165, 0.6027, 0.0156, 0.4402, 0.1336, 0.7562] +2026-04-14 05:01:20.152053: Epoch time: 104.25 s +2026-04-14 05:01:21.738907: +2026-04-14 05:01:21.740874: Epoch 3091 +2026-04-14 05:01:21.742814: Current learning rate: 0.00264 +2026-04-14 05:03:05.647910: train_loss -0.4024 +2026-04-14 05:03:05.653614: val_loss -0.3082 +2026-04-14 05:03:05.655372: Pseudo dice [0.4968, 0.1122, 0.6299, 0.2621, 0.5348, 0.1871, 0.8593] +2026-04-14 05:03:05.658047: Epoch time: 103.91 s +2026-04-14 05:03:07.248905: +2026-04-14 05:03:07.252011: Epoch 3092 +2026-04-14 05:03:07.254165: Current learning rate: 0.00263 +2026-04-14 05:04:51.350220: train_loss -0.4024 +2026-04-14 05:04:51.357103: val_loss -0.2748 +2026-04-14 05:04:51.359751: Pseudo dice [0.179, 0.1635, 0.5865, 0.3856, 0.6118, 0.0477, 0.7341] +2026-04-14 05:04:51.362925: Epoch time: 104.11 s +2026-04-14 05:04:52.975263: +2026-04-14 05:04:52.977564: Epoch 3093 +2026-04-14 05:04:52.979660: Current learning rate: 0.00263 +2026-04-14 05:06:37.194422: train_loss -0.4124 +2026-04-14 05:06:37.206179: val_loss -0.2815 +2026-04-14 05:06:37.209411: Pseudo dice [0.4533, 0.2602, 0.7281, 0.724, 0.4128, 0.0573, 0.7157] +2026-04-14 05:06:37.213298: Epoch time: 104.22 s +2026-04-14 05:06:38.847238: +2026-04-14 05:06:38.850375: Epoch 3094 +2026-04-14 05:06:38.855010: Current learning rate: 0.00263 +2026-04-14 05:08:23.531676: train_loss -0.4083 +2026-04-14 05:08:23.538406: val_loss -0.312 +2026-04-14 05:08:23.540689: Pseudo dice [0.624, 0.7381, 0.6778, 0.2908, 0.6399, 0.0789, 0.6267] +2026-04-14 05:08:23.543449: Epoch time: 104.69 s +2026-04-14 05:08:25.177748: +2026-04-14 05:08:25.179395: Epoch 3095 +2026-04-14 05:08:25.181513: Current learning rate: 0.00263 +2026-04-14 05:10:08.966642: train_loss -0.3848 +2026-04-14 05:10:08.972507: val_loss -0.3276 +2026-04-14 05:10:08.975517: Pseudo dice [0.6251, 0.2261, 0.5177, 0.7098, 0.4726, 0.8047, 0.7946] +2026-04-14 05:10:08.979168: Epoch time: 103.79 s +2026-04-14 05:10:10.549641: +2026-04-14 05:10:10.551718: Epoch 3096 +2026-04-14 05:10:10.553845: Current learning rate: 0.00262 +2026-04-14 05:11:54.564056: train_loss -0.41 +2026-04-14 05:11:54.575370: val_loss -0.3426 +2026-04-14 05:11:54.579328: Pseudo dice [0.781, 0.308, 0.7307, 0.558, 0.6256, 0.276, 0.75] +2026-04-14 05:11:54.582924: Epoch time: 104.02 s +2026-04-14 05:11:56.255370: +2026-04-14 05:11:56.257054: Epoch 3097 +2026-04-14 05:11:56.259383: Current learning rate: 0.00262 +2026-04-14 05:13:39.129759: train_loss -0.4087 +2026-04-14 05:13:39.136328: val_loss -0.2958 +2026-04-14 05:13:39.138594: Pseudo dice [0.5988, 0.3274, 0.4114, 0.6538, 0.5662, 0.0908, 0.8575] +2026-04-14 05:13:39.140692: Epoch time: 102.88 s +2026-04-14 05:13:40.663362: +2026-04-14 05:13:40.665485: Epoch 3098 +2026-04-14 05:13:40.667698: Current learning rate: 0.00262 +2026-04-14 05:15:24.140145: train_loss -0.4057 +2026-04-14 05:15:24.146499: val_loss -0.2696 +2026-04-14 05:15:24.148553: Pseudo dice [0.2955, 0.4022, 0.6223, 0.735, 0.4312, 0.0634, 0.8755] +2026-04-14 05:15:24.151090: Epoch time: 103.48 s +2026-04-14 05:15:25.762872: +2026-04-14 05:15:25.764696: Epoch 3099 +2026-04-14 05:15:25.766876: Current learning rate: 0.00261 +2026-04-14 05:17:09.401221: train_loss -0.4117 +2026-04-14 05:17:09.406963: val_loss -0.3255 +2026-04-14 05:17:09.409000: Pseudo dice [0.7655, 0.4509, 0.7903, 0.2714, 0.5967, 0.147, 0.8432] +2026-04-14 05:17:09.411621: Epoch time: 103.64 s +2026-04-14 05:17:13.102667: +2026-04-14 05:17:13.107067: Epoch 3100 +2026-04-14 05:17:13.112230: Current learning rate: 0.00261 +2026-04-14 05:18:57.652174: train_loss -0.3896 +2026-04-14 05:18:57.661186: val_loss -0.2327 +2026-04-14 05:18:57.664193: Pseudo dice [0.6851, 0.2469, 0.7541, 0.8194, 0.3374, 0.036, 0.3014] +2026-04-14 05:18:57.685853: Epoch time: 104.55 s +2026-04-14 05:18:59.331256: +2026-04-14 05:18:59.334496: Epoch 3101 +2026-04-14 05:18:59.337221: Current learning rate: 0.00261 +2026-04-14 05:20:43.651143: train_loss -0.4106 +2026-04-14 05:20:43.658924: val_loss -0.3266 +2026-04-14 05:20:43.660902: Pseudo dice [0.3805, 0.538, 0.6931, 0.4018, 0.6843, 0.1972, 0.7796] +2026-04-14 05:20:43.663594: Epoch time: 104.32 s +2026-04-14 05:20:46.405581: +2026-04-14 05:20:46.407502: Epoch 3102 +2026-04-14 05:20:46.409848: Current learning rate: 0.00261 +2026-04-14 05:22:29.901675: train_loss -0.4061 +2026-04-14 05:22:29.908430: val_loss -0.3368 +2026-04-14 05:22:29.910858: Pseudo dice [0.746, 0.5441, 0.7124, 0.8133, 0.3917, 0.544, 0.7852] +2026-04-14 05:22:29.913430: Epoch time: 103.5 s +2026-04-14 05:22:31.461459: +2026-04-14 05:22:31.464467: Epoch 3103 +2026-04-14 05:22:31.467836: Current learning rate: 0.0026 +2026-04-14 05:24:15.931846: train_loss -0.4041 +2026-04-14 05:24:15.939063: val_loss -0.3079 +2026-04-14 05:24:15.941355: Pseudo dice [0.7248, 0.7605, 0.3667, 0.8069, 0.4258, 0.1441, 0.7435] +2026-04-14 05:24:15.944396: Epoch time: 104.47 s +2026-04-14 05:24:17.514151: +2026-04-14 05:24:17.516456: Epoch 3104 +2026-04-14 05:24:17.518801: Current learning rate: 0.0026 +2026-04-14 05:26:01.501621: train_loss -0.3995 +2026-04-14 05:26:01.508104: val_loss -0.32 +2026-04-14 05:26:01.510311: Pseudo dice [0.3565, 0.0456, 0.49, 0.3314, 0.5389, 0.3609, 0.8602] +2026-04-14 05:26:01.512390: Epoch time: 103.99 s +2026-04-14 05:26:03.075184: +2026-04-14 05:26:03.076941: Epoch 3105 +2026-04-14 05:26:03.079585: Current learning rate: 0.0026 +2026-04-14 05:27:46.233238: train_loss -0.418 +2026-04-14 05:27:46.242360: val_loss -0.3319 +2026-04-14 05:27:46.245264: Pseudo dice [0.1457, 0.6001, 0.7734, 0.6174, 0.5592, 0.169, 0.8824] +2026-04-14 05:27:46.251123: Epoch time: 103.16 s +2026-04-14 05:27:47.856812: +2026-04-14 05:27:47.859422: Epoch 3106 +2026-04-14 05:27:47.862327: Current learning rate: 0.0026 +2026-04-14 05:29:31.168931: train_loss -0.4033 +2026-04-14 05:29:31.174600: val_loss -0.3645 +2026-04-14 05:29:31.176867: Pseudo dice [0.4556, 0.4269, 0.7298, 0.8596, 0.6379, 0.6005, 0.8894] +2026-04-14 05:29:31.179292: Epoch time: 103.32 s +2026-04-14 05:29:32.735617: +2026-04-14 05:29:32.737309: Epoch 3107 +2026-04-14 05:29:32.740169: Current learning rate: 0.00259 +2026-04-14 05:31:16.012024: train_loss -0.407 +2026-04-14 05:31:16.018508: val_loss -0.2871 +2026-04-14 05:31:16.020601: Pseudo dice [0.4876, 0.1331, 0.6909, 0.8553, 0.6081, 0.0774, 0.6339] +2026-04-14 05:31:16.022893: Epoch time: 103.28 s +2026-04-14 05:31:17.617176: +2026-04-14 05:31:17.618985: Epoch 3108 +2026-04-14 05:31:17.620847: Current learning rate: 0.00259 +2026-04-14 05:33:00.849170: train_loss -0.4162 +2026-04-14 05:33:00.855864: val_loss -0.2803 +2026-04-14 05:33:00.860322: Pseudo dice [0.2066, 0.3069, 0.594, 0.4959, 0.4643, 0.2241, 0.6698] +2026-04-14 05:33:00.862404: Epoch time: 103.24 s +2026-04-14 05:33:02.421515: +2026-04-14 05:33:02.423561: Epoch 3109 +2026-04-14 05:33:02.426448: Current learning rate: 0.00259 +2026-04-14 05:34:46.479523: train_loss -0.4151 +2026-04-14 05:34:46.485164: val_loss -0.3201 +2026-04-14 05:34:46.487279: Pseudo dice [0.7713, 0.3631, 0.5855, 0.7949, 0.4085, 0.5211, 0.8765] +2026-04-14 05:34:46.489630: Epoch time: 104.06 s +2026-04-14 05:34:48.064280: +2026-04-14 05:34:48.067569: Epoch 3110 +2026-04-14 05:34:48.070191: Current learning rate: 0.00259 +2026-04-14 05:36:33.185990: train_loss -0.3925 +2026-04-14 05:36:33.192147: val_loss -0.3554 +2026-04-14 05:36:33.194665: Pseudo dice [0.8555, 0.5106, 0.7216, 0.8551, 0.5827, 0.3395, 0.685] +2026-04-14 05:36:33.197202: Epoch time: 105.13 s +2026-04-14 05:36:34.850509: +2026-04-14 05:36:34.852567: Epoch 3111 +2026-04-14 05:36:34.854841: Current learning rate: 0.00258 +2026-04-14 05:38:19.299147: train_loss -0.4183 +2026-04-14 05:38:19.307122: val_loss -0.3362 +2026-04-14 05:38:19.309702: Pseudo dice [0.5572, 0.3791, 0.6777, 0.7176, 0.2855, 0.7694, 0.8918] +2026-04-14 05:38:19.312388: Epoch time: 104.45 s +2026-04-14 05:38:20.876213: +2026-04-14 05:38:20.877991: Epoch 3112 +2026-04-14 05:38:20.880003: Current learning rate: 0.00258 +2026-04-14 05:40:04.534965: train_loss -0.4056 +2026-04-14 05:40:04.542136: val_loss -0.2479 +2026-04-14 05:40:04.544427: Pseudo dice [0.7142, 0.5369, 0.4709, 0.4887, 0.6674, 0.0427, 0.651] +2026-04-14 05:40:04.547346: Epoch time: 103.66 s +2026-04-14 05:40:06.178968: +2026-04-14 05:40:06.181441: Epoch 3113 +2026-04-14 05:40:06.183857: Current learning rate: 0.00258 +2026-04-14 05:41:49.171752: train_loss -0.4018 +2026-04-14 05:41:49.177851: val_loss -0.3712 +2026-04-14 05:41:49.180037: Pseudo dice [0.8048, 0.6055, 0.7019, 0.0365, 0.6498, 0.686, 0.9271] +2026-04-14 05:41:49.182474: Epoch time: 103.0 s +2026-04-14 05:41:50.805646: +2026-04-14 05:41:50.807674: Epoch 3114 +2026-04-14 05:41:50.810418: Current learning rate: 0.00258 +2026-04-14 05:43:33.947346: train_loss -0.398 +2026-04-14 05:43:33.954559: val_loss -0.2934 +2026-04-14 05:43:33.956527: Pseudo dice [0.2937, 0.1923, 0.7516, 0.666, 0.7357, 0.3107, 0.6347] +2026-04-14 05:43:33.959176: Epoch time: 103.15 s +2026-04-14 05:43:35.528304: +2026-04-14 05:43:35.530401: Epoch 3115 +2026-04-14 05:43:35.532969: Current learning rate: 0.00257 +2026-04-14 05:45:19.034030: train_loss -0.4133 +2026-04-14 05:45:19.040393: val_loss -0.3633 +2026-04-14 05:45:19.042375: Pseudo dice [0.3121, 0.4746, 0.7567, 0.7588, 0.538, 0.8386, 0.7487] +2026-04-14 05:45:19.044741: Epoch time: 103.51 s +2026-04-14 05:45:20.620108: +2026-04-14 05:45:20.622023: Epoch 3116 +2026-04-14 05:45:20.623955: Current learning rate: 0.00257 +2026-04-14 05:47:04.147412: train_loss -0.3981 +2026-04-14 05:47:04.154710: val_loss -0.3161 +2026-04-14 05:47:04.157885: Pseudo dice [0.4113, 0.5698, 0.6584, 0.1001, 0.6603, 0.1603, 0.8025] +2026-04-14 05:47:04.159941: Epoch time: 103.53 s +2026-04-14 05:47:05.742031: +2026-04-14 05:47:05.743763: Epoch 3117 +2026-04-14 05:47:05.745734: Current learning rate: 0.00257 +2026-04-14 05:48:49.159374: train_loss -0.4093 +2026-04-14 05:48:49.166330: val_loss -0.3618 +2026-04-14 05:48:49.169882: Pseudo dice [0.7201, 0.515, 0.686, 0.8759, 0.6257, 0.8064, 0.8763] +2026-04-14 05:48:49.172530: Epoch time: 103.42 s +2026-04-14 05:48:50.737599: +2026-04-14 05:48:50.739782: Epoch 3118 +2026-04-14 05:48:50.742164: Current learning rate: 0.00256 +2026-04-14 05:50:34.588992: train_loss -0.3901 +2026-04-14 05:50:34.600908: val_loss -0.3081 +2026-04-14 05:50:34.602865: Pseudo dice [0.763, 0.7135, 0.5425, 0.506, 0.3605, 0.1041, 0.3604] +2026-04-14 05:50:34.607695: Epoch time: 103.85 s +2026-04-14 05:50:36.172489: +2026-04-14 05:50:36.174574: Epoch 3119 +2026-04-14 05:50:36.176680: Current learning rate: 0.00256 +2026-04-14 05:52:19.804492: train_loss -0.3949 +2026-04-14 05:52:19.814508: val_loss -0.2979 +2026-04-14 05:52:19.816833: Pseudo dice [0.6143, 0.4105, 0.5384, 0.5811, 0.3948, 0.083, 0.453] +2026-04-14 05:52:19.820271: Epoch time: 103.64 s +2026-04-14 05:52:21.349837: +2026-04-14 05:52:21.352166: Epoch 3120 +2026-04-14 05:52:21.354899: Current learning rate: 0.00256 +2026-04-14 05:54:04.467007: train_loss -0.3977 +2026-04-14 05:54:04.473160: val_loss -0.3125 +2026-04-14 05:54:04.475672: Pseudo dice [0.2503, 0.4524, 0.3874, 0.8323, 0.3813, 0.2302, 0.8462] +2026-04-14 05:54:04.479046: Epoch time: 103.12 s +2026-04-14 05:54:06.069222: +2026-04-14 05:54:06.071500: Epoch 3121 +2026-04-14 05:54:06.073684: Current learning rate: 0.00256 +2026-04-14 05:55:49.373463: train_loss -0.4031 +2026-04-14 05:55:49.379490: val_loss -0.3167 +2026-04-14 05:55:49.381470: Pseudo dice [0.6605, 0.6999, 0.6471, 0.8143, 0.2179, 0.0921, 0.4276] +2026-04-14 05:55:49.384999: Epoch time: 103.31 s +2026-04-14 05:55:52.105991: +2026-04-14 05:55:52.111465: Epoch 3122 +2026-04-14 05:55:52.114981: Current learning rate: 0.00255 +2026-04-14 05:57:35.619374: train_loss -0.3933 +2026-04-14 05:57:35.626321: val_loss -0.3521 +2026-04-14 05:57:35.628968: Pseudo dice [0.4595, 0.7665, 0.5867, 0.3273, 0.4698, 0.6493, 0.8854] +2026-04-14 05:57:35.631573: Epoch time: 103.52 s +2026-04-14 05:57:37.328609: +2026-04-14 05:57:37.330575: Epoch 3123 +2026-04-14 05:57:37.332730: Current learning rate: 0.00255 +2026-04-14 05:59:21.384539: train_loss -0.4008 +2026-04-14 05:59:21.397621: val_loss -0.1926 +2026-04-14 05:59:21.402296: Pseudo dice [0.6664, 0.8574, 0.5946, 0.3556, 0.3718, 0.0838, 0.7099] +2026-04-14 05:59:21.407380: Epoch time: 104.06 s +2026-04-14 05:59:23.011773: +2026-04-14 05:59:23.014087: Epoch 3124 +2026-04-14 05:59:23.016882: Current learning rate: 0.00255 +2026-04-14 06:01:08.019641: train_loss -0.3937 +2026-04-14 06:01:08.026702: val_loss -0.3355 +2026-04-14 06:01:08.028977: Pseudo dice [0.5931, 0.6769, 0.7478, 0.7778, 0.3841, 0.7441, 0.5973] +2026-04-14 06:01:08.031631: Epoch time: 105.01 s +2026-04-14 06:01:09.694501: +2026-04-14 06:01:09.697903: Epoch 3125 +2026-04-14 06:01:09.699997: Current learning rate: 0.00255 +2026-04-14 06:02:52.782120: train_loss -0.3988 +2026-04-14 06:02:52.788835: val_loss -0.3166 +2026-04-14 06:02:52.790887: Pseudo dice [0.8064, 0.4068, 0.5825, 0.2348, 0.5572, 0.181, 0.7948] +2026-04-14 06:02:52.793808: Epoch time: 103.09 s +2026-04-14 06:02:54.435107: +2026-04-14 06:02:54.437618: Epoch 3126 +2026-04-14 06:02:54.439574: Current learning rate: 0.00254 +2026-04-14 06:04:39.551240: train_loss -0.3922 +2026-04-14 06:04:39.558506: val_loss -0.2941 +2026-04-14 06:04:39.561436: Pseudo dice [0.7043, 0.5995, 0.7119, 0.6562, 0.4844, 0.493, 0.7562] +2026-04-14 06:04:39.564120: Epoch time: 105.12 s +2026-04-14 06:04:41.250001: +2026-04-14 06:04:41.252195: Epoch 3127 +2026-04-14 06:04:41.254621: Current learning rate: 0.00254 +2026-04-14 06:06:24.971486: train_loss -0.3953 +2026-04-14 06:06:24.978611: val_loss -0.3065 +2026-04-14 06:06:24.981209: Pseudo dice [0.3814, 0.4789, 0.6197, 0.9315, 0.2605, 0.1189, 0.8881] +2026-04-14 06:06:24.986300: Epoch time: 103.73 s +2026-04-14 06:06:26.571407: +2026-04-14 06:06:26.573507: Epoch 3128 +2026-04-14 06:06:26.576548: Current learning rate: 0.00254 +2026-04-14 06:08:10.341180: train_loss -0.3959 +2026-04-14 06:08:10.352166: val_loss -0.3611 +2026-04-14 06:08:10.355206: Pseudo dice [0.4388, 0.2409, 0.6531, 0.6173, 0.5774, 0.5455, 0.9024] +2026-04-14 06:08:10.362589: Epoch time: 103.77 s +2026-04-14 06:08:11.935704: +2026-04-14 06:08:11.942729: Epoch 3129 +2026-04-14 06:08:11.945940: Current learning rate: 0.00254 +2026-04-14 06:09:57.358839: train_loss -0.4064 +2026-04-14 06:09:57.367755: val_loss -0.3744 +2026-04-14 06:09:57.371027: Pseudo dice [0.7768, 0.7583, 0.7468, 0.5713, 0.5987, 0.8918, 0.8779] +2026-04-14 06:09:57.374721: Epoch time: 105.43 s +2026-04-14 06:09:58.953053: +2026-04-14 06:09:58.954970: Epoch 3130 +2026-04-14 06:09:58.957625: Current learning rate: 0.00253 +2026-04-14 06:11:42.156823: train_loss -0.4092 +2026-04-14 06:11:42.163017: val_loss -0.3526 +2026-04-14 06:11:42.165437: Pseudo dice [0.2421, 0.8826, 0.8068, 0.8115, 0.4248, 0.7305, 0.6267] +2026-04-14 06:11:42.168142: Epoch time: 103.21 s +2026-04-14 06:11:43.828077: +2026-04-14 06:11:43.830114: Epoch 3131 +2026-04-14 06:11:43.832021: Current learning rate: 0.00253 +2026-04-14 06:13:27.954048: train_loss -0.4092 +2026-04-14 06:13:27.962962: val_loss -0.2994 +2026-04-14 06:13:27.965043: Pseudo dice [0.529, 0.8908, 0.7374, 0.8948, 0.6319, 0.1898, 0.8784] +2026-04-14 06:13:27.967391: Epoch time: 104.13 s +2026-04-14 06:13:29.611317: +2026-04-14 06:13:29.612952: Epoch 3132 +2026-04-14 06:13:29.615313: Current learning rate: 0.00253 +2026-04-14 06:15:14.330374: train_loss -0.4272 +2026-04-14 06:15:14.339014: val_loss -0.2916 +2026-04-14 06:15:14.341659: Pseudo dice [0.8338, 0.7812, 0.5478, 0.1177, 0.5644, 0.4248, 0.4547] +2026-04-14 06:15:14.344760: Epoch time: 104.72 s +2026-04-14 06:15:15.938142: +2026-04-14 06:15:15.940146: Epoch 3133 +2026-04-14 06:15:15.942362: Current learning rate: 0.00253 +2026-04-14 06:16:59.401857: train_loss -0.4005 +2026-04-14 06:16:59.410152: val_loss -0.3154 +2026-04-14 06:16:59.412734: Pseudo dice [0.7304, 0.2127, 0.583, 0.3103, 0.4047, 0.5182, 0.5757] +2026-04-14 06:16:59.416415: Epoch time: 103.47 s +2026-04-14 06:17:00.987782: +2026-04-14 06:17:00.989651: Epoch 3134 +2026-04-14 06:17:00.991712: Current learning rate: 0.00252 +2026-04-14 06:18:44.791144: train_loss -0.4163 +2026-04-14 06:18:44.799911: val_loss -0.3568 +2026-04-14 06:18:44.804851: Pseudo dice [0.6326, 0.5546, 0.7113, 0.1779, 0.5751, 0.5453, 0.5184] +2026-04-14 06:18:44.807572: Epoch time: 103.81 s +2026-04-14 06:18:46.394337: +2026-04-14 06:18:46.396052: Epoch 3135 +2026-04-14 06:18:46.398414: Current learning rate: 0.00252 +2026-04-14 06:20:30.137189: train_loss -0.4015 +2026-04-14 06:20:30.142654: val_loss -0.3447 +2026-04-14 06:20:30.145268: Pseudo dice [0.4607, 0.3793, 0.75, 0.3922, 0.4377, 0.8193, 0.6634] +2026-04-14 06:20:30.147596: Epoch time: 103.75 s +2026-04-14 06:20:31.871166: +2026-04-14 06:20:31.875635: Epoch 3136 +2026-04-14 06:20:31.883789: Current learning rate: 0.00252 +2026-04-14 06:22:15.297945: train_loss -0.3921 +2026-04-14 06:22:15.305908: val_loss -0.3006 +2026-04-14 06:22:15.308182: Pseudo dice [0.5716, 0.8087, 0.7843, 0.5978, 0.5051, 0.0935, 0.8445] +2026-04-14 06:22:15.310732: Epoch time: 103.43 s +2026-04-14 06:22:16.920026: +2026-04-14 06:22:16.921822: Epoch 3137 +2026-04-14 06:22:16.925056: Current learning rate: 0.00252 +2026-04-14 06:24:00.386031: train_loss -0.4068 +2026-04-14 06:24:00.392246: val_loss -0.3293 +2026-04-14 06:24:00.394300: Pseudo dice [0.7747, 0.7645, 0.777, 0.5714, 0.6484, 0.1467, 0.733] +2026-04-14 06:24:00.397397: Epoch time: 103.47 s +2026-04-14 06:24:01.968077: +2026-04-14 06:24:01.969902: Epoch 3138 +2026-04-14 06:24:01.971954: Current learning rate: 0.00251 +2026-04-14 06:25:45.820964: train_loss -0.4171 +2026-04-14 06:25:45.827533: val_loss -0.3381 +2026-04-14 06:25:45.830232: Pseudo dice [0.7428, 0.8646, 0.5736, 0.7874, 0.3216, 0.2568, 0.8091] +2026-04-14 06:25:45.832507: Epoch time: 103.86 s +2026-04-14 06:25:47.402848: +2026-04-14 06:25:47.404632: Epoch 3139 +2026-04-14 06:25:47.407385: Current learning rate: 0.00251 +2026-04-14 06:27:30.692844: train_loss -0.4144 +2026-04-14 06:27:30.698970: val_loss -0.3379 +2026-04-14 06:27:30.701459: Pseudo dice [0.756, 0.7767, 0.7736, 0.4388, 0.3598, 0.5885, 0.5777] +2026-04-14 06:27:30.704249: Epoch time: 103.29 s +2026-04-14 06:27:32.299824: +2026-04-14 06:27:32.301908: Epoch 3140 +2026-04-14 06:27:32.304482: Current learning rate: 0.00251 +2026-04-14 06:29:15.514159: train_loss -0.4099 +2026-04-14 06:29:15.522303: val_loss -0.3142 +2026-04-14 06:29:15.524801: Pseudo dice [0.722, 0.6005, 0.6558, 0.3466, 0.5085, 0.4597, 0.7959] +2026-04-14 06:29:15.528195: Epoch time: 103.22 s +2026-04-14 06:29:17.096526: +2026-04-14 06:29:17.098755: Epoch 3141 +2026-04-14 06:29:17.101352: Current learning rate: 0.0025 +2026-04-14 06:30:59.976476: train_loss -0.4007 +2026-04-14 06:30:59.983395: val_loss -0.2462 +2026-04-14 06:30:59.985444: Pseudo dice [0.4275, 0.5427, 0.7114, 0.3758, 0.4724, 0.2497, 0.7415] +2026-04-14 06:30:59.987767: Epoch time: 102.88 s +2026-04-14 06:31:02.693981: +2026-04-14 06:31:02.696219: Epoch 3142 +2026-04-14 06:31:02.698239: Current learning rate: 0.0025 +2026-04-14 06:32:45.597069: train_loss -0.3877 +2026-04-14 06:32:45.602437: val_loss -0.2764 +2026-04-14 06:32:45.604477: Pseudo dice [0.3173, 0.5554, 0.7263, 0.0098, 0.5177, 0.4066, 0.8785] +2026-04-14 06:32:45.607107: Epoch time: 102.91 s +2026-04-14 06:32:47.170939: +2026-04-14 06:32:47.174625: Epoch 3143 +2026-04-14 06:32:47.178246: Current learning rate: 0.0025 +2026-04-14 06:34:30.026203: train_loss -0.4014 +2026-04-14 06:34:30.033499: val_loss -0.2479 +2026-04-14 06:34:30.036043: Pseudo dice [0.2885, 0.7719, 0.6001, 0.5393, 0.5658, 0.2923, 0.621] +2026-04-14 06:34:30.038803: Epoch time: 102.86 s +2026-04-14 06:34:31.616034: +2026-04-14 06:34:31.618128: Epoch 3144 +2026-04-14 06:34:31.620212: Current learning rate: 0.0025 +2026-04-14 06:36:15.321114: train_loss -0.4038 +2026-04-14 06:36:15.328327: val_loss -0.218 +2026-04-14 06:36:15.330930: Pseudo dice [0.5775, 0.6376, 0.3974, 0.0006, 0.3454, 0.0841, 0.1304] +2026-04-14 06:36:15.333501: Epoch time: 103.71 s +2026-04-14 06:36:16.929131: +2026-04-14 06:36:16.931992: Epoch 3145 +2026-04-14 06:36:16.934156: Current learning rate: 0.00249 +2026-04-14 06:38:02.459194: train_loss -0.3833 +2026-04-14 06:38:02.465754: val_loss -0.3354 +2026-04-14 06:38:02.467929: Pseudo dice [0.2829, 0.4017, 0.6749, 0.5142, 0.2985, 0.8392, 0.6934] +2026-04-14 06:38:02.471428: Epoch time: 105.53 s +2026-04-14 06:38:04.080426: +2026-04-14 06:38:04.082144: Epoch 3146 +2026-04-14 06:38:04.084326: Current learning rate: 0.00249 +2026-04-14 06:39:48.475693: train_loss -0.4022 +2026-04-14 06:39:48.484302: val_loss -0.3236 +2026-04-14 06:39:48.486412: Pseudo dice [0.3398, 0.6837, 0.7276, 0.7609, 0.6393, 0.1462, 0.7767] +2026-04-14 06:39:48.489321: Epoch time: 104.4 s +2026-04-14 06:39:50.097555: +2026-04-14 06:39:50.099465: Epoch 3147 +2026-04-14 06:39:50.101683: Current learning rate: 0.00249 +2026-04-14 06:41:34.232686: train_loss -0.3965 +2026-04-14 06:41:34.240602: val_loss -0.353 +2026-04-14 06:41:34.243697: Pseudo dice [0.6726, 0.5978, 0.7472, 0.664, 0.6428, 0.6491, 0.8418] +2026-04-14 06:41:34.246325: Epoch time: 104.14 s +2026-04-14 06:41:35.804233: +2026-04-14 06:41:35.806256: Epoch 3148 +2026-04-14 06:41:35.808224: Current learning rate: 0.00249 +2026-04-14 06:43:18.796151: train_loss -0.4052 +2026-04-14 06:43:18.801939: val_loss -0.2906 +2026-04-14 06:43:18.803747: Pseudo dice [0.4106, 0.4276, 0.6162, 0.1485, 0.4352, 0.1272, 0.497] +2026-04-14 06:43:18.806545: Epoch time: 103.0 s +2026-04-14 06:43:20.396430: +2026-04-14 06:43:20.398908: Epoch 3149 +2026-04-14 06:43:20.401734: Current learning rate: 0.00248 +2026-04-14 06:45:03.631964: train_loss -0.3998 +2026-04-14 06:45:03.640002: val_loss -0.2871 +2026-04-14 06:45:03.642899: Pseudo dice [0.3982, 0.741, 0.7037, 0.8233, 0.4499, 0.1307, 0.8196] +2026-04-14 06:45:03.645495: Epoch time: 103.24 s +2026-04-14 06:45:07.336107: +2026-04-14 06:45:07.338316: Epoch 3150 +2026-04-14 06:45:07.340422: Current learning rate: 0.00248 +2026-04-14 06:46:51.481935: train_loss -0.3827 +2026-04-14 06:46:51.488282: val_loss -0.3507 +2026-04-14 06:46:51.491079: Pseudo dice [0.6034, 0.7099, 0.5419, 0.0542, 0.4872, 0.7213, 0.8072] +2026-04-14 06:46:51.493374: Epoch time: 104.15 s +2026-04-14 06:46:53.125948: +2026-04-14 06:46:53.127789: Epoch 3151 +2026-04-14 06:46:53.130165: Current learning rate: 0.00248 +2026-04-14 06:48:37.846255: train_loss -0.3986 +2026-04-14 06:48:37.852649: val_loss -0.3358 +2026-04-14 06:48:37.854927: Pseudo dice [0.6368, 0.6938, 0.5584, 0.6077, 0.3774, 0.7901, 0.593] +2026-04-14 06:48:37.857063: Epoch time: 104.72 s +2026-04-14 06:48:39.446942: +2026-04-14 06:48:39.449265: Epoch 3152 +2026-04-14 06:48:39.451776: Current learning rate: 0.00248 +2026-04-14 06:50:22.921204: train_loss -0.4096 +2026-04-14 06:50:22.927824: val_loss -0.3046 +2026-04-14 06:50:22.929832: Pseudo dice [0.7031, 0.6744, 0.6499, 0.7047, 0.6356, 0.0471, 0.9027] +2026-04-14 06:50:22.932652: Epoch time: 103.48 s +2026-04-14 06:50:24.504020: +2026-04-14 06:50:24.510535: Epoch 3153 +2026-04-14 06:50:24.512563: Current learning rate: 0.00247 +2026-04-14 06:52:08.400208: train_loss -0.4194 +2026-04-14 06:52:08.407115: val_loss -0.2375 +2026-04-14 06:52:08.410011: Pseudo dice [0.4578, 0.6701, 0.3407, 0.0037, 0.5034, 0.0533, 0.83] +2026-04-14 06:52:08.412552: Epoch time: 103.9 s +2026-04-14 06:52:10.018013: +2026-04-14 06:52:10.020092: Epoch 3154 +2026-04-14 06:52:10.022118: Current learning rate: 0.00247 +2026-04-14 06:53:53.481472: train_loss -0.4167 +2026-04-14 06:53:53.488326: val_loss -0.3395 +2026-04-14 06:53:53.491008: Pseudo dice [0.7812, 0.3368, 0.6848, 0.762, 0.6333, 0.0589, 0.8303] +2026-04-14 06:53:53.494497: Epoch time: 103.47 s +2026-04-14 06:53:55.112025: +2026-04-14 06:53:55.114182: Epoch 3155 +2026-04-14 06:53:55.116529: Current learning rate: 0.00247 +2026-04-14 06:55:39.582806: train_loss -0.4151 +2026-04-14 06:55:39.588570: val_loss -0.2134 +2026-04-14 06:55:39.590867: Pseudo dice [0.2202, 0.4755, 0.4901, 0.1853, 0.5938, 0.0782, 0.763] +2026-04-14 06:55:39.592936: Epoch time: 104.47 s +2026-04-14 06:55:41.221916: +2026-04-14 06:55:41.224362: Epoch 3156 +2026-04-14 06:55:41.227001: Current learning rate: 0.00247 +2026-04-14 06:57:24.025169: train_loss -0.4101 +2026-04-14 06:57:24.033181: val_loss -0.281 +2026-04-14 06:57:24.035479: Pseudo dice [0.667, 0.6628, 0.5206, 0.0045, 0.6125, 0.3084, 0.887] +2026-04-14 06:57:24.038525: Epoch time: 102.81 s +2026-04-14 06:57:25.630605: +2026-04-14 06:57:25.633023: Epoch 3157 +2026-04-14 06:57:25.635100: Current learning rate: 0.00246 +2026-04-14 06:59:09.265383: train_loss -0.4115 +2026-04-14 06:59:09.271718: val_loss -0.3134 +2026-04-14 06:59:09.273618: Pseudo dice [0.0964, 0.4503, 0.724, 0.4219, 0.6155, 0.3137, 0.6824] +2026-04-14 06:59:09.275866: Epoch time: 103.64 s +2026-04-14 06:59:10.848278: +2026-04-14 06:59:10.850157: Epoch 3158 +2026-04-14 06:59:10.852709: Current learning rate: 0.00246 +2026-04-14 07:00:53.909226: train_loss -0.4069 +2026-04-14 07:00:53.917384: val_loss -0.2787 +2026-04-14 07:00:53.919936: Pseudo dice [0.6334, 0.562, 0.46, 0.7162, 0.532, 0.0475, 0.7921] +2026-04-14 07:00:53.923049: Epoch time: 103.06 s +2026-04-14 07:00:55.512071: +2026-04-14 07:00:55.514389: Epoch 3159 +2026-04-14 07:00:55.516463: Current learning rate: 0.00246 +2026-04-14 07:02:38.383673: train_loss -0.4029 +2026-04-14 07:02:38.392283: val_loss -0.3408 +2026-04-14 07:02:38.394233: Pseudo dice [0.6064, 0.6934, 0.6581, 0.5359, 0.5827, 0.6958, 0.7514] +2026-04-14 07:02:38.396807: Epoch time: 102.88 s +2026-04-14 07:02:39.979798: +2026-04-14 07:02:39.982540: Epoch 3160 +2026-04-14 07:02:39.984728: Current learning rate: 0.00245 +2026-04-14 07:04:23.369699: train_loss -0.4095 +2026-04-14 07:04:23.375489: val_loss -0.3434 +2026-04-14 07:04:23.378647: Pseudo dice [0.6419, 0.6987, 0.6638, 0.5802, 0.4617, 0.2522, 0.7056] +2026-04-14 07:04:23.380875: Epoch time: 103.39 s +2026-04-14 07:04:25.056438: +2026-04-14 07:04:25.059263: Epoch 3161 +2026-04-14 07:04:25.062496: Current learning rate: 0.00245 +2026-04-14 07:06:09.159749: train_loss -0.4098 +2026-04-14 07:06:09.164685: val_loss -0.3577 +2026-04-14 07:06:09.166809: Pseudo dice [0.6075, 0.7404, 0.7113, 0.4285, 0.5671, 0.6664, 0.7869] +2026-04-14 07:06:09.168706: Epoch time: 104.11 s +2026-04-14 07:06:10.804172: +2026-04-14 07:06:10.806159: Epoch 3162 +2026-04-14 07:06:10.808527: Current learning rate: 0.00245 +2026-04-14 07:07:53.755125: train_loss -0.3974 +2026-04-14 07:07:53.760581: val_loss -0.2501 +2026-04-14 07:07:53.762411: Pseudo dice [0.2932, 0.7106, 0.5341, 0.528, 0.5839, 0.1289, 0.8283] +2026-04-14 07:07:53.765803: Epoch time: 102.95 s +2026-04-14 07:07:55.319337: +2026-04-14 07:07:55.321034: Epoch 3163 +2026-04-14 07:07:55.322919: Current learning rate: 0.00245 +2026-04-14 07:09:38.532945: train_loss -0.414 +2026-04-14 07:09:38.542527: val_loss -0.349 +2026-04-14 07:09:38.545834: Pseudo dice [0.6074, 0.722, 0.4762, 0.62, 0.6537, 0.177, 0.8482] +2026-04-14 07:09:38.549177: Epoch time: 103.22 s +2026-04-14 07:09:40.205957: +2026-04-14 07:09:40.207878: Epoch 3164 +2026-04-14 07:09:40.210009: Current learning rate: 0.00244 +2026-04-14 07:11:23.247034: train_loss -0.4211 +2026-04-14 07:11:23.253738: val_loss -0.3862 +2026-04-14 07:11:23.257022: Pseudo dice [0.4331, 0.8134, 0.7327, 0.8024, 0.5767, 0.7883, 0.8535] +2026-04-14 07:11:23.259770: Epoch time: 103.04 s +2026-04-14 07:11:24.826377: +2026-04-14 07:11:24.828680: Epoch 3165 +2026-04-14 07:11:24.830917: Current learning rate: 0.00244 +2026-04-14 07:13:07.993520: train_loss -0.4231 +2026-04-14 07:13:08.002215: val_loss -0.2882 +2026-04-14 07:13:08.005392: Pseudo dice [0.3653, 0.4848, 0.7733, 0.4709, 0.3744, 0.1844, 0.8837] +2026-04-14 07:13:08.008318: Epoch time: 103.17 s +2026-04-14 07:13:09.597576: +2026-04-14 07:13:09.608995: Epoch 3166 +2026-04-14 07:13:09.611379: Current learning rate: 0.00244 +2026-04-14 07:14:52.722484: train_loss -0.41 +2026-04-14 07:14:52.729736: val_loss -0.3376 +2026-04-14 07:14:52.731666: Pseudo dice [0.3359, 0.4694, 0.7707, 0.8153, 0.0996, 0.654, 0.5579] +2026-04-14 07:14:52.733919: Epoch time: 103.13 s +2026-04-14 07:14:54.302727: +2026-04-14 07:14:54.305549: Epoch 3167 +2026-04-14 07:14:54.308474: Current learning rate: 0.00244 +2026-04-14 07:16:38.459195: train_loss -0.4139 +2026-04-14 07:16:38.465761: val_loss -0.3345 +2026-04-14 07:16:38.468023: Pseudo dice [0.6759, 0.7015, 0.696, 0.873, 0.4315, 0.3898, 0.8309] +2026-04-14 07:16:38.471897: Epoch time: 104.16 s +2026-04-14 07:16:40.085468: +2026-04-14 07:16:40.087694: Epoch 3168 +2026-04-14 07:16:40.089630: Current learning rate: 0.00243 +2026-04-14 07:18:22.827721: train_loss -0.3959 +2026-04-14 07:18:22.835236: val_loss -0.2992 +2026-04-14 07:18:22.837652: Pseudo dice [0.5823, 0.5886, 0.5754, 0.6605, 0.3808, 0.058, 0.9026] +2026-04-14 07:18:22.840870: Epoch time: 102.75 s +2026-04-14 07:18:24.400436: +2026-04-14 07:18:24.403171: Epoch 3169 +2026-04-14 07:18:24.405661: Current learning rate: 0.00243 +2026-04-14 07:20:07.693558: train_loss -0.3991 +2026-04-14 07:20:07.706416: val_loss -0.3658 +2026-04-14 07:20:07.709066: Pseudo dice [0.5508, 0.261, 0.825, 0.8019, 0.622, 0.7658, 0.873] +2026-04-14 07:20:07.710983: Epoch time: 103.3 s +2026-04-14 07:20:09.252734: +2026-04-14 07:20:09.255274: Epoch 3170 +2026-04-14 07:20:09.257226: Current learning rate: 0.00243 +2026-04-14 07:21:52.522689: train_loss -0.4164 +2026-04-14 07:21:52.530571: val_loss -0.3592 +2026-04-14 07:21:52.534491: Pseudo dice [0.6685, 0.8029, 0.7152, 0.5149, 0.4572, 0.5873, 0.8548] +2026-04-14 07:21:52.537288: Epoch time: 103.27 s +2026-04-14 07:21:54.190608: +2026-04-14 07:21:54.192563: Epoch 3171 +2026-04-14 07:21:54.194496: Current learning rate: 0.00243 +2026-04-14 07:23:37.280022: train_loss -0.4126 +2026-04-14 07:23:37.285372: val_loss -0.2555 +2026-04-14 07:23:37.287517: Pseudo dice [0.6303, 0.5445, 0.699, 0.3987, 0.6156, 0.0503, 0.5182] +2026-04-14 07:23:37.290619: Epoch time: 103.09 s +2026-04-14 07:23:38.862480: +2026-04-14 07:23:38.864759: Epoch 3172 +2026-04-14 07:23:38.867141: Current learning rate: 0.00242 +2026-04-14 07:25:21.739947: train_loss -0.4075 +2026-04-14 07:25:21.745234: val_loss -0.3311 +2026-04-14 07:25:21.746987: Pseudo dice [0.5795, 0.6078, 0.6242, 0.2563, 0.4256, 0.8444, 0.7059] +2026-04-14 07:25:21.749178: Epoch time: 102.88 s +2026-04-14 07:25:23.326327: +2026-04-14 07:25:23.327981: Epoch 3173 +2026-04-14 07:25:23.330920: Current learning rate: 0.00242 +2026-04-14 07:27:07.059253: train_loss -0.4085 +2026-04-14 07:27:07.066411: val_loss -0.2655 +2026-04-14 07:27:07.069287: Pseudo dice [0.6943, 0.3092, 0.3448, 0.7056, 0.6557, 0.0574, 0.8159] +2026-04-14 07:27:07.071887: Epoch time: 103.74 s +2026-04-14 07:27:08.629165: +2026-04-14 07:27:08.631173: Epoch 3174 +2026-04-14 07:27:08.633426: Current learning rate: 0.00242 +2026-04-14 07:28:51.634717: train_loss -0.411 +2026-04-14 07:28:51.640482: val_loss -0.3519 +2026-04-14 07:28:51.642425: Pseudo dice [0.7815, 0.8634, 0.7233, 0.6784, 0.6265, 0.7814, 0.8108] +2026-04-14 07:28:51.645241: Epoch time: 103.01 s +2026-04-14 07:28:53.256620: +2026-04-14 07:28:53.260594: Epoch 3175 +2026-04-14 07:28:53.264530: Current learning rate: 0.00242 +2026-04-14 07:30:35.893527: train_loss -0.4122 +2026-04-14 07:30:35.899564: val_loss -0.2765 +2026-04-14 07:30:35.901764: Pseudo dice [0.4007, 0.7724, 0.5452, 0.4601, 0.693, 0.1258, 0.7885] +2026-04-14 07:30:35.905190: Epoch time: 102.64 s +2026-04-14 07:30:37.499563: +2026-04-14 07:30:37.501676: Epoch 3176 +2026-04-14 07:30:37.503956: Current learning rate: 0.00241 +2026-04-14 07:32:21.122648: train_loss -0.4031 +2026-04-14 07:32:21.148183: val_loss -0.3555 +2026-04-14 07:32:21.150594: Pseudo dice [0.4956, 0.7155, 0.726, 0.461, 0.4701, 0.7903, 0.596] +2026-04-14 07:32:21.153202: Epoch time: 103.63 s +2026-04-14 07:32:22.779353: +2026-04-14 07:32:22.781235: Epoch 3177 +2026-04-14 07:32:22.783311: Current learning rate: 0.00241 +2026-04-14 07:34:06.135932: train_loss -0.3962 +2026-04-14 07:34:06.142237: val_loss -0.3489 +2026-04-14 07:34:06.144380: Pseudo dice [0.6262, 0.8078, 0.7123, 0.5143, 0.4003, 0.7789, 0.8668] +2026-04-14 07:34:06.146389: Epoch time: 103.36 s +2026-04-14 07:34:07.792948: +2026-04-14 07:34:07.795061: Epoch 3178 +2026-04-14 07:34:07.797194: Current learning rate: 0.00241 +2026-04-14 07:35:51.210565: train_loss -0.3951 +2026-04-14 07:35:51.217016: val_loss -0.3179 +2026-04-14 07:35:51.219600: Pseudo dice [0.5932, 0.6191, 0.6524, 0.185, 0.6103, 0.2075, 0.8472] +2026-04-14 07:35:51.221753: Epoch time: 103.42 s +2026-04-14 07:35:52.815680: +2026-04-14 07:35:52.817720: Epoch 3179 +2026-04-14 07:35:52.820342: Current learning rate: 0.0024 +2026-04-14 07:37:36.008232: train_loss -0.4067 +2026-04-14 07:37:36.016343: val_loss -0.2468 +2026-04-14 07:37:36.018362: Pseudo dice [0.3608, 0.7689, 0.6646, 0.5635, 0.5691, 0.2133, 0.7311] +2026-04-14 07:37:36.020661: Epoch time: 103.19 s +2026-04-14 07:37:37.608640: +2026-04-14 07:37:37.611191: Epoch 3180 +2026-04-14 07:37:37.613899: Current learning rate: 0.0024 +2026-04-14 07:39:20.541892: train_loss -0.4087 +2026-04-14 07:39:20.548425: val_loss -0.3002 +2026-04-14 07:39:20.550387: Pseudo dice [0.4872, 0.777, 0.6429, 0.3843, 0.4118, 0.0996, 0.6379] +2026-04-14 07:39:20.556259: Epoch time: 102.94 s +2026-04-14 07:39:22.112747: +2026-04-14 07:39:22.114435: Epoch 3181 +2026-04-14 07:39:22.116319: Current learning rate: 0.0024 +2026-04-14 07:41:05.669123: train_loss -0.4102 +2026-04-14 07:41:05.675369: val_loss -0.3225 +2026-04-14 07:41:05.678143: Pseudo dice [0.7048, 0.6666, 0.5924, 0.7658, 0.4452, 0.2571, 0.7829] +2026-04-14 07:41:05.680443: Epoch time: 103.56 s +2026-04-14 07:41:07.254247: +2026-04-14 07:41:07.256504: Epoch 3182 +2026-04-14 07:41:07.259170: Current learning rate: 0.0024 +2026-04-14 07:42:50.430470: train_loss -0.4084 +2026-04-14 07:42:50.437727: val_loss -0.3393 +2026-04-14 07:42:50.440198: Pseudo dice [0.7228, 0.5518, 0.7181, 0.7112, 0.6182, 0.2387, 0.7543] +2026-04-14 07:42:50.444390: Epoch time: 103.18 s +2026-04-14 07:42:51.976334: +2026-04-14 07:42:51.978408: Epoch 3183 +2026-04-14 07:42:51.980757: Current learning rate: 0.00239 +2026-04-14 07:44:35.087299: train_loss -0.4098 +2026-04-14 07:44:35.095350: val_loss -0.3099 +2026-04-14 07:44:35.098141: Pseudo dice [0.4917, 0.4888, 0.6939, 0.8017, 0.2575, 0.2932, 0.4547] +2026-04-14 07:44:35.101369: Epoch time: 103.11 s +2026-04-14 07:44:36.726046: +2026-04-14 07:44:36.728918: Epoch 3184 +2026-04-14 07:44:36.731192: Current learning rate: 0.00239 +2026-04-14 07:46:19.868765: train_loss -0.4197 +2026-04-14 07:46:19.874295: val_loss -0.3577 +2026-04-14 07:46:19.876281: Pseudo dice [0.6181, 0.5087, 0.5287, 0.7813, 0.6435, 0.3434, 0.8707] +2026-04-14 07:46:19.879581: Epoch time: 103.15 s +2026-04-14 07:46:21.419351: +2026-04-14 07:46:21.421043: Epoch 3185 +2026-04-14 07:46:21.423136: Current learning rate: 0.00239 +2026-04-14 07:48:04.202989: train_loss -0.4193 +2026-04-14 07:48:04.210679: val_loss -0.3469 +2026-04-14 07:48:04.213443: Pseudo dice [0.2424, 0.2505, 0.844, 0.7743, 0.4674, 0.7568, 0.7452] +2026-04-14 07:48:04.215702: Epoch time: 102.79 s +2026-04-14 07:48:05.789234: +2026-04-14 07:48:05.791382: Epoch 3186 +2026-04-14 07:48:05.793415: Current learning rate: 0.00239 +2026-04-14 07:49:48.648722: train_loss -0.4141 +2026-04-14 07:49:48.657157: val_loss -0.3252 +2026-04-14 07:49:48.659531: Pseudo dice [0.7428, 0.5769, 0.6638, 0.7909, 0.435, 0.2759, 0.7649] +2026-04-14 07:49:48.662297: Epoch time: 102.86 s +2026-04-14 07:49:50.205331: +2026-04-14 07:49:50.207249: Epoch 3187 +2026-04-14 07:49:50.209293: Current learning rate: 0.00238 +2026-04-14 07:51:32.910316: train_loss -0.4107 +2026-04-14 07:51:32.916673: val_loss -0.2264 +2026-04-14 07:51:32.919101: Pseudo dice [0.4484, 0.4947, 0.4592, 0.3211, 0.4724, 0.1287, 0.8307] +2026-04-14 07:51:32.921659: Epoch time: 102.71 s +2026-04-14 07:51:34.490741: +2026-04-14 07:51:34.492552: Epoch 3188 +2026-04-14 07:51:34.494663: Current learning rate: 0.00238 +2026-04-14 07:53:17.436692: train_loss -0.4073 +2026-04-14 07:53:17.443500: val_loss -0.269 +2026-04-14 07:53:17.445753: Pseudo dice [0.2783, 0.4482, 0.6461, 0.4882, 0.4605, 0.5292, 0.7122] +2026-04-14 07:53:17.447945: Epoch time: 102.95 s +2026-04-14 07:53:19.029444: +2026-04-14 07:53:19.031100: Epoch 3189 +2026-04-14 07:53:19.033210: Current learning rate: 0.00238 +2026-04-14 07:55:02.437707: train_loss -0.4073 +2026-04-14 07:55:02.444073: val_loss -0.3704 +2026-04-14 07:55:02.446351: Pseudo dice [0.4118, 0.6951, 0.8058, 0.8416, 0.596, 0.4984, 0.5713] +2026-04-14 07:55:02.448586: Epoch time: 103.41 s +2026-04-14 07:55:04.258325: +2026-04-14 07:55:04.260987: Epoch 3190 +2026-04-14 07:55:04.262972: Current learning rate: 0.00238 +2026-04-14 07:56:47.483537: train_loss -0.4136 +2026-04-14 07:56:47.490000: val_loss -0.249 +2026-04-14 07:56:47.492325: Pseudo dice [0.3409, 0.4811, 0.5408, 0.7251, 0.3819, 0.1769, 0.697] +2026-04-14 07:56:47.495542: Epoch time: 103.23 s +2026-04-14 07:56:49.061210: +2026-04-14 07:56:49.063944: Epoch 3191 +2026-04-14 07:56:49.066672: Current learning rate: 0.00237 +2026-04-14 07:58:32.641589: train_loss -0.3889 +2026-04-14 07:58:32.647612: val_loss -0.2779 +2026-04-14 07:58:32.649301: Pseudo dice [0.6392, 0.4503, 0.4343, 0.2884, 0.5247, 0.0584, 0.8339] +2026-04-14 07:58:32.651118: Epoch time: 103.58 s +2026-04-14 07:58:34.217089: +2026-04-14 07:58:34.219379: Epoch 3192 +2026-04-14 07:58:34.221340: Current learning rate: 0.00237 +2026-04-14 08:00:17.434362: train_loss -0.3777 +2026-04-14 08:00:17.440590: val_loss -0.3055 +2026-04-14 08:00:17.443038: Pseudo dice [0.7407, 0.4241, 0.7156, 0.7046, 0.564, 0.0783, 0.8712] +2026-04-14 08:00:17.445576: Epoch time: 103.22 s +2026-04-14 08:00:18.998875: +2026-04-14 08:00:19.000662: Epoch 3193 +2026-04-14 08:00:19.002650: Current learning rate: 0.00237 +2026-04-14 08:02:02.560405: train_loss -0.3916 +2026-04-14 08:02:02.566590: val_loss -0.2984 +2026-04-14 08:02:02.569010: Pseudo dice [0.433, 0.3024, 0.6319, 0.8088, 0.4589, 0.1673, 0.7691] +2026-04-14 08:02:02.571402: Epoch time: 103.57 s +2026-04-14 08:02:04.182044: +2026-04-14 08:02:04.184100: Epoch 3194 +2026-04-14 08:02:04.186221: Current learning rate: 0.00237 +2026-04-14 08:03:48.061056: train_loss -0.3936 +2026-04-14 08:03:48.073411: val_loss -0.3316 +2026-04-14 08:03:48.077483: Pseudo dice [0.7002, 0.5443, 0.6112, 0.7693, 0.6577, 0.0527, 0.8288] +2026-04-14 08:03:48.080670: Epoch time: 103.88 s +2026-04-14 08:03:49.640403: +2026-04-14 08:03:49.642480: Epoch 3195 +2026-04-14 08:03:49.646218: Current learning rate: 0.00236 +2026-04-14 08:05:32.844919: train_loss -0.3783 +2026-04-14 08:05:32.849928: val_loss -0.3337 +2026-04-14 08:05:32.853002: Pseudo dice [0.709, 0.5133, 0.7056, 0.6797, 0.3418, 0.4472, 0.7465] +2026-04-14 08:05:32.855321: Epoch time: 103.21 s +2026-04-14 08:05:34.462579: +2026-04-14 08:05:34.464241: Epoch 3196 +2026-04-14 08:05:34.466182: Current learning rate: 0.00236 +2026-04-14 08:07:18.612239: train_loss -0.4028 +2026-04-14 08:07:18.619610: val_loss -0.327 +2026-04-14 08:07:18.622211: Pseudo dice [0.6708, 0.7472, 0.7327, 0.7066, 0.4107, 0.1882, 0.7693] +2026-04-14 08:07:18.624623: Epoch time: 104.15 s +2026-04-14 08:07:20.247746: +2026-04-14 08:07:20.249551: Epoch 3197 +2026-04-14 08:07:20.252767: Current learning rate: 0.00236 +2026-04-14 08:09:03.943528: train_loss -0.4007 +2026-04-14 08:09:03.953890: val_loss -0.3443 +2026-04-14 08:09:03.956710: Pseudo dice [0.7216, 0.7308, 0.7024, 0.7768, 0.3717, 0.8172, 0.5616] +2026-04-14 08:09:03.959993: Epoch time: 103.7 s +2026-04-14 08:09:05.541035: +2026-04-14 08:09:05.543288: Epoch 3198 +2026-04-14 08:09:05.545305: Current learning rate: 0.00235 +2026-04-14 08:10:49.260737: train_loss -0.3927 +2026-04-14 08:10:49.266544: val_loss -0.3748 +2026-04-14 08:10:49.268787: Pseudo dice [0.5216, 0.5636, 0.7519, 0.6873, 0.6055, 0.5942, 0.6791] +2026-04-14 08:10:49.272565: Epoch time: 103.72 s +2026-04-14 08:10:50.930074: +2026-04-14 08:10:50.932302: Epoch 3199 +2026-04-14 08:10:50.934263: Current learning rate: 0.00235 +2026-04-14 08:12:34.447469: train_loss -0.4014 +2026-04-14 08:12:34.453759: val_loss -0.3571 +2026-04-14 08:12:34.455914: Pseudo dice [0.3467, 0.5062, 0.6812, 0.5875, 0.5625, 0.6957, 0.8687] +2026-04-14 08:12:34.458479: Epoch time: 103.52 s +2026-04-14 08:12:38.178495: +2026-04-14 08:12:38.181324: Epoch 3200 +2026-04-14 08:12:38.183884: Current learning rate: 0.00235 +2026-04-14 08:14:23.439932: train_loss -0.4162 +2026-04-14 08:14:23.446108: val_loss -0.3439 +2026-04-14 08:14:23.447948: Pseudo dice [0.5265, 0.6975, 0.7135, 0.1904, 0.5731, 0.1359, 0.8897] +2026-04-14 08:14:23.450146: Epoch time: 105.27 s +2026-04-14 08:14:25.065264: +2026-04-14 08:14:25.068473: Epoch 3201 +2026-04-14 08:14:25.070985: Current learning rate: 0.00235 +2026-04-14 08:16:08.501754: train_loss -0.4139 +2026-04-14 08:16:08.507515: val_loss -0.2814 +2026-04-14 08:16:08.509520: Pseudo dice [0.32, 0.8158, 0.704, 0.7812, 0.3533, 0.3102, 0.8109] +2026-04-14 08:16:08.511811: Epoch time: 103.44 s +2026-04-14 08:16:10.101074: +2026-04-14 08:16:10.103073: Epoch 3202 +2026-04-14 08:16:10.105323: Current learning rate: 0.00234 +2026-04-14 08:17:53.131407: train_loss -0.4128 +2026-04-14 08:17:53.138202: val_loss -0.3252 +2026-04-14 08:17:53.140564: Pseudo dice [0.6221, 0.2563, 0.6613, 0.858, 0.6493, 0.77, 0.671] +2026-04-14 08:17:53.149127: Epoch time: 103.03 s +2026-04-14 08:17:54.821990: +2026-04-14 08:17:54.824232: Epoch 3203 +2026-04-14 08:17:54.831371: Current learning rate: 0.00234 +2026-04-14 08:19:38.463392: train_loss -0.4104 +2026-04-14 08:19:38.469197: val_loss -0.3858 +2026-04-14 08:19:38.471283: Pseudo dice [0.4912, 0.2931, 0.802, 0.4162, 0.4839, 0.8318, 0.8284] +2026-04-14 08:19:38.473670: Epoch time: 103.65 s +2026-04-14 08:19:40.066298: +2026-04-14 08:19:40.068243: Epoch 3204 +2026-04-14 08:19:40.070699: Current learning rate: 0.00234 +2026-04-14 08:21:23.895748: train_loss -0.3886 +2026-04-14 08:21:23.903943: val_loss -0.2936 +2026-04-14 08:21:23.906235: Pseudo dice [0.4153, 0.1882, 0.5547, 0.2945, 0.4667, 0.6289, 0.8749] +2026-04-14 08:21:23.910039: Epoch time: 103.83 s +2026-04-14 08:21:25.530340: +2026-04-14 08:21:25.532444: Epoch 3205 +2026-04-14 08:21:25.534899: Current learning rate: 0.00234 +2026-04-14 08:23:08.581607: train_loss -0.3903 +2026-04-14 08:23:08.587198: val_loss -0.3252 +2026-04-14 08:23:08.588965: Pseudo dice [0.1919, 0.488, 0.424, 0.3439, 0.3732, 0.8843, 0.8065] +2026-04-14 08:23:08.591141: Epoch time: 103.06 s +2026-04-14 08:23:10.135051: +2026-04-14 08:23:10.137508: Epoch 3206 +2026-04-14 08:23:10.139603: Current learning rate: 0.00233 +2026-04-14 08:24:53.653608: train_loss -0.402 +2026-04-14 08:24:53.660416: val_loss -0.2903 +2026-04-14 08:24:53.662592: Pseudo dice [0.2975, 0.4304, 0.8282, 0.9078, 0.2837, 0.121, 0.86] +2026-04-14 08:24:53.665847: Epoch time: 103.52 s +2026-04-14 08:24:55.281893: +2026-04-14 08:24:55.284184: Epoch 3207 +2026-04-14 08:24:55.286288: Current learning rate: 0.00233 +2026-04-14 08:26:37.778414: train_loss -0.4106 +2026-04-14 08:26:37.784791: val_loss -0.3282 +2026-04-14 08:26:37.787655: Pseudo dice [0.4906, 0.7555, 0.7462, 0.4272, 0.2393, 0.7999, 0.7119] +2026-04-14 08:26:37.790181: Epoch time: 102.5 s +2026-04-14 08:26:39.362589: +2026-04-14 08:26:39.365360: Epoch 3208 +2026-04-14 08:26:39.368662: Current learning rate: 0.00233 +2026-04-14 08:28:22.175706: train_loss -0.4044 +2026-04-14 08:28:22.182267: val_loss -0.3211 +2026-04-14 08:28:22.184514: Pseudo dice [0.5727, 0.4337, 0.7567, 0.8733, 0.2113, 0.7343, 0.3071] +2026-04-14 08:28:22.186942: Epoch time: 102.82 s +2026-04-14 08:28:23.842975: +2026-04-14 08:28:23.845052: Epoch 3209 +2026-04-14 08:28:23.847282: Current learning rate: 0.00233 +2026-04-14 08:30:07.204267: train_loss -0.4035 +2026-04-14 08:30:07.212931: val_loss -0.2565 +2026-04-14 08:30:07.215334: Pseudo dice [0.8376, 0.4476, 0.7346, 0.8867, 0.3757, 0.2564, 0.6677] +2026-04-14 08:30:07.218155: Epoch time: 103.36 s +2026-04-14 08:30:08.835634: +2026-04-14 08:30:08.838088: Epoch 3210 +2026-04-14 08:30:08.840558: Current learning rate: 0.00232 +2026-04-14 08:31:52.181855: train_loss -0.3995 +2026-04-14 08:31:52.188889: val_loss -0.3602 +2026-04-14 08:31:52.191353: Pseudo dice [0.6514, 0.3401, 0.6705, 0.6917, 0.4087, 0.825, 0.7931] +2026-04-14 08:31:52.194161: Epoch time: 103.35 s +2026-04-14 08:31:53.792066: +2026-04-14 08:31:53.794088: Epoch 3211 +2026-04-14 08:31:53.796606: Current learning rate: 0.00232 +2026-04-14 08:33:36.430321: train_loss -0.3924 +2026-04-14 08:33:36.437135: val_loss -0.3332 +2026-04-14 08:33:36.448452: Pseudo dice [0.8162, 0.6137, 0.6079, 0.009, 0.5572, 0.375, 0.8686] +2026-04-14 08:33:36.451388: Epoch time: 102.64 s +2026-04-14 08:33:38.013688: +2026-04-14 08:33:38.015602: Epoch 3212 +2026-04-14 08:33:38.017887: Current learning rate: 0.00232 +2026-04-14 08:35:21.899300: train_loss -0.3969 +2026-04-14 08:35:21.910212: val_loss -0.3426 +2026-04-14 08:35:21.912394: Pseudo dice [0.7389, 0.524, 0.5154, 0.8959, 0.4532, 0.3648, 0.8622] +2026-04-14 08:35:21.915337: Epoch time: 103.89 s +2026-04-14 08:35:23.462053: +2026-04-14 08:35:23.463665: Epoch 3213 +2026-04-14 08:35:23.465581: Current learning rate: 0.00231 +2026-04-14 08:37:06.217466: train_loss -0.3835 +2026-04-14 08:37:06.223273: val_loss -0.2846 +2026-04-14 08:37:06.225564: Pseudo dice [0.7658, 0.6298, 0.7047, 0.8197, 0.4777, 0.2363, 0.8261] +2026-04-14 08:37:06.228124: Epoch time: 102.76 s +2026-04-14 08:37:07.834440: +2026-04-14 08:37:07.836508: Epoch 3214 +2026-04-14 08:37:07.838982: Current learning rate: 0.00231 +2026-04-14 08:38:51.458258: train_loss -0.3973 +2026-04-14 08:38:51.465925: val_loss -0.3843 +2026-04-14 08:38:51.467954: Pseudo dice [0.2731, 0.4093, 0.8568, 0.5455, 0.4091, 0.7528, 0.8893] +2026-04-14 08:38:51.471408: Epoch time: 103.63 s +2026-04-14 08:38:53.071985: +2026-04-14 08:38:53.073958: Epoch 3215 +2026-04-14 08:38:53.076134: Current learning rate: 0.00231 +2026-04-14 08:40:36.013265: train_loss -0.397 +2026-04-14 08:40:36.022479: val_loss -0.2975 +2026-04-14 08:40:36.024776: Pseudo dice [0.6003, 0.4943, 0.7095, 0.103, 0.2708, 0.3685, 0.8205] +2026-04-14 08:40:36.027450: Epoch time: 102.94 s +2026-04-14 08:40:37.624147: +2026-04-14 08:40:37.626601: Epoch 3216 +2026-04-14 08:40:37.629302: Current learning rate: 0.00231 +2026-04-14 08:42:20.804569: train_loss -0.3964 +2026-04-14 08:42:20.811872: val_loss -0.2934 +2026-04-14 08:42:20.815135: Pseudo dice [0.7429, 0.8317, 0.7362, 0.6557, 0.3974, 0.0949, 0.5997] +2026-04-14 08:42:20.817584: Epoch time: 103.18 s +2026-04-14 08:42:22.466796: +2026-04-14 08:42:22.469229: Epoch 3217 +2026-04-14 08:42:22.470982: Current learning rate: 0.0023 +2026-04-14 08:44:05.780475: train_loss -0.4114 +2026-04-14 08:44:05.789131: val_loss -0.2236 +2026-04-14 08:44:05.791317: Pseudo dice [0.4495, 0.6359, 0.5806, 0.8333, 0.2591, 0.2811, 0.8366] +2026-04-14 08:44:05.793447: Epoch time: 103.32 s +2026-04-14 08:44:07.453984: +2026-04-14 08:44:07.456756: Epoch 3218 +2026-04-14 08:44:07.459087: Current learning rate: 0.0023 +2026-04-14 08:45:50.167915: train_loss -0.4043 +2026-04-14 08:45:50.174004: val_loss -0.3377 +2026-04-14 08:45:50.176120: Pseudo dice [0.7153, 0.0904, 0.7249, 0.6226, 0.4509, 0.3612, 0.6944] +2026-04-14 08:45:50.178503: Epoch time: 102.72 s +2026-04-14 08:45:51.721607: +2026-04-14 08:45:51.723191: Epoch 3219 +2026-04-14 08:45:51.725030: Current learning rate: 0.0023 +2026-04-14 08:47:34.207621: train_loss -0.4073 +2026-04-14 08:47:34.217665: val_loss -0.3537 +2026-04-14 08:47:34.220732: Pseudo dice [0.6209, 0.7205, 0.6567, 0.7553, 0.6002, 0.3912, 0.8106] +2026-04-14 08:47:34.226802: Epoch time: 102.49 s +2026-04-14 08:47:37.013620: +2026-04-14 08:47:37.015756: Epoch 3220 +2026-04-14 08:47:37.017929: Current learning rate: 0.0023 +2026-04-14 08:49:19.763866: train_loss -0.4162 +2026-04-14 08:49:19.769635: val_loss -0.3611 +2026-04-14 08:49:19.771683: Pseudo dice [0.796, 0.7034, 0.764, 0.7753, 0.4582, 0.8837, 0.9155] +2026-04-14 08:49:19.774788: Epoch time: 102.75 s +2026-04-14 08:49:21.352941: +2026-04-14 08:49:21.355077: Epoch 3221 +2026-04-14 08:49:21.357416: Current learning rate: 0.00229 +2026-04-14 08:51:04.046068: train_loss -0.3992 +2026-04-14 08:51:04.052876: val_loss -0.3215 +2026-04-14 08:51:04.056105: Pseudo dice [0.7989, 0.6668, 0.4557, 0.2707, 0.4335, 0.3177, 0.8373] +2026-04-14 08:51:04.058980: Epoch time: 102.7 s +2026-04-14 08:51:05.667081: +2026-04-14 08:51:05.670170: Epoch 3222 +2026-04-14 08:51:05.672291: Current learning rate: 0.00229 +2026-04-14 08:52:48.154704: train_loss -0.3849 +2026-04-14 08:52:48.160598: val_loss -0.3499 +2026-04-14 08:52:48.162356: Pseudo dice [0.4125, 0.6788, 0.5938, 0.1917, 0.5008, 0.766, 0.8623] +2026-04-14 08:52:48.164993: Epoch time: 102.49 s +2026-04-14 08:52:49.798012: +2026-04-14 08:52:49.800605: Epoch 3223 +2026-04-14 08:52:49.803093: Current learning rate: 0.00229 +2026-04-14 08:54:33.254442: train_loss -0.3965 +2026-04-14 08:54:33.264719: val_loss -0.3391 +2026-04-14 08:54:33.267403: Pseudo dice [0.4075, 0.0, 0.6138, 0.8642, 0.4728, 0.7235, 0.7119] +2026-04-14 08:54:33.271004: Epoch time: 103.46 s +2026-04-14 08:54:34.853400: +2026-04-14 08:54:34.855308: Epoch 3224 +2026-04-14 08:54:34.857855: Current learning rate: 0.00229 +2026-04-14 08:56:17.973682: train_loss -0.3823 +2026-04-14 08:56:17.980555: val_loss -0.3285 +2026-04-14 08:56:17.982464: Pseudo dice [0.6915, 0.053, 0.6839, 0.5389, 0.2645, 0.7983, 0.7007] +2026-04-14 08:56:17.985175: Epoch time: 103.12 s +2026-04-14 08:56:19.591187: +2026-04-14 08:56:19.593125: Epoch 3225 +2026-04-14 08:56:19.594949: Current learning rate: 0.00228 +2026-04-14 08:58:03.683985: train_loss -0.4035 +2026-04-14 08:58:03.692513: val_loss -0.3451 +2026-04-14 08:58:03.694594: Pseudo dice [0.4723, 0.1705, 0.6658, 0.9122, 0.5468, 0.6907, 0.5841] +2026-04-14 08:58:03.697448: Epoch time: 104.1 s +2026-04-14 08:58:05.246799: +2026-04-14 08:58:05.248842: Epoch 3226 +2026-04-14 08:58:05.250628: Current learning rate: 0.00228 +2026-04-14 08:59:48.523343: train_loss -0.4089 +2026-04-14 08:59:48.529773: val_loss -0.271 +2026-04-14 08:59:48.532413: Pseudo dice [0.5493, 0.313, 0.5602, 0.8914, 0.6858, 0.2783, 0.7904] +2026-04-14 08:59:48.534984: Epoch time: 103.28 s +2026-04-14 08:59:50.143018: +2026-04-14 08:59:50.144923: Epoch 3227 +2026-04-14 08:59:50.146922: Current learning rate: 0.00228 +2026-04-14 09:01:33.196303: train_loss -0.4098 +2026-04-14 09:01:33.206798: val_loss -0.2437 +2026-04-14 09:01:33.210463: Pseudo dice [0.8602, 0.2827, 0.572, 0.6654, 0.468, 0.0429, 0.6902] +2026-04-14 09:01:33.213619: Epoch time: 103.05 s +2026-04-14 09:01:34.788185: +2026-04-14 09:01:34.789901: Epoch 3228 +2026-04-14 09:01:34.791868: Current learning rate: 0.00228 +2026-04-14 09:03:17.683293: train_loss -0.4048 +2026-04-14 09:03:17.691843: val_loss -0.3345 +2026-04-14 09:03:17.694110: Pseudo dice [0.3091, 0.0704, 0.6612, 0.557, 0.4915, 0.56, 0.8006] +2026-04-14 09:03:17.697059: Epoch time: 102.9 s +2026-04-14 09:03:19.286040: +2026-04-14 09:03:19.289651: Epoch 3229 +2026-04-14 09:03:19.293555: Current learning rate: 0.00227 +2026-04-14 09:05:02.407235: train_loss -0.4003 +2026-04-14 09:05:02.413882: val_loss -0.2848 +2026-04-14 09:05:02.415593: Pseudo dice [0.6018, 0.7525, 0.6647, 0.7909, 0.2959, 0.1711, 0.8143] +2026-04-14 09:05:02.418334: Epoch time: 103.12 s +2026-04-14 09:05:03.975255: +2026-04-14 09:05:03.977314: Epoch 3230 +2026-04-14 09:05:03.979738: Current learning rate: 0.00227 +2026-04-14 09:06:47.555109: train_loss -0.4058 +2026-04-14 09:06:47.563151: val_loss -0.3249 +2026-04-14 09:06:47.565914: Pseudo dice [0.6683, 0.3439, 0.6101, 0.8001, 0.5695, 0.1003, 0.849] +2026-04-14 09:06:47.569208: Epoch time: 103.58 s +2026-04-14 09:06:49.185211: +2026-04-14 09:06:49.187277: Epoch 3231 +2026-04-14 09:06:49.189183: Current learning rate: 0.00227 +2026-04-14 09:08:32.110911: train_loss -0.4042 +2026-04-14 09:08:32.117448: val_loss -0.3306 +2026-04-14 09:08:32.119875: Pseudo dice [0.6431, 0.1767, 0.6309, 0.7691, 0.5953, 0.0598, 0.7908] +2026-04-14 09:08:32.123756: Epoch time: 102.93 s +2026-04-14 09:08:33.740924: +2026-04-14 09:08:33.743009: Epoch 3232 +2026-04-14 09:08:33.744959: Current learning rate: 0.00226 +2026-04-14 09:10:17.050312: train_loss -0.3986 +2026-04-14 09:10:17.056484: val_loss -0.3226 +2026-04-14 09:10:17.058647: Pseudo dice [0.7466, 0.3761, 0.5637, 0.6338, 0.462, 0.583, 0.6815] +2026-04-14 09:10:17.062672: Epoch time: 103.31 s +2026-04-14 09:10:18.646320: +2026-04-14 09:10:18.648420: Epoch 3233 +2026-04-14 09:10:18.651037: Current learning rate: 0.00226 +2026-04-14 09:12:01.694573: train_loss -0.4131 +2026-04-14 09:12:01.702673: val_loss -0.3348 +2026-04-14 09:12:01.704585: Pseudo dice [0.8241, 0.7194, 0.7872, 0.5387, 0.2628, 0.8414, 0.4146] +2026-04-14 09:12:01.707139: Epoch time: 103.05 s +2026-04-14 09:12:03.320350: +2026-04-14 09:12:03.322756: Epoch 3234 +2026-04-14 09:12:03.324619: Current learning rate: 0.00226 +2026-04-14 09:13:46.242802: train_loss -0.4166 +2026-04-14 09:13:46.252074: val_loss -0.3322 +2026-04-14 09:13:46.255063: Pseudo dice [0.6399, 0.5701, 0.7485, 0.6641, 0.5746, 0.3116, 0.8867] +2026-04-14 09:13:46.258296: Epoch time: 102.92 s +2026-04-14 09:13:47.864643: +2026-04-14 09:13:47.867484: Epoch 3235 +2026-04-14 09:13:47.870177: Current learning rate: 0.00226 +2026-04-14 09:15:30.970000: train_loss -0.4092 +2026-04-14 09:15:30.975997: val_loss -0.3351 +2026-04-14 09:15:30.978719: Pseudo dice [0.8003, 0.692, 0.7584, 0.6598, 0.5022, 0.8936, 0.7751] +2026-04-14 09:15:30.981179: Epoch time: 103.11 s +2026-04-14 09:15:32.592421: +2026-04-14 09:15:32.594378: Epoch 3236 +2026-04-14 09:15:32.596304: Current learning rate: 0.00225 +2026-04-14 09:17:15.545432: train_loss -0.3974 +2026-04-14 09:17:15.552645: val_loss -0.3214 +2026-04-14 09:17:15.554803: Pseudo dice [0.4326, 0.807, 0.6429, 0.8208, 0.3287, 0.4202, 0.9054] +2026-04-14 09:17:15.557594: Epoch time: 102.96 s +2026-04-14 09:17:17.126224: +2026-04-14 09:17:17.127923: Epoch 3237 +2026-04-14 09:17:17.130233: Current learning rate: 0.00225 +2026-04-14 09:18:59.956670: train_loss -0.4028 +2026-04-14 09:18:59.964483: val_loss -0.3625 +2026-04-14 09:18:59.966730: Pseudo dice [0.7136, 0.7505, 0.8175, 0.8445, 0.3623, 0.578, 0.8324] +2026-04-14 09:18:59.968792: Epoch time: 102.83 s +2026-04-14 09:18:59.970842: Yayy! New best EMA pseudo Dice: 0.604 +2026-04-14 09:19:03.570632: +2026-04-14 09:19:03.572979: Epoch 3238 +2026-04-14 09:19:03.575322: Current learning rate: 0.00225 +2026-04-14 09:20:46.251491: train_loss -0.4097 +2026-04-14 09:20:46.259909: val_loss -0.3612 +2026-04-14 09:20:46.261774: Pseudo dice [0.8297, 0.755, 0.6217, 0.8387, 0.605, 0.7668, 0.7991] +2026-04-14 09:20:46.263860: Epoch time: 102.68 s +2026-04-14 09:20:46.266918: Yayy! New best EMA pseudo Dice: 0.6181 +2026-04-14 09:20:49.846787: +2026-04-14 09:20:49.850386: Epoch 3239 +2026-04-14 09:20:49.853249: Current learning rate: 0.00225 +2026-04-14 09:22:34.678682: train_loss -0.4096 +2026-04-14 09:22:34.684938: val_loss -0.2877 +2026-04-14 09:22:34.687065: Pseudo dice [0.623, 0.4536, 0.5806, 0.5508, 0.5144, 0.2685, 0.8213] +2026-04-14 09:22:34.689944: Epoch time: 104.84 s +2026-04-14 09:22:36.302757: +2026-04-14 09:22:36.304598: Epoch 3240 +2026-04-14 09:22:36.306476: Current learning rate: 0.00224 +2026-04-14 09:24:19.402730: train_loss -0.4047 +2026-04-14 09:24:19.409492: val_loss -0.3397 +2026-04-14 09:24:19.411862: Pseudo dice [0.7238, 0.7726, 0.6675, 0.6862, 0.4026, 0.8538, 0.4783] +2026-04-14 09:24:19.414744: Epoch time: 103.1 s +2026-04-14 09:24:21.062476: +2026-04-14 09:24:21.064470: Epoch 3241 +2026-04-14 09:24:21.066803: Current learning rate: 0.00224 +2026-04-14 09:26:03.927058: train_loss -0.4015 +2026-04-14 09:26:03.936140: val_loss -0.2339 +2026-04-14 09:26:03.938476: Pseudo dice [0.5746, 0.413, 0.5193, 0.5035, 0.3786, 0.0408, 0.85] +2026-04-14 09:26:03.941178: Epoch time: 102.87 s +2026-04-14 09:26:05.528886: +2026-04-14 09:26:05.531216: Epoch 3242 +2026-04-14 09:26:05.533140: Current learning rate: 0.00224 +2026-04-14 09:27:48.330105: train_loss -0.4002 +2026-04-14 09:27:48.337650: val_loss -0.3382 +2026-04-14 09:27:48.341780: Pseudo dice [0.6037, 0.7873, 0.6468, 0.2753, 0.5537, 0.803, 0.7913] +2026-04-14 09:27:48.344478: Epoch time: 102.8 s +2026-04-14 09:27:49.914899: +2026-04-14 09:27:49.917259: Epoch 3243 +2026-04-14 09:27:49.919630: Current learning rate: 0.00224 +2026-04-14 09:29:32.760367: train_loss -0.4099 +2026-04-14 09:29:32.765993: val_loss -0.3137 +2026-04-14 09:29:32.768091: Pseudo dice [0.6454, 0.4933, 0.7378, 0.0036, 0.4162, 0.4611, 0.6708] +2026-04-14 09:29:32.770469: Epoch time: 102.85 s +2026-04-14 09:29:34.353779: +2026-04-14 09:29:34.355570: Epoch 3244 +2026-04-14 09:29:34.357633: Current learning rate: 0.00223 +2026-04-14 09:31:17.521016: train_loss -0.4048 +2026-04-14 09:31:17.528973: val_loss -0.3336 +2026-04-14 09:31:17.532155: Pseudo dice [0.4003, 0.4872, 0.6931, 0.5988, 0.4176, 0.6755, 0.7439] +2026-04-14 09:31:17.536253: Epoch time: 103.17 s +2026-04-14 09:31:19.114303: +2026-04-14 09:31:19.116525: Epoch 3245 +2026-04-14 09:31:19.120174: Current learning rate: 0.00223 +2026-04-14 09:33:01.955293: train_loss -0.3989 +2026-04-14 09:33:01.963747: val_loss -0.3128 +2026-04-14 09:33:01.966097: Pseudo dice [0.777, 0.174, 0.6979, 0.684, 0.4599, 0.1848, 0.8715] +2026-04-14 09:33:01.968602: Epoch time: 102.84 s +2026-04-14 09:33:03.534128: +2026-04-14 09:33:03.536143: Epoch 3246 +2026-04-14 09:33:03.538415: Current learning rate: 0.00223 +2026-04-14 09:34:46.368516: train_loss -0.4122 +2026-04-14 09:34:46.373898: val_loss -0.3415 +2026-04-14 09:34:46.375845: Pseudo dice [0.7181, 0.8254, 0.6952, 0.7774, 0.3397, 0.8353, 0.6956] +2026-04-14 09:34:46.377746: Epoch time: 102.84 s +2026-04-14 09:34:47.998065: +2026-04-14 09:34:47.999786: Epoch 3247 +2026-04-14 09:34:48.001726: Current learning rate: 0.00222 +2026-04-14 09:36:31.060308: train_loss -0.4079 +2026-04-14 09:36:31.067268: val_loss -0.3195 +2026-04-14 09:36:31.070042: Pseudo dice [0.5412, 0.0271, 0.5792, 0.846, 0.4065, 0.3713, 0.6214] +2026-04-14 09:36:31.073089: Epoch time: 103.07 s +2026-04-14 09:36:32.661398: +2026-04-14 09:36:32.663432: Epoch 3248 +2026-04-14 09:36:32.665458: Current learning rate: 0.00222 +2026-04-14 09:38:15.536952: train_loss -0.4012 +2026-04-14 09:38:15.544125: val_loss -0.3226 +2026-04-14 09:38:15.546702: Pseudo dice [0.4727, 0.5379, 0.7585, 0.071, 0.3737, 0.1825, 0.7955] +2026-04-14 09:38:15.549205: Epoch time: 102.88 s +2026-04-14 09:38:17.150979: +2026-04-14 09:38:17.153002: Epoch 3249 +2026-04-14 09:38:17.154908: Current learning rate: 0.00222 +2026-04-14 09:39:59.580746: train_loss -0.4139 +2026-04-14 09:39:59.586481: val_loss -0.3368 +2026-04-14 09:39:59.588305: Pseudo dice [0.6699, 0.6279, 0.7925, 0.6446, 0.3954, 0.8047, 0.4913] +2026-04-14 09:39:59.590924: Epoch time: 102.43 s +2026-04-14 09:40:03.166250: +2026-04-14 09:40:03.168417: Epoch 3250 +2026-04-14 09:40:03.170034: Current learning rate: 0.00222 +2026-04-14 09:41:46.069993: train_loss -0.4187 +2026-04-14 09:41:46.079844: val_loss -0.3285 +2026-04-14 09:41:46.082348: Pseudo dice [0.3936, 0.6098, 0.7017, 0.8656, 0.3209, 0.1059, 0.8831] +2026-04-14 09:41:46.085215: Epoch time: 102.91 s +2026-04-14 09:41:47.651721: +2026-04-14 09:41:47.653359: Epoch 3251 +2026-04-14 09:41:47.654848: Current learning rate: 0.00221 +2026-04-14 09:43:30.037751: train_loss -0.4023 +2026-04-14 09:43:30.043736: val_loss -0.3435 +2026-04-14 09:43:30.045885: Pseudo dice [0.1998, 0.4483, 0.7227, 0.7534, 0.57, 0.8037, 0.6202] +2026-04-14 09:43:30.049341: Epoch time: 102.39 s +2026-04-14 09:43:31.692614: +2026-04-14 09:43:31.694270: Epoch 3252 +2026-04-14 09:43:31.696010: Current learning rate: 0.00221 +2026-04-14 09:45:14.513519: train_loss -0.4103 +2026-04-14 09:45:14.519910: val_loss -0.302 +2026-04-14 09:45:14.522258: Pseudo dice [0.4763, 0.7246, 0.6001, 0.1069, 0.5108, 0.2151, 0.7949] +2026-04-14 09:45:14.524699: Epoch time: 102.82 s +2026-04-14 09:45:16.102072: +2026-04-14 09:45:16.103804: Epoch 3253 +2026-04-14 09:45:16.105402: Current learning rate: 0.00221 +2026-04-14 09:46:58.652360: train_loss -0.4037 +2026-04-14 09:46:58.660338: val_loss -0.359 +2026-04-14 09:46:58.662497: Pseudo dice [0.6825, 0.52, 0.7802, 0.7894, 0.6079, 0.2506, 0.7226] +2026-04-14 09:46:58.664870: Epoch time: 102.55 s +2026-04-14 09:47:00.290334: +2026-04-14 09:47:00.292201: Epoch 3254 +2026-04-14 09:47:00.294101: Current learning rate: 0.00221 +2026-04-14 09:48:42.921445: train_loss -0.4148 +2026-04-14 09:48:42.930227: val_loss -0.2247 +2026-04-14 09:48:42.932432: Pseudo dice [0.693, 0.2268, 0.6354, 0.2207, 0.587, 0.0461, 0.8733] +2026-04-14 09:48:42.934488: Epoch time: 102.63 s +2026-04-14 09:48:44.508945: +2026-04-14 09:48:44.510988: Epoch 3255 +2026-04-14 09:48:44.512797: Current learning rate: 0.0022 +2026-04-14 09:50:27.203603: train_loss -0.4057 +2026-04-14 09:50:27.208766: val_loss -0.3414 +2026-04-14 09:50:27.210578: Pseudo dice [0.7251, 0.4384, 0.6523, 0.7858, 0.6195, 0.3718, 0.632] +2026-04-14 09:50:27.212901: Epoch time: 102.7 s +2026-04-14 09:50:28.764949: +2026-04-14 09:50:28.767161: Epoch 3256 +2026-04-14 09:50:28.768887: Current learning rate: 0.0022 +2026-04-14 09:52:11.470528: train_loss -0.3949 +2026-04-14 09:52:11.475834: val_loss -0.289 +2026-04-14 09:52:11.477596: Pseudo dice [0.5787, 0.5309, 0.6187, 0.8112, 0.5494, 0.2591, 0.4666] +2026-04-14 09:52:11.479770: Epoch time: 102.71 s +2026-04-14 09:52:13.071156: +2026-04-14 09:52:13.073932: Epoch 3257 +2026-04-14 09:52:13.075792: Current learning rate: 0.0022 +2026-04-14 09:53:56.546798: train_loss -0.4266 +2026-04-14 09:53:56.563903: val_loss -0.3087 +2026-04-14 09:53:56.569141: Pseudo dice [0.5906, 0.6039, 0.6818, 0.8522, 0.6286, 0.4433, 0.8616] +2026-04-14 09:53:56.574663: Epoch time: 103.48 s +2026-04-14 09:53:58.244299: +2026-04-14 09:53:58.246551: Epoch 3258 +2026-04-14 09:53:58.249784: Current learning rate: 0.0022 +2026-04-14 09:55:41.029828: train_loss -0.4217 +2026-04-14 09:55:41.036097: val_loss -0.3672 +2026-04-14 09:55:41.038278: Pseudo dice [0.6678, 0.4588, 0.7598, 0.3561, 0.6433, 0.5742, 0.7987] +2026-04-14 09:55:41.040572: Epoch time: 102.79 s +2026-04-14 09:55:43.846930: +2026-04-14 09:55:43.849438: Epoch 3259 +2026-04-14 09:55:43.851007: Current learning rate: 0.00219 +2026-04-14 09:57:27.111306: train_loss -0.4045 +2026-04-14 09:57:27.118639: val_loss -0.2835 +2026-04-14 09:57:27.120717: Pseudo dice [0.5648, 0.3534, 0.7988, 0.6795, 0.4178, 0.0936, 0.3944] +2026-04-14 09:57:27.123399: Epoch time: 103.27 s +2026-04-14 09:57:28.696638: +2026-04-14 09:57:28.698396: Epoch 3260 +2026-04-14 09:57:28.700258: Current learning rate: 0.00219 +2026-04-14 09:59:13.065247: train_loss -0.4101 +2026-04-14 09:59:13.071085: val_loss -0.2129 +2026-04-14 09:59:13.073074: Pseudo dice [0.4089, 0.0726, 0.6514, 0.9064, 0.4481, 0.1608, 0.5092] +2026-04-14 09:59:13.076253: Epoch time: 104.37 s +2026-04-14 09:59:14.697257: +2026-04-14 09:59:14.699325: Epoch 3261 +2026-04-14 09:59:14.701221: Current learning rate: 0.00219 +2026-04-14 10:00:57.781242: train_loss -0.4064 +2026-04-14 10:00:57.786308: val_loss -0.1828 +2026-04-14 10:00:57.788734: Pseudo dice [0.687, 0.3753, 0.6251, 0.4705, 0.3585, 0.1574, 0.5494] +2026-04-14 10:00:57.790939: Epoch time: 103.09 s +2026-04-14 10:00:59.354050: +2026-04-14 10:00:59.355897: Epoch 3262 +2026-04-14 10:00:59.359447: Current learning rate: 0.00218 +2026-04-14 10:02:42.628468: train_loss -0.4118 +2026-04-14 10:02:42.648789: val_loss -0.3608 +2026-04-14 10:02:42.663964: Pseudo dice [0.48, 0.6777, 0.6638, 0.869, 0.6944, 0.3561, 0.8709] +2026-04-14 10:02:42.677007: Epoch time: 103.28 s +2026-04-14 10:02:44.203903: +2026-04-14 10:02:44.210142: Epoch 3263 +2026-04-14 10:02:44.213043: Current learning rate: 0.00218 +2026-04-14 10:04:28.307944: train_loss -0.4195 +2026-04-14 10:04:28.317312: val_loss -0.3135 +2026-04-14 10:04:28.319484: Pseudo dice [0.48, 0.4557, 0.5703, 0.7783, 0.3722, 0.5478, 0.7024] +2026-04-14 10:04:28.322971: Epoch time: 104.11 s +2026-04-14 10:04:29.927217: +2026-04-14 10:04:29.928897: Epoch 3264 +2026-04-14 10:04:29.930450: Current learning rate: 0.00218 +2026-04-14 10:06:12.993767: train_loss -0.4121 +2026-04-14 10:06:13.002921: val_loss -0.3426 +2026-04-14 10:06:13.005211: Pseudo dice [0.2808, 0.3512, 0.6734, 0.7677, 0.5264, 0.7085, 0.7942] +2026-04-14 10:06:13.009099: Epoch time: 103.07 s +2026-04-14 10:06:14.655249: +2026-04-14 10:06:14.658893: Epoch 3265 +2026-04-14 10:06:14.661917: Current learning rate: 0.00218 +2026-04-14 10:07:58.892349: train_loss -0.4164 +2026-04-14 10:07:58.900578: val_loss -0.3644 +2026-04-14 10:07:58.903548: Pseudo dice [0.5059, 0.6443, 0.7912, 0.2031, 0.6212, 0.7911, 0.8426] +2026-04-14 10:07:58.906705: Epoch time: 104.24 s +2026-04-14 10:08:00.456686: +2026-04-14 10:08:00.459347: Epoch 3266 +2026-04-14 10:08:00.462304: Current learning rate: 0.00217 +2026-04-14 10:09:43.633066: train_loss -0.4101 +2026-04-14 10:09:43.639705: val_loss -0.3402 +2026-04-14 10:09:43.641491: Pseudo dice [0.7994, 0.4804, 0.6589, 0.6612, 0.5543, 0.576, 0.7875] +2026-04-14 10:09:43.643996: Epoch time: 103.18 s +2026-04-14 10:09:45.176122: +2026-04-14 10:09:45.178394: Epoch 3267 +2026-04-14 10:09:45.180016: Current learning rate: 0.00217 +2026-04-14 10:11:28.475162: train_loss -0.4158 +2026-04-14 10:11:28.485891: val_loss -0.2681 +2026-04-14 10:11:28.488950: Pseudo dice [0.4558, 0.6061, 0.5074, 0.52, 0.2482, 0.263, 0.7218] +2026-04-14 10:11:28.491284: Epoch time: 103.3 s +2026-04-14 10:11:30.069434: +2026-04-14 10:11:30.071321: Epoch 3268 +2026-04-14 10:11:30.073041: Current learning rate: 0.00217 +2026-04-14 10:13:13.635569: train_loss -0.4104 +2026-04-14 10:13:13.642974: val_loss -0.2929 +2026-04-14 10:13:13.645886: Pseudo dice [0.7338, 0.6857, 0.8174, 0.4437, 0.504, 0.0988, 0.8988] +2026-04-14 10:13:13.648856: Epoch time: 103.57 s +2026-04-14 10:13:15.277217: +2026-04-14 10:13:15.279137: Epoch 3269 +2026-04-14 10:13:15.280744: Current learning rate: 0.00217 +2026-04-14 10:14:58.475776: train_loss -0.4035 +2026-04-14 10:14:58.485543: val_loss -0.2729 +2026-04-14 10:14:58.488661: Pseudo dice [0.0887, 0.5042, 0.6796, 0.705, 0.1922, 0.3541, 0.3216] +2026-04-14 10:14:58.491664: Epoch time: 103.2 s +2026-04-14 10:15:00.108539: +2026-04-14 10:15:00.111644: Epoch 3270 +2026-04-14 10:15:00.113443: Current learning rate: 0.00216 +2026-04-14 10:16:43.828792: train_loss -0.4102 +2026-04-14 10:16:43.834923: val_loss -0.3283 +2026-04-14 10:16:43.837074: Pseudo dice [0.7886, 0.6046, 0.572, 0.5231, 0.6042, 0.9112, 0.5841] +2026-04-14 10:16:43.839142: Epoch time: 103.72 s +2026-04-14 10:16:45.507766: +2026-04-14 10:16:45.509526: Epoch 3271 +2026-04-14 10:16:45.511260: Current learning rate: 0.00216 +2026-04-14 10:18:28.812250: train_loss -0.417 +2026-04-14 10:18:28.819797: val_loss -0.3142 +2026-04-14 10:18:28.822192: Pseudo dice [0.6854, 0.8329, 0.7783, 0.5229, 0.53, 0.2305, 0.7655] +2026-04-14 10:18:28.824948: Epoch time: 103.31 s +2026-04-14 10:18:30.396957: +2026-04-14 10:18:30.406476: Epoch 3272 +2026-04-14 10:18:30.408223: Current learning rate: 0.00216 +2026-04-14 10:20:13.572076: train_loss -0.3985 +2026-04-14 10:20:13.577776: val_loss -0.3226 +2026-04-14 10:20:13.579795: Pseudo dice [0.7237, 0.8535, 0.6755, 0.5881, 0.5941, 0.2172, 0.519] +2026-04-14 10:20:13.582572: Epoch time: 103.18 s +2026-04-14 10:20:15.194292: +2026-04-14 10:20:15.196567: Epoch 3273 +2026-04-14 10:20:15.198388: Current learning rate: 0.00216 +2026-04-14 10:21:59.539994: train_loss -0.4 +2026-04-14 10:21:59.546682: val_loss -0.3384 +2026-04-14 10:21:59.549562: Pseudo dice [0.5603, 0.4931, 0.7016, 0.8601, 0.6492, 0.7518, 0.8469] +2026-04-14 10:21:59.552206: Epoch time: 104.35 s +2026-04-14 10:22:01.170863: +2026-04-14 10:22:01.172639: Epoch 3274 +2026-04-14 10:22:01.177201: Current learning rate: 0.00215 +2026-04-14 10:23:44.591105: train_loss -0.4127 +2026-04-14 10:23:44.604506: val_loss -0.3235 +2026-04-14 10:23:44.606752: Pseudo dice [0.4054, 0.4456, 0.7622, 0.7614, 0.4013, 0.7105, 0.6833] +2026-04-14 10:23:44.612014: Epoch time: 103.42 s +2026-04-14 10:23:46.206249: +2026-04-14 10:23:46.208462: Epoch 3275 +2026-04-14 10:23:46.211316: Current learning rate: 0.00215 +2026-04-14 10:25:29.304891: train_loss -0.4116 +2026-04-14 10:25:29.310731: val_loss -0.3799 +2026-04-14 10:25:29.312332: Pseudo dice [0.6548, 0.5697, 0.7036, 0.728, 0.3356, 0.8297, 0.7181] +2026-04-14 10:25:29.314430: Epoch time: 103.1 s +2026-04-14 10:25:30.860875: +2026-04-14 10:25:30.863113: Epoch 3276 +2026-04-14 10:25:30.864890: Current learning rate: 0.00215 +2026-04-14 10:27:14.211421: train_loss -0.3995 +2026-04-14 10:27:14.218251: val_loss -0.3278 +2026-04-14 10:27:14.219993: Pseudo dice [0.3085, 0.5088, 0.6579, 0.8243, 0.5541, 0.5801, 0.7276] +2026-04-14 10:27:14.223630: Epoch time: 103.35 s +2026-04-14 10:27:15.876148: +2026-04-14 10:27:15.878193: Epoch 3277 +2026-04-14 10:27:15.879956: Current learning rate: 0.00214 +2026-04-14 10:28:58.872755: train_loss -0.4135 +2026-04-14 10:28:58.890329: val_loss -0.3122 +2026-04-14 10:28:58.892213: Pseudo dice [0.8158, 0.172, 0.6287, 0.5091, 0.6324, 0.1401, 0.7147] +2026-04-14 10:28:58.897484: Epoch time: 103.0 s +2026-04-14 10:29:00.544726: +2026-04-14 10:29:00.546419: Epoch 3278 +2026-04-14 10:29:00.547870: Current learning rate: 0.00214 +2026-04-14 10:30:43.324456: train_loss -0.4101 +2026-04-14 10:30:43.332078: val_loss -0.3589 +2026-04-14 10:30:43.340029: Pseudo dice [0.6101, 0.5309, 0.6792, 0.8413, 0.6303, 0.7543, 0.8634] +2026-04-14 10:30:43.345692: Epoch time: 102.78 s +2026-04-14 10:30:46.015517: +2026-04-14 10:30:46.017569: Epoch 3279 +2026-04-14 10:30:46.019290: Current learning rate: 0.00214 +2026-04-14 10:32:31.029905: train_loss -0.4109 +2026-04-14 10:32:31.035974: val_loss -0.3817 +2026-04-14 10:32:31.037807: Pseudo dice [0.3984, 0.5288, 0.7475, 0.7587, 0.6257, 0.8231, 0.8554] +2026-04-14 10:32:31.040004: Epoch time: 105.02 s +2026-04-14 10:32:32.613577: +2026-04-14 10:32:32.615357: Epoch 3280 +2026-04-14 10:32:32.616833: Current learning rate: 0.00214 +2026-04-14 10:34:15.463208: train_loss -0.4118 +2026-04-14 10:34:15.471222: val_loss -0.3236 +2026-04-14 10:34:15.473146: Pseudo dice [0.852, 0.663, 0.6845, 0.5583, 0.4932, 0.2468, 0.9236] +2026-04-14 10:34:15.475661: Epoch time: 102.85 s +2026-04-14 10:34:17.050200: +2026-04-14 10:34:17.052457: Epoch 3281 +2026-04-14 10:34:17.054915: Current learning rate: 0.00213 +2026-04-14 10:35:59.452341: train_loss -0.4216 +2026-04-14 10:35:59.469112: val_loss -0.3489 +2026-04-14 10:35:59.473797: Pseudo dice [0.4383, 0.747, 0.7185, 0.6719, 0.639, 0.829, 0.6922] +2026-04-14 10:35:59.476403: Epoch time: 102.41 s +2026-04-14 10:36:01.012226: +2026-04-14 10:36:01.013876: Epoch 3282 +2026-04-14 10:36:01.015471: Current learning rate: 0.00213 +2026-04-14 10:37:43.859147: train_loss -0.4084 +2026-04-14 10:37:43.868806: val_loss -0.3407 +2026-04-14 10:37:43.870845: Pseudo dice [0.5245, 0.6613, 0.5878, 0.6785, 0.5782, 0.8693, 0.8224] +2026-04-14 10:37:43.875531: Epoch time: 102.85 s +2026-04-14 10:37:43.877419: Yayy! New best EMA pseudo Dice: 0.6197 +2026-04-14 10:37:47.449441: +2026-04-14 10:37:47.451947: Epoch 3283 +2026-04-14 10:37:47.453680: Current learning rate: 0.00213 +2026-04-14 10:39:30.198699: train_loss -0.4025 +2026-04-14 10:39:30.206845: val_loss -0.3577 +2026-04-14 10:39:30.211373: Pseudo dice [0.7685, 0.3153, 0.6857, 0.772, 0.6023, 0.8779, 0.8043] +2026-04-14 10:39:30.214210: Epoch time: 102.75 s +2026-04-14 10:39:30.216605: Yayy! New best EMA pseudo Dice: 0.6266 +2026-04-14 10:39:33.812099: +2026-04-14 10:39:33.814187: Epoch 3284 +2026-04-14 10:39:33.815865: Current learning rate: 0.00213 +2026-04-14 10:41:16.450645: train_loss -0.4103 +2026-04-14 10:41:16.459254: val_loss -0.2901 +2026-04-14 10:41:16.461241: Pseudo dice [0.6778, 0.3653, 0.7737, 0.818, 0.3598, 0.0459, 0.9247] +2026-04-14 10:41:16.463761: Epoch time: 102.64 s +2026-04-14 10:41:18.025086: +2026-04-14 10:41:18.026815: Epoch 3285 +2026-04-14 10:41:18.028546: Current learning rate: 0.00212 +2026-04-14 10:43:00.716384: train_loss -0.4226 +2026-04-14 10:43:00.730626: val_loss -0.331 +2026-04-14 10:43:00.746096: Pseudo dice [0.5864, 0.6612, 0.7467, 0.8179, 0.5541, 0.3965, 0.78] +2026-04-14 10:43:00.748494: Epoch time: 102.7 s +2026-04-14 10:43:02.349311: +2026-04-14 10:43:02.351420: Epoch 3286 +2026-04-14 10:43:02.353230: Current learning rate: 0.00212 +2026-04-14 10:44:45.089522: train_loss -0.412 +2026-04-14 10:44:45.097203: val_loss -0.2499 +2026-04-14 10:44:45.099395: Pseudo dice [0.7022, 0.4779, 0.6452, 0.393, 0.3868, 0.2229, 0.6934] +2026-04-14 10:44:45.102146: Epoch time: 102.74 s +2026-04-14 10:44:46.667022: +2026-04-14 10:44:46.669516: Epoch 3287 +2026-04-14 10:44:46.671571: Current learning rate: 0.00212 +2026-04-14 10:46:29.335294: train_loss -0.4194 +2026-04-14 10:46:29.341478: val_loss -0.3199 +2026-04-14 10:46:29.343342: Pseudo dice [0.8253, 0.6671, 0.6487, 0.3406, 0.6845, 0.4175, 0.7422] +2026-04-14 10:46:29.345994: Epoch time: 102.67 s +2026-04-14 10:46:30.994376: +2026-04-14 10:46:31.000346: Epoch 3288 +2026-04-14 10:46:31.004508: Current learning rate: 0.00212 +2026-04-14 10:48:13.861403: train_loss -0.4137 +2026-04-14 10:48:13.867772: val_loss -0.3222 +2026-04-14 10:48:13.869936: Pseudo dice [0.425, 0.0767, 0.6658, 0.8058, 0.6893, 0.4043, 0.8299] +2026-04-14 10:48:13.872066: Epoch time: 102.87 s +2026-04-14 10:48:15.470162: +2026-04-14 10:48:15.472549: Epoch 3289 +2026-04-14 10:48:15.474695: Current learning rate: 0.00211 +2026-04-14 10:49:58.001739: train_loss -0.4142 +2026-04-14 10:49:58.009231: val_loss -0.2959 +2026-04-14 10:49:58.014212: Pseudo dice [0.8129, 0.5576, 0.696, 0.8207, 0.3347, 0.0481, 0.8101] +2026-04-14 10:49:58.017918: Epoch time: 102.54 s +2026-04-14 10:49:59.586525: +2026-04-14 10:49:59.588584: Epoch 3290 +2026-04-14 10:49:59.590409: Current learning rate: 0.00211 +2026-04-14 10:51:42.215998: train_loss -0.4053 +2026-04-14 10:51:42.222026: val_loss -0.3144 +2026-04-14 10:51:42.223965: Pseudo dice [0.7686, 0.203, 0.7639, 0.6538, 0.4437, 0.1218, 0.4856] +2026-04-14 10:51:42.225951: Epoch time: 102.63 s +2026-04-14 10:51:43.768299: +2026-04-14 10:51:43.770988: Epoch 3291 +2026-04-14 10:51:43.773860: Current learning rate: 0.00211 +2026-04-14 10:53:27.203400: train_loss -0.4127 +2026-04-14 10:53:27.209356: val_loss -0.3398 +2026-04-14 10:53:27.212039: Pseudo dice [0.3548, 0.3963, 0.6101, 0.7508, 0.4538, 0.8164, 0.8375] +2026-04-14 10:53:27.214511: Epoch time: 103.44 s +2026-04-14 10:53:28.853402: +2026-04-14 10:53:28.855404: Epoch 3292 +2026-04-14 10:53:28.857114: Current learning rate: 0.0021 +2026-04-14 10:55:11.642963: train_loss -0.4052 +2026-04-14 10:55:11.648194: val_loss -0.304 +2026-04-14 10:55:11.650124: Pseudo dice [0.6707, 0.8793, 0.6698, 0.162, 0.1341, 0.292, 0.8609] +2026-04-14 10:55:11.652419: Epoch time: 102.79 s +2026-04-14 10:55:13.244539: +2026-04-14 10:55:13.246600: Epoch 3293 +2026-04-14 10:55:13.248804: Current learning rate: 0.0021 +2026-04-14 10:56:55.815222: train_loss -0.4091 +2026-04-14 10:56:55.820271: val_loss -0.2793 +2026-04-14 10:56:55.821943: Pseudo dice [0.7572, 0.0248, 0.7529, 0.6845, 0.5839, 0.4154, 0.8502] +2026-04-14 10:56:55.824410: Epoch time: 102.57 s +2026-04-14 10:56:57.387003: +2026-04-14 10:56:57.389409: Epoch 3294 +2026-04-14 10:56:57.391260: Current learning rate: 0.0021 +2026-04-14 10:58:40.425175: train_loss -0.4209 +2026-04-14 10:58:40.430995: val_loss -0.3194 +2026-04-14 10:58:40.433564: Pseudo dice [0.4506, 0.1262, 0.5503, 0.5608, 0.5066, 0.8533, 0.8931] +2026-04-14 10:58:40.436300: Epoch time: 103.04 s +2026-04-14 10:58:42.043748: +2026-04-14 10:58:42.045940: Epoch 3295 +2026-04-14 10:58:42.048291: Current learning rate: 0.0021 +2026-04-14 11:00:24.724196: train_loss -0.4134 +2026-04-14 11:00:24.732319: val_loss -0.3029 +2026-04-14 11:00:24.734884: Pseudo dice [0.5611, 0.2674, 0.8098, 0.8582, 0.3998, 0.3325, 0.8678] +2026-04-14 11:00:24.738145: Epoch time: 102.68 s +2026-04-14 11:00:26.392272: +2026-04-14 11:00:26.394175: Epoch 3296 +2026-04-14 11:00:26.395671: Current learning rate: 0.00209 +2026-04-14 11:02:09.122295: train_loss -0.4102 +2026-04-14 11:02:09.128491: val_loss -0.2991 +2026-04-14 11:02:09.130490: Pseudo dice [0.8441, 0.7085, 0.7046, 0.3067, 0.4857, 0.1727, 0.8633] +2026-04-14 11:02:09.132929: Epoch time: 102.73 s +2026-04-14 11:02:10.726341: +2026-04-14 11:02:10.728530: Epoch 3297 +2026-04-14 11:02:10.730563: Current learning rate: 0.00209 +2026-04-14 11:03:53.265091: train_loss -0.4046 +2026-04-14 11:03:53.271197: val_loss -0.3574 +2026-04-14 11:03:53.274652: Pseudo dice [0.7152, 0.6102, 0.7314, 0.8293, 0.6033, 0.8138, 0.7514] +2026-04-14 11:03:53.277263: Epoch time: 102.54 s +2026-04-14 11:03:54.859130: +2026-04-14 11:03:54.860775: Epoch 3298 +2026-04-14 11:03:54.862881: Current learning rate: 0.00209 +2026-04-14 11:05:38.452897: train_loss -0.417 +2026-04-14 11:05:38.458997: val_loss -0.3484 +2026-04-14 11:05:38.460558: Pseudo dice [0.2701, 0.7154, 0.6298, 0.4652, 0.519, 0.8323, 0.5395] +2026-04-14 11:05:38.463001: Epoch time: 103.6 s +2026-04-14 11:05:40.074095: +2026-04-14 11:05:40.076264: Epoch 3299 +2026-04-14 11:05:40.078098: Current learning rate: 0.00209 +2026-04-14 11:07:22.816647: train_loss -0.4142 +2026-04-14 11:07:22.822525: val_loss -0.3242 +2026-04-14 11:07:22.824518: Pseudo dice [0.5126, 0.4681, 0.828, 0.3118, 0.335, 0.201, 0.676] +2026-04-14 11:07:22.827142: Epoch time: 102.75 s +2026-04-14 11:07:26.562260: +2026-04-14 11:07:26.564701: Epoch 3300 +2026-04-14 11:07:26.566787: Current learning rate: 0.00208 +2026-04-14 11:09:09.404199: train_loss -0.4244 +2026-04-14 11:09:09.410233: val_loss -0.3662 +2026-04-14 11:09:09.412479: Pseudo dice [0.6118, 0.2878, 0.7206, 0.7362, 0.4592, 0.8903, 0.4685] +2026-04-14 11:09:09.415636: Epoch time: 102.85 s +2026-04-14 11:09:10.993553: +2026-04-14 11:09:10.995967: Epoch 3301 +2026-04-14 11:09:10.998206: Current learning rate: 0.00208 +2026-04-14 11:10:54.035875: train_loss -0.4157 +2026-04-14 11:10:54.042441: val_loss -0.3603 +2026-04-14 11:10:54.044839: Pseudo dice [0.4877, 0.7893, 0.7253, 0.7787, 0.5002, 0.823, 0.7036] +2026-04-14 11:10:54.047893: Epoch time: 103.05 s +2026-04-14 11:10:55.603956: +2026-04-14 11:10:55.606162: Epoch 3302 +2026-04-14 11:10:55.607952: Current learning rate: 0.00208 +2026-04-14 11:12:38.246027: train_loss -0.4071 +2026-04-14 11:12:38.253079: val_loss -0.1301 +2026-04-14 11:12:38.255777: Pseudo dice [0.1633, 0.5121, 0.5551, 0.8008, 0.3214, 0.0621, 0.9026] +2026-04-14 11:12:38.259257: Epoch time: 102.65 s +2026-04-14 11:12:39.802047: +2026-04-14 11:12:39.803700: Epoch 3303 +2026-04-14 11:12:39.805785: Current learning rate: 0.00208 +2026-04-14 11:14:22.276237: train_loss -0.4143 +2026-04-14 11:14:22.283455: val_loss -0.2912 +2026-04-14 11:14:22.286463: Pseudo dice [0.5272, 0.3895, 0.7584, 0.892, 0.4637, 0.0978, 0.6901] +2026-04-14 11:14:22.289568: Epoch time: 102.48 s +2026-04-14 11:14:23.876833: +2026-04-14 11:14:23.878716: Epoch 3304 +2026-04-14 11:14:23.880254: Current learning rate: 0.00207 +2026-04-14 11:16:06.548414: train_loss -0.4238 +2026-04-14 11:16:06.554249: val_loss -0.3134 +2026-04-14 11:16:06.556007: Pseudo dice [0.6336, 0.3149, 0.6428, 0.8132, 0.4898, 0.8145, 0.5531] +2026-04-14 11:16:06.558995: Epoch time: 102.68 s +2026-04-14 11:16:08.142467: +2026-04-14 11:16:08.144316: Epoch 3305 +2026-04-14 11:16:08.146127: Current learning rate: 0.00207 +2026-04-14 11:17:51.029907: train_loss -0.409 +2026-04-14 11:17:51.039061: val_loss -0.3188 +2026-04-14 11:17:51.041046: Pseudo dice [0.5343, 0.5791, 0.5369, 0.9232, 0.4616, 0.135, 0.8809] +2026-04-14 11:17:51.043493: Epoch time: 102.89 s +2026-04-14 11:17:52.627580: +2026-04-14 11:17:52.630747: Epoch 3306 +2026-04-14 11:17:52.632717: Current learning rate: 0.00207 +2026-04-14 11:19:35.723976: train_loss -0.4053 +2026-04-14 11:19:35.729366: val_loss -0.3211 +2026-04-14 11:19:35.731411: Pseudo dice [0.6674, 0.4205, 0.7486, 0.834, 0.4628, 0.2389, 0.8134] +2026-04-14 11:19:35.734941: Epoch time: 103.1 s +2026-04-14 11:19:37.290672: +2026-04-14 11:19:37.292580: Epoch 3307 +2026-04-14 11:19:37.294091: Current learning rate: 0.00206 +2026-04-14 11:21:20.827022: train_loss -0.4147 +2026-04-14 11:21:20.832657: val_loss -0.3036 +2026-04-14 11:21:20.834629: Pseudo dice [0.5285, 0.4969, 0.7528, 0.11, 0.5434, 0.1556, 0.8532] +2026-04-14 11:21:20.836977: Epoch time: 103.54 s +2026-04-14 11:21:22.438542: +2026-04-14 11:21:22.440242: Epoch 3308 +2026-04-14 11:21:22.442149: Current learning rate: 0.00206 +2026-04-14 11:23:06.280674: train_loss -0.4094 +2026-04-14 11:23:06.286653: val_loss -0.3473 +2026-04-14 11:23:06.289138: Pseudo dice [0.45, 0.4357, 0.7965, 0.3207, 0.5051, 0.7825, 0.6614] +2026-04-14 11:23:06.291668: Epoch time: 103.85 s +2026-04-14 11:23:07.898246: +2026-04-14 11:23:07.899945: Epoch 3309 +2026-04-14 11:23:07.901670: Current learning rate: 0.00206 +2026-04-14 11:24:50.997388: train_loss -0.4038 +2026-04-14 11:24:51.002891: val_loss -0.2255 +2026-04-14 11:24:51.004916: Pseudo dice [0.6361, 0.7919, 0.6478, 0.4886, 0.3902, 0.0618, 0.8323] +2026-04-14 11:24:51.008626: Epoch time: 103.1 s +2026-04-14 11:24:52.533318: +2026-04-14 11:24:52.534901: Epoch 3310 +2026-04-14 11:24:52.536402: Current learning rate: 0.00206 +2026-04-14 11:26:35.267167: train_loss -0.3982 +2026-04-14 11:26:35.273173: val_loss -0.2828 +2026-04-14 11:26:35.275227: Pseudo dice [0.5322, 0.2343, 0.7038, 0.5947, 0.4364, 0.1009, 0.8337] +2026-04-14 11:26:35.277417: Epoch time: 102.74 s +2026-04-14 11:26:36.853323: +2026-04-14 11:26:36.856286: Epoch 3311 +2026-04-14 11:26:36.858039: Current learning rate: 0.00205 +2026-04-14 11:28:20.502808: train_loss -0.4088 +2026-04-14 11:28:20.510545: val_loss -0.2489 +2026-04-14 11:28:20.513258: Pseudo dice [0.1929, 0.3773, 0.5604, 0.0113, 0.3976, 0.2989, 0.7087] +2026-04-14 11:28:20.515497: Epoch time: 103.65 s +2026-04-14 11:28:22.133473: +2026-04-14 11:28:22.135360: Epoch 3312 +2026-04-14 11:28:22.137089: Current learning rate: 0.00205 +2026-04-14 11:30:04.637393: train_loss -0.4138 +2026-04-14 11:30:04.642702: val_loss -0.3207 +2026-04-14 11:30:04.644511: Pseudo dice [0.7069, 0.4045, 0.6154, 0.7439, 0.5394, 0.4633, 0.8551] +2026-04-14 11:30:04.646694: Epoch time: 102.51 s +2026-04-14 11:30:06.218678: +2026-04-14 11:30:06.220536: Epoch 3313 +2026-04-14 11:30:06.222469: Current learning rate: 0.00205 +2026-04-14 11:31:49.927936: train_loss -0.4048 +2026-04-14 11:31:49.933569: val_loss -0.3332 +2026-04-14 11:31:49.935876: Pseudo dice [0.3743, 0.5947, 0.6475, 0.8058, 0.3742, 0.1351, 0.877] +2026-04-14 11:31:49.938354: Epoch time: 103.71 s +2026-04-14 11:31:51.497285: +2026-04-14 11:31:51.500856: Epoch 3314 +2026-04-14 11:31:51.504570: Current learning rate: 0.00205 +2026-04-14 11:33:35.225074: train_loss -0.4187 +2026-04-14 11:33:35.250343: val_loss -0.3091 +2026-04-14 11:33:35.252337: Pseudo dice [0.6071, 0.7536, 0.6608, 0.8101, 0.5046, 0.1416, 0.8788] +2026-04-14 11:33:35.254809: Epoch time: 103.73 s +2026-04-14 11:33:36.855583: +2026-04-14 11:33:36.857857: Epoch 3315 +2026-04-14 11:33:36.859780: Current learning rate: 0.00204 +2026-04-14 11:35:19.649651: train_loss -0.4159 +2026-04-14 11:35:19.655609: val_loss -0.2637 +2026-04-14 11:35:19.657243: Pseudo dice [0.7995, 0.244, 0.6287, 0.0759, 0.6423, 0.1535, 0.7091] +2026-04-14 11:35:19.660199: Epoch time: 102.8 s +2026-04-14 11:35:21.251940: +2026-04-14 11:35:21.253668: Epoch 3316 +2026-04-14 11:35:21.256230: Current learning rate: 0.00204 +2026-04-14 11:37:04.737046: train_loss -0.4108 +2026-04-14 11:37:04.745299: val_loss -0.3402 +2026-04-14 11:37:04.748172: Pseudo dice [0.4478, 0.4441, 0.7134, 0.5883, 0.6358, 0.3016, 0.8043] +2026-04-14 11:37:04.751085: Epoch time: 103.49 s +2026-04-14 11:37:06.442027: +2026-04-14 11:37:06.444159: Epoch 3317 +2026-04-14 11:37:06.446311: Current learning rate: 0.00204 +2026-04-14 11:38:49.341012: train_loss -0.4228 +2026-04-14 11:38:49.347322: val_loss -0.3544 +2026-04-14 11:38:49.349897: Pseudo dice [0.416, 0.8465, 0.7684, 0.8253, 0.3968, 0.8208, 0.8343] +2026-04-14 11:38:49.351951: Epoch time: 102.9 s +2026-04-14 11:38:52.083030: +2026-04-14 11:38:52.088347: Epoch 3318 +2026-04-14 11:38:52.092848: Current learning rate: 0.00203 +2026-04-14 11:40:35.028779: train_loss -0.4224 +2026-04-14 11:40:35.035196: val_loss -0.2109 +2026-04-14 11:40:35.037218: Pseudo dice [0.3451, 0.5597, 0.7669, 0.1334, 0.446, 0.0489, 0.7572] +2026-04-14 11:40:35.039388: Epoch time: 102.95 s +2026-04-14 11:40:36.612548: +2026-04-14 11:40:36.614634: Epoch 3319 +2026-04-14 11:40:36.616277: Current learning rate: 0.00203 +2026-04-14 11:42:19.755688: train_loss -0.4225 +2026-04-14 11:42:19.762495: val_loss -0.3215 +2026-04-14 11:42:19.765540: Pseudo dice [0.7654, 0.5531, 0.8054, 0.4889, 0.3494, 0.0534, 0.6859] +2026-04-14 11:42:19.769306: Epoch time: 103.15 s +2026-04-14 11:42:21.308914: +2026-04-14 11:42:21.311362: Epoch 3320 +2026-04-14 11:42:21.313560: Current learning rate: 0.00203 +2026-04-14 11:44:04.451346: train_loss -0.4146 +2026-04-14 11:44:04.459118: val_loss -0.3515 +2026-04-14 11:44:04.461302: Pseudo dice [0.5297, 0.784, 0.6621, 0.8094, 0.5905, 0.7582, 0.8591] +2026-04-14 11:44:04.463568: Epoch time: 103.15 s +2026-04-14 11:44:06.050944: +2026-04-14 11:44:06.052803: Epoch 3321 +2026-04-14 11:44:06.054372: Current learning rate: 0.00203 +2026-04-14 11:45:49.311810: train_loss -0.4094 +2026-04-14 11:45:49.323345: val_loss -0.348 +2026-04-14 11:45:49.326015: Pseudo dice [0.615, 0.7799, 0.7383, 0.6445, 0.3063, 0.787, 0.4439] +2026-04-14 11:45:49.328797: Epoch time: 103.26 s +2026-04-14 11:45:50.918444: +2026-04-14 11:45:50.920980: Epoch 3322 +2026-04-14 11:45:50.922796: Current learning rate: 0.00202 +2026-04-14 11:47:33.907763: train_loss -0.4241 +2026-04-14 11:47:33.915917: val_loss -0.2953 +2026-04-14 11:47:33.917992: Pseudo dice [0.7124, 0.2881, 0.5714, 0.1697, 0.4272, 0.4266, 0.4069] +2026-04-14 11:47:33.920744: Epoch time: 102.99 s +2026-04-14 11:47:35.488925: +2026-04-14 11:47:35.494390: Epoch 3323 +2026-04-14 11:47:35.496150: Current learning rate: 0.00202 +2026-04-14 11:49:18.383992: train_loss -0.4132 +2026-04-14 11:49:18.389282: val_loss -0.3677 +2026-04-14 11:49:18.391369: Pseudo dice [0.7534, 0.6225, 0.568, 0.7183, 0.6833, 0.828, 0.8498] +2026-04-14 11:49:18.394473: Epoch time: 102.9 s +2026-04-14 11:49:19.977658: +2026-04-14 11:49:19.979834: Epoch 3324 +2026-04-14 11:49:19.981646: Current learning rate: 0.00202 +2026-04-14 11:51:02.758273: train_loss -0.4035 +2026-04-14 11:51:02.766774: val_loss -0.3684 +2026-04-14 11:51:02.768801: Pseudo dice [0.1788, 0.6182, 0.7125, 0.7971, 0.5771, 0.5925, 0.642] +2026-04-14 11:51:02.772012: Epoch time: 102.78 s +2026-04-14 11:51:04.306796: +2026-04-14 11:51:04.309193: Epoch 3325 +2026-04-14 11:51:04.311246: Current learning rate: 0.00202 +2026-04-14 11:52:46.974554: train_loss -0.416 +2026-04-14 11:52:46.981073: val_loss -0.3707 +2026-04-14 11:52:46.983209: Pseudo dice [0.2886, 0.6994, 0.6789, 0.8393, 0.6325, 0.6963, 0.4797] +2026-04-14 11:52:46.985598: Epoch time: 102.67 s +2026-04-14 11:52:48.598820: +2026-04-14 11:52:48.603852: Epoch 3326 +2026-04-14 11:52:48.607561: Current learning rate: 0.00201 +2026-04-14 11:54:31.265062: train_loss -0.412 +2026-04-14 11:54:31.270714: val_loss -0.3462 +2026-04-14 11:54:31.273068: Pseudo dice [0.6044, 0.779, 0.7571, 0.5105, 0.5799, 0.7519, 0.4986] +2026-04-14 11:54:31.276133: Epoch time: 102.67 s +2026-04-14 11:54:32.867142: +2026-04-14 11:54:32.871014: Epoch 3327 +2026-04-14 11:54:32.873756: Current learning rate: 0.00201 +2026-04-14 11:56:15.666371: train_loss -0.4174 +2026-04-14 11:56:15.672833: val_loss -0.2742 +2026-04-14 11:56:15.675586: Pseudo dice [0.6464, 0.5796, 0.729, 0.3931, 0.5895, 0.085, 0.557] +2026-04-14 11:56:15.677824: Epoch time: 102.8 s +2026-04-14 11:56:17.275091: +2026-04-14 11:56:17.277240: Epoch 3328 +2026-04-14 11:56:17.278935: Current learning rate: 0.00201 +2026-04-14 11:58:00.272296: train_loss -0.4204 +2026-04-14 11:58:00.277457: val_loss -0.314 +2026-04-14 11:58:00.279904: Pseudo dice [0.7106, 0.5669, 0.6983, 0.3998, 0.4389, 0.3374, 0.7754] +2026-04-14 11:58:00.282744: Epoch time: 103.0 s +2026-04-14 11:58:01.848011: +2026-04-14 11:58:01.849830: Epoch 3329 +2026-04-14 11:58:01.851751: Current learning rate: 0.00201 +2026-04-14 11:59:46.017098: train_loss -0.4098 +2026-04-14 11:59:46.026794: val_loss -0.3433 +2026-04-14 11:59:46.030210: Pseudo dice [0.8367, 0.7107, 0.7498, 0.8268, 0.4788, 0.251, 0.8167] +2026-04-14 11:59:46.032515: Epoch time: 104.17 s +2026-04-14 11:59:47.726762: +2026-04-14 11:59:47.728533: Epoch 3330 +2026-04-14 11:59:47.730267: Current learning rate: 0.002 +2026-04-14 12:01:30.726675: train_loss -0.4114 +2026-04-14 12:01:30.731881: val_loss -0.357 +2026-04-14 12:01:30.733928: Pseudo dice [0.4396, 0.6746, 0.6943, 0.4883, 0.5504, 0.7581, 0.8406] +2026-04-14 12:01:30.735807: Epoch time: 103.0 s +2026-04-14 12:01:32.294316: +2026-04-14 12:01:32.296314: Epoch 3331 +2026-04-14 12:01:32.298204: Current learning rate: 0.002 +2026-04-14 12:03:15.037019: train_loss -0.4147 +2026-04-14 12:03:15.044931: val_loss -0.3113 +2026-04-14 12:03:15.046912: Pseudo dice [0.6913, 0.8107, 0.735, 0.6917, 0.3136, 0.5744, 0.5204] +2026-04-14 12:03:15.049907: Epoch time: 102.75 s +2026-04-14 12:03:16.603332: +2026-04-14 12:03:16.605314: Epoch 3332 +2026-04-14 12:03:16.607002: Current learning rate: 0.002 +2026-04-14 12:04:59.988380: train_loss -0.4092 +2026-04-14 12:04:59.997216: val_loss -0.361 +2026-04-14 12:04:59.999998: Pseudo dice [0.4713, 0.676, 0.8087, 0.7942, 0.5718, 0.7228, 0.7407] +2026-04-14 12:05:00.002658: Epoch time: 103.39 s +2026-04-14 12:05:01.558147: +2026-04-14 12:05:01.560055: Epoch 3333 +2026-04-14 12:05:01.561740: Current learning rate: 0.00199 +2026-04-14 12:06:44.323573: train_loss -0.4149 +2026-04-14 12:06:44.330493: val_loss -0.3024 +2026-04-14 12:06:44.332518: Pseudo dice [0.8001, 0.7486, 0.6783, 0.4247, 0.5228, 0.1826, 0.6994] +2026-04-14 12:06:44.334573: Epoch time: 102.77 s +2026-04-14 12:06:45.878989: +2026-04-14 12:06:45.880507: Epoch 3334 +2026-04-14 12:06:45.882248: Current learning rate: 0.00199 +2026-04-14 12:08:31.725729: train_loss -0.4195 +2026-04-14 12:08:31.730623: val_loss -0.2421 +2026-04-14 12:08:31.733005: Pseudo dice [0.6652, 0.7698, 0.6661, 0.678, 0.6015, 0.0395, 0.8627] +2026-04-14 12:08:31.735236: Epoch time: 105.85 s +2026-04-14 12:08:33.327270: +2026-04-14 12:08:33.328926: Epoch 3335 +2026-04-14 12:08:33.330925: Current learning rate: 0.00199 +2026-04-14 12:10:16.094146: train_loss -0.4151 +2026-04-14 12:10:16.102280: val_loss -0.3956 +2026-04-14 12:10:16.104566: Pseudo dice [0.708, 0.407, 0.7507, 0.8253, 0.6613, 0.72, 0.8534] +2026-04-14 12:10:16.106982: Epoch time: 102.77 s +2026-04-14 12:10:17.658469: +2026-04-14 12:10:17.660235: Epoch 3336 +2026-04-14 12:10:17.662149: Current learning rate: 0.00199 +2026-04-14 12:12:00.427144: train_loss -0.4077 +2026-04-14 12:12:00.434075: val_loss -0.2836 +2026-04-14 12:12:00.436605: Pseudo dice [0.6482, 0.4863, 0.7674, 0.733, 0.559, 0.1588, 0.8335] +2026-04-14 12:12:00.439077: Epoch time: 102.77 s +2026-04-14 12:12:02.008302: +2026-04-14 12:12:02.010210: Epoch 3337 +2026-04-14 12:12:02.012588: Current learning rate: 0.00198 +2026-04-14 12:13:44.533917: train_loss -0.4112 +2026-04-14 12:13:44.539549: val_loss -0.3102 +2026-04-14 12:13:44.542436: Pseudo dice [0.7232, 0.3274, 0.6822, 0.6565, 0.4026, 0.1445, 0.6512] +2026-04-14 12:13:44.544942: Epoch time: 102.53 s +2026-04-14 12:13:47.081692: +2026-04-14 12:13:47.083468: Epoch 3338 +2026-04-14 12:13:47.085351: Current learning rate: 0.00198 +2026-04-14 12:15:29.666867: train_loss -0.417 +2026-04-14 12:15:29.672411: val_loss -0.3505 +2026-04-14 12:15:29.674742: Pseudo dice [0.624, 0.5478, 0.7043, 0.3103, 0.5782, 0.6821, 0.7848] +2026-04-14 12:15:29.677779: Epoch time: 102.59 s +2026-04-14 12:15:31.247891: +2026-04-14 12:15:31.249608: Epoch 3339 +2026-04-14 12:15:31.251695: Current learning rate: 0.00198 +2026-04-14 12:17:14.794832: train_loss -0.415 +2026-04-14 12:17:14.800735: val_loss -0.2708 +2026-04-14 12:17:14.804541: Pseudo dice [0.6138, 0.8401, 0.4813, 0.8572, 0.3559, 0.0461, 0.8009] +2026-04-14 12:17:14.806958: Epoch time: 103.55 s +2026-04-14 12:17:16.401314: +2026-04-14 12:17:16.403661: Epoch 3340 +2026-04-14 12:17:16.405446: Current learning rate: 0.00198 +2026-04-14 12:18:58.930236: train_loss -0.4169 +2026-04-14 12:18:58.936178: val_loss -0.2419 +2026-04-14 12:18:58.938942: Pseudo dice [0.6716, 0.8097, 0.5748, 0.4566, 0.1238, 0.0417, 0.895] +2026-04-14 12:18:58.941917: Epoch time: 102.53 s +2026-04-14 12:19:00.519962: +2026-04-14 12:19:00.522484: Epoch 3341 +2026-04-14 12:19:00.524611: Current learning rate: 0.00197 +2026-04-14 12:20:43.073245: train_loss -0.3968 +2026-04-14 12:20:43.079370: val_loss -0.3341 +2026-04-14 12:20:43.081730: Pseudo dice [0.6721, 0.3765, 0.7316, 0.4194, 0.5322, 0.2084, 0.6404] +2026-04-14 12:20:43.084388: Epoch time: 102.56 s +2026-04-14 12:20:44.936528: +2026-04-14 12:20:44.938548: Epoch 3342 +2026-04-14 12:20:44.940108: Current learning rate: 0.00197 +2026-04-14 12:22:27.532561: train_loss -0.3982 +2026-04-14 12:22:27.538934: val_loss -0.2726 +2026-04-14 12:22:27.541398: Pseudo dice [0.4295, 0.4009, 0.7574, 0.044, 0.3623, 0.1687, 0.3712] +2026-04-14 12:22:27.544451: Epoch time: 102.6 s +2026-04-14 12:22:29.121981: +2026-04-14 12:22:29.123649: Epoch 3343 +2026-04-14 12:22:29.125171: Current learning rate: 0.00197 +2026-04-14 12:24:11.923326: train_loss -0.4077 +2026-04-14 12:24:11.928367: val_loss -0.2713 +2026-04-14 12:24:11.930418: Pseudo dice [0.7449, 0.6126, 0.5953, 0.6985, 0.5102, 0.1176, 0.7215] +2026-04-14 12:24:11.933040: Epoch time: 102.81 s +2026-04-14 12:24:13.561892: +2026-04-14 12:24:13.563967: Epoch 3344 +2026-04-14 12:24:13.565612: Current learning rate: 0.00196 +2026-04-14 12:25:56.317101: train_loss -0.4031 +2026-04-14 12:25:56.323286: val_loss -0.3461 +2026-04-14 12:25:56.325344: Pseudo dice [0.3785, 0.7664, 0.7895, 0.4956, 0.6366, 0.7419, 0.8458] +2026-04-14 12:25:56.327629: Epoch time: 102.76 s +2026-04-14 12:25:57.942175: +2026-04-14 12:25:57.944072: Epoch 3345 +2026-04-14 12:25:57.946150: Current learning rate: 0.00196 +2026-04-14 12:27:40.211210: train_loss -0.4085 +2026-04-14 12:27:40.223256: val_loss -0.3128 +2026-04-14 12:27:40.225431: Pseudo dice [0.5091, 0.3531, 0.5038, 0.8549, 0.6775, 0.0968, 0.8488] +2026-04-14 12:27:40.227872: Epoch time: 102.27 s +2026-04-14 12:27:41.806981: +2026-04-14 12:27:41.809152: Epoch 3346 +2026-04-14 12:27:41.810803: Current learning rate: 0.00196 +2026-04-14 12:29:24.374873: train_loss -0.4107 +2026-04-14 12:29:24.380536: val_loss -0.3287 +2026-04-14 12:29:24.382568: Pseudo dice [0.5678, 0.676, 0.6619, 0.7881, 0.3645, 0.2088, 0.8074] +2026-04-14 12:29:24.385059: Epoch time: 102.57 s +2026-04-14 12:29:26.029199: +2026-04-14 12:29:26.030843: Epoch 3347 +2026-04-14 12:29:26.032401: Current learning rate: 0.00196 +2026-04-14 12:31:08.329550: train_loss -0.4119 +2026-04-14 12:31:08.335616: val_loss -0.2783 +2026-04-14 12:31:08.337574: Pseudo dice [0.7127, 0.5314, 0.7478, 0.2793, 0.5525, 0.0377, 0.6416] +2026-04-14 12:31:08.339723: Epoch time: 102.3 s +2026-04-14 12:31:09.941249: +2026-04-14 12:31:09.942926: Epoch 3348 +2026-04-14 12:31:09.944530: Current learning rate: 0.00195 +2026-04-14 12:32:53.945604: train_loss -0.4149 +2026-04-14 12:32:53.951581: val_loss -0.3118 +2026-04-14 12:32:53.953944: Pseudo dice [0.4587, 0.345, 0.6037, 0.053, 0.5344, 0.2206, 0.9162] +2026-04-14 12:32:53.956187: Epoch time: 104.01 s +2026-04-14 12:32:55.631875: +2026-04-14 12:32:55.633622: Epoch 3349 +2026-04-14 12:32:55.635307: Current learning rate: 0.00195 +2026-04-14 12:34:38.305165: train_loss -0.4142 +2026-04-14 12:34:38.310601: val_loss -0.3324 +2026-04-14 12:34:38.312923: Pseudo dice [0.8573, 0.4881, 0.7468, 0.6425, 0.6192, 0.1425, 0.8315] +2026-04-14 12:34:38.316460: Epoch time: 102.68 s +2026-04-14 12:34:41.964621: +2026-04-14 12:34:41.966827: Epoch 3350 +2026-04-14 12:34:41.968613: Current learning rate: 0.00195 +2026-04-14 12:36:24.887425: train_loss -0.4019 +2026-04-14 12:36:24.893149: val_loss -0.3284 +2026-04-14 12:36:24.895121: Pseudo dice [0.17, 0.7522, 0.6898, 0.5404, 0.63, 0.1628, 0.7794] +2026-04-14 12:36:24.897786: Epoch time: 102.93 s +2026-04-14 12:36:26.505876: +2026-04-14 12:36:26.507710: Epoch 3351 +2026-04-14 12:36:26.510026: Current learning rate: 0.00195 +2026-04-14 12:38:09.219771: train_loss -0.4065 +2026-04-14 12:38:09.224643: val_loss -0.2884 +2026-04-14 12:38:09.226235: Pseudo dice [0.1365, 0.5596, 0.4462, 0.3655, 0.4501, 0.1471, 0.8466] +2026-04-14 12:38:09.228549: Epoch time: 102.72 s +2026-04-14 12:38:10.795988: +2026-04-14 12:38:10.797962: Epoch 3352 +2026-04-14 12:38:10.801508: Current learning rate: 0.00194 +2026-04-14 12:39:53.399856: train_loss -0.4089 +2026-04-14 12:39:53.407934: val_loss -0.3616 +2026-04-14 12:39:53.410133: Pseudo dice [0.6634, 0.8464, 0.6149, 0.8684, 0.6402, 0.7652, 0.8509] +2026-04-14 12:39:53.412738: Epoch time: 102.61 s +2026-04-14 12:39:55.062742: +2026-04-14 12:39:55.064389: Epoch 3353 +2026-04-14 12:39:55.066216: Current learning rate: 0.00194 +2026-04-14 12:41:37.509845: train_loss -0.4156 +2026-04-14 12:41:37.516279: val_loss -0.2709 +2026-04-14 12:41:37.518136: Pseudo dice [0.4838, 0.5396, 0.5346, 0.5975, 0.5953, 0.1903, 0.6368] +2026-04-14 12:41:37.520655: Epoch time: 102.45 s +2026-04-14 12:41:39.109302: +2026-04-14 12:41:39.110890: Epoch 3354 +2026-04-14 12:41:39.112444: Current learning rate: 0.00194 +2026-04-14 12:43:21.547709: train_loss -0.4123 +2026-04-14 12:43:21.552763: val_loss -0.2441 +2026-04-14 12:43:21.555100: Pseudo dice [0.8842, 0.5079, 0.6678, 0.1781, 0.2493, 0.0844, 0.7942] +2026-04-14 12:43:21.557419: Epoch time: 102.44 s +2026-04-14 12:43:23.127941: +2026-04-14 12:43:23.130382: Epoch 3355 +2026-04-14 12:43:23.132051: Current learning rate: 0.00194 +2026-04-14 12:45:05.918032: train_loss -0.4012 +2026-04-14 12:45:05.924166: val_loss -0.2956 +2026-04-14 12:45:05.925915: Pseudo dice [0.5032, 0.8799, 0.7991, 0.668, 0.526, 0.1301, 0.6373] +2026-04-14 12:45:05.928700: Epoch time: 102.79 s +2026-04-14 12:45:07.579449: +2026-04-14 12:45:07.581280: Epoch 3356 +2026-04-14 12:45:07.583300: Current learning rate: 0.00193 +2026-04-14 12:46:50.242893: train_loss -0.414 +2026-04-14 12:46:50.247949: val_loss -0.3455 +2026-04-14 12:46:50.249975: Pseudo dice [0.4868, 0.4356, 0.7559, 0.7274, 0.5393, 0.3506, 0.8684] +2026-04-14 12:46:50.253226: Epoch time: 102.67 s +2026-04-14 12:46:52.987371: +2026-04-14 12:46:52.989603: Epoch 3357 +2026-04-14 12:46:52.991439: Current learning rate: 0.00193 +2026-04-14 12:48:36.273082: train_loss -0.4141 +2026-04-14 12:48:36.279523: val_loss -0.2915 +2026-04-14 12:48:36.281890: Pseudo dice [0.62, 0.7116, 0.5921, 0.7202, 0.6147, 0.1587, 0.8724] +2026-04-14 12:48:36.284220: Epoch time: 103.29 s +2026-04-14 12:48:37.884468: +2026-04-14 12:48:37.886516: Epoch 3358 +2026-04-14 12:48:37.888231: Current learning rate: 0.00193 +2026-04-14 12:50:20.956181: train_loss -0.4158 +2026-04-14 12:50:20.964825: val_loss -0.2817 +2026-04-14 12:50:20.967398: Pseudo dice [0.4222, 0.7339, 0.6396, 0.4622, 0.6039, 0.3885, 0.7364] +2026-04-14 12:50:20.970545: Epoch time: 103.08 s +2026-04-14 12:50:22.632864: +2026-04-14 12:50:22.635059: Epoch 3359 +2026-04-14 12:50:22.637081: Current learning rate: 0.00192 +2026-04-14 12:52:05.983571: train_loss -0.4197 +2026-04-14 12:52:05.994451: val_loss -0.3416 +2026-04-14 12:52:06.005159: Pseudo dice [0.6369, 0.6591, 0.7069, 0.8011, 0.6017, 0.1447, 0.8925] +2026-04-14 12:52:06.008563: Epoch time: 103.36 s +2026-04-14 12:52:07.577430: +2026-04-14 12:52:07.579247: Epoch 3360 +2026-04-14 12:52:07.580872: Current learning rate: 0.00192 +2026-04-14 12:53:50.318657: train_loss -0.4168 +2026-04-14 12:53:50.324087: val_loss -0.3082 +2026-04-14 12:53:50.326139: Pseudo dice [0.6753, 0.2662, 0.5429, 0.1794, 0.5995, 0.0438, 0.8754] +2026-04-14 12:53:50.328528: Epoch time: 102.75 s +2026-04-14 12:53:51.919743: +2026-04-14 12:53:51.921751: Epoch 3361 +2026-04-14 12:53:51.923595: Current learning rate: 0.00192 +2026-04-14 12:55:34.826430: train_loss -0.4068 +2026-04-14 12:55:34.843981: val_loss -0.3498 +2026-04-14 12:55:34.847415: Pseudo dice [0.455, 0.6872, 0.7837, 0.4129, 0.6187, 0.7262, 0.7397] +2026-04-14 12:55:34.851151: Epoch time: 102.91 s +2026-04-14 12:55:36.447001: +2026-04-14 12:55:36.450714: Epoch 3362 +2026-04-14 12:55:36.452546: Current learning rate: 0.00192 +2026-04-14 12:57:19.139509: train_loss -0.4119 +2026-04-14 12:57:19.145632: val_loss -0.356 +2026-04-14 12:57:19.147535: Pseudo dice [0.7124, 0.6643, 0.7275, 0.7384, 0.6728, 0.5516, 0.8181] +2026-04-14 12:57:19.149884: Epoch time: 102.7 s +2026-04-14 12:57:20.744591: +2026-04-14 12:57:20.746319: Epoch 3363 +2026-04-14 12:57:20.747866: Current learning rate: 0.00191 +2026-04-14 12:59:03.410102: train_loss -0.4092 +2026-04-14 12:59:03.416629: val_loss -0.3293 +2026-04-14 12:59:03.418845: Pseudo dice [0.3296, 0.4253, 0.6391, 0.8291, 0.6536, 0.556, 0.728] +2026-04-14 12:59:03.420784: Epoch time: 102.67 s +2026-04-14 12:59:04.998760: +2026-04-14 12:59:05.000609: Epoch 3364 +2026-04-14 12:59:05.002288: Current learning rate: 0.00191 +2026-04-14 13:00:47.974536: train_loss -0.4183 +2026-04-14 13:00:47.979990: val_loss -0.3673 +2026-04-14 13:00:47.982908: Pseudo dice [0.8099, 0.4696, 0.7581, 0.8252, 0.593, 0.7374, 0.8042] +2026-04-14 13:00:47.985330: Epoch time: 102.98 s +2026-04-14 13:00:49.599594: +2026-04-14 13:00:49.601204: Epoch 3365 +2026-04-14 13:00:49.602939: Current learning rate: 0.00191 +2026-04-14 13:02:32.409800: train_loss -0.4143 +2026-04-14 13:02:32.414767: val_loss -0.2862 +2026-04-14 13:02:32.416943: Pseudo dice [0.7451, 0.6272, 0.7862, 0.7542, 0.6624, 0.1575, 0.5654] +2026-04-14 13:02:32.419852: Epoch time: 102.81 s +2026-04-14 13:02:34.019529: +2026-04-14 13:02:34.021506: Epoch 3366 +2026-04-14 13:02:34.023342: Current learning rate: 0.00191 +2026-04-14 13:04:17.197047: train_loss -0.4211 +2026-04-14 13:04:17.202014: val_loss -0.3804 +2026-04-14 13:04:17.203729: Pseudo dice [0.2162, 0.3193, 0.8259, 0.8934, 0.6481, 0.8831, 0.8557] +2026-04-14 13:04:17.205946: Epoch time: 103.18 s +2026-04-14 13:04:18.827369: +2026-04-14 13:04:18.829798: Epoch 3367 +2026-04-14 13:04:18.831484: Current learning rate: 0.0019 +2026-04-14 13:06:02.100710: train_loss -0.4194 +2026-04-14 13:06:02.108143: val_loss -0.3221 +2026-04-14 13:06:02.110299: Pseudo dice [0.6269, 0.5815, 0.8528, 0.8187, 0.61, 0.2059, 0.6527] +2026-04-14 13:06:02.113433: Epoch time: 103.28 s +2026-04-14 13:06:03.700454: +2026-04-14 13:06:03.702409: Epoch 3368 +2026-04-14 13:06:03.704607: Current learning rate: 0.0019 +2026-04-14 13:07:46.536444: train_loss -0.4289 +2026-04-14 13:07:46.541877: val_loss -0.3849 +2026-04-14 13:07:46.544495: Pseudo dice [0.6948, 0.6819, 0.7995, 0.7216, 0.649, 0.864, 0.8243] +2026-04-14 13:07:46.546661: Epoch time: 102.84 s +2026-04-14 13:07:48.157432: +2026-04-14 13:07:48.160053: Epoch 3369 +2026-04-14 13:07:48.162803: Current learning rate: 0.0019 +2026-04-14 13:09:32.596014: train_loss -0.4195 +2026-04-14 13:09:32.602332: val_loss -0.2467 +2026-04-14 13:09:32.604920: Pseudo dice [0.5595, 0.258, 0.7791, 0.8213, 0.4977, 0.1539, 0.5935] +2026-04-14 13:09:32.607899: Epoch time: 104.44 s +2026-04-14 13:09:34.220379: +2026-04-14 13:09:34.222777: Epoch 3370 +2026-04-14 13:09:34.225970: Current learning rate: 0.00189 +2026-04-14 13:11:16.895202: train_loss -0.4089 +2026-04-14 13:11:16.901109: val_loss -0.3615 +2026-04-14 13:11:16.903098: Pseudo dice [0.1864, 0.5118, 0.8185, 0.8041, 0.6512, 0.4556, 0.7401] +2026-04-14 13:11:16.906008: Epoch time: 102.68 s +2026-04-14 13:11:18.506159: +2026-04-14 13:11:18.507937: Epoch 3371 +2026-04-14 13:11:18.509534: Current learning rate: 0.00189 +2026-04-14 13:13:01.464110: train_loss -0.4199 +2026-04-14 13:13:01.469725: val_loss -0.2731 +2026-04-14 13:13:01.471944: Pseudo dice [0.7065, 0.5888, 0.7096, 0.8898, 0.5839, 0.2157, 0.7156] +2026-04-14 13:13:01.474535: Epoch time: 102.96 s +2026-04-14 13:13:03.115833: +2026-04-14 13:13:03.118400: Epoch 3372 +2026-04-14 13:13:03.120055: Current learning rate: 0.00189 +2026-04-14 13:14:45.635342: train_loss -0.4187 +2026-04-14 13:14:45.640503: val_loss -0.374 +2026-04-14 13:14:45.642113: Pseudo dice [0.5761, 0.3638, 0.7355, 0.8237, 0.5986, 0.2965, 0.874] +2026-04-14 13:14:45.644445: Epoch time: 102.52 s +2026-04-14 13:14:47.232986: +2026-04-14 13:14:47.234744: Epoch 3373 +2026-04-14 13:14:47.236444: Current learning rate: 0.00189 +2026-04-14 13:16:29.921371: train_loss -0.412 +2026-04-14 13:16:29.929036: val_loss -0.3389 +2026-04-14 13:16:29.932218: Pseudo dice [0.383, 0.6962, 0.7247, 0.769, 0.6655, 0.4718, 0.7341] +2026-04-14 13:16:29.934300: Epoch time: 102.69 s +2026-04-14 13:16:31.558415: +2026-04-14 13:16:31.560167: Epoch 3374 +2026-04-14 13:16:31.561728: Current learning rate: 0.00188 +2026-04-14 13:18:14.212947: train_loss -0.4273 +2026-04-14 13:18:14.218195: val_loss -0.3679 +2026-04-14 13:18:14.220102: Pseudo dice [0.6816, 0.3701, 0.6388, 0.8859, 0.6137, 0.6905, 0.8286] +2026-04-14 13:18:14.222379: Epoch time: 102.66 s +2026-04-14 13:18:15.788267: +2026-04-14 13:18:15.790179: Epoch 3375 +2026-04-14 13:18:15.791934: Current learning rate: 0.00188 +2026-04-14 13:19:58.991435: train_loss -0.4187 +2026-04-14 13:19:58.997345: val_loss -0.3132 +2026-04-14 13:19:58.999444: Pseudo dice [0.4729, 0.694, 0.7647, 0.7785, 0.5579, 0.271, 0.7473] +2026-04-14 13:19:59.002022: Epoch time: 103.21 s +2026-04-14 13:20:00.651899: +2026-04-14 13:20:00.653627: Epoch 3376 +2026-04-14 13:20:00.655121: Current learning rate: 0.00188 +2026-04-14 13:21:44.643498: train_loss -0.4036 +2026-04-14 13:21:44.647865: val_loss -0.2869 +2026-04-14 13:21:44.650173: Pseudo dice [0.6258, 0.2941, 0.6552, 0.7601, 0.3155, 0.0796, 0.8304] +2026-04-14 13:21:44.652162: Epoch time: 104.0 s +2026-04-14 13:21:46.310558: +2026-04-14 13:21:46.312590: Epoch 3377 +2026-04-14 13:21:46.314393: Current learning rate: 0.00188 +2026-04-14 13:23:29.113122: train_loss -0.4181 +2026-04-14 13:23:29.118371: val_loss -0.3645 +2026-04-14 13:23:29.120578: Pseudo dice [0.442, 0.3038, 0.7856, 0.7613, 0.6425, 0.777, 0.7902] +2026-04-14 13:23:29.122998: Epoch time: 102.81 s +2026-04-14 13:23:30.711987: +2026-04-14 13:23:30.714335: Epoch 3378 +2026-04-14 13:23:30.716511: Current learning rate: 0.00187 +2026-04-14 13:25:14.419971: train_loss -0.4103 +2026-04-14 13:25:14.424952: val_loss -0.1546 +2026-04-14 13:25:14.427246: Pseudo dice [0.6019, 0.5481, 0.4435, 0.3453, 0.3956, 0.3274, 0.8149] +2026-04-14 13:25:14.429396: Epoch time: 103.71 s +2026-04-14 13:25:16.027462: +2026-04-14 13:25:16.029173: Epoch 3379 +2026-04-14 13:25:16.030827: Current learning rate: 0.00187 +2026-04-14 13:26:58.757310: train_loss -0.41 +2026-04-14 13:26:58.768387: val_loss -0.3605 +2026-04-14 13:26:58.771789: Pseudo dice [0.6923, 0.3346, 0.8187, 0.6764, 0.6945, 0.1763, 0.8551] +2026-04-14 13:26:58.777797: Epoch time: 102.73 s +2026-04-14 13:27:00.416798: +2026-04-14 13:27:00.418361: Epoch 3380 +2026-04-14 13:27:00.419946: Current learning rate: 0.00187 +2026-04-14 13:28:43.287640: train_loss -0.4168 +2026-04-14 13:28:43.293392: val_loss -0.3226 +2026-04-14 13:28:43.295699: Pseudo dice [0.5203, 0.3026, 0.6332, 0.4293, 0.3402, 0.6461, 0.8819] +2026-04-14 13:28:43.297965: Epoch time: 102.87 s +2026-04-14 13:28:44.928017: +2026-04-14 13:28:44.929807: Epoch 3381 +2026-04-14 13:28:44.931955: Current learning rate: 0.00186 +2026-04-14 13:30:27.658697: train_loss -0.4181 +2026-04-14 13:30:27.664181: val_loss -0.3816 +2026-04-14 13:30:27.666066: Pseudo dice [0.596, 0.5203, 0.6638, 0.7391, 0.5508, 0.8441, 0.4944] +2026-04-14 13:30:27.668056: Epoch time: 102.73 s +2026-04-14 13:30:29.283669: +2026-04-14 13:30:29.285559: Epoch 3382 +2026-04-14 13:30:29.287267: Current learning rate: 0.00186 +2026-04-14 13:32:12.774812: train_loss -0.4225 +2026-04-14 13:32:12.780155: val_loss -0.2685 +2026-04-14 13:32:12.782231: Pseudo dice [0.407, 0.3989, 0.6873, 0.4484, 0.5329, 0.1625, 0.589] +2026-04-14 13:32:12.784555: Epoch time: 103.49 s +2026-04-14 13:32:14.401155: +2026-04-14 13:32:14.403241: Epoch 3383 +2026-04-14 13:32:14.404867: Current learning rate: 0.00186 +2026-04-14 13:33:57.389004: train_loss -0.4233 +2026-04-14 13:33:57.394331: val_loss -0.3639 +2026-04-14 13:33:57.396365: Pseudo dice [0.5631, 0.3535, 0.7616, 0.8717, 0.5842, 0.5724, 0.9085] +2026-04-14 13:33:57.398918: Epoch time: 102.99 s +2026-04-14 13:33:58.963578: +2026-04-14 13:33:58.965399: Epoch 3384 +2026-04-14 13:33:58.967048: Current learning rate: 0.00186 +2026-04-14 13:35:41.548675: train_loss -0.417 +2026-04-14 13:35:41.553529: val_loss -0.3288 +2026-04-14 13:35:41.555251: Pseudo dice [0.8057, 0.6729, 0.7467, 0.8961, 0.4149, 0.3349, 0.7238] +2026-04-14 13:35:41.557918: Epoch time: 102.59 s +2026-04-14 13:35:43.143330: +2026-04-14 13:35:43.145808: Epoch 3385 +2026-04-14 13:35:43.147683: Current learning rate: 0.00185 +2026-04-14 13:37:27.649781: train_loss -0.4144 +2026-04-14 13:37:27.655421: val_loss -0.3573 +2026-04-14 13:37:27.657614: Pseudo dice [0.6541, 0.831, 0.8304, 0.8833, 0.4293, 0.6908, 0.9] +2026-04-14 13:37:27.659960: Epoch time: 104.51 s +2026-04-14 13:37:29.229460: +2026-04-14 13:37:29.231350: Epoch 3386 +2026-04-14 13:37:29.233211: Current learning rate: 0.00185 +2026-04-14 13:39:11.974943: train_loss -0.4175 +2026-04-14 13:39:11.985339: val_loss -0.3526 +2026-04-14 13:39:11.988751: Pseudo dice [0.7128, 0.5448, 0.878, 0.8261, 0.5944, 0.244, 0.7365] +2026-04-14 13:39:11.992159: Epoch time: 102.75 s +2026-04-14 13:39:13.567090: +2026-04-14 13:39:13.570010: Epoch 3387 +2026-04-14 13:39:13.572201: Current learning rate: 0.00185 +2026-04-14 13:40:56.468742: train_loss -0.4192 +2026-04-14 13:40:56.475184: val_loss -0.2991 +2026-04-14 13:40:56.477305: Pseudo dice [0.688, 0.6465, 0.6059, 0.8931, 0.3292, 0.1541, 0.7255] +2026-04-14 13:40:56.479632: Epoch time: 102.91 s +2026-04-14 13:40:58.361962: +2026-04-14 13:40:58.364011: Epoch 3388 +2026-04-14 13:40:58.365499: Current learning rate: 0.00185 +2026-04-14 13:42:40.991322: train_loss -0.4225 +2026-04-14 13:42:40.997646: val_loss -0.3324 +2026-04-14 13:42:41.000433: Pseudo dice [0.5528, 0.7419, 0.7751, 0.0588, 0.3483, 0.1526, 0.4376] +2026-04-14 13:42:41.004578: Epoch time: 102.63 s +2026-04-14 13:42:42.653899: +2026-04-14 13:42:42.655700: Epoch 3389 +2026-04-14 13:42:42.657650: Current learning rate: 0.00184 +2026-04-14 13:44:26.002317: train_loss -0.4212 +2026-04-14 13:44:26.008501: val_loss -0.3588 +2026-04-14 13:44:26.011668: Pseudo dice [0.6704, 0.1153, 0.664, 0.4166, 0.6267, 0.609, 0.7603] +2026-04-14 13:44:26.014615: Epoch time: 103.35 s +2026-04-14 13:44:27.571524: +2026-04-14 13:44:27.574757: Epoch 3390 +2026-04-14 13:44:27.576647: Current learning rate: 0.00184 +2026-04-14 13:46:10.104236: train_loss -0.4106 +2026-04-14 13:46:10.109801: val_loss -0.2763 +2026-04-14 13:46:10.112065: Pseudo dice [0.663, 0.5755, 0.5911, 0.6072, 0.6109, 0.4166, 0.7216] +2026-04-14 13:46:10.114606: Epoch time: 102.54 s +2026-04-14 13:46:11.684285: +2026-04-14 13:46:11.686682: Epoch 3391 +2026-04-14 13:46:11.688466: Current learning rate: 0.00184 +2026-04-14 13:47:54.166117: train_loss -0.4102 +2026-04-14 13:47:54.171480: val_loss -0.2806 +2026-04-14 13:47:54.173314: Pseudo dice [0.7823, 0.7944, 0.7308, 0.492, 0.6149, 0.147, 0.3699] +2026-04-14 13:47:54.175422: Epoch time: 102.49 s +2026-04-14 13:47:55.776390: +2026-04-14 13:47:55.778173: Epoch 3392 +2026-04-14 13:47:55.780508: Current learning rate: 0.00184 +2026-04-14 13:49:38.610265: train_loss -0.4068 +2026-04-14 13:49:38.617527: val_loss -0.3265 +2026-04-14 13:49:38.620183: Pseudo dice [0.3368, 0.7235, 0.7427, 0.687, 0.3326, 0.4644, 0.3575] +2026-04-14 13:49:38.623477: Epoch time: 102.84 s +2026-04-14 13:49:40.212879: +2026-04-14 13:49:40.215661: Epoch 3393 +2026-04-14 13:49:40.217959: Current learning rate: 0.00183 +2026-04-14 13:51:24.247099: train_loss -0.3959 +2026-04-14 13:51:24.253551: val_loss -0.353 +2026-04-14 13:51:24.255186: Pseudo dice [0.5917, 0.2428, 0.7541, 0.7391, 0.5943, 0.6876, 0.9014] +2026-04-14 13:51:24.257765: Epoch time: 104.04 s +2026-04-14 13:51:25.853225: +2026-04-14 13:51:25.854982: Epoch 3394 +2026-04-14 13:51:25.856553: Current learning rate: 0.00183 +2026-04-14 13:53:08.672572: train_loss -0.4138 +2026-04-14 13:53:08.680187: val_loss -0.3513 +2026-04-14 13:53:08.683330: Pseudo dice [0.4834, 0.4901, 0.7842, 0.6513, 0.6449, 0.5609, 0.8665] +2026-04-14 13:53:08.685989: Epoch time: 102.82 s +2026-04-14 13:53:10.345238: +2026-04-14 13:53:10.347409: Epoch 3395 +2026-04-14 13:53:10.350999: Current learning rate: 0.00183 +2026-04-14 13:54:52.622695: train_loss -0.4041 +2026-04-14 13:54:52.628173: val_loss -0.3218 +2026-04-14 13:54:52.631838: Pseudo dice [0.5533, 0.2514, 0.6959, 0.5251, 0.5399, 0.1811, 0.9076] +2026-04-14 13:54:52.635495: Epoch time: 102.28 s +2026-04-14 13:54:55.370198: +2026-04-14 13:54:55.372514: Epoch 3396 +2026-04-14 13:54:55.374092: Current learning rate: 0.00182 +2026-04-14 13:56:37.589670: train_loss -0.4161 +2026-04-14 13:56:37.595432: val_loss -0.3426 +2026-04-14 13:56:37.597297: Pseudo dice [0.4368, 0.479, 0.3972, 0.514, 0.6094, 0.5201, 0.8832] +2026-04-14 13:56:37.599737: Epoch time: 102.22 s +2026-04-14 13:56:39.166030: +2026-04-14 13:56:39.169784: Epoch 3397 +2026-04-14 13:56:39.171658: Current learning rate: 0.00182 +2026-04-14 13:58:21.329016: train_loss -0.4159 +2026-04-14 13:58:21.335170: val_loss -0.2805 +2026-04-14 13:58:21.337499: Pseudo dice [0.6426, 0.312, 0.4445, 0.7664, 0.3775, 0.3292, 0.8519] +2026-04-14 13:58:21.339697: Epoch time: 102.17 s +2026-04-14 13:58:22.928937: +2026-04-14 13:58:22.931020: Epoch 3398 +2026-04-14 13:58:22.932546: Current learning rate: 0.00182 +2026-04-14 14:00:04.995337: train_loss -0.4143 +2026-04-14 14:00:05.001242: val_loss -0.3172 +2026-04-14 14:00:05.003182: Pseudo dice [0.6113, 0.7024, 0.5301, 0.4485, 0.5575, 0.3447, 0.8622] +2026-04-14 14:00:05.005104: Epoch time: 102.07 s +2026-04-14 14:00:06.586579: +2026-04-14 14:00:06.588127: Epoch 3399 +2026-04-14 14:00:06.589944: Current learning rate: 0.00182 +2026-04-14 14:01:48.725458: train_loss -0.4089 +2026-04-14 14:01:48.731174: val_loss -0.3374 +2026-04-14 14:01:48.733347: Pseudo dice [0.6353, 0.7683, 0.7501, 0.6471, 0.2663, 0.5907, 0.7681] +2026-04-14 14:01:48.736667: Epoch time: 102.14 s +2026-04-14 14:01:52.465292: +2026-04-14 14:01:52.467319: Epoch 3400 +2026-04-14 14:01:52.469033: Current learning rate: 0.00181 +2026-04-14 14:03:35.645621: train_loss -0.4131 +2026-04-14 14:03:35.650601: val_loss -0.2376 +2026-04-14 14:03:35.653423: Pseudo dice [0.3403, 0.7267, 0.7146, 0.2467, 0.6033, 0.1178, 0.6783] +2026-04-14 14:03:35.655974: Epoch time: 103.18 s +2026-04-14 14:03:37.265031: +2026-04-14 14:03:37.267255: Epoch 3401 +2026-04-14 14:03:37.269447: Current learning rate: 0.00181 +2026-04-14 14:05:19.427406: train_loss -0.419 +2026-04-14 14:05:19.433379: val_loss -0.3596 +2026-04-14 14:05:19.437977: Pseudo dice [0.6349, 0.6888, 0.7187, 0.631, 0.5286, 0.8062, 0.8596] +2026-04-14 14:05:19.441274: Epoch time: 102.17 s +2026-04-14 14:05:21.026588: +2026-04-14 14:05:21.028605: Epoch 3402 +2026-04-14 14:05:21.030328: Current learning rate: 0.00181 +2026-04-14 14:07:03.173499: train_loss -0.4061 +2026-04-14 14:07:03.179833: val_loss -0.3582 +2026-04-14 14:07:03.182074: Pseudo dice [0.5318, 0.5027, 0.8325, 0.7143, 0.3295, 0.8202, 0.8448] +2026-04-14 14:07:03.185351: Epoch time: 102.15 s +2026-04-14 14:07:04.746611: +2026-04-14 14:07:04.748724: Epoch 3403 +2026-04-14 14:07:04.750427: Current learning rate: 0.00181 +2026-04-14 14:08:46.953650: train_loss -0.411 +2026-04-14 14:08:46.958966: val_loss -0.292 +2026-04-14 14:08:46.961521: Pseudo dice [0.7923, 0.6544, 0.4481, 0.6865, 0.6263, 0.0818, 0.6339] +2026-04-14 14:08:46.964003: Epoch time: 102.21 s +2026-04-14 14:08:48.593922: +2026-04-14 14:08:48.596480: Epoch 3404 +2026-04-14 14:08:48.598350: Current learning rate: 0.0018 +2026-04-14 14:10:30.778090: train_loss -0.4053 +2026-04-14 14:10:30.785998: val_loss -0.3159 +2026-04-14 14:10:30.791025: Pseudo dice [0.561, 0.7143, 0.5987, 0.1549, 0.572, 0.1947, 0.8011] +2026-04-14 14:10:30.795498: Epoch time: 102.19 s +2026-04-14 14:10:32.431718: +2026-04-14 14:10:32.434016: Epoch 3405 +2026-04-14 14:10:32.436419: Current learning rate: 0.0018 +2026-04-14 14:12:15.072952: train_loss -0.4267 +2026-04-14 14:12:15.081716: val_loss -0.2603 +2026-04-14 14:12:15.084964: Pseudo dice [0.5477, 0.6225, 0.6721, 0.7258, 0.5921, 0.2745, 0.8874] +2026-04-14 14:12:15.087756: Epoch time: 102.65 s +2026-04-14 14:12:16.694350: +2026-04-14 14:12:16.696190: Epoch 3406 +2026-04-14 14:12:16.697860: Current learning rate: 0.0018 +2026-04-14 14:13:59.248416: train_loss -0.4253 +2026-04-14 14:13:59.259662: val_loss -0.2449 +2026-04-14 14:13:59.263263: Pseudo dice [0.4529, 0.7915, 0.6902, 0.7693, 0.5587, 0.1969, 0.7317] +2026-04-14 14:13:59.268971: Epoch time: 102.56 s +2026-04-14 14:14:00.880399: +2026-04-14 14:14:00.882648: Epoch 3407 +2026-04-14 14:14:00.884680: Current learning rate: 0.00179 +2026-04-14 14:15:43.030968: train_loss -0.4203 +2026-04-14 14:15:43.036366: val_loss -0.2644 +2026-04-14 14:15:43.038305: Pseudo dice [0.7844, 0.594, 0.6855, 0.6495, 0.2787, 0.156, 0.5191] +2026-04-14 14:15:43.040399: Epoch time: 102.15 s +2026-04-14 14:15:44.653621: +2026-04-14 14:15:44.655815: Epoch 3408 +2026-04-14 14:15:44.657741: Current learning rate: 0.00179 +2026-04-14 14:17:27.128825: train_loss -0.4145 +2026-04-14 14:17:27.137038: val_loss -0.3625 +2026-04-14 14:17:27.139270: Pseudo dice [0.4958, 0.4512, 0.7363, 0.7112, 0.5757, 0.8053, 0.8472] +2026-04-14 14:17:27.141499: Epoch time: 102.48 s +2026-04-14 14:17:28.971368: +2026-04-14 14:17:28.973634: Epoch 3409 +2026-04-14 14:17:28.975382: Current learning rate: 0.00179 +2026-04-14 14:19:11.047314: train_loss -0.4115 +2026-04-14 14:19:11.052591: val_loss -0.2398 +2026-04-14 14:19:11.055068: Pseudo dice [0.6917, 0.5034, 0.6754, 0.2384, 0.4656, 0.0326, 0.3893] +2026-04-14 14:19:11.057472: Epoch time: 102.08 s +2026-04-14 14:19:12.628315: +2026-04-14 14:19:12.633066: Epoch 3410 +2026-04-14 14:19:12.638609: Current learning rate: 0.00179 +2026-04-14 14:20:55.042087: train_loss -0.4088 +2026-04-14 14:20:55.054184: val_loss -0.2835 +2026-04-14 14:20:55.056496: Pseudo dice [0.6126, 0.501, 0.752, 0.0319, 0.6068, 0.2477, 0.9029] +2026-04-14 14:20:55.059117: Epoch time: 102.42 s +2026-04-14 14:20:56.618093: +2026-04-14 14:20:56.619855: Epoch 3411 +2026-04-14 14:20:56.622359: Current learning rate: 0.00178 +2026-04-14 14:22:38.773713: train_loss -0.4199 +2026-04-14 14:22:38.779788: val_loss -0.3663 +2026-04-14 14:22:38.781707: Pseudo dice [0.4519, 0.3929, 0.7407, 0.7607, 0.6178, 0.7766, 0.8323] +2026-04-14 14:22:38.784152: Epoch time: 102.16 s +2026-04-14 14:22:40.371978: +2026-04-14 14:22:40.373971: Epoch 3412 +2026-04-14 14:22:40.375864: Current learning rate: 0.00178 +2026-04-14 14:24:22.919380: train_loss -0.4254 +2026-04-14 14:24:22.924839: val_loss -0.3539 +2026-04-14 14:24:22.927187: Pseudo dice [0.7049, 0.271, 0.7057, 0.8556, 0.5747, 0.7717, 0.6135] +2026-04-14 14:24:22.929825: Epoch time: 102.55 s +2026-04-14 14:24:24.509180: +2026-04-14 14:24:24.511401: Epoch 3413 +2026-04-14 14:24:24.513573: Current learning rate: 0.00178 +2026-04-14 14:26:06.705576: train_loss -0.3907 +2026-04-14 14:26:06.711269: val_loss -0.3456 +2026-04-14 14:26:06.713471: Pseudo dice [0.7401, 0.4676, 0.6645, 0.5777, 0.4817, 0.2677, 0.8292] +2026-04-14 14:26:06.715701: Epoch time: 102.2 s +2026-04-14 14:26:08.324937: +2026-04-14 14:26:08.326867: Epoch 3414 +2026-04-14 14:26:08.328762: Current learning rate: 0.00178 +2026-04-14 14:27:51.538160: train_loss -0.4159 +2026-04-14 14:27:51.543728: val_loss -0.3281 +2026-04-14 14:27:51.546612: Pseudo dice [0.6681, 0.7285, 0.7558, 0.7186, 0.4516, 0.503, 0.7444] +2026-04-14 14:27:51.549300: Epoch time: 103.22 s +2026-04-14 14:27:53.121024: +2026-04-14 14:27:53.122792: Epoch 3415 +2026-04-14 14:27:53.124650: Current learning rate: 0.00177 +2026-04-14 14:29:36.599490: train_loss -0.4237 +2026-04-14 14:29:36.606653: val_loss -0.3656 +2026-04-14 14:29:36.608948: Pseudo dice [0.769, 0.8241, 0.8529, 0.7595, 0.5252, 0.7999, 0.7322] +2026-04-14 14:29:36.611795: Epoch time: 103.48 s +2026-04-14 14:29:38.198517: +2026-04-14 14:29:38.200901: Epoch 3416 +2026-04-14 14:29:38.202968: Current learning rate: 0.00177 +2026-04-14 14:31:20.423688: train_loss -0.4115 +2026-04-14 14:31:20.430441: val_loss -0.333 +2026-04-14 14:31:20.432564: Pseudo dice [0.595, 0.4489, 0.7263, 0.8228, 0.5389, 0.1343, 0.8505] +2026-04-14 14:31:20.435232: Epoch time: 102.23 s +2026-04-14 14:31:22.032919: +2026-04-14 14:31:22.039522: Epoch 3417 +2026-04-14 14:31:22.043768: Current learning rate: 0.00177 +2026-04-14 14:33:04.496274: train_loss -0.4102 +2026-04-14 14:33:04.501193: val_loss -0.3377 +2026-04-14 14:33:04.502926: Pseudo dice [0.6021, 0.4517, 0.767, 0.1448, 0.2719, 0.3025, 0.8599] +2026-04-14 14:33:04.505505: Epoch time: 102.47 s +2026-04-14 14:33:06.119355: +2026-04-14 14:33:06.121549: Epoch 3418 +2026-04-14 14:33:06.123329: Current learning rate: 0.00176 +2026-04-14 14:34:48.337108: train_loss -0.4212 +2026-04-14 14:34:48.342935: val_loss -0.1523 +2026-04-14 14:34:48.345026: Pseudo dice [0.8263, 0.4854, 0.6666, 0.6864, 0.2274, 0.2049, 0.6433] +2026-04-14 14:34:48.347218: Epoch time: 102.22 s +2026-04-14 14:34:49.919780: +2026-04-14 14:34:49.922149: Epoch 3419 +2026-04-14 14:34:49.923831: Current learning rate: 0.00176 +2026-04-14 14:36:32.205520: train_loss -0.4149 +2026-04-14 14:36:32.211107: val_loss -0.3414 +2026-04-14 14:36:32.213435: Pseudo dice [0.8283, 0.7535, 0.768, 0.8567, 0.6412, 0.7756, 0.8323] +2026-04-14 14:36:32.216191: Epoch time: 102.29 s +2026-04-14 14:36:33.767283: +2026-04-14 14:36:33.769436: Epoch 3420 +2026-04-14 14:36:33.771695: Current learning rate: 0.00176 +2026-04-14 14:38:15.857205: train_loss -0.4194 +2026-04-14 14:38:15.863738: val_loss -0.2845 +2026-04-14 14:38:15.865889: Pseudo dice [0.4845, 0.5857, 0.6916, 0.8807, 0.5202, 0.166, 0.8303] +2026-04-14 14:38:15.868304: Epoch time: 102.09 s +2026-04-14 14:38:17.422938: +2026-04-14 14:38:17.424736: Epoch 3421 +2026-04-14 14:38:17.426725: Current learning rate: 0.00176 +2026-04-14 14:40:00.186985: train_loss -0.4335 +2026-04-14 14:40:00.193110: val_loss -0.3436 +2026-04-14 14:40:00.196761: Pseudo dice [0.2995, 0.5628, 0.7281, 0.8333, 0.6406, 0.2527, 0.8812] +2026-04-14 14:40:00.199327: Epoch time: 102.77 s +2026-04-14 14:40:01.786457: +2026-04-14 14:40:01.788115: Epoch 3422 +2026-04-14 14:40:01.790377: Current learning rate: 0.00175 +2026-04-14 14:41:43.836823: train_loss -0.417 +2026-04-14 14:41:43.849727: val_loss -0.3308 +2026-04-14 14:41:43.852808: Pseudo dice [0.6357, 0.3546, 0.6778, 0.7906, 0.3649, 0.233, 0.9154] +2026-04-14 14:41:43.855741: Epoch time: 102.05 s +2026-04-14 14:41:45.496064: +2026-04-14 14:41:45.497782: Epoch 3423 +2026-04-14 14:41:45.500156: Current learning rate: 0.00175 +2026-04-14 14:43:27.993863: train_loss -0.4094 +2026-04-14 14:43:27.998665: val_loss -0.3196 +2026-04-14 14:43:28.000557: Pseudo dice [0.4209, 0.4397, 0.6438, 0.706, 0.4173, 0.4313, 0.8831] +2026-04-14 14:43:28.002937: Epoch time: 102.5 s +2026-04-14 14:43:29.646500: +2026-04-14 14:43:29.648995: Epoch 3424 +2026-04-14 14:43:29.650811: Current learning rate: 0.00175 +2026-04-14 14:45:12.313004: train_loss -0.4061 +2026-04-14 14:45:12.318564: val_loss -0.3522 +2026-04-14 14:45:12.320678: Pseudo dice [0.8282, 0.8079, 0.7041, 0.8441, 0.5169, 0.683, 0.9174] +2026-04-14 14:45:12.324460: Epoch time: 102.67 s +2026-04-14 14:45:13.960939: +2026-04-14 14:45:13.962686: Epoch 3425 +2026-04-14 14:45:13.964484: Current learning rate: 0.00175 +2026-04-14 14:46:56.141327: train_loss -0.4057 +2026-04-14 14:46:56.146992: val_loss -0.3466 +2026-04-14 14:46:56.148767: Pseudo dice [0.5281, 0.7481, 0.682, 0.8321, 0.5139, 0.2561, 0.5935] +2026-04-14 14:46:56.151263: Epoch time: 102.18 s +2026-04-14 14:46:57.698330: +2026-04-14 14:46:57.701389: Epoch 3426 +2026-04-14 14:46:57.703360: Current learning rate: 0.00174 +2026-04-14 14:48:40.623379: train_loss -0.4092 +2026-04-14 14:48:40.630792: val_loss -0.3355 +2026-04-14 14:48:40.634285: Pseudo dice [0.6985, 0.1614, 0.6714, 0.251, 0.5131, 0.2474, 0.3648] +2026-04-14 14:48:40.637915: Epoch time: 102.93 s +2026-04-14 14:48:42.285595: +2026-04-14 14:48:42.287390: Epoch 3427 +2026-04-14 14:48:42.288919: Current learning rate: 0.00174 +2026-04-14 14:50:24.590686: train_loss -0.4173 +2026-04-14 14:50:24.597172: val_loss -0.2772 +2026-04-14 14:50:24.599165: Pseudo dice [0.6024, 0.4315, 0.7873, 0.8253, 0.641, 0.1554, 0.8451] +2026-04-14 14:50:24.601300: Epoch time: 102.31 s +2026-04-14 14:50:26.186915: +2026-04-14 14:50:26.189672: Epoch 3428 +2026-04-14 14:50:26.191357: Current learning rate: 0.00174 +2026-04-14 14:52:08.906262: train_loss -0.4198 +2026-04-14 14:52:08.923557: val_loss -0.271 +2026-04-14 14:52:08.928969: Pseudo dice [0.8447, 0.3652, 0.715, 0.7892, 0.4605, 0.2299, 0.7769] +2026-04-14 14:52:08.934027: Epoch time: 102.72 s +2026-04-14 14:52:10.541655: +2026-04-14 14:52:10.544653: Epoch 3429 +2026-04-14 14:52:10.546400: Current learning rate: 0.00173 +2026-04-14 14:53:53.242213: train_loss -0.4154 +2026-04-14 14:53:53.249280: val_loss -0.2753 +2026-04-14 14:53:53.251522: Pseudo dice [0.6579, 0.6675, 0.5316, 0.8856, 0.6296, 0.1106, 0.8691] +2026-04-14 14:53:53.254508: Epoch time: 102.7 s +2026-04-14 14:53:54.862535: +2026-04-14 14:53:54.865030: Epoch 3430 +2026-04-14 14:53:54.866916: Current learning rate: 0.00173 +2026-04-14 14:55:37.000645: train_loss -0.427 +2026-04-14 14:55:37.006780: val_loss -0.3245 +2026-04-14 14:55:37.008786: Pseudo dice [0.3531, 0.3545, 0.7682, 0.5262, 0.4915, 0.3959, 0.8753] +2026-04-14 14:55:37.011110: Epoch time: 102.14 s +2026-04-14 14:55:38.598615: +2026-04-14 14:55:38.600923: Epoch 3431 +2026-04-14 14:55:38.602366: Current learning rate: 0.00173 +2026-04-14 14:57:21.172824: train_loss -0.4231 +2026-04-14 14:57:21.179022: val_loss -0.3526 +2026-04-14 14:57:21.181613: Pseudo dice [0.3295, 0.3403, 0.5847, 0.1877, 0.6633, 0.8733, 0.8103] +2026-04-14 14:57:21.184382: Epoch time: 102.58 s +2026-04-14 14:57:22.778555: +2026-04-14 14:57:22.780372: Epoch 3432 +2026-04-14 14:57:22.782378: Current learning rate: 0.00173 +2026-04-14 14:59:04.771039: train_loss -0.4268 +2026-04-14 14:59:04.776014: val_loss -0.3018 +2026-04-14 14:59:04.788078: Pseudo dice [0.6454, 0.3473, 0.7239, 0.8453, 0.6729, 0.1009, 0.945] +2026-04-14 14:59:04.791854: Epoch time: 102.0 s +2026-04-14 14:59:06.343697: +2026-04-14 14:59:06.346483: Epoch 3433 +2026-04-14 14:59:06.348274: Current learning rate: 0.00172 +2026-04-14 15:00:48.381085: train_loss -0.4184 +2026-04-14 15:00:48.387034: val_loss -0.3918 +2026-04-14 15:00:48.388995: Pseudo dice [0.6508, 0.6977, 0.8392, 0.2205, 0.622, 0.835, 0.8066] +2026-04-14 15:00:48.391837: Epoch time: 102.04 s +2026-04-14 15:00:49.948621: +2026-04-14 15:00:49.950499: Epoch 3434 +2026-04-14 15:00:49.952056: Current learning rate: 0.00172 +2026-04-14 15:02:32.032557: train_loss -0.4178 +2026-04-14 15:02:32.037180: val_loss -0.3494 +2026-04-14 15:02:32.038751: Pseudo dice [0.7282, 0.3758, 0.618, 0.7674, 0.4992, 0.8183, 0.901] +2026-04-14 15:02:32.041100: Epoch time: 102.09 s +2026-04-14 15:02:34.597066: +2026-04-14 15:02:34.598938: Epoch 3435 +2026-04-14 15:02:34.600703: Current learning rate: 0.00172 +2026-04-14 15:04:16.950614: train_loss -0.4205 +2026-04-14 15:04:16.956348: val_loss -0.3442 +2026-04-14 15:04:16.958168: Pseudo dice [0.6507, 0.2799, 0.719, 0.7907, 0.5023, 0.8606, 0.8782] +2026-04-14 15:04:16.960893: Epoch time: 102.36 s +2026-04-14 15:04:18.550741: +2026-04-14 15:04:18.552381: Epoch 3436 +2026-04-14 15:04:18.554094: Current learning rate: 0.00172 +2026-04-14 15:06:01.358621: train_loss -0.4323 +2026-04-14 15:06:01.364407: val_loss -0.3154 +2026-04-14 15:06:01.366701: Pseudo dice [0.8225, 0.2112, 0.6694, 0.6379, 0.4167, 0.0606, 0.8604] +2026-04-14 15:06:01.369758: Epoch time: 102.81 s +2026-04-14 15:06:02.928030: +2026-04-14 15:06:02.929998: Epoch 3437 +2026-04-14 15:06:02.932067: Current learning rate: 0.00171 +2026-04-14 15:07:45.181783: train_loss -0.4406 +2026-04-14 15:07:45.187044: val_loss -0.3263 +2026-04-14 15:07:45.189069: Pseudo dice [0.5853, 0.4706, 0.7283, 0.871, 0.3819, 0.0865, 0.8034] +2026-04-14 15:07:45.191257: Epoch time: 102.26 s +2026-04-14 15:07:46.775039: +2026-04-14 15:07:46.776726: Epoch 3438 +2026-04-14 15:07:46.778709: Current learning rate: 0.00171 +2026-04-14 15:09:28.989738: train_loss -0.4204 +2026-04-14 15:09:28.995536: val_loss -0.3063 +2026-04-14 15:09:28.998463: Pseudo dice [0.7582, 0.4311, 0.6438, 0.4742, 0.6711, 0.0404, 0.8739] +2026-04-14 15:09:29.001046: Epoch time: 102.22 s +2026-04-14 15:09:30.656409: +2026-04-14 15:09:30.658630: Epoch 3439 +2026-04-14 15:09:30.660859: Current learning rate: 0.00171 +2026-04-14 15:11:12.846804: train_loss -0.4215 +2026-04-14 15:11:12.852545: val_loss -0.3141 +2026-04-14 15:11:12.854388: Pseudo dice [0.7729, 0.671, 0.6806, 0.8904, 0.552, 0.1107, 0.8055] +2026-04-14 15:11:12.856596: Epoch time: 102.19 s +2026-04-14 15:11:14.442347: +2026-04-14 15:11:14.444419: Epoch 3440 +2026-04-14 15:11:14.446435: Current learning rate: 0.0017 +2026-04-14 15:12:57.256172: train_loss -0.425 +2026-04-14 15:12:57.261596: val_loss -0.2907 +2026-04-14 15:12:57.263700: Pseudo dice [0.4875, 0.5236, 0.5814, 0.768, 0.6583, 0.0998, 0.7767] +2026-04-14 15:12:57.266706: Epoch time: 102.82 s +2026-04-14 15:12:58.891466: +2026-04-14 15:12:58.893571: Epoch 3441 +2026-04-14 15:12:58.895317: Current learning rate: 0.0017 +2026-04-14 15:14:41.002737: train_loss -0.4287 +2026-04-14 15:14:41.008202: val_loss -0.2768 +2026-04-14 15:14:41.010277: Pseudo dice [0.3547, 0.6533, 0.4432, 0.8252, 0.4398, 0.0666, 0.6331] +2026-04-14 15:14:41.012779: Epoch time: 102.12 s +2026-04-14 15:14:42.577120: +2026-04-14 15:14:42.578755: Epoch 3442 +2026-04-14 15:14:42.580775: Current learning rate: 0.0017 +2026-04-14 15:16:25.154566: train_loss -0.4224 +2026-04-14 15:16:25.159431: val_loss -0.2858 +2026-04-14 15:16:25.161388: Pseudo dice [0.4549, 0.7686, 0.7198, 0.7895, 0.5035, 0.2111, 0.8434] +2026-04-14 15:16:25.163671: Epoch time: 102.58 s +2026-04-14 15:16:26.711542: +2026-04-14 15:16:26.713299: Epoch 3443 +2026-04-14 15:16:26.714814: Current learning rate: 0.0017 +2026-04-14 15:18:08.941024: train_loss -0.4067 +2026-04-14 15:18:08.948487: val_loss -0.2648 +2026-04-14 15:18:08.951185: Pseudo dice [0.3378, 0.606, 0.7295, 0.2061, 0.6023, 0.1138, 0.6787] +2026-04-14 15:18:08.953858: Epoch time: 102.23 s +2026-04-14 15:18:10.536765: +2026-04-14 15:18:10.539079: Epoch 3444 +2026-04-14 15:18:10.540759: Current learning rate: 0.00169 +2026-04-14 15:19:52.555038: train_loss -0.4127 +2026-04-14 15:19:52.562767: val_loss -0.2646 +2026-04-14 15:19:52.564766: Pseudo dice [0.5331, 0.7697, 0.6667, 0.3277, 0.4693, 0.1114, 0.5897] +2026-04-14 15:19:52.567668: Epoch time: 102.02 s +2026-04-14 15:19:54.137787: +2026-04-14 15:19:54.139748: Epoch 3445 +2026-04-14 15:19:54.141463: Current learning rate: 0.00169 +2026-04-14 15:21:36.865535: train_loss -0.4067 +2026-04-14 15:21:36.871527: val_loss -0.3556 +2026-04-14 15:21:36.873871: Pseudo dice [0.6051, 0.6714, 0.7085, 0.8122, 0.6438, 0.1895, 0.8915] +2026-04-14 15:21:36.876047: Epoch time: 102.73 s +2026-04-14 15:21:38.474631: +2026-04-14 15:21:38.476829: Epoch 3446 +2026-04-14 15:21:38.478456: Current learning rate: 0.00169 +2026-04-14 15:23:20.513079: train_loss -0.4197 +2026-04-14 15:23:20.518677: val_loss -0.3414 +2026-04-14 15:23:20.520640: Pseudo dice [0.4764, 0.5114, 0.7768, 0.6046, 0.3485, 0.7557, 0.6884] +2026-04-14 15:23:20.522604: Epoch time: 102.04 s +2026-04-14 15:23:22.087052: +2026-04-14 15:23:22.089199: Epoch 3447 +2026-04-14 15:23:22.090708: Current learning rate: 0.00168 +2026-04-14 15:25:04.135633: train_loss -0.4042 +2026-04-14 15:25:04.141248: val_loss -0.3737 +2026-04-14 15:25:04.144414: Pseudo dice [0.337, 0.3499, 0.7331, 0.5899, 0.6734, 0.6947, 0.8841] +2026-04-14 15:25:04.146928: Epoch time: 102.05 s +2026-04-14 15:25:05.726933: +2026-04-14 15:25:05.728823: Epoch 3448 +2026-04-14 15:25:05.730989: Current learning rate: 0.00168 +2026-04-14 15:26:47.705730: train_loss -0.4256 +2026-04-14 15:26:47.711246: val_loss -0.2667 +2026-04-14 15:26:47.714382: Pseudo dice [0.8136, 0.3865, 0.6747, 0.832, 0.573, 0.1234, 0.796] +2026-04-14 15:26:47.716504: Epoch time: 101.98 s +2026-04-14 15:26:49.258719: +2026-04-14 15:26:49.260856: Epoch 3449 +2026-04-14 15:26:49.263183: Current learning rate: 0.00168 +2026-04-14 15:28:33.503824: train_loss -0.4205 +2026-04-14 15:28:33.510768: val_loss -0.3462 +2026-04-14 15:28:33.512760: Pseudo dice [0.5775, 0.4947, 0.8236, 0.7365, 0.2822, 0.7301, 0.7354] +2026-04-14 15:28:33.515259: Epoch time: 104.25 s +2026-04-14 15:28:37.262318: +2026-04-14 15:28:37.263992: Epoch 3450 +2026-04-14 15:28:37.265533: Current learning rate: 0.00168 +2026-04-14 15:30:19.174537: train_loss -0.4351 +2026-04-14 15:30:19.181950: val_loss -0.2938 +2026-04-14 15:30:19.183859: Pseudo dice [0.8874, 0.3022, 0.6059, 0.3772, 0.6356, 0.2205, 0.826] +2026-04-14 15:30:19.186319: Epoch time: 101.92 s +2026-04-14 15:30:20.761307: +2026-04-14 15:30:20.762993: Epoch 3451 +2026-04-14 15:30:20.765288: Current learning rate: 0.00167 +2026-04-14 15:32:03.569178: train_loss -0.426 +2026-04-14 15:32:03.575020: val_loss -0.355 +2026-04-14 15:32:03.584836: Pseudo dice [0.3991, 0.5034, 0.7368, 0.8423, 0.4168, 0.4415, 0.6421] +2026-04-14 15:32:03.588199: Epoch time: 102.81 s +2026-04-14 15:32:05.324108: +2026-04-14 15:32:05.326398: Epoch 3452 +2026-04-14 15:32:05.328563: Current learning rate: 0.00167 +2026-04-14 15:33:48.531918: train_loss -0.4281 +2026-04-14 15:33:48.538059: val_loss -0.3345 +2026-04-14 15:33:48.541059: Pseudo dice [0.4926, 0.8356, 0.7713, 0.6303, 0.4606, 0.1818, 0.8681] +2026-04-14 15:33:48.544092: Epoch time: 103.21 s +2026-04-14 15:33:50.122573: +2026-04-14 15:33:50.124421: Epoch 3453 +2026-04-14 15:33:50.126774: Current learning rate: 0.00167 +2026-04-14 15:35:32.252994: train_loss -0.4286 +2026-04-14 15:35:32.278002: val_loss -0.3864 +2026-04-14 15:35:32.279921: Pseudo dice [0.7706, 0.7485, 0.8406, 0.775, 0.6087, 0.845, 0.9128] +2026-04-14 15:35:32.281785: Epoch time: 102.13 s +2026-04-14 15:35:34.078302: +2026-04-14 15:35:34.080058: Epoch 3454 +2026-04-14 15:35:34.081735: Current learning rate: 0.00167 +2026-04-14 15:37:16.560998: train_loss -0.4209 +2026-04-14 15:37:16.567122: val_loss -0.3192 +2026-04-14 15:37:16.569211: Pseudo dice [0.6371, 0.5928, 0.7423, 0.4729, 0.3816, 0.6956, 0.7225] +2026-04-14 15:37:16.571482: Epoch time: 102.49 s +2026-04-14 15:37:19.214986: +2026-04-14 15:37:19.216854: Epoch 3455 +2026-04-14 15:37:19.218518: Current learning rate: 0.00166 +2026-04-14 15:39:02.026796: train_loss -0.4053 +2026-04-14 15:39:02.032411: val_loss -0.2787 +2026-04-14 15:39:02.034304: Pseudo dice [0.8315, 0.4835, 0.4646, 0.7824, 0.6036, 0.2457, 0.8311] +2026-04-14 15:39:02.037542: Epoch time: 102.82 s +2026-04-14 15:39:03.620613: +2026-04-14 15:39:03.622747: Epoch 3456 +2026-04-14 15:39:03.624625: Current learning rate: 0.00166 +2026-04-14 15:40:46.314510: train_loss -0.4074 +2026-04-14 15:40:46.319940: val_loss -0.3238 +2026-04-14 15:40:46.321812: Pseudo dice [0.3223, 0.227, 0.7149, 0.7658, 0.4469, 0.78, 0.6834] +2026-04-14 15:40:46.324488: Epoch time: 102.7 s +2026-04-14 15:40:47.857885: +2026-04-14 15:40:47.859613: Epoch 3457 +2026-04-14 15:40:47.861895: Current learning rate: 0.00166 +2026-04-14 15:42:30.089562: train_loss -0.4109 +2026-04-14 15:42:30.096512: val_loss -0.2685 +2026-04-14 15:42:30.099100: Pseudo dice [0.7424, 0.377, 0.6232, 0.312, 0.4938, 0.0292, 0.6743] +2026-04-14 15:42:30.104636: Epoch time: 102.24 s +2026-04-14 15:42:31.684555: +2026-04-14 15:42:31.686742: Epoch 3458 +2026-04-14 15:42:31.688384: Current learning rate: 0.00165 +2026-04-14 15:44:13.880598: train_loss -0.4193 +2026-04-14 15:44:13.885745: val_loss -0.3561 +2026-04-14 15:44:13.887892: Pseudo dice [0.6534, 0.3643, 0.751, 0.6789, 0.6157, 0.5034, 0.7569] +2026-04-14 15:44:13.891000: Epoch time: 102.2 s +2026-04-14 15:44:15.549845: +2026-04-14 15:44:15.551768: Epoch 3459 +2026-04-14 15:44:15.553623: Current learning rate: 0.00165 +2026-04-14 15:45:57.850855: train_loss -0.4255 +2026-04-14 15:45:57.856673: val_loss -0.3313 +2026-04-14 15:45:57.858572: Pseudo dice [0.5289, 0.3984, 0.6271, 0.7982, 0.3122, 0.8241, 0.5444] +2026-04-14 15:45:57.860902: Epoch time: 102.31 s +2026-04-14 15:45:59.491506: +2026-04-14 15:45:59.493644: Epoch 3460 +2026-04-14 15:45:59.495250: Current learning rate: 0.00165 +2026-04-14 15:47:41.649443: train_loss -0.4294 +2026-04-14 15:47:41.655151: val_loss -0.3487 +2026-04-14 15:47:41.657087: Pseudo dice [0.5953, 0.714, 0.6994, 0.3747, 0.3741, 0.6709, 0.6899] +2026-04-14 15:47:41.659546: Epoch time: 102.16 s +2026-04-14 15:47:43.268809: +2026-04-14 15:47:43.271483: Epoch 3461 +2026-04-14 15:47:43.273863: Current learning rate: 0.00165 +2026-04-14 15:49:25.337010: train_loss -0.428 +2026-04-14 15:49:25.345846: val_loss -0.2904 +2026-04-14 15:49:25.349859: Pseudo dice [0.7215, 0.7, 0.6394, 0.7241, 0.0271, 0.1922, 0.8019] +2026-04-14 15:49:25.351910: Epoch time: 102.07 s +2026-04-14 15:49:26.949655: +2026-04-14 15:49:26.954904: Epoch 3462 +2026-04-14 15:49:26.962608: Current learning rate: 0.00164 +2026-04-14 15:51:09.073920: train_loss -0.4292 +2026-04-14 15:51:09.079864: val_loss -0.3906 +2026-04-14 15:51:09.081960: Pseudo dice [0.7514, 0.3734, 0.7147, 0.7982, 0.5778, 0.8331, 0.7056] +2026-04-14 15:51:09.084928: Epoch time: 102.13 s +2026-04-14 15:51:10.699757: +2026-04-14 15:51:10.702243: Epoch 3463 +2026-04-14 15:51:10.703932: Current learning rate: 0.00164 +2026-04-14 15:52:53.509400: train_loss -0.4205 +2026-04-14 15:52:53.517054: val_loss -0.3035 +2026-04-14 15:52:53.520625: Pseudo dice [0.7999, 0.6214, 0.648, 0.789, 0.2821, 0.1464, 0.8359] +2026-04-14 15:52:53.524580: Epoch time: 102.81 s +2026-04-14 15:52:55.136613: +2026-04-14 15:52:55.138874: Epoch 3464 +2026-04-14 15:52:55.140901: Current learning rate: 0.00164 +2026-04-14 15:54:37.439422: train_loss -0.4238 +2026-04-14 15:54:37.445179: val_loss -0.3108 +2026-04-14 15:54:37.446929: Pseudo dice [0.6642, 0.3085, 0.7496, 0.4777, 0.7152, 0.177, 0.8587] +2026-04-14 15:54:37.449323: Epoch time: 102.31 s +2026-04-14 15:54:39.083705: +2026-04-14 15:54:39.085718: Epoch 3465 +2026-04-14 15:54:39.087404: Current learning rate: 0.00164 +2026-04-14 15:56:21.558246: train_loss -0.4227 +2026-04-14 15:56:21.565559: val_loss -0.3614 +2026-04-14 15:56:21.567536: Pseudo dice [0.5802, 0.7845, 0.7547, 0.7638, 0.7599, 0.7936, 0.6931] +2026-04-14 15:56:21.570000: Epoch time: 102.48 s +2026-04-14 15:56:23.191211: +2026-04-14 15:56:23.193967: Epoch 3466 +2026-04-14 15:56:23.195630: Current learning rate: 0.00163 +2026-04-14 15:58:05.175504: train_loss -0.4192 +2026-04-14 15:58:05.182874: val_loss -0.373 +2026-04-14 15:58:05.184948: Pseudo dice [0.7027, 0.4086, 0.6303, 0.8266, 0.6067, 0.8169, 0.8764] +2026-04-14 15:58:05.187776: Epoch time: 101.99 s +2026-04-14 15:58:06.860152: +2026-04-14 15:58:06.862015: Epoch 3467 +2026-04-14 15:58:06.864090: Current learning rate: 0.00163 +2026-04-14 15:59:48.888710: train_loss -0.4255 +2026-04-14 15:59:48.894874: val_loss -0.3343 +2026-04-14 15:59:48.898037: Pseudo dice [0.4246, 0.2998, 0.7794, 0.8632, 0.2764, 0.7604, 0.7601] +2026-04-14 15:59:48.900492: Epoch time: 102.03 s +2026-04-14 15:59:50.486305: +2026-04-14 15:59:50.490504: Epoch 3468 +2026-04-14 15:59:50.493287: Current learning rate: 0.00163 +2026-04-14 16:01:32.434636: train_loss -0.4191 +2026-04-14 16:01:32.440043: val_loss -0.3138 +2026-04-14 16:01:32.442327: Pseudo dice [0.5206, 0.8678, 0.6503, 0.6569, 0.5399, 0.351, 0.7361] +2026-04-14 16:01:32.446679: Epoch time: 101.95 s +2026-04-14 16:01:34.040040: +2026-04-14 16:01:34.042301: Epoch 3469 +2026-04-14 16:01:34.044526: Current learning rate: 0.00162 +2026-04-14 16:03:18.038476: train_loss -0.4206 +2026-04-14 16:03:18.045451: val_loss -0.34 +2026-04-14 16:03:18.047763: Pseudo dice [0.4221, 0.3816, 0.7642, 0.8799, 0.6136, 0.4961, 0.8534] +2026-04-14 16:03:18.049903: Epoch time: 104.0 s +2026-04-14 16:03:19.673622: +2026-04-14 16:03:19.675673: Epoch 3470 +2026-04-14 16:03:19.677883: Current learning rate: 0.00162 +2026-04-14 16:05:02.017590: train_loss -0.4205 +2026-04-14 16:05:02.022812: val_loss -0.3145 +2026-04-14 16:05:02.024869: Pseudo dice [0.5684, 0.5645, 0.4608, 0.8346, 0.5342, 0.1517, 0.6192] +2026-04-14 16:05:02.027301: Epoch time: 102.35 s +2026-04-14 16:05:03.603001: +2026-04-14 16:05:03.604810: Epoch 3471 +2026-04-14 16:05:03.606309: Current learning rate: 0.00162 +2026-04-14 16:06:45.673927: train_loss -0.4211 +2026-04-14 16:06:45.679116: val_loss -0.2553 +2026-04-14 16:06:45.681103: Pseudo dice [0.7802, 0.2415, 0.6401, 0.7718, 0.3469, 0.2411, 0.8831] +2026-04-14 16:06:45.683470: Epoch time: 102.07 s +2026-04-14 16:06:47.233009: +2026-04-14 16:06:47.234702: Epoch 3472 +2026-04-14 16:06:47.236259: Current learning rate: 0.00162 +2026-04-14 16:08:29.802551: train_loss -0.418 +2026-04-14 16:08:29.808720: val_loss -0.342 +2026-04-14 16:08:29.812054: Pseudo dice [0.401, 0.2723, 0.5993, 0.79, 0.7035, 0.4819, 0.9138] +2026-04-14 16:08:29.815141: Epoch time: 102.57 s +2026-04-14 16:08:31.642891: +2026-04-14 16:08:31.644908: Epoch 3473 +2026-04-14 16:08:31.646484: Current learning rate: 0.00161 +2026-04-14 16:10:14.148384: train_loss -0.4361 +2026-04-14 16:10:14.156674: val_loss -0.3179 +2026-04-14 16:10:14.160041: Pseudo dice [0.6259, 0.313, 0.6926, 0.6872, 0.5191, 0.1348, 0.2646] +2026-04-14 16:10:14.164290: Epoch time: 102.51 s +2026-04-14 16:10:15.755050: +2026-04-14 16:10:15.756865: Epoch 3474 +2026-04-14 16:10:15.758612: Current learning rate: 0.00161 +2026-04-14 16:11:58.369409: train_loss -0.4249 +2026-04-14 16:11:58.374303: val_loss -0.3305 +2026-04-14 16:11:58.376292: Pseudo dice [0.6226, 0.8257, 0.8038, 0.7622, 0.3333, 0.2758, 0.6322] +2026-04-14 16:11:58.378363: Epoch time: 102.62 s +2026-04-14 16:12:01.157173: +2026-04-14 16:12:01.159369: Epoch 3475 +2026-04-14 16:12:01.161592: Current learning rate: 0.00161 +2026-04-14 16:13:43.175687: train_loss -0.4229 +2026-04-14 16:13:43.182046: val_loss -0.3064 +2026-04-14 16:13:43.188756: Pseudo dice [0.5873, 0.7885, 0.6698, 0.7457, 0.5062, 0.2727, 0.8411] +2026-04-14 16:13:43.193940: Epoch time: 102.02 s +2026-04-14 16:13:44.759126: +2026-04-14 16:13:44.761493: Epoch 3476 +2026-04-14 16:13:44.763856: Current learning rate: 0.00161 +2026-04-14 16:15:27.933171: train_loss -0.4265 +2026-04-14 16:15:27.941424: val_loss -0.373 +2026-04-14 16:15:27.944052: Pseudo dice [0.7843, 0.3617, 0.7608, 0.8409, 0.4053, 0.6721, 0.8001] +2026-04-14 16:15:27.946612: Epoch time: 103.18 s +2026-04-14 16:15:29.562234: +2026-04-14 16:15:29.564471: Epoch 3477 +2026-04-14 16:15:29.566434: Current learning rate: 0.0016 +2026-04-14 16:17:11.414104: train_loss -0.4175 +2026-04-14 16:17:11.419489: val_loss -0.3334 +2026-04-14 16:17:11.422127: Pseudo dice [0.472, 0.2629, 0.7239, 0.7636, 0.602, 0.6187, 0.681] +2026-04-14 16:17:11.424518: Epoch time: 101.86 s +2026-04-14 16:17:12.976095: +2026-04-14 16:17:12.977868: Epoch 3478 +2026-04-14 16:17:12.979804: Current learning rate: 0.0016 +2026-04-14 16:18:54.674423: train_loss -0.4153 +2026-04-14 16:18:54.679608: val_loss -0.3336 +2026-04-14 16:18:54.681894: Pseudo dice [0.8399, 0.5986, 0.6175, 0.208, 0.5206, 0.5418, 0.7523] +2026-04-14 16:18:54.684355: Epoch time: 101.7 s +2026-04-14 16:18:56.232240: +2026-04-14 16:18:56.234001: Epoch 3479 +2026-04-14 16:18:56.235570: Current learning rate: 0.0016 +2026-04-14 16:20:38.344111: train_loss -0.4171 +2026-04-14 16:20:38.361047: val_loss -0.2657 +2026-04-14 16:20:38.367030: Pseudo dice [0.8232, 0.2877, 0.6544, 0.9043, 0.5041, 0.2373, 0.8968] +2026-04-14 16:20:38.372644: Epoch time: 102.12 s +2026-04-14 16:20:39.945488: +2026-04-14 16:20:39.947374: Epoch 3480 +2026-04-14 16:20:39.949286: Current learning rate: 0.00159 +2026-04-14 16:22:22.364772: train_loss -0.4157 +2026-04-14 16:22:22.369604: val_loss -0.3238 +2026-04-14 16:22:22.371782: Pseudo dice [0.5723, 0.395, 0.6805, 0.4224, 0.5156, 0.6099, 0.866] +2026-04-14 16:22:22.374158: Epoch time: 102.42 s +2026-04-14 16:22:23.937440: +2026-04-14 16:22:23.939227: Epoch 3481 +2026-04-14 16:22:23.941501: Current learning rate: 0.00159 +2026-04-14 16:24:06.390763: train_loss -0.4188 +2026-04-14 16:24:06.397949: val_loss -0.2917 +2026-04-14 16:24:06.399999: Pseudo dice [0.759, 0.683, 0.8251, 0.6947, 0.6701, 0.1051, 0.8533] +2026-04-14 16:24:06.402640: Epoch time: 102.46 s +2026-04-14 16:24:07.976464: +2026-04-14 16:24:07.978255: Epoch 3482 +2026-04-14 16:24:07.979827: Current learning rate: 0.00159 +2026-04-14 16:25:50.411170: train_loss -0.4393 +2026-04-14 16:25:50.416725: val_loss -0.2744 +2026-04-14 16:25:50.419655: Pseudo dice [0.6614, 0.8291, 0.5228, 0.4171, 0.6235, 0.0273, 0.7367] +2026-04-14 16:25:50.422117: Epoch time: 102.44 s +2026-04-14 16:25:51.997581: +2026-04-14 16:25:52.008955: Epoch 3483 +2026-04-14 16:25:52.010910: Current learning rate: 0.00159 +2026-04-14 16:27:33.840456: train_loss -0.4594 +2026-04-14 16:27:33.847660: val_loss -0.4061 +2026-04-14 16:27:33.849744: Pseudo dice [0.6012, 0.3418, 0.7336, 0.1193, 0.6018, 0.8798, 0.8252] +2026-04-14 16:27:33.852810: Epoch time: 101.85 s +2026-04-14 16:27:35.427018: +2026-04-14 16:27:35.429698: Epoch 3484 +2026-04-14 16:27:35.431646: Current learning rate: 0.00158 +2026-04-14 16:29:17.528469: train_loss -0.4685 +2026-04-14 16:29:17.535257: val_loss -0.4118 +2026-04-14 16:29:17.537131: Pseudo dice [0.7151, 0.4696, 0.7242, 0.6696, 0.62, 0.1771, 0.6879] +2026-04-14 16:29:17.539397: Epoch time: 102.11 s +2026-04-14 16:29:19.088204: +2026-04-14 16:29:19.090189: Epoch 3485 +2026-04-14 16:29:19.091958: Current learning rate: 0.00158 +2026-04-14 16:31:01.428050: train_loss -0.483 +2026-04-14 16:31:01.433613: val_loss -0.4404 +2026-04-14 16:31:01.436006: Pseudo dice [0.7451, 0.7461, 0.7862, 0.7287, 0.628, 0.8597, 0.8718] +2026-04-14 16:31:01.438701: Epoch time: 102.34 s +2026-04-14 16:31:02.988627: +2026-04-14 16:31:02.990957: Epoch 3486 +2026-04-14 16:31:02.992796: Current learning rate: 0.00158 +2026-04-14 16:32:45.505172: train_loss -0.4985 +2026-04-14 16:32:45.511292: val_loss -0.4309 +2026-04-14 16:32:45.513361: Pseudo dice [0.3692, 0.6286, 0.8053, 0.8291, 0.5461, 0.7848, 0.8828] +2026-04-14 16:32:45.515687: Epoch time: 102.52 s +2026-04-14 16:32:47.087204: +2026-04-14 16:32:47.088823: Epoch 3487 +2026-04-14 16:32:47.090788: Current learning rate: 0.00157 +2026-04-14 16:34:28.786358: train_loss -0.4986 +2026-04-14 16:34:28.792683: val_loss -0.4204 +2026-04-14 16:34:28.794666: Pseudo dice [0.7899, 0.4678, 0.8501, 0.8354, 0.4735, 0.8528, 0.6377] +2026-04-14 16:34:28.797304: Epoch time: 101.7 s +2026-04-14 16:34:28.799315: Yayy! New best EMA pseudo Dice: 0.6279 +2026-04-14 16:34:32.437555: +2026-04-14 16:34:32.439755: Epoch 3488 +2026-04-14 16:34:32.442536: Current learning rate: 0.00157 +2026-04-14 16:36:14.777624: train_loss -0.5024 +2026-04-14 16:36:14.783718: val_loss -0.3866 +2026-04-14 16:36:14.786060: Pseudo dice [0.3137, 0.8444, 0.3788, 0.2472, 0.2531, 0.6555, 0.8775] +2026-04-14 16:36:14.788613: Epoch time: 102.34 s +2026-04-14 16:36:16.362597: +2026-04-14 16:36:16.364317: Epoch 3489 +2026-04-14 16:36:16.366383: Current learning rate: 0.00157 +2026-04-14 16:37:59.101017: train_loss -0.4886 +2026-04-14 16:37:59.107700: val_loss -0.4227 +2026-04-14 16:37:59.111265: Pseudo dice [0.8196, 0.1659, 0.857, 0.8526, 0.582, 0.2729, 0.9326] +2026-04-14 16:37:59.113950: Epoch time: 102.74 s +2026-04-14 16:38:00.695961: +2026-04-14 16:38:00.698171: Epoch 3490 +2026-04-14 16:38:00.699841: Current learning rate: 0.00157 +2026-04-14 16:39:43.626222: train_loss -0.4989 +2026-04-14 16:39:43.632104: val_loss -0.429 +2026-04-14 16:39:43.634232: Pseudo dice [0.5939, 0.32, 0.7734, 0.8749, 0.5601, 0.4984, 0.5121] +2026-04-14 16:39:43.637238: Epoch time: 102.93 s +2026-04-14 16:39:45.249309: +2026-04-14 16:39:45.250846: Epoch 3491 +2026-04-14 16:39:45.252583: Current learning rate: 0.00156 +2026-04-14 16:41:27.023915: train_loss -0.4987 +2026-04-14 16:41:27.029082: val_loss -0.3847 +2026-04-14 16:41:27.030804: Pseudo dice [0.5461, 0.5801, 0.7469, 0.8828, 0.4538, 0.1704, 0.8437] +2026-04-14 16:41:27.032961: Epoch time: 101.78 s +2026-04-14 16:41:28.575538: +2026-04-14 16:41:28.577391: Epoch 3492 +2026-04-14 16:41:28.579753: Current learning rate: 0.00156 +2026-04-14 16:43:10.406234: train_loss -0.5039 +2026-04-14 16:43:10.412570: val_loss -0.3283 +2026-04-14 16:43:10.414385: Pseudo dice [0.6704, 0.5874, 0.553, 0.634, 0.4854, 0.174, 0.8085] +2026-04-14 16:43:10.417913: Epoch time: 101.83 s +2026-04-14 16:43:12.002953: +2026-04-14 16:43:12.004960: Epoch 3493 +2026-04-14 16:43:12.007522: Current learning rate: 0.00156 +2026-04-14 16:44:53.808937: train_loss -0.4965 +2026-04-14 16:44:53.815156: val_loss -0.3697 +2026-04-14 16:44:53.817777: Pseudo dice [0.6791, 0.7148, 0.7392, 0.6326, 0.4896, 0.3831, 0.6604] +2026-04-14 16:44:53.820645: Epoch time: 101.81 s +2026-04-14 16:44:55.418860: +2026-04-14 16:44:55.420978: Epoch 3494 +2026-04-14 16:44:55.423149: Current learning rate: 0.00156 +2026-04-14 16:46:37.419585: train_loss -0.4961 +2026-04-14 16:46:37.427468: val_loss -0.3814 +2026-04-14 16:46:37.429573: Pseudo dice [0.8037, 0.6655, 0.5403, 0.7629, 0.4908, 0.1087, 0.8229] +2026-04-14 16:46:37.431891: Epoch time: 102.0 s +2026-04-14 16:46:40.186984: +2026-04-14 16:46:40.189385: Epoch 3495 +2026-04-14 16:46:40.191091: Current learning rate: 0.00155 +2026-04-14 16:48:22.008307: train_loss -0.4929 +2026-04-14 16:48:22.017169: val_loss -0.4293 +2026-04-14 16:48:22.020325: Pseudo dice [0.8105, 0.8226, 0.7204, 0.5997, 0.435, 0.825, 0.8536] +2026-04-14 16:48:22.022923: Epoch time: 101.83 s +2026-04-14 16:48:23.585204: +2026-04-14 16:48:23.587658: Epoch 3496 +2026-04-14 16:48:23.589585: Current learning rate: 0.00155 +2026-04-14 16:50:06.951314: train_loss -0.498 +2026-04-14 16:50:06.958304: val_loss -0.3504 +2026-04-14 16:50:06.960703: Pseudo dice [0.6623, 0.7127, 0.4978, 0.7518, 0.5574, 0.2428, 0.6917] +2026-04-14 16:50:06.963100: Epoch time: 103.37 s +2026-04-14 16:50:08.524398: +2026-04-14 16:50:08.526517: Epoch 3497 +2026-04-14 16:50:08.528285: Current learning rate: 0.00155 +2026-04-14 16:51:52.868548: train_loss -0.5154 +2026-04-14 16:51:52.874609: val_loss -0.4314 +2026-04-14 16:51:52.876773: Pseudo dice [0.6543, 0.7798, 0.7244, 0.4357, 0.5777, 0.4321, 0.8585] +2026-04-14 16:51:52.880113: Epoch time: 104.35 s +2026-04-14 16:51:54.435408: +2026-04-14 16:51:54.437142: Epoch 3498 +2026-04-14 16:51:54.439120: Current learning rate: 0.00154 +2026-04-14 16:53:36.605214: train_loss -0.5022 +2026-04-14 16:53:36.611535: val_loss -0.4059 +2026-04-14 16:53:36.613797: Pseudo dice [0.3199, 0.7797, 0.7587, 0.8311, 0.5409, 0.7483, 0.7653] +2026-04-14 16:53:36.616472: Epoch time: 102.17 s +2026-04-14 16:53:38.191633: +2026-04-14 16:53:38.195446: Epoch 3499 +2026-04-14 16:53:38.200243: Current learning rate: 0.00154 +2026-04-14 16:55:20.243356: train_loss -0.5108 +2026-04-14 16:55:20.252836: val_loss -0.4419 +2026-04-14 16:55:20.273052: Pseudo dice [0.7901, 0.2376, 0.7399, 0.874, 0.5235, 0.821, 0.8316] +2026-04-14 16:55:20.280764: Epoch time: 102.06 s +2026-04-14 16:55:22.449439: Yayy! New best EMA pseudo Dice: 0.6311 +2026-04-14 16:55:26.044642: +2026-04-14 16:55:26.046521: Epoch 3500 +2026-04-14 16:55:26.048424: Current learning rate: 0.00154 +2026-04-14 16:57:08.739259: train_loss -0.5 +2026-04-14 16:57:08.748347: val_loss -0.3378 +2026-04-14 16:57:08.759846: Pseudo dice [0.6033, 0.7376, 0.7019, 0.9212, 0.6224, 0.1693, 0.8132] +2026-04-14 16:57:08.766005: Epoch time: 102.7 s +2026-04-14 16:57:08.768440: Yayy! New best EMA pseudo Dice: 0.6333 +2026-04-14 16:57:12.409662: +2026-04-14 16:57:12.411901: Epoch 3501 +2026-04-14 16:57:12.413816: Current learning rate: 0.00154 +2026-04-14 16:58:54.937877: train_loss -0.4909 +2026-04-14 16:58:54.953213: val_loss -0.368 +2026-04-14 16:58:54.955841: Pseudo dice [0.7849, 0.572, 0.5993, 0.387, 0.3332, 0.1066, 0.8718] +2026-04-14 16:58:54.958403: Epoch time: 102.53 s +2026-04-14 16:58:56.518845: +2026-04-14 16:58:56.521066: Epoch 3502 +2026-04-14 16:58:56.522703: Current learning rate: 0.00153 +2026-04-14 17:00:38.475241: train_loss -0.5057 +2026-04-14 17:00:38.482266: val_loss -0.33 +2026-04-14 17:00:38.484412: Pseudo dice [0.5986, 0.6473, 0.8153, 0.6777, 0.4503, 0.1238, 0.5394] +2026-04-14 17:00:38.487051: Epoch time: 101.96 s +2026-04-14 17:00:40.024767: +2026-04-14 17:00:40.026647: Epoch 3503 +2026-04-14 17:00:40.028986: Current learning rate: 0.00153 +2026-04-14 17:02:22.114052: train_loss -0.4987 +2026-04-14 17:02:22.120188: val_loss -0.4178 +2026-04-14 17:02:22.122802: Pseudo dice [0.68, 0.5708, 0.6857, 0.8195, 0.4309, 0.6943, 0.634] +2026-04-14 17:02:22.125527: Epoch time: 102.09 s +2026-04-14 17:02:23.704338: +2026-04-14 17:02:23.706001: Epoch 3504 +2026-04-14 17:02:23.707558: Current learning rate: 0.00153 +2026-04-14 17:04:08.321206: train_loss -0.4914 +2026-04-14 17:04:08.326964: val_loss -0.3671 +2026-04-14 17:04:08.328888: Pseudo dice [0.4538, 0.4985, 0.7154, 0.6024, 0.5814, 0.1191, 0.7908] +2026-04-14 17:04:08.331854: Epoch time: 104.62 s +2026-04-14 17:04:09.882606: +2026-04-14 17:04:09.885039: Epoch 3505 +2026-04-14 17:04:09.887725: Current learning rate: 0.00153 +2026-04-14 17:05:51.864637: train_loss -0.4905 +2026-04-14 17:05:51.872537: val_loss -0.3443 +2026-04-14 17:05:51.874582: Pseudo dice [0.1894, 0.4855, 0.791, 0.8627, 0.3572, 0.1747, 0.7533] +2026-04-14 17:05:51.876826: Epoch time: 101.99 s +2026-04-14 17:05:53.403368: +2026-04-14 17:05:53.405223: Epoch 3506 +2026-04-14 17:05:53.407320: Current learning rate: 0.00152 +2026-04-14 17:07:35.409682: train_loss -0.4754 +2026-04-14 17:07:35.416811: val_loss -0.455 +2026-04-14 17:07:35.419193: Pseudo dice [0.5332, 0.6582, 0.8193, 0.4812, 0.655, 0.7721, 0.8221] +2026-04-14 17:07:35.421938: Epoch time: 102.01 s +2026-04-14 17:07:36.961962: +2026-04-14 17:07:36.963863: Epoch 3507 +2026-04-14 17:07:36.965808: Current learning rate: 0.00152 +2026-04-14 17:09:18.902102: train_loss -0.4992 +2026-04-14 17:09:18.912801: val_loss -0.3937 +2026-04-14 17:09:18.915508: Pseudo dice [0.3088, 0.7194, 0.6474, 0.7798, 0.5767, 0.1027, 0.6279] +2026-04-14 17:09:18.918381: Epoch time: 101.94 s +2026-04-14 17:09:20.505177: +2026-04-14 17:09:20.508037: Epoch 3508 +2026-04-14 17:09:20.510768: Current learning rate: 0.00152 +2026-04-14 17:11:04.099582: train_loss -0.5001 +2026-04-14 17:11:04.107625: val_loss -0.4262 +2026-04-14 17:11:04.109433: Pseudo dice [0.766, 0.627, 0.738, 0.7442, 0.564, 0.3776, 0.6103] +2026-04-14 17:11:04.112271: Epoch time: 103.6 s +2026-04-14 17:11:05.773548: +2026-04-14 17:11:05.775192: Epoch 3509 +2026-04-14 17:11:05.777054: Current learning rate: 0.00151 +2026-04-14 17:12:47.934736: train_loss -0.4881 +2026-04-14 17:12:47.941952: val_loss -0.4255 +2026-04-14 17:12:47.944351: Pseudo dice [0.4477, 0.7952, 0.7454, 0.8086, 0.461, 0.4775, 0.7135] +2026-04-14 17:12:47.947105: Epoch time: 102.16 s +2026-04-14 17:12:49.507882: +2026-04-14 17:12:49.510773: Epoch 3510 +2026-04-14 17:12:49.513033: Current learning rate: 0.00151 +2026-04-14 17:14:31.346123: train_loss -0.4979 +2026-04-14 17:14:31.354882: val_loss -0.4149 +2026-04-14 17:14:31.356867: Pseudo dice [0.5836, 0.3651, 0.7159, 0.7718, 0.5229, 0.8282, 0.5329] +2026-04-14 17:14:31.359771: Epoch time: 101.84 s +2026-04-14 17:14:32.934880: +2026-04-14 17:14:32.936551: Epoch 3511 +2026-04-14 17:14:32.939601: Current learning rate: 0.00151 +2026-04-14 17:16:15.004184: train_loss -0.5113 +2026-04-14 17:16:15.009260: val_loss -0.3855 +2026-04-14 17:16:15.011220: Pseudo dice [0.8721, 0.3129, 0.6791, 0.6818, 0.6941, 0.0598, 0.5967] +2026-04-14 17:16:15.013233: Epoch time: 102.07 s +2026-04-14 17:16:16.558457: +2026-04-14 17:16:16.560286: Epoch 3512 +2026-04-14 17:16:16.563288: Current learning rate: 0.00151 +2026-04-14 17:17:58.705465: train_loss -0.4877 +2026-04-14 17:17:58.711038: val_loss -0.4212 +2026-04-14 17:17:58.713055: Pseudo dice [0.8307, 0.4339, 0.7971, 0.8716, 0.5273, 0.7876, 0.8371] +2026-04-14 17:17:58.715729: Epoch time: 102.15 s +2026-04-14 17:18:00.256709: +2026-04-14 17:18:00.259278: Epoch 3513 +2026-04-14 17:18:00.261634: Current learning rate: 0.0015 +2026-04-14 17:19:43.663752: train_loss -0.4975 +2026-04-14 17:19:43.671879: val_loss -0.4293 +2026-04-14 17:19:43.679363: Pseudo dice [0.5585, 0.4023, 0.7746, 0.7945, 0.4882, 0.8345, 0.7407] +2026-04-14 17:19:43.682010: Epoch time: 103.41 s +2026-04-14 17:19:45.227407: +2026-04-14 17:19:45.230808: Epoch 3514 +2026-04-14 17:19:45.232730: Current learning rate: 0.0015 +2026-04-14 17:21:27.570695: train_loss -0.5156 +2026-04-14 17:21:27.577209: val_loss -0.3627 +2026-04-14 17:21:27.580680: Pseudo dice [0.4868, 0.7626, 0.7035, 0.2127, 0.4045, 0.2239, 0.8708] +2026-04-14 17:21:27.585678: Epoch time: 102.35 s +2026-04-14 17:21:29.118532: +2026-04-14 17:21:29.120783: Epoch 3515 +2026-04-14 17:21:29.122560: Current learning rate: 0.0015 +2026-04-14 17:23:11.369698: train_loss -0.5057 +2026-04-14 17:23:11.378654: val_loss -0.3084 +2026-04-14 17:23:11.381037: Pseudo dice [0.311, 0.6305, 0.6316, 0.8448, 0.4986, 0.1184, 0.6972] +2026-04-14 17:23:11.383644: Epoch time: 102.25 s +2026-04-14 17:23:12.948373: +2026-04-14 17:23:12.952362: Epoch 3516 +2026-04-14 17:23:12.954758: Current learning rate: 0.00149 +2026-04-14 17:24:55.387257: train_loss -0.5079 +2026-04-14 17:24:55.399585: val_loss -0.4365 +2026-04-14 17:24:55.401452: Pseudo dice [0.3245, 0.6858, 0.7364, 0.6115, 0.6875, 0.4835, 0.88] +2026-04-14 17:24:55.403377: Epoch time: 102.44 s +2026-04-14 17:24:56.940041: +2026-04-14 17:24:56.942006: Epoch 3517 +2026-04-14 17:24:56.944193: Current learning rate: 0.00149 +2026-04-14 17:26:39.120303: train_loss -0.4976 +2026-04-14 17:26:39.127672: val_loss -0.296 +2026-04-14 17:26:39.130957: Pseudo dice [0.4353, 0.3896, 0.5551, 0.675, 0.6946, 0.0186, 0.5756] +2026-04-14 17:26:39.133424: Epoch time: 102.18 s +2026-04-14 17:26:40.681979: +2026-04-14 17:26:40.683959: Epoch 3518 +2026-04-14 17:26:40.685732: Current learning rate: 0.00149 +2026-04-14 17:28:22.558024: train_loss -0.4928 +2026-04-14 17:28:22.563385: val_loss -0.3686 +2026-04-14 17:28:22.566290: Pseudo dice [0.2781, 0.1325, 0.6103, 0.8593, 0.3868, 0.2235, 0.8498] +2026-04-14 17:28:22.568708: Epoch time: 101.88 s +2026-04-14 17:28:24.111172: +2026-04-14 17:28:24.112952: Epoch 3519 +2026-04-14 17:28:24.115781: Current learning rate: 0.00149 +2026-04-14 17:30:08.388157: train_loss -0.5018 +2026-04-14 17:30:08.398002: val_loss -0.4209 +2026-04-14 17:30:08.400479: Pseudo dice [0.4546, 0.7402, 0.6067, 0.6188, 0.608, 0.851, 0.8684] +2026-04-14 17:30:08.402868: Epoch time: 104.28 s +2026-04-14 17:30:09.964747: +2026-04-14 17:30:09.973416: Epoch 3520 +2026-04-14 17:30:09.987093: Current learning rate: 0.00148 +2026-04-14 17:31:53.669382: train_loss -0.515 +2026-04-14 17:31:53.675989: val_loss -0.4384 +2026-04-14 17:31:53.677860: Pseudo dice [0.6181, 0.6372, 0.8317, 0.7408, 0.4432, 0.7936, 0.7989] +2026-04-14 17:31:53.680436: Epoch time: 103.71 s +2026-04-14 17:31:55.248140: +2026-04-14 17:31:55.249871: Epoch 3521 +2026-04-14 17:31:55.251633: Current learning rate: 0.00148 +2026-04-14 17:33:37.438214: train_loss -0.5027 +2026-04-14 17:33:37.443066: val_loss -0.3675 +2026-04-14 17:33:37.447187: Pseudo dice [0.7969, 0.4161, 0.7847, 0.7355, 0.3139, 0.1834, 0.6768] +2026-04-14 17:33:37.454576: Epoch time: 102.19 s +2026-04-14 17:33:39.007089: +2026-04-14 17:33:39.008950: Epoch 3522 +2026-04-14 17:33:39.010890: Current learning rate: 0.00148 +2026-04-14 17:35:21.470618: train_loss -0.5009 +2026-04-14 17:35:21.476101: val_loss -0.4044 +2026-04-14 17:35:21.478644: Pseudo dice [0.5482, 0.0881, 0.8154, 0.7228, 0.2721, 0.7872, 0.8413] +2026-04-14 17:35:21.481064: Epoch time: 102.47 s +2026-04-14 17:35:23.039198: +2026-04-14 17:35:23.041103: Epoch 3523 +2026-04-14 17:35:23.042647: Current learning rate: 0.00148 +2026-04-14 17:37:06.092737: train_loss -0.4951 +2026-04-14 17:37:06.097572: val_loss -0.351 +2026-04-14 17:37:06.099603: Pseudo dice [0.4224, 0.1854, 0.6742, 0.5268, 0.6247, 0.1037, 0.8788] +2026-04-14 17:37:06.102045: Epoch time: 103.06 s +2026-04-14 17:37:07.678411: +2026-04-14 17:37:07.683310: Epoch 3524 +2026-04-14 17:37:07.687396: Current learning rate: 0.00147 +2026-04-14 17:38:49.892648: train_loss -0.4926 +2026-04-14 17:38:49.904087: val_loss -0.437 +2026-04-14 17:38:49.906476: Pseudo dice [0.3509, 0.3799, 0.7957, 0.2463, 0.6426, 0.7498, 0.8052] +2026-04-14 17:38:49.910171: Epoch time: 102.22 s +2026-04-14 17:38:51.456937: +2026-04-14 17:38:51.459640: Epoch 3525 +2026-04-14 17:38:51.461786: Current learning rate: 0.00147 +2026-04-14 17:40:36.664081: train_loss -0.5117 +2026-04-14 17:40:36.669420: val_loss -0.3801 +2026-04-14 17:40:36.672486: Pseudo dice [0.5689, 0.3453, 0.7098, 0.6404, 0.5372, 0.0936, 0.8638] +2026-04-14 17:40:36.674998: Epoch time: 105.21 s +2026-04-14 17:40:38.230233: +2026-04-14 17:40:38.235765: Epoch 3526 +2026-04-14 17:40:38.238872: Current learning rate: 0.00147 +2026-04-14 17:42:22.385187: train_loss -0.5125 +2026-04-14 17:42:22.390378: val_loss -0.3641 +2026-04-14 17:42:22.392690: Pseudo dice [0.7754, 0.6455, 0.6249, 0.7572, 0.2405, 0.1488, 0.8666] +2026-04-14 17:42:22.395837: Epoch time: 104.16 s +2026-04-14 17:42:23.928227: +2026-04-14 17:42:23.931250: Epoch 3527 +2026-04-14 17:42:23.933557: Current learning rate: 0.00146 +2026-04-14 17:44:08.351218: train_loss -0.4954 +2026-04-14 17:44:08.363381: val_loss -0.2818 +2026-04-14 17:44:08.365578: Pseudo dice [0.6271, 0.5269, 0.6513, 0.3837, 0.6021, 0.1229, 0.8179] +2026-04-14 17:44:08.368528: Epoch time: 104.43 s +2026-04-14 17:44:09.917631: +2026-04-14 17:44:09.920146: Epoch 3528 +2026-04-14 17:44:09.922279: Current learning rate: 0.00146 +2026-04-14 17:45:54.621691: train_loss -0.5116 +2026-04-14 17:45:54.628425: val_loss -0.4026 +2026-04-14 17:45:54.631464: Pseudo dice [0.4384, 0.7485, 0.6677, 0.6374, 0.3778, 0.7499, 0.773] +2026-04-14 17:45:54.634216: Epoch time: 104.71 s +2026-04-14 17:45:56.182775: +2026-04-14 17:45:56.185213: Epoch 3529 +2026-04-14 17:45:56.187762: Current learning rate: 0.00146 +2026-04-14 17:47:38.414996: train_loss -0.5096 +2026-04-14 17:47:38.423631: val_loss -0.3367 +2026-04-14 17:47:38.426754: Pseudo dice [0.6615, 0.8228, 0.5986, 0.8699, 0.2925, 0.174, 0.4198] +2026-04-14 17:47:38.432847: Epoch time: 102.23 s +2026-04-14 17:47:40.030997: +2026-04-14 17:47:40.033469: Epoch 3530 +2026-04-14 17:47:40.035114: Current learning rate: 0.00146 +2026-04-14 17:49:24.176549: train_loss -0.5102 +2026-04-14 17:49:24.182599: val_loss -0.357 +2026-04-14 17:49:24.185048: Pseudo dice [0.7006, 0.5106, 0.7005, 0.5574, 0.4631, 0.1174, 0.2129] +2026-04-14 17:49:24.187038: Epoch time: 104.15 s +2026-04-14 17:49:25.739602: +2026-04-14 17:49:25.741486: Epoch 3531 +2026-04-14 17:49:25.743212: Current learning rate: 0.00145 +2026-04-14 17:51:07.868739: train_loss -0.509 +2026-04-14 17:51:07.874702: val_loss -0.3334 +2026-04-14 17:51:07.876459: Pseudo dice [0.5786, 0.5834, 0.7447, 0.7042, 0.4468, 0.094, 0.3581] +2026-04-14 17:51:07.879245: Epoch time: 102.13 s +2026-04-14 17:51:09.447834: +2026-04-14 17:51:09.450058: Epoch 3532 +2026-04-14 17:51:09.452478: Current learning rate: 0.00145 +2026-04-14 17:52:51.909242: train_loss -0.5063 +2026-04-14 17:52:51.916830: val_loss -0.452 +2026-04-14 17:52:51.919701: Pseudo dice [0.7915, 0.6722, 0.8543, 0.811, 0.5427, 0.7596, 0.857] +2026-04-14 17:52:51.923764: Epoch time: 102.47 s +2026-04-14 17:52:53.493340: +2026-04-14 17:52:53.495455: Epoch 3533 +2026-04-14 17:52:53.497400: Current learning rate: 0.00145 +2026-04-14 17:54:36.726029: train_loss -0.5018 +2026-04-14 17:54:36.732480: val_loss -0.3748 +2026-04-14 17:54:36.734858: Pseudo dice [0.8467, 0.7699, 0.7208, 0.8773, 0.4378, 0.089, 0.8399] +2026-04-14 17:54:36.738563: Epoch time: 103.24 s +2026-04-14 17:54:38.315326: +2026-04-14 17:54:38.317574: Epoch 3534 +2026-04-14 17:54:38.321510: Current learning rate: 0.00144 +2026-04-14 17:56:20.468717: train_loss -0.5004 +2026-04-14 17:56:20.477669: val_loss -0.4499 +2026-04-14 17:56:20.480559: Pseudo dice [0.6611, 0.3362, 0.811, 0.803, 0.6646, 0.5403, 0.8514] +2026-04-14 17:56:20.482910: Epoch time: 102.16 s +2026-04-14 17:56:22.069560: +2026-04-14 17:56:22.071343: Epoch 3535 +2026-04-14 17:56:22.073243: Current learning rate: 0.00144 +2026-04-14 17:58:04.007753: train_loss -0.5 +2026-04-14 17:58:04.014544: val_loss -0.3565 +2026-04-14 17:58:04.016584: Pseudo dice [0.5346, 0.8079, 0.7475, 0.7104, 0.5617, 0.2869, 0.7184] +2026-04-14 17:58:04.019013: Epoch time: 101.94 s +2026-04-14 17:58:05.571550: +2026-04-14 17:58:05.573416: Epoch 3536 +2026-04-14 17:58:05.575072: Current learning rate: 0.00144 +2026-04-14 17:59:47.418943: train_loss -0.5128 +2026-04-14 17:59:47.426394: val_loss -0.4328 +2026-04-14 17:59:47.429569: Pseudo dice [0.7221, 0.8633, 0.7296, 0.7213, 0.4532, 0.526, 0.8856] +2026-04-14 17:59:47.431960: Epoch time: 101.85 s +2026-04-14 17:59:48.969318: +2026-04-14 17:59:48.971091: Epoch 3537 +2026-04-14 17:59:48.972836: Current learning rate: 0.00144 +2026-04-14 18:01:30.890548: train_loss -0.4992 +2026-04-14 18:01:30.896061: val_loss -0.3856 +2026-04-14 18:01:30.898135: Pseudo dice [0.7056, 0.0425, 0.6166, 0.4639, 0.3497, 0.3316, 0.468] +2026-04-14 18:01:30.900349: Epoch time: 101.92 s +2026-04-14 18:01:32.460454: +2026-04-14 18:01:32.462344: Epoch 3538 +2026-04-14 18:01:32.464999: Current learning rate: 0.00143 +2026-04-14 18:03:15.113122: train_loss -0.5036 +2026-04-14 18:03:15.120183: val_loss -0.388 +2026-04-14 18:03:15.122902: Pseudo dice [0.7982, 0.4415, 0.8459, 0.637, 0.2611, 0.1174, 0.2729] +2026-04-14 18:03:15.126067: Epoch time: 102.66 s +2026-04-14 18:03:16.708279: +2026-04-14 18:03:16.710485: Epoch 3539 +2026-04-14 18:03:16.712480: Current learning rate: 0.00143 +2026-04-14 18:04:58.593582: train_loss -0.5103 +2026-04-14 18:04:58.600594: val_loss -0.3237 +2026-04-14 18:04:58.602913: Pseudo dice [0.7315, 0.6781, 0.6391, 0.6717, 0.3895, 0.1721, 0.7819] +2026-04-14 18:04:58.605666: Epoch time: 101.89 s +2026-04-14 18:05:00.172388: +2026-04-14 18:05:00.174174: Epoch 3540 +2026-04-14 18:05:00.176232: Current learning rate: 0.00143 +2026-04-14 18:06:41.954833: train_loss -0.4978 +2026-04-14 18:06:41.960883: val_loss -0.3929 +2026-04-14 18:06:41.963940: Pseudo dice [0.4127, 0.4085, 0.6635, 0.8163, 0.5979, 0.5411, 0.7104] +2026-04-14 18:06:41.971293: Epoch time: 101.79 s +2026-04-14 18:06:43.528051: +2026-04-14 18:06:43.529806: Epoch 3541 +2026-04-14 18:06:43.531402: Current learning rate: 0.00142 +2026-04-14 18:08:25.382077: train_loss -0.5063 +2026-04-14 18:08:25.387104: val_loss -0.4125 +2026-04-14 18:08:25.388784: Pseudo dice [0.8365, 0.8493, 0.7547, 0.8499, 0.1773, 0.7098, 0.4339] +2026-04-14 18:08:25.393199: Epoch time: 101.86 s +2026-04-14 18:08:26.981230: +2026-04-14 18:08:26.988391: Epoch 3542 +2026-04-14 18:08:26.989944: Current learning rate: 0.00142 +2026-04-14 18:10:09.088687: train_loss -0.5047 +2026-04-14 18:10:09.093507: val_loss -0.4587 +2026-04-14 18:10:09.095563: Pseudo dice [0.4695, 0.5401, 0.8108, 0.7973, 0.5683, 0.8367, 0.8382] +2026-04-14 18:10:09.097715: Epoch time: 102.11 s +2026-04-14 18:10:10.650841: +2026-04-14 18:10:10.652733: Epoch 3543 +2026-04-14 18:10:10.654429: Current learning rate: 0.00142 +2026-04-14 18:11:52.497869: train_loss -0.5042 +2026-04-14 18:11:52.504725: val_loss -0.4496 +2026-04-14 18:11:52.506923: Pseudo dice [0.6702, 0.5416, 0.7525, 0.8674, 0.4463, 0.8496, 0.4202] +2026-04-14 18:11:52.509607: Epoch time: 101.85 s +2026-04-14 18:11:54.072510: +2026-04-14 18:11:54.074292: Epoch 3544 +2026-04-14 18:11:54.076197: Current learning rate: 0.00142 +2026-04-14 18:13:36.230544: train_loss -0.5172 +2026-04-14 18:13:36.237672: val_loss -0.4107 +2026-04-14 18:13:36.243668: Pseudo dice [0.7158, 0.832, 0.7897, 0.7075, 0.5828, 0.1784, 0.7305] +2026-04-14 18:13:36.247377: Epoch time: 102.16 s +2026-04-14 18:13:37.797340: +2026-04-14 18:13:37.799293: Epoch 3545 +2026-04-14 18:13:37.800935: Current learning rate: 0.00141 +2026-04-14 18:15:21.099345: train_loss -0.5082 +2026-04-14 18:15:21.104858: val_loss -0.4386 +2026-04-14 18:15:21.106793: Pseudo dice [0.4586, 0.4647, 0.7414, 0.8019, 0.6937, 0.8118, 0.7616] +2026-04-14 18:15:21.108846: Epoch time: 103.31 s +2026-04-14 18:15:22.677619: +2026-04-14 18:15:22.679583: Epoch 3546 +2026-04-14 18:15:22.681193: Current learning rate: 0.00141 +2026-04-14 18:17:04.591809: train_loss -0.5044 +2026-04-14 18:17:04.597384: val_loss -0.4482 +2026-04-14 18:17:04.599729: Pseudo dice [0.7239, 0.7118, 0.7505, 0.8598, 0.3832, 0.78, 0.7356] +2026-04-14 18:17:04.602251: Epoch time: 101.92 s +2026-04-14 18:17:06.147809: +2026-04-14 18:17:06.149660: Epoch 3547 +2026-04-14 18:17:06.151766: Current learning rate: 0.00141 +2026-04-14 18:18:47.861394: train_loss -0.5067 +2026-04-14 18:18:47.866446: val_loss -0.4549 +2026-04-14 18:18:47.868990: Pseudo dice [0.4886, 0.8723, 0.6821, 0.8334, 0.6609, 0.3783, 0.7969] +2026-04-14 18:18:47.871377: Epoch time: 101.72 s +2026-04-14 18:18:49.424981: +2026-04-14 18:18:49.426908: Epoch 3548 +2026-04-14 18:18:49.428510: Current learning rate: 0.00141 +2026-04-14 18:20:31.262742: train_loss -0.5088 +2026-04-14 18:20:31.267934: val_loss -0.4481 +2026-04-14 18:20:31.270065: Pseudo dice [0.7162, 0.7873, 0.7813, 0.713, 0.5146, 0.8154, 0.8803] +2026-04-14 18:20:31.272395: Epoch time: 101.84 s +2026-04-14 18:20:31.274118: Yayy! New best EMA pseudo Dice: 0.6405 +2026-04-14 18:20:34.824057: +2026-04-14 18:20:34.825792: Epoch 3549 +2026-04-14 18:20:34.827439: Current learning rate: 0.0014 +2026-04-14 18:22:16.729850: train_loss -0.5112 +2026-04-14 18:22:16.735002: val_loss -0.4468 +2026-04-14 18:22:16.737036: Pseudo dice [0.5436, 0.4637, 0.8255, 0.8797, 0.5862, 0.5441, 0.9149] +2026-04-14 18:22:16.740466: Epoch time: 101.91 s +2026-04-14 18:22:18.742172: Yayy! New best EMA pseudo Dice: 0.6444 +2026-04-14 18:22:22.265585: +2026-04-14 18:22:22.267628: Epoch 3550 +2026-04-14 18:22:22.269124: Current learning rate: 0.0014 +2026-04-14 18:24:04.143325: train_loss -0.5048 +2026-04-14 18:24:04.148978: val_loss -0.3846 +2026-04-14 18:24:04.151170: Pseudo dice [0.5058, 0.6099, 0.7563, 0.413, 0.3352, 0.0735, 0.6834] +2026-04-14 18:24:04.153877: Epoch time: 101.88 s +2026-04-14 18:24:05.680276: +2026-04-14 18:24:05.682740: Epoch 3551 +2026-04-14 18:24:05.685277: Current learning rate: 0.0014 +2026-04-14 18:25:48.145637: train_loss -0.5104 +2026-04-14 18:25:48.153510: val_loss -0.3708 +2026-04-14 18:25:48.157581: Pseudo dice [0.7168, 0.4691, 0.7699, 0.4487, 0.5663, 0.1304, 0.8337] +2026-04-14 18:25:48.161713: Epoch time: 102.47 s +2026-04-14 18:25:50.843167: +2026-04-14 18:25:50.845082: Epoch 3552 +2026-04-14 18:25:50.846912: Current learning rate: 0.00139 +2026-04-14 18:27:33.008465: train_loss -0.5028 +2026-04-14 18:27:33.015737: val_loss -0.4173 +2026-04-14 18:27:33.017713: Pseudo dice [0.3269, 0.7735, 0.8211, 0.7823, 0.4357, 0.5717, 0.728] +2026-04-14 18:27:33.020120: Epoch time: 102.17 s +2026-04-14 18:27:34.560464: +2026-04-14 18:27:34.562987: Epoch 3553 +2026-04-14 18:27:34.565040: Current learning rate: 0.00139 +2026-04-14 18:29:16.633530: train_loss -0.5003 +2026-04-14 18:29:16.638522: val_loss -0.4008 +2026-04-14 18:29:16.640206: Pseudo dice [0.5981, 0.4916, 0.7542, 0.6224, 0.3636, 0.7824, 0.5133] +2026-04-14 18:29:16.642783: Epoch time: 102.08 s +2026-04-14 18:29:18.184598: +2026-04-14 18:29:18.186461: Epoch 3554 +2026-04-14 18:29:18.188274: Current learning rate: 0.00139 +2026-04-14 18:31:00.904858: train_loss -0.5077 +2026-04-14 18:31:00.913779: val_loss -0.416 +2026-04-14 18:31:00.916655: Pseudo dice [0.7202, 0.6701, 0.8539, 0.2306, 0.3894, 0.7804, 0.6538] +2026-04-14 18:31:00.919032: Epoch time: 102.72 s +2026-04-14 18:31:02.490514: +2026-04-14 18:31:02.492790: Epoch 3555 +2026-04-14 18:31:02.494324: Current learning rate: 0.00139 +2026-04-14 18:32:44.882277: train_loss -0.5075 +2026-04-14 18:32:44.891344: val_loss -0.4248 +2026-04-14 18:32:44.893179: Pseudo dice [0.32, 0.4456, 0.8404, 0.8347, 0.5675, 0.2741, 0.8208] +2026-04-14 18:32:44.897154: Epoch time: 102.4 s +2026-04-14 18:32:46.449104: +2026-04-14 18:32:46.451321: Epoch 3556 +2026-04-14 18:32:46.453108: Current learning rate: 0.00138 +2026-04-14 18:34:28.635051: train_loss -0.5099 +2026-04-14 18:34:28.642081: val_loss -0.4364 +2026-04-14 18:34:28.644269: Pseudo dice [0.408, 0.2055, 0.8546, 0.8462, 0.4251, 0.8266, 0.7203] +2026-04-14 18:34:28.646980: Epoch time: 102.19 s +2026-04-14 18:34:30.202688: +2026-04-14 18:34:30.204421: Epoch 3557 +2026-04-14 18:34:30.206718: Current learning rate: 0.00138 +2026-04-14 18:36:12.760232: train_loss -0.5134 +2026-04-14 18:36:12.766367: val_loss -0.3641 +2026-04-14 18:36:12.768367: Pseudo dice [0.4464, 0.353, 0.789, 0.734, 0.6148, 0.1842, 0.7625] +2026-04-14 18:36:12.771149: Epoch time: 102.56 s +2026-04-14 18:36:14.338870: +2026-04-14 18:36:14.340643: Epoch 3558 +2026-04-14 18:36:14.342519: Current learning rate: 0.00138 +2026-04-14 18:37:57.292503: train_loss -0.5125 +2026-04-14 18:37:57.297821: val_loss -0.3273 +2026-04-14 18:37:57.300043: Pseudo dice [0.6794, 0.3246, 0.3969, 0.5933, 0.6159, 0.3684, 0.7904] +2026-04-14 18:37:57.302901: Epoch time: 102.96 s +2026-04-14 18:37:58.886297: +2026-04-14 18:37:58.889187: Epoch 3559 +2026-04-14 18:37:58.891384: Current learning rate: 0.00137 +2026-04-14 18:39:41.067413: train_loss -0.5068 +2026-04-14 18:39:41.073454: val_loss -0.2878 +2026-04-14 18:39:41.075698: Pseudo dice [0.6571, 0.1357, 0.6385, 0.0553, 0.3485, 0.2293, 0.8178] +2026-04-14 18:39:41.078797: Epoch time: 102.19 s +2026-04-14 18:39:42.651715: +2026-04-14 18:39:42.653569: Epoch 3560 +2026-04-14 18:39:42.655205: Current learning rate: 0.00137 +2026-04-14 18:41:24.841137: train_loss -0.5069 +2026-04-14 18:41:24.847556: val_loss -0.4112 +2026-04-14 18:41:24.849708: Pseudo dice [0.4717, 0.7947, 0.5403, 0.1407, 0.6629, 0.4925, 0.7387] +2026-04-14 18:41:24.852325: Epoch time: 102.19 s +2026-04-14 18:41:26.419056: +2026-04-14 18:41:26.421296: Epoch 3561 +2026-04-14 18:41:26.423981: Current learning rate: 0.00137 +2026-04-14 18:43:08.965405: train_loss -0.5177 +2026-04-14 18:43:08.975957: val_loss -0.4245 +2026-04-14 18:43:08.978620: Pseudo dice [0.6193, 0.3306, 0.7825, 0.7046, 0.4388, 0.5624, 0.4757] +2026-04-14 18:43:08.981923: Epoch time: 102.55 s +2026-04-14 18:43:10.587193: +2026-04-14 18:43:10.589047: Epoch 3562 +2026-04-14 18:43:10.590761: Current learning rate: 0.00137 +2026-04-14 18:44:52.421590: train_loss -0.504 +2026-04-14 18:44:52.430487: val_loss -0.3739 +2026-04-14 18:44:52.433043: Pseudo dice [0.6404, 0.6519, 0.7947, 0.416, 0.6102, 0.0459, 0.8246] +2026-04-14 18:44:52.435869: Epoch time: 101.84 s +2026-04-14 18:44:54.001173: +2026-04-14 18:44:54.003617: Epoch 3563 +2026-04-14 18:44:54.005429: Current learning rate: 0.00136 +2026-04-14 18:46:35.883736: train_loss -0.498 +2026-04-14 18:46:35.888583: val_loss -0.4466 +2026-04-14 18:46:35.890344: Pseudo dice [0.7205, 0.5469, 0.8828, 0.4101, 0.6976, 0.4377, 0.8467] +2026-04-14 18:46:35.892446: Epoch time: 101.89 s +2026-04-14 18:46:37.452727: +2026-04-14 18:46:37.454716: Epoch 3564 +2026-04-14 18:46:37.456280: Current learning rate: 0.00136 +2026-04-14 18:48:19.263378: train_loss -0.5008 +2026-04-14 18:48:19.270117: val_loss -0.4415 +2026-04-14 18:48:19.272266: Pseudo dice [0.6918, 0.8238, 0.6342, 0.8356, 0.4923, 0.6768, 0.8796] +2026-04-14 18:48:19.274908: Epoch time: 101.81 s +2026-04-14 18:48:20.857810: +2026-04-14 18:48:20.860154: Epoch 3565 +2026-04-14 18:48:20.861851: Current learning rate: 0.00136 +2026-04-14 18:50:02.922186: train_loss -0.5044 +2026-04-14 18:50:02.931734: val_loss -0.3849 +2026-04-14 18:50:02.934824: Pseudo dice [0.7536, 0.5173, 0.6513, 0.796, 0.4197, 0.4197, 0.7272] +2026-04-14 18:50:02.937792: Epoch time: 102.07 s +2026-04-14 18:50:04.491442: +2026-04-14 18:50:04.493720: Epoch 3566 +2026-04-14 18:50:04.495723: Current learning rate: 0.00135 +2026-04-14 18:51:47.260786: train_loss -0.5102 +2026-04-14 18:51:47.270204: val_loss -0.4121 +2026-04-14 18:51:47.273210: Pseudo dice [0.8493, 0.0924, 0.7776, 0.7491, 0.6717, 0.322, 0.7083] +2026-04-14 18:51:47.276384: Epoch time: 102.77 s +2026-04-14 18:51:48.905715: +2026-04-14 18:51:48.908824: Epoch 3567 +2026-04-14 18:51:48.912125: Current learning rate: 0.00135 +2026-04-14 18:53:31.955554: train_loss -0.5027 +2026-04-14 18:53:31.963006: val_loss -0.4319 +2026-04-14 18:53:31.965034: Pseudo dice [0.5858, 0.7688, 0.6315, 0.4571, 0.6426, 0.7654, 0.9238] +2026-04-14 18:53:31.967778: Epoch time: 103.06 s +2026-04-14 18:53:33.521007: +2026-04-14 18:53:33.523389: Epoch 3568 +2026-04-14 18:53:33.526202: Current learning rate: 0.00135 +2026-04-14 18:55:16.606722: train_loss -0.4906 +2026-04-14 18:55:16.613672: val_loss -0.4293 +2026-04-14 18:55:16.616132: Pseudo dice [0.8935, 0.5456, 0.6598, 0.6359, 0.5601, 0.6848, 0.7377] +2026-04-14 18:55:16.619200: Epoch time: 103.09 s +2026-04-14 18:55:18.179540: +2026-04-14 18:55:18.181499: Epoch 3569 +2026-04-14 18:55:18.184391: Current learning rate: 0.00135 +2026-04-14 18:57:00.872553: train_loss -0.5014 +2026-04-14 18:57:00.879725: val_loss -0.3481 +2026-04-14 18:57:00.882998: Pseudo dice [0.8655, 0.4099, 0.6932, 0.7544, 0.6804, 0.2729, 0.74] +2026-04-14 18:57:00.885170: Epoch time: 102.7 s +2026-04-14 18:57:02.450452: +2026-04-14 18:57:02.452437: Epoch 3570 +2026-04-14 18:57:02.454946: Current learning rate: 0.00134 +2026-04-14 18:58:45.442919: train_loss -0.5053 +2026-04-14 18:58:45.454739: val_loss -0.3044 +2026-04-14 18:58:45.457996: Pseudo dice [0.4863, 0.182, 0.5382, 0.8441, 0.4468, 0.4394, 0.5118] +2026-04-14 18:58:45.462214: Epoch time: 103.0 s +2026-04-14 18:58:47.030887: +2026-04-14 18:58:47.032722: Epoch 3571 +2026-04-14 18:58:47.035089: Current learning rate: 0.00134 +2026-04-14 19:00:29.990501: train_loss -0.4977 +2026-04-14 19:00:29.998965: val_loss -0.3996 +2026-04-14 19:00:30.001422: Pseudo dice [0.3753, 0.7064, 0.7323, 0.4234, 0.4998, 0.173, 0.8165] +2026-04-14 19:00:30.004315: Epoch time: 102.96 s +2026-04-14 19:00:32.781646: +2026-04-14 19:00:32.783888: Epoch 3572 +2026-04-14 19:00:32.786048: Current learning rate: 0.00134 +2026-04-14 19:02:15.346755: train_loss -0.5175 +2026-04-14 19:02:15.356112: val_loss -0.4112 +2026-04-14 19:02:15.360013: Pseudo dice [0.4373, 0.8559, 0.5833, 0.8662, 0.6993, 0.2653, 0.8389] +2026-04-14 19:02:15.362648: Epoch time: 102.57 s +2026-04-14 19:02:16.960200: +2026-04-14 19:02:16.962263: Epoch 3573 +2026-04-14 19:02:16.965221: Current learning rate: 0.00134 +2026-04-14 19:04:00.444793: train_loss -0.5126 +2026-04-14 19:04:00.454840: val_loss -0.4509 +2026-04-14 19:04:00.457508: Pseudo dice [0.6845, 0.3664, 0.7468, 0.3534, 0.4932, 0.8413, 0.7974] +2026-04-14 19:04:00.459765: Epoch time: 103.49 s +2026-04-14 19:04:02.007338: +2026-04-14 19:04:02.009457: Epoch 3574 +2026-04-14 19:04:02.012100: Current learning rate: 0.00133 +2026-04-14 19:05:45.163433: train_loss -0.5174 +2026-04-14 19:05:45.170514: val_loss -0.4109 +2026-04-14 19:05:45.172953: Pseudo dice [0.4571, 0.6829, 0.6971, 0.7172, 0.4872, 0.4727, 0.4598] +2026-04-14 19:05:45.176360: Epoch time: 103.16 s +2026-04-14 19:05:46.743806: +2026-04-14 19:05:46.746519: Epoch 3575 +2026-04-14 19:05:46.748991: Current learning rate: 0.00133 +2026-04-14 19:07:29.508111: train_loss -0.5114 +2026-04-14 19:07:29.519750: val_loss -0.4015 +2026-04-14 19:07:29.522862: Pseudo dice [0.438, 0.3895, 0.6713, 0.9053, 0.4407, 0.2053, 0.6243] +2026-04-14 19:07:29.526345: Epoch time: 102.77 s +2026-04-14 19:07:31.129233: +2026-04-14 19:07:31.132367: Epoch 3576 +2026-04-14 19:07:31.135062: Current learning rate: 0.00133 +2026-04-14 19:09:13.700582: train_loss -0.5222 +2026-04-14 19:09:13.707742: val_loss -0.4627 +2026-04-14 19:09:13.710801: Pseudo dice [0.6905, 0.8298, 0.7512, 0.7713, 0.5188, 0.8868, 0.8406] +2026-04-14 19:09:13.714366: Epoch time: 102.58 s +2026-04-14 19:09:15.259878: +2026-04-14 19:09:15.262142: Epoch 3577 +2026-04-14 19:09:15.264711: Current learning rate: 0.00132 +2026-04-14 19:10:58.194623: train_loss -0.5093 +2026-04-14 19:10:58.201170: val_loss -0.3765 +2026-04-14 19:10:58.203615: Pseudo dice [0.8302, 0.7685, 0.7615, 0.6964, 0.4378, 0.1917, 0.7622] +2026-04-14 19:10:58.206746: Epoch time: 102.94 s +2026-04-14 19:10:59.792807: +2026-04-14 19:10:59.796087: Epoch 3578 +2026-04-14 19:10:59.806849: Current learning rate: 0.00132 +2026-04-14 19:12:43.426530: train_loss -0.5173 +2026-04-14 19:12:43.434166: val_loss -0.4283 +2026-04-14 19:12:43.436514: Pseudo dice [0.4856, 0.4985, 0.6034, 0.8069, 0.4462, 0.6939, 0.7832] +2026-04-14 19:12:43.439798: Epoch time: 103.64 s +2026-04-14 19:12:45.001218: +2026-04-14 19:12:45.003454: Epoch 3579 +2026-04-14 19:12:45.006126: Current learning rate: 0.00132 +2026-04-14 19:14:27.602071: train_loss -0.5144 +2026-04-14 19:14:27.610309: val_loss -0.4494 +2026-04-14 19:14:27.613323: Pseudo dice [0.8819, 0.7363, 0.6916, 0.7522, 0.7008, 0.8283, 0.8155] +2026-04-14 19:14:27.616085: Epoch time: 102.6 s +2026-04-14 19:14:29.169072: +2026-04-14 19:14:29.171417: Epoch 3580 +2026-04-14 19:14:29.174624: Current learning rate: 0.00132 +2026-04-14 19:16:10.940892: train_loss -0.5079 +2026-04-14 19:16:10.955827: val_loss -0.4443 +2026-04-14 19:16:10.964383: Pseudo dice [0.4519, 0.7178, 0.7523, 0.7791, 0.3591, 0.8658, 0.8495] +2026-04-14 19:16:10.978694: Epoch time: 101.78 s +2026-04-14 19:16:12.635005: +2026-04-14 19:16:12.636908: Epoch 3581 +2026-04-14 19:16:12.638814: Current learning rate: 0.00131 +2026-04-14 19:17:55.186556: train_loss -0.515 +2026-04-14 19:17:55.192717: val_loss -0.4186 +2026-04-14 19:17:55.195947: Pseudo dice [0.6948, 0.7436, 0.79, 0.5949, 0.4818, 0.1088, 0.7633] +2026-04-14 19:17:55.198888: Epoch time: 102.56 s +2026-04-14 19:17:56.776502: +2026-04-14 19:17:56.778444: Epoch 3582 +2026-04-14 19:17:56.780719: Current learning rate: 0.00131 +2026-04-14 19:19:39.101545: train_loss -0.5248 +2026-04-14 19:19:39.109220: val_loss -0.3906 +2026-04-14 19:19:39.114680: Pseudo dice [0.4136, 0.7351, 0.6835, 0.7428, 0.6052, 0.2665, 0.8615] +2026-04-14 19:19:39.118519: Epoch time: 102.33 s +2026-04-14 19:19:40.693004: +2026-04-14 19:19:40.697544: Epoch 3583 +2026-04-14 19:19:40.700679: Current learning rate: 0.00131 +2026-04-14 19:21:23.586082: train_loss -0.5049 +2026-04-14 19:21:23.599263: val_loss -0.3183 +2026-04-14 19:21:23.603754: Pseudo dice [0.4376, 0.5229, 0.4643, 0.3894, 0.4837, 0.1602, 0.7497] +2026-04-14 19:21:23.610109: Epoch time: 102.9 s +2026-04-14 19:21:25.277056: +2026-04-14 19:21:25.280106: Epoch 3584 +2026-04-14 19:21:25.282247: Current learning rate: 0.0013 +2026-04-14 19:23:07.767346: train_loss -0.5003 +2026-04-14 19:23:07.775679: val_loss -0.2997 +2026-04-14 19:23:07.778253: Pseudo dice [0.7105, 0.8089, 0.6367, 0.8053, 0.2777, 0.149, 0.5245] +2026-04-14 19:23:07.780716: Epoch time: 102.49 s +2026-04-14 19:23:09.337656: +2026-04-14 19:23:09.340463: Epoch 3585 +2026-04-14 19:23:09.343452: Current learning rate: 0.0013 +2026-04-14 19:24:52.356145: train_loss -0.5042 +2026-04-14 19:24:52.362347: val_loss -0.4424 +2026-04-14 19:24:52.364707: Pseudo dice [0.6551, 0.3845, 0.8058, 0.8179, 0.6614, 0.4616, 0.82] +2026-04-14 19:24:52.366884: Epoch time: 103.02 s +2026-04-14 19:24:53.931871: +2026-04-14 19:24:53.934512: Epoch 3586 +2026-04-14 19:24:53.936792: Current learning rate: 0.0013 +2026-04-14 19:26:38.480015: train_loss -0.5054 +2026-04-14 19:26:38.486314: val_loss -0.434 +2026-04-14 19:26:38.488173: Pseudo dice [0.7284, 0.8311, 0.8749, 0.8804, 0.5596, 0.7038, 0.8458] +2026-04-14 19:26:38.491337: Epoch time: 104.55 s +2026-04-14 19:26:40.052503: +2026-04-14 19:26:40.055465: Epoch 3587 +2026-04-14 19:26:40.058609: Current learning rate: 0.0013 +2026-04-14 19:28:23.171598: train_loss -0.518 +2026-04-14 19:28:23.177551: val_loss -0.3791 +2026-04-14 19:28:23.179825: Pseudo dice [0.7498, 0.7872, 0.7042, 0.776, 0.2606, 0.1227, 0.68] +2026-04-14 19:28:23.182904: Epoch time: 103.12 s +2026-04-14 19:28:24.752106: +2026-04-14 19:28:24.754521: Epoch 3588 +2026-04-14 19:28:24.756458: Current learning rate: 0.00129 +2026-04-14 19:30:07.423344: train_loss -0.5122 +2026-04-14 19:30:07.429425: val_loss -0.4209 +2026-04-14 19:30:07.431889: Pseudo dice [0.7849, 0.3038, 0.6821, 0.3961, 0.7285, 0.1138, 0.9083] +2026-04-14 19:30:07.435328: Epoch time: 102.67 s +2026-04-14 19:30:08.979611: +2026-04-14 19:30:08.982649: Epoch 3589 +2026-04-14 19:30:08.984779: Current learning rate: 0.00129 +2026-04-14 19:31:51.221915: train_loss -0.4956 +2026-04-14 19:31:51.228473: val_loss -0.4171 +2026-04-14 19:31:51.230650: Pseudo dice [0.8453, 0.4241, 0.7044, 0.6722, 0.4412, 0.7288, 0.8749] +2026-04-14 19:31:51.233490: Epoch time: 102.25 s +2026-04-14 19:31:52.795908: +2026-04-14 19:31:52.798271: Epoch 3590 +2026-04-14 19:31:52.800257: Current learning rate: 0.00129 +2026-04-14 19:33:35.037487: train_loss -0.5005 +2026-04-14 19:33:35.043689: val_loss -0.4316 +2026-04-14 19:33:35.045526: Pseudo dice [0.6274, 0.7195, 0.7254, 0.8703, 0.6096, 0.6572, 0.8542] +2026-04-14 19:33:35.048519: Epoch time: 102.25 s +2026-04-14 19:33:36.615190: +2026-04-14 19:33:36.618704: Epoch 3591 +2026-04-14 19:33:36.622155: Current learning rate: 0.00128 +2026-04-14 19:35:19.218147: train_loss -0.5021 +2026-04-14 19:35:19.225406: val_loss -0.4581 +2026-04-14 19:35:19.229113: Pseudo dice [0.6359, 0.7373, 0.8075, 0.5736, 0.345, 0.8576, 0.8839] +2026-04-14 19:35:19.232065: Epoch time: 102.61 s +2026-04-14 19:35:21.909198: +2026-04-14 19:35:21.911869: Epoch 3592 +2026-04-14 19:35:21.913875: Current learning rate: 0.00128 +2026-04-14 19:37:04.292785: train_loss -0.515 +2026-04-14 19:37:04.299277: val_loss -0.4247 +2026-04-14 19:37:04.301372: Pseudo dice [0.8284, 0.766, 0.685, 0.7413, 0.6661, 0.4935, 0.8501] +2026-04-14 19:37:04.303969: Epoch time: 102.39 s +2026-04-14 19:37:04.306289: Yayy! New best EMA pseudo Dice: 0.6461 +2026-04-14 19:37:07.962772: +2026-04-14 19:37:07.965711: Epoch 3593 +2026-04-14 19:37:07.967685: Current learning rate: 0.00128 +2026-04-14 19:38:51.796831: train_loss -0.5032 +2026-04-14 19:38:51.805297: val_loss -0.4142 +2026-04-14 19:38:51.810015: Pseudo dice [0.3459, 0.8075, 0.7253, 0.8701, 0.3897, 0.5493, 0.6231] +2026-04-14 19:38:51.813668: Epoch time: 103.84 s +2026-04-14 19:38:53.368676: +2026-04-14 19:38:53.370968: Epoch 3594 +2026-04-14 19:38:53.373795: Current learning rate: 0.00128 +2026-04-14 19:40:35.844852: train_loss -0.5169 +2026-04-14 19:40:35.854872: val_loss -0.4591 +2026-04-14 19:40:35.857950: Pseudo dice [0.5899, 0.8375, 0.6766, 0.9021, 0.6308, 0.5925, 0.9071] +2026-04-14 19:40:35.860778: Epoch time: 102.48 s +2026-04-14 19:40:35.863855: Yayy! New best EMA pseudo Dice: 0.6521 +2026-04-14 19:40:39.546183: +2026-04-14 19:40:39.548643: Epoch 3595 +2026-04-14 19:40:39.550835: Current learning rate: 0.00127 +2026-04-14 19:42:21.660903: train_loss -0.5132 +2026-04-14 19:42:21.667397: val_loss -0.4008 +2026-04-14 19:42:21.669472: Pseudo dice [0.4025, 0.6545, 0.7615, 0.0084, 0.5463, 0.3965, 0.795] +2026-04-14 19:42:21.671947: Epoch time: 102.12 s +2026-04-14 19:42:23.234305: +2026-04-14 19:42:23.236336: Epoch 3596 +2026-04-14 19:42:23.238659: Current learning rate: 0.00127 +2026-04-14 19:44:05.234621: train_loss -0.5072 +2026-04-14 19:44:05.243842: val_loss -0.3286 +2026-04-14 19:44:05.246007: Pseudo dice [0.1745, 0.4468, 0.471, 0.8678, 0.4055, 0.308, 0.864] +2026-04-14 19:44:05.248412: Epoch time: 102.0 s +2026-04-14 19:44:06.790348: +2026-04-14 19:44:06.795097: Epoch 3597 +2026-04-14 19:44:06.800167: Current learning rate: 0.00127 +2026-04-14 19:45:49.479744: train_loss -0.512 +2026-04-14 19:45:49.485651: val_loss -0.4505 +2026-04-14 19:45:49.487697: Pseudo dice [0.7941, 0.5932, 0.7663, 0.2539, 0.6196, 0.7958, 0.8963] +2026-04-14 19:45:49.490522: Epoch time: 102.69 s +2026-04-14 19:45:51.092569: +2026-04-14 19:45:51.095906: Epoch 3598 +2026-04-14 19:45:51.098275: Current learning rate: 0.00126 +2026-04-14 19:47:33.654651: train_loss -0.5119 +2026-04-14 19:47:33.661147: val_loss -0.4278 +2026-04-14 19:47:33.663618: Pseudo dice [0.5854, 0.5602, 0.7341, 0.8057, 0.3198, 0.877, 0.8331] +2026-04-14 19:47:33.666017: Epoch time: 102.57 s +2026-04-14 19:47:35.202497: +2026-04-14 19:47:35.204624: Epoch 3599 +2026-04-14 19:47:35.207951: Current learning rate: 0.00126 +2026-04-14 19:49:18.246466: train_loss -0.508 +2026-04-14 19:49:18.256260: val_loss -0.38 +2026-04-14 19:49:18.258233: Pseudo dice [0.6811, 0.4812, 0.3101, 0.3321, 0.5488, 0.1688, 0.8729] +2026-04-14 19:49:18.260483: Epoch time: 103.05 s +2026-04-14 19:49:21.856090: +2026-04-14 19:49:21.858530: Epoch 3600 +2026-04-14 19:49:21.860745: Current learning rate: 0.00126 +2026-04-14 19:51:04.703059: train_loss -0.5213 +2026-04-14 19:51:04.710074: val_loss -0.4418 +2026-04-14 19:51:04.712242: Pseudo dice [0.3764, 0.2658, 0.7692, 0.2121, 0.6861, 0.8034, 0.7593] +2026-04-14 19:51:04.715117: Epoch time: 102.85 s +2026-04-14 19:51:06.254190: +2026-04-14 19:51:06.255837: Epoch 3601 +2026-04-14 19:51:06.257631: Current learning rate: 0.00126 +2026-04-14 19:52:49.742767: train_loss -0.5161 +2026-04-14 19:52:49.748761: val_loss -0.4021 +2026-04-14 19:52:49.751094: Pseudo dice [0.3954, 0.7044, 0.7229, 0.0052, 0.6806, 0.2433, 0.6004] +2026-04-14 19:52:49.753875: Epoch time: 103.49 s +2026-04-14 19:52:51.317997: +2026-04-14 19:52:51.320463: Epoch 3602 +2026-04-14 19:52:51.323131: Current learning rate: 0.00125 +2026-04-14 19:54:34.479849: train_loss -0.509 +2026-04-14 19:54:34.486580: val_loss -0.4144 +2026-04-14 19:54:34.488983: Pseudo dice [0.3136, 0.7629, 0.8107, 0.5944, 0.6311, 0.4263, 0.7648] +2026-04-14 19:54:34.491873: Epoch time: 103.17 s +2026-04-14 19:54:36.029029: +2026-04-14 19:54:36.030642: Epoch 3603 +2026-04-14 19:54:36.032789: Current learning rate: 0.00125 +2026-04-14 19:56:18.245958: train_loss -0.5126 +2026-04-14 19:56:18.252338: val_loss -0.4394 +2026-04-14 19:56:18.254645: Pseudo dice [0.5736, 0.6095, 0.7781, 0.6856, 0.6473, 0.7814, 0.764] +2026-04-14 19:56:18.257231: Epoch time: 102.22 s +2026-04-14 19:56:19.806765: +2026-04-14 19:56:19.809098: Epoch 3604 +2026-04-14 19:56:19.811123: Current learning rate: 0.00125 +2026-04-14 19:58:05.115436: train_loss -0.5176 +2026-04-14 19:58:05.128292: val_loss -0.4193 +2026-04-14 19:58:05.132158: Pseudo dice [0.5357, 0.853, 0.754, 0.0825, 0.646, 0.5188, 0.727] +2026-04-14 19:58:05.137565: Epoch time: 105.31 s +2026-04-14 19:58:06.726140: +2026-04-14 19:58:06.728210: Epoch 3605 +2026-04-14 19:58:06.730371: Current learning rate: 0.00124 +2026-04-14 19:59:49.489606: train_loss -0.5253 +2026-04-14 19:59:49.498635: val_loss -0.379 +2026-04-14 19:59:49.500971: Pseudo dice [0.3399, 0.5818, 0.6951, 0.3848, 0.6113, 0.3334, 0.8399] +2026-04-14 19:59:49.503589: Epoch time: 102.77 s +2026-04-14 19:59:51.099947: +2026-04-14 19:59:51.102292: Epoch 3606 +2026-04-14 19:59:51.105552: Current learning rate: 0.00124 +2026-04-14 20:01:34.591664: train_loss -0.5155 +2026-04-14 20:01:34.598247: val_loss -0.3647 +2026-04-14 20:01:34.600524: Pseudo dice [0.4623, 0.8077, 0.7523, 0.8043, 0.3936, 0.2978, 0.5863] +2026-04-14 20:01:34.605041: Epoch time: 103.5 s +2026-04-14 20:01:36.154141: +2026-04-14 20:01:36.156523: Epoch 3607 +2026-04-14 20:01:36.160091: Current learning rate: 0.00124 +2026-04-14 20:03:18.112070: train_loss -0.5133 +2026-04-14 20:03:18.118142: val_loss -0.378 +2026-04-14 20:03:18.120084: Pseudo dice [0.2803, 0.6668, 0.6681, 0.4373, 0.6182, 0.2826, 0.7953] +2026-04-14 20:03:18.122446: Epoch time: 101.96 s +2026-04-14 20:03:19.660497: +2026-04-14 20:03:19.663091: Epoch 3608 +2026-04-14 20:03:19.666007: Current learning rate: 0.00124 +2026-04-14 20:05:02.047930: train_loss -0.5098 +2026-04-14 20:05:02.056178: val_loss -0.3071 +2026-04-14 20:05:02.058282: Pseudo dice [0.7827, 0.3808, 0.6964, 0.1994, 0.4499, 0.2314, 0.4985] +2026-04-14 20:05:02.063933: Epoch time: 102.39 s +2026-04-14 20:05:03.642172: +2026-04-14 20:05:03.643898: Epoch 3609 +2026-04-14 20:05:03.645857: Current learning rate: 0.00123 +2026-04-14 20:06:47.735100: train_loss -0.5011 +2026-04-14 20:06:47.743863: val_loss -0.429 +2026-04-14 20:06:47.746024: Pseudo dice [0.5232, 0.5117, 0.6832, 0.6478, 0.3954, 0.623, 0.7337] +2026-04-14 20:06:47.748386: Epoch time: 104.1 s +2026-04-14 20:06:49.390773: +2026-04-14 20:06:49.392928: Epoch 3610 +2026-04-14 20:06:49.395135: Current learning rate: 0.00123 +2026-04-14 20:08:33.928972: train_loss -0.5107 +2026-04-14 20:08:33.935843: val_loss -0.4016 +2026-04-14 20:08:33.938206: Pseudo dice [0.7117, 0.8462, 0.7619, 0.387, 0.5525, 0.3213, 0.7456] +2026-04-14 20:08:33.940990: Epoch time: 104.54 s +2026-04-14 20:08:35.500520: +2026-04-14 20:08:35.502341: Epoch 3611 +2026-04-14 20:08:35.505554: Current learning rate: 0.00123 +2026-04-14 20:10:20.065335: train_loss -0.5101 +2026-04-14 20:10:20.071889: val_loss -0.351 +2026-04-14 20:10:20.074296: Pseudo dice [0.4848, 0.6697, 0.73, 0.8901, 0.618, 0.2136, 0.7685] +2026-04-14 20:10:20.076973: Epoch time: 104.57 s +2026-04-14 20:10:21.627946: +2026-04-14 20:10:21.629832: Epoch 3612 +2026-04-14 20:10:21.631853: Current learning rate: 0.00122 +2026-04-14 20:12:03.696579: train_loss -0.5161 +2026-04-14 20:12:03.702201: val_loss -0.3167 +2026-04-14 20:12:03.705545: Pseudo dice [0.815, 0.6717, 0.5441, 0.8298, 0.6564, 0.0489, 0.8384] +2026-04-14 20:12:03.708072: Epoch time: 102.07 s +2026-04-14 20:12:05.267611: +2026-04-14 20:12:05.270120: Epoch 3613 +2026-04-14 20:12:05.272119: Current learning rate: 0.00122 +2026-04-14 20:13:48.089445: train_loss -0.5131 +2026-04-14 20:13:48.096864: val_loss -0.4345 +2026-04-14 20:13:48.104896: Pseudo dice [0.5344, 0.466, 0.8179, 0.8543, 0.5707, 0.8149, 0.7987] +2026-04-14 20:13:48.107183: Epoch time: 102.83 s +2026-04-14 20:13:49.654470: +2026-04-14 20:13:49.658655: Epoch 3614 +2026-04-14 20:13:49.661358: Current learning rate: 0.00122 +2026-04-14 20:15:31.639873: train_loss -0.514 +2026-04-14 20:15:31.647256: val_loss -0.4036 +2026-04-14 20:15:31.649000: Pseudo dice [0.3638, 0.702, 0.596, 0.9298, 0.6072, 0.0934, 0.892] +2026-04-14 20:15:31.651357: Epoch time: 101.99 s +2026-04-14 20:15:33.249801: +2026-04-14 20:15:33.251620: Epoch 3615 +2026-04-14 20:15:33.253629: Current learning rate: 0.00122 +2026-04-14 20:17:15.822812: train_loss -0.5214 +2026-04-14 20:17:15.830316: val_loss -0.47 +2026-04-14 20:17:15.833811: Pseudo dice [0.5314, 0.5395, 0.7277, 0.7667, 0.61, 0.7442, 0.8389] +2026-04-14 20:17:15.836681: Epoch time: 102.58 s +2026-04-14 20:17:17.397836: +2026-04-14 20:17:17.399628: Epoch 3616 +2026-04-14 20:17:17.402216: Current learning rate: 0.00121 +2026-04-14 20:19:00.255045: train_loss -0.5206 +2026-04-14 20:19:00.261599: val_loss -0.4499 +2026-04-14 20:19:00.263489: Pseudo dice [0.1505, 0.4751, 0.7585, 0.6845, 0.3146, 0.6913, 0.6665] +2026-04-14 20:19:00.266463: Epoch time: 102.86 s +2026-04-14 20:19:01.814451: +2026-04-14 20:19:01.818237: Epoch 3617 +2026-04-14 20:19:01.821495: Current learning rate: 0.00121 +2026-04-14 20:20:44.505934: train_loss -0.5268 +2026-04-14 20:20:44.513918: val_loss -0.398 +2026-04-14 20:20:44.516092: Pseudo dice [0.3779, 0.519, 0.5096, 0.5802, 0.3808, 0.2624, 0.7906] +2026-04-14 20:20:44.519035: Epoch time: 102.7 s +2026-04-14 20:20:46.090101: +2026-04-14 20:20:46.092567: Epoch 3618 +2026-04-14 20:20:46.096601: Current learning rate: 0.00121 +2026-04-14 20:22:28.476834: train_loss -0.5174 +2026-04-14 20:22:28.483690: val_loss -0.4131 +2026-04-14 20:22:28.486250: Pseudo dice [0.8519, 0.8552, 0.6591, 0.7322, 0.5833, 0.1513, 0.8434] +2026-04-14 20:22:28.488883: Epoch time: 102.39 s +2026-04-14 20:22:30.050268: +2026-04-14 20:22:30.052011: Epoch 3619 +2026-04-14 20:22:30.054352: Current learning rate: 0.0012 +2026-04-14 20:24:12.479227: train_loss -0.5189 +2026-04-14 20:24:12.485390: val_loss -0.4187 +2026-04-14 20:24:12.487937: Pseudo dice [0.1298, 0.6837, 0.6957, 0.8944, 0.5524, 0.574, 0.6147] +2026-04-14 20:24:12.490382: Epoch time: 102.43 s +2026-04-14 20:24:14.060267: +2026-04-14 20:24:14.062334: Epoch 3620 +2026-04-14 20:24:14.064815: Current learning rate: 0.0012 +2026-04-14 20:25:56.808284: train_loss -0.5143 +2026-04-14 20:25:56.816828: val_loss -0.4331 +2026-04-14 20:25:56.819541: Pseudo dice [0.6577, 0.7233, 0.7729, 0.3438, 0.3647, 0.7065, 0.7229] +2026-04-14 20:25:56.822390: Epoch time: 102.75 s +2026-04-14 20:25:58.361844: +2026-04-14 20:25:58.363755: Epoch 3621 +2026-04-14 20:25:58.365702: Current learning rate: 0.0012 +2026-04-14 20:27:40.331530: train_loss -0.5111 +2026-04-14 20:27:40.345085: val_loss -0.4051 +2026-04-14 20:27:40.348256: Pseudo dice [0.638, 0.8311, 0.4296, 0.8626, 0.5869, 0.1028, 0.8534] +2026-04-14 20:27:40.350638: Epoch time: 101.97 s +2026-04-14 20:27:41.892862: +2026-04-14 20:27:41.894876: Epoch 3622 +2026-04-14 20:27:41.897106: Current learning rate: 0.0012 +2026-04-14 20:29:24.636902: train_loss -0.5178 +2026-04-14 20:29:24.643067: val_loss -0.3718 +2026-04-14 20:29:24.644897: Pseudo dice [0.3079, 0.7959, 0.6104, 0.6107, 0.5468, 0.2168, 0.8268] +2026-04-14 20:29:24.647318: Epoch time: 102.75 s +2026-04-14 20:29:26.246660: +2026-04-14 20:29:26.250034: Epoch 3623 +2026-04-14 20:29:26.252577: Current learning rate: 0.00119 +2026-04-14 20:31:09.336052: train_loss -0.5159 +2026-04-14 20:31:09.342503: val_loss -0.4071 +2026-04-14 20:31:09.345755: Pseudo dice [0.7143, 0.6726, 0.7184, 0.8101, 0.6454, 0.1818, 0.7919] +2026-04-14 20:31:09.348155: Epoch time: 103.09 s +2026-04-14 20:31:10.899405: +2026-04-14 20:31:10.901953: Epoch 3624 +2026-04-14 20:31:10.904453: Current learning rate: 0.00119 +2026-04-14 20:32:52.986166: train_loss -0.505 +2026-04-14 20:32:52.994473: val_loss -0.3245 +2026-04-14 20:32:52.996717: Pseudo dice [0.7447, 0.7464, 0.5947, 0.8614, 0.3496, 0.1781, 0.487] +2026-04-14 20:32:52.999679: Epoch time: 102.09 s +2026-04-14 20:32:54.594345: +2026-04-14 20:32:54.596147: Epoch 3625 +2026-04-14 20:32:54.598339: Current learning rate: 0.00119 +2026-04-14 20:34:37.744935: train_loss -0.513 +2026-04-14 20:34:37.753552: val_loss -0.3922 +2026-04-14 20:34:37.759162: Pseudo dice [0.3982, 0.3783, 0.696, 0.015, 0.4981, 0.1364, 0.4785] +2026-04-14 20:34:37.763182: Epoch time: 103.15 s +2026-04-14 20:34:39.341732: +2026-04-14 20:34:39.343982: Epoch 3626 +2026-04-14 20:34:39.347155: Current learning rate: 0.00119 +2026-04-14 20:36:21.824571: train_loss -0.5121 +2026-04-14 20:36:21.830671: val_loss -0.392 +2026-04-14 20:36:21.832511: Pseudo dice [0.8794, 0.7497, 0.8018, 0.7037, 0.6088, 0.0574, 0.8049] +2026-04-14 20:36:21.835282: Epoch time: 102.49 s +2026-04-14 20:36:23.398151: +2026-04-14 20:36:23.400810: Epoch 3627 +2026-04-14 20:36:23.403431: Current learning rate: 0.00118 +2026-04-14 20:38:06.228214: train_loss -0.5129 +2026-04-14 20:38:06.239282: val_loss -0.4442 +2026-04-14 20:38:06.243799: Pseudo dice [0.4482, 0.7132, 0.8164, 0.8586, 0.559, 0.8388, 0.8297] +2026-04-14 20:38:06.247641: Epoch time: 102.83 s +2026-04-14 20:38:07.861165: +2026-04-14 20:38:07.863503: Epoch 3628 +2026-04-14 20:38:07.865480: Current learning rate: 0.00118 +2026-04-14 20:39:50.481369: train_loss -0.5184 +2026-04-14 20:39:50.488641: val_loss -0.3945 +2026-04-14 20:39:50.491240: Pseudo dice [0.6564, 0.5206, 0.7945, 0.5249, 0.4767, 0.1008, 0.6671] +2026-04-14 20:39:50.494290: Epoch time: 102.63 s +2026-04-14 20:39:52.090441: +2026-04-14 20:39:52.092543: Epoch 3629 +2026-04-14 20:39:52.094864: Current learning rate: 0.00118 +2026-04-14 20:41:34.436984: train_loss -0.5257 +2026-04-14 20:41:34.444077: val_loss -0.3631 +2026-04-14 20:41:34.447138: Pseudo dice [0.3182, 0.4178, 0.8522, 0.8632, 0.6796, 0.2162, 0.9255] +2026-04-14 20:41:34.450213: Epoch time: 102.35 s +2026-04-14 20:41:37.270848: +2026-04-14 20:41:37.273237: Epoch 3630 +2026-04-14 20:41:37.275353: Current learning rate: 0.00117 +2026-04-14 20:43:19.519586: train_loss -0.5249 +2026-04-14 20:43:19.525563: val_loss -0.3447 +2026-04-14 20:43:19.527435: Pseudo dice [0.5513, 0.724, 0.6036, 0.8502, 0.5907, 0.2151, 0.8954] +2026-04-14 20:43:19.529823: Epoch time: 102.25 s +2026-04-14 20:43:21.090955: +2026-04-14 20:43:21.094748: Epoch 3631 +2026-04-14 20:43:21.097350: Current learning rate: 0.00117 +2026-04-14 20:45:03.443178: train_loss -0.524 +2026-04-14 20:45:03.449386: val_loss -0.3648 +2026-04-14 20:45:03.452219: Pseudo dice [0.8395, 0.5436, 0.6338, 0.7245, 0.6588, 0.1184, 0.8929] +2026-04-14 20:45:03.454358: Epoch time: 102.36 s +2026-04-14 20:45:04.991498: +2026-04-14 20:45:04.993300: Epoch 3632 +2026-04-14 20:45:04.995862: Current learning rate: 0.00117 +2026-04-14 20:46:48.212212: train_loss -0.5281 +2026-04-14 20:46:48.221828: val_loss -0.4295 +2026-04-14 20:46:48.227116: Pseudo dice [0.5738, 0.4086, 0.735, 0.8356, 0.5653, 0.1366, 0.845] +2026-04-14 20:46:48.230194: Epoch time: 103.22 s +2026-04-14 20:46:49.796090: +2026-04-14 20:46:49.799031: Epoch 3633 +2026-04-14 20:46:49.800993: Current learning rate: 0.00117 +2026-04-14 20:48:32.503635: train_loss -0.5135 +2026-04-14 20:48:32.510710: val_loss -0.3725 +2026-04-14 20:48:32.513725: Pseudo dice [0.8062, 0.6678, 0.6119, 0.5765, 0.674, 0.1862, 0.9168] +2026-04-14 20:48:32.516176: Epoch time: 102.71 s +2026-04-14 20:48:34.167804: +2026-04-14 20:48:34.170429: Epoch 3634 +2026-04-14 20:48:34.172297: Current learning rate: 0.00116 +2026-04-14 20:50:17.281600: train_loss -0.5217 +2026-04-14 20:50:17.288067: val_loss -0.4358 +2026-04-14 20:50:17.290264: Pseudo dice [0.5646, 0.3161, 0.7833, 0.7289, 0.4388, 0.7152, 0.8084] +2026-04-14 20:50:17.292830: Epoch time: 103.12 s +2026-04-14 20:50:18.931057: +2026-04-14 20:50:18.933035: Epoch 3635 +2026-04-14 20:50:18.935171: Current learning rate: 0.00116 +2026-04-14 20:52:02.245452: train_loss -0.523 +2026-04-14 20:52:02.253792: val_loss -0.3841 +2026-04-14 20:52:02.257135: Pseudo dice [0.3802, 0.716, 0.7151, 0.8305, 0.5128, 0.4974, 0.7696] +2026-04-14 20:52:02.260740: Epoch time: 103.32 s +2026-04-14 20:52:03.848964: +2026-04-14 20:52:03.851883: Epoch 3636 +2026-04-14 20:52:03.854795: Current learning rate: 0.00116 +2026-04-14 20:53:48.024700: train_loss -0.5055 +2026-04-14 20:53:48.032663: val_loss -0.4695 +2026-04-14 20:53:48.036834: Pseudo dice [0.336, 0.8442, 0.8015, 0.7688, 0.7337, 0.9027, 0.7745] +2026-04-14 20:53:48.039564: Epoch time: 104.18 s +2026-04-14 20:53:49.607588: +2026-04-14 20:53:49.610560: Epoch 3637 +2026-04-14 20:53:49.616111: Current learning rate: 0.00115 +2026-04-14 20:55:32.286358: train_loss -0.5151 +2026-04-14 20:55:32.293462: val_loss -0.4164 +2026-04-14 20:55:32.297711: Pseudo dice [0.5742, 0.8545, 0.7492, 0.6598, 0.6147, 0.36, 0.8494] +2026-04-14 20:55:32.300800: Epoch time: 102.68 s +2026-04-14 20:55:33.905196: +2026-04-14 20:55:33.907605: Epoch 3638 +2026-04-14 20:55:33.910021: Current learning rate: 0.00115 +2026-04-14 20:57:16.604759: train_loss -0.5226 +2026-04-14 20:57:16.611870: val_loss -0.3711 +2026-04-14 20:57:16.614071: Pseudo dice [0.6931, 0.6931, 0.7765, 0.5524, 0.4299, 0.35, 0.233] +2026-04-14 20:57:16.616794: Epoch time: 102.7 s +2026-04-14 20:57:18.269464: +2026-04-14 20:57:18.271642: Epoch 3639 +2026-04-14 20:57:18.273766: Current learning rate: 0.00115 +2026-04-14 20:59:00.533887: train_loss -0.5207 +2026-04-14 20:59:00.540588: val_loss -0.4324 +2026-04-14 20:59:00.543103: Pseudo dice [0.8562, 0.6282, 0.6487, 0.5566, 0.5052, 0.5583, 0.7759] +2026-04-14 20:59:00.545078: Epoch time: 102.27 s +2026-04-14 20:59:02.109500: +2026-04-14 20:59:02.112000: Epoch 3640 +2026-04-14 20:59:02.113951: Current learning rate: 0.00115 +2026-04-14 21:00:46.111293: train_loss -0.5072 +2026-04-14 21:00:46.118191: val_loss -0.3978 +2026-04-14 21:00:46.121047: Pseudo dice [0.8409, 0.8215, 0.6958, 0.541, 0.5799, 0.3055, 0.5469] +2026-04-14 21:00:46.123841: Epoch time: 104.01 s +2026-04-14 21:00:47.712106: +2026-04-14 21:00:47.714289: Epoch 3641 +2026-04-14 21:00:47.716645: Current learning rate: 0.00114 +2026-04-14 21:02:30.731928: train_loss -0.5127 +2026-04-14 21:02:30.747992: val_loss -0.3081 +2026-04-14 21:02:30.751538: Pseudo dice [0.4483, 0.7649, 0.5876, 0.0459, 0.6577, 0.2515, 0.8734] +2026-04-14 21:02:30.755599: Epoch time: 103.02 s +2026-04-14 21:02:32.402992: +2026-04-14 21:02:32.404994: Epoch 3642 +2026-04-14 21:02:32.407089: Current learning rate: 0.00114 +2026-04-14 21:04:15.387565: train_loss -0.517 +2026-04-14 21:04:15.394523: val_loss -0.387 +2026-04-14 21:04:15.397016: Pseudo dice [0.7326, 0.6976, 0.7706, 0.4209, 0.3539, 0.2379, 0.7607] +2026-04-14 21:04:15.399222: Epoch time: 102.99 s +2026-04-14 21:04:16.994823: +2026-04-14 21:04:16.997969: Epoch 3643 +2026-04-14 21:04:17.001390: Current learning rate: 0.00114 +2026-04-14 21:06:00.247620: train_loss -0.5146 +2026-04-14 21:06:00.254085: val_loss -0.3966 +2026-04-14 21:06:00.256531: Pseudo dice [0.7063, 0.7294, 0.6145, 0.7588, 0.4503, 0.4043, 0.7095] +2026-04-14 21:06:00.264325: Epoch time: 103.26 s +2026-04-14 21:06:02.118074: +2026-04-14 21:06:02.120348: Epoch 3644 +2026-04-14 21:06:02.122586: Current learning rate: 0.00113 +2026-04-14 21:07:45.564584: train_loss -0.5154 +2026-04-14 21:07:45.571470: val_loss -0.4217 +2026-04-14 21:07:45.574468: Pseudo dice [0.5271, 0.8249, 0.791, 0.7353, 0.4189, 0.6718, 0.539] +2026-04-14 21:07:45.577630: Epoch time: 103.45 s +2026-04-14 21:07:47.194408: +2026-04-14 21:07:47.197982: Epoch 3645 +2026-04-14 21:07:47.200958: Current learning rate: 0.00113 +2026-04-14 21:09:31.362872: train_loss -0.5203 +2026-04-14 21:09:31.370768: val_loss -0.4129 +2026-04-14 21:09:31.373258: Pseudo dice [0.2976, 0.4585, 0.7159, 0.8495, 0.2701, 0.7478, 0.6476] +2026-04-14 21:09:31.378172: Epoch time: 104.17 s +2026-04-14 21:09:32.983628: +2026-04-14 21:09:32.986646: Epoch 3646 +2026-04-14 21:09:32.989012: Current learning rate: 0.00113 +2026-04-14 21:11:18.450503: train_loss -0.5183 +2026-04-14 21:11:18.457945: val_loss -0.4255 +2026-04-14 21:11:18.460042: Pseudo dice [0.7248, 0.6976, 0.5716, 0.4157, 0.3719, 0.4808, 0.7501] +2026-04-14 21:11:18.465396: Epoch time: 105.47 s +2026-04-14 21:11:20.057361: +2026-04-14 21:11:20.059166: Epoch 3647 +2026-04-14 21:11:20.061302: Current learning rate: 0.00112 +2026-04-14 21:13:06.741170: train_loss -0.5227 +2026-04-14 21:13:06.756948: val_loss -0.3979 +2026-04-14 21:13:06.762033: Pseudo dice [0.6237, 0.8092, 0.6913, 0.2443, 0.3897, 0.1096, 0.8529] +2026-04-14 21:13:06.768813: Epoch time: 106.69 s +2026-04-14 21:13:08.381705: +2026-04-14 21:13:08.384063: Epoch 3648 +2026-04-14 21:13:08.387858: Current learning rate: 0.00112 +2026-04-14 21:14:51.712503: train_loss -0.5136 +2026-04-14 21:14:51.721715: val_loss -0.3838 +2026-04-14 21:14:51.725418: Pseudo dice [0.5131, 0.5568, 0.5327, 0.6184, 0.6125, 0.3151, 0.8903] +2026-04-14 21:14:51.728257: Epoch time: 103.33 s +2026-04-14 21:14:53.356091: +2026-04-14 21:14:53.362535: Epoch 3649 +2026-04-14 21:14:53.367656: Current learning rate: 0.00112 +2026-04-14 21:16:37.937168: train_loss -0.5112 +2026-04-14 21:16:37.944097: val_loss -0.3745 +2026-04-14 21:16:37.947187: Pseudo dice [0.6605, 0.5326, 0.7144, 0.2222, 0.4921, 0.5422, 0.5625] +2026-04-14 21:16:37.950725: Epoch time: 104.58 s +2026-04-14 21:16:42.782115: +2026-04-14 21:16:42.785319: Epoch 3650 +2026-04-14 21:16:42.788025: Current learning rate: 0.00112 +2026-04-14 21:18:26.498522: train_loss -0.521 +2026-04-14 21:18:26.507718: val_loss -0.4296 +2026-04-14 21:18:26.511251: Pseudo dice [0.6703, 0.6551, 0.6762, 0.3125, 0.4112, 0.6275, 0.8942] +2026-04-14 21:18:26.525920: Epoch time: 103.72 s +2026-04-14 21:18:28.182043: +2026-04-14 21:18:28.185268: Epoch 3651 +2026-04-14 21:18:28.187352: Current learning rate: 0.00111 +2026-04-14 21:20:10.690502: train_loss -0.5105 +2026-04-14 21:20:10.696627: val_loss -0.317 +2026-04-14 21:20:10.699347: Pseudo dice [0.3037, 0.7719, 0.6566, 0.7947, 0.4836, 0.4425, 0.8717] +2026-04-14 21:20:10.702033: Epoch time: 102.51 s +2026-04-14 21:20:12.317778: +2026-04-14 21:20:12.319734: Epoch 3652 +2026-04-14 21:20:12.321884: Current learning rate: 0.00111 +2026-04-14 21:21:56.631246: train_loss -0.511 +2026-04-14 21:21:56.638053: val_loss -0.3326 +2026-04-14 21:21:56.640591: Pseudo dice [0.6146, 0.7875, 0.6206, 0.8024, 0.4519, 0.2384, 0.6531] +2026-04-14 21:21:56.643071: Epoch time: 104.32 s +2026-04-14 21:21:58.312700: +2026-04-14 21:21:58.316090: Epoch 3653 +2026-04-14 21:21:58.318075: Current learning rate: 0.00111 +2026-04-14 21:23:40.884592: train_loss -0.4992 +2026-04-14 21:23:40.891370: val_loss -0.4369 +2026-04-14 21:23:40.894177: Pseudo dice [0.5061, 0.7615, 0.8058, 0.6251, 0.2512, 0.8049, 0.9068] +2026-04-14 21:23:40.897557: Epoch time: 102.58 s +2026-04-14 21:23:42.520949: +2026-04-14 21:23:42.526071: Epoch 3654 +2026-04-14 21:23:42.530490: Current learning rate: 0.0011 +2026-04-14 21:25:25.498786: train_loss -0.5063 +2026-04-14 21:25:25.508094: val_loss -0.4104 +2026-04-14 21:25:25.510419: Pseudo dice [0.1983, 0.5843, 0.6844, 0.7964, 0.6307, 0.8521, 0.819] +2026-04-14 21:25:25.512317: Epoch time: 102.98 s +2026-04-14 21:25:27.087381: +2026-04-14 21:25:27.089729: Epoch 3655 +2026-04-14 21:25:27.092712: Current learning rate: 0.0011 +2026-04-14 21:27:10.240410: train_loss -0.5091 +2026-04-14 21:27:10.246301: val_loss -0.3659 +2026-04-14 21:27:10.248502: Pseudo dice [0.2361, 0.7245, 0.5453, 0.6989, 0.3613, 0.3164, 0.6574] +2026-04-14 21:27:10.251279: Epoch time: 103.16 s +2026-04-14 21:27:11.785456: +2026-04-14 21:27:11.787638: Epoch 3656 +2026-04-14 21:27:11.789762: Current learning rate: 0.0011 +2026-04-14 21:28:54.622664: train_loss -0.5168 +2026-04-14 21:28:54.631197: val_loss -0.43 +2026-04-14 21:28:54.635662: Pseudo dice [0.7744, 0.6417, 0.6984, 0.6168, 0.6445, 0.3133, 0.8948] +2026-04-14 21:28:54.639153: Epoch time: 102.84 s +2026-04-14 21:28:56.254951: +2026-04-14 21:28:56.257006: Epoch 3657 +2026-04-14 21:28:56.259355: Current learning rate: 0.0011 +2026-04-14 21:30:39.341409: train_loss -0.5231 +2026-04-14 21:30:39.347041: val_loss -0.4379 +2026-04-14 21:30:39.349298: Pseudo dice [0.4838, 0.6868, 0.7157, 0.6142, 0.6202, 0.8436, 0.8581] +2026-04-14 21:30:39.351585: Epoch time: 103.09 s +2026-04-14 21:30:40.972924: +2026-04-14 21:30:40.975050: Epoch 3658 +2026-04-14 21:30:40.977582: Current learning rate: 0.00109 +2026-04-14 21:32:25.523848: train_loss -0.5098 +2026-04-14 21:32:25.531954: val_loss -0.3901 +2026-04-14 21:32:25.535460: Pseudo dice [0.471, 0.6341, 0.569, 0.827, 0.2999, 0.6346, 0.7185] +2026-04-14 21:32:25.538445: Epoch time: 104.55 s +2026-04-14 21:32:27.118454: +2026-04-14 21:32:27.120716: Epoch 3659 +2026-04-14 21:32:27.123702: Current learning rate: 0.00109 +2026-04-14 21:34:09.795886: train_loss -0.5195 +2026-04-14 21:34:09.802093: val_loss -0.4236 +2026-04-14 21:34:09.804732: Pseudo dice [0.4482, 0.7308, 0.764, 0.8839, 0.4183, 0.8002, 0.8891] +2026-04-14 21:34:09.807595: Epoch time: 102.68 s +2026-04-14 21:34:11.425253: +2026-04-14 21:34:11.427437: Epoch 3660 +2026-04-14 21:34:11.429649: Current learning rate: 0.00109 +2026-04-14 21:35:54.064993: train_loss -0.5138 +2026-04-14 21:35:54.071439: val_loss -0.4834 +2026-04-14 21:35:54.074169: Pseudo dice [0.4647, 0.8267, 0.7782, 0.5679, 0.654, 0.8296, 0.9208] +2026-04-14 21:35:54.077038: Epoch time: 102.64 s +2026-04-14 21:35:55.700381: +2026-04-14 21:35:55.702582: Epoch 3661 +2026-04-14 21:35:55.705318: Current learning rate: 0.00108 +2026-04-14 21:37:37.634164: train_loss -0.5142 +2026-04-14 21:37:37.659585: val_loss -0.4496 +2026-04-14 21:37:37.662482: Pseudo dice [0.3789, 0.7982, 0.6224, 0.869, 0.6551, 0.7846, 0.8989] +2026-04-14 21:37:37.665357: Epoch time: 101.94 s +2026-04-14 21:37:39.275324: +2026-04-14 21:37:39.280484: Epoch 3662 +2026-04-14 21:37:39.284668: Current learning rate: 0.00108 +2026-04-14 21:39:21.907787: train_loss -0.5189 +2026-04-14 21:39:21.915139: val_loss -0.4349 +2026-04-14 21:39:21.917421: Pseudo dice [0.2497, 0.8679, 0.6865, 0.403, 0.4028, 0.8308, 0.6927] +2026-04-14 21:39:21.919879: Epoch time: 102.64 s +2026-04-14 21:39:23.496162: +2026-04-14 21:39:23.498093: Epoch 3663 +2026-04-14 21:39:23.500237: Current learning rate: 0.00108 +2026-04-14 21:41:06.415161: train_loss -0.5137 +2026-04-14 21:41:06.420649: val_loss -0.4292 +2026-04-14 21:41:06.423084: Pseudo dice [0.7244, 0.4374, 0.7319, 0.9101, 0.1839, 0.9021, 0.7609] +2026-04-14 21:41:06.425407: Epoch time: 102.92 s +2026-04-14 21:41:08.065598: +2026-04-14 21:41:08.067306: Epoch 3664 +2026-04-14 21:41:08.069265: Current learning rate: 0.00108 +2026-04-14 21:42:50.318028: train_loss -0.5157 +2026-04-14 21:42:50.324923: val_loss -0.3585 +2026-04-14 21:42:50.327077: Pseudo dice [0.7729, 0.7262, 0.6931, 0.5802, 0.415, 0.3666, 0.7906] +2026-04-14 21:42:50.329682: Epoch time: 102.26 s +2026-04-14 21:42:51.945704: +2026-04-14 21:42:51.947914: Epoch 3665 +2026-04-14 21:42:51.951217: Current learning rate: 0.00107 +2026-04-14 21:44:35.955702: train_loss -0.5242 +2026-04-14 21:44:35.961495: val_loss -0.3106 +2026-04-14 21:44:35.964656: Pseudo dice [0.2691, 0.689, 0.7271, 0.4846, 0.7026, 0.1608, 0.8856] +2026-04-14 21:44:35.967103: Epoch time: 104.01 s +2026-04-14 21:44:37.537232: +2026-04-14 21:44:37.539301: Epoch 3666 +2026-04-14 21:44:37.541589: Current learning rate: 0.00107 +2026-04-14 21:46:20.452608: train_loss -0.5288 +2026-04-14 21:46:20.459473: val_loss -0.4332 +2026-04-14 21:46:20.461723: Pseudo dice [0.6465, 0.8645, 0.7541, 0.8041, 0.4357, 0.7206, 0.8679] +2026-04-14 21:46:20.464128: Epoch time: 102.92 s +2026-04-14 21:46:22.081180: +2026-04-14 21:46:22.083405: Epoch 3667 +2026-04-14 21:46:22.085585: Current learning rate: 0.00107 +2026-04-14 21:48:04.709543: train_loss -0.5115 +2026-04-14 21:48:04.723054: val_loss -0.4524 +2026-04-14 21:48:04.727765: Pseudo dice [0.37, 0.6212, 0.6174, 0.6741, 0.6808, 0.868, 0.8874] +2026-04-14 21:48:04.732323: Epoch time: 102.63 s +2026-04-14 21:48:06.342122: +2026-04-14 21:48:06.345936: Epoch 3668 +2026-04-14 21:48:06.349897: Current learning rate: 0.00106 +2026-04-14 21:49:49.745827: train_loss -0.511 +2026-04-14 21:49:49.753674: val_loss -0.388 +2026-04-14 21:49:49.756180: Pseudo dice [0.3737, 0.687, 0.7544, 0.4959, 0.6019, 0.4239, 0.8484] +2026-04-14 21:49:49.759098: Epoch time: 103.41 s +2026-04-14 21:49:51.317218: +2026-04-14 21:49:51.319142: Epoch 3669 +2026-04-14 21:49:51.321656: Current learning rate: 0.00106 +2026-04-14 21:51:34.732231: train_loss -0.5172 +2026-04-14 21:51:34.739110: val_loss -0.4373 +2026-04-14 21:51:34.741447: Pseudo dice [0.8544, 0.4158, 0.7546, 0.8439, 0.2871, 0.4881, 0.8686] +2026-04-14 21:51:34.744625: Epoch time: 103.42 s +2026-04-14 21:51:37.565466: +2026-04-14 21:51:37.567263: Epoch 3670 +2026-04-14 21:51:37.569341: Current learning rate: 0.00106 +2026-04-14 21:53:20.540481: train_loss -0.5285 +2026-04-14 21:53:20.547518: val_loss -0.3976 +2026-04-14 21:53:20.549447: Pseudo dice [0.4419, 0.7584, 0.5841, 0.3812, 0.4815, 0.4106, 0.893] +2026-04-14 21:53:20.552718: Epoch time: 102.98 s +2026-04-14 21:53:22.112024: +2026-04-14 21:53:22.113805: Epoch 3671 +2026-04-14 21:53:22.117051: Current learning rate: 0.00106 +2026-04-14 21:55:05.091449: train_loss -0.5123 +2026-04-14 21:55:05.099843: val_loss -0.443 +2026-04-14 21:55:05.102140: Pseudo dice [0.4251, 0.8569, 0.7793, 0.842, 0.2778, 0.8477, 0.8187] +2026-04-14 21:55:05.104816: Epoch time: 102.98 s +2026-04-14 21:55:06.660091: +2026-04-14 21:55:06.663120: Epoch 3672 +2026-04-14 21:55:06.665287: Current learning rate: 0.00105 +2026-04-14 21:56:49.473433: train_loss -0.5162 +2026-04-14 21:56:49.481155: val_loss -0.449 +2026-04-14 21:56:49.483852: Pseudo dice [0.5179, 0.7701, 0.801, 0.7458, 0.2782, 0.8416, 0.8249] +2026-04-14 21:56:49.488051: Epoch time: 102.82 s +2026-04-14 21:56:51.069771: +2026-04-14 21:56:51.072438: Epoch 3673 +2026-04-14 21:56:51.076355: Current learning rate: 0.00105 +2026-04-14 21:58:34.102087: train_loss -0.5312 +2026-04-14 21:58:34.109949: val_loss -0.4132 +2026-04-14 21:58:34.114254: Pseudo dice [0.2955, 0.4244, 0.6979, 0.7765, 0.4831, 0.8767, 0.5758] +2026-04-14 21:58:34.116835: Epoch time: 103.04 s +2026-04-14 21:58:35.765325: +2026-04-14 21:58:35.767337: Epoch 3674 +2026-04-14 21:58:35.769169: Current learning rate: 0.00105 +2026-04-14 22:00:18.899997: train_loss -0.5008 +2026-04-14 22:00:18.906310: val_loss -0.441 +2026-04-14 22:00:18.908629: Pseudo dice [0.3871, 0.4022, 0.735, 0.824, 0.6653, 0.5937, 0.7472] +2026-04-14 22:00:18.911325: Epoch time: 103.14 s +2026-04-14 22:00:20.536943: +2026-04-14 22:00:20.539716: Epoch 3675 +2026-04-14 22:00:20.544554: Current learning rate: 0.00104 +2026-04-14 22:02:04.058211: train_loss -0.5139 +2026-04-14 22:02:04.064164: val_loss -0.3899 +2026-04-14 22:02:04.066291: Pseudo dice [0.7705, 0.7077, 0.7101, 0.7018, 0.44, 0.1108, 0.6745] +2026-04-14 22:02:04.070636: Epoch time: 103.53 s +2026-04-14 22:02:05.758183: +2026-04-14 22:02:05.760113: Epoch 3676 +2026-04-14 22:02:05.762115: Current learning rate: 0.00104 +2026-04-14 22:03:48.662616: train_loss -0.5219 +2026-04-14 22:03:48.672377: val_loss -0.4345 +2026-04-14 22:03:48.675357: Pseudo dice [0.7929, 0.505, 0.7428, 0.8198, 0.6399, 0.4432, 0.6161] +2026-04-14 22:03:48.678155: Epoch time: 102.91 s +2026-04-14 22:03:50.285538: +2026-04-14 22:03:50.287282: Epoch 3677 +2026-04-14 22:03:50.289318: Current learning rate: 0.00104 +2026-04-14 22:05:33.444306: train_loss -0.5187 +2026-04-14 22:05:33.450499: val_loss -0.4042 +2026-04-14 22:05:33.452991: Pseudo dice [0.8097, 0.7729, 0.6766, 0.5939, 0.609, 0.4282, 0.8862] +2026-04-14 22:05:33.455434: Epoch time: 103.16 s +2026-04-14 22:05:35.047209: +2026-04-14 22:05:35.049809: Epoch 3678 +2026-04-14 22:05:35.052174: Current learning rate: 0.00104 +2026-04-14 22:07:17.428476: train_loss -0.5118 +2026-04-14 22:07:17.435419: val_loss -0.3831 +2026-04-14 22:07:17.438040: Pseudo dice [0.8357, 0.7381, 0.6053, 0.4145, 0.5004, 0.1877, 0.3734] +2026-04-14 22:07:17.440445: Epoch time: 102.39 s +2026-04-14 22:07:19.028970: +2026-04-14 22:07:19.031272: Epoch 3679 +2026-04-14 22:07:19.033385: Current learning rate: 0.00103 +2026-04-14 22:09:02.389888: train_loss -0.5157 +2026-04-14 22:09:02.396003: val_loss -0.3153 +2026-04-14 22:09:02.398207: Pseudo dice [0.4126, 0.8248, 0.6612, 0.6043, 0.7447, 0.3549, 0.7381] +2026-04-14 22:09:02.400956: Epoch time: 103.36 s +2026-04-14 22:09:03.963568: +2026-04-14 22:09:03.966193: Epoch 3680 +2026-04-14 22:09:03.968337: Current learning rate: 0.00103 +2026-04-14 22:10:46.547959: train_loss -0.5243 +2026-04-14 22:10:46.553526: val_loss -0.4436 +2026-04-14 22:10:46.555669: Pseudo dice [0.512, 0.6363, 0.6337, 0.8153, 0.572, 0.9016, 0.7885] +2026-04-14 22:10:46.558330: Epoch time: 102.59 s +2026-04-14 22:10:48.204793: +2026-04-14 22:10:48.206599: Epoch 3681 +2026-04-14 22:10:48.208940: Current learning rate: 0.00103 +2026-04-14 22:12:31.068763: train_loss -0.5237 +2026-04-14 22:12:31.075187: val_loss -0.422 +2026-04-14 22:12:31.077780: Pseudo dice [0.6383, 0.697, 0.748, 0.0066, 0.6176, 0.2029, 0.7907] +2026-04-14 22:12:31.080420: Epoch time: 102.87 s +2026-04-14 22:12:32.660566: +2026-04-14 22:12:32.662921: Epoch 3682 +2026-04-14 22:12:32.665343: Current learning rate: 0.00102 +2026-04-14 22:14:15.673344: train_loss -0.5198 +2026-04-14 22:14:15.682476: val_loss -0.4471 +2026-04-14 22:14:15.684966: Pseudo dice [0.6967, 0.8654, 0.7214, 0.8503, 0.602, 0.8252, 0.8235] +2026-04-14 22:14:15.688420: Epoch time: 103.02 s +2026-04-14 22:14:17.349864: +2026-04-14 22:14:17.352624: Epoch 3683 +2026-04-14 22:14:17.354806: Current learning rate: 0.00102 +2026-04-14 22:16:01.128758: train_loss -0.5131 +2026-04-14 22:16:01.134462: val_loss -0.4502 +2026-04-14 22:16:01.136585: Pseudo dice [0.3504, 0.6374, 0.6705, 0.832, 0.5792, 0.816, 0.8766] +2026-04-14 22:16:01.139863: Epoch time: 103.78 s +2026-04-14 22:16:02.828834: +2026-04-14 22:16:02.830940: Epoch 3684 +2026-04-14 22:16:02.833917: Current learning rate: 0.00102 +2026-04-14 22:17:45.868248: train_loss -0.5297 +2026-04-14 22:17:45.875489: val_loss -0.4474 +2026-04-14 22:17:45.877883: Pseudo dice [0.8043, 0.8362, 0.6868, 0.6116, 0.7388, 0.8845, 0.6485] +2026-04-14 22:17:45.880185: Epoch time: 103.04 s +2026-04-14 22:17:47.477997: +2026-04-14 22:17:47.480506: Epoch 3685 +2026-04-14 22:17:47.482740: Current learning rate: 0.00102 +2026-04-14 22:19:33.023392: train_loss -0.5136 +2026-04-14 22:19:33.031611: val_loss -0.409 +2026-04-14 22:19:33.034028: Pseudo dice [0.8141, 0.7168, 0.7371, 0.7628, 0.4802, 0.369, 0.8189] +2026-04-14 22:19:33.036779: Epoch time: 105.55 s +2026-04-14 22:19:33.038703: Yayy! New best EMA pseudo Dice: 0.6531 +2026-04-14 22:19:36.791626: +2026-04-14 22:19:36.794271: Epoch 3686 +2026-04-14 22:19:36.796683: Current learning rate: 0.00101 +2026-04-14 22:21:19.958061: train_loss -0.5164 +2026-04-14 22:21:19.965727: val_loss -0.3687 +2026-04-14 22:21:19.968192: Pseudo dice [0.854, 0.6341, 0.8557, 0.227, 0.4716, 0.2215, 0.6211] +2026-04-14 22:21:19.970648: Epoch time: 103.17 s +2026-04-14 22:21:21.521223: +2026-04-14 22:21:21.523182: Epoch 3687 +2026-04-14 22:21:21.525227: Current learning rate: 0.00101 +2026-04-14 22:23:06.401869: train_loss -0.5096 +2026-04-14 22:23:06.408199: val_loss -0.3305 +2026-04-14 22:23:06.410290: Pseudo dice [0.1719, 0.8408, 0.7307, 0.6588, 0.3582, 0.4219, 0.8071] +2026-04-14 22:23:06.413116: Epoch time: 104.88 s +2026-04-14 22:23:08.002338: +2026-04-14 22:23:08.005833: Epoch 3688 +2026-04-14 22:23:08.009195: Current learning rate: 0.00101 +2026-04-14 22:24:50.561833: train_loss -0.5168 +2026-04-14 22:24:50.568483: val_loss -0.3859 +2026-04-14 22:24:50.570628: Pseudo dice [0.6207, 0.7941, 0.6408, 0.4053, 0.539, 0.1098, 0.7975] +2026-04-14 22:24:50.574176: Epoch time: 102.56 s +2026-04-14 22:24:52.230103: +2026-04-14 22:24:52.232348: Epoch 3689 +2026-04-14 22:24:52.235332: Current learning rate: 0.001 +2026-04-14 22:26:34.978553: train_loss -0.5041 +2026-04-14 22:26:34.985190: val_loss -0.3885 +2026-04-14 22:26:34.987686: Pseudo dice [0.7206, 0.6784, 0.81, 0.2817, 0.3016, 0.1961, 0.6493] +2026-04-14 22:26:34.992888: Epoch time: 102.75 s +2026-04-14 22:26:37.830035: +2026-04-14 22:26:37.832180: Epoch 3690 +2026-04-14 22:26:37.834426: Current learning rate: 0.001 +2026-04-14 22:28:21.556646: train_loss -0.5131 +2026-04-14 22:28:21.563933: val_loss -0.3779 +2026-04-14 22:28:21.567258: Pseudo dice [0.4423, 0.4633, 0.3769, 0.8339, 0.5111, 0.1811, 0.6119] +2026-04-14 22:28:21.570387: Epoch time: 103.73 s +2026-04-14 22:28:23.141805: +2026-04-14 22:28:23.144225: Epoch 3691 +2026-04-14 22:28:23.148331: Current learning rate: 0.001 +2026-04-14 22:30:07.606238: train_loss -0.5168 +2026-04-14 22:30:07.616478: val_loss -0.3752 +2026-04-14 22:30:07.619345: Pseudo dice [0.7168, 0.7564, 0.7015, 0.8019, 0.609, 0.1293, 0.7744] +2026-04-14 22:30:07.621987: Epoch time: 104.47 s +2026-04-14 22:30:09.231862: +2026-04-14 22:30:09.234028: Epoch 3692 +2026-04-14 22:30:09.235983: Current learning rate: 0.001 +2026-04-14 22:31:52.273542: train_loss -0.524 +2026-04-14 22:31:52.279436: val_loss -0.4261 +2026-04-14 22:31:52.282675: Pseudo dice [0.7193, 0.8126, 0.8415, 0.432, 0.3433, 0.6967, 0.8429] +2026-04-14 22:31:52.285463: Epoch time: 103.05 s +2026-04-14 22:31:53.844557: +2026-04-14 22:31:53.846353: Epoch 3693 +2026-04-14 22:31:53.848557: Current learning rate: 0.00099 +2026-04-14 22:33:38.036479: train_loss -0.5159 +2026-04-14 22:33:38.044090: val_loss -0.4154 +2026-04-14 22:33:38.048407: Pseudo dice [0.7089, 0.8469, 0.6929, 0.8664, 0.3588, 0.8777, 0.741] +2026-04-14 22:33:38.051449: Epoch time: 104.2 s +2026-04-14 22:33:39.641071: +2026-04-14 22:33:39.648399: Epoch 3694 +2026-04-14 22:33:39.658720: Current learning rate: 0.00099 +2026-04-14 22:35:22.636328: train_loss -0.5168 +2026-04-14 22:35:22.644439: val_loss -0.3813 +2026-04-14 22:35:22.646811: Pseudo dice [0.4652, 0.7462, 0.6127, 0.6186, 0.1285, 0.5043, 0.8478] +2026-04-14 22:35:22.649801: Epoch time: 103.0 s +2026-04-14 22:35:24.292702: +2026-04-14 22:35:24.295041: Epoch 3695 +2026-04-14 22:35:24.297402: Current learning rate: 0.00099 +2026-04-14 22:37:07.595521: train_loss -0.5159 +2026-04-14 22:37:07.601083: val_loss -0.4144 +2026-04-14 22:37:07.603007: Pseudo dice [0.534, 0.3478, 0.7021, 0.835, 0.5865, 0.2516, 0.8274] +2026-04-14 22:37:07.606184: Epoch time: 103.31 s +2026-04-14 22:37:09.165709: +2026-04-14 22:37:09.167849: Epoch 3696 +2026-04-14 22:37:09.170139: Current learning rate: 0.00098 +2026-04-14 22:38:52.701260: train_loss -0.5221 +2026-04-14 22:38:52.707061: val_loss -0.3071 +2026-04-14 22:38:52.710563: Pseudo dice [0.2976, 0.5404, 0.7567, 0.7517, 0.184, 0.3065, 0.923] +2026-04-14 22:38:52.714150: Epoch time: 103.54 s +2026-04-14 22:38:54.307091: +2026-04-14 22:38:54.308690: Epoch 3697 +2026-04-14 22:38:54.310931: Current learning rate: 0.00098 +2026-04-14 22:40:37.828224: train_loss -0.5208 +2026-04-14 22:40:37.834551: val_loss -0.4756 +2026-04-14 22:40:37.837064: Pseudo dice [0.6231, 0.8064, 0.7546, 0.8607, 0.7226, 0.8238, 0.8342] +2026-04-14 22:40:37.841171: Epoch time: 103.52 s +2026-04-14 22:40:39.423520: +2026-04-14 22:40:39.425667: Epoch 3698 +2026-04-14 22:40:39.427498: Current learning rate: 0.00098 +2026-04-14 22:42:23.176354: train_loss -0.5193 +2026-04-14 22:42:23.181978: val_loss -0.4487 +2026-04-14 22:42:23.184701: Pseudo dice [0.4964, 0.7556, 0.6551, 0.7571, 0.6237, 0.8518, 0.8984] +2026-04-14 22:42:23.187322: Epoch time: 103.76 s +2026-04-14 22:42:24.781840: +2026-04-14 22:42:24.783774: Epoch 3699 +2026-04-14 22:42:24.786162: Current learning rate: 0.00097 +2026-04-14 22:44:07.145781: train_loss -0.5155 +2026-04-14 22:44:07.152159: val_loss -0.4377 +2026-04-14 22:44:07.154602: Pseudo dice [0.4708, 0.6483, 0.7882, 0.7963, 0.6503, 0.8775, 0.6931] +2026-04-14 22:44:07.157174: Epoch time: 102.37 s +2026-04-14 22:44:10.903637: +2026-04-14 22:44:10.906689: Epoch 3700 +2026-04-14 22:44:10.909642: Current learning rate: 0.00097 +2026-04-14 22:45:53.315183: train_loss -0.5202 +2026-04-14 22:45:53.321514: val_loss -0.4358 +2026-04-14 22:45:53.323863: Pseudo dice [0.5799, 0.7519, 0.7363, 0.9155, 0.5656, 0.6285, 0.762] +2026-04-14 22:45:53.326502: Epoch time: 102.42 s +2026-04-14 22:45:54.929550: +2026-04-14 22:45:54.931661: Epoch 3701 +2026-04-14 22:45:54.933554: Current learning rate: 0.00097 +2026-04-14 22:47:37.371648: train_loss -0.5165 +2026-04-14 22:47:37.378107: val_loss -0.4601 +2026-04-14 22:47:37.381026: Pseudo dice [0.6356, 0.86, 0.5953, 0.1279, 0.5437, 0.818, 0.8154] +2026-04-14 22:47:37.383356: Epoch time: 102.45 s +2026-04-14 22:47:39.066215: +2026-04-14 22:47:39.067979: Epoch 3702 +2026-04-14 22:47:39.069971: Current learning rate: 0.00097 +2026-04-14 22:49:21.339580: train_loss -0.5235 +2026-04-14 22:49:21.357186: val_loss -0.4434 +2026-04-14 22:49:21.363400: Pseudo dice [0.3307, 0.5976, 0.746, 0.5251, 0.5412, 0.7713, 0.5584] +2026-04-14 22:49:21.369034: Epoch time: 102.28 s +2026-04-14 22:49:22.983835: +2026-04-14 22:49:22.985765: Epoch 3703 +2026-04-14 22:49:22.988324: Current learning rate: 0.00096 +2026-04-14 22:51:07.581714: train_loss -0.5105 +2026-04-14 22:51:07.588517: val_loss -0.4423 +2026-04-14 22:51:07.593331: Pseudo dice [0.3389, 0.8478, 0.6211, 0.4594, 0.5453, 0.642, 0.8777] +2026-04-14 22:51:07.600127: Epoch time: 104.6 s +2026-04-14 22:51:09.251930: +2026-04-14 22:51:09.254203: Epoch 3704 +2026-04-14 22:51:09.256407: Current learning rate: 0.00096 +2026-04-14 22:52:52.489694: train_loss -0.5155 +2026-04-14 22:52:52.497032: val_loss -0.2236 +2026-04-14 22:52:52.499494: Pseudo dice [0.5751, 0.3838, 0.4965, 0.2627, 0.1768, 0.0549, 0.8443] +2026-04-14 22:52:52.501880: Epoch time: 103.24 s +2026-04-14 22:52:54.118217: +2026-04-14 22:52:54.120083: Epoch 3705 +2026-04-14 22:52:54.122369: Current learning rate: 0.00096 +2026-04-14 22:54:38.506642: train_loss -0.5081 +2026-04-14 22:54:38.516028: val_loss -0.3354 +2026-04-14 22:54:38.518177: Pseudo dice [0.2119, 0.7949, 0.6334, 0.8276, 0.1858, 0.3479, 0.8532] +2026-04-14 22:54:38.520983: Epoch time: 104.39 s +2026-04-14 22:54:40.179260: +2026-04-14 22:54:40.181297: Epoch 3706 +2026-04-14 22:54:40.183647: Current learning rate: 0.00095 +2026-04-14 22:56:23.048671: train_loss -0.5165 +2026-04-14 22:56:23.054705: val_loss -0.4401 +2026-04-14 22:56:23.057451: Pseudo dice [0.6841, 0.4514, 0.7376, 0.908, 0.4828, 0.853, 0.7799] +2026-04-14 22:56:23.059914: Epoch time: 102.87 s +2026-04-14 22:56:24.767510: +2026-04-14 22:56:24.769598: Epoch 3707 +2026-04-14 22:56:24.771786: Current learning rate: 0.00095 +2026-04-14 22:58:07.372150: train_loss -0.5183 +2026-04-14 22:58:07.379317: val_loss -0.4734 +2026-04-14 22:58:07.382200: Pseudo dice [0.7848, 0.8559, 0.8785, 0.9284, 0.6838, 0.7545, 0.8562] +2026-04-14 22:58:07.385975: Epoch time: 102.61 s +2026-04-14 22:58:08.984925: +2026-04-14 22:58:08.986956: Epoch 3708 +2026-04-14 22:58:08.989059: Current learning rate: 0.00095 +2026-04-14 22:59:52.072723: train_loss -0.5223 +2026-04-14 22:59:52.078969: val_loss -0.3616 +2026-04-14 22:59:52.081180: Pseudo dice [0.3916, 0.7258, 0.7688, 0.4257, 0.2275, 0.2408, 0.5321] +2026-04-14 22:59:52.083445: Epoch time: 103.09 s +2026-04-14 22:59:54.802779: +2026-04-14 22:59:54.805387: Epoch 3709 +2026-04-14 22:59:54.807551: Current learning rate: 0.00095 +2026-04-14 23:01:38.045063: train_loss -0.5229 +2026-04-14 23:01:38.050764: val_loss -0.4521 +2026-04-14 23:01:38.052892: Pseudo dice [0.6958, 0.6662, 0.6928, 0.5118, 0.5665, 0.3246, 0.8486] +2026-04-14 23:01:38.055281: Epoch time: 103.25 s +2026-04-14 23:01:39.651177: +2026-04-14 23:01:39.654163: Epoch 3710 +2026-04-14 23:01:39.656662: Current learning rate: 0.00094 +2026-04-14 23:03:23.126770: train_loss -0.5223 +2026-04-14 23:03:23.133052: val_loss -0.402 +2026-04-14 23:03:23.135135: Pseudo dice [0.3722, 0.6467, 0.7694, 0.3606, 0.6882, 0.1745, 0.8213] +2026-04-14 23:03:23.138484: Epoch time: 103.48 s +2026-04-14 23:03:24.722402: +2026-04-14 23:03:24.724742: Epoch 3711 +2026-04-14 23:03:24.727066: Current learning rate: 0.00094 +2026-04-14 23:05:09.988266: train_loss -0.5177 +2026-04-14 23:05:09.999058: val_loss -0.4185 +2026-04-14 23:05:10.003374: Pseudo dice [0.7643, 0.8134, 0.6571, 0.8035, 0.3304, 0.8112, 0.8964] +2026-04-14 23:05:10.006499: Epoch time: 105.27 s +2026-04-14 23:05:11.615829: +2026-04-14 23:05:11.619994: Epoch 3712 +2026-04-14 23:05:11.622367: Current learning rate: 0.00094 +2026-04-14 23:06:54.384797: train_loss -0.5184 +2026-04-14 23:06:54.390968: val_loss -0.3499 +2026-04-14 23:06:54.393373: Pseudo dice [0.4198, 0.7534, 0.6932, 0.2149, 0.4605, 0.221, 0.7532] +2026-04-14 23:06:54.396345: Epoch time: 102.77 s +2026-04-14 23:06:55.998230: +2026-04-14 23:06:56.000858: Epoch 3713 +2026-04-14 23:06:56.003569: Current learning rate: 0.00093 +2026-04-14 23:08:40.549229: train_loss -0.5233 +2026-04-14 23:08:40.555508: val_loss -0.4214 +2026-04-14 23:08:40.558952: Pseudo dice [0.4289, 0.6466, 0.7475, 0.8428, 0.3758, 0.882, 0.7325] +2026-04-14 23:08:40.561393: Epoch time: 104.55 s +2026-04-14 23:08:42.178441: +2026-04-14 23:08:42.180483: Epoch 3714 +2026-04-14 23:08:42.182781: Current learning rate: 0.00093 +2026-04-14 23:10:24.859759: train_loss -0.514 +2026-04-14 23:10:24.866325: val_loss -0.353 +2026-04-14 23:10:24.868712: Pseudo dice [0.2375, 0.4591, 0.6978, 0.865, 0.662, 0.2441, 0.8797] +2026-04-14 23:10:24.871232: Epoch time: 102.69 s +2026-04-14 23:10:26.492177: +2026-04-14 23:10:26.494081: Epoch 3715 +2026-04-14 23:10:26.496884: Current learning rate: 0.00093 +2026-04-14 23:12:08.954607: train_loss -0.5183 +2026-04-14 23:12:08.961399: val_loss -0.3839 +2026-04-14 23:12:08.964243: Pseudo dice [0.5614, 0.4157, 0.5638, 0.8343, 0.3944, 0.1503, 0.7831] +2026-04-14 23:12:08.967309: Epoch time: 102.47 s +2026-04-14 23:12:10.572737: +2026-04-14 23:12:10.574707: Epoch 3716 +2026-04-14 23:12:10.576913: Current learning rate: 0.00092 +2026-04-14 23:13:52.655388: train_loss -0.5144 +2026-04-14 23:13:52.662642: val_loss -0.4164 +2026-04-14 23:13:52.665951: Pseudo dice [0.6966, 0.8443, 0.7027, 0.6788, 0.4072, 0.2861, 0.8032] +2026-04-14 23:13:52.670023: Epoch time: 102.09 s +2026-04-14 23:13:54.252340: +2026-04-14 23:13:54.255687: Epoch 3717 +2026-04-14 23:13:54.258139: Current learning rate: 0.00092 +2026-04-14 23:15:37.106577: train_loss -0.5177 +2026-04-14 23:15:37.112735: val_loss -0.2693 +2026-04-14 23:15:37.115233: Pseudo dice [0.7959, 0.4901, 0.7556, 0.8633, 0.5171, 0.2373, 0.8332] +2026-04-14 23:15:37.118335: Epoch time: 102.86 s +2026-04-14 23:15:38.719346: +2026-04-14 23:15:38.721230: Epoch 3718 +2026-04-14 23:15:38.724045: Current learning rate: 0.00092 +2026-04-14 23:17:22.135411: train_loss -0.5279 +2026-04-14 23:17:22.142049: val_loss -0.3653 +2026-04-14 23:17:22.144646: Pseudo dice [0.2229, 0.6331, 0.7128, 0.909, 0.3375, 0.1757, 0.6602] +2026-04-14 23:17:22.147530: Epoch time: 103.42 s +2026-04-14 23:17:23.706109: +2026-04-14 23:17:23.708022: Epoch 3719 +2026-04-14 23:17:23.710757: Current learning rate: 0.00092 +2026-04-14 23:19:06.762906: train_loss -0.5129 +2026-04-14 23:19:06.769621: val_loss -0.4117 +2026-04-14 23:19:06.772015: Pseudo dice [0.6763, 0.4142, 0.803, 0.4371, 0.5788, 0.4084, 0.7712] +2026-04-14 23:19:06.776117: Epoch time: 103.06 s +2026-04-14 23:19:08.364857: +2026-04-14 23:19:08.367437: Epoch 3720 +2026-04-14 23:19:08.369066: Current learning rate: 0.00091 +2026-04-14 23:20:53.008950: train_loss -0.5231 +2026-04-14 23:20:53.016155: val_loss -0.4308 +2026-04-14 23:20:53.018795: Pseudo dice [0.7293, 0.6771, 0.7309, 0.861, 0.6564, 0.7267, 0.6424] +2026-04-14 23:20:53.023550: Epoch time: 104.65 s +2026-04-14 23:20:54.578224: +2026-04-14 23:20:54.581638: Epoch 3721 +2026-04-14 23:20:54.583623: Current learning rate: 0.00091 +2026-04-14 23:22:37.550226: train_loss -0.5159 +2026-04-14 23:22:37.559034: val_loss -0.4123 +2026-04-14 23:22:37.562328: Pseudo dice [0.6505, 0.8404, 0.5991, 0.8388, 0.6421, 0.284, 0.5531] +2026-04-14 23:22:37.567637: Epoch time: 102.98 s +2026-04-14 23:22:39.117078: +2026-04-14 23:22:39.119437: Epoch 3722 +2026-04-14 23:22:39.121623: Current learning rate: 0.00091 +2026-04-14 23:24:21.588839: train_loss -0.5243 +2026-04-14 23:24:21.596086: val_loss -0.3851 +2026-04-14 23:24:21.598458: Pseudo dice [0.4648, 0.5548, 0.7481, 0.8193, 0.3119, 0.2187, 0.7908] +2026-04-14 23:24:21.601839: Epoch time: 102.48 s +2026-04-14 23:24:23.209492: +2026-04-14 23:24:23.211519: Epoch 3723 +2026-04-14 23:24:23.214112: Current learning rate: 0.0009 +2026-04-14 23:26:06.361688: train_loss -0.518 +2026-04-14 23:26:06.369551: val_loss -0.4309 +2026-04-14 23:26:06.371761: Pseudo dice [0.7669, 0.4864, 0.7939, 0.6177, 0.565, 0.1509, 0.8531] +2026-04-14 23:26:06.374475: Epoch time: 103.16 s +2026-04-14 23:26:07.991721: +2026-04-14 23:26:07.993672: Epoch 3724 +2026-04-14 23:26:07.995968: Current learning rate: 0.0009 +2026-04-14 23:27:52.328519: train_loss -0.5112 +2026-04-14 23:27:52.338028: val_loss -0.4288 +2026-04-14 23:27:52.340997: Pseudo dice [0.419, 0.852, 0.7279, 0.8713, 0.6917, 0.368, 0.8788] +2026-04-14 23:27:52.344314: Epoch time: 104.34 s +2026-04-14 23:27:53.928890: +2026-04-14 23:27:53.931993: Epoch 3725 +2026-04-14 23:27:53.935284: Current learning rate: 0.0009 +2026-04-14 23:29:36.122044: train_loss -0.524 +2026-04-14 23:29:36.128358: val_loss -0.4335 +2026-04-14 23:29:36.130528: Pseudo dice [0.2307, 0.7746, 0.6908, 0.9004, 0.3553, 0.8775, 0.8145] +2026-04-14 23:29:36.133160: Epoch time: 102.2 s +2026-04-14 23:29:37.724382: +2026-04-14 23:29:37.726719: Epoch 3726 +2026-04-14 23:29:37.730220: Current learning rate: 0.0009 +2026-04-14 23:31:20.974488: train_loss -0.529 +2026-04-14 23:31:20.979796: val_loss -0.4362 +2026-04-14 23:31:20.982351: Pseudo dice [0.8502, 0.7632, 0.7518, 0.7471, 0.6032, 0.3886, 0.8361] +2026-04-14 23:31:20.984939: Epoch time: 103.25 s +2026-04-14 23:31:22.517237: +2026-04-14 23:31:22.519801: Epoch 3727 +2026-04-14 23:31:22.521960: Current learning rate: 0.00089 +2026-04-14 23:33:04.855639: train_loss -0.5141 +2026-04-14 23:33:04.863365: val_loss -0.4007 +2026-04-14 23:33:04.866250: Pseudo dice [0.3923, 0.809, 0.6403, 0.6779, 0.5505, 0.6557, 0.8874] +2026-04-14 23:33:04.868788: Epoch time: 102.34 s +2026-04-14 23:33:06.673616: +2026-04-14 23:33:06.675284: Epoch 3728 +2026-04-14 23:33:06.677209: Current learning rate: 0.00089 +2026-04-14 23:34:50.542507: train_loss -0.5279 +2026-04-14 23:34:50.548398: val_loss -0.4484 +2026-04-14 23:34:50.550329: Pseudo dice [0.569, 0.7876, 0.7908, 0.6983, 0.5617, 0.6313, 0.8157] +2026-04-14 23:34:50.553315: Epoch time: 103.87 s +2026-04-14 23:34:52.211211: +2026-04-14 23:34:52.213222: Epoch 3729 +2026-04-14 23:34:52.215706: Current learning rate: 0.00089 +2026-04-14 23:36:37.881500: train_loss -0.5181 +2026-04-14 23:36:37.887980: val_loss -0.3441 +2026-04-14 23:36:37.890084: Pseudo dice [0.3209, 0.6581, 0.7247, 0.3906, 0.606, 0.19, 0.9227] +2026-04-14 23:36:37.892761: Epoch time: 105.67 s +2026-04-14 23:36:39.453165: +2026-04-14 23:36:39.455393: Epoch 3730 +2026-04-14 23:36:39.458309: Current learning rate: 0.00088 +2026-04-14 23:38:23.251505: train_loss -0.5266 +2026-04-14 23:38:23.259501: val_loss -0.4426 +2026-04-14 23:38:23.262382: Pseudo dice [0.8187, 0.6444, 0.6378, 0.1843, 0.6309, 0.7009, 0.6525] +2026-04-14 23:38:23.265021: Epoch time: 103.8 s +2026-04-14 23:38:24.843432: +2026-04-14 23:38:24.845557: Epoch 3731 +2026-04-14 23:38:24.848380: Current learning rate: 0.00088 +2026-04-14 23:40:09.688179: train_loss -0.5147 +2026-04-14 23:40:09.698705: val_loss -0.4365 +2026-04-14 23:40:09.700891: Pseudo dice [0.7866, 0.8798, 0.787, 0.4836, 0.4765, 0.7505, 0.8692] +2026-04-14 23:40:09.703192: Epoch time: 104.85 s +2026-04-14 23:40:11.311834: +2026-04-14 23:40:11.315095: Epoch 3732 +2026-04-14 23:40:11.317161: Current learning rate: 0.00088 +2026-04-14 23:41:56.835799: train_loss -0.5098 +2026-04-14 23:41:56.843587: val_loss -0.3398 +2026-04-14 23:41:56.846155: Pseudo dice [0.3035, 0.8468, 0.6025, 0.8455, 0.4363, 0.5183, 0.8371] +2026-04-14 23:41:56.848901: Epoch time: 105.53 s +2026-04-14 23:41:58.415003: +2026-04-14 23:41:58.417565: Epoch 3733 +2026-04-14 23:41:58.420315: Current learning rate: 0.00087 +2026-04-14 23:43:44.594766: train_loss -0.5226 +2026-04-14 23:43:44.601541: val_loss -0.443 +2026-04-14 23:43:44.604146: Pseudo dice [0.7662, 0.7993, 0.7845, 0.8951, 0.6661, 0.5308, 0.8814] +2026-04-14 23:43:44.606994: Epoch time: 106.18 s +2026-04-14 23:43:46.170086: +2026-04-14 23:43:46.172621: Epoch 3734 +2026-04-14 23:43:46.175386: Current learning rate: 0.00087 +2026-04-14 23:45:29.739963: train_loss -0.5065 +2026-04-14 23:45:29.745329: val_loss -0.4256 +2026-04-14 23:45:29.747241: Pseudo dice [0.7823, 0.8504, 0.8125, 0.7605, 0.3877, 0.0932, 0.8798] +2026-04-14 23:45:29.749497: Epoch time: 103.57 s +2026-04-14 23:45:31.337880: +2026-04-14 23:45:31.340149: Epoch 3735 +2026-04-14 23:45:31.344647: Current learning rate: 0.00087 +2026-04-14 23:47:13.629969: train_loss -0.5192 +2026-04-14 23:47:13.636964: val_loss -0.4295 +2026-04-14 23:47:13.639676: Pseudo dice [0.425, 0.6044, 0.8059, 0.7796, 0.4132, 0.4654, 0.6553] +2026-04-14 23:47:13.642382: Epoch time: 102.3 s +2026-04-14 23:47:15.230019: +2026-04-14 23:47:15.232944: Epoch 3736 +2026-04-14 23:47:15.235336: Current learning rate: 0.00087 +2026-04-14 23:48:57.985951: train_loss -0.5206 +2026-04-14 23:48:57.993358: val_loss -0.4354 +2026-04-14 23:48:57.996033: Pseudo dice [0.6884, 0.2419, 0.642, 0.8627, 0.6685, 0.1819, 0.8931] +2026-04-14 23:48:57.998163: Epoch time: 102.76 s +2026-04-14 23:48:59.577357: +2026-04-14 23:48:59.580198: Epoch 3737 +2026-04-14 23:48:59.582882: Current learning rate: 0.00086 +2026-04-14 23:50:44.780747: train_loss -0.5135 +2026-04-14 23:50:44.787749: val_loss -0.4076 +2026-04-14 23:50:44.790844: Pseudo dice [0.2586, 0.1414, 0.7338, 0.0344, 0.4999, 0.3102, 0.6988] +2026-04-14 23:50:44.794918: Epoch time: 105.21 s +2026-04-14 23:50:46.402844: +2026-04-14 23:50:46.407205: Epoch 3738 +2026-04-14 23:50:46.410744: Current learning rate: 0.00086 +2026-04-14 23:52:30.071300: train_loss -0.5191 +2026-04-14 23:52:30.079084: val_loss -0.4108 +2026-04-14 23:52:30.081519: Pseudo dice [0.4537, 0.4639, 0.7213, 0.761, 0.3184, 0.743, 0.3652] +2026-04-14 23:52:30.084183: Epoch time: 103.67 s +2026-04-14 23:52:31.690209: +2026-04-14 23:52:31.691925: Epoch 3739 +2026-04-14 23:52:31.693889: Current learning rate: 0.00086 +2026-04-14 23:54:14.938330: train_loss -0.513 +2026-04-14 23:54:14.944219: val_loss -0.3973 +2026-04-14 23:54:14.946700: Pseudo dice [0.6961, 0.5984, 0.776, 0.8593, 0.3011, 0.776, 0.3653] +2026-04-14 23:54:14.948850: Epoch time: 103.25 s +2026-04-14 23:54:16.529110: +2026-04-14 23:54:16.532458: Epoch 3740 +2026-04-14 23:54:16.534327: Current learning rate: 0.00085 +2026-04-14 23:55:59.168486: train_loss -0.5229 +2026-04-14 23:55:59.173803: val_loss -0.4202 +2026-04-14 23:55:59.176793: Pseudo dice [0.4257, 0.4061, 0.6852, 0.8905, 0.3496, 0.8028, 0.5947] +2026-04-14 23:55:59.180029: Epoch time: 102.64 s +2026-04-14 23:56:00.895572: +2026-04-14 23:56:00.908789: Epoch 3741 +2026-04-14 23:56:00.911205: Current learning rate: 0.00085 +2026-04-14 23:57:43.177016: train_loss -0.5283 +2026-04-14 23:57:43.186758: val_loss -0.3811 +2026-04-14 23:57:43.190370: Pseudo dice [0.4024, 0.529, 0.7194, 0.6369, 0.5118, 0.2844, 0.7349] +2026-04-14 23:57:43.194673: Epoch time: 102.29 s +2026-04-14 23:57:44.814529: +2026-04-14 23:57:44.816400: Epoch 3742 +2026-04-14 23:57:44.819700: Current learning rate: 0.00085 +2026-04-14 23:59:27.682656: train_loss -0.525 +2026-04-14 23:59:27.691061: val_loss -0.4184 +2026-04-14 23:59:27.693017: Pseudo dice [0.4857, 0.3364, 0.76, 0.4358, 0.5091, 0.7492, 0.8602] +2026-04-14 23:59:27.695454: Epoch time: 102.87 s +2026-04-14 23:59:29.249130: +2026-04-14 23:59:29.254946: Epoch 3743 +2026-04-14 23:59:29.260904: Current learning rate: 0.00085 +2026-04-15 00:01:13.914607: train_loss -0.5317 +2026-04-15 00:01:13.922664: val_loss -0.4359 +2026-04-15 00:01:13.925081: Pseudo dice [0.4755, 0.1085, 0.6424, 0.789, 0.5345, 0.5166, 0.8702] +2026-04-15 00:01:13.928322: Epoch time: 104.67 s +2026-04-15 00:01:15.524852: +2026-04-15 00:01:15.526767: Epoch 3744 +2026-04-15 00:01:15.529075: Current learning rate: 0.00084 +2026-04-15 00:02:59.761267: train_loss -0.53 +2026-04-15 00:02:59.787111: val_loss -0.4128 +2026-04-15 00:02:59.796300: Pseudo dice [0.7564, 0.7533, 0.7022, 0.08, 0.5536, 0.6598, 0.6467] +2026-04-15 00:02:59.803419: Epoch time: 104.24 s +2026-04-15 00:03:01.384315: +2026-04-15 00:03:01.386545: Epoch 3745 +2026-04-15 00:03:01.389037: Current learning rate: 0.00084 +2026-04-15 00:04:46.055958: train_loss -0.5241 +2026-04-15 00:04:46.064636: val_loss -0.3608 +2026-04-15 00:04:46.067769: Pseudo dice [0.4524, 0.4913, 0.6897, 0.8088, 0.483, 0.3794, 0.7181] +2026-04-15 00:04:46.070808: Epoch time: 104.68 s +2026-04-15 00:04:47.686487: +2026-04-15 00:04:47.690659: Epoch 3746 +2026-04-15 00:04:47.698569: Current learning rate: 0.00084 +2026-04-15 00:06:34.016318: train_loss -0.5349 +2026-04-15 00:06:34.026780: val_loss -0.4099 +2026-04-15 00:06:34.029523: Pseudo dice [0.4661, 0.4035, 0.69, 0.8014, 0.3101, 0.1968, 0.6055] +2026-04-15 00:06:34.033638: Epoch time: 106.33 s +2026-04-15 00:06:35.663998: +2026-04-15 00:06:35.666298: Epoch 3747 +2026-04-15 00:06:35.670089: Current learning rate: 0.00083 +2026-04-15 00:08:18.591023: train_loss -0.5213 +2026-04-15 00:08:18.597172: val_loss -0.4054 +2026-04-15 00:08:18.599077: Pseudo dice [0.1203, 0.6673, 0.6202, 0.8444, 0.5681, 0.1807, 0.6982] +2026-04-15 00:08:18.603000: Epoch time: 102.93 s +2026-04-15 00:08:20.272946: +2026-04-15 00:08:20.275341: Epoch 3748 +2026-04-15 00:08:20.278074: Current learning rate: 0.00083 +2026-04-15 00:10:03.888507: train_loss -0.5249 +2026-04-15 00:10:03.895144: val_loss -0.4504 +2026-04-15 00:10:03.897721: Pseudo dice [0.768, 0.3747, 0.7803, 0.8162, 0.7, 0.6067, 0.9179] +2026-04-15 00:10:03.900342: Epoch time: 103.62 s +2026-04-15 00:10:05.434570: +2026-04-15 00:10:05.438439: Epoch 3749 +2026-04-15 00:10:05.441027: Current learning rate: 0.00083 +2026-04-15 00:11:49.175617: train_loss -0.5208 +2026-04-15 00:11:49.184145: val_loss -0.3865 +2026-04-15 00:11:49.186050: Pseudo dice [0.7266, 0.3326, 0.768, 0.6772, 0.3308, 0.552, 0.6308] +2026-04-15 00:11:49.192296: Epoch time: 103.74 s +2026-04-15 00:11:52.873370: +2026-04-15 00:11:52.877182: Epoch 3750 +2026-04-15 00:11:52.881240: Current learning rate: 0.00082 +2026-04-15 00:13:36.053323: train_loss -0.5258 +2026-04-15 00:13:36.060125: val_loss -0.3256 +2026-04-15 00:13:36.062750: Pseudo dice [0.6165, 0.5478, 0.6754, 0.8926, 0.5643, 0.2331, 0.7573] +2026-04-15 00:13:36.066395: Epoch time: 103.18 s +2026-04-15 00:13:37.681628: +2026-04-15 00:13:37.687273: Epoch 3751 +2026-04-15 00:13:37.692621: Current learning rate: 0.00082 +2026-04-15 00:15:21.122598: train_loss -0.5272 +2026-04-15 00:15:21.129602: val_loss -0.3967 +2026-04-15 00:15:21.132699: Pseudo dice [0.6591, 0.7479, 0.7391, 0.8467, 0.6665, 0.0782, 0.8013] +2026-04-15 00:15:21.135319: Epoch time: 103.44 s +2026-04-15 00:15:22.761434: +2026-04-15 00:15:22.764097: Epoch 3752 +2026-04-15 00:15:22.766113: Current learning rate: 0.00082 +2026-04-15 00:17:04.915082: train_loss -0.524 +2026-04-15 00:17:04.921253: val_loss -0.4313 +2026-04-15 00:17:04.923268: Pseudo dice [0.775, 0.506, 0.7647, 0.7632, 0.3356, 0.4382, 0.5973] +2026-04-15 00:17:04.926039: Epoch time: 102.16 s +2026-04-15 00:17:06.477605: +2026-04-15 00:17:06.479791: Epoch 3753 +2026-04-15 00:17:06.482349: Current learning rate: 0.00082 +2026-04-15 00:18:48.956971: train_loss -0.5325 +2026-04-15 00:18:48.963570: val_loss -0.4611 +2026-04-15 00:18:48.965710: Pseudo dice [0.4549, 0.8022, 0.7623, 0.839, 0.6799, 0.8395, 0.8253] +2026-04-15 00:18:48.968419: Epoch time: 102.48 s +2026-04-15 00:18:50.567198: +2026-04-15 00:18:50.569091: Epoch 3754 +2026-04-15 00:18:50.571045: Current learning rate: 0.00081 +2026-04-15 00:20:33.161292: train_loss -0.5184 +2026-04-15 00:20:33.169305: val_loss -0.4069 +2026-04-15 00:20:33.173943: Pseudo dice [0.3882, 0.4414, 0.7681, 0.7809, 0.4898, 0.5974, 0.7667] +2026-04-15 00:20:33.176536: Epoch time: 102.6 s +2026-04-15 00:20:34.757517: +2026-04-15 00:20:34.759421: Epoch 3755 +2026-04-15 00:20:34.762995: Current learning rate: 0.00081 +2026-04-15 00:22:18.173734: train_loss -0.5301 +2026-04-15 00:22:18.180093: val_loss -0.4039 +2026-04-15 00:22:18.184124: Pseudo dice [0.518, 0.4033, 0.7269, 0.5296, 0.318, 0.124, 0.89] +2026-04-15 00:22:18.187189: Epoch time: 103.42 s +2026-04-15 00:22:19.721952: +2026-04-15 00:22:19.723709: Epoch 3756 +2026-04-15 00:22:19.725613: Current learning rate: 0.00081 +2026-04-15 00:24:02.701745: train_loss -0.5326 +2026-04-15 00:24:02.707174: val_loss -0.3594 +2026-04-15 00:24:02.709316: Pseudo dice [0.8316, 0.5794, 0.6545, 0.8671, 0.5408, 0.148, 0.7454] +2026-04-15 00:24:02.711645: Epoch time: 102.98 s +2026-04-15 00:24:04.300807: +2026-04-15 00:24:04.304281: Epoch 3757 +2026-04-15 00:24:04.306308: Current learning rate: 0.0008 +2026-04-15 00:25:47.975568: train_loss -0.5216 +2026-04-15 00:25:47.983015: val_loss -0.4433 +2026-04-15 00:25:47.985145: Pseudo dice [0.2637, 0.7784, 0.7224, 0.858, 0.4592, 0.8229, 0.6924] +2026-04-15 00:25:47.987491: Epoch time: 103.68 s +2026-04-15 00:25:49.563087: +2026-04-15 00:25:49.565991: Epoch 3758 +2026-04-15 00:25:49.568208: Current learning rate: 0.0008 +2026-04-15 00:27:31.867341: train_loss -0.5213 +2026-04-15 00:27:31.874365: val_loss -0.4275 +2026-04-15 00:27:31.876933: Pseudo dice [0.8084, 0.5815, 0.6246, 0.9044, 0.6151, 0.5518, 0.808] +2026-04-15 00:27:31.879830: Epoch time: 102.31 s +2026-04-15 00:27:33.461156: +2026-04-15 00:27:33.463264: Epoch 3759 +2026-04-15 00:27:33.466043: Current learning rate: 0.0008 +2026-04-15 00:29:16.232444: train_loss -0.5294 +2026-04-15 00:29:16.239010: val_loss -0.4265 +2026-04-15 00:29:16.241225: Pseudo dice [0.212, 0.8055, 0.686, 0.7947, 0.3729, 0.4559, 0.7817] +2026-04-15 00:29:16.244674: Epoch time: 102.77 s +2026-04-15 00:29:17.891979: +2026-04-15 00:29:17.894717: Epoch 3760 +2026-04-15 00:29:17.897150: Current learning rate: 0.00079 +2026-04-15 00:31:01.148614: train_loss -0.5194 +2026-04-15 00:31:01.155147: val_loss -0.3984 +2026-04-15 00:31:01.157620: Pseudo dice [0.1839, 0.7821, 0.7334, 0.4375, 0.3209, 0.1512, 0.6439] +2026-04-15 00:31:01.160653: Epoch time: 103.26 s +2026-04-15 00:31:02.985634: +2026-04-15 00:31:02.987868: Epoch 3761 +2026-04-15 00:31:02.990623: Current learning rate: 0.00079 +2026-04-15 00:32:45.644933: train_loss -0.5208 +2026-04-15 00:32:45.651668: val_loss -0.373 +2026-04-15 00:32:45.653760: Pseudo dice [0.4329, 0.6876, 0.7057, 0.6926, 0.5644, 0.316, 0.7103] +2026-04-15 00:32:45.658279: Epoch time: 102.66 s +2026-04-15 00:32:47.237993: +2026-04-15 00:32:47.240028: Epoch 3762 +2026-04-15 00:32:47.241965: Current learning rate: 0.00079 +2026-04-15 00:34:30.069966: train_loss -0.53 +2026-04-15 00:34:30.075078: val_loss -0.4344 +2026-04-15 00:34:30.077140: Pseudo dice [0.7752, 0.7686, 0.7718, 0.7371, 0.6194, 0.5007, 0.7942] +2026-04-15 00:34:30.079399: Epoch time: 102.84 s +2026-04-15 00:34:31.668762: +2026-04-15 00:34:31.670627: Epoch 3763 +2026-04-15 00:34:31.672686: Current learning rate: 0.00079 +2026-04-15 00:36:15.449513: train_loss -0.521 +2026-04-15 00:36:15.455863: val_loss -0.3983 +2026-04-15 00:36:15.458188: Pseudo dice [0.2478, 0.8158, 0.6592, 0.1015, 0.5541, 0.1503, 0.8017] +2026-04-15 00:36:15.461000: Epoch time: 103.78 s +2026-04-15 00:36:17.075368: +2026-04-15 00:36:17.077504: Epoch 3764 +2026-04-15 00:36:17.079735: Current learning rate: 0.00078 +2026-04-15 00:37:59.578295: train_loss -0.5271 +2026-04-15 00:37:59.588310: val_loss -0.4056 +2026-04-15 00:37:59.599324: Pseudo dice [0.8567, 0.4088, 0.7677, 0.884, 0.5122, 0.1142, 0.8376] +2026-04-15 00:37:59.602163: Epoch time: 102.51 s +2026-04-15 00:38:01.250585: +2026-04-15 00:38:01.252783: Epoch 3765 +2026-04-15 00:38:01.254961: Current learning rate: 0.00078 +2026-04-15 00:39:43.159879: train_loss -0.513 +2026-04-15 00:39:43.167360: val_loss -0.3955 +2026-04-15 00:39:43.171408: Pseudo dice [0.8225, 0.4263, 0.7636, 0.5992, 0.4856, 0.1622, 0.7504] +2026-04-15 00:39:43.173950: Epoch time: 101.91 s +2026-04-15 00:39:44.686487: +2026-04-15 00:39:44.688711: Epoch 3766 +2026-04-15 00:39:44.691093: Current learning rate: 0.00078 +2026-04-15 00:41:27.169685: train_loss -0.5216 +2026-04-15 00:41:27.176011: val_loss -0.3414 +2026-04-15 00:41:27.178481: Pseudo dice [0.4393, 0.7463, 0.7385, 0.8876, 0.5806, 0.204, 0.7693] +2026-04-15 00:41:27.180946: Epoch time: 102.49 s +2026-04-15 00:41:28.813313: +2026-04-15 00:41:28.815445: Epoch 3767 +2026-04-15 00:41:28.817419: Current learning rate: 0.00077 +2026-04-15 00:43:12.524994: train_loss -0.5091 +2026-04-15 00:43:12.531445: val_loss -0.4398 +2026-04-15 00:43:12.534475: Pseudo dice [0.5443, 0.615, 0.7578, 0.1453, 0.2928, 0.8231, 0.9132] +2026-04-15 00:43:12.537004: Epoch time: 103.72 s +2026-04-15 00:43:14.142586: +2026-04-15 00:43:14.145027: Epoch 3768 +2026-04-15 00:43:14.147228: Current learning rate: 0.00077 +2026-04-15 00:44:57.424975: train_loss -0.5251 +2026-04-15 00:44:57.431076: val_loss -0.4247 +2026-04-15 00:44:57.433044: Pseudo dice [0.8749, 0.7442, 0.7805, 0.8144, 0.5331, 0.2567, 0.7806] +2026-04-15 00:44:57.435633: Epoch time: 103.29 s +2026-04-15 00:44:59.060166: +2026-04-15 00:44:59.062338: Epoch 3769 +2026-04-15 00:44:59.064859: Current learning rate: 0.00077 +2026-04-15 00:46:43.751978: train_loss -0.5156 +2026-04-15 00:46:43.759053: val_loss -0.3916 +2026-04-15 00:46:43.762422: Pseudo dice [0.5364, 0.5293, 0.7108, 0.9092, 0.4682, 0.5098, 0.7882] +2026-04-15 00:46:43.765159: Epoch time: 104.7 s +2026-04-15 00:46:45.359247: +2026-04-15 00:46:45.361863: Epoch 3770 +2026-04-15 00:46:45.364800: Current learning rate: 0.00077 +2026-04-15 00:48:29.709805: train_loss -0.5214 +2026-04-15 00:48:29.718729: val_loss -0.3642 +2026-04-15 00:48:29.723461: Pseudo dice [0.4454, 0.6963, 0.7458, 0.705, 0.3214, 0.4464, 0.5919] +2026-04-15 00:48:29.728228: Epoch time: 104.35 s +2026-04-15 00:48:31.276981: +2026-04-15 00:48:31.278898: Epoch 3771 +2026-04-15 00:48:31.281041: Current learning rate: 0.00076 +2026-04-15 00:50:14.738000: train_loss -0.5286 +2026-04-15 00:50:14.744697: val_loss -0.4126 +2026-04-15 00:50:14.748053: Pseudo dice [0.4065, 0.7317, 0.8214, 0.7646, 0.6011, 0.359, 0.558] +2026-04-15 00:50:14.751237: Epoch time: 103.46 s +2026-04-15 00:50:16.339090: +2026-04-15 00:50:16.341149: Epoch 3772 +2026-04-15 00:50:16.343531: Current learning rate: 0.00076 +2026-04-15 00:52:00.156389: train_loss -0.5248 +2026-04-15 00:52:00.164274: val_loss -0.4057 +2026-04-15 00:52:00.167183: Pseudo dice [0.4318, 0.6554, 0.5955, 0.6369, 0.5454, 0.4231, 0.6498] +2026-04-15 00:52:00.170452: Epoch time: 103.82 s +2026-04-15 00:52:01.996100: +2026-04-15 00:52:01.998558: Epoch 3773 +2026-04-15 00:52:02.000524: Current learning rate: 0.00076 +2026-04-15 00:53:44.713118: train_loss -0.5337 +2026-04-15 00:53:44.719729: val_loss -0.4132 +2026-04-15 00:53:44.722142: Pseudo dice [0.5223, 0.8522, 0.6486, 0.6486, 0.5516, 0.511, 0.7678] +2026-04-15 00:53:44.724410: Epoch time: 102.72 s +2026-04-15 00:53:46.273769: +2026-04-15 00:53:46.275716: Epoch 3774 +2026-04-15 00:53:46.278458: Current learning rate: 0.00075 +2026-04-15 00:55:28.874826: train_loss -0.5214 +2026-04-15 00:55:28.881355: val_loss -0.433 +2026-04-15 00:55:28.883964: Pseudo dice [0.49, 0.1409, 0.7103, 0.8345, 0.5409, 0.6217, 0.8605] +2026-04-15 00:55:28.886562: Epoch time: 102.6 s +2026-04-15 00:55:30.469178: +2026-04-15 00:55:30.471344: Epoch 3775 +2026-04-15 00:55:30.473624: Current learning rate: 0.00075 +2026-04-15 00:57:12.560936: train_loss -0.5192 +2026-04-15 00:57:12.568690: val_loss -0.4485 +2026-04-15 00:57:12.571488: Pseudo dice [0.7901, 0.5338, 0.7032, 0.8547, 0.4013, 0.7963, 0.7751] +2026-04-15 00:57:12.574520: Epoch time: 102.1 s +2026-04-15 00:57:14.129554: +2026-04-15 00:57:14.131409: Epoch 3776 +2026-04-15 00:57:14.134591: Current learning rate: 0.00075 +2026-04-15 00:58:56.404713: train_loss -0.5173 +2026-04-15 00:58:56.410783: val_loss -0.4454 +2026-04-15 00:58:56.412786: Pseudo dice [0.5087, 0.6919, 0.7374, 0.8244, 0.4454, 0.777, 0.8557] +2026-04-15 00:58:56.415389: Epoch time: 102.28 s +2026-04-15 00:58:57.948292: +2026-04-15 00:58:57.950613: Epoch 3777 +2026-04-15 00:58:57.952772: Current learning rate: 0.00074 +2026-04-15 01:00:40.363793: train_loss -0.5263 +2026-04-15 01:00:40.370935: val_loss -0.4502 +2026-04-15 01:00:40.373319: Pseudo dice [0.4374, 0.3736, 0.7806, 0.7901, 0.6932, 0.6174, 0.8948] +2026-04-15 01:00:40.376176: Epoch time: 102.42 s +2026-04-15 01:00:41.932015: +2026-04-15 01:00:41.934125: Epoch 3778 +2026-04-15 01:00:41.936970: Current learning rate: 0.00074 +2026-04-15 01:02:25.534506: train_loss -0.519 +2026-04-15 01:02:25.543097: val_loss -0.4226 +2026-04-15 01:02:25.546343: Pseudo dice [0.7727, 0.3388, 0.6059, 0.8523, 0.2907, 0.7677, 0.9117] +2026-04-15 01:02:25.549216: Epoch time: 103.61 s +2026-04-15 01:02:27.167058: +2026-04-15 01:02:27.169252: Epoch 3779 +2026-04-15 01:02:27.171316: Current learning rate: 0.00074 +2026-04-15 01:04:09.742589: train_loss -0.515 +2026-04-15 01:04:09.749002: val_loss -0.4346 +2026-04-15 01:04:09.751227: Pseudo dice [0.6909, 0.6907, 0.7056, 0.7942, 0.4344, 0.8749, 0.7436] +2026-04-15 01:04:09.754070: Epoch time: 102.58 s +2026-04-15 01:04:11.299914: +2026-04-15 01:04:11.301762: Epoch 3780 +2026-04-15 01:04:11.304017: Current learning rate: 0.00074 +2026-04-15 01:05:53.707555: train_loss -0.5259 +2026-04-15 01:05:53.713663: val_loss -0.393 +2026-04-15 01:05:53.715842: Pseudo dice [0.5739, 0.7891, 0.7807, 0.9036, 0.582, 0.3017, 0.8873] +2026-04-15 01:05:53.718630: Epoch time: 102.41 s +2026-04-15 01:05:55.327098: +2026-04-15 01:05:55.328919: Epoch 3781 +2026-04-15 01:05:55.331272: Current learning rate: 0.00073 +2026-04-15 01:07:37.759488: train_loss -0.5245 +2026-04-15 01:07:37.766168: val_loss -0.4094 +2026-04-15 01:07:37.768028: Pseudo dice [0.8733, 0.7798, 0.7983, 0.8993, 0.5047, 0.498, 0.8476] +2026-04-15 01:07:37.770834: Epoch time: 102.44 s +2026-04-15 01:07:39.425306: +2026-04-15 01:07:39.427565: Epoch 3782 +2026-04-15 01:07:39.429767: Current learning rate: 0.00073 +2026-04-15 01:09:22.033533: train_loss -0.5215 +2026-04-15 01:09:22.041454: val_loss -0.4292 +2026-04-15 01:09:22.043596: Pseudo dice [0.8291, 0.7779, 0.8181, 0.7711, 0.5569, 0.7386, 0.5561] +2026-04-15 01:09:22.045868: Epoch time: 102.61 s +2026-04-15 01:09:22.047553: Yayy! New best EMA pseudo Dice: 0.6574 +2026-04-15 01:09:25.779315: +2026-04-15 01:09:25.783428: Epoch 3783 +2026-04-15 01:09:25.786430: Current learning rate: 0.00073 +2026-04-15 01:11:08.161102: train_loss -0.5153 +2026-04-15 01:11:08.167549: val_loss -0.4466 +2026-04-15 01:11:08.169956: Pseudo dice [0.8316, 0.6569, 0.7355, 0.4325, 0.615, 0.7629, 0.8919] +2026-04-15 01:11:08.173685: Epoch time: 102.39 s +2026-04-15 01:11:08.176075: Yayy! New best EMA pseudo Dice: 0.6621 +2026-04-15 01:11:11.834205: +2026-04-15 01:11:11.836910: Epoch 3784 +2026-04-15 01:11:11.839758: Current learning rate: 0.00072 +2026-04-15 01:12:55.115629: train_loss -0.5261 +2026-04-15 01:12:55.124078: val_loss -0.4472 +2026-04-15 01:12:55.126666: Pseudo dice [0.6174, 0.817, 0.8185, 0.8607, 0.6783, 0.4084, 0.922] +2026-04-15 01:12:55.130059: Epoch time: 103.29 s +2026-04-15 01:12:55.133118: Yayy! New best EMA pseudo Dice: 0.669 +2026-04-15 01:12:58.771158: +2026-04-15 01:12:58.773523: Epoch 3785 +2026-04-15 01:12:58.778001: Current learning rate: 0.00072 +2026-04-15 01:14:41.091962: train_loss -0.5285 +2026-04-15 01:14:41.097583: val_loss -0.4394 +2026-04-15 01:14:41.099409: Pseudo dice [0.3766, 0.8568, 0.7446, 0.7498, 0.5832, 0.8527, 0.8514] +2026-04-15 01:14:41.101870: Epoch time: 102.32 s +2026-04-15 01:14:41.104115: Yayy! New best EMA pseudo Dice: 0.6738 +2026-04-15 01:14:44.632335: +2026-04-15 01:14:44.635103: Epoch 3786 +2026-04-15 01:14:44.637277: Current learning rate: 0.00072 +2026-04-15 01:16:26.928762: train_loss -0.5202 +2026-04-15 01:16:26.935355: val_loss -0.3939 +2026-04-15 01:16:26.937278: Pseudo dice [0.6333, 0.8427, 0.6445, 0.8779, 0.3135, 0.3948, 0.677] +2026-04-15 01:16:26.939411: Epoch time: 102.3 s +2026-04-15 01:16:28.550575: +2026-04-15 01:16:28.552335: Epoch 3787 +2026-04-15 01:16:28.554163: Current learning rate: 0.00071 +2026-04-15 01:18:11.296249: train_loss -0.5218 +2026-04-15 01:18:11.303416: val_loss -0.3936 +2026-04-15 01:18:11.305780: Pseudo dice [0.5054, 0.8581, 0.6535, 0.8637, 0.6848, 0.3858, 0.8559] +2026-04-15 01:18:11.308873: Epoch time: 102.75 s +2026-04-15 01:18:14.012646: +2026-04-15 01:18:14.014576: Epoch 3788 +2026-04-15 01:18:14.016574: Current learning rate: 0.00071 +2026-04-15 01:19:56.572758: train_loss -0.5216 +2026-04-15 01:19:56.578632: val_loss -0.4416 +2026-04-15 01:19:56.581752: Pseudo dice [0.8176, 0.8156, 0.7149, 0.9133, 0.3147, 0.8114, 0.5311] +2026-04-15 01:19:56.584460: Epoch time: 102.56 s +2026-04-15 01:19:56.586966: Yayy! New best EMA pseudo Dice: 0.674 +2026-04-15 01:20:00.247401: +2026-04-15 01:20:00.263080: Epoch 3789 +2026-04-15 01:20:00.270466: Current learning rate: 0.00071 +2026-04-15 01:21:42.525088: train_loss -0.5223 +2026-04-15 01:21:42.532851: val_loss -0.3721 +2026-04-15 01:21:42.534800: Pseudo dice [0.2361, 0.5593, 0.7286, 0.862, 0.515, 0.0902, 0.6611] +2026-04-15 01:21:42.537015: Epoch time: 102.28 s +2026-04-15 01:21:44.081444: +2026-04-15 01:21:44.083323: Epoch 3790 +2026-04-15 01:21:44.085973: Current learning rate: 0.0007 +2026-04-15 01:23:27.691457: train_loss -0.5273 +2026-04-15 01:23:27.698493: val_loss -0.4314 +2026-04-15 01:23:27.700724: Pseudo dice [0.3983, 0.4468, 0.7399, 0.6655, 0.5196, 0.68, 0.8542] +2026-04-15 01:23:27.703391: Epoch time: 103.61 s +2026-04-15 01:23:29.257458: +2026-04-15 01:23:29.259229: Epoch 3791 +2026-04-15 01:23:29.261443: Current learning rate: 0.0007 +2026-04-15 01:25:11.614250: train_loss -0.5392 +2026-04-15 01:25:11.623162: val_loss -0.4306 +2026-04-15 01:25:11.625416: Pseudo dice [0.8106, 0.5592, 0.6999, 0.0868, 0.6726, 0.7889, 0.9031] +2026-04-15 01:25:11.628461: Epoch time: 102.36 s +2026-04-15 01:25:13.169728: +2026-04-15 01:25:13.171727: Epoch 3792 +2026-04-15 01:25:13.173981: Current learning rate: 0.0007 +2026-04-15 01:26:56.109402: train_loss -0.5327 +2026-04-15 01:26:56.119294: val_loss -0.4656 +2026-04-15 01:26:56.121457: Pseudo dice [0.5812, 0.777, 0.6881, 0.9272, 0.6266, 0.8495, 0.9112] +2026-04-15 01:26:56.124831: Epoch time: 102.94 s +2026-04-15 01:26:57.782730: +2026-04-15 01:26:57.784756: Epoch 3793 +2026-04-15 01:26:57.786647: Current learning rate: 0.0007 +2026-04-15 01:28:40.090925: train_loss -0.5262 +2026-04-15 01:28:40.098845: val_loss -0.4225 +2026-04-15 01:28:40.101553: Pseudo dice [0.6064, 0.6965, 0.8015, 0.7264, 0.5656, 0.3304, 0.843] +2026-04-15 01:28:40.104929: Epoch time: 102.31 s +2026-04-15 01:28:41.754475: +2026-04-15 01:28:41.756159: Epoch 3794 +2026-04-15 01:28:41.758322: Current learning rate: 0.00069 +2026-04-15 01:30:25.065199: train_loss -0.5286 +2026-04-15 01:30:25.072445: val_loss -0.3818 +2026-04-15 01:30:25.075709: Pseudo dice [0.7217, 0.7014, 0.7282, 0.7566, 0.6465, 0.3874, 0.748] +2026-04-15 01:30:25.078234: Epoch time: 103.31 s +2026-04-15 01:30:26.631132: +2026-04-15 01:30:26.633090: Epoch 3795 +2026-04-15 01:30:26.634975: Current learning rate: 0.00069 +2026-04-15 01:32:09.069084: train_loss -0.5233 +2026-04-15 01:32:09.077113: val_loss -0.4216 +2026-04-15 01:32:09.079566: Pseudo dice [0.6813, 0.7982, 0.7254, 0.0253, 0.6804, 0.3151, 0.6524] +2026-04-15 01:32:09.081769: Epoch time: 102.44 s +2026-04-15 01:32:10.675069: +2026-04-15 01:32:10.677327: Epoch 3796 +2026-04-15 01:32:10.680300: Current learning rate: 0.00069 +2026-04-15 01:33:53.888390: train_loss -0.5316 +2026-04-15 01:33:53.895427: val_loss -0.4564 +2026-04-15 01:33:53.898471: Pseudo dice [0.4103, 0.7874, 0.7704, 0.9191, 0.5625, 0.8158, 0.9085] +2026-04-15 01:33:53.901351: Epoch time: 103.22 s +2026-04-15 01:33:55.488258: +2026-04-15 01:33:55.490380: Epoch 3797 +2026-04-15 01:33:55.492568: Current learning rate: 0.00068 +2026-04-15 01:35:38.364908: train_loss -0.5165 +2026-04-15 01:35:38.373343: val_loss -0.4505 +2026-04-15 01:35:38.377748: Pseudo dice [0.6558, 0.8762, 0.8021, 0.8933, 0.6057, 0.7615, 0.9014] +2026-04-15 01:35:38.382866: Epoch time: 102.88 s +2026-04-15 01:35:38.387594: Yayy! New best EMA pseudo Dice: 0.6741 +2026-04-15 01:35:42.077055: +2026-04-15 01:35:42.079346: Epoch 3798 +2026-04-15 01:35:42.082586: Current learning rate: 0.00068 +2026-04-15 01:37:25.099726: train_loss -0.5147 +2026-04-15 01:37:25.113932: val_loss -0.3949 +2026-04-15 01:37:25.116709: Pseudo dice [0.5953, 0.7706, 0.511, 0.7289, 0.7005, 0.3598, 0.8295] +2026-04-15 01:37:25.119371: Epoch time: 103.03 s +2026-04-15 01:37:26.688068: +2026-04-15 01:37:26.691489: Epoch 3799 +2026-04-15 01:37:26.694174: Current learning rate: 0.00068 +2026-04-15 01:39:09.816811: train_loss -0.5179 +2026-04-15 01:39:09.824791: val_loss -0.4341 +2026-04-15 01:39:09.827800: Pseudo dice [0.7551, 0.8428, 0.5744, 0.7196, 0.4087, 0.7939, 0.7569] +2026-04-15 01:39:09.830952: Epoch time: 103.13 s +2026-04-15 01:39:13.558413: +2026-04-15 01:39:13.561213: Epoch 3800 +2026-04-15 01:39:13.563654: Current learning rate: 0.00067 +2026-04-15 01:40:56.105392: train_loss -0.531 +2026-04-15 01:40:56.112508: val_loss -0.3437 +2026-04-15 01:40:56.114931: Pseudo dice [0.7218, 0.8744, 0.7075, 0.441, 0.4188, 0.1266, 0.7506] +2026-04-15 01:40:56.118612: Epoch time: 102.55 s +2026-04-15 01:40:57.724917: +2026-04-15 01:40:57.727394: Epoch 3801 +2026-04-15 01:40:57.729678: Current learning rate: 0.00067 +2026-04-15 01:42:40.634083: train_loss -0.5256 +2026-04-15 01:42:40.639583: val_loss -0.4306 +2026-04-15 01:42:40.641857: Pseudo dice [0.3467, 0.752, 0.822, 0.7425, 0.5873, 0.3051, 0.8387] +2026-04-15 01:42:40.644197: Epoch time: 102.91 s +2026-04-15 01:42:42.226422: +2026-04-15 01:42:42.228569: Epoch 3802 +2026-04-15 01:42:42.231032: Current learning rate: 0.00067 +2026-04-15 01:44:26.136376: train_loss -0.5145 +2026-04-15 01:44:26.142000: val_loss -0.3871 +2026-04-15 01:44:26.144168: Pseudo dice [0.7922, 0.8466, 0.7678, 0.5294, 0.4643, 0.1778, 0.9023] +2026-04-15 01:44:26.146520: Epoch time: 103.91 s +2026-04-15 01:44:27.750341: +2026-04-15 01:44:27.752326: Epoch 3803 +2026-04-15 01:44:27.754278: Current learning rate: 0.00067 +2026-04-15 01:46:11.286019: train_loss -0.5251 +2026-04-15 01:46:11.291847: val_loss -0.4239 +2026-04-15 01:46:11.294191: Pseudo dice [0.5064, 0.6587, 0.7685, 0.7421, 0.414, 0.7311, 0.877] +2026-04-15 01:46:11.296552: Epoch time: 103.54 s +2026-04-15 01:46:12.869303: +2026-04-15 01:46:12.871110: Epoch 3804 +2026-04-15 01:46:12.873071: Current learning rate: 0.00066 +2026-04-15 01:47:56.255841: train_loss -0.5226 +2026-04-15 01:47:56.271636: val_loss -0.4562 +2026-04-15 01:47:56.278298: Pseudo dice [0.666, 0.8368, 0.6538, 0.8838, 0.6395, 0.7272, 0.9177] +2026-04-15 01:47:56.280921: Epoch time: 103.39 s +2026-04-15 01:47:57.888334: +2026-04-15 01:47:57.891424: Epoch 3805 +2026-04-15 01:47:57.894924: Current learning rate: 0.00066 +2026-04-15 01:49:40.645321: train_loss -0.5233 +2026-04-15 01:49:40.650813: val_loss -0.3296 +2026-04-15 01:49:40.653038: Pseudo dice [0.2777, 0.4881, 0.7256, 0.8223, 0.5461, 0.2519, 0.7214] +2026-04-15 01:49:40.655590: Epoch time: 102.76 s +2026-04-15 01:49:42.253107: +2026-04-15 01:49:42.255319: Epoch 3806 +2026-04-15 01:49:42.257247: Current learning rate: 0.00066 +2026-04-15 01:51:25.089775: train_loss -0.5284 +2026-04-15 01:51:25.101337: val_loss -0.2815 +2026-04-15 01:51:25.103303: Pseudo dice [0.7586, 0.7185, 0.7416, 0.7387, 0.4331, 0.2118, 0.5247] +2026-04-15 01:51:25.106564: Epoch time: 102.84 s +2026-04-15 01:51:27.835191: +2026-04-15 01:51:27.837291: Epoch 3807 +2026-04-15 01:51:27.839142: Current learning rate: 0.00065 +2026-04-15 01:53:10.203102: train_loss -0.521 +2026-04-15 01:53:10.211905: val_loss -0.4119 +2026-04-15 01:53:10.214392: Pseudo dice [0.4246, 0.7858, 0.6754, 0.8055, 0.6631, 0.2616, 0.807] +2026-04-15 01:53:10.217642: Epoch time: 102.37 s +2026-04-15 01:53:11.799629: +2026-04-15 01:53:11.801956: Epoch 3808 +2026-04-15 01:53:11.804299: Current learning rate: 0.00065 +2026-04-15 01:54:54.890728: train_loss -0.5186 +2026-04-15 01:54:54.898782: val_loss -0.4606 +2026-04-15 01:54:54.901215: Pseudo dice [0.8125, 0.8056, 0.7645, 0.8868, 0.3975, 0.7674, 0.8334] +2026-04-15 01:54:54.903910: Epoch time: 103.09 s +2026-04-15 01:54:56.487556: +2026-04-15 01:54:56.490134: Epoch 3809 +2026-04-15 01:54:56.492816: Current learning rate: 0.00065 +2026-04-15 01:56:39.326592: train_loss -0.5242 +2026-04-15 01:56:39.332869: val_loss -0.3452 +2026-04-15 01:56:39.335583: Pseudo dice [0.8101, 0.6691, 0.591, 0.3244, 0.5507, 0.1076, 0.8885] +2026-04-15 01:56:39.338784: Epoch time: 102.84 s +2026-04-15 01:56:40.934938: +2026-04-15 01:56:40.937623: Epoch 3810 +2026-04-15 01:56:40.939657: Current learning rate: 0.00064 +2026-04-15 01:58:23.421191: train_loss -0.5266 +2026-04-15 01:58:23.427291: val_loss -0.4502 +2026-04-15 01:58:23.429455: Pseudo dice [0.8033, 0.7208, 0.7627, 0.813, 0.5564, 0.8785, 0.8111] +2026-04-15 01:58:23.431616: Epoch time: 102.49 s +2026-04-15 01:58:25.049738: +2026-04-15 01:58:25.051963: Epoch 3811 +2026-04-15 01:58:25.054065: Current learning rate: 0.00064 +2026-04-15 02:00:08.036386: train_loss -0.5273 +2026-04-15 02:00:08.043029: val_loss -0.378 +2026-04-15 02:00:08.046214: Pseudo dice [0.7541, 0.5506, 0.8092, 0.8795, 0.522, 0.2309, 0.5862] +2026-04-15 02:00:08.049722: Epoch time: 102.99 s +2026-04-15 02:00:09.647535: +2026-04-15 02:00:09.650353: Epoch 3812 +2026-04-15 02:00:09.653336: Current learning rate: 0.00064 +2026-04-15 02:01:52.202907: train_loss -0.5319 +2026-04-15 02:01:52.210008: val_loss -0.3924 +2026-04-15 02:01:52.212311: Pseudo dice [0.553, 0.6457, 0.7808, 0.8866, 0.6944, 0.3423, 0.8272] +2026-04-15 02:01:52.214607: Epoch time: 102.56 s +2026-04-15 02:01:53.801178: +2026-04-15 02:01:53.803613: Epoch 3813 +2026-04-15 02:01:53.806130: Current learning rate: 0.00064 +2026-04-15 02:03:36.722000: train_loss -0.5232 +2026-04-15 02:03:36.728465: val_loss -0.394 +2026-04-15 02:03:36.748871: Pseudo dice [0.8548, 0.5536, 0.417, 0.1426, 0.5244, 0.0586, 0.8404] +2026-04-15 02:03:36.751439: Epoch time: 102.92 s +2026-04-15 02:03:38.370624: +2026-04-15 02:03:38.373334: Epoch 3814 +2026-04-15 02:03:38.375631: Current learning rate: 0.00063 +2026-04-15 02:05:21.436055: train_loss -0.5296 +2026-04-15 02:05:21.444503: val_loss -0.4225 +2026-04-15 02:05:21.447428: Pseudo dice [0.3309, 0.5784, 0.7563, 0.8679, 0.6373, 0.4372, 0.8305] +2026-04-15 02:05:21.452555: Epoch time: 103.07 s +2026-04-15 02:05:23.036471: +2026-04-15 02:05:23.039145: Epoch 3815 +2026-04-15 02:05:23.047789: Current learning rate: 0.00063 +2026-04-15 02:07:06.088233: train_loss -0.5292 +2026-04-15 02:07:06.093482: val_loss -0.3696 +2026-04-15 02:07:06.095561: Pseudo dice [0.6981, 0.6249, 0.6035, 0.8125, 0.4034, 0.2158, 0.7068] +2026-04-15 02:07:06.099453: Epoch time: 103.06 s +2026-04-15 02:07:07.697126: +2026-04-15 02:07:07.699085: Epoch 3816 +2026-04-15 02:07:07.701021: Current learning rate: 0.00063 +2026-04-15 02:08:52.110529: train_loss -0.5279 +2026-04-15 02:08:52.117734: val_loss -0.3171 +2026-04-15 02:08:52.121186: Pseudo dice [0.4518, 0.4788, 0.6454, 0.6473, 0.288, 0.2071, 0.8327] +2026-04-15 02:08:52.123964: Epoch time: 104.42 s +2026-04-15 02:08:53.734100: +2026-04-15 02:08:53.738126: Epoch 3817 +2026-04-15 02:08:53.742051: Current learning rate: 0.00062 +2026-04-15 02:10:36.745989: train_loss -0.5339 +2026-04-15 02:10:36.752033: val_loss -0.4403 +2026-04-15 02:10:36.755407: Pseudo dice [0.651, 0.8386, 0.804, 0.521, 0.6996, 0.1969, 0.9209] +2026-04-15 02:10:36.758658: Epoch time: 103.02 s +2026-04-15 02:10:38.337513: +2026-04-15 02:10:38.339510: Epoch 3818 +2026-04-15 02:10:38.342068: Current learning rate: 0.00062 +2026-04-15 02:12:21.111096: train_loss -0.5292 +2026-04-15 02:12:21.117944: val_loss -0.4537 +2026-04-15 02:12:21.120743: Pseudo dice [0.5693, 0.4118, 0.6593, 0.7419, 0.6239, 0.7769, 0.6439] +2026-04-15 02:12:21.123279: Epoch time: 102.78 s +2026-04-15 02:12:22.719519: +2026-04-15 02:12:22.724452: Epoch 3819 +2026-04-15 02:12:22.727512: Current learning rate: 0.00062 +2026-04-15 02:14:05.888015: train_loss -0.5221 +2026-04-15 02:14:05.894163: val_loss -0.4259 +2026-04-15 02:14:05.896038: Pseudo dice [0.6649, 0.638, 0.8081, 0.8998, 0.3595, 0.7694, 0.5292] +2026-04-15 02:14:05.898422: Epoch time: 103.17 s +2026-04-15 02:14:07.454244: +2026-04-15 02:14:07.456460: Epoch 3820 +2026-04-15 02:14:07.458385: Current learning rate: 0.00061 +2026-04-15 02:15:50.813029: train_loss -0.53 +2026-04-15 02:15:50.819356: val_loss -0.3514 +2026-04-15 02:15:50.821677: Pseudo dice [0.5026, 0.5721, 0.5753, 0.7805, 0.0423, 0.2111, 0.564] +2026-04-15 02:15:50.823929: Epoch time: 103.36 s +2026-04-15 02:15:52.462192: +2026-04-15 02:15:52.464009: Epoch 3821 +2026-04-15 02:15:52.466654: Current learning rate: 0.00061 +2026-04-15 02:17:35.607011: train_loss -0.5341 +2026-04-15 02:17:35.614048: val_loss -0.4217 +2026-04-15 02:17:35.616069: Pseudo dice [0.5809, 0.3558, 0.6827, 0.5333, 0.6302, 0.4259, 0.8692] +2026-04-15 02:17:35.618891: Epoch time: 103.15 s +2026-04-15 02:17:37.236845: +2026-04-15 02:17:37.238591: Epoch 3822 +2026-04-15 02:17:37.241038: Current learning rate: 0.00061 +2026-04-15 02:19:20.092920: train_loss -0.5307 +2026-04-15 02:19:20.101325: val_loss -0.4293 +2026-04-15 02:19:20.103651: Pseudo dice [0.6436, 0.5221, 0.7288, 0.8616, 0.7403, 0.3902, 0.7543] +2026-04-15 02:19:20.105889: Epoch time: 102.86 s +2026-04-15 02:19:21.746725: +2026-04-15 02:19:21.749176: Epoch 3823 +2026-04-15 02:19:21.752162: Current learning rate: 0.0006 +2026-04-15 02:21:04.878205: train_loss -0.5363 +2026-04-15 02:21:04.884559: val_loss -0.4181 +2026-04-15 02:21:04.887451: Pseudo dice [0.4012, 0.7685, 0.8174, 0.8402, 0.6218, 0.4433, 0.7562] +2026-04-15 02:21:04.890195: Epoch time: 103.14 s +2026-04-15 02:21:06.448239: +2026-04-15 02:21:06.450267: Epoch 3824 +2026-04-15 02:21:06.452600: Current learning rate: 0.0006 +2026-04-15 02:22:49.886420: train_loss -0.5338 +2026-04-15 02:22:49.894717: val_loss -0.4196 +2026-04-15 02:22:49.897054: Pseudo dice [0.8792, 0.7441, 0.7396, 0.9067, 0.5456, 0.3324, 0.9192] +2026-04-15 02:22:49.899607: Epoch time: 103.44 s +2026-04-15 02:22:51.469905: +2026-04-15 02:22:51.472412: Epoch 3825 +2026-04-15 02:22:51.474893: Current learning rate: 0.0006 +2026-04-15 02:24:34.310468: train_loss -0.5358 +2026-04-15 02:24:34.317308: val_loss -0.3857 +2026-04-15 02:24:34.319658: Pseudo dice [0.3784, 0.6349, 0.779, 0.6921, 0.6209, 0.3383, 0.7927] +2026-04-15 02:24:34.322141: Epoch time: 102.84 s +2026-04-15 02:24:35.864577: +2026-04-15 02:24:35.866851: Epoch 3826 +2026-04-15 02:24:35.869382: Current learning rate: 0.0006 +2026-04-15 02:26:18.913793: train_loss -0.5238 +2026-04-15 02:26:18.920741: val_loss -0.2755 +2026-04-15 02:26:18.922872: Pseudo dice [0.6483, 0.2745, 0.8076, 0.8656, 0.4776, 0.1721, 0.8473] +2026-04-15 02:26:18.927327: Epoch time: 103.05 s +2026-04-15 02:26:21.571396: +2026-04-15 02:26:21.573429: Epoch 3827 +2026-04-15 02:26:21.575414: Current learning rate: 0.00059 +2026-04-15 02:28:05.166291: train_loss -0.5334 +2026-04-15 02:28:05.172165: val_loss -0.3743 +2026-04-15 02:28:05.174006: Pseudo dice [0.7338, 0.2051, 0.8126, 0.074, 0.3762, 0.114, 0.5389] +2026-04-15 02:28:05.176153: Epoch time: 103.6 s +2026-04-15 02:28:06.785772: +2026-04-15 02:28:06.787593: Epoch 3828 +2026-04-15 02:28:06.789714: Current learning rate: 0.00059 +2026-04-15 02:29:49.450775: train_loss -0.5338 +2026-04-15 02:29:49.456619: val_loss -0.3554 +2026-04-15 02:29:49.458156: Pseudo dice [0.707, 0.6859, 0.7589, 0.678, 0.5245, 0.3866, 0.8318] +2026-04-15 02:29:49.460944: Epoch time: 102.67 s +2026-04-15 02:29:51.085344: +2026-04-15 02:29:51.087523: Epoch 3829 +2026-04-15 02:29:51.089851: Current learning rate: 0.00059 +2026-04-15 02:31:33.608557: train_loss -0.5328 +2026-04-15 02:31:33.616010: val_loss -0.4271 +2026-04-15 02:31:33.619023: Pseudo dice [0.3371, 0.4229, 0.7519, 0.8812, 0.405, 0.8362, 0.8076] +2026-04-15 02:31:33.622609: Epoch time: 102.53 s +2026-04-15 02:31:35.220598: +2026-04-15 02:31:35.222538: Epoch 3830 +2026-04-15 02:31:35.224801: Current learning rate: 0.00058 +2026-04-15 02:33:17.886947: train_loss -0.5326 +2026-04-15 02:33:17.893147: val_loss -0.3976 +2026-04-15 02:33:17.898742: Pseudo dice [0.717, 0.4351, 0.7554, 0.806, 0.2861, 0.4608, 0.4653] +2026-04-15 02:33:17.900909: Epoch time: 102.67 s +2026-04-15 02:33:19.482128: +2026-04-15 02:33:19.484336: Epoch 3831 +2026-04-15 02:33:19.486297: Current learning rate: 0.00058 +2026-04-15 02:35:01.799270: train_loss -0.528 +2026-04-15 02:35:01.809011: val_loss -0.4549 +2026-04-15 02:35:01.811852: Pseudo dice [0.7997, 0.378, 0.8476, 0.0587, 0.6991, 0.1957, 0.8191] +2026-04-15 02:35:01.814465: Epoch time: 102.32 s +2026-04-15 02:35:03.385663: +2026-04-15 02:35:03.388899: Epoch 3832 +2026-04-15 02:35:03.390970: Current learning rate: 0.00058 +2026-04-15 02:36:46.020587: train_loss -0.5285 +2026-04-15 02:36:46.026748: val_loss -0.4006 +2026-04-15 02:36:46.028731: Pseudo dice [0.3274, 0.2646, 0.7216, 0.6748, 0.2154, 0.3385, 0.3892] +2026-04-15 02:36:46.031181: Epoch time: 102.64 s +2026-04-15 02:36:47.606257: +2026-04-15 02:36:47.607858: Epoch 3833 +2026-04-15 02:36:47.610111: Current learning rate: 0.00057 +2026-04-15 02:38:30.067762: train_loss -0.5379 +2026-04-15 02:38:30.075810: val_loss -0.4371 +2026-04-15 02:38:30.078767: Pseudo dice [0.7573, 0.5999, 0.5329, 0.8845, 0.4587, 0.8324, 0.8098] +2026-04-15 02:38:30.081592: Epoch time: 102.47 s +2026-04-15 02:38:31.695340: +2026-04-15 02:38:31.698940: Epoch 3834 +2026-04-15 02:38:31.701089: Current learning rate: 0.00057 +2026-04-15 02:40:15.535835: train_loss -0.5355 +2026-04-15 02:40:15.541038: val_loss -0.4688 +2026-04-15 02:40:15.543258: Pseudo dice [0.6919, 0.4956, 0.8912, 0.8967, 0.429, 0.775, 0.7001] +2026-04-15 02:40:15.546106: Epoch time: 103.84 s +2026-04-15 02:40:17.163304: +2026-04-15 02:40:17.165450: Epoch 3835 +2026-04-15 02:40:17.167356: Current learning rate: 0.00057 +2026-04-15 02:42:00.119614: train_loss -0.5292 +2026-04-15 02:42:00.125597: val_loss -0.4564 +2026-04-15 02:42:00.128535: Pseudo dice [0.3151, 0.4465, 0.7661, 0.5394, 0.6273, 0.57, 0.7703] +2026-04-15 02:42:00.131580: Epoch time: 102.96 s +2026-04-15 02:42:01.697042: +2026-04-15 02:42:01.698934: Epoch 3836 +2026-04-15 02:42:01.700970: Current learning rate: 0.00056 +2026-04-15 02:43:44.079555: train_loss -0.5437 +2026-04-15 02:43:44.085780: val_loss -0.3755 +2026-04-15 02:43:44.088387: Pseudo dice [0.3437, 0.4085, 0.8491, 0.4685, 0.4723, 0.0641, 0.8181] +2026-04-15 02:43:44.091192: Epoch time: 102.39 s +2026-04-15 02:43:45.708365: +2026-04-15 02:43:45.711101: Epoch 3837 +2026-04-15 02:43:45.712837: Current learning rate: 0.00056 +2026-04-15 02:45:28.314981: train_loss -0.5311 +2026-04-15 02:45:28.322186: val_loss -0.3675 +2026-04-15 02:45:28.324623: Pseudo dice [0.4587, 0.4641, 0.8272, 0.3157, 0.6736, 0.194, 0.8678] +2026-04-15 02:45:28.327372: Epoch time: 102.61 s +2026-04-15 02:45:29.952722: +2026-04-15 02:45:29.955040: Epoch 3838 +2026-04-15 02:45:29.956702: Current learning rate: 0.00056 +2026-04-15 02:47:12.578688: train_loss -0.5283 +2026-04-15 02:47:12.584178: val_loss -0.4451 +2026-04-15 02:47:12.586107: Pseudo dice [0.8324, 0.7024, 0.8292, 0.0442, 0.6637, 0.8676, 0.8476] +2026-04-15 02:47:12.590081: Epoch time: 102.63 s +2026-04-15 02:47:14.146750: +2026-04-15 02:47:14.148358: Epoch 3839 +2026-04-15 02:47:14.150174: Current learning rate: 0.00055 +2026-04-15 02:48:56.808137: train_loss -0.5357 +2026-04-15 02:48:56.813901: val_loss -0.3991 +2026-04-15 02:48:56.816700: Pseudo dice [0.749, 0.549, 0.7382, 0.481, 0.4606, 0.138, 0.6799] +2026-04-15 02:48:56.819505: Epoch time: 102.67 s +2026-04-15 02:48:58.441858: +2026-04-15 02:48:58.454432: Epoch 3840 +2026-04-15 02:48:58.456700: Current learning rate: 0.00055 +2026-04-15 02:50:42.423895: train_loss -0.5402 +2026-04-15 02:50:42.430494: val_loss -0.407 +2026-04-15 02:50:42.432702: Pseudo dice [0.2874, 0.7462, 0.7088, 0.8544, 0.6654, 0.2731, 0.8887] +2026-04-15 02:50:42.435095: Epoch time: 103.99 s +2026-04-15 02:50:44.027966: +2026-04-15 02:50:44.029716: Epoch 3841 +2026-04-15 02:50:44.031785: Current learning rate: 0.00055 +2026-04-15 02:52:26.334099: train_loss -0.5359 +2026-04-15 02:52:26.340131: val_loss -0.4349 +2026-04-15 02:52:26.343329: Pseudo dice [0.2516, 0.5262, 0.7065, 0.0297, 0.6437, 0.3832, 0.7739] +2026-04-15 02:52:26.345681: Epoch time: 102.31 s +2026-04-15 02:52:27.936149: +2026-04-15 02:52:27.938560: Epoch 3842 +2026-04-15 02:52:27.940348: Current learning rate: 0.00055 +2026-04-15 02:54:10.625435: train_loss -0.5293 +2026-04-15 02:54:10.631244: val_loss -0.4242 +2026-04-15 02:54:10.633902: Pseudo dice [0.6722, 0.4372, 0.7356, 0.1396, 0.621, 0.4226, 0.8768] +2026-04-15 02:54:10.636966: Epoch time: 102.69 s +2026-04-15 02:54:12.235128: +2026-04-15 02:54:12.236843: Epoch 3843 +2026-04-15 02:54:12.239216: Current learning rate: 0.00054 +2026-04-15 02:55:54.764086: train_loss -0.5357 +2026-04-15 02:55:54.769903: val_loss -0.4591 +2026-04-15 02:55:54.771868: Pseudo dice [0.5852, 0.6676, 0.692, 0.8673, 0.6572, 0.8413, 0.8497] +2026-04-15 02:55:54.774812: Epoch time: 102.53 s +2026-04-15 02:55:56.360231: +2026-04-15 02:55:56.362150: Epoch 3844 +2026-04-15 02:55:56.364004: Current learning rate: 0.00054 +2026-04-15 02:57:38.941788: train_loss -0.5352 +2026-04-15 02:57:38.948171: val_loss -0.4244 +2026-04-15 02:57:38.955146: Pseudo dice [0.8553, 0.4559, 0.6616, 0.8955, 0.7088, 0.2346, 0.9185] +2026-04-15 02:57:38.958728: Epoch time: 102.59 s +2026-04-15 02:57:40.485328: +2026-04-15 02:57:40.488224: Epoch 3845 +2026-04-15 02:57:40.491024: Current learning rate: 0.00054 +2026-04-15 02:59:23.237983: train_loss -0.5371 +2026-04-15 02:59:23.243476: val_loss -0.3648 +2026-04-15 02:59:23.245316: Pseudo dice [0.7187, 0.4796, 0.5284, 0.265, 0.7034, 0.1457, 0.7374] +2026-04-15 02:59:23.249140: Epoch time: 102.76 s +2026-04-15 02:59:24.848357: +2026-04-15 02:59:24.850495: Epoch 3846 +2026-04-15 02:59:24.852399: Current learning rate: 0.00053 +2026-04-15 03:01:08.590566: train_loss -0.5394 +2026-04-15 03:01:08.597023: val_loss -0.4263 +2026-04-15 03:01:08.599158: Pseudo dice [0.777, 0.5405, 0.746, 0.2022, 0.5967, 0.7791, 0.7353] +2026-04-15 03:01:08.601609: Epoch time: 103.75 s +2026-04-15 03:01:10.193353: +2026-04-15 03:01:10.195459: Epoch 3847 +2026-04-15 03:01:10.197210: Current learning rate: 0.00053 +2026-04-15 03:02:53.518090: train_loss -0.5314 +2026-04-15 03:02:53.524928: val_loss -0.4401 +2026-04-15 03:02:53.527401: Pseudo dice [0.8201, 0.4458, 0.7298, 0.8807, 0.6185, 0.8783, 0.6287] +2026-04-15 03:02:53.529711: Epoch time: 103.33 s +2026-04-15 03:02:55.126128: +2026-04-15 03:02:55.127954: Epoch 3848 +2026-04-15 03:02:55.131326: Current learning rate: 0.00053 +2026-04-15 03:04:37.596871: train_loss -0.5294 +2026-04-15 03:04:37.602955: val_loss -0.4414 +2026-04-15 03:04:37.604928: Pseudo dice [0.5516, 0.7801, 0.6809, 0.3236, 0.6746, 0.3527, 0.8895] +2026-04-15 03:04:37.607483: Epoch time: 102.47 s +2026-04-15 03:04:39.234414: +2026-04-15 03:04:39.236831: Epoch 3849 +2026-04-15 03:04:39.239763: Current learning rate: 0.00052 +2026-04-15 03:06:21.626396: train_loss -0.5332 +2026-04-15 03:06:21.633239: val_loss -0.452 +2026-04-15 03:06:21.636050: Pseudo dice [0.7841, 0.5332, 0.5731, 0.8466, 0.5609, 0.2052, 0.9128] +2026-04-15 03:06:21.639609: Epoch time: 102.4 s +2026-04-15 03:06:25.355531: +2026-04-15 03:06:25.357978: Epoch 3850 +2026-04-15 03:06:25.359696: Current learning rate: 0.00052 +2026-04-15 03:08:07.860325: train_loss -0.5324 +2026-04-15 03:08:07.865489: val_loss -0.3838 +2026-04-15 03:08:07.867419: Pseudo dice [0.5151, 0.7313, 0.7303, 0.2093, 0.3599, 0.2735, 0.417] +2026-04-15 03:08:07.870255: Epoch time: 102.51 s +2026-04-15 03:08:09.463566: +2026-04-15 03:08:09.465490: Epoch 3851 +2026-04-15 03:08:09.467232: Current learning rate: 0.00052 +2026-04-15 03:09:52.345611: train_loss -0.5337 +2026-04-15 03:09:52.351033: val_loss -0.4447 +2026-04-15 03:09:52.353907: Pseudo dice [0.607, 0.5684, 0.7483, 0.8535, 0.5774, 0.8413, 0.6941] +2026-04-15 03:09:52.356368: Epoch time: 102.89 s +2026-04-15 03:09:53.932467: +2026-04-15 03:09:53.935301: Epoch 3852 +2026-04-15 03:09:53.937851: Current learning rate: 0.00051 +2026-04-15 03:11:38.165396: train_loss -0.5385 +2026-04-15 03:11:38.174992: val_loss -0.4514 +2026-04-15 03:11:38.177924: Pseudo dice [0.5321, 0.5338, 0.6978, 0.8262, 0.3891, 0.8231, 0.5376] +2026-04-15 03:11:38.181269: Epoch time: 104.24 s +2026-04-15 03:11:39.786663: +2026-04-15 03:11:39.793080: Epoch 3853 +2026-04-15 03:11:39.794947: Current learning rate: 0.00051 +2026-04-15 03:13:23.281288: train_loss -0.5358 +2026-04-15 03:13:23.289026: val_loss -0.414 +2026-04-15 03:13:23.291786: Pseudo dice [0.6142, 0.6591, 0.7034, 0.8967, 0.4318, 0.6013, 0.5706] +2026-04-15 03:13:23.294521: Epoch time: 103.5 s +2026-04-15 03:13:24.884694: +2026-04-15 03:13:24.887036: Epoch 3854 +2026-04-15 03:13:24.888983: Current learning rate: 0.00051 +2026-04-15 03:15:08.039538: train_loss -0.5335 +2026-04-15 03:15:08.046520: val_loss -0.2885 +2026-04-15 03:15:08.048762: Pseudo dice [0.4022, 0.5579, 0.6353, 0.3725, 0.5561, 0.2018, 0.8452] +2026-04-15 03:15:08.051162: Epoch time: 103.16 s +2026-04-15 03:15:09.630373: +2026-04-15 03:15:09.632287: Epoch 3855 +2026-04-15 03:15:09.634248: Current learning rate: 0.00051 +2026-04-15 03:16:53.162245: train_loss -0.5432 +2026-04-15 03:16:53.169847: val_loss -0.4409 +2026-04-15 03:16:53.172724: Pseudo dice [0.8066, 0.4526, 0.732, 0.7308, 0.5738, 0.8367, 0.7371] +2026-04-15 03:16:53.175070: Epoch time: 103.54 s +2026-04-15 03:16:54.758662: +2026-04-15 03:16:54.761070: Epoch 3856 +2026-04-15 03:16:54.762832: Current learning rate: 0.0005 +2026-04-15 03:18:38.344884: train_loss -0.5382 +2026-04-15 03:18:38.352376: val_loss -0.2763 +2026-04-15 03:18:38.354504: Pseudo dice [0.7251, 0.6694, 0.4226, 0.3159, 0.3301, 0.2648, 0.7637] +2026-04-15 03:18:38.357224: Epoch time: 103.59 s +2026-04-15 03:18:39.941114: +2026-04-15 03:18:39.943442: Epoch 3857 +2026-04-15 03:18:39.945410: Current learning rate: 0.0005 +2026-04-15 03:20:22.442744: train_loss -0.5443 +2026-04-15 03:20:22.449574: val_loss -0.4396 +2026-04-15 03:20:22.451799: Pseudo dice [0.3044, 0.4248, 0.8054, 0.6907, 0.6796, 0.3547, 0.697] +2026-04-15 03:20:22.454911: Epoch time: 102.51 s +2026-04-15 03:20:24.049685: +2026-04-15 03:20:24.051838: Epoch 3858 +2026-04-15 03:20:24.053651: Current learning rate: 0.0005 +2026-04-15 03:22:09.178047: train_loss -0.5379 +2026-04-15 03:22:09.184992: val_loss -0.4366 +2026-04-15 03:22:09.187549: Pseudo dice [0.7375, 0.7046, 0.8278, 0.7614, 0.6598, 0.807, 0.8072] +2026-04-15 03:22:09.190267: Epoch time: 105.13 s +2026-04-15 03:22:10.806717: +2026-04-15 03:22:10.808772: Epoch 3859 +2026-04-15 03:22:10.811168: Current learning rate: 0.00049 +2026-04-15 03:23:53.992702: train_loss -0.5367 +2026-04-15 03:23:53.998507: val_loss -0.4431 +2026-04-15 03:23:54.001294: Pseudo dice [0.7375, 0.2328, 0.6722, 0.7428, 0.6276, 0.8846, 0.8935] +2026-04-15 03:23:54.004231: Epoch time: 103.19 s +2026-04-15 03:23:55.594948: +2026-04-15 03:23:55.596648: Epoch 3860 +2026-04-15 03:23:55.598498: Current learning rate: 0.00049 +2026-04-15 03:25:38.129661: train_loss -0.53 +2026-04-15 03:25:38.135355: val_loss -0.4486 +2026-04-15 03:25:38.137397: Pseudo dice [0.4798, 0.7243, 0.7681, 0.8779, 0.6288, 0.7992, 0.8635] +2026-04-15 03:25:38.139400: Epoch time: 102.54 s +2026-04-15 03:25:39.694919: +2026-04-15 03:25:39.697145: Epoch 3861 +2026-04-15 03:25:39.699077: Current learning rate: 0.00049 +2026-04-15 03:27:22.716628: train_loss -0.5325 +2026-04-15 03:27:22.724059: val_loss -0.4477 +2026-04-15 03:27:22.726627: Pseudo dice [0.8419, 0.4683, 0.6957, 0.7831, 0.4559, 0.9056, 0.8682] +2026-04-15 03:27:22.729382: Epoch time: 103.03 s +2026-04-15 03:27:24.327456: +2026-04-15 03:27:24.329206: Epoch 3862 +2026-04-15 03:27:24.331024: Current learning rate: 0.00048 +2026-04-15 03:29:07.104782: train_loss -0.5479 +2026-04-15 03:29:07.113049: val_loss -0.4439 +2026-04-15 03:29:07.114993: Pseudo dice [0.1886, 0.7692, 0.6346, 0.8724, 0.5796, 0.8292, 0.8982] +2026-04-15 03:29:07.117442: Epoch time: 102.78 s +2026-04-15 03:29:08.726529: +2026-04-15 03:29:08.728382: Epoch 3863 +2026-04-15 03:29:08.730402: Current learning rate: 0.00048 +2026-04-15 03:30:51.693338: train_loss -0.5355 +2026-04-15 03:30:51.699278: val_loss -0.4561 +2026-04-15 03:30:51.701576: Pseudo dice [0.5186, 0.6434, 0.7775, 0.9002, 0.4078, 0.8517, 0.8777] +2026-04-15 03:30:51.703704: Epoch time: 102.97 s +2026-04-15 03:30:53.270281: +2026-04-15 03:30:53.273269: Epoch 3864 +2026-04-15 03:30:53.277586: Current learning rate: 0.00048 +2026-04-15 03:32:36.024160: train_loss -0.5328 +2026-04-15 03:32:36.031891: val_loss -0.3454 +2026-04-15 03:32:36.033996: Pseudo dice [0.1998, 0.7218, 0.5386, 0.6169, 0.5647, 0.3735, 0.8688] +2026-04-15 03:32:36.036973: Epoch time: 102.76 s +2026-04-15 03:32:37.655337: +2026-04-15 03:32:37.657210: Epoch 3865 +2026-04-15 03:32:37.659028: Current learning rate: 0.00047 +2026-04-15 03:34:20.673151: train_loss -0.5363 +2026-04-15 03:34:20.681604: val_loss -0.4247 +2026-04-15 03:34:20.684178: Pseudo dice [0.7219, 0.4717, 0.7225, 0.5149, 0.4866, 0.43, 0.921] +2026-04-15 03:34:20.687490: Epoch time: 103.02 s +2026-04-15 03:34:22.285794: +2026-04-15 03:34:22.287764: Epoch 3866 +2026-04-15 03:34:22.289360: Current learning rate: 0.00047 +2026-04-15 03:36:05.993579: train_loss -0.5341 +2026-04-15 03:36:06.001446: val_loss -0.4183 +2026-04-15 03:36:06.003278: Pseudo dice [0.7554, 0.7521, 0.665, 0.8052, 0.4973, 0.5592, 0.8992] +2026-04-15 03:36:06.005906: Epoch time: 103.71 s +2026-04-15 03:36:07.653285: +2026-04-15 03:36:07.655762: Epoch 3867 +2026-04-15 03:36:07.657651: Current learning rate: 0.00047 +2026-04-15 03:37:50.208082: train_loss -0.5367 +2026-04-15 03:37:50.216716: val_loss -0.4138 +2026-04-15 03:37:50.218789: Pseudo dice [0.4285, 0.5345, 0.6393, 0.6489, 0.6615, 0.4797, 0.8036] +2026-04-15 03:37:50.221206: Epoch time: 102.56 s +2026-04-15 03:37:51.815605: +2026-04-15 03:37:51.818029: Epoch 3868 +2026-04-15 03:37:51.819791: Current learning rate: 0.00046 +2026-04-15 03:39:34.116876: train_loss -0.5378 +2026-04-15 03:39:34.123173: val_loss -0.3937 +2026-04-15 03:39:34.125635: Pseudo dice [0.712, 0.8171, 0.771, 0.3004, 0.4599, 0.3087, 0.7312] +2026-04-15 03:39:34.128054: Epoch time: 102.31 s +2026-04-15 03:39:35.720492: +2026-04-15 03:39:35.722475: Epoch 3869 +2026-04-15 03:39:35.724370: Current learning rate: 0.00046 +2026-04-15 03:41:17.860497: train_loss -0.533 +2026-04-15 03:41:17.870020: val_loss -0.4329 +2026-04-15 03:41:17.872590: Pseudo dice [0.834, 0.5603, 0.7867, 0.8488, 0.6425, 0.5246, 0.9162] +2026-04-15 03:41:17.875870: Epoch time: 102.14 s +2026-04-15 03:41:19.489753: +2026-04-15 03:41:19.492660: Epoch 3870 +2026-04-15 03:41:19.495405: Current learning rate: 0.00046 +2026-04-15 03:43:01.490463: train_loss -0.5334 +2026-04-15 03:43:01.497931: val_loss -0.4081 +2026-04-15 03:43:01.500035: Pseudo dice [0.2465, 0.6784, 0.6707, 0.9067, 0.6014, 0.4773, 0.9428] +2026-04-15 03:43:01.502375: Epoch time: 102.0 s +2026-04-15 03:43:03.100289: +2026-04-15 03:43:03.102086: Epoch 3871 +2026-04-15 03:43:03.103881: Current learning rate: 0.00045 +2026-04-15 03:44:45.471760: train_loss -0.5327 +2026-04-15 03:44:45.480853: val_loss -0.4598 +2026-04-15 03:44:45.483435: Pseudo dice [0.5781, 0.7771, 0.7031, 0.8228, 0.5014, 0.8739, 0.7834] +2026-04-15 03:44:45.486130: Epoch time: 102.38 s +2026-04-15 03:44:47.106985: +2026-04-15 03:44:47.108855: Epoch 3872 +2026-04-15 03:44:47.110912: Current learning rate: 0.00045 +2026-04-15 03:46:29.856101: train_loss -0.5267 +2026-04-15 03:46:29.862056: val_loss -0.4531 +2026-04-15 03:46:29.863925: Pseudo dice [0.4469, 0.7837, 0.7619, 0.8859, 0.6143, 0.8926, 0.7438] +2026-04-15 03:46:29.866415: Epoch time: 102.75 s +2026-04-15 03:46:31.501893: +2026-04-15 03:46:31.504527: Epoch 3873 +2026-04-15 03:46:31.506358: Current learning rate: 0.00045 +2026-04-15 03:48:14.007917: train_loss -0.5413 +2026-04-15 03:48:14.013139: val_loss -0.4569 +2026-04-15 03:48:14.015553: Pseudo dice [0.6126, 0.7045, 0.8186, 0.8174, 0.6823, 0.5404, 0.925] +2026-04-15 03:48:14.018163: Epoch time: 102.51 s +2026-04-15 03:48:15.610751: +2026-04-15 03:48:15.612731: Epoch 3874 +2026-04-15 03:48:15.614581: Current learning rate: 0.00045 +2026-04-15 03:49:58.733006: train_loss -0.5312 +2026-04-15 03:49:58.739322: val_loss -0.4082 +2026-04-15 03:49:58.741368: Pseudo dice [0.8425, 0.3761, 0.7949, 0.8766, 0.4616, 0.6394, 0.57] +2026-04-15 03:49:58.744797: Epoch time: 103.13 s +2026-04-15 03:50:00.404010: +2026-04-15 03:50:00.405909: Epoch 3875 +2026-04-15 03:50:00.407645: Current learning rate: 0.00044 +2026-04-15 03:51:43.166214: train_loss -0.5325 +2026-04-15 03:51:43.172352: val_loss -0.4842 +2026-04-15 03:51:43.174365: Pseudo dice [0.4712, 0.5554, 0.8047, 0.8614, 0.7241, 0.831, 0.7723] +2026-04-15 03:51:43.176405: Epoch time: 102.77 s +2026-04-15 03:51:44.790609: +2026-04-15 03:51:44.804010: Epoch 3876 +2026-04-15 03:51:44.806851: Current learning rate: 0.00044 +2026-04-15 03:53:27.624529: train_loss -0.5369 +2026-04-15 03:53:27.632398: val_loss -0.4393 +2026-04-15 03:53:27.634187: Pseudo dice [0.5094, 0.7632, 0.8369, 0.8518, 0.4805, 0.323, 0.8398] +2026-04-15 03:53:27.636733: Epoch time: 102.84 s +2026-04-15 03:53:29.199314: +2026-04-15 03:53:29.201277: Epoch 3877 +2026-04-15 03:53:29.202883: Current learning rate: 0.00044 +2026-04-15 03:55:11.599957: train_loss -0.5273 +2026-04-15 03:55:11.606138: val_loss -0.463 +2026-04-15 03:55:11.608348: Pseudo dice [0.7742, 0.6503, 0.6107, 0.7924, 0.7587, 0.4803, 0.8247] +2026-04-15 03:55:11.611095: Epoch time: 102.4 s +2026-04-15 03:55:13.229811: +2026-04-15 03:55:13.231577: Epoch 3878 +2026-04-15 03:55:13.234365: Current learning rate: 0.00043 +2026-04-15 03:56:56.429808: train_loss -0.5271 +2026-04-15 03:56:56.435204: val_loss -0.3948 +2026-04-15 03:56:56.436984: Pseudo dice [0.7636, 0.5424, 0.6206, 0.8498, 0.6233, 0.4638, 0.8867] +2026-04-15 03:56:56.439425: Epoch time: 103.2 s +2026-04-15 03:56:58.107589: +2026-04-15 03:56:58.109815: Epoch 3879 +2026-04-15 03:56:58.111468: Current learning rate: 0.00043 +2026-04-15 03:58:41.499298: train_loss -0.5437 +2026-04-15 03:58:41.505597: val_loss -0.416 +2026-04-15 03:58:41.508628: Pseudo dice [0.5163, 0.4673, 0.7473, 0.902, 0.3838, 0.6281, 0.3418] +2026-04-15 03:58:41.511580: Epoch time: 103.4 s +2026-04-15 03:58:43.195127: +2026-04-15 03:58:43.197168: Epoch 3880 +2026-04-15 03:58:43.199152: Current learning rate: 0.00043 +2026-04-15 04:00:25.617893: train_loss -0.5338 +2026-04-15 04:00:25.623980: val_loss -0.4652 +2026-04-15 04:00:25.625863: Pseudo dice [0.5813, 0.6783, 0.8834, 0.88, 0.4658, 0.8108, 0.5232] +2026-04-15 04:00:25.628279: Epoch time: 102.43 s +2026-04-15 04:00:27.319621: +2026-04-15 04:00:27.321983: Epoch 3881 +2026-04-15 04:00:27.324127: Current learning rate: 0.00042 +2026-04-15 04:02:10.611380: train_loss -0.5366 +2026-04-15 04:02:10.618797: val_loss -0.4324 +2026-04-15 04:02:10.620896: Pseudo dice [0.4803, 0.8673, 0.7567, 0.8945, 0.4332, 0.2954, 0.4746] +2026-04-15 04:02:10.623892: Epoch time: 103.3 s +2026-04-15 04:02:12.204079: +2026-04-15 04:02:12.206243: Epoch 3882 +2026-04-15 04:02:12.208582: Current learning rate: 0.00042 +2026-04-15 04:03:55.541113: train_loss -0.536 +2026-04-15 04:03:55.546776: val_loss -0.4516 +2026-04-15 04:03:55.548647: Pseudo dice [0.6031, 0.754, 0.8168, 0.8667, 0.5444, 0.7547, 0.8655] +2026-04-15 04:03:55.552147: Epoch time: 103.34 s +2026-04-15 04:03:57.206544: +2026-04-15 04:03:57.211160: Epoch 3883 +2026-04-15 04:03:57.230027: Current learning rate: 0.00042 +2026-04-15 04:05:39.634813: train_loss -0.5272 +2026-04-15 04:05:39.640985: val_loss -0.4705 +2026-04-15 04:05:39.643789: Pseudo dice [0.7028, 0.319, 0.7191, 0.8468, 0.6727, 0.8416, 0.8686] +2026-04-15 04:05:39.646345: Epoch time: 102.43 s +2026-04-15 04:05:41.219653: +2026-04-15 04:05:41.221723: Epoch 3884 +2026-04-15 04:05:41.223641: Current learning rate: 0.00041 +2026-04-15 04:07:25.923628: train_loss -0.5412 +2026-04-15 04:07:25.929029: val_loss -0.4629 +2026-04-15 04:07:25.931033: Pseudo dice [0.8599, 0.7025, 0.7962, 0.8652, 0.3592, 0.8945, 0.6343] +2026-04-15 04:07:25.933551: Epoch time: 104.71 s +2026-04-15 04:07:25.935502: Yayy! New best EMA pseudo Dice: 0.6773 +2026-04-15 04:07:29.695342: +2026-04-15 04:07:29.697509: Epoch 3885 +2026-04-15 04:07:29.699469: Current learning rate: 0.00041 +2026-04-15 04:09:12.887738: train_loss -0.535 +2026-04-15 04:09:12.893339: val_loss -0.3292 +2026-04-15 04:09:12.895553: Pseudo dice [0.8411, 0.4961, 0.5795, 0.7454, 0.5485, 0.3465, 0.8335] +2026-04-15 04:09:12.900778: Epoch time: 103.2 s +2026-04-15 04:09:15.561030: +2026-04-15 04:09:15.562919: Epoch 3886 +2026-04-15 04:09:15.564508: Current learning rate: 0.00041 +2026-04-15 04:10:58.788807: train_loss -0.5303 +2026-04-15 04:10:58.797420: val_loss -0.4469 +2026-04-15 04:10:58.799886: Pseudo dice [0.6024, 0.6602, 0.7445, 0.6569, 0.3614, 0.8535, 0.6846] +2026-04-15 04:10:58.802469: Epoch time: 103.23 s +2026-04-15 04:11:00.393179: +2026-04-15 04:11:00.395361: Epoch 3887 +2026-04-15 04:11:00.396871: Current learning rate: 0.0004 +2026-04-15 04:12:42.858880: train_loss -0.5311 +2026-04-15 04:12:42.875168: val_loss -0.4075 +2026-04-15 04:12:42.880216: Pseudo dice [0.8345, 0.4311, 0.7498, 0.6055, 0.6469, 0.3801, 0.8269] +2026-04-15 04:12:42.886401: Epoch time: 102.47 s +2026-04-15 04:12:44.519964: +2026-04-15 04:12:44.522221: Epoch 3888 +2026-04-15 04:12:44.523990: Current learning rate: 0.0004 +2026-04-15 04:14:26.668912: train_loss -0.5387 +2026-04-15 04:14:26.674057: val_loss -0.4112 +2026-04-15 04:14:26.675912: Pseudo dice [0.5014, 0.4712, 0.6169, 0.7901, 0.6889, 0.3121, 0.7497] +2026-04-15 04:14:26.677855: Epoch time: 102.15 s +2026-04-15 04:14:28.264160: +2026-04-15 04:14:28.266504: Epoch 3889 +2026-04-15 04:14:28.268264: Current learning rate: 0.0004 +2026-04-15 04:16:11.503218: train_loss -0.5299 +2026-04-15 04:16:11.510901: val_loss -0.4217 +2026-04-15 04:16:11.513338: Pseudo dice [0.5203, 0.4408, 0.7774, 0.7552, 0.2946, 0.554, 0.8035] +2026-04-15 04:16:11.516490: Epoch time: 103.24 s +2026-04-15 04:16:13.130711: +2026-04-15 04:16:13.132983: Epoch 3890 +2026-04-15 04:16:13.134696: Current learning rate: 0.00039 +2026-04-15 04:17:55.843349: train_loss -0.5388 +2026-04-15 04:17:55.849694: val_loss -0.4417 +2026-04-15 04:17:55.851693: Pseudo dice [0.5034, 0.5163, 0.8174, 0.8775, 0.6136, 0.8215, 0.8761] +2026-04-15 04:17:55.853844: Epoch time: 102.72 s +2026-04-15 04:17:57.447679: +2026-04-15 04:17:57.449384: Epoch 3891 +2026-04-15 04:17:57.450968: Current learning rate: 0.00039 +2026-04-15 04:19:41.235362: train_loss -0.5345 +2026-04-15 04:19:41.240520: val_loss -0.3194 +2026-04-15 04:19:41.242840: Pseudo dice [0.8559, 0.4407, 0.641, 0.8919, 0.4241, 0.2462, 0.8011] +2026-04-15 04:19:41.247330: Epoch time: 103.79 s +2026-04-15 04:19:42.881715: +2026-04-15 04:19:42.884118: Epoch 3892 +2026-04-15 04:19:42.886276: Current learning rate: 0.00039 +2026-04-15 04:21:26.762203: train_loss -0.5281 +2026-04-15 04:21:26.775935: val_loss -0.4368 +2026-04-15 04:21:26.779215: Pseudo dice [0.5508, 0.339, 0.7874, 0.8437, 0.4383, 0.8973, 0.7971] +2026-04-15 04:21:26.782557: Epoch time: 103.88 s +2026-04-15 04:21:28.392841: +2026-04-15 04:21:28.394797: Epoch 3893 +2026-04-15 04:21:28.397816: Current learning rate: 0.00038 +2026-04-15 04:23:11.164571: train_loss -0.5386 +2026-04-15 04:23:11.170696: val_loss -0.2918 +2026-04-15 04:23:11.172627: Pseudo dice [0.5306, 0.268, 0.7221, 0.9067, 0.3379, 0.2253, 0.8822] +2026-04-15 04:23:11.174637: Epoch time: 102.78 s +2026-04-15 04:23:12.776902: +2026-04-15 04:23:12.779273: Epoch 3894 +2026-04-15 04:23:12.781054: Current learning rate: 0.00038 +2026-04-15 04:24:56.319578: train_loss -0.5413 +2026-04-15 04:24:56.325627: val_loss -0.4369 +2026-04-15 04:24:56.327518: Pseudo dice [0.8128, 0.3488, 0.6922, 0.8208, 0.4553, 0.8759, 0.9068] +2026-04-15 04:24:56.330027: Epoch time: 103.55 s +2026-04-15 04:24:57.940139: +2026-04-15 04:24:57.941960: Epoch 3895 +2026-04-15 04:24:57.943998: Current learning rate: 0.00038 +2026-04-15 04:26:40.937021: train_loss -0.529 +2026-04-15 04:26:40.944160: val_loss -0.365 +2026-04-15 04:26:40.946479: Pseudo dice [0.5574, 0.4171, 0.7083, 0.8953, 0.2761, 0.3783, 0.621] +2026-04-15 04:26:40.949340: Epoch time: 103.0 s +2026-04-15 04:26:42.593478: +2026-04-15 04:26:42.595427: Epoch 3896 +2026-04-15 04:26:42.597801: Current learning rate: 0.00037 +2026-04-15 04:28:25.731248: train_loss -0.5334 +2026-04-15 04:28:25.736359: val_loss -0.4284 +2026-04-15 04:28:25.738404: Pseudo dice [0.8205, 0.7097, 0.7171, 0.0706, 0.3647, 0.6221, 0.6205] +2026-04-15 04:28:25.740410: Epoch time: 103.14 s +2026-04-15 04:28:27.331916: +2026-04-15 04:28:27.337624: Epoch 3897 +2026-04-15 04:28:27.341997: Current learning rate: 0.00037 +2026-04-15 04:30:09.905602: train_loss -0.5358 +2026-04-15 04:30:09.911072: val_loss -0.4074 +2026-04-15 04:30:09.912930: Pseudo dice [0.5829, 0.6124, 0.7629, 0.8345, 0.4662, 0.452, 0.3584] +2026-04-15 04:30:09.915044: Epoch time: 102.58 s +2026-04-15 04:30:11.485990: +2026-04-15 04:30:11.488116: Epoch 3898 +2026-04-15 04:30:11.490410: Current learning rate: 0.00037 +2026-04-15 04:31:54.159648: train_loss -0.5409 +2026-04-15 04:31:54.164318: val_loss -0.4121 +2026-04-15 04:31:54.166202: Pseudo dice [0.7825, 0.2697, 0.7278, 0.699, 0.617, 0.4295, 0.75] +2026-04-15 04:31:54.168773: Epoch time: 102.68 s +2026-04-15 04:31:55.739974: +2026-04-15 04:31:55.741935: Epoch 3899 +2026-04-15 04:31:55.743862: Current learning rate: 0.00036 +2026-04-15 04:33:38.690815: train_loss -0.5362 +2026-04-15 04:33:38.698646: val_loss -0.3688 +2026-04-15 04:33:38.700791: Pseudo dice [0.6642, 0.6548, 0.6845, 0.718, 0.579, 0.301, 0.8391] +2026-04-15 04:33:38.703503: Epoch time: 102.95 s +2026-04-15 04:33:42.324762: +2026-04-15 04:33:42.326745: Epoch 3900 +2026-04-15 04:33:42.328448: Current learning rate: 0.00036 +2026-04-15 04:35:24.686227: train_loss -0.5321 +2026-04-15 04:35:24.700888: val_loss -0.4695 +2026-04-15 04:35:24.706283: Pseudo dice [0.8897, 0.3516, 0.764, 0.9036, 0.5622, 0.7889, 0.8597] +2026-04-15 04:35:24.712340: Epoch time: 102.37 s +2026-04-15 04:35:26.331650: +2026-04-15 04:35:26.333449: Epoch 3901 +2026-04-15 04:35:26.335780: Current learning rate: 0.00036 +2026-04-15 04:37:10.542920: train_loss -0.5279 +2026-04-15 04:37:10.549068: val_loss -0.4623 +2026-04-15 04:37:10.551046: Pseudo dice [0.7225, 0.6884, 0.7689, 0.8847, 0.5027, 0.7693, 0.8927] +2026-04-15 04:37:10.553148: Epoch time: 104.22 s +2026-04-15 04:37:12.172015: +2026-04-15 04:37:12.174058: Epoch 3902 +2026-04-15 04:37:12.177845: Current learning rate: 0.00036 +2026-04-15 04:38:55.706996: train_loss -0.5322 +2026-04-15 04:38:55.713064: val_loss -0.3894 +2026-04-15 04:38:55.715451: Pseudo dice [0.7376, 0.6762, 0.7431, 0.7416, 0.5757, 0.3629, 0.6146] +2026-04-15 04:38:55.718098: Epoch time: 103.54 s +2026-04-15 04:38:57.321370: +2026-04-15 04:38:57.323621: Epoch 3903 +2026-04-15 04:38:57.325284: Current learning rate: 0.00035 +2026-04-15 04:40:39.755055: train_loss -0.5372 +2026-04-15 04:40:39.759772: val_loss -0.4599 +2026-04-15 04:40:39.761583: Pseudo dice [0.8131, 0.6834, 0.6439, 0.8799, 0.7052, 0.762, 0.8004] +2026-04-15 04:40:39.765321: Epoch time: 102.44 s +2026-04-15 04:40:41.365395: +2026-04-15 04:40:41.370319: Epoch 3904 +2026-04-15 04:40:41.372186: Current learning rate: 0.00035 +2026-04-15 04:42:23.652293: train_loss -0.5408 +2026-04-15 04:42:23.661573: val_loss -0.4196 +2026-04-15 04:42:23.663942: Pseudo dice [0.3869, 0.515, 0.691, 0.7786, 0.6276, 0.876, 0.7761] +2026-04-15 04:42:23.666756: Epoch time: 102.29 s +2026-04-15 04:42:25.290439: +2026-04-15 04:42:25.292282: Epoch 3905 +2026-04-15 04:42:25.294122: Current learning rate: 0.00035 +2026-04-15 04:44:08.902536: train_loss -0.5364 +2026-04-15 04:44:08.907583: val_loss -0.4247 +2026-04-15 04:44:08.909573: Pseudo dice [0.5962, 0.3437, 0.79, 0.4757, 0.657, 0.332, 0.7835] +2026-04-15 04:44:08.912216: Epoch time: 103.62 s +2026-04-15 04:44:10.557768: +2026-04-15 04:44:10.560303: Epoch 3906 +2026-04-15 04:44:10.562540: Current learning rate: 0.00034 +2026-04-15 04:45:52.940732: train_loss -0.5319 +2026-04-15 04:45:52.948196: val_loss -0.4144 +2026-04-15 04:45:52.950552: Pseudo dice [0.3018, 0.6257, 0.7887, 0.8937, 0.6402, 0.4598, 0.9308] +2026-04-15 04:45:52.953264: Epoch time: 102.39 s +2026-04-15 04:45:54.547829: +2026-04-15 04:45:54.549811: Epoch 3907 +2026-04-15 04:45:54.551403: Current learning rate: 0.00034 +2026-04-15 04:47:36.809889: train_loss -0.5297 +2026-04-15 04:47:36.816767: val_loss -0.3431 +2026-04-15 04:47:36.819108: Pseudo dice [0.7774, 0.6511, 0.6886, 0.768, 0.4164, 0.4021, 0.7056] +2026-04-15 04:47:36.821449: Epoch time: 102.27 s +2026-04-15 04:47:38.422756: +2026-04-15 04:47:38.424546: Epoch 3908 +2026-04-15 04:47:38.426309: Current learning rate: 0.00034 +2026-04-15 04:49:21.304996: train_loss -0.5441 +2026-04-15 04:49:21.312006: val_loss -0.3896 +2026-04-15 04:49:21.313897: Pseudo dice [0.8347, 0.6114, 0.7205, 0.756, 0.6607, 0.3437, 0.7026] +2026-04-15 04:49:21.316653: Epoch time: 102.89 s +2026-04-15 04:49:22.896596: +2026-04-15 04:49:22.899366: Epoch 3909 +2026-04-15 04:49:22.901036: Current learning rate: 0.00033 +2026-04-15 04:51:06.197007: train_loss -0.5379 +2026-04-15 04:51:06.206215: val_loss -0.3839 +2026-04-15 04:51:06.208929: Pseudo dice [0.459, 0.6336, 0.7792, 0.816, 0.6723, 0.3493, 0.5748] +2026-04-15 04:51:06.212330: Epoch time: 103.3 s +2026-04-15 04:51:07.874114: +2026-04-15 04:51:07.876081: Epoch 3910 +2026-04-15 04:51:07.877922: Current learning rate: 0.00033 +2026-04-15 04:52:51.772542: train_loss -0.5397 +2026-04-15 04:52:51.779299: val_loss -0.4441 +2026-04-15 04:52:51.781401: Pseudo dice [0.4983, 0.4304, 0.6756, 0.8022, 0.5663, 0.7665, 0.8184] +2026-04-15 04:52:51.788768: Epoch time: 103.9 s +2026-04-15 04:52:53.398383: +2026-04-15 04:52:53.400241: Epoch 3911 +2026-04-15 04:52:53.403192: Current learning rate: 0.00033 +2026-04-15 04:54:36.099643: train_loss -0.536 +2026-04-15 04:54:36.106883: val_loss -0.3785 +2026-04-15 04:54:36.109154: Pseudo dice [0.4766, 0.4319, 0.7404, 0.7764, 0.6358, 0.329, 0.8588] +2026-04-15 04:54:36.111620: Epoch time: 102.71 s +2026-04-15 04:54:37.691708: +2026-04-15 04:54:37.693497: Epoch 3912 +2026-04-15 04:54:37.695032: Current learning rate: 0.00032 +2026-04-15 04:56:20.834695: train_loss -0.5434 +2026-04-15 04:56:20.841997: val_loss -0.4358 +2026-04-15 04:56:20.844376: Pseudo dice [0.7072, 0.4289, 0.7761, 0.7401, 0.5949, 0.4666, 0.9028] +2026-04-15 04:56:20.846754: Epoch time: 103.15 s +2026-04-15 04:56:22.414702: +2026-04-15 04:56:22.416545: Epoch 3913 +2026-04-15 04:56:22.418327: Current learning rate: 0.00032 +2026-04-15 04:58:05.741782: train_loss -0.5347 +2026-04-15 04:58:05.749890: val_loss -0.4671 +2026-04-15 04:58:05.753307: Pseudo dice [0.4525, 0.7126, 0.736, 0.887, 0.6078, 0.426, 0.8153] +2026-04-15 04:58:05.755302: Epoch time: 103.33 s +2026-04-15 04:58:07.315156: +2026-04-15 04:58:07.317181: Epoch 3914 +2026-04-15 04:58:07.319622: Current learning rate: 0.00032 +2026-04-15 04:59:51.391523: train_loss -0.5387 +2026-04-15 04:59:51.399874: val_loss -0.4051 +2026-04-15 04:59:51.402000: Pseudo dice [0.345, 0.764, 0.7602, 0.8395, 0.5225, 0.4555, 0.7] +2026-04-15 04:59:51.404359: Epoch time: 104.08 s +2026-04-15 04:59:53.052591: +2026-04-15 04:59:53.054240: Epoch 3915 +2026-04-15 04:59:53.056256: Current learning rate: 0.00031 +2026-04-15 05:01:35.753429: train_loss -0.534 +2026-04-15 05:01:35.764093: val_loss -0.3936 +2026-04-15 05:01:35.766736: Pseudo dice [0.119, 0.5108, 0.7395, 0.647, 0.6289, 0.433, 0.8871] +2026-04-15 05:01:35.771958: Epoch time: 102.7 s +2026-04-15 05:01:37.330744: +2026-04-15 05:01:37.332412: Epoch 3916 +2026-04-15 05:01:37.334813: Current learning rate: 0.00031 +2026-04-15 05:03:20.095924: train_loss -0.5423 +2026-04-15 05:03:20.104381: val_loss -0.4568 +2026-04-15 05:03:20.106125: Pseudo dice [0.2748, 0.3477, 0.6358, 0.7572, 0.6555, 0.8324, 0.7595] +2026-04-15 05:03:20.109024: Epoch time: 102.77 s +2026-04-15 05:03:21.712432: +2026-04-15 05:03:21.714302: Epoch 3917 +2026-04-15 05:03:21.715978: Current learning rate: 0.00031 +2026-04-15 05:05:04.468531: train_loss -0.543 +2026-04-15 05:05:04.476822: val_loss -0.4512 +2026-04-15 05:05:04.479243: Pseudo dice [0.2907, 0.6001, 0.7815, 0.8119, 0.5083, 0.5293, 0.8862] +2026-04-15 05:05:04.481444: Epoch time: 102.76 s +2026-04-15 05:05:06.056885: +2026-04-15 05:05:06.058843: Epoch 3918 +2026-04-15 05:05:06.060518: Current learning rate: 0.0003 +2026-04-15 05:06:48.693021: train_loss -0.5367 +2026-04-15 05:06:48.703323: val_loss -0.4466 +2026-04-15 05:06:48.706590: Pseudo dice [0.7521, 0.6785, 0.6675, 0.3856, 0.6854, 0.7808, 0.9168] +2026-04-15 05:06:48.709387: Epoch time: 102.64 s +2026-04-15 05:06:50.295266: +2026-04-15 05:06:50.297163: Epoch 3919 +2026-04-15 05:06:50.299304: Current learning rate: 0.0003 +2026-04-15 05:08:34.413538: train_loss -0.5338 +2026-04-15 05:08:34.422122: val_loss -0.4069 +2026-04-15 05:08:34.425851: Pseudo dice [0.7991, 0.8837, 0.7715, 0.9202, 0.3059, 0.4558, 0.9002] +2026-04-15 05:08:34.429098: Epoch time: 104.12 s +2026-04-15 05:08:36.056758: +2026-04-15 05:08:36.059469: Epoch 3920 +2026-04-15 05:08:36.062336: Current learning rate: 0.0003 +2026-04-15 05:10:19.323128: train_loss -0.5361 +2026-04-15 05:10:19.331396: val_loss -0.3876 +2026-04-15 05:10:19.333779: Pseudo dice [0.5558, 0.7555, 0.7522, 0.8445, 0.6481, 0.4689, 0.8257] +2026-04-15 05:10:19.336549: Epoch time: 103.27 s +2026-04-15 05:10:20.932046: +2026-04-15 05:10:20.933857: Epoch 3921 +2026-04-15 05:10:20.935433: Current learning rate: 0.00029 +2026-04-15 05:12:04.190495: train_loss -0.5465 +2026-04-15 05:12:04.198178: val_loss -0.4207 +2026-04-15 05:12:04.200167: Pseudo dice [0.8272, 0.8243, 0.7569, 0.6548, 0.4558, 0.9258, 0.6678] +2026-04-15 05:12:04.202893: Epoch time: 103.26 s +2026-04-15 05:12:05.794899: +2026-04-15 05:12:05.797482: Epoch 3922 +2026-04-15 05:12:05.799386: Current learning rate: 0.00029 +2026-04-15 05:13:48.281094: train_loss -0.5487 +2026-04-15 05:13:48.289554: val_loss -0.389 +2026-04-15 05:13:48.291776: Pseudo dice [0.3755, 0.7898, 0.7173, 0.8799, 0.5456, 0.4594, 0.8865] +2026-04-15 05:13:48.295755: Epoch time: 102.49 s +2026-04-15 05:13:49.897787: +2026-04-15 05:13:49.899711: Epoch 3923 +2026-04-15 05:13:49.901395: Current learning rate: 0.00029 +2026-04-15 05:15:32.325117: train_loss -0.5359 +2026-04-15 05:15:32.332915: val_loss -0.4306 +2026-04-15 05:15:32.334939: Pseudo dice [0.4266, 0.7565, 0.8031, 0.7698, 0.5325, 0.6296, 0.803] +2026-04-15 05:15:32.337577: Epoch time: 102.43 s +2026-04-15 05:15:33.903198: +2026-04-15 05:15:33.904940: Epoch 3924 +2026-04-15 05:15:33.906702: Current learning rate: 0.00028 +2026-04-15 05:17:16.286222: train_loss -0.53 +2026-04-15 05:17:16.294154: val_loss -0.3249 +2026-04-15 05:17:16.296750: Pseudo dice [0.6014, 0.5071, 0.6866, 0.8788, 0.3146, 0.3696, 0.8866] +2026-04-15 05:17:16.299266: Epoch time: 102.39 s +2026-04-15 05:17:19.014553: +2026-04-15 05:17:19.017272: Epoch 3925 +2026-04-15 05:17:19.018903: Current learning rate: 0.00028 +2026-04-15 05:19:01.392289: train_loss -0.5323 +2026-04-15 05:19:01.399972: val_loss -0.4212 +2026-04-15 05:19:01.401946: Pseudo dice [0.5186, 0.6562, 0.7819, 0.8945, 0.6347, 0.4191, 0.927] +2026-04-15 05:19:01.404510: Epoch time: 102.38 s +2026-04-15 05:19:02.986133: +2026-04-15 05:19:02.987952: Epoch 3926 +2026-04-15 05:19:02.989676: Current learning rate: 0.00028 +2026-04-15 05:20:45.189769: train_loss -0.5377 +2026-04-15 05:20:45.194783: val_loss -0.3887 +2026-04-15 05:20:45.196594: Pseudo dice [0.4699, 0.5653, 0.6241, 0.758, 0.5207, 0.5151, 0.888] +2026-04-15 05:20:45.199402: Epoch time: 102.21 s +2026-04-15 05:20:46.756518: +2026-04-15 05:20:46.758225: Epoch 3927 +2026-04-15 05:20:46.760074: Current learning rate: 0.00027 +2026-04-15 05:22:29.020643: train_loss -0.5385 +2026-04-15 05:22:29.026439: val_loss -0.4069 +2026-04-15 05:22:29.028349: Pseudo dice [0.7681, 0.5491, 0.6262, 0.7777, 0.6475, 0.3249, 0.8845] +2026-04-15 05:22:29.031666: Epoch time: 102.27 s +2026-04-15 05:22:30.627268: +2026-04-15 05:22:30.629307: Epoch 3928 +2026-04-15 05:22:30.631198: Current learning rate: 0.00027 +2026-04-15 05:24:12.958183: train_loss -0.5272 +2026-04-15 05:24:12.963947: val_loss -0.4302 +2026-04-15 05:24:12.965863: Pseudo dice [0.4871, 0.8102, 0.7874, 0.8759, 0.4982, 0.5946, 0.8544] +2026-04-15 05:24:12.968247: Epoch time: 102.33 s +2026-04-15 05:24:14.596614: +2026-04-15 05:24:14.598389: Epoch 3929 +2026-04-15 05:24:14.599894: Current learning rate: 0.00027 +2026-04-15 05:25:57.198787: train_loss -0.545 +2026-04-15 05:25:57.205125: val_loss -0.434 +2026-04-15 05:25:57.207681: Pseudo dice [0.7774, 0.8479, 0.8421, 0.6876, 0.6524, 0.5719, 0.8757] +2026-04-15 05:25:57.209686: Epoch time: 102.61 s +2026-04-15 05:25:58.799869: +2026-04-15 05:25:58.802321: Epoch 3930 +2026-04-15 05:25:58.804199: Current learning rate: 0.00026 +2026-04-15 05:27:41.144585: train_loss -0.5456 +2026-04-15 05:27:41.155400: val_loss -0.4037 +2026-04-15 05:27:41.157637: Pseudo dice [0.4943, 0.7261, 0.8483, 0.8732, 0.3734, 0.3506, 0.6441] +2026-04-15 05:27:41.160593: Epoch time: 102.35 s +2026-04-15 05:27:42.795822: +2026-04-15 05:27:42.798269: Epoch 3931 +2026-04-15 05:27:42.800215: Current learning rate: 0.00026 +2026-04-15 05:29:25.896743: train_loss -0.5385 +2026-04-15 05:29:25.905216: val_loss -0.4539 +2026-04-15 05:29:25.915245: Pseudo dice [0.7967, 0.6819, 0.8179, 0.3863, 0.536, 0.7832, 0.8159] +2026-04-15 05:29:25.917993: Epoch time: 103.1 s +2026-04-15 05:29:27.473228: +2026-04-15 05:29:27.475025: Epoch 3932 +2026-04-15 05:29:27.476909: Current learning rate: 0.00026 +2026-04-15 05:31:10.771567: train_loss -0.5368 +2026-04-15 05:31:10.777860: val_loss -0.3805 +2026-04-15 05:31:10.779932: Pseudo dice [0.8143, 0.7416, 0.7678, 0.8135, 0.5024, 0.1158, 0.8764] +2026-04-15 05:31:10.782256: Epoch time: 103.3 s +2026-04-15 05:31:12.368003: +2026-04-15 05:31:12.370405: Epoch 3933 +2026-04-15 05:31:12.372416: Current learning rate: 0.00025 +2026-04-15 05:32:54.587897: train_loss -0.5422 +2026-04-15 05:32:54.593532: val_loss -0.4469 +2026-04-15 05:32:54.596020: Pseudo dice [0.6414, 0.6801, 0.8243, 0.5339, 0.5496, 0.4593, 0.9162] +2026-04-15 05:32:54.598523: Epoch time: 102.22 s +2026-04-15 05:32:56.179058: +2026-04-15 05:32:56.180911: Epoch 3934 +2026-04-15 05:32:56.182776: Current learning rate: 0.00025 +2026-04-15 05:34:39.016793: train_loss -0.5291 +2026-04-15 05:34:39.022792: val_loss -0.4531 +2026-04-15 05:34:39.025374: Pseudo dice [0.2346, 0.8459, 0.7441, 0.8788, 0.4513, 0.911, 0.6747] +2026-04-15 05:34:39.029429: Epoch time: 102.84 s +2026-04-15 05:34:40.652935: +2026-04-15 05:34:40.657550: Epoch 3935 +2026-04-15 05:34:40.660858: Current learning rate: 0.00025 +2026-04-15 05:36:23.310977: train_loss -0.5411 +2026-04-15 05:36:23.318417: val_loss -0.239 +2026-04-15 05:36:23.320832: Pseudo dice [0.8333, 0.8513, 0.6495, 0.8536, 0.4061, 0.3557, 0.8287] +2026-04-15 05:36:23.323828: Epoch time: 102.66 s +2026-04-15 05:36:24.925074: +2026-04-15 05:36:24.927019: Epoch 3936 +2026-04-15 05:36:24.928789: Current learning rate: 0.00024 +2026-04-15 05:38:07.954312: train_loss -0.5362 +2026-04-15 05:38:07.970399: val_loss -0.3585 +2026-04-15 05:38:07.975737: Pseudo dice [0.8054, 0.8528, 0.7296, 0.6541, 0.4886, 0.292, 0.7857] +2026-04-15 05:38:07.980885: Epoch time: 103.03 s +2026-04-15 05:38:09.601887: +2026-04-15 05:38:09.603860: Epoch 3937 +2026-04-15 05:38:09.605942: Current learning rate: 0.00024 +2026-04-15 05:39:52.275441: train_loss -0.5374 +2026-04-15 05:39:52.280968: val_loss -0.4356 +2026-04-15 05:39:52.283116: Pseudo dice [0.732, 0.7561, 0.8127, 0.4, 0.5375, 0.3186, 0.831] +2026-04-15 05:39:52.285611: Epoch time: 102.68 s +2026-04-15 05:39:53.855455: +2026-04-15 05:39:53.857293: Epoch 3938 +2026-04-15 05:39:53.858990: Current learning rate: 0.00024 +2026-04-15 05:41:36.850952: train_loss -0.534 +2026-04-15 05:41:36.859316: val_loss -0.401 +2026-04-15 05:41:36.861703: Pseudo dice [0.1709, 0.8754, 0.842, 0.8473, 0.2955, 0.8194, 0.4905] +2026-04-15 05:41:36.864324: Epoch time: 103.0 s +2026-04-15 05:41:38.453367: +2026-04-15 05:41:38.455786: Epoch 3939 +2026-04-15 05:41:38.459626: Current learning rate: 0.00023 +2026-04-15 05:43:20.967702: train_loss -0.5393 +2026-04-15 05:43:20.975249: val_loss -0.3945 +2026-04-15 05:43:20.977180: Pseudo dice [0.277, 0.8857, 0.7441, 0.8483, 0.5139, 0.4436, 0.7978] +2026-04-15 05:43:20.979370: Epoch time: 102.52 s +2026-04-15 05:43:22.580633: +2026-04-15 05:43:22.582944: Epoch 3940 +2026-04-15 05:43:22.584513: Current learning rate: 0.00023 +2026-04-15 05:45:06.666066: train_loss -0.5433 +2026-04-15 05:45:06.671559: val_loss -0.42 +2026-04-15 05:45:06.674571: Pseudo dice [0.7674, 0.7799, 0.7204, 0.879, 0.4471, 0.6178, 0.4391] +2026-04-15 05:45:06.677592: Epoch time: 104.09 s +2026-04-15 05:45:08.304583: +2026-04-15 05:45:08.307003: Epoch 3941 +2026-04-15 05:45:08.308852: Current learning rate: 0.00022 +2026-04-15 05:46:52.382054: train_loss -0.5401 +2026-04-15 05:46:52.388501: val_loss -0.4621 +2026-04-15 05:46:52.390847: Pseudo dice [0.533, 0.6199, 0.7576, 0.556, 0.4096, 0.8442, 0.7536] +2026-04-15 05:46:52.392995: Epoch time: 104.08 s +2026-04-15 05:46:54.012357: +2026-04-15 05:46:54.014698: Epoch 3942 +2026-04-15 05:46:54.016608: Current learning rate: 0.00022 +2026-04-15 05:48:36.391332: train_loss -0.5366 +2026-04-15 05:48:36.399139: val_loss -0.3492 +2026-04-15 05:48:36.401110: Pseudo dice [0.7845, 0.6212, 0.7619, 0.7441, 0.6502, 0.402, 0.5682] +2026-04-15 05:48:36.403827: Epoch time: 102.38 s +2026-04-15 05:48:37.980748: +2026-04-15 05:48:37.983235: Epoch 3943 +2026-04-15 05:48:37.986483: Current learning rate: 0.00022 +2026-04-15 05:50:20.485142: train_loss -0.5321 +2026-04-15 05:50:20.491256: val_loss -0.4486 +2026-04-15 05:50:20.493437: Pseudo dice [0.7352, 0.6746, 0.7966, 0.4134, 0.6088, 0.6035, 0.8535] +2026-04-15 05:50:20.495567: Epoch time: 102.51 s +2026-04-15 05:50:22.392314: +2026-04-15 05:50:22.394337: Epoch 3944 +2026-04-15 05:50:22.396024: Current learning rate: 0.00021 +2026-04-15 05:52:05.012046: train_loss -0.5406 +2026-04-15 05:52:05.017855: val_loss -0.3993 +2026-04-15 05:52:05.019910: Pseudo dice [0.2437, 0.7183, 0.7156, 0.5621, 0.644, 0.3573, 0.7765] +2026-04-15 05:52:05.022591: Epoch time: 102.62 s +2026-04-15 05:52:07.796945: +2026-04-15 05:52:07.799295: Epoch 3945 +2026-04-15 05:52:07.800876: Current learning rate: 0.00021 +2026-04-15 05:53:50.585565: train_loss -0.539 +2026-04-15 05:53:50.590536: val_loss -0.3979 +2026-04-15 05:53:50.592632: Pseudo dice [0.7276, 0.6773, 0.6868, 0.9021, 0.4806, 0.3419, 0.7004] +2026-04-15 05:53:50.595937: Epoch time: 102.79 s +2026-04-15 05:53:52.176698: +2026-04-15 05:53:52.178342: Epoch 3946 +2026-04-15 05:53:52.180142: Current learning rate: 0.00021 +2026-04-15 05:55:35.039028: train_loss -0.5398 +2026-04-15 05:55:35.044808: val_loss -0.4486 +2026-04-15 05:55:35.047438: Pseudo dice [0.349, 0.8238, 0.8072, 0.8544, 0.5831, 0.8531, 0.8058] +2026-04-15 05:55:35.049800: Epoch time: 102.87 s +2026-04-15 05:55:36.649197: +2026-04-15 05:55:36.651452: Epoch 3947 +2026-04-15 05:55:36.653210: Current learning rate: 0.0002 +2026-04-15 05:57:18.909018: train_loss -0.5381 +2026-04-15 05:57:18.919657: val_loss -0.4061 +2026-04-15 05:57:18.923073: Pseudo dice [0.3816, 0.7872, 0.7736, 0.63, 0.4827, 0.1693, 0.7652] +2026-04-15 05:57:18.925867: Epoch time: 102.26 s +2026-04-15 05:57:20.521840: +2026-04-15 05:57:20.525755: Epoch 3948 +2026-04-15 05:57:20.528034: Current learning rate: 0.0002 +2026-04-15 05:59:03.320373: train_loss -0.5446 +2026-04-15 05:59:03.327403: val_loss -0.4581 +2026-04-15 05:59:03.329339: Pseudo dice [0.4012, 0.8283, 0.7791, 0.8202, 0.3829, 0.8447, 0.8371] +2026-04-15 05:59:03.331754: Epoch time: 102.8 s +2026-04-15 05:59:04.993887: +2026-04-15 05:59:04.995924: Epoch 3949 +2026-04-15 05:59:04.998737: Current learning rate: 0.0002 +2026-04-15 06:00:47.533250: train_loss -0.5345 +2026-04-15 06:00:47.540158: val_loss -0.2955 +2026-04-15 06:00:47.542276: Pseudo dice [0.4522, 0.8515, 0.7795, 0.7792, 0.3445, 0.3737, 0.7855] +2026-04-15 06:00:47.544740: Epoch time: 102.54 s +2026-04-15 06:00:51.277981: +2026-04-15 06:00:51.279831: Epoch 3950 +2026-04-15 06:00:51.281586: Current learning rate: 0.00019 +2026-04-15 06:02:34.319610: train_loss -0.5423 +2026-04-15 06:02:34.326876: val_loss -0.4084 +2026-04-15 06:02:34.330520: Pseudo dice [0.4212, 0.2918, 0.714, 0.8842, 0.682, 0.4598, 0.9057] +2026-04-15 06:02:34.333193: Epoch time: 103.05 s +2026-04-15 06:02:35.970187: +2026-04-15 06:02:35.972143: Epoch 3951 +2026-04-15 06:02:35.975478: Current learning rate: 0.00019 +2026-04-15 06:04:19.075785: train_loss -0.537 +2026-04-15 06:04:19.083207: val_loss -0.4585 +2026-04-15 06:04:19.086549: Pseudo dice [0.7936, 0.7833, 0.8587, 0.7148, 0.4132, 0.7704, 0.8447] +2026-04-15 06:04:19.088988: Epoch time: 103.11 s +2026-04-15 06:04:20.689120: +2026-04-15 06:04:20.691286: Epoch 3952 +2026-04-15 06:04:20.694630: Current learning rate: 0.00019 +2026-04-15 06:06:03.357762: train_loss -0.5284 +2026-04-15 06:06:03.362843: val_loss -0.4443 +2026-04-15 06:06:03.365800: Pseudo dice [0.8406, 0.8358, 0.7604, 0.1892, 0.6977, 0.8096, 0.7682] +2026-04-15 06:06:03.368831: Epoch time: 102.67 s +2026-04-15 06:06:04.970102: +2026-04-15 06:06:04.972119: Epoch 3953 +2026-04-15 06:06:04.973915: Current learning rate: 0.00018 +2026-04-15 06:07:47.577484: train_loss -0.5358 +2026-04-15 06:07:47.583460: val_loss -0.4552 +2026-04-15 06:07:47.585674: Pseudo dice [0.7314, 0.6758, 0.7788, 0.7921, 0.5568, 0.8887, 0.6422] +2026-04-15 06:07:47.588399: Epoch time: 102.61 s +2026-04-15 06:07:49.218052: +2026-04-15 06:07:49.220567: Epoch 3954 +2026-04-15 06:07:49.222255: Current learning rate: 0.00018 +2026-04-15 06:09:32.735245: train_loss -0.5413 +2026-04-15 06:09:32.741007: val_loss -0.4432 +2026-04-15 06:09:32.743471: Pseudo dice [0.6012, 0.6958, 0.7105, 0.1773, 0.6118, 0.8241, 0.7834] +2026-04-15 06:09:32.746650: Epoch time: 103.52 s +2026-04-15 06:09:34.374913: +2026-04-15 06:09:34.376502: Epoch 3955 +2026-04-15 06:09:34.378835: Current learning rate: 0.00018 +2026-04-15 06:11:16.861311: train_loss -0.5427 +2026-04-15 06:11:16.868437: val_loss -0.454 +2026-04-15 06:11:16.870602: Pseudo dice [0.549, 0.592, 0.4687, 0.8243, 0.4332, 0.8737, 0.8613] +2026-04-15 06:11:16.872771: Epoch time: 102.49 s +2026-04-15 06:11:18.432161: +2026-04-15 06:11:18.433770: Epoch 3956 +2026-04-15 06:11:18.435798: Current learning rate: 0.00017 +2026-04-15 06:13:00.911194: train_loss -0.5351 +2026-04-15 06:13:00.918315: val_loss -0.3554 +2026-04-15 06:13:00.921375: Pseudo dice [0.2328, 0.6396, 0.7558, 0.7879, 0.6221, 0.4577, 0.8014] +2026-04-15 06:13:00.924224: Epoch time: 102.48 s +2026-04-15 06:13:02.518154: +2026-04-15 06:13:02.519912: Epoch 3957 +2026-04-15 06:13:02.521417: Current learning rate: 0.00017 +2026-04-15 06:14:45.480880: train_loss -0.5277 +2026-04-15 06:14:45.489969: val_loss -0.4365 +2026-04-15 06:14:45.492460: Pseudo dice [0.7564, 0.8731, 0.8234, 0.8498, 0.6332, 0.4315, 0.8746] +2026-04-15 06:14:45.495208: Epoch time: 102.97 s +2026-04-15 06:14:47.096136: +2026-04-15 06:14:47.098349: Epoch 3958 +2026-04-15 06:14:47.099794: Current learning rate: 0.00017 +2026-04-15 06:16:29.723773: train_loss -0.5394 +2026-04-15 06:16:29.729788: val_loss -0.4338 +2026-04-15 06:16:29.733173: Pseudo dice [0.7815, 0.8791, 0.699, 0.721, 0.3634, 0.9084, 0.8015] +2026-04-15 06:16:29.736356: Epoch time: 102.63 s +2026-04-15 06:16:31.313405: +2026-04-15 06:16:31.315374: Epoch 3959 +2026-04-15 06:16:31.317742: Current learning rate: 0.00016 +2026-04-15 06:18:14.036895: train_loss -0.5332 +2026-04-15 06:18:14.041890: val_loss -0.4581 +2026-04-15 06:18:14.043755: Pseudo dice [0.7559, 0.7885, 0.642, 0.7496, 0.481, 0.8538, 0.7426] +2026-04-15 06:18:14.047637: Epoch time: 102.73 s +2026-04-15 06:18:14.050966: Yayy! New best EMA pseudo Dice: 0.678 +2026-04-15 06:18:17.922721: +2026-04-15 06:18:17.926607: Epoch 3960 +2026-04-15 06:18:17.929429: Current learning rate: 0.00016 +2026-04-15 06:20:01.370294: train_loss -0.5292 +2026-04-15 06:20:01.375699: val_loss -0.416 +2026-04-15 06:20:01.377753: Pseudo dice [0.5824, 0.6352, 0.8155, 0.6951, 0.6023, 0.2704, 0.9321] +2026-04-15 06:20:01.380295: Epoch time: 103.45 s +2026-04-15 06:20:02.985322: +2026-04-15 06:20:02.987446: Epoch 3961 +2026-04-15 06:20:02.989012: Current learning rate: 0.00015 +2026-04-15 06:21:45.258100: train_loss -0.5368 +2026-04-15 06:21:45.263207: val_loss -0.4408 +2026-04-15 06:21:45.264813: Pseudo dice [0.2599, 0.7027, 0.6778, 0.7336, 0.4076, 0.9156, 0.8412] +2026-04-15 06:21:45.267552: Epoch time: 102.28 s +2026-04-15 06:21:46.827043: +2026-04-15 06:21:46.829004: Epoch 3962 +2026-04-15 06:21:46.830624: Current learning rate: 0.00015 +2026-04-15 06:23:29.030493: train_loss -0.5366 +2026-04-15 06:23:29.036218: val_loss -0.3896 +2026-04-15 06:23:29.038594: Pseudo dice [0.8081, 0.7927, 0.7566, 0.6478, 0.429, 0.2593, 0.8596] +2026-04-15 06:23:29.040943: Epoch time: 102.21 s +2026-04-15 06:23:30.611872: +2026-04-15 06:23:30.614117: Epoch 3963 +2026-04-15 06:23:30.615942: Current learning rate: 0.00015 +2026-04-15 06:25:12.754592: train_loss -0.5339 +2026-04-15 06:25:12.761700: val_loss -0.4097 +2026-04-15 06:25:12.764652: Pseudo dice [0.8017, 0.8345, 0.6736, 0.7798, 0.5865, 0.4676, 0.8645] +2026-04-15 06:25:12.767130: Epoch time: 102.15 s +2026-04-15 06:25:15.502386: +2026-04-15 06:25:15.504475: Epoch 3964 +2026-04-15 06:25:15.506646: Current learning rate: 0.00014 +2026-04-15 06:26:57.840520: train_loss -0.5358 +2026-04-15 06:26:57.847207: val_loss -0.3521 +2026-04-15 06:26:57.849328: Pseudo dice [0.6241, 0.5628, 0.8692, 0.8506, 0.6642, 0.3984, 0.9149] +2026-04-15 06:26:57.851784: Epoch time: 102.34 s +2026-04-15 06:26:59.465953: +2026-04-15 06:26:59.467896: Epoch 3965 +2026-04-15 06:26:59.470241: Current learning rate: 0.00014 +2026-04-15 06:28:41.888004: train_loss -0.5388 +2026-04-15 06:28:41.893358: val_loss -0.4057 +2026-04-15 06:28:41.895006: Pseudo dice [0.2019, 0.6082, 0.7164, 0.6785, 0.2809, 0.8539, 0.7889] +2026-04-15 06:28:41.897157: Epoch time: 102.43 s +2026-04-15 06:28:43.500093: +2026-04-15 06:28:43.501884: Epoch 3966 +2026-04-15 06:28:43.503568: Current learning rate: 0.00014 +2026-04-15 06:30:25.981282: train_loss -0.5429 +2026-04-15 06:30:25.986210: val_loss -0.4497 +2026-04-15 06:30:25.988064: Pseudo dice [0.4745, 0.7211, 0.7857, 0.7242, 0.3185, 0.7591, 0.8925] +2026-04-15 06:30:25.990808: Epoch time: 102.48 s +2026-04-15 06:30:27.579074: +2026-04-15 06:30:27.581075: Epoch 3967 +2026-04-15 06:30:27.582847: Current learning rate: 0.00013 +2026-04-15 06:32:09.950025: train_loss -0.5405 +2026-04-15 06:32:09.955423: val_loss -0.4416 +2026-04-15 06:32:09.958019: Pseudo dice [0.8034, 0.8454, 0.6959, 0.4969, 0.6733, 0.8485, 0.7887] +2026-04-15 06:32:09.960577: Epoch time: 102.37 s +2026-04-15 06:32:11.545744: +2026-04-15 06:32:11.548299: Epoch 3968 +2026-04-15 06:32:11.550117: Current learning rate: 0.00013 +2026-04-15 06:33:54.467026: train_loss -0.5482 +2026-04-15 06:33:54.474149: val_loss -0.4498 +2026-04-15 06:33:54.476575: Pseudo dice [0.2466, 0.5065, 0.7869, 0.844, 0.4242, 0.8419, 0.9389] +2026-04-15 06:33:54.479984: Epoch time: 102.92 s +2026-04-15 06:33:56.091235: +2026-04-15 06:33:56.093730: Epoch 3969 +2026-04-15 06:33:56.095805: Current learning rate: 0.00013 +2026-04-15 06:35:38.453997: train_loss -0.534 +2026-04-15 06:35:38.459758: val_loss -0.466 +2026-04-15 06:35:38.461957: Pseudo dice [0.2285, 0.712, 0.7958, 0.8778, 0.6232, 0.8485, 0.9025] +2026-04-15 06:35:38.464163: Epoch time: 102.37 s +2026-04-15 06:35:40.136508: +2026-04-15 06:35:40.138694: Epoch 3970 +2026-04-15 06:35:40.140242: Current learning rate: 0.00012 +2026-04-15 06:37:22.274870: train_loss -0.5394 +2026-04-15 06:37:22.280090: val_loss -0.4441 +2026-04-15 06:37:22.281934: Pseudo dice [0.5632, 0.5543, 0.824, 0.7083, 0.6125, 0.7609, 0.7858] +2026-04-15 06:37:22.284231: Epoch time: 102.14 s +2026-04-15 06:37:22.285985: Yayy! New best EMA pseudo Dice: 0.678 +2026-04-15 06:37:25.957121: +2026-04-15 06:37:25.959507: Epoch 3971 +2026-04-15 06:37:25.961236: Current learning rate: 0.00012 +2026-04-15 06:39:08.405574: train_loss -0.5373 +2026-04-15 06:39:08.410917: val_loss -0.4497 +2026-04-15 06:39:08.413714: Pseudo dice [0.844, 0.4142, 0.7923, 0.7364, 0.3657, 0.4001, 0.7086] +2026-04-15 06:39:08.417381: Epoch time: 102.45 s +2026-04-15 06:39:09.953144: +2026-04-15 06:39:09.955525: Epoch 3972 +2026-04-15 06:39:09.957373: Current learning rate: 0.00011 +2026-04-15 06:40:52.301443: train_loss -0.545 +2026-04-15 06:40:52.307062: val_loss -0.4395 +2026-04-15 06:40:52.309362: Pseudo dice [0.6114, 0.4524, 0.7618, 0.7917, 0.448, 0.6815, 0.8963] +2026-04-15 06:40:52.311731: Epoch time: 102.35 s +2026-04-15 06:40:53.903397: +2026-04-15 06:40:53.905439: Epoch 3973 +2026-04-15 06:40:53.907063: Current learning rate: 0.00011 +2026-04-15 06:42:37.537996: train_loss -0.5405 +2026-04-15 06:42:37.546499: val_loss -0.4596 +2026-04-15 06:42:37.549217: Pseudo dice [0.7924, 0.4383, 0.8525, 0.8392, 0.5972, 0.6132, 0.8806] +2026-04-15 06:42:37.551575: Epoch time: 103.64 s +2026-04-15 06:42:39.210066: +2026-04-15 06:42:39.212072: Epoch 3974 +2026-04-15 06:42:39.213672: Current learning rate: 0.00011 +2026-04-15 06:44:21.912927: train_loss -0.5448 +2026-04-15 06:44:21.918027: val_loss -0.4477 +2026-04-15 06:44:21.920890: Pseudo dice [0.5183, 0.6463, 0.7974, 0.6736, 0.5023, 0.9002, 0.7166] +2026-04-15 06:44:21.923539: Epoch time: 102.71 s +2026-04-15 06:44:23.497904: +2026-04-15 06:44:23.500313: Epoch 3975 +2026-04-15 06:44:23.502033: Current learning rate: 0.0001 +2026-04-15 06:46:06.024204: train_loss -0.54 +2026-04-15 06:46:06.033061: val_loss -0.3799 +2026-04-15 06:46:06.035592: Pseudo dice [0.82, 0.8633, 0.7732, 0.8228, 0.5987, 0.3131, 0.8751] +2026-04-15 06:46:06.038289: Epoch time: 102.53 s +2026-04-15 06:46:06.040686: Yayy! New best EMA pseudo Dice: 0.6802 +2026-04-15 06:46:09.836208: +2026-04-15 06:46:09.838032: Epoch 3976 +2026-04-15 06:46:09.839901: Current learning rate: 0.0001 +2026-04-15 06:47:52.860065: train_loss -0.542 +2026-04-15 06:47:52.866748: val_loss -0.467 +2026-04-15 06:47:52.869307: Pseudo dice [0.7421, 0.6691, 0.8568, 0.8593, 0.64, 0.8503, 0.7631] +2026-04-15 06:47:52.872042: Epoch time: 103.03 s +2026-04-15 06:47:52.874192: Yayy! New best EMA pseudo Dice: 0.689 +2026-04-15 06:47:56.541967: +2026-04-15 06:47:56.544939: Epoch 3977 +2026-04-15 06:47:56.546772: Current learning rate: 0.0001 +2026-04-15 06:49:39.176043: train_loss -0.5453 +2026-04-15 06:49:39.181884: val_loss -0.3502 +2026-04-15 06:49:39.184939: Pseudo dice [0.3023, 0.8872, 0.7808, 0.853, 0.6744, 0.1917, 0.9429] +2026-04-15 06:49:39.188179: Epoch time: 102.64 s +2026-04-15 06:49:40.797133: +2026-04-15 06:49:40.799694: Epoch 3978 +2026-04-15 06:49:40.801619: Current learning rate: 9e-05 +2026-04-15 06:51:23.234337: train_loss -0.5398 +2026-04-15 06:51:23.240902: val_loss -0.4531 +2026-04-15 06:51:23.244122: Pseudo dice [0.7362, 0.4999, 0.7492, 0.7796, 0.689, 0.7543, 0.8336] +2026-04-15 06:51:23.247439: Epoch time: 102.44 s +2026-04-15 06:51:23.249903: Yayy! New best EMA pseudo Dice: 0.6897 +2026-04-15 06:51:26.908432: +2026-04-15 06:51:26.911768: Epoch 3979 +2026-04-15 06:51:26.913955: Current learning rate: 9e-05 +2026-04-15 06:53:09.517815: train_loss -0.5439 +2026-04-15 06:53:09.523259: val_loss -0.4386 +2026-04-15 06:53:09.525368: Pseudo dice [0.2717, 0.8998, 0.6297, 0.3469, 0.4089, 0.9044, 0.8386] +2026-04-15 06:53:09.527646: Epoch time: 102.61 s +2026-04-15 06:53:11.119143: +2026-04-15 06:53:11.120976: Epoch 3980 +2026-04-15 06:53:11.122548: Current learning rate: 8e-05 +2026-04-15 06:54:53.862828: train_loss -0.539 +2026-04-15 06:54:53.869932: val_loss -0.4339 +2026-04-15 06:54:53.873189: Pseudo dice [0.4073, 0.5203, 0.815, 0.6172, 0.2746, 0.5167, 0.8501] +2026-04-15 06:54:53.875785: Epoch time: 102.75 s +2026-04-15 06:54:55.540281: +2026-04-15 06:54:55.543314: Epoch 3981 +2026-04-15 06:54:55.546274: Current learning rate: 8e-05 +2026-04-15 06:56:37.747258: train_loss -0.5383 +2026-04-15 06:56:37.757999: val_loss -0.4247 +2026-04-15 06:56:37.762154: Pseudo dice [0.3565, 0.6439, 0.8492, 0.8367, 0.496, 0.8792, 0.803] +2026-04-15 06:56:37.767976: Epoch time: 102.21 s +2026-04-15 06:56:39.404451: +2026-04-15 06:56:39.407113: Epoch 3982 +2026-04-15 06:56:39.409163: Current learning rate: 8e-05 +2026-04-15 06:58:22.472127: train_loss -0.544 +2026-04-15 06:58:22.485563: val_loss -0.4285 +2026-04-15 06:58:22.491270: Pseudo dice [0.8137, 0.787, 0.7401, 0.8787, 0.1957, 0.5603, 0.8037] +2026-04-15 06:58:22.494134: Epoch time: 103.07 s +2026-04-15 06:58:24.072380: +2026-04-15 06:58:24.074260: Epoch 3983 +2026-04-15 06:58:24.075948: Current learning rate: 7e-05 +2026-04-15 07:00:06.921131: train_loss -0.5381 +2026-04-15 07:00:06.926258: val_loss -0.3937 +2026-04-15 07:00:06.929359: Pseudo dice [0.5037, 0.7878, 0.796, 0.8286, 0.6693, 0.3189, 0.7541] +2026-04-15 07:00:06.932374: Epoch time: 102.85 s +2026-04-15 07:00:08.585546: +2026-04-15 07:00:08.587366: Epoch 3984 +2026-04-15 07:00:08.589058: Current learning rate: 7e-05 +2026-04-15 07:01:51.227135: train_loss -0.5454 +2026-04-15 07:01:51.233090: val_loss -0.36 +2026-04-15 07:01:51.235204: Pseudo dice [0.7654, 0.8396, 0.8055, 0.8474, 0.5573, 0.3387, 0.6721] +2026-04-15 07:01:51.238224: Epoch time: 102.65 s +2026-04-15 07:01:52.848216: +2026-04-15 07:01:52.849855: Epoch 3985 +2026-04-15 07:01:52.851377: Current learning rate: 7e-05 +2026-04-15 07:03:35.350829: train_loss -0.5397 +2026-04-15 07:03:35.356782: val_loss -0.403 +2026-04-15 07:03:35.358356: Pseudo dice [0.7257, 0.8051, 0.7493, 0.094, 0.5294, 0.4792, 0.8241] +2026-04-15 07:03:35.360487: Epoch time: 102.51 s +2026-04-15 07:03:36.948984: +2026-04-15 07:03:36.950842: Epoch 3986 +2026-04-15 07:03:36.952749: Current learning rate: 6e-05 +2026-04-15 07:05:19.637072: train_loss -0.5332 +2026-04-15 07:05:19.642307: val_loss -0.3397 +2026-04-15 07:05:19.644267: Pseudo dice [0.6578, 0.608, 0.6446, 0.8352, 0.4255, 0.3892, 0.7052] +2026-04-15 07:05:19.646783: Epoch time: 102.69 s +2026-04-15 07:05:21.273611: +2026-04-15 07:05:21.276234: Epoch 3987 +2026-04-15 07:05:21.277850: Current learning rate: 6e-05 +2026-04-15 07:07:04.054913: train_loss -0.5359 +2026-04-15 07:07:04.060597: val_loss -0.4714 +2026-04-15 07:07:04.062768: Pseudo dice [0.511, 0.7782, 0.7343, 0.8377, 0.6949, 0.9077, 0.7493] +2026-04-15 07:07:04.065102: Epoch time: 102.79 s +2026-04-15 07:07:05.674396: +2026-04-15 07:07:05.676204: Epoch 3988 +2026-04-15 07:07:05.677752: Current learning rate: 5e-05 +2026-04-15 07:08:48.618891: train_loss -0.5329 +2026-04-15 07:08:48.625787: val_loss -0.4652 +2026-04-15 07:08:48.627571: Pseudo dice [0.3117, 0.8296, 0.7789, 0.8895, 0.6073, 0.8484, 0.8927] +2026-04-15 07:08:48.630141: Epoch time: 102.95 s +2026-04-15 07:08:50.212344: +2026-04-15 07:08:50.214717: Epoch 3989 +2026-04-15 07:08:50.216543: Current learning rate: 5e-05 +2026-04-15 07:10:33.027770: train_loss -0.5471 +2026-04-15 07:10:33.033458: val_loss -0.4153 +2026-04-15 07:10:33.035947: Pseudo dice [0.5218, 0.8253, 0.7259, 0.874, 0.6518, 0.5077, 0.8812] +2026-04-15 07:10:33.038805: Epoch time: 102.82 s +2026-04-15 07:10:34.661297: +2026-04-15 07:10:34.663453: Epoch 3990 +2026-04-15 07:10:34.665157: Current learning rate: 5e-05 +2026-04-15 07:12:19.640215: train_loss -0.5439 +2026-04-15 07:12:19.649269: val_loss -0.4721 +2026-04-15 07:12:19.651828: Pseudo dice [0.4894, 0.7278, 0.8014, 0.8544, 0.7353, 0.821, 0.9114] +2026-04-15 07:12:19.655758: Epoch time: 104.98 s +2026-04-15 07:12:21.269165: +2026-04-15 07:12:21.270963: Epoch 3991 +2026-04-15 07:12:21.273650: Current learning rate: 4e-05 +2026-04-15 07:14:03.594945: train_loss -0.5491 +2026-04-15 07:14:03.599849: val_loss -0.407 +2026-04-15 07:14:03.601431: Pseudo dice [0.7946, 0.4503, 0.5579, 0.7365, 0.4154, 0.9056, 0.5536] +2026-04-15 07:14:03.603544: Epoch time: 102.33 s +2026-04-15 07:14:05.165055: +2026-04-15 07:14:05.166802: Epoch 3992 +2026-04-15 07:14:05.168343: Current learning rate: 4e-05 +2026-04-15 07:15:48.669977: train_loss -0.5486 +2026-04-15 07:15:48.677409: val_loss -0.421 +2026-04-15 07:15:48.681399: Pseudo dice [0.5926, 0.5041, 0.7897, 0.8952, 0.598, 0.3292, 0.8405] +2026-04-15 07:15:48.683859: Epoch time: 103.51 s +2026-04-15 07:15:50.300226: +2026-04-15 07:15:50.302804: Epoch 3993 +2026-04-15 07:15:50.304792: Current learning rate: 3e-05 +2026-04-15 07:17:32.952328: train_loss -0.5472 +2026-04-15 07:17:32.958579: val_loss -0.3768 +2026-04-15 07:17:32.961288: Pseudo dice [0.7396, 0.6649, 0.8014, 0.0419, 0.4564, 0.2417, 0.6265] +2026-04-15 07:17:32.963678: Epoch time: 102.66 s +2026-04-15 07:17:34.583349: +2026-04-15 07:17:34.585542: Epoch 3994 +2026-04-15 07:17:34.587362: Current learning rate: 3e-05 +2026-04-15 07:19:17.914269: train_loss -0.5389 +2026-04-15 07:19:17.920919: val_loss -0.4287 +2026-04-15 07:19:17.922980: Pseudo dice [0.6255, 0.7846, 0.8741, 0.7835, 0.4278, 0.5645, 0.7332] +2026-04-15 07:19:17.925376: Epoch time: 103.33 s +2026-04-15 07:19:19.493241: +2026-04-15 07:19:19.495185: Epoch 3995 +2026-04-15 07:19:19.497611: Current learning rate: 2e-05 +2026-04-15 07:21:02.227161: train_loss -0.5337 +2026-04-15 07:21:02.242365: val_loss -0.4567 +2026-04-15 07:21:02.250197: Pseudo dice [0.5967, 0.8268, 0.6934, 0.7727, 0.5008, 0.8119, 0.7944] +2026-04-15 07:21:02.254512: Epoch time: 102.74 s +2026-04-15 07:21:03.857941: +2026-04-15 07:21:03.859816: Epoch 3996 +2026-04-15 07:21:03.861442: Current learning rate: 2e-05 +2026-04-15 07:22:46.991677: train_loss -0.5442 +2026-04-15 07:22:46.996714: val_loss -0.4343 +2026-04-15 07:22:46.999167: Pseudo dice [0.668, 0.6621, 0.6269, 0.8748, 0.6992, 0.289, 0.7737] +2026-04-15 07:22:47.001760: Epoch time: 103.14 s +2026-04-15 07:22:48.625115: +2026-04-15 07:22:48.627130: Epoch 3997 +2026-04-15 07:22:48.629168: Current learning rate: 2e-05 +2026-04-15 07:24:31.422385: train_loss -0.5384 +2026-04-15 07:24:31.428064: val_loss -0.4416 +2026-04-15 07:24:31.429929: Pseudo dice [0.6365, 0.5015, 0.6664, 0.8614, 0.503, 0.8455, 0.8533] +2026-04-15 07:24:31.432482: Epoch time: 102.8 s +2026-04-15 07:24:33.016080: +2026-04-15 07:24:33.018098: Epoch 3998 +2026-04-15 07:24:33.020243: Current learning rate: 1e-05 +2026-04-15 07:26:15.676651: train_loss -0.5353 +2026-04-15 07:26:15.682064: val_loss -0.3388 +2026-04-15 07:26:15.683888: Pseudo dice [0.7916, 0.6034, 0.7907, 0.6607, 0.5761, 0.3531, 0.8924] +2026-04-15 07:26:15.686208: Epoch time: 102.66 s +2026-04-15 07:26:17.270911: +2026-04-15 07:26:17.274696: Epoch 3999 +2026-04-15 07:26:17.276596: Current learning rate: 1e-05 +2026-04-15 07:28:00.459656: train_loss -0.5464 +2026-04-15 07:28:00.465223: val_loss -0.4047 +2026-04-15 07:28:00.467519: Pseudo dice [0.1096, 0.7468, 0.7933, 0.6751, 0.216, 0.8382, 0.5167] +2026-04-15 07:28:00.469555: Epoch time: 103.19 s +2026-04-15 07:28:04.227641: Training done. +2026-04-15 07:28:04.550687: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json +2026-04-15 07:28:04.555042: The split file contains 5 splits. +2026-04-15 07:28:04.557222: Desired fold for training: 1 +2026-04-15 07:28:04.559183: This split has 387 training and 97 validation cases. +2026-04-15 07:28:04.562054: predicting MSWAL_0008 +2026-04-15 07:28:04.572890: MSWAL_0008, shape torch.Size([1, 201, 537, 537]), rank 0 +2026-04-15 07:29:13.082150: predicting MSWAL_0009 +2026-04-15 07:29:13.108699: MSWAL_0009, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:29:25.882566: predicting MSWAL_0027 +2026-04-15 07:29:25.919401: MSWAL_0027, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 07:29:34.664903: predicting MSWAL_0029 +2026-04-15 07:29:34.683993: MSWAL_0029, shape torch.Size([1, 185, 527, 527]), rank 0 +2026-04-15 07:29:56.787251: predicting MSWAL_0032 +2026-04-15 07:29:56.830189: MSWAL_0032, shape torch.Size([1, 221, 507, 507]), rank 0 +2026-04-15 07:30:09.609750: predicting MSWAL_0034 +2026-04-15 07:30:09.626092: MSWAL_0034, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:30:22.589992: predicting MSWAL_0045 +2026-04-15 07:30:22.619472: MSWAL_0045, shape torch.Size([1, 209, 531, 531]), rank 0 +2026-04-15 07:30:44.978511: predicting MSWAL_0052 +2026-04-15 07:30:44.994012: MSWAL_0052, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:30:57.770267: predicting MSWAL_0056 +2026-04-15 07:30:57.800542: MSWAL_0056, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:31:10.590192: predicting MSWAL_0067 +2026-04-15 07:31:10.611620: MSWAL_0067, shape torch.Size([1, 177, 557, 557]), rank 0 +2026-04-15 07:31:32.693134: predicting MSWAL_0075 +2026-04-15 07:31:32.716805: MSWAL_0075, shape torch.Size([1, 193, 605, 605]), rank 0 +2026-04-15 07:31:56.721963: predicting MSWAL_0077 +2026-04-15 07:31:56.757861: MSWAL_0077, shape torch.Size([1, 165, 524, 524]), rank 0 +2026-04-15 07:32:11.882547: predicting MSWAL_0083 +2026-04-15 07:32:11.903194: MSWAL_0083, shape torch.Size([1, 177, 527, 527]), rank 0 +2026-04-15 07:32:34.231826: predicting MSWAL_0086 +2026-04-15 07:32:34.254787: MSWAL_0086, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:32:47.295762: predicting MSWAL_0092 +2026-04-15 07:32:47.324616: MSWAL_0092, shape torch.Size([1, 197, 507, 507]), rank 0 +2026-04-15 07:33:00.486913: predicting MSWAL_0101 +2026-04-15 07:33:00.520596: MSWAL_0101, shape torch.Size([1, 162, 444, 444]), rank 0 +2026-04-15 07:33:09.181566: predicting MSWAL_0105 +2026-04-15 07:33:09.203372: MSWAL_0105, shape torch.Size([1, 181, 572, 572]), rank 0 +2026-04-15 07:33:31.601806: predicting MSWAL_0108 +2026-04-15 07:33:31.623297: MSWAL_0108, shape torch.Size([1, 180, 507, 507]), rank 0 +2026-04-15 07:33:44.513804: predicting MSWAL_0110 +2026-04-15 07:33:44.527595: MSWAL_0110, shape torch.Size([1, 217, 507, 507]), rank 0 +2026-04-15 07:33:57.557522: predicting MSWAL_0128 +2026-04-15 07:33:57.570789: MSWAL_0128, shape torch.Size([1, 237, 575, 575]), rank 0 +2026-04-15 07:34:27.408011: predicting MSWAL_0151 +2026-04-15 07:34:27.439204: MSWAL_0151, shape torch.Size([1, 458, 535, 535]), rank 0 +2026-04-15 07:35:26.604573: predicting MSWAL_0165 +2026-04-15 07:35:26.636372: MSWAL_0165, shape torch.Size([1, 318, 480, 480]), rank 0 +2026-04-15 07:35:47.724387: predicting MSWAL_0166 +2026-04-15 07:35:47.751834: MSWAL_0166, shape torch.Size([1, 185, 553, 553]), rank 0 +2026-04-15 07:36:10.341408: predicting MSWAL_0167 +2026-04-15 07:36:10.355026: MSWAL_0167, shape torch.Size([1, 388, 539, 539]), rank 0 +2026-04-15 07:36:54.803152: predicting MSWAL_0182 +2026-04-15 07:36:54.841511: MSWAL_0182, shape torch.Size([1, 170, 465, 465]), rank 0 +2026-04-15 07:37:07.491515: predicting MSWAL_0184 +2026-04-15 07:37:07.509421: MSWAL_0184, shape torch.Size([1, 177, 581, 581]), rank 0 +2026-04-15 07:37:29.978651: predicting MSWAL_0186 +2026-04-15 07:37:30.004017: MSWAL_0186, shape torch.Size([1, 168, 507, 507]), rank 0 +2026-04-15 07:37:38.862445: predicting MSWAL_0219 +2026-04-15 07:37:38.886716: MSWAL_0219, shape torch.Size([1, 165, 543, 543]), rank 0 +2026-04-15 07:37:53.978269: predicting MSWAL_0228 +2026-04-15 07:37:53.997430: MSWAL_0228, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:38:06.951627: predicting MSWAL_0229 +2026-04-15 07:38:06.977192: MSWAL_0229, shape torch.Size([1, 165, 561, 561]), rank 0 +2026-04-15 07:38:22.215350: predicting MSWAL_0230 +2026-04-15 07:38:22.236116: MSWAL_0230, shape torch.Size([1, 391, 528, 528]), rank 0 +2026-04-15 07:39:06.730778: predicting MSWAL_0238 +2026-04-15 07:39:06.754604: MSWAL_0238, shape torch.Size([1, 440, 585, 585]), rank 0 +2026-04-15 07:39:58.972856: predicting MSWAL_0246 +2026-04-15 07:39:59.009219: MSWAL_0246, shape torch.Size([1, 300, 519, 519]), rank 0 +2026-04-15 07:40:36.227479: predicting MSWAL_0263 +2026-04-15 07:40:36.249964: MSWAL_0263, shape torch.Size([1, 252, 480, 480]), rank 0 +2026-04-15 07:40:53.152519: predicting MSWAL_0270 +2026-04-15 07:40:53.179084: MSWAL_0270, shape torch.Size([1, 319, 535, 535]), rank 0 +2026-04-15 07:41:30.270016: predicting MSWAL_0272 +2026-04-15 07:41:30.293463: MSWAL_0272, shape torch.Size([1, 397, 641, 641]), rank 0 +2026-04-15 07:42:50.660436: predicting MSWAL_0278 +2026-04-15 07:42:50.690127: MSWAL_0278, shape torch.Size([1, 304, 496, 496]), rank 0 +2026-04-15 07:43:12.302601: predicting MSWAL_0288 +2026-04-15 07:43:12.327617: MSWAL_0288, shape torch.Size([1, 217, 507, 507]), rank 0 +2026-04-15 07:43:25.589809: predicting MSWAL_0289 +2026-04-15 07:43:25.608478: MSWAL_0289, shape torch.Size([1, 177, 445, 445]), rank 0 +2026-04-15 07:43:39.095614: predicting MSWAL_0311 +2026-04-15 07:43:39.109324: MSWAL_0311, shape torch.Size([1, 197, 480, 480]), rank 0 +2026-04-15 07:43:51.861488: predicting MSWAL_0312 +2026-04-15 07:43:51.893115: MSWAL_0312, shape torch.Size([1, 165, 507, 507]), rank 0 +2026-04-15 07:44:00.586838: predicting MSWAL_0317 +2026-04-15 07:44:00.607023: MSWAL_0317, shape torch.Size([1, 213, 507, 507]), rank 0 +2026-04-15 07:44:13.548763: predicting MSWAL_0318 +2026-04-15 07:44:13.567346: MSWAL_0318, shape torch.Size([1, 181, 480, 480]), rank 0 +2026-04-15 07:44:26.310531: predicting MSWAL_0332 +2026-04-15 07:44:26.336295: MSWAL_0332, shape torch.Size([1, 197, 480, 480]), rank 0 +2026-04-15 07:44:39.275456: predicting MSWAL_0336 +2026-04-15 07:44:39.296481: MSWAL_0336, shape torch.Size([1, 301, 507, 507]), rank 0 +2026-04-15 07:45:00.442684: predicting MSWAL_0344 +2026-04-15 07:45:00.471427: MSWAL_0344, shape torch.Size([1, 309, 556, 556]), rank 0 +2026-04-15 07:45:37.670857: predicting MSWAL_0345 +2026-04-15 07:45:37.695623: MSWAL_0345, shape torch.Size([1, 200, 593, 593]), rank 0 +2026-04-15 07:46:00.347532: predicting MSWAL_0353 +2026-04-15 07:46:00.385285: MSWAL_0353, shape torch.Size([1, 544, 629, 629]), rank 0 +2026-04-15 07:47:06.820920: predicting MSWAL_0355 +2026-04-15 07:47:06.873562: MSWAL_0355, shape torch.Size([1, 453, 553, 553]), rank 0 +2026-04-15 07:48:05.934253: predicting MSWAL_0361 +2026-04-15 07:48:05.973294: MSWAL_0361, shape torch.Size([1, 317, 551, 551]), rank 0 +2026-04-15 07:48:44.567541: predicting MSWAL_0365 +2026-04-15 07:48:44.603812: MSWAL_0365, shape torch.Size([1, 333, 560, 560]), rank 0 +2026-04-15 07:49:23.358890: predicting MSWAL_0366 +2026-04-15 07:49:23.426398: MSWAL_0366, shape torch.Size([1, 293, 497, 497]), rank 0 +2026-04-15 07:49:45.661478: predicting MSWAL_0369 +2026-04-15 07:49:45.692140: MSWAL_0369, shape torch.Size([1, 379, 629, 629]), rank 0 +2026-04-15 07:50:30.667885: predicting MSWAL_0382 +2026-04-15 07:50:30.699780: MSWAL_0382, shape torch.Size([1, 305, 507, 507]), rank 0 +2026-04-15 07:50:52.074575: predicting MSWAL_0393 +2026-04-15 07:50:52.106329: MSWAL_0393, shape torch.Size([1, 307, 485, 485]), rank 0 +2026-04-15 07:51:13.277770: predicting MSWAL_0399 +2026-04-15 07:51:13.327585: MSWAL_0399, shape torch.Size([1, 253, 507, 507]), rank 0 +2026-04-15 07:51:30.399183: predicting MSWAL_0411 +2026-04-15 07:51:30.439930: MSWAL_0411, shape torch.Size([1, 217, 507, 507]), rank 0 +2026-04-15 07:51:43.152580: predicting MSWAL_0429 +2026-04-15 07:51:43.177957: MSWAL_0429, shape torch.Size([1, 337, 547, 547]), rank 0 +2026-04-15 07:52:27.552305: predicting MSWAL_0431 +2026-04-15 07:52:27.593076: MSWAL_0431, shape torch.Size([1, 349, 507, 507]), rank 0 +2026-04-15 07:52:52.953147: predicting MSWAL_0447 +2026-04-15 07:52:52.991698: MSWAL_0447, shape torch.Size([1, 237, 507, 507]), rank 0 +2026-04-15 07:53:10.037775: predicting MSWAL_0452 +2026-04-15 07:53:10.069090: MSWAL_0452, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 07:53:18.785904: predicting MSWAL_0455 +2026-04-15 07:53:18.842057: MSWAL_0455, shape torch.Size([1, 205, 508, 508]), rank 0 +2026-04-15 07:53:31.813638: predicting MSWAL_0461 +2026-04-15 07:53:31.854614: MSWAL_0461, shape torch.Size([1, 297, 608, 608]), rank 0 +2026-04-15 07:54:09.233316: predicting MSWAL_0479 +2026-04-15 07:54:09.281575: MSWAL_0479, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 07:54:18.047609: predicting MSWAL_0489 +2026-04-15 07:54:18.084145: MSWAL_0489, shape torch.Size([1, 217, 507, 507]), rank 0 +2026-04-15 07:54:30.871786: predicting MSWAL_0501 +2026-04-15 07:54:30.904008: MSWAL_0501, shape torch.Size([1, 209, 507, 507]), rank 0 +2026-04-15 07:54:43.798885: predicting MSWAL_0507 +2026-04-15 07:54:43.837042: MSWAL_0507, shape torch.Size([1, 197, 525, 525]), rank 0 +2026-04-15 07:55:06.185483: predicting MSWAL_0509 +2026-04-15 07:55:06.213918: MSWAL_0509, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:55:19.064963: predicting MSWAL_0512 +2026-04-15 07:55:19.090871: MSWAL_0512, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:55:32.640328: predicting MSWAL_0518 +2026-04-15 07:55:32.677747: MSWAL_0518, shape torch.Size([1, 261, 507, 507]), rank 0 +2026-04-15 07:55:49.723605: predicting MSWAL_0519 +2026-04-15 07:55:49.754616: MSWAL_0519, shape torch.Size([1, 177, 520, 520]), rank 0 +2026-04-15 07:56:12.167554: predicting MSWAL_0524 +2026-04-15 07:56:12.195753: MSWAL_0524, shape torch.Size([1, 149, 543, 543]), rank 0 +2026-04-15 07:56:27.270239: predicting MSWAL_0534 +2026-04-15 07:56:27.316433: MSWAL_0534, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 07:56:40.097417: predicting MSWAL_0555 +2026-04-15 07:56:40.123609: MSWAL_0555, shape torch.Size([1, 299, 467, 467]), rank 0 +2026-04-15 07:57:01.336433: predicting MSWAL_0566 +2026-04-15 07:57:01.380213: MSWAL_0566, shape torch.Size([1, 433, 643, 643]), rank 0 +2026-04-15 07:58:21.547315: predicting MSWAL_0567 +2026-04-15 07:58:21.590557: MSWAL_0567, shape torch.Size([1, 237, 495, 495]), rank 0 +2026-04-15 07:58:38.491556: predicting MSWAL_0575 +2026-04-15 07:58:38.530488: MSWAL_0575, shape torch.Size([1, 325, 583, 583]), rank 0 +2026-04-15 07:59:15.540418: predicting MSWAL_0578 +2026-04-15 07:59:15.569733: MSWAL_0578, shape torch.Size([1, 178, 480, 480]), rank 0 +2026-04-15 07:59:28.336528: predicting MSWAL_0580 +2026-04-15 07:59:28.365353: MSWAL_0580, shape torch.Size([1, 296, 591, 591]), rank 0 +2026-04-15 08:00:05.488258: predicting MSWAL_0583 +2026-04-15 08:00:05.518934: MSWAL_0583, shape torch.Size([1, 308, 480, 480]), rank 0 +2026-04-15 08:00:26.326109: predicting MSWAL_0584 +2026-04-15 08:00:26.359909: MSWAL_0584, shape torch.Size([1, 154, 512, 512]), rank 0 +2026-04-15 08:00:35.065499: predicting MSWAL_0586 +2026-04-15 08:00:35.097857: MSWAL_0586, shape torch.Size([1, 189, 568, 568]), rank 0 +2026-04-15 08:00:57.502532: predicting MSWAL_0614 +2026-04-15 08:00:57.525303: MSWAL_0614, shape torch.Size([1, 343, 575, 575]), rank 0 +2026-04-15 08:01:41.923954: predicting MSWAL_0617 +2026-04-15 08:01:41.975990: MSWAL_0617, shape torch.Size([1, 178, 497, 497]), rank 0 +2026-04-15 08:01:54.856427: predicting MSWAL_0629 +2026-04-15 08:01:54.878315: MSWAL_0629, shape torch.Size([1, 316, 541, 541]), rank 0 +2026-04-15 08:02:31.861533: predicting MSWAL_0636 +2026-04-15 08:02:31.903107: MSWAL_0636, shape torch.Size([1, 308, 539, 539]), rank 0 +2026-04-15 08:03:08.775203: predicting MSWAL_0638 +2026-04-15 08:03:08.811615: MSWAL_0638, shape torch.Size([1, 490, 480, 480]), rank 0 +2026-04-15 08:03:42.158608: predicting MSWAL_0650 +2026-04-15 08:03:42.215861: MSWAL_0650, shape torch.Size([1, 283, 507, 507]), rank 0 +2026-04-15 08:04:03.352846: predicting MSWAL_0654 +2026-04-15 08:04:03.379241: MSWAL_0654, shape torch.Size([1, 321, 507, 507]), rank 0 +2026-04-15 08:04:24.542153: predicting MSWAL_0655 +2026-04-15 08:04:24.571206: MSWAL_0655, shape torch.Size([1, 308, 556, 556]), rank 0 +2026-04-15 08:05:01.735373: predicting MSWAL_0663 +2026-04-15 08:05:01.764161: MSWAL_0663, shape torch.Size([1, 290, 511, 511]), rank 0 +2026-04-15 08:05:23.054113: predicting MSWAL_0667 +2026-04-15 08:05:23.091210: MSWAL_0667, shape torch.Size([1, 277, 508, 508]), rank 0 +2026-04-15 08:05:40.250867: predicting MSWAL_0670 +2026-04-15 08:05:40.293602: MSWAL_0670, shape torch.Size([1, 352, 549, 549]), rank 0 +2026-04-15 08:06:24.636453: predicting MSWAL_0673 +2026-04-15 08:06:24.673604: MSWAL_0673, shape torch.Size([1, 324, 528, 528]), rank 0 +2026-04-15 08:07:01.938680: predicting MSWAL_0674 +2026-04-15 08:07:01.982928: MSWAL_0674, shape torch.Size([1, 381, 599, 599]), rank 0 +2026-04-15 08:07:46.455165: predicting MSWAL_0681 +2026-04-15 08:07:46.491944: MSWAL_0681, shape torch.Size([1, 365, 543, 543]), rank 0 +2026-04-15 08:08:30.793171: predicting MSWAL_0694 +2026-04-15 08:08:30.822609: MSWAL_0694, shape torch.Size([1, 276, 544, 544]), rank 0 +2026-04-15 08:10:40.164544: Validation complete +2026-04-15 08:10:40.173229: Mean Validation Dice: 0.4703325529965783 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_best.pth b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_best.pth new file mode 100644 index 0000000000000000000000000000000000000000..203975555ee44e0486512444ba4ceabb1aba1308 --- 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'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}", + "configuration_name": "3d_fullres", + "cudnn_version": 90100, + "current_epoch": "0", + "dataloader_train": "", + "dataloader_train.generator": "", + "dataloader_train.num_processes": "12", + "dataloader_train.transform": "None", + "dataloader_val": "", + "dataloader_val.generator": "", + "dataloader_val.num_processes": "6", + "dataloader_val.transform": "None", + "dataset_json": "{'name': 'MSWAL', 'description': ' 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset', 'licence': 'CC BY-NC 4.0', 'relase': 'July 8, 2025', 'tensorImageSize': '3D', 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'gallstone': 1, 'kidney stone': 2, 'liver tumor': 3, 'kidney tumor': 4, 'pancreatic cancer': 5, 'liver cyst': 6, 'kidney cyst': 7}, 'numTraining': 484, 'numTest': 210, 'training': 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'./imagesTr/MSWAL_0034_0000.nii.gz', 'label': './labelsTr/MSWAL_0034.nii.gz'}, {'image': './imagesTr/MSWAL_0035_0000.nii.gz', 'label': './labelsTr/MSWAL_0035.nii.gz'}, {'image': './imagesTr/MSWAL_0037_0000.nii.gz', 'label': './labelsTr/MSWAL_0037.nii.gz'}, {'image': './imagesTr/MSWAL_0038_0000.nii.gz', 'label': './labelsTr/MSWAL_0038.nii.gz'}, {'image': './imagesTr/MSWAL_0039_0000.nii.gz', 'label': './labelsTr/MSWAL_0039.nii.gz'}, {'image': './imagesTr/MSWAL_0040_0000.nii.gz', 'label': './labelsTr/MSWAL_0040.nii.gz'}, {'image': './imagesTr/MSWAL_0041_0000.nii.gz', 'label': './labelsTr/MSWAL_0041.nii.gz'}, {'image': './imagesTr/MSWAL_0042_0000.nii.gz', 'label': './labelsTr/MSWAL_0042.nii.gz'}, {'image': './imagesTr/MSWAL_0045_0000.nii.gz', 'label': './labelsTr/MSWAL_0045.nii.gz'}, {'image': './imagesTr/MSWAL_0046_0000.nii.gz', 'label': './labelsTr/MSWAL_0046.nii.gz'}, {'image': './imagesTr/MSWAL_0049_0000.nii.gz', 'label': './labelsTr/MSWAL_0049.nii.gz'}, {'image': './imagesTr/MSWAL_0050_0000.nii.gz', 'label': './labelsTr/MSWAL_0050.nii.gz'}, {'image': './imagesTr/MSWAL_0051_0000.nii.gz', 'label': './labelsTr/MSWAL_0051.nii.gz'}, {'image': './imagesTr/MSWAL_0052_0000.nii.gz', 'label': './labelsTr/MSWAL_0052.nii.gz'}, {'image': './imagesTr/MSWAL_0054_0000.nii.gz', 'label': './labelsTr/MSWAL_0054.nii.gz'}, {'image': './imagesTr/MSWAL_0055_0000.nii.gz', 'label': './labelsTr/MSWAL_0055.nii.gz'}, {'image': './imagesTr/MSWAL_0056_0000.nii.gz', 'label': './labelsTr/MSWAL_0056.nii.gz'}, {'image': './imagesTr/MSWAL_0057_0000.nii.gz', 'label': './labelsTr/MSWAL_0057.nii.gz'}, {'image': './imagesTr/MSWAL_0059_0000.nii.gz', 'label': './labelsTr/MSWAL_0059.nii.gz'}, {'image': './imagesTr/MSWAL_0060_0000.nii.gz', 'label': './labelsTr/MSWAL_0060.nii.gz'}, {'image': './imagesTr/MSWAL_0061_0000.nii.gz', 'label': './labelsTr/MSWAL_0061.nii.gz'}, {'image': './imagesTr/MSWAL_0063_0000.nii.gz', 'label': './labelsTr/MSWAL_0063.nii.gz'}, {'image': './imagesTr/MSWAL_0064_0000.nii.gz', 'label': './labelsTr/MSWAL_0064.nii.gz'}, {'image': './imagesTr/MSWAL_0065_0000.nii.gz', 'label': './labelsTr/MSWAL_0065.nii.gz'}, {'image': './imagesTr/MSWAL_0066_0000.nii.gz', 'label': './labelsTr/MSWAL_0066.nii.gz'}, {'image': './imagesTr/MSWAL_0067_0000.nii.gz', 'label': './labelsTr/MSWAL_0067.nii.gz'}, {'image': './imagesTr/MSWAL_0069_0000.nii.gz', 'label': './labelsTr/MSWAL_0069.nii.gz'}, {'image': './imagesTr/MSWAL_0072_0000.nii.gz', 'label': './labelsTr/MSWAL_0072.nii.gz'}, {'image': './imagesTr/MSWAL_0075_0000.nii.gz', 'label': './labelsTr/MSWAL_0075.nii.gz'}, {'image': './imagesTr/MSWAL_0077_0000.nii.gz', 'label': './labelsTr/MSWAL_0077.nii.gz'}, {'image': './imagesTr/MSWAL_0080_0000.nii.gz', 'label': './labelsTr/MSWAL_0080.nii.gz'}, {'image': './imagesTr/MSWAL_0082_0000.nii.gz', 'label': './labelsTr/MSWAL_0082.nii.gz'}, {'image': './imagesTr/MSWAL_0083_0000.nii.gz', 'label': './labelsTr/MSWAL_0083.nii.gz'}, {'image': './imagesTr/MSWAL_0084_0000.nii.gz', 'label': './labelsTr/MSWAL_0084.nii.gz'}, {'image': './imagesTr/MSWAL_0085_0000.nii.gz', 'label': './labelsTr/MSWAL_0085.nii.gz'}, {'image': './imagesTr/MSWAL_0086_0000.nii.gz', 'label': './labelsTr/MSWAL_0086.nii.gz'}, {'image': './imagesTr/MSWAL_0088_0000.nii.gz', 'label': './labelsTr/MSWAL_0088.nii.gz'}, {'image': './imagesTr/MSWAL_0089_0000.nii.gz', 'label': './labelsTr/MSWAL_0089.nii.gz'}, {'image': './imagesTr/MSWAL_0092_0000.nii.gz', 'label': './labelsTr/MSWAL_0092.nii.gz'}, {'image': './imagesTr/MSWAL_0093_0000.nii.gz', 'label': './labelsTr/MSWAL_0093.nii.gz'}, {'image': './imagesTr/MSWAL_0094_0000.nii.gz', 'label': './labelsTr/MSWAL_0094.nii.gz'}, {'image': './imagesTr/MSWAL_0095_0000.nii.gz', 'label': './labelsTr/MSWAL_0095.nii.gz'}, {'image': './imagesTr/MSWAL_0096_0000.nii.gz', 'label': './labelsTr/MSWAL_0096.nii.gz'}, {'image': './imagesTr/MSWAL_0098_0000.nii.gz', 'label': './labelsTr/MSWAL_0098.nii.gz'}, {'image': 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320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 71.96339416503906, 'median': 45.0, 'min': -932.0, 'percentile_00_5': -93.0, 'percentile_99_5': 1052.0, 'std': 141.6230926513672}}}", + "preprocessed_dataset_folder": "/data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/nnUNetPlans_3d_fullres", + "preprocessed_dataset_folder_base": "/data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL", + "save_every": "50", + "torch_version": "2.5.0+cu121", + "unpack_dataset": "True", + "was_initialized": "True", + "weight_decay": "3e-05" +} \ No newline at end of file diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png new file mode 100644 index 0000000000000000000000000000000000000000..c7828ca099fb1e71c85416853bb3046b8f0aca20 --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:808a4ebdf0a8266508b00ceb6c0ef8b0d7dcce31ef03e33453db8fdd5455fec6 +size 1579464 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_2/training_log_2026_4_10_10_09_50.txt b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_2/training_log_2026_4_10_10_09_50.txt new file mode 100644 index 0000000000000000000000000000000000000000..4fbd3437ef892a03084a9bcbbc9a5386aa77e6f1 --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_2/training_log_2026_4_10_10_09_50.txt @@ -0,0 +1,28156 @@ + +####################################################################### +Please cite the following paper when using nnU-Net: +Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. +####################################################################### + +2026-04-10 10:09:50.182968: do_dummy_2d_data_aug: False +2026-04-10 10:09:50.254119: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json +2026-04-10 10:09:50.257095: The split file contains 5 splits. +2026-04-10 10:09:50.258432: Desired fold for training: 2 +2026-04-10 10:09:50.259755: This split has 387 training and 97 validation cases. +2026-04-10 10:10:04.355892: Using torch.compile... + +This is the configuration used by this training: +Configuration name: 3d_fullres + {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True} + +These are the global plan.json settings: + {'dataset_name': 'Dataset201_MSWAL', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.25, 0.75, 0.75], 'original_median_shape_after_transp': [261, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 71.96339416503906, 'median': 45.0, 'min': -932.0, 'percentile_00_5': -93.0, 'percentile_99_5': 1052.0, 'std': 141.6230926513672}}} + +2026-04-10 10:10:06.655386: unpacking dataset... +2026-04-10 10:10:13.473189: unpacking done... +2026-04-10 10:10:13.496329: Unable to plot network architecture: nnUNet_compile is enabled! +2026-04-10 10:10:13.564626: +2026-04-10 10:10:13.566216: Epoch 0 +2026-04-10 10:10:13.567675: Current learning rate: 0.01 +2026-04-10 10:15:02.727479: train_loss 0.2019 +2026-04-10 10:15:02.733771: val_loss 0.0505 +2026-04-10 10:15:02.736092: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:15:02.738071: Epoch time: 289.17 s +2026-04-10 10:15:02.739782: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 10:15:05.339037: +2026-04-10 10:15:05.340531: Epoch 1 +2026-04-10 10:15:05.341957: Current learning rate: 0.01 +2026-04-10 10:16:46.419481: train_loss 0.0817 +2026-04-10 10:16:46.424781: val_loss 0.0375 +2026-04-10 10:16:46.427215: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:16:46.429494: Epoch time: 101.08 s +2026-04-10 10:16:47.458282: +2026-04-10 10:16:47.459951: Epoch 2 +2026-04-10 10:16:47.461425: Current learning rate: 0.01 +2026-04-10 10:18:31.978346: train_loss 0.0689 +2026-04-10 10:18:31.983469: val_loss 0.0513 +2026-04-10 10:18:31.985460: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:18:31.987886: Epoch time: 104.52 s +2026-04-10 10:18:33.103621: +2026-04-10 10:18:33.106277: Epoch 3 +2026-04-10 10:18:33.107878: Current learning rate: 0.00999 +2026-04-10 10:21:09.410689: train_loss 0.0658 +2026-04-10 10:21:09.418806: val_loss 0.0469 +2026-04-10 10:21:09.420779: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:21:09.423115: Epoch time: 156.31 s +2026-04-10 10:21:10.506822: +2026-04-10 10:21:10.508525: Epoch 4 +2026-04-10 10:21:10.510998: Current learning rate: 0.00999 +2026-04-10 10:31:09.183725: train_loss 0.0616 +2026-04-10 10:31:09.192296: val_loss 0.031 +2026-04-10 10:31:09.194928: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:31:09.199471: Epoch time: 598.68 s +2026-04-10 10:31:11.165269: +2026-04-10 10:31:11.168099: Epoch 5 +2026-04-10 10:31:11.170577: Current learning rate: 0.00999 +2026-04-10 11:22:43.085929: train_loss 0.0562 +2026-04-10 11:22:43.093453: val_loss 0.0542 +2026-04-10 11:22:43.097899: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:22:43.100550: Epoch time: 3091.93 s +2026-04-10 11:22:45.422074: +2026-04-10 11:22:45.423789: Epoch 6 +2026-04-10 11:22:45.425327: Current learning rate: 0.00999 +2026-04-10 11:58:28.130752: train_loss 0.0646 +2026-04-10 11:58:28.138430: val_loss 0.0331 +2026-04-10 11:58:28.140615: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:58:28.142931: Epoch time: 2142.72 s +2026-04-10 11:58:29.317696: +2026-04-10 11:58:29.319317: Epoch 7 +2026-04-10 11:58:29.320677: Current learning rate: 0.00998 +2026-04-10 12:00:14.050245: train_loss 0.0528 +2026-04-10 12:00:14.056013: val_loss 0.0472 +2026-04-10 12:00:14.057813: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:00:14.059827: Epoch time: 104.74 s +2026-04-10 12:00:15.189089: +2026-04-10 12:00:15.190703: Epoch 8 +2026-04-10 12:00:15.192405: Current learning rate: 0.00998 +2026-04-10 12:01:59.803191: train_loss 0.0547 +2026-04-10 12:01:59.830623: val_loss 0.04 +2026-04-10 12:01:59.832483: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:01:59.834809: Epoch time: 104.62 s +2026-04-10 12:02:00.971060: +2026-04-10 12:02:00.972891: Epoch 9 +2026-04-10 12:02:00.974684: Current learning rate: 0.00998 +2026-04-10 12:03:43.326196: train_loss 0.0519 +2026-04-10 12:03:43.331934: val_loss 0.0437 +2026-04-10 12:03:43.333846: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:03:43.335999: Epoch time: 102.36 s +2026-04-10 12:03:44.419032: +2026-04-10 12:03:44.420755: Epoch 10 +2026-04-10 12:03:44.422256: Current learning rate: 0.00998 +2026-04-10 12:05:26.734016: train_loss 0.0692 +2026-04-10 12:05:26.749323: val_loss 0.0353 +2026-04-10 12:05:26.751138: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:05:26.755183: Epoch time: 102.32 s +2026-04-10 12:05:27.894021: +2026-04-10 12:05:27.896028: Epoch 11 +2026-04-10 12:05:27.897673: Current learning rate: 0.00998 +2026-04-10 12:07:09.731070: train_loss 0.0508 +2026-04-10 12:07:09.736709: val_loss 0.0457 +2026-04-10 12:07:09.739106: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:07:09.741531: Epoch time: 101.84 s +2026-04-10 12:07:10.786435: +2026-04-10 12:07:10.788126: Epoch 12 +2026-04-10 12:07:10.789697: Current learning rate: 0.00997 +2026-04-10 12:08:52.458757: train_loss 0.0594 +2026-04-10 12:08:52.464711: val_loss 0.0654 +2026-04-10 12:08:52.466669: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:08:52.469440: Epoch time: 101.68 s +2026-04-10 12:08:53.591434: +2026-04-10 12:08:53.593131: Epoch 13 +2026-04-10 12:08:53.594956: Current learning rate: 0.00997 +2026-04-10 12:10:35.557764: train_loss 0.0622 +2026-04-10 12:10:35.565198: val_loss 0.0403 +2026-04-10 12:10:35.567218: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:10:35.570009: Epoch time: 101.97 s +2026-04-10 12:10:36.686106: +2026-04-10 12:10:36.687966: Epoch 14 +2026-04-10 12:10:36.689443: Current learning rate: 0.00997 +2026-04-10 12:12:20.821908: train_loss 0.0532 +2026-04-10 12:12:20.828092: val_loss 0.0443 +2026-04-10 12:12:20.830469: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:12:20.832572: Epoch time: 104.14 s +2026-04-10 12:12:21.971401: +2026-04-10 12:12:21.973572: Epoch 15 +2026-04-10 12:12:21.975404: Current learning rate: 0.00997 +2026-04-10 12:14:14.471272: train_loss 0.0596 +2026-04-10 12:14:14.478776: val_loss 0.0447 +2026-04-10 12:14:14.480621: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:14:14.482981: Epoch time: 112.5 s +2026-04-10 12:14:15.594693: +2026-04-10 12:14:15.596356: Epoch 16 +2026-04-10 12:14:15.597715: Current learning rate: 0.00996 +2026-04-10 12:18:22.863421: train_loss 0.0546 +2026-04-10 12:18:22.868256: val_loss 0.0442 +2026-04-10 12:18:22.869757: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:18:22.871510: Epoch time: 247.27 s +2026-04-10 12:18:25.202751: +2026-04-10 12:18:25.204502: Epoch 17 +2026-04-10 12:18:25.205916: Current learning rate: 0.00996 +2026-04-10 12:22:26.460076: train_loss 0.052 +2026-04-10 12:22:26.466861: val_loss 0.0381 +2026-04-10 12:22:26.469316: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:22:26.471759: Epoch time: 241.26 s +2026-04-10 12:22:27.566279: +2026-04-10 12:22:27.568636: Epoch 18 +2026-04-10 12:22:27.570939: Current learning rate: 0.00996 +2026-04-10 12:24:11.310289: train_loss 0.0508 +2026-04-10 12:24:11.316500: val_loss 0.0352 +2026-04-10 12:24:11.319492: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:24:11.323270: Epoch time: 103.75 s +2026-04-10 12:24:12.441298: +2026-04-10 12:24:12.443167: Epoch 19 +2026-04-10 12:24:12.444687: Current learning rate: 0.00996 +2026-04-10 12:25:56.149782: train_loss 0.0527 +2026-04-10 12:25:56.155902: val_loss 0.0383 +2026-04-10 12:25:56.157979: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:25:56.160272: Epoch time: 103.71 s +2026-04-10 12:25:57.263453: +2026-04-10 12:25:57.265002: Epoch 20 +2026-04-10 12:25:57.266397: Current learning rate: 0.00995 +2026-04-10 12:27:39.529428: train_loss 0.0393 +2026-04-10 12:27:39.534564: val_loss 0.0497 +2026-04-10 12:27:39.536881: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:27:39.539079: Epoch time: 102.27 s +2026-04-10 12:27:40.636179: +2026-04-10 12:27:40.638386: Epoch 21 +2026-04-10 12:27:40.640278: Current learning rate: 0.00995 +2026-04-10 12:29:22.715688: train_loss 0.0414 +2026-04-10 12:29:22.722440: val_loss 0.0369 +2026-04-10 12:29:22.724538: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:29:22.727065: Epoch time: 102.08 s +2026-04-10 12:29:23.820418: +2026-04-10 12:29:23.821952: Epoch 22 +2026-04-10 12:29:23.823315: Current learning rate: 0.00995 +2026-04-10 12:31:05.883518: train_loss 0.0416 +2026-04-10 12:31:05.889093: val_loss 0.0304 +2026-04-10 12:31:05.891167: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:31:05.893830: Epoch time: 102.07 s +2026-04-10 12:31:06.969206: +2026-04-10 12:31:06.971038: Epoch 23 +2026-04-10 12:31:06.973083: Current learning rate: 0.00995 +2026-04-10 12:32:49.141253: train_loss 0.0437 +2026-04-10 12:32:49.146624: val_loss 0.028 +2026-04-10 12:32:49.148715: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:32:49.150986: Epoch time: 102.18 s +2026-04-10 12:32:50.226818: +2026-04-10 12:32:50.229321: Epoch 24 +2026-04-10 12:32:50.230845: Current learning rate: 0.00995 +2026-04-10 12:34:41.056906: train_loss 0.0386 +2026-04-10 12:34:41.063185: val_loss 0.031 +2026-04-10 12:34:41.065708: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:34:41.068056: Epoch time: 110.83 s +2026-04-10 12:34:42.126935: +2026-04-10 12:34:42.128995: Epoch 25 +2026-04-10 12:34:42.131926: Current learning rate: 0.00994 +2026-04-10 12:36:26.146008: train_loss 0.0461 +2026-04-10 12:36:26.153517: val_loss 0.0286 +2026-04-10 12:36:26.155365: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:36:26.157717: Epoch time: 104.02 s +2026-04-10 12:36:27.233391: +2026-04-10 12:36:27.235198: Epoch 26 +2026-04-10 12:36:27.237100: Current learning rate: 0.00994 +2026-04-10 12:40:17.598210: train_loss 0.0394 +2026-04-10 12:40:17.609715: val_loss 0.0316 +2026-04-10 12:40:17.611876: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:40:17.614092: Epoch time: 230.37 s +2026-04-10 12:40:19.132746: +2026-04-10 12:40:19.134646: Epoch 27 +2026-04-10 12:40:19.136774: Current learning rate: 0.00994 +2026-04-10 12:42:26.176952: train_loss 0.0446 +2026-04-10 12:42:26.183069: val_loss 0.0503 +2026-04-10 12:42:26.185185: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:42:26.187291: Epoch time: 127.05 s +2026-04-10 12:42:27.233227: +2026-04-10 12:42:27.235237: Epoch 28 +2026-04-10 12:42:27.237325: Current learning rate: 0.00994 +2026-04-10 12:44:10.572924: train_loss 0.0458 +2026-04-10 12:44:10.581226: val_loss 0.0317 +2026-04-10 12:44:10.583695: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:44:10.585914: Epoch time: 103.34 s +2026-04-10 12:44:11.688851: +2026-04-10 12:44:11.690598: Epoch 29 +2026-04-10 12:44:11.692360: Current learning rate: 0.00993 +2026-04-10 12:46:36.267853: train_loss 0.037 +2026-04-10 12:46:36.274177: val_loss 0.0381 +2026-04-10 12:46:36.276030: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:46:36.278801: Epoch time: 144.58 s +2026-04-10 12:46:37.771196: +2026-04-10 12:46:37.773546: Epoch 30 +2026-04-10 12:46:37.775117: Current learning rate: 0.00993 +2026-04-10 12:48:21.010939: train_loss 0.0435 +2026-04-10 12:48:21.018143: val_loss 0.045 +2026-04-10 12:48:21.020059: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:48:21.022590: Epoch time: 103.24 s +2026-04-10 12:48:22.131912: +2026-04-10 12:48:22.133645: Epoch 31 +2026-04-10 12:48:22.135329: Current learning rate: 0.00993 +2026-04-10 12:50:05.545109: train_loss 0.0479 +2026-04-10 12:50:05.552284: val_loss 0.0383 +2026-04-10 12:50:05.554400: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:50:05.557599: Epoch time: 103.42 s +2026-04-10 12:50:06.684161: +2026-04-10 12:50:06.685812: Epoch 32 +2026-04-10 12:50:06.687186: Current learning rate: 0.00993 +2026-04-10 12:51:48.993939: train_loss 0.0446 +2026-04-10 12:51:49.003516: val_loss 0.0219 +2026-04-10 12:51:49.009231: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:51:49.012983: Epoch time: 102.31 s +2026-04-10 12:51:50.078181: +2026-04-10 12:51:50.079851: Epoch 33 +2026-04-10 12:51:50.081391: Current learning rate: 0.00993 +2026-04-10 12:53:31.961267: train_loss 0.0475 +2026-04-10 12:53:31.967043: val_loss 0.0356 +2026-04-10 12:53:31.969351: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:53:31.971385: Epoch time: 101.89 s +2026-04-10 12:53:33.055971: +2026-04-10 12:53:33.057930: Epoch 34 +2026-04-10 12:53:33.059580: Current learning rate: 0.00992 +2026-04-10 12:55:15.861185: train_loss 0.0437 +2026-04-10 12:55:15.868685: val_loss 0.0327 +2026-04-10 12:55:15.870695: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:55:15.873227: Epoch time: 102.81 s +2026-04-10 12:55:16.983348: +2026-04-10 12:55:16.985427: Epoch 35 +2026-04-10 12:55:16.987239: Current learning rate: 0.00992 +2026-04-10 12:56:59.907925: train_loss 0.044 +2026-04-10 12:56:59.914001: val_loss 0.0468 +2026-04-10 12:56:59.916199: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:56:59.918370: Epoch time: 102.93 s +2026-04-10 12:57:01.043728: +2026-04-10 12:57:01.046138: Epoch 36 +2026-04-10 12:57:01.048412: Current learning rate: 0.00992 +2026-04-10 12:58:43.571627: train_loss 0.0394 +2026-04-10 12:58:43.578806: val_loss 0.02 +2026-04-10 12:58:43.580915: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 12:58:43.583494: Epoch time: 102.53 s +2026-04-10 12:58:45.905704: +2026-04-10 12:58:45.907350: Epoch 37 +2026-04-10 12:58:45.908818: Current learning rate: 0.00992 +2026-04-10 13:00:27.830194: train_loss 0.0396 +2026-04-10 13:00:27.836691: val_loss 0.0446 +2026-04-10 13:00:27.838408: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 13:00:27.840780: Epoch time: 101.93 s +2026-04-10 13:00:28.968265: +2026-04-10 13:00:28.969991: Epoch 38 +2026-04-10 13:00:28.971308: Current learning rate: 0.00991 +2026-04-10 13:02:10.811851: train_loss 0.0307 +2026-04-10 13:02:10.828126: val_loss 0.03 +2026-04-10 13:02:10.830205: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 13:02:10.832659: Epoch time: 101.85 s +2026-04-10 13:02:11.925171: +2026-04-10 13:02:11.926763: Epoch 39 +2026-04-10 13:02:11.928183: Current learning rate: 0.00991 +2026-04-10 13:03:53.793795: train_loss 0.0474 +2026-04-10 13:03:53.799236: val_loss 0.0279 +2026-04-10 13:03:53.801004: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 13:03:53.802780: Epoch time: 101.87 s +2026-04-10 13:03:54.902453: +2026-04-10 13:03:54.904310: Epoch 40 +2026-04-10 13:03:54.905848: Current learning rate: 0.00991 +2026-04-10 13:05:37.156296: train_loss 0.043 +2026-04-10 13:05:37.161867: val_loss 0.0281 +2026-04-10 13:05:37.163753: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 13:05:37.166056: Epoch time: 102.26 s +2026-04-10 13:05:38.306070: +2026-04-10 13:05:38.308181: Epoch 41 +2026-04-10 13:05:38.309820: Current learning rate: 0.00991 +2026-04-10 13:07:20.242234: train_loss 0.0234 +2026-04-10 13:07:20.249332: val_loss 0.0299 +2026-04-10 13:07:20.251573: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 13:07:20.253956: Epoch time: 101.94 s +2026-04-10 13:07:21.315503: +2026-04-10 13:07:21.317213: Epoch 42 +2026-04-10 13:07:21.318690: Current learning rate: 0.00991 +2026-04-10 13:09:03.253384: train_loss 0.0162 +2026-04-10 13:09:03.261090: val_loss 0.0309 +2026-04-10 13:09:03.263289: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 13:09:03.265768: Epoch time: 101.94 s +2026-04-10 13:09:04.298609: +2026-04-10 13:09:04.300154: Epoch 43 +2026-04-10 13:09:04.301486: Current learning rate: 0.0099 +2026-04-10 13:10:46.757522: train_loss 0.0304 +2026-04-10 13:10:46.764143: val_loss 0.0232 +2026-04-10 13:10:46.766130: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0012, 0.0] +2026-04-10 13:10:46.768694: Epoch time: 102.46 s +2026-04-10 13:10:46.770601: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 13:10:49.469421: +2026-04-10 13:10:49.471099: Epoch 44 +2026-04-10 13:10:49.472539: Current learning rate: 0.0099 +2026-04-10 13:12:31.511250: train_loss 0.0322 +2026-04-10 13:12:31.517411: val_loss 0.0167 +2026-04-10 13:12:31.519634: Pseudo dice [0.0, 0.0, 0.0746, 0.0, 0.0, 0.1673, 0.0] +2026-04-10 13:12:31.522147: Epoch time: 102.04 s +2026-04-10 13:12:31.524302: Yayy! New best EMA pseudo Dice: 0.0035 +2026-04-10 13:12:34.264259: +2026-04-10 13:12:34.266068: Epoch 45 +2026-04-10 13:12:34.267562: Current learning rate: 0.0099 +2026-04-10 13:14:16.861591: train_loss 0.0229 +2026-04-10 13:14:16.868293: val_loss 0.0081 +2026-04-10 13:14:16.870573: Pseudo dice [0.0, 0.0, 0.254, 0.0, 0.0, 0.1967, 0.0042] +2026-04-10 13:14:16.873646: Epoch time: 102.6 s +2026-04-10 13:14:16.877006: Yayy! New best EMA pseudo Dice: 0.0096 +2026-04-10 13:14:19.579046: +2026-04-10 13:14:19.580896: Epoch 46 +2026-04-10 13:14:19.582377: Current learning rate: 0.0099 +2026-04-10 13:16:01.659456: train_loss 0.03 +2026-04-10 13:16:01.665532: val_loss 0.0212 +2026-04-10 13:16:01.667510: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0462, 0.0906] +2026-04-10 13:16:01.669627: Epoch time: 102.08 s +2026-04-10 13:16:01.671478: Yayy! New best EMA pseudo Dice: 0.0106 +2026-04-10 13:16:04.312576: +2026-04-10 13:16:04.314222: Epoch 47 +2026-04-10 13:16:04.315902: Current learning rate: 0.00989 +2026-04-10 13:17:46.145188: train_loss 0.025 +2026-04-10 13:17:46.150608: val_loss 0.0053 +2026-04-10 13:17:46.152469: Pseudo dice [0.0, 0.0, 0.2159, 0.0, 0.0, 0.0154, 0.1369] +2026-04-10 13:17:46.154680: Epoch time: 101.84 s +2026-04-10 13:17:46.156414: Yayy! New best EMA pseudo Dice: 0.0148 +2026-04-10 13:17:48.844032: +2026-04-10 13:17:48.846491: Epoch 48 +2026-04-10 13:17:48.853805: Current learning rate: 0.00989 +2026-04-10 13:19:30.816392: train_loss 0.0066 +2026-04-10 13:19:30.822143: val_loss 0.0064 +2026-04-10 13:19:30.823800: Pseudo dice [0.0, 0.0, 0.3403, 0.0, 0.0, 0.0, 0.3111] +2026-04-10 13:19:30.826388: Epoch time: 101.98 s +2026-04-10 13:19:30.829460: Yayy! New best EMA pseudo Dice: 0.0226 +2026-04-10 13:19:33.517144: +2026-04-10 13:19:33.518771: Epoch 49 +2026-04-10 13:19:33.520189: Current learning rate: 0.00989 +2026-04-10 13:21:15.438369: train_loss 0.0228 +2026-04-10 13:21:15.444816: val_loss 0.0134 +2026-04-10 13:21:15.446567: Pseudo dice [0.0, 0.0, 0.212, 0.0, 0.0, 0.0, 0.1323] +2026-04-10 13:21:15.448808: Epoch time: 101.92 s +2026-04-10 13:21:17.029394: Yayy! New best EMA pseudo Dice: 0.0253 +2026-04-10 13:21:19.682597: +2026-04-10 13:21:19.684224: Epoch 50 +2026-04-10 13:21:19.685492: Current learning rate: 0.00989 +2026-04-10 13:23:01.636446: train_loss 0.0213 +2026-04-10 13:23:01.642415: val_loss 0.0028 +2026-04-10 13:23:01.644377: Pseudo dice [0.0, 0.0, 0.0436, 0.0, 0.0, 0.001, 0.2083] +2026-04-10 13:23:01.646910: Epoch time: 101.96 s +2026-04-10 13:23:01.648775: Yayy! New best EMA pseudo Dice: 0.0264 +2026-04-10 13:23:04.411363: +2026-04-10 13:23:04.412950: Epoch 51 +2026-04-10 13:23:04.414516: Current learning rate: 0.00989 +2026-04-10 13:24:46.353790: train_loss 0.0165 +2026-04-10 13:24:46.361419: val_loss 0.0199 +2026-04-10 13:24:46.363211: Pseudo dice [0.0, 0.0, 0.1513, 0.0, 0.0, 0.0, 0.4142] +2026-04-10 13:24:46.366133: Epoch time: 101.95 s +2026-04-10 13:24:46.368049: Yayy! New best EMA pseudo Dice: 0.0318 +2026-04-10 13:24:49.040553: +2026-04-10 13:24:49.042106: Epoch 52 +2026-04-10 13:24:49.043419: Current learning rate: 0.00988 +2026-04-10 13:26:30.859755: train_loss 0.0107 +2026-04-10 13:26:30.866084: val_loss 0.0168 +2026-04-10 13:26:30.868147: Pseudo dice [0.0, 0.0, 0.1436, 0.0, 0.0, 0.0, 0.3183] +2026-04-10 13:26:30.870499: Epoch time: 101.82 s +2026-04-10 13:26:30.872093: Yayy! New best EMA pseudo Dice: 0.0352 +2026-04-10 13:26:33.600321: +2026-04-10 13:26:33.601825: Epoch 53 +2026-04-10 13:26:33.603168: Current learning rate: 0.00988 +2026-04-10 13:28:15.509820: train_loss 0.0099 +2026-04-10 13:28:15.515873: val_loss 0.0229 +2026-04-10 13:28:15.517989: Pseudo dice [0.0, 0.0, 0.1482, 0.0, 0.0, 0.0, 0.1337] +2026-04-10 13:28:15.520554: Epoch time: 101.91 s +2026-04-10 13:28:15.522674: Yayy! New best EMA pseudo Dice: 0.0357 +2026-04-10 13:28:18.260655: +2026-04-10 13:28:18.262383: Epoch 54 +2026-04-10 13:28:18.263663: Current learning rate: 0.00988 +2026-04-10 13:30:01.538144: train_loss 0.0076 +2026-04-10 13:30:01.545306: val_loss -0.0002 +2026-04-10 13:30:01.546920: Pseudo dice [0.0, 0.0, 0.6082, 0.0, 0.0, 0.0, 0.4227] +2026-04-10 13:30:01.548921: Epoch time: 103.28 s +2026-04-10 13:30:01.550598: Yayy! New best EMA pseudo Dice: 0.0469 +2026-04-10 13:30:04.250202: +2026-04-10 13:30:04.251855: Epoch 55 +2026-04-10 13:30:04.253289: Current learning rate: 0.00988 +2026-04-10 13:31:46.494440: train_loss 0.003 +2026-04-10 13:31:46.499943: val_loss 0.0063 +2026-04-10 13:31:46.501793: Pseudo dice [0.0, 0.0, 0.2826, 0.0, 0.0, 0.0015, 0.2494] +2026-04-10 13:31:46.504207: Epoch time: 102.25 s +2026-04-10 13:31:46.505989: Yayy! New best EMA pseudo Dice: 0.0498 +2026-04-10 13:31:49.275377: +2026-04-10 13:31:49.276981: Epoch 56 +2026-04-10 13:31:49.278557: Current learning rate: 0.00987 +2026-04-10 13:33:31.309341: train_loss 0.0074 +2026-04-10 13:33:31.315603: val_loss 0.0003 +2026-04-10 13:33:31.317657: Pseudo dice [0.0, 0.0, 0.3362, 0.0, 0.0, 0.0705, 0.2532] +2026-04-10 13:33:31.320045: Epoch time: 102.04 s +2026-04-10 13:33:31.322076: Yayy! New best EMA pseudo Dice: 0.0543 +2026-04-10 13:33:34.042346: +2026-04-10 13:33:34.043960: Epoch 57 +2026-04-10 13:33:34.045246: Current learning rate: 0.00987 +2026-04-10 13:35:16.065081: train_loss 0.0188 +2026-04-10 13:35:16.071464: val_loss 0.0172 +2026-04-10 13:35:16.073696: Pseudo dice [0.0, 0.0, 0.2582, 0.0, 0.0, 0.111, 0.321] +2026-04-10 13:35:16.075943: Epoch time: 102.03 s +2026-04-10 13:35:16.078172: Yayy! New best EMA pseudo Dice: 0.0587 +2026-04-10 13:35:18.884965: +2026-04-10 13:35:18.887056: Epoch 58 +2026-04-10 13:35:18.888617: Current learning rate: 0.00987 +2026-04-10 13:37:00.933673: train_loss 0.0026 +2026-04-10 13:37:00.944998: val_loss 0.0034 +2026-04-10 13:37:00.947121: Pseudo dice [0.0, 0.0, 0.3682, 0.0, 0.0, 0.04, 0.2857] +2026-04-10 13:37:00.950282: Epoch time: 102.05 s +2026-04-10 13:37:00.952633: Yayy! New best EMA pseudo Dice: 0.0627 +2026-04-10 13:37:03.747835: +2026-04-10 13:37:03.749374: Epoch 59 +2026-04-10 13:37:03.750754: Current learning rate: 0.00987 +2026-04-10 13:38:45.810535: train_loss 0.0062 +2026-04-10 13:38:45.818881: val_loss 0.0177 +2026-04-10 13:38:45.820738: Pseudo dice [0.0, 0.0, 0.3163, 0.0, 0.0, 0.0503, 0.4198] +2026-04-10 13:38:45.823926: Epoch time: 102.07 s +2026-04-10 13:38:45.826545: Yayy! New best EMA pseudo Dice: 0.0677 +2026-04-10 13:38:48.604836: +2026-04-10 13:38:48.606572: Epoch 60 +2026-04-10 13:38:48.608090: Current learning rate: 0.00986 +2026-04-10 13:40:30.626426: train_loss 0.0055 +2026-04-10 13:40:30.633566: val_loss -0.0267 +2026-04-10 13:40:30.635755: Pseudo dice [0.0, 0.0, 0.5258, 0.0, 0.0, 0.1067, 0.5812] +2026-04-10 13:40:30.638530: Epoch time: 102.02 s +2026-04-10 13:40:30.641494: Yayy! New best EMA pseudo Dice: 0.0783 +2026-04-10 13:40:33.413368: +2026-04-10 13:40:33.414911: Epoch 61 +2026-04-10 13:40:33.416241: Current learning rate: 0.00986 +2026-04-10 13:42:15.706424: train_loss -0.0127 +2026-04-10 13:42:15.712448: val_loss -0.0083 +2026-04-10 13:42:15.714369: Pseudo dice [0.0, 0.0, 0.3402, 0.0, 0.0, 0.0753, 0.1715] +2026-04-10 13:42:15.716660: Epoch time: 102.3 s +2026-04-10 13:42:15.719199: Yayy! New best EMA pseudo Dice: 0.0788 +2026-04-10 13:42:18.568509: +2026-04-10 13:42:18.570101: Epoch 62 +2026-04-10 13:42:18.571442: Current learning rate: 0.00986 +2026-04-10 13:44:00.513953: train_loss -0.0053 +2026-04-10 13:44:00.520685: val_loss -0.0063 +2026-04-10 13:44:00.523072: Pseudo dice [0.0, 0.0, 0.5278, 0.0, 0.0, 0.2016, 0.3257] +2026-04-10 13:44:00.525898: Epoch time: 101.95 s +2026-04-10 13:44:00.528272: Yayy! New best EMA pseudo Dice: 0.086 +2026-04-10 13:44:03.372482: +2026-04-10 13:44:03.374218: Epoch 63 +2026-04-10 13:44:03.375594: Current learning rate: 0.00986 +2026-04-10 13:45:45.983703: train_loss -0.0024 +2026-04-10 13:45:45.990956: val_loss 0.0017 +2026-04-10 13:45:45.994328: Pseudo dice [0.0, 0.0, 0.4122, 0.0, 0.0, 0.2158, 0.4943] +2026-04-10 13:45:45.996903: Epoch time: 102.61 s +2026-04-10 13:45:45.998809: Yayy! New best EMA pseudo Dice: 0.0935 +2026-04-10 13:45:48.791649: +2026-04-10 13:45:48.793290: Epoch 64 +2026-04-10 13:45:48.794650: Current learning rate: 0.00986 +2026-04-10 13:47:31.222513: train_loss -0.0093 +2026-04-10 13:47:31.228887: val_loss -0.0152 +2026-04-10 13:47:31.230916: Pseudo dice [0.0, 0.0, 0.4323, 0.0, 0.0, 0.1188, 0.4965] +2026-04-10 13:47:31.233786: Epoch time: 102.43 s +2026-04-10 13:47:31.235785: Yayy! New best EMA pseudo Dice: 0.0991 +2026-04-10 13:47:34.096052: +2026-04-10 13:47:34.097741: Epoch 65 +2026-04-10 13:47:34.099141: Current learning rate: 0.00985 +2026-04-10 13:49:16.032135: train_loss -0.0116 +2026-04-10 13:49:16.038631: val_loss -0.0315 +2026-04-10 13:49:16.040663: Pseudo dice [0.0, 0.0, 0.4212, 0.0, 0.0, 0.3487, 0.4767] +2026-04-10 13:49:16.042637: Epoch time: 101.94 s +2026-04-10 13:49:16.045074: Yayy! New best EMA pseudo Dice: 0.107 +2026-04-10 13:49:18.923154: +2026-04-10 13:49:18.934775: Epoch 66 +2026-04-10 13:49:18.936242: Current learning rate: 0.00985 +2026-04-10 13:51:00.721744: train_loss -0.0145 +2026-04-10 13:51:00.728255: val_loss -0.0177 +2026-04-10 13:51:00.731282: Pseudo dice [0.0, 0.0, 0.5134, 0.0, 0.0, 0.1373, 0.5558] +2026-04-10 13:51:00.734498: Epoch time: 101.8 s +2026-04-10 13:51:00.736284: Yayy! New best EMA pseudo Dice: 0.1135 +2026-04-10 13:51:03.458702: +2026-04-10 13:51:03.460517: Epoch 67 +2026-04-10 13:51:03.461910: Current learning rate: 0.00985 +2026-04-10 13:52:45.590785: train_loss -0.0019 +2026-04-10 13:52:45.597474: val_loss -0.027 +2026-04-10 13:52:45.600504: Pseudo dice [0.0, 0.0, 0.3913, 0.0, 0.0, 0.2173, 0.458] +2026-04-10 13:52:45.603087: Epoch time: 102.14 s +2026-04-10 13:52:45.605528: Yayy! New best EMA pseudo Dice: 0.1174 +2026-04-10 13:52:48.454282: +2026-04-10 13:52:48.456135: Epoch 68 +2026-04-10 13:52:48.457553: Current learning rate: 0.00985 +2026-04-10 13:54:30.511356: train_loss -0.0122 +2026-04-10 13:54:30.518081: val_loss -0.0077 +2026-04-10 13:54:30.521278: Pseudo dice [0.0, 0.0, 0.4161, 0.0, 0.0, 0.1657, 0.2976] +2026-04-10 13:54:30.523290: Epoch time: 102.06 s +2026-04-10 13:54:30.524879: Yayy! New best EMA pseudo Dice: 0.1182 +2026-04-10 13:54:33.348625: +2026-04-10 13:54:33.350505: Epoch 69 +2026-04-10 13:54:33.351822: Current learning rate: 0.00984 +2026-04-10 13:56:15.080290: train_loss -0.0031 +2026-04-10 13:56:15.085792: val_loss -0.0214 +2026-04-10 13:56:15.087795: Pseudo dice [0.0, 0.0, 0.4821, 0.0, 0.0, 0.1491, 0.322] +2026-04-10 13:56:15.090998: Epoch time: 101.73 s +2026-04-10 13:56:15.092761: Yayy! New best EMA pseudo Dice: 0.12 +2026-04-10 13:56:18.963788: +2026-04-10 13:56:18.965306: Epoch 70 +2026-04-10 13:56:18.966724: Current learning rate: 0.00984 +2026-04-10 13:58:01.137290: train_loss -0.0112 +2026-04-10 13:58:01.144602: val_loss -0.0133 +2026-04-10 13:58:01.146436: Pseudo dice [0.0, 0.0, 0.3886, 0.0, 0.0, 0.1105, 0.3891] +2026-04-10 13:58:01.148731: Epoch time: 102.18 s +2026-04-10 13:58:01.150650: Yayy! New best EMA pseudo Dice: 0.1207 +2026-04-10 13:58:03.928165: +2026-04-10 13:58:03.929808: Epoch 71 +2026-04-10 13:58:03.931212: Current learning rate: 0.00984 +2026-04-10 13:59:45.951516: train_loss -0.0108 +2026-04-10 13:59:45.957706: val_loss -0.0276 +2026-04-10 13:59:45.959303: Pseudo dice [0.0, 0.0, 0.5596, 0.0, 0.0, 0.305, 0.4974] +2026-04-10 13:59:45.961304: Epoch time: 102.03 s +2026-04-10 13:59:45.962994: Yayy! New best EMA pseudo Dice: 0.1281 +2026-04-10 13:59:48.794023: +2026-04-10 13:59:48.795641: Epoch 72 +2026-04-10 13:59:48.796987: Current learning rate: 0.00984 +2026-04-10 14:01:30.816784: train_loss -0.0337 +2026-04-10 14:01:30.822852: val_loss -0.024 +2026-04-10 14:01:30.824697: Pseudo dice [0.0, 0.0, 0.3342, 0.0, 0.0, 0.2137, 0.3472] +2026-04-10 14:01:30.826749: Epoch time: 102.03 s +2026-04-10 14:01:31.996902: +2026-04-10 14:01:31.998811: Epoch 73 +2026-04-10 14:01:32.000336: Current learning rate: 0.00984 +2026-04-10 14:03:13.894991: train_loss -0.0185 +2026-04-10 14:03:13.900479: val_loss -0.0356 +2026-04-10 14:03:13.902160: Pseudo dice [0.0, 0.0, 0.5664, 0.0, 0.0, 0.3107, 0.4839] +2026-04-10 14:03:13.904024: Epoch time: 101.9 s +2026-04-10 14:03:13.905926: Yayy! New best EMA pseudo Dice: 0.1347 +2026-04-10 14:03:16.660496: +2026-04-10 14:03:16.662063: Epoch 74 +2026-04-10 14:03:16.663431: Current learning rate: 0.00983 +2026-04-10 14:04:58.742726: train_loss -0.0198 +2026-04-10 14:04:58.750772: val_loss -0.0209 +2026-04-10 14:04:58.752725: Pseudo dice [0.0, 0.0, 0.3876, 0.0, 0.0, 0.2386, 0.4744] +2026-04-10 14:04:58.755171: Epoch time: 102.09 s +2026-04-10 14:04:58.756880: Yayy! New best EMA pseudo Dice: 0.137 +2026-04-10 14:05:01.586435: +2026-04-10 14:05:01.588601: Epoch 75 +2026-04-10 14:05:01.590390: Current learning rate: 0.00983 +2026-04-10 14:06:44.436329: train_loss -0.0294 +2026-04-10 14:06:44.455100: val_loss -0.0158 +2026-04-10 14:06:44.458077: Pseudo dice [0.0, 0.0, 0.3003, 0.0, 0.0, 0.346, 0.3923] +2026-04-10 14:06:44.462351: Epoch time: 102.85 s +2026-04-10 14:06:44.464951: Yayy! New best EMA pseudo Dice: 0.1381 +2026-04-10 14:06:47.247723: +2026-04-10 14:06:47.249285: Epoch 76 +2026-04-10 14:06:47.250679: Current learning rate: 0.00983 +2026-04-10 14:08:29.752278: train_loss -0.0317 +2026-04-10 14:08:29.758924: val_loss -0.0284 +2026-04-10 14:08:29.760672: Pseudo dice [0.0, 0.0, 0.4773, 0.0, 0.0, 0.3552, 0.3422] +2026-04-10 14:08:29.762883: Epoch time: 102.51 s +2026-04-10 14:08:29.764731: Yayy! New best EMA pseudo Dice: 0.1411 +2026-04-10 14:08:32.548184: +2026-04-10 14:08:32.549896: Epoch 77 +2026-04-10 14:08:32.551512: Current learning rate: 0.00983 +2026-04-10 14:10:15.436897: train_loss -0.0201 +2026-04-10 14:10:15.444829: val_loss -0.0298 +2026-04-10 14:10:15.446730: Pseudo dice [0.0, 0.0, 0.4942, 0.0, 0.0, 0.2493, 0.5308] +2026-04-10 14:10:15.449186: Epoch time: 102.89 s +2026-04-10 14:10:15.451212: Yayy! New best EMA pseudo Dice: 0.1452 +2026-04-10 14:10:18.319199: +2026-04-10 14:10:18.321029: Epoch 78 +2026-04-10 14:10:18.322572: Current learning rate: 0.00982 +2026-04-10 14:12:00.549831: train_loss -0.0173 +2026-04-10 14:12:00.556471: val_loss -0.0254 +2026-04-10 14:12:00.559092: Pseudo dice [0.0, 0.0, 0.3602, 0.0, 0.0, 0.339, 0.4547] +2026-04-10 14:12:00.561756: Epoch time: 102.23 s +2026-04-10 14:12:00.563529: Yayy! New best EMA pseudo Dice: 0.1471 +2026-04-10 14:12:03.391693: +2026-04-10 14:12:03.393311: Epoch 79 +2026-04-10 14:12:03.394660: Current learning rate: 0.00982 +2026-04-10 14:14:05.360659: train_loss -0.0304 +2026-04-10 14:14:05.367982: val_loss -0.0326 +2026-04-10 14:14:05.369917: Pseudo dice [0.0, 0.0, 0.6281, 0.0, 0.0, 0.3336, 0.5559] +2026-04-10 14:14:05.372281: Epoch time: 121.97 s +2026-04-10 14:14:05.373989: Yayy! New best EMA pseudo Dice: 0.1541 +2026-04-10 14:14:08.115434: +2026-04-10 14:14:08.117224: Epoch 80 +2026-04-10 14:14:08.118798: Current learning rate: 0.00982 +2026-04-10 14:22:56.007560: train_loss -0.0346 +2026-04-10 14:22:56.014180: val_loss -0.0381 +2026-04-10 14:22:56.017421: Pseudo dice [0.0, 0.0, 0.5734, 0.0, 0.0, 0.2856, 0.5437] +2026-04-10 14:22:56.019890: Epoch time: 527.9 s +2026-04-10 14:22:56.022179: Yayy! New best EMA pseudo Dice: 0.1587 +2026-04-10 14:22:58.984746: +2026-04-10 14:22:58.986298: Epoch 81 +2026-04-10 14:22:58.987798: Current learning rate: 0.00982 +2026-04-10 15:14:51.812802: train_loss -0.0343 +2026-04-10 15:14:51.817597: val_loss -0.0402 +2026-04-10 15:14:51.819057: Pseudo dice [0.0, 0.0, 0.3904, 0.0, 0.0, 0.3549, 0.3525] +2026-04-10 15:14:51.820706: Epoch time: 3112.83 s +2026-04-10 15:14:53.646284: +2026-04-10 15:14:53.648090: Epoch 82 +2026-04-10 15:14:53.649680: Current learning rate: 0.00982 +2026-04-10 15:56:50.401591: train_loss -0.0213 +2026-04-10 15:56:50.408362: val_loss -0.0264 +2026-04-10 15:56:50.410450: Pseudo dice [0.0, 0.0, 0.5891, 0.0, 0.0, 0.2876, 0.4332] +2026-04-10 15:56:50.412604: Epoch time: 2516.76 s +2026-04-10 15:56:50.414390: Yayy! New best EMA pseudo Dice: 0.1614 +2026-04-10 15:56:56.380677: +2026-04-10 15:56:56.382671: Epoch 83 +2026-04-10 15:56:56.384578: Current learning rate: 0.00981 +2026-04-10 16:01:49.001008: train_loss -0.0288 +2026-04-10 16:01:49.008372: val_loss -0.0301 +2026-04-10 16:01:49.010900: Pseudo dice [0.0, 0.0, 0.4709, 0.0, 0.0, 0.1885, 0.4289] +2026-04-10 16:01:49.013128: Epoch time: 292.63 s +2026-04-10 16:01:50.106287: +2026-04-10 16:01:50.108442: Epoch 84 +2026-04-10 16:01:50.110418: Current learning rate: 0.00981 +2026-04-10 16:07:22.384877: train_loss -0.0263 +2026-04-10 16:07:22.392653: val_loss -0.0135 +2026-04-10 16:07:22.394873: Pseudo dice [0.0, 0.0, 0.3116, 0.0, 0.0, 0.305, 0.4905] +2026-04-10 16:07:22.397727: Epoch time: 332.28 s +2026-04-10 16:07:24.360180: +2026-04-10 16:07:24.362865: Epoch 85 +2026-04-10 16:07:24.365291: Current learning rate: 0.00981 +2026-04-10 16:13:05.886147: train_loss -0.0359 +2026-04-10 16:13:05.893887: val_loss -0.0051 +2026-04-10 16:13:05.896590: Pseudo dice [0.0, 0.0, 0.0324, 0.0, 0.0, 0.1301, 0.0527] +2026-04-10 16:13:05.899364: Epoch time: 341.53 s +2026-04-10 16:13:06.961975: +2026-04-10 16:13:06.964164: Epoch 86 +2026-04-10 16:13:06.965926: Current learning rate: 0.00981 +2026-04-10 16:15:30.276722: train_loss -0.0251 +2026-04-10 16:15:30.284257: val_loss -0.0368 +2026-04-10 16:15:30.287125: Pseudo dice [0.0, 0.0, 0.5177, 0.0, 0.0, 0.5441, 0.7492] +2026-04-10 16:15:30.291446: Epoch time: 143.32 s +2026-04-10 16:15:32.632629: +2026-04-10 16:15:32.634483: Epoch 87 +2026-04-10 16:15:32.635993: Current learning rate: 0.0098 +2026-04-10 16:17:15.131763: train_loss -0.0387 +2026-04-10 16:17:15.138370: val_loss -0.0278 +2026-04-10 16:17:15.140509: Pseudo dice [0.0, 0.0, 0.6248, 0.0, 0.0, 0.241, 0.4647] +2026-04-10 16:17:15.143233: Epoch time: 102.5 s +2026-04-10 16:17:15.145212: Yayy! New best EMA pseudo Dice: 0.1618 +2026-04-10 16:17:18.119721: +2026-04-10 16:17:18.122729: Epoch 88 +2026-04-10 16:17:18.124245: Current learning rate: 0.0098 +2026-04-10 16:19:00.910450: train_loss -0.0223 +2026-04-10 16:19:00.916386: val_loss -0.0381 +2026-04-10 16:19:00.918509: Pseudo dice [0.0, 0.0, 0.4619, 0.0, 0.0, 0.4322, 0.6004] +2026-04-10 16:19:00.921274: Epoch time: 102.79 s +2026-04-10 16:19:00.923433: Yayy! New best EMA pseudo Dice: 0.167 +2026-04-10 16:19:03.774317: +2026-04-10 16:19:03.775888: Epoch 89 +2026-04-10 16:19:03.777340: Current learning rate: 0.0098 +2026-04-10 16:20:46.507822: train_loss -0.0353 +2026-04-10 16:20:46.513155: val_loss -0.0371 +2026-04-10 16:20:46.515556: Pseudo dice [0.0, 0.0, 0.456, 0.0, 0.0, 0.2577, 0.5757] +2026-04-10 16:20:46.517688: Epoch time: 102.74 s +2026-04-10 16:20:46.519608: Yayy! New best EMA pseudo Dice: 0.1687 +2026-04-10 16:20:49.371512: +2026-04-10 16:20:49.373181: Epoch 90 +2026-04-10 16:20:49.374731: Current learning rate: 0.0098 +2026-04-10 16:22:32.087805: train_loss -0.0382 +2026-04-10 16:22:32.096205: val_loss -0.0266 +2026-04-10 16:22:32.099673: Pseudo dice [0.0, 0.0, 0.6032, 0.0, 0.0, 0.2926, 0.3885] +2026-04-10 16:22:32.102212: Epoch time: 102.72 s +2026-04-10 16:22:32.104363: Yayy! New best EMA pseudo Dice: 0.1702 +2026-04-10 16:22:34.933103: +2026-04-10 16:22:34.934958: Epoch 91 +2026-04-10 16:22:34.936356: Current learning rate: 0.0098 +2026-04-10 16:24:17.434593: train_loss -0.0415 +2026-04-10 16:24:17.442398: val_loss -0.0325 +2026-04-10 16:24:17.444499: Pseudo dice [0.0, 0.0, 0.264, 0.0, 0.0, 0.3516, 0.4926] +2026-04-10 16:24:17.447286: Epoch time: 102.5 s +2026-04-10 16:24:18.525794: +2026-04-10 16:24:18.528757: Epoch 92 +2026-04-10 16:24:18.531029: Current learning rate: 0.00979 +2026-04-10 16:26:00.594804: train_loss -0.0357 +2026-04-10 16:26:00.604208: val_loss -0.0471 +2026-04-10 16:26:00.606715: Pseudo dice [0.0, 0.0, 0.6388, 0.0, 0.0, 0.5235, 0.5123] +2026-04-10 16:26:00.609126: Epoch time: 102.07 s +2026-04-10 16:26:00.611032: Yayy! New best EMA pseudo Dice: 0.176 +2026-04-10 16:26:03.319317: +2026-04-10 16:26:03.321371: Epoch 93 +2026-04-10 16:26:03.322831: Current learning rate: 0.00979 +2026-04-10 16:27:45.649999: train_loss -0.033 +2026-04-10 16:27:45.656413: val_loss -0.0238 +2026-04-10 16:27:45.658349: Pseudo dice [0.0, 0.0, 0.4993, 0.0, 0.0, 0.1968, 0.5974] +2026-04-10 16:27:45.661218: Epoch time: 102.33 s +2026-04-10 16:27:45.663359: Yayy! New best EMA pseudo Dice: 0.1769 +2026-04-10 16:27:48.493819: +2026-04-10 16:27:48.495943: Epoch 94 +2026-04-10 16:27:48.497425: Current learning rate: 0.00979 +2026-04-10 16:29:30.784316: train_loss -0.0367 +2026-04-10 16:29:30.790211: val_loss -0.0543 +2026-04-10 16:29:30.792010: Pseudo dice [0.0, 0.0, 0.6775, 0.0, 0.0, 0.3065, 0.633] +2026-04-10 16:29:30.795061: Epoch time: 102.29 s +2026-04-10 16:29:30.796855: Yayy! New best EMA pseudo Dice: 0.1823 +2026-04-10 16:29:33.472057: +2026-04-10 16:29:33.473822: Epoch 95 +2026-04-10 16:29:33.475248: Current learning rate: 0.00979 +2026-04-10 16:31:16.102100: train_loss -0.0399 +2026-04-10 16:31:16.110776: val_loss -0.0485 +2026-04-10 16:31:16.113525: Pseudo dice [0.0, 0.0, 0.5525, 0.0, 0.0, 0.4648, 0.2335] +2026-04-10 16:31:16.115899: Epoch time: 102.63 s +2026-04-10 16:31:17.263669: +2026-04-10 16:31:17.266019: Epoch 96 +2026-04-10 16:31:17.267800: Current learning rate: 0.00978 +2026-04-10 16:32:59.340609: train_loss -0.033 +2026-04-10 16:32:59.347602: val_loss -0.0253 +2026-04-10 16:32:59.349912: Pseudo dice [0.0, 0.0, 0.4009, 0.0, 0.0, 0.3013, 0.6269] +2026-04-10 16:32:59.352477: Epoch time: 102.08 s +2026-04-10 16:32:59.354919: Yayy! New best EMA pseudo Dice: 0.1827 +2026-04-10 16:33:02.124733: +2026-04-10 16:33:02.126660: Epoch 97 +2026-04-10 16:33:02.128077: Current learning rate: 0.00978 +2026-04-10 16:34:44.211964: train_loss -0.0398 +2026-04-10 16:34:44.219414: val_loss -0.0424 +2026-04-10 16:34:44.222534: Pseudo dice [0.0, 0.0, 0.5218, 0.0, 0.0, 0.4129, 0.3935] +2026-04-10 16:34:44.224909: Epoch time: 102.09 s +2026-04-10 16:34:44.227348: Yayy! New best EMA pseudo Dice: 0.1834 +2026-04-10 16:34:46.938131: +2026-04-10 16:34:46.939961: Epoch 98 +2026-04-10 16:34:46.941833: Current learning rate: 0.00978 +2026-04-10 16:36:29.260459: train_loss -0.0434 +2026-04-10 16:36:29.266490: val_loss -0.0431 +2026-04-10 16:36:29.269391: Pseudo dice [0.0, 0.0, 0.3164, 0.0, 0.0, 0.5967, 0.6214] +2026-04-10 16:36:29.271712: Epoch time: 102.33 s +2026-04-10 16:36:29.273589: Yayy! New best EMA pseudo Dice: 0.187 +2026-04-10 16:36:32.099912: +2026-04-10 16:36:32.101753: Epoch 99 +2026-04-10 16:36:32.103473: Current learning rate: 0.00978 +2026-04-10 16:38:14.500515: train_loss -0.0292 +2026-04-10 16:38:14.507263: val_loss -0.0108 +2026-04-10 16:38:14.509044: Pseudo dice [0.0, 0.0, 0.0174, 0.0, 0.0, 0.2332, 0.5778] +2026-04-10 16:38:14.512655: Epoch time: 102.4 s +2026-04-10 16:38:17.183306: +2026-04-10 16:38:17.185363: Epoch 100 +2026-04-10 16:38:17.186978: Current learning rate: 0.00977 +2026-04-10 16:39:59.580978: train_loss -0.0364 +2026-04-10 16:39:59.588739: val_loss -0.0513 +2026-04-10 16:39:59.593351: Pseudo dice [0.0, 0.0, 0.3148, 0.0, 0.0, 0.4578, 0.5558] +2026-04-10 16:39:59.596192: Epoch time: 102.4 s +2026-04-10 16:40:00.664388: +2026-04-10 16:40:00.666298: Epoch 101 +2026-04-10 16:40:00.667863: Current learning rate: 0.00977 +2026-04-10 16:41:42.890127: train_loss -0.0428 +2026-04-10 16:41:42.896827: val_loss -0.027 +2026-04-10 16:41:42.898539: Pseudo dice [0.0, 0.0, 0.1136, 0.0, 0.0, 0.0886, 0.4373] +2026-04-10 16:41:42.900763: Epoch time: 102.23 s +2026-04-10 16:41:44.074358: +2026-04-10 16:41:44.076161: Epoch 102 +2026-04-10 16:41:44.077994: Current learning rate: 0.00977 +2026-04-10 16:43:26.820321: train_loss -0.042 +2026-04-10 16:43:26.827459: val_loss -0.0407 +2026-04-10 16:43:26.829926: Pseudo dice [0.0, 0.0, 0.4813, 0.0, 0.0, 0.5489, 0.4639] +2026-04-10 16:43:26.832465: Epoch time: 102.75 s +2026-04-10 16:43:27.938268: +2026-04-10 16:43:27.940675: Epoch 103 +2026-04-10 16:43:27.942932: Current learning rate: 0.00977 +2026-04-10 16:45:10.791519: train_loss -0.0527 +2026-04-10 16:45:10.798310: val_loss -0.0302 +2026-04-10 16:45:10.800337: Pseudo dice [0.0, 0.0, 0.6299, 0.0, 0.0, 0.2932, 0.5498] +2026-04-10 16:45:10.802542: Epoch time: 102.86 s +2026-04-10 16:45:13.000873: +2026-04-10 16:45:13.002599: Epoch 104 +2026-04-10 16:45:13.004031: Current learning rate: 0.00977 +2026-04-10 16:46:55.513127: train_loss -0.0371 +2026-04-10 16:46:55.519845: val_loss -0.0298 +2026-04-10 16:46:55.523085: Pseudo dice [0.0, 0.0, 0.6227, 0.0, 0.0, 0.1893, 0.3445] +2026-04-10 16:46:55.525695: Epoch time: 102.52 s +2026-04-10 16:46:56.613308: +2026-04-10 16:46:56.616138: Epoch 105 +2026-04-10 16:46:56.618105: Current learning rate: 0.00976 +2026-04-10 16:48:38.808080: train_loss -0.0585 +2026-04-10 16:48:38.814698: val_loss -0.0448 +2026-04-10 16:48:38.817541: Pseudo dice [0.0, 0.0, 0.1791, 0.0, 0.0, 0.4343, 0.6] +2026-04-10 16:48:38.819918: Epoch time: 102.2 s +2026-04-10 16:48:39.894467: +2026-04-10 16:48:39.896764: Epoch 106 +2026-04-10 16:48:39.898610: Current learning rate: 0.00976 +2026-04-10 16:50:22.098841: train_loss -0.056 +2026-04-10 16:50:22.104739: val_loss -0.0497 +2026-04-10 16:50:22.107091: Pseudo dice [0.0, 0.0, 0.616, 0.0, 0.0, 0.4224, 0.5609] +2026-04-10 16:50:22.109452: Epoch time: 102.21 s +2026-04-10 16:50:23.209835: +2026-04-10 16:50:23.211697: Epoch 107 +2026-04-10 16:50:23.216023: Current learning rate: 0.00976 +2026-04-10 16:52:05.747225: train_loss -0.0408 +2026-04-10 16:52:05.753216: val_loss -0.0641 +2026-04-10 16:52:05.756297: Pseudo dice [0.0, 0.0, 0.6208, 0.0001, 0.0, 0.2453, 0.5444] +2026-04-10 16:52:05.758864: Epoch time: 102.54 s +2026-04-10 16:52:06.879705: +2026-04-10 16:52:06.881633: Epoch 108 +2026-04-10 16:52:06.883467: Current learning rate: 0.00976 +2026-04-10 16:53:49.146759: train_loss -0.0517 +2026-04-10 16:53:49.152880: val_loss -0.0654 +2026-04-10 16:53:49.155239: Pseudo dice [0.0, 0.0, 0.608, 0.0, 0.0, 0.4596, 0.6105] +2026-04-10 16:53:49.157746: Epoch time: 102.27 s +2026-04-10 16:53:49.159505: Yayy! New best EMA pseudo Dice: 0.1902 +2026-04-10 16:53:51.935811: +2026-04-10 16:53:51.937495: Epoch 109 +2026-04-10 16:53:51.938957: Current learning rate: 0.00975 +2026-04-10 16:55:33.965031: train_loss -0.0574 +2026-04-10 16:55:33.971997: val_loss -0.0249 +2026-04-10 16:55:33.973648: Pseudo dice [0.0, 0.0, 0.3641, 0.0, 0.0, 0.2861, 0.3152] +2026-04-10 16:55:33.975924: Epoch time: 102.03 s +2026-04-10 16:55:35.032103: +2026-04-10 16:55:35.034070: Epoch 110 +2026-04-10 16:55:35.035540: Current learning rate: 0.00975 +2026-04-10 16:57:16.948853: train_loss -0.0468 +2026-04-10 16:57:16.954373: val_loss -0.0528 +2026-04-10 16:57:16.956620: Pseudo dice [0.0, 0.0, 0.7106, 0.0034, 0.0, 0.2683, 0.386] +2026-04-10 16:57:16.958795: Epoch time: 101.92 s +2026-04-10 16:57:18.051316: +2026-04-10 16:57:18.052982: Epoch 111 +2026-04-10 16:57:18.054559: Current learning rate: 0.00975 +2026-04-10 16:59:00.542669: train_loss -0.0559 +2026-04-10 16:59:00.548729: val_loss -0.0696 +2026-04-10 16:59:00.550750: Pseudo dice [0.0, 0.0, 0.6095, 0.0001, 0.0, 0.3886, 0.7375] +2026-04-10 16:59:00.554328: Epoch time: 102.49 s +2026-04-10 16:59:00.556278: Yayy! New best EMA pseudo Dice: 0.1922 +2026-04-10 16:59:03.361482: +2026-04-10 16:59:03.363451: Epoch 112 +2026-04-10 16:59:03.365034: Current learning rate: 0.00975 +2026-04-10 17:00:45.117973: train_loss -0.0587 +2026-04-10 17:00:45.128639: val_loss -0.0685 +2026-04-10 17:00:45.131160: Pseudo dice [0.0, 0.0, 0.5265, 0.0, 0.0, 0.4083, 0.5605] +2026-04-10 17:00:45.134923: Epoch time: 101.76 s +2026-04-10 17:00:45.137081: Yayy! New best EMA pseudo Dice: 0.1943 +2026-04-10 17:00:47.882390: +2026-04-10 17:00:47.884110: Epoch 113 +2026-04-10 17:00:47.885626: Current learning rate: 0.00975 +2026-04-10 17:02:30.148180: train_loss -0.0418 +2026-04-10 17:02:30.156835: val_loss -0.0317 +2026-04-10 17:02:30.159635: Pseudo dice [0.0, 0.0, 0.2116, 0.0, 0.0, 0.3444, 0.3057] +2026-04-10 17:02:30.162247: Epoch time: 102.27 s +2026-04-10 17:02:31.242424: +2026-04-10 17:02:31.244538: Epoch 114 +2026-04-10 17:02:31.246754: Current learning rate: 0.00974 +2026-04-10 17:04:13.734438: train_loss -0.0461 +2026-04-10 17:04:13.763507: val_loss -0.0603 +2026-04-10 17:04:13.774451: Pseudo dice [0.0, 0.0, 0.7379, 0.0, 0.0, 0.4663, 0.5128] +2026-04-10 17:04:13.776509: Epoch time: 102.5 s +2026-04-10 17:04:14.915310: +2026-04-10 17:04:14.918475: Epoch 115 +2026-04-10 17:04:14.922172: Current learning rate: 0.00974 +2026-04-10 17:05:56.860097: train_loss -0.0591 +2026-04-10 17:05:56.868615: val_loss -0.054 +2026-04-10 17:05:56.871141: Pseudo dice [0.3147, 0.0, 0.6623, 0.0028, 0.0, 0.4835, 0.7415] +2026-04-10 17:05:56.873851: Epoch time: 101.95 s +2026-04-10 17:05:56.876480: Yayy! New best EMA pseudo Dice: 0.2052 +2026-04-10 17:05:59.678734: +2026-04-10 17:05:59.681004: Epoch 116 +2026-04-10 17:05:59.682444: Current learning rate: 0.00974 +2026-04-10 17:07:41.710255: train_loss -0.0666 +2026-04-10 17:07:41.715896: val_loss -0.0135 +2026-04-10 17:07:41.718070: Pseudo dice [0.2353, 0.0, 0.0628, 0.0014, 0.0, 0.2559, 0.4815] +2026-04-10 17:07:41.720191: Epoch time: 102.03 s +2026-04-10 17:07:42.830813: +2026-04-10 17:07:42.832647: Epoch 117 +2026-04-10 17:07:42.834231: Current learning rate: 0.00974 +2026-04-10 17:09:24.754472: train_loss -0.0506 +2026-04-10 17:09:24.762482: val_loss -0.049 +2026-04-10 17:09:24.764985: Pseudo dice [0.1656, 0.0, 0.2194, 0.0, 0.0, 0.2411, 0.5163] +2026-04-10 17:09:24.767593: Epoch time: 101.93 s +2026-04-10 17:09:25.864935: +2026-04-10 17:09:25.867052: Epoch 118 +2026-04-10 17:09:25.869257: Current learning rate: 0.00973 +2026-04-10 17:11:07.738295: train_loss -0.0573 +2026-04-10 17:11:07.745676: val_loss -0.0413 +2026-04-10 17:11:07.748200: Pseudo dice [0.3783, 0.0, 0.4593, 0.0, 0.0, 0.0504, 0.6464] +2026-04-10 17:11:07.751589: Epoch time: 101.88 s +2026-04-10 17:11:08.857277: +2026-04-10 17:11:08.859566: Epoch 119 +2026-04-10 17:11:08.861140: Current learning rate: 0.00973 +2026-04-10 17:12:52.025352: train_loss -0.0648 +2026-04-10 17:12:52.033392: val_loss -0.0541 +2026-04-10 17:12:52.035402: Pseudo dice [0.4326, 0.0, 0.2961, 0.0047, 0.0, 0.303, 0.4339] +2026-04-10 17:12:52.038337: Epoch time: 103.17 s +2026-04-10 17:12:53.127302: +2026-04-10 17:12:53.129023: Epoch 120 +2026-04-10 17:12:53.130544: Current learning rate: 0.00973 +2026-04-10 17:14:35.285405: train_loss -0.05 +2026-04-10 17:14:35.292523: val_loss -0.0324 +2026-04-10 17:14:35.294914: Pseudo dice [0.4225, 0.0, 0.4356, 0.0003, 0.0, 0.2183, 0.1863] +2026-04-10 17:14:35.298294: Epoch time: 102.16 s +2026-04-10 17:14:36.403394: +2026-04-10 17:14:36.406112: Epoch 121 +2026-04-10 17:14:36.408478: Current learning rate: 0.00973 +2026-04-10 17:16:19.158425: train_loss -0.0704 +2026-04-10 17:16:19.164460: val_loss -0.0761 +2026-04-10 17:16:19.166565: Pseudo dice [0.315, 0.0, 0.5281, 0.0167, 0.0, 0.3312, 0.5858] +2026-04-10 17:16:19.169270: Epoch time: 102.76 s +2026-04-10 17:16:20.276587: +2026-04-10 17:16:20.278549: Epoch 122 +2026-04-10 17:16:20.280082: Current learning rate: 0.00973 +2026-04-10 17:18:02.617353: train_loss -0.0787 +2026-04-10 17:18:02.625485: val_loss -0.0672 +2026-04-10 17:18:02.628021: Pseudo dice [0.4184, 0.0, 0.3365, 0.0331, 0.0, 0.4149, 0.3686] +2026-04-10 17:18:02.630503: Epoch time: 102.34 s +2026-04-10 17:18:02.632748: Yayy! New best EMA pseudo Dice: 0.2053 +2026-04-10 17:18:05.412597: +2026-04-10 17:18:05.414359: Epoch 123 +2026-04-10 17:18:05.415915: Current learning rate: 0.00972 +2026-04-10 17:19:48.969008: train_loss -0.0699 +2026-04-10 17:19:48.976291: val_loss -0.0826 +2026-04-10 17:19:48.978310: Pseudo dice [0.4731, 0.0, 0.6662, 0.0008, 0.0, 0.5234, 0.6258] +2026-04-10 17:19:48.980736: Epoch time: 103.56 s +2026-04-10 17:19:48.982768: Yayy! New best EMA pseudo Dice: 0.2174 +2026-04-10 17:19:51.864295: +2026-04-10 17:19:51.866367: Epoch 124 +2026-04-10 17:19:51.867929: Current learning rate: 0.00972 +2026-04-10 17:21:34.595043: train_loss -0.0767 +2026-04-10 17:21:34.600232: val_loss -0.0875 +2026-04-10 17:21:34.601907: Pseudo dice [0.472, 0.0002, 0.6073, 0.033, 0.0, 0.408, 0.5224] +2026-04-10 17:21:34.603971: Epoch time: 102.73 s +2026-04-10 17:21:34.605573: Yayy! New best EMA pseudo Dice: 0.2249 +2026-04-10 17:21:37.341681: +2026-04-10 17:21:37.343288: Epoch 125 +2026-04-10 17:21:37.344647: Current learning rate: 0.00972 +2026-04-10 17:23:19.658856: train_loss -0.0725 +2026-04-10 17:23:19.666242: val_loss -0.0799 +2026-04-10 17:23:19.668392: Pseudo dice [0.2483, 0.0364, 0.4601, 0.0775, 0.0, 0.2982, 0.6352] +2026-04-10 17:23:19.670880: Epoch time: 102.32 s +2026-04-10 17:23:19.672609: Yayy! New best EMA pseudo Dice: 0.2275 +2026-04-10 17:23:22.486381: +2026-04-10 17:23:22.488086: Epoch 126 +2026-04-10 17:23:22.489565: Current learning rate: 0.00972 +2026-04-10 17:25:05.220772: train_loss -0.0641 +2026-04-10 17:25:05.226958: val_loss -0.047 +2026-04-10 17:25:05.229557: Pseudo dice [0.4285, 0.0237, 0.6196, 0.0002, 0.0, 0.2179, 0.6791] +2026-04-10 17:25:05.232446: Epoch time: 102.74 s +2026-04-10 17:25:05.234782: Yayy! New best EMA pseudo Dice: 0.2329 +2026-04-10 17:25:08.028766: +2026-04-10 17:25:08.030975: Epoch 127 +2026-04-10 17:25:08.032595: Current learning rate: 0.00971 +2026-04-10 17:26:50.605035: train_loss -0.063 +2026-04-10 17:26:50.611153: val_loss -0.0757 +2026-04-10 17:26:50.613330: Pseudo dice [0.4027, 0.3569, 0.7058, 0.0332, 0.0, 0.5292, 0.5392] +2026-04-10 17:26:50.615697: Epoch time: 102.58 s +2026-04-10 17:26:50.617433: Yayy! New best EMA pseudo Dice: 0.2462 +2026-04-10 17:26:53.466333: +2026-04-10 17:26:53.468325: Epoch 128 +2026-04-10 17:26:53.469815: Current learning rate: 0.00971 +2026-04-10 17:28:35.512058: train_loss -0.081 +2026-04-10 17:28:35.518387: val_loss -0.0802 +2026-04-10 17:28:35.520667: Pseudo dice [0.2568, 0.0836, 0.7519, 0.1532, 0.0533, 0.3857, 0.3763] +2026-04-10 17:28:35.523214: Epoch time: 102.05 s +2026-04-10 17:28:35.525323: Yayy! New best EMA pseudo Dice: 0.2511 +2026-04-10 17:28:38.297600: +2026-04-10 17:28:38.301073: Epoch 129 +2026-04-10 17:28:38.305129: Current learning rate: 0.00971 +2026-04-10 17:30:20.711705: train_loss -0.0851 +2026-04-10 17:30:20.717893: val_loss -0.0784 +2026-04-10 17:30:20.720060: Pseudo dice [0.5629, 0.4094, 0.6728, 0.0089, 0.0908, 0.2317, 0.5712] +2026-04-10 17:30:20.722481: Epoch time: 102.42 s +2026-04-10 17:30:20.724416: Yayy! New best EMA pseudo Dice: 0.2623 +2026-04-10 17:30:23.548591: +2026-04-10 17:30:23.550560: Epoch 130 +2026-04-10 17:30:23.552021: Current learning rate: 0.00971 +2026-04-10 17:32:06.390958: train_loss -0.0735 +2026-04-10 17:32:06.397619: val_loss -0.09 +2026-04-10 17:32:06.400015: Pseudo dice [0.4497, 0.2252, 0.6308, 0.0037, 0.0307, 0.7351, 0.6842] +2026-04-10 17:32:06.402025: Epoch time: 102.85 s +2026-04-10 17:32:06.404244: Yayy! New best EMA pseudo Dice: 0.2755 +2026-04-10 17:32:09.174708: +2026-04-10 17:32:09.176347: Epoch 131 +2026-04-10 17:32:09.183259: Current learning rate: 0.0097 +2026-04-10 17:33:51.835999: train_loss -0.0814 +2026-04-10 17:33:51.844078: val_loss -0.0632 +2026-04-10 17:33:51.846443: Pseudo dice [0.2057, 0.4923, 0.1213, 0.0, 0.1232, 0.2154, 0.4335] +2026-04-10 17:33:51.849144: Epoch time: 102.66 s +2026-04-10 17:33:52.952165: +2026-04-10 17:33:52.954293: Epoch 132 +2026-04-10 17:33:52.956174: Current learning rate: 0.0097 +2026-04-10 17:35:35.292330: train_loss -0.0823 +2026-04-10 17:35:35.305017: val_loss -0.0828 +2026-04-10 17:35:35.311004: Pseudo dice [0.3132, 0.5338, 0.6436, 0.0766, 0.2063, 0.5906, 0.7046] +2026-04-10 17:35:35.313710: Epoch time: 102.34 s +2026-04-10 17:35:35.316369: Yayy! New best EMA pseudo Dice: 0.2875 +2026-04-10 17:35:38.181764: +2026-04-10 17:35:38.183639: Epoch 133 +2026-04-10 17:35:38.185394: Current learning rate: 0.0097 +2026-04-10 17:37:20.614611: train_loss -0.0866 +2026-04-10 17:37:20.623144: val_loss -0.0887 +2026-04-10 17:37:20.625516: Pseudo dice [0.1797, 0.3148, 0.553, 0.0486, 0.3743, 0.4016, 0.6635] +2026-04-10 17:37:20.628175: Epoch time: 102.44 s +2026-04-10 17:37:20.630796: Yayy! New best EMA pseudo Dice: 0.295 +2026-04-10 17:37:23.578433: +2026-04-10 17:37:23.580210: Epoch 134 +2026-04-10 17:37:23.582914: Current learning rate: 0.0097 +2026-04-10 17:39:06.653224: train_loss -0.0865 +2026-04-10 17:39:06.660849: val_loss -0.1025 +2026-04-10 17:39:06.666457: Pseudo dice [0.4287, 0.4046, 0.581, 0.1114, 0.0818, 0.6033, 0.6621] +2026-04-10 17:39:06.669950: Epoch time: 103.08 s +2026-04-10 17:39:06.673236: Yayy! New best EMA pseudo Dice: 0.3065 +2026-04-10 17:39:09.550020: +2026-04-10 17:39:09.551870: Epoch 135 +2026-04-10 17:39:09.553410: Current learning rate: 0.0097 +2026-04-10 17:40:52.010986: train_loss -0.0808 +2026-04-10 17:40:52.017286: val_loss -0.0671 +2026-04-10 17:40:52.019661: Pseudo dice [0.2947, 0.0651, 0.6398, 0.0706, 0.3336, 0.1685, 0.3158] +2026-04-10 17:40:52.022265: Epoch time: 102.46 s +2026-04-10 17:40:53.165444: +2026-04-10 17:40:53.167958: Epoch 136 +2026-04-10 17:40:53.170092: Current learning rate: 0.00969 +2026-04-10 17:42:35.746218: train_loss -0.0938 +2026-04-10 17:42:35.753101: val_loss -0.0843 +2026-04-10 17:42:35.755065: Pseudo dice [0.4192, 0.0592, 0.7087, 0.1428, 0.0634, 0.4054, 0.588] +2026-04-10 17:42:35.757677: Epoch time: 102.58 s +2026-04-10 17:42:35.760213: Yayy! New best EMA pseudo Dice: 0.3066 +2026-04-10 17:42:38.609373: +2026-04-10 17:42:38.611564: Epoch 137 +2026-04-10 17:42:38.613191: Current learning rate: 0.00969 +2026-04-10 17:44:21.592213: train_loss -0.0846 +2026-04-10 17:44:21.600318: val_loss -0.0961 +2026-04-10 17:44:21.602576: Pseudo dice [0.3369, 0.5609, 0.5766, 0.3036, 0.2428, 0.3215, 0.4418] +2026-04-10 17:44:21.605047: Epoch time: 102.99 s +2026-04-10 17:44:21.607206: Yayy! New best EMA pseudo Dice: 0.3157 +2026-04-10 17:44:24.451174: +2026-04-10 17:44:24.452858: Epoch 138 +2026-04-10 17:44:24.454764: Current learning rate: 0.00969 +2026-04-10 17:46:06.852990: train_loss -0.0883 +2026-04-10 17:46:06.860992: val_loss -0.0832 +2026-04-10 17:46:06.863946: Pseudo dice [0.4635, 0.5509, 0.3297, 0.0461, 0.2169, 0.1281, 0.3898] +2026-04-10 17:46:06.866508: Epoch time: 102.4 s +2026-04-10 17:46:07.979441: +2026-04-10 17:46:07.982189: Epoch 139 +2026-04-10 17:46:07.984580: Current learning rate: 0.00969 +2026-04-10 17:47:52.883763: train_loss -0.0928 +2026-04-10 17:47:52.890465: val_loss -0.0687 +2026-04-10 17:47:52.892524: Pseudo dice [0.2791, 0.1131, 0.6689, 0.0675, 0.2747, 0.391, 0.229] +2026-04-10 17:47:52.895100: Epoch time: 104.91 s +2026-04-10 17:47:54.018731: +2026-04-10 17:47:54.021093: Epoch 140 +2026-04-10 17:47:54.023932: Current learning rate: 0.00968 +2026-04-10 17:49:36.549824: train_loss -0.0799 +2026-04-10 17:49:36.556074: val_loss -0.0907 +2026-04-10 17:49:36.558483: Pseudo dice [0.2766, 0.2557, 0.651, 0.0026, 0.2687, 0.5773, 0.6697] +2026-04-10 17:49:36.561402: Epoch time: 102.53 s +2026-04-10 17:49:36.564047: Yayy! New best EMA pseudo Dice: 0.3194 +2026-04-10 17:49:39.348005: +2026-04-10 17:49:39.349806: Epoch 141 +2026-04-10 17:49:39.351364: Current learning rate: 0.00968 +2026-04-10 17:51:21.638909: train_loss -0.0832 +2026-04-10 17:51:21.646490: val_loss -0.1059 +2026-04-10 17:51:21.649017: Pseudo dice [0.1462, 0.5368, 0.6106, 0.0974, 0.2982, 0.4952, 0.7161] +2026-04-10 17:51:21.652104: Epoch time: 102.29 s +2026-04-10 17:51:21.654061: Yayy! New best EMA pseudo Dice: 0.3289 +2026-04-10 17:51:24.457472: +2026-04-10 17:51:24.459269: Epoch 142 +2026-04-10 17:51:24.460871: Current learning rate: 0.00968 +2026-04-10 17:53:06.805732: train_loss -0.0909 +2026-04-10 17:53:06.812267: val_loss -0.0883 +2026-04-10 17:53:06.814117: Pseudo dice [0.5255, 0.1149, 0.359, 0.2059, 0.3269, 0.429, 0.3678] +2026-04-10 17:53:06.816605: Epoch time: 102.35 s +2026-04-10 17:53:06.818553: Yayy! New best EMA pseudo Dice: 0.3293 +2026-04-10 17:53:09.693270: +2026-04-10 17:53:09.695773: Epoch 143 +2026-04-10 17:53:09.697397: Current learning rate: 0.00968 +2026-04-10 17:54:53.110847: train_loss -0.0818 +2026-04-10 17:54:53.116917: val_loss -0.0695 +2026-04-10 17:54:53.119378: Pseudo dice [0.3037, 0.092, 0.5346, 0.1092, 0.2079, 0.4991, 0.4042] +2026-04-10 17:54:53.121960: Epoch time: 103.42 s +2026-04-10 17:54:54.294417: +2026-04-10 17:54:54.297098: Epoch 144 +2026-04-10 17:54:54.300453: Current learning rate: 0.00968 +2026-04-10 17:56:37.197464: train_loss -0.0984 +2026-04-10 17:56:37.205882: val_loss -0.1102 +2026-04-10 17:56:37.208407: Pseudo dice [0.4283, 0.13, 0.6614, 0.252, 0.1608, 0.4357, 0.8081] +2026-04-10 17:56:37.211899: Epoch time: 102.91 s +2026-04-10 17:56:37.214734: Yayy! New best EMA pseudo Dice: 0.3354 +2026-04-10 17:56:40.149795: +2026-04-10 17:56:40.152544: Epoch 145 +2026-04-10 17:56:40.154485: Current learning rate: 0.00967 +2026-04-10 17:58:23.523874: train_loss -0.1001 +2026-04-10 17:58:23.530412: val_loss -0.1046 +2026-04-10 17:58:23.532601: Pseudo dice [0.4924, 0.0538, 0.6136, 0.3612, 0.3668, 0.6528, 0.6948] +2026-04-10 17:58:23.535868: Epoch time: 103.38 s +2026-04-10 17:58:23.538453: Yayy! New best EMA pseudo Dice: 0.3481 +2026-04-10 17:58:26.468231: +2026-04-10 17:58:26.470358: Epoch 146 +2026-04-10 17:58:26.471955: Current learning rate: 0.00967 +2026-04-10 18:00:09.390254: train_loss -0.0983 +2026-04-10 18:00:09.397558: val_loss -0.1164 +2026-04-10 18:00:09.401058: Pseudo dice [0.5483, 0.329, 0.6455, 0.2989, 0.1955, 0.4414, 0.6915] +2026-04-10 18:00:09.404510: Epoch time: 102.93 s +2026-04-10 18:00:09.407080: Yayy! New best EMA pseudo Dice: 0.3583 +2026-04-10 18:00:12.301825: +2026-04-10 18:00:12.303913: Epoch 147 +2026-04-10 18:00:12.305660: Current learning rate: 0.00967 +2026-04-10 18:01:55.734733: train_loss -0.1001 +2026-04-10 18:01:55.742917: val_loss -0.1133 +2026-04-10 18:01:55.745253: Pseudo dice [0.4894, 0.4772, 0.5607, 0.283, 0.303, 0.6256, 0.7747] +2026-04-10 18:01:55.750015: Epoch time: 103.44 s +2026-04-10 18:01:55.753060: Yayy! New best EMA pseudo Dice: 0.3727 +2026-04-10 18:01:58.707521: +2026-04-10 18:01:58.709684: Epoch 148 +2026-04-10 18:01:58.714025: Current learning rate: 0.00967 +2026-04-10 18:03:41.555788: train_loss -0.107 +2026-04-10 18:03:41.563974: val_loss -0.0706 +2026-04-10 18:03:41.565797: Pseudo dice [0.5212, 0.2686, 0.5984, 0.0012, 0.2033, 0.2404, 0.4388] +2026-04-10 18:03:41.568254: Epoch time: 102.85 s +2026-04-10 18:03:42.687885: +2026-04-10 18:03:42.689610: Epoch 149 +2026-04-10 18:03:42.690974: Current learning rate: 0.00966 +2026-04-10 18:05:26.216157: train_loss -0.1209 +2026-04-10 18:05:26.222472: val_loss -0.1025 +2026-04-10 18:05:26.225056: Pseudo dice [0.3044, 0.6112, 0.5938, 0.1497, 0.2426, 0.5088, 0.6882] +2026-04-10 18:05:26.227406: Epoch time: 103.53 s +2026-04-10 18:05:28.038877: Yayy! New best EMA pseudo Dice: 0.3753 +2026-04-10 18:05:30.994060: +2026-04-10 18:05:30.995898: Epoch 150 +2026-04-10 18:05:30.998130: Current learning rate: 0.00966 +2026-04-10 18:07:14.090663: train_loss -0.0996 +2026-04-10 18:07:14.098055: val_loss -0.0886 +2026-04-10 18:07:14.101218: Pseudo dice [0.4748, 0.6664, 0.4763, 0.375, 0.3089, 0.253, 0.3657] +2026-04-10 18:07:14.103734: Epoch time: 103.1 s +2026-04-10 18:07:14.107106: Yayy! New best EMA pseudo Dice: 0.3795 +2026-04-10 18:07:17.085655: +2026-04-10 18:07:17.089423: Epoch 151 +2026-04-10 18:07:17.096666: Current learning rate: 0.00966 +2026-04-10 18:09:00.293411: train_loss -0.1036 +2026-04-10 18:09:00.302495: val_loss -0.0944 +2026-04-10 18:09:00.306611: Pseudo dice [0.2225, 0.2719, 0.6099, 0.1891, 0.183, 0.5549, 0.2168] +2026-04-10 18:09:00.310557: Epoch time: 103.21 s +2026-04-10 18:09:01.434005: +2026-04-10 18:09:01.436003: Epoch 152 +2026-04-10 18:09:01.438299: Current learning rate: 0.00966 +2026-04-10 18:10:44.744687: train_loss -0.1082 +2026-04-10 18:10:44.768831: val_loss -0.1138 +2026-04-10 18:10:44.784405: Pseudo dice [0.2356, 0.7109, 0.7298, 0.5526, 0.2832, 0.5978, 0.3116] +2026-04-10 18:10:44.786964: Epoch time: 103.31 s +2026-04-10 18:10:44.790092: Yayy! New best EMA pseudo Dice: 0.3852 +2026-04-10 18:10:47.695544: +2026-04-10 18:10:47.697175: Epoch 153 +2026-04-10 18:10:47.698872: Current learning rate: 0.00966 +2026-04-10 18:12:29.955609: train_loss -0.1227 +2026-04-10 18:12:29.961931: val_loss -0.1162 +2026-04-10 18:12:29.965590: Pseudo dice [0.5076, 0.5189, 0.7583, 0.072, 0.348, 0.4531, 0.7156] +2026-04-10 18:12:29.968986: Epoch time: 102.26 s +2026-04-10 18:12:29.971858: Yayy! New best EMA pseudo Dice: 0.3949 +2026-04-10 18:12:32.892488: +2026-04-10 18:12:32.894430: Epoch 154 +2026-04-10 18:12:32.895859: Current learning rate: 0.00965 +2026-04-10 18:14:15.323672: train_loss -0.1071 +2026-04-10 18:14:15.332694: val_loss -0.1019 +2026-04-10 18:14:15.336041: Pseudo dice [0.494, 0.1064, 0.3989, 0.0178, 0.3256, 0.3101, 0.6472] +2026-04-10 18:14:15.339432: Epoch time: 102.43 s +2026-04-10 18:14:17.581014: +2026-04-10 18:14:17.584039: Epoch 155 +2026-04-10 18:14:17.585862: Current learning rate: 0.00965 +2026-04-10 18:16:00.219958: train_loss -0.111 +2026-04-10 18:16:00.228462: val_loss -0.1098 +2026-04-10 18:16:00.230955: Pseudo dice [0.5194, 0.0501, 0.7559, 0.1299, 0.3682, 0.5052, 0.5892] +2026-04-10 18:16:00.234476: Epoch time: 102.64 s +2026-04-10 18:16:01.404546: +2026-04-10 18:16:01.407972: Epoch 156 +2026-04-10 18:16:01.410744: Current learning rate: 0.00965 +2026-04-10 18:17:44.449597: train_loss -0.1068 +2026-04-10 18:17:44.456383: val_loss -0.1011 +2026-04-10 18:17:44.459173: Pseudo dice [0.3826, 0.0692, 0.5501, 0.2343, 0.3945, 0.3938, 0.7695] +2026-04-10 18:17:44.462052: Epoch time: 103.05 s +2026-04-10 18:17:45.759657: +2026-04-10 18:17:45.761459: Epoch 157 +2026-04-10 18:17:45.763255: Current learning rate: 0.00965 +2026-04-10 18:19:28.167873: train_loss -0.1136 +2026-04-10 18:19:28.174157: val_loss -0.1075 +2026-04-10 18:19:28.176066: Pseudo dice [0.1975, 0.6464, 0.5462, 0.2652, 0.4555, 0.4261, 0.681] +2026-04-10 18:19:28.178527: Epoch time: 102.41 s +2026-04-10 18:19:28.181080: Yayy! New best EMA pseudo Dice: 0.3987 +2026-04-10 18:19:31.080785: +2026-04-10 18:19:31.082627: Epoch 158 +2026-04-10 18:19:31.084637: Current learning rate: 0.00964 +2026-04-10 18:21:14.472947: train_loss -0.1036 +2026-04-10 18:21:14.480814: val_loss -0.1191 +2026-04-10 18:21:14.483282: Pseudo dice [0.4216, 0.1169, 0.7202, 0.2992, 0.2167, 0.6375, 0.8034] +2026-04-10 18:21:14.486652: Epoch time: 103.4 s +2026-04-10 18:21:14.490939: Yayy! New best EMA pseudo Dice: 0.4048 +2026-04-10 18:21:17.386193: +2026-04-10 18:21:17.388332: Epoch 159 +2026-04-10 18:21:17.390420: Current learning rate: 0.00964 +2026-04-10 18:23:00.651218: train_loss -0.117 +2026-04-10 18:23:00.659162: val_loss -0.1167 +2026-04-10 18:23:00.661566: Pseudo dice [0.5573, 0.36, 0.5087, 0.3498, 0.3007, 0.6179, 0.6495] +2026-04-10 18:23:00.664274: Epoch time: 103.27 s +2026-04-10 18:23:00.666345: Yayy! New best EMA pseudo Dice: 0.412 +2026-04-10 18:23:03.517806: +2026-04-10 18:23:03.519557: Epoch 160 +2026-04-10 18:23:03.521004: Current learning rate: 0.00964 +2026-04-10 18:24:46.478378: train_loss -0.0956 +2026-04-10 18:24:46.485488: val_loss -0.1142 +2026-04-10 18:24:46.487696: Pseudo dice [0.3656, 0.6151, 0.4914, 0.369, 0.3376, 0.7512, 0.6172] +2026-04-10 18:24:46.490377: Epoch time: 102.96 s +2026-04-10 18:24:46.493032: Yayy! New best EMA pseudo Dice: 0.4215 +2026-04-10 18:24:49.332824: +2026-04-10 18:24:49.334927: Epoch 161 +2026-04-10 18:24:49.336636: Current learning rate: 0.00964 +2026-04-10 18:26:31.924533: train_loss -0.1017 +2026-04-10 18:26:31.931606: val_loss -0.1449 +2026-04-10 18:26:31.934840: Pseudo dice [0.5185, 0.1414, 0.6743, 0.2432, 0.3858, 0.7877, 0.5607] +2026-04-10 18:26:31.937731: Epoch time: 102.59 s +2026-04-10 18:26:31.939747: Yayy! New best EMA pseudo Dice: 0.4267 +2026-04-10 18:26:34.838695: +2026-04-10 18:26:34.841200: Epoch 162 +2026-04-10 18:26:34.843425: Current learning rate: 0.00963 +2026-04-10 18:28:17.601785: train_loss -0.1091 +2026-04-10 18:28:17.610087: val_loss -0.1033 +2026-04-10 18:28:17.612912: Pseudo dice [0.3409, 0.7313, 0.6284, 0.1483, 0.3313, 0.6406, 0.5402] +2026-04-10 18:28:17.615628: Epoch time: 102.77 s +2026-04-10 18:28:17.618523: Yayy! New best EMA pseudo Dice: 0.432 +2026-04-10 18:28:20.509403: +2026-04-10 18:28:20.511637: Epoch 163 +2026-04-10 18:28:20.514450: Current learning rate: 0.00963 +2026-04-10 18:30:03.567576: train_loss -0.1078 +2026-04-10 18:30:03.579405: val_loss -0.1003 +2026-04-10 18:30:03.582886: Pseudo dice [0.1817, 0.7423, 0.6594, 0.4141, 0.3014, 0.4811, 0.5] +2026-04-10 18:30:03.587862: Epoch time: 103.06 s +2026-04-10 18:30:03.590461: Yayy! New best EMA pseudo Dice: 0.4357 +2026-04-10 18:30:06.662843: +2026-04-10 18:30:06.665212: Epoch 164 +2026-04-10 18:30:06.667078: Current learning rate: 0.00963 +2026-04-10 18:31:50.651033: train_loss -0.1136 +2026-04-10 18:31:50.658931: val_loss -0.0895 +2026-04-10 18:31:50.661521: Pseudo dice [0.3783, 0.6978, 0.4853, 0.2797, 0.3706, 0.63, 0.5126] +2026-04-10 18:31:50.665930: Epoch time: 103.99 s +2026-04-10 18:31:50.669512: Yayy! New best EMA pseudo Dice: 0.44 +2026-04-10 18:31:53.643238: +2026-04-10 18:31:53.645961: Epoch 165 +2026-04-10 18:31:53.648200: Current learning rate: 0.00963 +2026-04-10 18:33:36.270125: train_loss -0.0913 +2026-04-10 18:33:36.278866: val_loss -0.1208 +2026-04-10 18:33:36.281112: Pseudo dice [0.5017, 0.4003, 0.5963, 0.0021, 0.3755, 0.302, 0.7326] +2026-04-10 18:33:36.283941: Epoch time: 102.63 s +2026-04-10 18:33:37.415871: +2026-04-10 18:33:37.418716: Epoch 166 +2026-04-10 18:33:37.421374: Current learning rate: 0.00963 +2026-04-10 18:35:20.645252: train_loss -0.0936 +2026-04-10 18:35:20.653095: val_loss -0.0974 +2026-04-10 18:35:20.655463: Pseudo dice [0.699, 0.7267, 0.5048, 0.0758, 0.4317, 0.1358, 0.8753] +2026-04-10 18:35:20.657881: Epoch time: 103.23 s +2026-04-10 18:35:20.660380: Yayy! New best EMA pseudo Dice: 0.4431 +2026-04-10 18:35:23.599730: +2026-04-10 18:35:23.601907: Epoch 167 +2026-04-10 18:35:23.604401: Current learning rate: 0.00962 +2026-04-10 18:37:06.544905: train_loss -0.1015 +2026-04-10 18:37:06.553056: val_loss -0.1291 +2026-04-10 18:37:06.556254: Pseudo dice [0.6963, 0.5857, 0.7612, 0.2755, 0.1468, 0.6326, 0.7432] +2026-04-10 18:37:06.558882: Epoch time: 102.95 s +2026-04-10 18:37:06.561817: Yayy! New best EMA pseudo Dice: 0.4537 +2026-04-10 18:37:09.454052: +2026-04-10 18:37:09.455706: Epoch 168 +2026-04-10 18:37:09.457564: Current learning rate: 0.00962 +2026-04-10 18:38:52.536952: train_loss -0.0864 +2026-04-10 18:38:52.545462: val_loss -0.091 +2026-04-10 18:38:52.548425: Pseudo dice [0.3401, 0.2513, 0.7013, 0.0, 0.254, 0.543, 0.4705] +2026-04-10 18:38:52.551717: Epoch time: 103.09 s +2026-04-10 18:38:53.746664: +2026-04-10 18:38:53.750523: Epoch 169 +2026-04-10 18:38:53.753259: Current learning rate: 0.00962 +2026-04-10 18:40:36.391873: train_loss -0.0957 +2026-04-10 18:40:36.400227: val_loss -0.113 +2026-04-10 18:40:36.402794: Pseudo dice [0.3644, 0.3853, 0.6826, 0.4697, 0.3411, 0.471, 0.6225] +2026-04-10 18:40:36.405304: Epoch time: 102.65 s +2026-04-10 18:40:37.544909: +2026-04-10 18:40:37.547235: Epoch 170 +2026-04-10 18:40:37.549244: Current learning rate: 0.00962 +2026-04-10 18:42:20.682469: train_loss -0.1154 +2026-04-10 18:42:20.688682: val_loss -0.1344 +2026-04-10 18:42:20.690964: Pseudo dice [0.4647, 0.3836, 0.7808, 0.5684, 0.4953, 0.4339, 0.4731] +2026-04-10 18:42:20.693507: Epoch time: 103.14 s +2026-04-10 18:42:20.696213: Yayy! New best EMA pseudo Dice: 0.4547 +2026-04-10 18:42:23.598321: +2026-04-10 18:42:23.600940: Epoch 171 +2026-04-10 18:42:23.602623: Current learning rate: 0.00961 +2026-04-10 18:44:07.258190: train_loss -0.1087 +2026-04-10 18:44:07.266351: val_loss -0.1111 +2026-04-10 18:44:07.269129: Pseudo dice [0.5516, 0.5219, 0.6112, 0.2818, 0.26, 0.5089, 0.5919] +2026-04-10 18:44:07.271765: Epoch time: 103.66 s +2026-04-10 18:44:07.275318: Yayy! New best EMA pseudo Dice: 0.4567 +2026-04-10 18:44:10.226423: +2026-04-10 18:44:10.229364: Epoch 172 +2026-04-10 18:44:10.231210: Current learning rate: 0.00961 +2026-04-10 18:45:53.679330: train_loss -0.1009 +2026-04-10 18:45:53.691502: val_loss -0.1064 +2026-04-10 18:45:53.694391: Pseudo dice [0.3632, 0.6947, 0.7127, 0.5847, 0.4484, 0.2536, 0.7513] +2026-04-10 18:45:53.697033: Epoch time: 103.46 s +2026-04-10 18:45:53.699616: Yayy! New best EMA pseudo Dice: 0.4655 +2026-04-10 18:45:56.623389: +2026-04-10 18:45:56.625074: Epoch 173 +2026-04-10 18:45:56.627036: Current learning rate: 0.00961 +2026-04-10 18:47:40.110348: train_loss -0.116 +2026-04-10 18:47:40.117208: val_loss -0.0967 +2026-04-10 18:47:40.119137: Pseudo dice [0.4107, 0.0426, 0.6331, 0.3718, 0.3523, 0.327, 0.4141] +2026-04-10 18:47:40.121516: Epoch time: 103.49 s +2026-04-10 18:47:41.259695: +2026-04-10 18:47:41.262510: Epoch 174 +2026-04-10 18:47:41.264620: Current learning rate: 0.00961 +2026-04-10 18:49:24.458802: train_loss -0.1127 +2026-04-10 18:49:24.472334: val_loss -0.1351 +2026-04-10 18:49:24.474448: Pseudo dice [0.6867, 0.1439, 0.5932, 0.1903, 0.4322, 0.3547, 0.8052] +2026-04-10 18:49:24.477642: Epoch time: 103.2 s +2026-04-10 18:49:25.632491: +2026-04-10 18:49:25.634539: Epoch 175 +2026-04-10 18:49:25.636728: Current learning rate: 0.00961 +2026-04-10 18:51:08.140836: train_loss -0.1245 +2026-04-10 18:51:08.147461: val_loss -0.1118 +2026-04-10 18:51:08.149711: Pseudo dice [0.548, 0.1575, 0.822, 0.2017, 0.3851, 0.313, 0.5735] +2026-04-10 18:51:08.152111: Epoch time: 102.51 s +2026-04-10 18:51:09.334755: +2026-04-10 18:51:09.336754: Epoch 176 +2026-04-10 18:51:09.338363: Current learning rate: 0.0096 +2026-04-10 18:52:52.165347: train_loss -0.1129 +2026-04-10 18:52:52.173026: val_loss -0.111 +2026-04-10 18:52:52.176241: Pseudo dice [0.5143, 0.0382, 0.5449, 0.3569, 0.4704, 0.3293, 0.7615] +2026-04-10 18:52:52.178936: Epoch time: 102.83 s +2026-04-10 18:52:53.292554: +2026-04-10 18:52:53.295395: Epoch 177 +2026-04-10 18:52:53.299081: Current learning rate: 0.0096 +2026-04-10 18:54:36.241139: train_loss -0.1114 +2026-04-10 18:54:36.250006: val_loss -0.099 +2026-04-10 18:54:36.252508: Pseudo dice [0.3976, 0.761, 0.5809, 0.1193, 0.5113, 0.4882, 0.2947] +2026-04-10 18:54:36.255186: Epoch time: 102.95 s +2026-04-10 18:54:37.433833: +2026-04-10 18:54:37.449599: Epoch 178 +2026-04-10 18:54:37.462242: Current learning rate: 0.0096 +2026-04-10 18:56:20.206641: train_loss -0.1172 +2026-04-10 18:56:20.215285: val_loss -0.1424 +2026-04-10 18:56:20.217913: Pseudo dice [0.4527, 0.6827, 0.6355, 0.2659, 0.4312, 0.2966, 0.772] +2026-04-10 18:56:20.220578: Epoch time: 102.78 s +2026-04-10 18:56:21.358355: +2026-04-10 18:56:21.361240: Epoch 179 +2026-04-10 18:56:21.363420: Current learning rate: 0.0096 +2026-04-10 18:58:05.354499: train_loss -0.1045 +2026-04-10 18:58:05.363105: val_loss -0.1036 +2026-04-10 18:58:05.365718: Pseudo dice [0.6668, 0.2685, 0.4368, 0.2206, 0.3654, 0.2477, 0.5099] +2026-04-10 18:58:05.368425: Epoch time: 104.0 s +2026-04-10 18:58:06.547029: +2026-04-10 18:58:06.549470: Epoch 180 +2026-04-10 18:58:06.551880: Current learning rate: 0.00959 +2026-04-10 18:59:50.216067: train_loss -0.1152 +2026-04-10 18:59:50.224041: val_loss -0.1057 +2026-04-10 18:59:50.226441: Pseudo dice [0.2885, 0.1036, 0.5933, 0.0569, 0.317, 0.5606, 0.8294] +2026-04-10 18:59:50.229104: Epoch time: 103.67 s +2026-04-10 18:59:51.366950: +2026-04-10 18:59:51.369212: Epoch 181 +2026-04-10 18:59:51.371099: Current learning rate: 0.00959 +2026-04-10 19:01:34.472560: train_loss -0.1306 +2026-04-10 19:01:34.480068: val_loss -0.1155 +2026-04-10 19:01:34.483076: Pseudo dice [0.6764, 0.5359, 0.6714, 0.0899, 0.4187, 0.3784, 0.4724] +2026-04-10 19:01:34.489719: Epoch time: 103.11 s +2026-04-10 19:01:35.664816: +2026-04-10 19:01:35.667756: Epoch 182 +2026-04-10 19:01:35.670614: Current learning rate: 0.00959 +2026-04-10 19:03:18.806160: train_loss -0.1125 +2026-04-10 19:03:18.813618: val_loss -0.0982 +2026-04-10 19:03:18.816727: Pseudo dice [0.5315, 0.4306, 0.6801, 0.3584, 0.2106, 0.2142, 0.5719] +2026-04-10 19:03:18.820332: Epoch time: 103.14 s +2026-04-10 19:03:19.971978: +2026-04-10 19:03:19.974074: Epoch 183 +2026-04-10 19:03:19.976413: Current learning rate: 0.00959 +2026-04-10 19:05:03.773175: train_loss -0.1222 +2026-04-10 19:05:03.809913: val_loss -0.0709 +2026-04-10 19:05:03.812743: Pseudo dice [0.6111, 0.1368, 0.5294, 0.0953, 0.4227, 0.3985, 0.2613] +2026-04-10 19:05:03.816718: Epoch time: 103.8 s +2026-04-10 19:05:04.957212: +2026-04-10 19:05:04.961062: Epoch 184 +2026-04-10 19:05:04.963928: Current learning rate: 0.00959 +2026-04-10 19:06:47.715189: train_loss -0.0997 +2026-04-10 19:06:47.722008: val_loss -0.1139 +2026-04-10 19:06:47.724340: Pseudo dice [0.5269, 0.224, 0.6432, 0.3605, 0.2997, 0.6064, 0.6282] +2026-04-10 19:06:47.727683: Epoch time: 102.76 s +2026-04-10 19:06:48.922760: +2026-04-10 19:06:48.924845: Epoch 185 +2026-04-10 19:06:48.926582: Current learning rate: 0.00958 +2026-04-10 19:08:31.742621: train_loss -0.1263 +2026-04-10 19:08:31.749862: val_loss -0.131 +2026-04-10 19:08:31.752204: Pseudo dice [0.4129, 0.2842, 0.6416, 0.6745, 0.4109, 0.6202, 0.6035] +2026-04-10 19:08:31.754917: Epoch time: 102.82 s +2026-04-10 19:08:32.944534: +2026-04-10 19:08:32.947291: Epoch 186 +2026-04-10 19:08:32.951261: Current learning rate: 0.00958 +2026-04-10 19:10:15.915832: train_loss -0.1232 +2026-04-10 19:10:15.923113: val_loss -0.1224 +2026-04-10 19:10:15.926519: Pseudo dice [0.5247, 0.1013, 0.6168, 0.0979, 0.5155, 0.4653, 0.7353] +2026-04-10 19:10:15.929733: Epoch time: 102.97 s +2026-04-10 19:10:17.050155: +2026-04-10 19:10:17.053080: Epoch 187 +2026-04-10 19:10:17.055854: Current learning rate: 0.00958 +2026-04-10 19:12:00.410533: train_loss -0.1144 +2026-04-10 19:12:00.419658: val_loss -0.0878 +2026-04-10 19:12:00.422364: Pseudo dice [0.6914, 0.3632, 0.5598, 0.1991, 0.3255, 0.3166, 0.441] +2026-04-10 19:12:00.425560: Epoch time: 103.36 s +2026-04-10 19:12:01.560798: +2026-04-10 19:12:01.563356: Epoch 188 +2026-04-10 19:12:01.565435: Current learning rate: 0.00958 +2026-04-10 19:13:45.045049: train_loss -0.1275 +2026-04-10 19:13:45.053576: val_loss -0.1216 +2026-04-10 19:13:45.055787: Pseudo dice [0.2078, 0.2306, 0.6231, 0.4617, 0.3183, 0.5732, 0.7059] +2026-04-10 19:13:45.059070: Epoch time: 103.49 s +2026-04-10 19:13:46.201084: +2026-04-10 19:13:46.203903: Epoch 189 +2026-04-10 19:13:46.206215: Current learning rate: 0.00957 +2026-04-10 19:15:29.824862: train_loss -0.1271 +2026-04-10 19:15:29.832983: val_loss -0.1392 +2026-04-10 19:15:29.836045: Pseudo dice [0.3837, 0.624, 0.7492, 0.6229, 0.3645, 0.6169, 0.6903] +2026-04-10 19:15:29.839753: Epoch time: 103.63 s +2026-04-10 19:15:30.963289: +2026-04-10 19:15:30.966015: Epoch 190 +2026-04-10 19:15:30.967884: Current learning rate: 0.00957 +2026-04-10 19:17:13.950897: train_loss -0.1144 +2026-04-10 19:17:13.957356: val_loss -0.1134 +2026-04-10 19:17:13.960048: Pseudo dice [0.358, 0.3795, 0.6701, 0.3118, 0.2528, 0.6103, 0.1813] +2026-04-10 19:17:13.962488: Epoch time: 102.99 s +2026-04-10 19:17:16.280928: +2026-04-10 19:17:16.282887: Epoch 191 +2026-04-10 19:17:16.285353: Current learning rate: 0.00957 +2026-04-10 19:18:59.025485: train_loss -0.1144 +2026-04-10 19:18:59.032120: val_loss -0.1233 +2026-04-10 19:18:59.035975: Pseudo dice [0.4727, 0.5904, 0.7474, 0.2952, 0.3616, 0.536, 0.7197] +2026-04-10 19:18:59.039770: Epoch time: 102.75 s +2026-04-10 19:19:00.672801: +2026-04-10 19:19:00.675429: Epoch 192 +2026-04-10 19:19:00.677110: Current learning rate: 0.00957 +2026-04-10 19:20:44.176499: train_loss -0.13 +2026-04-10 19:20:44.182494: val_loss -0.1141 +2026-04-10 19:20:44.184777: Pseudo dice [0.4651, 0.4393, 0.5862, 0.4423, 0.2419, 0.3351, 0.6617] +2026-04-10 19:20:44.187971: Epoch time: 103.51 s +2026-04-10 19:20:45.320636: +2026-04-10 19:20:45.324495: Epoch 193 +2026-04-10 19:20:45.326395: Current learning rate: 0.00956 +2026-04-10 19:22:28.802643: train_loss -0.1145 +2026-04-10 19:22:28.810299: val_loss -0.1106 +2026-04-10 19:22:28.812901: Pseudo dice [0.6548, 0.2663, 0.6449, 0.1245, 0.353, 0.4889, 0.6773] +2026-04-10 19:22:28.815363: Epoch time: 103.49 s +2026-04-10 19:22:30.006057: +2026-04-10 19:22:30.009672: Epoch 194 +2026-04-10 19:22:30.012088: Current learning rate: 0.00956 +2026-04-10 19:24:12.781864: train_loss -0.1189 +2026-04-10 19:24:12.788383: val_loss -0.1184 +2026-04-10 19:24:12.790790: Pseudo dice [0.5041, 0.4293, 0.7156, 0.6262, 0.45, 0.4013, 0.577] +2026-04-10 19:24:12.794153: Epoch time: 102.78 s +2026-04-10 19:24:13.961069: +2026-04-10 19:24:13.964668: Epoch 195 +2026-04-10 19:24:13.967227: Current learning rate: 0.00956 +2026-04-10 19:25:56.829363: train_loss -0.1042 +2026-04-10 19:25:56.835236: val_loss -0.1367 +2026-04-10 19:25:56.837531: Pseudo dice [0.7171, 0.858, 0.6521, 0.0534, 0.4538, 0.3512, 0.6878] +2026-04-10 19:25:56.840026: Epoch time: 102.87 s +2026-04-10 19:25:56.842298: Yayy! New best EMA pseudo Dice: 0.4724 +2026-04-10 19:25:59.762534: +2026-04-10 19:25:59.764460: Epoch 196 +2026-04-10 19:25:59.766067: Current learning rate: 0.00956 +2026-04-10 19:27:43.946660: train_loss -0.1359 +2026-04-10 19:27:43.958122: val_loss -0.1298 +2026-04-10 19:27:43.962113: Pseudo dice [0.5896, 0.7442, 0.761, 0.5349, 0.2634, 0.455, 0.6599] +2026-04-10 19:27:43.965428: Epoch time: 104.19 s +2026-04-10 19:27:43.968497: Yayy! New best EMA pseudo Dice: 0.4824 +2026-04-10 19:27:46.905150: +2026-04-10 19:27:46.907635: Epoch 197 +2026-04-10 19:27:46.910187: Current learning rate: 0.00956 +2026-04-10 19:29:30.558108: train_loss -0.1373 +2026-04-10 19:29:30.565367: val_loss -0.1392 +2026-04-10 19:29:30.568550: Pseudo dice [0.6086, 0.7738, 0.7854, 0.308, 0.3913, 0.4075, 0.4605] +2026-04-10 19:29:30.571090: Epoch time: 103.66 s +2026-04-10 19:29:30.573219: Yayy! New best EMA pseudo Dice: 0.4875 +2026-04-10 19:29:33.403194: +2026-04-10 19:29:33.406214: Epoch 198 +2026-04-10 19:29:33.408708: Current learning rate: 0.00955 +2026-04-10 19:31:16.772638: train_loss -0.1229 +2026-04-10 19:31:16.779623: val_loss -0.1209 +2026-04-10 19:31:16.782891: Pseudo dice [0.4279, 0.8225, 0.7436, 0.3922, 0.3719, 0.3073, 0.2955] +2026-04-10 19:31:16.785529: Epoch time: 103.37 s +2026-04-10 19:31:17.953847: +2026-04-10 19:31:17.956174: Epoch 199 +2026-04-10 19:31:17.958400: Current learning rate: 0.00955 +2026-04-10 19:33:01.424875: train_loss -0.1257 +2026-04-10 19:33:01.432433: val_loss -0.1091 +2026-04-10 19:33:01.434823: Pseudo dice [0.3507, 0.3729, 0.777, 0.0988, 0.4415, 0.4496, 0.5547] +2026-04-10 19:33:01.437881: Epoch time: 103.47 s +2026-04-10 19:33:04.338711: +2026-04-10 19:33:04.342028: Epoch 200 +2026-04-10 19:33:04.343802: Current learning rate: 0.00955 +2026-04-10 19:34:47.393096: train_loss -0.125 +2026-04-10 19:34:47.401241: val_loss -0.0855 +2026-04-10 19:34:47.403812: Pseudo dice [0.5165, 0.7099, 0.6117, 0.1667, 0.3901, 0.5769, 0.3117] +2026-04-10 19:34:47.406284: Epoch time: 103.06 s +2026-04-10 19:34:48.586832: +2026-04-10 19:34:48.588698: Epoch 201 +2026-04-10 19:34:48.590505: Current learning rate: 0.00955 +2026-04-10 19:36:31.729563: train_loss -0.1293 +2026-04-10 19:36:31.735158: val_loss -0.1288 +2026-04-10 19:36:31.737655: Pseudo dice [0.5907, 0.7903, 0.6292, 0.5239, 0.4021, 0.568, 0.3109] +2026-04-10 19:36:31.740773: Epoch time: 103.15 s +2026-04-10 19:36:32.895091: +2026-04-10 19:36:32.897603: Epoch 202 +2026-04-10 19:36:32.900133: Current learning rate: 0.00954 +2026-04-10 19:38:17.078464: train_loss -0.1395 +2026-04-10 19:38:17.087318: val_loss -0.1035 +2026-04-10 19:38:17.090239: Pseudo dice [0.5877, 0.581, 0.5225, 0.1539, 0.2422, 0.6949, 0.5193] +2026-04-10 19:38:17.094155: Epoch time: 104.19 s +2026-04-10 19:38:18.215038: +2026-04-10 19:38:18.217783: Epoch 203 +2026-04-10 19:38:18.221635: Current learning rate: 0.00954 +2026-04-10 19:40:01.290751: train_loss -0.1325 +2026-04-10 19:40:01.299320: val_loss -0.1331 +2026-04-10 19:40:01.303447: Pseudo dice [0.2675, 0.8519, 0.7585, 0.4185, 0.4389, 0.3959, 0.7216] +2026-04-10 19:40:01.306401: Epoch time: 103.08 s +2026-04-10 19:40:01.309718: Yayy! New best EMA pseudo Dice: 0.4918 +2026-04-10 19:40:04.321058: +2026-04-10 19:40:04.323322: Epoch 204 +2026-04-10 19:40:04.325389: Current learning rate: 0.00954 +2026-04-10 19:41:47.168029: train_loss -0.1184 +2026-04-10 19:41:47.175860: val_loss -0.1231 +2026-04-10 19:41:47.178198: Pseudo dice [0.5833, 0.2516, 0.7917, 0.334, 0.2717, 0.4175, 0.4422] +2026-04-10 19:41:47.180863: Epoch time: 102.85 s +2026-04-10 19:41:48.346803: +2026-04-10 19:41:48.349953: Epoch 205 +2026-04-10 19:41:48.352286: Current learning rate: 0.00954 +2026-04-10 19:43:30.820661: train_loss -0.1058 +2026-04-10 19:43:30.833656: val_loss -0.1364 +2026-04-10 19:43:30.846152: Pseudo dice [0.6899, 0.4803, 0.5315, 0.4713, 0.3307, 0.5681, 0.2593] +2026-04-10 19:43:30.848936: Epoch time: 102.48 s +2026-04-10 19:43:31.947638: +2026-04-10 19:43:31.950926: Epoch 206 +2026-04-10 19:43:31.953230: Current learning rate: 0.00954 +2026-04-10 19:45:14.724131: train_loss -0.1233 +2026-04-10 19:45:14.746467: val_loss -0.1315 +2026-04-10 19:45:14.749563: Pseudo dice [0.2456, 0.5883, 0.7863, 0.5078, 0.5893, 0.0481, 0.706] +2026-04-10 19:45:14.752060: Epoch time: 102.78 s +2026-04-10 19:45:15.829298: +2026-04-10 19:45:15.832009: Epoch 207 +2026-04-10 19:45:15.834504: Current learning rate: 0.00953 +2026-04-10 19:46:58.856204: train_loss -0.1353 +2026-04-10 19:46:58.863026: val_loss -0.1446 +2026-04-10 19:46:58.865663: Pseudo dice [0.6752, 0.4957, 0.7337, 0.0104, 0.5547, 0.4641, 0.864] +2026-04-10 19:46:58.868726: Epoch time: 103.03 s +2026-04-10 19:46:58.871406: Yayy! New best EMA pseudo Dice: 0.4923 +2026-04-10 19:47:01.633284: +2026-04-10 19:47:01.634944: Epoch 208 +2026-04-10 19:47:01.636509: Current learning rate: 0.00953 +2026-04-10 19:48:45.526330: train_loss -0.1266 +2026-04-10 19:48:45.533097: val_loss -0.1269 +2026-04-10 19:48:45.535243: Pseudo dice [0.2789, 0.6549, 0.721, 0.2308, 0.5158, 0.5874, 0.6236] +2026-04-10 19:48:45.537637: Epoch time: 103.9 s +2026-04-10 19:48:45.539557: Yayy! New best EMA pseudo Dice: 0.4947 +2026-04-10 19:48:48.319414: +2026-04-10 19:48:48.321582: Epoch 209 +2026-04-10 19:48:48.323759: Current learning rate: 0.00953 +2026-04-10 19:50:30.422933: train_loss -0.1393 +2026-04-10 19:50:30.428906: val_loss -0.1568 +2026-04-10 19:50:30.431607: Pseudo dice [0.4437, 0.65, 0.7658, 0.3186, 0.4715, 0.7905, 0.6772] +2026-04-10 19:50:30.434590: Epoch time: 102.11 s +2026-04-10 19:50:30.438307: Yayy! New best EMA pseudo Dice: 0.504 +2026-04-10 19:50:33.281970: +2026-04-10 19:50:33.284312: Epoch 210 +2026-04-10 19:50:33.285840: Current learning rate: 0.00953 +2026-04-10 19:52:16.428416: train_loss -0.1178 +2026-04-10 19:52:16.436493: val_loss -0.127 +2026-04-10 19:52:16.439512: Pseudo dice [0.5886, 0.401, 0.4824, 0.329, 0.4442, 0.4007, 0.7867] +2026-04-10 19:52:16.442762: Epoch time: 103.15 s +2026-04-10 19:52:17.536372: +2026-04-10 19:52:17.539253: Epoch 211 +2026-04-10 19:52:17.541532: Current learning rate: 0.00952 +2026-04-10 19:53:59.976183: train_loss -0.1312 +2026-04-10 19:53:59.983450: val_loss -0.1291 +2026-04-10 19:53:59.985959: Pseudo dice [0.627, 0.597, 0.6752, 0.1472, 0.3775, 0.421, 0.6828] +2026-04-10 19:53:59.989695: Epoch time: 102.44 s +2026-04-10 19:54:01.080735: +2026-04-10 19:54:01.082964: Epoch 212 +2026-04-10 19:54:01.084709: Current learning rate: 0.00952 +2026-04-10 19:55:45.726581: train_loss -0.1297 +2026-04-10 19:55:45.736769: val_loss -0.1063 +2026-04-10 19:55:45.739945: Pseudo dice [0.3925, 0.1071, 0.6855, 0.03, 0.3456, 0.321, 0.6148] +2026-04-10 19:55:45.743894: Epoch time: 104.65 s +2026-04-10 19:55:46.833693: +2026-04-10 19:55:46.836099: Epoch 213 +2026-04-10 19:55:46.837860: Current learning rate: 0.00952 +2026-04-10 19:57:30.919619: train_loss -0.1018 +2026-04-10 19:57:30.930662: val_loss -0.0757 +2026-04-10 19:57:30.934506: Pseudo dice [0.4054, 0.7357, 0.6952, 0.2783, 0.2682, 0.1462, 0.3014] +2026-04-10 19:57:30.938667: Epoch time: 104.09 s +2026-04-10 19:57:32.058846: +2026-04-10 19:57:32.061008: Epoch 214 +2026-04-10 19:57:32.063656: Current learning rate: 0.00952 +2026-04-10 19:59:15.552539: train_loss -0.1426 +2026-04-10 19:59:15.559724: val_loss -0.1089 +2026-04-10 19:59:15.562201: Pseudo dice [0.3755, 0.5734, 0.6795, 0.4378, 0.2198, 0.4258, 0.3885] +2026-04-10 19:59:15.565382: Epoch time: 103.5 s +2026-04-10 19:59:16.695953: +2026-04-10 19:59:16.698906: Epoch 215 +2026-04-10 19:59:16.701997: Current learning rate: 0.00951 +2026-04-10 20:00:59.932350: train_loss -0.1325 +2026-04-10 20:00:59.939814: val_loss -0.1253 +2026-04-10 20:00:59.942626: Pseudo dice [0.3951, 0.5476, 0.5426, 0.4407, 0.299, 0.7537, 0.5939] +2026-04-10 20:00:59.945465: Epoch time: 103.24 s +2026-04-10 20:01:01.025634: +2026-04-10 20:01:01.028368: Epoch 216 +2026-04-10 20:01:01.031168: Current learning rate: 0.00951 +2026-04-10 20:02:44.189487: train_loss -0.139 +2026-04-10 20:02:44.196780: val_loss -0.1234 +2026-04-10 20:02:44.198664: Pseudo dice [0.4568, 0.5647, 0.4485, 0.7309, 0.4884, 0.167, 0.713] +2026-04-10 20:02:44.201411: Epoch time: 103.17 s +2026-04-10 20:02:45.285419: +2026-04-10 20:02:45.287721: Epoch 217 +2026-04-10 20:02:45.289987: Current learning rate: 0.00951 +2026-04-10 20:04:27.967562: train_loss -0.1273 +2026-04-10 20:04:27.975728: val_loss -0.1213 +2026-04-10 20:04:27.978754: Pseudo dice [0.5893, 0.1676, 0.8192, 0.0533, 0.4782, 0.4692, 0.4526] +2026-04-10 20:04:27.982176: Epoch time: 102.69 s +2026-04-10 20:04:29.058867: +2026-04-10 20:04:29.061485: Epoch 218 +2026-04-10 20:04:29.063778: Current learning rate: 0.00951 +2026-04-10 20:06:13.403423: train_loss -0.1149 +2026-04-10 20:06:13.410871: val_loss -0.1083 +2026-04-10 20:06:13.413609: Pseudo dice [0.696, 0.4819, 0.6506, 0.0205, 0.4061, 0.3301, 0.1051] +2026-04-10 20:06:13.415939: Epoch time: 104.35 s +2026-04-10 20:06:14.517630: +2026-04-10 20:06:14.520679: Epoch 219 +2026-04-10 20:06:14.522287: Current learning rate: 0.00951 +2026-04-10 20:07:57.748109: train_loss -0.1267 +2026-04-10 20:07:57.755786: val_loss -0.149 +2026-04-10 20:07:57.758356: Pseudo dice [0.6884, 0.2314, 0.7611, 0.7145, 0.5127, 0.6498, 0.5214] +2026-04-10 20:07:57.761029: Epoch time: 103.23 s +2026-04-10 20:07:58.844464: +2026-04-10 20:07:58.846800: Epoch 220 +2026-04-10 20:07:58.848815: Current learning rate: 0.0095 +2026-04-10 20:09:41.830934: train_loss -0.1511 +2026-04-10 20:09:41.838520: val_loss -0.1235 +2026-04-10 20:09:41.841454: Pseudo dice [0.4606, 0.5969, 0.7218, 0.3052, 0.5747, 0.3952, 0.7385] +2026-04-10 20:09:41.844025: Epoch time: 102.99 s +2026-04-10 20:09:42.984278: +2026-04-10 20:09:42.986139: Epoch 221 +2026-04-10 20:09:42.989124: Current learning rate: 0.0095 +2026-04-10 20:11:26.576504: train_loss -0.1335 +2026-04-10 20:11:26.583477: val_loss -0.1267 +2026-04-10 20:11:26.585916: Pseudo dice [0.586, 0.3533, 0.5889, 0.0756, 0.2961, 0.5357, 0.5902] +2026-04-10 20:11:26.589894: Epoch time: 103.6 s +2026-04-10 20:11:27.685579: +2026-04-10 20:11:27.688285: Epoch 222 +2026-04-10 20:11:27.691142: Current learning rate: 0.0095 +2026-04-10 20:13:11.389421: train_loss -0.1189 +2026-04-10 20:13:11.395352: val_loss -0.131 +2026-04-10 20:13:11.397643: Pseudo dice [0.5413, 0.2629, 0.6674, 0.5936, 0.5427, 0.2818, 0.7293] +2026-04-10 20:13:11.400241: Epoch time: 103.71 s +2026-04-10 20:13:12.523661: +2026-04-10 20:13:12.526102: Epoch 223 +2026-04-10 20:13:12.528199: Current learning rate: 0.0095 +2026-04-10 20:14:55.524440: train_loss -0.1225 +2026-04-10 20:14:55.531394: val_loss -0.1078 +2026-04-10 20:14:55.534066: Pseudo dice [0.5333, 0.7173, 0.4106, 0.3486, 0.4736, 0.4916, 0.8088] +2026-04-10 20:14:55.536613: Epoch time: 103.0 s +2026-04-10 20:14:56.644444: +2026-04-10 20:14:56.646787: Epoch 224 +2026-04-10 20:14:56.650559: Current learning rate: 0.00949 +2026-04-10 20:16:38.813638: train_loss -0.1295 +2026-04-10 20:16:38.821263: val_loss -0.1473 +2026-04-10 20:16:38.823511: Pseudo dice [0.6588, 0.0431, 0.719, 0.2457, 0.3982, 0.4759, 0.7873] +2026-04-10 20:16:38.826942: Epoch time: 102.17 s +2026-04-10 20:16:39.899367: +2026-04-10 20:16:39.901326: Epoch 225 +2026-04-10 20:16:39.903242: Current learning rate: 0.00949 +2026-04-10 20:18:22.565989: train_loss -0.1202 +2026-04-10 20:18:22.574394: val_loss -0.1248 +2026-04-10 20:18:22.576760: Pseudo dice [0.2336, 0.8296, 0.7085, 0.3038, 0.5142, 0.6906, 0.65] +2026-04-10 20:18:22.579216: Epoch time: 102.67 s +2026-04-10 20:18:23.683287: +2026-04-10 20:18:23.687128: Epoch 226 +2026-04-10 20:18:23.693119: Current learning rate: 0.00949 +2026-04-10 20:20:05.965176: train_loss -0.1326 +2026-04-10 20:20:05.972700: val_loss -0.1257 +2026-04-10 20:20:05.974960: Pseudo dice [0.4288, 0.4241, 0.7067, 0.6654, 0.4736, 0.3982, 0.6663] +2026-04-10 20:20:05.978284: Epoch time: 102.29 s +2026-04-10 20:20:07.103638: +2026-04-10 20:20:07.106139: Epoch 227 +2026-04-10 20:20:07.108366: Current learning rate: 0.00949 +2026-04-10 20:21:50.242606: train_loss -0.1284 +2026-04-10 20:21:50.250896: val_loss -0.133 +2026-04-10 20:21:50.253766: Pseudo dice [0.539, 0.2288, 0.7398, 0.6038, 0.3926, 0.6046, 0.7118] +2026-04-10 20:21:50.256525: Epoch time: 103.14 s +2026-04-10 20:21:50.258930: Yayy! New best EMA pseudo Dice: 0.5045 +2026-04-10 20:21:54.033901: +2026-04-10 20:21:54.035939: Epoch 228 +2026-04-10 20:21:54.037835: Current learning rate: 0.00949 +2026-04-10 20:23:37.716163: train_loss -0.1235 +2026-04-10 20:23:37.723680: val_loss -0.1219 +2026-04-10 20:23:37.726269: Pseudo dice [0.5599, 0.7316, 0.3079, 0.6424, 0.4497, 0.4394, 0.6217] +2026-04-10 20:23:37.729249: Epoch time: 103.69 s +2026-04-10 20:23:37.732600: Yayy! New best EMA pseudo Dice: 0.5076 +2026-04-10 20:23:40.673030: +2026-04-10 20:23:40.675716: Epoch 229 +2026-04-10 20:23:40.677605: Current learning rate: 0.00948 +2026-04-10 20:25:24.453912: train_loss -0.145 +2026-04-10 20:25:24.460216: val_loss -0.1403 +2026-04-10 20:25:24.463134: Pseudo dice [0.645, 0.8096, 0.8135, 0.2432, 0.4789, 0.5662, 0.8364] +2026-04-10 20:25:24.466828: Epoch time: 103.78 s +2026-04-10 20:25:24.468881: Yayy! New best EMA pseudo Dice: 0.5196 +2026-04-10 20:25:27.223922: +2026-04-10 20:25:27.225823: Epoch 230 +2026-04-10 20:25:27.228276: Current learning rate: 0.00948 +2026-04-10 20:27:10.829645: train_loss -0.1403 +2026-04-10 20:27:10.838035: val_loss -0.1482 +2026-04-10 20:27:10.840252: Pseudo dice [0.7596, 0.5045, 0.8113, 0.7074, 0.5131, 0.4278, 0.7837] +2026-04-10 20:27:10.843442: Epoch time: 103.61 s +2026-04-10 20:27:10.845893: Yayy! New best EMA pseudo Dice: 0.5321 +2026-04-10 20:27:13.688097: +2026-04-10 20:27:13.689913: Epoch 231 +2026-04-10 20:27:13.692119: Current learning rate: 0.00948 +2026-04-10 20:28:56.934769: train_loss -0.1308 +2026-04-10 20:28:56.943878: val_loss -0.1208 +2026-04-10 20:28:56.946798: Pseudo dice [0.4383, 0.3953, 0.7236, 0.2262, 0.4742, 0.4335, 0.64] +2026-04-10 20:28:56.950053: Epoch time: 103.25 s +2026-04-10 20:28:58.036458: +2026-04-10 20:28:58.039948: Epoch 232 +2026-04-10 20:28:58.049094: Current learning rate: 0.00948 +2026-04-10 20:30:40.596163: train_loss -0.1434 +2026-04-10 20:30:40.606298: val_loss -0.1624 +2026-04-10 20:30:40.608831: Pseudo dice [0.7145, 0.7915, 0.8594, 0.351, 0.5575, 0.6904, 0.4698] +2026-04-10 20:30:40.611875: Epoch time: 102.56 s +2026-04-10 20:30:40.614250: Yayy! New best EMA pseudo Dice: 0.5371 +2026-04-10 20:30:43.503096: +2026-04-10 20:30:43.506629: Epoch 233 +2026-04-10 20:30:43.508573: Current learning rate: 0.00947 +2026-04-10 20:32:27.244080: train_loss -0.1335 +2026-04-10 20:32:27.252121: val_loss -0.1172 +2026-04-10 20:32:27.254921: Pseudo dice [0.5542, 0.6159, 0.5699, 0.2221, 0.373, 0.4717, 0.5853] +2026-04-10 20:32:27.257555: Epoch time: 103.74 s +2026-04-10 20:32:28.332119: +2026-04-10 20:32:28.334193: Epoch 234 +2026-04-10 20:32:28.337711: Current learning rate: 0.00947 +2026-04-10 20:34:10.801427: train_loss -0.1301 +2026-04-10 20:34:10.812190: val_loss -0.1414 +2026-04-10 20:34:10.815409: Pseudo dice [0.7386, 0.8709, 0.7283, 0.2633, 0.3986, 0.5595, 0.5335] +2026-04-10 20:34:10.818531: Epoch time: 102.47 s +2026-04-10 20:34:10.820854: Yayy! New best EMA pseudo Dice: 0.5372 +2026-04-10 20:34:13.622591: +2026-04-10 20:34:13.624508: Epoch 235 +2026-04-10 20:34:13.627199: Current learning rate: 0.00947 +2026-04-10 20:35:56.438637: train_loss -0.1456 +2026-04-10 20:35:56.445989: val_loss -0.1498 +2026-04-10 20:35:56.447987: Pseudo dice [0.4875, 0.2469, 0.8129, 0.5057, 0.4543, 0.7839, 0.7128] +2026-04-10 20:35:56.451450: Epoch time: 102.82 s +2026-04-10 20:35:56.455094: Yayy! New best EMA pseudo Dice: 0.5406 +2026-04-10 20:35:59.229370: +2026-04-10 20:35:59.231432: Epoch 236 +2026-04-10 20:35:59.233769: Current learning rate: 0.00947 +2026-04-10 20:37:41.849892: train_loss -0.1377 +2026-04-10 20:37:41.858157: val_loss -0.1404 +2026-04-10 20:37:41.860496: Pseudo dice [0.4127, 0.4577, 0.8355, 0.5002, 0.4519, 0.5742, 0.6585] +2026-04-10 20:37:41.862786: Epoch time: 102.62 s +2026-04-10 20:37:41.865241: Yayy! New best EMA pseudo Dice: 0.5422 +2026-04-10 20:37:44.760904: +2026-04-10 20:37:44.762961: Epoch 237 +2026-04-10 20:37:44.764796: Current learning rate: 0.00947 +2026-04-10 20:39:28.348750: train_loss -0.133 +2026-04-10 20:39:28.356266: val_loss -0.1072 +2026-04-10 20:39:28.358315: Pseudo dice [0.5661, 0.448, 0.5488, 0.3515, 0.2659, 0.2958, 0.6339] +2026-04-10 20:39:28.361020: Epoch time: 103.59 s +2026-04-10 20:39:29.470819: +2026-04-10 20:39:29.473755: Epoch 238 +2026-04-10 20:39:29.475523: Current learning rate: 0.00946 +2026-04-10 20:41:13.329901: train_loss -0.127 +2026-04-10 20:41:13.336090: val_loss -0.1526 +2026-04-10 20:41:13.339614: Pseudo dice [0.7284, 0.7907, 0.7085, 0.4309, 0.4283, 0.4321, 0.7879] +2026-04-10 20:41:13.343775: Epoch time: 103.86 s +2026-04-10 20:41:14.449881: +2026-04-10 20:41:14.453422: Epoch 239 +2026-04-10 20:41:14.456630: Current learning rate: 0.00946 +2026-04-10 20:42:57.910567: train_loss -0.1512 +2026-04-10 20:42:57.917976: val_loss -0.1369 +2026-04-10 20:42:57.919935: Pseudo dice [0.7874, 0.411, 0.7642, 0.3293, 0.504, 0.594, 0.6946] +2026-04-10 20:42:57.922766: Epoch time: 103.46 s +2026-04-10 20:42:57.924950: Yayy! New best EMA pseudo Dice: 0.5449 +2026-04-10 20:43:00.887025: +2026-04-10 20:43:00.888872: Epoch 240 +2026-04-10 20:43:00.890903: Current learning rate: 0.00946 +2026-04-10 20:44:44.505762: train_loss -0.1375 +2026-04-10 20:44:44.514785: val_loss -0.1145 +2026-04-10 20:44:44.517392: Pseudo dice [0.6581, 0.7418, 0.2295, 0.2438, 0.3383, 0.4893, 0.5596] +2026-04-10 20:44:44.519921: Epoch time: 103.62 s +2026-04-10 20:44:45.671444: +2026-04-10 20:44:45.673604: Epoch 241 +2026-04-10 20:44:45.676151: Current learning rate: 0.00946 +2026-04-10 20:46:28.367662: train_loss -0.1262 +2026-04-10 20:46:28.375207: val_loss -0.1214 +2026-04-10 20:46:28.379155: Pseudo dice [0.4257, 0.4805, 0.6768, 0.5524, 0.3546, 0.4461, 0.6273] +2026-04-10 20:46:28.382609: Epoch time: 102.7 s +2026-04-10 20:46:29.447561: +2026-04-10 20:46:29.449643: Epoch 242 +2026-04-10 20:46:29.452058: Current learning rate: 0.00945 +2026-04-10 20:48:12.283932: train_loss -0.1209 +2026-04-10 20:48:12.292527: val_loss -0.146 +2026-04-10 20:48:12.295385: Pseudo dice [0.4116, 0.294, 0.5986, 0.212, 0.5597, 0.548, 0.5737] +2026-04-10 20:48:12.299124: Epoch time: 102.84 s +2026-04-10 20:48:13.401195: +2026-04-10 20:48:13.403505: Epoch 243 +2026-04-10 20:48:13.405899: Current learning rate: 0.00945 +2026-04-10 20:49:56.233083: train_loss -0.1278 +2026-04-10 20:49:56.239629: val_loss -0.1075 +2026-04-10 20:49:56.241753: Pseudo dice [0.5451, 0.4819, 0.6621, 0.134, 0.3826, 0.6536, 0.2037] +2026-04-10 20:49:56.244129: Epoch time: 102.84 s +2026-04-10 20:49:57.368581: +2026-04-10 20:49:57.371180: Epoch 244 +2026-04-10 20:49:57.373774: Current learning rate: 0.00945 +2026-04-10 20:51:39.928831: train_loss -0.1364 +2026-04-10 20:51:39.934764: val_loss -0.1477 +2026-04-10 20:51:39.937056: Pseudo dice [0.5286, 0.3698, 0.7951, 0.4108, 0.5557, 0.4642, 0.4857] +2026-04-10 20:51:39.939098: Epoch time: 102.56 s +2026-04-10 20:51:41.061090: +2026-04-10 20:51:41.063849: Epoch 245 +2026-04-10 20:51:41.066166: Current learning rate: 0.00945 +2026-04-10 20:53:25.792754: train_loss -0.137 +2026-04-10 20:53:25.801057: val_loss -0.1378 +2026-04-10 20:53:25.803829: Pseudo dice [0.745, 0.7662, 0.7238, 0.5256, 0.5069, 0.6294, 0.7235] +2026-04-10 20:53:25.807184: Epoch time: 104.73 s +2026-04-10 20:53:26.942125: +2026-04-10 20:53:26.944179: Epoch 246 +2026-04-10 20:53:26.946403: Current learning rate: 0.00944 +2026-04-10 20:55:09.921462: train_loss -0.1219 +2026-04-10 20:55:09.930762: val_loss -0.0929 +2026-04-10 20:55:09.932605: Pseudo dice [0.5751, 0.1539, 0.6673, 0.2636, 0.3147, 0.3543, 0.5686] +2026-04-10 20:55:09.935116: Epoch time: 102.98 s +2026-04-10 20:55:11.063724: +2026-04-10 20:55:11.066078: Epoch 247 +2026-04-10 20:55:11.068368: Current learning rate: 0.00944 +2026-04-10 20:56:54.697456: train_loss -0.1362 +2026-04-10 20:56:54.705546: val_loss -0.1545 +2026-04-10 20:56:54.710718: Pseudo dice [0.6115, 0.3453, 0.8279, 0.6836, 0.5008, 0.7669, 0.7952] +2026-04-10 20:56:54.713510: Epoch time: 103.64 s +2026-04-10 20:56:55.821754: +2026-04-10 20:56:55.824777: Epoch 248 +2026-04-10 20:56:55.827104: Current learning rate: 0.00944 +2026-04-10 20:58:39.719431: train_loss -0.1333 +2026-04-10 20:58:39.727005: val_loss -0.127 +2026-04-10 20:58:39.729457: Pseudo dice [0.5978, 0.6434, 0.7945, 0.4714, 0.3593, 0.5479, 0.3967] +2026-04-10 20:58:39.732870: Epoch time: 103.9 s +2026-04-10 20:58:40.826298: +2026-04-10 20:58:40.828233: Epoch 249 +2026-04-10 20:58:40.830177: Current learning rate: 0.00944 +2026-04-10 21:00:25.108788: train_loss -0.1416 +2026-04-10 21:00:25.117491: val_loss -0.1351 +2026-04-10 21:00:25.122910: Pseudo dice [0.5766, 0.6403, 0.6205, 0.1447, 0.5774, 0.3843, 0.3811] +2026-04-10 21:00:25.126636: Epoch time: 104.29 s +2026-04-10 21:00:28.080986: +2026-04-10 21:00:28.083623: Epoch 250 +2026-04-10 21:00:28.085874: Current learning rate: 0.00944 +2026-04-10 21:02:12.659628: train_loss -0.1285 +2026-04-10 21:02:12.666723: val_loss -0.1372 +2026-04-10 21:02:12.670314: Pseudo dice [0.7145, 0.2055, 0.5509, 0.2974, 0.5283, 0.6217, 0.5645] +2026-04-10 21:02:12.673831: Epoch time: 104.58 s +2026-04-10 21:02:13.771861: +2026-04-10 21:02:13.775264: Epoch 251 +2026-04-10 21:02:13.777723: Current learning rate: 0.00943 +2026-04-10 21:03:57.249916: train_loss -0.1411 +2026-04-10 21:03:57.256589: val_loss -0.1387 +2026-04-10 21:03:57.261062: Pseudo dice [0.4519, 0.7577, 0.6137, 0.3142, 0.4282, 0.4452, 0.7821] +2026-04-10 21:03:57.264582: Epoch time: 103.48 s +2026-04-10 21:03:58.379504: +2026-04-10 21:03:58.382118: Epoch 252 +2026-04-10 21:03:58.384528: Current learning rate: 0.00943 +2026-04-10 21:05:41.874544: train_loss -0.135 +2026-04-10 21:05:41.894194: val_loss -0.1414 +2026-04-10 21:05:41.896926: Pseudo dice [0.589, 0.5972, 0.4871, 0.3788, 0.3565, 0.4798, 0.6648] +2026-04-10 21:05:41.900247: Epoch time: 103.5 s +2026-04-10 21:05:42.984858: +2026-04-10 21:05:42.988182: Epoch 253 +2026-04-10 21:05:42.990661: Current learning rate: 0.00943 +2026-04-10 21:07:26.189880: train_loss -0.1433 +2026-04-10 21:07:26.198874: val_loss -0.103 +2026-04-10 21:07:26.201258: Pseudo dice [0.6884, 0.3681, 0.6842, 0.4084, 0.2939, 0.3487, 0.5225] +2026-04-10 21:07:26.203508: Epoch time: 103.21 s +2026-04-10 21:07:27.349449: +2026-04-10 21:07:27.351569: Epoch 254 +2026-04-10 21:07:27.354291: Current learning rate: 0.00943 +2026-04-10 21:09:11.619198: train_loss -0.1327 +2026-04-10 21:09:11.626220: val_loss -0.1371 +2026-04-10 21:09:11.628520: Pseudo dice [0.708, 0.3533, 0.7395, 0.6068, 0.2133, 0.2892, 0.6577] +2026-04-10 21:09:11.631340: Epoch time: 104.27 s +2026-04-10 21:09:12.769639: +2026-04-10 21:09:12.775439: Epoch 255 +2026-04-10 21:09:12.778651: Current learning rate: 0.00942 +2026-04-10 21:10:56.697046: train_loss -0.1426 +2026-04-10 21:10:56.704157: val_loss -0.122 +2026-04-10 21:10:56.706469: Pseudo dice [0.5152, 0.8055, 0.4998, 0.1043, 0.4539, 0.3415, 0.7579] +2026-04-10 21:10:56.710264: Epoch time: 103.93 s +2026-04-10 21:10:57.867737: +2026-04-10 21:10:57.870258: Epoch 256 +2026-04-10 21:10:57.872911: Current learning rate: 0.00942 +2026-04-10 21:12:42.071633: train_loss -0.1352 +2026-04-10 21:12:42.078866: val_loss -0.1461 +2026-04-10 21:12:42.081476: Pseudo dice [0.7134, 0.1907, 0.6839, 0.5228, 0.3068, 0.5426, 0.6009] +2026-04-10 21:12:42.084695: Epoch time: 104.21 s +2026-04-10 21:12:43.210555: +2026-04-10 21:12:43.213110: Epoch 257 +2026-04-10 21:12:43.215230: Current learning rate: 0.00942 +2026-04-10 21:14:25.669504: train_loss -0.1422 +2026-04-10 21:14:25.677489: val_loss -0.1341 +2026-04-10 21:14:25.679765: Pseudo dice [0.3334, 0.8339, 0.7569, 0.7465, 0.6011, 0.107, 0.8226] +2026-04-10 21:14:25.684161: Epoch time: 102.46 s +2026-04-10 21:14:26.782026: +2026-04-10 21:14:26.784160: Epoch 258 +2026-04-10 21:14:26.786910: Current learning rate: 0.00942 +2026-04-10 21:16:10.011417: train_loss -0.139 +2026-04-10 21:16:10.018470: val_loss -0.1314 +2026-04-10 21:16:10.021446: Pseudo dice [0.7398, 0.0229, 0.7892, 0.4137, 0.4857, 0.4283, 0.8728] +2026-04-10 21:16:10.024159: Epoch time: 103.23 s +2026-04-10 21:16:11.097268: +2026-04-10 21:16:11.099361: Epoch 259 +2026-04-10 21:16:11.101642: Current learning rate: 0.00942 +2026-04-10 21:17:54.291808: train_loss -0.1324 +2026-04-10 21:17:54.301579: val_loss -0.1478 +2026-04-10 21:17:54.304593: Pseudo dice [0.3713, 0.0343, 0.8135, 0.5959, 0.4838, 0.3992, 0.5963] +2026-04-10 21:17:54.307101: Epoch time: 103.2 s +2026-04-10 21:17:55.422429: +2026-04-10 21:17:55.424925: Epoch 260 +2026-04-10 21:17:55.428152: Current learning rate: 0.00941 +2026-04-10 21:19:38.599510: train_loss -0.1436 +2026-04-10 21:19:38.607299: val_loss -0.1353 +2026-04-10 21:19:38.609826: Pseudo dice [0.5866, 0.7703, 0.6441, 0.6024, 0.5792, 0.6009, 0.5099] +2026-04-10 21:19:38.612396: Epoch time: 103.18 s +2026-04-10 21:19:39.713393: +2026-04-10 21:19:39.715565: Epoch 261 +2026-04-10 21:19:39.717980: Current learning rate: 0.00941 +2026-04-10 21:21:22.108901: train_loss -0.1501 +2026-04-10 21:21:22.114977: val_loss -0.1539 +2026-04-10 21:21:22.116737: Pseudo dice [0.5681, 0.781, 0.8077, 0.3232, 0.511, 0.4324, 0.8203] +2026-04-10 21:21:22.120179: Epoch time: 102.4 s +2026-04-10 21:21:23.223949: +2026-04-10 21:21:23.226211: Epoch 262 +2026-04-10 21:21:23.228744: Current learning rate: 0.00941 +2026-04-10 21:23:06.541261: train_loss -0.1646 +2026-04-10 21:23:06.550408: val_loss -0.1329 +2026-04-10 21:23:06.553631: Pseudo dice [0.6552, 0.2131, 0.4227, 0.7445, 0.5562, 0.6091, 0.1768] +2026-04-10 21:23:06.556395: Epoch time: 103.32 s +2026-04-10 21:23:07.699564: +2026-04-10 21:23:07.704225: Epoch 263 +2026-04-10 21:23:07.706835: Current learning rate: 0.00941 +2026-04-10 21:24:51.317860: train_loss -0.1588 +2026-04-10 21:24:51.325761: val_loss -0.1316 +2026-04-10 21:24:51.328705: Pseudo dice [0.2272, 0.8467, 0.5843, 0.4092, 0.4788, 0.6227, 0.7716] +2026-04-10 21:24:51.332751: Epoch time: 103.62 s +2026-04-10 21:24:52.464301: +2026-04-10 21:24:52.467108: Epoch 264 +2026-04-10 21:24:52.470722: Current learning rate: 0.0094 +2026-04-10 21:26:35.877310: train_loss -0.1335 +2026-04-10 21:26:35.884341: val_loss -0.1112 +2026-04-10 21:26:35.887646: Pseudo dice [0.814, 0.7275, 0.6957, 0.1753, 0.3502, 0.5947, 0.6558] +2026-04-10 21:26:35.891311: Epoch time: 103.42 s +2026-04-10 21:26:37.017328: +2026-04-10 21:26:37.021069: Epoch 265 +2026-04-10 21:26:37.023249: Current learning rate: 0.0094 +2026-04-10 21:28:21.410580: train_loss -0.1468 +2026-04-10 21:28:21.418744: val_loss -0.1314 +2026-04-10 21:28:21.421573: Pseudo dice [0.5705, 0.6526, 0.6154, 0.5046, 0.4682, 0.6636, 0.8428] +2026-04-10 21:28:21.424484: Epoch time: 104.4 s +2026-04-10 21:28:21.427913: Yayy! New best EMA pseudo Dice: 0.5463 +2026-04-10 21:28:24.318744: +2026-04-10 21:28:24.321033: Epoch 266 +2026-04-10 21:28:24.322994: Current learning rate: 0.0094 +2026-04-10 21:30:09.525110: train_loss -0.1485 +2026-04-10 21:30:09.536122: val_loss -0.1468 +2026-04-10 21:30:09.540922: Pseudo dice [0.6307, 0.4224, 0.7985, 0.493, 0.2957, 0.5502, 0.7935] +2026-04-10 21:30:09.546589: Epoch time: 105.21 s +2026-04-10 21:30:09.551616: Yayy! New best EMA pseudo Dice: 0.5486 +2026-04-10 21:30:12.600737: +2026-04-10 21:30:12.603203: Epoch 267 +2026-04-10 21:30:12.606338: Current learning rate: 0.0094 +2026-04-10 21:31:56.188705: train_loss -0.1503 +2026-04-10 21:31:56.196665: val_loss -0.1071 +2026-04-10 21:31:56.198720: Pseudo dice [0.6897, 0.283, 0.6213, 0.3326, 0.4289, 0.2475, 0.8174] +2026-04-10 21:31:56.201192: Epoch time: 103.59 s +2026-04-10 21:31:57.294373: +2026-04-10 21:31:57.297060: Epoch 268 +2026-04-10 21:31:57.298982: Current learning rate: 0.00939 +2026-04-10 21:33:41.291950: train_loss -0.1321 +2026-04-10 21:33:41.299124: val_loss -0.1381 +2026-04-10 21:33:41.301558: Pseudo dice [0.5024, 0.0687, 0.6446, 0.4702, 0.4329, 0.6966, 0.7883] +2026-04-10 21:33:41.304380: Epoch time: 104.0 s +2026-04-10 21:33:42.424009: +2026-04-10 21:33:42.426646: Epoch 269 +2026-04-10 21:33:42.428934: Current learning rate: 0.00939 +2026-04-10 21:35:24.996034: train_loss -0.146 +2026-04-10 21:35:25.003342: val_loss -0.1219 +2026-04-10 21:35:25.006601: Pseudo dice [0.6943, 0.1498, 0.6569, 0.4338, 0.5658, 0.5951, 0.2881] +2026-04-10 21:35:25.009527: Epoch time: 102.58 s +2026-04-10 21:35:26.108466: +2026-04-10 21:35:26.111293: Epoch 270 +2026-04-10 21:35:26.113431: Current learning rate: 0.00939 +2026-04-10 21:37:09.667761: train_loss -0.1436 +2026-04-10 21:37:09.676477: val_loss -0.1518 +2026-04-10 21:37:09.678771: Pseudo dice [0.7497, 0.8017, 0.7273, 0.4986, 0.3935, 0.4551, 0.8502] +2026-04-10 21:37:09.681568: Epoch time: 103.56 s +2026-04-10 21:37:10.777212: +2026-04-10 21:37:10.780587: Epoch 271 +2026-04-10 21:37:10.782887: Current learning rate: 0.00939 +2026-04-10 21:38:54.374755: train_loss -0.1558 +2026-04-10 21:38:54.381773: val_loss -0.1312 +2026-04-10 21:38:54.383727: Pseudo dice [0.6333, 0.5187, 0.6986, 0.5638, 0.3778, 0.8565, 0.7635] +2026-04-10 21:38:54.388061: Epoch time: 103.6 s +2026-04-10 21:38:54.391070: Yayy! New best EMA pseudo Dice: 0.5533 +2026-04-10 21:38:57.255894: +2026-04-10 21:38:57.258188: Epoch 272 +2026-04-10 21:38:57.259798: Current learning rate: 0.00939 +2026-04-10 21:40:40.747131: train_loss -0.1547 +2026-04-10 21:40:40.754766: val_loss -0.129 +2026-04-10 21:40:40.759214: Pseudo dice [0.23, 0.6111, 0.8078, 0.6588, 0.4834, 0.4656, 0.687] +2026-04-10 21:40:40.762034: Epoch time: 103.49 s +2026-04-10 21:40:40.764601: Yayy! New best EMA pseudo Dice: 0.5543 +2026-04-10 21:40:43.556747: +2026-04-10 21:40:43.559055: Epoch 273 +2026-04-10 21:40:43.560658: Current learning rate: 0.00938 +2026-04-10 21:42:26.636844: train_loss -0.1376 +2026-04-10 21:42:26.646027: val_loss -0.1486 +2026-04-10 21:42:26.649688: Pseudo dice [0.7951, 0.6501, 0.5637, 0.5135, 0.47, 0.3777, 0.8685] +2026-04-10 21:42:26.653017: Epoch time: 103.08 s +2026-04-10 21:42:26.655461: Yayy! New best EMA pseudo Dice: 0.5594 +2026-04-10 21:42:29.528972: +2026-04-10 21:42:29.531165: Epoch 274 +2026-04-10 21:42:29.532639: Current learning rate: 0.00938 +2026-04-10 21:44:13.223882: train_loss -0.1483 +2026-04-10 21:44:13.232581: val_loss -0.117 +2026-04-10 21:44:13.235585: Pseudo dice [0.4347, 0.7795, 0.7845, 0.3785, 0.1627, 0.2211, 0.7215] +2026-04-10 21:44:13.238482: Epoch time: 103.7 s +2026-04-10 21:44:14.389278: +2026-04-10 21:44:14.391099: Epoch 275 +2026-04-10 21:44:14.393866: Current learning rate: 0.00938 +2026-04-10 21:45:57.831439: train_loss -0.1424 +2026-04-10 21:45:57.839724: val_loss -0.1252 +2026-04-10 21:45:57.841769: Pseudo dice [0.71, 0.5315, 0.6184, 0.2615, 0.3427, 0.3537, 0.747] +2026-04-10 21:45:57.844729: Epoch time: 103.45 s +2026-04-10 21:45:58.969086: +2026-04-10 21:45:58.971602: Epoch 276 +2026-04-10 21:45:58.973767: Current learning rate: 0.00938 +2026-04-10 21:47:41.945737: train_loss -0.1563 +2026-04-10 21:47:41.954558: val_loss -0.1311 +2026-04-10 21:47:41.957742: Pseudo dice [0.5926, 0.2277, 0.5003, 0.0726, 0.4952, 0.7367, 0.5272] +2026-04-10 21:47:41.960475: Epoch time: 102.98 s +2026-04-10 21:47:43.100344: +2026-04-10 21:47:43.103420: Epoch 277 +2026-04-10 21:47:43.106601: Current learning rate: 0.00937 +2026-04-10 21:49:26.885514: train_loss -0.1452 +2026-04-10 21:49:26.892402: val_loss -0.1363 +2026-04-10 21:49:26.894895: Pseudo dice [0.6498, 0.7817, 0.5916, 0.6657, 0.5245, 0.2991, 0.7098] +2026-04-10 21:49:26.897508: Epoch time: 103.79 s +2026-04-10 21:49:28.009460: +2026-04-10 21:49:28.011710: Epoch 278 +2026-04-10 21:49:28.013826: Current learning rate: 0.00937 +2026-04-10 21:51:11.146131: train_loss -0.1499 +2026-04-10 21:51:11.152193: val_loss -0.1455 +2026-04-10 21:51:11.154295: Pseudo dice [0.6687, 0.401, 0.7582, 0.3866, 0.4018, 0.801, 0.7497] +2026-04-10 21:51:11.156861: Epoch time: 103.14 s +2026-04-10 21:51:12.314644: +2026-04-10 21:51:12.319741: Epoch 279 +2026-04-10 21:51:12.321602: Current learning rate: 0.00937 +2026-04-10 21:52:55.292710: train_loss -0.1388 +2026-04-10 21:52:55.299207: val_loss -0.1255 +2026-04-10 21:52:55.301877: Pseudo dice [0.7035, 0.5564, 0.4075, 0.0015, 0.3916, 0.3999, 0.8872] +2026-04-10 21:52:55.305790: Epoch time: 102.98 s +2026-04-10 21:52:56.464283: +2026-04-10 21:52:56.466637: Epoch 280 +2026-04-10 21:52:56.468707: Current learning rate: 0.00937 +2026-04-10 21:54:39.626111: train_loss -0.1418 +2026-04-10 21:54:39.634493: val_loss -0.1385 +2026-04-10 21:54:39.637388: Pseudo dice [0.5501, 0.8744, 0.6011, 0.1901, 0.3012, 0.5238, 0.7332] +2026-04-10 21:54:39.639745: Epoch time: 103.17 s +2026-04-10 21:54:40.771334: +2026-04-10 21:54:40.773610: Epoch 281 +2026-04-10 21:54:40.776272: Current learning rate: 0.00937 +2026-04-10 21:56:24.831137: train_loss -0.1495 +2026-04-10 21:56:24.840225: val_loss -0.1387 +2026-04-10 21:56:24.843421: Pseudo dice [0.5644, 0.1096, 0.7773, 0.3143, 0.423, 0.5659, 0.5586] +2026-04-10 21:56:24.846278: Epoch time: 104.06 s +2026-04-10 21:56:25.952036: +2026-04-10 21:56:25.955047: Epoch 282 +2026-04-10 21:56:25.958040: Current learning rate: 0.00936 +2026-04-10 21:58:09.356121: train_loss -0.15 +2026-04-10 21:58:09.363996: val_loss -0.1432 +2026-04-10 21:58:09.368328: Pseudo dice [0.7073, 0.1017, 0.8036, 0.7201, 0.4382, 0.2636, 0.6484] +2026-04-10 21:58:09.372509: Epoch time: 103.41 s +2026-04-10 21:58:10.472554: +2026-04-10 21:58:10.475355: Epoch 283 +2026-04-10 21:58:10.478530: Current learning rate: 0.00936 +2026-04-10 21:59:55.897882: train_loss -0.1421 +2026-04-10 21:59:55.905598: val_loss -0.1344 +2026-04-10 21:59:55.908620: Pseudo dice [0.68, 0.4714, 0.8168, 0.5101, 0.5097, 0.4863, 0.5737] +2026-04-10 21:59:55.912863: Epoch time: 105.43 s +2026-04-10 21:59:57.056878: +2026-04-10 21:59:57.062541: Epoch 284 +2026-04-10 21:59:57.066299: Current learning rate: 0.00936 +2026-04-10 22:01:40.888596: train_loss -0.1403 +2026-04-10 22:01:40.895469: val_loss -0.1422 +2026-04-10 22:01:40.897631: Pseudo dice [0.5047, 0.6624, 0.7095, 0.7174, 0.5648, 0.4096, 0.834] +2026-04-10 22:01:40.903924: Epoch time: 103.83 s +2026-04-10 22:01:42.035033: +2026-04-10 22:01:42.037985: Epoch 285 +2026-04-10 22:01:42.040810: Current learning rate: 0.00936 +2026-04-10 22:03:25.905711: train_loss -0.1407 +2026-04-10 22:03:25.914367: val_loss -0.1378 +2026-04-10 22:03:25.917308: Pseudo dice [0.7163, 0.3129, 0.7025, 0.4278, 0.2828, 0.732, 0.6784] +2026-04-10 22:03:25.920175: Epoch time: 103.87 s +2026-04-10 22:03:27.070771: +2026-04-10 22:03:27.073010: Epoch 286 +2026-04-10 22:03:27.075839: Current learning rate: 0.00935 +2026-04-10 22:05:11.051764: train_loss -0.1411 +2026-04-10 22:05:11.058215: val_loss -0.1418 +2026-04-10 22:05:11.061778: Pseudo dice [0.439, 0.6037, 0.748, 0.0429, 0.407, 0.3331, 0.6765] +2026-04-10 22:05:11.064843: Epoch time: 103.98 s +2026-04-10 22:05:12.226398: +2026-04-10 22:05:12.228699: Epoch 287 +2026-04-10 22:05:12.231575: Current learning rate: 0.00935 +2026-04-10 22:06:56.078682: train_loss -0.142 +2026-04-10 22:06:56.086290: val_loss -0.1423 +2026-04-10 22:06:56.088805: Pseudo dice [0.6233, 0.3058, 0.6107, 0.5087, 0.2486, 0.349, 0.5715] +2026-04-10 22:06:56.091673: Epoch time: 103.86 s +2026-04-10 22:06:57.243966: +2026-04-10 22:06:57.246139: Epoch 288 +2026-04-10 22:06:57.248368: Current learning rate: 0.00935 +2026-04-10 22:08:39.965755: train_loss -0.1384 +2026-04-10 22:08:39.974182: val_loss -0.1371 +2026-04-10 22:08:39.976693: Pseudo dice [0.6734, 0.7123, 0.7234, 0.5399, 0.3615, 0.7051, 0.695] +2026-04-10 22:08:39.984688: Epoch time: 102.72 s +2026-04-10 22:08:41.112337: +2026-04-10 22:08:41.114863: Epoch 289 +2026-04-10 22:08:41.117017: Current learning rate: 0.00935 +2026-04-10 22:10:24.321840: train_loss -0.1344 +2026-04-10 22:10:24.330716: val_loss -0.1389 +2026-04-10 22:10:24.333911: Pseudo dice [0.4158, 0.4573, 0.6716, 0.4984, 0.4472, 0.5441, 0.7814] +2026-04-10 22:10:24.337427: Epoch time: 103.21 s +2026-04-10 22:10:25.487266: +2026-04-10 22:10:25.489749: Epoch 290 +2026-04-10 22:10:25.492582: Current learning rate: 0.00935 +2026-04-10 22:12:08.482297: train_loss -0.1437 +2026-04-10 22:12:08.491417: val_loss -0.1774 +2026-04-10 22:12:08.494344: Pseudo dice [0.5454, 0.0774, 0.8242, 0.7466, 0.5377, 0.716, 0.8573] +2026-04-10 22:12:08.497925: Epoch time: 103.0 s +2026-04-10 22:12:09.609084: +2026-04-10 22:12:09.611867: Epoch 291 +2026-04-10 22:12:09.614233: Current learning rate: 0.00934 +2026-04-10 22:13:52.684286: train_loss -0.1511 +2026-04-10 22:13:52.691139: val_loss -0.1469 +2026-04-10 22:13:52.694093: Pseudo dice [0.717, 0.4119, 0.7532, 0.3642, 0.6138, 0.7456, 0.6809] +2026-04-10 22:13:52.697956: Epoch time: 103.08 s +2026-04-10 22:13:53.835801: +2026-04-10 22:13:53.838223: Epoch 292 +2026-04-10 22:13:53.840231: Current learning rate: 0.00934 +2026-04-10 22:15:37.361061: train_loss -0.1517 +2026-04-10 22:15:37.370723: val_loss -0.1384 +2026-04-10 22:15:37.374731: Pseudo dice [0.373, 0.4056, 0.4976, 0.5271, 0.5281, 0.4697, 0.7727] +2026-04-10 22:15:37.378724: Epoch time: 103.53 s +2026-04-10 22:15:38.470348: +2026-04-10 22:15:38.471947: Epoch 293 +2026-04-10 22:15:38.473714: Current learning rate: 0.00934 +2026-04-10 22:17:21.045449: train_loss -0.1431 +2026-04-10 22:17:21.053284: val_loss -0.1428 +2026-04-10 22:17:21.055364: Pseudo dice [0.5333, 0.5373, 0.5975, 0.5946, 0.466, 0.2977, 0.2884] +2026-04-10 22:17:21.058210: Epoch time: 102.58 s +2026-04-10 22:17:22.171210: +2026-04-10 22:17:22.173780: Epoch 294 +2026-04-10 22:17:22.175575: Current learning rate: 0.00934 +2026-04-10 22:19:04.912019: train_loss -0.1468 +2026-04-10 22:19:04.917948: val_loss -0.1178 +2026-04-10 22:19:04.920329: Pseudo dice [0.7472, 0.5218, 0.6782, 0.452, 0.5195, 0.2845, 0.5893] +2026-04-10 22:19:04.923064: Epoch time: 102.74 s +2026-04-10 22:19:06.068877: +2026-04-10 22:19:06.071956: Epoch 295 +2026-04-10 22:19:06.073883: Current learning rate: 0.00933 +2026-04-10 22:20:48.676191: train_loss -0.1488 +2026-04-10 22:20:48.682844: val_loss -0.1405 +2026-04-10 22:20:48.685632: Pseudo dice [0.6439, 0.5626, 0.7982, 0.427, 0.4275, 0.4898, 0.7197] +2026-04-10 22:20:48.689703: Epoch time: 102.61 s +2026-04-10 22:20:49.822041: +2026-04-10 22:20:49.826449: Epoch 296 +2026-04-10 22:20:49.828860: Current learning rate: 0.00933 +2026-04-10 22:22:32.653590: train_loss -0.1423 +2026-04-10 22:22:32.660543: val_loss -0.1298 +2026-04-10 22:22:32.663213: Pseudo dice [0.6261, 0.5014, 0.8269, 0.2979, 0.5341, 0.3602, 0.6648] +2026-04-10 22:22:32.667320: Epoch time: 102.83 s +2026-04-10 22:22:33.798905: +2026-04-10 22:22:33.801468: Epoch 297 +2026-04-10 22:22:33.805336: Current learning rate: 0.00933 +2026-04-10 22:24:17.094900: train_loss -0.1467 +2026-04-10 22:24:17.103201: val_loss -0.1607 +2026-04-10 22:24:17.105906: Pseudo dice [0.7821, 0.708, 0.836, 0.5043, 0.4782, 0.7865, 0.7578] +2026-04-10 22:24:17.108664: Epoch time: 103.3 s +2026-04-10 22:24:17.110649: Yayy! New best EMA pseudo Dice: 0.5614 +2026-04-10 22:24:19.981045: +2026-04-10 22:24:19.983033: Epoch 298 +2026-04-10 22:24:19.984643: Current learning rate: 0.00933 +2026-04-10 22:26:04.759661: train_loss -0.1721 +2026-04-10 22:26:04.766905: val_loss -0.1368 +2026-04-10 22:26:04.768753: Pseudo dice [0.4104, 0.6945, 0.8269, 0.5238, 0.5027, 0.4707, 0.8041] +2026-04-10 22:26:04.772126: Epoch time: 104.78 s +2026-04-10 22:26:04.774244: Yayy! New best EMA pseudo Dice: 0.5658 +2026-04-10 22:26:07.639893: +2026-04-10 22:26:07.643893: Epoch 299 +2026-04-10 22:26:07.646094: Current learning rate: 0.00932 +2026-04-10 22:27:51.985495: train_loss -0.1514 +2026-04-10 22:27:51.993413: val_loss -0.1391 +2026-04-10 22:27:51.996477: Pseudo dice [0.7412, 0.6861, 0.6498, 0.5607, 0.4474, 0.3794, 0.5312] +2026-04-10 22:27:51.999557: Epoch time: 104.35 s +2026-04-10 22:27:53.725749: Yayy! New best EMA pseudo Dice: 0.5663 +2026-04-10 22:27:56.596869: +2026-04-10 22:27:56.599467: Epoch 300 +2026-04-10 22:27:56.601873: Current learning rate: 0.00932 +2026-04-10 22:29:39.528543: train_loss -0.1483 +2026-04-10 22:29:39.537926: val_loss -0.1007 +2026-04-10 22:29:39.540670: Pseudo dice [0.5175, 0.7596, 0.7127, 0.5057, 0.3491, 0.5178, 0.7732] +2026-04-10 22:29:39.544251: Epoch time: 102.93 s +2026-04-10 22:29:39.550452: Yayy! New best EMA pseudo Dice: 0.5687 +2026-04-10 22:29:42.548159: +2026-04-10 22:29:42.550252: Epoch 301 +2026-04-10 22:29:42.552495: Current learning rate: 0.00932 +2026-04-10 22:31:27.288749: train_loss -0.129 +2026-04-10 22:31:27.297289: val_loss -0.1333 +2026-04-10 22:31:27.299408: Pseudo dice [0.5618, 0.2214, 0.8271, 0.3514, 0.4818, 0.4771, 0.7191] +2026-04-10 22:31:27.302084: Epoch time: 104.74 s +2026-04-10 22:31:28.453017: +2026-04-10 22:31:28.458349: Epoch 302 +2026-04-10 22:31:28.460433: Current learning rate: 0.00932 +2026-04-10 22:33:13.906243: train_loss -0.1498 +2026-04-10 22:33:13.919500: val_loss -0.1396 +2026-04-10 22:33:13.922907: Pseudo dice [0.6324, 0.4278, 0.5754, 0.3247, 0.3283, 0.2762, 0.6198] +2026-04-10 22:33:13.926397: Epoch time: 105.46 s +2026-04-10 22:33:15.099463: +2026-04-10 22:33:15.101678: Epoch 303 +2026-04-10 22:33:15.104877: Current learning rate: 0.00932 +2026-04-10 22:34:59.524382: train_loss -0.1394 +2026-04-10 22:34:59.533461: val_loss -0.1269 +2026-04-10 22:34:59.536673: Pseudo dice [0.5171, 0.4756, 0.7177, 0.0075, 0.2891, 0.4891, 0.4323] +2026-04-10 22:34:59.540063: Epoch time: 104.43 s +2026-04-10 22:35:00.714193: +2026-04-10 22:35:00.718255: Epoch 304 +2026-04-10 22:35:00.721432: Current learning rate: 0.00931 +2026-04-10 22:36:44.448172: train_loss -0.1421 +2026-04-10 22:36:44.457080: val_loss -0.1231 +2026-04-10 22:36:44.459597: Pseudo dice [0.3273, 0.7278, 0.7693, 0.7248, 0.4154, 0.4063, 0.6398] +2026-04-10 22:36:44.463598: Epoch time: 103.74 s +2026-04-10 22:36:45.683764: +2026-04-10 22:36:45.686161: Epoch 305 +2026-04-10 22:36:45.695556: Current learning rate: 0.00931 +2026-04-10 22:38:29.679446: train_loss -0.1488 +2026-04-10 22:38:29.689376: val_loss -0.1367 +2026-04-10 22:38:29.693251: Pseudo dice [0.1036, 0.1176, 0.3667, 0.4776, 0.5545, 0.4625, 0.7423] +2026-04-10 22:38:29.697827: Epoch time: 104.0 s +2026-04-10 22:38:30.822874: +2026-04-10 22:38:30.828076: Epoch 306 +2026-04-10 22:38:30.833185: Current learning rate: 0.00931 +2026-04-10 22:40:14.650726: train_loss -0.1532 +2026-04-10 22:40:14.656932: val_loss -0.1256 +2026-04-10 22:40:14.659630: Pseudo dice [0.2987, 0.7517, 0.8457, 0.433, 0.4687, 0.7711, 0.5538] +2026-04-10 22:40:14.662555: Epoch time: 103.83 s +2026-04-10 22:40:15.859837: +2026-04-10 22:40:15.862289: Epoch 307 +2026-04-10 22:40:15.864429: Current learning rate: 0.00931 +2026-04-10 22:42:00.133955: train_loss -0.1496 +2026-04-10 22:42:00.142605: val_loss -0.131 +2026-04-10 22:42:00.145014: Pseudo dice [0.7704, 0.5995, 0.7325, 0.5077, 0.4523, 0.4591, 0.8117] +2026-04-10 22:42:00.148607: Epoch time: 104.28 s +2026-04-10 22:42:01.369178: +2026-04-10 22:42:01.373506: Epoch 308 +2026-04-10 22:42:01.377297: Current learning rate: 0.0093 +2026-04-10 22:43:45.790102: train_loss -0.1579 +2026-04-10 22:43:45.801562: val_loss -0.1425 +2026-04-10 22:43:45.804911: Pseudo dice [0.6007, 0.7864, 0.582, 0.6823, 0.4865, 0.7257, 0.6821] +2026-04-10 22:43:45.808412: Epoch time: 104.42 s +2026-04-10 22:43:46.985191: +2026-04-10 22:43:46.987604: Epoch 309 +2026-04-10 22:43:46.991338: Current learning rate: 0.0093 +2026-04-10 22:45:29.892138: train_loss -0.1518 +2026-04-10 22:45:29.899298: val_loss -0.1275 +2026-04-10 22:45:29.902092: Pseudo dice [0.6603, 0.0499, 0.7977, 0.1054, 0.3538, 0.5711, 0.6243] +2026-04-10 22:45:29.904164: Epoch time: 102.91 s +2026-04-10 22:45:31.134588: +2026-04-10 22:45:31.137054: Epoch 310 +2026-04-10 22:45:31.140226: Current learning rate: 0.0093 +2026-04-10 22:47:14.289489: train_loss -0.1511 +2026-04-10 22:47:14.296929: val_loss -0.1036 +2026-04-10 22:47:14.300840: Pseudo dice [0.499, 0.7433, 0.7296, 0.4434, 0.4433, 0.3047, 0.6097] +2026-04-10 22:47:14.303562: Epoch time: 103.16 s +2026-04-10 22:47:15.441676: +2026-04-10 22:47:15.444436: Epoch 311 +2026-04-10 22:47:15.446679: Current learning rate: 0.0093 +2026-04-10 22:48:58.841299: train_loss -0.1479 +2026-04-10 22:48:58.847647: val_loss -0.1441 +2026-04-10 22:48:58.850653: Pseudo dice [0.6268, 0.6698, 0.6569, 0.5038, 0.4589, 0.4475, 0.5996] +2026-04-10 22:48:58.853349: Epoch time: 103.4 s +2026-04-10 22:48:59.992665: +2026-04-10 22:48:59.994279: Epoch 312 +2026-04-10 22:48:59.995883: Current learning rate: 0.0093 +2026-04-10 22:50:43.229655: train_loss -0.1374 +2026-04-10 22:50:43.252614: val_loss -0.1142 +2026-04-10 22:50:43.255594: Pseudo dice [0.3397, 0.4391, 0.6559, 0.3479, 0.4782, 0.5183, 0.7154] +2026-04-10 22:50:43.259032: Epoch time: 103.24 s +2026-04-10 22:50:44.455635: +2026-04-10 22:50:44.457658: Epoch 313 +2026-04-10 22:50:44.459774: Current learning rate: 0.00929 +2026-04-10 22:52:27.731775: train_loss -0.1444 +2026-04-10 22:52:27.739227: val_loss -0.1278 +2026-04-10 22:52:27.741261: Pseudo dice [0.5757, 0.1584, 0.8051, 0.7069, 0.485, 0.4722, 0.5715] +2026-04-10 22:52:27.744451: Epoch time: 103.28 s +2026-04-10 22:52:28.886254: +2026-04-10 22:52:28.889427: Epoch 314 +2026-04-10 22:52:28.892013: Current learning rate: 0.00929 +2026-04-10 22:54:12.327452: train_loss -0.1604 +2026-04-10 22:54:12.342078: val_loss -0.1417 +2026-04-10 22:54:12.344049: Pseudo dice [0.3842, 0.6215, 0.1608, 0.2987, 0.4464, 0.4224, 0.7464] +2026-04-10 22:54:12.347563: Epoch time: 103.44 s +2026-04-10 22:54:13.515974: +2026-04-10 22:54:13.518855: Epoch 315 +2026-04-10 22:54:13.520700: Current learning rate: 0.00929 +2026-04-10 22:55:56.381130: train_loss -0.1716 +2026-04-10 22:55:56.390625: val_loss -0.1499 +2026-04-10 22:55:56.393355: Pseudo dice [0.6658, 0.4053, 0.5361, 0.2595, 0.4466, 0.5679, 0.8392] +2026-04-10 22:55:56.396196: Epoch time: 102.87 s +2026-04-10 22:55:57.516994: +2026-04-10 22:55:57.520983: Epoch 316 +2026-04-10 22:55:57.523386: Current learning rate: 0.00929 +2026-04-10 22:57:41.641736: train_loss -0.1518 +2026-04-10 22:57:41.666597: val_loss -0.1117 +2026-04-10 22:57:41.668873: Pseudo dice [0.7142, 0.5097, 0.4469, 0.347, 0.3432, 0.5116, 0.815] +2026-04-10 22:57:41.672807: Epoch time: 104.13 s +2026-04-10 22:57:42.878305: +2026-04-10 22:57:42.881227: Epoch 317 +2026-04-10 22:57:42.883704: Current learning rate: 0.00928 +2026-04-10 22:59:26.925572: train_loss -0.1483 +2026-04-10 22:59:26.934289: val_loss -0.151 +2026-04-10 22:59:26.936790: Pseudo dice [0.697, 0.7141, 0.7968, 0.36, 0.4072, 0.4784, 0.9228] +2026-04-10 22:59:26.939036: Epoch time: 104.05 s +2026-04-10 22:59:28.111367: +2026-04-10 22:59:28.116167: Epoch 318 +2026-04-10 22:59:28.119765: Current learning rate: 0.00928 +2026-04-10 23:01:11.812914: train_loss -0.1457 +2026-04-10 23:01:11.825015: val_loss -0.1407 +2026-04-10 23:01:11.827667: Pseudo dice [0.3332, 0.7112, 0.758, 0.6847, 0.304, 0.7646, 0.7074] +2026-04-10 23:01:11.832219: Epoch time: 103.71 s +2026-04-10 23:01:12.971606: +2026-04-10 23:01:12.974535: Epoch 319 +2026-04-10 23:01:12.977069: Current learning rate: 0.00928 +2026-04-10 23:02:56.540855: train_loss -0.1423 +2026-04-10 23:02:56.550283: val_loss -0.1367 +2026-04-10 23:02:56.552994: Pseudo dice [0.327, 0.3276, 0.5459, 0.6549, 0.4527, 0.6327, 0.8228] +2026-04-10 23:02:56.556497: Epoch time: 103.57 s +2026-04-10 23:02:57.712325: +2026-04-10 23:02:57.716605: Epoch 320 +2026-04-10 23:02:57.719018: Current learning rate: 0.00928 +2026-04-10 23:04:40.397730: train_loss -0.1239 +2026-04-10 23:04:40.404773: val_loss -0.1381 +2026-04-10 23:04:40.407282: Pseudo dice [0.6084, 0.0453, 0.588, 0.5985, 0.4486, 0.5438, 0.8097] +2026-04-10 23:04:40.411097: Epoch time: 102.69 s +2026-04-10 23:04:42.674797: +2026-04-10 23:04:42.677107: Epoch 321 +2026-04-10 23:04:42.679104: Current learning rate: 0.00927 +2026-04-10 23:06:26.350219: train_loss -0.1467 +2026-04-10 23:06:26.357832: val_loss -0.1167 +2026-04-10 23:06:26.360896: Pseudo dice [0.234, 0.3331, 0.8019, 0.7093, 0.5677, 0.3386, 0.4729] +2026-04-10 23:06:26.363825: Epoch time: 103.68 s +2026-04-10 23:06:27.483405: +2026-04-10 23:06:27.486300: Epoch 322 +2026-04-10 23:06:27.488356: Current learning rate: 0.00927 +2026-04-10 23:08:10.678648: train_loss -0.1587 +2026-04-10 23:08:10.687052: val_loss -0.1328 +2026-04-10 23:08:10.689516: Pseudo dice [0.705, 0.7841, 0.3716, 0.2133, 0.6257, 0.6309, 0.3188] +2026-04-10 23:08:10.693059: Epoch time: 103.2 s +2026-04-10 23:08:11.883615: +2026-04-10 23:08:11.885696: Epoch 323 +2026-04-10 23:08:11.887904: Current learning rate: 0.00927 +2026-04-10 23:09:55.252303: train_loss -0.1466 +2026-04-10 23:09:55.258643: val_loss -0.1371 +2026-04-10 23:09:55.260804: Pseudo dice [0.6043, 0.4588, 0.7384, 0.4713, 0.4984, 0.6003, 0.793] +2026-04-10 23:09:55.264595: Epoch time: 103.37 s +2026-04-10 23:09:56.447691: +2026-04-10 23:09:56.451099: Epoch 324 +2026-04-10 23:09:56.453698: Current learning rate: 0.00927 +2026-04-10 23:11:40.168415: train_loss -0.1551 +2026-04-10 23:11:40.175619: val_loss -0.1376 +2026-04-10 23:11:40.178557: Pseudo dice [0.4955, 0.6776, 0.6731, 0.6372, 0.5231, 0.2911, 0.8404] +2026-04-10 23:11:40.181779: Epoch time: 103.72 s +2026-04-10 23:11:41.338482: +2026-04-10 23:11:41.340272: Epoch 325 +2026-04-10 23:11:41.342489: Current learning rate: 0.00927 +2026-04-10 23:13:25.356652: train_loss -0.1652 +2026-04-10 23:13:25.363169: val_loss -0.1481 +2026-04-10 23:13:25.365875: Pseudo dice [0.5736, 0.6211, 0.6225, 0.0506, 0.5028, 0.7533, 0.7039] +2026-04-10 23:13:25.368838: Epoch time: 104.02 s +2026-04-10 23:13:26.535589: +2026-04-10 23:13:26.538055: Epoch 326 +2026-04-10 23:13:26.540780: Current learning rate: 0.00926 +2026-04-10 23:15:09.754920: train_loss -0.1513 +2026-04-10 23:15:09.761351: val_loss -0.1579 +2026-04-10 23:15:09.763657: Pseudo dice [0.5701, 0.6096, 0.7196, 0.7393, 0.4995, 0.6648, 0.7028] +2026-04-10 23:15:09.766217: Epoch time: 103.22 s +2026-04-10 23:15:10.934836: +2026-04-10 23:15:10.936909: Epoch 327 +2026-04-10 23:15:10.939106: Current learning rate: 0.00926 +2026-04-10 23:16:54.129689: train_loss -0.1472 +2026-04-10 23:16:54.137275: val_loss -0.1179 +2026-04-10 23:16:54.140137: Pseudo dice [0.6686, 0.4482, 0.6717, 0.7058, 0.3989, 0.6899, 0.8431] +2026-04-10 23:16:54.142593: Epoch time: 103.2 s +2026-04-10 23:16:55.248316: +2026-04-10 23:16:55.250371: Epoch 328 +2026-04-10 23:16:55.252365: Current learning rate: 0.00926 +2026-04-10 23:18:38.776442: train_loss -0.1506 +2026-04-10 23:18:38.784111: val_loss -0.1404 +2026-04-10 23:18:38.786445: Pseudo dice [0.6121, 0.8098, 0.7537, 0.5624, 0.3512, 0.5294, 0.7822] +2026-04-10 23:18:38.789707: Epoch time: 103.53 s +2026-04-10 23:18:38.792188: Yayy! New best EMA pseudo Dice: 0.5709 +2026-04-10 23:18:41.643034: +2026-04-10 23:18:41.645651: Epoch 329 +2026-04-10 23:18:41.647660: Current learning rate: 0.00926 +2026-04-10 23:20:25.211447: train_loss -0.1599 +2026-04-10 23:20:25.219618: val_loss -0.1512 +2026-04-10 23:20:25.221422: Pseudo dice [0.5248, 0.8756, 0.7413, 0.7813, 0.475, 0.5469, 0.8553] +2026-04-10 23:20:25.223996: Epoch time: 103.57 s +2026-04-10 23:20:25.226233: Yayy! New best EMA pseudo Dice: 0.5824 +2026-04-10 23:20:28.137731: +2026-04-10 23:20:28.140151: Epoch 330 +2026-04-10 23:20:28.141726: Current learning rate: 0.00925 +2026-04-10 23:22:11.305214: train_loss -0.1635 +2026-04-10 23:22:11.311714: val_loss -0.1394 +2026-04-10 23:22:11.313973: Pseudo dice [0.6845, 0.4503, 0.6797, 0.4194, 0.6077, 0.291, 0.7109] +2026-04-10 23:22:11.316436: Epoch time: 103.17 s +2026-04-10 23:22:12.463187: +2026-04-10 23:22:12.465730: Epoch 331 +2026-04-10 23:22:12.469152: Current learning rate: 0.00925 +2026-04-10 23:23:56.536440: train_loss -0.1344 +2026-04-10 23:23:56.542645: val_loss -0.1408 +2026-04-10 23:23:56.544843: Pseudo dice [0.6013, 0.4123, 0.5611, 0.5362, 0.4758, 0.4283, 0.7671] +2026-04-10 23:23:56.547687: Epoch time: 104.08 s +2026-04-10 23:23:57.749786: +2026-04-10 23:23:57.753133: Epoch 332 +2026-04-10 23:23:57.755934: Current learning rate: 0.00925 +2026-04-10 23:25:40.793048: train_loss -0.1369 +2026-04-10 23:25:40.801376: val_loss -0.1404 +2026-04-10 23:25:40.804415: Pseudo dice [0.7234, 0.2138, 0.738, 0.2419, 0.3656, 0.6113, 0.6945] +2026-04-10 23:25:40.808395: Epoch time: 103.05 s +2026-04-10 23:25:42.008123: +2026-04-10 23:25:42.011606: Epoch 333 +2026-04-10 23:25:42.013949: Current learning rate: 0.00925 +2026-04-10 23:27:27.820034: train_loss -0.1496 +2026-04-10 23:27:27.828387: val_loss -0.142 +2026-04-10 23:27:27.833197: Pseudo dice [0.6865, 0.634, 0.5418, 0.3758, 0.6523, 0.4158, 0.6554] +2026-04-10 23:27:27.838137: Epoch time: 105.82 s +2026-04-10 23:27:28.953797: +2026-04-10 23:27:28.956910: Epoch 334 +2026-04-10 23:27:28.960878: Current learning rate: 0.00925 +2026-04-10 23:29:13.663806: train_loss -0.1474 +2026-04-10 23:29:13.673259: val_loss -0.1429 +2026-04-10 23:29:13.676344: Pseudo dice [0.3836, 0.1348, 0.8035, 0.1081, 0.3161, 0.3304, 0.7893] +2026-04-10 23:29:13.680541: Epoch time: 104.71 s +2026-04-10 23:29:14.885320: +2026-04-10 23:29:14.888185: Epoch 335 +2026-04-10 23:29:14.890807: Current learning rate: 0.00924 +2026-04-10 23:30:59.208042: train_loss -0.1488 +2026-04-10 23:30:59.218323: val_loss -0.1307 +2026-04-10 23:30:59.221363: Pseudo dice [0.5537, 0.731, 0.7742, 0.2724, 0.5205, 0.3823, 0.3941] +2026-04-10 23:30:59.225070: Epoch time: 104.33 s +2026-04-10 23:31:00.366196: +2026-04-10 23:31:00.369784: Epoch 336 +2026-04-10 23:31:00.372188: Current learning rate: 0.00924 +2026-04-10 23:32:44.644043: train_loss -0.1591 +2026-04-10 23:32:44.653089: val_loss -0.1294 +2026-04-10 23:32:44.655635: Pseudo dice [0.7284, 0.1253, 0.7839, 0.0484, 0.4925, 0.2456, 0.6738] +2026-04-10 23:32:44.660142: Epoch time: 104.28 s +2026-04-10 23:32:45.804991: +2026-04-10 23:32:45.807891: Epoch 337 +2026-04-10 23:32:45.810218: Current learning rate: 0.00924 +2026-04-10 23:34:29.948931: train_loss -0.1588 +2026-04-10 23:34:29.960359: val_loss -0.1194 +2026-04-10 23:34:29.962911: Pseudo dice [0.5628, 0.3236, 0.4238, 0.2073, 0.4605, 0.5687, 0.7599] +2026-04-10 23:34:29.966208: Epoch time: 104.15 s +2026-04-10 23:34:31.126087: +2026-04-10 23:34:31.128399: Epoch 338 +2026-04-10 23:34:31.131510: Current learning rate: 0.00924 +2026-04-10 23:36:16.197861: train_loss -0.1543 +2026-04-10 23:36:16.205950: val_loss -0.1262 +2026-04-10 23:36:16.208747: Pseudo dice [0.6716, 0.4144, 0.8079, 0.1707, 0.3921, 0.2475, 0.6082] +2026-04-10 23:36:16.211663: Epoch time: 105.07 s +2026-04-10 23:36:17.355952: +2026-04-10 23:36:17.358377: Epoch 339 +2026-04-10 23:36:17.361873: Current learning rate: 0.00923 +2026-04-10 23:38:01.348296: train_loss -0.1713 +2026-04-10 23:38:01.358332: val_loss -0.1564 +2026-04-10 23:38:01.360949: Pseudo dice [0.6983, 0.7419, 0.8459, 0.3865, 0.524, 0.4248, 0.8372] +2026-04-10 23:38:01.366961: Epoch time: 104.0 s +2026-04-10 23:38:03.759214: +2026-04-10 23:38:03.761044: Epoch 340 +2026-04-10 23:38:03.763017: Current learning rate: 0.00923 +2026-04-10 23:39:48.379581: train_loss -0.1721 +2026-04-10 23:39:48.386824: val_loss -0.1573 +2026-04-10 23:39:48.390075: Pseudo dice [0.8008, 0.6646, 0.7428, 0.4439, 0.4953, 0.6427, 0.7639] +2026-04-10 23:39:48.393980: Epoch time: 104.62 s +2026-04-10 23:39:49.573575: +2026-04-10 23:39:49.576937: Epoch 341 +2026-04-10 23:39:49.581102: Current learning rate: 0.00923 +2026-04-10 23:41:33.054518: train_loss -0.1557 +2026-04-10 23:41:33.060697: val_loss -0.1554 +2026-04-10 23:41:33.064962: Pseudo dice [0.5415, 0.7741, 0.8342, 0.5523, 0.5985, 0.6705, 0.797] +2026-04-10 23:41:33.068108: Epoch time: 103.48 s +2026-04-10 23:41:34.272972: +2026-04-10 23:41:34.275666: Epoch 342 +2026-04-10 23:41:34.278992: Current learning rate: 0.00923 +2026-04-10 23:43:18.268220: train_loss -0.1501 +2026-04-10 23:43:18.277374: val_loss -0.12 +2026-04-10 23:43:18.280613: Pseudo dice [0.4568, 0.8014, 0.645, 0.1529, 0.4006, 0.4132, 0.5549] +2026-04-10 23:43:18.283411: Epoch time: 104.0 s +2026-04-10 23:43:19.436962: +2026-04-10 23:43:19.439554: Epoch 343 +2026-04-10 23:43:19.442826: Current learning rate: 0.00922 +2026-04-10 23:45:02.615721: train_loss -0.1547 +2026-04-10 23:45:02.621509: val_loss -0.1516 +2026-04-10 23:45:02.623851: Pseudo dice [0.5582, 0.6656, 0.8151, 0.499, 0.5283, 0.6179, 0.6275] +2026-04-10 23:45:02.626561: Epoch time: 103.18 s +2026-04-10 23:45:03.854007: +2026-04-10 23:45:03.856543: Epoch 344 +2026-04-10 23:45:03.858774: Current learning rate: 0.00922 +2026-04-10 23:46:46.459492: train_loss -0.1565 +2026-04-10 23:46:46.467575: val_loss -0.1254 +2026-04-10 23:46:46.470883: Pseudo dice [0.6887, 0.4162, 0.5912, 0.5493, 0.3892, 0.4954, 0.5685] +2026-04-10 23:46:46.473467: Epoch time: 102.61 s +2026-04-10 23:46:47.624849: +2026-04-10 23:46:47.627318: Epoch 345 +2026-04-10 23:46:47.629408: Current learning rate: 0.00922 +2026-04-10 23:48:30.669300: train_loss -0.1549 +2026-04-10 23:48:30.676232: val_loss -0.1467 +2026-04-10 23:48:30.678244: Pseudo dice [0.4712, 0.766, 0.7665, 0.6714, 0.5708, 0.6023, 0.7627] +2026-04-10 23:48:30.681187: Epoch time: 103.05 s +2026-04-10 23:48:31.842527: +2026-04-10 23:48:31.844721: Epoch 346 +2026-04-10 23:48:31.846962: Current learning rate: 0.00922 +2026-04-10 23:50:13.879432: train_loss -0.1715 +2026-04-10 23:50:13.891581: val_loss -0.1387 +2026-04-10 23:50:13.893857: Pseudo dice [0.592, 0.6008, 0.6576, 0.3712, 0.5538, 0.3627, 0.5297] +2026-04-10 23:50:13.896003: Epoch time: 102.04 s +2026-04-10 23:50:15.077988: +2026-04-10 23:50:15.079844: Epoch 347 +2026-04-10 23:50:15.082610: Current learning rate: 0.00922 +2026-04-10 23:51:58.375671: train_loss -0.1618 +2026-04-10 23:51:58.383258: val_loss -0.1503 +2026-04-10 23:51:58.386078: Pseudo dice [0.3483, 0.6432, 0.7728, 0.6619, 0.4854, 0.7522, 0.8396] +2026-04-10 23:51:58.388856: Epoch time: 103.3 s +2026-04-10 23:51:59.566045: +2026-04-10 23:51:59.569433: Epoch 348 +2026-04-10 23:51:59.571626: Current learning rate: 0.00921 +2026-04-10 23:53:42.294131: train_loss -0.1515 +2026-04-10 23:53:42.300733: val_loss -0.1308 +2026-04-10 23:53:42.303231: Pseudo dice [0.3954, 0.1964, 0.5642, 0.2596, 0.5999, 0.3044, 0.8153] +2026-04-10 23:53:42.306382: Epoch time: 102.73 s +2026-04-10 23:53:43.480015: +2026-04-10 23:53:43.482514: Epoch 349 +2026-04-10 23:53:43.484490: Current learning rate: 0.00921 +2026-04-10 23:55:26.203092: train_loss -0.1625 +2026-04-10 23:55:26.211843: val_loss -0.148 +2026-04-10 23:55:26.214368: Pseudo dice [0.4443, 0.0965, 0.7035, 0.7375, 0.5795, 0.6796, 0.7174] +2026-04-10 23:55:26.217355: Epoch time: 102.73 s +2026-04-10 23:55:29.193880: +2026-04-10 23:55:29.195789: Epoch 350 +2026-04-10 23:55:29.197398: Current learning rate: 0.00921 +2026-04-10 23:57:12.567965: train_loss -0.144 +2026-04-10 23:57:12.575914: val_loss -0.1352 +2026-04-10 23:57:12.578160: Pseudo dice [0.5099, 0.5698, 0.6663, 0.4749, 0.5595, 0.2459, 0.8291] +2026-04-10 23:57:12.581853: Epoch time: 103.38 s +2026-04-10 23:57:13.763988: +2026-04-10 23:57:13.768901: Epoch 351 +2026-04-10 23:57:13.770649: Current learning rate: 0.00921 +2026-04-10 23:58:56.569087: train_loss -0.1324 +2026-04-10 23:58:56.575162: val_loss -0.1025 +2026-04-10 23:58:56.577152: Pseudo dice [0.6999, 0.0788, 0.624, 0.0978, 0.5163, 0.4273, 0.6007] +2026-04-10 23:58:56.579906: Epoch time: 102.81 s +2026-04-10 23:58:57.775706: +2026-04-10 23:58:57.777381: Epoch 352 +2026-04-10 23:58:57.779008: Current learning rate: 0.0092 +2026-04-11 00:00:42.004735: train_loss -0.1347 +2026-04-11 00:00:42.022780: val_loss -0.1318 +2026-04-11 00:00:42.026480: Pseudo dice [0.6023, 0.7872, 0.6823, 0.0738, 0.3914, 0.6117, 0.4407] +2026-04-11 00:00:42.030312: Epoch time: 104.23 s +2026-04-11 00:00:43.193132: +2026-04-11 00:00:43.196229: Epoch 353 +2026-04-11 00:00:43.199849: Current learning rate: 0.0092 +2026-04-11 00:02:30.440437: train_loss -0.1426 +2026-04-11 00:02:30.449068: val_loss -0.1547 +2026-04-11 00:02:30.452651: Pseudo dice [0.4791, 0.6418, 0.8744, 0.5431, 0.3655, 0.4115, 0.8946] +2026-04-11 00:02:30.456345: Epoch time: 107.25 s +2026-04-11 00:02:31.651883: +2026-04-11 00:02:31.655561: Epoch 354 +2026-04-11 00:02:31.664919: Current learning rate: 0.0092 +2026-04-11 00:04:16.196161: train_loss -0.162 +2026-04-11 00:04:16.206261: val_loss -0.1607 +2026-04-11 00:04:16.210233: Pseudo dice [0.5371, 0.2775, 0.7595, 0.2494, 0.4881, 0.5596, 0.873] +2026-04-11 00:04:16.214594: Epoch time: 104.55 s +2026-04-11 00:04:17.380470: +2026-04-11 00:04:17.383659: Epoch 355 +2026-04-11 00:04:17.385985: Current learning rate: 0.0092 +2026-04-11 00:06:02.331593: train_loss -0.1505 +2026-04-11 00:06:02.340091: val_loss -0.1227 +2026-04-11 00:06:02.342755: Pseudo dice [0.644, 0.6925, 0.7463, 0.5826, 0.4226, 0.5617, 0.6378] +2026-04-11 00:06:02.347636: Epoch time: 104.95 s +2026-04-11 00:06:03.496554: +2026-04-11 00:06:03.500101: Epoch 356 +2026-04-11 00:06:03.503726: Current learning rate: 0.0092 +2026-04-11 00:07:46.900970: train_loss -0.1675 +2026-04-11 00:07:46.912566: val_loss -0.1562 +2026-04-11 00:07:46.914841: Pseudo dice [0.5205, 0.7726, 0.8659, 0.5798, 0.4635, 0.6215, 0.6981] +2026-04-11 00:07:46.917426: Epoch time: 103.41 s +2026-04-11 00:07:48.066926: +2026-04-11 00:07:48.069096: Epoch 357 +2026-04-11 00:07:48.071918: Current learning rate: 0.00919 +2026-04-11 00:09:30.984990: train_loss -0.1601 +2026-04-11 00:09:30.992239: val_loss -0.1241 +2026-04-11 00:09:30.994081: Pseudo dice [0.5907, 0.4834, 0.734, 0.3573, 0.3585, 0.445, 0.6832] +2026-04-11 00:09:30.996807: Epoch time: 102.92 s +2026-04-11 00:09:32.149752: +2026-04-11 00:09:32.153684: Epoch 358 +2026-04-11 00:09:32.156533: Current learning rate: 0.00919 +2026-04-11 00:11:15.522485: train_loss -0.1619 +2026-04-11 00:11:15.532616: val_loss -0.1623 +2026-04-11 00:11:15.535114: Pseudo dice [0.4118, 0.5833, 0.7429, 0.5609, 0.4362, 0.5621, 0.8414] +2026-04-11 00:11:15.554525: Epoch time: 103.38 s +2026-04-11 00:11:16.772831: +2026-04-11 00:11:16.774590: Epoch 359 +2026-04-11 00:11:16.776542: Current learning rate: 0.00919 +2026-04-11 00:13:00.309085: train_loss -0.1586 +2026-04-11 00:13:00.315906: val_loss -0.121 +2026-04-11 00:13:00.318308: Pseudo dice [0.6017, 0.1275, 0.8243, 0.5062, 0.339, 0.3556, 0.6294] +2026-04-11 00:13:00.320392: Epoch time: 103.54 s +2026-04-11 00:13:01.481625: +2026-04-11 00:13:01.483392: Epoch 360 +2026-04-11 00:13:01.484843: Current learning rate: 0.00919 +2026-04-11 00:14:43.665952: train_loss -0.1509 +2026-04-11 00:14:43.673830: val_loss -0.1461 +2026-04-11 00:14:43.675780: Pseudo dice [0.6491, 0.7194, 0.6683, 0.3991, 0.5291, 0.1795, 0.565] +2026-04-11 00:14:43.677777: Epoch time: 102.19 s +2026-04-11 00:14:44.850950: +2026-04-11 00:14:44.852968: Epoch 361 +2026-04-11 00:14:44.854557: Current learning rate: 0.00918 +2026-04-11 00:16:28.096845: train_loss -0.1472 +2026-04-11 00:16:28.104116: val_loss -0.1356 +2026-04-11 00:16:28.106798: Pseudo dice [0.4613, 0.5622, 0.7954, 0.4714, 0.5317, 0.2468, 0.7238] +2026-04-11 00:16:28.109125: Epoch time: 103.25 s +2026-04-11 00:16:29.295699: +2026-04-11 00:16:29.298840: Epoch 362 +2026-04-11 00:16:29.300542: Current learning rate: 0.00918 +2026-04-11 00:18:11.776596: train_loss -0.163 +2026-04-11 00:18:11.783158: val_loss -0.1596 +2026-04-11 00:18:11.785245: Pseudo dice [0.6437, 0.3381, 0.7071, 0.5839, 0.4807, 0.5364, 0.3368] +2026-04-11 00:18:11.787970: Epoch time: 102.48 s +2026-04-11 00:18:12.957468: +2026-04-11 00:18:12.959606: Epoch 363 +2026-04-11 00:18:12.962198: Current learning rate: 0.00918 +2026-04-11 00:19:55.469908: train_loss -0.1597 +2026-04-11 00:19:55.477335: val_loss -0.1347 +2026-04-11 00:19:55.480822: Pseudo dice [0.323, 0.8671, 0.7473, 0.529, 0.5338, 0.5027, 0.2593] +2026-04-11 00:19:55.483545: Epoch time: 102.52 s +2026-04-11 00:19:56.655512: +2026-04-11 00:19:56.657724: Epoch 364 +2026-04-11 00:19:56.659604: Current learning rate: 0.00918 +2026-04-11 00:21:38.972406: train_loss -0.1214 +2026-04-11 00:21:38.980270: val_loss -0.1143 +2026-04-11 00:21:38.982500: Pseudo dice [0.6011, 0.4152, 0.3364, 0.3753, 0.4146, 0.7684, 0.7356] +2026-04-11 00:21:38.984989: Epoch time: 102.32 s +2026-04-11 00:21:40.206269: +2026-04-11 00:21:40.208085: Epoch 365 +2026-04-11 00:21:40.209675: Current learning rate: 0.00917 +2026-04-11 00:23:23.290478: train_loss -0.1549 +2026-04-11 00:23:23.298754: val_loss -0.1691 +2026-04-11 00:23:23.300844: Pseudo dice [0.5524, 0.4179, 0.8337, 0.6507, 0.5706, 0.7982, 0.7897] +2026-04-11 00:23:23.304019: Epoch time: 103.09 s +2026-04-11 00:23:24.488901: +2026-04-11 00:23:24.490838: Epoch 366 +2026-04-11 00:23:24.492397: Current learning rate: 0.00917 +2026-04-11 00:25:07.323561: train_loss -0.154 +2026-04-11 00:25:07.332067: val_loss -0.1518 +2026-04-11 00:25:07.334380: Pseudo dice [0.6782, 0.0935, 0.8086, 0.703, 0.3889, 0.2642, 0.8271] +2026-04-11 00:25:07.337928: Epoch time: 102.84 s +2026-04-11 00:25:08.507457: +2026-04-11 00:25:08.510048: Epoch 367 +2026-04-11 00:25:08.512834: Current learning rate: 0.00917 +2026-04-11 00:26:50.872564: train_loss -0.1557 +2026-04-11 00:26:50.879772: val_loss -0.1338 +2026-04-11 00:26:50.882076: Pseudo dice [0.347, 0.6788, 0.7533, 0.6591, 0.5145, 0.6832, 0.6682] +2026-04-11 00:26:50.885133: Epoch time: 102.37 s +2026-04-11 00:26:52.058162: +2026-04-11 00:26:52.060158: Epoch 368 +2026-04-11 00:26:52.062505: Current learning rate: 0.00917 +2026-04-11 00:28:35.172101: train_loss -0.1476 +2026-04-11 00:28:35.180066: val_loss -0.1069 +2026-04-11 00:28:35.182302: Pseudo dice [0.4831, 0.0796, 0.5477, 0.196, 0.4442, 0.5173, 0.6573] +2026-04-11 00:28:35.184437: Epoch time: 103.12 s +2026-04-11 00:28:36.351393: +2026-04-11 00:28:36.353390: Epoch 369 +2026-04-11 00:28:36.355705: Current learning rate: 0.00917 +2026-04-11 00:30:20.043799: train_loss -0.1366 +2026-04-11 00:30:20.051022: val_loss -0.1523 +2026-04-11 00:30:20.053186: Pseudo dice [0.6466, 0.7792, 0.7852, 0.6527, 0.5745, 0.4976, 0.5906] +2026-04-11 00:30:20.055949: Epoch time: 103.7 s +2026-04-11 00:30:21.223308: +2026-04-11 00:30:21.225464: Epoch 370 +2026-04-11 00:30:21.227934: Current learning rate: 0.00916 +2026-04-11 00:32:03.916580: train_loss -0.1449 +2026-04-11 00:32:03.927355: val_loss -0.1303 +2026-04-11 00:32:03.929569: Pseudo dice [0.5652, 0.8551, 0.5562, 0.2173, 0.3791, 0.7454, 0.8535] +2026-04-11 00:32:03.933444: Epoch time: 102.7 s +2026-04-11 00:32:05.122947: +2026-04-11 00:32:05.126157: Epoch 371 +2026-04-11 00:32:05.128239: Current learning rate: 0.00916 +2026-04-11 00:33:48.471260: train_loss -0.1733 +2026-04-11 00:33:48.478586: val_loss -0.1423 +2026-04-11 00:33:48.480220: Pseudo dice [0.4013, 0.4843, 0.832, 0.701, 0.3837, 0.6704, 0.6664] +2026-04-11 00:33:48.482898: Epoch time: 103.35 s +2026-04-11 00:33:49.695733: +2026-04-11 00:33:49.698186: Epoch 372 +2026-04-11 00:33:49.700330: Current learning rate: 0.00916 +2026-04-11 00:35:32.881661: train_loss -0.1576 +2026-04-11 00:35:32.889678: val_loss -0.1559 +2026-04-11 00:35:32.891929: Pseudo dice [0.4145, 0.2417, 0.8692, 0.4641, 0.4869, 0.7523, 0.5235] +2026-04-11 00:35:32.895068: Epoch time: 103.19 s +2026-04-11 00:35:34.083111: +2026-04-11 00:35:34.085742: Epoch 373 +2026-04-11 00:35:34.088011: Current learning rate: 0.00916 +2026-04-11 00:37:17.803047: train_loss -0.1509 +2026-04-11 00:37:17.810598: val_loss -0.1442 +2026-04-11 00:37:17.813283: Pseudo dice [0.7391, 0.8207, 0.6395, 0.6213, 0.5838, 0.5736, 0.7218] +2026-04-11 00:37:17.815820: Epoch time: 103.72 s +2026-04-11 00:37:18.974154: +2026-04-11 00:37:18.977238: Epoch 374 +2026-04-11 00:37:18.979931: Current learning rate: 0.00915 +2026-04-11 00:39:01.994795: train_loss -0.1482 +2026-04-11 00:39:02.002391: val_loss -0.1514 +2026-04-11 00:39:02.004801: Pseudo dice [0.5955, 0.272, 0.8328, 0.7347, 0.4552, 0.7304, 0.4402] +2026-04-11 00:39:02.007789: Epoch time: 103.02 s +2026-04-11 00:39:03.171729: +2026-04-11 00:39:03.175204: Epoch 375 +2026-04-11 00:39:03.177563: Current learning rate: 0.00915 +2026-04-11 00:40:46.303206: train_loss -0.1651 +2026-04-11 00:40:46.310255: val_loss -0.1387 +2026-04-11 00:40:46.312873: Pseudo dice [0.5329, 0.6892, 0.7543, 0.4298, 0.3965, 0.377, 0.7185] +2026-04-11 00:40:46.315185: Epoch time: 103.13 s +2026-04-11 00:40:47.459640: +2026-04-11 00:40:47.461729: Epoch 376 +2026-04-11 00:40:47.463493: Current learning rate: 0.00915 +2026-04-11 00:42:30.626695: train_loss -0.1561 +2026-04-11 00:42:30.634523: val_loss -0.1501 +2026-04-11 00:42:30.638190: Pseudo dice [0.547, 0.7913, 0.6239, 0.6134, 0.4105, 0.2842, 0.7789] +2026-04-11 00:42:30.642352: Epoch time: 103.17 s +2026-04-11 00:42:31.785053: +2026-04-11 00:42:31.787327: Epoch 377 +2026-04-11 00:42:31.790622: Current learning rate: 0.00915 +2026-04-11 00:44:14.024136: train_loss -0.1514 +2026-04-11 00:44:14.031913: val_loss -0.1249 +2026-04-11 00:44:14.036136: Pseudo dice [0.3625, 0.5257, 0.7667, 0.4602, 0.5959, 0.228, 0.6645] +2026-04-11 00:44:14.038409: Epoch time: 102.24 s +2026-04-11 00:44:15.227394: +2026-04-11 00:44:15.229104: Epoch 378 +2026-04-11 00:44:15.231454: Current learning rate: 0.00915 +2026-04-11 00:45:57.544860: train_loss -0.162 +2026-04-11 00:45:57.550858: val_loss -0.1409 +2026-04-11 00:45:57.553168: Pseudo dice [0.3459, 0.1361, 0.8235, 0.5841, 0.5765, 0.5079, 0.3475] +2026-04-11 00:45:57.555663: Epoch time: 102.32 s +2026-04-11 00:45:59.971173: +2026-04-11 00:45:59.974384: Epoch 379 +2026-04-11 00:45:59.976017: Current learning rate: 0.00914 +2026-04-11 00:47:42.395801: train_loss -0.1507 +2026-04-11 00:47:42.403181: val_loss -0.1178 +2026-04-11 00:47:42.405428: Pseudo dice [0.6902, 0.2793, 0.4824, 0.1617, 0.3105, 0.4924, 0.6901] +2026-04-11 00:47:42.407829: Epoch time: 102.43 s +2026-04-11 00:47:43.593597: +2026-04-11 00:47:43.597152: Epoch 380 +2026-04-11 00:47:43.599123: Current learning rate: 0.00914 +2026-04-11 00:49:26.201031: train_loss -0.1547 +2026-04-11 00:49:26.208331: val_loss -0.1312 +2026-04-11 00:49:26.210340: Pseudo dice [0.5126, 0.4783, 0.6151, 0.3541, 0.4417, 0.3144, 0.7376] +2026-04-11 00:49:26.212843: Epoch time: 102.61 s +2026-04-11 00:49:27.362822: +2026-04-11 00:49:27.364738: Epoch 381 +2026-04-11 00:49:27.366447: Current learning rate: 0.00914 +2026-04-11 00:51:10.632911: train_loss -0.1623 +2026-04-11 00:51:10.640579: val_loss -0.1685 +2026-04-11 00:51:10.643170: Pseudo dice [0.7101, 0.3925, 0.8065, 0.6629, 0.5875, 0.7977, 0.7562] +2026-04-11 00:51:10.645653: Epoch time: 103.27 s +2026-04-11 00:51:11.820933: +2026-04-11 00:51:11.823583: Epoch 382 +2026-04-11 00:51:11.826671: Current learning rate: 0.00914 +2026-04-11 00:52:54.771512: train_loss -0.1588 +2026-04-11 00:52:54.778017: val_loss -0.1344 +2026-04-11 00:52:54.780110: Pseudo dice [0.8259, 0.5709, 0.6741, 0.4367, 0.679, 0.5357, 0.7701] +2026-04-11 00:52:54.784473: Epoch time: 102.95 s +2026-04-11 00:52:55.975703: +2026-04-11 00:52:55.978232: Epoch 383 +2026-04-11 00:52:55.979940: Current learning rate: 0.00913 +2026-04-11 00:54:39.078554: train_loss -0.1457 +2026-04-11 00:54:39.084728: val_loss -0.1347 +2026-04-11 00:54:39.087250: Pseudo dice [0.5753, 0.8189, 0.8068, 0.6542, 0.5386, 0.4641, 0.6408] +2026-04-11 00:54:39.090632: Epoch time: 103.11 s +2026-04-11 00:54:40.294042: +2026-04-11 00:54:40.296200: Epoch 384 +2026-04-11 00:54:40.298533: Current learning rate: 0.00913 +2026-04-11 00:56:23.446323: train_loss -0.1534 +2026-04-11 00:56:23.460849: val_loss -0.1196 +2026-04-11 00:56:23.463485: Pseudo dice [0.648, 0.4685, 0.643, 0.3533, 0.3998, 0.4883, 0.7685] +2026-04-11 00:56:23.465818: Epoch time: 103.16 s +2026-04-11 00:56:24.612741: +2026-04-11 00:56:24.618846: Epoch 385 +2026-04-11 00:56:24.622500: Current learning rate: 0.00913 +2026-04-11 00:58:07.794507: train_loss -0.1482 +2026-04-11 00:58:07.800674: val_loss -0.1314 +2026-04-11 00:58:07.802837: Pseudo dice [0.7598, 0.4095, 0.6861, 0.5778, 0.2626, 0.4027, 0.7527] +2026-04-11 00:58:07.806113: Epoch time: 103.18 s +2026-04-11 00:58:08.999822: +2026-04-11 00:58:09.001723: Epoch 386 +2026-04-11 00:58:09.003277: Current learning rate: 0.00913 +2026-04-11 00:59:52.154953: train_loss -0.1728 +2026-04-11 00:59:52.162277: val_loss -0.1541 +2026-04-11 00:59:52.164382: Pseudo dice [0.6875, 0.1874, 0.836, 0.7654, 0.4966, 0.5506, 0.6084] +2026-04-11 00:59:52.167859: Epoch time: 103.16 s +2026-04-11 00:59:53.366305: +2026-04-11 00:59:53.368229: Epoch 387 +2026-04-11 00:59:53.370205: Current learning rate: 0.00912 +2026-04-11 01:01:36.055167: train_loss -0.145 +2026-04-11 01:01:36.060569: val_loss -0.133 +2026-04-11 01:01:36.062607: Pseudo dice [0.6911, 0.5927, 0.6686, 0.7418, 0.3992, 0.3601, 0.7017] +2026-04-11 01:01:36.065635: Epoch time: 102.69 s +2026-04-11 01:01:37.215047: +2026-04-11 01:01:37.218303: Epoch 388 +2026-04-11 01:01:37.219988: Current learning rate: 0.00912 +2026-04-11 01:03:20.196502: train_loss -0.1499 +2026-04-11 01:03:20.202309: val_loss -0.1308 +2026-04-11 01:03:20.204833: Pseudo dice [0.809, 0.7707, 0.0973, 0.5798, 0.3736, 0.7516, 0.7121] +2026-04-11 01:03:20.207110: Epoch time: 102.98 s +2026-04-11 01:03:21.367000: +2026-04-11 01:03:21.369055: Epoch 389 +2026-04-11 01:03:21.371310: Current learning rate: 0.00912 +2026-04-11 01:05:03.804674: train_loss -0.1587 +2026-04-11 01:05:03.810754: val_loss -0.1411 +2026-04-11 01:05:03.812415: Pseudo dice [0.3903, 0.6505, 0.8764, 0.5734, 0.5235, 0.5691, 0.5658] +2026-04-11 01:05:03.814758: Epoch time: 102.44 s +2026-04-11 01:05:04.979220: +2026-04-11 01:05:04.981589: Epoch 390 +2026-04-11 01:05:04.983266: Current learning rate: 0.00912 +2026-04-11 01:06:48.045823: train_loss -0.1468 +2026-04-11 01:06:48.052213: val_loss -0.1281 +2026-04-11 01:06:48.054299: Pseudo dice [0.8623, 0.8492, 0.7474, 0.2263, 0.327, 0.2802, 0.517] +2026-04-11 01:06:48.058221: Epoch time: 103.07 s +2026-04-11 01:06:49.246343: +2026-04-11 01:06:49.248640: Epoch 391 +2026-04-11 01:06:49.250725: Current learning rate: 0.00912 +2026-04-11 01:08:32.343259: train_loss -0.151 +2026-04-11 01:08:32.351278: val_loss -0.1607 +2026-04-11 01:08:32.353717: Pseudo dice [0.5997, 0.2419, 0.7509, 0.6916, 0.4564, 0.5482, 0.7738] +2026-04-11 01:08:32.356379: Epoch time: 103.1 s +2026-04-11 01:08:33.589105: +2026-04-11 01:08:33.591279: Epoch 392 +2026-04-11 01:08:33.592888: Current learning rate: 0.00911 +2026-04-11 01:10:16.907886: train_loss -0.1536 +2026-04-11 01:10:16.914778: val_loss -0.1261 +2026-04-11 01:10:16.917255: Pseudo dice [0.5738, 0.8042, 0.6209, 0.6782, 0.4773, 0.4674, 0.3208] +2026-04-11 01:10:16.920007: Epoch time: 103.32 s +2026-04-11 01:10:18.126086: +2026-04-11 01:10:18.127833: Epoch 393 +2026-04-11 01:10:18.129521: Current learning rate: 0.00911 +2026-04-11 01:12:00.066905: train_loss -0.1535 +2026-04-11 01:12:00.072677: val_loss -0.1224 +2026-04-11 01:12:00.075553: Pseudo dice [0.7611, 0.5948, 0.7583, 0.6581, 0.4244, 0.4629, 0.7502] +2026-04-11 01:12:00.078075: Epoch time: 101.94 s +2026-04-11 01:12:01.282726: +2026-04-11 01:12:01.284734: Epoch 394 +2026-04-11 01:12:01.286441: Current learning rate: 0.00911 +2026-04-11 01:13:43.732802: train_loss -0.1699 +2026-04-11 01:13:43.744075: val_loss -0.1502 +2026-04-11 01:13:43.747169: Pseudo dice [0.4632, 0.7899, 0.7533, 0.1198, 0.5353, 0.7421, 0.7825] +2026-04-11 01:13:43.749946: Epoch time: 102.45 s +2026-04-11 01:13:44.904230: +2026-04-11 01:13:44.905869: Epoch 395 +2026-04-11 01:13:44.907445: Current learning rate: 0.00911 +2026-04-11 01:15:27.384058: train_loss -0.145 +2026-04-11 01:15:27.393551: val_loss -0.1348 +2026-04-11 01:15:27.396623: Pseudo dice [0.6096, 0.1399, 0.7767, 0.4545, 0.4016, 0.5686, 0.6664] +2026-04-11 01:15:27.399125: Epoch time: 102.48 s +2026-04-11 01:15:28.544530: +2026-04-11 01:15:28.546335: Epoch 396 +2026-04-11 01:15:28.548213: Current learning rate: 0.0091 +2026-04-11 01:17:10.713451: train_loss -0.1712 +2026-04-11 01:17:10.720677: val_loss -0.1544 +2026-04-11 01:17:10.723113: Pseudo dice [0.5678, 0.611, 0.8081, 0.6361, 0.5293, 0.4301, 0.8359] +2026-04-11 01:17:10.726082: Epoch time: 102.17 s +2026-04-11 01:17:11.912899: +2026-04-11 01:17:11.914720: Epoch 397 +2026-04-11 01:17:11.916400: Current learning rate: 0.0091 +2026-04-11 01:18:54.543046: train_loss -0.144 +2026-04-11 01:18:54.549962: val_loss -0.1411 +2026-04-11 01:18:54.551840: Pseudo dice [0.5312, 0.8752, 0.7488, 0.0712, 0.3461, 0.4499, 0.6737] +2026-04-11 01:18:54.554617: Epoch time: 102.63 s +2026-04-11 01:18:55.710651: +2026-04-11 01:18:55.712753: Epoch 398 +2026-04-11 01:18:55.714913: Current learning rate: 0.0091 +2026-04-11 01:20:40.122452: train_loss -0.1611 +2026-04-11 01:20:40.128614: val_loss -0.1593 +2026-04-11 01:20:40.131519: Pseudo dice [0.6627, 0.6709, 0.8915, 0.6711, 0.6195, 0.7518, 0.5614] +2026-04-11 01:20:40.133679: Epoch time: 104.41 s +2026-04-11 01:20:40.136046: Yayy! New best EMA pseudo Dice: 0.5852 +2026-04-11 01:20:43.005808: +2026-04-11 01:20:43.011872: Epoch 399 +2026-04-11 01:20:43.013870: Current learning rate: 0.0091 +2026-04-11 01:22:26.455882: train_loss -0.1617 +2026-04-11 01:22:26.462906: val_loss -0.123 +2026-04-11 01:22:26.464712: Pseudo dice [0.8027, 0.3149, 0.6324, 0.6162, 0.3907, 0.446, 0.3477] +2026-04-11 01:22:26.467169: Epoch time: 103.45 s +2026-04-11 01:22:29.230727: +2026-04-11 01:22:29.232497: Epoch 400 +2026-04-11 01:22:29.234309: Current learning rate: 0.0091 +2026-04-11 01:24:11.938571: train_loss -0.174 +2026-04-11 01:24:11.944242: val_loss -0.1391 +2026-04-11 01:24:11.946449: Pseudo dice [0.3519, 0.6991, 0.8528, 0.6983, 0.46, 0.575, 0.7452] +2026-04-11 01:24:11.948797: Epoch time: 102.71 s +2026-04-11 01:24:13.116367: +2026-04-11 01:24:13.118541: Epoch 401 +2026-04-11 01:24:13.120203: Current learning rate: 0.00909 +2026-04-11 01:25:56.492373: train_loss -0.1668 +2026-04-11 01:25:56.498246: val_loss -0.1644 +2026-04-11 01:25:56.500309: Pseudo dice [0.7596, 0.332, 0.7375, 0.6301, 0.6396, 0.5237, 0.6589] +2026-04-11 01:25:56.502392: Epoch time: 103.38 s +2026-04-11 01:25:57.755939: +2026-04-11 01:25:57.757779: Epoch 402 +2026-04-11 01:25:57.759386: Current learning rate: 0.00909 +2026-04-11 01:27:40.266790: train_loss -0.1804 +2026-04-11 01:27:40.273463: val_loss -0.143 +2026-04-11 01:27:40.276361: Pseudo dice [0.3647, 0.3507, 0.7565, 0.6545, 0.3616, 0.6883, 0.7828] +2026-04-11 01:27:40.278979: Epoch time: 102.51 s +2026-04-11 01:27:41.467332: +2026-04-11 01:27:41.469142: Epoch 403 +2026-04-11 01:27:41.470762: Current learning rate: 0.00909 +2026-04-11 01:29:23.639259: train_loss -0.153 +2026-04-11 01:29:23.645314: val_loss -0.1352 +2026-04-11 01:29:23.647350: Pseudo dice [0.6776, 0.0925, 0.7259, 0.5545, 0.5389, 0.6562, 0.811] +2026-04-11 01:29:23.649914: Epoch time: 102.18 s +2026-04-11 01:29:24.835068: +2026-04-11 01:29:24.838220: Epoch 404 +2026-04-11 01:29:24.840093: Current learning rate: 0.00909 +2026-04-11 01:31:07.248731: train_loss -0.1633 +2026-04-11 01:31:07.256374: val_loss -0.1559 +2026-04-11 01:31:07.258695: Pseudo dice [0.8451, 0.1393, 0.5222, 0.8726, 0.5517, 0.404, 0.6977] +2026-04-11 01:31:07.262024: Epoch time: 102.42 s +2026-04-11 01:31:08.458472: +2026-04-11 01:31:08.460673: Epoch 405 +2026-04-11 01:31:08.462757: Current learning rate: 0.00908 +2026-04-11 01:32:50.798587: train_loss -0.1639 +2026-04-11 01:32:50.805727: val_loss -0.1516 +2026-04-11 01:32:50.807810: Pseudo dice [0.5499, 0.7724, 0.7417, 0.6748, 0.4211, 0.6292, 0.6174] +2026-04-11 01:32:50.810304: Epoch time: 102.34 s +2026-04-11 01:32:50.812145: Yayy! New best EMA pseudo Dice: 0.5869 +2026-04-11 01:32:53.656362: +2026-04-11 01:32:53.658890: Epoch 406 +2026-04-11 01:32:53.660301: Current learning rate: 0.00908 +2026-04-11 01:34:36.490885: train_loss -0.1693 +2026-04-11 01:34:36.496678: val_loss -0.1459 +2026-04-11 01:34:36.499000: Pseudo dice [0.6531, 0.8771, 0.4848, 0.2074, 0.5411, 0.6583, 0.7627] +2026-04-11 01:34:36.501702: Epoch time: 102.84 s +2026-04-11 01:34:36.504076: Yayy! New best EMA pseudo Dice: 0.588 +2026-04-11 01:34:39.341675: +2026-04-11 01:34:39.344041: Epoch 407 +2026-04-11 01:34:39.345646: Current learning rate: 0.00908 +2026-04-11 01:36:21.564309: train_loss -0.1716 +2026-04-11 01:36:21.570075: val_loss -0.1583 +2026-04-11 01:36:21.572220: Pseudo dice [0.4764, 0.3459, 0.747, 0.5532, 0.6466, 0.7728, 0.4048] +2026-04-11 01:36:21.574433: Epoch time: 102.23 s +2026-04-11 01:36:22.754980: +2026-04-11 01:36:22.757087: Epoch 408 +2026-04-11 01:36:22.758880: Current learning rate: 0.00908 +2026-04-11 01:38:05.106052: train_loss -0.1559 +2026-04-11 01:38:05.113909: val_loss -0.1778 +2026-04-11 01:38:05.115773: Pseudo dice [0.721, 0.5562, 0.7641, 0.7843, 0.6324, 0.5923, 0.4263] +2026-04-11 01:38:05.118157: Epoch time: 102.35 s +2026-04-11 01:38:05.120050: Yayy! New best EMA pseudo Dice: 0.591 +2026-04-11 01:38:07.951782: +2026-04-11 01:38:07.953653: Epoch 409 +2026-04-11 01:38:07.955412: Current learning rate: 0.00907 +2026-04-11 01:39:50.829108: train_loss -0.1729 +2026-04-11 01:39:50.841009: val_loss -0.1806 +2026-04-11 01:39:50.847488: Pseudo dice [0.4808, 0.7836, 0.8083, 0.4645, 0.5233, 0.7098, 0.7938] +2026-04-11 01:39:50.852103: Epoch time: 102.88 s +2026-04-11 01:39:50.854043: Yayy! New best EMA pseudo Dice: 0.5971 +2026-04-11 01:39:53.634708: +2026-04-11 01:39:53.637558: Epoch 410 +2026-04-11 01:39:53.639200: Current learning rate: 0.00907 +2026-04-11 01:41:36.634791: train_loss -0.152 +2026-04-11 01:41:36.642561: val_loss -0.1417 +2026-04-11 01:41:36.644998: Pseudo dice [0.7333, 0.6911, 0.5701, 0.2804, 0.3441, 0.7181, 0.3272] +2026-04-11 01:41:36.647893: Epoch time: 103.0 s +2026-04-11 01:41:37.734575: +2026-04-11 01:41:37.736612: Epoch 411 +2026-04-11 01:41:37.738372: Current learning rate: 0.00907 +2026-04-11 01:43:20.194334: train_loss -0.1795 +2026-04-11 01:43:20.201092: val_loss -0.1564 +2026-04-11 01:43:20.203161: Pseudo dice [0.6983, 0.499, 0.7329, 0.7228, 0.4934, 0.4745, 0.7847] +2026-04-11 01:43:20.205885: Epoch time: 102.46 s +2026-04-11 01:43:21.277003: +2026-04-11 01:43:21.278891: Epoch 412 +2026-04-11 01:43:21.281311: Current learning rate: 0.00907 +2026-04-11 01:45:04.499945: train_loss -0.1751 +2026-04-11 01:45:04.509286: val_loss -0.1077 +2026-04-11 01:45:04.511464: Pseudo dice [0.7216, 0.3875, 0.7717, 0.486, 0.3575, 0.7095, 0.4919] +2026-04-11 01:45:04.514844: Epoch time: 103.23 s +2026-04-11 01:45:05.654505: +2026-04-11 01:45:05.656537: Epoch 413 +2026-04-11 01:45:05.659376: Current learning rate: 0.00907 +2026-04-11 01:46:48.771244: train_loss -0.146 +2026-04-11 01:46:48.778070: val_loss -0.114 +2026-04-11 01:46:48.779922: Pseudo dice [0.6875, 0.1189, 0.73, 0.6387, 0.5085, 0.4193, 0.491] +2026-04-11 01:46:48.782220: Epoch time: 103.12 s +2026-04-11 01:46:49.899163: +2026-04-11 01:46:49.901256: Epoch 414 +2026-04-11 01:46:49.903242: Current learning rate: 0.00906 +2026-04-11 01:48:32.867830: train_loss -0.1535 +2026-04-11 01:48:32.874734: val_loss -0.1405 +2026-04-11 01:48:32.877734: Pseudo dice [0.6527, 0.616, 0.658, 0.6271, 0.5693, 0.1704, 0.5875] +2026-04-11 01:48:32.880489: Epoch time: 102.97 s +2026-04-11 01:48:33.977958: +2026-04-11 01:48:33.979925: Epoch 415 +2026-04-11 01:48:33.981935: Current learning rate: 0.00906 +2026-04-11 01:50:16.875045: train_loss -0.1529 +2026-04-11 01:50:16.883285: val_loss -0.1349 +2026-04-11 01:50:16.885129: Pseudo dice [0.6297, 0.3899, 0.8049, 0.153, 0.4065, 0.385, 0.4201] +2026-04-11 01:50:16.888229: Epoch time: 102.9 s +2026-04-11 01:50:18.018969: +2026-04-11 01:50:18.021171: Epoch 416 +2026-04-11 01:50:18.022938: Current learning rate: 0.00906 +2026-04-11 01:52:02.234258: train_loss -0.1581 +2026-04-11 01:52:02.243070: val_loss -0.1522 +2026-04-11 01:52:02.245411: Pseudo dice [0.5338, 0.6751, 0.7668, 0.4339, 0.3453, 0.6764, 0.6791] +2026-04-11 01:52:02.247943: Epoch time: 104.22 s +2026-04-11 01:52:03.340893: +2026-04-11 01:52:03.342988: Epoch 417 +2026-04-11 01:52:03.346263: Current learning rate: 0.00906 +2026-04-11 01:53:46.459783: train_loss -0.1619 +2026-04-11 01:53:46.466876: val_loss -0.1515 +2026-04-11 01:53:46.468625: Pseudo dice [0.7734, 0.7564, 0.6714, 0.2479, 0.4459, 0.6011, 0.7798] +2026-04-11 01:53:46.471170: Epoch time: 103.12 s +2026-04-11 01:53:47.580877: +2026-04-11 01:53:47.582958: Epoch 418 +2026-04-11 01:53:47.585096: Current learning rate: 0.00905 +2026-04-11 01:55:30.015153: train_loss -0.1539 +2026-04-11 01:55:30.021865: val_loss -0.14 +2026-04-11 01:55:30.023864: Pseudo dice [0.6584, 0.8829, 0.7589, 0.4936, 0.2553, 0.6767, 0.8116] +2026-04-11 01:55:30.027125: Epoch time: 102.44 s +2026-04-11 01:55:31.149592: +2026-04-11 01:55:31.151311: Epoch 419 +2026-04-11 01:55:31.152883: Current learning rate: 0.00905 +2026-04-11 01:57:13.887814: train_loss -0.1603 +2026-04-11 01:57:13.894721: val_loss -0.1476 +2026-04-11 01:57:13.896780: Pseudo dice [0.6059, 0.7756, 0.7915, 0.6763, 0.4927, 0.3731, 0.7474] +2026-04-11 01:57:13.899801: Epoch time: 102.74 s +2026-04-11 01:57:15.097173: +2026-04-11 01:57:15.099381: Epoch 420 +2026-04-11 01:57:15.101169: Current learning rate: 0.00905 +2026-04-11 01:58:57.475694: train_loss -0.1773 +2026-04-11 01:58:57.483691: val_loss -0.1576 +2026-04-11 01:58:57.486225: Pseudo dice [0.7323, 0.4792, 0.7198, 0.0581, 0.5241, 0.6415, 0.8503] +2026-04-11 01:58:57.488368: Epoch time: 102.38 s +2026-04-11 01:58:58.585202: +2026-04-11 01:58:58.586875: Epoch 421 +2026-04-11 01:58:58.588385: Current learning rate: 0.00905 +2026-04-11 02:00:41.053298: train_loss -0.1646 +2026-04-11 02:00:41.059236: val_loss -0.142 +2026-04-11 02:00:41.061682: Pseudo dice [0.6178, 0.6894, 0.6396, 0.69, 0.5493, 0.7625, 0.4542] +2026-04-11 02:00:41.064341: Epoch time: 102.47 s +2026-04-11 02:00:42.180760: +2026-04-11 02:00:42.182430: Epoch 422 +2026-04-11 02:00:42.183817: Current learning rate: 0.00905 +2026-04-11 02:02:24.741372: train_loss -0.1629 +2026-04-11 02:02:24.747294: val_loss -0.1468 +2026-04-11 02:02:24.749314: Pseudo dice [0.7774, 0.6012, 0.6592, 0.5256, 0.418, 0.541, 0.8254] +2026-04-11 02:02:24.752679: Epoch time: 102.56 s +2026-04-11 02:02:25.915012: +2026-04-11 02:02:25.917168: Epoch 423 +2026-04-11 02:02:25.918734: Current learning rate: 0.00904 +2026-04-11 02:04:08.027595: train_loss -0.1571 +2026-04-11 02:04:08.033997: val_loss -0.1549 +2026-04-11 02:04:08.036059: Pseudo dice [0.5918, 0.5805, 0.7293, 0.4402, 0.4452, 0.3768, 0.9053] +2026-04-11 02:04:08.038636: Epoch time: 102.12 s +2026-04-11 02:04:09.188585: +2026-04-11 02:04:09.190300: Epoch 424 +2026-04-11 02:04:09.192086: Current learning rate: 0.00904 +2026-04-11 02:05:51.733392: train_loss -0.1699 +2026-04-11 02:05:51.742329: val_loss -0.1422 +2026-04-11 02:05:51.744349: Pseudo dice [0.3565, 0.8329, 0.6032, 0.0067, 0.5524, 0.6396, 0.3729] +2026-04-11 02:05:51.746826: Epoch time: 102.55 s +2026-04-11 02:05:52.870717: +2026-04-11 02:05:52.872646: Epoch 425 +2026-04-11 02:05:52.874938: Current learning rate: 0.00904 +2026-04-11 02:07:35.326030: train_loss -0.172 +2026-04-11 02:07:35.332513: val_loss -0.1506 +2026-04-11 02:07:35.334365: Pseudo dice [0.5472, 0.6237, 0.6722, 0.6734, 0.5111, 0.3792, 0.6836] +2026-04-11 02:07:35.336471: Epoch time: 102.46 s +2026-04-11 02:07:36.450177: +2026-04-11 02:07:36.452290: Epoch 426 +2026-04-11 02:07:36.453893: Current learning rate: 0.00904 +2026-04-11 02:09:18.581391: train_loss -0.1639 +2026-04-11 02:09:18.587865: val_loss -0.1673 +2026-04-11 02:09:18.591115: Pseudo dice [0.6595, 0.8937, 0.7395, 0.7928, 0.5643, 0.3902, 0.7248] +2026-04-11 02:09:18.593583: Epoch time: 102.13 s +2026-04-11 02:09:19.696001: +2026-04-11 02:09:19.698130: Epoch 427 +2026-04-11 02:09:19.699783: Current learning rate: 0.00903 +2026-04-11 02:11:02.977880: train_loss -0.1589 +2026-04-11 02:11:02.987013: val_loss -0.1357 +2026-04-11 02:11:02.989603: Pseudo dice [0.6374, 0.6999, 0.6504, 0.62, 0.4723, 0.4946, 0.6399] +2026-04-11 02:11:02.993067: Epoch time: 103.29 s +2026-04-11 02:11:04.145409: +2026-04-11 02:11:04.147821: Epoch 428 +2026-04-11 02:11:04.152190: Current learning rate: 0.00903 +2026-04-11 02:12:47.439481: train_loss -0.1818 +2026-04-11 02:12:47.446259: val_loss -0.1537 +2026-04-11 02:12:47.448141: Pseudo dice [0.6393, 0.4974, 0.6872, 0.5761, 0.5563, 0.3489, 0.9055] +2026-04-11 02:12:47.450424: Epoch time: 103.3 s +2026-04-11 02:12:48.556336: +2026-04-11 02:12:48.558595: Epoch 429 +2026-04-11 02:12:48.560769: Current learning rate: 0.00903 +2026-04-11 02:14:31.378016: train_loss -0.1716 +2026-04-11 02:14:31.385069: val_loss -0.1497 +2026-04-11 02:14:31.387039: Pseudo dice [0.6185, 0.7336, 0.6521, 0.61, 0.4693, 0.6179, 0.6744] +2026-04-11 02:14:31.389519: Epoch time: 102.82 s +2026-04-11 02:14:32.484097: +2026-04-11 02:14:32.486548: Epoch 430 +2026-04-11 02:14:32.488702: Current learning rate: 0.00903 +2026-04-11 02:16:15.394961: train_loss -0.1634 +2026-04-11 02:16:15.402498: val_loss -0.1543 +2026-04-11 02:16:15.404781: Pseudo dice [0.7057, 0.1595, 0.807, 0.6281, 0.5879, 0.5843, 0.8452] +2026-04-11 02:16:15.407496: Epoch time: 102.91 s +2026-04-11 02:16:15.410073: Yayy! New best EMA pseudo Dice: 0.5982 +2026-04-11 02:16:18.327719: +2026-04-11 02:16:18.329454: Epoch 431 +2026-04-11 02:16:18.331268: Current learning rate: 0.00902 +2026-04-11 02:18:00.772799: train_loss -0.1571 +2026-04-11 02:18:00.780016: val_loss -0.1434 +2026-04-11 02:18:00.782625: Pseudo dice [0.6637, 0.3096, 0.8359, 0.6336, 0.5069, 0.3643, 0.6326] +2026-04-11 02:18:00.784753: Epoch time: 102.45 s +2026-04-11 02:18:01.898175: +2026-04-11 02:18:01.900741: Epoch 432 +2026-04-11 02:18:01.902953: Current learning rate: 0.00902 +2026-04-11 02:19:44.569928: train_loss -0.1755 +2026-04-11 02:19:44.577225: val_loss -0.1794 +2026-04-11 02:19:44.579002: Pseudo dice [0.6489, 0.8327, 0.8024, 0.5291, 0.4137, 0.5694, 0.624] +2026-04-11 02:19:44.581366: Epoch time: 102.67 s +2026-04-11 02:19:44.583502: Yayy! New best EMA pseudo Dice: 0.5985 +2026-04-11 02:19:47.364997: +2026-04-11 02:19:47.366903: Epoch 433 +2026-04-11 02:19:47.368446: Current learning rate: 0.00902 +2026-04-11 02:21:30.177075: train_loss -0.2446 +2026-04-11 02:21:30.184794: val_loss -0.2644 +2026-04-11 02:21:30.187098: Pseudo dice [0.569, 0.0, 0.8139, 0.1751, 0.5027, 0.6438, 0.492] +2026-04-11 02:21:30.189810: Epoch time: 102.82 s +2026-04-11 02:21:31.296336: +2026-04-11 02:21:31.298558: Epoch 434 +2026-04-11 02:21:31.300989: Current learning rate: 0.00902 +2026-04-11 02:23:13.379338: train_loss -0.2583 +2026-04-11 02:23:13.387091: val_loss -0.2716 +2026-04-11 02:23:13.389257: Pseudo dice [0.0437, 0.0, 0.4712, 0.2276, 0.2436, 0.4558, 0.668] +2026-04-11 02:23:13.391769: Epoch time: 102.09 s +2026-04-11 02:23:14.490047: +2026-04-11 02:23:14.491884: Epoch 435 +2026-04-11 02:23:14.493820: Current learning rate: 0.00902 +2026-04-11 02:24:57.110207: train_loss -0.3514 +2026-04-11 02:24:57.116797: val_loss -0.2813 +2026-04-11 02:24:57.118877: Pseudo dice [0.3624, 0.0, 0.6308, 0.6545, 0.4173, 0.3703, 0.863] +2026-04-11 02:24:57.121456: Epoch time: 102.62 s +2026-04-11 02:24:59.355392: +2026-04-11 02:24:59.357248: Epoch 436 +2026-04-11 02:24:59.358875: Current learning rate: 0.00901 +2026-04-11 02:26:41.482527: train_loss -0.3384 +2026-04-11 02:26:41.490309: val_loss -0.3055 +2026-04-11 02:26:41.492738: Pseudo dice [0.2597, 0.0, 0.4074, 0.4416, 0.4632, 0.6133, 0.7333] +2026-04-11 02:26:41.495760: Epoch time: 102.13 s +2026-04-11 02:26:42.608966: +2026-04-11 02:26:42.611159: Epoch 437 +2026-04-11 02:26:42.614010: Current learning rate: 0.00901 +2026-04-11 02:28:25.362511: train_loss -0.3313 +2026-04-11 02:28:25.370121: val_loss -0.3316 +2026-04-11 02:28:25.373103: Pseudo dice [0.0, 0.0, 0.7478, 0.4343, 0.5353, 0.5334, 0.7] +2026-04-11 02:28:25.376041: Epoch time: 102.76 s +2026-04-11 02:28:26.468676: +2026-04-11 02:28:26.470716: Epoch 438 +2026-04-11 02:28:26.472710: Current learning rate: 0.00901 +2026-04-11 02:30:09.322587: train_loss -0.3498 +2026-04-11 02:30:09.329073: val_loss -0.2694 +2026-04-11 02:30:09.331496: Pseudo dice [0.0, 0.0, 0.7334, 0.0807, 0.3968, 0.6267, 0.2674] +2026-04-11 02:30:09.333480: Epoch time: 102.86 s +2026-04-11 02:30:10.447690: +2026-04-11 02:30:10.449818: Epoch 439 +2026-04-11 02:30:10.451460: Current learning rate: 0.00901 +2026-04-11 02:31:52.707411: train_loss -0.3448 +2026-04-11 02:31:52.713580: val_loss -0.3382 +2026-04-11 02:31:52.716254: Pseudo dice [0.0, 0.0, 0.7126, 0.7529, 0.4844, 0.3705, 0.8698] +2026-04-11 02:31:52.718491: Epoch time: 102.26 s +2026-04-11 02:31:53.894219: +2026-04-11 02:31:53.896097: Epoch 440 +2026-04-11 02:31:53.897711: Current learning rate: 0.009 +2026-04-11 02:33:36.266261: train_loss -0.3421 +2026-04-11 02:33:36.272002: val_loss -0.3086 +2026-04-11 02:33:36.273866: Pseudo dice [0.0, 0.0, 0.6346, 0.3823, 0.2611, 0.2558, 0.7509] +2026-04-11 02:33:36.275889: Epoch time: 102.38 s +2026-04-11 02:33:37.407788: +2026-04-11 02:33:37.409995: Epoch 441 +2026-04-11 02:33:37.411694: Current learning rate: 0.009 +2026-04-11 02:35:20.144823: train_loss -0.3015 +2026-04-11 02:35:20.151417: val_loss -0.2732 +2026-04-11 02:35:20.153511: Pseudo dice [0.0, 0.0, 0.6016, 0.6039, 0.473, 0.4827, 0.667] +2026-04-11 02:35:20.156198: Epoch time: 102.74 s +2026-04-11 02:35:21.263377: +2026-04-11 02:35:21.265701: Epoch 442 +2026-04-11 02:35:21.267573: Current learning rate: 0.009 +2026-04-11 02:37:03.316144: train_loss -0.3479 +2026-04-11 02:37:03.323268: val_loss -0.3177 +2026-04-11 02:37:03.326261: Pseudo dice [0.0, 0.0, 0.6934, 0.6477, 0.3982, 0.5922, 0.5483] +2026-04-11 02:37:03.328681: Epoch time: 102.06 s +2026-04-11 02:37:04.454459: +2026-04-11 02:37:04.456756: Epoch 443 +2026-04-11 02:37:04.458825: Current learning rate: 0.009 +2026-04-11 02:38:47.251002: train_loss -0.3495 +2026-04-11 02:38:47.258237: val_loss -0.2888 +2026-04-11 02:38:47.260631: Pseudo dice [0.0, 0.0, 0.357, 0.4998, 0.4562, 0.5279, 0.3356] +2026-04-11 02:38:47.263406: Epoch time: 102.8 s +2026-04-11 02:38:48.388310: +2026-04-11 02:38:48.390555: Epoch 444 +2026-04-11 02:38:48.392201: Current learning rate: 0.009 +2026-04-11 02:40:31.166090: train_loss -0.3529 +2026-04-11 02:40:31.174238: val_loss -0.3287 +2026-04-11 02:40:31.177246: Pseudo dice [0.0, 0.0, 0.6201, 0.63, 0.3253, 0.5277, 0.7586] +2026-04-11 02:40:31.179719: Epoch time: 102.78 s +2026-04-11 02:40:32.285112: +2026-04-11 02:40:32.287499: Epoch 445 +2026-04-11 02:40:32.289530: Current learning rate: 0.00899 +2026-04-11 02:42:14.702266: train_loss -0.3506 +2026-04-11 02:42:14.712229: val_loss -0.3209 +2026-04-11 02:42:14.714515: Pseudo dice [0.0, 0.0, 0.7448, 0.5701, 0.4254, 0.1552, 0.7535] +2026-04-11 02:42:14.717074: Epoch time: 102.42 s +2026-04-11 02:42:15.838255: +2026-04-11 02:42:15.840430: Epoch 446 +2026-04-11 02:42:15.842571: Current learning rate: 0.00899 +2026-04-11 02:43:58.413171: train_loss -0.3482 +2026-04-11 02:43:58.420455: val_loss -0.3109 +2026-04-11 02:43:58.422356: Pseudo dice [0.0, 0.0, 0.6819, 0.2035, 0.4055, 0.3057, 0.6097] +2026-04-11 02:43:58.424808: Epoch time: 102.58 s +2026-04-11 02:43:59.580653: +2026-04-11 02:43:59.583179: Epoch 447 +2026-04-11 02:43:59.585313: Current learning rate: 0.00899 +2026-04-11 02:45:41.749092: train_loss -0.3738 +2026-04-11 02:45:41.755963: val_loss -0.3361 +2026-04-11 02:45:41.758911: Pseudo dice [0.0, 0.0, 0.8114, 0.7771, 0.6108, 0.2675, 0.8091] +2026-04-11 02:45:41.761759: Epoch time: 102.17 s +2026-04-11 02:45:42.873340: +2026-04-11 02:45:42.875174: Epoch 448 +2026-04-11 02:45:42.876858: Current learning rate: 0.00899 +2026-04-11 02:47:25.751096: train_loss -0.3672 +2026-04-11 02:47:25.759329: val_loss -0.3149 +2026-04-11 02:47:25.764143: Pseudo dice [0.0, 0.0, 0.6784, 0.7522, 0.4602, 0.2998, 0.4317] +2026-04-11 02:47:25.766576: Epoch time: 102.88 s +2026-04-11 02:47:26.855055: +2026-04-11 02:47:26.857419: Epoch 449 +2026-04-11 02:47:26.859431: Current learning rate: 0.00898 +2026-04-11 02:49:09.759061: train_loss -0.3732 +2026-04-11 02:49:09.765681: val_loss -0.3623 +2026-04-11 02:49:09.769418: Pseudo dice [0.0, 0.0, 0.8544, 0.5963, 0.5866, 0.811, 0.6293] +2026-04-11 02:49:09.771814: Epoch time: 102.91 s +2026-04-11 02:49:12.485850: +2026-04-11 02:49:12.487489: Epoch 450 +2026-04-11 02:49:12.488932: Current learning rate: 0.00898 +2026-04-11 02:50:54.889448: train_loss -0.3722 +2026-04-11 02:50:54.901647: val_loss -0.305 +2026-04-11 02:50:54.918682: Pseudo dice [0.0, 0.0, 0.6181, 0.5434, 0.3789, 0.3887, 0.7179] +2026-04-11 02:50:54.922497: Epoch time: 102.41 s +2026-04-11 02:50:56.052418: +2026-04-11 02:50:56.054424: Epoch 451 +2026-04-11 02:50:56.056329: Current learning rate: 0.00898 +2026-04-11 02:52:38.057559: train_loss -0.3528 +2026-04-11 02:52:38.066011: val_loss -0.3065 +2026-04-11 02:52:38.069404: Pseudo dice [0.0, 0.0, 0.5898, 0.3437, 0.5723, 0.7354, 0.7918] +2026-04-11 02:52:38.072099: Epoch time: 102.01 s +2026-04-11 02:52:39.169576: +2026-04-11 02:52:39.171351: Epoch 452 +2026-04-11 02:52:39.172817: Current learning rate: 0.00898 +2026-04-11 02:54:21.131232: train_loss -0.365 +2026-04-11 02:54:21.137713: val_loss -0.3365 +2026-04-11 02:54:21.140554: Pseudo dice [0.0, 0.0, 0.548, 0.1877, 0.5031, 0.7421, 0.802] +2026-04-11 02:54:21.144484: Epoch time: 101.96 s +2026-04-11 02:54:22.252701: +2026-04-11 02:54:22.254410: Epoch 453 +2026-04-11 02:54:22.256288: Current learning rate: 0.00897 +2026-04-11 02:56:04.265388: train_loss -0.3806 +2026-04-11 02:56:04.273205: val_loss -0.3512 +2026-04-11 02:56:04.275382: Pseudo dice [0.0, 0.0, 0.6437, 0.6658, 0.2971, 0.6882, 0.7124] +2026-04-11 02:56:04.277555: Epoch time: 102.02 s +2026-04-11 02:56:05.374365: +2026-04-11 02:56:05.376135: Epoch 454 +2026-04-11 02:56:05.377586: Current learning rate: 0.00897 +2026-04-11 02:57:47.582571: train_loss -0.3766 +2026-04-11 02:57:47.588892: val_loss -0.3046 +2026-04-11 02:57:47.590560: Pseudo dice [0.0, 0.0, 0.6361, 0.2218, 0.3321, 0.603, 0.729] +2026-04-11 02:57:47.592937: Epoch time: 102.21 s +2026-04-11 02:57:48.676175: +2026-04-11 02:57:48.678289: Epoch 455 +2026-04-11 02:57:48.683290: Current learning rate: 0.00897 +2026-04-11 02:59:30.751762: train_loss -0.3397 +2026-04-11 02:59:30.757725: val_loss -0.3231 +2026-04-11 02:59:30.759860: Pseudo dice [0.0, 0.0, 0.6502, 0.3214, 0.3656, 0.6796, 0.666] +2026-04-11 02:59:30.761834: Epoch time: 102.08 s +2026-04-11 02:59:31.878614: +2026-04-11 02:59:31.880954: Epoch 456 +2026-04-11 02:59:31.882753: Current learning rate: 0.00897 +2026-04-11 03:01:14.517004: train_loss -0.3766 +2026-04-11 03:01:14.523520: val_loss -0.35 +2026-04-11 03:01:14.525849: Pseudo dice [0.0, 0.0, 0.5945, 0.2509, 0.478, 0.6287, 0.7165] +2026-04-11 03:01:14.529537: Epoch time: 102.64 s +2026-04-11 03:01:16.779136: +2026-04-11 03:01:16.781480: Epoch 457 +2026-04-11 03:01:16.783042: Current learning rate: 0.00897 +2026-04-11 03:02:58.980353: train_loss -0.3552 +2026-04-11 03:02:58.985627: val_loss -0.3133 +2026-04-11 03:02:58.988018: Pseudo dice [0.0, 0.0, 0.7592, 0.3719, 0.4552, 0.4956, 0.8432] +2026-04-11 03:02:58.990666: Epoch time: 102.2 s +2026-04-11 03:03:00.119160: +2026-04-11 03:03:00.121393: Epoch 458 +2026-04-11 03:03:00.123188: Current learning rate: 0.00896 +2026-04-11 03:04:42.523997: train_loss -0.344 +2026-04-11 03:04:42.544715: val_loss -0.2968 +2026-04-11 03:04:42.547259: Pseudo dice [0.0, 0.0, 0.4678, 0.6693, 0.4134, 0.4702, 0.7556] +2026-04-11 03:04:42.549962: Epoch time: 102.41 s +2026-04-11 03:04:43.624546: +2026-04-11 03:04:43.626600: Epoch 459 +2026-04-11 03:04:43.628025: Current learning rate: 0.00896 +2026-04-11 03:06:26.000350: train_loss -0.3609 +2026-04-11 03:06:26.007078: val_loss -0.3228 +2026-04-11 03:06:26.009086: Pseudo dice [0.0, 0.0, 0.6799, 0.2023, 0.4395, 0.206, 0.7065] +2026-04-11 03:06:26.011586: Epoch time: 102.38 s +2026-04-11 03:06:27.115856: +2026-04-11 03:06:27.118422: Epoch 460 +2026-04-11 03:06:27.122459: Current learning rate: 0.00896 +2026-04-11 03:08:09.843783: train_loss -0.3635 +2026-04-11 03:08:09.868809: val_loss -0.3521 +2026-04-11 03:08:09.870631: Pseudo dice [0.0, 0.0, 0.7034, 0.4393, 0.2408, 0.6538, 0.9257] +2026-04-11 03:08:09.872758: Epoch time: 102.73 s +2026-04-11 03:08:10.948193: +2026-04-11 03:08:10.951228: Epoch 461 +2026-04-11 03:08:10.953256: Current learning rate: 0.00896 +2026-04-11 03:09:53.457436: train_loss -0.368 +2026-04-11 03:09:53.463983: val_loss -0.355 +2026-04-11 03:09:53.465778: Pseudo dice [0.0, 0.0, 0.7485, 0.2802, 0.4042, 0.6409, 0.8504] +2026-04-11 03:09:53.468212: Epoch time: 102.51 s +2026-04-11 03:09:54.615450: +2026-04-11 03:09:54.618429: Epoch 462 +2026-04-11 03:09:54.620425: Current learning rate: 0.00895 +2026-04-11 03:11:36.947133: train_loss -0.3754 +2026-04-11 03:11:36.953208: val_loss -0.3198 +2026-04-11 03:11:36.955599: Pseudo dice [0.0, 0.0, 0.7703, 0.0755, 0.4196, 0.63, 0.6479] +2026-04-11 03:11:36.957898: Epoch time: 102.33 s +2026-04-11 03:11:38.052712: +2026-04-11 03:11:38.054755: Epoch 463 +2026-04-11 03:11:38.056241: Current learning rate: 0.00895 +2026-04-11 03:13:20.665948: train_loss -0.3689 +2026-04-11 03:13:20.672597: val_loss -0.3364 +2026-04-11 03:13:20.675589: Pseudo dice [0.0, 0.0, 0.7652, 0.0628, 0.2769, 0.6927, 0.5514] +2026-04-11 03:13:20.677981: Epoch time: 102.62 s +2026-04-11 03:13:21.796083: +2026-04-11 03:13:21.798381: Epoch 464 +2026-04-11 03:13:21.800232: Current learning rate: 0.00895 +2026-04-11 03:15:03.999743: train_loss -0.3654 +2026-04-11 03:15:04.005538: val_loss -0.3062 +2026-04-11 03:15:04.007466: Pseudo dice [0.0, 0.0, 0.4019, 0.5226, 0.317, 0.6151, 0.4799] +2026-04-11 03:15:04.009915: Epoch time: 102.21 s +2026-04-11 03:15:05.126231: +2026-04-11 03:15:05.127851: Epoch 465 +2026-04-11 03:15:05.129222: Current learning rate: 0.00895 +2026-04-11 03:16:47.933858: train_loss -0.3588 +2026-04-11 03:16:47.940588: val_loss -0.3239 +2026-04-11 03:16:47.945076: Pseudo dice [0.0, 0.0, 0.7616, 0.5604, 0.5327, 0.419, 0.6928] +2026-04-11 03:16:47.947499: Epoch time: 102.81 s +2026-04-11 03:16:49.066853: +2026-04-11 03:16:49.069056: Epoch 466 +2026-04-11 03:16:49.070753: Current learning rate: 0.00895 +2026-04-11 03:18:31.669607: train_loss -0.3449 +2026-04-11 03:18:31.676003: val_loss -0.328 +2026-04-11 03:18:31.677989: Pseudo dice [0.0, 0.0, 0.7097, 0.6282, 0.4928, 0.3063, 0.7502] +2026-04-11 03:18:31.680297: Epoch time: 102.61 s +2026-04-11 03:18:32.807873: +2026-04-11 03:18:32.809803: Epoch 467 +2026-04-11 03:18:32.811525: Current learning rate: 0.00894 +2026-04-11 03:20:14.899188: train_loss -0.3611 +2026-04-11 03:20:14.905029: val_loss -0.2583 +2026-04-11 03:20:14.907166: Pseudo dice [0.1852, 0.0, 0.6775, 0.0325, 0.4926, 0.4653, 0.4285] +2026-04-11 03:20:14.910205: Epoch time: 102.09 s +2026-04-11 03:20:16.019332: +2026-04-11 03:20:16.021137: Epoch 468 +2026-04-11 03:20:16.024294: Current learning rate: 0.00894 +2026-04-11 03:21:58.801953: train_loss -0.3411 +2026-04-11 03:21:58.809860: val_loss -0.3286 +2026-04-11 03:21:58.814145: Pseudo dice [0.0, 0.0, 0.727, 0.0642, 0.358, 0.5732, 0.7447] +2026-04-11 03:21:58.816802: Epoch time: 102.79 s +2026-04-11 03:21:59.950132: +2026-04-11 03:21:59.952605: Epoch 469 +2026-04-11 03:21:59.955329: Current learning rate: 0.00894 +2026-04-11 03:23:42.122071: train_loss -0.326 +2026-04-11 03:23:42.128042: val_loss -0.2617 +2026-04-11 03:23:42.130129: Pseudo dice [0.0, 0.0, 0.6597, 0.2067, 0.3174, 0.5842, 0.1498] +2026-04-11 03:23:42.132865: Epoch time: 102.18 s +2026-04-11 03:23:43.227319: +2026-04-11 03:23:43.229325: Epoch 470 +2026-04-11 03:23:43.231329: Current learning rate: 0.00894 +2026-04-11 03:25:25.977662: train_loss -0.3421 +2026-04-11 03:25:25.983561: val_loss -0.2639 +2026-04-11 03:25:25.985013: Pseudo dice [0.0, 0.0, 0.4582, 0.005, 0.538, 0.5259, 0.6001] +2026-04-11 03:25:25.987464: Epoch time: 102.75 s +2026-04-11 03:25:27.113325: +2026-04-11 03:25:27.115390: Epoch 471 +2026-04-11 03:25:27.117216: Current learning rate: 0.00893 +2026-04-11 03:27:09.836869: train_loss -0.3186 +2026-04-11 03:27:09.843473: val_loss -0.3522 +2026-04-11 03:27:09.846516: Pseudo dice [0.0, 0.0, 0.8195, 0.6158, 0.5162, 0.7123, 0.7426] +2026-04-11 03:27:09.848761: Epoch time: 102.73 s +2026-04-11 03:27:10.973442: +2026-04-11 03:27:10.975659: Epoch 472 +2026-04-11 03:27:10.978025: Current learning rate: 0.00893 +2026-04-11 03:28:53.576497: train_loss -0.3473 +2026-04-11 03:28:53.583652: val_loss -0.327 +2026-04-11 03:28:53.585418: Pseudo dice [0.0, 0.0, 0.7738, 0.4313, 0.3537, 0.6723, 0.4111] +2026-04-11 03:28:53.588052: Epoch time: 102.61 s +2026-04-11 03:28:54.686626: +2026-04-11 03:28:54.689304: Epoch 473 +2026-04-11 03:28:54.691505: Current learning rate: 0.00893 +2026-04-11 03:30:36.816967: train_loss -0.3644 +2026-04-11 03:30:36.823441: val_loss -0.3539 +2026-04-11 03:30:36.825960: Pseudo dice [0.0, 0.0, 0.8397, 0.4495, 0.5599, 0.5051, 0.7463] +2026-04-11 03:30:36.828971: Epoch time: 102.13 s +2026-04-11 03:30:37.897829: +2026-04-11 03:30:37.899800: Epoch 474 +2026-04-11 03:30:37.901275: Current learning rate: 0.00893 +2026-04-11 03:32:19.963184: train_loss -0.3542 +2026-04-11 03:32:19.969120: val_loss -0.3118 +2026-04-11 03:32:19.970946: Pseudo dice [0.0, 0.0, 0.7949, 0.5502, 0.3726, 0.5127, 0.7005] +2026-04-11 03:32:19.973322: Epoch time: 102.07 s +2026-04-11 03:32:21.070410: +2026-04-11 03:32:21.072175: Epoch 475 +2026-04-11 03:32:21.073850: Current learning rate: 0.00892 +2026-04-11 03:34:03.766428: train_loss -0.3647 +2026-04-11 03:34:03.771935: val_loss -0.3503 +2026-04-11 03:34:03.774279: Pseudo dice [0.0, 0.0, 0.8421, 0.5716, 0.4675, 0.5, 0.7963] +2026-04-11 03:34:03.776504: Epoch time: 102.7 s +2026-04-11 03:34:04.930409: +2026-04-11 03:34:04.932728: Epoch 476 +2026-04-11 03:34:04.934553: Current learning rate: 0.00892 +2026-04-11 03:35:47.466429: train_loss -0.3811 +2026-04-11 03:35:47.474303: val_loss -0.2896 +2026-04-11 03:35:47.476961: Pseudo dice [0.0, 0.0, 0.2452, 0.4375, 0.6146, 0.6181, 0.7551] +2026-04-11 03:35:47.478983: Epoch time: 102.54 s +2026-04-11 03:35:48.576561: +2026-04-11 03:35:48.578542: Epoch 477 +2026-04-11 03:35:48.580341: Current learning rate: 0.00892 +2026-04-11 03:37:32.764447: train_loss -0.3687 +2026-04-11 03:37:32.771776: val_loss -0.3127 +2026-04-11 03:37:32.774179: Pseudo dice [0.0, 0.0, 0.6462, 0.3469, 0.3722, 0.3605, 0.6252] +2026-04-11 03:37:32.778066: Epoch time: 104.19 s +2026-04-11 03:37:33.879932: +2026-04-11 03:37:33.882576: Epoch 478 +2026-04-11 03:37:33.884344: Current learning rate: 0.00892 +2026-04-11 03:39:16.538000: train_loss -0.3639 +2026-04-11 03:39:16.543773: val_loss -0.3403 +2026-04-11 03:39:16.546105: Pseudo dice [0.0, 0.0, 0.8025, 0.4297, 0.4517, 0.5928, 0.7108] +2026-04-11 03:39:16.548411: Epoch time: 102.66 s +2026-04-11 03:39:17.664057: +2026-04-11 03:39:17.666802: Epoch 479 +2026-04-11 03:39:17.668693: Current learning rate: 0.00892 +2026-04-11 03:40:59.880453: train_loss -0.3646 +2026-04-11 03:40:59.886099: val_loss -0.3271 +2026-04-11 03:40:59.888147: Pseudo dice [0.0, 0.0, 0.7521, 0.0037, 0.293, 0.6097, 0.3037] +2026-04-11 03:40:59.890765: Epoch time: 102.22 s +2026-04-11 03:41:01.000006: +2026-04-11 03:41:01.002152: Epoch 480 +2026-04-11 03:41:01.004073: Current learning rate: 0.00891 +2026-04-11 03:42:43.844316: train_loss -0.3766 +2026-04-11 03:42:43.852539: val_loss -0.3524 +2026-04-11 03:42:43.855610: Pseudo dice [0.5677, 0.0, 0.8048, 0.5388, 0.5376, 0.3498, 0.7535] +2026-04-11 03:42:43.858687: Epoch time: 102.85 s +2026-04-11 03:42:45.002825: +2026-04-11 03:42:45.005093: Epoch 481 +2026-04-11 03:42:45.006727: Current learning rate: 0.00891 +2026-04-11 03:44:27.118985: train_loss -0.3238 +2026-04-11 03:44:27.126346: val_loss -0.2999 +2026-04-11 03:44:27.128438: Pseudo dice [0.2402, 0.0, 0.441, 0.2406, 0.5494, 0.159, 0.5477] +2026-04-11 03:44:27.130629: Epoch time: 102.12 s +2026-04-11 03:44:28.304509: +2026-04-11 03:44:28.306710: Epoch 482 +2026-04-11 03:44:28.308641: Current learning rate: 0.00891 +2026-04-11 03:46:10.262660: train_loss -0.3397 +2026-04-11 03:46:10.269591: val_loss -0.3069 +2026-04-11 03:46:10.271474: Pseudo dice [0.0, 0.0, 0.7196, 0.1349, 0.2851, 0.6856, 0.3998] +2026-04-11 03:46:10.274051: Epoch time: 101.96 s +2026-04-11 03:46:11.471287: +2026-04-11 03:46:11.473071: Epoch 483 +2026-04-11 03:46:11.474956: Current learning rate: 0.00891 +2026-04-11 03:47:53.525022: train_loss -0.3281 +2026-04-11 03:47:53.532234: val_loss -0.3065 +2026-04-11 03:47:53.533979: Pseudo dice [0.0, 0.0, 0.6557, 0.3576, 0.4179, 0.1232, 0.4422] +2026-04-11 03:47:53.536059: Epoch time: 102.06 s +2026-04-11 03:47:54.668686: +2026-04-11 03:47:54.670525: Epoch 484 +2026-04-11 03:47:54.672822: Current learning rate: 0.0089 +2026-04-11 03:49:37.283134: train_loss -0.356 +2026-04-11 03:49:37.290125: val_loss -0.3239 +2026-04-11 03:49:37.292362: Pseudo dice [0.0, 0.0, 0.8552, 0.5801, 0.4533, 0.6487, 0.7349] +2026-04-11 03:49:37.295053: Epoch time: 102.62 s +2026-04-11 03:49:38.411544: +2026-04-11 03:49:38.413870: Epoch 485 +2026-04-11 03:49:38.415654: Current learning rate: 0.0089 +2026-04-11 03:51:21.184098: train_loss -0.3765 +2026-04-11 03:51:21.193107: val_loss -0.3498 +2026-04-11 03:51:21.195589: Pseudo dice [0.6034, 0.0, 0.8271, 0.6488, 0.3224, 0.5585, 0.7728] +2026-04-11 03:51:21.198288: Epoch time: 102.78 s +2026-04-11 03:51:22.300031: +2026-04-11 03:51:22.302666: Epoch 486 +2026-04-11 03:51:22.304714: Current learning rate: 0.0089 +2026-04-11 03:53:04.378166: train_loss -0.379 +2026-04-11 03:53:04.384967: val_loss -0.3079 +2026-04-11 03:53:04.387080: Pseudo dice [0.0, 0.0, 0.6665, 0.3021, 0.4917, 0.2019, 0.2446] +2026-04-11 03:53:04.389916: Epoch time: 102.08 s +2026-04-11 03:53:05.557749: +2026-04-11 03:53:05.560294: Epoch 487 +2026-04-11 03:53:05.568941: Current learning rate: 0.0089 +2026-04-11 03:54:47.955913: train_loss -0.3382 +2026-04-11 03:54:47.962046: val_loss -0.2971 +2026-04-11 03:54:47.964329: Pseudo dice [0.0, 0.0, 0.7149, 0.307, 0.4145, 0.2476, 0.7978] +2026-04-11 03:54:47.966735: Epoch time: 102.4 s +2026-04-11 03:54:49.148618: +2026-04-11 03:54:49.150537: Epoch 488 +2026-04-11 03:54:49.152628: Current learning rate: 0.00889 +2026-04-11 03:56:31.235213: train_loss -0.3597 +2026-04-11 03:56:31.242621: val_loss -0.3033 +2026-04-11 03:56:31.244439: Pseudo dice [0.0131, 0.0, 0.6655, 0.2801, 0.4265, 0.4867, 0.6567] +2026-04-11 03:56:31.247331: Epoch time: 102.09 s +2026-04-11 03:56:32.417978: +2026-04-11 03:56:32.422078: Epoch 489 +2026-04-11 03:56:32.424287: Current learning rate: 0.00889 +2026-04-11 03:58:15.279599: train_loss -0.3673 +2026-04-11 03:58:15.286326: val_loss -0.321 +2026-04-11 03:58:15.288504: Pseudo dice [0.551, 0.0, 0.4923, 0.1748, 0.4044, 0.6458, 0.8047] +2026-04-11 03:58:15.290977: Epoch time: 102.86 s +2026-04-11 03:58:16.489249: +2026-04-11 03:58:16.496269: Epoch 490 +2026-04-11 03:58:16.498481: Current learning rate: 0.00889 +2026-04-11 03:59:58.878124: train_loss -0.3638 +2026-04-11 03:59:58.884306: val_loss -0.3441 +2026-04-11 03:59:58.886873: Pseudo dice [0.6114, 0.0, 0.7751, 0.313, 0.4038, 0.6103, 0.7434] +2026-04-11 03:59:58.889971: Epoch time: 102.39 s +2026-04-11 04:00:00.024273: +2026-04-11 04:00:00.026128: Epoch 491 +2026-04-11 04:00:00.027997: Current learning rate: 0.00889 +2026-04-11 04:01:42.399075: train_loss -0.3629 +2026-04-11 04:01:42.405800: val_loss -0.2892 +2026-04-11 04:01:42.409568: Pseudo dice [0.0, 0.0, 0.7514, 0.0385, 0.2412, 0.6133, 0.6977] +2026-04-11 04:01:42.411898: Epoch time: 102.38 s +2026-04-11 04:01:43.507371: +2026-04-11 04:01:43.509780: Epoch 492 +2026-04-11 04:01:43.511379: Current learning rate: 0.00889 +2026-04-11 04:03:26.049096: train_loss -0.3567 +2026-04-11 04:03:26.055355: val_loss -0.3429 +2026-04-11 04:03:26.057389: Pseudo dice [0.0, 0.0, 0.6143, 0.3953, 0.5577, 0.6979, 0.5816] +2026-04-11 04:03:26.059551: Epoch time: 102.55 s +2026-04-11 04:03:27.215259: +2026-04-11 04:03:27.217382: Epoch 493 +2026-04-11 04:03:27.219171: Current learning rate: 0.00888 +2026-04-11 04:05:09.400834: train_loss -0.3539 +2026-04-11 04:05:09.406532: val_loss -0.3177 +2026-04-11 04:05:09.409243: Pseudo dice [0.0, 0.0, 0.66, 0.5878, 0.4989, 0.3614, 0.6185] +2026-04-11 04:05:09.411214: Epoch time: 102.19 s +2026-04-11 04:05:10.518962: +2026-04-11 04:05:10.520625: Epoch 494 +2026-04-11 04:05:10.522212: Current learning rate: 0.00888 +2026-04-11 04:06:53.123867: train_loss -0.3504 +2026-04-11 04:06:53.131114: val_loss -0.3104 +2026-04-11 04:06:53.134121: Pseudo dice [0.7573, 0.0, 0.6508, 0.4764, 0.4643, 0.3783, 0.2751] +2026-04-11 04:06:53.136571: Epoch time: 102.61 s +2026-04-11 04:06:54.244159: +2026-04-11 04:06:54.245964: Epoch 495 +2026-04-11 04:06:54.247715: Current learning rate: 0.00888 +2026-04-11 04:08:36.369124: train_loss -0.3243 +2026-04-11 04:08:36.377409: val_loss -0.2949 +2026-04-11 04:08:36.379750: Pseudo dice [0.0, 0.0, 0.5139, 0.3791, 0.4494, 0.7289, 0.3164] +2026-04-11 04:08:36.383205: Epoch time: 102.13 s +2026-04-11 04:08:37.497276: +2026-04-11 04:08:37.499791: Epoch 496 +2026-04-11 04:08:37.502374: Current learning rate: 0.00888 +2026-04-11 04:10:19.773284: train_loss -0.3434 +2026-04-11 04:10:19.779706: val_loss -0.266 +2026-04-11 04:10:19.781699: Pseudo dice [0.0, 0.0, 0.1262, 0.1122, 0.3177, 0.2014, 0.7681] +2026-04-11 04:10:19.783651: Epoch time: 102.28 s +2026-04-11 04:10:20.900946: +2026-04-11 04:10:20.903519: Epoch 497 +2026-04-11 04:10:20.905658: Current learning rate: 0.00887 +2026-04-11 04:12:02.629532: train_loss -0.3198 +2026-04-11 04:12:02.634932: val_loss -0.3079 +2026-04-11 04:12:02.637313: Pseudo dice [0.0, 0.0, 0.6999, 0.2896, 0.3241, 0.5461, 0.4731] +2026-04-11 04:12:02.639947: Epoch time: 101.73 s +2026-04-11 04:12:04.970890: +2026-04-11 04:12:04.972728: Epoch 498 +2026-04-11 04:12:04.974679: Current learning rate: 0.00887 +2026-04-11 04:13:47.147526: train_loss -0.3265 +2026-04-11 04:13:47.154003: val_loss -0.3019 +2026-04-11 04:13:47.155929: Pseudo dice [0.0, 0.0, 0.5572, 0.0962, 0.4721, 0.59, 0.4995] +2026-04-11 04:13:47.158301: Epoch time: 102.18 s +2026-04-11 04:13:48.251358: +2026-04-11 04:13:48.252979: Epoch 499 +2026-04-11 04:13:48.254412: Current learning rate: 0.00887 +2026-04-11 04:15:31.223763: train_loss -0.3658 +2026-04-11 04:15:31.232305: val_loss -0.3272 +2026-04-11 04:15:31.234501: Pseudo dice [0.0, 0.0, 0.6696, 0.2662, 0.4343, 0.7151, 0.647] +2026-04-11 04:15:31.237691: Epoch time: 102.98 s +2026-04-11 04:15:34.065127: +2026-04-11 04:15:34.069544: Epoch 500 +2026-04-11 04:15:34.071117: Current learning rate: 0.00887 +2026-04-11 04:17:16.526253: train_loss -0.366 +2026-04-11 04:17:16.532678: val_loss -0.3403 +2026-04-11 04:17:16.535721: Pseudo dice [0.0, 0.0, 0.7095, 0.6444, 0.465, 0.625, 0.8638] +2026-04-11 04:17:16.538676: Epoch time: 102.46 s +2026-04-11 04:17:17.664955: +2026-04-11 04:17:17.667733: Epoch 501 +2026-04-11 04:17:17.670205: Current learning rate: 0.00887 +2026-04-11 04:19:00.919024: train_loss -0.3402 +2026-04-11 04:19:00.924814: val_loss -0.3157 +2026-04-11 04:19:00.927083: Pseudo dice [0.1452, 0.0, 0.7529, 0.2638, 0.4979, 0.7971, 0.2021] +2026-04-11 04:19:00.929710: Epoch time: 103.26 s +2026-04-11 04:19:02.030763: +2026-04-11 04:19:02.033514: Epoch 502 +2026-04-11 04:19:02.036683: Current learning rate: 0.00886 +2026-04-11 04:20:44.551424: train_loss -0.3722 +2026-04-11 04:20:44.559495: val_loss -0.3661 +2026-04-11 04:20:44.561514: Pseudo dice [0.1733, 0.0, 0.8116, 0.5892, 0.4151, 0.5317, 0.6716] +2026-04-11 04:20:44.564203: Epoch time: 102.52 s +2026-04-11 04:20:45.669721: +2026-04-11 04:20:45.671498: Epoch 503 +2026-04-11 04:20:45.673503: Current learning rate: 0.00886 +2026-04-11 04:22:28.119316: train_loss -0.385 +2026-04-11 04:22:28.124459: val_loss -0.3832 +2026-04-11 04:22:28.126432: Pseudo dice [0.5295, 0.0, 0.7041, 0.6417, 0.4434, 0.5314, 0.841] +2026-04-11 04:22:28.128888: Epoch time: 102.45 s +2026-04-11 04:22:29.236260: +2026-04-11 04:22:29.238647: Epoch 504 +2026-04-11 04:22:29.241398: Current learning rate: 0.00886 +2026-04-11 04:24:11.303175: train_loss -0.3678 +2026-04-11 04:24:11.309929: val_loss -0.3274 +2026-04-11 04:24:11.312151: Pseudo dice [0.0873, 0.0, 0.7541, 0.5963, 0.3465, 0.3234, 0.6849] +2026-04-11 04:24:11.315208: Epoch time: 102.07 s +2026-04-11 04:24:12.427883: +2026-04-11 04:24:12.429682: Epoch 505 +2026-04-11 04:24:12.431318: Current learning rate: 0.00886 +2026-04-11 04:25:55.145109: train_loss -0.3479 +2026-04-11 04:25:55.151959: val_loss -0.313 +2026-04-11 04:25:55.154401: Pseudo dice [0.0, 0.0, 0.8014, 0.1525, 0.2433, 0.4803, 0.4113] +2026-04-11 04:25:55.157248: Epoch time: 102.72 s +2026-04-11 04:25:56.322311: +2026-04-11 04:25:56.325563: Epoch 506 +2026-04-11 04:25:56.328907: Current learning rate: 0.00885 +2026-04-11 04:27:38.599297: train_loss -0.3522 +2026-04-11 04:27:38.604936: val_loss -0.2792 +2026-04-11 04:27:38.606970: Pseudo dice [0.0, 0.0, 0.6757, 0.2269, 0.4366, 0.6532, 0.2303] +2026-04-11 04:27:38.609098: Epoch time: 102.28 s +2026-04-11 04:27:39.726598: +2026-04-11 04:27:39.728697: Epoch 507 +2026-04-11 04:27:39.730494: Current learning rate: 0.00885 +2026-04-11 04:29:22.041448: train_loss -0.362 +2026-04-11 04:29:22.047426: val_loss -0.3108 +2026-04-11 04:29:22.049254: Pseudo dice [0.0, 0.0, 0.7632, 0.573, 0.4814, 0.5046, 0.75] +2026-04-11 04:29:22.051642: Epoch time: 102.32 s +2026-04-11 04:29:23.211928: +2026-04-11 04:29:23.214062: Epoch 508 +2026-04-11 04:29:23.215746: Current learning rate: 0.00885 +2026-04-11 04:31:05.654770: train_loss -0.3503 +2026-04-11 04:31:05.660948: val_loss -0.3501 +2026-04-11 04:31:05.662899: Pseudo dice [0.0, 0.0, 0.6432, 0.8208, 0.5018, 0.7164, 0.8078] +2026-04-11 04:31:05.666544: Epoch time: 102.45 s +2026-04-11 04:31:06.781121: +2026-04-11 04:31:06.783409: Epoch 509 +2026-04-11 04:31:06.784902: Current learning rate: 0.00885 +2026-04-11 04:32:48.713499: train_loss -0.3645 +2026-04-11 04:32:48.719441: val_loss -0.324 +2026-04-11 04:32:48.721305: Pseudo dice [0.0, 0.0, 0.7888, 0.228, 0.3144, 0.594, 0.8708] +2026-04-11 04:32:48.723719: Epoch time: 101.94 s +2026-04-11 04:32:49.852157: +2026-04-11 04:32:49.853818: Epoch 510 +2026-04-11 04:32:49.855564: Current learning rate: 0.00884 +2026-04-11 04:34:32.541375: train_loss -0.3707 +2026-04-11 04:34:32.548605: val_loss -0.3477 +2026-04-11 04:34:32.550607: Pseudo dice [0.0, 0.0, 0.7928, 0.5689, 0.434, 0.5363, 0.8392] +2026-04-11 04:34:32.553012: Epoch time: 102.69 s +2026-04-11 04:34:33.698782: +2026-04-11 04:34:33.700780: Epoch 511 +2026-04-11 04:34:33.702958: Current learning rate: 0.00884 +2026-04-11 04:36:15.926115: train_loss -0.3459 +2026-04-11 04:36:15.932636: val_loss -0.3285 +2026-04-11 04:36:15.934760: Pseudo dice [0.0, 0.0, 0.831, 0.486, 0.5914, 0.5848, 0.624] +2026-04-11 04:36:15.936944: Epoch time: 102.23 s +2026-04-11 04:36:17.059307: +2026-04-11 04:36:17.061172: Epoch 512 +2026-04-11 04:36:17.063344: Current learning rate: 0.00884 +2026-04-11 04:37:59.196355: train_loss -0.3747 +2026-04-11 04:37:59.203804: val_loss -0.319 +2026-04-11 04:37:59.205990: Pseudo dice [0.0, 0.0, 0.8124, 0.6092, 0.4704, 0.6931, 0.7266] +2026-04-11 04:37:59.209513: Epoch time: 102.14 s +2026-04-11 04:38:00.315989: +2026-04-11 04:38:00.318304: Epoch 513 +2026-04-11 04:38:00.320346: Current learning rate: 0.00884 +2026-04-11 04:39:42.607451: train_loss -0.3766 +2026-04-11 04:39:42.614472: val_loss -0.3466 +2026-04-11 04:39:42.616912: Pseudo dice [0.0, 0.0, 0.7798, 0.5767, 0.4681, 0.5956, 0.9119] +2026-04-11 04:39:42.619446: Epoch time: 102.29 s +2026-04-11 04:39:43.758517: +2026-04-11 04:39:43.760876: Epoch 514 +2026-04-11 04:39:43.762879: Current learning rate: 0.00884 +2026-04-11 04:41:25.924050: train_loss -0.3935 +2026-04-11 04:41:25.929357: val_loss -0.3248 +2026-04-11 04:41:25.931578: Pseudo dice [0.0, 0.0, 0.7936, 0.3355, 0.4812, 0.2883, 0.7351] +2026-04-11 04:41:25.934012: Epoch time: 102.17 s +2026-04-11 04:41:27.027448: +2026-04-11 04:41:27.029123: Epoch 515 +2026-04-11 04:41:27.030716: Current learning rate: 0.00883 +2026-04-11 04:43:09.250953: train_loss -0.37 +2026-04-11 04:43:09.257353: val_loss -0.3199 +2026-04-11 04:43:09.260649: Pseudo dice [0.0, 0.0, 0.7681, 0.3733, 0.2844, 0.3317, 0.373] +2026-04-11 04:43:09.263479: Epoch time: 102.23 s +2026-04-11 04:43:10.370153: +2026-04-11 04:43:10.372038: Epoch 516 +2026-04-11 04:43:10.373497: Current learning rate: 0.00883 +2026-04-11 04:44:52.733038: train_loss -0.349 +2026-04-11 04:44:52.739534: val_loss -0.3157 +2026-04-11 04:44:52.741440: Pseudo dice [0.0, 0.0, 0.6306, 0.6, 0.3537, 0.4861, 0.4475] +2026-04-11 04:44:52.743937: Epoch time: 102.37 s +2026-04-11 04:44:53.871473: +2026-04-11 04:44:53.873171: Epoch 517 +2026-04-11 04:44:53.875037: Current learning rate: 0.00883 +2026-04-11 04:46:35.938194: train_loss -0.3643 +2026-04-11 04:46:35.944028: val_loss -0.3342 +2026-04-11 04:46:35.946678: Pseudo dice [0.0, 0.0, 0.7851, 0.7496, 0.6432, 0.7788, 0.525] +2026-04-11 04:46:35.949591: Epoch time: 102.07 s +2026-04-11 04:46:38.175596: +2026-04-11 04:46:38.177190: Epoch 518 +2026-04-11 04:46:38.178539: Current learning rate: 0.00883 +2026-04-11 04:48:20.549583: train_loss -0.3296 +2026-04-11 04:48:20.556273: val_loss -0.3615 +2026-04-11 04:48:20.558045: Pseudo dice [0.0, 0.0, 0.7959, 0.2834, 0.5789, 0.6801, 0.66] +2026-04-11 04:48:20.560408: Epoch time: 102.38 s +2026-04-11 04:48:21.713529: +2026-04-11 04:48:21.715614: Epoch 519 +2026-04-11 04:48:21.717670: Current learning rate: 0.00882 +2026-04-11 04:50:04.359054: train_loss -0.3282 +2026-04-11 04:50:04.364441: val_loss -0.2913 +2026-04-11 04:50:04.366076: Pseudo dice [0.0, 0.0, 0.6293, 0.4213, 0.4319, 0.4964, 0.4227] +2026-04-11 04:50:04.368069: Epoch time: 102.65 s +2026-04-11 04:50:05.471961: +2026-04-11 04:50:05.473983: Epoch 520 +2026-04-11 04:50:05.475902: Current learning rate: 0.00882 +2026-04-11 04:51:48.080314: train_loss -0.3815 +2026-04-11 04:51:48.088041: val_loss -0.3106 +2026-04-11 04:51:48.090359: Pseudo dice [0.0, 0.0, 0.7068, 0.405, 0.4887, 0.6334, 0.7811] +2026-04-11 04:51:48.092678: Epoch time: 102.61 s +2026-04-11 04:51:49.280297: +2026-04-11 04:51:49.282164: Epoch 521 +2026-04-11 04:51:49.283930: Current learning rate: 0.00882 +2026-04-11 04:53:31.245100: train_loss -0.3769 +2026-04-11 04:53:31.251047: val_loss -0.3026 +2026-04-11 04:53:31.253254: Pseudo dice [0.0, 0.0, 0.7535, 0.0498, 0.3793, 0.2171, 0.7749] +2026-04-11 04:53:31.255547: Epoch time: 101.97 s +2026-04-11 04:53:32.372939: +2026-04-11 04:53:32.374508: Epoch 522 +2026-04-11 04:53:32.375986: Current learning rate: 0.00882 +2026-04-11 04:55:15.361575: train_loss -0.3339 +2026-04-11 04:55:15.368165: val_loss -0.3183 +2026-04-11 04:55:15.371006: Pseudo dice [0.0, 0.0, 0.6844, 0.0861, 0.4634, 0.4233, 0.2896] +2026-04-11 04:55:15.375187: Epoch time: 102.99 s +2026-04-11 04:55:16.503082: +2026-04-11 04:55:16.504815: Epoch 523 +2026-04-11 04:55:16.506325: Current learning rate: 0.00882 +2026-04-11 04:56:58.789426: train_loss -0.3681 +2026-04-11 04:56:58.796470: val_loss -0.3609 +2026-04-11 04:56:58.799437: Pseudo dice [0.0, 0.0, 0.8049, 0.3992, 0.5792, 0.7283, 0.9179] +2026-04-11 04:56:58.801468: Epoch time: 102.29 s +2026-04-11 04:56:59.941827: +2026-04-11 04:56:59.943615: Epoch 524 +2026-04-11 04:56:59.945281: Current learning rate: 0.00881 +2026-04-11 04:58:42.124955: train_loss -0.3624 +2026-04-11 04:58:42.131435: val_loss -0.3285 +2026-04-11 04:58:42.134079: Pseudo dice [0.0, 0.0, 0.8415, 0.8095, 0.5286, 0.6289, 0.7849] +2026-04-11 04:58:42.136658: Epoch time: 102.19 s +2026-04-11 04:58:43.298934: +2026-04-11 04:58:43.300942: Epoch 525 +2026-04-11 04:58:43.303472: Current learning rate: 0.00881 +2026-04-11 05:00:25.134185: train_loss -0.366 +2026-04-11 05:00:25.139526: val_loss -0.337 +2026-04-11 05:00:25.141170: Pseudo dice [0.0, 0.0, 0.6796, 0.5456, 0.3387, 0.6757, 0.7739] +2026-04-11 05:00:25.143174: Epoch time: 101.84 s +2026-04-11 05:00:26.234605: +2026-04-11 05:00:26.236365: Epoch 526 +2026-04-11 05:00:26.237889: Current learning rate: 0.00881 +2026-04-11 05:02:08.188884: train_loss -0.3352 +2026-04-11 05:02:08.195238: val_loss -0.2723 +2026-04-11 05:02:08.198107: Pseudo dice [0.0, 0.0, 0.4342, 0.4524, 0.3534, 0.1444, 0.2382] +2026-04-11 05:02:08.200788: Epoch time: 101.96 s +2026-04-11 05:02:09.332850: +2026-04-11 05:02:09.335281: Epoch 527 +2026-04-11 05:02:09.336672: Current learning rate: 0.00881 +2026-04-11 05:03:51.844499: train_loss -0.336 +2026-04-11 05:03:51.850513: val_loss -0.2889 +2026-04-11 05:03:51.853903: Pseudo dice [0.0, 0.0, 0.8248, 0.4554, 0.4512, 0.3047, 0.3456] +2026-04-11 05:03:51.856354: Epoch time: 102.51 s +2026-04-11 05:03:52.958056: +2026-04-11 05:03:52.961395: Epoch 528 +2026-04-11 05:03:52.963163: Current learning rate: 0.0088 +2026-04-11 05:05:35.156965: train_loss -0.3404 +2026-04-11 05:05:35.163645: val_loss -0.3153 +2026-04-11 05:05:35.165761: Pseudo dice [0.0, 0.0, 0.6288, 0.3589, 0.3623, 0.3446, 0.4955] +2026-04-11 05:05:35.168249: Epoch time: 102.2 s +2026-04-11 05:05:36.328265: +2026-04-11 05:05:36.330303: Epoch 529 +2026-04-11 05:05:36.332204: Current learning rate: 0.0088 +2026-04-11 05:07:18.559538: train_loss -0.3517 +2026-04-11 05:07:18.565145: val_loss -0.2952 +2026-04-11 05:07:18.567363: Pseudo dice [0.0, 0.0, 0.8539, 0.4197, 0.4016, 0.5062, 0.674] +2026-04-11 05:07:18.569211: Epoch time: 102.23 s +2026-04-11 05:07:19.701714: +2026-04-11 05:07:19.703363: Epoch 530 +2026-04-11 05:07:19.704852: Current learning rate: 0.0088 +2026-04-11 05:09:02.305143: train_loss -0.3809 +2026-04-11 05:09:02.312515: val_loss -0.329 +2026-04-11 05:09:02.314238: Pseudo dice [0.0, 0.0, 0.6663, 0.553, 0.422, 0.3227, 0.2765] +2026-04-11 05:09:02.317089: Epoch time: 102.61 s +2026-04-11 05:09:03.479732: +2026-04-11 05:09:03.481614: Epoch 531 +2026-04-11 05:09:03.483273: Current learning rate: 0.0088 +2026-04-11 05:10:45.367348: train_loss -0.3628 +2026-04-11 05:10:45.374637: val_loss -0.2824 +2026-04-11 05:10:45.376981: Pseudo dice [0.0, 0.0, 0.5849, 0.1457, 0.4319, 0.6271, 0.3479] +2026-04-11 05:10:45.379502: Epoch time: 101.89 s +2026-04-11 05:10:46.479472: +2026-04-11 05:10:46.481818: Epoch 532 +2026-04-11 05:10:46.483447: Current learning rate: 0.00879 +2026-04-11 05:12:29.596043: train_loss -0.3878 +2026-04-11 05:12:29.601511: val_loss -0.3206 +2026-04-11 05:12:29.603385: Pseudo dice [0.0, 0.0, 0.6114, 0.7292, 0.5401, 0.4009, 0.7816] +2026-04-11 05:12:29.605625: Epoch time: 103.12 s +2026-04-11 05:12:30.763153: +2026-04-11 05:12:30.764780: Epoch 533 +2026-04-11 05:12:30.766254: Current learning rate: 0.00879 +2026-04-11 05:14:12.618037: train_loss -0.3812 +2026-04-11 05:14:12.623847: val_loss -0.3796 +2026-04-11 05:14:12.625972: Pseudo dice [0.0, 0.0, 0.9075, 0.5745, 0.6217, 0.7088, 0.662] +2026-04-11 05:14:12.628736: Epoch time: 101.86 s +2026-04-11 05:14:13.842973: +2026-04-11 05:14:13.845008: Epoch 534 +2026-04-11 05:14:13.847278: Current learning rate: 0.00879 +2026-04-11 05:15:56.238619: train_loss -0.3817 +2026-04-11 05:15:56.245493: val_loss -0.3229 +2026-04-11 05:15:56.247713: Pseudo dice [0.0, 0.0, 0.7784, 0.589, 0.4561, 0.4072, 0.8233] +2026-04-11 05:15:56.250242: Epoch time: 102.4 s +2026-04-11 05:15:57.383185: +2026-04-11 05:15:57.385539: Epoch 535 +2026-04-11 05:15:57.387750: Current learning rate: 0.00879 +2026-04-11 05:17:39.473173: train_loss -0.3789 +2026-04-11 05:17:39.479143: val_loss -0.3512 +2026-04-11 05:17:39.481058: Pseudo dice [0.0, 0.0, 0.7028, 0.0, 0.5607, 0.4744, 0.8339] +2026-04-11 05:17:39.483625: Epoch time: 102.09 s +2026-04-11 05:17:40.607321: +2026-04-11 05:17:40.609040: Epoch 536 +2026-04-11 05:17:40.611009: Current learning rate: 0.00879 +2026-04-11 05:19:22.836098: train_loss -0.3567 +2026-04-11 05:19:22.842293: val_loss -0.3357 +2026-04-11 05:19:22.844222: Pseudo dice [0.0, 0.0, 0.6363, 0.7475, 0.4168, 0.6668, 0.8916] +2026-04-11 05:19:22.846360: Epoch time: 102.23 s +2026-04-11 05:19:23.964752: +2026-04-11 05:19:23.966765: Epoch 537 +2026-04-11 05:19:23.968241: Current learning rate: 0.00878 +2026-04-11 05:21:05.909082: train_loss -0.3634 +2026-04-11 05:21:05.914565: val_loss -0.2557 +2026-04-11 05:21:05.916189: Pseudo dice [0.0, 0.0, 0.3451, 0.0871, 0.2705, 0.4575, 0.0715] +2026-04-11 05:21:05.919841: Epoch time: 101.95 s +2026-04-11 05:21:08.227508: +2026-04-11 05:21:08.229199: Epoch 538 +2026-04-11 05:21:08.230556: Current learning rate: 0.00878 +2026-04-11 05:22:50.346442: train_loss -0.3398 +2026-04-11 05:22:50.354661: val_loss -0.3293 +2026-04-11 05:22:50.357028: Pseudo dice [0.0, 0.0, 0.656, 0.3258, 0.4226, 0.4736, 0.3698] +2026-04-11 05:22:50.359455: Epoch time: 102.12 s +2026-04-11 05:22:51.542763: +2026-04-11 05:22:51.544393: Epoch 539 +2026-04-11 05:22:51.545891: Current learning rate: 0.00878 +2026-04-11 05:24:33.896456: train_loss -0.3703 +2026-04-11 05:24:33.902463: val_loss -0.3195 +2026-04-11 05:24:33.904624: Pseudo dice [0.0, 0.0, 0.6411, 0.5701, 0.4385, 0.4291, 0.3394] +2026-04-11 05:24:33.906871: Epoch time: 102.36 s +2026-04-11 05:24:35.034266: +2026-04-11 05:24:35.036106: Epoch 540 +2026-04-11 05:24:35.037883: Current learning rate: 0.00878 +2026-04-11 05:26:17.178345: train_loss -0.3581 +2026-04-11 05:26:17.184316: val_loss -0.3558 +2026-04-11 05:26:17.186406: Pseudo dice [0.0, 0.0, 0.7047, 0.5877, 0.4904, 0.6278, 0.5572] +2026-04-11 05:26:17.188954: Epoch time: 102.15 s +2026-04-11 05:26:18.322807: +2026-04-11 05:26:18.324960: Epoch 541 +2026-04-11 05:26:18.326677: Current learning rate: 0.00877 +2026-04-11 05:28:00.540201: train_loss -0.3485 +2026-04-11 05:28:00.545802: val_loss -0.3082 +2026-04-11 05:28:00.547662: Pseudo dice [0.0, 0.0, 0.7816, 0.3426, 0.3632, 0.1529, 0.3474] +2026-04-11 05:28:00.550315: Epoch time: 102.22 s +2026-04-11 05:28:01.720389: +2026-04-11 05:28:01.722039: Epoch 542 +2026-04-11 05:28:01.723768: Current learning rate: 0.00877 +2026-04-11 05:29:44.563535: train_loss -0.3516 +2026-04-11 05:29:44.570879: val_loss -0.313 +2026-04-11 05:29:44.572573: Pseudo dice [0.0, 0.0, 0.7717, 0.4089, 0.3796, 0.6264, 0.6553] +2026-04-11 05:29:44.574824: Epoch time: 102.85 s +2026-04-11 05:29:45.694354: +2026-04-11 05:29:45.696689: Epoch 543 +2026-04-11 05:29:45.698529: Current learning rate: 0.00877 +2026-04-11 05:31:28.009303: train_loss -0.3663 +2026-04-11 05:31:28.015636: val_loss -0.3625 +2026-04-11 05:31:28.018018: Pseudo dice [0.0, 0.0, 0.8479, 0.7049, 0.608, 0.7619, 0.7778] +2026-04-11 05:31:28.020817: Epoch time: 102.32 s +2026-04-11 05:31:29.170226: +2026-04-11 05:31:29.171800: Epoch 544 +2026-04-11 05:31:29.173225: Current learning rate: 0.00877 +2026-04-11 05:33:11.191620: train_loss -0.3857 +2026-04-11 05:33:11.200582: val_loss -0.3322 +2026-04-11 05:33:11.203685: Pseudo dice [0.0, 0.0, 0.8298, 0.2765, 0.4506, 0.7744, 0.7594] +2026-04-11 05:33:11.205969: Epoch time: 102.02 s +2026-04-11 05:33:12.310633: +2026-04-11 05:33:12.312954: Epoch 545 +2026-04-11 05:33:12.314655: Current learning rate: 0.00876 +2026-04-11 05:34:54.800276: train_loss -0.3949 +2026-04-11 05:34:54.806413: val_loss -0.3392 +2026-04-11 05:34:54.808614: Pseudo dice [0.2694, 0.0, 0.7852, 0.6479, 0.5252, 0.5012, 0.6458] +2026-04-11 05:34:54.810845: Epoch time: 102.49 s +2026-04-11 05:34:55.991537: +2026-04-11 05:34:55.994026: Epoch 546 +2026-04-11 05:34:55.996005: Current learning rate: 0.00876 +2026-04-11 05:36:38.361946: train_loss -0.328 +2026-04-11 05:36:38.368499: val_loss -0.319 +2026-04-11 05:36:38.370195: Pseudo dice [0.0, 0.0, 0.831, 0.4202, 0.3632, 0.2446, 0.7079] +2026-04-11 05:36:38.372238: Epoch time: 102.37 s +2026-04-11 05:36:39.538273: +2026-04-11 05:36:39.539776: Epoch 547 +2026-04-11 05:36:39.541667: Current learning rate: 0.00876 +2026-04-11 05:38:21.777493: train_loss -0.3686 +2026-04-11 05:38:21.783646: val_loss -0.3548 +2026-04-11 05:38:21.785971: Pseudo dice [0.0, 0.0, 0.6673, 0.7687, 0.5736, 0.3375, 0.6621] +2026-04-11 05:38:21.788579: Epoch time: 102.24 s +2026-04-11 05:38:22.958944: +2026-04-11 05:38:22.960815: Epoch 548 +2026-04-11 05:38:22.962495: Current learning rate: 0.00876 +2026-04-11 05:40:05.074617: train_loss -0.3674 +2026-04-11 05:40:05.081457: val_loss -0.3442 +2026-04-11 05:40:05.084321: Pseudo dice [0.0, 0.0, 0.8087, 0.6307, 0.3727, 0.4824, 0.8042] +2026-04-11 05:40:05.086974: Epoch time: 102.12 s +2026-04-11 05:40:06.241843: +2026-04-11 05:40:06.243959: Epoch 549 +2026-04-11 05:40:06.245584: Current learning rate: 0.00876 +2026-04-11 05:41:48.392162: train_loss -0.3958 +2026-04-11 05:41:48.398650: val_loss -0.3467 +2026-04-11 05:41:48.400818: Pseudo dice [0.0, 0.0, 0.863, 0.2232, 0.3816, 0.466, 0.7878] +2026-04-11 05:41:48.402770: Epoch time: 102.15 s +2026-04-11 05:41:51.188199: +2026-04-11 05:41:51.190062: Epoch 550 +2026-04-11 05:41:51.191401: Current learning rate: 0.00875 +2026-04-11 05:43:33.648719: train_loss -0.3763 +2026-04-11 05:43:33.654867: val_loss -0.3237 +2026-04-11 05:43:33.656868: Pseudo dice [0.0, 0.0, 0.8122, 0.5806, 0.293, 0.6142, 0.3905] +2026-04-11 05:43:33.658912: Epoch time: 102.46 s +2026-04-11 05:43:34.796625: +2026-04-11 05:43:34.798533: Epoch 551 +2026-04-11 05:43:34.800044: Current learning rate: 0.00875 +2026-04-11 05:45:17.204560: train_loss -0.3664 +2026-04-11 05:45:17.210366: val_loss -0.3375 +2026-04-11 05:45:17.212396: Pseudo dice [0.0, 0.0, 0.755, 0.6412, 0.5278, 0.3966, 0.7463] +2026-04-11 05:45:17.214359: Epoch time: 102.41 s +2026-04-11 05:45:18.361834: +2026-04-11 05:45:18.364077: Epoch 552 +2026-04-11 05:45:18.365830: Current learning rate: 0.00875 +2026-04-11 05:47:00.445901: train_loss -0.3751 +2026-04-11 05:47:00.451747: val_loss -0.3288 +2026-04-11 05:47:00.453891: Pseudo dice [0.0, 0.0, 0.572, 0.7445, 0.3874, 0.5013, 0.5003] +2026-04-11 05:47:00.456516: Epoch time: 102.09 s +2026-04-11 05:47:01.583586: +2026-04-11 05:47:01.585780: Epoch 553 +2026-04-11 05:47:01.587473: Current learning rate: 0.00875 +2026-04-11 05:48:44.032030: train_loss -0.3682 +2026-04-11 05:48:44.039405: val_loss -0.3197 +2026-04-11 05:48:44.042081: Pseudo dice [0.0574, 0.0, 0.8007, 0.1271, 0.399, 0.3839, 0.6844] +2026-04-11 05:48:44.044444: Epoch time: 102.45 s +2026-04-11 05:48:45.169451: +2026-04-11 05:48:45.171481: Epoch 554 +2026-04-11 05:48:45.173394: Current learning rate: 0.00874 +2026-04-11 05:50:27.621472: train_loss -0.3648 +2026-04-11 05:50:27.627041: val_loss -0.3123 +2026-04-11 05:50:27.629507: Pseudo dice [0.0, 0.0, 0.715, 0.1932, 0.4327, 0.406, 0.7477] +2026-04-11 05:50:27.632048: Epoch time: 102.46 s +2026-04-11 05:50:28.779217: +2026-04-11 05:50:28.781019: Epoch 555 +2026-04-11 05:50:28.783238: Current learning rate: 0.00874 +2026-04-11 05:52:11.616855: train_loss -0.3595 +2026-04-11 05:52:11.626261: val_loss -0.3049 +2026-04-11 05:52:11.629294: Pseudo dice [0.0, 0.0, 0.3251, 0.035, 0.5714, 0.224, 0.2747] +2026-04-11 05:52:11.631664: Epoch time: 102.84 s +2026-04-11 05:52:12.748277: +2026-04-11 05:52:12.749998: Epoch 556 +2026-04-11 05:52:12.751484: Current learning rate: 0.00874 +2026-04-11 05:53:54.628045: train_loss -0.3775 +2026-04-11 05:53:54.634323: val_loss -0.3598 +2026-04-11 05:53:54.636862: Pseudo dice [0.0, 0.0, 0.8095, 0.1884, 0.4567, 0.4958, 0.7109] +2026-04-11 05:53:54.639133: Epoch time: 101.88 s +2026-04-11 05:53:55.757833: +2026-04-11 05:53:55.759571: Epoch 557 +2026-04-11 05:53:55.761165: Current learning rate: 0.00874 +2026-04-11 05:55:37.841711: train_loss -0.3672 +2026-04-11 05:55:37.848005: val_loss -0.3262 +2026-04-11 05:55:37.850434: Pseudo dice [0.0, 0.0, 0.8291, 0.0042, 0.5626, 0.4852, 0.5641] +2026-04-11 05:55:37.853048: Epoch time: 102.09 s +2026-04-11 05:55:40.090434: +2026-04-11 05:55:40.092299: Epoch 558 +2026-04-11 05:55:40.093786: Current learning rate: 0.00874 +2026-04-11 05:57:22.342479: train_loss -0.3723 +2026-04-11 05:57:22.349215: val_loss -0.3646 +2026-04-11 05:57:22.351848: Pseudo dice [0.0, 0.0, 0.7734, 0.3676, 0.4398, 0.7398, 0.6791] +2026-04-11 05:57:22.354857: Epoch time: 102.26 s +2026-04-11 05:57:23.490628: +2026-04-11 05:57:23.492360: Epoch 559 +2026-04-11 05:57:23.493716: Current learning rate: 0.00873 +2026-04-11 05:59:05.916058: train_loss -0.393 +2026-04-11 05:59:05.923162: val_loss -0.3359 +2026-04-11 05:59:05.925286: Pseudo dice [0.0, 0.0, 0.716, 0.704, 0.5487, 0.2811, 0.5844] +2026-04-11 05:59:05.928013: Epoch time: 102.43 s +2026-04-11 05:59:07.065822: +2026-04-11 05:59:07.067815: Epoch 560 +2026-04-11 05:59:07.069690: Current learning rate: 0.00873 +2026-04-11 06:00:49.213094: train_loss -0.3782 +2026-04-11 06:00:49.219358: val_loss -0.3356 +2026-04-11 06:00:49.221454: Pseudo dice [0.0, 0.0, 0.7314, 0.6166, 0.4343, 0.3317, 0.8179] +2026-04-11 06:00:49.223885: Epoch time: 102.15 s +2026-04-11 06:00:50.350529: +2026-04-11 06:00:50.352409: Epoch 561 +2026-04-11 06:00:50.354473: Current learning rate: 0.00873 +2026-04-11 06:02:32.639590: train_loss -0.3714 +2026-04-11 06:02:32.647174: val_loss -0.3314 +2026-04-11 06:02:32.649781: Pseudo dice [0.0, 0.0, 0.6763, 0.4656, 0.3829, 0.1772, 0.8435] +2026-04-11 06:02:32.652888: Epoch time: 102.29 s +2026-04-11 06:02:33.814376: +2026-04-11 06:02:33.816536: Epoch 562 +2026-04-11 06:02:33.818103: Current learning rate: 0.00873 +2026-04-11 06:04:15.726855: train_loss -0.3807 +2026-04-11 06:04:15.732535: val_loss -0.3341 +2026-04-11 06:04:15.733977: Pseudo dice [0.0, 0.0, 0.7975, 0.23, 0.4507, 0.4109, 0.7118] +2026-04-11 06:04:15.736771: Epoch time: 101.92 s +2026-04-11 06:04:16.857480: +2026-04-11 06:04:16.860291: Epoch 563 +2026-04-11 06:04:16.861807: Current learning rate: 0.00872 +2026-04-11 06:05:59.067980: train_loss -0.3601 +2026-04-11 06:05:59.073995: val_loss -0.2873 +2026-04-11 06:05:59.076315: Pseudo dice [0.0, 0.0, 0.3365, 0.5468, 0.429, 0.5065, 0.8026] +2026-04-11 06:05:59.078525: Epoch time: 102.21 s +2026-04-11 06:06:00.250949: +2026-04-11 06:06:00.252691: Epoch 564 +2026-04-11 06:06:00.254231: Current learning rate: 0.00872 +2026-04-11 06:07:42.473594: train_loss -0.3335 +2026-04-11 06:07:42.479352: val_loss -0.3503 +2026-04-11 06:07:42.481611: Pseudo dice [0.0, 0.0, 0.7251, 0.6509, 0.6314, 0.6552, 0.8464] +2026-04-11 06:07:42.484027: Epoch time: 102.23 s +2026-04-11 06:07:43.591188: +2026-04-11 06:07:43.592819: Epoch 565 +2026-04-11 06:07:43.594404: Current learning rate: 0.00872 +2026-04-11 06:09:25.669278: train_loss -0.3507 +2026-04-11 06:09:25.675633: val_loss -0.2975 +2026-04-11 06:09:25.677788: Pseudo dice [0.0, 0.0, 0.6057, 0.5242, 0.3364, 0.3387, 0.862] +2026-04-11 06:09:25.679900: Epoch time: 102.08 s +2026-04-11 06:09:26.866098: +2026-04-11 06:09:26.868381: Epoch 566 +2026-04-11 06:09:26.870062: Current learning rate: 0.00872 +2026-04-11 06:11:09.282364: train_loss -0.3577 +2026-04-11 06:11:09.289062: val_loss -0.3192 +2026-04-11 06:11:09.292001: Pseudo dice [0.0, 0.0, 0.7506, 0.1685, 0.6178, 0.7377, 0.7593] +2026-04-11 06:11:09.295205: Epoch time: 102.42 s +2026-04-11 06:11:10.432825: +2026-04-11 06:11:10.434622: Epoch 567 +2026-04-11 06:11:10.436325: Current learning rate: 0.00871 +2026-04-11 06:12:52.642518: train_loss -0.3732 +2026-04-11 06:12:52.649907: val_loss -0.3573 +2026-04-11 06:12:52.651917: Pseudo dice [0.0, 0.0, 0.8495, 0.3919, 0.5645, 0.431, 0.5037] +2026-04-11 06:12:52.654367: Epoch time: 102.21 s +2026-04-11 06:12:53.836983: +2026-04-11 06:12:53.838758: Epoch 568 +2026-04-11 06:12:53.840481: Current learning rate: 0.00871 +2026-04-11 06:14:36.419141: train_loss -0.3576 +2026-04-11 06:14:36.426320: val_loss -0.3373 +2026-04-11 06:14:36.428095: Pseudo dice [0.0, 0.0, 0.8539, 0.5465, 0.3903, 0.4155, 0.7767] +2026-04-11 06:14:36.430853: Epoch time: 102.59 s +2026-04-11 06:14:37.542159: +2026-04-11 06:14:37.544020: Epoch 569 +2026-04-11 06:14:37.545930: Current learning rate: 0.00871 +2026-04-11 06:16:19.745948: train_loss -0.3738 +2026-04-11 06:16:19.752134: val_loss -0.3478 +2026-04-11 06:16:19.753737: Pseudo dice [0.0, 0.0, 0.7963, 0.7198, 0.496, 0.2494, 0.8763] +2026-04-11 06:16:19.756514: Epoch time: 102.21 s +2026-04-11 06:16:20.887894: +2026-04-11 06:16:20.890241: Epoch 570 +2026-04-11 06:16:20.891715: Current learning rate: 0.00871 +2026-04-11 06:18:02.991693: train_loss -0.3762 +2026-04-11 06:18:02.997023: val_loss -0.3259 +2026-04-11 06:18:02.998965: Pseudo dice [0.0, 0.0, 0.5575, 0.5742, 0.4789, 0.4338, 0.5426] +2026-04-11 06:18:03.001291: Epoch time: 102.11 s +2026-04-11 06:18:04.108173: +2026-04-11 06:18:04.109792: Epoch 571 +2026-04-11 06:18:04.111176: Current learning rate: 0.00871 +2026-04-11 06:19:46.340365: train_loss -0.3732 +2026-04-11 06:19:46.346142: val_loss -0.2827 +2026-04-11 06:19:46.348215: Pseudo dice [0.0, 0.0, 0.6745, 0.2156, 0.2839, 0.4958, 0.6871] +2026-04-11 06:19:46.350436: Epoch time: 102.24 s +2026-04-11 06:19:47.521900: +2026-04-11 06:19:47.523536: Epoch 572 +2026-04-11 06:19:47.525051: Current learning rate: 0.0087 +2026-04-11 06:21:29.416662: train_loss -0.3622 +2026-04-11 06:21:29.425745: val_loss -0.3211 +2026-04-11 06:21:29.428051: Pseudo dice [0.0, 0.0, 0.7197, 0.6294, 0.5143, 0.3196, 0.5812] +2026-04-11 06:21:29.430755: Epoch time: 101.9 s +2026-04-11 06:21:30.579289: +2026-04-11 06:21:30.581726: Epoch 573 +2026-04-11 06:21:30.583407: Current learning rate: 0.0087 +2026-04-11 06:23:12.875241: train_loss -0.3717 +2026-04-11 06:23:12.881474: val_loss -0.3408 +2026-04-11 06:23:12.883298: Pseudo dice [0.0, 0.0, 0.6713, 0.4666, 0.4698, 0.8059, 0.7134] +2026-04-11 06:23:12.885766: Epoch time: 102.3 s +2026-04-11 06:23:14.080031: +2026-04-11 06:23:14.081965: Epoch 574 +2026-04-11 06:23:14.083756: Current learning rate: 0.0087 +2026-04-11 06:24:55.842227: train_loss -0.3718 +2026-04-11 06:24:55.847869: val_loss -0.3427 +2026-04-11 06:24:55.849702: Pseudo dice [0.0, 0.0, 0.655, 0.3603, 0.5857, 0.4023, 0.2087] +2026-04-11 06:24:55.851490: Epoch time: 101.77 s +2026-04-11 06:24:56.958514: +2026-04-11 06:24:56.960895: Epoch 575 +2026-04-11 06:24:56.962508: Current learning rate: 0.0087 +2026-04-11 06:26:38.818778: train_loss -0.3824 +2026-04-11 06:26:38.825724: val_loss -0.3333 +2026-04-11 06:26:38.828336: Pseudo dice [0.0, 0.0, 0.7138, 0.4034, 0.2119, 0.4327, 0.5172] +2026-04-11 06:26:38.830883: Epoch time: 101.86 s +2026-04-11 06:26:39.985769: +2026-04-11 06:26:39.987724: Epoch 576 +2026-04-11 06:26:39.989858: Current learning rate: 0.00869 +2026-04-11 06:28:21.561147: train_loss -0.3949 +2026-04-11 06:28:21.566725: val_loss -0.324 +2026-04-11 06:28:21.568245: Pseudo dice [0.0, 0.0, 0.7889, 0.7688, 0.4057, 0.3247, 0.8775] +2026-04-11 06:28:21.570422: Epoch time: 101.58 s +2026-04-11 06:28:22.696716: +2026-04-11 06:28:22.698400: Epoch 577 +2026-04-11 06:28:22.700081: Current learning rate: 0.00869 +2026-04-11 06:30:04.444786: train_loss -0.3662 +2026-04-11 06:30:04.450465: val_loss -0.3333 +2026-04-11 06:30:04.452381: Pseudo dice [0.0, 0.0, 0.6762, 0.0242, 0.5248, 0.5286, 0.5357] +2026-04-11 06:30:04.455116: Epoch time: 101.75 s +2026-04-11 06:30:05.577186: +2026-04-11 06:30:05.578921: Epoch 578 +2026-04-11 06:30:05.580289: Current learning rate: 0.00869 +2026-04-11 06:31:48.340024: train_loss -0.3706 +2026-04-11 06:31:48.346544: val_loss -0.3307 +2026-04-11 06:31:48.348750: Pseudo dice [0.0, 0.0, 0.7271, 0.0955, 0.537, 0.6184, 0.8058] +2026-04-11 06:31:48.352175: Epoch time: 102.77 s +2026-04-11 06:31:49.496688: +2026-04-11 06:31:49.498878: Epoch 579 +2026-04-11 06:31:49.500572: Current learning rate: 0.00869 +2026-04-11 06:33:31.338093: train_loss -0.3741 +2026-04-11 06:33:31.343373: val_loss -0.3043 +2026-04-11 06:33:31.345477: Pseudo dice [0.0, 0.0, 0.7765, 0.3222, 0.4122, 0.375, 0.7252] +2026-04-11 06:33:31.347854: Epoch time: 101.84 s +2026-04-11 06:33:32.478391: +2026-04-11 06:33:32.480155: Epoch 580 +2026-04-11 06:33:32.481763: Current learning rate: 0.00868 +2026-04-11 06:35:14.904394: train_loss -0.3613 +2026-04-11 06:35:14.923280: val_loss -0.3177 +2026-04-11 06:35:14.925856: Pseudo dice [0.0, 0.0, 0.5069, 0.4453, 0.5677, 0.3898, 0.6781] +2026-04-11 06:35:14.928154: Epoch time: 102.43 s +2026-04-11 06:35:16.072883: +2026-04-11 06:35:16.074785: Epoch 581 +2026-04-11 06:35:16.076292: Current learning rate: 0.00868 +2026-04-11 06:36:58.065454: train_loss -0.3599 +2026-04-11 06:36:58.072248: val_loss -0.3176 +2026-04-11 06:36:58.074423: Pseudo dice [0.0, 0.0, 0.6677, 0.1809, 0.4029, 0.7908, 0.7527] +2026-04-11 06:36:58.076857: Epoch time: 102.0 s +2026-04-11 06:36:59.218251: +2026-04-11 06:36:59.220093: Epoch 582 +2026-04-11 06:36:59.222063: Current learning rate: 0.00868 +2026-04-11 06:38:41.263649: train_loss -0.3637 +2026-04-11 06:38:41.269904: val_loss -0.3281 +2026-04-11 06:38:41.272525: Pseudo dice [0.0, 0.0, 0.7392, 0.0503, 0.5021, 0.5534, 0.8285] +2026-04-11 06:38:41.274765: Epoch time: 102.05 s +2026-04-11 06:38:42.511612: +2026-04-11 06:38:42.513462: Epoch 583 +2026-04-11 06:38:42.515459: Current learning rate: 0.00868 +2026-04-11 06:40:24.404143: train_loss -0.3781 +2026-04-11 06:40:24.410239: val_loss -0.3451 +2026-04-11 06:40:24.412572: Pseudo dice [0.0, 0.0, 0.7767, 0.4553, 0.3639, 0.5122, 0.6357] +2026-04-11 06:40:24.414644: Epoch time: 101.9 s +2026-04-11 06:40:25.555226: +2026-04-11 06:40:25.557159: Epoch 584 +2026-04-11 06:40:25.558831: Current learning rate: 0.00868 +2026-04-11 06:42:07.031875: train_loss -0.361 +2026-04-11 06:42:07.037803: val_loss -0.2885 +2026-04-11 06:42:07.040120: Pseudo dice [0.0, 0.0, 0.8463, 0.0228, 0.5594, 0.3887, 0.332] +2026-04-11 06:42:07.042561: Epoch time: 101.48 s +2026-04-11 06:42:08.183365: +2026-04-11 06:42:08.185467: Epoch 585 +2026-04-11 06:42:08.187535: Current learning rate: 0.00867 +2026-04-11 06:43:49.565382: train_loss -0.3788 +2026-04-11 06:43:49.571420: val_loss -0.3428 +2026-04-11 06:43:49.573553: Pseudo dice [0.0, 0.0, 0.8576, 0.053, 0.5782, 0.612, 0.4289] +2026-04-11 06:43:49.576661: Epoch time: 101.39 s +2026-04-11 06:43:50.693990: +2026-04-11 06:43:50.695802: Epoch 586 +2026-04-11 06:43:50.697394: Current learning rate: 0.00867 +2026-04-11 06:45:32.157530: train_loss -0.3547 +2026-04-11 06:45:32.163257: val_loss -0.3249 +2026-04-11 06:45:32.165208: Pseudo dice [0.0, 0.0, 0.3692, 0.4494, 0.4586, 0.1385, 0.9036] +2026-04-11 06:45:32.167763: Epoch time: 101.47 s +2026-04-11 06:45:33.301044: +2026-04-11 06:45:33.302573: Epoch 587 +2026-04-11 06:45:33.303930: Current learning rate: 0.00867 +2026-04-11 06:47:14.849944: train_loss -0.3549 +2026-04-11 06:47:14.856250: val_loss -0.3524 +2026-04-11 06:47:14.859154: Pseudo dice [0.0, 0.0, 0.6944, 0.646, 0.5188, 0.5869, 0.8534] +2026-04-11 06:47:14.861151: Epoch time: 101.55 s +2026-04-11 06:47:15.996916: +2026-04-11 06:47:15.998875: Epoch 588 +2026-04-11 06:47:16.000446: Current learning rate: 0.00867 +2026-04-11 06:48:57.515498: train_loss -0.3876 +2026-04-11 06:48:57.521112: val_loss -0.3126 +2026-04-11 06:48:57.522934: Pseudo dice [0.0, 0.0, 0.7798, 0.5787, 0.4939, 0.5935, 0.7873] +2026-04-11 06:48:57.525443: Epoch time: 101.52 s +2026-04-11 06:48:58.711387: +2026-04-11 06:48:58.712986: Epoch 589 +2026-04-11 06:48:58.714358: Current learning rate: 0.00866 +2026-04-11 06:50:40.310663: train_loss -0.3733 +2026-04-11 06:50:40.316293: val_loss -0.3273 +2026-04-11 06:50:40.318196: Pseudo dice [0.0, 0.0, 0.7406, 0.6452, 0.5662, 0.5617, 0.7557] +2026-04-11 06:50:40.320420: Epoch time: 101.6 s +2026-04-11 06:50:41.470585: +2026-04-11 06:50:41.473208: Epoch 590 +2026-04-11 06:50:41.477454: Current learning rate: 0.00866 +2026-04-11 06:52:23.254428: train_loss -0.3714 +2026-04-11 06:52:23.260169: val_loss -0.3448 +2026-04-11 06:52:23.262375: Pseudo dice [0.0, 0.0, 0.7192, 0.0349, 0.6854, 0.6131, 0.9042] +2026-04-11 06:52:23.264426: Epoch time: 101.79 s +2026-04-11 06:52:24.391539: +2026-04-11 06:52:24.393367: Epoch 591 +2026-04-11 06:52:24.394930: Current learning rate: 0.00866 +2026-04-11 06:54:05.849669: train_loss -0.3802 +2026-04-11 06:54:05.855877: val_loss -0.2743 +2026-04-11 06:54:05.857953: Pseudo dice [0.0, 0.0, 0.2872, 0.0113, 0.2929, 0.6861, 0.6822] +2026-04-11 06:54:05.860754: Epoch time: 101.46 s +2026-04-11 06:54:06.976532: +2026-04-11 06:54:06.977948: Epoch 592 +2026-04-11 06:54:06.979644: Current learning rate: 0.00866 +2026-04-11 06:55:48.753704: train_loss -0.3652 +2026-04-11 06:55:48.760614: val_loss -0.3819 +2026-04-11 06:55:48.763084: Pseudo dice [0.0, 0.0, 0.7453, 0.8238, 0.5456, 0.6271, 0.7609] +2026-04-11 06:55:48.766353: Epoch time: 101.78 s +2026-04-11 06:55:49.914576: +2026-04-11 06:55:49.916145: Epoch 593 +2026-04-11 06:55:49.917732: Current learning rate: 0.00866 +2026-04-11 06:57:31.262383: train_loss -0.3549 +2026-04-11 06:57:31.268238: val_loss -0.3347 +2026-04-11 06:57:31.270552: Pseudo dice [0.0, 0.0, 0.7315, 0.417, 0.3712, 0.4509, 0.781] +2026-04-11 06:57:31.273508: Epoch time: 101.35 s +2026-04-11 06:57:32.402734: +2026-04-11 06:57:32.404574: Epoch 594 +2026-04-11 06:57:32.406110: Current learning rate: 0.00865 +2026-04-11 06:59:13.908439: train_loss -0.3774 +2026-04-11 06:59:13.914378: val_loss -0.3646 +2026-04-11 06:59:13.916134: Pseudo dice [0.0, 0.0, 0.8486, 0.3406, 0.5273, 0.3058, 0.8196] +2026-04-11 06:59:13.918557: Epoch time: 101.51 s +2026-04-11 06:59:15.079807: +2026-04-11 06:59:15.081536: Epoch 595 +2026-04-11 06:59:15.083202: Current learning rate: 0.00865 +2026-04-11 07:00:56.805961: train_loss -0.3871 +2026-04-11 07:00:56.812073: val_loss -0.2761 +2026-04-11 07:00:56.814254: Pseudo dice [0.0, 0.0, 0.8304, 0.1099, 0.2647, 0.114, 0.5355] +2026-04-11 07:00:56.816691: Epoch time: 101.73 s +2026-04-11 07:00:57.957663: +2026-04-11 07:00:57.959448: Epoch 596 +2026-04-11 07:00:57.961486: Current learning rate: 0.00865 +2026-04-11 07:02:39.372375: train_loss -0.3736 +2026-04-11 07:02:39.378328: val_loss -0.3315 +2026-04-11 07:02:39.380230: Pseudo dice [0.0, 0.0, 0.833, 0.4848, 0.4617, 0.5851, 0.2258] +2026-04-11 07:02:39.382403: Epoch time: 101.42 s +2026-04-11 07:02:40.500915: +2026-04-11 07:02:40.503205: Epoch 597 +2026-04-11 07:02:40.504537: Current learning rate: 0.00865 +2026-04-11 07:04:22.206017: train_loss -0.3836 +2026-04-11 07:04:22.211649: val_loss -0.3124 +2026-04-11 07:04:22.213553: Pseudo dice [0.0, 0.0, 0.6013, 0.0061, 0.3977, 0.5498, 0.7051] +2026-04-11 07:04:22.215871: Epoch time: 101.71 s +2026-04-11 07:04:23.340391: +2026-04-11 07:04:23.342320: Epoch 598 +2026-04-11 07:04:23.343966: Current learning rate: 0.00864 +2026-04-11 07:06:06.227835: train_loss -0.3742 +2026-04-11 07:06:06.233221: val_loss -0.3469 +2026-04-11 07:06:06.235239: Pseudo dice [0.0, 0.0, 0.6644, 0.1367, 0.3766, 0.7037, 0.7837] +2026-04-11 07:06:06.236990: Epoch time: 102.89 s +2026-04-11 07:06:07.388992: +2026-04-11 07:06:07.390812: Epoch 599 +2026-04-11 07:06:07.392385: Current learning rate: 0.00864 +2026-04-11 07:07:49.182368: train_loss -0.383 +2026-04-11 07:07:49.188905: val_loss -0.3429 +2026-04-11 07:07:49.190594: Pseudo dice [0.0, 0.0, 0.7757, 0.6794, 0.5636, 0.5905, 0.746] +2026-04-11 07:07:49.192765: Epoch time: 101.8 s +2026-04-11 07:07:51.954635: +2026-04-11 07:07:51.956575: Epoch 600 +2026-04-11 07:07:51.957957: Current learning rate: 0.00864 +2026-04-11 07:09:33.704468: train_loss -0.3773 +2026-04-11 07:09:33.710580: val_loss -0.2899 +2026-04-11 07:09:33.712621: Pseudo dice [0.0, 0.0, 0.4563, 0.245, 0.4806, 0.734, 0.6346] +2026-04-11 07:09:33.714874: Epoch time: 101.75 s +2026-04-11 07:09:34.867186: +2026-04-11 07:09:34.868970: Epoch 601 +2026-04-11 07:09:34.870448: Current learning rate: 0.00864 +2026-04-11 07:11:16.266605: train_loss -0.3598 +2026-04-11 07:11:16.272555: val_loss -0.3591 +2026-04-11 07:11:16.274317: Pseudo dice [0.1285, 0.0, 0.8092, 0.6601, 0.5019, 0.6519, 0.4412] +2026-04-11 07:11:16.276797: Epoch time: 101.4 s +2026-04-11 07:11:17.412596: +2026-04-11 07:11:17.414375: Epoch 602 +2026-04-11 07:11:17.415860: Current learning rate: 0.00863 +2026-04-11 07:12:59.406105: train_loss -0.3618 +2026-04-11 07:12:59.412117: val_loss -0.3155 +2026-04-11 07:12:59.414121: Pseudo dice [0.0, 0.0, 0.715, 0.182, 0.2957, 0.3347, 0.6601] +2026-04-11 07:12:59.416178: Epoch time: 102.0 s +2026-04-11 07:13:00.590497: +2026-04-11 07:13:00.592131: Epoch 603 +2026-04-11 07:13:00.593827: Current learning rate: 0.00863 +2026-04-11 07:14:41.705579: train_loss -0.3507 +2026-04-11 07:14:41.710916: val_loss -0.3287 +2026-04-11 07:14:41.713143: Pseudo dice [0.1323, 0.0, 0.7305, 0.2916, 0.5015, 0.7486, 0.7664] +2026-04-11 07:14:41.715201: Epoch time: 101.12 s +2026-04-11 07:14:42.855000: +2026-04-11 07:14:42.856537: Epoch 604 +2026-04-11 07:14:42.858387: Current learning rate: 0.00863 +2026-04-11 07:16:24.695777: train_loss -0.3715 +2026-04-11 07:16:24.702500: val_loss -0.3489 +2026-04-11 07:16:24.704865: Pseudo dice [0.0, 0.0, 0.7521, 0.2183, 0.3831, 0.4688, 0.8511] +2026-04-11 07:16:24.706899: Epoch time: 101.84 s +2026-04-11 07:16:25.844216: +2026-04-11 07:16:25.845944: Epoch 605 +2026-04-11 07:16:25.847256: Current learning rate: 0.00863 +2026-04-11 07:18:07.198413: train_loss -0.3654 +2026-04-11 07:18:07.204094: val_loss -0.3197 +2026-04-11 07:18:07.206097: Pseudo dice [0.0177, 0.0, 0.4822, 0.0992, 0.4662, 0.3459, 0.7173] +2026-04-11 07:18:07.208112: Epoch time: 101.36 s +2026-04-11 07:18:08.382228: +2026-04-11 07:18:08.384530: Epoch 606 +2026-04-11 07:18:08.386838: Current learning rate: 0.00863 +2026-04-11 07:19:50.127385: train_loss -0.3651 +2026-04-11 07:19:50.134298: val_loss -0.3387 +2026-04-11 07:19:50.136355: Pseudo dice [0.476, 0.0, 0.7203, 0.41, 0.5199, 0.2832, 0.3079] +2026-04-11 07:19:50.138649: Epoch time: 101.75 s +2026-04-11 07:19:51.295967: +2026-04-11 07:19:51.297983: Epoch 607 +2026-04-11 07:19:51.299756: Current learning rate: 0.00862 +2026-04-11 07:21:32.761826: train_loss -0.3661 +2026-04-11 07:21:32.767428: val_loss -0.2958 +2026-04-11 07:21:32.769981: Pseudo dice [0.0, 0.0, 0.7433, 0.4899, 0.2778, 0.3348, 0.8853] +2026-04-11 07:21:32.773574: Epoch time: 101.47 s +2026-04-11 07:21:33.950257: +2026-04-11 07:21:33.951757: Epoch 608 +2026-04-11 07:21:33.953171: Current learning rate: 0.00862 +2026-04-11 07:23:15.650110: train_loss -0.3645 +2026-04-11 07:23:15.657446: val_loss -0.2918 +2026-04-11 07:23:15.659277: Pseudo dice [0.0, 0.0, 0.7576, 0.1288, 0.0, 0.7057, 0.8491] +2026-04-11 07:23:15.661722: Epoch time: 101.7 s +2026-04-11 07:23:16.837823: +2026-04-11 07:23:16.839418: Epoch 609 +2026-04-11 07:23:16.840753: Current learning rate: 0.00862 +2026-04-11 07:24:58.007344: train_loss -0.349 +2026-04-11 07:24:58.013880: val_loss -0.3339 +2026-04-11 07:24:58.015987: Pseudo dice [0.0, 0.0, 0.6239, 0.7278, 0.4262, 0.7265, 0.8437] +2026-04-11 07:24:58.018567: Epoch time: 101.17 s +2026-04-11 07:24:59.158447: +2026-04-11 07:24:59.160186: Epoch 610 +2026-04-11 07:24:59.161547: Current learning rate: 0.00862 +2026-04-11 07:26:40.402274: train_loss -0.3695 +2026-04-11 07:26:40.408174: val_loss -0.3262 +2026-04-11 07:26:40.410665: Pseudo dice [0.0, 0.0, 0.5784, 0.0907, 0.513, 0.2431, 0.7657] +2026-04-11 07:26:40.412845: Epoch time: 101.25 s +2026-04-11 07:26:41.547555: +2026-04-11 07:26:41.549393: Epoch 611 +2026-04-11 07:26:41.550817: Current learning rate: 0.00861 +2026-04-11 07:28:23.454039: train_loss -0.365 +2026-04-11 07:28:23.460976: val_loss -0.3206 +2026-04-11 07:28:23.464188: Pseudo dice [0.0, 0.0, 0.8095, 0.7895, 0.3395, 0.2491, 0.7239] +2026-04-11 07:28:23.466877: Epoch time: 101.91 s +2026-04-11 07:28:24.598356: +2026-04-11 07:28:24.600409: Epoch 612 +2026-04-11 07:28:24.602454: Current learning rate: 0.00861 +2026-04-11 07:30:06.392014: train_loss -0.3364 +2026-04-11 07:30:06.400114: val_loss -0.3173 +2026-04-11 07:30:06.401800: Pseudo dice [0.0, 0.0, 0.778, 0.4594, 0.4504, 0.6265, 0.8143] +2026-04-11 07:30:06.404180: Epoch time: 101.8 s +2026-04-11 07:30:07.534718: +2026-04-11 07:30:07.536587: Epoch 613 +2026-04-11 07:30:07.538094: Current learning rate: 0.00861 +2026-04-11 07:31:49.120311: train_loss -0.3731 +2026-04-11 07:31:49.127238: val_loss -0.3409 +2026-04-11 07:31:49.129282: Pseudo dice [0.0, 0.0, 0.4937, 0.3196, 0.4581, 0.7427, 0.3071] +2026-04-11 07:31:49.131559: Epoch time: 101.59 s +2026-04-11 07:31:50.288158: +2026-04-11 07:31:50.290034: Epoch 614 +2026-04-11 07:31:50.291707: Current learning rate: 0.00861 +2026-04-11 07:33:31.596106: train_loss -0.3438 +2026-04-11 07:33:31.601853: val_loss -0.2568 +2026-04-11 07:33:31.603592: Pseudo dice [0.0, 0.0, 0.4532, 0.3034, 0.2397, 0.1084, 0.6631] +2026-04-11 07:33:31.605722: Epoch time: 101.31 s +2026-04-11 07:33:32.753773: +2026-04-11 07:33:32.755510: Epoch 615 +2026-04-11 07:33:32.757053: Current learning rate: 0.0086 +2026-04-11 07:35:14.113409: train_loss -0.3316 +2026-04-11 07:35:14.119220: val_loss -0.2973 +2026-04-11 07:35:14.121388: Pseudo dice [0.0, 0.0, 0.8029, 0.1918, 0.209, 0.5801, 0.3535] +2026-04-11 07:35:14.123360: Epoch time: 101.36 s +2026-04-11 07:35:15.267770: +2026-04-11 07:35:15.269306: Epoch 616 +2026-04-11 07:35:15.271173: Current learning rate: 0.0086 +2026-04-11 07:36:56.974040: train_loss -0.3656 +2026-04-11 07:36:56.985002: val_loss -0.3209 +2026-04-11 07:36:56.987205: Pseudo dice [0.0, 0.0, 0.7104, 0.7861, 0.4785, 0.186, 0.8865] +2026-04-11 07:36:56.989620: Epoch time: 101.71 s +2026-04-11 07:36:58.119105: +2026-04-11 07:36:58.120827: Epoch 617 +2026-04-11 07:36:58.122455: Current learning rate: 0.0086 +2026-04-11 07:38:41.322774: train_loss -0.3505 +2026-04-11 07:38:41.328278: val_loss -0.3318 +2026-04-11 07:38:41.330092: Pseudo dice [0.0, 0.0, 0.7296, 0.563, 0.4186, 0.5849, 0.4265] +2026-04-11 07:38:41.332591: Epoch time: 103.21 s +2026-04-11 07:38:42.477991: +2026-04-11 07:38:42.480169: Epoch 618 +2026-04-11 07:38:42.482038: Current learning rate: 0.0086 +2026-04-11 07:40:23.890445: train_loss -0.3627 +2026-04-11 07:40:23.896732: val_loss -0.3235 +2026-04-11 07:40:23.898334: Pseudo dice [0.0, 0.0, 0.6721, 0.3641, 0.4292, 0.4438, 0.3205] +2026-04-11 07:40:23.900212: Epoch time: 101.42 s +2026-04-11 07:40:25.007348: +2026-04-11 07:40:25.008904: Epoch 619 +2026-04-11 07:40:25.010347: Current learning rate: 0.0086 +2026-04-11 07:42:06.974442: train_loss -0.3247 +2026-04-11 07:42:06.980589: val_loss -0.3418 +2026-04-11 07:42:06.982437: Pseudo dice [0.0, 0.0, 0.5645, 0.3307, 0.4797, 0.6498, 0.7047] +2026-04-11 07:42:06.985190: Epoch time: 101.97 s +2026-04-11 07:42:08.109580: +2026-04-11 07:42:08.111430: Epoch 620 +2026-04-11 07:42:08.113391: Current learning rate: 0.00859 +2026-04-11 07:43:49.776902: train_loss -0.3649 +2026-04-11 07:43:49.782967: val_loss -0.3266 +2026-04-11 07:43:49.784684: Pseudo dice [0.1636, 0.0, 0.6277, 0.5596, 0.4994, 0.3595, 0.8479] +2026-04-11 07:43:49.786545: Epoch time: 101.67 s +2026-04-11 07:43:51.094944: +2026-04-11 07:43:51.096956: Epoch 621 +2026-04-11 07:43:51.098553: Current learning rate: 0.00859 +2026-04-11 07:45:32.860843: train_loss -0.3677 +2026-04-11 07:45:32.869675: val_loss -0.3215 +2026-04-11 07:45:32.872621: Pseudo dice [0.0, 0.0, 0.5996, 0.571, 0.347, 0.7014, 0.796] +2026-04-11 07:45:32.874954: Epoch time: 101.77 s +2026-04-11 07:45:34.028032: +2026-04-11 07:45:34.029932: Epoch 622 +2026-04-11 07:45:34.031574: Current learning rate: 0.00859 +2026-04-11 07:47:15.790690: train_loss -0.3727 +2026-04-11 07:47:15.806415: val_loss -0.3213 +2026-04-11 07:47:15.808389: Pseudo dice [0.0, 0.0, 0.5935, 0.6415, 0.54, 0.7536, 0.7401] +2026-04-11 07:47:15.812841: Epoch time: 101.77 s +2026-04-11 07:47:16.961253: +2026-04-11 07:47:16.962830: Epoch 623 +2026-04-11 07:47:16.966027: Current learning rate: 0.00859 +2026-04-11 07:48:58.906858: train_loss -0.3682 +2026-04-11 07:48:58.916742: val_loss -0.32 +2026-04-11 07:48:58.919516: Pseudo dice [0.0, 0.0, 0.6649, 0.5944, 0.478, 0.5151, 0.8245] +2026-04-11 07:48:58.923195: Epoch time: 101.95 s +2026-04-11 07:49:00.072516: +2026-04-11 07:49:00.074528: Epoch 624 +2026-04-11 07:49:00.076433: Current learning rate: 0.00858 +2026-04-11 07:50:42.210559: train_loss -0.3721 +2026-04-11 07:50:42.216602: val_loss -0.3073 +2026-04-11 07:50:42.218292: Pseudo dice [0.1493, 0.0, 0.6373, 0.3851, 0.3701, 0.493, 0.444] +2026-04-11 07:50:42.220417: Epoch time: 102.14 s +2026-04-11 07:50:43.379519: +2026-04-11 07:50:43.381139: Epoch 625 +2026-04-11 07:50:43.382605: Current learning rate: 0.00858 +2026-04-11 07:52:25.344352: train_loss -0.3522 +2026-04-11 07:52:25.352551: val_loss -0.3401 +2026-04-11 07:52:25.354993: Pseudo dice [0.0, 0.0, 0.6413, 0.3923, 0.3575, 0.3597, 0.8376] +2026-04-11 07:52:25.357461: Epoch time: 101.97 s +2026-04-11 07:52:26.492188: +2026-04-11 07:52:26.494303: Epoch 626 +2026-04-11 07:52:26.495889: Current learning rate: 0.00858 +2026-04-11 07:54:08.655661: train_loss -0.3778 +2026-04-11 07:54:08.662209: val_loss -0.2763 +2026-04-11 07:54:08.664594: Pseudo dice [0.0, 0.0, 0.4372, 0.1526, 0.0529, 0.4007, 0.208] +2026-04-11 07:54:08.667077: Epoch time: 102.17 s +2026-04-11 07:54:09.801353: +2026-04-11 07:54:09.803643: Epoch 627 +2026-04-11 07:54:09.805208: Current learning rate: 0.00858 +2026-04-11 07:55:51.406739: train_loss -0.3927 +2026-04-11 07:55:51.413105: val_loss -0.3463 +2026-04-11 07:55:51.415456: Pseudo dice [0.0046, 0.0, 0.6697, 0.6522, 0.5381, 0.6145, 0.7517] +2026-04-11 07:55:51.417865: Epoch time: 101.61 s +2026-04-11 07:55:52.577069: +2026-04-11 07:55:52.578912: Epoch 628 +2026-04-11 07:55:52.580668: Current learning rate: 0.00858 +2026-04-11 07:57:34.464226: train_loss -0.3608 +2026-04-11 07:57:34.469743: val_loss -0.3241 +2026-04-11 07:57:34.471794: Pseudo dice [0.6851, 0.0, 0.7723, 0.4656, 0.5855, 0.7658, 0.5727] +2026-04-11 07:57:34.474219: Epoch time: 101.89 s +2026-04-11 07:57:35.596263: +2026-04-11 07:57:35.598026: Epoch 629 +2026-04-11 07:57:35.599549: Current learning rate: 0.00857 +2026-04-11 07:59:17.701905: train_loss -0.3924 +2026-04-11 07:59:17.708919: val_loss -0.3659 +2026-04-11 07:59:17.711505: Pseudo dice [0.0, 0.0, 0.8205, 0.7444, 0.6204, 0.7276, 0.7327] +2026-04-11 07:59:17.714054: Epoch time: 102.11 s +2026-04-11 07:59:18.915316: +2026-04-11 07:59:18.917567: Epoch 630 +2026-04-11 07:59:18.919455: Current learning rate: 0.00857 +2026-04-11 08:01:00.997233: train_loss -0.3753 +2026-04-11 08:01:01.003033: val_loss -0.3228 +2026-04-11 08:01:01.005757: Pseudo dice [0.408, 0.0, 0.7756, 0.1988, 0.5302, 0.4489, 0.7404] +2026-04-11 08:01:01.008260: Epoch time: 102.09 s +2026-04-11 08:01:02.150859: +2026-04-11 08:01:02.153183: Epoch 631 +2026-04-11 08:01:02.155365: Current learning rate: 0.00857 +2026-04-11 08:02:44.062913: train_loss -0.3613 +2026-04-11 08:02:44.070132: val_loss -0.3546 +2026-04-11 08:02:44.073946: Pseudo dice [0.1349, 0.0, 0.8254, 0.298, 0.4543, 0.7228, 0.6662] +2026-04-11 08:02:44.076201: Epoch time: 101.92 s +2026-04-11 08:02:45.239864: +2026-04-11 08:02:45.241691: Epoch 632 +2026-04-11 08:02:45.243301: Current learning rate: 0.00857 +2026-04-11 08:04:26.710781: train_loss -0.373 +2026-04-11 08:04:26.717574: val_loss -0.3222 +2026-04-11 08:04:26.720390: Pseudo dice [0.2774, 0.0, 0.6507, 0.2922, 0.5042, 0.2804, 0.7171] +2026-04-11 08:04:26.722994: Epoch time: 101.47 s +2026-04-11 08:04:27.879987: +2026-04-11 08:04:27.881684: Epoch 633 +2026-04-11 08:04:27.883253: Current learning rate: 0.00856 +2026-04-11 08:06:09.735157: train_loss -0.3696 +2026-04-11 08:06:09.741679: val_loss -0.3343 +2026-04-11 08:06:09.743712: Pseudo dice [0.0, 0.0, 0.7123, 0.4679, 0.5136, 0.4845, 0.7681] +2026-04-11 08:06:09.745712: Epoch time: 101.86 s +2026-04-11 08:06:10.885188: +2026-04-11 08:06:10.887336: Epoch 634 +2026-04-11 08:06:10.888893: Current learning rate: 0.00856 +2026-04-11 08:07:53.171038: train_loss -0.3824 +2026-04-11 08:07:53.180284: val_loss -0.3241 +2026-04-11 08:07:53.182225: Pseudo dice [0.2756, 0.0, 0.6943, 0.3121, 0.3421, 0.4982, 0.8276] +2026-04-11 08:07:53.184934: Epoch time: 102.29 s +2026-04-11 08:07:54.317282: +2026-04-11 08:07:54.319531: Epoch 635 +2026-04-11 08:07:54.321019: Current learning rate: 0.00856 +2026-04-11 08:09:36.188599: train_loss -0.3882 +2026-04-11 08:09:36.213989: val_loss -0.3317 +2026-04-11 08:09:36.216346: Pseudo dice [0.4269, 0.0, 0.5794, 0.4643, 0.4467, 0.6685, 0.522] +2026-04-11 08:09:36.218282: Epoch time: 101.87 s +2026-04-11 08:09:37.396555: +2026-04-11 08:09:37.402610: Epoch 636 +2026-04-11 08:09:37.404828: Current learning rate: 0.00856 +2026-04-11 08:11:18.949336: train_loss -0.3501 +2026-04-11 08:11:18.957063: val_loss -0.322 +2026-04-11 08:11:18.959403: Pseudo dice [0.0004, 0.0, 0.6974, 0.4685, 0.6158, 0.4884, 0.6428] +2026-04-11 08:11:18.962417: Epoch time: 101.56 s +2026-04-11 08:11:20.146512: +2026-04-11 08:11:20.148379: Epoch 637 +2026-04-11 08:11:20.150113: Current learning rate: 0.00855 +2026-04-11 08:13:03.414656: train_loss -0.383 +2026-04-11 08:13:03.421045: val_loss -0.3197 +2026-04-11 08:13:03.423497: Pseudo dice [0.0, 0.0, 0.6783, 0.4445, 0.5674, 0.2985, 0.6865] +2026-04-11 08:13:03.425890: Epoch time: 103.27 s +2026-04-11 08:13:04.558786: +2026-04-11 08:13:04.560690: Epoch 638 +2026-04-11 08:13:04.562374: Current learning rate: 0.00855 +2026-04-11 08:14:46.296403: train_loss -0.3767 +2026-04-11 08:14:46.303251: val_loss -0.3654 +2026-04-11 08:14:46.305051: Pseudo dice [0.5479, 0.0, 0.8331, 0.3549, 0.4346, 0.4911, 0.597] +2026-04-11 08:14:46.307501: Epoch time: 101.74 s +2026-04-11 08:14:47.472579: +2026-04-11 08:14:47.475057: Epoch 639 +2026-04-11 08:14:47.476749: Current learning rate: 0.00855 +2026-04-11 08:16:29.415396: train_loss -0.3618 +2026-04-11 08:16:29.421845: val_loss -0.3045 +2026-04-11 08:16:29.423982: Pseudo dice [0.0, 0.0, 0.5855, 0.4917, 0.3597, 0.5209, 0.6518] +2026-04-11 08:16:29.426217: Epoch time: 101.95 s +2026-04-11 08:16:30.552879: +2026-04-11 08:16:30.554520: Epoch 640 +2026-04-11 08:16:30.556779: Current learning rate: 0.00855 +2026-04-11 08:18:12.813008: train_loss -0.3786 +2026-04-11 08:18:12.820081: val_loss -0.3299 +2026-04-11 08:18:12.823502: Pseudo dice [0.0, 0.0, 0.7382, 0.3626, 0.4986, 0.7827, 0.5531] +2026-04-11 08:18:12.827171: Epoch time: 102.26 s +2026-04-11 08:18:13.964463: +2026-04-11 08:18:13.967021: Epoch 641 +2026-04-11 08:18:13.969433: Current learning rate: 0.00855 +2026-04-11 08:19:56.126187: train_loss -0.3935 +2026-04-11 08:19:56.132761: val_loss -0.3448 +2026-04-11 08:19:56.135250: Pseudo dice [0.0, 0.0, 0.74, 0.4358, 0.5465, 0.6665, 0.6519] +2026-04-11 08:19:56.137851: Epoch time: 102.16 s +2026-04-11 08:19:57.267670: +2026-04-11 08:19:57.269637: Epoch 642 +2026-04-11 08:19:57.271231: Current learning rate: 0.00854 +2026-04-11 08:21:38.972317: train_loss -0.3961 +2026-04-11 08:21:38.979032: val_loss -0.3292 +2026-04-11 08:21:38.980954: Pseudo dice [0.6211, 0.0, 0.2732, 0.4048, 0.4993, 0.741, 0.6879] +2026-04-11 08:21:38.983540: Epoch time: 101.71 s +2026-04-11 08:21:40.134048: +2026-04-11 08:21:40.135835: Epoch 643 +2026-04-11 08:21:40.137621: Current learning rate: 0.00854 +2026-04-11 08:23:22.583713: train_loss -0.3386 +2026-04-11 08:23:22.590216: val_loss -0.342 +2026-04-11 08:23:22.592355: Pseudo dice [0.5104, 0.0, 0.7758, 0.1652, 0.3567, 0.6605, 0.7961] +2026-04-11 08:23:22.595258: Epoch time: 102.45 s +2026-04-11 08:23:23.730417: +2026-04-11 08:23:23.732293: Epoch 644 +2026-04-11 08:23:23.733830: Current learning rate: 0.00854 +2026-04-11 08:25:05.340535: train_loss -0.3465 +2026-04-11 08:25:05.347986: val_loss -0.3105 +2026-04-11 08:25:05.350490: Pseudo dice [0.0011, 0.0, 0.7182, 0.5198, 0.1957, 0.4304, 0.8271] +2026-04-11 08:25:05.353406: Epoch time: 101.61 s +2026-04-11 08:25:06.497917: +2026-04-11 08:25:06.499748: Epoch 645 +2026-04-11 08:25:06.501632: Current learning rate: 0.00854 +2026-04-11 08:26:48.280731: train_loss -0.3496 +2026-04-11 08:26:48.287771: val_loss -0.3611 +2026-04-11 08:26:48.290830: Pseudo dice [0.05, 0.0, 0.8063, 0.6286, 0.4152, 0.2193, 0.7738] +2026-04-11 08:26:48.296667: Epoch time: 101.79 s +2026-04-11 08:26:49.424531: +2026-04-11 08:26:49.426862: Epoch 646 +2026-04-11 08:26:49.428521: Current learning rate: 0.00853 +2026-04-11 08:28:31.273472: train_loss -0.3731 +2026-04-11 08:28:31.280511: val_loss -0.3385 +2026-04-11 08:28:31.282849: Pseudo dice [0.5999, 0.0, 0.6512, 0.3908, 0.5247, 0.7071, 0.8871] +2026-04-11 08:28:31.285852: Epoch time: 101.85 s +2026-04-11 08:28:32.442121: +2026-04-11 08:28:32.443899: Epoch 647 +2026-04-11 08:28:32.445848: Current learning rate: 0.00853 +2026-04-11 08:30:14.591419: train_loss -0.3601 +2026-04-11 08:30:14.597674: val_loss -0.3518 +2026-04-11 08:30:14.599540: Pseudo dice [0.0419, 0.0, 0.7988, 0.739, 0.6174, 0.8864, 0.7818] +2026-04-11 08:30:14.602800: Epoch time: 102.15 s +2026-04-11 08:30:15.745114: +2026-04-11 08:30:15.747926: Epoch 648 +2026-04-11 08:30:15.750490: Current learning rate: 0.00853 +2026-04-11 08:31:57.686765: train_loss -0.3507 +2026-04-11 08:31:57.693683: val_loss -0.3232 +2026-04-11 08:31:57.695564: Pseudo dice [0.4791, 0.0, 0.7314, 0.3322, 0.5537, 0.4545, 0.6153] +2026-04-11 08:31:57.698544: Epoch time: 101.94 s +2026-04-11 08:31:58.831448: +2026-04-11 08:31:58.833765: Epoch 649 +2026-04-11 08:31:58.835263: Current learning rate: 0.00853 +2026-04-11 08:33:40.621518: train_loss -0.3707 +2026-04-11 08:33:40.627526: val_loss -0.3257 +2026-04-11 08:33:40.629403: Pseudo dice [0.5144, 0.0, 0.7436, 0.2021, 0.4307, 0.1546, 0.5988] +2026-04-11 08:33:40.631907: Epoch time: 101.79 s +2026-04-11 08:33:43.481096: +2026-04-11 08:33:43.483076: Epoch 650 +2026-04-11 08:33:43.484586: Current learning rate: 0.00852 +2026-04-11 08:35:25.552244: train_loss -0.3754 +2026-04-11 08:35:25.559795: val_loss -0.377 +2026-04-11 08:35:25.562376: Pseudo dice [0.3541, 0.0, 0.7216, 0.5433, 0.5424, 0.4633, 0.8927] +2026-04-11 08:35:25.565193: Epoch time: 102.07 s +2026-04-11 08:35:26.703121: +2026-04-11 08:35:26.705073: Epoch 651 +2026-04-11 08:35:26.706533: Current learning rate: 0.00852 +2026-04-11 08:37:08.378288: train_loss -0.3521 +2026-04-11 08:37:08.387305: val_loss -0.2854 +2026-04-11 08:37:08.389595: Pseudo dice [0.0, 0.0, 0.6686, 0.5869, 0.5329, 0.1544, 0.6029] +2026-04-11 08:37:08.393023: Epoch time: 101.68 s +2026-04-11 08:37:09.510718: +2026-04-11 08:37:09.512661: Epoch 652 +2026-04-11 08:37:09.514442: Current learning rate: 0.00852 +2026-04-11 08:38:51.264212: train_loss -0.3491 +2026-04-11 08:38:51.270515: val_loss -0.3257 +2026-04-11 08:38:51.272779: Pseudo dice [0.0, 0.0, 0.8336, 0.2754, 0.4593, 0.4884, 0.6085] +2026-04-11 08:38:51.275083: Epoch time: 101.76 s +2026-04-11 08:38:52.435539: +2026-04-11 08:38:52.437630: Epoch 653 +2026-04-11 08:38:52.440130: Current learning rate: 0.00852 +2026-04-11 08:40:34.316007: train_loss -0.3526 +2026-04-11 08:40:34.322638: val_loss -0.3173 +2026-04-11 08:40:34.324794: Pseudo dice [0.0, 0.0, 0.7649, 0.2635, 0.6669, 0.3209, 0.7277] +2026-04-11 08:40:34.326968: Epoch time: 101.88 s +2026-04-11 08:40:35.443853: +2026-04-11 08:40:35.445878: Epoch 654 +2026-04-11 08:40:35.447910: Current learning rate: 0.00852 +2026-04-11 08:42:17.214643: train_loss -0.355 +2026-04-11 08:42:17.222863: val_loss -0.3275 +2026-04-11 08:42:17.225628: Pseudo dice [0.0, 0.0, 0.6144, 0.1518, 0.4523, 0.3764, 0.7049] +2026-04-11 08:42:17.229129: Epoch time: 101.77 s +2026-04-11 08:42:18.392348: +2026-04-11 08:42:18.394611: Epoch 655 +2026-04-11 08:42:18.396648: Current learning rate: 0.00851 +2026-04-11 08:44:00.189900: train_loss -0.3517 +2026-04-11 08:44:00.196754: val_loss -0.2819 +2026-04-11 08:44:00.199855: Pseudo dice [0.0, 0.0, 0.5757, 0.0872, 0.546, 0.7974, 0.7831] +2026-04-11 08:44:00.203138: Epoch time: 101.8 s +2026-04-11 08:44:01.343371: +2026-04-11 08:44:01.345314: Epoch 656 +2026-04-11 08:44:01.347269: Current learning rate: 0.00851 +2026-04-11 08:45:43.522050: train_loss -0.3587 +2026-04-11 08:45:43.529526: val_loss -0.3617 +2026-04-11 08:45:43.532249: Pseudo dice [0.0, 0.0, 0.6202, 0.5883, 0.4682, 0.2926, 0.8903] +2026-04-11 08:45:43.534534: Epoch time: 102.18 s +2026-04-11 08:45:45.916713: +2026-04-11 08:45:45.918877: Epoch 657 +2026-04-11 08:45:45.920693: Current learning rate: 0.00851 +2026-04-11 08:47:28.582236: train_loss -0.3749 +2026-04-11 08:47:28.589487: val_loss -0.3549 +2026-04-11 08:47:28.591687: Pseudo dice [0.0, 0.0, 0.7377, 0.725, 0.6863, 0.6666, 0.8819] +2026-04-11 08:47:28.593775: Epoch time: 102.67 s +2026-04-11 08:47:29.729390: +2026-04-11 08:47:29.731440: Epoch 658 +2026-04-11 08:47:29.733280: Current learning rate: 0.00851 +2026-04-11 08:49:11.571120: train_loss -0.3834 +2026-04-11 08:49:11.579206: val_loss -0.3045 +2026-04-11 08:49:11.581945: Pseudo dice [0.0, 0.0, 0.801, 0.8032, 0.3503, 0.1803, 0.8103] +2026-04-11 08:49:11.584743: Epoch time: 101.84 s +2026-04-11 08:49:12.742556: +2026-04-11 08:49:12.744606: Epoch 659 +2026-04-11 08:49:12.746620: Current learning rate: 0.0085 +2026-04-11 08:50:54.365989: train_loss -0.3533 +2026-04-11 08:50:54.372746: val_loss -0.2858 +2026-04-11 08:50:54.374719: Pseudo dice [0.0, 0.0, 0.7399, 0.75, 0.6045, 0.6336, 0.3194] +2026-04-11 08:50:54.377213: Epoch time: 101.63 s +2026-04-11 08:50:55.544290: +2026-04-11 08:50:55.546789: Epoch 660 +2026-04-11 08:50:55.549254: Current learning rate: 0.0085 +2026-04-11 08:52:37.211916: train_loss -0.3712 +2026-04-11 08:52:37.217900: val_loss -0.3327 +2026-04-11 08:52:37.220126: Pseudo dice [0.0, 0.0, 0.4336, 0.4183, 0.3971, 0.5594, 0.7811] +2026-04-11 08:52:37.222583: Epoch time: 101.67 s +2026-04-11 08:52:38.397554: +2026-04-11 08:52:38.399405: Epoch 661 +2026-04-11 08:52:38.401957: Current learning rate: 0.0085 +2026-04-11 08:54:19.764967: train_loss -0.3732 +2026-04-11 08:54:19.771779: val_loss -0.3261 +2026-04-11 08:54:19.774888: Pseudo dice [0.0, 0.0, 0.6508, 0.6458, 0.2749, 0.5026, 0.6549] +2026-04-11 08:54:19.778343: Epoch time: 101.37 s +2026-04-11 08:54:21.000849: +2026-04-11 08:54:21.002711: Epoch 662 +2026-04-11 08:54:21.005234: Current learning rate: 0.0085 +2026-04-11 08:56:02.879163: train_loss -0.3782 +2026-04-11 08:56:02.888952: val_loss -0.3409 +2026-04-11 08:56:02.891636: Pseudo dice [0.0, 0.0, 0.7395, 0.191, 0.5885, 0.2644, 0.7783] +2026-04-11 08:56:02.894112: Epoch time: 101.88 s +2026-04-11 08:56:04.085160: +2026-04-11 08:56:04.101209: Epoch 663 +2026-04-11 08:56:04.103596: Current learning rate: 0.0085 +2026-04-11 08:57:46.052535: train_loss -0.3847 +2026-04-11 08:57:46.059238: val_loss -0.3116 +2026-04-11 08:57:46.061358: Pseudo dice [0.0, 0.0, 0.6895, 0.3383, 0.3965, 0.7097, 0.5311] +2026-04-11 08:57:46.064049: Epoch time: 101.97 s +2026-04-11 08:57:47.203251: +2026-04-11 08:57:47.205686: Epoch 664 +2026-04-11 08:57:47.208187: Current learning rate: 0.00849 +2026-04-11 08:59:28.778616: train_loss -0.3832 +2026-04-11 08:59:28.785536: val_loss -0.3513 +2026-04-11 08:59:28.788034: Pseudo dice [0.0, 0.0, 0.7622, 0.6993, 0.5559, 0.4697, 0.5613] +2026-04-11 08:59:28.790520: Epoch time: 101.58 s +2026-04-11 08:59:29.931198: +2026-04-11 08:59:29.933494: Epoch 665 +2026-04-11 08:59:29.936499: Current learning rate: 0.00849 +2026-04-11 09:01:11.868824: train_loss -0.3863 +2026-04-11 09:01:11.876397: val_loss -0.3416 +2026-04-11 09:01:11.878518: Pseudo dice [0.0, 0.0, 0.7722, 0.6185, 0.4773, 0.4619, 0.2042] +2026-04-11 09:01:11.880870: Epoch time: 101.94 s +2026-04-11 09:01:13.037638: +2026-04-11 09:01:13.039544: Epoch 666 +2026-04-11 09:01:13.041898: Current learning rate: 0.00849 +2026-04-11 09:02:55.496532: train_loss -0.3388 +2026-04-11 09:02:55.505285: val_loss -0.2823 +2026-04-11 09:02:55.507825: Pseudo dice [0.0, 0.0, 0.6912, 0.6252, 0.4883, 0.3143, 0.098] +2026-04-11 09:02:55.511068: Epoch time: 102.46 s +2026-04-11 09:02:56.656406: +2026-04-11 09:02:56.658509: Epoch 667 +2026-04-11 09:02:56.660797: Current learning rate: 0.00849 +2026-04-11 09:04:38.743447: train_loss -0.3578 +2026-04-11 09:04:38.750337: val_loss -0.3194 +2026-04-11 09:04:38.753175: Pseudo dice [0.0, 0.0, 0.5721, 0.5672, 0.326, 0.4475, 0.7989] +2026-04-11 09:04:38.756369: Epoch time: 102.09 s +2026-04-11 09:04:39.943747: +2026-04-11 09:04:39.945440: Epoch 668 +2026-04-11 09:04:39.948223: Current learning rate: 0.00848 +2026-04-11 09:06:22.146095: train_loss -0.3714 +2026-04-11 09:06:22.153117: val_loss -0.3232 +2026-04-11 09:06:22.154983: Pseudo dice [0.0, 0.0, 0.7323, 0.3264, 0.4368, 0.7492, 0.6895] +2026-04-11 09:06:22.157368: Epoch time: 102.21 s +2026-04-11 09:06:23.315345: +2026-04-11 09:06:23.317492: Epoch 669 +2026-04-11 09:06:23.320306: Current learning rate: 0.00848 +2026-04-11 09:08:05.748936: train_loss -0.3681 +2026-04-11 09:08:05.757072: val_loss -0.3449 +2026-04-11 09:08:05.759398: Pseudo dice [0.0013, 0.0, 0.7665, 0.4558, 0.4215, 0.4752, 0.6094] +2026-04-11 09:08:05.762471: Epoch time: 102.44 s +2026-04-11 09:08:06.915167: +2026-04-11 09:08:06.917316: Epoch 670 +2026-04-11 09:08:06.919445: Current learning rate: 0.00848 +2026-04-11 09:09:48.805033: train_loss -0.3823 +2026-04-11 09:09:48.822171: val_loss -0.3098 +2026-04-11 09:09:48.824410: Pseudo dice [0.0, 0.0, 0.756, 0.4978, 0.5062, 0.751, 0.5866] +2026-04-11 09:09:48.826966: Epoch time: 101.89 s +2026-04-11 09:09:49.998747: +2026-04-11 09:09:50.000729: Epoch 671 +2026-04-11 09:09:50.002590: Current learning rate: 0.00848 +2026-04-11 09:11:32.177471: train_loss -0.3806 +2026-04-11 09:11:32.183800: val_loss -0.3426 +2026-04-11 09:11:32.185649: Pseudo dice [0.0, 0.0, 0.6236, 0.7851, 0.5846, 0.7138, 0.8128] +2026-04-11 09:11:32.187976: Epoch time: 102.18 s +2026-04-11 09:11:33.372236: +2026-04-11 09:11:33.375124: Epoch 672 +2026-04-11 09:11:33.377081: Current learning rate: 0.00847 +2026-04-11 09:13:14.991261: train_loss -0.3817 +2026-04-11 09:13:14.999141: val_loss -0.3205 +2026-04-11 09:13:15.001544: Pseudo dice [0.0, 0.0, 0.7667, 0.6155, 0.518, 0.4194, 0.8467] +2026-04-11 09:13:15.004961: Epoch time: 101.62 s +2026-04-11 09:13:16.171633: +2026-04-11 09:13:16.175155: Epoch 673 +2026-04-11 09:13:16.177830: Current learning rate: 0.00847 +2026-04-11 09:14:58.357341: train_loss -0.3781 +2026-04-11 09:14:58.365393: val_loss -0.3339 +2026-04-11 09:14:58.368098: Pseudo dice [0.0, 0.0, 0.7563, 0.7306, 0.5332, 0.5686, 0.5382] +2026-04-11 09:14:58.370422: Epoch time: 102.19 s +2026-04-11 09:14:59.533146: +2026-04-11 09:14:59.535018: Epoch 674 +2026-04-11 09:14:59.537059: Current learning rate: 0.00847 +2026-04-11 09:16:41.437072: train_loss -0.3797 +2026-04-11 09:16:41.444876: val_loss -0.3063 +2026-04-11 09:16:41.447066: Pseudo dice [0.0, 0.0, 0.8011, 0.4848, 0.51, 0.7975, 0.2104] +2026-04-11 09:16:41.450161: Epoch time: 101.91 s +2026-04-11 09:16:42.661939: +2026-04-11 09:16:42.664130: Epoch 675 +2026-04-11 09:16:42.666409: Current learning rate: 0.00847 +2026-04-11 09:18:24.595627: train_loss -0.3787 +2026-04-11 09:18:24.602280: val_loss -0.3294 +2026-04-11 09:18:24.604909: Pseudo dice [0.0, 0.0, 0.6858, 0.1585, 0.5937, 0.2519, 0.8069] +2026-04-11 09:18:24.607667: Epoch time: 101.94 s +2026-04-11 09:18:25.750825: +2026-04-11 09:18:25.752756: Epoch 676 +2026-04-11 09:18:25.754881: Current learning rate: 0.00847 +2026-04-11 09:20:07.814823: train_loss -0.3864 +2026-04-11 09:20:07.822802: val_loss -0.382 +2026-04-11 09:20:07.824931: Pseudo dice [0.0, 0.0, 0.5089, 0.5605, 0.4548, 0.8282, 0.828] +2026-04-11 09:20:07.827689: Epoch time: 102.07 s +2026-04-11 09:20:10.146796: +2026-04-11 09:20:10.148406: Epoch 677 +2026-04-11 09:20:10.150292: Current learning rate: 0.00846 +2026-04-11 09:21:52.284654: train_loss -0.3706 +2026-04-11 09:21:52.291732: val_loss -0.3175 +2026-04-11 09:21:52.294281: Pseudo dice [0.0, 0.0, 0.7523, 0.0549, 0.5, 0.4168, 0.3921] +2026-04-11 09:21:52.297724: Epoch time: 102.14 s +2026-04-11 09:21:53.455734: +2026-04-11 09:21:53.457919: Epoch 678 +2026-04-11 09:21:53.461375: Current learning rate: 0.00846 +2026-04-11 09:23:35.406934: train_loss -0.3715 +2026-04-11 09:23:35.413811: val_loss -0.296 +2026-04-11 09:23:35.416106: Pseudo dice [0.0, 0.0, 0.7365, 0.4939, 0.4173, 0.4027, 0.6825] +2026-04-11 09:23:35.418545: Epoch time: 101.95 s +2026-04-11 09:23:36.615843: +2026-04-11 09:23:36.619645: Epoch 679 +2026-04-11 09:23:36.621823: Current learning rate: 0.00846 +2026-04-11 09:25:18.610909: train_loss -0.3743 +2026-04-11 09:25:18.618235: val_loss -0.3327 +2026-04-11 09:25:18.620617: Pseudo dice [0.0, 0.0, 0.6805, 0.6598, 0.5698, 0.7219, 0.8338] +2026-04-11 09:25:18.623007: Epoch time: 102.0 s +2026-04-11 09:25:19.876743: +2026-04-11 09:25:19.878954: Epoch 680 +2026-04-11 09:25:19.882130: Current learning rate: 0.00846 +2026-04-11 09:27:01.778357: train_loss -0.3819 +2026-04-11 09:27:01.787002: val_loss -0.3269 +2026-04-11 09:27:01.789270: Pseudo dice [0.0, 0.0, 0.7145, 0.1346, 0.4958, 0.6977, 0.7398] +2026-04-11 09:27:01.792559: Epoch time: 101.91 s +2026-04-11 09:27:02.954201: +2026-04-11 09:27:02.956537: Epoch 681 +2026-04-11 09:27:02.960179: Current learning rate: 0.00845 +2026-04-11 09:28:44.660484: train_loss -0.3634 +2026-04-11 09:28:44.667517: val_loss -0.3067 +2026-04-11 09:28:44.669749: Pseudo dice [0.0, 0.0, 0.5548, 0.5906, 0.6589, 0.588, 0.1769] +2026-04-11 09:28:44.672011: Epoch time: 101.71 s +2026-04-11 09:28:45.842438: +2026-04-11 09:28:45.844317: Epoch 682 +2026-04-11 09:28:45.846570: Current learning rate: 0.00845 +2026-04-11 09:30:27.761711: train_loss -0.3578 +2026-04-11 09:30:27.769647: val_loss -0.3422 +2026-04-11 09:30:27.772088: Pseudo dice [0.0, 0.0, 0.8243, 0.7199, 0.5338, 0.5905, 0.5358] +2026-04-11 09:30:27.775174: Epoch time: 101.92 s +2026-04-11 09:30:28.936735: +2026-04-11 09:30:28.938483: Epoch 683 +2026-04-11 09:30:28.940407: Current learning rate: 0.00845 +2026-04-11 09:32:10.702186: train_loss -0.3772 +2026-04-11 09:32:10.710049: val_loss -0.3063 +2026-04-11 09:32:10.713499: Pseudo dice [0.0, 0.0, 0.5128, 0.1489, 0.3153, 0.6491, 0.8815] +2026-04-11 09:32:10.717141: Epoch time: 101.77 s +2026-04-11 09:32:11.937743: +2026-04-11 09:32:11.939642: Epoch 684 +2026-04-11 09:32:11.941717: Current learning rate: 0.00845 +2026-04-11 09:33:53.656977: train_loss -0.3673 +2026-04-11 09:33:53.665267: val_loss -0.3339 +2026-04-11 09:33:53.667719: Pseudo dice [0.0, 0.0, 0.606, 0.7529, 0.5831, 0.4815, 0.2031] +2026-04-11 09:33:53.670678: Epoch time: 101.72 s +2026-04-11 09:33:54.839661: +2026-04-11 09:33:54.841612: Epoch 685 +2026-04-11 09:33:54.843667: Current learning rate: 0.00844 +2026-04-11 09:35:36.912299: train_loss -0.37 +2026-04-11 09:35:36.920014: val_loss -0.3569 +2026-04-11 09:35:36.922362: Pseudo dice [0.0, 0.0, 0.8682, 0.5899, 0.4544, 0.566, 0.2652] +2026-04-11 09:35:36.924878: Epoch time: 102.08 s +2026-04-11 09:35:38.091047: +2026-04-11 09:35:38.093856: Epoch 686 +2026-04-11 09:35:38.096679: Current learning rate: 0.00844 +2026-04-11 09:37:20.158237: train_loss -0.3607 +2026-04-11 09:37:20.163382: val_loss -0.2992 +2026-04-11 09:37:20.165284: Pseudo dice [0.0, 0.0, 0.8109, 0.4274, 0.4152, 0.1056, 0.6687] +2026-04-11 09:37:20.167854: Epoch time: 102.07 s +2026-04-11 09:37:21.352983: +2026-04-11 09:37:21.354970: Epoch 687 +2026-04-11 09:37:21.357043: Current learning rate: 0.00844 +2026-04-11 09:39:03.779929: train_loss -0.3688 +2026-04-11 09:39:03.786474: val_loss -0.3071 +2026-04-11 09:39:03.788532: Pseudo dice [0.0, 0.0, 0.6233, 0.1787, 0.259, 0.4642, 0.3627] +2026-04-11 09:39:03.791079: Epoch time: 102.43 s +2026-04-11 09:39:04.978938: +2026-04-11 09:39:04.981486: Epoch 688 +2026-04-11 09:39:04.983653: Current learning rate: 0.00844 +2026-04-11 09:40:46.943763: train_loss -0.3718 +2026-04-11 09:40:46.950312: val_loss -0.3506 +2026-04-11 09:40:46.953278: Pseudo dice [0.0, 0.0, 0.5997, 0.4147, 0.2899, 0.3939, 0.5197] +2026-04-11 09:40:46.956650: Epoch time: 101.97 s +2026-04-11 09:40:48.127982: +2026-04-11 09:40:48.129736: Epoch 689 +2026-04-11 09:40:48.131575: Current learning rate: 0.00844 +2026-04-11 09:42:30.120435: train_loss -0.3711 +2026-04-11 09:42:30.128296: val_loss -0.3186 +2026-04-11 09:42:30.130616: Pseudo dice [0.0, 0.0, 0.5289, 0.3185, 0.3646, 0.1768, 0.6661] +2026-04-11 09:42:30.133413: Epoch time: 102.0 s +2026-04-11 09:42:31.304815: +2026-04-11 09:42:31.306726: Epoch 690 +2026-04-11 09:42:31.310006: Current learning rate: 0.00843 +2026-04-11 09:44:13.474784: train_loss -0.3792 +2026-04-11 09:44:13.482020: val_loss -0.3344 +2026-04-11 09:44:13.483879: Pseudo dice [0.0, 0.0, 0.5934, 0.1296, 0.4143, 0.5285, 0.8077] +2026-04-11 09:44:13.487159: Epoch time: 102.17 s +2026-04-11 09:44:14.655143: +2026-04-11 09:44:14.659272: Epoch 691 +2026-04-11 09:44:14.668925: Current learning rate: 0.00843 +2026-04-11 09:45:56.726916: train_loss -0.3804 +2026-04-11 09:45:56.734309: val_loss -0.3127 +2026-04-11 09:45:56.737861: Pseudo dice [0.2977, 0.0, 0.4106, 0.2698, 0.2902, 0.6716, 0.714] +2026-04-11 09:45:56.740662: Epoch time: 102.07 s +2026-04-11 09:45:57.940542: +2026-04-11 09:45:57.942846: Epoch 692 +2026-04-11 09:45:57.945588: Current learning rate: 0.00843 +2026-04-11 09:47:39.766724: train_loss -0.3684 +2026-04-11 09:47:39.774722: val_loss -0.31 +2026-04-11 09:47:39.777247: Pseudo dice [0.0, 0.0, 0.7622, 0.5444, 0.3957, 0.1151, 0.5678] +2026-04-11 09:47:39.779976: Epoch time: 101.83 s +2026-04-11 09:47:41.001410: +2026-04-11 09:47:41.003114: Epoch 693 +2026-04-11 09:47:41.005127: Current learning rate: 0.00843 +2026-04-11 09:49:22.706774: train_loss -0.384 +2026-04-11 09:49:22.714296: val_loss -0.354 +2026-04-11 09:49:22.716467: Pseudo dice [0.2205, 0.0, 0.8853, 0.2737, 0.5454, 0.6932, 0.7403] +2026-04-11 09:49:22.719570: Epoch time: 101.71 s +2026-04-11 09:49:23.879591: +2026-04-11 09:49:23.881444: Epoch 694 +2026-04-11 09:49:23.883352: Current learning rate: 0.00842 +2026-04-11 09:51:05.558702: train_loss -0.3406 +2026-04-11 09:51:05.566815: val_loss -0.3404 +2026-04-11 09:51:05.569254: Pseudo dice [0.0, 0.0, 0.622, 0.3166, 0.5533, 0.2619, 0.763] +2026-04-11 09:51:05.572486: Epoch time: 101.68 s +2026-04-11 09:51:06.754197: +2026-04-11 09:51:06.756620: Epoch 695 +2026-04-11 09:51:06.758830: Current learning rate: 0.00842 +2026-04-11 09:52:48.863956: train_loss -0.3624 +2026-04-11 09:52:48.870714: val_loss -0.3202 +2026-04-11 09:52:48.874104: Pseudo dice [0.0, 0.0, 0.6783, 0.3433, 0.3257, 0.4913, 0.6191] +2026-04-11 09:52:48.876748: Epoch time: 102.11 s +2026-04-11 09:52:50.049074: +2026-04-11 09:52:50.051050: Epoch 696 +2026-04-11 09:52:50.053866: Current learning rate: 0.00842 +2026-04-11 09:54:33.258530: train_loss -0.3679 +2026-04-11 09:54:33.265874: val_loss -0.337 +2026-04-11 09:54:33.268549: Pseudo dice [0.0, 0.0, 0.5665, 0.7979, 0.4202, 0.3486, 0.851] +2026-04-11 09:54:33.271101: Epoch time: 103.21 s +2026-04-11 09:54:34.432187: +2026-04-11 09:54:34.434126: Epoch 697 +2026-04-11 09:54:34.435954: Current learning rate: 0.00842 +2026-04-11 09:56:15.990024: train_loss -0.3822 +2026-04-11 09:56:15.995970: val_loss -0.3365 +2026-04-11 09:56:15.998122: Pseudo dice [0.0, 0.0, 0.8086, 0.7064, 0.5152, 0.4763, 0.6012] +2026-04-11 09:56:16.000463: Epoch time: 101.56 s +2026-04-11 09:56:17.158850: +2026-04-11 09:56:17.160800: Epoch 698 +2026-04-11 09:56:17.162702: Current learning rate: 0.00841 +2026-04-11 09:57:59.082624: train_loss -0.3538 +2026-04-11 09:57:59.088635: val_loss -0.3281 +2026-04-11 09:57:59.090401: Pseudo dice [0.0, 0.0, 0.5775, 0.2867, 0.5211, 0.3292, 0.7657] +2026-04-11 09:57:59.092748: Epoch time: 101.93 s +2026-04-11 09:58:00.274646: +2026-04-11 09:58:00.276852: Epoch 699 +2026-04-11 09:58:00.279112: Current learning rate: 0.00841 +2026-04-11 09:59:41.954232: train_loss -0.3793 +2026-04-11 09:59:41.960854: val_loss -0.3043 +2026-04-11 09:59:41.963334: Pseudo dice [0.0, 0.0, 0.785, 0.5184, 0.4257, 0.288, 0.6293] +2026-04-11 09:59:41.966570: Epoch time: 101.68 s +2026-04-11 09:59:44.845723: +2026-04-11 09:59:44.847747: Epoch 700 +2026-04-11 09:59:44.849611: Current learning rate: 0.00841 +2026-04-11 10:01:26.911078: train_loss -0.3664 +2026-04-11 10:01:26.918037: val_loss -0.2935 +2026-04-11 10:01:26.920368: Pseudo dice [0.0, 0.0, 0.4901, 0.1866, 0.5026, 0.3627, 0.8398] +2026-04-11 10:01:26.923385: Epoch time: 102.07 s +2026-04-11 10:01:28.106444: +2026-04-11 10:01:28.108884: Epoch 701 +2026-04-11 10:01:28.111310: Current learning rate: 0.00841 +2026-04-11 10:03:10.598309: train_loss -0.3756 +2026-04-11 10:03:10.605567: val_loss -0.2851 +2026-04-11 10:03:10.608503: Pseudo dice [0.0, 0.0, 0.3825, 0.428, 0.5969, 0.3964, 0.1678] +2026-04-11 10:03:10.611557: Epoch time: 102.49 s +2026-04-11 10:03:11.787359: +2026-04-11 10:03:11.789171: Epoch 702 +2026-04-11 10:03:11.791302: Current learning rate: 0.00841 +2026-04-11 10:04:53.201560: train_loss -0.3935 +2026-04-11 10:04:53.209666: val_loss -0.3217 +2026-04-11 10:04:53.211939: Pseudo dice [0.0, 0.0, 0.7708, 0.2263, 0.4598, 0.2723, 0.5592] +2026-04-11 10:04:53.214881: Epoch time: 101.42 s +2026-04-11 10:04:54.373199: +2026-04-11 10:04:54.374978: Epoch 703 +2026-04-11 10:04:54.379007: Current learning rate: 0.0084 +2026-04-11 10:06:36.178964: train_loss -0.384 +2026-04-11 10:06:36.191987: val_loss -0.3087 +2026-04-11 10:06:36.194701: Pseudo dice [0.0, 0.0, 0.6433, 0.6647, 0.5221, 0.3706, 0.8502] +2026-04-11 10:06:36.197276: Epoch time: 101.81 s +2026-04-11 10:06:37.387884: +2026-04-11 10:06:37.389899: Epoch 704 +2026-04-11 10:06:37.392293: Current learning rate: 0.0084 +2026-04-11 10:08:19.350734: train_loss -0.3736 +2026-04-11 10:08:19.358143: val_loss -0.3343 +2026-04-11 10:08:19.360458: Pseudo dice [0.0, 0.0, 0.8147, 0.7313, 0.4968, 0.4408, 0.8137] +2026-04-11 10:08:19.362991: Epoch time: 101.97 s +2026-04-11 10:08:20.526845: +2026-04-11 10:08:20.528866: Epoch 705 +2026-04-11 10:08:20.531090: Current learning rate: 0.0084 +2026-04-11 10:10:02.861783: train_loss -0.381 +2026-04-11 10:10:02.888235: val_loss -0.3219 +2026-04-11 10:10:02.890125: Pseudo dice [0.0, 0.0, 0.6267, 0.5019, 0.4622, 0.5177, 0.8536] +2026-04-11 10:10:02.892892: Epoch time: 102.34 s +2026-04-11 10:10:04.069991: +2026-04-11 10:10:04.072233: Epoch 706 +2026-04-11 10:10:04.074302: Current learning rate: 0.0084 +2026-04-11 10:11:46.400639: train_loss -0.3765 +2026-04-11 10:11:46.406806: val_loss -0.3339 +2026-04-11 10:11:46.408948: Pseudo dice [0.0, 0.0, 0.6525, 0.217, 0.486, 0.4347, 0.7539] +2026-04-11 10:11:46.411276: Epoch time: 102.33 s +2026-04-11 10:11:47.587851: +2026-04-11 10:11:47.589756: Epoch 707 +2026-04-11 10:11:47.591570: Current learning rate: 0.00839 +2026-04-11 10:13:29.133988: train_loss -0.3859 +2026-04-11 10:13:29.140379: val_loss -0.3705 +2026-04-11 10:13:29.143260: Pseudo dice [0.0, 0.0, 0.85, 0.7688, 0.4287, 0.7302, 0.8483] +2026-04-11 10:13:29.146627: Epoch time: 101.55 s +2026-04-11 10:13:30.300812: +2026-04-11 10:13:30.302899: Epoch 708 +2026-04-11 10:13:30.305467: Current learning rate: 0.00839 +2026-04-11 10:15:12.154502: train_loss -0.3918 +2026-04-11 10:15:12.160611: val_loss -0.3493 +2026-04-11 10:15:12.162987: Pseudo dice [0.0, 0.0, 0.8628, 0.56, 0.4901, 0.8422, 0.5797] +2026-04-11 10:15:12.165463: Epoch time: 101.86 s +2026-04-11 10:15:13.328106: +2026-04-11 10:15:13.330025: Epoch 709 +2026-04-11 10:15:13.332298: Current learning rate: 0.00839 +2026-04-11 10:16:54.850369: train_loss -0.3681 +2026-04-11 10:16:54.856740: val_loss -0.3744 +2026-04-11 10:16:54.859060: Pseudo dice [0.0, 0.0, 0.6952, 0.7867, 0.3769, 0.7342, 0.9068] +2026-04-11 10:16:54.861488: Epoch time: 101.53 s +2026-04-11 10:16:56.069465: +2026-04-11 10:16:56.071706: Epoch 710 +2026-04-11 10:16:56.073912: Current learning rate: 0.00839 +2026-04-11 10:18:38.597395: train_loss -0.3948 +2026-04-11 10:18:38.609956: val_loss -0.3285 +2026-04-11 10:18:38.612430: Pseudo dice [0.0, 0.0, 0.4989, 0.6934, 0.3403, 0.4037, 0.6117] +2026-04-11 10:18:38.615684: Epoch time: 102.53 s +2026-04-11 10:18:39.835240: +2026-04-11 10:18:39.837266: Epoch 711 +2026-04-11 10:18:39.840265: Current learning rate: 0.00839 +2026-04-11 10:20:21.013195: train_loss -0.3653 +2026-04-11 10:20:21.019113: val_loss -0.3196 +2026-04-11 10:20:21.021788: Pseudo dice [0.0, 0.0, 0.6696, 0.2515, 0.1515, 0.3578, 0.5889] +2026-04-11 10:20:21.024160: Epoch time: 101.18 s +2026-04-11 10:20:22.194145: +2026-04-11 10:20:22.196338: Epoch 712 +2026-04-11 10:20:22.198223: Current learning rate: 0.00838 +2026-04-11 10:22:03.586461: train_loss -0.3714 +2026-04-11 10:22:03.592724: val_loss -0.2893 +2026-04-11 10:22:03.594778: Pseudo dice [0.0, 0.0, 0.7188, 0.378, 0.3991, 0.4497, 0.7336] +2026-04-11 10:22:03.597802: Epoch time: 101.4 s +2026-04-11 10:22:04.767562: +2026-04-11 10:22:04.769344: Epoch 713 +2026-04-11 10:22:04.771266: Current learning rate: 0.00838 +2026-04-11 10:23:45.959529: train_loss -0.3648 +2026-04-11 10:23:45.966185: val_loss -0.3644 +2026-04-11 10:23:45.968185: Pseudo dice [0.0, 0.0, 0.7477, 0.3074, 0.6111, 0.3142, 0.6431] +2026-04-11 10:23:45.970667: Epoch time: 101.2 s +2026-04-11 10:23:47.132346: +2026-04-11 10:23:47.134470: Epoch 714 +2026-04-11 10:23:47.136304: Current learning rate: 0.00838 +2026-04-11 10:25:29.105451: train_loss -0.3705 +2026-04-11 10:25:29.111951: val_loss -0.3372 +2026-04-11 10:25:29.114901: Pseudo dice [0.0, 0.0, 0.7873, 0.395, 0.5685, 0.3131, 0.7158] +2026-04-11 10:25:29.117492: Epoch time: 101.98 s +2026-04-11 10:25:30.280202: +2026-04-11 10:25:30.282141: Epoch 715 +2026-04-11 10:25:30.284277: Current learning rate: 0.00838 +2026-04-11 10:27:13.915999: train_loss -0.3747 +2026-04-11 10:27:13.922448: val_loss -0.3211 +2026-04-11 10:27:13.925218: Pseudo dice [0.0, 0.0, 0.4904, 0.2201, 0.3803, 0.3517, 0.8198] +2026-04-11 10:27:13.927577: Epoch time: 103.64 s +2026-04-11 10:27:15.103502: +2026-04-11 10:27:15.106094: Epoch 716 +2026-04-11 10:27:15.108279: Current learning rate: 0.00837 +2026-04-11 10:28:57.139964: train_loss -0.3865 +2026-04-11 10:28:57.146274: val_loss -0.3463 +2026-04-11 10:28:57.148600: Pseudo dice [0.0, 0.0, 0.8376, 0.1388, 0.3961, 0.5187, 0.5498] +2026-04-11 10:28:57.152307: Epoch time: 102.04 s +2026-04-11 10:28:58.337274: +2026-04-11 10:28:58.339365: Epoch 717 +2026-04-11 10:28:58.341348: Current learning rate: 0.00837 +2026-04-11 10:30:40.631498: train_loss -0.386 +2026-04-11 10:30:40.639526: val_loss -0.3407 +2026-04-11 10:30:40.641826: Pseudo dice [0.0, 0.0, 0.6749, 0.1926, 0.4526, 0.4785, 0.8342] +2026-04-11 10:30:40.644269: Epoch time: 102.3 s +2026-04-11 10:30:41.872284: +2026-04-11 10:30:41.874521: Epoch 718 +2026-04-11 10:30:41.876998: Current learning rate: 0.00837 +2026-04-11 10:32:23.276438: train_loss -0.3858 +2026-04-11 10:32:23.283836: val_loss -0.3426 +2026-04-11 10:32:23.287997: Pseudo dice [0.0, 0.0, 0.8079, 0.4617, 0.5881, 0.5725, 0.8173] +2026-04-11 10:32:23.291644: Epoch time: 101.41 s +2026-04-11 10:32:24.475873: +2026-04-11 10:32:24.478704: Epoch 719 +2026-04-11 10:32:24.482116: Current learning rate: 0.00837 +2026-04-11 10:34:06.064591: train_loss -0.3704 +2026-04-11 10:34:06.072771: val_loss -0.3373 +2026-04-11 10:34:06.075243: Pseudo dice [0.0, 0.0, 0.3255, 0.7563, 0.5448, 0.6795, 0.7741] +2026-04-11 10:34:06.077643: Epoch time: 101.59 s +2026-04-11 10:34:07.273546: +2026-04-11 10:34:07.275584: Epoch 720 +2026-04-11 10:34:07.278774: Current learning rate: 0.00836 +2026-04-11 10:35:49.138845: train_loss -0.3757 +2026-04-11 10:35:49.145791: val_loss -0.3722 +2026-04-11 10:35:49.148730: Pseudo dice [0.5015, 0.0, 0.8189, 0.4704, 0.6898, 0.6095, 0.8867] +2026-04-11 10:35:49.152304: Epoch time: 101.87 s +2026-04-11 10:35:50.366674: +2026-04-11 10:35:50.368918: Epoch 721 +2026-04-11 10:35:50.371206: Current learning rate: 0.00836 +2026-04-11 10:37:31.860699: train_loss -0.3635 +2026-04-11 10:37:31.868442: val_loss -0.3219 +2026-04-11 10:37:31.871327: Pseudo dice [0.0, 0.0, 0.7534, 0.5428, 0.1319, 0.5419, 0.8603] +2026-04-11 10:37:31.874416: Epoch time: 101.5 s +2026-04-11 10:37:33.112722: +2026-04-11 10:37:33.114790: Epoch 722 +2026-04-11 10:37:33.116757: Current learning rate: 0.00836 +2026-04-11 10:39:14.687875: train_loss -0.3431 +2026-04-11 10:39:14.694547: val_loss -0.2935 +2026-04-11 10:39:14.697254: Pseudo dice [0.0, 0.0, 0.4769, 0.0826, 0.2835, 0.2003, 0.1079] +2026-04-11 10:39:14.700560: Epoch time: 101.58 s +2026-04-11 10:39:15.946469: +2026-04-11 10:39:15.948691: Epoch 723 +2026-04-11 10:39:15.950485: Current learning rate: 0.00836 +2026-04-11 10:40:57.463016: train_loss -0.3532 +2026-04-11 10:40:57.470464: val_loss -0.3178 +2026-04-11 10:40:57.473017: Pseudo dice [0.0, 0.0, 0.8052, 0.6579, 0.5194, 0.5725, 0.5178] +2026-04-11 10:40:57.475672: Epoch time: 101.52 s +2026-04-11 10:40:58.643707: +2026-04-11 10:40:58.647377: Epoch 724 +2026-04-11 10:40:58.649737: Current learning rate: 0.00836 +2026-04-11 10:42:40.203967: train_loss -0.3725 +2026-04-11 10:42:40.211106: val_loss -0.3497 +2026-04-11 10:42:40.213338: Pseudo dice [0.0, 0.0, 0.8254, 0.3632, 0.3775, 0.3353, 0.7662] +2026-04-11 10:42:40.216175: Epoch time: 101.56 s +2026-04-11 10:42:41.366444: +2026-04-11 10:42:41.368551: Epoch 725 +2026-04-11 10:42:41.370174: Current learning rate: 0.00835 +2026-04-11 10:44:23.289658: train_loss -0.3574 +2026-04-11 10:44:23.297679: val_loss -0.3007 +2026-04-11 10:44:23.300023: Pseudo dice [0.0, 0.0, 0.622, 0.0548, 0.4392, 0.714, 0.3717] +2026-04-11 10:44:23.303463: Epoch time: 101.93 s +2026-04-11 10:44:24.486320: +2026-04-11 10:44:24.488593: Epoch 726 +2026-04-11 10:44:24.490735: Current learning rate: 0.00835 +2026-04-11 10:46:06.233438: train_loss -0.3502 +2026-04-11 10:46:06.239928: val_loss -0.2882 +2026-04-11 10:46:06.242168: Pseudo dice [0.0, 0.0, 0.6492, 0.1247, 0.4306, 0.8244, 0.4016] +2026-04-11 10:46:06.244805: Epoch time: 101.75 s +2026-04-11 10:46:07.421164: +2026-04-11 10:46:07.423194: Epoch 727 +2026-04-11 10:46:07.425310: Current learning rate: 0.00835 +2026-04-11 10:47:49.156633: train_loss -0.3655 +2026-04-11 10:47:49.163346: val_loss -0.3683 +2026-04-11 10:47:49.165716: Pseudo dice [0.0, 0.0, 0.8629, 0.0164, 0.5549, 0.4437, 0.5357] +2026-04-11 10:47:49.168042: Epoch time: 101.74 s +2026-04-11 10:47:50.351631: +2026-04-11 10:47:50.353975: Epoch 728 +2026-04-11 10:47:50.356182: Current learning rate: 0.00835 +2026-04-11 10:49:31.958723: train_loss -0.3832 +2026-04-11 10:49:31.965989: val_loss -0.3369 +2026-04-11 10:49:31.967827: Pseudo dice [0.0, 0.0, 0.7659, 0.2795, 0.4526, 0.7337, 0.7542] +2026-04-11 10:49:31.970380: Epoch time: 101.61 s +2026-04-11 10:49:33.133579: +2026-04-11 10:49:33.135562: Epoch 729 +2026-04-11 10:49:33.138064: Current learning rate: 0.00834 +2026-04-11 10:51:15.500615: train_loss -0.3827 +2026-04-11 10:51:15.508491: val_loss -0.3273 +2026-04-11 10:51:15.511276: Pseudo dice [0.0, 0.0, 0.6895, 0.6257, 0.4724, 0.3495, 0.7244] +2026-04-11 10:51:15.514132: Epoch time: 102.37 s +2026-04-11 10:51:16.651204: +2026-04-11 10:51:16.653087: Epoch 730 +2026-04-11 10:51:16.654943: Current learning rate: 0.00834 +2026-04-11 10:52:58.158882: train_loss -0.3817 +2026-04-11 10:52:58.168711: val_loss -0.3534 +2026-04-11 10:52:58.171903: Pseudo dice [0.0, 0.0, 0.7536, 0.6169, 0.4782, 0.6881, 0.5277] +2026-04-11 10:52:58.174729: Epoch time: 101.51 s +2026-04-11 10:52:59.350053: +2026-04-11 10:52:59.352402: Epoch 731 +2026-04-11 10:52:59.354442: Current learning rate: 0.00834 +2026-04-11 10:54:40.661808: train_loss -0.3903 +2026-04-11 10:54:40.669129: val_loss -0.3379 +2026-04-11 10:54:40.674597: Pseudo dice [0.0, 0.0, 0.8146, 0.6806, 0.5797, 0.4945, 0.8516] +2026-04-11 10:54:40.677091: Epoch time: 101.31 s +2026-04-11 10:54:41.842359: +2026-04-11 10:54:41.844039: Epoch 732 +2026-04-11 10:54:41.845980: Current learning rate: 0.00834 +2026-04-11 10:56:23.840091: train_loss -0.3697 +2026-04-11 10:56:23.847196: val_loss -0.3693 +2026-04-11 10:56:23.849596: Pseudo dice [0.0, 0.0, 0.8008, 0.6319, 0.5029, 0.6309, 0.7905] +2026-04-11 10:56:23.852398: Epoch time: 102.0 s +2026-04-11 10:56:25.036490: +2026-04-11 10:56:25.038605: Epoch 733 +2026-04-11 10:56:25.041637: Current learning rate: 0.00833 +2026-04-11 10:58:06.511212: train_loss -0.3958 +2026-04-11 10:58:06.518847: val_loss -0.3519 +2026-04-11 10:58:06.521051: Pseudo dice [0.0, 0.0, 0.6134, 0.6984, 0.6876, 0.701, 0.4727] +2026-04-11 10:58:06.523463: Epoch time: 101.48 s +2026-04-11 10:58:07.689218: +2026-04-11 10:58:07.691315: Epoch 734 +2026-04-11 10:58:07.693950: Current learning rate: 0.00833 +2026-04-11 10:59:49.371502: train_loss -0.363 +2026-04-11 10:59:49.378825: val_loss -0.3155 +2026-04-11 10:59:49.381114: Pseudo dice [0.0, 0.0, 0.7054, 0.6933, 0.4328, 0.1568, 0.8351] +2026-04-11 10:59:49.383344: Epoch time: 101.69 s +2026-04-11 10:59:51.714447: +2026-04-11 10:59:51.716901: Epoch 735 +2026-04-11 10:59:51.718826: Current learning rate: 0.00833 +2026-04-11 11:01:33.528786: train_loss -0.3567 +2026-04-11 11:01:33.543496: val_loss -0.2666 +2026-04-11 11:01:33.545738: Pseudo dice [0.0, 0.0, 0.3858, 0.1511, 0.4395, 0.3795, 0.8281] +2026-04-11 11:01:33.548934: Epoch time: 101.82 s +2026-04-11 11:01:34.730985: +2026-04-11 11:01:34.733197: Epoch 736 +2026-04-11 11:01:34.735339: Current learning rate: 0.00833 +2026-04-11 11:03:16.414304: train_loss -0.3854 +2026-04-11 11:03:16.421395: val_loss -0.3485 +2026-04-11 11:03:16.423564: Pseudo dice [0.0, 0.0, 0.6513, 0.7015, 0.3853, 0.4826, 0.8203] +2026-04-11 11:03:16.426283: Epoch time: 101.69 s +2026-04-11 11:03:17.608373: +2026-04-11 11:03:17.610632: Epoch 737 +2026-04-11 11:03:17.612916: Current learning rate: 0.00833 +2026-04-11 11:04:59.569350: train_loss -0.3796 +2026-04-11 11:04:59.576084: val_loss -0.3237 +2026-04-11 11:04:59.578417: Pseudo dice [0.0, 0.0, 0.8185, 0.5789, 0.367, 0.7161, 0.6447] +2026-04-11 11:04:59.581625: Epoch time: 101.96 s +2026-04-11 11:05:00.768173: +2026-04-11 11:05:00.771814: Epoch 738 +2026-04-11 11:05:00.774296: Current learning rate: 0.00832 +2026-04-11 11:06:42.256604: train_loss -0.3714 +2026-04-11 11:06:42.264599: val_loss -0.3293 +2026-04-11 11:06:42.266977: Pseudo dice [0.0, 0.0, 0.7825, 0.361, 0.2646, 0.7911, 0.7076] +2026-04-11 11:06:42.269669: Epoch time: 101.49 s +2026-04-11 11:06:43.424389: +2026-04-11 11:06:43.426636: Epoch 739 +2026-04-11 11:06:43.429034: Current learning rate: 0.00832 +2026-04-11 11:08:24.932949: train_loss -0.3856 +2026-04-11 11:08:24.939885: val_loss -0.3824 +2026-04-11 11:08:24.942161: Pseudo dice [0.0, 0.0, 0.8347, 0.7642, 0.441, 0.7096, 0.8832] +2026-04-11 11:08:24.945174: Epoch time: 101.51 s +2026-04-11 11:08:26.118458: +2026-04-11 11:08:26.121087: Epoch 740 +2026-04-11 11:08:26.123295: Current learning rate: 0.00832 +2026-04-11 11:10:08.086328: train_loss -0.4024 +2026-04-11 11:10:08.093283: val_loss -0.3285 +2026-04-11 11:10:08.095264: Pseudo dice [0.0, 0.0, 0.7362, 0.3827, 0.4822, 0.3271, 0.5354] +2026-04-11 11:10:08.097753: Epoch time: 101.97 s +2026-04-11 11:10:09.262590: +2026-04-11 11:10:09.264683: Epoch 741 +2026-04-11 11:10:09.266717: Current learning rate: 0.00832 +2026-04-11 11:11:50.776067: train_loss -0.3918 +2026-04-11 11:11:50.783140: val_loss -0.3316 +2026-04-11 11:11:50.785264: Pseudo dice [0.0, 0.0, 0.7926, 0.2891, 0.4929, 0.3033, 0.5734] +2026-04-11 11:11:50.788080: Epoch time: 101.52 s +2026-04-11 11:11:51.956234: +2026-04-11 11:11:51.957987: Epoch 742 +2026-04-11 11:11:51.959661: Current learning rate: 0.00831 +2026-04-11 11:13:33.786870: train_loss -0.3799 +2026-04-11 11:13:33.793595: val_loss -0.36 +2026-04-11 11:13:33.796779: Pseudo dice [0.0, 0.0, 0.6602, 0.7815, 0.1289, 0.5884, 0.7068] +2026-04-11 11:13:33.799437: Epoch time: 101.83 s +2026-04-11 11:13:34.964367: +2026-04-11 11:13:34.966488: Epoch 743 +2026-04-11 11:13:34.968565: Current learning rate: 0.00831 +2026-04-11 11:15:16.228261: train_loss -0.3819 +2026-04-11 11:15:16.235469: val_loss -0.3253 +2026-04-11 11:15:16.237847: Pseudo dice [0.0, 0.0, 0.5785, 0.7353, 0.5452, 0.6931, 0.5867] +2026-04-11 11:15:16.239918: Epoch time: 101.27 s +2026-04-11 11:15:17.431739: +2026-04-11 11:15:17.433921: Epoch 744 +2026-04-11 11:15:17.436025: Current learning rate: 0.00831 +2026-04-11 11:16:59.015367: train_loss -0.3817 +2026-04-11 11:16:59.022836: val_loss -0.3404 +2026-04-11 11:16:59.024920: Pseudo dice [0.0, 0.0, 0.7296, 0.6422, 0.5642, 0.7152, 0.5354] +2026-04-11 11:16:59.027525: Epoch time: 101.59 s +2026-04-11 11:17:00.202615: +2026-04-11 11:17:00.204603: Epoch 745 +2026-04-11 11:17:00.207054: Current learning rate: 0.00831 +2026-04-11 11:18:41.266552: train_loss -0.3661 +2026-04-11 11:18:41.274205: val_loss -0.2834 +2026-04-11 11:18:41.276334: Pseudo dice [0.0, 0.0, 0.1348, 0.5151, 0.3481, 0.5468, 0.6769] +2026-04-11 11:18:41.279417: Epoch time: 101.07 s +2026-04-11 11:18:42.424659: +2026-04-11 11:18:42.426674: Epoch 746 +2026-04-11 11:18:42.428716: Current learning rate: 0.0083 +2026-04-11 11:20:23.789139: train_loss -0.3529 +2026-04-11 11:20:23.797719: val_loss -0.3418 +2026-04-11 11:20:23.800100: Pseudo dice [0.0, 0.0, 0.7489, 0.6614, 0.4038, 0.4338, 0.7802] +2026-04-11 11:20:23.802566: Epoch time: 101.37 s +2026-04-11 11:20:24.996621: +2026-04-11 11:20:24.998332: Epoch 747 +2026-04-11 11:20:25.000319: Current learning rate: 0.0083 +2026-04-11 11:22:07.168281: train_loss -0.3734 +2026-04-11 11:22:07.175883: val_loss -0.3046 +2026-04-11 11:22:07.179334: Pseudo dice [0.0, 0.0, 0.7485, 0.6491, 0.5816, 0.5537, 0.8642] +2026-04-11 11:22:07.181963: Epoch time: 102.17 s +2026-04-11 11:22:08.368214: +2026-04-11 11:22:08.370136: Epoch 748 +2026-04-11 11:22:08.372693: Current learning rate: 0.0083 +2026-04-11 11:23:50.296649: train_loss -0.3786 +2026-04-11 11:23:50.303201: val_loss -0.3503 +2026-04-11 11:23:50.309439: Pseudo dice [0.0, 0.0, 0.7855, 0.2759, 0.4238, 0.5679, 0.6095] +2026-04-11 11:23:50.312112: Epoch time: 101.93 s +2026-04-11 11:23:51.494048: +2026-04-11 11:23:51.496216: Epoch 749 +2026-04-11 11:23:51.498326: Current learning rate: 0.0083 +2026-04-11 11:25:32.912037: train_loss -0.3561 +2026-04-11 11:25:32.919554: val_loss -0.3421 +2026-04-11 11:25:32.921792: Pseudo dice [0.0, 0.0, 0.6355, 0.4037, 0.5663, 0.612, 0.8842] +2026-04-11 11:25:32.923970: Epoch time: 101.42 s +2026-04-11 11:25:35.774770: +2026-04-11 11:25:35.776532: Epoch 750 +2026-04-11 11:25:35.778574: Current learning rate: 0.0083 +2026-04-11 11:27:17.103504: train_loss -0.372 +2026-04-11 11:27:17.110472: val_loss -0.3228 +2026-04-11 11:27:17.112610: Pseudo dice [0.0, 0.0, 0.7423, 0.2094, 0.5882, 0.6755, 0.8934] +2026-04-11 11:27:17.114876: Epoch time: 101.33 s +2026-04-11 11:27:18.313797: +2026-04-11 11:27:18.316006: Epoch 751 +2026-04-11 11:27:18.317919: Current learning rate: 0.00829 +2026-04-11 11:29:00.613342: train_loss -0.3539 +2026-04-11 11:29:00.620100: val_loss -0.3086 +2026-04-11 11:29:00.622472: Pseudo dice [0.0, 0.0, 0.6283, 0.6044, 0.4079, 0.5442, 0.5803] +2026-04-11 11:29:00.625792: Epoch time: 102.3 s +2026-04-11 11:29:01.807610: +2026-04-11 11:29:01.809873: Epoch 752 +2026-04-11 11:29:01.812261: Current learning rate: 0.00829 +2026-04-11 11:30:43.893742: train_loss -0.3579 +2026-04-11 11:30:43.901089: val_loss -0.3602 +2026-04-11 11:30:43.903793: Pseudo dice [0.0, 0.0, 0.7141, 0.6567, 0.5781, 0.5648, 0.8859] +2026-04-11 11:30:43.906228: Epoch time: 102.09 s +2026-04-11 11:30:45.053481: +2026-04-11 11:30:45.055306: Epoch 753 +2026-04-11 11:30:45.058517: Current learning rate: 0.00829 +2026-04-11 11:32:26.713649: train_loss -0.377 +2026-04-11 11:32:26.719586: val_loss -0.3107 +2026-04-11 11:32:26.721745: Pseudo dice [0.0, 0.0, 0.595, 0.2129, 0.418, 0.5492, 0.4953] +2026-04-11 11:32:26.724049: Epoch time: 101.66 s +2026-04-11 11:32:27.909750: +2026-04-11 11:32:27.911951: Epoch 754 +2026-04-11 11:32:27.913864: Current learning rate: 0.00829 +2026-04-11 11:34:11.099296: train_loss -0.3535 +2026-04-11 11:34:11.105456: val_loss -0.3393 +2026-04-11 11:34:11.107510: Pseudo dice [0.0, 0.0, 0.7007, 0.0316, 0.3773, 0.3777, 0.8561] +2026-04-11 11:34:11.109612: Epoch time: 103.19 s +2026-04-11 11:34:12.310321: +2026-04-11 11:34:12.312525: Epoch 755 +2026-04-11 11:34:12.316044: Current learning rate: 0.00828 +2026-04-11 11:35:53.907903: train_loss -0.3795 +2026-04-11 11:35:53.914613: val_loss -0.3221 +2026-04-11 11:35:53.918190: Pseudo dice [0.0, 0.0, 0.7482, 0.3377, 0.4339, 0.3564, 0.6484] +2026-04-11 11:35:53.921573: Epoch time: 101.6 s +2026-04-11 11:35:55.101554: +2026-04-11 11:35:55.103746: Epoch 756 +2026-04-11 11:35:55.105912: Current learning rate: 0.00828 +2026-04-11 11:37:36.651322: train_loss -0.3807 +2026-04-11 11:37:36.659859: val_loss -0.3261 +2026-04-11 11:37:36.661825: Pseudo dice [0.0, 0.0, 0.6589, 0.5511, 0.2145, 0.8207, 0.5791] +2026-04-11 11:37:36.665141: Epoch time: 101.55 s +2026-04-11 11:37:37.874999: +2026-04-11 11:37:37.877055: Epoch 757 +2026-04-11 11:37:37.879111: Current learning rate: 0.00828 +2026-04-11 11:39:19.811230: train_loss -0.3548 +2026-04-11 11:39:19.820116: val_loss -0.3134 +2026-04-11 11:39:19.822580: Pseudo dice [0.0, 0.0, 0.7526, 0.3033, 0.292, 0.3878, 0.781] +2026-04-11 11:39:19.826071: Epoch time: 101.94 s +2026-04-11 11:39:21.044114: +2026-04-11 11:39:21.046570: Epoch 758 +2026-04-11 11:39:21.049296: Current learning rate: 0.00828 +2026-04-11 11:41:02.986846: train_loss -0.3567 +2026-04-11 11:41:02.993229: val_loss -0.3687 +2026-04-11 11:41:02.995332: Pseudo dice [0.0, 0.0, 0.7897, 0.5653, 0.4286, 0.4595, 0.6544] +2026-04-11 11:41:02.997874: Epoch time: 101.95 s +2026-04-11 11:41:04.191191: +2026-04-11 11:41:04.193003: Epoch 759 +2026-04-11 11:41:04.195272: Current learning rate: 0.00827 +2026-04-11 11:42:46.132933: train_loss -0.3937 +2026-04-11 11:42:46.139758: val_loss -0.334 +2026-04-11 11:42:46.141830: Pseudo dice [0.0, 0.0, 0.4962, 0.4184, 0.4256, 0.8289, 0.6298] +2026-04-11 11:42:46.144014: Epoch time: 101.94 s +2026-04-11 11:42:47.320249: +2026-04-11 11:42:47.322434: Epoch 760 +2026-04-11 11:42:47.325776: Current learning rate: 0.00827 +2026-04-11 11:44:28.690134: train_loss -0.3868 +2026-04-11 11:44:28.696851: val_loss -0.3593 +2026-04-11 11:44:28.698993: Pseudo dice [0.0, 0.0, 0.7498, 0.3011, 0.5096, 0.7352, 0.7224] +2026-04-11 11:44:28.701476: Epoch time: 101.37 s +2026-04-11 11:44:29.913515: +2026-04-11 11:44:29.916479: Epoch 761 +2026-04-11 11:44:29.918731: Current learning rate: 0.00827 +2026-04-11 11:46:11.242822: train_loss -0.3583 +2026-04-11 11:46:11.249847: val_loss -0.3639 +2026-04-11 11:46:11.251682: Pseudo dice [0.0, 0.0, 0.8038, 0.4291, 0.6889, 0.6116, 0.8827] +2026-04-11 11:46:11.254019: Epoch time: 101.33 s +2026-04-11 11:46:12.409344: +2026-04-11 11:46:12.411150: Epoch 762 +2026-04-11 11:46:12.413193: Current learning rate: 0.00827 +2026-04-11 11:47:53.996403: train_loss -0.3901 +2026-04-11 11:47:54.004142: val_loss -0.307 +2026-04-11 11:47:54.007415: Pseudo dice [0.0, 0.0, 0.6194, 0.2318, 0.3223, 0.4903, 0.6802] +2026-04-11 11:47:54.011504: Epoch time: 101.59 s +2026-04-11 11:47:55.205298: +2026-04-11 11:47:55.206975: Epoch 763 +2026-04-11 11:47:55.208867: Current learning rate: 0.00827 +2026-04-11 11:49:36.978553: train_loss -0.3704 +2026-04-11 11:49:36.985684: val_loss -0.3482 +2026-04-11 11:49:36.988919: Pseudo dice [0.0, 0.0, 0.6226, 0.2307, 0.3518, 0.5687, 0.7335] +2026-04-11 11:49:36.991523: Epoch time: 101.78 s +2026-04-11 11:49:38.168429: +2026-04-11 11:49:38.171564: Epoch 764 +2026-04-11 11:49:38.174207: Current learning rate: 0.00826 +2026-04-11 11:51:19.639375: train_loss -0.3805 +2026-04-11 11:51:19.648401: val_loss -0.3602 +2026-04-11 11:51:19.650462: Pseudo dice [0.0, 0.0, 0.7222, 0.2803, 0.4808, 0.8403, 0.7957] +2026-04-11 11:51:19.653108: Epoch time: 101.47 s +2026-04-11 11:51:20.851192: +2026-04-11 11:51:20.853266: Epoch 765 +2026-04-11 11:51:20.855490: Current learning rate: 0.00826 +2026-04-11 11:53:02.604396: train_loss -0.3559 +2026-04-11 11:53:02.618951: val_loss -0.3158 +2026-04-11 11:53:02.622780: Pseudo dice [0.0, 0.0, 0.7182, 0.5622, 0.4486, 0.6895, 0.8236] +2026-04-11 11:53:02.629547: Epoch time: 101.76 s +2026-04-11 11:53:03.814083: +2026-04-11 11:53:03.816890: Epoch 766 +2026-04-11 11:53:03.819301: Current learning rate: 0.00826 +2026-04-11 11:54:45.769362: train_loss -0.3733 +2026-04-11 11:54:45.775774: val_loss -0.3299 +2026-04-11 11:54:45.777936: Pseudo dice [0.0, 0.0, 0.6264, 0.3084, 0.5539, 0.1794, 0.4107] +2026-04-11 11:54:45.780091: Epoch time: 101.96 s +2026-04-11 11:54:47.005360: +2026-04-11 11:54:47.007106: Epoch 767 +2026-04-11 11:54:47.008911: Current learning rate: 0.00826 +2026-04-11 11:56:28.831276: train_loss -0.3622 +2026-04-11 11:56:28.839246: val_loss -0.3313 +2026-04-11 11:56:28.841900: Pseudo dice [0.0, 0.0, 0.7373, 0.2876, 0.3675, 0.3334, 0.7718] +2026-04-11 11:56:28.844481: Epoch time: 101.83 s +2026-04-11 11:56:30.002429: +2026-04-11 11:56:30.004252: Epoch 768 +2026-04-11 11:56:30.006433: Current learning rate: 0.00825 +2026-04-11 11:58:11.368392: train_loss -0.3743 +2026-04-11 11:58:11.374931: val_loss -0.3156 +2026-04-11 11:58:11.377192: Pseudo dice [0.0, 0.0, 0.5917, 0.3956, 0.2825, 0.4156, 0.7509] +2026-04-11 11:58:11.380194: Epoch time: 101.37 s +2026-04-11 11:58:12.569502: +2026-04-11 11:58:12.571468: Epoch 769 +2026-04-11 11:58:12.574026: Current learning rate: 0.00825 +2026-04-11 11:59:54.178570: train_loss -0.3905 +2026-04-11 11:59:54.186434: val_loss -0.3335 +2026-04-11 11:59:54.188598: Pseudo dice [0.0, 0.0, 0.8556, 0.4924, 0.5167, 0.6188, 0.6507] +2026-04-11 11:59:54.191781: Epoch time: 101.61 s +2026-04-11 11:59:55.398216: +2026-04-11 11:59:55.400058: Epoch 770 +2026-04-11 11:59:55.402266: Current learning rate: 0.00825 +2026-04-11 12:01:37.172582: train_loss -0.4081 +2026-04-11 12:01:37.180609: val_loss -0.3914 +2026-04-11 12:01:37.183875: Pseudo dice [0.0, 0.0, 0.8246, 0.8311, 0.5706, 0.6516, 0.8887] +2026-04-11 12:01:37.186232: Epoch time: 101.78 s +2026-04-11 12:01:38.387224: +2026-04-11 12:01:38.389567: Epoch 771 +2026-04-11 12:01:38.391807: Current learning rate: 0.00825 +2026-04-11 12:03:20.022066: train_loss -0.36 +2026-04-11 12:03:20.029276: val_loss -0.3535 +2026-04-11 12:03:20.031576: Pseudo dice [0.0, 0.0, 0.7802, 0.0338, 0.5068, 0.4658, 0.6812] +2026-04-11 12:03:20.034428: Epoch time: 101.64 s +2026-04-11 12:03:21.221620: +2026-04-11 12:03:21.223467: Epoch 772 +2026-04-11 12:03:21.225492: Current learning rate: 0.00824 +2026-04-11 12:05:02.754058: train_loss -0.3774 +2026-04-11 12:05:02.761348: val_loss -0.3139 +2026-04-11 12:05:02.763625: Pseudo dice [0.0, 0.0, 0.8101, 0.4919, 0.3868, 0.5708, 0.6789] +2026-04-11 12:05:02.766238: Epoch time: 101.54 s +2026-04-11 12:05:03.962268: +2026-04-11 12:05:03.964783: Epoch 773 +2026-04-11 12:05:03.967954: Current learning rate: 0.00824 +2026-04-11 12:06:45.568044: train_loss -0.3902 +2026-04-11 12:06:45.584103: val_loss -0.3314 +2026-04-11 12:06:45.586189: Pseudo dice [0.0, 0.0, 0.7176, 0.4285, 0.4975, 0.4869, 0.3241] +2026-04-11 12:06:45.588512: Epoch time: 101.61 s +2026-04-11 12:06:47.884825: +2026-04-11 12:06:47.886582: Epoch 774 +2026-04-11 12:06:47.888512: Current learning rate: 0.00824 +2026-04-11 12:08:29.801475: train_loss -0.3788 +2026-04-11 12:08:29.808943: val_loss -0.3138 +2026-04-11 12:08:29.815277: Pseudo dice [0.0, 0.0, 0.7542, 0.7235, 0.5097, 0.3385, 0.5583] +2026-04-11 12:08:29.818394: Epoch time: 101.92 s +2026-04-11 12:08:31.017616: +2026-04-11 12:08:31.019917: Epoch 775 +2026-04-11 12:08:31.022124: Current learning rate: 0.00824 +2026-04-11 12:10:12.570960: train_loss -0.3593 +2026-04-11 12:10:12.579265: val_loss -0.3328 +2026-04-11 12:10:12.582477: Pseudo dice [0.0, 0.0, 0.7078, 0.5683, 0.4452, 0.5475, 0.3017] +2026-04-11 12:10:12.585717: Epoch time: 101.56 s +2026-04-11 12:10:13.786925: +2026-04-11 12:10:13.789221: Epoch 776 +2026-04-11 12:10:13.792138: Current learning rate: 0.00824 +2026-04-11 12:11:55.634345: train_loss -0.3733 +2026-04-11 12:11:55.641410: val_loss -0.343 +2026-04-11 12:11:55.643223: Pseudo dice [0.0, 0.0, 0.7359, 0.6875, 0.4702, 0.3654, 0.7715] +2026-04-11 12:11:55.645391: Epoch time: 101.85 s +2026-04-11 12:11:56.839668: +2026-04-11 12:11:56.841696: Epoch 777 +2026-04-11 12:11:56.843640: Current learning rate: 0.00823 +2026-04-11 12:13:38.848789: train_loss -0.3813 +2026-04-11 12:13:38.855471: val_loss -0.3364 +2026-04-11 12:13:38.857360: Pseudo dice [0.0, 0.0, 0.7606, 0.6169, 0.354, 0.4937, 0.8555] +2026-04-11 12:13:38.859966: Epoch time: 102.01 s +2026-04-11 12:13:40.030269: +2026-04-11 12:13:40.032302: Epoch 778 +2026-04-11 12:13:40.034663: Current learning rate: 0.00823 +2026-04-11 12:15:21.505774: train_loss -0.3812 +2026-04-11 12:15:21.512275: val_loss -0.3336 +2026-04-11 12:15:21.514220: Pseudo dice [0.0, 0.0, 0.6707, 0.1783, 0.4158, 0.8435, 0.7867] +2026-04-11 12:15:21.517322: Epoch time: 101.48 s +2026-04-11 12:15:22.733434: +2026-04-11 12:15:22.735118: Epoch 779 +2026-04-11 12:15:22.736965: Current learning rate: 0.00823 +2026-04-11 12:17:04.959807: train_loss -0.3482 +2026-04-11 12:17:04.981482: val_loss -0.3264 +2026-04-11 12:17:04.985311: Pseudo dice [0.0, 0.0, 0.5653, 0.2157, 0.4872, 0.2306, 0.602] +2026-04-11 12:17:04.988708: Epoch time: 102.23 s +2026-04-11 12:17:06.185841: +2026-04-11 12:17:06.187712: Epoch 780 +2026-04-11 12:17:06.190220: Current learning rate: 0.00823 +2026-04-11 12:18:47.920462: train_loss -0.3688 +2026-04-11 12:18:47.928336: val_loss -0.3198 +2026-04-11 12:18:47.931889: Pseudo dice [0.0, 0.0, 0.664, 0.3519, 0.5276, 0.3259, 0.4481] +2026-04-11 12:18:47.934287: Epoch time: 101.74 s +2026-04-11 12:18:49.123933: +2026-04-11 12:18:49.125676: Epoch 781 +2026-04-11 12:18:49.127358: Current learning rate: 0.00822 +2026-04-11 12:20:30.890989: train_loss -0.3771 +2026-04-11 12:20:30.897464: val_loss -0.3516 +2026-04-11 12:20:30.899495: Pseudo dice [0.0, 0.0, 0.6489, 0.4132, 0.4685, 0.7127, 0.8955] +2026-04-11 12:20:30.901760: Epoch time: 101.77 s +2026-04-11 12:20:32.090496: +2026-04-11 12:20:32.092647: Epoch 782 +2026-04-11 12:20:32.094735: Current learning rate: 0.00822 +2026-04-11 12:22:15.568353: train_loss -0.387 +2026-04-11 12:22:15.575730: val_loss -0.3498 +2026-04-11 12:22:15.578862: Pseudo dice [0.0, 0.0, 0.7822, 0.3574, 0.5774, 0.3916, 0.889] +2026-04-11 12:22:15.582497: Epoch time: 103.48 s +2026-04-11 12:22:16.773493: +2026-04-11 12:22:16.776705: Epoch 783 +2026-04-11 12:22:16.779475: Current learning rate: 0.00822 +2026-04-11 12:24:01.511820: train_loss -0.3889 +2026-04-11 12:24:01.520412: val_loss -0.3325 +2026-04-11 12:24:01.522852: Pseudo dice [0.0, 0.0, 0.7682, 0.5003, 0.4194, 0.4823, 0.7597] +2026-04-11 12:24:01.526895: Epoch time: 104.74 s +2026-04-11 12:24:02.697063: +2026-04-11 12:24:02.699753: Epoch 784 +2026-04-11 12:24:02.702034: Current learning rate: 0.00822 +2026-04-11 12:25:45.017459: train_loss -0.3922 +2026-04-11 12:25:45.024333: val_loss -0.3521 +2026-04-11 12:25:45.027813: Pseudo dice [0.0, 0.0, 0.6719, 0.2895, 0.3352, 0.7696, 0.7935] +2026-04-11 12:25:45.030873: Epoch time: 102.32 s +2026-04-11 12:25:46.262304: +2026-04-11 12:25:46.264557: Epoch 785 +2026-04-11 12:25:46.267314: Current learning rate: 0.00822 +2026-04-11 12:27:28.274796: train_loss -0.394 +2026-04-11 12:27:28.281871: val_loss -0.3364 +2026-04-11 12:27:28.284632: Pseudo dice [0.0, 0.0, 0.5812, 0.463, 0.4577, 0.7663, 0.5917] +2026-04-11 12:27:28.289468: Epoch time: 102.02 s +2026-04-11 12:27:29.497117: +2026-04-11 12:27:29.500788: Epoch 786 +2026-04-11 12:27:29.507301: Current learning rate: 0.00821 +2026-04-11 12:29:10.844386: train_loss -0.3828 +2026-04-11 12:29:10.851318: val_loss -0.2991 +2026-04-11 12:29:10.853554: Pseudo dice [0.0, 0.0, 0.4125, 0.3218, 0.517, 0.2314, 0.902] +2026-04-11 12:29:10.855913: Epoch time: 101.35 s +2026-04-11 12:29:12.057895: +2026-04-11 12:29:12.059634: Epoch 787 +2026-04-11 12:29:12.061722: Current learning rate: 0.00821 +2026-04-11 12:30:54.698551: train_loss -0.3867 +2026-04-11 12:30:54.705226: val_loss -0.3767 +2026-04-11 12:30:54.708000: Pseudo dice [0.0, 0.0, 0.824, 0.708, 0.6343, 0.6895, 0.7799] +2026-04-11 12:30:54.710364: Epoch time: 102.64 s +2026-04-11 12:30:55.901828: +2026-04-11 12:30:55.906551: Epoch 788 +2026-04-11 12:30:55.909051: Current learning rate: 0.00821 +2026-04-11 12:46:23.138855: train_loss -0.3717 +2026-04-11 12:46:23.148940: val_loss -0.3515 +2026-04-11 12:46:23.151621: Pseudo dice [0.0, 0.0, 0.7601, 0.5402, 0.5385, 0.738, 0.4729] +2026-04-11 12:46:23.154174: Epoch time: 927.24 s +2026-04-11 12:46:24.393408: +2026-04-11 12:46:24.395229: Epoch 789 +2026-04-11 12:46:24.397538: Current learning rate: 0.00821 +2026-04-11 12:48:05.615562: train_loss -0.3775 +2026-04-11 12:48:05.622153: val_loss -0.3328 +2026-04-11 12:48:05.624077: Pseudo dice [0.0, 0.0, 0.763, 0.7522, 0.6515, 0.6782, 0.2559] +2026-04-11 12:48:05.626626: Epoch time: 101.23 s +2026-04-11 12:48:06.803588: +2026-04-11 12:48:06.805851: Epoch 790 +2026-04-11 12:48:06.807948: Current learning rate: 0.0082 +2026-04-11 12:49:48.557580: train_loss -0.3816 +2026-04-11 12:49:48.564638: val_loss -0.34 +2026-04-11 12:49:48.566387: Pseudo dice [0.0, 0.0, 0.657, 0.5018, 0.4866, 0.5234, 0.7228] +2026-04-11 12:49:48.568605: Epoch time: 101.76 s +2026-04-11 12:49:49.742805: +2026-04-11 12:49:49.751966: Epoch 791 +2026-04-11 12:49:49.755865: Current learning rate: 0.0082 +2026-04-11 12:51:31.079082: train_loss -0.3834 +2026-04-11 12:51:31.085716: val_loss -0.303 +2026-04-11 12:51:31.087565: Pseudo dice [0.0, 0.0, 0.8323, 0.3522, 0.2255, 0.7501, 0.8754] +2026-04-11 12:51:31.090379: Epoch time: 101.34 s +2026-04-11 12:51:32.377839: +2026-04-11 12:51:32.380375: Epoch 792 +2026-04-11 12:51:32.382661: Current learning rate: 0.0082 +2026-04-11 12:53:14.280213: train_loss -0.3427 +2026-04-11 12:53:14.287115: val_loss -0.312 +2026-04-11 12:53:14.289623: Pseudo dice [0.0, 0.0, 0.4249, 0.33, 0.379, 0.2652, 0.7479] +2026-04-11 12:53:14.291960: Epoch time: 101.91 s +2026-04-11 12:53:16.557789: +2026-04-11 12:53:16.559813: Epoch 793 +2026-04-11 12:53:16.561805: Current learning rate: 0.0082 +2026-04-11 12:54:58.186709: train_loss -0.3673 +2026-04-11 12:54:58.196684: val_loss -0.2944 +2026-04-11 12:54:58.198961: Pseudo dice [0.0, 0.0, 0.5781, 0.6111, 0.228, 0.778, 0.644] +2026-04-11 12:54:58.201597: Epoch time: 101.63 s +2026-04-11 12:54:59.401356: +2026-04-11 12:54:59.403605: Epoch 794 +2026-04-11 12:54:59.405812: Current learning rate: 0.00819 +2026-04-11 12:56:41.832620: train_loss -0.3522 +2026-04-11 12:56:41.839077: val_loss -0.3163 +2026-04-11 12:56:41.841105: Pseudo dice [0.0, 0.0, 0.6439, 0.0732, 0.4266, 0.3271, 0.8227] +2026-04-11 12:56:41.843132: Epoch time: 102.43 s +2026-04-11 12:56:43.019500: +2026-04-11 12:56:43.023041: Epoch 795 +2026-04-11 12:56:43.025708: Current learning rate: 0.00819 +2026-04-11 12:58:24.348919: train_loss -0.3559 +2026-04-11 12:58:24.355922: val_loss -0.2959 +2026-04-11 12:58:24.358392: Pseudo dice [0.0, 0.0, 0.7572, 0.8572, 0.4283, 0.4985, 0.3009] +2026-04-11 12:58:24.360855: Epoch time: 101.33 s +2026-04-11 12:58:25.532674: +2026-04-11 12:58:25.534344: Epoch 796 +2026-04-11 12:58:25.536093: Current learning rate: 0.00819 +2026-04-11 13:00:06.767896: train_loss -0.3815 +2026-04-11 13:00:06.774972: val_loss -0.322 +2026-04-11 13:00:06.777343: Pseudo dice [0.0, 0.0, 0.7546, 0.0309, 0.6106, 0.5054, 0.7405] +2026-04-11 13:00:06.780257: Epoch time: 101.24 s +2026-04-11 13:00:07.948298: +2026-04-11 13:00:07.951231: Epoch 797 +2026-04-11 13:00:07.953532: Current learning rate: 0.00819 +2026-04-11 13:01:49.691745: train_loss -0.3678 +2026-04-11 13:01:49.697948: val_loss -0.3206 +2026-04-11 13:01:49.700489: Pseudo dice [0.0, 0.0, 0.6674, 0.6531, 0.1775, 0.6116, 0.7184] +2026-04-11 13:01:49.703045: Epoch time: 101.75 s +2026-04-11 13:01:50.897475: +2026-04-11 13:01:50.899131: Epoch 798 +2026-04-11 13:01:50.901134: Current learning rate: 0.00819 +2026-04-11 13:03:32.882750: train_loss -0.3832 +2026-04-11 13:03:32.890145: val_loss -0.3782 +2026-04-11 13:03:32.892842: Pseudo dice [0.0, 0.0, 0.8864, 0.7249, 0.5766, 0.5773, 0.7917] +2026-04-11 13:03:32.896750: Epoch time: 101.99 s +2026-04-11 13:03:34.128140: +2026-04-11 13:03:34.129978: Epoch 799 +2026-04-11 13:03:34.132071: Current learning rate: 0.00818 +2026-04-11 13:05:15.782773: train_loss -0.3736 +2026-04-11 13:05:15.789593: val_loss -0.3173 +2026-04-11 13:05:15.792515: Pseudo dice [0.0, 0.0, 0.6231, 0.2797, 0.3644, 0.2781, 0.7261] +2026-04-11 13:05:15.795157: Epoch time: 101.66 s +2026-04-11 13:05:18.692934: +2026-04-11 13:05:18.694987: Epoch 800 +2026-04-11 13:05:18.696807: Current learning rate: 0.00818 +2026-04-11 13:07:00.836125: train_loss -0.3533 +2026-04-11 13:07:00.842275: val_loss -0.3285 +2026-04-11 13:07:00.844645: Pseudo dice [0.0, 0.0, 0.8178, 0.0743, 0.4528, 0.4216, 0.5316] +2026-04-11 13:07:00.847273: Epoch time: 102.15 s +2026-04-11 13:07:02.061182: +2026-04-11 13:07:02.063117: Epoch 801 +2026-04-11 13:07:02.065343: Current learning rate: 0.00818 +2026-04-11 13:08:44.403871: train_loss -0.3758 +2026-04-11 13:08:44.412308: val_loss -0.3112 +2026-04-11 13:08:44.415312: Pseudo dice [0.0, 0.0, 0.8281, 0.3616, 0.0988, 0.7269, 0.6642] +2026-04-11 13:08:44.418134: Epoch time: 102.35 s +2026-04-11 13:08:45.597557: +2026-04-11 13:08:45.600164: Epoch 802 +2026-04-11 13:08:45.603256: Current learning rate: 0.00818 +2026-04-11 13:10:27.981231: train_loss -0.3361 +2026-04-11 13:10:27.987510: val_loss -0.3226 +2026-04-11 13:10:27.989352: Pseudo dice [0.0, 0.0, 0.6587, 0.2119, 0.5254, 0.4555, 0.5776] +2026-04-11 13:10:27.991837: Epoch time: 102.39 s +2026-04-11 13:10:29.160777: +2026-04-11 13:10:29.163757: Epoch 803 +2026-04-11 13:10:29.166445: Current learning rate: 0.00817 +2026-04-11 13:12:10.891711: train_loss -0.3862 +2026-04-11 13:12:10.897901: val_loss -0.3397 +2026-04-11 13:12:10.900097: Pseudo dice [0.0, 0.0, 0.7325, 0.1508, 0.3666, 0.4248, 0.5541] +2026-04-11 13:12:10.903278: Epoch time: 101.73 s +2026-04-11 13:12:12.182259: +2026-04-11 13:12:12.184007: Epoch 804 +2026-04-11 13:12:12.186218: Current learning rate: 0.00817 +2026-04-11 13:13:54.477580: train_loss -0.3865 +2026-04-11 13:13:54.491058: val_loss -0.338 +2026-04-11 13:13:54.494144: Pseudo dice [0.0, 0.0, 0.6665, 0.2214, 0.5279, 0.5623, 0.7722] +2026-04-11 13:13:54.496890: Epoch time: 102.3 s +2026-04-11 13:13:55.702984: +2026-04-11 13:13:55.705920: Epoch 805 +2026-04-11 13:13:55.708669: Current learning rate: 0.00817 +2026-04-11 13:15:37.468547: train_loss -0.383 +2026-04-11 13:15:37.474913: val_loss -0.3188 +2026-04-11 13:15:37.477881: Pseudo dice [0.0, 0.0, 0.6867, 0.5339, 0.5453, 0.4338, 0.63] +2026-04-11 13:15:37.481425: Epoch time: 101.77 s +2026-04-11 13:15:38.711810: +2026-04-11 13:15:38.713808: Epoch 806 +2026-04-11 13:15:38.716602: Current learning rate: 0.00817 +2026-04-11 13:17:21.122187: train_loss -0.3824 +2026-04-11 13:17:21.129631: val_loss -0.3188 +2026-04-11 13:17:21.131555: Pseudo dice [0.0, 0.0, 0.6196, 0.574, 0.5166, 0.5174, 0.5249] +2026-04-11 13:17:21.133752: Epoch time: 102.41 s +2026-04-11 13:17:22.345979: +2026-04-11 13:17:22.348350: Epoch 807 +2026-04-11 13:17:22.350631: Current learning rate: 0.00816 +2026-04-11 13:19:04.345820: train_loss -0.3655 +2026-04-11 13:19:04.352965: val_loss -0.2998 +2026-04-11 13:19:04.354908: Pseudo dice [0.0, 0.0, 0.7303, 0.6142, 0.0315, 0.2723, 0.5148] +2026-04-11 13:19:04.357000: Epoch time: 102.0 s +2026-04-11 13:19:05.550616: +2026-04-11 13:19:05.553353: Epoch 808 +2026-04-11 13:19:05.555956: Current learning rate: 0.00816 +2026-04-11 13:20:47.257565: train_loss -0.3351 +2026-04-11 13:20:47.288219: val_loss -0.2794 +2026-04-11 13:20:47.291305: Pseudo dice [0.0, 0.0, 0.2968, 0.7174, 0.269, 0.4108, 0.4253] +2026-04-11 13:20:47.294031: Epoch time: 101.71 s +2026-04-11 13:20:48.499886: +2026-04-11 13:20:48.502082: Epoch 809 +2026-04-11 13:20:48.508651: Current learning rate: 0.00816 +2026-04-11 13:22:30.122749: train_loss -0.3244 +2026-04-11 13:22:30.130176: val_loss -0.3099 +2026-04-11 13:22:30.134260: Pseudo dice [0.0, 0.0, 0.6858, 0.0513, 0.2115, 0.274, 0.3637] +2026-04-11 13:22:30.136754: Epoch time: 101.63 s +2026-04-11 13:22:31.306192: +2026-04-11 13:22:31.308266: Epoch 810 +2026-04-11 13:22:31.309854: Current learning rate: 0.00816 +2026-04-11 13:24:13.463328: train_loss -0.3578 +2026-04-11 13:24:13.471995: val_loss -0.3629 +2026-04-11 13:24:13.474735: Pseudo dice [0.0, 0.0, 0.8597, 0.2884, 0.6565, 0.6283, 0.7459] +2026-04-11 13:24:13.477209: Epoch time: 102.16 s +2026-04-11 13:24:14.656237: +2026-04-11 13:24:14.658569: Epoch 811 +2026-04-11 13:24:14.661090: Current learning rate: 0.00816 +2026-04-11 13:25:56.624994: train_loss -0.3718 +2026-04-11 13:25:56.631297: val_loss -0.3797 +2026-04-11 13:25:56.634300: Pseudo dice [0.0, 0.0, 0.864, 0.7439, 0.5217, 0.6259, 0.7175] +2026-04-11 13:25:56.636973: Epoch time: 101.97 s +2026-04-11 13:25:58.994458: +2026-04-11 13:25:58.996352: Epoch 812 +2026-04-11 13:25:58.998211: Current learning rate: 0.00815 +2026-04-11 13:27:41.100107: train_loss -0.3774 +2026-04-11 13:27:41.106713: val_loss -0.3288 +2026-04-11 13:27:41.108779: Pseudo dice [0.0, 0.0, 0.6502, 0.2155, 0.3013, 0.6802, 0.7607] +2026-04-11 13:27:41.111389: Epoch time: 102.11 s +2026-04-11 13:27:42.288970: +2026-04-11 13:27:42.290780: Epoch 813 +2026-04-11 13:27:42.292933: Current learning rate: 0.00815 +2026-04-11 13:29:24.417425: train_loss -0.3713 +2026-04-11 13:29:24.425530: val_loss -0.3325 +2026-04-11 13:29:24.428414: Pseudo dice [0.0, 0.0, 0.5627, 0.2138, 0.4544, 0.8083, 0.8887] +2026-04-11 13:29:24.431213: Epoch time: 102.13 s +2026-04-11 13:29:25.638384: +2026-04-11 13:29:25.640714: Epoch 814 +2026-04-11 13:29:25.642886: Current learning rate: 0.00815 +2026-04-11 13:31:07.676746: train_loss -0.3677 +2026-04-11 13:31:07.683216: val_loss -0.351 +2026-04-11 13:31:07.686066: Pseudo dice [0.0, 0.0, 0.7188, 0.0036, 0.4318, 0.6397, 0.7784] +2026-04-11 13:31:07.688893: Epoch time: 102.04 s +2026-04-11 13:31:08.894547: +2026-04-11 13:31:08.900870: Epoch 815 +2026-04-11 13:31:08.903854: Current learning rate: 0.00815 +2026-04-11 13:32:51.436327: train_loss -0.4023 +2026-04-11 13:32:51.443100: val_loss -0.3234 +2026-04-11 13:32:51.445049: Pseudo dice [0.0, 0.0, 0.4076, 0.5004, 0.0299, 0.3197, 0.8394] +2026-04-11 13:32:51.447416: Epoch time: 102.54 s +2026-04-11 13:32:52.640758: +2026-04-11 13:32:52.642669: Epoch 816 +2026-04-11 13:32:52.644496: Current learning rate: 0.00814 +2026-04-11 13:34:34.525375: train_loss -0.3666 +2026-04-11 13:34:34.532558: val_loss -0.3275 +2026-04-11 13:34:34.535306: Pseudo dice [0.0, 0.0, 0.7077, 0.497, 0.4285, 0.4503, 0.6763] +2026-04-11 13:34:34.538577: Epoch time: 101.89 s +2026-04-11 13:34:35.749186: +2026-04-11 13:34:35.751409: Epoch 817 +2026-04-11 13:34:35.753403: Current learning rate: 0.00814 +2026-04-11 13:36:17.542697: train_loss -0.3889 +2026-04-11 13:36:17.549375: val_loss -0.3627 +2026-04-11 13:36:17.551851: Pseudo dice [0.0, 0.0, 0.8032, 0.1974, 0.393, 0.4767, 0.6968] +2026-04-11 13:36:17.554638: Epoch time: 101.8 s +2026-04-11 13:36:18.764767: +2026-04-11 13:36:18.766878: Epoch 818 +2026-04-11 13:36:18.769010: Current learning rate: 0.00814 +2026-04-11 13:38:00.851617: train_loss -0.3803 +2026-04-11 13:38:00.859280: val_loss -0.3527 +2026-04-11 13:38:00.861238: Pseudo dice [0.0, 0.0, 0.7806, 0.5937, 0.5365, 0.764, 0.8585] +2026-04-11 13:38:00.867131: Epoch time: 102.09 s +2026-04-11 13:38:02.063532: +2026-04-11 13:38:02.065636: Epoch 819 +2026-04-11 13:38:02.067657: Current learning rate: 0.00814 +2026-04-11 13:39:45.055238: train_loss -0.377 +2026-04-11 13:39:45.062268: val_loss -0.3136 +2026-04-11 13:39:45.064438: Pseudo dice [0.0, 0.0, 0.827, 0.0254, 0.4662, 0.2067, 0.2698] +2026-04-11 13:39:45.067179: Epoch time: 102.99 s +2026-04-11 13:39:46.194772: +2026-04-11 13:39:46.197476: Epoch 820 +2026-04-11 13:39:46.201082: Current learning rate: 0.00813 +2026-04-11 13:41:27.903141: train_loss -0.4056 +2026-04-11 13:41:27.910117: val_loss -0.3298 +2026-04-11 13:41:27.912594: Pseudo dice [0.0, 0.0, 0.5572, 0.4047, 0.5819, 0.76, 0.7224] +2026-04-11 13:41:27.915308: Epoch time: 101.71 s +2026-04-11 13:41:29.013942: +2026-04-11 13:41:29.015712: Epoch 821 +2026-04-11 13:41:29.017549: Current learning rate: 0.00813 +2026-04-11 13:43:10.961142: train_loss -0.3835 +2026-04-11 13:43:10.967647: val_loss -0.3506 +2026-04-11 13:43:10.969840: Pseudo dice [0.0, 0.0, 0.8504, 0.7298, 0.3815, 0.8087, 0.6579] +2026-04-11 13:43:10.972492: Epoch time: 101.95 s +2026-04-11 13:43:12.134637: +2026-04-11 13:43:12.137092: Epoch 822 +2026-04-11 13:43:12.139858: Current learning rate: 0.00813 +2026-04-11 13:44:53.775867: train_loss -0.3852 +2026-04-11 13:44:53.782439: val_loss -0.362 +2026-04-11 13:44:53.785600: Pseudo dice [0.0, 0.0, 0.8015, 0.6464, 0.4093, 0.5467, 0.5315] +2026-04-11 13:44:53.788178: Epoch time: 101.64 s +2026-04-11 13:44:54.926559: +2026-04-11 13:44:54.929366: Epoch 823 +2026-04-11 13:44:54.932754: Current learning rate: 0.00813 +2026-04-11 13:46:36.961046: train_loss -0.3937 +2026-04-11 13:46:36.967947: val_loss -0.3584 +2026-04-11 13:46:36.971723: Pseudo dice [0.0, 0.0, 0.7277, 0.3408, 0.4342, 0.5322, 0.699] +2026-04-11 13:46:36.974474: Epoch time: 102.04 s +2026-04-11 13:46:38.082234: +2026-04-11 13:46:38.084325: Epoch 824 +2026-04-11 13:46:38.086324: Current learning rate: 0.00813 +2026-04-11 13:48:19.909129: train_loss -0.3892 +2026-04-11 13:48:19.916265: val_loss -0.3277 +2026-04-11 13:48:19.918393: Pseudo dice [0.0, 0.0, 0.6526, 0.6981, 0.3436, 0.6242, 0.4342] +2026-04-11 13:48:19.920886: Epoch time: 101.83 s +2026-04-11 13:48:21.032189: +2026-04-11 13:48:21.034384: Epoch 825 +2026-04-11 13:48:21.036645: Current learning rate: 0.00812 +2026-04-11 13:50:02.737051: train_loss -0.3548 +2026-04-11 13:50:02.749118: val_loss -0.3232 +2026-04-11 13:50:02.753000: Pseudo dice [0.0, 0.0, 0.8054, 0.147, 0.3875, 0.8024, 0.2733] +2026-04-11 13:50:02.757788: Epoch time: 101.71 s +2026-04-11 13:50:03.859768: +2026-04-11 13:50:03.861959: Epoch 826 +2026-04-11 13:50:03.863940: Current learning rate: 0.00812 +2026-04-11 13:51:45.815390: train_loss -0.3812 +2026-04-11 13:51:45.824130: val_loss -0.3173 +2026-04-11 13:51:45.826572: Pseudo dice [0.0, 0.0, 0.5936, 0.4787, 0.4551, 0.743, 0.8359] +2026-04-11 13:51:45.829500: Epoch time: 101.96 s +2026-04-11 13:51:46.963185: +2026-04-11 13:51:46.965204: Epoch 827 +2026-04-11 13:51:46.967306: Current learning rate: 0.00812 +2026-04-11 13:53:28.956912: train_loss -0.3926 +2026-04-11 13:53:28.964899: val_loss -0.3303 +2026-04-11 13:53:28.967466: Pseudo dice [0.0, 0.0, 0.6451, 0.6712, 0.5548, 0.6638, 0.4018] +2026-04-11 13:53:28.969994: Epoch time: 102.0 s +2026-04-11 13:53:30.078979: +2026-04-11 13:53:30.080882: Epoch 828 +2026-04-11 13:53:30.082741: Current learning rate: 0.00812 +2026-04-11 13:55:12.455346: train_loss -0.3553 +2026-04-11 13:55:12.461873: val_loss -0.2523 +2026-04-11 13:55:12.464081: Pseudo dice [0.0, 0.0, 0.6746, 0.1219, 0.1314, 0.4537, 0.2388] +2026-04-11 13:55:12.466645: Epoch time: 102.38 s +2026-04-11 13:55:13.602808: +2026-04-11 13:55:13.604589: Epoch 829 +2026-04-11 13:55:13.606548: Current learning rate: 0.00811 +2026-04-11 13:56:56.059535: train_loss -0.3399 +2026-04-11 13:56:56.067705: val_loss -0.348 +2026-04-11 13:56:56.069990: Pseudo dice [0.0, 0.0, 0.8197, 0.419, 0.5114, 0.4589, 0.6295] +2026-04-11 13:56:56.072233: Epoch time: 102.46 s +2026-04-11 13:56:57.192232: +2026-04-11 13:56:57.194681: Epoch 830 +2026-04-11 13:56:57.196650: Current learning rate: 0.00811 +2026-04-11 13:58:39.073930: train_loss -0.3847 +2026-04-11 13:58:39.081185: val_loss -0.3265 +2026-04-11 13:58:39.085281: Pseudo dice [0.0, 0.0, 0.7052, 0.5209, 0.6223, 0.208, 0.5471] +2026-04-11 13:58:39.087525: Epoch time: 101.88 s +2026-04-11 13:58:40.280576: +2026-04-11 13:58:40.282525: Epoch 831 +2026-04-11 13:58:40.285023: Current learning rate: 0.00811 +2026-04-11 14:00:21.891432: train_loss -0.3957 +2026-04-11 14:00:21.900368: val_loss -0.3213 +2026-04-11 14:00:21.902771: Pseudo dice [0.0, 0.0, 0.8333, 0.1713, 0.2852, 0.4425, 0.6568] +2026-04-11 14:00:21.906316: Epoch time: 101.61 s +2026-04-11 14:00:23.101177: +2026-04-11 14:00:23.103584: Epoch 832 +2026-04-11 14:00:23.105564: Current learning rate: 0.00811 +2026-04-11 14:02:05.834245: train_loss -0.3905 +2026-04-11 14:02:05.849617: val_loss -0.3586 +2026-04-11 14:02:05.865618: Pseudo dice [0.0, 0.0, 0.753, 0.5413, 0.579, 0.7061, 0.666] +2026-04-11 14:02:05.868458: Epoch time: 102.74 s +2026-04-11 14:02:06.984674: +2026-04-11 14:02:06.988449: Epoch 833 +2026-04-11 14:02:06.990548: Current learning rate: 0.0081 +2026-04-11 14:03:48.869915: train_loss -0.3709 +2026-04-11 14:03:48.876141: val_loss -0.3222 +2026-04-11 14:03:48.878680: Pseudo dice [0.0, 0.0, 0.738, 0.1481, 0.463, 0.3072, 0.7162] +2026-04-11 14:03:48.880933: Epoch time: 101.89 s +2026-04-11 14:03:50.146477: +2026-04-11 14:03:50.149291: Epoch 834 +2026-04-11 14:03:50.151305: Current learning rate: 0.0081 +2026-04-11 14:05:32.028266: train_loss -0.38 +2026-04-11 14:05:32.036437: val_loss -0.3332 +2026-04-11 14:05:32.038770: Pseudo dice [0.0, 0.0, 0.597, 0.6106, 0.6505, 0.6481, 0.8044] +2026-04-11 14:05:32.041384: Epoch time: 101.88 s +2026-04-11 14:05:33.243200: +2026-04-11 14:05:33.245219: Epoch 835 +2026-04-11 14:05:33.247831: Current learning rate: 0.0081 +2026-04-11 14:07:15.333641: train_loss -0.3492 +2026-04-11 14:07:15.341414: val_loss -0.2972 +2026-04-11 14:07:15.343564: Pseudo dice [0.0, 0.0, 0.7315, 0.0827, 0.3996, 0.69, 0.2933] +2026-04-11 14:07:15.346365: Epoch time: 102.09 s +2026-04-11 14:07:16.524257: +2026-04-11 14:07:16.526096: Epoch 836 +2026-04-11 14:07:16.528209: Current learning rate: 0.0081 +2026-04-11 14:08:58.432340: train_loss -0.3399 +2026-04-11 14:08:58.438776: val_loss -0.3642 +2026-04-11 14:08:58.442219: Pseudo dice [0.0, 0.0, 0.6494, 0.2567, 0.4498, 0.7672, 0.4993] +2026-04-11 14:08:58.444465: Epoch time: 101.91 s +2026-04-11 14:08:59.582496: +2026-04-11 14:08:59.584738: Epoch 837 +2026-04-11 14:08:59.586611: Current learning rate: 0.0081 +2026-04-11 14:10:41.474439: train_loss -0.3702 +2026-04-11 14:10:41.481157: val_loss -0.3311 +2026-04-11 14:10:41.485562: Pseudo dice [0.0, 0.0, 0.7653, 0.7422, 0.3295, 0.6725, 0.472] +2026-04-11 14:10:41.487861: Epoch time: 101.9 s +2026-04-11 14:10:42.590729: +2026-04-11 14:10:42.592742: Epoch 838 +2026-04-11 14:10:42.594951: Current learning rate: 0.00809 +2026-04-11 14:12:24.522914: train_loss -0.3513 +2026-04-11 14:12:24.528906: val_loss -0.3212 +2026-04-11 14:12:24.531226: Pseudo dice [0.0, 0.0, 0.4713, 0.1191, 0.3029, 0.5145, 0.807] +2026-04-11 14:12:24.533633: Epoch time: 101.94 s +2026-04-11 14:12:25.678994: +2026-04-11 14:12:25.680615: Epoch 839 +2026-04-11 14:12:25.682441: Current learning rate: 0.00809 +2026-04-11 14:14:07.412377: train_loss -0.3757 +2026-04-11 14:14:07.419295: val_loss -0.3209 +2026-04-11 14:14:07.422039: Pseudo dice [0.0, 0.0, 0.4198, 0.1211, 0.4426, 0.0989, 0.7525] +2026-04-11 14:14:07.424440: Epoch time: 101.74 s +2026-04-11 14:14:08.574728: +2026-04-11 14:14:08.576926: Epoch 840 +2026-04-11 14:14:08.581243: Current learning rate: 0.00809 +2026-04-11 14:15:50.174788: train_loss -0.3603 +2026-04-11 14:15:50.182443: val_loss -0.3376 +2026-04-11 14:15:50.188076: Pseudo dice [0.0, 0.0, 0.6931, 0.4938, 0.5551, 0.6857, 0.5689] +2026-04-11 14:15:50.190715: Epoch time: 101.6 s +2026-04-11 14:15:51.321514: +2026-04-11 14:15:51.323608: Epoch 841 +2026-04-11 14:15:51.325745: Current learning rate: 0.00809 +2026-04-11 14:17:32.751499: train_loss -0.3782 +2026-04-11 14:17:32.759426: val_loss -0.3062 +2026-04-11 14:17:32.763546: Pseudo dice [0.0, 0.0, 0.7447, 0.4101, 0.3165, 0.3363, 0.1942] +2026-04-11 14:17:32.765979: Epoch time: 101.43 s +2026-04-11 14:17:33.892975: +2026-04-11 14:17:33.895192: Epoch 842 +2026-04-11 14:17:33.898557: Current learning rate: 0.00808 +2026-04-11 14:19:15.393259: train_loss -0.3781 +2026-04-11 14:19:15.400009: val_loss -0.3164 +2026-04-11 14:19:15.402904: Pseudo dice [0.0, 0.0, 0.6408, 0.2373, 0.3833, 0.5029, 0.7557] +2026-04-11 14:19:15.405517: Epoch time: 101.5 s +2026-04-11 14:19:16.628298: +2026-04-11 14:19:16.629970: Epoch 843 +2026-04-11 14:19:16.631872: Current learning rate: 0.00808 +2026-04-11 14:20:58.535527: train_loss -0.3813 +2026-04-11 14:20:58.542482: val_loss -0.3102 +2026-04-11 14:20:58.544265: Pseudo dice [0.0, 0.0, 0.7068, 0.706, 0.3336, 0.4145, 0.7427] +2026-04-11 14:20:58.546871: Epoch time: 101.91 s +2026-04-11 14:20:59.682997: +2026-04-11 14:20:59.685640: Epoch 844 +2026-04-11 14:20:59.688442: Current learning rate: 0.00808 +2026-04-11 14:22:41.494892: train_loss -0.3638 +2026-04-11 14:22:41.502704: val_loss -0.3317 +2026-04-11 14:22:41.504649: Pseudo dice [0.0, 0.0, 0.5702, 0.3756, 0.4833, 0.6933, 0.859] +2026-04-11 14:22:41.507046: Epoch time: 101.82 s +2026-04-11 14:22:42.713886: +2026-04-11 14:22:42.716027: Epoch 845 +2026-04-11 14:22:42.717946: Current learning rate: 0.00808 +2026-04-11 14:24:24.657994: train_loss -0.371 +2026-04-11 14:24:24.664435: val_loss -0.3513 +2026-04-11 14:24:24.666619: Pseudo dice [0.0, 0.0, 0.773, 0.33, 0.5493, 0.4359, 0.7449] +2026-04-11 14:24:24.668970: Epoch time: 101.95 s +2026-04-11 14:24:25.806679: +2026-04-11 14:24:25.809114: Epoch 846 +2026-04-11 14:24:25.811399: Current learning rate: 0.00807 +2026-04-11 14:26:07.933997: train_loss -0.3744 +2026-04-11 14:26:07.941326: val_loss -0.3545 +2026-04-11 14:26:07.943872: Pseudo dice [0.0, 0.0, 0.6413, 0.5287, 0.506, 0.4699, 0.7511] +2026-04-11 14:26:07.946404: Epoch time: 102.13 s +2026-04-11 14:26:09.076163: +2026-04-11 14:26:09.079532: Epoch 847 +2026-04-11 14:26:09.081888: Current learning rate: 0.00807 +2026-04-11 14:27:50.536740: train_loss -0.3642 +2026-04-11 14:27:50.544058: val_loss -0.3446 +2026-04-11 14:27:50.546477: Pseudo dice [0.0, 0.0, 0.6765, 0.6814, 0.4029, 0.4807, 0.4002] +2026-04-11 14:27:50.550172: Epoch time: 101.46 s +2026-04-11 14:27:51.659918: +2026-04-11 14:27:51.661722: Epoch 848 +2026-04-11 14:27:51.663725: Current learning rate: 0.00807 +2026-04-11 14:29:33.647696: train_loss -0.3664 +2026-04-11 14:29:33.655036: val_loss -0.3085 +2026-04-11 14:29:33.656854: Pseudo dice [0.0, 0.0, 0.6547, 0.0968, 0.3076, 0.7144, 0.1967] +2026-04-11 14:29:33.660619: Epoch time: 101.99 s +2026-04-11 14:29:34.788812: +2026-04-11 14:29:34.790480: Epoch 849 +2026-04-11 14:29:34.792657: Current learning rate: 0.00807 +2026-04-11 14:31:17.052800: train_loss -0.3833 +2026-04-11 14:31:17.058850: val_loss -0.3472 +2026-04-11 14:31:17.061092: Pseudo dice [0.0, 0.0, 0.7399, 0.4052, 0.4921, 0.8254, 0.5674] +2026-04-11 14:31:17.063744: Epoch time: 102.27 s +2026-04-11 14:31:19.887807: +2026-04-11 14:31:19.889898: Epoch 850 +2026-04-11 14:31:19.891644: Current learning rate: 0.00807 +2026-04-11 14:33:01.827151: train_loss -0.3878 +2026-04-11 14:33:01.834412: val_loss -0.3389 +2026-04-11 14:33:01.836465: Pseudo dice [0.0, 0.0, 0.7954, 0.3323, 0.1324, 0.8173, 0.5973] +2026-04-11 14:33:01.839253: Epoch time: 101.94 s +2026-04-11 14:33:02.951723: +2026-04-11 14:33:02.953917: Epoch 851 +2026-04-11 14:33:02.955956: Current learning rate: 0.00806 +2026-04-11 14:34:44.872013: train_loss -0.3755 +2026-04-11 14:34:44.877958: val_loss -0.2842 +2026-04-11 14:34:44.880115: Pseudo dice [0.0, 0.0, 0.8068, 0.5131, 0.1167, 0.2882, 0.436] +2026-04-11 14:34:44.882492: Epoch time: 101.92 s +2026-04-11 14:34:45.984041: +2026-04-11 14:34:45.986253: Epoch 852 +2026-04-11 14:34:45.988510: Current learning rate: 0.00806 +2026-04-11 14:36:28.665947: train_loss -0.362 +2026-04-11 14:36:28.674169: val_loss -0.3057 +2026-04-11 14:36:28.676241: Pseudo dice [0.0, 0.0, 0.7239, 0.0397, 0.4278, 0.3223, 0.7953] +2026-04-11 14:36:28.678922: Epoch time: 102.68 s +2026-04-11 14:36:29.847158: +2026-04-11 14:36:29.850471: Epoch 853 +2026-04-11 14:36:29.852584: Current learning rate: 0.00806 +2026-04-11 14:38:11.490028: train_loss -0.3644 +2026-04-11 14:38:11.497751: val_loss -0.2788 +2026-04-11 14:38:11.501801: Pseudo dice [0.0, 0.0, 0.6002, 0.2298, 0.3889, 0.4338, 0.7347] +2026-04-11 14:38:11.504914: Epoch time: 101.65 s +2026-04-11 14:38:12.702976: +2026-04-11 14:38:12.705093: Epoch 854 +2026-04-11 14:38:12.708294: Current learning rate: 0.00806 +2026-04-11 14:39:54.908308: train_loss -0.3657 +2026-04-11 14:39:54.914453: val_loss -0.3288 +2026-04-11 14:39:54.916506: Pseudo dice [0.0, 0.0, 0.5931, 0.6899, 0.313, 0.5635, 0.6922] +2026-04-11 14:39:54.919393: Epoch time: 102.21 s +2026-04-11 14:39:56.064779: +2026-04-11 14:39:56.066576: Epoch 855 +2026-04-11 14:39:56.068622: Current learning rate: 0.00805 +2026-04-11 14:41:38.007358: train_loss -0.3831 +2026-04-11 14:41:38.013330: val_loss -0.3288 +2026-04-11 14:41:38.015831: Pseudo dice [0.0, 0.0, 0.7215, 0.4483, 0.7132, 0.5229, 0.5902] +2026-04-11 14:41:38.018127: Epoch time: 101.95 s +2026-04-11 14:41:39.172316: +2026-04-11 14:41:39.174406: Epoch 856 +2026-04-11 14:41:39.176343: Current learning rate: 0.00805 +2026-04-11 14:43:20.646770: train_loss -0.3709 +2026-04-11 14:43:20.652455: val_loss -0.3761 +2026-04-11 14:43:20.654599: Pseudo dice [0.0, 0.0, 0.7083, 0.7381, 0.3036, 0.7789, 0.8178] +2026-04-11 14:43:20.656676: Epoch time: 101.48 s +2026-04-11 14:43:21.802782: +2026-04-11 14:43:21.804523: Epoch 857 +2026-04-11 14:43:21.806657: Current learning rate: 0.00805 +2026-04-11 14:45:03.711528: train_loss -0.3736 +2026-04-11 14:45:03.717745: val_loss -0.3494 +2026-04-11 14:45:03.719650: Pseudo dice [0.0, 0.0, 0.765, 0.7364, 0.4355, 0.2709, 0.6619] +2026-04-11 14:45:03.721988: Epoch time: 101.91 s +2026-04-11 14:45:04.840176: +2026-04-11 14:45:04.842087: Epoch 858 +2026-04-11 14:45:04.845473: Current learning rate: 0.00805 +2026-04-11 14:46:46.349226: train_loss -0.4042 +2026-04-11 14:46:46.356007: val_loss -0.3638 +2026-04-11 14:46:46.360266: Pseudo dice [0.0, 0.0, 0.7726, 0.5993, 0.5845, 0.8062, 0.7864] +2026-04-11 14:46:46.362973: Epoch time: 101.51 s +2026-04-11 14:46:47.473617: +2026-04-11 14:46:47.475666: Epoch 859 +2026-04-11 14:46:47.478222: Current learning rate: 0.00804 +2026-04-11 14:48:28.674040: train_loss -0.3804 +2026-04-11 14:48:28.681735: val_loss -0.3477 +2026-04-11 14:48:28.683675: Pseudo dice [0.0, 0.0, 0.768, 0.6256, 0.3535, 0.5198, 0.7371] +2026-04-11 14:48:28.687232: Epoch time: 101.2 s +2026-04-11 14:48:29.821268: +2026-04-11 14:48:29.823374: Epoch 860 +2026-04-11 14:48:29.825063: Current learning rate: 0.00804 +2026-04-11 14:50:11.828562: train_loss -0.3705 +2026-04-11 14:50:11.835334: val_loss -0.3439 +2026-04-11 14:50:11.837470: Pseudo dice [0.0, 0.0, 0.6188, 0.6785, 0.4991, 0.6973, 0.5037] +2026-04-11 14:50:11.840607: Epoch time: 102.01 s +2026-04-11 14:50:12.977525: +2026-04-11 14:50:12.979376: Epoch 861 +2026-04-11 14:50:12.980828: Current learning rate: 0.00804 +2026-04-11 14:51:54.738747: train_loss -0.3677 +2026-04-11 14:51:54.745120: val_loss -0.335 +2026-04-11 14:51:54.747653: Pseudo dice [0.0, 0.0, 0.4429, 0.6484, 0.4657, 0.5834, 0.8626] +2026-04-11 14:51:54.749790: Epoch time: 101.76 s +2026-04-11 14:51:55.859550: +2026-04-11 14:51:55.861590: Epoch 862 +2026-04-11 14:51:55.863620: Current learning rate: 0.00804 +2026-04-11 14:53:37.605307: train_loss -0.3714 +2026-04-11 14:53:37.611167: val_loss -0.3458 +2026-04-11 14:53:37.613524: Pseudo dice [0.0, 0.0, 0.8345, 0.6343, 0.2611, 0.4048, 0.8682] +2026-04-11 14:53:37.616542: Epoch time: 101.75 s +2026-04-11 14:53:38.779972: +2026-04-11 14:53:38.782219: Epoch 863 +2026-04-11 14:53:38.783836: Current learning rate: 0.00804 +2026-04-11 14:55:20.152093: train_loss -0.3691 +2026-04-11 14:55:20.157266: val_loss -0.3192 +2026-04-11 14:55:20.158971: Pseudo dice [0.0, 0.0, 0.4623, 0.154, 0.3846, 0.5877, 0.3841] +2026-04-11 14:55:20.161182: Epoch time: 101.38 s +2026-04-11 14:55:21.267239: +2026-04-11 14:55:21.269434: Epoch 864 +2026-04-11 14:55:21.271242: Current learning rate: 0.00803 +2026-04-11 14:57:02.599787: train_loss -0.3799 +2026-04-11 14:57:02.606533: val_loss -0.3314 +2026-04-11 14:57:02.608914: Pseudo dice [0.0, 0.0, 0.7588, 0.5789, 0.4444, 0.7127, 0.7759] +2026-04-11 14:57:02.612401: Epoch time: 101.34 s +2026-04-11 14:57:03.748995: +2026-04-11 14:57:03.750733: Epoch 865 +2026-04-11 14:57:03.752203: Current learning rate: 0.00803 +2026-04-11 14:58:44.955497: train_loss -0.3948 +2026-04-11 14:58:44.963271: val_loss -0.333 +2026-04-11 14:58:44.965719: Pseudo dice [0.0, 0.0, 0.7133, 0.4274, 0.4291, 0.627, 0.5683] +2026-04-11 14:58:44.968533: Epoch time: 101.21 s +2026-04-11 14:58:46.083889: +2026-04-11 14:58:46.085547: Epoch 866 +2026-04-11 14:58:46.087185: Current learning rate: 0.00803 +2026-04-11 15:00:27.391947: train_loss -0.3985 +2026-04-11 15:00:27.400194: val_loss -0.3516 +2026-04-11 15:00:27.402777: Pseudo dice [0.0, 0.0, 0.7764, 0.6194, 0.4364, 0.721, 0.1751] +2026-04-11 15:00:27.406040: Epoch time: 101.31 s +2026-04-11 15:00:28.522727: +2026-04-11 15:00:28.525400: Epoch 867 +2026-04-11 15:00:28.527303: Current learning rate: 0.00803 +2026-04-11 15:02:10.385803: train_loss -0.3834 +2026-04-11 15:02:10.395766: val_loss -0.3322 +2026-04-11 15:02:10.398250: Pseudo dice [0.0, 0.0, 0.8227, 0.3569, 0.4282, 0.3995, 0.5648] +2026-04-11 15:02:10.401330: Epoch time: 101.87 s +2026-04-11 15:02:11.517906: +2026-04-11 15:02:11.519948: Epoch 868 +2026-04-11 15:02:11.521455: Current learning rate: 0.00802 +2026-04-11 15:03:52.821520: train_loss -0.3835 +2026-04-11 15:03:52.829031: val_loss -0.318 +2026-04-11 15:03:52.842957: Pseudo dice [0.0, 0.0, 0.7246, 0.6314, 0.4516, 0.3732, 0.8468] +2026-04-11 15:03:52.845646: Epoch time: 101.31 s +2026-04-11 15:03:53.969628: +2026-04-11 15:03:53.971568: Epoch 869 +2026-04-11 15:03:53.973252: Current learning rate: 0.00802 +2026-04-11 15:05:35.520575: train_loss -0.3969 +2026-04-11 15:05:35.526269: val_loss -0.3696 +2026-04-11 15:05:35.528586: Pseudo dice [0.0, 0.0, 0.7762, 0.5241, 0.6759, 0.5545, 0.7941] +2026-04-11 15:05:35.531004: Epoch time: 101.55 s +2026-04-11 15:05:36.671773: +2026-04-11 15:05:36.673762: Epoch 870 +2026-04-11 15:05:36.675509: Current learning rate: 0.00802 +2026-04-11 15:07:18.457789: train_loss -0.3869 +2026-04-11 15:07:18.465332: val_loss -0.361 +2026-04-11 15:07:18.467850: Pseudo dice [0.0, 0.0, 0.8079, 0.7604, 0.4152, 0.7723, 0.6508] +2026-04-11 15:07:18.470295: Epoch time: 101.79 s +2026-04-11 15:07:19.570483: +2026-04-11 15:07:19.572394: Epoch 871 +2026-04-11 15:07:19.574048: Current learning rate: 0.00802 +2026-04-11 15:09:01.654541: train_loss -0.3884 +2026-04-11 15:09:01.663521: val_loss -0.3159 +2026-04-11 15:09:01.665514: Pseudo dice [0.0, 0.0, 0.768, 0.1672, 0.4365, 0.691, 0.7693] +2026-04-11 15:09:01.668004: Epoch time: 102.09 s +2026-04-11 15:09:02.819534: +2026-04-11 15:09:02.821486: Epoch 872 +2026-04-11 15:09:02.823463: Current learning rate: 0.00801 +2026-04-11 15:10:44.002485: train_loss -0.3886 +2026-04-11 15:10:44.010696: val_loss -0.3467 +2026-04-11 15:10:44.012996: Pseudo dice [0.0, 0.0, 0.6994, 0.1816, 0.413, 0.5595, 0.6617] +2026-04-11 15:10:44.015748: Epoch time: 101.19 s +2026-04-11 15:10:46.202382: +2026-04-11 15:10:46.204130: Epoch 873 +2026-04-11 15:10:46.205830: Current learning rate: 0.00801 +2026-04-11 15:12:28.249056: train_loss -0.3946 +2026-04-11 15:12:28.255127: val_loss -0.3523 +2026-04-11 15:12:28.257681: Pseudo dice [0.0, 0.0, 0.7341, 0.2781, 0.6284, 0.6089, 0.8202] +2026-04-11 15:12:28.260454: Epoch time: 102.05 s +2026-04-11 15:12:29.448283: +2026-04-11 15:12:29.450207: Epoch 874 +2026-04-11 15:12:29.452187: Current learning rate: 0.00801 +2026-04-11 15:14:11.024747: train_loss -0.3989 +2026-04-11 15:14:11.030753: val_loss -0.3352 +2026-04-11 15:14:11.033220: Pseudo dice [0.0, 0.0, 0.6435, 0.5668, 0.5177, 0.6489, 0.7837] +2026-04-11 15:14:11.036777: Epoch time: 101.58 s +2026-04-11 15:14:12.146269: +2026-04-11 15:14:12.148315: Epoch 875 +2026-04-11 15:14:12.150398: Current learning rate: 0.00801 +2026-04-11 15:15:53.714255: train_loss -0.3938 +2026-04-11 15:15:53.720961: val_loss -0.3499 +2026-04-11 15:15:53.723403: Pseudo dice [0.0, 0.0, 0.6341, 0.049, 0.639, 0.8215, 0.511] +2026-04-11 15:15:53.726540: Epoch time: 101.57 s +2026-04-11 15:15:54.875117: +2026-04-11 15:15:54.876918: Epoch 876 +2026-04-11 15:15:54.879056: Current learning rate: 0.00801 +2026-04-11 15:17:36.554867: train_loss -0.3961 +2026-04-11 15:17:36.563698: val_loss -0.3296 +2026-04-11 15:17:36.565833: Pseudo dice [0.0, 0.0, 0.6155, 0.6795, 0.4484, 0.6062, 0.4704] +2026-04-11 15:17:36.567989: Epoch time: 101.68 s +2026-04-11 15:17:37.680913: +2026-04-11 15:17:37.683072: Epoch 877 +2026-04-11 15:17:37.684777: Current learning rate: 0.008 +2026-04-11 15:19:19.113258: train_loss -0.3939 +2026-04-11 15:19:19.119907: val_loss -0.3269 +2026-04-11 15:19:19.122617: Pseudo dice [0.0, 0.0, 0.6111, 0.3183, 0.4717, 0.6344, 0.3009] +2026-04-11 15:19:19.125189: Epoch time: 101.44 s +2026-04-11 15:19:20.248512: +2026-04-11 15:19:20.250490: Epoch 878 +2026-04-11 15:19:20.252356: Current learning rate: 0.008 +2026-04-11 15:21:01.701133: train_loss -0.3843 +2026-04-11 15:21:01.708121: val_loss -0.307 +2026-04-11 15:21:01.710096: Pseudo dice [0.0, 0.0, 0.3044, 0.3995, 0.3546, 0.759, 0.7512] +2026-04-11 15:21:01.712249: Epoch time: 101.46 s +2026-04-11 15:21:02.831317: +2026-04-11 15:21:02.833426: Epoch 879 +2026-04-11 15:21:02.836095: Current learning rate: 0.008 +2026-04-11 15:22:44.943465: train_loss -0.3796 +2026-04-11 15:22:44.950727: val_loss -0.3328 +2026-04-11 15:22:44.952526: Pseudo dice [0.0, 0.0, 0.8322, 0.6957, 0.4474, 0.4726, 0.6325] +2026-04-11 15:22:44.954977: Epoch time: 102.12 s +2026-04-11 15:22:46.064468: +2026-04-11 15:22:46.066375: Epoch 880 +2026-04-11 15:22:46.068155: Current learning rate: 0.008 +2026-04-11 15:24:27.358691: train_loss -0.3866 +2026-04-11 15:24:27.365272: val_loss -0.3436 +2026-04-11 15:24:27.367060: Pseudo dice [0.0, 0.0, 0.8069, 0.5355, 0.6401, 0.4996, 0.7593] +2026-04-11 15:24:27.369672: Epoch time: 101.3 s +2026-04-11 15:24:28.494625: +2026-04-11 15:24:28.496887: Epoch 881 +2026-04-11 15:24:28.498995: Current learning rate: 0.00799 +2026-04-11 15:26:09.871623: train_loss -0.3824 +2026-04-11 15:26:09.881605: val_loss -0.3866 +2026-04-11 15:26:09.883886: Pseudo dice [0.0, 0.0, 0.7988, 0.6483, 0.523, 0.6762, 0.8167] +2026-04-11 15:26:09.886565: Epoch time: 101.38 s +2026-04-11 15:26:11.002716: +2026-04-11 15:26:11.004697: Epoch 882 +2026-04-11 15:26:11.006333: Current learning rate: 0.00799 +2026-04-11 15:27:52.622256: train_loss -0.368 +2026-04-11 15:27:52.629419: val_loss -0.3459 +2026-04-11 15:27:52.631349: Pseudo dice [0.0, 0.0, 0.4884, 0.4598, 0.4663, 0.612, 0.716] +2026-04-11 15:27:52.633873: Epoch time: 101.62 s +2026-04-11 15:27:53.757332: +2026-04-11 15:27:53.759555: Epoch 883 +2026-04-11 15:27:53.761510: Current learning rate: 0.00799 +2026-04-11 15:29:34.973593: train_loss -0.3933 +2026-04-11 15:29:34.981168: val_loss -0.3538 +2026-04-11 15:29:34.983321: Pseudo dice [0.0, 0.0, 0.7377, 0.2597, 0.3341, 0.5039, 0.8722] +2026-04-11 15:29:34.985822: Epoch time: 101.22 s +2026-04-11 15:29:36.106559: +2026-04-11 15:29:36.108205: Epoch 884 +2026-04-11 15:29:36.109698: Current learning rate: 0.00799 +2026-04-11 15:31:17.778836: train_loss -0.3868 +2026-04-11 15:31:17.785678: val_loss -0.3426 +2026-04-11 15:31:17.787916: Pseudo dice [0.0, 0.0, 0.7981, 0.5979, 0.4418, 0.4826, 0.4718] +2026-04-11 15:31:17.790399: Epoch time: 101.68 s +2026-04-11 15:31:18.920214: +2026-04-11 15:31:18.921852: Epoch 885 +2026-04-11 15:31:18.923573: Current learning rate: 0.00798 +2026-04-11 15:33:00.359856: train_loss -0.3931 +2026-04-11 15:33:00.366237: val_loss -0.3418 +2026-04-11 15:33:00.368276: Pseudo dice [0.0, 0.0, 0.773, 0.6579, 0.5709, 0.6277, 0.7641] +2026-04-11 15:33:00.370764: Epoch time: 101.44 s +2026-04-11 15:33:01.514238: +2026-04-11 15:33:01.516578: Epoch 886 +2026-04-11 15:33:01.520347: Current learning rate: 0.00798 +2026-04-11 15:34:43.130702: train_loss -0.3865 +2026-04-11 15:34:43.138569: val_loss -0.3423 +2026-04-11 15:34:43.140706: Pseudo dice [0.0, 0.0, 0.7575, 0.4026, 0.6377, 0.0613, 0.8485] +2026-04-11 15:34:43.143492: Epoch time: 101.62 s +2026-04-11 15:34:44.285197: +2026-04-11 15:34:44.287401: Epoch 887 +2026-04-11 15:34:44.289240: Current learning rate: 0.00798 +2026-04-11 15:36:25.800132: train_loss -0.3906 +2026-04-11 15:36:25.811926: val_loss -0.3235 +2026-04-11 15:36:25.814419: Pseudo dice [0.0, 0.0, 0.565, 0.3038, 0.4174, 0.626, 0.7781] +2026-04-11 15:36:25.818475: Epoch time: 101.52 s +2026-04-11 15:36:26.933283: +2026-04-11 15:36:26.935672: Epoch 888 +2026-04-11 15:36:26.938228: Current learning rate: 0.00798 +2026-04-11 15:38:08.567095: train_loss -0.3874 +2026-04-11 15:38:08.573657: val_loss -0.329 +2026-04-11 15:38:08.575376: Pseudo dice [0.0, 0.0, 0.6546, 0.392, 0.4918, 0.5243, 0.3903] +2026-04-11 15:38:08.577753: Epoch time: 101.64 s +2026-04-11 15:38:09.703093: +2026-04-11 15:38:09.705721: Epoch 889 +2026-04-11 15:38:09.708731: Current learning rate: 0.00798 +2026-04-11 15:39:51.428845: train_loss -0.3934 +2026-04-11 15:39:51.436518: val_loss -0.3317 +2026-04-11 15:39:51.438712: Pseudo dice [0.0, 0.0, 0.4766, 0.1683, 0.5886, 0.3725, 0.2677] +2026-04-11 15:39:51.441091: Epoch time: 101.73 s +2026-04-11 15:39:52.566702: +2026-04-11 15:39:52.568644: Epoch 890 +2026-04-11 15:39:52.570416: Current learning rate: 0.00797 +2026-04-11 15:41:33.978526: train_loss -0.3564 +2026-04-11 15:41:33.987236: val_loss -0.3351 +2026-04-11 15:41:33.989643: Pseudo dice [0.0, 0.0, 0.5405, 0.5244, 0.331, 0.6264, 0.8123] +2026-04-11 15:41:33.992905: Epoch time: 101.42 s +2026-04-11 15:41:35.121195: +2026-04-11 15:41:35.122826: Epoch 891 +2026-04-11 15:41:35.124344: Current learning rate: 0.00797 +2026-04-11 15:43:16.534119: train_loss -0.3522 +2026-04-11 15:43:16.541675: val_loss -0.3296 +2026-04-11 15:43:16.544151: Pseudo dice [0.0, 0.0, 0.7028, 0.5689, 0.3571, 0.344, 0.7717] +2026-04-11 15:43:16.546345: Epoch time: 101.42 s +2026-04-11 15:43:17.663696: +2026-04-11 15:43:17.665419: Epoch 892 +2026-04-11 15:43:17.667163: Current learning rate: 0.00797 +2026-04-11 15:44:59.228059: train_loss -0.3326 +2026-04-11 15:44:59.235484: val_loss -0.2866 +2026-04-11 15:44:59.237511: Pseudo dice [0.0, 0.0, 0.3164, 0.1763, 0.3312, 0.1812, 0.8221] +2026-04-11 15:44:59.240310: Epoch time: 101.57 s +2026-04-11 15:45:01.434458: +2026-04-11 15:45:01.436538: Epoch 893 +2026-04-11 15:45:01.438561: Current learning rate: 0.00797 +2026-04-11 15:46:42.993876: train_loss -0.3469 +2026-04-11 15:46:42.999940: val_loss -0.3235 +2026-04-11 15:46:43.002239: Pseudo dice [0.0, 0.0, 0.6204, 0.3796, 0.3396, 0.4807, 0.6605] +2026-04-11 15:46:43.004642: Epoch time: 101.56 s +2026-04-11 15:46:44.116924: +2026-04-11 15:46:44.119126: Epoch 894 +2026-04-11 15:46:44.120852: Current learning rate: 0.00796 +2026-04-11 15:48:25.839395: train_loss -0.378 +2026-04-11 15:48:25.845728: val_loss -0.33 +2026-04-11 15:48:25.847594: Pseudo dice [0.0, 0.0, 0.5101, 0.038, 0.5208, 0.7231, 0.8438] +2026-04-11 15:48:25.849989: Epoch time: 101.73 s +2026-04-11 15:48:26.976470: +2026-04-11 15:48:26.978929: Epoch 895 +2026-04-11 15:48:26.980664: Current learning rate: 0.00796 +2026-04-11 15:50:08.523553: train_loss -0.3866 +2026-04-11 15:50:08.530169: val_loss -0.3254 +2026-04-11 15:50:08.532222: Pseudo dice [0.0, 0.0, 0.7239, 0.5678, 0.4236, 0.4562, 0.7796] +2026-04-11 15:50:08.535479: Epoch time: 101.55 s +2026-04-11 15:50:09.644321: +2026-04-11 15:50:09.646229: Epoch 896 +2026-04-11 15:50:09.648974: Current learning rate: 0.00796 +2026-04-11 15:51:51.554367: train_loss -0.3844 +2026-04-11 15:51:51.561626: val_loss -0.3355 +2026-04-11 15:51:51.563985: Pseudo dice [0.0, 0.0, 0.614, 0.3397, 0.3722, 0.6355, 0.6817] +2026-04-11 15:51:51.566807: Epoch time: 101.91 s +2026-04-11 15:51:52.692410: +2026-04-11 15:51:52.694179: Epoch 897 +2026-04-11 15:51:52.695873: Current learning rate: 0.00796 +2026-04-11 15:53:34.206582: train_loss -0.3861 +2026-04-11 15:53:34.212576: val_loss -0.3246 +2026-04-11 15:53:34.214634: Pseudo dice [0.0, 0.0, 0.8069, 0.5411, 0.5717, 0.475, 0.682] +2026-04-11 15:53:34.217001: Epoch time: 101.52 s +2026-04-11 15:53:35.346101: +2026-04-11 15:53:35.347950: Epoch 898 +2026-04-11 15:53:35.349566: Current learning rate: 0.00795 +2026-04-11 15:55:16.846239: train_loss -0.3669 +2026-04-11 15:55:16.853477: val_loss -0.3606 +2026-04-11 15:55:16.856214: Pseudo dice [0.0, 0.0, 0.6802, 0.6855, 0.5025, 0.6189, 0.7409] +2026-04-11 15:55:16.859425: Epoch time: 101.5 s +2026-04-11 15:55:17.986846: +2026-04-11 15:55:17.989456: Epoch 899 +2026-04-11 15:55:17.991893: Current learning rate: 0.00795 +2026-04-11 15:56:59.480168: train_loss -0.3958 +2026-04-11 15:56:59.487284: val_loss -0.305 +2026-04-11 15:56:59.489319: Pseudo dice [0.0, 0.0, 0.6694, 0.5695, 0.3056, 0.4768, 0.3335] +2026-04-11 15:56:59.491614: Epoch time: 101.5 s +2026-04-11 15:57:02.455368: +2026-04-11 15:57:02.458297: Epoch 900 +2026-04-11 15:57:02.461402: Current learning rate: 0.00795 +2026-04-11 15:58:44.015720: train_loss -0.3996 +2026-04-11 15:58:44.022645: val_loss -0.3172 +2026-04-11 15:58:44.025033: Pseudo dice [0.0, 0.0, 0.78, 0.2788, 0.5601, 0.3637, 0.3282] +2026-04-11 15:58:44.027920: Epoch time: 101.56 s +2026-04-11 15:58:45.136686: +2026-04-11 15:58:45.138495: Epoch 901 +2026-04-11 15:58:45.140491: Current learning rate: 0.00795 +2026-04-11 16:00:26.796526: train_loss -0.3938 +2026-04-11 16:00:26.803936: val_loss -0.3401 +2026-04-11 16:00:26.806463: Pseudo dice [0.0, 0.0, 0.7906, 0.2105, 0.0255, 0.5496, 0.6598] +2026-04-11 16:00:26.809334: Epoch time: 101.66 s +2026-04-11 16:00:27.949943: +2026-04-11 16:00:27.952262: Epoch 902 +2026-04-11 16:00:27.954751: Current learning rate: 0.00795 +2026-04-11 16:02:09.945337: train_loss -0.3936 +2026-04-11 16:02:09.950983: val_loss -0.3541 +2026-04-11 16:02:09.953100: Pseudo dice [0.0, 0.0, 0.771, 0.0808, 0.328, 0.6886, 0.9058] +2026-04-11 16:02:09.955419: Epoch time: 102.0 s +2026-04-11 16:02:11.067755: +2026-04-11 16:02:11.069827: Epoch 903 +2026-04-11 16:02:11.071789: Current learning rate: 0.00794 +2026-04-11 16:03:52.446743: train_loss -0.3666 +2026-04-11 16:03:52.452840: val_loss -0.3436 +2026-04-11 16:03:52.454862: Pseudo dice [0.0, 0.0, 0.5757, 0.2819, 0.407, 0.5508, 0.7402] +2026-04-11 16:03:52.457720: Epoch time: 101.38 s +2026-04-11 16:03:53.567959: +2026-04-11 16:03:53.570325: Epoch 904 +2026-04-11 16:03:53.572291: Current learning rate: 0.00794 +2026-04-11 16:05:35.520704: train_loss -0.3572 +2026-04-11 16:05:35.526545: val_loss -0.3137 +2026-04-11 16:05:35.528501: Pseudo dice [0.0, 0.0, 0.6768, 0.6082, 0.3319, 0.7413, 0.8322] +2026-04-11 16:05:35.530937: Epoch time: 101.96 s +2026-04-11 16:05:36.618171: +2026-04-11 16:05:36.620071: Epoch 905 +2026-04-11 16:05:36.621708: Current learning rate: 0.00794 +2026-04-11 16:07:18.633745: train_loss -0.361 +2026-04-11 16:07:18.641119: val_loss -0.3171 +2026-04-11 16:07:18.643275: Pseudo dice [0.0, 0.0, 0.7668, 0.347, 0.5943, 0.6985, 0.5412] +2026-04-11 16:07:18.646265: Epoch time: 102.02 s +2026-04-11 16:07:19.786537: +2026-04-11 16:07:19.788563: Epoch 906 +2026-04-11 16:07:19.790609: Current learning rate: 0.00794 +2026-04-11 16:09:01.348024: train_loss -0.3577 +2026-04-11 16:09:01.354305: val_loss -0.3197 +2026-04-11 16:09:01.356276: Pseudo dice [0.0, 0.0, 0.7431, 0.6262, 0.512, 0.5682, 0.8207] +2026-04-11 16:09:01.359130: Epoch time: 101.56 s +2026-04-11 16:09:02.467122: +2026-04-11 16:09:02.469239: Epoch 907 +2026-04-11 16:09:02.470783: Current learning rate: 0.00793 +2026-04-11 16:10:44.511088: train_loss -0.3445 +2026-04-11 16:10:44.516691: val_loss -0.3204 +2026-04-11 16:10:44.518806: Pseudo dice [0.0, 0.0, 0.7044, 0.2792, 0.4613, 0.5041, 0.3512] +2026-04-11 16:10:44.521460: Epoch time: 102.05 s +2026-04-11 16:10:45.651177: +2026-04-11 16:10:45.653659: Epoch 908 +2026-04-11 16:10:45.655683: Current learning rate: 0.00793 +2026-04-11 16:12:27.865579: train_loss -0.3614 +2026-04-11 16:12:27.891862: val_loss -0.3345 +2026-04-11 16:12:27.894030: Pseudo dice [0.0, 0.0, 0.6056, 0.4904, 0.4796, 0.2173, 0.7199] +2026-04-11 16:12:27.895952: Epoch time: 102.22 s +2026-04-11 16:12:29.013986: +2026-04-11 16:12:29.016362: Epoch 909 +2026-04-11 16:12:29.018112: Current learning rate: 0.00793 +2026-04-11 16:14:10.822988: train_loss -0.3828 +2026-04-11 16:14:10.829677: val_loss -0.3467 +2026-04-11 16:14:10.831764: Pseudo dice [0.0, 0.0, 0.7626, 0.4508, 0.3176, 0.4842, 0.857] +2026-04-11 16:14:10.834727: Epoch time: 101.81 s +2026-04-11 16:14:11.973988: +2026-04-11 16:14:11.976099: Epoch 910 +2026-04-11 16:14:11.978605: Current learning rate: 0.00793 +2026-04-11 16:15:54.216178: train_loss -0.3592 +2026-04-11 16:15:54.222824: val_loss -0.3427 +2026-04-11 16:15:54.225121: Pseudo dice [0.0, 0.0, 0.7944, 0.6465, 0.468, 0.7688, 0.8377] +2026-04-11 16:15:54.227496: Epoch time: 102.25 s +2026-04-11 16:15:55.361044: +2026-04-11 16:15:55.363231: Epoch 911 +2026-04-11 16:15:55.365028: Current learning rate: 0.00792 +2026-04-11 16:17:36.871678: train_loss -0.3837 +2026-04-11 16:17:36.877514: val_loss -0.3426 +2026-04-11 16:17:36.879290: Pseudo dice [0.0, 0.0, 0.7266, 0.3748, 0.5173, 0.7793, 0.6864] +2026-04-11 16:17:36.881755: Epoch time: 101.51 s +2026-04-11 16:17:38.030483: +2026-04-11 16:17:38.032137: Epoch 912 +2026-04-11 16:17:38.033731: Current learning rate: 0.00792 +2026-04-11 16:19:19.832768: train_loss -0.3895 +2026-04-11 16:19:19.840315: val_loss -0.3448 +2026-04-11 16:19:19.843042: Pseudo dice [0.0, 0.0, 0.7729, 0.2095, 0.4546, 0.6363, 0.4312] +2026-04-11 16:19:19.845759: Epoch time: 101.81 s +2026-04-11 16:19:21.015346: +2026-04-11 16:19:21.017227: Epoch 913 +2026-04-11 16:19:21.019011: Current learning rate: 0.00792 +2026-04-11 16:21:03.589086: train_loss -0.3811 +2026-04-11 16:21:03.596303: val_loss -0.3362 +2026-04-11 16:21:03.598811: Pseudo dice [0.0, 0.0, 0.7807, 0.1875, 0.3518, 0.4301, 0.7288] +2026-04-11 16:21:03.603390: Epoch time: 102.58 s +2026-04-11 16:21:04.734066: +2026-04-11 16:21:04.736475: Epoch 914 +2026-04-11 16:21:04.738208: Current learning rate: 0.00792 +2026-04-11 16:22:46.307329: train_loss -0.3688 +2026-04-11 16:22:46.316190: val_loss -0.3635 +2026-04-11 16:22:46.318478: Pseudo dice [0.0, 0.0, 0.6986, 0.4995, 0.545, 0.441, 0.904] +2026-04-11 16:22:46.321035: Epoch time: 101.58 s +2026-04-11 16:22:47.488928: +2026-04-11 16:22:47.493762: Epoch 915 +2026-04-11 16:22:47.498358: Current learning rate: 0.00792 +2026-04-11 16:24:29.220771: train_loss -0.3658 +2026-04-11 16:24:29.227659: val_loss -0.2796 +2026-04-11 16:24:29.229935: Pseudo dice [0.0, 0.0, 0.5885, 0.2224, 0.3257, 0.4163, 0.6136] +2026-04-11 16:24:29.232024: Epoch time: 101.73 s +2026-04-11 16:24:30.374608: +2026-04-11 16:24:30.376551: Epoch 916 +2026-04-11 16:24:30.378467: Current learning rate: 0.00791 +2026-04-11 16:26:12.319433: train_loss -0.3649 +2026-04-11 16:26:12.325376: val_loss -0.3284 +2026-04-11 16:26:12.327835: Pseudo dice [0.0, 0.0, 0.7913, 0.3959, 0.4258, 0.4847, 0.447] +2026-04-11 16:26:12.330136: Epoch time: 101.95 s +2026-04-11 16:26:13.453060: +2026-04-11 16:26:13.454795: Epoch 917 +2026-04-11 16:26:13.456489: Current learning rate: 0.00791 +2026-04-11 16:27:54.916093: train_loss -0.3839 +2026-04-11 16:27:54.922525: val_loss -0.3582 +2026-04-11 16:27:54.924604: Pseudo dice [0.0, 0.0, 0.8538, 0.6776, 0.3802, 0.5434, 0.3019] +2026-04-11 16:27:54.927051: Epoch time: 101.47 s +2026-04-11 16:27:56.069005: +2026-04-11 16:27:56.070824: Epoch 918 +2026-04-11 16:27:56.072548: Current learning rate: 0.00791 +2026-04-11 16:29:37.448092: train_loss -0.3363 +2026-04-11 16:29:37.454405: val_loss -0.3243 +2026-04-11 16:29:37.456407: Pseudo dice [0.0, 0.0, 0.589, 0.2423, 0.449, 0.2782, 0.7148] +2026-04-11 16:29:37.458712: Epoch time: 101.38 s +2026-04-11 16:29:38.594055: +2026-04-11 16:29:38.596081: Epoch 919 +2026-04-11 16:29:38.598125: Current learning rate: 0.00791 +2026-04-11 16:31:20.089734: train_loss -0.3635 +2026-04-11 16:31:20.096316: val_loss -0.3082 +2026-04-11 16:31:20.099136: Pseudo dice [0.0, 0.0, 0.2884, 0.5508, 0.3921, 0.3666, 0.5831] +2026-04-11 16:31:20.102220: Epoch time: 101.5 s +2026-04-11 16:31:21.234250: +2026-04-11 16:31:21.236070: Epoch 920 +2026-04-11 16:31:21.237711: Current learning rate: 0.0079 +2026-04-11 16:33:02.839639: train_loss -0.3332 +2026-04-11 16:33:02.849712: val_loss -0.3192 +2026-04-11 16:33:02.855724: Pseudo dice [0.0, 0.0, 0.6594, 0.5653, 0.4541, 0.4016, 0.808] +2026-04-11 16:33:02.860674: Epoch time: 101.61 s +2026-04-11 16:33:04.004793: +2026-04-11 16:33:04.007042: Epoch 921 +2026-04-11 16:33:04.008887: Current learning rate: 0.0079 +2026-04-11 16:34:45.377097: train_loss -0.367 +2026-04-11 16:34:45.383837: val_loss -0.3368 +2026-04-11 16:34:45.386184: Pseudo dice [0.0, 0.0, 0.7979, 0.6159, 0.4032, 0.7655, 0.7553] +2026-04-11 16:34:45.388424: Epoch time: 101.38 s +2026-04-11 16:34:46.519967: +2026-04-11 16:34:46.521938: Epoch 922 +2026-04-11 16:34:46.523616: Current learning rate: 0.0079 +2026-04-11 16:36:28.134460: train_loss -0.3798 +2026-04-11 16:36:28.140478: val_loss -0.3261 +2026-04-11 16:36:28.143361: Pseudo dice [0.0, 0.0, 0.6974, 0.2721, 0.5227, 0.5672, 0.7007] +2026-04-11 16:36:28.146302: Epoch time: 101.62 s +2026-04-11 16:36:29.297423: +2026-04-11 16:36:29.299771: Epoch 923 +2026-04-11 16:36:29.302701: Current learning rate: 0.0079 +2026-04-11 16:38:10.922787: train_loss -0.3622 +2026-04-11 16:38:10.930538: val_loss -0.3071 +2026-04-11 16:38:10.933339: Pseudo dice [0.0, 0.0, 0.5317, 0.5627, 0.2799, 0.3667, 0.5616] +2026-04-11 16:38:10.936704: Epoch time: 101.63 s +2026-04-11 16:38:12.059737: +2026-04-11 16:38:12.061724: Epoch 924 +2026-04-11 16:38:12.063447: Current learning rate: 0.00789 +2026-04-11 16:39:54.061584: train_loss -0.3605 +2026-04-11 16:39:54.068773: val_loss -0.3126 +2026-04-11 16:39:54.071300: Pseudo dice [0.0, 0.0, 0.7569, 0.5399, 0.4031, 0.5764, 0.469] +2026-04-11 16:39:54.074328: Epoch time: 102.01 s +2026-04-11 16:39:55.191841: +2026-04-11 16:39:55.194247: Epoch 925 +2026-04-11 16:39:55.196306: Current learning rate: 0.00789 +2026-04-11 16:41:36.903077: train_loss -0.3572 +2026-04-11 16:41:36.921951: val_loss -0.3607 +2026-04-11 16:41:36.937084: Pseudo dice [0.0, 0.0, 0.7325, 0.0261, 0.4356, 0.7144, 0.8781] +2026-04-11 16:41:36.940845: Epoch time: 101.71 s +2026-04-11 16:41:38.077778: +2026-04-11 16:41:38.079628: Epoch 926 +2026-04-11 16:41:38.081331: Current learning rate: 0.00789 +2026-04-11 16:43:19.663987: train_loss -0.3519 +2026-04-11 16:43:19.670091: val_loss -0.3109 +2026-04-11 16:43:19.672308: Pseudo dice [0.0, 0.0, 0.4862, 0.2903, 0.5335, 0.4815, 0.7768] +2026-04-11 16:43:19.674425: Epoch time: 101.59 s +2026-04-11 16:43:20.797711: +2026-04-11 16:43:20.799686: Epoch 927 +2026-04-11 16:43:20.801326: Current learning rate: 0.00789 +2026-04-11 16:45:02.619147: train_loss -0.3842 +2026-04-11 16:45:02.626195: val_loss -0.3354 +2026-04-11 16:45:02.628178: Pseudo dice [0.0, 0.0, 0.6483, 0.3659, 0.5426, 0.3435, 0.4301] +2026-04-11 16:45:02.630461: Epoch time: 101.82 s +2026-04-11 16:45:03.753621: +2026-04-11 16:45:03.755676: Epoch 928 +2026-04-11 16:45:03.757420: Current learning rate: 0.00789 +2026-04-11 16:46:45.271942: train_loss -0.3793 +2026-04-11 16:46:45.281713: val_loss -0.3352 +2026-04-11 16:46:45.283758: Pseudo dice [0.0, 0.0, 0.7309, 0.5087, 0.4907, 0.7018, 0.4958] +2026-04-11 16:46:45.286221: Epoch time: 101.52 s +2026-04-11 16:46:46.395326: +2026-04-11 16:46:46.397479: Epoch 929 +2026-04-11 16:46:46.399147: Current learning rate: 0.00788 +2026-04-11 16:48:27.937005: train_loss -0.389 +2026-04-11 16:48:27.943428: val_loss -0.3347 +2026-04-11 16:48:27.945924: Pseudo dice [0.0, 0.0, 0.702, 0.446, 0.4884, 0.4952, 0.7534] +2026-04-11 16:48:27.949783: Epoch time: 101.54 s +2026-04-11 16:48:29.023447: +2026-04-11 16:48:29.025163: Epoch 930 +2026-04-11 16:48:29.026764: Current learning rate: 0.00788 +2026-04-11 16:50:10.381625: train_loss -0.388 +2026-04-11 16:50:10.387788: val_loss -0.3371 +2026-04-11 16:50:10.390173: Pseudo dice [0.0, 0.0, 0.5455, 0.3438, 0.3377, 0.6798, 0.4192] +2026-04-11 16:50:10.392418: Epoch time: 101.36 s +2026-04-11 16:50:11.528689: +2026-04-11 16:50:11.530334: Epoch 931 +2026-04-11 16:50:11.531858: Current learning rate: 0.00788 +2026-04-11 16:51:52.908510: train_loss -0.3787 +2026-04-11 16:51:52.917167: val_loss -0.303 +2026-04-11 16:51:52.919374: Pseudo dice [0.0, 0.0, 0.771, 0.627, 0.3455, 0.5691, 0.6434] +2026-04-11 16:51:52.921699: Epoch time: 101.38 s +2026-04-11 16:51:54.003889: +2026-04-11 16:51:54.005561: Epoch 932 +2026-04-11 16:51:54.007128: Current learning rate: 0.00788 +2026-04-11 16:53:36.016594: train_loss -0.3724 +2026-04-11 16:53:36.023270: val_loss -0.354 +2026-04-11 16:53:36.025070: Pseudo dice [0.0, 0.0, 0.7303, 0.7589, 0.4091, 0.7599, 0.8459] +2026-04-11 16:53:36.027579: Epoch time: 102.02 s +2026-04-11 16:53:37.135519: +2026-04-11 16:53:37.137468: Epoch 933 +2026-04-11 16:53:37.139319: Current learning rate: 0.00787 +2026-04-11 16:55:18.873897: train_loss -0.3726 +2026-04-11 16:55:18.879301: val_loss -0.3138 +2026-04-11 16:55:18.881001: Pseudo dice [0.0, 0.0, 0.7141, 0.4476, 0.3614, 0.4529, 0.6075] +2026-04-11 16:55:18.883294: Epoch time: 101.74 s +2026-04-11 16:55:19.981606: +2026-04-11 16:55:19.983425: Epoch 934 +2026-04-11 16:55:19.984955: Current learning rate: 0.00787 +2026-04-11 16:57:02.663320: train_loss -0.3864 +2026-04-11 16:57:02.670164: val_loss -0.3307 +2026-04-11 16:57:02.672549: Pseudo dice [0.0, 0.0, 0.7587, 0.5773, 0.59, 0.6664, 0.6358] +2026-04-11 16:57:02.674813: Epoch time: 102.68 s +2026-04-11 16:57:03.771703: +2026-04-11 16:57:03.773449: Epoch 935 +2026-04-11 16:57:03.775132: Current learning rate: 0.00787 +2026-04-11 16:58:45.333251: train_loss -0.3925 +2026-04-11 16:58:45.339773: val_loss -0.3464 +2026-04-11 16:58:45.342007: Pseudo dice [0.0, 0.0, 0.6902, 0.5757, 0.5491, 0.7079, 0.7003] +2026-04-11 16:58:45.344526: Epoch time: 101.56 s +2026-04-11 16:58:46.493420: +2026-04-11 16:58:46.495490: Epoch 936 +2026-04-11 16:58:46.497212: Current learning rate: 0.00787 +2026-04-11 17:00:28.208058: train_loss -0.3935 +2026-04-11 17:00:28.217790: val_loss -0.377 +2026-04-11 17:00:28.219969: Pseudo dice [0.0, 0.0, 0.7261, 0.6744, 0.4127, 0.8263, 0.8171] +2026-04-11 17:00:28.222396: Epoch time: 101.72 s +2026-04-11 17:00:29.312769: +2026-04-11 17:00:29.314475: Epoch 937 +2026-04-11 17:00:29.316107: Current learning rate: 0.00786 +2026-04-11 17:02:10.849298: train_loss -0.3956 +2026-04-11 17:02:10.856547: val_loss -0.3536 +2026-04-11 17:02:10.858844: Pseudo dice [0.0, 0.0, 0.8337, 0.684, 0.4122, 0.4291, 0.5058] +2026-04-11 17:02:10.861469: Epoch time: 101.54 s +2026-04-11 17:02:12.021253: +2026-04-11 17:02:12.023108: Epoch 938 +2026-04-11 17:02:12.024843: Current learning rate: 0.00786 +2026-04-11 17:03:53.864968: train_loss -0.3786 +2026-04-11 17:03:53.871310: val_loss -0.3194 +2026-04-11 17:03:53.873355: Pseudo dice [0.0, 0.0, 0.3562, 0.7199, 0.398, 0.4766, 0.6369] +2026-04-11 17:03:53.876045: Epoch time: 101.85 s +2026-04-11 17:03:55.016627: +2026-04-11 17:03:55.018953: Epoch 939 +2026-04-11 17:03:55.021025: Current learning rate: 0.00786 +2026-04-11 17:05:36.521469: train_loss -0.3777 +2026-04-11 17:05:36.527880: val_loss -0.3075 +2026-04-11 17:05:36.530099: Pseudo dice [0.0, 0.0, 0.4807, 0.6165, 0.4754, 0.2512, 0.7572] +2026-04-11 17:05:36.532929: Epoch time: 101.51 s +2026-04-11 17:05:37.668500: +2026-04-11 17:05:37.670511: Epoch 940 +2026-04-11 17:05:37.672012: Current learning rate: 0.00786 +2026-04-11 17:07:19.083716: train_loss -0.3847 +2026-04-11 17:07:19.090603: val_loss -0.3597 +2026-04-11 17:07:19.092772: Pseudo dice [0.0, 0.0, 0.8029, 0.5345, 0.3404, 0.4381, 0.8891] +2026-04-11 17:07:19.095135: Epoch time: 101.42 s +2026-04-11 17:07:20.207589: +2026-04-11 17:07:20.209935: Epoch 941 +2026-04-11 17:07:20.211705: Current learning rate: 0.00786 +2026-04-11 17:09:02.297494: train_loss -0.3462 +2026-04-11 17:09:02.304682: val_loss -0.3296 +2026-04-11 17:09:02.307235: Pseudo dice [0.0, 0.0, 0.6418, 0.6942, 0.5183, 0.3149, 0.6517] +2026-04-11 17:09:02.309886: Epoch time: 102.09 s +2026-04-11 17:09:03.419333: +2026-04-11 17:09:03.421384: Epoch 942 +2026-04-11 17:09:03.423283: Current learning rate: 0.00785 +2026-04-11 17:10:45.007616: train_loss -0.3861 +2026-04-11 17:10:45.013993: val_loss -0.33 +2026-04-11 17:10:45.016380: Pseudo dice [0.0, 0.0, 0.7821, 0.744, 0.4286, 0.5202, 0.6042] +2026-04-11 17:10:45.018946: Epoch time: 101.59 s +2026-04-11 17:10:46.133746: +2026-04-11 17:10:46.135767: Epoch 943 +2026-04-11 17:10:46.137575: Current learning rate: 0.00785 +2026-04-11 17:12:27.524196: train_loss -0.401 +2026-04-11 17:12:27.534192: val_loss -0.3563 +2026-04-11 17:12:27.536510: Pseudo dice [0.0, 0.0, 0.6839, 0.0229, 0.4112, 0.4749, 0.7486] +2026-04-11 17:12:27.539079: Epoch time: 101.39 s +2026-04-11 17:12:28.630596: +2026-04-11 17:12:28.632344: Epoch 944 +2026-04-11 17:12:28.634015: Current learning rate: 0.00785 +2026-04-11 17:14:10.601600: train_loss -0.3815 +2026-04-11 17:14:10.608978: val_loss -0.3463 +2026-04-11 17:14:10.611248: Pseudo dice [0.0, 0.0, 0.8162, 0.5883, 0.6186, 0.6309, 0.7282] +2026-04-11 17:14:10.613614: Epoch time: 101.97 s +2026-04-11 17:14:11.713284: +2026-04-11 17:14:11.715342: Epoch 945 +2026-04-11 17:14:11.717316: Current learning rate: 0.00785 +2026-04-11 17:15:53.442364: train_loss -0.3977 +2026-04-11 17:15:53.448099: val_loss -0.3317 +2026-04-11 17:15:53.449861: Pseudo dice [0.0, 0.0, 0.6886, 0.1429, 0.2732, 0.8256, 0.8168] +2026-04-11 17:15:53.451866: Epoch time: 101.73 s +2026-04-11 17:15:54.563355: +2026-04-11 17:15:54.565260: Epoch 946 +2026-04-11 17:15:54.567093: Current learning rate: 0.00784 +2026-04-11 17:17:36.237986: train_loss -0.3881 +2026-04-11 17:17:36.244217: val_loss -0.3337 +2026-04-11 17:17:36.246258: Pseudo dice [0.0, 0.0, 0.7915, 0.0859, 0.4855, 0.448, 0.692] +2026-04-11 17:17:36.248542: Epoch time: 101.68 s +2026-04-11 17:17:37.375942: +2026-04-11 17:17:37.378008: Epoch 947 +2026-04-11 17:17:37.379594: Current learning rate: 0.00784 +2026-04-11 17:19:18.774826: train_loss -0.3678 +2026-04-11 17:19:18.781175: val_loss -0.3311 +2026-04-11 17:19:18.783098: Pseudo dice [0.0, 0.0, 0.8083, 0.4896, 0.4864, 0.6685, 0.6872] +2026-04-11 17:19:18.786771: Epoch time: 101.4 s +2026-04-11 17:19:19.890624: +2026-04-11 17:19:19.892286: Epoch 948 +2026-04-11 17:19:19.893858: Current learning rate: 0.00784 +2026-04-11 17:21:01.616548: train_loss -0.3803 +2026-04-11 17:21:01.621798: val_loss -0.3316 +2026-04-11 17:21:01.623712: Pseudo dice [0.0, 0.0, 0.5893, 0.4551, 0.5809, 0.723, 0.8847] +2026-04-11 17:21:01.625972: Epoch time: 101.73 s +2026-04-11 17:21:02.727556: +2026-04-11 17:21:02.729499: Epoch 949 +2026-04-11 17:21:02.731360: Current learning rate: 0.00784 +2026-04-11 17:22:44.175325: train_loss -0.3791 +2026-04-11 17:22:44.181738: val_loss -0.3388 +2026-04-11 17:22:44.184858: Pseudo dice [0.0, 0.0, 0.4453, 0.7272, 0.382, 0.4693, 0.4428] +2026-04-11 17:22:44.187375: Epoch time: 101.45 s +2026-04-11 17:22:46.966103: +2026-04-11 17:22:46.967714: Epoch 950 +2026-04-11 17:22:46.969241: Current learning rate: 0.00783 +2026-04-11 17:24:28.472893: train_loss -0.3903 +2026-04-11 17:24:28.481103: val_loss -0.3719 +2026-04-11 17:24:28.488143: Pseudo dice [0.0, 0.0, 0.7975, 0.6323, 0.4323, 0.8315, 0.9298] +2026-04-11 17:24:28.490298: Epoch time: 101.51 s +2026-04-11 17:24:29.641752: +2026-04-11 17:24:29.643495: Epoch 951 +2026-04-11 17:24:29.645144: Current learning rate: 0.00783 +2026-04-11 17:26:11.248400: train_loss -0.3709 +2026-04-11 17:26:11.255234: val_loss -0.3211 +2026-04-11 17:26:11.257263: Pseudo dice [0.0, 0.0, 0.5628, 0.464, 0.5208, 0.7153, 0.2456] +2026-04-11 17:26:11.259772: Epoch time: 101.61 s +2026-04-11 17:26:12.380352: +2026-04-11 17:26:12.381992: Epoch 952 +2026-04-11 17:26:12.383474: Current learning rate: 0.00783 +2026-04-11 17:27:54.046842: train_loss -0.3728 +2026-04-11 17:27:54.052823: val_loss -0.3509 +2026-04-11 17:27:54.055325: Pseudo dice [0.0, 0.0, 0.649, 0.254, 0.446, 0.762, 0.8558] +2026-04-11 17:27:54.057599: Epoch time: 101.67 s +2026-04-11 17:27:55.177105: +2026-04-11 17:27:55.179060: Epoch 953 +2026-04-11 17:27:55.180968: Current learning rate: 0.00783 +2026-04-11 17:29:36.675184: train_loss -0.3838 +2026-04-11 17:29:36.681257: val_loss -0.3563 +2026-04-11 17:29:36.683553: Pseudo dice [0.0, 0.0, 0.7846, 0.5004, 0.6461, 0.6019, 0.8574] +2026-04-11 17:29:36.685869: Epoch time: 101.5 s +2026-04-11 17:29:37.815318: +2026-04-11 17:29:37.817127: Epoch 954 +2026-04-11 17:29:37.818906: Current learning rate: 0.00783 +2026-04-11 17:31:20.154621: train_loss -0.3559 +2026-04-11 17:31:20.160697: val_loss -0.3124 +2026-04-11 17:31:20.162822: Pseudo dice [0.0, 0.0, 0.5758, 0.3291, 0.2931, 0.5438, 0.6553] +2026-04-11 17:31:20.164825: Epoch time: 102.34 s +2026-04-11 17:31:21.321009: +2026-04-11 17:31:21.322696: Epoch 955 +2026-04-11 17:31:21.324353: Current learning rate: 0.00782 +2026-04-11 17:33:03.237487: train_loss -0.3793 +2026-04-11 17:33:03.244124: val_loss -0.3216 +2026-04-11 17:33:03.246175: Pseudo dice [0.0, 0.0, 0.7043, 0.2745, 0.4801, 0.5681, 0.2276] +2026-04-11 17:33:03.248579: Epoch time: 101.92 s +2026-04-11 17:33:04.412009: +2026-04-11 17:33:04.413614: Epoch 956 +2026-04-11 17:33:04.415023: Current learning rate: 0.00782 +2026-04-11 17:34:46.567457: train_loss -0.3745 +2026-04-11 17:34:46.576653: val_loss -0.337 +2026-04-11 17:34:46.578781: Pseudo dice [0.0, 0.0, 0.6182, 0.2771, 0.3875, 0.8069, 0.6362] +2026-04-11 17:34:46.581380: Epoch time: 102.16 s +2026-04-11 17:34:47.783613: +2026-04-11 17:34:47.785821: Epoch 957 +2026-04-11 17:34:47.787968: Current learning rate: 0.00782 +2026-04-11 17:36:29.346826: train_loss -0.3709 +2026-04-11 17:36:29.353641: val_loss -0.3143 +2026-04-11 17:36:29.355542: Pseudo dice [0.0, 0.0, 0.5606, 0.1722, 0.5523, 0.5451, 0.7139] +2026-04-11 17:36:29.358041: Epoch time: 101.57 s +2026-04-11 17:36:30.575507: +2026-04-11 17:36:30.577268: Epoch 958 +2026-04-11 17:36:30.578714: Current learning rate: 0.00782 +2026-04-11 17:38:12.019299: train_loss -0.3737 +2026-04-11 17:38:12.027314: val_loss -0.3307 +2026-04-11 17:38:12.029467: Pseudo dice [0.0, 0.0, 0.6165, 0.002, 0.4392, 0.5992, 0.5237] +2026-04-11 17:38:12.032212: Epoch time: 101.45 s +2026-04-11 17:38:13.250689: +2026-04-11 17:38:13.252496: Epoch 959 +2026-04-11 17:38:13.254490: Current learning rate: 0.00781 +2026-04-11 17:39:54.879898: train_loss -0.3944 +2026-04-11 17:39:54.886138: val_loss -0.3069 +2026-04-11 17:39:54.888532: Pseudo dice [0.0, 0.0, 0.7243, 0.471, 0.5092, 0.3485, 0.7843] +2026-04-11 17:39:54.890851: Epoch time: 101.63 s +2026-04-11 17:39:56.028565: +2026-04-11 17:39:56.030752: Epoch 960 +2026-04-11 17:39:56.032357: Current learning rate: 0.00781 +2026-04-11 17:41:37.559249: train_loss -0.3768 +2026-04-11 17:41:37.565062: val_loss -0.3342 +2026-04-11 17:41:37.566671: Pseudo dice [0.0, 0.0, 0.5053, 0.2602, 0.4164, 0.58, 0.7981] +2026-04-11 17:41:37.568825: Epoch time: 101.53 s +2026-04-11 17:41:38.665221: +2026-04-11 17:41:38.667489: Epoch 961 +2026-04-11 17:41:38.669366: Current learning rate: 0.00781 +2026-04-11 17:43:20.091168: train_loss -0.3755 +2026-04-11 17:43:20.097501: val_loss -0.3423 +2026-04-11 17:43:20.100187: Pseudo dice [0.0, 0.0, 0.7568, 0.3044, 0.4099, 0.5494, 0.8539] +2026-04-11 17:43:20.102799: Epoch time: 101.43 s +2026-04-11 17:43:21.219781: +2026-04-11 17:43:21.221741: Epoch 962 +2026-04-11 17:43:21.223676: Current learning rate: 0.00781 +2026-04-11 17:45:02.770624: train_loss -0.3775 +2026-04-11 17:45:02.776543: val_loss -0.3613 +2026-04-11 17:45:02.778593: Pseudo dice [0.0, 0.0, 0.7497, 0.3651, 0.4663, 0.7509, 0.818] +2026-04-11 17:45:02.780822: Epoch time: 101.55 s +2026-04-11 17:45:03.957195: +2026-04-11 17:45:03.959432: Epoch 963 +2026-04-11 17:45:03.961182: Current learning rate: 0.0078 +2026-04-11 17:46:45.640762: train_loss -0.384 +2026-04-11 17:46:45.647405: val_loss -0.3255 +2026-04-11 17:46:45.649629: Pseudo dice [0.0, 0.0, 0.6377, 0.3565, 0.3848, 0.5906, 0.5367] +2026-04-11 17:46:45.651950: Epoch time: 101.69 s +2026-04-11 17:46:46.790476: +2026-04-11 17:46:46.792718: Epoch 964 +2026-04-11 17:46:46.794807: Current learning rate: 0.0078 +2026-04-11 17:48:28.293209: train_loss -0.3542 +2026-04-11 17:48:28.301050: val_loss -0.3382 +2026-04-11 17:48:28.302984: Pseudo dice [0.0, 0.0, 0.7562, 0.5231, 0.2553, 0.7968, 0.7282] +2026-04-11 17:48:28.305480: Epoch time: 101.51 s +2026-04-11 17:48:29.470308: +2026-04-11 17:48:29.472647: Epoch 965 +2026-04-11 17:48:29.474897: Current learning rate: 0.0078 +2026-04-11 17:50:11.403313: train_loss -0.3713 +2026-04-11 17:50:11.409392: val_loss -0.3226 +2026-04-11 17:50:11.411217: Pseudo dice [0.0, 0.0, 0.7939, 0.2879, 0.5814, 0.6748, 0.7532] +2026-04-11 17:50:11.413668: Epoch time: 101.94 s +2026-04-11 17:50:12.543387: +2026-04-11 17:50:12.545804: Epoch 966 +2026-04-11 17:50:12.547572: Current learning rate: 0.0078 +2026-04-11 17:51:54.028767: train_loss -0.3906 +2026-04-11 17:51:54.036556: val_loss -0.3614 +2026-04-11 17:51:54.038514: Pseudo dice [0.0, 0.0, 0.8683, 0.3284, 0.56, 0.8905, 0.913] +2026-04-11 17:51:54.040507: Epoch time: 101.49 s +2026-04-11 17:51:55.177798: +2026-04-11 17:51:55.179618: Epoch 967 +2026-04-11 17:51:55.181183: Current learning rate: 0.0078 +2026-04-11 17:53:37.124029: train_loss -0.3946 +2026-04-11 17:53:37.131104: val_loss -0.3692 +2026-04-11 17:53:37.133806: Pseudo dice [0.0, 0.0, 0.7943, 0.7582, 0.4811, 0.693, 0.8233] +2026-04-11 17:53:37.136516: Epoch time: 101.95 s +2026-04-11 17:53:38.286258: +2026-04-11 17:53:38.288382: Epoch 968 +2026-04-11 17:53:38.290235: Current learning rate: 0.00779 +2026-04-11 17:55:20.185941: train_loss -0.3938 +2026-04-11 17:55:20.192190: val_loss -0.3541 +2026-04-11 17:55:20.194472: Pseudo dice [0.0, 0.0, 0.7129, 0.0, 0.5204, 0.3918, 0.7543] +2026-04-11 17:55:20.197146: Epoch time: 101.9 s +2026-04-11 17:55:21.330995: +2026-04-11 17:55:21.333230: Epoch 969 +2026-04-11 17:55:21.334766: Current learning rate: 0.00779 +2026-04-11 17:57:03.173276: train_loss -0.3777 +2026-04-11 17:57:03.180008: val_loss -0.2942 +2026-04-11 17:57:03.182008: Pseudo dice [0.0, 0.0, 0.6112, 0.5084, 0.3963, 0.6828, 0.8812] +2026-04-11 17:57:03.185018: Epoch time: 101.85 s +2026-04-11 17:57:04.332919: +2026-04-11 17:57:04.334894: Epoch 970 +2026-04-11 17:57:04.336579: Current learning rate: 0.00779 +2026-04-11 17:58:46.115298: train_loss -0.3814 +2026-04-11 17:58:46.121820: val_loss -0.3541 +2026-04-11 17:58:46.123555: Pseudo dice [0.0, 0.0, 0.8679, 0.5472, 0.401, 0.5253, 0.6161] +2026-04-11 17:58:46.126179: Epoch time: 101.79 s +2026-04-11 17:58:47.264758: +2026-04-11 17:58:47.266605: Epoch 971 +2026-04-11 17:58:47.268239: Current learning rate: 0.00779 +2026-04-11 18:00:28.915064: train_loss -0.3881 +2026-04-11 18:00:28.922003: val_loss -0.3037 +2026-04-11 18:00:28.924045: Pseudo dice [0.0, 0.0, 0.4907, 0.3117, 0.4158, 0.8147, 0.7912] +2026-04-11 18:00:28.926471: Epoch time: 101.65 s +2026-04-11 18:00:30.115177: +2026-04-11 18:00:30.117092: Epoch 972 +2026-04-11 18:00:30.118737: Current learning rate: 0.00778 +2026-04-11 18:02:11.792510: train_loss -0.3847 +2026-04-11 18:02:11.799054: val_loss -0.3452 +2026-04-11 18:02:11.801491: Pseudo dice [0.0, 0.0, 0.7307, 0.4754, 0.4335, 0.8256, 0.8523] +2026-04-11 18:02:11.803655: Epoch time: 101.68 s +2026-04-11 18:02:12.952468: +2026-04-11 18:02:12.954571: Epoch 973 +2026-04-11 18:02:12.956299: Current learning rate: 0.00778 +2026-04-11 18:03:54.561673: train_loss -0.38 +2026-04-11 18:03:54.568417: val_loss -0.3253 +2026-04-11 18:03:54.570587: Pseudo dice [0.0, 0.0, 0.67, 0.1269, 0.4352, 0.6017, 0.8757] +2026-04-11 18:03:54.573012: Epoch time: 101.61 s +2026-04-11 18:03:55.714759: +2026-04-11 18:03:55.717091: Epoch 974 +2026-04-11 18:03:55.719214: Current learning rate: 0.00778 +2026-04-11 18:05:37.521603: train_loss -0.3753 +2026-04-11 18:05:37.527425: val_loss -0.3518 +2026-04-11 18:05:37.530152: Pseudo dice [0.0, 0.0, 0.5671, 0.0962, 0.6121, 0.5268, 0.9001] +2026-04-11 18:05:37.532402: Epoch time: 101.81 s +2026-04-11 18:05:39.834728: +2026-04-11 18:05:39.836647: Epoch 975 +2026-04-11 18:05:39.838171: Current learning rate: 0.00778 +2026-04-11 18:07:21.814658: train_loss -0.3981 +2026-04-11 18:07:21.820873: val_loss -0.3631 +2026-04-11 18:07:21.823127: Pseudo dice [0.0, 0.0, 0.7492, 0.6928, 0.5987, 0.8285, 0.678] +2026-04-11 18:07:21.825561: Epoch time: 101.98 s +2026-04-11 18:07:22.955344: +2026-04-11 18:07:22.957609: Epoch 976 +2026-04-11 18:07:22.959764: Current learning rate: 0.00777 +2026-04-11 18:09:04.456744: train_loss -0.3861 +2026-04-11 18:09:04.463034: val_loss -0.332 +2026-04-11 18:09:04.464777: Pseudo dice [0.0, 0.0, 0.8091, 0.605, 0.4385, 0.5728, 0.462] +2026-04-11 18:09:04.466766: Epoch time: 101.5 s +2026-04-11 18:09:05.620856: +2026-04-11 18:09:05.623052: Epoch 977 +2026-04-11 18:09:05.624936: Current learning rate: 0.00777 +2026-04-11 18:10:47.188058: train_loss -0.3703 +2026-04-11 18:10:47.194227: val_loss -0.3039 +2026-04-11 18:10:47.196452: Pseudo dice [0.0, 0.0, 0.7745, 0.4732, 0.5367, 0.0861, 0.2456] +2026-04-11 18:10:47.198833: Epoch time: 101.57 s +2026-04-11 18:10:48.341556: +2026-04-11 18:10:48.343735: Epoch 978 +2026-04-11 18:10:48.345602: Current learning rate: 0.00777 +2026-04-11 18:12:30.007002: train_loss -0.3807 +2026-04-11 18:12:30.014011: val_loss -0.3408 +2026-04-11 18:12:30.015738: Pseudo dice [0.0, 0.0, 0.6959, 0.5392, 0.4531, 0.4251, 0.7056] +2026-04-11 18:12:30.017902: Epoch time: 101.67 s +2026-04-11 18:12:31.192836: +2026-04-11 18:12:31.194592: Epoch 979 +2026-04-11 18:12:31.197119: Current learning rate: 0.00777 +2026-04-11 18:14:12.813004: train_loss -0.3773 +2026-04-11 18:14:12.819202: val_loss -0.3276 +2026-04-11 18:14:12.821681: Pseudo dice [0.0, 0.0, 0.7608, 0.0286, 0.2958, 0.1059, 0.7592] +2026-04-11 18:14:12.824017: Epoch time: 101.62 s +2026-04-11 18:14:13.993213: +2026-04-11 18:14:13.994928: Epoch 980 +2026-04-11 18:14:13.996501: Current learning rate: 0.00777 +2026-04-11 18:15:55.508626: train_loss -0.3735 +2026-04-11 18:15:55.514155: val_loss -0.3211 +2026-04-11 18:15:55.516095: Pseudo dice [0.0, 0.0, 0.4599, 0.5608, 0.3032, 0.7352, 0.8124] +2026-04-11 18:15:55.518368: Epoch time: 101.52 s +2026-04-11 18:15:56.703053: +2026-04-11 18:15:56.705674: Epoch 981 +2026-04-11 18:15:56.707492: Current learning rate: 0.00776 +2026-04-11 18:17:38.384947: train_loss -0.3871 +2026-04-11 18:17:38.390391: val_loss -0.3403 +2026-04-11 18:17:38.392712: Pseudo dice [0.0, 0.0, 0.728, 0.5415, 0.5012, 0.4901, 0.5947] +2026-04-11 18:17:38.394949: Epoch time: 101.69 s +2026-04-11 18:17:39.542258: +2026-04-11 18:17:39.543996: Epoch 982 +2026-04-11 18:17:39.545704: Current learning rate: 0.00776 +2026-04-11 18:19:21.039997: train_loss -0.319 +2026-04-11 18:19:21.045799: val_loss -0.354 +2026-04-11 18:19:21.047606: Pseudo dice [0.0, 0.0, 0.5906, 0.2699, 0.5188, 0.3582, 0.7855] +2026-04-11 18:19:21.049869: Epoch time: 101.5 s +2026-04-11 18:19:22.195135: +2026-04-11 18:19:22.197323: Epoch 983 +2026-04-11 18:19:22.199314: Current learning rate: 0.00776 +2026-04-11 18:21:04.519743: train_loss -0.3924 +2026-04-11 18:21:04.526088: val_loss -0.3219 +2026-04-11 18:21:04.528048: Pseudo dice [0.0, 0.0, 0.7218, 0.1429, 0.2837, 0.5527, 0.912] +2026-04-11 18:21:04.530613: Epoch time: 102.33 s +2026-04-11 18:21:05.665627: +2026-04-11 18:21:05.668200: Epoch 984 +2026-04-11 18:21:05.670522: Current learning rate: 0.00776 +2026-04-11 18:22:47.324703: train_loss -0.3836 +2026-04-11 18:22:47.330705: val_loss -0.3506 +2026-04-11 18:22:47.332806: Pseudo dice [0.0, 0.0, 0.5133, 0.0517, 0.5154, 0.7384, 0.7886] +2026-04-11 18:22:47.334843: Epoch time: 101.66 s +2026-04-11 18:22:48.471721: +2026-04-11 18:22:48.474571: Epoch 985 +2026-04-11 18:22:48.476467: Current learning rate: 0.00775 +2026-04-11 18:24:30.033964: train_loss -0.3772 +2026-04-11 18:24:30.040973: val_loss -0.3654 +2026-04-11 18:24:30.042921: Pseudo dice [0.0, 0.0, 0.6959, 0.44, 0.4519, 0.5779, 0.7244] +2026-04-11 18:24:30.045088: Epoch time: 101.57 s +2026-04-11 18:24:31.185440: +2026-04-11 18:24:31.187655: Epoch 986 +2026-04-11 18:24:31.189784: Current learning rate: 0.00775 +2026-04-11 18:26:12.813445: train_loss -0.3888 +2026-04-11 18:26:12.818697: val_loss -0.348 +2026-04-11 18:26:12.820485: Pseudo dice [0.0, 0.0, 0.8282, 0.6777, 0.4616, 0.5881, 0.5001] +2026-04-11 18:26:12.822839: Epoch time: 101.63 s +2026-04-11 18:26:13.954943: +2026-04-11 18:26:13.956713: Epoch 987 +2026-04-11 18:26:13.958274: Current learning rate: 0.00775 +2026-04-11 18:27:55.852260: train_loss -0.3744 +2026-04-11 18:27:55.858857: val_loss -0.3465 +2026-04-11 18:27:55.860645: Pseudo dice [0.0, 0.0, 0.615, 0.3875, 0.4049, 0.4645, 0.7184] +2026-04-11 18:27:55.862954: Epoch time: 101.9 s +2026-04-11 18:27:56.990326: +2026-04-11 18:27:56.992115: Epoch 988 +2026-04-11 18:27:56.993510: Current learning rate: 0.00775 +2026-04-11 18:29:38.729233: train_loss -0.3566 +2026-04-11 18:29:38.735667: val_loss -0.3318 +2026-04-11 18:29:38.738256: Pseudo dice [0.0, 0.0, 0.7942, 0.397, 0.5783, 0.792, 0.8557] +2026-04-11 18:29:38.740716: Epoch time: 101.74 s +2026-04-11 18:29:39.871789: +2026-04-11 18:29:39.874298: Epoch 989 +2026-04-11 18:29:39.876026: Current learning rate: 0.00774 +2026-04-11 18:31:21.614906: train_loss -0.3854 +2026-04-11 18:31:21.622112: val_loss -0.3372 +2026-04-11 18:31:21.624598: Pseudo dice [0.0, 0.0, 0.6971, 0.4735, 0.446, 0.6807, 0.5639] +2026-04-11 18:31:21.627293: Epoch time: 101.75 s +2026-04-11 18:31:22.752816: +2026-04-11 18:31:22.754741: Epoch 990 +2026-04-11 18:31:22.756646: Current learning rate: 0.00774 +2026-04-11 18:33:04.568009: train_loss -0.3965 +2026-04-11 18:33:04.574496: val_loss -0.3642 +2026-04-11 18:33:04.576396: Pseudo dice [0.0, 0.0, 0.7042, 0.393, 0.4942, 0.6874, 0.8004] +2026-04-11 18:33:04.578964: Epoch time: 101.82 s +2026-04-11 18:33:05.721086: +2026-04-11 18:33:05.723237: Epoch 991 +2026-04-11 18:33:05.724926: Current learning rate: 0.00774 +2026-04-11 18:34:47.257721: train_loss -0.3837 +2026-04-11 18:34:47.263970: val_loss -0.3534 +2026-04-11 18:34:47.265989: Pseudo dice [0.0, 0.0, 0.7093, 0.4084, 0.2332, 0.7676, 0.8346] +2026-04-11 18:34:47.268218: Epoch time: 101.54 s +2026-04-11 18:34:48.389838: +2026-04-11 18:34:48.391814: Epoch 992 +2026-04-11 18:34:48.393610: Current learning rate: 0.00774 +2026-04-11 18:36:29.925756: train_loss -0.3759 +2026-04-11 18:36:29.931522: val_loss -0.3431 +2026-04-11 18:36:29.934193: Pseudo dice [0.0, 0.0, 0.6759, 0.7665, 0.4262, 0.5916, 0.6291] +2026-04-11 18:36:29.936857: Epoch time: 101.54 s +2026-04-11 18:36:31.076638: +2026-04-11 18:36:31.078336: Epoch 993 +2026-04-11 18:36:31.079859: Current learning rate: 0.00774 +2026-04-11 18:38:12.666081: train_loss -0.3758 +2026-04-11 18:38:12.672437: val_loss -0.3853 +2026-04-11 18:38:12.674883: Pseudo dice [0.0, 0.0, 0.8227, 0.7774, 0.6025, 0.7403, 0.8472] +2026-04-11 18:38:12.676879: Epoch time: 101.59 s +2026-04-11 18:38:13.794033: +2026-04-11 18:38:13.796810: Epoch 994 +2026-04-11 18:38:13.799486: Current learning rate: 0.00773 +2026-04-11 18:39:55.460319: train_loss -0.3769 +2026-04-11 18:39:55.466177: val_loss -0.3232 +2026-04-11 18:39:55.468160: Pseudo dice [0.0, 0.0, 0.7281, 0.8112, 0.4941, 0.6732, 0.7285] +2026-04-11 18:39:55.470682: Epoch time: 101.67 s +2026-04-11 18:39:56.611344: +2026-04-11 18:39:56.612965: Epoch 995 +2026-04-11 18:39:56.614354: Current learning rate: 0.00773 +2026-04-11 18:41:38.334951: train_loss -0.3728 +2026-04-11 18:41:38.340850: val_loss -0.3448 +2026-04-11 18:41:38.343105: Pseudo dice [0.0, 0.0, 0.7371, 0.7267, 0.628, 0.4994, 0.8236] +2026-04-11 18:41:38.345143: Epoch time: 101.73 s +2026-04-11 18:41:40.618007: +2026-04-11 18:41:40.619899: Epoch 996 +2026-04-11 18:41:40.621424: Current learning rate: 0.00773 +2026-04-11 18:43:22.454319: train_loss -0.3931 +2026-04-11 18:43:22.460459: val_loss -0.3563 +2026-04-11 18:43:22.463160: Pseudo dice [0.0, 0.0, 0.8089, 0.515, 0.383, 0.6694, 0.8724] +2026-04-11 18:43:22.466086: Epoch time: 101.84 s +2026-04-11 18:43:23.602298: +2026-04-11 18:43:23.604336: Epoch 997 +2026-04-11 18:43:23.605762: Current learning rate: 0.00773 +2026-04-11 18:45:05.166114: train_loss -0.3463 +2026-04-11 18:45:05.172595: val_loss -0.3139 +2026-04-11 18:45:05.174542: Pseudo dice [0.0, 0.0, 0.6937, 0.4246, 0.3948, 0.5446, 0.7645] +2026-04-11 18:45:05.176893: Epoch time: 101.57 s +2026-04-11 18:45:06.325891: +2026-04-11 18:45:06.327598: Epoch 998 +2026-04-11 18:45:06.329096: Current learning rate: 0.00772 +2026-04-11 18:46:48.298951: train_loss -0.3539 +2026-04-11 18:46:48.305863: val_loss -0.2863 +2026-04-11 18:46:48.308041: Pseudo dice [0.0, 0.0, 0.4833, 0.3782, 0.4404, 0.0591, 0.5158] +2026-04-11 18:46:48.310689: Epoch time: 101.98 s +2026-04-11 18:46:49.452626: +2026-04-11 18:46:49.454763: Epoch 999 +2026-04-11 18:46:49.456734: Current learning rate: 0.00772 +2026-04-11 18:48:31.540627: train_loss -0.3519 +2026-04-11 18:48:31.546571: val_loss -0.326 +2026-04-11 18:48:31.548822: Pseudo dice [0.0, 0.0, 0.7109, 0.5503, 0.4869, 0.4234, 0.6976] +2026-04-11 18:48:31.551451: Epoch time: 102.09 s +2026-04-11 18:48:34.398465: +2026-04-11 18:48:34.400154: Epoch 1000 +2026-04-11 18:48:34.401723: Current learning rate: 0.00772 +2026-04-11 18:50:16.240363: train_loss -0.348 +2026-04-11 18:50:16.246718: val_loss -0.3521 +2026-04-11 18:50:16.248869: Pseudo dice [0.0, 0.0, 0.6613, 0.272, 0.5362, 0.524, 0.764] +2026-04-11 18:50:16.251219: Epoch time: 101.84 s +2026-04-11 18:50:17.380539: +2026-04-11 18:50:17.382823: Epoch 1001 +2026-04-11 18:50:17.384822: Current learning rate: 0.00772 +2026-04-11 18:51:59.301229: train_loss -0.3649 +2026-04-11 18:51:59.307529: val_loss -0.3142 +2026-04-11 18:51:59.310086: Pseudo dice [0.0, 0.0, 0.5675, 0.2428, 0.5164, 0.4502, 0.5266] +2026-04-11 18:51:59.312720: Epoch time: 101.92 s +2026-04-11 18:52:00.512840: +2026-04-11 18:52:00.514892: Epoch 1002 +2026-04-11 18:52:00.516647: Current learning rate: 0.00771 +2026-04-11 18:53:42.633281: train_loss -0.3687 +2026-04-11 18:53:42.639384: val_loss -0.33 +2026-04-11 18:53:42.641700: Pseudo dice [0.0, 0.0, 0.7421, 0.053, 0.2613, 0.624, 0.6388] +2026-04-11 18:53:42.644009: Epoch time: 102.12 s +2026-04-11 18:53:43.773682: +2026-04-11 18:53:43.775783: Epoch 1003 +2026-04-11 18:53:43.777417: Current learning rate: 0.00771 +2026-04-11 18:55:25.170503: train_loss -0.3794 +2026-04-11 18:55:25.177030: val_loss -0.3506 +2026-04-11 18:55:25.178872: Pseudo dice [0.0, 0.0, 0.7751, 0.345, 0.411, 0.5524, 0.913] +2026-04-11 18:55:25.181408: Epoch time: 101.4 s +2026-04-11 18:55:26.326018: +2026-04-11 18:55:26.328023: Epoch 1004 +2026-04-11 18:55:26.329685: Current learning rate: 0.00771 +2026-04-11 18:57:07.613000: train_loss -0.3697 +2026-04-11 18:57:07.618999: val_loss -0.3537 +2026-04-11 18:57:07.621974: Pseudo dice [0.0, 0.0, 0.7079, 0.0215, 0.5529, 0.6753, 0.6198] +2026-04-11 18:57:07.624271: Epoch time: 101.29 s +2026-04-11 18:57:08.778101: +2026-04-11 18:57:08.780975: Epoch 1005 +2026-04-11 18:57:08.782864: Current learning rate: 0.00771 +2026-04-11 18:58:50.507642: train_loss -0.3662 +2026-04-11 18:58:50.513592: val_loss -0.3191 +2026-04-11 18:58:50.515620: Pseudo dice [0.0, 0.0, 0.7655, 0.1057, 0.4803, 0.6323, 0.2808] +2026-04-11 18:58:50.517718: Epoch time: 101.73 s +2026-04-11 18:58:51.668356: +2026-04-11 18:58:51.670638: Epoch 1006 +2026-04-11 18:58:51.672243: Current learning rate: 0.0077 +2026-04-11 19:00:33.191222: train_loss -0.37 +2026-04-11 19:00:33.201454: val_loss -0.3414 +2026-04-11 19:00:33.204218: Pseudo dice [0.0, 0.0, 0.6291, 0.218, 0.575, 0.7213, 0.4651] +2026-04-11 19:00:33.207420: Epoch time: 101.53 s +2026-04-11 19:00:34.389773: +2026-04-11 19:00:34.391742: Epoch 1007 +2026-04-11 19:00:34.393597: Current learning rate: 0.0077 +2026-04-11 19:02:16.233214: train_loss -0.3884 +2026-04-11 19:02:16.239168: val_loss -0.3195 +2026-04-11 19:02:16.241243: Pseudo dice [0.0, 0.0, 0.806, 0.7892, 0.4911, 0.3288, 0.7236] +2026-04-11 19:02:16.243262: Epoch time: 101.85 s +2026-04-11 19:02:17.387036: +2026-04-11 19:02:17.388806: Epoch 1008 +2026-04-11 19:02:17.390243: Current learning rate: 0.0077 +2026-04-11 19:03:59.377174: train_loss -0.3817 +2026-04-11 19:03:59.383960: val_loss -0.2852 +2026-04-11 19:03:59.386133: Pseudo dice [0.0, 0.0, 0.456, 0.0207, 0.4153, 0.2419, 0.3643] +2026-04-11 19:03:59.388460: Epoch time: 101.99 s +2026-04-11 19:04:00.534546: +2026-04-11 19:04:00.536978: Epoch 1009 +2026-04-11 19:04:00.538489: Current learning rate: 0.0077 +2026-04-11 19:05:42.233765: train_loss -0.387 +2026-04-11 19:05:42.241806: val_loss -0.3369 +2026-04-11 19:05:42.243983: Pseudo dice [0.0, 0.0, 0.6711, 0.5386, 0.5271, 0.3441, 0.2998] +2026-04-11 19:05:42.246736: Epoch time: 101.7 s +2026-04-11 19:05:43.395304: +2026-04-11 19:05:43.398253: Epoch 1010 +2026-04-11 19:05:43.399831: Current learning rate: 0.0077 +2026-04-11 19:07:24.936956: train_loss -0.3726 +2026-04-11 19:07:24.943067: val_loss -0.3659 +2026-04-11 19:07:24.945808: Pseudo dice [0.0, 0.0, 0.8293, 0.364, 0.549, 0.5656, 0.7767] +2026-04-11 19:07:24.948241: Epoch time: 101.54 s +2026-04-11 19:07:26.086753: +2026-04-11 19:07:26.088844: Epoch 1011 +2026-04-11 19:07:26.090502: Current learning rate: 0.00769 +2026-04-11 19:09:07.648821: train_loss -0.3761 +2026-04-11 19:09:07.657286: val_loss -0.335 +2026-04-11 19:09:07.659691: Pseudo dice [0.0, 0.0, 0.5307, 0.2704, 0.5781, 0.3862, 0.2894] +2026-04-11 19:09:07.662613: Epoch time: 101.57 s +2026-04-11 19:09:08.819482: +2026-04-11 19:09:08.821929: Epoch 1012 +2026-04-11 19:09:08.823749: Current learning rate: 0.00769 +2026-04-11 19:10:50.720675: train_loss -0.3703 +2026-04-11 19:10:50.726043: val_loss -0.359 +2026-04-11 19:10:50.728357: Pseudo dice [0.0, 0.0, 0.896, 0.6167, 0.6029, 0.3907, 0.7274] +2026-04-11 19:10:50.731365: Epoch time: 101.9 s +2026-04-11 19:10:51.890916: +2026-04-11 19:10:51.892672: Epoch 1013 +2026-04-11 19:10:51.894168: Current learning rate: 0.00769 +2026-04-11 19:12:33.267351: train_loss -0.3913 +2026-04-11 19:12:33.274560: val_loss -0.3165 +2026-04-11 19:12:33.277407: Pseudo dice [0.0, 0.0, 0.4721, 0.4497, 0.4247, 0.5929, 0.1315] +2026-04-11 19:12:33.279520: Epoch time: 101.38 s +2026-04-11 19:12:34.429192: +2026-04-11 19:12:34.431599: Epoch 1014 +2026-04-11 19:12:34.433707: Current learning rate: 0.00769 +2026-04-11 19:14:16.080620: train_loss -0.3731 +2026-04-11 19:14:16.087486: val_loss -0.3051 +2026-04-11 19:14:16.089199: Pseudo dice [0.0, 0.0, 0.7104, 0.4935, 0.5047, 0.3693, 0.8054] +2026-04-11 19:14:16.091459: Epoch time: 101.65 s +2026-04-11 19:14:17.234826: +2026-04-11 19:14:17.236856: Epoch 1015 +2026-04-11 19:14:17.238449: Current learning rate: 0.00768 +2026-04-11 19:15:59.176152: train_loss -0.3874 +2026-04-11 19:15:59.182209: val_loss -0.3318 +2026-04-11 19:15:59.184193: Pseudo dice [0.0, 0.0, 0.5761, 0.6451, 0.4033, 0.4334, 0.622] +2026-04-11 19:15:59.187274: Epoch time: 101.94 s +2026-04-11 19:16:01.494524: +2026-04-11 19:16:01.496282: Epoch 1016 +2026-04-11 19:16:01.497841: Current learning rate: 0.00768 +2026-04-11 19:17:42.990810: train_loss -0.3947 +2026-04-11 19:17:42.997502: val_loss -0.3027 +2026-04-11 19:17:43.001083: Pseudo dice [0.0, 0.0, 0.3862, 0.2067, 0.4573, 0.401, 0.7446] +2026-04-11 19:17:43.003484: Epoch time: 101.5 s +2026-04-11 19:17:44.146321: +2026-04-11 19:17:44.148405: Epoch 1017 +2026-04-11 19:17:44.150574: Current learning rate: 0.00768 +2026-04-11 19:19:26.181912: train_loss -0.3898 +2026-04-11 19:19:26.188187: val_loss -0.3499 +2026-04-11 19:19:26.190264: Pseudo dice [0.0, 0.0, 0.8583, 0.6303, 0.5678, 0.8278, 0.8662] +2026-04-11 19:19:26.193067: Epoch time: 102.04 s +2026-04-11 19:19:27.325059: +2026-04-11 19:19:27.326906: Epoch 1018 +2026-04-11 19:19:27.328530: Current learning rate: 0.00768 +2026-04-11 19:21:09.023936: train_loss -0.3849 +2026-04-11 19:21:09.029962: val_loss -0.3488 +2026-04-11 19:21:09.032666: Pseudo dice [0.0, 0.0, 0.7804, 0.5542, 0.4099, 0.7517, 0.7055] +2026-04-11 19:21:09.034795: Epoch time: 101.7 s +2026-04-11 19:21:10.168412: +2026-04-11 19:21:10.170055: Epoch 1019 +2026-04-11 19:21:10.171566: Current learning rate: 0.00767 +2026-04-11 19:22:52.405597: train_loss -0.3856 +2026-04-11 19:22:52.411893: val_loss -0.3569 +2026-04-11 19:22:52.413727: Pseudo dice [0.0, 0.0, 0.6652, 0.2412, 0.4335, 0.6007, 0.7928] +2026-04-11 19:22:52.415806: Epoch time: 102.24 s +2026-04-11 19:22:53.527832: +2026-04-11 19:22:53.529520: Epoch 1020 +2026-04-11 19:22:53.531059: Current learning rate: 0.00767 +2026-04-11 19:24:35.354277: train_loss -0.3983 +2026-04-11 19:24:35.373451: val_loss -0.3578 +2026-04-11 19:24:35.378568: Pseudo dice [0.0, 0.0, 0.7729, 0.4995, 0.5613, 0.7172, 0.8156] +2026-04-11 19:24:35.382976: Epoch time: 101.83 s +2026-04-11 19:24:36.516387: +2026-04-11 19:24:36.518060: Epoch 1021 +2026-04-11 19:24:36.519628: Current learning rate: 0.00767 +2026-04-11 19:26:18.083524: train_loss -0.3624 +2026-04-11 19:26:18.090559: val_loss -0.3323 +2026-04-11 19:26:18.092655: Pseudo dice [0.0, 0.0, 0.7966, 0.5729, 0.5188, 0.788, 0.5111] +2026-04-11 19:26:18.094882: Epoch time: 101.57 s +2026-04-11 19:26:19.214605: +2026-04-11 19:26:19.216473: Epoch 1022 +2026-04-11 19:26:19.218425: Current learning rate: 0.00767 +2026-04-11 19:28:00.745502: train_loss -0.3815 +2026-04-11 19:28:00.751693: val_loss -0.3663 +2026-04-11 19:28:00.754054: Pseudo dice [0.0, 0.0, 0.7001, 0.8642, 0.603, 0.7881, 0.6172] +2026-04-11 19:28:00.756630: Epoch time: 101.53 s +2026-04-11 19:28:01.916924: +2026-04-11 19:28:01.919513: Epoch 1023 +2026-04-11 19:28:01.923152: Current learning rate: 0.00767 +2026-04-11 19:29:43.192264: train_loss -0.3955 +2026-04-11 19:29:43.199092: val_loss -0.3533 +2026-04-11 19:29:43.201257: Pseudo dice [0.0, 0.0, 0.7858, 0.4578, 0.5119, 0.1306, 0.8344] +2026-04-11 19:29:43.203630: Epoch time: 101.28 s +2026-04-11 19:29:44.339989: +2026-04-11 19:29:44.342074: Epoch 1024 +2026-04-11 19:29:44.343968: Current learning rate: 0.00766 +2026-04-11 19:31:25.980267: train_loss -0.4102 +2026-04-11 19:31:25.988361: val_loss -0.3367 +2026-04-11 19:31:25.990298: Pseudo dice [0.0, 0.0, 0.656, 0.2928, 0.6336, 0.5591, 0.8228] +2026-04-11 19:31:25.993720: Epoch time: 101.64 s +2026-04-11 19:31:27.115112: +2026-04-11 19:31:27.116961: Epoch 1025 +2026-04-11 19:31:27.118713: Current learning rate: 0.00766 +2026-04-11 19:33:08.688034: train_loss -0.4038 +2026-04-11 19:33:08.696462: val_loss -0.3295 +2026-04-11 19:33:08.698441: Pseudo dice [0.0, 0.0, 0.782, 0.6163, 0.5614, 0.4273, 0.7336] +2026-04-11 19:33:08.701831: Epoch time: 101.58 s +2026-04-11 19:33:09.828469: +2026-04-11 19:33:09.830444: Epoch 1026 +2026-04-11 19:33:09.832060: Current learning rate: 0.00766 +2026-04-11 19:34:51.216885: train_loss -0.3909 +2026-04-11 19:34:51.223582: val_loss -0.3337 +2026-04-11 19:34:51.225875: Pseudo dice [0.0, 0.0, 0.7306, 0.3502, 0.4639, 0.6434, 0.8802] +2026-04-11 19:34:51.228084: Epoch time: 101.39 s +2026-04-11 19:34:52.365776: +2026-04-11 19:34:52.367884: Epoch 1027 +2026-04-11 19:34:52.369776: Current learning rate: 0.00766 +2026-04-11 19:36:33.858673: train_loss -0.3844 +2026-04-11 19:36:33.864714: val_loss -0.3481 +2026-04-11 19:36:33.866512: Pseudo dice [0.0, 0.0, 0.7305, 0.1372, 0.3816, 0.6358, 0.8841] +2026-04-11 19:36:33.869460: Epoch time: 101.5 s +2026-04-11 19:36:35.044399: +2026-04-11 19:36:35.046851: Epoch 1028 +2026-04-11 19:36:35.048731: Current learning rate: 0.00765 +2026-04-11 19:38:16.904268: train_loss -0.3933 +2026-04-11 19:38:16.909538: val_loss -0.3415 +2026-04-11 19:38:16.911321: Pseudo dice [0.0, 0.0, 0.8753, 0.7115, 0.5326, 0.4682, 0.8251] +2026-04-11 19:38:16.913191: Epoch time: 101.86 s +2026-04-11 19:38:18.068793: +2026-04-11 19:38:18.070520: Epoch 1029 +2026-04-11 19:38:18.072064: Current learning rate: 0.00765 +2026-04-11 19:39:59.357822: train_loss -0.4042 +2026-04-11 19:39:59.363995: val_loss -0.3264 +2026-04-11 19:39:59.366245: Pseudo dice [0.0, 0.0, 0.7197, 0.7087, 0.4155, 0.6334, 0.8033] +2026-04-11 19:39:59.368911: Epoch time: 101.29 s +2026-04-11 19:40:00.495674: +2026-04-11 19:40:00.497454: Epoch 1030 +2026-04-11 19:40:00.498956: Current learning rate: 0.00765 +2026-04-11 19:41:41.792408: train_loss -0.3705 +2026-04-11 19:41:41.798504: val_loss -0.3147 +2026-04-11 19:41:41.801108: Pseudo dice [0.0, 0.0, 0.8483, 0.3572, 0.2373, 0.5908, 0.5449] +2026-04-11 19:41:41.803730: Epoch time: 101.3 s +2026-04-11 19:41:42.953248: +2026-04-11 19:41:42.955554: Epoch 1031 +2026-04-11 19:41:42.957166: Current learning rate: 0.00765 +2026-04-11 19:43:24.413809: train_loss -0.3704 +2026-04-11 19:43:24.419825: val_loss -0.3294 +2026-04-11 19:43:24.421712: Pseudo dice [0.0, 0.0, 0.4085, 0.2891, 0.5495, 0.6879, 0.6636] +2026-04-11 19:43:24.423737: Epoch time: 101.46 s +2026-04-11 19:43:25.568345: +2026-04-11 19:43:25.570247: Epoch 1032 +2026-04-11 19:43:25.571779: Current learning rate: 0.00764 +2026-04-11 19:45:06.941928: train_loss -0.3805 +2026-04-11 19:45:06.949261: val_loss -0.3576 +2026-04-11 19:45:06.951106: Pseudo dice [0.0, 0.0, 0.7173, 0.5078, 0.4862, 0.7448, 0.8945] +2026-04-11 19:45:06.953022: Epoch time: 101.38 s +2026-04-11 19:45:08.091631: +2026-04-11 19:45:08.093457: Epoch 1033 +2026-04-11 19:45:08.095170: Current learning rate: 0.00764 +2026-04-11 19:46:49.224234: train_loss -0.3892 +2026-04-11 19:46:49.235301: val_loss -0.3423 +2026-04-11 19:46:49.237438: Pseudo dice [0.0, 0.0, 0.5969, 0.6926, 0.402, 0.5569, 0.4187] +2026-04-11 19:46:49.239462: Epoch time: 101.13 s +2026-04-11 19:46:50.416320: +2026-04-11 19:46:50.418134: Epoch 1034 +2026-04-11 19:46:50.419943: Current learning rate: 0.00764 +2026-04-11 19:48:31.998232: train_loss -0.3854 +2026-04-11 19:48:32.004218: val_loss -0.3458 +2026-04-11 19:48:32.007030: Pseudo dice [0.0, 0.0, 0.7065, 0.2482, 0.4034, 0.8695, 0.367] +2026-04-11 19:48:32.010288: Epoch time: 101.59 s +2026-04-11 19:48:33.151396: +2026-04-11 19:48:33.154470: Epoch 1035 +2026-04-11 19:48:33.156823: Current learning rate: 0.00764 +2026-04-11 19:50:14.556718: train_loss -0.3814 +2026-04-11 19:50:14.564106: val_loss -0.3153 +2026-04-11 19:50:14.566552: Pseudo dice [0.0, 0.0, 0.2808, 0.6939, 0.4089, 0.8129, 0.6231] +2026-04-11 19:50:14.569176: Epoch time: 101.41 s +2026-04-11 19:50:15.716539: +2026-04-11 19:50:15.718422: Epoch 1036 +2026-04-11 19:50:15.720349: Current learning rate: 0.00764 +2026-04-11 19:51:58.000896: train_loss -0.3826 +2026-04-11 19:51:58.007672: val_loss -0.355 +2026-04-11 19:51:58.009995: Pseudo dice [0.0, 0.0, 0.8086, 0.4058, 0.4786, 0.859, 0.8817] +2026-04-11 19:51:58.012266: Epoch time: 102.29 s +2026-04-11 19:51:59.122088: +2026-04-11 19:51:59.123859: Epoch 1037 +2026-04-11 19:51:59.125410: Current learning rate: 0.00763 +2026-04-11 19:53:40.518472: train_loss -0.4071 +2026-04-11 19:53:40.525093: val_loss -0.3422 +2026-04-11 19:53:40.527061: Pseudo dice [0.0, 0.0, 0.487, 0.555, 0.5011, 0.5503, 0.7022] +2026-04-11 19:53:40.529213: Epoch time: 101.4 s +2026-04-11 19:53:41.655910: +2026-04-11 19:53:41.657804: Epoch 1038 +2026-04-11 19:53:41.659421: Current learning rate: 0.00763 +2026-04-11 19:55:23.035991: train_loss -0.387 +2026-04-11 19:55:23.042464: val_loss -0.3843 +2026-04-11 19:55:23.048372: Pseudo dice [0.0, 0.0, 0.7891, 0.7208, 0.5313, 0.6391, 0.7885] +2026-04-11 19:55:23.051342: Epoch time: 101.38 s +2026-04-11 19:55:24.178423: +2026-04-11 19:55:24.180140: Epoch 1039 +2026-04-11 19:55:24.181571: Current learning rate: 0.00763 +2026-04-11 19:57:06.070460: train_loss -0.3876 +2026-04-11 19:57:06.076367: val_loss -0.3404 +2026-04-11 19:57:06.078071: Pseudo dice [0.0, 0.0, 0.6318, 0.7452, 0.6351, 0.6885, 0.5609] +2026-04-11 19:57:06.084436: Epoch time: 101.9 s +2026-04-11 19:57:07.224835: +2026-04-11 19:57:07.226472: Epoch 1040 +2026-04-11 19:57:07.228040: Current learning rate: 0.00763 +2026-04-11 19:58:48.685458: train_loss -0.3963 +2026-04-11 19:58:48.691886: val_loss -0.3473 +2026-04-11 19:58:48.693878: Pseudo dice [0.0, 0.0, 0.6957, 0.7586, 0.5092, 0.3137, 0.7899] +2026-04-11 19:58:48.696335: Epoch time: 101.46 s +2026-04-11 19:58:49.838654: +2026-04-11 19:58:49.840324: Epoch 1041 +2026-04-11 19:58:49.841851: Current learning rate: 0.00762 +2026-04-11 20:00:31.239629: train_loss -0.3862 +2026-04-11 20:00:31.248486: val_loss -0.2997 +2026-04-11 20:00:31.250325: Pseudo dice [0.0, 0.0, 0.5049, 0.35, 0.624, 0.3248, 0.1066] +2026-04-11 20:00:31.253846: Epoch time: 101.4 s +2026-04-11 20:00:32.411820: +2026-04-11 20:00:32.413616: Epoch 1042 +2026-04-11 20:00:32.415123: Current learning rate: 0.00762 +2026-04-11 20:02:13.861853: train_loss -0.364 +2026-04-11 20:02:13.868603: val_loss -0.3709 +2026-04-11 20:02:13.870919: Pseudo dice [0.0, 0.0, 0.8035, 0.5746, 0.4141, 0.625, 0.8206] +2026-04-11 20:02:13.874576: Epoch time: 101.45 s +2026-04-11 20:02:15.025122: +2026-04-11 20:02:15.027022: Epoch 1043 +2026-04-11 20:02:15.028585: Current learning rate: 0.00762 +2026-04-11 20:03:56.403664: train_loss -0.3747 +2026-04-11 20:03:56.409952: val_loss -0.2569 +2026-04-11 20:03:56.412612: Pseudo dice [0.0, 0.0, 0.7421, 0.0537, 0.3316, 0.3432, 0.3039] +2026-04-11 20:03:56.415431: Epoch time: 101.38 s +2026-04-11 20:03:57.545715: +2026-04-11 20:03:57.547716: Epoch 1044 +2026-04-11 20:03:57.549598: Current learning rate: 0.00762 +2026-04-11 20:05:39.004280: train_loss -0.349 +2026-04-11 20:05:39.011624: val_loss -0.3418 +2026-04-11 20:05:39.013596: Pseudo dice [0.0, 0.0, 0.5591, 0.3869, 0.6067, 0.4487, 0.877] +2026-04-11 20:05:39.016174: Epoch time: 101.46 s +2026-04-11 20:05:40.152212: +2026-04-11 20:05:40.154647: Epoch 1045 +2026-04-11 20:05:40.159870: Current learning rate: 0.00761 +2026-04-11 20:07:21.944291: train_loss -0.3687 +2026-04-11 20:07:21.952049: val_loss -0.3547 +2026-04-11 20:07:21.954196: Pseudo dice [0.0, 0.0, 0.766, 0.6575, 0.5502, 0.3312, 0.817] +2026-04-11 20:07:21.956555: Epoch time: 101.8 s +2026-04-11 20:07:23.100840: +2026-04-11 20:07:23.102612: Epoch 1046 +2026-04-11 20:07:23.104230: Current learning rate: 0.00761 +2026-04-11 20:09:04.506603: train_loss -0.3656 +2026-04-11 20:09:04.512539: val_loss -0.3452 +2026-04-11 20:09:04.517143: Pseudo dice [0.0, 0.0, 0.6647, 0.771, 0.6001, 0.3023, 0.6671] +2026-04-11 20:09:04.519161: Epoch time: 101.41 s +2026-04-11 20:09:05.651963: +2026-04-11 20:09:05.653903: Epoch 1047 +2026-04-11 20:09:05.655679: Current learning rate: 0.00761 +2026-04-11 20:10:47.071701: train_loss -0.3836 +2026-04-11 20:10:47.077292: val_loss -0.3067 +2026-04-11 20:10:47.079206: Pseudo dice [0.0, 0.0, 0.78, 0.3148, 0.3037, 0.1868, 0.6104] +2026-04-11 20:10:47.081248: Epoch time: 101.42 s +2026-04-11 20:10:48.256458: +2026-04-11 20:10:48.258799: Epoch 1048 +2026-04-11 20:10:48.260984: Current learning rate: 0.00761 +2026-04-11 20:12:29.502652: train_loss -0.3573 +2026-04-11 20:12:29.511535: val_loss -0.327 +2026-04-11 20:12:29.514496: Pseudo dice [0.0, 0.0, 0.6355, 0.243, 0.4176, 0.8004, 0.6489] +2026-04-11 20:12:29.516973: Epoch time: 101.25 s +2026-04-11 20:12:30.663165: +2026-04-11 20:12:30.665291: Epoch 1049 +2026-04-11 20:12:30.667008: Current learning rate: 0.00761 +2026-04-11 20:14:12.508929: train_loss -0.3645 +2026-04-11 20:14:12.515504: val_loss -0.2915 +2026-04-11 20:14:12.517783: Pseudo dice [0.0, 0.0, 0.6484, 0.248, 0.612, 0.715, 0.6808] +2026-04-11 20:14:12.520153: Epoch time: 101.85 s +2026-04-11 20:14:15.304435: +2026-04-11 20:14:15.306565: Epoch 1050 +2026-04-11 20:14:15.308166: Current learning rate: 0.0076 +2026-04-11 20:15:57.371874: train_loss -0.3801 +2026-04-11 20:15:57.378619: val_loss -0.3294 +2026-04-11 20:15:57.380622: Pseudo dice [0.0, 0.0, 0.6875, 0.2454, 0.5605, 0.6491, 0.7422] +2026-04-11 20:15:57.382995: Epoch time: 102.07 s +2026-04-11 20:15:58.531700: +2026-04-11 20:15:58.533748: Epoch 1051 +2026-04-11 20:15:58.535473: Current learning rate: 0.0076 +2026-04-11 20:17:40.175270: train_loss -0.3693 +2026-04-11 20:17:40.182039: val_loss -0.3228 +2026-04-11 20:17:40.184278: Pseudo dice [0.0, 0.0, 0.816, 0.8043, 0.4533, 0.5824, 0.6498] +2026-04-11 20:17:40.187555: Epoch time: 101.65 s +2026-04-11 20:17:41.324422: +2026-04-11 20:17:41.327246: Epoch 1052 +2026-04-11 20:17:41.329094: Current learning rate: 0.0076 +2026-04-11 20:19:22.737608: train_loss -0.3894 +2026-04-11 20:19:22.744700: val_loss -0.3572 +2026-04-11 20:19:22.747773: Pseudo dice [0.0, 0.0, 0.8138, 0.6808, 0.3852, 0.6056, 0.4595] +2026-04-11 20:19:22.750891: Epoch time: 101.42 s +2026-04-11 20:19:23.880201: +2026-04-11 20:19:23.882065: Epoch 1053 +2026-04-11 20:19:23.883869: Current learning rate: 0.0076 +2026-04-11 20:21:05.325243: train_loss -0.3634 +2026-04-11 20:21:05.332746: val_loss -0.3286 +2026-04-11 20:21:05.335367: Pseudo dice [0.0, 0.0, 0.7797, 0.2574, 0.2925, 0.5477, 0.6552] +2026-04-11 20:21:05.338311: Epoch time: 101.45 s +2026-04-11 20:21:06.480570: +2026-04-11 20:21:06.482670: Epoch 1054 +2026-04-11 20:21:06.484258: Current learning rate: 0.00759 +2026-04-11 20:22:48.204114: train_loss -0.3682 +2026-04-11 20:22:48.211081: val_loss -0.2886 +2026-04-11 20:22:48.213353: Pseudo dice [0.0, 0.0, 0.696, 0.6367, 0.5539, 0.3802, 0.6033] +2026-04-11 20:22:48.215658: Epoch time: 101.73 s +2026-04-11 20:22:49.368207: +2026-04-11 20:22:49.370231: Epoch 1055 +2026-04-11 20:22:49.371979: Current learning rate: 0.00759 +2026-04-11 20:24:31.612752: train_loss -0.3592 +2026-04-11 20:24:31.620163: val_loss -0.3275 +2026-04-11 20:24:31.622108: Pseudo dice [0.0, 0.0, 0.6309, 0.2701, 0.3678, 0.4755, 0.77] +2026-04-11 20:24:31.624615: Epoch time: 102.25 s +2026-04-11 20:24:32.763885: +2026-04-11 20:24:32.766194: Epoch 1056 +2026-04-11 20:24:32.768695: Current learning rate: 0.00759 +2026-04-11 20:26:15.584642: train_loss -0.3797 +2026-04-11 20:26:15.591569: val_loss -0.3449 +2026-04-11 20:26:15.593766: Pseudo dice [0.0, 0.0, 0.7424, 0.6827, 0.4623, 0.3479, 0.6799] +2026-04-11 20:26:15.596084: Epoch time: 102.82 s +2026-04-11 20:26:16.719894: +2026-04-11 20:26:16.722731: Epoch 1057 +2026-04-11 20:26:16.724910: Current learning rate: 0.00759 +2026-04-11 20:27:57.963042: train_loss -0.3868 +2026-04-11 20:27:57.970633: val_loss -0.3354 +2026-04-11 20:27:57.973234: Pseudo dice [0.0, 0.0, 0.8052, 0.3783, 0.5924, 0.8612, 0.7391] +2026-04-11 20:27:57.975867: Epoch time: 101.25 s +2026-04-11 20:27:59.119175: +2026-04-11 20:27:59.121023: Epoch 1058 +2026-04-11 20:27:59.122608: Current learning rate: 0.00758 +2026-04-11 20:29:40.608256: train_loss -0.4059 +2026-04-11 20:29:40.613365: val_loss -0.2987 +2026-04-11 20:29:40.615111: Pseudo dice [0.0, 0.0, 0.6702, 0.3767, 0.2406, 0.5728, 0.4968] +2026-04-11 20:29:40.617575: Epoch time: 101.49 s +2026-04-11 20:29:41.799054: +2026-04-11 20:29:41.801217: Epoch 1059 +2026-04-11 20:29:41.803008: Current learning rate: 0.00758 +2026-04-11 20:31:23.206585: train_loss -0.3817 +2026-04-11 20:31:23.212761: val_loss -0.3159 +2026-04-11 20:31:23.215125: Pseudo dice [0.0, 0.0, 0.6674, 0.8183, 0.2587, 0.4452, 0.7558] +2026-04-11 20:31:23.217171: Epoch time: 101.41 s +2026-04-11 20:31:24.451301: +2026-04-11 20:31:24.453536: Epoch 1060 +2026-04-11 20:31:24.455092: Current learning rate: 0.00758 +2026-04-11 20:33:06.085044: train_loss -0.3655 +2026-04-11 20:33:06.091116: val_loss -0.346 +2026-04-11 20:33:06.093331: Pseudo dice [0.0, 0.0, 0.6803, 0.6108, 0.4947, 0.5166, 0.8831] +2026-04-11 20:33:06.095704: Epoch time: 101.64 s +2026-04-11 20:33:07.230928: +2026-04-11 20:33:07.232721: Epoch 1061 +2026-04-11 20:33:07.234418: Current learning rate: 0.00758 +2026-04-11 20:34:48.934534: train_loss -0.3529 +2026-04-11 20:34:48.940323: val_loss -0.3033 +2026-04-11 20:34:48.943212: Pseudo dice [0.0, 0.0, 0.4559, 0.7098, 0.3077, 0.3388, 0.4107] +2026-04-11 20:34:48.945708: Epoch time: 101.71 s +2026-04-11 20:34:50.095809: +2026-04-11 20:34:50.097530: Epoch 1062 +2026-04-11 20:34:50.099073: Current learning rate: 0.00758 +2026-04-11 20:36:31.552170: train_loss -0.389 +2026-04-11 20:36:31.569337: val_loss -0.3488 +2026-04-11 20:36:31.571862: Pseudo dice [0.0, 0.0, 0.8098, 0.131, 0.4953, 0.8887, 0.69] +2026-04-11 20:36:31.574413: Epoch time: 101.46 s +2026-04-11 20:36:32.707195: +2026-04-11 20:36:32.708985: Epoch 1063 +2026-04-11 20:36:32.710756: Current learning rate: 0.00757 +2026-04-11 20:38:14.125696: train_loss -0.3732 +2026-04-11 20:38:14.131374: val_loss -0.3386 +2026-04-11 20:38:14.133399: Pseudo dice [0.0, 0.0, 0.6967, 0.7373, 0.4626, 0.5395, 0.4502] +2026-04-11 20:38:14.135819: Epoch time: 101.42 s +2026-04-11 20:38:15.277415: +2026-04-11 20:38:15.279404: Epoch 1064 +2026-04-11 20:38:15.281260: Current learning rate: 0.00757 +2026-04-11 20:39:56.969676: train_loss -0.3741 +2026-04-11 20:39:56.976396: val_loss -0.3349 +2026-04-11 20:39:56.978262: Pseudo dice [0.0, 0.0, 0.8442, 0.5724, 0.3688, 0.8252, 0.5458] +2026-04-11 20:39:56.980765: Epoch time: 101.7 s +2026-04-11 20:39:58.102955: +2026-04-11 20:39:58.104645: Epoch 1065 +2026-04-11 20:39:58.106159: Current learning rate: 0.00757 +2026-04-11 20:41:39.823424: train_loss -0.3786 +2026-04-11 20:41:39.829906: val_loss -0.3153 +2026-04-11 20:41:39.831513: Pseudo dice [0.0, 0.0, 0.8167, 0.4751, 0.5385, 0.2897, 0.4543] +2026-04-11 20:41:39.833537: Epoch time: 101.72 s +2026-04-11 20:41:40.988442: +2026-04-11 20:41:40.990155: Epoch 1066 +2026-04-11 20:41:40.991668: Current learning rate: 0.00757 +2026-04-11 20:43:22.486804: train_loss -0.3842 +2026-04-11 20:43:22.491908: val_loss -0.3239 +2026-04-11 20:43:22.494072: Pseudo dice [0.0, 0.0, 0.8201, 0.4708, 0.4763, 0.7056, 0.691] +2026-04-11 20:43:22.496270: Epoch time: 101.5 s +2026-04-11 20:43:23.634523: +2026-04-11 20:43:23.637413: Epoch 1067 +2026-04-11 20:43:23.639042: Current learning rate: 0.00756 +2026-04-11 20:45:05.296280: train_loss -0.3732 +2026-04-11 20:45:05.302160: val_loss -0.3478 +2026-04-11 20:45:05.304173: Pseudo dice [0.0, 0.0, 0.7318, 0.7444, 0.4279, 0.5873, 0.765] +2026-04-11 20:45:05.307943: Epoch time: 101.66 s +2026-04-11 20:45:06.444474: +2026-04-11 20:45:06.446260: Epoch 1068 +2026-04-11 20:45:06.448174: Current learning rate: 0.00756 +2026-04-11 20:46:47.801548: train_loss -0.3981 +2026-04-11 20:46:47.807668: val_loss -0.3531 +2026-04-11 20:46:47.810167: Pseudo dice [0.0, 0.0, 0.7506, 0.7038, 0.4827, 0.7581, 0.4468] +2026-04-11 20:46:47.813139: Epoch time: 101.36 s +2026-04-11 20:46:48.951082: +2026-04-11 20:46:48.952776: Epoch 1069 +2026-04-11 20:46:48.954301: Current learning rate: 0.00756 +2026-04-11 20:48:30.188267: train_loss -0.4023 +2026-04-11 20:48:30.195369: val_loss -0.34 +2026-04-11 20:48:30.197571: Pseudo dice [0.0, 0.0, 0.7353, 0.7037, 0.5777, 0.6553, 0.7292] +2026-04-11 20:48:30.199832: Epoch time: 101.24 s +2026-04-11 20:48:31.335622: +2026-04-11 20:48:31.337229: Epoch 1070 +2026-04-11 20:48:31.338762: Current learning rate: 0.00756 +2026-04-11 20:50:12.981241: train_loss -0.3849 +2026-04-11 20:50:12.986969: val_loss -0.3554 +2026-04-11 20:50:12.989051: Pseudo dice [0.0, 0.0, 0.7439, 0.1683, 0.2988, 0.6747, 0.8351] +2026-04-11 20:50:12.991276: Epoch time: 101.65 s +2026-04-11 20:50:14.166395: +2026-04-11 20:50:14.168126: Epoch 1071 +2026-04-11 20:50:14.169827: Current learning rate: 0.00755 +2026-04-11 20:51:55.566072: train_loss -0.4086 +2026-04-11 20:51:55.572767: val_loss -0.3642 +2026-04-11 20:51:55.575244: Pseudo dice [0.0, 0.0, 0.628, 0.6541, 0.5754, 0.6277, 0.8237] +2026-04-11 20:51:55.577201: Epoch time: 101.4 s +2026-04-11 20:51:56.711110: +2026-04-11 20:51:56.713176: Epoch 1072 +2026-04-11 20:51:56.714683: Current learning rate: 0.00755 +2026-04-11 20:53:38.009113: train_loss -0.4045 +2026-04-11 20:53:38.015240: val_loss -0.3521 +2026-04-11 20:53:38.017430: Pseudo dice [0.0, 0.0, 0.8346, 0.2466, 0.6392, 0.2012, 0.5329] +2026-04-11 20:53:38.019944: Epoch time: 101.3 s +2026-04-11 20:53:39.157194: +2026-04-11 20:53:39.159142: Epoch 1073 +2026-04-11 20:53:39.160915: Current learning rate: 0.00755 +2026-04-11 20:55:20.644002: train_loss -0.388 +2026-04-11 20:55:20.650266: val_loss -0.3258 +2026-04-11 20:55:20.652347: Pseudo dice [0.0, 0.0, 0.7756, 0.414, 0.5216, 0.3031, 0.7151] +2026-04-11 20:55:20.654741: Epoch time: 101.49 s +2026-04-11 20:55:21.795905: +2026-04-11 20:55:21.797818: Epoch 1074 +2026-04-11 20:55:21.799638: Current learning rate: 0.00755 +2026-04-11 20:57:03.259542: train_loss -0.3978 +2026-04-11 20:57:03.265395: val_loss -0.3766 +2026-04-11 20:57:03.267448: Pseudo dice [0.0, 0.0, 0.84, 0.618, 0.5341, 0.4837, 0.755] +2026-04-11 20:57:03.269733: Epoch time: 101.47 s +2026-04-11 20:57:04.396564: +2026-04-11 20:57:04.398746: Epoch 1075 +2026-04-11 20:57:04.400744: Current learning rate: 0.00755 +2026-04-11 20:58:45.717782: train_loss -0.3889 +2026-04-11 20:58:45.724242: val_loss -0.3483 +2026-04-11 20:58:45.726432: Pseudo dice [0.0, 0.0, 0.7132, 0.676, 0.4744, 0.2622, 0.6007] +2026-04-11 20:58:45.729246: Epoch time: 101.32 s +2026-04-11 20:58:46.854826: +2026-04-11 20:58:46.856514: Epoch 1076 +2026-04-11 20:58:46.858113: Current learning rate: 0.00754 +2026-04-11 21:00:29.110534: train_loss -0.3763 +2026-04-11 21:00:29.116972: val_loss -0.3316 +2026-04-11 21:00:29.119342: Pseudo dice [0.0, 0.0, 0.5661, 0.6539, 0.2644, 0.62, 0.83] +2026-04-11 21:00:29.121830: Epoch time: 102.26 s +2026-04-11 21:00:30.241108: +2026-04-11 21:00:30.243048: Epoch 1077 +2026-04-11 21:00:30.244619: Current learning rate: 0.00754 +2026-04-11 21:02:11.708179: train_loss -0.3878 +2026-04-11 21:02:11.714279: val_loss -0.3449 +2026-04-11 21:02:11.716286: Pseudo dice [0.0, 0.0, 0.8047, 0.5892, 0.3846, 0.4258, 0.6367] +2026-04-11 21:02:11.719034: Epoch time: 101.47 s +2026-04-11 21:02:12.898398: +2026-04-11 21:02:12.900461: Epoch 1078 +2026-04-11 21:02:12.902125: Current learning rate: 0.00754 +2026-04-11 21:03:54.501488: train_loss -0.3756 +2026-04-11 21:03:54.508808: val_loss -0.3472 +2026-04-11 21:03:54.511510: Pseudo dice [0.0, 0.0, 0.7734, 0.2146, 0.5801, 0.4374, 0.8344] +2026-04-11 21:03:54.513766: Epoch time: 101.61 s +2026-04-11 21:03:55.671087: +2026-04-11 21:03:55.673137: Epoch 1079 +2026-04-11 21:03:55.675111: Current learning rate: 0.00754 +2026-04-11 21:05:37.511651: train_loss -0.3897 +2026-04-11 21:05:37.517946: val_loss -0.3574 +2026-04-11 21:05:37.519678: Pseudo dice [0.0, 0.0, 0.7952, 0.2243, 0.433, 0.5445, 0.8689] +2026-04-11 21:05:37.521965: Epoch time: 101.84 s +2026-04-11 21:05:38.651787: +2026-04-11 21:05:38.653569: Epoch 1080 +2026-04-11 21:05:38.655279: Current learning rate: 0.00753 +2026-04-11 21:07:20.208826: train_loss -0.3938 +2026-04-11 21:07:20.216399: val_loss -0.3103 +2026-04-11 21:07:20.218963: Pseudo dice [0.0, 0.0, 0.729, 0.2226, 0.3109, 0.7109, 0.8104] +2026-04-11 21:07:20.221737: Epoch time: 101.56 s +2026-04-11 21:07:21.379637: +2026-04-11 21:07:21.381386: Epoch 1081 +2026-04-11 21:07:21.383092: Current learning rate: 0.00753 +2026-04-11 21:09:02.780852: train_loss -0.3891 +2026-04-11 21:09:02.788127: val_loss -0.317 +2026-04-11 21:09:02.790227: Pseudo dice [0.0, 0.0, 0.7451, 0.0783, 0.2281, 0.7672, 0.8892] +2026-04-11 21:09:02.792331: Epoch time: 101.4 s +2026-04-11 21:09:03.944045: +2026-04-11 21:09:03.945766: Epoch 1082 +2026-04-11 21:09:03.950606: Current learning rate: 0.00753 +2026-04-11 21:10:45.628901: train_loss -0.3914 +2026-04-11 21:10:45.635618: val_loss -0.3553 +2026-04-11 21:10:45.639251: Pseudo dice [0.0, 0.0, 0.7922, 0.3934, 0.5123, 0.7891, 0.9129] +2026-04-11 21:10:45.642369: Epoch time: 101.69 s +2026-04-11 21:10:46.785089: +2026-04-11 21:10:46.786784: Epoch 1083 +2026-04-11 21:10:46.788356: Current learning rate: 0.00753 +2026-04-11 21:12:28.350445: train_loss -0.3772 +2026-04-11 21:12:28.357454: val_loss -0.3264 +2026-04-11 21:12:28.359228: Pseudo dice [0.0, 0.0, 0.5525, 0.2623, 0.364, 0.2207, 0.5899] +2026-04-11 21:12:28.361697: Epoch time: 101.57 s +2026-04-11 21:12:29.520118: +2026-04-11 21:12:29.521975: Epoch 1084 +2026-04-11 21:12:29.523468: Current learning rate: 0.00752 +2026-04-11 21:14:10.932961: train_loss -0.3537 +2026-04-11 21:14:10.938610: val_loss -0.2981 +2026-04-11 21:14:10.940507: Pseudo dice [0.0, 0.0, 0.5151, 0.392, 0.5033, 0.09, 0.8423] +2026-04-11 21:14:10.943285: Epoch time: 101.42 s +2026-04-11 21:14:12.110185: +2026-04-11 21:14:12.112104: Epoch 1085 +2026-04-11 21:14:12.113606: Current learning rate: 0.00752 +2026-04-11 21:15:53.551979: train_loss -0.3519 +2026-04-11 21:15:53.559464: val_loss -0.3368 +2026-04-11 21:15:53.561568: Pseudo dice [0.0, 0.0, 0.5734, 0.3446, 0.3478, 0.5234, 0.8725] +2026-04-11 21:15:53.564288: Epoch time: 101.44 s +2026-04-11 21:15:54.709037: +2026-04-11 21:15:54.710809: Epoch 1086 +2026-04-11 21:15:54.712339: Current learning rate: 0.00752 +2026-04-11 21:17:36.061538: train_loss -0.3927 +2026-04-11 21:17:36.067963: val_loss -0.3551 +2026-04-11 21:17:36.069629: Pseudo dice [0.0, 0.0, 0.7473, 0.4751, 0.4436, 0.6427, 0.2635] +2026-04-11 21:17:36.072016: Epoch time: 101.36 s +2026-04-11 21:17:37.233773: +2026-04-11 21:17:37.235750: Epoch 1087 +2026-04-11 21:17:37.237676: Current learning rate: 0.00752 +2026-04-11 21:19:18.714752: train_loss -0.4004 +2026-04-11 21:19:18.721900: val_loss -0.3689 +2026-04-11 21:19:18.724336: Pseudo dice [0.0, 0.0, 0.8682, 0.4941, 0.5916, 0.874, 0.7367] +2026-04-11 21:19:18.727035: Epoch time: 101.48 s +2026-04-11 21:19:19.872327: +2026-04-11 21:19:19.874512: Epoch 1088 +2026-04-11 21:19:19.876225: Current learning rate: 0.00751 +2026-04-11 21:21:01.548197: train_loss -0.3801 +2026-04-11 21:21:01.554168: val_loss -0.3474 +2026-04-11 21:21:01.556313: Pseudo dice [0.0, 0.0, 0.6936, 0.2958, 0.6053, 0.7644, 0.8376] +2026-04-11 21:21:01.558488: Epoch time: 101.68 s +2026-04-11 21:21:02.707819: +2026-04-11 21:21:02.709986: Epoch 1089 +2026-04-11 21:21:02.711664: Current learning rate: 0.00751 +2026-04-11 21:22:44.174477: train_loss -0.3905 +2026-04-11 21:22:44.180313: val_loss -0.3558 +2026-04-11 21:22:44.183056: Pseudo dice [0.0, 0.0, 0.7729, 0.5348, 0.525, 0.7, 0.8028] +2026-04-11 21:22:44.185071: Epoch time: 101.47 s +2026-04-11 21:22:45.335211: +2026-04-11 21:22:45.337020: Epoch 1090 +2026-04-11 21:22:45.338841: Current learning rate: 0.00751 +2026-04-11 21:24:26.963015: train_loss -0.3879 +2026-04-11 21:24:26.969449: val_loss -0.3524 +2026-04-11 21:24:26.972011: Pseudo dice [0.0, 0.0, 0.6042, 0.6087, 0.6496, 0.6206, 0.8492] +2026-04-11 21:24:26.974381: Epoch time: 101.63 s +2026-04-11 21:24:28.119016: +2026-04-11 21:24:28.120899: Epoch 1091 +2026-04-11 21:24:28.122495: Current learning rate: 0.00751 +2026-04-11 21:26:09.406290: train_loss -0.3955 +2026-04-11 21:26:09.412329: val_loss -0.3799 +2026-04-11 21:26:09.415174: Pseudo dice [0.0, 0.0, 0.8335, 0.7091, 0.4719, 0.4787, 0.7724] +2026-04-11 21:26:09.418493: Epoch time: 101.29 s +2026-04-11 21:26:10.558207: +2026-04-11 21:26:10.560032: Epoch 1092 +2026-04-11 21:26:10.561847: Current learning rate: 0.00751 +2026-04-11 21:27:52.037888: train_loss -0.3822 +2026-04-11 21:27:52.043447: val_loss -0.3128 +2026-04-11 21:27:52.045213: Pseudo dice [0.0, 0.0, 0.6483, 0.3454, 0.3183, 0.4552, 0.7733] +2026-04-11 21:27:52.047156: Epoch time: 101.48 s +2026-04-11 21:27:53.189794: +2026-04-11 21:27:53.191773: Epoch 1093 +2026-04-11 21:27:53.193410: Current learning rate: 0.0075 +2026-04-11 21:29:34.251287: train_loss -0.3886 +2026-04-11 21:29:34.258862: val_loss -0.3612 +2026-04-11 21:29:34.261643: Pseudo dice [0.0, 0.0, 0.7959, 0.7989, 0.54, 0.1601, 0.7333] +2026-04-11 21:29:34.265178: Epoch time: 101.06 s +2026-04-11 21:29:35.397557: +2026-04-11 21:29:35.399360: Epoch 1094 +2026-04-11 21:29:35.401009: Current learning rate: 0.0075 +2026-04-11 21:31:16.637474: train_loss -0.3833 +2026-04-11 21:31:16.643869: val_loss -0.3268 +2026-04-11 21:31:16.645882: Pseudo dice [0.0, 0.0, 0.6271, 0.7598, 0.3685, 0.0412, 0.5942] +2026-04-11 21:31:16.648001: Epoch time: 101.24 s +2026-04-11 21:31:17.778141: +2026-04-11 21:31:17.785127: Epoch 1095 +2026-04-11 21:31:17.787016: Current learning rate: 0.0075 +2026-04-11 21:32:59.079708: train_loss -0.382 +2026-04-11 21:32:59.085514: val_loss -0.3428 +2026-04-11 21:32:59.087716: Pseudo dice [0.0, 0.0, 0.6817, 0.3455, 0.5239, 0.3919, 0.7174] +2026-04-11 21:32:59.090121: Epoch time: 101.3 s +2026-04-11 21:33:00.249126: +2026-04-11 21:33:00.251109: Epoch 1096 +2026-04-11 21:33:00.253270: Current learning rate: 0.0075 +2026-04-11 21:34:41.780250: train_loss -0.3858 +2026-04-11 21:34:41.786401: val_loss -0.3286 +2026-04-11 21:34:41.788344: Pseudo dice [0.0, 0.0, 0.5408, 0.4876, 0.6776, 0.4055, 0.8859] +2026-04-11 21:34:41.790226: Epoch time: 101.53 s +2026-04-11 21:34:43.961944: +2026-04-11 21:34:43.964267: Epoch 1097 +2026-04-11 21:34:43.965968: Current learning rate: 0.00749 +2026-04-11 21:36:25.408825: train_loss -0.3809 +2026-04-11 21:36:25.415696: val_loss -0.3404 +2026-04-11 21:36:25.417789: Pseudo dice [0.0, 0.0, 0.6624, 0.4832, 0.5992, 0.6969, 0.7192] +2026-04-11 21:36:25.422127: Epoch time: 101.45 s +2026-04-11 21:36:26.565706: +2026-04-11 21:36:26.567809: Epoch 1098 +2026-04-11 21:36:26.569610: Current learning rate: 0.00749 +2026-04-11 21:38:07.960479: train_loss -0.3647 +2026-04-11 21:38:07.966920: val_loss -0.3086 +2026-04-11 21:38:07.969002: Pseudo dice [0.0, 0.0, 0.6987, 0.562, 0.6345, 0.4879, 0.4874] +2026-04-11 21:38:07.971303: Epoch time: 101.4 s +2026-04-11 21:38:09.158474: +2026-04-11 21:38:09.160389: Epoch 1099 +2026-04-11 21:38:09.162563: Current learning rate: 0.00749 +2026-04-11 21:39:50.465082: train_loss -0.3916 +2026-04-11 21:39:50.470968: val_loss -0.3381 +2026-04-11 21:39:50.472752: Pseudo dice [0.0, 0.0, 0.7908, 0.4852, 0.4279, 0.7813, 0.9202] +2026-04-11 21:39:50.474975: Epoch time: 101.31 s +2026-04-11 21:39:53.298169: +2026-04-11 21:39:53.300052: Epoch 1100 +2026-04-11 21:39:53.301551: Current learning rate: 0.00749 +2026-04-11 21:41:34.693525: train_loss -0.3942 +2026-04-11 21:41:34.700832: val_loss -0.3964 +2026-04-11 21:41:34.702905: Pseudo dice [0.0, 0.0, 0.8222, 0.6496, 0.3796, 0.7591, 0.6759] +2026-04-11 21:41:34.706745: Epoch time: 101.4 s +2026-04-11 21:41:35.862817: +2026-04-11 21:41:35.865413: Epoch 1101 +2026-04-11 21:41:35.867465: Current learning rate: 0.00748 +2026-04-11 21:43:17.395109: train_loss -0.3914 +2026-04-11 21:43:17.402742: val_loss -0.3149 +2026-04-11 21:43:17.404592: Pseudo dice [0.0, 0.0, 0.7363, 0.1949, 0.5186, 0.7087, 0.4219] +2026-04-11 21:43:17.407461: Epoch time: 101.54 s +2026-04-11 21:43:18.574254: +2026-04-11 21:43:18.576113: Epoch 1102 +2026-04-11 21:43:18.577602: Current learning rate: 0.00748 +2026-04-11 21:45:00.074914: train_loss -0.3947 +2026-04-11 21:45:00.080832: val_loss -0.3368 +2026-04-11 21:45:00.082565: Pseudo dice [0.0, 0.0, 0.811, 0.6649, 0.3378, 0.8361, 0.8136] +2026-04-11 21:45:00.085114: Epoch time: 101.5 s +2026-04-11 21:45:01.222077: +2026-04-11 21:45:01.223863: Epoch 1103 +2026-04-11 21:45:01.225428: Current learning rate: 0.00748 +2026-04-11 21:46:42.885080: train_loss -0.392 +2026-04-11 21:46:42.891518: val_loss -0.3436 +2026-04-11 21:46:42.893490: Pseudo dice [0.0, 0.0, 0.7994, 0.0157, 0.4385, 0.4637, 0.5382] +2026-04-11 21:46:42.896223: Epoch time: 101.67 s +2026-04-11 21:46:44.056489: +2026-04-11 21:46:44.058333: Epoch 1104 +2026-04-11 21:46:44.060249: Current learning rate: 0.00748 +2026-04-11 21:48:26.112198: train_loss -0.3878 +2026-04-11 21:48:26.118994: val_loss -0.3635 +2026-04-11 21:48:26.121372: Pseudo dice [0.0, 0.0, 0.8104, 0.6841, 0.4859, 0.4373, 0.814] +2026-04-11 21:48:26.124093: Epoch time: 102.06 s +2026-04-11 21:48:27.263710: +2026-04-11 21:48:27.266665: Epoch 1105 +2026-04-11 21:48:27.269825: Current learning rate: 0.00748 +2026-04-11 21:50:09.096005: train_loss -0.3932 +2026-04-11 21:50:09.102187: val_loss -0.3028 +2026-04-11 21:50:09.104299: Pseudo dice [0.0, 0.0, 0.7765, 0.2922, 0.3066, 0.3367, 0.6796] +2026-04-11 21:50:09.106813: Epoch time: 101.84 s +2026-04-11 21:50:10.274951: +2026-04-11 21:50:10.276822: Epoch 1106 +2026-04-11 21:50:10.278788: Current learning rate: 0.00747 +2026-04-11 21:51:52.020094: train_loss -0.3913 +2026-04-11 21:51:52.028011: val_loss -0.3362 +2026-04-11 21:51:52.030135: Pseudo dice [0.0, 0.0, 0.7339, 0.3783, 0.4893, 0.724, 0.5022] +2026-04-11 21:51:52.033076: Epoch time: 101.75 s +2026-04-11 21:51:53.227078: +2026-04-11 21:51:53.229184: Epoch 1107 +2026-04-11 21:51:53.231078: Current learning rate: 0.00747 +2026-04-11 21:53:35.425486: train_loss -0.3821 +2026-04-11 21:53:35.431711: val_loss -0.3228 +2026-04-11 21:53:35.435124: Pseudo dice [0.0, 0.0, 0.7544, 0.3041, 0.4558, 0.7114, 0.6721] +2026-04-11 21:53:35.437942: Epoch time: 102.2 s +2026-04-11 21:53:36.615001: +2026-04-11 21:53:36.617162: Epoch 1108 +2026-04-11 21:53:36.619539: Current learning rate: 0.00747 +2026-04-11 21:55:18.352238: train_loss -0.3884 +2026-04-11 21:55:18.359840: val_loss -0.3443 +2026-04-11 21:55:18.362803: Pseudo dice [0.0, 0.0, 0.8158, 0.5818, 0.4961, 0.6318, 0.726] +2026-04-11 21:55:18.365445: Epoch time: 101.74 s +2026-04-11 21:55:19.540165: +2026-04-11 21:55:19.542530: Epoch 1109 +2026-04-11 21:55:19.544410: Current learning rate: 0.00747 +2026-04-11 21:57:01.216501: train_loss -0.3606 +2026-04-11 21:57:01.222548: val_loss -0.2921 +2026-04-11 21:57:01.225215: Pseudo dice [0.0, 0.0, 0.5413, 0.6903, 0.3654, 0.2238, 0.5245] +2026-04-11 21:57:01.227834: Epoch time: 101.68 s +2026-04-11 21:57:02.382265: +2026-04-11 21:57:02.384580: Epoch 1110 +2026-04-11 21:57:02.386538: Current learning rate: 0.00746 +2026-04-11 21:58:44.203120: train_loss -0.3515 +2026-04-11 21:58:44.209260: val_loss -0.3524 +2026-04-11 21:58:44.225897: Pseudo dice [0.0, 0.0, 0.7469, 0.2268, 0.6656, 0.5381, 0.7017] +2026-04-11 21:58:44.228224: Epoch time: 101.82 s +2026-04-11 21:58:45.381835: +2026-04-11 21:58:45.384052: Epoch 1111 +2026-04-11 21:58:45.386284: Current learning rate: 0.00746 +2026-04-11 22:00:27.237576: train_loss -0.3838 +2026-04-11 22:00:27.245526: val_loss -0.3367 +2026-04-11 22:00:27.248431: Pseudo dice [0.0, 0.0, 0.7153, 0.6143, 0.4013, 0.3421, 0.8861] +2026-04-11 22:00:27.251513: Epoch time: 101.86 s +2026-04-11 22:00:28.576206: +2026-04-11 22:00:28.578171: Epoch 1112 +2026-04-11 22:00:28.580467: Current learning rate: 0.00746 +2026-04-11 22:02:10.466445: train_loss -0.3785 +2026-04-11 22:02:10.473737: val_loss -0.3246 +2026-04-11 22:02:10.476189: Pseudo dice [0.0, 0.0, 0.6745, 0.5952, 0.5477, 0.6178, 0.2089] +2026-04-11 22:02:10.478932: Epoch time: 101.89 s +2026-04-11 22:02:11.617588: +2026-04-11 22:02:11.619821: Epoch 1113 +2026-04-11 22:02:11.622125: Current learning rate: 0.00746 +2026-04-11 22:03:53.303499: train_loss -0.3674 +2026-04-11 22:03:53.310025: val_loss -0.3059 +2026-04-11 22:03:53.312279: Pseudo dice [0.0, 0.0, 0.7369, 0.0173, 0.3753, 0.1821, 0.8912] +2026-04-11 22:03:53.315357: Epoch time: 101.69 s +2026-04-11 22:03:54.476161: +2026-04-11 22:03:54.478114: Epoch 1114 +2026-04-11 22:03:54.480509: Current learning rate: 0.00745 +2026-04-11 22:05:36.330477: train_loss -0.3868 +2026-04-11 22:05:36.339384: val_loss -0.3596 +2026-04-11 22:05:36.347545: Pseudo dice [0.0, 0.0, 0.811, 0.4843, 0.4936, 0.8982, 0.8568] +2026-04-11 22:05:36.358483: Epoch time: 101.86 s +2026-04-11 22:05:37.493111: +2026-04-11 22:05:37.494721: Epoch 1115 +2026-04-11 22:05:37.496549: Current learning rate: 0.00745 +2026-04-11 22:07:19.301021: train_loss -0.3997 +2026-04-11 22:07:19.309519: val_loss -0.3256 +2026-04-11 22:07:19.312463: Pseudo dice [0.0, 0.0, 0.6894, 0.0006, 0.5205, 0.4353, 0.8079] +2026-04-11 22:07:19.315394: Epoch time: 101.81 s +2026-04-11 22:07:20.453241: +2026-04-11 22:07:20.456841: Epoch 1116 +2026-04-11 22:07:20.458932: Current learning rate: 0.00745 +2026-04-11 22:09:02.280264: train_loss -0.3866 +2026-04-11 22:09:02.286617: val_loss -0.3295 +2026-04-11 22:09:02.289208: Pseudo dice [0.0, 0.0, 0.7528, 0.2213, 0.4122, 0.4246, 0.8102] +2026-04-11 22:09:02.291622: Epoch time: 101.83 s +2026-04-11 22:09:04.674505: +2026-04-11 22:09:04.676278: Epoch 1117 +2026-04-11 22:09:04.678221: Current learning rate: 0.00745 +2026-04-11 22:10:46.417583: train_loss -0.394 +2026-04-11 22:10:46.425633: val_loss -0.3369 +2026-04-11 22:10:46.428275: Pseudo dice [0.0, 0.0, 0.5509, 0.4581, 0.579, 0.2786, 0.8606] +2026-04-11 22:10:46.431895: Epoch time: 101.75 s +2026-04-11 22:10:47.586183: +2026-04-11 22:10:47.589143: Epoch 1118 +2026-04-11 22:10:47.591597: Current learning rate: 0.00745 +2026-04-11 22:12:29.159273: train_loss -0.3857 +2026-04-11 22:12:29.166046: val_loss -0.3777 +2026-04-11 22:12:29.168285: Pseudo dice [0.0, 0.0, 0.846, 0.6478, 0.6094, 0.5377, 0.8718] +2026-04-11 22:12:29.170788: Epoch time: 101.58 s +2026-04-11 22:12:30.315829: +2026-04-11 22:12:30.317695: Epoch 1119 +2026-04-11 22:12:30.319652: Current learning rate: 0.00744 +2026-04-11 22:14:12.579201: train_loss -0.3923 +2026-04-11 22:14:12.587453: val_loss -0.3318 +2026-04-11 22:14:12.589702: Pseudo dice [0.0, 0.0, 0.7724, 0.6772, 0.4346, 0.2965, 0.6819] +2026-04-11 22:14:12.592143: Epoch time: 102.27 s +2026-04-11 22:14:13.756961: +2026-04-11 22:14:13.759075: Epoch 1120 +2026-04-11 22:14:13.761146: Current learning rate: 0.00744 +2026-04-11 22:15:55.494541: train_loss -0.3678 +2026-04-11 22:15:55.504698: val_loss -0.3294 +2026-04-11 22:15:55.507355: Pseudo dice [0.0, 0.0, 0.8105, 0.1215, 0.3202, 0.8223, 0.8256] +2026-04-11 22:15:55.510102: Epoch time: 101.74 s +2026-04-11 22:15:56.648406: +2026-04-11 22:15:56.650351: Epoch 1121 +2026-04-11 22:15:56.652101: Current learning rate: 0.00744 +2026-04-11 22:17:38.812622: train_loss -0.3811 +2026-04-11 22:17:38.821047: val_loss -0.3175 +2026-04-11 22:17:38.823422: Pseudo dice [0.0, 0.0, 0.7465, 0.3668, 0.3232, 0.6559, 0.5662] +2026-04-11 22:17:38.826212: Epoch time: 102.17 s +2026-04-11 22:17:39.976039: +2026-04-11 22:17:39.978071: Epoch 1122 +2026-04-11 22:17:39.980269: Current learning rate: 0.00744 +2026-04-11 22:19:22.098849: train_loss -0.3812 +2026-04-11 22:19:22.106584: val_loss -0.3102 +2026-04-11 22:19:22.108523: Pseudo dice [0.0, 0.0, 0.7645, 0.5516, 0.3851, 0.5762, 0.8655] +2026-04-11 22:19:22.111539: Epoch time: 102.13 s +2026-04-11 22:19:23.238625: +2026-04-11 22:19:23.240484: Epoch 1123 +2026-04-11 22:19:23.242972: Current learning rate: 0.00743 +2026-04-11 22:21:05.112082: train_loss -0.3912 +2026-04-11 22:21:05.119183: val_loss -0.2979 +2026-04-11 22:21:05.122269: Pseudo dice [0.0, 0.0, 0.7568, 0.5168, 0.5389, 0.7924, 0.0917] +2026-04-11 22:21:05.124939: Epoch time: 101.88 s +2026-04-11 22:21:06.273793: +2026-04-11 22:21:06.275642: Epoch 1124 +2026-04-11 22:21:06.277745: Current learning rate: 0.00743 +2026-04-11 22:22:48.045677: train_loss -0.3702 +2026-04-11 22:22:48.052756: val_loss -0.3425 +2026-04-11 22:22:48.055907: Pseudo dice [0.0, 0.0, 0.2477, 0.6831, 0.1235, 0.6883, 0.8956] +2026-04-11 22:22:48.060345: Epoch time: 101.78 s +2026-04-11 22:22:49.222062: +2026-04-11 22:22:49.224005: Epoch 1125 +2026-04-11 22:22:49.226179: Current learning rate: 0.00743 +2026-04-11 22:24:31.253883: train_loss -0.368 +2026-04-11 22:24:31.260143: val_loss -0.3081 +2026-04-11 22:24:31.262284: Pseudo dice [0.0, 0.0, 0.6071, 0.0116, 0.5802, 0.5127, 0.34] +2026-04-11 22:24:31.265787: Epoch time: 102.04 s +2026-04-11 22:24:32.432115: +2026-04-11 22:24:32.433981: Epoch 1126 +2026-04-11 22:24:32.435949: Current learning rate: 0.00743 +2026-04-11 22:26:13.778993: train_loss -0.4019 +2026-04-11 22:26:13.786008: val_loss -0.3542 +2026-04-11 22:26:13.789881: Pseudo dice [0.0, 0.0, 0.8199, 0.6056, 0.4633, 0.5156, 0.8846] +2026-04-11 22:26:13.792948: Epoch time: 101.35 s +2026-04-11 22:26:14.959255: +2026-04-11 22:26:14.961268: Epoch 1127 +2026-04-11 22:26:14.963430: Current learning rate: 0.00742 +2026-04-11 22:27:57.093611: train_loss -0.4036 +2026-04-11 22:27:57.099753: val_loss -0.3398 +2026-04-11 22:27:57.102178: Pseudo dice [0.0, 0.0, 0.8277, 0.3404, 0.4112, 0.2158, 0.4908] +2026-04-11 22:27:57.104620: Epoch time: 102.14 s +2026-04-11 22:27:58.311118: +2026-04-11 22:27:58.313080: Epoch 1128 +2026-04-11 22:27:58.315213: Current learning rate: 0.00742 +2026-04-11 22:29:40.030893: train_loss -0.378 +2026-04-11 22:29:40.037910: val_loss -0.3345 +2026-04-11 22:29:40.040795: Pseudo dice [0.0, 0.0, 0.7554, 0.6064, 0.613, 0.2015, 0.8337] +2026-04-11 22:29:40.045011: Epoch time: 101.72 s +2026-04-11 22:29:41.198001: +2026-04-11 22:29:41.200229: Epoch 1129 +2026-04-11 22:29:41.202353: Current learning rate: 0.00742 +2026-04-11 22:31:22.630496: train_loss -0.3866 +2026-04-11 22:31:22.638389: val_loss -0.3794 +2026-04-11 22:31:22.641496: Pseudo dice [0.0, 0.0, 0.6241, 0.548, 0.45, 0.5391, 0.832] +2026-04-11 22:31:22.644074: Epoch time: 101.44 s +2026-04-11 22:31:23.791569: +2026-04-11 22:31:23.793997: Epoch 1130 +2026-04-11 22:31:23.796548: Current learning rate: 0.00742 +2026-04-11 22:33:05.728174: train_loss -0.3984 +2026-04-11 22:33:05.735024: val_loss -0.358 +2026-04-11 22:33:05.737447: Pseudo dice [0.0, 0.0, 0.6679, 0.4431, 0.4319, 0.7191, 0.9005] +2026-04-11 22:33:05.739717: Epoch time: 101.94 s +2026-04-11 22:33:06.887686: +2026-04-11 22:33:06.889943: Epoch 1131 +2026-04-11 22:33:06.892551: Current learning rate: 0.00741 +2026-04-11 22:34:48.336593: train_loss -0.4036 +2026-04-11 22:34:48.343315: val_loss -0.362 +2026-04-11 22:34:48.345797: Pseudo dice [0.0, 0.0, 0.7101, 0.4845, 0.5382, 0.3148, 0.7834] +2026-04-11 22:34:48.348983: Epoch time: 101.45 s +2026-04-11 22:34:49.516546: +2026-04-11 22:34:49.521909: Epoch 1132 +2026-04-11 22:34:49.527117: Current learning rate: 0.00741 +2026-04-11 22:36:31.565422: train_loss -0.4031 +2026-04-11 22:36:31.571734: val_loss -0.3204 +2026-04-11 22:36:31.574362: Pseudo dice [0.0, 0.0, 0.4763, 0.4113, 0.5002, 0.6846, 0.7096] +2026-04-11 22:36:31.577108: Epoch time: 102.05 s +2026-04-11 22:36:32.740886: +2026-04-11 22:36:32.764303: Epoch 1133 +2026-04-11 22:36:32.766314: Current learning rate: 0.00741 +2026-04-11 22:38:14.530829: train_loss -0.3841 +2026-04-11 22:38:14.538327: val_loss -0.3354 +2026-04-11 22:38:14.540386: Pseudo dice [0.0, 0.0, 0.7373, 0.5344, 0.5476, 0.549, 0.8069] +2026-04-11 22:38:14.542898: Epoch time: 101.79 s +2026-04-11 22:38:15.697910: +2026-04-11 22:38:15.700475: Epoch 1134 +2026-04-11 22:38:15.702968: Current learning rate: 0.00741 +2026-04-11 22:39:57.646954: train_loss -0.4062 +2026-04-11 22:39:57.653642: val_loss -0.37 +2026-04-11 22:39:57.656146: Pseudo dice [0.0, 0.0, 0.7493, 0.5736, 0.3879, 0.6979, 0.8504] +2026-04-11 22:39:57.658480: Epoch time: 101.95 s +2026-04-11 22:39:58.777656: +2026-04-11 22:39:58.779698: Epoch 1135 +2026-04-11 22:39:58.781780: Current learning rate: 0.00741 +2026-04-11 22:41:40.470073: train_loss -0.408 +2026-04-11 22:41:40.478257: val_loss -0.3747 +2026-04-11 22:41:40.480588: Pseudo dice [0.0, 0.0, 0.8489, 0.797, 0.4615, 0.3327, 0.793] +2026-04-11 22:41:40.483812: Epoch time: 101.7 s +2026-04-11 22:41:41.614518: +2026-04-11 22:41:41.616826: Epoch 1136 +2026-04-11 22:41:41.619280: Current learning rate: 0.0074 +2026-04-11 22:43:23.963687: train_loss -0.3679 +2026-04-11 22:43:23.970748: val_loss -0.3215 +2026-04-11 22:43:23.974301: Pseudo dice [0.0, 0.0, 0.6595, 0.0299, 0.3555, 0.4559, 0.7809] +2026-04-11 22:43:23.977674: Epoch time: 102.35 s +2026-04-11 22:43:25.122996: +2026-04-11 22:43:25.129484: Epoch 1137 +2026-04-11 22:43:25.131895: Current learning rate: 0.0074 +2026-04-11 22:45:06.473719: train_loss -0.3535 +2026-04-11 22:45:06.482188: val_loss -0.3232 +2026-04-11 22:45:06.484535: Pseudo dice [0.0, 0.0, 0.5421, 0.1868, 0.4113, 0.5302, 0.4876] +2026-04-11 22:45:06.488307: Epoch time: 101.35 s +2026-04-11 22:45:08.774473: +2026-04-11 22:45:08.776315: Epoch 1138 +2026-04-11 22:45:08.778409: Current learning rate: 0.0074 +2026-04-11 22:46:51.691071: train_loss -0.3908 +2026-04-11 22:46:51.698886: val_loss -0.3868 +2026-04-11 22:46:51.701093: Pseudo dice [0.0, 0.0, 0.8315, 0.8197, 0.5741, 0.756, 0.695] +2026-04-11 22:46:51.703342: Epoch time: 102.92 s +2026-04-11 22:46:52.872647: +2026-04-11 22:46:52.874809: Epoch 1139 +2026-04-11 22:46:52.878970: Current learning rate: 0.0074 +2026-04-11 22:48:34.705756: train_loss -0.3919 +2026-04-11 22:48:34.715134: val_loss -0.3605 +2026-04-11 22:48:34.717685: Pseudo dice [0.0, 0.0, 0.7744, 0.5318, 0.6345, 0.3122, 0.7344] +2026-04-11 22:48:34.720671: Epoch time: 101.84 s +2026-04-11 22:48:35.862521: +2026-04-11 22:48:35.865060: Epoch 1140 +2026-04-11 22:48:35.867975: Current learning rate: 0.00739 +2026-04-11 22:50:18.195634: train_loss -0.3819 +2026-04-11 22:50:18.203881: val_loss -0.3712 +2026-04-11 22:50:18.206432: Pseudo dice [0.0, 0.0, 0.7359, 0.388, 0.579, 0.4402, 0.7462] +2026-04-11 22:50:18.210517: Epoch time: 102.34 s +2026-04-11 22:50:19.388244: +2026-04-11 22:50:19.390564: Epoch 1141 +2026-04-11 22:50:19.392974: Current learning rate: 0.00739 +2026-04-11 22:52:00.881438: train_loss -0.3801 +2026-04-11 22:52:00.889051: val_loss -0.297 +2026-04-11 22:52:00.892451: Pseudo dice [0.0, 0.0, 0.6084, 0.5941, 0.5144, 0.2667, 0.1754] +2026-04-11 22:52:00.895562: Epoch time: 101.5 s +2026-04-11 22:52:02.048240: +2026-04-11 22:52:02.050345: Epoch 1142 +2026-04-11 22:52:02.053441: Current learning rate: 0.00739 +2026-04-11 22:53:44.555185: train_loss -0.383 +2026-04-11 22:53:44.564690: val_loss -0.3515 +2026-04-11 22:53:44.567198: Pseudo dice [0.0, 0.0, 0.5802, 0.2904, 0.4162, 0.6114, 0.8578] +2026-04-11 22:53:44.569666: Epoch time: 102.51 s +2026-04-11 22:53:45.719158: +2026-04-11 22:53:45.721411: Epoch 1143 +2026-04-11 22:53:45.723636: Current learning rate: 0.00739 +2026-04-11 22:55:27.880970: train_loss -0.3978 +2026-04-11 22:55:27.889729: val_loss -0.3387 +2026-04-11 22:55:27.893340: Pseudo dice [0.0, 0.0, 0.6854, 0.792, 0.5208, 0.4826, 0.7683] +2026-04-11 22:55:27.896027: Epoch time: 102.16 s +2026-04-11 22:55:29.076112: +2026-04-11 22:55:29.078491: Epoch 1144 +2026-04-11 22:55:29.080188: Current learning rate: 0.00738 +2026-04-11 22:57:11.054788: train_loss -0.3853 +2026-04-11 22:57:11.074135: val_loss -0.351 +2026-04-11 22:57:11.076695: Pseudo dice [0.0, 0.0, 0.6131, 0.7634, 0.4165, 0.5759, 0.8971] +2026-04-11 22:57:11.079016: Epoch time: 101.98 s +2026-04-11 22:57:12.248128: +2026-04-11 22:57:12.250173: Epoch 1145 +2026-04-11 22:57:12.252059: Current learning rate: 0.00738 +2026-04-11 22:58:53.533869: train_loss -0.3957 +2026-04-11 22:58:53.542293: val_loss -0.3528 +2026-04-11 22:58:53.545869: Pseudo dice [0.0, 0.0, 0.6861, 0.1381, 0.3489, 0.5638, 0.7882] +2026-04-11 22:58:53.549049: Epoch time: 101.29 s +2026-04-11 22:58:54.710070: +2026-04-11 22:58:54.712843: Epoch 1146 +2026-04-11 22:58:54.714529: Current learning rate: 0.00738 +2026-04-11 23:00:36.092444: train_loss -0.3993 +2026-04-11 23:00:36.100267: val_loss -0.3232 +2026-04-11 23:00:36.102729: Pseudo dice [0.0, 0.0, 0.5707, 0.0129, 0.2847, 0.4596, 0.5664] +2026-04-11 23:00:36.105648: Epoch time: 101.39 s +2026-04-11 23:00:37.276259: +2026-04-11 23:00:37.278235: Epoch 1147 +2026-04-11 23:00:37.280344: Current learning rate: 0.00738 +2026-04-11 23:02:19.365741: train_loss -0.3963 +2026-04-11 23:02:19.373922: val_loss -0.3442 +2026-04-11 23:02:19.376031: Pseudo dice [0.0, 0.0, 0.8153, 0.7177, 0.4565, 0.3437, 0.82] +2026-04-11 23:02:19.378681: Epoch time: 102.09 s +2026-04-11 23:02:20.578534: +2026-04-11 23:02:20.581208: Epoch 1148 +2026-04-11 23:02:20.583103: Current learning rate: 0.00738 +2026-04-11 23:04:02.088008: train_loss -0.3371 +2026-04-11 23:04:02.100855: val_loss -0.2475 +2026-04-11 23:04:02.122055: Pseudo dice [0.0, 0.0, 0.4692, 0.0993, 0.1828, 0.2827, 0.4257] +2026-04-11 23:04:02.125922: Epoch time: 101.51 s +2026-04-11 23:04:03.304243: +2026-04-11 23:04:03.306205: Epoch 1149 +2026-04-11 23:04:03.308968: Current learning rate: 0.00737 +2026-04-11 23:05:45.229095: train_loss -0.363 +2026-04-11 23:05:45.235512: val_loss -0.3261 +2026-04-11 23:05:45.237541: Pseudo dice [0.0, 0.0, 0.6799, 0.5604, 0.5724, 0.6124, 0.9066] +2026-04-11 23:05:45.240199: Epoch time: 101.93 s +2026-04-11 23:05:47.966964: +2026-04-11 23:05:47.968737: Epoch 1150 +2026-04-11 23:05:47.971006: Current learning rate: 0.00737 +2026-04-11 23:07:29.399781: train_loss -0.3803 +2026-04-11 23:07:29.407848: val_loss -0.3459 +2026-04-11 23:07:29.410163: Pseudo dice [0.0, 0.0, 0.7555, 0.4099, 0.3094, 0.7801, 0.8265] +2026-04-11 23:07:29.413294: Epoch time: 101.44 s +2026-04-11 23:07:30.550775: +2026-04-11 23:07:30.552748: Epoch 1151 +2026-04-11 23:07:30.554464: Current learning rate: 0.00737 +2026-04-11 23:09:12.548496: train_loss -0.3772 +2026-04-11 23:09:12.556975: val_loss -0.3053 +2026-04-11 23:09:12.559475: Pseudo dice [0.0, 0.0, 0.4859, 0.4584, 0.5637, 0.3827, 0.3649] +2026-04-11 23:09:12.562302: Epoch time: 102.0 s +2026-04-11 23:09:13.791178: +2026-04-11 23:09:13.793604: Epoch 1152 +2026-04-11 23:09:13.796262: Current learning rate: 0.00737 +2026-04-11 23:10:55.773236: train_loss -0.3611 +2026-04-11 23:10:55.780258: val_loss -0.3319 +2026-04-11 23:10:55.782960: Pseudo dice [0.0, 0.0, 0.7626, 0.7434, 0.534, 0.799, 0.7204] +2026-04-11 23:10:55.785182: Epoch time: 101.99 s +2026-04-11 23:10:56.945122: +2026-04-11 23:10:56.946751: Epoch 1153 +2026-04-11 23:10:56.948960: Current learning rate: 0.00736 +2026-04-11 23:12:39.494824: train_loss -0.3959 +2026-04-11 23:12:39.502233: val_loss -0.3639 +2026-04-11 23:12:39.504285: Pseudo dice [0.0, 0.0, 0.7209, 0.5537, 0.4854, 0.7952, 0.7781] +2026-04-11 23:12:39.507434: Epoch time: 102.55 s +2026-04-11 23:12:40.681039: +2026-04-11 23:12:40.685227: Epoch 1154 +2026-04-11 23:12:40.689241: Current learning rate: 0.00736 +2026-04-11 23:14:23.317812: train_loss -0.3981 +2026-04-11 23:14:23.324460: val_loss -0.3467 +2026-04-11 23:14:23.326685: Pseudo dice [0.0, 0.0, 0.7277, 0.4821, 0.3679, 0.3894, 0.6377] +2026-04-11 23:14:23.329135: Epoch time: 102.64 s +2026-04-11 23:14:24.509006: +2026-04-11 23:14:24.510874: Epoch 1155 +2026-04-11 23:14:24.513093: Current learning rate: 0.00736 +2026-04-11 23:16:07.269765: train_loss -0.3676 +2026-04-11 23:16:07.278156: val_loss -0.345 +2026-04-11 23:16:07.280404: Pseudo dice [0.0, 0.0, 0.7228, 0.65, 0.4403, 0.7927, 0.7567] +2026-04-11 23:16:07.283512: Epoch time: 102.76 s +2026-04-11 23:16:08.534165: +2026-04-11 23:16:08.536003: Epoch 1156 +2026-04-11 23:16:08.537962: Current learning rate: 0.00736 +2026-04-11 23:17:50.837394: train_loss -0.3926 +2026-04-11 23:17:50.845695: val_loss -0.3398 +2026-04-11 23:17:50.848606: Pseudo dice [0.0, 0.0, 0.7174, 0.5269, 0.5477, 0.205, 0.8853] +2026-04-11 23:17:50.851349: Epoch time: 102.31 s +2026-04-11 23:17:52.058100: +2026-04-11 23:17:52.060123: Epoch 1157 +2026-04-11 23:17:52.062739: Current learning rate: 0.00735 +2026-04-11 23:19:34.821461: train_loss -0.3714 +2026-04-11 23:19:34.829697: val_loss -0.3101 +2026-04-11 23:19:34.832028: Pseudo dice [0.0, 0.0, 0.6519, 0.15, 0.4785, 0.525, 0.2815] +2026-04-11 23:19:34.834831: Epoch time: 102.77 s +2026-04-11 23:19:36.055627: +2026-04-11 23:19:36.058415: Epoch 1158 +2026-04-11 23:19:36.061276: Current learning rate: 0.00735 +2026-04-11 23:21:18.234583: train_loss -0.3897 +2026-04-11 23:21:18.241174: val_loss -0.3499 +2026-04-11 23:21:18.243083: Pseudo dice [0.0, 0.0, 0.6693, 0.6711, 0.464, 0.5876, 0.5281] +2026-04-11 23:21:18.245995: Epoch time: 102.18 s +2026-04-11 23:21:19.425022: +2026-04-11 23:21:19.426787: Epoch 1159 +2026-04-11 23:21:19.428967: Current learning rate: 0.00735 +2026-04-11 23:23:01.254419: train_loss -0.3767 +2026-04-11 23:23:01.260209: val_loss -0.3418 +2026-04-11 23:23:01.262379: Pseudo dice [0.0, 0.0, 0.8096, 0.6067, 0.3853, 0.4813, 0.8799] +2026-04-11 23:23:01.265234: Epoch time: 101.83 s +2026-04-11 23:23:02.463209: +2026-04-11 23:23:02.465004: Epoch 1160 +2026-04-11 23:23:02.467020: Current learning rate: 0.00735 +2026-04-11 23:24:44.560356: train_loss -0.392 +2026-04-11 23:24:44.568198: val_loss -0.3556 +2026-04-11 23:24:44.571413: Pseudo dice [0.0, 0.0, 0.588, 0.4056, 0.5352, 0.6844, 0.9259] +2026-04-11 23:24:44.574155: Epoch time: 102.1 s +2026-04-11 23:24:45.786650: +2026-04-11 23:24:45.788516: Epoch 1161 +2026-04-11 23:24:45.790618: Current learning rate: 0.00735 +2026-04-11 23:26:28.041527: train_loss -0.3467 +2026-04-11 23:26:28.047789: val_loss -0.3286 +2026-04-11 23:26:28.049969: Pseudo dice [0.0, 0.0, 0.6959, 0.2918, 0.4519, 0.3326, 0.8645] +2026-04-11 23:26:28.052460: Epoch time: 102.26 s +2026-04-11 23:26:29.258592: +2026-04-11 23:26:29.260754: Epoch 1162 +2026-04-11 23:26:29.262879: Current learning rate: 0.00734 +2026-04-11 23:28:12.005639: train_loss -0.3835 +2026-04-11 23:28:12.014031: val_loss -0.3026 +2026-04-11 23:28:12.016388: Pseudo dice [0.0, 0.0, 0.5908, 0.4304, 0.394, 0.4589, 0.5929] +2026-04-11 23:28:12.020283: Epoch time: 102.75 s +2026-04-11 23:28:13.218232: +2026-04-11 23:28:13.220505: Epoch 1163 +2026-04-11 23:28:13.222921: Current learning rate: 0.00734 +2026-04-11 23:29:55.422164: train_loss -0.3837 +2026-04-11 23:29:55.429926: val_loss -0.3703 +2026-04-11 23:29:55.433604: Pseudo dice [0.0, 0.0, 0.848, 0.656, 0.6076, 0.5043, 0.7216] +2026-04-11 23:29:55.436657: Epoch time: 102.21 s +2026-04-11 23:29:56.664358: +2026-04-11 23:29:56.666448: Epoch 1164 +2026-04-11 23:29:56.669308: Current learning rate: 0.00734 +2026-04-11 23:31:38.773232: train_loss -0.3986 +2026-04-11 23:31:38.780360: val_loss -0.3352 +2026-04-11 23:31:38.783214: Pseudo dice [0.0, 0.0, 0.7004, 0.2172, 0.6711, 0.4196, 0.9115] +2026-04-11 23:31:38.785558: Epoch time: 102.11 s +2026-04-11 23:31:39.986969: +2026-04-11 23:31:39.989214: Epoch 1165 +2026-04-11 23:31:39.990970: Current learning rate: 0.00734 +2026-04-11 23:33:22.536060: train_loss -0.3973 +2026-04-11 23:33:22.543523: val_loss -0.3799 +2026-04-11 23:33:22.545459: Pseudo dice [0.0, 0.0, 0.824, 0.2294, 0.5733, 0.3823, 0.7689] +2026-04-11 23:33:22.548001: Epoch time: 102.55 s +2026-04-11 23:33:23.730109: +2026-04-11 23:33:23.732412: Epoch 1166 +2026-04-11 23:33:23.734639: Current learning rate: 0.00733 +2026-04-11 23:35:06.452338: train_loss -0.3999 +2026-04-11 23:35:06.459938: val_loss -0.3721 +2026-04-11 23:35:06.461909: Pseudo dice [0.0, 0.0, 0.732, 0.215, 0.5528, 0.6823, 0.8058] +2026-04-11 23:35:06.464292: Epoch time: 102.73 s +2026-04-11 23:35:07.657319: +2026-04-11 23:35:07.659836: Epoch 1167 +2026-04-11 23:35:07.662735: Current learning rate: 0.00733 +2026-04-11 23:36:49.630425: train_loss -0.3946 +2026-04-11 23:36:49.637254: val_loss -0.3515 +2026-04-11 23:36:49.639614: Pseudo dice [0.0, 0.0, 0.7281, 0.2972, 0.247, 0.6182, 0.8919] +2026-04-11 23:36:49.642127: Epoch time: 101.97 s +2026-04-11 23:36:50.831798: +2026-04-11 23:36:50.834529: Epoch 1168 +2026-04-11 23:36:50.837914: Current learning rate: 0.00733 +2026-04-11 23:38:32.966129: train_loss -0.3908 +2026-04-11 23:38:32.976759: val_loss -0.3696 +2026-04-11 23:38:32.979353: Pseudo dice [0.0, 0.0, 0.8057, 0.0605, 0.4634, 0.9175, 0.9143] +2026-04-11 23:38:32.982118: Epoch time: 102.14 s +2026-04-11 23:38:34.379457: +2026-04-11 23:38:34.381480: Epoch 1169 +2026-04-11 23:38:34.383692: Current learning rate: 0.00733 +2026-04-11 23:40:17.496453: train_loss -0.4057 +2026-04-11 23:40:17.506351: val_loss -0.3155 +2026-04-11 23:40:17.508199: Pseudo dice [0.0, 0.0, 0.7254, 0.5746, 0.6223, 0.8048, 0.0771] +2026-04-11 23:40:17.511254: Epoch time: 103.12 s +2026-04-11 23:40:18.685299: +2026-04-11 23:40:18.687718: Epoch 1170 +2026-04-11 23:40:18.690180: Current learning rate: 0.00732 +2026-04-11 23:42:01.285257: train_loss -0.3952 +2026-04-11 23:42:01.293252: val_loss -0.3551 +2026-04-11 23:42:01.295886: Pseudo dice [0.0, 0.0, 0.8563, 0.5373, 0.6661, 0.6969, 0.6528] +2026-04-11 23:42:01.298294: Epoch time: 102.6 s +2026-04-11 23:42:02.491296: +2026-04-11 23:42:02.493585: Epoch 1171 +2026-04-11 23:42:02.496098: Current learning rate: 0.00732 +2026-04-11 23:43:44.793912: train_loss -0.4065 +2026-04-11 23:43:44.802468: val_loss -0.363 +2026-04-11 23:43:44.804666: Pseudo dice [0.0, 0.0, 0.6946, 0.3401, 0.5446, 0.321, 0.8429] +2026-04-11 23:43:44.807685: Epoch time: 102.31 s +2026-04-11 23:43:45.995274: +2026-04-11 23:43:45.999047: Epoch 1172 +2026-04-11 23:43:46.002290: Current learning rate: 0.00732 +2026-04-11 23:45:28.529946: train_loss -0.414 +2026-04-11 23:45:28.537715: val_loss -0.3373 +2026-04-11 23:45:28.540196: Pseudo dice [0.0, 0.0, 0.7077, 0.3854, 0.3431, 0.8415, 0.8144] +2026-04-11 23:45:28.542746: Epoch time: 102.54 s +2026-04-11 23:45:29.737267: +2026-04-11 23:45:29.739172: Epoch 1173 +2026-04-11 23:45:29.742278: Current learning rate: 0.00732 +2026-04-11 23:47:12.198263: train_loss -0.4005 +2026-04-11 23:47:12.207019: val_loss -0.2892 +2026-04-11 23:47:12.209976: Pseudo dice [0.0, 0.0, 0.8227, 0.0469, 0.2452, 0.1815, 0.8469] +2026-04-11 23:47:12.212803: Epoch time: 102.46 s +2026-04-11 23:47:13.407585: +2026-04-11 23:47:13.410033: Epoch 1174 +2026-04-11 23:47:13.412653: Current learning rate: 0.00731 +2026-04-11 23:48:55.354410: train_loss -0.3651 +2026-04-11 23:48:55.361214: val_loss -0.3028 +2026-04-11 23:48:55.363424: Pseudo dice [0.0, 0.0, 0.718, 0.295, 0.5561, 0.6574, 0.8622] +2026-04-11 23:48:55.366020: Epoch time: 101.95 s +2026-04-11 23:48:56.570207: +2026-04-11 23:48:56.572572: Epoch 1175 +2026-04-11 23:48:56.575609: Current learning rate: 0.00731 +2026-04-11 23:50:39.453530: train_loss -0.3881 +2026-04-11 23:50:39.460626: val_loss -0.3104 +2026-04-11 23:50:39.463073: Pseudo dice [0.0, 0.0, 0.7382, 0.5116, 0.2757, 0.1575, 0.7773] +2026-04-11 23:50:39.466889: Epoch time: 102.89 s +2026-04-11 23:50:40.643739: +2026-04-11 23:50:40.645827: Epoch 1176 +2026-04-11 23:50:40.647635: Current learning rate: 0.00731 +2026-04-11 23:52:23.013476: train_loss -0.3692 +2026-04-11 23:52:23.022290: val_loss -0.3715 +2026-04-11 23:52:23.024839: Pseudo dice [0.0, 0.0, 0.7617, 0.8264, 0.5978, 0.8813, 0.8506] +2026-04-11 23:52:23.027238: Epoch time: 102.37 s +2026-04-11 23:52:24.208184: +2026-04-11 23:52:24.211109: Epoch 1177 +2026-04-11 23:52:24.213270: Current learning rate: 0.00731 +2026-04-11 23:54:07.795249: train_loss -0.3761 +2026-04-11 23:54:07.816060: val_loss -0.379 +2026-04-11 23:54:07.818751: Pseudo dice [0.0, 0.0, 0.7532, 0.1103, 0.4875, 0.6053, 0.8802] +2026-04-11 23:54:07.821249: Epoch time: 103.59 s +2026-04-11 23:54:09.015222: +2026-04-11 23:54:09.017437: Epoch 1178 +2026-04-11 23:54:09.019314: Current learning rate: 0.00731 +2026-04-11 23:55:51.154903: train_loss -0.3702 +2026-04-11 23:55:51.161275: val_loss -0.333 +2026-04-11 23:55:51.164268: Pseudo dice [0.0, 0.0, 0.7085, 0.3938, 0.6216, 0.3106, 0.7522] +2026-04-11 23:55:51.167147: Epoch time: 102.14 s +2026-04-11 23:55:52.335611: +2026-04-11 23:55:52.337962: Epoch 1179 +2026-04-11 23:55:52.341928: Current learning rate: 0.0073 +2026-04-11 23:57:34.409376: train_loss -0.3919 +2026-04-11 23:57:34.417611: val_loss -0.3243 +2026-04-11 23:57:34.421133: Pseudo dice [0.0, 0.0, 0.741, 0.0815, 0.3862, 0.5233, 0.8496] +2026-04-11 23:57:34.423802: Epoch time: 102.08 s +2026-04-11 23:57:35.607025: +2026-04-11 23:57:35.609085: Epoch 1180 +2026-04-11 23:57:35.611415: Current learning rate: 0.0073 +2026-04-11 23:59:17.739419: train_loss -0.3901 +2026-04-11 23:59:17.746914: val_loss -0.3289 +2026-04-11 23:59:17.748680: Pseudo dice [0.0, 0.0, 0.6633, 0.0136, 0.5731, 0.4223, 0.7998] +2026-04-11 23:59:17.750827: Epoch time: 102.14 s +2026-04-11 23:59:18.928690: +2026-04-11 23:59:18.930427: Epoch 1181 +2026-04-11 23:59:18.932476: Current learning rate: 0.0073 +2026-04-12 00:01:03.595622: train_loss -0.382 +2026-04-12 00:01:03.610020: val_loss -0.3346 +2026-04-12 00:01:03.613641: Pseudo dice [0.0, 0.0, 0.8016, 0.3082, 0.5619, 0.7979, 0.6604] +2026-04-12 00:01:03.619009: Epoch time: 104.67 s +2026-04-12 00:01:04.830806: +2026-04-12 00:01:04.835023: Epoch 1182 +2026-04-12 00:01:04.838971: Current learning rate: 0.0073 +2026-04-12 00:02:48.735670: train_loss -0.3797 +2026-04-12 00:02:48.747500: val_loss -0.3472 +2026-04-12 00:02:48.753348: Pseudo dice [0.0, 0.0, 0.8208, 0.8152, 0.3906, 0.7367, 0.8084] +2026-04-12 00:02:48.758520: Epoch time: 103.9 s +2026-04-12 00:02:49.949960: +2026-04-12 00:02:49.952463: Epoch 1183 +2026-04-12 00:02:49.955485: Current learning rate: 0.00729 +2026-04-12 00:04:34.330913: train_loss -0.3738 +2026-04-12 00:04:34.340755: val_loss -0.3684 +2026-04-12 00:04:34.345327: Pseudo dice [0.0, 0.0, 0.7768, 0.4913, 0.4573, 0.6479, 0.8519] +2026-04-12 00:04:34.348181: Epoch time: 104.38 s +2026-04-12 00:04:35.526092: +2026-04-12 00:04:35.529765: Epoch 1184 +2026-04-12 00:04:35.533966: Current learning rate: 0.00729 +2026-04-12 00:06:17.338698: train_loss -0.3758 +2026-04-12 00:06:17.345904: val_loss -0.345 +2026-04-12 00:06:17.347925: Pseudo dice [0.0, 0.0, 0.8108, 0.251, 0.4432, 0.5594, 0.8828] +2026-04-12 00:06:17.350729: Epoch time: 101.82 s +2026-04-12 00:06:18.529806: +2026-04-12 00:06:18.531893: Epoch 1185 +2026-04-12 00:06:18.534649: Current learning rate: 0.00729 +2026-04-12 00:08:00.951665: train_loss -0.3449 +2026-04-12 00:08:00.958863: val_loss -0.3194 +2026-04-12 00:08:00.961529: Pseudo dice [0.0, 0.0, 0.631, 0.5222, 0.4864, 0.7995, 0.5438] +2026-04-12 00:08:00.963938: Epoch time: 102.43 s +2026-04-12 00:08:02.152891: +2026-04-12 00:08:02.155387: Epoch 1186 +2026-04-12 00:08:02.157861: Current learning rate: 0.00729 +2026-04-12 00:09:44.396281: train_loss -0.3934 +2026-04-12 00:09:44.403102: val_loss -0.3036 +2026-04-12 00:09:44.406883: Pseudo dice [0.0, 0.0, 0.3899, 0.099, 0.3055, 0.2278, 0.6079] +2026-04-12 00:09:44.409566: Epoch time: 102.25 s +2026-04-12 00:09:45.603093: +2026-04-12 00:09:45.605481: Epoch 1187 +2026-04-12 00:09:45.609282: Current learning rate: 0.00728 +2026-04-12 00:11:28.686755: train_loss -0.3984 +2026-04-12 00:11:28.695668: val_loss -0.3502 +2026-04-12 00:11:28.698198: Pseudo dice [0.0, 0.0, 0.6311, 0.5501, 0.4557, 0.7015, 0.7415] +2026-04-12 00:11:28.701084: Epoch time: 103.09 s +2026-04-12 00:11:29.907715: +2026-04-12 00:11:29.910041: Epoch 1188 +2026-04-12 00:11:29.913011: Current learning rate: 0.00728 +2026-04-12 00:13:12.521398: train_loss -0.4044 +2026-04-12 00:13:12.529982: val_loss -0.3708 +2026-04-12 00:13:12.533930: Pseudo dice [0.0, 0.0, 0.7881, 0.5328, 0.506, 0.6617, 0.9031] +2026-04-12 00:13:12.537315: Epoch time: 102.62 s +2026-04-12 00:13:13.714695: +2026-04-12 00:13:13.717995: Epoch 1189 +2026-04-12 00:13:13.721249: Current learning rate: 0.00728 +2026-04-12 00:14:56.291632: train_loss -0.3992 +2026-04-12 00:14:56.299253: val_loss -0.3494 +2026-04-12 00:14:56.301484: Pseudo dice [0.0, 0.0, 0.201, 0.8574, 0.4898, 0.5978, 0.9065] +2026-04-12 00:14:56.304096: Epoch time: 102.58 s +2026-04-12 00:14:57.510435: +2026-04-12 00:14:57.512399: Epoch 1190 +2026-04-12 00:14:57.514526: Current learning rate: 0.00728 +2026-04-12 00:16:39.734634: train_loss -0.4038 +2026-04-12 00:16:39.742254: val_loss -0.3497 +2026-04-12 00:16:39.744819: Pseudo dice [0.0, 0.0, 0.8485, 0.829, 0.3724, 0.3346, 0.8096] +2026-04-12 00:16:39.747732: Epoch time: 102.23 s +2026-04-12 00:16:40.918951: +2026-04-12 00:16:40.921078: Epoch 1191 +2026-04-12 00:16:40.923208: Current learning rate: 0.00728 +2026-04-12 00:18:23.513572: train_loss -0.394 +2026-04-12 00:18:23.521298: val_loss -0.3384 +2026-04-12 00:18:23.523962: Pseudo dice [0.0, 0.0, 0.7199, 0.6561, 0.5161, 0.4858, 0.544] +2026-04-12 00:18:23.527101: Epoch time: 102.6 s +2026-04-12 00:18:24.702287: +2026-04-12 00:18:24.704704: Epoch 1192 +2026-04-12 00:18:24.707128: Current learning rate: 0.00727 +2026-04-12 00:20:07.444854: train_loss -0.3947 +2026-04-12 00:20:07.453929: val_loss -0.3176 +2026-04-12 00:20:07.456293: Pseudo dice [0.0, 0.0, 0.7758, 0.541, 0.457, 0.4884, 0.3234] +2026-04-12 00:20:07.458949: Epoch time: 102.75 s +2026-04-12 00:20:08.630850: +2026-04-12 00:20:08.632960: Epoch 1193 +2026-04-12 00:20:08.634951: Current learning rate: 0.00727 +2026-04-12 00:21:51.216130: train_loss -0.4011 +2026-04-12 00:21:51.223939: val_loss -0.3633 +2026-04-12 00:21:51.226016: Pseudo dice [0.0, 0.0, 0.7555, 0.7772, 0.6977, 0.6824, 0.603] +2026-04-12 00:21:51.228579: Epoch time: 102.59 s +2026-04-12 00:21:52.390023: +2026-04-12 00:21:52.392582: Epoch 1194 +2026-04-12 00:21:52.394900: Current learning rate: 0.00727 +2026-04-12 00:23:34.493707: train_loss -0.3898 +2026-04-12 00:23:34.500339: val_loss -0.342 +2026-04-12 00:23:34.502572: Pseudo dice [0.0, 0.0, 0.8604, 0.6128, 0.4741, 0.4817, 0.8078] +2026-04-12 00:23:34.504877: Epoch time: 102.11 s +2026-04-12 00:23:35.687893: +2026-04-12 00:23:35.690044: Epoch 1195 +2026-04-12 00:23:35.692189: Current learning rate: 0.00727 +2026-04-12 00:25:18.030872: train_loss -0.3838 +2026-04-12 00:25:18.038209: val_loss -0.3453 +2026-04-12 00:25:18.040438: Pseudo dice [0.0, 0.0, 0.8192, 0.611, 0.6439, 0.5661, 0.7048] +2026-04-12 00:25:18.043913: Epoch time: 102.35 s +2026-04-12 00:25:19.205486: +2026-04-12 00:25:19.208020: Epoch 1196 +2026-04-12 00:25:19.210903: Current learning rate: 0.00726 +2026-04-12 00:27:01.343853: train_loss -0.3798 +2026-04-12 00:27:01.352395: val_loss -0.2917 +2026-04-12 00:27:01.355672: Pseudo dice [0.0, 0.0, 0.5781, 0.5888, 0.5295, 0.3701, 0.9035] +2026-04-12 00:27:01.358584: Epoch time: 102.14 s +2026-04-12 00:27:02.560680: +2026-04-12 00:27:02.563172: Epoch 1197 +2026-04-12 00:27:02.566068: Current learning rate: 0.00726 +2026-04-12 00:28:45.704809: train_loss -0.3867 +2026-04-12 00:28:45.712035: val_loss -0.3157 +2026-04-12 00:28:45.714277: Pseudo dice [0.0, 0.0, 0.7147, 0.5669, 0.2946, 0.4319, 0.3955] +2026-04-12 00:28:45.716859: Epoch time: 103.15 s +2026-04-12 00:28:46.887431: +2026-04-12 00:28:46.889631: Epoch 1198 +2026-04-12 00:28:46.891760: Current learning rate: 0.00726 +2026-04-12 00:30:29.294714: train_loss -0.3668 +2026-04-12 00:30:29.306172: val_loss -0.343 +2026-04-12 00:30:29.312400: Pseudo dice [0.0, 0.0, 0.7452, 0.8128, 0.4299, 0.6075, 0.4212] +2026-04-12 00:30:29.315622: Epoch time: 102.41 s +2026-04-12 00:30:30.497570: +2026-04-12 00:30:30.499641: Epoch 1199 +2026-04-12 00:30:30.501829: Current learning rate: 0.00726 +2026-04-12 00:32:12.293368: train_loss -0.3852 +2026-04-12 00:32:12.301514: val_loss -0.345 +2026-04-12 00:32:12.304495: Pseudo dice [0.0, 0.0, 0.8017, 0.7273, 0.4789, 0.6078, 0.8477] +2026-04-12 00:32:12.307936: Epoch time: 101.8 s +2026-04-12 00:32:15.225502: +2026-04-12 00:32:15.227326: Epoch 1200 +2026-04-12 00:32:15.229429: Current learning rate: 0.00725 +2026-04-12 00:33:57.548112: train_loss -0.3786 +2026-04-12 00:33:57.555746: val_loss -0.3139 +2026-04-12 00:33:57.558044: Pseudo dice [0.0, 0.0, 0.6818, 0.2212, 0.4287, 0.6662, 0.8386] +2026-04-12 00:33:57.560550: Epoch time: 102.33 s +2026-04-12 00:33:58.751344: +2026-04-12 00:33:58.753391: Epoch 1201 +2026-04-12 00:33:58.755542: Current learning rate: 0.00725 +2026-04-12 00:35:40.741196: train_loss -0.3878 +2026-04-12 00:35:40.747346: val_loss -0.3308 +2026-04-12 00:35:40.750419: Pseudo dice [0.0, 0.0, 0.6856, 0.0342, 0.5671, 0.2852, 0.2862] +2026-04-12 00:35:40.753089: Epoch time: 101.99 s +2026-04-12 00:35:41.920452: +2026-04-12 00:35:41.922248: Epoch 1202 +2026-04-12 00:35:41.924441: Current learning rate: 0.00725 +2026-04-12 00:37:23.901605: train_loss -0.3964 +2026-04-12 00:37:23.908487: val_loss -0.3455 +2026-04-12 00:37:23.911536: Pseudo dice [0.0, 0.0, 0.7349, 0.6662, 0.5419, 0.6211, 0.652] +2026-04-12 00:37:23.913791: Epoch time: 101.98 s +2026-04-12 00:37:25.093066: +2026-04-12 00:37:25.095031: Epoch 1203 +2026-04-12 00:37:25.096891: Current learning rate: 0.00725 +2026-04-12 00:39:07.126562: train_loss -0.4043 +2026-04-12 00:39:07.133252: val_loss -0.3628 +2026-04-12 00:39:07.135256: Pseudo dice [0.0, 0.0, 0.7241, 0.638, 0.3145, 0.7932, 0.8641] +2026-04-12 00:39:07.138003: Epoch time: 102.04 s +2026-04-12 00:39:08.288906: +2026-04-12 00:39:08.291158: Epoch 1204 +2026-04-12 00:39:08.293611: Current learning rate: 0.00724 +2026-04-12 00:40:50.339561: train_loss -0.389 +2026-04-12 00:40:50.349656: val_loss -0.3494 +2026-04-12 00:40:50.352040: Pseudo dice [0.0, 0.0, 0.423, 0.5521, 0.3754, 0.6675, 0.9006] +2026-04-12 00:40:50.355362: Epoch time: 102.05 s +2026-04-12 00:40:51.519920: +2026-04-12 00:40:51.523503: Epoch 1205 +2026-04-12 00:40:51.525333: Current learning rate: 0.00724 +2026-04-12 00:42:33.635077: train_loss -0.4033 +2026-04-12 00:42:33.642084: val_loss -0.3267 +2026-04-12 00:42:33.644277: Pseudo dice [0.0, 0.0, 0.5203, 0.6133, 0.5503, 0.7972, 0.7802] +2026-04-12 00:42:33.646727: Epoch time: 102.12 s +2026-04-12 00:42:34.829786: +2026-04-12 00:42:34.831879: Epoch 1206 +2026-04-12 00:42:34.833731: Current learning rate: 0.00724 +2026-04-12 00:44:16.813987: train_loss -0.4003 +2026-04-12 00:44:16.821471: val_loss -0.3561 +2026-04-12 00:44:16.823667: Pseudo dice [0.0, 0.0, 0.6184, 0.2074, 0.495, 0.6444, 0.7604] +2026-04-12 00:44:16.826415: Epoch time: 101.99 s +2026-04-12 00:44:18.020556: +2026-04-12 00:44:18.024623: Epoch 1207 +2026-04-12 00:44:18.027328: Current learning rate: 0.00724 +2026-04-12 00:45:59.986920: train_loss -0.3693 +2026-04-12 00:45:59.993990: val_loss -0.2943 +2026-04-12 00:45:59.998801: Pseudo dice [0.0, 0.0, 0.5441, 0.4937, 0.2843, 0.2935, 0.7827] +2026-04-12 00:46:00.001543: Epoch time: 101.97 s +2026-04-12 00:46:01.194293: +2026-04-12 00:46:01.196700: Epoch 1208 +2026-04-12 00:46:01.198895: Current learning rate: 0.00724 +2026-04-12 00:47:43.429568: train_loss -0.3856 +2026-04-12 00:47:43.437245: val_loss -0.3473 +2026-04-12 00:47:43.440128: Pseudo dice [0.0, 0.0, 0.6257, 0.2295, 0.3919, 0.5661, 0.9064] +2026-04-12 00:47:43.443044: Epoch time: 102.24 s +2026-04-12 00:47:44.614761: +2026-04-12 00:47:44.618419: Epoch 1209 +2026-04-12 00:47:44.621182: Current learning rate: 0.00723 +2026-04-12 00:49:27.010752: train_loss -0.3723 +2026-04-12 00:49:27.018580: val_loss -0.3612 +2026-04-12 00:49:27.020938: Pseudo dice [0.0, 0.0, 0.7263, 0.7054, 0.5244, 0.4638, 0.7473] +2026-04-12 00:49:27.023831: Epoch time: 102.4 s +2026-04-12 00:49:28.191168: +2026-04-12 00:49:28.194728: Epoch 1210 +2026-04-12 00:49:28.197099: Current learning rate: 0.00723 +2026-04-12 00:51:10.002572: train_loss -0.4044 +2026-04-12 00:51:10.009257: val_loss -0.3535 +2026-04-12 00:51:10.011821: Pseudo dice [0.0, 0.0, 0.6431, 0.7874, 0.5296, 0.4928, 0.8491] +2026-04-12 00:51:10.014138: Epoch time: 101.81 s +2026-04-12 00:51:11.166718: +2026-04-12 00:51:11.168950: Epoch 1211 +2026-04-12 00:51:11.171407: Current learning rate: 0.00723 +2026-04-12 00:52:52.873350: train_loss -0.4193 +2026-04-12 00:52:52.881613: val_loss -0.3129 +2026-04-12 00:52:52.883896: Pseudo dice [0.0, 0.0, 0.7797, 0.5813, 0.5147, 0.1425, 0.5533] +2026-04-12 00:52:52.886576: Epoch time: 101.71 s +2026-04-12 00:52:54.052850: +2026-04-12 00:52:54.055000: Epoch 1212 +2026-04-12 00:52:54.056826: Current learning rate: 0.00723 +2026-04-12 00:54:35.911277: train_loss -0.378 +2026-04-12 00:54:35.931764: val_loss -0.3249 +2026-04-12 00:54:35.933997: Pseudo dice [0.0, 0.0, 0.7222, 0.5907, 0.4141, 0.3629, 0.3351] +2026-04-12 00:54:35.936906: Epoch time: 101.86 s +2026-04-12 00:54:37.155054: +2026-04-12 00:54:37.158667: Epoch 1213 +2026-04-12 00:54:37.160772: Current learning rate: 0.00722 +2026-04-12 00:56:19.082694: train_loss -0.3855 +2026-04-12 00:56:19.090727: val_loss -0.3208 +2026-04-12 00:56:19.092949: Pseudo dice [0.0, 0.0, 0.6546, 0.4143, 0.5288, 0.4658, 0.8338] +2026-04-12 00:56:19.095934: Epoch time: 101.93 s +2026-04-12 00:56:20.244323: +2026-04-12 00:56:20.248395: Epoch 1214 +2026-04-12 00:56:20.250448: Current learning rate: 0.00722 +2026-04-12 00:58:02.423606: train_loss -0.3889 +2026-04-12 00:58:02.432227: val_loss -0.3426 +2026-04-12 00:58:02.434244: Pseudo dice [0.0, 0.0, 0.8211, 0.7935, 0.2587, 0.1896, 0.7291] +2026-04-12 00:58:02.436669: Epoch time: 102.18 s +2026-04-12 00:58:03.635045: +2026-04-12 00:58:03.638367: Epoch 1215 +2026-04-12 00:58:03.640293: Current learning rate: 0.00722 +2026-04-12 00:59:46.130841: train_loss -0.4085 +2026-04-12 00:59:46.139742: val_loss -0.3403 +2026-04-12 00:59:46.141776: Pseudo dice [0.0, 0.0, 0.7803, 0.5241, 0.4596, 0.8584, 0.8731] +2026-04-12 00:59:46.144178: Epoch time: 102.5 s +2026-04-12 00:59:47.339798: +2026-04-12 00:59:47.342187: Epoch 1216 +2026-04-12 00:59:47.344069: Current learning rate: 0.00722 +2026-04-12 01:01:31.976286: train_loss -0.4077 +2026-04-12 01:01:31.992800: val_loss -0.3545 +2026-04-12 01:01:32.000098: Pseudo dice [0.0, 0.0, 0.7252, 0.649, 0.5532, 0.6605, 0.7189] +2026-04-12 01:01:32.002892: Epoch time: 104.64 s +2026-04-12 01:01:34.307141: +2026-04-12 01:01:34.309789: Epoch 1217 +2026-04-12 01:01:34.311980: Current learning rate: 0.00721 +2026-04-12 01:03:17.884609: train_loss -0.4054 +2026-04-12 01:03:17.893433: val_loss -0.361 +2026-04-12 01:03:17.898295: Pseudo dice [0.0, 0.0, 0.7803, 0.2782, 0.4435, 0.8078, 0.6459] +2026-04-12 01:03:17.904111: Epoch time: 103.58 s +2026-04-12 01:03:19.072075: +2026-04-12 01:03:19.075643: Epoch 1218 +2026-04-12 01:03:19.078186: Current learning rate: 0.00721 +2026-04-12 01:05:02.895799: train_loss -0.3903 +2026-04-12 01:05:02.906896: val_loss -0.2967 +2026-04-12 01:05:02.910856: Pseudo dice [0.0, 0.0, 0.4571, 0.8179, 0.4984, 0.6065, 0.8544] +2026-04-12 01:05:02.915917: Epoch time: 103.82 s +2026-04-12 01:05:04.156359: +2026-04-12 01:05:04.160969: Epoch 1219 +2026-04-12 01:05:04.167377: Current learning rate: 0.00721 +2026-04-12 01:06:47.962578: train_loss -0.3787 +2026-04-12 01:06:47.973480: val_loss -0.3312 +2026-04-12 01:06:47.977380: Pseudo dice [0.0, 0.0, 0.7186, 0.4359, 0.1818, 0.6418, 0.2301] +2026-04-12 01:06:47.982153: Epoch time: 103.81 s +2026-04-12 01:06:49.166325: +2026-04-12 01:06:49.177642: Epoch 1220 +2026-04-12 01:06:49.181815: Current learning rate: 0.00721 +2026-04-12 01:08:33.144173: train_loss -0.3961 +2026-04-12 01:08:33.162234: val_loss -0.3663 +2026-04-12 01:08:33.172145: Pseudo dice [0.0, 0.0, 0.8181, 0.5815, 0.5051, 0.3188, 0.7761] +2026-04-12 01:08:33.178063: Epoch time: 103.98 s +2026-04-12 01:08:34.372600: +2026-04-12 01:08:34.376796: Epoch 1221 +2026-04-12 01:08:34.380374: Current learning rate: 0.00721 +2026-04-12 01:10:18.829696: train_loss -0.3932 +2026-04-12 01:10:18.842669: val_loss -0.2592 +2026-04-12 01:10:18.847047: Pseudo dice [0.0, 0.0, 0.1159, 0.3008, 0.1395, 0.4951, 0.3186] +2026-04-12 01:10:18.850533: Epoch time: 104.46 s +2026-04-12 01:10:20.060676: +2026-04-12 01:10:20.064408: Epoch 1222 +2026-04-12 01:10:20.068387: Current learning rate: 0.0072 +2026-04-12 01:12:03.552113: train_loss -0.3518 +2026-04-12 01:12:03.561340: val_loss -0.273 +2026-04-12 01:12:03.564014: Pseudo dice [0.0, 0.0, 0.7018, 0.114, 0.2912, 0.2093, 0.6434] +2026-04-12 01:12:03.567776: Epoch time: 103.49 s +2026-04-12 01:12:04.810547: +2026-04-12 01:12:04.814899: Epoch 1223 +2026-04-12 01:12:04.818038: Current learning rate: 0.0072 +2026-04-12 01:13:48.869201: train_loss -0.3845 +2026-04-12 01:13:48.878320: val_loss -0.3484 +2026-04-12 01:13:48.883152: Pseudo dice [0.0, 0.0, 0.8575, 0.5961, 0.5447, 0.8012, 0.5893] +2026-04-12 01:13:48.886125: Epoch time: 104.06 s +2026-04-12 01:13:50.104622: +2026-04-12 01:13:50.107356: Epoch 1224 +2026-04-12 01:13:50.109744: Current learning rate: 0.0072 +2026-04-12 01:15:33.569986: train_loss -0.3958 +2026-04-12 01:15:33.607595: val_loss -0.3403 +2026-04-12 01:15:33.613093: Pseudo dice [0.0, 0.0, 0.8269, 0.4341, 0.4357, 0.6694, 0.6812] +2026-04-12 01:15:33.618661: Epoch time: 103.47 s +2026-04-12 01:15:34.823391: +2026-04-12 01:15:34.831144: Epoch 1225 +2026-04-12 01:15:34.835695: Current learning rate: 0.0072 +2026-04-12 01:17:18.691945: train_loss -0.4029 +2026-04-12 01:17:18.710159: val_loss -0.327 +2026-04-12 01:17:18.714156: Pseudo dice [0.0, 0.0, 0.6868, 0.5022, 0.5457, 0.8622, 0.2722] +2026-04-12 01:17:18.718559: Epoch time: 103.87 s +2026-04-12 01:17:19.898300: +2026-04-12 01:17:19.903837: Epoch 1226 +2026-04-12 01:17:19.906205: Current learning rate: 0.00719 +2026-04-12 01:19:03.460590: train_loss -0.3874 +2026-04-12 01:19:03.469170: val_loss -0.3502 +2026-04-12 01:19:03.472552: Pseudo dice [0.0, 0.0, 0.7075, 0.0552, 0.5917, 0.6737, 0.869] +2026-04-12 01:19:03.476228: Epoch time: 103.57 s +2026-04-12 01:19:04.729069: +2026-04-12 01:19:04.732293: Epoch 1227 +2026-04-12 01:19:04.734793: Current learning rate: 0.00719 +2026-04-12 01:20:47.431819: train_loss -0.374 +2026-04-12 01:20:47.441405: val_loss -0.3645 +2026-04-12 01:20:47.443958: Pseudo dice [0.0, 0.0, 0.7302, 0.748, 0.6204, 0.3606, 0.8209] +2026-04-12 01:20:47.446992: Epoch time: 102.71 s +2026-04-12 01:20:48.618725: +2026-04-12 01:20:48.622356: Epoch 1228 +2026-04-12 01:20:48.625898: Current learning rate: 0.00719 +2026-04-12 01:22:31.666946: train_loss -0.3832 +2026-04-12 01:22:31.676971: val_loss -0.2898 +2026-04-12 01:22:31.679926: Pseudo dice [0.0, 0.0, 0.3894, 0.1471, 0.1654, 0.6541, 0.8184] +2026-04-12 01:22:31.684093: Epoch time: 103.05 s +2026-04-12 01:22:32.863730: +2026-04-12 01:22:32.870085: Epoch 1229 +2026-04-12 01:22:32.875860: Current learning rate: 0.00719 +2026-04-12 01:24:16.547264: train_loss -0.3686 +2026-04-12 01:24:16.559592: val_loss -0.3653 +2026-04-12 01:24:16.565457: Pseudo dice [0.0, 0.0, 0.7883, 0.5367, 0.328, 0.8262, 0.6159] +2026-04-12 01:24:16.572634: Epoch time: 103.69 s +2026-04-12 01:24:17.767285: +2026-04-12 01:24:17.772599: Epoch 1230 +2026-04-12 01:24:17.777838: Current learning rate: 0.00718 +2026-04-12 01:26:00.538521: train_loss -0.4018 +2026-04-12 01:26:00.556464: val_loss -0.3306 +2026-04-12 01:26:00.561187: Pseudo dice [0.0, 0.0, 0.7606, 0.7616, 0.4251, 0.4137, 0.2628] +2026-04-12 01:26:00.596534: Epoch time: 102.77 s +2026-04-12 01:26:01.779812: +2026-04-12 01:26:01.784467: Epoch 1231 +2026-04-12 01:26:01.787902: Current learning rate: 0.00718 +2026-04-12 01:27:45.360598: train_loss -0.3779 +2026-04-12 01:27:45.382257: val_loss -0.3395 +2026-04-12 01:27:45.386460: Pseudo dice [0.0, 0.0, 0.6828, 0.6158, 0.5304, 0.4094, 0.6915] +2026-04-12 01:27:45.390882: Epoch time: 103.58 s +2026-04-12 01:27:46.580814: +2026-04-12 01:27:46.583956: Epoch 1232 +2026-04-12 01:27:46.586559: Current learning rate: 0.00718 +2026-04-12 01:29:29.783522: train_loss -0.3869 +2026-04-12 01:29:29.791293: val_loss -0.3188 +2026-04-12 01:29:29.793594: Pseudo dice [0.0, 0.0, 0.8103, 0.2142, 0.393, 0.263, 0.7012] +2026-04-12 01:29:29.797515: Epoch time: 103.21 s +2026-04-12 01:29:30.999970: +2026-04-12 01:29:31.002034: Epoch 1233 +2026-04-12 01:29:31.004247: Current learning rate: 0.00718 +2026-04-12 01:31:14.567870: train_loss -0.3963 +2026-04-12 01:31:14.579090: val_loss -0.3377 +2026-04-12 01:31:14.583451: Pseudo dice [0.0, 0.0, 0.677, 0.1351, 0.5166, 0.5367, 0.5954] +2026-04-12 01:31:14.587384: Epoch time: 103.57 s +2026-04-12 01:31:15.796654: +2026-04-12 01:31:15.800124: Epoch 1234 +2026-04-12 01:31:15.803244: Current learning rate: 0.00717 +2026-04-12 01:32:59.853138: train_loss -0.3759 +2026-04-12 01:32:59.864331: val_loss -0.3546 +2026-04-12 01:32:59.867656: Pseudo dice [0.0, 0.0, 0.6821, 0.5661, 0.5214, 0.4339, 0.473] +2026-04-12 01:32:59.871288: Epoch time: 104.06 s +2026-04-12 01:33:01.050010: +2026-04-12 01:33:01.052729: Epoch 1235 +2026-04-12 01:33:01.055701: Current learning rate: 0.00717 +2026-04-12 01:34:45.512310: train_loss -0.381 +2026-04-12 01:34:45.522850: val_loss -0.3484 +2026-04-12 01:34:45.526483: Pseudo dice [0.0, 0.0, 0.8177, 0.5268, 0.4991, 0.5395, 0.3657] +2026-04-12 01:34:45.531972: Epoch time: 104.47 s +2026-04-12 01:34:46.717004: +2026-04-12 01:34:46.721198: Epoch 1236 +2026-04-12 01:34:46.725840: Current learning rate: 0.00717 +2026-04-12 01:36:29.963987: train_loss -0.4045 +2026-04-12 01:36:29.974018: val_loss -0.3333 +2026-04-12 01:36:29.978287: Pseudo dice [0.0, 0.0, 0.5122, 0.6398, 0.5713, 0.5907, 0.9192] +2026-04-12 01:36:29.982153: Epoch time: 103.25 s +2026-04-12 01:36:31.187985: +2026-04-12 01:36:31.191447: Epoch 1237 +2026-04-12 01:36:31.195688: Current learning rate: 0.00717 +2026-04-12 01:38:16.326824: train_loss -0.4121 +2026-04-12 01:38:16.333755: val_loss -0.327 +2026-04-12 01:38:16.336438: Pseudo dice [0.0, 0.0, 0.7212, 0.3979, 0.3761, 0.4686, 0.7418] +2026-04-12 01:38:16.341712: Epoch time: 105.14 s +2026-04-12 01:38:17.542194: +2026-04-12 01:38:17.545597: Epoch 1238 +2026-04-12 01:38:17.548789: Current learning rate: 0.00717 +2026-04-12 01:40:01.260935: train_loss -0.3744 +2026-04-12 01:40:01.269475: val_loss -0.3139 +2026-04-12 01:40:01.273484: Pseudo dice [0.0, 0.0, 0.5811, 0.3761, 0.3741, 0.7114, 0.8277] +2026-04-12 01:40:01.277064: Epoch time: 103.72 s +2026-04-12 01:40:02.539535: +2026-04-12 01:40:02.543976: Epoch 1239 +2026-04-12 01:40:02.547182: Current learning rate: 0.00716 +2026-04-12 01:41:45.816364: train_loss -0.4054 +2026-04-12 01:41:45.829258: val_loss -0.3215 +2026-04-12 01:41:45.832607: Pseudo dice [0.0, 0.0, 0.6339, 0.3402, 0.5023, 0.7348, 0.7329] +2026-04-12 01:41:45.840319: Epoch time: 103.28 s +2026-04-12 01:41:47.029560: +2026-04-12 01:41:47.032769: Epoch 1240 +2026-04-12 01:41:47.035184: Current learning rate: 0.00716 +2026-04-12 01:43:30.903360: train_loss -0.4056 +2026-04-12 01:43:30.911873: val_loss -0.3168 +2026-04-12 01:43:30.916107: Pseudo dice [0.0, 0.0, 0.6509, 0.3938, 0.441, 0.4778, 0.8351] +2026-04-12 01:43:30.919679: Epoch time: 103.88 s +2026-04-12 01:43:32.102844: +2026-04-12 01:43:32.110704: Epoch 1241 +2026-04-12 01:43:32.113415: Current learning rate: 0.00716 +2026-04-12 01:45:15.686676: train_loss -0.3592 +2026-04-12 01:45:15.696610: val_loss -0.319 +2026-04-12 01:45:15.705714: Pseudo dice [0.0, 0.0, 0.5916, 0.5999, 0.3951, 0.1043, 0.4575] +2026-04-12 01:45:15.709237: Epoch time: 103.59 s +2026-04-12 01:45:16.888493: +2026-04-12 01:45:16.916232: Epoch 1242 +2026-04-12 01:45:16.920167: Current learning rate: 0.00716 +2026-04-12 01:46:59.882778: train_loss -0.3836 +2026-04-12 01:46:59.890147: val_loss -0.2866 +2026-04-12 01:46:59.893491: Pseudo dice [0.0, 0.0, 0.3819, 0.4803, 0.5452, 0.7825, 0.3977] +2026-04-12 01:46:59.896936: Epoch time: 103.0 s +2026-04-12 01:47:01.141763: +2026-04-12 01:47:01.144390: Epoch 1243 +2026-04-12 01:47:01.146972: Current learning rate: 0.00715 +2026-04-12 01:48:45.159681: train_loss -0.3575 +2026-04-12 01:48:45.168862: val_loss -0.325 +2026-04-12 01:48:45.171356: Pseudo dice [0.0, 0.0, 0.7261, 0.0249, 0.3794, 0.561, 0.3445] +2026-04-12 01:48:45.175167: Epoch time: 104.02 s +2026-04-12 01:48:46.368975: +2026-04-12 01:48:46.374131: Epoch 1244 +2026-04-12 01:48:46.377959: Current learning rate: 0.00715 +2026-04-12 01:50:31.346781: train_loss -0.3913 +2026-04-12 01:50:31.359210: val_loss -0.3239 +2026-04-12 01:50:31.362692: Pseudo dice [0.0, 0.0, 0.6437, 0.1303, 0.4345, 0.5287, 0.1399] +2026-04-12 01:50:31.366686: Epoch time: 104.98 s +2026-04-12 01:50:32.626694: +2026-04-12 01:50:32.630442: Epoch 1245 +2026-04-12 01:50:32.633417: Current learning rate: 0.00715 +2026-04-12 01:52:16.324811: train_loss -0.3603 +2026-04-12 01:52:16.334608: val_loss -0.3171 +2026-04-12 01:52:16.338325: Pseudo dice [0.0, 0.0, 0.5635, 0.5349, 0.3131, 0.8443, 0.5645] +2026-04-12 01:52:16.341492: Epoch time: 103.7 s +2026-04-12 01:52:17.526058: +2026-04-12 01:52:17.530361: Epoch 1246 +2026-04-12 01:52:17.535258: Current learning rate: 0.00715 +2026-04-12 01:54:05.678167: train_loss -0.3823 +2026-04-12 01:54:05.687865: val_loss -0.3409 +2026-04-12 01:54:05.691612: Pseudo dice [0.0, 0.0, 0.7407, 0.6647, 0.5701, 0.4765, 0.8037] +2026-04-12 01:54:05.696626: Epoch time: 108.16 s +2026-04-12 01:54:06.864518: +2026-04-12 01:54:06.868078: Epoch 1247 +2026-04-12 01:54:06.871968: Current learning rate: 0.00714 +2026-04-12 01:55:51.124513: train_loss -0.4016 +2026-04-12 01:55:51.137776: val_loss -0.3423 +2026-04-12 01:55:51.142166: Pseudo dice [0.0, 0.0, 0.8677, 0.2954, 0.5808, 0.3637, 0.2273] +2026-04-12 01:55:51.145900: Epoch time: 104.26 s +2026-04-12 01:55:52.316649: +2026-04-12 01:55:52.321082: Epoch 1248 +2026-04-12 01:55:52.325882: Current learning rate: 0.00714 +2026-04-12 01:57:36.105885: train_loss -0.4041 +2026-04-12 01:57:36.117114: val_loss -0.3438 +2026-04-12 01:57:36.120241: Pseudo dice [0.0, 0.0, 0.6526, 0.3477, 0.5633, 0.5437, 0.7674] +2026-04-12 01:57:36.123849: Epoch time: 103.79 s +2026-04-12 01:57:37.299358: +2026-04-12 01:57:37.303864: Epoch 1249 +2026-04-12 01:57:37.308470: Current learning rate: 0.00714 +2026-04-12 01:59:21.516791: train_loss -0.4132 +2026-04-12 01:59:21.526621: val_loss -0.3677 +2026-04-12 01:59:21.533931: Pseudo dice [0.0, 0.0, 0.8449, 0.1991, 0.7507, 0.7408, 0.882] +2026-04-12 01:59:21.537515: Epoch time: 104.22 s +2026-04-12 01:59:24.716180: +2026-04-12 01:59:24.718562: Epoch 1250 +2026-04-12 01:59:24.721398: Current learning rate: 0.00714 +2026-04-12 02:01:07.920281: train_loss -0.4106 +2026-04-12 02:01:07.929224: val_loss -0.3562 +2026-04-12 02:01:07.932499: Pseudo dice [0.0, 0.0, 0.7176, 0.7638, 0.6061, 0.588, 0.8358] +2026-04-12 02:01:07.935745: Epoch time: 103.21 s +2026-04-12 02:01:09.107968: +2026-04-12 02:01:09.111789: Epoch 1251 +2026-04-12 02:01:09.114606: Current learning rate: 0.00714 +2026-04-12 02:02:53.337713: train_loss -0.4076 +2026-04-12 02:02:53.347304: val_loss -0.3712 +2026-04-12 02:02:53.354081: Pseudo dice [0.0, 0.0, 0.7896, 0.553, 0.6168, 0.8856, 0.696] +2026-04-12 02:02:53.368496: Epoch time: 104.23 s +2026-04-12 02:02:54.591057: +2026-04-12 02:02:54.596318: Epoch 1252 +2026-04-12 02:02:54.600001: Current learning rate: 0.00713 +2026-04-12 02:04:37.344381: train_loss -0.3926 +2026-04-12 02:04:37.365005: val_loss -0.3363 +2026-04-12 02:04:37.368190: Pseudo dice [0.0, 0.0, 0.6913, 0.5058, 0.5823, 0.7745, 0.5709] +2026-04-12 02:04:37.371513: Epoch time: 102.76 s +2026-04-12 02:04:38.547118: +2026-04-12 02:04:38.550040: Epoch 1253 +2026-04-12 02:04:38.552848: Current learning rate: 0.00713 +2026-04-12 02:06:23.014462: train_loss -0.3665 +2026-04-12 02:06:23.024356: val_loss -0.3275 +2026-04-12 02:06:23.028706: Pseudo dice [0.0, 0.0, 0.6993, 0.5166, 0.622, 0.7309, 0.3809] +2026-04-12 02:06:23.031879: Epoch time: 104.47 s +2026-04-12 02:06:24.228597: +2026-04-12 02:06:24.232967: Epoch 1254 +2026-04-12 02:06:24.236478: Current learning rate: 0.00713 +2026-04-12 02:08:08.004969: train_loss -0.3547 +2026-04-12 02:08:08.019906: val_loss -0.3107 +2026-04-12 02:08:08.023757: Pseudo dice [0.0, 0.0, 0.5032, 0.2105, 0.4572, 0.6783, 0.5562] +2026-04-12 02:08:08.029113: Epoch time: 103.78 s +2026-04-12 02:08:09.262461: +2026-04-12 02:08:09.268762: Epoch 1255 +2026-04-12 02:08:09.273419: Current learning rate: 0.00713 +2026-04-12 02:09:53.890982: train_loss -0.378 +2026-04-12 02:09:53.903114: val_loss -0.334 +2026-04-12 02:09:53.906301: Pseudo dice [0.0, 0.0, 0.8138, 0.6123, 0.5155, 0.6934, 0.8159] +2026-04-12 02:09:53.915537: Epoch time: 104.63 s +2026-04-12 02:09:55.113618: +2026-04-12 02:09:55.116642: Epoch 1256 +2026-04-12 02:09:55.120858: Current learning rate: 0.00712 +2026-04-12 02:11:39.071076: train_loss -0.3612 +2026-04-12 02:11:39.081338: val_loss -0.3165 +2026-04-12 02:11:39.085020: Pseudo dice [0.0, 0.0, 0.7579, 0.0589, 0.4912, 0.5385, 0.4282] +2026-04-12 02:11:39.089407: Epoch time: 103.96 s +2026-04-12 02:11:41.472321: +2026-04-12 02:11:41.475411: Epoch 1257 +2026-04-12 02:11:41.477503: Current learning rate: 0.00712 +2026-04-12 02:13:25.497490: train_loss -0.3844 +2026-04-12 02:13:25.510664: val_loss -0.3403 +2026-04-12 02:13:25.519282: Pseudo dice [0.0, 0.0, 0.6403, 0.6646, 0.6756, 0.4201, 0.4931] +2026-04-12 02:13:25.524142: Epoch time: 104.03 s +2026-04-12 02:13:26.724943: +2026-04-12 02:13:26.730684: Epoch 1258 +2026-04-12 02:13:26.738536: Current learning rate: 0.00712 +2026-04-12 02:15:10.488945: train_loss -0.408 +2026-04-12 02:15:10.498748: val_loss -0.3525 +2026-04-12 02:15:10.503011: Pseudo dice [0.0, 0.0, 0.7206, 0.4931, 0.466, 0.3183, 0.8934] +2026-04-12 02:15:10.506945: Epoch time: 103.77 s +2026-04-12 02:15:11.763294: +2026-04-12 02:15:11.766111: Epoch 1259 +2026-04-12 02:15:11.769285: Current learning rate: 0.00712 +2026-04-12 02:16:55.518486: train_loss -0.3933 +2026-04-12 02:16:55.550289: val_loss -0.3671 +2026-04-12 02:16:55.554054: Pseudo dice [0.0, 0.0, 0.7297, 0.7528, 0.5534, 0.6917, 0.8951] +2026-04-12 02:16:55.557854: Epoch time: 103.76 s +2026-04-12 02:16:56.774095: +2026-04-12 02:16:56.777109: Epoch 1260 +2026-04-12 02:16:56.780488: Current learning rate: 0.00711 +2026-04-12 02:18:41.078648: train_loss -0.3621 +2026-04-12 02:18:41.090660: val_loss -0.3547 +2026-04-12 02:18:41.094070: Pseudo dice [0.0, 0.0, 0.6609, 0.3312, 0.3304, 0.8039, 0.7274] +2026-04-12 02:18:41.101091: Epoch time: 104.31 s +2026-04-12 02:18:42.275272: +2026-04-12 02:18:42.280247: Epoch 1261 +2026-04-12 02:18:42.284204: Current learning rate: 0.00711 +2026-04-12 02:20:25.264879: train_loss -0.3417 +2026-04-12 02:20:25.275913: val_loss -0.3325 +2026-04-12 02:20:25.279638: Pseudo dice [0.0, 0.0, 0.7558, 0.3837, 0.5003, 0.3521, 0.7322] +2026-04-12 02:20:25.285057: Epoch time: 102.99 s +2026-04-12 02:20:26.513804: +2026-04-12 02:20:26.516542: Epoch 1262 +2026-04-12 02:20:26.518816: Current learning rate: 0.00711 +2026-04-12 02:22:10.500677: train_loss -0.3735 +2026-04-12 02:22:10.510196: val_loss -0.3601 +2026-04-12 02:22:10.513295: Pseudo dice [0.0, 0.0, 0.812, 0.4474, 0.497, 0.6469, 0.7783] +2026-04-12 02:22:10.516367: Epoch time: 103.99 s +2026-04-12 02:22:11.793885: +2026-04-12 02:22:11.796882: Epoch 1263 +2026-04-12 02:22:11.800010: Current learning rate: 0.00711 +2026-04-12 02:23:56.625034: train_loss -0.3862 +2026-04-12 02:23:56.634995: val_loss -0.3563 +2026-04-12 02:23:56.641283: Pseudo dice [0.0, 0.0, 0.8132, 0.5946, 0.6102, 0.7633, 0.5729] +2026-04-12 02:23:56.645120: Epoch time: 104.83 s +2026-04-12 02:23:57.843435: +2026-04-12 02:23:57.847645: Epoch 1264 +2026-04-12 02:23:57.851093: Current learning rate: 0.0071 +2026-04-12 02:25:41.263857: train_loss -0.3789 +2026-04-12 02:25:41.271487: val_loss -0.3401 +2026-04-12 02:25:41.275771: Pseudo dice [0.0, 0.0, 0.7472, 0.4266, 0.537, 0.7785, 0.4585] +2026-04-12 02:25:41.279715: Epoch time: 103.42 s +2026-04-12 02:25:42.484132: +2026-04-12 02:25:42.487746: Epoch 1265 +2026-04-12 02:25:42.491160: Current learning rate: 0.0071 +2026-04-12 02:27:25.064012: train_loss -0.3902 +2026-04-12 02:27:25.075376: val_loss -0.3507 +2026-04-12 02:27:25.080769: Pseudo dice [0.0, 0.0, 0.754, 0.6494, 0.4692, 0.3413, 0.9025] +2026-04-12 02:27:25.089082: Epoch time: 102.58 s +2026-04-12 02:27:26.358320: +2026-04-12 02:27:26.360836: Epoch 1266 +2026-04-12 02:27:26.363372: Current learning rate: 0.0071 +2026-04-12 02:29:09.703065: train_loss -0.3918 +2026-04-12 02:29:09.720104: val_loss -0.3089 +2026-04-12 02:29:09.723650: Pseudo dice [0.0, 0.0, 0.6953, 0.5812, 0.4925, 0.7184, 0.7489] +2026-04-12 02:29:09.729594: Epoch time: 103.35 s +2026-04-12 02:29:10.917775: +2026-04-12 02:29:10.926048: Epoch 1267 +2026-04-12 02:29:10.928441: Current learning rate: 0.0071 +2026-04-12 02:30:53.071470: train_loss -0.371 +2026-04-12 02:30:53.081044: val_loss -0.3211 +2026-04-12 02:30:53.084740: Pseudo dice [0.0, 0.0, 0.6309, 0.4348, 0.4523, 0.7366, 0.6565] +2026-04-12 02:30:53.087756: Epoch time: 102.16 s +2026-04-12 02:30:54.266264: +2026-04-12 02:30:54.269737: Epoch 1268 +2026-04-12 02:30:54.273176: Current learning rate: 0.0071 +2026-04-12 02:32:37.349746: train_loss -0.3633 +2026-04-12 02:32:37.365264: val_loss -0.3245 +2026-04-12 02:32:37.368782: Pseudo dice [0.0, 0.0, 0.7687, 0.5408, 0.4519, 0.4834, 0.652] +2026-04-12 02:32:37.373202: Epoch time: 103.09 s +2026-04-12 02:32:38.611153: +2026-04-12 02:32:38.613343: Epoch 1269 +2026-04-12 02:32:38.615477: Current learning rate: 0.00709 +2026-04-12 02:34:21.455784: train_loss -0.3988 +2026-04-12 02:34:21.464194: val_loss -0.3601 +2026-04-12 02:34:21.468041: Pseudo dice [0.0, 0.0, 0.751, 0.4262, 0.5621, 0.8296, 0.5481] +2026-04-12 02:34:21.474783: Epoch time: 102.85 s +2026-04-12 02:34:22.688095: +2026-04-12 02:34:22.691181: Epoch 1270 +2026-04-12 02:34:22.694407: Current learning rate: 0.00709 +2026-04-12 02:36:05.154501: train_loss -0.3972 +2026-04-12 02:36:05.168500: val_loss -0.3473 +2026-04-12 02:36:05.171489: Pseudo dice [0.0, 0.0, 0.7292, 0.6697, 0.5925, 0.6859, 0.6151] +2026-04-12 02:36:05.174048: Epoch time: 102.47 s +2026-04-12 02:36:06.415147: +2026-04-12 02:36:06.417324: Epoch 1271 +2026-04-12 02:36:06.419506: Current learning rate: 0.00709 +2026-04-12 02:37:48.795859: train_loss -0.3822 +2026-04-12 02:37:48.806504: val_loss -0.3496 +2026-04-12 02:37:48.809499: Pseudo dice [0.0, 0.0, 0.6988, 0.5849, 0.5408, 0.6161, 0.4925] +2026-04-12 02:37:48.812535: Epoch time: 102.38 s +2026-04-12 02:37:50.010458: +2026-04-12 02:37:50.012890: Epoch 1272 +2026-04-12 02:37:50.016241: Current learning rate: 0.00709 +2026-04-12 02:39:32.222739: train_loss -0.3686 +2026-04-12 02:39:32.234837: val_loss -0.3579 +2026-04-12 02:39:32.237719: Pseudo dice [0.0, 0.0, 0.7566, 0.2654, 0.3591, 0.4723, 0.8408] +2026-04-12 02:39:32.242709: Epoch time: 102.22 s +2026-04-12 02:39:33.442369: +2026-04-12 02:39:33.445431: Epoch 1273 +2026-04-12 02:39:33.448261: Current learning rate: 0.00708 +2026-04-12 02:41:15.920231: train_loss -0.3819 +2026-04-12 02:41:15.928760: val_loss -0.3452 +2026-04-12 02:41:15.931525: Pseudo dice [0.0, 0.0, 0.6508, 0.0835, 0.5027, 0.6909, 0.8537] +2026-04-12 02:41:15.933871: Epoch time: 102.48 s +2026-04-12 02:41:17.146935: +2026-04-12 02:41:17.149138: Epoch 1274 +2026-04-12 02:41:17.151614: Current learning rate: 0.00708 +2026-04-12 02:42:59.486359: train_loss -0.3958 +2026-04-12 02:42:59.493517: val_loss -0.3475 +2026-04-12 02:42:59.497038: Pseudo dice [0.0, 0.0, 0.7505, 0.5286, 0.2789, 0.5742, 0.8749] +2026-04-12 02:42:59.500304: Epoch time: 102.34 s +2026-04-12 02:43:00.708213: +2026-04-12 02:43:00.710673: Epoch 1275 +2026-04-12 02:43:00.713696: Current learning rate: 0.00708 +2026-04-12 02:44:43.053402: train_loss -0.3883 +2026-04-12 02:44:43.062666: val_loss -0.3269 +2026-04-12 02:44:43.066786: Pseudo dice [0.0, 0.0, 0.4881, 0.0831, 0.4973, 0.3663, 0.484] +2026-04-12 02:44:43.070635: Epoch time: 102.35 s +2026-04-12 02:44:44.253187: +2026-04-12 02:44:44.255602: Epoch 1276 +2026-04-12 02:44:44.259006: Current learning rate: 0.00708 +2026-04-12 02:46:27.101259: train_loss -0.3887 +2026-04-12 02:46:27.109403: val_loss -0.3589 +2026-04-12 02:46:27.112601: Pseudo dice [0.0, 0.0, 0.7794, 0.29, 0.5143, 0.67, 0.8699] +2026-04-12 02:46:27.115846: Epoch time: 102.85 s +2026-04-12 02:46:29.474143: +2026-04-12 02:46:29.475966: Epoch 1277 +2026-04-12 02:46:29.478015: Current learning rate: 0.00707 +2026-04-12 02:48:13.242860: train_loss -0.3626 +2026-04-12 02:48:13.251359: val_loss -0.3312 +2026-04-12 02:48:13.254679: Pseudo dice [0.0, 0.0, 0.542, 0.5542, 0.4014, 0.7041, 0.8424] +2026-04-12 02:48:13.258862: Epoch time: 103.77 s +2026-04-12 02:48:14.467021: +2026-04-12 02:48:14.470067: Epoch 1278 +2026-04-12 02:48:14.473397: Current learning rate: 0.00707 +2026-04-12 02:49:56.712409: train_loss -0.3844 +2026-04-12 02:49:56.720704: val_loss -0.3261 +2026-04-12 02:49:56.724761: Pseudo dice [0.0, 0.0, 0.7522, 0.6727, 0.4915, 0.2557, 0.6991] +2026-04-12 02:49:56.727788: Epoch time: 102.25 s +2026-04-12 02:49:57.935753: +2026-04-12 02:49:57.938370: Epoch 1279 +2026-04-12 02:49:57.941213: Current learning rate: 0.00707 +2026-04-12 02:51:40.415904: train_loss -0.3915 +2026-04-12 02:51:40.424670: val_loss -0.362 +2026-04-12 02:51:40.427825: Pseudo dice [0.0, 0.0, 0.7676, 0.2874, 0.5052, 0.8019, 0.9043] +2026-04-12 02:51:40.430960: Epoch time: 102.48 s +2026-04-12 02:51:41.682474: +2026-04-12 02:51:41.685129: Epoch 1280 +2026-04-12 02:51:41.688255: Current learning rate: 0.00707 +2026-04-12 02:53:23.270511: train_loss -0.3944 +2026-04-12 02:53:23.278277: val_loss -0.3128 +2026-04-12 02:53:23.281777: Pseudo dice [0.0, 0.0, 0.665, 0.5555, 0.3159, 0.7106, 0.8602] +2026-04-12 02:53:23.285047: Epoch time: 101.59 s +2026-04-12 02:53:24.464214: +2026-04-12 02:53:24.467289: Epoch 1281 +2026-04-12 02:53:24.469923: Current learning rate: 0.00707 +2026-04-12 02:55:07.163604: train_loss -0.3802 +2026-04-12 02:55:07.170676: val_loss -0.3429 +2026-04-12 02:55:07.173261: Pseudo dice [0.0, 0.0, 0.8085, 0.7258, 0.5751, 0.5275, 0.778] +2026-04-12 02:55:07.177269: Epoch time: 102.7 s +2026-04-12 02:55:08.376972: +2026-04-12 02:55:08.379848: Epoch 1282 +2026-04-12 02:55:08.382355: Current learning rate: 0.00706 +2026-04-12 02:56:50.125695: train_loss -0.4042 +2026-04-12 02:56:50.150957: val_loss -0.3561 +2026-04-12 02:56:50.154377: Pseudo dice [0.0, 0.0, 0.6042, 0.7641, 0.6112, 0.8704, 0.836] +2026-04-12 02:56:50.157799: Epoch time: 101.75 s +2026-04-12 02:56:51.336035: +2026-04-12 02:56:51.340406: Epoch 1283 +2026-04-12 02:56:51.344266: Current learning rate: 0.00706 +2026-04-12 02:58:33.875814: train_loss -0.4139 +2026-04-12 02:58:33.884686: val_loss -0.3266 +2026-04-12 02:58:33.887290: Pseudo dice [0.0, 0.0, 0.7785, 0.5159, 0.5531, 0.3012, 0.7625] +2026-04-12 02:58:33.889821: Epoch time: 102.54 s +2026-04-12 02:58:35.121747: +2026-04-12 02:58:35.124461: Epoch 1284 +2026-04-12 02:58:35.127189: Current learning rate: 0.00706 +2026-04-12 03:00:17.541488: train_loss -0.4088 +2026-04-12 03:00:17.550106: val_loss -0.3471 +2026-04-12 03:00:17.552486: Pseudo dice [0.0, 0.0, 0.7642, 0.3409, 0.5668, 0.6494, 0.5284] +2026-04-12 03:00:17.555300: Epoch time: 102.42 s +2026-04-12 03:00:18.747962: +2026-04-12 03:00:18.750268: Epoch 1285 +2026-04-12 03:00:18.757041: Current learning rate: 0.00706 +2026-04-12 03:02:02.037886: train_loss -0.3855 +2026-04-12 03:02:02.049552: val_loss -0.3477 +2026-04-12 03:02:02.053010: Pseudo dice [0.0, 0.0, 0.7595, 0.7208, 0.3752, 0.5665, 0.4921] +2026-04-12 03:02:02.057510: Epoch time: 103.29 s +2026-04-12 03:02:03.262508: +2026-04-12 03:02:03.267063: Epoch 1286 +2026-04-12 03:02:03.270247: Current learning rate: 0.00705 +2026-04-12 03:03:45.795987: train_loss -0.3942 +2026-04-12 03:03:45.805487: val_loss -0.3395 +2026-04-12 03:03:45.807758: Pseudo dice [0.0, 0.0, 0.7368, 0.1067, 0.5266, 0.518, 0.6463] +2026-04-12 03:03:45.811128: Epoch time: 102.54 s +2026-04-12 03:03:46.982121: +2026-04-12 03:03:46.984344: Epoch 1287 +2026-04-12 03:03:46.986491: Current learning rate: 0.00705 +2026-04-12 03:05:30.154586: train_loss -0.3865 +2026-04-12 03:05:30.163953: val_loss -0.3476 +2026-04-12 03:05:30.166564: Pseudo dice [0.0, 0.0, 0.7423, 0.5054, 0.5285, 0.6944, 0.8307] +2026-04-12 03:05:30.169463: Epoch time: 103.18 s +2026-04-12 03:05:31.412279: +2026-04-12 03:05:31.415189: Epoch 1288 +2026-04-12 03:05:31.422860: Current learning rate: 0.00705 +2026-04-12 03:07:14.122527: train_loss -0.3849 +2026-04-12 03:07:14.129613: val_loss -0.3448 +2026-04-12 03:07:14.131919: Pseudo dice [0.0, 0.0, 0.4115, 0.5427, 0.4264, 0.601, 0.5673] +2026-04-12 03:07:14.134744: Epoch time: 102.71 s +2026-04-12 03:07:15.314491: +2026-04-12 03:07:15.317244: Epoch 1289 +2026-04-12 03:07:15.320727: Current learning rate: 0.00705 +2026-04-12 03:08:58.505958: train_loss -0.393 +2026-04-12 03:08:58.515413: val_loss -0.3419 +2026-04-12 03:08:58.518839: Pseudo dice [0.0, 0.0, 0.8823, 0.3304, 0.5469, 0.6562, 0.6514] +2026-04-12 03:08:58.522449: Epoch time: 103.19 s +2026-04-12 03:08:59.743168: +2026-04-12 03:08:59.745679: Epoch 1290 +2026-04-12 03:08:59.748948: Current learning rate: 0.00704 +2026-04-12 03:10:42.291651: train_loss -0.3906 +2026-04-12 03:10:42.299506: val_loss -0.317 +2026-04-12 03:10:42.302341: Pseudo dice [0.0, 0.0, 0.6971, 0.2592, 0.3709, 0.4323, 0.636] +2026-04-12 03:10:42.305607: Epoch time: 102.55 s +2026-04-12 03:10:43.518496: +2026-04-12 03:10:43.521412: Epoch 1291 +2026-04-12 03:10:43.524531: Current learning rate: 0.00704 +2026-04-12 03:12:26.518027: train_loss -0.3974 +2026-04-12 03:12:26.528849: val_loss -0.3246 +2026-04-12 03:12:26.533314: Pseudo dice [0.0, 0.0, 0.5824, 0.0844, 0.3353, 0.5829, 0.3038] +2026-04-12 03:12:26.537006: Epoch time: 103.0 s +2026-04-12 03:12:27.771838: +2026-04-12 03:12:27.774753: Epoch 1292 +2026-04-12 03:12:27.777634: Current learning rate: 0.00704 +2026-04-12 03:14:10.371246: train_loss -0.3664 +2026-04-12 03:14:10.387636: val_loss -0.3316 +2026-04-12 03:14:10.390287: Pseudo dice [0.0, 0.0, 0.7565, 0.0023, 0.416, 0.2828, 0.6437] +2026-04-12 03:14:10.393217: Epoch time: 102.6 s +2026-04-12 03:14:11.561461: +2026-04-12 03:14:11.563750: Epoch 1293 +2026-04-12 03:14:11.566226: Current learning rate: 0.00704 +2026-04-12 03:15:54.100789: train_loss -0.3842 +2026-04-12 03:15:54.109529: val_loss -0.3276 +2026-04-12 03:15:54.112205: Pseudo dice [0.0, 0.0, 0.469, 0.4092, 0.4724, 0.7698, 0.6972] +2026-04-12 03:15:54.115782: Epoch time: 102.54 s +2026-04-12 03:15:55.289428: +2026-04-12 03:15:55.292130: Epoch 1294 +2026-04-12 03:15:55.296303: Current learning rate: 0.00703 +2026-04-12 03:17:38.456332: train_loss -0.3925 +2026-04-12 03:17:38.464462: val_loss -0.3605 +2026-04-12 03:17:38.467083: Pseudo dice [0.0, 0.0, 0.8189, 0.2426, 0.6205, 0.5665, 0.7394] +2026-04-12 03:17:38.470367: Epoch time: 103.17 s +2026-04-12 03:17:39.676178: +2026-04-12 03:17:39.678441: Epoch 1295 +2026-04-12 03:17:39.680637: Current learning rate: 0.00703 +2026-04-12 03:19:22.820065: train_loss -0.4087 +2026-04-12 03:19:22.832030: val_loss -0.3367 +2026-04-12 03:19:22.835258: Pseudo dice [0.0, 0.0, 0.6822, 0.2277, 0.4013, 0.4914, 0.9026] +2026-04-12 03:19:22.838957: Epoch time: 103.15 s +2026-04-12 03:19:24.051101: +2026-04-12 03:19:24.054926: Epoch 1296 +2026-04-12 03:19:24.057875: Current learning rate: 0.00703 +2026-04-12 03:21:06.375777: train_loss -0.3992 +2026-04-12 03:21:06.383597: val_loss -0.3394 +2026-04-12 03:21:06.385851: Pseudo dice [0.0, 0.0, 0.6754, 0.5515, 0.385, 0.2191, 0.7722] +2026-04-12 03:21:06.388695: Epoch time: 102.33 s +2026-04-12 03:21:07.582541: +2026-04-12 03:21:07.586091: Epoch 1297 +2026-04-12 03:21:07.588588: Current learning rate: 0.00703 +2026-04-12 03:22:52.187932: train_loss -0.3732 +2026-04-12 03:22:52.198328: val_loss -0.338 +2026-04-12 03:22:52.201204: Pseudo dice [0.0, 0.0, 0.6862, 0.2568, 0.4064, 0.8704, 0.9074] +2026-04-12 03:22:52.204813: Epoch time: 104.61 s +2026-04-12 03:22:53.361681: +2026-04-12 03:22:53.364483: Epoch 1298 +2026-04-12 03:22:53.367279: Current learning rate: 0.00703 +2026-04-12 03:24:36.512051: train_loss -0.4045 +2026-04-12 03:24:36.526981: val_loss -0.3543 +2026-04-12 03:24:36.530860: Pseudo dice [0.0, 0.0, 0.6855, 0.5463, 0.4315, 0.7522, 0.9179] +2026-04-12 03:24:36.535086: Epoch time: 103.15 s +2026-04-12 03:24:37.705177: +2026-04-12 03:24:37.707442: Epoch 1299 +2026-04-12 03:24:37.710001: Current learning rate: 0.00702 +2026-04-12 03:26:20.641229: train_loss -0.3616 +2026-04-12 03:26:20.649243: val_loss -0.3334 +2026-04-12 03:26:20.652231: Pseudo dice [0.0, 0.0, 0.6394, 0.0061, 0.5912, 0.4849, 0.633] +2026-04-12 03:26:20.656993: Epoch time: 102.94 s +2026-04-12 03:26:23.728396: +2026-04-12 03:26:23.730396: Epoch 1300 +2026-04-12 03:26:23.732741: Current learning rate: 0.00702 +2026-04-12 03:28:07.169163: train_loss -0.37 +2026-04-12 03:28:07.178868: val_loss -0.3295 +2026-04-12 03:28:07.184207: Pseudo dice [0.0, 0.0, 0.4278, 0.265, 0.5779, 0.706, 0.3381] +2026-04-12 03:28:07.188041: Epoch time: 103.44 s +2026-04-12 03:28:08.374598: +2026-04-12 03:28:08.376936: Epoch 1301 +2026-04-12 03:28:08.379403: Current learning rate: 0.00702 +2026-04-12 03:29:50.948655: train_loss -0.3928 +2026-04-12 03:29:50.957556: val_loss -0.3419 +2026-04-12 03:29:50.960143: Pseudo dice [0.0, 0.0, 0.8055, 0.5465, 0.6555, 0.5306, 0.6164] +2026-04-12 03:29:50.963035: Epoch time: 102.58 s +2026-04-12 03:29:52.185740: +2026-04-12 03:29:52.187766: Epoch 1302 +2026-04-12 03:29:52.189953: Current learning rate: 0.00702 +2026-04-12 03:31:35.560695: train_loss -0.3868 +2026-04-12 03:31:35.570060: val_loss -0.3555 +2026-04-12 03:31:35.573530: Pseudo dice [0.0, 0.0, 0.8122, 0.334, 0.5381, 0.4879, 0.8803] +2026-04-12 03:31:35.576970: Epoch time: 103.38 s +2026-04-12 03:31:36.777635: +2026-04-12 03:31:36.779933: Epoch 1303 +2026-04-12 03:31:36.782586: Current learning rate: 0.00701 +2026-04-12 03:33:19.584741: train_loss -0.4066 +2026-04-12 03:33:19.614221: val_loss -0.3772 +2026-04-12 03:33:19.635689: Pseudo dice [0.0, 0.0, 0.7805, 0.7547, 0.6321, 0.7789, 0.733] +2026-04-12 03:33:19.645309: Epoch time: 102.81 s +2026-04-12 03:33:20.823496: +2026-04-12 03:33:20.826108: Epoch 1304 +2026-04-12 03:33:20.830601: Current learning rate: 0.00701 +2026-04-12 03:35:02.929863: train_loss -0.4019 +2026-04-12 03:35:02.936141: val_loss -0.3031 +2026-04-12 03:35:02.938457: Pseudo dice [0.0, 0.0, 0.3969, 0.0738, 0.5686, 0.686, 0.0458] +2026-04-12 03:35:02.941136: Epoch time: 102.11 s +2026-04-12 03:35:04.177740: +2026-04-12 03:35:04.180461: Epoch 1305 +2026-04-12 03:35:04.183458: Current learning rate: 0.00701 +2026-04-12 03:36:46.736233: train_loss -0.3978 +2026-04-12 03:36:46.744635: val_loss -0.3652 +2026-04-12 03:36:46.747270: Pseudo dice [0.0, 0.0, 0.7748, 0.1799, 0.489, 0.706, 0.8224] +2026-04-12 03:36:46.750050: Epoch time: 102.56 s +2026-04-12 03:36:47.954162: +2026-04-12 03:36:47.956195: Epoch 1306 +2026-04-12 03:36:47.961463: Current learning rate: 0.00701 +2026-04-12 03:38:31.156043: train_loss -0.3947 +2026-04-12 03:38:31.165324: val_loss -0.3168 +2026-04-12 03:38:31.170937: Pseudo dice [0.0, 0.0, 0.7355, 0.2088, 0.5563, 0.5822, 0.839] +2026-04-12 03:38:31.176977: Epoch time: 103.2 s +2026-04-12 03:38:32.432807: +2026-04-12 03:38:32.436566: Epoch 1307 +2026-04-12 03:38:32.439739: Current learning rate: 0.007 +2026-04-12 03:40:14.911552: train_loss -0.3774 +2026-04-12 03:40:14.925371: val_loss -0.3473 +2026-04-12 03:40:14.928056: Pseudo dice [0.0, 0.0, 0.8485, 0.7391, 0.5811, 0.3925, 0.652] +2026-04-12 03:40:14.931396: Epoch time: 102.48 s +2026-04-12 03:40:16.129844: +2026-04-12 03:40:16.132166: Epoch 1308 +2026-04-12 03:40:16.134474: Current learning rate: 0.007 +2026-04-12 03:41:58.892229: train_loss -0.3776 +2026-04-12 03:41:58.902140: val_loss -0.3059 +2026-04-12 03:41:58.904364: Pseudo dice [0.0, 0.0, 0.8032, 0.1853, 0.3772, 0.4703, 0.1211] +2026-04-12 03:41:58.907966: Epoch time: 102.77 s +2026-04-12 03:42:00.163427: +2026-04-12 03:42:00.166529: Epoch 1309 +2026-04-12 03:42:00.169434: Current learning rate: 0.007 +2026-04-12 03:43:42.711422: train_loss -0.3762 +2026-04-12 03:43:42.718446: val_loss -0.3122 +2026-04-12 03:43:42.721359: Pseudo dice [0.0, 0.0, 0.716, 0.4191, 0.3934, 0.1959, 0.7754] +2026-04-12 03:43:42.724291: Epoch time: 102.55 s +2026-04-12 03:43:43.966467: +2026-04-12 03:43:43.969173: Epoch 1310 +2026-04-12 03:43:43.975558: Current learning rate: 0.007 +2026-04-12 03:45:27.035299: train_loss -0.3939 +2026-04-12 03:45:27.042204: val_loss -0.3489 +2026-04-12 03:45:27.045161: Pseudo dice [0.0, 0.0, 0.7864, 0.4206, 0.5801, 0.7608, 0.5186] +2026-04-12 03:45:27.048300: Epoch time: 103.07 s +2026-04-12 03:45:28.287101: +2026-04-12 03:45:28.289205: Epoch 1311 +2026-04-12 03:45:28.292012: Current learning rate: 0.00699 +2026-04-12 03:47:10.098769: train_loss -0.392 +2026-04-12 03:47:10.104944: val_loss -0.3145 +2026-04-12 03:47:10.107346: Pseudo dice [0.0, 0.0, 0.6016, 0.3946, 0.5918, 0.301, 0.6396] +2026-04-12 03:47:10.110445: Epoch time: 101.81 s +2026-04-12 03:47:11.321538: +2026-04-12 03:47:11.323785: Epoch 1312 +2026-04-12 03:47:11.326012: Current learning rate: 0.00699 +2026-04-12 03:48:54.583561: train_loss -0.397 +2026-04-12 03:48:54.590295: val_loss -0.3486 +2026-04-12 03:48:54.594263: Pseudo dice [0.0, 0.0, 0.5837, 0.1558, 0.5196, 0.8195, 0.8341] +2026-04-12 03:48:54.598155: Epoch time: 103.27 s +2026-04-12 03:48:55.778207: +2026-04-12 03:48:55.780587: Epoch 1313 +2026-04-12 03:48:55.782756: Current learning rate: 0.00699 +2026-04-12 03:50:38.432655: train_loss -0.3799 +2026-04-12 03:50:38.440316: val_loss -0.3123 +2026-04-12 03:50:38.443667: Pseudo dice [0.0, 0.0, 0.6062, 0.3457, 0.2996, 0.3376, 0.5097] +2026-04-12 03:50:38.446766: Epoch time: 102.66 s +2026-04-12 03:50:39.639126: +2026-04-12 03:50:39.642458: Epoch 1314 +2026-04-12 03:50:39.644840: Current learning rate: 0.00699 +2026-04-12 03:52:21.690795: train_loss -0.3635 +2026-04-12 03:52:21.696780: val_loss -0.3307 +2026-04-12 03:52:21.698982: Pseudo dice [0.0, 0.0, 0.6663, 0.396, 0.3718, 0.4141, 0.516] +2026-04-12 03:52:21.701522: Epoch time: 102.05 s +2026-04-12 03:52:22.909424: +2026-04-12 03:52:22.911556: Epoch 1315 +2026-04-12 03:52:22.913669: Current learning rate: 0.00699 +2026-04-12 03:54:05.069327: train_loss -0.4037 +2026-04-12 03:54:05.076123: val_loss -0.343 +2026-04-12 03:54:05.078172: Pseudo dice [0.0, 0.0, 0.7471, 0.3792, 0.5019, 0.7892, 0.8736] +2026-04-12 03:54:05.080803: Epoch time: 102.16 s +2026-04-12 03:54:06.303099: +2026-04-12 03:54:06.305408: Epoch 1316 +2026-04-12 03:54:06.307808: Current learning rate: 0.00698 +2026-04-12 03:55:48.650561: train_loss -0.3922 +2026-04-12 03:55:48.658275: val_loss -0.3699 +2026-04-12 03:55:48.660723: Pseudo dice [0.0, 0.0, 0.6574, 0.7799, 0.4745, 0.5048, 0.8962] +2026-04-12 03:55:48.663465: Epoch time: 102.35 s +2026-04-12 03:55:51.070750: +2026-04-12 03:55:51.072855: Epoch 1317 +2026-04-12 03:55:51.075155: Current learning rate: 0.00698 +2026-04-12 03:57:33.305437: train_loss -0.3701 +2026-04-12 03:57:33.312671: val_loss -0.2984 +2026-04-12 03:57:33.315211: Pseudo dice [0.0, 0.0, 0.5055, 0.4604, 0.3108, 0.5719, 0.5121] +2026-04-12 03:57:33.317812: Epoch time: 102.24 s +2026-04-12 03:57:34.557722: +2026-04-12 03:57:34.560246: Epoch 1318 +2026-04-12 03:57:34.562325: Current learning rate: 0.00698 +2026-04-12 03:59:16.681045: train_loss -0.381 +2026-04-12 03:59:16.688092: val_loss -0.3305 +2026-04-12 03:59:16.690689: Pseudo dice [0.0, 0.0, 0.5857, 0.4901, 0.4285, 0.5346, 0.7714] +2026-04-12 03:59:16.693089: Epoch time: 102.13 s +2026-04-12 03:59:17.906512: +2026-04-12 03:59:17.908769: Epoch 1319 +2026-04-12 03:59:17.911228: Current learning rate: 0.00698 +2026-04-12 04:01:00.523655: train_loss -0.3968 +2026-04-12 04:01:00.531517: val_loss -0.3439 +2026-04-12 04:01:00.534474: Pseudo dice [0.0, 0.0, 0.7897, 0.7397, 0.4872, 0.7683, 0.7083] +2026-04-12 04:01:00.537552: Epoch time: 102.62 s +2026-04-12 04:01:01.761379: +2026-04-12 04:01:01.764127: Epoch 1320 +2026-04-12 04:01:01.767094: Current learning rate: 0.00697 +2026-04-12 04:02:44.998794: train_loss -0.3986 +2026-04-12 04:02:45.005952: val_loss -0.3514 +2026-04-12 04:02:45.008578: Pseudo dice [0.0, 0.0, 0.6758, 0.6893, 0.3345, 0.7757, 0.3691] +2026-04-12 04:02:45.011858: Epoch time: 103.24 s +2026-04-12 04:02:46.185006: +2026-04-12 04:02:46.187005: Epoch 1321 +2026-04-12 04:02:46.189444: Current learning rate: 0.00697 +2026-04-12 04:04:28.301543: train_loss -0.3941 +2026-04-12 04:04:28.309338: val_loss -0.3246 +2026-04-12 04:04:28.312001: Pseudo dice [0.0, 0.0, 0.7978, 0.7895, 0.4477, 0.7373, 0.353] +2026-04-12 04:04:28.315342: Epoch time: 102.12 s +2026-04-12 04:04:29.532106: +2026-04-12 04:04:29.548060: Epoch 1322 +2026-04-12 04:04:29.561756: Current learning rate: 0.00697 +2026-04-12 04:06:11.398673: train_loss -0.3761 +2026-04-12 04:06:11.419901: val_loss -0.339 +2026-04-12 04:06:11.421994: Pseudo dice [0.0, 0.0, 0.8622, 0.784, 0.522, 0.5174, 0.8063] +2026-04-12 04:06:11.424998: Epoch time: 101.87 s +2026-04-12 04:06:12.618023: +2026-04-12 04:06:12.621296: Epoch 1323 +2026-04-12 04:06:12.625592: Current learning rate: 0.00697 +2026-04-12 04:07:55.153000: train_loss -0.3852 +2026-04-12 04:07:55.160612: val_loss -0.3506 +2026-04-12 04:07:55.162935: Pseudo dice [0.0, 0.0, 0.6876, 0.3043, 0.5946, 0.7749, 0.6103] +2026-04-12 04:07:55.165493: Epoch time: 102.54 s +2026-04-12 04:07:56.364310: +2026-04-12 04:07:56.367179: Epoch 1324 +2026-04-12 04:07:56.371003: Current learning rate: 0.00696 +2026-04-12 04:09:38.550126: train_loss -0.3883 +2026-04-12 04:09:38.557734: val_loss -0.352 +2026-04-12 04:09:38.559823: Pseudo dice [0.0, 0.0, 0.7737, 0.3684, 0.3688, 0.7396, 0.7743] +2026-04-12 04:09:38.562690: Epoch time: 102.19 s +2026-04-12 04:09:39.739126: +2026-04-12 04:09:39.741142: Epoch 1325 +2026-04-12 04:09:39.743675: Current learning rate: 0.00696 +2026-04-12 04:11:21.546420: train_loss -0.3759 +2026-04-12 04:11:21.555282: val_loss -0.3328 +2026-04-12 04:11:21.558205: Pseudo dice [0.0, 0.0, 0.6275, 0.4036, 0.5385, 0.6613, 0.5723] +2026-04-12 04:11:21.561516: Epoch time: 101.81 s +2026-04-12 04:11:22.746628: +2026-04-12 04:11:22.750004: Epoch 1326 +2026-04-12 04:11:22.753531: Current learning rate: 0.00696 +2026-04-12 04:13:05.424039: train_loss -0.3534 +2026-04-12 04:13:05.430707: val_loss -0.3473 +2026-04-12 04:13:05.432586: Pseudo dice [0.0, 0.0, 0.5886, 0.1126, 0.4015, 0.6739, 0.7817] +2026-04-12 04:13:05.435339: Epoch time: 102.68 s +2026-04-12 04:13:06.677671: +2026-04-12 04:13:06.680533: Epoch 1327 +2026-04-12 04:13:06.683301: Current learning rate: 0.00696 +2026-04-12 04:14:48.997473: train_loss -0.3818 +2026-04-12 04:14:49.003443: val_loss -0.3243 +2026-04-12 04:14:49.005444: Pseudo dice [0.0, 0.0, 0.7931, 0.5119, 0.4676, 0.6421, 0.3747] +2026-04-12 04:14:49.007552: Epoch time: 102.32 s +2026-04-12 04:14:50.250266: +2026-04-12 04:14:50.252678: Epoch 1328 +2026-04-12 04:14:50.255275: Current learning rate: 0.00696 +2026-04-12 04:16:32.653653: train_loss -0.3848 +2026-04-12 04:16:32.660930: val_loss -0.3516 +2026-04-12 04:16:32.664251: Pseudo dice [0.0, 0.0, 0.8007, 0.71, 0.4623, 0.3516, 0.4172] +2026-04-12 04:16:32.667795: Epoch time: 102.41 s +2026-04-12 04:16:33.883717: +2026-04-12 04:16:33.886126: Epoch 1329 +2026-04-12 04:16:33.888187: Current learning rate: 0.00695 +2026-04-12 04:18:16.968284: train_loss -0.4014 +2026-04-12 04:18:16.976331: val_loss -0.3479 +2026-04-12 04:18:16.979043: Pseudo dice [0.0, 0.0, 0.8001, 0.3008, 0.4826, 0.6617, 0.8894] +2026-04-12 04:18:16.982810: Epoch time: 103.09 s +2026-04-12 04:18:18.173391: +2026-04-12 04:18:18.175429: Epoch 1330 +2026-04-12 04:18:18.178138: Current learning rate: 0.00695 +2026-04-12 04:20:00.772290: train_loss -0.3933 +2026-04-12 04:20:00.780359: val_loss -0.3861 +2026-04-12 04:20:00.784587: Pseudo dice [0.0, 0.0, 0.6499, 0.3712, 0.5136, 0.721, 0.8366] +2026-04-12 04:20:00.787207: Epoch time: 102.6 s +2026-04-12 04:20:01.974382: +2026-04-12 04:20:01.976924: Epoch 1331 +2026-04-12 04:20:01.978952: Current learning rate: 0.00695 +2026-04-12 04:21:44.209769: train_loss -0.4023 +2026-04-12 04:21:44.217812: val_loss -0.3584 +2026-04-12 04:21:44.220176: Pseudo dice [0.0, 0.0, 0.7804, 0.5411, 0.6133, 0.342, 0.4082] +2026-04-12 04:21:44.222720: Epoch time: 102.24 s +2026-04-12 04:21:45.413349: +2026-04-12 04:21:45.415515: Epoch 1332 +2026-04-12 04:21:45.417722: Current learning rate: 0.00695 +2026-04-12 04:23:27.199945: train_loss -0.395 +2026-04-12 04:23:27.209323: val_loss -0.3438 +2026-04-12 04:23:27.214676: Pseudo dice [0.0, 0.0, 0.7629, 0.6444, 0.558, 0.553, 0.654] +2026-04-12 04:23:27.217441: Epoch time: 101.79 s +2026-04-12 04:23:28.388999: +2026-04-12 04:23:28.391397: Epoch 1333 +2026-04-12 04:23:28.393484: Current learning rate: 0.00694 +2026-04-12 04:25:11.360201: train_loss -0.3918 +2026-04-12 04:25:11.368498: val_loss -0.3306 +2026-04-12 04:25:11.371175: Pseudo dice [0.0, 0.0, 0.723, 0.0671, 0.4094, 0.4297, 0.3089] +2026-04-12 04:25:11.374014: Epoch time: 102.97 s +2026-04-12 04:25:12.559965: +2026-04-12 04:25:12.562287: Epoch 1334 +2026-04-12 04:25:12.564702: Current learning rate: 0.00694 +2026-04-12 04:26:55.019618: train_loss -0.3957 +2026-04-12 04:26:55.027275: val_loss -0.3317 +2026-04-12 04:26:55.032044: Pseudo dice [0.0, 0.0, 0.8094, 0.4942, 0.544, 0.4403, 0.7519] +2026-04-12 04:26:55.034760: Epoch time: 102.46 s +2026-04-12 04:26:56.304411: +2026-04-12 04:26:56.307163: Epoch 1335 +2026-04-12 04:26:56.310106: Current learning rate: 0.00694 +2026-04-12 04:28:38.039085: train_loss -0.3934 +2026-04-12 04:28:38.047689: val_loss -0.3451 +2026-04-12 04:28:38.050879: Pseudo dice [0.2492, 0.0, 0.3306, 0.5316, 0.4615, 0.7843, 0.9023] +2026-04-12 04:28:38.054253: Epoch time: 101.74 s +2026-04-12 04:28:39.303319: +2026-04-12 04:28:39.306298: Epoch 1336 +2026-04-12 04:28:39.308905: Current learning rate: 0.00694 +2026-04-12 04:30:21.912427: train_loss -0.3935 +2026-04-12 04:30:21.922603: val_loss -0.35 +2026-04-12 04:30:21.925690: Pseudo dice [0.0, 0.0, 0.8338, 0.2557, 0.6603, 0.4179, 0.7881] +2026-04-12 04:30:21.928606: Epoch time: 102.61 s +2026-04-12 04:30:24.332839: +2026-04-12 04:30:24.335320: Epoch 1337 +2026-04-12 04:30:24.337377: Current learning rate: 0.00693 +2026-04-12 04:32:07.097817: train_loss -0.3961 +2026-04-12 04:32:07.105709: val_loss -0.3254 +2026-04-12 04:32:07.108901: Pseudo dice [0.0, 0.0, 0.5971, 0.3914, 0.4209, 0.7678, 0.747] +2026-04-12 04:32:07.111996: Epoch time: 102.77 s +2026-04-12 04:32:08.322731: +2026-04-12 04:32:08.325043: Epoch 1338 +2026-04-12 04:32:08.327846: Current learning rate: 0.00693 +2026-04-12 04:33:50.878241: train_loss -0.4148 +2026-04-12 04:33:50.885438: val_loss -0.3335 +2026-04-12 04:33:50.887383: Pseudo dice [0.164, 0.0, 0.7809, 0.6018, 0.4139, 0.5318, 0.6517] +2026-04-12 04:33:50.889835: Epoch time: 102.56 s +2026-04-12 04:33:52.110640: +2026-04-12 04:33:52.112733: Epoch 1339 +2026-04-12 04:33:52.115006: Current learning rate: 0.00693 +2026-04-12 04:35:34.098360: train_loss -0.3971 +2026-04-12 04:35:34.104446: val_loss -0.3583 +2026-04-12 04:35:34.108115: Pseudo dice [0.4087, 0.0, 0.733, 0.4148, 0.5543, 0.8602, 0.5487] +2026-04-12 04:35:34.110980: Epoch time: 101.99 s +2026-04-12 04:35:35.343285: +2026-04-12 04:35:35.345516: Epoch 1340 +2026-04-12 04:35:35.347635: Current learning rate: 0.00693 +2026-04-12 04:37:17.963325: train_loss -0.364 +2026-04-12 04:37:17.970299: val_loss -0.3465 +2026-04-12 04:37:17.972205: Pseudo dice [0.329, 0.0, 0.6497, 0.2068, 0.3582, 0.7273, 0.9117] +2026-04-12 04:37:17.974360: Epoch time: 102.62 s +2026-04-12 04:37:19.215449: +2026-04-12 04:37:19.217937: Epoch 1341 +2026-04-12 04:37:19.220089: Current learning rate: 0.00692 +2026-04-12 04:39:01.265662: train_loss -0.3666 +2026-04-12 04:39:01.272063: val_loss -0.3079 +2026-04-12 04:39:01.274761: Pseudo dice [0.0, 0.0, 0.5491, 0.3347, 0.3636, 0.3976, 0.506] +2026-04-12 04:39:01.277601: Epoch time: 102.05 s +2026-04-12 04:39:02.509393: +2026-04-12 04:39:02.521089: Epoch 1342 +2026-04-12 04:39:02.528054: Current learning rate: 0.00692 +2026-04-12 04:40:45.270948: train_loss -0.3479 +2026-04-12 04:40:45.278843: val_loss -0.2843 +2026-04-12 04:40:45.281899: Pseudo dice [0.0763, 0.0, 0.7315, 0.597, 0.4393, 0.2989, 0.3492] +2026-04-12 04:40:45.286283: Epoch time: 102.76 s +2026-04-12 04:40:46.496857: +2026-04-12 04:40:46.499667: Epoch 1343 +2026-04-12 04:40:46.502137: Current learning rate: 0.00692 +2026-04-12 04:42:28.308463: train_loss -0.3784 +2026-04-12 04:42:28.318214: val_loss -0.336 +2026-04-12 04:42:28.322390: Pseudo dice [0.0011, 0.0, 0.8312, 0.6676, 0.5059, 0.2373, 0.7841] +2026-04-12 04:42:28.327042: Epoch time: 101.81 s +2026-04-12 04:42:29.551191: +2026-04-12 04:42:29.553199: Epoch 1344 +2026-04-12 04:42:29.555492: Current learning rate: 0.00692 +2026-04-12 04:44:11.642294: train_loss -0.3663 +2026-04-12 04:44:11.649587: val_loss -0.3129 +2026-04-12 04:44:11.651643: Pseudo dice [0.0, 0.0, 0.6388, 0.3899, 0.5063, 0.5736, 0.7173] +2026-04-12 04:44:11.654407: Epoch time: 102.09 s +2026-04-12 04:44:12.922266: +2026-04-12 04:44:12.924639: Epoch 1345 +2026-04-12 04:44:12.926931: Current learning rate: 0.00692 +2026-04-12 04:45:54.815611: train_loss -0.3579 +2026-04-12 04:45:54.823009: val_loss -0.3742 +2026-04-12 04:45:54.825028: Pseudo dice [0.0, 0.0, 0.6542, 0.7943, 0.4969, 0.3415, 0.4835] +2026-04-12 04:45:54.828378: Epoch time: 101.9 s +2026-04-12 04:45:56.028912: +2026-04-12 04:45:56.031361: Epoch 1346 +2026-04-12 04:45:56.033605: Current learning rate: 0.00691 +2026-04-12 04:47:38.542366: train_loss -0.3947 +2026-04-12 04:47:38.574789: val_loss -0.3605 +2026-04-12 04:47:38.585376: Pseudo dice [0.0, 0.0, 0.752, 0.5968, 0.5584, 0.6681, 0.6951] +2026-04-12 04:47:38.602714: Epoch time: 102.52 s +2026-04-12 04:47:39.855324: +2026-04-12 04:47:39.859392: Epoch 1347 +2026-04-12 04:47:39.864597: Current learning rate: 0.00691 +2026-04-12 04:49:21.610639: train_loss -0.3981 +2026-04-12 04:49:21.619635: val_loss -0.3472 +2026-04-12 04:49:21.622151: Pseudo dice [0.0, 0.0, 0.69, 0.6783, 0.4768, 0.4461, 0.7242] +2026-04-12 04:49:21.625545: Epoch time: 101.76 s +2026-04-12 04:49:22.897154: +2026-04-12 04:49:22.900303: Epoch 1348 +2026-04-12 04:49:22.902662: Current learning rate: 0.00691 +2026-04-12 04:51:04.850400: train_loss -0.3993 +2026-04-12 04:51:04.860056: val_loss -0.3432 +2026-04-12 04:51:04.862661: Pseudo dice [0.0, 0.0, 0.8034, 0.7206, 0.5289, 0.8552, 0.5512] +2026-04-12 04:51:04.865749: Epoch time: 101.96 s +2026-04-12 04:51:06.088940: +2026-04-12 04:51:06.091989: Epoch 1349 +2026-04-12 04:51:06.095252: Current learning rate: 0.00691 +2026-04-12 04:52:48.378957: train_loss -0.392 +2026-04-12 04:52:48.388340: val_loss -0.2976 +2026-04-12 04:52:48.390843: Pseudo dice [0.0, 0.0, 0.6202, 0.4489, 0.0505, 0.7888, 0.4885] +2026-04-12 04:52:48.394278: Epoch time: 102.29 s +2026-04-12 04:52:51.391530: +2026-04-12 04:52:51.394353: Epoch 1350 +2026-04-12 04:52:51.396167: Current learning rate: 0.0069 +2026-04-12 04:54:34.313238: train_loss -0.3708 +2026-04-12 04:54:34.320287: val_loss -0.3096 +2026-04-12 04:54:34.322752: Pseudo dice [0.0, 0.0, 0.7068, 0.2353, 0.2567, 0.6484, 0.1084] +2026-04-12 04:54:34.325084: Epoch time: 102.92 s +2026-04-12 04:54:35.543888: +2026-04-12 04:54:35.546077: Epoch 1351 +2026-04-12 04:54:35.548024: Current learning rate: 0.0069 +2026-04-12 04:56:17.154235: train_loss -0.398 +2026-04-12 04:56:17.160871: val_loss -0.3465 +2026-04-12 04:56:17.162935: Pseudo dice [0.0, 0.0, 0.8009, 0.823, 0.3617, 0.6314, 0.8307] +2026-04-12 04:56:17.165854: Epoch time: 101.61 s +2026-04-12 04:56:18.413908: +2026-04-12 04:56:18.416040: Epoch 1352 +2026-04-12 04:56:18.418245: Current learning rate: 0.0069 +2026-04-12 04:58:01.319620: train_loss -0.3983 +2026-04-12 04:58:01.327528: val_loss -0.3378 +2026-04-12 04:58:01.329985: Pseudo dice [0.2966, 0.0, 0.3749, 0.3595, 0.4734, 0.4812, 0.3694] +2026-04-12 04:58:01.332556: Epoch time: 102.91 s +2026-04-12 04:58:02.548619: +2026-04-12 04:58:02.550610: Epoch 1353 +2026-04-12 04:58:02.552833: Current learning rate: 0.0069 +2026-04-12 04:59:44.898620: train_loss -0.3904 +2026-04-12 04:59:44.909383: val_loss -0.3321 +2026-04-12 04:59:44.911555: Pseudo dice [0.5473, 0.0, 0.7382, 0.1696, 0.2601, 0.6841, 0.5717] +2026-04-12 04:59:44.914273: Epoch time: 102.35 s +2026-04-12 04:59:46.130375: +2026-04-12 04:59:46.132526: Epoch 1354 +2026-04-12 04:59:46.135169: Current learning rate: 0.00689 +2026-04-12 05:01:28.605491: train_loss -0.3785 +2026-04-12 05:01:28.616779: val_loss -0.3287 +2026-04-12 05:01:28.619679: Pseudo dice [0.0, 0.0, 0.735, 0.0154, 0.3708, 0.7538, 0.4762] +2026-04-12 05:01:28.625366: Epoch time: 102.48 s +2026-04-12 05:01:29.883751: +2026-04-12 05:01:29.886389: Epoch 1355 +2026-04-12 05:01:29.889117: Current learning rate: 0.00689 +2026-04-12 05:03:12.181293: train_loss -0.3901 +2026-04-12 05:03:12.189722: val_loss -0.3851 +2026-04-12 05:03:12.194285: Pseudo dice [0.0, 0.0, 0.8429, 0.5795, 0.4591, 0.871, 0.7429] +2026-04-12 05:03:12.199446: Epoch time: 102.3 s +2026-04-12 05:03:13.441403: +2026-04-12 05:03:13.443667: Epoch 1356 +2026-04-12 05:03:13.445992: Current learning rate: 0.00689 +2026-04-12 05:04:57.687667: train_loss -0.3906 +2026-04-12 05:04:57.693493: val_loss -0.3421 +2026-04-12 05:04:57.695427: Pseudo dice [0.2162, 0.0, 0.6972, 0.1326, 0.6133, 0.6352, 0.4586] +2026-04-12 05:04:57.698696: Epoch time: 104.25 s +2026-04-12 05:04:58.935579: +2026-04-12 05:04:58.937543: Epoch 1357 +2026-04-12 05:04:58.940137: Current learning rate: 0.00689 +2026-04-12 05:06:41.517286: train_loss -0.3897 +2026-04-12 05:06:41.527374: val_loss -0.3595 +2026-04-12 05:06:41.530538: Pseudo dice [0.4064, 0.0, 0.6332, 0.2611, 0.5768, 0.6337, 0.8077] +2026-04-12 05:06:41.533619: Epoch time: 102.58 s +2026-04-12 05:06:42.845723: +2026-04-12 05:06:42.847809: Epoch 1358 +2026-04-12 05:06:42.850028: Current learning rate: 0.00688 +2026-04-12 05:08:25.502400: train_loss -0.3877 +2026-04-12 05:08:25.510967: val_loss -0.3765 +2026-04-12 05:08:25.513203: Pseudo dice [0.5651, 0.0, 0.7754, 0.6436, 0.4349, 0.6704, 0.8558] +2026-04-12 05:08:25.515740: Epoch time: 102.66 s +2026-04-12 05:08:26.744062: +2026-04-12 05:08:26.746355: Epoch 1359 +2026-04-12 05:08:26.748307: Current learning rate: 0.00688 +2026-04-12 05:10:09.959219: train_loss -0.3843 +2026-04-12 05:10:09.967782: val_loss -0.3792 +2026-04-12 05:10:09.970310: Pseudo dice [0.4368, 0.0, 0.812, 0.8582, 0.2099, 0.4414, 0.8411] +2026-04-12 05:10:09.973240: Epoch time: 103.22 s +2026-04-12 05:10:11.212272: +2026-04-12 05:10:11.214690: Epoch 1360 +2026-04-12 05:10:11.217108: Current learning rate: 0.00688 +2026-04-12 05:11:55.034972: train_loss -0.3874 +2026-04-12 05:11:55.053090: val_loss -0.333 +2026-04-12 05:11:55.055810: Pseudo dice [0.5273, 0.0, 0.8153, 0.0661, 0.5341, 0.4937, 0.8844] +2026-04-12 05:11:55.058677: Epoch time: 103.83 s +2026-04-12 05:11:56.305228: +2026-04-12 05:11:56.307691: Epoch 1361 +2026-04-12 05:11:56.310762: Current learning rate: 0.00688 +2026-04-12 05:13:39.351605: train_loss -0.3956 +2026-04-12 05:13:39.358886: val_loss -0.3462 +2026-04-12 05:13:39.362333: Pseudo dice [0.0, 0.0, 0.7314, 0.3023, 0.5168, 0.8159, 0.6812] +2026-04-12 05:13:39.365308: Epoch time: 103.05 s +2026-04-12 05:13:40.647182: +2026-04-12 05:13:40.649177: Epoch 1362 +2026-04-12 05:13:40.651375: Current learning rate: 0.00688 +2026-04-12 05:15:22.861052: train_loss -0.3671 +2026-04-12 05:15:22.869252: val_loss -0.3424 +2026-04-12 05:15:22.871486: Pseudo dice [0.0, 0.0, 0.8223, 0.2433, 0.3302, 0.6483, 0.5102] +2026-04-12 05:15:22.874628: Epoch time: 102.22 s +2026-04-12 05:15:24.092326: +2026-04-12 05:15:24.094252: Epoch 1363 +2026-04-12 05:15:24.096726: Current learning rate: 0.00687 +2026-04-12 05:17:06.228692: train_loss -0.3761 +2026-04-12 05:17:06.236591: val_loss -0.3373 +2026-04-12 05:17:06.238792: Pseudo dice [0.0, 0.0, 0.6314, 0.0594, 0.5709, 0.4599, 0.8205] +2026-04-12 05:17:06.241848: Epoch time: 102.14 s +2026-04-12 05:17:07.450017: +2026-04-12 05:17:07.453042: Epoch 1364 +2026-04-12 05:17:07.456105: Current learning rate: 0.00687 +2026-04-12 05:18:49.438258: train_loss -0.3739 +2026-04-12 05:18:49.445321: val_loss -0.3367 +2026-04-12 05:18:49.447664: Pseudo dice [0.0, 0.0, 0.6617, 0.3229, 0.5026, 0.8246, 0.7756] +2026-04-12 05:18:49.450078: Epoch time: 101.99 s +2026-04-12 05:18:50.678569: +2026-04-12 05:18:50.684487: Epoch 1365 +2026-04-12 05:18:50.689930: Current learning rate: 0.00687 +2026-04-12 05:20:32.290466: train_loss -0.4053 +2026-04-12 05:20:32.297599: val_loss -0.3707 +2026-04-12 05:20:32.300242: Pseudo dice [0.0, 0.0, 0.8217, 0.3828, 0.376, 0.5565, 0.8287] +2026-04-12 05:20:32.302629: Epoch time: 101.62 s +2026-04-12 05:20:33.526064: +2026-04-12 05:20:33.529421: Epoch 1366 +2026-04-12 05:20:33.533106: Current learning rate: 0.00687 +2026-04-12 05:22:15.340245: train_loss -0.4052 +2026-04-12 05:22:15.346796: val_loss -0.336 +2026-04-12 05:22:15.349187: Pseudo dice [0.0, 0.0, 0.7957, 0.2667, 0.4163, 0.7888, 0.7993] +2026-04-12 05:22:15.352024: Epoch time: 101.82 s +2026-04-12 05:22:16.588154: +2026-04-12 05:22:16.590618: Epoch 1367 +2026-04-12 05:22:16.593561: Current learning rate: 0.00686 +2026-04-12 05:23:58.464974: train_loss -0.4015 +2026-04-12 05:23:58.476618: val_loss -0.3558 +2026-04-12 05:23:58.478820: Pseudo dice [0.0011, 0.0, 0.7449, 0.5002, 0.5897, 0.6783, 0.6017] +2026-04-12 05:23:58.481535: Epoch time: 101.88 s +2026-04-12 05:23:59.728477: +2026-04-12 05:23:59.730436: Epoch 1368 +2026-04-12 05:23:59.734216: Current learning rate: 0.00686 +2026-04-12 05:25:41.533928: train_loss -0.3652 +2026-04-12 05:25:41.539744: val_loss -0.3752 +2026-04-12 05:25:41.542093: Pseudo dice [0.0, 0.0, 0.742, 0.8489, 0.6189, 0.3892, 0.8162] +2026-04-12 05:25:41.544690: Epoch time: 101.81 s +2026-04-12 05:25:42.792890: +2026-04-12 05:25:42.795080: Epoch 1369 +2026-04-12 05:25:42.798081: Current learning rate: 0.00686 +2026-04-12 05:27:24.510795: train_loss -0.4007 +2026-04-12 05:27:24.517879: val_loss -0.3126 +2026-04-12 05:27:24.520779: Pseudo dice [0.2709, 0.0, 0.8355, 0.5446, 0.6235, 0.4805, 0.4423] +2026-04-12 05:27:24.523850: Epoch time: 101.72 s +2026-04-12 05:27:25.766840: +2026-04-12 05:27:25.768887: Epoch 1370 +2026-04-12 05:27:25.770946: Current learning rate: 0.00686 +2026-04-12 05:29:07.390950: train_loss -0.385 +2026-04-12 05:29:07.399778: val_loss -0.2952 +2026-04-12 05:29:07.402961: Pseudo dice [0.0, 0.0, 0.7375, 0.3765, 0.2614, 0.1987, 0.6765] +2026-04-12 05:29:07.405880: Epoch time: 101.63 s +2026-04-12 05:29:08.615126: +2026-04-12 05:29:08.617347: Epoch 1371 +2026-04-12 05:29:08.619630: Current learning rate: 0.00685 +2026-04-12 05:30:50.216902: train_loss -0.3591 +2026-04-12 05:30:50.222525: val_loss -0.346 +2026-04-12 05:30:50.224992: Pseudo dice [0.0, 0.0, 0.8374, 0.0047, 0.4826, 0.4627, 0.5644] +2026-04-12 05:30:50.227476: Epoch time: 101.6 s +2026-04-12 05:30:51.442999: +2026-04-12 05:30:51.444991: Epoch 1372 +2026-04-12 05:30:51.447120: Current learning rate: 0.00685 +2026-04-12 05:32:34.052136: train_loss -0.4048 +2026-04-12 05:32:34.059336: val_loss -0.3586 +2026-04-12 05:32:34.061725: Pseudo dice [0.0, 0.0, 0.775, 0.434, 0.5338, 0.8209, 0.7383] +2026-04-12 05:32:34.064806: Epoch time: 102.61 s +2026-04-12 05:32:35.315921: +2026-04-12 05:32:35.318096: Epoch 1373 +2026-04-12 05:32:35.320997: Current learning rate: 0.00685 +2026-04-12 05:34:17.561672: train_loss -0.3798 +2026-04-12 05:34:17.572909: val_loss -0.3235 +2026-04-12 05:34:17.577097: Pseudo dice [0.0, 0.0, 0.4125, 0.0959, 0.4795, 0.4905, 0.5583] +2026-04-12 05:34:17.580021: Epoch time: 102.25 s +2026-04-12 05:34:18.798739: +2026-04-12 05:34:18.801138: Epoch 1374 +2026-04-12 05:34:18.803882: Current learning rate: 0.00685 +2026-04-12 05:36:00.801891: train_loss -0.3939 +2026-04-12 05:36:00.810503: val_loss -0.3409 +2026-04-12 05:36:00.813680: Pseudo dice [0.4002, 0.0, 0.7672, 0.6375, 0.3466, 0.649, 0.5697] +2026-04-12 05:36:00.815845: Epoch time: 102.01 s +2026-04-12 05:36:02.029614: +2026-04-12 05:36:02.032795: Epoch 1375 +2026-04-12 05:36:02.035413: Current learning rate: 0.00684 +2026-04-12 05:37:43.650690: train_loss -0.3955 +2026-04-12 05:37:43.657249: val_loss -0.3264 +2026-04-12 05:37:43.659696: Pseudo dice [0.0, 0.0, 0.576, 0.1768, 0.4127, 0.5821, 0.6388] +2026-04-12 05:37:43.662321: Epoch time: 101.62 s +2026-04-12 05:37:44.853375: +2026-04-12 05:37:44.856044: Epoch 1376 +2026-04-12 05:37:44.858513: Current learning rate: 0.00684 +2026-04-12 05:39:27.918314: train_loss -0.4076 +2026-04-12 05:39:27.926334: val_loss -0.3232 +2026-04-12 05:39:27.929577: Pseudo dice [0.0, 0.0, 0.6471, 0.6644, 0.4634, 0.7788, 0.7864] +2026-04-12 05:39:27.932420: Epoch time: 103.07 s +2026-04-12 05:39:29.144770: +2026-04-12 05:39:29.146843: Epoch 1377 +2026-04-12 05:39:29.149354: Current learning rate: 0.00684 +2026-04-12 05:41:11.191275: train_loss -0.4023 +2026-04-12 05:41:11.199678: val_loss -0.3455 +2026-04-12 05:41:11.202063: Pseudo dice [0.0, 0.0, 0.7084, 0.6375, 0.3793, 0.5707, 0.8594] +2026-04-12 05:41:11.204569: Epoch time: 102.05 s +2026-04-12 05:41:12.414866: +2026-04-12 05:41:12.418200: Epoch 1378 +2026-04-12 05:41:12.420744: Current learning rate: 0.00684 +2026-04-12 05:42:55.147847: train_loss -0.4011 +2026-04-12 05:42:55.155545: val_loss -0.3315 +2026-04-12 05:42:55.158297: Pseudo dice [0.4382, 0.0, 0.7057, 0.0456, 0.5525, 0.7311, 0.5204] +2026-04-12 05:42:55.161628: Epoch time: 102.74 s +2026-04-12 05:42:56.376472: +2026-04-12 05:42:56.379115: Epoch 1379 +2026-04-12 05:42:56.382322: Current learning rate: 0.00684 +2026-04-12 05:44:38.541258: train_loss -0.4127 +2026-04-12 05:44:38.549397: val_loss -0.3478 +2026-04-12 05:44:38.552497: Pseudo dice [0.0, 0.0, 0.7715, 0.5475, 0.5002, 0.5961, 0.7308] +2026-04-12 05:44:38.555258: Epoch time: 102.17 s +2026-04-12 05:44:39.778525: +2026-04-12 05:44:39.781983: Epoch 1380 +2026-04-12 05:44:39.785058: Current learning rate: 0.00683 +2026-04-12 05:46:21.940696: train_loss -0.386 +2026-04-12 05:46:21.947941: val_loss -0.3236 +2026-04-12 05:46:21.950275: Pseudo dice [0.0, 0.0, 0.7779, 0.1545, 0.4197, 0.4416, 0.3238] +2026-04-12 05:46:21.953097: Epoch time: 102.17 s +2026-04-12 05:46:23.231377: +2026-04-12 05:46:23.233602: Epoch 1381 +2026-04-12 05:46:23.236383: Current learning rate: 0.00683 +2026-04-12 05:48:05.559516: train_loss -0.3834 +2026-04-12 05:48:05.567166: val_loss -0.3522 +2026-04-12 05:48:05.569959: Pseudo dice [0.0611, 0.0, 0.7695, 0.7108, 0.3472, 0.6757, 0.9067] +2026-04-12 05:48:05.573048: Epoch time: 102.33 s +2026-04-12 05:48:06.807097: +2026-04-12 05:48:06.810244: Epoch 1382 +2026-04-12 05:48:06.813569: Current learning rate: 0.00683 +2026-04-12 05:49:48.262194: train_loss -0.397 +2026-04-12 05:49:48.268834: val_loss -0.3364 +2026-04-12 05:49:48.271281: Pseudo dice [0.0, 0.0, 0.7809, 0.7509, 0.5441, 0.5182, 0.8914] +2026-04-12 05:49:48.273654: Epoch time: 101.46 s +2026-04-12 05:49:49.502318: +2026-04-12 05:49:49.504900: Epoch 1383 +2026-04-12 05:49:49.507676: Current learning rate: 0.00683 +2026-04-12 05:51:32.044356: train_loss -0.3866 +2026-04-12 05:51:32.054113: val_loss -0.3325 +2026-04-12 05:51:32.057371: Pseudo dice [0.0, 0.0, 0.7977, 0.6845, 0.566, 0.6873, 0.8533] +2026-04-12 05:51:32.060569: Epoch time: 102.55 s +2026-04-12 05:51:33.342570: +2026-04-12 05:51:33.345087: Epoch 1384 +2026-04-12 05:51:33.348348: Current learning rate: 0.00682 +2026-04-12 05:53:16.076313: train_loss -0.3996 +2026-04-12 05:53:16.083299: val_loss -0.3386 +2026-04-12 05:53:16.086630: Pseudo dice [0.0333, 0.0, 0.7805, 0.5375, 0.5721, 0.5294, 0.5983] +2026-04-12 05:53:16.089733: Epoch time: 102.74 s +2026-04-12 05:53:17.297315: +2026-04-12 05:53:17.299717: Epoch 1385 +2026-04-12 05:53:17.302035: Current learning rate: 0.00682 +2026-04-12 05:54:58.902797: train_loss -0.4023 +2026-04-12 05:54:58.916773: val_loss -0.3435 +2026-04-12 05:54:58.919508: Pseudo dice [0.3941, 0.0, 0.7976, 0.0, 0.4588, 0.5204, 0.8557] +2026-04-12 05:54:58.923005: Epoch time: 101.61 s +2026-04-12 05:55:00.143244: +2026-04-12 05:55:00.155085: Epoch 1386 +2026-04-12 05:55:00.157733: Current learning rate: 0.00682 +2026-04-12 05:56:41.790787: train_loss -0.3847 +2026-04-12 05:56:41.799270: val_loss -0.3184 +2026-04-12 05:56:41.801907: Pseudo dice [0.0, 0.0, 0.7778, 0.239, 0.1668, 0.2477, 0.8003] +2026-04-12 05:56:41.804617: Epoch time: 101.65 s +2026-04-12 05:56:43.007907: +2026-04-12 05:56:43.009725: Epoch 1387 +2026-04-12 05:56:43.011926: Current learning rate: 0.00682 +2026-04-12 05:58:25.357158: train_loss -0.3432 +2026-04-12 05:58:25.372327: val_loss -0.3212 +2026-04-12 05:58:25.376132: Pseudo dice [0.0, 0.0, 0.5791, 0.0954, 0.4314, 0.7924, 0.7739] +2026-04-12 05:58:25.380932: Epoch time: 102.35 s +2026-04-12 05:58:26.612880: +2026-04-12 05:58:26.615639: Epoch 1388 +2026-04-12 05:58:26.617833: Current learning rate: 0.00681 +2026-04-12 06:00:08.724217: train_loss -0.3839 +2026-04-12 06:00:08.733463: val_loss -0.3553 +2026-04-12 06:00:08.738306: Pseudo dice [0.0, 0.0, 0.8287, 0.2258, 0.4296, 0.7414, 0.7154] +2026-04-12 06:00:08.740923: Epoch time: 102.11 s +2026-04-12 06:00:10.022369: +2026-04-12 06:00:10.025337: Epoch 1389 +2026-04-12 06:00:10.027783: Current learning rate: 0.00681 +2026-04-12 06:01:51.825712: train_loss -0.396 +2026-04-12 06:01:51.833335: val_loss -0.343 +2026-04-12 06:01:51.836506: Pseudo dice [0.0, 0.0, 0.4224, 0.3643, 0.4997, 0.4155, 0.8772] +2026-04-12 06:01:51.840236: Epoch time: 101.81 s +2026-04-12 06:01:53.035440: +2026-04-12 06:01:53.038195: Epoch 1390 +2026-04-12 06:01:53.041295: Current learning rate: 0.00681 +2026-04-12 06:03:35.185245: train_loss -0.3963 +2026-04-12 06:03:35.194239: val_loss -0.3484 +2026-04-12 06:03:35.198305: Pseudo dice [0.0, 0.0, 0.6023, 0.225, 0.5148, 0.4782, 0.7039] +2026-04-12 06:03:35.201561: Epoch time: 102.15 s +2026-04-12 06:03:36.414170: +2026-04-12 06:03:36.417010: Epoch 1391 +2026-04-12 06:03:36.420283: Current learning rate: 0.00681 +2026-04-12 06:05:18.713079: train_loss -0.3944 +2026-04-12 06:05:18.720076: val_loss -0.3107 +2026-04-12 06:05:18.722369: Pseudo dice [0.0081, 0.0, 0.3424, 0.2295, 0.3699, 0.5711, 0.5249] +2026-04-12 06:05:18.724952: Epoch time: 102.3 s +2026-04-12 06:05:19.926555: +2026-04-12 06:05:19.930471: Epoch 1392 +2026-04-12 06:05:19.933252: Current learning rate: 0.0068 +2026-04-12 06:07:01.738937: train_loss -0.3862 +2026-04-12 06:07:01.746651: val_loss -0.3866 +2026-04-12 06:07:01.749361: Pseudo dice [0.4948, 0.0, 0.7688, 0.4394, 0.4962, 0.6103, 0.7887] +2026-04-12 06:07:01.751979: Epoch time: 101.82 s +2026-04-12 06:07:02.979969: +2026-04-12 06:07:02.982494: Epoch 1393 +2026-04-12 06:07:02.984666: Current learning rate: 0.0068 +2026-04-12 06:08:44.996256: train_loss -0.4187 +2026-04-12 06:08:45.005301: val_loss -0.3494 +2026-04-12 06:08:45.008800: Pseudo dice [0.4711, 0.0, 0.6943, 0.0819, 0.3307, 0.7072, 0.7006] +2026-04-12 06:08:45.012240: Epoch time: 102.02 s +2026-04-12 06:08:46.237057: +2026-04-12 06:08:46.239503: Epoch 1394 +2026-04-12 06:08:46.242077: Current learning rate: 0.0068 +2026-04-12 06:10:28.344527: train_loss -0.377 +2026-04-12 06:10:28.351846: val_loss -0.2863 +2026-04-12 06:10:28.354093: Pseudo dice [0.0, 0.0, 0.6691, 0.1694, 0.2896, 0.113, 0.8325] +2026-04-12 06:10:28.356589: Epoch time: 102.11 s +2026-04-12 06:10:29.598168: +2026-04-12 06:10:29.600917: Epoch 1395 +2026-04-12 06:10:29.604526: Current learning rate: 0.0068 +2026-04-12 06:12:11.771919: train_loss -0.3897 +2026-04-12 06:12:11.782369: val_loss -0.359 +2026-04-12 06:12:11.785192: Pseudo dice [0.5016, 0.0, 0.7563, 0.3411, 0.451, 0.4159, 0.8221] +2026-04-12 06:12:11.790244: Epoch time: 102.18 s +2026-04-12 06:12:14.167890: +2026-04-12 06:12:14.170130: Epoch 1396 +2026-04-12 06:12:14.172424: Current learning rate: 0.0068 +2026-04-12 06:13:56.843465: train_loss -0.3692 +2026-04-12 06:13:56.860165: val_loss -0.2878 +2026-04-12 06:13:56.862462: Pseudo dice [0.6754, 0.0, 0.5291, 0.1546, 0.1485, 0.424, 0.6187] +2026-04-12 06:13:56.873139: Epoch time: 102.68 s +2026-04-12 06:13:58.195213: +2026-04-12 06:13:58.197161: Epoch 1397 +2026-04-12 06:13:58.199617: Current learning rate: 0.00679 +2026-04-12 06:15:41.342359: train_loss -0.3652 +2026-04-12 06:15:41.351212: val_loss -0.2982 +2026-04-12 06:15:41.353809: Pseudo dice [0.4955, 0.0, 0.5768, 0.3485, 0.3798, 0.6202, 0.4942] +2026-04-12 06:15:41.356807: Epoch time: 103.15 s +2026-04-12 06:15:42.579517: +2026-04-12 06:15:42.581707: Epoch 1398 +2026-04-12 06:15:42.583971: Current learning rate: 0.00679 +2026-04-12 06:17:25.315828: train_loss -0.3844 +2026-04-12 06:17:25.324627: val_loss -0.3603 +2026-04-12 06:17:25.327492: Pseudo dice [0.3511, 0.0, 0.7213, 0.2447, 0.4973, 0.4914, 0.7085] +2026-04-12 06:17:25.336358: Epoch time: 102.74 s +2026-04-12 06:17:26.531595: +2026-04-12 06:17:26.533797: Epoch 1399 +2026-04-12 06:17:26.535968: Current learning rate: 0.00679 +2026-04-12 06:19:09.148029: train_loss -0.3694 +2026-04-12 06:19:09.159815: val_loss -0.3323 +2026-04-12 06:19:09.162890: Pseudo dice [0.0, 0.0, 0.6009, 0.7407, 0.3361, 0.6288, 0.7595] +2026-04-12 06:19:09.166803: Epoch time: 102.62 s +2026-04-12 06:19:12.188490: +2026-04-12 06:19:12.197193: Epoch 1400 +2026-04-12 06:19:12.200864: Current learning rate: 0.00679 +2026-04-12 06:20:54.663892: train_loss -0.3704 +2026-04-12 06:20:54.671734: val_loss -0.3448 +2026-04-12 06:20:54.674254: Pseudo dice [0.0, 0.0, 0.8371, 0.5934, 0.4145, 0.2318, 0.925] +2026-04-12 06:20:54.677835: Epoch time: 102.48 s +2026-04-12 06:20:55.972409: +2026-04-12 06:20:55.976211: Epoch 1401 +2026-04-12 06:20:55.978629: Current learning rate: 0.00678 +2026-04-12 06:22:38.442881: train_loss -0.3768 +2026-04-12 06:22:38.450164: val_loss -0.3113 +2026-04-12 06:22:38.455151: Pseudo dice [0.0, 0.0, 0.7387, 0.5651, 0.3459, 0.4504, 0.4195] +2026-04-12 06:22:38.459112: Epoch time: 102.47 s +2026-04-12 06:22:39.663131: +2026-04-12 06:22:39.666129: Epoch 1402 +2026-04-12 06:22:39.669783: Current learning rate: 0.00678 +2026-04-12 06:24:21.579731: train_loss -0.3904 +2026-04-12 06:24:21.589685: val_loss -0.3869 +2026-04-12 06:24:21.592262: Pseudo dice [0.0, 0.0, 0.8582, 0.726, 0.4541, 0.8374, 0.8085] +2026-04-12 06:24:21.596143: Epoch time: 101.92 s +2026-04-12 06:24:22.869442: +2026-04-12 06:24:22.871608: Epoch 1403 +2026-04-12 06:24:22.873980: Current learning rate: 0.00678 +2026-04-12 06:26:04.746356: train_loss -0.4014 +2026-04-12 06:26:04.757144: val_loss -0.3317 +2026-04-12 06:26:04.759518: Pseudo dice [0.0, 0.0, 0.7692, 0.5868, 0.333, 0.6229, 0.809] +2026-04-12 06:26:04.762681: Epoch time: 101.88 s +2026-04-12 06:26:05.959643: +2026-04-12 06:26:05.961951: Epoch 1404 +2026-04-12 06:26:05.963869: Current learning rate: 0.00678 +2026-04-12 06:27:48.516748: train_loss -0.3735 +2026-04-12 06:27:48.528122: val_loss -0.3363 +2026-04-12 06:27:48.530977: Pseudo dice [0.0, 0.0, 0.543, 0.5214, 0.5571, 0.7153, 0.7018] +2026-04-12 06:27:48.534227: Epoch time: 102.56 s +2026-04-12 06:27:49.837658: +2026-04-12 06:27:49.840401: Epoch 1405 +2026-04-12 06:27:49.843777: Current learning rate: 0.00677 +2026-04-12 06:29:32.179846: train_loss -0.3942 +2026-04-12 06:29:32.187427: val_loss -0.3684 +2026-04-12 06:29:32.190042: Pseudo dice [0.0, 0.0, 0.6944, 0.6249, 0.6309, 0.4743, 0.8117] +2026-04-12 06:29:32.192208: Epoch time: 102.35 s +2026-04-12 06:29:33.421838: +2026-04-12 06:29:33.423707: Epoch 1406 +2026-04-12 06:29:33.425731: Current learning rate: 0.00677 +2026-04-12 06:31:15.897593: train_loss -0.4065 +2026-04-12 06:31:15.908138: val_loss -0.3719 +2026-04-12 06:31:15.910418: Pseudo dice [0.0004, 0.0, 0.7531, 0.7125, 0.6024, 0.6531, 0.7415] +2026-04-12 06:31:15.913461: Epoch time: 102.48 s +2026-04-12 06:31:17.134287: +2026-04-12 06:31:17.139177: Epoch 1407 +2026-04-12 06:31:17.141313: Current learning rate: 0.00677 +2026-04-12 06:32:59.167910: train_loss -0.3932 +2026-04-12 06:32:59.177084: val_loss -0.3509 +2026-04-12 06:32:59.179708: Pseudo dice [0.0, 0.0, 0.739, 0.4951, 0.7108, 0.7503, 0.8548] +2026-04-12 06:32:59.183643: Epoch time: 102.04 s +2026-04-12 06:33:00.426188: +2026-04-12 06:33:00.429893: Epoch 1408 +2026-04-12 06:33:00.433003: Current learning rate: 0.00677 +2026-04-12 06:34:42.758029: train_loss -0.3837 +2026-04-12 06:34:42.766665: val_loss -0.3616 +2026-04-12 06:34:42.769322: Pseudo dice [0.0, 0.0, 0.7308, 0.7172, 0.582, 0.3579, 0.74] +2026-04-12 06:34:42.774003: Epoch time: 102.33 s +2026-04-12 06:34:44.054455: +2026-04-12 06:34:44.056824: Epoch 1409 +2026-04-12 06:34:44.059558: Current learning rate: 0.00676 +2026-04-12 06:36:25.991769: train_loss -0.3692 +2026-04-12 06:36:25.999392: val_loss -0.3398 +2026-04-12 06:36:26.001889: Pseudo dice [0.0, 0.0, 0.741, 0.2435, 0.512, 0.6613, 0.3452] +2026-04-12 06:36:26.004519: Epoch time: 101.94 s +2026-04-12 06:36:27.219814: +2026-04-12 06:36:27.221901: Epoch 1410 +2026-04-12 06:36:27.224474: Current learning rate: 0.00676 +2026-04-12 06:38:09.469805: train_loss -0.3972 +2026-04-12 06:38:09.480912: val_loss -0.3134 +2026-04-12 06:38:09.484403: Pseudo dice [0.0, 0.0, 0.4998, 0.2891, 0.4667, 0.4679, 0.5193] +2026-04-12 06:38:09.487187: Epoch time: 102.25 s +2026-04-12 06:38:10.706081: +2026-04-12 06:38:10.708517: Epoch 1411 +2026-04-12 06:38:10.712661: Current learning rate: 0.00676 +2026-04-12 06:39:52.859181: train_loss -0.4065 +2026-04-12 06:39:52.868678: val_loss -0.3145 +2026-04-12 06:39:52.871587: Pseudo dice [0.0, 0.0, 0.2113, 0.5503, 0.4598, 0.1787, 0.9051] +2026-04-12 06:39:52.874553: Epoch time: 102.16 s +2026-04-12 06:39:54.126496: +2026-04-12 06:39:54.128994: Epoch 1412 +2026-04-12 06:39:54.131840: Current learning rate: 0.00676 +2026-04-12 06:41:36.739466: train_loss -0.3878 +2026-04-12 06:41:36.746868: val_loss -0.2948 +2026-04-12 06:41:36.749670: Pseudo dice [0.0, 0.0, 0.6263, 0.1877, 0.3322, 0.511, 0.8532] +2026-04-12 06:41:36.752060: Epoch time: 102.62 s +2026-04-12 06:41:37.966271: +2026-04-12 06:41:37.968201: Epoch 1413 +2026-04-12 06:41:37.971483: Current learning rate: 0.00676 +2026-04-12 06:43:20.767742: train_loss -0.3784 +2026-04-12 06:43:20.775349: val_loss -0.3584 +2026-04-12 06:43:20.778358: Pseudo dice [0.0, 0.0, 0.7756, 0.0991, 0.4344, 0.7872, 0.7942] +2026-04-12 06:43:20.781011: Epoch time: 102.8 s +2026-04-12 06:43:22.012280: +2026-04-12 06:43:22.016385: Epoch 1414 +2026-04-12 06:43:22.018906: Current learning rate: 0.00675 +2026-04-12 06:45:04.594939: train_loss -0.377 +2026-04-12 06:45:04.607210: val_loss -0.3755 +2026-04-12 06:45:04.610429: Pseudo dice [0.0, 0.0, 0.8252, 0.5944, 0.5821, 0.6231, 0.6089] +2026-04-12 06:45:04.615277: Epoch time: 102.59 s +2026-04-12 06:45:05.856799: +2026-04-12 06:45:05.861082: Epoch 1415 +2026-04-12 06:45:05.864617: Current learning rate: 0.00675 +2026-04-12 06:46:49.440233: train_loss -0.3958 +2026-04-12 06:46:49.449911: val_loss -0.3457 +2026-04-12 06:46:49.452824: Pseudo dice [0.0, 0.0, 0.7874, 0.0883, 0.6068, 0.7135, 0.8581] +2026-04-12 06:46:49.456056: Epoch time: 103.59 s +2026-04-12 06:46:50.679234: +2026-04-12 06:46:50.681421: Epoch 1416 +2026-04-12 06:46:50.683671: Current learning rate: 0.00675 +2026-04-12 06:48:32.224592: train_loss -0.3966 +2026-04-12 06:48:32.233998: val_loss -0.3045 +2026-04-12 06:48:32.237546: Pseudo dice [0.0, 0.0, 0.6686, 0.4139, 0.4664, 0.5431, 0.7283] +2026-04-12 06:48:32.240556: Epoch time: 101.55 s +2026-04-12 06:48:33.451529: +2026-04-12 06:48:33.453624: Epoch 1417 +2026-04-12 06:48:33.456248: Current learning rate: 0.00675 +2026-04-12 06:50:16.037431: train_loss -0.4045 +2026-04-12 06:50:16.044899: val_loss -0.3553 +2026-04-12 06:50:16.047472: Pseudo dice [0.0, 0.0, 0.5795, 0.6636, 0.3111, 0.7175, 0.8408] +2026-04-12 06:50:16.050853: Epoch time: 102.59 s +2026-04-12 06:50:17.298294: +2026-04-12 06:50:17.300479: Epoch 1418 +2026-04-12 06:50:17.302975: Current learning rate: 0.00674 +2026-04-12 06:51:59.969696: train_loss -0.3945 +2026-04-12 06:51:59.977548: val_loss -0.3303 +2026-04-12 06:51:59.980631: Pseudo dice [0.0, 0.0, 0.819, 0.4979, 0.5203, 0.5389, 0.8438] +2026-04-12 06:51:59.983564: Epoch time: 102.67 s +2026-04-12 06:52:01.224826: +2026-04-12 06:52:01.227066: Epoch 1419 +2026-04-12 06:52:01.230649: Current learning rate: 0.00674 +2026-04-12 06:53:43.197417: train_loss -0.4152 +2026-04-12 06:53:43.205534: val_loss -0.3665 +2026-04-12 06:53:43.208049: Pseudo dice [0.0, 0.0, 0.5959, 0.6017, 0.5524, 0.683, 0.8684] +2026-04-12 06:53:43.210867: Epoch time: 101.98 s +2026-04-12 06:53:44.421497: +2026-04-12 06:53:44.423523: Epoch 1420 +2026-04-12 06:53:44.425839: Current learning rate: 0.00674 +2026-04-12 06:55:26.356280: train_loss -0.3883 +2026-04-12 06:55:26.363656: val_loss -0.3001 +2026-04-12 06:55:26.366569: Pseudo dice [0.0, 0.0, 0.6013, 0.2669, 0.4543, 0.4124, 0.6098] +2026-04-12 06:55:26.370289: Epoch time: 101.94 s +2026-04-12 06:55:27.590204: +2026-04-12 06:55:27.593038: Epoch 1421 +2026-04-12 06:55:27.595608: Current learning rate: 0.00674 +2026-04-12 06:57:10.104134: train_loss -0.3842 +2026-04-12 06:57:10.111081: val_loss -0.3734 +2026-04-12 06:57:10.113089: Pseudo dice [0.0, 0.0, 0.7758, 0.558, 0.5837, 0.765, 0.7173] +2026-04-12 06:57:10.117263: Epoch time: 102.52 s +2026-04-12 06:57:11.322767: +2026-04-12 06:57:11.325018: Epoch 1422 +2026-04-12 06:57:11.326914: Current learning rate: 0.00673 +2026-04-12 06:58:53.442337: train_loss -0.3693 +2026-04-12 06:58:53.449467: val_loss -0.2777 +2026-04-12 06:58:53.451955: Pseudo dice [0.0, 0.0, 0.5984, 0.4063, 0.5686, 0.2986, 0.5772] +2026-04-12 06:58:53.455664: Epoch time: 102.12 s +2026-04-12 06:58:54.678678: +2026-04-12 06:58:54.681520: Epoch 1423 +2026-04-12 06:58:54.684525: Current learning rate: 0.00673 +2026-04-12 07:00:36.751081: train_loss -0.3839 +2026-04-12 07:00:36.757001: val_loss -0.3335 +2026-04-12 07:00:36.759291: Pseudo dice [0.0, 0.0, 0.6495, 0.5941, 0.2764, 0.3913, 0.8072] +2026-04-12 07:00:36.762551: Epoch time: 102.08 s +2026-04-12 07:00:38.017618: +2026-04-12 07:00:38.019718: Epoch 1424 +2026-04-12 07:00:38.022558: Current learning rate: 0.00673 +2026-04-12 07:02:19.940075: train_loss -0.3823 +2026-04-12 07:02:19.947724: val_loss -0.3307 +2026-04-12 07:02:19.950966: Pseudo dice [0.0015, 0.0, 0.6005, 0.5801, 0.591, 0.7654, 0.6427] +2026-04-12 07:02:19.954087: Epoch time: 101.93 s +2026-04-12 07:02:21.295123: +2026-04-12 07:02:21.297311: Epoch 1425 +2026-04-12 07:02:21.299846: Current learning rate: 0.00673 +2026-04-12 07:04:02.991406: train_loss -0.3986 +2026-04-12 07:04:02.999584: val_loss -0.3761 +2026-04-12 07:04:03.002860: Pseudo dice [0.2051, 0.0, 0.7313, 0.5656, 0.5056, 0.8141, 0.876] +2026-04-12 07:04:03.006037: Epoch time: 101.7 s +2026-04-12 07:04:04.232394: +2026-04-12 07:04:04.235027: Epoch 1426 +2026-04-12 07:04:04.237114: Current learning rate: 0.00673 +2026-04-12 07:05:46.524029: train_loss -0.4011 +2026-04-12 07:05:46.532489: val_loss -0.3435 +2026-04-12 07:05:46.536067: Pseudo dice [0.5316, 0.0, 0.6987, 0.2964, 0.3956, 0.6952, 0.595] +2026-04-12 07:05:46.538752: Epoch time: 102.29 s +2026-04-12 07:05:47.800276: +2026-04-12 07:05:47.802613: Epoch 1427 +2026-04-12 07:05:47.804703: Current learning rate: 0.00672 +2026-04-12 07:07:29.546710: train_loss -0.3973 +2026-04-12 07:07:29.554507: val_loss -0.3684 +2026-04-12 07:07:29.558274: Pseudo dice [0.4566, 0.0, 0.7833, 0.4702, 0.4844, 0.73, 0.7458] +2026-04-12 07:07:29.561222: Epoch time: 101.75 s +2026-04-12 07:07:30.774592: +2026-04-12 07:07:30.776873: Epoch 1428 +2026-04-12 07:07:30.779271: Current learning rate: 0.00672 +2026-04-12 07:09:12.969025: train_loss -0.3908 +2026-04-12 07:09:12.976598: val_loss -0.3593 +2026-04-12 07:09:12.978811: Pseudo dice [0.3194, 0.0, 0.7738, 0.0082, 0.6173, 0.8104, 0.2372] +2026-04-12 07:09:12.981187: Epoch time: 102.2 s +2026-04-12 07:09:14.211587: +2026-04-12 07:09:14.213435: Epoch 1429 +2026-04-12 07:09:14.215479: Current learning rate: 0.00672 +2026-04-12 07:10:55.792178: train_loss -0.4131 +2026-04-12 07:10:55.799586: val_loss -0.337 +2026-04-12 07:10:55.802689: Pseudo dice [0.0, 0.0, 0.7973, 0.5469, 0.6273, 0.49, 0.849] +2026-04-12 07:10:55.805959: Epoch time: 101.58 s +2026-04-12 07:10:57.024996: +2026-04-12 07:10:57.026905: Epoch 1430 +2026-04-12 07:10:57.029425: Current learning rate: 0.00672 +2026-04-12 07:12:38.557889: train_loss -0.3694 +2026-04-12 07:12:38.566359: val_loss -0.3389 +2026-04-12 07:12:38.569540: Pseudo dice [0.0, 0.0, 0.7209, 0.792, 0.4642, 0.7283, 0.7796] +2026-04-12 07:12:38.573020: Epoch time: 101.54 s +2026-04-12 07:12:39.788151: +2026-04-12 07:12:39.790354: Epoch 1431 +2026-04-12 07:12:39.792954: Current learning rate: 0.00671 +2026-04-12 07:14:22.144799: train_loss -0.3897 +2026-04-12 07:14:22.151960: val_loss -0.3789 +2026-04-12 07:14:22.154394: Pseudo dice [0.3301, 0.0, 0.6455, 0.7096, 0.5115, 0.9038, 0.8648] +2026-04-12 07:14:22.157916: Epoch time: 102.36 s +2026-04-12 07:14:23.375402: +2026-04-12 07:14:23.377450: Epoch 1432 +2026-04-12 07:14:23.379425: Current learning rate: 0.00671 +2026-04-12 07:16:04.900862: train_loss -0.3996 +2026-04-12 07:16:04.910248: val_loss -0.3313 +2026-04-12 07:16:04.912213: Pseudo dice [0.0, 0.0, 0.7621, 0.8745, 0.0006, 0.735, 0.8531] +2026-04-12 07:16:04.914631: Epoch time: 101.53 s +2026-04-12 07:16:06.109118: +2026-04-12 07:16:06.111531: Epoch 1433 +2026-04-12 07:16:06.113422: Current learning rate: 0.00671 +2026-04-12 07:17:48.093622: train_loss -0.344 +2026-04-12 07:17:48.101411: val_loss -0.2833 +2026-04-12 07:17:48.103596: Pseudo dice [0.0091, 0.0, 0.304, 0.7215, 0.2031, 0.0523, 0.8042] +2026-04-12 07:17:48.106501: Epoch time: 101.99 s +2026-04-12 07:17:49.309128: +2026-04-12 07:17:49.311019: Epoch 1434 +2026-04-12 07:17:49.313654: Current learning rate: 0.00671 +2026-04-12 07:19:31.464691: train_loss -0.3533 +2026-04-12 07:19:31.471472: val_loss -0.336 +2026-04-12 07:19:31.473902: Pseudo dice [0.2125, 0.0, 0.6661, 0.1764, 0.2372, 0.6301, 0.7951] +2026-04-12 07:19:31.476418: Epoch time: 102.16 s +2026-04-12 07:19:33.857882: +2026-04-12 07:19:33.860134: Epoch 1435 +2026-04-12 07:19:33.862676: Current learning rate: 0.0067 +2026-04-12 07:21:16.024814: train_loss -0.3615 +2026-04-12 07:21:16.031664: val_loss -0.3514 +2026-04-12 07:21:16.033667: Pseudo dice [0.0, 0.0, 0.7846, 0.5607, 0.575, 0.6371, 0.763] +2026-04-12 07:21:16.036039: Epoch time: 102.17 s +2026-04-12 07:21:17.259782: +2026-04-12 07:21:17.262384: Epoch 1436 +2026-04-12 07:21:17.264746: Current learning rate: 0.0067 +2026-04-12 07:22:59.200466: train_loss -0.3803 +2026-04-12 07:22:59.207124: val_loss -0.3235 +2026-04-12 07:22:59.209167: Pseudo dice [0.0, 0.0, 0.7074, 0.3505, 0.6149, 0.6103, 0.795] +2026-04-12 07:22:59.212289: Epoch time: 101.94 s +2026-04-12 07:23:00.431611: +2026-04-12 07:23:00.433722: Epoch 1437 +2026-04-12 07:23:00.437595: Current learning rate: 0.0067 +2026-04-12 07:24:42.220855: train_loss -0.3752 +2026-04-12 07:24:42.230594: val_loss -0.3575 +2026-04-12 07:24:42.233697: Pseudo dice [0.1374, 0.0, 0.8269, 0.7569, 0.4797, 0.523, 0.4321] +2026-04-12 07:24:42.237001: Epoch time: 101.79 s +2026-04-12 07:24:43.475194: +2026-04-12 07:24:43.477572: Epoch 1438 +2026-04-12 07:24:43.479597: Current learning rate: 0.0067 +2026-04-12 07:26:25.466228: train_loss -0.3923 +2026-04-12 07:26:25.472735: val_loss -0.3446 +2026-04-12 07:26:25.475210: Pseudo dice [0.2374, 0.0, 0.8705, 0.1414, 0.3613, 0.4787, 0.3482] +2026-04-12 07:26:25.477938: Epoch time: 101.99 s +2026-04-12 07:26:26.709329: +2026-04-12 07:26:26.712537: Epoch 1439 +2026-04-12 07:26:26.715261: Current learning rate: 0.00669 +2026-04-12 07:28:08.101513: train_loss -0.3845 +2026-04-12 07:28:08.109970: val_loss -0.3283 +2026-04-12 07:28:08.113520: Pseudo dice [0.0, 0.0, 0.6546, 0.3479, 0.5498, 0.6748, 0.7224] +2026-04-12 07:28:08.116584: Epoch time: 101.4 s +2026-04-12 07:28:09.346274: +2026-04-12 07:28:09.348303: Epoch 1440 +2026-04-12 07:28:09.350686: Current learning rate: 0.00669 +2026-04-12 07:29:50.737795: train_loss -0.3832 +2026-04-12 07:29:50.743841: val_loss -0.3184 +2026-04-12 07:29:50.746809: Pseudo dice [0.0, 0.0, 0.6618, 0.5853, 0.3519, 0.2939, 0.6657] +2026-04-12 07:29:50.749922: Epoch time: 101.39 s +2026-04-12 07:29:51.982951: +2026-04-12 07:29:51.985179: Epoch 1441 +2026-04-12 07:29:51.987590: Current learning rate: 0.00669 +2026-04-12 07:31:34.392824: train_loss -0.3824 +2026-04-12 07:31:34.400476: val_loss -0.3022 +2026-04-12 07:31:34.402998: Pseudo dice [0.0316, 0.0, 0.6866, 0.0028, 0.4522, 0.3573, 0.8718] +2026-04-12 07:31:34.406159: Epoch time: 102.41 s +2026-04-12 07:31:35.612014: +2026-04-12 07:31:35.614246: Epoch 1442 +2026-04-12 07:31:35.616126: Current learning rate: 0.00669 +2026-04-12 07:33:18.266091: train_loss -0.4033 +2026-04-12 07:33:18.274728: val_loss -0.3777 +2026-04-12 07:33:18.279491: Pseudo dice [0.5701, 0.0, 0.7025, 0.2992, 0.6203, 0.8087, 0.5638] +2026-04-12 07:33:18.283526: Epoch time: 102.66 s +2026-04-12 07:33:19.494449: +2026-04-12 07:33:19.496573: Epoch 1443 +2026-04-12 07:33:19.498549: Current learning rate: 0.00669 +2026-04-12 07:35:01.223515: train_loss -0.3846 +2026-04-12 07:35:01.229419: val_loss -0.3349 +2026-04-12 07:35:01.231856: Pseudo dice [0.0648, 0.0, 0.6779, 0.1911, 0.6908, 0.5203, 0.8272] +2026-04-12 07:35:01.234385: Epoch time: 101.73 s +2026-04-12 07:35:02.427060: +2026-04-12 07:35:02.430207: Epoch 1444 +2026-04-12 07:35:02.432408: Current learning rate: 0.00668 +2026-04-12 07:36:44.335344: train_loss -0.3866 +2026-04-12 07:36:44.342008: val_loss -0.3312 +2026-04-12 07:36:44.344112: Pseudo dice [0.0, 0.0, 0.8035, 0.4046, 0.4756, 0.7258, 0.5814] +2026-04-12 07:36:44.347354: Epoch time: 101.91 s +2026-04-12 07:36:45.570967: +2026-04-12 07:36:45.573725: Epoch 1445 +2026-04-12 07:36:45.576998: Current learning rate: 0.00668 +2026-04-12 07:38:28.128537: train_loss -0.3916 +2026-04-12 07:38:28.136269: val_loss -0.3248 +2026-04-12 07:38:28.139031: Pseudo dice [0.0, 0.0, 0.7064, 0.1134, 0.5229, 0.4749, 0.8165] +2026-04-12 07:38:28.142781: Epoch time: 102.56 s +2026-04-12 07:38:29.370977: +2026-04-12 07:38:29.373577: Epoch 1446 +2026-04-12 07:38:29.375811: Current learning rate: 0.00668 +2026-04-12 07:40:11.443841: train_loss -0.4066 +2026-04-12 07:40:11.451589: val_loss -0.3623 +2026-04-12 07:40:11.455440: Pseudo dice [0.4348, 0.0, 0.7439, 0.0037, 0.5858, 0.8284, 0.8412] +2026-04-12 07:40:11.458464: Epoch time: 102.08 s +2026-04-12 07:40:12.710410: +2026-04-12 07:40:12.712859: Epoch 1447 +2026-04-12 07:40:12.715295: Current learning rate: 0.00668 +2026-04-12 07:41:54.531322: train_loss -0.3918 +2026-04-12 07:41:54.539495: val_loss -0.3248 +2026-04-12 07:41:54.542066: Pseudo dice [0.2452, 0.0, 0.7763, 0.4433, 0.5849, 0.4329, 0.2089] +2026-04-12 07:41:54.544844: Epoch time: 101.82 s +2026-04-12 07:41:55.783836: +2026-04-12 07:41:55.786569: Epoch 1448 +2026-04-12 07:41:55.788976: Current learning rate: 0.00667 +2026-04-12 07:43:37.898892: train_loss -0.3945 +2026-04-12 07:43:37.905357: val_loss -0.3543 +2026-04-12 07:43:37.908374: Pseudo dice [0.5163, 0.0, 0.8601, 0.7187, 0.5357, 0.5982, 0.6058] +2026-04-12 07:43:37.911397: Epoch time: 102.12 s +2026-04-12 07:43:39.133930: +2026-04-12 07:43:39.136191: Epoch 1449 +2026-04-12 07:43:39.138300: Current learning rate: 0.00667 +2026-04-12 07:45:20.912338: train_loss -0.3762 +2026-04-12 07:45:20.919514: val_loss -0.3034 +2026-04-12 07:45:20.921674: Pseudo dice [0.4561, 0.0, 0.6999, 0.3533, 0.4776, 0.6457, 0.2272] +2026-04-12 07:45:20.925241: Epoch time: 101.78 s +2026-04-12 07:45:23.847782: +2026-04-12 07:45:23.849639: Epoch 1450 +2026-04-12 07:45:23.851312: Current learning rate: 0.00667 +2026-04-12 07:47:05.707680: train_loss -0.3907 +2026-04-12 07:47:05.713965: val_loss -0.3131 +2026-04-12 07:47:05.716483: Pseudo dice [0.155, 0.0, 0.7707, 0.5473, 0.5949, 0.6582, 0.7706] +2026-04-12 07:47:05.719043: Epoch time: 101.86 s +2026-04-12 07:47:06.928794: +2026-04-12 07:47:06.930847: Epoch 1451 +2026-04-12 07:47:06.932665: Current learning rate: 0.00667 +2026-04-12 07:48:48.567276: train_loss -0.3863 +2026-04-12 07:48:48.576663: val_loss -0.3332 +2026-04-12 07:48:48.578778: Pseudo dice [0.0, 0.0, 0.72, 0.2903, 0.5342, 0.6827, 0.8521] +2026-04-12 07:48:48.581088: Epoch time: 101.64 s +2026-04-12 07:48:49.806596: +2026-04-12 07:48:49.809010: Epoch 1452 +2026-04-12 07:48:49.810690: Current learning rate: 0.00666 +2026-04-12 07:50:31.006095: train_loss -0.3915 +2026-04-12 07:50:31.012122: val_loss -0.3821 +2026-04-12 07:50:31.014360: Pseudo dice [0.0, 0.0, 0.8086, 0.0142, 0.5525, 0.705, 0.8746] +2026-04-12 07:50:31.016583: Epoch time: 101.2 s +2026-04-12 07:50:32.244091: +2026-04-12 07:50:32.246058: Epoch 1453 +2026-04-12 07:50:32.247780: Current learning rate: 0.00666 +2026-04-12 07:52:13.921386: train_loss -0.3966 +2026-04-12 07:52:13.932319: val_loss -0.3445 +2026-04-12 07:52:13.934060: Pseudo dice [0.0, 0.0, 0.7789, 0.6618, 0.116, 0.3697, 0.8186] +2026-04-12 07:52:13.937868: Epoch time: 101.68 s +2026-04-12 07:52:15.152087: +2026-04-12 07:52:15.154011: Epoch 1454 +2026-04-12 07:52:15.156284: Current learning rate: 0.00666 +2026-04-12 07:53:57.596571: train_loss -0.3642 +2026-04-12 07:53:57.603559: val_loss -0.3712 +2026-04-12 07:53:57.605819: Pseudo dice [0.0, 0.0, 0.6953, 0.7402, 0.5826, 0.5771, 0.7882] +2026-04-12 07:53:57.610062: Epoch time: 102.45 s +2026-04-12 07:53:58.868044: +2026-04-12 07:53:58.870033: Epoch 1455 +2026-04-12 07:53:58.871951: Current learning rate: 0.00666 +2026-04-12 07:55:40.324041: train_loss -0.3617 +2026-04-12 07:55:40.330708: val_loss -0.3725 +2026-04-12 07:55:40.333290: Pseudo dice [0.0011, 0.0, 0.8045, 0.7033, 0.4048, 0.4126, 0.7883] +2026-04-12 07:55:40.335863: Epoch time: 101.46 s +2026-04-12 07:55:41.540436: +2026-04-12 07:55:41.542480: Epoch 1456 +2026-04-12 07:55:41.544194: Current learning rate: 0.00665 +2026-04-12 07:57:22.963209: train_loss -0.3714 +2026-04-12 07:57:22.970067: val_loss -0.3395 +2026-04-12 07:57:22.972187: Pseudo dice [0.0323, 0.0, 0.7192, 0.4287, 0.48, 0.6391, 0.5909] +2026-04-12 07:57:22.975297: Epoch time: 101.43 s +2026-04-12 07:57:24.178594: +2026-04-12 07:57:24.180605: Epoch 1457 +2026-04-12 07:57:24.182387: Current learning rate: 0.00665 +2026-04-12 07:59:05.555714: train_loss -0.3715 +2026-04-12 07:59:05.564592: val_loss -0.3336 +2026-04-12 07:59:05.567386: Pseudo dice [0.1257, 0.0, 0.7557, 0.4356, 0.378, 0.537, 0.6927] +2026-04-12 07:59:05.570361: Epoch time: 101.38 s +2026-04-12 07:59:06.832095: +2026-04-12 07:59:06.834537: Epoch 1458 +2026-04-12 07:59:06.837283: Current learning rate: 0.00665 +2026-04-12 08:00:49.055109: train_loss -0.3938 +2026-04-12 08:00:49.061917: val_loss -0.3489 +2026-04-12 08:00:49.064560: Pseudo dice [0.1987, 0.0, 0.7746, 0.406, 0.3971, 0.5483, 0.6403] +2026-04-12 08:00:49.066826: Epoch time: 102.23 s +2026-04-12 08:00:50.311748: +2026-04-12 08:00:50.313830: Epoch 1459 +2026-04-12 08:00:50.316433: Current learning rate: 0.00665 +2026-04-12 08:02:31.911596: train_loss -0.412 +2026-04-12 08:02:31.920423: val_loss -0.3502 +2026-04-12 08:02:31.923417: Pseudo dice [0.5036, 0.0, 0.7189, 0.257, 0.707, 0.5031, 0.4951] +2026-04-12 08:02:31.926743: Epoch time: 101.6 s +2026-04-12 08:02:33.163574: +2026-04-12 08:02:33.165909: Epoch 1460 +2026-04-12 08:02:33.168792: Current learning rate: 0.00665 +2026-04-12 08:04:14.630849: train_loss -0.3747 +2026-04-12 08:04:14.638699: val_loss -0.3709 +2026-04-12 08:04:14.641540: Pseudo dice [0.6263, 0.0, 0.7872, 0.6903, 0.5281, 0.6276, 0.8614] +2026-04-12 08:04:14.644872: Epoch time: 101.47 s +2026-04-12 08:04:15.858572: +2026-04-12 08:04:15.861246: Epoch 1461 +2026-04-12 08:04:15.863211: Current learning rate: 0.00664 +2026-04-12 08:05:57.581571: train_loss -0.3855 +2026-04-12 08:05:57.600743: val_loss -0.358 +2026-04-12 08:05:57.603636: Pseudo dice [0.4414, 0.0, 0.5364, 0.0859, 0.4503, 0.8507, 0.8099] +2026-04-12 08:05:57.606466: Epoch time: 101.73 s +2026-04-12 08:05:58.854474: +2026-04-12 08:05:58.856102: Epoch 1462 +2026-04-12 08:05:58.857823: Current learning rate: 0.00664 +2026-04-12 08:07:40.506014: train_loss -0.3761 +2026-04-12 08:07:40.513360: val_loss -0.3804 +2026-04-12 08:07:40.515970: Pseudo dice [0.1022, 0.0, 0.8997, 0.6476, 0.5293, 0.2795, 0.909] +2026-04-12 08:07:40.518635: Epoch time: 101.65 s +2026-04-12 08:07:41.732622: +2026-04-12 08:07:41.734477: Epoch 1463 +2026-04-12 08:07:41.736521: Current learning rate: 0.00664 +2026-04-12 08:09:23.194463: train_loss -0.3618 +2026-04-12 08:09:23.202514: val_loss -0.3271 +2026-04-12 08:09:23.207362: Pseudo dice [0.0, 0.0, 0.8494, 0.5532, 0.4314, 0.404, 0.3023] +2026-04-12 08:09:23.210712: Epoch time: 101.46 s +2026-04-12 08:09:24.423369: +2026-04-12 08:09:24.425400: Epoch 1464 +2026-04-12 08:09:24.427354: Current learning rate: 0.00664 +2026-04-12 08:11:06.054161: train_loss -0.3849 +2026-04-12 08:11:06.062163: val_loss -0.3333 +2026-04-12 08:11:06.064373: Pseudo dice [0.0, 0.0, 0.7142, 0.5023, 0.573, 0.5267, 0.2769] +2026-04-12 08:11:06.067056: Epoch time: 101.63 s +2026-04-12 08:11:07.269510: +2026-04-12 08:11:07.271333: Epoch 1465 +2026-04-12 08:11:07.273140: Current learning rate: 0.00663 +2026-04-12 08:12:48.568611: train_loss -0.3847 +2026-04-12 08:12:48.574264: val_loss -0.3397 +2026-04-12 08:12:48.576416: Pseudo dice [0.0225, 0.0, 0.7795, 0.3805, 0.5381, 0.6314, 0.7338] +2026-04-12 08:12:48.578533: Epoch time: 101.3 s +2026-04-12 08:12:49.787278: +2026-04-12 08:12:49.789384: Epoch 1466 +2026-04-12 08:12:49.792217: Current learning rate: 0.00663 +2026-04-12 08:14:32.071803: train_loss -0.3941 +2026-04-12 08:14:32.079359: val_loss -0.3561 +2026-04-12 08:14:32.083196: Pseudo dice [0.3305, 0.0, 0.7619, 0.1992, 0.532, 0.5564, 0.6417] +2026-04-12 08:14:32.086208: Epoch time: 102.29 s +2026-04-12 08:14:33.325486: +2026-04-12 08:14:33.327669: Epoch 1467 +2026-04-12 08:14:33.330740: Current learning rate: 0.00663 +2026-04-12 08:16:14.712896: train_loss -0.4026 +2026-04-12 08:16:14.720433: val_loss -0.3117 +2026-04-12 08:16:14.722556: Pseudo dice [0.3454, 0.0, 0.7165, 0.4328, 0.4582, 0.3367, 0.3014] +2026-04-12 08:16:14.725907: Epoch time: 101.39 s +2026-04-12 08:16:15.983921: +2026-04-12 08:16:15.985873: Epoch 1468 +2026-04-12 08:16:15.987685: Current learning rate: 0.00663 +2026-04-12 08:17:58.024775: train_loss -0.3915 +2026-04-12 08:17:58.051816: val_loss -0.3359 +2026-04-12 08:17:58.053922: Pseudo dice [0.2902, 0.0, 0.833, 0.4708, 0.4327, 0.4641, 0.5108] +2026-04-12 08:17:58.057398: Epoch time: 102.04 s +2026-04-12 08:17:59.269260: +2026-04-12 08:17:59.271335: Epoch 1469 +2026-04-12 08:17:59.273128: Current learning rate: 0.00662 +2026-04-12 08:19:41.210926: train_loss -0.4026 +2026-04-12 08:19:41.220482: val_loss -0.3454 +2026-04-12 08:19:41.222762: Pseudo dice [0.0003, 0.0, 0.727, 0.4572, 0.4123, 0.3024, 0.8676] +2026-04-12 08:19:41.225490: Epoch time: 101.94 s +2026-04-12 08:19:42.440884: +2026-04-12 08:19:42.443254: Epoch 1470 +2026-04-12 08:19:42.446298: Current learning rate: 0.00662 +2026-04-12 08:21:24.278171: train_loss -0.3503 +2026-04-12 08:21:24.287328: val_loss -0.3249 +2026-04-12 08:21:24.290679: Pseudo dice [0.0, 0.0, 0.6151, 0.0038, 0.4935, 0.2495, 0.7358] +2026-04-12 08:21:24.294116: Epoch time: 101.84 s +2026-04-12 08:21:25.516847: +2026-04-12 08:21:25.519451: Epoch 1471 +2026-04-12 08:21:25.521493: Current learning rate: 0.00662 +2026-04-12 08:23:07.261554: train_loss -0.379 +2026-04-12 08:23:07.269998: val_loss -0.3648 +2026-04-12 08:23:07.272219: Pseudo dice [0.0, 0.0, 0.8039, 0.7636, 0.5524, 0.7615, 0.8005] +2026-04-12 08:23:07.275519: Epoch time: 101.75 s +2026-04-12 08:23:08.489708: +2026-04-12 08:23:08.492006: Epoch 1472 +2026-04-12 08:23:08.494591: Current learning rate: 0.00662 +2026-04-12 08:24:50.576599: train_loss -0.4023 +2026-04-12 08:24:50.584699: val_loss -0.3448 +2026-04-12 08:24:50.586529: Pseudo dice [0.4779, 0.0, 0.7161, 0.5359, 0.5282, 0.8433, 0.8765] +2026-04-12 08:24:50.588907: Epoch time: 102.09 s +2026-04-12 08:24:51.770735: +2026-04-12 08:24:51.772796: Epoch 1473 +2026-04-12 08:24:51.775176: Current learning rate: 0.00661 +2026-04-12 08:26:34.167222: train_loss -0.4126 +2026-04-12 08:26:34.174853: val_loss -0.3545 +2026-04-12 08:26:34.177632: Pseudo dice [0.5714, 0.0, 0.7571, 0.6872, 0.4358, 0.6054, 0.6296] +2026-04-12 08:26:34.179861: Epoch time: 102.4 s +2026-04-12 08:26:36.509853: +2026-04-12 08:26:36.512471: Epoch 1474 +2026-04-12 08:26:36.514647: Current learning rate: 0.00661 +2026-04-12 08:28:18.691100: train_loss -0.3881 +2026-04-12 08:28:18.697249: val_loss -0.3288 +2026-04-12 08:28:18.699103: Pseudo dice [0.4666, 0.0, 0.6902, 0.6242, 0.2855, 0.7748, 0.8712] +2026-04-12 08:28:18.701801: Epoch time: 102.18 s +2026-04-12 08:28:19.935063: +2026-04-12 08:28:19.937766: Epoch 1475 +2026-04-12 08:28:19.939660: Current learning rate: 0.00661 +2026-04-12 08:30:01.150466: train_loss -0.406 +2026-04-12 08:30:01.156873: val_loss -0.3343 +2026-04-12 08:30:01.158506: Pseudo dice [0.0, 0.0, 0.7771, 0.7236, 0.4818, 0.4136, 0.8821] +2026-04-12 08:30:01.161130: Epoch time: 101.22 s +2026-04-12 08:30:02.361413: +2026-04-12 08:30:02.363512: Epoch 1476 +2026-04-12 08:30:02.365490: Current learning rate: 0.00661 +2026-04-12 08:31:44.648307: train_loss -0.3928 +2026-04-12 08:31:44.655616: val_loss -0.3463 +2026-04-12 08:31:44.658102: Pseudo dice [0.0, 0.0, 0.6366, 0.5138, 0.3064, 0.5868, 0.723] +2026-04-12 08:31:44.660351: Epoch time: 102.29 s +2026-04-12 08:31:45.876134: +2026-04-12 08:31:45.878479: Epoch 1477 +2026-04-12 08:31:45.880138: Current learning rate: 0.0066 +2026-04-12 08:33:27.855294: train_loss -0.3985 +2026-04-12 08:33:27.864778: val_loss -0.353 +2026-04-12 08:33:27.869897: Pseudo dice [0.0, 0.0, 0.7436, 0.5026, 0.4626, 0.3093, 0.8426] +2026-04-12 08:33:27.872527: Epoch time: 101.98 s +2026-04-12 08:33:29.085695: +2026-04-12 08:33:29.088229: Epoch 1478 +2026-04-12 08:33:29.090498: Current learning rate: 0.0066 +2026-04-12 08:35:11.119348: train_loss -0.4097 +2026-04-12 08:35:11.131359: val_loss -0.3475 +2026-04-12 08:35:11.135386: Pseudo dice [0.0, 0.0, 0.7992, 0.3307, 0.3463, 0.552, 0.8514] +2026-04-12 08:35:11.138040: Epoch time: 102.04 s +2026-04-12 08:35:12.396921: +2026-04-12 08:35:12.398874: Epoch 1479 +2026-04-12 08:35:12.400647: Current learning rate: 0.0066 +2026-04-12 08:36:54.584854: train_loss -0.3967 +2026-04-12 08:36:54.592337: val_loss -0.341 +2026-04-12 08:36:54.594575: Pseudo dice [0.1183, 0.0, 0.4743, 0.528, 0.3708, 0.7336, 0.7335] +2026-04-12 08:36:54.598526: Epoch time: 102.19 s +2026-04-12 08:36:55.831649: +2026-04-12 08:36:55.833529: Epoch 1480 +2026-04-12 08:36:55.836518: Current learning rate: 0.0066 +2026-04-12 08:38:37.066655: train_loss -0.3837 +2026-04-12 08:38:37.073949: val_loss -0.3528 +2026-04-12 08:38:37.076384: Pseudo dice [0.196, 0.0, 0.8144, 0.1044, 0.2141, 0.696, 0.7347] +2026-04-12 08:38:37.079706: Epoch time: 101.24 s +2026-04-12 08:38:38.291467: +2026-04-12 08:38:38.293732: Epoch 1481 +2026-04-12 08:38:38.295979: Current learning rate: 0.0066 +2026-04-12 08:40:20.558885: train_loss -0.3841 +2026-04-12 08:40:20.566321: val_loss -0.3488 +2026-04-12 08:40:20.568475: Pseudo dice [0.7417, 0.0, 0.686, 0.241, 0.4926, 0.5672, 0.816] +2026-04-12 08:40:20.571588: Epoch time: 102.27 s +2026-04-12 08:40:21.778247: +2026-04-12 08:40:21.780190: Epoch 1482 +2026-04-12 08:40:21.782129: Current learning rate: 0.00659 +2026-04-12 08:42:03.401820: train_loss -0.3459 +2026-04-12 08:42:03.408977: val_loss -0.3337 +2026-04-12 08:42:03.411419: Pseudo dice [0.398, 0.0, 0.6744, 0.285, 0.3543, 0.4703, 0.4305] +2026-04-12 08:42:03.414279: Epoch time: 101.63 s +2026-04-12 08:42:04.641292: +2026-04-12 08:42:04.643827: Epoch 1483 +2026-04-12 08:42:04.645863: Current learning rate: 0.00659 +2026-04-12 08:43:46.084296: train_loss -0.3996 +2026-04-12 08:43:46.090390: val_loss -0.3306 +2026-04-12 08:43:46.092608: Pseudo dice [0.3429, 0.0, 0.6423, 0.1677, 0.6293, 0.3145, 0.1844] +2026-04-12 08:43:46.095044: Epoch time: 101.45 s +2026-04-12 08:43:47.324719: +2026-04-12 08:43:47.326921: Epoch 1484 +2026-04-12 08:43:47.328872: Current learning rate: 0.00659 +2026-04-12 08:45:28.894364: train_loss -0.3856 +2026-04-12 08:45:28.900226: val_loss -0.3213 +2026-04-12 08:45:28.902314: Pseudo dice [0.0, 0.0, 0.7049, 0.6868, 0.476, 0.5892, 0.4317] +2026-04-12 08:45:28.904817: Epoch time: 101.57 s +2026-04-12 08:45:30.119447: +2026-04-12 08:45:30.122220: Epoch 1485 +2026-04-12 08:45:30.124703: Current learning rate: 0.00659 +2026-04-12 08:47:12.562484: train_loss -0.3851 +2026-04-12 08:47:12.569580: val_loss -0.3396 +2026-04-12 08:47:12.574801: Pseudo dice [0.0, 0.0, 0.7708, 0.612, 0.3915, 0.7295, 0.6025] +2026-04-12 08:47:12.579008: Epoch time: 102.45 s +2026-04-12 08:47:13.818977: +2026-04-12 08:47:13.821128: Epoch 1486 +2026-04-12 08:47:13.824197: Current learning rate: 0.00658 +2026-04-12 08:48:56.009001: train_loss -0.3946 +2026-04-12 08:48:56.017123: val_loss -0.3537 +2026-04-12 08:48:56.020283: Pseudo dice [0.0, 0.0, 0.6018, 0.1698, 0.5811, 0.7345, 0.8386] +2026-04-12 08:48:56.024045: Epoch time: 102.19 s +2026-04-12 08:48:57.260564: +2026-04-12 08:48:57.263125: Epoch 1487 +2026-04-12 08:48:57.265048: Current learning rate: 0.00658 +2026-04-12 08:50:38.533138: train_loss -0.3902 +2026-04-12 08:50:38.541154: val_loss -0.3766 +2026-04-12 08:50:38.543631: Pseudo dice [0.0, 0.0, 0.7862, 0.739, 0.6115, 0.7285, 0.8817] +2026-04-12 08:50:38.547252: Epoch time: 101.28 s +2026-04-12 08:50:39.795466: +2026-04-12 08:50:39.797502: Epoch 1488 +2026-04-12 08:50:39.799510: Current learning rate: 0.00658 +2026-04-12 08:52:21.522116: train_loss -0.3926 +2026-04-12 08:52:21.530775: val_loss -0.369 +2026-04-12 08:52:21.534568: Pseudo dice [0.5054, 0.0, 0.7641, 0.5991, 0.4081, 0.624, 0.7738] +2026-04-12 08:52:21.537874: Epoch time: 101.73 s +2026-04-12 08:52:22.772810: +2026-04-12 08:52:22.776090: Epoch 1489 +2026-04-12 08:52:22.778634: Current learning rate: 0.00658 +2026-04-12 08:54:04.418078: train_loss -0.3989 +2026-04-12 08:54:04.430938: val_loss -0.329 +2026-04-12 08:54:04.434712: Pseudo dice [0.5219, 0.0, 0.6636, 0.6029, 0.5223, 0.8056, 0.8783] +2026-04-12 08:54:04.438347: Epoch time: 101.65 s +2026-04-12 08:54:05.642472: +2026-04-12 08:54:05.644703: Epoch 1490 +2026-04-12 08:54:05.646729: Current learning rate: 0.00657 +2026-04-12 08:55:47.765691: train_loss -0.4008 +2026-04-12 08:55:47.773799: val_loss -0.3427 +2026-04-12 08:55:47.777020: Pseudo dice [0.4683, 0.0, 0.7305, 0.542, 0.434, 0.4577, 0.8265] +2026-04-12 08:55:47.780045: Epoch time: 102.13 s +2026-04-12 08:55:48.992299: +2026-04-12 08:55:48.995007: Epoch 1491 +2026-04-12 08:55:48.997303: Current learning rate: 0.00657 +2026-04-12 08:57:31.322124: train_loss -0.4096 +2026-04-12 08:57:31.330810: val_loss -0.3649 +2026-04-12 08:57:31.334181: Pseudo dice [0.1441, 0.0, 0.8208, 0.3012, 0.601, 0.5567, 0.6653] +2026-04-12 08:57:31.337755: Epoch time: 102.33 s +2026-04-12 08:57:32.559862: +2026-04-12 08:57:32.562041: Epoch 1492 +2026-04-12 08:57:32.564230: Current learning rate: 0.00657 +2026-04-12 08:59:15.093946: train_loss -0.4197 +2026-04-12 08:59:15.100952: val_loss -0.3223 +2026-04-12 08:59:15.103192: Pseudo dice [0.3897, 0.0, 0.8639, 0.1778, 0.5005, 0.6336, 0.5323] +2026-04-12 08:59:15.106956: Epoch time: 102.54 s +2026-04-12 08:59:16.338988: +2026-04-12 08:59:16.340843: Epoch 1493 +2026-04-12 08:59:16.342902: Current learning rate: 0.00657 +2026-04-12 09:01:00.109921: train_loss -0.3935 +2026-04-12 09:01:00.115912: val_loss -0.3727 +2026-04-12 09:01:00.118329: Pseudo dice [0.6081, 0.0, 0.8171, 0.4151, 0.5047, 0.8966, 0.763] +2026-04-12 09:01:00.120832: Epoch time: 103.77 s +2026-04-12 09:01:01.348236: +2026-04-12 09:01:01.350337: Epoch 1494 +2026-04-12 09:01:01.352319: Current learning rate: 0.00656 +2026-04-12 09:02:43.680171: train_loss -0.3958 +2026-04-12 09:02:43.687535: val_loss -0.3149 +2026-04-12 09:02:43.694049: Pseudo dice [0.1773, 0.0, 0.8345, 0.1132, 0.5092, 0.224, 0.6454] +2026-04-12 09:02:43.700573: Epoch time: 102.34 s +2026-04-12 09:02:44.954035: +2026-04-12 09:02:44.957467: Epoch 1495 +2026-04-12 09:02:44.960016: Current learning rate: 0.00656 +2026-04-12 09:04:27.173609: train_loss -0.3949 +2026-04-12 09:04:27.181476: val_loss -0.3606 +2026-04-12 09:04:27.183995: Pseudo dice [0.2511, 0.0, 0.8903, 0.3806, 0.5561, 0.447, 0.8909] +2026-04-12 09:04:27.187435: Epoch time: 102.22 s +2026-04-12 09:04:28.418453: +2026-04-12 09:04:28.422531: Epoch 1496 +2026-04-12 09:04:28.425576: Current learning rate: 0.00656 +2026-04-12 09:06:10.088759: train_loss -0.3863 +2026-04-12 09:06:10.094555: val_loss -0.3401 +2026-04-12 09:06:10.096670: Pseudo dice [0.606, 0.0, 0.8152, 0.7632, 0.5278, 0.5745, 0.606] +2026-04-12 09:06:10.099511: Epoch time: 101.67 s +2026-04-12 09:06:11.309469: +2026-04-12 09:06:11.311366: Epoch 1497 +2026-04-12 09:06:11.313446: Current learning rate: 0.00656 +2026-04-12 09:07:53.320257: train_loss -0.388 +2026-04-12 09:07:53.333416: val_loss -0.3439 +2026-04-12 09:07:53.335732: Pseudo dice [0.0, 0.0, 0.814, 0.5984, 0.714, 0.6705, 0.8363] +2026-04-12 09:07:53.338401: Epoch time: 102.01 s +2026-04-12 09:07:54.572638: +2026-04-12 09:07:54.575138: Epoch 1498 +2026-04-12 09:07:54.576892: Current learning rate: 0.00656 +2026-04-12 09:09:36.242452: train_loss -0.3928 +2026-04-12 09:09:36.251403: val_loss -0.3285 +2026-04-12 09:09:36.254142: Pseudo dice [0.0, 0.0, 0.7541, 0.7872, 0.4069, 0.4453, 0.9323] +2026-04-12 09:09:36.257723: Epoch time: 101.67 s +2026-04-12 09:09:37.497137: +2026-04-12 09:09:37.499439: Epoch 1499 +2026-04-12 09:09:37.501631: Current learning rate: 0.00655 +2026-04-12 09:11:19.245742: train_loss -0.4045 +2026-04-12 09:11:19.252102: val_loss -0.3721 +2026-04-12 09:11:19.254938: Pseudo dice [0.0, 0.0, 0.7334, 0.6206, 0.4151, 0.6603, 0.8477] +2026-04-12 09:11:19.257835: Epoch time: 101.75 s +2026-04-12 09:11:22.279387: +2026-04-12 09:11:22.281554: Epoch 1500 +2026-04-12 09:11:22.283269: Current learning rate: 0.00655 +2026-04-12 09:13:04.780439: train_loss -0.4014 +2026-04-12 09:13:04.804057: val_loss -0.3472 +2026-04-12 09:13:04.806471: Pseudo dice [0.5041, 0.0, 0.7791, 0.7563, 0.5629, 0.6099, 0.8302] +2026-04-12 09:13:04.810241: Epoch time: 102.5 s +2026-04-12 09:13:06.049725: +2026-04-12 09:13:06.052243: Epoch 1501 +2026-04-12 09:13:06.054215: Current learning rate: 0.00655 +2026-04-12 09:14:48.487586: train_loss -0.4088 +2026-04-12 09:14:48.493918: val_loss -0.3654 +2026-04-12 09:14:48.496231: Pseudo dice [0.2495, 0.0, 0.8385, 0.6496, 0.3787, 0.6737, 0.8937] +2026-04-12 09:14:48.499118: Epoch time: 102.44 s +2026-04-12 09:14:49.754745: +2026-04-12 09:14:49.756911: Epoch 1502 +2026-04-12 09:14:49.758723: Current learning rate: 0.00655 +2026-04-12 09:16:31.277931: train_loss -0.3996 +2026-04-12 09:16:31.287283: val_loss -0.3625 +2026-04-12 09:16:31.289475: Pseudo dice [0.0, 0.0, 0.6988, 0.6954, 0.5346, 0.6684, 0.9029] +2026-04-12 09:16:31.292332: Epoch time: 101.53 s +2026-04-12 09:16:32.543285: +2026-04-12 09:16:32.545238: Epoch 1503 +2026-04-12 09:16:32.547029: Current learning rate: 0.00654 +2026-04-12 09:18:14.136209: train_loss -0.405 +2026-04-12 09:18:14.144370: val_loss -0.3548 +2026-04-12 09:18:14.146597: Pseudo dice [0.0, 0.0, 0.7958, 0.6853, 0.4676, 0.7704, 0.7011] +2026-04-12 09:18:14.149405: Epoch time: 101.6 s +2026-04-12 09:18:15.366235: +2026-04-12 09:18:15.368434: Epoch 1504 +2026-04-12 09:18:15.370424: Current learning rate: 0.00654 +2026-04-12 09:19:57.823835: train_loss -0.4048 +2026-04-12 09:19:57.831363: val_loss -0.3258 +2026-04-12 09:19:57.834288: Pseudo dice [0.0, 0.0, 0.6929, 0.4233, 0.6054, 0.3424, 0.55] +2026-04-12 09:19:57.837093: Epoch time: 102.46 s +2026-04-12 09:19:59.063377: +2026-04-12 09:19:59.065847: Epoch 1505 +2026-04-12 09:19:59.067873: Current learning rate: 0.00654 +2026-04-12 09:21:41.007766: train_loss -0.3849 +2026-04-12 09:21:41.014166: val_loss -0.3609 +2026-04-12 09:21:41.017199: Pseudo dice [0.4455, 0.0, 0.7975, 0.4706, 0.2756, 0.8981, 0.8592] +2026-04-12 09:21:41.020185: Epoch time: 101.95 s +2026-04-12 09:21:42.232610: +2026-04-12 09:21:42.239962: Epoch 1506 +2026-04-12 09:21:42.243396: Current learning rate: 0.00654 +2026-04-12 09:23:23.845608: train_loss -0.3959 +2026-04-12 09:23:23.853467: val_loss -0.385 +2026-04-12 09:23:23.856265: Pseudo dice [0.522, 0.0, 0.7948, 0.5619, 0.5545, 0.8666, 0.8921] +2026-04-12 09:23:23.859405: Epoch time: 101.62 s +2026-04-12 09:23:25.073688: +2026-04-12 09:23:25.075791: Epoch 1507 +2026-04-12 09:23:25.077795: Current learning rate: 0.00653 +2026-04-12 09:25:06.376065: train_loss -0.3987 +2026-04-12 09:25:06.382888: val_loss -0.3517 +2026-04-12 09:25:06.385035: Pseudo dice [0.0735, 0.0, 0.7324, 0.3316, 0.5514, 0.7851, 0.7532] +2026-04-12 09:25:06.387954: Epoch time: 101.31 s +2026-04-12 09:25:07.611860: +2026-04-12 09:25:07.614141: Epoch 1508 +2026-04-12 09:25:07.615800: Current learning rate: 0.00653 +2026-04-12 09:26:49.489902: train_loss -0.3916 +2026-04-12 09:26:49.497896: val_loss -0.3539 +2026-04-12 09:26:49.500619: Pseudo dice [0.7134, 0.0, 0.7112, 0.5973, 0.5138, 0.3649, 0.5658] +2026-04-12 09:26:49.503102: Epoch time: 101.88 s +2026-04-12 09:26:50.715077: +2026-04-12 09:26:50.716931: Epoch 1509 +2026-04-12 09:26:50.719193: Current learning rate: 0.00653 +2026-04-12 09:28:32.450027: train_loss -0.3978 +2026-04-12 09:28:32.456357: val_loss -0.3506 +2026-04-12 09:28:32.458540: Pseudo dice [0.3819, 0.0, 0.8198, 0.2284, 0.6112, 0.7866, 0.805] +2026-04-12 09:28:32.460928: Epoch time: 101.74 s +2026-04-12 09:28:33.715534: +2026-04-12 09:28:33.718370: Epoch 1510 +2026-04-12 09:28:33.720334: Current learning rate: 0.00653 +2026-04-12 09:30:15.780788: train_loss -0.3779 +2026-04-12 09:30:15.787985: val_loss -0.2783 +2026-04-12 09:30:15.790254: Pseudo dice [0.0, 0.0, 0.6668, 0.2914, 0.3537, 0.8124, 0.5271] +2026-04-12 09:30:15.793209: Epoch time: 102.07 s +2026-04-12 09:30:17.039397: +2026-04-12 09:30:17.041274: Epoch 1511 +2026-04-12 09:30:17.043092: Current learning rate: 0.00652 +2026-04-12 09:31:58.888868: train_loss -0.3667 +2026-04-12 09:31:58.896638: val_loss -0.3154 +2026-04-12 09:31:58.898679: Pseudo dice [0.0, 0.0, 0.6218, 0.1596, 0.2925, 0.6851, 0.7282] +2026-04-12 09:31:58.901172: Epoch time: 101.85 s +2026-04-12 09:32:00.105661: +2026-04-12 09:32:00.107607: Epoch 1512 +2026-04-12 09:32:00.109360: Current learning rate: 0.00652 +2026-04-12 09:33:41.596622: train_loss -0.3965 +2026-04-12 09:33:41.603324: val_loss -0.3618 +2026-04-12 09:33:41.605888: Pseudo dice [0.0, 0.0, 0.772, 0.3662, 0.4524, 0.8801, 0.8608] +2026-04-12 09:33:41.609041: Epoch time: 101.49 s +2026-04-12 09:33:43.799441: +2026-04-12 09:33:43.802395: Epoch 1513 +2026-04-12 09:33:43.805268: Current learning rate: 0.00652 +2026-04-12 09:35:25.782487: train_loss -0.3702 +2026-04-12 09:35:25.788732: val_loss -0.3054 +2026-04-12 09:35:25.791296: Pseudo dice [0.0, 0.0, 0.7643, 0.551, 0.4836, 0.5271, 0.3551] +2026-04-12 09:35:25.793652: Epoch time: 101.99 s +2026-04-12 09:35:27.014258: +2026-04-12 09:35:27.016659: Epoch 1514 +2026-04-12 09:35:27.019094: Current learning rate: 0.00652 +2026-04-12 09:37:08.976797: train_loss -0.3964 +2026-04-12 09:37:08.983147: val_loss -0.347 +2026-04-12 09:37:08.986989: Pseudo dice [0.0, 0.0, 0.8123, 0.1633, 0.3236, 0.8488, 0.8884] +2026-04-12 09:37:08.989974: Epoch time: 101.97 s +2026-04-12 09:37:10.237728: +2026-04-12 09:37:10.239812: Epoch 1515 +2026-04-12 09:37:10.241688: Current learning rate: 0.00652 +2026-04-12 09:38:52.344705: train_loss -0.3955 +2026-04-12 09:38:52.352248: val_loss -0.3477 +2026-04-12 09:38:52.355109: Pseudo dice [0.0, 0.0, 0.7546, 0.5569, 0.4146, 0.8838, 0.8684] +2026-04-12 09:38:52.357616: Epoch time: 102.11 s +2026-04-12 09:38:53.546230: +2026-04-12 09:38:53.548548: Epoch 1516 +2026-04-12 09:38:53.550581: Current learning rate: 0.00651 +2026-04-12 09:40:36.091627: train_loss -0.3946 +2026-04-12 09:40:36.097485: val_loss -0.346 +2026-04-12 09:40:36.099641: Pseudo dice [0.0, 0.0, 0.7214, 0.0228, 0.4747, 0.424, 0.301] +2026-04-12 09:40:36.102612: Epoch time: 102.55 s +2026-04-12 09:40:37.334656: +2026-04-12 09:40:37.336899: Epoch 1517 +2026-04-12 09:40:37.339156: Current learning rate: 0.00651 +2026-04-12 09:42:18.619865: train_loss -0.378 +2026-04-12 09:42:18.626057: val_loss -0.303 +2026-04-12 09:42:18.628327: Pseudo dice [0.0, 0.0, 0.5701, 0.6181, 0.5587, 0.5039, 0.397] +2026-04-12 09:42:18.630798: Epoch time: 101.29 s +2026-04-12 09:42:19.910417: +2026-04-12 09:42:19.912487: Epoch 1518 +2026-04-12 09:42:19.914566: Current learning rate: 0.00651 +2026-04-12 09:44:02.126709: train_loss -0.3926 +2026-04-12 09:44:02.133711: val_loss -0.3017 +2026-04-12 09:44:02.136974: Pseudo dice [0.0, 0.0, 0.5495, 0.3236, 0.3744, 0.4219, 0.8735] +2026-04-12 09:44:02.139638: Epoch time: 102.22 s +2026-04-12 09:44:03.369754: +2026-04-12 09:44:03.371482: Epoch 1519 +2026-04-12 09:44:03.373145: Current learning rate: 0.00651 +2026-04-12 09:45:45.512652: train_loss -0.3719 +2026-04-12 09:45:45.519956: val_loss -0.3462 +2026-04-12 09:45:45.522235: Pseudo dice [0.0, 0.0, 0.7396, 0.3467, 0.5323, 0.7302, 0.7864] +2026-04-12 09:45:45.524707: Epoch time: 102.15 s +2026-04-12 09:45:46.731291: +2026-04-12 09:45:46.733775: Epoch 1520 +2026-04-12 09:45:46.736468: Current learning rate: 0.0065 +2026-04-12 09:47:29.056722: train_loss -0.3788 +2026-04-12 09:47:29.069532: val_loss -0.3439 +2026-04-12 09:47:29.072943: Pseudo dice [0.0, 0.0, 0.7579, 0.3429, 0.5305, 0.6623, 0.6014] +2026-04-12 09:47:29.076195: Epoch time: 102.33 s +2026-04-12 09:47:30.291386: +2026-04-12 09:47:30.293749: Epoch 1521 +2026-04-12 09:47:30.295887: Current learning rate: 0.0065 +2026-04-12 09:49:12.278919: train_loss -0.4006 +2026-04-12 09:49:12.285370: val_loss -0.3395 +2026-04-12 09:49:12.287423: Pseudo dice [0.0, 0.0, 0.6049, 0.4705, 0.6129, 0.425, 0.7447] +2026-04-12 09:49:12.290320: Epoch time: 101.99 s +2026-04-12 09:49:13.518508: +2026-04-12 09:49:13.521442: Epoch 1522 +2026-04-12 09:49:13.524348: Current learning rate: 0.0065 +2026-04-12 09:50:55.127469: train_loss -0.4138 +2026-04-12 09:50:55.134945: val_loss -0.3654 +2026-04-12 09:50:55.137741: Pseudo dice [0.0, 0.0, 0.8054, 0.5138, 0.3878, 0.8652, 0.8246] +2026-04-12 09:50:55.140965: Epoch time: 101.61 s +2026-04-12 09:50:56.365882: +2026-04-12 09:50:56.367872: Epoch 1523 +2026-04-12 09:50:56.370185: Current learning rate: 0.0065 +2026-04-12 09:52:37.939542: train_loss -0.3888 +2026-04-12 09:52:37.947899: val_loss -0.347 +2026-04-12 09:52:37.950686: Pseudo dice [0.0, 0.0, 0.783, 0.7844, 0.3342, 0.7947, 0.6226] +2026-04-12 09:52:37.953942: Epoch time: 101.58 s +2026-04-12 09:52:39.165385: +2026-04-12 09:52:39.167665: Epoch 1524 +2026-04-12 09:52:39.169372: Current learning rate: 0.00649 +2026-04-12 09:54:21.249196: train_loss -0.3877 +2026-04-12 09:54:21.257457: val_loss -0.3497 +2026-04-12 09:54:21.260374: Pseudo dice [0.0, 0.0, 0.8337, 0.7086, 0.1174, 0.6033, 0.7541] +2026-04-12 09:54:21.263819: Epoch time: 102.09 s +2026-04-12 09:54:22.508574: +2026-04-12 09:54:22.510515: Epoch 1525 +2026-04-12 09:54:22.513247: Current learning rate: 0.00649 +2026-04-12 09:56:04.990819: train_loss -0.3837 +2026-04-12 09:56:04.999916: val_loss -0.3565 +2026-04-12 09:56:05.003157: Pseudo dice [0.0, 0.0, 0.8278, 0.6389, 0.4963, 0.5791, 0.5719] +2026-04-12 09:56:05.005669: Epoch time: 102.49 s +2026-04-12 09:56:06.304993: +2026-04-12 09:56:06.307406: Epoch 1526 +2026-04-12 09:56:06.310687: Current learning rate: 0.00649 +2026-04-12 09:57:47.851043: train_loss -0.3882 +2026-04-12 09:57:47.858540: val_loss -0.3431 +2026-04-12 09:57:47.860954: Pseudo dice [0.0, 0.0, 0.6843, 0.3899, 0.4449, 0.7898, 0.7455] +2026-04-12 09:57:47.863580: Epoch time: 101.55 s +2026-04-12 09:57:49.134515: +2026-04-12 09:57:49.136630: Epoch 1527 +2026-04-12 09:57:49.138637: Current learning rate: 0.00649 +2026-04-12 09:59:31.699720: train_loss -0.3876 +2026-04-12 09:59:31.708172: val_loss -0.3704 +2026-04-12 09:59:31.711311: Pseudo dice [0.0, 0.0, 0.8203, 0.3643, 0.438, 0.5759, 0.8529] +2026-04-12 09:59:31.714219: Epoch time: 102.57 s +2026-04-12 09:59:32.968912: +2026-04-12 09:59:32.971033: Epoch 1528 +2026-04-12 09:59:32.973274: Current learning rate: 0.00648 +2026-04-12 10:01:15.010355: train_loss -0.3854 +2026-04-12 10:01:15.018032: val_loss -0.3096 +2026-04-12 10:01:15.020421: Pseudo dice [0.0, 0.0, 0.6006, 0.2599, 0.3549, 0.3565, 0.7643] +2026-04-12 10:01:15.023072: Epoch time: 102.04 s +2026-04-12 10:01:16.252571: +2026-04-12 10:01:16.254578: Epoch 1529 +2026-04-12 10:01:16.256635: Current learning rate: 0.00648 +2026-04-12 10:02:57.998072: train_loss -0.3938 +2026-04-12 10:02:58.004629: val_loss -0.3238 +2026-04-12 10:02:58.006991: Pseudo dice [0.0, 0.0, 0.7869, 0.6008, 0.1692, 0.7904, 0.6847] +2026-04-12 10:02:58.010219: Epoch time: 101.75 s +2026-04-12 10:02:59.273877: +2026-04-12 10:02:59.276193: Epoch 1530 +2026-04-12 10:02:59.279531: Current learning rate: 0.00648 +2026-04-12 10:04:41.188615: train_loss -0.386 +2026-04-12 10:04:41.197418: val_loss -0.303 +2026-04-12 10:04:41.201983: Pseudo dice [0.0, 0.0, 0.685, 0.4539, 0.5345, 0.4496, 0.5392] +2026-04-12 10:04:41.205264: Epoch time: 101.92 s +2026-04-12 10:04:42.423256: +2026-04-12 10:04:42.426573: Epoch 1531 +2026-04-12 10:04:42.430014: Current learning rate: 0.00648 +2026-04-12 10:06:24.635330: train_loss -0.3738 +2026-04-12 10:06:24.641906: val_loss -0.3242 +2026-04-12 10:06:24.643837: Pseudo dice [0.0, 0.0, 0.686, 0.4618, 0.529, 0.4095, 0.4386] +2026-04-12 10:06:24.646281: Epoch time: 102.22 s +2026-04-12 10:06:27.010591: +2026-04-12 10:06:27.013107: Epoch 1532 +2026-04-12 10:06:27.018505: Current learning rate: 0.00648 +2026-04-12 10:08:10.395959: train_loss -0.3748 +2026-04-12 10:08:10.408254: val_loss -0.3353 +2026-04-12 10:08:10.412056: Pseudo dice [0.0, 0.0, 0.7261, 0.4834, 0.5045, 0.3317, 0.5702] +2026-04-12 10:08:10.418142: Epoch time: 103.39 s +2026-04-12 10:08:11.707201: +2026-04-12 10:08:11.710867: Epoch 1533 +2026-04-12 10:08:11.712921: Current learning rate: 0.00647 +2026-04-12 10:09:54.014669: train_loss -0.4071 +2026-04-12 10:09:54.021424: val_loss -0.33 +2026-04-12 10:09:54.023727: Pseudo dice [0.0, 0.0, 0.7555, 0.7111, 0.6073, 0.4353, 0.1351] +2026-04-12 10:09:54.026516: Epoch time: 102.31 s +2026-04-12 10:09:55.249121: +2026-04-12 10:09:55.251034: Epoch 1534 +2026-04-12 10:09:55.252690: Current learning rate: 0.00647 +2026-04-12 10:11:37.147047: train_loss -0.4042 +2026-04-12 10:11:37.154003: val_loss -0.3217 +2026-04-12 10:11:37.156683: Pseudo dice [0.0, 0.0, 0.5337, 0.4924, 0.3206, 0.1769, 0.8773] +2026-04-12 10:11:37.159200: Epoch time: 101.9 s +2026-04-12 10:11:38.421491: +2026-04-12 10:11:38.423546: Epoch 1535 +2026-04-12 10:11:38.425580: Current learning rate: 0.00647 +2026-04-12 10:13:20.563651: train_loss -0.3875 +2026-04-12 10:13:20.573382: val_loss -0.3409 +2026-04-12 10:13:20.577695: Pseudo dice [0.0, 0.0, 0.7767, 0.6573, 0.5948, 0.3947, 0.7074] +2026-04-12 10:13:20.580778: Epoch time: 102.15 s +2026-04-12 10:13:21.806526: +2026-04-12 10:13:21.809831: Epoch 1536 +2026-04-12 10:13:21.811903: Current learning rate: 0.00647 +2026-04-12 10:15:04.217116: train_loss -0.3962 +2026-04-12 10:15:04.223669: val_loss -0.374 +2026-04-12 10:15:04.225779: Pseudo dice [0.0, 0.0, 0.823, 0.6779, 0.5654, 0.4175, 0.8893] +2026-04-12 10:15:04.229651: Epoch time: 102.41 s +2026-04-12 10:15:05.476859: +2026-04-12 10:15:05.478866: Epoch 1537 +2026-04-12 10:15:05.481200: Current learning rate: 0.00646 +2026-04-12 10:16:46.958070: train_loss -0.4073 +2026-04-12 10:16:46.966386: val_loss -0.3642 +2026-04-12 10:16:46.969703: Pseudo dice [0.0, 0.0, 0.7152, 0.4821, 0.4477, 0.5655, 0.8679] +2026-04-12 10:16:46.972881: Epoch time: 101.48 s +2026-04-12 10:16:48.205775: +2026-04-12 10:16:48.207865: Epoch 1538 +2026-04-12 10:16:48.210598: Current learning rate: 0.00646 +2026-04-12 10:18:30.535407: train_loss -0.3777 +2026-04-12 10:18:30.542484: val_loss -0.3782 +2026-04-12 10:18:30.545257: Pseudo dice [0.0, 0.0, 0.7598, 0.7707, 0.5628, 0.4189, 0.8739] +2026-04-12 10:18:30.547717: Epoch time: 102.33 s +2026-04-12 10:18:31.807971: +2026-04-12 10:18:31.810330: Epoch 1539 +2026-04-12 10:18:31.812264: Current learning rate: 0.00646 +2026-04-12 10:20:13.808157: train_loss -0.4166 +2026-04-12 10:20:13.815214: val_loss -0.3446 +2026-04-12 10:20:13.817178: Pseudo dice [0.0, 0.0, 0.6721, 0.1404, 0.6094, 0.3059, 0.8925] +2026-04-12 10:20:13.819705: Epoch time: 102.0 s +2026-04-12 10:20:15.060768: +2026-04-12 10:20:15.062903: Epoch 1540 +2026-04-12 10:20:15.064636: Current learning rate: 0.00646 +2026-04-12 10:21:57.659476: train_loss -0.3975 +2026-04-12 10:21:57.667711: val_loss -0.3222 +2026-04-12 10:21:57.670318: Pseudo dice [0.0, 0.0, 0.7334, 0.6027, 0.6447, 0.5474, 0.565] +2026-04-12 10:21:57.673394: Epoch time: 102.6 s +2026-04-12 10:21:58.922432: +2026-04-12 10:21:58.925377: Epoch 1541 +2026-04-12 10:21:58.927785: Current learning rate: 0.00645 +2026-04-12 10:23:41.261570: train_loss -0.4005 +2026-04-12 10:23:41.270140: val_loss -0.3463 +2026-04-12 10:23:41.272427: Pseudo dice [0.0, 0.0, 0.7187, 0.6397, 0.5324, 0.7449, 0.5656] +2026-04-12 10:23:41.275005: Epoch time: 102.34 s +2026-04-12 10:23:42.513553: +2026-04-12 10:23:42.515584: Epoch 1542 +2026-04-12 10:23:42.517512: Current learning rate: 0.00645 +2026-04-12 10:25:25.187185: train_loss -0.4075 +2026-04-12 10:25:25.194966: val_loss -0.3443 +2026-04-12 10:25:25.197377: Pseudo dice [0.0, 0.0, 0.7568, 0.0235, 0.5525, 0.7236, 0.8868] +2026-04-12 10:25:25.200163: Epoch time: 102.68 s +2026-04-12 10:25:26.470324: +2026-04-12 10:25:26.472794: Epoch 1543 +2026-04-12 10:25:26.474876: Current learning rate: 0.00645 +2026-04-12 10:27:08.862921: train_loss -0.4148 +2026-04-12 10:27:08.870587: val_loss -0.3753 +2026-04-12 10:27:08.875143: Pseudo dice [0.0, 0.0, 0.6912, 0.5908, 0.5327, 0.7024, 0.9037] +2026-04-12 10:27:08.878074: Epoch time: 102.4 s +2026-04-12 10:27:10.212497: +2026-04-12 10:27:10.214862: Epoch 1544 +2026-04-12 10:27:10.216761: Current learning rate: 0.00645 +2026-04-12 10:28:52.406190: train_loss -0.4078 +2026-04-12 10:28:52.412289: val_loss -0.3458 +2026-04-12 10:28:52.414544: Pseudo dice [0.0, 0.0, 0.7168, 0.411, 0.4697, 0.8668, 0.9391] +2026-04-12 10:28:52.418913: Epoch time: 102.2 s +2026-04-12 10:28:53.672312: +2026-04-12 10:28:53.674217: Epoch 1545 +2026-04-12 10:28:53.676465: Current learning rate: 0.00644 +2026-04-12 10:30:36.344445: train_loss -0.3959 +2026-04-12 10:30:36.353223: val_loss -0.3436 +2026-04-12 10:30:36.356832: Pseudo dice [0.0, 0.0, 0.7995, 0.486, 0.3314, 0.8699, 0.8453] +2026-04-12 10:30:36.360851: Epoch time: 102.68 s +2026-04-12 10:30:37.642290: +2026-04-12 10:30:37.644563: Epoch 1546 +2026-04-12 10:30:37.646570: Current learning rate: 0.00644 +2026-04-12 10:32:18.796314: train_loss -0.4003 +2026-04-12 10:32:18.802860: val_loss -0.3122 +2026-04-12 10:32:18.804950: Pseudo dice [0.0, 0.0, 0.5178, 0.4031, 0.34, 0.5755, 0.8648] +2026-04-12 10:32:18.807890: Epoch time: 101.16 s +2026-04-12 10:32:20.034715: +2026-04-12 10:32:20.036568: Epoch 1547 +2026-04-12 10:32:20.038085: Current learning rate: 0.00644 +2026-04-12 10:34:01.417390: train_loss -0.3507 +2026-04-12 10:34:01.430632: val_loss -0.2757 +2026-04-12 10:34:01.433457: Pseudo dice [0.0, 0.0, 0.6297, 0.0972, 0.3499, 0.3027, 0.5132] +2026-04-12 10:34:01.436212: Epoch time: 101.39 s +2026-04-12 10:34:02.676905: +2026-04-12 10:34:02.679646: Epoch 1548 +2026-04-12 10:34:02.682430: Current learning rate: 0.00644 +2026-04-12 10:35:44.382221: train_loss -0.3575 +2026-04-12 10:35:44.388542: val_loss -0.3512 +2026-04-12 10:35:44.390487: Pseudo dice [0.0, 0.0, 0.7493, 0.2261, 0.4115, 0.5614, 0.7293] +2026-04-12 10:35:44.393262: Epoch time: 101.71 s +2026-04-12 10:35:45.648827: +2026-04-12 10:35:45.651932: Epoch 1549 +2026-04-12 10:35:45.654543: Current learning rate: 0.00644 +2026-04-12 10:37:28.107948: train_loss -0.3941 +2026-04-12 10:37:28.114938: val_loss -0.3442 +2026-04-12 10:37:28.117334: Pseudo dice [0.0, 0.0, 0.8047, 0.3569, 0.4336, 0.5247, 0.7097] +2026-04-12 10:37:28.120173: Epoch time: 102.46 s +2026-04-12 10:37:31.246814: +2026-04-12 10:37:31.248812: Epoch 1550 +2026-04-12 10:37:31.250328: Current learning rate: 0.00643 +2026-04-12 10:39:12.887216: train_loss -0.3979 +2026-04-12 10:39:12.892954: val_loss -0.3267 +2026-04-12 10:39:12.895185: Pseudo dice [0.0, 0.0, 0.8086, 0.0895, 0.4534, 0.48, 0.9128] +2026-04-12 10:39:12.899463: Epoch time: 101.64 s +2026-04-12 10:39:14.180403: +2026-04-12 10:39:14.182952: Epoch 1551 +2026-04-12 10:39:14.185035: Current learning rate: 0.00643 +2026-04-12 10:40:58.133486: train_loss -0.3756 +2026-04-12 10:40:58.140107: val_loss -0.3439 +2026-04-12 10:40:58.143560: Pseudo dice [0.0, 0.0, 0.7741, 0.4634, 0.4277, 0.557, 0.743] +2026-04-12 10:40:58.146645: Epoch time: 103.96 s +2026-04-12 10:40:59.375660: +2026-04-12 10:40:59.381203: Epoch 1552 +2026-04-12 10:40:59.384533: Current learning rate: 0.00643 +2026-04-12 10:42:41.959969: train_loss -0.3944 +2026-04-12 10:42:41.966553: val_loss -0.3645 +2026-04-12 10:42:41.968889: Pseudo dice [0.5219, 0.0, 0.7605, 0.5648, 0.6006, 0.5347, 0.6668] +2026-04-12 10:42:41.973081: Epoch time: 102.59 s +2026-04-12 10:42:43.262637: +2026-04-12 10:42:43.264545: Epoch 1553 +2026-04-12 10:42:43.266502: Current learning rate: 0.00643 +2026-04-12 10:44:25.288251: train_loss -0.381 +2026-04-12 10:44:25.295841: val_loss -0.3371 +2026-04-12 10:44:25.298426: Pseudo dice [0.4423, 0.0, 0.4756, 0.3772, 0.3182, 0.8887, 0.8482] +2026-04-12 10:44:25.301416: Epoch time: 102.03 s +2026-04-12 10:44:26.574709: +2026-04-12 10:44:26.577755: Epoch 1554 +2026-04-12 10:44:26.579897: Current learning rate: 0.00642 +2026-04-12 10:46:09.519303: train_loss -0.3951 +2026-04-12 10:46:09.528413: val_loss -0.3533 +2026-04-12 10:46:09.531498: Pseudo dice [0.0, 0.0, 0.7504, 0.0038, 0.3377, 0.6542, 0.7932] +2026-04-12 10:46:09.536720: Epoch time: 102.95 s +2026-04-12 10:46:10.777457: +2026-04-12 10:46:10.780143: Epoch 1555 +2026-04-12 10:46:10.782566: Current learning rate: 0.00642 +2026-04-12 10:47:53.311606: train_loss -0.3906 +2026-04-12 10:47:53.318417: val_loss -0.3566 +2026-04-12 10:47:53.321829: Pseudo dice [0.0, 0.0, 0.7737, 0.5125, 0.5071, 0.4971, 0.9156] +2026-04-12 10:47:53.325380: Epoch time: 102.54 s +2026-04-12 10:47:54.562067: +2026-04-12 10:47:54.566125: Epoch 1556 +2026-04-12 10:47:54.568537: Current learning rate: 0.00642 +2026-04-12 10:49:36.553617: train_loss -0.4026 +2026-04-12 10:49:36.563301: val_loss -0.3533 +2026-04-12 10:49:36.565276: Pseudo dice [0.0, 0.0, 0.7763, 0.7579, 0.369, 0.8344, 0.8068] +2026-04-12 10:49:36.567790: Epoch time: 101.99 s +2026-04-12 10:49:37.923385: +2026-04-12 10:49:37.925367: Epoch 1557 +2026-04-12 10:49:37.927258: Current learning rate: 0.00642 +2026-04-12 10:51:21.360918: train_loss -0.3964 +2026-04-12 10:51:21.371799: val_loss -0.3004 +2026-04-12 10:51:21.374232: Pseudo dice [0.465, 0.0, 0.4013, 0.3799, 0.3851, 0.6567, 0.5185] +2026-04-12 10:51:21.377378: Epoch time: 103.44 s +2026-04-12 10:51:22.655980: +2026-04-12 10:51:22.658179: Epoch 1558 +2026-04-12 10:51:22.660726: Current learning rate: 0.00641 +2026-04-12 10:53:04.365845: train_loss -0.3885 +2026-04-12 10:53:04.375688: val_loss -0.3383 +2026-04-12 10:53:04.378290: Pseudo dice [0.0, 0.0, 0.748, 0.3137, 0.3342, 0.7533, 0.8393] +2026-04-12 10:53:04.382198: Epoch time: 101.71 s +2026-04-12 10:53:05.646244: +2026-04-12 10:53:05.648458: Epoch 1559 +2026-04-12 10:53:05.650783: Current learning rate: 0.00641 +2026-04-12 10:54:48.190399: train_loss -0.3961 +2026-04-12 10:54:48.199102: val_loss -0.3232 +2026-04-12 10:54:48.201565: Pseudo dice [0.0, 0.0, 0.7233, 0.1932, 0.5558, 0.6992, 0.8466] +2026-04-12 10:54:48.204767: Epoch time: 102.55 s +2026-04-12 10:54:49.463468: +2026-04-12 10:54:49.467294: Epoch 1560 +2026-04-12 10:54:49.469254: Current learning rate: 0.00641 +2026-04-12 10:56:31.804443: train_loss -0.404 +2026-04-12 10:56:31.812232: val_loss -0.3323 +2026-04-12 10:56:31.815317: Pseudo dice [0.0, 0.0, 0.6731, 0.4136, 0.4172, 0.8579, 0.7275] +2026-04-12 10:56:31.818899: Epoch time: 102.34 s +2026-04-12 10:56:33.057087: +2026-04-12 10:56:33.059109: Epoch 1561 +2026-04-12 10:56:33.061565: Current learning rate: 0.00641 +2026-04-12 10:58:15.012929: train_loss -0.3997 +2026-04-12 10:58:15.021378: val_loss -0.3584 +2026-04-12 10:58:15.023648: Pseudo dice [0.037, 0.0, 0.8153, 0.8059, 0.5937, 0.7363, 0.7544] +2026-04-12 10:58:15.026342: Epoch time: 101.96 s +2026-04-12 10:58:16.279238: +2026-04-12 10:58:16.281103: Epoch 1562 +2026-04-12 10:58:16.282732: Current learning rate: 0.0064 +2026-04-12 10:59:58.790884: train_loss -0.3768 +2026-04-12 10:59:58.797021: val_loss -0.3599 +2026-04-12 10:59:58.799431: Pseudo dice [0.0413, 0.0, 0.6933, 0.1889, 0.5478, 0.405, 0.6521] +2026-04-12 10:59:58.801766: Epoch time: 102.51 s +2026-04-12 11:00:00.043380: +2026-04-12 11:00:00.045259: Epoch 1563 +2026-04-12 11:00:00.047087: Current learning rate: 0.0064 +2026-04-12 11:01:42.717752: train_loss -0.363 +2026-04-12 11:01:42.725614: val_loss -0.3314 +2026-04-12 11:01:42.730236: Pseudo dice [0.0, 0.0, 0.8134, 0.5379, 0.3229, 0.509, 0.677] +2026-04-12 11:01:42.734723: Epoch time: 102.68 s +2026-04-12 11:01:44.047289: +2026-04-12 11:01:44.050993: Epoch 1564 +2026-04-12 11:01:44.053884: Current learning rate: 0.0064 +2026-04-12 11:03:26.834356: train_loss -0.3775 +2026-04-12 11:03:26.843189: val_loss -0.3453 +2026-04-12 11:03:26.846373: Pseudo dice [0.0, 0.0, 0.7738, 0.8084, 0.4714, 0.4174, 0.4633] +2026-04-12 11:03:26.848916: Epoch time: 102.79 s +2026-04-12 11:03:28.109744: +2026-04-12 11:03:28.112082: Epoch 1565 +2026-04-12 11:03:28.113936: Current learning rate: 0.0064 +2026-04-12 11:05:09.757427: train_loss -0.3743 +2026-04-12 11:05:09.764994: val_loss -0.2989 +2026-04-12 11:05:09.767155: Pseudo dice [0.0, 0.0, 0.6883, 0.1487, 0.4021, 0.2268, 0.5406] +2026-04-12 11:05:09.769696: Epoch time: 101.65 s +2026-04-12 11:05:11.015961: +2026-04-12 11:05:11.018249: Epoch 1566 +2026-04-12 11:05:11.020680: Current learning rate: 0.00639 +2026-04-12 11:06:53.029356: train_loss -0.3775 +2026-04-12 11:06:53.035726: val_loss -0.3164 +2026-04-12 11:06:53.037782: Pseudo dice [0.0, 0.0, 0.6074, 0.3077, 0.4144, 0.4886, 0.6482] +2026-04-12 11:06:53.040551: Epoch time: 102.02 s +2026-04-12 11:06:54.299808: +2026-04-12 11:06:54.302350: Epoch 1567 +2026-04-12 11:06:54.304297: Current learning rate: 0.00639 +2026-04-12 11:08:36.146740: train_loss -0.3831 +2026-04-12 11:08:36.153463: val_loss -0.3151 +2026-04-12 11:08:36.155383: Pseudo dice [0.0, 0.0, 0.7578, 0.0666, 0.508, 0.5691, 0.5324] +2026-04-12 11:08:36.157907: Epoch time: 101.85 s +2026-04-12 11:08:37.411347: +2026-04-12 11:08:37.413256: Epoch 1568 +2026-04-12 11:08:37.415617: Current learning rate: 0.00639 +2026-04-12 11:10:20.276862: train_loss -0.3961 +2026-04-12 11:10:20.284858: val_loss -0.3429 +2026-04-12 11:10:20.286967: Pseudo dice [0.0, 0.0, 0.7866, 0.2457, 0.4319, 0.46, 0.7657] +2026-04-12 11:10:20.289716: Epoch time: 102.87 s +2026-04-12 11:10:21.531179: +2026-04-12 11:10:21.534004: Epoch 1569 +2026-04-12 11:10:21.536642: Current learning rate: 0.00639 +2026-04-12 11:12:03.620998: train_loss -0.4049 +2026-04-12 11:12:03.628440: val_loss -0.3284 +2026-04-12 11:12:03.630600: Pseudo dice [0.0, 0.0, 0.6855, 0.0693, 0.5601, 0.3806, 0.773] +2026-04-12 11:12:03.634048: Epoch time: 102.09 s +2026-04-12 11:12:04.910586: +2026-04-12 11:12:04.912988: Epoch 1570 +2026-04-12 11:12:04.915503: Current learning rate: 0.00639 +2026-04-12 11:13:48.337448: train_loss -0.3876 +2026-04-12 11:13:48.345032: val_loss -0.3617 +2026-04-12 11:13:48.347541: Pseudo dice [0.0, 0.0, 0.5272, 0.7433, 0.4815, 0.6052, 0.8493] +2026-04-12 11:13:48.350570: Epoch time: 103.43 s +2026-04-12 11:13:49.606799: +2026-04-12 11:13:49.608995: Epoch 1571 +2026-04-12 11:13:49.610994: Current learning rate: 0.00638 +2026-04-12 11:15:31.433351: train_loss -0.4116 +2026-04-12 11:15:31.443798: val_loss -0.3583 +2026-04-12 11:15:31.446088: Pseudo dice [0.0287, 0.0, 0.8106, 0.3262, 0.3123, 0.8148, 0.8061] +2026-04-12 11:15:31.449679: Epoch time: 101.83 s +2026-04-12 11:15:32.722953: +2026-04-12 11:15:32.724895: Epoch 1572 +2026-04-12 11:15:32.726512: Current learning rate: 0.00638 +2026-04-12 11:17:15.105098: train_loss -0.4072 +2026-04-12 11:17:15.110831: val_loss -0.3301 +2026-04-12 11:17:15.113270: Pseudo dice [0.4607, 0.0, 0.7978, 0.5872, 0.4505, 0.635, 0.6653] +2026-04-12 11:17:15.116293: Epoch time: 102.39 s +2026-04-12 11:17:16.379877: +2026-04-12 11:17:16.382177: Epoch 1573 +2026-04-12 11:17:16.384303: Current learning rate: 0.00638 +2026-04-12 11:18:58.145706: train_loss -0.3671 +2026-04-12 11:18:58.153909: val_loss -0.3602 +2026-04-12 11:18:58.158718: Pseudo dice [0.0288, 0.0, 0.7061, 0.7053, 0.5557, 0.7148, 0.7784] +2026-04-12 11:18:58.161616: Epoch time: 101.77 s +2026-04-12 11:18:59.435279: +2026-04-12 11:18:59.437754: Epoch 1574 +2026-04-12 11:18:59.439833: Current learning rate: 0.00638 +2026-04-12 11:20:41.273180: train_loss -0.4052 +2026-04-12 11:20:41.284502: val_loss -0.346 +2026-04-12 11:20:41.288301: Pseudo dice [0.0886, 0.0, 0.7632, 0.6425, 0.5638, 0.6593, 0.4953] +2026-04-12 11:20:41.292868: Epoch time: 101.84 s +2026-04-12 11:20:42.555266: +2026-04-12 11:20:42.560101: Epoch 1575 +2026-04-12 11:20:42.562425: Current learning rate: 0.00637 +2026-04-12 11:22:24.483822: train_loss -0.4041 +2026-04-12 11:22:24.490501: val_loss -0.3364 +2026-04-12 11:22:24.493613: Pseudo dice [0.1535, 0.0, 0.7847, 0.4275, 0.4498, 0.5306, 0.8753] +2026-04-12 11:22:24.499177: Epoch time: 101.93 s +2026-04-12 11:22:25.769257: +2026-04-12 11:22:25.771450: Epoch 1576 +2026-04-12 11:22:25.774139: Current learning rate: 0.00637 +2026-04-12 11:24:07.505509: train_loss -0.3829 +2026-04-12 11:24:07.513343: val_loss -0.3365 +2026-04-12 11:24:07.516269: Pseudo dice [0.3307, 0.0, 0.4441, 0.4928, 0.5178, 0.7651, 0.7407] +2026-04-12 11:24:07.519428: Epoch time: 101.74 s +2026-04-12 11:24:08.775438: +2026-04-12 11:24:08.777326: Epoch 1577 +2026-04-12 11:24:08.778994: Current learning rate: 0.00637 +2026-04-12 11:25:51.172569: train_loss -0.4057 +2026-04-12 11:25:51.178733: val_loss -0.385 +2026-04-12 11:25:51.180959: Pseudo dice [0.3263, 0.0, 0.7837, 0.8065, 0.5814, 0.7268, 0.8573] +2026-04-12 11:25:51.182890: Epoch time: 102.4 s +2026-04-12 11:25:52.424346: +2026-04-12 11:25:52.426209: Epoch 1578 +2026-04-12 11:25:52.427809: Current learning rate: 0.00637 +2026-04-12 11:27:34.721776: train_loss -0.4113 +2026-04-12 11:27:34.728894: val_loss -0.3412 +2026-04-12 11:27:34.733173: Pseudo dice [0.6564, 0.0, 0.7041, 0.6316, 0.5843, 0.1831, 0.666] +2026-04-12 11:27:34.736828: Epoch time: 102.3 s +2026-04-12 11:27:35.987236: +2026-04-12 11:27:35.989508: Epoch 1579 +2026-04-12 11:27:35.991553: Current learning rate: 0.00636 +2026-04-12 11:29:18.924580: train_loss -0.3816 +2026-04-12 11:29:18.932049: val_loss -0.3387 +2026-04-12 11:29:18.934022: Pseudo dice [0.5922, 0.0, 0.6637, 0.0505, 0.4557, 0.7799, 0.4743] +2026-04-12 11:29:18.936594: Epoch time: 102.94 s +2026-04-12 11:29:20.203887: +2026-04-12 11:29:20.206728: Epoch 1580 +2026-04-12 11:29:20.208959: Current learning rate: 0.00636 +2026-04-12 11:31:02.946949: train_loss -0.4032 +2026-04-12 11:31:02.954899: val_loss -0.3366 +2026-04-12 11:31:02.957645: Pseudo dice [0.1886, 0.0, 0.7767, 0.7493, 0.4808, 0.5359, 0.883] +2026-04-12 11:31:02.960211: Epoch time: 102.75 s +2026-04-12 11:31:04.248301: +2026-04-12 11:31:04.250139: Epoch 1581 +2026-04-12 11:31:04.252032: Current learning rate: 0.00636 +2026-04-12 11:32:46.510790: train_loss -0.3934 +2026-04-12 11:32:46.517102: val_loss -0.3527 +2026-04-12 11:32:46.519232: Pseudo dice [0.6452, 0.0, 0.5694, 0.0487, 0.5078, 0.7643, 0.7964] +2026-04-12 11:32:46.521446: Epoch time: 102.27 s +2026-04-12 11:32:47.780739: +2026-04-12 11:32:47.784943: Epoch 1582 +2026-04-12 11:32:47.787724: Current learning rate: 0.00636 +2026-04-12 11:34:30.327319: train_loss -0.4056 +2026-04-12 11:34:30.334012: val_loss -0.3686 +2026-04-12 11:34:30.336550: Pseudo dice [0.3825, 0.0, 0.8298, 0.3338, 0.587, 0.6609, 0.9021] +2026-04-12 11:34:30.339689: Epoch time: 102.55 s +2026-04-12 11:34:31.565071: +2026-04-12 11:34:31.567187: Epoch 1583 +2026-04-12 11:34:31.569074: Current learning rate: 0.00635 +2026-04-12 11:36:14.775668: train_loss -0.418 +2026-04-12 11:36:14.783077: val_loss -0.3851 +2026-04-12 11:36:14.786500: Pseudo dice [0.4485, 0.0, 0.815, 0.5916, 0.438, 0.3726, 0.7572] +2026-04-12 11:36:14.789929: Epoch time: 103.21 s +2026-04-12 11:36:16.073801: +2026-04-12 11:36:16.076018: Epoch 1584 +2026-04-12 11:36:16.078784: Current learning rate: 0.00635 +2026-04-12 11:37:58.967388: train_loss -0.4126 +2026-04-12 11:37:58.973704: val_loss -0.3177 +2026-04-12 11:37:58.977833: Pseudo dice [0.2354, 0.0, 0.6726, 0.4585, 0.3366, 0.2864, 0.6667] +2026-04-12 11:37:58.980312: Epoch time: 102.9 s +2026-04-12 11:38:00.221718: +2026-04-12 11:38:00.226925: Epoch 1585 +2026-04-12 11:38:00.228786: Current learning rate: 0.00635 +2026-04-12 11:39:42.483611: train_loss -0.3902 +2026-04-12 11:39:42.490932: val_loss -0.3215 +2026-04-12 11:39:42.493146: Pseudo dice [0.6588, 0.0, 0.3469, 0.6174, 0.4835, 0.623, 0.7835] +2026-04-12 11:39:42.497003: Epoch time: 102.26 s +2026-04-12 11:39:43.744405: +2026-04-12 11:39:43.746578: Epoch 1586 +2026-04-12 11:39:43.750718: Current learning rate: 0.00635 +2026-04-12 11:41:26.020764: train_loss -0.392 +2026-04-12 11:41:26.027821: val_loss -0.3469 +2026-04-12 11:41:26.029783: Pseudo dice [0.105, 0.0, 0.6493, 0.0795, 0.5401, 0.5444, 0.6694] +2026-04-12 11:41:26.032569: Epoch time: 102.28 s +2026-04-12 11:41:27.302915: +2026-04-12 11:41:27.304873: Epoch 1587 +2026-04-12 11:41:27.306549: Current learning rate: 0.00635 +2026-04-12 11:43:08.949269: train_loss -0.4065 +2026-04-12 11:43:08.956425: val_loss -0.3248 +2026-04-12 11:43:08.958574: Pseudo dice [0.4571, 0.0, 0.7644, 0.6845, 0.4251, 0.7016, 0.3117] +2026-04-12 11:43:08.961066: Epoch time: 101.65 s +2026-04-12 11:43:10.198886: +2026-04-12 11:43:10.201545: Epoch 1588 +2026-04-12 11:43:10.203824: Current learning rate: 0.00634 +2026-04-12 11:44:53.128747: train_loss -0.3996 +2026-04-12 11:44:53.136761: val_loss -0.3403 +2026-04-12 11:44:53.139748: Pseudo dice [0.3446, 0.0, 0.8072, 0.5265, 0.4661, 0.7821, 0.3658] +2026-04-12 11:44:53.143010: Epoch time: 102.93 s +2026-04-12 11:44:54.386423: +2026-04-12 11:44:54.388334: Epoch 1589 +2026-04-12 11:44:54.390206: Current learning rate: 0.00634 +2026-04-12 11:46:36.165230: train_loss -0.4137 +2026-04-12 11:46:36.172185: val_loss -0.4039 +2026-04-12 11:46:36.174598: Pseudo dice [0.5372, 0.0, 0.7965, 0.7065, 0.5316, 0.8758, 0.8536] +2026-04-12 11:46:36.177173: Epoch time: 101.78 s +2026-04-12 11:46:38.527601: +2026-04-12 11:46:38.529391: Epoch 1590 +2026-04-12 11:46:38.531091: Current learning rate: 0.00634 +2026-04-12 11:48:20.130256: train_loss -0.4136 +2026-04-12 11:48:20.138401: val_loss -0.3704 +2026-04-12 11:48:20.141861: Pseudo dice [0.7916, 0.0, 0.7992, 0.1105, 0.4234, 0.7977, 0.8616] +2026-04-12 11:48:20.147269: Epoch time: 101.61 s +2026-04-12 11:48:21.374749: +2026-04-12 11:48:21.376675: Epoch 1591 +2026-04-12 11:48:21.378721: Current learning rate: 0.00634 +2026-04-12 11:50:03.596669: train_loss -0.4114 +2026-04-12 11:50:03.605193: val_loss -0.3696 +2026-04-12 11:50:03.607568: Pseudo dice [0.6307, 0.0, 0.784, 0.1794, 0.4686, 0.8514, 0.692] +2026-04-12 11:50:03.610000: Epoch time: 102.22 s +2026-04-12 11:50:04.867254: +2026-04-12 11:50:04.869357: Epoch 1592 +2026-04-12 11:50:04.871442: Current learning rate: 0.00633 +2026-04-12 11:51:47.260593: train_loss -0.3805 +2026-04-12 11:51:47.267945: val_loss -0.3254 +2026-04-12 11:51:47.270454: Pseudo dice [0.0, 0.0, 0.6731, 0.3126, 0.4702, 0.7017, 0.8128] +2026-04-12 11:51:47.275739: Epoch time: 102.4 s +2026-04-12 11:51:48.525848: +2026-04-12 11:51:48.535459: Epoch 1593 +2026-04-12 11:51:48.539685: Current learning rate: 0.00633 +2026-04-12 11:53:30.651077: train_loss -0.3827 +2026-04-12 11:53:30.659017: val_loss -0.3047 +2026-04-12 11:53:30.661801: Pseudo dice [0.0205, 0.0, 0.7634, 0.3015, 0.2362, 0.8175, 0.7649] +2026-04-12 11:53:30.665351: Epoch time: 102.13 s +2026-04-12 11:53:31.921444: +2026-04-12 11:53:31.923803: Epoch 1594 +2026-04-12 11:53:31.926514: Current learning rate: 0.00633 +2026-04-12 11:55:13.732624: train_loss -0.3525 +2026-04-12 11:55:13.740275: val_loss -0.3122 +2026-04-12 11:55:13.742954: Pseudo dice [0.1838, 0.0, 0.6326, 0.1419, 0.6145, 0.3016, 0.2628] +2026-04-12 11:55:13.746037: Epoch time: 101.81 s +2026-04-12 11:55:15.006862: +2026-04-12 11:55:15.009265: Epoch 1595 +2026-04-12 11:55:15.011373: Current learning rate: 0.00633 +2026-04-12 11:56:57.541125: train_loss -0.3746 +2026-04-12 11:56:57.562669: val_loss -0.2851 +2026-04-12 11:56:57.564859: Pseudo dice [0.014, 0.0, 0.5561, 0.2608, 0.5018, 0.6461, 0.4448] +2026-04-12 11:56:57.567509: Epoch time: 102.54 s +2026-04-12 11:56:58.820517: +2026-04-12 11:56:58.823775: Epoch 1596 +2026-04-12 11:56:58.826367: Current learning rate: 0.00632 +2026-04-12 11:58:41.114260: train_loss -0.3762 +2026-04-12 11:58:41.124469: val_loss -0.344 +2026-04-12 11:58:41.127142: Pseudo dice [0.0, 0.0, 0.7373, 0.2359, 0.3666, 0.6352, 0.7474] +2026-04-12 11:58:41.130708: Epoch time: 102.3 s +2026-04-12 11:58:42.381925: +2026-04-12 11:58:42.384004: Epoch 1597 +2026-04-12 11:58:42.385943: Current learning rate: 0.00632 +2026-04-12 12:00:24.030923: train_loss -0.3753 +2026-04-12 12:00:24.037813: val_loss -0.3233 +2026-04-12 12:00:24.039841: Pseudo dice [0.5225, 0.0, 0.5926, 0.0438, 0.408, 0.8246, 0.7354] +2026-04-12 12:00:24.042840: Epoch time: 101.65 s +2026-04-12 12:00:25.281126: +2026-04-12 12:00:25.282970: Epoch 1598 +2026-04-12 12:00:25.284972: Current learning rate: 0.00632 +2026-04-12 12:02:06.680037: train_loss -0.3984 +2026-04-12 12:02:06.686481: val_loss -0.3457 +2026-04-12 12:02:06.688607: Pseudo dice [0.3688, 0.0, 0.6818, 0.2202, 0.506, 0.3889, 0.558] +2026-04-12 12:02:06.691483: Epoch time: 101.4 s +2026-04-12 12:02:07.933836: +2026-04-12 12:02:07.936075: Epoch 1599 +2026-04-12 12:02:07.937873: Current learning rate: 0.00632 +2026-04-12 12:03:49.723999: train_loss -0.4122 +2026-04-12 12:03:49.732235: val_loss -0.311 +2026-04-12 12:03:49.735279: Pseudo dice [0.572, 0.0, 0.6548, 0.0018, 0.4555, 0.4233, 0.6786] +2026-04-12 12:03:49.739493: Epoch time: 101.79 s +2026-04-12 12:03:52.562338: +2026-04-12 12:03:52.564307: Epoch 1600 +2026-04-12 12:03:52.566130: Current learning rate: 0.00631 +2026-04-12 12:05:34.247718: train_loss -0.3747 +2026-04-12 12:05:34.254892: val_loss -0.3407 +2026-04-12 12:05:34.257317: Pseudo dice [0.0, 0.0, 0.7446, 0.2915, 0.4917, 0.3806, 0.7409] +2026-04-12 12:05:34.259953: Epoch time: 101.69 s +2026-04-12 12:05:35.520859: +2026-04-12 12:05:35.523507: Epoch 1601 +2026-04-12 12:05:35.525576: Current learning rate: 0.00631 +2026-04-12 12:07:17.786831: train_loss -0.3817 +2026-04-12 12:07:17.793840: val_loss -0.3362 +2026-04-12 12:07:17.796788: Pseudo dice [0.0, 0.0, 0.7757, 0.2205, 0.568, 0.5779, 0.6976] +2026-04-12 12:07:17.798953: Epoch time: 102.27 s +2026-04-12 12:07:19.075585: +2026-04-12 12:07:19.078578: Epoch 1602 +2026-04-12 12:07:19.080946: Current learning rate: 0.00631 +2026-04-12 12:09:00.835641: train_loss -0.3962 +2026-04-12 12:09:00.843409: val_loss -0.3082 +2026-04-12 12:09:00.846070: Pseudo dice [0.0, 0.0, 0.8237, 0.6514, 0.5102, 0.4072, 0.524] +2026-04-12 12:09:00.848851: Epoch time: 101.76 s +2026-04-12 12:09:02.095992: +2026-04-12 12:09:02.098365: Epoch 1603 +2026-04-12 12:09:02.100392: Current learning rate: 0.00631 +2026-04-12 12:10:43.453916: train_loss -0.3694 +2026-04-12 12:10:43.463274: val_loss -0.2861 +2026-04-12 12:10:43.465617: Pseudo dice [0.0, 0.0, 0.1855, 0.3876, 0.4709, 0.4921, 0.6201] +2026-04-12 12:10:43.468392: Epoch time: 101.36 s +2026-04-12 12:10:44.772156: +2026-04-12 12:10:44.786781: Epoch 1604 +2026-04-12 12:10:44.789705: Current learning rate: 0.0063 +2026-04-12 12:12:26.305284: train_loss -0.3856 +2026-04-12 12:12:26.311893: val_loss -0.3031 +2026-04-12 12:12:26.313956: Pseudo dice [0.0, 0.0, 0.6065, 0.4482, 0.3153, 0.4086, 0.5699] +2026-04-12 12:12:26.316139: Epoch time: 101.54 s +2026-04-12 12:12:27.578048: +2026-04-12 12:12:27.579885: Epoch 1605 +2026-04-12 12:12:27.582001: Current learning rate: 0.0063 +2026-04-12 12:14:09.130262: train_loss -0.3923 +2026-04-12 12:14:09.137081: val_loss -0.3484 +2026-04-12 12:14:09.139258: Pseudo dice [0.0, 0.0, 0.7862, 0.2477, 0.4643, 0.5884, 0.6557] +2026-04-12 12:14:09.141506: Epoch time: 101.56 s +2026-04-12 12:14:10.385617: +2026-04-12 12:14:10.388128: Epoch 1606 +2026-04-12 12:14:10.390933: Current learning rate: 0.0063 +2026-04-12 12:15:52.672467: train_loss -0.3959 +2026-04-12 12:15:52.678528: val_loss -0.3597 +2026-04-12 12:15:52.681598: Pseudo dice [0.6013, 0.0, 0.6435, 0.415, 0.5101, 0.5936, 0.5773] +2026-04-12 12:15:52.684966: Epoch time: 102.29 s +2026-04-12 12:15:53.915810: +2026-04-12 12:15:53.917779: Epoch 1607 +2026-04-12 12:15:53.919481: Current learning rate: 0.0063 +2026-04-12 12:17:35.572111: train_loss -0.3691 +2026-04-12 12:17:35.579231: val_loss -0.3406 +2026-04-12 12:17:35.581491: Pseudo dice [0.2546, 0.0, 0.6338, 0.1846, 0.5996, 0.6113, 0.4778] +2026-04-12 12:17:35.583457: Epoch time: 101.66 s +2026-04-12 12:17:36.817193: +2026-04-12 12:17:36.819033: Epoch 1608 +2026-04-12 12:17:36.820607: Current learning rate: 0.0063 +2026-04-12 12:19:18.640851: train_loss -0.3876 +2026-04-12 12:19:18.649071: val_loss -0.3396 +2026-04-12 12:19:18.651695: Pseudo dice [0.0375, 0.0, 0.6993, 0.5532, 0.5157, 0.2369, 0.8497] +2026-04-12 12:19:18.655390: Epoch time: 101.83 s +2026-04-12 12:19:21.064301: +2026-04-12 12:19:21.066825: Epoch 1609 +2026-04-12 12:19:21.068628: Current learning rate: 0.00629 +2026-04-12 12:21:02.953529: train_loss -0.3899 +2026-04-12 12:21:02.960185: val_loss -0.3668 +2026-04-12 12:21:02.962219: Pseudo dice [0.2617, 0.0, 0.8757, 0.3604, 0.546, 0.841, 0.8483] +2026-04-12 12:21:02.964701: Epoch time: 101.89 s +2026-04-12 12:21:04.212037: +2026-04-12 12:21:04.214728: Epoch 1610 +2026-04-12 12:21:04.218277: Current learning rate: 0.00629 +2026-04-12 12:22:46.420606: train_loss -0.4035 +2026-04-12 12:22:46.427412: val_loss -0.3136 +2026-04-12 12:22:46.429658: Pseudo dice [0.0, 0.0, 0.7328, 0.2252, 0.6273, 0.3127, 0.4993] +2026-04-12 12:22:46.432218: Epoch time: 102.21 s +2026-04-12 12:22:47.701543: +2026-04-12 12:22:47.703571: Epoch 1611 +2026-04-12 12:22:47.705284: Current learning rate: 0.00629 +2026-04-12 12:24:29.577123: train_loss -0.3841 +2026-04-12 12:24:29.583316: val_loss -0.3589 +2026-04-12 12:24:29.585658: Pseudo dice [0.0, 0.0, 0.7439, 0.4319, 0.4623, 0.3273, 0.6982] +2026-04-12 12:24:29.587949: Epoch time: 101.88 s +2026-04-12 12:24:30.842492: +2026-04-12 12:24:30.844922: Epoch 1612 +2026-04-12 12:24:30.846561: Current learning rate: 0.00629 +2026-04-12 12:26:13.115418: train_loss -0.3757 +2026-04-12 12:26:13.123103: val_loss -0.3458 +2026-04-12 12:26:13.126976: Pseudo dice [0.0, 0.0, 0.771, 0.206, 0.4049, 0.3999, 0.7029] +2026-04-12 12:26:13.129542: Epoch time: 102.28 s +2026-04-12 12:26:14.381617: +2026-04-12 12:26:14.383650: Epoch 1613 +2026-04-12 12:26:14.385225: Current learning rate: 0.00628 +2026-04-12 12:27:56.435559: train_loss -0.3644 +2026-04-12 12:27:56.442448: val_loss -0.3401 +2026-04-12 12:27:56.444159: Pseudo dice [0.0, 0.0, 0.5267, 0.3436, 0.3164, 0.8125, 0.8496] +2026-04-12 12:27:56.446581: Epoch time: 102.06 s +2026-04-12 12:27:57.675161: +2026-04-12 12:27:57.677120: Epoch 1614 +2026-04-12 12:27:57.679137: Current learning rate: 0.00628 +2026-04-12 12:29:40.152940: train_loss -0.3927 +2026-04-12 12:29:40.163062: val_loss -0.3438 +2026-04-12 12:29:40.167324: Pseudo dice [0.0149, 0.0, 0.78, 0.7882, 0.5637, 0.6691, 0.8732] +2026-04-12 12:29:40.170409: Epoch time: 102.48 s +2026-04-12 12:29:41.433768: +2026-04-12 12:29:41.436919: Epoch 1615 +2026-04-12 12:29:41.439361: Current learning rate: 0.00628 +2026-04-12 12:31:23.617254: train_loss -0.4097 +2026-04-12 12:31:23.624429: val_loss -0.3385 +2026-04-12 12:31:23.626408: Pseudo dice [0.1264, 0.0, 0.5992, 0.6761, 0.401, 0.4033, 0.8955] +2026-04-12 12:31:23.628718: Epoch time: 102.19 s +2026-04-12 12:31:24.864239: +2026-04-12 12:31:24.866389: Epoch 1616 +2026-04-12 12:31:24.868195: Current learning rate: 0.00628 +2026-04-12 12:33:07.229628: train_loss -0.3907 +2026-04-12 12:33:07.238219: val_loss -0.3532 +2026-04-12 12:33:07.240589: Pseudo dice [0.5921, 0.0, 0.8476, 0.5819, 0.5629, 0.3844, 0.8135] +2026-04-12 12:33:07.243529: Epoch time: 102.37 s +2026-04-12 12:33:08.485311: +2026-04-12 12:33:08.487496: Epoch 1617 +2026-04-12 12:33:08.489702: Current learning rate: 0.00627 +2026-04-12 12:34:50.660851: train_loss -0.3963 +2026-04-12 12:34:50.667438: val_loss -0.3512 +2026-04-12 12:34:50.669412: Pseudo dice [0.5942, 0.0, 0.8155, 0.1622, 0.4427, 0.8311, 0.7322] +2026-04-12 12:34:50.671969: Epoch time: 102.18 s +2026-04-12 12:34:51.928786: +2026-04-12 12:34:51.931078: Epoch 1618 +2026-04-12 12:34:51.933285: Current learning rate: 0.00627 +2026-04-12 12:36:34.370817: train_loss -0.3838 +2026-04-12 12:36:34.377571: val_loss -0.3376 +2026-04-12 12:36:34.380969: Pseudo dice [0.274, 0.0, 0.7325, 0.3544, 0.5109, 0.6564, 0.8258] +2026-04-12 12:36:34.383314: Epoch time: 102.45 s +2026-04-12 12:36:35.643065: +2026-04-12 12:36:35.645120: Epoch 1619 +2026-04-12 12:36:35.646950: Current learning rate: 0.00627 +2026-04-12 12:38:17.118900: train_loss -0.4012 +2026-04-12 12:38:17.125826: val_loss -0.3935 +2026-04-12 12:38:17.128159: Pseudo dice [0.4814, 0.0, 0.6301, 0.8772, 0.5193, 0.7864, 0.8639] +2026-04-12 12:38:17.130824: Epoch time: 101.48 s +2026-04-12 12:38:18.380016: +2026-04-12 12:38:18.381826: Epoch 1620 +2026-04-12 12:38:18.383728: Current learning rate: 0.00627 +2026-04-12 12:40:00.756716: train_loss -0.3917 +2026-04-12 12:40:00.764013: val_loss -0.3285 +2026-04-12 12:40:00.766376: Pseudo dice [0.0, 0.0, 0.7025, 0.1218, 0.4338, 0.7027, 0.6652] +2026-04-12 12:40:00.769139: Epoch time: 102.38 s +2026-04-12 12:40:02.003495: +2026-04-12 12:40:02.005976: Epoch 1621 +2026-04-12 12:40:02.007753: Current learning rate: 0.00626 +2026-04-12 12:41:43.596900: train_loss -0.3841 +2026-04-12 12:41:43.602699: val_loss -0.3374 +2026-04-12 12:41:43.605305: Pseudo dice [0.5292, 0.0, 0.6635, 0.5476, 0.5507, 0.6393, 0.573] +2026-04-12 12:41:43.607732: Epoch time: 101.6 s +2026-04-12 12:41:44.847531: +2026-04-12 12:41:44.850455: Epoch 1622 +2026-04-12 12:41:44.852394: Current learning rate: 0.00626 +2026-04-12 12:43:26.866732: train_loss -0.3787 +2026-04-12 12:43:26.873785: val_loss -0.3516 +2026-04-12 12:43:26.876761: Pseudo dice [0.3924, 0.0, 0.7395, 0.7797, 0.5373, 0.5891, 0.6191] +2026-04-12 12:43:26.879524: Epoch time: 102.02 s +2026-04-12 12:43:28.160988: +2026-04-12 12:43:28.163424: Epoch 1623 +2026-04-12 12:43:28.165516: Current learning rate: 0.00626 +2026-04-12 12:45:10.903637: train_loss -0.4071 +2026-04-12 12:45:10.916762: val_loss -0.3853 +2026-04-12 12:45:10.918848: Pseudo dice [0.8315, 0.0, 0.7168, 0.2446, 0.4442, 0.4709, 0.8693] +2026-04-12 12:45:10.926966: Epoch time: 102.75 s +2026-04-12 12:45:12.177154: +2026-04-12 12:45:12.179276: Epoch 1624 +2026-04-12 12:45:12.181279: Current learning rate: 0.00626 +2026-04-12 12:46:53.797454: train_loss -0.4147 +2026-04-12 12:46:53.803794: val_loss -0.3674 +2026-04-12 12:46:53.806139: Pseudo dice [0.5829, 0.0, 0.8299, 0.0049, 0.5119, 0.7162, 0.8396] +2026-04-12 12:46:53.808655: Epoch time: 101.62 s +2026-04-12 12:46:55.031182: +2026-04-12 12:46:55.033215: Epoch 1625 +2026-04-12 12:46:55.034938: Current learning rate: 0.00626 +2026-04-12 12:48:37.433376: train_loss -0.3873 +2026-04-12 12:48:37.442700: val_loss -0.3229 +2026-04-12 12:48:37.445358: Pseudo dice [0.1122, 0.0, 0.7204, 0.0005, 0.5565, 0.5922, 0.1174] +2026-04-12 12:48:37.448525: Epoch time: 102.41 s +2026-04-12 12:48:38.699646: +2026-04-12 12:48:38.703360: Epoch 1626 +2026-04-12 12:48:38.705960: Current learning rate: 0.00625 +2026-04-12 12:50:21.293130: train_loss -0.3923 +2026-04-12 12:50:21.300629: val_loss -0.3387 +2026-04-12 12:50:21.303309: Pseudo dice [0.0012, 0.0, 0.7208, 0.7031, 0.3826, 0.5319, 0.6867] +2026-04-12 12:50:21.305769: Epoch time: 102.6 s +2026-04-12 12:50:22.577801: +2026-04-12 12:50:22.580762: Epoch 1627 +2026-04-12 12:50:22.583098: Current learning rate: 0.00625 +2026-04-12 12:52:04.891308: train_loss -0.3757 +2026-04-12 12:52:04.898019: val_loss -0.303 +2026-04-12 12:52:04.900228: Pseudo dice [0.0, 0.0, 0.722, 0.1426, 0.2867, 0.6012, 0.6873] +2026-04-12 12:52:04.902963: Epoch time: 102.32 s +2026-04-12 12:52:06.145295: +2026-04-12 12:52:06.147380: Epoch 1628 +2026-04-12 12:52:06.149173: Current learning rate: 0.00625 +2026-04-12 12:53:49.409570: train_loss -0.378 +2026-04-12 12:53:49.416213: val_loss -0.3588 +2026-04-12 12:53:49.418083: Pseudo dice [0.0, 0.0, 0.6487, 0.5418, 0.558, 0.7013, 0.7166] +2026-04-12 12:53:49.420068: Epoch time: 103.27 s +2026-04-12 12:53:50.664508: +2026-04-12 12:53:50.666528: Epoch 1629 +2026-04-12 12:53:50.668325: Current learning rate: 0.00625 +2026-04-12 12:55:32.773894: train_loss -0.3763 +2026-04-12 12:55:32.780809: val_loss -0.3224 +2026-04-12 12:55:32.783966: Pseudo dice [0.0, 0.0, 0.3092, 0.5183, 0.4628, 0.5991, 0.6982] +2026-04-12 12:55:32.786152: Epoch time: 102.11 s +2026-04-12 12:55:34.035886: +2026-04-12 12:55:34.038166: Epoch 1630 +2026-04-12 12:55:34.040853: Current learning rate: 0.00624 +2026-04-12 12:57:16.621361: train_loss -0.3998 +2026-04-12 12:57:16.636242: val_loss -0.3349 +2026-04-12 12:57:16.639292: Pseudo dice [0.0, 0.0, 0.842, 0.217, 0.3545, 0.6998, 0.8498] +2026-04-12 12:57:16.641675: Epoch time: 102.59 s +2026-04-12 12:57:17.895415: +2026-04-12 12:57:17.897969: Epoch 1631 +2026-04-12 12:57:17.899818: Current learning rate: 0.00624 +2026-04-12 12:58:59.603925: train_loss -0.403 +2026-04-12 12:58:59.610668: val_loss -0.3245 +2026-04-12 12:58:59.612821: Pseudo dice [0.0, 0.0, 0.6933, 0.0941, 0.6092, 0.3008, 0.3506] +2026-04-12 12:58:59.615081: Epoch time: 101.71 s +2026-04-12 12:59:00.836438: +2026-04-12 12:59:00.838326: Epoch 1632 +2026-04-12 12:59:00.840120: Current learning rate: 0.00624 +2026-04-12 13:00:43.049844: train_loss -0.3654 +2026-04-12 13:00:43.056962: val_loss -0.3514 +2026-04-12 13:00:43.062547: Pseudo dice [0.0, 0.0, 0.7817, 0.6999, 0.5397, 0.652, 0.7364] +2026-04-12 13:00:43.065305: Epoch time: 102.22 s +2026-04-12 13:00:44.304054: +2026-04-12 13:00:44.305997: Epoch 1633 +2026-04-12 13:00:44.308409: Current learning rate: 0.00624 +2026-04-12 13:02:26.479824: train_loss -0.3984 +2026-04-12 13:02:26.488319: val_loss -0.3355 +2026-04-12 13:02:26.490855: Pseudo dice [0.0, 0.0, 0.7102, 0.389, 0.5071, 0.8874, 0.3322] +2026-04-12 13:02:26.494400: Epoch time: 102.18 s +2026-04-12 13:02:27.747184: +2026-04-12 13:02:27.750286: Epoch 1634 +2026-04-12 13:02:27.752775: Current learning rate: 0.00623 +2026-04-12 13:04:09.426450: train_loss -0.4102 +2026-04-12 13:04:09.432223: val_loss -0.3737 +2026-04-12 13:04:09.434451: Pseudo dice [0.0, 0.0, 0.8591, 0.4092, 0.5393, 0.5479, 0.8241] +2026-04-12 13:04:09.436879: Epoch time: 101.68 s +2026-04-12 13:04:10.747755: +2026-04-12 13:04:10.750808: Epoch 1635 +2026-04-12 13:04:10.754487: Current learning rate: 0.00623 +2026-04-12 13:05:53.179536: train_loss -0.4163 +2026-04-12 13:05:53.186173: val_loss -0.3684 +2026-04-12 13:05:53.188092: Pseudo dice [0.3371, 0.0, 0.8109, 0.5789, 0.544, 0.5025, 0.8052] +2026-04-12 13:05:53.190413: Epoch time: 102.43 s +2026-04-12 13:05:54.434118: +2026-04-12 13:05:54.436306: Epoch 1636 +2026-04-12 13:05:54.438178: Current learning rate: 0.00623 +2026-04-12 13:07:35.977313: train_loss -0.4085 +2026-04-12 13:07:35.984447: val_loss -0.369 +2026-04-12 13:07:35.987285: Pseudo dice [0.6293, 0.0, 0.8065, 0.6364, 0.4296, 0.4189, 0.8536] +2026-04-12 13:07:35.990524: Epoch time: 101.55 s +2026-04-12 13:07:37.230805: +2026-04-12 13:07:37.232818: Epoch 1637 +2026-04-12 13:07:37.234469: Current learning rate: 0.00623 +2026-04-12 13:09:18.897925: train_loss -0.412 +2026-04-12 13:09:18.905533: val_loss -0.3365 +2026-04-12 13:09:18.910823: Pseudo dice [0.5795, 0.0, 0.8826, 0.0123, 0.4454, 0.4542, 0.5711] +2026-04-12 13:09:18.916186: Epoch time: 101.67 s +2026-04-12 13:09:20.130795: +2026-04-12 13:09:20.132950: Epoch 1638 +2026-04-12 13:09:20.134999: Current learning rate: 0.00622 +2026-04-12 13:11:02.326887: train_loss -0.3558 +2026-04-12 13:11:02.335938: val_loss -0.2721 +2026-04-12 13:11:02.338258: Pseudo dice [0.4764, 0.0, 0.6843, 0.5515, 0.4439, 0.2741, 0.1175] +2026-04-12 13:11:02.343765: Epoch time: 102.2 s +2026-04-12 13:11:03.550873: +2026-04-12 13:11:03.553368: Epoch 1639 +2026-04-12 13:11:03.555173: Current learning rate: 0.00622 +2026-04-12 13:12:45.288374: train_loss -0.3988 +2026-04-12 13:12:45.295506: val_loss -0.3358 +2026-04-12 13:12:45.298610: Pseudo dice [0.1163, 0.0, 0.7454, 0.6369, 0.4519, 0.5647, 0.7718] +2026-04-12 13:12:45.302409: Epoch time: 101.74 s +2026-04-12 13:12:46.548095: +2026-04-12 13:12:46.550373: Epoch 1640 +2026-04-12 13:12:46.552007: Current learning rate: 0.00622 +2026-04-12 13:14:28.167030: train_loss -0.3871 +2026-04-12 13:14:28.173474: val_loss -0.3264 +2026-04-12 13:14:28.175903: Pseudo dice [0.0015, 0.0, 0.7383, 0.222, 0.462, 0.8005, 0.7035] +2026-04-12 13:14:28.179138: Epoch time: 101.62 s +2026-04-12 13:14:29.410616: +2026-04-12 13:14:29.413364: Epoch 1641 +2026-04-12 13:14:29.415012: Current learning rate: 0.00622 +2026-04-12 13:16:11.160815: train_loss -0.3883 +2026-04-12 13:16:11.168454: val_loss -0.3412 +2026-04-12 13:16:11.171056: Pseudo dice [0.194, 0.0, 0.745, 0.4406, 0.3126, 0.4467, 0.8299] +2026-04-12 13:16:11.173764: Epoch time: 101.75 s +2026-04-12 13:16:12.373109: +2026-04-12 13:16:12.375747: Epoch 1642 +2026-04-12 13:16:12.378949: Current learning rate: 0.00621 +2026-04-12 13:17:53.599385: train_loss -0.396 +2026-04-12 13:17:53.606465: val_loss -0.2986 +2026-04-12 13:17:53.609037: Pseudo dice [0.1858, 0.0, 0.6906, 0.1694, 0.4964, 0.4058, 0.1224] +2026-04-12 13:17:53.611414: Epoch time: 101.23 s +2026-04-12 13:17:54.822316: +2026-04-12 13:17:54.824183: Epoch 1643 +2026-04-12 13:17:54.825852: Current learning rate: 0.00621 +2026-04-12 13:19:36.377316: train_loss -0.3912 +2026-04-12 13:19:36.403284: val_loss -0.346 +2026-04-12 13:19:36.405014: Pseudo dice [0.1544, 0.0, 0.6513, 0.2429, 0.5373, 0.7191, 0.4467] +2026-04-12 13:19:36.407536: Epoch time: 101.56 s +2026-04-12 13:19:37.652078: +2026-04-12 13:19:37.654784: Epoch 1644 +2026-04-12 13:19:37.656538: Current learning rate: 0.00621 +2026-04-12 13:21:19.047710: train_loss -0.3568 +2026-04-12 13:21:19.054271: val_loss -0.3302 +2026-04-12 13:21:19.056005: Pseudo dice [0.0, 0.0, 0.5757, 0.6539, 0.5206, 0.578, 0.583] +2026-04-12 13:21:19.058380: Epoch time: 101.4 s +2026-04-12 13:21:20.273063: +2026-04-12 13:21:20.275079: Epoch 1645 +2026-04-12 13:21:20.276858: Current learning rate: 0.00621 +2026-04-12 13:23:02.699173: train_loss -0.381 +2026-04-12 13:23:02.706503: val_loss -0.3082 +2026-04-12 13:23:02.708981: Pseudo dice [0.0, 0.0, 0.7778, 0.3866, 0.3698, 0.6488, 0.6297] +2026-04-12 13:23:02.711854: Epoch time: 102.43 s +2026-04-12 13:23:03.922719: +2026-04-12 13:23:03.924700: Epoch 1646 +2026-04-12 13:23:03.926558: Current learning rate: 0.00621 +2026-04-12 13:24:45.060846: train_loss -0.3818 +2026-04-12 13:24:45.069941: val_loss -0.3342 +2026-04-12 13:24:45.072886: Pseudo dice [0.0, 0.0, 0.8539, 0.8168, 0.6883, 0.4591, 0.7275] +2026-04-12 13:24:45.076479: Epoch time: 101.14 s +2026-04-12 13:24:46.279306: +2026-04-12 13:24:46.281298: Epoch 1647 +2026-04-12 13:24:46.283272: Current learning rate: 0.0062 +2026-04-12 13:26:28.423987: train_loss -0.3941 +2026-04-12 13:26:28.431592: val_loss -0.332 +2026-04-12 13:26:28.434512: Pseudo dice [0.1927, 0.0, 0.3424, 0.2423, 0.6144, 0.7491, 0.5861] +2026-04-12 13:26:28.439003: Epoch time: 102.15 s +2026-04-12 13:26:30.808389: +2026-04-12 13:26:30.810566: Epoch 1648 +2026-04-12 13:26:30.812186: Current learning rate: 0.0062 +2026-04-12 13:28:12.937259: train_loss -0.3857 +2026-04-12 13:28:12.944368: val_loss -0.3188 +2026-04-12 13:28:12.946320: Pseudo dice [0.0, 0.0, 0.7079, 0.284, 0.5871, 0.3849, 0.1059] +2026-04-12 13:28:12.948802: Epoch time: 102.13 s +2026-04-12 13:28:14.179093: +2026-04-12 13:28:14.181043: Epoch 1649 +2026-04-12 13:28:14.182881: Current learning rate: 0.0062 +2026-04-12 13:29:56.394428: train_loss -0.3944 +2026-04-12 13:29:56.400756: val_loss -0.3116 +2026-04-12 13:29:56.402618: Pseudo dice [0.0, 0.0, 0.5409, 0.0232, 0.503, 0.5062, 0.6844] +2026-04-12 13:29:56.405318: Epoch time: 102.22 s +2026-04-12 13:29:59.352926: +2026-04-12 13:29:59.354986: Epoch 1650 +2026-04-12 13:29:59.356632: Current learning rate: 0.0062 +2026-04-12 13:31:41.444168: train_loss -0.3803 +2026-04-12 13:31:41.450839: val_loss -0.3312 +2026-04-12 13:31:41.453303: Pseudo dice [0.0, 0.0, 0.8443, 0.7499, 0.579, 0.3888, 0.7076] +2026-04-12 13:31:41.455387: Epoch time: 102.09 s +2026-04-12 13:31:42.691538: +2026-04-12 13:31:42.693447: Epoch 1651 +2026-04-12 13:31:42.695157: Current learning rate: 0.00619 +2026-04-12 13:33:23.995560: train_loss -0.396 +2026-04-12 13:33:24.002558: val_loss -0.352 +2026-04-12 13:33:24.005009: Pseudo dice [0.0, 0.0, 0.7329, 0.5754, 0.4649, 0.7379, 0.5802] +2026-04-12 13:33:24.007533: Epoch time: 101.31 s +2026-04-12 13:33:25.192001: +2026-04-12 13:33:25.194283: Epoch 1652 +2026-04-12 13:33:25.196161: Current learning rate: 0.00619 +2026-04-12 13:35:06.761878: train_loss -0.4014 +2026-04-12 13:35:06.769275: val_loss -0.3543 +2026-04-12 13:35:06.771669: Pseudo dice [0.2957, 0.0, 0.8441, 0.4553, 0.3342, 0.5746, 0.8913] +2026-04-12 13:35:06.774750: Epoch time: 101.57 s +2026-04-12 13:35:07.966483: +2026-04-12 13:35:07.968576: Epoch 1653 +2026-04-12 13:35:07.970708: Current learning rate: 0.00619 +2026-04-12 13:36:49.517724: train_loss -0.38 +2026-04-12 13:36:49.525491: val_loss -0.3689 +2026-04-12 13:36:49.527827: Pseudo dice [0.045, 0.0, 0.8389, 0.3797, 0.4058, 0.7621, 0.7404] +2026-04-12 13:36:49.531458: Epoch time: 101.55 s +2026-04-12 13:36:50.752047: +2026-04-12 13:36:50.754396: Epoch 1654 +2026-04-12 13:36:50.756280: Current learning rate: 0.00619 +2026-04-12 13:38:32.584404: train_loss -0.3898 +2026-04-12 13:38:32.589878: val_loss -0.3567 +2026-04-12 13:38:32.591826: Pseudo dice [0.3932, 0.0, 0.8004, 0.8394, 0.5423, 0.4217, 0.7612] +2026-04-12 13:38:32.593952: Epoch time: 101.84 s +2026-04-12 13:38:33.809933: +2026-04-12 13:38:33.811769: Epoch 1655 +2026-04-12 13:38:33.813513: Current learning rate: 0.00618 +2026-04-12 13:40:15.076211: train_loss -0.4124 +2026-04-12 13:40:15.083630: val_loss -0.3131 +2026-04-12 13:40:15.085966: Pseudo dice [0.2362, 0.0, 0.7642, 0.6285, 0.6176, 0.2968, 0.6682] +2026-04-12 13:40:15.089551: Epoch time: 101.27 s +2026-04-12 13:40:16.311382: +2026-04-12 13:40:16.313229: Epoch 1656 +2026-04-12 13:40:16.315076: Current learning rate: 0.00618 +2026-04-12 13:41:58.527278: train_loss -0.3849 +2026-04-12 13:41:58.533774: val_loss -0.3383 +2026-04-12 13:41:58.536001: Pseudo dice [0.1783, 0.0, 0.7822, 0.378, 0.3362, 0.6593, 0.8597] +2026-04-12 13:41:58.538627: Epoch time: 102.22 s +2026-04-12 13:41:59.741090: +2026-04-12 13:41:59.742895: Epoch 1657 +2026-04-12 13:41:59.744599: Current learning rate: 0.00618 +2026-04-12 13:43:41.572622: train_loss -0.4116 +2026-04-12 13:43:41.580331: val_loss -0.3813 +2026-04-12 13:43:41.582895: Pseudo dice [0.4277, 0.0, 0.7486, 0.4348, 0.4385, 0.7189, 0.874] +2026-04-12 13:43:41.585201: Epoch time: 101.83 s +2026-04-12 13:43:42.821706: +2026-04-12 13:43:42.823899: Epoch 1658 +2026-04-12 13:43:42.827158: Current learning rate: 0.00618 +2026-04-12 13:45:24.664422: train_loss -0.3981 +2026-04-12 13:45:24.670357: val_loss -0.349 +2026-04-12 13:45:24.672839: Pseudo dice [0.3657, 0.0, 0.2803, 0.0026, 0.5216, 0.4876, 0.8658] +2026-04-12 13:45:24.675651: Epoch time: 101.85 s +2026-04-12 13:45:25.885462: +2026-04-12 13:45:25.888267: Epoch 1659 +2026-04-12 13:45:25.890732: Current learning rate: 0.00617 +2026-04-12 13:47:08.030021: train_loss -0.3655 +2026-04-12 13:47:08.036987: val_loss -0.3384 +2026-04-12 13:47:08.039036: Pseudo dice [0.6091, 0.0, 0.6478, 0.6106, 0.4748, 0.3986, 0.6206] +2026-04-12 13:47:08.041198: Epoch time: 102.15 s +2026-04-12 13:47:09.255357: +2026-04-12 13:47:09.258559: Epoch 1660 +2026-04-12 13:47:09.260281: Current learning rate: 0.00617 +2026-04-12 13:48:50.843439: train_loss -0.3836 +2026-04-12 13:48:50.850901: val_loss -0.3746 +2026-04-12 13:48:50.853226: Pseudo dice [0.6121, 0.0, 0.7444, 0.2607, 0.4012, 0.5797, 0.5] +2026-04-12 13:48:50.856001: Epoch time: 101.59 s +2026-04-12 13:48:52.058917: +2026-04-12 13:48:52.061010: Epoch 1661 +2026-04-12 13:48:52.062635: Current learning rate: 0.00617 +2026-04-12 13:50:34.164625: train_loss -0.3947 +2026-04-12 13:50:34.172617: val_loss -0.3347 +2026-04-12 13:50:34.174941: Pseudo dice [0.4412, 0.0, 0.4648, 0.0317, 0.5422, 0.7111, 0.6816] +2026-04-12 13:50:34.178214: Epoch time: 102.11 s +2026-04-12 13:50:35.373657: +2026-04-12 13:50:35.376189: Epoch 1662 +2026-04-12 13:50:35.378252: Current learning rate: 0.00617 +2026-04-12 13:52:17.334414: train_loss -0.4069 +2026-04-12 13:52:17.340528: val_loss -0.3571 +2026-04-12 13:52:17.343287: Pseudo dice [0.7761, 0.0, 0.743, 0.7237, 0.338, 0.7482, 0.232] +2026-04-12 13:52:17.345320: Epoch time: 101.96 s +2026-04-12 13:52:18.577786: +2026-04-12 13:52:18.582003: Epoch 1663 +2026-04-12 13:52:18.584146: Current learning rate: 0.00617 +2026-04-12 13:54:00.109402: train_loss -0.4088 +2026-04-12 13:54:00.115155: val_loss -0.3392 +2026-04-12 13:54:00.117652: Pseudo dice [0.6773, 0.0, 0.7292, 0.5563, 0.6094, 0.8272, 0.464] +2026-04-12 13:54:00.120152: Epoch time: 101.53 s +2026-04-12 13:54:01.315356: +2026-04-12 13:54:01.316896: Epoch 1664 +2026-04-12 13:54:01.318439: Current learning rate: 0.00616 +2026-04-12 13:55:42.588764: train_loss -0.3884 +2026-04-12 13:55:42.597697: val_loss -0.3384 +2026-04-12 13:55:42.600151: Pseudo dice [0.2033, 0.0, 0.7483, 0.6685, 0.3967, 0.7079, 0.6068] +2026-04-12 13:55:42.602928: Epoch time: 101.28 s +2026-04-12 13:55:43.803196: +2026-04-12 13:55:43.805062: Epoch 1665 +2026-04-12 13:55:43.807028: Current learning rate: 0.00616 +2026-04-12 13:57:25.587668: train_loss -0.409 +2026-04-12 13:57:25.598336: val_loss -0.3725 +2026-04-12 13:57:25.600594: Pseudo dice [0.0, 0.0, 0.8193, 0.6864, 0.5455, 0.6956, 0.7881] +2026-04-12 13:57:25.603876: Epoch time: 101.79 s +2026-04-12 13:57:26.808538: +2026-04-12 13:57:26.810541: Epoch 1666 +2026-04-12 13:57:26.812411: Current learning rate: 0.00616 +2026-04-12 13:59:08.321771: train_loss -0.3826 +2026-04-12 13:59:08.329663: val_loss -0.3479 +2026-04-12 13:59:08.332156: Pseudo dice [0.0, 0.0, 0.704, 0.6222, 0.6596, 0.867, 0.8048] +2026-04-12 13:59:08.334724: Epoch time: 101.52 s +2026-04-12 13:59:09.552834: +2026-04-12 13:59:09.555757: Epoch 1667 +2026-04-12 13:59:09.558540: Current learning rate: 0.00616 +2026-04-12 14:00:52.973177: train_loss -0.374 +2026-04-12 14:00:52.981957: val_loss -0.3101 +2026-04-12 14:00:52.984256: Pseudo dice [0.0, 0.0, 0.5984, 0.2433, 0.5598, 0.7132, 0.7019] +2026-04-12 14:00:52.987196: Epoch time: 103.42 s +2026-04-12 14:00:54.242910: +2026-04-12 14:00:54.244889: Epoch 1668 +2026-04-12 14:00:54.246818: Current learning rate: 0.00615 +2026-04-12 14:02:35.457049: train_loss -0.3982 +2026-04-12 14:02:35.468090: val_loss -0.3616 +2026-04-12 14:02:35.471552: Pseudo dice [0.0, 0.0, 0.8218, 0.8599, 0.5743, 0.6092, 0.8631] +2026-04-12 14:02:35.474827: Epoch time: 101.22 s +2026-04-12 14:02:36.730511: +2026-04-12 14:02:36.732364: Epoch 1669 +2026-04-12 14:02:36.734052: Current learning rate: 0.00615 +2026-04-12 14:04:18.815575: train_loss -0.4015 +2026-04-12 14:04:18.821927: val_loss -0.3579 +2026-04-12 14:04:18.824315: Pseudo dice [0.0, 0.0, 0.743, 0.4613, 0.5471, 0.5875, 0.7844] +2026-04-12 14:04:18.827080: Epoch time: 102.09 s +2026-04-12 14:04:20.099469: +2026-04-12 14:04:20.101301: Epoch 1670 +2026-04-12 14:04:20.103046: Current learning rate: 0.00615 +2026-04-12 14:06:01.276802: train_loss -0.3643 +2026-04-12 14:06:01.283681: val_loss -0.2617 +2026-04-12 14:06:01.285930: Pseudo dice [0.0, 0.0, 0.3831, 0.2634, 0.4481, 0.3246, 0.405] +2026-04-12 14:06:01.289083: Epoch time: 101.18 s +2026-04-12 14:06:02.543992: +2026-04-12 14:06:02.546203: Epoch 1671 +2026-04-12 14:06:02.548242: Current learning rate: 0.00615 +2026-04-12 14:07:44.029518: train_loss -0.3679 +2026-04-12 14:07:44.037922: val_loss -0.3099 +2026-04-12 14:07:44.040119: Pseudo dice [0.0, 0.0, 0.7382, 0.4656, 0.2221, 0.4037, 0.4816] +2026-04-12 14:07:44.042852: Epoch time: 101.49 s +2026-04-12 14:07:45.276586: +2026-04-12 14:07:45.278884: Epoch 1672 +2026-04-12 14:07:45.281179: Current learning rate: 0.00614 +2026-04-12 14:09:26.922261: train_loss -0.3897 +2026-04-12 14:09:26.928620: val_loss -0.3161 +2026-04-12 14:09:26.930797: Pseudo dice [0.0, 0.0, 0.8087, 0.4201, 0.2894, 0.5025, 0.8015] +2026-04-12 14:09:26.933090: Epoch time: 101.65 s +2026-04-12 14:09:28.168729: +2026-04-12 14:09:28.171123: Epoch 1673 +2026-04-12 14:09:28.173715: Current learning rate: 0.00614 +2026-04-12 14:11:09.437544: train_loss -0.3931 +2026-04-12 14:11:09.444822: val_loss -0.2932 +2026-04-12 14:11:09.447058: Pseudo dice [0.0, 0.0, 0.504, 0.0937, 0.336, 0.5576, 0.0909] +2026-04-12 14:11:09.449389: Epoch time: 101.27 s +2026-04-12 14:11:10.672447: +2026-04-12 14:11:10.674502: Epoch 1674 +2026-04-12 14:11:10.676182: Current learning rate: 0.00614 +2026-04-12 14:12:52.320878: train_loss -0.391 +2026-04-12 14:12:52.326584: val_loss -0.3558 +2026-04-12 14:12:52.329606: Pseudo dice [0.0, 0.0, 0.875, 0.7599, 0.427, 0.2573, 0.753] +2026-04-12 14:12:52.332175: Epoch time: 101.65 s +2026-04-12 14:12:53.555269: +2026-04-12 14:12:53.557304: Epoch 1675 +2026-04-12 14:12:53.559180: Current learning rate: 0.00614 +2026-04-12 14:14:35.715952: train_loss -0.3913 +2026-04-12 14:14:35.722538: val_loss -0.3368 +2026-04-12 14:14:35.724709: Pseudo dice [0.0, 0.0, 0.7475, 0.2336, 0.5807, 0.7503, 0.8024] +2026-04-12 14:14:35.728063: Epoch time: 102.16 s +2026-04-12 14:14:36.962846: +2026-04-12 14:14:36.965364: Epoch 1676 +2026-04-12 14:14:36.967129: Current learning rate: 0.00613 +2026-04-12 14:16:18.361124: train_loss -0.3971 +2026-04-12 14:16:18.366824: val_loss -0.3567 +2026-04-12 14:16:18.368730: Pseudo dice [0.0, 0.0, 0.7112, 0.355, 0.2811, 0.7966, 0.7547] +2026-04-12 14:16:18.371336: Epoch time: 101.4 s +2026-04-12 14:16:19.652538: +2026-04-12 14:16:19.654550: Epoch 1677 +2026-04-12 14:16:19.656096: Current learning rate: 0.00613 +2026-04-12 14:18:03.206625: train_loss -0.3901 +2026-04-12 14:18:03.213466: val_loss -0.3089 +2026-04-12 14:18:03.215971: Pseudo dice [0.0, 0.0, 0.7963, 0.1261, 0.2151, 0.4308, 0.4977] +2026-04-12 14:18:03.218334: Epoch time: 103.56 s +2026-04-12 14:18:04.439157: +2026-04-12 14:18:04.442217: Epoch 1678 +2026-04-12 14:18:04.444572: Current learning rate: 0.00613 +2026-04-12 14:19:46.182491: train_loss -0.3892 +2026-04-12 14:19:46.208906: val_loss -0.3603 +2026-04-12 14:19:46.211258: Pseudo dice [0.0, 0.0, 0.7856, 0.3402, 0.4967, 0.6435, 0.8062] +2026-04-12 14:19:46.213745: Epoch time: 101.75 s +2026-04-12 14:19:47.435897: +2026-04-12 14:19:47.437656: Epoch 1679 +2026-04-12 14:19:47.439165: Current learning rate: 0.00613 +2026-04-12 14:21:29.513335: train_loss -0.3859 +2026-04-12 14:21:29.521679: val_loss -0.3502 +2026-04-12 14:21:29.524404: Pseudo dice [0.0, 0.0, 0.6301, 0.4595, 0.5378, 0.7393, 0.6521] +2026-04-12 14:21:29.527707: Epoch time: 102.08 s +2026-04-12 14:21:30.761122: +2026-04-12 14:21:30.764999: Epoch 1680 +2026-04-12 14:21:30.772299: Current learning rate: 0.00612 +2026-04-12 14:23:19.253340: train_loss -0.382 +2026-04-12 14:23:19.259101: val_loss -0.322 +2026-04-12 14:23:19.262872: Pseudo dice [0.0, 0.0, 0.289, 0.4882, 0.4388, 0.2605, 0.5926] +2026-04-12 14:23:19.266579: Epoch time: 108.5 s +2026-04-12 14:23:20.484761: +2026-04-12 14:23:20.487239: Epoch 1681 +2026-04-12 14:23:20.488942: Current learning rate: 0.00612 +2026-04-12 14:25:25.317414: train_loss -0.3906 +2026-04-12 14:25:25.324092: val_loss -0.3567 +2026-04-12 14:25:25.327346: Pseudo dice [0.0, 0.0, 0.8012, 0.7061, 0.4115, 0.577, 0.8586] +2026-04-12 14:25:25.330879: Epoch time: 124.84 s +2026-04-12 14:25:26.565582: +2026-04-12 14:25:26.567539: Epoch 1682 +2026-04-12 14:25:26.569241: Current learning rate: 0.00612 +2026-04-12 14:46:57.522624: train_loss -0.4086 +2026-04-12 14:46:57.528977: val_loss -0.36 +2026-04-12 14:46:57.531418: Pseudo dice [0.734, 0.0, 0.7885, 0.255, 0.3999, 0.651, 0.8841] +2026-04-12 14:46:57.534459: Epoch time: 1290.96 s +2026-04-12 14:46:59.868740: +2026-04-12 14:46:59.871010: Epoch 1683 +2026-04-12 14:46:59.873318: Current learning rate: 0.00612 +2026-04-12 15:06:57.433045: train_loss -0.4059 +2026-04-12 15:06:57.440177: val_loss -0.37 +2026-04-12 15:06:57.442603: Pseudo dice [0.4723, 0.0, 0.6039, 0.4548, 0.4956, 0.5485, 0.8031] +2026-04-12 15:06:57.445285: Epoch time: 1197.57 s +2026-04-12 15:06:58.667044: +2026-04-12 15:06:58.669257: Epoch 1684 +2026-04-12 15:06:58.671504: Current learning rate: 0.00612 +2026-04-12 15:09:13.255930: train_loss -0.4064 +2026-04-12 15:09:13.261957: val_loss -0.3474 +2026-04-12 15:09:13.264311: Pseudo dice [0.4532, 0.0, 0.7908, 0.7087, 0.5971, 0.3898, 0.9003] +2026-04-12 15:09:13.266752: Epoch time: 134.59 s +2026-04-12 15:09:14.472283: +2026-04-12 15:09:14.474384: Epoch 1685 +2026-04-12 15:09:14.476614: Current learning rate: 0.00611 +2026-04-12 15:10:56.572555: train_loss -0.388 +2026-04-12 15:10:56.579992: val_loss -0.3697 +2026-04-12 15:10:56.582106: Pseudo dice [0.2749, 0.0, 0.6492, 0.8737, 0.5747, 0.3593, 0.6942] +2026-04-12 15:10:56.585188: Epoch time: 102.1 s +2026-04-12 15:10:57.824526: +2026-04-12 15:10:57.826423: Epoch 1686 +2026-04-12 15:10:57.828401: Current learning rate: 0.00611 +2026-04-12 15:12:39.589694: train_loss -0.3868 +2026-04-12 15:12:39.595512: val_loss -0.3559 +2026-04-12 15:12:39.597864: Pseudo dice [0.0, 0.0, 0.7365, 0.5937, 0.4809, 0.7762, 0.9068] +2026-04-12 15:12:39.600275: Epoch time: 101.77 s +2026-04-12 15:12:41.926896: +2026-04-12 15:12:41.928504: Epoch 1687 +2026-04-12 15:12:41.930017: Current learning rate: 0.00611 +2026-04-12 15:14:23.233820: train_loss -0.411 +2026-04-12 15:14:23.251089: val_loss -0.3626 +2026-04-12 15:14:23.253242: Pseudo dice [0.0, 0.0, 0.7503, 0.4929, 0.6056, 0.6519, 0.5981] +2026-04-12 15:14:23.255836: Epoch time: 101.31 s +2026-04-12 15:14:24.490237: +2026-04-12 15:14:24.492340: Epoch 1688 +2026-04-12 15:14:24.493999: Current learning rate: 0.00611 +2026-04-12 15:16:05.880486: train_loss -0.4032 +2026-04-12 15:16:05.886325: val_loss -0.3418 +2026-04-12 15:16:05.888604: Pseudo dice [0.5615, 0.0, 0.5614, 0.6485, 0.5077, 0.4272, 0.7992] +2026-04-12 15:16:05.890855: Epoch time: 101.39 s +2026-04-12 15:16:07.094883: +2026-04-12 15:16:07.096537: Epoch 1689 +2026-04-12 15:16:07.098010: Current learning rate: 0.0061 +2026-04-12 15:17:48.371142: train_loss -0.4145 +2026-04-12 15:17:48.377486: val_loss -0.3629 +2026-04-12 15:17:48.379570: Pseudo dice [0.5296, 0.0, 0.8862, 0.6329, 0.5312, 0.6112, 0.8594] +2026-04-12 15:17:48.382064: Epoch time: 101.28 s +2026-04-12 15:17:49.627245: +2026-04-12 15:17:49.629092: Epoch 1690 +2026-04-12 15:17:49.630898: Current learning rate: 0.0061 +2026-04-12 15:19:31.858274: train_loss -0.4109 +2026-04-12 15:19:31.864756: val_loss -0.3613 +2026-04-12 15:19:31.866892: Pseudo dice [0.6366, 0.0, 0.8565, 0.2934, 0.4462, 0.7483, 0.8146] +2026-04-12 15:19:31.869366: Epoch time: 102.23 s +2026-04-12 15:19:33.135396: +2026-04-12 15:19:33.137553: Epoch 1691 +2026-04-12 15:19:33.139521: Current learning rate: 0.0061 +2026-04-12 15:21:15.251190: train_loss -0.4002 +2026-04-12 15:21:15.257041: val_loss -0.311 +2026-04-12 15:21:15.259166: Pseudo dice [0.0, 0.0, 0.7616, 0.1039, 0.3451, 0.4614, 0.5438] +2026-04-12 15:21:15.261637: Epoch time: 102.12 s +2026-04-12 15:21:16.521209: +2026-04-12 15:21:16.522896: Epoch 1692 +2026-04-12 15:21:16.524574: Current learning rate: 0.0061 +2026-04-12 15:22:58.075342: train_loss -0.3836 +2026-04-12 15:22:58.082679: val_loss -0.3657 +2026-04-12 15:22:58.085544: Pseudo dice [0.0, 0.0, 0.7749, 0.7924, 0.6042, 0.6446, 0.3992] +2026-04-12 15:22:58.088305: Epoch time: 101.56 s +2026-04-12 15:22:59.310309: +2026-04-12 15:22:59.311968: Epoch 1693 +2026-04-12 15:22:59.313506: Current learning rate: 0.00609 +2026-04-12 15:24:41.269365: train_loss -0.413 +2026-04-12 15:24:41.274915: val_loss -0.3688 +2026-04-12 15:24:41.277003: Pseudo dice [0.3413, 0.0, 0.6218, 0.7415, 0.4827, 0.3948, 0.7051] +2026-04-12 15:24:41.279288: Epoch time: 101.96 s +2026-04-12 15:24:42.596746: +2026-04-12 15:24:42.598440: Epoch 1694 +2026-04-12 15:24:42.600056: Current learning rate: 0.00609 +2026-04-12 15:26:24.768559: train_loss -0.4098 +2026-04-12 15:26:24.775434: val_loss -0.3458 +2026-04-12 15:26:24.777900: Pseudo dice [0.0, 0.0, 0.7877, 0.1862, 0.5166, 0.2505, 0.6537] +2026-04-12 15:26:24.780901: Epoch time: 102.18 s +2026-04-12 15:26:26.021774: +2026-04-12 15:26:26.023986: Epoch 1695 +2026-04-12 15:26:26.025708: Current learning rate: 0.00609 +2026-04-12 15:28:08.015455: train_loss -0.3984 +2026-04-12 15:28:08.021552: val_loss -0.351 +2026-04-12 15:28:08.023309: Pseudo dice [0.4952, 0.0, 0.5862, 0.4321, 0.5245, 0.5184, 0.8923] +2026-04-12 15:28:08.026425: Epoch time: 102.0 s +2026-04-12 15:28:09.262569: +2026-04-12 15:28:09.264215: Epoch 1696 +2026-04-12 15:28:09.265785: Current learning rate: 0.00609 +2026-04-12 15:29:50.828983: train_loss -0.3768 +2026-04-12 15:29:50.835866: val_loss -0.3651 +2026-04-12 15:29:50.840631: Pseudo dice [0.4777, 0.0, 0.7174, 0.4181, 0.4046, 0.5433, 0.8358] +2026-04-12 15:29:50.843487: Epoch time: 101.57 s +2026-04-12 15:29:52.075950: +2026-04-12 15:29:52.077659: Epoch 1697 +2026-04-12 15:29:52.079387: Current learning rate: 0.00608 +2026-04-12 15:31:34.461362: train_loss -0.3957 +2026-04-12 15:31:34.468132: val_loss -0.3568 +2026-04-12 15:31:34.470463: Pseudo dice [0.6799, 0.0, 0.6718, 0.4811, 0.4962, 0.8132, 0.8622] +2026-04-12 15:31:34.473251: Epoch time: 102.39 s +2026-04-12 15:31:35.703201: +2026-04-12 15:31:35.705939: Epoch 1698 +2026-04-12 15:31:35.707487: Current learning rate: 0.00608 +2026-04-12 15:33:17.448289: train_loss -0.4233 +2026-04-12 15:33:17.454183: val_loss -0.3864 +2026-04-12 15:33:17.456014: Pseudo dice [0.6984, 0.0, 0.7628, 0.5418, 0.5171, 0.8268, 0.8959] +2026-04-12 15:33:17.458080: Epoch time: 101.75 s +2026-04-12 15:33:18.709998: +2026-04-12 15:33:18.711665: Epoch 1699 +2026-04-12 15:33:18.713178: Current learning rate: 0.00608 +2026-04-12 15:35:00.915183: train_loss -0.4096 +2026-04-12 15:35:00.921413: val_loss -0.4005 +2026-04-12 15:35:00.923713: Pseudo dice [0.7071, 0.0, 0.7917, 0.0864, 0.6068, 0.7618, 0.9013] +2026-04-12 15:35:00.926125: Epoch time: 102.21 s +2026-04-12 15:35:03.859217: +2026-04-12 15:35:03.861393: Epoch 1700 +2026-04-12 15:35:03.863195: Current learning rate: 0.00608 +2026-04-12 15:36:45.194210: train_loss -0.4197 +2026-04-12 15:36:45.201269: val_loss -0.3625 +2026-04-12 15:36:45.203734: Pseudo dice [0.4447, 0.0, 0.8208, 0.4611, 0.4999, 0.7339, 0.8646] +2026-04-12 15:36:45.206922: Epoch time: 101.34 s +2026-04-12 15:36:46.418688: +2026-04-12 15:36:46.420371: Epoch 1701 +2026-04-12 15:36:46.421903: Current learning rate: 0.00607 +2026-04-12 15:38:28.418199: train_loss -0.3943 +2026-04-12 15:38:28.426524: val_loss -0.3949 +2026-04-12 15:38:28.429001: Pseudo dice [0.5974, 0.0, 0.8039, 0.4668, 0.5787, 0.4601, 0.9154] +2026-04-12 15:38:28.431480: Epoch time: 102.0 s +2026-04-12 15:38:29.662543: +2026-04-12 15:38:29.664320: Epoch 1702 +2026-04-12 15:38:29.665996: Current learning rate: 0.00607 +2026-04-12 15:40:11.820589: train_loss -0.3895 +2026-04-12 15:40:11.828044: val_loss -0.3462 +2026-04-12 15:40:11.830327: Pseudo dice [0.5082, 0.0, 0.6862, 0.6227, 0.5555, 0.4032, 0.8192] +2026-04-12 15:40:11.833092: Epoch time: 102.16 s +2026-04-12 15:40:13.068501: +2026-04-12 15:40:13.070431: Epoch 1703 +2026-04-12 15:40:13.072350: Current learning rate: 0.00607 +2026-04-12 15:41:55.276740: train_loss -0.4069 +2026-04-12 15:41:55.286125: val_loss -0.3682 +2026-04-12 15:41:55.288248: Pseudo dice [0.4535, 0.0, 0.8048, 0.4862, 0.4524, 0.5843, 0.8231] +2026-04-12 15:41:55.290651: Epoch time: 102.21 s +2026-04-12 15:41:56.586962: +2026-04-12 15:41:56.589328: Epoch 1704 +2026-04-12 15:41:56.591624: Current learning rate: 0.00607 +2026-04-12 15:43:38.806300: train_loss -0.4054 +2026-04-12 15:43:38.813641: val_loss -0.3704 +2026-04-12 15:43:38.815296: Pseudo dice [0.3488, 0.0, 0.769, 0.4657, 0.433, 0.7918, 0.7092] +2026-04-12 15:43:38.817682: Epoch time: 102.22 s +2026-04-12 15:43:40.112107: +2026-04-12 15:43:40.113762: Epoch 1705 +2026-04-12 15:43:40.115333: Current learning rate: 0.00607 +2026-04-12 15:45:22.371322: train_loss -0.4073 +2026-04-12 15:45:22.378776: val_loss -0.3503 +2026-04-12 15:45:22.381039: Pseudo dice [0.3356, 0.0, 0.8144, 0.3433, 0.487, 0.3255, 0.6185] +2026-04-12 15:45:22.383660: Epoch time: 102.26 s +2026-04-12 15:45:24.818686: +2026-04-12 15:45:24.820519: Epoch 1706 +2026-04-12 15:45:24.822103: Current learning rate: 0.00606 +2026-04-12 15:47:06.364867: train_loss -0.3997 +2026-04-12 15:47:06.372464: val_loss -0.3607 +2026-04-12 15:47:06.374542: Pseudo dice [0.5351, 0.0, 0.7274, 0.2173, 0.5612, 0.758, 0.9167] +2026-04-12 15:47:06.377585: Epoch time: 101.55 s +2026-04-12 15:47:07.625792: +2026-04-12 15:47:07.627877: Epoch 1707 +2026-04-12 15:47:07.629628: Current learning rate: 0.00606 +2026-04-12 15:48:49.776405: train_loss -0.3994 +2026-04-12 15:48:49.785253: val_loss -0.3148 +2026-04-12 15:48:49.787365: Pseudo dice [0.0, 0.0, 0.7451, 0.4555, 0.2952, 0.4142, 0.6052] +2026-04-12 15:48:49.789704: Epoch time: 102.15 s +2026-04-12 15:48:51.035453: +2026-04-12 15:48:51.037493: Epoch 1708 +2026-04-12 15:48:51.039185: Current learning rate: 0.00606 +2026-04-12 15:50:32.451520: train_loss -0.3752 +2026-04-12 15:50:32.458106: val_loss -0.3274 +2026-04-12 15:50:32.460210: Pseudo dice [0.0, 0.0, 0.2617, 0.309, 0.4316, 0.8319, 0.6128] +2026-04-12 15:50:32.462390: Epoch time: 101.42 s +2026-04-12 15:50:33.710431: +2026-04-12 15:50:33.713134: Epoch 1709 +2026-04-12 15:50:33.717043: Current learning rate: 0.00606 +2026-04-12 15:52:15.654992: train_loss -0.3777 +2026-04-12 15:52:15.662115: val_loss -0.3499 +2026-04-12 15:52:15.664155: Pseudo dice [0.0, 0.0, 0.8644, 0.6932, 0.5174, 0.4598, 0.6985] +2026-04-12 15:52:15.666492: Epoch time: 101.95 s +2026-04-12 15:52:16.924796: +2026-04-12 15:52:16.927097: Epoch 1710 +2026-04-12 15:52:16.928819: Current learning rate: 0.00605 +2026-04-12 15:53:59.424968: train_loss -0.3661 +2026-04-12 15:53:59.431904: val_loss -0.3263 +2026-04-12 15:53:59.433958: Pseudo dice [0.0, 0.0, 0.7591, 0.7189, 0.5453, 0.4917, 0.5002] +2026-04-12 15:53:59.436145: Epoch time: 102.5 s +2026-04-12 15:54:00.748532: +2026-04-12 15:54:00.750298: Epoch 1711 +2026-04-12 15:54:00.751884: Current learning rate: 0.00605 +2026-04-12 15:55:42.200102: train_loss -0.3949 +2026-04-12 15:55:42.207114: val_loss -0.3247 +2026-04-12 15:55:42.209265: Pseudo dice [0.0, 0.0, 0.7152, 0.0395, 0.3461, 0.7491, 0.6329] +2026-04-12 15:55:42.211602: Epoch time: 101.45 s +2026-04-12 15:55:43.444870: +2026-04-12 15:55:43.447481: Epoch 1712 +2026-04-12 15:55:43.449387: Current learning rate: 0.00605 +2026-04-12 15:57:25.542412: train_loss -0.3911 +2026-04-12 15:57:25.550355: val_loss -0.3371 +2026-04-12 15:57:25.552835: Pseudo dice [0.0, 0.0, 0.6625, 0.2305, 0.4062, 0.2882, 0.7386] +2026-04-12 15:57:25.555391: Epoch time: 102.1 s +2026-04-12 15:57:26.840801: +2026-04-12 15:57:26.842572: Epoch 1713 +2026-04-12 15:57:26.844126: Current learning rate: 0.00605 +2026-04-12 15:59:09.132325: train_loss -0.4017 +2026-04-12 15:59:09.140956: val_loss -0.3396 +2026-04-12 15:59:09.143661: Pseudo dice [0.0, 0.0, 0.7391, 0.5998, 0.4568, 0.7368, 0.7814] +2026-04-12 15:59:09.145997: Epoch time: 102.29 s +2026-04-12 15:59:10.388952: +2026-04-12 15:59:10.391212: Epoch 1714 +2026-04-12 15:59:10.392868: Current learning rate: 0.00604 +2026-04-12 16:00:53.016980: train_loss -0.3922 +2026-04-12 16:00:53.028998: val_loss -0.3608 +2026-04-12 16:00:53.031083: Pseudo dice [0.0, 0.0, 0.8583, 0.5374, 0.4699, 0.6599, 0.7317] +2026-04-12 16:00:53.033468: Epoch time: 102.63 s +2026-04-12 16:00:54.338764: +2026-04-12 16:00:54.341053: Epoch 1715 +2026-04-12 16:00:54.343058: Current learning rate: 0.00604 +2026-04-12 16:02:35.833235: train_loss -0.3791 +2026-04-12 16:02:35.840181: val_loss -0.3107 +2026-04-12 16:02:35.842219: Pseudo dice [0.0, 0.0, 0.7393, 0.3455, 0.5583, 0.4039, 0.8949] +2026-04-12 16:02:35.845240: Epoch time: 101.5 s +2026-04-12 16:02:37.067839: +2026-04-12 16:02:37.070192: Epoch 1716 +2026-04-12 16:02:37.072182: Current learning rate: 0.00604 +2026-04-12 16:04:18.892871: train_loss -0.3962 +2026-04-12 16:04:18.899624: val_loss -0.3613 +2026-04-12 16:04:18.902355: Pseudo dice [0.0, 0.0, 0.784, 0.8283, 0.4121, 0.7265, 0.8485] +2026-04-12 16:04:18.904433: Epoch time: 101.83 s +2026-04-12 16:04:20.139258: +2026-04-12 16:04:20.141259: Epoch 1717 +2026-04-12 16:04:20.142915: Current learning rate: 0.00604 +2026-04-12 16:06:01.971925: train_loss -0.4147 +2026-04-12 16:06:01.978358: val_loss -0.335 +2026-04-12 16:06:01.980645: Pseudo dice [0.0, 0.0, 0.7575, 0.3442, 0.3986, 0.7165, 0.7647] +2026-04-12 16:06:01.983075: Epoch time: 101.84 s +2026-04-12 16:06:03.279384: +2026-04-12 16:06:03.281511: Epoch 1718 +2026-04-12 16:06:03.283198: Current learning rate: 0.00603 +2026-04-12 16:07:44.961647: train_loss -0.4038 +2026-04-12 16:07:44.972610: val_loss -0.3468 +2026-04-12 16:07:44.976087: Pseudo dice [0.0, 0.0, 0.7674, 0.2906, 0.4586, 0.6014, 0.5932] +2026-04-12 16:07:44.980097: Epoch time: 101.69 s +2026-04-12 16:07:46.223725: +2026-04-12 16:07:46.225558: Epoch 1719 +2026-04-12 16:07:46.227194: Current learning rate: 0.00603 +2026-04-12 16:09:27.561274: train_loss -0.3992 +2026-04-12 16:09:27.567598: val_loss -0.3376 +2026-04-12 16:09:27.572111: Pseudo dice [0.0, 0.0, 0.4652, 0.3583, 0.5648, 0.766, 0.8032] +2026-04-12 16:09:27.574692: Epoch time: 101.34 s +2026-04-12 16:09:28.826843: +2026-04-12 16:09:28.828653: Epoch 1720 +2026-04-12 16:09:28.830237: Current learning rate: 0.00603 +2026-04-12 16:11:10.498872: train_loss -0.4023 +2026-04-12 16:11:10.504989: val_loss -0.326 +2026-04-12 16:11:10.507818: Pseudo dice [0.0, 0.0, 0.6302, 0.8802, 0.4769, 0.4093, 0.479] +2026-04-12 16:11:10.510741: Epoch time: 101.68 s +2026-04-12 16:11:11.749202: +2026-04-12 16:11:11.751458: Epoch 1721 +2026-04-12 16:11:11.753365: Current learning rate: 0.00603 +2026-04-12 16:12:53.273558: train_loss -0.3992 +2026-04-12 16:12:53.280179: val_loss -0.3459 +2026-04-12 16:12:53.282239: Pseudo dice [0.0, 0.0, 0.8512, 0.6736, 0.3404, 0.6656, 0.9107] +2026-04-12 16:12:53.284779: Epoch time: 101.53 s +2026-04-12 16:12:54.559306: +2026-04-12 16:12:54.561318: Epoch 1722 +2026-04-12 16:12:54.562958: Current learning rate: 0.00602 +2026-04-12 16:14:36.186774: train_loss -0.4073 +2026-04-12 16:14:36.193558: val_loss -0.3454 +2026-04-12 16:14:36.195687: Pseudo dice [0.0, 0.0, 0.7352, 0.8695, 0.6386, 0.6987, 0.804] +2026-04-12 16:14:36.198061: Epoch time: 101.63 s +2026-04-12 16:14:37.424956: +2026-04-12 16:14:37.426718: Epoch 1723 +2026-04-12 16:14:37.428231: Current learning rate: 0.00602 +2026-04-12 16:16:18.857720: train_loss -0.4231 +2026-04-12 16:16:18.866181: val_loss -0.3259 +2026-04-12 16:16:18.868549: Pseudo dice [0.0, 0.0, 0.3313, 0.3716, 0.3558, 0.4761, 0.395] +2026-04-12 16:16:18.872532: Epoch time: 101.44 s +2026-04-12 16:16:20.182102: +2026-04-12 16:16:20.184206: Epoch 1724 +2026-04-12 16:16:20.187003: Current learning rate: 0.00602 +2026-04-12 16:18:01.878818: train_loss -0.4071 +2026-04-12 16:18:01.886829: val_loss -0.3333 +2026-04-12 16:18:01.888785: Pseudo dice [0.0, 0.0, 0.5294, 0.7857, 0.3488, 0.7406, 0.7796] +2026-04-12 16:18:01.891628: Epoch time: 101.7 s +2026-04-12 16:18:03.198097: +2026-04-12 16:18:03.200201: Epoch 1725 +2026-04-12 16:18:03.202000: Current learning rate: 0.00602 +2026-04-12 16:19:45.408725: train_loss -0.4085 +2026-04-12 16:19:45.414647: val_loss -0.3706 +2026-04-12 16:19:45.417014: Pseudo dice [0.0, 0.0, 0.5857, 0.5733, 0.4934, 0.4752, 0.8178] +2026-04-12 16:19:45.419280: Epoch time: 102.21 s +2026-04-12 16:19:47.735704: +2026-04-12 16:19:47.737837: Epoch 1726 +2026-04-12 16:19:47.739396: Current learning rate: 0.00602 +2026-04-12 16:21:29.400896: train_loss -0.4068 +2026-04-12 16:21:29.407746: val_loss -0.3955 +2026-04-12 16:21:29.410099: Pseudo dice [0.0, 0.0, 0.8015, 0.6679, 0.5994, 0.6599, 0.785] +2026-04-12 16:21:29.412757: Epoch time: 101.67 s +2026-04-12 16:21:30.649174: +2026-04-12 16:21:30.651350: Epoch 1727 +2026-04-12 16:21:30.653166: Current learning rate: 0.00601 +2026-04-12 16:23:13.075069: train_loss -0.3978 +2026-04-12 16:23:13.082955: val_loss -0.389 +2026-04-12 16:23:13.085202: Pseudo dice [0.0, 0.0, 0.8138, 0.2208, 0.5885, 0.8047, 0.7089] +2026-04-12 16:23:13.087831: Epoch time: 102.43 s +2026-04-12 16:23:14.371133: +2026-04-12 16:23:14.373766: Epoch 1728 +2026-04-12 16:23:14.376303: Current learning rate: 0.00601 +2026-04-12 16:24:56.314774: train_loss -0.4208 +2026-04-12 16:24:56.320250: val_loss -0.3675 +2026-04-12 16:24:56.322721: Pseudo dice [0.0, 0.0, 0.7658, 0.7068, 0.6226, 0.4154, 0.9073] +2026-04-12 16:24:56.324852: Epoch time: 101.95 s +2026-04-12 16:24:57.566756: +2026-04-12 16:24:57.568473: Epoch 1729 +2026-04-12 16:24:57.569947: Current learning rate: 0.00601 +2026-04-12 16:26:39.573390: train_loss -0.3994 +2026-04-12 16:26:39.581210: val_loss -0.3553 +2026-04-12 16:26:39.583317: Pseudo dice [0.0, 0.0, 0.7748, 0.6868, 0.4171, 0.4309, 0.8194] +2026-04-12 16:26:39.585316: Epoch time: 102.01 s +2026-04-12 16:26:40.882330: +2026-04-12 16:26:40.884468: Epoch 1730 +2026-04-12 16:26:40.886400: Current learning rate: 0.00601 +2026-04-12 16:28:23.241647: train_loss -0.3951 +2026-04-12 16:28:23.248297: val_loss -0.3491 +2026-04-12 16:28:23.250381: Pseudo dice [0.0, 0.0, 0.7093, 0.2591, 0.6029, 0.6086, 0.9014] +2026-04-12 16:28:23.253009: Epoch time: 102.36 s +2026-04-12 16:28:24.469145: +2026-04-12 16:28:24.471249: Epoch 1731 +2026-04-12 16:28:24.472897: Current learning rate: 0.006 +2026-04-12 16:30:05.868444: train_loss -0.4038 +2026-04-12 16:30:05.875972: val_loss -0.3748 +2026-04-12 16:30:05.878689: Pseudo dice [0.0, 0.0, 0.8584, 0.6868, 0.4791, 0.5851, 0.8427] +2026-04-12 16:30:05.881401: Epoch time: 101.4 s +2026-04-12 16:30:07.095861: +2026-04-12 16:30:07.097527: Epoch 1732 +2026-04-12 16:30:07.098986: Current learning rate: 0.006 +2026-04-12 16:31:49.132488: train_loss -0.4194 +2026-04-12 16:31:49.138653: val_loss -0.3402 +2026-04-12 16:31:49.141537: Pseudo dice [0.0, 0.0, 0.6225, 0.7364, 0.4304, 0.7882, 0.4246] +2026-04-12 16:31:49.145180: Epoch time: 102.04 s +2026-04-12 16:31:50.402322: +2026-04-12 16:31:50.404628: Epoch 1733 +2026-04-12 16:31:50.406134: Current learning rate: 0.006 +2026-04-12 16:33:32.858175: train_loss -0.417 +2026-04-12 16:33:32.863993: val_loss -0.3294 +2026-04-12 16:33:32.866101: Pseudo dice [0.0, 0.0, 0.648, 0.3398, 0.5081, 0.5886, 0.2762] +2026-04-12 16:33:32.868396: Epoch time: 102.46 s +2026-04-12 16:33:34.124693: +2026-04-12 16:33:34.126683: Epoch 1734 +2026-04-12 16:33:34.128769: Current learning rate: 0.006 +2026-04-12 16:35:16.052515: train_loss -0.412 +2026-04-12 16:35:16.059814: val_loss -0.3408 +2026-04-12 16:35:16.063331: Pseudo dice [0.0, 0.0, 0.8335, 0.3617, 0.3896, 0.7895, 0.7438] +2026-04-12 16:35:16.066938: Epoch time: 101.93 s +2026-04-12 16:35:17.290251: +2026-04-12 16:35:17.291901: Epoch 1735 +2026-04-12 16:35:17.293401: Current learning rate: 0.00599 +2026-04-12 16:36:59.311886: train_loss -0.4272 +2026-04-12 16:36:59.322387: val_loss -0.3631 +2026-04-12 16:36:59.324342: Pseudo dice [0.0, 0.0, 0.7943, 0.5542, 0.3371, 0.5954, 0.6381] +2026-04-12 16:36:59.333572: Epoch time: 102.02 s +2026-04-12 16:37:00.551609: +2026-04-12 16:37:00.553445: Epoch 1736 +2026-04-12 16:37:00.555246: Current learning rate: 0.00599 +2026-04-12 16:38:42.177838: train_loss -0.4169 +2026-04-12 16:38:42.184479: val_loss -0.2696 +2026-04-12 16:38:42.186384: Pseudo dice [0.0, 0.0, 0.6676, 0.0203, 0.2197, 0.2449, 0.8549] +2026-04-12 16:38:42.188955: Epoch time: 101.63 s +2026-04-12 16:38:43.439772: +2026-04-12 16:38:43.441983: Epoch 1737 +2026-04-12 16:38:43.443704: Current learning rate: 0.00599 +2026-04-12 16:40:25.196352: train_loss -0.3712 +2026-04-12 16:40:25.202942: val_loss -0.3608 +2026-04-12 16:40:25.204993: Pseudo dice [0.0, 0.0, 0.7942, 0.5887, 0.3664, 0.8284, 0.8519] +2026-04-12 16:40:25.207325: Epoch time: 101.76 s +2026-04-12 16:40:26.445057: +2026-04-12 16:40:26.447556: Epoch 1738 +2026-04-12 16:40:26.449412: Current learning rate: 0.00599 +2026-04-12 16:42:08.088943: train_loss -0.3967 +2026-04-12 16:42:08.096203: val_loss -0.3322 +2026-04-12 16:42:08.098136: Pseudo dice [0.1179, 0.0, 0.6151, 0.5469, 0.5418, 0.5781, 0.6334] +2026-04-12 16:42:08.100705: Epoch time: 101.65 s +2026-04-12 16:42:09.318051: +2026-04-12 16:42:09.320457: Epoch 1739 +2026-04-12 16:42:09.322235: Current learning rate: 0.00598 +2026-04-12 16:43:50.925424: train_loss -0.3929 +2026-04-12 16:43:50.933973: val_loss -0.3133 +2026-04-12 16:43:50.938953: Pseudo dice [0.0, 0.0, 0.7179, 0.616, 0.3278, 0.2244, 0.8291] +2026-04-12 16:43:50.941900: Epoch time: 101.61 s +2026-04-12 16:43:52.211727: +2026-04-12 16:43:52.213959: Epoch 1740 +2026-04-12 16:43:52.215854: Current learning rate: 0.00598 +2026-04-12 16:45:34.564202: train_loss -0.4081 +2026-04-12 16:45:34.570093: val_loss -0.3742 +2026-04-12 16:45:34.571885: Pseudo dice [0.0, 0.0, 0.5036, 0.8241, 0.5503, 0.5374, 0.8394] +2026-04-12 16:45:34.574143: Epoch time: 102.36 s +2026-04-12 16:45:35.786947: +2026-04-12 16:45:35.788676: Epoch 1741 +2026-04-12 16:45:35.790168: Current learning rate: 0.00598 +2026-04-12 16:47:17.214661: train_loss -0.4047 +2026-04-12 16:47:17.221272: val_loss -0.3741 +2026-04-12 16:47:17.223393: Pseudo dice [0.0, 0.0, 0.7615, 0.6864, 0.5521, 0.4174, 0.7653] +2026-04-12 16:47:17.225700: Epoch time: 101.43 s +2026-04-12 16:47:18.437534: +2026-04-12 16:47:18.439302: Epoch 1742 +2026-04-12 16:47:18.441003: Current learning rate: 0.00598 +2026-04-12 16:49:00.307647: train_loss -0.4175 +2026-04-12 16:49:00.314723: val_loss -0.3624 +2026-04-12 16:49:00.316651: Pseudo dice [0.0, 0.0, 0.7291, 0.812, 0.5694, 0.5628, 0.2795] +2026-04-12 16:49:00.319013: Epoch time: 101.87 s +2026-04-12 16:49:01.532729: +2026-04-12 16:49:01.534514: Epoch 1743 +2026-04-12 16:49:01.535949: Current learning rate: 0.00597 +2026-04-12 16:50:43.464386: train_loss -0.4118 +2026-04-12 16:50:43.471783: val_loss -0.3384 +2026-04-12 16:50:43.474098: Pseudo dice [0.0, 0.0, 0.8375, 0.5018, 0.4281, 0.8003, 0.7298] +2026-04-12 16:50:43.477245: Epoch time: 101.93 s +2026-04-12 16:50:44.723919: +2026-04-12 16:50:44.727276: Epoch 1744 +2026-04-12 16:50:44.729273: Current learning rate: 0.00597 +2026-04-12 16:52:27.266696: train_loss -0.3919 +2026-04-12 16:52:27.273221: val_loss -0.3356 +2026-04-12 16:52:27.275689: Pseudo dice [0.658, 0.0, 0.7066, 0.7989, 0.6015, 0.1547, 0.5929] +2026-04-12 16:52:27.278972: Epoch time: 102.55 s +2026-04-12 16:52:28.500589: +2026-04-12 16:52:28.503297: Epoch 1745 +2026-04-12 16:52:28.506620: Current learning rate: 0.00597 +2026-04-12 16:54:11.760778: train_loss -0.4053 +2026-04-12 16:54:11.768086: val_loss -0.3362 +2026-04-12 16:54:11.770295: Pseudo dice [0.7421, 0.0, 0.7964, 0.5734, 0.5131, 0.5918, 0.6751] +2026-04-12 16:54:11.772663: Epoch time: 103.26 s +2026-04-12 16:54:13.029369: +2026-04-12 16:54:13.032038: Epoch 1746 +2026-04-12 16:54:13.034498: Current learning rate: 0.00597 +2026-04-12 16:55:55.359551: train_loss -0.3994 +2026-04-12 16:55:55.373050: val_loss -0.3557 +2026-04-12 16:55:55.375122: Pseudo dice [0.33, 0.0, 0.5013, 0.2878, 0.2578, 0.6852, 0.7077] +2026-04-12 16:55:55.380366: Epoch time: 102.33 s +2026-04-12 16:55:56.647395: +2026-04-12 16:55:56.649291: Epoch 1747 +2026-04-12 16:55:56.651073: Current learning rate: 0.00597 +2026-04-12 16:57:38.676349: train_loss -0.4102 +2026-04-12 16:57:38.682926: val_loss -0.353 +2026-04-12 16:57:38.686593: Pseudo dice [0.248, 0.0, 0.857, 0.0937, 0.2461, 0.5017, 0.8884] +2026-04-12 16:57:38.689525: Epoch time: 102.03 s +2026-04-12 16:57:39.927433: +2026-04-12 16:57:39.929563: Epoch 1748 +2026-04-12 16:57:39.931786: Current learning rate: 0.00596 +2026-04-12 16:59:22.063673: train_loss -0.3806 +2026-04-12 16:59:22.074747: val_loss -0.3073 +2026-04-12 16:59:22.077813: Pseudo dice [0.013, 0.0, 0.6736, 0.7331, 0.4082, 0.4457, 0.8446] +2026-04-12 16:59:22.082857: Epoch time: 102.14 s +2026-04-12 16:59:23.344416: +2026-04-12 16:59:23.348987: Epoch 1749 +2026-04-12 16:59:23.354394: Current learning rate: 0.00596 +2026-04-12 17:01:05.416798: train_loss -0.398 +2026-04-12 17:01:05.423817: val_loss -0.3625 +2026-04-12 17:01:05.425749: Pseudo dice [0.4108, 0.0, 0.8174, 0.5592, 0.551, 0.5426, 0.247] +2026-04-12 17:01:05.428631: Epoch time: 102.08 s +2026-04-12 17:01:08.508430: +2026-04-12 17:01:08.511026: Epoch 1750 +2026-04-12 17:01:08.513032: Current learning rate: 0.00596 +2026-04-12 17:02:50.445997: train_loss -0.4203 +2026-04-12 17:02:50.454509: val_loss -0.3223 +2026-04-12 17:02:50.457102: Pseudo dice [0.2766, 0.0, 0.5251, 0.3437, 0.5094, 0.4213, 0.7154] +2026-04-12 17:02:50.459771: Epoch time: 101.94 s +2026-04-12 17:02:51.686602: +2026-04-12 17:02:51.688965: Epoch 1751 +2026-04-12 17:02:51.691274: Current learning rate: 0.00596 +2026-04-12 17:04:33.418743: train_loss -0.4033 +2026-04-12 17:04:33.425948: val_loss -0.3306 +2026-04-12 17:04:33.429189: Pseudo dice [0.2277, 0.0, 0.5392, 0.1481, 0.5483, 0.6926, 0.8569] +2026-04-12 17:04:33.431844: Epoch time: 101.74 s +2026-04-12 17:04:34.666523: +2026-04-12 17:04:34.668963: Epoch 1752 +2026-04-12 17:04:34.671253: Current learning rate: 0.00595 +2026-04-12 17:06:17.391431: train_loss -0.3939 +2026-04-12 17:06:17.405558: val_loss -0.3295 +2026-04-12 17:06:17.407809: Pseudo dice [0.3968, 0.0, 0.7178, 0.3814, 0.636, 0.4243, 0.1596] +2026-04-12 17:06:17.411048: Epoch time: 102.73 s +2026-04-12 17:06:18.659491: +2026-04-12 17:06:18.662574: Epoch 1753 +2026-04-12 17:06:18.664750: Current learning rate: 0.00595 +2026-04-12 17:08:01.320724: train_loss -0.4024 +2026-04-12 17:08:01.329871: val_loss -0.3889 +2026-04-12 17:08:01.332978: Pseudo dice [0.7281, 0.0, 0.7953, 0.4562, 0.5716, 0.4404, 0.3631] +2026-04-12 17:08:01.335417: Epoch time: 102.66 s +2026-04-12 17:08:02.577530: +2026-04-12 17:08:02.579443: Epoch 1754 +2026-04-12 17:08:02.581426: Current learning rate: 0.00595 +2026-04-12 17:09:44.882866: train_loss -0.3974 +2026-04-12 17:09:44.889648: val_loss -0.3197 +2026-04-12 17:09:44.891841: Pseudo dice [0.384, 0.0, 0.7053, 0.5217, 0.4113, 0.6454, 0.4922] +2026-04-12 17:09:44.894898: Epoch time: 102.31 s +2026-04-12 17:09:46.139784: +2026-04-12 17:09:46.142007: Epoch 1755 +2026-04-12 17:09:46.144281: Current learning rate: 0.00595 +2026-04-12 17:11:29.017001: train_loss -0.3752 +2026-04-12 17:11:29.024653: val_loss -0.3384 +2026-04-12 17:11:29.026718: Pseudo dice [0.4712, 0.0, 0.5358, 0.5974, 0.3995, 0.6663, 0.8123] +2026-04-12 17:11:29.028843: Epoch time: 102.88 s +2026-04-12 17:11:30.278166: +2026-04-12 17:11:30.280899: Epoch 1756 +2026-04-12 17:11:30.283600: Current learning rate: 0.00594 +2026-04-12 17:13:13.201698: train_loss -0.3687 +2026-04-12 17:13:13.210093: val_loss -0.3439 +2026-04-12 17:13:13.212777: Pseudo dice [0.5468, 0.0, 0.5636, 0.2363, 0.5944, 0.6614, 0.2574] +2026-04-12 17:13:13.215415: Epoch time: 102.93 s +2026-04-12 17:13:14.564861: +2026-04-12 17:13:14.567227: Epoch 1757 +2026-04-12 17:13:14.569491: Current learning rate: 0.00594 +2026-04-12 17:14:56.574886: train_loss -0.3975 +2026-04-12 17:14:56.583949: val_loss -0.3933 +2026-04-12 17:14:56.586532: Pseudo dice [0.4721, 0.0, 0.8445, 0.4694, 0.5433, 0.7249, 0.8231] +2026-04-12 17:14:56.591105: Epoch time: 102.01 s +2026-04-12 17:14:57.850987: +2026-04-12 17:14:57.852801: Epoch 1758 +2026-04-12 17:14:57.854988: Current learning rate: 0.00594 +2026-04-12 17:16:40.479988: train_loss -0.4079 +2026-04-12 17:16:40.487201: val_loss -0.3308 +2026-04-12 17:16:40.489711: Pseudo dice [0.3175, 0.0, 0.7326, 0.2706, 0.5291, 0.4226, 0.4268] +2026-04-12 17:16:40.492600: Epoch time: 102.63 s +2026-04-12 17:16:41.744052: +2026-04-12 17:16:41.746123: Epoch 1759 +2026-04-12 17:16:41.748038: Current learning rate: 0.00594 +2026-04-12 17:18:24.175365: train_loss -0.3835 +2026-04-12 17:18:24.184997: val_loss -0.3439 +2026-04-12 17:18:24.187309: Pseudo dice [0.0914, 0.0, 0.4818, 0.5341, 0.4011, 0.3223, 0.8683] +2026-04-12 17:18:24.190395: Epoch time: 102.43 s +2026-04-12 17:18:25.433001: +2026-04-12 17:18:25.435855: Epoch 1760 +2026-04-12 17:18:25.438886: Current learning rate: 0.00593 +2026-04-12 17:20:07.531573: train_loss -0.3965 +2026-04-12 17:20:07.538670: val_loss -0.3663 +2026-04-12 17:20:07.541097: Pseudo dice [0.4441, 0.0, 0.6364, 0.5367, 0.6127, 0.8696, 0.7741] +2026-04-12 17:20:07.544763: Epoch time: 102.1 s +2026-04-12 17:20:08.788376: +2026-04-12 17:20:08.790254: Epoch 1761 +2026-04-12 17:20:08.792390: Current learning rate: 0.00593 +2026-04-12 17:21:51.650761: train_loss -0.3803 +2026-04-12 17:21:51.659664: val_loss -0.368 +2026-04-12 17:21:51.661823: Pseudo dice [0.0, 0.0, 0.8428, 0.5206, 0.4968, 0.6043, 0.8823] +2026-04-12 17:21:51.665310: Epoch time: 102.86 s +2026-04-12 17:21:53.016173: +2026-04-12 17:21:53.018045: Epoch 1762 +2026-04-12 17:21:53.020437: Current learning rate: 0.00593 +2026-04-12 17:23:35.154105: train_loss -0.4062 +2026-04-12 17:23:35.160559: val_loss -0.39 +2026-04-12 17:23:35.163164: Pseudo dice [0.0, 0.0, 0.8156, 0.7778, 0.227, 0.7722, 0.9209] +2026-04-12 17:23:35.166239: Epoch time: 102.14 s +2026-04-12 17:23:36.433573: +2026-04-12 17:23:36.435833: Epoch 1763 +2026-04-12 17:23:36.438145: Current learning rate: 0.00593 +2026-04-12 17:25:18.813340: train_loss -0.3771 +2026-04-12 17:25:18.821172: val_loss -0.3316 +2026-04-12 17:25:18.823512: Pseudo dice [0.0, 0.0, 0.7183, 0.4491, 0.3645, 0.193, 0.2264] +2026-04-12 17:25:18.826258: Epoch time: 102.38 s +2026-04-12 17:25:20.064668: +2026-04-12 17:25:20.066820: Epoch 1764 +2026-04-12 17:25:20.069141: Current learning rate: 0.00592 +2026-04-12 17:27:03.030557: train_loss -0.3991 +2026-04-12 17:27:03.038742: val_loss -0.3494 +2026-04-12 17:27:03.040873: Pseudo dice [0.2798, 0.0, 0.7661, 0.0021, 0.5416, 0.786, 0.8943] +2026-04-12 17:27:03.044055: Epoch time: 102.97 s +2026-04-12 17:27:04.267690: +2026-04-12 17:27:04.269895: Epoch 1765 +2026-04-12 17:27:04.272184: Current learning rate: 0.00592 +2026-04-12 17:28:46.735176: train_loss -0.3975 +2026-04-12 17:28:46.742334: val_loss -0.3628 +2026-04-12 17:28:46.744644: Pseudo dice [0.3983, 0.0, 0.7494, 0.4147, 0.5983, 0.2032, 0.7697] +2026-04-12 17:28:46.746665: Epoch time: 102.47 s +2026-04-12 17:28:48.026647: +2026-04-12 17:28:48.028927: Epoch 1766 +2026-04-12 17:28:48.032941: Current learning rate: 0.00592 +2026-04-12 17:30:31.260259: train_loss -0.399 +2026-04-12 17:30:31.267578: val_loss -0.3547 +2026-04-12 17:30:31.269744: Pseudo dice [0.1541, 0.0, 0.8382, 0.5444, 0.4541, 0.8682, 0.661] +2026-04-12 17:30:31.271900: Epoch time: 103.24 s +2026-04-12 17:30:32.545851: +2026-04-12 17:30:32.547810: Epoch 1767 +2026-04-12 17:30:32.550158: Current learning rate: 0.00592 +2026-04-12 17:32:14.390520: train_loss -0.4087 +2026-04-12 17:32:14.399090: val_loss -0.3477 +2026-04-12 17:32:14.405859: Pseudo dice [0.4091, 0.0, 0.7358, 0.385, 0.4941, 0.6897, 0.3743] +2026-04-12 17:32:14.408338: Epoch time: 101.85 s +2026-04-12 17:32:15.660546: +2026-04-12 17:32:15.663294: Epoch 1768 +2026-04-12 17:32:15.665789: Current learning rate: 0.00592 +2026-04-12 17:33:57.892925: train_loss -0.4113 +2026-04-12 17:33:57.899968: val_loss -0.3003 +2026-04-12 17:33:57.902792: Pseudo dice [0.3802, 0.0, 0.6057, 0.1619, 0.2882, 0.5676, 0.6028] +2026-04-12 17:33:57.905496: Epoch time: 102.24 s +2026-04-12 17:33:59.169581: +2026-04-12 17:33:59.171771: Epoch 1769 +2026-04-12 17:33:59.174133: Current learning rate: 0.00591 +2026-04-12 17:35:42.089762: train_loss -0.3852 +2026-04-12 17:35:42.100135: val_loss -0.3249 +2026-04-12 17:35:42.103819: Pseudo dice [0.5, 0.0, 0.7854, 0.7869, 0.3819, 0.7726, 0.7291] +2026-04-12 17:35:42.108359: Epoch time: 102.92 s +2026-04-12 17:35:43.354140: +2026-04-12 17:35:43.357170: Epoch 1770 +2026-04-12 17:35:43.359367: Current learning rate: 0.00591 +2026-04-12 17:37:25.161413: train_loss -0.3711 +2026-04-12 17:37:25.168490: val_loss -0.2896 +2026-04-12 17:37:25.170585: Pseudo dice [0.0155, 0.0, 0.6649, 0.1082, 0.4716, 0.4495, 0.0451] +2026-04-12 17:37:25.173525: Epoch time: 101.81 s +2026-04-12 17:37:26.401611: +2026-04-12 17:37:26.403693: Epoch 1771 +2026-04-12 17:37:26.406039: Current learning rate: 0.00591 +2026-04-12 17:39:08.365829: train_loss -0.3652 +2026-04-12 17:39:08.373601: val_loss -0.3381 +2026-04-12 17:39:08.377030: Pseudo dice [0.0, 0.0, 0.7039, 0.7061, 0.6061, 0.4242, 0.7112] +2026-04-12 17:39:08.379317: Epoch time: 101.97 s +2026-04-12 17:39:09.636090: +2026-04-12 17:39:09.637981: Epoch 1772 +2026-04-12 17:39:09.640376: Current learning rate: 0.00591 +2026-04-12 17:40:51.520533: train_loss -0.3837 +2026-04-12 17:40:51.529119: val_loss -0.3009 +2026-04-12 17:40:51.531963: Pseudo dice [0.1887, 0.0, 0.389, 0.1321, 0.6675, 0.2804, 0.4821] +2026-04-12 17:40:51.534887: Epoch time: 101.89 s +2026-04-12 17:40:52.844684: +2026-04-12 17:40:52.846946: Epoch 1773 +2026-04-12 17:40:52.849424: Current learning rate: 0.0059 +2026-04-12 17:42:35.140749: train_loss -0.3592 +2026-04-12 17:42:35.148047: val_loss -0.3014 +2026-04-12 17:42:35.150604: Pseudo dice [0.2478, 0.0, 0.6122, 0.4979, 0.4155, 0.297, 0.4334] +2026-04-12 17:42:35.153479: Epoch time: 102.3 s +2026-04-12 17:42:36.388357: +2026-04-12 17:42:36.390099: Epoch 1774 +2026-04-12 17:42:36.393283: Current learning rate: 0.0059 +2026-04-12 17:44:18.730713: train_loss -0.3912 +2026-04-12 17:44:18.737393: val_loss -0.3422 +2026-04-12 17:44:18.739411: Pseudo dice [0.425, 0.0, 0.722, 0.2867, 0.2722, 0.68, 0.7491] +2026-04-12 17:44:18.741778: Epoch time: 102.35 s +2026-04-12 17:44:19.958813: +2026-04-12 17:44:19.961584: Epoch 1775 +2026-04-12 17:44:19.964251: Current learning rate: 0.0059 +2026-04-12 17:46:02.042121: train_loss -0.3628 +2026-04-12 17:46:02.048256: val_loss -0.2793 +2026-04-12 17:46:02.050757: Pseudo dice [0.0, 0.0, 0.5938, 0.4948, 0.4323, 0.5625, 0.593] +2026-04-12 17:46:02.053135: Epoch time: 102.09 s +2026-04-12 17:46:03.286531: +2026-04-12 17:46:03.288308: Epoch 1776 +2026-04-12 17:46:03.290351: Current learning rate: 0.0059 +2026-04-12 17:47:45.883029: train_loss -0.3683 +2026-04-12 17:47:45.892052: val_loss -0.3022 +2026-04-12 17:47:45.894856: Pseudo dice [0.0, 0.0, 0.563, 0.3292, 0.5524, 0.6593, 0.5657] +2026-04-12 17:47:45.897859: Epoch time: 102.6 s +2026-04-12 17:47:47.125640: +2026-04-12 17:47:47.127998: Epoch 1777 +2026-04-12 17:47:47.132043: Current learning rate: 0.00589 +2026-04-12 17:49:29.262307: train_loss -0.376 +2026-04-12 17:49:29.271269: val_loss -0.3417 +2026-04-12 17:49:29.273427: Pseudo dice [0.0, 0.0, 0.8038, 0.2344, 0.5621, 0.5161, 0.7614] +2026-04-12 17:49:29.276011: Epoch time: 102.14 s +2026-04-12 17:49:30.496973: +2026-04-12 17:49:30.498893: Epoch 1778 +2026-04-12 17:49:30.500928: Current learning rate: 0.00589 +2026-04-12 17:51:11.987919: train_loss -0.3782 +2026-04-12 17:51:11.995563: val_loss -0.375 +2026-04-12 17:51:11.998105: Pseudo dice [0.0, 0.0, 0.7061, 0.2947, 0.5499, 0.4901, 0.7854] +2026-04-12 17:51:12.000466: Epoch time: 101.49 s +2026-04-12 17:51:13.218397: +2026-04-12 17:51:13.220983: Epoch 1779 +2026-04-12 17:51:13.226345: Current learning rate: 0.00589 +2026-04-12 17:52:55.409247: train_loss -0.3798 +2026-04-12 17:52:55.416441: val_loss -0.3747 +2026-04-12 17:52:55.418692: Pseudo dice [0.0, 0.0, 0.8244, 0.3598, 0.4533, 0.5272, 0.7462] +2026-04-12 17:52:55.421365: Epoch time: 102.19 s +2026-04-12 17:52:56.678900: +2026-04-12 17:52:56.681204: Epoch 1780 +2026-04-12 17:52:56.683457: Current learning rate: 0.00589 +2026-04-12 17:54:39.203218: train_loss -0.3999 +2026-04-12 17:54:39.217807: val_loss -0.3384 +2026-04-12 17:54:39.220020: Pseudo dice [0.0, 0.0, 0.4074, 0.3219, 0.5739, 0.3203, 0.8577] +2026-04-12 17:54:39.223020: Epoch time: 102.53 s +2026-04-12 17:54:40.469254: +2026-04-12 17:54:40.471186: Epoch 1781 +2026-04-12 17:54:40.473490: Current learning rate: 0.00588 +2026-04-12 17:56:22.162957: train_loss -0.4095 +2026-04-12 17:56:22.172134: val_loss -0.3297 +2026-04-12 17:56:22.174292: Pseudo dice [0.0, 0.0, 0.7429, 0.4392, 0.4569, 0.781, 0.6618] +2026-04-12 17:56:22.176967: Epoch time: 101.7 s +2026-04-12 17:56:23.406145: +2026-04-12 17:56:23.409349: Epoch 1782 +2026-04-12 17:56:23.411609: Current learning rate: 0.00588 +2026-04-12 17:58:05.465914: train_loss -0.4095 +2026-04-12 17:58:05.475895: val_loss -0.3576 +2026-04-12 17:58:05.478209: Pseudo dice [0.0, 0.0, 0.6815, 0.6079, 0.4742, 0.7781, 0.5406] +2026-04-12 17:58:05.481178: Epoch time: 102.06 s +2026-04-12 17:58:06.687541: +2026-04-12 17:58:06.692050: Epoch 1783 +2026-04-12 17:58:06.694978: Current learning rate: 0.00588 +2026-04-12 17:59:49.387902: train_loss -0.3848 +2026-04-12 17:59:49.396603: val_loss -0.352 +2026-04-12 17:59:49.399181: Pseudo dice [0.0, 0.0, 0.8012, 0.4191, 0.4703, 0.3454, 0.8381] +2026-04-12 17:59:49.401917: Epoch time: 102.7 s +2026-04-12 17:59:50.645369: +2026-04-12 17:59:50.648067: Epoch 1784 +2026-04-12 17:59:50.651988: Current learning rate: 0.00588 +2026-04-12 18:01:34.108642: train_loss -0.3905 +2026-04-12 18:01:34.116169: val_loss -0.3625 +2026-04-12 18:01:34.118575: Pseudo dice [0.0, 0.0, 0.7537, 0.7805, 0.3849, 0.5562, 0.7773] +2026-04-12 18:01:34.121132: Epoch time: 103.47 s +2026-04-12 18:01:35.351517: +2026-04-12 18:01:35.354063: Epoch 1785 +2026-04-12 18:01:35.356063: Current learning rate: 0.00587 +2026-04-12 18:03:17.464197: train_loss -0.3652 +2026-04-12 18:03:17.470168: val_loss -0.2916 +2026-04-12 18:03:17.473180: Pseudo dice [0.0, 0.0, 0.5549, 0.7094, 0.4965, 0.5044, 0.8455] +2026-04-12 18:03:17.475636: Epoch time: 102.12 s +2026-04-12 18:03:18.769365: +2026-04-12 18:03:18.771763: Epoch 1786 +2026-04-12 18:03:18.773891: Current learning rate: 0.00587 +2026-04-12 18:05:00.849941: train_loss -0.3897 +2026-04-12 18:05:00.861379: val_loss -0.3344 +2026-04-12 18:05:00.863471: Pseudo dice [0.0, 0.0, 0.819, 0.42, 0.472, 0.5864, 0.3919] +2026-04-12 18:05:00.867169: Epoch time: 102.08 s +2026-04-12 18:05:02.142705: +2026-04-12 18:05:02.145198: Epoch 1787 +2026-04-12 18:05:02.147366: Current learning rate: 0.00587 +2026-04-12 18:06:44.603172: train_loss -0.4034 +2026-04-12 18:06:44.609977: val_loss -0.3564 +2026-04-12 18:06:44.612650: Pseudo dice [0.0, 0.0, 0.7733, 0.4595, 0.6109, 0.6501, 0.7551] +2026-04-12 18:06:44.615721: Epoch time: 102.46 s +2026-04-12 18:06:45.831300: +2026-04-12 18:06:45.833642: Epoch 1788 +2026-04-12 18:06:45.836222: Current learning rate: 0.00587 +2026-04-12 18:08:28.356803: train_loss -0.3987 +2026-04-12 18:08:28.363376: val_loss -0.3383 +2026-04-12 18:08:28.365507: Pseudo dice [0.0, 0.0, 0.798, 0.6913, 0.4757, 0.6129, 0.8325] +2026-04-12 18:08:28.368059: Epoch time: 102.53 s +2026-04-12 18:08:29.598911: +2026-04-12 18:08:29.601182: Epoch 1789 +2026-04-12 18:08:29.603492: Current learning rate: 0.00587 +2026-04-12 18:10:11.407007: train_loss -0.3904 +2026-04-12 18:10:11.414679: val_loss -0.3329 +2026-04-12 18:10:11.416858: Pseudo dice [0.0, 0.0, 0.6739, 0.2406, 0.3091, 0.3152, 0.8622] +2026-04-12 18:10:11.419797: Epoch time: 101.81 s +2026-04-12 18:10:12.716607: +2026-04-12 18:10:12.718525: Epoch 1790 +2026-04-12 18:10:12.720716: Current learning rate: 0.00586 +2026-04-12 18:11:54.806434: train_loss -0.3753 +2026-04-12 18:11:54.813847: val_loss -0.3433 +2026-04-12 18:11:54.816523: Pseudo dice [0.0, 0.0, 0.7583, 0.668, 0.5354, 0.852, 0.8921] +2026-04-12 18:11:54.821192: Epoch time: 102.09 s +2026-04-12 18:11:56.057861: +2026-04-12 18:11:56.060057: Epoch 1791 +2026-04-12 18:11:56.062286: Current learning rate: 0.00586 +2026-04-12 18:13:38.437143: train_loss -0.4032 +2026-04-12 18:13:38.444333: val_loss -0.3038 +2026-04-12 18:13:38.448184: Pseudo dice [0.0, 0.0, 0.641, 0.4102, 0.524, 0.4225, 0.5286] +2026-04-12 18:13:38.451095: Epoch time: 102.38 s +2026-04-12 18:13:39.704520: +2026-04-12 18:13:39.706709: Epoch 1792 +2026-04-12 18:13:39.709093: Current learning rate: 0.00586 +2026-04-12 18:15:22.191715: train_loss -0.403 +2026-04-12 18:15:22.198965: val_loss -0.3705 +2026-04-12 18:15:22.201029: Pseudo dice [0.0, 0.0, 0.8017, 0.7376, 0.4828, 0.6728, 0.7952] +2026-04-12 18:15:22.203340: Epoch time: 102.49 s +2026-04-12 18:15:23.432427: +2026-04-12 18:15:23.434385: Epoch 1793 +2026-04-12 18:15:23.436476: Current learning rate: 0.00586 +2026-04-12 18:17:05.508256: train_loss -0.3965 +2026-04-12 18:17:05.516868: val_loss -0.3624 +2026-04-12 18:17:05.519854: Pseudo dice [0.0, 0.0, 0.674, 0.6645, 0.4697, 0.4003, 0.8439] +2026-04-12 18:17:05.524717: Epoch time: 102.08 s +2026-04-12 18:17:06.764834: +2026-04-12 18:17:06.779032: Epoch 1794 +2026-04-12 18:17:06.781459: Current learning rate: 0.00585 +2026-04-12 18:18:48.613844: train_loss -0.395 +2026-04-12 18:18:48.621459: val_loss -0.345 +2026-04-12 18:18:48.623948: Pseudo dice [0.0, 0.0, 0.6342, 0.4366, 0.497, 0.3524, 0.8268] +2026-04-12 18:18:48.628637: Epoch time: 101.85 s +2026-04-12 18:18:49.910774: +2026-04-12 18:18:49.912791: Epoch 1795 +2026-04-12 18:18:49.914939: Current learning rate: 0.00585 +2026-04-12 18:20:31.490536: train_loss -0.4031 +2026-04-12 18:20:31.497163: val_loss -0.3766 +2026-04-12 18:20:31.499569: Pseudo dice [0.0, 0.0, 0.6998, 0.5945, 0.6082, 0.7021, 0.822] +2026-04-12 18:20:31.502261: Epoch time: 101.58 s +2026-04-12 18:20:32.735534: +2026-04-12 18:20:32.738516: Epoch 1796 +2026-04-12 18:20:32.740408: Current learning rate: 0.00585 +2026-04-12 18:22:14.391417: train_loss -0.404 +2026-04-12 18:22:14.398242: val_loss -0.3288 +2026-04-12 18:22:14.400323: Pseudo dice [0.0, 0.0, 0.7579, 0.1084, 0.4787, 0.537, 0.3729] +2026-04-12 18:22:14.402774: Epoch time: 101.66 s +2026-04-12 18:22:15.629643: +2026-04-12 18:22:15.631460: Epoch 1797 +2026-04-12 18:22:15.633946: Current learning rate: 0.00585 +2026-04-12 18:23:57.879935: train_loss -0.3997 +2026-04-12 18:23:57.887370: val_loss -0.3483 +2026-04-12 18:23:57.889422: Pseudo dice [0.0, 0.0, 0.7676, 0.466, 0.4136, 0.5373, 0.8674] +2026-04-12 18:23:57.892400: Epoch time: 102.25 s +2026-04-12 18:23:59.106774: +2026-04-12 18:23:59.108586: Epoch 1798 +2026-04-12 18:23:59.110631: Current learning rate: 0.00584 +2026-04-12 18:25:40.922948: train_loss -0.3781 +2026-04-12 18:25:40.930236: val_loss -0.358 +2026-04-12 18:25:40.932405: Pseudo dice [0.0, 0.0, 0.7769, 0.7576, 0.4851, 0.6949, 0.4174] +2026-04-12 18:25:40.935082: Epoch time: 101.82 s +2026-04-12 18:25:42.209274: +2026-04-12 18:25:42.210953: Epoch 1799 +2026-04-12 18:25:42.213020: Current learning rate: 0.00584 +2026-04-12 18:27:24.328686: train_loss -0.4034 +2026-04-12 18:27:24.336064: val_loss -0.3585 +2026-04-12 18:27:24.338586: Pseudo dice [0.0, 0.0, 0.8797, 0.6013, 0.5995, 0.8226, 0.1871] +2026-04-12 18:27:24.340978: Epoch time: 102.12 s +2026-04-12 18:27:27.331307: +2026-04-12 18:27:27.333094: Epoch 1800 +2026-04-12 18:27:27.334971: Current learning rate: 0.00584 +2026-04-12 18:29:09.743737: train_loss -0.3988 +2026-04-12 18:29:09.749784: val_loss -0.3641 +2026-04-12 18:29:09.752002: Pseudo dice [0.0, 0.0, 0.7313, 0.2497, 0.6793, 0.4359, 0.9033] +2026-04-12 18:29:09.754767: Epoch time: 102.42 s +2026-04-12 18:29:10.987012: +2026-04-12 18:29:10.988758: Epoch 1801 +2026-04-12 18:29:10.990671: Current learning rate: 0.00584 +2026-04-12 18:30:53.301509: train_loss -0.4021 +2026-04-12 18:30:53.308821: val_loss -0.3174 +2026-04-12 18:30:53.311953: Pseudo dice [0.0, 0.0, 0.7779, 0.5331, 0.2671, 0.5679, 0.8362] +2026-04-12 18:30:53.314598: Epoch time: 102.32 s +2026-04-12 18:30:54.555266: +2026-04-12 18:30:54.557519: Epoch 1802 +2026-04-12 18:30:54.559568: Current learning rate: 0.00583 +2026-04-12 18:32:36.545721: train_loss -0.3891 +2026-04-12 18:32:36.552793: val_loss -0.3406 +2026-04-12 18:32:36.555512: Pseudo dice [0.0, 0.0, 0.6815, 0.5321, 0.5255, 0.6715, 0.7168] +2026-04-12 18:32:36.557792: Epoch time: 101.99 s +2026-04-12 18:32:37.760925: +2026-04-12 18:32:37.763037: Epoch 1803 +2026-04-12 18:32:37.767636: Current learning rate: 0.00583 +2026-04-12 18:34:19.839705: train_loss -0.39 +2026-04-12 18:34:19.848942: val_loss -0.3559 +2026-04-12 18:34:19.851088: Pseudo dice [0.0, 0.0, 0.8211, 0.7341, 0.3951, 0.6782, 0.8112] +2026-04-12 18:34:19.853673: Epoch time: 102.08 s +2026-04-12 18:34:22.177010: +2026-04-12 18:34:22.178826: Epoch 1804 +2026-04-12 18:34:22.182214: Current learning rate: 0.00583 +2026-04-12 18:36:04.572819: train_loss -0.3764 +2026-04-12 18:36:04.579899: val_loss -0.3205 +2026-04-12 18:36:04.582282: Pseudo dice [0.6904, 0.0, 0.4385, 0.3916, 0.4968, 0.3802, 0.7797] +2026-04-12 18:36:04.585742: Epoch time: 102.4 s +2026-04-12 18:36:05.838838: +2026-04-12 18:36:05.841050: Epoch 1805 +2026-04-12 18:36:05.843006: Current learning rate: 0.00583 +2026-04-12 18:37:47.416527: train_loss -0.37 +2026-04-12 18:37:47.425862: val_loss -0.3343 +2026-04-12 18:37:47.429815: Pseudo dice [0.4611, 0.0, 0.7377, 0.2622, 0.4046, 0.6152, 0.7626] +2026-04-12 18:37:47.432960: Epoch time: 101.58 s +2026-04-12 18:37:48.711583: +2026-04-12 18:37:48.714818: Epoch 1806 +2026-04-12 18:37:48.717802: Current learning rate: 0.00582 +2026-04-12 18:39:30.745821: train_loss -0.3749 +2026-04-12 18:39:30.752522: val_loss -0.3255 +2026-04-12 18:39:30.754615: Pseudo dice [0.0, 0.0, 0.7863, 0.4965, 0.4238, 0.8787, 0.5022] +2026-04-12 18:39:30.756987: Epoch time: 102.04 s +2026-04-12 18:39:31.995270: +2026-04-12 18:39:31.997232: Epoch 1807 +2026-04-12 18:39:31.999239: Current learning rate: 0.00582 +2026-04-12 18:41:14.154784: train_loss -0.399 +2026-04-12 18:41:14.161224: val_loss -0.3146 +2026-04-12 18:41:14.163550: Pseudo dice [0.0, 0.0, 0.4974, 0.392, 0.4274, 0.3833, 0.7659] +2026-04-12 18:41:14.166241: Epoch time: 102.16 s +2026-04-12 18:41:15.409360: +2026-04-12 18:41:15.411310: Epoch 1808 +2026-04-12 18:41:15.413509: Current learning rate: 0.00582 +2026-04-12 18:42:58.093425: train_loss -0.3871 +2026-04-12 18:42:58.101414: val_loss -0.3073 +2026-04-12 18:42:58.103590: Pseudo dice [0.0, 0.0, 0.6582, 0.0196, 0.4554, 0.3654, 0.8476] +2026-04-12 18:42:58.106493: Epoch time: 102.69 s +2026-04-12 18:42:59.392817: +2026-04-12 18:42:59.396208: Epoch 1809 +2026-04-12 18:42:59.398340: Current learning rate: 0.00582 +2026-04-12 18:44:41.687190: train_loss -0.3833 +2026-04-12 18:44:41.694996: val_loss -0.3953 +2026-04-12 18:44:41.697390: Pseudo dice [0.0, 0.0, 0.6675, 0.6011, 0.4786, 0.8237, 0.8948] +2026-04-12 18:44:41.699960: Epoch time: 102.3 s +2026-04-12 18:44:42.922843: +2026-04-12 18:44:42.925156: Epoch 1810 +2026-04-12 18:44:42.927316: Current learning rate: 0.00581 +2026-04-12 18:46:24.413533: train_loss -0.4141 +2026-04-12 18:46:24.420491: val_loss -0.3607 +2026-04-12 18:46:24.423207: Pseudo dice [0.0, 0.0, 0.7872, 0.7749, 0.4063, 0.5767, 0.8628] +2026-04-12 18:46:24.426110: Epoch time: 101.49 s +2026-04-12 18:46:25.666772: +2026-04-12 18:46:25.669014: Epoch 1811 +2026-04-12 18:46:25.671547: Current learning rate: 0.00581 +2026-04-12 18:48:07.758485: train_loss -0.4111 +2026-04-12 18:48:07.765181: val_loss -0.379 +2026-04-12 18:48:07.774256: Pseudo dice [0.0, 0.0, 0.7477, 0.593, 0.5906, 0.6882, 0.884] +2026-04-12 18:48:07.778774: Epoch time: 102.09 s +2026-04-12 18:48:09.034944: +2026-04-12 18:48:09.037015: Epoch 1812 +2026-04-12 18:48:09.038885: Current learning rate: 0.00581 +2026-04-12 18:49:50.556560: train_loss -0.416 +2026-04-12 18:49:50.564122: val_loss -0.3475 +2026-04-12 18:49:50.566750: Pseudo dice [0.0, 0.0, 0.7738, 0.2211, 0.4728, 0.6351, 0.665] +2026-04-12 18:49:50.569088: Epoch time: 101.52 s +2026-04-12 18:49:51.774264: +2026-04-12 18:49:51.776192: Epoch 1813 +2026-04-12 18:49:51.778269: Current learning rate: 0.00581 +2026-04-12 18:51:33.447587: train_loss -0.4075 +2026-04-12 18:51:33.455304: val_loss -0.3588 +2026-04-12 18:51:33.459012: Pseudo dice [0.6209, 0.0, 0.7575, 0.787, 0.5033, 0.8442, 0.7833] +2026-04-12 18:51:33.461519: Epoch time: 101.68 s +2026-04-12 18:51:34.724801: +2026-04-12 18:51:34.726762: Epoch 1814 +2026-04-12 18:51:34.729002: Current learning rate: 0.00581 +2026-04-12 18:53:16.744774: train_loss -0.4102 +2026-04-12 18:53:16.752064: val_loss -0.3314 +2026-04-12 18:53:16.754560: Pseudo dice [0.5186, 0.0, 0.7594, 0.6784, 0.4312, 0.5759, 0.8231] +2026-04-12 18:53:16.757208: Epoch time: 102.02 s +2026-04-12 18:53:18.007942: +2026-04-12 18:53:18.013126: Epoch 1815 +2026-04-12 18:53:18.016348: Current learning rate: 0.0058 +2026-04-12 18:55:00.116049: train_loss -0.3844 +2026-04-12 18:55:00.123601: val_loss -0.3364 +2026-04-12 18:55:00.126388: Pseudo dice [0.4675, 0.0, 0.7089, 0.3412, 0.4011, 0.6235, 0.8319] +2026-04-12 18:55:00.128817: Epoch time: 102.11 s +2026-04-12 18:55:01.359121: +2026-04-12 18:55:01.361093: Epoch 1816 +2026-04-12 18:55:01.363327: Current learning rate: 0.0058 +2026-04-12 18:56:43.437002: train_loss -0.4256 +2026-04-12 18:56:43.443243: val_loss -0.3701 +2026-04-12 18:56:43.445194: Pseudo dice [0.5436, 0.0, 0.788, 0.7021, 0.4481, 0.5252, 0.8768] +2026-04-12 18:56:43.447864: Epoch time: 102.08 s +2026-04-12 18:56:44.677803: +2026-04-12 18:56:44.679606: Epoch 1817 +2026-04-12 18:56:44.682031: Current learning rate: 0.0058 +2026-04-12 18:58:26.603423: train_loss -0.4251 +2026-04-12 18:58:26.611161: val_loss -0.3651 +2026-04-12 18:58:26.613143: Pseudo dice [0.4403, 0.0, 0.7668, 0.7251, 0.596, 0.7487, 0.8136] +2026-04-12 18:58:26.616035: Epoch time: 101.93 s +2026-04-12 18:58:27.889189: +2026-04-12 18:58:27.891278: Epoch 1818 +2026-04-12 18:58:27.893614: Current learning rate: 0.0058 +2026-04-12 19:00:10.741938: train_loss -0.4106 +2026-04-12 19:00:10.749633: val_loss -0.3771 +2026-04-12 19:00:10.751836: Pseudo dice [0.2896, 0.0, 0.792, 0.0014, 0.5445, 0.8, 0.8449] +2026-04-12 19:00:10.755864: Epoch time: 102.86 s +2026-04-12 19:00:11.999291: +2026-04-12 19:00:12.002323: Epoch 1819 +2026-04-12 19:00:12.004813: Current learning rate: 0.00579 +2026-04-12 19:01:53.745938: train_loss -0.3816 +2026-04-12 19:01:53.754403: val_loss -0.3247 +2026-04-12 19:01:53.756809: Pseudo dice [0.0, 0.0, 0.609, 0.3921, 0.4232, 0.6844, 0.5688] +2026-04-12 19:01:53.759748: Epoch time: 101.75 s +2026-04-12 19:01:54.981796: +2026-04-12 19:01:54.983813: Epoch 1820 +2026-04-12 19:01:54.985866: Current learning rate: 0.00579 +2026-04-12 19:03:37.053861: train_loss -0.3816 +2026-04-12 19:03:37.062732: val_loss -0.3681 +2026-04-12 19:03:37.066884: Pseudo dice [0.4903, 0.0, 0.6414, 0.5237, 0.285, 0.7958, 0.8643] +2026-04-12 19:03:37.069631: Epoch time: 102.08 s +2026-04-12 19:03:38.315807: +2026-04-12 19:03:38.319071: Epoch 1821 +2026-04-12 19:03:38.321385: Current learning rate: 0.00579 +2026-04-12 19:05:19.895089: train_loss -0.4136 +2026-04-12 19:05:19.904961: val_loss -0.3302 +2026-04-12 19:05:19.907972: Pseudo dice [0.3041, 0.0, 0.71, 0.1041, 0.6328, 0.3926, 0.6285] +2026-04-12 19:05:19.910635: Epoch time: 101.58 s +2026-04-12 19:05:21.126630: +2026-04-12 19:05:21.128836: Epoch 1822 +2026-04-12 19:05:21.130909: Current learning rate: 0.00579 +2026-04-12 19:07:03.739662: train_loss -0.3788 +2026-04-12 19:07:03.746637: val_loss -0.3161 +2026-04-12 19:07:03.749237: Pseudo dice [0.0, 0.0, 0.5279, 0.1858, 0.4622, 0.4797, 0.7267] +2026-04-12 19:07:03.752705: Epoch time: 102.62 s +2026-04-12 19:07:04.989235: +2026-04-12 19:07:04.991911: Epoch 1823 +2026-04-12 19:07:04.994385: Current learning rate: 0.00578 +2026-04-12 19:08:46.805543: train_loss -0.3808 +2026-04-12 19:08:46.812907: val_loss -0.348 +2026-04-12 19:08:46.815565: Pseudo dice [0.0, 0.0, 0.8335, 0.3521, 0.459, 0.6635, 0.8945] +2026-04-12 19:08:46.818703: Epoch time: 101.82 s +2026-04-12 19:08:49.335093: +2026-04-12 19:08:49.337373: Epoch 1824 +2026-04-12 19:08:49.339440: Current learning rate: 0.00578 +2026-04-12 19:10:31.649705: train_loss -0.4073 +2026-04-12 19:10:31.674870: val_loss -0.3346 +2026-04-12 19:10:31.677867: Pseudo dice [0.0, 0.0, 0.6657, 0.6944, 0.3023, 0.696, 0.7113] +2026-04-12 19:10:31.680357: Epoch time: 102.32 s +2026-04-12 19:10:32.962800: +2026-04-12 19:10:32.964847: Epoch 1825 +2026-04-12 19:10:32.967017: Current learning rate: 0.00578 +2026-04-12 19:12:16.082262: train_loss -0.407 +2026-04-12 19:12:16.089676: val_loss -0.3474 +2026-04-12 19:12:16.091688: Pseudo dice [0.0, 0.0, 0.8174, 0.5341, 0.5004, 0.6246, 0.3869] +2026-04-12 19:12:16.093923: Epoch time: 103.12 s +2026-04-12 19:12:17.323367: +2026-04-12 19:12:17.325633: Epoch 1826 +2026-04-12 19:12:17.328881: Current learning rate: 0.00578 +2026-04-12 19:13:59.299031: train_loss -0.3948 +2026-04-12 19:13:59.305937: val_loss -0.377 +2026-04-12 19:13:59.308181: Pseudo dice [0.0, 0.0, 0.82, 0.6977, 0.3836, 0.8129, 0.5837] +2026-04-12 19:13:59.310739: Epoch time: 101.98 s +2026-04-12 19:14:00.587878: +2026-04-12 19:14:00.590749: Epoch 1827 +2026-04-12 19:14:00.593079: Current learning rate: 0.00577 +2026-04-12 19:15:42.678509: train_loss -0.4036 +2026-04-12 19:15:42.687199: val_loss -0.3625 +2026-04-12 19:15:42.689700: Pseudo dice [0.0, 0.0, 0.7922, 0.8159, 0.3836, 0.5799, 0.919] +2026-04-12 19:15:42.692191: Epoch time: 102.09 s +2026-04-12 19:15:43.941337: +2026-04-12 19:15:43.943284: Epoch 1828 +2026-04-12 19:15:43.945609: Current learning rate: 0.00577 +2026-04-12 19:17:25.659283: train_loss -0.4019 +2026-04-12 19:17:25.666243: val_loss -0.3417 +2026-04-12 19:17:25.668644: Pseudo dice [0.0, 0.0, 0.8094, 0.4966, 0.4434, 0.705, 0.8368] +2026-04-12 19:17:25.671277: Epoch time: 101.72 s +2026-04-12 19:17:26.907031: +2026-04-12 19:17:26.909868: Epoch 1829 +2026-04-12 19:17:26.912349: Current learning rate: 0.00577 +2026-04-12 19:19:08.901263: train_loss -0.4148 +2026-04-12 19:19:08.909696: val_loss -0.3442 +2026-04-12 19:19:08.912344: Pseudo dice [0.0, 0.0, 0.6974, 0.6456, 0.4223, 0.5171, 0.6546] +2026-04-12 19:19:08.915427: Epoch time: 102.0 s +2026-04-12 19:19:10.162794: +2026-04-12 19:19:10.164488: Epoch 1830 +2026-04-12 19:19:10.166518: Current learning rate: 0.00577 +2026-04-12 19:20:51.793066: train_loss -0.384 +2026-04-12 19:20:51.800412: val_loss -0.3408 +2026-04-12 19:20:51.802872: Pseudo dice [0.0, 0.0, 0.7598, 0.4569, 0.5114, 0.365, 0.8695] +2026-04-12 19:20:51.805407: Epoch time: 101.63 s +2026-04-12 19:20:53.706171: +2026-04-12 19:20:53.708413: Epoch 1831 +2026-04-12 19:20:53.710611: Current learning rate: 0.00576 +2026-04-12 19:22:35.574696: train_loss -0.419 +2026-04-12 19:22:35.583195: val_loss -0.3532 +2026-04-12 19:22:35.585364: Pseudo dice [0.0, 0.0, 0.6446, 0.1563, 0.5781, 0.8424, 0.8574] +2026-04-12 19:22:35.587436: Epoch time: 101.87 s +2026-04-12 19:22:36.806937: +2026-04-12 19:22:36.809391: Epoch 1832 +2026-04-12 19:22:36.811625: Current learning rate: 0.00576 +2026-04-12 19:24:18.515806: train_loss -0.4102 +2026-04-12 19:24:18.522724: val_loss -0.3473 +2026-04-12 19:24:18.525640: Pseudo dice [0.493, 0.0, 0.622, 0.282, 0.6558, 0.7909, 0.605] +2026-04-12 19:24:18.529063: Epoch time: 101.71 s +2026-04-12 19:24:19.751635: +2026-04-12 19:24:19.753610: Epoch 1833 +2026-04-12 19:24:19.755634: Current learning rate: 0.00576 +2026-04-12 19:26:01.504209: train_loss -0.4226 +2026-04-12 19:26:01.511528: val_loss -0.3538 +2026-04-12 19:26:01.514680: Pseudo dice [0.0001, 0.0, 0.6939, 0.7238, 0.5837, 0.5129, 0.8532] +2026-04-12 19:26:01.517300: Epoch time: 101.76 s +2026-04-12 19:26:02.738675: +2026-04-12 19:26:02.740879: Epoch 1834 +2026-04-12 19:26:02.743232: Current learning rate: 0.00576 +2026-04-12 19:27:45.080012: train_loss -0.4083 +2026-04-12 19:27:45.087513: val_loss -0.3737 +2026-04-12 19:27:45.089994: Pseudo dice [0.4603, 0.0, 0.7507, 0.7735, 0.51, 0.2892, 0.8918] +2026-04-12 19:27:45.093218: Epoch time: 102.34 s +2026-04-12 19:27:46.363356: +2026-04-12 19:27:46.365923: Epoch 1835 +2026-04-12 19:27:46.368164: Current learning rate: 0.00576 +2026-04-12 19:29:28.525163: train_loss -0.4141 +2026-04-12 19:29:28.531738: val_loss -0.4051 +2026-04-12 19:29:28.534240: Pseudo dice [0.7422, 0.0, 0.7662, 0.8467, 0.4585, 0.6743, 0.7544] +2026-04-12 19:29:28.537903: Epoch time: 102.17 s +2026-04-12 19:29:29.809631: +2026-04-12 19:29:29.811953: Epoch 1836 +2026-04-12 19:29:29.814435: Current learning rate: 0.00575 +2026-04-12 19:31:12.136768: train_loss -0.4109 +2026-04-12 19:31:12.142448: val_loss -0.3836 +2026-04-12 19:31:12.145477: Pseudo dice [0.3033, 0.0, 0.846, 0.754, 0.4507, 0.8353, 0.8877] +2026-04-12 19:31:12.148690: Epoch time: 102.33 s +2026-04-12 19:31:13.423417: +2026-04-12 19:31:13.425181: Epoch 1837 +2026-04-12 19:31:13.431847: Current learning rate: 0.00575 +2026-04-12 19:32:55.715963: train_loss -0.4191 +2026-04-12 19:32:55.723012: val_loss -0.3808 +2026-04-12 19:32:55.726104: Pseudo dice [0.3431, 0.0, 0.7812, 0.2269, 0.5947, 0.6709, 0.9312] +2026-04-12 19:32:55.728956: Epoch time: 102.3 s +2026-04-12 19:32:56.990152: +2026-04-12 19:32:56.992011: Epoch 1838 +2026-04-12 19:32:56.993808: Current learning rate: 0.00575 +2026-04-12 19:34:39.596481: train_loss -0.4072 +2026-04-12 19:34:39.605171: val_loss -0.3557 +2026-04-12 19:34:39.608819: Pseudo dice [0.4413, 0.0, 0.6398, 0.6357, 0.4905, 0.7331, 0.9027] +2026-04-12 19:34:39.611523: Epoch time: 102.61 s +2026-04-12 19:34:40.857734: +2026-04-12 19:34:40.859994: Epoch 1839 +2026-04-12 19:34:40.862061: Current learning rate: 0.00575 +2026-04-12 19:36:22.811755: train_loss -0.3993 +2026-04-12 19:36:22.818325: val_loss -0.3679 +2026-04-12 19:36:22.820493: Pseudo dice [0.4104, 0.0, 0.8143, 0.7634, 0.5723, 0.8072, 0.9103] +2026-04-12 19:36:22.823009: Epoch time: 101.96 s +2026-04-12 19:36:24.073342: +2026-04-12 19:36:24.075866: Epoch 1840 +2026-04-12 19:36:24.078227: Current learning rate: 0.00574 +2026-04-12 19:38:06.278965: train_loss -0.4024 +2026-04-12 19:38:06.287094: val_loss -0.3443 +2026-04-12 19:38:06.289886: Pseudo dice [0.5523, 0.0, 0.8395, 0.5064, 0.493, 0.7662, 0.7893] +2026-04-12 19:38:06.294168: Epoch time: 102.21 s +2026-04-12 19:38:07.576374: +2026-04-12 19:38:07.578620: Epoch 1841 +2026-04-12 19:38:07.580513: Current learning rate: 0.00574 +2026-04-12 19:39:49.513574: train_loss -0.4161 +2026-04-12 19:39:49.520091: val_loss -0.3695 +2026-04-12 19:39:49.522329: Pseudo dice [0.0, 0.0, 0.7891, 0.811, 0.6179, 0.4023, 0.774] +2026-04-12 19:39:49.524453: Epoch time: 101.94 s +2026-04-12 19:39:50.824171: +2026-04-12 19:39:50.826000: Epoch 1842 +2026-04-12 19:39:50.828096: Current learning rate: 0.00574 +2026-04-12 19:41:33.084759: train_loss -0.3763 +2026-04-12 19:41:33.092113: val_loss -0.3303 +2026-04-12 19:41:33.094118: Pseudo dice [0.0, 0.0, 0.7406, 0.5977, 0.4139, 0.6828, 0.7674] +2026-04-12 19:41:33.097064: Epoch time: 102.26 s +2026-04-12 19:41:34.319434: +2026-04-12 19:41:34.321338: Epoch 1843 +2026-04-12 19:41:34.323530: Current learning rate: 0.00574 +2026-04-12 19:43:16.045792: train_loss -0.3654 +2026-04-12 19:43:16.052552: val_loss -0.3396 +2026-04-12 19:43:16.054775: Pseudo dice [0.0119, 0.0, 0.7341, 0.4602, 0.4153, 0.4079, 0.8273] +2026-04-12 19:43:16.058122: Epoch time: 101.73 s +2026-04-12 19:43:18.244578: +2026-04-12 19:43:18.246288: Epoch 1844 +2026-04-12 19:43:18.248214: Current learning rate: 0.00573 +2026-04-12 19:44:59.988868: train_loss -0.3972 +2026-04-12 19:44:59.995331: val_loss -0.2946 +2026-04-12 19:44:59.997233: Pseudo dice [0.482, 0.0, 0.6802, 0.0023, 0.4181, 0.3321, 0.1598] +2026-04-12 19:44:59.999566: Epoch time: 101.75 s +2026-04-12 19:45:01.231931: +2026-04-12 19:45:01.233842: Epoch 1845 +2026-04-12 19:45:01.235726: Current learning rate: 0.00573 +2026-04-12 19:46:43.473429: train_loss -0.3861 +2026-04-12 19:46:43.479898: val_loss -0.3579 +2026-04-12 19:46:43.482137: Pseudo dice [0.5274, 0.0, 0.8301, 0.4124, 0.7057, 0.5049, 0.4099] +2026-04-12 19:46:43.484430: Epoch time: 102.24 s +2026-04-12 19:46:44.726293: +2026-04-12 19:46:44.729171: Epoch 1846 +2026-04-12 19:46:44.731397: Current learning rate: 0.00573 +2026-04-12 19:48:26.923430: train_loss -0.4106 +2026-04-12 19:48:26.930545: val_loss -0.4007 +2026-04-12 19:48:26.933244: Pseudo dice [0.5725, 0.0, 0.7812, 0.5424, 0.5178, 0.6907, 0.9245] +2026-04-12 19:48:26.935602: Epoch time: 102.2 s +2026-04-12 19:48:28.149730: +2026-04-12 19:48:28.151599: Epoch 1847 +2026-04-12 19:48:28.153514: Current learning rate: 0.00573 +2026-04-12 19:50:10.286945: train_loss -0.4012 +2026-04-12 19:50:10.294311: val_loss -0.3613 +2026-04-12 19:50:10.296809: Pseudo dice [0.4407, 0.0, 0.7268, 0.008, 0.5061, 0.7755, 0.8518] +2026-04-12 19:50:10.299291: Epoch time: 102.14 s +2026-04-12 19:50:11.552814: +2026-04-12 19:50:11.554600: Epoch 1848 +2026-04-12 19:50:11.557044: Current learning rate: 0.00572 +2026-04-12 19:51:53.355757: train_loss -0.3999 +2026-04-12 19:51:53.363415: val_loss -0.3575 +2026-04-12 19:51:53.366301: Pseudo dice [0.4602, 0.0, 0.6205, 0.5078, 0.39, 0.8343, 0.8471] +2026-04-12 19:51:53.370370: Epoch time: 101.81 s +2026-04-12 19:51:54.640939: +2026-04-12 19:51:54.643420: Epoch 1849 +2026-04-12 19:51:54.645827: Current learning rate: 0.00572 +2026-04-12 19:53:36.583995: train_loss -0.3999 +2026-04-12 19:53:36.591647: val_loss -0.321 +2026-04-12 19:53:36.594341: Pseudo dice [0.0, 0.0, 0.7628, 0.6153, 0.5503, 0.7331, 0.6975] +2026-04-12 19:53:36.597117: Epoch time: 101.95 s +2026-04-12 19:53:39.623223: +2026-04-12 19:53:39.624982: Epoch 1850 +2026-04-12 19:53:39.626820: Current learning rate: 0.00572 +2026-04-12 19:55:21.754289: train_loss -0.3766 +2026-04-12 19:55:21.762033: val_loss -0.3069 +2026-04-12 19:55:21.764547: Pseudo dice [0.1767, 0.0, 0.7122, 0.4658, 0.3809, 0.663, 0.5482] +2026-04-12 19:55:21.767118: Epoch time: 102.13 s +2026-04-12 19:55:22.983024: +2026-04-12 19:55:22.985188: Epoch 1851 +2026-04-12 19:55:22.987556: Current learning rate: 0.00572 +2026-04-12 19:57:05.348423: train_loss -0.4063 +2026-04-12 19:57:05.355917: val_loss -0.3505 +2026-04-12 19:57:05.357983: Pseudo dice [0.3502, 0.0, 0.7863, 0.5369, 0.558, 0.6552, 0.8112] +2026-04-12 19:57:05.360540: Epoch time: 102.37 s +2026-04-12 19:57:06.663461: +2026-04-12 19:57:06.665529: Epoch 1852 +2026-04-12 19:57:06.668176: Current learning rate: 0.00571 +2026-04-12 19:58:48.918031: train_loss -0.417 +2026-04-12 19:58:48.928556: val_loss -0.3761 +2026-04-12 19:58:48.930694: Pseudo dice [0.4779, 0.0, 0.684, 0.5224, 0.5084, 0.7351, 0.8442] +2026-04-12 19:58:48.933686: Epoch time: 102.26 s +2026-04-12 19:58:50.163910: +2026-04-12 19:58:50.166355: Epoch 1853 +2026-04-12 19:58:50.168918: Current learning rate: 0.00571 +2026-04-12 20:00:32.151563: train_loss -0.4033 +2026-04-12 20:00:32.163296: val_loss -0.3587 +2026-04-12 20:00:32.165469: Pseudo dice [0.4043, 0.0, 0.8377, 0.5217, 0.3539, 0.4382, 0.8887] +2026-04-12 20:00:32.169400: Epoch time: 101.99 s +2026-04-12 20:00:33.409663: +2026-04-12 20:00:33.412291: Epoch 1854 +2026-04-12 20:00:33.415041: Current learning rate: 0.00571 +2026-04-12 20:02:14.870873: train_loss -0.4065 +2026-04-12 20:02:14.877345: val_loss -0.3392 +2026-04-12 20:02:14.879619: Pseudo dice [0.4157, 0.0, 0.6626, 0.4549, 0.4849, 0.7582, 0.8718] +2026-04-12 20:02:14.882190: Epoch time: 101.46 s +2026-04-12 20:02:16.141685: +2026-04-12 20:02:16.143306: Epoch 1855 +2026-04-12 20:02:16.145302: Current learning rate: 0.00571 +2026-04-12 20:03:57.729180: train_loss -0.3753 +2026-04-12 20:03:57.735621: val_loss -0.3575 +2026-04-12 20:03:57.738226: Pseudo dice [0.0, 0.0, 0.6085, 0.3421, 0.5277, 0.8242, 0.8519] +2026-04-12 20:03:57.741663: Epoch time: 101.59 s +2026-04-12 20:03:59.001354: +2026-04-12 20:03:59.003154: Epoch 1856 +2026-04-12 20:03:59.005114: Current learning rate: 0.0057 +2026-04-12 20:05:41.051633: train_loss -0.3991 +2026-04-12 20:05:41.059453: val_loss -0.3893 +2026-04-12 20:05:41.061503: Pseudo dice [0.6793, 0.0, 0.6027, 0.0493, 0.5714, 0.8609, 0.8464] +2026-04-12 20:05:41.064387: Epoch time: 102.05 s +2026-04-12 20:05:42.315852: +2026-04-12 20:05:42.317828: Epoch 1857 +2026-04-12 20:05:42.319846: Current learning rate: 0.0057 +2026-04-12 20:07:24.397699: train_loss -0.3848 +2026-04-12 20:07:24.405406: val_loss -0.3463 +2026-04-12 20:07:24.408447: Pseudo dice [0.0, 0.0, 0.7845, 0.2058, 0.6255, 0.4191, 0.5801] +2026-04-12 20:07:24.411298: Epoch time: 102.08 s +2026-04-12 20:07:25.659476: +2026-04-12 20:07:25.661325: Epoch 1858 +2026-04-12 20:07:25.663484: Current learning rate: 0.0057 +2026-04-12 20:09:07.562179: train_loss -0.3729 +2026-04-12 20:09:07.570619: val_loss -0.3499 +2026-04-12 20:09:07.573069: Pseudo dice [0.0, 0.0, 0.6544, 0.5096, 0.4496, 0.6041, 0.8959] +2026-04-12 20:09:07.575997: Epoch time: 101.91 s +2026-04-12 20:09:08.813012: +2026-04-12 20:09:08.815204: Epoch 1859 +2026-04-12 20:09:08.817591: Current learning rate: 0.0057 +2026-04-12 20:10:50.740508: train_loss -0.3893 +2026-04-12 20:10:50.762708: val_loss -0.3465 +2026-04-12 20:10:50.767804: Pseudo dice [0.0, 0.0, 0.7731, 0.2178, 0.3529, 0.5478, 0.6308] +2026-04-12 20:10:50.770370: Epoch time: 101.93 s +2026-04-12 20:10:52.002410: +2026-04-12 20:10:52.004585: Epoch 1860 +2026-04-12 20:10:52.006718: Current learning rate: 0.0057 +2026-04-12 20:12:33.768124: train_loss -0.3628 +2026-04-12 20:12:33.775170: val_loss -0.3144 +2026-04-12 20:12:33.778406: Pseudo dice [0.0129, 0.0, 0.6757, 0.3769, 0.5004, 0.5927, 0.1376] +2026-04-12 20:12:33.781110: Epoch time: 101.77 s +2026-04-12 20:12:35.005892: +2026-04-12 20:12:35.008156: Epoch 1861 +2026-04-12 20:12:35.010541: Current learning rate: 0.00569 +2026-04-12 20:14:16.789719: train_loss -0.4117 +2026-04-12 20:14:16.797323: val_loss -0.3267 +2026-04-12 20:14:16.801162: Pseudo dice [0.0, 0.0, 0.7814, 0.6824, 0.5436, 0.5021, 0.6848] +2026-04-12 20:14:16.805147: Epoch time: 101.79 s +2026-04-12 20:14:18.010018: +2026-04-12 20:14:18.016203: Epoch 1862 +2026-04-12 20:14:18.019258: Current learning rate: 0.00569 +2026-04-12 20:16:00.480322: train_loss -0.4006 +2026-04-12 20:16:00.487543: val_loss -0.3279 +2026-04-12 20:16:00.489984: Pseudo dice [0.0, 0.0, 0.2806, 0.3703, 0.6354, 0.3765, 0.7687] +2026-04-12 20:16:00.492401: Epoch time: 102.47 s +2026-04-12 20:16:01.732674: +2026-04-12 20:16:01.734756: Epoch 1863 +2026-04-12 20:16:01.736708: Current learning rate: 0.00569 +2026-04-12 20:17:45.005002: train_loss -0.4113 +2026-04-12 20:17:45.012810: val_loss -0.3939 +2026-04-12 20:17:45.015465: Pseudo dice [0.0881, 0.0, 0.8159, 0.6434, 0.4666, 0.8773, 0.8571] +2026-04-12 20:17:45.018510: Epoch time: 103.28 s +2026-04-12 20:17:46.226483: +2026-04-12 20:17:46.228312: Epoch 1864 +2026-04-12 20:17:46.230374: Current learning rate: 0.00569 +2026-04-12 20:19:28.541019: train_loss -0.4126 +2026-04-12 20:19:28.547696: val_loss -0.3366 +2026-04-12 20:19:28.550914: Pseudo dice [0.0428, 0.0, 0.7838, 0.6803, 0.432, 0.5135, 0.286] +2026-04-12 20:19:28.558133: Epoch time: 102.32 s +2026-04-12 20:19:29.788303: +2026-04-12 20:19:29.790220: Epoch 1865 +2026-04-12 20:19:29.792460: Current learning rate: 0.00568 +2026-04-12 20:21:11.576534: train_loss -0.4252 +2026-04-12 20:21:11.584183: val_loss -0.3685 +2026-04-12 20:21:11.587135: Pseudo dice [0.6592, 0.0, 0.7193, 0.3717, 0.4072, 0.4715, 0.7854] +2026-04-12 20:21:11.589457: Epoch time: 101.79 s +2026-04-12 20:21:12.853147: +2026-04-12 20:21:12.855253: Epoch 1866 +2026-04-12 20:21:12.857191: Current learning rate: 0.00568 +2026-04-12 20:22:54.950483: train_loss -0.4159 +2026-04-12 20:22:54.956626: val_loss -0.3433 +2026-04-12 20:22:54.958843: Pseudo dice [0.5605, 0.0, 0.7514, 0.7233, 0.43, 0.694, 0.8447] +2026-04-12 20:22:54.961884: Epoch time: 102.1 s +2026-04-12 20:22:56.223654: +2026-04-12 20:22:56.225738: Epoch 1867 +2026-04-12 20:22:56.227809: Current learning rate: 0.00568 +2026-04-12 20:24:38.182755: train_loss -0.4137 +2026-04-12 20:24:38.189375: val_loss -0.3232 +2026-04-12 20:24:38.191699: Pseudo dice [0.5178, 0.0, 0.7215, 0.3172, 0.3236, 0.4109, 0.6165] +2026-04-12 20:24:38.194752: Epoch time: 101.96 s +2026-04-12 20:24:39.420570: +2026-04-12 20:24:39.422567: Epoch 1868 +2026-04-12 20:24:39.424718: Current learning rate: 0.00568 +2026-04-12 20:26:21.039704: train_loss -0.3924 +2026-04-12 20:26:21.048797: val_loss -0.3339 +2026-04-12 20:26:21.053680: Pseudo dice [0.2535, 0.0, 0.5286, 0.5268, 0.5289, 0.4612, 0.6367] +2026-04-12 20:26:21.058049: Epoch time: 101.62 s +2026-04-12 20:26:22.297321: +2026-04-12 20:26:22.299520: Epoch 1869 +2026-04-12 20:26:22.301787: Current learning rate: 0.00567 +2026-04-12 20:28:04.372422: train_loss -0.4032 +2026-04-12 20:28:04.380579: val_loss -0.3855 +2026-04-12 20:28:04.383177: Pseudo dice [0.5849, 0.0, 0.8253, 0.7067, 0.5411, 0.6386, 0.6821] +2026-04-12 20:28:04.386083: Epoch time: 102.08 s +2026-04-12 20:28:05.637830: +2026-04-12 20:28:05.640586: Epoch 1870 +2026-04-12 20:28:05.643314: Current learning rate: 0.00567 +2026-04-12 20:29:47.502957: train_loss -0.4145 +2026-04-12 20:29:47.509757: val_loss -0.3311 +2026-04-12 20:29:47.512233: Pseudo dice [0.4442, 0.0, 0.6943, 0.4272, 0.4502, 0.6236, 0.5157] +2026-04-12 20:29:47.514951: Epoch time: 101.87 s +2026-04-12 20:29:48.790922: +2026-04-12 20:29:48.792795: Epoch 1871 +2026-04-12 20:29:48.795019: Current learning rate: 0.00567 +2026-04-12 20:31:30.952995: train_loss -0.3903 +2026-04-12 20:31:30.959776: val_loss -0.3423 +2026-04-12 20:31:30.961585: Pseudo dice [0.0, 0.0, 0.5804, 0.3162, 0.4884, 0.4264, 0.8788] +2026-04-12 20:31:30.964080: Epoch time: 102.17 s +2026-04-12 20:31:32.214476: +2026-04-12 20:31:32.216553: Epoch 1872 +2026-04-12 20:31:32.219000: Current learning rate: 0.00567 +2026-04-12 20:33:14.181865: train_loss -0.4058 +2026-04-12 20:33:14.189481: val_loss -0.3692 +2026-04-12 20:33:14.195302: Pseudo dice [0.7187, 0.0, 0.7507, 0.7616, 0.5228, 0.4993, 0.8635] +2026-04-12 20:33:14.197945: Epoch time: 101.97 s +2026-04-12 20:33:15.476903: +2026-04-12 20:33:15.479230: Epoch 1873 +2026-04-12 20:33:15.481259: Current learning rate: 0.00566 +2026-04-12 20:34:57.710415: train_loss -0.3886 +2026-04-12 20:34:57.722687: val_loss -0.3563 +2026-04-12 20:34:57.726104: Pseudo dice [0.3091, 0.0, 0.8403, 0.599, 0.5095, 0.4102, 0.9387] +2026-04-12 20:34:57.729717: Epoch time: 102.24 s +2026-04-12 20:34:59.009721: +2026-04-12 20:34:59.011566: Epoch 1874 +2026-04-12 20:34:59.013689: Current learning rate: 0.00566 +2026-04-12 20:36:40.631157: train_loss -0.4016 +2026-04-12 20:36:40.639127: val_loss -0.3676 +2026-04-12 20:36:40.642016: Pseudo dice [0.4736, 0.0, 0.6966, 0.565, 0.4363, 0.5956, 0.8818] +2026-04-12 20:36:40.644633: Epoch time: 101.62 s +2026-04-12 20:36:41.898063: +2026-04-12 20:36:41.899831: Epoch 1875 +2026-04-12 20:36:41.901724: Current learning rate: 0.00566 +2026-04-12 20:38:23.700839: train_loss -0.4007 +2026-04-12 20:38:23.706966: val_loss -0.3102 +2026-04-12 20:38:23.709110: Pseudo dice [0.0, 0.0, 0.5868, 0.3517, 0.2898, 0.4147, 0.4363] +2026-04-12 20:38:23.711374: Epoch time: 101.81 s +2026-04-12 20:38:24.933420: +2026-04-12 20:38:24.935169: Epoch 1876 +2026-04-12 20:38:24.937050: Current learning rate: 0.00566 +2026-04-12 20:40:06.394323: train_loss -0.4009 +2026-04-12 20:40:06.400490: val_loss -0.3462 +2026-04-12 20:40:06.402496: Pseudo dice [0.0246, 0.0, 0.8542, 0.8244, 0.5293, 0.7098, 0.8917] +2026-04-12 20:40:06.405031: Epoch time: 101.46 s +2026-04-12 20:40:07.643508: +2026-04-12 20:40:07.645160: Epoch 1877 +2026-04-12 20:40:07.647026: Current learning rate: 0.00565 +2026-04-12 20:41:49.534453: train_loss -0.3932 +2026-04-12 20:41:49.540626: val_loss -0.3334 +2026-04-12 20:41:49.542754: Pseudo dice [0.1039, 0.0, 0.6927, 0.7811, 0.419, 0.771, 0.7284] +2026-04-12 20:41:49.545612: Epoch time: 101.89 s +2026-04-12 20:41:50.794871: +2026-04-12 20:41:50.797283: Epoch 1878 +2026-04-12 20:41:50.799478: Current learning rate: 0.00565 +2026-04-12 20:43:32.753201: train_loss -0.4029 +2026-04-12 20:43:32.760870: val_loss -0.3898 +2026-04-12 20:43:32.763538: Pseudo dice [0.4326, 0.0, 0.8121, 0.6087, 0.5056, 0.6603, 0.8475] +2026-04-12 20:43:32.766311: Epoch time: 101.96 s +2026-04-12 20:43:33.972361: +2026-04-12 20:43:33.974192: Epoch 1879 +2026-04-12 20:43:33.976080: Current learning rate: 0.00565 +2026-04-12 20:45:15.560428: train_loss -0.3887 +2026-04-12 20:45:15.567050: val_loss -0.3712 +2026-04-12 20:45:15.569142: Pseudo dice [0.6053, 0.0, 0.657, 0.0693, 0.7415, 0.647, 0.7376] +2026-04-12 20:45:15.571632: Epoch time: 101.59 s +2026-04-12 20:45:16.822042: +2026-04-12 20:45:16.823868: Epoch 1880 +2026-04-12 20:45:16.825840: Current learning rate: 0.00565 +2026-04-12 20:46:58.864997: train_loss -0.4215 +2026-04-12 20:46:58.873197: val_loss -0.3596 +2026-04-12 20:46:58.875486: Pseudo dice [0.1066, 0.0, 0.7981, 0.2132, 0.5419, 0.5634, 0.7772] +2026-04-12 20:46:58.877765: Epoch time: 102.05 s +2026-04-12 20:47:00.108022: +2026-04-12 20:47:00.110024: Epoch 1881 +2026-04-12 20:47:00.113639: Current learning rate: 0.00564 +2026-04-12 20:48:41.775480: train_loss -0.4092 +2026-04-12 20:48:41.782223: val_loss -0.3336 +2026-04-12 20:48:41.784514: Pseudo dice [0.6167, 0.0, 0.7558, 0.1354, 0.4866, 0.264, 0.5306] +2026-04-12 20:48:41.787374: Epoch time: 101.67 s +2026-04-12 20:48:43.028039: +2026-04-12 20:48:43.031289: Epoch 1882 +2026-04-12 20:48:43.034881: Current learning rate: 0.00564 +2026-04-12 20:50:24.921289: train_loss -0.3952 +2026-04-12 20:50:24.928351: val_loss -0.3729 +2026-04-12 20:50:24.930458: Pseudo dice [0.1159, 0.0, 0.5757, 0.6934, 0.5533, 0.775, 0.8986] +2026-04-12 20:50:24.932677: Epoch time: 101.9 s +2026-04-12 20:50:26.223805: +2026-04-12 20:50:26.225720: Epoch 1883 +2026-04-12 20:50:26.227754: Current learning rate: 0.00564 +2026-04-12 20:52:09.466794: train_loss -0.4015 +2026-04-12 20:52:09.477419: val_loss -0.3234 +2026-04-12 20:52:09.484712: Pseudo dice [0.0005, 0.0, 0.8054, 0.2644, 0.3387, 0.3982, 0.5898] +2026-04-12 20:52:09.487852: Epoch time: 103.25 s +2026-04-12 20:52:10.738618: +2026-04-12 20:52:10.741037: Epoch 1884 +2026-04-12 20:52:10.743378: Current learning rate: 0.00564 +2026-04-12 20:53:52.746572: train_loss -0.406 +2026-04-12 20:53:52.753424: val_loss -0.3625 +2026-04-12 20:53:52.756412: Pseudo dice [0.0951, 0.0, 0.6622, 0.6889, 0.4998, 0.5887, 0.7906] +2026-04-12 20:53:52.758906: Epoch time: 102.01 s +2026-04-12 20:53:54.015325: +2026-04-12 20:53:54.017413: Epoch 1885 +2026-04-12 20:53:54.019625: Current learning rate: 0.00564 +2026-04-12 20:55:36.057045: train_loss -0.4142 +2026-04-12 20:55:36.064821: val_loss -0.3473 +2026-04-12 20:55:36.067024: Pseudo dice [0.2765, 0.0, 0.6567, 0.8512, 0.6092, 0.8802, 0.916] +2026-04-12 20:55:36.070171: Epoch time: 102.04 s +2026-04-12 20:55:37.310319: +2026-04-12 20:55:37.313194: Epoch 1886 +2026-04-12 20:55:37.315961: Current learning rate: 0.00563 +2026-04-12 20:57:19.493432: train_loss -0.4222 +2026-04-12 20:57:19.500317: val_loss -0.3595 +2026-04-12 20:57:19.502056: Pseudo dice [0.5558, 0.0, 0.7535, 0.3288, 0.2769, 0.7829, 0.8552] +2026-04-12 20:57:19.504548: Epoch time: 102.19 s +2026-04-12 20:57:20.753424: +2026-04-12 20:57:20.756160: Epoch 1887 +2026-04-12 20:57:20.758810: Current learning rate: 0.00563 +2026-04-12 20:59:02.430523: train_loss -0.403 +2026-04-12 20:59:02.438181: val_loss -0.3567 +2026-04-12 20:59:02.441883: Pseudo dice [0.5839, 0.0, 0.6924, 0.4025, 0.4265, 0.6475, 0.7063] +2026-04-12 20:59:02.445020: Epoch time: 101.68 s +2026-04-12 20:59:03.683186: +2026-04-12 20:59:03.685371: Epoch 1888 +2026-04-12 20:59:03.687342: Current learning rate: 0.00563 +2026-04-12 21:00:45.572345: train_loss -0.3946 +2026-04-12 21:00:45.579503: val_loss -0.332 +2026-04-12 21:00:45.582139: Pseudo dice [0.2239, 0.0, 0.7914, 0.1723, 0.4251, 0.3896, 0.7989] +2026-04-12 21:00:45.584479: Epoch time: 101.89 s +2026-04-12 21:00:46.797636: +2026-04-12 21:00:46.800274: Epoch 1889 +2026-04-12 21:00:46.803920: Current learning rate: 0.00563 +2026-04-12 21:02:29.003396: train_loss -0.4123 +2026-04-12 21:02:29.010279: val_loss -0.3662 +2026-04-12 21:02:29.012299: Pseudo dice [0.047, 0.0, 0.7165, 0.4977, 0.4968, 0.4991, 0.8086] +2026-04-12 21:02:29.015034: Epoch time: 102.21 s +2026-04-12 21:02:30.238649: +2026-04-12 21:02:30.240619: Epoch 1890 +2026-04-12 21:02:30.242855: Current learning rate: 0.00562 +2026-04-12 21:04:11.771510: train_loss -0.41 +2026-04-12 21:04:11.777991: val_loss -0.3649 +2026-04-12 21:04:11.780325: Pseudo dice [0.094, 0.0, 0.8053, 0.8419, 0.4807, 0.5679, 0.9265] +2026-04-12 21:04:11.783712: Epoch time: 101.54 s +2026-04-12 21:04:13.054892: +2026-04-12 21:04:13.056668: Epoch 1891 +2026-04-12 21:04:13.059048: Current learning rate: 0.00562 +2026-04-12 21:05:55.224692: train_loss -0.4181 +2026-04-12 21:05:55.231534: val_loss -0.3594 +2026-04-12 21:05:55.233759: Pseudo dice [0.6714, 0.0, 0.7926, 0.7597, 0.741, 0.6261, 0.7428] +2026-04-12 21:05:55.236113: Epoch time: 102.17 s +2026-04-12 21:05:56.494300: +2026-04-12 21:05:56.496204: Epoch 1892 +2026-04-12 21:05:56.498215: Current learning rate: 0.00562 +2026-04-12 21:07:38.374247: train_loss -0.4163 +2026-04-12 21:07:38.387411: val_loss -0.3924 +2026-04-12 21:07:38.389761: Pseudo dice [0.6306, 0.0, 0.8829, 0.7294, 0.6565, 0.7971, 0.632] +2026-04-12 21:07:38.391909: Epoch time: 101.88 s +2026-04-12 21:07:39.643404: +2026-04-12 21:07:39.645388: Epoch 1893 +2026-04-12 21:07:39.647346: Current learning rate: 0.00562 +2026-04-12 21:09:21.512538: train_loss -0.3927 +2026-04-12 21:09:21.519131: val_loss -0.3555 +2026-04-12 21:09:21.521200: Pseudo dice [0.3727, 0.0, 0.8786, 0.2082, 0.4964, 0.7299, 0.9254] +2026-04-12 21:09:21.523568: Epoch time: 101.87 s +2026-04-12 21:09:22.751458: +2026-04-12 21:09:22.753924: Epoch 1894 +2026-04-12 21:09:22.756404: Current learning rate: 0.00561 +2026-04-12 21:11:04.745037: train_loss -0.371 +2026-04-12 21:11:04.760975: val_loss -0.3621 +2026-04-12 21:11:04.763287: Pseudo dice [0.004, 0.0, 0.6283, 0.528, 0.4211, 0.7691, 0.8819] +2026-04-12 21:11:04.766119: Epoch time: 102.0 s +2026-04-12 21:11:06.006515: +2026-04-12 21:11:06.008717: Epoch 1895 +2026-04-12 21:11:06.010916: Current learning rate: 0.00561 +2026-04-12 21:12:47.718208: train_loss -0.3807 +2026-04-12 21:12:47.725608: val_loss -0.3608 +2026-04-12 21:12:47.727618: Pseudo dice [0.6189, 0.0, 0.4818, 0.0711, 0.5146, 0.7577, 0.8904] +2026-04-12 21:12:47.733377: Epoch time: 101.71 s +2026-04-12 21:12:48.968340: +2026-04-12 21:12:48.970201: Epoch 1896 +2026-04-12 21:12:48.972023: Current learning rate: 0.00561 +2026-04-12 21:14:30.865201: train_loss -0.4074 +2026-04-12 21:14:30.872801: val_loss -0.3269 +2026-04-12 21:14:30.875238: Pseudo dice [0.2548, 0.0, 0.4719, 0.4402, 0.4918, 0.6178, 0.8338] +2026-04-12 21:14:30.878044: Epoch time: 101.9 s +2026-04-12 21:14:32.115672: +2026-04-12 21:14:32.117848: Epoch 1897 +2026-04-12 21:14:32.119518: Current learning rate: 0.00561 +2026-04-12 21:16:14.108924: train_loss -0.379 +2026-04-12 21:16:14.116394: val_loss -0.3674 +2026-04-12 21:16:14.119120: Pseudo dice [0.6493, 0.0, 0.8075, 0.7455, 0.4353, 0.4805, 0.9186] +2026-04-12 21:16:14.121740: Epoch time: 102.0 s +2026-04-12 21:16:15.419563: +2026-04-12 21:16:15.421624: Epoch 1898 +2026-04-12 21:16:15.423557: Current learning rate: 0.0056 +2026-04-12 21:17:57.469943: train_loss -0.4068 +2026-04-12 21:17:57.478287: val_loss -0.3364 +2026-04-12 21:17:57.481760: Pseudo dice [0.4176, 0.0, 0.6354, 0.2881, 0.5472, 0.7137, 0.4076] +2026-04-12 21:17:57.484228: Epoch time: 102.05 s +2026-04-12 21:17:58.712271: +2026-04-12 21:17:58.714109: Epoch 1899 +2026-04-12 21:17:58.716053: Current learning rate: 0.0056 +2026-04-12 21:19:41.217238: train_loss -0.4201 +2026-04-12 21:19:41.222936: val_loss -0.3515 +2026-04-12 21:19:41.225010: Pseudo dice [0.6338, 0.0, 0.5686, 0.8044, 0.6752, 0.8286, 0.86] +2026-04-12 21:19:41.227648: Epoch time: 102.51 s +2026-04-12 21:19:44.268896: +2026-04-12 21:19:44.274122: Epoch 1900 +2026-04-12 21:19:44.276595: Current learning rate: 0.0056 +2026-04-12 21:21:26.533717: train_loss -0.4115 +2026-04-12 21:21:26.544509: val_loss -0.3568 +2026-04-12 21:21:26.546335: Pseudo dice [0.6198, 0.0, 0.68, 0.0253, 0.4929, 0.5011, 0.6274] +2026-04-12 21:21:26.549054: Epoch time: 102.27 s +2026-04-12 21:21:27.760329: +2026-04-12 21:21:27.762156: Epoch 1901 +2026-04-12 21:21:27.764209: Current learning rate: 0.0056 +2026-04-12 21:23:10.028645: train_loss -0.4166 +2026-04-12 21:23:10.035982: val_loss -0.3208 +2026-04-12 21:23:10.038434: Pseudo dice [0.4737, 0.0, 0.6086, 0.113, 0.3859, 0.3956, 0.5431] +2026-04-12 21:23:10.041135: Epoch time: 102.27 s +2026-04-12 21:23:11.267047: +2026-04-12 21:23:11.269749: Epoch 1902 +2026-04-12 21:23:11.272051: Current learning rate: 0.00559 +2026-04-12 21:24:53.583302: train_loss -0.3788 +2026-04-12 21:24:53.592443: val_loss -0.3612 +2026-04-12 21:24:53.595819: Pseudo dice [0.4773, 0.0, 0.5586, 0.6574, 0.6078, 0.3705, 0.6496] +2026-04-12 21:24:53.598707: Epoch time: 102.32 s +2026-04-12 21:24:55.879186: +2026-04-12 21:24:55.884079: Epoch 1903 +2026-04-12 21:24:55.886331: Current learning rate: 0.00559 +2026-04-12 21:26:38.563314: train_loss -0.3828 +2026-04-12 21:26:38.569611: val_loss -0.3312 +2026-04-12 21:26:38.572558: Pseudo dice [0.3078, 0.0, 0.5519, 0.073, 0.345, 0.5742, 0.5585] +2026-04-12 21:26:38.575067: Epoch time: 102.69 s +2026-04-12 21:26:39.793045: +2026-04-12 21:26:39.795073: Epoch 1904 +2026-04-12 21:26:39.797772: Current learning rate: 0.00559 +2026-04-12 21:28:21.640037: train_loss -0.4097 +2026-04-12 21:28:21.649253: val_loss -0.3678 +2026-04-12 21:28:21.652481: Pseudo dice [0.4235, 0.0, 0.6746, 0.1412, 0.5173, 0.519, 0.8463] +2026-04-12 21:28:21.655647: Epoch time: 101.85 s +2026-04-12 21:28:22.895127: +2026-04-12 21:28:22.897437: Epoch 1905 +2026-04-12 21:28:22.899706: Current learning rate: 0.00559 +2026-04-12 21:30:05.991102: train_loss -0.3958 +2026-04-12 21:30:05.998923: val_loss -0.334 +2026-04-12 21:30:06.000868: Pseudo dice [0.3384, 0.0, 0.6506, 0.3672, 0.3511, 0.4253, 0.7624] +2026-04-12 21:30:06.003544: Epoch time: 103.1 s +2026-04-12 21:30:07.314152: +2026-04-12 21:30:07.327395: Epoch 1906 +2026-04-12 21:30:07.335549: Current learning rate: 0.00559 +2026-04-12 21:31:49.345903: train_loss -0.3757 +2026-04-12 21:31:49.357013: val_loss -0.2751 +2026-04-12 21:31:49.365799: Pseudo dice [0.1865, 0.0, 0.1127, 0.2744, 0.4116, 0.2611, 0.1675] +2026-04-12 21:31:49.373007: Epoch time: 102.04 s +2026-04-12 21:31:50.614933: +2026-04-12 21:31:50.617145: Epoch 1907 +2026-04-12 21:31:50.619329: Current learning rate: 0.00558 +2026-04-12 21:33:32.822117: train_loss -0.3722 +2026-04-12 21:33:32.829333: val_loss -0.3496 +2026-04-12 21:33:32.831807: Pseudo dice [0.2932, 0.0, 0.7417, 0.4732, 0.3938, 0.6959, 0.7415] +2026-04-12 21:33:32.833831: Epoch time: 102.21 s +2026-04-12 21:33:34.090089: +2026-04-12 21:33:34.092177: Epoch 1908 +2026-04-12 21:33:34.094213: Current learning rate: 0.00558 +2026-04-12 21:35:16.362823: train_loss -0.4098 +2026-04-12 21:35:16.369933: val_loss -0.3761 +2026-04-12 21:35:16.372288: Pseudo dice [0.6574, 0.0, 0.7339, 0.388, 0.469, 0.7905, 0.8359] +2026-04-12 21:35:16.374950: Epoch time: 102.28 s +2026-04-12 21:35:17.608231: +2026-04-12 21:35:17.610404: Epoch 1909 +2026-04-12 21:35:17.612514: Current learning rate: 0.00558 +2026-04-12 21:36:59.801454: train_loss -0.4231 +2026-04-12 21:36:59.810396: val_loss -0.4007 +2026-04-12 21:36:59.812903: Pseudo dice [0.6731, 0.0, 0.8166, 0.7341, 0.3909, 0.7559, 0.7687] +2026-04-12 21:36:59.815893: Epoch time: 102.2 s +2026-04-12 21:37:01.074101: +2026-04-12 21:37:01.076017: Epoch 1910 +2026-04-12 21:37:01.078707: Current learning rate: 0.00558 +2026-04-12 21:38:43.268852: train_loss -0.4011 +2026-04-12 21:38:43.276111: val_loss -0.3324 +2026-04-12 21:38:43.279344: Pseudo dice [0.0145, 0.0, 0.8081, 0.5181, 0.5133, 0.6519, 0.2934] +2026-04-12 21:38:43.282232: Epoch time: 102.2 s +2026-04-12 21:38:44.545406: +2026-04-12 21:38:44.547519: Epoch 1911 +2026-04-12 21:38:44.549878: Current learning rate: 0.00557 +2026-04-12 21:40:26.389513: train_loss -0.3922 +2026-04-12 21:40:26.395154: val_loss -0.386 +2026-04-12 21:40:26.396974: Pseudo dice [0.1198, 0.0, 0.8068, 0.7487, 0.4614, 0.7046, 0.7888] +2026-04-12 21:40:26.399195: Epoch time: 101.85 s +2026-04-12 21:40:27.648267: +2026-04-12 21:40:27.650111: Epoch 1912 +2026-04-12 21:40:27.652210: Current learning rate: 0.00557 +2026-04-12 21:42:09.550677: train_loss -0.3851 +2026-04-12 21:42:09.557988: val_loss -0.346 +2026-04-12 21:42:09.560912: Pseudo dice [0.5341, 0.0, 0.7343, 0.4474, 0.4872, 0.7244, 0.7755] +2026-04-12 21:42:09.563372: Epoch time: 101.91 s +2026-04-12 21:42:10.812691: +2026-04-12 21:42:10.814815: Epoch 1913 +2026-04-12 21:42:10.816914: Current learning rate: 0.00557 +2026-04-12 21:43:52.502442: train_loss -0.3899 +2026-04-12 21:43:52.510614: val_loss -0.3337 +2026-04-12 21:43:52.512938: Pseudo dice [0.5068, 0.0, 0.7228, 0.4903, 0.4228, 0.7682, 0.4162] +2026-04-12 21:43:52.515091: Epoch time: 101.69 s +2026-04-12 21:43:53.761577: +2026-04-12 21:43:53.763988: Epoch 1914 +2026-04-12 21:43:53.766179: Current learning rate: 0.00557 +2026-04-12 21:45:35.719563: train_loss -0.4003 +2026-04-12 21:45:35.728216: val_loss -0.3559 +2026-04-12 21:45:35.730749: Pseudo dice [0.2406, 0.0, 0.7153, 0.1325, 0.42, 0.5289, 0.7345] +2026-04-12 21:45:35.734443: Epoch time: 101.96 s +2026-04-12 21:45:36.966577: +2026-04-12 21:45:36.969049: Epoch 1915 +2026-04-12 21:45:36.971772: Current learning rate: 0.00556 +2026-04-12 21:47:19.571733: train_loss -0.4162 +2026-04-12 21:47:19.580070: val_loss -0.355 +2026-04-12 21:47:19.582195: Pseudo dice [0.6987, 0.0, 0.6538, 0.6591, 0.441, 0.548, 0.4033] +2026-04-12 21:47:19.584806: Epoch time: 102.61 s +2026-04-12 21:47:20.895746: +2026-04-12 21:47:20.897490: Epoch 1916 +2026-04-12 21:47:20.899364: Current learning rate: 0.00556 +2026-04-12 21:49:02.891465: train_loss -0.3973 +2026-04-12 21:49:02.902397: val_loss -0.3313 +2026-04-12 21:49:02.904708: Pseudo dice [0.297, 0.0, 0.7077, 0.6415, 0.4567, 0.5921, 0.7619] +2026-04-12 21:49:02.908495: Epoch time: 102.0 s +2026-04-12 21:49:04.162521: +2026-04-12 21:49:04.165563: Epoch 1917 +2026-04-12 21:49:04.168207: Current learning rate: 0.00556 +2026-04-12 21:50:45.942870: train_loss -0.3917 +2026-04-12 21:50:45.951936: val_loss -0.3049 +2026-04-12 21:50:45.954106: Pseudo dice [0.0, 0.0, 0.812, 0.2188, 0.5405, 0.8468, 0.1514] +2026-04-12 21:50:45.956113: Epoch time: 101.78 s +2026-04-12 21:50:47.257122: +2026-04-12 21:50:47.259279: Epoch 1918 +2026-04-12 21:50:47.262461: Current learning rate: 0.00556 +2026-04-12 21:52:30.002082: train_loss -0.4133 +2026-04-12 21:52:30.009758: val_loss -0.369 +2026-04-12 21:52:30.012257: Pseudo dice [0.0, 0.0, 0.7585, 0.6877, 0.4897, 0.6306, 0.8062] +2026-04-12 21:52:30.016104: Epoch time: 102.75 s +2026-04-12 21:52:31.276320: +2026-04-12 21:52:31.281364: Epoch 1919 +2026-04-12 21:52:31.283442: Current learning rate: 0.00555 +2026-04-12 21:54:13.422935: train_loss -0.4121 +2026-04-12 21:54:13.430167: val_loss -0.3479 +2026-04-12 21:54:13.432325: Pseudo dice [0.0, 0.0, 0.6707, 0.2449, 0.4956, 0.6216, 0.6657] +2026-04-12 21:54:13.434625: Epoch time: 102.15 s +2026-04-12 21:54:14.689857: +2026-04-12 21:54:14.691825: Epoch 1920 +2026-04-12 21:54:14.693996: Current learning rate: 0.00555 +2026-04-12 21:55:57.070269: train_loss -0.4158 +2026-04-12 21:55:57.077736: val_loss -0.3622 +2026-04-12 21:55:57.079867: Pseudo dice [0.0, 0.0, 0.698, 0.3787, 0.3854, 0.582, 0.6813] +2026-04-12 21:55:57.082275: Epoch time: 102.38 s +2026-04-12 21:55:58.402924: +2026-04-12 21:55:58.405091: Epoch 1921 +2026-04-12 21:55:58.407405: Current learning rate: 0.00555 +2026-04-12 21:57:40.380689: train_loss -0.405 +2026-04-12 21:57:40.387687: val_loss -0.3414 +2026-04-12 21:57:40.390531: Pseudo dice [0.0, 0.0, 0.7843, 0.779, 0.5913, 0.3631, 0.5786] +2026-04-12 21:57:40.393023: Epoch time: 101.98 s +2026-04-12 21:57:41.655166: +2026-04-12 21:57:41.660221: Epoch 1922 +2026-04-12 21:57:41.662462: Current learning rate: 0.00555 +2026-04-12 21:59:23.610341: train_loss -0.4208 +2026-04-12 21:59:23.617227: val_loss -0.3682 +2026-04-12 21:59:23.619469: Pseudo dice [0.0, 0.0, 0.755, 0.7409, 0.4444, 0.6894, 0.8191] +2026-04-12 21:59:23.621783: Epoch time: 101.96 s +2026-04-12 21:59:25.982867: +2026-04-12 21:59:25.984682: Epoch 1923 +2026-04-12 21:59:25.986635: Current learning rate: 0.00554 +2026-04-12 22:01:08.209437: train_loss -0.4289 +2026-04-12 22:01:08.216192: val_loss -0.3755 +2026-04-12 22:01:08.218658: Pseudo dice [0.0002, 0.0, 0.8071, 0.7787, 0.5143, 0.4064, 0.7074] +2026-04-12 22:01:08.221076: Epoch time: 102.23 s +2026-04-12 22:01:09.552687: +2026-04-12 22:01:09.554776: Epoch 1924 +2026-04-12 22:01:09.556859: Current learning rate: 0.00554 +2026-04-12 22:02:51.174201: train_loss -0.3991 +2026-04-12 22:02:51.181645: val_loss -0.3664 +2026-04-12 22:02:51.183860: Pseudo dice [0.5277, 0.0, 0.5798, 0.7806, 0.4195, 0.1433, 0.9349] +2026-04-12 22:02:51.186209: Epoch time: 101.62 s +2026-04-12 22:02:52.449191: +2026-04-12 22:02:52.450953: Epoch 1925 +2026-04-12 22:02:52.452770: Current learning rate: 0.00554 +2026-04-12 22:04:34.626882: train_loss -0.3895 +2026-04-12 22:04:34.633922: val_loss -0.3688 +2026-04-12 22:04:34.637867: Pseudo dice [0.7063, 0.0, 0.6147, 0.329, 0.4922, 0.4653, 0.2542] +2026-04-12 22:04:34.640500: Epoch time: 102.18 s +2026-04-12 22:04:35.885139: +2026-04-12 22:04:35.887877: Epoch 1926 +2026-04-12 22:04:35.890604: Current learning rate: 0.00554 +2026-04-12 22:06:17.584237: train_loss -0.4218 +2026-04-12 22:06:17.592227: val_loss -0.3525 +2026-04-12 22:06:17.595309: Pseudo dice [0.4888, 0.0, 0.856, 0.497, 0.4882, 0.7131, 0.4117] +2026-04-12 22:06:17.599006: Epoch time: 101.7 s +2026-04-12 22:06:18.861392: +2026-04-12 22:06:18.863692: Epoch 1927 +2026-04-12 22:06:18.865953: Current learning rate: 0.00553 +2026-04-12 22:08:01.274999: train_loss -0.4374 +2026-04-12 22:08:01.281659: val_loss -0.3721 +2026-04-12 22:08:01.286470: Pseudo dice [0.6697, 0.0, 0.667, 0.3555, 0.4932, 0.5031, 0.853] +2026-04-12 22:08:01.289254: Epoch time: 102.42 s +2026-04-12 22:08:02.551750: +2026-04-12 22:08:02.553459: Epoch 1928 +2026-04-12 22:08:02.555503: Current learning rate: 0.00553 +2026-04-12 22:09:44.603315: train_loss -0.4166 +2026-04-12 22:09:44.611472: val_loss -0.349 +2026-04-12 22:09:44.614405: Pseudo dice [0.0, 0.0, 0.8364, 0.6589, 0.5417, 0.6041, 0.8743] +2026-04-12 22:09:44.617340: Epoch time: 102.05 s +2026-04-12 22:09:45.854992: +2026-04-12 22:09:45.857199: Epoch 1929 +2026-04-12 22:09:45.862013: Current learning rate: 0.00553 +2026-04-12 22:11:27.432855: train_loss -0.3722 +2026-04-12 22:11:27.441653: val_loss -0.33 +2026-04-12 22:11:27.443985: Pseudo dice [0.0455, 0.0, 0.6968, 0.5898, 0.4493, 0.5227, 0.6534] +2026-04-12 22:11:27.447953: Epoch time: 101.58 s +2026-04-12 22:11:28.706240: +2026-04-12 22:11:28.708184: Epoch 1930 +2026-04-12 22:11:28.710101: Current learning rate: 0.00553 +2026-04-12 22:13:11.335271: train_loss -0.3926 +2026-04-12 22:13:11.342118: val_loss -0.3433 +2026-04-12 22:13:11.344274: Pseudo dice [0.1654, 0.0, 0.831, 0.5422, 0.3853, 0.544, 0.5736] +2026-04-12 22:13:11.346677: Epoch time: 102.63 s +2026-04-12 22:13:12.609785: +2026-04-12 22:13:12.611898: Epoch 1931 +2026-04-12 22:13:12.613786: Current learning rate: 0.00552 +2026-04-12 22:14:54.620448: train_loss -0.4023 +2026-04-12 22:14:54.628154: val_loss -0.3295 +2026-04-12 22:14:54.630547: Pseudo dice [0.0992, 0.0, 0.4579, 0.5421, 0.2978, 0.822, 0.8271] +2026-04-12 22:14:54.633169: Epoch time: 102.01 s +2026-04-12 22:14:55.904099: +2026-04-12 22:14:55.905895: Epoch 1932 +2026-04-12 22:14:55.907850: Current learning rate: 0.00552 +2026-04-12 22:16:38.450039: train_loss -0.3963 +2026-04-12 22:16:38.458362: val_loss -0.3886 +2026-04-12 22:16:38.461753: Pseudo dice [0.6064, 0.0, 0.8666, 0.5659, 0.4855, 0.7359, 0.8836] +2026-04-12 22:16:38.464361: Epoch time: 102.55 s +2026-04-12 22:16:39.717539: +2026-04-12 22:16:39.722437: Epoch 1933 +2026-04-12 22:16:39.724573: Current learning rate: 0.00552 +2026-04-12 22:18:21.563770: train_loss -0.4033 +2026-04-12 22:18:21.572627: val_loss -0.357 +2026-04-12 22:18:21.575414: Pseudo dice [0.7089, 0.0, 0.677, 0.6049, 0.2583, 0.7827, 0.8744] +2026-04-12 22:18:21.579266: Epoch time: 101.85 s +2026-04-12 22:18:22.819096: +2026-04-12 22:18:22.822097: Epoch 1934 +2026-04-12 22:18:22.824465: Current learning rate: 0.00552 +2026-04-12 22:20:05.053171: train_loss -0.4018 +2026-04-12 22:20:05.060232: val_loss -0.3252 +2026-04-12 22:20:05.062915: Pseudo dice [0.002, 0.0, 0.8176, 0.4948, 0.391, 0.4245, 0.8485] +2026-04-12 22:20:05.066100: Epoch time: 102.24 s +2026-04-12 22:20:06.339336: +2026-04-12 22:20:06.342109: Epoch 1935 +2026-04-12 22:20:06.347070: Current learning rate: 0.00552 +2026-04-12 22:21:48.250968: train_loss -0.4065 +2026-04-12 22:21:48.258742: val_loss -0.3365 +2026-04-12 22:21:48.260927: Pseudo dice [0.4295, 0.0, 0.7784, 0.6813, 0.565, 0.7453, 0.4465] +2026-04-12 22:21:48.263094: Epoch time: 101.91 s +2026-04-12 22:21:49.554063: +2026-04-12 22:21:49.557346: Epoch 1936 +2026-04-12 22:21:49.559548: Current learning rate: 0.00551 +2026-04-12 22:23:31.583257: train_loss -0.4041 +2026-04-12 22:23:31.610241: val_loss -0.3767 +2026-04-12 22:23:31.612622: Pseudo dice [0.6171, 0.0, 0.7961, 0.1858, 0.42, 0.7728, 0.7957] +2026-04-12 22:23:31.615735: Epoch time: 102.03 s +2026-04-12 22:23:32.879150: +2026-04-12 22:23:32.881345: Epoch 1937 +2026-04-12 22:23:32.884129: Current learning rate: 0.00551 +2026-04-12 22:25:14.888573: train_loss -0.3974 +2026-04-12 22:25:14.894947: val_loss -0.315 +2026-04-12 22:25:14.897187: Pseudo dice [0.1427, 0.0, 0.6231, 0.6314, 0.535, 0.7534, 0.641] +2026-04-12 22:25:14.899755: Epoch time: 102.01 s +2026-04-12 22:25:16.157979: +2026-04-12 22:25:16.160247: Epoch 1938 +2026-04-12 22:25:16.162684: Current learning rate: 0.00551 +2026-04-12 22:26:58.398126: train_loss -0.3791 +2026-04-12 22:26:58.404211: val_loss -0.3251 +2026-04-12 22:26:58.406404: Pseudo dice [0.0, 0.0, 0.7296, 0.3191, 0.6264, 0.5979, 0.4709] +2026-04-12 22:26:58.409051: Epoch time: 102.24 s +2026-04-12 22:26:59.651134: +2026-04-12 22:26:59.653217: Epoch 1939 +2026-04-12 22:26:59.655364: Current learning rate: 0.00551 +2026-04-12 22:28:41.880968: train_loss -0.3996 +2026-04-12 22:28:41.888798: val_loss -0.3608 +2026-04-12 22:28:41.893138: Pseudo dice [0.1006, 0.0, 0.7078, 0.8019, 0.5718, 0.6928, 0.8627] +2026-04-12 22:28:41.896663: Epoch time: 102.23 s +2026-04-12 22:28:43.131925: +2026-04-12 22:28:43.133952: Epoch 1940 +2026-04-12 22:28:43.136133: Current learning rate: 0.0055 +2026-04-12 22:30:25.432597: train_loss -0.4061 +2026-04-12 22:30:25.441947: val_loss -0.3627 +2026-04-12 22:30:25.446553: Pseudo dice [0.6075, 0.0, 0.81, 0.6951, 0.0, 0.7155, 0.8758] +2026-04-12 22:30:25.450515: Epoch time: 102.3 s +2026-04-12 22:30:26.697388: +2026-04-12 22:30:26.700052: Epoch 1941 +2026-04-12 22:30:26.703423: Current learning rate: 0.0055 +2026-04-12 22:32:08.833212: train_loss -0.4054 +2026-04-12 22:32:08.841835: val_loss -0.3422 +2026-04-12 22:32:08.844311: Pseudo dice [0.3184, 0.0, 0.695, 0.4649, 0.6461, 0.7293, 0.8941] +2026-04-12 22:32:08.847907: Epoch time: 102.14 s +2026-04-12 22:32:10.105303: +2026-04-12 22:32:10.107996: Epoch 1942 +2026-04-12 22:32:10.110063: Current learning rate: 0.0055 +2026-04-12 22:33:53.238290: train_loss -0.3901 +2026-04-12 22:33:53.245692: val_loss -0.3279 +2026-04-12 22:33:53.247998: Pseudo dice [0.1221, 0.0, 0.5922, 0.3437, 0.4782, 0.6967, 0.8416] +2026-04-12 22:33:53.250933: Epoch time: 103.14 s +2026-04-12 22:33:54.504791: +2026-04-12 22:33:54.506704: Epoch 1943 +2026-04-12 22:33:54.508699: Current learning rate: 0.0055 +2026-04-12 22:35:36.136873: train_loss -0.3981 +2026-04-12 22:35:36.144471: val_loss -0.3483 +2026-04-12 22:35:36.146531: Pseudo dice [0.5948, 0.0, 0.7643, 0.3926, 0.5351, 0.6215, 0.8505] +2026-04-12 22:35:36.148507: Epoch time: 101.64 s +2026-04-12 22:35:37.398803: +2026-04-12 22:35:37.400570: Epoch 1944 +2026-04-12 22:35:37.403262: Current learning rate: 0.00549 +2026-04-12 22:37:19.577487: train_loss -0.4101 +2026-04-12 22:37:19.584546: val_loss -0.3561 +2026-04-12 22:37:19.586347: Pseudo dice [0.2755, 0.0, 0.73, 0.6034, 0.3976, 0.2544, 0.8319] +2026-04-12 22:37:19.589178: Epoch time: 102.18 s +2026-04-12 22:37:20.830299: +2026-04-12 22:37:20.832776: Epoch 1945 +2026-04-12 22:37:20.835987: Current learning rate: 0.00549 +2026-04-12 22:39:02.909246: train_loss -0.4122 +2026-04-12 22:39:02.915649: val_loss -0.3666 +2026-04-12 22:39:02.917794: Pseudo dice [0.2773, 0.0, 0.8163, 0.7148, 0.5719, 0.6628, 0.8846] +2026-04-12 22:39:02.920788: Epoch time: 102.08 s +2026-04-12 22:39:04.241594: +2026-04-12 22:39:04.244324: Epoch 1946 +2026-04-12 22:39:04.246460: Current learning rate: 0.00549 +2026-04-12 22:40:46.245991: train_loss -0.4067 +2026-04-12 22:40:46.252522: val_loss -0.367 +2026-04-12 22:40:46.254937: Pseudo dice [0.4781, 0.0, 0.8316, 0.402, 0.5142, 0.4611, 0.886] +2026-04-12 22:40:46.257633: Epoch time: 102.01 s +2026-04-12 22:40:47.522284: +2026-04-12 22:40:47.523957: Epoch 1947 +2026-04-12 22:40:47.525759: Current learning rate: 0.00549 +2026-04-12 22:42:29.666792: train_loss -0.4121 +2026-04-12 22:42:29.673684: val_loss -0.3536 +2026-04-12 22:42:29.675644: Pseudo dice [0.4006, 0.0, 0.7611, 0.4022, 0.3417, 0.5192, 0.5917] +2026-04-12 22:42:29.678061: Epoch time: 102.15 s +2026-04-12 22:42:30.936271: +2026-04-12 22:42:30.945236: Epoch 1948 +2026-04-12 22:42:30.947066: Current learning rate: 0.00548 +2026-04-12 22:44:13.008270: train_loss -0.3966 +2026-04-12 22:44:13.015074: val_loss -0.3596 +2026-04-12 22:44:13.018280: Pseudo dice [0.6412, 0.0, 0.7665, 0.6923, 0.5153, 0.5125, 0.8386] +2026-04-12 22:44:13.020884: Epoch time: 102.08 s +2026-04-12 22:44:14.269756: +2026-04-12 22:44:14.271816: Epoch 1949 +2026-04-12 22:44:14.274261: Current learning rate: 0.00548 +2026-04-12 22:45:56.004419: train_loss -0.4012 +2026-04-12 22:45:56.011297: val_loss -0.3362 +2026-04-12 22:45:56.013566: Pseudo dice [0.2277, 0.0, 0.7707, 0.5373, 0.3279, 0.7948, 0.7647] +2026-04-12 22:45:56.015833: Epoch time: 101.74 s +2026-04-12 22:45:59.082504: +2026-04-12 22:45:59.084321: Epoch 1950 +2026-04-12 22:45:59.086374: Current learning rate: 0.00548 +2026-04-12 22:47:41.839892: train_loss -0.3824 +2026-04-12 22:47:41.849074: val_loss -0.3676 +2026-04-12 22:47:41.853613: Pseudo dice [0.509, 0.0, 0.7598, 0.628, 0.4222, 0.697, 0.729] +2026-04-12 22:47:41.855952: Epoch time: 102.76 s +2026-04-12 22:47:43.119929: +2026-04-12 22:47:43.121816: Epoch 1951 +2026-04-12 22:47:43.123807: Current learning rate: 0.00548 +2026-04-12 22:49:24.890389: train_loss -0.4065 +2026-04-12 22:49:24.897391: val_loss -0.3647 +2026-04-12 22:49:24.900155: Pseudo dice [0.351, 0.0, 0.7813, 0.6533, 0.4701, 0.6692, 0.8751] +2026-04-12 22:49:24.903429: Epoch time: 101.77 s +2026-04-12 22:49:26.131598: +2026-04-12 22:49:26.133604: Epoch 1952 +2026-04-12 22:49:26.135922: Current learning rate: 0.00547 +2026-04-12 22:51:08.202385: train_loss -0.4006 +2026-04-12 22:51:08.209941: val_loss -0.3667 +2026-04-12 22:51:08.212159: Pseudo dice [0.5292, 0.0, 0.8498, 0.7027, 0.4884, 0.8535, 0.736] +2026-04-12 22:51:08.214679: Epoch time: 102.07 s +2026-04-12 22:51:09.477714: +2026-04-12 22:51:09.480251: Epoch 1953 +2026-04-12 22:51:09.482494: Current learning rate: 0.00547 +2026-04-12 22:52:51.100305: train_loss -0.4079 +2026-04-12 22:52:51.107463: val_loss -0.3535 +2026-04-12 22:52:51.109719: Pseudo dice [0.0583, 0.0, 0.84, 0.7665, 0.4786, 0.7927, 0.4505] +2026-04-12 22:52:51.111806: Epoch time: 101.63 s +2026-04-12 22:52:52.384577: +2026-04-12 22:52:52.386668: Epoch 1954 +2026-04-12 22:52:52.389511: Current learning rate: 0.00547 +2026-04-12 22:54:34.228012: train_loss -0.3952 +2026-04-12 22:54:34.234744: val_loss -0.3397 +2026-04-12 22:54:34.236715: Pseudo dice [0.039, 0.0, 0.4773, 0.1412, 0.5369, 0.5871, 0.9235] +2026-04-12 22:54:34.239040: Epoch time: 101.85 s +2026-04-12 22:54:35.487602: +2026-04-12 22:54:35.489549: Epoch 1955 +2026-04-12 22:54:35.508293: Current learning rate: 0.00547 +2026-04-12 22:56:17.542996: train_loss -0.4067 +2026-04-12 22:56:17.549368: val_loss -0.3583 +2026-04-12 22:56:17.552288: Pseudo dice [0.2825, 0.0, 0.8285, 0.501, 0.4926, 0.6147, 0.3708] +2026-04-12 22:56:17.555050: Epoch time: 102.06 s +2026-04-12 22:56:18.860997: +2026-04-12 22:56:18.863002: Epoch 1956 +2026-04-12 22:56:18.865136: Current learning rate: 0.00546 +2026-04-12 22:58:00.496042: train_loss -0.4196 +2026-04-12 22:58:00.503772: val_loss -0.3598 +2026-04-12 22:58:00.506349: Pseudo dice [0.6228, 0.0, 0.8006, 0.2121, 0.493, 0.2926, 0.8099] +2026-04-12 22:58:00.508867: Epoch time: 101.64 s +2026-04-12 22:58:01.779177: +2026-04-12 22:58:01.781121: Epoch 1957 +2026-04-12 22:58:01.783404: Current learning rate: 0.00546 +2026-04-12 22:59:44.041077: train_loss -0.3732 +2026-04-12 22:59:44.047587: val_loss -0.3317 +2026-04-12 22:59:44.049829: Pseudo dice [0.0503, 0.0, 0.7688, 0.2632, 0.4184, 0.4653, 0.8157] +2026-04-12 22:59:44.053545: Epoch time: 102.27 s +2026-04-12 22:59:45.333240: +2026-04-12 22:59:45.335717: Epoch 1958 +2026-04-12 22:59:45.337993: Current learning rate: 0.00546 +2026-04-12 23:01:27.448998: train_loss -0.4001 +2026-04-12 23:01:27.458606: val_loss -0.3796 +2026-04-12 23:01:27.461622: Pseudo dice [0.683, 0.0, 0.7148, 0.2957, 0.4421, 0.377, 0.6313] +2026-04-12 23:01:27.466795: Epoch time: 102.12 s +2026-04-12 23:01:28.743745: +2026-04-12 23:01:28.745565: Epoch 1959 +2026-04-12 23:01:28.748038: Current learning rate: 0.00546 +2026-04-12 23:03:10.540944: train_loss -0.4178 +2026-04-12 23:03:10.547790: val_loss -0.3794 +2026-04-12 23:03:10.550046: Pseudo dice [0.2469, 0.0, 0.8532, 0.7337, 0.5103, 0.8606, 0.8481] +2026-04-12 23:03:10.552339: Epoch time: 101.8 s +2026-04-12 23:03:11.793955: +2026-04-12 23:03:11.796603: Epoch 1960 +2026-04-12 23:03:11.798783: Current learning rate: 0.00546 +2026-04-12 23:04:54.138653: train_loss -0.4176 +2026-04-12 23:04:54.145292: val_loss -0.3796 +2026-04-12 23:04:54.147760: Pseudo dice [0.6747, 0.0, 0.8071, 0.8088, 0.6207, 0.5184, 0.9139] +2026-04-12 23:04:54.150375: Epoch time: 102.35 s +2026-04-12 23:04:55.443369: +2026-04-12 23:04:55.445539: Epoch 1961 +2026-04-12 23:04:55.447780: Current learning rate: 0.00545 +2026-04-12 23:06:39.251906: train_loss -0.3974 +2026-04-12 23:06:39.258304: val_loss -0.3543 +2026-04-12 23:06:39.260329: Pseudo dice [0.3814, 0.0, 0.6675, 0.8493, 0.4143, 0.8274, 0.895] +2026-04-12 23:06:39.263861: Epoch time: 103.81 s +2026-04-12 23:06:40.594250: +2026-04-12 23:06:40.596192: Epoch 1962 +2026-04-12 23:06:40.598373: Current learning rate: 0.00545 +2026-04-12 23:08:22.830722: train_loss -0.4002 +2026-04-12 23:08:22.836746: val_loss -0.3701 +2026-04-12 23:08:22.839057: Pseudo dice [0.7524, 0.0, 0.6506, 0.3643, 0.5538, 0.4607, 0.7452] +2026-04-12 23:08:22.841342: Epoch time: 102.24 s +2026-04-12 23:08:24.180111: +2026-04-12 23:08:24.182621: Epoch 1963 +2026-04-12 23:08:24.185539: Current learning rate: 0.00545 +2026-04-12 23:10:05.936052: train_loss -0.4114 +2026-04-12 23:10:05.943564: val_loss -0.3708 +2026-04-12 23:10:05.946305: Pseudo dice [0.3557, 0.0, 0.8482, 0.6459, 0.4249, 0.7047, 0.8981] +2026-04-12 23:10:05.949024: Epoch time: 101.76 s +2026-04-12 23:10:07.233087: +2026-04-12 23:10:07.234896: Epoch 1964 +2026-04-12 23:10:07.237751: Current learning rate: 0.00545 +2026-04-12 23:11:49.646862: train_loss -0.428 +2026-04-12 23:11:49.657251: val_loss -0.338 +2026-04-12 23:11:49.660102: Pseudo dice [0.6881, 0.0, 0.693, 0.5217, 0.5189, 0.4533, 0.7611] +2026-04-12 23:11:49.663298: Epoch time: 102.42 s +2026-04-12 23:11:50.949390: +2026-04-12 23:11:50.951604: Epoch 1965 +2026-04-12 23:11:50.954180: Current learning rate: 0.00544 +2026-04-12 23:13:33.059746: train_loss -0.4108 +2026-04-12 23:13:33.066275: val_loss -0.3705 +2026-04-12 23:13:33.068508: Pseudo dice [0.64, 0.0, 0.7767, 0.6937, 0.5144, 0.6852, 0.9046] +2026-04-12 23:13:33.072810: Epoch time: 102.11 s +2026-04-12 23:13:34.340408: +2026-04-12 23:13:34.342766: Epoch 1966 +2026-04-12 23:13:34.345364: Current learning rate: 0.00544 +2026-04-12 23:15:16.593317: train_loss -0.4201 +2026-04-12 23:15:16.599956: val_loss -0.3571 +2026-04-12 23:15:16.602153: Pseudo dice [0.2573, 0.0, 0.786, 0.7732, 0.2291, 0.5853, 0.7919] +2026-04-12 23:15:16.604438: Epoch time: 102.26 s +2026-04-12 23:15:17.850979: +2026-04-12 23:15:17.852888: Epoch 1967 +2026-04-12 23:15:17.854854: Current learning rate: 0.00544 +2026-04-12 23:16:59.832762: train_loss -0.4056 +2026-04-12 23:16:59.839999: val_loss -0.3896 +2026-04-12 23:16:59.842497: Pseudo dice [0.6189, 0.0, 0.8729, 0.7177, 0.4027, 0.5975, 0.9348] +2026-04-12 23:16:59.845117: Epoch time: 101.98 s +2026-04-12 23:17:01.126970: +2026-04-12 23:17:01.129397: Epoch 1968 +2026-04-12 23:17:01.131713: Current learning rate: 0.00544 +2026-04-12 23:18:43.340773: train_loss -0.4233 +2026-04-12 23:18:43.349514: val_loss -0.3911 +2026-04-12 23:18:43.352433: Pseudo dice [0.7973, 0.0, 0.6038, 0.6901, 0.1668, 0.4665, 0.8405] +2026-04-12 23:18:43.354800: Epoch time: 102.22 s +2026-04-12 23:18:44.642527: +2026-04-12 23:18:44.646123: Epoch 1969 +2026-04-12 23:18:44.648393: Current learning rate: 0.00543 +2026-04-12 23:20:26.817215: train_loss -0.4183 +2026-04-12 23:20:26.827561: val_loss -0.3545 +2026-04-12 23:20:26.829795: Pseudo dice [0.548, 0.0, 0.8429, 0.1415, 0.5816, 0.3221, 0.8359] +2026-04-12 23:20:26.833015: Epoch time: 102.18 s +2026-04-12 23:20:28.086857: +2026-04-12 23:20:28.088734: Epoch 1970 +2026-04-12 23:20:28.090881: Current learning rate: 0.00543 +2026-04-12 23:22:10.025693: train_loss -0.4166 +2026-04-12 23:22:10.033099: val_loss -0.3471 +2026-04-12 23:22:10.035917: Pseudo dice [0.511, 0.0, 0.7319, 0.6114, 0.2589, 0.6878, 0.7991] +2026-04-12 23:22:10.038353: Epoch time: 101.94 s +2026-04-12 23:22:11.329437: +2026-04-12 23:22:11.331393: Epoch 1971 +2026-04-12 23:22:11.333396: Current learning rate: 0.00543 +2026-04-12 23:23:53.754830: train_loss -0.4026 +2026-04-12 23:23:53.783432: val_loss -0.3433 +2026-04-12 23:23:53.785612: Pseudo dice [0.3342, 0.0, 0.7231, 0.5013, 0.43, 0.2933, 0.8908] +2026-04-12 23:23:53.788384: Epoch time: 102.43 s +2026-04-12 23:23:55.052737: +2026-04-12 23:23:55.055815: Epoch 1972 +2026-04-12 23:23:55.058105: Current learning rate: 0.00543 +2026-04-12 23:25:37.196646: train_loss -0.4099 +2026-04-12 23:25:37.205233: val_loss -0.3585 +2026-04-12 23:25:37.207571: Pseudo dice [0.7447, 0.0, 0.8225, 0.0037, 0.5539, 0.5148, 0.2113] +2026-04-12 23:25:37.210139: Epoch time: 102.15 s +2026-04-12 23:25:38.478236: +2026-04-12 23:25:38.479958: Epoch 1973 +2026-04-12 23:25:38.481894: Current learning rate: 0.00542 +2026-04-12 23:27:20.668545: train_loss -0.411 +2026-04-12 23:27:20.675736: val_loss -0.3779 +2026-04-12 23:27:20.678146: Pseudo dice [0.539, 0.0, 0.8085, 0.7043, 0.58, 0.683, 0.732] +2026-04-12 23:27:20.680687: Epoch time: 102.19 s +2026-04-12 23:27:21.946391: +2026-04-12 23:27:21.948219: Epoch 1974 +2026-04-12 23:27:21.950510: Current learning rate: 0.00542 +2026-04-12 23:29:04.794653: train_loss -0.4062 +2026-04-12 23:29:04.802724: val_loss -0.328 +2026-04-12 23:29:04.805201: Pseudo dice [0.1166, 0.0, 0.6039, 0.0864, 0.5186, 0.7824, 0.6974] +2026-04-12 23:29:04.807910: Epoch time: 102.85 s +2026-04-12 23:29:06.109138: +2026-04-12 23:29:06.111676: Epoch 1975 +2026-04-12 23:29:06.114363: Current learning rate: 0.00542 +2026-04-12 23:30:48.018501: train_loss -0.3876 +2026-04-12 23:30:48.028276: val_loss -0.3895 +2026-04-12 23:30:48.030356: Pseudo dice [0.6147, 0.0, 0.7702, 0.4044, 0.4924, 0.7398, 0.7415] +2026-04-12 23:30:48.032776: Epoch time: 101.91 s +2026-04-12 23:30:49.350806: +2026-04-12 23:30:49.353126: Epoch 1976 +2026-04-12 23:30:49.355000: Current learning rate: 0.00542 +2026-04-12 23:32:31.401228: train_loss -0.3765 +2026-04-12 23:32:31.409704: val_loss -0.3298 +2026-04-12 23:32:31.412399: Pseudo dice [0.3628, 0.0, 0.776, 0.7412, 0.47, 0.3524, 0.8169] +2026-04-12 23:32:31.414614: Epoch time: 102.05 s +2026-04-12 23:32:32.667948: +2026-04-12 23:32:32.669902: Epoch 1977 +2026-04-12 23:32:32.672162: Current learning rate: 0.00541 +2026-04-12 23:34:14.504375: train_loss -0.3914 +2026-04-12 23:34:14.511349: val_loss -0.353 +2026-04-12 23:34:14.513185: Pseudo dice [0.4714, 0.0, 0.7835, 0.6011, 0.5505, 0.2586, 0.7436] +2026-04-12 23:34:14.515821: Epoch time: 101.84 s +2026-04-12 23:34:15.751778: +2026-04-12 23:34:15.753657: Epoch 1978 +2026-04-12 23:34:15.755404: Current learning rate: 0.00541 +2026-04-12 23:35:58.020557: train_loss -0.399 +2026-04-12 23:35:58.027549: val_loss -0.3514 +2026-04-12 23:35:58.030289: Pseudo dice [0.6064, 0.0, 0.6915, 0.7665, 0.559, 0.4775, 0.6452] +2026-04-12 23:35:58.032687: Epoch time: 102.27 s +2026-04-12 23:35:59.304872: +2026-04-12 23:35:59.306836: Epoch 1979 +2026-04-12 23:35:59.308879: Current learning rate: 0.00541 +2026-04-12 23:37:41.320196: train_loss -0.3948 +2026-04-12 23:37:41.326335: val_loss -0.3521 +2026-04-12 23:37:41.328415: Pseudo dice [0.4174, 0.0, 0.7346, 0.4828, 0.5735, 0.4273, 0.7945] +2026-04-12 23:37:41.330788: Epoch time: 102.02 s +2026-04-12 23:37:42.605405: +2026-04-12 23:37:42.607430: Epoch 1980 +2026-04-12 23:37:42.609558: Current learning rate: 0.00541 +2026-04-12 23:39:24.241212: train_loss -0.3744 +2026-04-12 23:39:24.250339: val_loss -0.3851 +2026-04-12 23:39:24.253249: Pseudo dice [0.6577, 0.0, 0.6455, 0.4478, 0.5051, 0.78, 0.895] +2026-04-12 23:39:24.255895: Epoch time: 101.64 s +2026-04-12 23:39:26.636700: +2026-04-12 23:39:26.638527: Epoch 1981 +2026-04-12 23:39:26.640457: Current learning rate: 0.0054 +2026-04-12 23:41:08.283914: train_loss -0.3926 +2026-04-12 23:41:08.291221: val_loss -0.3614 +2026-04-12 23:41:08.293435: Pseudo dice [0.5962, 0.0, 0.7182, 0.4402, 0.4984, 0.7387, 0.8123] +2026-04-12 23:41:08.295822: Epoch time: 101.65 s +2026-04-12 23:41:09.566182: +2026-04-12 23:41:09.567942: Epoch 1982 +2026-04-12 23:41:09.569845: Current learning rate: 0.0054 +2026-04-12 23:42:51.507558: train_loss -0.4059 +2026-04-12 23:42:51.516101: val_loss -0.3869 +2026-04-12 23:42:51.518576: Pseudo dice [0.3547, 0.0, 0.788, 0.5672, 0.4105, 0.284, 0.5286] +2026-04-12 23:42:51.520971: Epoch time: 101.94 s +2026-04-12 23:42:52.794410: +2026-04-12 23:42:52.796758: Epoch 1983 +2026-04-12 23:42:52.799139: Current learning rate: 0.0054 +2026-04-12 23:44:35.052066: train_loss -0.4217 +2026-04-12 23:44:35.059702: val_loss -0.3803 +2026-04-12 23:44:35.062904: Pseudo dice [0.4485, 0.0, 0.7988, 0.7684, 0.5695, 0.7984, 0.845] +2026-04-12 23:44:35.065946: Epoch time: 102.26 s +2026-04-12 23:44:36.396634: +2026-04-12 23:44:36.398995: Epoch 1984 +2026-04-12 23:44:36.401206: Current learning rate: 0.0054 +2026-04-12 23:46:18.333506: train_loss -0.416 +2026-04-12 23:46:18.341211: val_loss -0.3488 +2026-04-12 23:46:18.343619: Pseudo dice [0.5325, 0.0, 0.6969, 0.4936, 0.5711, 0.7718, 0.7437] +2026-04-12 23:46:18.346427: Epoch time: 101.94 s +2026-04-12 23:46:19.644118: +2026-04-12 23:46:19.646332: Epoch 1985 +2026-04-12 23:46:19.648725: Current learning rate: 0.0054 +2026-04-12 23:48:02.706774: train_loss -0.423 +2026-04-12 23:48:02.713181: val_loss -0.3985 +2026-04-12 23:48:02.715452: Pseudo dice [0.7268, 0.0, 0.7698, 0.7425, 0.5847, 0.7021, 0.8815] +2026-04-12 23:48:02.717721: Epoch time: 103.07 s +2026-04-12 23:48:03.964382: +2026-04-12 23:48:03.966829: Epoch 1986 +2026-04-12 23:48:03.969127: Current learning rate: 0.00539 +2026-04-12 23:49:45.815457: train_loss -0.4235 +2026-04-12 23:49:45.822504: val_loss -0.3691 +2026-04-12 23:49:45.824619: Pseudo dice [0.4834, 0.0, 0.8278, 0.6891, 0.4908, 0.2819, 0.8637] +2026-04-12 23:49:45.826899: Epoch time: 101.85 s +2026-04-12 23:49:47.062372: +2026-04-12 23:49:47.064657: Epoch 1987 +2026-04-12 23:49:47.066661: Current learning rate: 0.00539 +2026-04-12 23:51:28.991536: train_loss -0.4161 +2026-04-12 23:51:28.999027: val_loss -0.3957 +2026-04-12 23:51:29.000915: Pseudo dice [0.7653, 0.0, 0.7977, 0.7213, 0.4536, 0.7511, 0.9116] +2026-04-12 23:51:29.003655: Epoch time: 101.93 s +2026-04-12 23:51:30.260655: +2026-04-12 23:51:30.263750: Epoch 1988 +2026-04-12 23:51:30.265809: Current learning rate: 0.00539 +2026-04-12 23:53:11.997137: train_loss -0.412 +2026-04-12 23:53:12.004062: val_loss -0.3711 +2026-04-12 23:53:12.006546: Pseudo dice [0.8101, 0.0, 0.7266, 0.0078, 0.5125, 0.5669, 0.8787] +2026-04-12 23:53:12.008731: Epoch time: 101.74 s +2026-04-12 23:53:13.261261: +2026-04-12 23:53:13.263421: Epoch 1989 +2026-04-12 23:53:13.265759: Current learning rate: 0.00539 +2026-04-12 23:54:55.461645: train_loss -0.4126 +2026-04-12 23:54:55.468702: val_loss -0.3554 +2026-04-12 23:54:55.470862: Pseudo dice [0.6911, 0.0, 0.7824, 0.8097, 0.544, 0.7213, 0.5412] +2026-04-12 23:54:55.473131: Epoch time: 102.2 s +2026-04-12 23:54:56.772626: +2026-04-12 23:54:56.774527: Epoch 1990 +2026-04-12 23:54:56.776845: Current learning rate: 0.00538 +2026-04-12 23:56:38.573859: train_loss -0.3981 +2026-04-12 23:56:38.579265: val_loss -0.386 +2026-04-12 23:56:38.581733: Pseudo dice [0.6629, 0.0, 0.8551, 0.6207, 0.525, 0.5099, 0.9355] +2026-04-12 23:56:38.584140: Epoch time: 101.8 s +2026-04-12 23:56:39.853318: +2026-04-12 23:56:39.855127: Epoch 1991 +2026-04-12 23:56:39.856961: Current learning rate: 0.00538 +2026-04-12 23:58:22.086172: train_loss -0.3991 +2026-04-12 23:58:22.094563: val_loss -0.3443 +2026-04-12 23:58:22.096679: Pseudo dice [0.1499, 0.0, 0.6578, 0.6544, 0.4372, 0.3656, 0.6625] +2026-04-12 23:58:22.098978: Epoch time: 102.24 s +2026-04-12 23:58:23.349257: +2026-04-12 23:58:23.351562: Epoch 1992 +2026-04-12 23:58:23.354010: Current learning rate: 0.00538 +2026-04-13 00:00:05.545059: train_loss -0.4148 +2026-04-13 00:00:05.552705: val_loss -0.3978 +2026-04-13 00:00:05.556416: Pseudo dice [0.8589, 0.0, 0.7969, 0.6945, 0.4995, 0.5328, 0.8913] +2026-04-13 00:00:05.559558: Epoch time: 102.2 s +2026-04-13 00:00:06.840017: +2026-04-13 00:00:06.842842: Epoch 1993 +2026-04-13 00:00:06.845336: Current learning rate: 0.00538 +2026-04-13 00:01:48.993049: train_loss -0.4207 +2026-04-13 00:01:49.000231: val_loss -0.2991 +2026-04-13 00:01:49.004421: Pseudo dice [0.5008, 0.0, 0.5831, 0.2297, 0.392, 0.5249, 0.0847] +2026-04-13 00:01:49.007330: Epoch time: 102.16 s +2026-04-13 00:01:50.274669: +2026-04-13 00:01:50.276659: Epoch 1994 +2026-04-13 00:01:50.278701: Current learning rate: 0.00537 +2026-04-13 00:03:32.473759: train_loss -0.3839 +2026-04-13 00:03:32.481730: val_loss -0.3623 +2026-04-13 00:03:32.484137: Pseudo dice [0.6088, 0.0, 0.5683, 0.5053, 0.5919, 0.6552, 0.6595] +2026-04-13 00:03:32.487934: Epoch time: 102.2 s +2026-04-13 00:03:33.777401: +2026-04-13 00:03:33.779710: Epoch 1995 +2026-04-13 00:03:33.781851: Current learning rate: 0.00537 +2026-04-13 00:05:15.891566: train_loss -0.3829 +2026-04-13 00:05:15.899740: val_loss -0.3486 +2026-04-13 00:05:15.904129: Pseudo dice [0.2551, 0.0, 0.7418, 0.0423, 0.3908, 0.6948, 0.7281] +2026-04-13 00:05:15.906826: Epoch time: 102.12 s +2026-04-13 00:05:17.139762: +2026-04-13 00:05:17.142322: Epoch 1996 +2026-04-13 00:05:17.144608: Current learning rate: 0.00537 +2026-04-13 00:06:59.072489: train_loss -0.3927 +2026-04-13 00:06:59.078534: val_loss -0.3551 +2026-04-13 00:06:59.080477: Pseudo dice [0.5782, 0.0, 0.8582, 0.6112, 0.453, 0.2845, 0.8466] +2026-04-13 00:06:59.083162: Epoch time: 101.94 s +2026-04-13 00:07:00.370013: +2026-04-13 00:07:00.371822: Epoch 1997 +2026-04-13 00:07:00.374508: Current learning rate: 0.00537 +2026-04-13 00:08:43.156499: train_loss -0.3961 +2026-04-13 00:08:43.164478: val_loss -0.3357 +2026-04-13 00:08:43.167408: Pseudo dice [0.357, 0.0, 0.7395, 0.0468, 0.6003, 0.3168, 0.5116] +2026-04-13 00:08:43.169903: Epoch time: 102.79 s +2026-04-13 00:08:44.455585: +2026-04-13 00:08:44.458107: Epoch 1998 +2026-04-13 00:08:44.460111: Current learning rate: 0.00536 +2026-04-13 00:10:26.976441: train_loss -0.3988 +2026-04-13 00:10:26.987000: val_loss -0.3192 +2026-04-13 00:10:26.990410: Pseudo dice [0.028, 0.0, 0.7281, 0.6249, 0.4897, 0.4795, 0.7127] +2026-04-13 00:10:26.994498: Epoch time: 102.52 s +2026-04-13 00:10:28.296131: +2026-04-13 00:10:28.298707: Epoch 1999 +2026-04-13 00:10:28.300916: Current learning rate: 0.00536 +2026-04-13 00:12:10.059104: train_loss -0.3968 +2026-04-13 00:12:10.065474: val_loss -0.3722 +2026-04-13 00:12:10.067307: Pseudo dice [0.4217, 0.0, 0.7615, 0.4262, 0.4124, 0.4754, 0.6074] +2026-04-13 00:12:10.069258: Epoch time: 101.77 s +2026-04-13 00:12:13.209684: +2026-04-13 00:12:13.211233: Epoch 2000 +2026-04-13 00:12:13.213279: Current learning rate: 0.00536 +2026-04-13 00:13:55.921840: train_loss -0.39 +2026-04-13 00:13:55.929423: val_loss -0.3847 +2026-04-13 00:13:55.931788: Pseudo dice [0.469, 0.0, 0.6025, 0.743, 0.5442, 0.1416, 0.7222] +2026-04-13 00:13:55.934057: Epoch time: 102.72 s +2026-04-13 00:13:57.195354: +2026-04-13 00:13:57.197761: Epoch 2001 +2026-04-13 00:13:57.199802: Current learning rate: 0.00536 +2026-04-13 00:15:39.176252: train_loss -0.3995 +2026-04-13 00:15:39.183344: val_loss -0.3767 +2026-04-13 00:15:39.185489: Pseudo dice [0.6699, 0.0, 0.7497, 0.569, 0.612, 0.3282, 0.8326] +2026-04-13 00:15:39.188387: Epoch time: 101.98 s +2026-04-13 00:15:40.468537: +2026-04-13 00:15:40.471504: Epoch 2002 +2026-04-13 00:15:40.473571: Current learning rate: 0.00535 +2026-04-13 00:17:22.191519: train_loss -0.394 +2026-04-13 00:17:22.199268: val_loss -0.3565 +2026-04-13 00:17:22.201443: Pseudo dice [0.3656, 0.0, 0.6848, 0.5721, 0.432, 0.7075, 0.8207] +2026-04-13 00:17:22.203856: Epoch time: 101.73 s +2026-04-13 00:17:23.478975: +2026-04-13 00:17:23.480685: Epoch 2003 +2026-04-13 00:17:23.482478: Current learning rate: 0.00535 +2026-04-13 00:19:05.824167: train_loss -0.4052 +2026-04-13 00:19:05.830706: val_loss -0.3578 +2026-04-13 00:19:05.833014: Pseudo dice [0.5488, 0.0, 0.7534, 0.557, 0.6043, 0.7724, 0.7742] +2026-04-13 00:19:05.835816: Epoch time: 102.35 s +2026-04-13 00:19:07.109307: +2026-04-13 00:19:07.111104: Epoch 2004 +2026-04-13 00:19:07.112916: Current learning rate: 0.00535 +2026-04-13 00:20:49.031144: train_loss -0.4142 +2026-04-13 00:20:49.038258: val_loss -0.371 +2026-04-13 00:20:49.040457: Pseudo dice [0.5388, 0.0, 0.5355, 0.2104, 0.5178, 0.5623, 0.941] +2026-04-13 00:20:49.043247: Epoch time: 101.92 s +2026-04-13 00:20:50.311642: +2026-04-13 00:20:50.313593: Epoch 2005 +2026-04-13 00:20:50.315470: Current learning rate: 0.00535 +2026-04-13 00:22:32.040774: train_loss -0.4066 +2026-04-13 00:22:32.047467: val_loss -0.3667 +2026-04-13 00:22:32.051079: Pseudo dice [0.3086, 0.0, 0.7884, 0.5059, 0.5891, 0.7617, 0.866] +2026-04-13 00:22:32.054906: Epoch time: 101.73 s +2026-04-13 00:22:33.322496: +2026-04-13 00:22:33.324372: Epoch 2006 +2026-04-13 00:22:33.326500: Current learning rate: 0.00534 +2026-04-13 00:24:15.328681: train_loss -0.4022 +2026-04-13 00:24:15.354187: val_loss -0.3873 +2026-04-13 00:24:15.356511: Pseudo dice [0.7251, 0.0, 0.787, 0.6745, 0.6252, 0.3139, 0.8463] +2026-04-13 00:24:15.359445: Epoch time: 102.01 s +2026-04-13 00:24:16.632094: +2026-04-13 00:24:16.634107: Epoch 2007 +2026-04-13 00:24:16.635870: Current learning rate: 0.00534 +2026-04-13 00:25:58.137671: train_loss -0.3934 +2026-04-13 00:25:58.145221: val_loss -0.3651 +2026-04-13 00:25:58.147980: Pseudo dice [0.0031, 0.0, 0.7868, 0.5581, 0.4665, 0.8909, 0.8138] +2026-04-13 00:25:58.150822: Epoch time: 101.51 s +2026-04-13 00:25:59.479142: +2026-04-13 00:25:59.483332: Epoch 2008 +2026-04-13 00:25:59.485500: Current learning rate: 0.00534 +2026-04-13 00:27:41.906762: train_loss -0.3974 +2026-04-13 00:27:41.913174: val_loss -0.3439 +2026-04-13 00:27:41.915107: Pseudo dice [0.0796, 0.0, 0.7774, 0.0056, 0.5622, 0.6419, 0.7298] +2026-04-13 00:27:41.917282: Epoch time: 102.43 s +2026-04-13 00:27:43.185090: +2026-04-13 00:27:43.186854: Epoch 2009 +2026-04-13 00:27:43.188498: Current learning rate: 0.00534 +2026-04-13 00:29:24.795742: train_loss -0.4089 +2026-04-13 00:29:24.801683: val_loss -0.374 +2026-04-13 00:29:24.804439: Pseudo dice [0.5158, 0.0, 0.821, 0.5967, 0.3454, 0.6435, 0.8681] +2026-04-13 00:29:24.807444: Epoch time: 101.61 s +2026-04-13 00:29:26.092397: +2026-04-13 00:29:26.094166: Epoch 2010 +2026-04-13 00:29:26.095663: Current learning rate: 0.00533 +2026-04-13 00:31:08.031026: train_loss -0.4071 +2026-04-13 00:31:08.036218: val_loss -0.3508 +2026-04-13 00:31:08.038050: Pseudo dice [0.51, 0.0, 0.4967, 0.6014, 0.4391, 0.418, 0.804] +2026-04-13 00:31:08.040292: Epoch time: 101.94 s +2026-04-13 00:31:09.277858: +2026-04-13 00:31:09.279571: Epoch 2011 +2026-04-13 00:31:09.281130: Current learning rate: 0.00533 +2026-04-13 00:32:51.822501: train_loss -0.3998 +2026-04-13 00:32:51.828460: val_loss -0.3794 +2026-04-13 00:32:51.830606: Pseudo dice [0.4661, 0.0, 0.7986, 0.4923, 0.6133, 0.82, 0.8321] +2026-04-13 00:32:51.832931: Epoch time: 102.55 s +2026-04-13 00:32:53.335538: +2026-04-13 00:32:53.337481: Epoch 2012 +2026-04-13 00:32:53.339240: Current learning rate: 0.00533 +2026-04-13 00:34:34.745212: train_loss -0.3961 +2026-04-13 00:34:34.753495: val_loss -0.3719 +2026-04-13 00:34:34.755411: Pseudo dice [0.6796, 0.0, 0.6313, 0.2804, 0.5518, 0.6421, 0.8444] +2026-04-13 00:34:34.757920: Epoch time: 101.41 s +2026-04-13 00:34:36.017086: +2026-04-13 00:34:36.019289: Epoch 2013 +2026-04-13 00:34:36.021481: Current learning rate: 0.00533 +2026-04-13 00:36:17.638330: train_loss -0.4211 +2026-04-13 00:36:17.645291: val_loss -0.3101 +2026-04-13 00:36:17.647456: Pseudo dice [0.5138, 0.0, 0.6531, 0.4318, 0.6504, 0.3748, 0.292] +2026-04-13 00:36:17.649893: Epoch time: 101.62 s +2026-04-13 00:36:18.944405: +2026-04-13 00:36:18.946190: Epoch 2014 +2026-04-13 00:36:18.947928: Current learning rate: 0.00533 +2026-04-13 00:38:00.689396: train_loss -0.4059 +2026-04-13 00:38:00.695814: val_loss -0.3416 +2026-04-13 00:38:00.698134: Pseudo dice [0.5025, 0.0, 0.4837, 0.6941, 0.2731, 0.4024, 0.82] +2026-04-13 00:38:00.700559: Epoch time: 101.75 s +2026-04-13 00:38:01.969100: +2026-04-13 00:38:01.970924: Epoch 2015 +2026-04-13 00:38:01.972598: Current learning rate: 0.00532 +2026-04-13 00:39:43.793751: train_loss -0.4091 +2026-04-13 00:39:43.800103: val_loss -0.3688 +2026-04-13 00:39:43.801902: Pseudo dice [0.322, 0.0, 0.7356, 0.5172, 0.4445, 0.7319, 0.6739] +2026-04-13 00:39:43.803905: Epoch time: 101.83 s +2026-04-13 00:39:45.065803: +2026-04-13 00:39:45.067591: Epoch 2016 +2026-04-13 00:39:45.069309: Current learning rate: 0.00532 +2026-04-13 00:41:26.846790: train_loss -0.418 +2026-04-13 00:41:26.853585: val_loss -0.369 +2026-04-13 00:41:26.856053: Pseudo dice [0.382, 0.0, 0.7251, 0.6384, 0.532, 0.4507, 0.7262] +2026-04-13 00:41:26.858789: Epoch time: 101.78 s +2026-04-13 00:41:28.113982: +2026-04-13 00:41:28.115924: Epoch 2017 +2026-04-13 00:41:28.118031: Current learning rate: 0.00532 +2026-04-13 00:43:09.650659: train_loss -0.4056 +2026-04-13 00:43:09.657020: val_loss -0.337 +2026-04-13 00:43:09.659720: Pseudo dice [0.3435, 0.0, 0.7128, 0.5164, 0.5774, 0.6257, 0.2436] +2026-04-13 00:43:09.662051: Epoch time: 101.54 s +2026-04-13 00:43:10.907570: +2026-04-13 00:43:10.909398: Epoch 2018 +2026-04-13 00:43:10.911144: Current learning rate: 0.00532 +2026-04-13 00:44:53.213011: train_loss -0.3761 +2026-04-13 00:44:53.219232: val_loss -0.3223 +2026-04-13 00:44:53.221289: Pseudo dice [0.7781, 0.0, 0.6079, 0.1503, 0.4589, 0.3615, 0.1092] +2026-04-13 00:44:53.223840: Epoch time: 102.31 s +2026-04-13 00:44:54.494309: +2026-04-13 00:44:54.496207: Epoch 2019 +2026-04-13 00:44:54.497813: Current learning rate: 0.00531 +2026-04-13 00:46:36.076245: train_loss -0.3696 +2026-04-13 00:46:36.081632: val_loss -0.3471 +2026-04-13 00:46:36.083611: Pseudo dice [0.8091, 0.0, 0.6988, 0.4104, 0.5773, 0.8204, 0.9107] +2026-04-13 00:46:36.086018: Epoch time: 101.59 s +2026-04-13 00:46:38.452424: +2026-04-13 00:46:38.454765: Epoch 2020 +2026-04-13 00:46:38.456549: Current learning rate: 0.00531 +2026-04-13 00:48:20.201655: train_loss -0.3968 +2026-04-13 00:48:20.209002: val_loss -0.3651 +2026-04-13 00:48:20.211098: Pseudo dice [0.7407, 0.0, 0.7113, 0.3859, 0.4109, 0.4897, 0.8446] +2026-04-13 00:48:20.213217: Epoch time: 101.75 s +2026-04-13 00:48:21.473468: +2026-04-13 00:48:21.475331: Epoch 2021 +2026-04-13 00:48:21.476971: Current learning rate: 0.00531 +2026-04-13 00:50:03.333709: train_loss -0.4192 +2026-04-13 00:50:03.340146: val_loss -0.3544 +2026-04-13 00:50:03.343073: Pseudo dice [0.2689, 0.0, 0.8071, 0.3816, 0.5219, 0.7708, 0.873] +2026-04-13 00:50:03.345362: Epoch time: 101.86 s +2026-04-13 00:50:04.608134: +2026-04-13 00:50:04.609993: Epoch 2022 +2026-04-13 00:50:04.611516: Current learning rate: 0.00531 +2026-04-13 00:51:46.721949: train_loss -0.4167 +2026-04-13 00:51:46.729096: val_loss -0.3499 +2026-04-13 00:51:46.731501: Pseudo dice [0.7892, 0.0, 0.6715, 0.5717, 0.4526, 0.2963, 0.8818] +2026-04-13 00:51:46.733779: Epoch time: 102.12 s +2026-04-13 00:51:48.003549: +2026-04-13 00:51:48.006286: Epoch 2023 +2026-04-13 00:51:48.009218: Current learning rate: 0.0053 +2026-04-13 00:53:30.595914: train_loss -0.3976 +2026-04-13 00:53:30.603553: val_loss -0.3491 +2026-04-13 00:53:30.605562: Pseudo dice [0.5619, 0.0, 0.6926, 0.2231, 0.3775, 0.5888, 0.473] +2026-04-13 00:53:30.608275: Epoch time: 102.6 s +2026-04-13 00:53:31.910647: +2026-04-13 00:53:31.912670: Epoch 2024 +2026-04-13 00:53:31.914333: Current learning rate: 0.0053 +2026-04-13 00:55:13.524527: train_loss -0.4229 +2026-04-13 00:55:13.531973: val_loss -0.3442 +2026-04-13 00:55:13.533871: Pseudo dice [0.4779, 0.0, 0.7302, 0.0577, 0.3756, 0.6757, 0.816] +2026-04-13 00:55:13.536040: Epoch time: 101.62 s +2026-04-13 00:55:14.826534: +2026-04-13 00:55:14.828408: Epoch 2025 +2026-04-13 00:55:14.830213: Current learning rate: 0.0053 +2026-04-13 00:56:56.971462: train_loss -0.4231 +2026-04-13 00:56:56.980446: val_loss -0.357 +2026-04-13 00:56:56.983234: Pseudo dice [0.4155, 0.0, 0.8075, 0.022, 0.647, 0.4107, 0.5879] +2026-04-13 00:56:56.985884: Epoch time: 102.15 s +2026-04-13 00:56:58.259578: +2026-04-13 00:56:58.261636: Epoch 2026 +2026-04-13 00:56:58.263575: Current learning rate: 0.0053 +2026-04-13 00:58:40.191439: train_loss -0.4149 +2026-04-13 00:58:40.199223: val_loss -0.3481 +2026-04-13 00:58:40.201262: Pseudo dice [0.6882, 0.0, 0.714, 0.7039, 0.3036, 0.5849, 0.8171] +2026-04-13 00:58:40.203672: Epoch time: 101.94 s +2026-04-13 00:58:41.485216: +2026-04-13 00:58:41.487010: Epoch 2027 +2026-04-13 00:58:41.488533: Current learning rate: 0.00529 +2026-04-13 01:00:23.522448: train_loss -0.4089 +2026-04-13 01:00:23.529444: val_loss -0.3563 +2026-04-13 01:00:23.531629: Pseudo dice [0.6861, 0.0, 0.8213, 0.5602, 0.4732, 0.567, 0.8702] +2026-04-13 01:00:23.534323: Epoch time: 102.04 s +2026-04-13 01:00:24.784540: +2026-04-13 01:00:24.786628: Epoch 2028 +2026-04-13 01:00:24.788615: Current learning rate: 0.00529 +2026-04-13 01:02:06.306126: train_loss -0.4088 +2026-04-13 01:02:06.312754: val_loss -0.3602 +2026-04-13 01:02:06.314975: Pseudo dice [0.5642, 0.0, 0.6996, 0.5459, 0.629, 0.8128, 0.8466] +2026-04-13 01:02:06.317662: Epoch time: 101.52 s +2026-04-13 01:02:07.581634: +2026-04-13 01:02:07.583777: Epoch 2029 +2026-04-13 01:02:07.585642: Current learning rate: 0.00529 +2026-04-13 01:03:49.176821: train_loss -0.4057 +2026-04-13 01:03:49.184928: val_loss -0.3483 +2026-04-13 01:03:49.187085: Pseudo dice [0.3908, 0.0, 0.6929, 0.3231, 0.6071, 0.4247, 0.6366] +2026-04-13 01:03:49.189253: Epoch time: 101.6 s +2026-04-13 01:03:50.448252: +2026-04-13 01:03:50.450243: Epoch 2030 +2026-04-13 01:03:50.452033: Current learning rate: 0.00529 +2026-04-13 01:05:32.314750: train_loss -0.4073 +2026-04-13 01:05:32.322090: val_loss -0.3611 +2026-04-13 01:05:32.324500: Pseudo dice [0.8095, 0.0, 0.7535, 0.7425, 0.5357, 0.4487, 0.8083] +2026-04-13 01:05:32.326999: Epoch time: 101.87 s +2026-04-13 01:05:33.600094: +2026-04-13 01:05:33.602363: Epoch 2031 +2026-04-13 01:05:33.604073: Current learning rate: 0.00528 +2026-04-13 01:07:15.169822: train_loss -0.4046 +2026-04-13 01:07:15.176652: val_loss -0.3108 +2026-04-13 01:07:15.179219: Pseudo dice [0.7909, 0.0, 0.7363, 0.0836, 0.3906, 0.3569, 0.2174] +2026-04-13 01:07:15.182523: Epoch time: 101.57 s +2026-04-13 01:07:16.537665: +2026-04-13 01:07:16.539638: Epoch 2032 +2026-04-13 01:07:16.541843: Current learning rate: 0.00528 +2026-04-13 01:08:58.438105: train_loss -0.425 +2026-04-13 01:08:58.445298: val_loss -0.3571 +2026-04-13 01:08:58.447251: Pseudo dice [0.7399, 0.0, 0.4793, 0.4328, 0.6033, 0.6602, 0.737] +2026-04-13 01:08:58.449552: Epoch time: 101.9 s +2026-04-13 01:08:59.717050: +2026-04-13 01:08:59.718975: Epoch 2033 +2026-04-13 01:08:59.720663: Current learning rate: 0.00528 +2026-04-13 01:10:41.461715: train_loss -0.4061 +2026-04-13 01:10:41.468763: val_loss -0.35 +2026-04-13 01:10:41.471471: Pseudo dice [0.5113, 0.0, 0.7661, 0.2096, 0.3256, 0.7319, 0.8325] +2026-04-13 01:10:41.473542: Epoch time: 101.75 s +2026-04-13 01:10:42.713825: +2026-04-13 01:10:42.715776: Epoch 2034 +2026-04-13 01:10:42.717348: Current learning rate: 0.00528 +2026-04-13 01:12:24.372296: train_loss -0.4057 +2026-04-13 01:12:24.381136: val_loss -0.3306 +2026-04-13 01:12:24.383717: Pseudo dice [0.1286, 0.0, 0.7676, 0.7945, 0.6648, 0.6974, 0.8652] +2026-04-13 01:12:24.387226: Epoch time: 101.66 s +2026-04-13 01:12:25.666802: +2026-04-13 01:12:25.669532: Epoch 2035 +2026-04-13 01:12:25.672248: Current learning rate: 0.00527 +2026-04-13 01:14:07.373700: train_loss -0.3988 +2026-04-13 01:14:07.380421: val_loss -0.3778 +2026-04-13 01:14:07.382577: Pseudo dice [0.5143, 0.0, 0.7026, 0.0086, 0.5334, 0.8356, 0.9108] +2026-04-13 01:14:07.384981: Epoch time: 101.71 s +2026-04-13 01:14:08.630027: +2026-04-13 01:14:08.631916: Epoch 2036 +2026-04-13 01:14:08.633692: Current learning rate: 0.00527 +2026-04-13 01:15:50.330462: train_loss -0.4085 +2026-04-13 01:15:50.336433: val_loss -0.3784 +2026-04-13 01:15:50.339027: Pseudo dice [0.1018, 0.0, 0.7971, 0.6897, 0.6143, 0.7404, 0.6801] +2026-04-13 01:15:50.341460: Epoch time: 101.7 s +2026-04-13 01:15:51.597612: +2026-04-13 01:15:51.599557: Epoch 2037 +2026-04-13 01:15:51.601201: Current learning rate: 0.00527 +2026-04-13 01:17:33.575578: train_loss -0.4178 +2026-04-13 01:17:33.581247: val_loss -0.3186 +2026-04-13 01:17:33.583007: Pseudo dice [0.0047, 0.0, 0.8321, 0.6084, 0.2509, 0.6409, 0.7434] +2026-04-13 01:17:33.585110: Epoch time: 101.98 s +2026-04-13 01:17:34.864618: +2026-04-13 01:17:34.867468: Epoch 2038 +2026-04-13 01:17:34.869888: Current learning rate: 0.00527 +2026-04-13 01:19:16.678899: train_loss -0.4033 +2026-04-13 01:19:16.685718: val_loss -0.3194 +2026-04-13 01:19:16.687749: Pseudo dice [0.1749, 0.0, 0.6378, 0.2018, 0.4355, 0.4115, 0.5797] +2026-04-13 01:19:16.690412: Epoch time: 101.82 s +2026-04-13 01:19:17.945882: +2026-04-13 01:19:17.947719: Epoch 2039 +2026-04-13 01:19:17.949469: Current learning rate: 0.00526 +2026-04-13 01:21:00.923916: train_loss -0.4077 +2026-04-13 01:21:00.930417: val_loss -0.357 +2026-04-13 01:21:00.933740: Pseudo dice [0.2306, 0.0, 0.6793, 0.5669, 0.5135, 0.5342, 0.7366] +2026-04-13 01:21:00.935973: Epoch time: 102.98 s +2026-04-13 01:21:02.198239: +2026-04-13 01:21:02.200012: Epoch 2040 +2026-04-13 01:21:02.201849: Current learning rate: 0.00526 +2026-04-13 01:22:43.888209: train_loss -0.4193 +2026-04-13 01:22:43.896927: val_loss -0.3681 +2026-04-13 01:22:43.899117: Pseudo dice [0.812, 0.0, 0.7469, 0.7562, 0.5491, 0.6386, 0.6953] +2026-04-13 01:22:43.901846: Epoch time: 101.69 s +2026-04-13 01:22:45.251523: +2026-04-13 01:22:45.253400: Epoch 2041 +2026-04-13 01:22:45.255235: Current learning rate: 0.00526 +2026-04-13 01:24:26.936934: train_loss -0.4275 +2026-04-13 01:24:26.963487: val_loss -0.38 +2026-04-13 01:24:26.966022: Pseudo dice [0.7096, 0.0, 0.6389, 0.0702, 0.478, 0.4457, 0.8597] +2026-04-13 01:24:26.968595: Epoch time: 101.69 s +2026-04-13 01:24:28.208985: +2026-04-13 01:24:28.211248: Epoch 2042 +2026-04-13 01:24:28.213077: Current learning rate: 0.00526 +2026-04-13 01:26:09.854983: train_loss -0.4267 +2026-04-13 01:26:09.862228: val_loss -0.3563 +2026-04-13 01:26:09.865385: Pseudo dice [0.1974, 0.0, 0.8312, 0.584, 0.3931, 0.435, 0.9218] +2026-04-13 01:26:09.867971: Epoch time: 101.65 s +2026-04-13 01:26:11.124521: +2026-04-13 01:26:11.126822: Epoch 2043 +2026-04-13 01:26:11.129362: Current learning rate: 0.00526 +2026-04-13 01:27:53.699050: train_loss -0.4154 +2026-04-13 01:27:53.706117: val_loss -0.383 +2026-04-13 01:27:53.708062: Pseudo dice [0.6114, 0.0, 0.7838, 0.2913, 0.3779, 0.7266, 0.816] +2026-04-13 01:27:53.710322: Epoch time: 102.58 s +2026-04-13 01:27:54.938684: +2026-04-13 01:27:54.941470: Epoch 2044 +2026-04-13 01:27:54.943308: Current learning rate: 0.00525 +2026-04-13 01:29:37.126240: train_loss -0.3935 +2026-04-13 01:29:37.132182: val_loss -0.3459 +2026-04-13 01:29:37.134462: Pseudo dice [0.2507, 0.0, 0.7972, 0.3205, 0.5985, 0.7283, 0.8997] +2026-04-13 01:29:37.136996: Epoch time: 102.19 s +2026-04-13 01:29:38.406171: +2026-04-13 01:29:38.408903: Epoch 2045 +2026-04-13 01:29:38.410764: Current learning rate: 0.00525 +2026-04-13 01:31:20.680570: train_loss -0.3989 +2026-04-13 01:31:20.686525: val_loss -0.3081 +2026-04-13 01:31:20.688867: Pseudo dice [0.2174, 0.0, 0.4485, 0.1766, 0.4044, 0.1892, 0.9046] +2026-04-13 01:31:20.692714: Epoch time: 102.28 s +2026-04-13 01:31:21.960170: +2026-04-13 01:31:21.962223: Epoch 2046 +2026-04-13 01:31:21.963665: Current learning rate: 0.00525 +2026-04-13 01:33:03.982888: train_loss -0.3904 +2026-04-13 01:33:03.988926: val_loss -0.3606 +2026-04-13 01:33:03.990903: Pseudo dice [0.0, 0.0, 0.7268, 0.8385, 0.3303, 0.7354, 0.4425] +2026-04-13 01:33:03.992930: Epoch time: 102.03 s +2026-04-13 01:33:05.168453: +2026-04-13 01:33:05.170171: Epoch 2047 +2026-04-13 01:33:05.171806: Current learning rate: 0.00525 +2026-04-13 01:34:47.436277: train_loss -0.3999 +2026-04-13 01:34:47.442739: val_loss -0.3254 +2026-04-13 01:34:47.445032: Pseudo dice [0.0, 0.0, 0.4651, 0.5493, 0.5042, 0.459, 0.3675] +2026-04-13 01:34:47.447309: Epoch time: 102.27 s +2026-04-13 01:34:48.646538: +2026-04-13 01:34:48.649192: Epoch 2048 +2026-04-13 01:34:48.651936: Current learning rate: 0.00524 +2026-04-13 01:36:30.640421: train_loss -0.4324 +2026-04-13 01:36:30.646611: val_loss -0.3857 +2026-04-13 01:36:30.648511: Pseudo dice [0.4086, 0.0, 0.7961, 0.2752, 0.3442, 0.6948, 0.8135] +2026-04-13 01:36:30.650594: Epoch time: 102.0 s +2026-04-13 01:36:31.856512: +2026-04-13 01:36:31.859097: Epoch 2049 +2026-04-13 01:36:31.861066: Current learning rate: 0.00524 +2026-04-13 01:38:13.526524: train_loss -0.4099 +2026-04-13 01:38:13.533638: val_loss -0.376 +2026-04-13 01:38:13.536263: Pseudo dice [0.7459, 0.0, 0.8041, 0.7754, 0.3951, 0.5067, 0.4776] +2026-04-13 01:38:13.539016: Epoch time: 101.67 s +2026-04-13 01:38:16.499129: +2026-04-13 01:38:16.500991: Epoch 2050 +2026-04-13 01:38:16.503179: Current learning rate: 0.00524 +2026-04-13 01:39:58.678438: train_loss -0.4161 +2026-04-13 01:39:58.685068: val_loss -0.3476 +2026-04-13 01:39:58.687119: Pseudo dice [0.4031, 0.0, 0.7198, 0.1653, 0.5529, 0.8029, 0.2283] +2026-04-13 01:39:58.689487: Epoch time: 102.18 s +2026-04-13 01:39:59.882946: +2026-04-13 01:39:59.885525: Epoch 2051 +2026-04-13 01:39:59.887088: Current learning rate: 0.00524 +2026-04-13 01:41:41.673297: train_loss -0.4137 +2026-04-13 01:41:41.678775: val_loss -0.3732 +2026-04-13 01:41:41.681025: Pseudo dice [0.3517, 0.0, 0.8076, 0.7334, 0.5234, 0.394, 0.9451] +2026-04-13 01:41:41.683112: Epoch time: 101.79 s +2026-04-13 01:41:42.879934: +2026-04-13 01:41:42.881775: Epoch 2052 +2026-04-13 01:41:42.883308: Current learning rate: 0.00523 +2026-04-13 01:43:24.654249: train_loss -0.4088 +2026-04-13 01:43:24.661962: val_loss -0.3772 +2026-04-13 01:43:24.664090: Pseudo dice [0.7485, 0.0, 0.7346, 0.0487, 0.5002, 0.7884, 0.7935] +2026-04-13 01:43:24.666172: Epoch time: 101.78 s +2026-04-13 01:43:25.847567: +2026-04-13 01:43:25.849671: Epoch 2053 +2026-04-13 01:43:25.851550: Current learning rate: 0.00523 +2026-04-13 01:45:09.787579: train_loss -0.38 +2026-04-13 01:45:09.793340: val_loss -0.3439 +2026-04-13 01:45:09.797375: Pseudo dice [0.7464, 0.0, 0.6846, 0.0009, 0.4588, 0.5141, 0.8827] +2026-04-13 01:45:09.800065: Epoch time: 103.94 s +2026-04-13 01:45:11.019683: +2026-04-13 01:45:11.021513: Epoch 2054 +2026-04-13 01:45:11.024473: Current learning rate: 0.00523 +2026-04-13 01:46:52.809540: train_loss -0.4078 +2026-04-13 01:46:52.817968: val_loss -0.3963 +2026-04-13 01:46:52.820131: Pseudo dice [0.4777, 0.0, 0.7997, 0.5296, 0.5544, 0.7944, 0.9436] +2026-04-13 01:46:52.822747: Epoch time: 101.79 s +2026-04-13 01:46:54.003071: +2026-04-13 01:46:54.004962: Epoch 2055 +2026-04-13 01:46:54.006737: Current learning rate: 0.00523 +2026-04-13 01:48:35.981876: train_loss -0.3972 +2026-04-13 01:48:35.988472: val_loss -0.3124 +2026-04-13 01:48:35.991994: Pseudo dice [0.0124, 0.0, 0.7202, 0.2656, 0.0994, 0.5479, 0.7693] +2026-04-13 01:48:35.995389: Epoch time: 101.98 s +2026-04-13 01:48:37.190973: +2026-04-13 01:48:37.193269: Epoch 2056 +2026-04-13 01:48:37.195468: Current learning rate: 0.00522 +2026-04-13 01:50:18.969340: train_loss -0.3773 +2026-04-13 01:50:18.975939: val_loss -0.3145 +2026-04-13 01:50:18.977931: Pseudo dice [0.0332, 0.0, 0.6804, 0.0634, 0.43, 0.5629, 0.535] +2026-04-13 01:50:18.981243: Epoch time: 101.78 s +2026-04-13 01:50:20.152585: +2026-04-13 01:50:20.154553: Epoch 2057 +2026-04-13 01:50:20.156074: Current learning rate: 0.00522 +2026-04-13 01:52:01.782574: train_loss -0.3972 +2026-04-13 01:52:01.788228: val_loss -0.3508 +2026-04-13 01:52:01.790926: Pseudo dice [0.6197, 0.0, 0.8281, 0.2147, 0.5383, 0.2997, 0.5908] +2026-04-13 01:52:01.795379: Epoch time: 101.63 s +2026-04-13 01:52:02.995066: +2026-04-13 01:52:02.996809: Epoch 2058 +2026-04-13 01:52:02.998285: Current learning rate: 0.00522 +2026-04-13 01:53:44.695612: train_loss -0.4107 +2026-04-13 01:53:44.703376: val_loss -0.3807 +2026-04-13 01:53:44.705866: Pseudo dice [0.6718, 0.0, 0.7601, 0.5694, 0.4426, 0.5496, 0.8838] +2026-04-13 01:53:44.710208: Epoch time: 101.7 s +2026-04-13 01:53:45.946488: +2026-04-13 01:53:45.948736: Epoch 2059 +2026-04-13 01:53:45.950706: Current learning rate: 0.00522 +2026-04-13 01:55:29.185096: train_loss -0.3684 +2026-04-13 01:55:29.191994: val_loss -0.3594 +2026-04-13 01:55:29.193934: Pseudo dice [0.1696, 0.0, 0.7577, 0.235, 0.6967, 0.4441, 0.6322] +2026-04-13 01:55:29.196702: Epoch time: 103.24 s +2026-04-13 01:55:30.456164: +2026-04-13 01:55:30.457988: Epoch 2060 +2026-04-13 01:55:30.459970: Current learning rate: 0.00521 +2026-04-13 01:57:12.090111: train_loss -0.3817 +2026-04-13 01:57:12.095252: val_loss -0.3442 +2026-04-13 01:57:12.097489: Pseudo dice [0.1613, 0.0, 0.3941, 0.0192, 0.6633, 0.7726, 0.9178] +2026-04-13 01:57:12.100001: Epoch time: 101.64 s +2026-04-13 01:57:13.329896: +2026-04-13 01:57:13.331740: Epoch 2061 +2026-04-13 01:57:13.333348: Current learning rate: 0.00521 +2026-04-13 01:58:54.684646: train_loss -0.3945 +2026-04-13 01:58:54.690567: val_loss -0.357 +2026-04-13 01:58:54.694271: Pseudo dice [0.3009, 0.0, 0.6245, 0.7185, 0.5296, 0.4976, 0.6999] +2026-04-13 01:58:54.697164: Epoch time: 101.36 s +2026-04-13 01:58:55.877715: +2026-04-13 01:58:55.879551: Epoch 2062 +2026-04-13 01:58:55.881219: Current learning rate: 0.00521 +2026-04-13 02:00:37.673264: train_loss -0.3666 +2026-04-13 02:00:37.681125: val_loss -0.3636 +2026-04-13 02:00:37.683973: Pseudo dice [0.7173, 0.0, 0.5636, 0.5681, 0.4717, 0.1911, 0.878] +2026-04-13 02:00:37.686488: Epoch time: 101.8 s +2026-04-13 02:00:38.865748: +2026-04-13 02:00:38.867534: Epoch 2063 +2026-04-13 02:00:38.869209: Current learning rate: 0.00521 +2026-04-13 02:02:20.554065: train_loss -0.4059 +2026-04-13 02:02:20.559875: val_loss -0.3785 +2026-04-13 02:02:20.562234: Pseudo dice [0.6226, 0.0, 0.6297, 0.4957, 0.4848, 0.6301, 0.77] +2026-04-13 02:02:20.564578: Epoch time: 101.69 s +2026-04-13 02:02:21.741743: +2026-04-13 02:02:21.743719: Epoch 2064 +2026-04-13 02:02:21.745930: Current learning rate: 0.0052 +2026-04-13 02:04:03.620003: train_loss -0.3924 +2026-04-13 02:04:03.626288: val_loss -0.3498 +2026-04-13 02:04:03.628194: Pseudo dice [0.1418, 0.0, 0.7367, 0.4166, 0.3501, 0.5437, 0.6963] +2026-04-13 02:04:03.630224: Epoch time: 101.88 s +2026-04-13 02:04:04.814874: +2026-04-13 02:04:04.816895: Epoch 2065 +2026-04-13 02:04:04.819917: Current learning rate: 0.0052 +2026-04-13 02:05:46.694001: train_loss -0.4109 +2026-04-13 02:05:46.700253: val_loss -0.3896 +2026-04-13 02:05:46.702133: Pseudo dice [0.2684, 0.0, 0.7945, 0.8204, 0.4345, 0.3512, 0.7432] +2026-04-13 02:05:46.704603: Epoch time: 101.88 s +2026-04-13 02:05:47.906379: +2026-04-13 02:05:47.908345: Epoch 2066 +2026-04-13 02:05:47.910447: Current learning rate: 0.0052 +2026-04-13 02:07:29.799064: train_loss -0.3889 +2026-04-13 02:07:29.806066: val_loss -0.342 +2026-04-13 02:07:29.808085: Pseudo dice [0.6476, 0.0, 0.6969, 0.3301, 0.4115, 0.6136, 0.6971] +2026-04-13 02:07:29.810088: Epoch time: 101.9 s +2026-04-13 02:07:30.995635: +2026-04-13 02:07:30.998607: Epoch 2067 +2026-04-13 02:07:31.000596: Current learning rate: 0.0052 +2026-04-13 02:09:12.552001: train_loss -0.3938 +2026-04-13 02:09:12.559936: val_loss -0.3292 +2026-04-13 02:09:12.561781: Pseudo dice [0.3641, 0.0, 0.7906, 0.6296, 0.4057, 0.5447, 0.5802] +2026-04-13 02:09:12.564100: Epoch time: 101.56 s +2026-04-13 02:09:13.753317: +2026-04-13 02:09:13.755545: Epoch 2068 +2026-04-13 02:09:13.757271: Current learning rate: 0.00519 +2026-04-13 02:10:55.689802: train_loss -0.3955 +2026-04-13 02:10:55.696029: val_loss -0.3307 +2026-04-13 02:10:55.698181: Pseudo dice [0.0, 0.0, 0.6522, 0.2787, 0.5586, 0.7689, 0.7279] +2026-04-13 02:10:55.700707: Epoch time: 101.94 s +2026-04-13 02:10:56.903174: +2026-04-13 02:10:56.904857: Epoch 2069 +2026-04-13 02:10:56.906400: Current learning rate: 0.00519 +2026-04-13 02:12:38.737853: train_loss -0.388 +2026-04-13 02:12:38.744730: val_loss -0.3534 +2026-04-13 02:12:38.746907: Pseudo dice [0.0, 0.0, 0.7276, 0.0028, 0.4494, 0.3763, 0.8218] +2026-04-13 02:12:38.750981: Epoch time: 101.84 s +2026-04-13 02:12:39.950757: +2026-04-13 02:12:39.952614: Epoch 2070 +2026-04-13 02:12:39.954346: Current learning rate: 0.00519 +2026-04-13 02:14:21.564430: train_loss -0.3984 +2026-04-13 02:14:21.570430: val_loss -0.3431 +2026-04-13 02:14:21.572862: Pseudo dice [0.0, 0.0, 0.6509, 0.6055, 0.6487, 0.5775, 0.7611] +2026-04-13 02:14:21.575680: Epoch time: 101.62 s +2026-04-13 02:14:22.756100: +2026-04-13 02:14:22.757761: Epoch 2071 +2026-04-13 02:14:22.759317: Current learning rate: 0.00519 +2026-04-13 02:16:04.162224: train_loss -0.3988 +2026-04-13 02:16:04.168826: val_loss -0.3842 +2026-04-13 02:16:04.170999: Pseudo dice [0.0, 0.0, 0.7357, 0.7413, 0.5964, 0.6119, 0.9132] +2026-04-13 02:16:04.174165: Epoch time: 101.41 s +2026-04-13 02:16:05.363606: +2026-04-13 02:16:05.365669: Epoch 2072 +2026-04-13 02:16:05.367680: Current learning rate: 0.00518 +2026-04-13 02:17:46.958892: train_loss -0.3953 +2026-04-13 02:17:46.964267: val_loss -0.3989 +2026-04-13 02:17:46.966137: Pseudo dice [0.0, 0.0, 0.717, 0.6226, 0.4415, 0.1839, 0.656] +2026-04-13 02:17:46.968416: Epoch time: 101.6 s +2026-04-13 02:17:48.132120: +2026-04-13 02:17:48.134100: Epoch 2073 +2026-04-13 02:17:48.135741: Current learning rate: 0.00518 +2026-04-13 02:19:30.438363: train_loss -0.4154 +2026-04-13 02:19:30.444517: val_loss -0.3722 +2026-04-13 02:19:30.446125: Pseudo dice [0.4774, 0.0, 0.6662, 0.5423, 0.5819, 0.6647, 0.7957] +2026-04-13 02:19:30.448153: Epoch time: 102.31 s +2026-04-13 02:19:31.616023: +2026-04-13 02:19:31.618313: Epoch 2074 +2026-04-13 02:19:31.620178: Current learning rate: 0.00518 +2026-04-13 02:21:13.481660: train_loss -0.434 +2026-04-13 02:21:13.488534: val_loss -0.3782 +2026-04-13 02:21:13.490647: Pseudo dice [0.4629, 0.0, 0.8315, 0.7813, 0.4538, 0.7412, 0.6607] +2026-04-13 02:21:13.493140: Epoch time: 101.87 s +2026-04-13 02:21:14.678889: +2026-04-13 02:21:14.680570: Epoch 2075 +2026-04-13 02:21:14.682315: Current learning rate: 0.00518 +2026-04-13 02:22:56.431052: train_loss -0.4073 +2026-04-13 02:22:56.437778: val_loss -0.372 +2026-04-13 02:22:56.440076: Pseudo dice [0.5129, 0.0, 0.7471, 0.5315, 0.4909, 0.8132, 0.8648] +2026-04-13 02:22:56.442777: Epoch time: 101.76 s +2026-04-13 02:22:57.619891: +2026-04-13 02:22:57.622135: Epoch 2076 +2026-04-13 02:22:57.624399: Current learning rate: 0.00518 +2026-04-13 02:24:40.086286: train_loss -0.3943 +2026-04-13 02:24:40.112853: val_loss -0.3434 +2026-04-13 02:24:40.114808: Pseudo dice [0.672, 0.0, 0.677, 0.6796, 0.469, 0.0741, 0.5685] +2026-04-13 02:24:40.117419: Epoch time: 102.47 s +2026-04-13 02:24:41.385764: +2026-04-13 02:24:41.388019: Epoch 2077 +2026-04-13 02:24:41.389810: Current learning rate: 0.00517 +2026-04-13 02:26:23.046436: train_loss -0.3867 +2026-04-13 02:26:23.053786: val_loss -0.357 +2026-04-13 02:26:23.056576: Pseudo dice [0.2863, 0.0, 0.6556, 0.7757, 0.584, 0.7245, 0.826] +2026-04-13 02:26:23.059059: Epoch time: 101.66 s +2026-04-13 02:26:24.277871: +2026-04-13 02:26:24.280196: Epoch 2078 +2026-04-13 02:26:24.281752: Current learning rate: 0.00517 +2026-04-13 02:28:05.861098: train_loss -0.4092 +2026-04-13 02:28:05.866439: val_loss -0.3593 +2026-04-13 02:28:05.868517: Pseudo dice [0.7432, 0.0, 0.7989, 0.3149, 0.4062, 0.5418, 0.9079] +2026-04-13 02:28:05.870590: Epoch time: 101.59 s +2026-04-13 02:28:07.054888: +2026-04-13 02:28:07.056478: Epoch 2079 +2026-04-13 02:28:07.058000: Current learning rate: 0.00517 +2026-04-13 02:29:48.694429: train_loss -0.425 +2026-04-13 02:29:48.700734: val_loss -0.3419 +2026-04-13 02:29:48.702818: Pseudo dice [0.8269, 0.0, 0.642, 0.2832, 0.5668, 0.7258, 0.9255] +2026-04-13 02:29:48.705406: Epoch time: 101.64 s +2026-04-13 02:29:50.918041: +2026-04-13 02:29:50.919783: Epoch 2080 +2026-04-13 02:29:50.921304: Current learning rate: 0.00517 +2026-04-13 02:31:32.819915: train_loss -0.4194 +2026-04-13 02:31:32.826692: val_loss -0.3951 +2026-04-13 02:31:32.829563: Pseudo dice [0.6502, 0.0, 0.7667, 0.3866, 0.5253, 0.7595, 0.8803] +2026-04-13 02:31:32.831989: Epoch time: 101.9 s +2026-04-13 02:31:34.045305: +2026-04-13 02:31:34.047534: Epoch 2081 +2026-04-13 02:31:34.049867: Current learning rate: 0.00516 +2026-04-13 02:33:15.972813: train_loss -0.4105 +2026-04-13 02:33:15.980268: val_loss -0.3369 +2026-04-13 02:33:15.983216: Pseudo dice [0.3697, 0.0, 0.7852, 0.4406, 0.4241, 0.336, 0.2004] +2026-04-13 02:33:15.990084: Epoch time: 101.93 s +2026-04-13 02:33:17.186953: +2026-04-13 02:33:17.190113: Epoch 2082 +2026-04-13 02:33:17.192489: Current learning rate: 0.00516 +2026-04-13 02:34:59.125094: train_loss -0.4238 +2026-04-13 02:34:59.131619: val_loss -0.3622 +2026-04-13 02:34:59.133596: Pseudo dice [0.2328, 0.0, 0.8698, 0.4079, 0.4788, 0.3883, 0.8302] +2026-04-13 02:34:59.136312: Epoch time: 101.94 s +2026-04-13 02:35:00.342425: +2026-04-13 02:35:00.344412: Epoch 2083 +2026-04-13 02:35:00.345953: Current learning rate: 0.00516 +2026-04-13 02:36:42.624325: train_loss -0.4245 +2026-04-13 02:36:42.632612: val_loss -0.3515 +2026-04-13 02:36:42.634871: Pseudo dice [0.3575, 0.0, 0.7894, 0.3693, 0.5905, 0.5119, 0.7639] +2026-04-13 02:36:42.637547: Epoch time: 102.29 s +2026-04-13 02:36:43.849137: +2026-04-13 02:36:43.850931: Epoch 2084 +2026-04-13 02:36:43.852645: Current learning rate: 0.00516 +2026-04-13 02:38:25.993097: train_loss -0.4172 +2026-04-13 02:38:26.000112: val_loss -0.3412 +2026-04-13 02:38:26.002178: Pseudo dice [0.5203, 0.0, 0.6404, 0.6669, 0.5139, 0.7014, 0.4995] +2026-04-13 02:38:26.004830: Epoch time: 102.15 s +2026-04-13 02:38:27.215550: +2026-04-13 02:38:27.217528: Epoch 2085 +2026-04-13 02:38:27.219626: Current learning rate: 0.00515 +2026-04-13 02:40:09.046328: train_loss -0.4152 +2026-04-13 02:40:09.053567: val_loss -0.3218 +2026-04-13 02:40:09.055731: Pseudo dice [0.5069, 0.0, 0.7848, 0.1674, 0.6155, 0.6608, 0.234] +2026-04-13 02:40:09.058734: Epoch time: 101.83 s +2026-04-13 02:40:10.250978: +2026-04-13 02:40:10.253196: Epoch 2086 +2026-04-13 02:40:10.254793: Current learning rate: 0.00515 +2026-04-13 02:41:52.267437: train_loss -0.4115 +2026-04-13 02:41:52.273680: val_loss -0.3714 +2026-04-13 02:41:52.287918: Pseudo dice [0.7493, 0.0, 0.7437, 0.5686, 0.4205, 0.7009, 0.7092] +2026-04-13 02:41:52.290789: Epoch time: 102.02 s +2026-04-13 02:41:53.472019: +2026-04-13 02:41:53.473838: Epoch 2087 +2026-04-13 02:41:53.475325: Current learning rate: 0.00515 +2026-04-13 02:43:35.368445: train_loss -0.4303 +2026-04-13 02:43:35.379822: val_loss -0.3823 +2026-04-13 02:43:35.382803: Pseudo dice [0.5373, 0.0, 0.7054, 0.7253, 0.6362, 0.4967, 0.7616] +2026-04-13 02:43:35.386014: Epoch time: 101.9 s +2026-04-13 02:43:36.650759: +2026-04-13 02:43:36.652979: Epoch 2088 +2026-04-13 02:43:36.657424: Current learning rate: 0.00515 +2026-04-13 02:45:18.270946: train_loss -0.4236 +2026-04-13 02:45:18.277073: val_loss -0.386 +2026-04-13 02:45:18.279768: Pseudo dice [0.8315, 0.0, 0.7565, 0.8095, 0.6345, 0.6941, 0.6879] +2026-04-13 02:45:18.282272: Epoch time: 101.62 s +2026-04-13 02:45:19.482048: +2026-04-13 02:45:19.484546: Epoch 2089 +2026-04-13 02:45:19.487026: Current learning rate: 0.00514 +2026-04-13 02:47:01.117997: train_loss -0.4336 +2026-04-13 02:47:01.124013: val_loss -0.376 +2026-04-13 02:47:01.126329: Pseudo dice [0.4954, 0.0, 0.819, 0.6108, 0.6702, 0.7374, 0.3303] +2026-04-13 02:47:01.128443: Epoch time: 101.64 s +2026-04-13 02:47:02.332438: +2026-04-13 02:47:02.334514: Epoch 2090 +2026-04-13 02:47:02.336818: Current learning rate: 0.00514 +2026-04-13 02:48:43.737499: train_loss -0.4239 +2026-04-13 02:48:43.743438: val_loss -0.3144 +2026-04-13 02:48:43.745612: Pseudo dice [0.2499, 0.0, 0.8122, 0.6993, 0.3268, 0.609, 0.3443] +2026-04-13 02:48:43.748474: Epoch time: 101.41 s +2026-04-13 02:48:44.971053: +2026-04-13 02:48:44.973121: Epoch 2091 +2026-04-13 02:48:44.975005: Current learning rate: 0.00514 +2026-04-13 02:50:27.026253: train_loss -0.4121 +2026-04-13 02:50:27.032550: val_loss -0.3334 +2026-04-13 02:50:27.034501: Pseudo dice [0.0, 0.0, 0.8609, 0.6318, 0.4156, 0.8766, 0.6935] +2026-04-13 02:50:27.036644: Epoch time: 102.06 s +2026-04-13 02:50:28.215772: +2026-04-13 02:50:28.217891: Epoch 2092 +2026-04-13 02:50:28.219511: Current learning rate: 0.00514 +2026-04-13 02:52:09.926970: train_loss -0.4019 +2026-04-13 02:52:09.933613: val_loss -0.3466 +2026-04-13 02:52:09.935765: Pseudo dice [0.0, 0.0, 0.8509, 0.4676, 0.6511, 0.6861, 0.9213] +2026-04-13 02:52:09.939241: Epoch time: 101.71 s +2026-04-13 02:52:11.120693: +2026-04-13 02:52:11.122793: Epoch 2093 +2026-04-13 02:52:11.124959: Current learning rate: 0.00513 +2026-04-13 02:53:53.413495: train_loss -0.4272 +2026-04-13 02:53:53.420097: val_loss -0.3307 +2026-04-13 02:53:53.422661: Pseudo dice [0.0965, 0.0, 0.7072, 0.5902, 0.5776, 0.2144, 0.3857] +2026-04-13 02:53:53.424856: Epoch time: 102.3 s +2026-04-13 02:53:54.608663: +2026-04-13 02:53:54.610353: Epoch 2094 +2026-04-13 02:53:54.611758: Current learning rate: 0.00513 +2026-04-13 02:55:36.646941: train_loss -0.4155 +2026-04-13 02:55:36.652945: val_loss -0.3529 +2026-04-13 02:55:36.655281: Pseudo dice [0.7131, 0.0, 0.6684, 0.4694, 0.4779, 0.7003, 0.2004] +2026-04-13 02:55:36.657364: Epoch time: 102.04 s +2026-04-13 02:55:37.884044: +2026-04-13 02:55:37.885994: Epoch 2095 +2026-04-13 02:55:37.887541: Current learning rate: 0.00513 +2026-04-13 02:57:19.992177: train_loss -0.4138 +2026-04-13 02:57:19.998203: val_loss -0.4014 +2026-04-13 02:57:20.000456: Pseudo dice [0.6995, 0.0, 0.6929, 0.5594, 0.6278, 0.5521, 0.8691] +2026-04-13 02:57:20.003146: Epoch time: 102.11 s +2026-04-13 02:57:21.197646: +2026-04-13 02:57:21.199282: Epoch 2096 +2026-04-13 02:57:21.200772: Current learning rate: 0.00513 +2026-04-13 02:59:02.932357: train_loss -0.4197 +2026-04-13 02:59:02.938219: val_loss -0.3488 +2026-04-13 02:59:02.940460: Pseudo dice [0.3759, 0.0, 0.5954, 0.7495, 0.4644, 0.566, 0.7637] +2026-04-13 02:59:02.943005: Epoch time: 101.74 s +2026-04-13 02:59:04.127019: +2026-04-13 02:59:04.128786: Epoch 2097 +2026-04-13 02:59:04.130320: Current learning rate: 0.00512 +2026-04-13 03:00:45.706479: train_loss -0.4278 +2026-04-13 03:00:45.712790: val_loss -0.3468 +2026-04-13 03:00:45.715595: Pseudo dice [0.2434, 0.0, 0.8587, 0.531, 0.5078, 0.6278, 0.865] +2026-04-13 03:00:45.718672: Epoch time: 101.58 s +2026-04-13 03:00:46.900615: +2026-04-13 03:00:46.902351: Epoch 2098 +2026-04-13 03:00:46.903921: Current learning rate: 0.00512 +2026-04-13 03:02:29.251869: train_loss -0.4177 +2026-04-13 03:02:29.261863: val_loss -0.3334 +2026-04-13 03:02:29.265711: Pseudo dice [0.3542, 0.0, 0.7465, 0.1131, 0.5316, 0.7402, 0.4563] +2026-04-13 03:02:29.269014: Epoch time: 102.35 s +2026-04-13 03:02:30.456903: +2026-04-13 03:02:30.459266: Epoch 2099 +2026-04-13 03:02:30.461692: Current learning rate: 0.00512 +2026-04-13 03:04:12.111954: train_loss -0.4199 +2026-04-13 03:04:12.118251: val_loss -0.3878 +2026-04-13 03:04:12.120364: Pseudo dice [0.3755, 0.0, 0.7937, 0.6315, 0.6062, 0.4487, 0.8893] +2026-04-13 03:04:12.122405: Epoch time: 101.66 s +2026-04-13 03:04:16.039769: +2026-04-13 03:04:16.041771: Epoch 2100 +2026-04-13 03:04:16.043413: Current learning rate: 0.00512 +2026-04-13 03:05:57.986500: train_loss -0.4031 +2026-04-13 03:05:57.994089: val_loss -0.3671 +2026-04-13 03:05:57.996213: Pseudo dice [0.4224, 0.0, 0.7593, 0.4299, 0.5041, 0.7732, 0.8022] +2026-04-13 03:05:57.998490: Epoch time: 101.95 s +2026-04-13 03:05:59.212002: +2026-04-13 03:05:59.214355: Epoch 2101 +2026-04-13 03:05:59.216307: Current learning rate: 0.00511 +2026-04-13 03:07:40.988200: train_loss -0.3975 +2026-04-13 03:07:40.994968: val_loss -0.3539 +2026-04-13 03:07:40.997574: Pseudo dice [0.541, 0.0, 0.5776, 0.282, 0.5501, 0.4038, 0.9296] +2026-04-13 03:07:41.000160: Epoch time: 101.78 s +2026-04-13 03:07:42.176381: +2026-04-13 03:07:42.178449: Epoch 2102 +2026-04-13 03:07:42.180191: Current learning rate: 0.00511 +2026-04-13 03:09:24.167122: train_loss -0.4052 +2026-04-13 03:09:24.175976: val_loss -0.335 +2026-04-13 03:09:24.178469: Pseudo dice [0.5826, 0.0, 0.6537, 0.7577, 0.4819, 0.5891, 0.8922] +2026-04-13 03:09:24.181673: Epoch time: 101.99 s +2026-04-13 03:09:25.422126: +2026-04-13 03:09:25.424301: Epoch 2103 +2026-04-13 03:09:25.426353: Current learning rate: 0.00511 +2026-04-13 03:11:07.393117: train_loss -0.3991 +2026-04-13 03:11:07.399252: val_loss -0.3309 +2026-04-13 03:11:07.401264: Pseudo dice [0.0321, 0.0, 0.8294, 0.2183, 0.5022, 0.6645, 0.5151] +2026-04-13 03:11:07.404245: Epoch time: 101.97 s +2026-04-13 03:11:08.602375: +2026-04-13 03:11:08.604402: Epoch 2104 +2026-04-13 03:11:08.606531: Current learning rate: 0.00511 +2026-04-13 03:12:50.160761: train_loss -0.4087 +2026-04-13 03:12:50.166996: val_loss -0.3607 +2026-04-13 03:12:50.168909: Pseudo dice [0.6877, 0.0, 0.7789, 0.4453, 0.5169, 0.3786, 0.8493] +2026-04-13 03:12:50.171081: Epoch time: 101.56 s +2026-04-13 03:12:51.348098: +2026-04-13 03:12:51.349899: Epoch 2105 +2026-04-13 03:12:51.351510: Current learning rate: 0.0051 +2026-04-13 03:14:32.793252: train_loss -0.4093 +2026-04-13 03:14:32.799057: val_loss -0.3511 +2026-04-13 03:14:32.800912: Pseudo dice [0.4002, 0.0, 0.8073, 0.5025, 0.679, 0.0498, 0.636] +2026-04-13 03:14:32.803494: Epoch time: 101.45 s +2026-04-13 03:14:33.976559: +2026-04-13 03:14:33.978321: Epoch 2106 +2026-04-13 03:14:33.979814: Current learning rate: 0.0051 +2026-04-13 03:16:15.988311: train_loss -0.4084 +2026-04-13 03:16:15.995157: val_loss -0.3707 +2026-04-13 03:16:15.997787: Pseudo dice [0.6458, 0.0, 0.8238, 0.295, 0.5666, 0.519, 0.7105] +2026-04-13 03:16:16.000557: Epoch time: 102.01 s +2026-04-13 03:16:17.234391: +2026-04-13 03:16:17.237678: Epoch 2107 +2026-04-13 03:16:17.239379: Current learning rate: 0.0051 +2026-04-13 03:17:59.063160: train_loss -0.4153 +2026-04-13 03:17:59.081946: val_loss -0.4103 +2026-04-13 03:17:59.084250: Pseudo dice [0.4489, 0.0, 0.5021, 0.4617, 0.5575, 0.7067, 0.9049] +2026-04-13 03:17:59.086722: Epoch time: 101.83 s +2026-04-13 03:18:00.295240: +2026-04-13 03:18:00.296969: Epoch 2108 +2026-04-13 03:18:00.298537: Current learning rate: 0.0051 +2026-04-13 03:19:42.127760: train_loss -0.4052 +2026-04-13 03:19:42.135236: val_loss -0.3817 +2026-04-13 03:19:42.137312: Pseudo dice [0.3055, 0.0, 0.4819, 0.8303, 0.5822, 0.5369, 0.8571] +2026-04-13 03:19:42.139426: Epoch time: 101.84 s +2026-04-13 03:19:43.302778: +2026-04-13 03:19:43.305251: Epoch 2109 +2026-04-13 03:19:43.306842: Current learning rate: 0.0051 +2026-04-13 03:21:25.131164: train_loss -0.3983 +2026-04-13 03:21:25.138612: val_loss -0.3592 +2026-04-13 03:21:25.140969: Pseudo dice [0.0, 0.0, 0.757, 0.7726, 0.6735, 0.4771, 0.8786] +2026-04-13 03:21:25.143373: Epoch time: 101.83 s +2026-04-13 03:21:26.366754: +2026-04-13 03:21:26.368459: Epoch 2110 +2026-04-13 03:21:26.369860: Current learning rate: 0.00509 +2026-04-13 03:23:08.351060: train_loss -0.4055 +2026-04-13 03:23:08.357712: val_loss -0.3353 +2026-04-13 03:23:08.360197: Pseudo dice [0.0088, 0.0, 0.641, 0.0016, 0.5026, 0.5557, 0.8334] +2026-04-13 03:23:08.362301: Epoch time: 101.99 s +2026-04-13 03:23:09.553959: +2026-04-13 03:23:09.556076: Epoch 2111 +2026-04-13 03:23:09.559060: Current learning rate: 0.00509 +2026-04-13 03:24:51.172531: train_loss -0.4109 +2026-04-13 03:24:51.179425: val_loss -0.3446 +2026-04-13 03:24:51.181715: Pseudo dice [0.0077, 0.0, 0.7976, 0.0993, 0.6002, 0.3014, 0.8254] +2026-04-13 03:24:51.184476: Epoch time: 101.62 s +2026-04-13 03:24:52.372940: +2026-04-13 03:24:52.374572: Epoch 2112 +2026-04-13 03:24:52.376172: Current learning rate: 0.00509 +2026-04-13 03:26:34.260186: train_loss -0.4025 +2026-04-13 03:26:34.266166: val_loss -0.3676 +2026-04-13 03:26:34.267942: Pseudo dice [0.5909, 0.0, 0.8445, 0.4995, 0.5125, 0.4651, 0.8991] +2026-04-13 03:26:34.270553: Epoch time: 101.89 s +2026-04-13 03:26:35.452771: +2026-04-13 03:26:35.454480: Epoch 2113 +2026-04-13 03:26:35.455984: Current learning rate: 0.00509 +2026-04-13 03:28:17.266155: train_loss -0.4193 +2026-04-13 03:28:17.271990: val_loss -0.3488 +2026-04-13 03:28:17.273896: Pseudo dice [0.4438, 0.0, 0.6965, 0.3371, 0.4456, 0.6867, 0.809] +2026-04-13 03:28:17.276281: Epoch time: 101.82 s +2026-04-13 03:28:18.465853: +2026-04-13 03:28:18.467790: Epoch 2114 +2026-04-13 03:28:18.469582: Current learning rate: 0.00508 +2026-04-13 03:30:00.957271: train_loss -0.3964 +2026-04-13 03:30:00.964180: val_loss -0.3019 +2026-04-13 03:30:00.965886: Pseudo dice [0.4895, 0.0, 0.4997, 0.3118, 0.5978, 0.5529, 0.3322] +2026-04-13 03:30:00.969404: Epoch time: 102.49 s +2026-04-13 03:30:02.187563: +2026-04-13 03:30:02.191001: Epoch 2115 +2026-04-13 03:30:02.193498: Current learning rate: 0.00508 +2026-04-13 03:31:43.685170: train_loss -0.4 +2026-04-13 03:31:43.690965: val_loss -0.3301 +2026-04-13 03:31:43.692776: Pseudo dice [0.5782, 0.0, 0.6921, 0.2011, 0.4988, 0.4684, 0.6199] +2026-04-13 03:31:43.695530: Epoch time: 101.5 s +2026-04-13 03:31:44.864213: +2026-04-13 03:31:44.866214: Epoch 2116 +2026-04-13 03:31:44.868044: Current learning rate: 0.00508 +2026-04-13 03:33:26.677773: train_loss -0.4091 +2026-04-13 03:33:26.684334: val_loss -0.3683 +2026-04-13 03:33:26.686271: Pseudo dice [0.6654, 0.0, 0.6144, 0.2692, 0.5729, 0.4756, 0.8681] +2026-04-13 03:33:26.690116: Epoch time: 101.82 s +2026-04-13 03:33:27.898521: +2026-04-13 03:33:27.900252: Epoch 2117 +2026-04-13 03:33:27.901898: Current learning rate: 0.00508 +2026-04-13 03:35:09.407167: train_loss -0.4132 +2026-04-13 03:35:09.413233: val_loss -0.3572 +2026-04-13 03:35:09.415152: Pseudo dice [0.4053, 0.0, 0.8112, 0.0833, 0.5953, 0.6852, 0.823] +2026-04-13 03:35:09.417475: Epoch time: 101.51 s +2026-04-13 03:35:10.616120: +2026-04-13 03:35:10.617856: Epoch 2118 +2026-04-13 03:35:10.619324: Current learning rate: 0.00507 +2026-04-13 03:36:52.289305: train_loss -0.4134 +2026-04-13 03:36:52.295940: val_loss -0.373 +2026-04-13 03:36:52.298267: Pseudo dice [0.2386, 0.0, 0.6939, 0.022, 0.3035, 0.4647, 0.8267] +2026-04-13 03:36:52.300804: Epoch time: 101.68 s +2026-04-13 03:36:53.500205: +2026-04-13 03:36:53.502504: Epoch 2119 +2026-04-13 03:36:53.504682: Current learning rate: 0.00507 +2026-04-13 03:38:35.639536: train_loss -0.4196 +2026-04-13 03:38:35.646077: val_loss -0.344 +2026-04-13 03:38:35.648003: Pseudo dice [0.6301, 0.0, 0.7955, 0.2499, 0.5479, 0.6014, 0.9329] +2026-04-13 03:38:35.651103: Epoch time: 102.14 s +2026-04-13 03:38:36.877059: +2026-04-13 03:38:36.878783: Epoch 2120 +2026-04-13 03:38:36.880407: Current learning rate: 0.00507 +2026-04-13 03:40:18.768263: train_loss -0.4258 +2026-04-13 03:40:18.774195: val_loss -0.3845 +2026-04-13 03:40:18.775979: Pseudo dice [0.557, 0.0, 0.7991, 0.4773, 0.5811, 0.6134, 0.8664] +2026-04-13 03:40:18.777894: Epoch time: 101.89 s +2026-04-13 03:40:21.081426: +2026-04-13 03:40:21.083217: Epoch 2121 +2026-04-13 03:40:21.084691: Current learning rate: 0.00507 +2026-04-13 03:42:02.902567: train_loss -0.4171 +2026-04-13 03:42:02.909695: val_loss -0.3732 +2026-04-13 03:42:02.912552: Pseudo dice [0.3456, 0.0, 0.7377, 0.6939, 0.5047, 0.373, 0.8125] +2026-04-13 03:42:02.915916: Epoch time: 101.82 s +2026-04-13 03:42:04.101513: +2026-04-13 03:42:04.103506: Epoch 2122 +2026-04-13 03:42:04.105196: Current learning rate: 0.00506 +2026-04-13 03:43:46.174167: train_loss -0.3865 +2026-04-13 03:43:46.180501: val_loss -0.3147 +2026-04-13 03:43:46.182641: Pseudo dice [0.3013, 0.0, 0.7191, 0.2846, 0.2703, 0.3043, 0.6148] +2026-04-13 03:43:46.186312: Epoch time: 102.08 s +2026-04-13 03:43:47.359853: +2026-04-13 03:43:47.362113: Epoch 2123 +2026-04-13 03:43:47.364651: Current learning rate: 0.00506 +2026-04-13 03:45:28.993347: train_loss -0.3909 +2026-04-13 03:45:28.999528: val_loss -0.3694 +2026-04-13 03:45:29.001856: Pseudo dice [0.4, 0.0, 0.7387, 0.5082, 0.5101, 0.7776, 0.7748] +2026-04-13 03:45:29.004328: Epoch time: 101.64 s +2026-04-13 03:45:30.204275: +2026-04-13 03:45:30.206041: Epoch 2124 +2026-04-13 03:45:30.209550: Current learning rate: 0.00506 +2026-04-13 03:47:11.819516: train_loss -0.4111 +2026-04-13 03:47:11.826728: val_loss -0.3565 +2026-04-13 03:47:11.830321: Pseudo dice [0.5403, 0.0, 0.527, 0.2683, 0.4329, 0.3484, 0.4867] +2026-04-13 03:47:11.832423: Epoch time: 101.62 s +2026-04-13 03:47:13.026433: +2026-04-13 03:47:13.028610: Epoch 2125 +2026-04-13 03:47:13.030389: Current learning rate: 0.00506 +2026-04-13 03:48:54.787340: train_loss -0.42 +2026-04-13 03:48:54.794920: val_loss -0.3627 +2026-04-13 03:48:54.797527: Pseudo dice [0.135, 0.0, 0.6663, 0.4774, 0.3112, 0.678, 0.8694] +2026-04-13 03:48:54.799933: Epoch time: 101.76 s +2026-04-13 03:48:56.005714: +2026-04-13 03:48:56.008249: Epoch 2126 +2026-04-13 03:48:56.010179: Current learning rate: 0.00505 +2026-04-13 03:50:38.046414: train_loss -0.4113 +2026-04-13 03:50:38.053842: val_loss -0.3428 +2026-04-13 03:50:38.056537: Pseudo dice [0.1533, 0.0, 0.7458, 0.6726, 0.1538, 0.5833, 0.7773] +2026-04-13 03:50:38.059479: Epoch time: 102.04 s +2026-04-13 03:50:39.303878: +2026-04-13 03:50:39.305931: Epoch 2127 +2026-04-13 03:50:39.307745: Current learning rate: 0.00505 +2026-04-13 03:52:21.587496: train_loss -0.3961 +2026-04-13 03:52:21.595705: val_loss -0.3724 +2026-04-13 03:52:21.598173: Pseudo dice [0.7501, 0.0, 0.7819, 0.3846, 0.3984, 0.4399, 0.7011] +2026-04-13 03:52:21.602068: Epoch time: 102.29 s +2026-04-13 03:52:22.873117: +2026-04-13 03:52:22.874874: Epoch 2128 +2026-04-13 03:52:22.876350: Current learning rate: 0.00505 +2026-04-13 03:54:04.578679: train_loss -0.4213 +2026-04-13 03:54:04.585827: val_loss -0.3667 +2026-04-13 03:54:04.588324: Pseudo dice [0.6481, 0.0, 0.7752, 0.7878, 0.5284, 0.5142, 0.8146] +2026-04-13 03:54:04.590580: Epoch time: 101.71 s +2026-04-13 03:54:05.764971: +2026-04-13 03:54:05.766976: Epoch 2129 +2026-04-13 03:54:05.770156: Current learning rate: 0.00505 +2026-04-13 03:55:47.300152: train_loss -0.4137 +2026-04-13 03:55:47.307031: val_loss -0.3318 +2026-04-13 03:55:47.309085: Pseudo dice [0.3103, 0.0, 0.8616, 0.4146, 0.5114, 0.5325, 0.8293] +2026-04-13 03:55:47.311841: Epoch time: 101.54 s +2026-04-13 03:55:48.513531: +2026-04-13 03:55:48.515520: Epoch 2130 +2026-04-13 03:55:48.517345: Current learning rate: 0.00504 +2026-04-13 03:57:30.175981: train_loss -0.4108 +2026-04-13 03:57:30.181718: val_loss -0.3444 +2026-04-13 03:57:30.183433: Pseudo dice [0.5408, 0.0, 0.6936, 0.4965, 0.3336, 0.3824, 0.7148] +2026-04-13 03:57:30.185724: Epoch time: 101.67 s +2026-04-13 03:57:31.379633: +2026-04-13 03:57:31.381573: Epoch 2131 +2026-04-13 03:57:31.383141: Current learning rate: 0.00504 +2026-04-13 03:59:13.441724: train_loss -0.4082 +2026-04-13 03:59:13.447135: val_loss -0.3702 +2026-04-13 03:59:13.449037: Pseudo dice [0.5977, 0.0, 0.7307, 0.7289, 0.5464, 0.5145, 0.6618] +2026-04-13 03:59:13.451683: Epoch time: 102.07 s +2026-04-13 03:59:14.686129: +2026-04-13 03:59:14.688000: Epoch 2132 +2026-04-13 03:59:14.689923: Current learning rate: 0.00504 +2026-04-13 04:00:56.917879: train_loss -0.4076 +2026-04-13 04:00:56.923589: val_loss -0.3595 +2026-04-13 04:00:56.925725: Pseudo dice [0.655, 0.0, 0.7793, 0.0136, 0.5928, 0.5853, 0.9303] +2026-04-13 04:00:56.927934: Epoch time: 102.23 s +2026-04-13 04:00:58.118155: +2026-04-13 04:00:58.120131: Epoch 2133 +2026-04-13 04:00:58.121958: Current learning rate: 0.00504 +2026-04-13 04:02:40.081421: train_loss -0.4085 +2026-04-13 04:02:40.087809: val_loss -0.3508 +2026-04-13 04:02:40.089777: Pseudo dice [0.0862, 0.0, 0.7232, 0.5309, 0.5881, 0.359, 0.7156] +2026-04-13 04:02:40.092038: Epoch time: 101.97 s +2026-04-13 04:02:41.300527: +2026-04-13 04:02:41.302597: Epoch 2134 +2026-04-13 04:02:41.304278: Current learning rate: 0.00503 +2026-04-13 04:04:23.057378: train_loss -0.4248 +2026-04-13 04:04:23.063485: val_loss -0.3682 +2026-04-13 04:04:23.065748: Pseudo dice [0.5608, 0.0, 0.7152, 0.7351, 0.6359, 0.5041, 0.9127] +2026-04-13 04:04:23.068272: Epoch time: 101.76 s +2026-04-13 04:04:24.309000: +2026-04-13 04:04:24.310861: Epoch 2135 +2026-04-13 04:04:24.312722: Current learning rate: 0.00503 +2026-04-13 04:06:06.099002: train_loss -0.4246 +2026-04-13 04:06:06.105073: val_loss -0.349 +2026-04-13 04:06:06.107308: Pseudo dice [0.6046, 0.0, 0.748, 0.7899, 0.3978, 0.2686, 0.3242] +2026-04-13 04:06:06.109814: Epoch time: 101.79 s +2026-04-13 04:06:07.332541: +2026-04-13 04:06:07.334323: Epoch 2136 +2026-04-13 04:06:07.336489: Current learning rate: 0.00503 +2026-04-13 04:07:49.176287: train_loss -0.4077 +2026-04-13 04:07:49.182109: val_loss -0.3803 +2026-04-13 04:07:49.184008: Pseudo dice [0.3135, 0.0, 0.8217, 0.6674, 0.5808, 0.2716, 0.8653] +2026-04-13 04:07:49.186208: Epoch time: 101.85 s +2026-04-13 04:07:50.404386: +2026-04-13 04:07:50.406417: Epoch 2137 +2026-04-13 04:07:50.408023: Current learning rate: 0.00503 +2026-04-13 04:09:32.161233: train_loss -0.4044 +2026-04-13 04:09:32.167729: val_loss -0.3743 +2026-04-13 04:09:32.169688: Pseudo dice [0.5749, 0.0, 0.8421, 0.409, 0.3564, 0.6547, 0.8] +2026-04-13 04:09:32.172269: Epoch time: 101.76 s +2026-04-13 04:09:33.386466: +2026-04-13 04:09:33.388286: Epoch 2138 +2026-04-13 04:09:33.389970: Current learning rate: 0.00502 +2026-04-13 04:11:15.190534: train_loss -0.4243 +2026-04-13 04:11:15.196528: val_loss -0.3704 +2026-04-13 04:11:15.198903: Pseudo dice [0.6803, 0.0, 0.7856, 0.3947, 0.4118, 0.4098, 0.9014] +2026-04-13 04:11:15.201041: Epoch time: 101.81 s +2026-04-13 04:11:16.373400: +2026-04-13 04:11:16.375147: Epoch 2139 +2026-04-13 04:11:16.376706: Current learning rate: 0.00502 +2026-04-13 04:12:58.435190: train_loss -0.4077 +2026-04-13 04:12:58.440897: val_loss -0.3455 +2026-04-13 04:12:58.442977: Pseudo dice [0.3623, 0.0, 0.7235, 0.6604, 0.2397, 0.4326, 0.893] +2026-04-13 04:12:58.445129: Epoch time: 102.06 s +2026-04-13 04:12:59.656481: +2026-04-13 04:12:59.658523: Epoch 2140 +2026-04-13 04:12:59.660128: Current learning rate: 0.00502 +2026-04-13 04:14:41.527560: train_loss -0.3866 +2026-04-13 04:14:41.533531: val_loss -0.3488 +2026-04-13 04:14:41.535406: Pseudo dice [0.465, 0.0, 0.7712, 0.741, 0.3561, 0.4105, 0.878] +2026-04-13 04:14:41.537821: Epoch time: 101.87 s +2026-04-13 04:14:42.727056: +2026-04-13 04:14:42.728837: Epoch 2141 +2026-04-13 04:14:42.730473: Current learning rate: 0.00502 +2026-04-13 04:16:24.221682: train_loss -0.423 +2026-04-13 04:16:24.228363: val_loss -0.3755 +2026-04-13 04:16:24.230736: Pseudo dice [0.7525, 0.0, 0.6208, 0.6964, 0.4711, 0.7897, 0.6729] +2026-04-13 04:16:24.232881: Epoch time: 101.5 s +2026-04-13 04:16:26.499122: +2026-04-13 04:16:26.500899: Epoch 2142 +2026-04-13 04:16:26.502394: Current learning rate: 0.00502 +2026-04-13 04:18:07.901097: train_loss -0.4146 +2026-04-13 04:18:07.906858: val_loss -0.3659 +2026-04-13 04:18:07.908953: Pseudo dice [0.5375, 0.0, 0.7812, 0.2483, 0.2554, 0.6407, 0.8147] +2026-04-13 04:18:07.911386: Epoch time: 101.41 s +2026-04-13 04:18:09.098888: +2026-04-13 04:18:09.101175: Epoch 2143 +2026-04-13 04:18:09.102880: Current learning rate: 0.00501 +2026-04-13 04:19:50.603289: train_loss -0.4105 +2026-04-13 04:19:50.609886: val_loss -0.3765 +2026-04-13 04:19:50.611787: Pseudo dice [0.4241, 0.0, 0.8282, 0.5897, 0.2852, 0.4523, 0.9126] +2026-04-13 04:19:50.614214: Epoch time: 101.51 s +2026-04-13 04:19:51.821441: +2026-04-13 04:19:51.823184: Epoch 2144 +2026-04-13 04:19:51.824918: Current learning rate: 0.00501 +2026-04-13 04:21:33.244385: train_loss -0.4084 +2026-04-13 04:21:33.251484: val_loss -0.3435 +2026-04-13 04:21:33.253604: Pseudo dice [0.0813, 0.0, 0.7946, 0.6015, 0.3525, 0.5787, 0.8208] +2026-04-13 04:21:33.255971: Epoch time: 101.43 s +2026-04-13 04:21:34.439701: +2026-04-13 04:21:34.442673: Epoch 2145 +2026-04-13 04:21:34.444866: Current learning rate: 0.00501 +2026-04-13 04:23:15.791114: train_loss -0.3881 +2026-04-13 04:23:15.798573: val_loss -0.3371 +2026-04-13 04:23:15.801307: Pseudo dice [0.0, 0.0, 0.7453, 0.5591, 0.4825, 0.815, 0.7638] +2026-04-13 04:23:15.803608: Epoch time: 101.35 s +2026-04-13 04:23:16.997815: +2026-04-13 04:23:17.000044: Epoch 2146 +2026-04-13 04:23:17.002346: Current learning rate: 0.00501 +2026-04-13 04:24:58.624094: train_loss -0.4209 +2026-04-13 04:24:58.651265: val_loss -0.3553 +2026-04-13 04:24:58.653765: Pseudo dice [0.0, 0.0, 0.6571, 0.0844, 0.5044, 0.6294, 0.8239] +2026-04-13 04:24:58.656724: Epoch time: 101.63 s +2026-04-13 04:24:59.898944: +2026-04-13 04:24:59.901730: Epoch 2147 +2026-04-13 04:24:59.903578: Current learning rate: 0.005 +2026-04-13 04:26:41.745235: train_loss -0.425 +2026-04-13 04:26:41.751634: val_loss -0.3282 +2026-04-13 04:26:41.753410: Pseudo dice [0.0211, 0.0, 0.7488, 0.5003, 0.5565, 0.4786, 0.8071] +2026-04-13 04:26:41.755470: Epoch time: 101.85 s +2026-04-13 04:26:42.950486: +2026-04-13 04:26:42.952255: Epoch 2148 +2026-04-13 04:26:42.953843: Current learning rate: 0.005 +2026-04-13 04:28:24.539369: train_loss -0.4178 +2026-04-13 04:28:24.546876: val_loss -0.383 +2026-04-13 04:28:24.549057: Pseudo dice [0.4356, 0.0, 0.834, 0.7202, 0.5372, 0.8114, 0.9017] +2026-04-13 04:28:24.551484: Epoch time: 101.59 s +2026-04-13 04:28:25.774081: +2026-04-13 04:28:25.776067: Epoch 2149 +2026-04-13 04:28:25.778079: Current learning rate: 0.005 +2026-04-13 04:30:08.279298: train_loss -0.402 +2026-04-13 04:30:08.285642: val_loss -0.3862 +2026-04-13 04:30:08.288142: Pseudo dice [0.6303, 0.0, 0.6636, 0.6858, 0.4967, 0.5811, 0.8943] +2026-04-13 04:30:08.290334: Epoch time: 102.51 s +2026-04-13 04:30:11.231721: +2026-04-13 04:30:11.233417: Epoch 2150 +2026-04-13 04:30:11.234930: Current learning rate: 0.005 +2026-04-13 04:31:55.188298: train_loss -0.4204 +2026-04-13 04:31:55.194637: val_loss -0.3678 +2026-04-13 04:31:55.197007: Pseudo dice [0.7392, 0.0, 0.5834, 0.4551, 0.5259, 0.7315, 0.8453] +2026-04-13 04:31:55.200018: Epoch time: 103.96 s +2026-04-13 04:31:56.388117: +2026-04-13 04:31:56.389933: Epoch 2151 +2026-04-13 04:31:56.391603: Current learning rate: 0.00499 +2026-04-13 04:33:47.456351: train_loss -0.4366 +2026-04-13 04:33:47.463109: val_loss -0.3643 +2026-04-13 04:33:47.466236: Pseudo dice [0.5823, 0.0, 0.6762, 0.5119, 0.5443, 0.8344, 0.8721] +2026-04-13 04:33:47.468743: Epoch time: 111.07 s +2026-04-13 04:33:48.696205: +2026-04-13 04:33:48.698044: Epoch 2152 +2026-04-13 04:33:48.699926: Current learning rate: 0.00499 +2026-04-13 04:35:36.822974: train_loss -0.4275 +2026-04-13 04:35:36.832305: val_loss -0.3628 +2026-04-13 04:35:36.834510: Pseudo dice [0.5364, 0.0, 0.7382, 0.4029, 0.585, 0.3747, 0.8661] +2026-04-13 04:35:36.836852: Epoch time: 108.13 s +2026-04-13 04:35:38.048341: +2026-04-13 04:35:38.050838: Epoch 2153 +2026-04-13 04:35:38.052733: Current learning rate: 0.00499 +2026-04-13 04:37:20.301317: train_loss -0.4179 +2026-04-13 04:37:20.307713: val_loss -0.3741 +2026-04-13 04:37:20.309894: Pseudo dice [0.7713, 0.0, 0.8606, 0.7874, 0.541, 0.7542, 0.8567] +2026-04-13 04:37:20.313205: Epoch time: 102.26 s +2026-04-13 04:37:21.536638: +2026-04-13 04:37:21.538603: Epoch 2154 +2026-04-13 04:37:21.540818: Current learning rate: 0.00499 +2026-04-13 04:39:02.976577: train_loss -0.4067 +2026-04-13 04:39:02.982502: val_loss -0.3734 +2026-04-13 04:39:02.984391: Pseudo dice [0.338, 0.0, 0.7896, 0.6356, 0.5481, 0.5399, 0.8675] +2026-04-13 04:39:02.986457: Epoch time: 101.44 s +2026-04-13 04:39:04.201905: +2026-04-13 04:39:04.204635: Epoch 2155 +2026-04-13 04:39:04.208189: Current learning rate: 0.00498 +2026-04-13 04:40:45.782278: train_loss -0.4224 +2026-04-13 04:40:45.806689: val_loss -0.3907 +2026-04-13 04:40:45.809999: Pseudo dice [0.43, 0.0, 0.8627, 0.8551, 0.4603, 0.8592, 0.8844] +2026-04-13 04:40:45.812562: Epoch time: 101.58 s +2026-04-13 04:40:46.996649: +2026-04-13 04:40:46.998431: Epoch 2156 +2026-04-13 04:40:47.000029: Current learning rate: 0.00498 +2026-04-13 04:42:28.868410: train_loss -0.4257 +2026-04-13 04:42:28.874856: val_loss -0.3912 +2026-04-13 04:42:28.877157: Pseudo dice [0.5385, 0.0, 0.8639, 0.7963, 0.3686, 0.4574, 0.8783] +2026-04-13 04:42:28.879858: Epoch time: 101.87 s +2026-04-13 04:42:30.140404: +2026-04-13 04:42:30.142248: Epoch 2157 +2026-04-13 04:42:30.143791: Current learning rate: 0.00498 +2026-04-13 04:44:11.655038: train_loss -0.4286 +2026-04-13 04:44:11.661705: val_loss -0.4019 +2026-04-13 04:44:11.664005: Pseudo dice [0.6613, 0.0, 0.7908, 0.684, 0.6548, 0.7454, 0.8127] +2026-04-13 04:44:11.666334: Epoch time: 101.52 s +2026-04-13 04:44:12.880233: +2026-04-13 04:44:12.881896: Epoch 2158 +2026-04-13 04:44:12.883482: Current learning rate: 0.00498 +2026-04-13 04:45:54.170387: train_loss -0.4077 +2026-04-13 04:45:54.177621: val_loss -0.3703 +2026-04-13 04:45:54.179410: Pseudo dice [0.1459, 0.0, 0.7464, 0.6225, 0.4275, 0.529, 0.9092] +2026-04-13 04:45:54.181709: Epoch time: 101.29 s +2026-04-13 04:45:55.366562: +2026-04-13 04:45:55.368406: Epoch 2159 +2026-04-13 04:45:55.370188: Current learning rate: 0.00497 +2026-04-13 04:47:37.065172: train_loss -0.4121 +2026-04-13 04:47:37.071568: val_loss -0.3575 +2026-04-13 04:47:37.074030: Pseudo dice [0.0, 0.0, 0.7791, 0.7067, 0.5929, 0.574, 0.9317] +2026-04-13 04:47:37.076201: Epoch time: 101.7 s +2026-04-13 04:47:38.284778: +2026-04-13 04:47:38.286480: Epoch 2160 +2026-04-13 04:47:38.288131: Current learning rate: 0.00497 +2026-04-13 04:49:19.348165: train_loss -0.4295 +2026-04-13 04:49:19.354998: val_loss -0.3541 +2026-04-13 04:49:19.357145: Pseudo dice [0.6088, 0.0, 0.8467, 0.6398, 0.5034, 0.5226, 0.8633] +2026-04-13 04:49:19.359322: Epoch time: 101.07 s +2026-04-13 04:49:20.581247: +2026-04-13 04:49:20.583512: Epoch 2161 +2026-04-13 04:49:20.588192: Current learning rate: 0.00497 +2026-04-13 04:51:02.271293: train_loss -0.4214 +2026-04-13 04:51:02.277919: val_loss -0.3787 +2026-04-13 04:51:02.280354: Pseudo dice [0.7515, 0.0, 0.7222, 0.5625, 0.5735, 0.7382, 0.6776] +2026-04-13 04:51:02.282872: Epoch time: 101.69 s +2026-04-13 04:51:03.483711: +2026-04-13 04:51:03.485655: Epoch 2162 +2026-04-13 04:51:03.487328: Current learning rate: 0.00497 +2026-04-13 04:52:46.715486: train_loss -0.4323 +2026-04-13 04:52:46.721578: val_loss -0.3851 +2026-04-13 04:52:46.723909: Pseudo dice [0.6041, 0.0, 0.7602, 0.8692, 0.4465, 0.6942, 0.9152] +2026-04-13 04:52:46.726577: Epoch time: 103.23 s +2026-04-13 04:52:47.937312: +2026-04-13 04:52:47.939246: Epoch 2163 +2026-04-13 04:52:47.940703: Current learning rate: 0.00496 +2026-04-13 04:54:28.922177: train_loss -0.4133 +2026-04-13 04:54:28.930346: val_loss -0.3457 +2026-04-13 04:54:28.933657: Pseudo dice [0.4845, 0.0, 0.8394, 0.4461, 0.5182, 0.4638, 0.3932] +2026-04-13 04:54:28.936571: Epoch time: 100.99 s +2026-04-13 04:54:30.159931: +2026-04-13 04:54:30.161544: Epoch 2164 +2026-04-13 04:54:30.163140: Current learning rate: 0.00496 +2026-04-13 04:56:11.379634: train_loss -0.4144 +2026-04-13 04:56:11.385079: val_loss -0.4026 +2026-04-13 04:56:11.387595: Pseudo dice [0.6607, 0.0, 0.8067, 0.002, 0.4469, 0.6865, 0.8903] +2026-04-13 04:56:11.389859: Epoch time: 101.22 s +2026-04-13 04:56:12.634251: +2026-04-13 04:56:12.636000: Epoch 2165 +2026-04-13 04:56:12.637959: Current learning rate: 0.00496 +2026-04-13 04:57:53.959091: train_loss -0.4078 +2026-04-13 04:57:53.965990: val_loss -0.3761 +2026-04-13 04:57:53.968119: Pseudo dice [0.6869, 0.0, 0.798, 0.7172, 0.632, 0.3821, 0.6428] +2026-04-13 04:57:53.970726: Epoch time: 101.33 s +2026-04-13 04:57:55.152065: +2026-04-13 04:57:55.153745: Epoch 2166 +2026-04-13 04:57:55.155258: Current learning rate: 0.00496 +2026-04-13 04:59:37.007222: train_loss -0.4194 +2026-04-13 04:59:37.013517: val_loss -0.3746 +2026-04-13 04:59:37.015656: Pseudo dice [0.7541, 0.0, 0.6147, 0.7829, 0.4791, 0.7125, 0.9159] +2026-04-13 04:59:37.018100: Epoch time: 101.86 s +2026-04-13 04:59:38.213233: +2026-04-13 04:59:38.220006: Epoch 2167 +2026-04-13 04:59:38.228256: Current learning rate: 0.00495 +2026-04-13 05:01:19.628047: train_loss -0.3833 +2026-04-13 05:01:19.639847: val_loss -0.3316 +2026-04-13 05:01:19.641869: Pseudo dice [0.1241, 0.0, 0.6687, 0.0953, 0.2485, 0.3545, 0.8058] +2026-04-13 05:01:19.644275: Epoch time: 101.42 s +2026-04-13 05:01:20.842225: +2026-04-13 05:01:20.844219: Epoch 2168 +2026-04-13 05:01:20.846025: Current learning rate: 0.00495 +2026-04-13 05:03:02.675468: train_loss -0.3963 +2026-04-13 05:03:02.681357: val_loss -0.351 +2026-04-13 05:03:02.683296: Pseudo dice [0.2872, 0.0, 0.7799, 0.5789, 0.522, 0.7997, 0.5056] +2026-04-13 05:03:02.685427: Epoch time: 101.84 s +2026-04-13 05:03:03.896095: +2026-04-13 05:03:03.898046: Epoch 2169 +2026-04-13 05:03:03.899877: Current learning rate: 0.00495 +2026-04-13 05:04:45.696263: train_loss -0.3909 +2026-04-13 05:04:45.702539: val_loss -0.3676 +2026-04-13 05:04:45.704629: Pseudo dice [0.2801, 0.0, 0.7595, 0.7451, 0.5538, 0.8035, 0.778] +2026-04-13 05:04:45.706800: Epoch time: 101.8 s +2026-04-13 05:04:46.895044: +2026-04-13 05:04:46.898658: Epoch 2170 +2026-04-13 05:04:46.901930: Current learning rate: 0.00495 +2026-04-13 05:06:28.195812: train_loss -0.4077 +2026-04-13 05:06:28.202199: val_loss -0.3597 +2026-04-13 05:06:28.204294: Pseudo dice [0.3222, 0.0, 0.7074, 0.732, 0.5447, 0.6314, 0.7562] +2026-04-13 05:06:28.206489: Epoch time: 101.3 s +2026-04-13 05:06:29.397238: +2026-04-13 05:06:29.399070: Epoch 2171 +2026-04-13 05:06:29.400599: Current learning rate: 0.00494 +2026-04-13 05:08:11.225741: train_loss -0.4095 +2026-04-13 05:08:11.232798: val_loss -0.3223 +2026-04-13 05:08:11.234934: Pseudo dice [0.2314, 0.0, 0.6659, 0.5325, 0.4364, 0.6578, 0.2837] +2026-04-13 05:08:11.237282: Epoch time: 101.83 s +2026-04-13 05:08:12.407763: +2026-04-13 05:08:12.409453: Epoch 2172 +2026-04-13 05:08:12.411016: Current learning rate: 0.00494 +2026-04-13 05:09:54.423170: train_loss -0.3817 +2026-04-13 05:09:54.429559: val_loss -0.3195 +2026-04-13 05:09:54.433189: Pseudo dice [0.0541, 0.0, 0.7401, 0.7381, 0.3115, 0.2552, 0.4922] +2026-04-13 05:09:54.436054: Epoch time: 102.02 s +2026-04-13 05:09:55.627607: +2026-04-13 05:09:55.630728: Epoch 2173 +2026-04-13 05:09:55.632639: Current learning rate: 0.00494 +2026-04-13 05:11:36.802130: train_loss -0.4084 +2026-04-13 05:11:36.809514: val_loss -0.3392 +2026-04-13 05:11:36.812813: Pseudo dice [0.498, 0.0, 0.6049, 0.5426, 0.7035, 0.2562, 0.7979] +2026-04-13 05:11:36.815385: Epoch time: 101.18 s +2026-04-13 05:11:38.016375: +2026-04-13 05:11:38.018649: Epoch 2174 +2026-04-13 05:11:38.020674: Current learning rate: 0.00494 +2026-04-13 05:13:19.515446: train_loss -0.384 +2026-04-13 05:13:19.521643: val_loss -0.3236 +2026-04-13 05:13:19.523992: Pseudo dice [0.703, 0.0, 0.7844, 0.6432, 0.4806, 0.3209, 0.4248] +2026-04-13 05:13:19.526602: Epoch time: 101.5 s +2026-04-13 05:13:20.703818: +2026-04-13 05:13:20.705799: Epoch 2175 +2026-04-13 05:13:20.707273: Current learning rate: 0.00493 +2026-04-13 05:15:01.912270: train_loss -0.4016 +2026-04-13 05:15:01.918903: val_loss -0.3264 +2026-04-13 05:15:01.921568: Pseudo dice [0.4163, 0.0, 0.6652, 0.7645, 0.4667, 0.4602, 0.9356] +2026-04-13 05:15:01.923962: Epoch time: 101.21 s +2026-04-13 05:15:03.147209: +2026-04-13 05:15:03.148991: Epoch 2176 +2026-04-13 05:15:03.150524: Current learning rate: 0.00493 +2026-04-13 05:16:44.640539: train_loss -0.4016 +2026-04-13 05:16:44.645703: val_loss -0.3376 +2026-04-13 05:16:44.648184: Pseudo dice [0.6938, 0.0, 0.7699, 0.3957, 0.472, 0.5748, 0.7619] +2026-04-13 05:16:44.650446: Epoch time: 101.5 s +2026-04-13 05:16:45.848363: +2026-04-13 05:16:45.850310: Epoch 2177 +2026-04-13 05:16:45.853205: Current learning rate: 0.00493 +2026-04-13 05:18:27.115156: train_loss -0.4098 +2026-04-13 05:18:27.123861: val_loss -0.3676 +2026-04-13 05:18:27.126004: Pseudo dice [0.6254, 0.0, 0.7148, 0.3309, 0.6462, 0.8161, 0.7906] +2026-04-13 05:18:27.128211: Epoch time: 101.27 s +2026-04-13 05:18:28.320629: +2026-04-13 05:18:28.322633: Epoch 2178 +2026-04-13 05:18:28.324554: Current learning rate: 0.00493 +2026-04-13 05:20:09.808278: train_loss -0.3857 +2026-04-13 05:20:09.816678: val_loss -0.3792 +2026-04-13 05:20:09.818676: Pseudo dice [0.5774, 0.0, 0.6717, 0.7559, 0.4961, 0.8843, 0.8968] +2026-04-13 05:20:09.821909: Epoch time: 101.49 s +2026-04-13 05:20:11.039988: +2026-04-13 05:20:11.042107: Epoch 2179 +2026-04-13 05:20:11.043880: Current learning rate: 0.00493 +2026-04-13 05:21:52.604799: train_loss -0.4161 +2026-04-13 05:21:52.610534: val_loss -0.3616 +2026-04-13 05:21:52.612358: Pseudo dice [0.125, 0.0, 0.732, 0.2716, 0.3279, 0.5581, 0.9232] +2026-04-13 05:21:52.614497: Epoch time: 101.57 s +2026-04-13 05:21:53.812531: +2026-04-13 05:21:53.814510: Epoch 2180 +2026-04-13 05:21:53.816257: Current learning rate: 0.00492 +2026-04-13 05:23:35.418742: train_loss -0.4151 +2026-04-13 05:23:35.425227: val_loss -0.3559 +2026-04-13 05:23:35.427213: Pseudo dice [0.4969, 0.0, 0.7321, 0.6546, 0.5322, 0.5993, 0.7308] +2026-04-13 05:23:35.429375: Epoch time: 101.61 s +2026-04-13 05:23:36.625591: +2026-04-13 05:23:36.627230: Epoch 2181 +2026-04-13 05:23:36.628774: Current learning rate: 0.00492 +2026-04-13 05:25:17.877971: train_loss -0.409 +2026-04-13 05:25:17.906116: val_loss -0.3541 +2026-04-13 05:25:17.908104: Pseudo dice [0.62, 0.0, 0.7055, 0.5995, 0.5264, 0.2283, 0.5389] +2026-04-13 05:25:17.911788: Epoch time: 101.26 s +2026-04-13 05:25:19.117538: +2026-04-13 05:25:19.121643: Epoch 2182 +2026-04-13 05:25:19.125043: Current learning rate: 0.00492 +2026-04-13 05:27:00.336401: train_loss -0.4261 +2026-04-13 05:27:00.342171: val_loss -0.3365 +2026-04-13 05:27:00.346248: Pseudo dice [0.2804, 0.0, 0.7141, 0.6794, 0.4193, 0.2367, 0.8332] +2026-04-13 05:27:00.348635: Epoch time: 101.22 s +2026-04-13 05:27:02.659816: +2026-04-13 05:27:02.661585: Epoch 2183 +2026-04-13 05:27:02.663070: Current learning rate: 0.00492 +2026-04-13 05:28:44.057584: train_loss -0.4053 +2026-04-13 05:28:44.063728: val_loss -0.3459 +2026-04-13 05:28:44.065899: Pseudo dice [0.5237, 0.0, 0.7119, 0.4163, 0.6129, 0.1897, 0.8459] +2026-04-13 05:28:44.068120: Epoch time: 101.4 s +2026-04-13 05:28:45.282298: +2026-04-13 05:28:45.284139: Epoch 2184 +2026-04-13 05:28:45.285630: Current learning rate: 0.00491 +2026-04-13 05:30:26.488766: train_loss -0.429 +2026-04-13 05:30:26.494460: val_loss -0.3411 +2026-04-13 05:30:26.496899: Pseudo dice [0.8096, 0.0, 0.6816, 0.6309, 0.5794, 0.6731, 0.5621] +2026-04-13 05:30:26.500093: Epoch time: 101.21 s +2026-04-13 05:30:27.707444: +2026-04-13 05:30:27.709344: Epoch 2185 +2026-04-13 05:30:27.710956: Current learning rate: 0.00491 +2026-04-13 05:32:09.076792: train_loss -0.4129 +2026-04-13 05:32:09.083859: val_loss -0.3742 +2026-04-13 05:32:09.086616: Pseudo dice [0.2905, 0.0, 0.7946, 0.6106, 0.4172, 0.7809, 0.8118] +2026-04-13 05:32:09.089284: Epoch time: 101.37 s +2026-04-13 05:32:10.311410: +2026-04-13 05:32:10.313337: Epoch 2186 +2026-04-13 05:32:10.315173: Current learning rate: 0.00491 +2026-04-13 05:33:51.757365: train_loss -0.3902 +2026-04-13 05:33:51.763160: val_loss -0.3582 +2026-04-13 05:33:51.765699: Pseudo dice [0.5474, 0.0, 0.8559, 0.0005, 0.5348, 0.4009, 0.7274] +2026-04-13 05:33:51.768008: Epoch time: 101.45 s +2026-04-13 05:33:52.947526: +2026-04-13 05:33:52.949198: Epoch 2187 +2026-04-13 05:33:52.950762: Current learning rate: 0.00491 +2026-04-13 05:35:34.763409: train_loss -0.4155 +2026-04-13 05:35:34.769448: val_loss -0.3562 +2026-04-13 05:35:34.772978: Pseudo dice [0.6691, 0.0, 0.792, 0.3822, 0.6344, 0.6254, 0.87] +2026-04-13 05:35:34.775001: Epoch time: 101.82 s +2026-04-13 05:35:36.017035: +2026-04-13 05:35:36.018885: Epoch 2188 +2026-04-13 05:35:36.020501: Current learning rate: 0.0049 +2026-04-13 05:37:17.652098: train_loss -0.4066 +2026-04-13 05:37:17.659706: val_loss -0.3093 +2026-04-13 05:37:17.661497: Pseudo dice [0.6362, 0.0, 0.7173, 0.0513, 0.4805, 0.5133, 0.1876] +2026-04-13 05:37:17.663725: Epoch time: 101.64 s +2026-04-13 05:37:18.843264: +2026-04-13 05:37:18.845181: Epoch 2189 +2026-04-13 05:37:18.846706: Current learning rate: 0.0049 +2026-04-13 05:38:59.960203: train_loss -0.4085 +2026-04-13 05:38:59.966704: val_loss -0.3477 +2026-04-13 05:38:59.968577: Pseudo dice [0.5407, 0.0, 0.7827, 0.0906, 0.4418, 0.8499, 0.7782] +2026-04-13 05:38:59.971456: Epoch time: 101.12 s +2026-04-13 05:39:01.140071: +2026-04-13 05:39:01.142134: Epoch 2190 +2026-04-13 05:39:01.143773: Current learning rate: 0.0049 +2026-04-13 05:40:42.412513: train_loss -0.4291 +2026-04-13 05:40:42.417877: val_loss -0.3648 +2026-04-13 05:40:42.420250: Pseudo dice [0.6587, 0.0, 0.732, 0.5216, 0.4253, 0.8179, 0.6691] +2026-04-13 05:40:42.422935: Epoch time: 101.28 s +2026-04-13 05:40:43.609280: +2026-04-13 05:40:43.611156: Epoch 2191 +2026-04-13 05:40:43.612605: Current learning rate: 0.0049 +2026-04-13 05:42:24.906315: train_loss -0.4182 +2026-04-13 05:42:24.912927: val_loss -0.3516 +2026-04-13 05:42:24.915007: Pseudo dice [0.5065, 0.0, 0.5928, 0.5299, 0.5074, 0.5992, 0.8619] +2026-04-13 05:42:24.917426: Epoch time: 101.3 s +2026-04-13 05:42:26.110957: +2026-04-13 05:42:26.113263: Epoch 2192 +2026-04-13 05:42:26.115051: Current learning rate: 0.00489 +2026-04-13 05:44:07.161805: train_loss -0.4159 +2026-04-13 05:44:07.168641: val_loss -0.4224 +2026-04-13 05:44:07.171067: Pseudo dice [0.709, 0.0, 0.801, 0.4688, 0.4689, 0.7003, 0.8114] +2026-04-13 05:44:07.173513: Epoch time: 101.05 s +2026-04-13 05:44:08.371499: +2026-04-13 05:44:08.373379: Epoch 2193 +2026-04-13 05:44:08.374919: Current learning rate: 0.00489 +2026-04-13 05:45:49.664934: train_loss -0.4109 +2026-04-13 05:45:49.671533: val_loss -0.3581 +2026-04-13 05:45:49.673760: Pseudo dice [0.5548, 0.0, 0.7124, 0.377, 0.41, 0.8228, 0.7967] +2026-04-13 05:45:49.676434: Epoch time: 101.3 s +2026-04-13 05:45:50.852661: +2026-04-13 05:45:50.854716: Epoch 2194 +2026-04-13 05:45:50.856668: Current learning rate: 0.00489 +2026-04-13 05:47:32.285788: train_loss -0.3952 +2026-04-13 05:47:32.294855: val_loss -0.354 +2026-04-13 05:47:32.297074: Pseudo dice [0.5916, 0.0, 0.7115, 0.4467, 0.4637, 0.2987, 0.6993] +2026-04-13 05:47:32.299102: Epoch time: 101.44 s +2026-04-13 05:47:33.473482: +2026-04-13 05:47:33.475232: Epoch 2195 +2026-04-13 05:47:33.476888: Current learning rate: 0.00489 +2026-04-13 05:49:14.823166: train_loss -0.4103 +2026-04-13 05:49:14.829944: val_loss -0.3176 +2026-04-13 05:49:14.832005: Pseudo dice [0.273, 0.0, 0.5589, 0.1712, 0.3469, 0.5731, 0.8958] +2026-04-13 05:49:14.834326: Epoch time: 101.35 s +2026-04-13 05:49:16.044008: +2026-04-13 05:49:16.046023: Epoch 2196 +2026-04-13 05:49:16.047525: Current learning rate: 0.00488 +2026-04-13 05:50:57.731917: train_loss -0.4234 +2026-04-13 05:50:57.740041: val_loss -0.3686 +2026-04-13 05:50:57.742324: Pseudo dice [0.78, 0.0, 0.7589, 0.6574, 0.4899, 0.4954, 0.79] +2026-04-13 05:50:57.744958: Epoch time: 101.69 s +2026-04-13 05:50:58.965445: +2026-04-13 05:50:58.967473: Epoch 2197 +2026-04-13 05:50:58.969318: Current learning rate: 0.00488 +2026-04-13 05:52:40.903678: train_loss -0.4067 +2026-04-13 05:52:40.909343: val_loss -0.3676 +2026-04-13 05:52:40.911335: Pseudo dice [0.5634, 0.0, 0.7949, 0.5989, 0.478, 0.718, 0.8564] +2026-04-13 05:52:40.913373: Epoch time: 101.94 s +2026-04-13 05:52:42.106369: +2026-04-13 05:52:42.108354: Epoch 2198 +2026-04-13 05:52:42.110003: Current learning rate: 0.00488 +2026-04-13 05:54:25.220631: train_loss -0.3963 +2026-04-13 05:54:25.228045: val_loss -0.3776 +2026-04-13 05:54:25.230422: Pseudo dice [0.3817, 0.0, 0.721, 0.2491, 0.4633, 0.6309, 0.7352] +2026-04-13 05:54:25.233290: Epoch time: 103.12 s +2026-04-13 05:54:26.421326: +2026-04-13 05:54:26.423992: Epoch 2199 +2026-04-13 05:54:26.425369: Current learning rate: 0.00488 +2026-04-13 05:56:08.369612: train_loss -0.4032 +2026-04-13 05:56:08.377260: val_loss -0.3597 +2026-04-13 05:56:08.380527: Pseudo dice [0.1853, 0.0, 0.8354, 0.7654, 0.5666, 0.324, 0.8932] +2026-04-13 05:56:08.383196: Epoch time: 101.95 s +2026-04-13 05:56:11.342038: +2026-04-13 05:56:11.343836: Epoch 2200 +2026-04-13 05:56:11.346033: Current learning rate: 0.00487 +2026-04-13 05:57:53.131922: train_loss -0.3938 +2026-04-13 05:57:53.138022: val_loss -0.3978 +2026-04-13 05:57:53.140774: Pseudo dice [0.4038, 0.0, 0.7718, 0.7812, 0.44, 0.7069, 0.9305] +2026-04-13 05:57:53.143539: Epoch time: 101.79 s +2026-04-13 05:57:54.346012: +2026-04-13 05:57:54.348484: Epoch 2201 +2026-04-13 05:57:54.350132: Current learning rate: 0.00487 +2026-04-13 06:00:31.648566: train_loss -0.4346 +2026-04-13 06:00:31.655439: val_loss -0.353 +2026-04-13 06:00:31.657945: Pseudo dice [0.6393, 0.0, 0.7237, 0.669, 0.5936, 0.6538, 0.8236] +2026-04-13 06:00:31.660341: Epoch time: 157.31 s +2026-04-13 06:00:32.849899: +2026-04-13 06:00:32.851889: Epoch 2202 +2026-04-13 06:00:32.853641: Current learning rate: 0.00487 +2026-04-13 06:02:14.537059: train_loss -0.4114 +2026-04-13 06:02:14.546634: val_loss -0.3605 +2026-04-13 06:02:14.549970: Pseudo dice [0.5507, 0.0, 0.7077, 0.5035, 0.5146, 0.6774, 0.9093] +2026-04-13 06:02:14.553469: Epoch time: 101.69 s +2026-04-13 06:02:15.738402: +2026-04-13 06:02:15.740416: Epoch 2203 +2026-04-13 06:02:15.742059: Current learning rate: 0.00487 +2026-04-13 06:03:58.396196: train_loss -0.4179 +2026-04-13 06:03:58.402355: val_loss -0.3266 +2026-04-13 06:03:58.405371: Pseudo dice [0.635, 0.0, 0.745, 0.065, 0.4202, 0.5671, 0.2276] +2026-04-13 06:03:58.408822: Epoch time: 102.66 s +2026-04-13 06:03:59.583154: +2026-04-13 06:03:59.585108: Epoch 2204 +2026-04-13 06:03:59.587155: Current learning rate: 0.00486 +2026-04-13 06:05:41.386913: train_loss -0.4077 +2026-04-13 06:05:41.392589: val_loss -0.3407 +2026-04-13 06:05:41.394854: Pseudo dice [0.4829, 0.0, 0.6274, 0.6066, 0.3859, 0.3882, 0.722] +2026-04-13 06:05:41.397400: Epoch time: 101.81 s +2026-04-13 06:05:42.591038: +2026-04-13 06:05:42.593218: Epoch 2205 +2026-04-13 06:05:42.595049: Current learning rate: 0.00486 +2026-04-13 06:07:24.434730: train_loss -0.4019 +2026-04-13 06:07:24.441680: val_loss -0.316 +2026-04-13 06:07:24.443511: Pseudo dice [0.4228, 0.0, 0.4074, 0.0186, 0.384, 0.3736, 0.3874] +2026-04-13 06:07:24.446077: Epoch time: 101.85 s +2026-04-13 06:07:25.672175: +2026-04-13 06:07:25.673852: Epoch 2206 +2026-04-13 06:07:25.675857: Current learning rate: 0.00486 +2026-04-13 06:09:06.759106: train_loss -0.4108 +2026-04-13 06:09:06.765126: val_loss -0.3379 +2026-04-13 06:09:06.768357: Pseudo dice [0.7441, 0.0, 0.6675, 0.2069, 0.3406, 0.7686, 0.7077] +2026-04-13 06:09:06.770606: Epoch time: 101.09 s +2026-04-13 06:09:07.968107: +2026-04-13 06:09:07.969904: Epoch 2207 +2026-04-13 06:09:07.971704: Current learning rate: 0.00486 +2026-04-13 06:10:49.861805: train_loss -0.3964 +2026-04-13 06:10:49.870705: val_loss -0.3414 +2026-04-13 06:10:49.872979: Pseudo dice [0.1728, 0.0, 0.7676, 0.3305, 0.5009, 0.4202, 0.7504] +2026-04-13 06:10:49.875347: Epoch time: 101.9 s +2026-04-13 06:10:51.098320: +2026-04-13 06:10:51.100110: Epoch 2208 +2026-04-13 06:10:51.101760: Current learning rate: 0.00485 +2026-04-13 06:12:32.343403: train_loss -0.3826 +2026-04-13 06:12:32.349423: val_loss -0.374 +2026-04-13 06:12:32.351465: Pseudo dice [0.6112, 0.0, 0.642, 0.3268, 0.528, 0.5721, 0.8897] +2026-04-13 06:12:32.354109: Epoch time: 101.25 s +2026-04-13 06:12:33.545155: +2026-04-13 06:12:33.546879: Epoch 2209 +2026-04-13 06:12:33.548380: Current learning rate: 0.00485 +2026-04-13 06:14:14.909922: train_loss -0.4106 +2026-04-13 06:14:14.916579: val_loss -0.3594 +2026-04-13 06:14:14.918937: Pseudo dice [0.7813, 0.0, 0.713, 0.6246, 0.5524, 0.5377, 0.3983] +2026-04-13 06:14:14.922152: Epoch time: 101.37 s +2026-04-13 06:14:16.121562: +2026-04-13 06:14:16.123618: Epoch 2210 +2026-04-13 06:14:16.125562: Current learning rate: 0.00485 +2026-04-13 06:15:57.827773: train_loss -0.3968 +2026-04-13 06:15:57.835573: val_loss -0.3406 +2026-04-13 06:15:57.837762: Pseudo dice [0.4592, 0.0, 0.7762, 0.4325, 0.5771, 0.4329, 0.5311] +2026-04-13 06:15:57.840540: Epoch time: 101.71 s +2026-04-13 06:15:59.038710: +2026-04-13 06:15:59.040859: Epoch 2211 +2026-04-13 06:15:59.042764: Current learning rate: 0.00485 +2026-04-13 06:17:40.190454: train_loss -0.3939 +2026-04-13 06:17:40.196669: val_loss -0.3849 +2026-04-13 06:17:40.198658: Pseudo dice [0.6613, 0.0, 0.6756, 0.6246, 0.6359, 0.6532, 0.7111] +2026-04-13 06:17:40.201129: Epoch time: 101.15 s +2026-04-13 06:17:41.381124: +2026-04-13 06:17:41.382704: Epoch 2212 +2026-04-13 06:17:41.384068: Current learning rate: 0.00484 +2026-04-13 06:19:22.843211: train_loss -0.4022 +2026-04-13 06:19:22.850433: val_loss -0.3498 +2026-04-13 06:19:22.852356: Pseudo dice [0.5009, 0.0, 0.7144, 0.0207, 0.1973, 0.6697, 0.4733] +2026-04-13 06:19:22.855073: Epoch time: 101.47 s +2026-04-13 06:19:24.040809: +2026-04-13 06:19:24.042989: Epoch 2213 +2026-04-13 06:19:24.044661: Current learning rate: 0.00484 +2026-04-13 06:21:05.240791: train_loss -0.3868 +2026-04-13 06:21:05.246971: val_loss -0.3825 +2026-04-13 06:21:05.248932: Pseudo dice [0.7, 0.0, 0.815, 0.2467, 0.5235, 0.1438, 0.7247] +2026-04-13 06:21:05.251572: Epoch time: 101.2 s +2026-04-13 06:21:06.439556: +2026-04-13 06:21:06.442016: Epoch 2214 +2026-04-13 06:21:06.443892: Current learning rate: 0.00484 +2026-04-13 06:22:47.948350: train_loss -0.4231 +2026-04-13 06:22:47.954266: val_loss -0.3769 +2026-04-13 06:22:47.956120: Pseudo dice [0.3189, 0.0, 0.7907, 0.6458, 0.5913, 0.8164, 0.9098] +2026-04-13 06:22:47.958110: Epoch time: 101.51 s +2026-04-13 06:22:49.147795: +2026-04-13 06:22:49.149631: Epoch 2215 +2026-04-13 06:22:49.151155: Current learning rate: 0.00484 +2026-04-13 06:24:30.606205: train_loss -0.4143 +2026-04-13 06:24:30.613120: val_loss -0.3315 +2026-04-13 06:24:30.615225: Pseudo dice [0.4383, 0.0, 0.7228, 0.6808, 0.3905, 0.3086, 0.8165] +2026-04-13 06:24:30.618064: Epoch time: 101.46 s +2026-04-13 06:24:31.823987: +2026-04-13 06:24:31.827365: Epoch 2216 +2026-04-13 06:24:31.829103: Current learning rate: 0.00484 +2026-04-13 06:26:13.522091: train_loss -0.4062 +2026-04-13 06:26:13.547833: val_loss -0.3599 +2026-04-13 06:26:13.550206: Pseudo dice [0.6028, 0.0, 0.679, 0.5773, 0.6207, 0.7009, 0.555] +2026-04-13 06:26:13.552268: Epoch time: 101.7 s +2026-04-13 06:26:14.761273: +2026-04-13 06:26:14.763226: Epoch 2217 +2026-04-13 06:26:14.764815: Current learning rate: 0.00483 +2026-04-13 06:27:56.325814: train_loss -0.4323 +2026-04-13 06:27:56.331508: val_loss -0.3843 +2026-04-13 06:27:56.333614: Pseudo dice [0.6963, 0.0, 0.7996, 0.6168, 0.5214, 0.7714, 0.8462] +2026-04-13 06:27:56.336358: Epoch time: 101.57 s +2026-04-13 06:27:57.546340: +2026-04-13 06:27:57.548573: Epoch 2218 +2026-04-13 06:27:57.550519: Current learning rate: 0.00483 +2026-04-13 06:29:39.142139: train_loss -0.4241 +2026-04-13 06:29:39.148695: val_loss -0.3743 +2026-04-13 06:29:39.150383: Pseudo dice [0.3706, 0.0, 0.7846, 0.8428, 0.4895, 0.5954, 0.7645] +2026-04-13 06:29:39.153124: Epoch time: 101.6 s +2026-04-13 06:29:40.326144: +2026-04-13 06:29:40.328058: Epoch 2219 +2026-04-13 06:29:40.329609: Current learning rate: 0.00483 +2026-04-13 06:31:22.193167: train_loss -0.4035 +2026-04-13 06:31:22.199329: val_loss -0.3697 +2026-04-13 06:31:22.201646: Pseudo dice [0.8003, 0.0, 0.8105, 0.6249, 0.4507, 0.7428, 0.6802] +2026-04-13 06:31:22.205225: Epoch time: 101.87 s +2026-04-13 06:31:23.407808: +2026-04-13 06:31:23.409502: Epoch 2220 +2026-04-13 06:31:23.411134: Current learning rate: 0.00483 +2026-04-13 06:33:04.826782: train_loss -0.4071 +2026-04-13 06:33:04.832551: val_loss -0.3295 +2026-04-13 06:33:04.834672: Pseudo dice [0.229, 0.0, 0.6332, 0.2316, 0.3259, 0.5258, 0.9144] +2026-04-13 06:33:04.837138: Epoch time: 101.42 s +2026-04-13 06:33:06.036406: +2026-04-13 06:33:06.038246: Epoch 2221 +2026-04-13 06:33:06.039979: Current learning rate: 0.00482 +2026-04-13 06:34:47.225253: train_loss -0.3948 +2026-04-13 06:34:47.231124: val_loss -0.3307 +2026-04-13 06:34:47.233153: Pseudo dice [0.4769, 0.0, 0.5171, 0.1157, 0.5033, 0.6979, 0.7931] +2026-04-13 06:34:47.236366: Epoch time: 101.19 s +2026-04-13 06:34:48.436413: +2026-04-13 06:34:48.438143: Epoch 2222 +2026-04-13 06:34:48.439634: Current learning rate: 0.00482 +2026-04-13 06:36:29.787302: train_loss -0.3801 +2026-04-13 06:36:29.794804: val_loss -0.3205 +2026-04-13 06:36:29.800449: Pseudo dice [0.0, 0.0, 0.6045, 0.0732, 0.3893, 0.5293, 0.6026] +2026-04-13 06:36:29.803586: Epoch time: 101.35 s +2026-04-13 06:36:31.015741: +2026-04-13 06:36:31.017555: Epoch 2223 +2026-04-13 06:36:31.019298: Current learning rate: 0.00482 +2026-04-13 06:38:12.350143: train_loss -0.3889 +2026-04-13 06:38:12.355803: val_loss -0.3099 +2026-04-13 06:38:12.357602: Pseudo dice [0.0, 0.0, 0.5153, 0.3914, 0.411, 0.4809, 0.846] +2026-04-13 06:38:12.359651: Epoch time: 101.34 s +2026-04-13 06:38:14.618413: +2026-04-13 06:38:14.620352: Epoch 2224 +2026-04-13 06:38:14.621964: Current learning rate: 0.00482 +2026-04-13 06:39:56.184777: train_loss -0.3905 +2026-04-13 06:39:56.191675: val_loss -0.3659 +2026-04-13 06:39:56.194386: Pseudo dice [0.1434, 0.0, 0.8428, 0.3561, 0.5558, 0.5181, 0.6911] +2026-04-13 06:39:56.196979: Epoch time: 101.57 s +2026-04-13 06:39:57.384955: +2026-04-13 06:39:57.386909: Epoch 2225 +2026-04-13 06:39:57.388711: Current learning rate: 0.00481 +2026-04-13 06:41:39.336023: train_loss -0.4052 +2026-04-13 06:41:39.342347: val_loss -0.3838 +2026-04-13 06:41:39.344707: Pseudo dice [0.2503, 0.0, 0.7884, 0.5251, 0.4124, 0.5727, 0.8346] +2026-04-13 06:41:39.347564: Epoch time: 101.95 s +2026-04-13 06:41:40.585047: +2026-04-13 06:41:40.587733: Epoch 2226 +2026-04-13 06:41:40.590791: Current learning rate: 0.00481 +2026-04-13 06:43:21.693700: train_loss -0.4023 +2026-04-13 06:43:21.700111: val_loss -0.3453 +2026-04-13 06:43:21.702225: Pseudo dice [0.5926, 0.0, 0.8137, 0.2204, 0.4305, 0.6668, 0.2767] +2026-04-13 06:43:21.704157: Epoch time: 101.11 s +2026-04-13 06:43:22.910435: +2026-04-13 06:43:22.912532: Epoch 2227 +2026-04-13 06:43:22.914545: Current learning rate: 0.00481 +2026-04-13 06:45:04.317228: train_loss -0.417 +2026-04-13 06:45:04.324097: val_loss -0.3316 +2026-04-13 06:45:04.327545: Pseudo dice [0.8019, 0.0, 0.6945, 0.2546, 0.3857, 0.4458, 0.7668] +2026-04-13 06:45:04.329742: Epoch time: 101.41 s +2026-04-13 06:45:05.532451: +2026-04-13 06:45:05.534338: Epoch 2228 +2026-04-13 06:45:05.535844: Current learning rate: 0.00481 +2026-04-13 06:46:46.949152: train_loss -0.3865 +2026-04-13 06:46:46.957098: val_loss -0.3304 +2026-04-13 06:46:46.959699: Pseudo dice [0.4524, 0.0, 0.6893, 0.4666, 0.397, 0.6941, 0.5038] +2026-04-13 06:46:46.962592: Epoch time: 101.42 s +2026-04-13 06:46:48.150423: +2026-04-13 06:46:48.152348: Epoch 2229 +2026-04-13 06:46:48.153847: Current learning rate: 0.0048 +2026-04-13 06:48:29.672897: train_loss -0.3754 +2026-04-13 06:48:29.681854: val_loss -0.3401 +2026-04-13 06:48:29.684118: Pseudo dice [0.8035, 0.0, 0.7403, 0.7176, 0.3788, 0.5827, 0.5951] +2026-04-13 06:48:29.686818: Epoch time: 101.53 s +2026-04-13 06:48:30.877977: +2026-04-13 06:48:30.880167: Epoch 2230 +2026-04-13 06:48:30.881972: Current learning rate: 0.0048 +2026-04-13 06:50:12.082400: train_loss -0.3932 +2026-04-13 06:50:12.089839: val_loss -0.3626 +2026-04-13 06:50:12.092797: Pseudo dice [0.4643, 0.0, 0.8144, 0.6036, 0.4592, 0.6807, 0.5557] +2026-04-13 06:50:12.095277: Epoch time: 101.21 s +2026-04-13 06:50:13.289935: +2026-04-13 06:50:13.291740: Epoch 2231 +2026-04-13 06:50:13.293646: Current learning rate: 0.0048 +2026-04-13 06:51:55.166172: train_loss -0.3937 +2026-04-13 06:51:55.183877: val_loss -0.3563 +2026-04-13 06:51:55.187922: Pseudo dice [0.4327, 0.0, 0.7348, 0.2898, 0.4882, 0.7493, 0.6796] +2026-04-13 06:51:55.190237: Epoch time: 101.88 s +2026-04-13 06:51:56.405056: +2026-04-13 06:51:56.407227: Epoch 2232 +2026-04-13 06:51:56.409117: Current learning rate: 0.0048 +2026-04-13 06:53:37.581998: train_loss -0.4215 +2026-04-13 06:53:37.589024: val_loss -0.3619 +2026-04-13 06:53:37.590757: Pseudo dice [0.3437, 0.0, 0.6296, 0.1916, 0.526, 0.5367, 0.7443] +2026-04-13 06:53:37.592959: Epoch time: 101.18 s +2026-04-13 06:53:38.833866: +2026-04-13 06:53:38.835904: Epoch 2233 +2026-04-13 06:53:38.837450: Current learning rate: 0.00479 +2026-04-13 06:55:20.761911: train_loss -0.4118 +2026-04-13 06:55:20.768001: val_loss -0.3483 +2026-04-13 06:55:20.770059: Pseudo dice [0.3105, 0.0, 0.853, 0.5776, 0.5434, 0.4191, 0.7911] +2026-04-13 06:55:20.772738: Epoch time: 101.93 s +2026-04-13 06:55:21.986095: +2026-04-13 06:55:21.987752: Epoch 2234 +2026-04-13 06:55:21.989183: Current learning rate: 0.00479 +2026-04-13 06:57:03.507280: train_loss -0.3965 +2026-04-13 06:57:03.513337: val_loss -0.3184 +2026-04-13 06:57:03.515023: Pseudo dice [0.6356, 0.0, 0.5959, 0.0004, 0.2906, 0.8174, 0.7101] +2026-04-13 06:57:03.517905: Epoch time: 101.52 s +2026-04-13 06:57:04.721394: +2026-04-13 06:57:04.724055: Epoch 2235 +2026-04-13 06:57:04.725465: Current learning rate: 0.00479 +2026-04-13 06:58:46.047676: train_loss -0.3996 +2026-04-13 06:58:46.054245: val_loss -0.3693 +2026-04-13 06:58:46.056267: Pseudo dice [0.7275, 0.0, 0.8048, 0.3507, 0.3872, 0.8353, 0.746] +2026-04-13 06:58:46.058629: Epoch time: 101.33 s +2026-04-13 06:58:47.259220: +2026-04-13 06:58:47.261277: Epoch 2236 +2026-04-13 06:58:47.262840: Current learning rate: 0.00479 +2026-04-13 07:00:28.619126: train_loss -0.4041 +2026-04-13 07:00:28.624986: val_loss -0.3736 +2026-04-13 07:00:28.627388: Pseudo dice [0.1699, 0.0, 0.752, 0.6775, 0.5776, 0.6251, 0.8006] +2026-04-13 07:00:28.630372: Epoch time: 101.36 s +2026-04-13 07:00:29.832774: +2026-04-13 07:00:29.834600: Epoch 2237 +2026-04-13 07:00:29.836282: Current learning rate: 0.00478 +2026-04-13 07:02:11.407846: train_loss -0.4177 +2026-04-13 07:02:11.413550: val_loss -0.3674 +2026-04-13 07:02:11.415318: Pseudo dice [0.6361, 0.0, 0.6789, 0.7501, 0.6314, 0.625, 0.8789] +2026-04-13 07:02:11.417366: Epoch time: 101.58 s +2026-04-13 07:02:12.616784: +2026-04-13 07:02:12.618601: Epoch 2238 +2026-04-13 07:02:12.620254: Current learning rate: 0.00478 +2026-04-13 07:03:54.515267: train_loss -0.4115 +2026-04-13 07:03:54.521486: val_loss -0.3462 +2026-04-13 07:03:54.524599: Pseudo dice [0.4285, 0.0, 0.7131, 0.5111, 0.2363, 0.467, 0.913] +2026-04-13 07:03:54.527663: Epoch time: 101.9 s +2026-04-13 07:03:55.719340: +2026-04-13 07:03:55.721512: Epoch 2239 +2026-04-13 07:03:55.723131: Current learning rate: 0.00478 +2026-04-13 07:05:37.594240: train_loss -0.397 +2026-04-13 07:05:37.601552: val_loss -0.3705 +2026-04-13 07:05:37.604127: Pseudo dice [0.5283, 0.0, 0.6831, 0.6017, 0.4173, 0.4442, 0.9171] +2026-04-13 07:05:37.606533: Epoch time: 101.88 s +2026-04-13 07:05:38.775466: +2026-04-13 07:05:38.778459: Epoch 2240 +2026-04-13 07:05:38.780659: Current learning rate: 0.00478 +2026-04-13 07:07:20.313086: train_loss -0.4128 +2026-04-13 07:07:20.318868: val_loss -0.3648 +2026-04-13 07:07:20.322443: Pseudo dice [0.569, 0.0, 0.8, 0.3392, 0.5664, 0.7102, 0.6397] +2026-04-13 07:07:20.324862: Epoch time: 101.54 s +2026-04-13 07:07:21.512079: +2026-04-13 07:07:21.513891: Epoch 2241 +2026-04-13 07:07:21.515514: Current learning rate: 0.00477 +2026-04-13 07:09:02.805332: train_loss -0.4083 +2026-04-13 07:09:02.811892: val_loss -0.371 +2026-04-13 07:09:02.814007: Pseudo dice [0.7644, 0.0, 0.7932, 0.5785, 0.5541, 0.6896, 0.8402] +2026-04-13 07:09:02.816506: Epoch time: 101.3 s +2026-04-13 07:09:04.033069: +2026-04-13 07:09:04.035231: Epoch 2242 +2026-04-13 07:09:04.037118: Current learning rate: 0.00477 +2026-04-13 07:10:45.731008: train_loss -0.4147 +2026-04-13 07:10:45.737887: val_loss -0.3475 +2026-04-13 07:10:45.739639: Pseudo dice [0.5102, 0.0, 0.7386, 0.5395, 0.5092, 0.4079, 0.7441] +2026-04-13 07:10:45.742105: Epoch time: 101.7 s +2026-04-13 07:10:46.962681: +2026-04-13 07:10:46.964869: Epoch 2243 +2026-04-13 07:10:46.966922: Current learning rate: 0.00477 +2026-04-13 07:12:28.215866: train_loss -0.4255 +2026-04-13 07:12:28.222171: val_loss -0.3635 +2026-04-13 07:12:28.224538: Pseudo dice [0.3011, 0.0, 0.6963, 0.1178, 0.49, 0.5213, 0.7805] +2026-04-13 07:12:28.227305: Epoch time: 101.26 s +2026-04-13 07:12:29.431576: +2026-04-13 07:12:29.433183: Epoch 2244 +2026-04-13 07:12:29.434795: Current learning rate: 0.00477 +2026-04-13 07:14:10.651712: train_loss -0.4102 +2026-04-13 07:14:10.657789: val_loss -0.3587 +2026-04-13 07:14:10.660009: Pseudo dice [0.7648, 0.0, 0.8229, 0.4641, 0.2695, 0.5182, 0.8443] +2026-04-13 07:14:10.662484: Epoch time: 101.22 s +2026-04-13 07:14:13.008843: +2026-04-13 07:14:13.010574: Epoch 2245 +2026-04-13 07:14:13.012068: Current learning rate: 0.00476 +2026-04-13 07:15:55.047025: train_loss -0.4299 +2026-04-13 07:15:55.055598: val_loss -0.3612 +2026-04-13 07:15:55.058419: Pseudo dice [0.3887, 0.0, 0.7103, 0.8389, 0.5363, 0.539, 0.7939] +2026-04-13 07:15:55.060843: Epoch time: 102.04 s +2026-04-13 07:15:56.260511: +2026-04-13 07:15:56.262471: Epoch 2246 +2026-04-13 07:15:56.264547: Current learning rate: 0.00476 +2026-04-13 07:17:37.357420: train_loss -0.3753 +2026-04-13 07:17:37.363450: val_loss -0.3522 +2026-04-13 07:17:37.365681: Pseudo dice [0.0734, 0.0, 0.598, 0.3326, 0.4039, 0.371, 0.5182] +2026-04-13 07:17:37.367961: Epoch time: 101.1 s +2026-04-13 07:17:38.559437: +2026-04-13 07:17:38.561284: Epoch 2247 +2026-04-13 07:17:38.562931: Current learning rate: 0.00476 +2026-04-13 07:19:20.079061: train_loss -0.3761 +2026-04-13 07:19:20.085180: val_loss -0.3587 +2026-04-13 07:19:20.087429: Pseudo dice [0.542, 0.0, 0.7522, 0.5117, 0.5577, 0.3344, 0.7494] +2026-04-13 07:19:20.089823: Epoch time: 101.52 s +2026-04-13 07:19:21.267721: +2026-04-13 07:19:21.269486: Epoch 2248 +2026-04-13 07:19:21.270999: Current learning rate: 0.00476 +2026-04-13 07:21:02.860611: train_loss -0.4018 +2026-04-13 07:21:02.868101: val_loss -0.3714 +2026-04-13 07:21:02.870021: Pseudo dice [0.6261, 0.0, 0.8113, 0.7926, 0.3906, 0.573, 0.8928] +2026-04-13 07:21:02.872289: Epoch time: 101.6 s +2026-04-13 07:21:04.084886: +2026-04-13 07:21:04.086617: Epoch 2249 +2026-04-13 07:21:04.088177: Current learning rate: 0.00475 +2026-04-13 07:22:46.388514: train_loss -0.4206 +2026-04-13 07:22:46.394976: val_loss -0.3909 +2026-04-13 07:22:46.397159: Pseudo dice [0.6318, 0.0, 0.8337, 0.3315, 0.2672, 0.4388, 0.8159] +2026-04-13 07:22:46.400253: Epoch time: 102.31 s +2026-04-13 07:22:49.436474: +2026-04-13 07:22:49.438397: Epoch 2250 +2026-04-13 07:22:49.440028: Current learning rate: 0.00475 +2026-04-13 07:24:31.225647: train_loss -0.4062 +2026-04-13 07:24:31.231980: val_loss -0.3758 +2026-04-13 07:24:31.234227: Pseudo dice [0.6118, 0.0, 0.8517, 0.3641, 0.4722, 0.661, 0.7607] +2026-04-13 07:24:31.236884: Epoch time: 101.79 s +2026-04-13 07:24:32.463666: +2026-04-13 07:24:32.465388: Epoch 2251 +2026-04-13 07:24:32.467450: Current learning rate: 0.00475 +2026-04-13 07:26:14.064416: train_loss -0.3979 +2026-04-13 07:26:14.089553: val_loss -0.3502 +2026-04-13 07:26:14.091508: Pseudo dice [0.1992, 0.0, 0.6716, 0.3089, 0.3691, 0.6119, 0.8538] +2026-04-13 07:26:14.093652: Epoch time: 101.6 s +2026-04-13 07:26:15.275927: +2026-04-13 07:26:15.277605: Epoch 2252 +2026-04-13 07:26:15.279194: Current learning rate: 0.00475 +2026-04-13 07:27:56.821989: train_loss -0.4162 +2026-04-13 07:27:56.827645: val_loss -0.3587 +2026-04-13 07:27:56.830010: Pseudo dice [0.3377, 0.0, 0.7482, 0.4064, 0.4777, 0.7298, 0.892] +2026-04-13 07:27:56.832520: Epoch time: 101.55 s +2026-04-13 07:27:58.061357: +2026-04-13 07:27:58.063440: Epoch 2253 +2026-04-13 07:27:58.065394: Current learning rate: 0.00474 +2026-04-13 07:29:39.498046: train_loss -0.4319 +2026-04-13 07:29:39.505242: val_loss -0.3538 +2026-04-13 07:29:39.507511: Pseudo dice [0.7184, 0.0, 0.7776, 0.2848, 0.3918, 0.5073, 0.5897] +2026-04-13 07:29:39.510558: Epoch time: 101.44 s +2026-04-13 07:29:40.752691: +2026-04-13 07:29:40.754530: Epoch 2254 +2026-04-13 07:29:40.756231: Current learning rate: 0.00474 +2026-04-13 07:31:22.265839: train_loss -0.4075 +2026-04-13 07:31:22.272705: val_loss -0.3746 +2026-04-13 07:31:22.275100: Pseudo dice [0.5435, 0.0, 0.8087, 0.7092, 0.6262, 0.5912, 0.5851] +2026-04-13 07:31:22.277493: Epoch time: 101.52 s +2026-04-13 07:31:23.471146: +2026-04-13 07:31:23.473567: Epoch 2255 +2026-04-13 07:31:23.475528: Current learning rate: 0.00474 +2026-04-13 07:33:04.785224: train_loss -0.4056 +2026-04-13 07:33:04.790609: val_loss -0.3418 +2026-04-13 07:33:04.793083: Pseudo dice [0.1029, 0.0, 0.702, 0.7572, 0.3403, 0.7449, 0.6891] +2026-04-13 07:33:04.795374: Epoch time: 101.32 s +2026-04-13 07:33:06.068776: +2026-04-13 07:33:06.071009: Epoch 2256 +2026-04-13 07:33:06.072819: Current learning rate: 0.00474 +2026-04-13 07:34:47.634966: train_loss -0.4068 +2026-04-13 07:34:47.642789: val_loss -0.3526 +2026-04-13 07:34:47.645029: Pseudo dice [0.5078, 0.0, 0.7835, 0.2044, 0.5194, 0.5534, 0.787] +2026-04-13 07:34:47.647560: Epoch time: 101.57 s +2026-04-13 07:34:48.831755: +2026-04-13 07:34:48.833943: Epoch 2257 +2026-04-13 07:34:48.835840: Current learning rate: 0.00473 +2026-04-13 07:36:30.155938: train_loss -0.4318 +2026-04-13 07:36:30.162191: val_loss -0.3781 +2026-04-13 07:36:30.164563: Pseudo dice [0.681, 0.0, 0.713, 0.1935, 0.5885, 0.5447, 0.7739] +2026-04-13 07:36:30.167795: Epoch time: 101.33 s +2026-04-13 07:36:31.351395: +2026-04-13 07:36:31.353155: Epoch 2258 +2026-04-13 07:36:31.354834: Current learning rate: 0.00473 +2026-04-13 07:38:13.032419: train_loss -0.4209 +2026-04-13 07:38:13.037910: val_loss -0.3503 +2026-04-13 07:38:13.039765: Pseudo dice [0.4295, 0.0, 0.7923, 0.4867, 0.3723, 0.6849, 0.7822] +2026-04-13 07:38:13.041679: Epoch time: 101.68 s +2026-04-13 07:38:14.235836: +2026-04-13 07:38:14.237443: Epoch 2259 +2026-04-13 07:38:14.238974: Current learning rate: 0.00473 +2026-04-13 07:40:03.259445: train_loss -0.4044 +2026-04-13 07:40:03.266863: val_loss -0.3455 +2026-04-13 07:40:03.269351: Pseudo dice [0.3901, 0.0, 0.7998, 0.2056, 0.401, 0.6192, 0.8753] +2026-04-13 07:40:03.271621: Epoch time: 109.03 s +2026-04-13 07:40:04.459379: +2026-04-13 07:40:04.461062: Epoch 2260 +2026-04-13 07:40:04.462827: Current learning rate: 0.00473 +2026-04-13 07:41:45.667541: train_loss -0.4088 +2026-04-13 07:41:45.674186: val_loss -0.3574 +2026-04-13 07:41:45.676685: Pseudo dice [0.5534, 0.0, 0.7784, 0.6106, 0.2995, 0.54, 0.8913] +2026-04-13 07:41:45.679533: Epoch time: 101.21 s +2026-04-13 07:41:46.878083: +2026-04-13 07:41:46.881077: Epoch 2261 +2026-04-13 07:41:46.884433: Current learning rate: 0.00473 +2026-04-13 07:43:28.257107: train_loss -0.4053 +2026-04-13 07:43:28.263772: val_loss -0.3698 +2026-04-13 07:43:28.265931: Pseudo dice [0.5085, 0.0, 0.757, 0.5736, 0.5243, 0.603, 0.7095] +2026-04-13 07:43:28.268129: Epoch time: 101.38 s +2026-04-13 07:43:29.440892: +2026-04-13 07:43:29.442505: Epoch 2262 +2026-04-13 07:43:29.443969: Current learning rate: 0.00472 +2026-04-13 07:45:11.536180: train_loss -0.3961 +2026-04-13 07:45:11.541650: val_loss -0.2972 +2026-04-13 07:45:11.543808: Pseudo dice [0.0, 0.0, 0.6691, 0.2224, 0.5519, 0.3862, 0.8347] +2026-04-13 07:45:11.546298: Epoch time: 102.1 s +2026-04-13 07:45:12.732581: +2026-04-13 07:45:12.734531: Epoch 2263 +2026-04-13 07:45:12.736189: Current learning rate: 0.00472 +2026-04-13 07:46:54.111837: train_loss -0.3623 +2026-04-13 07:46:54.117911: val_loss -0.328 +2026-04-13 07:46:54.120017: Pseudo dice [0.0, 0.0, 0.6687, 0.505, 0.5619, 0.6912, 0.8989] +2026-04-13 07:46:54.122272: Epoch time: 101.38 s +2026-04-13 07:46:55.291954: +2026-04-13 07:46:55.293757: Epoch 2264 +2026-04-13 07:46:55.295229: Current learning rate: 0.00472 +2026-04-13 07:49:01.636754: train_loss -0.3669 +2026-04-13 07:49:01.643019: val_loss -0.2951 +2026-04-13 07:49:01.645081: Pseudo dice [0.0, 0.0, 0.2357, 0.0789, 0.2212, 0.38, 0.869] +2026-04-13 07:49:01.647728: Epoch time: 126.35 s +2026-04-13 07:49:02.852054: +2026-04-13 07:49:02.854261: Epoch 2265 +2026-04-13 07:49:02.856537: Current learning rate: 0.00472 +2026-04-13 07:50:45.976936: train_loss -0.4016 +2026-04-13 07:50:45.984138: val_loss -0.3722 +2026-04-13 07:50:45.986908: Pseudo dice [0.0, 0.0, 0.7639, 0.4238, 0.5002, 0.4002, 0.8668] +2026-04-13 07:50:45.989913: Epoch time: 103.13 s +2026-04-13 07:50:47.226322: +2026-04-13 07:50:47.227975: Epoch 2266 +2026-04-13 07:50:47.229452: Current learning rate: 0.00471 +2026-04-13 07:52:29.107786: train_loss -0.3989 +2026-04-13 07:52:29.113137: val_loss -0.3824 +2026-04-13 07:52:29.116255: Pseudo dice [0.1537, 0.0, 0.7964, 0.8, 0.5338, 0.6975, 0.8762] +2026-04-13 07:52:29.119662: Epoch time: 101.88 s +2026-04-13 07:52:30.294333: +2026-04-13 07:52:30.296040: Epoch 2267 +2026-04-13 07:52:30.297508: Current learning rate: 0.00471 +2026-04-13 07:54:12.271473: train_loss -0.4048 +2026-04-13 07:54:12.279487: val_loss -0.3797 +2026-04-13 07:54:12.282770: Pseudo dice [0.8201, 0.0, 0.843, 0.0233, 0.6224, 0.676, 0.7068] +2026-04-13 07:54:12.285425: Epoch time: 101.98 s +2026-04-13 07:54:13.475592: +2026-04-13 07:54:13.477860: Epoch 2268 +2026-04-13 07:54:13.480646: Current learning rate: 0.00471 +2026-04-13 07:55:54.938396: train_loss -0.4304 +2026-04-13 07:55:54.944858: val_loss -0.3467 +2026-04-13 07:55:54.947217: Pseudo dice [0.6189, 0.0, 0.7936, 0.3301, 0.511, 0.2975, 0.472] +2026-04-13 07:55:54.949409: Epoch time: 101.47 s +2026-04-13 07:55:56.177606: +2026-04-13 07:55:56.179730: Epoch 2269 +2026-04-13 07:55:56.181394: Current learning rate: 0.00471 +2026-04-13 07:57:37.648903: train_loss -0.4187 +2026-04-13 07:57:37.655438: val_loss -0.334 +2026-04-13 07:57:37.657633: Pseudo dice [0.6389, 0.0, 0.695, 0.1429, 0.4957, 0.8868, 0.7752] +2026-04-13 07:57:37.660260: Epoch time: 101.47 s +2026-04-13 07:57:38.875014: +2026-04-13 07:57:38.877715: Epoch 2270 +2026-04-13 07:57:38.879493: Current learning rate: 0.0047 +2026-04-13 07:59:20.309644: train_loss -0.4297 +2026-04-13 07:59:20.317865: val_loss -0.3426 +2026-04-13 07:59:20.323720: Pseudo dice [0.3898, 0.0, 0.8619, 0.0372, 0.4686, 0.7376, 0.7787] +2026-04-13 07:59:20.327281: Epoch time: 101.44 s +2026-04-13 07:59:21.571263: +2026-04-13 07:59:21.573884: Epoch 2271 +2026-04-13 07:59:21.577842: Current learning rate: 0.0047 +2026-04-13 08:01:02.393146: train_loss -0.4145 +2026-04-13 08:01:02.400298: val_loss -0.3552 +2026-04-13 08:01:02.403534: Pseudo dice [0.2074, 0.0, 0.708, 0.3603, 0.4565, 0.6542, 0.581] +2026-04-13 08:01:02.409368: Epoch time: 100.83 s +2026-04-13 08:01:03.640977: +2026-04-13 08:01:03.643885: Epoch 2272 +2026-04-13 08:01:03.646355: Current learning rate: 0.0047 +2026-04-13 08:02:45.107717: train_loss -0.4197 +2026-04-13 08:02:45.114527: val_loss -0.3507 +2026-04-13 08:02:45.116434: Pseudo dice [0.5045, 0.0, 0.6615, 0.756, 0.4958, 0.3768, 0.6852] +2026-04-13 08:02:45.118916: Epoch time: 101.47 s +2026-04-13 08:02:46.318280: +2026-04-13 08:02:46.320183: Epoch 2273 +2026-04-13 08:02:46.322244: Current learning rate: 0.0047 +2026-04-13 08:04:27.822572: train_loss -0.4119 +2026-04-13 08:04:27.829355: val_loss -0.3724 +2026-04-13 08:04:27.831353: Pseudo dice [0.7137, 0.0, 0.7874, 0.4282, 0.5931, 0.4517, 0.7961] +2026-04-13 08:04:27.833937: Epoch time: 101.51 s +2026-04-13 08:04:29.033082: +2026-04-13 08:04:29.035906: Epoch 2274 +2026-04-13 08:04:29.038492: Current learning rate: 0.00469 +2026-04-13 08:06:10.900068: train_loss -0.4139 +2026-04-13 08:06:10.909092: val_loss -0.3726 +2026-04-13 08:06:10.911889: Pseudo dice [0.4707, 0.0, 0.7892, 0.6871, 0.54, 0.6688, 0.8791] +2026-04-13 08:06:10.916101: Epoch time: 101.87 s +2026-04-13 08:06:12.134050: +2026-04-13 08:06:12.136307: Epoch 2275 +2026-04-13 08:06:12.138959: Current learning rate: 0.00469 +2026-04-13 08:07:54.286588: train_loss -0.4108 +2026-04-13 08:07:54.293526: val_loss -0.3687 +2026-04-13 08:07:54.297089: Pseudo dice [0.6559, 0.0, 0.8334, 0.4363, 0.3761, 0.6236, 0.8411] +2026-04-13 08:07:54.299816: Epoch time: 102.16 s +2026-04-13 08:07:55.503731: +2026-04-13 08:07:55.505829: Epoch 2276 +2026-04-13 08:07:55.508012: Current learning rate: 0.00469 +2026-04-13 08:09:36.710359: train_loss -0.4216 +2026-04-13 08:09:36.718290: val_loss -0.3601 +2026-04-13 08:09:36.721526: Pseudo dice [0.4115, 0.0, 0.8189, 0.6099, 0.4708, 0.5242, 0.4715] +2026-04-13 08:09:36.723747: Epoch time: 101.21 s +2026-04-13 08:09:37.919543: +2026-04-13 08:09:37.922506: Epoch 2277 +2026-04-13 08:09:37.924928: Current learning rate: 0.00469 +2026-04-13 08:11:19.608289: train_loss -0.4121 +2026-04-13 08:11:19.615183: val_loss -0.3594 +2026-04-13 08:11:19.617124: Pseudo dice [0.582, 0.0, 0.7286, 0.7221, 0.47, 0.5293, 0.8166] +2026-04-13 08:11:19.619214: Epoch time: 101.69 s +2026-04-13 08:11:20.828037: +2026-04-13 08:11:20.830424: Epoch 2278 +2026-04-13 08:11:20.832947: Current learning rate: 0.00468 +2026-04-13 08:13:02.539931: train_loss -0.4022 +2026-04-13 08:13:02.547872: val_loss -0.3811 +2026-04-13 08:13:02.550200: Pseudo dice [0.6395, 0.0, 0.6251, 0.3985, 0.4458, 0.4986, 0.8929] +2026-04-13 08:13:02.552705: Epoch time: 101.71 s +2026-04-13 08:13:03.763352: +2026-04-13 08:13:03.775067: Epoch 2279 +2026-04-13 08:13:03.777731: Current learning rate: 0.00468 +2026-04-13 08:14:45.355551: train_loss -0.4071 +2026-04-13 08:14:45.362786: val_loss -0.3581 +2026-04-13 08:14:45.365037: Pseudo dice [0.2527, 0.0, 0.8415, 0.3921, 0.4891, 0.1176, 0.9012] +2026-04-13 08:14:45.367916: Epoch time: 101.6 s +2026-04-13 08:14:46.562537: +2026-04-13 08:14:46.564425: Epoch 2280 +2026-04-13 08:14:46.566799: Current learning rate: 0.00468 +2026-04-13 08:16:27.791445: train_loss -0.4162 +2026-04-13 08:16:27.801118: val_loss -0.3427 +2026-04-13 08:16:27.803076: Pseudo dice [0.7094, 0.0, 0.7821, 0.4454, 0.5317, 0.4093, 0.8691] +2026-04-13 08:16:27.805366: Epoch time: 101.23 s +2026-04-13 08:16:29.014618: +2026-04-13 08:16:29.016356: Epoch 2281 +2026-04-13 08:16:29.018280: Current learning rate: 0.00468 +2026-04-13 08:18:11.179320: train_loss -0.4138 +2026-04-13 08:18:11.186920: val_loss -0.3262 +2026-04-13 08:18:11.189221: Pseudo dice [0.5361, 0.0, 0.6914, 0.2631, 0.3024, 0.5663, 0.4608] +2026-04-13 08:18:11.191920: Epoch time: 102.17 s +2026-04-13 08:18:12.389617: +2026-04-13 08:18:12.394946: Epoch 2282 +2026-04-13 08:18:12.397291: Current learning rate: 0.00467 +2026-04-13 08:19:54.091411: train_loss -0.4224 +2026-04-13 08:19:54.098536: val_loss -0.3905 +2026-04-13 08:19:54.101542: Pseudo dice [0.5973, 0.0, 0.7202, 0.7462, 0.5551, 0.8049, 0.8451] +2026-04-13 08:19:54.104137: Epoch time: 101.71 s +2026-04-13 08:19:55.286890: +2026-04-13 08:19:55.288957: Epoch 2283 +2026-04-13 08:19:55.290941: Current learning rate: 0.00467 +2026-04-13 08:21:36.642585: train_loss -0.403 +2026-04-13 08:21:36.649912: val_loss -0.3293 +2026-04-13 08:21:36.652087: Pseudo dice [0.4884, 0.0, 0.3421, 0.7061, 0.4842, 0.6759, 0.0546] +2026-04-13 08:21:36.654870: Epoch time: 101.36 s +2026-04-13 08:21:37.849329: +2026-04-13 08:21:37.851642: Epoch 2284 +2026-04-13 08:21:37.853853: Current learning rate: 0.00467 +2026-04-13 08:23:20.015391: train_loss -0.3653 +2026-04-13 08:23:20.033288: val_loss -0.272 +2026-04-13 08:23:20.036537: Pseudo dice [0.2553, 0.0, 0.1674, 0.0005, 0.3772, 0.1379, 0.553] +2026-04-13 08:23:20.039355: Epoch time: 102.17 s +2026-04-13 08:23:21.268299: +2026-04-13 08:23:21.270217: Epoch 2285 +2026-04-13 08:23:21.272340: Current learning rate: 0.00467 +2026-04-13 08:25:02.989454: train_loss -0.3743 +2026-04-13 08:25:02.997393: val_loss -0.3851 +2026-04-13 08:25:03.002141: Pseudo dice [0.3523, 0.0, 0.8034, 0.866, 0.625, 0.8083, 0.9302] +2026-04-13 08:25:03.007685: Epoch time: 101.72 s +2026-04-13 08:25:05.369291: +2026-04-13 08:25:05.373730: Epoch 2286 +2026-04-13 08:25:05.375623: Current learning rate: 0.00466 +2026-04-13 08:26:46.971619: train_loss -0.4171 +2026-04-13 08:26:46.989045: val_loss -0.3728 +2026-04-13 08:26:46.991557: Pseudo dice [0.53, 0.0, 0.8737, 0.5672, 0.4268, 0.7822, 0.8256] +2026-04-13 08:26:46.993994: Epoch time: 101.61 s +2026-04-13 08:26:48.203981: +2026-04-13 08:26:48.207028: Epoch 2287 +2026-04-13 08:26:48.209754: Current learning rate: 0.00466 +2026-04-13 08:28:29.631375: train_loss -0.4156 +2026-04-13 08:28:29.651039: val_loss -0.3898 +2026-04-13 08:28:29.655280: Pseudo dice [0.8135, 0.0, 0.8507, 0.4285, 0.4438, 0.5784, 0.8392] +2026-04-13 08:28:29.658382: Epoch time: 101.43 s +2026-04-13 08:28:30.858744: +2026-04-13 08:28:30.860617: Epoch 2288 +2026-04-13 08:28:30.862473: Current learning rate: 0.00466 +2026-04-13 08:30:12.501999: train_loss -0.4268 +2026-04-13 08:30:12.509651: val_loss -0.3788 +2026-04-13 08:30:12.512127: Pseudo dice [0.4125, 0.0, 0.8554, 0.7633, 0.469, 0.7046, 0.8508] +2026-04-13 08:30:12.514606: Epoch time: 101.65 s +2026-04-13 08:30:13.742835: +2026-04-13 08:30:13.744826: Epoch 2289 +2026-04-13 08:30:13.747323: Current learning rate: 0.00466 +2026-04-13 08:31:55.852936: train_loss -0.4302 +2026-04-13 08:31:55.860918: val_loss -0.3946 +2026-04-13 08:31:55.863455: Pseudo dice [0.7768, 0.0, 0.7947, 0.6358, 0.4307, 0.7892, 0.9308] +2026-04-13 08:31:55.867021: Epoch time: 102.11 s +2026-04-13 08:31:57.072021: +2026-04-13 08:31:57.074541: Epoch 2290 +2026-04-13 08:31:57.078772: Current learning rate: 0.00465 +2026-04-13 08:33:38.688137: train_loss -0.4067 +2026-04-13 08:33:38.695710: val_loss -0.3564 +2026-04-13 08:33:38.697962: Pseudo dice [0.4533, 0.0, 0.7577, 0.2888, 0.4633, 0.5644, 0.1722] +2026-04-13 08:33:38.700547: Epoch time: 101.62 s +2026-04-13 08:33:39.906661: +2026-04-13 08:33:39.908918: Epoch 2291 +2026-04-13 08:33:39.911574: Current learning rate: 0.00465 +2026-04-13 08:35:22.288540: train_loss -0.4216 +2026-04-13 08:35:22.295733: val_loss -0.3891 +2026-04-13 08:35:22.298656: Pseudo dice [0.7348, 0.0, 0.7598, 0.5156, 0.5128, 0.7907, 0.8941] +2026-04-13 08:35:22.301299: Epoch time: 102.38 s +2026-04-13 08:35:23.482351: +2026-04-13 08:35:23.484690: Epoch 2292 +2026-04-13 08:35:23.487735: Current learning rate: 0.00465 +2026-04-13 08:37:05.097720: train_loss -0.4242 +2026-04-13 08:37:05.105152: val_loss -0.3203 +2026-04-13 08:37:05.108226: Pseudo dice [0.1683, 0.0, 0.7528, 0.4401, 0.2199, 0.4217, 0.8554] +2026-04-13 08:37:05.110708: Epoch time: 101.62 s +2026-04-13 08:37:06.319879: +2026-04-13 08:37:06.321738: Epoch 2293 +2026-04-13 08:37:06.323823: Current learning rate: 0.00465 +2026-04-13 08:38:48.288305: train_loss -0.408 +2026-04-13 08:38:48.295398: val_loss -0.3854 +2026-04-13 08:38:48.297402: Pseudo dice [0.5171, 0.0, 0.8664, 0.5625, 0.5639, 0.7655, 0.7697] +2026-04-13 08:38:48.299921: Epoch time: 101.97 s +2026-04-13 08:38:49.510358: +2026-04-13 08:38:49.512264: Epoch 2294 +2026-04-13 08:38:49.514394: Current learning rate: 0.00464 +2026-04-13 08:40:31.170173: train_loss -0.4136 +2026-04-13 08:40:31.176972: val_loss -0.3773 +2026-04-13 08:40:31.179227: Pseudo dice [0.4229, 0.0, 0.8191, 0.5809, 0.5664, 0.867, 0.7432] +2026-04-13 08:40:31.181938: Epoch time: 101.66 s +2026-04-13 08:40:32.406013: +2026-04-13 08:40:32.408336: Epoch 2295 +2026-04-13 08:40:32.410912: Current learning rate: 0.00464 +2026-04-13 08:42:14.639222: train_loss -0.4036 +2026-04-13 08:42:14.646843: val_loss -0.3284 +2026-04-13 08:42:14.648880: Pseudo dice [0.5849, 0.0, 0.6773, 0.2166, 0.4472, 0.4169, 0.4072] +2026-04-13 08:42:14.651282: Epoch time: 102.24 s +2026-04-13 08:42:15.894587: +2026-04-13 08:42:15.896406: Epoch 2296 +2026-04-13 08:42:15.898534: Current learning rate: 0.00464 +2026-04-13 08:43:58.429312: train_loss -0.3971 +2026-04-13 08:43:58.436756: val_loss -0.3545 +2026-04-13 08:43:58.439265: Pseudo dice [0.7731, 0.0, 0.8387, 0.588, 0.5903, 0.8514, 0.2052] +2026-04-13 08:43:58.442085: Epoch time: 102.54 s +2026-04-13 08:43:59.665550: +2026-04-13 08:43:59.668607: Epoch 2297 +2026-04-13 08:43:59.670717: Current learning rate: 0.00464 +2026-04-13 08:45:42.341383: train_loss -0.4192 +2026-04-13 08:45:42.349424: val_loss -0.3252 +2026-04-13 08:45:42.352308: Pseudo dice [0.6888, 0.0, 0.8456, 0.1261, 0.2216, 0.2405, 0.4789] +2026-04-13 08:45:42.355019: Epoch time: 102.68 s +2026-04-13 08:45:43.596733: +2026-04-13 08:45:43.598394: Epoch 2298 +2026-04-13 08:45:43.600182: Current learning rate: 0.00463 +2026-04-13 08:47:25.211102: train_loss -0.4151 +2026-04-13 08:47:25.218118: val_loss -0.3516 +2026-04-13 08:47:25.220360: Pseudo dice [0.5926, 0.0, 0.7021, 0.3156, 0.4598, 0.8862, 0.1639] +2026-04-13 08:47:25.222610: Epoch time: 101.62 s +2026-04-13 08:47:26.424035: +2026-04-13 08:47:26.425748: Epoch 2299 +2026-04-13 08:47:26.427672: Current learning rate: 0.00463 +2026-04-13 08:49:08.488372: train_loss -0.4241 +2026-04-13 08:49:08.495137: val_loss -0.351 +2026-04-13 08:49:08.498018: Pseudo dice [0.0558, 0.0, 0.5872, 0.539, 0.4624, 0.637, 0.7491] +2026-04-13 08:49:08.500062: Epoch time: 102.07 s +2026-04-13 08:49:11.443641: +2026-04-13 08:49:11.445843: Epoch 2300 +2026-04-13 08:49:11.447670: Current learning rate: 0.00463 +2026-04-13 08:50:52.944637: train_loss -0.4052 +2026-04-13 08:50:52.951461: val_loss -0.3901 +2026-04-13 08:50:52.954036: Pseudo dice [0.4515, 0.0, 0.8342, 0.0097, 0.4705, 0.5989, 0.9383] +2026-04-13 08:50:52.956691: Epoch time: 101.5 s +2026-04-13 08:50:54.145178: +2026-04-13 08:50:54.147253: Epoch 2301 +2026-04-13 08:50:54.149347: Current learning rate: 0.00463 +2026-04-13 08:52:36.040888: train_loss -0.4215 +2026-04-13 08:52:36.047899: val_loss -0.3447 +2026-04-13 08:52:36.050012: Pseudo dice [0.7561, 0.0, 0.5555, 0.2614, 0.5872, 0.6816, 0.7008] +2026-04-13 08:52:36.053110: Epoch time: 101.9 s +2026-04-13 08:52:37.256970: +2026-04-13 08:52:37.267424: Epoch 2302 +2026-04-13 08:52:37.274845: Current learning rate: 0.00462 +2026-04-13 08:54:19.194408: train_loss -0.3995 +2026-04-13 08:54:19.201531: val_loss -0.3215 +2026-04-13 08:54:19.204034: Pseudo dice [0.4462, 0.0, 0.5773, 0.4211, 0.2792, 0.7463, 0.8919] +2026-04-13 08:54:19.206618: Epoch time: 101.94 s +2026-04-13 08:54:20.397495: +2026-04-13 08:54:20.399197: Epoch 2303 +2026-04-13 08:54:20.401776: Current learning rate: 0.00462 +2026-04-13 08:56:01.805603: train_loss -0.4151 +2026-04-13 08:56:01.813708: val_loss -0.3519 +2026-04-13 08:56:01.815977: Pseudo dice [0.2226, 0.0, 0.6978, 0.6749, 0.3623, 0.2607, 0.5939] +2026-04-13 08:56:01.819598: Epoch time: 101.41 s +2026-04-13 08:56:03.037251: +2026-04-13 08:56:03.038955: Epoch 2304 +2026-04-13 08:56:03.040874: Current learning rate: 0.00462 +2026-04-13 08:57:44.684049: train_loss -0.4042 +2026-04-13 08:57:44.690997: val_loss -0.3139 +2026-04-13 08:57:44.693484: Pseudo dice [0.4663, 0.0, 0.5187, 0.3728, 0.2624, 0.2737, 0.4375] +2026-04-13 08:57:44.696031: Epoch time: 101.65 s +2026-04-13 08:57:45.898961: +2026-04-13 08:57:45.900844: Epoch 2305 +2026-04-13 08:57:45.903106: Current learning rate: 0.00462 +2026-04-13 08:59:27.949816: train_loss -0.3935 +2026-04-13 08:59:27.956251: val_loss -0.3594 +2026-04-13 08:59:27.959208: Pseudo dice [0.5902, 0.0, 0.8124, 0.6167, 0.4287, 0.7904, 0.6343] +2026-04-13 08:59:27.962476: Epoch time: 102.05 s +2026-04-13 08:59:29.177122: +2026-04-13 08:59:29.179322: Epoch 2306 +2026-04-13 08:59:29.181876: Current learning rate: 0.00461 +2026-04-13 09:01:12.743698: train_loss -0.4151 +2026-04-13 09:01:12.750023: val_loss -0.3636 +2026-04-13 09:01:12.752913: Pseudo dice [0.7568, 0.0, 0.6636, 0.2389, 0.5628, 0.5252, 0.7783] +2026-04-13 09:01:12.755514: Epoch time: 103.57 s +2026-04-13 09:01:13.960459: +2026-04-13 09:01:13.962558: Epoch 2307 +2026-04-13 09:01:13.964478: Current learning rate: 0.00461 +2026-04-13 09:02:55.837941: train_loss -0.4127 +2026-04-13 09:02:55.845299: val_loss -0.3542 +2026-04-13 09:02:55.847860: Pseudo dice [0.7921, 0.0, 0.7598, 0.5821, 0.5266, 0.6263, 0.7188] +2026-04-13 09:02:55.850440: Epoch time: 101.88 s +2026-04-13 09:02:57.033827: +2026-04-13 09:02:57.035916: Epoch 2308 +2026-04-13 09:02:57.037764: Current learning rate: 0.00461 +2026-04-13 09:04:39.100210: train_loss -0.4267 +2026-04-13 09:04:39.106836: val_loss -0.3517 +2026-04-13 09:04:39.109879: Pseudo dice [0.715, 0.0, 0.7282, 0.1877, 0.5795, 0.4209, 0.6467] +2026-04-13 09:04:39.112244: Epoch time: 102.07 s +2026-04-13 09:04:40.286378: +2026-04-13 09:04:40.288925: Epoch 2309 +2026-04-13 09:04:40.291551: Current learning rate: 0.00461 +2026-04-13 09:06:22.254396: train_loss -0.407 +2026-04-13 09:06:22.261054: val_loss -0.3738 +2026-04-13 09:06:22.263079: Pseudo dice [0.525, 0.0, 0.7056, 0.4056, 0.5543, 0.7723, 0.6819] +2026-04-13 09:06:22.265538: Epoch time: 101.97 s +2026-04-13 09:06:23.455605: +2026-04-13 09:06:23.457390: Epoch 2310 +2026-04-13 09:06:23.459569: Current learning rate: 0.00461 +2026-04-13 09:08:05.348014: train_loss -0.4082 +2026-04-13 09:08:05.355197: val_loss -0.3413 +2026-04-13 09:08:05.357353: Pseudo dice [0.3781, 0.0, 0.77, 0.0229, 0.5676, 0.2798, 0.543] +2026-04-13 09:08:05.359982: Epoch time: 101.9 s +2026-04-13 09:08:06.551973: +2026-04-13 09:08:06.554261: Epoch 2311 +2026-04-13 09:08:06.556280: Current learning rate: 0.0046 +2026-04-13 09:09:48.012442: train_loss -0.4283 +2026-04-13 09:09:48.021361: val_loss -0.3538 +2026-04-13 09:09:48.024451: Pseudo dice [0.2202, 0.0, 0.6437, 0.2313, 0.5641, 0.677, 0.754] +2026-04-13 09:09:48.027105: Epoch time: 101.46 s +2026-04-13 09:09:49.204250: +2026-04-13 09:09:49.206265: Epoch 2312 +2026-04-13 09:09:49.208548: Current learning rate: 0.0046 +2026-04-13 09:11:30.515244: train_loss -0.4 +2026-04-13 09:11:30.523650: val_loss -0.3574 +2026-04-13 09:11:30.525959: Pseudo dice [0.44, 0.0, 0.7571, 0.2243, 0.5623, 0.519, 0.5914] +2026-04-13 09:11:30.532524: Epoch time: 101.31 s +2026-04-13 09:11:31.736573: +2026-04-13 09:11:31.740093: Epoch 2313 +2026-04-13 09:11:31.742250: Current learning rate: 0.0046 +2026-04-13 09:13:13.633747: train_loss -0.4219 +2026-04-13 09:13:13.641499: val_loss -0.3667 +2026-04-13 09:13:13.644044: Pseudo dice [0.5762, 0.0, 0.7791, 0.7875, 0.5834, 0.567, 0.5266] +2026-04-13 09:13:13.646775: Epoch time: 101.9 s +2026-04-13 09:13:14.913606: +2026-04-13 09:13:14.915392: Epoch 2314 +2026-04-13 09:13:14.917385: Current learning rate: 0.0046 +2026-04-13 09:14:56.824300: train_loss -0.4277 +2026-04-13 09:14:56.830754: val_loss -0.3798 +2026-04-13 09:14:56.833131: Pseudo dice [0.4227, 0.0, 0.682, 0.2814, 0.4824, 0.4106, 0.7749] +2026-04-13 09:14:56.835593: Epoch time: 101.91 s +2026-04-13 09:14:58.045941: +2026-04-13 09:14:58.048179: Epoch 2315 +2026-04-13 09:14:58.050270: Current learning rate: 0.00459 +2026-04-13 09:16:39.570512: train_loss -0.4102 +2026-04-13 09:16:39.579386: val_loss -0.3738 +2026-04-13 09:16:39.582953: Pseudo dice [0.6931, 0.0, 0.7913, 0.7877, 0.6046, 0.5799, 0.8772] +2026-04-13 09:16:39.585832: Epoch time: 101.53 s +2026-04-13 09:16:40.841572: +2026-04-13 09:16:40.843524: Epoch 2316 +2026-04-13 09:16:40.846175: Current learning rate: 0.00459 +2026-04-13 09:18:22.921516: train_loss -0.4183 +2026-04-13 09:18:22.928835: val_loss -0.3653 +2026-04-13 09:18:22.931827: Pseudo dice [0.5594, 0.0, 0.7588, 0.3246, 0.3832, 0.6731, 0.8415] +2026-04-13 09:18:22.935079: Epoch time: 102.08 s +2026-04-13 09:18:24.145458: +2026-04-13 09:18:24.147229: Epoch 2317 +2026-04-13 09:18:24.149253: Current learning rate: 0.00459 +2026-04-13 09:20:05.880317: train_loss -0.4079 +2026-04-13 09:20:05.887543: val_loss -0.3509 +2026-04-13 09:20:05.890089: Pseudo dice [0.4019, 0.0, 0.7729, 0.0101, 0.6199, 0.767, 0.7824] +2026-04-13 09:20:05.895135: Epoch time: 101.74 s +2026-04-13 09:20:07.091316: +2026-04-13 09:20:07.093068: Epoch 2318 +2026-04-13 09:20:07.095309: Current learning rate: 0.00459 +2026-04-13 09:21:48.439962: train_loss -0.3974 +2026-04-13 09:21:48.448505: val_loss -0.357 +2026-04-13 09:21:48.450875: Pseudo dice [0.8072, 0.0, 0.3878, 0.008, 0.3944, 0.3837, 0.8818] +2026-04-13 09:21:48.453305: Epoch time: 101.35 s +2026-04-13 09:21:49.634190: +2026-04-13 09:21:49.636051: Epoch 2319 +2026-04-13 09:21:49.638125: Current learning rate: 0.00458 +2026-04-13 09:23:31.693996: train_loss -0.4266 +2026-04-13 09:23:31.702060: val_loss -0.3409 +2026-04-13 09:23:31.704663: Pseudo dice [0.0294, 0.0, 0.8303, 0.4782, 0.7243, 0.6207, 0.5697] +2026-04-13 09:23:31.707640: Epoch time: 102.06 s +2026-04-13 09:23:32.945682: +2026-04-13 09:23:32.947947: Epoch 2320 +2026-04-13 09:23:32.953147: Current learning rate: 0.00458 +2026-04-13 09:25:14.845826: train_loss -0.4169 +2026-04-13 09:25:14.851883: val_loss -0.3783 +2026-04-13 09:25:14.854589: Pseudo dice [0.5756, 0.0, 0.8233, 0.6054, 0.3646, 0.773, 0.8613] +2026-04-13 09:25:14.857644: Epoch time: 101.9 s +2026-04-13 09:25:16.087030: +2026-04-13 09:25:16.094528: Epoch 2321 +2026-04-13 09:25:16.101975: Current learning rate: 0.00458 +2026-04-13 09:26:57.823939: train_loss -0.3768 +2026-04-13 09:26:57.850317: val_loss -0.3656 +2026-04-13 09:26:57.852729: Pseudo dice [0.6439, 0.0, 0.4571, 0.4732, 0.5412, 0.5925, 0.8054] +2026-04-13 09:26:57.855275: Epoch time: 101.74 s +2026-04-13 09:26:59.057603: +2026-04-13 09:26:59.059763: Epoch 2322 +2026-04-13 09:26:59.061626: Current learning rate: 0.00458 +2026-04-13 09:28:40.403184: train_loss -0.3908 +2026-04-13 09:28:40.410064: val_loss -0.4099 +2026-04-13 09:28:40.412364: Pseudo dice [0.5201, 0.0, 0.8258, 0.7263, 0.6916, 0.7793, 0.882] +2026-04-13 09:28:40.415046: Epoch time: 101.35 s +2026-04-13 09:28:41.670109: +2026-04-13 09:28:41.672042: Epoch 2323 +2026-04-13 09:28:41.675203: Current learning rate: 0.00457 +2026-04-13 09:30:23.450516: train_loss -0.4119 +2026-04-13 09:30:23.457286: val_loss -0.3474 +2026-04-13 09:30:23.460238: Pseudo dice [0.4709, 0.0, 0.7472, 0.1484, 0.5295, 0.5482, 0.8609] +2026-04-13 09:30:23.463429: Epoch time: 101.78 s +2026-04-13 09:30:24.676990: +2026-04-13 09:30:24.679735: Epoch 2324 +2026-04-13 09:30:24.682014: Current learning rate: 0.00457 +2026-04-13 09:32:06.460918: train_loss -0.4082 +2026-04-13 09:32:06.473675: val_loss -0.3785 +2026-04-13 09:32:06.476418: Pseudo dice [0.5997, 0.0, 0.6261, 0.4264, 0.4796, 0.8932, 0.8922] +2026-04-13 09:32:06.482737: Epoch time: 101.79 s +2026-04-13 09:32:07.667310: +2026-04-13 09:32:07.669660: Epoch 2325 +2026-04-13 09:32:07.672335: Current learning rate: 0.00457 +2026-04-13 09:33:49.015147: train_loss -0.4262 +2026-04-13 09:33:49.022281: val_loss -0.3477 +2026-04-13 09:33:49.024901: Pseudo dice [0.3732, 0.0, 0.8297, 0.2731, 0.5209, 0.4817, 0.8546] +2026-04-13 09:33:49.027470: Epoch time: 101.35 s +2026-04-13 09:33:50.303789: +2026-04-13 09:33:50.305796: Epoch 2326 +2026-04-13 09:33:50.307823: Current learning rate: 0.00457 +2026-04-13 09:35:31.753512: train_loss -0.391 +2026-04-13 09:35:31.760895: val_loss -0.3448 +2026-04-13 09:35:31.763165: Pseudo dice [0.69, 0.0, 0.6978, 0.4523, 0.1389, 0.5562, 0.9125] +2026-04-13 09:35:31.767677: Epoch time: 101.45 s +2026-04-13 09:35:34.138927: +2026-04-13 09:35:34.140699: Epoch 2327 +2026-04-13 09:35:34.142515: Current learning rate: 0.00456 +2026-04-13 09:37:15.375233: train_loss -0.4022 +2026-04-13 09:37:15.382235: val_loss -0.3958 +2026-04-13 09:37:15.384678: Pseudo dice [0.3632, 0.0, 0.7896, 0.5647, 0.5307, 0.8305, 0.7184] +2026-04-13 09:37:15.387691: Epoch time: 101.24 s +2026-04-13 09:37:16.603292: +2026-04-13 09:37:16.605780: Epoch 2328 +2026-04-13 09:37:16.607995: Current learning rate: 0.00456 +2026-04-13 09:38:58.584731: train_loss -0.4107 +2026-04-13 09:38:58.592075: val_loss -0.3745 +2026-04-13 09:38:58.594583: Pseudo dice [0.1725, 0.0, 0.5475, 0.3065, 0.5569, 0.82, 0.8886] +2026-04-13 09:38:58.598324: Epoch time: 101.98 s +2026-04-13 09:38:59.850154: +2026-04-13 09:38:59.852570: Epoch 2329 +2026-04-13 09:38:59.855419: Current learning rate: 0.00456 +2026-04-13 09:40:41.174935: train_loss -0.4264 +2026-04-13 09:40:41.181982: val_loss -0.3495 +2026-04-13 09:40:41.197613: Pseudo dice [0.4364, 0.0, 0.8778, 0.3645, 0.4778, 0.7009, 0.8985] +2026-04-13 09:40:41.213135: Epoch time: 101.33 s +2026-04-13 09:40:42.430608: +2026-04-13 09:40:42.432402: Epoch 2330 +2026-04-13 09:40:42.434244: Current learning rate: 0.00456 +2026-04-13 09:42:23.733315: train_loss -0.4154 +2026-04-13 09:42:23.741004: val_loss -0.3764 +2026-04-13 09:42:23.743728: Pseudo dice [0.4374, 0.0, 0.745, 0.5332, 0.54, 0.7955, 0.843] +2026-04-13 09:42:23.747234: Epoch time: 101.31 s +2026-04-13 09:42:24.955935: +2026-04-13 09:42:24.958354: Epoch 2331 +2026-04-13 09:42:24.961855: Current learning rate: 0.00455 +2026-04-13 09:44:07.106753: train_loss -0.4229 +2026-04-13 09:44:07.116473: val_loss -0.3611 +2026-04-13 09:44:07.119163: Pseudo dice [0.6388, 0.0, 0.6502, 0.4145, 0.6041, 0.4204, 0.9156] +2026-04-13 09:44:07.121571: Epoch time: 102.15 s +2026-04-13 09:44:08.323053: +2026-04-13 09:44:08.325832: Epoch 2332 +2026-04-13 09:44:08.328210: Current learning rate: 0.00455 +2026-04-13 09:45:53.599952: train_loss -0.4384 +2026-04-13 09:45:53.606663: val_loss -0.3721 +2026-04-13 09:45:53.608637: Pseudo dice [0.27, 0.0, 0.6077, 0.8036, 0.5488, 0.6142, 0.8933] +2026-04-13 09:45:53.611330: Epoch time: 105.28 s +2026-04-13 09:45:54.809505: +2026-04-13 09:45:54.812221: Epoch 2333 +2026-04-13 09:45:54.815159: Current learning rate: 0.00455 +2026-04-13 09:49:51.922421: train_loss -0.4216 +2026-04-13 09:49:51.932116: val_loss -0.3791 +2026-04-13 09:49:51.934387: Pseudo dice [0.6513, 0.0, 0.8385, 0.5014, 0.4968, 0.6056, 0.8086] +2026-04-13 09:49:51.938175: Epoch time: 237.12 s +2026-04-13 09:49:53.169330: +2026-04-13 09:49:53.171944: Epoch 2334 +2026-04-13 09:49:53.175350: Current learning rate: 0.00455 +2026-04-13 09:52:33.501348: train_loss -0.4291 +2026-04-13 09:52:33.509297: val_loss -0.3712 +2026-04-13 09:52:33.511640: Pseudo dice [0.5048, 0.0, 0.8241, 0.369, 0.5605, 0.663, 0.6503] +2026-04-13 09:52:33.515008: Epoch time: 160.34 s +2026-04-13 09:52:34.712734: +2026-04-13 09:52:34.714690: Epoch 2335 +2026-04-13 09:52:34.716731: Current learning rate: 0.00454 +2026-04-13 10:10:26.288790: train_loss -0.427 +2026-04-13 10:10:26.313311: val_loss -0.3739 +2026-04-13 10:10:26.315893: Pseudo dice [0.758, 0.0, 0.5969, 0.0611, 0.5224, 0.853, 0.7512] +2026-04-13 10:10:26.319048: Epoch time: 1071.58 s +2026-04-13 10:10:27.676183: +2026-04-13 10:10:27.678665: Epoch 2336 +2026-04-13 10:10:27.680916: Current learning rate: 0.00454 +2026-04-13 10:16:08.175809: train_loss -0.4253 +2026-04-13 10:16:08.182929: val_loss -0.3417 +2026-04-13 10:16:08.185272: Pseudo dice [0.7045, 0.0, 0.6078, 0.4691, 0.3039, 0.6326, 0.7896] +2026-04-13 10:16:08.189154: Epoch time: 340.5 s +2026-04-13 10:16:09.387125: +2026-04-13 10:16:09.389387: Epoch 2337 +2026-04-13 10:16:09.392121: Current learning rate: 0.00454 +2026-04-13 10:17:51.366920: train_loss -0.4219 +2026-04-13 10:17:51.373599: val_loss -0.3399 +2026-04-13 10:17:51.375777: Pseudo dice [0.4889, 0.0, 0.7128, 0.3842, 0.3376, 0.7083, 0.5284] +2026-04-13 10:17:51.379399: Epoch time: 101.98 s +2026-04-13 10:17:52.573407: +2026-04-13 10:17:52.576559: Epoch 2338 +2026-04-13 10:17:52.579485: Current learning rate: 0.00454 +2026-04-13 10:19:33.970334: train_loss -0.4334 +2026-04-13 10:19:33.979796: val_loss -0.3562 +2026-04-13 10:19:33.982593: Pseudo dice [0.7389, 0.0, 0.7599, 0.587, 0.4936, 0.5635, 0.5908] +2026-04-13 10:19:33.985261: Epoch time: 101.4 s +2026-04-13 10:19:35.179008: +2026-04-13 10:19:35.181274: Epoch 2339 +2026-04-13 10:19:35.183911: Current learning rate: 0.00453 +2026-04-13 10:21:16.906109: train_loss -0.4155 +2026-04-13 10:21:16.913728: val_loss -0.3911 +2026-04-13 10:21:16.916595: Pseudo dice [0.7751, 0.0, 0.6993, 0.6625, 0.4614, 0.4602, 0.7796] +2026-04-13 10:21:16.919991: Epoch time: 101.73 s +2026-04-13 10:21:18.154141: +2026-04-13 10:21:18.157167: Epoch 2340 +2026-04-13 10:21:18.159550: Current learning rate: 0.00453 +2026-04-13 10:23:00.118036: train_loss -0.4006 +2026-04-13 10:23:00.125296: val_loss -0.3551 +2026-04-13 10:23:00.128279: Pseudo dice [0.4886, 0.0, 0.7704, 0.3928, 0.4826, 0.6817, 0.575] +2026-04-13 10:23:00.130674: Epoch time: 101.97 s +2026-04-13 10:23:01.365201: +2026-04-13 10:23:01.367133: Epoch 2341 +2026-04-13 10:23:01.369094: Current learning rate: 0.00453 +2026-04-13 10:24:42.982080: train_loss -0.4231 +2026-04-13 10:24:42.990044: val_loss -0.3855 +2026-04-13 10:24:42.992897: Pseudo dice [0.7802, 0.0, 0.6962, 0.6148, 0.6509, 0.5981, 0.8651] +2026-04-13 10:24:42.996587: Epoch time: 101.62 s +2026-04-13 10:24:44.218953: +2026-04-13 10:24:44.221668: Epoch 2342 +2026-04-13 10:24:44.223549: Current learning rate: 0.00453 +2026-04-13 10:26:26.386859: train_loss -0.4146 +2026-04-13 10:26:26.393655: val_loss -0.3749 +2026-04-13 10:26:26.395887: Pseudo dice [0.3837, 0.0, 0.808, 0.7727, 0.4628, 0.3876, 0.6947] +2026-04-13 10:26:26.398179: Epoch time: 102.17 s +2026-04-13 10:26:27.614240: +2026-04-13 10:26:27.616395: Epoch 2343 +2026-04-13 10:26:27.618624: Current learning rate: 0.00452 +2026-04-13 10:28:08.951736: train_loss -0.4329 +2026-04-13 10:28:08.959681: val_loss -0.3125 +2026-04-13 10:28:08.970469: Pseudo dice [0.6023, 0.0, 0.502, 0.0783, 0.593, 0.5922, 0.8727] +2026-04-13 10:28:08.973192: Epoch time: 101.34 s +2026-04-13 10:28:10.177985: +2026-04-13 10:28:10.180009: Epoch 2344 +2026-04-13 10:28:10.182012: Current learning rate: 0.00452 +2026-04-13 10:29:52.023777: train_loss -0.4116 +2026-04-13 10:29:52.030318: val_loss -0.3755 +2026-04-13 10:29:52.032793: Pseudo dice [0.1596, 0.0, 0.7802, 0.6796, 0.5143, 0.6334, 0.6876] +2026-04-13 10:29:52.035289: Epoch time: 101.85 s +2026-04-13 10:29:53.293996: +2026-04-13 10:29:53.296146: Epoch 2345 +2026-04-13 10:29:53.298740: Current learning rate: 0.00452 +2026-04-13 10:31:35.746601: train_loss -0.405 +2026-04-13 10:31:35.753390: val_loss -0.3746 +2026-04-13 10:31:35.755622: Pseudo dice [0.7527, 0.0, 0.7728, 0.449, 0.5941, 0.5851, 0.8458] +2026-04-13 10:31:35.757974: Epoch time: 102.46 s +2026-04-13 10:31:36.953694: +2026-04-13 10:31:36.957027: Epoch 2346 +2026-04-13 10:31:36.959258: Current learning rate: 0.00452 +2026-04-13 10:33:19.559561: train_loss -0.397 +2026-04-13 10:33:19.567455: val_loss -0.3752 +2026-04-13 10:33:19.571531: Pseudo dice [0.7587, 0.0, 0.7706, 0.7718, 0.5957, 0.8076, 0.4954] +2026-04-13 10:33:19.574019: Epoch time: 102.61 s +2026-04-13 10:33:20.781347: +2026-04-13 10:33:20.783686: Epoch 2347 +2026-04-13 10:33:20.786024: Current learning rate: 0.00451 +2026-04-13 10:35:03.537862: train_loss -0.4195 +2026-04-13 10:35:03.544889: val_loss -0.3804 +2026-04-13 10:35:03.547152: Pseudo dice [0.6876, 0.0, 0.7265, 0.6738, 0.5407, 0.7344, 0.7171] +2026-04-13 10:35:03.549474: Epoch time: 102.76 s +2026-04-13 10:35:04.751941: +2026-04-13 10:35:04.754147: Epoch 2348 +2026-04-13 10:35:04.756361: Current learning rate: 0.00451 +2026-04-13 10:36:46.543701: train_loss -0.4311 +2026-04-13 10:36:46.552668: val_loss -0.3592 +2026-04-13 10:36:46.555768: Pseudo dice [0.5997, 0.0, 0.7634, 0.0313, 0.5498, 0.7127, 0.7217] +2026-04-13 10:36:46.558419: Epoch time: 101.79 s +2026-04-13 10:36:47.744524: +2026-04-13 10:36:47.747405: Epoch 2349 +2026-04-13 10:36:47.750113: Current learning rate: 0.00451 +2026-04-13 10:38:29.646505: train_loss -0.421 +2026-04-13 10:38:29.653939: val_loss -0.3643 +2026-04-13 10:38:29.656653: Pseudo dice [0.4147, 0.0, 0.7677, 0.6135, 0.5609, 0.6249, 0.9006] +2026-04-13 10:38:29.659212: Epoch time: 101.91 s +2026-04-13 10:38:32.640437: +2026-04-13 10:38:32.642234: Epoch 2350 +2026-04-13 10:38:32.644137: Current learning rate: 0.00451 +2026-04-13 10:40:14.690194: train_loss -0.4328 +2026-04-13 10:40:14.698103: val_loss -0.3299 +2026-04-13 10:40:14.700951: Pseudo dice [0.7723, 0.0, 0.7528, 0.0898, 0.2766, 0.5172, 0.4139] +2026-04-13 10:40:14.705656: Epoch time: 102.05 s +2026-04-13 10:40:15.911865: +2026-04-13 10:40:15.914288: Epoch 2351 +2026-04-13 10:40:15.917440: Current learning rate: 0.0045 +2026-04-13 10:41:58.212641: train_loss -0.4111 +2026-04-13 10:41:58.221056: val_loss -0.3723 +2026-04-13 10:41:58.223960: Pseudo dice [0.2533, 0.0, 0.7984, 0.1269, 0.626, 0.7183, 0.6761] +2026-04-13 10:41:58.228196: Epoch time: 102.3 s +2026-04-13 10:41:59.448829: +2026-04-13 10:41:59.450694: Epoch 2352 +2026-04-13 10:41:59.452538: Current learning rate: 0.0045 +2026-04-13 10:43:42.089430: train_loss -0.4244 +2026-04-13 10:43:42.100976: val_loss -0.3778 +2026-04-13 10:43:42.103936: Pseudo dice [0.6094, 0.0, 0.835, 0.289, 0.4487, 0.2787, 0.893] +2026-04-13 10:43:42.108137: Epoch time: 102.64 s +2026-04-13 10:43:43.316054: +2026-04-13 10:43:43.319268: Epoch 2353 +2026-04-13 10:43:43.322375: Current learning rate: 0.0045 +2026-04-13 10:45:25.413762: train_loss -0.4103 +2026-04-13 10:45:25.421170: val_loss -0.3674 +2026-04-13 10:45:25.426212: Pseudo dice [0.6155, 0.0, 0.7206, 0.3537, 0.4651, 0.3944, 0.8984] +2026-04-13 10:45:25.428846: Epoch time: 102.1 s +2026-04-13 10:45:26.667541: +2026-04-13 10:45:26.669490: Epoch 2354 +2026-04-13 10:45:26.671484: Current learning rate: 0.0045 +2026-04-13 10:47:08.333256: train_loss -0.4121 +2026-04-13 10:47:08.341141: val_loss -0.3667 +2026-04-13 10:47:08.343813: Pseudo dice [0.5238, 0.0, 0.4462, 0.2688, 0.465, 0.3699, 0.564] +2026-04-13 10:47:08.347738: Epoch time: 101.67 s +2026-04-13 10:47:09.532513: +2026-04-13 10:47:09.534966: Epoch 2355 +2026-04-13 10:47:09.540301: Current learning rate: 0.00449 +2026-04-13 10:48:51.538466: train_loss -0.4079 +2026-04-13 10:48:51.548467: val_loss -0.3551 +2026-04-13 10:48:51.554934: Pseudo dice [0.8103, 0.0, 0.6511, 0.477, 0.3892, 0.604, 0.6337] +2026-04-13 10:48:51.558195: Epoch time: 102.01 s +2026-04-13 10:48:52.762349: +2026-04-13 10:48:52.764943: Epoch 2356 +2026-04-13 10:48:52.766961: Current learning rate: 0.00449 +2026-04-13 10:50:35.010228: train_loss -0.3995 +2026-04-13 10:50:35.019720: val_loss -0.3792 +2026-04-13 10:50:35.022074: Pseudo dice [0.8131, 0.0, 0.7462, 0.6123, 0.468, 0.7367, 0.8744] +2026-04-13 10:50:35.024645: Epoch time: 102.25 s +2026-04-13 10:50:36.249860: +2026-04-13 10:50:36.252561: Epoch 2357 +2026-04-13 10:50:36.255511: Current learning rate: 0.00449 +2026-04-13 10:52:18.124648: train_loss -0.4301 +2026-04-13 10:52:18.132055: val_loss -0.3394 +2026-04-13 10:52:18.136255: Pseudo dice [0.5746, 0.0, 0.8161, 0.6189, 0.5118, 0.7313, 0.5229] +2026-04-13 10:52:18.138650: Epoch time: 101.88 s +2026-04-13 10:52:19.389645: +2026-04-13 10:52:19.392648: Epoch 2358 +2026-04-13 10:52:19.394368: Current learning rate: 0.00449 +2026-04-13 10:54:01.357371: train_loss -0.4185 +2026-04-13 10:54:01.364931: val_loss -0.3462 +2026-04-13 10:54:01.367852: Pseudo dice [0.3209, 0.0, 0.4398, 0.589, 0.4811, 0.6914, 0.6725] +2026-04-13 10:54:01.373036: Epoch time: 101.97 s +2026-04-13 10:54:02.593323: +2026-04-13 10:54:02.595279: Epoch 2359 +2026-04-13 10:54:02.597482: Current learning rate: 0.00448 +2026-04-13 10:55:44.734272: train_loss -0.4327 +2026-04-13 10:55:44.743210: val_loss -0.3784 +2026-04-13 10:55:44.745493: Pseudo dice [0.4599, 0.0, 0.8048, 0.7638, 0.5158, 0.5453, 0.9067] +2026-04-13 10:55:44.749048: Epoch time: 102.14 s +2026-04-13 10:55:45.952102: +2026-04-13 10:55:45.956160: Epoch 2360 +2026-04-13 10:55:45.958645: Current learning rate: 0.00448 +2026-04-13 10:57:28.399680: train_loss -0.4253 +2026-04-13 10:57:28.408011: val_loss -0.3716 +2026-04-13 10:57:28.410858: Pseudo dice [0.4155, 0.0, 0.564, 0.5389, 0.4908, 0.6427, 0.5936] +2026-04-13 10:57:28.414975: Epoch time: 102.45 s +2026-04-13 10:57:29.664112: +2026-04-13 10:57:29.666806: Epoch 2361 +2026-04-13 10:57:29.669559: Current learning rate: 0.00448 +2026-04-13 10:59:11.689841: train_loss -0.4133 +2026-04-13 10:59:11.697095: val_loss -0.3739 +2026-04-13 10:59:11.699855: Pseudo dice [0.7144, 0.0, 0.7648, 0.8543, 0.4977, 0.5747, 0.83] +2026-04-13 10:59:11.702646: Epoch time: 102.03 s +2026-04-13 10:59:12.913890: +2026-04-13 10:59:12.915729: Epoch 2362 +2026-04-13 10:59:12.917772: Current learning rate: 0.00448 +2026-04-13 11:00:55.851682: train_loss -0.4139 +2026-04-13 11:00:55.858641: val_loss -0.3441 +2026-04-13 11:00:55.862008: Pseudo dice [0.4675, 0.0, 0.3837, 0.0465, 0.3529, 0.664, 0.7182] +2026-04-13 11:00:55.864872: Epoch time: 102.94 s +2026-04-13 11:00:57.095756: +2026-04-13 11:00:57.099808: Epoch 2363 +2026-04-13 11:00:57.103558: Current learning rate: 0.00447 +2026-04-13 11:02:39.225339: train_loss -0.4247 +2026-04-13 11:02:39.233836: val_loss -0.3104 +2026-04-13 11:02:39.236464: Pseudo dice [0.0, 0.0, 0.6128, 0.614, 0.4477, 0.4839, 0.7783] +2026-04-13 11:02:39.239213: Epoch time: 102.13 s +2026-04-13 11:02:40.450815: +2026-04-13 11:02:40.452898: Epoch 2364 +2026-04-13 11:02:40.455324: Current learning rate: 0.00447 +2026-04-13 11:04:22.598556: train_loss -0.4033 +2026-04-13 11:04:22.605632: val_loss -0.3542 +2026-04-13 11:04:22.607981: Pseudo dice [0.0944, 0.0, 0.7053, 0.5503, 0.5729, 0.6019, 0.2668] +2026-04-13 11:04:22.611549: Epoch time: 102.15 s +2026-04-13 11:04:23.887974: +2026-04-13 11:04:23.890048: Epoch 2365 +2026-04-13 11:04:23.892258: Current learning rate: 0.00447 +2026-04-13 11:06:06.343213: train_loss -0.4019 +2026-04-13 11:06:06.358593: val_loss -0.3415 +2026-04-13 11:06:06.362523: Pseudo dice [0.0, 0.0, 0.8112, 0.7048, 0.5269, 0.776, 0.6066] +2026-04-13 11:06:06.366803: Epoch time: 102.46 s +2026-04-13 11:06:07.638760: +2026-04-13 11:06:07.642018: Epoch 2366 +2026-04-13 11:06:07.645276: Current learning rate: 0.00447 +2026-04-13 11:07:49.386393: train_loss -0.4173 +2026-04-13 11:07:49.398309: val_loss -0.3447 +2026-04-13 11:07:49.401792: Pseudo dice [0.0, 0.0, 0.7227, 0.5146, 0.5667, 0.3577, 0.6662] +2026-04-13 11:07:49.404673: Epoch time: 101.75 s +2026-04-13 11:07:50.607866: +2026-04-13 11:07:50.609997: Epoch 2367 +2026-04-13 11:07:50.612244: Current learning rate: 0.00447 +2026-04-13 11:09:32.614054: train_loss -0.3971 +2026-04-13 11:09:32.622630: val_loss -0.3417 +2026-04-13 11:09:32.625584: Pseudo dice [0.0, 0.0, 0.8026, 0.5215, 0.5552, 0.4659, 0.7988] +2026-04-13 11:09:32.628534: Epoch time: 102.01 s +2026-04-13 11:09:35.061658: +2026-04-13 11:09:35.063524: Epoch 2368 +2026-04-13 11:09:35.065521: Current learning rate: 0.00446 +2026-04-13 11:11:17.076967: train_loss -0.3932 +2026-04-13 11:11:17.084388: val_loss -0.3409 +2026-04-13 11:11:17.086943: Pseudo dice [0.237, 0.0, 0.7602, 0.3992, 0.4209, 0.1388, 0.795] +2026-04-13 11:11:17.099751: Epoch time: 102.02 s +2026-04-13 11:11:18.301543: +2026-04-13 11:11:18.304001: Epoch 2369 +2026-04-13 11:11:18.306508: Current learning rate: 0.00446 +2026-04-13 11:13:01.131139: train_loss -0.4129 +2026-04-13 11:13:01.139797: val_loss -0.3435 +2026-04-13 11:13:01.146312: Pseudo dice [0.6953, 0.0, 0.8147, 0.4895, 0.6742, 0.6031, 0.803] +2026-04-13 11:13:01.149336: Epoch time: 102.83 s +2026-04-13 11:13:02.367292: +2026-04-13 11:13:02.369285: Epoch 2370 +2026-04-13 11:13:02.371599: Current learning rate: 0.00446 +2026-04-13 11:14:43.934583: train_loss -0.4183 +2026-04-13 11:14:43.944914: val_loss -0.3559 +2026-04-13 11:14:43.948053: Pseudo dice [0.343, 0.0, 0.7346, 0.7172, 0.5756, 0.1076, 0.77] +2026-04-13 11:14:43.951288: Epoch time: 101.57 s +2026-04-13 11:14:45.141021: +2026-04-13 11:14:45.143417: Epoch 2371 +2026-04-13 11:14:45.145762: Current learning rate: 0.00446 +2026-04-13 11:16:27.226324: train_loss -0.4271 +2026-04-13 11:16:27.233486: val_loss -0.382 +2026-04-13 11:16:27.236113: Pseudo dice [0.7461, 0.0, 0.8458, 0.8795, 0.6187, 0.3866, 0.3999] +2026-04-13 11:16:27.238411: Epoch time: 102.09 s +2026-04-13 11:16:28.441430: +2026-04-13 11:16:28.443607: Epoch 2372 +2026-04-13 11:16:28.445818: Current learning rate: 0.00445 +2026-04-13 11:18:10.438679: train_loss -0.4316 +2026-04-13 11:18:10.446688: val_loss -0.3545 +2026-04-13 11:18:10.450468: Pseudo dice [0.4422, 0.0, 0.7289, 0.4509, 0.4685, 0.3883, 0.6612] +2026-04-13 11:18:10.453172: Epoch time: 102.0 s +2026-04-13 11:18:11.676281: +2026-04-13 11:18:11.678355: Epoch 2373 +2026-04-13 11:18:11.680437: Current learning rate: 0.00445 +2026-04-13 11:19:54.422481: train_loss -0.4234 +2026-04-13 11:19:54.430477: val_loss -0.3513 +2026-04-13 11:19:54.433320: Pseudo dice [0.5631, 0.0, 0.5597, 0.2912, 0.5516, 0.4256, 0.7408] +2026-04-13 11:19:54.438690: Epoch time: 102.75 s +2026-04-13 11:19:55.659138: +2026-04-13 11:19:55.661520: Epoch 2374 +2026-04-13 11:19:55.665397: Current learning rate: 0.00445 +2026-04-13 11:21:37.589557: train_loss -0.4177 +2026-04-13 11:21:37.597265: val_loss -0.4032 +2026-04-13 11:21:37.600252: Pseudo dice [0.6201, 0.0, 0.8083, 0.4859, 0.4375, 0.6987, 0.6425] +2026-04-13 11:21:37.602945: Epoch time: 101.93 s +2026-04-13 11:21:38.827688: +2026-04-13 11:21:38.830094: Epoch 2375 +2026-04-13 11:21:38.832463: Current learning rate: 0.00445 +2026-04-13 11:23:20.949586: train_loss -0.4228 +2026-04-13 11:23:20.956252: val_loss -0.355 +2026-04-13 11:23:20.958742: Pseudo dice [0.6224, 0.0, 0.7699, 0.25, 0.567, 0.8838, 0.8823] +2026-04-13 11:23:20.961942: Epoch time: 102.13 s +2026-04-13 11:23:22.192008: +2026-04-13 11:23:22.194168: Epoch 2376 +2026-04-13 11:23:22.196401: Current learning rate: 0.00444 +2026-04-13 11:25:05.264280: train_loss -0.4046 +2026-04-13 11:25:05.274288: val_loss -0.3395 +2026-04-13 11:25:05.280252: Pseudo dice [0.2345, 0.0, 0.6244, 0.1255, 0.3731, 0.4025, 0.1894] +2026-04-13 11:25:05.284159: Epoch time: 103.08 s +2026-04-13 11:25:06.486350: +2026-04-13 11:25:06.490350: Epoch 2377 +2026-04-13 11:25:06.494028: Current learning rate: 0.00444 +2026-04-13 11:26:48.993236: train_loss -0.3963 +2026-04-13 11:26:49.006412: val_loss -0.303 +2026-04-13 11:26:49.011679: Pseudo dice [0.294, 0.0, 0.7475, 0.5582, 0.2975, 0.337, 0.3935] +2026-04-13 11:26:49.014699: Epoch time: 102.51 s +2026-04-13 11:26:50.272873: +2026-04-13 11:26:50.275780: Epoch 2378 +2026-04-13 11:26:50.278426: Current learning rate: 0.00444 +2026-04-13 11:28:31.864345: train_loss -0.4001 +2026-04-13 11:28:31.871590: val_loss -0.3378 +2026-04-13 11:28:31.874120: Pseudo dice [0.6443, 0.0, 0.5718, 0.3034, 0.5574, 0.4393, 0.153] +2026-04-13 11:28:31.877572: Epoch time: 101.59 s +2026-04-13 11:28:33.090575: +2026-04-13 11:28:33.093369: Epoch 2379 +2026-04-13 11:28:33.096057: Current learning rate: 0.00444 +2026-04-13 11:30:15.324581: train_loss -0.4143 +2026-04-13 11:30:15.333094: val_loss -0.373 +2026-04-13 11:30:15.335663: Pseudo dice [0.6773, 0.0, 0.7827, 0.7835, 0.5849, 0.303, 0.8869] +2026-04-13 11:30:15.338629: Epoch time: 102.24 s +2026-04-13 11:30:16.561112: +2026-04-13 11:30:16.563474: Epoch 2380 +2026-04-13 11:30:16.566167: Current learning rate: 0.00443 +2026-04-13 11:31:58.453977: train_loss -0.3886 +2026-04-13 11:31:58.462957: val_loss -0.3508 +2026-04-13 11:31:58.465257: Pseudo dice [0.0, 0.0, 0.7358, 0.7353, 0.6505, 0.467, 0.8411] +2026-04-13 11:31:58.468085: Epoch time: 101.9 s +2026-04-13 11:31:59.687999: +2026-04-13 11:31:59.691992: Epoch 2381 +2026-04-13 11:31:59.694544: Current learning rate: 0.00443 +2026-04-13 11:33:42.006047: train_loss -0.3978 +2026-04-13 11:33:42.013004: val_loss -0.3388 +2026-04-13 11:33:42.015882: Pseudo dice [0.0, 0.0, 0.7455, 0.696, 0.4985, 0.7467, 0.7007] +2026-04-13 11:33:42.018445: Epoch time: 102.32 s +2026-04-13 11:33:43.230975: +2026-04-13 11:33:43.233076: Epoch 2382 +2026-04-13 11:33:43.235346: Current learning rate: 0.00443 +2026-04-13 11:35:25.025498: train_loss -0.3946 +2026-04-13 11:35:25.031946: val_loss -0.3655 +2026-04-13 11:35:25.038372: Pseudo dice [0.0, 0.0, 0.8755, 0.5636, 0.6484, 0.5487, 0.9197] +2026-04-13 11:35:25.041631: Epoch time: 101.8 s +2026-04-13 11:35:26.296316: +2026-04-13 11:35:26.298295: Epoch 2383 +2026-04-13 11:35:26.300315: Current learning rate: 0.00443 +2026-04-13 11:37:09.406037: train_loss -0.4189 +2026-04-13 11:37:09.418964: val_loss -0.3799 +2026-04-13 11:37:09.421963: Pseudo dice [0.2788, 0.0, 0.707, 0.6517, 0.4849, 0.7995, 0.9032] +2026-04-13 11:37:09.426240: Epoch time: 103.11 s +2026-04-13 11:37:10.707301: +2026-04-13 11:37:10.709480: Epoch 2384 +2026-04-13 11:37:10.712002: Current learning rate: 0.00442 +2026-04-13 11:38:53.162889: train_loss -0.4218 +2026-04-13 11:38:53.171708: val_loss -0.3692 +2026-04-13 11:38:53.173827: Pseudo dice [0.0, 0.0, 0.7398, 0.5779, 0.5293, 0.5482, 0.9153] +2026-04-13 11:38:53.176639: Epoch time: 102.46 s +2026-04-13 11:38:54.374131: +2026-04-13 11:38:54.376009: Epoch 2385 +2026-04-13 11:38:54.378122: Current learning rate: 0.00442 +2026-04-13 11:40:36.091901: train_loss -0.4234 +2026-04-13 11:40:36.097537: val_loss -0.3645 +2026-04-13 11:40:36.100080: Pseudo dice [0.0006, 0.0, 0.7935, 0.6946, 0.4128, 0.7209, 0.6403] +2026-04-13 11:40:36.102501: Epoch time: 101.72 s +2026-04-13 11:40:37.437885: +2026-04-13 11:40:37.440796: Epoch 2386 +2026-04-13 11:40:37.443911: Current learning rate: 0.00442 +2026-04-13 11:42:21.322336: train_loss -0.4219 +2026-04-13 11:42:21.330086: val_loss -0.3824 +2026-04-13 11:42:21.333182: Pseudo dice [0.592, 0.0, 0.6941, 0.6585, 0.4274, 0.5374, 0.7971] +2026-04-13 11:42:21.337567: Epoch time: 103.89 s +2026-04-13 11:42:22.581839: +2026-04-13 11:42:22.587414: Epoch 2387 +2026-04-13 11:42:22.593297: Current learning rate: 0.00442 +2026-04-13 11:44:04.956391: train_loss -0.4341 +2026-04-13 11:44:04.962840: val_loss -0.3641 +2026-04-13 11:44:04.967299: Pseudo dice [0.5912, 0.0, 0.6989, 0.5589, 0.5499, 0.4918, 0.8901] +2026-04-13 11:44:04.970773: Epoch time: 102.38 s +2026-04-13 11:44:06.217809: +2026-04-13 11:44:06.221072: Epoch 2388 +2026-04-13 11:44:06.223824: Current learning rate: 0.00441 +2026-04-13 11:45:48.669845: train_loss -0.4197 +2026-04-13 11:45:48.677386: val_loss -0.3703 +2026-04-13 11:45:48.679864: Pseudo dice [0.4762, 0.0, 0.6887, 0.7001, 0.5635, 0.7247, 0.9095] +2026-04-13 11:45:48.682675: Epoch time: 102.46 s +2026-04-13 11:45:51.036440: +2026-04-13 11:45:51.038744: Epoch 2389 +2026-04-13 11:45:51.040599: Current learning rate: 0.00441 +2026-04-13 11:47:34.072518: train_loss -0.4304 +2026-04-13 11:47:34.080450: val_loss -0.4016 +2026-04-13 11:47:34.083909: Pseudo dice [0.7529, 0.0, 0.8214, 0.6226, 0.3588, 0.6739, 0.8943] +2026-04-13 11:47:34.087100: Epoch time: 103.04 s +2026-04-13 11:47:35.332580: +2026-04-13 11:47:35.335584: Epoch 2390 +2026-04-13 11:47:35.337729: Current learning rate: 0.00441 +2026-04-13 11:49:16.715540: train_loss -0.41 +2026-04-13 11:49:16.725205: val_loss -0.3822 +2026-04-13 11:49:16.727920: Pseudo dice [0.819, 0.0, 0.76, 0.0845, 0.5452, 0.6533, 0.8728] +2026-04-13 11:49:16.731929: Epoch time: 101.39 s +2026-04-13 11:49:17.940039: +2026-04-13 11:49:17.942361: Epoch 2391 +2026-04-13 11:49:17.947851: Current learning rate: 0.00441 +2026-04-13 11:51:00.565667: train_loss -0.4244 +2026-04-13 11:51:00.589760: val_loss -0.378 +2026-04-13 11:51:00.592105: Pseudo dice [0.43, 0.0, 0.8047, 0.8833, 0.4833, 0.6101, 0.864] +2026-04-13 11:51:00.596737: Epoch time: 102.63 s +2026-04-13 11:51:01.801404: +2026-04-13 11:51:01.804534: Epoch 2392 +2026-04-13 11:51:01.806964: Current learning rate: 0.0044 +2026-04-13 11:52:43.982802: train_loss -0.4056 +2026-04-13 11:52:43.990811: val_loss -0.366 +2026-04-13 11:52:43.994427: Pseudo dice [0.5213, 0.0, 0.7471, 0.5948, 0.6667, 0.5525, 0.8441] +2026-04-13 11:52:43.996882: Epoch time: 102.18 s +2026-04-13 11:52:45.219772: +2026-04-13 11:52:45.226012: Epoch 2393 +2026-04-13 11:52:45.230517: Current learning rate: 0.0044 +2026-04-13 11:54:28.541280: train_loss -0.4012 +2026-04-13 11:54:28.548366: val_loss -0.3507 +2026-04-13 11:54:28.551172: Pseudo dice [0.3278, 0.0, 0.7221, 0.4184, 0.52, 0.699, 0.8922] +2026-04-13 11:54:28.554304: Epoch time: 103.32 s +2026-04-13 11:54:29.805615: +2026-04-13 11:54:29.809327: Epoch 2394 +2026-04-13 11:54:29.811435: Current learning rate: 0.0044 +2026-04-13 11:56:11.866209: train_loss -0.4164 +2026-04-13 11:56:11.873852: val_loss -0.3607 +2026-04-13 11:56:11.877396: Pseudo dice [0.6806, 0.0, 0.78, 0.5623, 0.4968, 0.5392, 0.8204] +2026-04-13 11:56:11.880392: Epoch time: 102.06 s +2026-04-13 11:56:13.108609: +2026-04-13 11:56:13.110778: Epoch 2395 +2026-04-13 11:56:13.113329: Current learning rate: 0.0044 +2026-04-13 11:57:55.118757: train_loss -0.408 +2026-04-13 11:57:55.125289: val_loss -0.3666 +2026-04-13 11:57:55.127647: Pseudo dice [0.5161, 0.0, 0.7771, 0.5324, 0.3985, 0.433, 0.6967] +2026-04-13 11:57:55.130824: Epoch time: 102.01 s +2026-04-13 11:57:56.414607: +2026-04-13 11:57:56.416783: Epoch 2396 +2026-04-13 11:57:56.419403: Current learning rate: 0.00439 +2026-04-13 11:59:39.697816: train_loss -0.4135 +2026-04-13 11:59:39.707395: val_loss -0.3698 +2026-04-13 11:59:39.711705: Pseudo dice [0.3363, 0.0, 0.6985, 0.4501, 0.6157, 0.5834, 0.1707] +2026-04-13 11:59:39.715361: Epoch time: 103.29 s +2026-04-13 11:59:40.955236: +2026-04-13 11:59:40.957723: Epoch 2397 +2026-04-13 11:59:40.959977: Current learning rate: 0.00439 +2026-04-13 12:01:22.624875: train_loss -0.4013 +2026-04-13 12:01:22.634525: val_loss -0.3596 +2026-04-13 12:01:22.637139: Pseudo dice [0.7479, 0.0, 0.7739, 0.4728, 0.4305, 0.3369, 0.8195] +2026-04-13 12:01:22.641233: Epoch time: 101.67 s +2026-04-13 12:01:23.874694: +2026-04-13 12:01:23.877234: Epoch 2398 +2026-04-13 12:01:23.879513: Current learning rate: 0.00439 +2026-04-13 12:03:05.681973: train_loss -0.3956 +2026-04-13 12:03:05.690101: val_loss -0.3441 +2026-04-13 12:03:05.693550: Pseudo dice [0.6045, 0.0, 0.842, 0.5979, 0.5019, 0.1986, 0.5979] +2026-04-13 12:03:05.695778: Epoch time: 101.81 s +2026-04-13 12:03:06.958341: +2026-04-13 12:03:06.960875: Epoch 2399 +2026-04-13 12:03:06.963115: Current learning rate: 0.00439 +2026-04-13 12:04:49.814199: train_loss -0.4144 +2026-04-13 12:04:49.821361: val_loss -0.3602 +2026-04-13 12:04:49.823994: Pseudo dice [0.7092, 0.0, 0.5365, 0.615, 0.4774, 0.654, 0.5181] +2026-04-13 12:04:49.826382: Epoch time: 102.86 s +2026-04-13 12:04:52.974569: +2026-04-13 12:04:52.977553: Epoch 2400 +2026-04-13 12:04:52.979484: Current learning rate: 0.00438 +2026-04-13 12:06:34.773355: train_loss -0.398 +2026-04-13 12:06:34.781023: val_loss -0.3675 +2026-04-13 12:06:34.785730: Pseudo dice [0.4237, 0.0, 0.8424, 0.8046, 0.4787, 0.8107, 0.7919] +2026-04-13 12:06:34.788421: Epoch time: 101.8 s +2026-04-13 12:06:36.035705: +2026-04-13 12:06:36.038445: Epoch 2401 +2026-04-13 12:06:36.043673: Current learning rate: 0.00438 +2026-04-13 12:08:17.883469: train_loss -0.4169 +2026-04-13 12:08:17.890814: val_loss -0.324 +2026-04-13 12:08:17.892497: Pseudo dice [0.3231, 0.0, 0.585, 0.1153, 0.2841, 0.7511, 0.7675] +2026-04-13 12:08:17.895074: Epoch time: 101.85 s +2026-04-13 12:08:19.139046: +2026-04-13 12:08:19.141651: Epoch 2402 +2026-04-13 12:08:19.144909: Current learning rate: 0.00438 +2026-04-13 12:10:01.760950: train_loss -0.4036 +2026-04-13 12:10:01.771245: val_loss -0.3962 +2026-04-13 12:10:01.773959: Pseudo dice [0.6024, 0.0, 0.8162, 0.6482, 0.5265, 0.5788, 0.8848] +2026-04-13 12:10:01.777215: Epoch time: 102.63 s +2026-04-13 12:10:03.071518: +2026-04-13 12:10:03.074950: Epoch 2403 +2026-04-13 12:10:03.077889: Current learning rate: 0.00438 +2026-04-13 12:11:45.730366: train_loss -0.4254 +2026-04-13 12:11:45.738564: val_loss -0.3936 +2026-04-13 12:11:45.741382: Pseudo dice [0.7215, 0.0, 0.6756, 0.7329, 0.5848, 0.7199, 0.9026] +2026-04-13 12:11:45.744024: Epoch time: 102.66 s +2026-04-13 12:11:46.951416: +2026-04-13 12:11:46.953418: Epoch 2404 +2026-04-13 12:11:46.955748: Current learning rate: 0.00437 +2026-04-13 12:13:29.308656: train_loss -0.4063 +2026-04-13 12:13:29.318551: val_loss -0.3689 +2026-04-13 12:13:29.322136: Pseudo dice [0.0, 0.0, 0.6958, 0.1526, 0.4608, 0.4589, 0.8901] +2026-04-13 12:13:29.329607: Epoch time: 102.36 s +2026-04-13 12:13:30.578589: +2026-04-13 12:13:30.581192: Epoch 2405 +2026-04-13 12:13:30.583517: Current learning rate: 0.00437 +2026-04-13 12:15:13.259895: train_loss -0.4029 +2026-04-13 12:15:13.268094: val_loss -0.3495 +2026-04-13 12:15:13.272410: Pseudo dice [0.0295, 0.0, 0.7005, 0.0197, 0.5709, 0.476, 0.739] +2026-04-13 12:15:13.276576: Epoch time: 102.68 s +2026-04-13 12:15:14.515079: +2026-04-13 12:15:14.518651: Epoch 2406 +2026-04-13 12:15:14.522916: Current learning rate: 0.00437 +2026-04-13 12:16:56.601901: train_loss -0.4063 +2026-04-13 12:16:56.609050: val_loss -0.3535 +2026-04-13 12:16:56.613242: Pseudo dice [0.4505, 0.0, 0.6161, 0.7656, 0.6378, 0.3155, 0.7789] +2026-04-13 12:16:56.615874: Epoch time: 102.09 s +2026-04-13 12:16:57.851379: +2026-04-13 12:16:57.855022: Epoch 2407 +2026-04-13 12:16:57.861733: Current learning rate: 0.00437 +2026-04-13 12:18:40.276998: train_loss -0.3935 +2026-04-13 12:18:40.285998: val_loss -0.3882 +2026-04-13 12:18:40.289616: Pseudo dice [0.7952, 0.0, 0.7965, 0.2033, 0.365, 0.5792, 0.8073] +2026-04-13 12:18:40.293517: Epoch time: 102.43 s +2026-04-13 12:18:41.548781: +2026-04-13 12:18:41.551685: Epoch 2408 +2026-04-13 12:18:41.557189: Current learning rate: 0.00436 +2026-04-13 12:20:23.448235: train_loss -0.3964 +2026-04-13 12:20:23.461031: val_loss -0.3553 +2026-04-13 12:20:23.464997: Pseudo dice [0.3214, 0.0, 0.7703, 0.4992, 0.4324, 0.7892, 0.8469] +2026-04-13 12:20:23.479887: Epoch time: 101.9 s +2026-04-13 12:20:25.886570: +2026-04-13 12:20:25.888869: Epoch 2409 +2026-04-13 12:20:25.891125: Current learning rate: 0.00436 +2026-04-13 12:22:08.347633: train_loss -0.421 +2026-04-13 12:22:08.357287: val_loss -0.3695 +2026-04-13 12:22:08.361865: Pseudo dice [0.4093, 0.0, 0.7091, 0.2058, 0.5719, 0.6896, 0.5732] +2026-04-13 12:22:08.370821: Epoch time: 102.46 s +2026-04-13 12:22:09.631125: +2026-04-13 12:22:09.634363: Epoch 2410 +2026-04-13 12:22:09.644567: Current learning rate: 0.00436 +2026-04-13 12:23:52.691871: train_loss -0.4109 +2026-04-13 12:23:52.699585: val_loss -0.3416 +2026-04-13 12:23:52.703602: Pseudo dice [0.7142, 0.0, 0.7855, 0.0711, 0.4718, 0.4347, 0.7046] +2026-04-13 12:23:52.707006: Epoch time: 103.06 s +2026-04-13 12:23:53.966123: +2026-04-13 12:23:53.968108: Epoch 2411 +2026-04-13 12:23:53.970451: Current learning rate: 0.00436 +2026-04-13 12:25:36.565138: train_loss -0.4144 +2026-04-13 12:25:36.574730: val_loss -0.3615 +2026-04-13 12:25:36.577766: Pseudo dice [0.5937, 0.0, 0.7197, 0.513, 0.5986, 0.7132, 0.9456] +2026-04-13 12:25:36.581084: Epoch time: 102.6 s +2026-04-13 12:25:37.804108: +2026-04-13 12:25:37.806773: Epoch 2412 +2026-04-13 12:25:37.809398: Current learning rate: 0.00435 +2026-04-13 12:27:20.946530: train_loss -0.4318 +2026-04-13 12:27:20.953440: val_loss -0.3677 +2026-04-13 12:27:20.956242: Pseudo dice [0.7387, 0.0, 0.7609, 0.7855, 0.4461, 0.6615, 0.9133] +2026-04-13 12:27:20.960165: Epoch time: 103.15 s +2026-04-13 12:27:22.237165: +2026-04-13 12:27:22.239569: Epoch 2413 +2026-04-13 12:27:22.241919: Current learning rate: 0.00435 +2026-04-13 12:29:05.309884: train_loss -0.4237 +2026-04-13 12:29:05.319174: val_loss -0.3376 +2026-04-13 12:29:05.323541: Pseudo dice [0.3558, 0.0, 0.6873, 0.6045, 0.5083, 0.6886, 0.7931] +2026-04-13 12:29:05.329285: Epoch time: 103.08 s +2026-04-13 12:29:06.610833: +2026-04-13 12:29:06.613812: Epoch 2414 +2026-04-13 12:29:06.616546: Current learning rate: 0.00435 +2026-04-13 12:30:48.975779: train_loss -0.417 +2026-04-13 12:30:48.983566: val_loss -0.326 +2026-04-13 12:30:48.987042: Pseudo dice [0.6097, 0.0, 0.5784, 0.0059, 0.4024, 0.4043, 0.2576] +2026-04-13 12:30:48.992755: Epoch time: 102.37 s +2026-04-13 12:30:50.253298: +2026-04-13 12:30:50.255947: Epoch 2415 +2026-04-13 12:30:50.258750: Current learning rate: 0.00435 +2026-04-13 12:32:33.073253: train_loss -0.4115 +2026-04-13 12:32:33.081251: val_loss -0.3683 +2026-04-13 12:32:33.083609: Pseudo dice [0.4224, 0.0, 0.7951, 0.2636, 0.6222, 0.7992, 0.7862] +2026-04-13 12:32:33.086788: Epoch time: 102.82 s +2026-04-13 12:32:34.309620: +2026-04-13 12:32:34.312356: Epoch 2416 +2026-04-13 12:32:34.315763: Current learning rate: 0.00434 +2026-04-13 12:34:16.864094: train_loss -0.4132 +2026-04-13 12:34:16.872533: val_loss -0.3486 +2026-04-13 12:34:16.875312: Pseudo dice [0.6848, 0.0, 0.7482, 0.5925, 0.3832, 0.5069, 0.8752] +2026-04-13 12:34:16.879553: Epoch time: 102.56 s +2026-04-13 12:34:18.098330: +2026-04-13 12:34:18.100834: Epoch 2417 +2026-04-13 12:34:18.103215: Current learning rate: 0.00434 +2026-04-13 12:36:00.082727: train_loss -0.4349 +2026-04-13 12:36:00.092487: val_loss -0.3752 +2026-04-13 12:36:00.096639: Pseudo dice [0.6849, 0.0, 0.6023, 0.484, 0.4118, 0.391, 0.4538] +2026-04-13 12:36:00.100763: Epoch time: 101.99 s +2026-04-13 12:36:01.355272: +2026-04-13 12:36:01.357935: Epoch 2418 +2026-04-13 12:36:01.362344: Current learning rate: 0.00434 +2026-04-13 12:37:43.815085: train_loss -0.4168 +2026-04-13 12:37:43.823949: val_loss -0.3791 +2026-04-13 12:37:43.827199: Pseudo dice [0.5367, 0.0, 0.7148, 0.5213, 0.492, 0.6468, 0.8538] +2026-04-13 12:37:43.830147: Epoch time: 102.46 s +2026-04-13 12:37:45.076388: +2026-04-13 12:37:45.079071: Epoch 2419 +2026-04-13 12:37:45.081908: Current learning rate: 0.00434 +2026-04-13 12:39:27.411616: train_loss -0.4185 +2026-04-13 12:39:27.426261: val_loss -0.3429 +2026-04-13 12:39:27.429062: Pseudo dice [0.4907, 0.0, 0.799, 0.5493, 0.5316, 0.6847, 0.2772] +2026-04-13 12:39:27.436845: Epoch time: 102.34 s +2026-04-13 12:39:28.683039: +2026-04-13 12:39:28.685749: Epoch 2420 +2026-04-13 12:39:28.688272: Current learning rate: 0.00433 +2026-04-13 12:41:11.106640: train_loss -0.4177 +2026-04-13 12:41:11.114563: val_loss -0.3522 +2026-04-13 12:41:11.120321: Pseudo dice [0.6659, 0.0, 0.6947, 0.4231, 0.3759, 0.2563, 0.3709] +2026-04-13 12:41:11.123082: Epoch time: 102.43 s +2026-04-13 12:41:12.352953: +2026-04-13 12:41:12.355495: Epoch 2421 +2026-04-13 12:41:12.358600: Current learning rate: 0.00433 +2026-04-13 12:42:55.603026: train_loss -0.4066 +2026-04-13 12:42:55.609891: val_loss -0.3527 +2026-04-13 12:42:55.612637: Pseudo dice [0.6257, 0.0, 0.7889, 0.5166, 0.3761, 0.6947, 0.8788] +2026-04-13 12:42:55.615484: Epoch time: 103.25 s +2026-04-13 12:42:56.846873: +2026-04-13 12:42:56.849448: Epoch 2422 +2026-04-13 12:42:56.852251: Current learning rate: 0.00433 +2026-04-13 12:44:39.337387: train_loss -0.4188 +2026-04-13 12:44:39.344351: val_loss -0.3842 +2026-04-13 12:44:39.346447: Pseudo dice [0.6536, 0.0, 0.2863, 0.4847, 0.4261, 0.8666, 0.803] +2026-04-13 12:44:39.349176: Epoch time: 102.49 s +2026-04-13 12:44:40.575141: +2026-04-13 12:44:40.578691: Epoch 2423 +2026-04-13 12:44:40.580979: Current learning rate: 0.00433 +2026-04-13 12:46:23.269312: train_loss -0.4397 +2026-04-13 12:46:23.276569: val_loss -0.3792 +2026-04-13 12:46:23.279202: Pseudo dice [0.4857, 0.0, 0.8635, 0.8043, 0.4383, 0.8113, 0.8118] +2026-04-13 12:46:23.282047: Epoch time: 102.7 s +2026-04-13 12:46:24.573033: +2026-04-13 12:46:24.575749: Epoch 2424 +2026-04-13 12:46:24.578537: Current learning rate: 0.00432 +2026-04-13 12:48:07.404429: train_loss -0.4209 +2026-04-13 12:48:07.413310: val_loss -0.375 +2026-04-13 12:48:07.416706: Pseudo dice [0.8075, 0.0, 0.6711, 0.0691, 0.4566, 0.4539, 0.8934] +2026-04-13 12:48:07.421143: Epoch time: 102.83 s +2026-04-13 12:48:08.630379: +2026-04-13 12:48:08.634873: Epoch 2425 +2026-04-13 12:48:08.637205: Current learning rate: 0.00432 +2026-04-13 12:49:51.164948: train_loss -0.4249 +2026-04-13 12:49:51.171834: val_loss -0.3506 +2026-04-13 12:49:51.174582: Pseudo dice [0.721, 0.0, 0.8524, 0.6734, 0.4861, 0.3534, 0.7826] +2026-04-13 12:49:51.177161: Epoch time: 102.54 s +2026-04-13 12:49:52.447438: +2026-04-13 12:49:52.450212: Epoch 2426 +2026-04-13 12:49:52.452478: Current learning rate: 0.00432 +2026-04-13 12:51:35.208976: train_loss -0.4226 +2026-04-13 12:51:35.215648: val_loss -0.3477 +2026-04-13 12:51:35.217930: Pseudo dice [0.592, 0.0, 0.7888, 0.555, 0.3437, 0.167, 0.8908] +2026-04-13 12:51:35.220279: Epoch time: 102.77 s +2026-04-13 12:51:36.435503: +2026-04-13 12:51:36.437837: Epoch 2427 +2026-04-13 12:51:36.440733: Current learning rate: 0.00432 +2026-04-13 12:53:20.127475: train_loss -0.3951 +2026-04-13 12:53:20.139223: val_loss -0.3653 +2026-04-13 12:53:20.142893: Pseudo dice [0.2416, 0.0, 0.6368, 0.3742, 0.6038, 0.7605, 0.8425] +2026-04-13 12:53:20.147212: Epoch time: 103.7 s +2026-04-13 12:53:21.384735: +2026-04-13 12:53:21.386972: Epoch 2428 +2026-04-13 12:53:21.389505: Current learning rate: 0.00431 +2026-04-13 12:55:03.285000: train_loss -0.411 +2026-04-13 12:55:03.295178: val_loss -0.3924 +2026-04-13 12:55:03.297978: Pseudo dice [0.7327, 0.0, 0.6837, 0.6447, 0.292, 0.7459, 0.8485] +2026-04-13 12:55:03.300595: Epoch time: 101.9 s +2026-04-13 12:55:04.524940: +2026-04-13 12:55:04.528841: Epoch 2429 +2026-04-13 12:55:04.531338: Current learning rate: 0.00431 +2026-04-13 12:56:47.641285: train_loss -0.4283 +2026-04-13 12:56:47.648485: val_loss -0.4029 +2026-04-13 12:56:47.651033: Pseudo dice [0.7499, 0.0, 0.8586, 0.6484, 0.367, 0.6893, 0.8829] +2026-04-13 12:56:47.655808: Epoch time: 103.12 s +2026-04-13 12:56:48.881173: +2026-04-13 12:56:48.885230: Epoch 2430 +2026-04-13 12:56:48.889133: Current learning rate: 0.00431 +2026-04-13 12:58:31.803863: train_loss -0.4189 +2026-04-13 12:58:31.812454: val_loss -0.3781 +2026-04-13 12:58:31.814961: Pseudo dice [0.5904, 0.0, 0.7957, 0.6372, 0.5009, 0.4134, 0.9199] +2026-04-13 12:58:31.823713: Epoch time: 102.93 s +2026-04-13 12:58:33.098749: +2026-04-13 12:58:33.105136: Epoch 2431 +2026-04-13 12:58:33.109065: Current learning rate: 0.00431 +2026-04-13 13:00:15.821805: train_loss -0.4148 +2026-04-13 13:00:15.828536: val_loss -0.3742 +2026-04-13 13:00:15.832937: Pseudo dice [0.4789, 0.0, 0.8146, 0.6666, 0.3359, 0.2116, 0.8451] +2026-04-13 13:00:15.836379: Epoch time: 102.73 s +2026-04-13 13:00:17.073554: +2026-04-13 13:00:17.075874: Epoch 2432 +2026-04-13 13:00:17.077917: Current learning rate: 0.0043 +2026-04-13 13:01:59.485338: train_loss -0.428 +2026-04-13 13:01:59.492575: val_loss -0.3738 +2026-04-13 13:01:59.495173: Pseudo dice [0.5399, 0.0, 0.8091, 0.7406, 0.3715, 0.5127, 0.7545] +2026-04-13 13:01:59.497840: Epoch time: 102.41 s +2026-04-13 13:02:00.724251: +2026-04-13 13:02:00.729137: Epoch 2433 +2026-04-13 13:02:00.732699: Current learning rate: 0.0043 +2026-04-13 13:03:42.490346: train_loss -0.4311 +2026-04-13 13:03:42.498658: val_loss -0.3876 +2026-04-13 13:03:42.501214: Pseudo dice [0.8413, 0.0, 0.7541, 0.4327, 0.5534, 0.8163, 0.9461] +2026-04-13 13:03:42.504737: Epoch time: 101.77 s +2026-04-13 13:03:43.759346: +2026-04-13 13:03:43.762593: Epoch 2434 +2026-04-13 13:03:43.765559: Current learning rate: 0.0043 +2026-04-13 13:05:26.597941: train_loss -0.4416 +2026-04-13 13:05:26.609950: val_loss -0.3563 +2026-04-13 13:05:26.612840: Pseudo dice [0.1927, 0.0, 0.8235, 0.6882, 0.4255, 0.562, 0.9141] +2026-04-13 13:05:26.617218: Epoch time: 102.84 s +2026-04-13 13:05:27.868840: +2026-04-13 13:05:27.871001: Epoch 2435 +2026-04-13 13:05:27.873304: Current learning rate: 0.0043 +2026-04-13 13:07:09.925352: train_loss -0.4115 +2026-04-13 13:07:09.933646: val_loss -0.3713 +2026-04-13 13:07:09.935967: Pseudo dice [0.8818, 0.0, 0.7041, 0.4369, 0.535, 0.5491, 0.8387] +2026-04-13 13:07:09.938793: Epoch time: 102.06 s +2026-04-13 13:07:11.143792: +2026-04-13 13:07:11.146786: Epoch 2436 +2026-04-13 13:07:11.149694: Current learning rate: 0.00429 +2026-04-13 13:08:53.515036: train_loss -0.4199 +2026-04-13 13:08:53.521360: val_loss -0.3785 +2026-04-13 13:08:53.523246: Pseudo dice [0.6334, 0.0, 0.7544, 0.5565, 0.3392, 0.6413, 0.9286] +2026-04-13 13:08:53.525802: Epoch time: 102.37 s +2026-04-13 13:08:54.759732: +2026-04-13 13:08:54.762033: Epoch 2437 +2026-04-13 13:08:54.764239: Current learning rate: 0.00429 +2026-04-13 13:10:38.228053: train_loss -0.3979 +2026-04-13 13:10:38.235284: val_loss -0.3677 +2026-04-13 13:10:38.238066: Pseudo dice [0.6865, 0.0, 0.6095, 0.5127, 0.5242, 0.5048, 0.9388] +2026-04-13 13:10:38.241583: Epoch time: 103.47 s +2026-04-13 13:10:39.495130: +2026-04-13 13:10:39.497423: Epoch 2438 +2026-04-13 13:10:39.500015: Current learning rate: 0.00429 +2026-04-13 13:12:21.828664: train_loss -0.4173 +2026-04-13 13:12:21.838003: val_loss -0.3648 +2026-04-13 13:12:21.840722: Pseudo dice [0.7375, 0.0, 0.5249, 0.449, 0.6193, 0.591, 0.9285] +2026-04-13 13:12:21.843203: Epoch time: 102.34 s +2026-04-13 13:12:23.145936: +2026-04-13 13:12:23.150055: Epoch 2439 +2026-04-13 13:12:23.152266: Current learning rate: 0.00429 +2026-04-13 13:14:05.409274: train_loss -0.4232 +2026-04-13 13:14:05.415737: val_loss -0.3561 +2026-04-13 13:14:05.418228: Pseudo dice [0.5599, 0.0, 0.7659, 0.5531, 0.5282, 0.4885, 0.2025] +2026-04-13 13:14:05.420654: Epoch time: 102.27 s +2026-04-13 13:14:06.651356: +2026-04-13 13:14:06.653667: Epoch 2440 +2026-04-13 13:14:06.656420: Current learning rate: 0.00429 +2026-04-13 13:15:49.027533: train_loss -0.4206 +2026-04-13 13:15:49.036634: val_loss -0.3676 +2026-04-13 13:15:49.044169: Pseudo dice [0.5705, 0.0, 0.7775, 0.4561, 0.4518, 0.6661, 0.607] +2026-04-13 13:15:49.048872: Epoch time: 102.38 s +2026-04-13 13:15:50.291389: +2026-04-13 13:15:50.293827: Epoch 2441 +2026-04-13 13:15:50.296417: Current learning rate: 0.00428 +2026-04-13 13:17:32.604735: train_loss -0.4246 +2026-04-13 13:17:32.613215: val_loss -0.3605 +2026-04-13 13:17:32.615929: Pseudo dice [0.3074, 0.0, 0.7061, 0.755, 0.5162, 0.7413, 0.8911] +2026-04-13 13:17:32.618568: Epoch time: 102.32 s +2026-04-13 13:17:33.848026: +2026-04-13 13:17:33.851475: Epoch 2442 +2026-04-13 13:17:33.853472: Current learning rate: 0.00428 +2026-04-13 13:19:16.333634: train_loss -0.4155 +2026-04-13 13:19:16.340950: val_loss -0.3379 +2026-04-13 13:19:16.343602: Pseudo dice [0.4346, 0.0, 0.6367, 0.5609, 0.6078, 0.3979, 0.4019] +2026-04-13 13:19:16.346723: Epoch time: 102.49 s +2026-04-13 13:19:17.562890: +2026-04-13 13:19:17.566399: Epoch 2443 +2026-04-13 13:19:17.569202: Current learning rate: 0.00428 +2026-04-13 13:21:00.097277: train_loss -0.4049 +2026-04-13 13:21:00.107230: val_loss -0.3653 +2026-04-13 13:21:00.110318: Pseudo dice [0.6672, 0.0, 0.6939, 0.7148, 0.6059, 0.7093, 0.5419] +2026-04-13 13:21:00.115507: Epoch time: 102.54 s +2026-04-13 13:21:01.368181: +2026-04-13 13:21:01.371051: Epoch 2444 +2026-04-13 13:21:01.373796: Current learning rate: 0.00428 +2026-04-13 13:22:43.406911: train_loss -0.4116 +2026-04-13 13:22:43.413456: val_loss -0.3793 +2026-04-13 13:22:43.415391: Pseudo dice [0.5581, 0.0, 0.7998, 0.8637, 0.562, 0.7353, 0.8894] +2026-04-13 13:22:43.418138: Epoch time: 102.04 s +2026-04-13 13:22:44.646560: +2026-04-13 13:22:44.650129: Epoch 2445 +2026-04-13 13:22:44.652583: Current learning rate: 0.00427 +2026-04-13 13:24:27.448675: train_loss -0.4329 +2026-04-13 13:24:27.458601: val_loss -0.357 +2026-04-13 13:24:27.461926: Pseudo dice [0.3645, 0.0, 0.7289, 0.6043, 0.4842, 0.7124, 0.4396] +2026-04-13 13:24:27.465346: Epoch time: 102.81 s +2026-04-13 13:24:28.742622: +2026-04-13 13:24:28.745731: Epoch 2446 +2026-04-13 13:24:28.748456: Current learning rate: 0.00427 +2026-04-13 13:26:11.110147: train_loss -0.4271 +2026-04-13 13:26:11.119820: val_loss -0.3503 +2026-04-13 13:26:11.123990: Pseudo dice [0.7297, 0.0, 0.7184, 0.8409, 0.4958, 0.4132, 0.5773] +2026-04-13 13:26:11.129287: Epoch time: 102.37 s +2026-04-13 13:26:12.376470: +2026-04-13 13:26:12.379791: Epoch 2447 +2026-04-13 13:26:12.388637: Current learning rate: 0.00427 +2026-04-13 13:27:55.301778: train_loss -0.4131 +2026-04-13 13:27:55.328550: val_loss -0.3739 +2026-04-13 13:27:55.331238: Pseudo dice [0.4929, 0.0, 0.7442, 0.3381, 0.5722, 0.4438, 0.766] +2026-04-13 13:27:55.333888: Epoch time: 102.93 s +2026-04-13 13:27:56.586039: +2026-04-13 13:27:56.588223: Epoch 2448 +2026-04-13 13:27:56.591016: Current learning rate: 0.00427 +2026-04-13 13:29:38.868265: train_loss -0.4222 +2026-04-13 13:29:38.876468: val_loss -0.351 +2026-04-13 13:29:38.879615: Pseudo dice [0.5212, 0.0, 0.819, 0.5539, 0.4461, 0.6982, 0.4011] +2026-04-13 13:29:38.885246: Epoch time: 102.29 s +2026-04-13 13:29:40.143228: +2026-04-13 13:29:40.147098: Epoch 2449 +2026-04-13 13:29:40.150087: Current learning rate: 0.00426 +2026-04-13 13:31:23.129297: train_loss -0.4033 +2026-04-13 13:31:23.135411: val_loss -0.3517 +2026-04-13 13:31:23.137803: Pseudo dice [0.7105, 0.0, 0.7123, 0.2195, 0.3719, 0.6624, 0.9042] +2026-04-13 13:31:23.140339: Epoch time: 102.99 s +2026-04-13 13:31:26.255950: +2026-04-13 13:31:26.258056: Epoch 2450 +2026-04-13 13:31:26.260287: Current learning rate: 0.00426 +2026-04-13 13:33:08.881728: train_loss -0.4122 +2026-04-13 13:33:08.888738: val_loss -0.3732 +2026-04-13 13:33:08.891413: Pseudo dice [0.2788, 0.0, 0.7841, 0.4859, 0.5175, 0.7913, 0.8125] +2026-04-13 13:33:08.894333: Epoch time: 102.63 s +2026-04-13 13:33:10.153482: +2026-04-13 13:33:10.155398: Epoch 2451 +2026-04-13 13:33:10.157700: Current learning rate: 0.00426 +2026-04-13 13:34:51.646301: train_loss -0.4313 +2026-04-13 13:34:51.666461: val_loss -0.3968 +2026-04-13 13:34:51.671039: Pseudo dice [0.7498, 0.0, 0.7956, 0.6118, 0.3752, 0.7791, 0.9188] +2026-04-13 13:34:51.673969: Epoch time: 101.5 s +2026-04-13 13:34:52.903591: +2026-04-13 13:34:52.907764: Epoch 2452 +2026-04-13 13:34:52.912301: Current learning rate: 0.00426 +2026-04-13 13:36:35.255290: train_loss -0.4231 +2026-04-13 13:36:35.262067: val_loss -0.376 +2026-04-13 13:36:35.265076: Pseudo dice [0.1672, 0.0, 0.7978, 0.8113, 0.4239, 0.6944, 0.9057] +2026-04-13 13:36:35.267409: Epoch time: 102.35 s +2026-04-13 13:36:36.503323: +2026-04-13 13:36:36.505491: Epoch 2453 +2026-04-13 13:36:36.507254: Current learning rate: 0.00425 +2026-04-13 13:38:19.208981: train_loss -0.4168 +2026-04-13 13:38:19.221443: val_loss -0.3608 +2026-04-13 13:38:19.225864: Pseudo dice [0.1637, 0.0, 0.8514, 0.4661, 0.5076, 0.36, 0.8992] +2026-04-13 13:38:19.228676: Epoch time: 102.71 s +2026-04-13 13:38:20.481496: +2026-04-13 13:38:20.483623: Epoch 2454 +2026-04-13 13:38:20.486193: Current learning rate: 0.00425 +2026-04-13 13:40:01.822054: train_loss -0.4097 +2026-04-13 13:40:01.833223: val_loss -0.364 +2026-04-13 13:40:01.836603: Pseudo dice [0.1696, 0.0, 0.8574, 0.5139, 0.4366, 0.5885, 0.8142] +2026-04-13 13:40:01.840620: Epoch time: 101.34 s +2026-04-13 13:40:03.086327: +2026-04-13 13:40:03.089517: Epoch 2455 +2026-04-13 13:40:03.092698: Current learning rate: 0.00425 +2026-04-13 13:41:45.492578: train_loss -0.4261 +2026-04-13 13:41:45.500068: val_loss -0.3616 +2026-04-13 13:41:45.502662: Pseudo dice [0.3638, 0.0, 0.7083, 0.6961, 0.4769, 0.6246, 0.8087] +2026-04-13 13:41:45.505950: Epoch time: 102.41 s +2026-04-13 13:41:46.754497: +2026-04-13 13:41:46.756986: Epoch 2456 +2026-04-13 13:41:46.759231: Current learning rate: 0.00425 +2026-04-13 13:43:29.055034: train_loss -0.4166 +2026-04-13 13:43:29.063402: val_loss -0.3815 +2026-04-13 13:43:29.066622: Pseudo dice [0.3873, 0.0, 0.8183, 0.347, 0.4649, 0.7454, 0.7749] +2026-04-13 13:43:29.069590: Epoch time: 102.3 s +2026-04-13 13:43:30.316930: +2026-04-13 13:43:30.319462: Epoch 2457 +2026-04-13 13:43:30.324968: Current learning rate: 0.00424 +2026-04-13 13:45:12.755353: train_loss -0.4179 +2026-04-13 13:45:12.762305: val_loss -0.359 +2026-04-13 13:45:12.764497: Pseudo dice [0.4557, 0.0, 0.849, 0.449, 0.5578, 0.6469, 0.6997] +2026-04-13 13:45:12.767112: Epoch time: 102.44 s +2026-04-13 13:45:13.992260: +2026-04-13 13:45:13.994161: Epoch 2458 +2026-04-13 13:45:13.996182: Current learning rate: 0.00424 +2026-04-13 13:46:56.832507: train_loss -0.4302 +2026-04-13 13:46:56.842618: val_loss -0.3263 +2026-04-13 13:46:56.845906: Pseudo dice [0.6131, 0.0, 0.7915, 0.0166, 0.4044, 0.5538, 0.1909] +2026-04-13 13:46:56.849028: Epoch time: 102.84 s +2026-04-13 13:46:58.055707: +2026-04-13 13:46:58.058225: Epoch 2459 +2026-04-13 13:46:58.060665: Current learning rate: 0.00424 +2026-04-13 13:48:40.247382: train_loss -0.422 +2026-04-13 13:48:40.255283: val_loss -0.3634 +2026-04-13 13:48:40.257348: Pseudo dice [0.5789, 0.0, 0.7577, 0.6181, 0.3706, 0.6611, 0.7841] +2026-04-13 13:48:40.260355: Epoch time: 102.19 s +2026-04-13 13:48:41.475017: +2026-04-13 13:48:41.477651: Epoch 2460 +2026-04-13 13:48:41.480370: Current learning rate: 0.00424 +2026-04-13 13:50:24.329495: train_loss -0.3895 +2026-04-13 13:50:24.336947: val_loss -0.3396 +2026-04-13 13:50:24.340315: Pseudo dice [0.3387, 0.0, 0.8547, 0.4325, 0.4235, 0.4739, 0.3865] +2026-04-13 13:50:24.343354: Epoch time: 102.86 s +2026-04-13 13:50:25.559906: +2026-04-13 13:50:25.562256: Epoch 2461 +2026-04-13 13:50:25.564498: Current learning rate: 0.00423 +2026-04-13 13:52:08.529514: train_loss -0.4194 +2026-04-13 13:52:08.536922: val_loss -0.3523 +2026-04-13 13:52:08.539579: Pseudo dice [0.4961, 0.0, 0.589, 0.3438, 0.3608, 0.7667, 0.8621] +2026-04-13 13:52:08.542399: Epoch time: 102.97 s +2026-04-13 13:52:09.788790: +2026-04-13 13:52:09.791685: Epoch 2462 +2026-04-13 13:52:09.794814: Current learning rate: 0.00423 +2026-04-13 13:53:52.473115: train_loss -0.416 +2026-04-13 13:53:52.478804: val_loss -0.3578 +2026-04-13 13:53:52.481142: Pseudo dice [0.5526, 0.0, 0.6602, 0.7322, 0.472, 0.6573, 0.5353] +2026-04-13 13:53:52.483827: Epoch time: 102.69 s +2026-04-13 13:53:53.709166: +2026-04-13 13:53:53.712033: Epoch 2463 +2026-04-13 13:53:53.714581: Current learning rate: 0.00423 +2026-04-13 13:55:35.887831: train_loss -0.4253 +2026-04-13 13:55:35.895066: val_loss -0.3626 +2026-04-13 13:55:35.898085: Pseudo dice [0.5551, 0.0, 0.4827, 0.6327, 0.511, 0.6315, 0.7864] +2026-04-13 13:55:35.901115: Epoch time: 102.18 s +2026-04-13 13:55:37.139965: +2026-04-13 13:55:37.143131: Epoch 2464 +2026-04-13 13:55:37.146312: Current learning rate: 0.00423 +2026-04-13 13:57:19.401864: train_loss -0.404 +2026-04-13 13:57:19.410313: val_loss -0.3407 +2026-04-13 13:57:19.412959: Pseudo dice [0.5005, 0.0, 0.7665, 0.3596, 0.4877, 0.7871, 0.8533] +2026-04-13 13:57:19.415468: Epoch time: 102.26 s +2026-04-13 13:57:20.659417: +2026-04-13 13:57:20.662020: Epoch 2465 +2026-04-13 13:57:20.664288: Current learning rate: 0.00422 +2026-04-13 13:59:02.692560: train_loss -0.4084 +2026-04-13 13:59:02.699197: val_loss -0.374 +2026-04-13 13:59:02.701523: Pseudo dice [0.5774, 0.0, 0.6777, 0.4913, 0.5599, 0.8272, 0.8718] +2026-04-13 13:59:02.705955: Epoch time: 102.04 s +2026-04-13 13:59:03.931721: +2026-04-13 13:59:03.934492: Epoch 2466 +2026-04-13 13:59:03.936974: Current learning rate: 0.00422 +2026-04-13 14:00:45.775160: train_loss -0.426 +2026-04-13 14:00:45.782162: val_loss -0.3808 +2026-04-13 14:00:45.784378: Pseudo dice [0.2185, 0.0, 0.7687, 0.6908, 0.3689, 0.7879, 0.8965] +2026-04-13 14:00:45.787099: Epoch time: 101.85 s +2026-04-13 14:00:46.996999: +2026-04-13 14:00:46.999011: Epoch 2467 +2026-04-13 14:00:47.001334: Current learning rate: 0.00422 +2026-04-13 14:02:29.259158: train_loss -0.4207 +2026-04-13 14:02:29.268447: val_loss -0.3593 +2026-04-13 14:02:29.270687: Pseudo dice [0.7921, 0.0, 0.7132, 0.5698, 0.4624, 0.5696, 0.9244] +2026-04-13 14:02:29.273424: Epoch time: 102.27 s +2026-04-13 14:02:30.516109: +2026-04-13 14:02:30.518193: Epoch 2468 +2026-04-13 14:02:30.521088: Current learning rate: 0.00422 +2026-04-13 14:04:13.644331: train_loss -0.3995 +2026-04-13 14:04:13.651758: val_loss -0.3437 +2026-04-13 14:04:13.654456: Pseudo dice [0.5173, 0.0, 0.7834, 0.0797, 0.5359, 0.6113, 0.7983] +2026-04-13 14:04:13.657307: Epoch time: 103.13 s +2026-04-13 14:04:14.881889: +2026-04-13 14:04:14.884473: Epoch 2469 +2026-04-13 14:04:14.886899: Current learning rate: 0.00421 +2026-04-13 14:05:58.565667: train_loss -0.3964 +2026-04-13 14:05:58.573553: val_loss -0.3461 +2026-04-13 14:05:58.576309: Pseudo dice [0.0091, 0.0, 0.7529, 0.5208, 0.4699, 0.7249, 0.7763] +2026-04-13 14:05:58.579249: Epoch time: 103.69 s +2026-04-13 14:06:00.328237: +2026-04-13 14:06:00.330157: Epoch 2470 +2026-04-13 14:06:00.332388: Current learning rate: 0.00421 +2026-04-13 14:07:43.345989: train_loss -0.4101 +2026-04-13 14:07:43.354612: val_loss -0.3552 +2026-04-13 14:07:43.357314: Pseudo dice [0.2387, 0.0, 0.7725, 0.4856, 0.4449, 0.8866, 0.8934] +2026-04-13 14:07:43.360736: Epoch time: 103.02 s +2026-04-13 14:07:44.630369: +2026-04-13 14:07:44.633013: Epoch 2471 +2026-04-13 14:07:44.636183: Current learning rate: 0.00421 +2026-04-13 14:09:27.466474: train_loss -0.4248 +2026-04-13 14:09:27.474387: val_loss -0.3689 +2026-04-13 14:09:27.476863: Pseudo dice [0.314, 0.0, 0.8623, 0.7038, 0.287, 0.7711, 0.8756] +2026-04-13 14:09:27.480236: Epoch time: 102.84 s +2026-04-13 14:09:28.686693: +2026-04-13 14:09:28.689158: Epoch 2472 +2026-04-13 14:09:28.691615: Current learning rate: 0.00421 +2026-04-13 14:11:11.168924: train_loss -0.4051 +2026-04-13 14:11:11.175760: val_loss -0.3802 +2026-04-13 14:11:11.178559: Pseudo dice [0.3513, 0.0, 0.8231, 0.7049, 0.6261, 0.6246, 0.9131] +2026-04-13 14:11:11.181393: Epoch time: 102.49 s +2026-04-13 14:11:12.390193: +2026-04-13 14:11:12.392425: Epoch 2473 +2026-04-13 14:11:12.394804: Current learning rate: 0.0042 +2026-04-13 14:12:54.769502: train_loss -0.4321 +2026-04-13 14:12:54.776811: val_loss -0.3669 +2026-04-13 14:12:54.780376: Pseudo dice [0.4869, 0.0, 0.8227, 0.5773, 0.5135, 0.6984, 0.7374] +2026-04-13 14:12:54.783606: Epoch time: 102.38 s +2026-04-13 14:12:56.040934: +2026-04-13 14:12:56.044075: Epoch 2474 +2026-04-13 14:12:56.046085: Current learning rate: 0.0042 +2026-04-13 14:14:37.912611: train_loss -0.4093 +2026-04-13 14:14:37.922014: val_loss -0.3638 +2026-04-13 14:14:37.924286: Pseudo dice [0.2244, 0.0, 0.7229, 0.7729, 0.5598, 0.8098, 0.921] +2026-04-13 14:14:37.927440: Epoch time: 101.87 s +2026-04-13 14:14:39.150309: +2026-04-13 14:14:39.153051: Epoch 2475 +2026-04-13 14:14:39.156593: Current learning rate: 0.0042 +2026-04-13 14:16:21.438991: train_loss -0.4277 +2026-04-13 14:16:21.451735: val_loss -0.3434 +2026-04-13 14:16:21.453904: Pseudo dice [0.6129, 0.0, 0.6572, 0.0056, 0.4746, 0.4788, 0.7186] +2026-04-13 14:16:21.457541: Epoch time: 102.29 s +2026-04-13 14:16:22.680253: +2026-04-13 14:16:22.683765: Epoch 2476 +2026-04-13 14:16:22.685993: Current learning rate: 0.0042 +2026-04-13 14:18:04.832494: train_loss -0.4148 +2026-04-13 14:18:04.841175: val_loss -0.3724 +2026-04-13 14:18:04.844226: Pseudo dice [0.3002, 0.0, 0.8026, 0.2336, 0.5581, 0.8765, 0.9101] +2026-04-13 14:18:04.847021: Epoch time: 102.16 s +2026-04-13 14:18:06.062604: +2026-04-13 14:18:06.066344: Epoch 2477 +2026-04-13 14:18:06.069092: Current learning rate: 0.00419 +2026-04-13 14:19:48.099730: train_loss -0.4281 +2026-04-13 14:19:48.111011: val_loss -0.3695 +2026-04-13 14:19:48.112983: Pseudo dice [0.4527, 0.0, 0.8581, 0.7548, 0.5455, 0.566, 0.7067] +2026-04-13 14:19:48.116238: Epoch time: 102.04 s +2026-04-13 14:19:49.338624: +2026-04-13 14:19:49.340808: Epoch 2478 +2026-04-13 14:19:49.342977: Current learning rate: 0.00419 +2026-04-13 14:21:32.027497: train_loss -0.4209 +2026-04-13 14:21:32.035998: val_loss -0.3475 +2026-04-13 14:21:32.039076: Pseudo dice [0.4754, 0.0, 0.8318, 0.566, 0.3114, 0.5029, 0.8555] +2026-04-13 14:21:32.041803: Epoch time: 102.69 s +2026-04-13 14:21:33.278734: +2026-04-13 14:21:33.280715: Epoch 2479 +2026-04-13 14:21:33.283041: Current learning rate: 0.00419 +2026-04-13 14:23:15.382087: train_loss -0.4323 +2026-04-13 14:23:15.388601: val_loss -0.378 +2026-04-13 14:23:15.391582: Pseudo dice [0.4271, 0.0, 0.7497, 0.8749, 0.6457, 0.6573, 0.861] +2026-04-13 14:23:15.395119: Epoch time: 102.11 s +2026-04-13 14:23:16.633126: +2026-04-13 14:23:16.635335: Epoch 2480 +2026-04-13 14:23:16.637569: Current learning rate: 0.00419 +2026-04-13 14:24:59.220138: train_loss -0.4217 +2026-04-13 14:24:59.228386: val_loss -0.3398 +2026-04-13 14:24:59.231978: Pseudo dice [0.1311, 0.0, 0.6526, 0.6553, 0.4893, 0.4983, 0.7264] +2026-04-13 14:24:59.234938: Epoch time: 102.59 s +2026-04-13 14:25:00.483362: +2026-04-13 14:25:00.486316: Epoch 2481 +2026-04-13 14:25:00.490247: Current learning rate: 0.00418 +2026-04-13 14:26:42.108025: train_loss -0.4067 +2026-04-13 14:26:42.116203: val_loss -0.3822 +2026-04-13 14:26:42.118871: Pseudo dice [0.5684, 0.0, 0.8433, 0.7249, 0.3938, 0.6763, 0.7029] +2026-04-13 14:26:42.122029: Epoch time: 101.63 s +2026-04-13 14:26:43.388343: +2026-04-13 14:26:43.391076: Epoch 2482 +2026-04-13 14:26:43.393609: Current learning rate: 0.00418 +2026-04-13 14:28:26.014762: train_loss -0.4226 +2026-04-13 14:28:26.021908: val_loss -0.3875 +2026-04-13 14:28:26.024030: Pseudo dice [0.5338, 0.0, 0.77, 0.6987, 0.6335, 0.6291, 0.8928] +2026-04-13 14:28:26.026133: Epoch time: 102.63 s +2026-04-13 14:28:27.339925: +2026-04-13 14:28:27.342038: Epoch 2483 +2026-04-13 14:28:27.344714: Current learning rate: 0.00418 +2026-04-13 14:30:09.408950: train_loss -0.4205 +2026-04-13 14:30:09.416623: val_loss -0.3633 +2026-04-13 14:30:09.418917: Pseudo dice [0.5754, 0.0, 0.7597, 0.0805, 0.4207, 0.4448, 0.8971] +2026-04-13 14:30:09.422629: Epoch time: 102.07 s +2026-04-13 14:30:10.674579: +2026-04-13 14:30:10.678795: Epoch 2484 +2026-04-13 14:30:10.682640: Current learning rate: 0.00418 +2026-04-13 14:31:52.417900: train_loss -0.4191 +2026-04-13 14:31:52.426803: val_loss -0.3828 +2026-04-13 14:31:52.429306: Pseudo dice [0.6203, 0.0, 0.7869, 0.8836, 0.3504, 0.6983, 0.9476] +2026-04-13 14:31:52.432023: Epoch time: 101.75 s +2026-04-13 14:31:53.670419: +2026-04-13 14:31:53.672597: Epoch 2485 +2026-04-13 14:31:53.674609: Current learning rate: 0.00417 +2026-04-13 14:33:36.447375: train_loss -0.4188 +2026-04-13 14:33:36.456661: val_loss -0.3298 +2026-04-13 14:33:36.459648: Pseudo dice [0.1589, 0.0, 0.7852, 0.3848, 0.5079, 0.3433, 0.5282] +2026-04-13 14:33:36.463981: Epoch time: 102.78 s +2026-04-13 14:33:37.708266: +2026-04-13 14:33:37.712945: Epoch 2486 +2026-04-13 14:33:37.719178: Current learning rate: 0.00417 +2026-04-13 14:35:19.615699: train_loss -0.4145 +2026-04-13 14:35:19.623558: val_loss -0.3508 +2026-04-13 14:35:19.625947: Pseudo dice [0.0, 0.0, 0.6529, 0.5105, 0.6041, 0.787, 0.7727] +2026-04-13 14:35:19.628613: Epoch time: 101.91 s +2026-04-13 14:35:20.851041: +2026-04-13 14:35:20.853246: Epoch 2487 +2026-04-13 14:35:20.855435: Current learning rate: 0.00417 +2026-04-13 14:37:02.383233: train_loss -0.4077 +2026-04-13 14:37:02.394171: val_loss -0.3704 +2026-04-13 14:37:02.396944: Pseudo dice [0.648, 0.0, 0.6747, 0.3799, 0.512, 0.8143, 0.8153] +2026-04-13 14:37:02.400675: Epoch time: 101.54 s +2026-04-13 14:37:03.644313: +2026-04-13 14:37:03.648856: Epoch 2488 +2026-04-13 14:37:03.651237: Current learning rate: 0.00417 +2026-04-13 14:38:45.369812: train_loss -0.4241 +2026-04-13 14:38:45.377069: val_loss -0.3876 +2026-04-13 14:38:45.379229: Pseudo dice [0.3849, 0.0, 0.6797, 0.7033, 0.5288, 0.879, 0.8831] +2026-04-13 14:38:45.381460: Epoch time: 101.73 s +2026-04-13 14:38:46.599533: +2026-04-13 14:38:46.602610: Epoch 2489 +2026-04-13 14:38:46.604793: Current learning rate: 0.00416 +2026-04-13 14:40:28.915632: train_loss -0.4228 +2026-04-13 14:40:28.923159: val_loss -0.3469 +2026-04-13 14:40:28.925195: Pseudo dice [0.5205, 0.0, 0.8488, 0.6254, 0.4842, 0.525, 0.8411] +2026-04-13 14:40:28.927604: Epoch time: 102.32 s +2026-04-13 14:40:31.246843: +2026-04-13 14:40:31.248950: Epoch 2490 +2026-04-13 14:40:31.251001: Current learning rate: 0.00416 +2026-04-13 14:42:12.877119: train_loss -0.411 +2026-04-13 14:42:12.884037: val_loss -0.374 +2026-04-13 14:42:12.887006: Pseudo dice [0.6709, 0.0, 0.6914, 0.6393, 0.5148, 0.8445, 0.8759] +2026-04-13 14:42:12.889555: Epoch time: 101.63 s +2026-04-13 14:42:14.147120: +2026-04-13 14:42:14.149504: Epoch 2491 +2026-04-13 14:42:14.152492: Current learning rate: 0.00416 +2026-04-13 14:43:57.286356: train_loss -0.4243 +2026-04-13 14:43:57.295335: val_loss -0.3251 +2026-04-13 14:43:57.298542: Pseudo dice [0.6231, 0.0, 0.7076, 0.164, 0.3839, 0.5878, 0.9129] +2026-04-13 14:43:57.301221: Epoch time: 103.14 s +2026-04-13 14:43:58.612799: +2026-04-13 14:43:58.615150: Epoch 2492 +2026-04-13 14:43:58.617492: Current learning rate: 0.00416 +2026-04-13 14:45:41.216335: train_loss -0.4378 +2026-04-13 14:45:41.226963: val_loss -0.3761 +2026-04-13 14:45:41.229669: Pseudo dice [0.51, 0.0, 0.6563, 0.6867, 0.502, 0.7241, 0.5439] +2026-04-13 14:45:41.234637: Epoch time: 102.61 s +2026-04-13 14:45:42.474832: +2026-04-13 14:45:42.477252: Epoch 2493 +2026-04-13 14:45:42.481623: Current learning rate: 0.00415 +2026-04-13 14:47:25.249078: train_loss -0.4412 +2026-04-13 14:47:25.257640: val_loss -0.3871 +2026-04-13 14:47:25.260120: Pseudo dice [0.8226, 0.0, 0.5924, 0.0208, 0.4944, 0.7776, 0.947] +2026-04-13 14:47:25.262702: Epoch time: 102.78 s +2026-04-13 14:47:26.547785: +2026-04-13 14:47:26.550255: Epoch 2494 +2026-04-13 14:47:26.552673: Current learning rate: 0.00415 +2026-04-13 14:49:08.191298: train_loss -0.4262 +2026-04-13 14:49:08.200161: val_loss -0.3433 +2026-04-13 14:49:08.203348: Pseudo dice [0.0287, 0.0, 0.5738, 0.4809, 0.4766, 0.6558, 0.7584] +2026-04-13 14:49:08.206541: Epoch time: 101.65 s +2026-04-13 14:49:09.458559: +2026-04-13 14:49:09.460771: Epoch 2495 +2026-04-13 14:49:09.463459: Current learning rate: 0.00415 +2026-04-13 14:50:51.506220: train_loss -0.4223 +2026-04-13 14:50:51.514607: val_loss -0.3786 +2026-04-13 14:50:51.518942: Pseudo dice [0.1592, 0.0, 0.8555, 0.5872, 0.6373, 0.7677, 0.6936] +2026-04-13 14:50:51.523781: Epoch time: 102.05 s +2026-04-13 14:50:52.746981: +2026-04-13 14:50:52.749014: Epoch 2496 +2026-04-13 14:50:52.751191: Current learning rate: 0.00415 +2026-04-13 14:52:34.977629: train_loss -0.4073 +2026-04-13 14:52:34.986371: val_loss -0.3859 +2026-04-13 14:52:34.989833: Pseudo dice [0.3737, 0.0, 0.4741, 0.7323, 0.4927, 0.719, 0.8156] +2026-04-13 14:52:34.992129: Epoch time: 102.23 s +2026-04-13 14:52:36.266748: +2026-04-13 14:52:36.269468: Epoch 2497 +2026-04-13 14:52:36.272452: Current learning rate: 0.00414 +2026-04-13 14:54:18.015882: train_loss -0.4276 +2026-04-13 14:54:18.024209: val_loss -0.3721 +2026-04-13 14:54:18.027020: Pseudo dice [0.5703, 0.0, 0.7486, 0.6497, 0.5976, 0.8835, 0.8177] +2026-04-13 14:54:18.030601: Epoch time: 101.75 s +2026-04-13 14:54:19.293965: +2026-04-13 14:54:19.295784: Epoch 2498 +2026-04-13 14:54:19.298140: Current learning rate: 0.00414 +2026-04-13 14:56:01.494337: train_loss -0.4097 +2026-04-13 14:56:01.504251: val_loss -0.3671 +2026-04-13 14:56:01.507369: Pseudo dice [0.739, 0.0, 0.7841, 0.5621, 0.5041, 0.6757, 0.6386] +2026-04-13 14:56:01.511416: Epoch time: 102.2 s +2026-04-13 14:56:02.744690: +2026-04-13 14:56:02.746829: Epoch 2499 +2026-04-13 14:56:02.749209: Current learning rate: 0.00414 +2026-04-13 14:57:44.931702: train_loss -0.4261 +2026-04-13 14:57:44.938495: val_loss -0.3515 +2026-04-13 14:57:44.941265: Pseudo dice [0.5339, 0.0, 0.7565, 0.0485, 0.5855, 0.6712, 0.8371] +2026-04-13 14:57:44.944222: Epoch time: 102.19 s +2026-04-13 14:57:47.980742: +2026-04-13 14:57:47.982624: Epoch 2500 +2026-04-13 14:57:47.984635: Current learning rate: 0.00414 +2026-04-13 14:59:29.644998: train_loss -0.4187 +2026-04-13 14:59:29.652524: val_loss -0.3664 +2026-04-13 14:59:29.654403: Pseudo dice [0.5846, 0.0, 0.777, 0.805, 0.4553, 0.8836, 0.6659] +2026-04-13 14:59:29.657391: Epoch time: 101.67 s +2026-04-13 14:59:30.887045: +2026-04-13 14:59:30.889371: Epoch 2501 +2026-04-13 14:59:30.891852: Current learning rate: 0.00413 +2026-04-13 15:01:13.373303: train_loss -0.4045 +2026-04-13 15:01:13.381322: val_loss -0.3479 +2026-04-13 15:01:13.384153: Pseudo dice [0.2961, 0.0, 0.7554, 0.53, 0.3896, 0.4936, 0.69] +2026-04-13 15:01:13.387326: Epoch time: 102.49 s +2026-04-13 15:01:14.637738: +2026-04-13 15:01:14.640598: Epoch 2502 +2026-04-13 15:01:14.642946: Current learning rate: 0.00413 +2026-04-13 15:02:56.738612: train_loss -0.4115 +2026-04-13 15:02:56.745293: val_loss -0.3717 +2026-04-13 15:02:56.747739: Pseudo dice [0.6031, 0.0, 0.7459, 0.5958, 0.5319, 0.7637, 0.8065] +2026-04-13 15:02:56.750175: Epoch time: 102.1 s +2026-04-13 15:02:57.991459: +2026-04-13 15:02:57.994022: Epoch 2503 +2026-04-13 15:02:57.995991: Current learning rate: 0.00413 +2026-04-13 15:04:39.921373: train_loss -0.4256 +2026-04-13 15:04:39.928316: val_loss -0.368 +2026-04-13 15:04:39.930433: Pseudo dice [0.5722, 0.0, 0.8188, 0.4127, 0.4931, 0.9015, 0.8812] +2026-04-13 15:04:39.934547: Epoch time: 101.93 s +2026-04-13 15:04:41.170047: +2026-04-13 15:04:41.172274: Epoch 2504 +2026-04-13 15:04:41.174542: Current learning rate: 0.00413 +2026-04-13 15:06:23.423491: train_loss -0.4095 +2026-04-13 15:06:23.429978: val_loss -0.3786 +2026-04-13 15:06:23.432380: Pseudo dice [0.3396, 0.0, 0.5864, 0.2525, 0.5617, 0.7234, 0.6911] +2026-04-13 15:06:23.436157: Epoch time: 102.26 s +2026-04-13 15:06:24.675299: +2026-04-13 15:06:24.677498: Epoch 2505 +2026-04-13 15:06:24.679515: Current learning rate: 0.00412 +2026-04-13 15:08:07.324580: train_loss -0.4157 +2026-04-13 15:08:07.332529: val_loss -0.3584 +2026-04-13 15:08:07.335423: Pseudo dice [0.5296, 0.0, 0.7381, 0.6992, 0.5094, 0.6267, 0.8142] +2026-04-13 15:08:07.337841: Epoch time: 102.65 s +2026-04-13 15:08:08.560666: +2026-04-13 15:08:08.563620: Epoch 2506 +2026-04-13 15:08:08.565502: Current learning rate: 0.00412 +2026-04-13 15:09:50.350417: train_loss -0.4269 +2026-04-13 15:09:50.359099: val_loss -0.3781 +2026-04-13 15:09:50.362128: Pseudo dice [0.2484, 0.0, 0.7853, 0.7909, 0.5864, 0.4615, 0.8485] +2026-04-13 15:09:50.365062: Epoch time: 101.79 s +2026-04-13 15:09:51.596646: +2026-04-13 15:09:51.598640: Epoch 2507 +2026-04-13 15:09:51.601084: Current learning rate: 0.00412 +2026-04-13 15:11:33.739531: train_loss -0.4372 +2026-04-13 15:11:33.748980: val_loss -0.3643 +2026-04-13 15:11:33.751106: Pseudo dice [0.6328, 0.0, 0.8656, 0.4642, 0.4226, 0.8085, 0.7278] +2026-04-13 15:11:33.754069: Epoch time: 102.15 s +2026-04-13 15:11:34.970871: +2026-04-13 15:11:34.972984: Epoch 2508 +2026-04-13 15:11:34.975363: Current learning rate: 0.00412 +2026-04-13 15:13:16.244655: train_loss -0.4209 +2026-04-13 15:13:16.252529: val_loss -0.3684 +2026-04-13 15:13:16.255188: Pseudo dice [0.698, 0.0, 0.5002, 0.3735, 0.453, 0.6753, 0.8876] +2026-04-13 15:13:16.257847: Epoch time: 101.28 s +2026-04-13 15:13:17.501588: +2026-04-13 15:13:17.503940: Epoch 2509 +2026-04-13 15:13:17.506024: Current learning rate: 0.00411 +2026-04-13 15:14:59.078222: train_loss -0.4157 +2026-04-13 15:14:59.084772: val_loss -0.3385 +2026-04-13 15:14:59.087570: Pseudo dice [0.4649, 0.0, 0.857, 0.1206, 0.4756, 0.6334, 0.8959] +2026-04-13 15:14:59.089774: Epoch time: 101.58 s +2026-04-13 15:15:01.369830: +2026-04-13 15:15:01.372044: Epoch 2510 +2026-04-13 15:15:01.374115: Current learning rate: 0.00411 +2026-04-13 15:16:43.507526: train_loss -0.4278 +2026-04-13 15:16:43.515748: val_loss -0.3485 +2026-04-13 15:16:43.518404: Pseudo dice [0.5824, 0.0, 0.6308, 0.5802, 0.5983, 0.7287, 0.6391] +2026-04-13 15:16:43.523828: Epoch time: 102.14 s +2026-04-13 15:16:44.742300: +2026-04-13 15:16:44.744877: Epoch 2511 +2026-04-13 15:16:44.748029: Current learning rate: 0.00411 +2026-04-13 15:18:26.410052: train_loss -0.4037 +2026-04-13 15:18:26.417842: val_loss -0.3556 +2026-04-13 15:18:26.419973: Pseudo dice [0.724, 0.0, 0.548, 0.5553, 0.4032, 0.6995, 0.8509] +2026-04-13 15:18:26.422761: Epoch time: 101.67 s +2026-04-13 15:18:27.663853: +2026-04-13 15:18:27.666376: Epoch 2512 +2026-04-13 15:18:27.668871: Current learning rate: 0.00411 +2026-04-13 15:20:09.668209: train_loss -0.4197 +2026-04-13 15:20:09.675467: val_loss -0.3343 +2026-04-13 15:20:09.678555: Pseudo dice [0.3753, 0.0, 0.6174, 0.32, 0.4884, 0.5744, 0.6486] +2026-04-13 15:20:09.681254: Epoch time: 102.01 s +2026-04-13 15:20:10.928235: +2026-04-13 15:20:10.933137: Epoch 2513 +2026-04-13 15:20:10.935971: Current learning rate: 0.0041 +2026-04-13 15:21:52.764824: train_loss -0.4344 +2026-04-13 15:21:52.771632: val_loss -0.3706 +2026-04-13 15:21:52.773628: Pseudo dice [0.4486, 0.0, 0.6684, 0.4876, 0.3781, 0.8108, 0.836] +2026-04-13 15:21:52.776476: Epoch time: 101.84 s +2026-04-13 15:21:54.005183: +2026-04-13 15:21:54.008877: Epoch 2514 +2026-04-13 15:21:54.011359: Current learning rate: 0.0041 +2026-04-13 15:23:35.587184: train_loss -0.4268 +2026-04-13 15:23:35.595016: val_loss -0.3779 +2026-04-13 15:23:35.599478: Pseudo dice [0.7787, 0.0, 0.7339, 0.7657, 0.4604, 0.7638, 0.9177] +2026-04-13 15:23:35.602657: Epoch time: 101.59 s +2026-04-13 15:23:36.830175: +2026-04-13 15:23:36.832851: Epoch 2515 +2026-04-13 15:23:36.835967: Current learning rate: 0.0041 +2026-04-13 15:25:19.489425: train_loss -0.4305 +2026-04-13 15:25:19.496835: val_loss -0.3862 +2026-04-13 15:25:19.498667: Pseudo dice [0.3297, 0.0, 0.7939, 0.7974, 0.5571, 0.817, 0.8506] +2026-04-13 15:25:19.501428: Epoch time: 102.66 s +2026-04-13 15:25:20.729082: +2026-04-13 15:25:20.730979: Epoch 2516 +2026-04-13 15:25:20.733127: Current learning rate: 0.0041 +2026-04-13 15:27:02.467951: train_loss -0.4315 +2026-04-13 15:27:02.474971: val_loss -0.3793 +2026-04-13 15:27:02.477357: Pseudo dice [0.5268, 0.0, 0.7913, 0.6887, 0.5436, 0.5463, 0.6396] +2026-04-13 15:27:02.479810: Epoch time: 101.74 s +2026-04-13 15:27:03.721888: +2026-04-13 15:27:03.723823: Epoch 2517 +2026-04-13 15:27:03.725708: Current learning rate: 0.00409 +2026-04-13 15:28:44.937487: train_loss -0.4327 +2026-04-13 15:28:44.964129: val_loss -0.3846 +2026-04-13 15:28:44.967173: Pseudo dice [0.5637, 0.0, 0.8529, 0.5932, 0.5766, 0.4614, 0.8499] +2026-04-13 15:28:44.970040: Epoch time: 101.22 s +2026-04-13 15:28:46.185855: +2026-04-13 15:28:46.189772: Epoch 2518 +2026-04-13 15:28:46.192357: Current learning rate: 0.00409 +2026-04-13 15:30:27.590088: train_loss -0.4267 +2026-04-13 15:30:27.598419: val_loss -0.4186 +2026-04-13 15:30:27.601310: Pseudo dice [0.6435, 0.0, 0.8652, 0.1799, 0.5471, 0.7532, 0.817] +2026-04-13 15:30:27.604716: Epoch time: 101.41 s +2026-04-13 15:30:28.873520: +2026-04-13 15:30:28.876485: Epoch 2519 +2026-04-13 15:30:28.880160: Current learning rate: 0.00409 +2026-04-13 15:32:10.509342: train_loss -0.4396 +2026-04-13 15:32:10.516193: val_loss -0.402 +2026-04-13 15:32:10.518240: Pseudo dice [0.7677, 0.0, 0.7819, 0.4426, 0.4998, 0.745, 0.8575] +2026-04-13 15:32:10.521381: Epoch time: 101.64 s +2026-04-13 15:32:11.759819: +2026-04-13 15:32:11.762107: Epoch 2520 +2026-04-13 15:32:11.763922: Current learning rate: 0.00409 +2026-04-13 15:33:53.860283: train_loss -0.4519 +2026-04-13 15:33:53.867519: val_loss -0.3403 +2026-04-13 15:33:53.870004: Pseudo dice [0.6799, 0.0, 0.6889, 0.2667, 0.4095, 0.6976, 0.8464] +2026-04-13 15:33:53.872397: Epoch time: 102.1 s +2026-04-13 15:33:55.073608: +2026-04-13 15:33:55.075626: Epoch 2521 +2026-04-13 15:33:55.077990: Current learning rate: 0.00408 +2026-04-13 15:35:37.591825: train_loss -0.4347 +2026-04-13 15:35:37.598684: val_loss -0.3731 +2026-04-13 15:35:37.601308: Pseudo dice [0.4773, 0.0, 0.7967, 0.5726, 0.4813, 0.4932, 0.6817] +2026-04-13 15:35:37.604533: Epoch time: 102.52 s +2026-04-13 15:35:38.831639: +2026-04-13 15:35:38.833950: Epoch 2522 +2026-04-13 15:35:38.836224: Current learning rate: 0.00408 +2026-04-13 15:37:21.022349: train_loss -0.4421 +2026-04-13 15:37:21.028839: val_loss -0.3727 +2026-04-13 15:37:21.031104: Pseudo dice [0.3611, 0.0, 0.8468, 0.7828, 0.3995, 0.3887, 0.8979] +2026-04-13 15:37:21.033534: Epoch time: 102.19 s +2026-04-13 15:37:22.227717: +2026-04-13 15:37:22.232391: Epoch 2523 +2026-04-13 15:37:22.234941: Current learning rate: 0.00408 +2026-04-13 15:39:04.137832: train_loss -0.4255 +2026-04-13 15:39:04.150562: val_loss -0.3708 +2026-04-13 15:39:04.156087: Pseudo dice [0.6013, 0.0, 0.7651, 0.154, 0.5057, 0.7128, 0.9014] +2026-04-13 15:39:04.158900: Epoch time: 101.91 s +2026-04-13 15:39:05.400640: +2026-04-13 15:39:05.402656: Epoch 2524 +2026-04-13 15:39:05.404578: Current learning rate: 0.00408 +2026-04-13 15:40:46.934725: train_loss -0.4315 +2026-04-13 15:40:46.941092: val_loss -0.3357 +2026-04-13 15:40:46.943538: Pseudo dice [0.5169, 0.0, 0.6842, 0.0259, 0.3723, 0.5463, 0.8696] +2026-04-13 15:40:46.945965: Epoch time: 101.54 s +2026-04-13 15:40:48.182083: +2026-04-13 15:40:48.184295: Epoch 2525 +2026-04-13 15:40:48.186727: Current learning rate: 0.00407 +2026-04-13 15:42:30.027265: train_loss -0.4227 +2026-04-13 15:42:30.037887: val_loss -0.3538 +2026-04-13 15:42:30.040295: Pseudo dice [0.5734, 0.0, 0.7921, 0.745, 0.5579, 0.3825, 0.6818] +2026-04-13 15:42:30.043392: Epoch time: 101.85 s +2026-04-13 15:42:31.300602: +2026-04-13 15:42:31.304071: Epoch 2526 +2026-04-13 15:42:31.310007: Current learning rate: 0.00407 +2026-04-13 15:44:13.112957: train_loss -0.4144 +2026-04-13 15:44:13.119472: val_loss -0.327 +2026-04-13 15:44:13.122153: Pseudo dice [0.489, 0.0, 0.7431, 0.4216, 0.2854, 0.4941, 0.849] +2026-04-13 15:44:13.124555: Epoch time: 101.82 s +2026-04-13 15:44:14.363971: +2026-04-13 15:44:14.365986: Epoch 2527 +2026-04-13 15:44:14.368232: Current learning rate: 0.00407 +2026-04-13 15:45:56.672220: train_loss -0.3883 +2026-04-13 15:45:56.678884: val_loss -0.3293 +2026-04-13 15:45:56.681009: Pseudo dice [0.7151, 0.0, 0.7287, 0.3275, 0.3968, 0.2444, 0.8694] +2026-04-13 15:45:56.684177: Epoch time: 102.31 s +2026-04-13 15:45:57.895106: +2026-04-13 15:45:57.897193: Epoch 2528 +2026-04-13 15:45:57.899151: Current learning rate: 0.00407 +2026-04-13 15:47:40.164334: train_loss -0.3939 +2026-04-13 15:47:40.171589: val_loss -0.3144 +2026-04-13 15:47:40.173743: Pseudo dice [0.6993, 0.0, 0.4522, 0.047, 0.1071, 0.6392, 0.8635] +2026-04-13 15:47:40.175937: Epoch time: 102.27 s +2026-04-13 15:47:41.432045: +2026-04-13 15:47:41.434758: Epoch 2529 +2026-04-13 15:47:41.438948: Current learning rate: 0.00406 +2026-04-13 15:49:23.341079: train_loss -0.4164 +2026-04-13 15:49:23.348665: val_loss -0.3423 +2026-04-13 15:49:23.351321: Pseudo dice [0.207, 0.0, 0.7279, 0.1726, 0.5744, 0.4039, 0.7897] +2026-04-13 15:49:23.354368: Epoch time: 101.91 s +2026-04-13 15:49:24.570603: +2026-04-13 15:49:24.572383: Epoch 2530 +2026-04-13 15:49:24.574382: Current learning rate: 0.00406 +2026-04-13 15:51:07.705057: train_loss -0.421 +2026-04-13 15:51:07.712602: val_loss -0.3347 +2026-04-13 15:51:07.716376: Pseudo dice [0.6196, 0.0, 0.6504, 0.2691, 0.4532, 0.8296, 0.878] +2026-04-13 15:51:07.719193: Epoch time: 103.14 s +2026-04-13 15:51:08.949440: +2026-04-13 15:51:08.951687: Epoch 2531 +2026-04-13 15:51:08.953613: Current learning rate: 0.00406 +2026-04-13 15:52:50.982460: train_loss -0.4312 +2026-04-13 15:52:50.989318: val_loss -0.3688 +2026-04-13 15:52:50.991980: Pseudo dice [0.733, 0.0, 0.7602, 0.2424, 0.3846, 0.6097, 0.6188] +2026-04-13 15:52:50.996274: Epoch time: 102.04 s +2026-04-13 15:52:52.222495: +2026-04-13 15:52:52.225142: Epoch 2532 +2026-04-13 15:52:52.227897: Current learning rate: 0.00406 +2026-04-13 15:54:34.664647: train_loss -0.4216 +2026-04-13 15:54:34.671615: val_loss -0.3569 +2026-04-13 15:54:34.673757: Pseudo dice [0.7681, 0.0, 0.8454, 0.5115, 0.4009, 0.7909, 0.5538] +2026-04-13 15:54:34.679394: Epoch time: 102.45 s +2026-04-13 15:54:35.942941: +2026-04-13 15:54:35.944920: Epoch 2533 +2026-04-13 15:54:35.947272: Current learning rate: 0.00405 +2026-04-13 15:56:18.105817: train_loss -0.4207 +2026-04-13 15:56:18.113151: val_loss -0.3665 +2026-04-13 15:56:18.115322: Pseudo dice [0.1749, 0.0, 0.7063, 0.4434, 0.5359, 0.8288, 0.8814] +2026-04-13 15:56:18.117962: Epoch time: 102.17 s +2026-04-13 15:56:19.355698: +2026-04-13 15:56:19.357696: Epoch 2534 +2026-04-13 15:56:19.359533: Current learning rate: 0.00405 +2026-04-13 15:58:01.404138: train_loss -0.4136 +2026-04-13 15:58:01.410607: val_loss -0.36 +2026-04-13 15:58:01.413722: Pseudo dice [0.4829, 0.0, 0.8246, 0.681, 0.4739, 0.4619, 0.8354] +2026-04-13 15:58:01.415875: Epoch time: 102.05 s +2026-04-13 15:58:02.637339: +2026-04-13 15:58:02.639095: Epoch 2535 +2026-04-13 15:58:02.641074: Current learning rate: 0.00405 +2026-04-13 15:59:44.377495: train_loss -0.4303 +2026-04-13 15:59:44.385634: val_loss -0.3408 +2026-04-13 15:59:44.389065: Pseudo dice [0.6101, 0.0, 0.691, 0.0783, 0.4414, 0.6096, 0.7129] +2026-04-13 15:59:44.398273: Epoch time: 101.74 s +2026-04-13 15:59:45.628258: +2026-04-13 15:59:45.630459: Epoch 2536 +2026-04-13 15:59:45.632579: Current learning rate: 0.00405 +2026-04-13 16:01:27.376872: train_loss -0.427 +2026-04-13 16:01:27.385065: val_loss -0.3821 +2026-04-13 16:01:27.387147: Pseudo dice [0.3942, 0.0, 0.7738, 0.7946, 0.4712, 0.6001, 0.7841] +2026-04-13 16:01:27.389536: Epoch time: 101.75 s +2026-04-13 16:01:28.611902: +2026-04-13 16:01:28.614476: Epoch 2537 +2026-04-13 16:01:28.616771: Current learning rate: 0.00404 +2026-04-13 16:03:10.221820: train_loss -0.4264 +2026-04-13 16:03:10.227520: val_loss -0.3487 +2026-04-13 16:03:10.229625: Pseudo dice [0.4268, 0.0, 0.7966, 0.5216, 0.226, 0.684, 0.8898] +2026-04-13 16:03:10.232213: Epoch time: 101.61 s +2026-04-13 16:03:11.435309: +2026-04-13 16:03:11.438170: Epoch 2538 +2026-04-13 16:03:11.440737: Current learning rate: 0.00404 +2026-04-13 16:04:53.204032: train_loss -0.4285 +2026-04-13 16:04:53.211020: val_loss -0.3204 +2026-04-13 16:04:53.213306: Pseudo dice [0.1827, 0.0, 0.6608, 0.3634, 0.5322, 0.1298, 0.8287] +2026-04-13 16:04:53.216549: Epoch time: 101.77 s +2026-04-13 16:04:54.434289: +2026-04-13 16:04:54.436610: Epoch 2539 +2026-04-13 16:04:54.438437: Current learning rate: 0.00404 +2026-04-13 16:06:36.234168: train_loss -0.4055 +2026-04-13 16:06:36.240650: val_loss -0.3599 +2026-04-13 16:06:36.242566: Pseudo dice [0.0985, 0.0, 0.6191, 0.277, 0.5836, 0.4929, 0.715] +2026-04-13 16:06:36.245079: Epoch time: 101.8 s +2026-04-13 16:06:37.467469: +2026-04-13 16:06:37.470061: Epoch 2540 +2026-04-13 16:06:37.472335: Current learning rate: 0.00404 +2026-04-13 16:08:19.391011: train_loss -0.4021 +2026-04-13 16:08:19.399500: val_loss -0.3805 +2026-04-13 16:08:19.403039: Pseudo dice [0.6823, 0.0, 0.7573, 0.2576, 0.4816, 0.2815, 0.8287] +2026-04-13 16:08:19.406396: Epoch time: 101.93 s +2026-04-13 16:08:20.703574: +2026-04-13 16:08:20.706260: Epoch 2541 +2026-04-13 16:08:20.711323: Current learning rate: 0.00403 +2026-04-13 16:10:02.009041: train_loss -0.4357 +2026-04-13 16:10:02.017260: val_loss -0.375 +2026-04-13 16:10:02.019747: Pseudo dice [0.6669, 0.0, 0.8886, 0.6784, 0.4389, 0.4133, 0.764] +2026-04-13 16:10:02.026279: Epoch time: 101.31 s +2026-04-13 16:10:03.249840: +2026-04-13 16:10:03.252831: Epoch 2542 +2026-04-13 16:10:03.255894: Current learning rate: 0.00403 +2026-04-13 16:11:45.876399: train_loss -0.4093 +2026-04-13 16:11:45.885749: val_loss -0.3535 +2026-04-13 16:11:45.888559: Pseudo dice [0.0102, 0.0, 0.6274, 0.6244, 0.5256, 0.3932, 0.8599] +2026-04-13 16:11:45.897459: Epoch time: 102.63 s +2026-04-13 16:11:47.176174: +2026-04-13 16:11:47.178998: Epoch 2543 +2026-04-13 16:11:47.184429: Current learning rate: 0.00403 +2026-04-13 16:13:28.634920: train_loss -0.4182 +2026-04-13 16:13:28.642347: val_loss -0.3805 +2026-04-13 16:13:28.644685: Pseudo dice [0.4479, 0.0, 0.7926, 0.7883, 0.5177, 0.3251, 0.8943] +2026-04-13 16:13:28.648128: Epoch time: 101.46 s +2026-04-13 16:13:29.879108: +2026-04-13 16:13:29.883049: Epoch 2544 +2026-04-13 16:13:29.885857: Current learning rate: 0.00403 +2026-04-13 16:15:11.315494: train_loss -0.4434 +2026-04-13 16:15:11.328535: val_loss -0.3832 +2026-04-13 16:15:11.330776: Pseudo dice [0.4023, 0.0, 0.7712, 0.0835, 0.3284, 0.7791, 0.8927] +2026-04-13 16:15:11.335406: Epoch time: 101.44 s +2026-04-13 16:15:12.616126: +2026-04-13 16:15:12.619266: Epoch 2545 +2026-04-13 16:15:12.621530: Current learning rate: 0.00402 +2026-04-13 16:16:54.421165: train_loss -0.4241 +2026-04-13 16:16:54.429368: val_loss -0.3621 +2026-04-13 16:16:54.432603: Pseudo dice [0.6523, 0.0, 0.4128, 0.5041, 0.5764, 0.5124, 0.7516] +2026-04-13 16:16:54.435576: Epoch time: 101.81 s +2026-04-13 16:16:55.680897: +2026-04-13 16:16:55.682773: Epoch 2546 +2026-04-13 16:16:55.684794: Current learning rate: 0.00402 +2026-04-13 16:18:38.185035: train_loss -0.4142 +2026-04-13 16:18:38.193588: val_loss -0.3561 +2026-04-13 16:18:38.196287: Pseudo dice [0.4205, 0.0, 0.7646, 0.5884, 0.4988, 0.5929, 0.5768] +2026-04-13 16:18:38.200233: Epoch time: 102.51 s +2026-04-13 16:18:39.456898: +2026-04-13 16:18:39.458969: Epoch 2547 +2026-04-13 16:18:39.462295: Current learning rate: 0.00402 +2026-04-13 16:20:22.067324: train_loss -0.4032 +2026-04-13 16:20:22.082898: val_loss -0.3578 +2026-04-13 16:20:22.086898: Pseudo dice [0.7615, 0.0, 0.6916, 0.4032, 0.5821, 0.7402, 0.7659] +2026-04-13 16:20:22.091287: Epoch time: 102.61 s +2026-04-13 16:20:23.377748: +2026-04-13 16:20:23.380232: Epoch 2548 +2026-04-13 16:20:23.382381: Current learning rate: 0.00402 +2026-04-13 16:22:05.350501: train_loss -0.4173 +2026-04-13 16:22:05.360356: val_loss -0.3672 +2026-04-13 16:22:05.364570: Pseudo dice [0.5409, 0.0, 0.8444, 0.5864, 0.541, 0.6655, 0.8215] +2026-04-13 16:22:05.368629: Epoch time: 101.98 s +2026-04-13 16:22:06.579002: +2026-04-13 16:22:06.581146: Epoch 2549 +2026-04-13 16:22:06.585880: Current learning rate: 0.00401 +2026-04-13 16:23:49.063103: train_loss -0.4226 +2026-04-13 16:23:49.070582: val_loss -0.3687 +2026-04-13 16:23:49.073261: Pseudo dice [0.5347, 0.0, 0.8042, 0.5327, 0.4991, 0.8058, 0.6828] +2026-04-13 16:23:49.076567: Epoch time: 102.49 s +2026-04-13 16:23:53.162104: +2026-04-13 16:23:53.164219: Epoch 2550 +2026-04-13 16:23:53.166129: Current learning rate: 0.00401 +2026-04-13 16:25:34.803834: train_loss -0.4195 +2026-04-13 16:25:34.810919: val_loss -0.3516 +2026-04-13 16:25:34.812935: Pseudo dice [0.3269, 0.0, 0.6996, 0.388, 0.3375, 0.6152, 0.8157] +2026-04-13 16:25:34.815913: Epoch time: 101.64 s +2026-04-13 16:25:36.060212: +2026-04-13 16:25:36.063580: Epoch 2551 +2026-04-13 16:25:36.066813: Current learning rate: 0.00401 +2026-04-13 16:27:18.260962: train_loss -0.4286 +2026-04-13 16:27:18.267748: val_loss -0.3781 +2026-04-13 16:27:18.270407: Pseudo dice [0.7699, 0.0, 0.7097, 0.5567, 0.4866, 0.7711, 0.787] +2026-04-13 16:27:18.273282: Epoch time: 102.2 s +2026-04-13 16:27:19.472448: +2026-04-13 16:27:19.474599: Epoch 2552 +2026-04-13 16:27:19.477115: Current learning rate: 0.00401 +2026-04-13 16:29:01.660951: train_loss -0.4067 +2026-04-13 16:29:01.687850: val_loss -0.3536 +2026-04-13 16:29:01.690319: Pseudo dice [0.5071, 0.0, 0.7632, 0.1756, 0.3852, 0.7205, 0.8894] +2026-04-13 16:29:01.692635: Epoch time: 102.19 s +2026-04-13 16:29:02.930314: +2026-04-13 16:29:02.932265: Epoch 2553 +2026-04-13 16:29:02.934213: Current learning rate: 0.004 +2026-04-13 16:30:44.396903: train_loss -0.4278 +2026-04-13 16:30:44.403795: val_loss -0.334 +2026-04-13 16:30:44.406348: Pseudo dice [0.515, 0.0, 0.649, 0.0337, 0.4537, 0.4843, 0.7423] +2026-04-13 16:30:44.408935: Epoch time: 101.47 s +2026-04-13 16:30:45.634097: +2026-04-13 16:30:45.636009: Epoch 2554 +2026-04-13 16:30:45.639616: Current learning rate: 0.004 +2026-04-13 16:32:27.660639: train_loss -0.389 +2026-04-13 16:32:27.667623: val_loss -0.3534 +2026-04-13 16:32:27.669968: Pseudo dice [0.4796, 0.0, 0.8197, 0.6162, 0.5044, 0.5137, 0.8459] +2026-04-13 16:32:27.672485: Epoch time: 102.03 s +2026-04-13 16:32:28.923741: +2026-04-13 16:32:28.925955: Epoch 2555 +2026-04-13 16:32:28.927999: Current learning rate: 0.004 +2026-04-13 16:34:10.562902: train_loss -0.4119 +2026-04-13 16:34:10.570156: val_loss -0.3467 +2026-04-13 16:34:10.572734: Pseudo dice [0.3455, 0.0, 0.7034, 0.4612, 0.5309, 0.6515, 0.8615] +2026-04-13 16:34:10.575446: Epoch time: 101.64 s +2026-04-13 16:34:11.822308: +2026-04-13 16:34:11.824554: Epoch 2556 +2026-04-13 16:34:11.826631: Current learning rate: 0.004 +2026-04-13 16:35:53.320110: train_loss -0.424 +2026-04-13 16:35:53.326280: val_loss -0.3672 +2026-04-13 16:35:53.328629: Pseudo dice [0.7791, 0.0, 0.6934, 0.1525, 0.327, 0.4172, 0.7578] +2026-04-13 16:35:53.330917: Epoch time: 101.5 s +2026-04-13 16:35:54.586110: +2026-04-13 16:35:54.587977: Epoch 2557 +2026-04-13 16:35:54.589634: Current learning rate: 0.00399 +2026-04-13 16:37:36.509700: train_loss -0.4261 +2026-04-13 16:37:36.516886: val_loss -0.3665 +2026-04-13 16:37:36.519117: Pseudo dice [0.317, 0.0, 0.8001, 0.1756, 0.4474, 0.5892, 0.7933] +2026-04-13 16:37:36.521956: Epoch time: 101.93 s +2026-04-13 16:37:37.761998: +2026-04-13 16:37:37.764169: Epoch 2558 +2026-04-13 16:37:37.765875: Current learning rate: 0.00399 +2026-04-13 16:39:19.454647: train_loss -0.4302 +2026-04-13 16:39:19.463109: val_loss -0.3686 +2026-04-13 16:39:19.466466: Pseudo dice [0.4027, 0.0, 0.6159, 0.8082, 0.5561, 0.363, 0.9011] +2026-04-13 16:39:19.469217: Epoch time: 101.7 s +2026-04-13 16:39:20.704070: +2026-04-13 16:39:20.707895: Epoch 2559 +2026-04-13 16:39:20.710735: Current learning rate: 0.00399 +2026-04-13 16:41:02.584854: train_loss -0.4326 +2026-04-13 16:41:02.594167: val_loss -0.3289 +2026-04-13 16:41:02.597262: Pseudo dice [0.4323, 0.0, 0.6915, 0.0391, 0.3939, 0.4745, 0.9248] +2026-04-13 16:41:02.600349: Epoch time: 101.88 s +2026-04-13 16:41:03.838603: +2026-04-13 16:41:03.841008: Epoch 2560 +2026-04-13 16:41:03.842847: Current learning rate: 0.00399 +2026-04-13 16:42:46.315021: train_loss -0.4093 +2026-04-13 16:42:46.328124: val_loss -0.3582 +2026-04-13 16:42:46.333978: Pseudo dice [0.7221, 0.0, 0.801, 0.1737, 0.6181, 0.7074, 0.7185] +2026-04-13 16:42:46.337711: Epoch time: 102.48 s +2026-04-13 16:42:47.632398: +2026-04-13 16:42:47.634314: Epoch 2561 +2026-04-13 16:42:47.636347: Current learning rate: 0.00398 +2026-04-13 16:44:29.537378: train_loss -0.406 +2026-04-13 16:44:29.544667: val_loss -0.3483 +2026-04-13 16:44:29.547789: Pseudo dice [0.514, 0.0, 0.8726, 0.7471, 0.3838, 0.5266, 0.7469] +2026-04-13 16:44:29.550433: Epoch time: 101.91 s +2026-04-13 16:44:30.758086: +2026-04-13 16:44:30.759882: Epoch 2562 +2026-04-13 16:44:30.762114: Current learning rate: 0.00398 +2026-04-13 16:46:12.196635: train_loss -0.4077 +2026-04-13 16:46:12.203767: val_loss -0.3465 +2026-04-13 16:46:12.206340: Pseudo dice [0.6188, 0.0, 0.8017, 0.7235, 0.3327, 0.4688, 0.5803] +2026-04-13 16:46:12.210339: Epoch time: 101.44 s +2026-04-13 16:46:13.424083: +2026-04-13 16:46:13.426200: Epoch 2563 +2026-04-13 16:46:13.427935: Current learning rate: 0.00398 +2026-04-13 16:47:54.986211: train_loss -0.4184 +2026-04-13 16:47:54.992680: val_loss -0.3617 +2026-04-13 16:47:54.996060: Pseudo dice [0.3339, 0.0, 0.6986, 0.0605, 0.6334, 0.6567, 0.7237] +2026-04-13 16:47:54.998817: Epoch time: 101.57 s +2026-04-13 16:47:56.229716: +2026-04-13 16:47:56.235555: Epoch 2564 +2026-04-13 16:47:56.238290: Current learning rate: 0.00398 +2026-04-13 16:49:38.049666: train_loss -0.416 +2026-04-13 16:49:38.056058: val_loss -0.3544 +2026-04-13 16:49:38.058593: Pseudo dice [0.8451, 0.0, 0.8109, 0.1168, 0.5366, 0.8003, 0.8761] +2026-04-13 16:49:38.061233: Epoch time: 101.82 s +2026-04-13 16:49:39.283628: +2026-04-13 16:49:39.286835: Epoch 2565 +2026-04-13 16:49:39.288726: Current learning rate: 0.00397 +2026-04-13 16:51:20.586102: train_loss -0.4355 +2026-04-13 16:51:20.593295: val_loss -0.3707 +2026-04-13 16:51:20.595915: Pseudo dice [0.7333, 0.0, 0.8555, 0.744, 0.5174, 0.754, 0.9354] +2026-04-13 16:51:20.599029: Epoch time: 101.31 s +2026-04-13 16:51:21.827901: +2026-04-13 16:51:21.829751: Epoch 2566 +2026-04-13 16:51:21.831510: Current learning rate: 0.00397 +2026-04-13 16:53:04.171903: train_loss -0.4456 +2026-04-13 16:53:04.179364: val_loss -0.4182 +2026-04-13 16:53:04.181545: Pseudo dice [0.7585, 0.0, 0.8353, 0.7977, 0.4479, 0.8162, 0.9005] +2026-04-13 16:53:04.183806: Epoch time: 102.35 s +2026-04-13 16:53:05.425966: +2026-04-13 16:53:05.428500: Epoch 2567 +2026-04-13 16:53:05.430609: Current learning rate: 0.00397 +2026-04-13 16:54:47.595088: train_loss -0.4313 +2026-04-13 16:54:47.603338: val_loss -0.3612 +2026-04-13 16:54:47.605717: Pseudo dice [0.7516, 0.0, 0.6208, 0.4509, 0.5328, 0.593, 0.9068] +2026-04-13 16:54:47.608401: Epoch time: 102.17 s +2026-04-13 16:54:48.847683: +2026-04-13 16:54:48.849998: Epoch 2568 +2026-04-13 16:54:48.851770: Current learning rate: 0.00397 +2026-04-13 16:56:30.695412: train_loss -0.4295 +2026-04-13 16:56:30.703286: val_loss -0.4007 +2026-04-13 16:56:30.705947: Pseudo dice [0.5099, 0.0, 0.8192, 0.8062, 0.5054, 0.7825, 0.8896] +2026-04-13 16:56:30.711390: Epoch time: 101.85 s +2026-04-13 16:56:31.925242: +2026-04-13 16:56:31.927042: Epoch 2569 +2026-04-13 16:56:31.929112: Current learning rate: 0.00396 +2026-04-13 16:58:14.560112: train_loss -0.4423 +2026-04-13 16:58:14.566643: val_loss -0.3812 +2026-04-13 16:58:14.569280: Pseudo dice [0.6765, 0.0, 0.7857, 0.7876, 0.5077, 0.5097, 0.9037] +2026-04-13 16:58:14.571859: Epoch time: 102.64 s +2026-04-13 16:58:15.790355: +2026-04-13 16:58:15.792667: Epoch 2570 +2026-04-13 16:58:15.794987: Current learning rate: 0.00396 +2026-04-13 16:59:57.831222: train_loss -0.4216 +2026-04-13 16:59:57.837662: val_loss -0.3908 +2026-04-13 16:59:57.840225: Pseudo dice [0.8103, 0.0, 0.7428, 0.4737, 0.412, 0.7516, 0.9131] +2026-04-13 16:59:57.842581: Epoch time: 102.04 s +2026-04-13 17:00:00.299710: +2026-04-13 17:00:00.301898: Epoch 2571 +2026-04-13 17:00:00.303936: Current learning rate: 0.00396 +2026-04-13 17:01:42.717206: train_loss -0.4357 +2026-04-13 17:01:42.728102: val_loss -0.3586 +2026-04-13 17:01:42.730946: Pseudo dice [0.7507, 0.0, 0.7979, 0.443, 0.2848, 0.7601, 0.8003] +2026-04-13 17:01:42.735067: Epoch time: 102.42 s +2026-04-13 17:01:43.990558: +2026-04-13 17:01:43.993932: Epoch 2572 +2026-04-13 17:01:43.999118: Current learning rate: 0.00396 +2026-04-13 17:03:26.428357: train_loss -0.4358 +2026-04-13 17:03:26.437357: val_loss -0.3633 +2026-04-13 17:03:26.440244: Pseudo dice [0.5962, 0.0, 0.6584, 0.6755, 0.49, 0.425, 0.9277] +2026-04-13 17:03:26.445940: Epoch time: 102.44 s +2026-04-13 17:03:27.725305: +2026-04-13 17:03:27.728164: Epoch 2573 +2026-04-13 17:03:27.731490: Current learning rate: 0.00395 +2026-04-13 17:05:09.761729: train_loss -0.4469 +2026-04-13 17:05:09.769698: val_loss -0.354 +2026-04-13 17:05:09.772086: Pseudo dice [0.3613, 0.0, 0.8395, 0.5274, 0.3643, 0.7952, 0.6393] +2026-04-13 17:05:09.774672: Epoch time: 102.04 s +2026-04-13 17:05:11.096299: +2026-04-13 17:05:11.098474: Epoch 2574 +2026-04-13 17:05:11.100211: Current learning rate: 0.00395 +2026-04-13 17:06:53.148453: train_loss -0.4212 +2026-04-13 17:06:53.156372: val_loss -0.3329 +2026-04-13 17:06:53.158942: Pseudo dice [0.5124, 0.0, 0.836, 0.6281, 0.3558, 0.4963, 0.7595] +2026-04-13 17:06:53.161287: Epoch time: 102.06 s +2026-04-13 17:06:54.399163: +2026-04-13 17:06:54.401371: Epoch 2575 +2026-04-13 17:06:54.403622: Current learning rate: 0.00395 +2026-04-13 17:08:37.078809: train_loss -0.4239 +2026-04-13 17:08:37.089536: val_loss -0.3479 +2026-04-13 17:08:37.092100: Pseudo dice [0.3994, 0.0, 0.7082, 0.2721, 0.4856, 0.4961, 0.8512] +2026-04-13 17:08:37.095907: Epoch time: 102.68 s +2026-04-13 17:08:38.326011: +2026-04-13 17:08:38.327911: Epoch 2576 +2026-04-13 17:08:38.329642: Current learning rate: 0.00395 +2026-04-13 17:10:19.763372: train_loss -0.4319 +2026-04-13 17:10:19.770701: val_loss -0.3843 +2026-04-13 17:10:19.773434: Pseudo dice [0.7455, 0.0, 0.785, 0.3294, 0.477, 0.494, 0.7174] +2026-04-13 17:10:19.776398: Epoch time: 101.44 s +2026-04-13 17:10:21.021451: +2026-04-13 17:10:21.023634: Epoch 2577 +2026-04-13 17:10:21.025387: Current learning rate: 0.00394 +2026-04-13 17:12:03.050057: train_loss -0.4071 +2026-04-13 17:12:03.060097: val_loss -0.3803 +2026-04-13 17:12:03.062288: Pseudo dice [0.7013, 0.0, 0.656, 0.6633, 0.4113, 0.8683, 0.8309] +2026-04-13 17:12:03.064745: Epoch time: 102.03 s +2026-04-13 17:12:04.326151: +2026-04-13 17:12:04.328002: Epoch 2578 +2026-04-13 17:12:04.329662: Current learning rate: 0.00394 +2026-04-13 17:13:46.479621: train_loss -0.4161 +2026-04-13 17:13:46.489290: val_loss -0.3516 +2026-04-13 17:13:46.491977: Pseudo dice [0.5932, 0.0, 0.7036, 0.6777, 0.415, 0.729, 0.5856] +2026-04-13 17:13:46.495764: Epoch time: 102.16 s +2026-04-13 17:13:47.734803: +2026-04-13 17:13:47.736890: Epoch 2579 +2026-04-13 17:13:47.738692: Current learning rate: 0.00394 +2026-04-13 17:15:30.146311: train_loss -0.4118 +2026-04-13 17:15:30.154634: val_loss -0.3768 +2026-04-13 17:15:30.159009: Pseudo dice [0.7304, 0.0, 0.7452, 0.6632, 0.4339, 0.5986, 0.7974] +2026-04-13 17:15:30.161292: Epoch time: 102.41 s +2026-04-13 17:15:31.394797: +2026-04-13 17:15:31.396608: Epoch 2580 +2026-04-13 17:15:31.398335: Current learning rate: 0.00394 +2026-04-13 17:17:13.424375: train_loss -0.4151 +2026-04-13 17:17:13.430889: val_loss -0.3584 +2026-04-13 17:17:13.432535: Pseudo dice [0.4874, 0.0, 0.7679, 0.5632, 0.4552, 0.858, 0.6771] +2026-04-13 17:17:13.436489: Epoch time: 102.03 s +2026-04-13 17:17:14.656045: +2026-04-13 17:17:14.658986: Epoch 2581 +2026-04-13 17:17:14.663182: Current learning rate: 0.00393 +2026-04-13 17:18:56.841454: train_loss -0.42 +2026-04-13 17:18:56.851344: val_loss -0.3687 +2026-04-13 17:18:56.853792: Pseudo dice [0.578, 0.0, 0.6858, 0.5835, 0.4117, 0.5533, 0.8709] +2026-04-13 17:18:56.856730: Epoch time: 102.19 s +2026-04-13 17:18:58.091981: +2026-04-13 17:18:58.094379: Epoch 2582 +2026-04-13 17:18:58.096607: Current learning rate: 0.00393 +2026-04-13 17:20:39.421789: train_loss -0.4098 +2026-04-13 17:20:39.428782: val_loss -0.3521 +2026-04-13 17:20:39.430971: Pseudo dice [0.8148, 0.0, 0.7798, 0.4894, 0.3691, 0.5593, 0.6618] +2026-04-13 17:20:39.433752: Epoch time: 101.33 s +2026-04-13 17:20:40.651761: +2026-04-13 17:20:40.653867: Epoch 2583 +2026-04-13 17:20:40.655779: Current learning rate: 0.00393 +2026-04-13 17:22:22.643744: train_loss -0.4229 +2026-04-13 17:22:22.650624: val_loss -0.3435 +2026-04-13 17:22:22.653400: Pseudo dice [0.6704, 0.0, 0.791, 0.3261, 0.5651, 0.8632, 0.7616] +2026-04-13 17:22:22.656342: Epoch time: 102.0 s +2026-04-13 17:22:23.937080: +2026-04-13 17:22:23.939786: Epoch 2584 +2026-04-13 17:22:23.942033: Current learning rate: 0.00393 +2026-04-13 17:24:05.882572: train_loss -0.4118 +2026-04-13 17:24:05.889627: val_loss -0.3796 +2026-04-13 17:24:05.892014: Pseudo dice [0.8141, 0.0, 0.6467, 0.2382, 0.4466, 0.6529, 0.8241] +2026-04-13 17:24:05.895693: Epoch time: 101.95 s +2026-04-13 17:24:07.127624: +2026-04-13 17:24:07.130200: Epoch 2585 +2026-04-13 17:24:07.132161: Current learning rate: 0.00392 +2026-04-13 17:25:49.235887: train_loss -0.4191 +2026-04-13 17:25:49.244238: val_loss -0.3414 +2026-04-13 17:25:49.247344: Pseudo dice [0.6871, 0.0, 0.5491, 0.3947, 0.4591, 0.4162, 0.8468] +2026-04-13 17:25:49.250717: Epoch time: 102.11 s +2026-04-13 17:25:50.474697: +2026-04-13 17:25:50.476720: Epoch 2586 +2026-04-13 17:25:50.478553: Current learning rate: 0.00392 +2026-04-13 17:27:32.284442: train_loss -0.4126 +2026-04-13 17:27:32.293798: val_loss -0.3834 +2026-04-13 17:27:32.295953: Pseudo dice [0.3767, 0.0, 0.6914, 0.6751, 0.5995, 0.2842, 0.7681] +2026-04-13 17:27:32.298658: Epoch time: 101.81 s +2026-04-13 17:27:33.548312: +2026-04-13 17:27:33.551020: Epoch 2587 +2026-04-13 17:27:33.554526: Current learning rate: 0.00392 +2026-04-13 17:29:15.355500: train_loss -0.4299 +2026-04-13 17:29:15.382356: val_loss -0.3868 +2026-04-13 17:29:15.385427: Pseudo dice [0.5543, 0.0, 0.8236, 0.6808, 0.4802, 0.8252, 0.8702] +2026-04-13 17:29:15.389071: Epoch time: 101.81 s +2026-04-13 17:29:16.630660: +2026-04-13 17:29:16.632621: Epoch 2588 +2026-04-13 17:29:16.634753: Current learning rate: 0.00392 +2026-04-13 17:30:58.264645: train_loss -0.433 +2026-04-13 17:30:58.271287: val_loss -0.3513 +2026-04-13 17:30:58.273376: Pseudo dice [0.3256, 0.0, 0.7459, 0.6095, 0.5014, 0.2929, 0.5036] +2026-04-13 17:30:58.275748: Epoch time: 101.64 s +2026-04-13 17:30:59.508367: +2026-04-13 17:30:59.510248: Epoch 2589 +2026-04-13 17:30:59.511721: Current learning rate: 0.00391 +2026-04-13 17:32:41.482496: train_loss -0.4281 +2026-04-13 17:32:41.492471: val_loss -0.3486 +2026-04-13 17:32:41.495199: Pseudo dice [0.4899, 0.0, 0.799, 0.5946, 0.4594, 0.4694, 0.5844] +2026-04-13 17:32:41.501222: Epoch time: 101.98 s +2026-04-13 17:32:42.730448: +2026-04-13 17:32:42.732915: Epoch 2590 +2026-04-13 17:32:42.735403: Current learning rate: 0.00391 +2026-04-13 17:34:24.847566: train_loss -0.4179 +2026-04-13 17:34:24.854236: val_loss -0.3633 +2026-04-13 17:34:24.856627: Pseudo dice [0.4257, 0.0, 0.7703, 0.4274, 0.5713, 0.728, 0.8179] +2026-04-13 17:34:24.858904: Epoch time: 102.12 s +2026-04-13 17:34:27.239103: +2026-04-13 17:34:27.240990: Epoch 2591 +2026-04-13 17:34:27.242642: Current learning rate: 0.00391 +2026-04-13 17:36:09.056426: train_loss -0.4212 +2026-04-13 17:36:09.064502: val_loss -0.3401 +2026-04-13 17:36:09.066612: Pseudo dice [0.1263, 0.0, 0.7127, 0.7198, 0.4546, 0.4916, 0.5297] +2026-04-13 17:36:09.068833: Epoch time: 101.82 s +2026-04-13 17:36:10.268375: +2026-04-13 17:36:10.270466: Epoch 2592 +2026-04-13 17:36:10.272313: Current learning rate: 0.00391 +2026-04-13 17:37:52.311536: train_loss -0.4366 +2026-04-13 17:37:52.319339: val_loss -0.3661 +2026-04-13 17:37:52.321858: Pseudo dice [0.7275, 0.0, 0.8607, 0.4551, 0.5806, 0.819, 0.9427] +2026-04-13 17:37:52.324135: Epoch time: 102.05 s +2026-04-13 17:37:53.569615: +2026-04-13 17:37:53.572689: Epoch 2593 +2026-04-13 17:37:53.574619: Current learning rate: 0.0039 +2026-04-13 17:39:35.965622: train_loss -0.4237 +2026-04-13 17:39:35.974428: val_loss -0.3648 +2026-04-13 17:39:35.977944: Pseudo dice [0.7324, 0.0, 0.8055, 0.0122, 0.3997, 0.6388, 0.7183] +2026-04-13 17:39:35.980755: Epoch time: 102.4 s +2026-04-13 17:39:37.256827: +2026-04-13 17:39:37.260341: Epoch 2594 +2026-04-13 17:39:37.263066: Current learning rate: 0.0039 +2026-04-13 17:41:19.266523: train_loss -0.4236 +2026-04-13 17:41:19.274859: val_loss -0.3755 +2026-04-13 17:41:19.277389: Pseudo dice [0.773, 0.0, 0.8255, 0.5807, 0.3735, 0.6217, 0.8134] +2026-04-13 17:41:19.279563: Epoch time: 102.01 s +2026-04-13 17:41:20.514223: +2026-04-13 17:41:20.521623: Epoch 2595 +2026-04-13 17:41:20.524310: Current learning rate: 0.0039 +2026-04-13 17:43:02.298814: train_loss -0.4353 +2026-04-13 17:43:02.305688: val_loss -0.3758 +2026-04-13 17:43:02.308045: Pseudo dice [0.6049, 0.0, 0.7882, 0.7654, 0.5604, 0.7933, 0.952] +2026-04-13 17:43:02.310678: Epoch time: 101.79 s +2026-04-13 17:43:03.544975: +2026-04-13 17:43:03.546874: Epoch 2596 +2026-04-13 17:43:03.548962: Current learning rate: 0.0039 +2026-04-13 17:44:45.352170: train_loss -0.4484 +2026-04-13 17:44:45.358897: val_loss -0.3854 +2026-04-13 17:44:45.361585: Pseudo dice [0.691, 0.0, 0.8156, 0.3177, 0.3432, 0.853, 0.9505] +2026-04-13 17:44:45.364563: Epoch time: 101.81 s +2026-04-13 17:44:46.621447: +2026-04-13 17:44:46.624430: Epoch 2597 +2026-04-13 17:44:46.626696: Current learning rate: 0.00389 +2026-04-13 17:46:28.538045: train_loss -0.4263 +2026-04-13 17:46:28.548916: val_loss -0.3679 +2026-04-13 17:46:28.552063: Pseudo dice [0.3891, 0.0, 0.8245, 0.4153, 0.5391, 0.7128, 0.7643] +2026-04-13 17:46:28.555022: Epoch time: 101.92 s +2026-04-13 17:46:29.816523: +2026-04-13 17:46:29.819242: Epoch 2598 +2026-04-13 17:46:29.821371: Current learning rate: 0.00389 +2026-04-13 17:48:11.600433: train_loss -0.4266 +2026-04-13 17:48:11.607287: val_loss -0.3487 +2026-04-13 17:48:11.611197: Pseudo dice [0.3143, 0.0, 0.6861, 0.6292, 0.3723, 0.7478, 0.9003] +2026-04-13 17:48:11.613755: Epoch time: 101.79 s +2026-04-13 17:48:12.846820: +2026-04-13 17:48:12.848754: Epoch 2599 +2026-04-13 17:48:12.850568: Current learning rate: 0.00389 +2026-04-13 17:49:54.646337: train_loss -0.4271 +2026-04-13 17:49:54.654475: val_loss -0.3649 +2026-04-13 17:49:54.657344: Pseudo dice [0.2563, 0.0, 0.6696, 0.8175, 0.4823, 0.6934, 0.9484] +2026-04-13 17:49:54.660139: Epoch time: 101.8 s +2026-04-13 17:49:57.651412: +2026-04-13 17:49:57.653995: Epoch 2600 +2026-04-13 17:49:57.656163: Current learning rate: 0.00389 +2026-04-13 17:51:39.266290: train_loss -0.4255 +2026-04-13 17:51:39.273549: val_loss -0.3908 +2026-04-13 17:51:39.277100: Pseudo dice [0.6484, 0.0, 0.7697, 0.8799, 0.5937, 0.4174, 0.832] +2026-04-13 17:51:39.281446: Epoch time: 101.62 s +2026-04-13 17:51:40.502852: +2026-04-13 17:51:40.505214: Epoch 2601 +2026-04-13 17:51:40.508888: Current learning rate: 0.00388 +2026-04-13 17:53:22.305901: train_loss -0.4389 +2026-04-13 17:53:22.313701: val_loss -0.3753 +2026-04-13 17:53:22.315799: Pseudo dice [0.7925, 0.0, 0.8476, 0.181, 0.3905, 0.6291, 0.4609] +2026-04-13 17:53:22.318558: Epoch time: 101.81 s +2026-04-13 17:53:23.595822: +2026-04-13 17:53:23.597675: Epoch 2602 +2026-04-13 17:53:23.599570: Current learning rate: 0.00388 +2026-04-13 17:55:05.516500: train_loss -0.4403 +2026-04-13 17:55:05.525064: val_loss -0.3681 +2026-04-13 17:55:05.527026: Pseudo dice [0.7042, 0.0, 0.8432, 0.6337, 0.3263, 0.413, 0.8308] +2026-04-13 17:55:05.529921: Epoch time: 101.92 s +2026-04-13 17:55:06.737470: +2026-04-13 17:55:06.739531: Epoch 2603 +2026-04-13 17:55:06.741090: Current learning rate: 0.00388 +2026-04-13 17:56:48.083009: train_loss -0.4449 +2026-04-13 17:56:48.090786: val_loss -0.3535 +2026-04-13 17:56:48.092834: Pseudo dice [0.5656, 0.0, 0.7121, 0.6811, 0.5906, 0.2781, 0.7792] +2026-04-13 17:56:48.095063: Epoch time: 101.35 s +2026-04-13 17:56:49.353654: +2026-04-13 17:56:49.356030: Epoch 2604 +2026-04-13 17:56:49.358188: Current learning rate: 0.00388 +2026-04-13 17:58:30.931095: train_loss -0.448 +2026-04-13 17:58:30.937982: val_loss -0.3767 +2026-04-13 17:58:30.940190: Pseudo dice [0.5897, 0.0, 0.6993, 0.7759, 0.6583, 0.8189, 0.7824] +2026-04-13 17:58:30.943405: Epoch time: 101.58 s +2026-04-13 17:58:32.184480: +2026-04-13 17:58:32.187653: Epoch 2605 +2026-04-13 17:58:32.189519: Current learning rate: 0.00387 +2026-04-13 18:00:14.391083: train_loss -0.4396 +2026-04-13 18:00:14.402325: val_loss -0.3819 +2026-04-13 18:00:14.404428: Pseudo dice [0.5381, 0.0, 0.7953, 0.5216, 0.5215, 0.5917, 0.8124] +2026-04-13 18:00:14.406997: Epoch time: 102.21 s +2026-04-13 18:00:15.652091: +2026-04-13 18:00:15.654055: Epoch 2606 +2026-04-13 18:00:15.655915: Current learning rate: 0.00387 +2026-04-13 18:01:57.161217: train_loss -0.4364 +2026-04-13 18:01:57.168067: val_loss -0.3625 +2026-04-13 18:01:57.170632: Pseudo dice [0.5491, 0.0, 0.5249, 0.5319, 0.3564, 0.4896, 0.8048] +2026-04-13 18:01:57.174197: Epoch time: 101.51 s +2026-04-13 18:01:58.401863: +2026-04-13 18:01:58.404433: Epoch 2607 +2026-04-13 18:01:58.406317: Current learning rate: 0.00387 +2026-04-13 18:03:40.443165: train_loss -0.431 +2026-04-13 18:03:40.452981: val_loss -0.4008 +2026-04-13 18:03:40.455740: Pseudo dice [0.6057, 0.0, 0.6754, 0.7216, 0.5869, 0.3776, 0.9225] +2026-04-13 18:03:40.458502: Epoch time: 102.04 s +2026-04-13 18:03:41.681284: +2026-04-13 18:03:41.683135: Epoch 2608 +2026-04-13 18:03:41.685065: Current learning rate: 0.00387 +2026-04-13 18:05:23.938183: train_loss -0.4377 +2026-04-13 18:05:23.945051: val_loss -0.3861 +2026-04-13 18:05:23.947405: Pseudo dice [0.7869, 0.0, 0.6971, 0.0046, 0.5517, 0.313, 0.9218] +2026-04-13 18:05:23.951416: Epoch time: 102.26 s +2026-04-13 18:05:25.189617: +2026-04-13 18:05:25.191527: Epoch 2609 +2026-04-13 18:05:25.193697: Current learning rate: 0.00386 +2026-04-13 18:07:07.394777: train_loss -0.4319 +2026-04-13 18:07:07.401998: val_loss -0.3747 +2026-04-13 18:07:07.404267: Pseudo dice [0.4472, 0.0, 0.7524, 0.3436, 0.4123, 0.5677, 0.9142] +2026-04-13 18:07:07.406730: Epoch time: 102.21 s +2026-04-13 18:07:08.691410: +2026-04-13 18:07:08.693282: Epoch 2610 +2026-04-13 18:07:08.694943: Current learning rate: 0.00386 +2026-04-13 18:08:51.057469: train_loss -0.4263 +2026-04-13 18:08:51.067214: val_loss -0.3697 +2026-04-13 18:08:51.069537: Pseudo dice [0.5221, 0.0, 0.7552, 0.5824, 0.5397, 0.7701, 0.8897] +2026-04-13 18:08:51.071788: Epoch time: 102.37 s +2026-04-13 18:08:53.422814: +2026-04-13 18:08:53.424592: Epoch 2611 +2026-04-13 18:08:53.426864: Current learning rate: 0.00386 +2026-04-13 18:10:35.313188: train_loss -0.4229 +2026-04-13 18:10:35.319700: val_loss -0.3865 +2026-04-13 18:10:35.322273: Pseudo dice [0.4926, 0.0, 0.8377, 0.8214, 0.503, 0.4182, 0.8173] +2026-04-13 18:10:35.324948: Epoch time: 101.89 s +2026-04-13 18:10:36.619237: +2026-04-13 18:10:36.621130: Epoch 2612 +2026-04-13 18:10:36.624463: Current learning rate: 0.00386 +2026-04-13 18:12:18.311303: train_loss -0.4208 +2026-04-13 18:12:18.318696: val_loss -0.3596 +2026-04-13 18:12:18.320977: Pseudo dice [0.5996, 0.0, 0.8112, 0.5194, 0.547, 0.7021, 0.7276] +2026-04-13 18:12:18.323777: Epoch time: 101.7 s +2026-04-13 18:12:19.545923: +2026-04-13 18:12:19.548231: Epoch 2613 +2026-04-13 18:12:19.549939: Current learning rate: 0.00385 +2026-04-13 18:14:00.913582: train_loss -0.4329 +2026-04-13 18:14:00.922814: val_loss -0.3206 +2026-04-13 18:14:00.924856: Pseudo dice [0.4602, 0.0, 0.832, 0.2307, 0.4023, 0.3227, 0.2204] +2026-04-13 18:14:00.930122: Epoch time: 101.37 s +2026-04-13 18:14:02.158043: +2026-04-13 18:14:02.159775: Epoch 2614 +2026-04-13 18:14:02.161546: Current learning rate: 0.00385 +2026-04-13 18:15:44.578786: train_loss -0.4231 +2026-04-13 18:15:44.586853: val_loss -0.376 +2026-04-13 18:15:44.589686: Pseudo dice [0.4319, 0.0, 0.7786, 0.6422, 0.4617, 0.6655, 0.8996] +2026-04-13 18:15:44.592417: Epoch time: 102.42 s +2026-04-13 18:15:45.814844: +2026-04-13 18:15:45.824563: Epoch 2615 +2026-04-13 18:15:45.827009: Current learning rate: 0.00385 +2026-04-13 18:17:27.570735: train_loss -0.4201 +2026-04-13 18:17:27.576343: val_loss -0.3323 +2026-04-13 18:17:27.578838: Pseudo dice [0.4738, 0.0, 0.6814, 0.637, 0.3719, 0.7218, 0.6438] +2026-04-13 18:17:27.581300: Epoch time: 101.76 s +2026-04-13 18:17:28.844018: +2026-04-13 18:17:28.847852: Epoch 2616 +2026-04-13 18:17:28.851566: Current learning rate: 0.00385 +2026-04-13 18:19:10.603839: train_loss -0.4179 +2026-04-13 18:19:10.614201: val_loss -0.3359 +2026-04-13 18:19:10.617361: Pseudo dice [0.3435, 0.0, 0.7233, 0.6732, 0.5949, 0.4091, 0.571] +2026-04-13 18:19:10.620248: Epoch time: 101.76 s +2026-04-13 18:19:11.850972: +2026-04-13 18:19:11.853086: Epoch 2617 +2026-04-13 18:19:11.854743: Current learning rate: 0.00384 +2026-04-13 18:20:53.578275: train_loss -0.4001 +2026-04-13 18:20:53.587275: val_loss -0.3744 +2026-04-13 18:20:53.590573: Pseudo dice [0.7, 0.0, 0.6528, 0.6969, 0.3676, 0.6436, 0.8645] +2026-04-13 18:20:53.593175: Epoch time: 101.73 s +2026-04-13 18:20:54.819186: +2026-04-13 18:20:54.821282: Epoch 2618 +2026-04-13 18:20:54.822870: Current learning rate: 0.00384 +2026-04-13 18:22:36.459243: train_loss -0.4182 +2026-04-13 18:22:36.466677: val_loss -0.3487 +2026-04-13 18:22:36.468568: Pseudo dice [0.7135, 0.0, 0.7522, 0.1049, 0.4368, 0.552, 0.2662] +2026-04-13 18:22:36.471693: Epoch time: 101.64 s +2026-04-13 18:22:37.707222: +2026-04-13 18:22:37.709976: Epoch 2619 +2026-04-13 18:22:37.712043: Current learning rate: 0.00384 +2026-04-13 18:24:19.812862: train_loss -0.4296 +2026-04-13 18:24:19.819778: val_loss -0.3597 +2026-04-13 18:24:19.822186: Pseudo dice [0.42, 0.0, 0.6613, 0.6826, 0.6014, 0.7528, 0.7972] +2026-04-13 18:24:19.825265: Epoch time: 102.11 s +2026-04-13 18:24:21.100749: +2026-04-13 18:24:21.102693: Epoch 2620 +2026-04-13 18:24:21.104607: Current learning rate: 0.00384 +2026-04-13 18:26:02.557160: train_loss -0.4234 +2026-04-13 18:26:02.563335: val_loss -0.3361 +2026-04-13 18:26:02.565584: Pseudo dice [0.5576, 0.0, 0.7325, 0.7597, 0.5051, 0.3643, 0.8272] +2026-04-13 18:26:02.567721: Epoch time: 101.46 s +2026-04-13 18:26:03.799989: +2026-04-13 18:26:03.803549: Epoch 2621 +2026-04-13 18:26:03.805226: Current learning rate: 0.00383 +2026-04-13 18:27:45.540269: train_loss -0.397 +2026-04-13 18:27:45.547627: val_loss -0.3785 +2026-04-13 18:27:45.551012: Pseudo dice [0.5609, 0.0, 0.7396, 0.6998, 0.43, 0.7735, 0.8046] +2026-04-13 18:27:45.554416: Epoch time: 101.74 s +2026-04-13 18:27:46.778747: +2026-04-13 18:27:46.780898: Epoch 2622 +2026-04-13 18:27:46.782943: Current learning rate: 0.00383 +2026-04-13 18:29:28.329028: train_loss -0.4013 +2026-04-13 18:29:28.348391: val_loss -0.3651 +2026-04-13 18:29:28.351836: Pseudo dice [0.6343, 0.0, 0.6112, 0.6148, 0.2697, 0.8776, 0.9475] +2026-04-13 18:29:28.357474: Epoch time: 101.55 s +2026-04-13 18:29:29.623375: +2026-04-13 18:29:29.626090: Epoch 2623 +2026-04-13 18:29:29.627796: Current learning rate: 0.00383 +2026-04-13 18:31:11.828509: train_loss -0.4274 +2026-04-13 18:31:11.835803: val_loss -0.3559 +2026-04-13 18:31:11.839154: Pseudo dice [0.4795, 0.0, 0.7544, 0.6683, 0.5732, 0.2758, 0.6549] +2026-04-13 18:31:11.841835: Epoch time: 102.21 s +2026-04-13 18:31:13.051051: +2026-04-13 18:31:13.053637: Epoch 2624 +2026-04-13 18:31:13.055879: Current learning rate: 0.00383 +2026-04-13 18:32:54.556298: train_loss -0.421 +2026-04-13 18:32:54.562408: val_loss -0.3754 +2026-04-13 18:32:54.564419: Pseudo dice [0.7259, 0.0, 0.7823, 0.7925, 0.4749, 0.7054, 0.8944] +2026-04-13 18:32:54.566812: Epoch time: 101.51 s +2026-04-13 18:32:55.786359: +2026-04-13 18:32:55.788985: Epoch 2625 +2026-04-13 18:32:55.790915: Current learning rate: 0.00382 +2026-04-13 18:34:37.313895: train_loss -0.4332 +2026-04-13 18:34:37.320774: val_loss -0.3709 +2026-04-13 18:34:37.323213: Pseudo dice [0.1682, 0.0, 0.7971, 0.8333, 0.5573, 0.59, 0.9159] +2026-04-13 18:34:37.326980: Epoch time: 101.53 s +2026-04-13 18:34:38.599732: +2026-04-13 18:34:38.601671: Epoch 2626 +2026-04-13 18:34:38.603318: Current learning rate: 0.00382 +2026-04-13 18:36:20.720399: train_loss -0.4213 +2026-04-13 18:36:20.726410: val_loss -0.3711 +2026-04-13 18:36:20.728392: Pseudo dice [0.7382, 0.0, 0.7836, 0.5514, 0.422, 0.8758, 0.8467] +2026-04-13 18:36:20.730547: Epoch time: 102.12 s +2026-04-13 18:36:21.999201: +2026-04-13 18:36:22.001188: Epoch 2627 +2026-04-13 18:36:22.008434: Current learning rate: 0.00382 +2026-04-13 18:38:03.841240: train_loss -0.4111 +2026-04-13 18:38:03.852478: val_loss -0.3995 +2026-04-13 18:38:03.855220: Pseudo dice [0.6842, 0.0, 0.8338, 0.3706, 0.5447, 0.8232, 0.8018] +2026-04-13 18:38:03.857611: Epoch time: 101.85 s +2026-04-13 18:38:05.080319: +2026-04-13 18:38:05.082537: Epoch 2628 +2026-04-13 18:38:05.084354: Current learning rate: 0.00382 +2026-04-13 18:39:46.775620: train_loss -0.4168 +2026-04-13 18:39:46.784793: val_loss -0.3803 +2026-04-13 18:39:46.787364: Pseudo dice [0.5126, 0.0, 0.8637, 0.723, 0.6226, 0.5938, 0.6718] +2026-04-13 18:39:46.790476: Epoch time: 101.7 s +2026-04-13 18:39:48.021474: +2026-04-13 18:39:48.023497: Epoch 2629 +2026-04-13 18:39:48.025613: Current learning rate: 0.00381 +2026-04-13 18:41:30.487864: train_loss -0.4272 +2026-04-13 18:41:30.496902: val_loss -0.3701 +2026-04-13 18:41:30.504465: Pseudo dice [0.8165, 0.0, 0.8446, 0.7393, 0.4736, 0.794, 0.9038] +2026-04-13 18:41:30.507564: Epoch time: 102.47 s +2026-04-13 18:41:31.780634: +2026-04-13 18:41:31.784210: Epoch 2630 +2026-04-13 18:41:31.787210: Current learning rate: 0.00381 +2026-04-13 18:43:14.086223: train_loss -0.415 +2026-04-13 18:43:14.093461: val_loss -0.3554 +2026-04-13 18:43:14.095936: Pseudo dice [0.6722, 0.0, 0.6392, 0.5962, 0.3499, 0.8596, 0.2975] +2026-04-13 18:43:14.098524: Epoch time: 102.31 s +2026-04-13 18:43:15.326520: +2026-04-13 18:43:15.329176: Epoch 2631 +2026-04-13 18:43:15.330828: Current learning rate: 0.00381 +2026-04-13 18:44:57.866464: train_loss -0.4414 +2026-04-13 18:44:57.876048: val_loss -0.3497 +2026-04-13 18:44:57.878542: Pseudo dice [0.5172, 0.0, 0.8246, 0.0275, 0.3075, 0.6368, 0.714] +2026-04-13 18:44:57.881300: Epoch time: 102.54 s +2026-04-13 18:44:59.130536: +2026-04-13 18:44:59.132945: Epoch 2632 +2026-04-13 18:44:59.135403: Current learning rate: 0.00381 +2026-04-13 18:46:41.410700: train_loss -0.431 +2026-04-13 18:46:41.417074: val_loss -0.3603 +2026-04-13 18:46:41.418842: Pseudo dice [0.8171, 0.0, 0.7096, 0.5502, 0.5206, 0.4299, 0.7881] +2026-04-13 18:46:41.421313: Epoch time: 102.28 s +2026-04-13 18:46:42.637862: +2026-04-13 18:46:42.640228: Epoch 2633 +2026-04-13 18:46:42.642084: Current learning rate: 0.0038 +2026-04-13 18:48:24.695726: train_loss -0.4224 +2026-04-13 18:48:24.702418: val_loss -0.3702 +2026-04-13 18:48:24.704915: Pseudo dice [0.3283, 0.0, 0.8524, 0.4297, 0.6032, 0.6342, 0.8726] +2026-04-13 18:48:24.707164: Epoch time: 102.06 s +2026-04-13 18:48:25.921578: +2026-04-13 18:48:25.924807: Epoch 2634 +2026-04-13 18:48:25.926777: Current learning rate: 0.0038 +2026-04-13 18:50:08.658516: train_loss -0.4185 +2026-04-13 18:50:08.666584: val_loss -0.3354 +2026-04-13 18:50:08.668597: Pseudo dice [0.4275, 0.0, 0.7021, 0.4209, 0.4019, 0.5236, 0.8506] +2026-04-13 18:50:08.671159: Epoch time: 102.74 s +2026-04-13 18:50:09.885008: +2026-04-13 18:50:09.887938: Epoch 2635 +2026-04-13 18:50:09.890915: Current learning rate: 0.0038 +2026-04-13 18:51:51.386633: train_loss -0.4166 +2026-04-13 18:51:51.393224: val_loss -0.3582 +2026-04-13 18:51:51.397210: Pseudo dice [0.6105, 0.0, 0.4424, 0.1645, 0.4297, 0.8953, 0.8921] +2026-04-13 18:51:51.399997: Epoch time: 101.5 s +2026-04-13 18:51:52.643946: +2026-04-13 18:51:52.645922: Epoch 2636 +2026-04-13 18:51:52.647956: Current learning rate: 0.0038 +2026-04-13 18:53:34.657209: train_loss -0.4135 +2026-04-13 18:53:34.664526: val_loss -0.3834 +2026-04-13 18:53:34.666773: Pseudo dice [0.6872, 0.0, 0.8829, 0.6706, 0.403, 0.6638, 0.8855] +2026-04-13 18:53:34.669778: Epoch time: 102.02 s +2026-04-13 18:53:35.987562: +2026-04-13 18:53:35.989654: Epoch 2637 +2026-04-13 18:53:35.991656: Current learning rate: 0.00379 +2026-04-13 18:55:17.638099: train_loss -0.4034 +2026-04-13 18:55:17.645576: val_loss -0.3525 +2026-04-13 18:55:17.647439: Pseudo dice [0.5132, 0.0, 0.6661, 0.1252, 0.3514, 0.5599, 0.8652] +2026-04-13 18:55:17.650020: Epoch time: 101.65 s +2026-04-13 18:55:18.911111: +2026-04-13 18:55:18.913141: Epoch 2638 +2026-04-13 18:55:18.915071: Current learning rate: 0.00379 +2026-04-13 18:57:00.610803: train_loss -0.4104 +2026-04-13 18:57:00.617290: val_loss -0.3707 +2026-04-13 18:57:00.623448: Pseudo dice [0.7934, 0.0, 0.6266, 0.9032, 0.5957, 0.4249, 0.8562] +2026-04-13 18:57:00.626098: Epoch time: 101.7 s +2026-04-13 18:57:01.838564: +2026-04-13 18:57:01.840748: Epoch 2639 +2026-04-13 18:57:01.842708: Current learning rate: 0.00379 +2026-04-13 18:58:43.310880: train_loss -0.4108 +2026-04-13 18:58:43.319910: val_loss -0.3503 +2026-04-13 18:58:43.322633: Pseudo dice [0.7441, 0.0, 0.6773, 0.4534, 0.4031, 0.619, 0.6404] +2026-04-13 18:58:43.326126: Epoch time: 101.48 s +2026-04-13 18:58:44.564705: +2026-04-13 18:58:44.566662: Epoch 2640 +2026-04-13 18:58:44.568534: Current learning rate: 0.00379 +2026-04-13 19:00:25.963177: train_loss -0.4268 +2026-04-13 19:00:25.969642: val_loss -0.3536 +2026-04-13 19:00:25.972276: Pseudo dice [0.524, 0.0, 0.5919, 0.1657, 0.3941, 0.7557, 0.8706] +2026-04-13 19:00:25.976802: Epoch time: 101.4 s +2026-04-13 19:00:27.185905: +2026-04-13 19:00:27.187984: Epoch 2641 +2026-04-13 19:00:27.191080: Current learning rate: 0.00378 +2026-04-13 19:02:09.161126: train_loss -0.4322 +2026-04-13 19:02:09.168386: val_loss -0.3414 +2026-04-13 19:02:09.171112: Pseudo dice [0.3436, 0.0, 0.8328, 0.2273, 0.4682, 0.6641, 0.8658] +2026-04-13 19:02:09.173905: Epoch time: 101.98 s +2026-04-13 19:02:10.394162: +2026-04-13 19:02:10.396188: Epoch 2642 +2026-04-13 19:02:10.397847: Current learning rate: 0.00378 +2026-04-13 19:03:51.804533: train_loss -0.4089 +2026-04-13 19:03:51.811723: val_loss -0.3351 +2026-04-13 19:03:51.814731: Pseudo dice [0.3862, 0.0, 0.6595, 0.5714, 0.2823, 0.4972, 0.5978] +2026-04-13 19:03:51.817218: Epoch time: 101.41 s +2026-04-13 19:03:53.036753: +2026-04-13 19:03:53.038544: Epoch 2643 +2026-04-13 19:03:53.040182: Current learning rate: 0.00378 +2026-04-13 19:05:34.596687: train_loss -0.4012 +2026-04-13 19:05:34.604032: val_loss -0.3236 +2026-04-13 19:05:34.606218: Pseudo dice [0.8344, 0.0, 0.7912, 0.0646, 0.5154, 0.6943, 0.3221] +2026-04-13 19:05:34.608546: Epoch time: 101.56 s +2026-04-13 19:05:35.834462: +2026-04-13 19:05:35.836327: Epoch 2644 +2026-04-13 19:05:35.838190: Current learning rate: 0.00378 +2026-04-13 19:07:17.784129: train_loss -0.4205 +2026-04-13 19:07:17.804113: val_loss -0.3434 +2026-04-13 19:07:17.807204: Pseudo dice [0.352, 0.0, 0.8249, 0.844, 0.4267, 0.4995, 0.6319] +2026-04-13 19:07:17.811776: Epoch time: 101.95 s +2026-04-13 19:07:19.051348: +2026-04-13 19:07:19.054711: Epoch 2645 +2026-04-13 19:07:19.057014: Current learning rate: 0.00377 +2026-04-13 19:09:00.652589: train_loss -0.4121 +2026-04-13 19:09:00.658844: val_loss -0.372 +2026-04-13 19:09:00.661222: Pseudo dice [0.6403, 0.0, 0.6494, 0.6941, 0.411, 0.64, 0.9237] +2026-04-13 19:09:00.663522: Epoch time: 101.6 s +2026-04-13 19:09:01.930485: +2026-04-13 19:09:01.932429: Epoch 2646 +2026-04-13 19:09:01.934358: Current learning rate: 0.00377 +2026-04-13 19:10:43.642213: train_loss -0.4322 +2026-04-13 19:10:43.648383: val_loss -0.3483 +2026-04-13 19:10:43.650237: Pseudo dice [0.5731, 0.0, 0.8362, 0.3201, 0.6348, 0.4731, 0.6038] +2026-04-13 19:10:43.652643: Epoch time: 101.71 s +2026-04-13 19:10:44.912367: +2026-04-13 19:10:44.914214: Epoch 2647 +2026-04-13 19:10:44.915870: Current learning rate: 0.00377 +2026-04-13 19:12:26.283111: train_loss -0.4194 +2026-04-13 19:12:26.292244: val_loss -0.3584 +2026-04-13 19:12:26.294910: Pseudo dice [0.6483, 0.0, 0.7344, 0.4264, 0.5298, 0.6357, 0.8908] +2026-04-13 19:12:26.297649: Epoch time: 101.37 s +2026-04-13 19:12:27.530693: +2026-04-13 19:12:27.532833: Epoch 2648 +2026-04-13 19:12:27.534501: Current learning rate: 0.00377 +2026-04-13 19:14:09.310140: train_loss -0.4175 +2026-04-13 19:14:09.316126: val_loss -0.3824 +2026-04-13 19:14:09.318376: Pseudo dice [0.6697, 0.0, 0.4671, 0.6684, 0.5557, 0.5102, 0.9018] +2026-04-13 19:14:09.320389: Epoch time: 101.78 s +2026-04-13 19:14:10.547101: +2026-04-13 19:14:10.549132: Epoch 2649 +2026-04-13 19:14:10.551167: Current learning rate: 0.00376 +2026-04-13 19:15:51.882677: train_loss -0.433 +2026-04-13 19:15:51.889046: val_loss -0.3852 +2026-04-13 19:15:51.891660: Pseudo dice [0.7247, 0.0, 0.7963, 0.3585, 0.6557, 0.8696, 0.8364] +2026-04-13 19:15:51.894565: Epoch time: 101.34 s +2026-04-13 19:15:55.092925: +2026-04-13 19:15:55.094810: Epoch 2650 +2026-04-13 19:15:55.096546: Current learning rate: 0.00376 +2026-04-13 19:17:37.002216: train_loss -0.4345 +2026-04-13 19:17:37.015490: val_loss -0.3928 +2026-04-13 19:17:37.018447: Pseudo dice [0.607, 0.0, 0.7971, 0.5237, 0.5624, 0.5753, 0.9294] +2026-04-13 19:17:37.021163: Epoch time: 101.91 s +2026-04-13 19:17:38.252504: +2026-04-13 19:17:38.254947: Epoch 2651 +2026-04-13 19:17:38.256802: Current learning rate: 0.00376 +2026-04-13 19:19:21.381319: train_loss -0.4331 +2026-04-13 19:19:21.388466: val_loss -0.3525 +2026-04-13 19:19:21.390543: Pseudo dice [0.5798, 0.0, 0.8036, 0.0948, 0.2697, 0.4029, 0.7785] +2026-04-13 19:19:21.393505: Epoch time: 103.13 s +2026-04-13 19:19:22.678092: +2026-04-13 19:19:22.680009: Epoch 2652 +2026-04-13 19:19:22.681895: Current learning rate: 0.00376 +2026-04-13 19:21:04.138802: train_loss -0.4151 +2026-04-13 19:21:04.146747: val_loss -0.3169 +2026-04-13 19:21:04.148736: Pseudo dice [0.8178, 0.0, 0.7249, 0.2651, 0.3843, 0.2753, 0.1884] +2026-04-13 19:21:04.152051: Epoch time: 101.46 s +2026-04-13 19:21:05.355621: +2026-04-13 19:21:05.357491: Epoch 2653 +2026-04-13 19:21:05.359138: Current learning rate: 0.00375 +2026-04-13 19:22:47.369720: train_loss -0.4172 +2026-04-13 19:22:47.376108: val_loss -0.3773 +2026-04-13 19:22:47.378255: Pseudo dice [0.8122, 0.0, 0.7975, 0.4237, 0.5832, 0.8984, 0.7737] +2026-04-13 19:22:47.380485: Epoch time: 102.02 s +2026-04-13 19:22:48.613378: +2026-04-13 19:22:48.615443: Epoch 2654 +2026-04-13 19:22:48.617081: Current learning rate: 0.00375 +2026-04-13 19:24:30.754106: train_loss -0.4301 +2026-04-13 19:24:30.761062: val_loss -0.3967 +2026-04-13 19:24:30.763511: Pseudo dice [0.7157, 0.0, 0.8251, 0.8107, 0.421, 0.673, 0.92] +2026-04-13 19:24:30.765826: Epoch time: 102.14 s +2026-04-13 19:24:32.020170: +2026-04-13 19:24:32.022041: Epoch 2655 +2026-04-13 19:24:32.023789: Current learning rate: 0.00375 +2026-04-13 19:26:13.470444: train_loss -0.4322 +2026-04-13 19:26:13.476171: val_loss -0.3816 +2026-04-13 19:26:13.478813: Pseudo dice [0.6957, 0.0, 0.8028, 0.8055, 0.519, 0.5993, 0.8553] +2026-04-13 19:26:13.481228: Epoch time: 101.45 s +2026-04-13 19:26:14.778563: +2026-04-13 19:26:14.780279: Epoch 2656 +2026-04-13 19:26:14.782170: Current learning rate: 0.00375 +2026-04-13 19:27:56.685802: train_loss -0.429 +2026-04-13 19:27:56.693089: val_loss -0.3606 +2026-04-13 19:27:56.696455: Pseudo dice [0.6563, 0.0, 0.8597, 0.541, 0.5963, 0.5038, 0.8584] +2026-04-13 19:27:56.698756: Epoch time: 101.91 s +2026-04-13 19:27:57.960145: +2026-04-13 19:27:57.962252: Epoch 2657 +2026-04-13 19:27:57.964276: Current learning rate: 0.00374 +2026-04-13 19:29:39.368196: train_loss -0.4256 +2026-04-13 19:29:39.375865: val_loss -0.3978 +2026-04-13 19:29:39.378024: Pseudo dice [0.7858, 0.0, 0.7995, 0.5737, 0.5906, 0.5265, 0.705] +2026-04-13 19:29:39.380476: Epoch time: 101.41 s +2026-04-13 19:29:40.595869: +2026-04-13 19:29:40.597580: Epoch 2658 +2026-04-13 19:29:40.599192: Current learning rate: 0.00374 +2026-04-13 19:31:22.049082: train_loss -0.4239 +2026-04-13 19:31:22.056121: val_loss -0.3021 +2026-04-13 19:31:22.059124: Pseudo dice [0.2713, 0.0, 0.6945, 0.0188, 0.4636, 0.7532, 0.811] +2026-04-13 19:31:22.062389: Epoch time: 101.46 s +2026-04-13 19:31:23.293739: +2026-04-13 19:31:23.295742: Epoch 2659 +2026-04-13 19:31:23.297462: Current learning rate: 0.00374 +2026-04-13 19:33:05.264578: train_loss -0.4133 +2026-04-13 19:33:05.272434: val_loss -0.3831 +2026-04-13 19:33:05.274611: Pseudo dice [0.5677, 0.0, 0.7703, 0.7121, 0.4792, 0.7895, 0.8777] +2026-04-13 19:33:05.277341: Epoch time: 101.97 s +2026-04-13 19:33:06.546232: +2026-04-13 19:33:06.548653: Epoch 2660 +2026-04-13 19:33:06.550288: Current learning rate: 0.00374 +2026-04-13 19:34:48.380209: train_loss -0.4364 +2026-04-13 19:34:48.386740: val_loss -0.4152 +2026-04-13 19:34:48.388938: Pseudo dice [0.6747, 0.0, 0.8113, 0.7377, 0.4382, 0.6553, 0.9147] +2026-04-13 19:34:48.391414: Epoch time: 101.84 s +2026-04-13 19:34:49.656331: +2026-04-13 19:34:49.658425: Epoch 2661 +2026-04-13 19:34:49.660275: Current learning rate: 0.00373 +2026-04-13 19:36:31.390251: train_loss -0.4368 +2026-04-13 19:36:31.397384: val_loss -0.396 +2026-04-13 19:36:31.399576: Pseudo dice [0.4276, 0.0, 0.8377, 0.7018, 0.5238, 0.7437, 0.9238] +2026-04-13 19:36:31.402012: Epoch time: 101.74 s +2026-04-13 19:36:32.647354: +2026-04-13 19:36:32.649750: Epoch 2662 +2026-04-13 19:36:32.651865: Current learning rate: 0.00373 +2026-04-13 19:38:14.986297: train_loss -0.4436 +2026-04-13 19:38:14.994998: val_loss -0.3493 +2026-04-13 19:38:14.999025: Pseudo dice [0.6672, 0.0, 0.7436, 0.113, 0.5704, 0.5574, 0.8406] +2026-04-13 19:38:15.002005: Epoch time: 102.34 s +2026-04-13 19:38:16.247358: +2026-04-13 19:38:16.249402: Epoch 2663 +2026-04-13 19:38:16.251207: Current learning rate: 0.00373 +2026-04-13 19:39:57.999733: train_loss -0.3947 +2026-04-13 19:39:58.008098: val_loss -0.3581 +2026-04-13 19:39:58.010325: Pseudo dice [0.6478, 0.0, 0.7453, 0.4963, 0.4224, 0.4644, 0.9297] +2026-04-13 19:39:58.013460: Epoch time: 101.76 s +2026-04-13 19:39:59.289727: +2026-04-13 19:39:59.291845: Epoch 2664 +2026-04-13 19:39:59.294563: Current learning rate: 0.00373 +2026-04-13 19:41:40.729051: train_loss -0.3744 +2026-04-13 19:41:40.735459: val_loss -0.33 +2026-04-13 19:41:40.737352: Pseudo dice [0.0097, 0.0, 0.7127, 0.4331, 0.1667, 0.2641, 0.7925] +2026-04-13 19:41:40.739718: Epoch time: 101.44 s +2026-04-13 19:41:41.951472: +2026-04-13 19:41:41.953129: Epoch 2665 +2026-04-13 19:41:41.954630: Current learning rate: 0.00372 +2026-04-13 19:43:24.342987: train_loss -0.4227 +2026-04-13 19:43:24.351501: val_loss -0.3634 +2026-04-13 19:43:24.354349: Pseudo dice [0.7517, 0.0, 0.812, 0.2539, 0.3777, 0.5467, 0.8095] +2026-04-13 19:43:24.357097: Epoch time: 102.39 s +2026-04-13 19:43:25.580288: +2026-04-13 19:43:25.583286: Epoch 2666 +2026-04-13 19:43:25.585631: Current learning rate: 0.00372 +2026-04-13 19:45:06.980256: train_loss -0.4228 +2026-04-13 19:45:06.988978: val_loss -0.3503 +2026-04-13 19:45:06.992012: Pseudo dice [0.3409, 0.0, 0.8043, 0.0199, 0.5318, 0.6503, 0.7787] +2026-04-13 19:45:06.994848: Epoch time: 101.4 s +2026-04-13 19:45:08.215949: +2026-04-13 19:45:08.219522: Epoch 2667 +2026-04-13 19:45:08.224535: Current learning rate: 0.00372 +2026-04-13 19:46:49.581116: train_loss -0.4246 +2026-04-13 19:46:49.586853: val_loss -0.3509 +2026-04-13 19:46:49.589217: Pseudo dice [0.618, 0.0, 0.6566, 0.4072, 0.3545, 0.39, 0.8338] +2026-04-13 19:46:49.591368: Epoch time: 101.37 s +2026-04-13 19:46:50.812250: +2026-04-13 19:46:50.813986: Epoch 2668 +2026-04-13 19:46:50.815917: Current learning rate: 0.00372 +2026-04-13 19:48:32.732905: train_loss -0.4228 +2026-04-13 19:48:32.742991: val_loss -0.3731 +2026-04-13 19:48:32.745728: Pseudo dice [0.4996, 0.0, 0.6074, 0.498, 0.5424, 0.4904, 0.7927] +2026-04-13 19:48:32.748842: Epoch time: 101.92 s +2026-04-13 19:48:33.981812: +2026-04-13 19:48:33.983638: Epoch 2669 +2026-04-13 19:48:33.985262: Current learning rate: 0.00371 +2026-04-13 19:50:16.146705: train_loss -0.4195 +2026-04-13 19:50:16.153594: val_loss -0.3768 +2026-04-13 19:50:16.155840: Pseudo dice [0.5641, 0.0, 0.842, 0.8083, 0.4381, 0.4349, 0.8355] +2026-04-13 19:50:16.158210: Epoch time: 102.17 s +2026-04-13 19:50:17.418101: +2026-04-13 19:50:17.420289: Epoch 2670 +2026-04-13 19:50:17.421818: Current learning rate: 0.00371 +2026-04-13 19:51:58.821806: train_loss -0.4299 +2026-04-13 19:51:58.828195: val_loss -0.3588 +2026-04-13 19:51:58.830388: Pseudo dice [0.3334, 0.0, 0.7709, 0.5208, 0.4896, 0.5735, 0.8585] +2026-04-13 19:51:58.834552: Epoch time: 101.41 s +2026-04-13 19:52:00.066297: +2026-04-13 19:52:00.068072: Epoch 2671 +2026-04-13 19:52:00.069620: Current learning rate: 0.00371 +2026-04-13 19:53:43.137459: train_loss -0.4113 +2026-04-13 19:53:43.144342: val_loss -0.3881 +2026-04-13 19:53:43.147157: Pseudo dice [0.7001, 0.0, 0.7519, 0.7381, 0.5546, 0.6585, 0.9401] +2026-04-13 19:53:43.150404: Epoch time: 103.07 s +2026-04-13 19:53:44.363114: +2026-04-13 19:53:44.365765: Epoch 2672 +2026-04-13 19:53:44.367460: Current learning rate: 0.00371 +2026-04-13 19:55:26.003323: train_loss -0.4035 +2026-04-13 19:55:26.013225: val_loss -0.3743 +2026-04-13 19:55:26.015843: Pseudo dice [0.3613, 0.0, 0.8055, 0.7313, 0.6401, 0.791, 0.8312] +2026-04-13 19:55:26.018128: Epoch time: 101.64 s +2026-04-13 19:55:27.286220: +2026-04-13 19:55:27.288469: Epoch 2673 +2026-04-13 19:55:27.290273: Current learning rate: 0.0037 +2026-04-13 19:57:08.855918: train_loss -0.4237 +2026-04-13 19:57:08.862980: val_loss -0.3692 +2026-04-13 19:57:08.865573: Pseudo dice [0.5812, 0.0, 0.744, 0.7434, 0.5869, 0.795, 0.8896] +2026-04-13 19:57:08.867890: Epoch time: 101.57 s +2026-04-13 19:57:10.101506: +2026-04-13 19:57:10.103258: Epoch 2674 +2026-04-13 19:57:10.104736: Current learning rate: 0.0037 +2026-04-13 19:58:51.574240: train_loss -0.4203 +2026-04-13 19:58:51.580968: val_loss -0.3725 +2026-04-13 19:58:51.583042: Pseudo dice [0.6743, 0.0, 0.7012, 0.6267, 0.5755, 0.7214, 0.3661] +2026-04-13 19:58:51.585580: Epoch time: 101.48 s +2026-04-13 19:58:52.838708: +2026-04-13 19:58:52.841375: Epoch 2675 +2026-04-13 19:58:52.844578: Current learning rate: 0.0037 +2026-04-13 20:00:34.714996: train_loss -0.4141 +2026-04-13 20:00:34.722100: val_loss -0.3537 +2026-04-13 20:00:34.723758: Pseudo dice [0.752, 0.0, 0.604, 0.6949, 0.3183, 0.4138, 0.3266] +2026-04-13 20:00:34.727173: Epoch time: 101.88 s +2026-04-13 20:00:35.987903: +2026-04-13 20:00:35.990188: Epoch 2676 +2026-04-13 20:00:35.992045: Current learning rate: 0.0037 +2026-04-13 20:02:17.600410: train_loss -0.4332 +2026-04-13 20:02:17.606647: val_loss -0.3823 +2026-04-13 20:02:17.608505: Pseudo dice [0.4542, 0.0, 0.7518, 0.7737, 0.5528, 0.6558, 0.3964] +2026-04-13 20:02:17.611080: Epoch time: 101.62 s +2026-04-13 20:02:18.852967: +2026-04-13 20:02:18.854913: Epoch 2677 +2026-04-13 20:02:18.856824: Current learning rate: 0.00369 +2026-04-13 20:04:00.214221: train_loss -0.429 +2026-04-13 20:04:00.220527: val_loss -0.3424 +2026-04-13 20:04:00.222449: Pseudo dice [0.3341, 0.0, 0.4704, 0.4424, 0.4868, 0.6686, 0.929] +2026-04-13 20:04:00.224618: Epoch time: 101.36 s +2026-04-13 20:04:01.528937: +2026-04-13 20:04:01.530911: Epoch 2678 +2026-04-13 20:04:01.532603: Current learning rate: 0.00369 +2026-04-13 20:05:43.186045: train_loss -0.4168 +2026-04-13 20:05:43.192654: val_loss -0.3425 +2026-04-13 20:05:43.194787: Pseudo dice [0.5291, 0.0, 0.776, 0.5667, 0.4264, 0.5395, 0.267] +2026-04-13 20:05:43.197129: Epoch time: 101.66 s +2026-04-13 20:05:44.451826: +2026-04-13 20:05:44.453603: Epoch 2679 +2026-04-13 20:05:44.455199: Current learning rate: 0.00369 +2026-04-13 20:07:26.171955: train_loss -0.4102 +2026-04-13 20:07:26.178927: val_loss -0.3487 +2026-04-13 20:07:26.180655: Pseudo dice [0.5271, 0.0, 0.4895, 0.2885, 0.54, 0.5998, 0.682] +2026-04-13 20:07:26.183602: Epoch time: 101.72 s +2026-04-13 20:07:27.399544: +2026-04-13 20:07:27.401526: Epoch 2680 +2026-04-13 20:07:27.403105: Current learning rate: 0.00369 +2026-04-13 20:09:09.335846: train_loss -0.4225 +2026-04-13 20:09:09.343223: val_loss -0.391 +2026-04-13 20:09:09.345336: Pseudo dice [0.5811, 0.0, 0.8315, 0.871, 0.5861, 0.7652, 0.9001] +2026-04-13 20:09:09.347790: Epoch time: 101.94 s +2026-04-13 20:09:10.579514: +2026-04-13 20:09:10.581380: Epoch 2681 +2026-04-13 20:09:10.583011: Current learning rate: 0.00368 +2026-04-13 20:10:52.348602: train_loss -0.4333 +2026-04-13 20:10:52.354605: val_loss -0.3716 +2026-04-13 20:10:52.356588: Pseudo dice [0.4815, 0.0, 0.5007, 0.5475, 0.5611, 0.6989, 0.9126] +2026-04-13 20:10:52.358903: Epoch time: 101.77 s +2026-04-13 20:10:53.600879: +2026-04-13 20:10:53.602885: Epoch 2682 +2026-04-13 20:10:53.604488: Current learning rate: 0.00368 +2026-04-13 20:12:35.558686: train_loss -0.4357 +2026-04-13 20:12:35.566274: val_loss -0.3786 +2026-04-13 20:12:35.569137: Pseudo dice [0.6983, 0.0, 0.7717, 0.18, 0.4308, 0.7929, 0.8928] +2026-04-13 20:12:35.572066: Epoch time: 101.96 s +2026-04-13 20:12:36.801797: +2026-04-13 20:12:36.804224: Epoch 2683 +2026-04-13 20:12:36.806447: Current learning rate: 0.00368 +2026-04-13 20:14:18.182905: train_loss -0.4349 +2026-04-13 20:14:18.190499: val_loss -0.3937 +2026-04-13 20:14:18.192818: Pseudo dice [0.6505, 0.0, 0.8552, 0.7524, 0.529, 0.8316, 0.8437] +2026-04-13 20:14:18.195353: Epoch time: 101.38 s +2026-04-13 20:14:19.435896: +2026-04-13 20:14:19.437776: Epoch 2684 +2026-04-13 20:14:19.439534: Current learning rate: 0.00368 +2026-04-13 20:16:01.385909: train_loss -0.4197 +2026-04-13 20:16:01.391531: val_loss -0.3838 +2026-04-13 20:16:01.393599: Pseudo dice [0.723, 0.0, 0.7178, 0.7175, 0.6778, 0.8176, 0.9319] +2026-04-13 20:16:01.396664: Epoch time: 101.95 s +2026-04-13 20:16:02.623219: +2026-04-13 20:16:02.625365: Epoch 2685 +2026-04-13 20:16:02.627172: Current learning rate: 0.00367 +2026-04-13 20:17:44.133707: train_loss -0.4368 +2026-04-13 20:17:44.143535: val_loss -0.3359 +2026-04-13 20:17:44.146078: Pseudo dice [0.6198, 0.0, 0.6821, 0.5796, 0.5098, 0.6204, 0.914] +2026-04-13 20:17:44.148159: Epoch time: 101.51 s +2026-04-13 20:17:45.357017: +2026-04-13 20:17:45.359042: Epoch 2686 +2026-04-13 20:17:45.360871: Current learning rate: 0.00367 +2026-04-13 20:19:26.895661: train_loss -0.4414 +2026-04-13 20:19:26.901709: val_loss -0.3736 +2026-04-13 20:19:26.903803: Pseudo dice [0.6575, 0.0, 0.7756, 0.2751, 0.5508, 0.7008, 0.7704] +2026-04-13 20:19:26.906295: Epoch time: 101.54 s +2026-04-13 20:19:28.172935: +2026-04-13 20:19:28.174572: Epoch 2687 +2026-04-13 20:19:28.176138: Current learning rate: 0.00367 +2026-04-13 20:21:09.782011: train_loss -0.4295 +2026-04-13 20:21:09.788569: val_loss -0.3792 +2026-04-13 20:21:09.791107: Pseudo dice [0.7048, 0.0, 0.646, 0.1929, 0.6313, 0.5553, 0.8899] +2026-04-13 20:21:09.793756: Epoch time: 101.61 s +2026-04-13 20:21:11.075162: +2026-04-13 20:21:11.077557: Epoch 2688 +2026-04-13 20:21:11.079210: Current learning rate: 0.00367 +2026-04-13 20:22:52.487421: train_loss -0.4334 +2026-04-13 20:22:52.493768: val_loss -0.3706 +2026-04-13 20:22:52.497032: Pseudo dice [0.6692, 0.0, 0.6987, 0.6018, 0.4513, 0.6142, 0.9074] +2026-04-13 20:22:52.499557: Epoch time: 101.42 s +2026-04-13 20:22:53.739369: +2026-04-13 20:22:53.741471: Epoch 2689 +2026-04-13 20:22:53.743323: Current learning rate: 0.00366 +2026-04-13 20:24:34.905767: train_loss -0.4358 +2026-04-13 20:24:34.912829: val_loss -0.3819 +2026-04-13 20:24:34.914974: Pseudo dice [0.5882, 0.0, 0.7632, 0.4147, 0.3393, 0.7498, 0.9434] +2026-04-13 20:24:34.918689: Epoch time: 101.17 s +2026-04-13 20:24:36.174810: +2026-04-13 20:24:36.178527: Epoch 2690 +2026-04-13 20:24:36.180435: Current learning rate: 0.00366 +2026-04-13 20:26:17.453457: train_loss -0.433 +2026-04-13 20:26:17.460748: val_loss -0.3818 +2026-04-13 20:26:17.463308: Pseudo dice [0.6605, 0.0, 0.7843, 0.2545, 0.5616, 0.7562, 0.9282] +2026-04-13 20:26:17.465817: Epoch time: 101.28 s +2026-04-13 20:26:18.690658: +2026-04-13 20:26:18.692805: Epoch 2691 +2026-04-13 20:26:18.695164: Current learning rate: 0.00366 +2026-04-13 20:28:00.155951: train_loss -0.4366 +2026-04-13 20:28:00.162132: val_loss -0.3696 +2026-04-13 20:28:00.164910: Pseudo dice [0.7088, 0.0, 0.8187, 0.1992, 0.2709, 0.778, 0.9258] +2026-04-13 20:28:00.167620: Epoch time: 101.47 s +2026-04-13 20:28:02.563901: +2026-04-13 20:28:02.577041: Epoch 2692 +2026-04-13 20:28:02.579983: Current learning rate: 0.00366 +2026-04-13 20:29:44.378789: train_loss -0.4598 +2026-04-13 20:29:44.407284: val_loss -0.3625 +2026-04-13 20:29:44.409756: Pseudo dice [0.728, 0.0, 0.6935, 0.2688, 0.5634, 0.7561, 0.8476] +2026-04-13 20:29:44.412297: Epoch time: 101.82 s +2026-04-13 20:29:45.649448: +2026-04-13 20:29:45.651471: Epoch 2693 +2026-04-13 20:29:45.653304: Current learning rate: 0.00365 +2026-04-13 20:31:27.063438: train_loss -0.427 +2026-04-13 20:31:27.069007: val_loss -0.3495 +2026-04-13 20:31:27.071080: Pseudo dice [0.5948, 0.0, 0.6617, 0.4209, 0.1858, 0.4132, 0.7978] +2026-04-13 20:31:27.074067: Epoch time: 101.42 s +2026-04-13 20:31:28.304864: +2026-04-13 20:31:28.306766: Epoch 2694 +2026-04-13 20:31:28.309345: Current learning rate: 0.00365 +2026-04-13 20:33:09.752838: train_loss -0.4245 +2026-04-13 20:33:09.758913: val_loss -0.4 +2026-04-13 20:33:09.761039: Pseudo dice [0.6131, 0.0, 0.8653, 0.7201, 0.5514, 0.6407, 0.9283] +2026-04-13 20:33:09.763436: Epoch time: 101.45 s +2026-04-13 20:33:10.978323: +2026-04-13 20:33:10.980820: Epoch 2695 +2026-04-13 20:33:10.982917: Current learning rate: 0.00365 +2026-04-13 20:34:52.511468: train_loss -0.4416 +2026-04-13 20:34:52.525181: val_loss -0.3569 +2026-04-13 20:34:52.527123: Pseudo dice [0.7347, 0.0, 0.6991, 0.4968, 0.5887, 0.7613, 0.9142] +2026-04-13 20:34:52.529630: Epoch time: 101.54 s +2026-04-13 20:34:53.755071: +2026-04-13 20:34:53.756994: Epoch 2696 +2026-04-13 20:34:53.758997: Current learning rate: 0.00365 +2026-04-13 20:36:35.196635: train_loss -0.414 +2026-04-13 20:36:35.201938: val_loss -0.3941 +2026-04-13 20:36:35.204087: Pseudo dice [0.5439, 0.0, 0.7468, 0.7393, 0.4457, 0.8796, 0.9178] +2026-04-13 20:36:35.206184: Epoch time: 101.44 s +2026-04-13 20:36:36.428412: +2026-04-13 20:36:36.430690: Epoch 2697 +2026-04-13 20:36:36.433301: Current learning rate: 0.00364 +2026-04-13 20:38:17.780046: train_loss -0.4359 +2026-04-13 20:38:17.786180: val_loss -0.3911 +2026-04-13 20:38:17.788744: Pseudo dice [0.7515, 0.0, 0.8264, 0.49, 0.602, 0.604, 0.8776] +2026-04-13 20:38:17.790936: Epoch time: 101.35 s +2026-04-13 20:38:19.023205: +2026-04-13 20:38:19.025052: Epoch 2698 +2026-04-13 20:38:19.026732: Current learning rate: 0.00364 +2026-04-13 20:40:00.733103: train_loss -0.4394 +2026-04-13 20:40:00.741128: val_loss -0.3775 +2026-04-13 20:40:00.743040: Pseudo dice [0.5369, 0.0, 0.759, 0.3957, 0.6352, 0.7994, 0.8864] +2026-04-13 20:40:00.745546: Epoch time: 101.71 s +2026-04-13 20:40:01.980921: +2026-04-13 20:40:01.984565: Epoch 2699 +2026-04-13 20:40:01.986263: Current learning rate: 0.00364 +2026-04-13 20:41:44.461390: train_loss -0.4164 +2026-04-13 20:41:44.469434: val_loss -0.369 +2026-04-13 20:41:44.474365: Pseudo dice [0.6468, 0.0, 0.7589, 0.3548, 0.4536, 0.5322, 0.7079] +2026-04-13 20:41:44.479363: Epoch time: 102.48 s +2026-04-13 20:41:47.664721: +2026-04-13 20:41:47.667000: Epoch 2700 +2026-04-13 20:41:47.670385: Current learning rate: 0.00364 +2026-04-13 20:43:30.674206: train_loss -0.4219 +2026-04-13 20:43:30.680649: val_loss -0.3903 +2026-04-13 20:43:30.683367: Pseudo dice [0.5298, 0.0, 0.8033, 0.5061, 0.5923, 0.6973, 0.8382] +2026-04-13 20:43:30.686218: Epoch time: 103.01 s +2026-04-13 20:43:31.965473: +2026-04-13 20:43:31.967955: Epoch 2701 +2026-04-13 20:43:31.970384: Current learning rate: 0.00363 +2026-04-13 20:45:13.451813: train_loss -0.4146 +2026-04-13 20:45:13.457547: val_loss -0.3509 +2026-04-13 20:45:13.460439: Pseudo dice [0.2135, 0.0, 0.6168, 0.3247, 0.496, 0.6769, 0.8323] +2026-04-13 20:45:13.462710: Epoch time: 101.49 s +2026-04-13 20:45:14.700842: +2026-04-13 20:45:14.702910: Epoch 2702 +2026-04-13 20:45:14.705592: Current learning rate: 0.00363 +2026-04-13 20:46:57.246184: train_loss -0.4054 +2026-04-13 20:46:57.256037: val_loss -0.3757 +2026-04-13 20:46:57.260198: Pseudo dice [0.6955, 0.0, 0.8671, 0.6677, 0.4115, 0.2823, 0.8888] +2026-04-13 20:46:57.264863: Epoch time: 102.55 s +2026-04-13 20:46:58.472862: +2026-04-13 20:46:58.474917: Epoch 2703 +2026-04-13 20:46:58.476751: Current learning rate: 0.00363 +2026-04-13 20:48:40.378383: train_loss -0.4114 +2026-04-13 20:48:40.387307: val_loss -0.3862 +2026-04-13 20:48:40.389452: Pseudo dice [0.7072, 0.0, 0.5834, 0.7988, 0.5566, 0.7688, 0.6639] +2026-04-13 20:48:40.392200: Epoch time: 101.91 s +2026-04-13 20:48:41.614971: +2026-04-13 20:48:41.617605: Epoch 2704 +2026-04-13 20:48:41.619441: Current learning rate: 0.00363 +2026-04-13 20:50:23.556725: train_loss -0.4369 +2026-04-13 20:50:23.566783: val_loss -0.4125 +2026-04-13 20:50:23.569081: Pseudo dice [0.691, 0.0, 0.8676, 0.7412, 0.5152, 0.6198, 0.7882] +2026-04-13 20:50:23.571617: Epoch time: 101.94 s +2026-04-13 20:50:24.823286: +2026-04-13 20:50:24.825485: Epoch 2705 +2026-04-13 20:50:24.827287: Current learning rate: 0.00362 +2026-04-13 20:52:06.127314: train_loss -0.4254 +2026-04-13 20:52:06.133626: val_loss -0.3613 +2026-04-13 20:52:06.136458: Pseudo dice [0.8014, 0.0, 0.8473, 0.0797, 0.47, 0.5092, 0.6428] +2026-04-13 20:52:06.139010: Epoch time: 101.31 s +2026-04-13 20:52:07.335687: +2026-04-13 20:52:07.337925: Epoch 2706 +2026-04-13 20:52:07.339437: Current learning rate: 0.00362 +2026-04-13 20:53:49.387261: train_loss -0.4207 +2026-04-13 20:53:49.393705: val_loss -0.4024 +2026-04-13 20:53:49.396111: Pseudo dice [0.6317, 0.0, 0.7356, 0.5478, 0.5381, 0.5658, 0.8835] +2026-04-13 20:53:49.398175: Epoch time: 102.05 s +2026-04-13 20:53:50.632694: +2026-04-13 20:53:50.634386: Epoch 2707 +2026-04-13 20:53:50.636079: Current learning rate: 0.00362 +2026-04-13 20:55:32.149438: train_loss -0.4166 +2026-04-13 20:55:32.156209: val_loss -0.3699 +2026-04-13 20:55:32.158387: Pseudo dice [0.2172, 0.0, 0.6665, 0.5643, 0.6122, 0.6892, 0.8878] +2026-04-13 20:55:32.160737: Epoch time: 101.52 s +2026-04-13 20:55:33.416900: +2026-04-13 20:55:33.418926: Epoch 2708 +2026-04-13 20:55:33.420738: Current learning rate: 0.00362 +2026-04-13 20:57:14.754731: train_loss -0.4178 +2026-04-13 20:57:14.761701: val_loss -0.3746 +2026-04-13 20:57:14.763749: Pseudo dice [0.6686, 0.0, 0.867, 0.488, 0.5592, 0.5047, 0.7985] +2026-04-13 20:57:14.766354: Epoch time: 101.34 s +2026-04-13 20:57:16.005180: +2026-04-13 20:57:16.006909: Epoch 2709 +2026-04-13 20:57:16.008415: Current learning rate: 0.00361 +2026-04-13 20:58:57.605946: train_loss -0.421 +2026-04-13 20:58:57.612017: val_loss -0.3716 +2026-04-13 20:58:57.614417: Pseudo dice [0.6462, 0.0, 0.8547, 0.6717, 0.514, 0.4196, 0.6621] +2026-04-13 20:58:57.616850: Epoch time: 101.6 s +2026-04-13 20:58:58.846073: +2026-04-13 20:58:58.848066: Epoch 2710 +2026-04-13 20:58:58.850102: Current learning rate: 0.00361 +2026-04-13 21:00:40.467012: train_loss -0.4518 +2026-04-13 21:00:40.473325: val_loss -0.3764 +2026-04-13 21:00:40.475672: Pseudo dice [0.7745, 0.0, 0.795, 0.5111, 0.1855, 0.597, 0.8898] +2026-04-13 21:00:40.477782: Epoch time: 101.62 s +2026-04-13 21:00:41.695594: +2026-04-13 21:00:41.698434: Epoch 2711 +2026-04-13 21:00:41.700140: Current learning rate: 0.00361 +2026-04-13 21:02:23.110470: train_loss -0.4367 +2026-04-13 21:02:23.116850: val_loss -0.3654 +2026-04-13 21:02:23.119756: Pseudo dice [0.807, 0.0, 0.6538, 0.1119, 0.5189, 0.8627, 0.8716] +2026-04-13 21:02:23.122550: Epoch time: 101.42 s +2026-04-13 21:02:25.387839: +2026-04-13 21:02:25.390238: Epoch 2712 +2026-04-13 21:02:25.391727: Current learning rate: 0.00361 +2026-04-13 21:04:06.930794: train_loss -0.4274 +2026-04-13 21:04:06.937598: val_loss -0.352 +2026-04-13 21:04:06.939887: Pseudo dice [0.7616, 0.0, 0.7576, 0.3897, 0.3955, 0.3638, 0.6907] +2026-04-13 21:04:06.942628: Epoch time: 101.55 s +2026-04-13 21:04:08.165834: +2026-04-13 21:04:08.168283: Epoch 2713 +2026-04-13 21:04:08.171429: Current learning rate: 0.0036 +2026-04-13 21:05:49.964922: train_loss -0.4435 +2026-04-13 21:05:49.971162: val_loss -0.3924 +2026-04-13 21:05:49.973247: Pseudo dice [0.8209, 0.0, 0.8255, 0.8245, 0.3745, 0.76, 0.8379] +2026-04-13 21:05:49.975756: Epoch time: 101.8 s +2026-04-13 21:05:51.231300: +2026-04-13 21:05:51.234713: Epoch 2714 +2026-04-13 21:05:51.236853: Current learning rate: 0.0036 +2026-04-13 21:07:32.730025: train_loss -0.4494 +2026-04-13 21:07:32.735968: val_loss -0.3948 +2026-04-13 21:07:32.738081: Pseudo dice [0.6494, 0.0, 0.8357, 0.5574, 0.4944, 0.5185, 0.8868] +2026-04-13 21:07:32.740425: Epoch time: 101.5 s +2026-04-13 21:07:33.961180: +2026-04-13 21:07:33.963135: Epoch 2715 +2026-04-13 21:07:33.964812: Current learning rate: 0.0036 +2026-04-13 21:09:15.650817: train_loss -0.4381 +2026-04-13 21:09:15.658590: val_loss -0.3668 +2026-04-13 21:09:15.660727: Pseudo dice [0.7082, 0.0, 0.8733, 0.7965, 0.6367, 0.4914, 0.7057] +2026-04-13 21:09:15.663465: Epoch time: 101.69 s +2026-04-13 21:09:16.957979: +2026-04-13 21:09:16.961694: Epoch 2716 +2026-04-13 21:09:16.964670: Current learning rate: 0.0036 +2026-04-13 21:10:58.953821: train_loss -0.4165 +2026-04-13 21:10:58.960807: val_loss -0.3772 +2026-04-13 21:10:58.963392: Pseudo dice [0.5827, 0.0, 0.7259, 0.6882, 0.3269, 0.6597, 0.8614] +2026-04-13 21:10:58.966665: Epoch time: 102.0 s +2026-04-13 21:11:00.208292: +2026-04-13 21:11:00.210200: Epoch 2717 +2026-04-13 21:11:00.213263: Current learning rate: 0.00359 +2026-04-13 21:12:41.627091: train_loss -0.4398 +2026-04-13 21:12:41.634936: val_loss -0.3766 +2026-04-13 21:12:41.637282: Pseudo dice [0.6274, 0.0, 0.5548, 0.489, 0.5337, 0.6313, 0.8984] +2026-04-13 21:12:41.639434: Epoch time: 101.42 s +2026-04-13 21:12:42.888192: +2026-04-13 21:12:42.890292: Epoch 2718 +2026-04-13 21:12:42.891933: Current learning rate: 0.00359 +2026-04-13 21:14:24.707490: train_loss -0.4327 +2026-04-13 21:14:24.713584: val_loss -0.3832 +2026-04-13 21:14:24.715554: Pseudo dice [0.8314, 0.0, 0.8436, 0.849, 0.6308, 0.673, 0.8151] +2026-04-13 21:14:24.717876: Epoch time: 101.82 s +2026-04-13 21:14:25.935794: +2026-04-13 21:14:25.937875: Epoch 2719 +2026-04-13 21:14:25.939803: Current learning rate: 0.00359 +2026-04-13 21:16:07.420398: train_loss -0.4346 +2026-04-13 21:16:07.427011: val_loss -0.3359 +2026-04-13 21:16:07.428920: Pseudo dice [0.7892, 0.0, 0.8102, 0.4535, 0.268, 0.5793, 0.0786] +2026-04-13 21:16:07.431339: Epoch time: 101.49 s +2026-04-13 21:16:08.659947: +2026-04-13 21:16:08.662841: Epoch 2720 +2026-04-13 21:16:08.665405: Current learning rate: 0.00359 +2026-04-13 21:17:50.133907: train_loss -0.3985 +2026-04-13 21:17:50.141486: val_loss -0.3445 +2026-04-13 21:17:50.147685: Pseudo dice [0.7111, 0.0, 0.7315, 0.1594, 0.5154, 0.7083, 0.6899] +2026-04-13 21:17:50.151207: Epoch time: 101.48 s +2026-04-13 21:17:51.358836: +2026-04-13 21:17:51.360874: Epoch 2721 +2026-04-13 21:17:51.362457: Current learning rate: 0.00358 +2026-04-13 21:19:33.828120: train_loss -0.4357 +2026-04-13 21:19:33.837624: val_loss -0.3814 +2026-04-13 21:19:33.840586: Pseudo dice [0.7443, 0.0, 0.8381, 0.3962, 0.4704, 0.6091, 0.7226] +2026-04-13 21:19:33.844155: Epoch time: 102.47 s +2026-04-13 21:19:35.097400: +2026-04-13 21:19:35.100129: Epoch 2722 +2026-04-13 21:19:35.102735: Current learning rate: 0.00358 +2026-04-13 21:21:17.245462: train_loss -0.4275 +2026-04-13 21:21:17.252416: val_loss -0.3366 +2026-04-13 21:21:17.254960: Pseudo dice [0.765, 0.0, 0.6194, 0.132, 0.4729, 0.4063, 0.8611] +2026-04-13 21:21:17.257668: Epoch time: 102.15 s +2026-04-13 21:21:18.511754: +2026-04-13 21:21:18.513792: Epoch 2723 +2026-04-13 21:21:18.515670: Current learning rate: 0.00358 +2026-04-13 21:23:00.192222: train_loss -0.4414 +2026-04-13 21:23:00.199312: val_loss -0.4085 +2026-04-13 21:23:00.201430: Pseudo dice [0.538, 0.0, 0.7849, 0.6534, 0.4864, 0.8724, 0.8784] +2026-04-13 21:23:00.203718: Epoch time: 101.68 s +2026-04-13 21:23:01.441449: +2026-04-13 21:23:01.443491: Epoch 2724 +2026-04-13 21:23:01.445310: Current learning rate: 0.00358 +2026-04-13 21:24:43.645123: train_loss -0.4504 +2026-04-13 21:24:43.652348: val_loss -0.3575 +2026-04-13 21:24:43.654254: Pseudo dice [0.6688, 0.0, 0.7575, 0.2561, 0.3983, 0.7864, 0.8912] +2026-04-13 21:24:43.656615: Epoch time: 102.21 s +2026-04-13 21:24:44.931353: +2026-04-13 21:24:44.933368: Epoch 2725 +2026-04-13 21:24:44.935228: Current learning rate: 0.00357 +2026-04-13 21:26:27.159453: train_loss -0.4327 +2026-04-13 21:26:27.166029: val_loss -0.3877 +2026-04-13 21:26:27.168074: Pseudo dice [0.7093, 0.0, 0.8511, 0.5782, 0.5397, 0.7831, 0.9033] +2026-04-13 21:26:27.170783: Epoch time: 102.23 s +2026-04-13 21:26:28.449943: +2026-04-13 21:26:28.452021: Epoch 2726 +2026-04-13 21:26:28.453586: Current learning rate: 0.00357 +2026-04-13 21:28:11.023360: train_loss -0.4493 +2026-04-13 21:28:11.029980: val_loss -0.3929 +2026-04-13 21:28:11.032171: Pseudo dice [0.7169, 0.0, 0.8476, 0.3472, 0.5716, 0.6538, 0.8654] +2026-04-13 21:28:11.034642: Epoch time: 102.58 s +2026-04-13 21:28:12.259363: +2026-04-13 21:28:12.266753: Epoch 2727 +2026-04-13 21:28:12.270977: Current learning rate: 0.00357 +2026-04-13 21:29:54.216288: train_loss -0.451 +2026-04-13 21:29:54.242722: val_loss -0.3953 +2026-04-13 21:29:54.246029: Pseudo dice [0.6254, 0.0, 0.8542, 0.8086, 0.5024, 0.8686, 0.8519] +2026-04-13 21:29:54.249386: Epoch time: 101.96 s +2026-04-13 21:29:55.502865: +2026-04-13 21:29:55.504848: Epoch 2728 +2026-04-13 21:29:55.506338: Current learning rate: 0.00357 +2026-04-13 21:31:36.840010: train_loss -0.4519 +2026-04-13 21:31:36.848924: val_loss -0.3581 +2026-04-13 21:31:36.851365: Pseudo dice [0.67, 0.0, 0.6477, 0.5054, 0.5218, 0.7763, 0.5191] +2026-04-13 21:31:36.853744: Epoch time: 101.34 s +2026-04-13 21:31:38.084764: +2026-04-13 21:31:38.086644: Epoch 2729 +2026-04-13 21:31:38.088387: Current learning rate: 0.00356 +2026-04-13 21:33:20.179057: train_loss -0.4445 +2026-04-13 21:33:20.187680: val_loss -0.3782 +2026-04-13 21:33:20.191410: Pseudo dice [0.4426, 0.0, 0.8531, 0.1209, 0.4668, 0.5646, 0.4027] +2026-04-13 21:33:20.195936: Epoch time: 102.1 s +2026-04-13 21:33:21.478242: +2026-04-13 21:33:21.480455: Epoch 2730 +2026-04-13 21:33:21.482161: Current learning rate: 0.00356 +2026-04-13 21:35:02.778895: train_loss -0.4421 +2026-04-13 21:35:02.787064: val_loss -0.3882 +2026-04-13 21:35:02.789085: Pseudo dice [0.7721, 0.0, 0.6044, 0.5345, 0.5294, 0.3183, 0.8981] +2026-04-13 21:35:02.793332: Epoch time: 101.3 s +2026-04-13 21:35:04.021273: +2026-04-13 21:35:04.023203: Epoch 2731 +2026-04-13 21:35:04.025612: Current learning rate: 0.00356 +2026-04-13 21:36:46.399534: train_loss -0.4364 +2026-04-13 21:36:46.407602: val_loss -0.3606 +2026-04-13 21:36:46.412023: Pseudo dice [0.551, 0.0, 0.6459, 0.1477, 0.4138, 0.5795, 0.4385] +2026-04-13 21:36:46.415751: Epoch time: 102.38 s +2026-04-13 21:36:47.672884: +2026-04-13 21:36:47.676437: Epoch 2732 +2026-04-13 21:36:47.682684: Current learning rate: 0.00356 +2026-04-13 21:38:30.419863: train_loss -0.4206 +2026-04-13 21:38:30.426982: val_loss -0.3723 +2026-04-13 21:38:30.429218: Pseudo dice [0.488, 0.0, 0.4839, 0.3232, 0.5269, 0.8511, 0.8945] +2026-04-13 21:38:30.431361: Epoch time: 102.75 s +2026-04-13 21:38:31.674010: +2026-04-13 21:38:31.677049: Epoch 2733 +2026-04-13 21:38:31.680563: Current learning rate: 0.00355 +2026-04-13 21:40:13.566726: train_loss -0.4308 +2026-04-13 21:40:13.580190: val_loss -0.3266 +2026-04-13 21:40:13.583781: Pseudo dice [0.5357, 0.0, 0.8352, 0.0285, 0.4068, 0.66, 0.3001] +2026-04-13 21:40:13.588330: Epoch time: 101.9 s +2026-04-13 21:40:14.838242: +2026-04-13 21:40:14.844029: Epoch 2734 +2026-04-13 21:40:14.847647: Current learning rate: 0.00355 +2026-04-13 21:41:57.404851: train_loss -0.443 +2026-04-13 21:41:57.411216: val_loss -0.3845 +2026-04-13 21:41:57.414617: Pseudo dice [0.5299, 0.0, 0.8019, 0.4157, 0.4949, 0.648, 0.7081] +2026-04-13 21:41:57.417218: Epoch time: 102.57 s +2026-04-13 21:41:58.637367: +2026-04-13 21:41:58.639339: Epoch 2735 +2026-04-13 21:41:58.641663: Current learning rate: 0.00355 +2026-04-13 21:43:40.038997: train_loss -0.425 +2026-04-13 21:43:40.047058: val_loss -0.3462 +2026-04-13 21:43:40.050140: Pseudo dice [0.5321, 0.0, 0.7809, 0.0275, 0.5127, 0.4248, 0.658] +2026-04-13 21:43:40.052733: Epoch time: 101.4 s +2026-04-13 21:43:41.280526: +2026-04-13 21:43:41.282576: Epoch 2736 +2026-04-13 21:43:41.284512: Current learning rate: 0.00355 +2026-04-13 21:45:23.643209: train_loss -0.4378 +2026-04-13 21:45:23.651195: val_loss -0.3855 +2026-04-13 21:45:23.654235: Pseudo dice [0.6447, 0.0, 0.8208, 0.6448, 0.5143, 0.7458, 0.8709] +2026-04-13 21:45:23.657905: Epoch time: 102.37 s +2026-04-13 21:45:24.882960: +2026-04-13 21:45:24.884952: Epoch 2737 +2026-04-13 21:45:24.886825: Current learning rate: 0.00354 +2026-04-13 21:47:06.743957: train_loss -0.4307 +2026-04-13 21:47:06.751835: val_loss -0.3746 +2026-04-13 21:47:06.753975: Pseudo dice [0.5834, 0.0, 0.7548, 0.1816, 0.3216, 0.1358, 0.9341] +2026-04-13 21:47:06.756395: Epoch time: 101.86 s +2026-04-13 21:47:07.977408: +2026-04-13 21:47:07.979250: Epoch 2738 +2026-04-13 21:47:07.982723: Current learning rate: 0.00354 +2026-04-13 21:48:49.918054: train_loss -0.4489 +2026-04-13 21:48:49.924711: val_loss -0.3897 +2026-04-13 21:48:49.926807: Pseudo dice [0.8184, 0.0, 0.7968, 0.2395, 0.6468, 0.5715, 0.52] +2026-04-13 21:48:49.928898: Epoch time: 101.94 s +2026-04-13 21:48:51.168591: +2026-04-13 21:48:51.170523: Epoch 2739 +2026-04-13 21:48:51.172144: Current learning rate: 0.00354 +2026-04-13 21:50:33.672935: train_loss -0.4329 +2026-04-13 21:50:33.681919: val_loss -0.4104 +2026-04-13 21:50:33.687827: Pseudo dice [0.5239, 0.0, 0.8318, 0.5448, 0.5501, 0.9023, 0.9129] +2026-04-13 21:50:33.691241: Epoch time: 102.51 s +2026-04-13 21:50:35.010154: +2026-04-13 21:50:35.014040: Epoch 2740 +2026-04-13 21:50:35.016353: Current learning rate: 0.00354 +2026-04-13 21:52:16.668664: train_loss -0.4389 +2026-04-13 21:52:16.675382: val_loss -0.3579 +2026-04-13 21:52:16.679829: Pseudo dice [0.7684, 0.0, 0.6723, 0.1267, 0.5068, 0.2471, 0.8228] +2026-04-13 21:52:16.682272: Epoch time: 101.66 s +2026-04-13 21:52:17.901789: +2026-04-13 21:52:17.903526: Epoch 2741 +2026-04-13 21:52:17.905360: Current learning rate: 0.00353 +2026-04-13 21:53:59.243809: train_loss -0.4401 +2026-04-13 21:53:59.250954: val_loss -0.3441 +2026-04-13 21:53:59.253133: Pseudo dice [0.7263, 0.0, 0.7452, 0.0002, 0.4134, 0.6479, 0.8973] +2026-04-13 21:53:59.257076: Epoch time: 101.35 s +2026-04-13 21:54:00.489805: +2026-04-13 21:54:00.491827: Epoch 2742 +2026-04-13 21:54:00.493911: Current learning rate: 0.00353 +2026-04-13 21:55:42.085460: train_loss -0.4459 +2026-04-13 21:55:42.104141: val_loss -0.3867 +2026-04-13 21:55:42.106596: Pseudo dice [0.3936, 0.0, 0.7489, 0.7112, 0.5274, 0.7437, 0.9147] +2026-04-13 21:55:42.109179: Epoch time: 101.6 s +2026-04-13 21:55:43.342227: +2026-04-13 21:55:43.344304: Epoch 2743 +2026-04-13 21:55:43.346287: Current learning rate: 0.00353 +2026-04-13 21:57:25.526672: train_loss -0.4438 +2026-04-13 21:57:25.536076: val_loss -0.3749 +2026-04-13 21:57:25.538742: Pseudo dice [0.797, 0.0, 0.7329, 0.2053, 0.1956, 0.7277, 0.7998] +2026-04-13 21:57:25.542698: Epoch time: 102.19 s +2026-04-13 21:57:26.763888: +2026-04-13 21:57:26.765645: Epoch 2744 +2026-04-13 21:57:26.767939: Current learning rate: 0.00353 +2026-04-13 21:59:09.115550: train_loss -0.441 +2026-04-13 21:59:09.130880: val_loss -0.3966 +2026-04-13 21:59:09.138937: Pseudo dice [0.6498, 0.0, 0.5974, 0.8636, 0.4687, 0.4877, 0.8338] +2026-04-13 21:59:09.144852: Epoch time: 102.35 s +2026-04-13 21:59:10.403278: +2026-04-13 21:59:10.405396: Epoch 2745 +2026-04-13 21:59:10.407094: Current learning rate: 0.00352 +2026-04-13 22:00:52.232728: train_loss -0.4526 +2026-04-13 22:00:52.239293: val_loss -0.4075 +2026-04-13 22:00:52.242184: Pseudo dice [0.6184, 0.0, 0.859, 0.8203, 0.5189, 0.6321, 0.7206] +2026-04-13 22:00:52.244850: Epoch time: 101.83 s +2026-04-13 22:00:53.490850: +2026-04-13 22:00:53.492698: Epoch 2746 +2026-04-13 22:00:53.494230: Current learning rate: 0.00352 +2026-04-13 22:02:35.897878: train_loss -0.4514 +2026-04-13 22:02:35.904036: val_loss -0.3635 +2026-04-13 22:02:35.906175: Pseudo dice [0.6957, 0.0, 0.7381, 0.3924, 0.2469, 0.4796, 0.9099] +2026-04-13 22:02:35.908262: Epoch time: 102.41 s +2026-04-13 22:02:37.135097: +2026-04-13 22:02:37.136965: Epoch 2747 +2026-04-13 22:02:37.138816: Current learning rate: 0.00352 +2026-04-13 22:04:19.118989: train_loss -0.4456 +2026-04-13 22:04:19.126054: val_loss -0.337 +2026-04-13 22:04:19.128705: Pseudo dice [0.4914, 0.0, 0.6974, 0.2233, 0.5598, 0.1887, 0.8676] +2026-04-13 22:04:19.131921: Epoch time: 101.99 s +2026-04-13 22:04:20.357644: +2026-04-13 22:04:20.362455: Epoch 2748 +2026-04-13 22:04:20.365167: Current learning rate: 0.00352 +2026-04-13 22:06:02.080866: train_loss -0.4406 +2026-04-13 22:06:02.087376: val_loss -0.3523 +2026-04-13 22:06:02.089596: Pseudo dice [0.8292, 0.0, 0.6057, 0.4809, 0.532, 0.6085, 0.5958] +2026-04-13 22:06:02.092360: Epoch time: 101.73 s +2026-04-13 22:06:03.342752: +2026-04-13 22:06:03.344664: Epoch 2749 +2026-04-13 22:06:03.347040: Current learning rate: 0.00351 +2026-04-13 22:07:45.387732: train_loss -0.4395 +2026-04-13 22:07:45.393121: val_loss -0.3747 +2026-04-13 22:07:45.394941: Pseudo dice [0.516, 0.0, 0.8027, 0.6156, 0.4825, 0.663, 0.6962] +2026-04-13 22:07:45.397064: Epoch time: 102.05 s +2026-04-13 22:07:48.431850: +2026-04-13 22:07:48.433581: Epoch 2750 +2026-04-13 22:07:48.435071: Current learning rate: 0.00351 +2026-04-13 22:09:30.182720: train_loss -0.4523 +2026-04-13 22:09:30.190154: val_loss -0.3868 +2026-04-13 22:09:30.192400: Pseudo dice [0.6888, 0.0, 0.8122, 0.605, 0.5047, 0.5121, 0.9188] +2026-04-13 22:09:30.195020: Epoch time: 101.75 s +2026-04-13 22:09:31.459275: +2026-04-13 22:09:31.461562: Epoch 2751 +2026-04-13 22:09:31.463352: Current learning rate: 0.00351 +2026-04-13 22:11:13.294798: train_loss -0.4308 +2026-04-13 22:11:13.304605: val_loss -0.3503 +2026-04-13 22:11:13.311971: Pseudo dice [0.071, 0.0, 0.5506, 0.2864, 0.611, 0.4988, 0.9025] +2026-04-13 22:11:13.314921: Epoch time: 101.84 s +2026-04-13 22:11:14.648190: +2026-04-13 22:11:14.650139: Epoch 2752 +2026-04-13 22:11:14.652631: Current learning rate: 0.00351 +2026-04-13 22:12:57.093525: train_loss -0.4266 +2026-04-13 22:12:57.101106: val_loss -0.3878 +2026-04-13 22:12:57.104696: Pseudo dice [0.3921, 0.0, 0.7475, 0.8554, 0.4901, 0.7356, 0.8433] +2026-04-13 22:12:57.107194: Epoch time: 102.45 s +2026-04-13 22:12:58.337099: +2026-04-13 22:12:58.339631: Epoch 2753 +2026-04-13 22:12:58.341604: Current learning rate: 0.0035 +2026-04-13 22:14:39.807118: train_loss -0.4462 +2026-04-13 22:14:39.816174: val_loss -0.3952 +2026-04-13 22:14:39.818794: Pseudo dice [0.4681, 0.0, 0.8376, 0.562, 0.5216, 0.7419, 0.915] +2026-04-13 22:14:39.821411: Epoch time: 101.47 s +2026-04-13 22:14:41.053925: +2026-04-13 22:14:41.056352: Epoch 2754 +2026-04-13 22:14:41.058128: Current learning rate: 0.0035 +2026-04-13 22:16:22.627026: train_loss -0.4261 +2026-04-13 22:16:22.635219: val_loss -0.3678 +2026-04-13 22:16:22.637649: Pseudo dice [0.7078, 0.0, 0.7515, 0.8251, 0.5148, 0.6478, 0.9265] +2026-04-13 22:16:22.639877: Epoch time: 101.58 s +2026-04-13 22:16:23.890900: +2026-04-13 22:16:23.893314: Epoch 2755 +2026-04-13 22:16:23.895966: Current learning rate: 0.0035 +2026-04-13 22:18:05.317914: train_loss -0.4275 +2026-04-13 22:18:05.327078: val_loss -0.3451 +2026-04-13 22:18:05.329995: Pseudo dice [0.4839, 0.0, 0.8225, 0.5881, 0.3881, 0.4971, 0.3324] +2026-04-13 22:18:05.332877: Epoch time: 101.43 s +2026-04-13 22:18:06.585070: +2026-04-13 22:18:06.587254: Epoch 2756 +2026-04-13 22:18:06.589437: Current learning rate: 0.0035 +2026-04-13 22:19:48.835200: train_loss -0.4311 +2026-04-13 22:19:48.847135: val_loss -0.3582 +2026-04-13 22:19:48.852816: Pseudo dice [0.6492, 0.0, 0.7735, 0.4798, 0.4212, 0.7374, 0.8175] +2026-04-13 22:19:48.855866: Epoch time: 102.25 s +2026-04-13 22:19:50.095027: +2026-04-13 22:19:50.098213: Epoch 2757 +2026-04-13 22:19:50.101427: Current learning rate: 0.00349 +2026-04-13 22:21:32.333829: train_loss -0.4533 +2026-04-13 22:21:32.339935: val_loss -0.3696 +2026-04-13 22:21:32.342777: Pseudo dice [0.4184, 0.0, 0.9124, 0.7739, 0.5154, 0.4509, 0.8329] +2026-04-13 22:21:32.345436: Epoch time: 102.24 s +2026-04-13 22:21:33.570756: +2026-04-13 22:21:33.572580: Epoch 2758 +2026-04-13 22:21:33.574283: Current learning rate: 0.00349 +2026-04-13 22:23:15.258580: train_loss -0.4398 +2026-04-13 22:23:15.265531: val_loss -0.3924 +2026-04-13 22:23:15.273172: Pseudo dice [0.486, 0.0, 0.7824, 0.4234, 0.5572, 0.5679, 0.9316] +2026-04-13 22:23:15.276560: Epoch time: 101.69 s +2026-04-13 22:23:16.564780: +2026-04-13 22:23:16.566928: Epoch 2759 +2026-04-13 22:23:16.568666: Current learning rate: 0.00349 +2026-04-13 22:24:58.287204: train_loss -0.4585 +2026-04-13 22:24:58.293098: val_loss -0.4008 +2026-04-13 22:24:58.295326: Pseudo dice [0.8179, 0.0, 0.8207, 0.7874, 0.6397, 0.7661, 0.8791] +2026-04-13 22:24:58.297566: Epoch time: 101.73 s +2026-04-13 22:24:59.595315: +2026-04-13 22:24:59.597193: Epoch 2760 +2026-04-13 22:24:59.599416: Current learning rate: 0.00349 +2026-04-13 22:26:41.503018: train_loss -0.4271 +2026-04-13 22:26:41.510185: val_loss -0.347 +2026-04-13 22:26:41.513034: Pseudo dice [0.6134, 0.0, 0.7739, 0.1591, 0.344, 0.5324, 0.7311] +2026-04-13 22:26:41.515656: Epoch time: 101.91 s +2026-04-13 22:26:42.776466: +2026-04-13 22:26:42.778512: Epoch 2761 +2026-04-13 22:26:42.780278: Current learning rate: 0.00348 +2026-04-13 22:28:24.847127: train_loss -0.4207 +2026-04-13 22:28:24.857208: val_loss -0.3769 +2026-04-13 22:28:24.859428: Pseudo dice [0.5569, 0.0, 0.885, 0.711, 0.4871, 0.7125, 0.9049] +2026-04-13 22:28:24.863228: Epoch time: 102.07 s +2026-04-13 22:28:26.102925: +2026-04-13 22:28:26.105384: Epoch 2762 +2026-04-13 22:28:26.107495: Current learning rate: 0.00348 +2026-04-13 22:30:07.672542: train_loss -0.4109 +2026-04-13 22:30:07.699261: val_loss -0.3393 +2026-04-13 22:30:07.701983: Pseudo dice [0.6297, 0.0, 0.6191, 0.2598, 0.357, 0.5517, 0.859] +2026-04-13 22:30:07.704336: Epoch time: 101.57 s +2026-04-13 22:30:08.953748: +2026-04-13 22:30:08.955591: Epoch 2763 +2026-04-13 22:30:08.957268: Current learning rate: 0.00348 +2026-04-13 22:31:50.529371: train_loss -0.4157 +2026-04-13 22:31:50.537802: val_loss -0.3374 +2026-04-13 22:31:50.540989: Pseudo dice [0.1166, 0.0, 0.8548, 0.7775, 0.5446, 0.6653, 0.2135] +2026-04-13 22:31:50.543582: Epoch time: 101.58 s +2026-04-13 22:31:51.800169: +2026-04-13 22:31:51.803386: Epoch 2764 +2026-04-13 22:31:51.805407: Current learning rate: 0.00348 +2026-04-13 22:33:33.499558: train_loss -0.4107 +2026-04-13 22:33:33.507246: val_loss -0.3178 +2026-04-13 22:33:33.509675: Pseudo dice [0.2116, 0.0, 0.7632, 0.0982, 0.5173, 0.8409, 0.7571] +2026-04-13 22:33:33.513085: Epoch time: 101.7 s +2026-04-13 22:33:34.768075: +2026-04-13 22:33:34.770940: Epoch 2765 +2026-04-13 22:33:34.773183: Current learning rate: 0.00347 +2026-04-13 22:35:16.048240: train_loss -0.435 +2026-04-13 22:35:16.054709: val_loss -0.3385 +2026-04-13 22:35:16.057299: Pseudo dice [0.6921, 0.0, 0.7582, 0.3094, 0.5324, 0.8637, 0.9097] +2026-04-13 22:35:16.059813: Epoch time: 101.28 s +2026-04-13 22:35:17.290647: +2026-04-13 22:35:17.292300: Epoch 2766 +2026-04-13 22:35:17.293933: Current learning rate: 0.00347 +2026-04-13 22:36:59.220296: train_loss -0.4382 +2026-04-13 22:36:59.227211: val_loss -0.353 +2026-04-13 22:36:59.229612: Pseudo dice [0.4127, 0.0, 0.7741, 0.1184, 0.633, 0.7464, 0.3819] +2026-04-13 22:36:59.231922: Epoch time: 101.93 s +2026-04-13 22:37:00.453416: +2026-04-13 22:37:00.455723: Epoch 2767 +2026-04-13 22:37:00.457473: Current learning rate: 0.00347 +2026-04-13 22:38:42.546349: train_loss -0.4305 +2026-04-13 22:38:42.554847: val_loss -0.3467 +2026-04-13 22:38:42.558400: Pseudo dice [0.6404, 0.0, 0.6551, 0.3465, 0.308, 0.7981, 0.8009] +2026-04-13 22:38:42.562264: Epoch time: 102.1 s +2026-04-13 22:38:43.808930: +2026-04-13 22:38:43.811651: Epoch 2768 +2026-04-13 22:38:43.815155: Current learning rate: 0.00346 +2026-04-13 22:40:25.585343: train_loss -0.4358 +2026-04-13 22:40:25.592103: val_loss -0.3675 +2026-04-13 22:40:25.594663: Pseudo dice [0.8528, 0.0, 0.7671, 0.0819, 0.4257, 0.6997, 0.8203] +2026-04-13 22:40:25.597724: Epoch time: 101.78 s +2026-04-13 22:40:26.828272: +2026-04-13 22:40:26.830805: Epoch 2769 +2026-04-13 22:40:26.832946: Current learning rate: 0.00346 +2026-04-13 22:42:08.759016: train_loss -0.4448 +2026-04-13 22:42:08.766118: val_loss -0.3899 +2026-04-13 22:42:08.768443: Pseudo dice [0.7221, 0.0, 0.7626, 0.6224, 0.5942, 0.8581, 0.9028] +2026-04-13 22:42:08.771434: Epoch time: 101.93 s +2026-04-13 22:42:10.052551: +2026-04-13 22:42:10.055915: Epoch 2770 +2026-04-13 22:42:10.058310: Current learning rate: 0.00346 +2026-04-13 22:43:51.931266: train_loss -0.4477 +2026-04-13 22:43:51.941441: val_loss -0.3816 +2026-04-13 22:43:51.944268: Pseudo dice [0.7522, 0.0, 0.8909, 0.8607, 0.4586, 0.3573, 0.3844] +2026-04-13 22:43:51.947547: Epoch time: 101.88 s +2026-04-13 22:43:53.182057: +2026-04-13 22:43:53.184386: Epoch 2771 +2026-04-13 22:43:53.186343: Current learning rate: 0.00346 +2026-04-13 22:45:35.399489: train_loss -0.445 +2026-04-13 22:45:35.406246: val_loss -0.3876 +2026-04-13 22:45:35.408619: Pseudo dice [0.7621, 0.0, 0.7232, 0.2398, 0.5514, 0.8663, 0.891] +2026-04-13 22:45:35.410872: Epoch time: 102.22 s +2026-04-13 22:45:36.688872: +2026-04-13 22:45:36.691065: Epoch 2772 +2026-04-13 22:45:36.692724: Current learning rate: 0.00345 +2026-04-13 22:47:18.565985: train_loss -0.4565 +2026-04-13 22:47:18.572472: val_loss -0.3899 +2026-04-13 22:47:18.574612: Pseudo dice [0.7637, 0.0, 0.7867, 0.5343, 0.6801, 0.7791, 0.8408] +2026-04-13 22:47:18.576820: Epoch time: 101.88 s +2026-04-13 22:47:20.946742: +2026-04-13 22:47:20.948726: Epoch 2773 +2026-04-13 22:47:20.950915: Current learning rate: 0.00345 +2026-04-13 22:49:03.726664: train_loss -0.434 +2026-04-13 22:49:03.734362: val_loss -0.3497 +2026-04-13 22:49:03.737435: Pseudo dice [0.6038, 0.0, 0.7724, 0.2589, 0.5817, 0.6438, 0.9239] +2026-04-13 22:49:03.739703: Epoch time: 102.78 s +2026-04-13 22:49:05.045290: +2026-04-13 22:49:05.048007: Epoch 2774 +2026-04-13 22:49:05.050626: Current learning rate: 0.00345 +2026-04-13 22:50:47.033994: train_loss -0.4284 +2026-04-13 22:50:47.041788: val_loss -0.4019 +2026-04-13 22:50:47.045634: Pseudo dice [0.4053, 0.0, 0.7494, 0.901, 0.4555, 0.8101, 0.9421] +2026-04-13 22:50:47.050488: Epoch time: 101.99 s +2026-04-13 22:50:48.298049: +2026-04-13 22:50:48.301537: Epoch 2775 +2026-04-13 22:50:48.304001: Current learning rate: 0.00345 +2026-04-13 22:52:30.623769: train_loss -0.4345 +2026-04-13 22:52:30.643834: val_loss -0.3739 +2026-04-13 22:52:30.647807: Pseudo dice [0.6631, 0.0, 0.7321, 0.8161, 0.4886, 0.6559, 0.8677] +2026-04-13 22:52:30.652514: Epoch time: 102.33 s +2026-04-13 22:52:31.961879: +2026-04-13 22:52:31.964256: Epoch 2776 +2026-04-13 22:52:31.966528: Current learning rate: 0.00344 +2026-04-13 22:54:14.189681: train_loss -0.4413 +2026-04-13 22:54:14.196698: val_loss -0.3856 +2026-04-13 22:54:14.199402: Pseudo dice [0.4455, 0.0, 0.8011, 0.4309, 0.6123, 0.7073, 0.843] +2026-04-13 22:54:14.202326: Epoch time: 102.23 s +2026-04-13 22:54:15.484015: +2026-04-13 22:54:15.487052: Epoch 2777 +2026-04-13 22:54:15.489719: Current learning rate: 0.00344 +2026-04-13 22:55:57.303549: train_loss -0.4357 +2026-04-13 22:55:57.310570: val_loss -0.3872 +2026-04-13 22:55:57.312793: Pseudo dice [0.7038, 0.0, 0.3981, 0.762, 0.5229, 0.8201, 0.8586] +2026-04-13 22:55:57.316178: Epoch time: 101.82 s +2026-04-13 22:55:58.602187: +2026-04-13 22:55:58.604445: Epoch 2778 +2026-04-13 22:55:58.606575: Current learning rate: 0.00344 +2026-04-13 22:57:40.183746: train_loss -0.4376 +2026-04-13 22:57:40.190535: val_loss -0.3296 +2026-04-13 22:57:40.192976: Pseudo dice [0.2281, 0.0, 0.6131, 0.079, 0.4919, 0.699, 0.8188] +2026-04-13 22:57:40.195624: Epoch time: 101.58 s +2026-04-13 22:57:41.445854: +2026-04-13 22:57:41.447819: Epoch 2779 +2026-04-13 22:57:41.449760: Current learning rate: 0.00344 +2026-04-13 22:59:23.290457: train_loss -0.442 +2026-04-13 22:59:23.296510: val_loss -0.3563 +2026-04-13 22:59:23.298672: Pseudo dice [0.448, 0.0, 0.8039, 0.51, 0.3007, 0.4927, 0.4618] +2026-04-13 22:59:23.301286: Epoch time: 101.85 s +2026-04-13 22:59:24.550341: +2026-04-13 22:59:24.552161: Epoch 2780 +2026-04-13 22:59:24.553798: Current learning rate: 0.00343 +2026-04-13 23:01:06.451830: train_loss -0.4424 +2026-04-13 23:01:06.460259: val_loss -0.395 +2026-04-13 23:01:06.462557: Pseudo dice [0.6818, 0.0, 0.8393, 0.8433, 0.4092, 0.6072, 0.6807] +2026-04-13 23:01:06.466677: Epoch time: 101.9 s +2026-04-13 23:01:07.711755: +2026-04-13 23:01:07.713761: Epoch 2781 +2026-04-13 23:01:07.715327: Current learning rate: 0.00343 +2026-04-13 23:02:49.038301: train_loss -0.4259 +2026-04-13 23:02:49.045494: val_loss -0.38 +2026-04-13 23:02:49.047865: Pseudo dice [0.7542, 0.0, 0.8068, 0.5553, 0.5462, 0.8548, 0.6945] +2026-04-13 23:02:49.050398: Epoch time: 101.33 s +2026-04-13 23:02:50.272925: +2026-04-13 23:02:50.275434: Epoch 2782 +2026-04-13 23:02:50.277445: Current learning rate: 0.00343 +2026-04-13 23:04:31.666734: train_loss -0.4285 +2026-04-13 23:04:31.675494: val_loss -0.3445 +2026-04-13 23:04:31.677762: Pseudo dice [0.7454, 0.0, 0.3522, 0.2892, 0.4874, 0.4872, 0.5547] +2026-04-13 23:04:31.680152: Epoch time: 101.4 s +2026-04-13 23:04:32.883533: +2026-04-13 23:04:32.890843: Epoch 2783 +2026-04-13 23:04:32.892867: Current learning rate: 0.00343 +2026-04-13 23:06:14.684569: train_loss -0.442 +2026-04-13 23:06:14.692394: val_loss -0.3661 +2026-04-13 23:06:14.694438: Pseudo dice [0.4843, 0.0, 0.8039, 0.5615, 0.5861, 0.6696, 0.5029] +2026-04-13 23:06:14.696692: Epoch time: 101.8 s +2026-04-13 23:06:15.917702: +2026-04-13 23:06:15.919415: Epoch 2784 +2026-04-13 23:06:15.920969: Current learning rate: 0.00342 +2026-04-13 23:07:58.066956: train_loss -0.4435 +2026-04-13 23:07:58.073340: val_loss -0.393 +2026-04-13 23:07:58.075375: Pseudo dice [0.2739, 0.0, 0.8518, 0.1197, 0.6173, 0.8621, 0.9271] +2026-04-13 23:07:58.077953: Epoch time: 102.15 s +2026-04-13 23:07:59.341417: +2026-04-13 23:07:59.344402: Epoch 2785 +2026-04-13 23:07:59.346205: Current learning rate: 0.00342 +2026-04-13 23:09:40.897053: train_loss -0.4283 +2026-04-13 23:09:40.905544: val_loss -0.3555 +2026-04-13 23:09:40.908786: Pseudo dice [0.4999, 0.0, 0.6349, 0.1602, 0.5796, 0.7744, 0.8436] +2026-04-13 23:09:40.912267: Epoch time: 101.56 s +2026-04-13 23:09:42.168990: +2026-04-13 23:09:42.170989: Epoch 2786 +2026-04-13 23:09:42.172675: Current learning rate: 0.00342 +2026-04-13 23:11:24.222278: train_loss -0.4113 +2026-04-13 23:11:24.230603: val_loss -0.3857 +2026-04-13 23:11:24.232865: Pseudo dice [0.6579, 0.0, 0.6216, 0.6296, 0.5269, 0.759, 0.8111] +2026-04-13 23:11:24.236144: Epoch time: 102.06 s +2026-04-13 23:11:25.471342: +2026-04-13 23:11:25.473353: Epoch 2787 +2026-04-13 23:11:25.475115: Current learning rate: 0.00342 +2026-04-13 23:13:08.048947: train_loss -0.4308 +2026-04-13 23:13:08.057563: val_loss -0.3694 +2026-04-13 23:13:08.059693: Pseudo dice [0.4958, 0.0, 0.8191, 0.4548, 0.5214, 0.7422, 0.8273] +2026-04-13 23:13:08.061868: Epoch time: 102.58 s +2026-04-13 23:13:09.309376: +2026-04-13 23:13:09.311527: Epoch 2788 +2026-04-13 23:13:09.313383: Current learning rate: 0.00341 +2026-04-13 23:14:50.861040: train_loss -0.4189 +2026-04-13 23:14:50.868326: val_loss -0.3769 +2026-04-13 23:14:50.870388: Pseudo dice [0.0, 0.0, 0.8389, 0.7235, 0.4592, 0.7587, 0.8734] +2026-04-13 23:14:50.872671: Epoch time: 101.55 s +2026-04-13 23:14:52.100809: +2026-04-13 23:14:52.102702: Epoch 2789 +2026-04-13 23:14:52.104580: Current learning rate: 0.00341 +2026-04-13 23:16:33.761549: train_loss -0.3926 +2026-04-13 23:16:33.768935: val_loss -0.3311 +2026-04-13 23:16:33.771005: Pseudo dice [0.0, 0.0, 0.855, 0.4706, 0.4201, 0.5509, 0.2789] +2026-04-13 23:16:33.773413: Epoch time: 101.66 s +2026-04-13 23:16:35.003447: +2026-04-13 23:16:35.005746: Epoch 2790 +2026-04-13 23:16:35.007487: Current learning rate: 0.00341 +2026-04-13 23:18:17.521703: train_loss -0.3934 +2026-04-13 23:18:17.528260: val_loss -0.3344 +2026-04-13 23:18:17.531083: Pseudo dice [0.0, 0.0, 0.6484, 0.4109, 0.4878, 0.5703, 0.5931] +2026-04-13 23:18:17.535222: Epoch time: 102.52 s +2026-04-13 23:18:18.809372: +2026-04-13 23:18:18.811594: Epoch 2791 +2026-04-13 23:18:18.813567: Current learning rate: 0.00341 +2026-04-13 23:20:00.740893: train_loss -0.4208 +2026-04-13 23:20:00.748034: val_loss -0.3923 +2026-04-13 23:20:00.750112: Pseudo dice [0.0, 0.0, 0.6606, 0.5082, 0.6729, 0.819, 0.8855] +2026-04-13 23:20:00.752085: Epoch time: 101.93 s +2026-04-13 23:20:01.977594: +2026-04-13 23:20:01.979778: Epoch 2792 +2026-04-13 23:20:01.981816: Current learning rate: 0.0034 +2026-04-13 23:21:43.642462: train_loss -0.4305 +2026-04-13 23:21:43.649008: val_loss -0.3363 +2026-04-13 23:21:43.650614: Pseudo dice [0.0803, 0.0, 0.7471, 0.6894, 0.5135, 0.5588, 0.4504] +2026-04-13 23:21:43.652740: Epoch time: 101.67 s +2026-04-13 23:21:45.939502: +2026-04-13 23:21:45.941208: Epoch 2793 +2026-04-13 23:21:45.942774: Current learning rate: 0.0034 +2026-04-13 23:23:27.501341: train_loss -0.4343 +2026-04-13 23:23:27.509027: val_loss -0.3775 +2026-04-13 23:23:27.515910: Pseudo dice [0.5056, 0.0, 0.8914, 0.5465, 0.6687, 0.7011, 0.8051] +2026-04-13 23:23:27.518059: Epoch time: 101.56 s +2026-04-13 23:23:28.764295: +2026-04-13 23:23:28.766769: Epoch 2794 +2026-04-13 23:23:28.768974: Current learning rate: 0.0034 +2026-04-13 23:25:10.305580: train_loss -0.4392 +2026-04-13 23:25:10.314145: val_loss -0.3563 +2026-04-13 23:25:10.317099: Pseudo dice [0.3033, 0.0, 0.8195, 0.6374, 0.5794, 0.5202, 0.7728] +2026-04-13 23:25:10.321927: Epoch time: 101.54 s +2026-04-13 23:25:11.546333: +2026-04-13 23:25:11.548281: Epoch 2795 +2026-04-13 23:25:11.550157: Current learning rate: 0.0034 +2026-04-13 23:26:53.623405: train_loss -0.4327 +2026-04-13 23:26:53.632186: val_loss -0.3945 +2026-04-13 23:26:53.634502: Pseudo dice [0.5272, 0.0, 0.8384, 0.709, 0.7161, 0.892, 0.8652] +2026-04-13 23:26:53.637606: Epoch time: 102.08 s +2026-04-13 23:26:54.873849: +2026-04-13 23:26:54.876017: Epoch 2796 +2026-04-13 23:26:54.878407: Current learning rate: 0.00339 +2026-04-13 23:28:36.519773: train_loss -0.4361 +2026-04-13 23:28:36.525973: val_loss -0.3219 +2026-04-13 23:28:36.528110: Pseudo dice [0.4963, 0.0, 0.8256, 0.0877, 0.3992, 0.7848, 0.2157] +2026-04-13 23:28:36.530839: Epoch time: 101.65 s +2026-04-13 23:28:37.770487: +2026-04-13 23:28:37.774863: Epoch 2797 +2026-04-13 23:28:37.783596: Current learning rate: 0.00339 +2026-04-13 23:30:19.820995: train_loss -0.442 +2026-04-13 23:30:19.847518: val_loss -0.3826 +2026-04-13 23:30:19.849405: Pseudo dice [0.5887, 0.0, 0.7898, 0.028, 0.5455, 0.6828, 0.5481] +2026-04-13 23:30:19.852153: Epoch time: 102.05 s +2026-04-13 23:30:21.079079: +2026-04-13 23:30:21.081848: Epoch 2798 +2026-04-13 23:30:21.084112: Current learning rate: 0.00339 +2026-04-13 23:32:02.918267: train_loss -0.4442 +2026-04-13 23:32:02.926143: val_loss -0.3562 +2026-04-13 23:32:02.930310: Pseudo dice [0.6257, 0.0, 0.8243, 0.102, 0.5188, 0.715, 0.7222] +2026-04-13 23:32:02.932931: Epoch time: 101.84 s +2026-04-13 23:32:04.172847: +2026-04-13 23:32:04.174916: Epoch 2799 +2026-04-13 23:32:04.177071: Current learning rate: 0.00339 +2026-04-13 23:33:46.690475: train_loss -0.4388 +2026-04-13 23:33:46.697478: val_loss -0.401 +2026-04-13 23:33:46.699763: Pseudo dice [0.6201, 0.0, 0.8682, 0.6749, 0.521, 0.6324, 0.9243] +2026-04-13 23:33:46.702022: Epoch time: 102.52 s +2026-04-13 23:33:49.733723: +2026-04-13 23:33:49.735284: Epoch 2800 +2026-04-13 23:33:49.736814: Current learning rate: 0.00338 +2026-04-13 23:35:31.518506: train_loss -0.4488 +2026-04-13 23:35:31.528940: val_loss -0.347 +2026-04-13 23:35:31.530882: Pseudo dice [0.5508, 0.0, 0.7033, 0.1185, 0.5216, 0.484, 0.9019] +2026-04-13 23:35:31.533440: Epoch time: 101.79 s +2026-04-13 23:35:32.771871: +2026-04-13 23:35:32.774361: Epoch 2801 +2026-04-13 23:35:32.776337: Current learning rate: 0.00338 +2026-04-13 23:37:14.979223: train_loss -0.4105 +2026-04-13 23:37:14.987450: val_loss -0.3758 +2026-04-13 23:37:14.990836: Pseudo dice [0.2483, 0.0, 0.716, 0.0034, 0.4789, 0.6781, 0.837] +2026-04-13 23:37:14.993706: Epoch time: 102.21 s +2026-04-13 23:37:16.233310: +2026-04-13 23:37:16.235551: Epoch 2802 +2026-04-13 23:37:16.237890: Current learning rate: 0.00338 +2026-04-13 23:38:57.847943: train_loss -0.4409 +2026-04-13 23:38:57.855556: val_loss -0.3856 +2026-04-13 23:38:57.858131: Pseudo dice [0.7772, 0.0, 0.809, 0.6792, 0.4192, 0.6796, 0.6868] +2026-04-13 23:38:57.860865: Epoch time: 101.62 s +2026-04-13 23:38:59.090093: +2026-04-13 23:38:59.097352: Epoch 2803 +2026-04-13 23:38:59.099597: Current learning rate: 0.00338 +2026-04-13 23:40:41.311016: train_loss -0.4472 +2026-04-13 23:40:41.317529: val_loss -0.3688 +2026-04-13 23:40:41.319508: Pseudo dice [0.5085, 0.0, 0.6768, 0.6994, 0.6572, 0.8453, 0.9328] +2026-04-13 23:40:41.321794: Epoch time: 102.22 s +2026-04-13 23:40:42.556023: +2026-04-13 23:40:42.558052: Epoch 2804 +2026-04-13 23:40:42.559839: Current learning rate: 0.00337 +2026-04-13 23:42:24.967542: train_loss -0.4368 +2026-04-13 23:42:24.976059: val_loss -0.3838 +2026-04-13 23:42:24.979612: Pseudo dice [0.7695, 0.0, 0.7889, 0.5887, 0.6227, 0.6914, 0.8794] +2026-04-13 23:42:24.982566: Epoch time: 102.41 s +2026-04-13 23:42:26.246593: +2026-04-13 23:42:26.248888: Epoch 2805 +2026-04-13 23:42:26.251084: Current learning rate: 0.00337 +2026-04-13 23:44:08.294729: train_loss -0.4497 +2026-04-13 23:44:08.301138: val_loss -0.354 +2026-04-13 23:44:08.303420: Pseudo dice [0.4836, 0.0, 0.8325, 0.3924, 0.3851, 0.8275, 0.7273] +2026-04-13 23:44:08.305989: Epoch time: 102.05 s +2026-04-13 23:44:09.517156: +2026-04-13 23:44:09.518945: Epoch 2806 +2026-04-13 23:44:09.520998: Current learning rate: 0.00337 +2026-04-13 23:45:51.808433: train_loss -0.4464 +2026-04-13 23:45:51.817295: val_loss -0.3618 +2026-04-13 23:45:51.819350: Pseudo dice [0.8037, 0.0, 0.448, 0.2256, 0.6268, 0.8421, 0.9097] +2026-04-13 23:45:51.822964: Epoch time: 102.29 s +2026-04-13 23:45:53.078279: +2026-04-13 23:45:53.080400: Epoch 2807 +2026-04-13 23:45:53.082234: Current learning rate: 0.00337 +2026-04-13 23:47:35.115130: train_loss -0.4413 +2026-04-13 23:47:35.126292: val_loss -0.3664 +2026-04-13 23:47:35.128425: Pseudo dice [0.7862, 0.0, 0.8319, 0.706, 0.475, 0.6325, 0.9423] +2026-04-13 23:47:35.132892: Epoch time: 102.04 s +2026-04-13 23:47:36.441842: +2026-04-13 23:47:36.466363: Epoch 2808 +2026-04-13 23:47:36.469078: Current learning rate: 0.00336 +2026-04-13 23:49:19.197393: train_loss -0.4446 +2026-04-13 23:49:19.203836: val_loss -0.3783 +2026-04-13 23:49:19.206188: Pseudo dice [0.7341, 0.0, 0.7407, 0.8603, 0.4178, 0.7511, 0.903] +2026-04-13 23:49:19.209817: Epoch time: 102.76 s +2026-04-13 23:49:20.426851: +2026-04-13 23:49:20.429308: Epoch 2809 +2026-04-13 23:49:20.431668: Current learning rate: 0.00336 +2026-04-13 23:51:02.341323: train_loss -0.4324 +2026-04-13 23:51:02.347630: val_loss -0.4024 +2026-04-13 23:51:02.350082: Pseudo dice [0.7087, 0.0, 0.7679, 0.7049, 0.3846, 0.8776, 0.7673] +2026-04-13 23:51:02.352893: Epoch time: 101.92 s +2026-04-13 23:51:03.593064: +2026-04-13 23:51:03.596299: Epoch 2810 +2026-04-13 23:51:03.600610: Current learning rate: 0.00336 +2026-04-13 23:52:45.487750: train_loss -0.4109 +2026-04-13 23:52:45.497477: val_loss -0.3384 +2026-04-13 23:52:45.501433: Pseudo dice [0.7068, 0.0, 0.7746, 0.2145, 0.4465, 0.4843, 0.6545] +2026-04-13 23:52:45.506170: Epoch time: 101.9 s +2026-04-13 23:52:46.735781: +2026-04-13 23:52:46.738261: Epoch 2811 +2026-04-13 23:52:46.740258: Current learning rate: 0.00336 +2026-04-13 23:54:28.722161: train_loss -0.3902 +2026-04-13 23:54:28.728675: val_loss -0.3617 +2026-04-13 23:54:28.731104: Pseudo dice [0.7293, 0.0, 0.692, 0.1369, 0.3704, 0.6138, 0.8251] +2026-04-13 23:54:28.733481: Epoch time: 101.99 s +2026-04-13 23:54:29.959349: +2026-04-13 23:54:29.961758: Epoch 2812 +2026-04-13 23:54:29.963890: Current learning rate: 0.00335 +2026-04-13 23:56:11.683157: train_loss -0.4356 +2026-04-13 23:56:11.689201: val_loss -0.3859 +2026-04-13 23:56:11.691411: Pseudo dice [0.5301, 0.0, 0.7877, 0.6518, 0.545, 0.8438, 0.7615] +2026-04-13 23:56:11.694224: Epoch time: 101.73 s +2026-04-13 23:56:13.992695: +2026-04-13 23:56:13.994702: Epoch 2813 +2026-04-13 23:56:13.996359: Current learning rate: 0.00335 +2026-04-13 23:57:55.856205: train_loss -0.4378 +2026-04-13 23:57:55.863240: val_loss -0.3518 +2026-04-13 23:57:55.867314: Pseudo dice [0.6999, 0.0, 0.8654, 0.5449, 0.3616, 0.6519, 0.8343] +2026-04-13 23:57:55.870296: Epoch time: 101.87 s +2026-04-13 23:57:57.116313: +2026-04-13 23:57:57.118638: Epoch 2814 +2026-04-13 23:57:57.122467: Current learning rate: 0.00335 +2026-04-13 23:59:38.710484: train_loss -0.4157 +2026-04-13 23:59:38.717027: val_loss -0.3722 +2026-04-13 23:59:38.718931: Pseudo dice [0.433, 0.0, 0.6444, 0.0029, 0.4696, 0.5518, 0.8086] +2026-04-13 23:59:38.721889: Epoch time: 101.6 s +2026-04-13 23:59:39.930959: +2026-04-13 23:59:39.933120: Epoch 2815 +2026-04-13 23:59:39.935127: Current learning rate: 0.00335 +2026-04-14 00:01:22.884109: train_loss -0.4229 +2026-04-14 00:01:22.894299: val_loss -0.3416 +2026-04-14 00:01:22.897322: Pseudo dice [0.2932, 0.0, 0.7625, 0.113, 0.3148, 0.1829, 0.7044] +2026-04-14 00:01:22.900408: Epoch time: 102.96 s +2026-04-14 00:01:24.147758: +2026-04-14 00:01:24.150944: Epoch 2816 +2026-04-14 00:01:24.153305: Current learning rate: 0.00334 +2026-04-14 00:03:06.355867: train_loss -0.4193 +2026-04-14 00:03:06.362707: val_loss -0.3693 +2026-04-14 00:03:06.365134: Pseudo dice [0.6534, 0.0, 0.6418, 0.3161, 0.5449, 0.8026, 0.8793] +2026-04-14 00:03:06.367584: Epoch time: 102.21 s +2026-04-14 00:03:07.612740: +2026-04-14 00:03:07.615336: Epoch 2817 +2026-04-14 00:03:07.617823: Current learning rate: 0.00334 +2026-04-14 00:04:49.306086: train_loss -0.4212 +2026-04-14 00:04:49.315619: val_loss -0.3499 +2026-04-14 00:04:49.318442: Pseudo dice [0.4765, 0.0, 0.7186, 0.0573, 0.5237, 0.5725, 0.6674] +2026-04-14 00:04:49.321429: Epoch time: 101.7 s +2026-04-14 00:04:50.565805: +2026-04-14 00:04:50.567911: Epoch 2818 +2026-04-14 00:04:50.570018: Current learning rate: 0.00334 +2026-04-14 00:06:32.370649: train_loss -0.4206 +2026-04-14 00:06:32.377626: val_loss -0.3749 +2026-04-14 00:06:32.380569: Pseudo dice [0.7489, 0.0, 0.8549, 0.1484, 0.4212, 0.4979, 0.8808] +2026-04-14 00:06:32.383254: Epoch time: 101.81 s +2026-04-14 00:06:33.609679: +2026-04-14 00:06:33.611640: Epoch 2819 +2026-04-14 00:06:33.613564: Current learning rate: 0.00334 +2026-04-14 00:08:15.607591: train_loss -0.4138 +2026-04-14 00:08:15.614143: val_loss -0.3645 +2026-04-14 00:08:15.616649: Pseudo dice [0.7204, 0.0, 0.7982, 0.6863, 0.2686, 0.4488, 0.8653] +2026-04-14 00:08:15.618885: Epoch time: 102.0 s +2026-04-14 00:08:16.849021: +2026-04-14 00:08:16.851297: Epoch 2820 +2026-04-14 00:08:16.853183: Current learning rate: 0.00333 +2026-04-14 00:09:58.424068: train_loss -0.4041 +2026-04-14 00:09:58.434994: val_loss -0.345 +2026-04-14 00:09:58.438175: Pseudo dice [0.6192, 0.0, 0.7718, 0.5915, 0.5868, 0.6581, 0.7041] +2026-04-14 00:09:58.443806: Epoch time: 101.58 s +2026-04-14 00:09:59.674454: +2026-04-14 00:09:59.676852: Epoch 2821 +2026-04-14 00:09:59.678521: Current learning rate: 0.00333 +2026-04-14 00:11:41.382931: train_loss -0.406 +2026-04-14 00:11:41.389976: val_loss -0.3368 +2026-04-14 00:11:41.392088: Pseudo dice [0.4119, 0.0, 0.7904, 0.5336, 0.388, 0.5196, 0.6893] +2026-04-14 00:11:41.394618: Epoch time: 101.71 s +2026-04-14 00:11:42.665970: +2026-04-14 00:11:42.668095: Epoch 2822 +2026-04-14 00:11:42.669789: Current learning rate: 0.00333 +2026-04-14 00:13:24.329468: train_loss -0.4327 +2026-04-14 00:13:24.338115: val_loss -0.3506 +2026-04-14 00:13:24.340747: Pseudo dice [0.4865, 0.0, 0.6704, 0.704, 0.373, 0.8605, 0.7816] +2026-04-14 00:13:24.343347: Epoch time: 101.67 s +2026-04-14 00:13:25.588298: +2026-04-14 00:13:25.590280: Epoch 2823 +2026-04-14 00:13:25.592203: Current learning rate: 0.00333 +2026-04-14 00:15:07.181245: train_loss -0.3676 +2026-04-14 00:15:07.188355: val_loss -0.3608 +2026-04-14 00:15:07.190764: Pseudo dice [0.2759, 0.0, 0.7417, 0.0696, 0.4054, 0.4092, 0.5313] +2026-04-14 00:15:07.193627: Epoch time: 101.6 s +2026-04-14 00:15:08.422935: +2026-04-14 00:15:08.424646: Epoch 2824 +2026-04-14 00:15:08.426263: Current learning rate: 0.00332 +2026-04-14 00:16:50.599412: train_loss -0.3904 +2026-04-14 00:16:50.610291: val_loss -0.3663 +2026-04-14 00:16:50.614726: Pseudo dice [0.1464, 0.0, 0.66, 0.4785, 0.6949, 0.3486, 0.8863] +2026-04-14 00:16:50.618115: Epoch time: 102.18 s +2026-04-14 00:16:51.928546: +2026-04-14 00:16:51.930540: Epoch 2825 +2026-04-14 00:16:51.932105: Current learning rate: 0.00332 +2026-04-14 00:18:33.298609: train_loss -0.4154 +2026-04-14 00:18:33.306326: val_loss -0.3461 +2026-04-14 00:18:33.308861: Pseudo dice [0.4399, 0.0, 0.6409, 0.3651, 0.4698, 0.3401, 0.8858] +2026-04-14 00:18:33.311841: Epoch time: 101.37 s +2026-04-14 00:18:34.541595: +2026-04-14 00:18:34.544888: Epoch 2826 +2026-04-14 00:18:34.547525: Current learning rate: 0.00332 +2026-04-14 00:20:16.318577: train_loss -0.4127 +2026-04-14 00:20:16.325178: val_loss -0.3583 +2026-04-14 00:20:16.327164: Pseudo dice [0.3034, 0.0, 0.6229, 0.5973, 0.4025, 0.7143, 0.8264] +2026-04-14 00:20:16.329692: Epoch time: 101.78 s +2026-04-14 00:20:17.571777: +2026-04-14 00:20:17.573644: Epoch 2827 +2026-04-14 00:20:17.575270: Current learning rate: 0.00332 +2026-04-14 00:21:59.372642: train_loss -0.3962 +2026-04-14 00:21:59.378700: val_loss -0.3486 +2026-04-14 00:21:59.380637: Pseudo dice [0.0, 0.0, 0.8515, 0.7993, 0.4583, 0.7258, 0.7891] +2026-04-14 00:21:59.382712: Epoch time: 101.8 s +2026-04-14 00:22:00.622359: +2026-04-14 00:22:00.624336: Epoch 2828 +2026-04-14 00:22:00.626101: Current learning rate: 0.00331 +2026-04-14 00:23:42.404039: train_loss -0.4137 +2026-04-14 00:23:42.410801: val_loss -0.3216 +2026-04-14 00:23:42.413105: Pseudo dice [0.0, 0.0, 0.8275, 0.4438, 0.2139, 0.4128, 0.6149] +2026-04-14 00:23:42.415517: Epoch time: 101.79 s +2026-04-14 00:23:43.667725: +2026-04-14 00:23:43.669861: Epoch 2829 +2026-04-14 00:23:43.671811: Current learning rate: 0.00331 +2026-04-14 00:25:25.418750: train_loss -0.421 +2026-04-14 00:25:25.425105: val_loss -0.3533 +2026-04-14 00:25:25.427732: Pseudo dice [0.0, 0.0, 0.6534, 0.5432, 0.7066, 0.3289, 0.6553] +2026-04-14 00:25:25.430503: Epoch time: 101.75 s +2026-04-14 00:25:26.651577: +2026-04-14 00:25:26.653620: Epoch 2830 +2026-04-14 00:25:26.655318: Current learning rate: 0.00331 +2026-04-14 00:27:08.078885: train_loss -0.4199 +2026-04-14 00:27:08.084496: val_loss -0.3764 +2026-04-14 00:27:08.086680: Pseudo dice [0.0, 0.0, 0.8008, 0.0042, 0.4329, 0.7217, 0.8285] +2026-04-14 00:27:08.089242: Epoch time: 101.43 s +2026-04-14 00:27:09.322675: +2026-04-14 00:27:09.326157: Epoch 2831 +2026-04-14 00:27:09.328743: Current learning rate: 0.00331 +2026-04-14 00:28:50.863044: train_loss -0.4399 +2026-04-14 00:28:50.870013: val_loss -0.3198 +2026-04-14 00:28:50.873446: Pseudo dice [0.0, 0.0, 0.7114, 0.4524, 0.5162, 0.2804, 0.8709] +2026-04-14 00:28:50.876022: Epoch time: 101.54 s +2026-04-14 00:28:52.151480: +2026-04-14 00:28:52.153323: Epoch 2832 +2026-04-14 00:28:52.155039: Current learning rate: 0.0033 +2026-04-14 00:30:33.550917: train_loss -0.4411 +2026-04-14 00:30:33.557744: val_loss -0.4033 +2026-04-14 00:30:33.560536: Pseudo dice [0.0, 0.0, 0.8646, 0.4907, 0.6109, 0.5924, 0.9146] +2026-04-14 00:30:33.562720: Epoch time: 101.4 s +2026-04-14 00:30:34.853900: +2026-04-14 00:30:34.855606: Epoch 2833 +2026-04-14 00:30:34.857374: Current learning rate: 0.0033 +2026-04-14 00:32:17.417904: train_loss -0.4184 +2026-04-14 00:32:17.425799: val_loss -0.321 +2026-04-14 00:32:17.427867: Pseudo dice [0.0, 0.0, 0.7716, 0.7516, 0.489, 0.5136, 0.4716] +2026-04-14 00:32:17.430133: Epoch time: 102.57 s +2026-04-14 00:32:18.680284: +2026-04-14 00:32:18.682230: Epoch 2834 +2026-04-14 00:32:18.684042: Current learning rate: 0.0033 +2026-04-14 00:34:00.084030: train_loss -0.422 +2026-04-14 00:34:00.094966: val_loss -0.3642 +2026-04-14 00:34:00.098726: Pseudo dice [0.0, 0.0, 0.761, 0.019, 0.4218, 0.248, 0.9358] +2026-04-14 00:34:00.103937: Epoch time: 101.41 s +2026-04-14 00:34:01.350986: +2026-04-14 00:34:01.352980: Epoch 2835 +2026-04-14 00:34:01.354688: Current learning rate: 0.00329 +2026-04-14 00:35:43.876098: train_loss -0.4167 +2026-04-14 00:35:43.882268: val_loss -0.3969 +2026-04-14 00:35:43.884431: Pseudo dice [0.0, 0.0, 0.8346, 0.3888, 0.5854, 0.5857, 0.6312] +2026-04-14 00:35:43.887092: Epoch time: 102.53 s +2026-04-14 00:35:45.131771: +2026-04-14 00:35:45.134180: Epoch 2836 +2026-04-14 00:35:45.136180: Current learning rate: 0.00329 +2026-04-14 00:37:27.037248: train_loss -0.4173 +2026-04-14 00:37:27.043789: val_loss -0.361 +2026-04-14 00:37:27.045770: Pseudo dice [0.0, 0.0, 0.8151, 0.654, 0.5837, 0.7186, 0.8638] +2026-04-14 00:37:27.048233: Epoch time: 101.91 s +2026-04-14 00:37:28.268507: +2026-04-14 00:37:28.270416: Epoch 2837 +2026-04-14 00:37:28.272132: Current learning rate: 0.00329 +2026-04-14 00:39:09.933927: train_loss -0.4325 +2026-04-14 00:39:09.940063: val_loss -0.3291 +2026-04-14 00:39:09.942399: Pseudo dice [0.0, 0.0, 0.5147, 0.0415, 0.3949, 0.7091, 0.8429] +2026-04-14 00:39:09.944788: Epoch time: 101.67 s +2026-04-14 00:39:11.187485: +2026-04-14 00:39:11.189573: Epoch 2838 +2026-04-14 00:39:11.191866: Current learning rate: 0.00329 +2026-04-14 00:40:53.112077: train_loss -0.4197 +2026-04-14 00:40:53.118131: val_loss -0.3657 +2026-04-14 00:40:53.120246: Pseudo dice [0.0, 0.0, 0.8089, 0.6971, 0.5201, 0.7389, 0.8813] +2026-04-14 00:40:53.122697: Epoch time: 101.93 s +2026-04-14 00:40:54.375234: +2026-04-14 00:40:54.377151: Epoch 2839 +2026-04-14 00:40:54.378926: Current learning rate: 0.00328 +2026-04-14 00:42:36.096482: train_loss -0.4196 +2026-04-14 00:42:36.103016: val_loss -0.3587 +2026-04-14 00:42:36.105062: Pseudo dice [0.0, 0.0, 0.6028, 0.7662, 0.5567, 0.5779, 0.9325] +2026-04-14 00:42:36.107144: Epoch time: 101.72 s +2026-04-14 00:42:37.359323: +2026-04-14 00:42:37.360954: Epoch 2840 +2026-04-14 00:42:37.362586: Current learning rate: 0.00328 +2026-04-14 00:44:19.541601: train_loss -0.4268 +2026-04-14 00:44:19.548284: val_loss -0.374 +2026-04-14 00:44:19.550143: Pseudo dice [0.0, 0.0, 0.7418, 0.6425, 0.537, 0.7621, 0.8869] +2026-04-14 00:44:19.552282: Epoch time: 102.19 s +2026-04-14 00:44:20.780283: +2026-04-14 00:44:20.782452: Epoch 2841 +2026-04-14 00:44:20.783978: Current learning rate: 0.00328 +2026-04-14 00:46:02.420580: train_loss -0.4365 +2026-04-14 00:46:02.427204: val_loss -0.3554 +2026-04-14 00:46:02.429406: Pseudo dice [0.0, 0.0, 0.7805, 0.601, 0.4883, 0.7597, 0.8928] +2026-04-14 00:46:02.431711: Epoch time: 101.64 s +2026-04-14 00:46:03.652247: +2026-04-14 00:46:03.654287: Epoch 2842 +2026-04-14 00:46:03.656259: Current learning rate: 0.00328 +2026-04-14 00:47:45.440856: train_loss -0.4183 +2026-04-14 00:47:45.447227: val_loss -0.388 +2026-04-14 00:47:45.449741: Pseudo dice [0.0, 0.0, 0.7968, 0.6655, 0.5773, 0.5543, 0.6526] +2026-04-14 00:47:45.452796: Epoch time: 101.79 s +2026-04-14 00:47:46.670345: +2026-04-14 00:47:46.672215: Epoch 2843 +2026-04-14 00:47:46.675003: Current learning rate: 0.00327 +2026-04-14 00:49:28.627119: train_loss -0.4324 +2026-04-14 00:49:28.633852: val_loss -0.3533 +2026-04-14 00:49:28.635809: Pseudo dice [0.0, 0.0, 0.828, 0.7136, 0.6025, 0.3694, 0.8968] +2026-04-14 00:49:28.638598: Epoch time: 101.96 s +2026-04-14 00:49:29.875190: +2026-04-14 00:49:29.877383: Epoch 2844 +2026-04-14 00:49:29.879862: Current learning rate: 0.00327 +2026-04-14 00:51:11.620485: train_loss -0.4113 +2026-04-14 00:51:11.626624: val_loss -0.3268 +2026-04-14 00:51:11.628500: Pseudo dice [0.0, 0.0, 0.6771, 0.6534, 0.4079, 0.3586, 0.5739] +2026-04-14 00:51:11.630862: Epoch time: 101.75 s +2026-04-14 00:51:12.855329: +2026-04-14 00:51:12.857239: Epoch 2845 +2026-04-14 00:51:12.858822: Current learning rate: 0.00327 +2026-04-14 00:52:54.445026: train_loss -0.4393 +2026-04-14 00:52:54.451921: val_loss -0.3618 +2026-04-14 00:52:54.454659: Pseudo dice [0.0, 0.0, 0.8056, 0.4268, 0.4426, 0.8389, 0.8437] +2026-04-14 00:52:54.457279: Epoch time: 101.59 s +2026-04-14 00:52:55.755224: +2026-04-14 00:52:55.757438: Epoch 2846 +2026-04-14 00:52:55.759197: Current learning rate: 0.00327 +2026-04-14 00:54:37.917345: train_loss -0.4421 +2026-04-14 00:54:37.925144: val_loss -0.3458 +2026-04-14 00:54:37.927542: Pseudo dice [0.2241, 0.0, 0.8144, 0.4356, 0.3972, 0.7945, 0.893] +2026-04-14 00:54:37.929972: Epoch time: 102.17 s +2026-04-14 00:54:39.135458: +2026-04-14 00:54:39.137820: Epoch 2847 +2026-04-14 00:54:39.140069: Current learning rate: 0.00326 +2026-04-14 00:56:20.739537: train_loss -0.445 +2026-04-14 00:56:20.746916: val_loss -0.3992 +2026-04-14 00:56:20.749058: Pseudo dice [0.4869, 0.0, 0.6282, 0.1871, 0.5154, 0.5869, 0.8691] +2026-04-14 00:56:20.752194: Epoch time: 101.61 s +2026-04-14 00:56:21.974248: +2026-04-14 00:56:21.976458: Epoch 2848 +2026-04-14 00:56:21.978082: Current learning rate: 0.00326 +2026-04-14 00:58:03.896671: train_loss -0.4444 +2026-04-14 00:58:03.903627: val_loss -0.3302 +2026-04-14 00:58:03.905773: Pseudo dice [0.5326, 0.0, 0.7878, 0.5049, 0.426, 0.7097, 0.364] +2026-04-14 00:58:03.908323: Epoch time: 101.93 s +2026-04-14 00:58:05.145323: +2026-04-14 00:58:05.147260: Epoch 2849 +2026-04-14 00:58:05.148803: Current learning rate: 0.00326 +2026-04-14 00:59:47.495502: train_loss -0.4379 +2026-04-14 00:59:47.502691: val_loss -0.3766 +2026-04-14 00:59:47.505262: Pseudo dice [0.5762, 0.0, 0.8523, 0.7683, 0.5777, 0.6821, 0.7563] +2026-04-14 00:59:47.508512: Epoch time: 102.35 s +2026-04-14 00:59:50.590544: +2026-04-14 00:59:50.592525: Epoch 2850 +2026-04-14 00:59:50.594118: Current learning rate: 0.00326 +2026-04-14 01:01:32.589252: train_loss -0.4408 +2026-04-14 01:01:32.595637: val_loss -0.3702 +2026-04-14 01:01:32.597424: Pseudo dice [0.3584, 0.0, 0.7165, 0.5128, 0.3839, 0.7914, 0.923] +2026-04-14 01:01:32.600145: Epoch time: 102.0 s +2026-04-14 01:01:33.891605: +2026-04-14 01:01:33.893512: Epoch 2851 +2026-04-14 01:01:33.895019: Current learning rate: 0.00325 +2026-04-14 01:03:15.312195: train_loss -0.4325 +2026-04-14 01:03:15.319028: val_loss -0.3698 +2026-04-14 01:03:15.320977: Pseudo dice [0.3346, 0.0, 0.5941, 0.8722, 0.4183, 0.9388, 0.9342] +2026-04-14 01:03:15.323394: Epoch time: 101.42 s +2026-04-14 01:03:16.652851: +2026-04-14 01:03:16.655016: Epoch 2852 +2026-04-14 01:03:16.656697: Current learning rate: 0.00325 +2026-04-14 01:04:59.309334: train_loss -0.419 +2026-04-14 01:04:59.315999: val_loss -0.3278 +2026-04-14 01:04:59.320533: Pseudo dice [0.2314, 0.0, 0.5496, 0.4046, 0.6558, 0.8499, 0.8759] +2026-04-14 01:04:59.327184: Epoch time: 102.66 s +2026-04-14 01:05:00.573301: +2026-04-14 01:05:00.575433: Epoch 2853 +2026-04-14 01:05:00.577311: Current learning rate: 0.00325 +2026-04-14 01:06:43.716169: train_loss -0.4338 +2026-04-14 01:06:43.725178: val_loss -0.3553 +2026-04-14 01:06:43.727297: Pseudo dice [0.685, 0.0, 0.7343, 0.4715, 0.468, 0.2323, 0.8382] +2026-04-14 01:06:43.734816: Epoch time: 103.15 s +2026-04-14 01:06:44.986215: +2026-04-14 01:06:44.988209: Epoch 2854 +2026-04-14 01:06:44.990038: Current learning rate: 0.00325 +2026-04-14 01:08:26.463582: train_loss -0.4359 +2026-04-14 01:08:26.474839: val_loss -0.3931 +2026-04-14 01:08:26.477046: Pseudo dice [0.729, 0.0, 0.7337, 0.2387, 0.5904, 0.5018, 0.8404] +2026-04-14 01:08:26.479786: Epoch time: 101.48 s +2026-04-14 01:08:27.691940: +2026-04-14 01:08:27.693596: Epoch 2855 +2026-04-14 01:08:27.695130: Current learning rate: 0.00324 +2026-04-14 01:10:09.403618: train_loss -0.4442 +2026-04-14 01:10:09.410973: val_loss -0.3789 +2026-04-14 01:10:09.413314: Pseudo dice [0.6247, 0.0, 0.8718, 0.6304, 0.454, 0.4557, 0.9113] +2026-04-14 01:10:09.417295: Epoch time: 101.71 s +2026-04-14 01:10:10.702616: +2026-04-14 01:10:10.706450: Epoch 2856 +2026-04-14 01:10:10.708360: Current learning rate: 0.00324 +2026-04-14 01:11:52.373939: train_loss -0.4287 +2026-04-14 01:11:52.380398: val_loss -0.3297 +2026-04-14 01:11:52.383938: Pseudo dice [0.6408, 0.0, 0.7716, 0.6318, 0.5727, 0.6047, 0.3869] +2026-04-14 01:11:52.386810: Epoch time: 101.67 s +2026-04-14 01:11:53.638154: +2026-04-14 01:11:53.640043: Epoch 2857 +2026-04-14 01:11:53.641802: Current learning rate: 0.00324 +2026-04-14 01:13:34.996664: train_loss -0.4419 +2026-04-14 01:13:35.003254: val_loss -0.3568 +2026-04-14 01:13:35.005429: Pseudo dice [0.6075, 0.0, 0.7924, 0.2877, 0.3741, 0.714, 0.9136] +2026-04-14 01:13:35.007970: Epoch time: 101.36 s +2026-04-14 01:13:36.227284: +2026-04-14 01:13:36.229005: Epoch 2858 +2026-04-14 01:13:36.230609: Current learning rate: 0.00324 +2026-04-14 01:15:17.842878: train_loss -0.4436 +2026-04-14 01:15:17.849484: val_loss -0.3746 +2026-04-14 01:15:17.852135: Pseudo dice [0.8478, 0.0, 0.8674, 0.7418, 0.5904, 0.8032, 0.9345] +2026-04-14 01:15:17.855211: Epoch time: 101.62 s +2026-04-14 01:15:19.313689: +2026-04-14 01:15:19.315275: Epoch 2859 +2026-04-14 01:15:19.316790: Current learning rate: 0.00323 +2026-04-14 01:17:00.817077: train_loss -0.4476 +2026-04-14 01:17:00.823696: val_loss -0.387 +2026-04-14 01:17:00.826699: Pseudo dice [0.5852, 0.0, 0.6252, 0.6708, 0.614, 0.8779, 0.8899] +2026-04-14 01:17:00.829241: Epoch time: 101.51 s +2026-04-14 01:17:02.113230: +2026-04-14 01:17:02.115493: Epoch 2860 +2026-04-14 01:17:02.117057: Current learning rate: 0.00323 +2026-04-14 01:18:43.954945: train_loss -0.4097 +2026-04-14 01:18:43.961246: val_loss -0.3281 +2026-04-14 01:18:43.964890: Pseudo dice [0.4199, 0.0, 0.6159, 0.4621, 0.5471, 0.5358, 0.8822] +2026-04-14 01:18:43.967304: Epoch time: 101.84 s +2026-04-14 01:18:45.203261: +2026-04-14 01:18:45.205369: Epoch 2861 +2026-04-14 01:18:45.207300: Current learning rate: 0.00323 +2026-04-14 01:20:26.774255: train_loss -0.4314 +2026-04-14 01:20:26.780671: val_loss -0.3419 +2026-04-14 01:20:26.783238: Pseudo dice [0.4692, 0.0, 0.7105, 0.1721, 0.4844, 0.6283, 0.9044] +2026-04-14 01:20:26.786665: Epoch time: 101.57 s +2026-04-14 01:20:28.047428: +2026-04-14 01:20:28.049378: Epoch 2862 +2026-04-14 01:20:28.050959: Current learning rate: 0.00323 +2026-04-14 01:22:09.678182: train_loss -0.45 +2026-04-14 01:22:09.685399: val_loss -0.3744 +2026-04-14 01:22:09.687679: Pseudo dice [0.6313, 0.0, 0.8346, 0.6361, 0.4464, 0.7635, 0.8618] +2026-04-14 01:22:09.690495: Epoch time: 101.63 s +2026-04-14 01:22:10.942097: +2026-04-14 01:22:10.943968: Epoch 2863 +2026-04-14 01:22:10.945580: Current learning rate: 0.00322 +2026-04-14 01:23:53.435468: train_loss -0.4304 +2026-04-14 01:23:53.442861: val_loss -0.3338 +2026-04-14 01:23:53.445362: Pseudo dice [0.5068, 0.0, 0.5171, 0.7899, 0.415, 0.72, 0.2256] +2026-04-14 01:23:53.447722: Epoch time: 102.5 s +2026-04-14 01:23:54.684859: +2026-04-14 01:23:54.687122: Epoch 2864 +2026-04-14 01:23:54.688954: Current learning rate: 0.00322 +2026-04-14 01:25:36.053019: train_loss -0.4318 +2026-04-14 01:25:36.062345: val_loss -0.3752 +2026-04-14 01:25:36.064799: Pseudo dice [0.508, 0.0, 0.8345, 0.5262, 0.3798, 0.7811, 0.7084] +2026-04-14 01:25:36.068407: Epoch time: 101.37 s +2026-04-14 01:25:37.384699: +2026-04-14 01:25:37.386529: Epoch 2865 +2026-04-14 01:25:37.388050: Current learning rate: 0.00322 +2026-04-14 01:27:18.847173: train_loss -0.4262 +2026-04-14 01:27:18.853808: val_loss -0.3703 +2026-04-14 01:27:18.856326: Pseudo dice [0.4599, 0.0, 0.9071, 0.4192, 0.4641, 0.579, 0.8792] +2026-04-14 01:27:18.858887: Epoch time: 101.47 s +2026-04-14 01:27:20.107740: +2026-04-14 01:27:20.109568: Epoch 2866 +2026-04-14 01:27:20.111317: Current learning rate: 0.00322 +2026-04-14 01:29:01.952153: train_loss -0.4347 +2026-04-14 01:29:01.958893: val_loss -0.3297 +2026-04-14 01:29:01.960869: Pseudo dice [0.5154, 0.0, 0.7183, 0.1325, 0.5067, 0.4282, 0.6692] +2026-04-14 01:29:01.963664: Epoch time: 101.85 s +2026-04-14 01:29:03.225320: +2026-04-14 01:29:03.227275: Epoch 2867 +2026-04-14 01:29:03.229919: Current learning rate: 0.00321 +2026-04-14 01:30:45.146669: train_loss -0.4264 +2026-04-14 01:30:45.171915: val_loss -0.4235 +2026-04-14 01:30:45.174093: Pseudo dice [0.736, 0.0, 0.8081, 0.8877, 0.5614, 0.4892, 0.7899] +2026-04-14 01:30:45.176727: Epoch time: 101.92 s +2026-04-14 01:30:46.460665: +2026-04-14 01:30:46.462445: Epoch 2868 +2026-04-14 01:30:46.463993: Current learning rate: 0.00321 +2026-04-14 01:32:27.907796: train_loss -0.4539 +2026-04-14 01:32:27.915667: val_loss -0.3792 +2026-04-14 01:32:27.917639: Pseudo dice [0.7009, 0.0, 0.7419, 0.3268, 0.5566, 0.4317, 0.9122] +2026-04-14 01:32:27.919463: Epoch time: 101.45 s +2026-04-14 01:32:29.203916: +2026-04-14 01:32:29.205718: Epoch 2869 +2026-04-14 01:32:29.207200: Current learning rate: 0.00321 +2026-04-14 01:34:11.000927: train_loss -0.433 +2026-04-14 01:34:11.008301: val_loss -0.3708 +2026-04-14 01:34:11.010777: Pseudo dice [0.6046, 0.0, 0.7868, 0.1248, 0.5185, 0.7761, 0.5579] +2026-04-14 01:34:11.012787: Epoch time: 101.8 s +2026-04-14 01:34:12.352699: +2026-04-14 01:34:12.354355: Epoch 2870 +2026-04-14 01:34:12.356203: Current learning rate: 0.00321 +2026-04-14 01:35:53.902609: train_loss -0.4272 +2026-04-14 01:35:53.910082: val_loss -0.3562 +2026-04-14 01:35:53.912732: Pseudo dice [0.5709, 0.0, 0.7492, 0.6455, 0.6595, 0.4368, 0.9013] +2026-04-14 01:35:53.916301: Epoch time: 101.55 s +2026-04-14 01:35:55.175335: +2026-04-14 01:35:55.177036: Epoch 2871 +2026-04-14 01:35:55.178523: Current learning rate: 0.0032 +2026-04-14 01:37:37.075261: train_loss -0.4419 +2026-04-14 01:37:37.081782: val_loss -0.3623 +2026-04-14 01:37:37.084231: Pseudo dice [0.6177, 0.0, 0.8295, 0.7859, 0.4605, 0.5723, 0.9036] +2026-04-14 01:37:37.086629: Epoch time: 101.9 s +2026-04-14 01:37:38.343727: +2026-04-14 01:37:38.345437: Epoch 2872 +2026-04-14 01:37:38.347051: Current learning rate: 0.0032 +2026-04-14 01:39:19.838399: train_loss -0.4551 +2026-04-14 01:39:19.844555: val_loss -0.3767 +2026-04-14 01:39:19.846802: Pseudo dice [0.3877, 0.0, 0.7789, 0.0115, 0.4061, 0.7737, 0.8959] +2026-04-14 01:39:19.849200: Epoch time: 101.5 s +2026-04-14 01:39:22.179513: +2026-04-14 01:39:22.181352: Epoch 2873 +2026-04-14 01:39:22.182859: Current learning rate: 0.0032 +2026-04-14 01:41:03.697147: train_loss -0.4273 +2026-04-14 01:41:03.704316: val_loss -0.3778 +2026-04-14 01:41:03.707313: Pseudo dice [0.7937, 0.0, 0.7437, 0.6962, 0.6072, 0.8393, 0.8985] +2026-04-14 01:41:03.709634: Epoch time: 101.52 s +2026-04-14 01:41:04.995785: +2026-04-14 01:41:04.997813: Epoch 2874 +2026-04-14 01:41:04.999493: Current learning rate: 0.0032 +2026-04-14 01:42:46.687747: train_loss -0.4272 +2026-04-14 01:42:46.696213: val_loss -0.3542 +2026-04-14 01:42:46.698601: Pseudo dice [0.5694, 0.0, 0.8199, 0.4856, 0.5499, 0.8724, 0.8988] +2026-04-14 01:42:46.702802: Epoch time: 101.7 s +2026-04-14 01:42:48.039844: +2026-04-14 01:42:48.043511: Epoch 2875 +2026-04-14 01:42:48.045488: Current learning rate: 0.00319 +2026-04-14 01:44:29.456044: train_loss -0.4421 +2026-04-14 01:44:29.464011: val_loss -0.3864 +2026-04-14 01:44:29.466373: Pseudo dice [0.2858, 0.0, 0.7879, 0.8305, 0.5427, 0.5346, 0.9223] +2026-04-14 01:44:29.468967: Epoch time: 101.42 s +2026-04-14 01:44:30.722706: +2026-04-14 01:44:30.726111: Epoch 2876 +2026-04-14 01:44:30.728260: Current learning rate: 0.00319 +2026-04-14 01:46:12.351042: train_loss -0.4357 +2026-04-14 01:46:12.358860: val_loss -0.376 +2026-04-14 01:46:12.361631: Pseudo dice [0.526, 0.0, 0.5878, 0.4751, 0.2893, 0.8015, 0.8566] +2026-04-14 01:46:12.364584: Epoch time: 101.63 s +2026-04-14 01:46:13.615398: +2026-04-14 01:46:13.617294: Epoch 2877 +2026-04-14 01:46:13.619015: Current learning rate: 0.00319 +2026-04-14 01:47:55.028460: train_loss -0.4194 +2026-04-14 01:47:55.035241: val_loss -0.3547 +2026-04-14 01:47:55.039732: Pseudo dice [0.2379, 0.0, 0.7416, 0.1432, 0.4652, 0.3959, 0.7555] +2026-04-14 01:47:55.043741: Epoch time: 101.42 s +2026-04-14 01:47:56.309329: +2026-04-14 01:47:56.311448: Epoch 2878 +2026-04-14 01:47:56.313322: Current learning rate: 0.00319 +2026-04-14 01:49:38.068331: train_loss -0.4295 +2026-04-14 01:49:38.074357: val_loss -0.377 +2026-04-14 01:49:38.075990: Pseudo dice [0.7729, 0.0, 0.5841, 0.6799, 0.4804, 0.6883, 0.7047] +2026-04-14 01:49:38.078249: Epoch time: 101.76 s +2026-04-14 01:49:39.362286: +2026-04-14 01:49:39.364127: Epoch 2879 +2026-04-14 01:49:39.365919: Current learning rate: 0.00318 +2026-04-14 01:51:20.899105: train_loss -0.4552 +2026-04-14 01:51:20.905670: val_loss -0.3786 +2026-04-14 01:51:20.907834: Pseudo dice [0.6097, 0.0, 0.7595, 0.7099, 0.5601, 0.4401, 0.9021] +2026-04-14 01:51:20.910533: Epoch time: 101.54 s +2026-04-14 01:51:22.195761: +2026-04-14 01:51:22.197688: Epoch 2880 +2026-04-14 01:51:22.199368: Current learning rate: 0.00318 +2026-04-14 01:53:03.550861: train_loss -0.4245 +2026-04-14 01:53:03.557964: val_loss -0.3733 +2026-04-14 01:53:03.559937: Pseudo dice [0.4351, 0.0, 0.8535, 0.0099, 0.2876, 0.5715, 0.7594] +2026-04-14 01:53:03.562145: Epoch time: 101.36 s +2026-04-14 01:53:04.829125: +2026-04-14 01:53:04.831104: Epoch 2881 +2026-04-14 01:53:04.832942: Current learning rate: 0.00318 +2026-04-14 01:54:46.287280: train_loss -0.4284 +2026-04-14 01:54:46.294715: val_loss -0.405 +2026-04-14 01:54:46.297077: Pseudo dice [0.8324, 0.0, 0.7507, 0.4097, 0.6098, 0.7563, 0.8845] +2026-04-14 01:54:46.299314: Epoch time: 101.46 s +2026-04-14 01:54:47.551953: +2026-04-14 01:54:47.553652: Epoch 2882 +2026-04-14 01:54:47.555196: Current learning rate: 0.00317 +2026-04-14 01:56:28.982290: train_loss -0.4384 +2026-04-14 01:56:28.989604: val_loss -0.3934 +2026-04-14 01:56:28.991425: Pseudo dice [0.8224, 0.0, 0.6568, 0.7249, 0.4957, 0.7902, 0.777] +2026-04-14 01:56:28.995334: Epoch time: 101.43 s +2026-04-14 01:56:30.244695: +2026-04-14 01:56:30.247299: Epoch 2883 +2026-04-14 01:56:30.248850: Current learning rate: 0.00317 +2026-04-14 01:58:11.730534: train_loss -0.4305 +2026-04-14 01:58:11.740430: val_loss -0.4079 +2026-04-14 01:58:11.743699: Pseudo dice [0.6518, 0.0, 0.8146, 0.4942, 0.4099, 0.6994, 0.8905] +2026-04-14 01:58:11.755393: Epoch time: 101.49 s +2026-04-14 01:58:13.005916: +2026-04-14 01:58:13.007825: Epoch 2884 +2026-04-14 01:58:13.009512: Current learning rate: 0.00317 +2026-04-14 01:59:54.755826: train_loss -0.4314 +2026-04-14 01:59:54.762456: val_loss -0.3587 +2026-04-14 01:59:54.765297: Pseudo dice [0.0, 0.0, 0.7818, 0.6814, 0.5606, 0.7326, 0.8891] +2026-04-14 01:59:54.767421: Epoch time: 101.75 s +2026-04-14 01:59:56.017326: +2026-04-14 01:59:56.019264: Epoch 2885 +2026-04-14 01:59:56.021144: Current learning rate: 0.00317 +2026-04-14 02:01:37.931047: train_loss -0.4285 +2026-04-14 02:01:37.938178: val_loss -0.38 +2026-04-14 02:01:37.940588: Pseudo dice [0.802, 0.0, 0.7719, 0.1825, 0.5514, 0.7131, 0.5752] +2026-04-14 02:01:37.942906: Epoch time: 101.92 s +2026-04-14 02:01:39.224020: +2026-04-14 02:01:39.226411: Epoch 2886 +2026-04-14 02:01:39.228215: Current learning rate: 0.00316 +2026-04-14 02:03:21.097108: train_loss -0.4421 +2026-04-14 02:03:21.105165: val_loss -0.3725 +2026-04-14 02:03:21.107746: Pseudo dice [0.5388, 0.0, 0.8682, 0.5384, 0.6296, 0.6735, 0.7499] +2026-04-14 02:03:21.111191: Epoch time: 101.88 s +2026-04-14 02:03:22.364390: +2026-04-14 02:03:22.366995: Epoch 2887 +2026-04-14 02:03:22.369817: Current learning rate: 0.00316 +2026-04-14 02:05:04.162550: train_loss -0.4484 +2026-04-14 02:05:04.169265: val_loss -0.3966 +2026-04-14 02:05:04.171531: Pseudo dice [0.7231, 0.0, 0.7792, 0.8527, 0.6484, 0.4137, 0.8678] +2026-04-14 02:05:04.174072: Epoch time: 101.8 s +2026-04-14 02:05:05.453076: +2026-04-14 02:05:05.455408: Epoch 2888 +2026-04-14 02:05:05.457554: Current learning rate: 0.00316 +2026-04-14 02:06:47.075341: train_loss -0.4342 +2026-04-14 02:06:47.082370: val_loss -0.3688 +2026-04-14 02:06:47.085320: Pseudo dice [0.5657, 0.0, 0.4387, 0.1853, 0.4629, 0.4239, 0.8839] +2026-04-14 02:06:47.088059: Epoch time: 101.63 s +2026-04-14 02:06:48.356411: +2026-04-14 02:06:48.358230: Epoch 2889 +2026-04-14 02:06:48.360394: Current learning rate: 0.00316 +2026-04-14 02:08:29.876694: train_loss -0.4374 +2026-04-14 02:08:29.884346: val_loss -0.3687 +2026-04-14 02:08:29.886735: Pseudo dice [0.4865, 0.0, 0.6494, 0.5518, 0.6254, 0.462, 0.8889] +2026-04-14 02:08:29.888982: Epoch time: 101.52 s +2026-04-14 02:08:31.216697: +2026-04-14 02:08:31.221704: Epoch 2890 +2026-04-14 02:08:31.224259: Current learning rate: 0.00315 +2026-04-14 02:10:13.216484: train_loss -0.4353 +2026-04-14 02:10:13.226766: val_loss -0.3777 +2026-04-14 02:10:13.230132: Pseudo dice [0.7522, 0.0, 0.8279, 0.6232, 0.545, 0.8218, 0.8002] +2026-04-14 02:10:13.233186: Epoch time: 102.0 s +2026-04-14 02:10:14.517288: +2026-04-14 02:10:14.519820: Epoch 2891 +2026-04-14 02:10:14.522911: Current learning rate: 0.00315 +2026-04-14 02:11:56.106192: train_loss -0.4232 +2026-04-14 02:11:56.114638: val_loss -0.3635 +2026-04-14 02:11:56.116882: Pseudo dice [0.612, 0.0, 0.7334, 0.4463, 0.5046, 0.5457, 0.8386] +2026-04-14 02:11:56.119465: Epoch time: 101.59 s +2026-04-14 02:11:57.358663: +2026-04-14 02:11:57.360890: Epoch 2892 +2026-04-14 02:11:57.362821: Current learning rate: 0.00315 +2026-04-14 02:13:38.770321: train_loss -0.4358 +2026-04-14 02:13:38.777205: val_loss -0.362 +2026-04-14 02:13:38.779572: Pseudo dice [0.8685, 0.0, 0.5855, 0.5826, 0.4287, 0.5364, 0.739] +2026-04-14 02:13:38.783339: Epoch time: 101.41 s +2026-04-14 02:13:40.031684: +2026-04-14 02:13:40.033587: Epoch 2893 +2026-04-14 02:13:40.036011: Current learning rate: 0.00315 +2026-04-14 02:15:23.004273: train_loss -0.4451 +2026-04-14 02:15:23.013759: val_loss -0.3887 +2026-04-14 02:15:23.016514: Pseudo dice [0.4647, 0.0, 0.7931, 0.9065, 0.5621, 0.3673, 0.9488] +2026-04-14 02:15:23.020056: Epoch time: 102.98 s +2026-04-14 02:15:24.291248: +2026-04-14 02:15:24.293313: Epoch 2894 +2026-04-14 02:15:24.296965: Current learning rate: 0.00314 +2026-04-14 02:17:06.131839: train_loss -0.4487 +2026-04-14 02:17:06.139952: val_loss -0.4056 +2026-04-14 02:17:06.142339: Pseudo dice [0.5634, 0.0, 0.8003, 0.8228, 0.6443, 0.9059, 0.815] +2026-04-14 02:17:06.145312: Epoch time: 101.84 s +2026-04-14 02:17:07.406412: +2026-04-14 02:17:07.408259: Epoch 2895 +2026-04-14 02:17:07.410090: Current learning rate: 0.00314 +2026-04-14 02:18:49.191217: train_loss -0.4389 +2026-04-14 02:18:49.197376: val_loss -0.3449 +2026-04-14 02:18:49.200996: Pseudo dice [0.4013, 0.0, 0.5602, 0.4519, 0.3413, 0.8218, 0.8815] +2026-04-14 02:18:49.203957: Epoch time: 101.79 s +2026-04-14 02:18:50.447325: +2026-04-14 02:18:50.449450: Epoch 2896 +2026-04-14 02:18:50.451528: Current learning rate: 0.00314 +2026-04-14 02:20:32.515882: train_loss -0.4382 +2026-04-14 02:20:32.523097: val_loss -0.3656 +2026-04-14 02:20:32.525752: Pseudo dice [0.7069, 0.0, 0.7889, 0.6011, 0.4378, 0.5947, 0.746] +2026-04-14 02:20:32.528321: Epoch time: 102.07 s +2026-04-14 02:20:33.779413: +2026-04-14 02:20:33.781586: Epoch 2897 +2026-04-14 02:20:33.783614: Current learning rate: 0.00314 +2026-04-14 02:22:15.891767: train_loss -0.4307 +2026-04-14 02:22:15.898196: val_loss -0.39 +2026-04-14 02:22:15.900436: Pseudo dice [0.5112, 0.0, 0.6703, 0.5365, 0.5341, 0.7341, 0.9264] +2026-04-14 02:22:15.903983: Epoch time: 102.12 s +2026-04-14 02:22:17.201059: +2026-04-14 02:22:17.203202: Epoch 2898 +2026-04-14 02:22:17.205251: Current learning rate: 0.00313 +2026-04-14 02:23:59.273795: train_loss -0.4353 +2026-04-14 02:23:59.280577: val_loss -0.3712 +2026-04-14 02:23:59.282811: Pseudo dice [0.5678, 0.0, 0.3414, 0.0488, 0.5203, 0.8535, 0.922] +2026-04-14 02:23:59.285389: Epoch time: 102.08 s +2026-04-14 02:24:00.523292: +2026-04-14 02:24:00.525353: Epoch 2899 +2026-04-14 02:24:00.527274: Current learning rate: 0.00313 +2026-04-14 02:25:42.209393: train_loss -0.4446 +2026-04-14 02:25:42.220866: val_loss -0.3791 +2026-04-14 02:25:42.223077: Pseudo dice [0.6743, 0.0, 0.7764, 0.1366, 0.5956, 0.6289, 0.9267] +2026-04-14 02:25:42.225581: Epoch time: 101.69 s +2026-04-14 02:25:45.280809: +2026-04-14 02:25:45.282586: Epoch 2900 +2026-04-14 02:25:45.285229: Current learning rate: 0.00313 +2026-04-14 02:27:26.908377: train_loss -0.4427 +2026-04-14 02:27:26.915074: val_loss -0.3816 +2026-04-14 02:27:26.917334: Pseudo dice [0.7004, 0.0, 0.8187, 0.5362, 0.6905, 0.7309, 0.9375] +2026-04-14 02:27:26.920394: Epoch time: 101.63 s +2026-04-14 02:27:28.191629: +2026-04-14 02:27:28.193492: Epoch 2901 +2026-04-14 02:27:28.195558: Current learning rate: 0.00313 +2026-04-14 02:29:09.717510: train_loss -0.46 +2026-04-14 02:29:09.724260: val_loss -0.3956 +2026-04-14 02:29:09.726855: Pseudo dice [0.4632, 0.0, 0.8536, 0.6131, 0.3909, 0.7371, 0.8198] +2026-04-14 02:29:09.730245: Epoch time: 101.53 s +2026-04-14 02:29:11.048619: +2026-04-14 02:29:11.050608: Epoch 2902 +2026-04-14 02:29:11.052544: Current learning rate: 0.00312 +2026-04-14 02:30:52.578264: train_loss -0.4244 +2026-04-14 02:30:52.585185: val_loss -0.357 +2026-04-14 02:30:52.588444: Pseudo dice [0.6972, 0.0, 0.5835, 0.6281, 0.3081, 0.8492, 0.7627] +2026-04-14 02:30:52.590889: Epoch time: 101.53 s +2026-04-14 02:30:53.863844: +2026-04-14 02:30:53.865870: Epoch 2903 +2026-04-14 02:30:53.868490: Current learning rate: 0.00312 +2026-04-14 02:32:36.002720: train_loss -0.4137 +2026-04-14 02:32:36.009459: val_loss -0.3597 +2026-04-14 02:32:36.012343: Pseudo dice [0.5747, 0.0, 0.7778, 0.3754, 0.5196, 0.806, 0.5663] +2026-04-14 02:32:36.015057: Epoch time: 102.14 s +2026-04-14 02:32:37.266253: +2026-04-14 02:32:37.268414: Epoch 2904 +2026-04-14 02:32:37.270813: Current learning rate: 0.00312 +2026-04-14 02:34:18.902627: train_loss -0.4229 +2026-04-14 02:34:18.909674: val_loss -0.3701 +2026-04-14 02:34:18.912226: Pseudo dice [0.7624, 0.0, 0.6895, 0.64, 0.4947, 0.6607, 0.8513] +2026-04-14 02:34:18.915814: Epoch time: 101.64 s +2026-04-14 02:34:20.189068: +2026-04-14 02:34:20.191397: Epoch 2905 +2026-04-14 02:34:20.193519: Current learning rate: 0.00312 +2026-04-14 02:36:01.596035: train_loss -0.4225 +2026-04-14 02:36:01.602946: val_loss -0.377 +2026-04-14 02:36:01.605112: Pseudo dice [0.6312, 0.0, 0.8197, 0.5061, 0.5354, 0.7068, 0.5268] +2026-04-14 02:36:01.608275: Epoch time: 101.41 s +2026-04-14 02:36:02.861376: +2026-04-14 02:36:02.863229: Epoch 2906 +2026-04-14 02:36:02.865182: Current learning rate: 0.00311 +2026-04-14 02:37:44.355703: train_loss -0.4326 +2026-04-14 02:37:44.362567: val_loss -0.3203 +2026-04-14 02:37:44.364669: Pseudo dice [0.6074, 0.0, 0.6694, 0.1319, 0.5379, 0.4765, 0.7156] +2026-04-14 02:37:44.367126: Epoch time: 101.5 s +2026-04-14 02:37:45.664662: +2026-04-14 02:37:45.668085: Epoch 2907 +2026-04-14 02:37:45.670226: Current learning rate: 0.00311 +2026-04-14 02:39:27.014064: train_loss -0.4265 +2026-04-14 02:39:27.023663: val_loss -0.4021 +2026-04-14 02:39:27.028947: Pseudo dice [0.7206, 0.0, 0.7854, 0.7826, 0.6039, 0.747, 0.9272] +2026-04-14 02:39:27.031529: Epoch time: 101.35 s +2026-04-14 02:39:28.284661: +2026-04-14 02:39:28.287268: Epoch 2908 +2026-04-14 02:39:28.290730: Current learning rate: 0.00311 +2026-04-14 02:41:10.171258: train_loss -0.4425 +2026-04-14 02:41:10.182769: val_loss -0.3891 +2026-04-14 02:41:10.187867: Pseudo dice [0.4516, 0.0, 0.7279, 0.1656, 0.5426, 0.5556, 0.7342] +2026-04-14 02:41:10.194055: Epoch time: 101.89 s +2026-04-14 02:41:11.459703: +2026-04-14 02:41:11.461710: Epoch 2909 +2026-04-14 02:41:11.464510: Current learning rate: 0.00311 +2026-04-14 02:42:53.011456: train_loss -0.4338 +2026-04-14 02:42:53.019226: val_loss -0.3327 +2026-04-14 02:42:53.021430: Pseudo dice [0.7379, 0.0, 0.6676, 0.4304, 0.3878, 0.5808, 0.5362] +2026-04-14 02:42:53.024406: Epoch time: 101.55 s +2026-04-14 02:42:54.282637: +2026-04-14 02:42:54.284657: Epoch 2910 +2026-04-14 02:42:54.286785: Current learning rate: 0.0031 +2026-04-14 02:44:37.084401: train_loss -0.4214 +2026-04-14 02:44:37.092541: val_loss -0.37 +2026-04-14 02:44:37.094728: Pseudo dice [0.5951, 0.0, 0.7008, 0.7264, 0.5867, 0.5802, 0.8619] +2026-04-14 02:44:37.097499: Epoch time: 102.8 s +2026-04-14 02:44:38.348499: +2026-04-14 02:44:38.350244: Epoch 2911 +2026-04-14 02:44:38.352072: Current learning rate: 0.0031 +2026-04-14 02:46:20.282433: train_loss -0.4496 +2026-04-14 02:46:20.289373: val_loss -0.3683 +2026-04-14 02:46:20.292414: Pseudo dice [0.227, 0.0, 0.7535, 0.7363, 0.5242, 0.7803, 0.878] +2026-04-14 02:46:20.294799: Epoch time: 101.94 s +2026-04-14 02:46:21.554820: +2026-04-14 02:46:21.556785: Epoch 2912 +2026-04-14 02:46:21.559004: Current learning rate: 0.0031 +2026-04-14 02:48:03.245159: train_loss -0.4438 +2026-04-14 02:48:03.253118: val_loss -0.3529 +2026-04-14 02:48:03.255640: Pseudo dice [0.5062, 0.0, 0.8366, 0.4575, 0.6929, 0.7594, 0.8453] +2026-04-14 02:48:03.258378: Epoch time: 101.69 s +2026-04-14 02:48:05.700397: +2026-04-14 02:48:05.702389: Epoch 2913 +2026-04-14 02:48:05.704329: Current learning rate: 0.0031 +2026-04-14 02:49:47.712163: train_loss -0.4436 +2026-04-14 02:49:47.719023: val_loss -0.3772 +2026-04-14 02:49:47.721986: Pseudo dice [0.5076, 0.0, 0.6481, 0.4169, 0.6051, 0.8572, 0.8763] +2026-04-14 02:49:47.725409: Epoch time: 102.01 s +2026-04-14 02:49:49.003779: +2026-04-14 02:49:49.005668: Epoch 2914 +2026-04-14 02:49:49.007910: Current learning rate: 0.00309 +2026-04-14 02:51:30.694101: train_loss -0.4485 +2026-04-14 02:51:30.701763: val_loss -0.3781 +2026-04-14 02:51:30.705677: Pseudo dice [0.5506, 0.0, 0.6553, 0.2949, 0.3527, 0.8082, 0.5779] +2026-04-14 02:51:30.709810: Epoch time: 101.69 s +2026-04-14 02:51:31.969052: +2026-04-14 02:51:31.972157: Epoch 2915 +2026-04-14 02:51:31.975732: Current learning rate: 0.00309 +2026-04-14 02:53:13.887444: train_loss -0.4455 +2026-04-14 02:53:13.894571: val_loss -0.3654 +2026-04-14 02:53:13.897747: Pseudo dice [0.6239, 0.0, 0.7522, 0.4932, 0.5825, 0.2737, 0.9153] +2026-04-14 02:53:13.900349: Epoch time: 101.92 s +2026-04-14 02:53:15.143736: +2026-04-14 02:53:15.146235: Epoch 2916 +2026-04-14 02:53:15.148421: Current learning rate: 0.00309 +2026-04-14 02:54:57.283030: train_loss -0.4514 +2026-04-14 02:54:57.290670: val_loss -0.3958 +2026-04-14 02:54:57.293072: Pseudo dice [0.8139, 0.0, 0.849, 0.7564, 0.5078, 0.6862, 0.7965] +2026-04-14 02:54:57.296528: Epoch time: 102.14 s +2026-04-14 02:54:58.549234: +2026-04-14 02:54:58.552115: Epoch 2917 +2026-04-14 02:54:58.554517: Current learning rate: 0.00309 +2026-04-14 02:56:40.998955: train_loss -0.4318 +2026-04-14 02:56:41.005092: val_loss -0.4114 +2026-04-14 02:56:41.008124: Pseudo dice [0.7923, 0.0, 0.7558, 0.8754, 0.6143, 0.4487, 0.8542] +2026-04-14 02:56:41.010516: Epoch time: 102.45 s +2026-04-14 02:56:42.564174: +2026-04-14 02:56:42.566367: Epoch 2918 +2026-04-14 02:56:42.568354: Current learning rate: 0.00308 +2026-04-14 02:58:24.867522: train_loss -0.4464 +2026-04-14 02:58:24.878112: val_loss -0.3711 +2026-04-14 02:58:24.880951: Pseudo dice [0.6173, 0.0, 0.78, 0.215, 0.281, 0.7161, 0.8783] +2026-04-14 02:58:24.883780: Epoch time: 102.31 s +2026-04-14 02:58:26.201831: +2026-04-14 02:58:26.204101: Epoch 2919 +2026-04-14 02:58:26.206166: Current learning rate: 0.00308 +2026-04-14 03:00:08.261167: train_loss -0.4525 +2026-04-14 03:00:08.268667: val_loss -0.3884 +2026-04-14 03:00:08.271513: Pseudo dice [0.6565, 0.0, 0.715, 0.2624, 0.6217, 0.9211, 0.9321] +2026-04-14 03:00:08.274296: Epoch time: 102.06 s +2026-04-14 03:00:09.569907: +2026-04-14 03:00:09.572057: Epoch 2920 +2026-04-14 03:00:09.574581: Current learning rate: 0.00308 +2026-04-14 03:01:51.901794: train_loss -0.4547 +2026-04-14 03:01:51.909212: val_loss -0.3881 +2026-04-14 03:01:51.911387: Pseudo dice [0.4247, 0.0, 0.8511, 0.66, 0.5135, 0.5666, 0.8182] +2026-04-14 03:01:51.914584: Epoch time: 102.33 s +2026-04-14 03:01:53.167107: +2026-04-14 03:01:53.169965: Epoch 2921 +2026-04-14 03:01:53.172543: Current learning rate: 0.00308 +2026-04-14 03:03:35.746193: train_loss -0.4469 +2026-04-14 03:03:35.758003: val_loss -0.3723 +2026-04-14 03:03:35.780274: Pseudo dice [0.7114, 0.0, 0.8067, 0.1695, 0.5109, 0.585, 0.8614] +2026-04-14 03:03:35.783156: Epoch time: 102.58 s +2026-04-14 03:03:37.078220: +2026-04-14 03:03:37.097408: Epoch 2922 +2026-04-14 03:03:37.100578: Current learning rate: 0.00307 +2026-04-14 03:05:18.984912: train_loss -0.43 +2026-04-14 03:05:18.993027: val_loss -0.3434 +2026-04-14 03:05:18.996528: Pseudo dice [0.6869, 0.0, 0.7722, 0.1374, 0.4171, 0.846, 0.9313] +2026-04-14 03:05:18.999124: Epoch time: 101.91 s +2026-04-14 03:05:20.296424: +2026-04-14 03:05:20.298455: Epoch 2923 +2026-04-14 03:05:20.300522: Current learning rate: 0.00307 +2026-04-14 03:07:02.454421: train_loss -0.4468 +2026-04-14 03:07:02.463214: val_loss -0.3835 +2026-04-14 03:07:02.465582: Pseudo dice [0.5194, 0.0, 0.6839, 0.4601, 0.4628, 0.5039, 0.915] +2026-04-14 03:07:02.468413: Epoch time: 102.16 s +2026-04-14 03:07:03.764186: +2026-04-14 03:07:03.766449: Epoch 2924 +2026-04-14 03:07:03.768656: Current learning rate: 0.00307 +2026-04-14 03:08:45.353762: train_loss -0.4356 +2026-04-14 03:08:45.361254: val_loss -0.3713 +2026-04-14 03:08:45.364865: Pseudo dice [0.707, 0.0, 0.7158, 0.2497, 0.4304, 0.2389, 0.8765] +2026-04-14 03:08:45.367628: Epoch time: 101.59 s +2026-04-14 03:08:46.655562: +2026-04-14 03:08:46.658275: Epoch 2925 +2026-04-14 03:08:46.661164: Current learning rate: 0.00306 +2026-04-14 03:10:28.522994: train_loss -0.4327 +2026-04-14 03:10:28.531019: val_loss -0.3494 +2026-04-14 03:10:28.533465: Pseudo dice [0.7818, 0.0, 0.7638, 0.1837, 0.3831, 0.8293, 0.9232] +2026-04-14 03:10:28.536001: Epoch time: 101.87 s +2026-04-14 03:10:29.849158: +2026-04-14 03:10:29.851422: Epoch 2926 +2026-04-14 03:10:29.853750: Current learning rate: 0.00306 +2026-04-14 03:12:11.952999: train_loss -0.4418 +2026-04-14 03:12:11.961808: val_loss -0.3692 +2026-04-14 03:12:11.964198: Pseudo dice [0.8494, 0.0, 0.552, 0.4874, 0.605, 0.3757, 0.8805] +2026-04-14 03:12:11.968499: Epoch time: 102.11 s +2026-04-14 03:12:13.230350: +2026-04-14 03:12:13.232077: Epoch 2927 +2026-04-14 03:12:13.234011: Current learning rate: 0.00306 +2026-04-14 03:13:54.962126: train_loss -0.438 +2026-04-14 03:13:54.970061: val_loss -0.3812 +2026-04-14 03:13:54.972429: Pseudo dice [0.7422, 0.0, 0.8313, 0.4825, 0.3578, 0.3979, 0.7349] +2026-04-14 03:13:54.976028: Epoch time: 101.74 s +2026-04-14 03:13:56.281343: +2026-04-14 03:13:56.283577: Epoch 2928 +2026-04-14 03:13:56.285532: Current learning rate: 0.00306 +2026-04-14 03:15:38.473443: train_loss -0.4382 +2026-04-14 03:15:38.482015: val_loss -0.3335 +2026-04-14 03:15:38.484305: Pseudo dice [0.3612, 0.0, 0.6497, 0.4578, 0.4807, 0.6215, 0.7652] +2026-04-14 03:15:38.487268: Epoch time: 102.2 s +2026-04-14 03:15:39.778609: +2026-04-14 03:15:39.780985: Epoch 2929 +2026-04-14 03:15:39.783378: Current learning rate: 0.00305 +2026-04-14 03:17:21.844501: train_loss -0.4405 +2026-04-14 03:17:21.853499: val_loss -0.3185 +2026-04-14 03:17:21.855848: Pseudo dice [0.48, 0.0, 0.6785, 0.3422, 0.361, 0.433, 0.6313] +2026-04-14 03:17:21.858506: Epoch time: 102.07 s +2026-04-14 03:17:23.292449: +2026-04-14 03:17:23.294552: Epoch 2930 +2026-04-14 03:17:23.296826: Current learning rate: 0.00305 +2026-04-14 03:19:05.365310: train_loss -0.4488 +2026-04-14 03:19:05.373044: val_loss -0.353 +2026-04-14 03:19:05.375436: Pseudo dice [0.6133, 0.0, 0.664, 0.2301, 0.5177, 0.8686, 0.8577] +2026-04-14 03:19:05.377978: Epoch time: 102.08 s +2026-04-14 03:19:06.642931: +2026-04-14 03:19:06.645301: Epoch 2931 +2026-04-14 03:19:06.648472: Current learning rate: 0.00305 +2026-04-14 03:20:48.327761: train_loss -0.4365 +2026-04-14 03:20:48.338470: val_loss -0.395 +2026-04-14 03:20:48.340491: Pseudo dice [0.5134, 0.0, 0.8549, 0.6894, 0.6557, 0.8258, 0.8946] +2026-04-14 03:20:48.343208: Epoch time: 101.69 s +2026-04-14 03:20:49.576101: +2026-04-14 03:20:49.578234: Epoch 2932 +2026-04-14 03:20:49.580864: Current learning rate: 0.00305 +2026-04-14 03:22:31.978498: train_loss -0.4433 +2026-04-14 03:22:31.985955: val_loss -0.3621 +2026-04-14 03:22:31.988746: Pseudo dice [0.6867, 0.0, 0.7499, 0.3645, 0.4359, 0.5663, 0.8898] +2026-04-14 03:22:31.993126: Epoch time: 102.41 s +2026-04-14 03:22:34.381082: +2026-04-14 03:22:34.383120: Epoch 2933 +2026-04-14 03:22:34.385057: Current learning rate: 0.00304 +2026-04-14 03:24:16.140488: train_loss -0.4458 +2026-04-14 03:24:16.148625: val_loss -0.3547 +2026-04-14 03:24:16.151347: Pseudo dice [0.8589, 0.0, 0.6762, 0.5494, 0.4106, 0.6344, 0.8477] +2026-04-14 03:24:16.154154: Epoch time: 101.76 s +2026-04-14 03:24:17.421880: +2026-04-14 03:24:17.425008: Epoch 2934 +2026-04-14 03:24:17.429608: Current learning rate: 0.00304 +2026-04-14 03:25:59.831444: train_loss -0.4231 +2026-04-14 03:25:59.837827: val_loss -0.3487 +2026-04-14 03:25:59.840715: Pseudo dice [0.6161, 0.0, 0.552, 0.8607, 0.4487, 0.6389, 0.6698] +2026-04-14 03:25:59.842989: Epoch time: 102.41 s +2026-04-14 03:26:01.117385: +2026-04-14 03:26:01.119761: Epoch 2935 +2026-04-14 03:26:01.122460: Current learning rate: 0.00304 +2026-04-14 03:27:43.618649: train_loss -0.4153 +2026-04-14 03:27:43.627780: val_loss -0.3841 +2026-04-14 03:27:43.630197: Pseudo dice [0.6872, 0.0, 0.8311, 0.7555, 0.5661, 0.8405, 0.2052] +2026-04-14 03:27:43.633835: Epoch time: 102.5 s +2026-04-14 03:27:44.894475: +2026-04-14 03:27:44.898124: Epoch 2936 +2026-04-14 03:27:44.901365: Current learning rate: 0.00304 +2026-04-14 03:29:27.464193: train_loss -0.4343 +2026-04-14 03:29:27.471818: val_loss -0.3516 +2026-04-14 03:29:27.476614: Pseudo dice [0.6315, 0.0, 0.4963, 0.209, 0.7065, 0.4107, 0.742] +2026-04-14 03:29:27.479331: Epoch time: 102.57 s +2026-04-14 03:29:28.815573: +2026-04-14 03:29:28.818475: Epoch 2937 +2026-04-14 03:29:28.822094: Current learning rate: 0.00303 +2026-04-14 03:31:10.903543: train_loss -0.4377 +2026-04-14 03:31:10.933050: val_loss -0.3828 +2026-04-14 03:31:10.935655: Pseudo dice [0.5736, 0.0, 0.7845, 0.4063, 0.6698, 0.5511, 0.7722] +2026-04-14 03:31:10.938251: Epoch time: 102.09 s +2026-04-14 03:31:12.210808: +2026-04-14 03:31:12.213116: Epoch 2938 +2026-04-14 03:31:12.215863: Current learning rate: 0.00303 +2026-04-14 03:32:54.469992: train_loss -0.439 +2026-04-14 03:32:54.477015: val_loss -0.3436 +2026-04-14 03:32:54.479000: Pseudo dice [0.319, 0.0, 0.8614, 0.7035, 0.4507, 0.7405, 0.8943] +2026-04-14 03:32:54.482119: Epoch time: 102.26 s +2026-04-14 03:32:55.745062: +2026-04-14 03:32:55.747279: Epoch 2939 +2026-04-14 03:32:55.749397: Current learning rate: 0.00303 +2026-04-14 03:34:37.930829: train_loss -0.4223 +2026-04-14 03:34:37.938762: val_loss -0.3651 +2026-04-14 03:34:37.941088: Pseudo dice [0.3889, 0.0, 0.6374, 0.767, 0.4182, 0.7851, 0.8881] +2026-04-14 03:34:37.943984: Epoch time: 102.19 s +2026-04-14 03:34:39.197627: +2026-04-14 03:34:39.199678: Epoch 2940 +2026-04-14 03:34:39.201619: Current learning rate: 0.00303 +2026-04-14 03:36:21.006357: train_loss -0.4298 +2026-04-14 03:36:21.015231: val_loss -0.3628 +2026-04-14 03:36:21.017795: Pseudo dice [0.4185, 0.0, 0.8047, 0.5958, 0.4372, 0.7343, 0.8797] +2026-04-14 03:36:21.021407: Epoch time: 101.81 s +2026-04-14 03:36:22.287947: +2026-04-14 03:36:22.293907: Epoch 2941 +2026-04-14 03:36:22.300491: Current learning rate: 0.00302 +2026-04-14 03:38:04.446391: train_loss -0.4241 +2026-04-14 03:38:04.454865: val_loss -0.3327 +2026-04-14 03:38:04.473642: Pseudo dice [0.5398, 0.0, 0.7861, 0.6887, 0.144, 0.5988, 0.3029] +2026-04-14 03:38:04.477021: Epoch time: 102.16 s +2026-04-14 03:38:05.748251: +2026-04-14 03:38:05.750662: Epoch 2942 +2026-04-14 03:38:05.752993: Current learning rate: 0.00302 +2026-04-14 03:39:49.016453: train_loss -0.4481 +2026-04-14 03:39:49.023914: val_loss -0.3768 +2026-04-14 03:39:49.025985: Pseudo dice [0.5043, 0.0, 0.7828, 0.7033, 0.5145, 0.5705, 0.8919] +2026-04-14 03:39:49.028604: Epoch time: 103.27 s +2026-04-14 03:39:50.331632: +2026-04-14 03:39:50.333732: Epoch 2943 +2026-04-14 03:39:50.335675: Current learning rate: 0.00302 +2026-04-14 03:41:32.633389: train_loss -0.4277 +2026-04-14 03:41:32.640962: val_loss -0.3613 +2026-04-14 03:41:32.643169: Pseudo dice [0.6647, 0.0, 0.5753, 0.3775, 0.4056, 0.6722, 0.8821] +2026-04-14 03:41:32.646102: Epoch time: 102.3 s +2026-04-14 03:41:33.918858: +2026-04-14 03:41:33.921534: Epoch 2944 +2026-04-14 03:41:33.926123: Current learning rate: 0.00302 +2026-04-14 03:43:15.381370: train_loss -0.4293 +2026-04-14 03:43:15.387506: val_loss -0.3403 +2026-04-14 03:43:15.389546: Pseudo dice [0.5614, 0.0, 0.7729, 0.0307, 0.6352, 0.1208, 0.655] +2026-04-14 03:43:15.391833: Epoch time: 101.47 s +2026-04-14 03:43:16.670845: +2026-04-14 03:43:16.672773: Epoch 2945 +2026-04-14 03:43:16.674931: Current learning rate: 0.00301 +2026-04-14 03:44:59.463070: train_loss -0.4251 +2026-04-14 03:44:59.470446: val_loss -0.3347 +2026-04-14 03:44:59.472956: Pseudo dice [0.7401, 0.0, 0.8424, 0.7613, 0.5032, 0.7958, 0.116] +2026-04-14 03:44:59.476030: Epoch time: 102.8 s +2026-04-14 03:45:00.781040: +2026-04-14 03:45:00.784188: Epoch 2946 +2026-04-14 03:45:00.787051: Current learning rate: 0.00301 +2026-04-14 03:46:43.017638: train_loss -0.4299 +2026-04-14 03:46:43.026427: val_loss -0.3709 +2026-04-14 03:46:43.028932: Pseudo dice [0.6943, 0.0, 0.7555, 0.4715, 0.4609, 0.5914, 0.7402] +2026-04-14 03:46:43.033716: Epoch time: 102.24 s +2026-04-14 03:46:44.287704: +2026-04-14 03:46:44.291389: Epoch 2947 +2026-04-14 03:46:44.295999: Current learning rate: 0.00301 +2026-04-14 03:48:26.014806: train_loss -0.4466 +2026-04-14 03:48:26.020794: val_loss -0.4075 +2026-04-14 03:48:26.022933: Pseudo dice [0.7929, 0.0, 0.8031, 0.7618, 0.5333, 0.8727, 0.8823] +2026-04-14 03:48:26.025581: Epoch time: 101.73 s +2026-04-14 03:48:27.254318: +2026-04-14 03:48:27.256140: Epoch 2948 +2026-04-14 03:48:27.258412: Current learning rate: 0.00301 +2026-04-14 03:50:09.257987: train_loss -0.4631 +2026-04-14 03:50:09.265067: val_loss -0.3564 +2026-04-14 03:50:09.268466: Pseudo dice [0.4827, 0.0, 0.4973, 0.1895, 0.4789, 0.7767, 0.5913] +2026-04-14 03:50:09.270988: Epoch time: 102.01 s +2026-04-14 03:50:10.535471: +2026-04-14 03:50:10.537455: Epoch 2949 +2026-04-14 03:50:10.540240: Current learning rate: 0.003 +2026-04-14 03:51:53.093681: train_loss -0.4351 +2026-04-14 03:51:53.102250: val_loss -0.386 +2026-04-14 03:51:53.104286: Pseudo dice [0.4692, 0.0, 0.8023, 0.7741, 0.5711, 0.6099, 0.7583] +2026-04-14 03:51:53.107005: Epoch time: 102.56 s +2026-04-14 03:51:56.275973: +2026-04-14 03:51:56.277768: Epoch 2950 +2026-04-14 03:51:56.279893: Current learning rate: 0.003 +2026-04-14 03:53:38.006914: train_loss -0.4372 +2026-04-14 03:53:38.014911: val_loss -0.3338 +2026-04-14 03:53:38.019154: Pseudo dice [0.4214, 0.0, 0.7526, 0.0604, 0.2823, 0.7619, 0.7658] +2026-04-14 03:53:38.022239: Epoch time: 101.73 s +2026-04-14 03:53:39.284780: +2026-04-14 03:53:39.286866: Epoch 2951 +2026-04-14 03:53:39.289358: Current learning rate: 0.003 +2026-04-14 03:55:20.670622: train_loss -0.4391 +2026-04-14 03:55:20.677779: val_loss -0.3967 +2026-04-14 03:55:20.681534: Pseudo dice [0.7862, 0.0, 0.7369, 0.7579, 0.5306, 0.6667, 0.8851] +2026-04-14 03:55:20.685192: Epoch time: 101.39 s +2026-04-14 03:55:21.949215: +2026-04-14 03:55:21.951575: Epoch 2952 +2026-04-14 03:55:21.953638: Current learning rate: 0.003 +2026-04-14 03:57:03.822894: train_loss -0.426 +2026-04-14 03:57:03.840271: val_loss -0.3827 +2026-04-14 03:57:03.846914: Pseudo dice [0.7985, 0.0, 0.6953, 0.1185, 0.3896, 0.3624, 0.8567] +2026-04-14 03:57:03.853268: Epoch time: 101.88 s +2026-04-14 03:57:06.227975: +2026-04-14 03:57:06.229814: Epoch 2953 +2026-04-14 03:57:06.231850: Current learning rate: 0.00299 +2026-04-14 03:58:48.799415: train_loss -0.4257 +2026-04-14 03:58:48.806155: val_loss -0.3659 +2026-04-14 03:58:48.808524: Pseudo dice [0.7839, 0.0, 0.8075, 0.0818, 0.5235, 0.4435, 0.486] +2026-04-14 03:58:48.811196: Epoch time: 102.57 s +2026-04-14 03:58:50.063305: +2026-04-14 03:58:50.065759: Epoch 2954 +2026-04-14 03:58:50.068285: Current learning rate: 0.00299 +2026-04-14 04:00:32.366394: train_loss -0.4428 +2026-04-14 04:00:32.374344: val_loss -0.3551 +2026-04-14 04:00:32.376859: Pseudo dice [0.3091, 0.0, 0.8814, 0.263, 0.4199, 0.6644, 0.7867] +2026-04-14 04:00:32.379489: Epoch time: 102.31 s +2026-04-14 04:00:33.623101: +2026-04-14 04:00:33.625335: Epoch 2955 +2026-04-14 04:00:33.627671: Current learning rate: 0.00299 +2026-04-14 04:02:15.767215: train_loss -0.4423 +2026-04-14 04:02:15.776748: val_loss -0.399 +2026-04-14 04:02:15.779429: Pseudo dice [0.5981, 0.0, 0.7811, 0.605, 0.6095, 0.8357, 0.9039] +2026-04-14 04:02:15.782818: Epoch time: 102.15 s +2026-04-14 04:02:17.034598: +2026-04-14 04:02:17.036760: Epoch 2956 +2026-04-14 04:02:17.039255: Current learning rate: 0.00299 +2026-04-14 04:03:58.742907: train_loss -0.439 +2026-04-14 04:03:58.750479: val_loss -0.3762 +2026-04-14 04:03:58.752814: Pseudo dice [0.6741, 0.0, 0.8207, 0.461, 0.4694, 0.6804, 0.868] +2026-04-14 04:03:58.755341: Epoch time: 101.71 s +2026-04-14 04:04:00.031425: +2026-04-14 04:04:00.033316: Epoch 2957 +2026-04-14 04:04:00.035600: Current learning rate: 0.00298 +2026-04-14 04:05:42.143908: train_loss -0.4224 +2026-04-14 04:05:42.154644: val_loss -0.3571 +2026-04-14 04:05:42.157216: Pseudo dice [0.4438, 0.0, 0.6616, 0.7743, 0.5264, 0.2526, 0.7344] +2026-04-14 04:05:42.160553: Epoch time: 102.12 s +2026-04-14 04:05:43.445263: +2026-04-14 04:05:43.447741: Epoch 2958 +2026-04-14 04:05:43.450024: Current learning rate: 0.00298 +2026-04-14 04:07:25.054366: train_loss -0.4108 +2026-04-14 04:07:25.062027: val_loss -0.3906 +2026-04-14 04:07:25.064002: Pseudo dice [0.4627, 0.0, 0.8145, 0.6971, 0.5858, 0.7701, 0.8973] +2026-04-14 04:07:25.067134: Epoch time: 101.61 s +2026-04-14 04:07:26.321604: +2026-04-14 04:07:26.323912: Epoch 2959 +2026-04-14 04:07:26.325990: Current learning rate: 0.00298 +2026-04-14 04:09:08.121358: train_loss -0.4258 +2026-04-14 04:09:08.130200: val_loss -0.4021 +2026-04-14 04:09:08.133529: Pseudo dice [0.7559, 0.0, 0.7732, 0.4165, 0.5983, 0.6043, 0.6546] +2026-04-14 04:09:08.136761: Epoch time: 101.8 s +2026-04-14 04:09:09.411023: +2026-04-14 04:09:09.413465: Epoch 2960 +2026-04-14 04:09:09.416177: Current learning rate: 0.00297 +2026-04-14 04:10:51.291605: train_loss -0.4357 +2026-04-14 04:10:51.299171: val_loss -0.3872 +2026-04-14 04:10:51.301716: Pseudo dice [0.5951, 0.0, 0.8438, 0.4408, 0.5391, 0.6465, 0.9164] +2026-04-14 04:10:51.304454: Epoch time: 101.88 s +2026-04-14 04:10:52.567386: +2026-04-14 04:10:52.570642: Epoch 2961 +2026-04-14 04:10:52.573572: Current learning rate: 0.00297 +2026-04-14 04:12:34.482786: train_loss -0.4245 +2026-04-14 04:12:34.489697: val_loss -0.3632 +2026-04-14 04:12:34.491992: Pseudo dice [0.4576, 0.0, 0.6822, 0.5457, 0.526, 0.5236, 0.723] +2026-04-14 04:12:34.494276: Epoch time: 101.92 s +2026-04-14 04:12:35.793997: +2026-04-14 04:12:35.795831: Epoch 2962 +2026-04-14 04:12:35.797832: Current learning rate: 0.00297 +2026-04-14 04:14:17.850164: train_loss -0.4414 +2026-04-14 04:14:17.858572: val_loss -0.3813 +2026-04-14 04:14:17.861076: Pseudo dice [0.1234, 0.0, 0.7926, 0.6479, 0.558, 0.7144, 0.9253] +2026-04-14 04:14:17.865067: Epoch time: 102.06 s +2026-04-14 04:14:19.127805: +2026-04-14 04:14:19.129823: Epoch 2963 +2026-04-14 04:14:19.132045: Current learning rate: 0.00297 +2026-04-14 04:16:01.703852: train_loss -0.3976 +2026-04-14 04:16:01.711054: val_loss -0.3654 +2026-04-14 04:16:01.713563: Pseudo dice [0.4871, 0.0, 0.5278, 0.5907, 0.3554, 0.806, 0.909] +2026-04-14 04:16:01.716481: Epoch time: 102.58 s +2026-04-14 04:16:02.965515: +2026-04-14 04:16:02.967556: Epoch 2964 +2026-04-14 04:16:02.969884: Current learning rate: 0.00296 +2026-04-14 04:17:44.975513: train_loss -0.411 +2026-04-14 04:17:44.982037: val_loss -0.3512 +2026-04-14 04:17:44.984496: Pseudo dice [0.2881, 0.0, 0.7951, 0.7029, 0.5471, 0.6342, 0.8578] +2026-04-14 04:17:44.987689: Epoch time: 102.01 s +2026-04-14 04:17:46.241541: +2026-04-14 04:17:46.243937: Epoch 2965 +2026-04-14 04:17:46.246916: Current learning rate: 0.00296 +2026-04-14 04:19:28.845233: train_loss -0.4391 +2026-04-14 04:19:28.852260: val_loss -0.3997 +2026-04-14 04:19:28.855821: Pseudo dice [0.8773, 0.0, 0.7341, 0.7613, 0.438, 0.8292, 0.9224] +2026-04-14 04:19:28.859278: Epoch time: 102.61 s +2026-04-14 04:19:30.146447: +2026-04-14 04:19:30.148490: Epoch 2966 +2026-04-14 04:19:30.151238: Current learning rate: 0.00296 +2026-04-14 04:21:12.308927: train_loss -0.4512 +2026-04-14 04:21:12.319110: val_loss -0.3957 +2026-04-14 04:21:12.321833: Pseudo dice [0.3767, 0.0, 0.6694, 0.834, 0.4474, 0.6352, 0.9194] +2026-04-14 04:21:12.325082: Epoch time: 102.17 s +2026-04-14 04:21:13.559371: +2026-04-14 04:21:13.562338: Epoch 2967 +2026-04-14 04:21:13.567074: Current learning rate: 0.00296 +2026-04-14 04:22:56.308644: train_loss -0.455 +2026-04-14 04:22:56.317490: val_loss -0.3806 +2026-04-14 04:22:56.320638: Pseudo dice [0.5645, 0.0, 0.7873, 0.2261, 0.5149, 0.77, 0.8582] +2026-04-14 04:22:56.323474: Epoch time: 102.75 s +2026-04-14 04:22:57.596420: +2026-04-14 04:22:57.598471: Epoch 2968 +2026-04-14 04:22:57.600565: Current learning rate: 0.00295 +2026-04-14 04:24:40.314283: train_loss -0.4296 +2026-04-14 04:24:40.322176: val_loss -0.3976 +2026-04-14 04:24:40.324411: Pseudo dice [0.501, 0.0, 0.877, 0.7453, 0.4029, 0.7669, 0.7274] +2026-04-14 04:24:40.328061: Epoch time: 102.72 s +2026-04-14 04:24:41.612832: +2026-04-14 04:24:41.614861: Epoch 2969 +2026-04-14 04:24:41.617271: Current learning rate: 0.00295 +2026-04-14 04:26:23.273195: train_loss -0.434 +2026-04-14 04:26:23.280352: val_loss -0.3629 +2026-04-14 04:26:23.283082: Pseudo dice [0.3641, 0.0, 0.579, 0.5189, 0.5204, 0.7215, 0.5274] +2026-04-14 04:26:23.287074: Epoch time: 101.66 s +2026-04-14 04:26:24.550053: +2026-04-14 04:26:24.552773: Epoch 2970 +2026-04-14 04:26:24.554924: Current learning rate: 0.00295 +2026-04-14 04:28:06.750443: train_loss -0.4196 +2026-04-14 04:28:06.758034: val_loss -0.3685 +2026-04-14 04:28:06.760135: Pseudo dice [0.777, 0.0, 0.7156, 0.3776, 0.6244, 0.785, 0.5127] +2026-04-14 04:28:06.762662: Epoch time: 102.2 s +2026-04-14 04:28:08.019476: +2026-04-14 04:28:08.021759: Epoch 2971 +2026-04-14 04:28:08.026335: Current learning rate: 0.00295 +2026-04-14 04:29:49.624268: train_loss -0.4301 +2026-04-14 04:29:49.632386: val_loss -0.3556 +2026-04-14 04:29:49.635596: Pseudo dice [0.6032, 0.0, 0.5525, 0.4622, 0.5512, 0.8205, 0.8742] +2026-04-14 04:29:49.638206: Epoch time: 101.61 s +2026-04-14 04:29:50.885083: +2026-04-14 04:29:50.887424: Epoch 2972 +2026-04-14 04:29:50.889752: Current learning rate: 0.00294 +2026-04-14 04:31:33.012439: train_loss -0.4395 +2026-04-14 04:31:33.039131: val_loss -0.3589 +2026-04-14 04:31:33.041531: Pseudo dice [0.8618, 0.0, 0.7742, 0.2938, 0.5749, 0.6722, 0.8512] +2026-04-14 04:31:33.044103: Epoch time: 102.13 s +2026-04-14 04:31:35.409506: +2026-04-14 04:31:35.411444: Epoch 2973 +2026-04-14 04:31:35.413444: Current learning rate: 0.00294 +2026-04-14 04:33:16.967040: train_loss -0.4149 +2026-04-14 04:33:16.973937: val_loss -0.3536 +2026-04-14 04:33:16.976397: Pseudo dice [0.0, 0.0, 0.8305, 0.725, 0.513, 0.5234, 0.8028] +2026-04-14 04:33:16.980082: Epoch time: 101.56 s +2026-04-14 04:33:18.239358: +2026-04-14 04:33:18.241221: Epoch 2974 +2026-04-14 04:33:18.243165: Current learning rate: 0.00294 +2026-04-14 04:34:59.900920: train_loss -0.4205 +2026-04-14 04:34:59.906948: val_loss -0.3384 +2026-04-14 04:34:59.909365: Pseudo dice [0.7107, 0.0, 0.7257, 0.2164, 0.2429, 0.6751, 0.8503] +2026-04-14 04:34:59.911759: Epoch time: 101.66 s +2026-04-14 04:35:01.156583: +2026-04-14 04:35:01.159914: Epoch 2975 +2026-04-14 04:35:01.162371: Current learning rate: 0.00294 +2026-04-14 04:36:42.745972: train_loss -0.4236 +2026-04-14 04:36:42.753563: val_loss -0.3162 +2026-04-14 04:36:42.755873: Pseudo dice [0.6745, 0.0, 0.8745, 0.4451, 0.4934, 0.5777, 0.1934] +2026-04-14 04:36:42.758908: Epoch time: 101.59 s +2026-04-14 04:36:44.025041: +2026-04-14 04:36:44.027172: Epoch 2976 +2026-04-14 04:36:44.030333: Current learning rate: 0.00293 +2026-04-14 04:38:26.285338: train_loss -0.4451 +2026-04-14 04:38:26.296101: val_loss -0.3975 +2026-04-14 04:38:26.299395: Pseudo dice [0.3066, 0.0, 0.7936, 0.7376, 0.5568, 0.9064, 0.8726] +2026-04-14 04:38:26.302297: Epoch time: 102.26 s +2026-04-14 04:38:27.596013: +2026-04-14 04:38:27.598248: Epoch 2977 +2026-04-14 04:38:27.601190: Current learning rate: 0.00293 +2026-04-14 04:40:09.272266: train_loss -0.4507 +2026-04-14 04:40:09.279205: val_loss -0.3743 +2026-04-14 04:40:09.281760: Pseudo dice [0.6552, 0.0, 0.8254, 0.4584, 0.2887, 0.5056, 0.7999] +2026-04-14 04:40:09.284654: Epoch time: 101.68 s +2026-04-14 04:40:10.529848: +2026-04-14 04:40:10.531809: Epoch 2978 +2026-04-14 04:40:10.535744: Current learning rate: 0.00293 +2026-04-14 04:41:53.197888: train_loss -0.4377 +2026-04-14 04:41:53.206369: val_loss -0.3757 +2026-04-14 04:41:53.209093: Pseudo dice [0.7924, 0.0, 0.7971, 0.6129, 0.6299, 0.4716, 0.7742] +2026-04-14 04:41:53.212259: Epoch time: 102.67 s +2026-04-14 04:41:54.477195: +2026-04-14 04:41:54.479198: Epoch 2979 +2026-04-14 04:41:54.481673: Current learning rate: 0.00293 +2026-04-14 04:43:37.080224: train_loss -0.4434 +2026-04-14 04:43:37.085851: val_loss -0.3834 +2026-04-14 04:43:37.088320: Pseudo dice [0.5059, 0.0, 0.8338, 0.3471, 0.5849, 0.8787, 0.8937] +2026-04-14 04:43:37.091073: Epoch time: 102.61 s +2026-04-14 04:43:38.352549: +2026-04-14 04:43:38.355421: Epoch 2980 +2026-04-14 04:43:38.357471: Current learning rate: 0.00292 +2026-04-14 04:45:20.945451: train_loss -0.4568 +2026-04-14 04:45:20.952060: val_loss -0.3966 +2026-04-14 04:45:20.954555: Pseudo dice [0.4376, 0.0, 0.6373, 0.7066, 0.6448, 0.7868, 0.9105] +2026-04-14 04:45:20.957362: Epoch time: 102.6 s +2026-04-14 04:45:22.228519: +2026-04-14 04:45:22.230538: Epoch 2981 +2026-04-14 04:45:22.232777: Current learning rate: 0.00292 +2026-04-14 04:47:04.952533: train_loss -0.4349 +2026-04-14 04:47:04.959028: val_loss -0.3566 +2026-04-14 04:47:04.961709: Pseudo dice [0.3668, 0.0, 0.8249, 0.7538, 0.5236, 0.6599, 0.5733] +2026-04-14 04:47:04.964600: Epoch time: 102.73 s +2026-04-14 04:47:06.197446: +2026-04-14 04:47:06.199634: Epoch 2982 +2026-04-14 04:47:06.201852: Current learning rate: 0.00292 +2026-04-14 04:48:48.660112: train_loss -0.4407 +2026-04-14 04:48:48.668378: val_loss -0.3812 +2026-04-14 04:48:48.670927: Pseudo dice [0.4658, 0.0, 0.8754, 0.4462, 0.6397, 0.3689, 0.7022] +2026-04-14 04:48:48.674644: Epoch time: 102.47 s +2026-04-14 04:48:49.912552: +2026-04-14 04:48:49.914408: Epoch 2983 +2026-04-14 04:48:49.916363: Current learning rate: 0.00292 +2026-04-14 04:50:32.254638: train_loss -0.4254 +2026-04-14 04:50:32.261589: val_loss -0.418 +2026-04-14 04:50:32.264517: Pseudo dice [0.7448, 0.0, 0.8547, 0.6347, 0.6595, 0.4114, 0.8165] +2026-04-14 04:50:32.267872: Epoch time: 102.35 s +2026-04-14 04:50:33.551801: +2026-04-14 04:50:33.554028: Epoch 2984 +2026-04-14 04:50:33.556655: Current learning rate: 0.00291 +2026-04-14 04:52:15.332883: train_loss -0.4425 +2026-04-14 04:52:15.341038: val_loss -0.4104 +2026-04-14 04:52:15.344051: Pseudo dice [0.6225, 0.0, 0.8103, 0.7695, 0.6097, 0.4823, 0.934] +2026-04-14 04:52:15.348368: Epoch time: 101.78 s +2026-04-14 04:52:16.579655: +2026-04-14 04:52:16.581878: Epoch 2985 +2026-04-14 04:52:16.583822: Current learning rate: 0.00291 +2026-04-14 04:53:58.961764: train_loss -0.4358 +2026-04-14 04:53:58.971983: val_loss -0.3925 +2026-04-14 04:53:58.974640: Pseudo dice [0.7962, 0.0, 0.8911, 0.5049, 0.2504, 0.7563, 0.8725] +2026-04-14 04:53:58.977436: Epoch time: 102.39 s +2026-04-14 04:54:00.225708: +2026-04-14 04:54:00.227989: Epoch 2986 +2026-04-14 04:54:00.230332: Current learning rate: 0.00291 +2026-04-14 04:55:42.216221: train_loss -0.4336 +2026-04-14 04:55:42.223278: val_loss -0.352 +2026-04-14 04:55:42.226530: Pseudo dice [0.8512, 0.0, 0.7833, 0.3514, 0.5604, 0.5215, 0.879] +2026-04-14 04:55:42.229753: Epoch time: 101.99 s +2026-04-14 04:55:43.449923: +2026-04-14 04:55:43.452022: Epoch 2987 +2026-04-14 04:55:43.453968: Current learning rate: 0.00291 +2026-04-14 04:57:25.623552: train_loss -0.428 +2026-04-14 04:57:25.631057: val_loss -0.3662 +2026-04-14 04:57:25.633218: Pseudo dice [0.8067, 0.0, 0.7308, 0.7115, 0.5575, 0.7539, 0.7531] +2026-04-14 04:57:25.636977: Epoch time: 102.18 s +2026-04-14 04:57:26.937119: +2026-04-14 04:57:26.943010: Epoch 2988 +2026-04-14 04:57:26.949952: Current learning rate: 0.0029 +2026-04-14 04:59:08.431602: train_loss -0.4437 +2026-04-14 04:59:08.439622: val_loss -0.37 +2026-04-14 04:59:08.442004: Pseudo dice [0.7298, 0.0, 0.8137, 0.8199, 0.4987, 0.6662, 0.8472] +2026-04-14 04:59:08.444810: Epoch time: 101.5 s +2026-04-14 04:59:09.692126: +2026-04-14 04:59:09.694306: Epoch 2989 +2026-04-14 04:59:09.696428: Current learning rate: 0.0029 +2026-04-14 05:00:51.382900: train_loss -0.4378 +2026-04-14 05:00:51.390515: val_loss -0.3465 +2026-04-14 05:00:51.392837: Pseudo dice [0.3636, 0.0, 0.5136, 0.0002, 0.4938, 0.5111, 0.4272] +2026-04-14 05:00:51.395498: Epoch time: 101.69 s +2026-04-14 05:00:52.636287: +2026-04-14 05:00:52.638413: Epoch 2990 +2026-04-14 05:00:52.640683: Current learning rate: 0.0029 +2026-04-14 05:02:34.356703: train_loss -0.4528 +2026-04-14 05:02:34.363339: val_loss -0.3942 +2026-04-14 05:02:34.365447: Pseudo dice [0.7589, 0.0, 0.8562, 0.599, 0.5959, 0.572, 0.9049] +2026-04-14 05:02:34.368338: Epoch time: 101.72 s +2026-04-14 05:02:35.615963: +2026-04-14 05:02:35.618380: Epoch 2991 +2026-04-14 05:02:35.620636: Current learning rate: 0.00289 +2026-04-14 05:04:17.390437: train_loss -0.4501 +2026-04-14 05:04:17.398300: val_loss -0.3849 +2026-04-14 05:04:17.401147: Pseudo dice [0.526, 0.0, 0.7795, 0.8665, 0.4664, 0.2746, 0.9122] +2026-04-14 05:04:17.403614: Epoch time: 101.78 s +2026-04-14 05:04:18.656866: +2026-04-14 05:04:18.659506: Epoch 2992 +2026-04-14 05:04:18.661425: Current learning rate: 0.00289 +2026-04-14 05:06:00.545103: train_loss -0.4531 +2026-04-14 05:06:00.552230: val_loss -0.3874 +2026-04-14 05:06:00.554739: Pseudo dice [0.6508, 0.0, 0.8195, 0.5245, 0.5615, 0.6142, 0.8901] +2026-04-14 05:06:00.557159: Epoch time: 101.89 s +2026-04-14 05:06:02.925999: +2026-04-14 05:06:02.927858: Epoch 2993 +2026-04-14 05:06:02.929666: Current learning rate: 0.00289 +2026-04-14 05:07:45.461495: train_loss -0.4571 +2026-04-14 05:07:45.467771: val_loss -0.3779 +2026-04-14 05:07:45.470313: Pseudo dice [0.7227, 0.0, 0.8004, 0.3365, 0.4145, 0.7516, 0.7443] +2026-04-14 05:07:45.472594: Epoch time: 102.54 s +2026-04-14 05:07:46.728602: +2026-04-14 05:07:46.730623: Epoch 2994 +2026-04-14 05:07:46.732692: Current learning rate: 0.00289 +2026-04-14 05:09:28.745051: train_loss -0.458 +2026-04-14 05:09:28.751301: val_loss -0.3709 +2026-04-14 05:09:28.753600: Pseudo dice [0.6597, 0.0, 0.5905, 0.637, 0.7259, 0.8584, 0.8958] +2026-04-14 05:09:28.756639: Epoch time: 102.02 s +2026-04-14 05:09:30.006518: +2026-04-14 05:09:30.008595: Epoch 2995 +2026-04-14 05:09:30.011328: Current learning rate: 0.00288 +2026-04-14 05:11:12.552224: train_loss -0.4492 +2026-04-14 05:11:12.558435: val_loss -0.3911 +2026-04-14 05:11:12.561849: Pseudo dice [0.595, 0.0, 0.7667, 0.5122, 0.4592, 0.3134, 0.9341] +2026-04-14 05:11:12.564571: Epoch time: 102.55 s +2026-04-14 05:11:13.817118: +2026-04-14 05:11:13.821777: Epoch 2996 +2026-04-14 05:11:13.826712: Current learning rate: 0.00288 +2026-04-14 05:12:55.710246: train_loss -0.4585 +2026-04-14 05:12:55.717803: val_loss -0.384 +2026-04-14 05:12:55.721428: Pseudo dice [0.5035, 0.0, 0.7223, 0.7714, 0.5768, 0.8411, 0.69] +2026-04-14 05:12:55.724074: Epoch time: 101.9 s +2026-04-14 05:12:57.050177: +2026-04-14 05:12:57.052632: Epoch 2997 +2026-04-14 05:12:57.055233: Current learning rate: 0.00288 +2026-04-14 05:14:38.958400: train_loss -0.4436 +2026-04-14 05:14:38.966090: val_loss -0.3649 +2026-04-14 05:14:38.968663: Pseudo dice [0.6428, 0.0, 0.8147, 0.8652, 0.3713, 0.7914, 0.6038] +2026-04-14 05:14:38.972711: Epoch time: 101.91 s +2026-04-14 05:14:40.248940: +2026-04-14 05:14:40.251103: Epoch 2998 +2026-04-14 05:14:40.253571: Current learning rate: 0.00288 +2026-04-14 05:16:22.338027: train_loss -0.4398 +2026-04-14 05:16:22.345869: val_loss -0.3719 +2026-04-14 05:16:22.348616: Pseudo dice [0.394, 0.0, 0.8351, 0.5866, 0.5007, 0.5925, 0.8371] +2026-04-14 05:16:22.351388: Epoch time: 102.09 s +2026-04-14 05:16:23.641882: +2026-04-14 05:16:23.644215: Epoch 2999 +2026-04-14 05:16:23.646665: Current learning rate: 0.00287 +2026-04-14 05:18:05.207753: train_loss -0.4432 +2026-04-14 05:18:05.214372: val_loss -0.3789 +2026-04-14 05:18:05.216798: Pseudo dice [0.6146, 0.0, 0.7799, 0.8004, 0.5539, 0.6706, 0.8636] +2026-04-14 05:18:05.219195: Epoch time: 101.57 s +2026-04-14 05:18:08.303864: +2026-04-14 05:18:08.306930: Epoch 3000 +2026-04-14 05:18:08.308993: Current learning rate: 0.00287 +2026-04-14 05:19:49.919383: train_loss -0.4435 +2026-04-14 05:19:49.927703: val_loss -0.4111 +2026-04-14 05:19:49.930212: Pseudo dice [0.6677, 0.0, 0.5995, 0.7822, 0.6633, 0.7809, 0.8968] +2026-04-14 05:19:49.933196: Epoch time: 101.62 s +2026-04-14 05:19:51.180321: +2026-04-14 05:19:51.182367: Epoch 3001 +2026-04-14 05:19:51.184539: Current learning rate: 0.00287 +2026-04-14 05:21:32.679196: train_loss -0.4427 +2026-04-14 05:21:32.685677: val_loss -0.3825 +2026-04-14 05:21:32.687842: Pseudo dice [0.3402, 0.0, 0.8487, 0.8119, 0.5723, 0.5666, 0.7138] +2026-04-14 05:21:32.690508: Epoch time: 101.5 s +2026-04-14 05:21:33.925076: +2026-04-14 05:21:33.927269: Epoch 3002 +2026-04-14 05:21:33.929638: Current learning rate: 0.00287 +2026-04-14 05:23:15.458630: train_loss -0.46 +2026-04-14 05:23:15.465575: val_loss -0.3937 +2026-04-14 05:23:15.467942: Pseudo dice [0.3966, 0.0, 0.7569, 0.7559, 0.6079, 0.7638, 0.885] +2026-04-14 05:23:15.470445: Epoch time: 101.54 s +2026-04-14 05:23:16.819774: +2026-04-14 05:23:16.822673: Epoch 3003 +2026-04-14 05:23:16.827350: Current learning rate: 0.00286 +2026-04-14 05:24:58.438870: train_loss -0.4628 +2026-04-14 05:24:58.446550: val_loss -0.3735 +2026-04-14 05:24:58.448699: Pseudo dice [0.6465, 0.0, 0.7631, 0.1597, 0.457, 0.8921, 0.8913] +2026-04-14 05:24:58.451034: Epoch time: 101.62 s +2026-04-14 05:24:59.723650: +2026-04-14 05:24:59.730390: Epoch 3004 +2026-04-14 05:24:59.732612: Current learning rate: 0.00286 +2026-04-14 05:26:42.202325: train_loss -0.4545 +2026-04-14 05:26:42.210710: val_loss -0.4023 +2026-04-14 05:26:42.213914: Pseudo dice [0.5572, 0.0, 0.8258, 0.5258, 0.5944, 0.2823, 0.8927] +2026-04-14 05:26:42.216487: Epoch time: 102.48 s +2026-04-14 05:26:43.491505: +2026-04-14 05:26:43.493935: Epoch 3005 +2026-04-14 05:26:43.495674: Current learning rate: 0.00286 +2026-04-14 05:28:25.401494: train_loss -0.4505 +2026-04-14 05:28:25.409424: val_loss -0.3576 +2026-04-14 05:28:25.412027: Pseudo dice [0.7629, 0.0, 0.7416, 0.0007, 0.449, 0.8126, 0.8778] +2026-04-14 05:28:25.415119: Epoch time: 101.91 s +2026-04-14 05:28:26.667374: +2026-04-14 05:28:26.669153: Epoch 3006 +2026-04-14 05:28:26.671075: Current learning rate: 0.00286 +2026-04-14 05:30:08.413138: train_loss -0.4389 +2026-04-14 05:30:08.420642: val_loss -0.3449 +2026-04-14 05:30:08.423307: Pseudo dice [0.6694, 0.0, 0.6759, 0.1995, 0.5368, 0.8393, 0.8733] +2026-04-14 05:30:08.425776: Epoch time: 101.75 s +2026-04-14 05:30:09.693604: +2026-04-14 05:30:09.695364: Epoch 3007 +2026-04-14 05:30:09.697273: Current learning rate: 0.00285 +2026-04-14 05:31:51.607045: train_loss -0.4231 +2026-04-14 05:31:51.614849: val_loss -0.3856 +2026-04-14 05:31:51.616964: Pseudo dice [0.7107, 0.0, 0.841, 0.1386, 0.5692, 0.6002, 0.8076] +2026-04-14 05:31:51.619241: Epoch time: 101.92 s +2026-04-14 05:31:52.910253: +2026-04-14 05:31:52.913006: Epoch 3008 +2026-04-14 05:31:52.915748: Current learning rate: 0.00285 +2026-04-14 05:33:34.938755: train_loss -0.4366 +2026-04-14 05:33:34.955363: val_loss -0.4117 +2026-04-14 05:33:34.958323: Pseudo dice [0.7176, 0.0, 0.7944, 0.6671, 0.6199, 0.8783, 0.9247] +2026-04-14 05:33:34.960882: Epoch time: 102.03 s +2026-04-14 05:33:36.218323: +2026-04-14 05:33:36.220522: Epoch 3009 +2026-04-14 05:33:36.222899: Current learning rate: 0.00285 +2026-04-14 05:35:17.891013: train_loss -0.4402 +2026-04-14 05:35:17.899402: val_loss -0.3805 +2026-04-14 05:35:17.901787: Pseudo dice [0.5058, 0.0, 0.7984, 0.6937, 0.5383, 0.8967, 0.8566] +2026-04-14 05:35:17.906000: Epoch time: 101.68 s +2026-04-14 05:35:19.165552: +2026-04-14 05:35:19.167850: Epoch 3010 +2026-04-14 05:35:19.170355: Current learning rate: 0.00285 +2026-04-14 05:37:00.952665: train_loss -0.4479 +2026-04-14 05:37:00.959394: val_loss -0.3711 +2026-04-14 05:37:00.961970: Pseudo dice [0.636, 0.0, 0.7705, 0.7473, 0.374, 0.5105, 0.7094] +2026-04-14 05:37:00.965494: Epoch time: 101.79 s +2026-04-14 05:37:02.225740: +2026-04-14 05:37:02.227543: Epoch 3011 +2026-04-14 05:37:02.229665: Current learning rate: 0.00284 +2026-04-14 05:38:43.700960: train_loss -0.4433 +2026-04-14 05:38:43.709182: val_loss -0.3387 +2026-04-14 05:38:43.711761: Pseudo dice [0.7099, 0.0, 0.6858, 0.5075, 0.3236, 0.7029, 0.9285] +2026-04-14 05:38:43.714782: Epoch time: 101.48 s +2026-04-14 05:38:44.948806: +2026-04-14 05:38:44.951410: Epoch 3012 +2026-04-14 05:38:44.954387: Current learning rate: 0.00284 +2026-04-14 05:40:28.661538: train_loss -0.4385 +2026-04-14 05:40:28.668188: val_loss -0.3759 +2026-04-14 05:40:28.670604: Pseudo dice [0.7323, 0.0, 0.8018, 0.8433, 0.4493, 0.5482, 0.8709] +2026-04-14 05:40:28.673455: Epoch time: 103.72 s +2026-04-14 05:40:29.954881: +2026-04-14 05:40:29.956990: Epoch 3013 +2026-04-14 05:40:29.961103: Current learning rate: 0.00284 +2026-04-14 05:42:11.897253: train_loss -0.4254 +2026-04-14 05:42:11.904841: val_loss -0.3661 +2026-04-14 05:42:11.907657: Pseudo dice [0.8056, 0.0, 0.8068, 0.7011, 0.184, 0.6964, 0.6074] +2026-04-14 05:42:11.910190: Epoch time: 101.95 s +2026-04-14 05:42:13.216571: +2026-04-14 05:42:13.219325: Epoch 3014 +2026-04-14 05:42:13.221859: Current learning rate: 0.00284 +2026-04-14 05:43:54.889749: train_loss -0.435 +2026-04-14 05:43:54.895911: val_loss -0.3851 +2026-04-14 05:43:54.898058: Pseudo dice [0.7185, 0.0, 0.8551, 0.6364, 0.4068, 0.8999, 0.4893] +2026-04-14 05:43:54.900787: Epoch time: 101.68 s +2026-04-14 05:43:56.220601: +2026-04-14 05:43:56.223119: Epoch 3015 +2026-04-14 05:43:56.225277: Current learning rate: 0.00283 +2026-04-14 05:45:38.756616: train_loss -0.4503 +2026-04-14 05:45:38.763723: val_loss -0.4105 +2026-04-14 05:45:38.765798: Pseudo dice [0.7066, 0.0, 0.7283, 0.6337, 0.5794, 0.8052, 0.9286] +2026-04-14 05:45:38.768512: Epoch time: 102.54 s +2026-04-14 05:45:40.054778: +2026-04-14 05:45:40.057101: Epoch 3016 +2026-04-14 05:45:40.059266: Current learning rate: 0.00283 +2026-04-14 05:47:22.123179: train_loss -0.4466 +2026-04-14 05:47:22.130372: val_loss -0.3811 +2026-04-14 05:47:22.132970: Pseudo dice [0.7799, 0.0, 0.8115, 0.8426, 0.4274, 0.6791, 0.5956] +2026-04-14 05:47:22.135618: Epoch time: 102.07 s +2026-04-14 05:47:23.429943: +2026-04-14 05:47:23.432735: Epoch 3017 +2026-04-14 05:47:23.434676: Current learning rate: 0.00283 +2026-04-14 05:49:05.655518: train_loss -0.4486 +2026-04-14 05:49:05.666560: val_loss -0.3544 +2026-04-14 05:49:05.669476: Pseudo dice [0.7169, 0.0, 0.852, 0.0049, 0.3932, 0.8663, 0.5728] +2026-04-14 05:49:05.672637: Epoch time: 102.23 s +2026-04-14 05:49:06.953584: +2026-04-14 05:49:06.955446: Epoch 3018 +2026-04-14 05:49:06.959424: Current learning rate: 0.00283 +2026-04-14 05:50:48.815895: train_loss -0.4387 +2026-04-14 05:50:48.822643: val_loss -0.3744 +2026-04-14 05:50:48.824884: Pseudo dice [0.6781, 0.0, 0.7766, 0.3632, 0.1798, 0.7619, 0.926] +2026-04-14 05:50:48.827531: Epoch time: 101.87 s +2026-04-14 05:50:50.080008: +2026-04-14 05:50:50.081932: Epoch 3019 +2026-04-14 05:50:50.084008: Current learning rate: 0.00282 +2026-04-14 05:52:32.124367: train_loss -0.4377 +2026-04-14 05:52:32.132215: val_loss -0.3521 +2026-04-14 05:52:32.134417: Pseudo dice [0.6164, 0.0, 0.824, 0.1611, 0.5516, 0.6375, 0.8214] +2026-04-14 05:52:32.138185: Epoch time: 102.05 s +2026-04-14 05:52:33.409718: +2026-04-14 05:52:33.411912: Epoch 3020 +2026-04-14 05:52:33.414855: Current learning rate: 0.00282 +2026-04-14 05:54:15.408453: train_loss -0.4513 +2026-04-14 05:54:15.416610: val_loss -0.3889 +2026-04-14 05:54:15.418787: Pseudo dice [0.7868, 0.0, 0.8809, 0.3143, 0.5418, 0.4683, 0.8811] +2026-04-14 05:54:15.422278: Epoch time: 102.0 s +2026-04-14 05:54:16.667081: +2026-04-14 05:54:16.669464: Epoch 3021 +2026-04-14 05:54:16.672086: Current learning rate: 0.00282 +2026-04-14 05:55:58.268856: train_loss -0.4458 +2026-04-14 05:55:58.275061: val_loss -0.3753 +2026-04-14 05:55:58.276925: Pseudo dice [0.5934, 0.0, 0.8122, 0.5568, 0.5392, 0.4071, 0.9274] +2026-04-14 05:55:58.279345: Epoch time: 101.61 s +2026-04-14 05:55:59.558710: +2026-04-14 05:55:59.560999: Epoch 3022 +2026-04-14 05:55:59.563072: Current learning rate: 0.00281 +2026-04-14 05:57:41.537215: train_loss -0.4603 +2026-04-14 05:57:41.545099: val_loss -0.3782 +2026-04-14 05:57:41.548254: Pseudo dice [0.2904, 0.0, 0.8302, 0.7592, 0.5549, 0.8493, 0.8417] +2026-04-14 05:57:41.551508: Epoch time: 101.98 s +2026-04-14 05:57:42.799968: +2026-04-14 05:57:42.802363: Epoch 3023 +2026-04-14 05:57:42.805315: Current learning rate: 0.00281 +2026-04-14 05:59:25.091637: train_loss -0.4646 +2026-04-14 05:59:25.098619: val_loss -0.3604 +2026-04-14 05:59:25.100965: Pseudo dice [0.6089, 0.0, 0.8206, 0.6682, 0.4174, 0.8362, 0.873] +2026-04-14 05:59:25.103826: Epoch time: 102.29 s +2026-04-14 05:59:26.369747: +2026-04-14 05:59:26.372621: Epoch 3024 +2026-04-14 05:59:26.374731: Current learning rate: 0.00281 +2026-04-14 06:01:08.285152: train_loss -0.4516 +2026-04-14 06:01:08.294667: val_loss -0.3733 +2026-04-14 06:01:08.305315: Pseudo dice [0.7222, 0.0, 0.7803, 0.7242, 0.6001, 0.3027, 0.8473] +2026-04-14 06:01:08.308985: Epoch time: 101.92 s +2026-04-14 06:01:09.571283: +2026-04-14 06:01:09.574169: Epoch 3025 +2026-04-14 06:01:09.578444: Current learning rate: 0.00281 +2026-04-14 06:02:51.711250: train_loss -0.4572 +2026-04-14 06:02:51.718128: val_loss -0.381 +2026-04-14 06:02:51.720544: Pseudo dice [0.7534, 0.0, 0.7821, 0.8918, 0.4756, 0.5666, 0.91] +2026-04-14 06:02:51.724520: Epoch time: 102.14 s +2026-04-14 06:02:53.020832: +2026-04-14 06:02:53.022867: Epoch 3026 +2026-04-14 06:02:53.024874: Current learning rate: 0.0028 +2026-04-14 06:04:34.893155: train_loss -0.4389 +2026-04-14 06:04:34.914037: val_loss -0.3731 +2026-04-14 06:04:34.916294: Pseudo dice [0.1803, 0.0, 0.8195, 0.3882, 0.37, 0.6239, 0.8693] +2026-04-14 06:04:34.922909: Epoch time: 101.88 s +2026-04-14 06:04:36.165099: +2026-04-14 06:04:36.168914: Epoch 3027 +2026-04-14 06:04:36.171405: Current learning rate: 0.0028 +2026-04-14 06:06:17.506572: train_loss -0.4226 +2026-04-14 06:06:17.515768: val_loss -0.3734 +2026-04-14 06:06:17.518808: Pseudo dice [0.4668, 0.0, 0.7424, 0.002, 0.5674, 0.8087, 0.6322] +2026-04-14 06:06:17.522988: Epoch time: 101.35 s +2026-04-14 06:06:18.799284: +2026-04-14 06:06:18.801038: Epoch 3028 +2026-04-14 06:06:18.803013: Current learning rate: 0.0028 +2026-04-14 06:08:00.235854: train_loss -0.4329 +2026-04-14 06:08:00.246914: val_loss -0.3757 +2026-04-14 06:08:00.249017: Pseudo dice [0.4036, 0.0, 0.7885, 0.6802, 0.5329, 0.6762, 0.9139] +2026-04-14 06:08:00.252275: Epoch time: 101.44 s +2026-04-14 06:08:01.490949: +2026-04-14 06:08:01.493459: Epoch 3029 +2026-04-14 06:08:01.496393: Current learning rate: 0.0028 +2026-04-14 06:09:43.659469: train_loss -0.4395 +2026-04-14 06:09:43.666715: val_loss -0.3781 +2026-04-14 06:09:43.668927: Pseudo dice [0.484, 0.0, 0.7785, 0.7588, 0.4951, 0.5961, 0.8316] +2026-04-14 06:09:43.671416: Epoch time: 102.17 s +2026-04-14 06:09:44.925000: +2026-04-14 06:09:44.926941: Epoch 3030 +2026-04-14 06:09:44.929202: Current learning rate: 0.00279 +2026-04-14 06:11:26.333664: train_loss -0.4452 +2026-04-14 06:11:26.343824: val_loss -0.4036 +2026-04-14 06:11:26.345910: Pseudo dice [0.7566, 0.0, 0.8332, 0.6485, 0.5182, 0.7419, 0.8637] +2026-04-14 06:11:26.348283: Epoch time: 101.41 s +2026-04-14 06:11:27.597353: +2026-04-14 06:11:27.599179: Epoch 3031 +2026-04-14 06:11:27.601535: Current learning rate: 0.00279 +2026-04-14 06:13:09.992851: train_loss -0.4455 +2026-04-14 06:13:10.001171: val_loss -0.3556 +2026-04-14 06:13:10.003705: Pseudo dice [0.6128, 0.0, 0.7404, 0.4705, 0.5221, 0.6036, 0.8756] +2026-04-14 06:13:10.006378: Epoch time: 102.4 s +2026-04-14 06:13:11.306928: +2026-04-14 06:13:11.309001: Epoch 3032 +2026-04-14 06:13:11.311743: Current learning rate: 0.00279 +2026-04-14 06:14:55.044086: train_loss -0.4425 +2026-04-14 06:14:55.049870: val_loss -0.3853 +2026-04-14 06:14:55.052400: Pseudo dice [0.0811, 0.0, 0.8338, 0.2546, 0.5243, 0.7466, 0.8391] +2026-04-14 06:14:55.055194: Epoch time: 103.74 s +2026-04-14 06:14:56.339167: +2026-04-14 06:14:56.341361: Epoch 3033 +2026-04-14 06:14:56.343457: Current learning rate: 0.00279 +2026-04-14 06:16:38.327115: train_loss -0.4324 +2026-04-14 06:16:38.334200: val_loss -0.3862 +2026-04-14 06:16:38.336542: Pseudo dice [0.4776, 0.0, 0.6991, 0.8401, 0.6336, 0.7572, 0.8359] +2026-04-14 06:16:38.338667: Epoch time: 101.99 s +2026-04-14 06:16:39.614016: +2026-04-14 06:16:39.616330: Epoch 3034 +2026-04-14 06:16:39.618598: Current learning rate: 0.00278 +2026-04-14 06:18:21.608453: train_loss -0.4386 +2026-04-14 06:18:21.616222: val_loss -0.4 +2026-04-14 06:18:21.619198: Pseudo dice [0.7221, 0.0, 0.7691, 0.6958, 0.5997, 0.7369, 0.89] +2026-04-14 06:18:21.622213: Epoch time: 102.0 s +2026-04-14 06:18:22.874034: +2026-04-14 06:18:22.876102: Epoch 3035 +2026-04-14 06:18:22.878541: Current learning rate: 0.00278 +2026-04-14 06:20:04.656252: train_loss -0.4434 +2026-04-14 06:20:04.664809: val_loss -0.3876 +2026-04-14 06:20:04.667494: Pseudo dice [0.6813, 0.0, 0.7812, 0.6594, 0.5408, 0.7434, 0.8524] +2026-04-14 06:20:04.670901: Epoch time: 101.79 s +2026-04-14 06:20:05.914114: +2026-04-14 06:20:05.916389: Epoch 3036 +2026-04-14 06:20:05.918906: Current learning rate: 0.00278 +2026-04-14 06:21:47.313397: train_loss -0.4297 +2026-04-14 06:21:47.321306: val_loss -0.3434 +2026-04-14 06:21:47.323589: Pseudo dice [0.7261, 0.0, 0.6729, 0.1975, 0.6449, 0.6583, 0.254] +2026-04-14 06:21:47.326815: Epoch time: 101.4 s +2026-04-14 06:21:48.561468: +2026-04-14 06:21:48.563358: Epoch 3037 +2026-04-14 06:21:48.565481: Current learning rate: 0.00278 +2026-04-14 06:23:29.787460: train_loss -0.4449 +2026-04-14 06:23:29.798599: val_loss -0.3803 +2026-04-14 06:23:29.804269: Pseudo dice [0.7722, 0.0, 0.7249, 0.3682, 0.5326, 0.7949, 0.7971] +2026-04-14 06:23:29.806869: Epoch time: 101.23 s +2026-04-14 06:23:31.067415: +2026-04-14 06:23:31.070076: Epoch 3038 +2026-04-14 06:23:31.072673: Current learning rate: 0.00277 +2026-04-14 06:25:13.095186: train_loss -0.4505 +2026-04-14 06:25:13.102588: val_loss -0.3858 +2026-04-14 06:25:13.104825: Pseudo dice [0.7662, 0.0, 0.7638, 0.5611, 0.5182, 0.3807, 0.8173] +2026-04-14 06:25:13.107386: Epoch time: 102.03 s +2026-04-14 06:25:14.361887: +2026-04-14 06:25:14.363680: Epoch 3039 +2026-04-14 06:25:14.365571: Current learning rate: 0.00277 +2026-04-14 06:26:56.227552: train_loss -0.4346 +2026-04-14 06:26:56.234215: val_loss -0.3686 +2026-04-14 06:26:56.236490: Pseudo dice [0.4639, 0.0, 0.4816, 0.3867, 0.5826, 0.7502, 0.8819] +2026-04-14 06:26:56.239166: Epoch time: 101.87 s +2026-04-14 06:26:57.508406: +2026-04-14 06:26:57.510755: Epoch 3040 +2026-04-14 06:26:57.513088: Current learning rate: 0.00277 +2026-04-14 06:28:39.821285: train_loss -0.4478 +2026-04-14 06:28:39.828819: val_loss -0.3514 +2026-04-14 06:28:39.831130: Pseudo dice [0.5952, 0.0, 0.8737, 0.6261, 0.5177, 0.4417, 0.8867] +2026-04-14 06:28:39.833431: Epoch time: 102.32 s +2026-04-14 06:28:41.189688: +2026-04-14 06:28:41.191490: Epoch 3041 +2026-04-14 06:28:41.193406: Current learning rate: 0.00277 +2026-04-14 06:30:23.214269: train_loss -0.4399 +2026-04-14 06:30:23.221677: val_loss -0.3632 +2026-04-14 06:30:23.224989: Pseudo dice [0.6013, 0.0, 0.6227, 0.5994, 0.5608, 0.2405, 0.8] +2026-04-14 06:30:23.227421: Epoch time: 102.03 s +2026-04-14 06:30:24.462051: +2026-04-14 06:30:24.464391: Epoch 3042 +2026-04-14 06:30:24.467306: Current learning rate: 0.00276 +2026-04-14 06:32:07.110078: train_loss -0.4389 +2026-04-14 06:32:07.136359: val_loss -0.3541 +2026-04-14 06:32:07.138408: Pseudo dice [0.6417, 0.0, 0.7341, 0.5464, 0.5471, 0.4985, 0.7006] +2026-04-14 06:32:07.141026: Epoch time: 102.65 s +2026-04-14 06:32:08.360732: +2026-04-14 06:32:08.362370: Epoch 3043 +2026-04-14 06:32:08.364263: Current learning rate: 0.00276 +2026-04-14 06:33:50.590216: train_loss -0.4117 +2026-04-14 06:33:50.600071: val_loss -0.3784 +2026-04-14 06:33:50.603801: Pseudo dice [0.6304, 0.0, 0.8662, 0.2059, 0.476, 0.5347, 0.8391] +2026-04-14 06:33:50.606730: Epoch time: 102.23 s +2026-04-14 06:33:51.861479: +2026-04-14 06:33:51.863942: Epoch 3044 +2026-04-14 06:33:51.866517: Current learning rate: 0.00276 +2026-04-14 06:35:34.179526: train_loss -0.421 +2026-04-14 06:35:34.188838: val_loss -0.3709 +2026-04-14 06:35:34.191051: Pseudo dice [0.5195, 0.0, 0.6831, 0.6271, 0.4543, 0.4494, 0.7501] +2026-04-14 06:35:34.194425: Epoch time: 102.32 s +2026-04-14 06:35:35.493569: +2026-04-14 06:35:35.495602: Epoch 3045 +2026-04-14 06:35:35.498053: Current learning rate: 0.00276 +2026-04-14 06:37:17.470126: train_loss -0.4363 +2026-04-14 06:37:17.493543: val_loss -0.3452 +2026-04-14 06:37:17.496091: Pseudo dice [0.5109, 0.0, 0.7629, 0.326, 0.3736, 0.5569, 0.7175] +2026-04-14 06:37:17.499069: Epoch time: 101.98 s +2026-04-14 06:37:18.731464: +2026-04-14 06:37:18.733842: Epoch 3046 +2026-04-14 06:37:18.736102: Current learning rate: 0.00275 +2026-04-14 06:39:00.577275: train_loss -0.4396 +2026-04-14 06:39:00.585986: val_loss -0.3607 +2026-04-14 06:39:00.589282: Pseudo dice [0.7633, 0.0, 0.7085, 0.8263, 0.4682, 0.6845, 0.8785] +2026-04-14 06:39:00.591845: Epoch time: 101.85 s +2026-04-14 06:39:01.867386: +2026-04-14 06:39:01.869776: Epoch 3047 +2026-04-14 06:39:01.872303: Current learning rate: 0.00275 +2026-04-14 06:40:43.590145: train_loss -0.4441 +2026-04-14 06:40:43.596840: val_loss -0.3848 +2026-04-14 06:40:43.599487: Pseudo dice [0.8104, 0.0, 0.5025, 0.7556, 0.4895, 0.5623, 0.8028] +2026-04-14 06:40:43.602447: Epoch time: 101.73 s +2026-04-14 06:40:44.837295: +2026-04-14 06:40:44.839664: Epoch 3048 +2026-04-14 06:40:44.841844: Current learning rate: 0.00275 +2026-04-14 06:42:26.078603: train_loss -0.4537 +2026-04-14 06:42:26.086056: val_loss -0.3595 +2026-04-14 06:42:26.088192: Pseudo dice [0.5895, 0.0, 0.7773, 0.4929, 0.4825, 0.7597, 0.9024] +2026-04-14 06:42:26.090250: Epoch time: 101.24 s +2026-04-14 06:42:27.309376: +2026-04-14 06:42:27.312253: Epoch 3049 +2026-04-14 06:42:27.314470: Current learning rate: 0.00274 +2026-04-14 06:44:08.863433: train_loss -0.4513 +2026-04-14 06:44:08.870461: val_loss -0.3717 +2026-04-14 06:44:08.873558: Pseudo dice [0.6588, 0.0, 0.8509, 0.3196, 0.4263, 0.849, 0.9266] +2026-04-14 06:44:08.876384: Epoch time: 101.56 s +2026-04-14 06:44:11.888081: +2026-04-14 06:44:11.890125: Epoch 3050 +2026-04-14 06:44:11.891917: Current learning rate: 0.00274 +2026-04-14 06:45:53.918310: train_loss -0.4517 +2026-04-14 06:45:53.926546: val_loss -0.364 +2026-04-14 06:45:53.928859: Pseudo dice [0.2678, 0.0, 0.7666, 0.5266, 0.5102, 0.9205, 0.9048] +2026-04-14 06:45:53.931230: Epoch time: 102.03 s +2026-04-14 06:45:55.173071: +2026-04-14 06:45:55.174801: Epoch 3051 +2026-04-14 06:45:55.176832: Current learning rate: 0.00274 +2026-04-14 06:47:36.870272: train_loss -0.4445 +2026-04-14 06:47:36.877548: val_loss -0.4131 +2026-04-14 06:47:36.879551: Pseudo dice [0.5743, 0.0, 0.7935, 0.4645, 0.5837, 0.6558, 0.8835] +2026-04-14 06:47:36.882728: Epoch time: 101.7 s +2026-04-14 06:47:39.228082: +2026-04-14 06:47:39.230169: Epoch 3052 +2026-04-14 06:47:39.232480: Current learning rate: 0.00274 +2026-04-14 06:49:21.146270: train_loss -0.4477 +2026-04-14 06:49:21.153186: val_loss -0.3844 +2026-04-14 06:49:21.157338: Pseudo dice [0.7153, 0.0, 0.774, 0.5715, 0.5069, 0.6816, 0.7733] +2026-04-14 06:49:21.159778: Epoch time: 101.92 s +2026-04-14 06:49:22.428748: +2026-04-14 06:49:22.430906: Epoch 3053 +2026-04-14 06:49:22.432869: Current learning rate: 0.00273 +2026-04-14 06:51:04.395669: train_loss -0.4572 +2026-04-14 06:51:04.402551: val_loss -0.3849 +2026-04-14 06:51:04.405299: Pseudo dice [0.7089, 0.0, 0.8565, 0.7488, 0.4767, 0.7075, 0.8973] +2026-04-14 06:51:04.407678: Epoch time: 101.97 s +2026-04-14 06:51:05.674655: +2026-04-14 06:51:05.676414: Epoch 3054 +2026-04-14 06:51:05.678224: Current learning rate: 0.00273 +2026-04-14 06:52:47.234298: train_loss -0.4474 +2026-04-14 06:52:47.241827: val_loss -0.4218 +2026-04-14 06:52:47.244111: Pseudo dice [0.7786, 0.0, 0.842, 0.7128, 0.6118, 0.7164, 0.9585] +2026-04-14 06:52:47.247296: Epoch time: 101.56 s +2026-04-14 06:52:48.476179: +2026-04-14 06:52:48.478290: Epoch 3055 +2026-04-14 06:52:48.480477: Current learning rate: 0.00273 +2026-04-14 06:54:30.018890: train_loss -0.4312 +2026-04-14 06:54:30.025232: val_loss -0.4017 +2026-04-14 06:54:30.027636: Pseudo dice [0.7031, 0.0, 0.8147, 0.443, 0.5425, 0.7976, 0.9124] +2026-04-14 06:54:30.031016: Epoch time: 101.55 s +2026-04-14 06:54:31.286809: +2026-04-14 06:54:31.289645: Epoch 3056 +2026-04-14 06:54:31.292190: Current learning rate: 0.00273 +2026-04-14 06:56:13.089658: train_loss -0.4305 +2026-04-14 06:56:13.096060: val_loss -0.3656 +2026-04-14 06:56:13.098463: Pseudo dice [0.6861, 0.0, 0.7439, 0.17, 0.5611, 0.7924, 0.9004] +2026-04-14 06:56:13.100835: Epoch time: 101.81 s +2026-04-14 06:56:14.332929: +2026-04-14 06:56:14.334679: Epoch 3057 +2026-04-14 06:56:14.336600: Current learning rate: 0.00272 +2026-04-14 06:57:56.154391: train_loss -0.4492 +2026-04-14 06:57:56.162030: val_loss -0.3893 +2026-04-14 06:57:56.164072: Pseudo dice [0.3594, 0.0, 0.9128, 0.6653, 0.465, 0.7817, 0.9121] +2026-04-14 06:57:56.167085: Epoch time: 101.82 s +2026-04-14 06:57:57.452852: +2026-04-14 06:57:57.455073: Epoch 3058 +2026-04-14 06:57:57.457711: Current learning rate: 0.00272 +2026-04-14 06:59:38.825073: train_loss -0.4358 +2026-04-14 06:59:38.831775: val_loss -0.392 +2026-04-14 06:59:38.834136: Pseudo dice [0.7028, 0.0, 0.7406, 0.702, 0.6116, 0.7806, 0.6496] +2026-04-14 06:59:38.836369: Epoch time: 101.38 s +2026-04-14 06:59:40.087888: +2026-04-14 06:59:40.090143: Epoch 3059 +2026-04-14 06:59:40.092321: Current learning rate: 0.00272 +2026-04-14 07:01:22.002042: train_loss -0.452 +2026-04-14 07:01:22.008444: val_loss -0.3943 +2026-04-14 07:01:22.010534: Pseudo dice [0.7804, 0.0, 0.8167, 0.7763, 0.5822, 0.7688, 0.6357] +2026-04-14 07:01:22.012626: Epoch time: 101.92 s +2026-04-14 07:01:23.257251: +2026-04-14 07:01:23.259633: Epoch 3060 +2026-04-14 07:01:23.262371: Current learning rate: 0.00272 +2026-04-14 07:03:04.654712: train_loss -0.4492 +2026-04-14 07:03:04.662097: val_loss -0.4005 +2026-04-14 07:03:04.664747: Pseudo dice [0.7252, 0.0, 0.6846, 0.4982, 0.6282, 0.7256, 0.7187] +2026-04-14 07:03:04.667906: Epoch time: 101.4 s +2026-04-14 07:03:05.948916: +2026-04-14 07:03:05.951034: Epoch 3061 +2026-04-14 07:03:05.953384: Current learning rate: 0.00271 +2026-04-14 07:04:47.869506: train_loss -0.442 +2026-04-14 07:04:47.876921: val_loss -0.3679 +2026-04-14 07:04:47.880202: Pseudo dice [0.4823, 0.0, 0.7737, 0.3806, 0.5728, 0.4858, 0.7099] +2026-04-14 07:04:47.883306: Epoch time: 101.92 s +2026-04-14 07:04:49.148992: +2026-04-14 07:04:49.150797: Epoch 3062 +2026-04-14 07:04:49.152819: Current learning rate: 0.00271 +2026-04-14 07:06:30.912930: train_loss -0.4494 +2026-04-14 07:06:30.919986: val_loss -0.3925 +2026-04-14 07:06:30.922677: Pseudo dice [0.3664, 0.0, 0.7393, 0.6123, 0.6685, 0.7118, 0.8409] +2026-04-14 07:06:30.925261: Epoch time: 101.77 s +2026-04-14 07:06:32.169479: +2026-04-14 07:06:32.171677: Epoch 3063 +2026-04-14 07:06:32.173873: Current learning rate: 0.00271 +2026-04-14 07:08:14.055782: train_loss -0.451 +2026-04-14 07:08:14.063169: val_loss -0.3611 +2026-04-14 07:08:14.066290: Pseudo dice [0.5048, 0.0, 0.7746, 0.3714, 0.6677, 0.7709, 0.7517] +2026-04-14 07:08:14.069252: Epoch time: 101.89 s +2026-04-14 07:08:15.340397: +2026-04-14 07:08:15.342236: Epoch 3064 +2026-04-14 07:08:15.344015: Current learning rate: 0.00271 +2026-04-14 07:09:57.037107: train_loss -0.4474 +2026-04-14 07:09:57.042838: val_loss -0.3769 +2026-04-14 07:09:57.046361: Pseudo dice [0.7681, 0.0, 0.8189, 0.6889, 0.623, 0.8032, 0.8562] +2026-04-14 07:09:57.049138: Epoch time: 101.7 s +2026-04-14 07:09:58.291161: +2026-04-14 07:09:58.293432: Epoch 3065 +2026-04-14 07:09:58.295310: Current learning rate: 0.0027 +2026-04-14 07:11:40.129117: train_loss -0.4505 +2026-04-14 07:11:40.136365: val_loss -0.3778 +2026-04-14 07:11:40.138659: Pseudo dice [0.6226, 0.0, 0.63, 0.8566, 0.5905, 0.5151, 0.8547] +2026-04-14 07:11:40.141780: Epoch time: 101.84 s +2026-04-14 07:11:41.403136: +2026-04-14 07:11:41.404857: Epoch 3066 +2026-04-14 07:11:41.406802: Current learning rate: 0.0027 +2026-04-14 07:13:23.130135: train_loss -0.4629 +2026-04-14 07:13:23.137041: val_loss -0.4009 +2026-04-14 07:13:23.139237: Pseudo dice [0.4675, 0.0, 0.8479, 0.6891, 0.6393, 0.5553, 0.8814] +2026-04-14 07:13:23.142371: Epoch time: 101.73 s +2026-04-14 07:13:24.418077: +2026-04-14 07:13:24.420127: Epoch 3067 +2026-04-14 07:13:24.422196: Current learning rate: 0.0027 +2026-04-14 07:15:05.908982: train_loss -0.4567 +2026-04-14 07:15:05.916291: val_loss -0.3852 +2026-04-14 07:15:05.918491: Pseudo dice [0.3938, 0.0, 0.7864, 0.6181, 0.5138, 0.5637, 0.7095] +2026-04-14 07:15:05.921396: Epoch time: 101.49 s +2026-04-14 07:15:07.160461: +2026-04-14 07:15:07.162414: Epoch 3068 +2026-04-14 07:15:07.164385: Current learning rate: 0.0027 +2026-04-14 07:16:49.068162: train_loss -0.4551 +2026-04-14 07:16:49.076528: val_loss -0.3642 +2026-04-14 07:16:49.078898: Pseudo dice [0.75, 0.0, 0.6967, 0.4532, 0.4445, 0.6549, 0.8735] +2026-04-14 07:16:49.092728: Epoch time: 101.91 s +2026-04-14 07:16:50.351006: +2026-04-14 07:16:50.353018: Epoch 3069 +2026-04-14 07:16:50.355458: Current learning rate: 0.00269 +2026-04-14 07:18:32.248262: train_loss -0.446 +2026-04-14 07:18:32.255365: val_loss -0.3934 +2026-04-14 07:18:32.257416: Pseudo dice [0.4237, 0.0, 0.851, 0.6473, 0.5482, 0.7609, 0.7649] +2026-04-14 07:18:32.260046: Epoch time: 101.9 s +2026-04-14 07:18:33.502792: +2026-04-14 07:18:33.504636: Epoch 3070 +2026-04-14 07:18:33.506974: Current learning rate: 0.00269 +2026-04-14 07:20:15.384340: train_loss -0.4522 +2026-04-14 07:20:15.392073: val_loss -0.3668 +2026-04-14 07:20:15.394534: Pseudo dice [0.7893, 0.0, 0.7235, 0.6221, 0.091, 0.6198, 0.6035] +2026-04-14 07:20:15.398022: Epoch time: 101.88 s +2026-04-14 07:20:16.665698: +2026-04-14 07:20:16.668334: Epoch 3071 +2026-04-14 07:20:16.670785: Current learning rate: 0.00269 +2026-04-14 07:21:59.384395: train_loss -0.4475 +2026-04-14 07:21:59.391521: val_loss -0.369 +2026-04-14 07:21:59.394629: Pseudo dice [0.5197, 0.0, 0.8573, 0.2507, 0.53, 0.9023, 0.448] +2026-04-14 07:21:59.397096: Epoch time: 102.72 s +2026-04-14 07:22:00.653432: +2026-04-14 07:22:00.655408: Epoch 3072 +2026-04-14 07:22:00.657690: Current learning rate: 0.00268 +2026-04-14 07:23:42.111532: train_loss -0.4604 +2026-04-14 07:23:42.119165: val_loss -0.3281 +2026-04-14 07:23:42.121886: Pseudo dice [0.5458, 0.0, 0.5945, 0.536, 0.4046, 0.8383, 0.5149] +2026-04-14 07:23:42.124445: Epoch time: 101.46 s +2026-04-14 07:23:43.346430: +2026-04-14 07:23:43.348341: Epoch 3073 +2026-04-14 07:23:43.350265: Current learning rate: 0.00268 +2026-04-14 07:25:25.210988: train_loss -0.4526 +2026-04-14 07:25:25.217498: val_loss -0.3776 +2026-04-14 07:25:25.219828: Pseudo dice [0.7644, 0.0, 0.7369, 0.2538, 0.4138, 0.7767, 0.8824] +2026-04-14 07:25:25.223167: Epoch time: 101.87 s +2026-04-14 07:25:26.488645: +2026-04-14 07:25:26.490628: Epoch 3074 +2026-04-14 07:25:26.492873: Current learning rate: 0.00268 +2026-04-14 07:27:08.209934: train_loss -0.4324 +2026-04-14 07:27:08.219948: val_loss -0.3746 +2026-04-14 07:27:08.222454: Pseudo dice [0.6571, 0.0, 0.8455, 0.0266, 0.4649, 0.8987, 0.5466] +2026-04-14 07:27:08.225827: Epoch time: 101.72 s +2026-04-14 07:27:09.512996: +2026-04-14 07:27:09.515217: Epoch 3075 +2026-04-14 07:27:09.517235: Current learning rate: 0.00268 +2026-04-14 07:28:51.696481: train_loss -0.4475 +2026-04-14 07:28:51.703003: val_loss -0.3839 +2026-04-14 07:28:51.705555: Pseudo dice [0.7093, 0.0, 0.8414, 0.2777, 0.3789, 0.6364, 0.9327] +2026-04-14 07:28:51.708559: Epoch time: 102.19 s +2026-04-14 07:28:53.015045: +2026-04-14 07:28:53.016955: Epoch 3076 +2026-04-14 07:28:53.018971: Current learning rate: 0.00267 +2026-04-14 07:30:34.849005: train_loss -0.4536 +2026-04-14 07:30:34.855423: val_loss -0.3606 +2026-04-14 07:30:34.857538: Pseudo dice [0.3658, 0.0, 0.7011, 0.4652, 0.4204, 0.8962, 0.8504] +2026-04-14 07:30:34.860047: Epoch time: 101.84 s +2026-04-14 07:30:36.178787: +2026-04-14 07:30:36.180517: Epoch 3077 +2026-04-14 07:30:36.182333: Current learning rate: 0.00267 +2026-04-14 07:32:18.042051: train_loss -0.4502 +2026-04-14 07:32:18.067858: val_loss -0.3635 +2026-04-14 07:32:18.071014: Pseudo dice [0.7071, 0.0, 0.8199, 0.8907, 0.4221, 0.742, 0.224] +2026-04-14 07:32:18.073510: Epoch time: 101.87 s +2026-04-14 07:32:19.380473: +2026-04-14 07:32:19.382362: Epoch 3078 +2026-04-14 07:32:19.384412: Current learning rate: 0.00267 +2026-04-14 07:34:01.408521: train_loss -0.444 +2026-04-14 07:34:01.415065: val_loss -0.344 +2026-04-14 07:34:01.416960: Pseudo dice [0.5543, 0.0, 0.7286, 0.1874, 0.3643, 0.7887, 0.4055] +2026-04-14 07:34:01.419734: Epoch time: 102.03 s +2026-04-14 07:34:02.693926: +2026-04-14 07:34:02.696088: Epoch 3079 +2026-04-14 07:34:02.698195: Current learning rate: 0.00267 +2026-04-14 07:35:44.396569: train_loss -0.4391 +2026-04-14 07:35:44.403030: val_loss -0.3763 +2026-04-14 07:35:44.404997: Pseudo dice [0.6607, 0.0, 0.8905, 0.6046, 0.4209, 0.2775, 0.8138] +2026-04-14 07:35:44.408167: Epoch time: 101.71 s +2026-04-14 07:35:45.661397: +2026-04-14 07:35:45.663490: Epoch 3080 +2026-04-14 07:35:45.665587: Current learning rate: 0.00266 +2026-04-14 07:37:27.792136: train_loss -0.4088 +2026-04-14 07:37:27.799522: val_loss -0.3483 +2026-04-14 07:37:27.802312: Pseudo dice [0.6715, 0.0, 0.4691, 0.8375, 0.596, 0.827, 0.1503] +2026-04-14 07:37:27.804843: Epoch time: 102.13 s +2026-04-14 07:37:29.073574: +2026-04-14 07:37:29.076076: Epoch 3081 +2026-04-14 07:37:29.078167: Current learning rate: 0.00266 +2026-04-14 07:39:10.921746: train_loss -0.4366 +2026-04-14 07:39:10.928198: val_loss -0.3602 +2026-04-14 07:39:10.931112: Pseudo dice [0.2951, 0.0, 0.8446, 0.1708, 0.5599, 0.711, 0.8945] +2026-04-14 07:39:10.935806: Epoch time: 101.85 s +2026-04-14 07:39:12.190414: +2026-04-14 07:39:12.192378: Epoch 3082 +2026-04-14 07:39:12.194533: Current learning rate: 0.00266 +2026-04-14 07:40:53.941049: train_loss -0.4581 +2026-04-14 07:40:53.947432: val_loss -0.3888 +2026-04-14 07:40:53.949950: Pseudo dice [0.8488, 0.0, 0.8266, 0.6263, 0.584, 0.8604, 0.4626] +2026-04-14 07:40:53.952327: Epoch time: 101.75 s +2026-04-14 07:40:55.192122: +2026-04-14 07:40:55.193912: Epoch 3083 +2026-04-14 07:40:55.195812: Current learning rate: 0.00266 +2026-04-14 07:42:36.714478: train_loss -0.452 +2026-04-14 07:42:36.721212: val_loss -0.3516 +2026-04-14 07:42:36.723208: Pseudo dice [0.4525, 0.0, 0.8421, 0.4566, 0.6342, 0.5773, 0.7895] +2026-04-14 07:42:36.725559: Epoch time: 101.53 s +2026-04-14 07:42:38.002109: +2026-04-14 07:42:38.004339: Epoch 3084 +2026-04-14 07:42:38.006492: Current learning rate: 0.00265 +2026-04-14 07:44:19.655214: train_loss -0.4618 +2026-04-14 07:44:19.661954: val_loss -0.3718 +2026-04-14 07:44:19.664319: Pseudo dice [0.7756, 0.0, 0.8172, 0.6462, 0.4393, 0.349, 0.9236] +2026-04-14 07:44:19.667038: Epoch time: 101.66 s +2026-04-14 07:44:20.980226: +2026-04-14 07:44:20.982208: Epoch 3085 +2026-04-14 07:44:20.984319: Current learning rate: 0.00265 +2026-04-14 07:46:02.944556: train_loss -0.4544 +2026-04-14 07:46:02.952391: val_loss -0.3982 +2026-04-14 07:46:02.955102: Pseudo dice [0.7541, 0.0, 0.7577, 0.636, 0.5061, 0.6785, 0.7328] +2026-04-14 07:46:02.957851: Epoch time: 101.97 s +2026-04-14 07:46:04.206121: +2026-04-14 07:46:04.208164: Epoch 3086 +2026-04-14 07:46:04.210107: Current learning rate: 0.00265 +2026-04-14 07:47:46.250653: train_loss -0.4601 +2026-04-14 07:47:46.258214: val_loss -0.3866 +2026-04-14 07:47:46.270077: Pseudo dice [0.7644, 0.0, 0.766, 0.2932, 0.5104, 0.7277, 0.8581] +2026-04-14 07:47:46.277219: Epoch time: 102.05 s +2026-04-14 07:47:47.547334: +2026-04-14 07:47:47.549382: Epoch 3087 +2026-04-14 07:47:47.551473: Current learning rate: 0.00265 +2026-04-14 07:49:29.413038: train_loss -0.4484 +2026-04-14 07:49:29.419961: val_loss -0.341 +2026-04-14 07:49:29.422344: Pseudo dice [0.7428, 0.0, 0.5133, 0.6649, 0.5969, 0.5868, 0.5223] +2026-04-14 07:49:29.425318: Epoch time: 101.87 s +2026-04-14 07:49:30.672776: +2026-04-14 07:49:30.674886: Epoch 3088 +2026-04-14 07:49:30.677878: Current learning rate: 0.00264 +2026-04-14 07:51:12.309314: train_loss -0.4358 +2026-04-14 07:51:12.316398: val_loss -0.3714 +2026-04-14 07:51:12.318421: Pseudo dice [0.8554, 0.0, 0.8646, 0.4376, 0.5019, 0.7737, 0.799] +2026-04-14 07:51:12.321052: Epoch time: 101.64 s +2026-04-14 07:51:13.548673: +2026-04-14 07:51:13.550601: Epoch 3089 +2026-04-14 07:51:13.552575: Current learning rate: 0.00264 +2026-04-14 07:52:55.420462: train_loss -0.4404 +2026-04-14 07:52:55.427710: val_loss -0.3847 +2026-04-14 07:52:55.430136: Pseudo dice [0.7101, 0.0, 0.7031, 0.593, 0.5571, 0.927, 0.887] +2026-04-14 07:52:55.432427: Epoch time: 101.87 s +2026-04-14 07:52:56.710252: +2026-04-14 07:52:56.712350: Epoch 3090 +2026-04-14 07:52:56.714729: Current learning rate: 0.00264 +2026-04-14 07:54:37.987850: train_loss -0.4314 +2026-04-14 07:54:37.995633: val_loss -0.3725 +2026-04-14 07:54:37.998291: Pseudo dice [0.6516, 0.0, 0.7645, 0.5754, 0.2889, 0.5275, 0.4594] +2026-04-14 07:54:38.000828: Epoch time: 101.28 s +2026-04-14 07:54:39.273470: +2026-04-14 07:54:39.275508: Epoch 3091 +2026-04-14 07:54:39.277929: Current learning rate: 0.00264 +2026-04-14 07:56:22.024025: train_loss -0.4361 +2026-04-14 07:56:22.030116: val_loss -0.4033 +2026-04-14 07:56:22.032708: Pseudo dice [0.8118, 0.0, 0.8522, 0.5172, 0.4672, 0.7194, 0.8395] +2026-04-14 07:56:22.036822: Epoch time: 102.76 s +2026-04-14 07:56:23.300704: +2026-04-14 07:56:23.302778: Epoch 3092 +2026-04-14 07:56:23.305204: Current learning rate: 0.00263 +2026-04-14 07:58:04.818092: train_loss -0.4266 +2026-04-14 07:58:04.823940: val_loss -0.3584 +2026-04-14 07:58:04.825973: Pseudo dice [0.7268, 0.0, 0.6045, 0.2274, 0.5512, 0.8639, 0.5389] +2026-04-14 07:58:04.829137: Epoch time: 101.52 s +2026-04-14 07:58:06.113943: +2026-04-14 07:58:06.116004: Epoch 3093 +2026-04-14 07:58:06.118093: Current learning rate: 0.00263 +2026-04-14 07:59:48.087832: train_loss -0.4371 +2026-04-14 07:59:48.098390: val_loss -0.3592 +2026-04-14 07:59:48.101240: Pseudo dice [0.6889, 0.0, 0.8221, 0.1301, 0.3189, 0.6729, 0.7593] +2026-04-14 07:59:48.104044: Epoch time: 101.98 s +2026-04-14 07:59:49.339764: +2026-04-14 07:59:49.341761: Epoch 3094 +2026-04-14 07:59:49.344152: Current learning rate: 0.00263 +2026-04-14 08:01:31.071594: train_loss -0.4491 +2026-04-14 08:01:31.079788: val_loss -0.3701 +2026-04-14 08:01:31.082008: Pseudo dice [0.4508, 0.0, 0.8423, 0.5113, 0.5479, 0.6755, 0.7839] +2026-04-14 08:01:31.084722: Epoch time: 101.73 s +2026-04-14 08:01:32.381818: +2026-04-14 08:01:32.383920: Epoch 3095 +2026-04-14 08:01:32.385850: Current learning rate: 0.00263 +2026-04-14 08:03:13.836530: train_loss -0.437 +2026-04-14 08:03:13.843376: val_loss -0.356 +2026-04-14 08:03:13.846128: Pseudo dice [0.2809, 0.0, 0.7435, 0.1168, 0.5299, 0.8583, 0.7777] +2026-04-14 08:03:13.848727: Epoch time: 101.46 s +2026-04-14 08:03:15.133535: +2026-04-14 08:03:15.136189: Epoch 3096 +2026-04-14 08:03:15.138463: Current learning rate: 0.00262 +2026-04-14 08:04:56.886369: train_loss -0.4405 +2026-04-14 08:04:56.895551: val_loss -0.3906 +2026-04-14 08:04:56.901171: Pseudo dice [0.6204, 0.0, 0.7295, 0.7248, 0.3867, 0.6963, 0.7994] +2026-04-14 08:04:56.911996: Epoch time: 101.76 s +2026-04-14 08:04:58.171763: +2026-04-14 08:04:58.174304: Epoch 3097 +2026-04-14 08:04:58.176284: Current learning rate: 0.00262 +2026-04-14 08:06:40.553968: train_loss -0.4577 +2026-04-14 08:06:40.561005: val_loss -0.3812 +2026-04-14 08:06:40.562879: Pseudo dice [0.4062, 0.0, 0.785, 0.6655, 0.382, 0.6773, 0.8118] +2026-04-14 08:06:40.565143: Epoch time: 102.39 s +2026-04-14 08:06:41.854143: +2026-04-14 08:06:41.856102: Epoch 3098 +2026-04-14 08:06:41.858104: Current learning rate: 0.00262 +2026-04-14 08:08:23.064105: train_loss -0.453 +2026-04-14 08:08:23.072399: val_loss -0.3639 +2026-04-14 08:08:23.074871: Pseudo dice [0.5494, 0.0, 0.8115, 0.7821, 0.4571, 0.8274, 0.9217] +2026-04-14 08:08:23.077348: Epoch time: 101.21 s +2026-04-14 08:08:24.333648: +2026-04-14 08:08:24.335322: Epoch 3099 +2026-04-14 08:08:24.337255: Current learning rate: 0.00261 +2026-04-14 08:10:06.415301: train_loss -0.4633 +2026-04-14 08:10:06.423960: val_loss -0.3949 +2026-04-14 08:10:06.426739: Pseudo dice [0.6188, 0.0, 0.7665, 0.3322, 0.5043, 0.7317, 0.903] +2026-04-14 08:10:06.429946: Epoch time: 102.08 s +2026-04-14 08:10:09.687627: +2026-04-14 08:10:09.689702: Epoch 3100 +2026-04-14 08:10:09.691638: Current learning rate: 0.00261 +2026-04-14 08:11:51.154683: train_loss -0.447 +2026-04-14 08:11:51.161557: val_loss -0.426 +2026-04-14 08:11:51.164253: Pseudo dice [0.5418, 0.0, 0.8136, 0.8814, 0.5888, 0.9277, 0.9205] +2026-04-14 08:11:51.166765: Epoch time: 101.47 s +2026-04-14 08:11:52.440428: +2026-04-14 08:11:52.442473: Epoch 3101 +2026-04-14 08:11:52.444818: Current learning rate: 0.00261 +2026-04-14 08:13:33.932339: train_loss -0.4487 +2026-04-14 08:13:33.939578: val_loss -0.3763 +2026-04-14 08:13:33.941902: Pseudo dice [0.5949, 0.0, 0.8461, 0.7198, 0.5714, 0.6786, 0.6633] +2026-04-14 08:13:33.944386: Epoch time: 101.49 s +2026-04-14 08:13:35.203758: +2026-04-14 08:13:35.206060: Epoch 3102 +2026-04-14 08:13:35.208145: Current learning rate: 0.00261 +2026-04-14 08:15:16.855340: train_loss -0.4436 +2026-04-14 08:15:16.862538: val_loss -0.3584 +2026-04-14 08:15:16.865330: Pseudo dice [0.7526, 0.0, 0.804, 0.3322, 0.5424, 0.6171, 0.5088] +2026-04-14 08:15:16.867991: Epoch time: 101.65 s +2026-04-14 08:15:18.130147: +2026-04-14 08:15:18.132995: Epoch 3103 +2026-04-14 08:15:18.137067: Current learning rate: 0.0026 +2026-04-14 08:16:59.960926: train_loss -0.4423 +2026-04-14 08:16:59.967873: val_loss -0.385 +2026-04-14 08:16:59.970571: Pseudo dice [0.439, 0.0, 0.8403, 0.2021, 0.5677, 0.8863, 0.7584] +2026-04-14 08:16:59.972952: Epoch time: 101.83 s +2026-04-14 08:17:01.231875: +2026-04-14 08:17:01.234452: Epoch 3104 +2026-04-14 08:17:01.236997: Current learning rate: 0.0026 +2026-04-14 08:18:42.919313: train_loss -0.4373 +2026-04-14 08:18:42.927587: val_loss -0.3505 +2026-04-14 08:18:42.930570: Pseudo dice [0.5241, 0.0, 0.7359, 0.1015, 0.5848, 0.4602, 0.5889] +2026-04-14 08:18:42.933176: Epoch time: 101.69 s +2026-04-14 08:18:44.237361: +2026-04-14 08:18:44.240018: Epoch 3105 +2026-04-14 08:18:44.242314: Current learning rate: 0.0026 +2026-04-14 08:20:25.596064: train_loss -0.4406 +2026-04-14 08:20:25.603448: val_loss -0.334 +2026-04-14 08:20:25.606113: Pseudo dice [0.4388, 0.0, 0.734, 0.1078, 0.5381, 0.4161, 0.46] +2026-04-14 08:20:25.608600: Epoch time: 101.36 s +2026-04-14 08:20:26.830521: +2026-04-14 08:20:26.832162: Epoch 3106 +2026-04-14 08:20:26.834069: Current learning rate: 0.0026 +2026-04-14 08:22:08.641095: train_loss -0.4518 +2026-04-14 08:22:08.647212: val_loss -0.3759 +2026-04-14 08:22:08.649584: Pseudo dice [0.4853, 0.0, 0.6694, 0.8117, 0.4433, 0.6567, 0.8319] +2026-04-14 08:22:08.651959: Epoch time: 101.81 s +2026-04-14 08:22:09.899597: +2026-04-14 08:22:09.902163: Epoch 3107 +2026-04-14 08:22:09.904229: Current learning rate: 0.00259 +2026-04-14 08:23:51.903867: train_loss -0.4495 +2026-04-14 08:23:51.910408: val_loss -0.3446 +2026-04-14 08:23:51.912571: Pseudo dice [0.644, 0.0, 0.7353, 0.0269, 0.5594, 0.515, 0.7856] +2026-04-14 08:23:51.915136: Epoch time: 102.01 s +2026-04-14 08:23:53.248409: +2026-04-14 08:23:53.250216: Epoch 3108 +2026-04-14 08:23:53.252341: Current learning rate: 0.00259 +2026-04-14 08:25:34.763069: train_loss -0.4478 +2026-04-14 08:25:34.781729: val_loss -0.3691 +2026-04-14 08:25:34.784836: Pseudo dice [0.4105, 0.0, 0.7784, 0.875, 0.5441, 0.6531, 0.8201] +2026-04-14 08:25:34.787305: Epoch time: 101.52 s +2026-04-14 08:25:36.044938: +2026-04-14 08:25:36.046810: Epoch 3109 +2026-04-14 08:25:36.048669: Current learning rate: 0.00259 +2026-04-14 08:27:17.475985: train_loss -0.4641 +2026-04-14 08:27:17.483755: val_loss -0.4037 +2026-04-14 08:27:17.486158: Pseudo dice [0.8177, 0.0, 0.8354, 0.6607, 0.2495, 0.6669, 0.7664] +2026-04-14 08:27:17.489306: Epoch time: 101.43 s +2026-04-14 08:27:18.717428: +2026-04-14 08:27:18.719441: Epoch 3110 +2026-04-14 08:27:18.721600: Current learning rate: 0.00259 +2026-04-14 08:29:01.036423: train_loss -0.4537 +2026-04-14 08:29:01.043412: val_loss -0.3652 +2026-04-14 08:29:01.045275: Pseudo dice [0.5189, 0.0, 0.7247, 0.5207, 0.2535, 0.565, 0.7486] +2026-04-14 08:29:01.048745: Epoch time: 102.32 s +2026-04-14 08:29:03.478143: +2026-04-14 08:29:03.480379: Epoch 3111 +2026-04-14 08:29:03.482501: Current learning rate: 0.00258 +2026-04-14 08:30:45.263031: train_loss -0.4456 +2026-04-14 08:30:45.270561: val_loss -0.3574 +2026-04-14 08:30:45.272754: Pseudo dice [0.586, 0.0, 0.8527, 0.4061, 0.2316, 0.6383, 0.414] +2026-04-14 08:30:45.275831: Epoch time: 101.79 s +2026-04-14 08:30:46.513237: +2026-04-14 08:30:46.515347: Epoch 3112 +2026-04-14 08:30:46.517763: Current learning rate: 0.00258 +2026-04-14 08:32:28.212646: train_loss -0.4409 +2026-04-14 08:32:28.238146: val_loss -0.4154 +2026-04-14 08:32:28.240108: Pseudo dice [0.7666, 0.0, 0.8076, 0.4301, 0.5602, 0.852, 0.92] +2026-04-14 08:32:28.242489: Epoch time: 101.7 s +2026-04-14 08:32:29.533675: +2026-04-14 08:32:29.535478: Epoch 3113 +2026-04-14 08:32:29.537364: Current learning rate: 0.00258 +2026-04-14 08:34:10.913034: train_loss -0.4515 +2026-04-14 08:34:10.920423: val_loss -0.403 +2026-04-14 08:34:10.923203: Pseudo dice [0.6365, 0.0, 0.8202, 0.6625, 0.6502, 0.86, 0.9267] +2026-04-14 08:34:10.926724: Epoch time: 101.38 s +2026-04-14 08:34:12.199466: +2026-04-14 08:34:12.201219: Epoch 3114 +2026-04-14 08:34:12.203133: Current learning rate: 0.00258 +2026-04-14 08:35:54.206769: train_loss -0.4492 +2026-04-14 08:35:54.214828: val_loss -0.3667 +2026-04-14 08:35:54.217460: Pseudo dice [0.4343, 0.0, 0.8666, 0.0934, 0.4307, 0.5829, 0.8868] +2026-04-14 08:35:54.220308: Epoch time: 102.01 s +2026-04-14 08:35:55.507010: +2026-04-14 08:35:55.509166: Epoch 3115 +2026-04-14 08:35:55.511052: Current learning rate: 0.00257 +2026-04-14 08:37:37.683327: train_loss -0.4417 +2026-04-14 08:37:37.689496: val_loss -0.3735 +2026-04-14 08:37:37.691790: Pseudo dice [0.7403, 0.0, 0.8508, 0.2526, 0.581, 0.7589, 0.8196] +2026-04-14 08:37:37.694323: Epoch time: 102.18 s +2026-04-14 08:37:39.018752: +2026-04-14 08:37:39.020673: Epoch 3116 +2026-04-14 08:37:39.022648: Current learning rate: 0.00257 +2026-04-14 08:39:21.228007: train_loss -0.4572 +2026-04-14 08:39:21.236627: val_loss -0.4096 +2026-04-14 08:39:21.238740: Pseudo dice [0.7344, 0.0, 0.8213, 0.61, 0.4473, 0.8323, 0.925] +2026-04-14 08:39:21.241445: Epoch time: 102.21 s +2026-04-14 08:39:22.510645: +2026-04-14 08:39:22.512459: Epoch 3117 +2026-04-14 08:39:22.514766: Current learning rate: 0.00257 +2026-04-14 08:41:03.980075: train_loss -0.4493 +2026-04-14 08:41:03.987133: val_loss -0.3375 +2026-04-14 08:41:03.989617: Pseudo dice [0.6601, 0.0, 0.7781, 0.2728, 0.2242, 0.384, 0.7497] +2026-04-14 08:41:03.992790: Epoch time: 101.47 s +2026-04-14 08:41:05.278858: +2026-04-14 08:41:05.280755: Epoch 3118 +2026-04-14 08:41:05.282804: Current learning rate: 0.00256 +2026-04-14 08:42:46.741015: train_loss -0.4317 +2026-04-14 08:42:46.761418: val_loss -0.3888 +2026-04-14 08:42:46.763965: Pseudo dice [0.7709, 0.0, 0.8344, 0.822, 0.5632, 0.7346, 0.9139] +2026-04-14 08:42:46.766312: Epoch time: 101.47 s +2026-04-14 08:42:48.005982: +2026-04-14 08:42:48.007793: Epoch 3119 +2026-04-14 08:42:48.009777: Current learning rate: 0.00256 +2026-04-14 08:44:29.497575: train_loss -0.4261 +2026-04-14 08:44:29.503672: val_loss -0.3697 +2026-04-14 08:44:29.505566: Pseudo dice [0.3567, 0.0, 0.7879, 0.5572, 0.2639, 0.2786, 0.7461] +2026-04-14 08:44:29.507828: Epoch time: 101.49 s +2026-04-14 08:44:30.786107: +2026-04-14 08:44:30.788013: Epoch 3120 +2026-04-14 08:44:30.789902: Current learning rate: 0.00256 +2026-04-14 08:46:12.591338: train_loss -0.4514 +2026-04-14 08:46:12.607577: val_loss -0.3711 +2026-04-14 08:46:12.610199: Pseudo dice [0.8132, 0.0, 0.8142, 0.4597, 0.5085, 0.7234, 0.7094] +2026-04-14 08:46:12.613902: Epoch time: 101.81 s +2026-04-14 08:46:13.873202: +2026-04-14 08:46:13.875231: Epoch 3121 +2026-04-14 08:46:13.877271: Current learning rate: 0.00256 +2026-04-14 08:47:55.571071: train_loss -0.4349 +2026-04-14 08:47:55.577751: val_loss -0.3505 +2026-04-14 08:47:55.582543: Pseudo dice [0.5701, 0.0, 0.8555, 0.0552, 0.3853, 0.358, 0.9236] +2026-04-14 08:47:55.585063: Epoch time: 101.7 s +2026-04-14 08:47:56.862571: +2026-04-14 08:47:56.865230: Epoch 3122 +2026-04-14 08:47:56.868980: Current learning rate: 0.00255 +2026-04-14 08:49:38.704030: train_loss -0.4544 +2026-04-14 08:49:38.710445: val_loss -0.3973 +2026-04-14 08:49:38.712324: Pseudo dice [0.7318, 0.0, 0.8103, 0.7937, 0.5652, 0.7411, 0.8645] +2026-04-14 08:49:38.714645: Epoch time: 101.84 s +2026-04-14 08:49:40.069456: +2026-04-14 08:49:40.071635: Epoch 3123 +2026-04-14 08:49:40.073476: Current learning rate: 0.00255 +2026-04-14 08:51:21.625538: train_loss -0.4465 +2026-04-14 08:51:21.633527: val_loss -0.3708 +2026-04-14 08:51:21.635859: Pseudo dice [0.5209, 0.0, 0.657, 0.3636, 0.5833, 0.7382, 0.8991] +2026-04-14 08:51:21.638251: Epoch time: 101.56 s +2026-04-14 08:51:22.901563: +2026-04-14 08:51:22.903333: Epoch 3124 +2026-04-14 08:51:22.905239: Current learning rate: 0.00255 +2026-04-14 08:53:04.361081: train_loss -0.4521 +2026-04-14 08:53:04.368028: val_loss -0.4077 +2026-04-14 08:53:04.370370: Pseudo dice [0.7512, 0.0, 0.7854, 0.7913, 0.5517, 0.7714, 0.9224] +2026-04-14 08:53:04.372999: Epoch time: 101.46 s +2026-04-14 08:53:05.650563: +2026-04-14 08:53:05.652349: Epoch 3125 +2026-04-14 08:53:05.654297: Current learning rate: 0.00255 +2026-04-14 08:54:47.411936: train_loss -0.4433 +2026-04-14 08:54:47.419057: val_loss -0.3553 +2026-04-14 08:54:47.420968: Pseudo dice [0.493, 0.0, 0.8066, 0.6864, 0.4889, 0.3349, 0.6836] +2026-04-14 08:54:47.423631: Epoch time: 101.76 s +2026-04-14 08:54:48.681562: +2026-04-14 08:54:48.683518: Epoch 3126 +2026-04-14 08:54:48.685460: Current learning rate: 0.00254 +2026-04-14 08:56:30.505110: train_loss -0.4378 +2026-04-14 08:56:30.516903: val_loss -0.4031 +2026-04-14 08:56:30.519511: Pseudo dice [0.3232, 0.0, 0.8096, 0.7059, 0.5387, 0.8304, 0.7974] +2026-04-14 08:56:30.522316: Epoch time: 101.83 s +2026-04-14 08:56:31.851877: +2026-04-14 08:56:31.853926: Epoch 3127 +2026-04-14 08:56:31.856326: Current learning rate: 0.00254 +2026-04-14 08:58:13.793910: train_loss -0.4287 +2026-04-14 08:58:13.799268: val_loss -0.3123 +2026-04-14 08:58:13.801024: Pseudo dice [0.5386, 0.0, 0.6889, 0.0977, 0.3098, 0.6045, 0.6313] +2026-04-14 08:58:13.803541: Epoch time: 101.95 s +2026-04-14 08:58:15.084531: +2026-04-14 08:58:15.086347: Epoch 3128 +2026-04-14 08:58:15.088438: Current learning rate: 0.00254 +2026-04-14 08:59:56.380992: train_loss -0.4425 +2026-04-14 08:59:56.391778: val_loss -0.3183 +2026-04-14 08:59:56.395130: Pseudo dice [0.4432, 0.0, 0.5114, 0.7329, 0.5015, 0.6763, 0.5998] +2026-04-14 08:59:56.398805: Epoch time: 101.3 s +2026-04-14 08:59:57.738259: +2026-04-14 08:59:57.740440: Epoch 3129 +2026-04-14 08:59:57.742858: Current learning rate: 0.00254 +2026-04-14 09:01:39.721634: train_loss -0.431 +2026-04-14 09:01:39.728455: val_loss -0.366 +2026-04-14 09:01:39.730643: Pseudo dice [0.8465, 0.0, 0.7861, 0.2016, 0.526, 0.6965, 0.7724] +2026-04-14 09:01:39.733813: Epoch time: 101.99 s +2026-04-14 09:01:40.994934: +2026-04-14 09:01:40.996958: Epoch 3130 +2026-04-14 09:01:40.999185: Current learning rate: 0.00253 +2026-04-14 09:03:22.622332: train_loss -0.4479 +2026-04-14 09:03:22.628920: val_loss -0.3533 +2026-04-14 09:03:22.631596: Pseudo dice [0.6561, 0.0, 0.6889, 0.3779, 0.4097, 0.6637, 0.6362] +2026-04-14 09:03:22.634782: Epoch time: 101.63 s +2026-04-14 09:03:25.097797: +2026-04-14 09:03:25.099551: Epoch 3131 +2026-04-14 09:03:25.101327: Current learning rate: 0.00253 +2026-04-14 09:05:06.567078: train_loss -0.451 +2026-04-14 09:05:06.572777: val_loss -0.3803 +2026-04-14 09:05:06.575119: Pseudo dice [0.5118, 0.0, 0.7423, 0.5791, 0.5177, 0.4596, 0.9266] +2026-04-14 09:05:06.577352: Epoch time: 101.47 s +2026-04-14 09:05:07.853801: +2026-04-14 09:05:07.856054: Epoch 3132 +2026-04-14 09:05:07.858226: Current learning rate: 0.00253 +2026-04-14 09:06:49.619035: train_loss -0.4481 +2026-04-14 09:06:49.625845: val_loss -0.3781 +2026-04-14 09:06:49.628383: Pseudo dice [0.2986, 0.0, 0.7994, 0.472, 0.571, 0.7621, 0.2342] +2026-04-14 09:06:49.630823: Epoch time: 101.77 s +2026-04-14 09:06:50.891870: +2026-04-14 09:06:50.893645: Epoch 3133 +2026-04-14 09:06:50.895581: Current learning rate: 0.00253 +2026-04-14 09:08:32.472172: train_loss -0.4394 +2026-04-14 09:08:32.480678: val_loss -0.366 +2026-04-14 09:08:32.482843: Pseudo dice [0.1937, 0.0, 0.8052, 0.7654, 0.581, 0.5634, 0.7799] +2026-04-14 09:08:32.485691: Epoch time: 101.58 s +2026-04-14 09:08:33.748976: +2026-04-14 09:08:33.750973: Epoch 3134 +2026-04-14 09:08:33.752907: Current learning rate: 0.00252 +2026-04-14 09:10:15.719848: train_loss -0.4497 +2026-04-14 09:10:15.726316: val_loss -0.3515 +2026-04-14 09:10:15.728535: Pseudo dice [0.6381, 0.0, 0.8343, 0.6198, 0.4353, 0.6593, 0.2352] +2026-04-14 09:10:15.731010: Epoch time: 101.97 s +2026-04-14 09:10:16.983115: +2026-04-14 09:10:16.985748: Epoch 3135 +2026-04-14 09:10:16.988452: Current learning rate: 0.00252 +2026-04-14 09:11:58.258065: train_loss -0.4312 +2026-04-14 09:11:58.265126: val_loss -0.3764 +2026-04-14 09:11:58.267505: Pseudo dice [0.6488, 0.0, 0.7038, 0.356, 0.5794, 0.5773, 0.8961] +2026-04-14 09:11:58.270337: Epoch time: 101.28 s +2026-04-14 09:11:59.542983: +2026-04-14 09:11:59.546627: Epoch 3136 +2026-04-14 09:11:59.549043: Current learning rate: 0.00252 +2026-04-14 09:13:41.072396: train_loss -0.4555 +2026-04-14 09:13:41.078928: val_loss -0.3743 +2026-04-14 09:13:41.080960: Pseudo dice [0.7247, 0.0, 0.711, 0.6598, 0.443, 0.5826, 0.8173] +2026-04-14 09:13:41.084299: Epoch time: 101.53 s +2026-04-14 09:13:42.371355: +2026-04-14 09:13:42.373039: Epoch 3137 +2026-04-14 09:13:42.374721: Current learning rate: 0.00252 +2026-04-14 09:15:23.862273: train_loss -0.4423 +2026-04-14 09:15:23.869155: val_loss -0.3514 +2026-04-14 09:15:23.871426: Pseudo dice [0.6256, 0.0, 0.8577, 0.5449, 0.1753, 0.5051, 0.734] +2026-04-14 09:15:23.874614: Epoch time: 101.49 s +2026-04-14 09:15:25.166033: +2026-04-14 09:15:25.168190: Epoch 3138 +2026-04-14 09:15:25.170589: Current learning rate: 0.00251 +2026-04-14 09:17:06.608511: train_loss -0.4413 +2026-04-14 09:17:06.616558: val_loss -0.4153 +2026-04-14 09:17:06.618667: Pseudo dice [0.6469, 0.0, 0.9034, 0.7279, 0.6433, 0.5913, 0.8273] +2026-04-14 09:17:06.621601: Epoch time: 101.45 s +2026-04-14 09:17:07.874754: +2026-04-14 09:17:07.876602: Epoch 3139 +2026-04-14 09:17:07.878618: Current learning rate: 0.00251 +2026-04-14 09:18:49.502521: train_loss -0.4512 +2026-04-14 09:18:49.509318: val_loss -0.3825 +2026-04-14 09:18:49.511190: Pseudo dice [0.5879, 0.0, 0.6626, 0.6073, 0.4549, 0.726, 0.8845] +2026-04-14 09:18:49.513708: Epoch time: 101.63 s +2026-04-14 09:18:50.774246: +2026-04-14 09:18:50.776469: Epoch 3140 +2026-04-14 09:18:50.778846: Current learning rate: 0.00251 +2026-04-14 09:20:32.545199: train_loss -0.4419 +2026-04-14 09:20:32.552078: val_loss -0.4095 +2026-04-14 09:20:32.554023: Pseudo dice [0.5064, 0.0, 0.8287, 0.3608, 0.2996, 0.8386, 0.8928] +2026-04-14 09:20:32.556425: Epoch time: 101.77 s +2026-04-14 09:20:33.833199: +2026-04-14 09:20:33.834983: Epoch 3141 +2026-04-14 09:20:33.836977: Current learning rate: 0.0025 +2026-04-14 09:22:15.570944: train_loss -0.4584 +2026-04-14 09:22:15.578175: val_loss -0.3904 +2026-04-14 09:22:15.581038: Pseudo dice [0.6061, 0.0, 0.7052, 0.4724, 0.151, 0.6412, 0.9095] +2026-04-14 09:22:15.584029: Epoch time: 101.74 s +2026-04-14 09:22:16.843696: +2026-04-14 09:22:16.845828: Epoch 3142 +2026-04-14 09:22:16.848270: Current learning rate: 0.0025 +2026-04-14 09:23:58.216936: train_loss -0.4436 +2026-04-14 09:23:58.225354: val_loss -0.3252 +2026-04-14 09:23:58.227916: Pseudo dice [0.3665, 0.0, 0.7539, 0.2108, 0.4259, 0.756, 0.6834] +2026-04-14 09:23:58.230772: Epoch time: 101.38 s +2026-04-14 09:23:59.467469: +2026-04-14 09:23:59.469430: Epoch 3143 +2026-04-14 09:23:59.471421: Current learning rate: 0.0025 +2026-04-14 09:25:41.090425: train_loss -0.4406 +2026-04-14 09:25:41.097199: val_loss -0.3663 +2026-04-14 09:25:41.099222: Pseudo dice [0.2095, 0.0, 0.8042, 0.7119, 0.4213, 0.8657, 0.6053] +2026-04-14 09:25:41.102403: Epoch time: 101.63 s +2026-04-14 09:25:42.341102: +2026-04-14 09:25:42.342709: Epoch 3144 +2026-04-14 09:25:42.344459: Current learning rate: 0.0025 +2026-04-14 09:27:24.080023: train_loss -0.4521 +2026-04-14 09:27:24.085714: val_loss -0.3506 +2026-04-14 09:27:24.087487: Pseudo dice [0.7177, 0.0, 0.7495, 0.2686, 0.3161, 0.7275, 0.8724] +2026-04-14 09:27:24.089898: Epoch time: 101.74 s +2026-04-14 09:27:25.350037: +2026-04-14 09:27:25.351880: Epoch 3145 +2026-04-14 09:27:25.353904: Current learning rate: 0.00249 +2026-04-14 09:29:07.038826: train_loss -0.4165 +2026-04-14 09:29:07.046146: val_loss -0.3691 +2026-04-14 09:29:07.048297: Pseudo dice [0.7884, 0.0, 0.7335, 0.5762, 0.5398, 0.5321, 0.6432] +2026-04-14 09:29:07.051210: Epoch time: 101.69 s +2026-04-14 09:29:08.317987: +2026-04-14 09:29:08.319830: Epoch 3146 +2026-04-14 09:29:08.321784: Current learning rate: 0.00249 +2026-04-14 09:30:49.822361: train_loss -0.4244 +2026-04-14 09:30:49.829376: val_loss -0.3775 +2026-04-14 09:30:49.832074: Pseudo dice [0.5329, 0.0, 0.8274, 0.6582, 0.3537, 0.5567, 0.6645] +2026-04-14 09:30:49.834891: Epoch time: 101.51 s +2026-04-14 09:30:51.101092: +2026-04-14 09:30:51.103490: Epoch 3147 +2026-04-14 09:30:51.105507: Current learning rate: 0.00249 +2026-04-14 09:32:33.017319: train_loss -0.4382 +2026-04-14 09:32:33.023435: val_loss -0.3789 +2026-04-14 09:32:33.025511: Pseudo dice [0.3313, 0.0, 0.7899, 0.832, 0.5912, 0.7822, 0.872] +2026-04-14 09:32:33.027989: Epoch time: 101.92 s +2026-04-14 09:32:34.288848: +2026-04-14 09:32:34.290730: Epoch 3148 +2026-04-14 09:32:34.292588: Current learning rate: 0.00249 +2026-04-14 09:34:15.692990: train_loss -0.4454 +2026-04-14 09:34:15.701395: val_loss -0.3573 +2026-04-14 09:34:15.703324: Pseudo dice [0.4542, 0.0, 0.8468, 0.5834, 0.5827, 0.4927, 0.9192] +2026-04-14 09:34:15.706013: Epoch time: 101.41 s +2026-04-14 09:34:16.968225: +2026-04-14 09:34:16.970366: Epoch 3149 +2026-04-14 09:34:16.972471: Current learning rate: 0.00248 +2026-04-14 09:35:58.650245: train_loss -0.4531 +2026-04-14 09:35:58.658091: val_loss -0.3614 +2026-04-14 09:35:58.660191: Pseudo dice [0.8102, 0.0, 0.7033, 0.4815, 0.4101, 0.4028, 0.8225] +2026-04-14 09:35:58.662471: Epoch time: 101.69 s +2026-04-14 09:36:01.722301: +2026-04-14 09:36:01.724425: Epoch 3150 +2026-04-14 09:36:01.726346: Current learning rate: 0.00248 +2026-04-14 09:37:44.390052: train_loss -0.4481 +2026-04-14 09:37:44.396573: val_loss -0.3976 +2026-04-14 09:37:44.399077: Pseudo dice [0.7536, 0.0, 0.8299, 0.7235, 0.6159, 0.6319, 0.8807] +2026-04-14 09:37:44.402220: Epoch time: 102.67 s +2026-04-14 09:37:45.638718: +2026-04-14 09:37:45.641194: Epoch 3151 +2026-04-14 09:37:45.643231: Current learning rate: 0.00248 +2026-04-14 09:39:27.249982: train_loss -0.4376 +2026-04-14 09:39:27.255993: val_loss -0.3287 +2026-04-14 09:39:27.257915: Pseudo dice [0.5631, 0.0, 0.7269, 0.0792, 0.5125, 0.3689, 0.765] +2026-04-14 09:39:27.260021: Epoch time: 101.61 s +2026-04-14 09:39:28.501142: +2026-04-14 09:39:28.502833: Epoch 3152 +2026-04-14 09:39:28.504787: Current learning rate: 0.00248 +2026-04-14 09:41:09.954344: train_loss -0.4368 +2026-04-14 09:41:09.962446: val_loss -0.3759 +2026-04-14 09:41:09.964367: Pseudo dice [0.5577, 0.0, 0.8506, 0.0244, 0.6011, 0.7074, 0.6299] +2026-04-14 09:41:09.966761: Epoch time: 101.46 s +2026-04-14 09:41:11.232856: +2026-04-14 09:41:11.234890: Epoch 3153 +2026-04-14 09:41:11.236683: Current learning rate: 0.00247 +2026-04-14 09:42:52.967418: train_loss -0.456 +2026-04-14 09:42:52.972598: val_loss -0.3667 +2026-04-14 09:42:52.974590: Pseudo dice [0.7251, 0.0, 0.5975, 0.4413, 0.5069, 0.8029, 0.7789] +2026-04-14 09:42:52.976821: Epoch time: 101.74 s +2026-04-14 09:42:54.251338: +2026-04-14 09:42:54.253397: Epoch 3154 +2026-04-14 09:42:54.255277: Current learning rate: 0.00247 +2026-04-14 09:44:36.123204: train_loss -0.4446 +2026-04-14 09:44:36.129164: val_loss -0.3905 +2026-04-14 09:44:36.131803: Pseudo dice [0.7161, 0.0, 0.8414, 0.2207, 0.4187, 0.8588, 0.7148] +2026-04-14 09:44:36.134215: Epoch time: 101.87 s +2026-04-14 09:44:37.403050: +2026-04-14 09:44:37.405053: Epoch 3155 +2026-04-14 09:44:37.406767: Current learning rate: 0.00247 +2026-04-14 09:46:19.578037: train_loss -0.4394 +2026-04-14 09:46:19.585966: val_loss -0.341 +2026-04-14 09:46:19.587924: Pseudo dice [0.5658, 0.0, 0.7472, 0.1489, 0.3133, 0.5042, 0.853] +2026-04-14 09:46:19.593039: Epoch time: 102.18 s +2026-04-14 09:46:20.868054: +2026-04-14 09:46:20.869850: Epoch 3156 +2026-04-14 09:46:20.871506: Current learning rate: 0.00247 +2026-04-14 09:48:02.138238: train_loss -0.4507 +2026-04-14 09:48:02.144930: val_loss -0.3541 +2026-04-14 09:48:02.147192: Pseudo dice [0.3692, 0.0, 0.6973, 0.0643, 0.2088, 0.8016, 0.8399] +2026-04-14 09:48:02.150658: Epoch time: 101.27 s +2026-04-14 09:48:03.385618: +2026-04-14 09:48:03.387731: Epoch 3157 +2026-04-14 09:48:03.389199: Current learning rate: 0.00246 +2026-04-14 09:49:45.074695: train_loss -0.4425 +2026-04-14 09:49:45.081187: val_loss -0.3807 +2026-04-14 09:49:45.083532: Pseudo dice [0.8236, 0.0, 0.793, 0.2578, 0.435, 0.5424, 0.8547] +2026-04-14 09:49:45.086107: Epoch time: 101.69 s +2026-04-14 09:49:46.335389: +2026-04-14 09:49:46.337353: Epoch 3158 +2026-04-14 09:49:46.339115: Current learning rate: 0.00246 +2026-04-14 09:51:27.961051: train_loss -0.4534 +2026-04-14 09:51:27.967813: val_loss -0.3531 +2026-04-14 09:51:27.970089: Pseudo dice [0.768, 0.0, 0.7402, 0.7327, 0.4815, 0.4473, 0.8199] +2026-04-14 09:51:27.972383: Epoch time: 101.63 s +2026-04-14 09:51:29.208910: +2026-04-14 09:51:29.211526: Epoch 3159 +2026-04-14 09:51:29.213655: Current learning rate: 0.00246 +2026-04-14 09:53:10.670161: train_loss -0.4481 +2026-04-14 09:53:10.677220: val_loss -0.3908 +2026-04-14 09:53:10.679707: Pseudo dice [0.5372, 0.0, 0.6472, 0.8608, 0.4913, 0.6174, 0.7737] +2026-04-14 09:53:10.683192: Epoch time: 101.46 s +2026-04-14 09:53:11.957252: +2026-04-14 09:53:11.959186: Epoch 3160 +2026-04-14 09:53:11.960743: Current learning rate: 0.00245 +2026-04-14 09:54:53.615190: train_loss -0.4594 +2026-04-14 09:54:53.620866: val_loss -0.3811 +2026-04-14 09:54:53.623034: Pseudo dice [0.8034, 0.0, 0.8844, 0.7208, 0.522, 0.4222, 0.8401] +2026-04-14 09:54:53.625510: Epoch time: 101.66 s +2026-04-14 09:54:54.878945: +2026-04-14 09:54:54.880734: Epoch 3161 +2026-04-14 09:54:54.882482: Current learning rate: 0.00245 +2026-04-14 09:56:36.407600: train_loss -0.4611 +2026-04-14 09:56:36.415488: val_loss -0.4069 +2026-04-14 09:56:36.417760: Pseudo dice [0.6748, 0.0, 0.8247, 0.7885, 0.5966, 0.8189, 0.7865] +2026-04-14 09:56:36.420318: Epoch time: 101.53 s +2026-04-14 09:56:37.729005: +2026-04-14 09:56:37.731037: Epoch 3162 +2026-04-14 09:56:37.732875: Current learning rate: 0.00245 +2026-04-14 09:58:19.159320: train_loss -0.4528 +2026-04-14 09:58:19.168377: val_loss -0.3842 +2026-04-14 09:58:19.170739: Pseudo dice [0.7677, 0.0, 0.6532, 0.7226, 0.5647, 0.3554, 0.9277] +2026-04-14 09:58:19.172932: Epoch time: 101.43 s +2026-04-14 09:58:20.439797: +2026-04-14 09:58:20.441513: Epoch 3163 +2026-04-14 09:58:20.443461: Current learning rate: 0.00245 +2026-04-14 10:00:01.886602: train_loss -0.4553 +2026-04-14 10:00:01.893756: val_loss -0.3616 +2026-04-14 10:00:01.895910: Pseudo dice [0.6432, 0.0, 0.8473, 0.4078, 0.7346, 0.5565, 0.8404] +2026-04-14 10:00:01.898690: Epoch time: 101.45 s +2026-04-14 10:00:03.220155: +2026-04-14 10:00:03.224557: Epoch 3164 +2026-04-14 10:00:03.226418: Current learning rate: 0.00244 +2026-04-14 10:01:44.809572: train_loss -0.4634 +2026-04-14 10:01:44.816194: val_loss -0.3943 +2026-04-14 10:01:44.818252: Pseudo dice [0.7905, 0.0, 0.7856, 0.8243, 0.574, 0.8365, 0.9028] +2026-04-14 10:01:44.820764: Epoch time: 101.59 s +2026-04-14 10:01:46.051039: +2026-04-14 10:01:46.052889: Epoch 3165 +2026-04-14 10:01:46.054675: Current learning rate: 0.00244 +2026-04-14 10:03:27.516435: train_loss -0.4571 +2026-04-14 10:03:27.522360: val_loss -0.4265 +2026-04-14 10:03:27.524684: Pseudo dice [0.4793, 0.0, 0.9018, 0.765, 0.6894, 0.5717, 0.932] +2026-04-14 10:03:27.527412: Epoch time: 101.47 s +2026-04-14 10:03:28.814216: +2026-04-14 10:03:28.816452: Epoch 3166 +2026-04-14 10:03:28.818058: Current learning rate: 0.00244 +2026-04-14 10:05:10.116610: train_loss -0.4421 +2026-04-14 10:05:10.122571: val_loss -0.3608 +2026-04-14 10:05:10.125315: Pseudo dice [0.5764, 0.0, 0.6206, 0.1507, 0.5753, 0.2909, 0.8972] +2026-04-14 10:05:10.127414: Epoch time: 101.31 s +2026-04-14 10:05:11.599616: +2026-04-14 10:05:11.601748: Epoch 3167 +2026-04-14 10:05:11.603741: Current learning rate: 0.00244 +2026-04-14 10:06:53.028051: train_loss -0.4521 +2026-04-14 10:06:53.037568: val_loss -0.369 +2026-04-14 10:06:53.040284: Pseudo dice [0.1466, 0.0, 0.8588, 0.7771, 0.6481, 0.4568, 0.8215] +2026-04-14 10:06:53.043198: Epoch time: 101.43 s +2026-04-14 10:06:54.293511: +2026-04-14 10:06:54.295570: Epoch 3168 +2026-04-14 10:06:54.297318: Current learning rate: 0.00243 +2026-04-14 10:08:36.121314: train_loss -0.4519 +2026-04-14 10:08:36.127625: val_loss -0.3866 +2026-04-14 10:08:36.129352: Pseudo dice [0.5612, 0.0, 0.6694, 0.1463, 0.5275, 0.7191, 0.8525] +2026-04-14 10:08:36.131615: Epoch time: 101.83 s +2026-04-14 10:08:37.408304: +2026-04-14 10:08:37.410011: Epoch 3169 +2026-04-14 10:08:37.411937: Current learning rate: 0.00243 +2026-04-14 10:10:19.293713: train_loss -0.471 +2026-04-14 10:10:19.301205: val_loss -0.4017 +2026-04-14 10:10:19.303835: Pseudo dice [0.6407, 0.0, 0.8567, 0.6874, 0.5702, 0.8203, 0.8794] +2026-04-14 10:10:19.306366: Epoch time: 101.89 s +2026-04-14 10:10:21.696213: +2026-04-14 10:10:21.698128: Epoch 3170 +2026-04-14 10:10:21.699733: Current learning rate: 0.00243 +2026-04-14 10:12:03.251210: train_loss -0.4591 +2026-04-14 10:12:03.257095: val_loss -0.3699 +2026-04-14 10:12:03.259258: Pseudo dice [0.6433, 0.0, 0.8476, 0.7414, 0.5875, 0.3775, 0.8603] +2026-04-14 10:12:03.261813: Epoch time: 101.56 s +2026-04-14 10:12:04.510098: +2026-04-14 10:12:04.513430: Epoch 3171 +2026-04-14 10:12:04.515515: Current learning rate: 0.00243 +2026-04-14 10:13:46.366386: train_loss -0.4577 +2026-04-14 10:13:46.376729: val_loss -0.3818 +2026-04-14 10:13:46.379256: Pseudo dice [0.599, 0.0, 0.7575, 0.4668, 0.6017, 0.7269, 0.8759] +2026-04-14 10:13:46.382318: Epoch time: 101.86 s +2026-04-14 10:13:47.653297: +2026-04-14 10:13:47.655186: Epoch 3172 +2026-04-14 10:13:47.656952: Current learning rate: 0.00242 +2026-04-14 10:15:29.136748: train_loss -0.4402 +2026-04-14 10:15:29.144495: val_loss -0.3233 +2026-04-14 10:15:29.146873: Pseudo dice [0.5764, 0.0, 0.6522, 0.1179, 0.2096, 0.4517, 0.8902] +2026-04-14 10:15:29.149030: Epoch time: 101.49 s +2026-04-14 10:15:30.411858: +2026-04-14 10:15:30.413669: Epoch 3173 +2026-04-14 10:15:30.415221: Current learning rate: 0.00242 +2026-04-14 10:17:11.998110: train_loss -0.4379 +2026-04-14 10:17:12.005431: val_loss -0.4056 +2026-04-14 10:17:12.008680: Pseudo dice [0.6693, 0.0, 0.7948, 0.8074, 0.5783, 0.4932, 0.9263] +2026-04-14 10:17:12.011155: Epoch time: 101.59 s +2026-04-14 10:17:13.259908: +2026-04-14 10:17:13.261567: Epoch 3174 +2026-04-14 10:17:13.263207: Current learning rate: 0.00242 +2026-04-14 10:18:54.636003: train_loss -0.4657 +2026-04-14 10:18:54.643896: val_loss -0.3776 +2026-04-14 10:18:54.646345: Pseudo dice [0.4968, 0.0, 0.827, 0.802, 0.3899, 0.6635, 0.8818] +2026-04-14 10:18:54.649536: Epoch time: 101.38 s +2026-04-14 10:18:55.881574: +2026-04-14 10:18:55.883500: Epoch 3175 +2026-04-14 10:18:55.885204: Current learning rate: 0.00242 +2026-04-14 10:20:37.636959: train_loss -0.4518 +2026-04-14 10:20:37.643963: val_loss -0.4046 +2026-04-14 10:20:37.645888: Pseudo dice [0.5004, 0.0, 0.854, 0.8775, 0.4656, 0.6919, 0.8359] +2026-04-14 10:20:37.650379: Epoch time: 101.76 s +2026-04-14 10:20:38.902673: +2026-04-14 10:20:38.905508: Epoch 3176 +2026-04-14 10:20:38.907865: Current learning rate: 0.00241 +2026-04-14 10:22:20.560998: train_loss -0.4343 +2026-04-14 10:22:20.569006: val_loss -0.374 +2026-04-14 10:22:20.571128: Pseudo dice [0.5593, 0.0, 0.807, 0.6141, 0.3142, 0.6756, 0.8501] +2026-04-14 10:22:20.574321: Epoch time: 101.66 s +2026-04-14 10:22:21.821296: +2026-04-14 10:22:21.823190: Epoch 3177 +2026-04-14 10:22:21.824762: Current learning rate: 0.00241 +2026-04-14 10:24:03.351930: train_loss -0.4401 +2026-04-14 10:24:03.359608: val_loss -0.3715 +2026-04-14 10:24:03.361568: Pseudo dice [0.5628, 0.0, 0.6007, 0.4666, 0.4908, 0.7409, 0.727] +2026-04-14 10:24:03.364780: Epoch time: 101.53 s +2026-04-14 10:24:04.644401: +2026-04-14 10:24:04.646439: Epoch 3178 +2026-04-14 10:24:04.648022: Current learning rate: 0.00241 +2026-04-14 10:25:46.581393: train_loss -0.4365 +2026-04-14 10:25:46.587829: val_loss -0.4017 +2026-04-14 10:25:46.590381: Pseudo dice [0.645, 0.0, 0.8235, 0.824, 0.5298, 0.8087, 0.7765] +2026-04-14 10:25:46.593265: Epoch time: 101.94 s +2026-04-14 10:25:47.944914: +2026-04-14 10:25:47.946599: Epoch 3179 +2026-04-14 10:25:47.948570: Current learning rate: 0.0024 +2026-04-14 10:27:29.407981: train_loss -0.4558 +2026-04-14 10:27:29.415041: val_loss -0.3695 +2026-04-14 10:27:29.417231: Pseudo dice [0.7076, 0.0, 0.7708, 0.0368, 0.5327, 0.5285, 0.4078] +2026-04-14 10:27:29.419291: Epoch time: 101.47 s +2026-04-14 10:27:30.727746: +2026-04-14 10:27:30.729687: Epoch 3180 +2026-04-14 10:27:30.731811: Current learning rate: 0.0024 +2026-04-14 10:29:12.262339: train_loss -0.4477 +2026-04-14 10:29:12.269463: val_loss -0.385 +2026-04-14 10:29:12.272462: Pseudo dice [0.764, 0.0, 0.8468, 0.6889, 0.5614, 0.7484, 0.7615] +2026-04-14 10:29:12.274613: Epoch time: 101.54 s +2026-04-14 10:29:13.534844: +2026-04-14 10:29:13.536776: Epoch 3181 +2026-04-14 10:29:13.538627: Current learning rate: 0.0024 +2026-04-14 10:30:55.391341: train_loss -0.4491 +2026-04-14 10:30:55.398521: val_loss -0.4034 +2026-04-14 10:30:55.400948: Pseudo dice [0.7919, 0.0, 0.8452, 0.0, 0.5307, 0.8789, 0.8723] +2026-04-14 10:30:55.403345: Epoch time: 101.86 s +2026-04-14 10:30:56.700060: +2026-04-14 10:30:56.701818: Epoch 3182 +2026-04-14 10:30:56.703396: Current learning rate: 0.0024 +2026-04-14 10:32:37.961882: train_loss -0.4516 +2026-04-14 10:32:37.969254: val_loss -0.3622 +2026-04-14 10:32:37.971442: Pseudo dice [0.3244, 0.0, 0.7744, 0.5828, 0.4993, 0.7944, 0.8918] +2026-04-14 10:32:37.974134: Epoch time: 101.26 s +2026-04-14 10:32:39.238035: +2026-04-14 10:32:39.240611: Epoch 3183 +2026-04-14 10:32:39.242278: Current learning rate: 0.00239 +2026-04-14 10:34:20.819147: train_loss -0.4552 +2026-04-14 10:34:20.827644: val_loss -0.3677 +2026-04-14 10:34:20.829798: Pseudo dice [0.5986, 0.0, 0.8195, 0.4267, 0.5243, 0.7457, 0.8134] +2026-04-14 10:34:20.832253: Epoch time: 101.58 s +2026-04-14 10:34:22.092357: +2026-04-14 10:34:22.094158: Epoch 3184 +2026-04-14 10:34:22.095782: Current learning rate: 0.00239 +2026-04-14 10:36:03.803593: train_loss -0.4606 +2026-04-14 10:36:03.810127: val_loss -0.3834 +2026-04-14 10:36:03.812434: Pseudo dice [0.6665, 0.0, 0.7184, 0.4853, 0.6277, 0.806, 0.9388] +2026-04-14 10:36:03.815018: Epoch time: 101.71 s +2026-04-14 10:36:05.081443: +2026-04-14 10:36:05.083781: Epoch 3185 +2026-04-14 10:36:05.085586: Current learning rate: 0.00239 +2026-04-14 10:37:46.522479: train_loss -0.4618 +2026-04-14 10:37:46.530007: val_loss -0.3544 +2026-04-14 10:37:46.532318: Pseudo dice [0.5909, 0.0, 0.7385, 0.6028, 0.5209, 0.8819, 0.8323] +2026-04-14 10:37:46.535087: Epoch time: 101.44 s +2026-04-14 10:37:47.851330: +2026-04-14 10:37:47.853274: Epoch 3186 +2026-04-14 10:37:47.854795: Current learning rate: 0.00239 +2026-04-14 10:39:30.324986: train_loss -0.4318 +2026-04-14 10:39:30.331269: val_loss -0.3306 +2026-04-14 10:39:30.334693: Pseudo dice [0.5298, 0.0, 0.8665, 0.2504, 0.2012, 0.2582, 0.8326] +2026-04-14 10:39:30.337881: Epoch time: 102.48 s +2026-04-14 10:39:31.629292: +2026-04-14 10:39:31.634637: Epoch 3187 +2026-04-14 10:39:31.636822: Current learning rate: 0.00238 +2026-04-14 10:41:14.183283: train_loss -0.3915 +2026-04-14 10:41:14.194141: val_loss -0.3591 +2026-04-14 10:41:14.196296: Pseudo dice [0.3429, 0.0, 0.7616, 0.7635, 0.2784, 0.8494, 0.9022] +2026-04-14 10:41:14.198710: Epoch time: 102.56 s +2026-04-14 10:41:15.488719: +2026-04-14 10:41:15.494374: Epoch 3188 +2026-04-14 10:41:15.497042: Current learning rate: 0.00238 +2026-04-14 10:42:56.972869: train_loss -0.4244 +2026-04-14 10:42:56.980934: val_loss -0.3407 +2026-04-14 10:42:56.984124: Pseudo dice [0.5268, 0.0, 0.7275, 0.3715, 0.404, 0.7558, 0.7757] +2026-04-14 10:42:56.987628: Epoch time: 101.49 s +2026-04-14 10:42:58.264033: +2026-04-14 10:42:58.265930: Epoch 3189 +2026-04-14 10:42:58.269928: Current learning rate: 0.00238 +2026-04-14 10:44:39.997616: train_loss -0.4395 +2026-04-14 10:44:40.004157: val_loss -0.4038 +2026-04-14 10:44:40.006245: Pseudo dice [0.8886, 0.0, 0.7133, 0.4541, 0.4237, 0.8078, 0.8264] +2026-04-14 10:44:40.009171: Epoch time: 101.74 s +2026-04-14 10:44:42.350851: +2026-04-14 10:44:42.352629: Epoch 3190 +2026-04-14 10:44:42.354292: Current learning rate: 0.00238 +2026-04-14 10:46:23.903599: train_loss -0.461 +2026-04-14 10:46:23.912001: val_loss -0.4048 +2026-04-14 10:46:23.914656: Pseudo dice [0.2145, 0.0, 0.8514, 0.5265, 0.4069, 0.8468, 0.7996] +2026-04-14 10:46:23.917885: Epoch time: 101.56 s +2026-04-14 10:46:25.182230: +2026-04-14 10:46:25.184276: Epoch 3191 +2026-04-14 10:46:25.185799: Current learning rate: 0.00237 +2026-04-14 10:48:06.566388: train_loss -0.4474 +2026-04-14 10:48:06.572981: val_loss -0.4008 +2026-04-14 10:48:06.575328: Pseudo dice [0.4194, 0.0, 0.7438, 0.2814, 0.4151, 0.7353, 0.9202] +2026-04-14 10:48:06.577578: Epoch time: 101.39 s +2026-04-14 10:48:07.859507: +2026-04-14 10:48:07.861402: Epoch 3192 +2026-04-14 10:48:07.863057: Current learning rate: 0.00237 +2026-04-14 10:49:49.391228: train_loss -0.4562 +2026-04-14 10:49:49.396945: val_loss -0.3965 +2026-04-14 10:49:49.398917: Pseudo dice [0.5495, 0.0, 0.8872, 0.8846, 0.4439, 0.2951, 0.8423] +2026-04-14 10:49:49.401042: Epoch time: 101.53 s +2026-04-14 10:49:50.718528: +2026-04-14 10:49:50.720616: Epoch 3193 +2026-04-14 10:49:50.723005: Current learning rate: 0.00237 +2026-04-14 10:51:32.543677: train_loss -0.4402 +2026-04-14 10:51:32.552500: val_loss -0.3732 +2026-04-14 10:51:32.555321: Pseudo dice [0.487, 0.0, 0.7003, 0.7683, 0.5364, 0.7664, 0.8933] +2026-04-14 10:51:32.557679: Epoch time: 101.83 s +2026-04-14 10:51:33.903926: +2026-04-14 10:51:33.905573: Epoch 3194 +2026-04-14 10:51:33.907064: Current learning rate: 0.00237 +2026-04-14 10:53:15.282607: train_loss -0.4578 +2026-04-14 10:53:15.289615: val_loss -0.3747 +2026-04-14 10:53:15.291897: Pseudo dice [0.59, 0.0, 0.7978, 0.4559, 0.6034, 0.8817, 0.8597] +2026-04-14 10:53:15.294413: Epoch time: 101.38 s +2026-04-14 10:53:16.564494: +2026-04-14 10:53:16.566204: Epoch 3195 +2026-04-14 10:53:16.568184: Current learning rate: 0.00236 +2026-04-14 10:54:58.204312: train_loss -0.4557 +2026-04-14 10:54:58.210938: val_loss -0.3783 +2026-04-14 10:54:58.213328: Pseudo dice [0.3253, 0.0, 0.8609, 0.6434, 0.5555, 0.6949, 0.9175] +2026-04-14 10:54:58.216035: Epoch time: 101.64 s +2026-04-14 10:54:59.486110: +2026-04-14 10:54:59.487964: Epoch 3196 +2026-04-14 10:54:59.489487: Current learning rate: 0.00236 +2026-04-14 10:56:40.707398: train_loss -0.4479 +2026-04-14 10:56:40.713544: val_loss -0.3937 +2026-04-14 10:56:40.715993: Pseudo dice [0.7769, 0.0, 0.7659, 0.6179, 0.4582, 0.8367, 0.9226] +2026-04-14 10:56:40.718458: Epoch time: 101.22 s +2026-04-14 10:56:42.006898: +2026-04-14 10:56:42.008703: Epoch 3197 +2026-04-14 10:56:42.010241: Current learning rate: 0.00236 +2026-04-14 10:58:23.572884: train_loss -0.4435 +2026-04-14 10:58:23.580786: val_loss -0.3252 +2026-04-14 10:58:23.583156: Pseudo dice [0.6009, 0.0, 0.5246, 0.3003, 0.2239, 0.4512, 0.8968] +2026-04-14 10:58:23.585995: Epoch time: 101.57 s +2026-04-14 10:58:24.846492: +2026-04-14 10:58:24.848842: Epoch 3198 +2026-04-14 10:58:24.850879: Current learning rate: 0.00235 +2026-04-14 11:00:06.108505: train_loss -0.4335 +2026-04-14 11:00:06.116389: val_loss -0.3291 +2026-04-14 11:00:06.119039: Pseudo dice [0.4042, 0.0, 0.6676, 0.1731, 0.3842, 0.5817, 0.9041] +2026-04-14 11:00:06.122844: Epoch time: 101.27 s +2026-04-14 11:00:07.382564: +2026-04-14 11:00:07.384280: Epoch 3199 +2026-04-14 11:00:07.385807: Current learning rate: 0.00235 +2026-04-14 11:01:49.278943: train_loss -0.4513 +2026-04-14 11:01:49.285811: val_loss -0.3207 +2026-04-14 11:01:49.288390: Pseudo dice [0.4626, 0.0, 0.8271, 0.5485, 0.1693, 0.702, 0.8987] +2026-04-14 11:01:49.290814: Epoch time: 101.9 s +2026-04-14 11:01:52.341263: +2026-04-14 11:01:52.343151: Epoch 3200 +2026-04-14 11:01:52.344676: Current learning rate: 0.00235 +2026-04-14 11:03:33.865452: train_loss -0.4471 +2026-04-14 11:03:33.871477: val_loss -0.3316 +2026-04-14 11:03:33.873688: Pseudo dice [0.4822, 0.0, 0.7846, 0.2842, 0.4171, 0.4931, 0.8208] +2026-04-14 11:03:33.876437: Epoch time: 101.53 s +2026-04-14 11:03:35.163247: +2026-04-14 11:03:35.165100: Epoch 3201 +2026-04-14 11:03:35.167198: Current learning rate: 0.00235 +2026-04-14 11:05:16.647905: train_loss -0.4418 +2026-04-14 11:05:16.654260: val_loss -0.3719 +2026-04-14 11:05:16.656299: Pseudo dice [0.3719, 0.0, 0.698, 0.1858, 0.4501, 0.7538, 0.8664] +2026-04-14 11:05:16.659182: Epoch time: 101.49 s +2026-04-14 11:05:17.911969: +2026-04-14 11:05:17.914127: Epoch 3202 +2026-04-14 11:05:17.915856: Current learning rate: 0.00234 +2026-04-14 11:06:59.282353: train_loss -0.428 +2026-04-14 11:06:59.290774: val_loss -0.3862 +2026-04-14 11:06:59.294147: Pseudo dice [0.4354, 0.0, 0.7716, 0.7903, 0.5218, 0.7708, 0.7967] +2026-04-14 11:06:59.296701: Epoch time: 101.37 s +2026-04-14 11:07:00.570858: +2026-04-14 11:07:00.573066: Epoch 3203 +2026-04-14 11:07:00.575108: Current learning rate: 0.00234 +2026-04-14 11:08:42.327070: train_loss -0.4282 +2026-04-14 11:08:42.335638: val_loss -0.3701 +2026-04-14 11:08:42.337594: Pseudo dice [0.4705, 0.0, 0.7116, 0.6427, 0.4553, 0.8629, 0.8817] +2026-04-14 11:08:42.339988: Epoch time: 101.76 s +2026-04-14 11:08:43.658909: +2026-04-14 11:08:43.660741: Epoch 3204 +2026-04-14 11:08:43.662230: Current learning rate: 0.00234 +2026-04-14 11:10:25.428885: train_loss -0.4296 +2026-04-14 11:10:25.436589: val_loss -0.3151 +2026-04-14 11:10:25.439038: Pseudo dice [0.6366, 0.0, 0.3904, 0.6386, 0.6237, 0.6287, 0.6334] +2026-04-14 11:10:25.441446: Epoch time: 101.77 s +2026-04-14 11:10:26.704936: +2026-04-14 11:10:26.707799: Epoch 3205 +2026-04-14 11:10:26.710541: Current learning rate: 0.00234 +2026-04-14 11:12:08.493801: train_loss -0.4318 +2026-04-14 11:12:08.499569: val_loss -0.4154 +2026-04-14 11:12:08.501637: Pseudo dice [0.7641, 0.0, 0.7607, 0.7416, 0.3873, 0.6416, 0.8791] +2026-04-14 11:12:08.504532: Epoch time: 101.79 s +2026-04-14 11:12:09.788336: +2026-04-14 11:12:09.790375: Epoch 3206 +2026-04-14 11:12:09.791900: Current learning rate: 0.00233 +2026-04-14 11:13:51.578103: train_loss -0.4593 +2026-04-14 11:13:51.585281: val_loss -0.3886 +2026-04-14 11:13:51.587271: Pseudo dice [0.8132, 0.0, 0.7307, 0.6029, 0.4536, 0.5006, 0.7936] +2026-04-14 11:13:51.589674: Epoch time: 101.79 s +2026-04-14 11:13:52.874015: +2026-04-14 11:13:52.875837: Epoch 3207 +2026-04-14 11:13:52.877526: Current learning rate: 0.00233 +2026-04-14 11:15:34.300547: train_loss -0.4484 +2026-04-14 11:15:34.307380: val_loss -0.3768 +2026-04-14 11:15:34.309465: Pseudo dice [0.7802, 0.0, 0.8108, 0.5384, 0.5461, 0.4769, 0.6698] +2026-04-14 11:15:34.311791: Epoch time: 101.43 s +2026-04-14 11:15:35.571614: +2026-04-14 11:15:35.573419: Epoch 3208 +2026-04-14 11:15:35.575012: Current learning rate: 0.00233 +2026-04-14 11:17:16.869619: train_loss -0.4452 +2026-04-14 11:17:16.876495: val_loss -0.3924 +2026-04-14 11:17:16.878618: Pseudo dice [0.7867, 0.0, 0.8713, 0.6315, 0.3543, 0.7906, 0.7614] +2026-04-14 11:17:16.881172: Epoch time: 101.3 s +2026-04-14 11:17:18.134261: +2026-04-14 11:17:18.136208: Epoch 3209 +2026-04-14 11:17:18.138086: Current learning rate: 0.00233 +2026-04-14 11:19:00.861862: train_loss -0.445 +2026-04-14 11:19:00.867917: val_loss -0.4003 +2026-04-14 11:19:00.870225: Pseudo dice [0.7883, 0.0, 0.8525, 0.7864, 0.6267, 0.5331, 0.8642] +2026-04-14 11:19:00.872795: Epoch time: 102.73 s +2026-04-14 11:19:02.173532: +2026-04-14 11:19:02.175328: Epoch 3210 +2026-04-14 11:19:02.177351: Current learning rate: 0.00232 +2026-04-14 11:20:43.839921: train_loss -0.4315 +2026-04-14 11:20:43.846604: val_loss -0.3835 +2026-04-14 11:20:43.850886: Pseudo dice [0.6895, 0.0, 0.6863, 0.5789, 0.2757, 0.7197, 0.8887] +2026-04-14 11:20:43.853221: Epoch time: 101.67 s +2026-04-14 11:20:45.111243: +2026-04-14 11:20:45.113639: Epoch 3211 +2026-04-14 11:20:45.115253: Current learning rate: 0.00232 +2026-04-14 11:22:26.804560: train_loss -0.4415 +2026-04-14 11:22:26.812379: val_loss -0.3557 +2026-04-14 11:22:26.814803: Pseudo dice [0.7535, 0.0, 0.7704, 0.6763, 0.4931, 0.7208, 0.3587] +2026-04-14 11:22:26.818833: Epoch time: 101.7 s +2026-04-14 11:22:28.092746: +2026-04-14 11:22:28.095005: Epoch 3212 +2026-04-14 11:22:28.096781: Current learning rate: 0.00232 +2026-04-14 11:24:09.454808: train_loss -0.4474 +2026-04-14 11:24:09.461986: val_loss -0.3617 +2026-04-14 11:24:09.464591: Pseudo dice [0.7864, 0.0, 0.6905, 0.7073, 0.7323, 0.3096, 0.8198] +2026-04-14 11:24:09.466739: Epoch time: 101.37 s +2026-04-14 11:24:10.705656: +2026-04-14 11:24:10.707566: Epoch 3213 +2026-04-14 11:24:10.709227: Current learning rate: 0.00231 +2026-04-14 11:25:52.086712: train_loss -0.4551 +2026-04-14 11:25:52.093212: val_loss -0.4008 +2026-04-14 11:25:52.095662: Pseudo dice [0.7786, 0.0, 0.8192, 0.2052, 0.4513, 0.8027, 0.8988] +2026-04-14 11:25:52.098802: Epoch time: 101.38 s +2026-04-14 11:25:53.375403: +2026-04-14 11:25:53.377246: Epoch 3214 +2026-04-14 11:25:53.379213: Current learning rate: 0.00231 +2026-04-14 11:27:34.469724: train_loss -0.456 +2026-04-14 11:27:34.475477: val_loss -0.3726 +2026-04-14 11:27:34.477405: Pseudo dice [0.4195, 0.0, 0.847, 0.7944, 0.3748, 0.6376, 0.9299] +2026-04-14 11:27:34.479712: Epoch time: 101.1 s +2026-04-14 11:27:35.715732: +2026-04-14 11:27:35.717559: Epoch 3215 +2026-04-14 11:27:35.719205: Current learning rate: 0.00231 +2026-04-14 11:29:16.921095: train_loss -0.4579 +2026-04-14 11:29:16.928561: val_loss -0.3847 +2026-04-14 11:29:16.931159: Pseudo dice [0.6045, 0.0, 0.8593, 0.8523, 0.6114, 0.4284, 0.6013] +2026-04-14 11:29:16.933723: Epoch time: 101.21 s +2026-04-14 11:29:18.281557: +2026-04-14 11:29:18.283566: Epoch 3216 +2026-04-14 11:29:18.285484: Current learning rate: 0.00231 +2026-04-14 11:30:59.466700: train_loss -0.4626 +2026-04-14 11:30:59.474448: val_loss -0.3543 +2026-04-14 11:30:59.477047: Pseudo dice [0.626, 0.0, 0.7275, 0.1072, 0.6438, 0.625, 0.7839] +2026-04-14 11:30:59.479212: Epoch time: 101.19 s +2026-04-14 11:31:00.744436: +2026-04-14 11:31:00.746715: Epoch 3217 +2026-04-14 11:31:00.748659: Current learning rate: 0.0023 +2026-04-14 11:32:41.977208: train_loss -0.467 +2026-04-14 11:32:41.984179: val_loss -0.3932 +2026-04-14 11:32:41.986156: Pseudo dice [0.5823, 0.0, 0.7863, 0.5051, 0.4182, 0.7578, 0.8906] +2026-04-14 11:32:41.989042: Epoch time: 101.24 s +2026-04-14 11:32:43.266597: +2026-04-14 11:32:43.268550: Epoch 3218 +2026-04-14 11:32:43.270309: Current learning rate: 0.0023 +2026-04-14 11:34:24.510161: train_loss -0.451 +2026-04-14 11:34:24.536212: val_loss -0.417 +2026-04-14 11:34:24.538466: Pseudo dice [0.6132, 0.0, 0.8479, 0.8651, 0.5945, 0.7622, 0.9432] +2026-04-14 11:34:24.540873: Epoch time: 101.25 s +2026-04-14 11:34:25.829237: +2026-04-14 11:34:25.831240: Epoch 3219 +2026-04-14 11:34:25.832745: Current learning rate: 0.0023 +2026-04-14 11:36:07.045600: train_loss -0.451 +2026-04-14 11:36:07.050792: val_loss -0.3904 +2026-04-14 11:36:07.052636: Pseudo dice [0.6786, 0.0, 0.7046, 0.636, 0.6605, 0.6941, 0.9161] +2026-04-14 11:36:07.054815: Epoch time: 101.22 s +2026-04-14 11:36:08.332297: +2026-04-14 11:36:08.334188: Epoch 3220 +2026-04-14 11:36:08.335860: Current learning rate: 0.0023 +2026-04-14 11:37:49.744284: train_loss -0.4619 +2026-04-14 11:37:49.751683: val_loss -0.4123 +2026-04-14 11:37:49.753861: Pseudo dice [0.7014, 0.0, 0.7789, 0.7894, 0.5253, 0.6491, 0.808] +2026-04-14 11:37:49.756389: Epoch time: 101.42 s +2026-04-14 11:37:51.026067: +2026-04-14 11:37:51.028761: Epoch 3221 +2026-04-14 11:37:51.030351: Current learning rate: 0.00229 +2026-04-14 11:39:32.551613: train_loss -0.4511 +2026-04-14 11:39:32.558209: val_loss -0.4012 +2026-04-14 11:39:32.561102: Pseudo dice [0.5039, 0.0, 0.8398, 0.7758, 0.4976, 0.6742, 0.7957] +2026-04-14 11:39:32.563473: Epoch time: 101.53 s +2026-04-14 11:39:33.837228: +2026-04-14 11:39:33.838862: Epoch 3222 +2026-04-14 11:39:33.840314: Current learning rate: 0.00229 +2026-04-14 11:41:15.472554: train_loss -0.472 +2026-04-14 11:41:15.480042: val_loss -0.374 +2026-04-14 11:41:15.482313: Pseudo dice [0.8149, 0.0, 0.7986, 0.8499, 0.4759, 0.8191, 0.6263] +2026-04-14 11:41:15.485048: Epoch time: 101.64 s +2026-04-14 11:41:16.842311: +2026-04-14 11:41:16.844209: Epoch 3223 +2026-04-14 11:41:16.845932: Current learning rate: 0.00229 +2026-04-14 11:42:58.450841: train_loss -0.4631 +2026-04-14 11:42:58.457222: val_loss -0.3572 +2026-04-14 11:42:58.459569: Pseudo dice [0.4858, 0.0, 0.6817, 0.0893, 0.5328, 0.6779, 0.8643] +2026-04-14 11:42:58.461953: Epoch time: 101.61 s +2026-04-14 11:42:59.748065: +2026-04-14 11:42:59.749938: Epoch 3224 +2026-04-14 11:42:59.752077: Current learning rate: 0.00229 +2026-04-14 11:44:41.207370: train_loss -0.4566 +2026-04-14 11:44:41.213723: val_loss -0.3979 +2026-04-14 11:44:41.216278: Pseudo dice [0.6408, 0.0, 0.8491, 0.8131, 0.5743, 0.5861, 0.9315] +2026-04-14 11:44:41.219261: Epoch time: 101.46 s +2026-04-14 11:44:42.484714: +2026-04-14 11:44:42.486431: Epoch 3225 +2026-04-14 11:44:42.488050: Current learning rate: 0.00228 +2026-04-14 11:46:24.051023: train_loss -0.4546 +2026-04-14 11:46:24.057284: val_loss -0.4104 +2026-04-14 11:46:24.063017: Pseudo dice [0.8153, 0.0, 0.7289, 0.8321, 0.527, 0.5082, 0.9471] +2026-04-14 11:46:24.066038: Epoch time: 101.57 s +2026-04-14 11:46:25.364367: +2026-04-14 11:46:25.366437: Epoch 3226 +2026-04-14 11:46:25.368004: Current learning rate: 0.00228 +2026-04-14 11:48:06.766221: train_loss -0.4491 +2026-04-14 11:48:06.772835: val_loss -0.3862 +2026-04-14 11:48:06.775505: Pseudo dice [0.6878, 0.0, 0.5369, 0.6173, 0.5844, 0.78, 0.8771] +2026-04-14 11:48:06.778801: Epoch time: 101.4 s +2026-04-14 11:48:08.028863: +2026-04-14 11:48:08.030681: Epoch 3227 +2026-04-14 11:48:08.032377: Current learning rate: 0.00228 +2026-04-14 11:49:49.355704: train_loss -0.4591 +2026-04-14 11:49:49.362592: val_loss -0.3928 +2026-04-14 11:49:49.364801: Pseudo dice [0.5067, 0.0, 0.895, 0.2211, 0.4941, 0.758, 0.7806] +2026-04-14 11:49:49.367042: Epoch time: 101.33 s +2026-04-14 11:49:50.623883: +2026-04-14 11:49:50.625679: Epoch 3228 +2026-04-14 11:49:50.627411: Current learning rate: 0.00228 +2026-04-14 11:51:32.182761: train_loss -0.4531 +2026-04-14 11:51:32.189756: val_loss -0.3766 +2026-04-14 11:51:32.194611: Pseudo dice [0.3723, 0.0, 0.8545, 0.45, 0.4309, 0.734, 0.9352] +2026-04-14 11:51:32.197462: Epoch time: 101.56 s +2026-04-14 11:51:34.581294: +2026-04-14 11:51:34.583185: Epoch 3229 +2026-04-14 11:51:34.584768: Current learning rate: 0.00227 +2026-04-14 11:53:16.450054: train_loss -0.4431 +2026-04-14 11:53:16.455777: val_loss -0.3725 +2026-04-14 11:53:16.458204: Pseudo dice [0.6096, 0.0, 0.8237, 0.6775, 0.4519, 0.6032, 0.8358] +2026-04-14 11:53:16.460327: Epoch time: 101.87 s +2026-04-14 11:53:17.730062: +2026-04-14 11:53:17.732508: Epoch 3230 +2026-04-14 11:53:17.734308: Current learning rate: 0.00227 +2026-04-14 11:54:59.785411: train_loss -0.4434 +2026-04-14 11:54:59.791533: val_loss -0.3572 +2026-04-14 11:54:59.793281: Pseudo dice [0.6695, 0.0, 0.7424, 0.8493, 0.2684, 0.8979, 0.615] +2026-04-14 11:54:59.795992: Epoch time: 102.06 s +2026-04-14 11:55:01.071811: +2026-04-14 11:55:01.073808: Epoch 3231 +2026-04-14 11:55:01.076296: Current learning rate: 0.00227 +2026-04-14 11:56:42.943449: train_loss -0.4391 +2026-04-14 11:56:42.949656: val_loss -0.3651 +2026-04-14 11:56:42.951744: Pseudo dice [0.5004, 0.0, 0.7752, 0.7492, 0.4289, 0.4063, 0.8377] +2026-04-14 11:56:42.954249: Epoch time: 101.87 s +2026-04-14 11:56:44.196681: +2026-04-14 11:56:44.198806: Epoch 3232 +2026-04-14 11:56:44.200439: Current learning rate: 0.00226 +2026-04-14 11:58:25.803557: train_loss -0.4593 +2026-04-14 11:58:25.809769: val_loss -0.4248 +2026-04-14 11:58:25.812289: Pseudo dice [0.8128, 0.0, 0.8247, 0.8823, 0.5749, 0.6729, 0.7651] +2026-04-14 11:58:25.814772: Epoch time: 101.61 s +2026-04-14 11:58:27.049975: +2026-04-14 11:58:27.052131: Epoch 3233 +2026-04-14 11:58:27.054077: Current learning rate: 0.00226 +2026-04-14 12:00:08.471843: train_loss -0.4514 +2026-04-14 12:00:08.478943: val_loss -0.3517 +2026-04-14 12:00:08.481183: Pseudo dice [0.4381, 0.0, 0.8396, 0.7565, 0.4575, 0.5203, 0.8874] +2026-04-14 12:00:08.483568: Epoch time: 101.43 s +2026-04-14 12:00:09.747943: +2026-04-14 12:00:09.749909: Epoch 3234 +2026-04-14 12:00:09.751416: Current learning rate: 0.00226 +2026-04-14 12:01:51.102970: train_loss -0.4506 +2026-04-14 12:01:51.124818: val_loss -0.3528 +2026-04-14 12:01:51.126931: Pseudo dice [0.069, 0.0, 0.828, 0.776, 0.4581, 0.8923, 0.709] +2026-04-14 12:01:51.129802: Epoch time: 101.36 s +2026-04-14 12:01:52.409110: +2026-04-14 12:01:52.410985: Epoch 3235 +2026-04-14 12:01:52.412453: Current learning rate: 0.00226 +2026-04-14 12:03:33.953971: train_loss -0.4447 +2026-04-14 12:03:33.964238: val_loss -0.3821 +2026-04-14 12:03:33.966479: Pseudo dice [0.4582, 0.0, 0.8185, 0.6967, 0.4512, 0.5769, 0.8654] +2026-04-14 12:03:33.969464: Epoch time: 101.55 s +2026-04-14 12:03:35.237827: +2026-04-14 12:03:35.239768: Epoch 3236 +2026-04-14 12:03:35.241597: Current learning rate: 0.00225 +2026-04-14 12:05:17.010193: train_loss -0.4551 +2026-04-14 12:05:17.017079: val_loss -0.3854 +2026-04-14 12:05:17.019345: Pseudo dice [0.6719, 0.0, 0.822, 0.821, 0.4408, 0.666, 0.9105] +2026-04-14 12:05:17.022578: Epoch time: 101.78 s +2026-04-14 12:05:18.291585: +2026-04-14 12:05:18.293298: Epoch 3237 +2026-04-14 12:05:18.294934: Current learning rate: 0.00225 +2026-04-14 12:06:59.787369: train_loss -0.4513 +2026-04-14 12:06:59.794811: val_loss -0.3576 +2026-04-14 12:06:59.797155: Pseudo dice [0.6149, 0.0, 0.7391, 0.5056, 0.4642, 0.7326, 0.8645] +2026-04-14 12:06:59.799849: Epoch time: 101.5 s +2026-04-14 12:07:01.073563: +2026-04-14 12:07:01.075413: Epoch 3238 +2026-04-14 12:07:01.078025: Current learning rate: 0.00225 +2026-04-14 12:08:42.772647: train_loss -0.4366 +2026-04-14 12:08:42.778048: val_loss -0.395 +2026-04-14 12:08:42.780241: Pseudo dice [0.7162, 0.0, 0.8088, 0.5621, 0.5486, 0.692, 0.7461] +2026-04-14 12:08:42.782718: Epoch time: 101.7 s +2026-04-14 12:08:44.072716: +2026-04-14 12:08:44.074572: Epoch 3239 +2026-04-14 12:08:44.076075: Current learning rate: 0.00225 +2026-04-14 12:10:25.575047: train_loss -0.4387 +2026-04-14 12:10:25.582040: val_loss -0.3812 +2026-04-14 12:10:25.585504: Pseudo dice [0.5446, 0.0, 0.8394, 0.8652, 0.6346, 0.7919, 0.8461] +2026-04-14 12:10:25.588235: Epoch time: 101.51 s +2026-04-14 12:10:26.854203: +2026-04-14 12:10:26.856076: Epoch 3240 +2026-04-14 12:10:26.857732: Current learning rate: 0.00224 +2026-04-14 12:12:08.740776: train_loss -0.4231 +2026-04-14 12:12:08.749058: val_loss -0.3333 +2026-04-14 12:12:08.752054: Pseudo dice [0.6029, 0.0, 0.4073, 0.3127, 0.5283, 0.4128, 0.7589] +2026-04-14 12:12:08.755008: Epoch time: 101.89 s +2026-04-14 12:12:10.025027: +2026-04-14 12:12:10.026803: Epoch 3241 +2026-04-14 12:12:10.028328: Current learning rate: 0.00224 +2026-04-14 12:13:51.479599: train_loss -0.4411 +2026-04-14 12:13:51.486169: val_loss -0.3674 +2026-04-14 12:13:51.489205: Pseudo dice [0.8348, 0.0, 0.7732, 0.842, 0.6019, 0.3793, 0.703] +2026-04-14 12:13:51.491808: Epoch time: 101.46 s +2026-04-14 12:13:52.770374: +2026-04-14 12:13:52.772568: Epoch 3242 +2026-04-14 12:13:52.774235: Current learning rate: 0.00224 +2026-04-14 12:15:34.187380: train_loss -0.4383 +2026-04-14 12:15:34.194840: val_loss -0.3735 +2026-04-14 12:15:34.196964: Pseudo dice [0.7333, 0.0, 0.7851, 0.5783, 0.4732, 0.4714, 0.8063] +2026-04-14 12:15:34.199484: Epoch time: 101.42 s +2026-04-14 12:15:35.497098: +2026-04-14 12:15:35.499273: Epoch 3243 +2026-04-14 12:15:35.501444: Current learning rate: 0.00224 +2026-04-14 12:17:17.073257: train_loss -0.4445 +2026-04-14 12:17:17.079009: val_loss -0.4157 +2026-04-14 12:17:17.081085: Pseudo dice [0.6103, 0.0, 0.8392, 0.8408, 0.5402, 0.5581, 0.8963] +2026-04-14 12:17:17.083498: Epoch time: 101.58 s +2026-04-14 12:17:18.338607: +2026-04-14 12:17:18.340217: Epoch 3244 +2026-04-14 12:17:18.341618: Current learning rate: 0.00223 +2026-04-14 12:18:59.693041: train_loss -0.463 +2026-04-14 12:18:59.700033: val_loss -0.372 +2026-04-14 12:18:59.702517: Pseudo dice [0.4281, 0.0, 0.8619, 0.479, 0.3404, 0.378, 0.8636] +2026-04-14 12:18:59.705041: Epoch time: 101.36 s +2026-04-14 12:19:00.962883: +2026-04-14 12:19:00.965339: Epoch 3245 +2026-04-14 12:19:00.967197: Current learning rate: 0.00223 +2026-04-14 12:20:42.289500: train_loss -0.4586 +2026-04-14 12:20:42.296194: val_loss -0.3963 +2026-04-14 12:20:42.298638: Pseudo dice [0.5622, 0.0, 0.832, 0.8107, 0.4928, 0.6075, 0.873] +2026-04-14 12:20:42.301132: Epoch time: 101.33 s +2026-04-14 12:20:43.584313: +2026-04-14 12:20:43.586215: Epoch 3246 +2026-04-14 12:20:43.587833: Current learning rate: 0.00223 +2026-04-14 12:22:25.705455: train_loss -0.4509 +2026-04-14 12:22:25.712779: val_loss -0.4014 +2026-04-14 12:22:25.714898: Pseudo dice [0.5577, 0.0, 0.8098, 0.7596, 0.501, 0.9077, 0.8588] +2026-04-14 12:22:25.717513: Epoch time: 102.12 s +2026-04-14 12:22:27.033184: +2026-04-14 12:22:27.035688: Epoch 3247 +2026-04-14 12:22:27.037276: Current learning rate: 0.00222 +2026-04-14 12:24:08.659492: train_loss -0.4492 +2026-04-14 12:24:08.666359: val_loss -0.3886 +2026-04-14 12:24:08.668577: Pseudo dice [0.6121, 0.0, 0.7657, 0.3066, 0.4208, 0.8645, 0.8654] +2026-04-14 12:24:08.670761: Epoch time: 101.63 s +2026-04-14 12:24:09.987768: +2026-04-14 12:24:09.989430: Epoch 3248 +2026-04-14 12:24:09.991141: Current learning rate: 0.00222 +2026-04-14 12:25:51.622179: train_loss -0.4463 +2026-04-14 12:25:51.628118: val_loss -0.3808 +2026-04-14 12:25:51.630161: Pseudo dice [0.4248, 0.0, 0.7709, 0.7934, 0.4751, 0.9158, 0.9168] +2026-04-14 12:25:51.632789: Epoch time: 101.64 s +2026-04-14 12:25:54.017348: +2026-04-14 12:25:54.019115: Epoch 3249 +2026-04-14 12:25:54.020859: Current learning rate: 0.00222 +2026-04-14 12:27:35.704456: train_loss -0.4547 +2026-04-14 12:27:35.710595: val_loss -0.397 +2026-04-14 12:27:35.712363: Pseudo dice [0.8363, 0.0, 0.7871, 0.8668, 0.5081, 0.9042, 0.8715] +2026-04-14 12:27:35.714308: Epoch time: 101.69 s +2026-04-14 12:27:38.855193: +2026-04-14 12:27:38.857079: Epoch 3250 +2026-04-14 12:27:38.858600: Current learning rate: 0.00222 +2026-04-14 12:29:20.396783: train_loss -0.442 +2026-04-14 12:29:20.403034: val_loss -0.3708 +2026-04-14 12:29:20.405039: Pseudo dice [0.2514, 0.0, 0.8721, 0.8, 0.5752, 0.7391, 0.8835] +2026-04-14 12:29:20.407593: Epoch time: 101.54 s +2026-04-14 12:29:21.689729: +2026-04-14 12:29:21.691644: Epoch 3251 +2026-04-14 12:29:21.693196: Current learning rate: 0.00221 +2026-04-14 12:31:03.660368: train_loss -0.464 +2026-04-14 12:31:03.667773: val_loss -0.3793 +2026-04-14 12:31:03.670556: Pseudo dice [0.4404, 0.0, 0.8209, 0.6675, 0.5294, 0.8843, 0.7754] +2026-04-14 12:31:03.673716: Epoch time: 101.97 s +2026-04-14 12:31:04.950222: +2026-04-14 12:31:04.952267: Epoch 3252 +2026-04-14 12:31:04.953800: Current learning rate: 0.00221 +2026-04-14 12:32:46.421257: train_loss -0.4601 +2026-04-14 12:32:46.427476: val_loss -0.3784 +2026-04-14 12:32:46.429976: Pseudo dice [0.701, 0.0, 0.856, 0.8503, 0.4969, 0.6194, 0.8682] +2026-04-14 12:32:46.432292: Epoch time: 101.47 s +2026-04-14 12:32:47.713392: +2026-04-14 12:32:47.715202: Epoch 3253 +2026-04-14 12:32:47.717080: Current learning rate: 0.00221 +2026-04-14 12:34:29.691524: train_loss -0.453 +2026-04-14 12:34:29.697365: val_loss -0.3894 +2026-04-14 12:34:29.702159: Pseudo dice [0.4725, 0.0, 0.8515, 0.8565, 0.6572, 0.6925, 0.8857] +2026-04-14 12:34:29.704489: Epoch time: 101.98 s +2026-04-14 12:34:30.977639: +2026-04-14 12:34:30.979843: Epoch 3254 +2026-04-14 12:34:30.981657: Current learning rate: 0.00221 +2026-04-14 12:36:12.687515: train_loss -0.4599 +2026-04-14 12:36:12.693077: val_loss -0.4131 +2026-04-14 12:36:12.695213: Pseudo dice [0.7641, 0.0, 0.7888, 0.7157, 0.3677, 0.9135, 0.8844] +2026-04-14 12:36:12.697918: Epoch time: 101.71 s +2026-04-14 12:36:14.017745: +2026-04-14 12:36:14.019726: Epoch 3255 +2026-04-14 12:36:14.021689: Current learning rate: 0.0022 +2026-04-14 12:37:55.692849: train_loss -0.4374 +2026-04-14 12:37:55.698092: val_loss -0.3719 +2026-04-14 12:37:55.700309: Pseudo dice [0.7342, 0.0, 0.8303, 0.3513, 0.4482, 0.7872, 0.8345] +2026-04-14 12:37:55.703034: Epoch time: 101.68 s +2026-04-14 12:37:56.972859: +2026-04-14 12:37:56.974616: Epoch 3256 +2026-04-14 12:37:56.976268: Current learning rate: 0.0022 +2026-04-14 12:39:38.815179: train_loss -0.4495 +2026-04-14 12:39:38.822420: val_loss -0.3555 +2026-04-14 12:39:38.825260: Pseudo dice [0.7209, 0.0, 0.8225, 0.0792, 0.2414, 0.6963, 0.8926] +2026-04-14 12:39:38.828325: Epoch time: 101.85 s +2026-04-14 12:39:40.127356: +2026-04-14 12:39:40.129779: Epoch 3257 +2026-04-14 12:39:40.131514: Current learning rate: 0.0022 +2026-04-14 12:41:21.569408: train_loss -0.4521 +2026-04-14 12:41:21.576130: val_loss -0.413 +2026-04-14 12:41:21.578303: Pseudo dice [0.7006, 0.0, 0.853, 0.8494, 0.5229, 0.6933, 0.9398] +2026-04-14 12:41:21.581592: Epoch time: 101.45 s +2026-04-14 12:41:22.855506: +2026-04-14 12:41:22.857227: Epoch 3258 +2026-04-14 12:41:22.858696: Current learning rate: 0.0022 +2026-04-14 12:43:04.310904: train_loss -0.4528 +2026-04-14 12:43:04.317430: val_loss -0.3845 +2026-04-14 12:43:04.319607: Pseudo dice [0.7322, 0.0, 0.7664, 0.6084, 0.5627, 0.6485, 0.8587] +2026-04-14 12:43:04.322016: Epoch time: 101.46 s +2026-04-14 12:43:05.571247: +2026-04-14 12:43:05.573300: Epoch 3259 +2026-04-14 12:43:05.575189: Current learning rate: 0.00219 +2026-04-14 12:44:47.064003: train_loss -0.4541 +2026-04-14 12:44:47.070737: val_loss -0.3876 +2026-04-14 12:44:47.073033: Pseudo dice [0.515, 0.0, 0.7952, 0.544, 0.5886, 0.5631, 0.8719] +2026-04-14 12:44:47.075652: Epoch time: 101.5 s +2026-04-14 12:44:48.369306: +2026-04-14 12:44:48.371022: Epoch 3260 +2026-04-14 12:44:48.373254: Current learning rate: 0.00219 +2026-04-14 12:46:30.530979: train_loss -0.4657 +2026-04-14 12:46:30.537256: val_loss -0.3756 +2026-04-14 12:46:30.539639: Pseudo dice [0.7178, 0.0, 0.827, 0.7248, 0.4154, 0.6699, 0.8679] +2026-04-14 12:46:30.542026: Epoch time: 102.16 s +2026-04-14 12:46:31.816864: +2026-04-14 12:46:31.818823: Epoch 3261 +2026-04-14 12:46:31.820583: Current learning rate: 0.00219 +2026-04-14 12:48:13.110579: train_loss -0.452 +2026-04-14 12:48:13.117557: val_loss -0.3776 +2026-04-14 12:48:13.119984: Pseudo dice [0.3882, 0.0, 0.7373, 0.2297, 0.5777, 0.7261, 0.9189] +2026-04-14 12:48:13.122582: Epoch time: 101.3 s +2026-04-14 12:48:14.369353: +2026-04-14 12:48:14.371142: Epoch 3262 +2026-04-14 12:48:14.372838: Current learning rate: 0.00218 +2026-04-14 12:49:56.088342: train_loss -0.4661 +2026-04-14 12:49:56.097396: val_loss -0.37 +2026-04-14 12:49:56.105505: Pseudo dice [0.4493, 0.0, 0.6974, 0.2218, 0.4488, 0.8248, 0.8293] +2026-04-14 12:49:56.108134: Epoch time: 101.72 s +2026-04-14 12:49:57.359488: +2026-04-14 12:49:57.361866: Epoch 3263 +2026-04-14 12:49:57.363577: Current learning rate: 0.00218 +2026-04-14 12:51:38.733540: train_loss -0.4639 +2026-04-14 12:51:38.740228: val_loss -0.3964 +2026-04-14 12:51:38.742406: Pseudo dice [0.6322, 0.0, 0.856, 0.4085, 0.5434, 0.7763, 0.4872] +2026-04-14 12:51:38.744837: Epoch time: 101.38 s +2026-04-14 12:51:39.979608: +2026-04-14 12:51:39.981291: Epoch 3264 +2026-04-14 12:51:39.982812: Current learning rate: 0.00218 +2026-04-14 12:53:21.842614: train_loss -0.4489 +2026-04-14 12:53:21.873104: val_loss -0.3619 +2026-04-14 12:53:21.875362: Pseudo dice [0.4824, 0.0, 0.7778, 0.6009, 0.3951, 0.8508, 0.8872] +2026-04-14 12:53:21.878035: Epoch time: 101.87 s +2026-04-14 12:53:23.154932: +2026-04-14 12:53:23.157175: Epoch 3265 +2026-04-14 12:53:23.159867: Current learning rate: 0.00218 +2026-04-14 12:55:04.455055: train_loss -0.4487 +2026-04-14 12:55:04.461247: val_loss -0.4144 +2026-04-14 12:55:04.463543: Pseudo dice [0.7171, 0.0, 0.7494, 0.5901, 0.4737, 0.7044, 0.8794] +2026-04-14 12:55:04.466391: Epoch time: 101.3 s +2026-04-14 12:55:05.713153: +2026-04-14 12:55:05.715268: Epoch 3266 +2026-04-14 12:55:05.717113: Current learning rate: 0.00217 +2026-04-14 12:56:47.430129: train_loss -0.452 +2026-04-14 12:56:47.435737: val_loss -0.3624 +2026-04-14 12:56:47.437764: Pseudo dice [0.5889, 0.0, 0.8939, 0.4259, 0.2209, 0.8046, 0.634] +2026-04-14 12:56:47.440198: Epoch time: 101.72 s +2026-04-14 12:56:48.695480: +2026-04-14 12:56:48.697296: Epoch 3267 +2026-04-14 12:56:48.699121: Current learning rate: 0.00217 +2026-04-14 12:58:29.973589: train_loss -0.4577 +2026-04-14 12:58:29.979734: val_loss -0.4219 +2026-04-14 12:58:29.981759: Pseudo dice [0.8013, 0.0, 0.8823, 0.6806, 0.638, 0.7489, 0.9236] +2026-04-14 12:58:29.983824: Epoch time: 101.28 s +2026-04-14 12:58:31.254955: +2026-04-14 12:58:31.257000: Epoch 3268 +2026-04-14 12:58:31.258426: Current learning rate: 0.00217 +2026-04-14 13:00:13.945087: train_loss -0.4475 +2026-04-14 13:00:13.952252: val_loss -0.374 +2026-04-14 13:00:13.954947: Pseudo dice [0.7453, 0.0, 0.7423, 0.0455, 0.594, 0.7518, 0.8881] +2026-04-14 13:00:13.958360: Epoch time: 102.69 s +2026-04-14 13:00:15.254036: +2026-04-14 13:00:15.256581: Epoch 3269 +2026-04-14 13:00:15.259104: Current learning rate: 0.00217 +2026-04-14 13:01:57.157351: train_loss -0.4568 +2026-04-14 13:01:57.165981: val_loss -0.3798 +2026-04-14 13:01:57.167951: Pseudo dice [0.7032, 0.0, 0.8557, 0.5884, 0.4375, 0.8186, 0.7997] +2026-04-14 13:01:57.170813: Epoch time: 101.91 s +2026-04-14 13:01:58.441294: +2026-04-14 13:01:58.443281: Epoch 3270 +2026-04-14 13:01:58.445804: Current learning rate: 0.00216 +2026-04-14 13:03:40.418011: train_loss -0.4364 +2026-04-14 13:03:40.423311: val_loss -0.3673 +2026-04-14 13:03:40.425621: Pseudo dice [0.4031, 0.0, 0.8065, 0.6496, 0.332, 0.3797, 0.9029] +2026-04-14 13:03:40.428046: Epoch time: 101.98 s +2026-04-14 13:03:41.693513: +2026-04-14 13:03:41.695704: Epoch 3271 +2026-04-14 13:03:41.697970: Current learning rate: 0.00216 +2026-04-14 13:05:23.361970: train_loss -0.4493 +2026-04-14 13:05:23.368840: val_loss -0.4057 +2026-04-14 13:05:23.370883: Pseudo dice [0.79, 0.0, 0.8433, 0.5644, 0.5889, 0.7986, 0.9162] +2026-04-14 13:05:23.375325: Epoch time: 101.67 s +2026-04-14 13:05:24.658075: +2026-04-14 13:05:24.660057: Epoch 3272 +2026-04-14 13:05:24.661572: Current learning rate: 0.00216 +2026-04-14 13:07:05.885045: train_loss -0.4352 +2026-04-14 13:07:05.892782: val_loss -0.3329 +2026-04-14 13:07:05.895009: Pseudo dice [0.373, 0.0, 0.6733, 0.0341, 0.5097, 0.8102, 0.8512] +2026-04-14 13:07:05.897234: Epoch time: 101.23 s +2026-04-14 13:07:07.137481: +2026-04-14 13:07:07.139480: Epoch 3273 +2026-04-14 13:07:07.140918: Current learning rate: 0.00216 +2026-04-14 13:08:48.739668: train_loss -0.4514 +2026-04-14 13:08:48.747469: val_loss -0.3704 +2026-04-14 13:08:48.749986: Pseudo dice [0.5948, 0.0, 0.7235, 0.4084, 0.5177, 0.5159, 0.918] +2026-04-14 13:08:48.752923: Epoch time: 101.61 s +2026-04-14 13:08:50.066429: +2026-04-14 13:08:50.068186: Epoch 3274 +2026-04-14 13:08:50.069938: Current learning rate: 0.00215 +2026-04-14 13:10:31.361325: train_loss -0.442 +2026-04-14 13:10:31.367682: val_loss -0.3893 +2026-04-14 13:10:31.369595: Pseudo dice [0.414, 0.0, 0.8295, 0.1461, 0.5454, 0.6617, 0.8302] +2026-04-14 13:10:31.371988: Epoch time: 101.3 s +2026-04-14 13:10:32.647685: +2026-04-14 13:10:32.650526: Epoch 3275 +2026-04-14 13:10:32.652558: Current learning rate: 0.00215 +2026-04-14 13:12:14.575944: train_loss -0.4523 +2026-04-14 13:12:14.582235: val_loss -0.3766 +2026-04-14 13:12:14.586589: Pseudo dice [0.6662, 0.0, 0.8464, 0.4321, 0.5182, 0.6444, 0.8821] +2026-04-14 13:12:14.589154: Epoch time: 101.93 s +2026-04-14 13:12:15.831121: +2026-04-14 13:12:15.833489: Epoch 3276 +2026-04-14 13:12:15.835428: Current learning rate: 0.00215 +2026-04-14 13:13:57.594102: train_loss -0.4579 +2026-04-14 13:13:57.601973: val_loss -0.3932 +2026-04-14 13:13:57.604529: Pseudo dice [0.3965, 0.0, 0.8934, 0.3434, 0.5241, 0.8884, 0.7209] +2026-04-14 13:13:57.607735: Epoch time: 101.77 s +2026-04-14 13:13:58.871579: +2026-04-14 13:13:58.874589: Epoch 3277 +2026-04-14 13:13:58.876927: Current learning rate: 0.00214 +2026-04-14 13:15:40.454154: train_loss -0.4579 +2026-04-14 13:15:40.460494: val_loss -0.3909 +2026-04-14 13:15:40.462613: Pseudo dice [0.7096, 0.0, 0.6397, 0.8011, 0.4806, 0.6385, 0.6417] +2026-04-14 13:15:40.464801: Epoch time: 101.59 s +2026-04-14 13:15:41.747350: +2026-04-14 13:15:41.749282: Epoch 3278 +2026-04-14 13:15:41.750899: Current learning rate: 0.00214 +2026-04-14 13:17:23.481848: train_loss -0.4506 +2026-04-14 13:17:23.489953: val_loss -0.3455 +2026-04-14 13:17:23.491838: Pseudo dice [0.5902, 0.0, 0.8469, 0.3244, 0.4535, 0.7046, 0.9069] +2026-04-14 13:17:23.494995: Epoch time: 101.74 s +2026-04-14 13:17:24.763034: +2026-04-14 13:17:24.764913: Epoch 3279 +2026-04-14 13:17:24.766405: Current learning rate: 0.00214 +2026-04-14 13:19:05.823744: train_loss -0.4522 +2026-04-14 13:19:05.830244: val_loss -0.3932 +2026-04-14 13:19:05.832824: Pseudo dice [0.6541, 0.0, 0.7631, 0.5081, 0.5749, 0.6933, 0.9366] +2026-04-14 13:19:05.836009: Epoch time: 101.06 s +2026-04-14 13:19:07.136799: +2026-04-14 13:19:07.138487: Epoch 3280 +2026-04-14 13:19:07.140017: Current learning rate: 0.00214 +2026-04-14 13:20:48.706172: train_loss -0.4651 +2026-04-14 13:20:48.712906: val_loss -0.3812 +2026-04-14 13:20:48.715199: Pseudo dice [0.5824, 0.0, 0.6788, 0.3599, 0.6443, 0.8351, 0.9116] +2026-04-14 13:20:48.717802: Epoch time: 101.57 s +2026-04-14 13:20:49.966929: +2026-04-14 13:20:49.968926: Epoch 3281 +2026-04-14 13:20:49.970594: Current learning rate: 0.00213 +2026-04-14 13:22:31.419924: train_loss -0.4412 +2026-04-14 13:22:31.426112: val_loss -0.3483 +2026-04-14 13:22:31.428333: Pseudo dice [0.8181, 0.0, 0.7243, 0.128, 0.3958, 0.468, 0.803] +2026-04-14 13:22:31.431144: Epoch time: 101.46 s +2026-04-14 13:22:32.745255: +2026-04-14 13:22:32.747027: Epoch 3282 +2026-04-14 13:22:32.748450: Current learning rate: 0.00213 +2026-04-14 13:24:14.267685: train_loss -0.4603 +2026-04-14 13:24:14.273557: val_loss -0.3821 +2026-04-14 13:24:14.275899: Pseudo dice [0.8221, 0.0, 0.7761, 0.6461, 0.4038, 0.7177, 0.8948] +2026-04-14 13:24:14.278309: Epoch time: 101.53 s +2026-04-14 13:24:15.542721: +2026-04-14 13:24:15.544543: Epoch 3283 +2026-04-14 13:24:15.546130: Current learning rate: 0.00213 +2026-04-14 13:25:57.211022: train_loss -0.4487 +2026-04-14 13:25:57.219102: val_loss -0.3971 +2026-04-14 13:25:57.221236: Pseudo dice [0.7742, 0.0, 0.7915, 0.5961, 0.2115, 0.7888, 0.7266] +2026-04-14 13:25:57.223529: Epoch time: 101.67 s +2026-04-14 13:25:58.543455: +2026-04-14 13:25:58.545625: Epoch 3284 +2026-04-14 13:25:58.547507: Current learning rate: 0.00213 +2026-04-14 13:27:39.784736: train_loss -0.4377 +2026-04-14 13:27:39.791358: val_loss -0.38 +2026-04-14 13:27:39.793444: Pseudo dice [0.7469, 0.0, 0.7027, 0.8556, 0.472, 0.7091, 0.7807] +2026-04-14 13:27:39.795623: Epoch time: 101.24 s +2026-04-14 13:27:41.021277: +2026-04-14 13:27:41.023168: Epoch 3285 +2026-04-14 13:27:41.025089: Current learning rate: 0.00212 +2026-04-14 13:29:22.474316: train_loss -0.449 +2026-04-14 13:29:22.481066: val_loss -0.3849 +2026-04-14 13:29:22.483404: Pseudo dice [0.5324, 0.0, 0.8041, 0.8102, 0.5822, 0.7448, 0.8955] +2026-04-14 13:29:22.486006: Epoch time: 101.46 s +2026-04-14 13:29:23.761992: +2026-04-14 13:29:23.764051: Epoch 3286 +2026-04-14 13:29:23.765670: Current learning rate: 0.00212 +2026-04-14 13:31:05.359133: train_loss -0.4519 +2026-04-14 13:31:05.364850: val_loss -0.3828 +2026-04-14 13:31:05.366826: Pseudo dice [0.6963, 0.0, 0.6212, 0.14, 0.5875, 0.525, 0.906] +2026-04-14 13:31:05.369692: Epoch time: 101.6 s +2026-04-14 13:31:06.643932: +2026-04-14 13:31:06.647026: Epoch 3287 +2026-04-14 13:31:06.648563: Current learning rate: 0.00212 +2026-04-14 13:32:47.955903: train_loss -0.4426 +2026-04-14 13:32:47.962650: val_loss -0.3816 +2026-04-14 13:32:47.964809: Pseudo dice [0.5245, 0.0, 0.8357, 0.6079, 0.441, 0.5748, 0.9463] +2026-04-14 13:32:47.967313: Epoch time: 101.32 s +2026-04-14 13:32:50.270470: +2026-04-14 13:32:50.272533: Epoch 3288 +2026-04-14 13:32:50.274158: Current learning rate: 0.00212 +2026-04-14 13:34:32.128699: train_loss -0.4655 +2026-04-14 13:34:32.134797: val_loss -0.384 +2026-04-14 13:34:32.137038: Pseudo dice [0.7592, 0.0, 0.8062, 0.5322, 0.5451, 0.5258, 0.7048] +2026-04-14 13:34:32.139702: Epoch time: 101.86 s +2026-04-14 13:34:33.426372: +2026-04-14 13:34:33.428861: Epoch 3289 +2026-04-14 13:34:33.430726: Current learning rate: 0.00211 +2026-04-14 13:36:14.705117: train_loss -0.4388 +2026-04-14 13:36:14.711315: val_loss -0.3758 +2026-04-14 13:36:14.713651: Pseudo dice [0.3916, 0.0, 0.8336, 0.7526, 0.6076, 0.7961, 0.9239] +2026-04-14 13:36:14.716252: Epoch time: 101.28 s +2026-04-14 13:36:15.980289: +2026-04-14 13:36:15.981979: Epoch 3290 +2026-04-14 13:36:15.983497: Current learning rate: 0.00211 +2026-04-14 13:37:57.962905: train_loss -0.4444 +2026-04-14 13:37:57.971228: val_loss -0.3973 +2026-04-14 13:37:57.973391: Pseudo dice [0.7607, 0.0, 0.8327, 0.9053, 0.4637, 0.5885, 0.9026] +2026-04-14 13:37:57.975634: Epoch time: 101.99 s +2026-04-14 13:37:59.264835: +2026-04-14 13:37:59.266923: Epoch 3291 +2026-04-14 13:37:59.268418: Current learning rate: 0.00211 +2026-04-14 13:39:40.654224: train_loss -0.449 +2026-04-14 13:39:40.661032: val_loss -0.4042 +2026-04-14 13:39:40.663527: Pseudo dice [0.5013, 0.0, 0.8694, 0.6228, 0.5282, 0.5483, 0.8995] +2026-04-14 13:39:40.666174: Epoch time: 101.39 s +2026-04-14 13:39:41.962036: +2026-04-14 13:39:41.963842: Epoch 3292 +2026-04-14 13:39:41.965598: Current learning rate: 0.0021 +2026-04-14 13:41:23.476672: train_loss -0.4611 +2026-04-14 13:41:23.483345: val_loss -0.3511 +2026-04-14 13:41:23.486767: Pseudo dice [0.2014, 0.0, 0.6735, 0.0973, 0.527, 0.7209, 0.9398] +2026-04-14 13:41:23.489080: Epoch time: 101.52 s +2026-04-14 13:41:24.776585: +2026-04-14 13:41:24.779293: Epoch 3293 +2026-04-14 13:41:24.781191: Current learning rate: 0.0021 +2026-04-14 13:43:06.506393: train_loss -0.4583 +2026-04-14 13:43:06.513000: val_loss -0.3692 +2026-04-14 13:43:06.515031: Pseudo dice [0.403, 0.0, 0.8141, 0.3695, 0.476, 0.8054, 0.6499] +2026-04-14 13:43:06.517861: Epoch time: 101.73 s +2026-04-14 13:43:07.828591: +2026-04-14 13:43:07.831025: Epoch 3294 +2026-04-14 13:43:07.833206: Current learning rate: 0.0021 +2026-04-14 13:44:49.312634: train_loss -0.4688 +2026-04-14 13:44:49.318312: val_loss -0.4137 +2026-04-14 13:44:49.321251: Pseudo dice [0.7836, 0.0, 0.878, 0.8556, 0.5572, 0.89, 0.8119] +2026-04-14 13:44:49.324427: Epoch time: 101.49 s +2026-04-14 13:44:50.574688: +2026-04-14 13:44:50.576833: Epoch 3295 +2026-04-14 13:44:50.578473: Current learning rate: 0.0021 +2026-04-14 13:46:31.974774: train_loss -0.4524 +2026-04-14 13:46:31.981718: val_loss -0.3994 +2026-04-14 13:46:31.984755: Pseudo dice [0.7536, 0.0, 0.828, 0.6775, 0.5207, 0.5402, 0.9101] +2026-04-14 13:46:31.987372: Epoch time: 101.4 s +2026-04-14 13:46:33.223943: +2026-04-14 13:46:33.225806: Epoch 3296 +2026-04-14 13:46:33.227692: Current learning rate: 0.00209 +2026-04-14 13:48:14.847374: train_loss -0.4534 +2026-04-14 13:48:14.854170: val_loss -0.3631 +2026-04-14 13:48:14.856217: Pseudo dice [0.4286, 0.0, 0.6807, 0.0101, 0.5199, 0.7059, 0.8268] +2026-04-14 13:48:14.858779: Epoch time: 101.63 s +2026-04-14 13:48:16.189653: +2026-04-14 13:48:16.191369: Epoch 3297 +2026-04-14 13:48:16.193346: Current learning rate: 0.00209 +2026-04-14 13:49:57.635263: train_loss -0.446 +2026-04-14 13:49:57.642062: val_loss -0.3603 +2026-04-14 13:49:57.644499: Pseudo dice [0.4891, 0.0, 0.789, 0.2796, 0.4612, 0.7701, 0.8358] +2026-04-14 13:49:57.647406: Epoch time: 101.45 s +2026-04-14 13:49:58.923894: +2026-04-14 13:49:58.925974: Epoch 3298 +2026-04-14 13:49:58.927555: Current learning rate: 0.00209 +2026-04-14 13:51:40.345592: train_loss -0.4501 +2026-04-14 13:51:40.352852: val_loss -0.3725 +2026-04-14 13:51:40.355560: Pseudo dice [0.3957, 0.0, 0.7911, 0.4585, 0.5692, 0.5958, 0.739] +2026-04-14 13:51:40.358502: Epoch time: 101.42 s +2026-04-14 13:51:41.599407: +2026-04-14 13:51:41.601398: Epoch 3299 +2026-04-14 13:51:41.602991: Current learning rate: 0.00209 +2026-04-14 13:53:23.003194: train_loss -0.4404 +2026-04-14 13:53:23.011089: val_loss -0.3749 +2026-04-14 13:53:23.013707: Pseudo dice [0.5547, 0.0, 0.8467, 0.4498, 0.3732, 0.7019, 0.8922] +2026-04-14 13:53:23.016575: Epoch time: 101.41 s +2026-04-14 13:53:26.059207: +2026-04-14 13:53:26.061874: Epoch 3300 +2026-04-14 13:53:26.063371: Current learning rate: 0.00208 +2026-04-14 13:55:07.388634: train_loss -0.4368 +2026-04-14 13:55:07.395557: val_loss -0.3834 +2026-04-14 13:55:07.398232: Pseudo dice [0.358, 0.0, 0.7694, 0.5161, 0.4221, 0.7129, 0.7643] +2026-04-14 13:55:07.401061: Epoch time: 101.33 s +2026-04-14 13:55:08.682817: +2026-04-14 13:55:08.684469: Epoch 3301 +2026-04-14 13:55:08.686024: Current learning rate: 0.00208 +2026-04-14 13:56:49.885593: train_loss -0.4512 +2026-04-14 13:56:49.893151: val_loss -0.4051 +2026-04-14 13:56:49.895590: Pseudo dice [0.7285, 0.0, 0.8428, 0.7995, 0.5884, 0.9246, 0.7126] +2026-04-14 13:56:49.897865: Epoch time: 101.21 s +2026-04-14 13:56:51.178772: +2026-04-14 13:56:51.180506: Epoch 3302 +2026-04-14 13:56:51.182553: Current learning rate: 0.00208 +2026-04-14 13:58:32.737917: train_loss -0.4602 +2026-04-14 13:58:32.744898: val_loss -0.3533 +2026-04-14 13:58:32.747350: Pseudo dice [0.5733, 0.0, 0.6887, 0.3735, 0.3895, 0.6787, 0.891] +2026-04-14 13:58:32.752390: Epoch time: 101.56 s +2026-04-14 13:58:34.002977: +2026-04-14 13:58:34.004866: Epoch 3303 +2026-04-14 13:58:34.006714: Current learning rate: 0.00208 +2026-04-14 14:00:15.729025: train_loss -0.4505 +2026-04-14 14:00:15.736129: val_loss -0.3642 +2026-04-14 14:00:15.738769: Pseudo dice [0.4454, 0.0, 0.8391, 0.6072, 0.5999, 0.4625, 0.9277] +2026-04-14 14:00:15.742050: Epoch time: 101.73 s +2026-04-14 14:00:16.988549: +2026-04-14 14:00:16.990739: Epoch 3304 +2026-04-14 14:00:16.992349: Current learning rate: 0.00207 +2026-04-14 14:01:58.569449: train_loss -0.4493 +2026-04-14 14:01:58.575316: val_loss -0.4207 +2026-04-14 14:01:58.577218: Pseudo dice [0.753, 0.0, 0.861, 0.8034, 0.5621, 0.5718, 0.9472] +2026-04-14 14:01:58.579748: Epoch time: 101.58 s +2026-04-14 14:01:59.821402: +2026-04-14 14:01:59.823282: Epoch 3305 +2026-04-14 14:01:59.824970: Current learning rate: 0.00207 +2026-04-14 14:03:41.622446: train_loss -0.4532 +2026-04-14 14:03:41.634156: val_loss -0.3711 +2026-04-14 14:03:41.637612: Pseudo dice [0.497, 0.0, 0.7691, 0.3634, 0.4429, 0.9075, 0.8175] +2026-04-14 14:03:41.640982: Epoch time: 101.8 s +2026-04-14 14:03:42.897636: +2026-04-14 14:03:42.901834: Epoch 3306 +2026-04-14 14:03:42.905017: Current learning rate: 0.00207 +2026-04-14 14:05:24.101500: train_loss -0.463 +2026-04-14 14:05:24.109073: val_loss -0.3991 +2026-04-14 14:05:24.111089: Pseudo dice [0.5586, 0.0, 0.8089, 0.4389, 0.4723, 0.91, 0.939] +2026-04-14 14:05:24.113487: Epoch time: 101.21 s +2026-04-14 14:05:26.540007: +2026-04-14 14:05:26.542020: Epoch 3307 +2026-04-14 14:05:26.543652: Current learning rate: 0.00206 +2026-04-14 14:07:08.009132: train_loss -0.4445 +2026-04-14 14:07:08.016287: val_loss -0.4159 +2026-04-14 14:07:08.018669: Pseudo dice [0.857, 0.0, 0.8734, 0.4915, 0.3515, 0.476, 0.914] +2026-04-14 14:07:08.021201: Epoch time: 101.47 s +2026-04-14 14:07:09.270310: +2026-04-14 14:07:09.272635: Epoch 3308 +2026-04-14 14:07:09.274390: Current learning rate: 0.00206 +2026-04-14 14:08:50.848449: train_loss -0.4593 +2026-04-14 14:08:50.854337: val_loss -0.3707 +2026-04-14 14:08:50.858679: Pseudo dice [0.5311, 0.0, 0.5527, 0.3214, 0.5072, 0.6487, 0.8893] +2026-04-14 14:08:50.862194: Epoch time: 101.58 s +2026-04-14 14:08:52.161642: +2026-04-14 14:08:52.163807: Epoch 3309 +2026-04-14 14:08:52.165677: Current learning rate: 0.00206 +2026-04-14 14:10:33.758343: train_loss -0.4353 +2026-04-14 14:10:33.764349: val_loss -0.3525 +2026-04-14 14:10:33.766687: Pseudo dice [0.436, 0.0, 0.5358, 0.1652, 0.489, 0.6139, 0.8078] +2026-04-14 14:10:33.769484: Epoch time: 101.6 s +2026-04-14 14:10:35.010696: +2026-04-14 14:10:35.013176: Epoch 3310 +2026-04-14 14:10:35.014907: Current learning rate: 0.00206 +2026-04-14 14:12:16.236133: train_loss -0.4465 +2026-04-14 14:12:16.242346: val_loss -0.3972 +2026-04-14 14:12:16.244542: Pseudo dice [0.709, 0.0, 0.695, 0.7578, 0.5723, 0.7646, 0.9137] +2026-04-14 14:12:16.247662: Epoch time: 101.23 s +2026-04-14 14:12:17.513993: +2026-04-14 14:12:17.515986: Epoch 3311 +2026-04-14 14:12:17.517591: Current learning rate: 0.00205 +2026-04-14 14:13:59.087040: train_loss -0.4486 +2026-04-14 14:13:59.094684: val_loss -0.3981 +2026-04-14 14:13:59.098471: Pseudo dice [0.5332, 0.0, 0.7715, 0.9343, 0.3787, 0.7288, 0.7147] +2026-04-14 14:13:59.100740: Epoch time: 101.58 s +2026-04-14 14:14:00.355016: +2026-04-14 14:14:00.356747: Epoch 3312 +2026-04-14 14:14:00.358329: Current learning rate: 0.00205 +2026-04-14 14:15:41.545613: train_loss -0.4552 +2026-04-14 14:15:41.551263: val_loss -0.3991 +2026-04-14 14:15:41.553902: Pseudo dice [0.6657, 0.0, 0.85, 0.8034, 0.5005, 0.9129, 0.6227] +2026-04-14 14:15:41.556544: Epoch time: 101.19 s +2026-04-14 14:15:42.838262: +2026-04-14 14:15:42.839904: Epoch 3313 +2026-04-14 14:15:42.841391: Current learning rate: 0.00205 +2026-04-14 14:17:23.973489: train_loss -0.4669 +2026-04-14 14:17:23.980393: val_loss -0.4162 +2026-04-14 14:17:23.982407: Pseudo dice [0.8071, 0.0, 0.8527, 0.7719, 0.5842, 0.7486, 0.9201] +2026-04-14 14:17:23.986112: Epoch time: 101.14 s +2026-04-14 14:17:25.231232: +2026-04-14 14:17:25.233314: Epoch 3314 +2026-04-14 14:17:25.235308: Current learning rate: 0.00205 +2026-04-14 14:19:06.455274: train_loss -0.4608 +2026-04-14 14:19:06.464055: val_loss -0.3733 +2026-04-14 14:19:06.467347: Pseudo dice [0.7866, 0.0, 0.6452, 0.257, 0.59, 0.5882, 0.9229] +2026-04-14 14:19:06.469669: Epoch time: 101.23 s +2026-04-14 14:19:07.759327: +2026-04-14 14:19:07.760985: Epoch 3315 +2026-04-14 14:19:07.762493: Current learning rate: 0.00204 +2026-04-14 14:20:49.403835: train_loss -0.442 +2026-04-14 14:20:49.410788: val_loss -0.3475 +2026-04-14 14:20:49.413546: Pseudo dice [0.6542, 0.0, 0.5788, 0.3653, 0.4065, 0.7139, 0.928] +2026-04-14 14:20:49.415721: Epoch time: 101.65 s +2026-04-14 14:20:50.681334: +2026-04-14 14:20:50.683330: Epoch 3316 +2026-04-14 14:20:50.685020: Current learning rate: 0.00204 +2026-04-14 14:22:32.463885: train_loss -0.4407 +2026-04-14 14:22:32.469544: val_loss -0.3884 +2026-04-14 14:22:32.471543: Pseudo dice [0.6053, 0.0, 0.7114, 0.3313, 0.3211, 0.5087, 0.9168] +2026-04-14 14:22:32.474172: Epoch time: 101.79 s +2026-04-14 14:22:33.767950: +2026-04-14 14:22:33.770048: Epoch 3317 +2026-04-14 14:22:33.771628: Current learning rate: 0.00204 +2026-04-14 14:24:15.346183: train_loss -0.442 +2026-04-14 14:24:15.351615: val_loss -0.3885 +2026-04-14 14:24:15.353727: Pseudo dice [0.7795, 0.0, 0.83, 0.0365, 0.2226, 0.7932, 0.8339] +2026-04-14 14:24:15.356564: Epoch time: 101.58 s +2026-04-14 14:24:16.676327: +2026-04-14 14:24:16.678526: Epoch 3318 +2026-04-14 14:24:16.680262: Current learning rate: 0.00203 +2026-04-14 14:25:58.024120: train_loss -0.4533 +2026-04-14 14:25:58.029724: val_loss -0.3688 +2026-04-14 14:25:58.031423: Pseudo dice [0.8493, 0.0, 0.4904, 0.0878, 0.5923, 0.6044, 0.865] +2026-04-14 14:25:58.033818: Epoch time: 101.35 s +2026-04-14 14:25:59.294837: +2026-04-14 14:25:59.296652: Epoch 3319 +2026-04-14 14:25:59.298144: Current learning rate: 0.00203 +2026-04-14 14:27:40.623418: train_loss -0.4504 +2026-04-14 14:27:40.629267: val_loss -0.391 +2026-04-14 14:27:40.631627: Pseudo dice [0.7797, 0.0, 0.4587, 0.1941, 0.5312, 0.6333, 0.8467] +2026-04-14 14:27:40.634725: Epoch time: 101.33 s +2026-04-14 14:27:41.878297: +2026-04-14 14:27:41.880735: Epoch 3320 +2026-04-14 14:27:41.882331: Current learning rate: 0.00203 +2026-04-14 14:29:23.174516: train_loss -0.4506 +2026-04-14 14:29:23.183837: val_loss -0.3694 +2026-04-14 14:29:23.186169: Pseudo dice [0.2773, 0.0, 0.7616, 0.4879, 0.4942, 0.7228, 0.9086] +2026-04-14 14:29:23.188832: Epoch time: 101.3 s +2026-04-14 14:29:24.457621: +2026-04-14 14:29:24.459982: Epoch 3321 +2026-04-14 14:29:24.461660: Current learning rate: 0.00203 +2026-04-14 14:31:06.420157: train_loss -0.4697 +2026-04-14 14:31:06.429946: val_loss -0.4194 +2026-04-14 14:31:06.433547: Pseudo dice [0.5683, 0.0, 0.8697, 0.8835, 0.6207, 0.8496, 0.9338] +2026-04-14 14:31:06.436213: Epoch time: 101.97 s +2026-04-14 14:31:07.677686: +2026-04-14 14:31:07.679827: Epoch 3322 +2026-04-14 14:31:07.681833: Current learning rate: 0.00202 +2026-04-14 14:32:49.448395: train_loss -0.4764 +2026-04-14 14:32:49.454918: val_loss -0.3677 +2026-04-14 14:32:49.457324: Pseudo dice [0.8132, 0.0, 0.6145, 0.058, 0.4579, 0.853, 0.898] +2026-04-14 14:32:49.459904: Epoch time: 101.77 s +2026-04-14 14:32:50.748886: +2026-04-14 14:32:50.751109: Epoch 3323 +2026-04-14 14:32:50.752627: Current learning rate: 0.00202 +2026-04-14 14:34:32.011576: train_loss -0.4624 +2026-04-14 14:34:32.017985: val_loss -0.3583 +2026-04-14 14:34:32.020022: Pseudo dice [0.4985, 0.0, 0.7853, 0.1677, 0.5693, 0.6804, 0.7368] +2026-04-14 14:34:32.022534: Epoch time: 101.27 s +2026-04-14 14:34:33.305666: +2026-04-14 14:34:33.308046: Epoch 3324 +2026-04-14 14:34:33.311168: Current learning rate: 0.00202 +2026-04-14 14:36:14.720408: train_loss -0.4634 +2026-04-14 14:36:14.727658: val_loss -0.3881 +2026-04-14 14:36:14.730082: Pseudo dice [0.7847, 0.0, 0.8016, 0.6845, 0.3676, 0.6931, 0.8651] +2026-04-14 14:36:14.732956: Epoch time: 101.42 s +2026-04-14 14:36:15.980235: +2026-04-14 14:36:15.983017: Epoch 3325 +2026-04-14 14:36:15.985433: Current learning rate: 0.00202 +2026-04-14 14:37:57.797353: train_loss -0.467 +2026-04-14 14:37:57.804237: val_loss -0.3832 +2026-04-14 14:37:57.806290: Pseudo dice [0.4899, 0.0, 0.7908, 0.8273, 0.4052, 0.4619, 0.747] +2026-04-14 14:37:57.809299: Epoch time: 101.82 s +2026-04-14 14:37:59.060578: +2026-04-14 14:37:59.062480: Epoch 3326 +2026-04-14 14:37:59.064161: Current learning rate: 0.00201 +2026-04-14 14:39:40.291889: train_loss -0.4643 +2026-04-14 14:39:40.298512: val_loss -0.4107 +2026-04-14 14:39:40.300955: Pseudo dice [0.657, 0.0, 0.8434, 0.8636, 0.513, 0.8445, 0.8868] +2026-04-14 14:39:40.304596: Epoch time: 101.23 s +2026-04-14 14:39:42.787353: +2026-04-14 14:39:42.789972: Epoch 3327 +2026-04-14 14:39:42.792627: Current learning rate: 0.00201 +2026-04-14 14:41:24.274827: train_loss -0.4648 +2026-04-14 14:41:24.280853: val_loss -0.3954 +2026-04-14 14:41:24.283026: Pseudo dice [0.8555, 0.0, 0.8712, 0.3903, 0.1505, 0.5489, 0.9059] +2026-04-14 14:41:24.285984: Epoch time: 101.49 s +2026-04-14 14:41:25.546477: +2026-04-14 14:41:25.549070: Epoch 3328 +2026-04-14 14:41:25.550876: Current learning rate: 0.00201 +2026-04-14 14:43:07.241894: train_loss -0.453 +2026-04-14 14:43:07.249145: val_loss -0.3788 +2026-04-14 14:43:07.251291: Pseudo dice [0.4738, 0.0, 0.7717, 0.2306, 0.479, 0.5901, 0.9174] +2026-04-14 14:43:07.254190: Epoch time: 101.7 s +2026-04-14 14:43:08.514461: +2026-04-14 14:43:08.516718: Epoch 3329 +2026-04-14 14:43:08.518451: Current learning rate: 0.00201 +2026-04-14 14:44:49.847157: train_loss -0.4453 +2026-04-14 14:44:49.854239: val_loss -0.3602 +2026-04-14 14:44:49.856200: Pseudo dice [0.5149, 0.0, 0.7926, 0.4568, 0.2479, 0.4592, 0.9087] +2026-04-14 14:44:49.858371: Epoch time: 101.34 s +2026-04-14 14:44:51.117817: +2026-04-14 14:44:51.119930: Epoch 3330 +2026-04-14 14:44:51.121592: Current learning rate: 0.002 +2026-04-14 14:46:33.152254: train_loss -0.4413 +2026-04-14 14:46:33.161361: val_loss -0.3926 +2026-04-14 14:46:33.164901: Pseudo dice [0.8116, 0.0, 0.7903, 0.1868, 0.5253, 0.8554, 0.7715] +2026-04-14 14:46:33.167906: Epoch time: 102.04 s +2026-04-14 14:46:34.442703: +2026-04-14 14:46:34.444620: Epoch 3331 +2026-04-14 14:46:34.446251: Current learning rate: 0.002 +2026-04-14 14:48:16.200484: train_loss -0.4524 +2026-04-14 14:48:16.209231: val_loss -0.3337 +2026-04-14 14:48:16.212183: Pseudo dice [0.6515, 0.0, 0.7192, 0.429, 0.3136, 0.6003, 0.8716] +2026-04-14 14:48:16.215182: Epoch time: 101.76 s +2026-04-14 14:48:17.491426: +2026-04-14 14:48:17.493390: Epoch 3332 +2026-04-14 14:48:17.495664: Current learning rate: 0.002 +2026-04-14 14:49:58.834427: train_loss -0.4449 +2026-04-14 14:49:58.846504: val_loss -0.3381 +2026-04-14 14:49:58.849278: Pseudo dice [0.8289, 0.0, 0.5522, 0.105, 0.336, 0.7893, 0.7532] +2026-04-14 14:49:58.852361: Epoch time: 101.35 s +2026-04-14 14:50:00.108392: +2026-04-14 14:50:00.110492: Epoch 3333 +2026-04-14 14:50:00.112180: Current learning rate: 0.00199 +2026-04-14 14:51:41.574859: train_loss -0.4555 +2026-04-14 14:51:41.585730: val_loss -0.3502 +2026-04-14 14:51:41.588140: Pseudo dice [0.5766, 0.0, 0.6668, 0.6021, 0.4521, 0.8482, 0.8449] +2026-04-14 14:51:41.595178: Epoch time: 101.47 s +2026-04-14 14:51:42.847819: +2026-04-14 14:51:42.849885: Epoch 3334 +2026-04-14 14:51:42.856846: Current learning rate: 0.00199 +2026-04-14 14:53:24.179522: train_loss -0.4567 +2026-04-14 14:53:24.188951: val_loss -0.3936 +2026-04-14 14:53:24.191278: Pseudo dice [0.5973, 0.0, 0.7586, 0.7576, 0.4717, 0.586, 0.8993] +2026-04-14 14:53:24.193974: Epoch time: 101.33 s +2026-04-14 14:53:25.468073: +2026-04-14 14:53:25.469719: Epoch 3335 +2026-04-14 14:53:25.471220: Current learning rate: 0.00199 +2026-04-14 14:55:06.849411: train_loss -0.4545 +2026-04-14 14:55:06.856282: val_loss -0.3704 +2026-04-14 14:55:06.858810: Pseudo dice [0.744, 0.0, 0.7047, 0.6434, 0.503, 0.5451, 0.9353] +2026-04-14 14:55:06.861716: Epoch time: 101.38 s +2026-04-14 14:55:08.137112: +2026-04-14 14:55:08.146622: Epoch 3336 +2026-04-14 14:55:08.148406: Current learning rate: 0.00199 +2026-04-14 14:56:49.575951: train_loss -0.4437 +2026-04-14 14:56:49.582295: val_loss -0.3869 +2026-04-14 14:56:49.584501: Pseudo dice [0.662, 0.0, 0.8813, 0.3284, 0.4302, 0.5497, 0.8691] +2026-04-14 14:56:49.587430: Epoch time: 101.44 s +2026-04-14 14:56:50.880679: +2026-04-14 14:56:50.882806: Epoch 3337 +2026-04-14 14:56:50.884624: Current learning rate: 0.00198 +2026-04-14 14:58:32.260414: train_loss -0.4622 +2026-04-14 14:58:32.266769: val_loss -0.3532 +2026-04-14 14:58:32.268936: Pseudo dice [0.8251, 0.0, 0.7957, 0.3243, 0.5098, 0.7465, 0.5608] +2026-04-14 14:58:32.271806: Epoch time: 101.38 s +2026-04-14 14:58:33.572868: +2026-04-14 14:58:33.574800: Epoch 3338 +2026-04-14 14:58:33.576384: Current learning rate: 0.00198 +2026-04-14 15:00:14.878546: train_loss -0.4671 +2026-04-14 15:00:14.885122: val_loss -0.4024 +2026-04-14 15:00:14.887611: Pseudo dice [0.6818, 0.0, 0.7989, 0.749, 0.3721, 0.5299, 0.9027] +2026-04-14 15:00:14.890755: Epoch time: 101.31 s +2026-04-14 15:00:16.168457: +2026-04-14 15:00:16.170669: Epoch 3339 +2026-04-14 15:00:16.172287: Current learning rate: 0.00198 +2026-04-14 15:01:58.304341: train_loss -0.4724 +2026-04-14 15:01:58.310633: val_loss -0.394 +2026-04-14 15:01:58.312684: Pseudo dice [0.8647, 0.0, 0.7905, 0.303, 0.4588, 0.6882, 0.8528] +2026-04-14 15:01:58.315593: Epoch time: 102.14 s +2026-04-14 15:01:59.708530: +2026-04-14 15:01:59.710190: Epoch 3340 +2026-04-14 15:01:59.711720: Current learning rate: 0.00198 +2026-04-14 15:03:41.001943: train_loss -0.4691 +2026-04-14 15:03:41.007638: val_loss -0.3417 +2026-04-14 15:03:41.011127: Pseudo dice [0.704, 0.0, 0.8434, 0.2832, 0.3526, 0.1406, 0.845] +2026-04-14 15:03:41.013272: Epoch time: 101.3 s +2026-04-14 15:03:42.278034: +2026-04-14 15:03:42.279786: Epoch 3341 +2026-04-14 15:03:42.281533: Current learning rate: 0.00197 +2026-04-14 15:05:23.596511: train_loss -0.4588 +2026-04-14 15:05:23.603750: val_loss -0.4125 +2026-04-14 15:05:23.606814: Pseudo dice [0.2622, 0.0, 0.8257, 0.8035, 0.4968, 0.6455, 0.8624] +2026-04-14 15:05:23.609433: Epoch time: 101.32 s +2026-04-14 15:05:24.907892: +2026-04-14 15:05:24.910143: Epoch 3342 +2026-04-14 15:05:24.911716: Current learning rate: 0.00197 +2026-04-14 15:07:06.169144: train_loss -0.4688 +2026-04-14 15:07:06.175692: val_loss -0.3668 +2026-04-14 15:07:06.177870: Pseudo dice [0.6189, 0.0, 0.8191, 0.0816, 0.3302, 0.7347, 0.9379] +2026-04-14 15:07:06.180274: Epoch time: 101.26 s +2026-04-14 15:07:07.447684: +2026-04-14 15:07:07.449619: Epoch 3343 +2026-04-14 15:07:07.451216: Current learning rate: 0.00197 +2026-04-14 15:08:48.970558: train_loss -0.4631 +2026-04-14 15:08:48.978920: val_loss -0.3707 +2026-04-14 15:08:48.982039: Pseudo dice [0.2553, 0.0, 0.8218, 0.8447, 0.5072, 0.8277, 0.9245] +2026-04-14 15:08:48.990736: Epoch time: 101.53 s +2026-04-14 15:08:50.292099: +2026-04-14 15:08:50.295409: Epoch 3344 +2026-04-14 15:08:50.298268: Current learning rate: 0.00196 +2026-04-14 15:10:32.184129: train_loss -0.4545 +2026-04-14 15:10:32.193080: val_loss -0.3714 +2026-04-14 15:10:32.195680: Pseudo dice [0.7155, 0.0, 0.878, 0.5336, 0.454, 0.5285, 0.8319] +2026-04-14 15:10:32.198897: Epoch time: 101.9 s +2026-04-14 15:10:33.501473: +2026-04-14 15:10:33.503879: Epoch 3345 +2026-04-14 15:10:33.505682: Current learning rate: 0.00196 +2026-04-14 15:12:15.211433: train_loss -0.4625 +2026-04-14 15:12:15.218455: val_loss -0.3781 +2026-04-14 15:12:15.220638: Pseudo dice [0.5619, 0.0, 0.7717, 0.5323, 0.1396, 0.6991, 0.931] +2026-04-14 15:12:15.222904: Epoch time: 101.71 s +2026-04-14 15:12:16.541591: +2026-04-14 15:12:16.543479: Epoch 3346 +2026-04-14 15:12:16.545161: Current learning rate: 0.00196 +2026-04-14 15:13:58.050257: train_loss -0.4599 +2026-04-14 15:13:58.056462: val_loss -0.4109 +2026-04-14 15:13:58.058655: Pseudo dice [0.8247, 0.0, 0.8611, 0.6009, 0.4912, 0.4819, 0.9372] +2026-04-14 15:13:58.061628: Epoch time: 101.51 s +2026-04-14 15:14:00.390512: +2026-04-14 15:14:00.392325: Epoch 3347 +2026-04-14 15:14:00.393895: Current learning rate: 0.00196 +2026-04-14 15:15:41.876953: train_loss -0.4582 +2026-04-14 15:15:41.889492: val_loss -0.3887 +2026-04-14 15:15:41.914307: Pseudo dice [0.843, 0.0, 0.873, 0.7052, 0.3826, 0.842, 0.727] +2026-04-14 15:15:41.917457: Epoch time: 101.49 s +2026-04-14 15:15:43.288726: +2026-04-14 15:15:43.291239: Epoch 3348 +2026-04-14 15:15:43.293361: Current learning rate: 0.00195 +2026-04-14 15:17:24.764219: train_loss -0.4684 +2026-04-14 15:17:24.770655: val_loss -0.3653 +2026-04-14 15:17:24.772952: Pseudo dice [0.569, 0.0, 0.8275, 0.5396, 0.3623, 0.4493, 0.8814] +2026-04-14 15:17:24.775745: Epoch time: 101.48 s +2026-04-14 15:17:26.042392: +2026-04-14 15:17:26.044291: Epoch 3349 +2026-04-14 15:17:26.046144: Current learning rate: 0.00195 +2026-04-14 15:19:07.461927: train_loss -0.4613 +2026-04-14 15:19:07.470106: val_loss -0.3967 +2026-04-14 15:19:07.473203: Pseudo dice [0.4912, 0.0, 0.7996, 0.444, 0.4187, 0.6724, 0.9304] +2026-04-14 15:19:07.475796: Epoch time: 101.42 s +2026-04-14 15:19:10.648844: +2026-04-14 15:19:10.654014: Epoch 3350 +2026-04-14 15:19:10.657140: Current learning rate: 0.00195 +2026-04-14 15:20:51.950735: train_loss -0.4607 +2026-04-14 15:20:51.957273: val_loss -0.3876 +2026-04-14 15:20:51.960193: Pseudo dice [0.4362, 0.0, 0.8726, 0.6354, 0.3778, 0.7379, 0.9207] +2026-04-14 15:20:51.963239: Epoch time: 101.3 s +2026-04-14 15:20:53.246437: +2026-04-14 15:20:53.249025: Epoch 3351 +2026-04-14 15:20:53.250822: Current learning rate: 0.00195 +2026-04-14 15:22:35.142223: train_loss -0.4716 +2026-04-14 15:22:35.150122: val_loss -0.3933 +2026-04-14 15:22:35.153993: Pseudo dice [0.6535, 0.0, 0.8068, 0.3784, 0.5342, 0.6975, 0.8685] +2026-04-14 15:22:35.157865: Epoch time: 101.9 s +2026-04-14 15:22:36.434780: +2026-04-14 15:22:36.436653: Epoch 3352 +2026-04-14 15:22:36.438722: Current learning rate: 0.00194 +2026-04-14 15:24:18.658121: train_loss -0.4619 +2026-04-14 15:24:18.664682: val_loss -0.342 +2026-04-14 15:24:18.666883: Pseudo dice [0.654, 0.0, 0.734, 0.3353, 0.4584, 0.6983, 0.6807] +2026-04-14 15:24:18.670095: Epoch time: 102.23 s +2026-04-14 15:24:19.953188: +2026-04-14 15:24:19.954842: Epoch 3353 +2026-04-14 15:24:19.956248: Current learning rate: 0.00194 +2026-04-14 15:26:01.347445: train_loss -0.4664 +2026-04-14 15:26:01.354402: val_loss -0.4001 +2026-04-14 15:26:01.356710: Pseudo dice [0.5296, 0.0, 0.8788, 0.5901, 0.6177, 0.494, 0.9225] +2026-04-14 15:26:01.358777: Epoch time: 101.4 s +2026-04-14 15:26:02.622205: +2026-04-14 15:26:02.624116: Epoch 3354 +2026-04-14 15:26:02.626131: Current learning rate: 0.00194 +2026-04-14 15:27:44.037853: train_loss -0.4568 +2026-04-14 15:27:44.044863: val_loss -0.3873 +2026-04-14 15:27:44.047600: Pseudo dice [0.8319, 0.0, 0.659, 0.4769, 0.4745, 0.621, 0.9024] +2026-04-14 15:27:44.050265: Epoch time: 101.42 s +2026-04-14 15:27:45.323096: +2026-04-14 15:27:45.324830: Epoch 3355 +2026-04-14 15:27:45.326497: Current learning rate: 0.00194 +2026-04-14 15:29:26.743943: train_loss -0.4579 +2026-04-14 15:29:26.751381: val_loss -0.4058 +2026-04-14 15:29:26.753689: Pseudo dice [0.6641, 0.0, 0.858, 0.4952, 0.4607, 0.6643, 0.6427] +2026-04-14 15:29:26.756718: Epoch time: 101.42 s +2026-04-14 15:29:28.031633: +2026-04-14 15:29:28.033406: Epoch 3356 +2026-04-14 15:29:28.034990: Current learning rate: 0.00193 +2026-04-14 15:31:09.739714: train_loss -0.4726 +2026-04-14 15:31:09.746727: val_loss -0.3829 +2026-04-14 15:31:09.748953: Pseudo dice [0.7328, 0.0, 0.764, 0.6425, 0.6319, 0.6733, 0.8612] +2026-04-14 15:31:09.751363: Epoch time: 101.71 s +2026-04-14 15:31:11.003884: +2026-04-14 15:31:11.005684: Epoch 3357 +2026-04-14 15:31:11.007237: Current learning rate: 0.00193 +2026-04-14 15:32:52.084315: train_loss -0.4468 +2026-04-14 15:32:52.092807: val_loss -0.4185 +2026-04-14 15:32:52.095475: Pseudo dice [0.7987, 0.0, 0.7531, 0.7274, 0.5895, 0.7358, 0.8718] +2026-04-14 15:32:52.098543: Epoch time: 101.08 s +2026-04-14 15:32:53.388468: +2026-04-14 15:32:53.390563: Epoch 3358 +2026-04-14 15:32:53.392571: Current learning rate: 0.00193 +2026-04-14 15:34:34.719370: train_loss -0.4666 +2026-04-14 15:34:34.727123: val_loss -0.3917 +2026-04-14 15:34:34.729298: Pseudo dice [0.2982, 0.0, 0.8163, 0.8557, 0.4252, 0.7638, 0.923] +2026-04-14 15:34:34.731941: Epoch time: 101.33 s +2026-04-14 15:34:36.075611: +2026-04-14 15:34:36.077691: Epoch 3359 +2026-04-14 15:34:36.079426: Current learning rate: 0.00192 +2026-04-14 15:36:17.643940: train_loss -0.4713 +2026-04-14 15:36:17.649864: val_loss -0.3928 +2026-04-14 15:36:17.651979: Pseudo dice [0.7986, 0.0, 0.6663, 0.1539, 0.4971, 0.8437, 0.8294] +2026-04-14 15:36:17.654198: Epoch time: 101.57 s +2026-04-14 15:36:18.917405: +2026-04-14 15:36:18.919524: Epoch 3360 +2026-04-14 15:36:18.921160: Current learning rate: 0.00192 +2026-04-14 15:38:00.426363: train_loss -0.4705 +2026-04-14 15:38:00.452952: val_loss -0.3637 +2026-04-14 15:38:00.455003: Pseudo dice [0.5951, 0.0, 0.722, 0.115, 0.5216, 0.9035, 0.7805] +2026-04-14 15:38:00.458332: Epoch time: 101.51 s +2026-04-14 15:38:01.746585: +2026-04-14 15:38:01.748266: Epoch 3361 +2026-04-14 15:38:01.749969: Current learning rate: 0.00192 +2026-04-14 15:39:43.382010: train_loss -0.4694 +2026-04-14 15:39:43.388674: val_loss -0.4001 +2026-04-14 15:39:43.391894: Pseudo dice [0.4865, 0.0, 0.8929, 0.8399, 0.4936, 0.4177, 0.9405] +2026-04-14 15:39:43.394188: Epoch time: 101.64 s +2026-04-14 15:39:44.671848: +2026-04-14 15:39:44.673820: Epoch 3362 +2026-04-14 15:39:44.675421: Current learning rate: 0.00192 +2026-04-14 15:41:26.305097: train_loss -0.4676 +2026-04-14 15:41:26.311765: val_loss -0.3916 +2026-04-14 15:41:26.313729: Pseudo dice [0.7136, 0.0, 0.8863, 0.5549, 0.5897, 0.6912, 0.8373] +2026-04-14 15:41:26.316349: Epoch time: 101.64 s +2026-04-14 15:41:27.645460: +2026-04-14 15:41:27.647198: Epoch 3363 +2026-04-14 15:41:27.648755: Current learning rate: 0.00191 +2026-04-14 15:43:09.480339: train_loss -0.4469 +2026-04-14 15:43:09.487454: val_loss -0.3922 +2026-04-14 15:43:09.489548: Pseudo dice [0.7486, 0.0, 0.6753, 0.5155, 0.5684, 0.4367, 0.8447] +2026-04-14 15:43:09.492761: Epoch time: 101.84 s +2026-04-14 15:43:10.777145: +2026-04-14 15:43:10.778934: Epoch 3364 +2026-04-14 15:43:10.781280: Current learning rate: 0.00191 +2026-04-14 15:44:52.117548: train_loss -0.4566 +2026-04-14 15:44:52.124859: val_loss -0.3488 +2026-04-14 15:44:52.127098: Pseudo dice [0.6677, 0.0, 0.7059, 0.3821, 0.3313, 0.2944, 0.8743] +2026-04-14 15:44:52.129698: Epoch time: 101.34 s +2026-04-14 15:44:53.403486: +2026-04-14 15:44:53.405186: Epoch 3365 +2026-04-14 15:44:53.406696: Current learning rate: 0.00191 +2026-04-14 15:46:35.080020: train_loss -0.4516 +2026-04-14 15:46:35.087501: val_loss -0.4165 +2026-04-14 15:46:35.090078: Pseudo dice [0.7635, 0.0, 0.8309, 0.007, 0.6053, 0.6172, 0.8504] +2026-04-14 15:46:35.094157: Epoch time: 101.68 s +2026-04-14 15:46:37.651852: +2026-04-14 15:46:37.653826: Epoch 3366 +2026-04-14 15:46:37.655524: Current learning rate: 0.00191 +2026-04-14 15:48:19.182734: train_loss -0.4702 +2026-04-14 15:48:19.194464: val_loss -0.4172 +2026-04-14 15:48:19.199990: Pseudo dice [0.5792, 0.0, 0.8153, 0.6949, 0.5911, 0.9161, 0.9232] +2026-04-14 15:48:19.204722: Epoch time: 101.53 s +2026-04-14 15:48:20.565323: +2026-04-14 15:48:20.567328: Epoch 3367 +2026-04-14 15:48:20.569751: Current learning rate: 0.0019 +2026-04-14 15:50:02.394455: train_loss -0.4696 +2026-04-14 15:50:02.402587: val_loss -0.4119 +2026-04-14 15:50:02.404904: Pseudo dice [0.4618, 0.0, 0.6901, 0.886, 0.5504, 0.7353, 0.8212] +2026-04-14 15:50:02.407645: Epoch time: 101.83 s +2026-04-14 15:50:03.699489: +2026-04-14 15:50:03.701515: Epoch 3368 +2026-04-14 15:50:03.703330: Current learning rate: 0.0019 +2026-04-14 15:51:45.072975: train_loss -0.4602 +2026-04-14 15:51:45.081074: val_loss -0.4132 +2026-04-14 15:51:45.083376: Pseudo dice [0.4144, 0.0, 0.8339, 0.6673, 0.5073, 0.8329, 0.9486] +2026-04-14 15:51:45.086309: Epoch time: 101.38 s +2026-04-14 15:51:46.366768: +2026-04-14 15:51:46.368670: Epoch 3369 +2026-04-14 15:51:46.370798: Current learning rate: 0.0019 +2026-04-14 15:53:27.872502: train_loss -0.4756 +2026-04-14 15:53:27.878944: val_loss -0.3724 +2026-04-14 15:53:27.880923: Pseudo dice [0.5552, 0.0, 0.6869, 0.853, 0.4677, 0.7493, 0.845] +2026-04-14 15:53:27.883321: Epoch time: 101.51 s +2026-04-14 15:53:29.192489: +2026-04-14 15:53:29.194253: Epoch 3370 +2026-04-14 15:53:29.195765: Current learning rate: 0.00189 +2026-04-14 15:55:10.453640: train_loss -0.4545 +2026-04-14 15:55:10.460166: val_loss -0.4103 +2026-04-14 15:55:10.462186: Pseudo dice [0.7693, 0.0, 0.8743, 0.8489, 0.4635, 0.7963, 0.9303] +2026-04-14 15:55:10.464728: Epoch time: 101.26 s +2026-04-14 15:55:11.740880: +2026-04-14 15:55:11.742843: Epoch 3371 +2026-04-14 15:55:11.744406: Current learning rate: 0.00189 +2026-04-14 15:56:53.420195: train_loss -0.4667 +2026-04-14 15:56:53.426423: val_loss -0.3431 +2026-04-14 15:56:53.428353: Pseudo dice [0.77, 0.0, 0.7872, 0.2996, 0.3463, 0.2013, 0.7451] +2026-04-14 15:56:53.431043: Epoch time: 101.68 s +2026-04-14 15:56:54.703049: +2026-04-14 15:56:54.705118: Epoch 3372 +2026-04-14 15:56:54.706779: Current learning rate: 0.00189 +2026-04-14 15:58:35.969671: train_loss -0.4592 +2026-04-14 15:58:35.976807: val_loss -0.4007 +2026-04-14 15:58:35.979983: Pseudo dice [0.7348, 0.0, 0.8625, 0.6956, 0.4855, 0.8267, 0.922] +2026-04-14 15:58:35.982917: Epoch time: 101.27 s +2026-04-14 15:58:37.247777: +2026-04-14 15:58:37.249659: Epoch 3373 +2026-04-14 15:58:37.251690: Current learning rate: 0.00189 +2026-04-14 16:00:18.463566: train_loss -0.4748 +2026-04-14 16:00:18.480064: val_loss -0.4008 +2026-04-14 16:00:18.485762: Pseudo dice [0.6358, 0.0, 0.7988, 0.5316, 0.5411, 0.6079, 0.9244] +2026-04-14 16:00:18.488315: Epoch time: 101.22 s +2026-04-14 16:00:19.775850: +2026-04-14 16:00:19.777452: Epoch 3374 +2026-04-14 16:00:19.778947: Current learning rate: 0.00188 +2026-04-14 16:02:01.013182: train_loss -0.4504 +2026-04-14 16:02:01.020177: val_loss -0.3754 +2026-04-14 16:02:01.022554: Pseudo dice [0.7465, 0.0, 0.809, 0.7262, 0.5092, 0.7234, 0.767] +2026-04-14 16:02:01.025056: Epoch time: 101.24 s +2026-04-14 16:02:02.315141: +2026-04-14 16:02:02.316998: Epoch 3375 +2026-04-14 16:02:02.318651: Current learning rate: 0.00188 +2026-04-14 16:03:43.789580: train_loss -0.4538 +2026-04-14 16:03:43.797505: val_loss -0.3789 +2026-04-14 16:03:43.800104: Pseudo dice [0.5242, 0.0, 0.7433, 0.4712, 0.6338, 0.4007, 0.8552] +2026-04-14 16:03:43.802753: Epoch time: 101.48 s +2026-04-14 16:03:45.054014: +2026-04-14 16:03:45.056193: Epoch 3376 +2026-04-14 16:03:45.057997: Current learning rate: 0.00188 +2026-04-14 16:05:26.455290: train_loss -0.4562 +2026-04-14 16:05:26.464737: val_loss -0.4061 +2026-04-14 16:05:26.466771: Pseudo dice [0.7674, 0.0, 0.8451, 0.5553, 0.55, 0.7844, 0.9092] +2026-04-14 16:05:26.469232: Epoch time: 101.4 s +2026-04-14 16:05:27.747897: +2026-04-14 16:05:27.749934: Epoch 3377 +2026-04-14 16:05:27.751498: Current learning rate: 0.00188 +2026-04-14 16:07:09.503780: train_loss -0.4503 +2026-04-14 16:07:09.511011: val_loss -0.3702 +2026-04-14 16:07:09.513519: Pseudo dice [0.3414, 0.0, 0.7834, 0.703, 0.5537, 0.5163, 0.6201] +2026-04-14 16:07:09.516194: Epoch time: 101.76 s +2026-04-14 16:07:10.807220: +2026-04-14 16:07:10.809642: Epoch 3378 +2026-04-14 16:07:10.811342: Current learning rate: 0.00187 +2026-04-14 16:08:52.157508: train_loss -0.4465 +2026-04-14 16:08:52.164428: val_loss -0.3447 +2026-04-14 16:08:52.167005: Pseudo dice [0.7389, 0.0, 0.6188, 0.633, 0.4798, 0.3211, 0.9084] +2026-04-14 16:08:52.169304: Epoch time: 101.35 s +2026-04-14 16:08:53.425335: +2026-04-14 16:08:53.427815: Epoch 3379 +2026-04-14 16:08:53.429488: Current learning rate: 0.00187 +2026-04-14 16:10:34.688537: train_loss -0.4537 +2026-04-14 16:10:34.703276: val_loss -0.3482 +2026-04-14 16:10:34.705255: Pseudo dice [0.7698, 0.0, 0.8311, 0.222, 0.4913, 0.3277, 0.3774] +2026-04-14 16:10:34.707564: Epoch time: 101.27 s +2026-04-14 16:10:35.987029: +2026-04-14 16:10:35.988676: Epoch 3380 +2026-04-14 16:10:35.990254: Current learning rate: 0.00187 +2026-04-14 16:12:17.339244: train_loss -0.4463 +2026-04-14 16:12:17.346255: val_loss -0.3995 +2026-04-14 16:12:17.348230: Pseudo dice [0.0944, 0.0, 0.8066, 0.858, 0.5693, 0.5611, 0.9413] +2026-04-14 16:12:17.350277: Epoch time: 101.36 s +2026-04-14 16:12:18.621448: +2026-04-14 16:12:18.623136: Epoch 3381 +2026-04-14 16:12:18.624988: Current learning rate: 0.00186 +2026-04-14 16:14:00.099825: train_loss -0.4528 +2026-04-14 16:14:00.106897: val_loss -0.4159 +2026-04-14 16:14:00.109636: Pseudo dice [0.7293, 0.0, 0.8095, 0.6546, 0.5306, 0.8003, 0.9262] +2026-04-14 16:14:00.112155: Epoch time: 101.48 s +2026-04-14 16:14:01.375658: +2026-04-14 16:14:01.377383: Epoch 3382 +2026-04-14 16:14:01.379210: Current learning rate: 0.00186 +2026-04-14 16:15:42.975647: train_loss -0.4597 +2026-04-14 16:15:42.981496: val_loss -0.4122 +2026-04-14 16:15:42.983780: Pseudo dice [0.6553, 0.0, 0.8762, 0.9124, 0.5458, 0.8678, 0.9277] +2026-04-14 16:15:42.986566: Epoch time: 101.6 s +2026-04-14 16:15:44.258169: +2026-04-14 16:15:44.260175: Epoch 3383 +2026-04-14 16:15:44.262266: Current learning rate: 0.00186 +2026-04-14 16:17:25.805508: train_loss -0.4653 +2026-04-14 16:17:25.821015: val_loss -0.3937 +2026-04-14 16:17:25.823754: Pseudo dice [0.6373, 0.0, 0.8728, 0.6954, 0.583, 0.7494, 0.9431] +2026-04-14 16:17:25.831151: Epoch time: 101.55 s +2026-04-14 16:17:27.118701: +2026-04-14 16:17:27.120786: Epoch 3384 +2026-04-14 16:17:27.122454: Current learning rate: 0.00186 +2026-04-14 16:19:09.099345: train_loss -0.4767 +2026-04-14 16:19:09.106125: val_loss -0.3888 +2026-04-14 16:19:09.108286: Pseudo dice [0.625, 0.0, 0.8285, 0.2138, 0.4163, 0.6714, 0.9282] +2026-04-14 16:19:09.114230: Epoch time: 101.98 s +2026-04-14 16:19:10.464134: +2026-04-14 16:19:10.467239: Epoch 3385 +2026-04-14 16:19:10.471094: Current learning rate: 0.00185 +2026-04-14 16:20:52.116512: train_loss -0.4693 +2026-04-14 16:20:52.123827: val_loss -0.3944 +2026-04-14 16:20:52.126897: Pseudo dice [0.5977, 0.0, 0.8046, 0.8685, 0.6406, 0.8035, 0.8552] +2026-04-14 16:20:52.129140: Epoch time: 101.66 s +2026-04-14 16:20:54.540852: +2026-04-14 16:20:54.543078: Epoch 3386 +2026-04-14 16:20:54.544627: Current learning rate: 0.00185 +2026-04-14 16:22:36.201102: train_loss -0.4554 +2026-04-14 16:22:36.207897: val_loss -0.3794 +2026-04-14 16:22:36.210175: Pseudo dice [0.6058, 0.0, 0.8393, 0.3726, 0.3626, 0.7011, 0.9142] +2026-04-14 16:22:36.216436: Epoch time: 101.66 s +2026-04-14 16:22:37.522290: +2026-04-14 16:22:37.524316: Epoch 3387 +2026-04-14 16:22:37.526877: Current learning rate: 0.00185 +2026-04-14 16:24:19.097726: train_loss -0.4549 +2026-04-14 16:24:19.104250: val_loss -0.4018 +2026-04-14 16:24:19.107127: Pseudo dice [0.8649, 0.0, 0.7492, 0.6045, 0.5258, 0.8035, 0.9163] +2026-04-14 16:24:19.109568: Epoch time: 101.58 s +2026-04-14 16:24:20.405161: +2026-04-14 16:24:20.407064: Epoch 3388 +2026-04-14 16:24:20.409025: Current learning rate: 0.00185 +2026-04-14 16:26:01.976007: train_loss -0.4603 +2026-04-14 16:26:01.982339: val_loss -0.3799 +2026-04-14 16:26:01.984465: Pseudo dice [0.6512, 0.0, 0.9038, 0.848, 0.4313, 0.5747, 0.7776] +2026-04-14 16:26:01.987027: Epoch time: 101.57 s +2026-04-14 16:26:03.309497: +2026-04-14 16:26:03.311790: Epoch 3389 +2026-04-14 16:26:03.313337: Current learning rate: 0.00184 +2026-04-14 16:27:45.645267: train_loss -0.4667 +2026-04-14 16:27:45.652098: val_loss -0.4015 +2026-04-14 16:27:45.654356: Pseudo dice [0.5548, 0.0, 0.8046, 0.8198, 0.529, 0.7432, 0.9031] +2026-04-14 16:27:45.656693: Epoch time: 102.34 s +2026-04-14 16:27:46.937740: +2026-04-14 16:27:46.939548: Epoch 3390 +2026-04-14 16:27:46.942646: Current learning rate: 0.00184 +2026-04-14 16:29:28.805108: train_loss -0.4526 +2026-04-14 16:29:28.811257: val_loss -0.3733 +2026-04-14 16:29:28.813281: Pseudo dice [0.6074, 0.0, 0.8213, 0.709, 0.4908, 0.5237, 0.9335] +2026-04-14 16:29:28.816175: Epoch time: 101.87 s +2026-04-14 16:29:30.115268: +2026-04-14 16:29:30.117052: Epoch 3391 +2026-04-14 16:29:30.118860: Current learning rate: 0.00184 +2026-04-14 16:31:11.860204: train_loss -0.4561 +2026-04-14 16:31:11.865910: val_loss -0.3477 +2026-04-14 16:31:11.868058: Pseudo dice [0.711, 0.0, 0.6196, 0.3033, 0.4349, 0.4094, 0.7571] +2026-04-14 16:31:11.870348: Epoch time: 101.75 s +2026-04-14 16:31:13.124333: +2026-04-14 16:31:13.126512: Epoch 3392 +2026-04-14 16:31:13.128682: Current learning rate: 0.00184 +2026-04-14 16:32:54.973002: train_loss -0.4753 +2026-04-14 16:32:54.979902: val_loss -0.3655 +2026-04-14 16:32:54.982542: Pseudo dice [0.746, 0.0, 0.6661, 0.3974, 0.472, 0.4877, 0.7868] +2026-04-14 16:32:54.985135: Epoch time: 101.85 s +2026-04-14 16:32:56.281733: +2026-04-14 16:32:56.283708: Epoch 3393 +2026-04-14 16:32:56.285792: Current learning rate: 0.00183 +2026-04-14 16:34:37.932209: train_loss -0.4686 +2026-04-14 16:34:37.939466: val_loss -0.3668 +2026-04-14 16:34:37.941450: Pseudo dice [0.3947, 0.0, 0.8364, 0.8588, 0.397, 0.5511, 0.9213] +2026-04-14 16:34:37.943723: Epoch time: 101.65 s +2026-04-14 16:34:39.208960: +2026-04-14 16:34:39.210803: Epoch 3394 +2026-04-14 16:34:39.212532: Current learning rate: 0.00183 +2026-04-14 16:36:20.570879: train_loss -0.4533 +2026-04-14 16:36:20.577638: val_loss -0.4018 +2026-04-14 16:36:20.581862: Pseudo dice [0.779, 0.0, 0.9179, 0.9265, 0.3075, 0.7307, 0.3302] +2026-04-14 16:36:20.584639: Epoch time: 101.36 s +2026-04-14 16:36:21.899801: +2026-04-14 16:36:21.901633: Epoch 3395 +2026-04-14 16:36:21.903345: Current learning rate: 0.00183 +2026-04-14 16:38:03.997112: train_loss -0.4571 +2026-04-14 16:38:04.004113: val_loss -0.3653 +2026-04-14 16:38:04.006299: Pseudo dice [0.7082, 0.0, 0.8323, 0.7424, 0.454, 0.397, 0.7315] +2026-04-14 16:38:04.009362: Epoch time: 102.1 s +2026-04-14 16:38:05.312666: +2026-04-14 16:38:05.314761: Epoch 3396 +2026-04-14 16:38:05.316512: Current learning rate: 0.00182 +2026-04-14 16:39:46.762637: train_loss -0.4661 +2026-04-14 16:39:46.770179: val_loss -0.377 +2026-04-14 16:39:46.773943: Pseudo dice [0.7136, 0.0, 0.6714, 0.3556, 0.4577, 0.8798, 0.9263] +2026-04-14 16:39:46.776887: Epoch time: 101.45 s +2026-04-14 16:39:48.036469: +2026-04-14 16:39:48.038158: Epoch 3397 +2026-04-14 16:39:48.039902: Current learning rate: 0.00182 +2026-04-14 16:41:29.635176: train_loss -0.4659 +2026-04-14 16:41:29.640942: val_loss -0.3718 +2026-04-14 16:41:29.643104: Pseudo dice [0.4579, 0.0, 0.6456, 0.3718, 0.6049, 0.5997, 0.872] +2026-04-14 16:41:29.645300: Epoch time: 101.6 s +2026-04-14 16:41:30.907583: +2026-04-14 16:41:30.909470: Epoch 3398 +2026-04-14 16:41:30.911085: Current learning rate: 0.00182 +2026-04-14 16:43:12.477099: train_loss -0.4675 +2026-04-14 16:43:12.489702: val_loss -0.3873 +2026-04-14 16:43:12.492504: Pseudo dice [0.6453, 0.0, 0.8327, 0.6749, 0.6547, 0.4959, 0.8428] +2026-04-14 16:43:12.495781: Epoch time: 101.57 s +2026-04-14 16:43:13.793123: +2026-04-14 16:43:13.796471: Epoch 3399 +2026-04-14 16:43:13.798322: Current learning rate: 0.00182 +2026-04-14 16:44:55.392915: train_loss -0.4788 +2026-04-14 16:44:55.400688: val_loss -0.4152 +2026-04-14 16:44:55.403855: Pseudo dice [0.7508, 0.0, 0.6557, 0.521, 0.5744, 0.8895, 0.869] +2026-04-14 16:44:55.408271: Epoch time: 101.6 s +2026-04-14 16:44:58.545437: +2026-04-14 16:44:58.547440: Epoch 3400 +2026-04-14 16:44:58.549041: Current learning rate: 0.00181 +2026-04-14 16:46:40.005418: train_loss -0.458 +2026-04-14 16:46:40.011378: val_loss -0.4059 +2026-04-14 16:46:40.014747: Pseudo dice [0.7349, 0.0, 0.8537, 0.407, 0.4281, 0.7945, 0.9431] +2026-04-14 16:46:40.017293: Epoch time: 101.46 s +2026-04-14 16:46:41.303824: +2026-04-14 16:46:41.305770: Epoch 3401 +2026-04-14 16:46:41.307991: Current learning rate: 0.00181 +2026-04-14 16:48:22.703093: train_loss -0.4604 +2026-04-14 16:48:22.709301: val_loss -0.3922 +2026-04-14 16:48:22.711099: Pseudo dice [0.7464, 0.0, 0.8181, 0.5481, 0.5162, 0.7499, 0.903] +2026-04-14 16:48:22.713478: Epoch time: 101.4 s +2026-04-14 16:48:23.946096: +2026-04-14 16:48:23.948630: Epoch 3402 +2026-04-14 16:48:23.950246: Current learning rate: 0.00181 +2026-04-14 16:50:05.547859: train_loss -0.4638 +2026-04-14 16:50:05.554383: val_loss -0.3867 +2026-04-14 16:50:05.556674: Pseudo dice [0.6886, 0.0, 0.8254, 0.6415, 0.1269, 0.6619, 0.818] +2026-04-14 16:50:05.560032: Epoch time: 101.61 s +2026-04-14 16:50:06.817159: +2026-04-14 16:50:06.821656: Epoch 3403 +2026-04-14 16:50:06.827141: Current learning rate: 0.00181 +2026-04-14 16:51:48.678216: train_loss -0.4529 +2026-04-14 16:51:48.685084: val_loss -0.3647 +2026-04-14 16:51:48.687885: Pseudo dice [0.784, 0.0, 0.4837, 0.1766, 0.4889, 0.5159, 0.8393] +2026-04-14 16:51:48.690502: Epoch time: 101.86 s +2026-04-14 16:51:49.971807: +2026-04-14 16:51:49.973546: Epoch 3404 +2026-04-14 16:51:49.975398: Current learning rate: 0.0018 +2026-04-14 16:53:31.624985: train_loss -0.4615 +2026-04-14 16:53:31.631587: val_loss -0.4068 +2026-04-14 16:53:31.633908: Pseudo dice [0.8307, 0.0, 0.6727, 0.8473, 0.4804, 0.4597, 0.9384] +2026-04-14 16:53:31.636435: Epoch time: 101.66 s +2026-04-14 16:53:34.022016: +2026-04-14 16:53:34.024221: Epoch 3405 +2026-04-14 16:53:34.025776: Current learning rate: 0.0018 +2026-04-14 16:55:15.705862: train_loss -0.4636 +2026-04-14 16:55:15.712547: val_loss -0.4125 +2026-04-14 16:55:15.714578: Pseudo dice [0.8238, 0.0, 0.856, 0.9157, 0.6215, 0.6708, 0.9378] +2026-04-14 16:55:15.716922: Epoch time: 101.69 s +2026-04-14 16:55:17.004377: +2026-04-14 16:55:17.006129: Epoch 3406 +2026-04-14 16:55:17.007736: Current learning rate: 0.0018 +2026-04-14 16:56:58.433017: train_loss -0.4698 +2026-04-14 16:56:58.441367: val_loss -0.3541 +2026-04-14 16:56:58.443594: Pseudo dice [0.6882, 0.0, 0.7966, 0.0662, 0.5696, 0.7942, 0.9458] +2026-04-14 16:56:58.446153: Epoch time: 101.43 s +2026-04-14 16:56:59.742020: +2026-04-14 16:56:59.744894: Epoch 3407 +2026-04-14 16:56:59.747309: Current learning rate: 0.00179 +2026-04-14 16:58:41.063337: train_loss -0.4558 +2026-04-14 16:58:41.071251: val_loss -0.3754 +2026-04-14 16:58:41.073378: Pseudo dice [0.7725, 0.0, 0.6634, 0.231, 0.3531, 0.8995, 0.9211] +2026-04-14 16:58:41.076894: Epoch time: 101.32 s +2026-04-14 16:58:42.385753: +2026-04-14 16:58:42.387826: Epoch 3408 +2026-04-14 16:58:42.389500: Current learning rate: 0.00179 +2026-04-14 17:00:23.624444: train_loss -0.4637 +2026-04-14 17:00:23.638877: val_loss -0.3874 +2026-04-14 17:00:23.640856: Pseudo dice [0.7325, 0.0, 0.8043, 0.5185, 0.1884, 0.6253, 0.7775] +2026-04-14 17:00:23.643906: Epoch time: 101.24 s +2026-04-14 17:00:25.014418: +2026-04-14 17:00:25.016276: Epoch 3409 +2026-04-14 17:00:25.018012: Current learning rate: 0.00179 +2026-04-14 17:02:06.396294: train_loss -0.4652 +2026-04-14 17:02:06.402769: val_loss -0.3769 +2026-04-14 17:02:06.405178: Pseudo dice [0.6681, 0.0, 0.8519, 0.5796, 0.3837, 0.4965, 0.9018] +2026-04-14 17:02:06.408102: Epoch time: 101.38 s +2026-04-14 17:02:07.676634: +2026-04-14 17:02:07.679469: Epoch 3410 +2026-04-14 17:02:07.682430: Current learning rate: 0.00179 +2026-04-14 17:03:49.691755: train_loss -0.4717 +2026-04-14 17:03:49.697741: val_loss -0.3798 +2026-04-14 17:03:49.700525: Pseudo dice [0.7925, 0.0, 0.7827, 0.374, 0.3855, 0.7876, 0.9061] +2026-04-14 17:03:49.703676: Epoch time: 102.02 s +2026-04-14 17:03:50.989404: +2026-04-14 17:03:50.999105: Epoch 3411 +2026-04-14 17:03:51.001639: Current learning rate: 0.00178 +2026-04-14 17:05:32.346992: train_loss -0.4733 +2026-04-14 17:05:32.353053: val_loss -0.4014 +2026-04-14 17:05:32.355123: Pseudo dice [0.6853, 0.0, 0.6867, 0.6625, 0.4956, 0.3851, 0.9104] +2026-04-14 17:05:32.357589: Epoch time: 101.36 s +2026-04-14 17:05:33.601822: +2026-04-14 17:05:33.603787: Epoch 3412 +2026-04-14 17:05:33.605706: Current learning rate: 0.00178 +2026-04-14 17:07:15.172103: train_loss -0.4656 +2026-04-14 17:07:15.185740: val_loss -0.3966 +2026-04-14 17:07:15.187385: Pseudo dice [0.5788, 0.0, 0.8459, 0.8621, 0.4318, 0.7793, 0.9163] +2026-04-14 17:07:15.193147: Epoch time: 101.57 s +2026-04-14 17:07:16.524365: +2026-04-14 17:07:16.526606: Epoch 3413 +2026-04-14 17:07:16.529044: Current learning rate: 0.00178 +2026-04-14 17:08:58.341469: train_loss -0.4533 +2026-04-14 17:08:58.348666: val_loss -0.3525 +2026-04-14 17:08:58.354261: Pseudo dice [0.6302, 0.0, 0.6496, 0.1531, 0.6596, 0.6067, 0.7521] +2026-04-14 17:08:58.358582: Epoch time: 101.82 s +2026-04-14 17:08:59.651836: +2026-04-14 17:08:59.653931: Epoch 3414 +2026-04-14 17:08:59.656289: Current learning rate: 0.00178 +2026-04-14 17:10:41.804230: train_loss -0.4656 +2026-04-14 17:10:41.813215: val_loss -0.3887 +2026-04-14 17:10:41.815407: Pseudo dice [0.3999, 0.0, 0.8267, 0.2171, 0.5122, 0.6384, 0.8362] +2026-04-14 17:10:41.818586: Epoch time: 102.16 s +2026-04-14 17:10:43.253104: +2026-04-14 17:10:43.255220: Epoch 3415 +2026-04-14 17:10:43.257132: Current learning rate: 0.00177 +2026-04-14 17:12:25.194452: train_loss -0.4546 +2026-04-14 17:12:25.200687: val_loss -0.3959 +2026-04-14 17:12:25.202930: Pseudo dice [0.6928, 0.0, 0.8512, 0.7433, 0.52, 0.8351, 0.9319] +2026-04-14 17:12:25.205656: Epoch time: 101.94 s +2026-04-14 17:12:26.587635: +2026-04-14 17:12:26.589543: Epoch 3416 +2026-04-14 17:12:26.591300: Current learning rate: 0.00177 +2026-04-14 17:14:08.453305: train_loss -0.4626 +2026-04-14 17:14:08.461873: val_loss -0.4134 +2026-04-14 17:14:08.465988: Pseudo dice [0.7489, 0.0, 0.8926, 0.7578, 0.6771, 0.7446, 0.7191] +2026-04-14 17:14:08.468897: Epoch time: 101.87 s +2026-04-14 17:14:09.793833: +2026-04-14 17:14:09.796088: Epoch 3417 +2026-04-14 17:14:09.797713: Current learning rate: 0.00177 +2026-04-14 17:15:51.818394: train_loss -0.467 +2026-04-14 17:15:51.825732: val_loss -0.395 +2026-04-14 17:15:51.827852: Pseudo dice [0.531, 0.0, 0.7793, 0.7124, 0.3632, 0.8028, 0.918] +2026-04-14 17:15:51.830249: Epoch time: 102.03 s +2026-04-14 17:15:53.117277: +2026-04-14 17:15:53.119047: Epoch 3418 +2026-04-14 17:15:53.120512: Current learning rate: 0.00176 +2026-04-14 17:17:34.825066: train_loss -0.4578 +2026-04-14 17:17:34.848574: val_loss -0.3754 +2026-04-14 17:17:34.850959: Pseudo dice [0.6291, 0.0, 0.7566, 0.1466, 0.6216, 0.8185, 0.8669] +2026-04-14 17:17:34.853641: Epoch time: 101.71 s +2026-04-14 17:17:36.158959: +2026-04-14 17:17:36.160773: Epoch 3419 +2026-04-14 17:17:36.162633: Current learning rate: 0.00176 +2026-04-14 17:19:17.742647: train_loss -0.4644 +2026-04-14 17:19:17.750357: val_loss -0.3803 +2026-04-14 17:19:17.757946: Pseudo dice [0.6168, 0.0, 0.8732, 0.7297, 0.4919, 0.6876, 0.4794] +2026-04-14 17:19:17.760296: Epoch time: 101.59 s +2026-04-14 17:19:19.068877: +2026-04-14 17:19:19.070637: Epoch 3420 +2026-04-14 17:19:19.072181: Current learning rate: 0.00176 +2026-04-14 17:21:00.639209: train_loss -0.4541 +2026-04-14 17:21:00.647305: val_loss -0.3642 +2026-04-14 17:21:00.649433: Pseudo dice [0.6096, 0.0, 0.6222, 0.5141, 0.5004, 0.3813, 0.8378] +2026-04-14 17:21:00.652337: Epoch time: 101.57 s +2026-04-14 17:21:01.983433: +2026-04-14 17:21:01.986045: Epoch 3421 +2026-04-14 17:21:01.989058: Current learning rate: 0.00176 +2026-04-14 17:22:43.419006: train_loss -0.4624 +2026-04-14 17:22:43.425483: val_loss -0.3412 +2026-04-14 17:22:43.429036: Pseudo dice [0.7187, 0.0, 0.7675, 0.8737, 0.5014, 0.7578, 0.8876] +2026-04-14 17:22:43.432394: Epoch time: 101.44 s +2026-04-14 17:22:44.712736: +2026-04-14 17:22:44.714629: Epoch 3422 +2026-04-14 17:22:44.716256: Current learning rate: 0.00175 +2026-04-14 17:24:26.245406: train_loss -0.4529 +2026-04-14 17:24:26.251661: val_loss -0.4081 +2026-04-14 17:24:26.254567: Pseudo dice [0.6901, 0.0, 0.8364, 0.5109, 0.5878, 0.7121, 0.6617] +2026-04-14 17:24:26.256922: Epoch time: 101.54 s +2026-04-14 17:24:27.521530: +2026-04-14 17:24:27.523206: Epoch 3423 +2026-04-14 17:24:27.524755: Current learning rate: 0.00175 +2026-04-14 17:26:09.013438: train_loss -0.4582 +2026-04-14 17:26:09.019493: val_loss -0.3746 +2026-04-14 17:26:09.021300: Pseudo dice [0.5115, 0.0, 0.6918, 0.3103, 0.6155, 0.4125, 0.8318] +2026-04-14 17:26:09.023661: Epoch time: 101.49 s +2026-04-14 17:26:10.310468: +2026-04-14 17:26:10.313557: Epoch 3424 +2026-04-14 17:26:10.315364: Current learning rate: 0.00175 +2026-04-14 17:27:52.918200: train_loss -0.4613 +2026-04-14 17:27:52.925808: val_loss -0.3956 +2026-04-14 17:27:52.932532: Pseudo dice [0.6207, 0.0, 0.8562, 0.8122, 0.5114, 0.8098, 0.7357] +2026-04-14 17:27:52.935655: Epoch time: 102.61 s +2026-04-14 17:27:54.201723: +2026-04-14 17:27:54.203719: Epoch 3425 +2026-04-14 17:27:54.205356: Current learning rate: 0.00175 +2026-04-14 17:29:35.824388: train_loss -0.4528 +2026-04-14 17:29:35.832101: val_loss -0.3997 +2026-04-14 17:29:35.834487: Pseudo dice [0.8769, 0.0, 0.848, 0.7297, 0.5827, 0.7894, 0.9073] +2026-04-14 17:29:35.837045: Epoch time: 101.63 s +2026-04-14 17:29:37.133283: +2026-04-14 17:29:37.135274: Epoch 3426 +2026-04-14 17:29:37.137054: Current learning rate: 0.00174 +2026-04-14 17:31:18.669498: train_loss -0.4586 +2026-04-14 17:31:18.679653: val_loss -0.3819 +2026-04-14 17:31:18.681562: Pseudo dice [0.7886, 0.0, 0.7668, 0.6383, 0.4804, 0.7592, 0.8799] +2026-04-14 17:31:18.684006: Epoch time: 101.54 s +2026-04-14 17:31:19.961409: +2026-04-14 17:31:19.964175: Epoch 3427 +2026-04-14 17:31:19.966255: Current learning rate: 0.00174 +2026-04-14 17:33:01.813318: train_loss -0.4576 +2026-04-14 17:33:01.820520: val_loss -0.3685 +2026-04-14 17:33:01.822788: Pseudo dice [0.7395, 0.0, 0.8405, 0.7681, 0.3693, 0.6958, 0.8869] +2026-04-14 17:33:01.825210: Epoch time: 101.86 s +2026-04-14 17:33:03.089455: +2026-04-14 17:33:03.091091: Epoch 3428 +2026-04-14 17:33:03.092619: Current learning rate: 0.00174 +2026-04-14 17:34:45.149780: train_loss -0.4533 +2026-04-14 17:34:45.157142: val_loss -0.3592 +2026-04-14 17:34:45.162083: Pseudo dice [0.6791, 0.0, 0.8125, 0.0969, 0.5368, 0.501, 0.8683] +2026-04-14 17:34:45.166262: Epoch time: 102.06 s +2026-04-14 17:34:46.437697: +2026-04-14 17:34:46.439610: Epoch 3429 +2026-04-14 17:34:46.441893: Current learning rate: 0.00173 +2026-04-14 17:36:28.151895: train_loss -0.4536 +2026-04-14 17:36:28.157855: val_loss -0.4108 +2026-04-14 17:36:28.160181: Pseudo dice [0.6391, 0.0, 0.8342, 0.5483, 0.4706, 0.4318, 0.9084] +2026-04-14 17:36:28.162529: Epoch time: 101.72 s +2026-04-14 17:36:29.462980: +2026-04-14 17:36:29.464872: Epoch 3430 +2026-04-14 17:36:29.466609: Current learning rate: 0.00173 +2026-04-14 17:38:11.436281: train_loss -0.4514 +2026-04-14 17:38:11.442297: val_loss -0.3614 +2026-04-14 17:38:11.444507: Pseudo dice [0.4431, 0.0, 0.828, 0.4709, 0.6313, 0.7767, 0.7628] +2026-04-14 17:38:11.447521: Epoch time: 101.98 s +2026-04-14 17:38:12.718771: +2026-04-14 17:38:12.723286: Epoch 3431 +2026-04-14 17:38:12.728183: Current learning rate: 0.00173 +2026-04-14 17:39:54.725719: train_loss -0.4694 +2026-04-14 17:39:54.734431: val_loss -0.3705 +2026-04-14 17:39:54.739646: Pseudo dice [0.4911, 0.0, 0.8386, 0.5201, 0.2025, 0.7264, 0.6297] +2026-04-14 17:39:54.742173: Epoch time: 102.01 s +2026-04-14 17:39:56.013121: +2026-04-14 17:39:56.014904: Epoch 3432 +2026-04-14 17:39:56.016595: Current learning rate: 0.00173 +2026-04-14 17:41:38.054131: train_loss -0.4623 +2026-04-14 17:41:38.064111: val_loss -0.3429 +2026-04-14 17:41:38.067543: Pseudo dice [0.1753, 0.0, 0.8282, 0.7588, 0.4788, 0.5052, 0.5299] +2026-04-14 17:41:38.070236: Epoch time: 102.04 s +2026-04-14 17:41:39.354612: +2026-04-14 17:41:39.357024: Epoch 3433 +2026-04-14 17:41:39.358863: Current learning rate: 0.00172 +2026-04-14 17:43:20.672707: train_loss -0.4469 +2026-04-14 17:43:20.678309: val_loss -0.4014 +2026-04-14 17:43:20.680469: Pseudo dice [0.5076, 0.0, 0.5958, 0.5012, 0.6582, 0.6243, 0.8351] +2026-04-14 17:43:20.683274: Epoch time: 101.32 s +2026-04-14 17:43:21.947751: +2026-04-14 17:43:21.949891: Epoch 3434 +2026-04-14 17:43:21.951876: Current learning rate: 0.00172 +2026-04-14 17:45:03.184169: train_loss -0.4518 +2026-04-14 17:45:03.190326: val_loss -0.389 +2026-04-14 17:45:03.193610: Pseudo dice [0.5945, 0.0, 0.8444, 0.6556, 0.6062, 0.6436, 0.776] +2026-04-14 17:45:03.196167: Epoch time: 101.24 s +2026-04-14 17:45:04.496453: +2026-04-14 17:45:04.498513: Epoch 3435 +2026-04-14 17:45:04.500222: Current learning rate: 0.00172 +2026-04-14 17:46:46.148808: train_loss -0.4555 +2026-04-14 17:46:46.155147: val_loss -0.3625 +2026-04-14 17:46:46.157043: Pseudo dice [0.5548, 0.0, 0.7436, 0.2839, 0.4042, 0.6443, 0.8233] +2026-04-14 17:46:46.159720: Epoch time: 101.66 s +2026-04-14 17:46:47.454872: +2026-04-14 17:46:47.457099: Epoch 3436 +2026-04-14 17:46:47.459062: Current learning rate: 0.00172 +2026-04-14 17:48:29.469658: train_loss -0.4681 +2026-04-14 17:48:29.482070: val_loss -0.3564 +2026-04-14 17:48:29.484221: Pseudo dice [0.3224, 0.0, 0.6817, 0.3292, 0.4508, 0.756, 0.8228] +2026-04-14 17:48:29.489454: Epoch time: 102.02 s +2026-04-14 17:48:30.763347: +2026-04-14 17:48:30.767528: Epoch 3437 +2026-04-14 17:48:30.770567: Current learning rate: 0.00171 +2026-04-14 17:50:12.895073: train_loss -0.4639 +2026-04-14 17:50:12.901785: val_loss -0.3711 +2026-04-14 17:50:12.903783: Pseudo dice [0.6113, 0.0, 0.8131, 0.3117, 0.2594, 0.7589, 0.8334] +2026-04-14 17:50:12.906265: Epoch time: 102.13 s +2026-04-14 17:50:14.189658: +2026-04-14 17:50:14.191491: Epoch 3438 +2026-04-14 17:50:14.194063: Current learning rate: 0.00171 +2026-04-14 17:51:55.450555: train_loss -0.4739 +2026-04-14 17:51:55.456651: val_loss -0.4069 +2026-04-14 17:51:55.459118: Pseudo dice [0.7647, 0.0, 0.7737, 0.8829, 0.4189, 0.7827, 0.8798] +2026-04-14 17:51:55.462270: Epoch time: 101.26 s +2026-04-14 17:51:56.772974: +2026-04-14 17:51:56.775042: Epoch 3439 +2026-04-14 17:51:56.776509: Current learning rate: 0.00171 +2026-04-14 17:53:38.312928: train_loss -0.4764 +2026-04-14 17:53:38.321090: val_loss -0.4087 +2026-04-14 17:53:38.323325: Pseudo dice [0.7205, 0.0, 0.8458, 0.3953, 0.607, 0.7003, 0.8091] +2026-04-14 17:53:38.325893: Epoch time: 101.54 s +2026-04-14 17:53:39.697497: +2026-04-14 17:53:39.699579: Epoch 3440 +2026-04-14 17:53:39.701490: Current learning rate: 0.0017 +2026-04-14 17:55:22.753484: train_loss -0.4615 +2026-04-14 17:55:22.766238: val_loss -0.3923 +2026-04-14 17:55:22.769820: Pseudo dice [0.6262, 0.0, 0.8723, 0.4016, 0.5004, 0.6261, 0.914] +2026-04-14 17:55:22.778580: Epoch time: 103.06 s +2026-04-14 17:55:24.077498: +2026-04-14 17:55:24.079821: Epoch 3441 +2026-04-14 17:55:24.081941: Current learning rate: 0.0017 +2026-04-14 17:57:06.113904: train_loss -0.4508 +2026-04-14 17:57:06.122262: val_loss -0.4026 +2026-04-14 17:57:06.126386: Pseudo dice [0.7611, 0.0, 0.8635, 0.7174, 0.6665, 0.6103, 0.7337] +2026-04-14 17:57:06.129174: Epoch time: 102.04 s +2026-04-14 17:57:07.399478: +2026-04-14 17:57:07.401658: Epoch 3442 +2026-04-14 17:57:07.404090: Current learning rate: 0.0017 +2026-04-14 17:58:49.210669: train_loss -0.4634 +2026-04-14 17:58:49.218613: val_loss -0.381 +2026-04-14 17:58:49.223024: Pseudo dice [0.615, 0.0, 0.7811, 0.7818, 0.5769, 0.7754, 0.9037] +2026-04-14 17:58:49.225482: Epoch time: 101.81 s +2026-04-14 17:58:50.528505: +2026-04-14 17:58:50.530731: Epoch 3443 +2026-04-14 17:58:50.532569: Current learning rate: 0.0017 +2026-04-14 18:00:32.024063: train_loss -0.4551 +2026-04-14 18:00:32.031110: val_loss -0.3724 +2026-04-14 18:00:32.033203: Pseudo dice [0.6812, 0.0, 0.876, 0.3866, 0.5582, 0.7783, 0.9243] +2026-04-14 18:00:32.035849: Epoch time: 101.5 s +2026-04-14 18:00:33.309675: +2026-04-14 18:00:33.311387: Epoch 3444 +2026-04-14 18:00:33.312921: Current learning rate: 0.00169 +2026-04-14 18:02:15.826717: train_loss -0.4631 +2026-04-14 18:02:15.832438: val_loss -0.3439 +2026-04-14 18:02:15.834860: Pseudo dice [0.8451, 0.0, 0.7691, 0.0001, 0.7092, 0.8932, 0.7125] +2026-04-14 18:02:15.837447: Epoch time: 102.52 s +2026-04-14 18:02:17.104881: +2026-04-14 18:02:17.106571: Epoch 3445 +2026-04-14 18:02:17.108083: Current learning rate: 0.00169 +2026-04-14 18:03:58.417121: train_loss -0.4552 +2026-04-14 18:03:58.424626: val_loss -0.408 +2026-04-14 18:03:58.427287: Pseudo dice [0.8188, 0.0, 0.8275, 0.5783, 0.5469, 0.8928, 0.9062] +2026-04-14 18:03:58.429760: Epoch time: 101.32 s +2026-04-14 18:03:59.702340: +2026-04-14 18:03:59.704183: Epoch 3446 +2026-04-14 18:03:59.705865: Current learning rate: 0.00169 +2026-04-14 18:05:41.422345: train_loss -0.4724 +2026-04-14 18:05:41.428281: val_loss -0.4214 +2026-04-14 18:05:41.430539: Pseudo dice [0.8705, 0.0, 0.9023, 0.9167, 0.6012, 0.601, 0.9245] +2026-04-14 18:05:41.432854: Epoch time: 101.72 s +2026-04-14 18:05:42.723928: +2026-04-14 18:05:42.725905: Epoch 3447 +2026-04-14 18:05:42.727696: Current learning rate: 0.00168 +2026-04-14 18:07:24.074995: train_loss -0.4651 +2026-04-14 18:07:24.081188: val_loss -0.3813 +2026-04-14 18:07:24.083071: Pseudo dice [0.6197, 0.0, 0.8644, 0.8386, 0.1882, 0.8532, 0.3895] +2026-04-14 18:07:24.085127: Epoch time: 101.35 s +2026-04-14 18:07:25.378217: +2026-04-14 18:07:25.380019: Epoch 3448 +2026-04-14 18:07:25.387257: Current learning rate: 0.00168 +2026-04-14 18:09:06.980476: train_loss -0.4616 +2026-04-14 18:09:06.987898: val_loss -0.3912 +2026-04-14 18:09:06.990986: Pseudo dice [0.6652, 0.0, 0.8689, 0.4061, 0.5094, 0.8932, 0.9343] +2026-04-14 18:09:06.993078: Epoch time: 101.61 s +2026-04-14 18:09:08.261601: +2026-04-14 18:09:08.263969: Epoch 3449 +2026-04-14 18:09:08.266632: Current learning rate: 0.00168 +2026-04-14 18:10:49.804421: train_loss -0.4758 +2026-04-14 18:10:49.810828: val_loss -0.3868 +2026-04-14 18:10:49.813123: Pseudo dice [0.7305, 0.0, 0.7704, 0.6216, 0.4475, 0.7739, 0.7645] +2026-04-14 18:10:49.815859: Epoch time: 101.55 s +2026-04-14 18:10:52.925895: +2026-04-14 18:10:52.927787: Epoch 3450 +2026-04-14 18:10:52.929255: Current learning rate: 0.00168 +2026-04-14 18:12:34.680628: train_loss -0.4635 +2026-04-14 18:12:34.688453: val_loss -0.3516 +2026-04-14 18:12:34.691082: Pseudo dice [0.7125, 0.0, 0.7857, 0.5466, 0.4715, 0.8156, 0.938] +2026-04-14 18:12:34.695816: Epoch time: 101.76 s +2026-04-14 18:12:35.994426: +2026-04-14 18:12:35.996789: Epoch 3451 +2026-04-14 18:12:35.998627: Current learning rate: 0.00167 +2026-04-14 18:14:17.210320: train_loss -0.461 +2026-04-14 18:14:17.216380: val_loss -0.3779 +2026-04-14 18:14:17.218786: Pseudo dice [0.795, 0.0, 0.8793, 0.5531, 0.5369, 0.4184, 0.8804] +2026-04-14 18:14:17.221279: Epoch time: 101.22 s +2026-04-14 18:14:18.588197: +2026-04-14 18:14:18.589956: Epoch 3452 +2026-04-14 18:14:18.591644: Current learning rate: 0.00167 +2026-04-14 18:16:00.176309: train_loss -0.4627 +2026-04-14 18:16:00.182304: val_loss -0.4058 +2026-04-14 18:16:00.184198: Pseudo dice [0.6735, 0.0, 0.841, 0.8086, 0.5052, 0.4012, 0.8609] +2026-04-14 18:16:00.186405: Epoch time: 101.59 s +2026-04-14 18:16:01.456232: +2026-04-14 18:16:01.458177: Epoch 3453 +2026-04-14 18:16:01.459649: Current learning rate: 0.00167 +2026-04-14 18:17:42.716788: train_loss -0.4613 +2026-04-14 18:17:42.722438: val_loss -0.3896 +2026-04-14 18:17:42.725061: Pseudo dice [0.429, 0.0, 0.6915, 0.0853, 0.4783, 0.681, 0.9443] +2026-04-14 18:17:42.727628: Epoch time: 101.26 s +2026-04-14 18:17:43.972864: +2026-04-14 18:17:43.975279: Epoch 3454 +2026-04-14 18:17:43.977242: Current learning rate: 0.00167 +2026-04-14 18:19:25.283663: train_loss -0.4609 +2026-04-14 18:19:25.290296: val_loss -0.3896 +2026-04-14 18:19:25.292506: Pseudo dice [0.8108, 0.0, 0.8042, 0.0255, 0.5922, 0.8263, 0.8895] +2026-04-14 18:19:25.295130: Epoch time: 101.31 s +2026-04-14 18:19:26.593361: +2026-04-14 18:19:26.595419: Epoch 3455 +2026-04-14 18:19:26.597002: Current learning rate: 0.00166 +2026-04-14 18:21:08.905062: train_loss -0.455 +2026-04-14 18:21:08.914751: val_loss -0.3905 +2026-04-14 18:21:08.918664: Pseudo dice [0.7238, 0.0, 0.74, 0.2266, 0.555, 0.6458, 0.9126] +2026-04-14 18:21:08.922433: Epoch time: 102.31 s +2026-04-14 18:21:10.193427: +2026-04-14 18:21:10.195167: Epoch 3456 +2026-04-14 18:21:10.198511: Current learning rate: 0.00166 +2026-04-14 18:22:51.518123: train_loss -0.4539 +2026-04-14 18:22:51.525045: val_loss -0.3936 +2026-04-14 18:22:51.527825: Pseudo dice [0.3084, 0.0, 0.7793, 0.6603, 0.4454, 0.7645, 0.8429] +2026-04-14 18:22:51.529835: Epoch time: 101.33 s +2026-04-14 18:22:52.847403: +2026-04-14 18:22:52.849386: Epoch 3457 +2026-04-14 18:22:52.850975: Current learning rate: 0.00166 +2026-04-14 18:24:34.299944: train_loss -0.4607 +2026-04-14 18:24:34.306397: val_loss -0.3703 +2026-04-14 18:24:34.309216: Pseudo dice [0.8637, 0.0, 0.7359, 0.727, 0.4307, 0.6518, 0.8599] +2026-04-14 18:24:34.311575: Epoch time: 101.46 s +2026-04-14 18:24:35.567154: +2026-04-14 18:24:35.570483: Epoch 3458 +2026-04-14 18:24:35.572126: Current learning rate: 0.00165 +2026-04-14 18:26:16.790252: train_loss -0.4326 +2026-04-14 18:26:16.797570: val_loss -0.3634 +2026-04-14 18:26:16.799964: Pseudo dice [0.8164, 0.0, 0.6911, 0.0785, 0.5951, 0.2413, 0.8115] +2026-04-14 18:26:16.802611: Epoch time: 101.23 s +2026-04-14 18:26:18.079353: +2026-04-14 18:26:18.081700: Epoch 3459 +2026-04-14 18:26:18.083607: Current learning rate: 0.00165 +2026-04-14 18:27:59.390306: train_loss -0.4595 +2026-04-14 18:27:59.398170: val_loss -0.4187 +2026-04-14 18:27:59.400184: Pseudo dice [0.531, 0.0, 0.8916, 0.8558, 0.4713, 0.8829, 0.8913] +2026-04-14 18:27:59.408703: Epoch time: 101.31 s +2026-04-14 18:28:00.710523: +2026-04-14 18:28:00.712301: Epoch 3460 +2026-04-14 18:28:00.713829: Current learning rate: 0.00165 +2026-04-14 18:29:41.996035: train_loss -0.4749 +2026-04-14 18:29:42.003184: val_loss -0.3978 +2026-04-14 18:29:42.005335: Pseudo dice [0.8167, 0.0, 0.8049, 0.8524, 0.3188, 0.5335, 0.8454] +2026-04-14 18:29:42.008248: Epoch time: 101.29 s +2026-04-14 18:29:43.274361: +2026-04-14 18:29:43.276160: Epoch 3461 +2026-04-14 18:29:43.277772: Current learning rate: 0.00165 +2026-04-14 18:31:24.668032: train_loss -0.4613 +2026-04-14 18:31:24.674559: val_loss -0.4162 +2026-04-14 18:31:24.676668: Pseudo dice [0.8187, 0.0, 0.7477, 0.8368, 0.6305, 0.6849, 0.9359] +2026-04-14 18:31:24.679453: Epoch time: 101.4 s +2026-04-14 18:31:25.954792: +2026-04-14 18:31:25.956617: Epoch 3462 +2026-04-14 18:31:25.958329: Current learning rate: 0.00164 +2026-04-14 18:33:07.055828: train_loss -0.4724 +2026-04-14 18:33:07.062658: val_loss -0.3857 +2026-04-14 18:33:07.068298: Pseudo dice [0.6775, 0.0, 0.7646, 0.3666, 0.3378, 0.755, 0.8969] +2026-04-14 18:33:07.070721: Epoch time: 101.1 s +2026-04-14 18:33:08.350816: +2026-04-14 18:33:08.353096: Epoch 3463 +2026-04-14 18:33:08.355031: Current learning rate: 0.00164 +2026-04-14 18:34:49.991935: train_loss -0.4792 +2026-04-14 18:34:49.999343: val_loss -0.3838 +2026-04-14 18:34:50.001910: Pseudo dice [0.5849, 0.0, 0.78, 0.5767, 0.5096, 0.7354, 0.648] +2026-04-14 18:34:50.004505: Epoch time: 101.64 s +2026-04-14 18:34:52.401528: +2026-04-14 18:34:52.403263: Epoch 3464 +2026-04-14 18:34:52.404811: Current learning rate: 0.00164 +2026-04-14 18:36:34.080729: train_loss -0.4509 +2026-04-14 18:36:34.088885: val_loss -0.3513 +2026-04-14 18:36:34.091145: Pseudo dice [0.4704, 0.0, 0.7063, 0.1943, 0.424, 0.5717, 0.7996] +2026-04-14 18:36:34.094096: Epoch time: 101.68 s +2026-04-14 18:36:35.393528: +2026-04-14 18:36:35.395918: Epoch 3465 +2026-04-14 18:36:35.397738: Current learning rate: 0.00164 +2026-04-14 18:38:17.221707: train_loss -0.4679 +2026-04-14 18:38:17.228992: val_loss -0.3941 +2026-04-14 18:38:17.231263: Pseudo dice [0.3333, 0.0, 0.7659, 0.3321, 0.5046, 0.4225, 0.9443] +2026-04-14 18:38:17.233737: Epoch time: 101.83 s +2026-04-14 18:38:18.497385: +2026-04-14 18:38:18.499907: Epoch 3466 +2026-04-14 18:38:18.502269: Current learning rate: 0.00163 +2026-04-14 18:40:00.168411: train_loss -0.468 +2026-04-14 18:40:00.195402: val_loss -0.4065 +2026-04-14 18:40:00.198353: Pseudo dice [0.6877, 0.0, 0.8466, 0.862, 0.6577, 0.8914, 0.686] +2026-04-14 18:40:00.200980: Epoch time: 101.67 s +2026-04-14 18:40:01.469246: +2026-04-14 18:40:01.474425: Epoch 3467 +2026-04-14 18:40:01.477534: Current learning rate: 0.00163 +2026-04-14 18:41:43.021571: train_loss -0.4683 +2026-04-14 18:41:43.029551: val_loss -0.3851 +2026-04-14 18:41:43.032226: Pseudo dice [0.6139, 0.0, 0.819, 0.4365, 0.4851, 0.5695, 0.84] +2026-04-14 18:41:43.034791: Epoch time: 101.56 s +2026-04-14 18:41:44.365245: +2026-04-14 18:41:44.367841: Epoch 3468 +2026-04-14 18:41:44.370406: Current learning rate: 0.00163 +2026-04-14 18:43:25.789799: train_loss -0.4611 +2026-04-14 18:43:25.797527: val_loss -0.3632 +2026-04-14 18:43:25.799980: Pseudo dice [0.6685, 0.0, 0.7464, 0.7731, 0.4923, 0.5717, 0.9083] +2026-04-14 18:43:25.803134: Epoch time: 101.43 s +2026-04-14 18:43:27.056179: +2026-04-14 18:43:27.057904: Epoch 3469 +2026-04-14 18:43:27.059491: Current learning rate: 0.00162 +2026-04-14 18:45:08.353823: train_loss -0.4613 +2026-04-14 18:45:08.359632: val_loss -0.3745 +2026-04-14 18:45:08.361860: Pseudo dice [0.4849, 0.0, 0.6482, 0.1027, 0.6209, 0.4877, 0.8755] +2026-04-14 18:45:08.364551: Epoch time: 101.3 s +2026-04-14 18:45:09.601884: +2026-04-14 18:45:09.604015: Epoch 3470 +2026-04-14 18:45:09.605894: Current learning rate: 0.00162 +2026-04-14 18:46:50.966695: train_loss -0.4629 +2026-04-14 18:46:50.973494: val_loss -0.4314 +2026-04-14 18:46:50.975396: Pseudo dice [0.6242, 0.0, 0.9015, 0.8546, 0.5281, 0.8409, 0.9047] +2026-04-14 18:46:50.977617: Epoch time: 101.37 s +2026-04-14 18:46:52.224223: +2026-04-14 18:46:52.226290: Epoch 3471 +2026-04-14 18:46:52.227921: Current learning rate: 0.00162 +2026-04-14 18:48:33.620714: train_loss -0.4667 +2026-04-14 18:48:33.628595: val_loss -0.3839 +2026-04-14 18:48:33.631135: Pseudo dice [0.7391, 0.0, 0.8799, 0.7048, 0.6507, 0.4485, 0.8704] +2026-04-14 18:48:33.633554: Epoch time: 101.4 s +2026-04-14 18:48:34.935297: +2026-04-14 18:48:34.937051: Epoch 3472 +2026-04-14 18:48:34.938688: Current learning rate: 0.00162 +2026-04-14 18:50:16.302747: train_loss -0.4636 +2026-04-14 18:50:16.310971: val_loss -0.403 +2026-04-14 18:50:16.313074: Pseudo dice [0.3817, 0.0, 0.8675, 0.7072, 0.4606, 0.6447, 0.7455] +2026-04-14 18:50:16.315904: Epoch time: 101.37 s +2026-04-14 18:50:17.568548: +2026-04-14 18:50:17.570622: Epoch 3473 +2026-04-14 18:50:17.572675: Current learning rate: 0.00161 +2026-04-14 18:51:59.714467: train_loss -0.4649 +2026-04-14 18:51:59.722163: val_loss -0.3708 +2026-04-14 18:51:59.725297: Pseudo dice [0.6704, 0.0, 0.7278, 0.4528, 0.3671, 0.7203, 0.7532] +2026-04-14 18:51:59.728267: Epoch time: 102.15 s +2026-04-14 18:52:01.036407: +2026-04-14 18:52:01.038190: Epoch 3474 +2026-04-14 18:52:01.040979: Current learning rate: 0.00161 +2026-04-14 18:53:43.325434: train_loss -0.4679 +2026-04-14 18:53:43.332299: val_loss -0.3885 +2026-04-14 18:53:43.335120: Pseudo dice [0.4259, 0.0, 0.7569, 0.8084, 0.3481, 0.6604, 0.8266] +2026-04-14 18:53:43.337667: Epoch time: 102.29 s +2026-04-14 18:53:44.578568: +2026-04-14 18:53:44.580405: Epoch 3475 +2026-04-14 18:53:44.582722: Current learning rate: 0.00161 +2026-04-14 18:55:26.424336: train_loss -0.4728 +2026-04-14 18:55:26.435117: val_loss -0.4005 +2026-04-14 18:55:26.438280: Pseudo dice [0.8304, 0.0, 0.8573, 0.5236, 0.5416, 0.872, 0.7856] +2026-04-14 18:55:26.455012: Epoch time: 101.85 s +2026-04-14 18:55:27.737211: +2026-04-14 18:55:27.739645: Epoch 3476 +2026-04-14 18:55:27.742427: Current learning rate: 0.00161 +2026-04-14 18:57:09.345675: train_loss -0.465 +2026-04-14 18:57:09.352605: val_loss -0.359 +2026-04-14 18:57:09.354886: Pseudo dice [0.759, 0.0, 0.8507, 0.5665, 0.3496, 0.4923, 0.9409] +2026-04-14 18:57:09.357392: Epoch time: 101.61 s +2026-04-14 18:57:10.633703: +2026-04-14 18:57:10.635770: Epoch 3477 +2026-04-14 18:57:10.637892: Current learning rate: 0.0016 +2026-04-14 18:58:52.804405: train_loss -0.4621 +2026-04-14 18:58:52.810868: val_loss -0.3657 +2026-04-14 18:58:52.813277: Pseudo dice [0.5064, 0.0, 0.8266, 0.5493, 0.4795, 0.8616, 0.8675] +2026-04-14 18:58:52.816295: Epoch time: 102.17 s +2026-04-14 18:58:54.072414: +2026-04-14 18:58:54.074367: Epoch 3478 +2026-04-14 18:58:54.076539: Current learning rate: 0.0016 +2026-04-14 19:00:35.558496: train_loss -0.4765 +2026-04-14 19:00:35.565358: val_loss -0.4131 +2026-04-14 19:00:35.567615: Pseudo dice [0.7012, 0.0, 0.7001, 0.8095, 0.5962, 0.4369, 0.9407] +2026-04-14 19:00:35.570458: Epoch time: 101.49 s +2026-04-14 19:00:36.835605: +2026-04-14 19:00:36.837623: Epoch 3479 +2026-04-14 19:00:36.841518: Current learning rate: 0.0016 +2026-04-14 19:02:18.934442: train_loss -0.4352 +2026-04-14 19:02:18.941453: val_loss -0.3841 +2026-04-14 19:02:18.943605: Pseudo dice [0.5775, 0.0, 0.8356, 0.8378, 0.4538, 0.6458, 0.882] +2026-04-14 19:02:18.945919: Epoch time: 102.1 s +2026-04-14 19:02:20.212952: +2026-04-14 19:02:20.216270: Epoch 3480 +2026-04-14 19:02:20.218934: Current learning rate: 0.00159 +2026-04-14 19:04:02.260808: train_loss -0.4626 +2026-04-14 19:04:02.268167: val_loss -0.3746 +2026-04-14 19:04:02.271528: Pseudo dice [0.7074, 0.0, 0.7387, 0.4583, 0.4036, 0.7107, 0.8403] +2026-04-14 19:04:02.274258: Epoch time: 102.05 s +2026-04-14 19:04:03.608190: +2026-04-14 19:04:03.610507: Epoch 3481 +2026-04-14 19:04:03.612605: Current learning rate: 0.00159 +2026-04-14 19:05:45.730016: train_loss -0.4553 +2026-04-14 19:05:45.737469: val_loss -0.3989 +2026-04-14 19:05:45.740307: Pseudo dice [0.6783, 0.0, 0.8843, 0.7931, 0.609, 0.8287, 0.9156] +2026-04-14 19:05:45.743236: Epoch time: 102.12 s +2026-04-14 19:05:47.021199: +2026-04-14 19:05:47.023674: Epoch 3482 +2026-04-14 19:05:47.026932: Current learning rate: 0.00159 +2026-04-14 19:07:28.452998: train_loss -0.4654 +2026-04-14 19:07:28.459627: val_loss -0.3818 +2026-04-14 19:07:28.461938: Pseudo dice [0.6931, 0.0, 0.7362, 0.2293, 0.5598, 0.8565, 0.9126] +2026-04-14 19:07:28.464311: Epoch time: 101.44 s +2026-04-14 19:07:29.747534: +2026-04-14 19:07:29.749813: Epoch 3483 +2026-04-14 19:07:29.752155: Current learning rate: 0.00159 +2026-04-14 19:09:12.598409: train_loss -0.4727 +2026-04-14 19:09:12.606061: val_loss -0.3929 +2026-04-14 19:09:12.609257: Pseudo dice [0.8271, 0.0, 0.8609, 0.2639, 0.2854, 0.8345, 0.8415] +2026-04-14 19:09:12.612111: Epoch time: 102.85 s +2026-04-14 19:09:13.889404: +2026-04-14 19:09:13.891212: Epoch 3484 +2026-04-14 19:09:13.895280: Current learning rate: 0.00158 +2026-04-14 19:10:55.607088: train_loss -0.4689 +2026-04-14 19:10:55.615613: val_loss -0.3805 +2026-04-14 19:10:55.618097: Pseudo dice [0.6416, 0.0, 0.7004, 0.2311, 0.4302, 0.2417, 0.6858] +2026-04-14 19:10:55.620996: Epoch time: 101.72 s +2026-04-14 19:10:56.926424: +2026-04-14 19:10:56.929474: Epoch 3485 +2026-04-14 19:10:56.932687: Current learning rate: 0.00158 +2026-04-14 19:12:39.134601: train_loss -0.464 +2026-04-14 19:12:39.141278: val_loss -0.3901 +2026-04-14 19:12:39.143714: Pseudo dice [0.6273, 0.0, 0.7457, 0.5857, 0.5339, 0.8232, 0.8786] +2026-04-14 19:12:39.146566: Epoch time: 102.21 s +2026-04-14 19:12:40.445204: +2026-04-14 19:12:40.447010: Epoch 3486 +2026-04-14 19:12:40.449024: Current learning rate: 0.00158 +2026-04-14 19:14:22.062941: train_loss -0.4781 +2026-04-14 19:14:22.072672: val_loss -0.4027 +2026-04-14 19:14:22.074779: Pseudo dice [0.8606, 0.0, 0.5476, 0.8221, 0.4515, 0.8856, 0.4528] +2026-04-14 19:14:22.077514: Epoch time: 101.62 s +2026-04-14 19:14:23.366498: +2026-04-14 19:14:23.369281: Epoch 3487 +2026-04-14 19:14:23.372228: Current learning rate: 0.00157 +2026-04-14 19:16:05.602253: train_loss -0.4645 +2026-04-14 19:16:05.609028: val_loss -0.3894 +2026-04-14 19:16:05.611527: Pseudo dice [0.6534, 0.0, 0.851, 0.7744, 0.4278, 0.7222, 0.851] +2026-04-14 19:16:05.613788: Epoch time: 102.24 s +2026-04-14 19:16:06.893494: +2026-04-14 19:16:06.895394: Epoch 3488 +2026-04-14 19:16:06.897653: Current learning rate: 0.00157 +2026-04-14 19:17:48.630962: train_loss -0.4801 +2026-04-14 19:17:48.637701: val_loss -0.3854 +2026-04-14 19:17:48.640255: Pseudo dice [0.8267, 0.0, 0.7896, 0.6786, 0.6713, 0.9039, 0.7871] +2026-04-14 19:17:48.642835: Epoch time: 101.74 s +2026-04-14 19:17:49.951358: +2026-04-14 19:17:49.953913: Epoch 3489 +2026-04-14 19:17:49.955973: Current learning rate: 0.00157 +2026-04-14 19:19:31.499018: train_loss -0.4647 +2026-04-14 19:19:31.507389: val_loss -0.3908 +2026-04-14 19:19:31.510111: Pseudo dice [0.4821, 0.0, 0.8371, 0.5319, 0.5832, 0.6708, 0.8133] +2026-04-14 19:19:31.512714: Epoch time: 101.55 s +2026-04-14 19:19:32.813976: +2026-04-14 19:19:32.816298: Epoch 3490 +2026-04-14 19:19:32.818484: Current learning rate: 0.00157 +2026-04-14 19:21:14.338190: train_loss -0.4836 +2026-04-14 19:21:14.346265: val_loss -0.3782 +2026-04-14 19:21:14.348832: Pseudo dice [0.4529, 0.0, 0.8764, 0.4083, 0.4203, 0.9383, 0.8763] +2026-04-14 19:21:14.352300: Epoch time: 101.53 s +2026-04-14 19:21:15.658369: +2026-04-14 19:21:15.663415: Epoch 3491 +2026-04-14 19:21:15.665477: Current learning rate: 0.00156 +2026-04-14 19:22:57.264987: train_loss -0.4872 +2026-04-14 19:22:57.272330: val_loss -0.3988 +2026-04-14 19:22:57.275612: Pseudo dice [0.686, 0.0, 0.727, 0.249, 0.6322, 0.5284, 0.9026] +2026-04-14 19:22:57.278067: Epoch time: 101.61 s +2026-04-14 19:22:58.563258: +2026-04-14 19:22:58.565551: Epoch 3492 +2026-04-14 19:22:58.567720: Current learning rate: 0.00156 +2026-04-14 19:24:40.691879: train_loss -0.4822 +2026-04-14 19:24:40.699831: val_loss -0.3805 +2026-04-14 19:24:40.701933: Pseudo dice [0.4638, 0.0, 0.7336, 0.8762, 0.5017, 0.7421, 0.8149] +2026-04-14 19:24:40.703985: Epoch time: 102.13 s +2026-04-14 19:24:41.986196: +2026-04-14 19:24:41.988303: Epoch 3493 +2026-04-14 19:24:41.990241: Current learning rate: 0.00156 +2026-04-14 19:26:23.513495: train_loss -0.4648 +2026-04-14 19:26:23.521428: val_loss -0.4159 +2026-04-14 19:26:23.524325: Pseudo dice [0.5236, 0.0, 0.8529, 0.4117, 0.4044, 0.4482, 0.9415] +2026-04-14 19:26:23.527628: Epoch time: 101.53 s +2026-04-14 19:26:24.797530: +2026-04-14 19:26:24.799562: Epoch 3494 +2026-04-14 19:26:24.801672: Current learning rate: 0.00156 +2026-04-14 19:28:06.463068: train_loss -0.4552 +2026-04-14 19:28:06.469664: val_loss -0.3616 +2026-04-14 19:28:06.473123: Pseudo dice [0.5285, 0.0, 0.8483, 0.7902, 0.5404, 0.3629, 0.8827] +2026-04-14 19:28:06.475942: Epoch time: 101.67 s +2026-04-14 19:28:07.747746: +2026-04-14 19:28:07.749713: Epoch 3495 +2026-04-14 19:28:07.751771: Current learning rate: 0.00155 +2026-04-14 19:29:49.508369: train_loss -0.4567 +2026-04-14 19:29:49.518158: val_loss -0.3741 +2026-04-14 19:29:49.520620: Pseudo dice [0.471, 0.0, 0.8445, 0.6223, 0.5164, 0.7018, 0.8497] +2026-04-14 19:29:49.523006: Epoch time: 101.76 s +2026-04-14 19:29:50.807811: +2026-04-14 19:29:50.810351: Epoch 3496 +2026-04-14 19:29:50.813565: Current learning rate: 0.00155 +2026-04-14 19:31:32.901884: train_loss -0.4709 +2026-04-14 19:31:32.910117: val_loss -0.3285 +2026-04-14 19:31:32.912131: Pseudo dice [0.4496, 0.0, 0.6518, 0.0973, 0.4517, 0.5545, 0.8541] +2026-04-14 19:31:32.915672: Epoch time: 102.1 s +2026-04-14 19:31:34.199035: +2026-04-14 19:31:34.200826: Epoch 3497 +2026-04-14 19:31:34.202660: Current learning rate: 0.00155 +2026-04-14 19:33:16.436675: train_loss -0.4641 +2026-04-14 19:33:16.445954: val_loss -0.4026 +2026-04-14 19:33:16.449008: Pseudo dice [0.5433, 0.0, 0.8202, 0.8281, 0.552, 0.7737, 0.916] +2026-04-14 19:33:16.451540: Epoch time: 102.24 s +2026-04-14 19:33:17.725693: +2026-04-14 19:33:17.727937: Epoch 3498 +2026-04-14 19:33:17.730138: Current learning rate: 0.00154 +2026-04-14 19:34:59.244621: train_loss -0.4752 +2026-04-14 19:34:59.251004: val_loss -0.3675 +2026-04-14 19:34:59.253572: Pseudo dice [0.6736, 0.0, 0.8015, 0.8199, 0.5746, 0.7828, 0.8929] +2026-04-14 19:34:59.256097: Epoch time: 101.52 s +2026-04-14 19:35:00.516729: +2026-04-14 19:35:00.518744: Epoch 3499 +2026-04-14 19:35:00.521018: Current learning rate: 0.00154 +2026-04-14 19:36:42.528478: train_loss -0.4848 +2026-04-14 19:36:42.537183: val_loss -0.3976 +2026-04-14 19:36:42.541226: Pseudo dice [0.7831, 0.0, 0.8123, 0.7896, 0.652, 0.7496, 0.7952] +2026-04-14 19:36:42.544474: Epoch time: 102.01 s +2026-04-14 19:36:45.729350: +2026-04-14 19:36:45.731263: Epoch 3500 +2026-04-14 19:36:45.733753: Current learning rate: 0.00154 +2026-04-14 19:38:27.652645: train_loss -0.4622 +2026-04-14 19:38:27.660254: val_loss -0.3915 +2026-04-14 19:38:27.662829: Pseudo dice [0.6854, 0.0, 0.878, 0.6326, 0.487, 0.8249, 0.8927] +2026-04-14 19:38:27.666468: Epoch time: 101.93 s +2026-04-14 19:38:28.987200: +2026-04-14 19:38:28.992499: Epoch 3501 +2026-04-14 19:38:28.995485: Current learning rate: 0.00154 +2026-04-14 19:40:10.508986: train_loss -0.4539 +2026-04-14 19:40:10.516292: val_loss -0.3978 +2026-04-14 19:40:10.518373: Pseudo dice [0.3302, 0.0, 0.8401, 0.8378, 0.5369, 0.8184, 0.9311] +2026-04-14 19:40:10.521127: Epoch time: 101.53 s +2026-04-14 19:40:11.807310: +2026-04-14 19:40:11.809470: Epoch 3502 +2026-04-14 19:40:11.811860: Current learning rate: 0.00153 +2026-04-14 19:41:53.750952: train_loss -0.4596 +2026-04-14 19:41:53.760465: val_loss -0.3848 +2026-04-14 19:41:53.762918: Pseudo dice [0.5635, 0.0, 0.7865, 0.6536, 0.4726, 0.5518, 0.91] +2026-04-14 19:41:53.765716: Epoch time: 101.95 s +2026-04-14 19:41:56.112029: +2026-04-14 19:41:56.123378: Epoch 3503 +2026-04-14 19:41:56.125467: Current learning rate: 0.00153 +2026-04-14 19:43:37.888861: train_loss -0.4716 +2026-04-14 19:43:37.894937: val_loss -0.4076 +2026-04-14 19:43:37.897338: Pseudo dice [0.7567, 0.0, 0.8848, 0.8339, 0.3049, 0.4782, 0.9014] +2026-04-14 19:43:37.900740: Epoch time: 101.78 s +2026-04-14 19:43:39.204592: +2026-04-14 19:43:39.213574: Epoch 3504 +2026-04-14 19:43:39.217046: Current learning rate: 0.00153 +2026-04-14 19:45:21.537747: train_loss -0.464 +2026-04-14 19:45:21.548493: val_loss -0.3302 +2026-04-14 19:45:21.551662: Pseudo dice [0.7673, 0.0, 0.7746, 0.1926, 0.2782, 0.7937, 0.787] +2026-04-14 19:45:21.554722: Epoch time: 102.34 s +2026-04-14 19:45:22.858121: +2026-04-14 19:45:22.860447: Epoch 3505 +2026-04-14 19:45:22.862767: Current learning rate: 0.00153 +2026-04-14 19:47:05.119555: train_loss -0.4773 +2026-04-14 19:47:05.126893: val_loss -0.3648 +2026-04-14 19:47:05.129598: Pseudo dice [0.7796, 0.0, 0.7985, 0.2569, 0.4293, 0.4567, 0.8596] +2026-04-14 19:47:05.132104: Epoch time: 102.26 s +2026-04-14 19:47:06.443683: +2026-04-14 19:47:06.445808: Epoch 3506 +2026-04-14 19:47:06.447769: Current learning rate: 0.00152 +2026-04-14 19:48:48.632407: train_loss -0.4622 +2026-04-14 19:48:48.639399: val_loss -0.3832 +2026-04-14 19:48:48.643181: Pseudo dice [0.3156, 0.0, 0.6619, 0.2294, 0.5118, 0.736, 0.9226] +2026-04-14 19:48:48.646951: Epoch time: 102.19 s +2026-04-14 19:48:49.984111: +2026-04-14 19:48:49.986058: Epoch 3507 +2026-04-14 19:48:49.989474: Current learning rate: 0.00152 +2026-04-14 19:50:31.505561: train_loss -0.4814 +2026-04-14 19:50:31.512455: val_loss -0.385 +2026-04-14 19:50:31.515615: Pseudo dice [0.1928, 0.0, 0.8787, 0.8357, 0.6101, 0.3662, 0.8617] +2026-04-14 19:50:31.519411: Epoch time: 101.52 s +2026-04-14 19:50:32.808043: +2026-04-14 19:50:32.809848: Epoch 3508 +2026-04-14 19:50:32.811728: Current learning rate: 0.00152 +2026-04-14 19:52:14.989215: train_loss -0.4689 +2026-04-14 19:52:15.000687: val_loss -0.3562 +2026-04-14 19:52:15.004237: Pseudo dice [0.8875, 0.0, 0.8678, 0.8831, 0.5393, 0.8304, 0.1695] +2026-04-14 19:52:15.008033: Epoch time: 102.18 s +2026-04-14 19:52:16.276378: +2026-04-14 19:52:16.278969: Epoch 3509 +2026-04-14 19:52:16.281290: Current learning rate: 0.00151 +2026-04-14 19:53:58.125801: train_loss -0.4679 +2026-04-14 19:53:58.135166: val_loss -0.38 +2026-04-14 19:53:58.137654: Pseudo dice [0.4534, 0.0, 0.8325, 0.5375, 0.5097, 0.6741, 0.7353] +2026-04-14 19:53:58.140985: Epoch time: 101.85 s +2026-04-14 19:53:59.397470: +2026-04-14 19:53:59.400592: Epoch 3510 +2026-04-14 19:53:59.403101: Current learning rate: 0.00151 +2026-04-14 19:55:40.900971: train_loss -0.4608 +2026-04-14 19:55:40.907885: val_loss -0.3776 +2026-04-14 19:55:40.910280: Pseudo dice [0.7748, 0.0, 0.8241, 0.7251, 0.5402, 0.8498, 0.9267] +2026-04-14 19:55:40.913296: Epoch time: 101.51 s +2026-04-14 19:55:42.180763: +2026-04-14 19:55:42.183250: Epoch 3511 +2026-04-14 19:55:42.186099: Current learning rate: 0.00151 +2026-04-14 19:57:24.176801: train_loss -0.4482 +2026-04-14 19:57:24.183705: val_loss -0.3725 +2026-04-14 19:57:24.185920: Pseudo dice [0.6272, 0.0, 0.8126, 0.7545, 0.6185, 0.6244, 0.848] +2026-04-14 19:57:24.188124: Epoch time: 102.0 s +2026-04-14 19:57:25.451128: +2026-04-14 19:57:25.453376: Epoch 3512 +2026-04-14 19:57:25.455603: Current learning rate: 0.00151 +2026-04-14 19:59:06.996823: train_loss -0.4597 +2026-04-14 19:59:07.005795: val_loss -0.3348 +2026-04-14 19:59:07.008490: Pseudo dice [0.4544, 0.0, 0.8131, 0.2959, 0.37, 0.3514, 0.8938] +2026-04-14 19:59:07.011982: Epoch time: 101.55 s +2026-04-14 19:59:08.304006: +2026-04-14 19:59:08.306973: Epoch 3513 +2026-04-14 19:59:08.309108: Current learning rate: 0.0015 +2026-04-14 20:00:50.690672: train_loss -0.4456 +2026-04-14 20:00:50.698656: val_loss -0.3733 +2026-04-14 20:00:50.701805: Pseudo dice [0.548, 0.0, 0.8537, 0.8392, 0.5351, 0.5381, 0.7084] +2026-04-14 20:00:50.704796: Epoch time: 102.39 s +2026-04-14 20:00:51.973027: +2026-04-14 20:00:51.975207: Epoch 3514 +2026-04-14 20:00:51.977295: Current learning rate: 0.0015 +2026-04-14 20:02:33.450609: train_loss -0.4659 +2026-04-14 20:02:33.457184: val_loss -0.3633 +2026-04-14 20:02:33.459973: Pseudo dice [0.4998, 0.0, 0.7925, 0.3151, 0.5984, 0.4959, 0.6439] +2026-04-14 20:02:33.463099: Epoch time: 101.48 s +2026-04-14 20:02:34.753618: +2026-04-14 20:02:34.755406: Epoch 3515 +2026-04-14 20:02:34.757594: Current learning rate: 0.0015 +2026-04-14 20:04:16.681481: train_loss -0.4601 +2026-04-14 20:04:16.693569: val_loss -0.3935 +2026-04-14 20:04:16.697567: Pseudo dice [0.6603, 0.0, 0.7878, 0.5159, 0.5102, 0.565, 0.8853] +2026-04-14 20:04:16.701061: Epoch time: 101.93 s +2026-04-14 20:04:17.951103: +2026-04-14 20:04:17.953725: Epoch 3516 +2026-04-14 20:04:17.956468: Current learning rate: 0.00149 +2026-04-14 20:06:00.047281: train_loss -0.4611 +2026-04-14 20:06:00.056900: val_loss -0.3661 +2026-04-14 20:06:00.058987: Pseudo dice [0.6803, 0.0, 0.8654, 0.004, 0.3923, 0.684, 0.9241] +2026-04-14 20:06:00.063258: Epoch time: 102.1 s +2026-04-14 20:06:01.350104: +2026-04-14 20:06:01.352129: Epoch 3517 +2026-04-14 20:06:01.354213: Current learning rate: 0.00149 +2026-04-14 20:07:43.049568: train_loss -0.4603 +2026-04-14 20:07:43.057828: val_loss -0.3984 +2026-04-14 20:07:43.060123: Pseudo dice [0.7933, 0.0, 0.8133, 0.4838, 0.3476, 0.4496, 0.8843] +2026-04-14 20:07:43.063355: Epoch time: 101.7 s +2026-04-14 20:07:44.325052: +2026-04-14 20:07:44.329241: Epoch 3518 +2026-04-14 20:07:44.331782: Current learning rate: 0.00149 +2026-04-14 20:09:25.893580: train_loss -0.4744 +2026-04-14 20:09:25.900238: val_loss -0.3526 +2026-04-14 20:09:25.903043: Pseudo dice [0.7409, 0.0, 0.7881, 0.6724, 0.3833, 0.8827, 0.9424] +2026-04-14 20:09:25.905438: Epoch time: 101.57 s +2026-04-14 20:09:27.171092: +2026-04-14 20:09:27.172966: Epoch 3519 +2026-04-14 20:09:27.175010: Current learning rate: 0.00149 +2026-04-14 20:11:08.717602: train_loss -0.4709 +2026-04-14 20:11:08.724947: val_loss -0.4107 +2026-04-14 20:11:08.727499: Pseudo dice [0.7081, 0.0, 0.8509, 0.691, 0.4296, 0.8299, 0.9263] +2026-04-14 20:11:08.729835: Epoch time: 101.55 s +2026-04-14 20:11:09.999591: +2026-04-14 20:11:10.001667: Epoch 3520 +2026-04-14 20:11:10.004245: Current learning rate: 0.00148 +2026-04-14 20:12:51.484625: train_loss -0.4775 +2026-04-14 20:12:51.492605: val_loss -0.4011 +2026-04-14 20:12:51.496308: Pseudo dice [0.8277, 0.0, 0.8136, 0.1648, 0.4739, 0.8017, 0.8763] +2026-04-14 20:12:51.503345: Epoch time: 101.49 s +2026-04-14 20:12:52.772901: +2026-04-14 20:12:52.774876: Epoch 3521 +2026-04-14 20:12:52.777180: Current learning rate: 0.00148 +2026-04-14 20:14:34.495802: train_loss -0.4759 +2026-04-14 20:14:34.504309: val_loss -0.4151 +2026-04-14 20:14:34.506697: Pseudo dice [0.601, 0.0, 0.8818, 0.8401, 0.3988, 0.9268, 0.8608] +2026-04-14 20:14:34.509260: Epoch time: 101.73 s +2026-04-14 20:14:35.765749: +2026-04-14 20:14:35.768364: Epoch 3522 +2026-04-14 20:14:35.770493: Current learning rate: 0.00148 +2026-04-14 20:16:17.339949: train_loss -0.47 +2026-04-14 20:16:17.353530: val_loss -0.4014 +2026-04-14 20:16:17.356714: Pseudo dice [0.5874, 0.0, 0.8161, 0.4221, 0.5783, 0.8238, 0.8787] +2026-04-14 20:16:17.359433: Epoch time: 101.58 s +2026-04-14 20:16:19.680397: +2026-04-14 20:16:19.682268: Epoch 3523 +2026-04-14 20:16:19.684312: Current learning rate: 0.00148 +2026-04-14 20:18:01.542924: train_loss -0.4636 +2026-04-14 20:18:01.551541: val_loss -0.3924 +2026-04-14 20:18:01.554907: Pseudo dice [0.6079, 0.0, 0.8195, 0.7746, 0.5465, 0.4851, 0.9388] +2026-04-14 20:18:01.557189: Epoch time: 101.87 s +2026-04-14 20:18:02.843228: +2026-04-14 20:18:02.844951: Epoch 3524 +2026-04-14 20:18:02.846854: Current learning rate: 0.00147 +2026-04-14 20:19:44.519467: train_loss -0.466 +2026-04-14 20:19:44.526211: val_loss -0.3889 +2026-04-14 20:19:44.529093: Pseudo dice [0.5539, 0.0, 0.8945, 0.758, 0.4146, 0.6044, 0.8464] +2026-04-14 20:19:44.532655: Epoch time: 101.68 s +2026-04-14 20:19:45.794560: +2026-04-14 20:19:45.800948: Epoch 3525 +2026-04-14 20:19:45.805404: Current learning rate: 0.00147 +2026-04-14 20:21:27.435086: train_loss -0.4683 +2026-04-14 20:21:27.442529: val_loss -0.4056 +2026-04-14 20:21:27.444754: Pseudo dice [0.4579, 0.0, 0.8917, 0.8373, 0.5621, 0.5129, 0.9112] +2026-04-14 20:21:27.447371: Epoch time: 101.64 s +2026-04-14 20:21:28.780450: +2026-04-14 20:21:28.782622: Epoch 3526 +2026-04-14 20:21:28.785012: Current learning rate: 0.00147 +2026-04-14 20:23:10.299339: train_loss -0.4616 +2026-04-14 20:23:10.305086: val_loss -0.3879 +2026-04-14 20:23:10.307231: Pseudo dice [0.8185, 0.0, 0.8197, 0.4497, 0.4552, 0.2234, 0.9239] +2026-04-14 20:23:10.310166: Epoch time: 101.52 s +2026-04-14 20:23:11.561341: +2026-04-14 20:23:11.563096: Epoch 3527 +2026-04-14 20:23:11.565060: Current learning rate: 0.00146 +2026-04-14 20:24:53.435561: train_loss -0.4778 +2026-04-14 20:24:53.442171: val_loss -0.3988 +2026-04-14 20:24:53.444551: Pseudo dice [0.5668, 0.0, 0.7701, 0.8133, 0.5942, 0.5761, 0.86] +2026-04-14 20:24:53.447645: Epoch time: 101.88 s +2026-04-14 20:24:54.764360: +2026-04-14 20:24:54.767409: Epoch 3528 +2026-04-14 20:24:54.770127: Current learning rate: 0.00146 +2026-04-14 20:26:36.629331: train_loss -0.4675 +2026-04-14 20:26:36.637483: val_loss -0.372 +2026-04-14 20:26:36.640025: Pseudo dice [0.6346, 0.0, 0.8543, 0.7229, 0.5174, 0.6803, 0.9409] +2026-04-14 20:26:36.642596: Epoch time: 101.87 s +2026-04-14 20:26:37.929045: +2026-04-14 20:26:37.931087: Epoch 3529 +2026-04-14 20:26:37.933147: Current learning rate: 0.00146 +2026-04-14 20:28:19.377052: train_loss -0.4734 +2026-04-14 20:28:19.384000: val_loss -0.4001 +2026-04-14 20:28:19.386949: Pseudo dice [0.5684, 0.0, 0.6535, 0.8741, 0.4166, 0.7122, 0.9314] +2026-04-14 20:28:19.389237: Epoch time: 101.45 s +2026-04-14 20:28:20.686582: +2026-04-14 20:28:20.689039: Epoch 3530 +2026-04-14 20:28:20.691373: Current learning rate: 0.00146 +2026-04-14 20:30:02.206833: train_loss -0.4647 +2026-04-14 20:30:02.214265: val_loss -0.4068 +2026-04-14 20:30:02.217353: Pseudo dice [0.5858, 0.0, 0.823, 0.9071, 0.499, 0.8321, 0.842] +2026-04-14 20:30:02.220865: Epoch time: 101.52 s +2026-04-14 20:30:03.534088: +2026-04-14 20:30:03.537156: Epoch 3531 +2026-04-14 20:30:03.544975: Current learning rate: 0.00145 +2026-04-14 20:31:45.073412: train_loss -0.4674 +2026-04-14 20:31:45.080534: val_loss -0.4082 +2026-04-14 20:31:45.082681: Pseudo dice [0.5324, 0.0, 0.7877, 0.7051, 0.5019, 0.6196, 0.9258] +2026-04-14 20:31:45.085637: Epoch time: 101.54 s +2026-04-14 20:31:46.364810: +2026-04-14 20:31:46.366973: Epoch 3532 +2026-04-14 20:31:46.369846: Current learning rate: 0.00145 +2026-04-14 20:33:28.085579: train_loss -0.4713 +2026-04-14 20:33:28.092201: val_loss -0.3731 +2026-04-14 20:33:28.094638: Pseudo dice [0.6465, 0.0, 0.8532, 0.6796, 0.4776, 0.7721, 0.8434] +2026-04-14 20:33:28.097634: Epoch time: 101.72 s +2026-04-14 20:33:29.374227: +2026-04-14 20:33:29.377574: Epoch 3533 +2026-04-14 20:33:29.379616: Current learning rate: 0.00145 +2026-04-14 20:35:10.747983: train_loss -0.4729 +2026-04-14 20:35:10.755817: val_loss -0.3973 +2026-04-14 20:35:10.758826: Pseudo dice [0.5763, 0.0, 0.7914, 0.8219, 0.4714, 0.3161, 0.8898] +2026-04-14 20:35:10.761286: Epoch time: 101.38 s +2026-04-14 20:35:12.045408: +2026-04-14 20:35:12.047613: Epoch 3534 +2026-04-14 20:35:12.050061: Current learning rate: 0.00144 +2026-04-14 20:36:53.347389: train_loss -0.456 +2026-04-14 20:36:53.359345: val_loss -0.3719 +2026-04-14 20:36:53.361987: Pseudo dice [0.5401, 0.0, 0.8673, 0.2837, 0.5536, 0.8872, 0.931] +2026-04-14 20:36:53.365133: Epoch time: 101.31 s +2026-04-14 20:36:54.637154: +2026-04-14 20:36:54.639051: Epoch 3535 +2026-04-14 20:36:54.641362: Current learning rate: 0.00144 +2026-04-14 20:38:36.008245: train_loss -0.4602 +2026-04-14 20:38:36.017921: val_loss -0.3807 +2026-04-14 20:38:36.020270: Pseudo dice [0.5077, 0.0, 0.6996, 0.5746, 0.5556, 0.6949, 0.8977] +2026-04-14 20:38:36.024774: Epoch time: 101.37 s +2026-04-14 20:38:37.272587: +2026-04-14 20:38:37.275157: Epoch 3536 +2026-04-14 20:38:37.277701: Current learning rate: 0.00144 +2026-04-14 20:40:18.955456: train_loss -0.4724 +2026-04-14 20:40:18.962715: val_loss -0.3984 +2026-04-14 20:40:18.965629: Pseudo dice [0.736, 0.0, 0.8406, 0.6196, 0.3191, 0.9127, 0.9308] +2026-04-14 20:40:18.969335: Epoch time: 101.69 s +2026-04-14 20:40:20.239588: +2026-04-14 20:40:20.241231: Epoch 3537 +2026-04-14 20:40:20.243241: Current learning rate: 0.00144 +2026-04-14 20:42:02.200881: train_loss -0.4554 +2026-04-14 20:42:02.207680: val_loss -0.3686 +2026-04-14 20:42:02.209999: Pseudo dice [0.4831, 0.0, 0.8393, 0.403, 0.4252, 0.8037, 0.8604] +2026-04-14 20:42:02.214754: Epoch time: 101.96 s +2026-04-14 20:42:03.519604: +2026-04-14 20:42:03.522163: Epoch 3538 +2026-04-14 20:42:03.524495: Current learning rate: 0.00143 +2026-04-14 20:43:45.278314: train_loss -0.4813 +2026-04-14 20:43:45.284817: val_loss -0.3848 +2026-04-14 20:43:45.288397: Pseudo dice [0.4819, 0.0, 0.908, 0.8629, 0.4524, 0.4572, 0.8767] +2026-04-14 20:43:45.291666: Epoch time: 101.76 s +2026-04-14 20:43:46.631057: +2026-04-14 20:43:46.633507: Epoch 3539 +2026-04-14 20:43:46.635571: Current learning rate: 0.00143 +2026-04-14 20:45:27.952007: train_loss -0.4716 +2026-04-14 20:45:27.959531: val_loss -0.4119 +2026-04-14 20:45:27.962204: Pseudo dice [0.6024, 0.0, 0.8335, 0.6078, 0.3995, 0.8369, 0.9248] +2026-04-14 20:45:27.964990: Epoch time: 101.32 s +2026-04-14 20:45:29.207110: +2026-04-14 20:45:29.209342: Epoch 3540 +2026-04-14 20:45:29.211700: Current learning rate: 0.00143 +2026-04-14 20:47:11.108665: train_loss -0.4798 +2026-04-14 20:47:11.116884: val_loss -0.4091 +2026-04-14 20:47:11.119595: Pseudo dice [0.6371, 0.0, 0.8138, 0.7999, 0.4858, 0.6454, 0.831] +2026-04-14 20:47:11.122450: Epoch time: 101.9 s +2026-04-14 20:47:12.432042: +2026-04-14 20:47:12.434231: Epoch 3541 +2026-04-14 20:47:12.436077: Current learning rate: 0.00142 +2026-04-14 20:48:54.033530: train_loss -0.458 +2026-04-14 20:48:54.040644: val_loss -0.352 +2026-04-14 20:48:54.043515: Pseudo dice [0.5641, 0.0, 0.538, 0.2419, 0.3808, 0.6183, 0.7864] +2026-04-14 20:48:54.045914: Epoch time: 101.6 s +2026-04-14 20:48:55.328728: +2026-04-14 20:48:55.330459: Epoch 3542 +2026-04-14 20:48:55.332350: Current learning rate: 0.00142 +2026-04-14 20:50:37.610572: train_loss -0.4645 +2026-04-14 20:50:37.617789: val_loss -0.396 +2026-04-14 20:50:37.620110: Pseudo dice [0.6843, 0.0, 0.7339, 0.7374, 0.5166, 0.5546, 0.9121] +2026-04-14 20:50:37.622605: Epoch time: 102.28 s +2026-04-14 20:50:40.004519: +2026-04-14 20:50:40.006590: Epoch 3543 +2026-04-14 20:50:40.008561: Current learning rate: 0.00142 +2026-04-14 20:52:21.307856: train_loss -0.468 +2026-04-14 20:52:21.314998: val_loss -0.4156 +2026-04-14 20:52:21.317502: Pseudo dice [0.8528, 0.0, 0.877, 0.8635, 0.4538, 0.7719, 0.9486] +2026-04-14 20:52:21.320236: Epoch time: 101.31 s +2026-04-14 20:52:22.584490: +2026-04-14 20:52:22.586797: Epoch 3544 +2026-04-14 20:52:22.589094: Current learning rate: 0.00142 +2026-04-14 20:54:05.076536: train_loss -0.4736 +2026-04-14 20:54:05.084225: val_loss -0.4021 +2026-04-14 20:54:05.088095: Pseudo dice [0.7956, 0.0, 0.7917, 0.336, 0.7105, 0.8339, 0.8023] +2026-04-14 20:54:05.090599: Epoch time: 102.5 s +2026-04-14 20:54:06.464177: +2026-04-14 20:54:06.466155: Epoch 3545 +2026-04-14 20:54:06.468452: Current learning rate: 0.00141 +2026-04-14 20:55:48.722826: train_loss -0.4628 +2026-04-14 20:55:48.731068: val_loss -0.4087 +2026-04-14 20:55:48.734244: Pseudo dice [0.5579, 0.0, 0.8665, 0.7821, 0.5327, 0.8917, 0.9217] +2026-04-14 20:55:48.737261: Epoch time: 102.26 s +2026-04-14 20:55:50.013066: +2026-04-14 20:55:50.014931: Epoch 3546 +2026-04-14 20:55:50.017644: Current learning rate: 0.00141 +2026-04-14 20:57:31.480501: train_loss -0.4628 +2026-04-14 20:57:31.488494: val_loss -0.4167 +2026-04-14 20:57:31.491737: Pseudo dice [0.7367, 0.0, 0.8226, 0.8423, 0.472, 0.6168, 0.8673] +2026-04-14 20:57:31.495748: Epoch time: 101.47 s +2026-04-14 20:57:32.777553: +2026-04-14 20:57:32.779490: Epoch 3547 +2026-04-14 20:57:32.781467: Current learning rate: 0.00141 +2026-04-14 20:59:14.941583: train_loss -0.4715 +2026-04-14 20:59:14.949012: val_loss -0.3913 +2026-04-14 20:59:14.951330: Pseudo dice [0.6149, 0.0, 0.8372, 0.7837, 0.5418, 0.6898, 0.874] +2026-04-14 20:59:14.953779: Epoch time: 102.17 s +2026-04-14 20:59:14.956724: Yayy! New best EMA pseudo Dice: 0.5988 +2026-04-14 20:59:18.111493: +2026-04-14 20:59:18.113986: Epoch 3548 +2026-04-14 20:59:18.115762: Current learning rate: 0.00141 +2026-04-14 21:00:59.825440: train_loss -0.4763 +2026-04-14 21:00:59.833423: val_loss -0.4345 +2026-04-14 21:00:59.835845: Pseudo dice [0.6607, 0.0, 0.8696, 0.9108, 0.57, 0.5797, 0.9012] +2026-04-14 21:00:59.839833: Epoch time: 101.72 s +2026-04-14 21:00:59.842135: Yayy! New best EMA pseudo Dice: 0.6031 +2026-04-14 21:01:03.017652: +2026-04-14 21:01:03.019682: Epoch 3549 +2026-04-14 21:01:03.021546: Current learning rate: 0.0014 +2026-04-14 21:02:44.827703: train_loss -0.4728 +2026-04-14 21:02:44.835315: val_loss -0.3927 +2026-04-14 21:02:44.837888: Pseudo dice [0.5618, 0.0, 0.8664, 0.7153, 0.5573, 0.6533, 0.8501] +2026-04-14 21:02:44.840631: Epoch time: 101.81 s +2026-04-14 21:02:47.975188: +2026-04-14 21:02:47.977092: Epoch 3550 +2026-04-14 21:02:47.979527: Current learning rate: 0.0014 +2026-04-14 21:04:30.159921: train_loss -0.4668 +2026-04-14 21:04:30.167206: val_loss -0.3778 +2026-04-14 21:04:30.169438: Pseudo dice [0.4496, 0.0, 0.5648, 0.7629, 0.6445, 0.7447, 0.86] +2026-04-14 21:04:30.172891: Epoch time: 102.19 s +2026-04-14 21:04:31.446233: +2026-04-14 21:04:31.449177: Epoch 3551 +2026-04-14 21:04:31.451699: Current learning rate: 0.0014 +2026-04-14 21:06:13.835393: train_loss -0.4671 +2026-04-14 21:06:13.845423: val_loss -0.3813 +2026-04-14 21:06:13.847707: Pseudo dice [0.5965, 0.0, 0.8412, 0.6194, 0.4989, 0.7535, 0.8718] +2026-04-14 21:06:13.850752: Epoch time: 102.39 s +2026-04-14 21:06:15.156478: +2026-04-14 21:06:15.160574: Epoch 3552 +2026-04-14 21:06:15.162865: Current learning rate: 0.00139 +2026-04-14 21:07:58.000950: train_loss -0.47 +2026-04-14 21:07:58.007940: val_loss -0.4117 +2026-04-14 21:07:58.010269: Pseudo dice [0.7213, 0.0, 0.695, 0.8777, 0.5366, 0.2427, 0.9561] +2026-04-14 21:07:58.013424: Epoch time: 102.85 s +2026-04-14 21:07:59.277113: +2026-04-14 21:07:59.278851: Epoch 3553 +2026-04-14 21:07:59.281061: Current learning rate: 0.00139 +2026-04-14 21:09:40.578640: train_loss -0.4591 +2026-04-14 21:09:40.585538: val_loss -0.4205 +2026-04-14 21:09:40.587514: Pseudo dice [0.6778, 0.0, 0.8995, 0.7808, 0.5068, 0.7054, 0.8751] +2026-04-14 21:09:40.592383: Epoch time: 101.3 s +2026-04-14 21:09:41.885127: +2026-04-14 21:09:41.887278: Epoch 3554 +2026-04-14 21:09:41.889813: Current learning rate: 0.00139 +2026-04-14 21:11:23.803596: train_loss -0.4748 +2026-04-14 21:11:23.813230: val_loss -0.4068 +2026-04-14 21:11:23.815638: Pseudo dice [0.2598, 0.0, 0.9119, 0.8144, 0.492, 0.7497, 0.9116] +2026-04-14 21:11:23.818224: Epoch time: 101.92 s +2026-04-14 21:11:25.141602: +2026-04-14 21:11:25.143973: Epoch 3555 +2026-04-14 21:11:25.148057: Current learning rate: 0.00139 +2026-04-14 21:13:07.368572: train_loss -0.4734 +2026-04-14 21:13:07.378736: val_loss -0.3833 +2026-04-14 21:13:07.381626: Pseudo dice [0.3159, 0.0, 0.8813, 0.5454, 0.4474, 0.5529, 0.8782] +2026-04-14 21:13:07.384942: Epoch time: 102.23 s +2026-04-14 21:13:08.686604: +2026-04-14 21:13:08.689290: Epoch 3556 +2026-04-14 21:13:08.692106: Current learning rate: 0.00138 +2026-04-14 21:14:50.643066: train_loss -0.4743 +2026-04-14 21:14:50.649964: val_loss -0.3927 +2026-04-14 21:14:50.652133: Pseudo dice [0.7425, 0.0, 0.7901, 0.147, 0.3885, 0.7745, 0.9197] +2026-04-14 21:14:50.654423: Epoch time: 101.96 s +2026-04-14 21:14:51.928718: +2026-04-14 21:14:51.931018: Epoch 3557 +2026-04-14 21:14:51.933270: Current learning rate: 0.00138 +2026-04-14 21:16:33.944775: train_loss -0.4735 +2026-04-14 21:16:33.955835: val_loss -0.3779 +2026-04-14 21:16:33.959136: Pseudo dice [0.4994, 0.0, 0.8296, 0.7913, 0.3345, 0.3561, 0.8863] +2026-04-14 21:16:33.963461: Epoch time: 102.02 s +2026-04-14 21:16:35.266906: +2026-04-14 21:16:35.269397: Epoch 3558 +2026-04-14 21:16:35.272001: Current learning rate: 0.00138 +2026-04-14 21:18:17.700111: train_loss -0.4689 +2026-04-14 21:18:17.709855: val_loss -0.407 +2026-04-14 21:18:17.712558: Pseudo dice [0.4364, 0.0, 0.833, 0.8078, 0.5107, 0.7951, 0.8776] +2026-04-14 21:18:17.719280: Epoch time: 102.44 s +2026-04-14 21:18:19.001958: +2026-04-14 21:18:19.004299: Epoch 3559 +2026-04-14 21:18:19.007241: Current learning rate: 0.00137 +2026-04-14 21:20:00.938555: train_loss -0.4725 +2026-04-14 21:20:00.947281: val_loss -0.3933 +2026-04-14 21:20:00.949649: Pseudo dice [0.6743, 0.0, 0.792, 0.7424, 0.5065, 0.5878, 0.8308] +2026-04-14 21:20:00.951924: Epoch time: 101.94 s +2026-04-14 21:20:02.200888: +2026-04-14 21:20:02.203620: Epoch 3560 +2026-04-14 21:20:02.206058: Current learning rate: 0.00137 +2026-04-14 21:21:43.986579: train_loss -0.4725 +2026-04-14 21:21:43.992447: val_loss -0.3961 +2026-04-14 21:21:43.994753: Pseudo dice [0.6366, 0.0, 0.7757, 0.4982, 0.4116, 0.5166, 0.9486] +2026-04-14 21:21:43.997496: Epoch time: 101.79 s +2026-04-14 21:21:45.268771: +2026-04-14 21:21:45.270606: Epoch 3561 +2026-04-14 21:21:45.272578: Current learning rate: 0.00137 +2026-04-14 21:23:28.615732: train_loss -0.4795 +2026-04-14 21:23:28.623115: val_loss -0.3534 +2026-04-14 21:23:28.625345: Pseudo dice [0.8181, 0.0, 0.7178, 0.1732, 0.4293, 0.7711, 0.9057] +2026-04-14 21:23:28.627482: Epoch time: 103.35 s +2026-04-14 21:23:29.862386: +2026-04-14 21:23:29.864708: Epoch 3562 +2026-04-14 21:23:29.867734: Current learning rate: 0.00137 +2026-04-14 21:25:12.023330: train_loss -0.4816 +2026-04-14 21:25:12.030567: val_loss -0.3898 +2026-04-14 21:25:12.033076: Pseudo dice [0.8001, 0.0, 0.8017, 0.2724, 0.5514, 0.7977, 0.9561] +2026-04-14 21:25:12.035504: Epoch time: 102.16 s +2026-04-14 21:25:13.313449: +2026-04-14 21:25:13.318331: Epoch 3563 +2026-04-14 21:25:13.321147: Current learning rate: 0.00136 +2026-04-14 21:26:55.056287: train_loss -0.4533 +2026-04-14 21:26:55.062590: val_loss -0.3532 +2026-04-14 21:26:55.066086: Pseudo dice [0.6081, 0.0, 0.6057, 0.422, 0.4014, 0.5908, 0.8662] +2026-04-14 21:26:55.068640: Epoch time: 101.75 s +2026-04-14 21:26:56.383174: +2026-04-14 21:26:56.385756: Epoch 3564 +2026-04-14 21:26:56.387987: Current learning rate: 0.00136 +2026-04-14 21:28:38.863913: train_loss -0.4625 +2026-04-14 21:28:38.870721: val_loss -0.3803 +2026-04-14 21:28:38.873595: Pseudo dice [0.7565, 0.0, 0.7609, 0.8409, 0.5838, 0.6351, 0.8739] +2026-04-14 21:28:38.876191: Epoch time: 102.48 s +2026-04-14 21:28:40.122949: +2026-04-14 21:28:40.126949: Epoch 3565 +2026-04-14 21:28:40.129997: Current learning rate: 0.00136 +2026-04-14 21:30:22.146400: train_loss -0.4646 +2026-04-14 21:30:22.154110: val_loss -0.4022 +2026-04-14 21:30:22.157692: Pseudo dice [0.7618, 0.0, 0.8495, 0.7346, 0.4852, 0.5504, 0.8264] +2026-04-14 21:30:22.161668: Epoch time: 102.03 s +2026-04-14 21:30:23.495175: +2026-04-14 21:30:23.500063: Epoch 3566 +2026-04-14 21:30:23.510211: Current learning rate: 0.00135 +2026-04-14 21:32:05.849287: train_loss -0.4682 +2026-04-14 21:32:05.860846: val_loss -0.3995 +2026-04-14 21:32:05.863345: Pseudo dice [0.412, 0.0, 0.8463, 0.6586, 0.4529, 0.6882, 0.8551] +2026-04-14 21:32:05.866087: Epoch time: 102.36 s +2026-04-14 21:32:07.145959: +2026-04-14 21:32:07.147817: Epoch 3567 +2026-04-14 21:32:07.150088: Current learning rate: 0.00135 +2026-04-14 21:33:48.468741: train_loss -0.4668 +2026-04-14 21:33:48.476368: val_loss -0.4037 +2026-04-14 21:33:48.480227: Pseudo dice [0.578, 0.0, 0.864, 0.6575, 0.3898, 0.5699, 0.8545] +2026-04-14 21:33:48.483371: Epoch time: 101.33 s +2026-04-14 21:33:49.765044: +2026-04-14 21:33:49.767108: Epoch 3568 +2026-04-14 21:33:49.769096: Current learning rate: 0.00135 +2026-04-14 21:35:31.963356: train_loss -0.462 +2026-04-14 21:35:31.970856: val_loss -0.4125 +2026-04-14 21:35:31.973229: Pseudo dice [0.5567, 0.0, 0.8278, 0.9136, 0.6199, 0.6237, 0.804] +2026-04-14 21:35:31.976051: Epoch time: 102.2 s +2026-04-14 21:35:33.253705: +2026-04-14 21:35:33.255715: Epoch 3569 +2026-04-14 21:35:33.257896: Current learning rate: 0.00135 +2026-04-14 21:37:15.398561: train_loss -0.4658 +2026-04-14 21:37:15.407098: val_loss -0.3554 +2026-04-14 21:37:15.409621: Pseudo dice [0.7466, 0.0, 0.7212, 0.6497, 0.5357, 0.77, 0.7608] +2026-04-14 21:37:15.412247: Epoch time: 102.15 s +2026-04-14 21:37:16.940867: +2026-04-14 21:37:16.942796: Epoch 3570 +2026-04-14 21:37:16.945047: Current learning rate: 0.00134 +2026-04-14 21:38:58.881251: train_loss -0.4653 +2026-04-14 21:38:58.888511: val_loss -0.3909 +2026-04-14 21:38:58.891407: Pseudo dice [0.7769, 0.0, 0.8088, 0.7556, 0.4804, 0.828, 0.8383] +2026-04-14 21:38:58.894031: Epoch time: 101.94 s +2026-04-14 21:39:00.196452: +2026-04-14 21:39:00.199528: Epoch 3571 +2026-04-14 21:39:00.201993: Current learning rate: 0.00134 +2026-04-14 21:40:42.182479: train_loss -0.4731 +2026-04-14 21:40:42.189779: val_loss -0.4125 +2026-04-14 21:40:42.192108: Pseudo dice [0.72, 0.0, 0.8603, 0.7028, 0.532, 0.5919, 0.9357] +2026-04-14 21:40:42.194654: Epoch time: 101.99 s +2026-04-14 21:40:43.483495: +2026-04-14 21:40:43.486278: Epoch 3572 +2026-04-14 21:40:43.489217: Current learning rate: 0.00134 +2026-04-14 21:42:25.317857: train_loss -0.4756 +2026-04-14 21:42:25.345642: val_loss -0.3663 +2026-04-14 21:42:25.348468: Pseudo dice [0.5554, 0.0, 0.8216, 0.5837, 0.518, 0.689, 0.8935] +2026-04-14 21:42:25.350820: Epoch time: 101.84 s +2026-04-14 21:42:26.626141: +2026-04-14 21:42:26.628576: Epoch 3573 +2026-04-14 21:42:26.632356: Current learning rate: 0.00134 +2026-04-14 21:44:08.333410: train_loss -0.4728 +2026-04-14 21:44:08.342212: val_loss -0.3886 +2026-04-14 21:44:08.345071: Pseudo dice [0.3127, 0.0, 0.8302, 0.4083, 0.585, 0.7068, 0.9237] +2026-04-14 21:44:08.348268: Epoch time: 101.71 s +2026-04-14 21:44:09.624945: +2026-04-14 21:44:09.628910: Epoch 3574 +2026-04-14 21:44:09.631502: Current learning rate: 0.00133 +2026-04-14 21:45:51.032526: train_loss -0.4624 +2026-04-14 21:45:51.039664: val_loss -0.3935 +2026-04-14 21:45:51.042032: Pseudo dice [0.7268, 0.0, 0.7678, 0.867, 0.5297, 0.3746, 0.9062] +2026-04-14 21:45:51.044582: Epoch time: 101.41 s +2026-04-14 21:45:52.298527: +2026-04-14 21:45:52.300256: Epoch 3575 +2026-04-14 21:45:52.302214: Current learning rate: 0.00133 +2026-04-14 21:47:33.799898: train_loss -0.4752 +2026-04-14 21:47:33.808204: val_loss -0.3634 +2026-04-14 21:47:33.810521: Pseudo dice [0.3234, 0.0, 0.7343, 0.1872, 0.489, 0.5404, 0.9243] +2026-04-14 21:47:33.812829: Epoch time: 101.5 s +2026-04-14 21:47:35.129504: +2026-04-14 21:47:35.132265: Epoch 3576 +2026-04-14 21:47:35.135086: Current learning rate: 0.00133 +2026-04-14 21:49:16.878034: train_loss -0.4674 +2026-04-14 21:49:16.884951: val_loss -0.3954 +2026-04-14 21:49:16.888575: Pseudo dice [0.7632, 0.0, 0.8404, 0.7835, 0.5597, 0.6948, 0.8919] +2026-04-14 21:49:16.891537: Epoch time: 101.75 s +2026-04-14 21:49:18.175168: +2026-04-14 21:49:18.176916: Epoch 3577 +2026-04-14 21:49:18.178901: Current learning rate: 0.00132 +2026-04-14 21:51:00.397141: train_loss -0.4692 +2026-04-14 21:51:00.405166: val_loss -0.3747 +2026-04-14 21:51:00.408145: Pseudo dice [0.7075, 0.0, 0.8393, 0.1921, 0.4108, 0.7705, 0.6615] +2026-04-14 21:51:00.411026: Epoch time: 102.23 s +2026-04-14 21:51:01.667765: +2026-04-14 21:51:01.670284: Epoch 3578 +2026-04-14 21:51:01.673042: Current learning rate: 0.00132 +2026-04-14 21:52:42.944357: train_loss -0.4692 +2026-04-14 21:52:42.951649: val_loss -0.391 +2026-04-14 21:52:42.953810: Pseudo dice [0.539, 0.0, 0.8154, 0.524, 0.6032, 0.8225, 0.8869] +2026-04-14 21:52:42.956967: Epoch time: 101.28 s +2026-04-14 21:52:44.194656: +2026-04-14 21:52:44.197210: Epoch 3579 +2026-04-14 21:52:44.199433: Current learning rate: 0.00132 +2026-04-14 21:54:26.581606: train_loss -0.4651 +2026-04-14 21:54:26.588191: val_loss -0.3663 +2026-04-14 21:54:26.590262: Pseudo dice [0.514, 0.0, 0.8534, 0.7115, 0.3667, 0.3799, 0.8056] +2026-04-14 21:54:26.592336: Epoch time: 102.39 s +2026-04-14 21:54:27.866845: +2026-04-14 21:54:27.868844: Epoch 3580 +2026-04-14 21:54:27.871030: Current learning rate: 0.00132 +2026-04-14 21:56:09.647871: train_loss -0.4758 +2026-04-14 21:56:09.655651: val_loss -0.3983 +2026-04-14 21:56:09.658521: Pseudo dice [0.508, 0.0, 0.6098, 0.7069, 0.5768, 0.748, 0.9272] +2026-04-14 21:56:09.661443: Epoch time: 101.78 s +2026-04-14 21:56:10.947497: +2026-04-14 21:56:10.949816: Epoch 3581 +2026-04-14 21:56:10.951741: Current learning rate: 0.00131 +2026-04-14 21:57:54.133700: train_loss -0.4758 +2026-04-14 21:57:54.141183: val_loss -0.4174 +2026-04-14 21:57:54.143492: Pseudo dice [0.7772, 0.0, 0.8614, 0.2093, 0.3164, 0.7584, 0.9024] +2026-04-14 21:57:54.146185: Epoch time: 103.19 s +2026-04-14 21:57:55.407679: +2026-04-14 21:57:55.409831: Epoch 3582 +2026-04-14 21:57:55.411960: Current learning rate: 0.00131 +2026-04-14 21:59:37.053623: train_loss -0.462 +2026-04-14 21:59:37.060402: val_loss -0.3605 +2026-04-14 21:59:37.062629: Pseudo dice [0.6628, 0.0, 0.7854, 0.1146, 0.3951, 0.4975, 0.7396] +2026-04-14 21:59:37.065093: Epoch time: 101.65 s +2026-04-14 21:59:38.336147: +2026-04-14 21:59:38.338236: Epoch 3583 +2026-04-14 21:59:38.340647: Current learning rate: 0.00131 +2026-04-14 22:01:20.108819: train_loss -0.471 +2026-04-14 22:01:20.118606: val_loss -0.4098 +2026-04-14 22:01:20.120640: Pseudo dice [0.5035, 0.0, 0.8696, 0.5178, 0.4906, 0.5867, 0.8792] +2026-04-14 22:01:20.124766: Epoch time: 101.78 s +2026-04-14 22:01:21.360284: +2026-04-14 22:01:21.363552: Epoch 3584 +2026-04-14 22:01:21.366350: Current learning rate: 0.0013 +2026-04-14 22:03:02.856539: train_loss -0.4943 +2026-04-14 22:03:02.863178: val_loss -0.4044 +2026-04-14 22:03:02.871071: Pseudo dice [0.6628, 0.0, 0.8702, 0.4503, 0.2764, 0.6339, 0.7502] +2026-04-14 22:03:02.874170: Epoch time: 101.5 s +2026-04-14 22:03:04.141519: +2026-04-14 22:03:04.143788: Epoch 3585 +2026-04-14 22:03:04.146178: Current learning rate: 0.0013 +2026-04-14 22:04:46.035306: train_loss -0.5028 +2026-04-14 22:04:46.041889: val_loss -0.4557 +2026-04-14 22:04:46.044224: Pseudo dice [0.7349, 0.0, 0.8969, 0.7963, 0.3667, 0.6387, 0.7203] +2026-04-14 22:04:46.047186: Epoch time: 101.9 s +2026-04-14 22:04:47.358483: +2026-04-14 22:04:47.361672: Epoch 3586 +2026-04-14 22:04:47.366670: Current learning rate: 0.0013 +2026-04-14 22:06:28.762333: train_loss -0.5405 +2026-04-14 22:06:28.769086: val_loss -0.451 +2026-04-14 22:06:28.772335: Pseudo dice [0.5947, 0.0, 0.848, 0.7626, 0.5889, 0.7267, 0.8927] +2026-04-14 22:06:28.775170: Epoch time: 101.41 s +2026-04-14 22:06:30.075758: +2026-04-14 22:06:30.080107: Epoch 3587 +2026-04-14 22:06:30.084138: Current learning rate: 0.0013 +2026-04-14 22:08:11.708133: train_loss -0.551 +2026-04-14 22:08:11.716449: val_loss -0.4241 +2026-04-14 22:08:11.720170: Pseudo dice [0.4469, 0.0, 0.8585, 0.2329, 0.2921, 0.6456, 0.7522] +2026-04-14 22:08:11.723496: Epoch time: 101.64 s +2026-04-14 22:08:12.989974: +2026-04-14 22:08:12.995698: Epoch 3588 +2026-04-14 22:08:12.998097: Current learning rate: 0.00129 +2026-04-14 22:09:54.963602: train_loss -0.5324 +2026-04-14 22:09:54.971664: val_loss -0.3947 +2026-04-14 22:09:54.974736: Pseudo dice [0.7393, 0.0, 0.7544, 0.4082, 0.405, 0.7655, 0.9302] +2026-04-14 22:09:54.978393: Epoch time: 101.98 s +2026-04-14 22:09:56.272512: +2026-04-14 22:09:56.274531: Epoch 3589 +2026-04-14 22:09:56.276527: Current learning rate: 0.00129 +2026-04-14 22:11:38.542620: train_loss -0.5417 +2026-04-14 22:11:38.550170: val_loss -0.4126 +2026-04-14 22:11:38.552994: Pseudo dice [0.6334, 0.0, 0.883, 0.04, 0.1226, 0.7759, 0.9279] +2026-04-14 22:11:38.556028: Epoch time: 102.27 s +2026-04-14 22:11:39.783863: +2026-04-14 22:11:39.789407: Epoch 3590 +2026-04-14 22:11:39.794120: Current learning rate: 0.00129 +2026-04-14 22:13:21.818887: train_loss -0.5398 +2026-04-14 22:13:21.829183: val_loss -0.4731 +2026-04-14 22:13:21.831746: Pseudo dice [0.871, 0.0, 0.805, 0.7242, 0.5605, 0.8231, 0.893] +2026-04-14 22:13:21.834039: Epoch time: 102.04 s +2026-04-14 22:13:23.081934: +2026-04-14 22:13:23.084938: Epoch 3591 +2026-04-14 22:13:23.087124: Current learning rate: 0.00128 +2026-04-14 22:15:04.633597: train_loss -0.5407 +2026-04-14 22:15:04.640687: val_loss -0.4852 +2026-04-14 22:15:04.643389: Pseudo dice [0.7227, 0.0, 0.8639, 0.9018, 0.5276, 0.9053, 0.9013] +2026-04-14 22:15:04.646168: Epoch time: 101.55 s +2026-04-14 22:15:05.891298: +2026-04-14 22:15:05.893262: Epoch 3592 +2026-04-14 22:15:05.895228: Current learning rate: 0.00128 +2026-04-14 22:16:47.524981: train_loss -0.5498 +2026-04-14 22:16:47.536465: val_loss -0.4586 +2026-04-14 22:16:47.539145: Pseudo dice [0.856, 0.0, 0.2912, 0.3693, 0.5281, 0.7891, 0.8569] +2026-04-14 22:16:47.542523: Epoch time: 101.64 s +2026-04-14 22:16:48.823812: +2026-04-14 22:16:48.826363: Epoch 3593 +2026-04-14 22:16:48.829969: Current learning rate: 0.00128 +2026-04-14 22:18:31.076183: train_loss -0.5629 +2026-04-14 22:18:31.083044: val_loss -0.4344 +2026-04-14 22:18:31.085440: Pseudo dice [0.5813, 0.0, 0.5516, 0.5403, 0.4803, 0.9294, 0.9003] +2026-04-14 22:18:31.088717: Epoch time: 102.26 s +2026-04-14 22:18:32.350753: +2026-04-14 22:18:32.353176: Epoch 3594 +2026-04-14 22:18:32.356160: Current learning rate: 0.00128 +2026-04-14 22:20:14.354516: train_loss -0.5485 +2026-04-14 22:20:14.362427: val_loss -0.4521 +2026-04-14 22:20:14.364950: Pseudo dice [0.5157, 0.0, 0.8614, 0.517, 0.4418, 0.3819, 0.8874] +2026-04-14 22:20:14.367460: Epoch time: 102.01 s +2026-04-14 22:20:15.624173: +2026-04-14 22:20:15.626502: Epoch 3595 +2026-04-14 22:20:15.628627: Current learning rate: 0.00127 +2026-04-14 22:21:57.930113: train_loss -0.5557 +2026-04-14 22:21:57.937234: val_loss -0.4679 +2026-04-14 22:21:57.941762: Pseudo dice [0.8361, 0.0, 0.8485, 0.8535, 0.5543, 0.538, 0.9] +2026-04-14 22:21:57.946919: Epoch time: 102.31 s +2026-04-14 22:21:59.207628: +2026-04-14 22:21:59.209890: Epoch 3596 +2026-04-14 22:21:59.212760: Current learning rate: 0.00127 +2026-04-14 22:23:40.882716: train_loss -0.5524 +2026-04-14 22:23:40.889649: val_loss -0.4656 +2026-04-14 22:23:40.892186: Pseudo dice [0.4492, 0.0, 0.7737, 0.572, 0.4389, 0.7575, 0.8829] +2026-04-14 22:23:40.894608: Epoch time: 101.68 s +2026-04-14 22:23:42.153385: +2026-04-14 22:23:42.156323: Epoch 3597 +2026-04-14 22:23:42.162197: Current learning rate: 0.00127 +2026-04-14 22:25:24.201494: train_loss -0.5507 +2026-04-14 22:25:24.209172: val_loss -0.4792 +2026-04-14 22:25:24.211810: Pseudo dice [0.4771, 0.0, 0.8977, 0.5709, 0.4864, 0.8245, 0.895] +2026-04-14 22:25:24.215491: Epoch time: 102.05 s +2026-04-14 22:25:25.486430: +2026-04-14 22:25:25.488125: Epoch 3598 +2026-04-14 22:25:25.490280: Current learning rate: 0.00126 +2026-04-14 22:27:07.095851: train_loss -0.5431 +2026-04-14 22:27:07.103407: val_loss -0.4433 +2026-04-14 22:27:07.105598: Pseudo dice [0.4897, 0.0, 0.8231, 0.5389, 0.4692, 0.565, 0.9465] +2026-04-14 22:27:07.108211: Epoch time: 101.61 s +2026-04-14 22:27:08.410616: +2026-04-14 22:27:08.413247: Epoch 3599 +2026-04-14 22:27:08.416700: Current learning rate: 0.00126 +2026-04-14 22:28:51.067599: train_loss -0.5446 +2026-04-14 22:28:51.074142: val_loss -0.4755 +2026-04-14 22:28:51.076483: Pseudo dice [0.7634, 0.0, 0.8349, 0.8139, 0.4231, 0.7642, 0.9364] +2026-04-14 22:28:51.078792: Epoch time: 102.66 s +2026-04-14 22:28:54.274575: +2026-04-14 22:28:54.276954: Epoch 3600 +2026-04-14 22:28:54.278800: Current learning rate: 0.00126 +2026-04-14 22:30:36.342964: train_loss -0.5566 +2026-04-14 22:30:36.349995: val_loss -0.4604 +2026-04-14 22:30:36.352427: Pseudo dice [0.7093, 0.0, 0.8285, 0.8891, 0.4516, 0.9112, 0.933] +2026-04-14 22:30:36.355273: Epoch time: 102.07 s +2026-04-14 22:30:38.702389: +2026-04-14 22:30:38.704248: Epoch 3601 +2026-04-14 22:30:38.706360: Current learning rate: 0.00126 +2026-04-14 22:32:21.022091: train_loss -0.5502 +2026-04-14 22:32:21.030189: val_loss -0.4704 +2026-04-14 22:32:21.032897: Pseudo dice [0.7769, 0.0, 0.8178, 0.5844, 0.5948, 0.5529, 0.8478] +2026-04-14 22:32:21.035381: Epoch time: 102.32 s +2026-04-14 22:32:22.340544: +2026-04-14 22:32:22.342467: Epoch 3602 +2026-04-14 22:32:22.344417: Current learning rate: 0.00125 +2026-04-14 22:34:04.498185: train_loss -0.5523 +2026-04-14 22:34:04.504644: val_loss -0.4816 +2026-04-14 22:34:04.507699: Pseudo dice [0.3618, 0.0, 0.8598, 0.77, 0.5579, 0.7928, 0.9171] +2026-04-14 22:34:04.510044: Epoch time: 102.16 s +2026-04-14 22:34:05.760300: +2026-04-14 22:34:05.762676: Epoch 3603 +2026-04-14 22:34:05.764948: Current learning rate: 0.00125 +2026-04-14 22:35:47.655167: train_loss -0.5438 +2026-04-14 22:35:47.662198: val_loss -0.4483 +2026-04-14 22:35:47.664911: Pseudo dice [0.7183, 0.0, 0.6209, 0.7097, 0.5124, 0.6014, 0.8953] +2026-04-14 22:35:47.667320: Epoch time: 101.9 s +2026-04-14 22:35:48.933674: +2026-04-14 22:35:48.935920: Epoch 3604 +2026-04-14 22:35:48.937891: Current learning rate: 0.00125 +2026-04-14 22:37:31.091608: train_loss -0.5504 +2026-04-14 22:37:31.104463: val_loss -0.4671 +2026-04-14 22:37:31.108164: Pseudo dice [0.6691, 0.0, 0.8516, 0.8918, 0.5321, 0.8769, 0.9059] +2026-04-14 22:37:31.110710: Epoch time: 102.16 s +2026-04-14 22:37:32.390012: +2026-04-14 22:37:32.392313: Epoch 3605 +2026-04-14 22:37:32.395280: Current learning rate: 0.00124 +2026-04-14 22:39:14.193861: train_loss -0.5462 +2026-04-14 22:39:14.201390: val_loss -0.4631 +2026-04-14 22:39:14.203713: Pseudo dice [0.5925, 0.0, 0.7762, 0.8267, 0.4839, 0.5751, 0.9365] +2026-04-14 22:39:14.208077: Epoch time: 101.81 s +2026-04-14 22:39:15.494595: +2026-04-14 22:39:15.496511: Epoch 3606 +2026-04-14 22:39:15.498546: Current learning rate: 0.00124 +2026-04-14 22:40:57.218530: train_loss -0.5521 +2026-04-14 22:40:57.227663: val_loss -0.4941 +2026-04-14 22:40:57.229994: Pseudo dice [0.6574, 0.0, 0.9134, 0.8092, 0.587, 0.6117, 0.9066] +2026-04-14 22:40:57.232907: Epoch time: 101.73 s +2026-04-14 22:40:58.501364: +2026-04-14 22:40:58.504472: Epoch 3607 +2026-04-14 22:40:58.506422: Current learning rate: 0.00124 +2026-04-14 22:42:40.294998: train_loss -0.5502 +2026-04-14 22:42:40.323539: val_loss -0.4929 +2026-04-14 22:42:40.326655: Pseudo dice [0.7687, 0.0, 0.7652, 0.8559, 0.6084, 0.8628, 0.943] +2026-04-14 22:42:40.330212: Epoch time: 101.8 s +2026-04-14 22:42:40.333630: Yayy! New best EMA pseudo Dice: 0.6106 +2026-04-14 22:42:43.586408: +2026-04-14 22:42:43.589051: Epoch 3608 +2026-04-14 22:42:43.590695: Current learning rate: 0.00124 +2026-04-14 22:44:25.699730: train_loss -0.5434 +2026-04-14 22:44:25.707117: val_loss -0.4602 +2026-04-14 22:44:25.709342: Pseudo dice [0.7441, 0.0, 0.8445, 0.7045, 0.4897, 0.8879, 0.7781] +2026-04-14 22:44:25.711804: Epoch time: 102.12 s +2026-04-14 22:44:25.713899: Yayy! New best EMA pseudo Dice: 0.6131 +2026-04-14 22:44:28.857473: +2026-04-14 22:44:28.859545: Epoch 3609 +2026-04-14 22:44:28.861209: Current learning rate: 0.00123 +2026-04-14 22:46:11.295475: train_loss -0.5493 +2026-04-14 22:46:11.302671: val_loss -0.4556 +2026-04-14 22:46:11.305260: Pseudo dice [0.6218, 0.0, 0.7529, 0.8181, 0.5739, 0.6239, 0.9197] +2026-04-14 22:46:11.307988: Epoch time: 102.44 s +2026-04-14 22:46:11.310420: Yayy! New best EMA pseudo Dice: 0.6134 +2026-04-14 22:46:14.514525: +2026-04-14 22:46:14.516992: Epoch 3610 +2026-04-14 22:46:14.518691: Current learning rate: 0.00123 +2026-04-14 22:47:56.616266: train_loss -0.5505 +2026-04-14 22:47:56.623521: val_loss -0.4696 +2026-04-14 22:47:56.625786: Pseudo dice [0.5141, 0.0, 0.743, 0.8984, 0.4385, 0.6841, 0.946] +2026-04-14 22:47:56.628048: Epoch time: 102.1 s +2026-04-14 22:47:57.897172: +2026-04-14 22:47:57.899272: Epoch 3611 +2026-04-14 22:47:57.901914: Current learning rate: 0.00123 +2026-04-14 22:49:39.820238: train_loss -0.5523 +2026-04-14 22:49:39.826509: val_loss -0.4747 +2026-04-14 22:49:39.828940: Pseudo dice [0.6257, 0.0, 0.8209, 0.8801, 0.5377, 0.8375, 0.8266] +2026-04-14 22:49:39.831493: Epoch time: 101.93 s +2026-04-14 22:49:39.833342: Yayy! New best EMA pseudo Dice: 0.6158 +2026-04-14 22:49:42.940477: +2026-04-14 22:49:42.942763: Epoch 3612 +2026-04-14 22:49:42.944625: Current learning rate: 0.00122 +2026-04-14 22:51:24.399230: train_loss -0.5641 +2026-04-14 22:51:24.406147: val_loss -0.4606 +2026-04-14 22:51:24.411057: Pseudo dice [0.5128, 0.0, 0.7272, 0.7002, 0.5322, 0.6847, 0.8786] +2026-04-14 22:51:24.414036: Epoch time: 101.46 s +2026-04-14 22:51:25.739276: +2026-04-14 22:51:25.741289: Epoch 3613 +2026-04-14 22:51:25.744016: Current learning rate: 0.00122 +2026-04-14 22:53:07.400803: train_loss -0.5585 +2026-04-14 22:53:07.407507: val_loss -0.4573 +2026-04-14 22:53:07.410721: Pseudo dice [0.6485, 0.0, 0.7349, 0.9086, 0.5251, 0.8191, 0.8515] +2026-04-14 22:53:07.413365: Epoch time: 101.66 s +2026-04-14 22:53:08.698437: +2026-04-14 22:53:08.700695: Epoch 3614 +2026-04-14 22:53:08.703217: Current learning rate: 0.00122 +2026-04-14 22:54:50.998208: train_loss -0.5471 +2026-04-14 22:54:51.005396: val_loss -0.4205 +2026-04-14 22:54:51.007892: Pseudo dice [0.5642, 0.0, 0.7703, 0.2661, 0.3172, 0.7064, 0.9382] +2026-04-14 22:54:51.011160: Epoch time: 102.3 s +2026-04-14 22:54:52.328338: +2026-04-14 22:54:52.330438: Epoch 3615 +2026-04-14 22:54:52.333022: Current learning rate: 0.00122 +2026-04-14 22:56:34.330458: train_loss -0.542 +2026-04-14 22:56:34.342349: val_loss -0.4496 +2026-04-14 22:56:34.344569: Pseudo dice [0.5046, 0.0, 0.7297, 0.5339, 0.5089, 0.797, 0.9204] +2026-04-14 22:56:34.346847: Epoch time: 102.01 s +2026-04-14 22:56:35.692985: +2026-04-14 22:56:35.695205: Epoch 3616 +2026-04-14 22:56:35.697810: Current learning rate: 0.00121 +2026-04-14 22:58:17.603051: train_loss -0.5676 +2026-04-14 22:58:17.613396: val_loss -0.449 +2026-04-14 22:58:17.616076: Pseudo dice [0.452, 0.0, 0.6071, 0.8769, 0.4147, 0.8934, 0.7354] +2026-04-14 22:58:17.619809: Epoch time: 101.91 s +2026-04-14 22:58:18.892306: +2026-04-14 22:58:18.894647: Epoch 3617 +2026-04-14 22:58:18.897741: Current learning rate: 0.00121 +2026-04-14 23:00:01.060122: train_loss -0.556 +2026-04-14 23:00:01.067662: val_loss -0.4869 +2026-04-14 23:00:01.069931: Pseudo dice [0.8256, 0.0, 0.8923, 0.7842, 0.5542, 0.6649, 0.9334] +2026-04-14 23:00:01.073245: Epoch time: 102.17 s +2026-04-14 23:00:02.324206: +2026-04-14 23:00:02.326031: Epoch 3618 +2026-04-14 23:00:02.329256: Current learning rate: 0.00121 +2026-04-14 23:01:44.589440: train_loss -0.5559 +2026-04-14 23:01:44.596987: val_loss -0.4667 +2026-04-14 23:01:44.599094: Pseudo dice [0.7006, 0.0, 0.8467, 0.5989, 0.506, 0.566, 0.7714] +2026-04-14 23:01:44.601619: Epoch time: 102.27 s +2026-04-14 23:01:46.910549: +2026-04-14 23:01:46.912615: Epoch 3619 +2026-04-14 23:01:46.914527: Current learning rate: 0.0012 +2026-04-14 23:03:28.903990: train_loss -0.5635 +2026-04-14 23:03:28.912511: val_loss -0.4694 +2026-04-14 23:03:28.916023: Pseudo dice [0.5958, 0.0, 0.7863, 0.9128, 0.6331, 0.4274, 0.9542] +2026-04-14 23:03:28.920462: Epoch time: 102.0 s +2026-04-14 23:03:30.196564: +2026-04-14 23:03:30.201504: Epoch 3620 +2026-04-14 23:03:30.203980: Current learning rate: 0.0012 +2026-04-14 23:05:12.994334: train_loss -0.5556 +2026-04-14 23:05:13.003441: val_loss -0.4761 +2026-04-14 23:05:13.005933: Pseudo dice [0.5645, 0.0, 0.8713, 0.7779, 0.6283, 0.677, 0.8175] +2026-04-14 23:05:13.008844: Epoch time: 102.8 s +2026-04-14 23:05:14.327320: +2026-04-14 23:05:14.329817: Epoch 3621 +2026-04-14 23:05:14.332711: Current learning rate: 0.0012 +2026-04-14 23:06:55.709769: train_loss -0.5561 +2026-04-14 23:06:55.715364: val_loss -0.4699 +2026-04-14 23:06:55.717340: Pseudo dice [0.4185, 0.0, 0.8645, 0.0327, 0.5207, 0.7393, 0.8809] +2026-04-14 23:06:55.719610: Epoch time: 101.39 s +2026-04-14 23:06:57.002011: +2026-04-14 23:06:57.004424: Epoch 3622 +2026-04-14 23:06:57.006910: Current learning rate: 0.0012 +2026-04-14 23:08:38.660283: train_loss -0.5549 +2026-04-14 23:08:38.666393: val_loss -0.4782 +2026-04-14 23:08:38.669098: Pseudo dice [0.6104, 0.0, 0.8652, 0.6393, 0.5306, 0.8143, 0.8922] +2026-04-14 23:08:38.671413: Epoch time: 101.66 s +2026-04-14 23:08:39.964874: +2026-04-14 23:08:39.967961: Epoch 3623 +2026-04-14 23:08:39.970343: Current learning rate: 0.00119 +2026-04-14 23:10:21.278734: train_loss -0.5495 +2026-04-14 23:10:21.288913: val_loss -0.4907 +2026-04-14 23:10:21.291481: Pseudo dice [0.7424, 0.0, 0.8709, 0.8551, 0.4924, 0.879, 0.865] +2026-04-14 23:10:21.294568: Epoch time: 101.32 s +2026-04-14 23:10:22.615920: +2026-04-14 23:10:22.617751: Epoch 3624 +2026-04-14 23:10:22.620957: Current learning rate: 0.00119 +2026-04-14 23:12:04.717501: train_loss -0.5582 +2026-04-14 23:12:04.731102: val_loss -0.4761 +2026-04-14 23:12:04.734299: Pseudo dice [0.5609, 0.0, 0.5333, 0.8725, 0.5453, 0.6199, 0.9033] +2026-04-14 23:12:04.737035: Epoch time: 102.1 s +2026-04-14 23:12:06.081947: +2026-04-14 23:12:06.084195: Epoch 3625 +2026-04-14 23:12:06.086453: Current learning rate: 0.00119 +2026-04-14 23:13:47.954777: train_loss -0.5656 +2026-04-14 23:13:47.962725: val_loss -0.4649 +2026-04-14 23:13:47.966588: Pseudo dice [0.81, 0.0, 0.8317, 0.8622, 0.6515, 0.4434, 0.8395] +2026-04-14 23:13:47.977126: Epoch time: 101.88 s +2026-04-14 23:13:49.282341: +2026-04-14 23:13:49.289254: Epoch 3626 +2026-04-14 23:13:49.293806: Current learning rate: 0.00119 +2026-04-14 23:15:31.475305: train_loss -0.5633 +2026-04-14 23:15:31.482737: val_loss -0.477 +2026-04-14 23:15:31.485259: Pseudo dice [0.6078, 0.0, 0.8362, 0.7038, 0.3708, 0.7592, 0.8687] +2026-04-14 23:15:31.488019: Epoch time: 102.2 s +2026-04-14 23:15:32.764461: +2026-04-14 23:15:32.766530: Epoch 3627 +2026-04-14 23:15:32.768683: Current learning rate: 0.00118 +2026-04-14 23:17:14.387851: train_loss -0.5554 +2026-04-14 23:17:14.395387: val_loss -0.4799 +2026-04-14 23:17:14.397755: Pseudo dice [0.6421, 0.0, 0.8773, 0.8131, 0.3283, 0.5569, 0.6048] +2026-04-14 23:17:14.401268: Epoch time: 101.63 s +2026-04-14 23:17:15.730681: +2026-04-14 23:17:15.732659: Epoch 3628 +2026-04-14 23:17:15.734714: Current learning rate: 0.00118 +2026-04-14 23:18:57.041847: train_loss -0.5483 +2026-04-14 23:18:57.049343: val_loss -0.4713 +2026-04-14 23:18:57.051471: Pseudo dice [0.7862, 0.0, 0.835, 0.3429, 0.5307, 0.868, 0.9068] +2026-04-14 23:18:57.056271: Epoch time: 101.31 s +2026-04-14 23:18:58.335252: +2026-04-14 23:18:58.337021: Epoch 3629 +2026-04-14 23:18:58.338927: Current learning rate: 0.00118 +2026-04-14 23:20:40.510100: train_loss -0.555 +2026-04-14 23:20:40.516656: val_loss -0.5055 +2026-04-14 23:20:40.518973: Pseudo dice [0.7549, 0.0, 0.9218, 0.8584, 0.5809, 0.7914, 0.9329] +2026-04-14 23:20:40.521448: Epoch time: 102.18 s +2026-04-14 23:20:41.844048: +2026-04-14 23:20:41.847351: Epoch 3630 +2026-04-14 23:20:41.849850: Current learning rate: 0.00117 +2026-04-14 23:22:23.902429: train_loss -0.5607 +2026-04-14 23:22:23.909434: val_loss -0.4984 +2026-04-14 23:22:23.912045: Pseudo dice [0.7941, 0.0, 0.8191, 0.8625, 0.5807, 0.8042, 0.9085] +2026-04-14 23:22:23.914934: Epoch time: 102.06 s +2026-04-14 23:22:25.209521: +2026-04-14 23:22:25.211557: Epoch 3631 +2026-04-14 23:22:25.213622: Current learning rate: 0.00117 +2026-04-14 23:24:07.646054: train_loss -0.5621 +2026-04-14 23:24:07.654463: val_loss -0.4692 +2026-04-14 23:24:07.657062: Pseudo dice [0.7994, 0.0, 0.8272, 0.585, 0.5856, 0.6685, 0.9274] +2026-04-14 23:24:07.659774: Epoch time: 102.44 s +2026-04-14 23:24:07.661791: Yayy! New best EMA pseudo Dice: 0.6164 +2026-04-14 23:24:10.830581: +2026-04-14 23:24:10.849362: Epoch 3632 +2026-04-14 23:24:10.851717: Current learning rate: 0.00117 +2026-04-14 23:25:53.277009: train_loss -0.5581 +2026-04-14 23:25:53.284259: val_loss -0.4859 +2026-04-14 23:25:53.286619: Pseudo dice [0.528, 0.0, 0.9041, 0.7578, 0.4762, 0.7247, 0.8415] +2026-04-14 23:25:53.289584: Epoch time: 102.45 s +2026-04-14 23:25:54.594060: +2026-04-14 23:25:54.596741: Epoch 3633 +2026-04-14 23:25:54.599457: Current learning rate: 0.00117 +2026-04-14 23:27:36.180110: train_loss -0.5536 +2026-04-14 23:27:36.188885: val_loss -0.479 +2026-04-14 23:27:36.191373: Pseudo dice [0.7213, 0.0, 0.7259, 0.448, 0.378, 0.7044, 0.9223] +2026-04-14 23:27:36.195262: Epoch time: 101.59 s +2026-04-14 23:27:37.457522: +2026-04-14 23:27:37.459595: Epoch 3634 +2026-04-14 23:27:37.461950: Current learning rate: 0.00116 +2026-04-14 23:29:19.383163: train_loss -0.5575 +2026-04-14 23:29:19.389623: val_loss -0.4768 +2026-04-14 23:29:19.392509: Pseudo dice [0.4752, 0.0, 0.7664, 0.7361, 0.5433, 0.9333, 0.9374] +2026-04-14 23:29:19.395278: Epoch time: 101.93 s +2026-04-14 23:29:20.663004: +2026-04-14 23:29:20.665529: Epoch 3635 +2026-04-14 23:29:20.667986: Current learning rate: 0.00116 +2026-04-14 23:31:02.650723: train_loss -0.5556 +2026-04-14 23:31:02.658403: val_loss -0.4663 +2026-04-14 23:31:02.660489: Pseudo dice [0.318, 0.0, 0.8826, 0.5256, 0.5709, 0.5534, 0.9099] +2026-04-14 23:31:02.662925: Epoch time: 101.99 s +2026-04-14 23:31:03.956122: +2026-04-14 23:31:03.958460: Epoch 3636 +2026-04-14 23:31:03.960666: Current learning rate: 0.00116 +2026-04-14 23:32:45.567592: train_loss -0.5481 +2026-04-14 23:32:45.575227: val_loss -0.4265 +2026-04-14 23:32:45.577358: Pseudo dice [0.8039, 0.0, 0.3213, 0.1765, 0.4194, 0.7977, 0.9076] +2026-04-14 23:32:45.579795: Epoch time: 101.61 s +2026-04-14 23:32:46.869587: +2026-04-14 23:32:46.871502: Epoch 3637 +2026-04-14 23:32:46.873914: Current learning rate: 0.00115 +2026-04-14 23:34:28.380559: train_loss -0.5582 +2026-04-14 23:34:28.389610: val_loss -0.4871 +2026-04-14 23:34:28.391970: Pseudo dice [0.8198, 0.0, 0.9197, 0.3153, 0.4306, 0.5965, 0.9406] +2026-04-14 23:34:28.395888: Epoch time: 101.51 s +2026-04-14 23:34:30.754700: +2026-04-14 23:34:30.756539: Epoch 3638 +2026-04-14 23:34:30.758469: Current learning rate: 0.00115 +2026-04-14 23:36:12.620220: train_loss -0.5561 +2026-04-14 23:36:12.627939: val_loss -0.5035 +2026-04-14 23:36:12.629872: Pseudo dice [0.4897, 0.0, 0.8329, 0.8119, 0.5434, 0.7291, 0.9535] +2026-04-14 23:36:12.633490: Epoch time: 101.87 s +2026-04-14 23:36:13.912735: +2026-04-14 23:36:13.918469: Epoch 3639 +2026-04-14 23:36:13.921438: Current learning rate: 0.00115 +2026-04-14 23:37:55.824488: train_loss -0.5503 +2026-04-14 23:37:55.833459: val_loss -0.4742 +2026-04-14 23:37:55.836538: Pseudo dice [0.7281, 0.0, 0.8484, 0.1608, 0.5667, 0.5822, 0.922] +2026-04-14 23:37:55.840483: Epoch time: 101.91 s +2026-04-14 23:37:57.111313: +2026-04-14 23:37:57.114911: Epoch 3640 +2026-04-14 23:37:57.117108: Current learning rate: 0.00115 +2026-04-14 23:39:38.522815: train_loss -0.5582 +2026-04-14 23:39:38.530056: val_loss -0.4981 +2026-04-14 23:39:38.534455: Pseudo dice [0.8187, 0.0, 0.8159, 0.8715, 0.6361, 0.7992, 0.9335] +2026-04-14 23:39:38.537610: Epoch time: 101.41 s +2026-04-14 23:39:39.805474: +2026-04-14 23:39:39.807948: Epoch 3641 +2026-04-14 23:39:39.810262: Current learning rate: 0.00114 +2026-04-14 23:41:21.404841: train_loss -0.5661 +2026-04-14 23:41:21.413985: val_loss -0.4684 +2026-04-14 23:41:21.416661: Pseudo dice [0.6317, 0.0, 0.7893, 0.488, 0.5844, 0.6814, 0.9427] +2026-04-14 23:41:21.419177: Epoch time: 101.6 s +2026-04-14 23:41:22.712356: +2026-04-14 23:41:22.714711: Epoch 3642 +2026-04-14 23:41:22.717023: Current learning rate: 0.00114 +2026-04-14 23:43:04.526622: train_loss -0.551 +2026-04-14 23:43:04.554133: val_loss -0.4592 +2026-04-14 23:43:04.556400: Pseudo dice [0.551, 0.0, 0.8382, 0.4878, 0.5145, 0.63, 0.953] +2026-04-14 23:43:04.559696: Epoch time: 101.82 s +2026-04-14 23:43:05.883514: +2026-04-14 23:43:05.886041: Epoch 3643 +2026-04-14 23:43:05.888683: Current learning rate: 0.00114 +2026-04-14 23:44:49.347959: train_loss -0.5525 +2026-04-14 23:44:49.355714: val_loss -0.4716 +2026-04-14 23:44:49.358765: Pseudo dice [0.408, 0.0, 0.8025, 0.749, 0.5312, 0.8121, 0.8122] +2026-04-14 23:44:49.361343: Epoch time: 103.47 s +2026-04-14 23:44:50.639945: +2026-04-14 23:44:50.642200: Epoch 3644 +2026-04-14 23:44:50.645521: Current learning rate: 0.00113 +2026-04-14 23:46:32.980503: train_loss -0.5629 +2026-04-14 23:46:32.987228: val_loss -0.4957 +2026-04-14 23:46:32.989648: Pseudo dice [0.7406, 0.0, 0.8842, 0.783, 0.4122, 0.7027, 0.919] +2026-04-14 23:46:32.992925: Epoch time: 102.34 s +2026-04-14 23:46:34.286173: +2026-04-14 23:46:34.288380: Epoch 3645 +2026-04-14 23:46:34.290525: Current learning rate: 0.00113 +2026-04-14 23:48:15.957116: train_loss -0.5453 +2026-04-14 23:48:15.965279: val_loss -0.4917 +2026-04-14 23:48:15.967338: Pseudo dice [0.6606, 0.0, 0.8291, 0.9032, 0.481, 0.6193, 0.9179] +2026-04-14 23:48:15.970386: Epoch time: 101.67 s +2026-04-14 23:48:17.213189: +2026-04-14 23:48:17.215704: Epoch 3646 +2026-04-14 23:48:17.218510: Current learning rate: 0.00113 +2026-04-14 23:49:59.598899: train_loss -0.5548 +2026-04-14 23:49:59.605338: val_loss -0.4803 +2026-04-14 23:49:59.607758: Pseudo dice [0.8206, 0.0, 0.8591, 0.766, 0.5797, 0.9089, 0.9254] +2026-04-14 23:49:59.610357: Epoch time: 102.39 s +2026-04-14 23:50:00.887989: +2026-04-14 23:50:00.889789: Epoch 3647 +2026-04-14 23:50:00.892204: Current learning rate: 0.00112 +2026-04-14 23:51:42.965703: train_loss -0.5641 +2026-04-14 23:51:42.973248: val_loss -0.4839 +2026-04-14 23:51:42.975667: Pseudo dice [0.4764, 0.0, 0.853, 0.8658, 0.4989, 0.67, 0.9062] +2026-04-14 23:51:42.978143: Epoch time: 102.08 s +2026-04-14 23:51:44.248484: +2026-04-14 23:51:44.250902: Epoch 3648 +2026-04-14 23:51:44.253485: Current learning rate: 0.00112 +2026-04-14 23:53:25.974977: train_loss -0.5574 +2026-04-14 23:53:25.982936: val_loss -0.4638 +2026-04-14 23:53:25.985380: Pseudo dice [0.67, 0.0, 0.8026, 0.7413, 0.5146, 0.3286, 0.8126] +2026-04-14 23:53:25.988029: Epoch time: 101.73 s +2026-04-14 23:53:27.337892: +2026-04-14 23:53:27.340781: Epoch 3649 +2026-04-14 23:53:27.343214: Current learning rate: 0.00112 +2026-04-14 23:55:09.425555: train_loss -0.5536 +2026-04-14 23:55:09.435112: val_loss -0.4554 +2026-04-14 23:55:09.441099: Pseudo dice [0.6038, 0.0, 0.8015, 0.8798, 0.5573, 0.8258, 0.8232] +2026-04-14 23:55:09.445001: Epoch time: 102.09 s +2026-04-14 23:55:12.603747: +2026-04-14 23:55:12.606452: Epoch 3650 +2026-04-14 23:55:12.608221: Current learning rate: 0.00112 +2026-04-14 23:56:54.214078: train_loss -0.5468 +2026-04-14 23:56:54.220503: val_loss -0.4227 +2026-04-14 23:56:54.222805: Pseudo dice [0.8525, 0.0, 0.8647, 0.7563, 0.2671, 0.6285, 0.3541] +2026-04-14 23:56:54.224669: Epoch time: 101.61 s +2026-04-14 23:56:55.541971: +2026-04-14 23:56:55.543810: Epoch 3651 +2026-04-14 23:56:55.546000: Current learning rate: 0.00111 +2026-04-14 23:58:37.917174: train_loss -0.5538 +2026-04-14 23:58:37.927158: val_loss -0.498 +2026-04-14 23:58:37.929482: Pseudo dice [0.8262, 0.0, 0.8999, 0.4968, 0.5413, 0.8684, 0.894] +2026-04-14 23:58:37.932404: Epoch time: 102.38 s +2026-04-14 23:58:39.287489: +2026-04-14 23:58:39.289752: Epoch 3652 +2026-04-14 23:58:39.292512: Current learning rate: 0.00111 +2026-04-15 00:00:21.706342: train_loss -0.5389 +2026-04-15 00:00:21.715659: val_loss -0.4775 +2026-04-15 00:00:21.719460: Pseudo dice [0.839, 0.0, 0.8373, 0.2227, 0.6066, 0.8839, 0.9554] +2026-04-15 00:00:21.723911: Epoch time: 102.42 s +2026-04-15 00:00:23.027573: +2026-04-15 00:00:23.031023: Epoch 3653 +2026-04-15 00:00:23.035308: Current learning rate: 0.00111 +2026-04-15 00:02:05.609473: train_loss -0.5482 +2026-04-15 00:02:05.615707: val_loss -0.4822 +2026-04-15 00:02:05.617909: Pseudo dice [0.7778, 0.0, 0.7598, 0.8199, 0.4856, 0.8033, 0.8375] +2026-04-15 00:02:05.620762: Epoch time: 102.59 s +2026-04-15 00:02:06.905668: +2026-04-15 00:02:06.908085: Epoch 3654 +2026-04-15 00:02:06.910439: Current learning rate: 0.0011 +2026-04-15 00:03:48.942080: train_loss -0.5543 +2026-04-15 00:03:48.950001: val_loss -0.3963 +2026-04-15 00:03:48.952877: Pseudo dice [0.6532, 0.0, 0.4042, 0.1114, 0.5314, 0.84, 0.9135] +2026-04-15 00:03:48.955746: Epoch time: 102.04 s +2026-04-15 00:03:50.214059: +2026-04-15 00:03:50.217092: Epoch 3655 +2026-04-15 00:03:50.219984: Current learning rate: 0.0011 +2026-04-15 00:05:32.273523: train_loss -0.543 +2026-04-15 00:05:32.284648: val_loss -0.4732 +2026-04-15 00:05:32.287485: Pseudo dice [0.7306, 0.0, 0.8498, 0.7519, 0.4643, 0.9094, 0.8952] +2026-04-15 00:05:32.290463: Epoch time: 102.06 s +2026-04-15 00:05:33.585808: +2026-04-15 00:05:33.588023: Epoch 3656 +2026-04-15 00:05:33.590147: Current learning rate: 0.0011 +2026-04-15 00:07:15.368455: train_loss -0.5545 +2026-04-15 00:07:15.376194: val_loss -0.454 +2026-04-15 00:07:15.379332: Pseudo dice [0.5891, 0.0, 0.8318, 0.4463, 0.5652, 0.7521, 0.9114] +2026-04-15 00:07:15.381709: Epoch time: 101.79 s +2026-04-15 00:07:16.739444: +2026-04-15 00:07:16.744574: Epoch 3657 +2026-04-15 00:07:16.747459: Current learning rate: 0.0011 +2026-04-15 00:08:59.485645: train_loss -0.541 +2026-04-15 00:08:59.493781: val_loss -0.4666 +2026-04-15 00:08:59.496330: Pseudo dice [0.6078, 0.0, 0.8747, 0.7356, 0.4915, 0.8211, 0.9298] +2026-04-15 00:08:59.499101: Epoch time: 102.75 s +2026-04-15 00:09:00.812486: +2026-04-15 00:09:00.814405: Epoch 3658 +2026-04-15 00:09:00.816482: Current learning rate: 0.00109 +2026-04-15 00:10:42.391662: train_loss -0.5507 +2026-04-15 00:10:42.397554: val_loss -0.4556 +2026-04-15 00:10:42.400241: Pseudo dice [0.3253, 0.0, 0.6199, 0.4511, 0.3477, 0.7882, 0.9371] +2026-04-15 00:10:42.402670: Epoch time: 101.58 s +2026-04-15 00:10:43.673179: +2026-04-15 00:10:43.675848: Epoch 3659 +2026-04-15 00:10:43.678699: Current learning rate: 0.00109 +2026-04-15 00:12:25.776696: train_loss -0.5499 +2026-04-15 00:12:25.782451: val_loss -0.4896 +2026-04-15 00:12:25.785195: Pseudo dice [0.7754, 0.0, 0.8635, 0.6796, 0.5077, 0.8312, 0.9274] +2026-04-15 00:12:25.787810: Epoch time: 102.11 s +2026-04-15 00:12:27.052779: +2026-04-15 00:12:27.054579: Epoch 3660 +2026-04-15 00:12:27.057004: Current learning rate: 0.00109 +2026-04-15 00:14:08.545126: train_loss -0.5636 +2026-04-15 00:14:08.555346: val_loss -0.4705 +2026-04-15 00:14:08.558048: Pseudo dice [0.7399, 0.0, 0.6851, 0.3043, 0.5623, 0.805, 0.7749] +2026-04-15 00:14:08.560837: Epoch time: 101.5 s +2026-04-15 00:14:09.817916: +2026-04-15 00:14:09.820051: Epoch 3661 +2026-04-15 00:14:09.822179: Current learning rate: 0.00108 +2026-04-15 00:15:51.223310: train_loss -0.5547 +2026-04-15 00:15:51.231224: val_loss -0.4843 +2026-04-15 00:15:51.233860: Pseudo dice [0.4955, 0.0, 0.8358, 0.8362, 0.4987, 0.8854, 0.9291] +2026-04-15 00:15:51.236421: Epoch time: 101.41 s +2026-04-15 00:15:52.494298: +2026-04-15 00:15:52.496757: Epoch 3662 +2026-04-15 00:15:52.498842: Current learning rate: 0.00108 +2026-04-15 00:17:34.555645: train_loss -0.5628 +2026-04-15 00:17:34.562486: val_loss -0.467 +2026-04-15 00:17:34.565274: Pseudo dice [0.6908, 0.0, 0.7986, 0.8444, 0.5857, 0.8651, 0.6745] +2026-04-15 00:17:34.567849: Epoch time: 102.06 s +2026-04-15 00:17:35.885861: +2026-04-15 00:17:35.887803: Epoch 3663 +2026-04-15 00:17:35.890011: Current learning rate: 0.00108 +2026-04-15 00:19:17.862321: train_loss -0.5506 +2026-04-15 00:19:17.869541: val_loss -0.4478 +2026-04-15 00:19:17.872374: Pseudo dice [0.5629, 0.0, 0.8191, 0.5275, 0.4954, 0.6891, 0.8477] +2026-04-15 00:19:17.874572: Epoch time: 101.98 s +2026-04-15 00:19:19.111827: +2026-04-15 00:19:19.113839: Epoch 3664 +2026-04-15 00:19:19.115976: Current learning rate: 0.00108 +2026-04-15 00:21:00.487907: train_loss -0.5575 +2026-04-15 00:21:00.496554: val_loss -0.4676 +2026-04-15 00:21:00.498911: Pseudo dice [0.7792, 0.0, 0.8233, 0.6876, 0.4054, 0.8289, 0.6598] +2026-04-15 00:21:00.502089: Epoch time: 101.38 s +2026-04-15 00:21:01.790076: +2026-04-15 00:21:01.791905: Epoch 3665 +2026-04-15 00:21:01.793750: Current learning rate: 0.00107 +2026-04-15 00:22:43.197641: train_loss -0.5689 +2026-04-15 00:22:43.206208: val_loss -0.4871 +2026-04-15 00:22:43.208840: Pseudo dice [0.4644, 0.0, 0.8505, 0.6487, 0.5323, 0.8858, 0.9234] +2026-04-15 00:22:43.211665: Epoch time: 101.41 s +2026-04-15 00:22:44.470144: +2026-04-15 00:22:44.471874: Epoch 3666 +2026-04-15 00:22:44.473796: Current learning rate: 0.00107 +2026-04-15 00:24:26.261184: train_loss -0.5675 +2026-04-15 00:24:26.267967: val_loss -0.4609 +2026-04-15 00:24:26.270500: Pseudo dice [0.727, 0.0, 0.8235, 0.058, 0.5358, 0.3074, 0.8812] +2026-04-15 00:24:26.273954: Epoch time: 101.79 s +2026-04-15 00:24:27.591220: +2026-04-15 00:24:27.593258: Epoch 3667 +2026-04-15 00:24:27.596155: Current learning rate: 0.00107 +2026-04-15 00:26:09.487012: train_loss -0.558 +2026-04-15 00:26:09.500377: val_loss -0.4926 +2026-04-15 00:26:09.502469: Pseudo dice [0.5046, 0.0, 0.8558, 0.8218, 0.5154, 0.6443, 0.9252] +2026-04-15 00:26:09.504842: Epoch time: 101.9 s +2026-04-15 00:26:10.794914: +2026-04-15 00:26:10.797407: Epoch 3668 +2026-04-15 00:26:10.799396: Current learning rate: 0.00106 +2026-04-15 00:27:52.534438: train_loss -0.5661 +2026-04-15 00:27:52.542558: val_loss -0.459 +2026-04-15 00:27:52.544542: Pseudo dice [0.573, 0.0, 0.575, 0.2223, 0.5587, 0.7894, 0.8708] +2026-04-15 00:27:52.546659: Epoch time: 101.74 s +2026-04-15 00:27:53.864307: +2026-04-15 00:27:53.866344: Epoch 3669 +2026-04-15 00:27:53.868677: Current learning rate: 0.00106 +2026-04-15 00:29:35.906499: train_loss -0.5691 +2026-04-15 00:29:35.912854: val_loss -0.4651 +2026-04-15 00:29:35.914789: Pseudo dice [0.6265, 0.0, 0.8666, 0.8132, 0.5607, 0.603, 0.9236] +2026-04-15 00:29:35.917274: Epoch time: 102.05 s +2026-04-15 00:29:37.202120: +2026-04-15 00:29:37.203838: Epoch 3670 +2026-04-15 00:29:37.205636: Current learning rate: 0.00106 +2026-04-15 00:31:18.833349: train_loss -0.5576 +2026-04-15 00:31:18.840298: val_loss -0.4831 +2026-04-15 00:31:18.842451: Pseudo dice [0.7825, 0.0, 0.8558, 0.8874, 0.6597, 0.7779, 0.871] +2026-04-15 00:31:18.844864: Epoch time: 101.63 s +2026-04-15 00:31:20.133047: +2026-04-15 00:31:20.134872: Epoch 3671 +2026-04-15 00:31:20.136908: Current learning rate: 0.00106 +2026-04-15 00:33:01.837727: train_loss -0.5604 +2026-04-15 00:33:01.844107: val_loss -0.488 +2026-04-15 00:33:01.846823: Pseudo dice [0.4796, 0.0, 0.8211, 0.7254, 0.4728, 0.799, 0.9475] +2026-04-15 00:33:01.851993: Epoch time: 101.71 s +2026-04-15 00:33:03.177342: +2026-04-15 00:33:03.179659: Epoch 3672 +2026-04-15 00:33:03.181597: Current learning rate: 0.00105 +2026-04-15 00:34:44.894012: train_loss -0.5745 +2026-04-15 00:34:44.901458: val_loss -0.468 +2026-04-15 00:34:44.903909: Pseudo dice [0.7888, 0.0, 0.8438, 0.637, 0.5085, 0.871, 0.8238] +2026-04-15 00:34:44.906520: Epoch time: 101.72 s +2026-04-15 00:34:46.177273: +2026-04-15 00:34:46.179278: Epoch 3673 +2026-04-15 00:34:46.181137: Current learning rate: 0.00105 +2026-04-15 00:36:27.874276: train_loss -0.5596 +2026-04-15 00:36:27.886255: val_loss -0.4618 +2026-04-15 00:36:27.896237: Pseudo dice [0.7624, 0.0, 0.7111, 0.4493, 0.5027, 0.6966, 0.9208] +2026-04-15 00:36:27.900040: Epoch time: 101.7 s +2026-04-15 00:36:29.215012: +2026-04-15 00:36:29.217324: Epoch 3674 +2026-04-15 00:36:29.219658: Current learning rate: 0.00105 +2026-04-15 00:38:10.734942: train_loss -0.5555 +2026-04-15 00:38:10.742023: val_loss -0.4788 +2026-04-15 00:38:10.744513: Pseudo dice [0.7013, 0.0, 0.7993, 0.7802, 0.4872, 0.8815, 0.9097] +2026-04-15 00:38:10.746984: Epoch time: 101.52 s +2026-04-15 00:38:12.069653: +2026-04-15 00:38:12.071337: Epoch 3675 +2026-04-15 00:38:12.073112: Current learning rate: 0.00104 +2026-04-15 00:39:54.211843: train_loss -0.5555 +2026-04-15 00:39:54.220220: val_loss -0.4716 +2026-04-15 00:39:54.222522: Pseudo dice [0.5576, 0.0, 0.7311, 0.6793, 0.4351, 0.8218, 0.8129] +2026-04-15 00:39:54.225296: Epoch time: 102.15 s +2026-04-15 00:39:55.511794: +2026-04-15 00:39:55.514286: Epoch 3676 +2026-04-15 00:39:55.517554: Current learning rate: 0.00104 +2026-04-15 00:41:37.146951: train_loss -0.5642 +2026-04-15 00:41:37.156821: val_loss -0.5134 +2026-04-15 00:41:37.159678: Pseudo dice [0.6469, 0.0, 0.7937, 0.7912, 0.5747, 0.8237, 0.9132] +2026-04-15 00:41:37.163185: Epoch time: 101.64 s +2026-04-15 00:41:38.405080: +2026-04-15 00:41:38.407040: Epoch 3677 +2026-04-15 00:41:38.409173: Current learning rate: 0.00104 +2026-04-15 00:43:21.344281: train_loss -0.567 +2026-04-15 00:43:21.351540: val_loss -0.4179 +2026-04-15 00:43:21.354035: Pseudo dice [0.5588, 0.0, 0.7148, 0.2975, 0.518, 0.3218, 0.7128] +2026-04-15 00:43:21.356524: Epoch time: 102.94 s +2026-04-15 00:43:22.676144: +2026-04-15 00:43:22.678484: Epoch 3678 +2026-04-15 00:43:22.680422: Current learning rate: 0.00104 +2026-04-15 00:45:04.612607: train_loss -0.5691 +2026-04-15 00:45:04.619951: val_loss -0.4911 +2026-04-15 00:45:04.622249: Pseudo dice [0.7475, 0.0, 0.9205, 0.9288, 0.2687, 0.7603, 0.9473] +2026-04-15 00:45:04.625047: Epoch time: 101.94 s +2026-04-15 00:45:05.891360: +2026-04-15 00:45:05.893235: Epoch 3679 +2026-04-15 00:45:05.895110: Current learning rate: 0.00103 +2026-04-15 00:46:47.788511: train_loss -0.5593 +2026-04-15 00:46:47.797194: val_loss -0.4655 +2026-04-15 00:46:47.799428: Pseudo dice [0.7384, 0.0, 0.7943, 0.8034, 0.449, 0.7193, 0.9282] +2026-04-15 00:46:47.802918: Epoch time: 101.9 s +2026-04-15 00:46:49.131169: +2026-04-15 00:46:49.133232: Epoch 3680 +2026-04-15 00:46:49.135811: Current learning rate: 0.00103 +2026-04-15 00:48:31.136935: train_loss -0.5578 +2026-04-15 00:48:31.144092: val_loss -0.4616 +2026-04-15 00:48:31.146785: Pseudo dice [0.7867, 0.0, 0.8714, 0.6127, 0.537, 0.828, 0.8624] +2026-04-15 00:48:31.149111: Epoch time: 102.01 s +2026-04-15 00:48:32.419382: +2026-04-15 00:48:32.421785: Epoch 3681 +2026-04-15 00:48:32.424798: Current learning rate: 0.00103 +2026-04-15 00:50:14.328042: train_loss -0.5601 +2026-04-15 00:50:14.336066: val_loss -0.4662 +2026-04-15 00:50:14.338516: Pseudo dice [0.5977, 0.0, 0.8617, 0.7784, 0.4952, 0.8424, 0.9039] +2026-04-15 00:50:14.341599: Epoch time: 101.91 s +2026-04-15 00:50:15.652877: +2026-04-15 00:50:15.656358: Epoch 3682 +2026-04-15 00:50:15.658707: Current learning rate: 0.00102 +2026-04-15 00:51:58.097734: train_loss -0.5628 +2026-04-15 00:51:58.105527: val_loss -0.4641 +2026-04-15 00:51:58.108240: Pseudo dice [0.8119, 0.0, 0.8163, 0.5707, 0.5919, 0.6495, 0.6806] +2026-04-15 00:51:58.111277: Epoch time: 102.45 s +2026-04-15 00:51:59.390781: +2026-04-15 00:51:59.392857: Epoch 3683 +2026-04-15 00:51:59.395116: Current learning rate: 0.00102 +2026-04-15 00:53:41.175022: train_loss -0.5488 +2026-04-15 00:53:41.184233: val_loss -0.4947 +2026-04-15 00:53:41.186465: Pseudo dice [0.65, 0.0, 0.7054, 0.6293, 0.501, 0.3685, 0.8138] +2026-04-15 00:53:41.188944: Epoch time: 101.79 s +2026-04-15 00:53:42.521576: +2026-04-15 00:53:42.524097: Epoch 3684 +2026-04-15 00:53:42.526111: Current learning rate: 0.00102 +2026-04-15 00:55:24.607514: train_loss -0.5654 +2026-04-15 00:55:24.614185: val_loss -0.4629 +2026-04-15 00:55:24.620473: Pseudo dice [0.5537, 0.0, 0.7706, 0.6156, 0.3837, 0.8305, 0.7648] +2026-04-15 00:55:24.623260: Epoch time: 102.09 s +2026-04-15 00:55:25.912614: +2026-04-15 00:55:25.915009: Epoch 3685 +2026-04-15 00:55:25.917325: Current learning rate: 0.00102 +2026-04-15 00:57:07.626715: train_loss -0.5614 +2026-04-15 00:57:07.633796: val_loss -0.4942 +2026-04-15 00:57:07.635985: Pseudo dice [0.5794, 0.0, 0.8925, 0.8779, 0.422, 0.8065, 0.9331] +2026-04-15 00:57:07.638596: Epoch time: 101.72 s +2026-04-15 00:57:08.924188: +2026-04-15 00:57:08.926266: Epoch 3686 +2026-04-15 00:57:08.928736: Current learning rate: 0.00101 +2026-04-15 00:58:50.553701: train_loss -0.5712 +2026-04-15 00:58:50.564713: val_loss -0.4846 +2026-04-15 00:58:50.568308: Pseudo dice [0.8597, 0.0, 0.8899, 0.5239, 0.4075, 0.5668, 0.8346] +2026-04-15 00:58:50.570886: Epoch time: 101.63 s +2026-04-15 00:58:51.846174: +2026-04-15 00:58:51.848058: Epoch 3687 +2026-04-15 00:58:51.850205: Current learning rate: 0.00101 +2026-04-15 01:00:33.556117: train_loss -0.5657 +2026-04-15 01:00:33.564031: val_loss -0.4922 +2026-04-15 01:00:33.567004: Pseudo dice [0.722, 0.0, 0.8652, 0.761, 0.3988, 0.6896, 0.8562] +2026-04-15 01:00:33.569660: Epoch time: 101.71 s +2026-04-15 01:00:34.855143: +2026-04-15 01:00:34.857187: Epoch 3688 +2026-04-15 01:00:34.859294: Current learning rate: 0.00101 +2026-04-15 01:02:16.261369: train_loss -0.5658 +2026-04-15 01:02:16.267811: val_loss -0.4824 +2026-04-15 01:02:16.269578: Pseudo dice [0.8413, 0.0, 0.8263, 0.8114, 0.5565, 0.5752, 0.4812] +2026-04-15 01:02:16.272466: Epoch time: 101.41 s +2026-04-15 01:02:17.615267: +2026-04-15 01:02:17.616971: Epoch 3689 +2026-04-15 01:02:17.618849: Current learning rate: 0.001 +2026-04-15 01:03:59.130944: train_loss -0.572 +2026-04-15 01:03:59.137459: val_loss -0.4702 +2026-04-15 01:03:59.139824: Pseudo dice [0.6758, 0.0, 0.8506, 0.8505, 0.3312, 0.8581, 0.9198] +2026-04-15 01:03:59.142408: Epoch time: 101.52 s +2026-04-15 01:04:00.417675: +2026-04-15 01:04:00.419731: Epoch 3690 +2026-04-15 01:04:00.421826: Current learning rate: 0.001 +2026-04-15 01:05:42.270530: train_loss -0.552 +2026-04-15 01:05:42.277058: val_loss -0.4799 +2026-04-15 01:05:42.279214: Pseudo dice [0.5777, 0.0, 0.8984, 0.8992, 0.5309, 0.521, 0.9274] +2026-04-15 01:05:42.281713: Epoch time: 101.86 s +2026-04-15 01:05:43.558228: +2026-04-15 01:05:43.560564: Epoch 3691 +2026-04-15 01:05:43.562616: Current learning rate: 0.001 +2026-04-15 01:07:25.112487: train_loss -0.5558 +2026-04-15 01:07:25.120688: val_loss -0.496 +2026-04-15 01:07:25.123218: Pseudo dice [0.5987, 0.0, 0.8438, 0.8464, 0.4806, 0.7604, 0.6999] +2026-04-15 01:07:25.125641: Epoch time: 101.56 s +2026-04-15 01:07:26.381174: +2026-04-15 01:07:26.382909: Epoch 3692 +2026-04-15 01:07:26.384828: Current learning rate: 0.001 +2026-04-15 01:09:08.971222: train_loss -0.5617 +2026-04-15 01:09:08.977153: val_loss -0.4606 +2026-04-15 01:09:08.979423: Pseudo dice [0.5344, 0.0, 0.8732, 0.7905, 0.3963, 0.7943, 0.6841] +2026-04-15 01:09:08.982235: Epoch time: 102.59 s +2026-04-15 01:09:10.241281: +2026-04-15 01:09:10.243308: Epoch 3693 +2026-04-15 01:09:10.245197: Current learning rate: 0.00099 +2026-04-15 01:10:52.143009: train_loss -0.566 +2026-04-15 01:10:52.150523: val_loss -0.4769 +2026-04-15 01:10:52.153298: Pseudo dice [0.5922, 0.0, 0.8727, 0.7902, 0.5172, 0.8091, 0.9175] +2026-04-15 01:10:52.156529: Epoch time: 101.9 s +2026-04-15 01:10:53.403013: +2026-04-15 01:10:53.405276: Epoch 3694 +2026-04-15 01:10:53.407363: Current learning rate: 0.00099 +2026-04-15 01:12:35.044560: train_loss -0.5541 +2026-04-15 01:12:35.054518: val_loss -0.4693 +2026-04-15 01:12:35.056840: Pseudo dice [0.5256, 0.0, 0.837, 0.4721, 0.3904, 0.6363, 0.8885] +2026-04-15 01:12:35.060569: Epoch time: 101.64 s +2026-04-15 01:12:36.359676: +2026-04-15 01:12:36.361630: Epoch 3695 +2026-04-15 01:12:36.363892: Current learning rate: 0.00099 +2026-04-15 01:14:18.222694: train_loss -0.5615 +2026-04-15 01:14:18.229850: val_loss -0.4565 +2026-04-15 01:14:18.232242: Pseudo dice [0.7536, 0.0, 0.7449, 0.5561, 0.4159, 0.515, 0.9347] +2026-04-15 01:14:18.234977: Epoch time: 101.87 s +2026-04-15 01:14:19.503280: +2026-04-15 01:14:19.507819: Epoch 3696 +2026-04-15 01:14:19.510161: Current learning rate: 0.00098 +2026-04-15 01:16:00.785655: train_loss -0.5682 +2026-04-15 01:16:00.799368: val_loss -0.4648 +2026-04-15 01:16:00.801470: Pseudo dice [0.6867, 0.0, 0.7787, 0.8666, 0.5476, 0.6305, 0.1859] +2026-04-15 01:16:00.803515: Epoch time: 101.29 s +2026-04-15 01:16:03.192508: +2026-04-15 01:16:03.194418: Epoch 3697 +2026-04-15 01:16:03.196247: Current learning rate: 0.00098 +2026-04-15 01:17:45.056504: train_loss -0.5531 +2026-04-15 01:17:45.064777: val_loss -0.4967 +2026-04-15 01:17:45.066843: Pseudo dice [0.7779, 0.0, 0.7697, 0.8163, 0.503, 0.6864, 0.8605] +2026-04-15 01:17:45.069802: Epoch time: 101.87 s +2026-04-15 01:17:46.360645: +2026-04-15 01:17:46.363801: Epoch 3698 +2026-04-15 01:17:46.366302: Current learning rate: 0.00098 +2026-04-15 01:19:28.026617: train_loss -0.5668 +2026-04-15 01:19:28.032385: val_loss -0.4936 +2026-04-15 01:19:28.034462: Pseudo dice [0.6133, 0.0, 0.8787, 0.8319, 0.1784, 0.9184, 0.8459] +2026-04-15 01:19:28.037189: Epoch time: 101.67 s +2026-04-15 01:19:29.332459: +2026-04-15 01:19:29.334448: Epoch 3699 +2026-04-15 01:19:29.336406: Current learning rate: 0.00097 +2026-04-15 01:21:11.517658: train_loss -0.5715 +2026-04-15 01:21:11.527244: val_loss -0.4902 +2026-04-15 01:21:11.530146: Pseudo dice [0.7833, 0.0, 0.8252, 0.7812, 0.5158, 0.8763, 0.9128] +2026-04-15 01:21:11.532394: Epoch time: 102.19 s +2026-04-15 01:21:14.683852: +2026-04-15 01:21:14.686281: Epoch 3700 +2026-04-15 01:21:14.687933: Current learning rate: 0.00097 +2026-04-15 01:22:56.259046: train_loss -0.567 +2026-04-15 01:22:56.266170: val_loss -0.4728 +2026-04-15 01:22:56.269306: Pseudo dice [0.6058, 0.0, 0.8205, 0.8458, 0.573, 0.8427, 0.9401] +2026-04-15 01:22:56.274713: Epoch time: 101.58 s +2026-04-15 01:22:57.537964: +2026-04-15 01:22:57.539611: Epoch 3701 +2026-04-15 01:22:57.541389: Current learning rate: 0.00097 +2026-04-15 01:24:38.992775: train_loss -0.5606 +2026-04-15 01:24:39.000637: val_loss -0.4851 +2026-04-15 01:24:39.003137: Pseudo dice [0.5511, 0.0, 0.7755, 0.8793, 0.5087, 0.761, 0.8926] +2026-04-15 01:24:39.006202: Epoch time: 101.46 s +2026-04-15 01:24:40.249735: +2026-04-15 01:24:40.251647: Epoch 3702 +2026-04-15 01:24:40.253734: Current learning rate: 0.00097 +2026-04-15 01:26:22.106323: train_loss -0.5498 +2026-04-15 01:26:22.114426: val_loss -0.497 +2026-04-15 01:26:22.117058: Pseudo dice [0.6907, 0.0, 0.8685, 0.727, 0.6471, 0.6143, 0.9569] +2026-04-15 01:26:22.119895: Epoch time: 101.86 s +2026-04-15 01:26:23.423114: +2026-04-15 01:26:23.424962: Epoch 3703 +2026-04-15 01:26:23.426995: Current learning rate: 0.00096 +2026-04-15 01:28:04.945176: train_loss -0.5695 +2026-04-15 01:28:04.951713: val_loss -0.4916 +2026-04-15 01:28:04.953894: Pseudo dice [0.5783, 0.0, 0.8922, 0.6703, 0.4681, 0.6308, 0.9094] +2026-04-15 01:28:04.958524: Epoch time: 101.53 s +2026-04-15 01:28:06.279986: +2026-04-15 01:28:06.282404: Epoch 3704 +2026-04-15 01:28:06.284877: Current learning rate: 0.00096 +2026-04-15 01:29:47.974799: train_loss -0.5628 +2026-04-15 01:29:47.980896: val_loss -0.4754 +2026-04-15 01:29:47.984175: Pseudo dice [0.4537, 0.0, 0.8185, 0.6846, 0.45, 0.8923, 0.8804] +2026-04-15 01:29:47.987041: Epoch time: 101.7 s +2026-04-15 01:29:49.262488: +2026-04-15 01:29:49.265130: Epoch 3705 +2026-04-15 01:29:49.267599: Current learning rate: 0.00096 +2026-04-15 01:31:30.448104: train_loss -0.563 +2026-04-15 01:31:30.455717: val_loss -0.4583 +2026-04-15 01:31:30.458272: Pseudo dice [0.4973, 0.0, 0.7647, 0.0435, 0.4875, 0.751, 0.9081] +2026-04-15 01:31:30.461050: Epoch time: 101.19 s +2026-04-15 01:31:31.722303: +2026-04-15 01:31:31.724340: Epoch 3706 +2026-04-15 01:31:31.726294: Current learning rate: 0.00095 +2026-04-15 01:33:13.218736: train_loss -0.5484 +2026-04-15 01:33:13.226464: val_loss -0.4598 +2026-04-15 01:33:13.228574: Pseudo dice [0.8151, 0.0, 0.7612, 0.7677, 0.407, 0.6877, 0.7664] +2026-04-15 01:33:13.231127: Epoch time: 101.5 s +2026-04-15 01:33:14.498209: +2026-04-15 01:33:14.500002: Epoch 3707 +2026-04-15 01:33:14.502336: Current learning rate: 0.00095 +2026-04-15 01:34:55.689842: train_loss -0.5621 +2026-04-15 01:34:55.697053: val_loss -0.504 +2026-04-15 01:34:55.699256: Pseudo dice [0.7257, 0.0, 0.8504, 0.7244, 0.5305, 0.8303, 0.8983] +2026-04-15 01:34:55.701996: Epoch time: 101.19 s +2026-04-15 01:34:56.964837: +2026-04-15 01:34:56.966690: Epoch 3708 +2026-04-15 01:34:56.968608: Current learning rate: 0.00095 +2026-04-15 01:36:38.651037: train_loss -0.5733 +2026-04-15 01:36:38.658875: val_loss -0.4671 +2026-04-15 01:36:38.662210: Pseudo dice [0.5, 0.0, 0.7471, 0.7661, 0.5481, 0.911, 0.9237] +2026-04-15 01:36:38.665446: Epoch time: 101.69 s +2026-04-15 01:36:39.942841: +2026-04-15 01:36:39.944607: Epoch 3709 +2026-04-15 01:36:39.946856: Current learning rate: 0.00095 +2026-04-15 01:38:21.587977: train_loss -0.5582 +2026-04-15 01:38:21.598040: val_loss -0.496 +2026-04-15 01:38:21.600574: Pseudo dice [0.5949, 0.0, 0.8952, 0.9258, 0.5942, 0.496, 0.9501] +2026-04-15 01:38:21.603011: Epoch time: 101.65 s +2026-04-15 01:38:22.857010: +2026-04-15 01:38:22.858763: Epoch 3710 +2026-04-15 01:38:22.860735: Current learning rate: 0.00094 +2026-04-15 01:40:04.561205: train_loss -0.5549 +2026-04-15 01:40:04.571462: val_loss -0.4723 +2026-04-15 01:40:04.574651: Pseudo dice [0.7515, 0.0, 0.7553, 0.7316, 0.4853, 0.7658, 0.9149] +2026-04-15 01:40:04.578540: Epoch time: 101.71 s +2026-04-15 01:40:05.867445: +2026-04-15 01:40:05.869463: Epoch 3711 +2026-04-15 01:40:05.871477: Current learning rate: 0.00094 +2026-04-15 01:41:47.364319: train_loss -0.5545 +2026-04-15 01:41:47.372188: val_loss -0.47 +2026-04-15 01:41:47.375243: Pseudo dice [0.8401, 0.0, 0.6141, 0.7679, 0.4322, 0.8248, 0.9168] +2026-04-15 01:41:47.377820: Epoch time: 101.5 s +2026-04-15 01:41:48.633965: +2026-04-15 01:41:48.635636: Epoch 3712 +2026-04-15 01:41:48.637755: Current learning rate: 0.00094 +2026-04-15 01:43:30.702666: train_loss -0.5727 +2026-04-15 01:43:30.708010: val_loss -0.4461 +2026-04-15 01:43:30.710944: Pseudo dice [0.2639, 0.0, 0.7748, 0.5367, 0.2803, 0.7297, 0.8973] +2026-04-15 01:43:30.713519: Epoch time: 102.07 s +2026-04-15 01:43:31.998746: +2026-04-15 01:43:32.000894: Epoch 3713 +2026-04-15 01:43:32.003211: Current learning rate: 0.00093 +2026-04-15 01:45:13.166772: train_loss -0.5671 +2026-04-15 01:45:13.174292: val_loss -0.4708 +2026-04-15 01:45:13.177111: Pseudo dice [0.7848, 0.0, 0.6799, 0.7354, 0.3634, 0.83, 0.8706] +2026-04-15 01:45:13.179869: Epoch time: 101.17 s +2026-04-15 01:45:14.449636: +2026-04-15 01:45:14.451341: Epoch 3714 +2026-04-15 01:45:14.453351: Current learning rate: 0.00093 +2026-04-15 01:46:56.449813: train_loss -0.5724 +2026-04-15 01:46:56.456894: val_loss -0.4532 +2026-04-15 01:46:56.459069: Pseudo dice [0.7937, 0.0, 0.7799, 0.0486, 0.4953, 0.8397, 0.7938] +2026-04-15 01:46:56.461648: Epoch time: 102.0 s +2026-04-15 01:46:57.742708: +2026-04-15 01:46:57.744565: Epoch 3715 +2026-04-15 01:46:57.746412: Current learning rate: 0.00093 +2026-04-15 01:48:39.354854: train_loss -0.5662 +2026-04-15 01:48:39.361160: val_loss -0.4782 +2026-04-15 01:48:39.363264: Pseudo dice [0.6096, 0.0, 0.8715, 0.8604, 0.4882, 0.7975, 0.9392] +2026-04-15 01:48:39.365602: Epoch time: 101.62 s +2026-04-15 01:48:40.636415: +2026-04-15 01:48:40.638569: Epoch 3716 +2026-04-15 01:48:40.640708: Current learning rate: 0.00092 +2026-04-15 01:50:23.577368: train_loss -0.5555 +2026-04-15 01:50:23.584050: val_loss -0.4603 +2026-04-15 01:50:23.586238: Pseudo dice [0.5427, 0.0, 0.8418, 0.2813, 0.5615, 0.8352, 0.9412] +2026-04-15 01:50:23.588663: Epoch time: 102.94 s +2026-04-15 01:50:24.841444: +2026-04-15 01:50:24.843166: Epoch 3717 +2026-04-15 01:50:24.844975: Current learning rate: 0.00092 +2026-04-15 01:52:06.580170: train_loss -0.5689 +2026-04-15 01:52:06.586951: val_loss -0.4567 +2026-04-15 01:52:06.588786: Pseudo dice [0.8083, 0.0, 0.8223, 0.7222, 0.239, 0.7243, 0.7597] +2026-04-15 01:52:06.591518: Epoch time: 101.74 s +2026-04-15 01:52:07.853803: +2026-04-15 01:52:07.856110: Epoch 3718 +2026-04-15 01:52:07.858226: Current learning rate: 0.00092 +2026-04-15 01:53:49.358316: train_loss -0.5725 +2026-04-15 01:53:49.365295: val_loss -0.4341 +2026-04-15 01:53:49.367864: Pseudo dice [0.7505, 0.0, 0.5988, 0.7767, 0.4515, 0.592, 0.606] +2026-04-15 01:53:49.370166: Epoch time: 101.51 s +2026-04-15 01:53:50.617244: +2026-04-15 01:53:50.619418: Epoch 3719 +2026-04-15 01:53:50.621472: Current learning rate: 0.00092 +2026-04-15 01:55:31.943036: train_loss -0.5786 +2026-04-15 01:55:31.950046: val_loss -0.4536 +2026-04-15 01:55:31.952586: Pseudo dice [0.4911, 0.0, 0.8402, 0.6413, 0.3157, 0.8525, 0.5377] +2026-04-15 01:55:31.955670: Epoch time: 101.33 s +2026-04-15 01:55:33.232270: +2026-04-15 01:55:33.234307: Epoch 3720 +2026-04-15 01:55:33.236566: Current learning rate: 0.00091 +2026-04-15 01:57:15.823800: train_loss -0.5561 +2026-04-15 01:57:15.830455: val_loss -0.447 +2026-04-15 01:57:15.832580: Pseudo dice [0.8812, 0.0, 0.6485, 0.2185, 0.2789, 0.5005, 0.8443] +2026-04-15 01:57:15.834956: Epoch time: 102.59 s +2026-04-15 01:57:17.092404: +2026-04-15 01:57:17.094556: Epoch 3721 +2026-04-15 01:57:17.098876: Current learning rate: 0.00091 +2026-04-15 01:58:58.712339: train_loss -0.5629 +2026-04-15 01:58:58.723469: val_loss -0.4951 +2026-04-15 01:58:58.726062: Pseudo dice [0.8162, 0.0, 0.8699, 0.7007, 0.4639, 0.6847, 0.7012] +2026-04-15 01:58:58.728332: Epoch time: 101.62 s +2026-04-15 01:58:59.978457: +2026-04-15 01:58:59.980286: Epoch 3722 +2026-04-15 01:58:59.982615: Current learning rate: 0.00091 +2026-04-15 02:00:41.674017: train_loss -0.5612 +2026-04-15 02:00:41.680455: val_loss -0.4657 +2026-04-15 02:00:41.682917: Pseudo dice [0.7011, 0.0, 0.6998, 0.4566, 0.5846, 0.8828, 0.8912] +2026-04-15 02:00:41.685475: Epoch time: 101.7 s +2026-04-15 02:00:42.945999: +2026-04-15 02:00:42.948514: Epoch 3723 +2026-04-15 02:00:42.950515: Current learning rate: 0.0009 +2026-04-15 02:02:24.895827: train_loss -0.5618 +2026-04-15 02:02:24.903457: val_loss -0.4766 +2026-04-15 02:02:24.905609: Pseudo dice [0.6806, 0.0, 0.827, 0.8074, 0.5011, 0.7644, 0.8643] +2026-04-15 02:02:24.908076: Epoch time: 101.95 s +2026-04-15 02:02:26.178432: +2026-04-15 02:02:26.180464: Epoch 3724 +2026-04-15 02:02:26.182525: Current learning rate: 0.0009 +2026-04-15 02:04:07.452797: train_loss -0.5728 +2026-04-15 02:04:07.460236: val_loss -0.4735 +2026-04-15 02:04:07.462831: Pseudo dice [0.4764, 0.0, 0.7245, 0.8836, 0.4972, 0.5518, 0.8716] +2026-04-15 02:04:07.465377: Epoch time: 101.28 s +2026-04-15 02:04:08.736270: +2026-04-15 02:04:08.738152: Epoch 3725 +2026-04-15 02:04:08.740235: Current learning rate: 0.0009 +2026-04-15 02:05:50.473924: train_loss -0.5631 +2026-04-15 02:05:50.480854: val_loss -0.4595 +2026-04-15 02:05:50.482792: Pseudo dice [0.6327, 0.0, 0.8377, 0.5468, 0.5819, 0.7259, 0.9318] +2026-04-15 02:05:50.485135: Epoch time: 101.74 s +2026-04-15 02:05:51.791823: +2026-04-15 02:05:51.793691: Epoch 3726 +2026-04-15 02:05:51.795650: Current learning rate: 0.0009 +2026-04-15 02:07:33.263898: train_loss -0.5573 +2026-04-15 02:07:33.271216: val_loss -0.46 +2026-04-15 02:07:33.273490: Pseudo dice [0.8217, 0.0, 0.736, 0.3538, 0.3861, 0.7755, 0.9123] +2026-04-15 02:07:33.276111: Epoch time: 101.48 s +2026-04-15 02:07:34.520627: +2026-04-15 02:07:34.522507: Epoch 3727 +2026-04-15 02:07:34.524432: Current learning rate: 0.00089 +2026-04-15 02:09:16.265234: train_loss -0.5581 +2026-04-15 02:09:16.272312: val_loss -0.4858 +2026-04-15 02:09:16.274423: Pseudo dice [0.7063, 0.0, 0.8359, 0.5315, 0.5035, 0.6231, 0.9129] +2026-04-15 02:09:16.276983: Epoch time: 101.75 s +2026-04-15 02:09:17.578297: +2026-04-15 02:09:17.580206: Epoch 3728 +2026-04-15 02:09:17.582054: Current learning rate: 0.00089 +2026-04-15 02:10:59.378521: train_loss -0.5515 +2026-04-15 02:10:59.386306: val_loss -0.461 +2026-04-15 02:10:59.388650: Pseudo dice [0.7821, 0.0, 0.7761, 0.7606, 0.4074, 0.8156, 0.7005] +2026-04-15 02:10:59.391518: Epoch time: 101.8 s +2026-04-15 02:11:00.643887: +2026-04-15 02:11:00.645526: Epoch 3729 +2026-04-15 02:11:00.647419: Current learning rate: 0.00089 +2026-04-15 02:12:42.536910: train_loss -0.5635 +2026-04-15 02:12:42.543996: val_loss -0.4211 +2026-04-15 02:12:42.546270: Pseudo dice [0.7743, 0.0, 0.6631, 0.0493, 0.7179, 0.4635, 0.8104] +2026-04-15 02:12:42.549711: Epoch time: 101.9 s +2026-04-15 02:12:43.856420: +2026-04-15 02:12:43.858325: Epoch 3730 +2026-04-15 02:12:43.860125: Current learning rate: 0.00088 +2026-04-15 02:14:25.496257: train_loss -0.5597 +2026-04-15 02:14:25.503306: val_loss -0.4738 +2026-04-15 02:14:25.505969: Pseudo dice [0.5617, 0.0, 0.8274, 0.6132, 0.4055, 0.8137, 0.9502] +2026-04-15 02:14:25.508692: Epoch time: 101.64 s +2026-04-15 02:14:26.788918: +2026-04-15 02:14:26.791109: Epoch 3731 +2026-04-15 02:14:26.793183: Current learning rate: 0.00088 +2026-04-15 02:16:08.736351: train_loss -0.5806 +2026-04-15 02:16:08.742572: val_loss -0.4574 +2026-04-15 02:16:08.744642: Pseudo dice [0.774, 0.0, 0.7161, 0.0034, 0.5876, 0.8649, 0.8857] +2026-04-15 02:16:08.746950: Epoch time: 101.95 s +2026-04-15 02:16:10.062260: +2026-04-15 02:16:10.064304: Epoch 3732 +2026-04-15 02:16:10.066190: Current learning rate: 0.00088 +2026-04-15 02:17:51.682578: train_loss -0.5621 +2026-04-15 02:17:51.689353: val_loss -0.4979 +2026-04-15 02:17:51.693089: Pseudo dice [0.7477, 0.0, 0.8244, 0.7173, 0.691, 0.771, 0.7579] +2026-04-15 02:17:51.696319: Epoch time: 101.62 s +2026-04-15 02:17:52.976163: +2026-04-15 02:17:52.977914: Epoch 3733 +2026-04-15 02:17:52.979942: Current learning rate: 0.00087 +2026-04-15 02:19:35.324900: train_loss -0.5661 +2026-04-15 02:19:35.331841: val_loss -0.4974 +2026-04-15 02:19:35.334167: Pseudo dice [0.693, 0.0, 0.8158, 0.8011, 0.5982, 0.9123, 0.9058] +2026-04-15 02:19:35.336764: Epoch time: 102.35 s +2026-04-15 02:19:36.611093: +2026-04-15 02:19:36.615071: Epoch 3734 +2026-04-15 02:19:36.618410: Current learning rate: 0.00087 +2026-04-15 02:21:18.064301: train_loss -0.5675 +2026-04-15 02:21:18.069793: val_loss -0.5186 +2026-04-15 02:21:18.072313: Pseudo dice [0.6326, 0.0, 0.8654, 0.915, 0.5502, 0.9172, 0.957] +2026-04-15 02:21:18.074879: Epoch time: 101.46 s +2026-04-15 02:21:19.416432: +2026-04-15 02:21:19.418857: Epoch 3735 +2026-04-15 02:21:19.421320: Current learning rate: 0.00087 +2026-04-15 02:23:00.954345: train_loss -0.5639 +2026-04-15 02:23:00.960850: val_loss -0.4842 +2026-04-15 02:23:00.963138: Pseudo dice [0.6559, 0.0, 0.8446, 0.7726, 0.5149, 0.6053, 0.7964] +2026-04-15 02:23:00.965624: Epoch time: 101.54 s +2026-04-15 02:23:03.352164: +2026-04-15 02:23:03.353874: Epoch 3736 +2026-04-15 02:23:03.355767: Current learning rate: 0.00087 +2026-04-15 02:24:45.024594: train_loss -0.5716 +2026-04-15 02:24:45.030784: val_loss -0.4962 +2026-04-15 02:24:45.034156: Pseudo dice [0.7616, 0.0, 0.8383, 0.7512, 0.5708, 0.6295, 0.9584] +2026-04-15 02:24:45.036805: Epoch time: 101.68 s +2026-04-15 02:24:46.318748: +2026-04-15 02:24:46.320694: Epoch 3737 +2026-04-15 02:24:46.323156: Current learning rate: 0.00086 +2026-04-15 02:26:28.524006: train_loss -0.5676 +2026-04-15 02:26:28.529896: val_loss -0.4724 +2026-04-15 02:26:28.531951: Pseudo dice [0.5868, 0.0, 0.7857, 0.7148, 0.2097, 0.3087, 0.9253] +2026-04-15 02:26:28.534076: Epoch time: 102.21 s +2026-04-15 02:26:29.782012: +2026-04-15 02:26:29.783762: Epoch 3738 +2026-04-15 02:26:29.785494: Current learning rate: 0.00086 +2026-04-15 02:28:11.590549: train_loss -0.5731 +2026-04-15 02:28:11.597769: val_loss -0.4497 +2026-04-15 02:28:11.600110: Pseudo dice [0.7831, 0.0, 0.9017, 0.6788, 0.5226, 0.2425, 0.6766] +2026-04-15 02:28:11.602842: Epoch time: 101.81 s +2026-04-15 02:28:12.886273: +2026-04-15 02:28:12.900881: Epoch 3739 +2026-04-15 02:28:12.915084: Current learning rate: 0.00086 +2026-04-15 02:29:54.741367: train_loss -0.559 +2026-04-15 02:29:54.751743: val_loss -0.5091 +2026-04-15 02:29:54.754576: Pseudo dice [0.649, 0.0, 0.8575, 0.7266, 0.5881, 0.6073, 0.9004] +2026-04-15 02:29:54.757221: Epoch time: 101.86 s +2026-04-15 02:29:56.138205: +2026-04-15 02:29:56.140592: Epoch 3740 +2026-04-15 02:29:56.142805: Current learning rate: 0.00085 +2026-04-15 02:31:37.847587: train_loss -0.5636 +2026-04-15 02:31:37.853244: val_loss -0.451 +2026-04-15 02:31:37.855245: Pseudo dice [0.5848, 0.0, 0.8554, 0.5557, 0.4071, 0.594, 0.8777] +2026-04-15 02:31:37.857798: Epoch time: 101.71 s +2026-04-15 02:31:39.197666: +2026-04-15 02:31:39.199337: Epoch 3741 +2026-04-15 02:31:39.201255: Current learning rate: 0.00085 +2026-04-15 02:33:20.818866: train_loss -0.5573 +2026-04-15 02:33:20.825334: val_loss -0.4617 +2026-04-15 02:33:20.827411: Pseudo dice [0.5318, 0.0, 0.8731, 0.7693, 0.5865, 0.4887, 0.9292] +2026-04-15 02:33:20.829479: Epoch time: 101.62 s +2026-04-15 02:33:22.093633: +2026-04-15 02:33:22.095460: Epoch 3742 +2026-04-15 02:33:22.097312: Current learning rate: 0.00085 +2026-04-15 02:35:03.512460: train_loss -0.5633 +2026-04-15 02:35:03.518330: val_loss -0.5148 +2026-04-15 02:35:03.520184: Pseudo dice [0.7629, 0.0, 0.8725, 0.8849, 0.4721, 0.7774, 0.8906] +2026-04-15 02:35:03.522548: Epoch time: 101.42 s +2026-04-15 02:35:04.780311: +2026-04-15 02:35:04.783313: Epoch 3743 +2026-04-15 02:35:04.785714: Current learning rate: 0.00085 +2026-04-15 02:36:46.459257: train_loss -0.5575 +2026-04-15 02:36:46.465705: val_loss -0.4562 +2026-04-15 02:36:46.467515: Pseudo dice [0.6373, 0.0, 0.8275, 0.4171, 0.3186, 0.7666, 0.6258] +2026-04-15 02:36:46.470099: Epoch time: 101.68 s +2026-04-15 02:36:47.735982: +2026-04-15 02:36:47.738999: Epoch 3744 +2026-04-15 02:36:47.741069: Current learning rate: 0.00084 +2026-04-15 02:38:29.776612: train_loss -0.561 +2026-04-15 02:38:29.786652: val_loss -0.4959 +2026-04-15 02:38:29.788763: Pseudo dice [0.8105, 0.0, 0.877, 0.7627, 0.4048, 0.5798, 0.9184] +2026-04-15 02:38:29.791611: Epoch time: 102.04 s +2026-04-15 02:38:31.036347: +2026-04-15 02:38:31.041874: Epoch 3745 +2026-04-15 02:38:31.044193: Current learning rate: 0.00084 +2026-04-15 02:40:12.497400: train_loss -0.5655 +2026-04-15 02:40:12.505422: val_loss -0.474 +2026-04-15 02:40:12.508118: Pseudo dice [0.7498, 0.0, 0.8013, 0.7816, 0.4816, 0.2818, 0.9226] +2026-04-15 02:40:12.510650: Epoch time: 101.46 s +2026-04-15 02:40:13.783421: +2026-04-15 02:40:13.785224: Epoch 3746 +2026-04-15 02:40:13.787024: Current learning rate: 0.00084 +2026-04-15 02:41:55.487469: train_loss -0.5576 +2026-04-15 02:41:55.495072: val_loss -0.4763 +2026-04-15 02:41:55.497699: Pseudo dice [0.7474, 0.0, 0.8492, 0.8623, 0.3463, 0.6664, 0.8887] +2026-04-15 02:41:55.500115: Epoch time: 101.71 s +2026-04-15 02:41:56.773514: +2026-04-15 02:41:56.776940: Epoch 3747 +2026-04-15 02:41:56.779351: Current learning rate: 0.00083 +2026-04-15 02:43:38.507890: train_loss -0.5615 +2026-04-15 02:43:38.514516: val_loss -0.4786 +2026-04-15 02:43:38.516942: Pseudo dice [0.7254, 0.0, 0.6698, 0.2487, 0.402, 0.6385, 0.8344] +2026-04-15 02:43:38.519564: Epoch time: 101.74 s +2026-04-15 02:43:39.780410: +2026-04-15 02:43:39.782794: Epoch 3748 +2026-04-15 02:43:39.784883: Current learning rate: 0.00083 +2026-04-15 02:45:21.702221: train_loss -0.5624 +2026-04-15 02:45:21.709102: val_loss -0.4321 +2026-04-15 02:45:21.711341: Pseudo dice [0.3498, 0.0, 0.8597, 0.1513, 0.3759, 0.538, 0.798] +2026-04-15 02:45:21.713726: Epoch time: 101.92 s +2026-04-15 02:45:22.973511: +2026-04-15 02:45:22.975269: Epoch 3749 +2026-04-15 02:45:22.976789: Current learning rate: 0.00083 +2026-04-15 02:47:04.278279: train_loss -0.5673 +2026-04-15 02:47:04.284582: val_loss -0.4941 +2026-04-15 02:47:04.287129: Pseudo dice [0.8469, 0.0, 0.8756, 0.7781, 0.5607, 0.6438, 0.8356] +2026-04-15 02:47:04.289739: Epoch time: 101.31 s +2026-04-15 02:47:07.368886: +2026-04-15 02:47:07.370686: Epoch 3750 +2026-04-15 02:47:07.372078: Current learning rate: 0.00082 +2026-04-15 02:48:48.752567: train_loss -0.5792 +2026-04-15 02:48:48.760196: val_loss -0.4789 +2026-04-15 02:48:48.762235: Pseudo dice [0.5896, 0.0, 0.8809, 0.4607, 0.4826, 0.5009, 0.9073] +2026-04-15 02:48:48.764837: Epoch time: 101.39 s +2026-04-15 02:48:50.040247: +2026-04-15 02:48:50.042226: Epoch 3751 +2026-04-15 02:48:50.043854: Current learning rate: 0.00082 +2026-04-15 02:50:31.171723: train_loss -0.5519 +2026-04-15 02:50:31.179801: val_loss -0.4944 +2026-04-15 02:50:31.182753: Pseudo dice [0.7351, 0.0, 0.8821, 0.8598, 0.3786, 0.5539, 0.9092] +2026-04-15 02:50:31.186018: Epoch time: 101.13 s +2026-04-15 02:50:32.449553: +2026-04-15 02:50:32.451766: Epoch 3752 +2026-04-15 02:50:32.453551: Current learning rate: 0.00082 +2026-04-15 02:52:13.805544: train_loss -0.5562 +2026-04-15 02:52:13.812711: val_loss -0.4548 +2026-04-15 02:52:13.815360: Pseudo dice [0.7379, 0.0, 0.7862, 0.3224, 0.6782, 0.8603, 0.8733] +2026-04-15 02:52:13.817697: Epoch time: 101.36 s +2026-04-15 02:52:15.091711: +2026-04-15 02:52:15.093832: Epoch 3753 +2026-04-15 02:52:15.095734: Current learning rate: 0.00082 +2026-04-15 02:53:56.769206: train_loss -0.5704 +2026-04-15 02:53:56.775168: val_loss -0.4934 +2026-04-15 02:53:56.777432: Pseudo dice [0.66, 0.0, 0.7829, 0.3264, 0.5107, 0.6179, 0.8329] +2026-04-15 02:53:56.780176: Epoch time: 101.68 s +2026-04-15 02:53:58.071754: +2026-04-15 02:53:58.073968: Epoch 3754 +2026-04-15 02:53:58.076287: Current learning rate: 0.00081 +2026-04-15 02:55:39.241474: train_loss -0.5656 +2026-04-15 02:55:39.246927: val_loss -0.4588 +2026-04-15 02:55:39.249192: Pseudo dice [0.7546, 0.0, 0.8077, 0.3276, 0.514, 0.646, 0.7946] +2026-04-15 02:55:39.252027: Epoch time: 101.17 s +2026-04-15 02:55:40.502649: +2026-04-15 02:55:40.505185: Epoch 3755 +2026-04-15 02:55:40.506725: Current learning rate: 0.00081 +2026-04-15 02:57:22.955073: train_loss -0.5643 +2026-04-15 02:57:22.960971: val_loss -0.4761 +2026-04-15 02:57:22.962940: Pseudo dice [0.8546, 0.0, 0.7466, 0.3374, 0.5268, 0.544, 0.8252] +2026-04-15 02:57:22.964936: Epoch time: 102.46 s +2026-04-15 02:57:24.234292: +2026-04-15 02:57:24.236153: Epoch 3756 +2026-04-15 02:57:24.238195: Current learning rate: 0.00081 +2026-04-15 02:59:05.841378: train_loss -0.5637 +2026-04-15 02:59:05.854565: val_loss -0.4497 +2026-04-15 02:59:05.865862: Pseudo dice [0.4799, 0.0, 0.7755, 0.4426, 0.6886, 0.699, 0.8708] +2026-04-15 02:59:05.869919: Epoch time: 101.61 s +2026-04-15 02:59:07.142520: +2026-04-15 02:59:07.144541: Epoch 3757 +2026-04-15 02:59:07.146352: Current learning rate: 0.0008 +2026-04-15 03:00:48.893433: train_loss -0.5657 +2026-04-15 03:00:48.900549: val_loss -0.4801 +2026-04-15 03:00:48.902593: Pseudo dice [0.7217, 0.0, 0.8632, 0.5818, 0.5452, 0.6553, 0.8798] +2026-04-15 03:00:48.905885: Epoch time: 101.75 s +2026-04-15 03:00:50.164862: +2026-04-15 03:00:50.166836: Epoch 3758 +2026-04-15 03:00:50.168505: Current learning rate: 0.0008 +2026-04-15 03:02:32.820440: train_loss -0.5641 +2026-04-15 03:02:32.826655: val_loss -0.4389 +2026-04-15 03:02:32.828817: Pseudo dice [0.4462, 0.0, 0.7964, 0.0003, 0.0824, 0.724, 0.9291] +2026-04-15 03:02:32.832072: Epoch time: 102.66 s +2026-04-15 03:02:34.183369: +2026-04-15 03:02:34.185040: Epoch 3759 +2026-04-15 03:02:34.186789: Current learning rate: 0.0008 +2026-04-15 03:04:15.503221: train_loss -0.5633 +2026-04-15 03:04:15.510730: val_loss -0.4578 +2026-04-15 03:04:15.512743: Pseudo dice [0.4801, 0.0, 0.8629, 0.3894, 0.3792, 0.712, 0.8421] +2026-04-15 03:04:15.515423: Epoch time: 101.32 s +2026-04-15 03:04:16.800942: +2026-04-15 03:04:16.802736: Epoch 3760 +2026-04-15 03:04:16.804235: Current learning rate: 0.00079 +2026-04-15 03:05:58.248859: train_loss -0.5656 +2026-04-15 03:05:58.256684: val_loss -0.4737 +2026-04-15 03:05:58.259192: Pseudo dice [0.5918, 0.0, 0.8988, 0.4201, 0.4188, 0.4902, 0.9171] +2026-04-15 03:05:58.262146: Epoch time: 101.45 s +2026-04-15 03:05:59.582655: +2026-04-15 03:05:59.584544: Epoch 3761 +2026-04-15 03:05:59.586293: Current learning rate: 0.00079 +2026-04-15 03:07:41.042795: train_loss -0.5718 +2026-04-15 03:07:41.048956: val_loss -0.4541 +2026-04-15 03:07:41.051159: Pseudo dice [0.516, 0.0, 0.5844, 0.4945, 0.5513, 0.6834, 0.9274] +2026-04-15 03:07:41.055321: Epoch time: 101.46 s +2026-04-15 03:07:42.322924: +2026-04-15 03:07:42.325241: Epoch 3762 +2026-04-15 03:07:42.327140: Current learning rate: 0.00079 +2026-04-15 03:09:23.832787: train_loss -0.5653 +2026-04-15 03:09:23.839699: val_loss -0.4662 +2026-04-15 03:09:23.842235: Pseudo dice [0.0742, 0.0, 0.8503, 0.8909, 0.494, 0.6245, 0.8482] +2026-04-15 03:09:23.845299: Epoch time: 101.51 s +2026-04-15 03:09:25.192318: +2026-04-15 03:09:25.194018: Epoch 3763 +2026-04-15 03:09:25.195824: Current learning rate: 0.00079 +2026-04-15 03:11:06.595570: train_loss -0.5625 +2026-04-15 03:11:06.603478: val_loss -0.4548 +2026-04-15 03:11:06.607229: Pseudo dice [0.7861, 0.0, 0.7988, 0.6413, 0.5039, 0.8287, 0.9276] +2026-04-15 03:11:06.609983: Epoch time: 101.41 s +2026-04-15 03:11:07.858411: +2026-04-15 03:11:07.861639: Epoch 3764 +2026-04-15 03:11:07.863369: Current learning rate: 0.00078 +2026-04-15 03:12:49.504486: train_loss -0.5719 +2026-04-15 03:12:49.510825: val_loss -0.4922 +2026-04-15 03:12:49.513587: Pseudo dice [0.8398, 0.0, 0.8002, 0.5966, 0.3272, 0.9082, 0.9415] +2026-04-15 03:12:49.515860: Epoch time: 101.65 s +2026-04-15 03:12:50.775524: +2026-04-15 03:12:50.777308: Epoch 3765 +2026-04-15 03:12:50.779023: Current learning rate: 0.00078 +2026-04-15 03:14:32.450525: train_loss -0.5589 +2026-04-15 03:14:32.456737: val_loss -0.47 +2026-04-15 03:14:32.459466: Pseudo dice [0.8502, 0.0, 0.7374, 0.8222, 0.5647, 0.6364, 0.7214] +2026-04-15 03:14:32.464051: Epoch time: 101.68 s +2026-04-15 03:14:33.731803: +2026-04-15 03:14:33.733802: Epoch 3766 +2026-04-15 03:14:33.735353: Current learning rate: 0.00078 +2026-04-15 03:16:15.315086: train_loss -0.5566 +2026-04-15 03:16:15.323230: val_loss -0.4654 +2026-04-15 03:16:15.325524: Pseudo dice [0.8252, 0.0, 0.7844, 0.6275, 0.4484, 0.6602, 0.8775] +2026-04-15 03:16:15.328656: Epoch time: 101.59 s +2026-04-15 03:16:16.642958: +2026-04-15 03:16:16.645292: Epoch 3767 +2026-04-15 03:16:16.647119: Current learning rate: 0.00077 +2026-04-15 03:17:58.289140: train_loss -0.569 +2026-04-15 03:17:58.300828: val_loss -0.4573 +2026-04-15 03:17:58.303229: Pseudo dice [0.3377, 0.0, 0.8475, 0.445, 0.5498, 0.6143, 0.8224] +2026-04-15 03:17:58.305651: Epoch time: 101.65 s +2026-04-15 03:17:59.590343: +2026-04-15 03:17:59.592301: Epoch 3768 +2026-04-15 03:17:59.593997: Current learning rate: 0.00077 +2026-04-15 03:19:41.238435: train_loss -0.5562 +2026-04-15 03:19:41.246421: val_loss -0.4483 +2026-04-15 03:19:41.249078: Pseudo dice [0.5248, 0.0, 0.8567, 0.391, 0.4577, 0.7685, 0.9044] +2026-04-15 03:19:41.252648: Epoch time: 101.65 s +2026-04-15 03:19:42.543442: +2026-04-15 03:19:42.545928: Epoch 3769 +2026-04-15 03:19:42.548467: Current learning rate: 0.00077 +2026-04-15 03:21:23.979347: train_loss -0.5627 +2026-04-15 03:21:23.987388: val_loss -0.4705 +2026-04-15 03:21:23.989692: Pseudo dice [0.8019, 0.0, 0.4461, 0.1234, 0.5129, 0.7601, 0.9536] +2026-04-15 03:21:23.992706: Epoch time: 101.44 s +2026-04-15 03:21:25.270926: +2026-04-15 03:21:25.272636: Epoch 3770 +2026-04-15 03:21:25.274188: Current learning rate: 0.00077 +2026-04-15 03:23:07.422311: train_loss -0.5727 +2026-04-15 03:23:07.429669: val_loss -0.4849 +2026-04-15 03:23:07.431690: Pseudo dice [0.7503, 0.0, 0.7741, 0.4024, 0.4097, 0.6035, 0.9239] +2026-04-15 03:23:07.433964: Epoch time: 102.15 s +2026-04-15 03:23:08.706588: +2026-04-15 03:23:08.708849: Epoch 3771 +2026-04-15 03:23:08.711121: Current learning rate: 0.00076 +2026-04-15 03:24:50.166027: train_loss -0.5661 +2026-04-15 03:24:50.174359: val_loss -0.4855 +2026-04-15 03:24:50.176317: Pseudo dice [0.7886, 0.0, 0.8766, 0.7551, 0.4835, 0.6264, 0.8387] +2026-04-15 03:24:50.179564: Epoch time: 101.46 s +2026-04-15 03:24:51.458113: +2026-04-15 03:24:51.460293: Epoch 3772 +2026-04-15 03:24:51.462039: Current learning rate: 0.00076 +2026-04-15 03:26:33.021837: train_loss -0.5732 +2026-04-15 03:26:33.028977: val_loss -0.4722 +2026-04-15 03:26:33.031629: Pseudo dice [0.8512, 0.0, 0.7745, 0.7469, 0.4611, 0.8119, 0.936] +2026-04-15 03:26:33.034306: Epoch time: 101.57 s +2026-04-15 03:26:34.308575: +2026-04-15 03:26:34.310251: Epoch 3773 +2026-04-15 03:26:34.312155: Current learning rate: 0.00076 +2026-04-15 03:28:15.899558: train_loss -0.5676 +2026-04-15 03:28:15.906424: val_loss -0.4702 +2026-04-15 03:28:15.908629: Pseudo dice [0.796, 0.0, 0.8396, 0.7368, 0.5505, 0.5396, 0.8798] +2026-04-15 03:28:15.911500: Epoch time: 101.59 s +2026-04-15 03:28:17.167178: +2026-04-15 03:28:17.169238: Epoch 3774 +2026-04-15 03:28:17.170999: Current learning rate: 0.00075 +2026-04-15 03:29:59.344538: train_loss -0.5752 +2026-04-15 03:29:59.353170: val_loss -0.4448 +2026-04-15 03:29:59.357075: Pseudo dice [0.5539, 0.0, 0.7838, 0.6255, 0.4449, 0.8018, 0.8103] +2026-04-15 03:29:59.360975: Epoch time: 102.18 s +2026-04-15 03:30:01.793952: +2026-04-15 03:30:01.795743: Epoch 3775 +2026-04-15 03:30:01.797267: Current learning rate: 0.00075 +2026-04-15 03:31:43.495251: train_loss -0.571 +2026-04-15 03:31:43.503031: val_loss -0.5022 +2026-04-15 03:31:43.508604: Pseudo dice [0.8948, 0.0, 0.7307, 0.7957, 0.4452, 0.6701, 0.8549] +2026-04-15 03:31:43.512010: Epoch time: 101.7 s +2026-04-15 03:31:44.781857: +2026-04-15 03:31:44.784427: Epoch 3776 +2026-04-15 03:31:44.787007: Current learning rate: 0.00075 +2026-04-15 03:33:26.788710: train_loss -0.5836 +2026-04-15 03:33:26.798219: val_loss -0.4214 +2026-04-15 03:33:26.801038: Pseudo dice [0.7938, 0.0, 0.6629, 0.446, 0.5208, 0.7002, 0.9109] +2026-04-15 03:33:26.803412: Epoch time: 102.01 s +2026-04-15 03:33:28.090133: +2026-04-15 03:33:28.092063: Epoch 3777 +2026-04-15 03:33:28.093867: Current learning rate: 0.00074 +2026-04-15 03:35:10.397207: train_loss -0.5771 +2026-04-15 03:35:10.405083: val_loss -0.4908 +2026-04-15 03:35:10.407104: Pseudo dice [0.6277, 0.0, 0.8437, 0.8381, 0.5835, 0.6733, 0.8647] +2026-04-15 03:35:10.409706: Epoch time: 102.31 s +2026-04-15 03:35:11.683538: +2026-04-15 03:35:11.687317: Epoch 3778 +2026-04-15 03:35:11.689644: Current learning rate: 0.00074 +2026-04-15 03:36:53.638513: train_loss -0.5761 +2026-04-15 03:36:53.646994: val_loss -0.4584 +2026-04-15 03:36:53.648955: Pseudo dice [0.6849, 0.0, 0.79, 0.5734, 0.5289, 0.5911, 0.7442] +2026-04-15 03:36:53.651304: Epoch time: 101.96 s +2026-04-15 03:36:54.919092: +2026-04-15 03:36:54.920822: Epoch 3779 +2026-04-15 03:36:54.922437: Current learning rate: 0.00074 +2026-04-15 03:38:36.508501: train_loss -0.5737 +2026-04-15 03:38:36.517190: val_loss -0.4742 +2026-04-15 03:38:36.520231: Pseudo dice [0.5269, 0.0, 0.8405, 0.5831, 0.4444, 0.8722, 0.8982] +2026-04-15 03:38:36.522583: Epoch time: 101.59 s +2026-04-15 03:38:37.803542: +2026-04-15 03:38:37.805427: Epoch 3780 +2026-04-15 03:38:37.807273: Current learning rate: 0.00074 +2026-04-15 03:40:19.531015: train_loss -0.5645 +2026-04-15 03:40:19.540199: val_loss -0.4571 +2026-04-15 03:40:19.542431: Pseudo dice [0.4791, 0.0, 0.8174, 0.523, 0.4864, 0.5355, 0.8722] +2026-04-15 03:40:19.544943: Epoch time: 101.73 s +2026-04-15 03:40:20.794281: +2026-04-15 03:40:20.796530: Epoch 3781 +2026-04-15 03:40:20.798170: Current learning rate: 0.00073 +2026-04-15 03:42:02.510717: train_loss -0.5746 +2026-04-15 03:42:02.519464: val_loss -0.4324 +2026-04-15 03:42:02.521569: Pseudo dice [0.6857, 0.0, 0.8099, 0.4384, 0.3979, 0.6295, 0.8838] +2026-04-15 03:42:02.524901: Epoch time: 101.72 s +2026-04-15 03:42:03.886615: +2026-04-15 03:42:03.888596: Epoch 3782 +2026-04-15 03:42:03.890426: Current learning rate: 0.00073 +2026-04-15 03:43:45.266454: train_loss -0.5556 +2026-04-15 03:43:45.275081: val_loss -0.4878 +2026-04-15 03:43:45.277708: Pseudo dice [0.7476, 0.0, 0.8619, 0.917, 0.4732, 0.827, 0.91] +2026-04-15 03:43:45.280784: Epoch time: 101.38 s +2026-04-15 03:43:46.585963: +2026-04-15 03:43:46.588344: Epoch 3783 +2026-04-15 03:43:46.590521: Current learning rate: 0.00073 +2026-04-15 03:45:28.068300: train_loss -0.5628 +2026-04-15 03:45:28.076499: val_loss -0.4597 +2026-04-15 03:45:28.079001: Pseudo dice [0.7357, 0.0, 0.7975, 0.8068, 0.6444, 0.8534, 0.8777] +2026-04-15 03:45:28.081730: Epoch time: 101.49 s +2026-04-15 03:45:29.352053: +2026-04-15 03:45:29.354187: Epoch 3784 +2026-04-15 03:45:29.355925: Current learning rate: 0.00072 +2026-04-15 03:47:10.906729: train_loss -0.5591 +2026-04-15 03:47:10.914519: val_loss -0.5101 +2026-04-15 03:47:10.917228: Pseudo dice [0.4856, 0.0, 0.8295, 0.12, 0.4908, 0.8182, 0.9047] +2026-04-15 03:47:10.920164: Epoch time: 101.56 s +2026-04-15 03:47:12.181094: +2026-04-15 03:47:12.183090: Epoch 3785 +2026-04-15 03:47:12.184837: Current learning rate: 0.00072 +2026-04-15 03:48:53.832395: train_loss -0.5801 +2026-04-15 03:48:53.840183: val_loss -0.4597 +2026-04-15 03:48:53.842162: Pseudo dice [0.5484, 0.0, 0.8296, 0.345, 0.539, 0.7692, 0.8517] +2026-04-15 03:48:53.845251: Epoch time: 101.65 s +2026-04-15 03:48:55.145586: +2026-04-15 03:48:55.147855: Epoch 3786 +2026-04-15 03:48:55.149789: Current learning rate: 0.00072 +2026-04-15 03:50:36.508659: train_loss -0.5771 +2026-04-15 03:50:36.516244: val_loss -0.4141 +2026-04-15 03:50:36.518092: Pseudo dice [0.47, 0.0, 0.7943, 0.6071, 0.4461, 0.7813, 0.8551] +2026-04-15 03:50:36.521168: Epoch time: 101.37 s +2026-04-15 03:50:37.790401: +2026-04-15 03:50:37.793096: Epoch 3787 +2026-04-15 03:50:37.794833: Current learning rate: 0.00071 +2026-04-15 03:52:19.416953: train_loss -0.5712 +2026-04-15 03:52:19.425629: val_loss -0.4885 +2026-04-15 03:52:19.428184: Pseudo dice [0.8529, 0.0, 0.8845, 0.8033, 0.413, 0.8559, 0.931] +2026-04-15 03:52:19.430555: Epoch time: 101.63 s +2026-04-15 03:52:20.717827: +2026-04-15 03:52:20.719735: Epoch 3788 +2026-04-15 03:52:20.721433: Current learning rate: 0.00071 +2026-04-15 03:54:02.275115: train_loss -0.5715 +2026-04-15 03:54:02.281577: val_loss -0.4855 +2026-04-15 03:54:02.283062: Pseudo dice [0.7298, 0.0, 0.7657, 0.5312, 0.6276, 0.8859, 0.8941] +2026-04-15 03:54:02.284878: Epoch time: 101.56 s +2026-04-15 03:54:03.537159: +2026-04-15 03:54:03.539089: Epoch 3789 +2026-04-15 03:54:03.540640: Current learning rate: 0.00071 +2026-04-15 03:55:44.963959: train_loss -0.5723 +2026-04-15 03:55:44.971908: val_loss -0.4606 +2026-04-15 03:55:44.974209: Pseudo dice [0.6481, 0.0, 0.745, 0.8993, 0.4706, 0.6314, 0.7762] +2026-04-15 03:55:44.977346: Epoch time: 101.43 s +2026-04-15 03:55:46.250691: +2026-04-15 03:55:46.252641: Epoch 3790 +2026-04-15 03:55:46.254214: Current learning rate: 0.0007 +2026-04-15 03:57:27.984574: train_loss -0.5686 +2026-04-15 03:57:27.997409: val_loss -0.4934 +2026-04-15 03:57:28.002039: Pseudo dice [0.485, 0.0, 0.8817, 0.902, 0.253, 0.4106, 0.901] +2026-04-15 03:57:28.009184: Epoch time: 101.74 s +2026-04-15 03:57:29.262300: +2026-04-15 03:57:29.264030: Epoch 3791 +2026-04-15 03:57:29.265533: Current learning rate: 0.0007 +2026-04-15 03:59:10.963104: train_loss -0.5703 +2026-04-15 03:59:10.971513: val_loss -0.4715 +2026-04-15 03:59:10.975167: Pseudo dice [0.8175, 0.0, 0.7543, 0.4818, 0.3841, 0.5515, 0.8929] +2026-04-15 03:59:10.978461: Epoch time: 101.7 s +2026-04-15 03:59:12.254336: +2026-04-15 03:59:12.256070: Epoch 3792 +2026-04-15 03:59:12.257573: Current learning rate: 0.0007 +2026-04-15 04:00:53.714706: train_loss -0.5563 +2026-04-15 04:00:53.721999: val_loss -0.4683 +2026-04-15 04:00:53.724340: Pseudo dice [0.4202, 0.0, 0.8469, 0.9013, 0.402, 0.523, 0.6388] +2026-04-15 04:00:53.726768: Epoch time: 101.46 s +2026-04-15 04:00:55.011131: +2026-04-15 04:00:55.013040: Epoch 3793 +2026-04-15 04:00:55.014597: Current learning rate: 0.0007 +2026-04-15 04:02:36.737979: train_loss -0.5762 +2026-04-15 04:02:36.744447: val_loss -0.4685 +2026-04-15 04:02:36.747297: Pseudo dice [0.6268, 0.0, 0.7938, 0.752, 0.4724, 0.7246, 0.915] +2026-04-15 04:02:36.749676: Epoch time: 101.73 s +2026-04-15 04:02:38.110456: +2026-04-15 04:02:38.112401: Epoch 3794 +2026-04-15 04:02:38.113997: Current learning rate: 0.00069 +2026-04-15 04:04:20.565173: train_loss -0.5629 +2026-04-15 04:04:20.571661: val_loss -0.4715 +2026-04-15 04:04:20.574024: Pseudo dice [0.7543, 0.0, 0.8262, 0.8148, 0.459, 0.8511, 0.9539] +2026-04-15 04:04:20.576489: Epoch time: 102.46 s +2026-04-15 04:04:21.863975: +2026-04-15 04:04:21.865778: Epoch 3795 +2026-04-15 04:04:21.867954: Current learning rate: 0.00069 +2026-04-15 04:06:03.218502: train_loss -0.5696 +2026-04-15 04:06:03.226125: val_loss -0.5247 +2026-04-15 04:06:03.228682: Pseudo dice [0.8753, 0.0, 0.8778, 0.7015, 0.5459, 0.8442, 0.9315] +2026-04-15 04:06:03.231294: Epoch time: 101.36 s +2026-04-15 04:06:04.513573: +2026-04-15 04:06:04.515584: Epoch 3796 +2026-04-15 04:06:04.517927: Current learning rate: 0.00069 +2026-04-15 04:07:46.049962: train_loss -0.5696 +2026-04-15 04:07:46.056246: val_loss -0.4449 +2026-04-15 04:07:46.058326: Pseudo dice [0.6561, 0.0, 0.5815, 0.3405, 0.4947, 0.7676, 0.9446] +2026-04-15 04:07:46.060493: Epoch time: 101.54 s +2026-04-15 04:07:47.327794: +2026-04-15 04:07:47.329926: Epoch 3797 +2026-04-15 04:07:47.332003: Current learning rate: 0.00068 +2026-04-15 04:09:29.506967: train_loss -0.5732 +2026-04-15 04:09:29.513856: val_loss -0.4747 +2026-04-15 04:09:29.516836: Pseudo dice [0.6214, 0.0, 0.8198, 0.6272, 0.5386, 0.5254, 0.9277] +2026-04-15 04:09:29.519557: Epoch time: 102.18 s +2026-04-15 04:09:30.756143: +2026-04-15 04:09:30.757852: Epoch 3798 +2026-04-15 04:09:30.759450: Current learning rate: 0.00068 +2026-04-15 04:11:12.140145: train_loss -0.5565 +2026-04-15 04:11:12.149311: val_loss -0.4573 +2026-04-15 04:11:12.151910: Pseudo dice [0.8579, 0.0, 0.9014, 0.816, 0.4588, 0.7617, 0.9194] +2026-04-15 04:11:12.156780: Epoch time: 101.39 s +2026-04-15 04:11:13.456825: +2026-04-15 04:11:13.460435: Epoch 3799 +2026-04-15 04:11:13.462770: Current learning rate: 0.00068 +2026-04-15 04:12:55.508189: train_loss -0.5667 +2026-04-15 04:12:55.514122: val_loss -0.4481 +2026-04-15 04:12:55.516266: Pseudo dice [0.3126, 0.0, 0.7418, 0.4993, 0.5549, 0.7867, 0.8929] +2026-04-15 04:12:55.518931: Epoch time: 102.05 s +2026-04-15 04:12:58.578748: +2026-04-15 04:12:58.580372: Epoch 3800 +2026-04-15 04:12:58.581977: Current learning rate: 0.00067 +2026-04-15 04:14:40.361375: train_loss -0.5565 +2026-04-15 04:14:40.367001: val_loss -0.4696 +2026-04-15 04:14:40.369312: Pseudo dice [0.4088, 0.0, 0.8754, 0.7648, 0.5128, 0.7597, 0.9313] +2026-04-15 04:14:40.372442: Epoch time: 101.79 s +2026-04-15 04:14:41.685269: +2026-04-15 04:14:41.687047: Epoch 3801 +2026-04-15 04:14:41.688599: Current learning rate: 0.00067 +2026-04-15 04:16:23.120271: train_loss -0.5695 +2026-04-15 04:16:23.130319: val_loss -0.471 +2026-04-15 04:16:23.132275: Pseudo dice [0.4931, 0.0, 0.7999, 0.6184, 0.5671, 0.6861, 0.8851] +2026-04-15 04:16:23.135200: Epoch time: 101.44 s +2026-04-15 04:16:24.421689: +2026-04-15 04:16:24.424687: Epoch 3802 +2026-04-15 04:16:24.427376: Current learning rate: 0.00067 +2026-04-15 04:18:05.599650: train_loss -0.5697 +2026-04-15 04:18:05.606447: val_loss -0.4826 +2026-04-15 04:18:05.609142: Pseudo dice [0.6823, 0.0, 0.7446, 0.3388, 0.5665, 0.49, 0.8356] +2026-04-15 04:18:05.611985: Epoch time: 101.18 s +2026-04-15 04:18:06.873045: +2026-04-15 04:18:06.875251: Epoch 3803 +2026-04-15 04:18:06.876820: Current learning rate: 0.00067 +2026-04-15 04:19:48.484037: train_loss -0.5679 +2026-04-15 04:19:48.491786: val_loss -0.4367 +2026-04-15 04:19:48.494193: Pseudo dice [0.4083, 0.0, 0.6946, 0.3783, 0.578, 0.677, 0.8064] +2026-04-15 04:19:48.497640: Epoch time: 101.61 s +2026-04-15 04:19:49.814470: +2026-04-15 04:19:49.816449: Epoch 3804 +2026-04-15 04:19:49.818097: Current learning rate: 0.00066 +2026-04-15 04:21:31.143990: train_loss -0.5691 +2026-04-15 04:21:31.162707: val_loss -0.4876 +2026-04-15 04:21:31.165358: Pseudo dice [0.4768, 0.0, 0.7165, 0.8515, 0.4598, 0.8276, 0.9056] +2026-04-15 04:21:31.168268: Epoch time: 101.33 s +2026-04-15 04:21:32.434546: +2026-04-15 04:21:32.437184: Epoch 3805 +2026-04-15 04:21:32.438948: Current learning rate: 0.00066 +2026-04-15 04:23:13.560447: train_loss -0.5674 +2026-04-15 04:23:13.566286: val_loss -0.4884 +2026-04-15 04:23:13.568347: Pseudo dice [0.3251, 0.0, 0.804, 0.8364, 0.5916, 0.7946, 0.9313] +2026-04-15 04:23:13.570694: Epoch time: 101.13 s +2026-04-15 04:23:14.837335: +2026-04-15 04:23:14.839239: Epoch 3806 +2026-04-15 04:23:14.840995: Current learning rate: 0.00066 +2026-04-15 04:24:56.368446: train_loss -0.5759 +2026-04-15 04:24:56.374675: val_loss -0.4924 +2026-04-15 04:24:56.377815: Pseudo dice [0.819, 0.0, 0.8276, 0.892, 0.6001, 0.8586, 0.9196] +2026-04-15 04:24:56.380395: Epoch time: 101.53 s +2026-04-15 04:24:57.645823: +2026-04-15 04:24:57.648263: Epoch 3807 +2026-04-15 04:24:57.650092: Current learning rate: 0.00065 +2026-04-15 04:26:39.423085: train_loss -0.5666 +2026-04-15 04:26:39.431694: val_loss -0.4975 +2026-04-15 04:26:39.433963: Pseudo dice [0.8183, 0.0, 0.8757, 0.9, 0.5241, 0.6871, 0.9382] +2026-04-15 04:26:39.438547: Epoch time: 101.78 s +2026-04-15 04:26:40.726390: +2026-04-15 04:26:40.728563: Epoch 3808 +2026-04-15 04:26:40.730147: Current learning rate: 0.00065 +2026-04-15 04:28:22.391132: train_loss -0.5682 +2026-04-15 04:28:22.398576: val_loss -0.471 +2026-04-15 04:28:22.400922: Pseudo dice [0.5888, 0.0, 0.8613, 0.4898, 0.5075, 0.7783, 0.9089] +2026-04-15 04:28:22.403050: Epoch time: 101.67 s +2026-04-15 04:28:23.712742: +2026-04-15 04:28:23.714560: Epoch 3809 +2026-04-15 04:28:23.716578: Current learning rate: 0.00065 +2026-04-15 04:30:05.179021: train_loss -0.5814 +2026-04-15 04:30:05.185723: val_loss -0.4982 +2026-04-15 04:30:05.188133: Pseudo dice [0.3761, 0.0, 0.8424, 0.9054, 0.6644, 0.9079, 0.7907] +2026-04-15 04:30:05.190406: Epoch time: 101.47 s +2026-04-15 04:30:06.431502: +2026-04-15 04:30:06.433279: Epoch 3810 +2026-04-15 04:30:06.434841: Current learning rate: 0.00064 +2026-04-15 04:31:48.113977: train_loss -0.5728 +2026-04-15 04:31:48.119609: val_loss -0.5045 +2026-04-15 04:31:48.121763: Pseudo dice [0.7504, 0.0, 0.8842, 0.8385, 0.5441, 0.6788, 0.8945] +2026-04-15 04:31:48.124144: Epoch time: 101.69 s +2026-04-15 04:31:49.441045: +2026-04-15 04:31:49.443310: Epoch 3811 +2026-04-15 04:31:49.445230: Current learning rate: 0.00064 +2026-04-15 04:33:31.334578: train_loss -0.5682 +2026-04-15 04:33:31.340951: val_loss -0.4883 +2026-04-15 04:33:31.343242: Pseudo dice [0.3954, 0.0, 0.8765, 0.7934, 0.4951, 0.8047, 0.6805] +2026-04-15 04:33:31.345495: Epoch time: 101.9 s +2026-04-15 04:33:32.634174: +2026-04-15 04:33:32.639009: Epoch 3812 +2026-04-15 04:33:32.641416: Current learning rate: 0.00064 +2026-04-15 04:35:14.674273: train_loss -0.5711 +2026-04-15 04:35:14.681216: val_loss -0.4643 +2026-04-15 04:35:14.683207: Pseudo dice [0.3455, 0.0, 0.7818, 0.8448, 0.6088, 0.7909, 0.8666] +2026-04-15 04:35:14.686341: Epoch time: 102.04 s +2026-04-15 04:35:15.971909: +2026-04-15 04:35:15.974013: Epoch 3813 +2026-04-15 04:35:15.975786: Current learning rate: 0.00064 +2026-04-15 04:36:57.900409: train_loss -0.5751 +2026-04-15 04:36:57.907050: val_loss -0.4935 +2026-04-15 04:36:57.909183: Pseudo dice [0.8017, 0.0, 0.8754, 0.8354, 0.405, 0.7638, 0.9563] +2026-04-15 04:36:57.911521: Epoch time: 101.93 s +2026-04-15 04:37:00.307160: +2026-04-15 04:37:00.308833: Epoch 3814 +2026-04-15 04:37:00.310352: Current learning rate: 0.00063 +2026-04-15 04:38:41.595644: train_loss -0.5706 +2026-04-15 04:38:41.601846: val_loss -0.4774 +2026-04-15 04:38:41.603885: Pseudo dice [0.8066, 0.0, 0.8244, 0.5804, 0.3642, 0.6159, 0.9027] +2026-04-15 04:38:41.605931: Epoch time: 101.29 s +2026-04-15 04:38:42.889946: +2026-04-15 04:38:42.892123: Epoch 3815 +2026-04-15 04:38:42.893585: Current learning rate: 0.00063 +2026-04-15 04:40:24.774915: train_loss -0.5682 +2026-04-15 04:40:24.780934: val_loss -0.451 +2026-04-15 04:40:24.783286: Pseudo dice [0.7952, 0.0, 0.765, 0.4685, 0.5287, 0.5561, 0.9357] +2026-04-15 04:40:24.785800: Epoch time: 101.89 s +2026-04-15 04:40:26.137330: +2026-04-15 04:40:26.139107: Epoch 3816 +2026-04-15 04:40:26.140627: Current learning rate: 0.00063 +2026-04-15 04:42:07.375051: train_loss -0.5742 +2026-04-15 04:42:07.380955: val_loss -0.4889 +2026-04-15 04:42:07.383005: Pseudo dice [0.749, 0.0, 0.8475, 0.7984, 0.4423, 0.8048, 0.7433] +2026-04-15 04:42:07.385508: Epoch time: 101.24 s +2026-04-15 04:42:08.669454: +2026-04-15 04:42:08.671359: Epoch 3817 +2026-04-15 04:42:08.673024: Current learning rate: 0.00062 +2026-04-15 04:43:50.501541: train_loss -0.5691 +2026-04-15 04:43:50.507472: val_loss -0.499 +2026-04-15 04:43:50.510056: Pseudo dice [0.7606, 0.0, 0.9002, 0.803, 0.4293, 0.814, 0.9537] +2026-04-15 04:43:50.512861: Epoch time: 101.84 s +2026-04-15 04:43:51.817679: +2026-04-15 04:43:51.819465: Epoch 3818 +2026-04-15 04:43:51.821059: Current learning rate: 0.00062 +2026-04-15 04:45:34.006102: train_loss -0.5783 +2026-04-15 04:45:34.014002: val_loss -0.4577 +2026-04-15 04:45:34.016656: Pseudo dice [0.428, 0.0, 0.7784, 0.3673, 0.4548, 0.8078, 0.951] +2026-04-15 04:45:34.019524: Epoch time: 102.19 s +2026-04-15 04:45:35.303640: +2026-04-15 04:45:35.305779: Epoch 3819 +2026-04-15 04:45:35.307414: Current learning rate: 0.00062 +2026-04-15 04:47:16.817280: train_loss -0.5654 +2026-04-15 04:47:16.823842: val_loss -0.4657 +2026-04-15 04:47:16.826193: Pseudo dice [0.5434, 0.0, 0.8526, 0.8735, 0.5638, 0.6029, 0.9445] +2026-04-15 04:47:16.828524: Epoch time: 101.52 s +2026-04-15 04:47:18.127965: +2026-04-15 04:47:18.129994: Epoch 3820 +2026-04-15 04:47:18.131742: Current learning rate: 0.00061 +2026-04-15 04:48:59.887411: train_loss -0.5737 +2026-04-15 04:48:59.893254: val_loss -0.44 +2026-04-15 04:48:59.895589: Pseudo dice [0.6079, 0.0, 0.8025, 0.2563, 0.5319, 0.7678, 0.8137] +2026-04-15 04:48:59.898244: Epoch time: 101.76 s +2026-04-15 04:49:01.232674: +2026-04-15 04:49:01.235204: Epoch 3821 +2026-04-15 04:49:01.236950: Current learning rate: 0.00061 +2026-04-15 04:50:42.947392: train_loss -0.5661 +2026-04-15 04:50:42.953975: val_loss -0.4621 +2026-04-15 04:50:42.956342: Pseudo dice [0.6175, 0.0, 0.8685, 0.2293, 0.3302, 0.9159, 0.9264] +2026-04-15 04:50:42.958678: Epoch time: 101.72 s +2026-04-15 04:50:44.280122: +2026-04-15 04:50:44.282305: Epoch 3822 +2026-04-15 04:50:44.287982: Current learning rate: 0.00061 +2026-04-15 04:52:26.233215: train_loss -0.5753 +2026-04-15 04:52:26.244066: val_loss -0.5109 +2026-04-15 04:52:26.246682: Pseudo dice [0.8939, 0.0, 0.8243, 0.8485, 0.5335, 0.79, 0.8512] +2026-04-15 04:52:26.250228: Epoch time: 101.96 s +2026-04-15 04:52:27.575508: +2026-04-15 04:52:27.578607: Epoch 3823 +2026-04-15 04:52:27.580977: Current learning rate: 0.0006 +2026-04-15 04:54:09.027671: train_loss -0.5783 +2026-04-15 04:54:09.034630: val_loss -0.4918 +2026-04-15 04:54:09.037324: Pseudo dice [0.3292, 0.0, 0.868, 0.7478, 0.4558, 0.8372, 0.9101] +2026-04-15 04:54:09.039582: Epoch time: 101.46 s +2026-04-15 04:54:10.320357: +2026-04-15 04:54:10.322770: Epoch 3824 +2026-04-15 04:54:10.327554: Current learning rate: 0.0006 +2026-04-15 04:55:51.663136: train_loss -0.5793 +2026-04-15 04:55:51.669382: val_loss -0.4794 +2026-04-15 04:55:51.671288: Pseudo dice [0.6768, 0.0, 0.8297, 0.7327, 0.5184, 0.8667, 0.911] +2026-04-15 04:55:51.673970: Epoch time: 101.35 s +2026-04-15 04:55:53.059933: +2026-04-15 04:55:53.062554: Epoch 3825 +2026-04-15 04:55:53.064805: Current learning rate: 0.0006 +2026-04-15 04:57:35.120297: train_loss -0.5783 +2026-04-15 04:57:35.126313: val_loss -0.4587 +2026-04-15 04:57:35.128335: Pseudo dice [0.7442, 0.0, 0.7172, 0.5751, 0.5187, 0.7829, 0.9258] +2026-04-15 04:57:35.130565: Epoch time: 102.06 s +2026-04-15 04:57:36.407005: +2026-04-15 04:57:36.409371: Epoch 3826 +2026-04-15 04:57:36.411406: Current learning rate: 0.0006 +2026-04-15 04:59:18.011658: train_loss -0.5687 +2026-04-15 04:59:18.017652: val_loss -0.4696 +2026-04-15 04:59:18.020110: Pseudo dice [0.5237, 0.0, 0.7652, 0.6365, 0.5855, 0.5657, 0.8014] +2026-04-15 04:59:18.022464: Epoch time: 101.61 s +2026-04-15 04:59:19.308943: +2026-04-15 04:59:19.311190: Epoch 3827 +2026-04-15 04:59:19.313337: Current learning rate: 0.00059 +2026-04-15 05:01:00.885875: train_loss -0.5708 +2026-04-15 05:01:00.893015: val_loss -0.454 +2026-04-15 05:01:00.895073: Pseudo dice [0.7146, 0.0, 0.7765, 0.2677, 0.5185, 0.3212, 0.9264] +2026-04-15 05:01:00.897618: Epoch time: 101.58 s +2026-04-15 05:01:02.201274: +2026-04-15 05:01:02.203340: Epoch 3828 +2026-04-15 05:01:02.205488: Current learning rate: 0.00059 +2026-04-15 05:02:43.542668: train_loss -0.5701 +2026-04-15 05:02:43.549038: val_loss -0.4608 +2026-04-15 05:02:43.551015: Pseudo dice [0.4783, 0.0, 0.8839, 0.6767, 0.5635, 0.9033, 0.7547] +2026-04-15 05:02:43.553108: Epoch time: 101.34 s +2026-04-15 05:02:44.830382: +2026-04-15 05:02:44.832058: Epoch 3829 +2026-04-15 05:02:44.833585: Current learning rate: 0.00059 +2026-04-15 05:04:26.224583: train_loss -0.5759 +2026-04-15 05:04:26.231877: val_loss -0.4641 +2026-04-15 05:04:26.234255: Pseudo dice [0.6167, 0.0, 0.8233, 0.7116, 0.5015, 0.7914, 0.8209] +2026-04-15 05:04:26.236546: Epoch time: 101.4 s +2026-04-15 05:04:27.515725: +2026-04-15 05:04:27.517702: Epoch 3830 +2026-04-15 05:04:27.519513: Current learning rate: 0.00058 +2026-04-15 05:06:08.962281: train_loss -0.569 +2026-04-15 05:06:08.968609: val_loss -0.4473 +2026-04-15 05:06:08.970753: Pseudo dice [0.4564, 0.0, 0.7542, 0.6851, 0.2905, 0.7403, 0.8848] +2026-04-15 05:06:08.972516: Epoch time: 101.45 s +2026-04-15 05:06:10.302699: +2026-04-15 05:06:10.304714: Epoch 3831 +2026-04-15 05:06:10.306284: Current learning rate: 0.00058 +2026-04-15 05:07:51.951272: train_loss -0.563 +2026-04-15 05:07:51.959020: val_loss -0.4695 +2026-04-15 05:07:51.963425: Pseudo dice [0.7039, 0.0, 0.8197, 0.7266, 0.3162, 0.7551, 0.8928] +2026-04-15 05:07:51.966449: Epoch time: 101.65 s +2026-04-15 05:07:53.262428: +2026-04-15 05:07:53.264139: Epoch 3832 +2026-04-15 05:07:53.265661: Current learning rate: 0.00058 +2026-04-15 05:09:34.713379: train_loss -0.5747 +2026-04-15 05:09:34.720403: val_loss -0.4985 +2026-04-15 05:09:34.723255: Pseudo dice [0.8053, 0.0, 0.6111, 0.6147, 0.3841, 0.68, 0.8625] +2026-04-15 05:09:34.726543: Epoch time: 101.45 s +2026-04-15 05:09:36.919191: +2026-04-15 05:09:36.920990: Epoch 3833 +2026-04-15 05:09:36.922554: Current learning rate: 0.00057 +2026-04-15 05:11:18.697547: train_loss -0.5737 +2026-04-15 05:11:18.703115: val_loss -0.4578 +2026-04-15 05:11:18.705256: Pseudo dice [0.8445, 0.0, 0.7957, 0.6503, 0.4304, 0.8958, 0.8407] +2026-04-15 05:11:18.707644: Epoch time: 101.78 s +2026-04-15 05:11:19.994580: +2026-04-15 05:11:19.998341: Epoch 3834 +2026-04-15 05:11:20.000107: Current learning rate: 0.00057 +2026-04-15 05:13:02.128374: train_loss -0.5837 +2026-04-15 05:13:02.134802: val_loss -0.4497 +2026-04-15 05:13:02.137135: Pseudo dice [0.3843, 0.0, 0.8147, 0.7231, 0.4616, 0.8489, 0.946] +2026-04-15 05:13:02.139771: Epoch time: 102.14 s +2026-04-15 05:13:03.420321: +2026-04-15 05:13:03.422053: Epoch 3835 +2026-04-15 05:13:03.423508: Current learning rate: 0.00057 +2026-04-15 05:14:44.871097: train_loss -0.5698 +2026-04-15 05:14:44.878500: val_loss -0.4827 +2026-04-15 05:14:44.881615: Pseudo dice [0.7097, 0.0, 0.7007, 0.5581, 0.6545, 0.9046, 0.7298] +2026-04-15 05:14:44.884279: Epoch time: 101.45 s +2026-04-15 05:14:46.165950: +2026-04-15 05:14:46.168142: Epoch 3836 +2026-04-15 05:14:46.169586: Current learning rate: 0.00056 +2026-04-15 05:16:28.218075: train_loss -0.5822 +2026-04-15 05:16:28.225605: val_loss -0.4951 +2026-04-15 05:16:28.228589: Pseudo dice [0.6301, 0.0, 0.8941, 0.3464, 0.4981, 0.81, 0.878] +2026-04-15 05:16:28.231771: Epoch time: 102.06 s +2026-04-15 05:16:29.520921: +2026-04-15 05:16:29.522971: Epoch 3837 +2026-04-15 05:16:29.524666: Current learning rate: 0.00056 +2026-04-15 05:18:11.446374: train_loss -0.5718 +2026-04-15 05:18:11.453847: val_loss -0.4903 +2026-04-15 05:18:11.456633: Pseudo dice [0.4824, 0.0, 0.8947, 0.733, 0.4588, 0.8869, 0.927] +2026-04-15 05:18:11.459139: Epoch time: 101.93 s +2026-04-15 05:18:12.826942: +2026-04-15 05:18:12.828792: Epoch 3838 +2026-04-15 05:18:12.830390: Current learning rate: 0.00056 +2026-04-15 05:19:54.629603: train_loss -0.5828 +2026-04-15 05:19:54.635916: val_loss -0.4736 +2026-04-15 05:19:54.637976: Pseudo dice [0.5895, 0.0, 0.8976, 0.7981, 0.4596, 0.5552, 0.9312] +2026-04-15 05:19:54.640102: Epoch time: 101.81 s +2026-04-15 05:19:55.926883: +2026-04-15 05:19:55.928834: Epoch 3839 +2026-04-15 05:19:55.930444: Current learning rate: 0.00055 +2026-04-15 05:21:37.330080: train_loss -0.5805 +2026-04-15 05:21:37.336787: val_loss -0.4852 +2026-04-15 05:21:37.339079: Pseudo dice [0.8988, 0.0, 0.8595, 0.7714, 0.6018, 0.8447, 0.9242] +2026-04-15 05:21:37.340906: Epoch time: 101.41 s +2026-04-15 05:21:38.636526: +2026-04-15 05:21:38.639199: Epoch 3840 +2026-04-15 05:21:38.641304: Current learning rate: 0.00055 +2026-04-15 05:23:20.158314: train_loss -0.5771 +2026-04-15 05:23:20.166317: val_loss -0.4843 +2026-04-15 05:23:20.168712: Pseudo dice [0.5936, 0.0, 0.886, 0.8094, 0.5253, 0.8842, 0.88] +2026-04-15 05:23:20.171801: Epoch time: 101.52 s +2026-04-15 05:23:21.468965: +2026-04-15 05:23:21.472707: Epoch 3841 +2026-04-15 05:23:21.476801: Current learning rate: 0.00055 +2026-04-15 05:25:02.977616: train_loss -0.578 +2026-04-15 05:25:02.984669: val_loss -0.482 +2026-04-15 05:25:02.986673: Pseudo dice [0.3785, 0.0, 0.8863, 0.6653, 0.3562, 0.7624, 0.9355] +2026-04-15 05:25:02.989095: Epoch time: 101.51 s +2026-04-15 05:25:04.274134: +2026-04-15 05:25:04.275703: Epoch 3842 +2026-04-15 05:25:04.277219: Current learning rate: 0.00055 +2026-04-15 05:26:45.444695: train_loss -0.5791 +2026-04-15 05:26:45.451490: val_loss -0.5192 +2026-04-15 05:26:45.454027: Pseudo dice [0.8069, 0.0, 0.807, 0.89, 0.5552, 0.9198, 0.9553] +2026-04-15 05:26:45.457192: Epoch time: 101.17 s +2026-04-15 05:26:45.459583: Yayy! New best EMA pseudo Dice: 0.6182 +2026-04-15 05:26:48.570028: +2026-04-15 05:26:48.572058: Epoch 3843 +2026-04-15 05:26:48.573761: Current learning rate: 0.00054 +2026-04-15 05:28:30.269528: train_loss -0.5764 +2026-04-15 05:28:30.276338: val_loss -0.4944 +2026-04-15 05:28:30.278930: Pseudo dice [0.7651, 0.0, 0.8691, 0.5895, 0.513, 0.8814, 0.9445] +2026-04-15 05:28:30.282007: Epoch time: 101.7 s +2026-04-15 05:28:30.283985: Yayy! New best EMA pseudo Dice: 0.6216 +2026-04-15 05:28:33.363802: +2026-04-15 05:28:33.365638: Epoch 3844 +2026-04-15 05:28:33.367428: Current learning rate: 0.00054 +2026-04-15 05:30:14.888404: train_loss -0.5738 +2026-04-15 05:30:14.895581: val_loss -0.5133 +2026-04-15 05:30:14.897768: Pseudo dice [0.7127, 0.0, 0.9303, 0.7966, 0.4425, 0.8435, 0.7276] +2026-04-15 05:30:14.900429: Epoch time: 101.53 s +2026-04-15 05:30:14.902817: Yayy! New best EMA pseudo Dice: 0.623 +2026-04-15 05:30:18.005996: +2026-04-15 05:30:18.007729: Epoch 3845 +2026-04-15 05:30:18.009216: Current learning rate: 0.00054 +2026-04-15 05:31:59.228420: train_loss -0.5723 +2026-04-15 05:31:59.234830: val_loss -0.4872 +2026-04-15 05:31:59.237005: Pseudo dice [0.449, 0.0, 0.869, 0.7288, 0.4921, 0.8551, 0.9571] +2026-04-15 05:31:59.239625: Epoch time: 101.23 s +2026-04-15 05:32:00.522773: +2026-04-15 05:32:00.524602: Epoch 3846 +2026-04-15 05:32:00.526015: Current learning rate: 0.00053 +2026-04-15 05:33:41.967174: train_loss -0.5855 +2026-04-15 05:33:41.974430: val_loss -0.4835 +2026-04-15 05:33:41.976293: Pseudo dice [0.4003, 0.0, 0.8498, 0.7059, 0.4179, 0.832, 0.9357] +2026-04-15 05:33:41.978585: Epoch time: 101.45 s +2026-04-15 05:33:43.247681: +2026-04-15 05:33:43.249450: Epoch 3847 +2026-04-15 05:33:43.251045: Current learning rate: 0.00053 +2026-04-15 05:35:24.488326: train_loss -0.5781 +2026-04-15 05:35:24.495335: val_loss -0.4656 +2026-04-15 05:35:24.498252: Pseudo dice [0.8524, 0.0, 0.7559, 0.6142, 0.363, 0.575, 0.9563] +2026-04-15 05:35:24.500623: Epoch time: 101.24 s +2026-04-15 05:35:25.768755: +2026-04-15 05:35:25.771302: Epoch 3848 +2026-04-15 05:35:25.774779: Current learning rate: 0.00053 +2026-04-15 05:37:07.556433: train_loss -0.5754 +2026-04-15 05:37:07.564735: val_loss -0.4735 +2026-04-15 05:37:07.566975: Pseudo dice [0.8067, 0.0, 0.784, 0.6787, 0.5634, 0.8353, 0.9482] +2026-04-15 05:37:07.569114: Epoch time: 101.79 s +2026-04-15 05:37:08.835596: +2026-04-15 05:37:08.837266: Epoch 3849 +2026-04-15 05:37:08.838833: Current learning rate: 0.00052 +2026-04-15 05:38:50.026565: train_loss -0.5723 +2026-04-15 05:38:50.032675: val_loss -0.4854 +2026-04-15 05:38:50.034885: Pseudo dice [0.7773, 0.0, 0.7888, 0.6732, 0.7053, 0.824, 0.9082] +2026-04-15 05:38:50.037317: Epoch time: 101.19 s +2026-04-15 05:38:51.899598: Yayy! New best EMA pseudo Dice: 0.6256 +2026-04-15 05:38:54.874553: +2026-04-15 05:38:54.876433: Epoch 3850 +2026-04-15 05:38:54.877902: Current learning rate: 0.00052 +2026-04-15 05:40:36.747977: train_loss -0.5779 +2026-04-15 05:40:36.754959: val_loss -0.4701 +2026-04-15 05:40:36.757868: Pseudo dice [0.7028, 0.0, 0.8629, 0.2113, 0.4409, 0.9014, 0.9283] +2026-04-15 05:40:36.761180: Epoch time: 101.88 s +2026-04-15 05:40:39.100577: +2026-04-15 05:40:39.102499: Epoch 3851 +2026-04-15 05:40:39.103992: Current learning rate: 0.00052 +2026-04-15 05:42:20.881302: train_loss -0.5721 +2026-04-15 05:42:20.886875: val_loss -0.4789 +2026-04-15 05:42:20.888480: Pseudo dice [0.39, 0.0, 0.8455, 0.6934, 0.3361, 0.7738, 0.9107] +2026-04-15 05:42:20.890776: Epoch time: 101.78 s +2026-04-15 05:42:22.209040: +2026-04-15 05:42:22.212408: Epoch 3852 +2026-04-15 05:42:22.214945: Current learning rate: 0.00051 +2026-04-15 05:44:03.468899: train_loss -0.5658 +2026-04-15 05:44:03.476774: val_loss -0.4882 +2026-04-15 05:44:03.479331: Pseudo dice [0.8458, 0.0, 0.8935, 0.4833, 0.4412, 0.574, 0.9326] +2026-04-15 05:44:03.481920: Epoch time: 101.26 s +2026-04-15 05:44:04.758578: +2026-04-15 05:44:04.760702: Epoch 3853 +2026-04-15 05:44:04.762400: Current learning rate: 0.00051 +2026-04-15 05:45:46.092814: train_loss -0.5669 +2026-04-15 05:45:46.118200: val_loss -0.4974 +2026-04-15 05:45:46.120554: Pseudo dice [0.7055, 0.0, 0.8874, 0.584, 0.5292, 0.3624, 0.9271] +2026-04-15 05:45:46.123054: Epoch time: 101.34 s +2026-04-15 05:45:47.388356: +2026-04-15 05:45:47.390458: Epoch 3854 +2026-04-15 05:45:47.392153: Current learning rate: 0.00051 +2026-04-15 05:47:29.420364: train_loss -0.5698 +2026-04-15 05:47:29.426444: val_loss -0.4462 +2026-04-15 05:47:29.428677: Pseudo dice [0.2719, 0.0, 0.5787, 0.2778, 0.5831, 0.8176, 0.9242] +2026-04-15 05:47:29.430943: Epoch time: 102.04 s +2026-04-15 05:47:30.697991: +2026-04-15 05:47:30.699903: Epoch 3855 +2026-04-15 05:47:30.701553: Current learning rate: 0.00051 +2026-04-15 05:49:12.719972: train_loss -0.585 +2026-04-15 05:49:12.728072: val_loss -0.4797 +2026-04-15 05:49:12.731861: Pseudo dice [0.5883, 0.0, 0.8189, 0.6385, 0.4135, 0.6808, 0.949] +2026-04-15 05:49:12.734539: Epoch time: 102.03 s +2026-04-15 05:49:14.024831: +2026-04-15 05:49:14.027580: Epoch 3856 +2026-04-15 05:49:14.029197: Current learning rate: 0.0005 +2026-04-15 05:50:55.453628: train_loss -0.5771 +2026-04-15 05:50:55.460036: val_loss -0.502 +2026-04-15 05:50:55.462036: Pseudo dice [0.8732, 0.0, 0.8707, 0.8314, 0.4589, 0.851, 0.9324] +2026-04-15 05:50:55.464620: Epoch time: 101.43 s +2026-04-15 05:50:56.743798: +2026-04-15 05:50:56.745814: Epoch 3857 +2026-04-15 05:50:56.747464: Current learning rate: 0.0005 +2026-04-15 05:52:38.441761: train_loss -0.5737 +2026-04-15 05:52:38.447857: val_loss -0.478 +2026-04-15 05:52:38.449962: Pseudo dice [0.3234, 0.0, 0.8147, 0.5692, 0.5015, 0.7603, 0.9433] +2026-04-15 05:52:38.453184: Epoch time: 101.7 s +2026-04-15 05:52:39.735898: +2026-04-15 05:52:39.737739: Epoch 3858 +2026-04-15 05:52:39.739343: Current learning rate: 0.0005 +2026-04-15 05:54:21.230889: train_loss -0.5698 +2026-04-15 05:54:21.237234: val_loss -0.4905 +2026-04-15 05:54:21.239459: Pseudo dice [0.6458, 0.0, 0.8713, 0.8594, 0.4133, 0.6414, 0.9539] +2026-04-15 05:54:21.241875: Epoch time: 101.5 s +2026-04-15 05:54:22.530303: +2026-04-15 05:54:22.532312: Epoch 3859 +2026-04-15 05:54:22.534007: Current learning rate: 0.00049 +2026-04-15 05:56:04.086679: train_loss -0.5701 +2026-04-15 05:56:04.093134: val_loss -0.5107 +2026-04-15 05:56:04.095406: Pseudo dice [0.7081, 0.0, 0.8467, 0.6455, 0.5132, 0.7393, 0.9028] +2026-04-15 05:56:04.097782: Epoch time: 101.56 s +2026-04-15 05:56:05.365003: +2026-04-15 05:56:05.366821: Epoch 3860 +2026-04-15 05:56:05.368456: Current learning rate: 0.00049 +2026-04-15 05:57:46.905565: train_loss -0.5747 +2026-04-15 05:57:46.912928: val_loss -0.4901 +2026-04-15 05:57:46.914971: Pseudo dice [0.5986, 0.0, 0.8238, 0.5559, 0.3779, 0.7083, 0.906] +2026-04-15 05:57:46.917212: Epoch time: 101.54 s +2026-04-15 05:57:48.193384: +2026-04-15 05:57:48.195468: Epoch 3861 +2026-04-15 05:57:48.197391: Current learning rate: 0.00049 +2026-04-15 05:59:29.711773: train_loss -0.5804 +2026-04-15 05:59:29.719707: val_loss -0.4947 +2026-04-15 05:59:29.722283: Pseudo dice [0.754, 0.0, 0.8676, 0.537, 0.4255, 0.8539, 0.8183] +2026-04-15 05:59:29.725976: Epoch time: 101.52 s +2026-04-15 05:59:31.039350: +2026-04-15 05:59:31.041188: Epoch 3862 +2026-04-15 05:59:31.042828: Current learning rate: 0.00048 +2026-04-15 06:01:12.524443: train_loss -0.5738 +2026-04-15 06:01:12.530854: val_loss -0.4888 +2026-04-15 06:01:12.532539: Pseudo dice [0.7769, 0.0, 0.7491, 0.3576, 0.5534, 0.7523, 0.9397] +2026-04-15 06:01:12.535083: Epoch time: 101.49 s +2026-04-15 06:01:13.855118: +2026-04-15 06:01:13.856726: Epoch 3863 +2026-04-15 06:01:13.858263: Current learning rate: 0.00048 +2026-04-15 06:02:56.089761: train_loss -0.5786 +2026-04-15 06:02:56.097060: val_loss -0.4346 +2026-04-15 06:02:56.099267: Pseudo dice [0.5523, 0.0, 0.7649, 0.2035, 0.4559, 0.4286, 0.8892] +2026-04-15 06:02:56.101856: Epoch time: 102.24 s +2026-04-15 06:02:57.402354: +2026-04-15 06:02:57.404474: Epoch 3864 +2026-04-15 06:02:57.406407: Current learning rate: 0.00048 +2026-04-15 06:04:39.031785: train_loss -0.582 +2026-04-15 06:04:39.039465: val_loss -0.4839 +2026-04-15 06:04:39.042021: Pseudo dice [0.5816, 0.0, 0.862, 0.8786, 0.6384, 0.5735, 0.9493] +2026-04-15 06:04:39.044631: Epoch time: 101.63 s +2026-04-15 06:04:40.348573: +2026-04-15 06:04:40.350489: Epoch 3865 +2026-04-15 06:04:40.352189: Current learning rate: 0.00047 +2026-04-15 06:06:21.786044: train_loss -0.5717 +2026-04-15 06:06:21.792572: val_loss -0.495 +2026-04-15 06:06:21.794888: Pseudo dice [0.7037, 0.0, 0.8617, 0.6925, 0.4919, 0.8773, 0.9152] +2026-04-15 06:06:21.797306: Epoch time: 101.44 s +2026-04-15 06:06:23.105534: +2026-04-15 06:06:23.107209: Epoch 3866 +2026-04-15 06:06:23.108723: Current learning rate: 0.00047 +2026-04-15 06:08:04.513780: train_loss -0.5737 +2026-04-15 06:08:04.520765: val_loss -0.522 +2026-04-15 06:08:04.523576: Pseudo dice [0.7576, 0.0, 0.8667, 0.7232, 0.364, 0.7901, 0.9265] +2026-04-15 06:08:04.526652: Epoch time: 101.41 s +2026-04-15 06:08:05.820935: +2026-04-15 06:08:05.823003: Epoch 3867 +2026-04-15 06:08:05.824827: Current learning rate: 0.00047 +2026-04-15 06:09:47.115902: train_loss -0.5842 +2026-04-15 06:09:47.129833: val_loss -0.4917 +2026-04-15 06:09:47.132130: Pseudo dice [0.7914, 0.0, 0.8634, 0.8979, 0.379, 0.4633, 0.7781] +2026-04-15 06:09:47.134933: Epoch time: 101.3 s +2026-04-15 06:09:48.450870: +2026-04-15 06:09:48.452605: Epoch 3868 +2026-04-15 06:09:48.454100: Current learning rate: 0.00046 +2026-04-15 06:11:30.114120: train_loss -0.5783 +2026-04-15 06:11:30.124639: val_loss -0.4953 +2026-04-15 06:11:30.126823: Pseudo dice [0.769, 0.0, 0.8856, 0.4542, 0.5022, 0.7493, 0.8852] +2026-04-15 06:11:30.131508: Epoch time: 101.67 s +2026-04-15 06:11:31.479521: +2026-04-15 06:11:31.481661: Epoch 3869 +2026-04-15 06:11:31.483252: Current learning rate: 0.00046 +2026-04-15 06:13:12.865326: train_loss -0.5662 +2026-04-15 06:13:12.872610: val_loss -0.4344 +2026-04-15 06:13:12.875165: Pseudo dice [0.2248, 0.0, 0.685, 0.5902, 0.5045, 0.5571, 0.8567] +2026-04-15 06:13:12.877925: Epoch time: 101.39 s +2026-04-15 06:13:14.186486: +2026-04-15 06:13:14.188789: Epoch 3870 +2026-04-15 06:13:14.190378: Current learning rate: 0.00046 +2026-04-15 06:14:57.353884: train_loss -0.5813 +2026-04-15 06:14:57.361691: val_loss -0.477 +2026-04-15 06:14:57.364525: Pseudo dice [0.7884, 0.0, 0.7918, 0.4213, 0.4789, 0.6313, 0.9213] +2026-04-15 06:14:57.367423: Epoch time: 103.17 s +2026-04-15 06:14:58.699805: +2026-04-15 06:14:58.701532: Epoch 3871 +2026-04-15 06:14:58.703039: Current learning rate: 0.00045 +2026-04-15 06:16:40.009297: train_loss -0.5701 +2026-04-15 06:16:40.017052: val_loss -0.5242 +2026-04-15 06:16:40.019236: Pseudo dice [0.5874, 0.0, 0.8881, 0.8544, 0.6291, 0.8555, 0.8933] +2026-04-15 06:16:40.024801: Epoch time: 101.31 s +2026-04-15 06:16:41.330486: +2026-04-15 06:16:41.332352: Epoch 3872 +2026-04-15 06:16:41.334108: Current learning rate: 0.00045 +2026-04-15 06:18:22.758378: train_loss -0.5754 +2026-04-15 06:18:22.765536: val_loss -0.4984 +2026-04-15 06:18:22.767510: Pseudo dice [0.8702, 0.0, 0.8201, 0.9128, 0.5633, 0.5754, 0.942] +2026-04-15 06:18:22.770786: Epoch time: 101.43 s +2026-04-15 06:18:24.106011: +2026-04-15 06:18:24.108518: Epoch 3873 +2026-04-15 06:18:24.110271: Current learning rate: 0.00045 +2026-04-15 06:20:05.340047: train_loss -0.5834 +2026-04-15 06:20:05.349474: val_loss -0.4437 +2026-04-15 06:20:05.351382: Pseudo dice [0.7808, 0.0, 0.689, 0.5124, 0.5969, 0.5958, 0.8836] +2026-04-15 06:20:05.355912: Epoch time: 101.24 s +2026-04-15 06:20:06.723647: +2026-04-15 06:20:06.725428: Epoch 3874 +2026-04-15 06:20:06.727005: Current learning rate: 0.00045 +2026-04-15 06:21:47.771748: train_loss -0.5837 +2026-04-15 06:21:47.779439: val_loss -0.4762 +2026-04-15 06:21:47.782973: Pseudo dice [0.6725, 0.0, 0.7735, 0.5045, 0.633, 0.6163, 0.9223] +2026-04-15 06:21:47.786245: Epoch time: 101.05 s +2026-04-15 06:21:49.128160: +2026-04-15 06:21:49.129989: Epoch 3875 +2026-04-15 06:21:49.131541: Current learning rate: 0.00044 +2026-04-15 06:23:30.530824: train_loss -0.5818 +2026-04-15 06:23:30.538082: val_loss -0.4792 +2026-04-15 06:23:30.540542: Pseudo dice [0.5628, 0.0, 0.81, 0.8149, 0.4355, 0.5408, 0.8522] +2026-04-15 06:23:30.543666: Epoch time: 101.41 s +2026-04-15 06:23:31.855241: +2026-04-15 06:23:31.856998: Epoch 3876 +2026-04-15 06:23:31.858606: Current learning rate: 0.00044 +2026-04-15 06:25:13.065663: train_loss -0.5818 +2026-04-15 06:25:13.072456: val_loss -0.5 +2026-04-15 06:25:13.074632: Pseudo dice [0.7713, 0.0, 0.879, 0.6479, 0.6029, 0.8424, 0.8634] +2026-04-15 06:25:13.077117: Epoch time: 101.21 s +2026-04-15 06:25:14.372466: +2026-04-15 06:25:14.374193: Epoch 3877 +2026-04-15 06:25:14.375637: Current learning rate: 0.00044 +2026-04-15 06:26:56.031407: train_loss -0.5815 +2026-04-15 06:26:56.037489: val_loss -0.4741 +2026-04-15 06:26:56.039275: Pseudo dice [0.5799, 0.0, 0.7448, 0.3037, 0.3634, 0.8293, 0.9238] +2026-04-15 06:26:56.041425: Epoch time: 101.66 s +2026-04-15 06:26:57.373952: +2026-04-15 06:26:57.375762: Epoch 3878 +2026-04-15 06:26:57.377759: Current learning rate: 0.00043 +2026-04-15 06:28:38.871037: train_loss -0.5644 +2026-04-15 06:28:38.876669: val_loss -0.4643 +2026-04-15 06:28:38.878732: Pseudo dice [0.5169, 0.0, 0.8468, 0.831, 0.5027, 0.2979, 0.9009] +2026-04-15 06:28:38.881001: Epoch time: 101.5 s +2026-04-15 06:28:40.174439: +2026-04-15 06:28:40.176125: Epoch 3879 +2026-04-15 06:28:40.178003: Current learning rate: 0.00043 +2026-04-15 06:30:21.596933: train_loss -0.5748 +2026-04-15 06:30:21.604815: val_loss -0.4768 +2026-04-15 06:30:21.606942: Pseudo dice [0.4943, 0.0, 0.8124, 0.9278, 0.3642, 0.8603, 0.9517] +2026-04-15 06:30:21.609311: Epoch time: 101.43 s +2026-04-15 06:30:22.954018: +2026-04-15 06:30:22.956131: Epoch 3880 +2026-04-15 06:30:22.957861: Current learning rate: 0.00043 +2026-04-15 06:32:04.072438: train_loss -0.576 +2026-04-15 06:32:04.079207: val_loss -0.4803 +2026-04-15 06:32:04.081315: Pseudo dice [0.6144, 0.0, 0.7675, 0.5009, 0.3615, 0.65, 0.9441] +2026-04-15 06:32:04.084011: Epoch time: 101.12 s +2026-04-15 06:32:05.404247: +2026-04-15 06:32:05.405994: Epoch 3881 +2026-04-15 06:32:05.407545: Current learning rate: 0.00042 +2026-04-15 06:33:46.970024: train_loss -0.58 +2026-04-15 06:33:46.976521: val_loss -0.4885 +2026-04-15 06:33:46.978637: Pseudo dice [0.7971, 0.0, 0.8205, 0.8848, 0.5852, 0.6978, 0.9243] +2026-04-15 06:33:46.981466: Epoch time: 101.57 s +2026-04-15 06:33:48.265474: +2026-04-15 06:33:48.267378: Epoch 3882 +2026-04-15 06:33:48.269005: Current learning rate: 0.00042 +2026-04-15 06:35:29.723547: train_loss -0.574 +2026-04-15 06:35:29.731912: val_loss -0.4605 +2026-04-15 06:35:29.734462: Pseudo dice [0.3497, 0.0, 0.8674, 0.5842, 0.3811, 0.91, 0.7241] +2026-04-15 06:35:29.738144: Epoch time: 101.46 s +2026-04-15 06:35:31.043620: +2026-04-15 06:35:31.045331: Epoch 3883 +2026-04-15 06:35:31.046882: Current learning rate: 0.00042 +2026-04-15 06:37:12.570579: train_loss -0.5858 +2026-04-15 06:37:12.576444: val_loss -0.4823 +2026-04-15 06:37:12.578713: Pseudo dice [0.5898, 0.0, 0.8655, 0.2914, 0.4853, 0.1879, 0.9323] +2026-04-15 06:37:12.580805: Epoch time: 101.53 s +2026-04-15 06:37:13.901679: +2026-04-15 06:37:13.903663: Epoch 3884 +2026-04-15 06:37:13.905510: Current learning rate: 0.00041 +2026-04-15 06:38:55.359441: train_loss -0.5813 +2026-04-15 06:38:55.365614: val_loss -0.5103 +2026-04-15 06:38:55.367643: Pseudo dice [0.8583, 0.0, 0.9095, 0.8388, 0.4387, 0.8174, 0.8949] +2026-04-15 06:38:55.369942: Epoch time: 101.46 s +2026-04-15 06:38:56.702649: +2026-04-15 06:38:56.704824: Epoch 3885 +2026-04-15 06:38:56.707286: Current learning rate: 0.00041 +2026-04-15 06:40:38.371734: train_loss -0.5886 +2026-04-15 06:40:38.378071: val_loss -0.4858 +2026-04-15 06:40:38.381299: Pseudo dice [0.5347, 0.0, 0.9077, 0.8131, 0.4422, 0.9015, 0.9327] +2026-04-15 06:40:38.383798: Epoch time: 101.67 s +2026-04-15 06:40:39.666995: +2026-04-15 06:40:39.669293: Epoch 3886 +2026-04-15 06:40:39.672384: Current learning rate: 0.00041 +2026-04-15 06:42:21.480152: train_loss -0.57 +2026-04-15 06:42:21.486545: val_loss -0.4698 +2026-04-15 06:42:21.488197: Pseudo dice [0.6781, 0.0, 0.7935, 0.8771, 0.2521, 0.7266, 0.8479] +2026-04-15 06:42:21.490568: Epoch time: 101.82 s +2026-04-15 06:42:22.860797: +2026-04-15 06:42:22.863192: Epoch 3887 +2026-04-15 06:42:22.864807: Current learning rate: 0.0004 +2026-04-15 06:44:04.553127: train_loss -0.5751 +2026-04-15 06:44:04.562675: val_loss -0.5005 +2026-04-15 06:44:04.565064: Pseudo dice [0.6334, 0.0, 0.7021, 0.6509, 0.2566, 0.8841, 0.9521] +2026-04-15 06:44:04.567715: Epoch time: 101.7 s +2026-04-15 06:44:05.858519: +2026-04-15 06:44:05.860609: Epoch 3888 +2026-04-15 06:44:05.862921: Current learning rate: 0.0004 +2026-04-15 06:45:47.489218: train_loss -0.5814 +2026-04-15 06:45:47.496128: val_loss -0.4926 +2026-04-15 06:45:47.499674: Pseudo dice [0.738, 0.0, 0.8842, 0.9445, 0.6589, 0.8559, 0.9321] +2026-04-15 06:45:47.503167: Epoch time: 101.63 s +2026-04-15 06:45:48.823735: +2026-04-15 06:45:48.826419: Epoch 3889 +2026-04-15 06:45:48.828161: Current learning rate: 0.0004 +2026-04-15 06:47:31.000228: train_loss -0.5715 +2026-04-15 06:47:31.007896: val_loss -0.478 +2026-04-15 06:47:31.009883: Pseudo dice [0.7895, 0.0, 0.8362, 0.6663, 0.4166, 0.828, 0.9465] +2026-04-15 06:47:31.012661: Epoch time: 102.18 s +2026-04-15 06:47:33.463008: +2026-04-15 06:47:33.465740: Epoch 3890 +2026-04-15 06:47:33.467380: Current learning rate: 0.00039 +2026-04-15 06:49:15.440939: train_loss -0.586 +2026-04-15 06:49:15.447041: val_loss -0.4821 +2026-04-15 06:49:15.449774: Pseudo dice [0.6785, 0.0, 0.8477, 0.7181, 0.5221, 0.8969, 0.8985] +2026-04-15 06:49:15.452740: Epoch time: 101.98 s +2026-04-15 06:49:16.805413: +2026-04-15 06:49:16.807412: Epoch 3891 +2026-04-15 06:49:16.809680: Current learning rate: 0.00039 +2026-04-15 06:50:58.119781: train_loss -0.5744 +2026-04-15 06:50:58.134294: val_loss -0.4832 +2026-04-15 06:50:58.146350: Pseudo dice [0.6104, 0.0, 0.8426, 0.934, 0.3146, 0.6288, 0.94] +2026-04-15 06:50:58.150941: Epoch time: 101.32 s +2026-04-15 06:50:59.457011: +2026-04-15 06:50:59.459684: Epoch 3892 +2026-04-15 06:50:59.461679: Current learning rate: 0.00039 +2026-04-15 06:52:40.907308: train_loss -0.5811 +2026-04-15 06:52:40.913312: val_loss -0.4966 +2026-04-15 06:52:40.915357: Pseudo dice [0.627, 0.0, 0.8879, 0.8665, 0.471, 0.5004, 0.9435] +2026-04-15 06:52:40.918665: Epoch time: 101.45 s +2026-04-15 06:52:42.230767: +2026-04-15 06:52:42.232984: Epoch 3893 +2026-04-15 06:52:42.234697: Current learning rate: 0.00038 +2026-04-15 06:54:23.517205: train_loss -0.5672 +2026-04-15 06:54:23.523056: val_loss -0.4897 +2026-04-15 06:54:23.525083: Pseudo dice [0.6583, 0.0, 0.8483, 0.9085, 0.4731, 0.8999, 0.9074] +2026-04-15 06:54:23.527146: Epoch time: 101.29 s +2026-04-15 06:54:24.853260: +2026-04-15 06:54:24.855100: Epoch 3894 +2026-04-15 06:54:24.856551: Current learning rate: 0.00038 +2026-04-15 06:56:06.724212: train_loss -0.5774 +2026-04-15 06:56:06.731544: val_loss -0.4832 +2026-04-15 06:56:06.733709: Pseudo dice [0.8233, 0.0, 0.8408, 0.622, 0.5918, 0.8584, 0.9191] +2026-04-15 06:56:06.736444: Epoch time: 101.87 s +2026-04-15 06:56:08.012864: +2026-04-15 06:56:08.015263: Epoch 3895 +2026-04-15 06:56:08.016971: Current learning rate: 0.00038 +2026-04-15 06:57:49.667603: train_loss -0.572 +2026-04-15 06:57:49.684242: val_loss -0.5015 +2026-04-15 06:57:49.693259: Pseudo dice [0.6923, 0.0, 0.8015, 0.6215, 0.4234, 0.5296, 0.8969] +2026-04-15 06:57:49.696724: Epoch time: 101.66 s +2026-04-15 06:57:51.013494: +2026-04-15 06:57:51.015838: Epoch 3896 +2026-04-15 06:57:51.017633: Current learning rate: 0.00037 +2026-04-15 06:59:32.421971: train_loss -0.5547 +2026-04-15 06:59:32.427809: val_loss -0.5068 +2026-04-15 06:59:32.429841: Pseudo dice [0.8286, 0.0, 0.8385, 0.9041, 0.4188, 0.868, 0.9271] +2026-04-15 06:59:32.432239: Epoch time: 101.41 s +2026-04-15 06:59:33.703365: +2026-04-15 06:59:33.705641: Epoch 3897 +2026-04-15 06:59:33.707528: Current learning rate: 0.00037 +2026-04-15 07:01:14.962017: train_loss -0.5827 +2026-04-15 07:01:14.968792: val_loss -0.4833 +2026-04-15 07:01:14.971599: Pseudo dice [0.6273, 0.0, 0.8409, 0.0409, 0.4357, 0.8302, 0.8483] +2026-04-15 07:01:14.974273: Epoch time: 101.26 s +2026-04-15 07:01:16.304588: +2026-04-15 07:01:16.306516: Epoch 3898 +2026-04-15 07:01:16.308083: Current learning rate: 0.00037 +2026-04-15 07:02:57.638313: train_loss -0.568 +2026-04-15 07:02:57.647241: val_loss -0.481 +2026-04-15 07:02:57.649315: Pseudo dice [0.6463, 0.0, 0.7645, 0.3437, 0.459, 0.5011, 0.89] +2026-04-15 07:02:57.651742: Epoch time: 101.34 s +2026-04-15 07:02:58.953283: +2026-04-15 07:02:58.955137: Epoch 3899 +2026-04-15 07:02:58.956630: Current learning rate: 0.00036 +2026-04-15 07:04:40.421665: train_loss -0.5761 +2026-04-15 07:04:40.427708: val_loss -0.4824 +2026-04-15 07:04:40.430328: Pseudo dice [0.6569, 0.0, 0.87, 0.7591, 0.4847, 0.5483, 0.8806] +2026-04-15 07:04:40.433697: Epoch time: 101.47 s +2026-04-15 07:04:43.589185: +2026-04-15 07:04:43.591053: Epoch 3900 +2026-04-15 07:04:43.592846: Current learning rate: 0.00036 +2026-04-15 07:06:24.947244: train_loss -0.5845 +2026-04-15 07:06:24.953508: val_loss -0.4163 +2026-04-15 07:06:24.955368: Pseudo dice [0.5837, 0.0, 0.1763, 0.1138, 0.4703, 0.5597, 0.7269] +2026-04-15 07:06:24.959126: Epoch time: 101.36 s +2026-04-15 07:06:26.265200: +2026-04-15 07:06:26.267012: Epoch 3901 +2026-04-15 07:06:26.268696: Current learning rate: 0.00036 +2026-04-15 07:08:08.145975: train_loss -0.58 +2026-04-15 07:08:08.152445: val_loss -0.4748 +2026-04-15 07:08:08.155619: Pseudo dice [0.858, 0.0, 0.3622, 0.2127, 0.3649, 0.8226, 0.9366] +2026-04-15 07:08:08.157825: Epoch time: 101.88 s +2026-04-15 07:08:09.439257: +2026-04-15 07:08:09.441827: Epoch 3902 +2026-04-15 07:08:09.443846: Current learning rate: 0.00036 +2026-04-15 07:09:51.206738: train_loss -0.5724 +2026-04-15 07:09:51.212898: val_loss -0.4537 +2026-04-15 07:09:51.215603: Pseudo dice [0.6589, 0.0, 0.7308, 0.3698, 0.4274, 0.8241, 0.8949] +2026-04-15 07:09:51.217942: Epoch time: 101.77 s +2026-04-15 07:09:52.499195: +2026-04-15 07:09:52.501419: Epoch 3903 +2026-04-15 07:09:52.503075: Current learning rate: 0.00035 +2026-04-15 07:11:34.009219: train_loss -0.5714 +2026-04-15 07:11:34.019503: val_loss -0.5155 +2026-04-15 07:11:34.037863: Pseudo dice [0.8627, 0.0, 0.823, 0.6578, 0.5533, 0.8599, 0.9241] +2026-04-15 07:11:34.040440: Epoch time: 101.51 s +2026-04-15 07:11:35.328366: +2026-04-15 07:11:35.330208: Epoch 3904 +2026-04-15 07:11:35.331881: Current learning rate: 0.00035 +2026-04-15 07:13:16.531579: train_loss -0.5872 +2026-04-15 07:13:16.538661: val_loss -0.5022 +2026-04-15 07:13:16.540645: Pseudo dice [0.4942, 0.0, 0.9117, 0.8192, 0.5769, 0.8205, 0.949] +2026-04-15 07:13:16.543451: Epoch time: 101.21 s +2026-04-15 07:13:17.823629: +2026-04-15 07:13:17.825595: Epoch 3905 +2026-04-15 07:13:17.827296: Current learning rate: 0.00035 +2026-04-15 07:14:59.453213: train_loss -0.5842 +2026-04-15 07:14:59.461428: val_loss -0.5086 +2026-04-15 07:14:59.463671: Pseudo dice [0.7114, 0.0, 0.8176, 0.8774, 0.3883, 0.6495, 0.93] +2026-04-15 07:14:59.466189: Epoch time: 101.63 s +2026-04-15 07:15:00.765352: +2026-04-15 07:15:00.767470: Epoch 3906 +2026-04-15 07:15:00.769133: Current learning rate: 0.00034 +2026-04-15 07:16:42.034108: train_loss -0.5774 +2026-04-15 07:16:42.039892: val_loss -0.4931 +2026-04-15 07:16:42.041695: Pseudo dice [0.6344, 0.0, 0.8566, 0.7857, 0.5582, 0.4643, 0.9415] +2026-04-15 07:16:42.043793: Epoch time: 101.27 s +2026-04-15 07:16:43.316229: +2026-04-15 07:16:43.318825: Epoch 3907 +2026-04-15 07:16:43.320367: Current learning rate: 0.00034 +2026-04-15 07:18:24.710164: train_loss -0.5814 +2026-04-15 07:18:24.716673: val_loss -0.4833 +2026-04-15 07:18:24.719151: Pseudo dice [0.6053, 0.0, 0.7941, 0.2759, 0.5, 0.8243, 0.9109] +2026-04-15 07:18:24.722276: Epoch time: 101.4 s +2026-04-15 07:18:25.992112: +2026-04-15 07:18:25.994117: Epoch 3908 +2026-04-15 07:18:25.996052: Current learning rate: 0.00034 +2026-04-15 07:20:08.243854: train_loss -0.5843 +2026-04-15 07:20:08.251674: val_loss -0.4645 +2026-04-15 07:20:08.254035: Pseudo dice [0.7823, 0.0, 0.8762, 0.6807, 0.4466, 0.7596, 0.7002] +2026-04-15 07:20:08.256476: Epoch time: 102.25 s +2026-04-15 07:20:09.550572: +2026-04-15 07:20:09.556647: Epoch 3909 +2026-04-15 07:20:09.558257: Current learning rate: 0.00033 +2026-04-15 07:21:51.187244: train_loss -0.5762 +2026-04-15 07:21:51.196494: val_loss -0.4718 +2026-04-15 07:21:51.198999: Pseudo dice [0.5914, 0.0, 0.8859, 0.6105, 0.4416, 0.8731, 0.8951] +2026-04-15 07:21:51.202379: Epoch time: 101.64 s +2026-04-15 07:21:52.500171: +2026-04-15 07:21:52.502242: Epoch 3910 +2026-04-15 07:21:52.503991: Current learning rate: 0.00033 +2026-04-15 07:23:33.839841: train_loss -0.5891 +2026-04-15 07:23:33.845881: val_loss -0.4916 +2026-04-15 07:23:33.849264: Pseudo dice [0.6387, 0.0, 0.8833, 0.6651, 0.6391, 0.8828, 0.833] +2026-04-15 07:23:33.851952: Epoch time: 101.34 s +2026-04-15 07:23:35.180314: +2026-04-15 07:23:35.182188: Epoch 3911 +2026-04-15 07:23:35.183935: Current learning rate: 0.00033 +2026-04-15 07:25:16.712488: train_loss -0.5726 +2026-04-15 07:25:16.718503: val_loss -0.4655 +2026-04-15 07:25:16.720298: Pseudo dice [0.6015, 0.0, 0.8025, 0.7795, 0.4433, 0.8083, 0.9146] +2026-04-15 07:25:16.722532: Epoch time: 101.54 s +2026-04-15 07:25:18.077830: +2026-04-15 07:25:18.079830: Epoch 3912 +2026-04-15 07:25:18.081807: Current learning rate: 0.00032 +2026-04-15 07:26:59.545912: train_loss -0.5815 +2026-04-15 07:26:59.551974: val_loss -0.4758 +2026-04-15 07:26:59.554259: Pseudo dice [0.5564, 0.0, 0.8251, 0.8781, 0.531, 0.6854, 0.9101] +2026-04-15 07:26:59.557908: Epoch time: 101.47 s +2026-04-15 07:27:00.855182: +2026-04-15 07:27:00.857277: Epoch 3913 +2026-04-15 07:27:00.859018: Current learning rate: 0.00032 +2026-04-15 07:28:42.203913: train_loss -0.5798 +2026-04-15 07:28:42.210321: val_loss -0.4632 +2026-04-15 07:28:42.212325: Pseudo dice [0.7202, 0.0, 0.7913, 0.7356, 0.5286, 0.7551, 0.8586] +2026-04-15 07:28:42.214772: Epoch time: 101.35 s +2026-04-15 07:28:43.527207: +2026-04-15 07:28:43.528857: Epoch 3914 +2026-04-15 07:28:43.530457: Current learning rate: 0.00032 +2026-04-15 07:30:25.399006: train_loss -0.585 +2026-04-15 07:30:25.404799: val_loss -0.4432 +2026-04-15 07:30:25.406884: Pseudo dice [0.23, 0.0, 0.7585, 0.8658, 0.4748, 0.4369, 0.8328] +2026-04-15 07:30:25.409538: Epoch time: 101.87 s +2026-04-15 07:30:26.687062: +2026-04-15 07:30:26.689106: Epoch 3915 +2026-04-15 07:30:26.691392: Current learning rate: 0.00031 +2026-04-15 07:32:07.889216: train_loss -0.571 +2026-04-15 07:32:07.896051: val_loss -0.4975 +2026-04-15 07:32:07.898344: Pseudo dice [0.6971, 0.0, 0.8657, 0.8726, 0.6903, 0.8031, 0.8062] +2026-04-15 07:32:07.900970: Epoch time: 101.21 s +2026-04-15 07:32:09.210110: +2026-04-15 07:32:09.212536: Epoch 3916 +2026-04-15 07:32:09.214341: Current learning rate: 0.00031 +2026-04-15 07:33:50.913469: train_loss -0.5784 +2026-04-15 07:33:50.921262: val_loss -0.4502 +2026-04-15 07:33:50.924006: Pseudo dice [0.7166, 0.0, 0.8722, 0.9363, 0.549, 0.7348, 0.8852] +2026-04-15 07:33:50.926344: Epoch time: 101.71 s +2026-04-15 07:33:52.208103: +2026-04-15 07:33:52.211316: Epoch 3917 +2026-04-15 07:33:52.213105: Current learning rate: 0.00031 +2026-04-15 07:35:33.717449: train_loss -0.5783 +2026-04-15 07:35:33.724086: val_loss -0.4958 +2026-04-15 07:35:33.726212: Pseudo dice [0.8564, 0.0, 0.8561, 0.874, 0.67, 0.7938, 0.9164] +2026-04-15 07:35:33.729753: Epoch time: 101.51 s +2026-04-15 07:35:35.061152: +2026-04-15 07:35:35.063088: Epoch 3918 +2026-04-15 07:35:35.064866: Current learning rate: 0.0003 +2026-04-15 07:37:16.548043: train_loss -0.5784 +2026-04-15 07:37:16.555824: val_loss -0.5238 +2026-04-15 07:37:16.558672: Pseudo dice [0.7894, 0.0, 0.8679, 0.8902, 0.4734, 0.9015, 0.9212] +2026-04-15 07:37:16.562313: Epoch time: 101.49 s +2026-04-15 07:37:16.564197: Yayy! New best EMA pseudo Dice: 0.6286 +2026-04-15 07:37:19.730805: +2026-04-15 07:37:19.732774: Epoch 3919 +2026-04-15 07:37:19.734255: Current learning rate: 0.0003 +2026-04-15 07:39:00.795850: train_loss -0.5769 +2026-04-15 07:39:00.802325: val_loss -0.4562 +2026-04-15 07:39:00.804362: Pseudo dice [0.6401, 0.0, 0.7439, 0.5747, 0.4423, 0.7653, 0.9314] +2026-04-15 07:39:00.806347: Epoch time: 101.07 s +2026-04-15 07:39:02.084688: +2026-04-15 07:39:02.086999: Epoch 3920 +2026-04-15 07:39:02.089742: Current learning rate: 0.0003 +2026-04-15 07:40:43.888890: train_loss -0.5814 +2026-04-15 07:40:43.894852: val_loss -0.4765 +2026-04-15 07:40:43.897077: Pseudo dice [0.797, 0.0, 0.7466, 0.6895, 0.4595, 0.804, 0.9086] +2026-04-15 07:40:43.898994: Epoch time: 101.81 s +2026-04-15 07:40:45.226283: +2026-04-15 07:40:45.228272: Epoch 3921 +2026-04-15 07:40:45.229778: Current learning rate: 0.00029 +2026-04-15 07:42:26.616558: train_loss -0.5827 +2026-04-15 07:42:26.622648: val_loss -0.4436 +2026-04-15 07:42:26.625145: Pseudo dice [0.7315, 0.0, 0.7733, 0.5432, 0.3988, 0.4316, 0.7303] +2026-04-15 07:42:26.627434: Epoch time: 101.39 s +2026-04-15 07:42:27.903691: +2026-04-15 07:42:27.906868: Epoch 3922 +2026-04-15 07:42:27.910213: Current learning rate: 0.00029 +2026-04-15 07:44:09.527259: train_loss -0.5926 +2026-04-15 07:44:09.535841: val_loss -0.51 +2026-04-15 07:44:09.538273: Pseudo dice [0.5518, 0.0, 0.8962, 0.6704, 0.6676, 0.777, 0.9005] +2026-04-15 07:44:09.541965: Epoch time: 101.63 s +2026-04-15 07:44:10.919189: +2026-04-15 07:44:10.922246: Epoch 3923 +2026-04-15 07:44:10.923948: Current learning rate: 0.00029 +2026-04-15 07:45:52.217400: train_loss -0.581 +2026-04-15 07:45:52.225050: val_loss -0.4352 +2026-04-15 07:45:52.227865: Pseudo dice [0.5126, 0.0, 0.8314, 0.712, 0.3537, 0.7688, 0.9235] +2026-04-15 07:45:52.230709: Epoch time: 101.3 s +2026-04-15 07:45:53.511701: +2026-04-15 07:45:53.513599: Epoch 3924 +2026-04-15 07:45:53.515333: Current learning rate: 0.00028 +2026-04-15 07:47:34.907450: train_loss -0.585 +2026-04-15 07:47:34.913408: val_loss -0.495 +2026-04-15 07:47:34.915117: Pseudo dice [0.6802, 0.0, 0.7843, 0.918, 0.5219, 0.8997, 0.9257] +2026-04-15 07:47:34.917440: Epoch time: 101.4 s +2026-04-15 07:47:36.217989: +2026-04-15 07:47:36.219719: Epoch 3925 +2026-04-15 07:47:36.221354: Current learning rate: 0.00028 +2026-04-15 07:49:17.821047: train_loss -0.5805 +2026-04-15 07:49:17.829379: val_loss -0.4709 +2026-04-15 07:49:17.831701: Pseudo dice [0.7698, 0.0, 0.8474, 0.5326, 0.2695, 0.7905, 0.751] +2026-04-15 07:49:17.834615: Epoch time: 101.61 s +2026-04-15 07:49:19.180915: +2026-04-15 07:49:19.184036: Epoch 3926 +2026-04-15 07:49:19.186706: Current learning rate: 0.00028 +2026-04-15 07:51:00.381179: train_loss -0.5702 +2026-04-15 07:51:00.387200: val_loss -0.4791 +2026-04-15 07:51:00.389258: Pseudo dice [0.8731, 0.0, 0.8238, 0.2193, 0.503, 0.6388, 0.7055] +2026-04-15 07:51:00.391685: Epoch time: 101.2 s +2026-04-15 07:51:01.650233: +2026-04-15 07:51:01.652166: Epoch 3927 +2026-04-15 07:51:01.654403: Current learning rate: 0.00027 +2026-04-15 07:52:44.268075: train_loss -0.5783 +2026-04-15 07:52:44.276680: val_loss -0.4625 +2026-04-15 07:52:44.279312: Pseudo dice [0.6304, 0.0, 0.7235, 0.5713, 0.3518, 0.5374, 0.5419] +2026-04-15 07:52:44.281513: Epoch time: 102.62 s +2026-04-15 07:52:45.584753: +2026-04-15 07:52:45.586853: Epoch 3928 +2026-04-15 07:52:45.588628: Current learning rate: 0.00027 +2026-04-15 07:54:27.553607: train_loss -0.5805 +2026-04-15 07:54:27.560226: val_loss -0.4821 +2026-04-15 07:54:27.563353: Pseudo dice [0.7243, 0.0, 0.8181, 0.1936, 0.5897, 0.8078, 0.8231] +2026-04-15 07:54:27.566164: Epoch time: 101.97 s +2026-04-15 07:54:28.863615: +2026-04-15 07:54:28.866038: Epoch 3929 +2026-04-15 07:54:28.867744: Current learning rate: 0.00027 +2026-04-15 07:56:10.633802: train_loss -0.5678 +2026-04-15 07:56:10.640929: val_loss -0.4522 +2026-04-15 07:56:10.643359: Pseudo dice [0.7123, 0.0, 0.5576, 0.5754, 0.4631, 0.4815, 0.8806] +2026-04-15 07:56:10.645727: Epoch time: 101.77 s +2026-04-15 07:56:11.955316: +2026-04-15 07:56:11.957074: Epoch 3930 +2026-04-15 07:56:11.958537: Current learning rate: 0.00026 +2026-04-15 07:57:53.406047: train_loss -0.5828 +2026-04-15 07:57:53.413166: val_loss -0.4858 +2026-04-15 07:57:53.415790: Pseudo dice [0.6807, 0.0, 0.8167, 0.8429, 0.6011, 0.8781, 0.9479] +2026-04-15 07:57:53.418353: Epoch time: 101.45 s +2026-04-15 07:57:54.738250: +2026-04-15 07:57:54.740330: Epoch 3931 +2026-04-15 07:57:54.741981: Current learning rate: 0.00026 +2026-04-15 07:59:36.721556: train_loss -0.5725 +2026-04-15 07:59:36.727491: val_loss -0.4812 +2026-04-15 07:59:36.730136: Pseudo dice [0.7441, 0.0, 0.7794, 0.1417, 0.5918, 0.3803, 0.9219] +2026-04-15 07:59:36.732669: Epoch time: 101.99 s +2026-04-15 07:59:38.108364: +2026-04-15 07:59:38.110251: Epoch 3932 +2026-04-15 07:59:38.111740: Current learning rate: 0.00026 +2026-04-15 08:01:19.908348: train_loss -0.569 +2026-04-15 08:01:19.914578: val_loss -0.4354 +2026-04-15 08:01:19.917431: Pseudo dice [0.6602, 0.0, 0.4925, 0.4847, 0.5962, 0.4998, 0.9066] +2026-04-15 08:01:19.920169: Epoch time: 101.8 s +2026-04-15 08:01:21.270201: +2026-04-15 08:01:21.272499: Epoch 3933 +2026-04-15 08:01:21.274823: Current learning rate: 0.00025 +2026-04-15 08:03:02.623939: train_loss -0.5776 +2026-04-15 08:03:02.631082: val_loss -0.4741 +2026-04-15 08:03:02.633998: Pseudo dice [0.603, 0.0, 0.805, 0.3755, 0.6577, 0.5451, 0.8762] +2026-04-15 08:03:02.637711: Epoch time: 101.36 s +2026-04-15 08:03:03.927853: +2026-04-15 08:03:03.929610: Epoch 3934 +2026-04-15 08:03:03.931175: Current learning rate: 0.00025 +2026-04-15 08:04:45.692191: train_loss -0.5815 +2026-04-15 08:04:45.712968: val_loss -0.4905 +2026-04-15 08:04:45.715117: Pseudo dice [0.5616, 0.0, 0.8161, 0.6441, 0.6001, 0.8858, 0.8288] +2026-04-15 08:04:45.718094: Epoch time: 101.77 s +2026-04-15 08:04:47.039996: +2026-04-15 08:04:47.041884: Epoch 3935 +2026-04-15 08:04:47.043562: Current learning rate: 0.00025 +2026-04-15 08:06:28.448380: train_loss -0.5779 +2026-04-15 08:06:28.455112: val_loss -0.4795 +2026-04-15 08:06:28.457240: Pseudo dice [0.8203, 0.0, 0.8813, 0.6841, 0.4218, 0.679, 0.8486] +2026-04-15 08:06:28.459533: Epoch time: 101.41 s +2026-04-15 08:06:29.730317: +2026-04-15 08:06:29.732010: Epoch 3936 +2026-04-15 08:06:29.733513: Current learning rate: 0.00024 +2026-04-15 08:08:11.917830: train_loss -0.5822 +2026-04-15 08:08:11.924412: val_loss -0.4554 +2026-04-15 08:08:11.926797: Pseudo dice [0.554, 0.0, 0.8344, 0.0864, 0.3384, 0.598, 0.9356] +2026-04-15 08:08:11.929404: Epoch time: 102.19 s +2026-04-15 08:08:13.208159: +2026-04-15 08:08:13.209981: Epoch 3937 +2026-04-15 08:08:13.211640: Current learning rate: 0.00024 +2026-04-15 08:09:54.561864: train_loss -0.5955 +2026-04-15 08:09:54.568554: val_loss -0.4884 +2026-04-15 08:09:54.570612: Pseudo dice [0.453, 0.0, 0.8195, 0.6472, 0.5568, 0.76, 0.881] +2026-04-15 08:09:54.573522: Epoch time: 101.36 s +2026-04-15 08:09:55.869991: +2026-04-15 08:09:55.871767: Epoch 3938 +2026-04-15 08:09:55.873596: Current learning rate: 0.00024 +2026-04-15 08:11:37.935519: train_loss -0.5757 +2026-04-15 08:11:37.941450: val_loss -0.4828 +2026-04-15 08:11:37.943452: Pseudo dice [0.8675, 0.0, 0.8385, 0.8354, 0.6034, 0.6895, 0.8116] +2026-04-15 08:11:37.946138: Epoch time: 102.07 s +2026-04-15 08:11:39.263268: +2026-04-15 08:11:39.265260: Epoch 3939 +2026-04-15 08:11:39.266772: Current learning rate: 0.00023 +2026-04-15 08:13:20.943936: train_loss -0.5881 +2026-04-15 08:13:20.950681: val_loss -0.4845 +2026-04-15 08:13:20.952799: Pseudo dice [0.3859, 0.0, 0.8018, 0.8043, 0.4238, 0.8803, 0.9136] +2026-04-15 08:13:20.955413: Epoch time: 101.68 s +2026-04-15 08:13:22.250624: +2026-04-15 08:13:22.252451: Epoch 3940 +2026-04-15 08:13:22.254209: Current learning rate: 0.00023 +2026-04-15 08:15:03.864692: train_loss -0.5759 +2026-04-15 08:15:03.872252: val_loss -0.4575 +2026-04-15 08:15:03.874497: Pseudo dice [0.7588, 0.0, 0.8412, 0.4264, 0.4464, 0.6742, 0.9301] +2026-04-15 08:15:03.877032: Epoch time: 101.62 s +2026-04-15 08:15:05.158736: +2026-04-15 08:15:05.160362: Epoch 3941 +2026-04-15 08:15:05.161942: Current learning rate: 0.00022 +2026-04-15 08:16:46.644410: train_loss -0.5822 +2026-04-15 08:16:46.653270: val_loss -0.4979 +2026-04-15 08:16:46.655455: Pseudo dice [0.6472, 0.0, 0.8965, 0.9031, 0.4999, 0.7679, 0.9111] +2026-04-15 08:16:46.658081: Epoch time: 101.49 s +2026-04-15 08:16:47.968484: +2026-04-15 08:16:47.970901: Epoch 3942 +2026-04-15 08:16:47.972823: Current learning rate: 0.00022 +2026-04-15 08:18:29.647333: train_loss -0.5761 +2026-04-15 08:18:29.654262: val_loss -0.4931 +2026-04-15 08:18:29.656572: Pseudo dice [0.4699, 0.0, 0.8497, 0.7155, 0.5334, 0.6846, 0.9153] +2026-04-15 08:18:29.660892: Epoch time: 101.68 s +2026-04-15 08:18:30.945020: +2026-04-15 08:18:30.947356: Epoch 3943 +2026-04-15 08:18:30.949232: Current learning rate: 0.00022 +2026-04-15 08:20:12.399567: train_loss -0.5836 +2026-04-15 08:20:12.406024: val_loss -0.4646 +2026-04-15 08:20:12.408259: Pseudo dice [0.7807, 0.0, 0.8544, 0.2273, 0.2956, 0.7733, 0.8892] +2026-04-15 08:20:12.411009: Epoch time: 101.46 s +2026-04-15 08:20:13.723863: +2026-04-15 08:20:13.725558: Epoch 3944 +2026-04-15 08:20:13.727106: Current learning rate: 0.00021 +2026-04-15 08:21:55.698596: train_loss -0.5872 +2026-04-15 08:21:55.705026: val_loss -0.4825 +2026-04-15 08:21:55.706918: Pseudo dice [0.6826, 0.0, 0.8705, 0.7953, 0.4875, 0.7078, 0.9263] +2026-04-15 08:21:55.709547: Epoch time: 101.98 s +2026-04-15 08:21:57.008247: +2026-04-15 08:21:57.013377: Epoch 3945 +2026-04-15 08:21:57.016101: Current learning rate: 0.00021 +2026-04-15 08:23:38.659928: train_loss -0.568 +2026-04-15 08:23:38.666067: val_loss -0.5077 +2026-04-15 08:23:38.668075: Pseudo dice [0.6011, 0.0, 0.8112, 0.8016, 0.4175, 0.9049, 0.9068] +2026-04-15 08:23:38.670353: Epoch time: 101.65 s +2026-04-15 08:23:39.941578: +2026-04-15 08:23:39.943368: Epoch 3946 +2026-04-15 08:23:39.944823: Current learning rate: 0.00021 +2026-04-15 08:25:21.596497: train_loss -0.5672 +2026-04-15 08:25:21.604442: val_loss -0.4656 +2026-04-15 08:25:21.606813: Pseudo dice [0.6678, 0.0, 0.8503, 0.5108, 0.2783, 0.7518, 0.9326] +2026-04-15 08:25:21.610859: Epoch time: 101.66 s +2026-04-15 08:25:24.023545: +2026-04-15 08:25:24.025208: Epoch 3947 +2026-04-15 08:25:24.026868: Current learning rate: 0.0002 +2026-04-15 08:27:06.152254: train_loss -0.5866 +2026-04-15 08:27:06.158389: val_loss -0.5051 +2026-04-15 08:27:06.161127: Pseudo dice [0.7164, 0.0, 0.7554, 0.8421, 0.3822, 0.5347, 0.9111] +2026-04-15 08:27:06.163486: Epoch time: 102.13 s +2026-04-15 08:27:07.445561: +2026-04-15 08:27:07.447568: Epoch 3948 +2026-04-15 08:27:07.449258: Current learning rate: 0.0002 +2026-04-15 08:28:49.301162: train_loss -0.5793 +2026-04-15 08:28:49.307604: val_loss -0.5216 +2026-04-15 08:28:49.309774: Pseudo dice [0.5236, 0.0, 0.8833, 0.5835, 0.619, 0.9128, 0.9472] +2026-04-15 08:28:49.312254: Epoch time: 101.86 s +2026-04-15 08:28:50.603379: +2026-04-15 08:28:50.605217: Epoch 3949 +2026-04-15 08:28:50.607318: Current learning rate: 0.0002 +2026-04-15 08:30:32.094475: train_loss -0.5818 +2026-04-15 08:30:32.100744: val_loss -0.4906 +2026-04-15 08:30:32.117964: Pseudo dice [0.6434, 0.0, 0.7616, 0.9153, 0.6888, 0.8359, 0.9496] +2026-04-15 08:30:32.121285: Epoch time: 101.49 s +2026-04-15 08:30:35.218566: +2026-04-15 08:30:35.220518: Epoch 3950 +2026-04-15 08:30:35.222032: Current learning rate: 0.00019 +2026-04-15 08:32:16.582490: train_loss -0.5695 +2026-04-15 08:32:16.588817: val_loss -0.4899 +2026-04-15 08:32:16.590636: Pseudo dice [0.4896, 0.0, 0.8557, 0.5217, 0.5742, 0.9068, 0.9083] +2026-04-15 08:32:16.593174: Epoch time: 101.37 s +2026-04-15 08:32:17.890339: +2026-04-15 08:32:17.892543: Epoch 3951 +2026-04-15 08:32:17.894213: Current learning rate: 0.00019 +2026-04-15 08:33:59.250507: train_loss -0.5718 +2026-04-15 08:33:59.258144: val_loss -0.5074 +2026-04-15 08:33:59.260545: Pseudo dice [0.7493, 0.0, 0.8304, 0.7905, 0.5543, 0.827, 0.8839] +2026-04-15 08:33:59.263240: Epoch time: 101.36 s +2026-04-15 08:34:00.557018: +2026-04-15 08:34:00.558792: Epoch 3952 +2026-04-15 08:34:00.560746: Current learning rate: 0.00019 +2026-04-15 08:35:42.121340: train_loss -0.5756 +2026-04-15 08:35:42.127416: val_loss -0.5083 +2026-04-15 08:35:42.132416: Pseudo dice [0.649, 0.0, 0.6952, 0.8472, 0.6398, 0.9189, 0.9061] +2026-04-15 08:35:42.134944: Epoch time: 101.57 s +2026-04-15 08:35:43.506773: +2026-04-15 08:35:43.508939: Epoch 3953 +2026-04-15 08:35:43.510681: Current learning rate: 0.00018 +2026-04-15 08:37:25.207640: train_loss -0.5872 +2026-04-15 08:37:25.214278: val_loss -0.4601 +2026-04-15 08:37:25.216765: Pseudo dice [0.3589, 0.0, 0.7576, 0.2598, 0.5224, 0.606, 0.8538] +2026-04-15 08:37:25.219887: Epoch time: 101.7 s +2026-04-15 08:37:26.492771: +2026-04-15 08:37:26.494845: Epoch 3954 +2026-04-15 08:37:26.496653: Current learning rate: 0.00018 +2026-04-15 08:39:08.153979: train_loss -0.5845 +2026-04-15 08:39:08.160684: val_loss -0.4618 +2026-04-15 08:39:08.163161: Pseudo dice [0.8074, 0.0, 0.6862, 0.0407, 0.5613, 0.7253, 0.7871] +2026-04-15 08:39:08.165729: Epoch time: 101.66 s +2026-04-15 08:39:09.457798: +2026-04-15 08:39:09.460438: Epoch 3955 +2026-04-15 08:39:09.462720: Current learning rate: 0.00018 +2026-04-15 08:40:51.023396: train_loss -0.5936 +2026-04-15 08:40:51.029125: val_loss -0.4619 +2026-04-15 08:40:51.031113: Pseudo dice [0.5051, 0.0, 0.6436, 0.7172, 0.3611, 0.4191, 0.9516] +2026-04-15 08:40:51.033614: Epoch time: 101.57 s +2026-04-15 08:40:52.343721: +2026-04-15 08:40:52.345896: Epoch 3956 +2026-04-15 08:40:52.347643: Current learning rate: 0.00017 +2026-04-15 08:42:33.769142: train_loss -0.5763 +2026-04-15 08:42:33.775799: val_loss -0.4817 +2026-04-15 08:42:33.778353: Pseudo dice [0.5649, 0.0, 0.8719, 0.6001, 0.4652, 0.8656, 0.7885] +2026-04-15 08:42:33.780875: Epoch time: 101.43 s +2026-04-15 08:42:35.055572: +2026-04-15 08:42:35.057632: Epoch 3957 +2026-04-15 08:42:35.059930: Current learning rate: 0.00017 +2026-04-15 08:44:16.505498: train_loss -0.5837 +2026-04-15 08:44:16.512970: val_loss -0.4963 +2026-04-15 08:44:16.516982: Pseudo dice [0.3133, 0.0, 0.883, 0.7125, 0.52, 0.9089, 0.9292] +2026-04-15 08:44:16.520061: Epoch time: 101.45 s +2026-04-15 08:44:17.790315: +2026-04-15 08:44:17.792604: Epoch 3958 +2026-04-15 08:44:17.794326: Current learning rate: 0.00017 +2026-04-15 08:45:59.050289: train_loss -0.5802 +2026-04-15 08:45:59.056810: val_loss -0.4849 +2026-04-15 08:45:59.059930: Pseudo dice [0.6512, 0.0, 0.718, 0.5929, 0.6027, 0.7278, 0.9096] +2026-04-15 08:45:59.062502: Epoch time: 101.26 s +2026-04-15 08:46:00.401130: +2026-04-15 08:46:00.403665: Epoch 3959 +2026-04-15 08:46:00.405581: Current learning rate: 0.00016 +2026-04-15 08:47:41.860644: train_loss -0.5787 +2026-04-15 08:47:41.888542: val_loss -0.4919 +2026-04-15 08:47:41.890695: Pseudo dice [0.6846, 0.0, 0.7664, 0.7952, 0.2978, 0.7461, 0.8852] +2026-04-15 08:47:41.893291: Epoch time: 101.46 s +2026-04-15 08:47:43.226727: +2026-04-15 08:47:43.228327: Epoch 3960 +2026-04-15 08:47:43.229825: Current learning rate: 0.00016 +2026-04-15 08:49:24.995342: train_loss -0.5837 +2026-04-15 08:49:25.001812: val_loss -0.4914 +2026-04-15 08:49:25.004363: Pseudo dice [0.6062, 0.0, 0.6269, 0.5621, 0.5272, 0.6514, 0.9199] +2026-04-15 08:49:25.006745: Epoch time: 101.77 s +2026-04-15 08:49:26.308114: +2026-04-15 08:49:26.309800: Epoch 3961 +2026-04-15 08:49:26.311397: Current learning rate: 0.00015 +2026-04-15 08:51:08.089124: train_loss -0.5829 +2026-04-15 08:51:08.095357: val_loss -0.4501 +2026-04-15 08:51:08.097950: Pseudo dice [0.698, 0.0, 0.6232, 0.393, 0.4095, 0.9057, 0.9182] +2026-04-15 08:51:08.100212: Epoch time: 101.78 s +2026-04-15 08:51:09.362831: +2026-04-15 08:51:09.364668: Epoch 3962 +2026-04-15 08:51:09.366128: Current learning rate: 0.00015 +2026-04-15 08:52:50.727655: train_loss -0.5925 +2026-04-15 08:52:50.736090: val_loss -0.5052 +2026-04-15 08:52:50.738579: Pseudo dice [0.7849, 0.0, 0.8807, 0.906, 0.4998, 0.7288, 0.9535] +2026-04-15 08:52:50.741757: Epoch time: 101.37 s +2026-04-15 08:52:52.013057: +2026-04-15 08:52:52.014791: Epoch 3963 +2026-04-15 08:52:52.016357: Current learning rate: 0.00015 +2026-04-15 08:54:33.502251: train_loss -0.5734 +2026-04-15 08:54:33.509696: val_loss -0.4618 +2026-04-15 08:54:33.512439: Pseudo dice [0.8399, 0.0, 0.7663, 0.702, 0.5403, 0.5218, 0.9344] +2026-04-15 08:54:33.514947: Epoch time: 101.49 s +2026-04-15 08:54:34.800457: +2026-04-15 08:54:34.802643: Epoch 3964 +2026-04-15 08:54:34.804559: Current learning rate: 0.00014 +2026-04-15 08:56:16.144687: train_loss -0.5849 +2026-04-15 08:56:16.153868: val_loss -0.4701 +2026-04-15 08:56:16.156776: Pseudo dice [0.6781, 0.0, 0.8938, 0.7503, 0.3588, 0.8876, 0.7811] +2026-04-15 08:56:16.159484: Epoch time: 101.35 s +2026-04-15 08:56:17.472674: +2026-04-15 08:56:17.475315: Epoch 3965 +2026-04-15 08:56:17.477502: Current learning rate: 0.00014 +2026-04-15 08:57:58.672725: train_loss -0.5737 +2026-04-15 08:57:58.679502: val_loss -0.4843 +2026-04-15 08:57:58.681202: Pseudo dice [0.2905, 0.0, 0.8697, 0.9076, 0.2023, 0.6908, 0.9368] +2026-04-15 08:57:58.683243: Epoch time: 101.2 s +2026-04-15 08:58:00.988515: +2026-04-15 08:58:00.990390: Epoch 3966 +2026-04-15 08:58:00.991773: Current learning rate: 0.00014 +2026-04-15 08:59:42.596634: train_loss -0.59 +2026-04-15 08:59:42.602802: val_loss -0.4828 +2026-04-15 08:59:42.604481: Pseudo dice [0.7236, 0.0, 0.8574, 0.5044, 0.5449, 0.8493, 0.9353] +2026-04-15 08:59:42.607158: Epoch time: 101.61 s +2026-04-15 08:59:43.890735: +2026-04-15 08:59:43.893789: Epoch 3967 +2026-04-15 08:59:43.895532: Current learning rate: 0.00013 +2026-04-15 09:01:25.310591: train_loss -0.5811 +2026-04-15 09:01:25.317532: val_loss -0.4497 +2026-04-15 09:01:25.319895: Pseudo dice [0.4615, 0.0, 0.7589, 0.5063, 0.5184, 0.7772, 0.9488] +2026-04-15 09:01:25.322248: Epoch time: 101.42 s +2026-04-15 09:01:26.608832: +2026-04-15 09:01:26.610865: Epoch 3968 +2026-04-15 09:01:26.612506: Current learning rate: 0.00013 +2026-04-15 09:03:08.106432: train_loss -0.5865 +2026-04-15 09:03:08.113364: val_loss -0.4899 +2026-04-15 09:03:08.115475: Pseudo dice [0.8227, 0.0, 0.8307, 0.0026, 0.4396, 0.8383, 0.9069] +2026-04-15 09:03:08.118791: Epoch time: 101.5 s +2026-04-15 09:03:09.438345: +2026-04-15 09:03:09.441073: Epoch 3969 +2026-04-15 09:03:09.442976: Current learning rate: 0.00013 +2026-04-15 09:04:51.025860: train_loss -0.5886 +2026-04-15 09:04:51.031959: val_loss -0.4546 +2026-04-15 09:04:51.034321: Pseudo dice [0.7627, 0.0, 0.6901, 0.4419, 0.2853, 0.5256, 0.929] +2026-04-15 09:04:51.036520: Epoch time: 101.59 s +2026-04-15 09:04:52.301965: +2026-04-15 09:04:52.303736: Epoch 3970 +2026-04-15 09:04:52.305177: Current learning rate: 0.00012 +2026-04-15 09:06:34.212946: train_loss -0.5833 +2026-04-15 09:06:34.219343: val_loss -0.4281 +2026-04-15 09:06:34.226397: Pseudo dice [0.5671, 0.0, 0.6328, 0.5045, 0.5254, 0.842, 0.8321] +2026-04-15 09:06:34.236623: Epoch time: 101.91 s +2026-04-15 09:06:35.563817: +2026-04-15 09:06:35.565478: Epoch 3971 +2026-04-15 09:06:35.567045: Current learning rate: 0.00012 +2026-04-15 09:08:16.952291: train_loss -0.5765 +2026-04-15 09:08:16.959783: val_loss -0.4885 +2026-04-15 09:08:16.962204: Pseudo dice [0.8632, 0.0, 0.7568, 0.6017, 0.5565, 0.8266, 0.9287] +2026-04-15 09:08:16.964563: Epoch time: 101.39 s +2026-04-15 09:08:18.252450: +2026-04-15 09:08:18.263309: Epoch 3972 +2026-04-15 09:08:18.265214: Current learning rate: 0.00011 +2026-04-15 09:09:59.951888: train_loss -0.5815 +2026-04-15 09:09:59.958866: val_loss -0.4837 +2026-04-15 09:09:59.960938: Pseudo dice [0.5191, 0.0, 0.8623, 0.0922, 0.4563, 0.8754, 0.9174] +2026-04-15 09:09:59.963300: Epoch time: 101.7 s +2026-04-15 09:10:01.227482: +2026-04-15 09:10:01.229401: Epoch 3973 +2026-04-15 09:10:01.231004: Current learning rate: 0.00011 +2026-04-15 09:11:42.994499: train_loss -0.5849 +2026-04-15 09:11:43.001355: val_loss -0.4702 +2026-04-15 09:11:43.004699: Pseudo dice [0.7586, 0.0, 0.8743, 0.6192, 0.536, 0.8669, 0.857] +2026-04-15 09:11:43.009489: Epoch time: 101.77 s +2026-04-15 09:11:44.300190: +2026-04-15 09:11:44.302405: Epoch 3974 +2026-04-15 09:11:44.304741: Current learning rate: 0.00011 +2026-04-15 09:13:25.707080: train_loss -0.5843 +2026-04-15 09:13:25.713435: val_loss -0.4723 +2026-04-15 09:13:25.715681: Pseudo dice [0.6216, 0.0, 0.7656, 0.8701, 0.0495, 0.8804, 0.9136] +2026-04-15 09:13:25.718098: Epoch time: 101.41 s +2026-04-15 09:13:26.995968: +2026-04-15 09:13:26.997969: Epoch 3975 +2026-04-15 09:13:27.000025: Current learning rate: 0.0001 +2026-04-15 09:15:08.711433: train_loss -0.5807 +2026-04-15 09:15:08.719175: val_loss -0.4602 +2026-04-15 09:15:08.723595: Pseudo dice [0.6644, 0.0, 0.7828, 0.8147, 0.5236, 0.7336, 0.9018] +2026-04-15 09:15:08.726462: Epoch time: 101.72 s +2026-04-15 09:15:10.013634: +2026-04-15 09:15:10.015976: Epoch 3976 +2026-04-15 09:15:10.017625: Current learning rate: 0.0001 +2026-04-15 09:16:52.058394: train_loss -0.5867 +2026-04-15 09:16:52.064475: val_loss -0.4806 +2026-04-15 09:16:52.067385: Pseudo dice [0.7432, 0.0, 0.8947, 0.5011, 0.4791, 0.8428, 0.9182] +2026-04-15 09:16:52.069747: Epoch time: 102.05 s +2026-04-15 09:16:53.362808: +2026-04-15 09:16:53.364777: Epoch 3977 +2026-04-15 09:16:53.366470: Current learning rate: 0.0001 +2026-04-15 09:18:35.313973: train_loss -0.5903 +2026-04-15 09:18:35.321110: val_loss -0.4979 +2026-04-15 09:18:35.324030: Pseudo dice [0.5536, 0.0, 0.8562, 0.7816, 0.5407, 0.8609, 0.9038] +2026-04-15 09:18:35.327315: Epoch time: 101.95 s +2026-04-15 09:18:36.635334: +2026-04-15 09:18:36.637061: Epoch 3978 +2026-04-15 09:18:36.638697: Current learning rate: 9e-05 +2026-04-15 09:20:18.012699: train_loss -0.5844 +2026-04-15 09:20:18.019435: val_loss -0.4736 +2026-04-15 09:20:18.021326: Pseudo dice [0.7017, 0.0, 0.7247, 0.4788, 0.5974, 0.8375, 0.8422] +2026-04-15 09:20:18.023505: Epoch time: 101.38 s +2026-04-15 09:20:19.343885: +2026-04-15 09:20:19.345558: Epoch 3979 +2026-04-15 09:20:19.347102: Current learning rate: 9e-05 +2026-04-15 09:22:01.081230: train_loss -0.5801 +2026-04-15 09:22:01.087199: val_loss -0.4801 +2026-04-15 09:22:01.089756: Pseudo dice [0.7101, 0.0, 0.7837, 0.5976, 0.4679, 0.8305, 0.8759] +2026-04-15 09:22:01.092518: Epoch time: 101.74 s +2026-04-15 09:22:02.426274: +2026-04-15 09:22:02.428020: Epoch 3980 +2026-04-15 09:22:02.429480: Current learning rate: 8e-05 +2026-04-15 09:23:44.331971: train_loss -0.5842 +2026-04-15 09:23:44.341933: val_loss -0.4899 +2026-04-15 09:23:44.344316: Pseudo dice [0.7873, 0.0, 0.8347, 0.4711, 0.4377, 0.3913, 0.8] +2026-04-15 09:23:44.346815: Epoch time: 101.91 s +2026-04-15 09:23:45.663242: +2026-04-15 09:23:45.664952: Epoch 3981 +2026-04-15 09:23:45.666450: Current learning rate: 8e-05 +2026-04-15 09:25:27.001118: train_loss -0.5757 +2026-04-15 09:25:27.010221: val_loss -0.4884 +2026-04-15 09:25:27.012357: Pseudo dice [0.8079, 0.0, 0.8527, 0.9205, 0.6086, 0.79, 0.9336] +2026-04-15 09:25:27.014557: Epoch time: 101.34 s +2026-04-15 09:25:28.331777: +2026-04-15 09:25:28.333553: Epoch 3982 +2026-04-15 09:25:28.335184: Current learning rate: 8e-05 +2026-04-15 09:27:09.995771: train_loss -0.5842 +2026-04-15 09:27:10.003372: val_loss -0.4742 +2026-04-15 09:27:10.005375: Pseudo dice [0.6334, 0.0, 0.8339, 0.8535, 0.4618, 0.8685, 0.9206] +2026-04-15 09:27:10.008253: Epoch time: 101.67 s +2026-04-15 09:27:11.274496: +2026-04-15 09:27:11.276480: Epoch 3983 +2026-04-15 09:27:11.278331: Current learning rate: 7e-05 +2026-04-15 09:28:52.869177: train_loss -0.5708 +2026-04-15 09:28:52.874717: val_loss -0.4837 +2026-04-15 09:28:52.876688: Pseudo dice [0.5039, 0.0, 0.8108, 0.7791, 0.4514, 0.7648, 0.9298] +2026-04-15 09:28:52.879428: Epoch time: 101.6 s +2026-04-15 09:28:54.153678: +2026-04-15 09:28:54.155384: Epoch 3984 +2026-04-15 09:28:54.157112: Current learning rate: 7e-05 +2026-04-15 09:30:35.692067: train_loss -0.5773 +2026-04-15 09:30:35.699321: val_loss -0.4383 +2026-04-15 09:30:35.701464: Pseudo dice [0.8517, 0.0, 0.8201, 0.0952, 0.4348, 0.7367, 0.9527] +2026-04-15 09:30:35.703719: Epoch time: 101.54 s +2026-04-15 09:30:37.017254: +2026-04-15 09:30:37.019254: Epoch 3985 +2026-04-15 09:30:37.021065: Current learning rate: 7e-05 +2026-04-15 09:32:19.798650: train_loss -0.5948 +2026-04-15 09:32:19.804539: val_loss -0.4736 +2026-04-15 09:32:19.806971: Pseudo dice [0.7268, 0.0, 0.7501, 0.5135, 0.3727, 0.8176, 0.8464] +2026-04-15 09:32:19.809267: Epoch time: 102.78 s +2026-04-15 09:32:21.092527: +2026-04-15 09:32:21.094167: Epoch 3986 +2026-04-15 09:32:21.095647: Current learning rate: 6e-05 +2026-04-15 09:34:02.917121: train_loss -0.5763 +2026-04-15 09:34:02.924920: val_loss -0.4548 +2026-04-15 09:34:02.927130: Pseudo dice [0.7233, 0.0, 0.7276, 0.4664, 0.601, 0.5278, 0.899] +2026-04-15 09:34:02.929763: Epoch time: 101.83 s +2026-04-15 09:34:04.253245: +2026-04-15 09:34:04.255182: Epoch 3987 +2026-04-15 09:34:04.257111: Current learning rate: 6e-05 +2026-04-15 09:35:45.893569: train_loss -0.5842 +2026-04-15 09:35:45.901051: val_loss -0.4578 +2026-04-15 09:35:45.903325: Pseudo dice [0.4655, 0.0, 0.86, 0.6451, 0.6142, 0.8132, 0.9086] +2026-04-15 09:35:45.906499: Epoch time: 101.64 s +2026-04-15 09:35:47.188122: +2026-04-15 09:35:47.190145: Epoch 3988 +2026-04-15 09:35:47.191835: Current learning rate: 5e-05 +2026-04-15 09:37:29.482633: train_loss -0.5935 +2026-04-15 09:37:29.490450: val_loss -0.4782 +2026-04-15 09:37:29.493057: Pseudo dice [0.7295, 0.0, 0.8678, 0.107, 0.4652, 0.6035, 0.8893] +2026-04-15 09:37:29.496259: Epoch time: 102.3 s +2026-04-15 09:37:30.837683: +2026-04-15 09:37:30.839457: Epoch 3989 +2026-04-15 09:37:30.841480: Current learning rate: 5e-05 +2026-04-15 09:39:12.497970: train_loss -0.5953 +2026-04-15 09:39:12.503854: val_loss -0.4937 +2026-04-15 09:39:12.505790: Pseudo dice [0.7243, 0.0, 0.8878, 0.4074, 0.4153, 0.8837, 0.9064] +2026-04-15 09:39:12.508453: Epoch time: 101.66 s +2026-04-15 09:39:13.820582: +2026-04-15 09:39:13.822491: Epoch 3990 +2026-04-15 09:39:13.826133: Current learning rate: 5e-05 +2026-04-15 09:40:55.228325: train_loss -0.5713 +2026-04-15 09:40:55.248194: val_loss -0.5012 +2026-04-15 09:40:55.250841: Pseudo dice [0.8063, 0.0, 0.8615, 0.902, 0.6096, 0.8216, 0.9519] +2026-04-15 09:40:55.253984: Epoch time: 101.41 s +2026-04-15 09:40:56.576787: +2026-04-15 09:40:56.579042: Epoch 3991 +2026-04-15 09:40:56.581094: Current learning rate: 4e-05 +2026-04-15 09:42:37.939701: train_loss -0.5777 +2026-04-15 09:42:37.946409: val_loss -0.474 +2026-04-15 09:42:37.948447: Pseudo dice [0.4496, 0.0, 0.8082, 0.8813, 0.3547, 0.8948, 0.9002] +2026-04-15 09:42:37.951845: Epoch time: 101.37 s +2026-04-15 09:42:39.240865: +2026-04-15 09:42:39.243016: Epoch 3992 +2026-04-15 09:42:39.244773: Current learning rate: 4e-05 +2026-04-15 09:44:20.594008: train_loss -0.5807 +2026-04-15 09:44:20.599958: val_loss -0.4548 +2026-04-15 09:44:20.602050: Pseudo dice [0.6789, 0.0, 0.8706, 0.6316, 0.3764, 0.742, 0.9437] +2026-04-15 09:44:20.604218: Epoch time: 101.36 s +2026-04-15 09:44:21.888742: +2026-04-15 09:44:21.890538: Epoch 3993 +2026-04-15 09:44:21.892083: Current learning rate: 3e-05 +2026-04-15 09:46:03.608999: train_loss -0.5838 +2026-04-15 09:46:03.615506: val_loss -0.4967 +2026-04-15 09:46:03.618384: Pseudo dice [0.8329, 0.0, 0.8117, 0.6537, 0.6097, 0.7979, 0.9369] +2026-04-15 09:46:03.620780: Epoch time: 101.72 s +2026-04-15 09:46:04.899459: +2026-04-15 09:46:04.901416: Epoch 3994 +2026-04-15 09:46:04.903181: Current learning rate: 3e-05 +2026-04-15 09:47:46.226332: train_loss -0.5887 +2026-04-15 09:47:46.233127: val_loss -0.4768 +2026-04-15 09:47:46.236268: Pseudo dice [0.8143, 0.0, 0.8886, 0.6355, 0.5018, 0.8395, 0.9156] +2026-04-15 09:47:46.239143: Epoch time: 101.33 s +2026-04-15 09:47:47.525799: +2026-04-15 09:47:47.529160: Epoch 3995 +2026-04-15 09:47:47.530811: Current learning rate: 2e-05 +2026-04-15 09:49:29.466067: train_loss -0.5776 +2026-04-15 09:49:29.473977: val_loss -0.445 +2026-04-15 09:49:29.476014: Pseudo dice [0.6518, 0.0, 0.6627, 0.5363, 0.4482, 0.8753, 0.9125] +2026-04-15 09:49:29.478108: Epoch time: 101.94 s +2026-04-15 09:49:30.750844: +2026-04-15 09:49:30.752610: Epoch 3996 +2026-04-15 09:49:30.755389: Current learning rate: 2e-05 +2026-04-15 09:51:12.445720: train_loss -0.5911 +2026-04-15 09:51:12.451557: val_loss -0.4999 +2026-04-15 09:51:12.454207: Pseudo dice [0.8466, 0.0, 0.8263, 0.6348, 0.5074, 0.5688, 0.7955] +2026-04-15 09:51:12.456482: Epoch time: 101.7 s +2026-04-15 09:51:13.762192: +2026-04-15 09:51:13.763922: Epoch 3997 +2026-04-15 09:51:13.765502: Current learning rate: 2e-05 +2026-04-15 09:52:55.415640: train_loss -0.5986 +2026-04-15 09:52:55.421617: val_loss -0.457 +2026-04-15 09:52:55.424026: Pseudo dice [0.7994, 0.0, 0.8249, 0.5066, 0.541, 0.8158, 0.8882] +2026-04-15 09:52:55.426309: Epoch time: 101.66 s +2026-04-15 09:52:56.670370: +2026-04-15 09:52:56.672203: Epoch 3998 +2026-04-15 09:52:56.674130: Current learning rate: 1e-05 +2026-04-15 09:54:37.998265: train_loss -0.5766 +2026-04-15 09:54:38.005899: val_loss -0.4934 +2026-04-15 09:54:38.011226: Pseudo dice [0.5127, 0.0, 0.8247, 0.6688, 0.5594, 0.8272, 0.92] +2026-04-15 09:54:38.013585: Epoch time: 101.33 s +2026-04-15 09:54:39.373667: +2026-04-15 09:54:39.375706: Epoch 3999 +2026-04-15 09:54:39.377146: Current learning rate: 1e-05 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_3/checkpoint_best.pth b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_3/checkpoint_best.pth new file mode 100644 index 0000000000000000000000000000000000000000..883e9f6f719da7cb371e2d40ea6ed83b44614cc4 --- /dev/null +++ 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'./imagesTs/MSWAL_0647_0000.nii.gz', 'label': './labelsTs/MSWAL_0647.nii.gz'}, {'image': './imagesTs/MSWAL_0652_0000.nii.gz', 'label': './labelsTs/MSWAL_0652.nii.gz'}, {'image': './imagesTs/MSWAL_0657_0000.nii.gz', 'label': './labelsTs/MSWAL_0657.nii.gz'}, {'image': './imagesTs/MSWAL_0659_0000.nii.gz', 'label': './labelsTs/MSWAL_0659.nii.gz'}, {'image': './imagesTs/MSWAL_0664_0000.nii.gz', 'label': './labelsTs/MSWAL_0664.nii.gz'}, {'image': './imagesTs/MSWAL_0665_0000.nii.gz', 'label': './labelsTs/MSWAL_0665.nii.gz'}, {'image': './imagesTs/MSWAL_0672_0000.nii.gz', 'label': './labelsTs/MSWAL_0672.nii.gz'}, {'image': './imagesTs/MSWAL_0678_0000.nii.gz', 'label': './labelsTs/MSWAL_0678.nii.gz'}, {'image': './imagesTs/MSWAL_0683_0000.nii.gz', 'label': './labelsTs/MSWAL_0683.nii.gz'}, {'image': './imagesTs/MSWAL_0684_0000.nii.gz', 'label': './labelsTs/MSWAL_0684.nii.gz'}, {'image': './imagesTs/MSWAL_0689_0000.nii.gz', 'label': './labelsTs/MSWAL_0689.nii.gz'}, {'image': './imagesTs/MSWAL_0691_0000.nii.gz', 'label': './labelsTs/MSWAL_0691.nii.gz'}]}, 'unpack_dataset': True, 'device': device(type='cuda')}", + "network": "OptimizedModule", + "num_epochs": "4000", + "num_input_channels": "1", + "num_iterations_per_epoch": "250", + "num_val_iterations_per_epoch": "50", + "optimizer": "SGD (\nParameter Group 0\n dampening: 0\n differentiable: False\n foreach: None\n fused: None\n initial_lr: 0.01\n lr: 0.01\n maximize: False\n momentum: 0.99\n nesterov: True\n weight_decay: 3e-05\n)", + "output_folder": "/data/houbb/nnunetv2/nnUNet_results/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_3", + "output_folder_base": "/data/houbb/nnunetv2/nnUNet_results/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres", + "oversample_foreground_percent": "0.33", + "plans_manager": "{'dataset_name': 'Dataset201_MSWAL', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.25, 0.75, 0.75], 'original_median_shape_after_transp': [261, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 35, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [190, 381, 381], 'spacing': [1.6798954741801528, 1.0079372845080916, 1.0079372845080916], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 71.96339416503906, 'median': 45.0, 'min': -932.0, 'percentile_00_5': -93.0, 'percentile_99_5': 1052.0, 'std': 141.6230926513672}}}", + "preprocessed_dataset_folder": "/data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/nnUNetPlans_3d_fullres", + "preprocessed_dataset_folder_base": "/data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL", + "save_every": "50", + "torch_version": "2.5.0+cu121", + "unpack_dataset": "True", + "was_initialized": "True", + "weight_decay": "3e-05" +} \ No newline at end of file diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_3/progress.png b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_3/progress.png new file mode 100644 index 0000000000000000000000000000000000000000..2f2fe421eada9c7f0c3df5957742ef02119ecb3f --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_3/progress.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ec829319b821026e3d9ecad0a401998882b2856751846e583b110d850e6f26f +size 1464631 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_3/training_log_2026_4_10_10_09_49.txt b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_3/training_log_2026_4_10_10_09_49.txt new file mode 100644 index 0000000000000000000000000000000000000000..c6df360b98fe9c129f007b870d2c4549609fa842 --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_3/training_log_2026_4_10_10_09_49.txt @@ -0,0 +1,28393 @@ + +####################################################################### +Please cite the following paper when using nnU-Net: +Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. +####################################################################### + +2026-04-10 10:09:49.894189: do_dummy_2d_data_aug: False +2026-04-10 10:09:49.950217: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json +2026-04-10 10:09:49.953585: The split file contains 5 splits. +2026-04-10 10:09:49.955227: Desired fold for training: 3 +2026-04-10 10:09:49.956886: This split has 387 training and 97 validation cases. +2026-04-10 10:09:56.259859: Using torch.compile... + +This is the configuration used by this training: +Configuration name: 3d_fullres + {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True} + +These are the global plan.json settings: + {'dataset_name': 'Dataset201_MSWAL', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.25, 0.75, 0.75], 'original_median_shape_after_transp': [261, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 71.96339416503906, 'median': 45.0, 'min': -932.0, 'percentile_00_5': -93.0, 'percentile_99_5': 1052.0, 'std': 141.6230926513672}}} + +2026-04-10 10:09:57.385280: unpacking dataset... +2026-04-10 10:10:03.009487: unpacking done... +2026-04-10 10:10:03.030667: Unable to plot network architecture: nnUNet_compile is enabled! +2026-04-10 10:10:03.089330: +2026-04-10 10:10:03.090862: Epoch 0 +2026-04-10 10:10:03.092522: Current learning rate: 0.01 +2026-04-10 10:13:58.755176: train_loss 0.2162 +2026-04-10 10:13:58.760451: val_loss 0.0875 +2026-04-10 10:13:58.761916: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:13:58.763871: Epoch time: 235.67 s +2026-04-10 10:13:58.765784: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 10:14:01.256080: +2026-04-10 10:14:01.257389: Epoch 1 +2026-04-10 10:14:01.258594: Current learning rate: 0.01 +2026-04-10 10:15:42.857744: train_loss 0.0561 +2026-04-10 10:15:42.866269: val_loss 0.0558 +2026-04-10 10:15:42.867746: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:15:42.868918: Epoch time: 101.6 s +2026-04-10 10:15:43.907279: +2026-04-10 10:15:43.908564: Epoch 2 +2026-04-10 10:15:43.909663: Current learning rate: 0.01 +2026-04-10 10:17:25.193470: train_loss 0.066 +2026-04-10 10:17:25.199062: val_loss 0.0505 +2026-04-10 10:17:25.200799: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:17:25.205251: Epoch time: 101.29 s +2026-04-10 10:17:26.263960: +2026-04-10 10:17:26.265638: Epoch 3 +2026-04-10 10:17:26.267021: Current learning rate: 0.00999 +2026-04-10 10:19:07.571655: train_loss 0.062 +2026-04-10 10:19:07.575867: val_loss 0.0618 +2026-04-10 10:19:07.577808: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:19:07.579448: Epoch time: 101.31 s +2026-04-10 10:19:08.616368: +2026-04-10 10:19:08.617960: Epoch 4 +2026-04-10 10:19:08.619520: Current learning rate: 0.00999 +2026-04-10 10:20:50.048275: train_loss 0.056 +2026-04-10 10:20:50.052596: val_loss 0.0459 +2026-04-10 10:20:50.054182: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:20:50.055707: Epoch time: 101.44 s +2026-04-10 10:20:51.093123: +2026-04-10 10:20:51.095069: Epoch 5 +2026-04-10 10:20:51.096994: Current learning rate: 0.00999 +2026-04-10 10:22:32.516978: train_loss 0.0579 +2026-04-10 10:22:32.520808: val_loss 0.0451 +2026-04-10 10:22:32.522513: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:22:32.523524: Epoch time: 101.43 s +2026-04-10 10:22:33.519223: +2026-04-10 10:22:33.520583: Epoch 6 +2026-04-10 10:22:33.521809: Current learning rate: 0.00999 +2026-04-10 10:24:14.898183: train_loss 0.048 +2026-04-10 10:24:14.903731: val_loss 0.0479 +2026-04-10 10:24:14.905397: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:24:14.907236: Epoch time: 101.38 s +2026-04-10 10:24:15.920944: +2026-04-10 10:24:15.922854: Epoch 7 +2026-04-10 10:24:15.924162: Current learning rate: 0.00998 +2026-04-10 10:25:57.383948: train_loss 0.0551 +2026-04-10 10:25:57.389237: val_loss 0.0566 +2026-04-10 10:25:57.390471: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:25:57.391956: Epoch time: 101.47 s +2026-04-10 10:25:58.429888: +2026-04-10 10:25:58.431713: Epoch 8 +2026-04-10 10:25:58.433223: Current learning rate: 0.00998 +2026-04-10 10:27:40.023829: train_loss 0.0567 +2026-04-10 10:27:40.028435: val_loss 0.0465 +2026-04-10 10:27:40.029909: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:27:40.031610: Epoch time: 101.6 s +2026-04-10 10:27:41.081136: +2026-04-10 10:27:41.082481: Epoch 9 +2026-04-10 10:27:41.083816: Current learning rate: 0.00998 +2026-04-10 10:29:22.781529: train_loss 0.0584 +2026-04-10 10:29:22.787905: val_loss 0.051 +2026-04-10 10:29:22.789886: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:29:22.791712: Epoch time: 101.7 s +2026-04-10 10:29:23.787814: +2026-04-10 10:29:23.789329: Epoch 10 +2026-04-10 10:29:23.790784: Current learning rate: 0.00998 +2026-04-10 10:31:05.435620: train_loss 0.0625 +2026-04-10 10:31:05.442490: val_loss 0.0522 +2026-04-10 10:31:05.444170: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:31:05.445896: Epoch time: 101.65 s +2026-04-10 10:31:06.422694: +2026-04-10 10:31:06.424444: Epoch 11 +2026-04-10 10:31:06.425812: Current learning rate: 0.00998 +2026-04-10 10:32:47.930864: train_loss 0.0472 +2026-04-10 10:32:47.935435: val_loss 0.068 +2026-04-10 10:32:47.936933: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:32:47.938151: Epoch time: 101.51 s +2026-04-10 10:32:48.925517: +2026-04-10 10:32:48.927190: Epoch 12 +2026-04-10 10:32:48.928918: Current learning rate: 0.00997 +2026-04-10 10:34:30.750248: train_loss 0.0556 +2026-04-10 10:34:30.754535: val_loss 0.0567 +2026-04-10 10:34:30.756185: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:34:30.757303: Epoch time: 101.83 s +2026-04-10 10:34:31.757312: +2026-04-10 10:34:31.758549: Epoch 13 +2026-04-10 10:34:31.759649: Current learning rate: 0.00997 +2026-04-10 10:36:13.391390: train_loss 0.0618 +2026-04-10 10:36:13.395287: val_loss 0.0337 +2026-04-10 10:36:13.408802: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:36:13.410587: Epoch time: 101.64 s +2026-04-10 10:36:14.431941: +2026-04-10 10:36:14.433443: Epoch 14 +2026-04-10 10:36:14.434568: Current learning rate: 0.00997 +2026-04-10 10:37:56.151163: train_loss 0.0497 +2026-04-10 10:37:56.155182: val_loss 0.0437 +2026-04-10 10:37:56.156647: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:37:56.158388: Epoch time: 101.72 s +2026-04-10 10:37:57.205203: +2026-04-10 10:37:57.206823: Epoch 15 +2026-04-10 10:37:57.208263: Current learning rate: 0.00997 +2026-04-10 10:39:38.913790: train_loss 0.0541 +2026-04-10 10:39:38.917771: val_loss 0.0404 +2026-04-10 10:39:38.919281: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:39:38.920759: Epoch time: 101.71 s +2026-04-10 10:39:39.961041: +2026-04-10 10:39:39.962587: Epoch 16 +2026-04-10 10:39:39.963789: Current learning rate: 0.00996 +2026-04-10 10:41:21.700373: train_loss 0.0385 +2026-04-10 10:41:21.705825: val_loss 0.0382 +2026-04-10 10:41:21.707875: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:41:21.709328: Epoch time: 101.74 s +2026-04-10 10:41:22.758684: +2026-04-10 10:41:22.760483: Epoch 17 +2026-04-10 10:41:22.761795: Current learning rate: 0.00996 +2026-04-10 10:43:04.656834: train_loss 0.044 +2026-04-10 10:43:04.661488: val_loss 0.0395 +2026-04-10 10:43:04.663065: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:43:04.664403: Epoch time: 101.9 s +2026-04-10 10:43:05.715381: +2026-04-10 10:43:05.716691: Epoch 18 +2026-04-10 10:43:05.717952: Current learning rate: 0.00996 +2026-04-10 10:44:47.433271: train_loss 0.0405 +2026-04-10 10:44:47.437358: val_loss 0.0443 +2026-04-10 10:44:47.438500: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:44:47.439564: Epoch time: 101.72 s +2026-04-10 10:44:49.534582: +2026-04-10 10:44:49.535946: Epoch 19 +2026-04-10 10:44:49.537193: Current learning rate: 0.00996 +2026-04-10 10:46:31.630777: train_loss 0.0438 +2026-04-10 10:46:31.637127: val_loss 0.04 +2026-04-10 10:46:31.638690: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:46:31.640268: Epoch time: 102.1 s +2026-04-10 10:46:32.672569: +2026-04-10 10:46:32.675908: Epoch 20 +2026-04-10 10:46:32.677345: Current learning rate: 0.00995 +2026-04-10 10:48:14.614270: train_loss 0.0427 +2026-04-10 10:48:14.619027: val_loss 0.0578 +2026-04-10 10:48:14.621232: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:48:14.622882: Epoch time: 101.95 s +2026-04-10 10:48:15.682478: +2026-04-10 10:48:15.684111: Epoch 21 +2026-04-10 10:48:15.685611: Current learning rate: 0.00995 +2026-04-10 10:49:57.692997: train_loss 0.0587 +2026-04-10 10:49:57.700007: val_loss 0.0434 +2026-04-10 10:49:57.701707: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:49:57.703174: Epoch time: 102.01 s +2026-04-10 10:49:58.720891: +2026-04-10 10:49:58.722697: Epoch 22 +2026-04-10 10:49:58.724441: Current learning rate: 0.00995 +2026-04-10 10:51:40.776703: train_loss 0.0431 +2026-04-10 10:51:40.782187: val_loss 0.049 +2026-04-10 10:51:40.783700: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:51:40.785048: Epoch time: 102.06 s +2026-04-10 10:51:41.791428: +2026-04-10 10:51:41.792912: Epoch 23 +2026-04-10 10:51:41.794280: Current learning rate: 0.00995 +2026-04-10 10:53:23.708344: train_loss 0.0429 +2026-04-10 10:53:23.713850: val_loss 0.0457 +2026-04-10 10:53:23.715908: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:53:23.717473: Epoch time: 101.92 s +2026-04-10 10:53:24.716785: +2026-04-10 10:53:24.718332: Epoch 24 +2026-04-10 10:53:24.719916: Current learning rate: 0.00995 +2026-04-10 10:55:06.779521: train_loss 0.0494 +2026-04-10 10:55:06.792736: val_loss 0.0419 +2026-04-10 10:55:06.797861: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:55:06.799168: Epoch time: 102.07 s +2026-04-10 10:55:07.794837: +2026-04-10 10:55:07.796315: Epoch 25 +2026-04-10 10:55:07.797750: Current learning rate: 0.00994 +2026-04-10 10:56:49.891959: train_loss 0.0485 +2026-04-10 10:56:49.912524: val_loss 0.035 +2026-04-10 10:56:49.925242: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:56:49.926792: Epoch time: 102.1 s +2026-04-10 10:56:50.944951: +2026-04-10 10:56:50.957954: Epoch 26 +2026-04-10 10:56:50.963377: Current learning rate: 0.00994 +2026-04-10 10:58:32.758304: train_loss 0.0403 +2026-04-10 10:58:32.763058: val_loss 0.0548 +2026-04-10 10:58:32.764751: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:58:32.767179: Epoch time: 101.82 s +2026-04-10 10:58:33.790951: +2026-04-10 10:58:33.792804: Epoch 27 +2026-04-10 10:58:33.794248: Current learning rate: 0.00994 +2026-04-10 11:00:15.787311: train_loss 0.0385 +2026-04-10 11:00:15.797875: val_loss 0.0381 +2026-04-10 11:00:15.799896: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:00:15.802190: Epoch time: 102.0 s +2026-04-10 11:00:16.827970: +2026-04-10 11:00:16.830394: Epoch 28 +2026-04-10 11:00:16.835977: Current learning rate: 0.00994 +2026-04-10 11:01:58.772789: train_loss 0.0391 +2026-04-10 11:01:58.777719: val_loss 0.0323 +2026-04-10 11:01:58.779342: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:01:58.780954: Epoch time: 101.95 s +2026-04-10 11:01:59.806821: +2026-04-10 11:01:59.808150: Epoch 29 +2026-04-10 11:01:59.809375: Current learning rate: 0.00993 +2026-04-10 11:03:41.850798: train_loss 0.035 +2026-04-10 11:03:41.855433: val_loss 0.0312 +2026-04-10 11:03:41.857077: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:03:41.859056: Epoch time: 102.05 s +2026-04-10 11:03:42.885200: +2026-04-10 11:03:42.886626: Epoch 30 +2026-04-10 11:03:42.887910: Current learning rate: 0.00993 +2026-04-10 11:05:24.845844: train_loss 0.0428 +2026-04-10 11:05:24.851037: val_loss 0.0421 +2026-04-10 11:05:24.852706: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:05:24.854548: Epoch time: 101.96 s +2026-04-10 11:05:24.856134: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 11:05:27.369673: +2026-04-10 11:05:27.371221: Epoch 31 +2026-04-10 11:05:27.372725: Current learning rate: 0.00993 +2026-04-10 11:07:09.500411: train_loss 0.0488 +2026-04-10 11:07:09.506064: val_loss 0.0375 +2026-04-10 11:07:09.508160: Pseudo dice [0.0, 0.0, 0.0001, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:07:09.510040: Epoch time: 102.13 s +2026-04-10 11:07:09.511754: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 11:07:12.087025: +2026-04-10 11:07:12.088493: Epoch 32 +2026-04-10 11:07:12.089862: Current learning rate: 0.00993 +2026-04-10 11:08:54.047428: train_loss 0.0332 +2026-04-10 11:08:54.052874: val_loss 0.0535 +2026-04-10 11:08:54.054376: Pseudo dice [0.0, 0.0, 0.0606, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:08:54.055933: Epoch time: 101.96 s +2026-04-10 11:08:54.057307: Yayy! New best EMA pseudo Dice: 0.0009 +2026-04-10 11:08:56.596455: +2026-04-10 11:08:56.597845: Epoch 33 +2026-04-10 11:08:56.599130: Current learning rate: 0.00993 +2026-04-10 11:10:38.763604: train_loss 0.0404 +2026-04-10 11:10:38.768342: val_loss 0.0423 +2026-04-10 11:10:38.769852: Pseudo dice [0.0, 0.0, 0.0119, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:10:38.771683: Epoch time: 102.17 s +2026-04-10 11:10:38.773219: Yayy! New best EMA pseudo Dice: 0.001 +2026-04-10 11:10:41.332414: +2026-04-10 11:10:41.333836: Epoch 34 +2026-04-10 11:10:41.335057: Current learning rate: 0.00992 +2026-04-10 11:12:23.357552: train_loss 0.042 +2026-04-10 11:12:23.380501: val_loss 0.0278 +2026-04-10 11:12:23.381787: Pseudo dice [0.0, 0.0, 0.0107, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:12:23.382949: Epoch time: 102.03 s +2026-04-10 11:12:23.383962: Yayy! New best EMA pseudo Dice: 0.001 +2026-04-10 11:12:25.970891: +2026-04-10 11:12:25.972284: Epoch 35 +2026-04-10 11:12:25.973664: Current learning rate: 0.00992 +2026-04-10 11:14:07.978839: train_loss 0.0305 +2026-04-10 11:14:07.983535: val_loss 0.0218 +2026-04-10 11:14:07.985084: Pseudo dice [0.0, 0.0, 0.3609, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:14:07.986943: Epoch time: 102.01 s +2026-04-10 11:14:07.988475: Yayy! New best EMA pseudo Dice: 0.0061 +2026-04-10 11:14:10.586864: +2026-04-10 11:14:10.588604: Epoch 36 +2026-04-10 11:14:10.590012: Current learning rate: 0.00992 +2026-04-10 11:15:52.645047: train_loss 0.0384 +2026-04-10 11:15:52.649358: val_loss 0.0353 +2026-04-10 11:15:52.651155: Pseudo dice [0.0, 0.0, 0.0154, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:15:52.652887: Epoch time: 102.06 s +2026-04-10 11:15:53.717977: +2026-04-10 11:15:53.719630: Epoch 37 +2026-04-10 11:15:53.721174: Current learning rate: 0.00992 +2026-04-10 11:17:35.743225: train_loss 0.0345 +2026-04-10 11:17:35.747216: val_loss 0.0401 +2026-04-10 11:17:35.748428: Pseudo dice [0.0, 0.0, 0.0001, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:17:35.749557: Epoch time: 102.03 s +2026-04-10 11:17:37.870708: +2026-04-10 11:17:37.872131: Epoch 38 +2026-04-10 11:17:37.873309: Current learning rate: 0.00991 +2026-04-10 11:19:20.023568: train_loss 0.0355 +2026-04-10 11:19:20.031367: val_loss 0.0381 +2026-04-10 11:19:20.038703: Pseudo dice [0.0, 0.0, 0.0273, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:19:20.041346: Epoch time: 102.16 s +2026-04-10 11:19:21.113181: +2026-04-10 11:19:21.114511: Epoch 39 +2026-04-10 11:19:21.115655: Current learning rate: 0.00991 +2026-04-10 11:21:03.075465: train_loss 0.0374 +2026-04-10 11:21:03.079453: val_loss 0.0357 +2026-04-10 11:21:03.081068: Pseudo dice [0.0, 0.0, 0.0709, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:21:03.082494: Epoch time: 101.97 s +2026-04-10 11:21:04.138196: +2026-04-10 11:21:04.139976: Epoch 40 +2026-04-10 11:21:04.141869: Current learning rate: 0.00991 +2026-04-10 11:22:46.055792: train_loss 0.0264 +2026-04-10 11:22:46.080665: val_loss 0.0337 +2026-04-10 11:22:46.082113: Pseudo dice [0.0, 0.0, 0.111, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:22:46.084058: Epoch time: 101.92 s +2026-04-10 11:22:46.085624: Yayy! New best EMA pseudo Dice: 0.0065 +2026-04-10 11:22:48.689016: +2026-04-10 11:22:48.690663: Epoch 41 +2026-04-10 11:22:48.691914: Current learning rate: 0.00991 +2026-04-10 11:24:30.820891: train_loss 0.0355 +2026-04-10 11:24:30.825001: val_loss 0.0449 +2026-04-10 11:24:30.826458: Pseudo dice [0.0, 0.0, 0.2437, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:24:30.827906: Epoch time: 102.13 s +2026-04-10 11:24:30.829033: Yayy! New best EMA pseudo Dice: 0.0094 +2026-04-10 11:24:33.357307: +2026-04-10 11:24:33.358606: Epoch 42 +2026-04-10 11:24:33.359664: Current learning rate: 0.00991 +2026-04-10 11:26:15.371091: train_loss 0.023 +2026-04-10 11:26:15.375903: val_loss 0.0262 +2026-04-10 11:26:15.378619: Pseudo dice [0.0, 0.0, 0.405, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:26:15.380452: Epoch time: 102.02 s +2026-04-10 11:26:15.381955: Yayy! New best EMA pseudo Dice: 0.0142 +2026-04-10 11:26:17.906492: +2026-04-10 11:26:17.907987: Epoch 43 +2026-04-10 11:26:17.909313: Current learning rate: 0.0099 +2026-04-10 11:27:59.809781: train_loss 0.0385 +2026-04-10 11:27:59.813954: val_loss 0.0295 +2026-04-10 11:27:59.815691: Pseudo dice [0.0, 0.0, 0.0598, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:27:59.817056: Epoch time: 101.91 s +2026-04-10 11:28:00.842947: +2026-04-10 11:28:00.844347: Epoch 44 +2026-04-10 11:28:00.845541: Current learning rate: 0.0099 +2026-04-10 11:29:42.834548: train_loss 0.0302 +2026-04-10 11:29:42.840286: val_loss 0.0417 +2026-04-10 11:29:42.842175: Pseudo dice [0.0, 0.0, 0.1128, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:29:42.844007: Epoch time: 101.99 s +2026-04-10 11:29:43.841895: +2026-04-10 11:29:43.843299: Epoch 45 +2026-04-10 11:29:43.844499: Current learning rate: 0.0099 +2026-04-10 11:31:25.844946: train_loss 0.0411 +2026-04-10 11:31:25.849209: val_loss 0.0304 +2026-04-10 11:31:25.850846: Pseudo dice [0.0, 0.0, 0.4097, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:31:25.852787: Epoch time: 102.01 s +2026-04-10 11:31:25.854596: Yayy! New best EMA pseudo Dice: 0.0184 +2026-04-10 11:31:28.396571: +2026-04-10 11:31:28.398474: Epoch 46 +2026-04-10 11:31:28.400171: Current learning rate: 0.0099 +2026-04-10 11:33:10.460345: train_loss 0.0288 +2026-04-10 11:33:10.464333: val_loss 0.0333 +2026-04-10 11:33:10.465828: Pseudo dice [0.0, 0.0, 0.1571, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:33:10.467960: Epoch time: 102.07 s +2026-04-10 11:33:10.469228: Yayy! New best EMA pseudo Dice: 0.0188 +2026-04-10 11:33:12.967779: +2026-04-10 11:33:12.969199: Epoch 47 +2026-04-10 11:33:12.970502: Current learning rate: 0.00989 +2026-04-10 11:34:54.968310: train_loss 0.0327 +2026-04-10 11:34:54.972512: val_loss 0.0356 +2026-04-10 11:34:54.974705: Pseudo dice [0.0, 0.0, 0.0206, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:34:54.975811: Epoch time: 102.0 s +2026-04-10 11:34:55.973476: +2026-04-10 11:34:55.974897: Epoch 48 +2026-04-10 11:34:55.976163: Current learning rate: 0.00989 +2026-04-10 11:36:38.141582: train_loss 0.0327 +2026-04-10 11:36:38.145829: val_loss 0.0174 +2026-04-10 11:36:38.147000: Pseudo dice [0.0, 0.0, 0.1082, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:36:38.148841: Epoch time: 102.17 s +2026-04-10 11:36:39.162137: +2026-04-10 11:36:39.163738: Epoch 49 +2026-04-10 11:36:39.165012: Current learning rate: 0.00989 +2026-04-10 11:38:21.352189: train_loss 0.0205 +2026-04-10 11:38:21.356373: val_loss 0.0256 +2026-04-10 11:38:21.357951: Pseudo dice [0.0, 0.0, 0.3439, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:38:21.360279: Epoch time: 102.19 s +2026-04-10 11:38:22.896315: Yayy! New best EMA pseudo Dice: 0.0202 +2026-04-10 11:38:25.339224: +2026-04-10 11:38:25.340792: Epoch 50 +2026-04-10 11:38:25.342417: Current learning rate: 0.00989 +2026-04-10 11:40:07.436988: train_loss 0.0239 +2026-04-10 11:40:07.441332: val_loss 0.036 +2026-04-10 11:40:07.442802: Pseudo dice [0.0, 0.0, 0.2438, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:40:07.444016: Epoch time: 102.1 s +2026-04-10 11:40:07.445650: Yayy! New best EMA pseudo Dice: 0.0217 +2026-04-10 11:40:09.949651: +2026-04-10 11:40:09.951605: Epoch 51 +2026-04-10 11:40:09.952898: Current learning rate: 0.00989 +2026-04-10 11:41:52.015728: train_loss 0.0214 +2026-04-10 11:41:52.019925: val_loss 0.0125 +2026-04-10 11:41:52.021349: Pseudo dice [0.0, 0.0, 0.4393, 0.0, 0.0, 0.0, 0.0003] +2026-04-10 11:41:52.022461: Epoch time: 102.07 s +2026-04-10 11:41:52.023816: Yayy! New best EMA pseudo Dice: 0.0258 +2026-04-10 11:41:54.554397: +2026-04-10 11:41:54.556613: Epoch 52 +2026-04-10 11:41:54.558218: Current learning rate: 0.00988 +2026-04-10 11:43:36.725737: train_loss 0.0117 +2026-04-10 11:43:36.729740: val_loss 0.0241 +2026-04-10 11:43:36.731722: Pseudo dice [0.0, 0.0, 0.4618, 0.0, 0.0, 0.0, 0.1236] +2026-04-10 11:43:36.733197: Epoch time: 102.17 s +2026-04-10 11:43:36.734405: Yayy! New best EMA pseudo Dice: 0.0316 +2026-04-10 11:43:39.266702: +2026-04-10 11:43:39.268202: Epoch 53 +2026-04-10 11:43:39.269475: Current learning rate: 0.00988 +2026-04-10 11:45:21.293169: train_loss 0.0161 +2026-04-10 11:45:21.297102: val_loss 0.0269 +2026-04-10 11:45:21.298469: Pseudo dice [0.0, 0.0, 0.1346, 0.0, 0.0, 0.0, 0.2729] +2026-04-10 11:45:21.299905: Epoch time: 102.03 s +2026-04-10 11:45:21.303756: Yayy! New best EMA pseudo Dice: 0.0342 +2026-04-10 11:45:23.891235: +2026-04-10 11:45:23.892872: Epoch 54 +2026-04-10 11:45:23.894100: Current learning rate: 0.00988 +2026-04-10 11:47:06.015743: train_loss 0.0023 +2026-04-10 11:47:06.021023: val_loss 0.0307 +2026-04-10 11:47:06.022823: Pseudo dice [0.0, 0.0, 0.0687, 0.0, 0.0, 0.0, 0.3724] +2026-04-10 11:47:06.024210: Epoch time: 102.13 s +2026-04-10 11:47:06.025723: Yayy! New best EMA pseudo Dice: 0.0371 +2026-04-10 11:47:08.582417: +2026-04-10 11:47:08.584078: Epoch 55 +2026-04-10 11:47:08.585363: Current learning rate: 0.00988 +2026-04-10 11:48:50.610773: train_loss 0.0135 +2026-04-10 11:48:50.614700: val_loss 0.0178 +2026-04-10 11:48:50.615927: Pseudo dice [0.0, 0.0, 0.4517, 0.0, 0.0, 0.0, 0.1694] +2026-04-10 11:48:50.617301: Epoch time: 102.03 s +2026-04-10 11:48:50.618604: Yayy! New best EMA pseudo Dice: 0.0423 +2026-04-10 11:48:54.104991: +2026-04-10 11:48:54.106545: Epoch 56 +2026-04-10 11:48:54.107939: Current learning rate: 0.00987 +2026-04-10 11:50:36.059641: train_loss 0.0036 +2026-04-10 11:50:36.064366: val_loss 0.0194 +2026-04-10 11:50:36.066002: Pseudo dice [0.0, 0.0, 0.447, 0.0, 0.0, 0.0498, 0.1698] +2026-04-10 11:50:36.067671: Epoch time: 101.96 s +2026-04-10 11:50:36.069515: Yayy! New best EMA pseudo Dice: 0.0476 +2026-04-10 11:50:38.575882: +2026-04-10 11:50:38.577367: Epoch 57 +2026-04-10 11:50:38.578703: Current learning rate: 0.00987 +2026-04-10 11:52:20.491592: train_loss 0.0019 +2026-04-10 11:52:20.495764: val_loss 0.0138 +2026-04-10 11:52:20.497161: Pseudo dice [0.0, 0.0, 0.0818, 0.0, 0.0, 0.0477, 0.104] +2026-04-10 11:52:20.498383: Epoch time: 101.92 s +2026-04-10 11:52:21.495250: +2026-04-10 11:52:21.496951: Epoch 58 +2026-04-10 11:52:21.498447: Current learning rate: 0.00987 +2026-04-10 11:54:03.452211: train_loss 0.0146 +2026-04-10 11:54:03.457009: val_loss -0.0009 +2026-04-10 11:54:03.458854: Pseudo dice [0.0, 0.0, 0.1095, 0.0, 0.0, 0.1737, 0.2012] +2026-04-10 11:54:03.460326: Epoch time: 101.96 s +2026-04-10 11:54:03.461652: Yayy! New best EMA pseudo Dice: 0.0485 +2026-04-10 11:54:06.000864: +2026-04-10 11:54:06.002520: Epoch 59 +2026-04-10 11:54:06.003929: Current learning rate: 0.00987 +2026-04-10 11:55:48.074540: train_loss -0.0031 +2026-04-10 11:55:48.078905: val_loss 0.0028 +2026-04-10 11:55:48.080265: Pseudo dice [0.0, 0.0, 0.5344, 0.0, 0.0, 0.1495, 0.3992] +2026-04-10 11:55:48.082017: Epoch time: 102.08 s +2026-04-10 11:55:48.083383: Yayy! New best EMA pseudo Dice: 0.0591 +2026-04-10 11:55:50.831098: +2026-04-10 11:55:50.832566: Epoch 60 +2026-04-10 11:55:50.833907: Current learning rate: 0.00986 +2026-04-10 11:57:32.817948: train_loss 0.0084 +2026-04-10 11:57:32.822762: val_loss -0.0122 +2026-04-10 11:57:32.824191: Pseudo dice [0.0, 0.0, 0.4531, 0.0, 0.0, 0.2508, 0.6159] +2026-04-10 11:57:32.825640: Epoch time: 101.99 s +2026-04-10 11:57:32.827026: Yayy! New best EMA pseudo Dice: 0.072 +2026-04-10 11:57:35.349854: +2026-04-10 11:57:35.351317: Epoch 61 +2026-04-10 11:57:35.352674: Current learning rate: 0.00986 +2026-04-10 11:59:17.241517: train_loss -0.0018 +2026-04-10 11:59:17.245343: val_loss -0.0099 +2026-04-10 11:59:17.246563: Pseudo dice [0.0, 0.0295, 0.0185, 0.0, 0.0, 0.401, 0.1343] +2026-04-10 11:59:17.247591: Epoch time: 101.9 s +2026-04-10 11:59:17.248773: Yayy! New best EMA pseudo Dice: 0.0732 +2026-04-10 11:59:19.773558: +2026-04-10 11:59:19.774995: Epoch 62 +2026-04-10 11:59:19.776220: Current learning rate: 0.00986 +2026-04-10 12:01:01.858727: train_loss -0.0088 +2026-04-10 12:01:01.863859: val_loss -0.0165 +2026-04-10 12:01:01.865496: Pseudo dice [0.0, 0.0125, 0.3682, 0.0, 0.0, 0.3182, 0.4874] +2026-04-10 12:01:01.867453: Epoch time: 102.09 s +2026-04-10 12:01:01.869082: Yayy! New best EMA pseudo Dice: 0.0828 +2026-04-10 12:01:04.415169: +2026-04-10 12:01:04.416712: Epoch 63 +2026-04-10 12:01:04.418194: Current learning rate: 0.00986 +2026-04-10 12:02:46.668504: train_loss 0.0005 +2026-04-10 12:02:46.674195: val_loss -0.0069 +2026-04-10 12:02:46.676195: Pseudo dice [0.0, 0.0, 0.3503, 0.0, 0.0, 0.1391, 0.3742] +2026-04-10 12:02:46.677884: Epoch time: 102.26 s +2026-04-10 12:02:46.679621: Yayy! New best EMA pseudo Dice: 0.0868 +2026-04-10 12:02:49.211940: +2026-04-10 12:02:49.213421: Epoch 64 +2026-04-10 12:02:49.214765: Current learning rate: 0.00986 +2026-04-10 12:04:31.139787: train_loss -0.0103 +2026-04-10 12:04:31.144061: val_loss 0.0066 +2026-04-10 12:04:31.146076: Pseudo dice [0.0, 0.1108, 0.0516, 0.0, 0.0, 0.2364, 0.1856] +2026-04-10 12:04:31.147742: Epoch time: 101.93 s +2026-04-10 12:04:32.182491: +2026-04-10 12:04:32.184016: Epoch 65 +2026-04-10 12:04:32.185604: Current learning rate: 0.00985 +2026-04-10 12:06:14.061317: train_loss -0.0041 +2026-04-10 12:06:14.065702: val_loss 0.0101 +2026-04-10 12:06:14.067271: Pseudo dice [0.0, 0.1482, 0.0281, 0.0, 0.0, 0.1747, 0.2076] +2026-04-10 12:06:14.068897: Epoch time: 101.88 s +2026-04-10 12:06:15.113968: +2026-04-10 12:06:15.115530: Epoch 66 +2026-04-10 12:06:15.117048: Current learning rate: 0.00985 +2026-04-10 12:07:57.298226: train_loss -0.0002 +2026-04-10 12:07:57.303670: val_loss -0.011 +2026-04-10 12:07:57.305450: Pseudo dice [0.0, 0.0137, 0.4553, 0.0, 0.0, 0.3186, 0.376] +2026-04-10 12:07:57.307178: Epoch time: 102.19 s +2026-04-10 12:07:57.308826: Yayy! New best EMA pseudo Dice: 0.0939 +2026-04-10 12:07:59.841280: +2026-04-10 12:07:59.842640: Epoch 67 +2026-04-10 12:07:59.844154: Current learning rate: 0.00985 +2026-04-10 12:09:41.905001: train_loss -0.0137 +2026-04-10 12:09:41.912454: val_loss -0.0283 +2026-04-10 12:09:41.913939: Pseudo dice [0.0, 0.125, 0.4473, 0.0, 0.0, 0.1612, 0.4051] +2026-04-10 12:09:41.915567: Epoch time: 102.07 s +2026-04-10 12:09:41.916960: Yayy! New best EMA pseudo Dice: 0.1008 +2026-04-10 12:09:44.485596: +2026-04-10 12:09:44.487029: Epoch 68 +2026-04-10 12:09:44.488421: Current learning rate: 0.00985 +2026-04-10 12:11:26.470340: train_loss -0.0124 +2026-04-10 12:11:26.475202: val_loss -0.0081 +2026-04-10 12:11:26.477002: Pseudo dice [0.0, 0.1111, 0.4737, 0.0, 0.0, 0.1723, 0.2478] +2026-04-10 12:11:26.478381: Epoch time: 101.99 s +2026-04-10 12:11:26.479766: Yayy! New best EMA pseudo Dice: 0.105 +2026-04-10 12:11:29.126780: +2026-04-10 12:11:29.128301: Epoch 69 +2026-04-10 12:11:29.129719: Current learning rate: 0.00984 +2026-04-10 12:13:10.978781: train_loss -0.0312 +2026-04-10 12:13:10.982955: val_loss -0.0262 +2026-04-10 12:13:10.984554: Pseudo dice [0.0153, 0.0564, 0.2287, 0.0, 0.0, 0.2486, 0.3136] +2026-04-10 12:13:10.986068: Epoch time: 101.86 s +2026-04-10 12:13:10.987485: Yayy! New best EMA pseudo Dice: 0.1069 +2026-04-10 12:13:13.587461: +2026-04-10 12:13:13.589132: Epoch 70 +2026-04-10 12:13:13.590675: Current learning rate: 0.00984 +2026-04-10 12:14:55.741354: train_loss -0.0137 +2026-04-10 12:14:55.747177: val_loss -0.0098 +2026-04-10 12:14:55.748864: Pseudo dice [0.0094, 0.0703, 0.382, 0.0, 0.0, 0.0572, 0.5063] +2026-04-10 12:14:55.750442: Epoch time: 102.16 s +2026-04-10 12:14:55.752644: Yayy! New best EMA pseudo Dice: 0.1108 +2026-04-10 12:14:58.312706: +2026-04-10 12:14:58.314380: Epoch 71 +2026-04-10 12:14:58.316187: Current learning rate: 0.00984 +2026-04-10 12:16:40.382714: train_loss -0.0129 +2026-04-10 12:16:40.387260: val_loss -0.0146 +2026-04-10 12:16:40.388853: Pseudo dice [0.0, 0.0723, 0.3511, 0.0, 0.0, 0.2922, 0.2797] +2026-04-10 12:16:40.390175: Epoch time: 102.07 s +2026-04-10 12:16:40.391630: Yayy! New best EMA pseudo Dice: 0.114 +2026-04-10 12:16:42.969445: +2026-04-10 12:16:42.971420: Epoch 72 +2026-04-10 12:16:42.973054: Current learning rate: 0.00984 +2026-04-10 12:18:24.981781: train_loss -0.0288 +2026-04-10 12:18:24.987566: val_loss -0.0318 +2026-04-10 12:18:24.989182: Pseudo dice [0.1313, 0.3065, 0.0852, 0.0, 0.0, 0.2409, 0.5265] +2026-04-10 12:18:24.991277: Epoch time: 102.02 s +2026-04-10 12:18:24.992823: Yayy! New best EMA pseudo Dice: 0.121 +2026-04-10 12:18:28.465466: +2026-04-10 12:18:28.467107: Epoch 73 +2026-04-10 12:18:28.468385: Current learning rate: 0.00984 +2026-04-10 12:20:10.434727: train_loss -0.0225 +2026-04-10 12:20:10.439706: val_loss -0.0146 +2026-04-10 12:20:10.441446: Pseudo dice [0.0896, 0.3082, 0.1336, 0.0, 0.0, 0.4253, 0.3449] +2026-04-10 12:20:10.443085: Epoch time: 101.97 s +2026-04-10 12:20:10.444624: Yayy! New best EMA pseudo Dice: 0.1275 +2026-04-10 12:20:13.031347: +2026-04-10 12:20:13.033021: Epoch 74 +2026-04-10 12:20:13.034690: Current learning rate: 0.00983 +2026-04-10 12:21:54.863365: train_loss -0.0179 +2026-04-10 12:21:54.867964: val_loss -0.0245 +2026-04-10 12:21:54.869511: Pseudo dice [0.0227, 0.0525, 0.1073, 0.0, 0.0, 0.2852, 0.4227] +2026-04-10 12:21:54.870965: Epoch time: 101.84 s +2026-04-10 12:21:55.936622: +2026-04-10 12:21:55.938112: Epoch 75 +2026-04-10 12:21:55.939463: Current learning rate: 0.00983 +2026-04-10 12:23:37.803583: train_loss -0.0331 +2026-04-10 12:23:37.808260: val_loss -0.0384 +2026-04-10 12:23:37.809669: Pseudo dice [0.0317, 0.0494, 0.5978, 0.0, 0.0, 0.2624, 0.39] +2026-04-10 12:23:37.811584: Epoch time: 101.87 s +2026-04-10 12:23:37.812831: Yayy! New best EMA pseudo Dice: 0.1337 +2026-04-10 12:23:40.451445: +2026-04-10 12:23:40.453086: Epoch 76 +2026-04-10 12:23:40.454569: Current learning rate: 0.00983 +2026-04-10 12:25:22.542912: train_loss -0.0372 +2026-04-10 12:25:22.547130: val_loss -0.0424 +2026-04-10 12:25:22.548744: Pseudo dice [0.128, 0.1411, 0.3049, 0.0, 0.0, 0.4355, 0.4305] +2026-04-10 12:25:22.550763: Epoch time: 102.09 s +2026-04-10 12:25:22.552385: Yayy! New best EMA pseudo Dice: 0.1409 +2026-04-10 12:25:25.236249: +2026-04-10 12:25:25.237776: Epoch 77 +2026-04-10 12:25:25.239183: Current learning rate: 0.00983 +2026-04-10 12:27:07.329604: train_loss -0.0451 +2026-04-10 12:27:07.333812: val_loss -0.0541 +2026-04-10 12:27:07.335251: Pseudo dice [0.1826, 0.2205, 0.1331, 0.0, 0.0, 0.2689, 0.4896] +2026-04-10 12:27:07.336819: Epoch time: 102.1 s +2026-04-10 12:27:07.338219: Yayy! New best EMA pseudo Dice: 0.1453 +2026-04-10 12:27:09.951797: +2026-04-10 12:27:09.953515: Epoch 78 +2026-04-10 12:27:09.954924: Current learning rate: 0.00982 +2026-04-10 12:28:52.100513: train_loss -0.0575 +2026-04-10 12:28:52.109893: val_loss -0.0411 +2026-04-10 12:28:52.111626: Pseudo dice [0.2131, 0.308, 0.4854, 0.0, 0.0, 0.3427, 0.4207] +2026-04-10 12:28:52.113559: Epoch time: 102.15 s +2026-04-10 12:28:52.114797: Yayy! New best EMA pseudo Dice: 0.1561 +2026-04-10 12:28:54.770854: +2026-04-10 12:28:54.772253: Epoch 79 +2026-04-10 12:28:54.773689: Current learning rate: 0.00982 +2026-04-10 12:30:36.695878: train_loss -0.0319 +2026-04-10 12:30:36.700274: val_loss -0.0469 +2026-04-10 12:30:36.702300: Pseudo dice [0.2941, 0.0513, 0.3283, 0.0, 0.0, 0.3493, 0.469] +2026-04-10 12:30:36.703935: Epoch time: 101.93 s +2026-04-10 12:30:36.705353: Yayy! New best EMA pseudo Dice: 0.1618 +2026-04-10 12:30:39.360528: +2026-04-10 12:30:39.362164: Epoch 80 +2026-04-10 12:30:39.363619: Current learning rate: 0.00982 +2026-04-10 12:32:21.592603: train_loss -0.0331 +2026-04-10 12:32:21.597172: val_loss -0.0287 +2026-04-10 12:32:21.598792: Pseudo dice [0.1336, 0.1729, 0.4552, 0.0, 0.0, 0.2883, 0.3318] +2026-04-10 12:32:21.600144: Epoch time: 102.24 s +2026-04-10 12:32:21.601587: Yayy! New best EMA pseudo Dice: 0.1654 +2026-04-10 12:32:24.222284: +2026-04-10 12:32:24.224036: Epoch 81 +2026-04-10 12:32:24.225752: Current learning rate: 0.00982 +2026-04-10 12:34:06.218424: train_loss -0.0448 +2026-04-10 12:34:06.223774: val_loss -0.0592 +2026-04-10 12:34:06.225247: Pseudo dice [0.4654, 0.0912, 0.5844, 0.0, 0.0, 0.2449, 0.4804] +2026-04-10 12:34:06.226792: Epoch time: 102.0 s +2026-04-10 12:34:06.228637: Yayy! New best EMA pseudo Dice: 0.1755 +2026-04-10 12:34:08.789291: +2026-04-10 12:34:08.790757: Epoch 82 +2026-04-10 12:34:08.792146: Current learning rate: 0.00982 +2026-04-10 12:35:50.807771: train_loss -0.0407 +2026-04-10 12:35:50.813030: val_loss -0.0414 +2026-04-10 12:35:50.814687: Pseudo dice [0.0131, 0.091, 0.3231, 0.0, 0.0, 0.2291, 0.4881] +2026-04-10 12:35:50.816520: Epoch time: 102.02 s +2026-04-10 12:35:51.809863: +2026-04-10 12:35:51.811227: Epoch 83 +2026-04-10 12:35:51.814742: Current learning rate: 0.00981 +2026-04-10 12:37:33.749099: train_loss -0.0433 +2026-04-10 12:37:33.753210: val_loss -0.0566 +2026-04-10 12:37:33.754561: Pseudo dice [0.2592, 0.3557, 0.2999, 0.0, 0.0, 0.1924, 0.5404] +2026-04-10 12:37:33.756007: Epoch time: 101.94 s +2026-04-10 12:37:33.757452: Yayy! New best EMA pseudo Dice: 0.1804 +2026-04-10 12:37:36.278019: +2026-04-10 12:37:36.279637: Epoch 84 +2026-04-10 12:37:36.280996: Current learning rate: 0.00981 +2026-04-10 12:39:18.434415: train_loss -0.0432 +2026-04-10 12:39:18.440633: val_loss -0.0463 +2026-04-10 12:39:18.442296: Pseudo dice [0.1692, 0.0895, 0.2493, 0.0, 0.0, 0.1462, 0.4704] +2026-04-10 12:39:18.443809: Epoch time: 102.16 s +2026-04-10 12:39:19.443490: +2026-04-10 12:39:19.445036: Epoch 85 +2026-04-10 12:39:19.446574: Current learning rate: 0.00981 +2026-04-10 12:41:01.361579: train_loss -0.0537 +2026-04-10 12:41:01.365836: val_loss -0.0377 +2026-04-10 12:41:01.368130: Pseudo dice [0.0736, 0.2193, 0.1159, 0.0, 0.0, 0.343, 0.3789] +2026-04-10 12:41:01.369591: Epoch time: 101.92 s +2026-04-10 12:41:02.354749: +2026-04-10 12:41:02.356354: Epoch 86 +2026-04-10 12:41:02.358055: Current learning rate: 0.00981 +2026-04-10 12:42:44.185539: train_loss -0.0533 +2026-04-10 12:42:44.189591: val_loss -0.0472 +2026-04-10 12:42:44.190828: Pseudo dice [0.0842, 0.2916, 0.499, 0.0, 0.0, 0.2687, 0.3214] +2026-04-10 12:42:44.192011: Epoch time: 101.83 s +2026-04-10 12:42:45.169473: +2026-04-10 12:42:45.170679: Epoch 87 +2026-04-10 12:42:45.171880: Current learning rate: 0.0098 +2026-04-10 12:44:27.138687: train_loss -0.0456 +2026-04-10 12:44:27.143001: val_loss -0.0467 +2026-04-10 12:44:27.144799: Pseudo dice [0.2283, 0.2608, 0.4651, 0.0, 0.0, 0.1199, 0.4874] +2026-04-10 12:44:27.146620: Epoch time: 101.97 s +2026-04-10 12:44:27.148085: Yayy! New best EMA pseudo Dice: 0.1843 +2026-04-10 12:44:29.765816: +2026-04-10 12:44:29.767470: Epoch 88 +2026-04-10 12:44:29.768685: Current learning rate: 0.0098 +2026-04-10 12:46:11.825037: train_loss -0.0588 +2026-04-10 12:46:11.830195: val_loss -0.0723 +2026-04-10 12:46:11.831829: Pseudo dice [0.4701, 0.1572, 0.2979, 0.0, 0.0, 0.53, 0.45] +2026-04-10 12:46:11.833486: Epoch time: 102.06 s +2026-04-10 12:46:11.834786: Yayy! New best EMA pseudo Dice: 0.1931 +2026-04-10 12:46:14.469585: +2026-04-10 12:46:14.471240: Epoch 89 +2026-04-10 12:46:14.472913: Current learning rate: 0.0098 +2026-04-10 12:47:57.005934: train_loss -0.0512 +2026-04-10 12:47:57.010565: val_loss -0.0578 +2026-04-10 12:47:57.012110: Pseudo dice [0.2148, 0.1204, 0.7116, 0.0, 0.0, 0.4557, 0.2375] +2026-04-10 12:47:57.013709: Epoch time: 102.54 s +2026-04-10 12:47:57.015229: Yayy! New best EMA pseudo Dice: 0.1986 +2026-04-10 12:47:59.585021: +2026-04-10 12:47:59.586485: Epoch 90 +2026-04-10 12:47:59.587868: Current learning rate: 0.0098 +2026-04-10 12:49:42.817132: train_loss -0.0568 +2026-04-10 12:49:42.822669: val_loss -0.029 +2026-04-10 12:49:42.825303: Pseudo dice [0.1429, 0.2655, 0.2647, 0.0, 0.0, 0.2749, 0.1502] +2026-04-10 12:49:42.827634: Epoch time: 103.24 s +2026-04-10 12:49:43.830947: +2026-04-10 12:49:43.832410: Epoch 91 +2026-04-10 12:49:43.833976: Current learning rate: 0.0098 +2026-04-10 12:51:26.009426: train_loss -0.0576 +2026-04-10 12:51:26.013600: val_loss -0.0452 +2026-04-10 12:51:26.015026: Pseudo dice [0.3981, 0.0308, 0.2904, 0.0, 0.0, 0.5622, 0.2923] +2026-04-10 12:51:26.016306: Epoch time: 102.18 s +2026-04-10 12:51:27.018257: +2026-04-10 12:51:27.020113: Epoch 92 +2026-04-10 12:51:27.021950: Current learning rate: 0.00979 +2026-04-10 12:53:09.114857: train_loss -0.0433 +2026-04-10 12:53:09.120513: val_loss -0.0585 +2026-04-10 12:53:09.122028: Pseudo dice [0.4089, 0.0611, 0.1386, 0.0, 0.0, 0.4608, 0.2507] +2026-04-10 12:53:09.123918: Epoch time: 102.1 s +2026-04-10 12:53:10.114054: +2026-04-10 12:53:10.115590: Epoch 93 +2026-04-10 12:53:10.116914: Current learning rate: 0.00979 +2026-04-10 12:54:52.063207: train_loss -0.0568 +2026-04-10 12:54:52.067844: val_loss -0.0649 +2026-04-10 12:54:52.069531: Pseudo dice [0.3287, 0.2064, 0.5896, 0.0, 0.0, 0.3381, 0.3913] +2026-04-10 12:54:52.071032: Epoch time: 101.95 s +2026-04-10 12:54:52.072383: Yayy! New best EMA pseudo Dice: 0.2034 +2026-04-10 12:54:54.622202: +2026-04-10 12:54:54.623950: Epoch 94 +2026-04-10 12:54:54.625337: Current learning rate: 0.00979 +2026-04-10 12:56:36.725562: train_loss -0.0513 +2026-04-10 12:56:36.729386: val_loss -0.042 +2026-04-10 12:56:36.730857: Pseudo dice [0.0793, 0.3079, 0.5557, 0.0, 0.0, 0.4174, 0.5408] +2026-04-10 12:56:36.732084: Epoch time: 102.11 s +2026-04-10 12:56:36.733273: Yayy! New best EMA pseudo Dice: 0.2102 +2026-04-10 12:56:39.218859: +2026-04-10 12:56:39.220447: Epoch 95 +2026-04-10 12:56:39.221797: Current learning rate: 0.00979 +2026-04-10 12:58:21.176702: train_loss -0.0615 +2026-04-10 12:58:21.182168: val_loss -0.0536 +2026-04-10 12:58:21.183907: Pseudo dice [0.19, 0.4011, 0.3413, 0.0, 0.0, 0.2772, 0.3176] +2026-04-10 12:58:21.185449: Epoch time: 101.96 s +2026-04-10 12:58:21.186997: Yayy! New best EMA pseudo Dice: 0.211 +2026-04-10 12:58:23.739493: +2026-04-10 12:58:23.741203: Epoch 96 +2026-04-10 12:58:23.742580: Current learning rate: 0.00978 +2026-04-10 13:00:05.724107: train_loss -0.0512 +2026-04-10 13:00:05.728160: val_loss -0.0499 +2026-04-10 13:00:05.729348: Pseudo dice [0.1501, 0.1137, 0.4983, 0.0, 0.0, 0.275, 0.6462] +2026-04-10 13:00:05.731074: Epoch time: 101.99 s +2026-04-10 13:00:05.732370: Yayy! New best EMA pseudo Dice: 0.214 +2026-04-10 13:00:08.249646: +2026-04-10 13:00:08.251113: Epoch 97 +2026-04-10 13:00:08.252411: Current learning rate: 0.00978 +2026-04-10 13:01:50.267607: train_loss -0.0779 +2026-04-10 13:01:50.272048: val_loss -0.0704 +2026-04-10 13:01:50.273481: Pseudo dice [0.1852, 0.3077, 0.4085, 0.0, 0.0, 0.4626, 0.2719] +2026-04-10 13:01:50.274912: Epoch time: 102.02 s +2026-04-10 13:01:50.276144: Yayy! New best EMA pseudo Dice: 0.216 +2026-04-10 13:01:52.843064: +2026-04-10 13:01:52.844947: Epoch 98 +2026-04-10 13:01:52.846399: Current learning rate: 0.00978 +2026-04-10 13:03:34.859153: train_loss -0.0621 +2026-04-10 13:03:34.863501: val_loss -0.0575 +2026-04-10 13:03:34.864908: Pseudo dice [0.428, 0.1079, 0.5342, 0.0, 0.0, 0.5052, 0.4899] +2026-04-10 13:03:34.866188: Epoch time: 102.02 s +2026-04-10 13:03:34.867754: Yayy! New best EMA pseudo Dice: 0.2239 +2026-04-10 13:03:37.503118: +2026-04-10 13:03:37.504688: Epoch 99 +2026-04-10 13:03:37.506182: Current learning rate: 0.00978 +2026-04-10 13:05:19.601013: train_loss -0.0514 +2026-04-10 13:05:19.605299: val_loss -0.0734 +2026-04-10 13:05:19.607098: Pseudo dice [0.2755, 0.3754, 0.4752, 0.0, 0.0, 0.4815, 0.5607] +2026-04-10 13:05:19.608637: Epoch time: 102.1 s +2026-04-10 13:05:21.165416: Yayy! New best EMA pseudo Dice: 0.2325 +2026-04-10 13:05:23.731493: +2026-04-10 13:05:23.732965: Epoch 100 +2026-04-10 13:05:23.734344: Current learning rate: 0.00977 +2026-04-10 13:07:05.769871: train_loss -0.0767 +2026-04-10 13:07:05.775395: val_loss -0.0493 +2026-04-10 13:07:05.779101: Pseudo dice [0.3651, 0.052, 0.0925, 0.0, 0.0, 0.5508, 0.5993] +2026-04-10 13:07:05.781697: Epoch time: 102.04 s +2026-04-10 13:07:05.783763: Yayy! New best EMA pseudo Dice: 0.2329 +2026-04-10 13:07:08.349584: +2026-04-10 13:07:08.351020: Epoch 101 +2026-04-10 13:07:08.352393: Current learning rate: 0.00977 +2026-04-10 13:08:50.440107: train_loss -0.0705 +2026-04-10 13:08:50.443700: val_loss -0.0362 +2026-04-10 13:08:50.445167: Pseudo dice [0.2629, 0.267, 0.3572, 0.0, 0.0, 0.3405, 0.52] +2026-04-10 13:08:50.446221: Epoch time: 102.09 s +2026-04-10 13:08:50.447905: Yayy! New best EMA pseudo Dice: 0.2346 +2026-04-10 13:08:53.033032: +2026-04-10 13:08:53.034410: Epoch 102 +2026-04-10 13:08:53.035995: Current learning rate: 0.00977 +2026-04-10 13:10:35.011653: train_loss -0.0677 +2026-04-10 13:10:35.015828: val_loss -0.0798 +2026-04-10 13:10:35.017347: Pseudo dice [0.1733, 0.1061, 0.6941, 0.0, 0.0, 0.7165, 0.635] +2026-04-10 13:10:35.018923: Epoch time: 101.98 s +2026-04-10 13:10:35.020430: Yayy! New best EMA pseudo Dice: 0.2443 +2026-04-10 13:10:37.607136: +2026-04-10 13:10:37.608873: Epoch 103 +2026-04-10 13:10:37.610152: Current learning rate: 0.00977 +2026-04-10 13:12:19.576149: train_loss -0.0718 +2026-04-10 13:12:19.580263: val_loss -0.0511 +2026-04-10 13:12:19.581721: Pseudo dice [0.2917, 0.2385, 0.2782, 0.0, 0.0, 0.4846, 0.2816] +2026-04-10 13:12:19.583359: Epoch time: 101.97 s +2026-04-10 13:12:20.597256: +2026-04-10 13:12:20.598742: Epoch 104 +2026-04-10 13:12:20.600156: Current learning rate: 0.00977 +2026-04-10 13:14:02.538719: train_loss -0.0521 +2026-04-10 13:14:02.544105: val_loss -0.0521 +2026-04-10 13:14:02.545730: Pseudo dice [0.1545, 0.0182, 0.5892, 0.0, 0.0, 0.5088, 0.2201] +2026-04-10 13:14:02.547397: Epoch time: 101.94 s +2026-04-10 13:14:03.558465: +2026-04-10 13:14:03.562957: Epoch 105 +2026-04-10 13:14:03.564539: Current learning rate: 0.00976 +2026-04-10 13:15:45.445110: train_loss -0.0576 +2026-04-10 13:15:45.450501: val_loss -0.0673 +2026-04-10 13:15:45.452228: Pseudo dice [0.3843, 0.1263, 0.458, 0.0, 0.0, 0.5205, 0.4927] +2026-04-10 13:15:45.454038: Epoch time: 101.89 s +2026-04-10 13:15:46.482695: +2026-04-10 13:15:46.484404: Epoch 106 +2026-04-10 13:15:46.485738: Current learning rate: 0.00976 +2026-04-10 13:17:28.384295: train_loss -0.0751 +2026-04-10 13:17:28.388013: val_loss -0.0809 +2026-04-10 13:17:28.389380: Pseudo dice [0.1668, 0.0181, 0.4638, 0.0, 0.0, 0.4766, 0.7302] +2026-04-10 13:17:28.390883: Epoch time: 101.9 s +2026-04-10 13:17:28.392206: Yayy! New best EMA pseudo Dice: 0.246 +2026-04-10 13:17:30.935813: +2026-04-10 13:17:30.937017: Epoch 107 +2026-04-10 13:17:30.938324: Current learning rate: 0.00976 +2026-04-10 13:19:12.924141: train_loss -0.0642 +2026-04-10 13:19:12.928265: val_loss -0.074 +2026-04-10 13:19:12.929564: Pseudo dice [0.4768, 0.2155, 0.4043, 0.0, 0.0, 0.5635, 0.5889] +2026-04-10 13:19:12.930813: Epoch time: 101.99 s +2026-04-10 13:19:12.932321: Yayy! New best EMA pseudo Dice: 0.2535 +2026-04-10 13:19:16.387303: +2026-04-10 13:19:16.389123: Epoch 108 +2026-04-10 13:19:16.390482: Current learning rate: 0.00976 +2026-04-10 13:20:58.269741: train_loss -0.0482 +2026-04-10 13:20:58.274714: val_loss -0.077 +2026-04-10 13:20:58.276246: Pseudo dice [0.1575, 0.2567, 0.5499, 0.0, 0.0, 0.4778, 0.4975] +2026-04-10 13:20:58.277885: Epoch time: 101.89 s +2026-04-10 13:20:58.279464: Yayy! New best EMA pseudo Dice: 0.2558 +2026-04-10 13:21:00.906738: +2026-04-10 13:21:00.908280: Epoch 109 +2026-04-10 13:21:00.909718: Current learning rate: 0.00975 +2026-04-10 13:22:42.951226: train_loss -0.0552 +2026-04-10 13:22:42.955432: val_loss -0.077 +2026-04-10 13:22:42.957113: Pseudo dice [0.1736, 0.3561, 0.3233, 0.0, 0.0, 0.3059, 0.6429] +2026-04-10 13:22:42.958557: Epoch time: 102.05 s +2026-04-10 13:22:42.959885: Yayy! New best EMA pseudo Dice: 0.256 +2026-04-10 13:22:45.551570: +2026-04-10 13:22:45.553054: Epoch 110 +2026-04-10 13:22:45.554463: Current learning rate: 0.00975 +2026-04-10 13:24:27.432087: train_loss -0.0772 +2026-04-10 13:24:27.435974: val_loss -0.083 +2026-04-10 13:24:27.437481: Pseudo dice [0.2115, 0.1754, 0.6433, 0.0, 0.0, 0.4363, 0.5055] +2026-04-10 13:24:27.438804: Epoch time: 101.88 s +2026-04-10 13:24:27.440012: Yayy! New best EMA pseudo Dice: 0.2586 +2026-04-10 13:24:29.988075: +2026-04-10 13:24:29.989474: Epoch 111 +2026-04-10 13:24:29.990839: Current learning rate: 0.00975 +2026-04-10 13:26:12.001111: train_loss -0.0669 +2026-04-10 13:26:12.005311: val_loss -0.0693 +2026-04-10 13:26:12.006807: Pseudo dice [0.2385, 0.0903, 0.5838, 0.0, 0.0, 0.3828, 0.3263] +2026-04-10 13:26:12.008009: Epoch time: 102.02 s +2026-04-10 13:26:13.016842: +2026-04-10 13:26:13.018506: Epoch 112 +2026-04-10 13:26:13.019670: Current learning rate: 0.00975 +2026-04-10 13:27:55.005098: train_loss -0.0729 +2026-04-10 13:27:55.027518: val_loss -0.0434 +2026-04-10 13:27:55.029181: Pseudo dice [0.4038, 0.1696, 0.3771, 0.0, 0.0, 0.1943, 0.4252] +2026-04-10 13:27:55.030889: Epoch time: 101.99 s +2026-04-10 13:27:56.060193: +2026-04-10 13:27:56.062132: Epoch 113 +2026-04-10 13:27:56.063626: Current learning rate: 0.00975 +2026-04-10 13:29:38.235307: train_loss -0.0637 +2026-04-10 13:29:38.239634: val_loss -0.0858 +2026-04-10 13:29:38.241100: Pseudo dice [0.2935, 0.2423, 0.5874, 0.0, 0.0, 0.448, 0.4611] +2026-04-10 13:29:38.242469: Epoch time: 102.18 s +2026-04-10 13:29:39.282826: +2026-04-10 13:29:39.284388: Epoch 114 +2026-04-10 13:29:39.286176: Current learning rate: 0.00974 +2026-04-10 13:31:21.247312: train_loss -0.0855 +2026-04-10 13:31:21.252152: val_loss -0.0866 +2026-04-10 13:31:21.253799: Pseudo dice [0.2565, 0.1484, 0.6308, 0.0, 0.0, 0.606, 0.5303] +2026-04-10 13:31:21.255064: Epoch time: 101.97 s +2026-04-10 13:31:21.256482: Yayy! New best EMA pseudo Dice: 0.2619 +2026-04-10 13:31:23.879764: +2026-04-10 13:31:23.884694: Epoch 115 +2026-04-10 13:31:23.886410: Current learning rate: 0.00974 +2026-04-10 13:33:05.776326: train_loss -0.0926 +2026-04-10 13:33:05.780431: val_loss -0.093 +2026-04-10 13:33:05.781608: Pseudo dice [0.4428, 0.3502, 0.288, 0.0, 0.0, 0.3374, 0.6818] +2026-04-10 13:33:05.783190: Epoch time: 101.9 s +2026-04-10 13:33:05.784328: Yayy! New best EMA pseudo Dice: 0.2657 +2026-04-10 13:33:08.432487: +2026-04-10 13:33:08.434700: Epoch 116 +2026-04-10 13:33:08.436258: Current learning rate: 0.00974 +2026-04-10 13:34:50.499521: train_loss -0.0791 +2026-04-10 13:34:50.504387: val_loss -0.0776 +2026-04-10 13:34:50.506036: Pseudo dice [0.3522, 0.0204, 0.2875, 0.0, 0.0, 0.5847, 0.6238] +2026-04-10 13:34:50.507622: Epoch time: 102.07 s +2026-04-10 13:34:50.509567: Yayy! New best EMA pseudo Dice: 0.2658 +2026-04-10 13:34:53.060299: +2026-04-10 13:34:53.061687: Epoch 117 +2026-04-10 13:34:53.062908: Current learning rate: 0.00974 +2026-04-10 13:36:35.090373: train_loss -0.0688 +2026-04-10 13:36:35.094750: val_loss -0.0741 +2026-04-10 13:36:35.101303: Pseudo dice [0.2538, 0.291, 0.654, 0.0, 0.0, 0.5416, 0.6347] +2026-04-10 13:36:35.111207: Epoch time: 102.03 s +2026-04-10 13:36:35.115181: Yayy! New best EMA pseudo Dice: 0.2732 +2026-04-10 13:36:37.699993: +2026-04-10 13:36:37.701468: Epoch 118 +2026-04-10 13:36:37.703004: Current learning rate: 0.00973 +2026-04-10 13:38:19.689844: train_loss -0.0868 +2026-04-10 13:38:19.693843: val_loss -0.0927 +2026-04-10 13:38:19.695382: Pseudo dice [0.2675, 0.2303, 0.3116, 0.0, 0.0, 0.6271, 0.7516] +2026-04-10 13:38:19.696769: Epoch time: 101.99 s +2026-04-10 13:38:19.698140: Yayy! New best EMA pseudo Dice: 0.2771 +2026-04-10 13:38:22.281100: +2026-04-10 13:38:22.282469: Epoch 119 +2026-04-10 13:38:22.283668: Current learning rate: 0.00973 +2026-04-10 13:40:04.470387: train_loss -0.0895 +2026-04-10 13:40:04.475014: val_loss -0.0679 +2026-04-10 13:40:04.476593: Pseudo dice [0.2658, 0.2709, 0.5096, 0.0, 0.0, 0.2768, 0.3041] +2026-04-10 13:40:04.478275: Epoch time: 102.19 s +2026-04-10 13:40:05.512790: +2026-04-10 13:40:05.514416: Epoch 120 +2026-04-10 13:40:05.516323: Current learning rate: 0.00973 +2026-04-10 13:41:47.542964: train_loss -0.095 +2026-04-10 13:41:47.546895: val_loss -0.0825 +2026-04-10 13:41:47.548365: Pseudo dice [0.2441, 0.2799, 0.695, 0.0, 0.0, 0.609, 0.401] +2026-04-10 13:41:47.549547: Epoch time: 102.03 s +2026-04-10 13:41:47.550744: Yayy! New best EMA pseudo Dice: 0.2772 +2026-04-10 13:41:50.136047: +2026-04-10 13:41:50.137557: Epoch 121 +2026-04-10 13:41:50.139041: Current learning rate: 0.00973 +2026-04-10 13:43:32.018691: train_loss -0.0875 +2026-04-10 13:43:32.023644: val_loss -0.0927 +2026-04-10 13:43:32.024916: Pseudo dice [0.3297, 0.114, 0.4537, 0.0, 0.0, 0.4981, 0.7247] +2026-04-10 13:43:32.026720: Epoch time: 101.89 s +2026-04-10 13:43:32.028511: Yayy! New best EMA pseudo Dice: 0.2798 +2026-04-10 13:43:34.613379: +2026-04-10 13:43:34.614946: Epoch 122 +2026-04-10 13:43:34.616656: Current learning rate: 0.00973 +2026-04-10 13:45:16.618021: train_loss -0.0775 +2026-04-10 13:45:16.622894: val_loss -0.0461 +2026-04-10 13:45:16.624472: Pseudo dice [0.3408, 0.2518, 0.2499, 0.0, 0.0, 0.4193, 0.3381] +2026-04-10 13:45:16.626351: Epoch time: 102.01 s +2026-04-10 13:45:17.684624: +2026-04-10 13:45:17.686197: Epoch 123 +2026-04-10 13:45:17.687793: Current learning rate: 0.00972 +2026-04-10 13:46:59.562790: train_loss -0.0831 +2026-04-10 13:46:59.567342: val_loss -0.0486 +2026-04-10 13:46:59.569002: Pseudo dice [0.3283, 0.1, 0.5125, 0.0, 0.0, 0.4639, 0.4794] +2026-04-10 13:46:59.570539: Epoch time: 101.88 s +2026-04-10 13:47:00.620239: +2026-04-10 13:47:00.626070: Epoch 124 +2026-04-10 13:47:00.627509: Current learning rate: 0.00972 +2026-04-10 13:48:42.479889: train_loss -0.0872 +2026-04-10 13:48:42.483986: val_loss -0.0852 +2026-04-10 13:48:42.485361: Pseudo dice [0.5568, 0.2942, 0.7615, 0.0, 0.0, 0.3089, 0.5787] +2026-04-10 13:48:42.486609: Epoch time: 101.86 s +2026-04-10 13:48:42.488075: Yayy! New best EMA pseudo Dice: 0.2824 +2026-04-10 13:48:45.906442: +2026-04-10 13:48:45.907776: Epoch 125 +2026-04-10 13:48:45.909084: Current learning rate: 0.00972 +2026-04-10 13:50:27.930654: train_loss -0.0904 +2026-04-10 13:50:27.934702: val_loss -0.0905 +2026-04-10 13:50:27.936173: Pseudo dice [0.2087, 0.2448, 0.5815, 0.0, 0.0, 0.341, 0.6333] +2026-04-10 13:50:27.937666: Epoch time: 102.03 s +2026-04-10 13:50:27.938813: Yayy! New best EMA pseudo Dice: 0.2829 +2026-04-10 13:50:30.508754: +2026-04-10 13:50:30.510392: Epoch 126 +2026-04-10 13:50:30.511632: Current learning rate: 0.00972 +2026-04-10 13:52:12.354846: train_loss -0.0988 +2026-04-10 13:52:12.363558: val_loss -0.0713 +2026-04-10 13:52:12.365471: Pseudo dice [0.1565, 0.4383, 0.6152, 0.0, 0.0, 0.7069, 0.7198] +2026-04-10 13:52:12.367190: Epoch time: 101.85 s +2026-04-10 13:52:12.369512: Yayy! New best EMA pseudo Dice: 0.2923 +2026-04-10 13:52:14.990973: +2026-04-10 13:52:14.992634: Epoch 127 +2026-04-10 13:52:14.994255: Current learning rate: 0.00971 +2026-04-10 13:53:56.906949: train_loss -0.0781 +2026-04-10 13:53:56.910863: val_loss -0.0734 +2026-04-10 13:53:56.912700: Pseudo dice [0.4734, 0.2763, 0.6355, 0.0, 0.0, 0.5722, 0.1544] +2026-04-10 13:53:56.914116: Epoch time: 101.92 s +2026-04-10 13:53:56.915351: Yayy! New best EMA pseudo Dice: 0.2932 +2026-04-10 13:53:59.535419: +2026-04-10 13:53:59.536874: Epoch 128 +2026-04-10 13:53:59.538269: Current learning rate: 0.00971 +2026-04-10 13:55:41.394416: train_loss -0.0921 +2026-04-10 13:55:41.399323: val_loss -0.0662 +2026-04-10 13:55:41.401039: Pseudo dice [0.5916, 0.3757, 0.332, 0.0, 0.0, 0.4239, 0.294] +2026-04-10 13:55:41.402725: Epoch time: 101.86 s +2026-04-10 13:55:42.456686: +2026-04-10 13:55:42.458412: Epoch 129 +2026-04-10 13:55:42.459912: Current learning rate: 0.00971 +2026-04-10 13:57:24.314612: train_loss -0.0982 +2026-04-10 13:57:24.319214: val_loss -0.0848 +2026-04-10 13:57:24.320947: Pseudo dice [0.1788, 0.5868, 0.5318, 0.0, 0.001, 0.4888, 0.2199] +2026-04-10 13:57:24.322612: Epoch time: 101.86 s +2026-04-10 13:57:25.364463: +2026-04-10 13:57:25.366119: Epoch 130 +2026-04-10 13:57:25.367324: Current learning rate: 0.00971 +2026-04-10 13:59:07.389782: train_loss -0.0874 +2026-04-10 13:59:07.394923: val_loss -0.0916 +2026-04-10 13:59:07.396245: Pseudo dice [0.2398, 0.4099, 0.6109, 0.0, 0.0002, 0.4341, 0.6202] +2026-04-10 13:59:07.397876: Epoch time: 102.03 s +2026-04-10 13:59:07.399058: Yayy! New best EMA pseudo Dice: 0.296 +2026-04-10 13:59:09.970118: +2026-04-10 13:59:09.971680: Epoch 131 +2026-04-10 13:59:09.973062: Current learning rate: 0.0097 +2026-04-10 14:00:51.763414: train_loss -0.088 +2026-04-10 14:00:51.768504: val_loss -0.095 +2026-04-10 14:00:51.769971: Pseudo dice [0.2727, 0.1463, 0.4542, 0.0, 0.1173, 0.5304, 0.4209] +2026-04-10 14:00:51.771311: Epoch time: 101.8 s +2026-04-10 14:00:52.808754: +2026-04-10 14:00:52.810387: Epoch 132 +2026-04-10 14:00:52.812053: Current learning rate: 0.0097 +2026-04-10 14:02:34.789701: train_loss -0.0949 +2026-04-10 14:02:34.794403: val_loss -0.0733 +2026-04-10 14:02:34.796032: Pseudo dice [0.3143, 0.0771, 0.2843, 0.0, 0.0715, 0.4779, 0.5681] +2026-04-10 14:02:34.797503: Epoch time: 101.98 s +2026-04-10 14:02:35.849831: +2026-04-10 14:02:35.851726: Epoch 133 +2026-04-10 14:02:35.853187: Current learning rate: 0.0097 +2026-04-10 14:04:17.776643: train_loss -0.0931 +2026-04-10 14:04:17.781342: val_loss -0.1001 +2026-04-10 14:04:17.783356: Pseudo dice [0.5195, 0.4088, 0.6045, 0.0, 0.0928, 0.4315, 0.5888] +2026-04-10 14:04:17.785109: Epoch time: 101.93 s +2026-04-10 14:04:17.787239: Yayy! New best EMA pseudo Dice: 0.2991 +2026-04-10 14:04:20.364405: +2026-04-10 14:04:20.365910: Epoch 134 +2026-04-10 14:04:20.367241: Current learning rate: 0.0097 +2026-04-10 14:06:02.243988: train_loss -0.1034 +2026-04-10 14:06:02.248783: val_loss -0.1098 +2026-04-10 14:06:02.250728: Pseudo dice [0.3075, 0.1712, 0.6323, 0.0, 0.0486, 0.6262, 0.7406] +2026-04-10 14:06:02.252288: Epoch time: 101.88 s +2026-04-10 14:06:02.254323: Yayy! New best EMA pseudo Dice: 0.3053 +2026-04-10 14:06:04.874262: +2026-04-10 14:06:04.875814: Epoch 135 +2026-04-10 14:06:04.877242: Current learning rate: 0.0097 +2026-04-10 14:07:47.039247: train_loss -0.0937 +2026-04-10 14:07:47.051605: val_loss -0.0836 +2026-04-10 14:07:47.053360: Pseudo dice [0.545, 0.0653, 0.5464, 0.0, 0.0062, 0.2461, 0.5168] +2026-04-10 14:07:47.054833: Epoch time: 102.17 s +2026-04-10 14:07:48.107556: +2026-04-10 14:07:48.109164: Epoch 136 +2026-04-10 14:07:48.110695: Current learning rate: 0.00969 +2026-04-10 14:09:30.068239: train_loss -0.0983 +2026-04-10 14:09:30.072386: val_loss -0.0801 +2026-04-10 14:09:30.073848: Pseudo dice [0.1054, 0.2312, 0.1857, 0.0, 0.0408, 0.4981, 0.7199] +2026-04-10 14:09:30.075084: Epoch time: 101.96 s +2026-04-10 14:09:31.146257: +2026-04-10 14:09:31.147575: Epoch 137 +2026-04-10 14:09:31.148788: Current learning rate: 0.00969 +2026-04-10 14:11:13.067484: train_loss -0.0955 +2026-04-10 14:11:13.072644: val_loss -0.1058 +2026-04-10 14:11:13.074583: Pseudo dice [0.3702, 0.1261, 0.2882, 0.0, 0.2135, 0.491, 0.4641] +2026-04-10 14:11:13.076160: Epoch time: 101.92 s +2026-04-10 14:11:14.146430: +2026-04-10 14:11:14.148223: Epoch 138 +2026-04-10 14:11:14.149908: Current learning rate: 0.00969 +2026-04-10 14:12:56.085945: train_loss -0.089 +2026-04-10 14:12:56.089597: val_loss -0.0831 +2026-04-10 14:12:56.090980: Pseudo dice [0.6295, 0.33, 0.4592, 0.0, 0.1375, 0.2663, 0.3823] +2026-04-10 14:12:56.093163: Epoch time: 101.94 s +2026-04-10 14:12:57.140878: +2026-04-10 14:12:57.142599: Epoch 139 +2026-04-10 14:12:57.143949: Current learning rate: 0.00969 +2026-04-10 14:14:39.129933: train_loss -0.1015 +2026-04-10 14:14:39.134836: val_loss -0.092 +2026-04-10 14:14:39.136717: Pseudo dice [0.497, 0.1002, 0.5886, 0.0, 0.1992, 0.3679, 0.6411] +2026-04-10 14:14:39.138183: Epoch time: 101.99 s +2026-04-10 14:14:40.205357: +2026-04-10 14:14:40.207080: Epoch 140 +2026-04-10 14:14:40.208425: Current learning rate: 0.00968 +2026-04-10 14:16:21.999686: train_loss -0.0957 +2026-04-10 14:16:22.004969: val_loss -0.0763 +2026-04-10 14:16:22.006793: Pseudo dice [0.2324, 0.0596, 0.3152, 0.0, 0.08, 0.3819, 0.4467] +2026-04-10 14:16:22.008156: Epoch time: 101.8 s +2026-04-10 14:16:23.081307: +2026-04-10 14:16:23.082743: Epoch 141 +2026-04-10 14:16:23.083984: Current learning rate: 0.00968 +2026-04-10 14:18:05.070268: train_loss -0.0975 +2026-04-10 14:18:05.075242: val_loss -0.0699 +2026-04-10 14:18:05.077595: Pseudo dice [0.1612, 0.3023, 0.3647, 0.0, 0.2383, 0.387, 0.5842] +2026-04-10 14:18:05.079175: Epoch time: 101.99 s +2026-04-10 14:18:06.143356: +2026-04-10 14:18:06.144716: Epoch 142 +2026-04-10 14:18:06.145951: Current learning rate: 0.00968 +2026-04-10 14:19:48.050067: train_loss -0.106 +2026-04-10 14:19:48.054819: val_loss -0.0914 +2026-04-10 14:19:48.056602: Pseudo dice [0.2155, 0.0691, 0.74, 0.0, 0.1935, 0.5496, 0.404] +2026-04-10 14:19:48.058085: Epoch time: 101.91 s +2026-04-10 14:19:50.190337: +2026-04-10 14:19:50.191885: Epoch 143 +2026-04-10 14:19:50.193189: Current learning rate: 0.00968 +2026-04-10 14:21:32.025757: train_loss -0.101 +2026-04-10 14:21:32.031067: val_loss -0.0909 +2026-04-10 14:21:32.032934: Pseudo dice [0.4807, 0.1414, 0.6411, 0.0, 0.2479, 0.5843, 0.6115] +2026-04-10 14:21:32.034480: Epoch time: 101.84 s +2026-04-10 14:21:33.095188: +2026-04-10 14:21:33.097087: Epoch 144 +2026-04-10 14:21:33.098457: Current learning rate: 0.00968 +2026-04-10 14:23:14.981219: train_loss -0.1022 +2026-04-10 14:23:14.986432: val_loss -0.1115 +2026-04-10 14:23:14.987915: Pseudo dice [0.4712, 0.0554, 0.7475, 0.0, 0.1895, 0.7723, 0.6227] +2026-04-10 14:23:14.989720: Epoch time: 101.89 s +2026-04-10 14:23:14.991200: Yayy! New best EMA pseudo Dice: 0.3145 +2026-04-10 14:23:17.584841: +2026-04-10 14:23:17.586403: Epoch 145 +2026-04-10 14:23:17.587966: Current learning rate: 0.00967 +2026-04-10 14:24:59.451246: train_loss -0.1019 +2026-04-10 14:24:59.457354: val_loss -0.075 +2026-04-10 14:24:59.459473: Pseudo dice [0.2831, 0.0603, 0.5771, 0.0, 0.2899, 0.211, 0.5742] +2026-04-10 14:24:59.461075: Epoch time: 101.87 s +2026-04-10 14:25:00.544558: +2026-04-10 14:25:00.546648: Epoch 146 +2026-04-10 14:25:00.548249: Current learning rate: 0.00967 +2026-04-10 14:26:42.551554: train_loss -0.1039 +2026-04-10 14:26:42.555915: val_loss -0.0775 +2026-04-10 14:26:42.557255: Pseudo dice [0.4003, 0.2479, 0.6368, 0.0, 0.2374, 0.1171, 0.597] +2026-04-10 14:26:42.558735: Epoch time: 102.01 s +2026-04-10 14:26:43.635516: +2026-04-10 14:26:43.636974: Epoch 147 +2026-04-10 14:26:43.638339: Current learning rate: 0.00967 +2026-04-10 14:28:25.543079: train_loss -0.1051 +2026-04-10 14:28:25.547640: val_loss -0.0923 +2026-04-10 14:28:25.549553: Pseudo dice [0.3051, 0.3372, 0.5217, 0.0, 0.1011, 0.3252, 0.685] +2026-04-10 14:28:25.551466: Epoch time: 101.91 s +2026-04-10 14:28:26.619829: +2026-04-10 14:28:26.621444: Epoch 148 +2026-04-10 14:28:26.622826: Current learning rate: 0.00967 +2026-04-10 14:30:08.695118: train_loss -0.1084 +2026-04-10 14:30:08.699050: val_loss -0.0914 +2026-04-10 14:30:08.700316: Pseudo dice [0.2098, 0.4618, 0.2044, 0.0, 0.1472, 0.6202, 0.562] +2026-04-10 14:30:08.702187: Epoch time: 102.08 s +2026-04-10 14:30:09.784062: +2026-04-10 14:30:09.785396: Epoch 149 +2026-04-10 14:30:09.786591: Current learning rate: 0.00966 +2026-04-10 14:31:51.753331: train_loss -0.1002 +2026-04-10 14:31:51.757729: val_loss -0.1131 +2026-04-10 14:31:51.759833: Pseudo dice [0.5746, 0.1163, 0.5677, 0.0, 0.198, 0.6493, 0.7034] +2026-04-10 14:31:51.761365: Epoch time: 101.97 s +2026-04-10 14:31:53.324911: Yayy! New best EMA pseudo Dice: 0.3225 +2026-04-10 14:31:55.911900: +2026-04-10 14:31:55.913481: Epoch 150 +2026-04-10 14:31:55.914866: Current learning rate: 0.00966 +2026-04-10 14:33:37.893056: train_loss -0.1142 +2026-04-10 14:33:37.897005: val_loss -0.0934 +2026-04-10 14:33:37.898476: Pseudo dice [0.2842, 0.3635, 0.6098, 0.0, 0.2732, 0.4408, 0.6268] +2026-04-10 14:33:37.899698: Epoch time: 101.98 s +2026-04-10 14:33:37.901083: Yayy! New best EMA pseudo Dice: 0.3274 +2026-04-10 14:33:40.482071: +2026-04-10 14:33:40.483569: Epoch 151 +2026-04-10 14:33:40.485054: Current learning rate: 0.00966 +2026-04-10 14:35:22.417910: train_loss -0.1059 +2026-04-10 14:35:22.421834: val_loss -0.0816 +2026-04-10 14:35:22.423305: Pseudo dice [0.2707, 0.0566, 0.5369, 0.0, 0.1102, 0.5789, 0.5522] +2026-04-10 14:35:22.428321: Epoch time: 101.94 s +2026-04-10 14:35:23.498880: +2026-04-10 14:35:23.500497: Epoch 152 +2026-04-10 14:35:23.501912: Current learning rate: 0.00966 +2026-04-10 14:37:05.442371: train_loss -0.1149 +2026-04-10 14:37:05.446943: val_loss -0.1105 +2026-04-10 14:37:05.448578: Pseudo dice [0.2612, 0.1864, 0.6702, 0.0, 0.3224, 0.7473, 0.5528] +2026-04-10 14:37:05.450249: Epoch time: 101.95 s +2026-04-10 14:37:05.451690: Yayy! New best EMA pseudo Dice: 0.3314 +2026-04-10 14:37:08.073257: +2026-04-10 14:37:08.076066: Epoch 153 +2026-04-10 14:37:08.077579: Current learning rate: 0.00966 +2026-04-10 14:38:50.086802: train_loss -0.1095 +2026-04-10 14:38:50.091312: val_loss -0.0921 +2026-04-10 14:38:50.092855: Pseudo dice [0.3957, 0.1051, 0.5807, 0.0, 0.1944, 0.5375, 0.6651] +2026-04-10 14:38:50.094506: Epoch time: 102.02 s +2026-04-10 14:38:50.096012: Yayy! New best EMA pseudo Dice: 0.3337 +2026-04-10 14:38:52.769155: +2026-04-10 14:38:52.770771: Epoch 154 +2026-04-10 14:38:52.772363: Current learning rate: 0.00965 +2026-04-10 14:40:34.729414: train_loss -0.1037 +2026-04-10 14:40:34.734014: val_loss -0.0887 +2026-04-10 14:40:34.735883: Pseudo dice [0.331, 0.2583, 0.5411, 0.0, 0.2053, 0.6129, 0.5931] +2026-04-10 14:40:34.737675: Epoch time: 101.96 s +2026-04-10 14:40:34.739250: Yayy! New best EMA pseudo Dice: 0.3366 +2026-04-10 14:40:37.331588: +2026-04-10 14:40:37.333222: Epoch 155 +2026-04-10 14:40:37.334656: Current learning rate: 0.00965 +2026-04-10 14:42:19.480227: train_loss -0.1173 +2026-04-10 14:42:19.484561: val_loss -0.097 +2026-04-10 14:42:19.485894: Pseudo dice [0.4442, 0.4103, 0.5462, 0.0, 0.0927, 0.5725, 0.5176] +2026-04-10 14:42:19.487380: Epoch time: 102.15 s +2026-04-10 14:42:19.488497: Yayy! New best EMA pseudo Dice: 0.3399 +2026-04-10 14:42:22.072339: +2026-04-10 14:42:22.073814: Epoch 156 +2026-04-10 14:42:22.075293: Current learning rate: 0.00965 +2026-04-10 14:44:04.091113: train_loss -0.1156 +2026-04-10 14:44:04.096497: val_loss -0.105 +2026-04-10 14:44:04.098053: Pseudo dice [0.7661, 0.3253, 0.6757, 0.0, 0.0972, 0.5386, 0.5361] +2026-04-10 14:44:04.099623: Epoch time: 102.02 s +2026-04-10 14:44:04.101037: Yayy! New best EMA pseudo Dice: 0.3479 +2026-04-10 14:44:06.703224: +2026-04-10 14:44:06.704902: Epoch 157 +2026-04-10 14:44:06.706270: Current learning rate: 0.00965 +2026-04-10 14:45:48.709415: train_loss -0.1128 +2026-04-10 14:45:48.713956: val_loss -0.1003 +2026-04-10 14:45:48.715322: Pseudo dice [0.5541, 0.1605, 0.73, 0.0, 0.2299, 0.3906, 0.5175] +2026-04-10 14:45:48.717737: Epoch time: 102.01 s +2026-04-10 14:45:48.719135: Yayy! New best EMA pseudo Dice: 0.35 +2026-04-10 14:45:51.298829: +2026-04-10 14:45:51.300578: Epoch 158 +2026-04-10 14:45:51.302070: Current learning rate: 0.00964 +2026-04-10 14:47:33.399734: train_loss -0.1242 +2026-04-10 14:47:33.404377: val_loss -0.0892 +2026-04-10 14:47:33.406315: Pseudo dice [0.3, 0.3065, 0.6667, 0.0003, 0.2455, 0.5403, 0.5106] +2026-04-10 14:47:33.407569: Epoch time: 102.1 s +2026-04-10 14:47:33.408860: Yayy! New best EMA pseudo Dice: 0.3517 +2026-04-10 14:47:36.149938: +2026-04-10 14:47:36.152089: Epoch 159 +2026-04-10 14:47:36.153613: Current learning rate: 0.00964 +2026-04-10 14:49:18.231826: train_loss -0.1123 +2026-04-10 14:49:18.235863: val_loss -0.1202 +2026-04-10 14:49:18.237335: Pseudo dice [0.5374, 0.1564, 0.6352, 0.0, 0.2455, 0.5268, 0.7891] +2026-04-10 14:49:18.239216: Epoch time: 102.09 s +2026-04-10 14:49:18.240478: Yayy! New best EMA pseudo Dice: 0.3578 +2026-04-10 14:49:21.940268: +2026-04-10 14:49:21.941842: Epoch 160 +2026-04-10 14:49:21.943089: Current learning rate: 0.00964 +2026-04-10 14:51:04.211740: train_loss -0.1127 +2026-04-10 14:51:04.216601: val_loss -0.1036 +2026-04-10 14:51:04.218432: Pseudo dice [0.4832, 0.1892, 0.5939, 0.0001, 0.2419, 0.7262, 0.6762] +2026-04-10 14:51:04.219733: Epoch time: 102.27 s +2026-04-10 14:51:04.221087: Yayy! New best EMA pseudo Dice: 0.3636 +2026-04-10 14:51:06.845624: +2026-04-10 14:51:06.847791: Epoch 161 +2026-04-10 14:51:06.849432: Current learning rate: 0.00964 +2026-04-10 14:52:48.972316: train_loss -0.1061 +2026-04-10 14:52:48.977069: val_loss -0.0878 +2026-04-10 14:52:48.978587: Pseudo dice [0.2898, 0.3319, 0.6466, 0.0007, 0.1831, 0.4308, 0.5778] +2026-04-10 14:52:48.980078: Epoch time: 102.13 s +2026-04-10 14:52:50.062758: +2026-04-10 14:52:50.064437: Epoch 162 +2026-04-10 14:52:50.065711: Current learning rate: 0.00963 +2026-04-10 14:54:32.058382: train_loss -0.1287 +2026-04-10 14:54:32.073695: val_loss -0.1158 +2026-04-10 14:54:32.075050: Pseudo dice [0.4305, 0.1654, 0.7076, 0.0, 0.3117, 0.702, 0.6387] +2026-04-10 14:54:32.076181: Epoch time: 102.0 s +2026-04-10 14:54:32.077880: Yayy! New best EMA pseudo Dice: 0.3684 +2026-04-10 14:54:34.746108: +2026-04-10 14:54:34.747824: Epoch 163 +2026-04-10 14:54:34.749245: Current learning rate: 0.00963 +2026-04-10 14:56:16.720320: train_loss -0.1353 +2026-04-10 14:56:16.724618: val_loss -0.0784 +2026-04-10 14:56:16.726848: Pseudo dice [0.6526, 0.1629, 0.5818, 0.0004, 0.1669, 0.5915, 0.2724] +2026-04-10 14:56:16.728538: Epoch time: 101.98 s +2026-04-10 14:56:17.818655: +2026-04-10 14:56:17.820197: Epoch 164 +2026-04-10 14:56:17.821681: Current learning rate: 0.00963 +2026-04-10 14:57:59.789621: train_loss -0.1138 +2026-04-10 14:57:59.793847: val_loss -0.0922 +2026-04-10 14:57:59.795352: Pseudo dice [0.3337, 0.6145, 0.3046, 0.0, 0.1071, 0.3008, 0.5825] +2026-04-10 14:57:59.796545: Epoch time: 101.97 s +2026-04-10 14:58:00.845205: +2026-04-10 14:58:00.846943: Epoch 165 +2026-04-10 14:58:00.848366: Current learning rate: 0.00963 +2026-04-10 14:59:42.793043: train_loss -0.1261 +2026-04-10 14:59:42.797462: val_loss -0.0625 +2026-04-10 14:59:42.799210: Pseudo dice [0.4738, 0.4546, 0.5682, 0.0256, 0.0747, 0.4597, 0.6522] +2026-04-10 14:59:42.800735: Epoch time: 101.95 s +2026-04-10 14:59:43.844851: +2026-04-10 14:59:43.846880: Epoch 166 +2026-04-10 14:59:43.848316: Current learning rate: 0.00963 +2026-04-10 15:01:25.975619: train_loss -0.1181 +2026-04-10 15:01:25.983083: val_loss -0.0832 +2026-04-10 15:01:25.984555: Pseudo dice [0.4301, 0.3834, 0.5831, 0.2643, 0.3326, 0.3011, 0.7104] +2026-04-10 15:01:25.985909: Epoch time: 102.13 s +2026-04-10 15:01:25.987380: Yayy! New best EMA pseudo Dice: 0.3707 +2026-04-10 15:01:28.622886: +2026-04-10 15:01:28.624910: Epoch 167 +2026-04-10 15:01:28.626331: Current learning rate: 0.00962 +2026-04-10 15:03:10.667573: train_loss -0.1177 +2026-04-10 15:03:10.673476: val_loss -0.0993 +2026-04-10 15:03:10.675257: Pseudo dice [0.4432, 0.1134, 0.7643, 0.1454, 0.4155, 0.4036, 0.7817] +2026-04-10 15:03:10.676792: Epoch time: 102.05 s +2026-04-10 15:03:10.678447: Yayy! New best EMA pseudo Dice: 0.3774 +2026-04-10 15:03:13.228161: +2026-04-10 15:03:13.230028: Epoch 168 +2026-04-10 15:03:13.231528: Current learning rate: 0.00962 +2026-04-10 15:04:55.341046: train_loss -0.1204 +2026-04-10 15:04:55.346008: val_loss -0.0928 +2026-04-10 15:04:55.347657: Pseudo dice [0.3878, 0.1533, 0.5874, 0.0, 0.1136, 0.5106, 0.2644] +2026-04-10 15:04:55.349659: Epoch time: 102.12 s +2026-04-10 15:04:56.428662: +2026-04-10 15:04:56.430437: Epoch 169 +2026-04-10 15:04:56.432072: Current learning rate: 0.00962 +2026-04-10 15:06:38.363303: train_loss -0.111 +2026-04-10 15:06:38.368031: val_loss -0.1086 +2026-04-10 15:06:38.369263: Pseudo dice [0.7385, 0.2008, 0.6058, 0.0, 0.0554, 0.5283, 0.5721] +2026-04-10 15:06:38.371085: Epoch time: 101.94 s +2026-04-10 15:06:39.461855: +2026-04-10 15:06:39.463722: Epoch 170 +2026-04-10 15:06:39.465087: Current learning rate: 0.00962 +2026-04-10 15:08:21.449041: train_loss -0.13 +2026-04-10 15:08:21.453542: val_loss -0.0788 +2026-04-10 15:08:21.454859: Pseudo dice [0.5477, 0.0052, 0.7178, 0.0005, 0.1416, 0.4497, 0.2227] +2026-04-10 15:08:21.456391: Epoch time: 101.99 s +2026-04-10 15:08:22.529909: +2026-04-10 15:08:22.531701: Epoch 171 +2026-04-10 15:08:22.532984: Current learning rate: 0.00961 +2026-04-10 15:10:04.388633: train_loss -0.1195 +2026-04-10 15:10:04.393657: val_loss -0.126 +2026-04-10 15:10:04.395271: Pseudo dice [0.3282, 0.1849, 0.7704, 0.4561, 0.323, 0.6754, 0.7476] +2026-04-10 15:10:04.397020: Epoch time: 101.86 s +2026-04-10 15:10:05.485624: +2026-04-10 15:10:05.487791: Epoch 172 +2026-04-10 15:10:05.489434: Current learning rate: 0.00961 +2026-04-10 15:11:47.339550: train_loss -0.1119 +2026-04-10 15:11:47.344463: val_loss -0.0914 +2026-04-10 15:11:47.346069: Pseudo dice [0.3024, 0.0838, 0.5469, 0.5126, 0.2143, 0.4734, 0.5156] +2026-04-10 15:11:47.347474: Epoch time: 101.86 s +2026-04-10 15:11:48.422360: +2026-04-10 15:11:48.424155: Epoch 173 +2026-04-10 15:11:48.425734: Current learning rate: 0.00961 +2026-04-10 15:13:30.469673: train_loss -0.1297 +2026-04-10 15:13:30.473950: val_loss -0.108 +2026-04-10 15:13:30.476102: Pseudo dice [0.2588, 0.0897, 0.3723, 0.1069, 0.1902, 0.5584, 0.6391] +2026-04-10 15:13:30.477288: Epoch time: 102.05 s +2026-04-10 15:13:31.546477: +2026-04-10 15:13:31.548011: Epoch 174 +2026-04-10 15:13:31.549334: Current learning rate: 0.00961 +2026-04-10 15:15:13.510576: train_loss -0.1323 +2026-04-10 15:15:13.514571: val_loss -0.1339 +2026-04-10 15:15:13.516094: Pseudo dice [0.5263, 0.2077, 0.7368, 0.4568, 0.2999, 0.6242, 0.4157] +2026-04-10 15:15:13.517614: Epoch time: 101.97 s +2026-04-10 15:15:13.518798: Yayy! New best EMA pseudo Dice: 0.3803 +2026-04-10 15:15:16.146880: +2026-04-10 15:15:16.148341: Epoch 175 +2026-04-10 15:15:16.149607: Current learning rate: 0.00961 +2026-04-10 15:16:58.076865: train_loss -0.1327 +2026-04-10 15:16:58.081616: val_loss -0.1198 +2026-04-10 15:16:58.083792: Pseudo dice [0.6536, 0.0997, 0.7083, 0.4036, 0.2914, 0.6751, 0.6251] +2026-04-10 15:16:58.085863: Epoch time: 101.93 s +2026-04-10 15:16:58.087411: Yayy! New best EMA pseudo Dice: 0.3916 +2026-04-10 15:17:00.700934: +2026-04-10 15:17:00.703011: Epoch 176 +2026-04-10 15:17:00.704380: Current learning rate: 0.0096 +2026-04-10 15:18:42.569252: train_loss -0.1257 +2026-04-10 15:18:42.573267: val_loss -0.097 +2026-04-10 15:18:42.574842: Pseudo dice [0.1546, 0.1892, 0.5953, 0.2566, 0.149, 0.4305, 0.3711] +2026-04-10 15:18:42.576681: Epoch time: 101.87 s +2026-04-10 15:18:43.652328: +2026-04-10 15:18:43.653823: Epoch 177 +2026-04-10 15:18:43.655083: Current learning rate: 0.0096 +2026-04-10 15:20:26.658277: train_loss -0.1181 +2026-04-10 15:20:26.664712: val_loss -0.1092 +2026-04-10 15:20:26.666148: Pseudo dice [0.5482, 0.2949, 0.5733, 0.4594, 0.2583, 0.5953, 0.6953] +2026-04-10 15:20:26.667430: Epoch time: 103.01 s +2026-04-10 15:20:26.668559: Yayy! New best EMA pseudo Dice: 0.3937 +2026-04-10 15:20:29.336157: +2026-04-10 15:20:29.338069: Epoch 178 +2026-04-10 15:20:29.339638: Current learning rate: 0.0096 +2026-04-10 15:22:11.177508: train_loss -0.1138 +2026-04-10 15:22:11.181319: val_loss -0.0986 +2026-04-10 15:22:11.183282: Pseudo dice [0.597, 0.2775, 0.6495, 0.5365, 0.1559, 0.494, 0.3306] +2026-04-10 15:22:11.184667: Epoch time: 101.84 s +2026-04-10 15:22:11.186312: Yayy! New best EMA pseudo Dice: 0.3978 +2026-04-10 15:22:13.927797: +2026-04-10 15:22:13.929468: Epoch 179 +2026-04-10 15:22:13.930779: Current learning rate: 0.0096 +2026-04-10 15:23:55.945154: train_loss -0.1169 +2026-04-10 15:23:55.969102: val_loss -0.0936 +2026-04-10 15:23:55.970654: Pseudo dice [0.346, 0.2202, 0.7965, 0.0197, 0.3684, 0.5362, 0.1625] +2026-04-10 15:23:55.972086: Epoch time: 102.02 s +2026-04-10 15:23:57.052254: +2026-04-10 15:23:57.054035: Epoch 180 +2026-04-10 15:23:57.056132: Current learning rate: 0.00959 +2026-04-10 15:25:39.019655: train_loss -0.1396 +2026-04-10 15:25:39.024410: val_loss -0.1088 +2026-04-10 15:25:39.026020: Pseudo dice [0.5268, 0.2233, 0.7096, 0.537, 0.2415, 0.5364, 0.5073] +2026-04-10 15:25:39.027635: Epoch time: 101.97 s +2026-04-10 15:25:39.029017: Yayy! New best EMA pseudo Dice: 0.4006 +2026-04-10 15:25:41.623327: +2026-04-10 15:25:41.624768: Epoch 181 +2026-04-10 15:25:41.626197: Current learning rate: 0.00959 +2026-04-10 15:27:23.560660: train_loss -0.12 +2026-04-10 15:27:23.566209: val_loss -0.1156 +2026-04-10 15:27:23.567678: Pseudo dice [0.5592, 0.211, 0.5438, 0.6194, 0.2947, 0.3902, 0.5598] +2026-04-10 15:27:23.569262: Epoch time: 101.94 s +2026-04-10 15:27:23.570760: Yayy! New best EMA pseudo Dice: 0.406 +2026-04-10 15:27:26.184142: +2026-04-10 15:27:26.185780: Epoch 182 +2026-04-10 15:27:26.187658: Current learning rate: 0.00959 +2026-04-10 15:29:08.214949: train_loss -0.1252 +2026-04-10 15:29:08.219484: val_loss -0.0927 +2026-04-10 15:29:08.220958: Pseudo dice [0.2906, 0.3589, 0.6336, 0.2055, 0.3104, 0.4255, 0.76] +2026-04-10 15:29:08.222594: Epoch time: 102.03 s +2026-04-10 15:29:08.224029: Yayy! New best EMA pseudo Dice: 0.408 +2026-04-10 15:29:10.788885: +2026-04-10 15:29:10.790377: Epoch 183 +2026-04-10 15:29:10.791946: Current learning rate: 0.00959 +2026-04-10 15:30:52.679179: train_loss -0.1066 +2026-04-10 15:30:52.683594: val_loss -0.1186 +2026-04-10 15:30:52.685582: Pseudo dice [0.3631, 0.219, 0.5699, 0.6577, 0.2718, 0.7051, 0.7597] +2026-04-10 15:30:52.687159: Epoch time: 101.89 s +2026-04-10 15:30:52.688760: Yayy! New best EMA pseudo Dice: 0.4179 +2026-04-10 15:30:55.423168: +2026-04-10 15:30:55.424813: Epoch 184 +2026-04-10 15:30:55.426280: Current learning rate: 0.00959 +2026-04-10 15:32:37.268353: train_loss -0.1246 +2026-04-10 15:32:37.273435: val_loss -0.1057 +2026-04-10 15:32:37.275059: Pseudo dice [0.3445, 0.4825, 0.5124, 0.2501, 0.3369, 0.5112, 0.4055] +2026-04-10 15:32:37.276330: Epoch time: 101.85 s +2026-04-10 15:32:38.365850: +2026-04-10 15:32:38.367340: Epoch 185 +2026-04-10 15:32:38.368763: Current learning rate: 0.00958 +2026-04-10 15:34:20.378283: train_loss -0.1265 +2026-04-10 15:34:20.382938: val_loss -0.1054 +2026-04-10 15:34:20.384300: Pseudo dice [0.6525, 0.3501, 0.5172, 0.4899, 0.0922, 0.6671, 0.7676] +2026-04-10 15:34:20.385878: Epoch time: 102.02 s +2026-04-10 15:34:20.387511: Yayy! New best EMA pseudo Dice: 0.4255 +2026-04-10 15:34:23.037087: +2026-04-10 15:34:23.038917: Epoch 186 +2026-04-10 15:34:23.040450: Current learning rate: 0.00958 +2026-04-10 15:36:04.990428: train_loss -0.1309 +2026-04-10 15:36:04.995386: val_loss -0.1132 +2026-04-10 15:36:04.996889: Pseudo dice [0.5657, 0.1094, 0.309, 0.1404, 0.1927, 0.5565, 0.5931] +2026-04-10 15:36:04.998434: Epoch time: 101.96 s +2026-04-10 15:36:06.048065: +2026-04-10 15:36:06.049973: Epoch 187 +2026-04-10 15:36:06.051702: Current learning rate: 0.00958 +2026-04-10 15:37:47.839481: train_loss -0.142 +2026-04-10 15:37:47.844622: val_loss -0.1162 +2026-04-10 15:37:47.846288: Pseudo dice [0.221, 0.4608, 0.6962, 0.7983, 0.2237, 0.6056, 0.5777] +2026-04-10 15:37:47.847730: Epoch time: 101.79 s +2026-04-10 15:37:47.849617: Yayy! New best EMA pseudo Dice: 0.4276 +2026-04-10 15:37:50.540726: +2026-04-10 15:37:50.542458: Epoch 188 +2026-04-10 15:37:50.543998: Current learning rate: 0.00958 +2026-04-10 15:39:32.393568: train_loss -0.1262 +2026-04-10 15:39:32.397850: val_loss -0.0902 +2026-04-10 15:39:32.399463: Pseudo dice [0.5525, 0.1184, 0.1271, 0.6601, 0.2553, 0.2505, 0.5591] +2026-04-10 15:39:32.401290: Epoch time: 101.86 s +2026-04-10 15:39:33.476960: +2026-04-10 15:39:33.478958: Epoch 189 +2026-04-10 15:39:33.480599: Current learning rate: 0.00957 +2026-04-10 15:41:15.411161: train_loss -0.1106 +2026-04-10 15:41:15.416691: val_loss -0.0883 +2026-04-10 15:41:15.418071: Pseudo dice [0.5012, 0.1852, 0.5789, 0.2996, 0.2932, 0.2756, 0.6339] +2026-04-10 15:41:15.420097: Epoch time: 101.94 s +2026-04-10 15:41:16.497332: +2026-04-10 15:41:16.498866: Epoch 190 +2026-04-10 15:41:16.500770: Current learning rate: 0.00957 +2026-04-10 15:42:58.377143: train_loss -0.1367 +2026-04-10 15:42:58.383082: val_loss -0.1138 +2026-04-10 15:42:58.384709: Pseudo dice [0.3994, 0.2596, 0.6337, 0.3213, 0.2834, 0.463, 0.8437] +2026-04-10 15:42:58.386503: Epoch time: 101.88 s +2026-04-10 15:42:59.467471: +2026-04-10 15:42:59.468853: Epoch 191 +2026-04-10 15:42:59.470279: Current learning rate: 0.00957 +2026-04-10 15:44:41.362887: train_loss -0.1307 +2026-04-10 15:44:41.368689: val_loss -0.1307 +2026-04-10 15:44:41.370247: Pseudo dice [0.4739, 0.1456, 0.7228, 0.7243, 0.2466, 0.6072, 0.5493] +2026-04-10 15:44:41.371864: Epoch time: 101.9 s +2026-04-10 15:44:41.373396: Yayy! New best EMA pseudo Dice: 0.4296 +2026-04-10 15:44:43.989462: +2026-04-10 15:44:43.991031: Epoch 192 +2026-04-10 15:44:43.992598: Current learning rate: 0.00957 +2026-04-10 15:46:25.931089: train_loss -0.136 +2026-04-10 15:46:25.935526: val_loss -0.1164 +2026-04-10 15:46:25.937245: Pseudo dice [0.1735, 0.1686, 0.4581, 0.6959, 0.22, 0.7946, 0.8093] +2026-04-10 15:46:25.938674: Epoch time: 101.94 s +2026-04-10 15:46:25.940126: Yayy! New best EMA pseudo Dice: 0.4341 +2026-04-10 15:46:28.571249: +2026-04-10 15:46:28.572640: Epoch 193 +2026-04-10 15:46:28.573832: Current learning rate: 0.00956 +2026-04-10 15:48:10.566399: train_loss -0.1256 +2026-04-10 15:48:10.571267: val_loss -0.0971 +2026-04-10 15:48:10.573275: Pseudo dice [0.3963, 0.1882, 0.5507, 0.6275, 0.29, 0.5268, 0.7975] +2026-04-10 15:48:10.574919: Epoch time: 102.0 s +2026-04-10 15:48:10.576630: Yayy! New best EMA pseudo Dice: 0.4389 +2026-04-10 15:48:14.267998: +2026-04-10 15:48:14.269998: Epoch 194 +2026-04-10 15:48:14.271509: Current learning rate: 0.00956 +2026-04-10 15:49:56.113900: train_loss -0.1317 +2026-04-10 15:49:56.119354: val_loss -0.1194 +2026-04-10 15:49:56.120888: Pseudo dice [0.4946, 0.1347, 0.5322, 0.4939, 0.3071, 0.5335, 0.6402] +2026-04-10 15:49:56.122573: Epoch time: 101.85 s +2026-04-10 15:49:56.123890: Yayy! New best EMA pseudo Dice: 0.4398 +2026-04-10 15:49:58.765197: +2026-04-10 15:49:58.766649: Epoch 195 +2026-04-10 15:49:58.767997: Current learning rate: 0.00956 +2026-04-10 15:51:40.861684: train_loss -0.1467 +2026-04-10 15:51:40.866570: val_loss -0.0964 +2026-04-10 15:51:40.868185: Pseudo dice [0.664, 0.1108, 0.6763, 0.474, 0.3238, 0.5585, 0.3954] +2026-04-10 15:51:40.870112: Epoch time: 102.1 s +2026-04-10 15:51:40.871400: Yayy! New best EMA pseudo Dice: 0.4416 +2026-04-10 15:51:43.526994: +2026-04-10 15:51:43.528909: Epoch 196 +2026-04-10 15:51:43.530293: Current learning rate: 0.00956 +2026-04-10 15:53:25.738187: train_loss -0.1327 +2026-04-10 15:53:25.742135: val_loss -0.1158 +2026-04-10 15:53:25.744763: Pseudo dice [0.4941, 0.1954, 0.6596, 0.2162, 0.2978, 0.6729, 0.6007] +2026-04-10 15:53:25.746269: Epoch time: 102.21 s +2026-04-10 15:53:25.747641: Yayy! New best EMA pseudo Dice: 0.4422 +2026-04-10 15:53:28.495110: +2026-04-10 15:53:28.496912: Epoch 197 +2026-04-10 15:53:28.498311: Current learning rate: 0.00956 +2026-04-10 15:55:10.406734: train_loss -0.1253 +2026-04-10 15:55:10.413703: val_loss -0.0978 +2026-04-10 15:55:10.415781: Pseudo dice [0.587, 0.0712, 0.7219, 0.0336, 0.1599, 0.5546, 0.2781] +2026-04-10 15:55:10.418448: Epoch time: 101.91 s +2026-04-10 15:55:11.505441: +2026-04-10 15:55:11.506939: Epoch 198 +2026-04-10 15:55:11.508357: Current learning rate: 0.00955 +2026-04-10 15:56:53.599210: train_loss -0.1149 +2026-04-10 15:56:53.604490: val_loss -0.1053 +2026-04-10 15:56:53.606018: Pseudo dice [0.4366, 0.148, 0.7159, 0.5623, 0.2338, 0.495, 0.4061] +2026-04-10 15:56:53.607629: Epoch time: 102.1 s +2026-04-10 15:56:54.677337: +2026-04-10 15:56:54.678904: Epoch 199 +2026-04-10 15:56:54.680284: Current learning rate: 0.00955 +2026-04-10 15:58:36.739955: train_loss -0.1403 +2026-04-10 15:58:36.745588: val_loss -0.1381 +2026-04-10 15:58:36.747397: Pseudo dice [0.5663, 0.0635, 0.5444, 0.6746, 0.3658, 0.4721, 0.6354] +2026-04-10 15:58:36.749153: Epoch time: 102.07 s +2026-04-10 15:58:39.401032: +2026-04-10 15:58:39.402959: Epoch 200 +2026-04-10 15:58:39.404724: Current learning rate: 0.00955 +2026-04-10 16:00:21.296887: train_loss -0.1321 +2026-04-10 16:00:21.301101: val_loss -0.082 +2026-04-10 16:00:21.302403: Pseudo dice [0.1595, 0.1372, 0.5344, 0.5777, 0.1932, 0.6347, 0.4249] +2026-04-10 16:00:21.303831: Epoch time: 101.9 s +2026-04-10 16:00:22.391483: +2026-04-10 16:00:22.393582: Epoch 201 +2026-04-10 16:00:22.394964: Current learning rate: 0.00955 +2026-04-10 16:02:04.478998: train_loss -0.1376 +2026-04-10 16:02:04.484350: val_loss -0.1038 +2026-04-10 16:02:04.486586: Pseudo dice [0.2765, 0.2253, 0.6476, 0.6619, 0.1085, 0.4574, 0.6405] +2026-04-10 16:02:04.488032: Epoch time: 102.09 s +2026-04-10 16:02:05.560238: +2026-04-10 16:02:05.561764: Epoch 202 +2026-04-10 16:02:05.563024: Current learning rate: 0.00954 +2026-04-10 16:03:47.670600: train_loss -0.1321 +2026-04-10 16:03:47.676873: val_loss -0.1203 +2026-04-10 16:03:47.679553: Pseudo dice [0.6515, 0.2998, 0.77, 0.8087, 0.138, 0.6492, 0.6932] +2026-04-10 16:03:47.681965: Epoch time: 102.11 s +2026-04-10 16:03:47.683408: Yayy! New best EMA pseudo Dice: 0.4449 +2026-04-10 16:03:50.396047: +2026-04-10 16:03:50.397779: Epoch 203 +2026-04-10 16:03:50.399051: Current learning rate: 0.00954 +2026-04-10 16:05:32.431662: train_loss -0.1403 +2026-04-10 16:05:32.436633: val_loss -0.1049 +2026-04-10 16:05:32.438610: Pseudo dice [0.4611, 0.6234, 0.7207, 0.3686, 0.3494, 0.2756, 0.5599] +2026-04-10 16:05:32.440500: Epoch time: 102.04 s +2026-04-10 16:05:32.442842: Yayy! New best EMA pseudo Dice: 0.4484 +2026-04-10 16:05:35.149657: +2026-04-10 16:05:35.151296: Epoch 204 +2026-04-10 16:05:35.152924: Current learning rate: 0.00954 +2026-04-10 16:07:17.233734: train_loss -0.1166 +2026-04-10 16:07:17.240139: val_loss -0.0926 +2026-04-10 16:07:17.241971: Pseudo dice [0.351, 0.1208, 0.2748, 0.4392, 0.366, 0.3228, 0.4515] +2026-04-10 16:07:17.243705: Epoch time: 102.09 s +2026-04-10 16:07:18.319781: +2026-04-10 16:07:18.321596: Epoch 205 +2026-04-10 16:07:18.322973: Current learning rate: 0.00954 +2026-04-10 16:09:00.271384: train_loss -0.1258 +2026-04-10 16:09:00.276728: val_loss -0.1209 +2026-04-10 16:09:00.278474: Pseudo dice [0.4898, 0.1525, 0.7201, 0.4976, 0.1738, 0.6581, 0.6623] +2026-04-10 16:09:00.280142: Epoch time: 101.95 s +2026-04-10 16:09:01.291524: +2026-04-10 16:09:01.293208: Epoch 206 +2026-04-10 16:09:01.294631: Current learning rate: 0.00954 +2026-04-10 16:10:43.370128: train_loss -0.1395 +2026-04-10 16:10:43.374935: val_loss -0.1378 +2026-04-10 16:10:43.376753: Pseudo dice [0.4514, 0.0133, 0.5784, 0.4749, 0.4318, 0.6734, 0.803] +2026-04-10 16:10:43.378479: Epoch time: 102.08 s +2026-04-10 16:10:44.388445: +2026-04-10 16:10:44.389959: Epoch 207 +2026-04-10 16:10:44.391361: Current learning rate: 0.00953 +2026-04-10 16:12:26.413137: train_loss -0.1414 +2026-04-10 16:12:26.417860: val_loss -0.1083 +2026-04-10 16:12:26.419420: Pseudo dice [0.3818, 0.3505, 0.6869, 0.5571, 0.2458, 0.6368, 0.3263] +2026-04-10 16:12:26.421167: Epoch time: 102.03 s +2026-04-10 16:12:27.438148: +2026-04-10 16:12:27.442958: Epoch 208 +2026-04-10 16:12:27.444427: Current learning rate: 0.00953 +2026-04-10 16:14:09.477425: train_loss -0.14 +2026-04-10 16:14:09.482105: val_loss -0.0976 +2026-04-10 16:14:09.484092: Pseudo dice [0.1513, 0.1556, 0.5452, 0.6407, 0.2766, 0.5381, 0.3831] +2026-04-10 16:14:09.485705: Epoch time: 102.04 s +2026-04-10 16:14:10.517592: +2026-04-10 16:14:10.519097: Epoch 209 +2026-04-10 16:14:10.520535: Current learning rate: 0.00953 +2026-04-10 16:15:52.391185: train_loss -0.14 +2026-04-10 16:15:52.396255: val_loss -0.1164 +2026-04-10 16:15:52.398236: Pseudo dice [0.6103, 0.4248, 0.715, 0.3647, 0.2265, 0.5364, 0.5945] +2026-04-10 16:15:52.400587: Epoch time: 101.88 s +2026-04-10 16:15:53.426576: +2026-04-10 16:15:53.428268: Epoch 210 +2026-04-10 16:15:53.429752: Current learning rate: 0.00953 +2026-04-10 16:17:35.287624: train_loss -0.1448 +2026-04-10 16:17:35.292608: val_loss -0.1256 +2026-04-10 16:17:35.294689: Pseudo dice [0.4464, 0.2803, 0.8213, 0.1216, 0.166, 0.6383, 0.4221] +2026-04-10 16:17:35.296685: Epoch time: 101.86 s +2026-04-10 16:17:36.306408: +2026-04-10 16:17:36.308450: Epoch 211 +2026-04-10 16:17:36.310667: Current learning rate: 0.00952 +2026-04-10 16:19:18.089504: train_loss -0.1322 +2026-04-10 16:19:18.094830: val_loss -0.112 +2026-04-10 16:19:18.096648: Pseudo dice [0.8043, 0.2186, 0.2306, 0.3582, 0.2041, 0.6267, 0.8074] +2026-04-10 16:19:18.098266: Epoch time: 101.79 s +2026-04-10 16:19:20.207086: +2026-04-10 16:19:20.208959: Epoch 212 +2026-04-10 16:19:20.210503: Current learning rate: 0.00952 +2026-04-10 16:21:02.111284: train_loss -0.1402 +2026-04-10 16:21:02.117599: val_loss -0.1205 +2026-04-10 16:21:02.119337: Pseudo dice [0.2609, 0.2646, 0.4838, 0.7624, 0.2963, 0.5797, 0.7578] +2026-04-10 16:21:02.120765: Epoch time: 101.91 s +2026-04-10 16:21:02.122567: Yayy! New best EMA pseudo Dice: 0.4491 +2026-04-10 16:21:04.730061: +2026-04-10 16:21:04.732088: Epoch 213 +2026-04-10 16:21:04.733808: Current learning rate: 0.00952 +2026-04-10 16:22:46.781056: train_loss -0.1403 +2026-04-10 16:22:46.785685: val_loss -0.1054 +2026-04-10 16:22:46.787405: Pseudo dice [0.5864, 0.3158, 0.5477, 0.2569, 0.189, 0.3355, 0.5526] +2026-04-10 16:22:46.789415: Epoch time: 102.05 s +2026-04-10 16:22:47.807707: +2026-04-10 16:22:47.809265: Epoch 214 +2026-04-10 16:22:47.810760: Current learning rate: 0.00952 +2026-04-10 16:24:29.900802: train_loss -0.1353 +2026-04-10 16:24:29.905724: val_loss -0.1261 +2026-04-10 16:24:29.907144: Pseudo dice [0.36, 0.5637, 0.6348, 0.2683, 0.3936, 0.6684, 0.3129] +2026-04-10 16:24:29.908365: Epoch time: 102.1 s +2026-04-10 16:24:30.929682: +2026-04-10 16:24:30.931752: Epoch 215 +2026-04-10 16:24:30.933321: Current learning rate: 0.00951 +2026-04-10 16:26:12.904326: train_loss -0.1385 +2026-04-10 16:26:12.908834: val_loss -0.139 +2026-04-10 16:26:12.910580: Pseudo dice [0.6634, 0.4437, 0.545, 0.2988, 0.382, 0.4879, 0.2375] +2026-04-10 16:26:12.912358: Epoch time: 101.98 s +2026-04-10 16:26:13.917435: +2026-04-10 16:26:13.918915: Epoch 216 +2026-04-10 16:26:13.920203: Current learning rate: 0.00951 +2026-04-10 16:27:56.030545: train_loss -0.1301 +2026-04-10 16:27:56.034651: val_loss -0.1093 +2026-04-10 16:27:56.036128: Pseudo dice [0.5248, 0.4922, 0.3417, 0.6138, 0.2654, 0.4248, 0.6321] +2026-04-10 16:27:56.037343: Epoch time: 102.12 s +2026-04-10 16:27:57.061876: +2026-04-10 16:27:57.063552: Epoch 217 +2026-04-10 16:27:57.064899: Current learning rate: 0.00951 +2026-04-10 16:29:38.976183: train_loss -0.1322 +2026-04-10 16:29:38.981224: val_loss -0.115 +2026-04-10 16:29:38.982782: Pseudo dice [0.6172, 0.1392, 0.7675, 0.5665, 0.3107, 0.378, 0.5699] +2026-04-10 16:29:38.984496: Epoch time: 101.92 s +2026-04-10 16:29:38.986280: Yayy! New best EMA pseudo Dice: 0.4503 +2026-04-10 16:29:41.563526: +2026-04-10 16:29:41.565927: Epoch 218 +2026-04-10 16:29:41.567759: Current learning rate: 0.00951 +2026-04-10 16:31:23.579697: train_loss -0.1366 +2026-04-10 16:31:23.584718: val_loss -0.1074 +2026-04-10 16:31:23.586367: Pseudo dice [0.5336, 0.2656, 0.606, 0.156, 0.2298, 0.5902, 0.5266] +2026-04-10 16:31:23.588104: Epoch time: 102.02 s +2026-04-10 16:31:24.605706: +2026-04-10 16:31:24.607378: Epoch 219 +2026-04-10 16:31:24.608774: Current learning rate: 0.00951 +2026-04-10 16:33:06.575074: train_loss -0.1586 +2026-04-10 16:33:06.579644: val_loss -0.0962 +2026-04-10 16:33:06.581372: Pseudo dice [0.6848, 0.3527, 0.6279, 0.3568, 0.1399, 0.6257, 0.5462] +2026-04-10 16:33:06.583774: Epoch time: 101.97 s +2026-04-10 16:33:07.610612: +2026-04-10 16:33:07.612402: Epoch 220 +2026-04-10 16:33:07.614030: Current learning rate: 0.0095 +2026-04-10 16:34:49.703457: train_loss -0.1533 +2026-04-10 16:34:49.707556: val_loss -0.1226 +2026-04-10 16:34:49.709516: Pseudo dice [0.4441, 0.2955, 0.5183, 0.8433, 0.1591, 0.709, 0.5108] +2026-04-10 16:34:49.711304: Epoch time: 102.1 s +2026-04-10 16:34:49.712800: Yayy! New best EMA pseudo Dice: 0.4545 +2026-04-10 16:34:52.346547: +2026-04-10 16:34:52.348196: Epoch 221 +2026-04-10 16:34:52.349966: Current learning rate: 0.0095 +2026-04-10 16:36:34.448492: train_loss -0.1436 +2026-04-10 16:36:34.454437: val_loss -0.1161 +2026-04-10 16:36:34.456137: Pseudo dice [0.1607, 0.2849, 0.6625, 0.3308, 0.1763, 0.7512, 0.5983] +2026-04-10 16:36:34.458292: Epoch time: 102.11 s +2026-04-10 16:36:35.468491: +2026-04-10 16:36:35.470255: Epoch 222 +2026-04-10 16:36:35.471958: Current learning rate: 0.0095 +2026-04-10 16:38:17.388603: train_loss -0.1448 +2026-04-10 16:38:17.393631: val_loss -0.1065 +2026-04-10 16:38:17.395212: Pseudo dice [0.5924, 0.2196, 0.5683, 0.7632, 0.374, 0.6813, 0.1145] +2026-04-10 16:38:17.396421: Epoch time: 101.92 s +2026-04-10 16:38:18.424725: +2026-04-10 16:38:18.426344: Epoch 223 +2026-04-10 16:38:18.427851: Current learning rate: 0.0095 +2026-04-10 16:40:00.338617: train_loss -0.1377 +2026-04-10 16:40:00.343900: val_loss -0.1217 +2026-04-10 16:40:00.345733: Pseudo dice [0.6191, 0.4195, 0.4921, 0.4605, 0.142, 0.4803, 0.6188] +2026-04-10 16:40:00.347353: Epoch time: 101.92 s +2026-04-10 16:40:01.380504: +2026-04-10 16:40:01.382168: Epoch 224 +2026-04-10 16:40:01.383520: Current learning rate: 0.00949 +2026-04-10 16:41:43.285699: train_loss -0.1368 +2026-04-10 16:41:43.299116: val_loss -0.1263 +2026-04-10 16:41:43.301925: Pseudo dice [0.3356, 0.0336, 0.5708, 0.7367, 0.3437, 0.5232, 0.5512] +2026-04-10 16:41:43.303573: Epoch time: 101.91 s +2026-04-10 16:41:44.312841: +2026-04-10 16:41:44.314560: Epoch 225 +2026-04-10 16:41:44.315935: Current learning rate: 0.00949 +2026-04-10 16:43:26.243396: train_loss -0.146 +2026-04-10 16:43:26.248736: val_loss -0.1378 +2026-04-10 16:43:26.250265: Pseudo dice [0.6734, 0.4683, 0.8292, 0.5781, 0.2673, 0.6547, 0.6623] +2026-04-10 16:43:26.251867: Epoch time: 101.93 s +2026-04-10 16:43:26.253434: Yayy! New best EMA pseudo Dice: 0.4669 +2026-04-10 16:43:28.863577: +2026-04-10 16:43:28.865745: Epoch 226 +2026-04-10 16:43:28.867320: Current learning rate: 0.00949 +2026-04-10 16:45:10.320468: train_loss -0.1349 +2026-04-10 16:45:10.325849: val_loss -0.103 +2026-04-10 16:45:10.327971: Pseudo dice [0.3446, 0.3039, 0.7647, 0.6402, 0.1885, 0.7052, 0.6945] +2026-04-10 16:45:10.331945: Epoch time: 101.46 s +2026-04-10 16:45:10.333761: Yayy! New best EMA pseudo Dice: 0.4722 +2026-04-10 16:45:12.991632: +2026-04-10 16:45:12.993222: Epoch 227 +2026-04-10 16:45:12.994569: Current learning rate: 0.00949 +2026-04-10 16:46:54.163538: train_loss -0.158 +2026-04-10 16:46:54.168836: val_loss -0.1149 +2026-04-10 16:46:54.170795: Pseudo dice [0.3018, 0.1872, 0.6507, 0.8464, 0.1975, 0.6711, 0.6238] +2026-04-10 16:46:54.172208: Epoch time: 101.17 s +2026-04-10 16:46:54.173864: Yayy! New best EMA pseudo Dice: 0.4747 +2026-04-10 16:46:56.739269: +2026-04-10 16:46:56.741127: Epoch 228 +2026-04-10 16:46:56.742568: Current learning rate: 0.00949 +2026-04-10 16:48:37.997902: train_loss -0.1342 +2026-04-10 16:48:38.002724: val_loss -0.1267 +2026-04-10 16:48:38.004196: Pseudo dice [0.5116, 0.0819, 0.7315, 0.6959, 0.2612, 0.2996, 0.7274] +2026-04-10 16:48:38.006575: Epoch time: 101.26 s +2026-04-10 16:48:39.028744: +2026-04-10 16:48:39.030218: Epoch 229 +2026-04-10 16:48:39.031570: Current learning rate: 0.00948 +2026-04-10 16:50:20.172592: train_loss -0.1318 +2026-04-10 16:50:20.177650: val_loss -0.1228 +2026-04-10 16:50:20.179558: Pseudo dice [0.3993, 0.1225, 0.7274, 0.4409, 0.3516, 0.3222, 0.7312] +2026-04-10 16:50:20.181439: Epoch time: 101.15 s +2026-04-10 16:50:21.197839: +2026-04-10 16:50:21.199480: Epoch 230 +2026-04-10 16:50:21.201047: Current learning rate: 0.00948 +2026-04-10 16:52:03.297151: train_loss -0.1386 +2026-04-10 16:52:03.304402: val_loss -0.1138 +2026-04-10 16:52:03.306002: Pseudo dice [0.4989, 0.0301, 0.4918, 0.3384, 0.2679, 0.495, 0.2259] +2026-04-10 16:52:03.308439: Epoch time: 102.1 s +2026-04-10 16:52:04.334066: +2026-04-10 16:52:04.335735: Epoch 231 +2026-04-10 16:52:04.337228: Current learning rate: 0.00948 +2026-04-10 16:53:45.604231: train_loss -0.1486 +2026-04-10 16:53:45.608929: val_loss -0.1187 +2026-04-10 16:53:45.610645: Pseudo dice [0.3192, 0.3573, 0.6977, 0.3402, 0.266, 0.6046, 0.625] +2026-04-10 16:53:45.611952: Epoch time: 101.27 s +2026-04-10 16:53:46.614103: +2026-04-10 16:53:46.615634: Epoch 232 +2026-04-10 16:53:46.616927: Current learning rate: 0.00948 +2026-04-10 16:55:28.016670: train_loss -0.1353 +2026-04-10 16:55:28.021330: val_loss -0.1046 +2026-04-10 16:55:28.023047: Pseudo dice [0.3991, 0.4625, 0.6271, 0.2856, 0.3037, 0.434, 0.5179] +2026-04-10 16:55:28.025438: Epoch time: 101.41 s +2026-04-10 16:55:29.053518: +2026-04-10 16:55:29.054921: Epoch 233 +2026-04-10 16:55:29.058932: Current learning rate: 0.00947 +2026-04-10 16:57:10.370605: train_loss -0.1455 +2026-04-10 16:57:10.376963: val_loss -0.0827 +2026-04-10 16:57:10.378800: Pseudo dice [0.352, 0.4952, 0.4303, 0.1828, 0.2104, 0.4881, 0.5479] +2026-04-10 16:57:10.380553: Epoch time: 101.32 s +2026-04-10 16:57:11.425848: +2026-04-10 16:57:11.427416: Epoch 234 +2026-04-10 16:57:11.429013: Current learning rate: 0.00947 +2026-04-10 16:58:52.724803: train_loss -0.1506 +2026-04-10 16:58:52.730122: val_loss -0.11 +2026-04-10 16:58:52.731997: Pseudo dice [0.5647, 0.2384, 0.6787, 0.0992, 0.3873, 0.6035, 0.6339] +2026-04-10 16:58:52.734016: Epoch time: 101.3 s +2026-04-10 16:58:53.747876: +2026-04-10 16:58:53.749941: Epoch 235 +2026-04-10 16:58:53.751502: Current learning rate: 0.00947 +2026-04-10 17:00:34.916846: train_loss -0.1447 +2026-04-10 17:00:34.921747: val_loss -0.1211 +2026-04-10 17:00:34.923917: Pseudo dice [0.7876, 0.6366, 0.7359, 0.6325, 0.2393, 0.5061, 0.8442] +2026-04-10 17:00:34.925557: Epoch time: 101.17 s +2026-04-10 17:00:35.948038: +2026-04-10 17:00:35.949651: Epoch 236 +2026-04-10 17:00:35.951157: Current learning rate: 0.00947 +2026-04-10 17:02:17.048403: train_loss -0.1476 +2026-04-10 17:02:17.053705: val_loss -0.1203 +2026-04-10 17:02:17.055643: Pseudo dice [0.4266, 0.1072, 0.6888, 0.46, 0.3537, 0.6758, 0.6595] +2026-04-10 17:02:17.057922: Epoch time: 101.1 s +2026-04-10 17:02:18.089996: +2026-04-10 17:02:18.093599: Epoch 237 +2026-04-10 17:02:18.094939: Current learning rate: 0.00947 +2026-04-10 17:03:59.214305: train_loss -0.1381 +2026-04-10 17:03:59.218625: val_loss -0.1025 +2026-04-10 17:03:59.220225: Pseudo dice [0.1878, 0.1454, 0.5695, 0.6179, 0.1681, 0.6623, 0.7068] +2026-04-10 17:03:59.222118: Epoch time: 101.13 s +2026-04-10 17:04:00.268210: +2026-04-10 17:04:00.269562: Epoch 238 +2026-04-10 17:04:00.270882: Current learning rate: 0.00946 +2026-04-10 17:05:41.466155: train_loss -0.1629 +2026-04-10 17:05:41.471177: val_loss -0.1379 +2026-04-10 17:05:41.472704: Pseudo dice [0.7, 0.2331, 0.6981, 0.6622, 0.2308, 0.7832, 0.7415] +2026-04-10 17:05:41.474149: Epoch time: 101.2 s +2026-04-10 17:05:41.476153: Yayy! New best EMA pseudo Dice: 0.4766 +2026-04-10 17:05:44.119396: +2026-04-10 17:05:44.121365: Epoch 239 +2026-04-10 17:05:44.123009: Current learning rate: 0.00946 +2026-04-10 17:07:25.366667: train_loss -0.1537 +2026-04-10 17:07:25.371519: val_loss -0.1324 +2026-04-10 17:07:25.373173: Pseudo dice [0.7132, 0.1966, 0.6797, 0.7487, 0.1828, 0.7865, 0.602] +2026-04-10 17:07:25.375052: Epoch time: 101.25 s +2026-04-10 17:07:25.376764: Yayy! New best EMA pseudo Dice: 0.4848 +2026-04-10 17:07:27.996510: +2026-04-10 17:07:28.013788: Epoch 240 +2026-04-10 17:07:28.015352: Current learning rate: 0.00946 +2026-04-10 17:09:09.299908: train_loss -0.1469 +2026-04-10 17:09:09.304498: val_loss -0.1127 +2026-04-10 17:09:09.305869: Pseudo dice [0.4576, 0.1416, 0.2457, 0.4371, 0.356, 0.5119, 0.7238] +2026-04-10 17:09:09.307956: Epoch time: 101.31 s +2026-04-10 17:09:10.344658: +2026-04-10 17:09:10.346663: Epoch 241 +2026-04-10 17:09:10.348800: Current learning rate: 0.00946 +2026-04-10 17:10:51.614974: train_loss -0.143 +2026-04-10 17:10:51.620747: val_loss -0.1228 +2026-04-10 17:10:51.622455: Pseudo dice [0.3797, 0.3654, 0.7266, 0.6613, 0.1974, 0.7644, 0.7688] +2026-04-10 17:10:51.625547: Epoch time: 101.27 s +2026-04-10 17:10:51.627226: Yayy! New best EMA pseudo Dice: 0.4849 +2026-04-10 17:10:54.256660: +2026-04-10 17:10:54.258451: Epoch 242 +2026-04-10 17:10:54.259847: Current learning rate: 0.00945 +2026-04-10 17:12:35.663794: train_loss -0.1559 +2026-04-10 17:12:35.667968: val_loss -0.1062 +2026-04-10 17:12:35.669379: Pseudo dice [0.7753, 0.5197, 0.5246, 0.4886, 0.3075, 0.6001, 0.5665] +2026-04-10 17:12:35.671049: Epoch time: 101.41 s +2026-04-10 17:12:35.672257: Yayy! New best EMA pseudo Dice: 0.4904 +2026-04-10 17:12:38.248293: +2026-04-10 17:12:38.250064: Epoch 243 +2026-04-10 17:12:38.251584: Current learning rate: 0.00945 +2026-04-10 17:14:19.492584: train_loss -0.1534 +2026-04-10 17:14:19.499672: val_loss -0.1121 +2026-04-10 17:14:19.501194: Pseudo dice [0.2431, 0.2014, 0.7317, 0.7087, 0.4287, 0.5674, 0.7826] +2026-04-10 17:14:19.503877: Epoch time: 101.25 s +2026-04-10 17:14:19.505525: Yayy! New best EMA pseudo Dice: 0.4937 +2026-04-10 17:14:22.110520: +2026-04-10 17:14:22.112160: Epoch 244 +2026-04-10 17:14:22.113378: Current learning rate: 0.00945 +2026-04-10 17:16:03.387129: train_loss -0.1649 +2026-04-10 17:16:03.391824: val_loss -0.1142 +2026-04-10 17:16:03.393340: Pseudo dice [0.4946, 0.3329, 0.5311, 0.7074, 0.35, 0.3299, 0.5445] +2026-04-10 17:16:03.394660: Epoch time: 101.28 s +2026-04-10 17:16:04.428810: +2026-04-10 17:16:04.430937: Epoch 245 +2026-04-10 17:16:04.432491: Current learning rate: 0.00945 +2026-04-10 17:17:45.538594: train_loss -0.1466 +2026-04-10 17:17:45.545271: val_loss -0.1267 +2026-04-10 17:17:45.546885: Pseudo dice [0.4401, 0.2908, 0.5924, 0.6342, 0.2215, 0.476, 0.734] +2026-04-10 17:17:45.548831: Epoch time: 101.11 s +2026-04-10 17:17:46.603527: +2026-04-10 17:17:46.605476: Epoch 246 +2026-04-10 17:17:46.607008: Current learning rate: 0.00944 +2026-04-10 17:19:27.913785: train_loss -0.1449 +2026-04-10 17:19:27.919976: val_loss -0.1011 +2026-04-10 17:19:27.921896: Pseudo dice [0.324, 0.2643, 0.4668, 0.5226, 0.2225, 0.6697, 0.551] +2026-04-10 17:19:27.923916: Epoch time: 101.31 s +2026-04-10 17:19:28.970845: +2026-04-10 17:19:28.972272: Epoch 247 +2026-04-10 17:19:28.973613: Current learning rate: 0.00944 +2026-04-10 17:21:10.273321: train_loss -0.1632 +2026-04-10 17:21:10.278044: val_loss -0.1048 +2026-04-10 17:21:10.279577: Pseudo dice [0.5248, 0.3553, 0.6455, 0.2486, 0.1661, 0.4009, 0.5036] +2026-04-10 17:21:10.281718: Epoch time: 101.31 s +2026-04-10 17:21:11.301292: +2026-04-10 17:21:11.302741: Epoch 248 +2026-04-10 17:21:11.304190: Current learning rate: 0.00944 +2026-04-10 17:22:52.501598: train_loss -0.1452 +2026-04-10 17:22:52.507516: val_loss -0.1127 +2026-04-10 17:22:52.509108: Pseudo dice [0.6937, 0.2971, 0.5029, 0.708, 0.3871, 0.3925, 0.8021] +2026-04-10 17:22:52.510992: Epoch time: 101.2 s +2026-04-10 17:22:54.615719: +2026-04-10 17:22:54.619935: Epoch 249 +2026-04-10 17:22:54.621430: Current learning rate: 0.00944 +2026-04-10 17:24:35.690400: train_loss -0.1466 +2026-04-10 17:24:35.696437: val_loss -0.1011 +2026-04-10 17:24:35.698473: Pseudo dice [0.231, 0.0231, 0.7275, 0.0765, 0.1986, 0.6213, 0.5831] +2026-04-10 17:24:35.700095: Epoch time: 101.08 s +2026-04-10 17:24:38.333681: +2026-04-10 17:24:38.335901: Epoch 250 +2026-04-10 17:24:38.337371: Current learning rate: 0.00944 +2026-04-10 17:26:19.566018: train_loss -0.1477 +2026-04-10 17:26:19.573210: val_loss -0.1023 +2026-04-10 17:26:19.575200: Pseudo dice [0.6187, 0.2523, 0.6917, 0.3442, 0.1941, 0.1799, 0.5186] +2026-04-10 17:26:19.576674: Epoch time: 101.24 s +2026-04-10 17:26:20.607466: +2026-04-10 17:26:20.609024: Epoch 251 +2026-04-10 17:26:20.610612: Current learning rate: 0.00943 +2026-04-10 17:28:01.881196: train_loss -0.1423 +2026-04-10 17:28:01.885895: val_loss -0.1277 +2026-04-10 17:28:01.887761: Pseudo dice [0.5677, 0.1263, 0.6813, 0.5031, 0.0, 0.5392, 0.6863] +2026-04-10 17:28:01.889660: Epoch time: 101.28 s +2026-04-10 17:28:02.922904: +2026-04-10 17:28:02.924709: Epoch 252 +2026-04-10 17:28:02.926007: Current learning rate: 0.00943 +2026-04-10 17:29:44.345450: train_loss -0.1477 +2026-04-10 17:29:44.349411: val_loss -0.1286 +2026-04-10 17:29:44.350535: Pseudo dice [0.3723, 0.0, 0.3945, 0.6555, 0.2973, 0.458, 0.3675] +2026-04-10 17:29:44.351751: Epoch time: 101.43 s +2026-04-10 17:29:45.385673: +2026-04-10 17:29:45.387348: Epoch 253 +2026-04-10 17:29:45.388789: Current learning rate: 0.00943 +2026-04-10 17:31:26.844104: train_loss -0.1376 +2026-04-10 17:31:26.849485: val_loss -0.1133 +2026-04-10 17:31:26.851027: Pseudo dice [0.7421, 0.306, 0.5575, 0.0016, 0.2622, 0.6397, 0.7428] +2026-04-10 17:31:26.852399: Epoch time: 101.46 s +2026-04-10 17:31:27.875944: +2026-04-10 17:31:27.877566: Epoch 254 +2026-04-10 17:31:27.878814: Current learning rate: 0.00943 +2026-04-10 17:33:09.132674: train_loss -0.1478 +2026-04-10 17:33:09.137794: val_loss -0.1226 +2026-04-10 17:33:09.139872: Pseudo dice [0.336, 0.0614, 0.7558, 0.004, 0.1852, 0.4033, 0.7067] +2026-04-10 17:33:09.141980: Epoch time: 101.26 s +2026-04-10 17:33:10.184988: +2026-04-10 17:33:10.186649: Epoch 255 +2026-04-10 17:33:10.188554: Current learning rate: 0.00942 +2026-04-10 17:34:51.446912: train_loss -0.1628 +2026-04-10 17:34:51.452069: val_loss -0.1118 +2026-04-10 17:34:51.453771: Pseudo dice [0.357, 0.1017, 0.7508, 0.7806, 0.215, 0.2408, 0.5425] +2026-04-10 17:34:51.455923: Epoch time: 101.27 s +2026-04-10 17:34:52.514758: +2026-04-10 17:34:52.516565: Epoch 256 +2026-04-10 17:34:52.517834: Current learning rate: 0.00942 +2026-04-10 17:36:33.717380: train_loss -0.1453 +2026-04-10 17:36:33.722339: val_loss -0.1159 +2026-04-10 17:36:33.723640: Pseudo dice [0.2549, 0.256, 0.7171, 0.6111, 0.2536, 0.1133, 0.5828] +2026-04-10 17:36:33.725274: Epoch time: 101.21 s +2026-04-10 17:36:34.753172: +2026-04-10 17:36:34.754853: Epoch 257 +2026-04-10 17:36:34.756300: Current learning rate: 0.00942 +2026-04-10 17:38:16.227633: train_loss -0.1514 +2026-04-10 17:38:16.233725: val_loss -0.1346 +2026-04-10 17:38:16.235951: Pseudo dice [0.5687, 0.245, 0.4377, 0.6576, 0.1847, 0.739, 0.5675] +2026-04-10 17:38:16.237979: Epoch time: 101.48 s +2026-04-10 17:38:17.346568: +2026-04-10 17:38:17.348230: Epoch 258 +2026-04-10 17:38:17.349915: Current learning rate: 0.00942 +2026-04-10 17:39:59.684758: train_loss -0.1476 +2026-04-10 17:39:59.691465: val_loss -0.1212 +2026-04-10 17:39:59.696477: Pseudo dice [0.4408, 0.4115, 0.7441, 0.0558, 0.3629, 0.5057, 0.7027] +2026-04-10 17:39:59.698613: Epoch time: 102.34 s +2026-04-10 17:40:00.777441: +2026-04-10 17:40:00.779624: Epoch 259 +2026-04-10 17:40:00.781223: Current learning rate: 0.00942 +2026-04-10 17:41:42.420367: train_loss -0.1365 +2026-04-10 17:41:42.426296: val_loss -0.1149 +2026-04-10 17:41:42.428073: Pseudo dice [0.7349, 0.1929, 0.4116, 0.7416, 0.2672, 0.6144, 0.6845] +2026-04-10 17:41:42.430355: Epoch time: 101.65 s +2026-04-10 17:41:43.512132: +2026-04-10 17:41:43.514249: Epoch 260 +2026-04-10 17:41:43.516515: Current learning rate: 0.00941 +2026-04-10 17:43:25.309155: train_loss -0.1468 +2026-04-10 17:43:25.313780: val_loss -0.1275 +2026-04-10 17:43:25.315520: Pseudo dice [0.6543, 0.1487, 0.448, 0.6503, 0.2139, 0.6815, 0.7211] +2026-04-10 17:43:25.318619: Epoch time: 101.8 s +2026-04-10 17:43:26.369737: +2026-04-10 17:43:26.371496: Epoch 261 +2026-04-10 17:43:26.373002: Current learning rate: 0.00941 +2026-04-10 17:45:08.050122: train_loss -0.1456 +2026-04-10 17:45:08.055375: val_loss -0.1129 +2026-04-10 17:45:08.056789: Pseudo dice [0.5522, 0.1753, 0.7647, 0.5474, 0.2586, 0.5903, 0.5294] +2026-04-10 17:45:08.058403: Epoch time: 101.68 s +2026-04-10 17:45:09.107491: +2026-04-10 17:45:09.108938: Epoch 262 +2026-04-10 17:45:09.110168: Current learning rate: 0.00941 +2026-04-10 17:46:50.605958: train_loss -0.1584 +2026-04-10 17:46:50.612056: val_loss -0.1449 +2026-04-10 17:46:50.614109: Pseudo dice [0.4582, 0.3408, 0.6184, 0.7815, 0.3039, 0.6872, 0.6031] +2026-04-10 17:46:50.616633: Epoch time: 101.5 s +2026-04-10 17:46:51.678931: +2026-04-10 17:46:51.680331: Epoch 263 +2026-04-10 17:46:51.681570: Current learning rate: 0.00941 +2026-04-10 17:48:33.025467: train_loss -0.1506 +2026-04-10 17:48:33.030827: val_loss -0.1228 +2026-04-10 17:48:33.032517: Pseudo dice [0.7617, 0.0365, 0.7381, 0.6849, 0.2856, 0.6763, 0.4112] +2026-04-10 17:48:33.034012: Epoch time: 101.35 s +2026-04-10 17:48:34.080989: +2026-04-10 17:48:34.082942: Epoch 264 +2026-04-10 17:48:34.084560: Current learning rate: 0.0094 +2026-04-10 17:50:15.763443: train_loss -0.1437 +2026-04-10 17:50:15.771667: val_loss -0.1175 +2026-04-10 17:50:15.773629: Pseudo dice [0.4289, 0.0581, 0.7634, 0.5053, 0.0883, 0.4783, 0.6278] +2026-04-10 17:50:15.775321: Epoch time: 101.69 s +2026-04-10 17:50:16.829754: +2026-04-10 17:50:16.831010: Epoch 265 +2026-04-10 17:50:16.832820: Current learning rate: 0.0094 +2026-04-10 17:51:58.396525: train_loss -0.1599 +2026-04-10 17:51:58.400758: val_loss -0.1054 +2026-04-10 17:51:58.402314: Pseudo dice [0.6035, 0.2711, 0.8222, 0.7058, 0.0653, 0.3215, 0.4133] +2026-04-10 17:51:58.403826: Epoch time: 101.57 s +2026-04-10 17:51:59.440029: +2026-04-10 17:51:59.442159: Epoch 266 +2026-04-10 17:51:59.443696: Current learning rate: 0.0094 +2026-04-10 17:53:40.935110: train_loss -0.1604 +2026-04-10 17:53:40.939826: val_loss -0.1144 +2026-04-10 17:53:40.942171: Pseudo dice [0.5363, 0.0288, 0.5704, 0.7659, 0.4675, 0.4308, 0.5398] +2026-04-10 17:53:40.943900: Epoch time: 101.5 s +2026-04-10 17:53:41.991098: +2026-04-10 17:53:41.992839: Epoch 267 +2026-04-10 17:53:41.994253: Current learning rate: 0.0094 +2026-04-10 17:55:23.476092: train_loss -0.1515 +2026-04-10 17:55:23.482962: val_loss -0.1171 +2026-04-10 17:55:23.487272: Pseudo dice [0.2694, 0.1479, 0.6623, 0.4397, 0.319, 0.3852, 0.7186] +2026-04-10 17:55:23.490042: Epoch time: 101.49 s +2026-04-10 17:55:24.550742: +2026-04-10 17:55:24.552343: Epoch 268 +2026-04-10 17:55:24.553720: Current learning rate: 0.00939 +2026-04-10 17:57:05.978095: train_loss -0.154 +2026-04-10 17:57:05.982921: val_loss -0.1194 +2026-04-10 17:57:05.984444: Pseudo dice [0.2333, 0.0603, 0.5717, 0.7881, 0.2936, 0.7552, 0.6975] +2026-04-10 17:57:05.985965: Epoch time: 101.43 s +2026-04-10 17:57:08.133371: +2026-04-10 17:57:08.134894: Epoch 269 +2026-04-10 17:57:08.136189: Current learning rate: 0.00939 +2026-04-10 17:58:49.671893: train_loss -0.1477 +2026-04-10 17:58:49.677883: val_loss -0.0788 +2026-04-10 17:58:49.679887: Pseudo dice [0.728, 0.0516, 0.1497, 0.1094, 0.3647, 0.7789, 0.5477] +2026-04-10 17:58:49.681597: Epoch time: 101.54 s +2026-04-10 17:58:50.736097: +2026-04-10 17:58:50.737835: Epoch 270 +2026-04-10 17:58:50.739452: Current learning rate: 0.00939 +2026-04-10 18:00:32.423635: train_loss -0.1488 +2026-04-10 18:00:32.431585: val_loss -0.126 +2026-04-10 18:00:32.433232: Pseudo dice [0.3621, 0.4525, 0.7154, 0.613, 0.2492, 0.6316, 0.835] +2026-04-10 18:00:32.435307: Epoch time: 101.69 s +2026-04-10 18:00:33.494026: +2026-04-10 18:00:33.496029: Epoch 271 +2026-04-10 18:00:33.498425: Current learning rate: 0.00939 +2026-04-10 18:02:15.610392: train_loss -0.1332 +2026-04-10 18:02:15.617034: val_loss -0.0926 +2026-04-10 18:02:15.619744: Pseudo dice [0.6764, 0.256, 0.6637, 0.5314, 0.1065, 0.3002, 0.6405] +2026-04-10 18:02:15.621558: Epoch time: 102.12 s +2026-04-10 18:02:16.723215: +2026-04-10 18:02:16.725917: Epoch 272 +2026-04-10 18:02:16.727659: Current learning rate: 0.00939 +2026-04-10 18:03:58.207964: train_loss -0.1412 +2026-04-10 18:03:58.213608: val_loss -0.109 +2026-04-10 18:03:58.216869: Pseudo dice [0.6295, 0.1999, 0.5176, 0.3344, 0.1577, 0.6394, 0.6483] +2026-04-10 18:03:58.224011: Epoch time: 101.49 s +2026-04-10 18:03:59.270415: +2026-04-10 18:03:59.272509: Epoch 273 +2026-04-10 18:03:59.274428: Current learning rate: 0.00938 +2026-04-10 18:05:42.142330: train_loss -0.1603 +2026-04-10 18:05:42.148871: val_loss -0.1293 +2026-04-10 18:05:42.150988: Pseudo dice [0.3291, 0.4234, 0.4828, 0.4935, 0.2821, 0.6759, 0.6501] +2026-04-10 18:05:42.154998: Epoch time: 102.88 s +2026-04-10 18:05:43.219173: +2026-04-10 18:05:43.221423: Epoch 274 +2026-04-10 18:05:43.222868: Current learning rate: 0.00938 +2026-04-10 18:07:25.628364: train_loss -0.146 +2026-04-10 18:07:25.634192: val_loss -0.1068 +2026-04-10 18:07:25.636173: Pseudo dice [0.3478, 0.041, 0.7173, 0.5938, 0.3574, 0.6775, 0.4434] +2026-04-10 18:07:25.638402: Epoch time: 102.41 s +2026-04-10 18:07:26.670280: +2026-04-10 18:07:26.672287: Epoch 275 +2026-04-10 18:07:26.675049: Current learning rate: 0.00938 +2026-04-10 18:09:08.283894: train_loss -0.1392 +2026-04-10 18:09:08.289189: val_loss -0.1244 +2026-04-10 18:09:08.291473: Pseudo dice [0.4698, 0.4923, 0.7157, 0.2014, 0.2388, 0.5327, 0.7669] +2026-04-10 18:09:08.294115: Epoch time: 101.62 s +2026-04-10 18:09:09.354394: +2026-04-10 18:09:09.357170: Epoch 276 +2026-04-10 18:09:09.359389: Current learning rate: 0.00938 +2026-04-10 18:10:52.124707: train_loss -0.1408 +2026-04-10 18:10:52.131200: val_loss -0.1209 +2026-04-10 18:10:52.133749: Pseudo dice [0.575, 0.1826, 0.7753, 0.8504, 0.1808, 0.5887, 0.3463] +2026-04-10 18:10:52.135409: Epoch time: 102.77 s +2026-04-10 18:10:53.205767: +2026-04-10 18:10:53.207485: Epoch 277 +2026-04-10 18:10:53.210621: Current learning rate: 0.00937 +2026-04-10 18:12:34.895656: train_loss -0.1593 +2026-04-10 18:12:34.900957: val_loss -0.1253 +2026-04-10 18:12:34.903885: Pseudo dice [0.4005, 0.2133, 0.7037, 0.531, 0.3673, 0.8317, 0.5799] +2026-04-10 18:12:34.906365: Epoch time: 101.69 s +2026-04-10 18:12:35.989796: +2026-04-10 18:12:35.991704: Epoch 278 +2026-04-10 18:12:35.993219: Current learning rate: 0.00937 +2026-04-10 18:14:17.570574: train_loss -0.1636 +2026-04-10 18:14:17.574910: val_loss -0.1162 +2026-04-10 18:14:17.576704: Pseudo dice [0.4902, 0.2531, 0.6822, 0.0524, 0.035, 0.616, 0.5845] +2026-04-10 18:14:17.578474: Epoch time: 101.58 s +2026-04-10 18:14:18.631781: +2026-04-10 18:14:18.634048: Epoch 279 +2026-04-10 18:14:18.635370: Current learning rate: 0.00937 +2026-04-10 18:16:00.373926: train_loss -0.158 +2026-04-10 18:16:00.379074: val_loss -0.1047 +2026-04-10 18:16:00.381438: Pseudo dice [0.7312, 0.2195, 0.7738, 0.519, 0.1046, 0.4049, 0.8178] +2026-04-10 18:16:00.383966: Epoch time: 101.75 s +2026-04-10 18:16:01.440152: +2026-04-10 18:16:01.442402: Epoch 280 +2026-04-10 18:16:01.444007: Current learning rate: 0.00937 +2026-04-10 18:17:43.140944: train_loss -0.1646 +2026-04-10 18:17:43.147059: val_loss -0.0784 +2026-04-10 18:17:43.148963: Pseudo dice [0.4522, 0.3316, 0.4568, 0.7696, 0.084, 0.2199, 0.3958] +2026-04-10 18:17:43.150584: Epoch time: 101.7 s +2026-04-10 18:17:44.230134: +2026-04-10 18:17:44.232094: Epoch 281 +2026-04-10 18:17:44.234055: Current learning rate: 0.00937 +2026-04-10 18:19:25.931820: train_loss -0.1365 +2026-04-10 18:19:25.936682: val_loss -0.1423 +2026-04-10 18:19:25.938089: Pseudo dice [0.6895, 0.1727, 0.5509, 0.2625, 0.3827, 0.7717, 0.7855] +2026-04-10 18:19:25.940428: Epoch time: 101.7 s +2026-04-10 18:19:26.979950: +2026-04-10 18:19:26.981450: Epoch 282 +2026-04-10 18:19:26.982893: Current learning rate: 0.00936 +2026-04-10 18:21:08.582061: train_loss -0.1497 +2026-04-10 18:21:08.588086: val_loss -0.1415 +2026-04-10 18:21:08.590543: Pseudo dice [0.5573, 0.4152, 0.7416, 0.7347, 0.2999, 0.1977, 0.6939] +2026-04-10 18:21:08.592753: Epoch time: 101.61 s +2026-04-10 18:21:09.662451: +2026-04-10 18:21:09.664113: Epoch 283 +2026-04-10 18:21:09.666749: Current learning rate: 0.00936 +2026-04-10 18:22:51.162979: train_loss -0.1554 +2026-04-10 18:22:51.166933: val_loss -0.1295 +2026-04-10 18:22:51.168490: Pseudo dice [0.525, 0.3421, 0.5723, 0.3012, 0.2982, 0.5342, 0.7995] +2026-04-10 18:22:51.169882: Epoch time: 101.5 s +2026-04-10 18:22:52.225641: +2026-04-10 18:22:52.227189: Epoch 284 +2026-04-10 18:22:52.229390: Current learning rate: 0.00936 +2026-04-10 18:24:33.789800: train_loss -0.144 +2026-04-10 18:24:33.795285: val_loss -0.1049 +2026-04-10 18:24:33.797162: Pseudo dice [0.3352, 0.1453, 0.5793, 0.4972, 0.1573, 0.2008, 0.2834] +2026-04-10 18:24:33.798975: Epoch time: 101.57 s +2026-04-10 18:24:34.842404: +2026-04-10 18:24:34.843987: Epoch 285 +2026-04-10 18:24:34.845379: Current learning rate: 0.00936 +2026-04-10 18:26:16.415414: train_loss -0.1549 +2026-04-10 18:26:16.421744: val_loss -0.11 +2026-04-10 18:26:16.423378: Pseudo dice [0.5367, 0.1854, 0.649, 0.7205, 0.2218, 0.2644, 0.7802] +2026-04-10 18:26:16.424874: Epoch time: 101.58 s +2026-04-10 18:26:17.473725: +2026-04-10 18:26:17.475890: Epoch 286 +2026-04-10 18:26:17.477581: Current learning rate: 0.00935 +2026-04-10 18:27:59.021395: train_loss -0.14 +2026-04-10 18:27:59.027113: val_loss -0.1043 +2026-04-10 18:27:59.028589: Pseudo dice [0.2266, 0.3957, 0.6935, 0.879, 0.4511, 0.2274, 0.6742] +2026-04-10 18:27:59.030200: Epoch time: 101.55 s +2026-04-10 18:28:00.098505: +2026-04-10 18:28:00.099956: Epoch 287 +2026-04-10 18:28:00.101169: Current learning rate: 0.00935 +2026-04-10 18:29:41.812790: train_loss -0.1554 +2026-04-10 18:29:41.818685: val_loss -0.143 +2026-04-10 18:29:41.820581: Pseudo dice [0.5486, 0.1966, 0.6577, 0.6914, 0.3221, 0.7033, 0.7229] +2026-04-10 18:29:41.822525: Epoch time: 101.72 s +2026-04-10 18:29:42.893453: +2026-04-10 18:29:42.894879: Epoch 288 +2026-04-10 18:29:42.896350: Current learning rate: 0.00935 +2026-04-10 18:31:24.604811: train_loss -0.1499 +2026-04-10 18:31:24.609879: val_loss -0.16 +2026-04-10 18:31:24.612241: Pseudo dice [0.4788, 0.1387, 0.7369, 0.2786, 0.3432, 0.5106, 0.8213] +2026-04-10 18:31:24.614193: Epoch time: 101.71 s +2026-04-10 18:31:25.671647: +2026-04-10 18:31:25.673753: Epoch 289 +2026-04-10 18:31:25.675254: Current learning rate: 0.00935 +2026-04-10 18:33:07.527384: train_loss -0.1653 +2026-04-10 18:33:07.532389: val_loss -0.1165 +2026-04-10 18:33:07.534188: Pseudo dice [0.4611, 0.3219, 0.4101, 0.7819, 0.2458, 0.6554, 0.6456] +2026-04-10 18:33:07.536350: Epoch time: 101.86 s +2026-04-10 18:33:09.777629: +2026-04-10 18:33:09.779587: Epoch 290 +2026-04-10 18:33:09.781476: Current learning rate: 0.00935 +2026-04-10 18:34:51.515774: train_loss -0.157 +2026-04-10 18:34:51.520540: val_loss -0.1223 +2026-04-10 18:34:51.522110: Pseudo dice [0.5675, 0.1186, 0.4373, 0.6931, 0.4045, 0.5612, 0.7984] +2026-04-10 18:34:51.523594: Epoch time: 101.74 s +2026-04-10 18:34:52.593963: +2026-04-10 18:34:52.595854: Epoch 291 +2026-04-10 18:34:52.597169: Current learning rate: 0.00934 +2026-04-10 18:36:34.197906: train_loss -0.1622 +2026-04-10 18:36:34.203955: val_loss -0.1002 +2026-04-10 18:36:34.206692: Pseudo dice [0.4525, 0.412, 0.3754, 0.6928, 0.1406, 0.716, 0.5089] +2026-04-10 18:36:34.208328: Epoch time: 101.61 s +2026-04-10 18:36:35.290910: +2026-04-10 18:36:35.292613: Epoch 292 +2026-04-10 18:36:35.294780: Current learning rate: 0.00934 +2026-04-10 18:38:16.845621: train_loss -0.1629 +2026-04-10 18:38:16.851667: val_loss -0.1343 +2026-04-10 18:38:16.853260: Pseudo dice [0.3636, 0.5036, 0.7422, 0.703, 0.3916, 0.6524, 0.6099] +2026-04-10 18:38:16.855404: Epoch time: 101.56 s +2026-04-10 18:38:17.992056: +2026-04-10 18:38:17.993706: Epoch 293 +2026-04-10 18:38:17.995448: Current learning rate: 0.00934 +2026-04-10 18:40:00.262151: train_loss -0.1576 +2026-04-10 18:40:00.268201: val_loss -0.1226 +2026-04-10 18:40:00.270044: Pseudo dice [0.3634, 0.3124, 0.723, 0.2871, 0.2512, 0.6222, 0.6682] +2026-04-10 18:40:00.273747: Epoch time: 102.27 s +2026-04-10 18:40:01.365626: +2026-04-10 18:40:01.367302: Epoch 294 +2026-04-10 18:40:01.368991: Current learning rate: 0.00934 +2026-04-10 18:41:43.480178: train_loss -0.1719 +2026-04-10 18:41:43.489712: val_loss -0.1317 +2026-04-10 18:41:43.491787: Pseudo dice [0.7375, 0.4816, 0.7736, 0.4082, 0.2252, 0.7131, 0.6449] +2026-04-10 18:41:43.494410: Epoch time: 102.12 s +2026-04-10 18:41:44.586368: +2026-04-10 18:41:44.590895: Epoch 295 +2026-04-10 18:41:44.593954: Current learning rate: 0.00933 +2026-04-10 18:43:27.012502: train_loss -0.1535 +2026-04-10 18:43:27.019702: val_loss -0.1278 +2026-04-10 18:43:27.021656: Pseudo dice [0.2694, 0.489, 0.7291, 0.1512, 0.3494, 0.7804, 0.8448] +2026-04-10 18:43:27.023951: Epoch time: 102.43 s +2026-04-10 18:43:27.025633: Yayy! New best EMA pseudo Dice: 0.4955 +2026-04-10 18:43:29.885734: +2026-04-10 18:43:29.888595: Epoch 296 +2026-04-10 18:43:29.890668: Current learning rate: 0.00933 +2026-04-10 18:45:11.630393: train_loss -0.1281 +2026-04-10 18:45:11.634966: val_loss -0.0988 +2026-04-10 18:45:11.636541: Pseudo dice [0.2762, 0.3241, 0.4681, 0.6188, 0.0107, 0.5886, 0.8148] +2026-04-10 18:45:11.638168: Epoch time: 101.75 s +2026-04-10 18:45:12.712414: +2026-04-10 18:45:12.714669: Epoch 297 +2026-04-10 18:45:12.716732: Current learning rate: 0.00933 +2026-04-10 18:46:54.130273: train_loss -0.1573 +2026-04-10 18:46:54.136636: val_loss -0.1236 +2026-04-10 18:46:54.138457: Pseudo dice [0.5903, 0.3423, 0.7603, 0.6513, 0.4469, 0.4855, 0.8649] +2026-04-10 18:46:54.140072: Epoch time: 101.42 s +2026-04-10 18:46:54.142360: Yayy! New best EMA pseudo Dice: 0.5004 +2026-04-10 18:46:56.885422: +2026-04-10 18:46:56.886982: Epoch 298 +2026-04-10 18:46:56.888530: Current learning rate: 0.00933 +2026-04-10 18:48:39.151849: train_loss -0.1602 +2026-04-10 18:48:39.157213: val_loss -0.0985 +2026-04-10 18:48:39.159832: Pseudo dice [0.471, 0.1646, 0.5927, 0.5707, 0.1101, 0.8413, 0.5764] +2026-04-10 18:48:39.162488: Epoch time: 102.27 s +2026-04-10 18:48:40.273020: +2026-04-10 18:48:40.275124: Epoch 299 +2026-04-10 18:48:40.276880: Current learning rate: 0.00932 +2026-04-10 18:50:21.853248: train_loss -0.1636 +2026-04-10 18:50:21.859570: val_loss -0.1051 +2026-04-10 18:50:21.861823: Pseudo dice [0.1465, 0.0817, 0.7565, 0.7243, 0.2441, 0.5735, 0.5543] +2026-04-10 18:50:21.864070: Epoch time: 101.58 s +2026-04-10 18:50:24.482622: +2026-04-10 18:50:24.484721: Epoch 300 +2026-04-10 18:50:24.486497: Current learning rate: 0.00932 +2026-04-10 18:52:05.865338: train_loss -0.1636 +2026-04-10 18:52:05.869493: val_loss -0.1164 +2026-04-10 18:52:05.871311: Pseudo dice [0.7693, 0.2734, 0.6418, 0.134, 0.1588, 0.4776, 0.7512] +2026-04-10 18:52:05.873075: Epoch time: 101.39 s +2026-04-10 18:52:06.952242: +2026-04-10 18:52:06.953915: Epoch 301 +2026-04-10 18:52:06.955393: Current learning rate: 0.00932 +2026-04-10 18:53:48.548585: train_loss -0.154 +2026-04-10 18:53:48.553666: val_loss -0.1074 +2026-04-10 18:53:48.555253: Pseudo dice [0.3931, 0.4064, 0.1833, 0.8213, 0.2506, 0.4561, 0.6233] +2026-04-10 18:53:48.556789: Epoch time: 101.6 s +2026-04-10 18:53:49.643001: +2026-04-10 18:53:49.644396: Epoch 302 +2026-04-10 18:53:49.645766: Current learning rate: 0.00932 +2026-04-10 18:55:31.320183: train_loss -0.1354 +2026-04-10 18:55:31.326023: val_loss -0.1194 +2026-04-10 18:55:31.328426: Pseudo dice [0.3948, 0.2172, 0.6141, 0.5035, 0.4092, 0.6103, 0.6137] +2026-04-10 18:55:31.330065: Epoch time: 101.68 s +2026-04-10 18:55:32.424761: +2026-04-10 18:55:32.426802: Epoch 303 +2026-04-10 18:55:32.428575: Current learning rate: 0.00932 +2026-04-10 18:57:13.807185: train_loss -0.1598 +2026-04-10 18:57:13.812958: val_loss -0.1184 +2026-04-10 18:57:13.814994: Pseudo dice [0.4905, 0.1442, 0.4502, 0.6522, 0.181, 0.687, 0.6247] +2026-04-10 18:57:13.816819: Epoch time: 101.39 s +2026-04-10 18:57:14.910416: +2026-04-10 18:57:14.912093: Epoch 304 +2026-04-10 18:57:14.913279: Current learning rate: 0.00931 +2026-04-10 18:58:56.448982: train_loss -0.1625 +2026-04-10 18:58:56.453578: val_loss -0.1207 +2026-04-10 18:58:56.455184: Pseudo dice [0.4094, 0.0137, 0.605, 0.7833, 0.2762, 0.7146, 0.6167] +2026-04-10 18:58:56.456775: Epoch time: 101.54 s +2026-04-10 18:58:57.534168: +2026-04-10 18:58:57.536853: Epoch 305 +2026-04-10 18:58:57.539221: Current learning rate: 0.00931 +2026-04-10 19:00:39.189728: train_loss -0.1412 +2026-04-10 19:00:39.195526: val_loss -0.1073 +2026-04-10 19:00:39.197302: Pseudo dice [0.3419, 0.1163, 0.5445, 0.4683, 0.3081, 0.4334, 0.6229] +2026-04-10 19:00:39.200144: Epoch time: 101.66 s +2026-04-10 19:00:40.261267: +2026-04-10 19:00:40.263391: Epoch 306 +2026-04-10 19:00:40.264949: Current learning rate: 0.00931 +2026-04-10 19:02:21.776924: train_loss -0.153 +2026-04-10 19:02:21.782538: val_loss -0.1242 +2026-04-10 19:02:21.784286: Pseudo dice [0.5705, 0.1648, 0.6815, 0.7464, 0.2495, 0.6249, 0.5745] +2026-04-10 19:02:21.787050: Epoch time: 101.52 s +2026-04-10 19:02:22.853803: +2026-04-10 19:02:22.856048: Epoch 307 +2026-04-10 19:02:22.857777: Current learning rate: 0.00931 +2026-04-10 19:04:04.288034: train_loss -0.1595 +2026-04-10 19:04:04.293646: val_loss -0.1434 +2026-04-10 19:04:04.296091: Pseudo dice [0.2608, 0.3399, 0.7278, 0.1159, 0.3855, 0.1616, 0.8346] +2026-04-10 19:04:04.297760: Epoch time: 101.44 s +2026-04-10 19:04:05.418509: +2026-04-10 19:04:05.420681: Epoch 308 +2026-04-10 19:04:05.422465: Current learning rate: 0.0093 +2026-04-10 19:05:47.250318: train_loss -0.1565 +2026-04-10 19:05:47.260715: val_loss -0.1194 +2026-04-10 19:05:47.272397: Pseudo dice [0.481, 0.1333, 0.772, 0.2518, 0.1826, 0.5576, 0.6515] +2026-04-10 19:05:47.275715: Epoch time: 101.83 s +2026-04-10 19:05:49.523263: +2026-04-10 19:05:49.525875: Epoch 309 +2026-04-10 19:05:49.527555: Current learning rate: 0.0093 +2026-04-10 19:07:31.011842: train_loss -0.1555 +2026-04-10 19:07:31.017021: val_loss -0.1135 +2026-04-10 19:07:31.019137: Pseudo dice [0.2172, 0.0947, 0.7582, 0.3682, 0.2365, 0.5248, 0.4141] +2026-04-10 19:07:31.020851: Epoch time: 101.49 s +2026-04-10 19:07:32.104974: +2026-04-10 19:07:32.107852: Epoch 310 +2026-04-10 19:07:32.109472: Current learning rate: 0.0093 +2026-04-10 19:09:14.482105: train_loss -0.1568 +2026-04-10 19:09:14.489362: val_loss -0.1467 +2026-04-10 19:09:14.493808: Pseudo dice [0.6619, 0.3832, 0.4515, 0.6794, 0.0776, 0.8024, 0.8585] +2026-04-10 19:09:14.495786: Epoch time: 102.38 s +2026-04-10 19:09:15.578625: +2026-04-10 19:09:15.581101: Epoch 311 +2026-04-10 19:09:15.582819: Current learning rate: 0.0093 +2026-04-10 19:10:58.211572: train_loss -0.1633 +2026-04-10 19:10:58.221100: val_loss -0.1312 +2026-04-10 19:10:58.223200: Pseudo dice [0.317, 0.5097, 0.3629, 0.0996, 0.1188, 0.6454, 0.7755] +2026-04-10 19:10:58.225152: Epoch time: 102.64 s +2026-04-10 19:10:59.318969: +2026-04-10 19:10:59.321060: Epoch 312 +2026-04-10 19:10:59.322889: Current learning rate: 0.0093 +2026-04-10 19:12:41.841850: train_loss -0.161 +2026-04-10 19:12:41.850254: val_loss -0.1403 +2026-04-10 19:12:41.853283: Pseudo dice [0.6223, 0.3887, 0.6581, 0.8102, 0.2905, 0.7023, 0.7545] +2026-04-10 19:12:41.856114: Epoch time: 102.53 s +2026-04-10 19:12:42.959441: +2026-04-10 19:12:42.961739: Epoch 313 +2026-04-10 19:12:42.963505: Current learning rate: 0.00929 +2026-04-10 19:14:24.589091: train_loss -0.1665 +2026-04-10 19:14:24.596140: val_loss -0.1284 +2026-04-10 19:14:24.597775: Pseudo dice [0.5859, 0.1717, 0.6766, 0.5816, 0.3618, 0.6323, 0.8211] +2026-04-10 19:14:24.599214: Epoch time: 101.63 s +2026-04-10 19:14:25.716848: +2026-04-10 19:14:25.718688: Epoch 314 +2026-04-10 19:14:25.721033: Current learning rate: 0.00929 +2026-04-10 19:16:07.557623: train_loss -0.1437 +2026-04-10 19:16:07.567930: val_loss -0.1228 +2026-04-10 19:16:07.571604: Pseudo dice [0.8417, 0.2465, 0.6799, 0.4787, 0.2231, 0.2149, 0.8862] +2026-04-10 19:16:07.574358: Epoch time: 101.84 s +2026-04-10 19:16:08.675548: +2026-04-10 19:16:08.677263: Epoch 315 +2026-04-10 19:16:08.678722: Current learning rate: 0.00929 +2026-04-10 19:17:50.454033: train_loss -0.1686 +2026-04-10 19:17:50.458915: val_loss -0.1116 +2026-04-10 19:17:50.460672: Pseudo dice [0.3511, 0.0412, 0.6937, 0.1037, 0.2211, 0.6414, 0.709] +2026-04-10 19:17:50.470411: Epoch time: 101.78 s +2026-04-10 19:17:51.557878: +2026-04-10 19:17:51.560074: Epoch 316 +2026-04-10 19:17:51.563593: Current learning rate: 0.00929 +2026-04-10 19:19:33.196180: train_loss -0.1599 +2026-04-10 19:19:33.202441: val_loss -0.0991 +2026-04-10 19:19:33.205111: Pseudo dice [0.3731, 0.1925, 0.7135, 0.1562, 0.1281, 0.5099, 0.5062] +2026-04-10 19:19:33.207229: Epoch time: 101.64 s +2026-04-10 19:19:34.279255: +2026-04-10 19:19:34.281244: Epoch 317 +2026-04-10 19:19:34.282907: Current learning rate: 0.00928 +2026-04-10 19:21:15.682725: train_loss -0.1534 +2026-04-10 19:21:15.689302: val_loss -0.1423 +2026-04-10 19:21:15.691321: Pseudo dice [0.4976, 0.2039, 0.7888, 0.6828, 0.2706, 0.6439, 0.5883] +2026-04-10 19:21:15.693062: Epoch time: 101.41 s +2026-04-10 19:21:16.787213: +2026-04-10 19:21:16.789412: Epoch 318 +2026-04-10 19:21:16.790802: Current learning rate: 0.00928 +2026-04-10 19:22:58.317169: train_loss -0.1529 +2026-04-10 19:22:58.324126: val_loss -0.1408 +2026-04-10 19:22:58.326035: Pseudo dice [0.4639, 0.1709, 0.7191, 0.2701, 0.3063, 0.3513, 0.7564] +2026-04-10 19:22:58.327513: Epoch time: 101.53 s +2026-04-10 19:22:59.395580: +2026-04-10 19:22:59.397430: Epoch 319 +2026-04-10 19:22:59.400248: Current learning rate: 0.00928 +2026-04-10 19:24:40.588264: train_loss -0.1527 +2026-04-10 19:24:40.593505: val_loss -0.1293 +2026-04-10 19:24:40.595394: Pseudo dice [0.6644, 0.319, 0.7736, 0.5721, 0.4571, 0.375, 0.6815] +2026-04-10 19:24:40.597121: Epoch time: 101.2 s +2026-04-10 19:24:41.657841: +2026-04-10 19:24:41.660306: Epoch 320 +2026-04-10 19:24:41.665085: Current learning rate: 0.00928 +2026-04-10 19:26:23.225292: train_loss -0.141 +2026-04-10 19:26:23.230896: val_loss -0.0944 +2026-04-10 19:26:23.232606: Pseudo dice [0.3313, 0.124, 0.5951, 0.5168, 0.3203, 0.608, 0.4679] +2026-04-10 19:26:23.234254: Epoch time: 101.57 s +2026-04-10 19:26:24.308744: +2026-04-10 19:26:24.310978: Epoch 321 +2026-04-10 19:26:24.312934: Current learning rate: 0.00927 +2026-04-10 19:28:06.070524: train_loss -0.1678 +2026-04-10 19:28:06.075487: val_loss -0.1051 +2026-04-10 19:28:06.077337: Pseudo dice [0.3618, 0.057, 0.3824, 0.2638, 0.3343, 0.4623, 0.6125] +2026-04-10 19:28:06.078887: Epoch time: 101.77 s +2026-04-10 19:28:07.158247: +2026-04-10 19:28:07.174345: Epoch 322 +2026-04-10 19:28:07.175946: Current learning rate: 0.00927 +2026-04-10 19:29:48.596815: train_loss -0.1625 +2026-04-10 19:29:48.602752: val_loss -0.1278 +2026-04-10 19:29:48.605094: Pseudo dice [0.6676, 0.0811, 0.7356, 0.8132, 0.368, 0.7355, 0.421] +2026-04-10 19:29:48.606956: Epoch time: 101.44 s +2026-04-10 19:29:49.688911: +2026-04-10 19:29:49.690777: Epoch 323 +2026-04-10 19:29:49.692265: Current learning rate: 0.00927 +2026-04-10 19:31:31.152999: train_loss -0.1691 +2026-04-10 19:31:31.160505: val_loss -0.1227 +2026-04-10 19:31:31.162225: Pseudo dice [0.675, 0.1052, 0.6017, 0.3187, 0.3111, 0.0979, 0.6862] +2026-04-10 19:31:31.163987: Epoch time: 101.47 s +2026-04-10 19:31:32.252315: +2026-04-10 19:31:32.254238: Epoch 324 +2026-04-10 19:31:32.255724: Current learning rate: 0.00927 +2026-04-10 19:33:14.098829: train_loss -0.1501 +2026-04-10 19:33:14.103474: val_loss -0.1003 +2026-04-10 19:33:14.105900: Pseudo dice [0.4076, 0.2086, 0.6682, 0.6492, 0.1397, 0.6884, 0.2375] +2026-04-10 19:33:14.108077: Epoch time: 101.85 s +2026-04-10 19:33:15.226706: +2026-04-10 19:33:15.228979: Epoch 325 +2026-04-10 19:33:15.230389: Current learning rate: 0.00927 +2026-04-10 19:34:56.776159: train_loss -0.1502 +2026-04-10 19:34:56.782688: val_loss -0.1362 +2026-04-10 19:34:56.785086: Pseudo dice [0.5379, 0.2164, 0.6015, 0.7015, 0.2548, 0.749, 0.5651] +2026-04-10 19:34:56.787397: Epoch time: 101.55 s +2026-04-10 19:34:57.872365: +2026-04-10 19:34:57.874693: Epoch 326 +2026-04-10 19:34:57.876248: Current learning rate: 0.00926 +2026-04-10 19:36:39.417900: train_loss -0.1547 +2026-04-10 19:36:39.424623: val_loss -0.1026 +2026-04-10 19:36:39.426714: Pseudo dice [0.2148, 0.1657, 0.6935, 0.5431, 0.1311, 0.7293, 0.6802] +2026-04-10 19:36:39.428651: Epoch time: 101.55 s +2026-04-10 19:36:40.512971: +2026-04-10 19:36:40.514366: Epoch 327 +2026-04-10 19:36:40.515969: Current learning rate: 0.00926 +2026-04-10 19:38:22.784655: train_loss -0.1604 +2026-04-10 19:38:22.798193: val_loss -0.1197 +2026-04-10 19:38:22.800922: Pseudo dice [0.5774, 0.1121, 0.5584, 0.6138, 0.1557, 0.5054, 0.373] +2026-04-10 19:38:22.802834: Epoch time: 102.28 s +2026-04-10 19:38:23.920017: +2026-04-10 19:38:23.922014: Epoch 328 +2026-04-10 19:38:23.926450: Current learning rate: 0.00926 +2026-04-10 19:40:05.708440: train_loss -0.1583 +2026-04-10 19:40:05.714729: val_loss -0.1073 +2026-04-10 19:40:05.716102: Pseudo dice [0.6122, 0.1615, 0.5007, 0.6915, 0.0947, 0.5969, 0.2405] +2026-04-10 19:40:05.718116: Epoch time: 101.79 s +2026-04-10 19:40:06.792216: +2026-04-10 19:40:06.794048: Epoch 329 +2026-04-10 19:40:06.796703: Current learning rate: 0.00926 +2026-04-10 19:41:50.053538: train_loss -0.159 +2026-04-10 19:41:50.059129: val_loss -0.1384 +2026-04-10 19:41:50.061050: Pseudo dice [0.3303, 0.4422, 0.4271, 0.5969, 0.2007, 0.765, 0.7618] +2026-04-10 19:41:50.062737: Epoch time: 103.26 s +2026-04-10 19:41:51.141365: +2026-04-10 19:41:51.143410: Epoch 330 +2026-04-10 19:41:51.145074: Current learning rate: 0.00925 +2026-04-10 19:43:32.844983: train_loss -0.1662 +2026-04-10 19:43:32.849517: val_loss -0.1139 +2026-04-10 19:43:32.851130: Pseudo dice [0.3667, 0.0351, 0.6499, 0.5045, 0.2934, 0.6837, 0.4517] +2026-04-10 19:43:32.853373: Epoch time: 101.71 s +2026-04-10 19:43:33.932374: +2026-04-10 19:43:33.933874: Epoch 331 +2026-04-10 19:43:33.935183: Current learning rate: 0.00925 +2026-04-10 19:45:16.349942: train_loss -0.1598 +2026-04-10 19:45:16.355093: val_loss -0.1134 +2026-04-10 19:45:16.356794: Pseudo dice [0.2155, 0.2834, 0.5128, 0.8006, 0.2705, 0.7611, 0.502] +2026-04-10 19:45:16.359341: Epoch time: 102.42 s +2026-04-10 19:45:17.440396: +2026-04-10 19:45:17.442925: Epoch 332 +2026-04-10 19:45:17.444679: Current learning rate: 0.00925 +2026-04-10 19:46:59.068717: train_loss -0.1458 +2026-04-10 19:46:59.073607: val_loss -0.1084 +2026-04-10 19:46:59.075690: Pseudo dice [0.2816, 0.3451, 0.6142, 0.7361, 0.275, 0.7358, 0.4469] +2026-04-10 19:46:59.077455: Epoch time: 101.63 s +2026-04-10 19:47:00.135136: +2026-04-10 19:47:00.136566: Epoch 333 +2026-04-10 19:47:00.138053: Current learning rate: 0.00925 +2026-04-10 19:48:41.550618: train_loss -0.1285 +2026-04-10 19:48:41.557409: val_loss -0.0976 +2026-04-10 19:48:41.559551: Pseudo dice [0.3235, 0.1655, 0.6849, 0.1534, 0.1851, 0.7656, 0.4191] +2026-04-10 19:48:41.561457: Epoch time: 101.42 s +2026-04-10 19:48:42.618361: +2026-04-10 19:48:42.619772: Epoch 334 +2026-04-10 19:48:42.621200: Current learning rate: 0.00925 +2026-04-10 19:50:24.316599: train_loss -0.1576 +2026-04-10 19:50:24.321938: val_loss -0.1367 +2026-04-10 19:50:24.323490: Pseudo dice [0.4245, 0.4036, 0.6208, 0.6397, 0.5923, 0.5528, 0.6854] +2026-04-10 19:50:24.324773: Epoch time: 101.7 s +2026-04-10 19:50:25.420417: +2026-04-10 19:50:25.422115: Epoch 335 +2026-04-10 19:50:25.424545: Current learning rate: 0.00924 +2026-04-10 19:52:06.697126: train_loss -0.1467 +2026-04-10 19:52:06.702038: val_loss -0.1326 +2026-04-10 19:52:06.704145: Pseudo dice [0.6685, 0.634, 0.6325, 0.5413, 0.083, 0.6803, 0.7387] +2026-04-10 19:52:06.706031: Epoch time: 101.28 s +2026-04-10 19:52:07.822657: +2026-04-10 19:52:07.824022: Epoch 336 +2026-04-10 19:52:07.825777: Current learning rate: 0.00924 +2026-04-10 19:53:49.798875: train_loss -0.1703 +2026-04-10 19:53:49.804293: val_loss -0.0891 +2026-04-10 19:53:49.806406: Pseudo dice [0.2931, 0.0267, 0.5376, 0.4543, 0.2567, 0.5426, 0.7604] +2026-04-10 19:53:49.808093: Epoch time: 101.98 s +2026-04-10 19:53:50.907577: +2026-04-10 19:53:50.909198: Epoch 337 +2026-04-10 19:53:50.910410: Current learning rate: 0.00924 +2026-04-10 19:55:32.504306: train_loss -0.157 +2026-04-10 19:55:32.509611: val_loss -0.1385 +2026-04-10 19:55:32.511278: Pseudo dice [0.5517, 0.3559, 0.5806, 0.4972, 0.0474, 0.4754, 0.7975] +2026-04-10 19:55:32.513502: Epoch time: 101.6 s +2026-04-10 19:55:33.601737: +2026-04-10 19:55:33.604417: Epoch 338 +2026-04-10 19:55:33.606049: Current learning rate: 0.00924 +2026-04-10 19:57:14.841446: train_loss -0.1565 +2026-04-10 19:57:14.846556: val_loss -0.1409 +2026-04-10 19:57:14.848500: Pseudo dice [0.7786, 0.5737, 0.5091, 0.7962, 0.3878, 0.8103, 0.7227] +2026-04-10 19:57:14.850103: Epoch time: 101.24 s +2026-04-10 19:57:15.954114: +2026-04-10 19:57:15.955891: Epoch 339 +2026-04-10 19:57:15.957299: Current learning rate: 0.00923 +2026-04-10 19:58:57.302900: train_loss -0.1561 +2026-04-10 19:58:57.308921: val_loss -0.1544 +2026-04-10 19:58:57.311172: Pseudo dice [0.638, 0.0726, 0.7016, 0.3072, 0.3678, 0.781, 0.8322] +2026-04-10 19:58:57.312932: Epoch time: 101.35 s +2026-04-10 19:58:58.423970: +2026-04-10 19:58:58.426027: Epoch 340 +2026-04-10 19:58:58.427312: Current learning rate: 0.00923 +2026-04-10 20:00:40.218986: train_loss -0.171 +2026-04-10 20:00:40.224716: val_loss -0.1325 +2026-04-10 20:00:40.226491: Pseudo dice [0.5076, 0.301, 0.5626, 0.388, 0.2108, 0.6435, 0.7684] +2026-04-10 20:00:40.229154: Epoch time: 101.8 s +2026-04-10 20:00:41.361203: +2026-04-10 20:00:41.362795: Epoch 341 +2026-04-10 20:00:41.364036: Current learning rate: 0.00923 +2026-04-10 20:02:23.117800: train_loss -0.1666 +2026-04-10 20:02:23.124305: val_loss -0.1329 +2026-04-10 20:02:23.127593: Pseudo dice [0.5341, 0.1472, 0.6636, 0.781, 0.224, 0.6962, 0.7907] +2026-04-10 20:02:23.129489: Epoch time: 101.76 s +2026-04-10 20:02:24.238905: +2026-04-10 20:02:24.240547: Epoch 342 +2026-04-10 20:02:24.241960: Current learning rate: 0.00923 +2026-04-10 20:04:05.919440: train_loss -0.1566 +2026-04-10 20:04:05.925490: val_loss -0.1501 +2026-04-10 20:04:05.928001: Pseudo dice [0.6008, 0.0628, 0.4346, 0.817, 0.3837, 0.7401, 0.7983] +2026-04-10 20:04:05.931584: Epoch time: 101.68 s +2026-04-10 20:04:05.933730: Yayy! New best EMA pseudo Dice: 0.5013 +2026-04-10 20:04:08.648100: +2026-04-10 20:04:08.650468: Epoch 343 +2026-04-10 20:04:08.651967: Current learning rate: 0.00922 +2026-04-10 20:05:50.685317: train_loss -0.1617 +2026-04-10 20:05:50.690338: val_loss -0.1165 +2026-04-10 20:05:50.692400: Pseudo dice [0.4889, 0.5543, 0.687, 0.6926, 0.288, 0.4958, 0.6244] +2026-04-10 20:05:50.693950: Epoch time: 102.04 s +2026-04-10 20:05:50.695860: Yayy! New best EMA pseudo Dice: 0.5059 +2026-04-10 20:05:53.402045: +2026-04-10 20:05:53.403519: Epoch 344 +2026-04-10 20:05:53.405015: Current learning rate: 0.00922 +2026-04-10 20:07:35.274581: train_loss -0.1552 +2026-04-10 20:07:35.279269: val_loss -0.1346 +2026-04-10 20:07:35.281254: Pseudo dice [0.3978, 0.0033, 0.5663, 0.644, 0.3882, 0.2385, 0.723] +2026-04-10 20:07:35.283039: Epoch time: 101.88 s +2026-04-10 20:07:36.403651: +2026-04-10 20:07:36.405729: Epoch 345 +2026-04-10 20:07:36.407675: Current learning rate: 0.00922 +2026-04-10 20:09:18.794829: train_loss -0.1681 +2026-04-10 20:09:18.802711: val_loss -0.1275 +2026-04-10 20:09:18.804792: Pseudo dice [0.2122, 0.4233, 0.7299, 0.797, 0.3517, 0.654, 0.7757] +2026-04-10 20:09:18.806857: Epoch time: 102.39 s +2026-04-10 20:09:19.924382: +2026-04-10 20:09:19.929156: Epoch 346 +2026-04-10 20:09:19.930746: Current learning rate: 0.00922 +2026-04-10 20:11:02.166780: train_loss -0.1678 +2026-04-10 20:11:02.174957: val_loss -0.11 +2026-04-10 20:11:02.177352: Pseudo dice [0.3814, 0.2588, 0.5212, 0.4028, 0.2623, 0.5186, 0.6087] +2026-04-10 20:11:02.178710: Epoch time: 102.25 s +2026-04-10 20:11:03.297602: +2026-04-10 20:11:03.299988: Epoch 347 +2026-04-10 20:11:03.302258: Current learning rate: 0.00922 +2026-04-10 20:12:45.789197: train_loss -0.1676 +2026-04-10 20:12:45.797813: val_loss -0.1285 +2026-04-10 20:12:45.801550: Pseudo dice [0.4641, 0.1054, 0.6245, 0.7915, 0.4932, 0.4963, 0.6573] +2026-04-10 20:12:45.803393: Epoch time: 102.49 s +2026-04-10 20:12:46.926089: +2026-04-10 20:12:46.928869: Epoch 348 +2026-04-10 20:12:46.930499: Current learning rate: 0.00921 +2026-04-10 20:14:29.690630: train_loss -0.1544 +2026-04-10 20:14:29.697937: val_loss -0.1241 +2026-04-10 20:14:29.699972: Pseudo dice [0.638, 0.3669, 0.5055, 0.5962, 0.2076, 0.6939, 0.6976] +2026-04-10 20:14:29.701805: Epoch time: 102.77 s +2026-04-10 20:14:30.798320: +2026-04-10 20:14:30.802227: Epoch 349 +2026-04-10 20:14:30.804204: Current learning rate: 0.00921 +2026-04-10 20:16:13.354677: train_loss -0.1678 +2026-04-10 20:16:13.361221: val_loss -0.1364 +2026-04-10 20:16:13.363562: Pseudo dice [0.4084, 0.2002, 0.6185, 0.7056, 0.2358, 0.5942, 0.6983] +2026-04-10 20:16:13.365293: Epoch time: 102.56 s +2026-04-10 20:16:16.259280: +2026-04-10 20:16:16.260819: Epoch 350 +2026-04-10 20:16:16.262460: Current learning rate: 0.00921 +2026-04-10 20:17:58.772441: train_loss -0.169 +2026-04-10 20:17:58.778033: val_loss -0.1271 +2026-04-10 20:17:58.780155: Pseudo dice [0.6781, 0.1908, 0.6105, 0.4045, 0.1351, 0.3597, 0.8313] +2026-04-10 20:17:58.781631: Epoch time: 102.52 s +2026-04-10 20:17:59.861418: +2026-04-10 20:17:59.862859: Epoch 351 +2026-04-10 20:17:59.864771: Current learning rate: 0.00921 +2026-04-10 20:19:41.378506: train_loss -0.1577 +2026-04-10 20:19:41.386999: val_loss -0.1139 +2026-04-10 20:19:41.388790: Pseudo dice [0.3882, 0.1375, 0.6249, 0.5198, 0.2828, 0.7216, 0.6346] +2026-04-10 20:19:41.390415: Epoch time: 101.52 s +2026-04-10 20:19:42.502664: +2026-04-10 20:19:42.506505: Epoch 352 +2026-04-10 20:19:42.508607: Current learning rate: 0.0092 +2026-04-10 20:21:23.874594: train_loss -0.1566 +2026-04-10 20:21:23.879746: val_loss -0.1039 +2026-04-10 20:21:23.881328: Pseudo dice [0.4351, 0.3596, 0.6056, 0.512, 0.1598, 0.1958, 0.7721] +2026-04-10 20:21:23.882932: Epoch time: 101.38 s +2026-04-10 20:21:24.979012: +2026-04-10 20:21:24.980968: Epoch 353 +2026-04-10 20:21:24.982590: Current learning rate: 0.0092 +2026-04-10 20:23:06.561727: train_loss -0.163 +2026-04-10 20:23:06.566774: val_loss -0.0936 +2026-04-10 20:23:06.569290: Pseudo dice [0.367, 0.3421, 0.6793, 0.4155, 0.2219, 0.5166, 0.5191] +2026-04-10 20:23:06.570893: Epoch time: 101.59 s +2026-04-10 20:23:07.684742: +2026-04-10 20:23:07.686187: Epoch 354 +2026-04-10 20:23:07.687846: Current learning rate: 0.0092 +2026-04-10 20:24:49.328973: train_loss -0.1607 +2026-04-10 20:24:49.334380: val_loss -0.1358 +2026-04-10 20:24:49.336650: Pseudo dice [0.489, 0.1649, 0.6987, 0.0797, 0.3945, 0.7157, 0.1755] +2026-04-10 20:24:49.338427: Epoch time: 101.65 s +2026-04-10 20:24:50.417277: +2026-04-10 20:24:50.419250: Epoch 355 +2026-04-10 20:24:50.420748: Current learning rate: 0.0092 +2026-04-10 20:26:32.101404: train_loss -0.151 +2026-04-10 20:26:32.106642: val_loss -0.1157 +2026-04-10 20:26:32.108768: Pseudo dice [0.306, 0.1204, 0.6923, 0.7752, 0.3304, 0.6933, 0.5356] +2026-04-10 20:26:32.110457: Epoch time: 101.69 s +2026-04-10 20:26:33.220534: +2026-04-10 20:26:33.222092: Epoch 356 +2026-04-10 20:26:33.223513: Current learning rate: 0.0092 +2026-04-10 20:28:14.709476: train_loss -0.1522 +2026-04-10 20:28:14.714957: val_loss -0.1044 +2026-04-10 20:28:14.716713: Pseudo dice [0.6605, 0.1313, 0.4055, 0.0997, 0.3857, 0.5504, 0.7857] +2026-04-10 20:28:14.718375: Epoch time: 101.49 s +2026-04-10 20:28:15.825082: +2026-04-10 20:28:15.827251: Epoch 357 +2026-04-10 20:28:15.828664: Current learning rate: 0.00919 +2026-04-10 20:29:57.404942: train_loss -0.1511 +2026-04-10 20:29:57.409839: val_loss -0.1354 +2026-04-10 20:29:57.412709: Pseudo dice [0.2972, 0.3006, 0.6424, 0.7195, 0.2632, 0.7007, 0.8051] +2026-04-10 20:29:57.414490: Epoch time: 101.58 s +2026-04-10 20:29:58.530594: +2026-04-10 20:29:58.532498: Epoch 358 +2026-04-10 20:29:58.533849: Current learning rate: 0.00919 +2026-04-10 20:31:40.085272: train_loss -0.1593 +2026-04-10 20:31:40.091390: val_loss -0.1213 +2026-04-10 20:31:40.093412: Pseudo dice [0.4032, 0.2844, 0.4252, 0.6052, 0.3401, 0.0822, 0.7548] +2026-04-10 20:31:40.095347: Epoch time: 101.56 s +2026-04-10 20:31:41.189043: +2026-04-10 20:31:41.190986: Epoch 359 +2026-04-10 20:31:41.192475: Current learning rate: 0.00919 +2026-04-10 20:33:22.394499: train_loss -0.1606 +2026-04-10 20:33:22.398403: val_loss -0.1053 +2026-04-10 20:33:22.400787: Pseudo dice [0.5769, 0.3288, 0.7374, 0.0801, 0.1974, 0.7999, 0.189] +2026-04-10 20:33:22.402905: Epoch time: 101.21 s +2026-04-10 20:33:23.521742: +2026-04-10 20:33:23.523609: Epoch 360 +2026-04-10 20:33:23.525281: Current learning rate: 0.00919 +2026-04-10 20:35:05.004906: train_loss -0.1758 +2026-04-10 20:35:05.010243: val_loss -0.0998 +2026-04-10 20:35:05.011889: Pseudo dice [0.2444, 0.5879, 0.4598, 0.5318, 0.2192, 0.477, 0.8166] +2026-04-10 20:35:05.013575: Epoch time: 101.49 s +2026-04-10 20:35:06.115904: +2026-04-10 20:35:06.117962: Epoch 361 +2026-04-10 20:35:06.119647: Current learning rate: 0.00918 +2026-04-10 20:36:48.138253: train_loss -0.1574 +2026-04-10 20:36:48.144522: val_loss -0.1346 +2026-04-10 20:36:48.146724: Pseudo dice [0.7868, 0.4921, 0.5936, 0.8003, 0.4421, 0.7582, 0.4488] +2026-04-10 20:36:48.162209: Epoch time: 102.03 s +2026-04-10 20:36:49.249171: +2026-04-10 20:36:49.251456: Epoch 362 +2026-04-10 20:36:49.252877: Current learning rate: 0.00918 +2026-04-10 20:38:30.973278: train_loss -0.1672 +2026-04-10 20:38:30.978543: val_loss -0.1177 +2026-04-10 20:38:30.980314: Pseudo dice [0.4323, 0.5859, 0.8342, 0.2551, 0.1472, 0.7033, 0.3031] +2026-04-10 20:38:30.982037: Epoch time: 101.73 s +2026-04-10 20:38:32.067618: +2026-04-10 20:38:32.069398: Epoch 363 +2026-04-10 20:38:32.070800: Current learning rate: 0.00918 +2026-04-10 20:40:13.612117: train_loss -0.1464 +2026-04-10 20:40:13.616961: val_loss -0.1068 +2026-04-10 20:40:13.618418: Pseudo dice [0.5323, 0.083, 0.6037, 0.5554, 0.3985, 0.693, 0.5223] +2026-04-10 20:40:13.620379: Epoch time: 101.55 s +2026-04-10 20:40:14.713580: +2026-04-10 20:40:14.716348: Epoch 364 +2026-04-10 20:40:14.717861: Current learning rate: 0.00918 +2026-04-10 20:41:57.051166: train_loss -0.1615 +2026-04-10 20:41:57.057346: val_loss -0.118 +2026-04-10 20:41:57.059092: Pseudo dice [0.4553, 0.3254, 0.6967, 0.1404, 0.0756, 0.3507, 0.5699] +2026-04-10 20:41:57.062765: Epoch time: 102.34 s +2026-04-10 20:41:58.162219: +2026-04-10 20:41:58.164195: Epoch 365 +2026-04-10 20:41:58.165558: Current learning rate: 0.00917 +2026-04-10 20:43:39.775388: train_loss -0.1622 +2026-04-10 20:43:39.781072: val_loss -0.1283 +2026-04-10 20:43:39.782925: Pseudo dice [0.6814, 0.1263, 0.7854, 0.7896, 0.2498, 0.7621, 0.658] +2026-04-10 20:43:39.788732: Epoch time: 101.62 s +2026-04-10 20:43:40.897238: +2026-04-10 20:43:40.898607: Epoch 366 +2026-04-10 20:43:40.899873: Current learning rate: 0.00917 +2026-04-10 20:45:22.392710: train_loss -0.1748 +2026-04-10 20:45:22.411011: val_loss -0.1311 +2026-04-10 20:45:22.414454: Pseudo dice [0.7551, 0.4386, 0.4898, 0.7849, 0.3375, 0.2886, 0.7707] +2026-04-10 20:45:22.416294: Epoch time: 101.5 s +2026-04-10 20:45:23.487571: +2026-04-10 20:45:23.489354: Epoch 367 +2026-04-10 20:45:23.490772: Current learning rate: 0.00917 +2026-04-10 20:47:05.828618: train_loss -0.1522 +2026-04-10 20:47:05.834743: val_loss -0.1333 +2026-04-10 20:47:05.837065: Pseudo dice [0.4444, 0.0735, 0.6345, 0.8192, 0.256, 0.6989, 0.6609] +2026-04-10 20:47:05.838635: Epoch time: 102.34 s +2026-04-10 20:47:08.061811: +2026-04-10 20:47:08.065234: Epoch 368 +2026-04-10 20:47:08.067369: Current learning rate: 0.00917 +2026-04-10 20:48:50.011088: train_loss -0.1663 +2026-04-10 20:48:50.016095: val_loss -0.0996 +2026-04-10 20:48:50.017503: Pseudo dice [0.4802, 0.2601, 0.4435, 0.555, 0.2492, 0.6761, 0.5616] +2026-04-10 20:48:50.018911: Epoch time: 101.95 s +2026-04-10 20:48:51.103428: +2026-04-10 20:48:51.104944: Epoch 369 +2026-04-10 20:48:51.106205: Current learning rate: 0.00917 +2026-04-10 20:50:32.694029: train_loss -0.1725 +2026-04-10 20:50:32.702044: val_loss -0.1536 +2026-04-10 20:50:32.704841: Pseudo dice [0.3055, 0.2727, 0.6145, 0.6829, 0.3006, 0.7589, 0.7933] +2026-04-10 20:50:32.706433: Epoch time: 101.59 s +2026-04-10 20:50:33.784757: +2026-04-10 20:50:33.786682: Epoch 370 +2026-04-10 20:50:33.788227: Current learning rate: 0.00916 +2026-04-10 20:52:15.294923: train_loss -0.1676 +2026-04-10 20:52:15.301148: val_loss -0.1116 +2026-04-10 20:52:15.302881: Pseudo dice [0.6539, 0.2777, 0.2577, 0.2025, 0.2787, 0.4082, 0.5978] +2026-04-10 20:52:15.305148: Epoch time: 101.51 s +2026-04-10 20:52:16.392395: +2026-04-10 20:52:16.394774: Epoch 371 +2026-04-10 20:52:16.396600: Current learning rate: 0.00916 +2026-04-10 20:53:58.067063: train_loss -0.1633 +2026-04-10 20:53:58.072736: val_loss -0.1275 +2026-04-10 20:53:58.074653: Pseudo dice [0.27, 0.5562, 0.7889, 0.8988, 0.4493, 0.7479, 0.6059] +2026-04-10 20:53:58.076315: Epoch time: 101.68 s +2026-04-10 20:53:59.175314: +2026-04-10 20:53:59.178260: Epoch 372 +2026-04-10 20:53:59.179936: Current learning rate: 0.00916 +2026-04-10 20:55:41.641542: train_loss -0.1677 +2026-04-10 20:55:41.646828: val_loss -0.1362 +2026-04-10 20:55:41.648287: Pseudo dice [0.2047, 0.2441, 0.7049, 0.7936, 0.2864, 0.5072, 0.7748] +2026-04-10 20:55:41.649830: Epoch time: 102.47 s +2026-04-10 20:55:42.731625: +2026-04-10 20:55:42.733291: Epoch 373 +2026-04-10 20:55:42.735169: Current learning rate: 0.00916 +2026-04-10 20:57:24.272552: train_loss -0.1622 +2026-04-10 20:57:24.277257: val_loss -0.1068 +2026-04-10 20:57:24.279078: Pseudo dice [0.3377, 0.2884, 0.6294, 0.5311, 0.3541, 0.703, 0.6401] +2026-04-10 20:57:24.280674: Epoch time: 101.54 s +2026-04-10 20:57:25.389291: +2026-04-10 20:57:25.391385: Epoch 374 +2026-04-10 20:57:25.393330: Current learning rate: 0.00915 +2026-04-10 20:59:07.659490: train_loss -0.1624 +2026-04-10 20:59:07.666255: val_loss -0.1476 +2026-04-10 20:59:07.668726: Pseudo dice [0.6028, 0.2405, 0.7036, 0.7895, 0.3819, 0.7626, 0.6703] +2026-04-10 20:59:07.670411: Epoch time: 102.27 s +2026-04-10 20:59:08.781607: +2026-04-10 20:59:08.784085: Epoch 375 +2026-04-10 20:59:08.786695: Current learning rate: 0.00915 +2026-04-10 21:00:50.364627: train_loss -0.1756 +2026-04-10 21:00:50.376513: val_loss -0.1244 +2026-04-10 21:00:50.378482: Pseudo dice [0.518, 0.2247, 0.8371, 0.5905, 0.2697, 0.2945, 0.588] +2026-04-10 21:00:50.380150: Epoch time: 101.59 s +2026-04-10 21:00:51.467889: +2026-04-10 21:00:51.469887: Epoch 376 +2026-04-10 21:00:51.471568: Current learning rate: 0.00915 +2026-04-10 21:02:32.927306: train_loss -0.1676 +2026-04-10 21:02:32.932735: val_loss -0.1227 +2026-04-10 21:02:32.934270: Pseudo dice [0.7581, 0.0656, 0.5933, 0.2025, 0.3216, 0.5872, 0.4708] +2026-04-10 21:02:32.935748: Epoch time: 101.46 s +2026-04-10 21:02:34.027122: +2026-04-10 21:02:34.028832: Epoch 377 +2026-04-10 21:02:34.030159: Current learning rate: 0.00915 +2026-04-10 21:04:15.470912: train_loss -0.1758 +2026-04-10 21:04:15.474945: val_loss -0.1377 +2026-04-10 21:04:15.476723: Pseudo dice [0.3412, 0.0498, 0.7035, 0.5839, 0.2478, 0.7424, 0.8201] +2026-04-10 21:04:15.478291: Epoch time: 101.45 s +2026-04-10 21:04:16.580585: +2026-04-10 21:04:16.582156: Epoch 378 +2026-04-10 21:04:16.584685: Current learning rate: 0.00915 +2026-04-10 21:05:58.586953: train_loss -0.1669 +2026-04-10 21:05:58.592592: val_loss -0.1223 +2026-04-10 21:05:58.595381: Pseudo dice [0.5804, 0.3922, 0.5165, 0.677, 0.1769, 0.8362, 0.8368] +2026-04-10 21:05:58.597583: Epoch time: 102.01 s +2026-04-10 21:05:59.708148: +2026-04-10 21:05:59.709695: Epoch 379 +2026-04-10 21:05:59.711298: Current learning rate: 0.00914 +2026-04-10 21:07:41.333594: train_loss -0.1586 +2026-04-10 21:07:41.339110: val_loss -0.1502 +2026-04-10 21:07:41.341270: Pseudo dice [0.7792, 0.0413, 0.7054, 0.8027, 0.409, 0.5548, 0.7827] +2026-04-10 21:07:41.342836: Epoch time: 101.63 s +2026-04-10 21:07:41.344606: Yayy! New best EMA pseudo Dice: 0.5109 +2026-04-10 21:07:43.998781: +2026-04-10 21:07:44.000593: Epoch 380 +2026-04-10 21:07:44.002169: Current learning rate: 0.00914 +2026-04-10 21:09:25.518488: train_loss -0.1529 +2026-04-10 21:09:25.528433: val_loss -0.1335 +2026-04-10 21:09:25.530202: Pseudo dice [0.3277, 0.0182, 0.7482, 0.7609, 0.2306, 0.6402, 0.6882] +2026-04-10 21:09:25.532662: Epoch time: 101.52 s +2026-04-10 21:09:26.615766: +2026-04-10 21:09:26.617683: Epoch 381 +2026-04-10 21:09:26.619220: Current learning rate: 0.00914 +2026-04-10 21:11:08.793836: train_loss -0.1573 +2026-04-10 21:11:08.800448: val_loss -0.1369 +2026-04-10 21:11:08.802237: Pseudo dice [0.4953, 0.3556, 0.7443, 0.6755, 0.4217, 0.7582, 0.7892] +2026-04-10 21:11:08.804508: Epoch time: 102.18 s +2026-04-10 21:11:08.806251: Yayy! New best EMA pseudo Dice: 0.5183 +2026-04-10 21:11:11.557668: +2026-04-10 21:11:11.559658: Epoch 382 +2026-04-10 21:11:11.561330: Current learning rate: 0.00914 +2026-04-10 21:12:53.501826: train_loss -0.1829 +2026-04-10 21:12:53.507612: val_loss -0.1352 +2026-04-10 21:12:53.509388: Pseudo dice [0.6447, 0.3123, 0.6477, 0.7688, 0.3653, 0.5125, 0.7103] +2026-04-10 21:12:53.511187: Epoch time: 101.95 s +2026-04-10 21:12:53.514196: Yayy! New best EMA pseudo Dice: 0.5231 +2026-04-10 21:12:56.210440: +2026-04-10 21:12:56.212360: Epoch 383 +2026-04-10 21:12:56.213886: Current learning rate: 0.00913 +2026-04-10 21:14:37.967652: train_loss -0.1601 +2026-04-10 21:14:37.973996: val_loss -0.1378 +2026-04-10 21:14:37.975752: Pseudo dice [0.6647, 0.5013, 0.6113, 0.8359, 0.2629, 0.7282, 0.7977] +2026-04-10 21:14:37.977496: Epoch time: 101.76 s +2026-04-10 21:14:37.979379: Yayy! New best EMA pseudo Dice: 0.5337 +2026-04-10 21:14:40.714743: +2026-04-10 21:14:40.716936: Epoch 384 +2026-04-10 21:14:40.718732: Current learning rate: 0.00913 +2026-04-10 21:16:22.387660: train_loss -0.1638 +2026-04-10 21:16:22.394652: val_loss -0.1183 +2026-04-10 21:16:22.399437: Pseudo dice [0.3857, 0.3132, 0.6211, 0.6878, 0.2914, 0.6529, 0.8609] +2026-04-10 21:16:22.401081: Epoch time: 101.68 s +2026-04-10 21:16:22.402695: Yayy! New best EMA pseudo Dice: 0.5348 +2026-04-10 21:16:25.068483: +2026-04-10 21:16:25.070048: Epoch 385 +2026-04-10 21:16:25.071268: Current learning rate: 0.00913 +2026-04-10 21:18:07.322796: train_loss -0.1734 +2026-04-10 21:18:07.328504: val_loss -0.1519 +2026-04-10 21:18:07.330756: Pseudo dice [0.6532, 0.3539, 0.779, 0.8128, 0.4023, 0.5686, 0.8266] +2026-04-10 21:18:07.332298: Epoch time: 102.26 s +2026-04-10 21:18:07.334032: Yayy! New best EMA pseudo Dice: 0.5441 +2026-04-10 21:18:11.028806: +2026-04-10 21:18:11.031896: Epoch 386 +2026-04-10 21:18:11.033585: Current learning rate: 0.00913 +2026-04-10 21:19:53.048423: train_loss -0.1546 +2026-04-10 21:19:53.054649: val_loss -0.0994 +2026-04-10 21:19:53.056360: Pseudo dice [0.5135, 0.1203, 0.5491, 0.584, 0.4052, 0.2024, 0.5362] +2026-04-10 21:19:53.059055: Epoch time: 102.02 s +2026-04-10 21:19:54.177633: +2026-04-10 21:19:54.180001: Epoch 387 +2026-04-10 21:19:54.181810: Current learning rate: 0.00912 +2026-04-10 21:21:35.465365: train_loss -0.1741 +2026-04-10 21:21:35.472195: val_loss -0.1251 +2026-04-10 21:21:35.474120: Pseudo dice [0.6547, 0.3255, 0.6511, 0.6257, 0.2908, 0.7055, 0.7628] +2026-04-10 21:21:35.476279: Epoch time: 101.29 s +2026-04-10 21:21:36.580124: +2026-04-10 21:21:36.583176: Epoch 388 +2026-04-10 21:21:36.585003: Current learning rate: 0.00912 +2026-04-10 21:23:18.020638: train_loss -0.1625 +2026-04-10 21:23:18.026134: val_loss -0.1477 +2026-04-10 21:23:18.028139: Pseudo dice [0.5993, 0.364, 0.8576, 0.4111, 0.1814, 0.6555, 0.841] +2026-04-10 21:23:18.030317: Epoch time: 101.44 s +2026-04-10 21:23:19.152982: +2026-04-10 21:23:19.159650: Epoch 389 +2026-04-10 21:23:19.169642: Current learning rate: 0.00912 +2026-04-10 21:25:01.066896: train_loss -0.1406 +2026-04-10 21:25:01.072991: val_loss -0.1328 +2026-04-10 21:25:01.075211: Pseudo dice [0.4094, 0.3358, 0.6294, 0.6576, 0.3493, 0.408, 0.6437] +2026-04-10 21:25:01.076939: Epoch time: 101.92 s +2026-04-10 21:25:02.206534: +2026-04-10 21:25:02.208836: Epoch 390 +2026-04-10 21:25:02.210978: Current learning rate: 0.00912 +2026-04-10 21:26:44.335042: train_loss -0.1666 +2026-04-10 21:26:44.340088: val_loss -0.1281 +2026-04-10 21:26:44.342212: Pseudo dice [0.4065, 0.4694, 0.4575, 0.8731, 0.3963, 0.2186, 0.6209] +2026-04-10 21:26:44.343997: Epoch time: 102.13 s +2026-04-10 21:26:45.475247: +2026-04-10 21:26:45.477203: Epoch 391 +2026-04-10 21:26:45.479156: Current learning rate: 0.00912 +2026-04-10 21:28:27.306334: train_loss -0.1706 +2026-04-10 21:28:27.312384: val_loss -0.1384 +2026-04-10 21:28:27.315667: Pseudo dice [0.3431, 0.3041, 0.6628, 0.458, 0.4495, 0.7335, 0.6976] +2026-04-10 21:28:27.319714: Epoch time: 101.83 s +2026-04-10 21:28:28.427025: +2026-04-10 21:28:28.429120: Epoch 392 +2026-04-10 21:28:28.430690: Current learning rate: 0.00911 +2026-04-10 21:30:10.508022: train_loss -0.166 +2026-04-10 21:30:10.514043: val_loss -0.0479 +2026-04-10 21:30:10.516188: Pseudo dice [0.6959, 0.6554, 0.2327, 0.5106, 0.156, 0.5212, 0.5082] +2026-04-10 21:30:10.518002: Epoch time: 102.08 s +2026-04-10 21:30:11.665984: +2026-04-10 21:30:11.668616: Epoch 393 +2026-04-10 21:30:11.670597: Current learning rate: 0.00911 +2026-04-10 21:31:53.166143: train_loss -0.1278 +2026-04-10 21:31:53.171152: val_loss -0.1283 +2026-04-10 21:31:53.173009: Pseudo dice [0.3003, 0.1722, 0.8426, 0.5071, 0.2682, 0.6099, 0.5895] +2026-04-10 21:31:53.175133: Epoch time: 101.5 s +2026-04-10 21:31:54.287331: +2026-04-10 21:31:54.289091: Epoch 394 +2026-04-10 21:31:54.291020: Current learning rate: 0.00911 +2026-04-10 21:33:35.739346: train_loss -0.1583 +2026-04-10 21:33:35.745556: val_loss -0.0987 +2026-04-10 21:33:35.747445: Pseudo dice [0.3482, 0.3433, 0.6637, 0.7388, 0.278, 0.6381, 0.5455] +2026-04-10 21:33:35.749045: Epoch time: 101.46 s +2026-04-10 21:33:36.843884: +2026-04-10 21:33:36.846024: Epoch 395 +2026-04-10 21:33:36.847972: Current learning rate: 0.00911 +2026-04-10 21:35:18.523319: train_loss -0.138 +2026-04-10 21:35:18.530752: val_loss -0.0894 +2026-04-10 21:35:18.533974: Pseudo dice [0.5206, 0.5699, 0.4325, 0.0243, 0.178, 0.5483, 0.3648] +2026-04-10 21:35:18.535788: Epoch time: 101.68 s +2026-04-10 21:35:19.689283: +2026-04-10 21:35:19.691663: Epoch 396 +2026-04-10 21:35:19.693056: Current learning rate: 0.0091 +2026-04-10 21:37:01.635302: train_loss -0.14 +2026-04-10 21:37:01.642853: val_loss -0.1247 +2026-04-10 21:37:01.646152: Pseudo dice [0.25, 0.2591, 0.6959, 0.1038, 0.2437, 0.4914, 0.6223] +2026-04-10 21:37:01.648842: Epoch time: 101.95 s +2026-04-10 21:37:02.772291: +2026-04-10 21:37:02.775043: Epoch 397 +2026-04-10 21:37:02.789958: Current learning rate: 0.0091 +2026-04-10 21:38:44.143359: train_loss -0.1473 +2026-04-10 21:38:44.149257: val_loss -0.1387 +2026-04-10 21:38:44.151076: Pseudo dice [0.4256, 0.2028, 0.7677, 0.5923, 0.283, 0.5006, 0.8685] +2026-04-10 21:38:44.152700: Epoch time: 101.37 s +2026-04-10 21:38:45.278235: +2026-04-10 21:38:45.280209: Epoch 398 +2026-04-10 21:38:45.281501: Current learning rate: 0.0091 +2026-04-10 21:40:26.879822: train_loss -0.1562 +2026-04-10 21:40:26.884751: val_loss -0.1103 +2026-04-10 21:40:26.887116: Pseudo dice [0.3717, 0.0899, 0.6485, 0.2427, 0.1296, 0.6222, 0.4176] +2026-04-10 21:40:26.888821: Epoch time: 101.6 s +2026-04-10 21:40:27.997059: +2026-04-10 21:40:27.998576: Epoch 399 +2026-04-10 21:40:28.000054: Current learning rate: 0.0091 +2026-04-10 21:42:09.475784: train_loss -0.1559 +2026-04-10 21:42:09.480483: val_loss -0.096 +2026-04-10 21:42:09.482604: Pseudo dice [0.2727, 0.3466, 0.5335, 0.0848, 0.3778, 0.4771, 0.7136] +2026-04-10 21:42:09.484295: Epoch time: 101.48 s +2026-04-10 21:42:12.274976: +2026-04-10 21:42:12.276659: Epoch 400 +2026-04-10 21:42:12.278332: Current learning rate: 0.0091 +2026-04-10 21:43:53.653804: train_loss -0.1534 +2026-04-10 21:43:53.659383: val_loss -0.138 +2026-04-10 21:43:53.661368: Pseudo dice [0.737, 0.1076, 0.7652, 0.1632, 0.323, 0.5903, 0.4947] +2026-04-10 21:43:53.662873: Epoch time: 101.38 s +2026-04-10 21:43:54.791737: +2026-04-10 21:43:54.794293: Epoch 401 +2026-04-10 21:43:54.796495: Current learning rate: 0.00909 +2026-04-10 21:45:37.146297: train_loss -0.1699 +2026-04-10 21:45:37.151846: val_loss -0.1274 +2026-04-10 21:45:37.153712: Pseudo dice [0.6296, 0.5104, 0.467, 0.7803, 0.3413, 0.2869, 0.7984] +2026-04-10 21:45:37.155241: Epoch time: 102.36 s +2026-04-10 21:45:38.295835: +2026-04-10 21:45:38.297314: Epoch 402 +2026-04-10 21:45:38.298946: Current learning rate: 0.00909 +2026-04-10 21:47:19.849096: train_loss -0.1803 +2026-04-10 21:47:19.854566: val_loss -0.1227 +2026-04-10 21:47:19.857162: Pseudo dice [0.5543, 0.2727, 0.7413, 0.6779, 0.4975, 0.343, 0.5644] +2026-04-10 21:47:19.859351: Epoch time: 101.56 s +2026-04-10 21:47:20.964685: +2026-04-10 21:47:20.966526: Epoch 403 +2026-04-10 21:47:20.968542: Current learning rate: 0.00909 +2026-04-10 21:49:02.527060: train_loss -0.1703 +2026-04-10 21:49:02.537263: val_loss -0.146 +2026-04-10 21:49:02.539711: Pseudo dice [0.7526, 0.2542, 0.7693, 0.0377, 0.4656, 0.5959, 0.7801] +2026-04-10 21:49:02.542111: Epoch time: 101.57 s +2026-04-10 21:49:03.657409: +2026-04-10 21:49:03.659441: Epoch 404 +2026-04-10 21:49:03.661212: Current learning rate: 0.00909 +2026-04-10 21:50:45.685041: train_loss -0.1743 +2026-04-10 21:50:45.690128: val_loss -0.1552 +2026-04-10 21:50:45.691471: Pseudo dice [0.715, 0.3339, 0.7236, 0.7862, 0.4529, 0.635, 0.6858] +2026-04-10 21:50:45.693557: Epoch time: 102.03 s +2026-04-10 21:50:47.885761: +2026-04-10 21:50:47.887516: Epoch 405 +2026-04-10 21:50:47.889391: Current learning rate: 0.00908 +2026-04-10 21:52:29.641647: train_loss -0.164 +2026-04-10 21:52:29.647404: val_loss -0.0924 +2026-04-10 21:52:29.649464: Pseudo dice [0.3285, 0.2016, 0.5723, 0.6726, 0.4367, 0.7248, 0.091] +2026-04-10 21:52:29.651334: Epoch time: 101.76 s +2026-04-10 21:52:30.751931: +2026-04-10 21:52:30.755571: Epoch 406 +2026-04-10 21:52:30.757088: Current learning rate: 0.00908 +2026-04-10 21:54:12.730125: train_loss -0.1673 +2026-04-10 21:54:12.736998: val_loss -0.1449 +2026-04-10 21:54:12.739202: Pseudo dice [0.3292, 0.0275, 0.6439, 0.5142, 0.5138, 0.7606, 0.7573] +2026-04-10 21:54:12.740869: Epoch time: 101.98 s +2026-04-10 21:54:13.871742: +2026-04-10 21:54:13.874256: Epoch 407 +2026-04-10 21:54:13.875895: Current learning rate: 0.00908 +2026-04-10 21:55:55.735876: train_loss -0.1697 +2026-04-10 21:55:55.743837: val_loss -0.1123 +2026-04-10 21:55:55.745411: Pseudo dice [0.642, 0.2726, 0.6609, 0.5647, 0.1121, 0.5627, 0.6081] +2026-04-10 21:55:55.748137: Epoch time: 101.87 s +2026-04-10 21:55:56.877849: +2026-04-10 21:55:56.879953: Epoch 408 +2026-04-10 21:55:56.881587: Current learning rate: 0.00908 +2026-04-10 21:57:39.475193: train_loss -0.1637 +2026-04-10 21:57:39.480675: val_loss -0.1267 +2026-04-10 21:57:39.483106: Pseudo dice [0.628, 0.2658, 0.7672, 0.8143, 0.132, 0.7238, 0.4033] +2026-04-10 21:57:39.486359: Epoch time: 102.6 s +2026-04-10 21:57:40.615216: +2026-04-10 21:57:40.617504: Epoch 409 +2026-04-10 21:57:40.619263: Current learning rate: 0.00907 +2026-04-10 21:59:22.637098: train_loss -0.1603 +2026-04-10 21:59:22.642690: val_loss -0.1471 +2026-04-10 21:59:22.645384: Pseudo dice [0.6274, 0.069, 0.7368, 0.2698, 0.4126, 0.7424, 0.5764] +2026-04-10 21:59:22.647898: Epoch time: 102.02 s +2026-04-10 21:59:23.758550: +2026-04-10 21:59:23.760082: Epoch 410 +2026-04-10 21:59:23.761874: Current learning rate: 0.00907 +2026-04-10 22:01:05.358116: train_loss -0.1484 +2026-04-10 22:01:05.362890: val_loss -0.1359 +2026-04-10 22:01:05.364575: Pseudo dice [0.211, 0.4791, 0.6548, 0.6761, 0.1719, 0.7859, 0.7785] +2026-04-10 22:01:05.366104: Epoch time: 101.6 s +2026-04-10 22:01:06.398118: +2026-04-10 22:01:06.400816: Epoch 411 +2026-04-10 22:01:06.402805: Current learning rate: 0.00907 +2026-04-10 22:02:48.105709: train_loss -0.1548 +2026-04-10 22:02:48.110966: val_loss -0.0631 +2026-04-10 22:02:48.115005: Pseudo dice [0.3253, 0.1721, 0.6641, 0.1163, 0.496, 0.4511, 0.03] +2026-04-10 22:02:48.116922: Epoch time: 101.71 s +2026-04-10 22:02:49.163779: +2026-04-10 22:02:49.165872: Epoch 412 +2026-04-10 22:02:49.167833: Current learning rate: 0.00907 +2026-04-10 22:04:31.000418: train_loss -0.1508 +2026-04-10 22:04:31.006900: val_loss -0.1314 +2026-04-10 22:04:31.008831: Pseudo dice [0.4543, 0.4567, 0.6326, 0.6165, 0.2966, 0.7259, 0.5015] +2026-04-10 22:04:31.012371: Epoch time: 101.84 s +2026-04-10 22:04:32.087365: +2026-04-10 22:04:32.089453: Epoch 413 +2026-04-10 22:04:32.091125: Current learning rate: 0.00907 +2026-04-10 22:06:13.815975: train_loss -0.1408 +2026-04-10 22:06:13.820775: val_loss -0.1606 +2026-04-10 22:06:13.822831: Pseudo dice [0.3856, 0.645, 0.7362, 0.7564, 0.3162, 0.8451, 0.6485] +2026-04-10 22:06:13.824461: Epoch time: 101.73 s +2026-04-10 22:06:14.873415: +2026-04-10 22:06:14.875022: Epoch 414 +2026-04-10 22:06:14.876567: Current learning rate: 0.00906 +2026-04-10 22:07:56.552905: train_loss -0.1569 +2026-04-10 22:07:56.557468: val_loss -0.0901 +2026-04-10 22:07:56.559041: Pseudo dice [0.2589, 0.4088, 0.731, 0.0857, 0.2873, 0.5356, 0.661] +2026-04-10 22:07:56.560719: Epoch time: 101.68 s +2026-04-10 22:07:57.625338: +2026-04-10 22:07:57.629316: Epoch 415 +2026-04-10 22:07:57.631621: Current learning rate: 0.00906 +2026-04-10 22:09:39.075580: train_loss -0.1602 +2026-04-10 22:09:39.082400: val_loss -0.1452 +2026-04-10 22:09:39.084177: Pseudo dice [0.3457, 0.318, 0.7893, 0.6227, 0.4418, 0.4517, 0.6527] +2026-04-10 22:09:39.086566: Epoch time: 101.45 s +2026-04-10 22:09:40.113047: +2026-04-10 22:09:40.115011: Epoch 416 +2026-04-10 22:09:40.116644: Current learning rate: 0.00906 +2026-04-10 22:11:22.209434: train_loss -0.1718 +2026-04-10 22:11:22.214550: val_loss -0.1682 +2026-04-10 22:11:22.216705: Pseudo dice [0.6907, 0.1276, 0.686, 0.7737, 0.391, 0.7969, 0.6164] +2026-04-10 22:11:22.218428: Epoch time: 102.1 s +2026-04-10 22:11:23.280146: +2026-04-10 22:11:23.281650: Epoch 417 +2026-04-10 22:11:23.283399: Current learning rate: 0.00906 +2026-04-10 22:13:05.516366: train_loss -0.1602 +2026-04-10 22:13:05.521701: val_loss -0.1354 +2026-04-10 22:13:05.523396: Pseudo dice [0.4458, 0.3639, 0.7739, 0.4657, 0.3657, 0.7957, 0.7684] +2026-04-10 22:13:05.524731: Epoch time: 102.24 s +2026-04-10 22:13:06.598772: +2026-04-10 22:13:06.600209: Epoch 418 +2026-04-10 22:13:06.601536: Current learning rate: 0.00905 +2026-04-10 22:14:48.520854: train_loss -0.1752 +2026-04-10 22:14:48.526818: val_loss -0.1517 +2026-04-10 22:14:48.529557: Pseudo dice [0.4457, 0.0018, 0.7357, 0.5585, 0.4506, 0.6303, 0.6843] +2026-04-10 22:14:48.531473: Epoch time: 101.93 s +2026-04-10 22:14:49.630937: +2026-04-10 22:14:49.632698: Epoch 419 +2026-04-10 22:14:49.634927: Current learning rate: 0.00905 +2026-04-10 22:16:31.140404: train_loss -0.1782 +2026-04-10 22:16:31.147448: val_loss -0.1399 +2026-04-10 22:16:31.149005: Pseudo dice [0.4799, 0.0586, 0.7199, 0.4826, 0.3959, 0.3987, 0.516] +2026-04-10 22:16:31.150891: Epoch time: 101.51 s +2026-04-10 22:16:32.197023: +2026-04-10 22:16:32.199214: Epoch 420 +2026-04-10 22:16:32.200835: Current learning rate: 0.00905 +2026-04-10 22:18:13.455451: train_loss -0.1667 +2026-04-10 22:18:13.460721: val_loss -0.1182 +2026-04-10 22:18:13.462356: Pseudo dice [0.4718, 0.0875, 0.7945, 0.554, 0.3057, 0.6457, 0.6204] +2026-04-10 22:18:13.464303: Epoch time: 101.26 s +2026-04-10 22:18:14.537112: +2026-04-10 22:18:14.539175: Epoch 421 +2026-04-10 22:18:14.540516: Current learning rate: 0.00905 +2026-04-10 22:19:56.559386: train_loss -0.1744 +2026-04-10 22:19:56.564632: val_loss -0.1215 +2026-04-10 22:19:56.566321: Pseudo dice [0.7667, 0.1129, 0.5375, 0.8186, 0.2311, 0.4014, 0.571] +2026-04-10 22:19:56.568265: Epoch time: 102.03 s +2026-04-10 22:19:57.616224: +2026-04-10 22:19:57.618010: Epoch 422 +2026-04-10 22:19:57.619859: Current learning rate: 0.00905 +2026-04-10 22:21:39.034822: train_loss -0.1657 +2026-04-10 22:21:39.039705: val_loss -0.1394 +2026-04-10 22:21:39.041605: Pseudo dice [0.7936, 0.5418, 0.6057, 0.6217, 0.3426, 0.3058, 0.7184] +2026-04-10 22:21:39.043475: Epoch time: 101.42 s +2026-04-10 22:21:40.102833: +2026-04-10 22:21:40.104212: Epoch 423 +2026-04-10 22:21:40.105880: Current learning rate: 0.00904 +2026-04-10 22:23:22.175504: train_loss -0.1588 +2026-04-10 22:23:22.180671: val_loss -0.1454 +2026-04-10 22:23:22.182112: Pseudo dice [0.7219, 0.6185, 0.7625, 0.635, 0.3952, 0.6959, 0.7304] +2026-04-10 22:23:22.183855: Epoch time: 102.08 s +2026-04-10 22:23:23.237729: +2026-04-10 22:23:23.239432: Epoch 424 +2026-04-10 22:23:23.241232: Current learning rate: 0.00904 +2026-04-10 22:25:04.994743: train_loss -0.1714 +2026-04-10 22:25:05.001773: val_loss -0.1112 +2026-04-10 22:25:05.004415: Pseudo dice [0.4304, 0.2505, 0.6787, 0.4591, 0.2213, 0.6229, 0.5755] +2026-04-10 22:25:05.007561: Epoch time: 101.76 s +2026-04-10 22:25:06.072366: +2026-04-10 22:25:06.074247: Epoch 425 +2026-04-10 22:25:06.076102: Current learning rate: 0.00904 +2026-04-10 22:26:49.348120: train_loss -0.1642 +2026-04-10 22:26:49.353459: val_loss -0.1076 +2026-04-10 22:26:49.355228: Pseudo dice [0.698, 0.0599, 0.6499, 0.8096, 0.1172, 0.5177, 0.6861] +2026-04-10 22:26:49.356833: Epoch time: 103.28 s +2026-04-10 22:26:50.456727: +2026-04-10 22:26:50.459167: Epoch 426 +2026-04-10 22:26:50.460912: Current learning rate: 0.00904 +2026-04-10 22:28:32.202484: train_loss -0.1738 +2026-04-10 22:28:32.208994: val_loss -0.1036 +2026-04-10 22:28:32.211071: Pseudo dice [0.3175, 0.033, 0.6038, 0.8288, 0.265, 0.6495, 0.6519] +2026-04-10 22:28:32.212786: Epoch time: 101.75 s +2026-04-10 22:28:33.267556: +2026-04-10 22:28:33.269538: Epoch 427 +2026-04-10 22:28:33.271069: Current learning rate: 0.00903 +2026-04-10 22:30:15.145602: train_loss -0.1669 +2026-04-10 22:30:15.151724: val_loss -0.1224 +2026-04-10 22:30:15.153562: Pseudo dice [0.3203, 0.4321, 0.5804, 0.5317, 0.125, 0.4702, 0.4783] +2026-04-10 22:30:15.162504: Epoch time: 101.88 s +2026-04-10 22:30:16.210496: +2026-04-10 22:30:16.212046: Epoch 428 +2026-04-10 22:30:16.213765: Current learning rate: 0.00903 +2026-04-10 22:31:57.862773: train_loss -0.1678 +2026-04-10 22:31:57.868793: val_loss -0.1397 +2026-04-10 22:31:57.870660: Pseudo dice [0.6886, 0.1136, 0.7084, 0.8335, 0.1804, 0.7166, 0.7133] +2026-04-10 22:31:57.872664: Epoch time: 101.66 s +2026-04-10 22:31:58.942147: +2026-04-10 22:31:58.943529: Epoch 429 +2026-04-10 22:31:58.945475: Current learning rate: 0.00903 +2026-04-10 22:33:41.802367: train_loss -0.1838 +2026-04-10 22:33:41.809454: val_loss -0.1344 +2026-04-10 22:33:41.811698: Pseudo dice [0.6814, 0.2228, 0.7233, 0.638, 0.3813, 0.5466, 0.6358] +2026-04-10 22:33:41.813517: Epoch time: 102.86 s +2026-04-10 22:33:42.885070: +2026-04-10 22:33:42.887145: Epoch 430 +2026-04-10 22:33:42.890095: Current learning rate: 0.00903 +2026-04-10 22:35:25.252985: train_loss -0.1585 +2026-04-10 22:35:25.257695: val_loss -0.1122 +2026-04-10 22:35:25.259946: Pseudo dice [0.5325, 0.3248, 0.228, 0.4484, 0.367, 0.4534, 0.4169] +2026-04-10 22:35:25.274729: Epoch time: 102.37 s +2026-04-10 22:35:26.314144: +2026-04-10 22:35:26.315999: Epoch 431 +2026-04-10 22:35:26.317480: Current learning rate: 0.00902 +2026-04-10 22:37:08.184709: train_loss -0.164 +2026-04-10 22:37:08.191999: val_loss -0.1032 +2026-04-10 22:37:08.194249: Pseudo dice [0.3508, 0.4446, 0.6559, 0.7196, 0.2072, 0.6156, 0.3691] +2026-04-10 22:37:08.196237: Epoch time: 101.87 s +2026-04-10 22:37:09.243519: +2026-04-10 22:37:09.245271: Epoch 432 +2026-04-10 22:37:09.246932: Current learning rate: 0.00902 +2026-04-10 22:38:50.727712: train_loss -0.1655 +2026-04-10 22:38:50.733822: val_loss -0.1375 +2026-04-10 22:38:50.735893: Pseudo dice [0.2131, 0.1511, 0.6226, 0.6786, 0.2362, 0.7775, 0.7868] +2026-04-10 22:38:50.737625: Epoch time: 101.49 s +2026-04-10 22:38:51.783883: +2026-04-10 22:38:51.785616: Epoch 433 +2026-04-10 22:38:51.787325: Current learning rate: 0.00902 +2026-04-10 22:40:33.842610: train_loss -0.1561 +2026-04-10 22:40:33.848073: val_loss -0.1152 +2026-04-10 22:40:33.850825: Pseudo dice [0.4334, 0.4212, 0.5187, 0.367, 0.2998, 0.352, 0.6476] +2026-04-10 22:40:33.852847: Epoch time: 102.06 s +2026-04-10 22:40:34.922931: +2026-04-10 22:40:34.924420: Epoch 434 +2026-04-10 22:40:34.926026: Current learning rate: 0.00902 +2026-04-10 22:42:17.260146: train_loss -0.1738 +2026-04-10 22:42:17.271730: val_loss -0.1196 +2026-04-10 22:42:17.273991: Pseudo dice [0.3426, 0.0757, 0.753, 0.2357, 0.321, 0.6762, 0.4531] +2026-04-10 22:42:17.275905: Epoch time: 102.34 s +2026-04-10 22:42:18.351951: +2026-04-10 22:42:18.354285: Epoch 435 +2026-04-10 22:42:18.356621: Current learning rate: 0.00902 +2026-04-10 22:44:00.626374: train_loss -0.1705 +2026-04-10 22:44:00.632160: val_loss -0.093 +2026-04-10 22:44:00.634133: Pseudo dice [0.2677, 0.5979, 0.3205, 0.6798, 0.1794, 0.5442, 0.6768] +2026-04-10 22:44:00.636486: Epoch time: 102.28 s +2026-04-10 22:44:01.687972: +2026-04-10 22:44:01.690134: Epoch 436 +2026-04-10 22:44:01.692183: Current learning rate: 0.00901 +2026-04-10 22:45:43.927749: train_loss -0.1735 +2026-04-10 22:45:43.937357: val_loss -0.1435 +2026-04-10 22:45:43.939546: Pseudo dice [0.5652, 0.2555, 0.8162, 0.5532, 0.3983, 0.6409, 0.7812] +2026-04-10 22:45:43.941287: Epoch time: 102.24 s +2026-04-10 22:45:44.988772: +2026-04-10 22:45:44.990305: Epoch 437 +2026-04-10 22:45:44.991883: Current learning rate: 0.00901 +2026-04-10 22:47:26.786948: train_loss -0.1705 +2026-04-10 22:47:26.795467: val_loss -0.127 +2026-04-10 22:47:26.797988: Pseudo dice [0.3316, 0.2169, 0.6613, 0.6126, 0.4976, 0.7009, 0.6603] +2026-04-10 22:47:26.800285: Epoch time: 101.8 s +2026-04-10 22:47:27.852847: +2026-04-10 22:47:27.855234: Epoch 438 +2026-04-10 22:47:27.856848: Current learning rate: 0.00901 +2026-04-10 22:49:09.726905: train_loss -0.1697 +2026-04-10 22:49:09.731714: val_loss -0.1232 +2026-04-10 22:49:09.734205: Pseudo dice [0.2607, 0.1403, 0.8114, 0.742, 0.2554, 0.6868, 0.9377] +2026-04-10 22:49:09.735655: Epoch time: 101.88 s +2026-04-10 22:49:10.796159: +2026-04-10 22:49:10.797787: Epoch 439 +2026-04-10 22:49:10.799199: Current learning rate: 0.00901 +2026-04-10 22:50:52.804919: train_loss -0.1638 +2026-04-10 22:50:52.809998: val_loss -0.1272 +2026-04-10 22:50:52.813007: Pseudo dice [0.2776, 0.1042, 0.7434, 0.7401, 0.3871, 0.7912, 0.6749] +2026-04-10 22:50:52.815516: Epoch time: 102.01 s +2026-04-10 22:50:53.876371: +2026-04-10 22:50:53.879024: Epoch 440 +2026-04-10 22:50:53.881219: Current learning rate: 0.009 +2026-04-10 22:52:35.551400: train_loss -0.1747 +2026-04-10 22:52:35.560535: val_loss -0.1183 +2026-04-10 22:52:35.570130: Pseudo dice [0.3421, 0.2428, 0.5168, 0.5216, 0.2171, 0.6737, 0.6218] +2026-04-10 22:52:35.572160: Epoch time: 101.68 s +2026-04-10 22:52:36.645488: +2026-04-10 22:52:36.647583: Epoch 441 +2026-04-10 22:52:36.649338: Current learning rate: 0.009 +2026-04-10 22:54:18.249491: train_loss -0.1765 +2026-04-10 22:54:18.255083: val_loss -0.1196 +2026-04-10 22:54:18.256840: Pseudo dice [0.6387, 0.4545, 0.6886, 0.7963, 0.3634, 0.6164, 0.701] +2026-04-10 22:54:18.258251: Epoch time: 101.61 s +2026-04-10 22:54:19.284706: +2026-04-10 22:54:19.286432: Epoch 442 +2026-04-10 22:54:19.288512: Current learning rate: 0.009 +2026-04-10 22:56:00.767183: train_loss -0.166 +2026-04-10 22:56:00.771584: val_loss -0.1181 +2026-04-10 22:56:00.773265: Pseudo dice [0.4103, 0.1241, 0.7794, 0.2941, 0.3361, 0.2369, 0.3818] +2026-04-10 22:56:00.775380: Epoch time: 101.49 s +2026-04-10 22:56:01.820828: +2026-04-10 22:56:01.822312: Epoch 443 +2026-04-10 22:56:01.823774: Current learning rate: 0.009 +2026-04-10 22:57:43.236969: train_loss -0.1688 +2026-04-10 22:57:43.242888: val_loss -0.1508 +2026-04-10 22:57:43.245215: Pseudo dice [0.517, 0.2591, 0.8209, 0.5993, 0.4393, 0.7037, 0.8826] +2026-04-10 22:57:43.247617: Epoch time: 101.42 s +2026-04-10 22:57:44.317915: +2026-04-10 22:57:44.319371: Epoch 444 +2026-04-10 22:57:44.320682: Current learning rate: 0.009 +2026-04-10 22:59:26.853875: train_loss -0.1697 +2026-04-10 22:59:26.861650: val_loss -0.1542 +2026-04-10 22:59:26.864640: Pseudo dice [0.6951, 0.373, 0.6628, 0.4784, 0.1622, 0.7733, 0.6755] +2026-04-10 22:59:26.866439: Epoch time: 102.54 s +2026-04-10 22:59:27.923661: +2026-04-10 22:59:27.925800: Epoch 445 +2026-04-10 22:59:27.929489: Current learning rate: 0.00899 +2026-04-10 23:01:09.473121: train_loss -0.1619 +2026-04-10 23:01:09.480615: val_loss -0.1093 +2026-04-10 23:01:09.482137: Pseudo dice [0.558, 0.2506, 0.698, 0.0009, 0.1889, 0.3491, 0.7166] +2026-04-10 23:01:09.483484: Epoch time: 101.55 s +2026-04-10 23:01:11.583977: +2026-04-10 23:01:11.585541: Epoch 446 +2026-04-10 23:01:11.587379: Current learning rate: 0.00899 +2026-04-10 23:02:53.421456: train_loss -0.1641 +2026-04-10 23:02:53.429004: val_loss -0.1348 +2026-04-10 23:02:53.430922: Pseudo dice [0.7456, 0.2115, 0.7004, 0.6219, 0.2707, 0.6473, 0.7862] +2026-04-10 23:02:53.432646: Epoch time: 101.84 s +2026-04-10 23:02:54.495860: +2026-04-10 23:02:54.498371: Epoch 447 +2026-04-10 23:02:54.499871: Current learning rate: 0.00899 +2026-04-10 23:04:36.065354: train_loss -0.1445 +2026-04-10 23:04:36.070875: val_loss -0.1105 +2026-04-10 23:04:36.072513: Pseudo dice [0.1625, 0.2899, 0.565, 0.6237, 0.4212, 0.4666, 0.7132] +2026-04-10 23:04:36.074718: Epoch time: 101.57 s +2026-04-10 23:04:37.099940: +2026-04-10 23:04:37.101418: Epoch 448 +2026-04-10 23:04:37.103481: Current learning rate: 0.00899 +2026-04-10 23:06:19.393295: train_loss -0.166 +2026-04-10 23:06:19.400193: val_loss -0.1432 +2026-04-10 23:06:19.402485: Pseudo dice [0.7534, 0.0, 0.8309, 0.7085, 0.284, 0.6574, 0.7862] +2026-04-10 23:06:19.405352: Epoch time: 102.3 s +2026-04-10 23:06:20.473928: +2026-04-10 23:06:20.476108: Epoch 449 +2026-04-10 23:06:20.479396: Current learning rate: 0.00898 +2026-04-10 23:08:02.723455: train_loss -0.161 +2026-04-10 23:08:02.727329: val_loss -0.1116 +2026-04-10 23:08:02.728574: Pseudo dice [0.2618, 0.1612, 0.6183, 0.3604, 0.3516, 0.6409, 0.6755] +2026-04-10 23:08:02.730084: Epoch time: 102.25 s +2026-04-10 23:08:05.390233: +2026-04-10 23:08:05.392110: Epoch 450 +2026-04-10 23:08:05.393543: Current learning rate: 0.00898 +2026-04-10 23:09:46.947212: train_loss -0.161 +2026-04-10 23:09:46.952521: val_loss -0.131 +2026-04-10 23:09:46.954080: Pseudo dice [0.2913, 0.0584, 0.3259, 0.5821, 0.4595, 0.5499, 0.7757] +2026-04-10 23:09:46.956068: Epoch time: 101.56 s +2026-04-10 23:09:48.000952: +2026-04-10 23:09:48.002811: Epoch 451 +2026-04-10 23:09:48.004352: Current learning rate: 0.00898 +2026-04-10 23:11:29.422234: train_loss -0.172 +2026-04-10 23:11:29.428899: val_loss -0.1413 +2026-04-10 23:11:29.430658: Pseudo dice [0.7659, 0.2701, 0.5754, 0.7943, 0.2742, 0.5449, 0.8056] +2026-04-10 23:11:29.432999: Epoch time: 101.42 s +2026-04-10 23:11:30.470682: +2026-04-10 23:11:30.472560: Epoch 452 +2026-04-10 23:11:30.474180: Current learning rate: 0.00898 +2026-04-10 23:13:12.080698: train_loss -0.1572 +2026-04-10 23:13:12.090143: val_loss -0.1275 +2026-04-10 23:13:12.092296: Pseudo dice [0.5382, 0.2566, 0.6152, 0.6702, 0.4143, 0.1047, 0.7363] +2026-04-10 23:13:12.094541: Epoch time: 101.61 s +2026-04-10 23:13:13.149323: +2026-04-10 23:13:13.151623: Epoch 453 +2026-04-10 23:13:13.153486: Current learning rate: 0.00897 +2026-04-10 23:14:54.635975: train_loss -0.1583 +2026-04-10 23:14:54.639957: val_loss -0.1552 +2026-04-10 23:14:54.641190: Pseudo dice [0.7577, 0.1581, 0.7398, 0.7288, 0.4895, 0.709, 0.7704] +2026-04-10 23:14:54.642566: Epoch time: 101.49 s +2026-04-10 23:14:55.716809: +2026-04-10 23:14:55.718126: Epoch 454 +2026-04-10 23:14:55.719423: Current learning rate: 0.00897 +2026-04-10 23:16:37.180187: train_loss -0.1477 +2026-04-10 23:16:37.186772: val_loss -0.1206 +2026-04-10 23:16:37.188958: Pseudo dice [0.3465, 0.4587, 0.4689, 0.8093, 0.2086, 0.7224, 0.4178] +2026-04-10 23:16:37.191134: Epoch time: 101.47 s +2026-04-10 23:16:38.246875: +2026-04-10 23:16:38.249232: Epoch 455 +2026-04-10 23:16:38.251054: Current learning rate: 0.00897 +2026-04-10 23:18:19.805309: train_loss -0.1668 +2026-04-10 23:18:19.810703: val_loss -0.1113 +2026-04-10 23:18:19.812394: Pseudo dice [0.1575, 0.2736, 0.4491, 0.7742, 0.128, 0.7179, 0.6121] +2026-04-10 23:18:19.814599: Epoch time: 101.56 s +2026-04-10 23:18:20.865227: +2026-04-10 23:18:20.866806: Epoch 456 +2026-04-10 23:18:20.868248: Current learning rate: 0.00897 +2026-04-10 23:20:02.469421: train_loss -0.1632 +2026-04-10 23:20:02.475910: val_loss -0.1235 +2026-04-10 23:20:02.477414: Pseudo dice [0.3832, 0.2018, 0.4252, 0.2721, 0.4077, 0.645, 0.6528] +2026-04-10 23:20:02.479013: Epoch time: 101.61 s +2026-04-10 23:20:03.543976: +2026-04-10 23:20:03.545728: Epoch 457 +2026-04-10 23:20:03.547279: Current learning rate: 0.00897 +2026-04-10 23:21:45.226082: train_loss -0.1656 +2026-04-10 23:21:45.235667: val_loss -0.1486 +2026-04-10 23:21:45.237510: Pseudo dice [0.6249, 0.3388, 0.6325, 0.5723, 0.3121, 0.7617, 0.879] +2026-04-10 23:21:45.239918: Epoch time: 101.69 s +2026-04-10 23:21:46.295588: +2026-04-10 23:21:46.297328: Epoch 458 +2026-04-10 23:21:46.298924: Current learning rate: 0.00896 +2026-04-10 23:23:28.018078: train_loss -0.1693 +2026-04-10 23:23:28.027066: val_loss -0.1495 +2026-04-10 23:23:28.028713: Pseudo dice [0.5031, 0.2013, 0.6865, 0.3345, 0.5038, 0.6504, 0.7519] +2026-04-10 23:23:28.030494: Epoch time: 101.73 s +2026-04-10 23:23:29.084302: +2026-04-10 23:23:29.086157: Epoch 459 +2026-04-10 23:23:29.087604: Current learning rate: 0.00896 +2026-04-10 23:25:10.791841: train_loss -0.191 +2026-04-10 23:25:10.796923: val_loss -0.1165 +2026-04-10 23:25:10.798504: Pseudo dice [0.6025, 0.5114, 0.561, 0.8416, 0.3192, 0.7185, 0.4961] +2026-04-10 23:25:10.800092: Epoch time: 101.71 s +2026-04-10 23:25:11.855274: +2026-04-10 23:25:11.856775: Epoch 460 +2026-04-10 23:25:11.858159: Current learning rate: 0.00896 +2026-04-10 23:26:53.532949: train_loss -0.1776 +2026-04-10 23:26:53.539146: val_loss -0.1401 +2026-04-10 23:26:53.540795: Pseudo dice [0.6002, 0.2343, 0.6813, 0.6702, 0.4068, 0.7802, 0.8116] +2026-04-10 23:26:53.543298: Epoch time: 101.68 s +2026-04-10 23:26:54.597203: +2026-04-10 23:26:54.598700: Epoch 461 +2026-04-10 23:26:54.600836: Current learning rate: 0.00896 +2026-04-10 23:28:36.189586: train_loss -0.175 +2026-04-10 23:28:36.195496: val_loss -0.1271 +2026-04-10 23:28:36.198640: Pseudo dice [0.5005, 0.4261, 0.7744, 0.8885, 0.3603, 0.5296, 0.6909] +2026-04-10 23:28:36.200710: Epoch time: 101.6 s +2026-04-10 23:28:37.282187: +2026-04-10 23:28:37.283721: Epoch 462 +2026-04-10 23:28:37.285005: Current learning rate: 0.00895 +2026-04-10 23:30:19.899698: train_loss -0.169 +2026-04-10 23:30:19.905164: val_loss -0.1544 +2026-04-10 23:30:19.906784: Pseudo dice [0.3868, 0.1366, 0.6721, 0.4907, 0.3199, 0.4529, 0.8775] +2026-04-10 23:30:19.908375: Epoch time: 102.62 s +2026-04-10 23:30:20.976192: +2026-04-10 23:30:20.978933: Epoch 463 +2026-04-10 23:30:20.981462: Current learning rate: 0.00895 +2026-04-10 23:32:02.596668: train_loss -0.178 +2026-04-10 23:32:02.601909: val_loss -0.1263 +2026-04-10 23:32:02.603762: Pseudo dice [0.5245, 0.0928, 0.7024, 0.9073, 0.4427, 0.6881, 0.5646] +2026-04-10 23:32:02.605772: Epoch time: 101.62 s +2026-04-10 23:32:03.676524: +2026-04-10 23:32:03.678171: Epoch 464 +2026-04-10 23:32:03.679693: Current learning rate: 0.00895 +2026-04-10 23:33:45.161955: train_loss -0.1777 +2026-04-10 23:33:45.167530: val_loss -0.135 +2026-04-10 23:33:45.169097: Pseudo dice [0.5234, 0.282, 0.6931, 0.5117, 0.1548, 0.5728, 0.529] +2026-04-10 23:33:45.172441: Epoch time: 101.49 s +2026-04-10 23:33:46.244380: +2026-04-10 23:33:46.246104: Epoch 465 +2026-04-10 23:33:46.247923: Current learning rate: 0.00895 +2026-04-10 23:35:27.850118: train_loss -0.1772 +2026-04-10 23:35:27.854978: val_loss -0.131 +2026-04-10 23:35:27.858068: Pseudo dice [0.2646, 0.088, 0.7114, 0.6569, 0.2882, 0.6213, 0.7181] +2026-04-10 23:35:27.860059: Epoch time: 101.61 s +2026-04-10 23:35:28.921730: +2026-04-10 23:35:28.923625: Epoch 466 +2026-04-10 23:35:28.924968: Current learning rate: 0.00895 +2026-04-10 23:37:10.571517: train_loss -0.1516 +2026-04-10 23:37:10.576735: val_loss -0.1378 +2026-04-10 23:37:10.578124: Pseudo dice [0.3408, 0.5011, 0.7565, 0.7616, 0.4311, 0.7614, 0.5874] +2026-04-10 23:37:10.579946: Epoch time: 101.65 s +2026-04-10 23:37:12.780570: +2026-04-10 23:37:12.782375: Epoch 467 +2026-04-10 23:37:12.783841: Current learning rate: 0.00894 +2026-04-10 23:38:54.584455: train_loss -0.1764 +2026-04-10 23:38:54.592736: val_loss -0.125 +2026-04-10 23:38:54.594738: Pseudo dice [0.3881, 0.3913, 0.64, 0.6797, 0.2356, 0.5411, 0.7485] +2026-04-10 23:38:54.597118: Epoch time: 101.81 s +2026-04-10 23:38:55.651819: +2026-04-10 23:38:55.653606: Epoch 468 +2026-04-10 23:38:55.655540: Current learning rate: 0.00894 +2026-04-10 23:40:37.343015: train_loss -0.1755 +2026-04-10 23:40:37.347842: val_loss -0.1457 +2026-04-10 23:40:37.349384: Pseudo dice [0.4037, 0.5752, 0.6442, 0.3868, 0.4243, 0.6819, 0.6808] +2026-04-10 23:40:37.351734: Epoch time: 101.69 s +2026-04-10 23:40:38.409103: +2026-04-10 23:40:38.411294: Epoch 469 +2026-04-10 23:40:38.412981: Current learning rate: 0.00894 +2026-04-10 23:42:19.888817: train_loss -0.1558 +2026-04-10 23:42:19.894211: val_loss -0.1049 +2026-04-10 23:42:19.895907: Pseudo dice [0.48, 0.1038, 0.3436, 0.1939, 0.4967, 0.5543, 0.7227] +2026-04-10 23:42:19.897493: Epoch time: 101.48 s +2026-04-10 23:42:20.953888: +2026-04-10 23:42:20.955511: Epoch 470 +2026-04-10 23:42:20.957295: Current learning rate: 0.00894 +2026-04-10 23:44:02.529655: train_loss -0.1551 +2026-04-10 23:44:02.540702: val_loss -0.1371 +2026-04-10 23:44:02.542214: Pseudo dice [0.3989, 0.1404, 0.7331, 0.4172, 0.2639, 0.6021, 0.6387] +2026-04-10 23:44:02.543483: Epoch time: 101.58 s +2026-04-10 23:44:03.599215: +2026-04-10 23:44:03.600629: Epoch 471 +2026-04-10 23:44:03.602037: Current learning rate: 0.00893 +2026-04-10 23:45:45.003871: train_loss -0.1816 +2026-04-10 23:45:45.008473: val_loss -0.1433 +2026-04-10 23:45:45.010373: Pseudo dice [0.4535, 0.3914, 0.707, 0.8303, 0.2005, 0.7261, 0.8434] +2026-04-10 23:45:45.012667: Epoch time: 101.41 s +2026-04-10 23:45:46.060386: +2026-04-10 23:45:46.061719: Epoch 472 +2026-04-10 23:45:46.063622: Current learning rate: 0.00893 +2026-04-10 23:47:27.474180: train_loss -0.1637 +2026-04-10 23:47:27.478667: val_loss -0.1187 +2026-04-10 23:47:27.480428: Pseudo dice [0.5499, 0.6321, 0.5184, 0.6546, 0.146, 0.5912, 0.7746] +2026-04-10 23:47:27.482288: Epoch time: 101.42 s +2026-04-10 23:47:28.522576: +2026-04-10 23:47:28.524930: Epoch 473 +2026-04-10 23:47:28.526904: Current learning rate: 0.00893 +2026-04-10 23:49:10.242593: train_loss -0.1695 +2026-04-10 23:49:10.246945: val_loss -0.1452 +2026-04-10 23:49:10.248506: Pseudo dice [0.6982, 0.2012, 0.7137, 0.3717, 0.2741, 0.5713, 0.5427] +2026-04-10 23:49:10.250210: Epoch time: 101.72 s +2026-04-10 23:49:11.312111: +2026-04-10 23:49:11.315514: Epoch 474 +2026-04-10 23:49:11.316994: Current learning rate: 0.00893 +2026-04-10 23:50:52.845204: train_loss -0.1604 +2026-04-10 23:50:52.850160: val_loss -0.141 +2026-04-10 23:50:52.851515: Pseudo dice [0.1399, 0.1881, 0.6164, 0.4008, 0.3909, 0.8254, 0.7743] +2026-04-10 23:50:52.852910: Epoch time: 101.54 s +2026-04-10 23:50:53.920798: +2026-04-10 23:50:53.922891: Epoch 475 +2026-04-10 23:50:53.924328: Current learning rate: 0.00892 +2026-04-10 23:52:35.676294: train_loss -0.1642 +2026-04-10 23:52:35.682357: val_loss -0.1182 +2026-04-10 23:52:35.683837: Pseudo dice [0.5616, 0.4777, 0.5343, 0.0496, 0.2134, 0.7716, 0.5206] +2026-04-10 23:52:35.686143: Epoch time: 101.76 s +2026-04-10 23:52:36.756172: +2026-04-10 23:52:36.758237: Epoch 476 +2026-04-10 23:52:36.759975: Current learning rate: 0.00892 +2026-04-10 23:54:18.373403: train_loss -0.1682 +2026-04-10 23:54:18.380924: val_loss -0.1324 +2026-04-10 23:54:18.382869: Pseudo dice [0.6324, 0.4493, 0.7536, 0.1576, 0.3785, 0.4513, 0.5211] +2026-04-10 23:54:18.385287: Epoch time: 101.62 s +2026-04-10 23:54:19.450478: +2026-04-10 23:54:19.452577: Epoch 477 +2026-04-10 23:54:19.454093: Current learning rate: 0.00892 +2026-04-10 23:56:00.851219: train_loss -0.1693 +2026-04-10 23:56:00.855933: val_loss -0.156 +2026-04-10 23:56:00.857595: Pseudo dice [0.7131, 0.1806, 0.7206, 0.7277, 0.4731, 0.8277, 0.7435] +2026-04-10 23:56:00.859233: Epoch time: 101.4 s +2026-04-10 23:56:01.932888: +2026-04-10 23:56:01.934568: Epoch 478 +2026-04-10 23:56:01.936442: Current learning rate: 0.00892 +2026-04-10 23:57:43.535008: train_loss -0.1756 +2026-04-10 23:57:43.543063: val_loss -0.1216 +2026-04-10 23:57:43.544697: Pseudo dice [0.6458, 0.5693, 0.757, 0.1533, 0.3148, 0.5819, 0.6423] +2026-04-10 23:57:43.546557: Epoch time: 101.61 s +2026-04-10 23:57:44.604934: +2026-04-10 23:57:44.606888: Epoch 479 +2026-04-10 23:57:44.608383: Current learning rate: 0.00892 +2026-04-10 23:59:26.665118: train_loss -0.1491 +2026-04-10 23:59:26.671140: val_loss -0.1366 +2026-04-10 23:59:26.673847: Pseudo dice [0.4977, 0.602, 0.5906, 0.7482, 0.4042, 0.3458, 0.3125] +2026-04-10 23:59:26.676118: Epoch time: 102.06 s +2026-04-10 23:59:27.773167: +2026-04-10 23:59:27.775300: Epoch 480 +2026-04-10 23:59:27.777133: Current learning rate: 0.00891 +2026-04-11 00:01:11.361469: train_loss -0.1679 +2026-04-11 00:01:11.371620: val_loss -0.1509 +2026-04-11 00:01:11.374051: Pseudo dice [0.7125, 0.1692, 0.6847, 0.829, 0.3367, 0.7024, 0.6556] +2026-04-11 00:01:11.377072: Epoch time: 103.59 s +2026-04-11 00:01:12.473821: +2026-04-11 00:01:12.476025: Epoch 481 +2026-04-11 00:01:12.478047: Current learning rate: 0.00891 +2026-04-11 00:02:54.856696: train_loss -0.162 +2026-04-11 00:02:54.863818: val_loss -0.124 +2026-04-11 00:02:54.866222: Pseudo dice [0.3257, 0.1428, 0.6273, 0.1397, 0.4017, 0.6962, 0.6873] +2026-04-11 00:02:54.868521: Epoch time: 102.39 s +2026-04-11 00:02:55.945552: +2026-04-11 00:02:55.947248: Epoch 482 +2026-04-11 00:02:55.949308: Current learning rate: 0.00891 +2026-04-11 00:04:37.528616: train_loss -0.1632 +2026-04-11 00:04:37.533689: val_loss -0.1098 +2026-04-11 00:04:37.535304: Pseudo dice [0.3006, 0.4535, 0.6388, 0.0603, 0.2715, 0.6284, 0.8461] +2026-04-11 00:04:37.536909: Epoch time: 101.59 s +2026-04-11 00:04:38.583097: +2026-04-11 00:04:38.585066: Epoch 483 +2026-04-11 00:04:38.586664: Current learning rate: 0.00891 +2026-04-11 00:06:20.229610: train_loss -0.1761 +2026-04-11 00:06:20.236986: val_loss -0.1229 +2026-04-11 00:06:20.239257: Pseudo dice [0.5145, 0.1841, 0.4836, 0.357, 0.3846, 0.7751, 0.6729] +2026-04-11 00:06:20.241641: Epoch time: 101.65 s +2026-04-11 00:06:21.316608: +2026-04-11 00:06:21.319685: Epoch 484 +2026-04-11 00:06:21.321644: Current learning rate: 0.0089 +2026-04-11 00:08:03.504576: train_loss -0.175 +2026-04-11 00:08:03.509963: val_loss -0.1439 +2026-04-11 00:08:03.511796: Pseudo dice [0.8181, 0.0324, 0.5815, 0.5555, 0.4549, 0.3054, 0.7691] +2026-04-11 00:08:03.513775: Epoch time: 102.19 s +2026-04-11 00:08:04.580689: +2026-04-11 00:08:04.583040: Epoch 485 +2026-04-11 00:08:04.584920: Current learning rate: 0.0089 +2026-04-11 00:09:46.220929: train_loss -0.1661 +2026-04-11 00:09:46.225210: val_loss -0.1237 +2026-04-11 00:09:46.227645: Pseudo dice [0.474, 0.5222, 0.8313, 0.8412, 0.2925, 0.3765, 0.4217] +2026-04-11 00:09:46.229340: Epoch time: 101.64 s +2026-04-11 00:09:47.289211: +2026-04-11 00:09:47.290661: Epoch 486 +2026-04-11 00:09:47.291917: Current learning rate: 0.0089 +2026-04-11 00:11:28.602716: train_loss -0.187 +2026-04-11 00:11:28.613107: val_loss -0.1187 +2026-04-11 00:11:28.615201: Pseudo dice [0.4489, 0.2089, 0.7378, 0.4535, 0.2997, 0.4202, 0.835] +2026-04-11 00:11:28.618194: Epoch time: 101.32 s +2026-04-11 00:11:29.710234: +2026-04-11 00:11:29.712305: Epoch 487 +2026-04-11 00:11:29.714080: Current learning rate: 0.0089 +2026-04-11 00:13:12.683914: train_loss -0.1835 +2026-04-11 00:13:12.690215: val_loss -0.1529 +2026-04-11 00:13:12.691721: Pseudo dice [0.4297, 0.3606, 0.805, 0.3925, 0.2909, 0.7754, 0.7941] +2026-04-11 00:13:12.693692: Epoch time: 102.98 s +2026-04-11 00:13:13.780192: +2026-04-11 00:13:13.782661: Epoch 488 +2026-04-11 00:13:13.784304: Current learning rate: 0.00889 +2026-04-11 00:14:55.326080: train_loss -0.1525 +2026-04-11 00:14:55.331413: val_loss -0.1332 +2026-04-11 00:14:55.333133: Pseudo dice [0.7402, 0.2359, 0.7491, 0.7013, 0.2686, 0.7003, 0.6435] +2026-04-11 00:14:55.334654: Epoch time: 101.55 s +2026-04-11 00:14:56.404882: +2026-04-11 00:14:56.406814: Epoch 489 +2026-04-11 00:14:56.409124: Current learning rate: 0.00889 +2026-04-11 00:16:38.118544: train_loss -0.1735 +2026-04-11 00:16:38.123510: val_loss -0.1264 +2026-04-11 00:16:38.125051: Pseudo dice [0.4202, 0.2577, 0.6979, 0.4174, 0.5089, 0.7346, 0.7453] +2026-04-11 00:16:38.126705: Epoch time: 101.72 s +2026-04-11 00:16:39.182432: +2026-04-11 00:16:39.184213: Epoch 490 +2026-04-11 00:16:39.185734: Current learning rate: 0.00889 +2026-04-11 00:18:20.974120: train_loss -0.1762 +2026-04-11 00:18:20.978337: val_loss -0.1246 +2026-04-11 00:18:20.979790: Pseudo dice [0.3641, 0.0021, 0.7264, 0.8212, 0.1829, 0.5056, 0.7264] +2026-04-11 00:18:20.981103: Epoch time: 101.79 s +2026-04-11 00:18:22.051103: +2026-04-11 00:18:22.052863: Epoch 491 +2026-04-11 00:18:22.054389: Current learning rate: 0.00889 +2026-04-11 00:20:03.883965: train_loss -0.1752 +2026-04-11 00:20:03.890790: val_loss -0.129 +2026-04-11 00:20:03.893198: Pseudo dice [0.4425, 0.1746, 0.6103, 0.6234, 0.353, 0.7568, 0.8016] +2026-04-11 00:20:03.895280: Epoch time: 101.84 s +2026-04-11 00:20:04.982933: +2026-04-11 00:20:04.984839: Epoch 492 +2026-04-11 00:20:04.986557: Current learning rate: 0.00889 +2026-04-11 00:21:46.568292: train_loss -0.176 +2026-04-11 00:21:46.573046: val_loss -0.1447 +2026-04-11 00:21:46.574433: Pseudo dice [0.47, 0.3985, 0.7558, 0.7505, 0.2594, 0.6748, 0.7418] +2026-04-11 00:21:46.576066: Epoch time: 101.59 s +2026-04-11 00:21:47.653939: +2026-04-11 00:21:47.655711: Epoch 493 +2026-04-11 00:21:47.657077: Current learning rate: 0.00888 +2026-04-11 00:23:29.467308: train_loss -0.1707 +2026-04-11 00:23:29.472475: val_loss -0.1229 +2026-04-11 00:23:29.474334: Pseudo dice [0.503, 0.076, 0.6209, 0.791, 0.3805, 0.351, 0.5113] +2026-04-11 00:23:29.476169: Epoch time: 101.82 s +2026-04-11 00:23:30.994699: +2026-04-11 00:23:30.996372: Epoch 494 +2026-04-11 00:23:30.998358: Current learning rate: 0.00888 +2026-04-11 00:25:12.525941: train_loss -0.1802 +2026-04-11 00:25:12.531238: val_loss -0.1194 +2026-04-11 00:25:12.533366: Pseudo dice [0.5462, 0.0749, 0.6462, 0.8085, 0.2445, 0.3832, 0.8093] +2026-04-11 00:25:12.534848: Epoch time: 101.53 s +2026-04-11 00:25:13.617200: +2026-04-11 00:25:13.618742: Epoch 495 +2026-04-11 00:25:13.620750: Current learning rate: 0.00888 +2026-04-11 00:26:55.269899: train_loss -0.1749 +2026-04-11 00:26:55.275608: val_loss -0.1219 +2026-04-11 00:26:55.277421: Pseudo dice [0.3348, 0.3323, 0.7172, 0.7477, 0.0462, 0.3911, 0.6417] +2026-04-11 00:26:55.278731: Epoch time: 101.66 s +2026-04-11 00:26:56.349739: +2026-04-11 00:26:56.351984: Epoch 496 +2026-04-11 00:26:56.353534: Current learning rate: 0.00888 +2026-04-11 00:28:38.061978: train_loss -0.1721 +2026-04-11 00:28:38.066568: val_loss -0.1463 +2026-04-11 00:28:38.068120: Pseudo dice [0.3372, 0.2172, 0.5952, 0.8325, 0.2772, 0.4501, 0.842] +2026-04-11 00:28:38.070226: Epoch time: 101.72 s +2026-04-11 00:28:39.139877: +2026-04-11 00:28:39.141344: Epoch 497 +2026-04-11 00:28:39.142571: Current learning rate: 0.00887 +2026-04-11 00:30:20.790438: train_loss -0.1787 +2026-04-11 00:30:20.795121: val_loss -0.1615 +2026-04-11 00:30:20.796579: Pseudo dice [0.5675, 0.5504, 0.4284, 0.8819, 0.4148, 0.458, 0.8796] +2026-04-11 00:30:20.798092: Epoch time: 101.65 s +2026-04-11 00:30:21.876052: +2026-04-11 00:30:21.877784: Epoch 498 +2026-04-11 00:30:21.879261: Current learning rate: 0.00887 +2026-04-11 00:32:03.547486: train_loss -0.1724 +2026-04-11 00:32:03.553953: val_loss -0.1411 +2026-04-11 00:32:03.556697: Pseudo dice [0.3486, 0.5278, 0.6729, 0.7777, 0.4354, 0.6877, 0.3602] +2026-04-11 00:32:03.557983: Epoch time: 101.67 s +2026-04-11 00:32:04.621596: +2026-04-11 00:32:04.623599: Epoch 499 +2026-04-11 00:32:04.625604: Current learning rate: 0.00887 +2026-04-11 00:33:46.141173: train_loss -0.1684 +2026-04-11 00:33:46.145638: val_loss -0.1522 +2026-04-11 00:33:46.147631: Pseudo dice [0.2122, 0.5618, 0.7605, 0.6701, 0.5002, 0.5159, 0.8556] +2026-04-11 00:33:46.149704: Epoch time: 101.52 s +2026-04-11 00:33:48.828775: +2026-04-11 00:33:48.830172: Epoch 500 +2026-04-11 00:33:48.831545: Current learning rate: 0.00887 +2026-04-11 00:35:30.413745: train_loss -0.162 +2026-04-11 00:35:30.419107: val_loss -0.139 +2026-04-11 00:35:30.420671: Pseudo dice [0.49, 0.5049, 0.718, 0.8248, 0.2641, 0.3798, 0.8715] +2026-04-11 00:35:30.422210: Epoch time: 101.59 s +2026-04-11 00:35:31.492112: +2026-04-11 00:35:31.494303: Epoch 501 +2026-04-11 00:35:31.496218: Current learning rate: 0.00887 +2026-04-11 00:37:13.130118: train_loss -0.1682 +2026-04-11 00:37:13.136113: val_loss -0.1422 +2026-04-11 00:37:13.137746: Pseudo dice [0.485, 0.1711, 0.6933, 0.6554, 0.3238, 0.806, 0.7185] +2026-04-11 00:37:13.139732: Epoch time: 101.64 s +2026-04-11 00:37:14.223447: +2026-04-11 00:37:14.225382: Epoch 502 +2026-04-11 00:37:14.227011: Current learning rate: 0.00886 +2026-04-11 00:38:55.898790: train_loss -0.169 +2026-04-11 00:38:55.904623: val_loss -0.1258 +2026-04-11 00:38:55.906365: Pseudo dice [0.5298, 0.0167, 0.6806, 0.6111, 0.4008, 0.64, 0.4957] +2026-04-11 00:38:55.908567: Epoch time: 101.68 s +2026-04-11 00:38:57.000205: +2026-04-11 00:38:57.002048: Epoch 503 +2026-04-11 00:38:57.003900: Current learning rate: 0.00886 +2026-04-11 00:40:38.640839: train_loss -0.1706 +2026-04-11 00:40:38.645723: val_loss -0.115 +2026-04-11 00:40:38.648352: Pseudo dice [0.366, 0.075, 0.6492, 0.3158, 0.5221, 0.3007, 0.8279] +2026-04-11 00:40:38.650150: Epoch time: 101.64 s +2026-04-11 00:40:39.717259: +2026-04-11 00:40:39.719110: Epoch 504 +2026-04-11 00:40:39.720579: Current learning rate: 0.00886 +2026-04-11 00:42:21.350538: train_loss -0.1324 +2026-04-11 00:42:21.354868: val_loss -0.1047 +2026-04-11 00:42:21.356717: Pseudo dice [0.3636, 0.0328, 0.6603, 0.8158, 0.4366, 0.4223, 0.6298] +2026-04-11 00:42:21.358677: Epoch time: 101.64 s +2026-04-11 00:42:22.429443: +2026-04-11 00:42:22.430844: Epoch 505 +2026-04-11 00:42:22.432333: Current learning rate: 0.00886 +2026-04-11 00:44:03.676155: train_loss -0.1533 +2026-04-11 00:44:03.682445: val_loss -0.0965 +2026-04-11 00:44:03.684047: Pseudo dice [0.6802, 0.6092, 0.5657, 0.2343, 0.2246, 0.0657, 0.6902] +2026-04-11 00:44:03.685719: Epoch time: 101.25 s +2026-04-11 00:44:04.766068: +2026-04-11 00:44:04.767609: Epoch 506 +2026-04-11 00:44:04.769038: Current learning rate: 0.00885 +2026-04-11 00:45:46.473731: train_loss -0.1631 +2026-04-11 00:45:46.478583: val_loss -0.1241 +2026-04-11 00:45:46.480265: Pseudo dice [0.7993, 0.3643, 0.7211, 0.7181, 0.288, 0.6333, 0.6824] +2026-04-11 00:45:46.482814: Epoch time: 101.71 s +2026-04-11 00:45:47.553630: +2026-04-11 00:45:47.555118: Epoch 507 +2026-04-11 00:45:47.556520: Current learning rate: 0.00885 +2026-04-11 00:47:30.363091: train_loss -0.1694 +2026-04-11 00:47:30.368888: val_loss -0.145 +2026-04-11 00:47:30.371390: Pseudo dice [0.1809, 0.4879, 0.7113, 0.6228, 0.4371, 0.6745, 0.745] +2026-04-11 00:47:30.373440: Epoch time: 102.81 s +2026-04-11 00:47:31.449121: +2026-04-11 00:47:31.451509: Epoch 508 +2026-04-11 00:47:31.453199: Current learning rate: 0.00885 +2026-04-11 00:49:12.889548: train_loss -0.1742 +2026-04-11 00:49:12.894948: val_loss -0.1286 +2026-04-11 00:49:12.896310: Pseudo dice [0.4234, 0.158, 0.4523, 0.7303, 0.4591, 0.4096, 0.6578] +2026-04-11 00:49:12.897686: Epoch time: 101.44 s +2026-04-11 00:49:13.975472: +2026-04-11 00:49:13.977133: Epoch 509 +2026-04-11 00:49:13.978580: Current learning rate: 0.00885 +2026-04-11 00:50:55.427863: train_loss -0.1771 +2026-04-11 00:50:55.438616: val_loss -0.1049 +2026-04-11 00:50:55.440134: Pseudo dice [0.2638, 0.4223, 0.7639, 0.3907, 0.3395, 0.5828, 0.7659] +2026-04-11 00:50:55.441751: Epoch time: 101.46 s +2026-04-11 00:50:56.505398: +2026-04-11 00:50:56.507058: Epoch 510 +2026-04-11 00:50:56.508556: Current learning rate: 0.00884 +2026-04-11 00:52:38.225024: train_loss -0.165 +2026-04-11 00:52:38.230485: val_loss -0.1318 +2026-04-11 00:52:38.232037: Pseudo dice [0.2704, 0.3837, 0.796, 0.0741, 0.3656, 0.5848, 0.8843] +2026-04-11 00:52:38.233401: Epoch time: 101.72 s +2026-04-11 00:52:39.291390: +2026-04-11 00:52:39.293184: Epoch 511 +2026-04-11 00:52:39.294686: Current learning rate: 0.00884 +2026-04-11 00:54:20.852282: train_loss -0.1691 +2026-04-11 00:54:20.858802: val_loss -0.117 +2026-04-11 00:54:20.861725: Pseudo dice [0.5875, 0.2097, 0.712, 0.2727, 0.392, 0.5644, 0.7181] +2026-04-11 00:54:20.863647: Epoch time: 101.56 s +2026-04-11 00:54:21.955067: +2026-04-11 00:54:21.956743: Epoch 512 +2026-04-11 00:54:21.958324: Current learning rate: 0.00884 +2026-04-11 00:56:03.636655: train_loss -0.1682 +2026-04-11 00:56:03.641912: val_loss -0.1457 +2026-04-11 00:56:03.643604: Pseudo dice [0.4229, 0.0934, 0.5203, 0.7083, 0.4763, 0.4531, 0.6705] +2026-04-11 00:56:03.645192: Epoch time: 101.68 s +2026-04-11 00:56:04.736680: +2026-04-11 00:56:04.738627: Epoch 513 +2026-04-11 00:56:04.740497: Current learning rate: 0.00884 +2026-04-11 00:57:46.276609: train_loss -0.1629 +2026-04-11 00:57:46.287644: val_loss -0.103 +2026-04-11 00:57:46.289864: Pseudo dice [0.2968, 0.5729, 0.2716, 0.4053, 0.1599, 0.2378, 0.433] +2026-04-11 00:57:46.291561: Epoch time: 101.54 s +2026-04-11 00:57:47.372294: +2026-04-11 00:57:47.373789: Epoch 514 +2026-04-11 00:57:47.375054: Current learning rate: 0.00884 +2026-04-11 00:59:28.943791: train_loss -0.1733 +2026-04-11 00:59:28.947982: val_loss -0.1343 +2026-04-11 00:59:28.949426: Pseudo dice [0.3305, 0.258, 0.3997, 0.4186, 0.3631, 0.5846, 0.7046] +2026-04-11 00:59:28.950802: Epoch time: 101.57 s +2026-04-11 00:59:30.028386: +2026-04-11 00:59:30.029896: Epoch 515 +2026-04-11 00:59:30.031432: Current learning rate: 0.00883 +2026-04-11 01:01:11.894605: train_loss -0.1703 +2026-04-11 01:01:11.901331: val_loss -0.1552 +2026-04-11 01:01:11.903282: Pseudo dice [0.3022, 0.5813, 0.6292, 0.6702, 0.478, 0.7983, 0.8823] +2026-04-11 01:01:11.904947: Epoch time: 101.87 s +2026-04-11 01:01:12.971418: +2026-04-11 01:01:12.972847: Epoch 516 +2026-04-11 01:01:12.974294: Current learning rate: 0.00883 +2026-04-11 01:02:54.780850: train_loss -0.1664 +2026-04-11 01:02:54.786038: val_loss -0.1152 +2026-04-11 01:02:54.787907: Pseudo dice [0.1426, 0.2963, 0.5949, 0.2021, 0.4304, 0.2182, 0.5452] +2026-04-11 01:02:54.789662: Epoch time: 101.81 s +2026-04-11 01:02:55.885556: +2026-04-11 01:02:55.887163: Epoch 517 +2026-04-11 01:02:55.888592: Current learning rate: 0.00883 +2026-04-11 01:04:37.279180: train_loss -0.169 +2026-04-11 01:04:37.284683: val_loss -0.1466 +2026-04-11 01:04:37.286453: Pseudo dice [0.7143, 0.6327, 0.672, 0.7312, 0.3203, 0.4872, 0.6089] +2026-04-11 01:04:37.287953: Epoch time: 101.4 s +2026-04-11 01:04:38.355426: +2026-04-11 01:04:38.356913: Epoch 518 +2026-04-11 01:04:38.359017: Current learning rate: 0.00883 +2026-04-11 01:06:19.989776: train_loss -0.1801 +2026-04-11 01:06:19.993646: val_loss -0.1324 +2026-04-11 01:06:19.995063: Pseudo dice [0.5408, 0.2399, 0.2903, 0.4483, 0.219, 0.4209, 0.8576] +2026-04-11 01:06:19.996698: Epoch time: 101.64 s +2026-04-11 01:06:21.082095: +2026-04-11 01:06:21.083658: Epoch 519 +2026-04-11 01:06:21.084864: Current learning rate: 0.00882 +2026-04-11 01:08:02.811016: train_loss -0.1801 +2026-04-11 01:08:02.815340: val_loss -0.1344 +2026-04-11 01:08:02.816735: Pseudo dice [0.678, 0.485, 0.5474, 0.5205, 0.317, 0.5683, 0.8525] +2026-04-11 01:08:02.818391: Epoch time: 101.73 s +2026-04-11 01:08:03.875196: +2026-04-11 01:08:03.876727: Epoch 520 +2026-04-11 01:08:03.878339: Current learning rate: 0.00882 +2026-04-11 01:09:45.582045: train_loss -0.1885 +2026-04-11 01:09:45.587564: val_loss -0.1102 +2026-04-11 01:09:45.589057: Pseudo dice [0.7078, 0.171, 0.1331, 0.6416, 0.2253, 0.7129, 0.1964] +2026-04-11 01:09:45.590764: Epoch time: 101.71 s +2026-04-11 01:09:46.669209: +2026-04-11 01:09:46.670488: Epoch 521 +2026-04-11 01:09:46.671687: Current learning rate: 0.00882 +2026-04-11 01:11:28.326793: train_loss -0.1763 +2026-04-11 01:11:28.331351: val_loss -0.119 +2026-04-11 01:11:28.333458: Pseudo dice [0.4058, 0.303, 0.3253, 0.7466, 0.2447, 0.6796, 0.7282] +2026-04-11 01:11:28.335624: Epoch time: 101.66 s +2026-04-11 01:11:29.439835: +2026-04-11 01:11:29.441424: Epoch 522 +2026-04-11 01:11:29.442951: Current learning rate: 0.00882 +2026-04-11 01:13:11.151433: train_loss -0.1833 +2026-04-11 01:13:11.157459: val_loss -0.1537 +2026-04-11 01:13:11.159204: Pseudo dice [0.6051, 0.1863, 0.6852, 0.5719, 0.3232, 0.7725, 0.6115] +2026-04-11 01:13:11.161047: Epoch time: 101.71 s +2026-04-11 01:13:12.245605: +2026-04-11 01:13:12.247399: Epoch 523 +2026-04-11 01:13:12.249030: Current learning rate: 0.00882 +2026-04-11 01:14:53.754281: train_loss -0.1781 +2026-04-11 01:14:53.759663: val_loss -0.1254 +2026-04-11 01:14:53.761461: Pseudo dice [0.6461, 0.4088, 0.5773, 0.7458, 0.1401, 0.7263, 0.6556] +2026-04-11 01:14:53.763397: Epoch time: 101.51 s +2026-04-11 01:14:54.842672: +2026-04-11 01:14:54.844027: Epoch 524 +2026-04-11 01:14:54.845434: Current learning rate: 0.00881 +2026-04-11 01:16:36.385123: train_loss -0.1793 +2026-04-11 01:16:36.389551: val_loss -0.1598 +2026-04-11 01:16:36.390934: Pseudo dice [0.4804, 0.1905, 0.465, 0.4476, 0.1442, 0.7687, 0.861] +2026-04-11 01:16:36.392377: Epoch time: 101.55 s +2026-04-11 01:16:37.470606: +2026-04-11 01:16:37.472060: Epoch 525 +2026-04-11 01:16:37.473300: Current learning rate: 0.00881 +2026-04-11 01:18:19.132755: train_loss -0.173 +2026-04-11 01:18:19.137662: val_loss -0.1239 +2026-04-11 01:18:19.139502: Pseudo dice [0.5358, 0.0426, 0.6877, 0.4023, 0.4484, 0.3804, 0.6093] +2026-04-11 01:18:19.141527: Epoch time: 101.67 s +2026-04-11 01:18:20.239413: +2026-04-11 01:18:20.240968: Epoch 526 +2026-04-11 01:18:20.242894: Current learning rate: 0.00881 +2026-04-11 01:20:01.807295: train_loss -0.1659 +2026-04-11 01:20:01.811403: val_loss -0.1268 +2026-04-11 01:20:01.812637: Pseudo dice [0.6262, 0.4451, 0.6229, 0.8256, 0.2357, 0.3259, 0.7227] +2026-04-11 01:20:01.813968: Epoch time: 101.57 s +2026-04-11 01:20:02.884733: +2026-04-11 01:20:02.886046: Epoch 527 +2026-04-11 01:20:02.887310: Current learning rate: 0.00881 +2026-04-11 01:21:45.793338: train_loss -0.178 +2026-04-11 01:21:45.797666: val_loss -0.1265 +2026-04-11 01:21:45.799155: Pseudo dice [0.7002, 0.4223, 0.526, 0.6787, 0.218, 0.6728, 0.6457] +2026-04-11 01:21:45.800446: Epoch time: 102.91 s +2026-04-11 01:21:46.888133: +2026-04-11 01:21:46.889383: Epoch 528 +2026-04-11 01:21:46.890572: Current learning rate: 0.0088 +2026-04-11 01:23:28.644939: train_loss -0.1849 +2026-04-11 01:23:28.649070: val_loss -0.1564 +2026-04-11 01:23:28.650860: Pseudo dice [0.8149, 0.1382, 0.8258, 0.7294, 0.4261, 0.532, 0.8356] +2026-04-11 01:23:28.652272: Epoch time: 101.76 s +2026-04-11 01:23:29.730457: +2026-04-11 01:23:29.731954: Epoch 529 +2026-04-11 01:23:29.733296: Current learning rate: 0.0088 +2026-04-11 01:25:11.339071: train_loss -0.1655 +2026-04-11 01:25:11.343262: val_loss -0.1361 +2026-04-11 01:25:11.344870: Pseudo dice [0.5263, 0.2098, 0.8136, 0.6464, 0.4194, 0.5371, 0.6315] +2026-04-11 01:25:11.346432: Epoch time: 101.61 s +2026-04-11 01:25:12.423381: +2026-04-11 01:25:12.424959: Epoch 530 +2026-04-11 01:25:12.426253: Current learning rate: 0.0088 +2026-04-11 01:26:54.064495: train_loss -0.1937 +2026-04-11 01:26:54.069512: val_loss -0.1107 +2026-04-11 01:26:54.071045: Pseudo dice [0.6974, 0.3409, 0.6617, 0.5469, 0.1882, 0.7733, 0.7003] +2026-04-11 01:26:54.072652: Epoch time: 101.64 s +2026-04-11 01:26:55.150567: +2026-04-11 01:26:55.152156: Epoch 531 +2026-04-11 01:26:55.153565: Current learning rate: 0.0088 +2026-04-11 01:28:36.765211: train_loss -0.1868 +2026-04-11 01:28:36.769099: val_loss -0.1515 +2026-04-11 01:28:36.770536: Pseudo dice [0.5276, 0.277, 0.6635, 0.6599, 0.364, 0.7067, 0.7764] +2026-04-11 01:28:36.771759: Epoch time: 101.62 s +2026-04-11 01:28:37.827008: +2026-04-11 01:28:37.828591: Epoch 532 +2026-04-11 01:28:37.830775: Current learning rate: 0.00879 +2026-04-11 01:30:19.134414: train_loss -0.1746 +2026-04-11 01:30:19.138584: val_loss -0.1226 +2026-04-11 01:30:19.140047: Pseudo dice [0.2799, 0.0899, 0.6174, 0.6637, 0.4444, 0.5661, 0.6546] +2026-04-11 01:30:19.141262: Epoch time: 101.31 s +2026-04-11 01:30:20.202778: +2026-04-11 01:30:20.204090: Epoch 533 +2026-04-11 01:30:20.205249: Current learning rate: 0.00879 +2026-04-11 01:32:01.737141: train_loss -0.1728 +2026-04-11 01:32:01.741298: val_loss -0.1574 +2026-04-11 01:32:01.742673: Pseudo dice [0.4472, 0.5288, 0.8207, 0.5345, 0.5171, 0.4588, 0.8099] +2026-04-11 01:32:01.744003: Epoch time: 101.54 s +2026-04-11 01:32:02.812092: +2026-04-11 01:32:02.813491: Epoch 534 +2026-04-11 01:32:02.814608: Current learning rate: 0.00879 +2026-04-11 01:33:44.483633: train_loss -0.1587 +2026-04-11 01:33:44.487662: val_loss -0.1158 +2026-04-11 01:33:44.489202: Pseudo dice [0.2134, 0.3901, 0.6665, 0.2032, 0.2733, 0.5949, 0.709] +2026-04-11 01:33:44.490339: Epoch time: 101.67 s +2026-04-11 01:33:45.540236: +2026-04-11 01:33:45.541857: Epoch 535 +2026-04-11 01:33:45.543333: Current learning rate: 0.00879 +2026-04-11 01:35:26.999236: train_loss -0.1712 +2026-04-11 01:35:27.003690: val_loss -0.1321 +2026-04-11 01:35:27.004979: Pseudo dice [0.449, 0.0845, 0.6617, 0.795, 0.3698, 0.5739, 0.698] +2026-04-11 01:35:27.006564: Epoch time: 101.46 s +2026-04-11 01:35:28.059276: +2026-04-11 01:35:28.060759: Epoch 536 +2026-04-11 01:35:28.062310: Current learning rate: 0.00879 +2026-04-11 01:37:09.318697: train_loss -0.1491 +2026-04-11 01:37:09.323123: val_loss -0.1364 +2026-04-11 01:37:09.324857: Pseudo dice [0.5141, 0.0916, 0.6158, 0.7148, 0.1996, 0.6153, 0.4408] +2026-04-11 01:37:09.326152: Epoch time: 101.26 s +2026-04-11 01:37:10.393468: +2026-04-11 01:37:10.394896: Epoch 537 +2026-04-11 01:37:10.396220: Current learning rate: 0.00878 +2026-04-11 01:38:51.956384: train_loss -0.146 +2026-04-11 01:38:51.960848: val_loss -0.1056 +2026-04-11 01:38:51.962308: Pseudo dice [0.4793, 0.1044, 0.6445, 0.373, 0.2971, 0.8124, 0.345] +2026-04-11 01:38:51.963800: Epoch time: 101.57 s +2026-04-11 01:38:53.023474: +2026-04-11 01:38:53.026981: Epoch 538 +2026-04-11 01:38:53.028550: Current learning rate: 0.00878 +2026-04-11 01:40:34.523047: train_loss -0.1633 +2026-04-11 01:40:34.527335: val_loss -0.1387 +2026-04-11 01:40:34.529376: Pseudo dice [0.2732, 0.19, 0.7044, 0.8218, 0.1332, 0.7477, 0.7847] +2026-04-11 01:40:34.531178: Epoch time: 101.5 s +2026-04-11 01:40:35.629410: +2026-04-11 01:40:35.631012: Epoch 539 +2026-04-11 01:40:35.633054: Current learning rate: 0.00878 +2026-04-11 01:42:16.849757: train_loss -0.1679 +2026-04-11 01:42:16.854120: val_loss -0.1529 +2026-04-11 01:42:16.855590: Pseudo dice [0.2919, 0.063, 0.6331, 0.6567, 0.5839, 0.5835, 0.6768] +2026-04-11 01:42:16.856884: Epoch time: 101.22 s +2026-04-11 01:42:17.920920: +2026-04-11 01:42:17.923823: Epoch 540 +2026-04-11 01:42:17.925466: Current learning rate: 0.00878 +2026-04-11 01:43:59.325655: train_loss -0.167 +2026-04-11 01:43:59.332510: val_loss -0.1289 +2026-04-11 01:43:59.334544: Pseudo dice [0.616, 0.0731, 0.5474, 0.2273, 0.2128, 0.7127, 0.5991] +2026-04-11 01:43:59.336417: Epoch time: 101.41 s +2026-04-11 01:44:00.396501: +2026-04-11 01:44:00.398200: Epoch 541 +2026-04-11 01:44:00.399637: Current learning rate: 0.00877 +2026-04-11 01:45:41.725593: train_loss -0.1488 +2026-04-11 01:45:41.731354: val_loss -0.14 +2026-04-11 01:45:41.732949: Pseudo dice [0.3599, 0.3162, 0.6399, 0.6384, 0.4357, 0.2788, 0.5072] +2026-04-11 01:45:41.734542: Epoch time: 101.33 s +2026-04-11 01:45:42.799442: +2026-04-11 01:45:42.801015: Epoch 542 +2026-04-11 01:45:42.802717: Current learning rate: 0.00877 +2026-04-11 01:47:24.192786: train_loss -0.1494 +2026-04-11 01:47:24.196692: val_loss -0.1195 +2026-04-11 01:47:24.198375: Pseudo dice [0.5814, 0.3241, 0.6187, 0.6009, 0.3497, 0.5339, 0.7524] +2026-04-11 01:47:24.200279: Epoch time: 101.4 s +2026-04-11 01:47:25.259612: +2026-04-11 01:47:25.261573: Epoch 543 +2026-04-11 01:47:25.263091: Current learning rate: 0.00877 +2026-04-11 01:49:06.765028: train_loss -0.1704 +2026-04-11 01:49:06.770115: val_loss -0.1672 +2026-04-11 01:49:06.771960: Pseudo dice [0.7154, 0.1972, 0.7964, 0.5252, 0.3462, 0.7458, 0.7938] +2026-04-11 01:49:06.773669: Epoch time: 101.51 s +2026-04-11 01:49:07.822982: +2026-04-11 01:49:07.824243: Epoch 544 +2026-04-11 01:49:07.825452: Current learning rate: 0.00877 +2026-04-11 01:50:49.274104: train_loss -0.1765 +2026-04-11 01:50:49.279133: val_loss -0.1385 +2026-04-11 01:50:49.280690: Pseudo dice [0.1688, 0.153, 0.5076, 0.726, 0.2726, 0.8708, 0.8629] +2026-04-11 01:50:49.282189: Epoch time: 101.45 s +2026-04-11 01:50:50.320700: +2026-04-11 01:50:50.322337: Epoch 545 +2026-04-11 01:50:50.323911: Current learning rate: 0.00876 +2026-04-11 01:52:31.581027: train_loss -0.1742 +2026-04-11 01:52:31.585450: val_loss -0.1078 +2026-04-11 01:52:31.587004: Pseudo dice [0.2842, 0.5966, 0.5172, 0.8809, 0.4004, 0.3941, 0.6165] +2026-04-11 01:52:31.588392: Epoch time: 101.26 s +2026-04-11 01:52:32.643194: +2026-04-11 01:52:32.644608: Epoch 546 +2026-04-11 01:52:32.645991: Current learning rate: 0.00876 +2026-04-11 01:54:14.297487: train_loss -0.1459 +2026-04-11 01:54:14.302813: val_loss -0.1197 +2026-04-11 01:54:14.304217: Pseudo dice [0.3171, 0.1626, 0.6529, 0.291, 0.3394, 0.7826, 0.6505] +2026-04-11 01:54:14.306399: Epoch time: 101.66 s +2026-04-11 01:54:15.352249: +2026-04-11 01:54:15.353791: Epoch 547 +2026-04-11 01:54:15.355384: Current learning rate: 0.00876 +2026-04-11 01:55:56.852243: train_loss -0.1472 +2026-04-11 01:55:56.857427: val_loss -0.1352 +2026-04-11 01:55:56.859127: Pseudo dice [0.6697, 0.4387, 0.5261, 0.3262, 0.3153, 0.3724, 0.7354] +2026-04-11 01:55:56.860771: Epoch time: 101.5 s +2026-04-11 01:55:57.914737: +2026-04-11 01:55:57.917557: Epoch 548 +2026-04-11 01:55:57.919286: Current learning rate: 0.00876 +2026-04-11 01:57:40.409466: train_loss -0.1654 +2026-04-11 01:57:40.414315: val_loss -0.0797 +2026-04-11 01:57:40.416097: Pseudo dice [0.3259, 0.2653, 0.4735, 0.0376, 0.1941, 0.6843, 0.2871] +2026-04-11 01:57:40.417858: Epoch time: 102.5 s +2026-04-11 01:57:41.477729: +2026-04-11 01:57:41.479228: Epoch 549 +2026-04-11 01:57:41.480763: Current learning rate: 0.00876 +2026-04-11 01:59:23.118394: train_loss -0.163 +2026-04-11 01:59:23.123426: val_loss -0.1427 +2026-04-11 01:59:23.124546: Pseudo dice [0.5242, 0.2366, 0.7257, 0.6507, 0.4126, 0.7238, 0.7086] +2026-04-11 01:59:23.125818: Epoch time: 101.64 s +2026-04-11 01:59:25.742702: +2026-04-11 01:59:25.744126: Epoch 550 +2026-04-11 01:59:25.745502: Current learning rate: 0.00875 +2026-04-11 02:01:07.355642: train_loss -0.1726 +2026-04-11 02:01:07.360198: val_loss -0.1238 +2026-04-11 02:01:07.361884: Pseudo dice [0.4002, 0.3248, 0.7994, 0.5969, 0.4475, 0.6641, 0.3705] +2026-04-11 02:01:07.363242: Epoch time: 101.62 s +2026-04-11 02:01:08.434027: +2026-04-11 02:01:08.435852: Epoch 551 +2026-04-11 02:01:08.437023: Current learning rate: 0.00875 +2026-04-11 02:02:49.663376: train_loss -0.1624 +2026-04-11 02:02:49.668748: val_loss -0.1383 +2026-04-11 02:02:49.670470: Pseudo dice [0.3324, 0.2987, 0.7796, 0.7005, 0.3631, 0.6767, 0.698] +2026-04-11 02:02:49.672494: Epoch time: 101.23 s +2026-04-11 02:02:50.732764: +2026-04-11 02:02:50.734210: Epoch 552 +2026-04-11 02:02:50.735714: Current learning rate: 0.00875 +2026-04-11 02:04:32.258637: train_loss -0.1569 +2026-04-11 02:04:32.263235: val_loss -0.1275 +2026-04-11 02:04:32.265501: Pseudo dice [0.6032, 0.0748, 0.5582, 0.4764, 0.4153, 0.697, 0.6958] +2026-04-11 02:04:32.266966: Epoch time: 101.53 s +2026-04-11 02:04:33.344105: +2026-04-11 02:04:33.345591: Epoch 553 +2026-04-11 02:04:33.347011: Current learning rate: 0.00875 +2026-04-11 02:06:14.993137: train_loss -0.1668 +2026-04-11 02:06:14.998048: val_loss -0.1359 +2026-04-11 02:06:14.999760: Pseudo dice [0.4003, 0.1302, 0.6751, 0.6825, 0.168, 0.703, 0.7214] +2026-04-11 02:06:15.001481: Epoch time: 101.65 s +2026-04-11 02:06:16.085284: +2026-04-11 02:06:16.086687: Epoch 554 +2026-04-11 02:06:16.088302: Current learning rate: 0.00874 +2026-04-11 02:07:57.558191: train_loss -0.191 +2026-04-11 02:07:57.565470: val_loss -0.1487 +2026-04-11 02:07:57.566766: Pseudo dice [0.6954, 0.4512, 0.7597, 0.6386, 0.4882, 0.5936, 0.7627] +2026-04-11 02:07:57.573874: Epoch time: 101.48 s +2026-04-11 02:07:58.655887: +2026-04-11 02:07:58.657377: Epoch 555 +2026-04-11 02:07:58.658869: Current learning rate: 0.00874 +2026-04-11 02:09:40.357670: train_loss -0.159 +2026-04-11 02:09:40.361842: val_loss -0.1445 +2026-04-11 02:09:40.363366: Pseudo dice [0.5636, 0.5625, 0.8469, 0.7172, 0.3452, 0.7691, 0.8066] +2026-04-11 02:09:40.364658: Epoch time: 101.7 s +2026-04-11 02:09:41.411891: +2026-04-11 02:09:41.413682: Epoch 556 +2026-04-11 02:09:41.414827: Current learning rate: 0.00874 +2026-04-11 02:11:22.937611: train_loss -0.1757 +2026-04-11 02:11:22.942180: val_loss -0.1243 +2026-04-11 02:11:22.943941: Pseudo dice [0.3036, 0.453, 0.5165, 0.5759, 0.2751, 0.6848, 0.838] +2026-04-11 02:11:22.945413: Epoch time: 101.53 s +2026-04-11 02:11:24.030549: +2026-04-11 02:11:24.031816: Epoch 557 +2026-04-11 02:11:24.033325: Current learning rate: 0.00874 +2026-04-11 02:13:05.395295: train_loss -0.1815 +2026-04-11 02:13:05.399841: val_loss -0.1189 +2026-04-11 02:13:05.401377: Pseudo dice [0.2088, 0.4611, 0.7552, 0.4111, 0.5669, 0.7747, 0.4573] +2026-04-11 02:13:05.402902: Epoch time: 101.37 s +2026-04-11 02:13:06.448298: +2026-04-11 02:13:06.449945: Epoch 558 +2026-04-11 02:13:06.451458: Current learning rate: 0.00874 +2026-04-11 02:14:47.649936: train_loss -0.1787 +2026-04-11 02:14:47.654390: val_loss -0.1299 +2026-04-11 02:14:47.656414: Pseudo dice [0.4551, 0.188, 0.7546, 0.5778, 0.2262, 0.659, 0.6393] +2026-04-11 02:14:47.658599: Epoch time: 101.2 s +2026-04-11 02:14:48.716655: +2026-04-11 02:14:48.718601: Epoch 559 +2026-04-11 02:14:48.719943: Current learning rate: 0.00873 +2026-04-11 02:16:30.458142: train_loss -0.1849 +2026-04-11 02:16:30.462463: val_loss -0.1354 +2026-04-11 02:16:30.464177: Pseudo dice [0.2689, 0.0599, 0.753, 0.8204, 0.4223, 0.6394, 0.8176] +2026-04-11 02:16:30.465565: Epoch time: 101.74 s +2026-04-11 02:16:31.533546: +2026-04-11 02:16:31.534951: Epoch 560 +2026-04-11 02:16:31.536338: Current learning rate: 0.00873 +2026-04-11 02:18:13.454728: train_loss -0.1882 +2026-04-11 02:18:13.460898: val_loss -0.1286 +2026-04-11 02:18:13.462497: Pseudo dice [0.1883, 0.5596, 0.7618, 0.7005, 0.1706, 0.4941, 0.5658] +2026-04-11 02:18:13.464083: Epoch time: 101.92 s +2026-04-11 02:18:14.540087: +2026-04-11 02:18:14.541693: Epoch 561 +2026-04-11 02:18:14.543176: Current learning rate: 0.00873 +2026-04-11 02:19:56.304595: train_loss -0.1765 +2026-04-11 02:19:56.309607: val_loss -0.1469 +2026-04-11 02:19:56.311060: Pseudo dice [0.6846, 0.353, 0.7777, 0.8927, 0.1811, 0.6699, 0.4414] +2026-04-11 02:19:56.312283: Epoch time: 101.77 s +2026-04-11 02:19:57.373158: +2026-04-11 02:19:57.374893: Epoch 562 +2026-04-11 02:19:57.376380: Current learning rate: 0.00873 +2026-04-11 02:21:39.202594: train_loss -0.1761 +2026-04-11 02:21:39.206579: val_loss -0.1316 +2026-04-11 02:21:39.208053: Pseudo dice [0.5625, 0.0781, 0.5481, 0.8751, 0.1374, 0.5852, 0.7079] +2026-04-11 02:21:39.209336: Epoch time: 101.83 s +2026-04-11 02:21:40.266253: +2026-04-11 02:21:40.267750: Epoch 563 +2026-04-11 02:21:40.269001: Current learning rate: 0.00872 +2026-04-11 02:23:22.399803: train_loss -0.1809 +2026-04-11 02:23:22.404980: val_loss -0.1436 +2026-04-11 02:23:22.406587: Pseudo dice [0.5789, 0.2195, 0.6749, 0.6861, 0.332, 0.7229, 0.8677] +2026-04-11 02:23:22.408404: Epoch time: 102.14 s +2026-04-11 02:23:23.468734: +2026-04-11 02:23:23.470703: Epoch 564 +2026-04-11 02:23:23.472012: Current learning rate: 0.00872 +2026-04-11 02:25:05.317909: train_loss -0.1813 +2026-04-11 02:25:05.323930: val_loss -0.1205 +2026-04-11 02:25:05.325834: Pseudo dice [0.415, 0.1658, 0.411, 0.6728, 0.2035, 0.5915, 0.516] +2026-04-11 02:25:05.328542: Epoch time: 101.85 s +2026-04-11 02:25:06.399274: +2026-04-11 02:25:06.400530: Epoch 565 +2026-04-11 02:25:06.401858: Current learning rate: 0.00872 +2026-04-11 02:26:48.336976: train_loss -0.1703 +2026-04-11 02:26:48.340845: val_loss -0.1242 +2026-04-11 02:26:48.342663: Pseudo dice [0.3686, 0.0741, 0.5438, 0.7281, 0.2698, 0.5695, 0.7758] +2026-04-11 02:26:48.344188: Epoch time: 101.94 s +2026-04-11 02:26:49.481001: +2026-04-11 02:26:49.482935: Epoch 566 +2026-04-11 02:26:49.485050: Current learning rate: 0.00872 +2026-04-11 02:28:31.061872: train_loss -0.163 +2026-04-11 02:28:31.065691: val_loss -0.1193 +2026-04-11 02:28:31.067418: Pseudo dice [0.6647, 0.4594, 0.5267, 0.051, 0.3377, 0.7556, 0.79] +2026-04-11 02:28:31.068892: Epoch time: 101.58 s +2026-04-11 02:28:32.121706: +2026-04-11 02:28:32.123942: Epoch 567 +2026-04-11 02:28:32.125298: Current learning rate: 0.00871 +2026-04-11 02:30:14.075802: train_loss -0.1726 +2026-04-11 02:30:14.080015: val_loss -0.1106 +2026-04-11 02:30:14.082042: Pseudo dice [0.2296, 0.5748, 0.6251, 0.7344, 0.2951, 0.5573, 0.6052] +2026-04-11 02:30:14.083717: Epoch time: 101.96 s +2026-04-11 02:30:15.155581: +2026-04-11 02:30:15.157059: Epoch 568 +2026-04-11 02:30:15.158690: Current learning rate: 0.00871 +2026-04-11 02:31:58.237991: train_loss -0.1782 +2026-04-11 02:31:58.242820: val_loss -0.0846 +2026-04-11 02:31:58.244348: Pseudo dice [0.3012, 0.3099, 0.429, 0.6265, 0.1871, 0.5097, 0.8664] +2026-04-11 02:31:58.246058: Epoch time: 103.09 s +2026-04-11 02:31:59.312134: +2026-04-11 02:31:59.314080: Epoch 569 +2026-04-11 02:31:59.315526: Current learning rate: 0.00871 +2026-04-11 02:33:41.313002: train_loss -0.1739 +2026-04-11 02:33:41.317992: val_loss -0.1308 +2026-04-11 02:33:41.319330: Pseudo dice [0.7009, 0.0919, 0.3792, 0.6387, 0.198, 0.5485, 0.5822] +2026-04-11 02:33:41.321070: Epoch time: 102.0 s +2026-04-11 02:33:42.407983: +2026-04-11 02:33:42.409246: Epoch 570 +2026-04-11 02:33:42.410301: Current learning rate: 0.00871 +2026-04-11 02:35:24.154284: train_loss -0.1751 +2026-04-11 02:35:24.158116: val_loss -0.1276 +2026-04-11 02:35:24.159330: Pseudo dice [0.2998, 0.1992, 0.564, 0.7132, 0.0453, 0.7351, 0.8081] +2026-04-11 02:35:24.160584: Epoch time: 101.75 s +2026-04-11 02:35:25.228649: +2026-04-11 02:35:25.230024: Epoch 571 +2026-04-11 02:35:25.231174: Current learning rate: 0.00871 +2026-04-11 02:37:07.306924: train_loss -0.1728 +2026-04-11 02:37:07.311078: val_loss -0.1342 +2026-04-11 02:37:07.312653: Pseudo dice [0.4408, 0.4868, 0.6112, 0.5796, 0.3747, 0.7023, 0.6034] +2026-04-11 02:37:07.314334: Epoch time: 102.08 s +2026-04-11 02:37:08.377405: +2026-04-11 02:37:08.379057: Epoch 572 +2026-04-11 02:37:08.380828: Current learning rate: 0.0087 +2026-04-11 02:38:49.940634: train_loss -0.1673 +2026-04-11 02:38:49.945475: val_loss -0.1204 +2026-04-11 02:38:49.947300: Pseudo dice [0.4768, 0.411, 0.749, 0.5647, 0.5407, 0.6587, 0.3005] +2026-04-11 02:38:49.948883: Epoch time: 101.57 s +2026-04-11 02:38:51.027956: +2026-04-11 02:38:51.029690: Epoch 573 +2026-04-11 02:38:51.031117: Current learning rate: 0.0087 +2026-04-11 02:40:32.996741: train_loss -0.1469 +2026-04-11 02:40:33.002727: val_loss -0.1212 +2026-04-11 02:40:33.004395: Pseudo dice [0.5086, 0.1074, 0.6448, 0.7882, 0.1662, 0.7345, 0.7629] +2026-04-11 02:40:33.006037: Epoch time: 101.97 s +2026-04-11 02:40:34.076785: +2026-04-11 02:40:34.078228: Epoch 574 +2026-04-11 02:40:34.079539: Current learning rate: 0.0087 +2026-04-11 02:42:15.868155: train_loss -0.1696 +2026-04-11 02:42:15.872276: val_loss -0.1178 +2026-04-11 02:42:15.875447: Pseudo dice [0.5546, 0.1955, 0.6482, 0.7916, 0.5205, 0.5393, 0.3708] +2026-04-11 02:42:15.877340: Epoch time: 101.79 s +2026-04-11 02:42:16.980634: +2026-04-11 02:42:16.982360: Epoch 575 +2026-04-11 02:42:16.983922: Current learning rate: 0.0087 +2026-04-11 02:43:58.625697: train_loss -0.1801 +2026-04-11 02:43:58.631075: val_loss -0.1435 +2026-04-11 02:43:58.632686: Pseudo dice [0.5153, 0.2188, 0.8321, 0.7538, 0.3819, 0.7726, 0.697] +2026-04-11 02:43:58.634218: Epoch time: 101.65 s +2026-04-11 02:43:59.721256: +2026-04-11 02:43:59.723059: Epoch 576 +2026-04-11 02:43:59.724476: Current learning rate: 0.00869 +2026-04-11 02:45:41.724118: train_loss -0.1724 +2026-04-11 02:45:41.728643: val_loss -0.1656 +2026-04-11 02:45:41.730738: Pseudo dice [0.6997, 0.3581, 0.7691, 0.7659, 0.4238, 0.7807, 0.8602] +2026-04-11 02:45:41.732036: Epoch time: 102.01 s +2026-04-11 02:45:42.796422: +2026-04-11 02:45:42.798086: Epoch 577 +2026-04-11 02:45:42.799584: Current learning rate: 0.00869 +2026-04-11 02:47:24.879314: train_loss -0.1801 +2026-04-11 02:47:24.883548: val_loss -0.1265 +2026-04-11 02:47:24.885075: Pseudo dice [0.4997, 0.4754, 0.736, 0.8752, 0.4473, 0.6369, 0.4673] +2026-04-11 02:47:24.886368: Epoch time: 102.09 s +2026-04-11 02:47:25.980406: +2026-04-11 02:47:25.981926: Epoch 578 +2026-04-11 02:47:25.983249: Current learning rate: 0.00869 +2026-04-11 02:49:07.729963: train_loss -0.1696 +2026-04-11 02:49:07.735744: val_loss -0.1354 +2026-04-11 02:49:07.737161: Pseudo dice [0.5602, 0.1456, 0.7359, 0.8634, 0.2874, 0.8003, 0.7173] +2026-04-11 02:49:07.738544: Epoch time: 101.75 s +2026-04-11 02:49:07.740003: Yayy! New best EMA pseudo Dice: 0.5443 +2026-04-11 02:49:10.439535: +2026-04-11 02:49:10.441139: Epoch 579 +2026-04-11 02:49:10.442545: Current learning rate: 0.00869 +2026-04-11 02:50:52.296064: train_loss -0.1742 +2026-04-11 02:50:52.300084: val_loss -0.1201 +2026-04-11 02:50:52.301558: Pseudo dice [0.3516, 0.4046, 0.7525, 0.255, 0.1872, 0.4661, 0.4098] +2026-04-11 02:50:52.302944: Epoch time: 101.86 s +2026-04-11 02:50:53.376144: +2026-04-11 02:50:53.377491: Epoch 580 +2026-04-11 02:50:53.379341: Current learning rate: 0.00868 +2026-04-11 02:52:35.199461: train_loss -0.176 +2026-04-11 02:52:35.203560: val_loss -0.1416 +2026-04-11 02:52:35.205381: Pseudo dice [0.523, 0.0772, 0.8121, 0.3672, 0.3997, 0.6071, 0.8707] +2026-04-11 02:52:35.206700: Epoch time: 101.83 s +2026-04-11 02:52:36.285297: +2026-04-11 02:52:36.286896: Epoch 581 +2026-04-11 02:52:36.288230: Current learning rate: 0.00868 +2026-04-11 02:54:18.155613: train_loss -0.1668 +2026-04-11 02:54:18.160379: val_loss -0.146 +2026-04-11 02:54:18.161806: Pseudo dice [0.5311, 0.6542, 0.5123, 0.8908, 0.3543, 0.5956, 0.7255] +2026-04-11 02:54:18.163128: Epoch time: 101.87 s +2026-04-11 02:54:19.250287: +2026-04-11 02:54:19.251919: Epoch 582 +2026-04-11 02:54:19.253631: Current learning rate: 0.00868 +2026-04-11 02:56:01.234185: train_loss -0.1765 +2026-04-11 02:56:01.242166: val_loss -0.1452 +2026-04-11 02:56:01.243809: Pseudo dice [0.5894, 0.5263, 0.7502, 0.2175, 0.3955, 0.2887, 0.6884] +2026-04-11 02:56:01.245634: Epoch time: 101.99 s +2026-04-11 02:56:02.350397: +2026-04-11 02:56:02.352593: Epoch 583 +2026-04-11 02:56:02.354606: Current learning rate: 0.00868 +2026-04-11 02:57:44.604165: train_loss -0.186 +2026-04-11 02:57:44.608291: val_loss -0.139 +2026-04-11 02:57:44.609545: Pseudo dice [0.5731, 0.5009, 0.7498, 0.9075, 0.2458, 0.6121, 0.8385] +2026-04-11 02:57:44.610996: Epoch time: 102.26 s +2026-04-11 02:57:45.708051: +2026-04-11 02:57:45.709409: Epoch 584 +2026-04-11 02:57:45.710694: Current learning rate: 0.00868 +2026-04-11 02:59:27.850048: train_loss -0.174 +2026-04-11 02:59:27.853957: val_loss -0.1118 +2026-04-11 02:59:27.855616: Pseudo dice [0.2214, 0.4042, 0.7775, 0.4742, 0.2111, 0.6682, 0.8275] +2026-04-11 02:59:27.857352: Epoch time: 102.15 s +2026-04-11 02:59:28.939271: +2026-04-11 02:59:28.940800: Epoch 585 +2026-04-11 02:59:28.942113: Current learning rate: 0.00867 +2026-04-11 03:01:10.811485: train_loss -0.168 +2026-04-11 03:01:10.815801: val_loss -0.1015 +2026-04-11 03:01:10.817853: Pseudo dice [0.2209, 0.4082, 0.7158, 0.2252, 0.3775, 0.3338, 0.4965] +2026-04-11 03:01:10.819854: Epoch time: 101.88 s +2026-04-11 03:01:11.898919: +2026-04-11 03:01:11.900741: Epoch 586 +2026-04-11 03:01:11.902124: Current learning rate: 0.00867 +2026-04-11 03:02:53.641220: train_loss -0.1528 +2026-04-11 03:02:53.645474: val_loss -0.1016 +2026-04-11 03:02:53.646869: Pseudo dice [0.4351, 0.4049, 0.6017, 0.29, 0.2185, 0.3921, 0.4988] +2026-04-11 03:02:53.648171: Epoch time: 101.75 s +2026-04-11 03:02:54.731167: +2026-04-11 03:02:54.732544: Epoch 587 +2026-04-11 03:02:54.733946: Current learning rate: 0.00867 +2026-04-11 03:04:36.446041: train_loss -0.158 +2026-04-11 03:04:36.449939: val_loss -0.1178 +2026-04-11 03:04:36.451685: Pseudo dice [0.2657, 0.1232, 0.5707, 0.5712, 0.3892, 0.5062, 0.8572] +2026-04-11 03:04:36.453164: Epoch time: 101.72 s +2026-04-11 03:04:37.548677: +2026-04-11 03:04:37.550460: Epoch 588 +2026-04-11 03:04:37.551934: Current learning rate: 0.00867 +2026-04-11 03:06:20.462460: train_loss -0.1914 +2026-04-11 03:06:20.468641: val_loss -0.1214 +2026-04-11 03:06:20.470649: Pseudo dice [0.2237, 0.3896, 0.6464, 0.6191, 0.2469, 0.6484, 0.7743] +2026-04-11 03:06:20.472567: Epoch time: 102.92 s +2026-04-11 03:06:21.538699: +2026-04-11 03:06:21.540339: Epoch 589 +2026-04-11 03:06:21.542054: Current learning rate: 0.00866 +2026-04-11 03:08:03.666875: train_loss -0.1852 +2026-04-11 03:08:03.670839: val_loss -0.1634 +2026-04-11 03:08:03.672009: Pseudo dice [0.649, 0.3738, 0.7371, 0.8347, 0.2723, 0.7602, 0.7052] +2026-04-11 03:08:03.673437: Epoch time: 102.13 s +2026-04-11 03:08:04.747263: +2026-04-11 03:08:04.748911: Epoch 590 +2026-04-11 03:08:04.750269: Current learning rate: 0.00866 +2026-04-11 03:09:46.851040: train_loss -0.1743 +2026-04-11 03:09:46.855222: val_loss -0.1382 +2026-04-11 03:09:46.856665: Pseudo dice [0.4102, 0.1669, 0.6076, 0.3378, 0.4301, 0.7424, 0.8183] +2026-04-11 03:09:46.858289: Epoch time: 102.11 s +2026-04-11 03:09:47.941830: +2026-04-11 03:09:47.943079: Epoch 591 +2026-04-11 03:09:47.944173: Current learning rate: 0.00866 +2026-04-11 03:11:29.756460: train_loss -0.19 +2026-04-11 03:11:29.761387: val_loss -0.1494 +2026-04-11 03:11:29.763579: Pseudo dice [0.637, 0.5139, 0.7223, 0.6967, 0.4483, 0.742, 0.8423] +2026-04-11 03:11:29.765582: Epoch time: 101.82 s +2026-04-11 03:11:30.846817: +2026-04-11 03:11:30.848341: Epoch 592 +2026-04-11 03:11:30.849899: Current learning rate: 0.00866 +2026-04-11 03:13:12.703165: train_loss -0.2016 +2026-04-11 03:13:12.708350: val_loss -0.2031 +2026-04-11 03:13:12.710649: Pseudo dice [0.2621, 0.4947, 0.5096, 0.7955, 0.4811, 0.4661, 0.8201] +2026-04-11 03:13:12.712383: Epoch time: 101.86 s +2026-04-11 03:13:13.797193: +2026-04-11 03:13:13.798819: Epoch 593 +2026-04-11 03:13:13.800580: Current learning rate: 0.00866 +2026-04-11 03:14:56.102463: train_loss -0.2502 +2026-04-11 03:14:56.106629: val_loss -0.217 +2026-04-11 03:14:56.108027: Pseudo dice [0.0, 0.0, 0.5029, 0.1515, 0.3388, 0.6765, 0.364] +2026-04-11 03:14:56.109628: Epoch time: 102.31 s +2026-04-11 03:14:57.210008: +2026-04-11 03:14:57.211270: Epoch 594 +2026-04-11 03:14:57.212468: Current learning rate: 0.00865 +2026-04-11 03:16:39.531849: train_loss -0.2371 +2026-04-11 03:16:39.536671: val_loss -0.233 +2026-04-11 03:16:39.538029: Pseudo dice [0.0, 0.0, 0.4522, 0.8632, 0.3244, 0.5397, 0.5753] +2026-04-11 03:16:39.539526: Epoch time: 102.33 s +2026-04-11 03:16:40.627740: +2026-04-11 03:16:40.629360: Epoch 595 +2026-04-11 03:16:40.630761: Current learning rate: 0.00865 +2026-04-11 03:18:22.749799: train_loss -0.2578 +2026-04-11 03:18:22.754361: val_loss -0.2341 +2026-04-11 03:18:22.756268: Pseudo dice [0.0, 0.0, 0.6981, 0.3901, 0.377, 0.6831, 0.8692] +2026-04-11 03:18:22.757727: Epoch time: 102.13 s +2026-04-11 03:18:23.855021: +2026-04-11 03:18:23.856551: Epoch 596 +2026-04-11 03:18:23.858010: Current learning rate: 0.00865 +2026-04-11 03:20:06.290967: train_loss -0.2578 +2026-04-11 03:20:06.295217: val_loss -0.2246 +2026-04-11 03:20:06.296612: Pseudo dice [0.0, 0.0, 0.301, 0.6756, 0.1022, 0.8161, 0.6866] +2026-04-11 03:20:06.298238: Epoch time: 102.44 s +2026-04-11 03:20:07.396714: +2026-04-11 03:20:07.398160: Epoch 597 +2026-04-11 03:20:07.399513: Current learning rate: 0.00865 +2026-04-11 03:21:49.506370: train_loss -0.2697 +2026-04-11 03:21:49.510540: val_loss -0.2219 +2026-04-11 03:21:49.512150: Pseudo dice [0.0, 0.321, 0.5595, 0.5334, 0.48, 0.2648, 0.763] +2026-04-11 03:21:49.513777: Epoch time: 102.11 s +2026-04-11 03:21:50.597699: +2026-04-11 03:21:50.599230: Epoch 598 +2026-04-11 03:21:50.600554: Current learning rate: 0.00864 +2026-04-11 03:23:32.703420: train_loss -0.2662 +2026-04-11 03:23:32.708134: val_loss -0.2252 +2026-04-11 03:23:32.710724: Pseudo dice [0.0, 0.3123, 0.5869, 0.5209, 0.2511, 0.7021, 0.7019] +2026-04-11 03:23:32.713088: Epoch time: 102.11 s +2026-04-11 03:23:33.790345: +2026-04-11 03:23:33.791828: Epoch 599 +2026-04-11 03:23:33.793316: Current learning rate: 0.00864 +2026-04-11 03:25:15.321268: train_loss -0.2544 +2026-04-11 03:25:15.325292: val_loss -0.2047 +2026-04-11 03:25:15.326937: Pseudo dice [0.0, 0.0, 0.6634, 0.6843, 0.1735, 0.4579, 0.7671] +2026-04-11 03:25:15.328361: Epoch time: 101.53 s +2026-04-11 03:25:18.061204: +2026-04-11 03:25:18.063256: Epoch 600 +2026-04-11 03:25:18.064711: Current learning rate: 0.00864 +2026-04-11 03:26:59.889506: train_loss -0.2388 +2026-04-11 03:26:59.894922: val_loss -0.1704 +2026-04-11 03:26:59.896689: Pseudo dice [0.0, 0.0, 0.6193, 0.2204, 0.1439, 0.8319, 0.7086] +2026-04-11 03:26:59.898244: Epoch time: 101.83 s +2026-04-11 03:27:01.009300: +2026-04-11 03:27:01.011082: Epoch 601 +2026-04-11 03:27:01.012393: Current learning rate: 0.00864 +2026-04-11 03:28:42.965000: train_loss -0.2418 +2026-04-11 03:28:42.988914: val_loss -0.2278 +2026-04-11 03:28:42.990193: Pseudo dice [0.0, 0.0, 0.4893, 0.7735, 0.4247, 0.2342, 0.8713] +2026-04-11 03:28:42.992613: Epoch time: 101.96 s +2026-04-11 03:28:44.095404: +2026-04-11 03:28:44.096920: Epoch 602 +2026-04-11 03:28:44.098440: Current learning rate: 0.00863 +2026-04-11 03:30:26.133497: train_loss -0.2585 +2026-04-11 03:30:26.138368: val_loss -0.213 +2026-04-11 03:30:26.139820: Pseudo dice [0.0, 0.0, 0.735, 0.1431, 0.4348, 0.3611, 0.58] +2026-04-11 03:30:26.141189: Epoch time: 102.04 s +2026-04-11 03:30:27.215989: +2026-04-11 03:30:27.217538: Epoch 603 +2026-04-11 03:30:27.219076: Current learning rate: 0.00863 +2026-04-11 03:32:09.168678: train_loss -0.2345 +2026-04-11 03:32:09.173545: val_loss -0.2074 +2026-04-11 03:32:09.175251: Pseudo dice [0.0, 0.0, 0.4382, 0.717, 0.3194, 0.6894, 0.755] +2026-04-11 03:32:09.177372: Epoch time: 101.96 s +2026-04-11 03:32:10.261943: +2026-04-11 03:32:10.263874: Epoch 604 +2026-04-11 03:32:10.265300: Current learning rate: 0.00863 +2026-04-11 03:33:52.171215: train_loss -0.2649 +2026-04-11 03:33:52.175588: val_loss -0.256 +2026-04-11 03:33:52.177088: Pseudo dice [0.0, 0.0, 0.7372, 0.9042, 0.2988, 0.7242, 0.6525] +2026-04-11 03:33:52.178790: Epoch time: 101.91 s +2026-04-11 03:33:53.261702: +2026-04-11 03:33:53.263334: Epoch 605 +2026-04-11 03:33:53.264708: Current learning rate: 0.00863 +2026-04-11 03:35:35.041925: train_loss -0.2625 +2026-04-11 03:35:35.046430: val_loss -0.2172 +2026-04-11 03:35:35.047961: Pseudo dice [0.0, 0.0, 0.6298, 0.7526, 0.3502, 0.7116, 0.8727] +2026-04-11 03:35:35.049559: Epoch time: 101.78 s +2026-04-11 03:35:36.145317: +2026-04-11 03:35:36.146671: Epoch 606 +2026-04-11 03:35:36.147894: Current learning rate: 0.00863 +2026-04-11 03:37:17.946661: train_loss -0.266 +2026-04-11 03:37:17.951139: val_loss -0.24 +2026-04-11 03:37:17.952758: Pseudo dice [0.0, 0.0, 0.7878, 0.5671, 0.2862, 0.7548, 0.6047] +2026-04-11 03:37:17.954798: Epoch time: 101.8 s +2026-04-11 03:37:19.042896: +2026-04-11 03:37:19.044700: Epoch 607 +2026-04-11 03:37:19.046235: Current learning rate: 0.00862 +2026-04-11 03:39:01.030243: train_loss -0.2718 +2026-04-11 03:39:01.035698: val_loss -0.2456 +2026-04-11 03:39:01.037437: Pseudo dice [0.0, 0.0, 0.5764, 0.7903, 0.2402, 0.7117, 0.7521] +2026-04-11 03:39:01.039706: Epoch time: 101.99 s +2026-04-11 03:39:02.133553: +2026-04-11 03:39:02.134848: Epoch 608 +2026-04-11 03:39:02.136527: Current learning rate: 0.00862 +2026-04-11 03:40:45.070027: train_loss -0.2752 +2026-04-11 03:40:45.074598: val_loss -0.2101 +2026-04-11 03:40:45.076252: Pseudo dice [0.0, 0.0, 0.571, 0.6362, 0.1454, 0.3868, 0.691] +2026-04-11 03:40:45.077875: Epoch time: 102.94 s +2026-04-11 03:40:46.171179: +2026-04-11 03:40:46.173024: Epoch 609 +2026-04-11 03:40:46.174645: Current learning rate: 0.00862 +2026-04-11 03:42:27.955728: train_loss -0.2705 +2026-04-11 03:42:27.959956: val_loss -0.2123 +2026-04-11 03:42:27.962081: Pseudo dice [0.0, 0.0, 0.3352, 0.4406, 0.3806, 0.443, 0.3145] +2026-04-11 03:42:27.964018: Epoch time: 101.79 s +2026-04-11 03:42:29.057618: +2026-04-11 03:42:29.059169: Epoch 610 +2026-04-11 03:42:29.060554: Current learning rate: 0.00862 +2026-04-11 03:44:11.065592: train_loss -0.2619 +2026-04-11 03:44:11.070154: val_loss -0.2316 +2026-04-11 03:44:11.071765: Pseudo dice [0.0, 0.0, 0.7892, 0.8761, 0.412, 0.5984, 0.5657] +2026-04-11 03:44:11.073037: Epoch time: 102.01 s +2026-04-11 03:44:12.162609: +2026-04-11 03:44:12.164069: Epoch 611 +2026-04-11 03:44:12.165569: Current learning rate: 0.00861 +2026-04-11 03:45:54.324894: train_loss -0.2674 +2026-04-11 03:45:54.329256: val_loss -0.2316 +2026-04-11 03:45:54.330812: Pseudo dice [0.0, 0.0, 0.5618, 0.6116, 0.3281, 0.6584, 0.7287] +2026-04-11 03:45:54.332810: Epoch time: 102.17 s +2026-04-11 03:45:55.418831: +2026-04-11 03:45:55.420381: Epoch 612 +2026-04-11 03:45:55.422508: Current learning rate: 0.00861 +2026-04-11 03:47:37.243787: train_loss -0.2678 +2026-04-11 03:47:37.248101: val_loss -0.2049 +2026-04-11 03:47:37.249501: Pseudo dice [0.0, 0.0, 0.8363, 0.3896, 0.2696, 0.8039, 0.6056] +2026-04-11 03:47:37.250968: Epoch time: 101.83 s +2026-04-11 03:47:38.330108: +2026-04-11 03:47:38.331681: Epoch 613 +2026-04-11 03:47:38.333112: Current learning rate: 0.00861 +2026-04-11 03:49:20.131967: train_loss -0.2471 +2026-04-11 03:49:20.136061: val_loss -0.2519 +2026-04-11 03:49:20.137752: Pseudo dice [0.0, 0.0, 0.8272, 0.7219, 0.4838, 0.6488, 0.7913] +2026-04-11 03:49:20.139239: Epoch time: 101.8 s +2026-04-11 03:49:21.235601: +2026-04-11 03:49:21.237067: Epoch 614 +2026-04-11 03:49:21.238412: Current learning rate: 0.00861 +2026-04-11 03:51:03.088703: train_loss -0.2726 +2026-04-11 03:51:03.093616: val_loss -0.229 +2026-04-11 03:51:03.095154: Pseudo dice [0.0, 0.0, 0.7702, 0.7813, 0.3292, 0.7699, 0.8962] +2026-04-11 03:51:03.096677: Epoch time: 101.86 s +2026-04-11 03:51:04.197627: +2026-04-11 03:51:04.200569: Epoch 615 +2026-04-11 03:51:04.203631: Current learning rate: 0.0086 +2026-04-11 03:52:46.000761: train_loss -0.2789 +2026-04-11 03:52:46.005275: val_loss -0.2303 +2026-04-11 03:52:46.006766: Pseudo dice [0.0, 0.0, 0.7049, 0.7354, 0.418, 0.8182, 0.5429] +2026-04-11 03:52:46.008835: Epoch time: 101.81 s +2026-04-11 03:52:47.102618: +2026-04-11 03:52:47.104297: Epoch 616 +2026-04-11 03:52:47.105830: Current learning rate: 0.0086 +2026-04-11 03:54:28.723644: train_loss -0.2671 +2026-04-11 03:54:28.727820: val_loss -0.2318 +2026-04-11 03:54:28.729311: Pseudo dice [0.0, 0.0, 0.7016, 0.7898, 0.1563, 0.719, 0.6333] +2026-04-11 03:54:28.730444: Epoch time: 101.62 s +2026-04-11 03:54:29.836837: +2026-04-11 03:54:29.838181: Epoch 617 +2026-04-11 03:54:29.839439: Current learning rate: 0.0086 +2026-04-11 03:56:11.950379: train_loss -0.273 +2026-04-11 03:56:11.954280: val_loss -0.2398 +2026-04-11 03:56:11.955937: Pseudo dice [0.0, 0.0, 0.6231, 0.7526, 0.3018, 0.6046, 0.8819] +2026-04-11 03:56:11.957366: Epoch time: 102.12 s +2026-04-11 03:56:13.070986: +2026-04-11 03:56:13.072499: Epoch 618 +2026-04-11 03:56:13.073727: Current learning rate: 0.0086 +2026-04-11 03:57:54.914615: train_loss -0.2673 +2026-04-11 03:57:54.918629: val_loss -0.2138 +2026-04-11 03:57:54.920102: Pseudo dice [0.0, 0.0, 0.6589, 0.8329, 0.2559, 0.5712, 0.3137] +2026-04-11 03:57:54.921596: Epoch time: 101.85 s +2026-04-11 03:57:56.014602: +2026-04-11 03:57:56.016484: Epoch 619 +2026-04-11 03:57:56.017898: Current learning rate: 0.0086 +2026-04-11 03:59:37.749709: train_loss -0.2738 +2026-04-11 03:59:37.754780: val_loss -0.2357 +2026-04-11 03:59:37.757073: Pseudo dice [0.0, 0.2105, 0.6623, 0.6797, 0.4205, 0.2935, 0.6831] +2026-04-11 03:59:37.758461: Epoch time: 101.74 s +2026-04-11 03:59:38.846896: +2026-04-11 03:59:38.848977: Epoch 620 +2026-04-11 03:59:38.850435: Current learning rate: 0.00859 +2026-04-11 04:01:20.859971: train_loss -0.2776 +2026-04-11 04:01:20.864820: val_loss -0.2361 +2026-04-11 04:01:20.866339: Pseudo dice [0.0, 0.0158, 0.7273, 0.7991, 0.3809, 0.6519, 0.7581] +2026-04-11 04:01:20.868070: Epoch time: 102.02 s +2026-04-11 04:01:21.943197: +2026-04-11 04:01:21.944747: Epoch 621 +2026-04-11 04:01:21.946145: Current learning rate: 0.00859 +2026-04-11 04:03:03.657170: train_loss -0.273 +2026-04-11 04:03:03.661554: val_loss -0.2447 +2026-04-11 04:03:03.663111: Pseudo dice [0.0, 0.2898, 0.5993, 0.5519, 0.3498, 0.7523, 0.7606] +2026-04-11 04:03:03.664861: Epoch time: 101.72 s +2026-04-11 04:03:04.750188: +2026-04-11 04:03:04.752036: Epoch 622 +2026-04-11 04:03:04.753588: Current learning rate: 0.00859 +2026-04-11 04:04:46.447433: train_loss -0.2534 +2026-04-11 04:04:46.452391: val_loss -0.2288 +2026-04-11 04:04:46.454063: Pseudo dice [0.0, 0.0, 0.5602, 0.8304, 0.4014, 0.6212, 0.5609] +2026-04-11 04:04:46.455871: Epoch time: 101.7 s +2026-04-11 04:04:47.549196: +2026-04-11 04:04:47.550936: Epoch 623 +2026-04-11 04:04:47.552666: Current learning rate: 0.00859 +2026-04-11 04:06:29.512918: train_loss -0.2615 +2026-04-11 04:06:29.517917: val_loss -0.2163 +2026-04-11 04:06:29.519276: Pseudo dice [0.0, 0.0, 0.697, 0.5444, 0.429, 0.728, 0.3142] +2026-04-11 04:06:29.520745: Epoch time: 101.97 s +2026-04-11 04:06:30.608781: +2026-04-11 04:06:30.610512: Epoch 624 +2026-04-11 04:06:30.611672: Current learning rate: 0.00858 +2026-04-11 04:08:12.791631: train_loss -0.2641 +2026-04-11 04:08:12.796461: val_loss -0.2163 +2026-04-11 04:08:12.797924: Pseudo dice [0.0, 0.0, 0.7747, 0.5931, 0.3505, 0.2914, 0.8471] +2026-04-11 04:08:12.799426: Epoch time: 102.19 s +2026-04-11 04:08:13.874047: +2026-04-11 04:08:13.875611: Epoch 625 +2026-04-11 04:08:13.877103: Current learning rate: 0.00858 +2026-04-11 04:09:55.537214: train_loss -0.2611 +2026-04-11 04:09:55.541600: val_loss -0.2699 +2026-04-11 04:09:55.543489: Pseudo dice [0.0, 0.0, 0.7563, 0.9069, 0.4281, 0.5738, 0.8269] +2026-04-11 04:09:55.544909: Epoch time: 101.67 s +2026-04-11 04:09:56.636969: +2026-04-11 04:09:56.638491: Epoch 626 +2026-04-11 04:09:56.639752: Current learning rate: 0.00858 +2026-04-11 04:11:38.675295: train_loss -0.2696 +2026-04-11 04:11:38.679698: val_loss -0.2042 +2026-04-11 04:11:38.681237: Pseudo dice [0.0, 0.0438, 0.7389, 0.7182, 0.3863, 0.5086, 0.6406] +2026-04-11 04:11:38.682827: Epoch time: 102.04 s +2026-04-11 04:11:39.780250: +2026-04-11 04:11:39.781899: Epoch 627 +2026-04-11 04:11:39.783429: Current learning rate: 0.00858 +2026-04-11 04:13:21.927752: train_loss -0.2702 +2026-04-11 04:13:21.934805: val_loss -0.2101 +2026-04-11 04:13:21.936023: Pseudo dice [0.0, 0.0468, 0.5883, 0.2495, 0.1174, 0.567, 0.8556] +2026-04-11 04:13:21.937240: Epoch time: 102.15 s +2026-04-11 04:13:23.016076: +2026-04-11 04:13:23.018232: Epoch 628 +2026-04-11 04:13:23.019803: Current learning rate: 0.00858 +2026-04-11 04:15:06.103638: train_loss -0.2685 +2026-04-11 04:15:06.108115: val_loss -0.2536 +2026-04-11 04:15:06.109666: Pseudo dice [0.1538, 0.0007, 0.7479, 0.6364, 0.4135, 0.8024, 0.774] +2026-04-11 04:15:06.111523: Epoch time: 103.09 s +2026-04-11 04:15:07.196599: +2026-04-11 04:15:07.198697: Epoch 629 +2026-04-11 04:15:07.200166: Current learning rate: 0.00857 +2026-04-11 04:16:49.032453: train_loss -0.2644 +2026-04-11 04:16:49.038530: val_loss -0.1872 +2026-04-11 04:16:49.040387: Pseudo dice [0.0, 0.4146, 0.6275, 0.6019, 0.2828, 0.6344, 0.8262] +2026-04-11 04:16:49.042468: Epoch time: 101.84 s +2026-04-11 04:16:50.147914: +2026-04-11 04:16:50.149406: Epoch 630 +2026-04-11 04:16:50.150987: Current learning rate: 0.00857 +2026-04-11 04:18:32.155612: train_loss -0.2311 +2026-04-11 04:18:32.160245: val_loss -0.1663 +2026-04-11 04:18:32.162810: Pseudo dice [0.0, 0.0, 0.6052, 0.5337, 0.1198, 0.6122, 0.2703] +2026-04-11 04:18:32.164257: Epoch time: 102.01 s +2026-04-11 04:18:33.254077: +2026-04-11 04:18:33.255682: Epoch 631 +2026-04-11 04:18:33.257091: Current learning rate: 0.00857 +2026-04-11 04:20:15.599593: train_loss -0.2271 +2026-04-11 04:20:15.604448: val_loss -0.165 +2026-04-11 04:20:15.606009: Pseudo dice [0.0, 0.0, 0.1139, 0.5608, 0.1445, 0.2463, 0.5448] +2026-04-11 04:20:15.607406: Epoch time: 102.35 s +2026-04-11 04:20:16.682827: +2026-04-11 04:20:16.684297: Epoch 632 +2026-04-11 04:20:16.686012: Current learning rate: 0.00857 +2026-04-11 04:21:58.473809: train_loss -0.2226 +2026-04-11 04:21:58.478328: val_loss -0.2082 +2026-04-11 04:21:58.479729: Pseudo dice [0.0, 0.0, 0.5933, 0.485, 0.2296, 0.6928, 0.8023] +2026-04-11 04:21:58.481297: Epoch time: 101.79 s +2026-04-11 04:21:59.593413: +2026-04-11 04:21:59.594854: Epoch 633 +2026-04-11 04:21:59.596525: Current learning rate: 0.00856 +2026-04-11 04:23:41.483791: train_loss -0.2666 +2026-04-11 04:23:41.488314: val_loss -0.1957 +2026-04-11 04:23:41.489943: Pseudo dice [0.0, 0.2714, 0.6462, 0.6527, 0.2961, 0.5864, 0.5302] +2026-04-11 04:23:41.491473: Epoch time: 101.89 s +2026-04-11 04:23:42.583840: +2026-04-11 04:23:42.585766: Epoch 634 +2026-04-11 04:23:42.587116: Current learning rate: 0.00856 +2026-04-11 04:25:24.663520: train_loss -0.2767 +2026-04-11 04:25:24.668372: val_loss -0.2033 +2026-04-11 04:25:24.669691: Pseudo dice [0.0127, 0.1758, 0.7376, 0.6621, 0.2647, 0.432, 0.7532] +2026-04-11 04:25:24.671077: Epoch time: 102.08 s +2026-04-11 04:25:25.768811: +2026-04-11 04:25:25.770203: Epoch 635 +2026-04-11 04:25:25.771575: Current learning rate: 0.00856 +2026-04-11 04:27:07.734470: train_loss -0.2594 +2026-04-11 04:27:07.738703: val_loss -0.2068 +2026-04-11 04:27:07.740458: Pseudo dice [0.0334, 0.0, 0.6307, 0.6243, 0.2489, 0.385, 0.6107] +2026-04-11 04:27:07.742012: Epoch time: 101.97 s +2026-04-11 04:27:08.845258: +2026-04-11 04:27:08.846830: Epoch 636 +2026-04-11 04:27:08.848295: Current learning rate: 0.00856 +2026-04-11 04:28:50.615635: train_loss -0.2258 +2026-04-11 04:28:50.643964: val_loss -0.2006 +2026-04-11 04:28:50.646061: Pseudo dice [0.0, 0.0, 0.7691, 0.4265, 0.1754, 0.2768, 0.719] +2026-04-11 04:28:50.647924: Epoch time: 101.77 s +2026-04-11 04:28:51.740918: +2026-04-11 04:28:51.742456: Epoch 637 +2026-04-11 04:28:51.744218: Current learning rate: 0.00855 +2026-04-11 04:30:33.805415: train_loss -0.2569 +2026-04-11 04:30:33.810358: val_loss -0.2122 +2026-04-11 04:30:33.812795: Pseudo dice [0.0, 0.0, 0.4625, 0.5867, 0.1378, 0.4866, 0.421] +2026-04-11 04:30:33.814574: Epoch time: 102.07 s +2026-04-11 04:30:34.900425: +2026-04-11 04:30:34.902289: Epoch 638 +2026-04-11 04:30:34.903803: Current learning rate: 0.00855 +2026-04-11 04:32:17.191572: train_loss -0.2712 +2026-04-11 04:32:17.195509: val_loss -0.2454 +2026-04-11 04:32:17.197034: Pseudo dice [0.0, 0.0, 0.7591, 0.6139, 0.4764, 0.6496, 0.8428] +2026-04-11 04:32:17.198210: Epoch time: 102.29 s +2026-04-11 04:32:18.285322: +2026-04-11 04:32:18.289122: Epoch 639 +2026-04-11 04:32:18.290620: Current learning rate: 0.00855 +2026-04-11 04:34:00.187637: train_loss -0.2697 +2026-04-11 04:34:00.192088: val_loss -0.2149 +2026-04-11 04:34:00.193722: Pseudo dice [0.0, 0.101, 0.7146, 0.808, 0.2668, 0.5798, 0.7546] +2026-04-11 04:34:00.195271: Epoch time: 101.91 s +2026-04-11 04:34:01.266338: +2026-04-11 04:34:01.267781: Epoch 640 +2026-04-11 04:34:01.268988: Current learning rate: 0.00855 +2026-04-11 04:35:43.188594: train_loss -0.2705 +2026-04-11 04:35:43.192955: val_loss -0.2691 +2026-04-11 04:35:43.194478: Pseudo dice [0.0, 0.3364, 0.8062, 0.2031, 0.4157, 0.817, 0.79] +2026-04-11 04:35:43.196018: Epoch time: 101.93 s +2026-04-11 04:35:44.286467: +2026-04-11 04:35:44.288756: Epoch 641 +2026-04-11 04:35:44.290389: Current learning rate: 0.00855 +2026-04-11 04:37:26.144661: train_loss -0.2627 +2026-04-11 04:37:26.154886: val_loss -0.2224 +2026-04-11 04:37:26.156310: Pseudo dice [0.0, 0.0, 0.7044, 0.6625, 0.2939, 0.2143, 0.7911] +2026-04-11 04:37:26.157984: Epoch time: 101.86 s +2026-04-11 04:37:27.254784: +2026-04-11 04:37:27.256577: Epoch 642 +2026-04-11 04:37:27.257981: Current learning rate: 0.00854 +2026-04-11 04:39:09.188093: train_loss -0.2568 +2026-04-11 04:39:09.192621: val_loss -0.2177 +2026-04-11 04:39:09.194083: Pseudo dice [0.1648, 0.2266, 0.5806, 0.4199, 0.2252, 0.4816, 0.866] +2026-04-11 04:39:09.195505: Epoch time: 101.94 s +2026-04-11 04:39:10.310348: +2026-04-11 04:39:10.312266: Epoch 643 +2026-04-11 04:39:10.313911: Current learning rate: 0.00854 +2026-04-11 04:40:52.377257: train_loss -0.2627 +2026-04-11 04:40:52.382205: val_loss -0.2716 +2026-04-11 04:40:52.383738: Pseudo dice [0.7004, 0.1021, 0.8233, 0.6547, 0.5162, 0.6616, 0.8313] +2026-04-11 04:40:52.384924: Epoch time: 102.07 s +2026-04-11 04:40:53.442102: +2026-04-11 04:40:53.443794: Epoch 644 +2026-04-11 04:40:53.445284: Current learning rate: 0.00854 +2026-04-11 04:42:35.390128: train_loss -0.2209 +2026-04-11 04:42:35.395064: val_loss -0.1899 +2026-04-11 04:42:35.397127: Pseudo dice [0.161, 0.0, 0.4206, 0.2598, 0.0493, 0.6551, 0.549] +2026-04-11 04:42:35.399031: Epoch time: 101.95 s +2026-04-11 04:42:36.517466: +2026-04-11 04:42:36.519106: Epoch 645 +2026-04-11 04:42:36.520718: Current learning rate: 0.00854 +2026-04-11 04:44:18.316887: train_loss -0.2421 +2026-04-11 04:44:18.321389: val_loss -0.1861 +2026-04-11 04:44:18.322829: Pseudo dice [0.0, 0.0, 0.5862, 0.8648, 0.2756, 0.702, 0.594] +2026-04-11 04:44:18.324278: Epoch time: 101.8 s +2026-04-11 04:44:19.423750: +2026-04-11 04:44:19.425343: Epoch 646 +2026-04-11 04:44:19.427004: Current learning rate: 0.00853 +2026-04-11 04:46:01.548745: train_loss -0.2478 +2026-04-11 04:46:01.553456: val_loss -0.2214 +2026-04-11 04:46:01.554980: Pseudo dice [0.0035, 0.0633, 0.833, 0.7723, 0.4276, 0.6878, 0.6546] +2026-04-11 04:46:01.556561: Epoch time: 102.13 s +2026-04-11 04:46:02.644912: +2026-04-11 04:46:02.646541: Epoch 647 +2026-04-11 04:46:02.648065: Current learning rate: 0.00853 +2026-04-11 04:47:44.750301: train_loss -0.2417 +2026-04-11 04:47:44.755730: val_loss -0.1914 +2026-04-11 04:47:44.757460: Pseudo dice [0.0, 0.0, 0.4552, 0.2158, 0.0838, 0.6422, 0.8225] +2026-04-11 04:47:44.758772: Epoch time: 102.11 s +2026-04-11 04:47:45.846819: +2026-04-11 04:47:45.848618: Epoch 648 +2026-04-11 04:47:45.850079: Current learning rate: 0.00853 +2026-04-11 04:49:29.102744: train_loss -0.251 +2026-04-11 04:49:29.107546: val_loss -0.2136 +2026-04-11 04:49:29.108863: Pseudo dice [0.0076, 0.0, 0.6318, 0.2994, 0.5274, 0.273, 0.876] +2026-04-11 04:49:29.110063: Epoch time: 103.26 s +2026-04-11 04:49:30.209104: +2026-04-11 04:49:30.211028: Epoch 649 +2026-04-11 04:49:30.212656: Current learning rate: 0.00853 +2026-04-11 04:51:12.144383: train_loss -0.2635 +2026-04-11 04:51:12.148818: val_loss -0.2104 +2026-04-11 04:51:12.150439: Pseudo dice [0.0012, 0.0, 0.6859, 0.7384, 0.2329, 0.3446, 0.6956] +2026-04-11 04:51:12.152244: Epoch time: 101.94 s +2026-04-11 04:51:14.853988: +2026-04-11 04:51:14.855553: Epoch 650 +2026-04-11 04:51:14.856910: Current learning rate: 0.00852 +2026-04-11 04:52:56.972493: train_loss -0.2375 +2026-04-11 04:52:56.976995: val_loss -0.2193 +2026-04-11 04:52:56.978542: Pseudo dice [0.0142, 0.0, 0.7571, 0.6837, 0.2544, 0.6337, 0.3828] +2026-04-11 04:52:56.980355: Epoch time: 102.12 s +2026-04-11 04:52:58.083750: +2026-04-11 04:52:58.085952: Epoch 651 +2026-04-11 04:52:58.087264: Current learning rate: 0.00852 +2026-04-11 04:54:40.222022: train_loss -0.2671 +2026-04-11 04:54:40.227866: val_loss -0.2384 +2026-04-11 04:54:40.229367: Pseudo dice [0.4941, 0.6215, 0.6456, 0.7762, 0.4783, 0.266, 0.3082] +2026-04-11 04:54:40.230898: Epoch time: 102.14 s +2026-04-11 04:54:41.330267: +2026-04-11 04:54:41.332014: Epoch 652 +2026-04-11 04:54:41.333938: Current learning rate: 0.00852 +2026-04-11 04:56:23.086187: train_loss -0.2516 +2026-04-11 04:56:23.089740: val_loss -0.2044 +2026-04-11 04:56:23.091273: Pseudo dice [0.0703, 0.0, 0.5308, 0.7259, 0.159, 0.4063, 0.4519] +2026-04-11 04:56:23.092477: Epoch time: 101.76 s +2026-04-11 04:56:24.182174: +2026-04-11 04:56:24.183445: Epoch 653 +2026-04-11 04:56:24.184555: Current learning rate: 0.00852 +2026-04-11 04:58:05.985146: train_loss -0.2562 +2026-04-11 04:58:05.990003: val_loss -0.219 +2026-04-11 04:58:05.991766: Pseudo dice [0.1347, 0.1117, 0.7459, 0.6586, 0.3832, 0.4545, 0.8268] +2026-04-11 04:58:05.993794: Epoch time: 101.81 s +2026-04-11 04:58:07.087453: +2026-04-11 04:58:07.088885: Epoch 654 +2026-04-11 04:58:07.090875: Current learning rate: 0.00852 +2026-04-11 04:59:49.171873: train_loss -0.2719 +2026-04-11 04:59:49.176157: val_loss -0.185 +2026-04-11 04:59:49.178041: Pseudo dice [0.0, 0.0092, 0.7669, 0.1635, 0.0434, 0.4515, 0.561] +2026-04-11 04:59:49.179648: Epoch time: 102.09 s +2026-04-11 04:59:50.257641: +2026-04-11 04:59:50.260559: Epoch 655 +2026-04-11 04:59:50.261992: Current learning rate: 0.00851 +2026-04-11 05:01:32.338582: train_loss -0.2386 +2026-04-11 05:01:32.344142: val_loss -0.1766 +2026-04-11 05:01:32.346972: Pseudo dice [0.0, 0.0, 0.7149, 0.6106, 0.1277, 0.6489, 0.5404] +2026-04-11 05:01:32.349040: Epoch time: 102.08 s +2026-04-11 05:01:33.439571: +2026-04-11 05:01:33.440863: Epoch 656 +2026-04-11 05:01:33.442069: Current learning rate: 0.00851 +2026-04-11 05:03:15.602679: train_loss -0.2479 +2026-04-11 05:03:15.607573: val_loss -0.1885 +2026-04-11 05:03:15.608841: Pseudo dice [0.0, 0.0, 0.3152, 0.5213, 0.3273, 0.3924, 0.7703] +2026-04-11 05:03:15.610431: Epoch time: 102.17 s +2026-04-11 05:03:16.693920: +2026-04-11 05:03:16.695768: Epoch 657 +2026-04-11 05:03:16.697233: Current learning rate: 0.00851 +2026-04-11 05:04:58.647197: train_loss -0.241 +2026-04-11 05:04:58.652364: val_loss -0.2381 +2026-04-11 05:04:58.654042: Pseudo dice [0.0, 0.2094, 0.7428, 0.7278, 0.2571, 0.6407, 0.7141] +2026-04-11 05:04:58.655709: Epoch time: 101.96 s +2026-04-11 05:04:59.752519: +2026-04-11 05:04:59.754056: Epoch 658 +2026-04-11 05:04:59.755463: Current learning rate: 0.00851 +2026-04-11 05:06:41.629845: train_loss -0.2548 +2026-04-11 05:06:41.633950: val_loss -0.2378 +2026-04-11 05:06:41.635453: Pseudo dice [0.0, 0.6722, 0.1658, 0.249, 0.5082, 0.6795, 0.8986] +2026-04-11 05:06:41.636839: Epoch time: 101.88 s +2026-04-11 05:06:42.723997: +2026-04-11 05:06:42.725589: Epoch 659 +2026-04-11 05:06:42.726851: Current learning rate: 0.0085 +2026-04-11 05:08:24.636085: train_loss -0.2653 +2026-04-11 05:08:24.641097: val_loss -0.2184 +2026-04-11 05:08:24.643166: Pseudo dice [0.3741, 0.3658, 0.3728, 0.0075, 0.3603, 0.8237, 0.6323] +2026-04-11 05:08:24.644747: Epoch time: 101.92 s +2026-04-11 05:08:25.725638: +2026-04-11 05:08:25.727414: Epoch 660 +2026-04-11 05:08:25.729091: Current learning rate: 0.0085 +2026-04-11 05:10:07.512390: train_loss -0.259 +2026-04-11 05:10:07.516723: val_loss -0.2027 +2026-04-11 05:10:07.518183: Pseudo dice [0.0, 0.1384, 0.5777, 0.6006, 0.1829, 0.7264, 0.6273] +2026-04-11 05:10:07.519457: Epoch time: 101.79 s +2026-04-11 05:10:08.596448: +2026-04-11 05:10:08.598164: Epoch 661 +2026-04-11 05:10:08.599406: Current learning rate: 0.0085 +2026-04-11 05:11:50.589569: train_loss -0.2455 +2026-04-11 05:11:50.593906: val_loss -0.212 +2026-04-11 05:11:50.595655: Pseudo dice [0.0, 0.0, 0.7571, 0.8141, 0.3269, 0.5312, 0.8494] +2026-04-11 05:11:50.597218: Epoch time: 102.0 s +2026-04-11 05:11:51.695095: +2026-04-11 05:11:51.697754: Epoch 662 +2026-04-11 05:11:51.699279: Current learning rate: 0.0085 +2026-04-11 05:13:33.498773: train_loss -0.265 +2026-04-11 05:13:33.502751: val_loss -0.2181 +2026-04-11 05:13:33.504614: Pseudo dice [0.0, 0.0, 0.5791, 0.464, 0.0696, 0.711, 0.2594] +2026-04-11 05:13:33.506243: Epoch time: 101.81 s +2026-04-11 05:13:34.599519: +2026-04-11 05:13:34.601042: Epoch 663 +2026-04-11 05:13:34.602409: Current learning rate: 0.0085 +2026-04-11 05:15:16.493547: train_loss -0.2723 +2026-04-11 05:15:16.497471: val_loss -0.2756 +2026-04-11 05:15:16.499060: Pseudo dice [0.521, 0.1114, 0.7911, 0.4761, 0.3689, 0.7462, 0.8398] +2026-04-11 05:15:16.500424: Epoch time: 101.9 s +2026-04-11 05:15:17.580276: +2026-04-11 05:15:17.581538: Epoch 664 +2026-04-11 05:15:17.582927: Current learning rate: 0.00849 +2026-04-11 05:16:59.364083: train_loss -0.265 +2026-04-11 05:16:59.368592: val_loss -0.2638 +2026-04-11 05:16:59.369874: Pseudo dice [0.4196, 0.0, 0.664, 0.6427, 0.5456, 0.6622, 0.7516] +2026-04-11 05:16:59.371037: Epoch time: 101.79 s +2026-04-11 05:17:00.459038: +2026-04-11 05:17:00.460606: Epoch 665 +2026-04-11 05:17:00.461829: Current learning rate: 0.00849 +2026-04-11 05:18:42.222255: train_loss -0.2655 +2026-04-11 05:18:42.227242: val_loss -0.2333 +2026-04-11 05:18:42.229409: Pseudo dice [0.2632, 0.2442, 0.5091, 0.7107, 0.4244, 0.8651, 0.7895] +2026-04-11 05:18:42.231388: Epoch time: 101.77 s +2026-04-11 05:18:43.314908: +2026-04-11 05:18:43.316905: Epoch 666 +2026-04-11 05:18:43.318410: Current learning rate: 0.00849 +2026-04-11 05:20:25.444280: train_loss -0.2555 +2026-04-11 05:20:25.448344: val_loss -0.2359 +2026-04-11 05:20:25.449734: Pseudo dice [0.2362, 0.057, 0.7779, 0.1486, 0.293, 0.6274, 0.8826] +2026-04-11 05:20:25.451059: Epoch time: 102.13 s +2026-04-11 05:20:26.725321: +2026-04-11 05:20:26.727047: Epoch 667 +2026-04-11 05:20:26.728488: Current learning rate: 0.00849 +2026-04-11 05:22:08.699395: train_loss -0.269 +2026-04-11 05:22:08.703372: val_loss -0.2405 +2026-04-11 05:22:08.704676: Pseudo dice [0.2753, 0.5795, 0.6658, 0.85, 0.5433, 0.6172, 0.6191] +2026-04-11 05:22:08.705831: Epoch time: 101.98 s +2026-04-11 05:22:09.805952: +2026-04-11 05:22:09.807329: Epoch 668 +2026-04-11 05:22:09.808651: Current learning rate: 0.00848 +2026-04-11 05:23:52.662884: train_loss -0.2886 +2026-04-11 05:23:52.667430: val_loss -0.2276 +2026-04-11 05:23:52.669495: Pseudo dice [0.2665, 0.3935, 0.6717, 0.267, 0.3275, 0.6869, 0.8404] +2026-04-11 05:23:52.672048: Epoch time: 102.86 s +2026-04-11 05:23:53.792407: +2026-04-11 05:23:53.800052: Epoch 669 +2026-04-11 05:23:53.801461: Current learning rate: 0.00848 +2026-04-11 05:25:35.825509: train_loss -0.2586 +2026-04-11 05:25:35.830143: val_loss -0.2279 +2026-04-11 05:25:35.831753: Pseudo dice [0.0, 0.0, 0.651, 0.6807, 0.2324, 0.5683, 0.6081] +2026-04-11 05:25:35.833098: Epoch time: 102.04 s +2026-04-11 05:25:36.947510: +2026-04-11 05:25:36.948916: Epoch 670 +2026-04-11 05:25:36.950305: Current learning rate: 0.00848 +2026-04-11 05:27:18.821009: train_loss -0.2338 +2026-04-11 05:27:18.827066: val_loss -0.2061 +2026-04-11 05:27:18.828522: Pseudo dice [0.0, 0.0, 0.7919, 0.7453, 0.3485, 0.4051, 0.6491] +2026-04-11 05:27:18.830000: Epoch time: 101.88 s +2026-04-11 05:27:19.924286: +2026-04-11 05:27:19.925544: Epoch 671 +2026-04-11 05:27:19.926662: Current learning rate: 0.00848 +2026-04-11 05:29:01.645852: train_loss -0.2582 +2026-04-11 05:29:01.650209: val_loss -0.2417 +2026-04-11 05:29:01.652419: Pseudo dice [0.0, 0.0, 0.6677, 0.846, 0.3297, 0.6913, 0.6037] +2026-04-11 05:29:01.654517: Epoch time: 101.72 s +2026-04-11 05:29:02.753270: +2026-04-11 05:29:02.754929: Epoch 672 +2026-04-11 05:29:02.756142: Current learning rate: 0.00847 +2026-04-11 05:30:44.467448: train_loss -0.2658 +2026-04-11 05:30:44.471561: val_loss -0.2256 +2026-04-11 05:30:44.473079: Pseudo dice [0.0, 0.3404, 0.6881, 0.6936, 0.2288, 0.779, 0.6661] +2026-04-11 05:30:44.474469: Epoch time: 101.72 s +2026-04-11 05:30:45.602078: +2026-04-11 05:30:45.603634: Epoch 673 +2026-04-11 05:30:45.604962: Current learning rate: 0.00847 +2026-04-11 05:32:27.327850: train_loss -0.2657 +2026-04-11 05:32:27.331688: val_loss -0.2226 +2026-04-11 05:32:27.333058: Pseudo dice [0.0031, 0.0829, 0.7468, 0.8186, 0.3014, 0.3844, 0.5318] +2026-04-11 05:32:27.334385: Epoch time: 101.73 s +2026-04-11 05:32:28.454609: +2026-04-11 05:32:28.455995: Epoch 674 +2026-04-11 05:32:28.457175: Current learning rate: 0.00847 +2026-04-11 05:34:10.064194: train_loss -0.2832 +2026-04-11 05:34:10.068453: val_loss -0.2395 +2026-04-11 05:34:10.070065: Pseudo dice [0.4322, 0.649, 0.5254, 0.7928, 0.3026, 0.7276, 0.6584] +2026-04-11 05:34:10.071529: Epoch time: 101.61 s +2026-04-11 05:34:11.185183: +2026-04-11 05:34:11.186813: Epoch 675 +2026-04-11 05:34:11.188179: Current learning rate: 0.00847 +2026-04-11 05:35:52.787272: train_loss -0.2802 +2026-04-11 05:35:52.791322: val_loss -0.2376 +2026-04-11 05:35:52.793216: Pseudo dice [0.3421, 0.358, 0.6886, 0.8193, 0.2568, 0.6901, 0.555] +2026-04-11 05:35:52.794749: Epoch time: 101.61 s +2026-04-11 05:35:53.921740: +2026-04-11 05:35:53.923314: Epoch 676 +2026-04-11 05:35:53.924513: Current learning rate: 0.00847 +2026-04-11 05:37:35.733083: train_loss -0.2756 +2026-04-11 05:37:35.738122: val_loss -0.2443 +2026-04-11 05:37:35.739608: Pseudo dice [0.3895, 0.5408, 0.7061, 0.2383, 0.2838, 0.7489, 0.7336] +2026-04-11 05:37:35.741502: Epoch time: 101.81 s +2026-04-11 05:37:36.866650: +2026-04-11 05:37:36.868161: Epoch 677 +2026-04-11 05:37:36.869650: Current learning rate: 0.00846 +2026-04-11 05:39:18.552025: train_loss -0.2625 +2026-04-11 05:39:18.557082: val_loss -0.2547 +2026-04-11 05:39:18.560361: Pseudo dice [0.1729, 0.1142, 0.4801, 0.6439, 0.16, 0.7185, 0.8471] +2026-04-11 05:39:18.562413: Epoch time: 101.69 s +2026-04-11 05:39:19.680545: +2026-04-11 05:39:19.681991: Epoch 678 +2026-04-11 05:39:19.683584: Current learning rate: 0.00846 +2026-04-11 05:41:01.036590: train_loss -0.2591 +2026-04-11 05:41:01.041196: val_loss -0.2222 +2026-04-11 05:41:01.043166: Pseudo dice [0.1017, 0.692, 0.5821, 0.3517, 0.1398, 0.7599, 0.5143] +2026-04-11 05:41:01.044898: Epoch time: 101.36 s +2026-04-11 05:41:02.161394: +2026-04-11 05:41:02.163054: Epoch 679 +2026-04-11 05:41:02.164421: Current learning rate: 0.00846 +2026-04-11 05:42:43.868117: train_loss -0.2581 +2026-04-11 05:42:43.873086: val_loss -0.2552 +2026-04-11 05:42:43.874712: Pseudo dice [0.2755, 0.5888, 0.6775, 0.6688, 0.3021, 0.5826, 0.831] +2026-04-11 05:42:43.876140: Epoch time: 101.71 s +2026-04-11 05:42:45.000309: +2026-04-11 05:42:45.002024: Epoch 680 +2026-04-11 05:42:45.003561: Current learning rate: 0.00846 +2026-04-11 05:44:26.675235: train_loss -0.252 +2026-04-11 05:44:26.679819: val_loss -0.2333 +2026-04-11 05:44:26.681549: Pseudo dice [0.0, 0.001, 0.6872, 0.7693, 0.4117, 0.7856, 0.7014] +2026-04-11 05:44:26.683355: Epoch time: 101.68 s +2026-04-11 05:44:27.813490: +2026-04-11 05:44:27.815074: Epoch 681 +2026-04-11 05:44:27.816671: Current learning rate: 0.00845 +2026-04-11 05:46:09.411156: train_loss -0.2598 +2026-04-11 05:46:09.415191: val_loss -0.1917 +2026-04-11 05:46:09.417065: Pseudo dice [0.0, 0.5012, 0.5039, 0.6062, 0.1193, 0.605, 0.6803] +2026-04-11 05:46:09.418651: Epoch time: 101.6 s +2026-04-11 05:46:10.521012: +2026-04-11 05:46:10.522361: Epoch 682 +2026-04-11 05:46:10.523458: Current learning rate: 0.00845 +2026-04-11 05:47:52.194681: train_loss -0.2386 +2026-04-11 05:47:52.198431: val_loss -0.2119 +2026-04-11 05:47:52.199882: Pseudo dice [0.1833, 0.4651, 0.5169, 0.8098, 0.0744, 0.3219, 0.555] +2026-04-11 05:47:52.201137: Epoch time: 101.68 s +2026-04-11 05:47:53.325289: +2026-04-11 05:47:53.327048: Epoch 683 +2026-04-11 05:47:53.328426: Current learning rate: 0.00845 +2026-04-11 05:49:35.116405: train_loss -0.2456 +2026-04-11 05:49:35.121119: val_loss -0.2178 +2026-04-11 05:49:35.122725: Pseudo dice [0.2009, 0.0852, 0.7297, 0.6075, 0.2849, 0.3667, 0.6527] +2026-04-11 05:49:35.124169: Epoch time: 101.79 s +2026-04-11 05:49:36.275172: +2026-04-11 05:49:36.277154: Epoch 684 +2026-04-11 05:49:36.278493: Current learning rate: 0.00845 +2026-04-11 05:51:17.996687: train_loss -0.2568 +2026-04-11 05:51:18.000627: val_loss -0.248 +2026-04-11 05:51:18.002399: Pseudo dice [0.0095, 0.2628, 0.7612, 0.6812, 0.4591, 0.7084, 0.6264] +2026-04-11 05:51:18.004075: Epoch time: 101.72 s +2026-04-11 05:51:19.147560: +2026-04-11 05:51:19.149580: Epoch 685 +2026-04-11 05:51:19.151011: Current learning rate: 0.00844 +2026-04-11 05:53:00.972874: train_loss -0.2868 +2026-04-11 05:53:00.979112: val_loss -0.2205 +2026-04-11 05:53:00.980820: Pseudo dice [0.1009, 0.7553, 0.7522, 0.7348, 0.4792, 0.262, 0.8143] +2026-04-11 05:53:00.982607: Epoch time: 101.83 s +2026-04-11 05:53:02.086539: +2026-04-11 05:53:02.087757: Epoch 686 +2026-04-11 05:53:02.089044: Current learning rate: 0.00844 +2026-04-11 05:54:43.901390: train_loss -0.2565 +2026-04-11 05:54:43.907670: val_loss -0.1984 +2026-04-11 05:54:43.909580: Pseudo dice [0.0, 0.0, 0.6799, 0.6433, 0.2882, 0.709, 0.5697] +2026-04-11 05:54:43.912543: Epoch time: 101.82 s +2026-04-11 05:54:45.045461: +2026-04-11 05:54:45.047131: Epoch 687 +2026-04-11 05:54:45.048723: Current learning rate: 0.00844 +2026-04-11 05:56:26.763481: train_loss -0.249 +2026-04-11 05:56:26.767507: val_loss -0.2434 +2026-04-11 05:56:26.768952: Pseudo dice [0.1871, 0.6776, 0.4586, 0.6212, 0.3131, 0.663, 0.7274] +2026-04-11 05:56:26.770696: Epoch time: 101.72 s +2026-04-11 05:56:27.992837: +2026-04-11 05:56:27.994604: Epoch 688 +2026-04-11 05:56:27.996154: Current learning rate: 0.00844 +2026-04-11 05:58:10.857583: train_loss -0.2685 +2026-04-11 05:58:10.862167: val_loss -0.2667 +2026-04-11 05:58:10.864630: Pseudo dice [0.2288, 0.6357, 0.7643, 0.7621, 0.3708, 0.8371, 0.6425] +2026-04-11 05:58:10.866112: Epoch time: 102.87 s +2026-04-11 05:58:11.985543: +2026-04-11 05:58:11.986995: Epoch 689 +2026-04-11 05:58:11.988394: Current learning rate: 0.00844 +2026-04-11 05:59:53.784288: train_loss -0.2737 +2026-04-11 05:59:53.789303: val_loss -0.2252 +2026-04-11 05:59:53.790866: Pseudo dice [0.4152, 0.0796, 0.7746, 0.7587, 0.2517, 0.3366, 0.8301] +2026-04-11 05:59:53.792371: Epoch time: 101.8 s +2026-04-11 05:59:54.913214: +2026-04-11 05:59:54.914768: Epoch 690 +2026-04-11 05:59:54.916208: Current learning rate: 0.00843 +2026-04-11 06:01:36.859495: train_loss -0.2542 +2026-04-11 06:01:36.863539: val_loss -0.1717 +2026-04-11 06:01:36.866096: Pseudo dice [0.0, 0.0, 0.5153, 0.6571, 0.2483, 0.5958, 0.2281] +2026-04-11 06:01:36.867910: Epoch time: 101.95 s +2026-04-11 06:01:37.990781: +2026-04-11 06:01:37.992160: Epoch 691 +2026-04-11 06:01:37.993610: Current learning rate: 0.00843 +2026-04-11 06:03:20.210063: train_loss -0.2498 +2026-04-11 06:03:20.215603: val_loss -0.1914 +2026-04-11 06:03:20.217708: Pseudo dice [0.0, 0.0, 0.4848, 0.4118, 0.294, 0.7141, 0.6063] +2026-04-11 06:03:20.219876: Epoch time: 102.22 s +2026-04-11 06:03:21.356096: +2026-04-11 06:03:21.358058: Epoch 692 +2026-04-11 06:03:21.360481: Current learning rate: 0.00843 +2026-04-11 06:05:03.515401: train_loss -0.2405 +2026-04-11 06:05:03.520368: val_loss -0.2107 +2026-04-11 06:05:03.521610: Pseudo dice [0.0107, 0.0, 0.6379, 0.524, 0.2556, 0.4397, 0.718] +2026-04-11 06:05:03.522959: Epoch time: 102.16 s +2026-04-11 06:05:04.632130: +2026-04-11 06:05:04.634568: Epoch 693 +2026-04-11 06:05:04.636208: Current learning rate: 0.00843 +2026-04-11 06:06:46.801418: train_loss -0.2477 +2026-04-11 06:06:46.805914: val_loss -0.2652 +2026-04-11 06:06:46.807683: Pseudo dice [0.4349, 0.2685, 0.69, 0.1524, 0.5212, 0.649, 0.7558] +2026-04-11 06:06:46.809271: Epoch time: 102.17 s +2026-04-11 06:06:47.928378: +2026-04-11 06:06:47.929745: Epoch 694 +2026-04-11 06:06:47.931276: Current learning rate: 0.00842 +2026-04-11 06:08:29.817040: train_loss -0.2551 +2026-04-11 06:08:29.821067: val_loss -0.2538 +2026-04-11 06:08:29.822260: Pseudo dice [0.0143, 0.1091, 0.6924, 0.6506, 0.0734, 0.767, 0.7231] +2026-04-11 06:08:29.823529: Epoch time: 101.89 s +2026-04-11 06:08:30.947129: +2026-04-11 06:08:30.948390: Epoch 695 +2026-04-11 06:08:30.949481: Current learning rate: 0.00842 +2026-04-11 06:10:12.739056: train_loss -0.2628 +2026-04-11 06:10:13.884984: val_loss -0.2465 +2026-04-11 06:10:13.886230: Pseudo dice [0.0469, 0.1912, 0.7167, 0.7337, 0.4064, 0.5879, 0.7751] +2026-04-11 06:10:13.887677: Epoch time: 101.8 s +2026-04-11 06:10:15.004904: +2026-04-11 06:10:15.006850: Epoch 696 +2026-04-11 06:10:15.008155: Current learning rate: 0.00842 +2026-04-11 06:11:57.140703: train_loss -0.2588 +2026-04-11 06:11:57.145679: val_loss -0.2424 +2026-04-11 06:11:57.146953: Pseudo dice [0.0747, 0.088, 0.6969, 0.326, 0.3614, 0.7855, 0.5624] +2026-04-11 06:11:57.148475: Epoch time: 102.14 s +2026-04-11 06:11:58.271880: +2026-04-11 06:11:58.273265: Epoch 697 +2026-04-11 06:11:58.274567: Current learning rate: 0.00842 +2026-04-11 06:13:40.459537: train_loss -0.2348 +2026-04-11 06:13:40.463880: val_loss -0.2142 +2026-04-11 06:13:40.465527: Pseudo dice [0.0148, 0.5582, 0.6995, 0.6804, 0.1709, 0.6758, 0.4314] +2026-04-11 06:13:40.467380: Epoch time: 102.19 s +2026-04-11 06:13:41.604211: +2026-04-11 06:13:41.605746: Epoch 698 +2026-04-11 06:13:41.607112: Current learning rate: 0.00841 +2026-04-11 06:15:23.524935: train_loss -0.2339 +2026-04-11 06:15:23.529124: val_loss -0.1566 +2026-04-11 06:15:23.530627: Pseudo dice [0.0, 0.0, 0.5996, 0.5356, 0.4161, 0.3494, 0.2243] +2026-04-11 06:15:23.534555: Epoch time: 101.92 s +2026-04-11 06:15:24.650124: +2026-04-11 06:15:24.651904: Epoch 699 +2026-04-11 06:15:24.653328: Current learning rate: 0.00841 +2026-04-11 06:17:06.774791: train_loss -0.249 +2026-04-11 06:17:06.778942: val_loss -0.2193 +2026-04-11 06:17:06.780405: Pseudo dice [0.0, 0.0, 0.5125, 0.8022, 0.1738, 0.5591, 0.8375] +2026-04-11 06:17:06.781930: Epoch time: 102.13 s +2026-04-11 06:17:09.439890: +2026-04-11 06:17:09.441221: Epoch 700 +2026-04-11 06:17:09.442295: Current learning rate: 0.00841 +2026-04-11 06:18:51.464234: train_loss -0.2434 +2026-04-11 06:18:51.468474: val_loss -0.1926 +2026-04-11 06:18:51.470062: Pseudo dice [0.0454, 0.0, 0.2621, 0.576, 0.0461, 0.697, 0.5762] +2026-04-11 06:18:51.471716: Epoch time: 102.03 s +2026-04-11 06:18:52.603749: +2026-04-11 06:18:52.605686: Epoch 701 +2026-04-11 06:18:52.606976: Current learning rate: 0.00841 +2026-04-11 06:20:34.454508: train_loss -0.2502 +2026-04-11 06:20:34.458579: val_loss -0.2248 +2026-04-11 06:20:34.459858: Pseudo dice [0.2111, 0.0, 0.5615, 0.5343, 0.2145, 0.2012, 0.7957] +2026-04-11 06:20:34.461133: Epoch time: 101.85 s +2026-04-11 06:20:35.581731: +2026-04-11 06:20:35.583231: Epoch 702 +2026-04-11 06:20:35.584750: Current learning rate: 0.00841 +2026-04-11 06:22:17.226859: train_loss -0.2523 +2026-04-11 06:22:17.231876: val_loss -0.2358 +2026-04-11 06:22:17.233497: Pseudo dice [0.2424, 0.0048, 0.7457, 0.6468, 0.3559, 0.7131, 0.7212] +2026-04-11 06:22:17.235346: Epoch time: 101.65 s +2026-04-11 06:22:18.345680: +2026-04-11 06:22:18.347726: Epoch 703 +2026-04-11 06:22:18.349231: Current learning rate: 0.0084 +2026-04-11 06:24:00.533260: train_loss -0.2693 +2026-04-11 06:24:00.537818: val_loss -0.2499 +2026-04-11 06:24:00.539448: Pseudo dice [0.3904, 0.1459, 0.826, 0.8017, 0.3769, 0.7808, 0.6367] +2026-04-11 06:24:00.540908: Epoch time: 102.19 s +2026-04-11 06:24:01.662136: +2026-04-11 06:24:01.663476: Epoch 704 +2026-04-11 06:24:01.664741: Current learning rate: 0.0084 +2026-04-11 06:25:43.698620: train_loss -0.2804 +2026-04-11 06:25:43.703155: val_loss -0.2081 +2026-04-11 06:25:43.704830: Pseudo dice [0.0244, 0.0, 0.7412, 0.7281, 0.2146, 0.643, 0.2727] +2026-04-11 06:25:43.706386: Epoch time: 102.04 s +2026-04-11 06:25:44.830718: +2026-04-11 06:25:44.832385: Epoch 705 +2026-04-11 06:25:44.834259: Current learning rate: 0.0084 +2026-04-11 06:27:26.809052: train_loss -0.2793 +2026-04-11 06:27:26.813560: val_loss -0.2151 +2026-04-11 06:27:26.815143: Pseudo dice [0.0, 0.3841, 0.3435, 0.6553, 0.3216, 0.7765, 0.47] +2026-04-11 06:27:26.817183: Epoch time: 101.98 s +2026-04-11 06:27:27.919805: +2026-04-11 06:27:27.921263: Epoch 706 +2026-04-11 06:27:27.922698: Current learning rate: 0.0084 +2026-04-11 06:29:10.063292: train_loss -0.2585 +2026-04-11 06:29:10.087799: val_loss -0.2429 +2026-04-11 06:29:10.089626: Pseudo dice [0.5531, 0.8543, 0.8726, 0.0482, 0.2552, 0.5563, 0.7201] +2026-04-11 06:29:10.091618: Epoch time: 102.15 s +2026-04-11 06:29:11.215436: +2026-04-11 06:29:11.216743: Epoch 707 +2026-04-11 06:29:11.218272: Current learning rate: 0.00839 +2026-04-11 06:30:53.373463: train_loss -0.2468 +2026-04-11 06:30:53.377803: val_loss -0.2409 +2026-04-11 06:30:53.379336: Pseudo dice [0.279, 0.3588, 0.7178, 0.3728, 0.3828, 0.7674, 0.6471] +2026-04-11 06:30:53.380605: Epoch time: 102.16 s +2026-04-11 06:30:55.669129: +2026-04-11 06:30:55.670656: Epoch 708 +2026-04-11 06:30:55.672057: Current learning rate: 0.00839 +2026-04-11 06:32:37.835736: train_loss -0.2444 +2026-04-11 06:32:37.840557: val_loss -0.1894 +2026-04-11 06:32:37.842801: Pseudo dice [0.0401, 0.0122, 0.3196, 0.5019, 0.203, 0.2917, 0.6086] +2026-04-11 06:32:37.844187: Epoch time: 102.17 s +2026-04-11 06:32:38.946491: +2026-04-11 06:32:38.947982: Epoch 709 +2026-04-11 06:32:38.953072: Current learning rate: 0.00839 +2026-04-11 06:34:21.132163: train_loss -0.2564 +2026-04-11 06:34:21.136626: val_loss -0.2035 +2026-04-11 06:34:21.137948: Pseudo dice [0.0733, 0.0, 0.5872, 0.5685, 0.1737, 0.6263, 0.6657] +2026-04-11 06:34:21.139973: Epoch time: 102.19 s +2026-04-11 06:34:22.260426: +2026-04-11 06:34:22.261667: Epoch 710 +2026-04-11 06:34:22.262746: Current learning rate: 0.00839 +2026-04-11 06:36:03.820222: train_loss -0.2651 +2026-04-11 06:36:03.825056: val_loss -0.2304 +2026-04-11 06:36:03.827060: Pseudo dice [0.0654, 0.0, 0.7597, 0.5767, 0.3872, 0.6423, 0.65] +2026-04-11 06:36:03.829052: Epoch time: 101.56 s +2026-04-11 06:36:04.953766: +2026-04-11 06:36:04.955453: Epoch 711 +2026-04-11 06:36:04.957079: Current learning rate: 0.00839 +2026-04-11 06:37:46.602057: train_loss -0.2589 +2026-04-11 06:37:46.607237: val_loss -0.1967 +2026-04-11 06:37:46.608953: Pseudo dice [0.0, 0.0, 0.6966, 0.5267, 0.2992, 0.7518, 0.3835] +2026-04-11 06:37:46.610438: Epoch time: 101.65 s +2026-04-11 06:37:47.751120: +2026-04-11 06:37:47.753032: Epoch 712 +2026-04-11 06:37:47.754523: Current learning rate: 0.00838 +2026-04-11 06:39:29.698832: train_loss -0.2617 +2026-04-11 06:39:29.703064: val_loss -0.2389 +2026-04-11 06:39:29.704831: Pseudo dice [0.0006, 0.095, 0.7571, 0.7208, 0.4504, 0.6574, 0.8312] +2026-04-11 06:39:29.706347: Epoch time: 101.95 s +2026-04-11 06:39:30.845522: +2026-04-11 06:39:30.846974: Epoch 713 +2026-04-11 06:39:30.848396: Current learning rate: 0.00838 +2026-04-11 06:41:12.832958: train_loss -0.2563 +2026-04-11 06:41:12.839219: val_loss -0.2325 +2026-04-11 06:41:12.841162: Pseudo dice [0.0769, 0.5147, 0.5339, 0.8012, 0.2865, 0.795, 0.6148] +2026-04-11 06:41:12.843629: Epoch time: 101.99 s +2026-04-11 06:41:13.954787: +2026-04-11 06:41:13.956430: Epoch 714 +2026-04-11 06:41:13.957809: Current learning rate: 0.00838 +2026-04-11 06:42:55.935105: train_loss -0.2693 +2026-04-11 06:42:55.940344: val_loss -0.2166 +2026-04-11 06:42:55.941923: Pseudo dice [0.2179, 0.0, 0.734, 0.34, 0.4694, 0.4744, 0.3908] +2026-04-11 06:42:55.943664: Epoch time: 101.98 s +2026-04-11 06:42:57.059920: +2026-04-11 06:42:57.061545: Epoch 715 +2026-04-11 06:42:57.063336: Current learning rate: 0.00838 +2026-04-11 06:44:39.232139: train_loss -0.2574 +2026-04-11 06:44:39.236156: val_loss -0.2002 +2026-04-11 06:44:39.237550: Pseudo dice [0.0663, 0.069, 0.6277, 0.6626, 0.1968, 0.6586, 0.5449] +2026-04-11 06:44:39.238745: Epoch time: 102.18 s +2026-04-11 06:44:40.355608: +2026-04-11 06:44:40.356826: Epoch 716 +2026-04-11 06:44:40.357941: Current learning rate: 0.00837 +2026-04-11 06:46:22.344237: train_loss -0.2568 +2026-04-11 06:46:22.348364: val_loss -0.2201 +2026-04-11 06:46:22.349937: Pseudo dice [0.0, 0.0, 0.6444, 0.6389, 0.151, 0.7276, 0.408] +2026-04-11 06:46:22.351289: Epoch time: 101.99 s +2026-04-11 06:46:23.492549: +2026-04-11 06:46:23.493834: Epoch 717 +2026-04-11 06:46:23.494876: Current learning rate: 0.00837 +2026-04-11 06:48:05.453226: train_loss -0.2462 +2026-04-11 06:48:05.457273: val_loss -0.2406 +2026-04-11 06:48:05.458684: Pseudo dice [0.0, 0.5822, 0.6733, 0.7654, 0.4305, 0.5312, 0.5779] +2026-04-11 06:48:05.459965: Epoch time: 101.96 s +2026-04-11 06:48:06.597792: +2026-04-11 06:48:06.599268: Epoch 718 +2026-04-11 06:48:06.600482: Current learning rate: 0.00837 +2026-04-11 06:49:48.668616: train_loss -0.2621 +2026-04-11 06:49:48.672878: val_loss -0.2287 +2026-04-11 06:49:48.674557: Pseudo dice [0.0, 0.3828, 0.6575, 0.3995, 0.5002, 0.808, 0.8153] +2026-04-11 06:49:48.676129: Epoch time: 102.07 s +2026-04-11 06:49:49.807497: +2026-04-11 06:49:49.809238: Epoch 719 +2026-04-11 06:49:49.810619: Current learning rate: 0.00837 +2026-04-11 06:51:32.038603: train_loss -0.263 +2026-04-11 06:51:32.042465: val_loss -0.2427 +2026-04-11 06:51:32.043749: Pseudo dice [0.0, 0.0007, 0.6852, 0.7943, 0.4649, 0.6643, 0.7449] +2026-04-11 06:51:32.045080: Epoch time: 102.23 s +2026-04-11 06:51:33.165400: +2026-04-11 06:51:33.166730: Epoch 720 +2026-04-11 06:51:33.167803: Current learning rate: 0.00836 +2026-04-11 06:53:15.283058: train_loss -0.2627 +2026-04-11 06:53:15.287594: val_loss -0.215 +2026-04-11 06:53:15.289885: Pseudo dice [0.3022, 0.0, 0.6791, 0.4434, 0.2659, 0.7776, 0.3457] +2026-04-11 06:53:15.292032: Epoch time: 102.12 s +2026-04-11 06:53:16.423472: +2026-04-11 06:53:16.425256: Epoch 721 +2026-04-11 06:53:16.426829: Current learning rate: 0.00836 +2026-04-11 06:54:58.493582: train_loss -0.271 +2026-04-11 06:54:58.498682: val_loss -0.201 +2026-04-11 06:54:58.500299: Pseudo dice [0.2472, 0.0, 0.7253, 0.2024, 0.2532, 0.5325, 0.6935] +2026-04-11 06:54:58.501944: Epoch time: 102.07 s +2026-04-11 06:54:59.588382: +2026-04-11 06:54:59.589697: Epoch 722 +2026-04-11 06:54:59.591065: Current learning rate: 0.00836 +2026-04-11 06:56:41.501194: train_loss -0.2686 +2026-04-11 06:56:41.505085: val_loss -0.2304 +2026-04-11 06:56:41.506552: Pseudo dice [0.4574, 0.1405, 0.7064, 0.4437, 0.3413, 0.4248, 0.7857] +2026-04-11 06:56:41.507694: Epoch time: 101.92 s +2026-04-11 06:56:42.637326: +2026-04-11 06:56:42.638522: Epoch 723 +2026-04-11 06:56:42.639656: Current learning rate: 0.00836 +2026-04-11 06:58:24.844585: train_loss -0.2648 +2026-04-11 06:58:24.848797: val_loss -0.2145 +2026-04-11 06:58:24.850261: Pseudo dice [0.0947, 0.1378, 0.6927, 0.7798, 0.4458, 0.1445, 0.6908] +2026-04-11 06:58:24.851595: Epoch time: 102.21 s +2026-04-11 06:58:25.965460: +2026-04-11 06:58:25.967149: Epoch 724 +2026-04-11 06:58:25.968576: Current learning rate: 0.00836 +2026-04-11 07:00:08.044807: train_loss -0.2664 +2026-04-11 07:00:08.049398: val_loss -0.2419 +2026-04-11 07:00:08.050886: Pseudo dice [0.6346, 0.2495, 0.7011, 0.6828, 0.2938, 0.7025, 0.8365] +2026-04-11 07:00:08.052330: Epoch time: 102.08 s +2026-04-11 07:00:09.155805: +2026-04-11 07:00:09.157243: Epoch 725 +2026-04-11 07:00:09.158697: Current learning rate: 0.00835 +2026-04-11 07:01:51.257837: train_loss -0.2736 +2026-04-11 07:01:51.262083: val_loss -0.1989 +2026-04-11 07:01:51.263387: Pseudo dice [0.0, 0.0, 0.4621, 0.5927, 0.0082, 0.4413, 0.7344] +2026-04-11 07:01:51.265188: Epoch time: 102.11 s +2026-04-11 07:01:52.388338: +2026-04-11 07:01:52.390121: Epoch 726 +2026-04-11 07:01:52.391536: Current learning rate: 0.00835 +2026-04-11 07:03:34.114799: train_loss -0.2434 +2026-04-11 07:03:34.120752: val_loss -0.2304 +2026-04-11 07:03:34.122407: Pseudo dice [0.0002, 0.1887, 0.6676, 0.677, 0.2713, 0.6784, 0.7178] +2026-04-11 07:03:34.123959: Epoch time: 101.73 s +2026-04-11 07:03:35.252650: +2026-04-11 07:03:35.254027: Epoch 727 +2026-04-11 07:03:35.255319: Current learning rate: 0.00835 +2026-04-11 07:05:17.247201: train_loss -0.2706 +2026-04-11 07:05:17.251831: val_loss -0.233 +2026-04-11 07:05:17.253291: Pseudo dice [0.2792, 0.3253, 0.8382, 0.3181, 0.3767, 0.0638, 0.5715] +2026-04-11 07:05:17.254531: Epoch time: 102.0 s +2026-04-11 07:05:19.474673: +2026-04-11 07:05:19.476206: Epoch 728 +2026-04-11 07:05:19.477636: Current learning rate: 0.00835 +2026-04-11 07:07:01.546836: train_loss -0.2732 +2026-04-11 07:07:01.550800: val_loss -0.2504 +2026-04-11 07:07:01.553094: Pseudo dice [0.4488, 0.1601, 0.6933, 0.3026, 0.2962, 0.6629, 0.8486] +2026-04-11 07:07:01.554823: Epoch time: 102.08 s +2026-04-11 07:07:02.700271: +2026-04-11 07:07:02.701991: Epoch 729 +2026-04-11 07:07:02.703440: Current learning rate: 0.00834 +2026-04-11 07:08:44.675839: train_loss -0.2633 +2026-04-11 07:08:44.681313: val_loss -0.2351 +2026-04-11 07:08:44.682952: Pseudo dice [0.0033, 0.0684, 0.6607, 0.8018, 0.363, 0.6167, 0.8214] +2026-04-11 07:08:44.684836: Epoch time: 101.98 s +2026-04-11 07:08:45.795423: +2026-04-11 07:08:45.797148: Epoch 730 +2026-04-11 07:08:45.798661: Current learning rate: 0.00834 +2026-04-11 07:10:27.759066: train_loss -0.2537 +2026-04-11 07:10:27.764934: val_loss -0.2173 +2026-04-11 07:10:27.767274: Pseudo dice [0.0, 0.2455, 0.7577, 0.6658, 0.3248, 0.4433, 0.846] +2026-04-11 07:10:27.768891: Epoch time: 101.97 s +2026-04-11 07:10:28.874811: +2026-04-11 07:10:28.876248: Epoch 731 +2026-04-11 07:10:28.877640: Current learning rate: 0.00834 +2026-04-11 07:12:10.974693: train_loss -0.2641 +2026-04-11 07:12:10.979490: val_loss -0.2326 +2026-04-11 07:12:10.980944: Pseudo dice [0.0, 0.2991, 0.5623, 0.8893, 0.346, 0.7321, 0.709] +2026-04-11 07:12:10.982331: Epoch time: 102.1 s +2026-04-11 07:12:12.126841: +2026-04-11 07:12:12.128439: Epoch 732 +2026-04-11 07:12:12.129778: Current learning rate: 0.00834 +2026-04-11 07:13:54.085922: train_loss -0.2742 +2026-04-11 07:13:54.089564: val_loss -0.2206 +2026-04-11 07:13:54.091176: Pseudo dice [0.0107, 0.076, 0.4203, 0.6487, 0.2816, 0.5617, 0.6315] +2026-04-11 07:13:54.092546: Epoch time: 101.96 s +2026-04-11 07:13:55.212285: +2026-04-11 07:13:55.213559: Epoch 733 +2026-04-11 07:13:55.214830: Current learning rate: 0.00833 +2026-04-11 07:15:37.125586: train_loss -0.2696 +2026-04-11 07:15:37.133236: val_loss -0.2204 +2026-04-11 07:15:37.135710: Pseudo dice [0.0859, 0.3826, 0.5478, 0.4345, 0.4029, 0.5539, 0.9095] +2026-04-11 07:15:37.137650: Epoch time: 101.92 s +2026-04-11 07:15:38.240402: +2026-04-11 07:15:38.241849: Epoch 734 +2026-04-11 07:15:38.243398: Current learning rate: 0.00833 +2026-04-11 07:17:20.157380: train_loss -0.2661 +2026-04-11 07:17:20.162113: val_loss -0.2179 +2026-04-11 07:17:20.163744: Pseudo dice [0.1998, 0.2948, 0.67, 0.6984, 0.3249, 0.8343, 0.6875] +2026-04-11 07:17:20.165215: Epoch time: 101.92 s +2026-04-11 07:17:21.281393: +2026-04-11 07:17:21.282705: Epoch 735 +2026-04-11 07:17:21.283865: Current learning rate: 0.00833 +2026-04-11 07:19:03.273186: train_loss -0.2677 +2026-04-11 07:19:03.277923: val_loss -0.2048 +2026-04-11 07:19:03.280292: Pseudo dice [0.0, 0.1701, 0.7004, 0.667, 0.1468, 0.7962, 0.7536] +2026-04-11 07:19:03.282458: Epoch time: 101.99 s +2026-04-11 07:19:04.410524: +2026-04-11 07:19:04.412259: Epoch 736 +2026-04-11 07:19:04.413765: Current learning rate: 0.00833 +2026-04-11 07:20:46.483487: train_loss -0.2484 +2026-04-11 07:20:46.489754: val_loss -0.2178 +2026-04-11 07:20:46.491280: Pseudo dice [0.0, 0.3041, 0.6331, 0.7115, 0.2749, 0.6512, 0.5254] +2026-04-11 07:20:46.492842: Epoch time: 102.08 s +2026-04-11 07:20:47.593263: +2026-04-11 07:20:47.594627: Epoch 737 +2026-04-11 07:20:47.595985: Current learning rate: 0.00833 +2026-04-11 07:22:29.386486: train_loss -0.2781 +2026-04-11 07:22:29.392681: val_loss -0.1559 +2026-04-11 07:22:29.394151: Pseudo dice [0.0618, 0.0, 0.5311, 0.4929, 0.1668, 0.417, 0.593] +2026-04-11 07:22:29.396027: Epoch time: 101.8 s +2026-04-11 07:22:30.524662: +2026-04-11 07:22:30.526228: Epoch 738 +2026-04-11 07:22:30.527843: Current learning rate: 0.00832 +2026-04-11 07:24:12.573959: train_loss -0.2549 +2026-04-11 07:24:12.580629: val_loss -0.2126 +2026-04-11 07:24:12.585451: Pseudo dice [0.0, 0.0, 0.4735, 0.5337, 0.2333, 0.7737, 0.8977] +2026-04-11 07:24:12.587276: Epoch time: 102.05 s +2026-04-11 07:24:13.797525: +2026-04-11 07:24:13.799014: Epoch 739 +2026-04-11 07:24:13.800441: Current learning rate: 0.00832 +2026-04-11 07:25:55.917578: train_loss -0.2671 +2026-04-11 07:25:55.927723: val_loss -0.2408 +2026-04-11 07:25:55.929637: Pseudo dice [0.0, 0.0, 0.6392, 0.0008, 0.3425, 0.6897, 0.6206] +2026-04-11 07:25:55.931504: Epoch time: 102.12 s +2026-04-11 07:25:57.079235: +2026-04-11 07:25:57.080844: Epoch 740 +2026-04-11 07:25:57.082043: Current learning rate: 0.00832 +2026-04-11 07:27:39.032112: train_loss -0.2499 +2026-04-11 07:27:39.036192: val_loss -0.228 +2026-04-11 07:27:39.037457: Pseudo dice [0.1282, 0.0, 0.7832, 0.3725, 0.3426, 0.7389, 0.7122] +2026-04-11 07:27:39.039141: Epoch time: 101.96 s +2026-04-11 07:27:40.183582: +2026-04-11 07:27:40.184792: Epoch 741 +2026-04-11 07:27:40.185973: Current learning rate: 0.00832 +2026-04-11 07:29:22.004571: train_loss -0.2582 +2026-04-11 07:29:22.032365: val_loss -0.2189 +2026-04-11 07:29:22.034320: Pseudo dice [0.2287, 0.0, 0.6458, 0.8248, 0.2436, 0.6705, 0.8005] +2026-04-11 07:29:22.036074: Epoch time: 101.82 s +2026-04-11 07:29:23.154783: +2026-04-11 07:29:23.156265: Epoch 742 +2026-04-11 07:29:23.157724: Current learning rate: 0.00831 +2026-04-11 07:31:05.105358: train_loss -0.264 +2026-04-11 07:31:05.109905: val_loss -0.1919 +2026-04-11 07:31:05.111302: Pseudo dice [0.0012, 0.0, 0.6644, 0.4812, 0.4301, 0.2127, 0.4428] +2026-04-11 07:31:05.112792: Epoch time: 101.95 s +2026-04-11 07:31:06.225528: +2026-04-11 07:31:06.227109: Epoch 743 +2026-04-11 07:31:06.228722: Current learning rate: 0.00831 +2026-04-11 07:32:48.317338: train_loss -0.2472 +2026-04-11 07:32:48.323396: val_loss -0.2405 +2026-04-11 07:32:48.326117: Pseudo dice [0.4183, 0.0, 0.6383, 0.6504, 0.3763, 0.8233, 0.7084] +2026-04-11 07:32:48.327755: Epoch time: 102.1 s +2026-04-11 07:32:49.440334: +2026-04-11 07:32:49.442065: Epoch 744 +2026-04-11 07:32:49.443517: Current learning rate: 0.00831 +2026-04-11 07:34:31.147699: train_loss -0.2632 +2026-04-11 07:34:31.152377: val_loss -0.2372 +2026-04-11 07:34:31.154365: Pseudo dice [0.3064, 0.0, 0.6498, 0.8977, 0.2781, 0.6244, 0.8756] +2026-04-11 07:34:31.155996: Epoch time: 101.71 s +2026-04-11 07:34:32.270768: +2026-04-11 07:34:32.272458: Epoch 745 +2026-04-11 07:34:32.274169: Current learning rate: 0.00831 +2026-04-11 07:36:14.233091: train_loss -0.2699 +2026-04-11 07:36:14.237810: val_loss -0.2335 +2026-04-11 07:36:14.239518: Pseudo dice [0.6614, 0.0, 0.7191, 0.8398, 0.4203, 0.6787, 0.6957] +2026-04-11 07:36:14.240929: Epoch time: 101.97 s +2026-04-11 07:36:15.366107: +2026-04-11 07:36:15.367855: Epoch 746 +2026-04-11 07:36:15.369694: Current learning rate: 0.0083 +2026-04-11 07:37:57.382279: train_loss -0.276 +2026-04-11 07:37:57.387478: val_loss -0.2159 +2026-04-11 07:37:57.390417: Pseudo dice [0.8256, 0.0, 0.5856, 0.502, 0.1559, 0.6605, 0.6518] +2026-04-11 07:37:57.392139: Epoch time: 102.02 s +2026-04-11 07:37:58.504518: +2026-04-11 07:37:58.506317: Epoch 747 +2026-04-11 07:37:58.508338: Current learning rate: 0.0083 +2026-04-11 07:39:41.686147: train_loss -0.2581 +2026-04-11 07:39:41.690945: val_loss -0.2403 +2026-04-11 07:39:41.692595: Pseudo dice [0.6469, 0.0, 0.4871, 0.6379, 0.2012, 0.4897, 0.841] +2026-04-11 07:39:41.693877: Epoch time: 103.18 s +2026-04-11 07:39:42.809945: +2026-04-11 07:39:42.811570: Epoch 748 +2026-04-11 07:39:42.813077: Current learning rate: 0.0083 +2026-04-11 07:41:24.674057: train_loss -0.2637 +2026-04-11 07:41:24.682497: val_loss -0.2136 +2026-04-11 07:41:24.684431: Pseudo dice [0.3731, 0.0, 0.6063, 0.7234, 0.2313, 0.751, 0.5908] +2026-04-11 07:41:24.686466: Epoch time: 101.87 s +2026-04-11 07:41:25.818747: +2026-04-11 07:41:25.820613: Epoch 749 +2026-04-11 07:41:25.823021: Current learning rate: 0.0083 +2026-04-11 07:43:07.571862: train_loss -0.2694 +2026-04-11 07:43:07.576536: val_loss -0.2206 +2026-04-11 07:43:07.578585: Pseudo dice [0.0, 0.0, 0.6889, 0.6389, 0.3202, 0.695, 0.8234] +2026-04-11 07:43:07.580260: Epoch time: 101.76 s +2026-04-11 07:43:10.286603: +2026-04-11 07:43:10.288165: Epoch 750 +2026-04-11 07:43:10.290227: Current learning rate: 0.0083 +2026-04-11 07:44:51.941654: train_loss -0.2575 +2026-04-11 07:44:51.947645: val_loss -0.2227 +2026-04-11 07:44:51.949606: Pseudo dice [0.5338, 0.047, 0.2839, 0.1826, 0.3898, 0.3688, 0.7731] +2026-04-11 07:44:51.951649: Epoch time: 101.66 s +2026-04-11 07:44:53.071552: +2026-04-11 07:44:53.073237: Epoch 751 +2026-04-11 07:44:53.074878: Current learning rate: 0.00829 +2026-04-11 07:46:35.175405: train_loss -0.2432 +2026-04-11 07:46:35.180120: val_loss -0.2248 +2026-04-11 07:46:35.181916: Pseudo dice [0.0026, 0.0, 0.7025, 0.8644, 0.0365, 0.8236, 0.8874] +2026-04-11 07:46:35.183596: Epoch time: 102.11 s +2026-04-11 07:46:36.309832: +2026-04-11 07:46:36.311411: Epoch 752 +2026-04-11 07:46:36.313132: Current learning rate: 0.00829 +2026-04-11 07:48:17.926420: train_loss -0.2689 +2026-04-11 07:48:17.931427: val_loss -0.1869 +2026-04-11 07:48:17.933115: Pseudo dice [0.6229, 0.0, 0.8176, 0.224, 0.0551, 0.7012, 0.5181] +2026-04-11 07:48:17.934673: Epoch time: 101.62 s +2026-04-11 07:48:19.061402: +2026-04-11 07:48:19.062897: Epoch 753 +2026-04-11 07:48:19.064605: Current learning rate: 0.00829 +2026-04-11 07:50:00.738021: train_loss -0.2813 +2026-04-11 07:50:00.742835: val_loss -0.2424 +2026-04-11 07:50:00.744177: Pseudo dice [0.6116, 0.3444, 0.7595, 0.7988, 0.4038, 0.6413, 0.4689] +2026-04-11 07:50:00.745737: Epoch time: 101.68 s +2026-04-11 07:50:01.898713: +2026-04-11 07:50:01.900729: Epoch 754 +2026-04-11 07:50:01.902858: Current learning rate: 0.00829 +2026-04-11 07:51:43.787378: train_loss -0.2787 +2026-04-11 07:51:43.792753: val_loss -0.2242 +2026-04-11 07:51:43.795160: Pseudo dice [0.27, 0.3578, 0.4115, 0.8131, 0.3965, 0.4968, 0.7862] +2026-04-11 07:51:43.796577: Epoch time: 101.89 s +2026-04-11 07:51:44.927778: +2026-04-11 07:51:44.929909: Epoch 755 +2026-04-11 07:51:44.932728: Current learning rate: 0.00828 +2026-04-11 07:53:26.979477: train_loss -0.2678 +2026-04-11 07:53:26.984659: val_loss -0.2254 +2026-04-11 07:53:26.986384: Pseudo dice [0.5289, 0.0, 0.7515, 0.4668, 0.2987, 0.6829, 0.5241] +2026-04-11 07:53:26.987980: Epoch time: 102.05 s +2026-04-11 07:53:28.117034: +2026-04-11 07:53:28.118936: Epoch 756 +2026-04-11 07:53:28.120869: Current learning rate: 0.00828 +2026-04-11 07:55:10.184563: train_loss -0.2458 +2026-04-11 07:55:10.189074: val_loss -0.2205 +2026-04-11 07:55:10.190594: Pseudo dice [0.1913, 0.0, 0.4332, 0.7727, 0.3579, 0.6709, 0.7234] +2026-04-11 07:55:10.191821: Epoch time: 102.07 s +2026-04-11 07:55:11.330914: +2026-04-11 07:55:11.332811: Epoch 757 +2026-04-11 07:55:11.334337: Current learning rate: 0.00828 +2026-04-11 07:56:53.411258: train_loss -0.2492 +2026-04-11 07:56:53.415893: val_loss -0.2325 +2026-04-11 07:56:53.417839: Pseudo dice [0.0, 0.0, 0.6661, 0.637, 0.4092, 0.6901, 0.8641] +2026-04-11 07:56:53.419291: Epoch time: 102.08 s +2026-04-11 07:56:54.531636: +2026-04-11 07:56:54.532998: Epoch 758 +2026-04-11 07:56:54.534999: Current learning rate: 0.00828 +2026-04-11 07:58:36.737621: train_loss -0.2523 +2026-04-11 07:58:36.742192: val_loss -0.2011 +2026-04-11 07:58:36.743693: Pseudo dice [0.0, 0.0, 0.6486, 0.1996, 0.2061, 0.5693, 0.7218] +2026-04-11 07:58:36.745041: Epoch time: 102.21 s +2026-04-11 07:58:37.871120: +2026-04-11 07:58:37.873115: Epoch 759 +2026-04-11 07:58:37.874650: Current learning rate: 0.00827 +2026-04-11 08:00:20.040919: train_loss -0.2627 +2026-04-11 08:00:20.045817: val_loss -0.244 +2026-04-11 08:00:20.047871: Pseudo dice [0.002, 0.1073, 0.7124, 0.4474, 0.4863, 0.7904, 0.8816] +2026-04-11 08:00:20.049536: Epoch time: 102.17 s +2026-04-11 08:00:21.140393: +2026-04-11 08:00:21.142238: Epoch 760 +2026-04-11 08:00:21.144087: Current learning rate: 0.00827 +2026-04-11 08:02:03.162855: train_loss -0.2624 +2026-04-11 08:02:03.167407: val_loss -0.2117 +2026-04-11 08:02:03.169029: Pseudo dice [0.0972, 0.2417, 0.8456, 0.7298, 0.2363, 0.7842, 0.6141] +2026-04-11 08:02:03.171198: Epoch time: 102.03 s +2026-04-11 08:02:04.289771: +2026-04-11 08:02:04.291427: Epoch 761 +2026-04-11 08:02:04.293034: Current learning rate: 0.00827 +2026-04-11 08:03:46.277764: train_loss -0.2681 +2026-04-11 08:03:46.282922: val_loss -0.2376 +2026-04-11 08:03:46.284415: Pseudo dice [0.0999, 0.3173, 0.779, 0.381, 0.4027, 0.834, 0.6607] +2026-04-11 08:03:46.285886: Epoch time: 101.99 s +2026-04-11 08:03:47.393242: +2026-04-11 08:03:47.394667: Epoch 762 +2026-04-11 08:03:47.396383: Current learning rate: 0.00827 +2026-04-11 08:05:29.544042: train_loss -0.282 +2026-04-11 08:05:29.550523: val_loss -0.2351 +2026-04-11 08:05:29.552232: Pseudo dice [0.6558, 0.0771, 0.7361, 0.2847, 0.1029, 0.5665, 0.8555] +2026-04-11 08:05:29.554256: Epoch time: 102.15 s +2026-04-11 08:05:30.694658: +2026-04-11 08:05:30.696488: Epoch 763 +2026-04-11 08:05:30.698215: Current learning rate: 0.00827 +2026-04-11 08:07:12.850736: train_loss -0.2627 +2026-04-11 08:07:12.856669: val_loss -0.1921 +2026-04-11 08:07:12.858387: Pseudo dice [0.1381, 0.4706, 0.7684, 0.8054, 0.3207, 0.4317, 0.5538] +2026-04-11 08:07:12.861015: Epoch time: 102.16 s +2026-04-11 08:07:13.975245: +2026-04-11 08:07:13.976849: Epoch 764 +2026-04-11 08:07:13.979690: Current learning rate: 0.00826 +2026-04-11 08:08:56.225738: train_loss -0.2644 +2026-04-11 08:08:56.232330: val_loss -0.2262 +2026-04-11 08:08:56.234090: Pseudo dice [0.0973, 0.0462, 0.7924, 0.6881, 0.3921, 0.7996, 0.5494] +2026-04-11 08:08:56.235978: Epoch time: 102.25 s +2026-04-11 08:08:57.375700: +2026-04-11 08:08:57.378301: Epoch 765 +2026-04-11 08:08:57.380453: Current learning rate: 0.00826 +2026-04-11 08:10:39.365590: train_loss -0.2513 +2026-04-11 08:10:39.372797: val_loss -0.2255 +2026-04-11 08:10:39.374612: Pseudo dice [0.0, 0.0, 0.6947, 0.4137, 0.3929, 0.7609, 0.6364] +2026-04-11 08:10:39.376272: Epoch time: 101.99 s +2026-04-11 08:10:40.514777: +2026-04-11 08:10:40.516500: Epoch 766 +2026-04-11 08:10:40.518862: Current learning rate: 0.00826 +2026-04-11 08:12:22.422817: train_loss -0.2498 +2026-04-11 08:12:22.428438: val_loss -0.2359 +2026-04-11 08:12:22.430303: Pseudo dice [0.007, 0.3391, 0.7351, 0.6074, 0.2146, 0.8441, 0.5704] +2026-04-11 08:12:22.431744: Epoch time: 101.91 s +2026-04-11 08:12:24.668819: +2026-04-11 08:12:24.670923: Epoch 767 +2026-04-11 08:12:24.672780: Current learning rate: 0.00826 +2026-04-11 08:14:06.755652: train_loss -0.2531 +2026-04-11 08:14:06.760481: val_loss -0.2366 +2026-04-11 08:14:06.762096: Pseudo dice [0.1519, 0.4694, 0.2306, 0.8277, 0.252, 0.513, 0.7113] +2026-04-11 08:14:06.764411: Epoch time: 102.09 s +2026-04-11 08:14:07.898438: +2026-04-11 08:14:07.900132: Epoch 768 +2026-04-11 08:14:07.902253: Current learning rate: 0.00825 +2026-04-11 08:15:49.413134: train_loss -0.2443 +2026-04-11 08:15:49.418763: val_loss -0.2349 +2026-04-11 08:15:49.420787: Pseudo dice [0.6012, 0.0405, 0.7229, 0.8342, 0.395, 0.713, 0.5191] +2026-04-11 08:15:49.422261: Epoch time: 101.52 s +2026-04-11 08:15:50.578624: +2026-04-11 08:15:50.580719: Epoch 769 +2026-04-11 08:15:50.582561: Current learning rate: 0.00825 +2026-04-11 08:17:32.379387: train_loss -0.2646 +2026-04-11 08:17:32.386081: val_loss -0.2289 +2026-04-11 08:17:32.387986: Pseudo dice [0.0756, 0.0, 0.5739, 0.4068, 0.3847, 0.4975, 0.7829] +2026-04-11 08:17:32.389878: Epoch time: 101.8 s +2026-04-11 08:17:33.529465: +2026-04-11 08:17:33.531882: Epoch 770 +2026-04-11 08:17:33.534908: Current learning rate: 0.00825 +2026-04-11 08:19:15.323161: train_loss -0.2374 +2026-04-11 08:19:15.328185: val_loss -0.2285 +2026-04-11 08:19:15.329932: Pseudo dice [0.1498, 0.0, 0.7114, 0.6049, 0.3687, 0.4514, 0.7834] +2026-04-11 08:19:15.331720: Epoch time: 101.8 s +2026-04-11 08:19:16.478522: +2026-04-11 08:19:16.480945: Epoch 771 +2026-04-11 08:19:16.482855: Current learning rate: 0.00825 +2026-04-11 08:20:58.488213: train_loss -0.266 +2026-04-11 08:20:58.492585: val_loss -0.2358 +2026-04-11 08:20:58.494304: Pseudo dice [0.3598, 0.0, 0.5891, 0.6182, 0.2424, 0.7614, 0.8444] +2026-04-11 08:20:58.496001: Epoch time: 102.01 s +2026-04-11 08:20:59.631840: +2026-04-11 08:20:59.633609: Epoch 772 +2026-04-11 08:20:59.635297: Current learning rate: 0.00824 +2026-04-11 08:22:41.415857: train_loss -0.2552 +2026-04-11 08:22:41.421050: val_loss -0.2233 +2026-04-11 08:22:41.422625: Pseudo dice [0.1894, 0.0, 0.5328, 0.6869, 0.2299, 0.7109, 0.8191] +2026-04-11 08:22:41.424129: Epoch time: 101.79 s +2026-04-11 08:22:42.575333: +2026-04-11 08:22:42.576985: Epoch 773 +2026-04-11 08:22:42.579113: Current learning rate: 0.00824 +2026-04-11 08:24:24.440894: train_loss -0.2664 +2026-04-11 08:24:24.445147: val_loss -0.2284 +2026-04-11 08:24:24.447049: Pseudo dice [0.2364, 0.0048, 0.6016, 0.8608, 0.252, 0.4205, 0.6923] +2026-04-11 08:24:24.450174: Epoch time: 101.87 s +2026-04-11 08:24:25.599757: +2026-04-11 08:24:25.601849: Epoch 774 +2026-04-11 08:24:25.604051: Current learning rate: 0.00824 +2026-04-11 08:26:07.111777: train_loss -0.2918 +2026-04-11 08:26:07.116677: val_loss -0.2422 +2026-04-11 08:26:07.118764: Pseudo dice [0.1792, 0.2592, 0.6762, 0.8364, 0.3186, 0.8079, 0.5975] +2026-04-11 08:26:07.120370: Epoch time: 101.52 s +2026-04-11 08:26:08.253762: +2026-04-11 08:26:08.255747: Epoch 775 +2026-04-11 08:26:08.257574: Current learning rate: 0.00824 +2026-04-11 08:27:50.007676: train_loss -0.2959 +2026-04-11 08:27:50.014871: val_loss -0.2244 +2026-04-11 08:27:50.017041: Pseudo dice [0.1629, 0.2134, 0.5218, 0.7225, 0.1445, 0.1604, 0.8278] +2026-04-11 08:27:50.019244: Epoch time: 101.76 s +2026-04-11 08:27:51.156065: +2026-04-11 08:27:51.157712: Epoch 776 +2026-04-11 08:27:51.159599: Current learning rate: 0.00824 +2026-04-11 08:29:32.915001: train_loss -0.2751 +2026-04-11 08:29:32.939367: val_loss -0.2596 +2026-04-11 08:29:32.941531: Pseudo dice [0.7561, 0.1567, 0.6264, 0.8593, 0.2025, 0.765, 0.7236] +2026-04-11 08:29:32.942954: Epoch time: 101.76 s +2026-04-11 08:29:34.089840: +2026-04-11 08:29:34.091395: Epoch 777 +2026-04-11 08:29:34.093652: Current learning rate: 0.00823 +2026-04-11 08:31:16.095392: train_loss -0.2886 +2026-04-11 08:31:16.101234: val_loss -0.2402 +2026-04-11 08:31:16.102772: Pseudo dice [0.0, 0.3129, 0.4466, 0.6974, 0.2829, 0.6674, 0.7781] +2026-04-11 08:31:16.104255: Epoch time: 102.01 s +2026-04-11 08:31:17.228128: +2026-04-11 08:31:17.229928: Epoch 778 +2026-04-11 08:31:17.231687: Current learning rate: 0.00823 +2026-04-11 08:32:59.362357: train_loss -0.3069 +2026-04-11 08:32:59.367840: val_loss -0.322 +2026-04-11 08:32:59.369188: Pseudo dice [0.459, 0.3622, 0.4438, 0.3337, 0.3537, 0.8178, 0.7129] +2026-04-11 08:32:59.370477: Epoch time: 102.14 s +2026-04-11 08:33:00.501280: +2026-04-11 08:33:00.502933: Epoch 779 +2026-04-11 08:33:00.504675: Current learning rate: 0.00823 +2026-04-11 08:34:42.265843: train_loss -0.3703 +2026-04-11 08:34:42.270751: val_loss -0.3118 +2026-04-11 08:34:42.272494: Pseudo dice [0.0781, 0.081, 0.7491, 0.3489, 0.2662, 0.6704, 0.5976] +2026-04-11 08:34:42.274080: Epoch time: 101.77 s +2026-04-11 08:34:43.414607: +2026-04-11 08:34:43.416025: Epoch 780 +2026-04-11 08:34:43.417619: Current learning rate: 0.00823 +2026-04-11 08:36:25.113905: train_loss -0.3844 +2026-04-11 08:36:25.118459: val_loss -0.3485 +2026-04-11 08:36:25.120479: Pseudo dice [0.1801, 0.111, 0.5891, 0.6649, 0.2552, 0.6935, 0.1801] +2026-04-11 08:36:25.121897: Epoch time: 101.7 s +2026-04-11 08:36:26.266117: +2026-04-11 08:36:26.268111: Epoch 781 +2026-04-11 08:36:26.270074: Current learning rate: 0.00822 +2026-04-11 08:38:08.208594: train_loss -0.3529 +2026-04-11 08:38:08.214520: val_loss -0.3067 +2026-04-11 08:38:08.216939: Pseudo dice [0.2074, 0.2733, 0.5912, 0.8623, 0.362, 0.1211, 0.6956] +2026-04-11 08:38:08.218939: Epoch time: 101.95 s +2026-04-11 08:38:09.387717: +2026-04-11 08:38:09.390050: Epoch 782 +2026-04-11 08:38:09.392329: Current learning rate: 0.00822 +2026-04-11 08:39:51.307894: train_loss -0.329 +2026-04-11 08:39:51.312636: val_loss -0.3263 +2026-04-11 08:39:51.314267: Pseudo dice [0.3361, 0.5015, 0.6625, 0.3904, 0.4227, 0.1198, 0.5222] +2026-04-11 08:39:51.315894: Epoch time: 101.92 s +2026-04-11 08:39:52.455180: +2026-04-11 08:39:52.457362: Epoch 783 +2026-04-11 08:39:52.459559: Current learning rate: 0.00822 +2026-04-11 08:41:34.463295: train_loss -0.3486 +2026-04-11 08:41:34.468051: val_loss -0.287 +2026-04-11 08:41:34.470273: Pseudo dice [0.2092, 0.1466, 0.7349, 0.4187, 0.1542, 0.6988, 0.4003] +2026-04-11 08:41:34.471773: Epoch time: 102.01 s +2026-04-11 08:41:35.628675: +2026-04-11 08:41:35.630354: Epoch 784 +2026-04-11 08:41:35.632191: Current learning rate: 0.00822 +2026-04-11 08:43:17.312668: train_loss -0.3593 +2026-04-11 08:43:17.317249: val_loss -0.2977 +2026-04-11 08:43:17.318666: Pseudo dice [0.2377, 0.1314, 0.7422, 0.4391, 0.3535, 0.3671, 0.2657] +2026-04-11 08:43:17.320011: Epoch time: 101.69 s +2026-04-11 08:43:18.456083: +2026-04-11 08:43:18.457870: Epoch 785 +2026-04-11 08:43:18.459604: Current learning rate: 0.00822 +2026-04-11 08:45:00.321435: train_loss -0.3437 +2026-04-11 08:45:00.326759: val_loss -0.336 +2026-04-11 08:45:00.328298: Pseudo dice [0.1551, 0.2273, 0.789, 0.6165, 0.1182, 0.693, 0.6364] +2026-04-11 08:45:00.330078: Epoch time: 101.87 s +2026-04-11 08:45:01.471558: +2026-04-11 08:45:01.473574: Epoch 786 +2026-04-11 08:45:01.475879: Current learning rate: 0.00821 +2026-04-11 08:46:43.230755: train_loss -0.3634 +2026-04-11 08:46:43.236839: val_loss -0.3046 +2026-04-11 08:46:43.238588: Pseudo dice [0.1804, 0.3768, 0.5418, 0.045, 0.2062, 0.6729, 0.4658] +2026-04-11 08:46:43.240722: Epoch time: 101.76 s +2026-04-11 08:46:45.510047: +2026-04-11 08:46:45.511926: Epoch 787 +2026-04-11 08:46:45.513939: Current learning rate: 0.00821 +2026-04-11 08:48:27.620430: train_loss -0.3461 +2026-04-11 08:48:27.626407: val_loss -0.3309 +2026-04-11 08:48:27.628319: Pseudo dice [0.1705, 0.0, 0.707, 0.521, 0.0161, 0.7672, 0.8186] +2026-04-11 08:48:27.630155: Epoch time: 102.11 s +2026-04-11 08:48:28.786588: +2026-04-11 08:48:28.788368: Epoch 788 +2026-04-11 08:48:28.790454: Current learning rate: 0.00821 +2026-04-11 08:50:10.435539: train_loss -0.3754 +2026-04-11 08:50:10.440393: val_loss -0.3485 +2026-04-11 08:50:10.442694: Pseudo dice [0.6216, 0.1043, 0.4558, 0.7772, 0.0909, 0.7427, 0.8301] +2026-04-11 08:50:10.444662: Epoch time: 101.65 s +2026-04-11 08:50:11.573925: +2026-04-11 08:50:11.576139: Epoch 789 +2026-04-11 08:50:11.578029: Current learning rate: 0.00821 +2026-04-11 08:51:53.050225: train_loss -0.3941 +2026-04-11 08:51:53.056038: val_loss -0.322 +2026-04-11 08:51:53.057644: Pseudo dice [0.0296, 0.3987, 0.6017, 0.7781, 0.0682, 0.7274, 0.431] +2026-04-11 08:51:53.059175: Epoch time: 101.48 s +2026-04-11 08:51:54.226980: +2026-04-11 08:51:54.228645: Epoch 790 +2026-04-11 08:51:54.230411: Current learning rate: 0.0082 +2026-04-11 08:53:36.310059: train_loss -0.38 +2026-04-11 08:53:36.316469: val_loss -0.3442 +2026-04-11 08:53:36.319317: Pseudo dice [0.2608, 0.2274, 0.7423, 0.4738, 0.084, 0.5517, 0.6569] +2026-04-11 08:53:36.327363: Epoch time: 102.09 s +2026-04-11 08:53:37.460909: +2026-04-11 08:53:37.462709: Epoch 791 +2026-04-11 08:53:37.464980: Current learning rate: 0.0082 +2026-04-11 08:55:19.388389: train_loss -0.3905 +2026-04-11 08:55:19.393887: val_loss -0.3561 +2026-04-11 08:55:19.396219: Pseudo dice [0.3338, 0.5087, 0.7095, 0.5672, 0.3175, 0.7957, 0.7056] +2026-04-11 08:55:19.398699: Epoch time: 101.93 s +2026-04-11 08:55:20.545877: +2026-04-11 08:55:20.547562: Epoch 792 +2026-04-11 08:55:20.549403: Current learning rate: 0.0082 +2026-04-11 08:57:02.359300: train_loss -0.3801 +2026-04-11 08:57:02.363948: val_loss -0.3229 +2026-04-11 08:57:02.366074: Pseudo dice [0.1333, 0.1154, 0.6595, 0.6422, 0.4362, 0.3856, 0.6463] +2026-04-11 08:57:02.367611: Epoch time: 101.82 s +2026-04-11 08:57:03.492800: +2026-04-11 08:57:03.494506: Epoch 793 +2026-04-11 08:57:03.496302: Current learning rate: 0.0082 +2026-04-11 08:58:45.392597: train_loss -0.3817 +2026-04-11 08:58:45.398028: val_loss -0.3384 +2026-04-11 08:58:45.399772: Pseudo dice [0.1317, 0.4724, 0.691, 0.6775, 0.2485, 0.631, 0.8691] +2026-04-11 08:58:45.401250: Epoch time: 101.9 s +2026-04-11 08:58:46.525584: +2026-04-11 08:58:46.527359: Epoch 794 +2026-04-11 08:58:46.529235: Current learning rate: 0.00819 +2026-04-11 09:00:27.942251: train_loss -0.3914 +2026-04-11 09:00:27.948940: val_loss -0.3049 +2026-04-11 09:00:27.950804: Pseudo dice [0.3757, 0.2202, 0.5857, 0.7353, 0.2462, 0.5165, 0.654] +2026-04-11 09:00:27.952391: Epoch time: 101.42 s +2026-04-11 09:00:29.140173: +2026-04-11 09:00:29.141966: Epoch 795 +2026-04-11 09:00:29.143989: Current learning rate: 0.00819 +2026-04-11 09:02:10.800688: train_loss -0.3774 +2026-04-11 09:02:10.805454: val_loss -0.3182 +2026-04-11 09:02:10.807549: Pseudo dice [0.0167, 0.5123, 0.813, 0.6902, 0.4624, 0.4137, 0.7407] +2026-04-11 09:02:10.809286: Epoch time: 101.66 s +2026-04-11 09:02:11.950816: +2026-04-11 09:02:11.952353: Epoch 796 +2026-04-11 09:02:11.954462: Current learning rate: 0.00819 +2026-04-11 09:03:53.706934: train_loss -0.3636 +2026-04-11 09:03:53.711473: val_loss -0.2998 +2026-04-11 09:03:53.713157: Pseudo dice [0.0602, 0.0243, 0.7079, 0.5178, 0.0976, 0.3992, 0.8068] +2026-04-11 09:03:53.714881: Epoch time: 101.76 s +2026-04-11 09:03:54.852553: +2026-04-11 09:03:54.854622: Epoch 797 +2026-04-11 09:03:54.856817: Current learning rate: 0.00819 +2026-04-11 09:05:36.591210: train_loss -0.362 +2026-04-11 09:05:36.599255: val_loss -0.3371 +2026-04-11 09:05:36.601106: Pseudo dice [0.7772, 0.1042, 0.7039, 0.7832, 0.2322, 0.5856, 0.7101] +2026-04-11 09:05:36.602775: Epoch time: 101.74 s +2026-04-11 09:05:37.742805: +2026-04-11 09:05:37.745427: Epoch 798 +2026-04-11 09:05:37.749968: Current learning rate: 0.00819 +2026-04-11 09:07:19.632576: train_loss -0.3455 +2026-04-11 09:07:19.637163: val_loss -0.3242 +2026-04-11 09:07:19.639133: Pseudo dice [0.2776, 0.139, 0.588, 0.7112, 0.2255, 0.7731, 0.6524] +2026-04-11 09:07:19.640664: Epoch time: 101.89 s +2026-04-11 09:07:20.783242: +2026-04-11 09:07:20.784810: Epoch 799 +2026-04-11 09:07:20.786525: Current learning rate: 0.00818 +2026-04-11 09:09:02.589794: train_loss -0.3462 +2026-04-11 09:09:02.594768: val_loss -0.275 +2026-04-11 09:09:02.596586: Pseudo dice [0.0, 0.1763, 0.5815, 0.6719, 0.1497, 0.4953, 0.1388] +2026-04-11 09:09:02.598708: Epoch time: 101.81 s +2026-04-11 09:09:05.383680: +2026-04-11 09:09:05.385293: Epoch 800 +2026-04-11 09:09:05.386945: Current learning rate: 0.00818 +2026-04-11 09:10:47.091221: train_loss -0.3736 +2026-04-11 09:10:47.096891: val_loss -0.358 +2026-04-11 09:10:47.098698: Pseudo dice [0.1162, 0.2905, 0.6921, 0.8183, 0.3351, 0.7522, 0.8393] +2026-04-11 09:10:47.102265: Epoch time: 101.71 s +2026-04-11 09:10:48.247399: +2026-04-11 09:10:48.249580: Epoch 801 +2026-04-11 09:10:48.251307: Current learning rate: 0.00818 +2026-04-11 09:12:30.087158: train_loss -0.3957 +2026-04-11 09:12:30.091847: val_loss -0.3563 +2026-04-11 09:12:30.093221: Pseudo dice [0.2489, 0.0, 0.724, 0.8188, 0.3978, 0.6094, 0.8715] +2026-04-11 09:12:30.094569: Epoch time: 101.84 s +2026-04-11 09:12:31.215030: +2026-04-11 09:12:31.216412: Epoch 802 +2026-04-11 09:12:31.217882: Current learning rate: 0.00818 +2026-04-11 09:14:12.581562: train_loss -0.3639 +2026-04-11 09:14:12.587490: val_loss -0.3381 +2026-04-11 09:14:12.589224: Pseudo dice [0.1729, 0.0, 0.7001, 0.4833, 0.1536, 0.6803, 0.7534] +2026-04-11 09:14:12.590460: Epoch time: 101.37 s +2026-04-11 09:14:13.737425: +2026-04-11 09:14:13.739405: Epoch 803 +2026-04-11 09:14:13.740945: Current learning rate: 0.00817 +2026-04-11 09:15:55.398391: train_loss -0.3542 +2026-04-11 09:15:55.403962: val_loss -0.3266 +2026-04-11 09:15:55.406310: Pseudo dice [0.0, 0.0, 0.5383, 0.512, 0.4495, 0.5818, 0.4448] +2026-04-11 09:15:55.408280: Epoch time: 101.66 s +2026-04-11 09:15:56.575867: +2026-04-11 09:15:56.577541: Epoch 804 +2026-04-11 09:15:56.579229: Current learning rate: 0.00817 +2026-04-11 09:17:38.218417: train_loss -0.3654 +2026-04-11 09:17:38.224457: val_loss -0.2291 +2026-04-11 09:17:38.225820: Pseudo dice [0.0, 0.0, 0.5963, 0.5896, 0.1978, 0.1193, 0.0846] +2026-04-11 09:17:38.227979: Epoch time: 101.65 s +2026-04-11 09:17:39.352857: +2026-04-11 09:17:39.354311: Epoch 805 +2026-04-11 09:17:39.356308: Current learning rate: 0.00817 +2026-04-11 09:19:21.012786: train_loss -0.3299 +2026-04-11 09:19:21.019375: val_loss -0.3049 +2026-04-11 09:19:21.021394: Pseudo dice [0.0, 0.0, 0.2027, 0.7838, 0.2594, 0.8252, 0.6512] +2026-04-11 09:19:21.023247: Epoch time: 101.66 s +2026-04-11 09:19:22.166640: +2026-04-11 09:19:22.168267: Epoch 806 +2026-04-11 09:19:22.170340: Current learning rate: 0.00817 +2026-04-11 09:21:05.584331: train_loss -0.3375 +2026-04-11 09:21:05.589993: val_loss -0.3202 +2026-04-11 09:21:05.591699: Pseudo dice [0.0, 0.0, 0.6347, 0.1407, 0.0844, 0.8004, 0.5848] +2026-04-11 09:21:05.593618: Epoch time: 103.42 s +2026-04-11 09:21:06.710415: +2026-04-11 09:21:06.712474: Epoch 807 +2026-04-11 09:21:06.714406: Current learning rate: 0.00816 +2026-04-11 09:22:48.457435: train_loss -0.3419 +2026-04-11 09:22:48.463451: val_loss -0.3371 +2026-04-11 09:22:48.466440: Pseudo dice [0.0, 0.0, 0.6073, 0.7073, 0.476, 0.2813, 0.7121] +2026-04-11 09:22:48.468291: Epoch time: 101.75 s +2026-04-11 09:22:49.604641: +2026-04-11 09:22:49.606106: Epoch 808 +2026-04-11 09:22:49.607796: Current learning rate: 0.00816 +2026-04-11 09:24:31.259098: train_loss -0.3726 +2026-04-11 09:24:31.263793: val_loss -0.3623 +2026-04-11 09:24:31.265623: Pseudo dice [0.5899, 0.2462, 0.6816, 0.398, 0.5071, 0.6542, 0.7526] +2026-04-11 09:24:31.266888: Epoch time: 101.66 s +2026-04-11 09:24:32.417628: +2026-04-11 09:24:32.419249: Epoch 809 +2026-04-11 09:24:32.421168: Current learning rate: 0.00816 +2026-04-11 09:26:14.330666: train_loss -0.3568 +2026-04-11 09:26:14.335217: val_loss -0.2993 +2026-04-11 09:26:14.337496: Pseudo dice [0.0, 0.0, 0.3834, 0.275, 0.2719, 0.278, 0.3948] +2026-04-11 09:26:14.339263: Epoch time: 101.92 s +2026-04-11 09:26:15.483518: +2026-04-11 09:26:15.485166: Epoch 810 +2026-04-11 09:26:15.486735: Current learning rate: 0.00816 +2026-04-11 09:27:57.442762: train_loss -0.3706 +2026-04-11 09:27:57.447942: val_loss -0.3589 +2026-04-11 09:27:57.449578: Pseudo dice [0.0, 0.0, 0.6251, 0.6411, 0.1951, 0.34, 0.6536] +2026-04-11 09:27:57.451391: Epoch time: 101.96 s +2026-04-11 09:27:58.601892: +2026-04-11 09:27:58.603444: Epoch 811 +2026-04-11 09:27:58.605285: Current learning rate: 0.00816 +2026-04-11 09:29:40.364963: train_loss -0.3676 +2026-04-11 09:29:40.389910: val_loss -0.3442 +2026-04-11 09:29:40.392188: Pseudo dice [0.3114, 0.2102, 0.5467, 0.7023, 0.4954, 0.4253, 0.7305] +2026-04-11 09:29:40.393800: Epoch time: 101.77 s +2026-04-11 09:29:41.560124: +2026-04-11 09:29:41.561858: Epoch 812 +2026-04-11 09:29:41.563776: Current learning rate: 0.00815 +2026-04-11 09:31:23.585581: train_loss -0.3715 +2026-04-11 09:31:23.591285: val_loss -0.3197 +2026-04-11 09:31:23.592697: Pseudo dice [0.0, 0.0, 0.3096, 0.3331, 0.3136, 0.4031, 0.8518] +2026-04-11 09:31:23.594325: Epoch time: 102.03 s +2026-04-11 09:31:24.748458: +2026-04-11 09:31:24.750405: Epoch 813 +2026-04-11 09:31:24.752189: Current learning rate: 0.00815 +2026-04-11 09:33:06.720240: train_loss -0.3395 +2026-04-11 09:33:06.725823: val_loss -0.2973 +2026-04-11 09:33:06.728011: Pseudo dice [0.0, 0.0314, 0.4515, 0.2836, 0.3223, 0.5145, 0.5816] +2026-04-11 09:33:06.729635: Epoch time: 101.97 s +2026-04-11 09:33:07.876277: +2026-04-11 09:33:07.877992: Epoch 814 +2026-04-11 09:33:07.879773: Current learning rate: 0.00815 +2026-04-11 09:34:49.824011: train_loss -0.36 +2026-04-11 09:34:49.829392: val_loss -0.3213 +2026-04-11 09:34:49.830979: Pseudo dice [0.4649, 0.3791, 0.4936, 0.7733, 0.1145, 0.3428, 0.3083] +2026-04-11 09:34:49.832646: Epoch time: 101.95 s +2026-04-11 09:34:50.983393: +2026-04-11 09:34:50.984961: Epoch 815 +2026-04-11 09:34:50.987021: Current learning rate: 0.00815 +2026-04-11 09:36:32.909499: train_loss -0.3882 +2026-04-11 09:36:32.914013: val_loss -0.3591 +2026-04-11 09:36:32.915637: Pseudo dice [0.1942, 0.1236, 0.7654, 0.7041, 0.22, 0.7817, 0.6807] +2026-04-11 09:36:32.916860: Epoch time: 101.93 s +2026-04-11 09:36:34.064777: +2026-04-11 09:36:34.066871: Epoch 816 +2026-04-11 09:36:34.069199: Current learning rate: 0.00814 +2026-04-11 09:38:16.039999: train_loss -0.407 +2026-04-11 09:38:16.045951: val_loss -0.3744 +2026-04-11 09:38:16.048015: Pseudo dice [0.2023, 0.1106, 0.7826, 0.6985, 0.3763, 0.6567, 0.744] +2026-04-11 09:38:16.050077: Epoch time: 101.98 s +2026-04-11 09:38:17.206807: +2026-04-11 09:38:17.208571: Epoch 817 +2026-04-11 09:38:17.210199: Current learning rate: 0.00814 +2026-04-11 09:39:58.975749: train_loss -0.3839 +2026-04-11 09:39:58.980800: val_loss -0.3112 +2026-04-11 09:39:58.982562: Pseudo dice [0.4736, 0.281, 0.1866, 0.349, 0.1672, 0.2658, 0.8545] +2026-04-11 09:39:58.984653: Epoch time: 101.77 s +2026-04-11 09:40:00.142796: +2026-04-11 09:40:00.144507: Epoch 818 +2026-04-11 09:40:00.146386: Current learning rate: 0.00814 +2026-04-11 09:41:41.819968: train_loss -0.3712 +2026-04-11 09:41:41.824614: val_loss -0.3103 +2026-04-11 09:41:41.826214: Pseudo dice [0.2504, 0.3247, 0.6297, 0.3624, 0.2027, 0.4322, 0.7307] +2026-04-11 09:41:41.828228: Epoch time: 101.68 s +2026-04-11 09:41:42.971826: +2026-04-11 09:41:42.973732: Epoch 819 +2026-04-11 09:41:42.975822: Current learning rate: 0.00814 +2026-04-11 09:43:24.717875: train_loss -0.3539 +2026-04-11 09:43:24.722992: val_loss -0.3019 +2026-04-11 09:43:24.724879: Pseudo dice [0.0, 0.3296, 0.5191, 0.2961, 0.2921, 0.379, 0.1766] +2026-04-11 09:43:24.726249: Epoch time: 101.75 s +2026-04-11 09:43:25.794227: +2026-04-11 09:43:25.799474: Epoch 820 +2026-04-11 09:43:25.801354: Current learning rate: 0.00813 +2026-04-11 09:45:07.629208: train_loss -0.3706 +2026-04-11 09:45:07.633673: val_loss -0.3003 +2026-04-11 09:45:07.635092: Pseudo dice [0.0, 0.0, 0.5518, 0.7514, 0.2384, 0.5388, 0.3439] +2026-04-11 09:45:07.636502: Epoch time: 101.84 s +2026-04-11 09:45:08.713689: +2026-04-11 09:45:08.715905: Epoch 821 +2026-04-11 09:45:08.718180: Current learning rate: 0.00813 +2026-04-11 09:46:50.413654: train_loss -0.3667 +2026-04-11 09:46:50.419126: val_loss -0.3227 +2026-04-11 09:46:50.420797: Pseudo dice [0.0, 0.0, 0.58, 0.7422, 0.3193, 0.5387, 0.5899] +2026-04-11 09:46:50.422477: Epoch time: 101.7 s +2026-04-11 09:46:51.482768: +2026-04-11 09:46:51.484433: Epoch 822 +2026-04-11 09:46:51.486171: Current learning rate: 0.00813 +2026-04-11 09:48:33.328159: train_loss -0.3773 +2026-04-11 09:48:33.332903: val_loss -0.325 +2026-04-11 09:48:33.334730: Pseudo dice [0.0, 0.0, 0.5692, 0.4236, 0.3126, 0.3751, 0.7372] +2026-04-11 09:48:33.336487: Epoch time: 101.85 s +2026-04-11 09:48:34.410185: +2026-04-11 09:48:34.414676: Epoch 823 +2026-04-11 09:48:34.416899: Current learning rate: 0.00813 +2026-04-11 09:50:16.018295: train_loss -0.3788 +2026-04-11 09:50:16.024294: val_loss -0.2883 +2026-04-11 09:50:16.026465: Pseudo dice [0.0, 0.0, 0.3959, 0.4666, 0.078, 0.562, 0.632] +2026-04-11 09:50:16.028083: Epoch time: 101.61 s +2026-04-11 09:50:17.094862: +2026-04-11 09:50:17.097137: Epoch 824 +2026-04-11 09:50:17.099150: Current learning rate: 0.00813 +2026-04-11 09:51:58.950773: train_loss -0.3701 +2026-04-11 09:51:58.955961: val_loss -0.3248 +2026-04-11 09:51:58.958210: Pseudo dice [0.0, 0.0, 0.7523, 0.6334, 0.2708, 0.4347, 0.8219] +2026-04-11 09:51:58.960093: Epoch time: 101.86 s +2026-04-11 09:52:00.033325: +2026-04-11 09:52:00.035177: Epoch 825 +2026-04-11 09:52:00.036999: Current learning rate: 0.00812 +2026-04-11 09:53:41.631810: train_loss -0.3813 +2026-04-11 09:53:41.637067: val_loss -0.2879 +2026-04-11 09:53:41.638697: Pseudo dice [0.0, 0.1716, 0.7714, 0.2095, 0.2167, 0.5587, 0.5772] +2026-04-11 09:53:41.640198: Epoch time: 101.6 s +2026-04-11 09:53:42.713552: +2026-04-11 09:53:42.715115: Epoch 826 +2026-04-11 09:53:42.716855: Current learning rate: 0.00812 +2026-04-11 09:55:25.647122: train_loss -0.3524 +2026-04-11 09:55:25.652256: val_loss -0.2908 +2026-04-11 09:55:25.654232: Pseudo dice [0.0, 0.2004, 0.4615, 0.5116, 0.0709, 0.7782, 0.5233] +2026-04-11 09:55:25.656012: Epoch time: 102.94 s +2026-04-11 09:55:26.742470: +2026-04-11 09:55:26.744239: Epoch 827 +2026-04-11 09:55:26.746568: Current learning rate: 0.00812 +2026-04-11 09:57:08.838174: train_loss -0.3603 +2026-04-11 09:57:08.843726: val_loss -0.2807 +2026-04-11 09:57:08.845582: Pseudo dice [0.0, 0.1383, 0.4205, 0.1775, 0.1975, 0.7239, 0.6765] +2026-04-11 09:57:08.848033: Epoch time: 102.1 s +2026-04-11 09:57:09.918819: +2026-04-11 09:57:09.942065: Epoch 828 +2026-04-11 09:57:09.944344: Current learning rate: 0.00812 +2026-04-11 09:58:51.982207: train_loss -0.3733 +2026-04-11 09:58:51.987505: val_loss -0.3294 +2026-04-11 09:58:51.989130: Pseudo dice [0.0, 0.1172, 0.4177, 0.2487, 0.4037, 0.7239, 0.7237] +2026-04-11 09:58:51.990551: Epoch time: 102.07 s +2026-04-11 09:58:53.079387: +2026-04-11 09:58:53.080865: Epoch 829 +2026-04-11 09:58:53.082797: Current learning rate: 0.00811 +2026-04-11 10:00:35.023906: train_loss -0.3647 +2026-04-11 10:00:35.031051: val_loss -0.3595 +2026-04-11 10:00:35.033770: Pseudo dice [0.0, 0.1032, 0.6964, 0.1471, 0.1855, 0.649, 0.7434] +2026-04-11 10:00:35.036056: Epoch time: 101.95 s +2026-04-11 10:00:36.125619: +2026-04-11 10:00:36.128021: Epoch 830 +2026-04-11 10:00:36.130562: Current learning rate: 0.00811 +2026-04-11 10:02:17.862801: train_loss -0.3925 +2026-04-11 10:02:17.867284: val_loss -0.3416 +2026-04-11 10:02:17.868798: Pseudo dice [0.0, 0.0023, 0.6801, 0.75, 0.4049, 0.6956, 0.8081] +2026-04-11 10:02:17.870180: Epoch time: 101.74 s +2026-04-11 10:02:18.941683: +2026-04-11 10:02:18.943193: Epoch 831 +2026-04-11 10:02:18.944719: Current learning rate: 0.00811 +2026-04-11 10:04:00.717415: train_loss -0.374 +2026-04-11 10:04:00.723355: val_loss -0.3433 +2026-04-11 10:04:00.724870: Pseudo dice [0.0, 0.295, 0.5555, 0.0011, 0.4763, 0.1902, 0.6646] +2026-04-11 10:04:00.726856: Epoch time: 101.78 s +2026-04-11 10:04:01.795810: +2026-04-11 10:04:01.798404: Epoch 832 +2026-04-11 10:04:01.800561: Current learning rate: 0.00811 +2026-04-11 10:05:43.342563: train_loss -0.3538 +2026-04-11 10:05:43.347487: val_loss -0.2918 +2026-04-11 10:05:43.349560: Pseudo dice [0.0, 0.1146, 0.606, 0.6009, 0.4139, 0.4788, 0.762] +2026-04-11 10:05:43.351427: Epoch time: 101.55 s +2026-04-11 10:05:44.428726: +2026-04-11 10:05:44.430625: Epoch 833 +2026-04-11 10:05:44.432526: Current learning rate: 0.0081 +2026-04-11 10:07:25.840921: train_loss -0.3295 +2026-04-11 10:07:25.846421: val_loss -0.3114 +2026-04-11 10:07:25.848280: Pseudo dice [0.0, 0.0712, 0.5842, 0.5193, 0.1109, 0.3071, 0.7525] +2026-04-11 10:07:25.851229: Epoch time: 101.42 s +2026-04-11 10:07:26.936064: +2026-04-11 10:07:26.937708: Epoch 834 +2026-04-11 10:07:26.939525: Current learning rate: 0.0081 +2026-04-11 10:09:08.483897: train_loss -0.3431 +2026-04-11 10:09:08.495998: val_loss -0.2967 +2026-04-11 10:09:08.501817: Pseudo dice [0.0, 0.0014, 0.6356, 0.4534, 0.1723, 0.15, 0.3218] +2026-04-11 10:09:08.503583: Epoch time: 101.55 s +2026-04-11 10:09:09.592747: +2026-04-11 10:09:09.594531: Epoch 835 +2026-04-11 10:09:09.596461: Current learning rate: 0.0081 +2026-04-11 10:10:50.919865: train_loss -0.3658 +2026-04-11 10:10:50.926324: val_loss -0.2881 +2026-04-11 10:10:50.927958: Pseudo dice [0.0176, 0.0362, 0.6302, 0.6767, 0.2285, 0.5963, 0.7836] +2026-04-11 10:10:50.929527: Epoch time: 101.33 s +2026-04-11 10:10:52.022073: +2026-04-11 10:10:52.024161: Epoch 836 +2026-04-11 10:10:52.026743: Current learning rate: 0.0081 +2026-04-11 10:12:33.371890: train_loss -0.3388 +2026-04-11 10:12:33.377198: val_loss -0.2637 +2026-04-11 10:12:33.379158: Pseudo dice [0.0277, 0.0536, 0.4442, 0.6521, 0.3793, 0.6299, 0.2877] +2026-04-11 10:12:33.381067: Epoch time: 101.35 s +2026-04-11 10:12:34.453878: +2026-04-11 10:12:34.456272: Epoch 837 +2026-04-11 10:12:34.458345: Current learning rate: 0.0081 +2026-04-11 10:14:15.643777: train_loss -0.3696 +2026-04-11 10:14:15.653364: val_loss -0.3413 +2026-04-11 10:14:15.656478: Pseudo dice [0.0, 0.0, 0.6385, 0.9021, 0.3864, 0.5538, 0.6996] +2026-04-11 10:14:15.660801: Epoch time: 101.19 s +2026-04-11 10:14:16.748571: +2026-04-11 10:14:16.750376: Epoch 838 +2026-04-11 10:14:16.752100: Current learning rate: 0.00809 +2026-04-11 10:15:58.014680: train_loss -0.3658 +2026-04-11 10:15:58.019834: val_loss -0.3518 +2026-04-11 10:15:58.021718: Pseudo dice [0.0, 0.0, 0.7463, 0.7802, 0.3526, 0.5954, 0.8048] +2026-04-11 10:15:58.023441: Epoch time: 101.27 s +2026-04-11 10:15:59.104203: +2026-04-11 10:15:59.105736: Epoch 839 +2026-04-11 10:15:59.107525: Current learning rate: 0.00809 +2026-04-11 10:17:40.493474: train_loss -0.3921 +2026-04-11 10:17:40.498004: val_loss -0.3774 +2026-04-11 10:17:40.499484: Pseudo dice [0.0, 0.5758, 0.5599, 0.6922, 0.4184, 0.8527, 0.7114] +2026-04-11 10:17:40.501148: Epoch time: 101.39 s +2026-04-11 10:17:41.566740: +2026-04-11 10:17:41.568511: Epoch 840 +2026-04-11 10:17:41.570223: Current learning rate: 0.00809 +2026-04-11 10:19:22.885102: train_loss -0.3939 +2026-04-11 10:19:22.889898: val_loss -0.341 +2026-04-11 10:19:22.891417: Pseudo dice [0.0, 0.4177, 0.6957, 0.3525, 0.3222, 0.7254, 0.6449] +2026-04-11 10:19:22.892851: Epoch time: 101.32 s +2026-04-11 10:19:23.972955: +2026-04-11 10:19:23.974504: Epoch 841 +2026-04-11 10:19:23.976621: Current learning rate: 0.00809 +2026-04-11 10:21:05.358815: train_loss -0.3744 +2026-04-11 10:21:05.365202: val_loss -0.3255 +2026-04-11 10:21:05.366817: Pseudo dice [0.0, 0.4103, 0.6719, 0.4184, 0.3895, 0.7469, 0.415] +2026-04-11 10:21:05.368596: Epoch time: 101.39 s +2026-04-11 10:21:06.434957: +2026-04-11 10:21:06.436741: Epoch 842 +2026-04-11 10:21:06.438650: Current learning rate: 0.00808 +2026-04-11 10:22:48.094742: train_loss -0.3802 +2026-04-11 10:22:48.100094: val_loss -0.352 +2026-04-11 10:22:48.101743: Pseudo dice [0.0, 0.3916, 0.7223, 0.7588, 0.5005, 0.4905, 0.6316] +2026-04-11 10:22:48.103610: Epoch time: 101.66 s +2026-04-11 10:22:49.170909: +2026-04-11 10:22:49.172618: Epoch 843 +2026-04-11 10:22:49.174539: Current learning rate: 0.00808 +2026-04-11 10:24:30.671886: train_loss -0.38 +2026-04-11 10:24:30.678374: val_loss -0.3082 +2026-04-11 10:24:30.681322: Pseudo dice [0.0, 0.1293, 0.6273, 0.508, 0.0006, 0.6505, 0.5014] +2026-04-11 10:24:30.683540: Epoch time: 101.5 s +2026-04-11 10:24:31.769158: +2026-04-11 10:24:31.771446: Epoch 844 +2026-04-11 10:24:31.774153: Current learning rate: 0.00808 +2026-04-11 10:26:13.576896: train_loss -0.3665 +2026-04-11 10:26:13.586231: val_loss -0.3595 +2026-04-11 10:26:13.589407: Pseudo dice [0.0, 0.1717, 0.7348, 0.7598, 0.3285, 0.569, 0.8496] +2026-04-11 10:26:13.591026: Epoch time: 101.81 s +2026-04-11 10:26:14.697381: +2026-04-11 10:26:14.699368: Epoch 845 +2026-04-11 10:26:14.701309: Current learning rate: 0.00808 +2026-04-11 10:27:56.505954: train_loss -0.3724 +2026-04-11 10:27:56.510778: val_loss -0.3445 +2026-04-11 10:27:56.512314: Pseudo dice [0.0, 0.3907, 0.7652, 0.782, 0.1723, 0.6758, 0.6973] +2026-04-11 10:27:56.513784: Epoch time: 101.81 s +2026-04-11 10:27:57.588239: +2026-04-11 10:27:57.589844: Epoch 846 +2026-04-11 10:27:57.591711: Current learning rate: 0.00807 +2026-04-11 10:29:39.448035: train_loss -0.3741 +2026-04-11 10:29:39.452831: val_loss -0.3641 +2026-04-11 10:29:39.454681: Pseudo dice [0.2856, 0.0, 0.7627, 0.6342, 0.4012, 0.8004, 0.6253] +2026-04-11 10:29:39.456490: Epoch time: 101.86 s +2026-04-11 10:29:41.663254: +2026-04-11 10:29:41.664860: Epoch 847 +2026-04-11 10:29:41.666788: Current learning rate: 0.00807 +2026-04-11 10:31:23.417890: train_loss -0.3454 +2026-04-11 10:31:23.422429: val_loss -0.3249 +2026-04-11 10:31:23.424863: Pseudo dice [0.1804, 0.1704, 0.6504, 0.2356, 0.2477, 0.4893, 0.8311] +2026-04-11 10:31:23.426764: Epoch time: 101.76 s +2026-04-11 10:31:24.519210: +2026-04-11 10:31:24.521243: Epoch 848 +2026-04-11 10:31:24.523232: Current learning rate: 0.00807 +2026-04-11 10:33:06.453608: train_loss -0.3746 +2026-04-11 10:33:06.458647: val_loss -0.3252 +2026-04-11 10:33:06.460673: Pseudo dice [0.1883, 0.1868, 0.5752, 0.7481, 0.2595, 0.5804, 0.4598] +2026-04-11 10:33:06.462463: Epoch time: 101.94 s +2026-04-11 10:33:07.562093: +2026-04-11 10:33:07.563873: Epoch 849 +2026-04-11 10:33:07.565525: Current learning rate: 0.00807 +2026-04-11 10:34:49.478300: train_loss -0.3675 +2026-04-11 10:34:49.482650: val_loss -0.3625 +2026-04-11 10:34:49.484124: Pseudo dice [0.119, 0.1421, 0.5436, 0.6359, 0.396, 0.6495, 0.7813] +2026-04-11 10:34:49.485437: Epoch time: 101.92 s +2026-04-11 10:34:52.149918: +2026-04-11 10:34:52.152028: Epoch 850 +2026-04-11 10:34:52.153908: Current learning rate: 0.00807 +2026-04-11 10:36:33.975754: train_loss -0.3677 +2026-04-11 10:36:33.980677: val_loss -0.3005 +2026-04-11 10:36:33.982320: Pseudo dice [0.0, 0.2521, 0.7118, 0.6205, 0.2125, 0.4394, 0.5651] +2026-04-11 10:36:33.984474: Epoch time: 101.83 s +2026-04-11 10:36:35.072550: +2026-04-11 10:36:35.074493: Epoch 851 +2026-04-11 10:36:35.076466: Current learning rate: 0.00806 +2026-04-11 10:38:16.833979: train_loss -0.3694 +2026-04-11 10:38:16.841672: val_loss -0.3355 +2026-04-11 10:38:16.843464: Pseudo dice [0.1444, 0.0414, 0.7265, 0.7943, 0.3628, 0.7762, 0.8634] +2026-04-11 10:38:16.852955: Epoch time: 101.76 s +2026-04-11 10:38:17.985110: +2026-04-11 10:38:17.987324: Epoch 852 +2026-04-11 10:38:17.989172: Current learning rate: 0.00806 +2026-04-11 10:39:59.480367: train_loss -0.3761 +2026-04-11 10:39:59.485231: val_loss -0.3902 +2026-04-11 10:39:59.487871: Pseudo dice [0.0901, 0.1831, 0.8028, 0.79, 0.059, 0.7655, 0.8592] +2026-04-11 10:39:59.489727: Epoch time: 101.5 s +2026-04-11 10:40:00.585636: +2026-04-11 10:40:00.587431: Epoch 853 +2026-04-11 10:40:00.589464: Current learning rate: 0.00806 +2026-04-11 10:41:42.466387: train_loss -0.3851 +2026-04-11 10:41:42.472168: val_loss -0.3334 +2026-04-11 10:41:42.474584: Pseudo dice [0.1261, 0.4221, 0.7605, 0.179, 0.2462, 0.7997, 0.8114] +2026-04-11 10:41:42.477021: Epoch time: 101.88 s +2026-04-11 10:41:43.569635: +2026-04-11 10:41:43.571507: Epoch 854 +2026-04-11 10:41:43.573757: Current learning rate: 0.00806 +2026-04-11 10:43:25.453962: train_loss -0.3616 +2026-04-11 10:43:25.458842: val_loss -0.3505 +2026-04-11 10:43:25.460342: Pseudo dice [0.0, 0.4752, 0.8529, 0.1857, 0.4865, 0.8588, 0.5864] +2026-04-11 10:43:25.462474: Epoch time: 101.89 s +2026-04-11 10:43:26.530462: +2026-04-11 10:43:26.532102: Epoch 855 +2026-04-11 10:43:26.533869: Current learning rate: 0.00805 +2026-04-11 10:45:08.286871: train_loss -0.367 +2026-04-11 10:45:08.292953: val_loss -0.349 +2026-04-11 10:45:08.295295: Pseudo dice [0.0008, 0.3782, 0.7131, 0.3962, 0.3024, 0.3803, 0.7908] +2026-04-11 10:45:08.297196: Epoch time: 101.76 s +2026-04-11 10:45:09.395809: +2026-04-11 10:45:09.397768: Epoch 856 +2026-04-11 10:45:09.399858: Current learning rate: 0.00805 +2026-04-11 10:46:51.013840: train_loss -0.3942 +2026-04-11 10:46:51.018633: val_loss -0.3347 +2026-04-11 10:46:51.019993: Pseudo dice [0.0328, 0.2036, 0.78, 0.521, 0.0491, 0.7343, 0.7462] +2026-04-11 10:46:51.021594: Epoch time: 101.62 s +2026-04-11 10:46:52.084829: +2026-04-11 10:46:52.086838: Epoch 857 +2026-04-11 10:46:52.088435: Current learning rate: 0.00805 +2026-04-11 10:48:33.872744: train_loss -0.3671 +2026-04-11 10:48:33.878676: val_loss -0.2995 +2026-04-11 10:48:33.880285: Pseudo dice [0.1539, 0.0362, 0.3627, 0.1686, 0.2193, 0.2993, 0.675] +2026-04-11 10:48:33.881791: Epoch time: 101.79 s +2026-04-11 10:48:34.964885: +2026-04-11 10:48:34.966644: Epoch 858 +2026-04-11 10:48:34.968674: Current learning rate: 0.00805 +2026-04-11 10:50:16.840929: train_loss -0.3707 +2026-04-11 10:50:16.845493: val_loss -0.2927 +2026-04-11 10:50:16.846910: Pseudo dice [0.0097, 0.0, 0.6873, 0.6289, 0.3916, 0.1311, 0.3108] +2026-04-11 10:50:16.848658: Epoch time: 101.88 s +2026-04-11 10:50:17.931211: +2026-04-11 10:50:17.933254: Epoch 859 +2026-04-11 10:50:17.935219: Current learning rate: 0.00804 +2026-04-11 10:51:59.663973: train_loss -0.3693 +2026-04-11 10:51:59.668283: val_loss -0.3312 +2026-04-11 10:51:59.669940: Pseudo dice [0.3369, 0.126, 0.4768, 0.8247, 0.154, 0.6843, 0.7369] +2026-04-11 10:51:59.671475: Epoch time: 101.74 s +2026-04-11 10:52:00.761903: +2026-04-11 10:52:00.763720: Epoch 860 +2026-04-11 10:52:00.765611: Current learning rate: 0.00804 +2026-04-11 10:53:42.536716: train_loss -0.3791 +2026-04-11 10:53:42.541708: val_loss -0.3313 +2026-04-11 10:53:42.543130: Pseudo dice [0.3896, 0.0, 0.3531, 0.6164, 0.2809, 0.7168, 0.712] +2026-04-11 10:53:42.544355: Epoch time: 101.78 s +2026-04-11 10:53:43.636813: +2026-04-11 10:53:43.638349: Epoch 861 +2026-04-11 10:53:43.639941: Current learning rate: 0.00804 +2026-04-11 10:55:25.644614: train_loss -0.3726 +2026-04-11 10:55:25.649732: val_loss -0.3065 +2026-04-11 10:55:25.651337: Pseudo dice [0.0062, 0.1787, 0.5502, 0.5687, 0.0195, 0.319, 0.7266] +2026-04-11 10:55:25.652957: Epoch time: 102.01 s +2026-04-11 10:55:26.733023: +2026-04-11 10:55:26.735297: Epoch 862 +2026-04-11 10:55:26.737158: Current learning rate: 0.00804 +2026-04-11 10:57:08.341012: train_loss -0.3674 +2026-04-11 10:57:08.347403: val_loss -0.3373 +2026-04-11 10:57:08.349144: Pseudo dice [0.3283, 0.0859, 0.3455, 0.7374, 0.1874, 0.6401, 0.8087] +2026-04-11 10:57:08.350545: Epoch time: 101.61 s +2026-04-11 10:57:09.448006: +2026-04-11 10:57:09.449571: Epoch 863 +2026-04-11 10:57:09.451516: Current learning rate: 0.00804 +2026-04-11 10:58:51.013033: train_loss -0.3823 +2026-04-11 10:58:51.018070: val_loss -0.3734 +2026-04-11 10:58:51.020044: Pseudo dice [0.3307, 0.2326, 0.7655, 0.6301, 0.3885, 0.7042, 0.6392] +2026-04-11 10:58:51.021923: Epoch time: 101.57 s +2026-04-11 10:58:52.107087: +2026-04-11 10:58:52.108854: Epoch 864 +2026-04-11 10:58:52.110915: Current learning rate: 0.00803 +2026-04-11 11:00:33.867204: train_loss -0.3691 +2026-04-11 11:00:33.873344: val_loss -0.3446 +2026-04-11 11:00:33.875422: Pseudo dice [0.3049, 0.2733, 0.6549, 0.3483, 0.178, 0.3301, 0.649] +2026-04-11 11:00:33.877008: Epoch time: 101.76 s +2026-04-11 11:00:34.944166: +2026-04-11 11:00:34.945815: Epoch 865 +2026-04-11 11:00:34.947537: Current learning rate: 0.00803 +2026-04-11 11:02:16.628462: train_loss -0.3863 +2026-04-11 11:02:16.633795: val_loss -0.3415 +2026-04-11 11:02:16.635604: Pseudo dice [0.2915, 0.276, 0.764, 0.6687, 0.1432, 0.6377, 0.4563] +2026-04-11 11:02:16.637274: Epoch time: 101.69 s +2026-04-11 11:02:17.707947: +2026-04-11 11:02:17.709694: Epoch 866 +2026-04-11 11:02:17.711673: Current learning rate: 0.00803 +2026-04-11 11:03:59.492818: train_loss -0.3913 +2026-04-11 11:03:59.497352: val_loss -0.3465 +2026-04-11 11:03:59.500196: Pseudo dice [0.5043, 0.6735, 0.7588, 0.8715, 0.3845, 0.5021, 0.3753] +2026-04-11 11:03:59.501874: Epoch time: 101.79 s +2026-04-11 11:04:00.575866: +2026-04-11 11:04:00.577551: Epoch 867 +2026-04-11 11:04:00.579472: Current learning rate: 0.00803 +2026-04-11 11:05:43.590172: train_loss -0.3861 +2026-04-11 11:05:43.595164: val_loss -0.3551 +2026-04-11 11:05:43.596702: Pseudo dice [0.2842, 0.0, 0.4313, 0.444, 0.392, 0.7182, 0.6253] +2026-04-11 11:05:43.598572: Epoch time: 103.02 s +2026-04-11 11:05:44.691828: +2026-04-11 11:05:44.694908: Epoch 868 +2026-04-11 11:05:44.697186: Current learning rate: 0.00802 +2026-04-11 11:07:26.744340: train_loss -0.375 +2026-04-11 11:07:26.749784: val_loss -0.362 +2026-04-11 11:07:26.752413: Pseudo dice [0.0468, 0.387, 0.7195, 0.7316, 0.3428, 0.7058, 0.6653] +2026-04-11 11:07:26.754508: Epoch time: 102.06 s +2026-04-11 11:07:27.837824: +2026-04-11 11:07:27.839517: Epoch 869 +2026-04-11 11:07:27.841968: Current learning rate: 0.00802 +2026-04-11 11:09:09.862289: train_loss -0.3904 +2026-04-11 11:09:09.867620: val_loss -0.3447 +2026-04-11 11:09:09.869456: Pseudo dice [0.424, 0.0, 0.6237, 0.5974, 0.3693, 0.7884, 0.4316] +2026-04-11 11:09:09.871164: Epoch time: 102.03 s +2026-04-11 11:09:10.969656: +2026-04-11 11:09:10.971390: Epoch 870 +2026-04-11 11:09:10.973556: Current learning rate: 0.00802 +2026-04-11 11:10:52.499614: train_loss -0.3638 +2026-04-11 11:10:52.504999: val_loss -0.3525 +2026-04-11 11:10:52.506816: Pseudo dice [0.0344, 0.0, 0.4393, 0.6223, 0.4063, 0.4213, 0.7532] +2026-04-11 11:10:52.508289: Epoch time: 101.53 s +2026-04-11 11:10:53.596220: +2026-04-11 11:10:53.597816: Epoch 871 +2026-04-11 11:10:53.600680: Current learning rate: 0.00802 +2026-04-11 11:12:35.130758: train_loss -0.3464 +2026-04-11 11:12:35.136308: val_loss -0.3259 +2026-04-11 11:12:35.137813: Pseudo dice [0.0, 0.0, 0.6289, 0.3333, 0.3856, 0.7116, 0.688] +2026-04-11 11:12:35.139477: Epoch time: 101.54 s +2026-04-11 11:12:36.212927: +2026-04-11 11:12:36.214976: Epoch 872 +2026-04-11 11:12:36.216851: Current learning rate: 0.00801 +2026-04-11 11:14:18.078570: train_loss -0.3703 +2026-04-11 11:14:18.083848: val_loss -0.3593 +2026-04-11 11:14:18.085706: Pseudo dice [0.0, 0.0, 0.8626, 0.7787, 0.4139, 0.6064, 0.4833] +2026-04-11 11:14:18.087341: Epoch time: 101.87 s +2026-04-11 11:14:19.164232: +2026-04-11 11:14:19.165694: Epoch 873 +2026-04-11 11:14:19.167336: Current learning rate: 0.00801 +2026-04-11 11:16:00.995836: train_loss -0.3847 +2026-04-11 11:16:01.000998: val_loss -0.3467 +2026-04-11 11:16:01.002538: Pseudo dice [0.0115, 0.0, 0.8143, 0.6247, 0.3869, 0.6087, 0.6506] +2026-04-11 11:16:01.004313: Epoch time: 101.83 s +2026-04-11 11:16:02.078121: +2026-04-11 11:16:02.079725: Epoch 874 +2026-04-11 11:16:02.081261: Current learning rate: 0.00801 +2026-04-11 11:17:43.967780: train_loss -0.3882 +2026-04-11 11:17:43.972749: val_loss -0.3582 +2026-04-11 11:17:43.974358: Pseudo dice [0.4401, 0.3043, 0.6922, 0.6549, 0.521, 0.6438, 0.2997] +2026-04-11 11:17:43.976102: Epoch time: 101.89 s +2026-04-11 11:17:45.075024: +2026-04-11 11:17:45.076963: Epoch 875 +2026-04-11 11:17:45.078889: Current learning rate: 0.00801 +2026-04-11 11:19:26.988713: train_loss -0.3867 +2026-04-11 11:19:26.993437: val_loss -0.3412 +2026-04-11 11:19:26.995903: Pseudo dice [0.0, 0.3555, 0.6771, 0.5518, 0.5114, 0.8134, 0.3676] +2026-04-11 11:19:26.997113: Epoch time: 101.92 s +2026-04-11 11:19:28.081017: +2026-04-11 11:19:28.082580: Epoch 876 +2026-04-11 11:19:28.084433: Current learning rate: 0.00801 +2026-04-11 11:21:09.811526: train_loss -0.386 +2026-04-11 11:21:09.816385: val_loss -0.3114 +2026-04-11 11:21:09.817946: Pseudo dice [0.6064, 0.0668, 0.7663, 0.8209, 0.4009, 0.3786, 0.4153] +2026-04-11 11:21:09.819607: Epoch time: 101.73 s +2026-04-11 11:21:10.961785: +2026-04-11 11:21:10.963193: Epoch 877 +2026-04-11 11:21:10.965049: Current learning rate: 0.008 +2026-04-11 11:22:53.026699: train_loss -0.3815 +2026-04-11 11:22:53.032958: val_loss -0.338 +2026-04-11 11:22:53.034980: Pseudo dice [0.2821, 0.5189, 0.5289, 0.7328, 0.1678, 0.3129, 0.2563] +2026-04-11 11:22:53.036938: Epoch time: 102.07 s +2026-04-11 11:22:54.117961: +2026-04-11 11:22:54.119315: Epoch 878 +2026-04-11 11:22:54.120793: Current learning rate: 0.008 +2026-04-11 11:24:35.874927: train_loss -0.3639 +2026-04-11 11:24:35.890985: val_loss -0.3583 +2026-04-11 11:24:35.892782: Pseudo dice [0.2692, 0.2124, 0.6424, 0.4568, 0.4353, 0.6533, 0.6444] +2026-04-11 11:24:35.895076: Epoch time: 101.76 s +2026-04-11 11:24:36.994750: +2026-04-11 11:24:36.996442: Epoch 879 +2026-04-11 11:24:36.998279: Current learning rate: 0.008 +2026-04-11 11:26:18.784021: train_loss -0.3965 +2026-04-11 11:26:18.789770: val_loss -0.3801 +2026-04-11 11:26:18.791359: Pseudo dice [0.2597, 0.0, 0.7596, 0.0069, 0.1496, 0.7852, 0.8807] +2026-04-11 11:26:18.792672: Epoch time: 101.79 s +2026-04-11 11:26:19.860590: +2026-04-11 11:26:19.862272: Epoch 880 +2026-04-11 11:26:19.864066: Current learning rate: 0.008 +2026-04-11 11:28:01.974924: train_loss -0.3656 +2026-04-11 11:28:01.980310: val_loss -0.286 +2026-04-11 11:28:01.982676: Pseudo dice [0.19, 0.0, 0.4796, 0.6537, 0.006, 0.2315, 0.5126] +2026-04-11 11:28:01.985124: Epoch time: 102.12 s +2026-04-11 11:28:03.071990: +2026-04-11 11:28:03.083277: Epoch 881 +2026-04-11 11:28:03.084486: Current learning rate: 0.00799 +2026-04-11 11:29:44.810662: train_loss -0.3617 +2026-04-11 11:29:44.816409: val_loss -0.3158 +2026-04-11 11:29:44.820678: Pseudo dice [0.4908, 0.3738, 0.3519, 0.0672, 0.1391, 0.6151, 0.3312] +2026-04-11 11:29:44.822571: Epoch time: 101.74 s +2026-04-11 11:29:45.897211: +2026-04-11 11:29:45.899391: Epoch 882 +2026-04-11 11:29:45.901742: Current learning rate: 0.00799 +2026-04-11 11:31:27.419550: train_loss -0.3497 +2026-04-11 11:31:27.424272: val_loss -0.3343 +2026-04-11 11:31:27.425904: Pseudo dice [0.3477, 0.0354, 0.5664, 0.6216, 0.1097, 0.7778, 0.5557] +2026-04-11 11:31:27.427291: Epoch time: 101.53 s +2026-04-11 11:31:28.497503: +2026-04-11 11:31:28.498976: Epoch 883 +2026-04-11 11:31:28.500541: Current learning rate: 0.00799 +2026-04-11 11:33:10.182340: train_loss -0.4085 +2026-04-11 11:33:10.186850: val_loss -0.3521 +2026-04-11 11:33:10.188976: Pseudo dice [0.2066, 0.0948, 0.7369, 0.7917, 0.1344, 0.6923, 0.5726] +2026-04-11 11:33:10.190619: Epoch time: 101.69 s +2026-04-11 11:33:11.267307: +2026-04-11 11:33:11.268581: Epoch 884 +2026-04-11 11:33:11.270138: Current learning rate: 0.00799 +2026-04-11 11:34:52.784989: train_loss -0.3765 +2026-04-11 11:34:52.789953: val_loss -0.3209 +2026-04-11 11:34:52.791740: Pseudo dice [0.082, 0.0, 0.7451, 0.5133, 0.2143, 0.1258, 0.7648] +2026-04-11 11:34:52.793369: Epoch time: 101.52 s +2026-04-11 11:34:53.865114: +2026-04-11 11:34:53.866750: Epoch 885 +2026-04-11 11:34:53.868595: Current learning rate: 0.00798 +2026-04-11 11:36:35.559784: train_loss -0.3716 +2026-04-11 11:36:35.564524: val_loss -0.3109 +2026-04-11 11:36:35.566113: Pseudo dice [0.0, 0.0, 0.5389, 0.3678, 0.2967, 0.6075, 0.5638] +2026-04-11 11:36:35.568135: Epoch time: 101.7 s +2026-04-11 11:36:36.647601: +2026-04-11 11:36:36.649337: Epoch 886 +2026-04-11 11:36:36.651259: Current learning rate: 0.00798 +2026-04-11 11:38:18.282785: train_loss -0.357 +2026-04-11 11:38:18.287645: val_loss -0.3017 +2026-04-11 11:38:18.289240: Pseudo dice [0.3897, 0.004, 0.471, 0.4927, 0.225, 0.6932, 0.4348] +2026-04-11 11:38:18.290896: Epoch time: 101.64 s +2026-04-11 11:38:19.368323: +2026-04-11 11:38:19.369902: Epoch 887 +2026-04-11 11:38:19.371684: Current learning rate: 0.00798 +2026-04-11 11:40:00.930919: train_loss -0.3781 +2026-04-11 11:40:00.935809: val_loss -0.3293 +2026-04-11 11:40:00.937726: Pseudo dice [0.2847, 0.5845, 0.3207, 0.8443, 0.3998, 0.2631, 0.3512] +2026-04-11 11:40:00.939506: Epoch time: 101.57 s +2026-04-11 11:40:03.105866: +2026-04-11 11:40:03.107695: Epoch 888 +2026-04-11 11:40:03.109614: Current learning rate: 0.00798 +2026-04-11 11:41:44.672163: train_loss -0.3531 +2026-04-11 11:41:44.677358: val_loss -0.3001 +2026-04-11 11:41:44.679203: Pseudo dice [0.0772, 0.0, 0.3591, 0.5427, 0.2943, 0.1808, 0.7297] +2026-04-11 11:41:44.680788: Epoch time: 101.57 s +2026-04-11 11:41:45.754584: +2026-04-11 11:41:45.758990: Epoch 889 +2026-04-11 11:41:45.761057: Current learning rate: 0.00798 +2026-04-11 11:43:27.568106: train_loss -0.3463 +2026-04-11 11:43:27.574656: val_loss -0.3364 +2026-04-11 11:43:27.576686: Pseudo dice [0.0905, 0.6541, 0.7202, 0.6598, 0.4139, 0.4671, 0.5761] +2026-04-11 11:43:27.578022: Epoch time: 101.82 s +2026-04-11 11:43:28.647090: +2026-04-11 11:43:28.648739: Epoch 890 +2026-04-11 11:43:28.650673: Current learning rate: 0.00797 +2026-04-11 11:45:10.445618: train_loss -0.3776 +2026-04-11 11:45:10.450299: val_loss -0.3442 +2026-04-11 11:45:10.451663: Pseudo dice [0.3485, 0.336, 0.6694, 0.6328, 0.0764, 0.6398, 0.7384] +2026-04-11 11:45:10.453442: Epoch time: 101.8 s +2026-04-11 11:45:11.549407: +2026-04-11 11:45:11.550943: Epoch 891 +2026-04-11 11:45:11.552834: Current learning rate: 0.00797 +2026-04-11 11:46:53.445356: train_loss -0.3487 +2026-04-11 11:46:53.450372: val_loss -0.2855 +2026-04-11 11:46:53.451887: Pseudo dice [0.199, 0.0, 0.5942, 0.175, 0.3562, 0.2632, 0.6777] +2026-04-11 11:46:53.454051: Epoch time: 101.9 s +2026-04-11 11:46:54.534163: +2026-04-11 11:46:54.535849: Epoch 892 +2026-04-11 11:46:54.537581: Current learning rate: 0.00797 +2026-04-11 11:48:36.220805: train_loss -0.3615 +2026-04-11 11:48:36.228740: val_loss -0.3367 +2026-04-11 11:48:36.230771: Pseudo dice [0.1617, 0.6411, 0.5992, 0.8192, 0.2415, 0.4474, 0.7656] +2026-04-11 11:48:36.232805: Epoch time: 101.69 s +2026-04-11 11:48:37.301066: +2026-04-11 11:48:37.302670: Epoch 893 +2026-04-11 11:48:37.304654: Current learning rate: 0.00797 +2026-04-11 11:50:19.118420: train_loss -0.3848 +2026-04-11 11:50:19.123406: val_loss -0.2931 +2026-04-11 11:50:19.125182: Pseudo dice [0.0071, 0.3933, 0.4552, 0.407, 0.1801, 0.6638, 0.2651] +2026-04-11 11:50:19.126871: Epoch time: 101.82 s +2026-04-11 11:50:20.223107: +2026-04-11 11:50:20.225315: Epoch 894 +2026-04-11 11:50:20.227100: Current learning rate: 0.00796 +2026-04-11 11:52:02.144635: train_loss -0.3491 +2026-04-11 11:52:02.152868: val_loss -0.3412 +2026-04-11 11:52:02.154722: Pseudo dice [0.0, 0.3953, 0.6539, 0.3667, 0.2214, 0.5163, 0.4069] +2026-04-11 11:52:02.157615: Epoch time: 101.92 s +2026-04-11 11:52:03.235122: +2026-04-11 11:52:03.236939: Epoch 895 +2026-04-11 11:52:03.238635: Current learning rate: 0.00796 +2026-04-11 11:53:44.877173: train_loss -0.3622 +2026-04-11 11:53:44.881939: val_loss -0.3059 +2026-04-11 11:53:44.884043: Pseudo dice [0.0, 0.0483, 0.4988, 0.656, 0.1526, 0.3388, 0.7024] +2026-04-11 11:53:44.885770: Epoch time: 101.65 s +2026-04-11 11:53:45.996136: +2026-04-11 11:53:45.997928: Epoch 896 +2026-04-11 11:53:45.999620: Current learning rate: 0.00796 +2026-04-11 11:55:27.687181: train_loss -0.3722 +2026-04-11 11:55:27.693455: val_loss -0.3603 +2026-04-11 11:55:27.695068: Pseudo dice [0.0, 0.342, 0.8392, 0.8189, 0.3587, 0.8474, 0.7067] +2026-04-11 11:55:27.697654: Epoch time: 101.69 s +2026-04-11 11:55:28.778778: +2026-04-11 11:55:28.780485: Epoch 897 +2026-04-11 11:55:28.782603: Current learning rate: 0.00796 +2026-04-11 11:57:10.481773: train_loss -0.3744 +2026-04-11 11:57:10.487212: val_loss -0.3205 +2026-04-11 11:57:10.488928: Pseudo dice [0.0012, 0.4065, 0.2727, 0.7508, 0.4358, 0.5763, 0.6107] +2026-04-11 11:57:10.490587: Epoch time: 101.71 s +2026-04-11 11:57:11.562446: +2026-04-11 11:57:11.564022: Epoch 898 +2026-04-11 11:57:11.566598: Current learning rate: 0.00795 +2026-04-11 11:58:53.107321: train_loss -0.3874 +2026-04-11 11:58:53.112594: val_loss -0.3432 +2026-04-11 11:58:53.114279: Pseudo dice [0.0, 0.2319, 0.7223, 0.6091, 0.4497, 0.3642, 0.8424] +2026-04-11 11:58:53.115807: Epoch time: 101.55 s +2026-04-11 11:58:54.186814: +2026-04-11 11:58:54.188922: Epoch 899 +2026-04-11 11:58:54.190913: Current learning rate: 0.00795 +2026-04-11 12:00:36.030535: train_loss -0.4009 +2026-04-11 12:00:36.035850: val_loss -0.3153 +2026-04-11 12:00:36.037717: Pseudo dice [0.0065, 0.2893, 0.5608, 0.8487, 0.4432, 0.2912, 0.5802] +2026-04-11 12:00:36.039546: Epoch time: 101.85 s +2026-04-11 12:00:38.749724: +2026-04-11 12:00:38.751252: Epoch 900 +2026-04-11 12:00:38.753087: Current learning rate: 0.00795 +2026-04-11 12:02:20.449558: train_loss -0.3931 +2026-04-11 12:02:20.455226: val_loss -0.3395 +2026-04-11 12:02:20.457048: Pseudo dice [0.0, 0.1361, 0.3513, 0.7831, 0.2571, 0.3656, 0.8274] +2026-04-11 12:02:20.458769: Epoch time: 101.7 s +2026-04-11 12:02:21.526395: +2026-04-11 12:02:21.528226: Epoch 901 +2026-04-11 12:02:21.530204: Current learning rate: 0.00795 +2026-04-11 12:04:03.104658: train_loss -0.3639 +2026-04-11 12:04:03.109468: val_loss -0.2989 +2026-04-11 12:04:03.110766: Pseudo dice [0.2949, 0.0, 0.5641, 0.6667, 0.0284, 0.4741, 0.834] +2026-04-11 12:04:03.112338: Epoch time: 101.58 s +2026-04-11 12:04:04.380858: +2026-04-11 12:04:04.382774: Epoch 902 +2026-04-11 12:04:04.384643: Current learning rate: 0.00795 +2026-04-11 12:05:46.061329: train_loss -0.379 +2026-04-11 12:05:46.067685: val_loss -0.3225 +2026-04-11 12:05:46.069609: Pseudo dice [0.1715, 0.0141, 0.5246, 0.1952, 0.3662, 0.6414, 0.5508] +2026-04-11 12:05:46.071027: Epoch time: 101.68 s +2026-04-11 12:05:47.139345: +2026-04-11 12:05:47.141063: Epoch 903 +2026-04-11 12:05:47.142924: Current learning rate: 0.00794 +2026-04-11 12:07:28.992049: train_loss -0.3628 +2026-04-11 12:07:28.996940: val_loss -0.3516 +2026-04-11 12:07:28.998457: Pseudo dice [0.2773, 0.2057, 0.7043, 0.7736, 0.495, 0.6618, 0.7087] +2026-04-11 12:07:28.999755: Epoch time: 101.86 s +2026-04-11 12:07:30.064380: +2026-04-11 12:07:30.066015: Epoch 904 +2026-04-11 12:07:30.067677: Current learning rate: 0.00794 +2026-04-11 12:09:11.645708: train_loss -0.3817 +2026-04-11 12:09:11.650926: val_loss -0.3645 +2026-04-11 12:09:11.652828: Pseudo dice [0.3305, 0.2095, 0.5564, 0.7054, 0.3689, 0.7752, 0.6929] +2026-04-11 12:09:11.655246: Epoch time: 101.58 s +2026-04-11 12:09:12.726437: +2026-04-11 12:09:12.728203: Epoch 905 +2026-04-11 12:09:12.730619: Current learning rate: 0.00794 +2026-04-11 12:10:54.462973: train_loss -0.3896 +2026-04-11 12:10:54.467720: val_loss -0.3477 +2026-04-11 12:10:54.469336: Pseudo dice [0.1871, 0.0159, 0.611, 0.7946, 0.3443, 0.7691, 0.5332] +2026-04-11 12:10:54.470850: Epoch time: 101.74 s +2026-04-11 12:10:55.554286: +2026-04-11 12:10:55.555848: Epoch 906 +2026-04-11 12:10:55.557823: Current learning rate: 0.00794 +2026-04-11 12:12:37.235494: train_loss -0.3875 +2026-04-11 12:12:37.240940: val_loss -0.3451 +2026-04-11 12:12:37.242998: Pseudo dice [0.1398, 0.0906, 0.8283, 0.7492, 0.2482, 0.6451, 0.7164] +2026-04-11 12:12:37.244579: Epoch time: 101.68 s +2026-04-11 12:12:38.296041: +2026-04-11 12:12:38.297698: Epoch 907 +2026-04-11 12:12:38.299381: Current learning rate: 0.00793 +2026-04-11 12:14:20.027071: train_loss -0.3801 +2026-04-11 12:14:20.032398: val_loss -0.3241 +2026-04-11 12:14:20.034596: Pseudo dice [0.2104, 0.2399, 0.5361, 0.6832, 0.2368, 0.6687, 0.4187] +2026-04-11 12:14:20.036471: Epoch time: 101.73 s +2026-04-11 12:14:22.150253: +2026-04-11 12:14:22.161416: Epoch 908 +2026-04-11 12:14:22.167865: Current learning rate: 0.00793 +2026-04-11 12:16:03.728436: train_loss -0.3485 +2026-04-11 12:16:03.732868: val_loss -0.3626 +2026-04-11 12:16:03.734479: Pseudo dice [0.0, 0.187, 0.7698, 0.7786, 0.2827, 0.6296, 0.8034] +2026-04-11 12:16:03.735637: Epoch time: 101.58 s +2026-04-11 12:16:04.801452: +2026-04-11 12:16:04.803146: Epoch 909 +2026-04-11 12:16:04.804852: Current learning rate: 0.00793 +2026-04-11 12:17:46.443405: train_loss -0.3893 +2026-04-11 12:17:46.449049: val_loss -0.3616 +2026-04-11 12:17:46.450663: Pseudo dice [0.2474, 0.1778, 0.8164, 0.4461, 0.3569, 0.7358, 0.583] +2026-04-11 12:17:46.452366: Epoch time: 101.65 s +2026-04-11 12:17:47.541276: +2026-04-11 12:17:47.542899: Epoch 910 +2026-04-11 12:17:47.544528: Current learning rate: 0.00793 +2026-04-11 12:19:29.347836: train_loss -0.3844 +2026-04-11 12:19:29.353645: val_loss -0.3442 +2026-04-11 12:19:29.355368: Pseudo dice [0.6763, 0.218, 0.3846, 0.5035, 0.4873, 0.4353, 0.695] +2026-04-11 12:19:29.356755: Epoch time: 101.81 s +2026-04-11 12:19:30.421547: +2026-04-11 12:19:30.423211: Epoch 911 +2026-04-11 12:19:30.424957: Current learning rate: 0.00792 +2026-04-11 12:21:12.077901: train_loss -0.3996 +2026-04-11 12:21:12.082945: val_loss -0.3686 +2026-04-11 12:21:12.084942: Pseudo dice [0.427, 0.3498, 0.7636, 0.8487, 0.228, 0.5242, 0.6786] +2026-04-11 12:21:12.086639: Epoch time: 101.66 s +2026-04-11 12:21:13.160071: +2026-04-11 12:21:13.161790: Epoch 912 +2026-04-11 12:21:13.163800: Current learning rate: 0.00792 +2026-04-11 12:23:02.547838: train_loss -0.41 +2026-04-11 12:23:02.553303: val_loss -0.3488 +2026-04-11 12:23:02.556843: Pseudo dice [0.5226, 0.2778, 0.7402, 0.7457, 0.2949, 0.6063, 0.4665] +2026-04-11 12:23:02.559749: Epoch time: 109.39 s +2026-04-11 12:23:03.607806: +2026-04-11 12:23:03.609482: Epoch 913 +2026-04-11 12:23:03.612351: Current learning rate: 0.00792 +2026-04-11 12:24:45.443500: train_loss -0.3848 +2026-04-11 12:24:45.448455: val_loss -0.3106 +2026-04-11 12:24:45.450120: Pseudo dice [0.0, 0.1627, 0.6515, 0.8486, 0.1401, 0.8035, 0.3709] +2026-04-11 12:24:45.452040: Epoch time: 101.84 s +2026-04-11 12:24:46.525365: +2026-04-11 12:24:46.528219: Epoch 914 +2026-04-11 12:24:46.530164: Current learning rate: 0.00792 +2026-04-11 12:26:28.337433: train_loss -0.3735 +2026-04-11 12:26:28.342508: val_loss -0.3437 +2026-04-11 12:26:28.344049: Pseudo dice [0.0, 0.1596, 0.7348, 0.8016, 0.1241, 0.4214, 0.7149] +2026-04-11 12:26:28.345541: Epoch time: 101.82 s +2026-04-11 12:26:29.408634: +2026-04-11 12:26:29.410162: Epoch 915 +2026-04-11 12:26:29.411749: Current learning rate: 0.00792 +2026-04-11 12:28:10.888255: train_loss -0.3868 +2026-04-11 12:28:10.892616: val_loss -0.355 +2026-04-11 12:28:10.893998: Pseudo dice [0.1275, 0.1781, 0.5051, 0.5754, 0.2206, 0.5687, 0.8446] +2026-04-11 12:28:10.895893: Epoch time: 101.48 s +2026-04-11 12:28:11.967437: +2026-04-11 12:28:11.968992: Epoch 916 +2026-04-11 12:28:11.971010: Current learning rate: 0.00791 +2026-04-11 12:29:53.559335: train_loss -0.3857 +2026-04-11 12:29:53.563905: val_loss -0.3071 +2026-04-11 12:29:53.565731: Pseudo dice [0.2121, 0.0, 0.6023, 0.5847, 0.141, 0.607, 0.7643] +2026-04-11 12:29:53.567201: Epoch time: 101.6 s +2026-04-11 12:29:54.653476: +2026-04-11 12:29:54.655000: Epoch 917 +2026-04-11 12:29:54.656481: Current learning rate: 0.00791 +2026-04-11 12:31:36.610833: train_loss -0.3791 +2026-04-11 12:31:36.615482: val_loss -0.3553 +2026-04-11 12:31:36.617342: Pseudo dice [0.4434, 0.0, 0.6381, 0.4413, 0.3671, 0.6609, 0.5079] +2026-04-11 12:31:36.619097: Epoch time: 101.96 s +2026-04-11 12:31:39.585094: +2026-04-11 12:31:39.588281: Epoch 918 +2026-04-11 12:31:39.589912: Current learning rate: 0.00791 +2026-04-11 12:39:28.588755: train_loss -0.388 +2026-04-11 12:39:28.595090: val_loss -0.3649 +2026-04-11 12:39:28.597883: Pseudo dice [0.5094, 0.0, 0.6884, 0.6585, 0.2406, 0.6593, 0.8038] +2026-04-11 12:39:28.599920: Epoch time: 469.01 s +2026-04-11 12:39:38.012197: +2026-04-11 12:39:38.013958: Epoch 919 +2026-04-11 12:39:38.015769: Current learning rate: 0.00791 +2026-04-11 12:47:14.763323: train_loss -0.3504 +2026-04-11 12:47:14.768238: val_loss -0.327 +2026-04-11 12:47:14.769633: Pseudo dice [0.6448, 0.0, 0.5015, 0.4855, 0.3481, 0.3453, 0.2276] +2026-04-11 12:47:14.771234: Epoch time: 456.75 s +2026-04-11 12:47:15.842353: +2026-04-11 12:47:15.844231: Epoch 920 +2026-04-11 12:47:15.846106: Current learning rate: 0.0079 +2026-04-11 12:48:57.394254: train_loss -0.3771 +2026-04-11 12:48:57.399204: val_loss -0.3185 +2026-04-11 12:48:57.401356: Pseudo dice [0.1228, 0.3706, 0.6911, 0.8187, 0.2827, 0.2899, 0.4597] +2026-04-11 12:48:57.403248: Epoch time: 101.55 s +2026-04-11 12:48:58.479665: +2026-04-11 12:48:58.481607: Epoch 921 +2026-04-11 12:48:58.483488: Current learning rate: 0.0079 +2026-04-11 12:50:40.347300: train_loss -0.3906 +2026-04-11 12:50:40.352127: val_loss -0.3245 +2026-04-11 12:50:40.353767: Pseudo dice [0.2907, 0.2594, 0.5446, 0.4999, 0.2983, 0.6004, 0.4283] +2026-04-11 12:50:40.355663: Epoch time: 101.87 s +2026-04-11 12:50:41.416783: +2026-04-11 12:50:41.418463: Epoch 922 +2026-04-11 12:50:41.420345: Current learning rate: 0.0079 +2026-04-11 12:52:23.363796: train_loss -0.3706 +2026-04-11 12:52:23.368913: val_loss -0.3599 +2026-04-11 12:52:23.371289: Pseudo dice [0.2078, 0.4093, 0.7231, 0.1035, 0.3482, 0.7151, 0.5361] +2026-04-11 12:52:23.373025: Epoch time: 101.95 s +2026-04-11 12:52:24.459004: +2026-04-11 12:52:24.461076: Epoch 923 +2026-04-11 12:52:24.463096: Current learning rate: 0.0079 +2026-04-11 12:54:06.145194: train_loss -0.3957 +2026-04-11 12:54:06.150631: val_loss -0.3298 +2026-04-11 12:54:06.152390: Pseudo dice [0.2184, 0.1787, 0.6801, 0.8605, 0.3503, 0.3795, 0.5863] +2026-04-11 12:54:06.154111: Epoch time: 101.69 s +2026-04-11 12:54:07.223479: +2026-04-11 12:54:07.225034: Epoch 924 +2026-04-11 12:54:07.226662: Current learning rate: 0.00789 +2026-04-11 12:55:48.912874: train_loss -0.3913 +2026-04-11 12:55:48.918117: val_loss -0.3235 +2026-04-11 12:55:48.920030: Pseudo dice [0.4932, 0.0913, 0.6735, 0.7116, 0.2071, 0.6745, 0.5491] +2026-04-11 12:55:48.922096: Epoch time: 101.69 s +2026-04-11 12:55:49.971794: +2026-04-11 12:55:49.973336: Epoch 925 +2026-04-11 12:55:49.975234: Current learning rate: 0.00789 +2026-04-11 12:57:31.882780: train_loss -0.3477 +2026-04-11 12:57:31.888591: val_loss -0.3244 +2026-04-11 12:57:31.891447: Pseudo dice [0.0, 0.0, 0.6138, 0.7225, 0.3851, 0.3699, 0.7906] +2026-04-11 12:57:31.895004: Epoch time: 101.91 s +2026-04-11 12:57:32.958320: +2026-04-11 12:57:32.960147: Epoch 926 +2026-04-11 12:57:32.962075: Current learning rate: 0.00789 +2026-04-11 12:59:14.714900: train_loss -0.3673 +2026-04-11 12:59:14.719291: val_loss -0.3113 +2026-04-11 12:59:14.720584: Pseudo dice [0.0, 0.0, 0.7002, 0.5332, 0.1621, 0.6772, 0.711] +2026-04-11 12:59:14.722198: Epoch time: 101.76 s +2026-04-11 12:59:15.786364: +2026-04-11 12:59:15.788110: Epoch 927 +2026-04-11 12:59:15.790467: Current learning rate: 0.00789 +2026-04-11 13:00:57.430900: train_loss -0.3914 +2026-04-11 13:00:57.435585: val_loss -0.3668 +2026-04-11 13:00:57.437613: Pseudo dice [0.0084, 0.2551, 0.81, 0.7647, 0.5379, 0.5622, 0.8007] +2026-04-11 13:00:57.439387: Epoch time: 101.65 s +2026-04-11 13:00:58.635100: +2026-04-11 13:00:58.636479: Epoch 928 +2026-04-11 13:00:58.638250: Current learning rate: 0.00789 +2026-04-11 13:02:40.390448: train_loss -0.3865 +2026-04-11 13:02:40.394667: val_loss -0.3459 +2026-04-11 13:02:40.396380: Pseudo dice [0.0008, 0.2273, 0.7623, 0.4867, 0.2935, 0.6609, 0.8155] +2026-04-11 13:02:40.398536: Epoch time: 101.76 s +2026-04-11 13:02:41.465283: +2026-04-11 13:02:41.468683: Epoch 929 +2026-04-11 13:02:41.470804: Current learning rate: 0.00788 +2026-04-11 13:04:23.925848: train_loss -0.3924 +2026-04-11 13:04:23.930718: val_loss -0.3454 +2026-04-11 13:04:23.932346: Pseudo dice [0.4345, 0.0563, 0.6341, 0.1444, 0.2575, 0.7876, 0.7827] +2026-04-11 13:04:23.934025: Epoch time: 102.46 s +2026-04-11 13:04:25.004122: +2026-04-11 13:04:25.005748: Epoch 930 +2026-04-11 13:04:25.007595: Current learning rate: 0.00788 +2026-04-11 13:06:06.657659: train_loss -0.3362 +2026-04-11 13:06:06.664351: val_loss -0.3382 +2026-04-11 13:06:06.666077: Pseudo dice [0.098, 0.5776, 0.6543, 0.7035, 0.2843, 0.1644, 0.3158] +2026-04-11 13:06:06.667759: Epoch time: 101.66 s +2026-04-11 13:06:07.730907: +2026-04-11 13:06:07.732370: Epoch 931 +2026-04-11 13:06:07.734275: Current learning rate: 0.00788 +2026-04-11 13:07:49.126947: train_loss -0.3591 +2026-04-11 13:07:49.131973: val_loss -0.2537 +2026-04-11 13:07:49.133990: Pseudo dice [0.16, 0.1621, 0.2436, 0.1562, 0.1872, 0.4967, 0.7819] +2026-04-11 13:07:49.136384: Epoch time: 101.4 s +2026-04-11 13:07:50.212323: +2026-04-11 13:07:50.213960: Epoch 932 +2026-04-11 13:07:50.215775: Current learning rate: 0.00788 +2026-04-11 13:09:31.792592: train_loss -0.3677 +2026-04-11 13:09:31.798066: val_loss -0.3424 +2026-04-11 13:09:31.799927: Pseudo dice [0.0852, 0.1891, 0.8488, 0.0093, 0.3985, 0.7188, 0.6217] +2026-04-11 13:09:31.801398: Epoch time: 101.58 s +2026-04-11 13:09:32.877538: +2026-04-11 13:09:32.879562: Epoch 933 +2026-04-11 13:09:32.881631: Current learning rate: 0.00787 +2026-04-11 13:11:14.353369: train_loss -0.374 +2026-04-11 13:11:14.359024: val_loss -0.3271 +2026-04-11 13:11:14.360771: Pseudo dice [0.0, 0.1118, 0.5651, 0.3457, 0.3114, 0.4328, 0.7642] +2026-04-11 13:11:14.362329: Epoch time: 101.48 s +2026-04-11 13:11:15.442688: +2026-04-11 13:11:15.444466: Epoch 934 +2026-04-11 13:11:15.446427: Current learning rate: 0.00787 +2026-04-11 13:12:56.826693: train_loss -0.3643 +2026-04-11 13:12:56.832361: val_loss -0.3059 +2026-04-11 13:12:56.834180: Pseudo dice [0.12, 0.0251, 0.6357, 0.766, 0.3237, 0.4807, 0.6039] +2026-04-11 13:12:56.840103: Epoch time: 101.39 s +2026-04-11 13:12:57.919170: +2026-04-11 13:12:57.920805: Epoch 935 +2026-04-11 13:12:57.922606: Current learning rate: 0.00787 +2026-04-11 13:14:39.575094: train_loss -0.382 +2026-04-11 13:14:39.580150: val_loss -0.2994 +2026-04-11 13:14:39.581442: Pseudo dice [0.0756, 0.0283, 0.3018, 0.5565, 0.0331, 0.4596, 0.7938] +2026-04-11 13:14:39.583326: Epoch time: 101.66 s +2026-04-11 13:14:40.644044: +2026-04-11 13:14:40.645825: Epoch 936 +2026-04-11 13:14:40.647551: Current learning rate: 0.00787 +2026-04-11 13:16:22.281014: train_loss -0.3854 +2026-04-11 13:16:22.287322: val_loss -0.3419 +2026-04-11 13:16:22.288897: Pseudo dice [0.3057, 0.1044, 0.5731, 0.3001, 0.1537, 0.6706, 0.5989] +2026-04-11 13:16:22.291062: Epoch time: 101.64 s +2026-04-11 13:16:23.364435: +2026-04-11 13:16:23.366286: Epoch 937 +2026-04-11 13:16:23.368185: Current learning rate: 0.00786 +2026-04-11 13:18:04.796378: train_loss -0.3734 +2026-04-11 13:18:04.801368: val_loss -0.3522 +2026-04-11 13:18:04.802977: Pseudo dice [0.0, 0.2453, 0.6944, 0.8466, 0.4554, 0.7914, 0.6548] +2026-04-11 13:18:04.804460: Epoch time: 101.43 s +2026-04-11 13:18:05.887714: +2026-04-11 13:18:05.889149: Epoch 938 +2026-04-11 13:18:05.890713: Current learning rate: 0.00786 +2026-04-11 13:19:47.445734: train_loss -0.3929 +2026-04-11 13:19:47.450130: val_loss -0.3414 +2026-04-11 13:19:47.451871: Pseudo dice [0.0, 0.0, 0.7113, 0.4756, 0.173, 0.7267, 0.7038] +2026-04-11 13:19:47.453479: Epoch time: 101.56 s +2026-04-11 13:19:48.529175: +2026-04-11 13:19:48.530743: Epoch 939 +2026-04-11 13:19:48.532496: Current learning rate: 0.00786 +2026-04-11 13:21:30.026316: train_loss -0.3726 +2026-04-11 13:21:30.030870: val_loss -0.3393 +2026-04-11 13:21:30.032757: Pseudo dice [0.035, 0.5019, 0.7259, 0.845, 0.2653, 0.6987, 0.6568] +2026-04-11 13:21:30.034092: Epoch time: 101.5 s +2026-04-11 13:21:31.112881: +2026-04-11 13:21:31.114620: Epoch 940 +2026-04-11 13:21:31.116565: Current learning rate: 0.00786 +2026-04-11 13:23:12.396322: train_loss -0.3941 +2026-04-11 13:23:12.401904: val_loss -0.3437 +2026-04-11 13:23:12.404148: Pseudo dice [0.1954, 0.2591, 0.8001, 0.634, 0.2265, 0.7094, 0.7824] +2026-04-11 13:23:12.406098: Epoch time: 101.29 s +2026-04-11 13:23:13.486124: +2026-04-11 13:23:13.487814: Epoch 941 +2026-04-11 13:23:13.489701: Current learning rate: 0.00786 +2026-04-11 13:24:54.922632: train_loss -0.3966 +2026-04-11 13:24:54.927589: val_loss -0.3516 +2026-04-11 13:24:54.929640: Pseudo dice [0.5731, 0.2721, 0.6503, 0.8633, 0.3909, 0.1666, 0.8302] +2026-04-11 13:24:54.931235: Epoch time: 101.44 s +2026-04-11 13:24:56.003721: +2026-04-11 13:24:56.005649: Epoch 942 +2026-04-11 13:24:56.007457: Current learning rate: 0.00785 +2026-04-11 13:26:37.370566: train_loss -0.4106 +2026-04-11 13:26:37.374716: val_loss -0.3434 +2026-04-11 13:26:37.375980: Pseudo dice [0.7053, 0.2587, 0.5372, 0.825, 0.171, 0.6529, 0.8476] +2026-04-11 13:26:37.377214: Epoch time: 101.37 s +2026-04-11 13:26:38.443131: +2026-04-11 13:26:38.444375: Epoch 943 +2026-04-11 13:26:38.445846: Current learning rate: 0.00785 +2026-04-11 13:28:20.170921: train_loss -0.3955 +2026-04-11 13:28:20.177217: val_loss -0.3615 +2026-04-11 13:28:20.179254: Pseudo dice [0.7144, 0.6125, 0.7634, 0.853, 0.2657, 0.7391, 0.6246] +2026-04-11 13:28:20.181019: Epoch time: 101.73 s +2026-04-11 13:28:21.256410: +2026-04-11 13:28:21.258102: Epoch 944 +2026-04-11 13:28:21.260259: Current learning rate: 0.00785 +2026-04-11 13:30:02.844253: train_loss -0.3837 +2026-04-11 13:30:02.848602: val_loss -0.3107 +2026-04-11 13:30:02.850470: Pseudo dice [0.0, 0.3534, 0.6736, 0.4452, 0.1616, 0.3713, 0.8207] +2026-04-11 13:30:02.851882: Epoch time: 101.59 s +2026-04-11 13:30:03.919820: +2026-04-11 13:30:03.921185: Epoch 945 +2026-04-11 13:30:03.922724: Current learning rate: 0.00785 +2026-04-11 13:31:45.633944: train_loss -0.3544 +2026-04-11 13:31:45.638491: val_loss -0.3329 +2026-04-11 13:31:45.640293: Pseudo dice [0.0, 0.0, 0.7672, 0.7058, 0.2327, 0.5435, 0.7073] +2026-04-11 13:31:45.642336: Epoch time: 101.72 s +2026-04-11 13:31:46.721329: +2026-04-11 13:31:46.722791: Epoch 946 +2026-04-11 13:31:46.724865: Current learning rate: 0.00784 +2026-04-11 13:33:28.233366: train_loss -0.374 +2026-04-11 13:33:28.237886: val_loss -0.3059 +2026-04-11 13:33:28.239644: Pseudo dice [0.0, 0.1033, 0.6537, 0.521, 0.3223, 0.4921, 0.7004] +2026-04-11 13:33:28.241041: Epoch time: 101.52 s +2026-04-11 13:33:29.318740: +2026-04-11 13:33:29.320264: Epoch 947 +2026-04-11 13:33:29.322004: Current learning rate: 0.00784 +2026-04-11 13:35:11.066979: train_loss -0.3583 +2026-04-11 13:35:11.071604: val_loss -0.295 +2026-04-11 13:35:11.073178: Pseudo dice [0.0, 0.1748, 0.61, 0.7117, 0.3386, 0.3426, 0.3758] +2026-04-11 13:35:11.074661: Epoch time: 101.75 s +2026-04-11 13:35:12.160592: +2026-04-11 13:35:12.162341: Epoch 948 +2026-04-11 13:35:12.163963: Current learning rate: 0.00784 +2026-04-11 13:36:53.854232: train_loss -0.3895 +2026-04-11 13:36:53.859825: val_loss -0.3381 +2026-04-11 13:36:53.861654: Pseudo dice [0.0, 0.3205, 0.6816, 0.8241, 0.2937, 0.6997, 0.5672] +2026-04-11 13:36:53.863366: Epoch time: 101.7 s +2026-04-11 13:36:54.920451: +2026-04-11 13:36:54.922424: Epoch 949 +2026-04-11 13:36:54.924374: Current learning rate: 0.00784 +2026-04-11 13:38:36.712928: train_loss -0.371 +2026-04-11 13:38:36.718812: val_loss -0.3528 +2026-04-11 13:38:36.720496: Pseudo dice [0.0, 0.2157, 0.3323, 0.8291, 0.4141, 0.6669, 0.6192] +2026-04-11 13:38:36.722684: Epoch time: 101.8 s +2026-04-11 13:38:40.472005: +2026-04-11 13:38:40.473292: Epoch 950 +2026-04-11 13:38:40.474930: Current learning rate: 0.00783 +2026-04-11 13:40:21.735257: train_loss -0.3828 +2026-04-11 13:40:21.741035: val_loss -0.3346 +2026-04-11 13:40:21.742997: Pseudo dice [0.0, 0.0284, 0.396, 0.5145, 0.1594, 0.8213, 0.6635] +2026-04-11 13:40:21.744660: Epoch time: 101.27 s +2026-04-11 13:40:22.831009: +2026-04-11 13:40:22.832580: Epoch 951 +2026-04-11 13:40:22.834388: Current learning rate: 0.00783 +2026-04-11 13:42:04.254757: train_loss -0.3877 +2026-04-11 13:42:04.259201: val_loss -0.364 +2026-04-11 13:42:04.260823: Pseudo dice [0.0, 0.054, 0.6654, 0.3716, 0.4113, 0.7733, 0.7451] +2026-04-11 13:42:04.262312: Epoch time: 101.43 s +2026-04-11 13:42:05.340155: +2026-04-11 13:42:05.345768: Epoch 952 +2026-04-11 13:42:05.347255: Current learning rate: 0.00783 +2026-04-11 13:43:47.129822: train_loss -0.4004 +2026-04-11 13:43:47.135045: val_loss -0.3277 +2026-04-11 13:43:47.137002: Pseudo dice [0.0, 0.1837, 0.8028, 0.8362, 0.1306, 0.5955, 0.6751] +2026-04-11 13:43:47.139083: Epoch time: 101.79 s +2026-04-11 13:43:48.222451: +2026-04-11 13:43:48.223995: Epoch 953 +2026-04-11 13:43:48.225856: Current learning rate: 0.00783 +2026-04-11 13:45:30.078034: train_loss -0.3969 +2026-04-11 13:45:30.083955: val_loss -0.3645 +2026-04-11 13:45:30.085652: Pseudo dice [0.0, 0.0, 0.8938, 0.5215, 0.2684, 0.714, 0.8558] +2026-04-11 13:45:30.087073: Epoch time: 101.86 s +2026-04-11 13:45:31.177774: +2026-04-11 13:45:31.179267: Epoch 954 +2026-04-11 13:45:31.180870: Current learning rate: 0.00783 +2026-04-11 13:47:12.846841: train_loss -0.38 +2026-04-11 13:47:12.852035: val_loss -0.3281 +2026-04-11 13:47:12.853774: Pseudo dice [0.0, 0.0, 0.6088, 0.8382, 0.1884, 0.6521, 0.6212] +2026-04-11 13:47:12.855182: Epoch time: 101.67 s +2026-04-11 13:47:13.943990: +2026-04-11 13:47:13.945730: Epoch 955 +2026-04-11 13:47:13.947351: Current learning rate: 0.00782 +2026-04-11 13:48:55.783946: train_loss -0.3733 +2026-04-11 13:48:55.789588: val_loss -0.3423 +2026-04-11 13:48:55.794836: Pseudo dice [0.0, 0.0, 0.6575, 0.614, 0.3047, 0.7158, 0.4233] +2026-04-11 13:48:55.800320: Epoch time: 101.84 s +2026-04-11 13:48:56.900757: +2026-04-11 13:48:56.902559: Epoch 956 +2026-04-11 13:48:56.904460: Current learning rate: 0.00782 +2026-04-11 13:50:38.764936: train_loss -0.3767 +2026-04-11 13:50:38.770227: val_loss -0.2684 +2026-04-11 13:50:38.772079: Pseudo dice [0.0, 0.0, 0.2428, 0.1167, 0.4328, 0.0844, 0.5861] +2026-04-11 13:50:38.773712: Epoch time: 101.87 s +2026-04-11 13:50:39.869351: +2026-04-11 13:50:39.870864: Epoch 957 +2026-04-11 13:50:39.872594: Current learning rate: 0.00782 +2026-04-11 13:52:21.656144: train_loss -0.3756 +2026-04-11 13:52:21.662333: val_loss -0.3358 +2026-04-11 13:52:21.664365: Pseudo dice [0.0, 0.0, 0.7363, 0.7676, 0.2133, 0.7385, 0.4866] +2026-04-11 13:52:21.665754: Epoch time: 101.79 s +2026-04-11 13:52:22.777366: +2026-04-11 13:52:22.779333: Epoch 958 +2026-04-11 13:52:22.781214: Current learning rate: 0.00782 +2026-04-11 13:54:04.190511: train_loss -0.3799 +2026-04-11 13:54:04.199288: val_loss -0.3558 +2026-04-11 13:54:04.201019: Pseudo dice [0.0, 0.0, 0.7133, 0.8288, 0.2677, 0.7462, 0.8203] +2026-04-11 13:54:04.202205: Epoch time: 101.42 s +2026-04-11 13:54:05.296335: +2026-04-11 13:54:05.297984: Epoch 959 +2026-04-11 13:54:05.300076: Current learning rate: 0.00781 +2026-04-11 13:55:46.886395: train_loss -0.3959 +2026-04-11 13:55:46.891394: val_loss -0.3386 +2026-04-11 13:55:46.893209: Pseudo dice [0.0, 0.0, 0.584, 0.7273, 0.2612, 0.576, 0.8676] +2026-04-11 13:55:46.894931: Epoch time: 101.59 s +2026-04-11 13:55:47.980736: +2026-04-11 13:55:47.982324: Epoch 960 +2026-04-11 13:55:47.984601: Current learning rate: 0.00781 +2026-04-11 13:57:29.366307: train_loss -0.3765 +2026-04-11 13:57:29.372042: val_loss -0.3275 +2026-04-11 13:57:29.373467: Pseudo dice [0.0, 0.0, 0.7487, 0.7756, 0.229, 0.7334, 0.6235] +2026-04-11 13:57:29.375087: Epoch time: 101.39 s +2026-04-11 13:57:30.477403: +2026-04-11 13:57:30.479295: Epoch 961 +2026-04-11 13:57:30.480978: Current learning rate: 0.00781 +2026-04-11 13:59:12.096051: train_loss -0.397 +2026-04-11 13:59:12.101164: val_loss -0.3458 +2026-04-11 13:59:12.102925: Pseudo dice [0.0, 0.0, 0.6208, 0.8367, 0.376, 0.4429, 0.839] +2026-04-11 13:59:12.104419: Epoch time: 101.62 s +2026-04-11 13:59:13.204665: +2026-04-11 13:59:13.206363: Epoch 962 +2026-04-11 13:59:13.208494: Current learning rate: 0.00781 +2026-04-11 14:00:54.992648: train_loss -0.3978 +2026-04-11 14:00:54.997306: val_loss -0.3428 +2026-04-11 14:00:54.998807: Pseudo dice [0.0, 0.0, 0.3723, 0.8046, 0.2471, 0.8074, 0.7674] +2026-04-11 14:00:55.000488: Epoch time: 101.79 s +2026-04-11 14:00:56.080671: +2026-04-11 14:00:56.082422: Epoch 963 +2026-04-11 14:00:56.084316: Current learning rate: 0.0078 +2026-04-11 14:02:37.570212: train_loss -0.3998 +2026-04-11 14:02:37.575215: val_loss -0.3473 +2026-04-11 14:02:37.576878: Pseudo dice [0.0, 0.0, 0.841, 0.7467, 0.5744, 0.701, 0.4756] +2026-04-11 14:02:37.578742: Epoch time: 101.49 s +2026-04-11 14:02:38.673512: +2026-04-11 14:02:38.674995: Epoch 964 +2026-04-11 14:02:38.677029: Current learning rate: 0.0078 +2026-04-11 14:04:20.120266: train_loss -0.3995 +2026-04-11 14:04:20.125402: val_loss -0.3608 +2026-04-11 14:04:20.127181: Pseudo dice [0.0, 0.0, 0.8278, 0.7922, 0.3546, 0.7811, 0.6315] +2026-04-11 14:04:20.128802: Epoch time: 101.45 s +2026-04-11 14:04:21.218921: +2026-04-11 14:04:21.220644: Epoch 965 +2026-04-11 14:04:21.222645: Current learning rate: 0.0078 +2026-04-11 14:06:02.872272: train_loss -0.3886 +2026-04-11 14:06:02.877940: val_loss -0.2798 +2026-04-11 14:06:02.880097: Pseudo dice [0.0, 0.0, 0.0753, 0.7672, 0.3246, 0.3936, 0.6237] +2026-04-11 14:06:02.881962: Epoch time: 101.66 s +2026-04-11 14:06:03.975369: +2026-04-11 14:06:03.977761: Epoch 966 +2026-04-11 14:06:03.979755: Current learning rate: 0.0078 +2026-04-11 14:07:45.714455: train_loss -0.3747 +2026-04-11 14:07:45.734355: val_loss -0.2981 +2026-04-11 14:07:45.736231: Pseudo dice [0.0, 0.0, 0.4577, 0.5996, 0.3296, 0.3731, 0.6247] +2026-04-11 14:07:45.738000: Epoch time: 101.74 s +2026-04-11 14:07:46.836162: +2026-04-11 14:07:46.838356: Epoch 967 +2026-04-11 14:07:46.840406: Current learning rate: 0.0078 +2026-04-11 14:09:28.481530: train_loss -0.3937 +2026-04-11 14:09:28.487268: val_loss -0.3361 +2026-04-11 14:09:28.489167: Pseudo dice [0.0, 0.0, 0.5065, 0.8226, 0.2463, 0.6256, 0.6162] +2026-04-11 14:09:28.490839: Epoch time: 101.65 s +2026-04-11 14:09:29.601235: +2026-04-11 14:09:29.603285: Epoch 968 +2026-04-11 14:09:29.605667: Current learning rate: 0.00779 +2026-04-11 14:11:11.353028: train_loss -0.3996 +2026-04-11 14:11:11.358995: val_loss -0.3095 +2026-04-11 14:11:11.360849: Pseudo dice [0.0, 0.0, 0.6249, 0.8152, 0.2609, 0.6861, 0.4464] +2026-04-11 14:11:11.362589: Epoch time: 101.76 s +2026-04-11 14:11:12.447233: +2026-04-11 14:11:12.449193: Epoch 969 +2026-04-11 14:11:12.451066: Current learning rate: 0.00779 +2026-04-11 14:12:53.646256: train_loss -0.3789 +2026-04-11 14:12:53.651964: val_loss -0.3689 +2026-04-11 14:12:53.653853: Pseudo dice [0.0, 0.0, 0.7283, 0.2273, 0.539, 0.7477, 0.4534] +2026-04-11 14:12:53.656453: Epoch time: 101.2 s +2026-04-11 14:12:54.764720: +2026-04-11 14:12:54.766406: Epoch 970 +2026-04-11 14:12:54.768192: Current learning rate: 0.00779 +2026-04-11 14:14:36.155262: train_loss -0.388 +2026-04-11 14:14:36.163625: val_loss -0.3423 +2026-04-11 14:14:36.166677: Pseudo dice [0.0, 0.0, 0.506, 0.5754, 0.1354, 0.7358, 0.7149] +2026-04-11 14:14:36.168496: Epoch time: 101.39 s +2026-04-11 14:14:37.257676: +2026-04-11 14:14:37.259034: Epoch 971 +2026-04-11 14:14:37.260922: Current learning rate: 0.00779 +2026-04-11 14:16:19.917079: train_loss -0.3784 +2026-04-11 14:16:19.922567: val_loss -0.3384 +2026-04-11 14:16:19.924668: Pseudo dice [0.0, 0.0, 0.5804, 0.8992, 0.1994, 0.7218, 0.8623] +2026-04-11 14:16:19.926471: Epoch time: 102.66 s +2026-04-11 14:16:21.020000: +2026-04-11 14:16:21.021374: Epoch 972 +2026-04-11 14:16:21.023287: Current learning rate: 0.00778 +2026-04-11 14:18:02.507685: train_loss -0.3719 +2026-04-11 14:18:02.513575: val_loss -0.3305 +2026-04-11 14:18:02.515867: Pseudo dice [0.0, 0.0, 0.7053, 0.6661, 0.2211, 0.5459, 0.545] +2026-04-11 14:18:02.518180: Epoch time: 101.49 s +2026-04-11 14:18:03.627844: +2026-04-11 14:18:03.629824: Epoch 973 +2026-04-11 14:18:03.631459: Current learning rate: 0.00778 +2026-04-11 14:19:45.338640: train_loss -0.3927 +2026-04-11 14:19:45.344645: val_loss -0.3548 +2026-04-11 14:19:45.346371: Pseudo dice [0.0, 0.0, 0.7358, 0.5624, 0.3034, 0.7066, 0.5697] +2026-04-11 14:19:45.348759: Epoch time: 101.71 s +2026-04-11 14:19:46.438499: +2026-04-11 14:19:46.439950: Epoch 974 +2026-04-11 14:19:46.441978: Current learning rate: 0.00778 +2026-04-11 14:21:27.724358: train_loss -0.3894 +2026-04-11 14:21:27.729032: val_loss -0.3174 +2026-04-11 14:21:27.730758: Pseudo dice [0.0, 0.0, 0.6726, 0.8103, 0.3835, 0.4451, 0.7085] +2026-04-11 14:21:27.732348: Epoch time: 101.29 s +2026-04-11 14:21:28.825261: +2026-04-11 14:21:28.827607: Epoch 975 +2026-04-11 14:21:28.829499: Current learning rate: 0.00778 +2026-04-11 14:23:10.011283: train_loss -0.3932 +2026-04-11 14:23:10.015910: val_loss -0.333 +2026-04-11 14:23:10.017363: Pseudo dice [0.0, 0.0, 0.7293, 0.5832, 0.3437, 0.6876, 0.7325] +2026-04-11 14:23:10.018598: Epoch time: 101.19 s +2026-04-11 14:23:11.117054: +2026-04-11 14:23:11.118510: Epoch 976 +2026-04-11 14:23:11.120130: Current learning rate: 0.00777 +2026-04-11 14:24:52.479930: train_loss -0.3905 +2026-04-11 14:24:52.484969: val_loss -0.3357 +2026-04-11 14:24:52.486381: Pseudo dice [0.0, 0.0, 0.5776, 0.4845, 0.3118, 0.4812, 0.5166] +2026-04-11 14:24:52.488309: Epoch time: 101.37 s +2026-04-11 14:24:53.578855: +2026-04-11 14:24:53.580714: Epoch 977 +2026-04-11 14:24:53.582851: Current learning rate: 0.00777 +2026-04-11 14:26:34.920393: train_loss -0.4064 +2026-04-11 14:26:34.925431: val_loss -0.3567 +2026-04-11 14:26:34.926878: Pseudo dice [0.0, 0.0, 0.739, 0.7047, 0.4625, 0.6851, 0.6678] +2026-04-11 14:26:34.928275: Epoch time: 101.34 s +2026-04-11 14:26:36.033524: +2026-04-11 14:26:36.035309: Epoch 978 +2026-04-11 14:26:36.037292: Current learning rate: 0.00777 +2026-04-11 14:28:17.266231: train_loss -0.3821 +2026-04-11 14:28:17.271210: val_loss -0.3097 +2026-04-11 14:28:17.273089: Pseudo dice [0.0, 0.0, 0.4638, 0.1194, 0.2577, 0.5511, 0.7952] +2026-04-11 14:28:17.274652: Epoch time: 101.24 s +2026-04-11 14:28:18.369332: +2026-04-11 14:28:18.370692: Epoch 979 +2026-04-11 14:28:18.372459: Current learning rate: 0.00777 +2026-04-11 14:29:59.503756: train_loss -0.391 +2026-04-11 14:29:59.509821: val_loss -0.3528 +2026-04-11 14:29:59.511736: Pseudo dice [0.0, 0.0, 0.8018, 0.8254, 0.0929, 0.7454, 0.7666] +2026-04-11 14:29:59.513325: Epoch time: 101.14 s +2026-04-11 14:30:00.612675: +2026-04-11 14:30:00.614821: Epoch 980 +2026-04-11 14:30:00.616749: Current learning rate: 0.00777 +2026-04-11 14:31:42.051242: train_loss -0.3781 +2026-04-11 14:31:42.056208: val_loss -0.3337 +2026-04-11 14:31:42.057973: Pseudo dice [0.1399, 0.0, 0.5173, 0.8374, 0.2379, 0.5916, 0.6761] +2026-04-11 14:31:42.059697: Epoch time: 101.44 s +2026-04-11 14:31:43.171394: +2026-04-11 14:31:43.173017: Epoch 981 +2026-04-11 14:31:43.174849: Current learning rate: 0.00776 +2026-04-11 14:33:24.448950: train_loss -0.3935 +2026-04-11 14:33:24.454518: val_loss -0.3455 +2026-04-11 14:33:24.456349: Pseudo dice [0.1907, 0.0173, 0.7767, 0.6762, 0.4531, 0.7516, 0.7042] +2026-04-11 14:33:24.458244: Epoch time: 101.28 s +2026-04-11 14:33:25.559139: +2026-04-11 14:33:25.560700: Epoch 982 +2026-04-11 14:33:25.562508: Current learning rate: 0.00776 +2026-04-11 14:35:06.743265: train_loss -0.3691 +2026-04-11 14:35:06.748330: val_loss -0.2949 +2026-04-11 14:35:06.749912: Pseudo dice [0.0, 0.0, 0.515, 0.4589, 0.0038, 0.6604, 0.577] +2026-04-11 14:35:06.751461: Epoch time: 101.19 s +2026-04-11 14:35:07.841575: +2026-04-11 14:35:07.842736: Epoch 983 +2026-04-11 14:35:07.844219: Current learning rate: 0.00776 +2026-04-11 14:36:49.242512: train_loss -0.3593 +2026-04-11 14:36:49.247223: val_loss -0.332 +2026-04-11 14:36:49.248730: Pseudo dice [0.0, 0.0, 0.6359, 0.2066, 0.1807, 0.5039, 0.6733] +2026-04-11 14:36:49.250144: Epoch time: 101.4 s +2026-04-11 14:36:50.330446: +2026-04-11 14:36:50.331864: Epoch 984 +2026-04-11 14:36:50.333488: Current learning rate: 0.00776 +2026-04-11 14:38:31.666682: train_loss -0.3617 +2026-04-11 14:38:31.671536: val_loss -0.3164 +2026-04-11 14:38:31.673005: Pseudo dice [0.0, 0.0, 0.507, 0.7618, 0.2522, 0.7051, 0.7355] +2026-04-11 14:38:31.674452: Epoch time: 101.34 s +2026-04-11 14:38:32.775858: +2026-04-11 14:38:32.777684: Epoch 985 +2026-04-11 14:38:32.779624: Current learning rate: 0.00775 +2026-04-11 14:40:14.085812: train_loss -0.3896 +2026-04-11 14:40:14.090585: val_loss -0.2965 +2026-04-11 14:40:14.092335: Pseudo dice [0.0, 0.0, 0.5723, 0.7766, 0.1293, 0.58, 0.5392] +2026-04-11 14:40:14.093958: Epoch time: 101.31 s +2026-04-11 14:40:15.187611: +2026-04-11 14:40:15.189290: Epoch 986 +2026-04-11 14:40:15.190960: Current learning rate: 0.00775 +2026-04-11 14:41:56.411582: train_loss -0.3848 +2026-04-11 14:41:56.416980: val_loss -0.3598 +2026-04-11 14:41:56.418799: Pseudo dice [0.0059, 0.0, 0.6474, 0.7369, 0.6098, 0.6881, 0.7022] +2026-04-11 14:41:56.420605: Epoch time: 101.23 s +2026-04-11 14:41:57.502882: +2026-04-11 14:41:57.504878: Epoch 987 +2026-04-11 14:41:57.506987: Current learning rate: 0.00775 +2026-04-11 14:43:38.909575: train_loss -0.3792 +2026-04-11 14:43:38.914849: val_loss -0.3492 +2026-04-11 14:43:38.916896: Pseudo dice [0.5954, 0.0, 0.5539, 0.0572, 0.4392, 0.2683, 0.7855] +2026-04-11 14:43:38.918612: Epoch time: 101.41 s +2026-04-11 14:43:40.009457: +2026-04-11 14:43:40.010829: Epoch 988 +2026-04-11 14:43:40.013032: Current learning rate: 0.00775 +2026-04-11 14:45:21.194166: train_loss -0.4052 +2026-04-11 14:45:21.199661: val_loss -0.4031 +2026-04-11 14:45:21.201381: Pseudo dice [0.6338, 0.014, 0.67, 0.7259, 0.5768, 0.8495, 0.8472] +2026-04-11 14:45:21.202958: Epoch time: 101.19 s +2026-04-11 14:45:22.303219: +2026-04-11 14:45:22.304815: Epoch 989 +2026-04-11 14:45:22.307117: Current learning rate: 0.00774 +2026-04-11 14:47:03.578117: train_loss -0.3784 +2026-04-11 14:47:03.583167: val_loss -0.335 +2026-04-11 14:47:03.584980: Pseudo dice [0.4473, 0.0, 0.6261, 0.3472, 0.1901, 0.7419, 0.2932] +2026-04-11 14:47:03.586465: Epoch time: 101.28 s +2026-04-11 14:47:04.677325: +2026-04-11 14:47:04.678654: Epoch 990 +2026-04-11 14:47:04.680451: Current learning rate: 0.00774 +2026-04-11 14:48:46.120863: train_loss -0.3723 +2026-04-11 14:48:46.125650: val_loss -0.3536 +2026-04-11 14:48:46.128331: Pseudo dice [0.4398, 0.0, 0.6027, 0.7365, 0.1127, 0.6999, 0.8399] +2026-04-11 14:48:46.129972: Epoch time: 101.45 s +2026-04-11 14:48:47.211275: +2026-04-11 14:48:47.213100: Epoch 991 +2026-04-11 14:48:47.215033: Current learning rate: 0.00774 +2026-04-11 14:50:28.375925: train_loss -0.3712 +2026-04-11 14:50:28.380212: val_loss -0.3754 +2026-04-11 14:50:28.381669: Pseudo dice [0.0, 0.8152, 0.5332, 0.6615, 0.3578, 0.5557, 0.7916] +2026-04-11 14:50:28.383415: Epoch time: 101.17 s +2026-04-11 14:50:30.504402: +2026-04-11 14:50:30.505929: Epoch 992 +2026-04-11 14:50:30.507593: Current learning rate: 0.00774 +2026-04-11 14:52:11.624732: train_loss -0.3833 +2026-04-11 14:52:11.629341: val_loss -0.3456 +2026-04-11 14:52:11.630644: Pseudo dice [0.0, 0.4478, 0.6543, 0.4303, 0.4553, 0.6861, 0.5922] +2026-04-11 14:52:11.632297: Epoch time: 101.12 s +2026-04-11 14:52:12.719423: +2026-04-11 14:52:12.721085: Epoch 993 +2026-04-11 14:52:12.722584: Current learning rate: 0.00774 +2026-04-11 14:53:54.052899: train_loss -0.3744 +2026-04-11 14:53:54.057896: val_loss -0.3531 +2026-04-11 14:53:54.059745: Pseudo dice [0.0702, 0.0043, 0.4356, 0.743, 0.1987, 0.6731, 0.768] +2026-04-11 14:53:54.061393: Epoch time: 101.34 s +2026-04-11 14:53:55.160578: +2026-04-11 14:53:55.162225: Epoch 994 +2026-04-11 14:53:55.163719: Current learning rate: 0.00773 +2026-04-11 14:55:36.331177: train_loss -0.3884 +2026-04-11 14:55:36.336864: val_loss -0.3568 +2026-04-11 14:55:36.338464: Pseudo dice [0.482, 0.1347, 0.3913, 0.6979, 0.3737, 0.77, 0.5268] +2026-04-11 14:55:36.339976: Epoch time: 101.17 s +2026-04-11 14:55:37.433597: +2026-04-11 14:55:37.443055: Epoch 995 +2026-04-11 14:55:37.444509: Current learning rate: 0.00773 +2026-04-11 14:57:18.633715: train_loss -0.3775 +2026-04-11 14:57:18.638592: val_loss -0.3546 +2026-04-11 14:57:18.640153: Pseudo dice [0.303, 0.5833, 0.7124, 0.6732, 0.2685, 0.7266, 0.5526] +2026-04-11 14:57:18.641640: Epoch time: 101.2 s +2026-04-11 14:57:19.740546: +2026-04-11 14:57:19.742090: Epoch 996 +2026-04-11 14:57:19.743549: Current learning rate: 0.00773 +2026-04-11 14:59:01.014573: train_loss -0.343 +2026-04-11 14:59:01.019970: val_loss -0.2133 +2026-04-11 14:59:01.021326: Pseudo dice [0.0, 0.0, 0.412, 0.0439, 0.2447, 0.1831, 0.0481] +2026-04-11 14:59:01.022468: Epoch time: 101.28 s +2026-04-11 14:59:02.094903: +2026-04-11 14:59:02.096697: Epoch 997 +2026-04-11 14:59:02.098480: Current learning rate: 0.00773 +2026-04-11 15:00:43.249051: train_loss -0.3372 +2026-04-11 15:00:43.254886: val_loss -0.3123 +2026-04-11 15:00:43.256739: Pseudo dice [0.0, 0.232, 0.7405, 0.5671, 0.4581, 0.6149, 0.4489] +2026-04-11 15:00:43.258260: Epoch time: 101.16 s +2026-04-11 15:00:44.375660: +2026-04-11 15:00:44.377619: Epoch 998 +2026-04-11 15:00:44.379133: Current learning rate: 0.00772 +2026-04-11 15:02:25.635753: train_loss -0.3568 +2026-04-11 15:02:25.640005: val_loss -0.3448 +2026-04-11 15:02:25.641540: Pseudo dice [0.0, 0.0023, 0.5806, 0.0179, 0.4258, 0.6126, 0.7249] +2026-04-11 15:02:25.642933: Epoch time: 101.26 s +2026-04-11 15:02:26.757303: +2026-04-11 15:02:26.758754: Epoch 999 +2026-04-11 15:02:26.760039: Current learning rate: 0.00772 +2026-04-11 15:04:08.067491: train_loss -0.3683 +2026-04-11 15:04:08.072841: val_loss -0.3174 +2026-04-11 15:04:08.074881: Pseudo dice [0.0, 0.0, 0.6744, 0.4088, 0.2911, 0.7369, 0.7272] +2026-04-11 15:04:08.077023: Epoch time: 101.31 s +2026-04-11 15:04:10.769363: +2026-04-11 15:04:10.771221: Epoch 1000 +2026-04-11 15:04:10.772864: Current learning rate: 0.00772 +2026-04-11 15:05:52.133070: train_loss -0.3427 +2026-04-11 15:05:52.138594: val_loss -0.3014 +2026-04-11 15:05:52.140301: Pseudo dice [0.0, 0.0, 0.5989, 0.0318, 0.2603, 0.327, 0.2028] +2026-04-11 15:05:52.141629: Epoch time: 101.37 s +2026-04-11 15:05:53.247715: +2026-04-11 15:05:53.249536: Epoch 1001 +2026-04-11 15:05:53.251478: Current learning rate: 0.00772 +2026-04-11 15:07:34.543951: train_loss -0.3662 +2026-04-11 15:07:34.548851: val_loss -0.336 +2026-04-11 15:07:34.550544: Pseudo dice [0.0, 0.0, 0.8062, 0.7249, 0.3553, 0.743, 0.6573] +2026-04-11 15:07:34.552067: Epoch time: 101.3 s +2026-04-11 15:07:35.673677: +2026-04-11 15:07:35.675262: Epoch 1002 +2026-04-11 15:07:35.676675: Current learning rate: 0.00771 +2026-04-11 15:09:16.908836: train_loss -0.3463 +2026-04-11 15:09:16.913985: val_loss -0.3002 +2026-04-11 15:09:16.915481: Pseudo dice [0.0, 0.0, 0.7448, 0.5571, 0.4433, 0.6953, 0.3346] +2026-04-11 15:09:16.917154: Epoch time: 101.24 s +2026-04-11 15:09:18.019698: +2026-04-11 15:09:18.021169: Epoch 1003 +2026-04-11 15:09:18.022652: Current learning rate: 0.00771 +2026-04-11 15:10:59.180922: train_loss -0.3741 +2026-04-11 15:10:59.186624: val_loss -0.3554 +2026-04-11 15:10:59.188172: Pseudo dice [0.0, 0.0, 0.5472, 0.7922, 0.4614, 0.5224, 0.6467] +2026-04-11 15:10:59.189836: Epoch time: 101.16 s +2026-04-11 15:11:00.292525: +2026-04-11 15:11:00.294011: Epoch 1004 +2026-04-11 15:11:00.295230: Current learning rate: 0.00771 +2026-04-11 15:12:41.500243: train_loss -0.3951 +2026-04-11 15:12:41.504609: val_loss -0.3587 +2026-04-11 15:12:41.506088: Pseudo dice [0.0, 0.0, 0.5503, 0.8538, 0.4767, 0.6252, 0.739] +2026-04-11 15:12:41.507488: Epoch time: 101.21 s +2026-04-11 15:12:42.620157: +2026-04-11 15:12:42.621642: Epoch 1005 +2026-04-11 15:12:42.623199: Current learning rate: 0.00771 +2026-04-11 15:14:23.920778: train_loss -0.3823 +2026-04-11 15:14:23.925167: val_loss -0.3538 +2026-04-11 15:14:23.926719: Pseudo dice [0.1896, 0.0, 0.732, 0.816, 0.3392, 0.8196, 0.896] +2026-04-11 15:14:23.928002: Epoch time: 101.3 s +2026-04-11 15:14:25.016615: +2026-04-11 15:14:25.017934: Epoch 1006 +2026-04-11 15:14:25.019251: Current learning rate: 0.0077 +2026-04-11 15:16:06.170221: train_loss -0.375 +2026-04-11 15:16:06.175360: val_loss -0.3442 +2026-04-11 15:16:06.177550: Pseudo dice [0.0, 0.0819, 0.708, 0.7588, 0.241, 0.6288, 0.7415] +2026-04-11 15:16:06.178879: Epoch time: 101.16 s +2026-04-11 15:16:07.273269: +2026-04-11 15:16:07.274926: Epoch 1007 +2026-04-11 15:16:07.276420: Current learning rate: 0.0077 +2026-04-11 15:17:48.589720: train_loss -0.3943 +2026-04-11 15:17:48.595459: val_loss -0.331 +2026-04-11 15:17:48.596986: Pseudo dice [0.2387, 0.041, 0.6482, 0.444, 0.4199, 0.4966, 0.7722] +2026-04-11 15:17:48.598539: Epoch time: 101.32 s +2026-04-11 15:17:49.701184: +2026-04-11 15:17:49.702680: Epoch 1008 +2026-04-11 15:17:49.704129: Current learning rate: 0.0077 +2026-04-11 15:19:31.083925: train_loss -0.3969 +2026-04-11 15:19:31.088720: val_loss -0.3562 +2026-04-11 15:19:31.090293: Pseudo dice [0.3151, 0.0728, 0.5795, 0.7818, 0.1783, 0.5536, 0.7806] +2026-04-11 15:19:31.091878: Epoch time: 101.39 s +2026-04-11 15:19:32.215233: +2026-04-11 15:19:32.216765: Epoch 1009 +2026-04-11 15:19:32.218193: Current learning rate: 0.0077 +2026-04-11 15:21:13.549162: train_loss -0.3755 +2026-04-11 15:21:13.556072: val_loss -0.3419 +2026-04-11 15:21:13.557468: Pseudo dice [0.0, 0.0, 0.7444, 0.6446, 0.417, 0.5146, 0.8488] +2026-04-11 15:21:13.558723: Epoch time: 101.34 s +2026-04-11 15:21:14.666142: +2026-04-11 15:21:14.667789: Epoch 1010 +2026-04-11 15:21:14.669263: Current learning rate: 0.0077 +2026-04-11 15:22:56.011009: train_loss -0.3547 +2026-04-11 15:22:56.015018: val_loss -0.2525 +2026-04-11 15:22:56.016565: Pseudo dice [0.0, 0.0907, 0.3225, 0.7848, 0.229, 0.1239, 0.5067] +2026-04-11 15:22:56.018080: Epoch time: 101.35 s +2026-04-11 15:22:57.115438: +2026-04-11 15:22:57.116975: Epoch 1011 +2026-04-11 15:22:57.118451: Current learning rate: 0.00769 +2026-04-11 15:24:38.384972: train_loss -0.3611 +2026-04-11 15:24:38.407066: val_loss -0.3391 +2026-04-11 15:24:38.409770: Pseudo dice [0.0, 0.113, 0.6466, 0.5796, 0.2765, 0.7777, 0.5944] +2026-04-11 15:24:38.417422: Epoch time: 101.27 s +2026-04-11 15:24:40.597465: +2026-04-11 15:24:40.599343: Epoch 1012 +2026-04-11 15:24:40.601197: Current learning rate: 0.00769 +2026-04-11 15:26:21.917365: train_loss -0.35 +2026-04-11 15:26:21.921588: val_loss -0.2924 +2026-04-11 15:26:21.923215: Pseudo dice [0.0, 0.0, 0.6964, 0.5493, 0.2789, 0.1823, 0.7154] +2026-04-11 15:26:21.924694: Epoch time: 101.32 s +2026-04-11 15:26:23.025234: +2026-04-11 15:26:23.026752: Epoch 1013 +2026-04-11 15:26:23.028161: Current learning rate: 0.00769 +2026-04-11 15:28:04.462343: train_loss -0.3838 +2026-04-11 15:28:04.466641: val_loss -0.341 +2026-04-11 15:28:04.468052: Pseudo dice [0.0, 0.0, 0.6078, 0.7174, 0.177, 0.7274, 0.6814] +2026-04-11 15:28:04.469720: Epoch time: 101.44 s +2026-04-11 15:28:05.570219: +2026-04-11 15:28:05.571599: Epoch 1014 +2026-04-11 15:28:05.573086: Current learning rate: 0.00769 +2026-04-11 15:29:46.914716: train_loss -0.3893 +2026-04-11 15:29:46.919502: val_loss -0.3665 +2026-04-11 15:29:46.921091: Pseudo dice [0.1348, 0.1773, 0.723, 0.5781, 0.3609, 0.6958, 0.7081] +2026-04-11 15:29:46.923489: Epoch time: 101.35 s +2026-04-11 15:29:48.007109: +2026-04-11 15:29:48.008610: Epoch 1015 +2026-04-11 15:29:48.009862: Current learning rate: 0.00768 +2026-04-11 15:31:29.418784: train_loss -0.3628 +2026-04-11 15:31:29.442969: val_loss -0.3065 +2026-04-11 15:31:29.444275: Pseudo dice [0.2652, 0.2037, 0.5153, 0.6397, 0.2802, 0.7877, 0.3187] +2026-04-11 15:31:29.445549: Epoch time: 101.41 s +2026-04-11 15:31:30.537721: +2026-04-11 15:31:30.539063: Epoch 1016 +2026-04-11 15:31:30.540371: Current learning rate: 0.00768 +2026-04-11 15:33:11.755450: train_loss -0.3906 +2026-04-11 15:33:11.759690: val_loss -0.3338 +2026-04-11 15:33:11.761052: Pseudo dice [0.2059, 0.3222, 0.7133, 0.7796, 0.3164, 0.6746, 0.8014] +2026-04-11 15:33:11.762648: Epoch time: 101.22 s +2026-04-11 15:33:12.865371: +2026-04-11 15:33:12.867088: Epoch 1017 +2026-04-11 15:33:12.868466: Current learning rate: 0.00768 +2026-04-11 15:34:54.148620: train_loss -0.3851 +2026-04-11 15:34:54.152919: val_loss -0.3098 +2026-04-11 15:34:54.154322: Pseudo dice [0.227, 0.1977, 0.5951, 0.7653, 0.1224, 0.2692, 0.639] +2026-04-11 15:34:54.155641: Epoch time: 101.29 s +2026-04-11 15:34:55.264860: +2026-04-11 15:34:55.266246: Epoch 1018 +2026-04-11 15:34:55.267631: Current learning rate: 0.00768 +2026-04-11 15:36:36.694468: train_loss -0.3817 +2026-04-11 15:36:36.698679: val_loss -0.3418 +2026-04-11 15:36:36.699990: Pseudo dice [0.2943, 0.036, 0.5301, 0.6029, 0.2566, 0.778, 0.8409] +2026-04-11 15:36:36.701277: Epoch time: 101.43 s +2026-04-11 15:36:37.804831: +2026-04-11 15:36:37.806273: Epoch 1019 +2026-04-11 15:36:37.807694: Current learning rate: 0.00767 +2026-04-11 15:38:18.928052: train_loss -0.3874 +2026-04-11 15:38:18.932582: val_loss -0.3217 +2026-04-11 15:38:18.934046: Pseudo dice [0.074, 0.2841, 0.7415, 0.7713, 0.4364, 0.4685, 0.7888] +2026-04-11 15:38:18.935434: Epoch time: 101.13 s +2026-04-11 15:38:20.038624: +2026-04-11 15:38:20.040099: Epoch 1020 +2026-04-11 15:38:20.041531: Current learning rate: 0.00767 +2026-04-11 15:40:01.117866: train_loss -0.3917 +2026-04-11 15:40:01.123387: val_loss -0.32 +2026-04-11 15:40:01.124833: Pseudo dice [0.0269, 0.307, 0.7761, 0.7552, 0.2589, 0.4947, 0.6467] +2026-04-11 15:40:01.127112: Epoch time: 101.08 s +2026-04-11 15:40:02.242928: +2026-04-11 15:40:02.244829: Epoch 1021 +2026-04-11 15:40:02.246355: Current learning rate: 0.00767 +2026-04-11 15:41:43.494405: train_loss -0.41 +2026-04-11 15:41:43.498748: val_loss -0.3404 +2026-04-11 15:41:43.500282: Pseudo dice [0.3245, 0.1528, 0.5717, 0.2746, 0.0661, 0.4703, 0.8492] +2026-04-11 15:41:43.501723: Epoch time: 101.25 s +2026-04-11 15:41:44.604200: +2026-04-11 15:41:44.605485: Epoch 1022 +2026-04-11 15:41:44.606677: Current learning rate: 0.00767 +2026-04-11 15:43:25.750026: train_loss -0.3714 +2026-04-11 15:43:25.756007: val_loss -0.3314 +2026-04-11 15:43:25.757815: Pseudo dice [0.3444, 0.2047, 0.5948, 0.8804, 0.2446, 0.5246, 0.6731] +2026-04-11 15:43:25.759538: Epoch time: 101.15 s +2026-04-11 15:43:26.867177: +2026-04-11 15:43:26.868682: Epoch 1023 +2026-04-11 15:43:26.870281: Current learning rate: 0.00767 +2026-04-11 15:45:07.950876: train_loss -0.3721 +2026-04-11 15:45:07.957553: val_loss -0.335 +2026-04-11 15:45:07.959185: Pseudo dice [0.2522, 0.0, 0.7607, 0.6646, 0.2258, 0.658, 0.6553] +2026-04-11 15:45:07.960654: Epoch time: 101.09 s +2026-04-11 15:45:09.070651: +2026-04-11 15:45:09.072432: Epoch 1024 +2026-04-11 15:45:09.074153: Current learning rate: 0.00766 +2026-04-11 15:46:50.152010: train_loss -0.376 +2026-04-11 15:46:50.156203: val_loss -0.3358 +2026-04-11 15:46:50.157702: Pseudo dice [0.5426, 0.0, 0.5892, 0.5801, 0.4135, 0.7367, 0.6428] +2026-04-11 15:46:50.159761: Epoch time: 101.08 s +2026-04-11 15:46:51.258234: +2026-04-11 15:46:51.260333: Epoch 1025 +2026-04-11 15:46:51.261899: Current learning rate: 0.00766 +2026-04-11 15:48:32.247415: train_loss -0.3833 +2026-04-11 15:48:32.251461: val_loss -0.3223 +2026-04-11 15:48:32.252593: Pseudo dice [0.1735, 0.1035, 0.518, 0.6318, 0.2536, 0.7869, 0.8644] +2026-04-11 15:48:32.253994: Epoch time: 100.99 s +2026-04-11 15:48:33.355737: +2026-04-11 15:48:33.357066: Epoch 1026 +2026-04-11 15:48:33.358301: Current learning rate: 0.00766 +2026-04-11 15:50:14.325023: train_loss -0.3898 +2026-04-11 15:50:14.329755: val_loss -0.3145 +2026-04-11 15:50:14.331460: Pseudo dice [0.0958, 0.0214, 0.5534, 0.491, 0.3694, 0.3185, 0.761] +2026-04-11 15:50:14.332974: Epoch time: 100.97 s +2026-04-11 15:50:15.434959: +2026-04-11 15:50:15.436543: Epoch 1027 +2026-04-11 15:50:15.438084: Current learning rate: 0.00766 +2026-04-11 15:51:56.545502: train_loss -0.3757 +2026-04-11 15:51:56.550159: val_loss -0.3379 +2026-04-11 15:51:56.551477: Pseudo dice [0.2883, 0.0012, 0.806, 0.7868, 0.1392, 0.632, 0.7104] +2026-04-11 15:51:56.552740: Epoch time: 101.11 s +2026-04-11 15:51:57.666451: +2026-04-11 15:51:57.667975: Epoch 1028 +2026-04-11 15:51:57.669436: Current learning rate: 0.00765 +2026-04-11 15:53:38.545334: train_loss -0.3532 +2026-04-11 15:53:38.551632: val_loss -0.3304 +2026-04-11 15:53:38.553249: Pseudo dice [0.0692, 0.0, 0.779, 0.5133, 0.2274, 0.5632, 0.8682] +2026-04-11 15:53:38.555202: Epoch time: 100.88 s +2026-04-11 15:53:39.646274: +2026-04-11 15:53:39.648052: Epoch 1029 +2026-04-11 15:53:39.649685: Current learning rate: 0.00765 +2026-04-11 15:55:20.838231: train_loss -0.4032 +2026-04-11 15:55:20.843057: val_loss -0.3278 +2026-04-11 15:55:20.845179: Pseudo dice [0.3738, 0.0, 0.6307, 0.6229, 0.2327, 0.3779, 0.7492] +2026-04-11 15:55:20.846602: Epoch time: 101.19 s +2026-04-11 15:55:21.947986: +2026-04-11 15:55:21.949882: Epoch 1030 +2026-04-11 15:55:21.951453: Current learning rate: 0.00765 +2026-04-11 15:57:03.024379: train_loss -0.3926 +2026-04-11 15:57:03.030848: val_loss -0.3594 +2026-04-11 15:57:03.032594: Pseudo dice [0.0632, 0.0, 0.8459, 0.3103, 0.2517, 0.8534, 0.8218] +2026-04-11 15:57:03.034548: Epoch time: 101.08 s +2026-04-11 15:57:04.140375: +2026-04-11 15:57:04.141907: Epoch 1031 +2026-04-11 15:57:04.143395: Current learning rate: 0.00765 +2026-04-11 15:58:45.232169: train_loss -0.3886 +2026-04-11 15:58:45.236868: val_loss -0.3157 +2026-04-11 15:58:45.238691: Pseudo dice [0.4189, 0.0, 0.7052, 0.8408, 0.2314, 0.616, 0.6763] +2026-04-11 15:58:45.240093: Epoch time: 101.09 s +2026-04-11 15:58:46.333632: +2026-04-11 15:58:46.335091: Epoch 1032 +2026-04-11 15:58:46.336613: Current learning rate: 0.00764 +2026-04-11 16:00:27.435250: train_loss -0.3829 +2026-04-11 16:00:27.440403: val_loss -0.3413 +2026-04-11 16:00:27.442031: Pseudo dice [0.0, 0.0, 0.742, 0.777, 0.182, 0.5709, 0.6659] +2026-04-11 16:00:27.443490: Epoch time: 101.1 s +2026-04-11 16:00:29.657589: +2026-04-11 16:00:29.659647: Epoch 1033 +2026-04-11 16:00:29.661292: Current learning rate: 0.00764 +2026-04-11 16:02:10.827056: train_loss -0.3807 +2026-04-11 16:02:10.833814: val_loss -0.3451 +2026-04-11 16:02:10.835965: Pseudo dice [0.0, 0.0, 0.6078, 0.5565, 0.1735, 0.4655, 0.8887] +2026-04-11 16:02:10.837573: Epoch time: 101.17 s +2026-04-11 16:02:11.910013: +2026-04-11 16:02:11.911793: Epoch 1034 +2026-04-11 16:02:11.913398: Current learning rate: 0.00764 +2026-04-11 16:03:53.075397: train_loss -0.3562 +2026-04-11 16:03:53.079679: val_loss -0.3645 +2026-04-11 16:03:53.081171: Pseudo dice [0.0, 0.0, 0.5895, 0.8343, 0.2645, 0.6978, 0.6651] +2026-04-11 16:03:53.082345: Epoch time: 101.17 s +2026-04-11 16:03:54.169788: +2026-04-11 16:03:54.171440: Epoch 1035 +2026-04-11 16:03:54.172955: Current learning rate: 0.00764 +2026-04-11 16:05:35.303180: train_loss -0.3773 +2026-04-11 16:05:35.308443: val_loss -0.3307 +2026-04-11 16:05:35.310061: Pseudo dice [0.0, 0.0, 0.6693, 0.7284, 0.3404, 0.5326, 0.6066] +2026-04-11 16:05:35.312894: Epoch time: 101.14 s +2026-04-11 16:05:36.413352: +2026-04-11 16:05:36.414739: Epoch 1036 +2026-04-11 16:05:36.416257: Current learning rate: 0.00764 +2026-04-11 16:07:17.700525: train_loss -0.3796 +2026-04-11 16:07:17.705914: val_loss -0.3397 +2026-04-11 16:07:17.707707: Pseudo dice [0.1046, 0.2019, 0.7213, 0.7585, 0.3244, 0.7417, 0.865] +2026-04-11 16:07:17.709191: Epoch time: 101.29 s +2026-04-11 16:07:18.803818: +2026-04-11 16:07:18.805573: Epoch 1037 +2026-04-11 16:07:18.807190: Current learning rate: 0.00763 +2026-04-11 16:09:00.137432: train_loss -0.3881 +2026-04-11 16:09:00.142580: val_loss -0.3344 +2026-04-11 16:09:00.144361: Pseudo dice [0.1896, 0.1857, 0.8036, 0.8389, 0.2172, 0.6213, 0.766] +2026-04-11 16:09:00.146060: Epoch time: 101.34 s +2026-04-11 16:09:01.245769: +2026-04-11 16:09:01.255094: Epoch 1038 +2026-04-11 16:09:01.256681: Current learning rate: 0.00763 +2026-04-11 16:10:42.490434: train_loss -0.3852 +2026-04-11 16:10:42.494816: val_loss -0.3709 +2026-04-11 16:10:42.496073: Pseudo dice [0.1862, 0.0556, 0.6198, 0.4897, 0.2331, 0.5105, 0.7315] +2026-04-11 16:10:42.497589: Epoch time: 101.25 s +2026-04-11 16:10:43.603806: +2026-04-11 16:10:43.605189: Epoch 1039 +2026-04-11 16:10:43.606414: Current learning rate: 0.00763 +2026-04-11 16:12:24.875079: train_loss -0.37 +2026-04-11 16:12:24.879970: val_loss -0.3223 +2026-04-11 16:12:24.881552: Pseudo dice [0.0, 0.0974, 0.7557, 0.5505, 0.1986, 0.7357, 0.6123] +2026-04-11 16:12:24.883080: Epoch time: 101.27 s +2026-04-11 16:12:25.983827: +2026-04-11 16:12:25.985525: Epoch 1040 +2026-04-11 16:12:25.986924: Current learning rate: 0.00763 +2026-04-11 16:14:07.193396: train_loss -0.386 +2026-04-11 16:14:07.197422: val_loss -0.3162 +2026-04-11 16:14:07.198933: Pseudo dice [0.0, 0.061, 0.6637, 0.785, 0.2192, 0.5535, 0.6659] +2026-04-11 16:14:07.200334: Epoch time: 101.21 s +2026-04-11 16:14:08.298619: +2026-04-11 16:14:08.300013: Epoch 1041 +2026-04-11 16:14:08.301217: Current learning rate: 0.00762 +2026-04-11 16:15:49.563363: train_loss -0.3847 +2026-04-11 16:15:49.568281: val_loss -0.3143 +2026-04-11 16:15:49.570026: Pseudo dice [0.0008, 0.2145, 0.6307, 0.4961, 0.3574, 0.5821, 0.6273] +2026-04-11 16:15:49.571686: Epoch time: 101.27 s +2026-04-11 16:15:50.691694: +2026-04-11 16:15:50.693610: Epoch 1042 +2026-04-11 16:15:50.695290: Current learning rate: 0.00762 +2026-04-11 16:17:32.130545: train_loss -0.3751 +2026-04-11 16:17:32.135378: val_loss -0.3569 +2026-04-11 16:17:32.136993: Pseudo dice [0.1361, 0.3715, 0.6333, 0.5755, 0.3805, 0.6078, 0.7105] +2026-04-11 16:17:32.138233: Epoch time: 101.44 s +2026-04-11 16:17:33.242504: +2026-04-11 16:17:33.243920: Epoch 1043 +2026-04-11 16:17:33.245595: Current learning rate: 0.00762 +2026-04-11 16:19:14.565152: train_loss -0.3962 +2026-04-11 16:19:14.569393: val_loss -0.3587 +2026-04-11 16:19:14.570864: Pseudo dice [0.3894, 0.1125, 0.5754, 0.3335, 0.5213, 0.7892, 0.8197] +2026-04-11 16:19:14.572379: Epoch time: 101.33 s +2026-04-11 16:19:15.673826: +2026-04-11 16:19:15.675123: Epoch 1044 +2026-04-11 16:19:15.676458: Current learning rate: 0.00762 +2026-04-11 16:20:56.832080: train_loss -0.4074 +2026-04-11 16:20:56.837226: val_loss -0.3538 +2026-04-11 16:20:56.839290: Pseudo dice [0.1962, 0.0767, 0.731, 0.8717, 0.4341, 0.8554, 0.3788] +2026-04-11 16:20:56.841108: Epoch time: 101.16 s +2026-04-11 16:20:57.921816: +2026-04-11 16:20:57.923809: Epoch 1045 +2026-04-11 16:20:57.925326: Current learning rate: 0.00761 +2026-04-11 16:22:39.078652: train_loss -0.3949 +2026-04-11 16:22:39.083157: val_loss -0.3553 +2026-04-11 16:22:39.084855: Pseudo dice [0.5287, 0.0285, 0.7313, 0.6624, 0.0461, 0.4527, 0.5673] +2026-04-11 16:22:39.086321: Epoch time: 101.16 s +2026-04-11 16:22:40.172254: +2026-04-11 16:22:40.173744: Epoch 1046 +2026-04-11 16:22:40.175075: Current learning rate: 0.00761 +2026-04-11 16:24:21.379671: train_loss -0.3792 +2026-04-11 16:24:21.385233: val_loss -0.3501 +2026-04-11 16:24:21.386812: Pseudo dice [0.1402, 0.5227, 0.7685, 0.8711, 0.3325, 0.7184, 0.69] +2026-04-11 16:24:21.388698: Epoch time: 101.21 s +2026-04-11 16:24:22.508169: +2026-04-11 16:24:22.510082: Epoch 1047 +2026-04-11 16:24:22.511515: Current learning rate: 0.00761 +2026-04-11 16:26:03.649576: train_loss -0.3782 +2026-04-11 16:26:03.653665: val_loss -0.3009 +2026-04-11 16:26:03.655292: Pseudo dice [0.0, 0.7286, 0.6517, 0.4558, 0.2816, 0.4106, 0.6812] +2026-04-11 16:26:03.656814: Epoch time: 101.14 s +2026-04-11 16:26:04.757403: +2026-04-11 16:26:04.758812: Epoch 1048 +2026-04-11 16:26:04.760026: Current learning rate: 0.00761 +2026-04-11 16:27:45.995813: train_loss -0.3742 +2026-04-11 16:27:45.999935: val_loss -0.3479 +2026-04-11 16:27:46.001482: Pseudo dice [0.0, 0.4285, 0.658, 0.7401, 0.5665, 0.816, 0.8779] +2026-04-11 16:27:46.002915: Epoch time: 101.24 s +2026-04-11 16:27:47.129030: +2026-04-11 16:27:47.130473: Epoch 1049 +2026-04-11 16:27:47.131826: Current learning rate: 0.00761 +2026-04-11 16:29:28.363005: train_loss -0.3913 +2026-04-11 16:29:28.367210: val_loss -0.3231 +2026-04-11 16:29:28.368670: Pseudo dice [0.0017, 0.2596, 0.6105, 0.6955, 0.2962, 0.7102, 0.6761] +2026-04-11 16:29:28.370080: Epoch time: 101.24 s +2026-04-11 16:29:31.054398: +2026-04-11 16:29:31.057174: Epoch 1050 +2026-04-11 16:29:31.058495: Current learning rate: 0.0076 +2026-04-11 16:31:12.192708: train_loss -0.3963 +2026-04-11 16:31:12.198016: val_loss -0.3459 +2026-04-11 16:31:12.199719: Pseudo dice [0.0, 0.0, 0.8006, 0.5917, 0.3759, 0.4733, 0.5662] +2026-04-11 16:31:12.201407: Epoch time: 101.14 s +2026-04-11 16:31:13.306860: +2026-04-11 16:31:13.308955: Epoch 1051 +2026-04-11 16:31:13.310834: Current learning rate: 0.0076 +2026-04-11 16:32:54.423230: train_loss -0.3826 +2026-04-11 16:32:54.429333: val_loss -0.2958 +2026-04-11 16:32:54.431523: Pseudo dice [0.0, 0.167, 0.7725, 0.4253, 0.1029, 0.7204, 0.3118] +2026-04-11 16:32:54.433176: Epoch time: 101.12 s +2026-04-11 16:32:55.536086: +2026-04-11 16:32:55.538183: Epoch 1052 +2026-04-11 16:32:55.540014: Current learning rate: 0.0076 +2026-04-11 16:34:36.641840: train_loss -0.3622 +2026-04-11 16:34:36.646098: val_loss -0.3308 +2026-04-11 16:34:36.647949: Pseudo dice [0.0, 0.2865, 0.759, 0.8087, 0.3036, 0.5956, 0.2463] +2026-04-11 16:34:36.649362: Epoch time: 101.11 s +2026-04-11 16:34:37.758269: +2026-04-11 16:34:37.760020: Epoch 1053 +2026-04-11 16:34:37.761517: Current learning rate: 0.0076 +2026-04-11 16:36:20.025044: train_loss -0.3874 +2026-04-11 16:36:20.030797: val_loss -0.3504 +2026-04-11 16:36:20.032199: Pseudo dice [0.0, 0.3519, 0.8064, 0.2664, 0.1836, 0.7356, 0.669] +2026-04-11 16:36:20.034426: Epoch time: 102.27 s +2026-04-11 16:36:21.138499: +2026-04-11 16:36:21.140171: Epoch 1054 +2026-04-11 16:36:21.141771: Current learning rate: 0.00759 +2026-04-11 16:38:02.402899: train_loss -0.3974 +2026-04-11 16:38:02.407501: val_loss -0.3231 +2026-04-11 16:38:02.409286: Pseudo dice [0.0, 0.0974, 0.6041, 0.8479, 0.38, 0.7469, 0.8291] +2026-04-11 16:38:02.410899: Epoch time: 101.27 s +2026-04-11 16:38:03.549308: +2026-04-11 16:38:03.551444: Epoch 1055 +2026-04-11 16:38:03.552705: Current learning rate: 0.00759 +2026-04-11 16:39:44.999733: train_loss -0.3691 +2026-04-11 16:39:45.005270: val_loss -0.3031 +2026-04-11 16:39:45.006869: Pseudo dice [0.0, 0.0, 0.224, 0.003, 0.0917, 0.6299, 0.7009] +2026-04-11 16:39:45.008432: Epoch time: 101.45 s +2026-04-11 16:39:46.115880: +2026-04-11 16:39:46.120805: Epoch 1056 +2026-04-11 16:39:46.122208: Current learning rate: 0.00759 +2026-04-11 16:41:27.405759: train_loss -0.384 +2026-04-11 16:41:27.410920: val_loss -0.3131 +2026-04-11 16:41:27.412468: Pseudo dice [0.0, 0.0, 0.6439, 0.8001, 0.2247, 0.6385, 0.5495] +2026-04-11 16:41:27.414047: Epoch time: 101.29 s +2026-04-11 16:41:28.529464: +2026-04-11 16:41:28.530959: Epoch 1057 +2026-04-11 16:41:28.532379: Current learning rate: 0.00759 +2026-04-11 16:43:09.795332: train_loss -0.3777 +2026-04-11 16:43:09.799559: val_loss -0.3334 +2026-04-11 16:43:09.801821: Pseudo dice [0.0, 0.0, 0.5141, 0.4052, 0.4112, 0.5091, 0.5383] +2026-04-11 16:43:09.803618: Epoch time: 101.27 s +2026-04-11 16:43:10.917006: +2026-04-11 16:43:10.918563: Epoch 1058 +2026-04-11 16:43:10.920218: Current learning rate: 0.00758 +2026-04-11 16:44:52.264702: train_loss -0.3914 +2026-04-11 16:44:52.269720: val_loss -0.3795 +2026-04-11 16:44:52.271219: Pseudo dice [0.0, 0.0, 0.7832, 0.6435, 0.2816, 0.6793, 0.6644] +2026-04-11 16:44:52.273015: Epoch time: 101.35 s +2026-04-11 16:44:53.396678: +2026-04-11 16:44:53.398082: Epoch 1059 +2026-04-11 16:44:53.399440: Current learning rate: 0.00758 +2026-04-11 16:46:34.815102: train_loss -0.3495 +2026-04-11 16:46:34.819176: val_loss -0.2917 +2026-04-11 16:46:34.820781: Pseudo dice [0.0, 0.0, 0.466, 0.268, 0.3592, 0.4325, 0.5322] +2026-04-11 16:46:34.822672: Epoch time: 101.42 s +2026-04-11 16:46:35.956882: +2026-04-11 16:46:35.958430: Epoch 1060 +2026-04-11 16:46:35.959761: Current learning rate: 0.00758 +2026-04-11 16:48:17.192929: train_loss -0.3639 +2026-04-11 16:48:17.197753: val_loss -0.3695 +2026-04-11 16:48:17.199440: Pseudo dice [0.0, 0.0, 0.5749, 0.8464, 0.2693, 0.6275, 0.8031] +2026-04-11 16:48:17.201004: Epoch time: 101.24 s +2026-04-11 16:48:18.310445: +2026-04-11 16:48:18.312005: Epoch 1061 +2026-04-11 16:48:18.313497: Current learning rate: 0.00758 +2026-04-11 16:49:59.454899: train_loss -0.3749 +2026-04-11 16:49:59.459648: val_loss -0.3187 +2026-04-11 16:49:59.461814: Pseudo dice [0.1872, 0.1801, 0.5249, 0.4696, 0.2427, 0.7291, 0.6143] +2026-04-11 16:49:59.463784: Epoch time: 101.15 s +2026-04-11 16:50:00.597969: +2026-04-11 16:50:00.599575: Epoch 1062 +2026-04-11 16:50:00.600943: Current learning rate: 0.00758 +2026-04-11 16:51:41.712197: train_loss -0.389 +2026-04-11 16:51:41.716420: val_loss -0.3385 +2026-04-11 16:51:41.718157: Pseudo dice [0.0585, 0.5077, 0.5987, 0.7221, 0.3376, 0.6796, 0.7955] +2026-04-11 16:51:41.719529: Epoch time: 101.12 s +2026-04-11 16:51:42.854357: +2026-04-11 16:51:42.856141: Epoch 1063 +2026-04-11 16:51:42.857521: Current learning rate: 0.00757 +2026-04-11 16:53:24.153672: train_loss -0.3602 +2026-04-11 16:53:24.158341: val_loss -0.335 +2026-04-11 16:53:24.160084: Pseudo dice [0.0, 0.1801, 0.5591, 0.6846, 0.151, 0.7937, 0.7865] +2026-04-11 16:53:24.161543: Epoch time: 101.3 s +2026-04-11 16:53:25.263345: +2026-04-11 16:53:25.264810: Epoch 1064 +2026-04-11 16:53:25.266045: Current learning rate: 0.00757 +2026-04-11 16:55:06.609047: train_loss -0.3714 +2026-04-11 16:55:06.613376: val_loss -0.338 +2026-04-11 16:55:06.614900: Pseudo dice [0.0, 0.2644, 0.7479, 0.0, 0.2702, 0.444, 0.4798] +2026-04-11 16:55:06.616371: Epoch time: 101.35 s +2026-04-11 16:55:07.727610: +2026-04-11 16:55:07.728990: Epoch 1065 +2026-04-11 16:55:07.730240: Current learning rate: 0.00757 +2026-04-11 16:56:48.959574: train_loss -0.3788 +2026-04-11 16:56:48.964046: val_loss -0.3496 +2026-04-11 16:56:48.965657: Pseudo dice [0.262, 0.681, 0.1687, 0.6514, 0.2536, 0.4733, 0.837] +2026-04-11 16:56:48.967142: Epoch time: 101.24 s +2026-04-11 16:56:50.076844: +2026-04-11 16:56:50.078457: Epoch 1066 +2026-04-11 16:56:50.079618: Current learning rate: 0.00757 +2026-04-11 16:58:31.422309: train_loss -0.3727 +2026-04-11 16:58:31.426525: val_loss -0.3387 +2026-04-11 16:58:31.428259: Pseudo dice [0.0, 0.0, 0.7209, 0.585, 0.1141, 0.2459, 0.2156] +2026-04-11 16:58:31.431536: Epoch time: 101.35 s +2026-04-11 16:58:32.543976: +2026-04-11 16:58:32.545228: Epoch 1067 +2026-04-11 16:58:32.546424: Current learning rate: 0.00756 +2026-04-11 17:00:13.882931: train_loss -0.3515 +2026-04-11 17:00:13.888343: val_loss -0.372 +2026-04-11 17:00:13.890181: Pseudo dice [0.0, 0.0, 0.7224, 0.8537, 0.3457, 0.769, 0.727] +2026-04-11 17:00:13.891513: Epoch time: 101.34 s +2026-04-11 17:00:15.009413: +2026-04-11 17:00:15.011097: Epoch 1068 +2026-04-11 17:00:15.012504: Current learning rate: 0.00756 +2026-04-11 17:01:56.333421: train_loss -0.3739 +2026-04-11 17:01:56.337953: val_loss -0.3189 +2026-04-11 17:01:56.339486: Pseudo dice [0.0738, 0.0, 0.5917, 0.3892, 0.3444, 0.2561, 0.5699] +2026-04-11 17:01:56.340983: Epoch time: 101.33 s +2026-04-11 17:01:57.440292: +2026-04-11 17:01:57.441648: Epoch 1069 +2026-04-11 17:01:57.442949: Current learning rate: 0.00756 +2026-04-11 17:03:38.746360: train_loss -0.3717 +2026-04-11 17:03:38.751451: val_loss -0.3005 +2026-04-11 17:03:38.754285: Pseudo dice [0.33, 0.0, 0.6095, 0.0808, 0.4028, 0.4497, 0.653] +2026-04-11 17:03:38.756025: Epoch time: 101.31 s +2026-04-11 17:03:39.867364: +2026-04-11 17:03:39.868875: Epoch 1070 +2026-04-11 17:03:39.870349: Current learning rate: 0.00756 +2026-04-11 17:05:21.071404: train_loss -0.368 +2026-04-11 17:05:21.076149: val_loss -0.3145 +2026-04-11 17:05:21.077622: Pseudo dice [0.2635, 0.1885, 0.7027, 0.0344, 0.2635, 0.2896, 0.5908] +2026-04-11 17:05:21.079235: Epoch time: 101.21 s +2026-04-11 17:05:22.184344: +2026-04-11 17:05:22.185895: Epoch 1071 +2026-04-11 17:05:22.187793: Current learning rate: 0.00755 +2026-04-11 17:07:03.569710: train_loss -0.3586 +2026-04-11 17:07:03.574400: val_loss -0.3257 +2026-04-11 17:07:03.576252: Pseudo dice [0.5614, 0.0886, 0.4307, 0.3031, 0.1679, 0.4979, 0.7091] +2026-04-11 17:07:03.577759: Epoch time: 101.39 s +2026-04-11 17:07:04.718111: +2026-04-11 17:07:04.719320: Epoch 1072 +2026-04-11 17:07:04.720478: Current learning rate: 0.00755 +2026-04-11 17:08:46.077389: train_loss -0.4036 +2026-04-11 17:08:46.081605: val_loss -0.3506 +2026-04-11 17:08:46.083183: Pseudo dice [0.6657, 0.0, 0.6331, 0.7332, 0.2345, 0.6399, 0.5688] +2026-04-11 17:08:46.084732: Epoch time: 101.36 s +2026-04-11 17:08:47.213626: +2026-04-11 17:08:47.215394: Epoch 1073 +2026-04-11 17:08:47.216808: Current learning rate: 0.00755 +2026-04-11 17:10:28.670712: train_loss -0.3786 +2026-04-11 17:10:28.674922: val_loss -0.3345 +2026-04-11 17:10:28.676221: Pseudo dice [0.4908, 0.3129, 0.478, 0.6455, 0.2945, 0.8009, 0.5373] +2026-04-11 17:10:28.677463: Epoch time: 101.46 s +2026-04-11 17:10:30.883056: +2026-04-11 17:10:30.884542: Epoch 1074 +2026-04-11 17:10:30.885855: Current learning rate: 0.00755 +2026-04-11 17:12:12.357623: train_loss -0.3623 +2026-04-11 17:12:12.362636: val_loss -0.3673 +2026-04-11 17:12:12.364605: Pseudo dice [0.0, 0.2656, 0.5597, 0.8151, 0.3418, 0.7039, 0.7936] +2026-04-11 17:12:12.366370: Epoch time: 101.48 s +2026-04-11 17:12:13.479779: +2026-04-11 17:12:13.481496: Epoch 1075 +2026-04-11 17:12:13.482891: Current learning rate: 0.00755 +2026-04-11 17:13:55.018594: train_loss -0.396 +2026-04-11 17:13:55.022770: val_loss -0.3692 +2026-04-11 17:13:55.024351: Pseudo dice [0.0, 0.0187, 0.795, 0.8051, 0.0632, 0.7797, 0.8429] +2026-04-11 17:13:55.025758: Epoch time: 101.54 s +2026-04-11 17:13:56.145795: +2026-04-11 17:13:56.147406: Epoch 1076 +2026-04-11 17:13:56.148558: Current learning rate: 0.00754 +2026-04-11 17:15:37.444560: train_loss -0.3626 +2026-04-11 17:15:37.448763: val_loss -0.3334 +2026-04-11 17:15:37.450434: Pseudo dice [0.1465, 0.0479, 0.4741, 0.7026, 0.2075, 0.6634, 0.7666] +2026-04-11 17:15:37.452098: Epoch time: 101.3 s +2026-04-11 17:15:38.561329: +2026-04-11 17:15:38.562947: Epoch 1077 +2026-04-11 17:15:38.564376: Current learning rate: 0.00754 +2026-04-11 17:17:19.959756: train_loss -0.375 +2026-04-11 17:17:19.964343: val_loss -0.3265 +2026-04-11 17:17:19.965776: Pseudo dice [0.0149, 0.5264, 0.738, 0.5084, 0.3538, 0.4224, 0.6529] +2026-04-11 17:17:19.967250: Epoch time: 101.4 s +2026-04-11 17:17:21.078311: +2026-04-11 17:17:21.080020: Epoch 1078 +2026-04-11 17:17:21.081382: Current learning rate: 0.00754 +2026-04-11 17:19:02.447984: train_loss -0.3671 +2026-04-11 17:19:02.452016: val_loss -0.3387 +2026-04-11 17:19:02.453583: Pseudo dice [0.1096, 0.1094, 0.5459, 0.4964, 0.041, 0.6498, 0.8089] +2026-04-11 17:19:02.454873: Epoch time: 101.37 s +2026-04-11 17:19:03.568944: +2026-04-11 17:19:03.570574: Epoch 1079 +2026-04-11 17:19:03.571790: Current learning rate: 0.00754 +2026-04-11 17:20:45.090365: train_loss -0.3795 +2026-04-11 17:20:45.095726: val_loss -0.3062 +2026-04-11 17:20:45.097275: Pseudo dice [0.1364, 0.1287, 0.391, 0.102, 0.304, 0.3688, 0.8101] +2026-04-11 17:20:45.098689: Epoch time: 101.52 s +2026-04-11 17:20:46.218536: +2026-04-11 17:20:46.219830: Epoch 1080 +2026-04-11 17:20:46.221214: Current learning rate: 0.00753 +2026-04-11 17:22:27.700330: train_loss -0.3628 +2026-04-11 17:22:27.705225: val_loss -0.3378 +2026-04-11 17:22:27.706629: Pseudo dice [0.1386, 0.5455, 0.444, 0.3981, 0.2661, 0.7393, 0.563] +2026-04-11 17:22:27.708217: Epoch time: 101.49 s +2026-04-11 17:22:28.806479: +2026-04-11 17:22:28.807999: Epoch 1081 +2026-04-11 17:22:28.809412: Current learning rate: 0.00753 +2026-04-11 17:24:10.103268: train_loss -0.383 +2026-04-11 17:24:10.109183: val_loss -0.3284 +2026-04-11 17:24:10.111070: Pseudo dice [0.026, 0.0473, 0.6798, 0.1749, 0.2159, 0.6707, 0.8015] +2026-04-11 17:24:10.113806: Epoch time: 101.3 s +2026-04-11 17:24:11.234253: +2026-04-11 17:24:11.236091: Epoch 1082 +2026-04-11 17:24:11.237564: Current learning rate: 0.00753 +2026-04-11 17:25:52.621967: train_loss -0.3713 +2026-04-11 17:25:52.627501: val_loss -0.3303 +2026-04-11 17:25:52.629118: Pseudo dice [0.0008, 0.0, 0.7248, 0.678, 0.5428, 0.3869, 0.8271] +2026-04-11 17:25:52.630576: Epoch time: 101.39 s +2026-04-11 17:25:53.751826: +2026-04-11 17:25:53.753152: Epoch 1083 +2026-04-11 17:25:53.754416: Current learning rate: 0.00753 +2026-04-11 17:27:35.132838: train_loss -0.3942 +2026-04-11 17:27:35.138517: val_loss -0.3138 +2026-04-11 17:27:35.139826: Pseudo dice [0.5077, 0.41, 0.6802, 0.221, 0.1271, 0.5977, 0.4765] +2026-04-11 17:27:35.143486: Epoch time: 101.38 s +2026-04-11 17:27:36.258772: +2026-04-11 17:27:36.260938: Epoch 1084 +2026-04-11 17:27:36.262442: Current learning rate: 0.00752 +2026-04-11 17:29:17.693824: train_loss -0.3875 +2026-04-11 17:29:17.698248: val_loss -0.3591 +2026-04-11 17:29:17.700021: Pseudo dice [0.5196, 0.0, 0.6596, 0.1797, 0.4013, 0.5233, 0.7287] +2026-04-11 17:29:17.701473: Epoch time: 101.44 s +2026-04-11 17:29:18.794243: +2026-04-11 17:29:18.795957: Epoch 1085 +2026-04-11 17:29:18.797270: Current learning rate: 0.00752 +2026-04-11 17:31:00.302588: train_loss -0.3834 +2026-04-11 17:31:00.306718: val_loss -0.3214 +2026-04-11 17:31:00.308110: Pseudo dice [0.0, 0.5267, 0.2916, 0.7662, 0.3889, 0.7014, 0.6549] +2026-04-11 17:31:00.309491: Epoch time: 101.51 s +2026-04-11 17:31:01.434042: +2026-04-11 17:31:01.435462: Epoch 1086 +2026-04-11 17:31:01.436765: Current learning rate: 0.00752 +2026-04-11 17:32:42.785787: train_loss -0.3741 +2026-04-11 17:32:42.789979: val_loss -0.3287 +2026-04-11 17:32:42.791566: Pseudo dice [0.0, 0.5694, 0.6372, 0.7243, 0.3405, 0.6593, 0.7604] +2026-04-11 17:32:42.792910: Epoch time: 101.35 s +2026-04-11 17:32:43.906247: +2026-04-11 17:32:43.907725: Epoch 1087 +2026-04-11 17:32:43.909132: Current learning rate: 0.00752 +2026-04-11 17:34:25.044580: train_loss -0.38 +2026-04-11 17:34:25.048730: val_loss -0.31 +2026-04-11 17:34:25.050205: Pseudo dice [0.0, 0.0682, 0.3818, 0.7461, 0.0793, 0.6712, 0.6531] +2026-04-11 17:34:25.051609: Epoch time: 101.14 s +2026-04-11 17:34:26.143793: +2026-04-11 17:34:26.145303: Epoch 1088 +2026-04-11 17:34:26.146740: Current learning rate: 0.00751 +2026-04-11 17:36:07.395163: train_loss -0.3843 +2026-04-11 17:36:07.399812: val_loss -0.3627 +2026-04-11 17:36:07.401422: Pseudo dice [0.0, 0.6888, 0.8161, 0.5242, 0.432, 0.5603, 0.585] +2026-04-11 17:36:07.403322: Epoch time: 101.25 s +2026-04-11 17:36:08.520039: +2026-04-11 17:36:08.521929: Epoch 1089 +2026-04-11 17:36:08.523417: Current learning rate: 0.00751 +2026-04-11 17:37:49.905486: train_loss -0.3767 +2026-04-11 17:37:49.909598: val_loss -0.3559 +2026-04-11 17:37:49.911110: Pseudo dice [0.0, 0.0225, 0.6846, 0.5526, 0.4125, 0.4517, 0.749] +2026-04-11 17:37:49.912396: Epoch time: 101.39 s +2026-04-11 17:37:51.027094: +2026-04-11 17:37:51.028619: Epoch 1090 +2026-04-11 17:37:51.030041: Current learning rate: 0.00751 +2026-04-11 17:39:32.187476: train_loss -0.375 +2026-04-11 17:39:32.191558: val_loss -0.3451 +2026-04-11 17:39:32.193126: Pseudo dice [0.0015, 0.5135, 0.6775, 0.5929, 0.0909, 0.6118, 0.7645] +2026-04-11 17:39:32.194640: Epoch time: 101.16 s +2026-04-11 17:39:33.297430: +2026-04-11 17:39:33.298772: Epoch 1091 +2026-04-11 17:39:33.300040: Current learning rate: 0.00751 +2026-04-11 17:41:14.592607: train_loss -0.3904 +2026-04-11 17:41:14.596699: val_loss -0.3034 +2026-04-11 17:41:14.598266: Pseudo dice [0.4331, 0.0773, 0.5773, 0.0606, 0.1402, 0.5312, 0.7565] +2026-04-11 17:41:14.599618: Epoch time: 101.3 s +2026-04-11 17:41:15.705295: +2026-04-11 17:41:15.706766: Epoch 1092 +2026-04-11 17:41:15.708025: Current learning rate: 0.00751 +2026-04-11 17:42:56.916133: train_loss -0.3747 +2026-04-11 17:42:56.922506: val_loss -0.2759 +2026-04-11 17:42:56.924517: Pseudo dice [0.0, 0.0, 0.6596, 0.8267, 0.1787, 0.5312, 0.1478] +2026-04-11 17:42:56.926322: Epoch time: 101.21 s +2026-04-11 17:42:58.048740: +2026-04-11 17:42:58.050319: Epoch 1093 +2026-04-11 17:42:58.051784: Current learning rate: 0.0075 +2026-04-11 17:44:39.316184: train_loss -0.3391 +2026-04-11 17:44:39.320399: val_loss -0.3533 +2026-04-11 17:44:39.322190: Pseudo dice [0.0, 0.0, 0.4142, 0.8599, 0.4456, 0.7427, 0.6488] +2026-04-11 17:44:39.324013: Epoch time: 101.27 s +2026-04-11 17:44:40.415767: +2026-04-11 17:44:40.417291: Epoch 1094 +2026-04-11 17:44:40.418587: Current learning rate: 0.0075 +2026-04-11 17:46:21.910906: train_loss -0.354 +2026-04-11 17:46:21.916187: val_loss -0.335 +2026-04-11 17:46:21.918012: Pseudo dice [0.0, 0.0, 0.497, 0.5145, 0.1371, 0.7713, 0.6263] +2026-04-11 17:46:21.919829: Epoch time: 101.5 s +2026-04-11 17:46:23.948827: +2026-04-11 17:46:23.950312: Epoch 1095 +2026-04-11 17:46:23.951830: Current learning rate: 0.0075 +2026-04-11 17:48:05.111932: train_loss -0.3733 +2026-04-11 17:48:05.115919: val_loss -0.3274 +2026-04-11 17:48:05.117509: Pseudo dice [0.0, 0.2746, 0.8033, 0.6746, 0.2952, 0.7907, 0.5331] +2026-04-11 17:48:05.119202: Epoch time: 101.17 s +2026-04-11 17:48:06.233299: +2026-04-11 17:48:06.234660: Epoch 1096 +2026-04-11 17:48:06.235918: Current learning rate: 0.0075 +2026-04-11 17:49:47.843280: train_loss -0.3917 +2026-04-11 17:49:47.847561: val_loss -0.2875 +2026-04-11 17:49:47.848834: Pseudo dice [0.0, 0.2758, 0.355, 0.7479, 0.1662, 0.3587, 0.5336] +2026-04-11 17:49:47.850568: Epoch time: 101.61 s +2026-04-11 17:49:48.969079: +2026-04-11 17:49:48.970652: Epoch 1097 +2026-04-11 17:49:48.972120: Current learning rate: 0.00749 +2026-04-11 17:51:30.153466: train_loss -0.381 +2026-04-11 17:51:30.157280: val_loss -0.3741 +2026-04-11 17:51:30.158647: Pseudo dice [0.0, 0.5074, 0.7467, 0.7896, 0.5671, 0.7642, 0.7436] +2026-04-11 17:51:30.159865: Epoch time: 101.19 s +2026-04-11 17:51:31.266367: +2026-04-11 17:51:31.267810: Epoch 1098 +2026-04-11 17:51:31.269028: Current learning rate: 0.00749 +2026-04-11 17:53:12.864851: train_loss -0.4122 +2026-04-11 17:53:12.869988: val_loss -0.3745 +2026-04-11 17:53:12.871869: Pseudo dice [0.0, 0.0783, 0.7789, 0.817, 0.4053, 0.7135, 0.7994] +2026-04-11 17:53:12.873444: Epoch time: 101.6 s +2026-04-11 17:53:13.995159: +2026-04-11 17:53:13.997617: Epoch 1099 +2026-04-11 17:53:14.001476: Current learning rate: 0.00749 +2026-04-11 17:54:55.483842: train_loss -0.3755 +2026-04-11 17:54:55.488209: val_loss -0.331 +2026-04-11 17:54:55.489620: Pseudo dice [0.0, 0.0, 0.5021, 0.735, 0.202, 0.3952, 0.6123] +2026-04-11 17:54:55.491054: Epoch time: 101.49 s +2026-04-11 17:54:58.161559: +2026-04-11 17:54:58.163009: Epoch 1100 +2026-04-11 17:54:58.164218: Current learning rate: 0.00749 +2026-04-11 17:56:39.906430: train_loss -0.3604 +2026-04-11 17:56:39.910703: val_loss -0.3048 +2026-04-11 17:56:39.912320: Pseudo dice [0.0, 0.0, 0.7332, 0.0024, 0.1953, 0.5813, 0.4711] +2026-04-11 17:56:39.913967: Epoch time: 101.75 s +2026-04-11 17:56:41.030094: +2026-04-11 17:56:41.032035: Epoch 1101 +2026-04-11 17:56:41.033713: Current learning rate: 0.00748 +2026-04-11 17:58:22.653388: train_loss -0.3595 +2026-04-11 17:58:22.657254: val_loss -0.3437 +2026-04-11 17:58:22.658710: Pseudo dice [0.0, 0.0, 0.7079, 0.8037, 0.3858, 0.476, 0.653] +2026-04-11 17:58:22.660011: Epoch time: 101.63 s +2026-04-11 17:58:23.771387: +2026-04-11 17:58:23.772865: Epoch 1102 +2026-04-11 17:58:23.774091: Current learning rate: 0.00748 +2026-04-11 18:00:05.507576: train_loss -0.3908 +2026-04-11 18:00:05.511629: val_loss -0.3254 +2026-04-11 18:00:05.513435: Pseudo dice [0.0, 0.0, 0.5363, 0.7537, 0.0952, 0.7616, 0.5807] +2026-04-11 18:00:05.514863: Epoch time: 101.74 s +2026-04-11 18:00:06.625450: +2026-04-11 18:00:06.626867: Epoch 1103 +2026-04-11 18:00:06.628210: Current learning rate: 0.00748 +2026-04-11 18:01:48.212374: train_loss -0.3906 +2026-04-11 18:01:48.216500: val_loss -0.3303 +2026-04-11 18:01:48.217891: Pseudo dice [0.0, 0.0, 0.3832, 0.3507, 0.2649, 0.6649, 0.7602] +2026-04-11 18:01:48.219297: Epoch time: 101.59 s +2026-04-11 18:01:49.335475: +2026-04-11 18:01:49.336961: Epoch 1104 +2026-04-11 18:01:49.338211: Current learning rate: 0.00748 +2026-04-11 18:03:30.595402: train_loss -0.3873 +2026-04-11 18:03:30.599233: val_loss -0.2988 +2026-04-11 18:03:30.600587: Pseudo dice [0.0, 0.0617, 0.5518, 0.7764, 0.0869, 0.5451, 0.8726] +2026-04-11 18:03:30.601928: Epoch time: 101.26 s +2026-04-11 18:03:31.716501: +2026-04-11 18:03:31.718669: Epoch 1105 +2026-04-11 18:03:31.720257: Current learning rate: 0.00748 +2026-04-11 18:05:12.939509: train_loss -0.3957 +2026-04-11 18:05:12.943329: val_loss -0.3543 +2026-04-11 18:05:12.944686: Pseudo dice [0.0, 0.1287, 0.8036, 0.7861, 0.1605, 0.8781, 0.6701] +2026-04-11 18:05:12.946028: Epoch time: 101.23 s +2026-04-11 18:05:14.049320: +2026-04-11 18:05:14.051085: Epoch 1106 +2026-04-11 18:05:14.052454: Current learning rate: 0.00747 +2026-04-11 18:06:55.503188: train_loss -0.3813 +2026-04-11 18:06:55.507515: val_loss -0.3582 +2026-04-11 18:06:55.509103: Pseudo dice [0.0, 0.0874, 0.7105, 0.3091, 0.191, 0.644, 0.7645] +2026-04-11 18:06:55.510845: Epoch time: 101.46 s +2026-04-11 18:06:56.638556: +2026-04-11 18:06:56.641288: Epoch 1107 +2026-04-11 18:06:56.642742: Current learning rate: 0.00747 +2026-04-11 18:08:38.238240: train_loss -0.4033 +2026-04-11 18:08:38.242878: val_loss -0.3659 +2026-04-11 18:08:38.244505: Pseudo dice [0.6528, 0.1886, 0.8134, 0.8312, 0.3074, 0.7939, 0.5014] +2026-04-11 18:08:38.246039: Epoch time: 101.6 s +2026-04-11 18:08:39.337440: +2026-04-11 18:08:39.338842: Epoch 1108 +2026-04-11 18:08:39.340002: Current learning rate: 0.00747 +2026-04-11 18:10:20.792714: train_loss -0.3955 +2026-04-11 18:10:20.797257: val_loss -0.3652 +2026-04-11 18:10:20.799219: Pseudo dice [0.2851, 0.7694, 0.615, 0.8936, 0.1628, 0.6597, 0.4842] +2026-04-11 18:10:20.800666: Epoch time: 101.46 s +2026-04-11 18:10:21.913057: +2026-04-11 18:10:21.914828: Epoch 1109 +2026-04-11 18:10:21.916437: Current learning rate: 0.00747 +2026-04-11 18:12:03.493151: train_loss -0.3947 +2026-04-11 18:12:03.506496: val_loss -0.3472 +2026-04-11 18:12:03.508345: Pseudo dice [0.2378, 0.1431, 0.422, 0.4266, 0.3431, 0.6183, 0.8016] +2026-04-11 18:12:03.509674: Epoch time: 101.58 s +2026-04-11 18:12:04.622083: +2026-04-11 18:12:04.623587: Epoch 1110 +2026-04-11 18:12:04.625028: Current learning rate: 0.00746 +2026-04-11 18:13:46.216559: train_loss -0.4035 +2026-04-11 18:13:46.221348: val_loss -0.3495 +2026-04-11 18:13:46.222946: Pseudo dice [0.0821, 0.3721, 0.7224, 0.2237, 0.0949, 0.7878, 0.6628] +2026-04-11 18:13:46.224488: Epoch time: 101.6 s +2026-04-11 18:13:47.341298: +2026-04-11 18:13:47.342808: Epoch 1111 +2026-04-11 18:13:47.344172: Current learning rate: 0.00746 +2026-04-11 18:15:30.385318: train_loss -0.4001 +2026-04-11 18:15:30.393568: val_loss -0.3494 +2026-04-11 18:15:30.395235: Pseudo dice [0.5265, 0.5035, 0.3099, 0.5709, 0.1241, 0.6021, 0.6084] +2026-04-11 18:15:30.397479: Epoch time: 103.05 s +2026-04-11 18:15:31.616378: +2026-04-11 18:15:31.618316: Epoch 1112 +2026-04-11 18:15:31.620046: Current learning rate: 0.00746 +2026-04-11 18:17:13.265212: train_loss -0.3881 +2026-04-11 18:17:13.269890: val_loss -0.3109 +2026-04-11 18:17:13.271235: Pseudo dice [0.0, 0.1382, 0.5643, 0.0621, 0.0702, 0.6704, 0.5897] +2026-04-11 18:17:13.272829: Epoch time: 101.65 s +2026-04-11 18:17:14.381749: +2026-04-11 18:17:14.383263: Epoch 1113 +2026-04-11 18:17:14.384633: Current learning rate: 0.00746 +2026-04-11 18:18:56.015268: train_loss -0.3951 +2026-04-11 18:18:56.019475: val_loss -0.3516 +2026-04-11 18:18:56.022009: Pseudo dice [0.5656, 0.384, 0.7065, 0.5853, 0.4047, 0.7884, 0.2713] +2026-04-11 18:18:56.023444: Epoch time: 101.64 s +2026-04-11 18:18:57.145899: +2026-04-11 18:18:57.147516: Epoch 1114 +2026-04-11 18:18:57.149016: Current learning rate: 0.00745 +2026-04-11 18:20:38.254628: train_loss -0.3758 +2026-04-11 18:20:38.258654: val_loss -0.3131 +2026-04-11 18:20:38.259951: Pseudo dice [0.043, 0.0, 0.6031, 0.8694, 0.2746, 0.2895, 0.6471] +2026-04-11 18:20:38.261673: Epoch time: 101.11 s +2026-04-11 18:20:39.374174: +2026-04-11 18:20:39.375695: Epoch 1115 +2026-04-11 18:20:39.377060: Current learning rate: 0.00745 +2026-04-11 18:22:21.693651: train_loss -0.3582 +2026-04-11 18:22:21.698052: val_loss -0.3468 +2026-04-11 18:22:21.699564: Pseudo dice [0.0003, 0.0, 0.6765, 0.3779, 0.3284, 0.4715, 0.7682] +2026-04-11 18:22:21.701062: Epoch time: 102.32 s +2026-04-11 18:22:22.812609: +2026-04-11 18:22:22.814040: Epoch 1116 +2026-04-11 18:22:22.815578: Current learning rate: 0.00745 +2026-04-11 18:24:04.063834: train_loss -0.3691 +2026-04-11 18:24:04.068054: val_loss -0.3364 +2026-04-11 18:24:04.080714: Pseudo dice [0.6418, 0.0747, 0.5061, 0.641, 0.2807, 0.6546, 0.3479] +2026-04-11 18:24:04.082098: Epoch time: 101.25 s +2026-04-11 18:24:05.189755: +2026-04-11 18:24:05.191861: Epoch 1117 +2026-04-11 18:24:05.193247: Current learning rate: 0.00745 +2026-04-11 18:25:46.621615: train_loss -0.3665 +2026-04-11 18:25:46.626170: val_loss -0.3305 +2026-04-11 18:25:46.627998: Pseudo dice [0.0, 0.0, 0.5246, 0.396, 0.2244, 0.7912, 0.618] +2026-04-11 18:25:46.629751: Epoch time: 101.43 s +2026-04-11 18:25:47.753324: +2026-04-11 18:25:47.754932: Epoch 1118 +2026-04-11 18:25:47.756265: Current learning rate: 0.00745 +2026-04-11 18:27:29.334823: train_loss -0.3844 +2026-04-11 18:27:29.340535: val_loss -0.3241 +2026-04-11 18:27:29.342252: Pseudo dice [0.0, 0.0, 0.5133, 0.426, 0.3491, 0.6266, 0.8457] +2026-04-11 18:27:29.343860: Epoch time: 101.58 s +2026-04-11 18:27:30.448628: +2026-04-11 18:27:30.450154: Epoch 1119 +2026-04-11 18:27:30.451633: Current learning rate: 0.00744 +2026-04-11 18:29:12.228894: train_loss -0.381 +2026-04-11 18:29:12.232900: val_loss -0.3566 +2026-04-11 18:29:12.234461: Pseudo dice [0.0, 0.0, 0.7374, 0.5127, 0.2811, 0.7341, 0.779] +2026-04-11 18:29:12.235930: Epoch time: 101.78 s +2026-04-11 18:29:13.365131: +2026-04-11 18:29:13.369007: Epoch 1120 +2026-04-11 18:29:13.370486: Current learning rate: 0.00744 +2026-04-11 18:30:55.013543: train_loss -0.3747 +2026-04-11 18:30:55.019225: val_loss -0.3554 +2026-04-11 18:30:55.021185: Pseudo dice [0.0, 0.0, 0.3764, 0.6133, 0.4257, 0.8422, 0.8048] +2026-04-11 18:30:55.022615: Epoch time: 101.65 s +2026-04-11 18:30:56.130339: +2026-04-11 18:30:56.131787: Epoch 1121 +2026-04-11 18:30:56.133189: Current learning rate: 0.00744 +2026-04-11 18:32:37.875338: train_loss -0.3774 +2026-04-11 18:32:37.901845: val_loss -0.3039 +2026-04-11 18:32:37.903332: Pseudo dice [0.1013, 0.0, 0.5996, 0.7508, 0.2747, 0.661, 0.3712] +2026-04-11 18:32:37.904939: Epoch time: 101.75 s +2026-04-11 18:32:39.020699: +2026-04-11 18:32:39.022036: Epoch 1122 +2026-04-11 18:32:39.023413: Current learning rate: 0.00744 +2026-04-11 18:34:20.440403: train_loss -0.3615 +2026-04-11 18:34:20.444768: val_loss -0.2936 +2026-04-11 18:34:20.446378: Pseudo dice [0.0, 0.3484, 0.5414, 0.7645, 0.1256, 0.3358, 0.6455] +2026-04-11 18:34:20.447613: Epoch time: 101.42 s +2026-04-11 18:34:21.550802: +2026-04-11 18:34:21.552225: Epoch 1123 +2026-04-11 18:34:21.558133: Current learning rate: 0.00743 +2026-04-11 18:36:03.185357: train_loss -0.3903 +2026-04-11 18:36:03.189883: val_loss -0.3485 +2026-04-11 18:36:03.191366: Pseudo dice [0.6427, 0.0574, 0.6769, 0.5351, 0.3781, 0.339, 0.8152] +2026-04-11 18:36:03.192810: Epoch time: 101.64 s +2026-04-11 18:36:04.311828: +2026-04-11 18:36:04.313500: Epoch 1124 +2026-04-11 18:36:04.314833: Current learning rate: 0.00743 +2026-04-11 18:37:45.831685: train_loss -0.3981 +2026-04-11 18:37:45.835866: val_loss -0.2852 +2026-04-11 18:37:45.837607: Pseudo dice [0.1571, 0.0, 0.6425, 0.6019, 0.1678, 0.5661, 0.5007] +2026-04-11 18:37:45.839252: Epoch time: 101.52 s +2026-04-11 18:37:46.945098: +2026-04-11 18:37:46.946864: Epoch 1125 +2026-04-11 18:37:46.948378: Current learning rate: 0.00743 +2026-04-11 18:39:28.582822: train_loss -0.372 +2026-04-11 18:39:28.587931: val_loss -0.3016 +2026-04-11 18:39:28.589595: Pseudo dice [0.0117, 0.1388, 0.7604, 0.6432, 0.0817, 0.6945, 0.5231] +2026-04-11 18:39:28.591160: Epoch time: 101.64 s +2026-04-11 18:39:29.713371: +2026-04-11 18:39:29.714752: Epoch 1126 +2026-04-11 18:39:29.715980: Current learning rate: 0.00743 +2026-04-11 18:41:11.815897: train_loss -0.3827 +2026-04-11 18:41:11.820349: val_loss -0.3525 +2026-04-11 18:41:11.821904: Pseudo dice [0.0194, 0.0, 0.6788, 0.8173, 0.2929, 0.4424, 0.7327] +2026-04-11 18:41:11.823364: Epoch time: 102.11 s +2026-04-11 18:41:12.932880: +2026-04-11 18:41:12.934181: Epoch 1127 +2026-04-11 18:41:12.935565: Current learning rate: 0.00742 +2026-04-11 18:42:54.392665: train_loss -0.3697 +2026-04-11 18:42:54.396710: val_loss -0.3108 +2026-04-11 18:42:54.398253: Pseudo dice [0.1397, 0.0, 0.526, 0.6699, 0.2159, 0.7656, 0.577] +2026-04-11 18:42:54.399682: Epoch time: 101.46 s +2026-04-11 18:42:55.514704: +2026-04-11 18:42:55.516151: Epoch 1128 +2026-04-11 18:42:55.517388: Current learning rate: 0.00742 +2026-04-11 18:44:37.471774: train_loss -0.3613 +2026-04-11 18:44:37.476008: val_loss -0.307 +2026-04-11 18:44:37.477625: Pseudo dice [0.0492, 0.0, 0.8635, 0.6985, 0.156, 0.6246, 0.3497] +2026-04-11 18:44:37.478901: Epoch time: 101.96 s +2026-04-11 18:44:38.593657: +2026-04-11 18:44:38.595149: Epoch 1129 +2026-04-11 18:44:38.596374: Current learning rate: 0.00742 +2026-04-11 18:46:19.992203: train_loss -0.3782 +2026-04-11 18:46:19.996226: val_loss -0.3432 +2026-04-11 18:46:19.997796: Pseudo dice [0.2821, 0.0, 0.559, 0.1941, 0.345, 0.5313, 0.5578] +2026-04-11 18:46:19.999111: Epoch time: 101.4 s +2026-04-11 18:46:21.098160: +2026-04-11 18:46:21.099740: Epoch 1130 +2026-04-11 18:46:21.101017: Current learning rate: 0.00742 +2026-04-11 18:48:02.171978: train_loss -0.3844 +2026-04-11 18:48:02.177128: val_loss -0.3511 +2026-04-11 18:48:02.178823: Pseudo dice [0.039, 0.3785, 0.7839, 0.6143, 0.165, 0.8354, 0.7209] +2026-04-11 18:48:02.181242: Epoch time: 101.08 s +2026-04-11 18:48:03.648108: +2026-04-11 18:48:03.650321: Epoch 1131 +2026-04-11 18:48:03.652296: Current learning rate: 0.00741 +2026-04-11 18:49:44.961607: train_loss -0.4095 +2026-04-11 18:49:44.966203: val_loss -0.3702 +2026-04-11 18:49:44.968403: Pseudo dice [0.1927, 0.2291, 0.8018, 0.5925, 0.3587, 0.5543, 0.697] +2026-04-11 18:49:44.970555: Epoch time: 101.32 s +2026-04-11 18:49:46.085860: +2026-04-11 18:49:46.087732: Epoch 1132 +2026-04-11 18:49:46.089424: Current learning rate: 0.00741 +2026-04-11 18:51:27.553744: train_loss -0.3989 +2026-04-11 18:51:27.559163: val_loss -0.3144 +2026-04-11 18:51:27.561154: Pseudo dice [0.2847, 0.2495, 0.6651, 0.2667, 0.1709, 0.2334, 0.8335] +2026-04-11 18:51:27.563118: Epoch time: 101.47 s +2026-04-11 18:51:28.674826: +2026-04-11 18:51:28.676758: Epoch 1133 +2026-04-11 18:51:28.678432: Current learning rate: 0.00741 +2026-04-11 18:53:10.185701: train_loss -0.3792 +2026-04-11 18:53:10.191109: val_loss -0.2911 +2026-04-11 18:53:10.192998: Pseudo dice [0.0089, 0.1412, 0.3901, 0.5134, 0.2747, 0.3106, 0.6989] +2026-04-11 18:53:10.195128: Epoch time: 101.51 s +2026-04-11 18:53:11.303797: +2026-04-11 18:53:11.305512: Epoch 1134 +2026-04-11 18:53:11.307029: Current learning rate: 0.00741 +2026-04-11 18:54:52.978666: train_loss -0.3677 +2026-04-11 18:54:52.982794: val_loss -0.3605 +2026-04-11 18:54:52.984214: Pseudo dice [0.0, 0.0, 0.5184, 0.74, 0.3251, 0.7995, 0.5593] +2026-04-11 18:54:52.985557: Epoch time: 101.68 s +2026-04-11 18:54:54.089006: +2026-04-11 18:54:54.090476: Epoch 1135 +2026-04-11 18:54:54.091766: Current learning rate: 0.00741 +2026-04-11 18:56:35.604192: train_loss -0.3847 +2026-04-11 18:56:35.609677: val_loss -0.3434 +2026-04-11 18:56:35.611381: Pseudo dice [0.0, 0.0691, 0.6291, 0.8371, 0.4047, 0.7354, 0.4886] +2026-04-11 18:56:35.612863: Epoch time: 101.52 s +2026-04-11 18:56:37.768920: +2026-04-11 18:56:37.770599: Epoch 1136 +2026-04-11 18:56:37.772175: Current learning rate: 0.0074 +2026-04-11 18:58:19.402506: train_loss -0.3905 +2026-04-11 18:58:19.408679: val_loss -0.3661 +2026-04-11 18:58:19.410634: Pseudo dice [0.3346, 0.2947, 0.8013, 0.5997, 0.2868, 0.5327, 0.6736] +2026-04-11 18:58:19.412117: Epoch time: 101.64 s +2026-04-11 18:58:20.527883: +2026-04-11 18:58:20.529804: Epoch 1137 +2026-04-11 18:58:20.531453: Current learning rate: 0.0074 +2026-04-11 19:00:02.147484: train_loss -0.3996 +2026-04-11 19:00:02.151942: val_loss -0.3372 +2026-04-11 19:00:02.153994: Pseudo dice [0.4065, 0.1693, 0.6535, 0.5718, 0.1863, 0.2812, 0.6773] +2026-04-11 19:00:02.155632: Epoch time: 101.62 s +2026-04-11 19:00:03.275228: +2026-04-11 19:00:03.277003: Epoch 1138 +2026-04-11 19:00:03.278448: Current learning rate: 0.0074 +2026-04-11 19:01:44.773311: train_loss -0.4093 +2026-04-11 19:01:44.778185: val_loss -0.3455 +2026-04-11 19:01:44.780010: Pseudo dice [0.3419, 0.345, 0.7766, 0.3517, 0.1383, 0.7528, 0.8788] +2026-04-11 19:01:44.781719: Epoch time: 101.5 s +2026-04-11 19:01:45.902830: +2026-04-11 19:01:45.904435: Epoch 1139 +2026-04-11 19:01:45.905844: Current learning rate: 0.0074 +2026-04-11 19:03:27.617537: train_loss -0.4198 +2026-04-11 19:03:27.621802: val_loss -0.3747 +2026-04-11 19:03:27.623133: Pseudo dice [0.1634, 0.5902, 0.5584, 0.8488, 0.4011, 0.771, 0.714] +2026-04-11 19:03:27.624475: Epoch time: 101.72 s +2026-04-11 19:03:28.729864: +2026-04-11 19:03:28.731342: Epoch 1140 +2026-04-11 19:03:28.732695: Current learning rate: 0.00739 +2026-04-11 19:05:10.089656: train_loss -0.3995 +2026-04-11 19:05:10.093823: val_loss -0.3183 +2026-04-11 19:05:10.095170: Pseudo dice [0.1403, 0.1316, 0.6046, 0.604, 0.116, 0.6512, 0.5887] +2026-04-11 19:05:10.096913: Epoch time: 101.36 s +2026-04-11 19:05:11.203997: +2026-04-11 19:05:11.217203: Epoch 1141 +2026-04-11 19:05:11.219149: Current learning rate: 0.00739 +2026-04-11 19:06:53.099835: train_loss -0.3926 +2026-04-11 19:06:53.110621: val_loss -0.3668 +2026-04-11 19:06:53.113774: Pseudo dice [0.4523, 0.5262, 0.5863, 0.7721, 0.4283, 0.7385, 0.8795] +2026-04-11 19:06:53.115309: Epoch time: 101.9 s +2026-04-11 19:06:54.227593: +2026-04-11 19:06:54.229131: Epoch 1142 +2026-04-11 19:06:54.230582: Current learning rate: 0.00739 +2026-04-11 19:08:35.737037: train_loss -0.3951 +2026-04-11 19:08:35.741381: val_loss -0.2879 +2026-04-11 19:08:35.742825: Pseudo dice [0.0166, 0.0399, 0.6041, 0.879, 0.1292, 0.4186, 0.6926] +2026-04-11 19:08:35.744364: Epoch time: 101.51 s +2026-04-11 19:08:36.853430: +2026-04-11 19:08:36.855344: Epoch 1143 +2026-04-11 19:08:36.856838: Current learning rate: 0.00739 +2026-04-11 19:10:18.162026: train_loss -0.3963 +2026-04-11 19:10:18.166981: val_loss -0.3546 +2026-04-11 19:10:18.169039: Pseudo dice [0.1693, 0.0074, 0.6048, 0.7615, 0.4624, 0.748, 0.7907] +2026-04-11 19:10:18.171217: Epoch time: 101.31 s +2026-04-11 19:10:19.295969: +2026-04-11 19:10:19.297725: Epoch 1144 +2026-04-11 19:10:19.299507: Current learning rate: 0.00738 +2026-04-11 19:12:00.534478: train_loss -0.3959 +2026-04-11 19:12:00.539558: val_loss -0.3567 +2026-04-11 19:12:00.541130: Pseudo dice [0.2756, 0.0, 0.7813, 0.6659, 0.3911, 0.7371, 0.7988] +2026-04-11 19:12:00.542872: Epoch time: 101.24 s +2026-04-11 19:12:01.669206: +2026-04-11 19:12:01.670629: Epoch 1145 +2026-04-11 19:12:01.671985: Current learning rate: 0.00738 +2026-04-11 19:13:43.013537: train_loss -0.3974 +2026-04-11 19:13:43.017317: val_loss -0.3365 +2026-04-11 19:13:43.019108: Pseudo dice [0.7716, 0.0323, 0.5123, 0.6437, 0.4439, 0.8066, 0.5926] +2026-04-11 19:13:43.020510: Epoch time: 101.35 s +2026-04-11 19:13:44.151102: +2026-04-11 19:13:44.152904: Epoch 1146 +2026-04-11 19:13:44.154253: Current learning rate: 0.00738 +2026-04-11 19:15:25.751717: train_loss -0.3827 +2026-04-11 19:15:25.756082: val_loss -0.3215 +2026-04-11 19:15:25.757756: Pseudo dice [0.0, 0.0, 0.796, 0.382, 0.1024, 0.5741, 0.214] +2026-04-11 19:15:25.759185: Epoch time: 101.6 s +2026-04-11 19:15:26.902326: +2026-04-11 19:15:26.903841: Epoch 1147 +2026-04-11 19:15:26.905167: Current learning rate: 0.00738 +2026-04-11 19:17:08.228718: train_loss -0.3614 +2026-04-11 19:17:08.233853: val_loss -0.3074 +2026-04-11 19:17:08.235601: Pseudo dice [0.1027, 0.0, 0.6065, 0.5693, 0.2257, 0.5348, 0.4493] +2026-04-11 19:17:08.237270: Epoch time: 101.33 s +2026-04-11 19:17:09.382342: +2026-04-11 19:17:09.384174: Epoch 1148 +2026-04-11 19:17:09.385866: Current learning rate: 0.00738 +2026-04-11 19:18:50.755724: train_loss -0.4013 +2026-04-11 19:18:50.761236: val_loss -0.35 +2026-04-11 19:18:50.762854: Pseudo dice [0.2578, 0.2124, 0.7212, 0.7748, 0.4645, 0.3115, 0.8248] +2026-04-11 19:18:50.764850: Epoch time: 101.38 s +2026-04-11 19:18:51.901992: +2026-04-11 19:18:51.904062: Epoch 1149 +2026-04-11 19:18:51.905661: Current learning rate: 0.00737 +2026-04-11 19:20:33.472047: train_loss -0.3842 +2026-04-11 19:20:33.476867: val_loss -0.3828 +2026-04-11 19:20:33.478141: Pseudo dice [0.7064, 0.1062, 0.7488, 0.8088, 0.2614, 0.8405, 0.7438] +2026-04-11 19:20:33.479444: Epoch time: 101.57 s +2026-04-11 19:20:36.266233: +2026-04-11 19:20:36.267546: Epoch 1150 +2026-04-11 19:20:36.268718: Current learning rate: 0.00737 +2026-04-11 19:22:17.667995: train_loss -0.3747 +2026-04-11 19:22:17.673017: val_loss -0.3268 +2026-04-11 19:22:17.674491: Pseudo dice [0.0138, 0.0, 0.6852, 0.1154, 0.2829, 0.3327, 0.6155] +2026-04-11 19:22:17.675629: Epoch time: 101.41 s +2026-04-11 19:22:18.822831: +2026-04-11 19:22:18.824252: Epoch 1151 +2026-04-11 19:22:18.825451: Current learning rate: 0.00737 +2026-04-11 19:24:00.127662: train_loss -0.3664 +2026-04-11 19:24:00.131648: val_loss -0.3196 +2026-04-11 19:24:00.133186: Pseudo dice [0.0, 0.0, 0.489, 0.3328, 0.4086, 0.7591, 0.5387] +2026-04-11 19:24:00.134666: Epoch time: 101.31 s +2026-04-11 19:24:01.282778: +2026-04-11 19:24:01.284581: Epoch 1152 +2026-04-11 19:24:01.285900: Current learning rate: 0.00737 +2026-04-11 19:25:42.715483: train_loss -0.3775 +2026-04-11 19:25:42.720606: val_loss -0.3033 +2026-04-11 19:25:42.722082: Pseudo dice [0.05, 0.0, 0.5757, 0.8452, 0.1612, 0.6685, 0.6668] +2026-04-11 19:25:42.723648: Epoch time: 101.44 s +2026-04-11 19:25:43.847458: +2026-04-11 19:25:43.848867: Epoch 1153 +2026-04-11 19:25:43.850213: Current learning rate: 0.00736 +2026-04-11 19:27:25.415367: train_loss -0.3941 +2026-04-11 19:27:25.420240: val_loss -0.3395 +2026-04-11 19:27:25.421881: Pseudo dice [0.2848, 0.3343, 0.5089, 0.2275, 0.4842, 0.6797, 0.5922] +2026-04-11 19:27:25.423582: Epoch time: 101.57 s +2026-04-11 19:27:26.557821: +2026-04-11 19:27:26.559366: Epoch 1154 +2026-04-11 19:27:26.560958: Current learning rate: 0.00736 +2026-04-11 19:29:08.133768: train_loss -0.4173 +2026-04-11 19:29:08.138126: val_loss -0.351 +2026-04-11 19:29:08.139675: Pseudo dice [0.2672, 0.6989, 0.7768, 0.7777, 0.1102, 0.754, 0.4142] +2026-04-11 19:29:08.141078: Epoch time: 101.58 s +2026-04-11 19:29:09.260127: +2026-04-11 19:29:09.261904: Epoch 1155 +2026-04-11 19:29:09.263145: Current learning rate: 0.00736 +2026-04-11 19:30:50.719963: train_loss -0.3684 +2026-04-11 19:30:50.724108: val_loss -0.3258 +2026-04-11 19:30:50.725502: Pseudo dice [0.0, 0.0777, 0.6813, 0.3162, 0.1578, 0.3998, 0.6585] +2026-04-11 19:30:50.726668: Epoch time: 101.46 s +2026-04-11 19:30:53.078880: +2026-04-11 19:30:53.080526: Epoch 1156 +2026-04-11 19:30:53.081784: Current learning rate: 0.00736 +2026-04-11 19:32:34.648291: train_loss -0.3762 +2026-04-11 19:32:34.653786: val_loss -0.3442 +2026-04-11 19:32:34.656013: Pseudo dice [0.0, 0.4868, 0.1757, 0.7973, 0.3285, 0.8219, 0.4821] +2026-04-11 19:32:34.657751: Epoch time: 101.57 s +2026-04-11 19:32:35.818867: +2026-04-11 19:32:35.820436: Epoch 1157 +2026-04-11 19:32:35.824932: Current learning rate: 0.00735 +2026-04-11 19:34:17.380249: train_loss -0.3873 +2026-04-11 19:34:17.384962: val_loss -0.3559 +2026-04-11 19:34:17.386918: Pseudo dice [0.0, 0.5895, 0.8136, 0.3022, 0.113, 0.5691, 0.5246] +2026-04-11 19:34:17.388305: Epoch time: 101.56 s +2026-04-11 19:34:18.539159: +2026-04-11 19:34:18.540614: Epoch 1158 +2026-04-11 19:34:18.541885: Current learning rate: 0.00735 +2026-04-11 19:36:00.090402: train_loss -0.3792 +2026-04-11 19:36:00.095556: val_loss -0.3463 +2026-04-11 19:36:00.098119: Pseudo dice [0.0, 0.3424, 0.6147, 0.8073, 0.5131, 0.3526, 0.7892] +2026-04-11 19:36:00.099496: Epoch time: 101.55 s +2026-04-11 19:36:01.244534: +2026-04-11 19:36:01.246571: Epoch 1159 +2026-04-11 19:36:01.248404: Current learning rate: 0.00735 +2026-04-11 19:37:42.882313: train_loss -0.3747 +2026-04-11 19:37:42.886291: val_loss -0.358 +2026-04-11 19:37:42.887823: Pseudo dice [0.0, 0.0, 0.4248, 0.7346, 0.2461, 0.7714, 0.846] +2026-04-11 19:37:42.889136: Epoch time: 101.64 s +2026-04-11 19:37:44.025048: +2026-04-11 19:37:44.026657: Epoch 1160 +2026-04-11 19:37:44.027870: Current learning rate: 0.00735 +2026-04-11 19:39:25.487934: train_loss -0.3758 +2026-04-11 19:39:25.493432: val_loss -0.3403 +2026-04-11 19:39:25.496621: Pseudo dice [0.0, 0.0, 0.7577, 0.7927, 0.3823, 0.6136, 0.6691] +2026-04-11 19:39:25.498891: Epoch time: 101.47 s +2026-04-11 19:39:26.620098: +2026-04-11 19:39:26.621875: Epoch 1161 +2026-04-11 19:39:26.623516: Current learning rate: 0.00735 +2026-04-11 19:41:08.175903: train_loss -0.3704 +2026-04-11 19:41:08.180114: val_loss -0.3007 +2026-04-11 19:41:08.181555: Pseudo dice [0.0, 0.0, 0.6776, 0.5266, 0.392, 0.6163, 0.6446] +2026-04-11 19:41:08.183045: Epoch time: 101.56 s +2026-04-11 19:41:09.314327: +2026-04-11 19:41:09.315820: Epoch 1162 +2026-04-11 19:41:09.317191: Current learning rate: 0.00734 +2026-04-11 19:42:50.641559: train_loss -0.3812 +2026-04-11 19:42:50.646626: val_loss -0.3562 +2026-04-11 19:42:50.648408: Pseudo dice [0.0, 0.0, 0.5706, 0.7211, 0.5536, 0.7173, 0.761] +2026-04-11 19:42:50.649954: Epoch time: 101.33 s +2026-04-11 19:42:51.774698: +2026-04-11 19:42:51.776440: Epoch 1163 +2026-04-11 19:42:51.777827: Current learning rate: 0.00734 +2026-04-11 19:44:33.343704: train_loss -0.3892 +2026-04-11 19:44:33.347666: val_loss -0.3299 +2026-04-11 19:44:33.349189: Pseudo dice [0.2149, 0.3709, 0.6773, 0.7202, 0.1859, 0.7184, 0.8518] +2026-04-11 19:44:33.350703: Epoch time: 101.57 s +2026-04-11 19:44:34.477771: +2026-04-11 19:44:34.479205: Epoch 1164 +2026-04-11 19:44:34.480526: Current learning rate: 0.00734 +2026-04-11 19:46:15.909890: train_loss -0.4014 +2026-04-11 19:46:15.913735: val_loss -0.3643 +2026-04-11 19:46:15.915110: Pseudo dice [0.0, 0.2577, 0.3109, 0.8934, 0.3932, 0.8468, 0.8492] +2026-04-11 19:46:15.916490: Epoch time: 101.44 s +2026-04-11 19:46:17.065857: +2026-04-11 19:46:17.067262: Epoch 1165 +2026-04-11 19:46:17.068492: Current learning rate: 0.00734 +2026-04-11 19:47:58.465344: train_loss -0.3694 +2026-04-11 19:47:58.470512: val_loss -0.3545 +2026-04-11 19:47:58.472252: Pseudo dice [0.0, 0.4989, 0.6301, 0.684, 0.1211, 0.6487, 0.6374] +2026-04-11 19:47:58.474279: Epoch time: 101.4 s +2026-04-11 19:47:59.611531: +2026-04-11 19:47:59.613352: Epoch 1166 +2026-04-11 19:47:59.615627: Current learning rate: 0.00733 +2026-04-11 19:49:41.248040: train_loss -0.3745 +2026-04-11 19:49:41.252426: val_loss -0.3255 +2026-04-11 19:49:41.253941: Pseudo dice [0.0, 0.2332, 0.5821, 0.4541, 0.3577, 0.2431, 0.6554] +2026-04-11 19:49:41.255355: Epoch time: 101.64 s +2026-04-11 19:49:42.394980: +2026-04-11 19:49:42.396485: Epoch 1167 +2026-04-11 19:49:42.397895: Current learning rate: 0.00733 +2026-04-11 19:51:23.874668: train_loss -0.4088 +2026-04-11 19:51:23.878896: val_loss -0.349 +2026-04-11 19:51:23.880517: Pseudo dice [0.0, 0.2206, 0.7476, 0.7342, 0.2357, 0.6129, 0.8065] +2026-04-11 19:51:23.882156: Epoch time: 101.48 s +2026-04-11 19:51:25.013821: +2026-04-11 19:51:25.015243: Epoch 1168 +2026-04-11 19:51:25.016552: Current learning rate: 0.00733 +2026-04-11 19:53:06.382748: train_loss -0.3838 +2026-04-11 19:53:06.387311: val_loss -0.3317 +2026-04-11 19:53:06.389135: Pseudo dice [0.0, 0.0, 0.6412, 0.7426, 0.3626, 0.5327, 0.5528] +2026-04-11 19:53:06.390749: Epoch time: 101.37 s +2026-04-11 19:53:07.524592: +2026-04-11 19:53:07.525959: Epoch 1169 +2026-04-11 19:53:07.527143: Current learning rate: 0.00733 +2026-04-11 19:54:48.995788: train_loss -0.3694 +2026-04-11 19:54:49.000250: val_loss -0.3581 +2026-04-11 19:54:49.001648: Pseudo dice [0.0, 0.0, 0.4688, 0.7448, 0.2332, 0.6633, 0.5579] +2026-04-11 19:54:49.003114: Epoch time: 101.47 s +2026-04-11 19:54:50.139626: +2026-04-11 19:54:50.141052: Epoch 1170 +2026-04-11 19:54:50.142315: Current learning rate: 0.00732 +2026-04-11 19:56:31.678422: train_loss -0.3872 +2026-04-11 19:56:31.682457: val_loss -0.3371 +2026-04-11 19:56:31.683744: Pseudo dice [0.0, 0.0, 0.6972, 0.8315, 0.0, 0.8177, 0.7333] +2026-04-11 19:56:31.685101: Epoch time: 101.54 s +2026-04-11 19:56:32.799595: +2026-04-11 19:56:32.801033: Epoch 1171 +2026-04-11 19:56:32.802440: Current learning rate: 0.00732 +2026-04-11 19:58:14.331861: train_loss -0.3654 +2026-04-11 19:58:14.336588: val_loss -0.369 +2026-04-11 19:58:14.338065: Pseudo dice [0.0, 0.0, 0.7432, 0.6866, 0.2302, 0.7156, 0.646] +2026-04-11 19:58:14.339609: Epoch time: 101.54 s +2026-04-11 19:58:15.470602: +2026-04-11 19:58:15.472539: Epoch 1172 +2026-04-11 19:58:15.474004: Current learning rate: 0.00732 +2026-04-11 19:59:56.963151: train_loss -0.3865 +2026-04-11 19:59:56.968326: val_loss -0.3354 +2026-04-11 19:59:56.970004: Pseudo dice [0.0, 0.0046, 0.4968, 0.8896, 0.1858, 0.7372, 0.759] +2026-04-11 19:59:56.971599: Epoch time: 101.5 s +2026-04-11 19:59:58.114381: +2026-04-11 19:59:58.116221: Epoch 1173 +2026-04-11 19:59:58.117712: Current learning rate: 0.00732 +2026-04-11 20:01:39.580981: train_loss -0.4066 +2026-04-11 20:01:39.585020: val_loss -0.3658 +2026-04-11 20:01:39.586666: Pseudo dice [0.0, 0.1505, 0.6577, 0.8384, 0.3576, 0.7608, 0.4802] +2026-04-11 20:01:39.588598: Epoch time: 101.47 s +2026-04-11 20:01:40.716393: +2026-04-11 20:01:40.718175: Epoch 1174 +2026-04-11 20:01:40.719402: Current learning rate: 0.00731 +2026-04-11 20:03:22.057637: train_loss -0.3979 +2026-04-11 20:03:22.062112: val_loss -0.3277 +2026-04-11 20:03:22.064003: Pseudo dice [0.0, 0.0, 0.785, 0.7032, 0.2472, 0.7731, 0.7032] +2026-04-11 20:03:22.065396: Epoch time: 101.34 s +2026-04-11 20:03:23.176947: +2026-04-11 20:03:23.178696: Epoch 1175 +2026-04-11 20:03:23.180261: Current learning rate: 0.00731 +2026-04-11 20:05:04.725816: train_loss -0.399 +2026-04-11 20:05:04.730482: val_loss -0.3227 +2026-04-11 20:05:04.732336: Pseudo dice [0.0, 0.0, 0.7233, 0.8415, 0.1752, 0.6872, 0.7432] +2026-04-11 20:05:04.734252: Epoch time: 101.55 s +2026-04-11 20:05:05.868253: +2026-04-11 20:05:05.869825: Epoch 1176 +2026-04-11 20:05:05.871211: Current learning rate: 0.00731 +2026-04-11 20:06:47.327107: train_loss -0.3737 +2026-04-11 20:06:47.331217: val_loss -0.3295 +2026-04-11 20:06:47.332712: Pseudo dice [0.0, 0.0, 0.7261, 0.6707, 0.4383, 0.59, 0.7552] +2026-04-11 20:06:47.334659: Epoch time: 101.46 s +2026-04-11 20:06:49.605980: +2026-04-11 20:06:49.607563: Epoch 1177 +2026-04-11 20:06:49.609032: Current learning rate: 0.00731 +2026-04-11 20:08:31.097073: train_loss -0.3529 +2026-04-11 20:08:31.107220: val_loss -0.3333 +2026-04-11 20:08:31.126662: Pseudo dice [0.0, 0.0, 0.4434, 0.7556, 0.2979, 0.6954, 0.8339] +2026-04-11 20:08:31.128700: Epoch time: 101.49 s +2026-04-11 20:08:32.283854: +2026-04-11 20:08:32.285747: Epoch 1178 +2026-04-11 20:08:32.286942: Current learning rate: 0.00731 +2026-04-11 20:10:13.804568: train_loss -0.3876 +2026-04-11 20:10:13.808525: val_loss -0.34 +2026-04-11 20:10:13.810086: Pseudo dice [0.612, 0.0, 0.5639, 0.6281, 0.2281, 0.7043, 0.8672] +2026-04-11 20:10:13.811441: Epoch time: 101.52 s +2026-04-11 20:10:14.967896: +2026-04-11 20:10:14.969714: Epoch 1179 +2026-04-11 20:10:14.971146: Current learning rate: 0.0073 +2026-04-11 20:11:56.381161: train_loss -0.3734 +2026-04-11 20:11:56.385767: val_loss -0.3572 +2026-04-11 20:11:56.387300: Pseudo dice [0.0135, 0.0, 0.5065, 0.7983, 0.3681, 0.6206, 0.5995] +2026-04-11 20:11:56.388814: Epoch time: 101.42 s +2026-04-11 20:11:57.535939: +2026-04-11 20:11:57.537333: Epoch 1180 +2026-04-11 20:11:57.538579: Current learning rate: 0.0073 +2026-04-11 20:13:39.080436: train_loss -0.3797 +2026-04-11 20:13:39.084752: val_loss -0.3706 +2026-04-11 20:13:39.086459: Pseudo dice [0.0, 0.0, 0.4201, 0.613, 0.5229, 0.7719, 0.841] +2026-04-11 20:13:39.087727: Epoch time: 101.55 s +2026-04-11 20:13:40.224792: +2026-04-11 20:13:40.226166: Epoch 1181 +2026-04-11 20:13:40.227544: Current learning rate: 0.0073 +2026-04-11 20:15:21.753955: train_loss -0.3726 +2026-04-11 20:15:21.759745: val_loss -0.2947 +2026-04-11 20:15:21.762778: Pseudo dice [0.2481, 0.0, 0.72, 0.6232, 0.0982, 0.4498, 0.4971] +2026-04-11 20:15:21.764659: Epoch time: 101.53 s +2026-04-11 20:15:22.907513: +2026-04-11 20:15:22.909485: Epoch 1182 +2026-04-11 20:15:22.911122: Current learning rate: 0.0073 +2026-04-11 20:17:04.519730: train_loss -0.3805 +2026-04-11 20:17:04.524479: val_loss -0.3666 +2026-04-11 20:17:04.526279: Pseudo dice [0.0466, 0.0, 0.7244, 0.0463, 0.3915, 0.5638, 0.5925] +2026-04-11 20:17:04.527710: Epoch time: 101.62 s +2026-04-11 20:17:05.674776: +2026-04-11 20:17:05.676451: Epoch 1183 +2026-04-11 20:17:05.677875: Current learning rate: 0.00729 +2026-04-11 20:18:47.143838: train_loss -0.3888 +2026-04-11 20:18:47.148173: val_loss -0.3028 +2026-04-11 20:18:47.150047: Pseudo dice [0.0116, 0.0, 0.3122, 0.417, 0.3393, 0.172, 0.6695] +2026-04-11 20:18:47.151493: Epoch time: 101.47 s +2026-04-11 20:18:48.301353: +2026-04-11 20:18:48.302973: Epoch 1184 +2026-04-11 20:18:48.304264: Current learning rate: 0.00729 +2026-04-11 20:20:29.698247: train_loss -0.3666 +2026-04-11 20:20:29.702427: val_loss -0.3365 +2026-04-11 20:20:29.703723: Pseudo dice [0.0919, 0.0, 0.7249, 0.417, 0.2279, 0.7191, 0.3903] +2026-04-11 20:20:29.705065: Epoch time: 101.4 s +2026-04-11 20:20:30.852167: +2026-04-11 20:20:30.853580: Epoch 1185 +2026-04-11 20:20:30.854999: Current learning rate: 0.00729 +2026-04-11 20:22:12.416260: train_loss -0.4005 +2026-04-11 20:22:12.423764: val_loss -0.3153 +2026-04-11 20:22:12.425271: Pseudo dice [0.4346, 0.1215, 0.5738, 0.4874, 0.231, 0.7876, 0.565] +2026-04-11 20:22:12.426761: Epoch time: 101.57 s +2026-04-11 20:22:13.565917: +2026-04-11 20:22:13.567570: Epoch 1186 +2026-04-11 20:22:13.568799: Current learning rate: 0.00729 +2026-04-11 20:23:55.061984: train_loss -0.3704 +2026-04-11 20:23:55.067293: val_loss -0.3512 +2026-04-11 20:23:55.068838: Pseudo dice [0.0, 0.196, 0.7094, 0.3207, 0.4522, 0.8435, 0.3693] +2026-04-11 20:23:55.070413: Epoch time: 101.5 s +2026-04-11 20:23:56.224576: +2026-04-11 20:23:56.226112: Epoch 1187 +2026-04-11 20:23:56.227338: Current learning rate: 0.00728 +2026-04-11 20:25:37.660624: train_loss -0.3943 +2026-04-11 20:25:37.664676: val_loss -0.3054 +2026-04-11 20:25:37.666566: Pseudo dice [0.0, 0.2086, 0.4904, 0.6917, 0.5069, 0.5258, 0.7605] +2026-04-11 20:25:37.668081: Epoch time: 101.44 s +2026-04-11 20:25:38.805074: +2026-04-11 20:25:38.806744: Epoch 1188 +2026-04-11 20:25:38.808123: Current learning rate: 0.00728 +2026-04-11 20:27:20.001681: train_loss -0.4068 +2026-04-11 20:27:20.006413: val_loss -0.3611 +2026-04-11 20:27:20.008445: Pseudo dice [0.0, 0.1655, 0.6436, 0.8445, 0.1579, 0.6495, 0.7155] +2026-04-11 20:27:20.010303: Epoch time: 101.2 s +2026-04-11 20:27:21.157801: +2026-04-11 20:27:21.159487: Epoch 1189 +2026-04-11 20:27:21.161071: Current learning rate: 0.00728 +2026-04-11 20:29:02.424736: train_loss -0.3944 +2026-04-11 20:29:02.429317: val_loss -0.3466 +2026-04-11 20:29:02.430762: Pseudo dice [0.0, 0.331, 0.7639, 0.7478, 0.5366, 0.7005, 0.5022] +2026-04-11 20:29:02.432165: Epoch time: 101.27 s +2026-04-11 20:29:03.555233: +2026-04-11 20:29:03.557283: Epoch 1190 +2026-04-11 20:29:03.559155: Current learning rate: 0.00728 +2026-04-11 20:30:45.025837: train_loss -0.3884 +2026-04-11 20:30:45.030189: val_loss -0.3631 +2026-04-11 20:30:45.031762: Pseudo dice [0.0, 0.3124, 0.7414, 0.7078, 0.4092, 0.8061, 0.7895] +2026-04-11 20:30:45.033397: Epoch time: 101.47 s +2026-04-11 20:30:46.165416: +2026-04-11 20:30:46.166890: Epoch 1191 +2026-04-11 20:30:46.168281: Current learning rate: 0.00728 +2026-04-11 20:32:27.495245: train_loss -0.3971 +2026-04-11 20:32:27.500556: val_loss -0.3001 +2026-04-11 20:32:27.502656: Pseudo dice [0.1708, 0.1161, 0.3155, 0.6693, 0.1252, 0.4543, 0.6763] +2026-04-11 20:32:27.504966: Epoch time: 101.33 s +2026-04-11 20:32:28.634365: +2026-04-11 20:32:28.636132: Epoch 1192 +2026-04-11 20:32:28.637696: Current learning rate: 0.00727 +2026-04-11 20:34:09.940032: train_loss -0.3892 +2026-04-11 20:34:09.944307: val_loss -0.3372 +2026-04-11 20:34:09.945766: Pseudo dice [0.2628, 0.2754, 0.622, 0.4575, 0.3689, 0.7085, 0.7639] +2026-04-11 20:34:09.947250: Epoch time: 101.31 s +2026-04-11 20:34:11.084719: +2026-04-11 20:34:11.086071: Epoch 1193 +2026-04-11 20:34:11.087523: Current learning rate: 0.00727 +2026-04-11 20:35:52.503558: train_loss -0.3864 +2026-04-11 20:35:52.508165: val_loss -0.3672 +2026-04-11 20:35:52.510289: Pseudo dice [0.4877, 0.0, 0.6382, 0.823, 0.4318, 0.7094, 0.7831] +2026-04-11 20:35:52.513034: Epoch time: 101.42 s +2026-04-11 20:35:53.659592: +2026-04-11 20:35:53.661544: Epoch 1194 +2026-04-11 20:35:53.663165: Current learning rate: 0.00727 +2026-04-11 20:37:35.060966: train_loss -0.3717 +2026-04-11 20:37:35.065705: val_loss -0.3291 +2026-04-11 20:37:35.067352: Pseudo dice [0.3064, 0.0, 0.6004, 0.5173, 0.3435, 0.7448, 0.4594] +2026-04-11 20:37:35.068995: Epoch time: 101.4 s +2026-04-11 20:37:36.197199: +2026-04-11 20:37:36.198806: Epoch 1195 +2026-04-11 20:37:36.200217: Current learning rate: 0.00727 +2026-04-11 20:39:17.664874: train_loss -0.3623 +2026-04-11 20:39:17.669441: val_loss -0.3318 +2026-04-11 20:39:17.670726: Pseudo dice [0.1704, 0.2401, 0.5172, 0.6922, 0.2644, 0.6482, 0.7104] +2026-04-11 20:39:17.672248: Epoch time: 101.47 s +2026-04-11 20:39:18.796847: +2026-04-11 20:39:18.798455: Epoch 1196 +2026-04-11 20:39:18.799796: Current learning rate: 0.00726 +2026-04-11 20:41:00.297803: train_loss -0.385 +2026-04-11 20:41:00.303347: val_loss -0.3539 +2026-04-11 20:41:00.304891: Pseudo dice [0.0, 0.1745, 0.8118, 0.4252, 0.2735, 0.8313, 0.7008] +2026-04-11 20:41:00.306195: Epoch time: 101.5 s +2026-04-11 20:41:02.714097: +2026-04-11 20:41:02.715863: Epoch 1197 +2026-04-11 20:41:02.717359: Current learning rate: 0.00726 +2026-04-11 20:42:44.313241: train_loss -0.3959 +2026-04-11 20:42:44.317347: val_loss -0.3341 +2026-04-11 20:42:44.318628: Pseudo dice [0.0, 0.2428, 0.739, 0.8104, 0.3757, 0.5245, 0.8232] +2026-04-11 20:42:44.319905: Epoch time: 101.6 s +2026-04-11 20:42:45.454415: +2026-04-11 20:42:45.455837: Epoch 1198 +2026-04-11 20:42:45.457252: Current learning rate: 0.00726 +2026-04-11 20:44:27.176865: train_loss -0.382 +2026-04-11 20:44:27.180965: val_loss -0.3135 +2026-04-11 20:44:27.182593: Pseudo dice [0.0, 0.0, 0.5125, 0.4202, 0.1625, 0.6118, 0.781] +2026-04-11 20:44:27.183995: Epoch time: 101.73 s +2026-04-11 20:44:28.324570: +2026-04-11 20:44:28.326186: Epoch 1199 +2026-04-11 20:44:28.327549: Current learning rate: 0.00726 +2026-04-11 20:46:09.890905: train_loss -0.3778 +2026-04-11 20:46:09.895942: val_loss -0.322 +2026-04-11 20:46:09.897574: Pseudo dice [0.0003, 0.2002, 0.6007, 0.8289, 0.1624, 0.5519, 0.7837] +2026-04-11 20:46:09.899314: Epoch time: 101.57 s +2026-04-11 20:46:12.643999: +2026-04-11 20:46:12.662938: Epoch 1200 +2026-04-11 20:46:12.673230: Current learning rate: 0.00725 +2026-04-11 20:47:54.050238: train_loss -0.4019 +2026-04-11 20:47:54.054235: val_loss -0.3206 +2026-04-11 20:47:54.055965: Pseudo dice [0.6177, 0.2136, 0.544, 0.0001, 0.178, 0.2839, 0.4851] +2026-04-11 20:47:54.057302: Epoch time: 101.41 s +2026-04-11 20:47:55.211777: +2026-04-11 20:47:55.213686: Epoch 1201 +2026-04-11 20:47:55.215384: Current learning rate: 0.00725 +2026-04-11 20:49:36.572832: train_loss -0.3653 +2026-04-11 20:49:36.577357: val_loss -0.3226 +2026-04-11 20:49:36.578768: Pseudo dice [0.0852, 0.0585, 0.6314, 0.6189, 0.1633, 0.446, 0.3601] +2026-04-11 20:49:36.580276: Epoch time: 101.36 s +2026-04-11 20:49:37.727158: +2026-04-11 20:49:37.728953: Epoch 1202 +2026-04-11 20:49:37.730431: Current learning rate: 0.00725 +2026-04-11 20:51:19.398520: train_loss -0.3479 +2026-04-11 20:51:19.402731: val_loss -0.3094 +2026-04-11 20:51:19.404114: Pseudo dice [0.0, 0.0836, 0.6102, 0.8014, 0.2565, 0.5375, 0.2792] +2026-04-11 20:51:19.405652: Epoch time: 101.67 s +2026-04-11 20:51:20.539639: +2026-04-11 20:51:20.543608: Epoch 1203 +2026-04-11 20:51:20.545231: Current learning rate: 0.00725 +2026-04-11 20:53:02.179700: train_loss -0.365 +2026-04-11 20:53:02.184563: val_loss -0.3349 +2026-04-11 20:53:02.186349: Pseudo dice [0.0, 0.1553, 0.436, 0.1416, 0.4176, 0.5372, 0.8485] +2026-04-11 20:53:02.188284: Epoch time: 101.64 s +2026-04-11 20:53:03.328738: +2026-04-11 20:53:03.331995: Epoch 1204 +2026-04-11 20:53:03.334200: Current learning rate: 0.00724 +2026-04-11 20:54:45.127012: train_loss -0.3962 +2026-04-11 20:54:45.131093: val_loss -0.3457 +2026-04-11 20:54:45.132562: Pseudo dice [0.2428, 0.2641, 0.7234, 0.8832, 0.3744, 0.818, 0.8169] +2026-04-11 20:54:45.133814: Epoch time: 101.8 s +2026-04-11 20:54:46.259197: +2026-04-11 20:54:46.260630: Epoch 1205 +2026-04-11 20:54:46.261958: Current learning rate: 0.00724 +2026-04-11 20:56:27.862310: train_loss -0.3947 +2026-04-11 20:56:27.866724: val_loss -0.3682 +2026-04-11 20:56:27.868130: Pseudo dice [0.3635, 0.0, 0.691, 0.7755, 0.2868, 0.7458, 0.8057] +2026-04-11 20:56:27.869379: Epoch time: 101.61 s +2026-04-11 20:56:28.994110: +2026-04-11 20:56:28.995637: Epoch 1206 +2026-04-11 20:56:28.996940: Current learning rate: 0.00724 +2026-04-11 20:58:10.710190: train_loss -0.3948 +2026-04-11 20:58:10.714965: val_loss -0.3955 +2026-04-11 20:58:10.716338: Pseudo dice [0.7619, 0.8382, 0.7866, 0.6514, 0.459, 0.5549, 0.7305] +2026-04-11 20:58:10.718366: Epoch time: 101.72 s +2026-04-11 20:58:11.855700: +2026-04-11 20:58:11.857438: Epoch 1207 +2026-04-11 20:58:11.859022: Current learning rate: 0.00724 +2026-04-11 20:59:53.615941: train_loss -0.3959 +2026-04-11 20:59:53.620258: val_loss -0.3203 +2026-04-11 20:59:53.621955: Pseudo dice [0.04, 0.2303, 0.6307, 0.7135, 0.2686, 0.5773, 0.645] +2026-04-11 20:59:53.623294: Epoch time: 101.76 s +2026-04-11 20:59:54.769065: +2026-04-11 20:59:54.770750: Epoch 1208 +2026-04-11 20:59:54.771948: Current learning rate: 0.00724 +2026-04-11 21:01:36.154687: train_loss -0.3795 +2026-04-11 21:01:36.159118: val_loss -0.3398 +2026-04-11 21:01:36.160465: Pseudo dice [0.3799, 0.0474, 0.7745, 0.6343, 0.1938, 0.7546, 0.8059] +2026-04-11 21:01:36.161709: Epoch time: 101.39 s +2026-04-11 21:01:37.289306: +2026-04-11 21:01:37.290763: Epoch 1209 +2026-04-11 21:01:37.292016: Current learning rate: 0.00723 +2026-04-11 21:03:19.095004: train_loss -0.4146 +2026-04-11 21:03:19.099340: val_loss -0.3503 +2026-04-11 21:03:19.100696: Pseudo dice [0.292, 0.3291, 0.6665, 0.5577, 0.3194, 0.6503, 0.8564] +2026-04-11 21:03:19.101969: Epoch time: 101.81 s +2026-04-11 21:03:20.235422: +2026-04-11 21:03:20.236765: Epoch 1210 +2026-04-11 21:03:20.238088: Current learning rate: 0.00723 +2026-04-11 21:05:01.785231: train_loss -0.4011 +2026-04-11 21:05:01.789713: val_loss -0.3354 +2026-04-11 21:05:01.791278: Pseudo dice [0.2489, 0.115, 0.7683, 0.3995, 0.2257, 0.7069, 0.5282] +2026-04-11 21:05:01.792932: Epoch time: 101.55 s +2026-04-11 21:05:02.940628: +2026-04-11 21:05:02.942176: Epoch 1211 +2026-04-11 21:05:02.943499: Current learning rate: 0.00723 +2026-04-11 21:06:44.682229: train_loss -0.3983 +2026-04-11 21:06:44.686611: val_loss -0.3342 +2026-04-11 21:06:44.688059: Pseudo dice [0.2674, 0.4974, 0.3683, 0.6448, 0.4862, 0.7708, 0.5931] +2026-04-11 21:06:44.690202: Epoch time: 101.74 s +2026-04-11 21:06:45.845234: +2026-04-11 21:06:45.848355: Epoch 1212 +2026-04-11 21:06:45.862677: Current learning rate: 0.00723 +2026-04-11 21:08:27.409015: train_loss -0.3935 +2026-04-11 21:08:27.413132: val_loss -0.2924 +2026-04-11 21:08:27.414606: Pseudo dice [0.0718, 0.0, 0.4273, 0.3468, 0.1887, 0.8003, 0.2967] +2026-04-11 21:08:27.415972: Epoch time: 101.57 s +2026-04-11 21:08:28.640013: +2026-04-11 21:08:28.641437: Epoch 1213 +2026-04-11 21:08:28.642702: Current learning rate: 0.00722 +2026-04-11 21:10:09.950666: train_loss -0.3894 +2026-04-11 21:10:09.954543: val_loss -0.3523 +2026-04-11 21:10:09.955905: Pseudo dice [0.4922, 0.5825, 0.542, 0.1607, 0.2804, 0.3114, 0.5241] +2026-04-11 21:10:09.957417: Epoch time: 101.31 s +2026-04-11 21:10:11.095879: +2026-04-11 21:10:11.097381: Epoch 1214 +2026-04-11 21:10:11.098676: Current learning rate: 0.00722 +2026-04-11 21:11:52.655201: train_loss -0.4004 +2026-04-11 21:11:52.659645: val_loss -0.3827 +2026-04-11 21:11:52.661425: Pseudo dice [0.7157, 0.5663, 0.7004, 0.5408, 0.4276, 0.7895, 0.6847] +2026-04-11 21:11:52.663000: Epoch time: 101.56 s +2026-04-11 21:11:53.802114: +2026-04-11 21:11:53.803833: Epoch 1215 +2026-04-11 21:11:53.805394: Current learning rate: 0.00722 +2026-04-11 21:13:35.221921: train_loss -0.4032 +2026-04-11 21:13:35.226659: val_loss -0.3345 +2026-04-11 21:13:35.228276: Pseudo dice [0.4664, 0.0648, 0.6427, 0.4303, 0.2413, 0.7449, 0.1185] +2026-04-11 21:13:35.230144: Epoch time: 101.42 s +2026-04-11 21:13:36.377946: +2026-04-11 21:13:36.379735: Epoch 1216 +2026-04-11 21:13:36.381129: Current learning rate: 0.00722 +2026-04-11 21:15:17.914479: train_loss -0.3854 +2026-04-11 21:15:17.922078: val_loss -0.3807 +2026-04-11 21:15:17.923691: Pseudo dice [0.0, 0.728, 0.7281, 0.8136, 0.2394, 0.6843, 0.8707] +2026-04-11 21:15:17.925047: Epoch time: 101.54 s +2026-04-11 21:15:20.186315: +2026-04-11 21:15:20.188200: Epoch 1217 +2026-04-11 21:15:20.189620: Current learning rate: 0.00721 +2026-04-11 21:17:01.665694: train_loss -0.3827 +2026-04-11 21:17:01.670333: val_loss -0.3964 +2026-04-11 21:17:01.671968: Pseudo dice [0.3672, 0.0, 0.6355, 0.7833, 0.3181, 0.7033, 0.7582] +2026-04-11 21:17:01.673203: Epoch time: 101.48 s +2026-04-11 21:17:02.832032: +2026-04-11 21:17:02.833379: Epoch 1218 +2026-04-11 21:17:02.834633: Current learning rate: 0.00721 +2026-04-11 21:18:44.374884: train_loss -0.3936 +2026-04-11 21:18:44.379051: val_loss -0.3477 +2026-04-11 21:18:44.380748: Pseudo dice [0.6884, 0.5491, 0.7172, 0.6091, 0.1353, 0.6355, 0.6781] +2026-04-11 21:18:44.382221: Epoch time: 101.55 s +2026-04-11 21:18:45.525589: +2026-04-11 21:18:45.527344: Epoch 1219 +2026-04-11 21:18:45.528782: Current learning rate: 0.00721 +2026-04-11 21:20:27.144886: train_loss -0.3844 +2026-04-11 21:20:27.150169: val_loss -0.3383 +2026-04-11 21:20:27.151945: Pseudo dice [0.3235, 0.3291, 0.6054, 0.445, 0.2507, 0.7132, 0.6976] +2026-04-11 21:20:27.153755: Epoch time: 101.62 s +2026-04-11 21:20:28.290889: +2026-04-11 21:20:28.292639: Epoch 1220 +2026-04-11 21:20:28.293988: Current learning rate: 0.00721 +2026-04-11 21:22:09.856156: train_loss -0.4 +2026-04-11 21:22:09.860372: val_loss -0.3615 +2026-04-11 21:22:09.861774: Pseudo dice [0.5444, 0.3832, 0.5797, 0.7884, 0.422, 0.5627, 0.4027] +2026-04-11 21:22:09.863030: Epoch time: 101.57 s +2026-04-11 21:22:11.006767: +2026-04-11 21:22:11.008091: Epoch 1221 +2026-04-11 21:22:11.009393: Current learning rate: 0.00721 +2026-04-11 21:23:52.465359: train_loss -0.3955 +2026-04-11 21:23:52.469701: val_loss -0.3433 +2026-04-11 21:23:52.471340: Pseudo dice [0.2852, 0.5387, 0.6168, 0.8428, 0.1978, 0.2396, 0.6555] +2026-04-11 21:23:52.472630: Epoch time: 101.46 s +2026-04-11 21:23:53.744462: +2026-04-11 21:23:53.746146: Epoch 1222 +2026-04-11 21:23:53.747540: Current learning rate: 0.0072 +2026-04-11 21:25:35.479592: train_loss -0.3745 +2026-04-11 21:25:35.483621: val_loss -0.3593 +2026-04-11 21:25:35.485257: Pseudo dice [0.0899, 0.2312, 0.6625, 0.7094, 0.4663, 0.802, 0.3894] +2026-04-11 21:25:35.486952: Epoch time: 101.74 s +2026-04-11 21:25:36.613293: +2026-04-11 21:25:36.620974: Epoch 1223 +2026-04-11 21:25:36.632806: Current learning rate: 0.0072 +2026-04-11 21:27:18.064377: train_loss -0.3931 +2026-04-11 21:27:18.069771: val_loss -0.3446 +2026-04-11 21:27:18.072972: Pseudo dice [0.009, 0.0056, 0.5127, 0.7994, 0.258, 0.78, 0.5205] +2026-04-11 21:27:18.074708: Epoch time: 101.45 s +2026-04-11 21:27:19.228565: +2026-04-11 21:27:19.230276: Epoch 1224 +2026-04-11 21:27:19.231934: Current learning rate: 0.0072 +2026-04-11 21:29:00.717226: train_loss -0.3964 +2026-04-11 21:29:00.721468: val_loss -0.3656 +2026-04-11 21:29:00.722851: Pseudo dice [0.1731, 0.2387, 0.7403, 0.597, 0.3495, 0.6419, 0.6842] +2026-04-11 21:29:00.724149: Epoch time: 101.49 s +2026-04-11 21:29:01.867630: +2026-04-11 21:29:01.869340: Epoch 1225 +2026-04-11 21:29:01.870857: Current learning rate: 0.0072 +2026-04-11 21:30:43.544662: train_loss -0.4204 +2026-04-11 21:30:43.551512: val_loss -0.3879 +2026-04-11 21:30:43.553178: Pseudo dice [0.728, 0.1483, 0.6479, 0.8871, 0.2246, 0.7872, 0.8045] +2026-04-11 21:30:43.555111: Epoch time: 101.68 s +2026-04-11 21:30:44.706373: +2026-04-11 21:30:44.708416: Epoch 1226 +2026-04-11 21:30:44.710400: Current learning rate: 0.00719 +2026-04-11 21:32:26.096588: train_loss -0.4137 +2026-04-11 21:32:26.100829: val_loss -0.3189 +2026-04-11 21:32:26.102262: Pseudo dice [0.1254, 0.2151, 0.2668, 0.6562, 0.4649, 0.5184, 0.7197] +2026-04-11 21:32:26.103895: Epoch time: 101.39 s +2026-04-11 21:32:27.252897: +2026-04-11 21:32:27.254352: Epoch 1227 +2026-04-11 21:32:27.255583: Current learning rate: 0.00719 +2026-04-11 21:34:08.942117: train_loss -0.3765 +2026-04-11 21:34:08.965893: val_loss -0.3158 +2026-04-11 21:34:08.967481: Pseudo dice [0.0035, 0.4711, 0.6426, 0.6848, 0.1245, 0.7559, 0.8352] +2026-04-11 21:34:08.968995: Epoch time: 101.69 s +2026-04-11 21:34:10.093389: +2026-04-11 21:34:10.094933: Epoch 1228 +2026-04-11 21:34:10.096284: Current learning rate: 0.00719 +2026-04-11 21:35:51.630481: train_loss -0.3902 +2026-04-11 21:35:51.634695: val_loss -0.3225 +2026-04-11 21:35:51.636177: Pseudo dice [0.1054, 0.0987, 0.545, 0.8382, 0.0925, 0.4611, 0.5148] +2026-04-11 21:35:51.637914: Epoch time: 101.54 s +2026-04-11 21:35:52.771857: +2026-04-11 21:35:52.773432: Epoch 1229 +2026-04-11 21:35:52.774726: Current learning rate: 0.00719 +2026-04-11 21:37:34.329031: train_loss -0.3624 +2026-04-11 21:37:34.333612: val_loss -0.3679 +2026-04-11 21:37:34.335287: Pseudo dice [0.6006, 0.5357, 0.6845, 0.8246, 0.1939, 0.6757, 0.7928] +2026-04-11 21:37:34.337500: Epoch time: 101.56 s +2026-04-11 21:37:35.481176: +2026-04-11 21:37:35.482494: Epoch 1230 +2026-04-11 21:37:35.483712: Current learning rate: 0.00718 +2026-04-11 21:39:17.062864: train_loss -0.3903 +2026-04-11 21:39:17.067369: val_loss -0.3507 +2026-04-11 21:39:17.069003: Pseudo dice [0.1638, 0.5233, 0.4336, 0.6336, 0.4233, 0.6592, 0.531] +2026-04-11 21:39:17.070406: Epoch time: 101.58 s +2026-04-11 21:39:18.208454: +2026-04-11 21:39:18.210006: Epoch 1231 +2026-04-11 21:39:18.211330: Current learning rate: 0.00718 +2026-04-11 21:40:59.794481: train_loss -0.3889 +2026-04-11 21:40:59.799013: val_loss -0.382 +2026-04-11 21:40:59.800696: Pseudo dice [0.4455, 0.1926, 0.8171, 0.7403, 0.2054, 0.8159, 0.8473] +2026-04-11 21:40:59.802274: Epoch time: 101.59 s +2026-04-11 21:41:00.936795: +2026-04-11 21:41:00.938646: Epoch 1232 +2026-04-11 21:41:00.940695: Current learning rate: 0.00718 +2026-04-11 21:42:42.207941: train_loss -0.3708 +2026-04-11 21:42:42.211666: val_loss -0.2792 +2026-04-11 21:42:42.213209: Pseudo dice [0.0766, 0.2071, 0.5157, 0.2573, 0.0, 0.6423, 0.2055] +2026-04-11 21:42:42.214520: Epoch time: 101.27 s +2026-04-11 21:42:43.353447: +2026-04-11 21:42:43.354938: Epoch 1233 +2026-04-11 21:42:43.356164: Current learning rate: 0.00718 +2026-04-11 21:44:24.902744: train_loss -0.3671 +2026-04-11 21:44:24.906966: val_loss -0.3632 +2026-04-11 21:44:24.908662: Pseudo dice [0.3493, 0.3113, 0.6392, 0.8553, 0.2129, 0.5264, 0.7892] +2026-04-11 21:44:24.910377: Epoch time: 101.55 s +2026-04-11 21:44:26.051125: +2026-04-11 21:44:26.052638: Epoch 1234 +2026-04-11 21:44:26.053937: Current learning rate: 0.00717 +2026-04-11 21:46:07.280710: train_loss -0.3669 +2026-04-11 21:46:07.285186: val_loss -0.2966 +2026-04-11 21:46:07.286781: Pseudo dice [0.2357, 0.0912, 0.5041, 0.5987, 0.0516, 0.4385, 0.6033] +2026-04-11 21:46:07.288319: Epoch time: 101.23 s +2026-04-11 21:46:08.422421: +2026-04-11 21:46:08.423983: Epoch 1235 +2026-04-11 21:46:08.425615: Current learning rate: 0.00717 +2026-04-11 21:47:49.767596: train_loss -0.353 +2026-04-11 21:47:49.772453: val_loss -0.3472 +2026-04-11 21:47:49.774661: Pseudo dice [0.0568, 0.0, 0.7261, 0.8521, 0.2425, 0.7535, 0.5721] +2026-04-11 21:47:49.776022: Epoch time: 101.35 s +2026-04-11 21:47:50.915397: +2026-04-11 21:47:50.916923: Epoch 1236 +2026-04-11 21:47:50.918545: Current learning rate: 0.00717 +2026-04-11 21:49:32.500155: train_loss -0.38 +2026-04-11 21:49:32.508462: val_loss -0.3481 +2026-04-11 21:49:32.511192: Pseudo dice [0.6902, 0.55, 0.4249, 0.878, 0.3467, 0.7658, 0.8546] +2026-04-11 21:49:32.513091: Epoch time: 101.59 s +2026-04-11 21:49:33.662941: +2026-04-11 21:49:33.664953: Epoch 1237 +2026-04-11 21:49:33.667076: Current learning rate: 0.00717 +2026-04-11 21:51:16.096119: train_loss -0.3726 +2026-04-11 21:51:16.105763: val_loss -0.3184 +2026-04-11 21:51:16.115916: Pseudo dice [0.0, 0.3519, 0.5387, 0.3904, 0.2771, 0.5914, 0.1929] +2026-04-11 21:51:16.123844: Epoch time: 102.44 s +2026-04-11 21:51:17.272664: +2026-04-11 21:51:17.274609: Epoch 1238 +2026-04-11 21:51:17.276907: Current learning rate: 0.00717 +2026-04-11 21:52:58.915263: train_loss -0.3807 +2026-04-11 21:52:58.919661: val_loss -0.3355 +2026-04-11 21:52:58.921308: Pseudo dice [0.002, 0.6184, 0.7633, 0.3492, 0.4613, 0.8102, 0.4092] +2026-04-11 21:52:58.922807: Epoch time: 101.65 s +2026-04-11 21:53:00.077745: +2026-04-11 21:53:00.079577: Epoch 1239 +2026-04-11 21:53:00.081214: Current learning rate: 0.00716 +2026-04-11 21:54:41.708063: train_loss -0.3773 +2026-04-11 21:54:41.721194: val_loss -0.3301 +2026-04-11 21:54:41.722851: Pseudo dice [0.0977, 0.0838, 0.5316, 0.3313, 0.3736, 0.6684, 0.7099] +2026-04-11 21:54:41.724548: Epoch time: 101.63 s +2026-04-11 21:54:42.885999: +2026-04-11 21:54:42.887412: Epoch 1240 +2026-04-11 21:54:42.888913: Current learning rate: 0.00716 +2026-04-11 21:56:24.314322: train_loss -0.4016 +2026-04-11 21:56:24.320398: val_loss -0.3408 +2026-04-11 21:56:24.322287: Pseudo dice [0.1952, 0.5292, 0.6078, 0.4916, 0.4466, 0.6708, 0.7941] +2026-04-11 21:56:24.324839: Epoch time: 101.43 s +2026-04-11 21:56:25.459147: +2026-04-11 21:56:25.461667: Epoch 1241 +2026-04-11 21:56:25.464413: Current learning rate: 0.00716 +2026-04-11 21:58:06.798718: train_loss -0.3715 +2026-04-11 21:58:06.803438: val_loss -0.3301 +2026-04-11 21:58:06.805317: Pseudo dice [0.2169, 0.0721, 0.7518, 0.1747, 0.3167, 0.4335, 0.6558] +2026-04-11 21:58:06.806979: Epoch time: 101.34 s +2026-04-11 21:58:07.943467: +2026-04-11 21:58:07.945262: Epoch 1242 +2026-04-11 21:58:07.947077: Current learning rate: 0.00716 +2026-04-11 21:59:49.453921: train_loss -0.3873 +2026-04-11 21:59:49.460254: val_loss -0.3215 +2026-04-11 21:59:49.462450: Pseudo dice [0.2732, 0.0396, 0.4622, 0.7507, 0.1568, 0.4129, 0.7968] +2026-04-11 21:59:49.464000: Epoch time: 101.51 s +2026-04-11 21:59:50.620970: +2026-04-11 21:59:50.623057: Epoch 1243 +2026-04-11 21:59:50.625002: Current learning rate: 0.00715 +2026-04-11 22:01:32.097626: train_loss -0.3876 +2026-04-11 22:01:32.103171: val_loss -0.3299 +2026-04-11 22:01:32.104673: Pseudo dice [0.0, 0.4612, 0.662, 0.069, 0.2696, 0.6197, 0.5299] +2026-04-11 22:01:32.106486: Epoch time: 101.48 s +2026-04-11 22:01:33.259909: +2026-04-11 22:01:33.261526: Epoch 1244 +2026-04-11 22:01:33.263299: Current learning rate: 0.00715 +2026-04-11 22:03:14.799410: train_loss -0.3581 +2026-04-11 22:03:14.805106: val_loss -0.322 +2026-04-11 22:03:14.807490: Pseudo dice [0.0, 0.2425, 0.7469, 0.3715, 0.4657, 0.4406, 0.5223] +2026-04-11 22:03:14.809670: Epoch time: 101.54 s +2026-04-11 22:03:15.946908: +2026-04-11 22:03:15.948708: Epoch 1245 +2026-04-11 22:03:15.950452: Current learning rate: 0.00715 +2026-04-11 22:04:57.511222: train_loss -0.3963 +2026-04-11 22:04:57.515684: val_loss -0.3423 +2026-04-11 22:04:57.517604: Pseudo dice [0.0951, 0.6728, 0.5838, 0.5158, 0.183, 0.6853, 0.5962] +2026-04-11 22:04:57.519028: Epoch time: 101.57 s +2026-04-11 22:04:58.674966: +2026-04-11 22:04:58.676880: Epoch 1246 +2026-04-11 22:04:58.678793: Current learning rate: 0.00715 +2026-04-11 22:06:40.235155: train_loss -0.3804 +2026-04-11 22:06:40.240335: val_loss -0.3148 +2026-04-11 22:06:40.241966: Pseudo dice [0.0673, 0.2511, 0.6131, 0.8727, 0.2548, 0.1767, 0.7707] +2026-04-11 22:06:40.243660: Epoch time: 101.56 s +2026-04-11 22:06:41.372690: +2026-04-11 22:06:41.374550: Epoch 1247 +2026-04-11 22:06:41.376526: Current learning rate: 0.00714 +2026-04-11 22:08:22.887421: train_loss -0.3687 +2026-04-11 22:08:22.894010: val_loss -0.3748 +2026-04-11 22:08:22.896068: Pseudo dice [0.2941, 0.3428, 0.7274, 0.8337, 0.341, 0.6339, 0.6979] +2026-04-11 22:08:22.898268: Epoch time: 101.52 s +2026-04-11 22:08:24.060179: +2026-04-11 22:08:24.061664: Epoch 1248 +2026-04-11 22:08:24.063478: Current learning rate: 0.00714 +2026-04-11 22:10:05.592898: train_loss -0.378 +2026-04-11 22:10:05.598590: val_loss -0.3616 +2026-04-11 22:10:05.600304: Pseudo dice [0.3767, 0.0, 0.7973, 0.7835, 0.5703, 0.4137, 0.7824] +2026-04-11 22:10:05.602511: Epoch time: 101.54 s +2026-04-11 22:10:07.073064: +2026-04-11 22:10:07.074788: Epoch 1249 +2026-04-11 22:10:07.076800: Current learning rate: 0.00714 +2026-04-11 22:11:48.511944: train_loss -0.3894 +2026-04-11 22:11:48.517523: val_loss -0.3736 +2026-04-11 22:11:48.519125: Pseudo dice [0.2311, 0.0032, 0.7314, 0.8641, 0.2185, 0.6847, 0.7276] +2026-04-11 22:11:48.520650: Epoch time: 101.44 s +2026-04-11 22:11:51.294226: +2026-04-11 22:11:51.296774: Epoch 1250 +2026-04-11 22:11:51.298561: Current learning rate: 0.00714 +2026-04-11 22:13:32.690681: train_loss -0.3989 +2026-04-11 22:13:32.696157: val_loss -0.3604 +2026-04-11 22:13:32.697907: Pseudo dice [0.2589, 0.4511, 0.6828, 0.6116, 0.3115, 0.4961, 0.5905] +2026-04-11 22:13:32.700379: Epoch time: 101.4 s +2026-04-11 22:13:33.856192: +2026-04-11 22:13:33.857969: Epoch 1251 +2026-04-11 22:13:33.859856: Current learning rate: 0.00714 +2026-04-11 22:15:15.374308: train_loss -0.3991 +2026-04-11 22:15:15.378694: val_loss -0.3725 +2026-04-11 22:15:15.380290: Pseudo dice [0.288, 0.4897, 0.777, 0.7802, 0.223, 0.8187, 0.7682] +2026-04-11 22:15:15.381599: Epoch time: 101.52 s +2026-04-11 22:15:16.531757: +2026-04-11 22:15:16.533432: Epoch 1252 +2026-04-11 22:15:16.535143: Current learning rate: 0.00713 +2026-04-11 22:16:57.870278: train_loss -0.3921 +2026-04-11 22:16:57.876024: val_loss -0.3639 +2026-04-11 22:16:57.879737: Pseudo dice [0.3532, 0.4028, 0.7878, 0.4585, 0.2234, 0.6446, 0.7322] +2026-04-11 22:16:57.881743: Epoch time: 101.34 s +2026-04-11 22:16:59.027204: +2026-04-11 22:16:59.029218: Epoch 1253 +2026-04-11 22:16:59.031307: Current learning rate: 0.00713 +2026-04-11 22:18:40.479349: train_loss -0.4001 +2026-04-11 22:18:40.491952: val_loss -0.3471 +2026-04-11 22:18:40.495314: Pseudo dice [0.0538, 0.0796, 0.8057, 0.8189, 0.1336, 0.8523, 0.6846] +2026-04-11 22:18:40.506585: Epoch time: 101.46 s +2026-04-11 22:18:41.663944: +2026-04-11 22:18:41.665709: Epoch 1254 +2026-04-11 22:18:41.667493: Current learning rate: 0.00713 +2026-04-11 22:20:23.219198: train_loss -0.4008 +2026-04-11 22:20:23.225487: val_loss -0.3388 +2026-04-11 22:20:23.228313: Pseudo dice [0.6048, 0.0782, 0.7356, 0.7411, 0.2355, 0.6627, 0.6519] +2026-04-11 22:20:23.230657: Epoch time: 101.56 s +2026-04-11 22:20:24.389653: +2026-04-11 22:20:24.391212: Epoch 1255 +2026-04-11 22:20:24.392938: Current learning rate: 0.00713 +2026-04-11 22:22:05.910297: train_loss -0.3795 +2026-04-11 22:22:05.915137: val_loss -0.3159 +2026-04-11 22:22:05.917335: Pseudo dice [0.0, 0.292, 0.2751, 0.5744, 0.3096, 0.8388, 0.7794] +2026-04-11 22:22:05.919132: Epoch time: 101.52 s +2026-04-11 22:22:07.076351: +2026-04-11 22:22:07.078176: Epoch 1256 +2026-04-11 22:22:07.080101: Current learning rate: 0.00712 +2026-04-11 22:23:48.376354: train_loss -0.3735 +2026-04-11 22:23:48.381300: val_loss -0.3215 +2026-04-11 22:23:48.383768: Pseudo dice [0.0, 0.1587, 0.4533, 0.7876, 0.3493, 0.5671, 0.5279] +2026-04-11 22:23:48.385829: Epoch time: 101.3 s +2026-04-11 22:23:50.626867: +2026-04-11 22:23:50.628571: Epoch 1257 +2026-04-11 22:23:50.630547: Current learning rate: 0.00712 +2026-04-11 22:25:32.039974: train_loss -0.3756 +2026-04-11 22:25:32.044907: val_loss -0.3061 +2026-04-11 22:25:32.046551: Pseudo dice [0.0, 0.0807, 0.5786, 0.5968, 0.1158, 0.4854, 0.7] +2026-04-11 22:25:32.048135: Epoch time: 101.42 s +2026-04-11 22:25:33.175135: +2026-04-11 22:25:33.176926: Epoch 1258 +2026-04-11 22:25:33.179028: Current learning rate: 0.00712 +2026-04-11 22:27:14.758567: train_loss -0.4003 +2026-04-11 22:27:14.766604: val_loss -0.3905 +2026-04-11 22:27:14.768924: Pseudo dice [0.0, 0.1857, 0.7519, 0.8277, 0.317, 0.8499, 0.8443] +2026-04-11 22:27:14.770614: Epoch time: 101.59 s +2026-04-11 22:27:15.962308: +2026-04-11 22:27:15.963721: Epoch 1259 +2026-04-11 22:27:15.965379: Current learning rate: 0.00712 +2026-04-11 22:28:57.569429: train_loss -0.3753 +2026-04-11 22:28:57.574445: val_loss -0.3534 +2026-04-11 22:28:57.576223: Pseudo dice [0.0, 0.033, 0.753, 0.6779, 0.2569, 0.7969, 0.7838] +2026-04-11 22:28:57.578017: Epoch time: 101.61 s +2026-04-11 22:28:58.729864: +2026-04-11 22:28:58.732027: Epoch 1260 +2026-04-11 22:28:58.734032: Current learning rate: 0.00711 +2026-04-11 22:30:40.456335: train_loss -0.389 +2026-04-11 22:30:40.463022: val_loss -0.3794 +2026-04-11 22:30:40.464769: Pseudo dice [0.6101, 0.3699, 0.7126, 0.7035, 0.4105, 0.662, 0.5451] +2026-04-11 22:30:40.466764: Epoch time: 101.73 s +2026-04-11 22:30:41.618471: +2026-04-11 22:30:41.620294: Epoch 1261 +2026-04-11 22:30:41.622251: Current learning rate: 0.00711 +2026-04-11 22:32:23.193241: train_loss -0.3867 +2026-04-11 22:32:23.199531: val_loss -0.3205 +2026-04-11 22:32:23.201234: Pseudo dice [0.1252, 0.5107, 0.4682, 0.7833, 0.4044, 0.2731, 0.6637] +2026-04-11 22:32:23.202855: Epoch time: 101.58 s +2026-04-11 22:32:24.341614: +2026-04-11 22:32:24.343201: Epoch 1262 +2026-04-11 22:32:24.344995: Current learning rate: 0.00711 +2026-04-11 22:34:05.981663: train_loss -0.3947 +2026-04-11 22:34:05.988242: val_loss -0.3581 +2026-04-11 22:34:05.990185: Pseudo dice [0.4977, 0.0513, 0.3428, 0.8792, 0.3303, 0.4555, 0.7192] +2026-04-11 22:34:05.991755: Epoch time: 101.64 s +2026-04-11 22:34:07.139219: +2026-04-11 22:34:07.140935: Epoch 1263 +2026-04-11 22:34:07.142744: Current learning rate: 0.00711 +2026-04-11 22:35:48.479594: train_loss -0.3879 +2026-04-11 22:35:48.484174: val_loss -0.3268 +2026-04-11 22:35:48.485851: Pseudo dice [0.2999, 0.0452, 0.5293, 0.0805, 0.3902, 0.7244, 0.4621] +2026-04-11 22:35:48.487290: Epoch time: 101.34 s +2026-04-11 22:35:49.640413: +2026-04-11 22:35:49.642159: Epoch 1264 +2026-04-11 22:35:49.643869: Current learning rate: 0.0071 +2026-04-11 22:37:31.107847: train_loss -0.4139 +2026-04-11 22:37:31.113601: val_loss -0.3595 +2026-04-11 22:37:31.115155: Pseudo dice [0.4652, 0.2542, 0.6997, 0.6799, 0.4549, 0.5468, 0.5526] +2026-04-11 22:37:31.117034: Epoch time: 101.47 s +2026-04-11 22:37:32.265361: +2026-04-11 22:37:32.266893: Epoch 1265 +2026-04-11 22:37:32.268959: Current learning rate: 0.0071 +2026-04-11 22:39:13.691876: train_loss -0.3982 +2026-04-11 22:39:13.696462: val_loss -0.3593 +2026-04-11 22:39:13.698078: Pseudo dice [0.0, 0.4116, 0.7602, 0.7733, 0.429, 0.5846, 0.7642] +2026-04-11 22:39:13.700078: Epoch time: 101.43 s +2026-04-11 22:39:14.834501: +2026-04-11 22:39:14.836143: Epoch 1266 +2026-04-11 22:39:14.837918: Current learning rate: 0.0071 +2026-04-11 22:40:55.882107: train_loss -0.3935 +2026-04-11 22:40:55.886642: val_loss -0.3448 +2026-04-11 22:40:55.888338: Pseudo dice [0.0, 0.6193, 0.7889, 0.6518, 0.3302, 0.7159, 0.6424] +2026-04-11 22:40:55.889662: Epoch time: 101.05 s +2026-04-11 22:40:57.057795: +2026-04-11 22:40:57.059429: Epoch 1267 +2026-04-11 22:40:57.061070: Current learning rate: 0.0071 +2026-04-11 22:42:38.461695: train_loss -0.3752 +2026-04-11 22:42:38.467124: val_loss -0.3627 +2026-04-11 22:42:38.469222: Pseudo dice [0.0, 0.109, 0.7854, 0.7651, 0.1498, 0.5423, 0.809] +2026-04-11 22:42:38.470732: Epoch time: 101.41 s +2026-04-11 22:42:39.629555: +2026-04-11 22:42:39.631261: Epoch 1268 +2026-04-11 22:42:39.633235: Current learning rate: 0.0071 +2026-04-11 22:44:21.070286: train_loss -0.384 +2026-04-11 22:44:21.075481: val_loss -0.3699 +2026-04-11 22:44:21.077012: Pseudo dice [0.0, 0.6526, 0.666, 0.8208, 0.3656, 0.8701, 0.5707] +2026-04-11 22:44:21.078371: Epoch time: 101.44 s +2026-04-11 22:44:22.238415: +2026-04-11 22:44:22.240235: Epoch 1269 +2026-04-11 22:44:22.247001: Current learning rate: 0.00709 +2026-04-11 22:46:03.581444: train_loss -0.3769 +2026-04-11 22:46:03.585678: val_loss -0.3411 +2026-04-11 22:46:03.587371: Pseudo dice [0.2872, 0.0, 0.4734, 0.3999, 0.347, 0.4299, 0.4826] +2026-04-11 22:46:03.588906: Epoch time: 101.35 s +2026-04-11 22:46:04.732677: +2026-04-11 22:46:04.734140: Epoch 1270 +2026-04-11 22:46:04.735736: Current learning rate: 0.00709 +2026-04-11 22:47:45.879439: train_loss -0.3798 +2026-04-11 22:47:45.884857: val_loss -0.347 +2026-04-11 22:47:45.886630: Pseudo dice [0.3389, 0.0655, 0.7889, 0.736, 0.3322, 0.7447, 0.1678] +2026-04-11 22:47:45.888128: Epoch time: 101.15 s +2026-04-11 22:47:47.032896: +2026-04-11 22:47:47.034745: Epoch 1271 +2026-04-11 22:47:47.036933: Current learning rate: 0.00709 +2026-04-11 22:49:28.227804: train_loss -0.3762 +2026-04-11 22:49:28.232922: val_loss -0.3566 +2026-04-11 22:49:28.234992: Pseudo dice [0.2054, 0.2583, 0.5869, 0.8277, 0.4061, 0.5173, 0.7832] +2026-04-11 22:49:28.236746: Epoch time: 101.2 s +2026-04-11 22:49:29.401034: +2026-04-11 22:49:29.402504: Epoch 1272 +2026-04-11 22:49:29.404387: Current learning rate: 0.00709 +2026-04-11 22:51:10.610039: train_loss -0.3686 +2026-04-11 22:51:10.614843: val_loss -0.348 +2026-04-11 22:51:10.616686: Pseudo dice [0.6192, 0.1392, 0.5164, 0.6256, 0.2234, 0.6341, 0.9144] +2026-04-11 22:51:10.618289: Epoch time: 101.21 s +2026-04-11 22:51:11.783142: +2026-04-11 22:51:11.784540: Epoch 1273 +2026-04-11 22:51:11.786108: Current learning rate: 0.00708 +2026-04-11 22:52:53.081618: train_loss -0.3753 +2026-04-11 22:52:53.086396: val_loss -0.3665 +2026-04-11 22:52:53.088459: Pseudo dice [0.0, 0.4562, 0.7444, 0.7593, 0.3074, 0.5019, 0.7873] +2026-04-11 22:52:53.090648: Epoch time: 101.3 s +2026-04-11 22:52:54.242915: +2026-04-11 22:52:54.244992: Epoch 1274 +2026-04-11 22:52:54.246923: Current learning rate: 0.00708 +2026-04-11 22:54:35.528003: train_loss -0.3877 +2026-04-11 22:54:35.533190: val_loss -0.323 +2026-04-11 22:54:35.534989: Pseudo dice [0.0, 0.1777, 0.7275, 0.3389, 0.3551, 0.5428, 0.5862] +2026-04-11 22:54:35.538315: Epoch time: 101.29 s +2026-04-11 22:54:36.686117: +2026-04-11 22:54:36.687642: Epoch 1275 +2026-04-11 22:54:36.689630: Current learning rate: 0.00708 +2026-04-11 22:56:17.800012: train_loss -0.3728 +2026-04-11 22:56:17.804855: val_loss -0.336 +2026-04-11 22:56:17.806538: Pseudo dice [0.0, 0.2955, 0.6063, 0.6498, 0.2875, 0.407, 0.6179] +2026-04-11 22:56:17.808345: Epoch time: 101.12 s +2026-04-11 22:56:18.957682: +2026-04-11 22:56:18.959278: Epoch 1276 +2026-04-11 22:56:18.961087: Current learning rate: 0.00708 +2026-04-11 22:58:00.212676: train_loss -0.3509 +2026-04-11 22:58:00.217692: val_loss -0.2898 +2026-04-11 22:58:00.219358: Pseudo dice [0.0, 0.3619, 0.4422, 0.8242, 0.2438, 0.4242, 0.6046] +2026-04-11 22:58:00.221091: Epoch time: 101.26 s +2026-04-11 22:58:01.379362: +2026-04-11 22:58:01.380871: Epoch 1277 +2026-04-11 22:58:01.382714: Current learning rate: 0.00707 +2026-04-11 22:59:43.917779: train_loss -0.376 +2026-04-11 22:59:43.923785: val_loss -0.3382 +2026-04-11 22:59:43.925403: Pseudo dice [0.0, 0.0, 0.5367, 0.8421, 0.3588, 0.2619, 0.6587] +2026-04-11 22:59:43.927027: Epoch time: 102.54 s +2026-04-11 22:59:45.077960: +2026-04-11 22:59:45.079819: Epoch 1278 +2026-04-11 22:59:45.082003: Current learning rate: 0.00707 +2026-04-11 23:01:26.398320: train_loss -0.3916 +2026-04-11 23:01:26.403288: val_loss -0.3037 +2026-04-11 23:01:26.405035: Pseudo dice [0.0, 0.349, 0.0873, 0.6317, 0.3422, 0.4794, 0.7541] +2026-04-11 23:01:26.406874: Epoch time: 101.32 s +2026-04-11 23:01:27.567452: +2026-04-11 23:01:27.569639: Epoch 1279 +2026-04-11 23:01:27.571656: Current learning rate: 0.00707 +2026-04-11 23:03:09.042484: train_loss -0.3981 +2026-04-11 23:03:09.047831: val_loss -0.3536 +2026-04-11 23:03:09.049891: Pseudo dice [0.0, 0.5006, 0.6023, 0.7582, 0.4438, 0.7418, 0.4748] +2026-04-11 23:03:09.051658: Epoch time: 101.48 s +2026-04-11 23:03:10.205255: +2026-04-11 23:03:10.207923: Epoch 1280 +2026-04-11 23:03:10.209773: Current learning rate: 0.00707 +2026-04-11 23:04:51.626704: train_loss -0.3889 +2026-04-11 23:04:51.634253: val_loss -0.3272 +2026-04-11 23:04:51.636310: Pseudo dice [0.0, 0.008, 0.7326, 0.6142, 0.023, 0.4044, 0.4438] +2026-04-11 23:04:51.638049: Epoch time: 101.42 s +2026-04-11 23:04:52.783026: +2026-04-11 23:04:52.784732: Epoch 1281 +2026-04-11 23:04:52.787078: Current learning rate: 0.00707 +2026-04-11 23:06:34.114756: train_loss -0.3785 +2026-04-11 23:06:34.120684: val_loss -0.3081 +2026-04-11 23:06:34.123310: Pseudo dice [0.0, 0.037, 0.4595, 0.3961, 0.216, 0.405, 0.4874] +2026-04-11 23:06:34.125018: Epoch time: 101.33 s +2026-04-11 23:06:35.272917: +2026-04-11 23:06:35.274424: Epoch 1282 +2026-04-11 23:06:35.276388: Current learning rate: 0.00706 +2026-04-11 23:08:16.496707: train_loss -0.3802 +2026-04-11 23:08:16.501844: val_loss -0.3353 +2026-04-11 23:08:16.503573: Pseudo dice [0.0007, 0.5402, 0.7775, 0.8824, 0.0732, 0.2756, 0.6899] +2026-04-11 23:08:16.505061: Epoch time: 101.23 s +2026-04-11 23:08:17.655178: +2026-04-11 23:08:17.656829: Epoch 1283 +2026-04-11 23:08:17.658588: Current learning rate: 0.00706 +2026-04-11 23:09:58.803257: train_loss -0.381 +2026-04-11 23:09:58.807800: val_loss -0.3622 +2026-04-11 23:09:58.809511: Pseudo dice [0.1181, 0.2098, 0.5687, 0.7862, 0.436, 0.7026, 0.8049] +2026-04-11 23:09:58.811049: Epoch time: 101.15 s +2026-04-11 23:09:59.963281: +2026-04-11 23:09:59.965640: Epoch 1284 +2026-04-11 23:09:59.968059: Current learning rate: 0.00706 +2026-04-11 23:11:41.308503: train_loss -0.3893 +2026-04-11 23:11:41.315290: val_loss -0.2546 +2026-04-11 23:11:41.317352: Pseudo dice [0.0, 0.4398, 0.4808, 0.2842, 0.2529, 0.4311, 0.2841] +2026-04-11 23:11:41.319185: Epoch time: 101.35 s +2026-04-11 23:11:42.473281: +2026-04-11 23:11:42.475283: Epoch 1285 +2026-04-11 23:11:42.477377: Current learning rate: 0.00706 +2026-04-11 23:13:23.792768: train_loss -0.3773 +2026-04-11 23:13:23.796942: val_loss -0.3366 +2026-04-11 23:13:23.798496: Pseudo dice [0.0, 0.6575, 0.6656, 0.6916, 0.1838, 0.4051, 0.6759] +2026-04-11 23:13:23.799941: Epoch time: 101.32 s +2026-04-11 23:13:24.965471: +2026-04-11 23:13:24.966903: Epoch 1286 +2026-04-11 23:13:24.968727: Current learning rate: 0.00705 +2026-04-11 23:15:06.260395: train_loss -0.3851 +2026-04-11 23:15:06.265610: val_loss -0.3171 +2026-04-11 23:15:06.267545: Pseudo dice [0.0, 0.3197, 0.5121, 0.8431, 0.1266, 0.6851, 0.2139] +2026-04-11 23:15:06.269109: Epoch time: 101.3 s +2026-04-11 23:15:07.420250: +2026-04-11 23:15:07.422197: Epoch 1287 +2026-04-11 23:15:07.424403: Current learning rate: 0.00705 +2026-04-11 23:16:49.001645: train_loss -0.3788 +2026-04-11 23:16:49.007263: val_loss -0.325 +2026-04-11 23:16:49.009155: Pseudo dice [0.0, 0.6106, 0.7525, 0.7077, 0.3844, 0.4807, 0.6319] +2026-04-11 23:16:49.011217: Epoch time: 101.58 s +2026-04-11 23:16:50.176095: +2026-04-11 23:16:50.177998: Epoch 1288 +2026-04-11 23:16:50.179982: Current learning rate: 0.00705 +2026-04-11 23:18:31.289871: train_loss -0.3647 +2026-04-11 23:18:31.296441: val_loss -0.3184 +2026-04-11 23:18:31.297983: Pseudo dice [0.0, 0.2831, 0.7947, 0.0259, 0.4165, 0.7892, 0.6785] +2026-04-11 23:18:31.299460: Epoch time: 101.12 s +2026-04-11 23:18:32.449972: +2026-04-11 23:18:32.451620: Epoch 1289 +2026-04-11 23:18:32.453402: Current learning rate: 0.00705 +2026-04-11 23:20:13.704157: train_loss -0.3688 +2026-04-11 23:20:13.709326: val_loss -0.3411 +2026-04-11 23:20:13.710697: Pseudo dice [0.0, 0.0471, 0.5925, 0.8428, 0.1846, 0.672, 0.8323] +2026-04-11 23:20:13.712602: Epoch time: 101.26 s +2026-04-11 23:20:14.861005: +2026-04-11 23:20:14.862499: Epoch 1290 +2026-04-11 23:20:14.864262: Current learning rate: 0.00704 +2026-04-11 23:21:56.110272: train_loss -0.3974 +2026-04-11 23:21:56.115624: val_loss -0.3794 +2026-04-11 23:21:56.117319: Pseudo dice [0.0, 0.3971, 0.8038, 0.5964, 0.3729, 0.8368, 0.4626] +2026-04-11 23:21:56.119136: Epoch time: 101.25 s +2026-04-11 23:21:57.285922: +2026-04-11 23:21:57.287359: Epoch 1291 +2026-04-11 23:21:57.288914: Current learning rate: 0.00704 +2026-04-11 23:23:38.507112: train_loss -0.3925 +2026-04-11 23:23:38.511990: val_loss -0.319 +2026-04-11 23:23:38.513736: Pseudo dice [0.0, 0.167, 0.3304, 0.2128, 0.0786, 0.8135, 0.6825] +2026-04-11 23:23:38.515393: Epoch time: 101.22 s +2026-04-11 23:23:39.655577: +2026-04-11 23:23:39.657297: Epoch 1292 +2026-04-11 23:23:39.659432: Current learning rate: 0.00704 +2026-04-11 23:25:21.259286: train_loss -0.3941 +2026-04-11 23:25:21.264200: val_loss -0.3741 +2026-04-11 23:25:21.265630: Pseudo dice [0.0, 0.5305, 0.784, 0.7781, 0.2987, 0.7116, 0.7761] +2026-04-11 23:25:21.267067: Epoch time: 101.61 s +2026-04-11 23:25:22.404476: +2026-04-11 23:25:22.406383: Epoch 1293 +2026-04-11 23:25:22.408546: Current learning rate: 0.00704 +2026-04-11 23:27:03.712868: train_loss -0.4078 +2026-04-11 23:27:03.718615: val_loss -0.3953 +2026-04-11 23:27:03.720598: Pseudo dice [0.0, 0.3233, 0.7433, 0.6941, 0.49, 0.7311, 0.7003] +2026-04-11 23:27:03.722022: Epoch time: 101.31 s +2026-04-11 23:27:04.869960: +2026-04-11 23:27:04.871799: Epoch 1294 +2026-04-11 23:27:04.873576: Current learning rate: 0.00703 +2026-04-11 23:28:46.341034: train_loss -0.4158 +2026-04-11 23:28:46.346018: val_loss -0.3438 +2026-04-11 23:28:46.347575: Pseudo dice [0.0775, 0.1503, 0.4016, 0.6847, 0.2204, 0.6159, 0.7871] +2026-04-11 23:28:46.349236: Epoch time: 101.47 s +2026-04-11 23:28:47.500161: +2026-04-11 23:28:47.501575: Epoch 1295 +2026-04-11 23:28:47.503002: Current learning rate: 0.00703 +2026-04-11 23:30:29.006213: train_loss -0.3965 +2026-04-11 23:30:29.011837: val_loss -0.3547 +2026-04-11 23:30:29.013653: Pseudo dice [0.1216, 0.5029, 0.5545, 0.3484, 0.4036, 0.7986, 0.8547] +2026-04-11 23:30:29.015411: Epoch time: 101.51 s +2026-04-11 23:30:30.176206: +2026-04-11 23:30:30.177998: Epoch 1296 +2026-04-11 23:30:30.179823: Current learning rate: 0.00703 +2026-04-11 23:32:12.325153: train_loss -0.395 +2026-04-11 23:32:12.331089: val_loss -0.3587 +2026-04-11 23:32:12.333336: Pseudo dice [0.3001, 0.183, 0.4615, 0.5714, 0.3769, 0.5544, 0.565] +2026-04-11 23:32:12.334787: Epoch time: 102.15 s +2026-04-11 23:32:13.481045: +2026-04-11 23:32:13.482708: Epoch 1297 +2026-04-11 23:32:13.484550: Current learning rate: 0.00703 +2026-04-11 23:33:55.607021: train_loss -0.3515 +2026-04-11 23:33:55.618414: val_loss -0.3167 +2026-04-11 23:33:55.620162: Pseudo dice [0.2117, 0.0, 0.5748, 0.4174, 0.186, 0.7089, 0.6362] +2026-04-11 23:33:55.635304: Epoch time: 102.13 s +2026-04-11 23:33:57.857038: +2026-04-11 23:33:57.859001: Epoch 1298 +2026-04-11 23:33:57.860925: Current learning rate: 0.00703 +2026-04-11 23:35:39.716058: train_loss -0.369 +2026-04-11 23:35:39.721317: val_loss -0.3736 +2026-04-11 23:35:39.723044: Pseudo dice [0.0372, 0.0, 0.6158, 0.6533, 0.4323, 0.7917, 0.8527] +2026-04-11 23:35:39.724698: Epoch time: 101.86 s +2026-04-11 23:35:40.866808: +2026-04-11 23:35:40.868989: Epoch 1299 +2026-04-11 23:35:40.870660: Current learning rate: 0.00702 +2026-04-11 23:37:22.375309: train_loss -0.3825 +2026-04-11 23:37:22.381107: val_loss -0.3153 +2026-04-11 23:37:22.382823: Pseudo dice [0.2279, 0.0, 0.6992, 0.5207, 0.0374, 0.7755, 0.362] +2026-04-11 23:37:22.384380: Epoch time: 101.51 s +2026-04-11 23:37:25.198838: +2026-04-11 23:37:25.200593: Epoch 1300 +2026-04-11 23:37:25.202727: Current learning rate: 0.00702 +2026-04-11 23:39:06.636859: train_loss -0.3592 +2026-04-11 23:39:06.642170: val_loss -0.3605 +2026-04-11 23:39:06.644246: Pseudo dice [0.2765, 0.0, 0.7198, 0.8642, 0.3307, 0.3879, 0.8623] +2026-04-11 23:39:06.645864: Epoch time: 101.44 s +2026-04-11 23:39:07.797715: +2026-04-11 23:39:07.799299: Epoch 1301 +2026-04-11 23:39:07.801110: Current learning rate: 0.00702 +2026-04-11 23:40:49.125064: train_loss -0.3998 +2026-04-11 23:40:49.129423: val_loss -0.367 +2026-04-11 23:40:49.131309: Pseudo dice [0.178, 0.0928, 0.8063, 0.7807, 0.4721, 0.5443, 0.4469] +2026-04-11 23:40:49.132869: Epoch time: 101.33 s +2026-04-11 23:40:50.269738: +2026-04-11 23:40:50.271125: Epoch 1302 +2026-04-11 23:40:50.272568: Current learning rate: 0.00702 +2026-04-11 23:42:31.617737: train_loss -0.394 +2026-04-11 23:42:31.622883: val_loss -0.3527 +2026-04-11 23:42:31.624783: Pseudo dice [0.4699, 0.511, 0.5664, 0.42, 0.3897, 0.7947, 0.5753] +2026-04-11 23:42:31.626865: Epoch time: 101.35 s +2026-04-11 23:42:32.773306: +2026-04-11 23:42:32.775040: Epoch 1303 +2026-04-11 23:42:32.776862: Current learning rate: 0.00701 +2026-04-11 23:44:14.123102: train_loss -0.4187 +2026-04-11 23:44:14.127700: val_loss -0.3714 +2026-04-11 23:44:14.128993: Pseudo dice [0.4493, 0.1144, 0.7203, 0.7499, 0.4914, 0.7797, 0.8394] +2026-04-11 23:44:14.130392: Epoch time: 101.35 s +2026-04-11 23:44:15.269478: +2026-04-11 23:44:15.271145: Epoch 1304 +2026-04-11 23:44:15.272720: Current learning rate: 0.00701 +2026-04-11 23:45:56.991407: train_loss -0.412 +2026-04-11 23:45:56.998002: val_loss -0.3921 +2026-04-11 23:45:56.999967: Pseudo dice [0.4799, 0.0, 0.5417, 0.8765, 0.2751, 0.7673, 0.7793] +2026-04-11 23:45:57.001818: Epoch time: 101.73 s +2026-04-11 23:45:58.173019: +2026-04-11 23:45:58.174699: Epoch 1305 +2026-04-11 23:45:58.176596: Current learning rate: 0.00701 +2026-04-11 23:47:39.661420: train_loss -0.3919 +2026-04-11 23:47:39.666290: val_loss -0.3262 +2026-04-11 23:47:39.668100: Pseudo dice [0.1312, 0.4029, 0.5699, 0.7196, 0.2467, 0.4431, 0.6641] +2026-04-11 23:47:39.669619: Epoch time: 101.49 s +2026-04-11 23:47:40.830728: +2026-04-11 23:47:40.832396: Epoch 1306 +2026-04-11 23:47:40.834094: Current learning rate: 0.00701 +2026-04-11 23:49:22.567494: train_loss -0.4034 +2026-04-11 23:49:22.573645: val_loss -0.3497 +2026-04-11 23:49:22.577956: Pseudo dice [0.3634, 0.3686, 0.7147, 0.7711, 0.3966, 0.5765, 0.263] +2026-04-11 23:49:22.579736: Epoch time: 101.74 s +2026-04-11 23:49:23.746374: +2026-04-11 23:49:23.748226: Epoch 1307 +2026-04-11 23:49:23.750258: Current learning rate: 0.007 +2026-04-11 23:51:05.328434: train_loss -0.4054 +2026-04-11 23:51:05.334148: val_loss -0.359 +2026-04-11 23:51:05.346375: Pseudo dice [0.5665, 0.1083, 0.4265, 0.8327, 0.5198, 0.6856, 0.8523] +2026-04-11 23:51:05.348571: Epoch time: 101.59 s +2026-04-11 23:51:06.509232: +2026-04-11 23:51:06.511001: Epoch 1308 +2026-04-11 23:51:06.514469: Current learning rate: 0.007 +2026-04-11 23:52:48.129132: train_loss -0.3953 +2026-04-11 23:52:48.133998: val_loss -0.3141 +2026-04-11 23:52:48.135994: Pseudo dice [0.3033, 0.0, 0.3313, 0.918, 0.2579, 0.7255, 0.3066] +2026-04-11 23:52:48.138048: Epoch time: 101.62 s +2026-04-11 23:52:49.286888: +2026-04-11 23:52:49.288755: Epoch 1309 +2026-04-11 23:52:49.290663: Current learning rate: 0.007 +2026-04-11 23:54:30.838121: train_loss -0.3916 +2026-04-11 23:54:30.843681: val_loss -0.3671 +2026-04-11 23:54:30.845387: Pseudo dice [0.6649, 0.1468, 0.4034, 0.7861, 0.1968, 0.7393, 0.6655] +2026-04-11 23:54:30.847467: Epoch time: 101.55 s +2026-04-11 23:54:31.978049: +2026-04-11 23:54:31.979673: Epoch 1310 +2026-04-11 23:54:31.981615: Current learning rate: 0.007 +2026-04-11 23:56:13.313139: train_loss -0.3988 +2026-04-11 23:56:13.318608: val_loss -0.3415 +2026-04-11 23:56:13.321019: Pseudo dice [0.1682, 0.5111, 0.796, 0.848, 0.3707, 0.7826, 0.5546] +2026-04-11 23:56:13.322989: Epoch time: 101.34 s +2026-04-11 23:56:14.495154: +2026-04-11 23:56:14.497658: Epoch 1311 +2026-04-11 23:56:14.500428: Current learning rate: 0.00699 +2026-04-11 23:57:55.886370: train_loss -0.4146 +2026-04-11 23:57:55.891335: val_loss -0.3373 +2026-04-11 23:57:55.893080: Pseudo dice [0.524, 0.2683, 0.5882, 0.6882, 0.3484, 0.6642, 0.7706] +2026-04-11 23:57:55.894625: Epoch time: 101.4 s +2026-04-11 23:57:57.049062: +2026-04-11 23:57:57.050753: Epoch 1312 +2026-04-11 23:57:57.052889: Current learning rate: 0.00699 +2026-04-11 23:59:38.393174: train_loss -0.3879 +2026-04-11 23:59:38.398601: val_loss -0.3508 +2026-04-11 23:59:38.400624: Pseudo dice [0.3883, 0.1631, 0.6663, 0.4214, 0.3713, 0.6354, 0.7266] +2026-04-11 23:59:38.402316: Epoch time: 101.35 s +2026-04-11 23:59:39.560427: +2026-04-11 23:59:39.561922: Epoch 1313 +2026-04-11 23:59:39.563660: Current learning rate: 0.00699 +2026-04-12 00:01:21.439542: train_loss -0.3988 +2026-04-12 00:01:21.445611: val_loss -0.3103 +2026-04-12 00:01:21.447438: Pseudo dice [0.0535, 0.0306, 0.4243, 0.7801, 0.2103, 0.7765, 0.4573] +2026-04-12 00:01:21.449553: Epoch time: 101.88 s +2026-04-12 00:01:22.617722: +2026-04-12 00:01:22.619546: Epoch 1314 +2026-04-12 00:01:22.622372: Current learning rate: 0.00699 +2026-04-12 00:03:03.982803: train_loss -0.4072 +2026-04-12 00:03:03.989328: val_loss -0.3397 +2026-04-12 00:03:03.991134: Pseudo dice [0.2588, 0.0, 0.6271, 0.6837, 0.2599, 0.5538, 0.7541] +2026-04-12 00:03:03.992895: Epoch time: 101.37 s +2026-04-12 00:03:05.153070: +2026-04-12 00:03:05.154743: Epoch 1315 +2026-04-12 00:03:05.156423: Current learning rate: 0.00699 +2026-04-12 00:04:46.493115: train_loss -0.4018 +2026-04-12 00:04:46.500952: val_loss -0.3327 +2026-04-12 00:04:46.503849: Pseudo dice [0.3292, 0.6387, 0.6624, 0.6318, 0.3707, 0.8263, 0.4623] +2026-04-12 00:04:46.506025: Epoch time: 101.34 s +2026-04-12 00:04:47.660986: +2026-04-12 00:04:47.662567: Epoch 1316 +2026-04-12 00:04:47.664516: Current learning rate: 0.00698 +2026-04-12 00:06:29.212352: train_loss -0.405 +2026-04-12 00:06:29.217027: val_loss -0.3631 +2026-04-12 00:06:29.218676: Pseudo dice [0.3927, 0.5591, 0.743, 0.8076, 0.4031, 0.6427, 0.5773] +2026-04-12 00:06:29.220321: Epoch time: 101.55 s +2026-04-12 00:06:30.371354: +2026-04-12 00:06:30.372816: Epoch 1317 +2026-04-12 00:06:30.374267: Current learning rate: 0.00698 +2026-04-12 00:08:11.820697: train_loss -0.4029 +2026-04-12 00:08:11.826043: val_loss -0.3369 +2026-04-12 00:08:11.827497: Pseudo dice [0.1812, 0.0709, 0.5836, 0.5999, 0.2907, 0.6236, 0.5458] +2026-04-12 00:08:11.828827: Epoch time: 101.45 s +2026-04-12 00:08:14.119644: +2026-04-12 00:08:14.121332: Epoch 1318 +2026-04-12 00:08:14.123376: Current learning rate: 0.00698 +2026-04-12 00:09:55.397310: train_loss -0.3955 +2026-04-12 00:09:55.402283: val_loss -0.3685 +2026-04-12 00:09:55.404394: Pseudo dice [0.3341, 0.065, 0.7557, 0.5288, 0.1671, 0.5863, 0.7334] +2026-04-12 00:09:55.406475: Epoch time: 101.28 s +2026-04-12 00:09:56.564914: +2026-04-12 00:09:56.566521: Epoch 1319 +2026-04-12 00:09:56.568279: Current learning rate: 0.00698 +2026-04-12 00:11:38.066493: train_loss -0.3892 +2026-04-12 00:11:38.073150: val_loss -0.3642 +2026-04-12 00:11:38.074655: Pseudo dice [0.5702, 0.6555, 0.2253, 0.7868, 0.3344, 0.6782, 0.5705] +2026-04-12 00:11:38.076652: Epoch time: 101.5 s +2026-04-12 00:11:39.254638: +2026-04-12 00:11:39.256702: Epoch 1320 +2026-04-12 00:11:39.259132: Current learning rate: 0.00697 +2026-04-12 00:13:20.841991: train_loss -0.3758 +2026-04-12 00:13:20.848139: val_loss -0.3336 +2026-04-12 00:13:20.849777: Pseudo dice [0.0618, 0.2156, 0.7469, 0.2501, 0.4399, 0.6756, 0.6525] +2026-04-12 00:13:20.851700: Epoch time: 101.59 s +2026-04-12 00:13:21.995755: +2026-04-12 00:13:21.997604: Epoch 1321 +2026-04-12 00:13:21.999835: Current learning rate: 0.00697 +2026-04-12 00:15:03.425371: train_loss -0.396 +2026-04-12 00:15:03.431156: val_loss -0.3406 +2026-04-12 00:15:03.433055: Pseudo dice [0.1544, 0.5546, 0.5924, 0.5762, 0.4304, 0.6315, 0.4962] +2026-04-12 00:15:03.434855: Epoch time: 101.43 s +2026-04-12 00:15:04.607737: +2026-04-12 00:15:04.609374: Epoch 1322 +2026-04-12 00:15:04.611306: Current learning rate: 0.00697 +2026-04-12 00:16:46.062430: train_loss -0.4104 +2026-04-12 00:16:46.067825: val_loss -0.3558 +2026-04-12 00:16:46.069381: Pseudo dice [0.336, 0.239, 0.6286, 0.4243, 0.3297, 0.8103, 0.723] +2026-04-12 00:16:46.071187: Epoch time: 101.46 s +2026-04-12 00:16:47.225804: +2026-04-12 00:16:47.227337: Epoch 1323 +2026-04-12 00:16:47.229057: Current learning rate: 0.00697 +2026-04-12 00:18:28.657586: train_loss -0.4107 +2026-04-12 00:18:28.664456: val_loss -0.3604 +2026-04-12 00:18:28.666136: Pseudo dice [0.2173, 0.1423, 0.6408, 0.4053, 0.4551, 0.431, 0.905] +2026-04-12 00:18:28.667902: Epoch time: 101.43 s +2026-04-12 00:18:29.830237: +2026-04-12 00:18:29.831944: Epoch 1324 +2026-04-12 00:18:29.834045: Current learning rate: 0.00696 +2026-04-12 00:20:10.966403: train_loss -0.4126 +2026-04-12 00:20:10.973079: val_loss -0.3144 +2026-04-12 00:20:10.975227: Pseudo dice [0.5957, 0.2659, 0.8309, 0.4529, 0.4477, 0.4338, 0.543] +2026-04-12 00:20:10.977229: Epoch time: 101.14 s +2026-04-12 00:20:12.126997: +2026-04-12 00:20:12.128866: Epoch 1325 +2026-04-12 00:20:12.131071: Current learning rate: 0.00696 +2026-04-12 00:21:53.305385: train_loss -0.3856 +2026-04-12 00:21:53.310310: val_loss -0.3454 +2026-04-12 00:21:53.312203: Pseudo dice [0.2774, 0.1146, 0.6084, 0.5282, 0.2911, 0.5423, 0.7757] +2026-04-12 00:21:53.314343: Epoch time: 101.18 s +2026-04-12 00:21:54.472953: +2026-04-12 00:21:54.474685: Epoch 1326 +2026-04-12 00:21:54.476815: Current learning rate: 0.00696 +2026-04-12 00:23:36.019366: train_loss -0.3875 +2026-04-12 00:23:36.024965: val_loss -0.3677 +2026-04-12 00:23:36.026734: Pseudo dice [0.318, 0.0, 0.6803, 0.3807, 0.511, 0.6292, 0.4327] +2026-04-12 00:23:36.028441: Epoch time: 101.55 s +2026-04-12 00:23:37.181091: +2026-04-12 00:23:37.182678: Epoch 1327 +2026-04-12 00:23:37.184493: Current learning rate: 0.00696 +2026-04-12 00:25:18.480747: train_loss -0.4211 +2026-04-12 00:25:18.485843: val_loss -0.3782 +2026-04-12 00:25:18.487830: Pseudo dice [0.4406, 0.5179, 0.5381, 0.8011, 0.3485, 0.6015, 0.6879] +2026-04-12 00:25:18.489365: Epoch time: 101.3 s +2026-04-12 00:25:19.641278: +2026-04-12 00:25:19.643286: Epoch 1328 +2026-04-12 00:25:19.645497: Current learning rate: 0.00696 +2026-04-12 00:27:01.028546: train_loss -0.3583 +2026-04-12 00:27:01.033931: val_loss -0.3364 +2026-04-12 00:27:01.035788: Pseudo dice [0.0, 0.0, 0.7478, 0.4697, 0.254, 0.5021, 0.6913] +2026-04-12 00:27:01.037525: Epoch time: 101.39 s +2026-04-12 00:27:02.213201: +2026-04-12 00:27:02.215050: Epoch 1329 +2026-04-12 00:27:02.217391: Current learning rate: 0.00695 +2026-04-12 00:28:43.759470: train_loss -0.3431 +2026-04-12 00:28:43.764744: val_loss -0.3292 +2026-04-12 00:28:43.766758: Pseudo dice [0.0, 0.0, 0.581, 0.6055, 0.39, 0.2363, 0.7646] +2026-04-12 00:28:43.768663: Epoch time: 101.55 s +2026-04-12 00:28:44.918592: +2026-04-12 00:28:44.920757: Epoch 1330 +2026-04-12 00:28:44.922812: Current learning rate: 0.00695 +2026-04-12 00:30:26.103938: train_loss -0.3575 +2026-04-12 00:30:26.108644: val_loss -0.2898 +2026-04-12 00:30:26.110332: Pseudo dice [0.1604, 0.0, 0.5248, 0.0482, 0.1347, 0.3144, 0.6901] +2026-04-12 00:30:26.111875: Epoch time: 101.19 s +2026-04-12 00:30:27.276124: +2026-04-12 00:30:27.278070: Epoch 1331 +2026-04-12 00:30:27.280265: Current learning rate: 0.00695 +2026-04-12 00:32:08.579239: train_loss -0.3651 +2026-04-12 00:32:08.584290: val_loss -0.3173 +2026-04-12 00:32:08.586265: Pseudo dice [0.1892, 0.0, 0.7291, 0.7924, 0.3489, 0.3513, 0.6487] +2026-04-12 00:32:08.588232: Epoch time: 101.31 s +2026-04-12 00:32:09.735202: +2026-04-12 00:32:09.736709: Epoch 1332 +2026-04-12 00:32:09.738706: Current learning rate: 0.00695 +2026-04-12 00:33:51.365323: train_loss -0.3793 +2026-04-12 00:33:51.369922: val_loss -0.3635 +2026-04-12 00:33:51.371318: Pseudo dice [0.6615, 0.0, 0.7014, 0.6963, 0.4169, 0.613, 0.5322] +2026-04-12 00:33:51.372715: Epoch time: 101.63 s +2026-04-12 00:33:52.513469: +2026-04-12 00:33:52.516453: Epoch 1333 +2026-04-12 00:33:52.520367: Current learning rate: 0.00694 +2026-04-12 00:35:33.887450: train_loss -0.399 +2026-04-12 00:35:33.893361: val_loss -0.3724 +2026-04-12 00:35:33.895689: Pseudo dice [0.152, 0.0, 0.7553, 0.7674, 0.3926, 0.4809, 0.5373] +2026-04-12 00:35:33.897501: Epoch time: 101.38 s +2026-04-12 00:35:35.051845: +2026-04-12 00:35:35.053377: Epoch 1334 +2026-04-12 00:35:35.055021: Current learning rate: 0.00694 +2026-04-12 00:37:16.567119: train_loss -0.3937 +2026-04-12 00:37:16.572199: val_loss -0.3269 +2026-04-12 00:37:16.574244: Pseudo dice [0.4066, 0.0558, 0.794, 0.328, 0.3145, 0.6969, 0.5606] +2026-04-12 00:37:16.575831: Epoch time: 101.52 s +2026-04-12 00:37:17.743171: +2026-04-12 00:37:17.744889: Epoch 1335 +2026-04-12 00:37:17.746958: Current learning rate: 0.00694 +2026-04-12 00:38:59.267831: train_loss -0.3726 +2026-04-12 00:38:59.272992: val_loss -0.3241 +2026-04-12 00:38:59.274720: Pseudo dice [0.0578, 0.2802, 0.6671, 0.6444, 0.2126, 0.6428, 0.6641] +2026-04-12 00:38:59.276478: Epoch time: 101.53 s +2026-04-12 00:39:00.455244: +2026-04-12 00:39:00.457175: Epoch 1336 +2026-04-12 00:39:00.459153: Current learning rate: 0.00694 +2026-04-12 00:40:41.997916: train_loss -0.3729 +2026-04-12 00:40:42.002750: val_loss -0.3321 +2026-04-12 00:40:42.004779: Pseudo dice [0.0886, 0.0341, 0.7263, 0.0, 0.2998, 0.5382, 0.6846] +2026-04-12 00:40:42.006457: Epoch time: 101.55 s +2026-04-12 00:40:43.178393: +2026-04-12 00:40:43.179873: Epoch 1337 +2026-04-12 00:40:43.181738: Current learning rate: 0.00693 +2026-04-12 00:42:24.599795: train_loss -0.3543 +2026-04-12 00:42:24.605186: val_loss -0.3474 +2026-04-12 00:42:24.607051: Pseudo dice [0.0369, 0.1223, 0.6039, 0.007, 0.3089, 0.7942, 0.4129] +2026-04-12 00:42:24.608907: Epoch time: 101.42 s +2026-04-12 00:42:25.785663: +2026-04-12 00:42:25.787251: Epoch 1338 +2026-04-12 00:42:25.789239: Current learning rate: 0.00693 +2026-04-12 00:44:08.304185: train_loss -0.3817 +2026-04-12 00:44:08.309295: val_loss -0.3521 +2026-04-12 00:44:08.310831: Pseudo dice [0.3403, 0.5151, 0.5695, 0.3362, 0.3436, 0.7422, 0.6657] +2026-04-12 00:44:08.312236: Epoch time: 102.52 s +2026-04-12 00:44:09.462794: +2026-04-12 00:44:09.464838: Epoch 1339 +2026-04-12 00:44:09.466879: Current learning rate: 0.00693 +2026-04-12 00:45:50.983215: train_loss -0.3938 +2026-04-12 00:45:50.988479: val_loss -0.3769 +2026-04-12 00:45:50.990627: Pseudo dice [0.418, 0.1082, 0.5106, 0.77, 0.4515, 0.7708, 0.6679] +2026-04-12 00:45:50.992373: Epoch time: 101.52 s +2026-04-12 00:45:52.162224: +2026-04-12 00:45:52.164326: Epoch 1340 +2026-04-12 00:45:52.166481: Current learning rate: 0.00693 +2026-04-12 00:47:33.654727: train_loss -0.3792 +2026-04-12 00:47:33.661187: val_loss -0.3447 +2026-04-12 00:47:33.663602: Pseudo dice [0.0612, 0.0618, 0.5039, 0.7323, 0.3929, 0.7652, 0.2287] +2026-04-12 00:47:33.665395: Epoch time: 101.5 s +2026-04-12 00:47:34.834818: +2026-04-12 00:47:34.836736: Epoch 1341 +2026-04-12 00:47:34.838744: Current learning rate: 0.00692 +2026-04-12 00:49:16.535892: train_loss -0.3853 +2026-04-12 00:49:16.540608: val_loss -0.3565 +2026-04-12 00:49:16.542704: Pseudo dice [0.322, 0.4486, 0.7117, 0.6947, 0.3016, 0.5985, 0.6954] +2026-04-12 00:49:16.544452: Epoch time: 101.7 s +2026-04-12 00:49:17.721790: +2026-04-12 00:49:17.723594: Epoch 1342 +2026-04-12 00:49:17.726008: Current learning rate: 0.00692 +2026-04-12 00:50:58.930371: train_loss -0.3995 +2026-04-12 00:50:58.936871: val_loss -0.3262 +2026-04-12 00:50:58.938824: Pseudo dice [0.0882, 0.2522, 0.5643, 0.597, 0.4036, 0.7666, 0.3403] +2026-04-12 00:50:58.940584: Epoch time: 101.21 s +2026-04-12 00:51:00.111035: +2026-04-12 00:51:00.112860: Epoch 1343 +2026-04-12 00:51:00.114922: Current learning rate: 0.00692 +2026-04-12 00:52:41.226899: train_loss -0.4 +2026-04-12 00:52:41.232049: val_loss -0.3801 +2026-04-12 00:52:41.233524: Pseudo dice [0.254, 0.3712, 0.7253, 0.5547, 0.445, 0.7136, 0.7003] +2026-04-12 00:52:41.235181: Epoch time: 101.12 s +2026-04-12 00:52:42.394839: +2026-04-12 00:52:42.397579: Epoch 1344 +2026-04-12 00:52:42.401449: Current learning rate: 0.00692 +2026-04-12 00:54:23.757394: train_loss -0.4141 +2026-04-12 00:54:23.762871: val_loss -0.3351 +2026-04-12 00:54:23.764940: Pseudo dice [0.1797, 0.1373, 0.4357, 0.7176, 0.2586, 0.6689, 0.7077] +2026-04-12 00:54:23.766605: Epoch time: 101.37 s +2026-04-12 00:54:24.924734: +2026-04-12 00:54:24.926649: Epoch 1345 +2026-04-12 00:54:24.928744: Current learning rate: 0.00692 +2026-04-12 00:56:06.712564: train_loss -0.3768 +2026-04-12 00:56:06.718737: val_loss -0.3577 +2026-04-12 00:56:06.721061: Pseudo dice [0.4389, 0.058, 0.5526, 0.76, 0.3459, 0.7013, 0.7214] +2026-04-12 00:56:06.722657: Epoch time: 101.79 s +2026-04-12 00:56:07.910679: +2026-04-12 00:56:07.912452: Epoch 1346 +2026-04-12 00:56:07.914551: Current learning rate: 0.00691 +2026-04-12 00:57:49.196491: train_loss -0.4134 +2026-04-12 00:57:49.201759: val_loss -0.3445 +2026-04-12 00:57:49.204270: Pseudo dice [0.0014, 0.2017, 0.7267, 0.6834, 0.334, 0.7094, 0.7017] +2026-04-12 00:57:49.207277: Epoch time: 101.29 s +2026-04-12 00:57:50.390023: +2026-04-12 00:57:50.392035: Epoch 1347 +2026-04-12 00:57:50.394051: Current learning rate: 0.00691 +2026-04-12 00:59:31.808702: train_loss -0.3831 +2026-04-12 00:59:31.814917: val_loss -0.2631 +2026-04-12 00:59:31.816516: Pseudo dice [0.0, 0.0933, 0.3891, 0.3002, 0.2509, 0.3102, 0.4233] +2026-04-12 00:59:31.818459: Epoch time: 101.42 s +2026-04-12 00:59:33.008811: +2026-04-12 00:59:33.010333: Epoch 1348 +2026-04-12 00:59:33.011967: Current learning rate: 0.00691 +2026-04-12 01:01:14.882942: train_loss -0.3803 +2026-04-12 01:01:14.894549: val_loss -0.3339 +2026-04-12 01:01:14.896408: Pseudo dice [0.0, 0.0, 0.7615, 0.577, 0.436, 0.5476, 0.824] +2026-04-12 01:01:14.898502: Epoch time: 101.88 s +2026-04-12 01:01:16.074605: +2026-04-12 01:01:16.076816: Epoch 1349 +2026-04-12 01:01:16.079243: Current learning rate: 0.00691 +2026-04-12 01:02:57.549541: train_loss -0.3913 +2026-04-12 01:02:57.554576: val_loss -0.3168 +2026-04-12 01:02:57.556735: Pseudo dice [0.0, 0.0, 0.5752, 0.2595, 0.1849, 0.3706, 0.7584] +2026-04-12 01:02:57.558382: Epoch time: 101.48 s +2026-04-12 01:03:00.457457: +2026-04-12 01:03:00.459267: Epoch 1350 +2026-04-12 01:03:00.461282: Current learning rate: 0.0069 +2026-04-12 01:04:42.084870: train_loss -0.4081 +2026-04-12 01:04:42.090276: val_loss -0.3574 +2026-04-12 01:04:42.092002: Pseudo dice [0.0, 0.1211, 0.4993, 0.7804, 0.4019, 0.8363, 0.5963] +2026-04-12 01:04:42.096851: Epoch time: 101.63 s +2026-04-12 01:04:43.271555: +2026-04-12 01:04:43.273556: Epoch 1351 +2026-04-12 01:04:43.275409: Current learning rate: 0.0069 +2026-04-12 01:06:24.472680: train_loss -0.3836 +2026-04-12 01:06:24.479479: val_loss -0.3339 +2026-04-12 01:06:24.481998: Pseudo dice [0.0, 0.1356, 0.6831, 0.2027, 0.3076, 0.8668, 0.7916] +2026-04-12 01:06:24.484027: Epoch time: 101.21 s +2026-04-12 01:06:25.674405: +2026-04-12 01:06:25.676049: Epoch 1352 +2026-04-12 01:06:25.678551: Current learning rate: 0.0069 +2026-04-12 01:08:07.064634: train_loss -0.409 +2026-04-12 01:08:07.070382: val_loss -0.3078 +2026-04-12 01:08:07.072360: Pseudo dice [0.0, 0.1126, 0.5541, 0.2273, 0.1988, 0.5797, 0.8316] +2026-04-12 01:08:07.075659: Epoch time: 101.39 s +2026-04-12 01:08:08.255115: +2026-04-12 01:08:08.257255: Epoch 1353 +2026-04-12 01:08:08.259363: Current learning rate: 0.0069 +2026-04-12 01:09:49.723344: train_loss -0.3919 +2026-04-12 01:09:49.729019: val_loss -0.2932 +2026-04-12 01:09:49.730988: Pseudo dice [0.0, 0.1109, 0.5034, 0.9286, 0.1549, 0.7252, 0.1536] +2026-04-12 01:09:49.733190: Epoch time: 101.47 s +2026-04-12 01:09:50.906809: +2026-04-12 01:09:50.908307: Epoch 1354 +2026-04-12 01:09:50.910026: Current learning rate: 0.00689 +2026-04-12 01:11:32.483557: train_loss -0.4082 +2026-04-12 01:11:32.488700: val_loss -0.3465 +2026-04-12 01:11:32.490687: Pseudo dice [0.0, 0.4453, 0.6302, 0.8306, 0.2324, 0.6271, 0.5848] +2026-04-12 01:11:32.494905: Epoch time: 101.58 s +2026-04-12 01:11:33.660556: +2026-04-12 01:11:33.662458: Epoch 1355 +2026-04-12 01:11:33.664873: Current learning rate: 0.00689 +2026-04-12 01:13:14.998367: train_loss -0.4092 +2026-04-12 01:13:15.004774: val_loss -0.3317 +2026-04-12 01:13:15.006690: Pseudo dice [0.0, 0.394, 0.6526, 0.5327, 0.2316, 0.4998, 0.7236] +2026-04-12 01:13:15.009031: Epoch time: 101.34 s +2026-04-12 01:13:16.192287: +2026-04-12 01:13:16.194126: Epoch 1356 +2026-04-12 01:13:16.196565: Current learning rate: 0.00689 +2026-04-12 01:14:57.374887: train_loss -0.3824 +2026-04-12 01:14:57.379934: val_loss -0.3318 +2026-04-12 01:14:57.381810: Pseudo dice [0.0, 0.0, 0.6639, 0.417, 0.2005, 0.1658, 0.7624] +2026-04-12 01:14:57.383868: Epoch time: 101.19 s +2026-04-12 01:14:58.553596: +2026-04-12 01:14:58.555092: Epoch 1357 +2026-04-12 01:14:58.556879: Current learning rate: 0.00689 +2026-04-12 01:16:39.979210: train_loss -0.3798 +2026-04-12 01:16:39.985944: val_loss -0.361 +2026-04-12 01:16:39.987851: Pseudo dice [0.0, 0.5608, 0.6033, 0.8331, 0.3557, 0.808, 0.6746] +2026-04-12 01:16:39.989497: Epoch time: 101.43 s +2026-04-12 01:16:42.224160: +2026-04-12 01:16:42.226129: Epoch 1358 +2026-04-12 01:16:42.228064: Current learning rate: 0.00688 +2026-04-12 01:18:23.861813: train_loss -0.4033 +2026-04-12 01:18:23.869427: val_loss -0.3528 +2026-04-12 01:18:23.871984: Pseudo dice [0.0, 0.4844, 0.7099, 0.2495, 0.446, 0.692, 0.7594] +2026-04-12 01:18:23.873957: Epoch time: 101.64 s +2026-04-12 01:18:25.048947: +2026-04-12 01:18:25.050656: Epoch 1359 +2026-04-12 01:18:25.053041: Current learning rate: 0.00688 +2026-04-12 01:20:07.118011: train_loss -0.3991 +2026-04-12 01:20:07.123022: val_loss -0.3461 +2026-04-12 01:20:07.124622: Pseudo dice [0.0, 0.0, 0.7591, 0.1485, 0.3631, 0.5355, 0.7773] +2026-04-12 01:20:07.126684: Epoch time: 102.07 s +2026-04-12 01:20:08.308897: +2026-04-12 01:20:08.310932: Epoch 1360 +2026-04-12 01:20:08.312824: Current learning rate: 0.00688 +2026-04-12 01:21:50.389438: train_loss -0.3921 +2026-04-12 01:21:50.395241: val_loss -0.3699 +2026-04-12 01:21:50.397141: Pseudo dice [0.0, 0.1369, 0.6653, 0.3147, 0.1367, 0.7515, 0.8711] +2026-04-12 01:21:50.398681: Epoch time: 102.08 s +2026-04-12 01:21:51.579095: +2026-04-12 01:21:51.581001: Epoch 1361 +2026-04-12 01:21:51.583045: Current learning rate: 0.00688 +2026-04-12 01:23:33.252787: train_loss -0.3895 +2026-04-12 01:23:33.258508: val_loss -0.3235 +2026-04-12 01:23:33.261015: Pseudo dice [0.0, 0.0, 0.748, 0.6532, 0.5218, 0.4062, 0.7646] +2026-04-12 01:23:33.263411: Epoch time: 101.68 s +2026-04-12 01:23:34.432888: +2026-04-12 01:23:34.434463: Epoch 1362 +2026-04-12 01:23:34.437812: Current learning rate: 0.00688 +2026-04-12 01:25:16.375334: train_loss -0.3734 +2026-04-12 01:25:16.382237: val_loss -0.358 +2026-04-12 01:25:16.385974: Pseudo dice [0.0426, 0.375, 0.8024, 0.8329, 0.3804, 0.4912, 0.6921] +2026-04-12 01:25:16.388574: Epoch time: 101.95 s +2026-04-12 01:25:17.557679: +2026-04-12 01:25:17.571189: Epoch 1363 +2026-04-12 01:25:17.574023: Current learning rate: 0.00687 +2026-04-12 01:26:59.768767: train_loss -0.4033 +2026-04-12 01:26:59.780937: val_loss -0.3623 +2026-04-12 01:26:59.784123: Pseudo dice [0.0073, 0.0222, 0.8259, 0.8707, 0.5739, 0.7507, 0.6816] +2026-04-12 01:26:59.786325: Epoch time: 102.21 s +2026-04-12 01:27:01.235853: +2026-04-12 01:27:01.241035: Epoch 1364 +2026-04-12 01:27:01.242859: Current learning rate: 0.00687 +2026-04-12 01:28:42.980142: train_loss -0.4107 +2026-04-12 01:28:42.985264: val_loss -0.3488 +2026-04-12 01:28:42.987494: Pseudo dice [0.5517, 0.2542, 0.6398, 0.6418, 0.0445, 0.6875, 0.8034] +2026-04-12 01:28:42.989502: Epoch time: 101.75 s +2026-04-12 01:28:44.167637: +2026-04-12 01:28:44.169375: Epoch 1365 +2026-04-12 01:28:44.171929: Current learning rate: 0.00687 +2026-04-12 01:30:25.947866: train_loss -0.3968 +2026-04-12 01:30:25.961930: val_loss -0.3627 +2026-04-12 01:30:25.967625: Pseudo dice [0.07, 0.3254, 0.7175, 0.6936, 0.2451, 0.7964, 0.772] +2026-04-12 01:30:25.970758: Epoch time: 101.78 s +2026-04-12 01:30:27.149256: +2026-04-12 01:30:27.151090: Epoch 1366 +2026-04-12 01:30:27.153194: Current learning rate: 0.00687 +2026-04-12 01:32:09.019088: train_loss -0.4148 +2026-04-12 01:32:09.035594: val_loss -0.3503 +2026-04-12 01:32:09.040717: Pseudo dice [0.6071, 0.4744, 0.468, 0.6996, 0.3114, 0.4703, 0.8002] +2026-04-12 01:32:09.056740: Epoch time: 101.87 s +2026-04-12 01:32:10.221583: +2026-04-12 01:32:10.228477: Epoch 1367 +2026-04-12 01:32:10.233140: Current learning rate: 0.00686 +2026-04-12 01:33:52.263946: train_loss -0.4069 +2026-04-12 01:33:52.269182: val_loss -0.3372 +2026-04-12 01:33:52.271544: Pseudo dice [0.0481, 0.2013, 0.6135, 0.6419, 0.4527, 0.6423, 0.7673] +2026-04-12 01:33:52.273421: Epoch time: 102.05 s +2026-04-12 01:33:53.435232: +2026-04-12 01:33:53.437126: Epoch 1368 +2026-04-12 01:33:53.439006: Current learning rate: 0.00686 +2026-04-12 01:35:35.292674: train_loss -0.3952 +2026-04-12 01:35:35.298631: val_loss -0.3516 +2026-04-12 01:35:35.300861: Pseudo dice [0.3197, 0.0243, 0.4167, 0.7362, 0.2273, 0.7924, 0.8025] +2026-04-12 01:35:35.302958: Epoch time: 101.86 s +2026-04-12 01:35:36.510944: +2026-04-12 01:35:36.513402: Epoch 1369 +2026-04-12 01:35:36.515261: Current learning rate: 0.00686 +2026-04-12 01:37:17.984136: train_loss -0.3551 +2026-04-12 01:37:17.991664: val_loss -0.3421 +2026-04-12 01:37:17.994999: Pseudo dice [0.0, 0.0323, 0.7745, 0.1377, 0.2599, 0.4824, 0.6072] +2026-04-12 01:37:17.996932: Epoch time: 101.48 s +2026-04-12 01:37:19.193560: +2026-04-12 01:37:19.195468: Epoch 1370 +2026-04-12 01:37:19.197424: Current learning rate: 0.00686 +2026-04-12 01:39:00.783600: train_loss -0.355 +2026-04-12 01:39:00.790068: val_loss -0.3503 +2026-04-12 01:39:00.792392: Pseudo dice [0.0, 0.0268, 0.7436, 0.4743, 0.2417, 0.8603, 0.7533] +2026-04-12 01:39:00.796568: Epoch time: 101.59 s +2026-04-12 01:39:01.991363: +2026-04-12 01:39:01.992911: Epoch 1371 +2026-04-12 01:39:01.995023: Current learning rate: 0.00685 +2026-04-12 01:40:44.173446: train_loss -0.3727 +2026-04-12 01:40:44.178707: val_loss -0.387 +2026-04-12 01:40:44.180604: Pseudo dice [0.0, 0.0734, 0.8512, 0.738, 0.2629, 0.6857, 0.6792] +2026-04-12 01:40:44.182220: Epoch time: 102.19 s +2026-04-12 01:40:45.346187: +2026-04-12 01:40:45.347908: Epoch 1372 +2026-04-12 01:40:45.349618: Current learning rate: 0.00685 +2026-04-12 01:42:27.026083: train_loss -0.3896 +2026-04-12 01:42:27.034065: val_loss -0.3496 +2026-04-12 01:42:27.036843: Pseudo dice [0.0, 0.4032, 0.7262, 0.587, 0.5247, 0.7856, 0.5744] +2026-04-12 01:42:27.038777: Epoch time: 101.68 s +2026-04-12 01:42:28.216983: +2026-04-12 01:42:28.218672: Epoch 1373 +2026-04-12 01:42:28.220452: Current learning rate: 0.00685 +2026-04-12 01:44:09.612190: train_loss -0.3992 +2026-04-12 01:44:09.618731: val_loss -0.3497 +2026-04-12 01:44:09.620829: Pseudo dice [0.0, 0.1826, 0.4246, 0.6719, 0.3731, 0.3262, 0.8041] +2026-04-12 01:44:09.622750: Epoch time: 101.4 s +2026-04-12 01:44:10.784776: +2026-04-12 01:44:10.786514: Epoch 1374 +2026-04-12 01:44:10.788060: Current learning rate: 0.00685 +2026-04-12 01:45:52.493684: train_loss -0.3723 +2026-04-12 01:45:52.500802: val_loss -0.3603 +2026-04-12 01:45:52.502829: Pseudo dice [0.0, 0.287, 0.5923, 0.7555, 0.422, 0.1911, 0.7538] +2026-04-12 01:45:52.505918: Epoch time: 101.71 s +2026-04-12 01:45:53.686192: +2026-04-12 01:45:53.687835: Epoch 1375 +2026-04-12 01:45:53.690496: Current learning rate: 0.00684 +2026-04-12 01:47:35.792778: train_loss -0.3647 +2026-04-12 01:47:35.798647: val_loss -0.2874 +2026-04-12 01:47:35.801065: Pseudo dice [0.0, 0.2901, 0.346, 0.7018, 0.2377, 0.6034, 0.5553] +2026-04-12 01:47:35.804466: Epoch time: 102.11 s +2026-04-12 01:47:36.974340: +2026-04-12 01:47:36.976188: Epoch 1376 +2026-04-12 01:47:36.978266: Current learning rate: 0.00684 +2026-04-12 01:49:18.590559: train_loss -0.3494 +2026-04-12 01:49:18.596797: val_loss -0.3159 +2026-04-12 01:49:18.598603: Pseudo dice [0.0, 0.2172, 0.7907, 0.584, 0.4621, 0.2308, 0.6708] +2026-04-12 01:49:18.600529: Epoch time: 101.62 s +2026-04-12 01:49:19.775029: +2026-04-12 01:49:19.777255: Epoch 1377 +2026-04-12 01:49:19.779664: Current learning rate: 0.00684 +2026-04-12 01:51:01.617301: train_loss -0.3829 +2026-04-12 01:51:01.622267: val_loss -0.3528 +2026-04-12 01:51:01.623877: Pseudo dice [0.0, 0.0292, 0.7543, 0.832, 0.2246, 0.4008, 0.7321] +2026-04-12 01:51:01.625778: Epoch time: 101.85 s +2026-04-12 01:51:03.897836: +2026-04-12 01:51:03.900352: Epoch 1378 +2026-04-12 01:51:03.902864: Current learning rate: 0.00684 +2026-04-12 01:52:45.503862: train_loss -0.3858 +2026-04-12 01:52:45.509963: val_loss -0.3385 +2026-04-12 01:52:45.513655: Pseudo dice [0.3551, 0.2397, 0.4967, 0.1778, 0.3724, 0.6678, 0.5557] +2026-04-12 01:52:45.516877: Epoch time: 101.61 s +2026-04-12 01:52:46.676371: +2026-04-12 01:52:46.677985: Epoch 1379 +2026-04-12 01:52:46.680780: Current learning rate: 0.00684 +2026-04-12 01:54:28.329855: train_loss -0.3912 +2026-04-12 01:54:28.335335: val_loss -0.3636 +2026-04-12 01:54:28.337517: Pseudo dice [0.0002, 0.1675, 0.7727, 0.7273, 0.3533, 0.796, 0.8301] +2026-04-12 01:54:28.339442: Epoch time: 101.66 s +2026-04-12 01:54:29.523004: +2026-04-12 01:54:29.524843: Epoch 1380 +2026-04-12 01:54:29.527024: Current learning rate: 0.00683 +2026-04-12 01:56:11.563947: train_loss -0.4013 +2026-04-12 01:56:11.570898: val_loss -0.3596 +2026-04-12 01:56:11.572796: Pseudo dice [0.375, 0.4479, 0.5452, 0.787, 0.2058, 0.7489, 0.6462] +2026-04-12 01:56:11.574721: Epoch time: 102.04 s +2026-04-12 01:56:12.772709: +2026-04-12 01:56:12.774832: Epoch 1381 +2026-04-12 01:56:12.777224: Current learning rate: 0.00683 +2026-04-12 01:57:54.235056: train_loss -0.3953 +2026-04-12 01:57:54.240345: val_loss -0.3554 +2026-04-12 01:57:54.242519: Pseudo dice [0.3558, 0.0, 0.4836, 0.7363, 0.46, 0.7483, 0.5665] +2026-04-12 01:57:54.244461: Epoch time: 101.47 s +2026-04-12 01:57:55.421790: +2026-04-12 01:57:55.424396: Epoch 1382 +2026-04-12 01:57:55.427616: Current learning rate: 0.00683 +2026-04-12 01:59:36.851130: train_loss -0.3933 +2026-04-12 01:59:36.856993: val_loss -0.309 +2026-04-12 01:59:36.859166: Pseudo dice [0.0969, 0.3206, 0.433, 0.0574, 0.3991, 0.7052, 0.4399] +2026-04-12 01:59:36.861119: Epoch time: 101.43 s +2026-04-12 01:59:38.022891: +2026-04-12 01:59:38.024474: Epoch 1383 +2026-04-12 01:59:38.026309: Current learning rate: 0.00683 +2026-04-12 02:01:19.268462: train_loss -0.3903 +2026-04-12 02:01:19.273568: val_loss -0.3721 +2026-04-12 02:01:19.275357: Pseudo dice [0.5904, 0.3223, 0.6613, 0.7335, 0.1887, 0.5228, 0.7104] +2026-04-12 02:01:19.277324: Epoch time: 101.25 s +2026-04-12 02:01:20.462854: +2026-04-12 02:01:20.465556: Epoch 1384 +2026-04-12 02:01:20.490182: Current learning rate: 0.00682 +2026-04-12 02:03:02.464930: train_loss -0.3457 +2026-04-12 02:03:02.473924: val_loss -0.3128 +2026-04-12 02:03:02.477607: Pseudo dice [0.0, 0.0006, 0.3223, 0.8574, 0.3293, 0.2265, 0.4489] +2026-04-12 02:03:02.480465: Epoch time: 102.01 s +2026-04-12 02:03:03.665442: +2026-04-12 02:03:03.668851: Epoch 1385 +2026-04-12 02:03:03.671196: Current learning rate: 0.00682 +2026-04-12 02:04:45.069785: train_loss -0.3684 +2026-04-12 02:04:45.075670: val_loss -0.3035 +2026-04-12 02:04:45.077408: Pseudo dice [0.0, 0.1945, 0.6271, 0.5011, 0.2443, 0.4157, 0.5091] +2026-04-12 02:04:45.079242: Epoch time: 101.41 s +2026-04-12 02:04:46.272650: +2026-04-12 02:04:46.274434: Epoch 1386 +2026-04-12 02:04:46.276680: Current learning rate: 0.00682 +2026-04-12 02:06:27.703928: train_loss -0.4008 +2026-04-12 02:06:27.709375: val_loss -0.3459 +2026-04-12 02:06:27.711200: Pseudo dice [0.1534, 0.08, 0.6437, 0.7376, 0.3086, 0.7372, 0.7105] +2026-04-12 02:06:27.714150: Epoch time: 101.43 s +2026-04-12 02:06:28.904494: +2026-04-12 02:06:28.906383: Epoch 1387 +2026-04-12 02:06:28.908658: Current learning rate: 0.00682 +2026-04-12 02:08:10.349436: train_loss -0.3908 +2026-04-12 02:08:10.355145: val_loss -0.3519 +2026-04-12 02:08:10.357129: Pseudo dice [0.4406, 0.4724, 0.7911, 0.2815, 0.3989, 0.7689, 0.8013] +2026-04-12 02:08:10.359650: Epoch time: 101.45 s +2026-04-12 02:08:11.524365: +2026-04-12 02:08:11.526444: Epoch 1388 +2026-04-12 02:08:11.528633: Current learning rate: 0.00681 +2026-04-12 02:09:52.774450: train_loss -0.3991 +2026-04-12 02:09:52.783781: val_loss -0.3024 +2026-04-12 02:09:52.787106: Pseudo dice [0.1933, 0.0727, 0.612, 0.6467, 0.0372, 0.6004, 0.6405] +2026-04-12 02:09:52.789513: Epoch time: 101.25 s +2026-04-12 02:09:53.977432: +2026-04-12 02:09:53.979682: Epoch 1389 +2026-04-12 02:09:53.981689: Current learning rate: 0.00681 +2026-04-12 02:11:35.499441: train_loss -0.3842 +2026-04-12 02:11:35.514893: val_loss -0.3178 +2026-04-12 02:11:35.517247: Pseudo dice [0.1302, 0.0013, 0.5927, 0.0309, 0.1221, 0.444, 0.6868] +2026-04-12 02:11:35.522433: Epoch time: 101.53 s +2026-04-12 02:11:36.727111: +2026-04-12 02:11:36.728941: Epoch 1390 +2026-04-12 02:11:36.730627: Current learning rate: 0.00681 +2026-04-12 02:13:18.151693: train_loss -0.4001 +2026-04-12 02:13:18.157598: val_loss -0.3622 +2026-04-12 02:13:18.159347: Pseudo dice [0.137, 0.1873, 0.8211, 0.7814, 0.3207, 0.6621, 0.6202] +2026-04-12 02:13:18.161088: Epoch time: 101.43 s +2026-04-12 02:13:19.339751: +2026-04-12 02:13:19.341901: Epoch 1391 +2026-04-12 02:13:19.344492: Current learning rate: 0.00681 +2026-04-12 02:15:00.722806: train_loss -0.3961 +2026-04-12 02:15:00.728766: val_loss -0.3312 +2026-04-12 02:15:00.730747: Pseudo dice [0.2365, 0.0711, 0.67, 0.8731, 0.2036, 0.2681, 0.8134] +2026-04-12 02:15:00.732489: Epoch time: 101.39 s +2026-04-12 02:15:01.916192: +2026-04-12 02:15:01.918556: Epoch 1392 +2026-04-12 02:15:01.920644: Current learning rate: 0.0068 +2026-04-12 02:16:43.465899: train_loss -0.3392 +2026-04-12 02:16:43.476956: val_loss -0.2949 +2026-04-12 02:16:43.480739: Pseudo dice [0.0, 0.2328, 0.5172, 0.7445, 0.2523, 0.3802, 0.3568] +2026-04-12 02:16:43.483972: Epoch time: 101.55 s +2026-04-12 02:16:44.674045: +2026-04-12 02:16:44.676043: Epoch 1393 +2026-04-12 02:16:44.678025: Current learning rate: 0.0068 +2026-04-12 02:18:26.106784: train_loss -0.3617 +2026-04-12 02:18:26.111847: val_loss -0.328 +2026-04-12 02:18:26.114085: Pseudo dice [0.0, 0.0812, 0.6334, 0.6488, 0.1833, 0.179, 0.7611] +2026-04-12 02:18:26.116032: Epoch time: 101.44 s +2026-04-12 02:18:27.301790: +2026-04-12 02:18:27.304187: Epoch 1394 +2026-04-12 02:18:27.306124: Current learning rate: 0.0068 +2026-04-12 02:20:08.647747: train_loss -0.3956 +2026-04-12 02:20:08.653560: val_loss -0.3424 +2026-04-12 02:20:08.655622: Pseudo dice [0.0, 0.5211, 0.7175, 0.3667, 0.4288, 0.7496, 0.3236] +2026-04-12 02:20:08.657911: Epoch time: 101.35 s +2026-04-12 02:20:09.840624: +2026-04-12 02:20:09.842388: Epoch 1395 +2026-04-12 02:20:09.844478: Current learning rate: 0.0068 +2026-04-12 02:21:51.314238: train_loss -0.4017 +2026-04-12 02:21:51.319095: val_loss -0.3637 +2026-04-12 02:21:51.321210: Pseudo dice [0.0, 0.388, 0.5789, 0.6903, 0.255, 0.8231, 0.8183] +2026-04-12 02:21:51.322812: Epoch time: 101.48 s +2026-04-12 02:21:52.569233: +2026-04-12 02:21:52.572123: Epoch 1396 +2026-04-12 02:21:52.575125: Current learning rate: 0.0068 +2026-04-12 02:23:34.049945: train_loss -0.3949 +2026-04-12 02:23:34.055516: val_loss -0.379 +2026-04-12 02:23:34.057348: Pseudo dice [0.0, 0.7246, 0.7076, 0.8085, 0.4248, 0.8323, 0.8182] +2026-04-12 02:23:34.059136: Epoch time: 101.48 s +2026-04-12 02:23:35.235026: +2026-04-12 02:23:35.236604: Epoch 1397 +2026-04-12 02:23:35.238414: Current learning rate: 0.00679 +2026-04-12 02:25:16.425527: train_loss -0.4102 +2026-04-12 02:25:16.430448: val_loss -0.3575 +2026-04-12 02:25:16.432168: Pseudo dice [0.0, 0.1425, 0.7323, 0.7096, 0.537, 0.8079, 0.5253] +2026-04-12 02:25:16.433930: Epoch time: 101.19 s +2026-04-12 02:25:18.769688: +2026-04-12 02:25:18.771936: Epoch 1398 +2026-04-12 02:25:18.773979: Current learning rate: 0.00679 +2026-04-12 02:27:00.153462: train_loss -0.3676 +2026-04-12 02:27:00.160095: val_loss -0.3143 +2026-04-12 02:27:00.162094: Pseudo dice [0.0, 0.1325, 0.5337, 0.5156, 0.2225, 0.7924, 0.7564] +2026-04-12 02:27:00.165109: Epoch time: 101.39 s +2026-04-12 02:27:01.382317: +2026-04-12 02:27:01.384734: Epoch 1399 +2026-04-12 02:27:01.387282: Current learning rate: 0.00679 +2026-04-12 02:28:42.481238: train_loss -0.4097 +2026-04-12 02:28:42.486669: val_loss -0.356 +2026-04-12 02:28:42.488386: Pseudo dice [0.0, 0.2559, 0.7516, 0.0026, 0.3563, 0.6354, 0.5965] +2026-04-12 02:28:42.489941: Epoch time: 101.1 s +2026-04-12 02:28:45.061937: +2026-04-12 02:28:45.064745: Epoch 1400 +2026-04-12 02:28:45.066519: Current learning rate: 0.00679 +2026-04-12 02:30:26.554033: train_loss -0.3958 +2026-04-12 02:30:26.560647: val_loss -0.3702 +2026-04-12 02:30:26.563158: Pseudo dice [0.6092, 0.5179, 0.6681, 0.5872, 0.5016, 0.4822, 0.5388] +2026-04-12 02:30:26.564651: Epoch time: 101.5 s +2026-04-12 02:30:27.763186: +2026-04-12 02:30:27.764899: Epoch 1401 +2026-04-12 02:30:27.766484: Current learning rate: 0.00678 +2026-04-12 02:32:09.344982: train_loss -0.3942 +2026-04-12 02:32:09.350147: val_loss -0.3283 +2026-04-12 02:32:09.352316: Pseudo dice [0.0, 0.0626, 0.5907, 0.7588, 0.2507, 0.6454, 0.461] +2026-04-12 02:32:09.354325: Epoch time: 101.58 s +2026-04-12 02:32:10.543058: +2026-04-12 02:32:10.544976: Epoch 1402 +2026-04-12 02:32:10.546856: Current learning rate: 0.00678 +2026-04-12 02:33:51.940090: train_loss -0.3867 +2026-04-12 02:33:51.947587: val_loss -0.3395 +2026-04-12 02:33:51.949738: Pseudo dice [0.0, 0.2417, 0.7144, 0.1276, 0.2807, 0.7009, 0.7971] +2026-04-12 02:33:51.951864: Epoch time: 101.4 s +2026-04-12 02:33:53.151100: +2026-04-12 02:33:53.152934: Epoch 1403 +2026-04-12 02:33:53.155621: Current learning rate: 0.00678 +2026-04-12 02:35:34.517714: train_loss -0.3841 +2026-04-12 02:35:34.543386: val_loss -0.3624 +2026-04-12 02:35:34.545957: Pseudo dice [0.0289, 0.0, 0.5676, 0.7071, 0.4238, 0.7166, 0.6316] +2026-04-12 02:35:34.547789: Epoch time: 101.37 s +2026-04-12 02:35:35.727754: +2026-04-12 02:35:35.729441: Epoch 1404 +2026-04-12 02:35:35.731379: Current learning rate: 0.00678 +2026-04-12 02:37:17.299558: train_loss -0.3807 +2026-04-12 02:37:17.307351: val_loss -0.363 +2026-04-12 02:37:17.309925: Pseudo dice [0.7066, 0.5866, 0.3986, 0.6667, 0.348, 0.5362, 0.7634] +2026-04-12 02:37:17.311914: Epoch time: 101.57 s +2026-04-12 02:37:18.490638: +2026-04-12 02:37:18.492301: Epoch 1405 +2026-04-12 02:37:18.494440: Current learning rate: 0.00677 +2026-04-12 02:38:59.995802: train_loss -0.3853 +2026-04-12 02:39:00.000764: val_loss -0.3021 +2026-04-12 02:39:00.003160: Pseudo dice [0.3432, 0.2371, 0.4362, 0.1931, 0.3766, 0.6445, 0.2128] +2026-04-12 02:39:00.006554: Epoch time: 101.51 s +2026-04-12 02:39:01.209800: +2026-04-12 02:39:01.211637: Epoch 1406 +2026-04-12 02:39:01.213627: Current learning rate: 0.00677 +2026-04-12 02:40:42.577331: train_loss -0.3891 +2026-04-12 02:40:42.583844: val_loss -0.3128 +2026-04-12 02:40:42.588675: Pseudo dice [0.2496, 0.0, 0.6393, 0.7432, 0.3252, 0.7734, 0.429] +2026-04-12 02:40:42.591951: Epoch time: 101.37 s +2026-04-12 02:40:43.794564: +2026-04-12 02:40:43.796962: Epoch 1407 +2026-04-12 02:40:43.799495: Current learning rate: 0.00677 +2026-04-12 02:42:25.202665: train_loss -0.367 +2026-04-12 02:42:25.209056: val_loss -0.331 +2026-04-12 02:42:25.210978: Pseudo dice [0.6481, 0.2383, 0.4748, 0.5961, 0.2717, 0.7714, 0.7246] +2026-04-12 02:42:25.213468: Epoch time: 101.41 s +2026-04-12 02:42:26.399492: +2026-04-12 02:42:26.402379: Epoch 1408 +2026-04-12 02:42:26.405539: Current learning rate: 0.00677 +2026-04-12 02:44:07.800827: train_loss -0.3867 +2026-04-12 02:44:07.806490: val_loss -0.3766 +2026-04-12 02:44:07.808682: Pseudo dice [0.2186, 0.1227, 0.78, 0.8648, 0.0594, 0.6332, 0.8638] +2026-04-12 02:44:07.810640: Epoch time: 101.4 s +2026-04-12 02:44:09.000997: +2026-04-12 02:44:09.003013: Epoch 1409 +2026-04-12 02:44:09.004996: Current learning rate: 0.00676 +2026-04-12 02:45:50.270299: train_loss -0.3909 +2026-04-12 02:45:50.276822: val_loss -0.3455 +2026-04-12 02:45:50.278863: Pseudo dice [0.2186, 0.3544, 0.6988, 0.7019, 0.2647, 0.8243, 0.6808] +2026-04-12 02:45:50.280728: Epoch time: 101.27 s +2026-04-12 02:45:51.454529: +2026-04-12 02:45:51.456317: Epoch 1410 +2026-04-12 02:45:51.458453: Current learning rate: 0.00676 +2026-04-12 02:47:32.747110: train_loss -0.4051 +2026-04-12 02:47:32.753129: val_loss -0.3569 +2026-04-12 02:47:32.756147: Pseudo dice [0.4037, 0.1701, 0.6787, 0.4345, 0.3266, 0.7538, 0.7887] +2026-04-12 02:47:32.758234: Epoch time: 101.3 s +2026-04-12 02:47:33.937805: +2026-04-12 02:47:33.939746: Epoch 1411 +2026-04-12 02:47:33.942418: Current learning rate: 0.00676 +2026-04-12 02:49:15.307975: train_loss -0.412 +2026-04-12 02:49:15.313289: val_loss -0.3297 +2026-04-12 02:49:15.315039: Pseudo dice [0.0061, 0.6979, 0.7864, 0.7079, 0.1634, 0.7621, 0.6423] +2026-04-12 02:49:15.316835: Epoch time: 101.37 s +2026-04-12 02:49:16.506037: +2026-04-12 02:49:16.507939: Epoch 1412 +2026-04-12 02:49:16.509626: Current learning rate: 0.00676 +2026-04-12 02:50:57.913719: train_loss -0.3988 +2026-04-12 02:50:57.918900: val_loss -0.3759 +2026-04-12 02:50:57.920993: Pseudo dice [0.8043, 0.3797, 0.6851, 0.7666, 0.5346, 0.5831, 0.784] +2026-04-12 02:50:57.923334: Epoch time: 101.41 s +2026-04-12 02:50:59.100347: +2026-04-12 02:50:59.102159: Epoch 1413 +2026-04-12 02:50:59.103996: Current learning rate: 0.00676 +2026-04-12 02:52:40.412663: train_loss -0.3805 +2026-04-12 02:52:40.418506: val_loss -0.3507 +2026-04-12 02:52:40.420434: Pseudo dice [0.0972, 0.0, 0.4667, 0.8221, 0.2864, 0.6837, 0.7332] +2026-04-12 02:52:40.422185: Epoch time: 101.32 s +2026-04-12 02:52:41.618096: +2026-04-12 02:52:41.620085: Epoch 1414 +2026-04-12 02:52:41.622403: Current learning rate: 0.00675 +2026-04-12 02:54:22.864974: train_loss -0.3894 +2026-04-12 02:54:22.874270: val_loss -0.3273 +2026-04-12 02:54:22.876804: Pseudo dice [0.2177, 0.0, 0.6702, 0.1072, 0.1963, 0.5798, 0.6564] +2026-04-12 02:54:22.879298: Epoch time: 101.25 s +2026-04-12 02:54:24.056572: +2026-04-12 02:54:24.058328: Epoch 1415 +2026-04-12 02:54:24.060487: Current learning rate: 0.00675 +2026-04-12 02:56:05.663176: train_loss -0.3792 +2026-04-12 02:56:05.668197: val_loss -0.3201 +2026-04-12 02:56:05.670064: Pseudo dice [0.0, 0.0, 0.4555, 0.1612, 0.4624, 0.7284, 0.7202] +2026-04-12 02:56:05.671780: Epoch time: 101.61 s +2026-04-12 02:56:06.864427: +2026-04-12 02:56:06.866245: Epoch 1416 +2026-04-12 02:56:06.868324: Current learning rate: 0.00675 +2026-04-12 02:57:48.262635: train_loss -0.3875 +2026-04-12 02:57:48.268714: val_loss -0.3673 +2026-04-12 02:57:48.270547: Pseudo dice [0.0, 0.0, 0.6776, 0.0119, 0.3692, 0.3895, 0.5459] +2026-04-12 02:57:48.272395: Epoch time: 101.4 s +2026-04-12 02:57:49.490748: +2026-04-12 02:57:49.493901: Epoch 1417 +2026-04-12 02:57:49.496449: Current learning rate: 0.00675 +2026-04-12 02:59:33.955333: train_loss -0.3888 +2026-04-12 02:59:33.960701: val_loss -0.3205 +2026-04-12 02:59:33.962615: Pseudo dice [0.0007, 0.0, 0.6246, 0.0033, 0.2491, 0.7493, 0.9123] +2026-04-12 02:59:33.964319: Epoch time: 104.47 s +2026-04-12 02:59:35.172686: +2026-04-12 02:59:35.174286: Epoch 1418 +2026-04-12 02:59:35.176941: Current learning rate: 0.00674 +2026-04-12 03:01:16.705667: train_loss -0.3956 +2026-04-12 03:01:16.712714: val_loss -0.3758 +2026-04-12 03:01:16.714629: Pseudo dice [0.5076, 0.209, 0.6994, 0.0988, 0.434, 0.7572, 0.7787] +2026-04-12 03:01:16.716445: Epoch time: 101.54 s +2026-04-12 03:01:18.154216: +2026-04-12 03:01:18.156302: Epoch 1419 +2026-04-12 03:01:18.158871: Current learning rate: 0.00674 +2026-04-12 03:02:59.852510: train_loss -0.3868 +2026-04-12 03:02:59.860957: val_loss -0.3245 +2026-04-12 03:02:59.865449: Pseudo dice [0.1188, 0.0753, 0.4756, 0.8635, 0.1794, 0.2886, 0.3831] +2026-04-12 03:02:59.868525: Epoch time: 101.7 s +2026-04-12 03:03:01.074086: +2026-04-12 03:03:01.075896: Epoch 1420 +2026-04-12 03:03:01.077975: Current learning rate: 0.00674 +2026-04-12 03:04:42.837607: train_loss -0.3757 +2026-04-12 03:04:42.844255: val_loss -0.3005 +2026-04-12 03:04:42.846492: Pseudo dice [0.7914, 0.0, 0.6175, 0.7226, 0.1074, 0.6693, 0.5361] +2026-04-12 03:04:42.848088: Epoch time: 101.77 s +2026-04-12 03:04:44.032237: +2026-04-12 03:04:44.035080: Epoch 1421 +2026-04-12 03:04:44.037516: Current learning rate: 0.00674 +2026-04-12 03:06:25.082657: train_loss -0.3835 +2026-04-12 03:06:25.091856: val_loss -0.3331 +2026-04-12 03:06:25.093796: Pseudo dice [0.1183, 0.3177, 0.5457, 0.3831, 0.5439, 0.6186, 0.1779] +2026-04-12 03:06:25.095925: Epoch time: 101.05 s +2026-04-12 03:06:26.291659: +2026-04-12 03:06:26.293549: Epoch 1422 +2026-04-12 03:06:26.295353: Current learning rate: 0.00673 +2026-04-12 03:08:07.553095: train_loss -0.3902 +2026-04-12 03:08:07.558460: val_loss -0.3616 +2026-04-12 03:08:07.560162: Pseudo dice [0.5913, 0.296, 0.6831, 0.8105, 0.1444, 0.6097, 0.7651] +2026-04-12 03:08:07.562491: Epoch time: 101.26 s +2026-04-12 03:08:08.774363: +2026-04-12 03:08:08.776069: Epoch 1423 +2026-04-12 03:08:08.777692: Current learning rate: 0.00673 +2026-04-12 03:09:50.277764: train_loss -0.4016 +2026-04-12 03:09:50.283684: val_loss -0.3373 +2026-04-12 03:09:50.285391: Pseudo dice [0.3851, 0.0529, 0.6457, 0.0, 0.1071, 0.5286, 0.7726] +2026-04-12 03:09:50.287542: Epoch time: 101.51 s +2026-04-12 03:09:51.465949: +2026-04-12 03:09:51.467788: Epoch 1424 +2026-04-12 03:09:51.469697: Current learning rate: 0.00673 +2026-04-12 03:11:33.193722: train_loss -0.4064 +2026-04-12 03:11:33.199380: val_loss -0.3661 +2026-04-12 03:11:33.201179: Pseudo dice [0.5167, 0.2372, 0.6585, 0.5565, 0.0641, 0.8346, 0.7372] +2026-04-12 03:11:33.203678: Epoch time: 101.73 s +2026-04-12 03:11:34.364235: +2026-04-12 03:11:34.365926: Epoch 1425 +2026-04-12 03:11:34.367676: Current learning rate: 0.00673 +2026-04-12 03:13:15.663780: train_loss -0.3984 +2026-04-12 03:13:15.669094: val_loss -0.3878 +2026-04-12 03:13:15.670854: Pseudo dice [0.3663, 0.2522, 0.7156, 0.8172, 0.3726, 0.8556, 0.5956] +2026-04-12 03:13:15.673063: Epoch time: 101.3 s +2026-04-12 03:13:16.848276: +2026-04-12 03:13:16.850005: Epoch 1426 +2026-04-12 03:13:16.852155: Current learning rate: 0.00673 +2026-04-12 03:14:58.310396: train_loss -0.3949 +2026-04-12 03:14:58.316153: val_loss -0.366 +2026-04-12 03:14:58.318063: Pseudo dice [0.3309, 0.59, 0.4336, 0.5595, 0.5233, 0.5873, 0.7909] +2026-04-12 03:14:58.319682: Epoch time: 101.47 s +2026-04-12 03:14:59.503259: +2026-04-12 03:14:59.505430: Epoch 1427 +2026-04-12 03:14:59.507689: Current learning rate: 0.00672 +2026-04-12 03:16:40.919620: train_loss -0.4098 +2026-04-12 03:16:40.924473: val_loss -0.3568 +2026-04-12 03:16:40.926400: Pseudo dice [0.3245, 0.2446, 0.3692, 0.7791, 0.3811, 0.649, 0.7633] +2026-04-12 03:16:40.928528: Epoch time: 101.42 s +2026-04-12 03:16:42.096163: +2026-04-12 03:16:42.097661: Epoch 1428 +2026-04-12 03:16:42.099821: Current learning rate: 0.00672 +2026-04-12 03:18:23.558257: train_loss -0.3916 +2026-04-12 03:18:23.565351: val_loss -0.3553 +2026-04-12 03:18:23.567385: Pseudo dice [0.3254, 0.1761, 0.5965, 0.8017, 0.2693, 0.5537, 0.8446] +2026-04-12 03:18:23.569576: Epoch time: 101.47 s +2026-04-12 03:18:24.733210: +2026-04-12 03:18:24.735157: Epoch 1429 +2026-04-12 03:18:24.737293: Current learning rate: 0.00672 +2026-04-12 03:20:06.115345: train_loss -0.3875 +2026-04-12 03:20:06.120595: val_loss -0.3305 +2026-04-12 03:20:06.123692: Pseudo dice [0.2669, 0.1752, 0.4967, 0.5915, 0.214, 0.369, 0.7677] +2026-04-12 03:20:06.126255: Epoch time: 101.39 s +2026-04-12 03:20:07.287042: +2026-04-12 03:20:07.289623: Epoch 1430 +2026-04-12 03:20:07.292209: Current learning rate: 0.00672 +2026-04-12 03:21:48.808869: train_loss -0.3861 +2026-04-12 03:21:48.814674: val_loss -0.3327 +2026-04-12 03:21:48.816404: Pseudo dice [0.5198, 0.1694, 0.5075, 0.7344, 0.2306, 0.2513, 0.8453] +2026-04-12 03:21:48.818225: Epoch time: 101.52 s +2026-04-12 03:21:49.978248: +2026-04-12 03:21:49.980468: Epoch 1431 +2026-04-12 03:21:49.983204: Current learning rate: 0.00671 +2026-04-12 03:23:31.401106: train_loss -0.3852 +2026-04-12 03:23:31.407544: val_loss -0.3201 +2026-04-12 03:23:31.409485: Pseudo dice [0.2913, 0.089, 0.7615, 0.6121, 0.0992, 0.4359, 0.5241] +2026-04-12 03:23:31.411798: Epoch time: 101.43 s +2026-04-12 03:23:32.587565: +2026-04-12 03:23:32.589453: Epoch 1432 +2026-04-12 03:23:32.591639: Current learning rate: 0.00671 +2026-04-12 03:25:13.914271: train_loss -0.375 +2026-04-12 03:25:13.918873: val_loss -0.3415 +2026-04-12 03:25:13.920771: Pseudo dice [0.4803, 0.4191, 0.7482, 0.3363, 0.3166, 0.4914, 0.7502] +2026-04-12 03:25:13.922860: Epoch time: 101.33 s +2026-04-12 03:25:15.079506: +2026-04-12 03:25:15.081526: Epoch 1433 +2026-04-12 03:25:15.083928: Current learning rate: 0.00671 +2026-04-12 03:27:00.579249: train_loss -0.3824 +2026-04-12 03:27:00.588984: val_loss -0.313 +2026-04-12 03:27:00.591039: Pseudo dice [0.128, 0.0014, 0.5349, 0.4578, 0.0892, 0.6943, 0.6093] +2026-04-12 03:27:00.593889: Epoch time: 105.5 s +2026-04-12 03:27:01.774077: +2026-04-12 03:27:01.776202: Epoch 1434 +2026-04-12 03:27:01.778219: Current learning rate: 0.00671 +2026-04-12 03:28:42.833695: train_loss -0.3882 +2026-04-12 03:28:42.839216: val_loss -0.3544 +2026-04-12 03:28:42.841221: Pseudo dice [0.4725, 0.2734, 0.7827, 0.3193, 0.0451, 0.5653, 0.7355] +2026-04-12 03:28:42.843103: Epoch time: 101.06 s +2026-04-12 03:28:44.005292: +2026-04-12 03:28:44.016810: Epoch 1435 +2026-04-12 03:28:44.022193: Current learning rate: 0.0067 +2026-04-12 03:30:24.996606: train_loss -0.3803 +2026-04-12 03:30:25.002104: val_loss -0.363 +2026-04-12 03:30:25.003838: Pseudo dice [0.5282, 0.0, 0.6778, 0.8555, 0.3777, 0.7303, 0.5117] +2026-04-12 03:30:25.006540: Epoch time: 100.99 s +2026-04-12 03:30:26.193718: +2026-04-12 03:30:26.195996: Epoch 1436 +2026-04-12 03:30:26.199987: Current learning rate: 0.0067 +2026-04-12 03:32:07.262833: train_loss -0.3826 +2026-04-12 03:32:07.267493: val_loss -0.3548 +2026-04-12 03:32:07.269369: Pseudo dice [0.2124, 0.2372, 0.801, 0.7096, 0.3871, 0.6816, 0.6] +2026-04-12 03:32:07.271171: Epoch time: 101.07 s +2026-04-12 03:32:08.463048: +2026-04-12 03:32:08.464698: Epoch 1437 +2026-04-12 03:32:08.466879: Current learning rate: 0.0067 +2026-04-12 03:33:50.658927: train_loss -0.3996 +2026-04-12 03:33:50.665108: val_loss -0.3622 +2026-04-12 03:33:50.667435: Pseudo dice [0.0, 0.0, 0.7557, 0.0002, 0.2732, 0.7707, 0.6629] +2026-04-12 03:33:50.669080: Epoch time: 102.2 s +2026-04-12 03:33:51.842579: +2026-04-12 03:33:51.844652: Epoch 1438 +2026-04-12 03:33:51.847081: Current learning rate: 0.0067 +2026-04-12 03:35:33.376038: train_loss -0.3725 +2026-04-12 03:35:33.382120: val_loss -0.3034 +2026-04-12 03:35:33.384082: Pseudo dice [0.0385, 0.0, 0.4969, 0.4696, 0.4454, 0.1746, 0.5354] +2026-04-12 03:35:33.386157: Epoch time: 101.54 s +2026-04-12 03:35:34.610643: +2026-04-12 03:35:34.612802: Epoch 1439 +2026-04-12 03:35:34.615245: Current learning rate: 0.00669 +2026-04-12 03:37:15.992174: train_loss -0.3663 +2026-04-12 03:37:15.996503: val_loss -0.3532 +2026-04-12 03:37:15.997899: Pseudo dice [0.1696, 0.0, 0.8178, 0.6202, 0.2103, 0.7876, 0.4688] +2026-04-12 03:37:15.999311: Epoch time: 101.38 s +2026-04-12 03:37:17.175329: +2026-04-12 03:37:17.177164: Epoch 1440 +2026-04-12 03:37:17.179173: Current learning rate: 0.00669 +2026-04-12 03:38:58.551525: train_loss -0.3975 +2026-04-12 03:38:58.558340: val_loss -0.3327 +2026-04-12 03:38:58.560926: Pseudo dice [0.0951, 0.0428, 0.7485, 0.7477, 0.3422, 0.7281, 0.7911] +2026-04-12 03:38:58.563239: Epoch time: 101.38 s +2026-04-12 03:38:59.732555: +2026-04-12 03:38:59.734281: Epoch 1441 +2026-04-12 03:38:59.736187: Current learning rate: 0.00669 +2026-04-12 03:40:41.347899: train_loss -0.4009 +2026-04-12 03:40:41.353176: val_loss -0.3967 +2026-04-12 03:40:41.354921: Pseudo dice [0.4653, 0.3209, 0.7103, 0.5299, 0.3085, 0.7851, 0.8627] +2026-04-12 03:40:41.356558: Epoch time: 101.62 s +2026-04-12 03:40:42.521506: +2026-04-12 03:40:42.523237: Epoch 1442 +2026-04-12 03:40:42.525381: Current learning rate: 0.00669 +2026-04-12 03:42:23.766472: train_loss -0.398 +2026-04-12 03:42:23.788390: val_loss -0.3488 +2026-04-12 03:42:23.795587: Pseudo dice [0.5372, 0.4695, 0.4899, 0.4718, 0.3729, 0.2097, 0.8944] +2026-04-12 03:42:23.803899: Epoch time: 101.25 s +2026-04-12 03:42:24.960449: +2026-04-12 03:42:24.962326: Epoch 1443 +2026-04-12 03:42:24.964135: Current learning rate: 0.00669 +2026-04-12 03:44:06.456850: train_loss -0.3982 +2026-04-12 03:44:06.462765: val_loss -0.3487 +2026-04-12 03:44:06.464657: Pseudo dice [0.3923, 0.0, 0.6696, 0.2457, 0.4503, 0.66, 0.7412] +2026-04-12 03:44:06.466473: Epoch time: 101.5 s +2026-04-12 03:44:07.634600: +2026-04-12 03:44:07.636266: Epoch 1444 +2026-04-12 03:44:07.638371: Current learning rate: 0.00668 +2026-04-12 03:45:49.017051: train_loss -0.4052 +2026-04-12 03:45:49.022576: val_loss -0.2992 +2026-04-12 03:45:49.024217: Pseudo dice [0.0529, 0.3652, 0.5425, 0.0914, 0.0771, 0.6297, 0.4193] +2026-04-12 03:45:49.025867: Epoch time: 101.39 s +2026-04-12 03:45:50.203535: +2026-04-12 03:45:50.205586: Epoch 1445 +2026-04-12 03:45:50.207539: Current learning rate: 0.00668 +2026-04-12 03:47:31.939924: train_loss -0.4151 +2026-04-12 03:47:31.945075: val_loss -0.3815 +2026-04-12 03:47:31.946670: Pseudo dice [0.3079, 0.2108, 0.5834, 0.8438, 0.2316, 0.7482, 0.6604] +2026-04-12 03:47:31.949119: Epoch time: 101.74 s +2026-04-12 03:47:33.125509: +2026-04-12 03:47:33.136262: Epoch 1446 +2026-04-12 03:47:33.140854: Current learning rate: 0.00668 +2026-04-12 03:49:14.487621: train_loss -0.4059 +2026-04-12 03:49:14.493725: val_loss -0.3428 +2026-04-12 03:49:14.495621: Pseudo dice [0.222, 0.2372, 0.6167, 0.5717, 0.3689, 0.5727, 0.449] +2026-04-12 03:49:14.497501: Epoch time: 101.37 s +2026-04-12 03:49:15.689056: +2026-04-12 03:49:15.691317: Epoch 1447 +2026-04-12 03:49:15.693959: Current learning rate: 0.00668 +2026-04-12 03:50:57.216601: train_loss -0.3918 +2026-04-12 03:50:57.221402: val_loss -0.3451 +2026-04-12 03:50:57.223154: Pseudo dice [0.6411, 0.1536, 0.6981, 0.2613, 0.1611, 0.7713, 0.7862] +2026-04-12 03:50:57.224578: Epoch time: 101.53 s +2026-04-12 03:50:58.401098: +2026-04-12 03:50:58.404189: Epoch 1448 +2026-04-12 03:50:58.406656: Current learning rate: 0.00667 +2026-04-12 03:52:39.775994: train_loss -0.4073 +2026-04-12 03:52:39.781863: val_loss -0.3824 +2026-04-12 03:52:39.783510: Pseudo dice [0.5146, 0.4021, 0.6488, 0.8777, 0.561, 0.6482, 0.9357] +2026-04-12 03:52:39.785312: Epoch time: 101.38 s +2026-04-12 03:52:40.978226: +2026-04-12 03:52:40.979829: Epoch 1449 +2026-04-12 03:52:40.981677: Current learning rate: 0.00667 +2026-04-12 03:54:22.211758: train_loss -0.3948 +2026-04-12 03:54:22.216238: val_loss -0.3252 +2026-04-12 03:54:22.217700: Pseudo dice [0.0171, 0.2495, 0.6014, 0.3299, 0.1933, 0.2794, 0.7436] +2026-04-12 03:54:22.219110: Epoch time: 101.24 s +2026-04-12 03:54:24.996097: +2026-04-12 03:54:24.998100: Epoch 1450 +2026-04-12 03:54:25.000353: Current learning rate: 0.00667 +2026-04-12 03:56:06.311120: train_loss -0.3823 +2026-04-12 03:56:06.315986: val_loss -0.3484 +2026-04-12 03:56:06.317464: Pseudo dice [0.4459, 0.09, 0.6066, 0.8092, 0.2207, 0.5723, 0.9125] +2026-04-12 03:56:06.319161: Epoch time: 101.32 s +2026-04-12 03:56:07.494336: +2026-04-12 03:56:07.496349: Epoch 1451 +2026-04-12 03:56:07.498354: Current learning rate: 0.00667 +2026-04-12 03:57:48.983088: train_loss -0.3681 +2026-04-12 03:57:48.989180: val_loss -0.3408 +2026-04-12 03:57:48.990710: Pseudo dice [0.219, 0.5673, 0.5376, 0.7561, 0.2605, 0.6962, 0.4729] +2026-04-12 03:57:48.992832: Epoch time: 101.49 s +2026-04-12 03:57:50.195178: +2026-04-12 03:57:50.196857: Epoch 1452 +2026-04-12 03:57:50.199069: Current learning rate: 0.00666 +2026-04-12 03:59:31.705670: train_loss -0.3882 +2026-04-12 03:59:31.710044: val_loss -0.348 +2026-04-12 03:59:31.711541: Pseudo dice [0.2958, 0.0347, 0.4652, 0.412, 0.1473, 0.6741, 0.614] +2026-04-12 03:59:31.713054: Epoch time: 101.51 s +2026-04-12 03:59:32.881797: +2026-04-12 03:59:32.883567: Epoch 1453 +2026-04-12 03:59:32.885493: Current learning rate: 0.00666 +2026-04-12 04:01:14.223024: train_loss -0.3698 +2026-04-12 04:01:14.227968: val_loss -0.2965 +2026-04-12 04:01:14.230032: Pseudo dice [0.1235, 0.0, 0.5892, 0.1995, 0.4287, 0.3162, 0.4854] +2026-04-12 04:01:14.231575: Epoch time: 101.34 s +2026-04-12 04:01:15.424699: +2026-04-12 04:01:15.428014: Epoch 1454 +2026-04-12 04:01:15.430303: Current learning rate: 0.00666 +2026-04-12 04:02:56.957222: train_loss -0.3831 +2026-04-12 04:02:56.964982: val_loss -0.3733 +2026-04-12 04:02:56.967335: Pseudo dice [0.3069, 0.2559, 0.6767, 0.422, 0.3183, 0.6948, 0.8895] +2026-04-12 04:02:56.970080: Epoch time: 101.54 s +2026-04-12 04:02:58.149250: +2026-04-12 04:02:58.151425: Epoch 1455 +2026-04-12 04:02:58.153876: Current learning rate: 0.00666 +2026-04-12 04:04:39.353800: train_loss -0.3204 +2026-04-12 04:04:39.359630: val_loss -0.3379 +2026-04-12 04:04:39.362185: Pseudo dice [0.2481, 0.0323, 0.436, 0.7432, 0.1622, 0.6183, 0.6589] +2026-04-12 04:04:39.364073: Epoch time: 101.21 s +2026-04-12 04:04:40.609560: +2026-04-12 04:04:40.611537: Epoch 1456 +2026-04-12 04:04:40.614013: Current learning rate: 0.00665 +2026-04-12 04:06:22.060332: train_loss -0.3792 +2026-04-12 04:06:22.065892: val_loss -0.3238 +2026-04-12 04:06:22.067511: Pseudo dice [0.2678, 0.2278, 0.656, 0.7501, 0.29, 0.535, 0.379] +2026-04-12 04:06:22.069628: Epoch time: 101.45 s +2026-04-12 04:06:24.230554: +2026-04-12 04:06:24.232574: Epoch 1457 +2026-04-12 04:06:24.234786: Current learning rate: 0.00665 +2026-04-12 04:08:05.634094: train_loss -0.3899 +2026-04-12 04:08:05.639862: val_loss -0.3396 +2026-04-12 04:08:05.643258: Pseudo dice [0.4094, 0.5133, 0.8534, 0.4009, 0.0984, 0.7048, 0.4882] +2026-04-12 04:08:05.646090: Epoch time: 101.41 s +2026-04-12 04:08:06.823502: +2026-04-12 04:08:06.825188: Epoch 1458 +2026-04-12 04:08:06.827093: Current learning rate: 0.00665 +2026-04-12 04:09:48.309444: train_loss -0.4114 +2026-04-12 04:09:48.314653: val_loss -0.3559 +2026-04-12 04:09:48.316702: Pseudo dice [0.4479, 0.6694, 0.6561, 0.7908, 0.2233, 0.755, 0.3385] +2026-04-12 04:09:48.318738: Epoch time: 101.49 s +2026-04-12 04:09:49.488420: +2026-04-12 04:09:49.490405: Epoch 1459 +2026-04-12 04:09:49.492408: Current learning rate: 0.00665 +2026-04-12 04:11:30.860547: train_loss -0.4188 +2026-04-12 04:11:30.867431: val_loss -0.3615 +2026-04-12 04:11:30.869577: Pseudo dice [0.701, 0.1086, 0.5235, 0.6472, 0.3638, 0.8098, 0.4593] +2026-04-12 04:11:30.872222: Epoch time: 101.38 s +2026-04-12 04:11:32.046838: +2026-04-12 04:11:32.048897: Epoch 1460 +2026-04-12 04:11:32.051098: Current learning rate: 0.00665 +2026-04-12 04:13:13.381330: train_loss -0.4003 +2026-04-12 04:13:13.387516: val_loss -0.3374 +2026-04-12 04:13:13.389304: Pseudo dice [0.4751, 0.2247, 0.5917, 0.0741, 0.311, 0.463, 0.6117] +2026-04-12 04:13:13.391046: Epoch time: 101.34 s +2026-04-12 04:13:14.659929: +2026-04-12 04:13:14.661795: Epoch 1461 +2026-04-12 04:13:14.663881: Current learning rate: 0.00664 +2026-04-12 04:14:56.125830: train_loss -0.4042 +2026-04-12 04:14:56.131505: val_loss -0.3727 +2026-04-12 04:14:56.133337: Pseudo dice [0.4932, 0.0915, 0.7882, 0.6056, 0.4067, 0.7472, 0.6746] +2026-04-12 04:14:56.134898: Epoch time: 101.47 s +2026-04-12 04:14:57.315539: +2026-04-12 04:14:57.317347: Epoch 1462 +2026-04-12 04:14:57.319630: Current learning rate: 0.00664 +2026-04-12 04:16:38.517848: train_loss -0.3804 +2026-04-12 04:16:38.522318: val_loss -0.3207 +2026-04-12 04:16:38.524295: Pseudo dice [0.0, 0.0417, 0.5934, 0.6804, 0.2742, 0.6198, 0.5602] +2026-04-12 04:16:38.525879: Epoch time: 101.21 s +2026-04-12 04:16:39.719373: +2026-04-12 04:16:39.721300: Epoch 1463 +2026-04-12 04:16:39.723440: Current learning rate: 0.00664 +2026-04-12 04:18:20.955455: train_loss -0.3833 +2026-04-12 04:18:20.959970: val_loss -0.3058 +2026-04-12 04:18:20.962158: Pseudo dice [0.0, 0.8089, 0.4768, 0.5882, 0.0554, 0.5095, 0.5601] +2026-04-12 04:18:20.963904: Epoch time: 101.24 s +2026-04-12 04:18:22.141900: +2026-04-12 04:18:22.143563: Epoch 1464 +2026-04-12 04:18:22.145336: Current learning rate: 0.00664 +2026-04-12 04:20:03.495727: train_loss -0.3963 +2026-04-12 04:20:03.501059: val_loss -0.3752 +2026-04-12 04:20:03.503118: Pseudo dice [0.7147, 0.2269, 0.7252, 0.8089, 0.1937, 0.1817, 0.771] +2026-04-12 04:20:03.505369: Epoch time: 101.36 s +2026-04-12 04:20:04.689709: +2026-04-12 04:20:04.691361: Epoch 1465 +2026-04-12 04:20:04.693423: Current learning rate: 0.00663 +2026-04-12 04:21:46.023682: train_loss -0.3929 +2026-04-12 04:21:46.028567: val_loss -0.3213 +2026-04-12 04:21:46.030246: Pseudo dice [0.0095, 0.0166, 0.7114, 0.6398, 0.3534, 0.7258, 0.8057] +2026-04-12 04:21:46.032048: Epoch time: 101.34 s +2026-04-12 04:21:47.206877: +2026-04-12 04:21:47.208415: Epoch 1466 +2026-04-12 04:21:47.210168: Current learning rate: 0.00663 +2026-04-12 04:23:28.475111: train_loss -0.369 +2026-04-12 04:23:28.480897: val_loss -0.3308 +2026-04-12 04:23:28.482736: Pseudo dice [0.2293, 0.0, 0.5912, 0.3978, 0.3022, 0.6811, 0.7244] +2026-04-12 04:23:28.484574: Epoch time: 101.27 s +2026-04-12 04:23:29.654210: +2026-04-12 04:23:29.656401: Epoch 1467 +2026-04-12 04:23:29.658454: Current learning rate: 0.00663 +2026-04-12 04:25:10.950378: train_loss -0.4028 +2026-04-12 04:25:10.956354: val_loss -0.363 +2026-04-12 04:25:10.958075: Pseudo dice [0.3436, 0.1343, 0.7121, 0.7991, 0.2897, 0.6448, 0.6876] +2026-04-12 04:25:10.959828: Epoch time: 101.3 s +2026-04-12 04:25:12.151856: +2026-04-12 04:25:12.153513: Epoch 1468 +2026-04-12 04:25:12.158826: Current learning rate: 0.00663 +2026-04-12 04:26:53.620739: train_loss -0.4084 +2026-04-12 04:26:53.626342: val_loss -0.3703 +2026-04-12 04:26:53.628253: Pseudo dice [0.4147, 0.6293, 0.7147, 0.7937, 0.2934, 0.5954, 0.7318] +2026-04-12 04:26:53.630323: Epoch time: 101.47 s +2026-04-12 04:26:55.071289: +2026-04-12 04:26:55.073368: Epoch 1469 +2026-04-12 04:26:55.075323: Current learning rate: 0.00662 +2026-04-12 04:28:36.291750: train_loss -0.3756 +2026-04-12 04:28:36.298881: val_loss -0.3102 +2026-04-12 04:28:36.300832: Pseudo dice [0.5092, 0.3486, 0.5866, 0.6458, 0.1572, 0.7187, 0.0742] +2026-04-12 04:28:36.302767: Epoch time: 101.22 s +2026-04-12 04:28:37.468951: +2026-04-12 04:28:37.470803: Epoch 1470 +2026-04-12 04:28:37.472807: Current learning rate: 0.00662 +2026-04-12 04:30:18.985517: train_loss -0.3854 +2026-04-12 04:30:18.996282: val_loss -0.3116 +2026-04-12 04:30:18.999568: Pseudo dice [0.5739, 0.1132, 0.2246, 0.3654, 0.2461, 0.4526, 0.8035] +2026-04-12 04:30:19.001999: Epoch time: 101.52 s +2026-04-12 04:30:20.185156: +2026-04-12 04:30:20.187036: Epoch 1471 +2026-04-12 04:30:20.189284: Current learning rate: 0.00662 +2026-04-12 04:32:01.545987: train_loss -0.3958 +2026-04-12 04:32:01.551025: val_loss -0.3639 +2026-04-12 04:32:01.552524: Pseudo dice [0.1168, 0.7306, 0.5485, 0.2622, 0.2709, 0.573, 0.8529] +2026-04-12 04:32:01.554700: Epoch time: 101.36 s +2026-04-12 04:32:02.733068: +2026-04-12 04:32:02.734848: Epoch 1472 +2026-04-12 04:32:02.736876: Current learning rate: 0.00662 +2026-04-12 04:33:44.007565: train_loss -0.3964 +2026-04-12 04:33:44.024357: val_loss -0.3331 +2026-04-12 04:33:44.025896: Pseudo dice [0.0996, 0.1984, 0.7446, 0.7674, 0.3273, 0.1192, 0.7746] +2026-04-12 04:33:44.027905: Epoch time: 101.28 s +2026-04-12 04:33:45.201544: +2026-04-12 04:33:45.203640: Epoch 1473 +2026-04-12 04:33:45.205598: Current learning rate: 0.00661 +2026-04-12 04:35:26.511023: train_loss -0.3894 +2026-04-12 04:35:26.516477: val_loss -0.3233 +2026-04-12 04:35:26.518823: Pseudo dice [0.3562, 0.2692, 0.4595, 0.7835, 0.3125, 0.2961, 0.7827] +2026-04-12 04:35:26.520770: Epoch time: 101.31 s +2026-04-12 04:35:27.694988: +2026-04-12 04:35:27.696736: Epoch 1474 +2026-04-12 04:35:27.698524: Current learning rate: 0.00661 +2026-04-12 04:37:08.818112: train_loss -0.3906 +2026-04-12 04:37:08.824518: val_loss -0.3237 +2026-04-12 04:37:08.826377: Pseudo dice [0.3814, 0.1895, 0.631, 0.5916, 0.3385, 0.8341, 0.7593] +2026-04-12 04:37:08.827845: Epoch time: 101.13 s +2026-04-12 04:37:10.007325: +2026-04-12 04:37:10.009250: Epoch 1475 +2026-04-12 04:37:10.011011: Current learning rate: 0.00661 +2026-04-12 04:38:51.319071: train_loss -0.396 +2026-04-12 04:38:51.324797: val_loss -0.3245 +2026-04-12 04:38:51.327226: Pseudo dice [0.2192, 0.1555, 0.7074, 0.0013, 0.1515, 0.6775, 0.6032] +2026-04-12 04:38:51.329100: Epoch time: 101.32 s +2026-04-12 04:38:52.507215: +2026-04-12 04:38:52.509219: Epoch 1476 +2026-04-12 04:38:52.511166: Current learning rate: 0.00661 +2026-04-12 04:40:34.736794: train_loss -0.3831 +2026-04-12 04:40:34.743635: val_loss -0.2914 +2026-04-12 04:40:34.746830: Pseudo dice [0.0, 0.45, 0.6058, 0.3181, 0.2591, 0.2286, 0.6008] +2026-04-12 04:40:34.748449: Epoch time: 102.23 s +2026-04-12 04:40:35.914732: +2026-04-12 04:40:35.916851: Epoch 1477 +2026-04-12 04:40:35.919715: Current learning rate: 0.0066 +2026-04-12 04:42:17.476219: train_loss -0.3861 +2026-04-12 04:42:17.480478: val_loss -0.3085 +2026-04-12 04:42:17.481913: Pseudo dice [0.0043, 0.282, 0.6717, 0.6184, 0.2359, 0.8165, 0.325] +2026-04-12 04:42:17.483415: Epoch time: 101.56 s +2026-04-12 04:42:18.647255: +2026-04-12 04:42:18.648762: Epoch 1478 +2026-04-12 04:42:18.650464: Current learning rate: 0.0066 +2026-04-12 04:44:00.135293: train_loss -0.4086 +2026-04-12 04:44:00.141614: val_loss -0.3384 +2026-04-12 04:44:00.143717: Pseudo dice [0.0921, 0.5811, 0.6339, 0.7501, 0.3446, 0.5722, 0.3703] +2026-04-12 04:44:00.146190: Epoch time: 101.49 s +2026-04-12 04:44:01.313355: +2026-04-12 04:44:01.315534: Epoch 1479 +2026-04-12 04:44:01.318076: Current learning rate: 0.0066 +2026-04-12 04:45:42.402106: train_loss -0.4064 +2026-04-12 04:45:42.407423: val_loss -0.314 +2026-04-12 04:45:42.409250: Pseudo dice [0.5091, 0.284, 0.565, 0.7108, 0.0935, 0.7401, 0.7215] +2026-04-12 04:45:42.410949: Epoch time: 101.09 s +2026-04-12 04:45:43.592624: +2026-04-12 04:45:43.594542: Epoch 1480 +2026-04-12 04:45:43.596751: Current learning rate: 0.0066 +2026-04-12 04:47:24.541058: train_loss -0.4032 +2026-04-12 04:47:24.546280: val_loss -0.3289 +2026-04-12 04:47:24.548010: Pseudo dice [0.0135, 0.2889, 0.5338, 0.2477, 0.3268, 0.7668, 0.7011] +2026-04-12 04:47:24.549546: Epoch time: 100.95 s +2026-04-12 04:47:25.718064: +2026-04-12 04:47:25.719909: Epoch 1481 +2026-04-12 04:47:25.721969: Current learning rate: 0.0066 +2026-04-12 04:49:06.780934: train_loss -0.3975 +2026-04-12 04:49:06.786030: val_loss -0.3773 +2026-04-12 04:49:06.788189: Pseudo dice [0.4349, 0.6292, 0.7562, 0.7179, 0.3648, 0.8247, 0.6295] +2026-04-12 04:49:06.789829: Epoch time: 101.07 s +2026-04-12 04:49:07.989878: +2026-04-12 04:49:07.992063: Epoch 1482 +2026-04-12 04:49:07.994275: Current learning rate: 0.00659 +2026-04-12 04:50:49.239741: train_loss -0.4087 +2026-04-12 04:50:49.245989: val_loss -0.3718 +2026-04-12 04:50:49.248315: Pseudo dice [0.5988, 0.5687, 0.6148, 0.6703, 0.5656, 0.6821, 0.6065] +2026-04-12 04:50:49.250097: Epoch time: 101.25 s +2026-04-12 04:50:50.447394: +2026-04-12 04:50:50.449293: Epoch 1483 +2026-04-12 04:50:50.451242: Current learning rate: 0.00659 +2026-04-12 04:52:31.804043: train_loss -0.3907 +2026-04-12 04:52:31.810797: val_loss -0.3624 +2026-04-12 04:52:31.813056: Pseudo dice [0.3663, 0.4828, 0.3624, 0.7715, 0.3544, 0.749, 0.8435] +2026-04-12 04:52:31.814821: Epoch time: 101.36 s +2026-04-12 04:52:33.008014: +2026-04-12 04:52:33.010092: Epoch 1484 +2026-04-12 04:52:33.012372: Current learning rate: 0.00659 +2026-04-12 04:54:14.394063: train_loss -0.3945 +2026-04-12 04:54:14.399397: val_loss -0.3254 +2026-04-12 04:54:14.401355: Pseudo dice [0.0745, 0.0439, 0.7885, 0.6714, 0.2328, 0.7599, 0.7327] +2026-04-12 04:54:14.402963: Epoch time: 101.39 s +2026-04-12 04:54:15.598239: +2026-04-12 04:54:15.600028: Epoch 1485 +2026-04-12 04:54:15.601718: Current learning rate: 0.00659 +2026-04-12 04:55:56.889302: train_loss -0.3877 +2026-04-12 04:55:56.895302: val_loss -0.3669 +2026-04-12 04:55:56.897593: Pseudo dice [0.4382, 0.0, 0.7639, 0.4648, 0.3601, 0.7044, 0.7945] +2026-04-12 04:55:56.899365: Epoch time: 101.29 s +2026-04-12 04:55:58.081152: +2026-04-12 04:55:58.083486: Epoch 1486 +2026-04-12 04:55:58.085838: Current learning rate: 0.00658 +2026-04-12 04:57:39.123591: train_loss -0.3846 +2026-04-12 04:57:39.129163: val_loss -0.2996 +2026-04-12 04:57:39.130884: Pseudo dice [0.1718, 0.0, 0.5539, 0.6942, 0.2501, 0.3311, 0.4305] +2026-04-12 04:57:39.132700: Epoch time: 101.05 s +2026-04-12 04:57:40.311414: +2026-04-12 04:57:40.313161: Epoch 1487 +2026-04-12 04:57:40.315024: Current learning rate: 0.00658 +2026-04-12 04:59:21.462824: train_loss -0.4145 +2026-04-12 04:59:21.469460: val_loss -0.3744 +2026-04-12 04:59:21.470938: Pseudo dice [0.2532, 0.0, 0.5554, 0.489, 0.3036, 0.6459, 0.8874] +2026-04-12 04:59:21.473072: Epoch time: 101.15 s +2026-04-12 04:59:22.657643: +2026-04-12 04:59:22.659688: Epoch 1488 +2026-04-12 04:59:22.661712: Current learning rate: 0.00658 +2026-04-12 05:01:03.990735: train_loss -0.4182 +2026-04-12 05:01:03.995531: val_loss -0.3444 +2026-04-12 05:01:03.996983: Pseudo dice [0.2685, 0.1099, 0.4434, 0.6765, 0.1528, 0.7975, 0.823] +2026-04-12 05:01:03.998420: Epoch time: 101.34 s +2026-04-12 05:01:05.177361: +2026-04-12 05:01:05.179096: Epoch 1489 +2026-04-12 05:01:05.180927: Current learning rate: 0.00658 +2026-04-12 05:02:46.474429: train_loss -0.4042 +2026-04-12 05:02:46.480293: val_loss -0.3178 +2026-04-12 05:02:46.482233: Pseudo dice [0.2386, 0.2288, 0.6889, 0.4521, 0.3401, 0.2013, 0.7548] +2026-04-12 05:02:46.484312: Epoch time: 101.3 s +2026-04-12 05:02:47.655043: +2026-04-12 05:02:47.656667: Epoch 1490 +2026-04-12 05:02:47.658231: Current learning rate: 0.00657 +2026-04-12 05:04:29.183414: train_loss -0.41 +2026-04-12 05:04:29.189730: val_loss -0.3172 +2026-04-12 05:04:29.191720: Pseudo dice [0.0, 0.3132, 0.7092, 0.8058, 0.3007, 0.4854, 0.5907] +2026-04-12 05:04:29.194283: Epoch time: 101.53 s +2026-04-12 05:04:30.392777: +2026-04-12 05:04:30.395662: Epoch 1491 +2026-04-12 05:04:30.398075: Current learning rate: 0.00657 +2026-04-12 05:06:11.691463: train_loss -0.3779 +2026-04-12 05:06:11.696563: val_loss -0.3106 +2026-04-12 05:06:11.698352: Pseudo dice [0.0, 0.0564, 0.2015, 0.7037, 0.2908, 0.4751, 0.6594] +2026-04-12 05:06:11.700331: Epoch time: 101.3 s +2026-04-12 05:06:12.888752: +2026-04-12 05:06:12.890365: Epoch 1492 +2026-04-12 05:06:12.892288: Current learning rate: 0.00657 +2026-04-12 05:07:54.159568: train_loss -0.3718 +2026-04-12 05:07:54.163814: val_loss -0.3437 +2026-04-12 05:07:54.165809: Pseudo dice [0.0, 0.0664, 0.752, 0.824, 0.5654, 0.4117, 0.7624] +2026-04-12 05:07:54.167480: Epoch time: 101.27 s +2026-04-12 05:07:55.343879: +2026-04-12 05:07:55.345916: Epoch 1493 +2026-04-12 05:07:55.348195: Current learning rate: 0.00657 +2026-04-12 05:09:36.597958: train_loss -0.3485 +2026-04-12 05:09:36.611524: val_loss -0.3427 +2026-04-12 05:09:36.614266: Pseudo dice [0.387, 0.0548, 0.6511, 0.4543, 0.3932, 0.5573, 0.3214] +2026-04-12 05:09:36.616579: Epoch time: 101.26 s +2026-04-12 05:09:37.837500: +2026-04-12 05:09:37.839445: Epoch 1494 +2026-04-12 05:09:37.842096: Current learning rate: 0.00656 +2026-04-12 05:11:18.984704: train_loss -0.3501 +2026-04-12 05:11:18.990965: val_loss -0.297 +2026-04-12 05:11:18.992941: Pseudo dice [0.0771, 0.0, 0.5002, 0.0448, 0.214, 0.3704, 0.4084] +2026-04-12 05:11:18.994798: Epoch time: 101.15 s +2026-04-12 05:11:20.204997: +2026-04-12 05:11:20.206884: Epoch 1495 +2026-04-12 05:11:20.208774: Current learning rate: 0.00656 +2026-04-12 05:13:01.476843: train_loss -0.3858 +2026-04-12 05:13:01.485186: val_loss -0.3611 +2026-04-12 05:13:01.487433: Pseudo dice [0.3822, 0.2043, 0.6554, 0.4774, 0.3573, 0.6455, 0.7776] +2026-04-12 05:13:01.489838: Epoch time: 101.27 s +2026-04-12 05:13:03.661867: +2026-04-12 05:13:03.664012: Epoch 1496 +2026-04-12 05:13:03.666603: Current learning rate: 0.00656 +2026-04-12 05:14:44.996312: train_loss -0.3932 +2026-04-12 05:14:45.006540: val_loss -0.3495 +2026-04-12 05:14:45.010895: Pseudo dice [0.1959, 0.5779, 0.6414, 0.0922, 0.0506, 0.4938, 0.8083] +2026-04-12 05:14:45.013093: Epoch time: 101.34 s +2026-04-12 05:14:46.373601: +2026-04-12 05:14:46.375590: Epoch 1497 +2026-04-12 05:14:46.378089: Current learning rate: 0.00656 +2026-04-12 05:16:27.811529: train_loss -0.3799 +2026-04-12 05:16:27.816838: val_loss -0.3397 +2026-04-12 05:16:27.819417: Pseudo dice [0.2846, 0.0406, 0.8457, 0.025, 0.3209, 0.6179, 0.478] +2026-04-12 05:16:27.820992: Epoch time: 101.44 s +2026-04-12 05:16:28.984295: +2026-04-12 05:16:28.986165: Epoch 1498 +2026-04-12 05:16:28.989079: Current learning rate: 0.00656 +2026-04-12 05:18:10.234299: train_loss -0.3823 +2026-04-12 05:18:10.239171: val_loss -0.3296 +2026-04-12 05:18:10.241125: Pseudo dice [0.1795, 0.0, 0.3075, 0.8355, 0.3628, 0.5243, 0.8446] +2026-04-12 05:18:10.243419: Epoch time: 101.25 s +2026-04-12 05:18:11.411193: +2026-04-12 05:18:11.412939: Epoch 1499 +2026-04-12 05:18:11.414775: Current learning rate: 0.00655 +2026-04-12 05:19:52.718189: train_loss -0.4002 +2026-04-12 05:19:52.723134: val_loss -0.2992 +2026-04-12 05:19:52.724774: Pseudo dice [0.0143, 0.0718, 0.4034, 0.8488, 0.1343, 0.5786, 0.5683] +2026-04-12 05:19:52.726767: Epoch time: 101.31 s +2026-04-12 05:19:55.555860: +2026-04-12 05:19:55.558455: Epoch 1500 +2026-04-12 05:19:55.560071: Current learning rate: 0.00655 +2026-04-12 05:21:36.889295: train_loss -0.3947 +2026-04-12 05:21:36.896422: val_loss -0.3342 +2026-04-12 05:21:36.898230: Pseudo dice [0.0623, 0.2942, 0.6772, 0.5557, 0.155, 0.6361, 0.8251] +2026-04-12 05:21:36.899726: Epoch time: 101.34 s +2026-04-12 05:21:38.084093: +2026-04-12 05:21:38.085691: Epoch 1501 +2026-04-12 05:21:38.087620: Current learning rate: 0.00655 +2026-04-12 05:23:19.345280: train_loss -0.3674 +2026-04-12 05:23:19.350906: val_loss -0.3414 +2026-04-12 05:23:19.352680: Pseudo dice [0.006, 0.088, 0.1711, 0.7153, 0.3686, 0.765, 0.7361] +2026-04-12 05:23:19.354563: Epoch time: 101.26 s +2026-04-12 05:23:20.544363: +2026-04-12 05:23:20.546264: Epoch 1502 +2026-04-12 05:23:20.548163: Current learning rate: 0.00655 +2026-04-12 05:25:01.851362: train_loss -0.3565 +2026-04-12 05:25:01.861797: val_loss -0.3282 +2026-04-12 05:25:01.864987: Pseudo dice [0.4161, 0.2848, 0.6607, 0.3339, 0.3738, 0.7663, 0.7] +2026-04-12 05:25:01.880601: Epoch time: 101.31 s +2026-04-12 05:25:03.040610: +2026-04-12 05:25:03.042288: Epoch 1503 +2026-04-12 05:25:03.044118: Current learning rate: 0.00654 +2026-04-12 05:26:44.289044: train_loss -0.3704 +2026-04-12 05:26:44.294260: val_loss -0.3332 +2026-04-12 05:26:44.296506: Pseudo dice [0.1879, 0.1678, 0.5846, 0.7605, 0.2289, 0.7518, 0.751] +2026-04-12 05:26:44.297831: Epoch time: 101.25 s +2026-04-12 05:26:45.486413: +2026-04-12 05:26:45.488663: Epoch 1504 +2026-04-12 05:26:45.490870: Current learning rate: 0.00654 +2026-04-12 05:28:26.626865: train_loss -0.4102 +2026-04-12 05:28:26.633171: val_loss -0.336 +2026-04-12 05:28:26.635173: Pseudo dice [0.3903, 0.0, 0.3035, 0.6512, 0.0171, 0.7709, 0.3058] +2026-04-12 05:28:26.636909: Epoch time: 101.14 s +2026-04-12 05:28:27.832982: +2026-04-12 05:28:27.834545: Epoch 1505 +2026-04-12 05:28:27.836245: Current learning rate: 0.00654 +2026-04-12 05:30:09.637320: train_loss -0.3953 +2026-04-12 05:30:09.644205: val_loss -0.3501 +2026-04-12 05:30:09.646675: Pseudo dice [0.2957, 0.5482, 0.554, 0.7034, 0.3131, 0.8419, 0.5925] +2026-04-12 05:30:09.648643: Epoch time: 101.81 s +2026-04-12 05:30:10.853757: +2026-04-12 05:30:10.855678: Epoch 1506 +2026-04-12 05:30:10.857811: Current learning rate: 0.00654 +2026-04-12 05:31:52.257491: train_loss -0.3968 +2026-04-12 05:31:52.263127: val_loss -0.3047 +2026-04-12 05:31:52.265010: Pseudo dice [0.1719, 0.0812, 0.5408, 0.5201, 0.4179, 0.3789, 0.3546] +2026-04-12 05:31:52.266682: Epoch time: 101.41 s +2026-04-12 05:31:53.454885: +2026-04-12 05:31:53.456564: Epoch 1507 +2026-04-12 05:31:53.458620: Current learning rate: 0.00653 +2026-04-12 05:33:34.908418: train_loss -0.3581 +2026-04-12 05:33:34.913711: val_loss -0.3077 +2026-04-12 05:33:34.915415: Pseudo dice [0.1314, 0.3373, 0.5498, 0.6299, 0.381, 0.2249, 0.2934] +2026-04-12 05:33:34.916843: Epoch time: 101.46 s +2026-04-12 05:33:36.106367: +2026-04-12 05:33:36.108063: Epoch 1508 +2026-04-12 05:33:36.109814: Current learning rate: 0.00653 +2026-04-12 05:35:17.462604: train_loss -0.3827 +2026-04-12 05:35:17.467926: val_loss -0.3195 +2026-04-12 05:35:17.470781: Pseudo dice [0.054, 0.0026, 0.7066, 0.5839, 0.3645, 0.7162, 0.7019] +2026-04-12 05:35:17.472557: Epoch time: 101.36 s +2026-04-12 05:35:18.663439: +2026-04-12 05:35:18.665239: Epoch 1509 +2026-04-12 05:35:18.667403: Current learning rate: 0.00653 +2026-04-12 05:37:00.143226: train_loss -0.3741 +2026-04-12 05:37:00.168257: val_loss -0.3361 +2026-04-12 05:37:00.170434: Pseudo dice [0.0593, 0.018, 0.6489, 0.8004, 0.2175, 0.6486, 0.8008] +2026-04-12 05:37:00.172847: Epoch time: 101.48 s +2026-04-12 05:37:01.355895: +2026-04-12 05:37:01.357818: Epoch 1510 +2026-04-12 05:37:01.360080: Current learning rate: 0.00653 +2026-04-12 05:38:42.688561: train_loss -0.3987 +2026-04-12 05:38:42.693549: val_loss -0.3667 +2026-04-12 05:38:42.695184: Pseudo dice [0.3507, 0.2107, 0.6334, 0.8246, 0.3605, 0.6852, 0.6639] +2026-04-12 05:38:42.696716: Epoch time: 101.34 s +2026-04-12 05:38:43.903987: +2026-04-12 05:38:43.923476: Epoch 1511 +2026-04-12 05:38:43.938797: Current learning rate: 0.00652 +2026-04-12 05:40:25.278843: train_loss -0.3944 +2026-04-12 05:40:25.285184: val_loss -0.3142 +2026-04-12 05:40:25.286882: Pseudo dice [0.0, 0.1285, 0.6672, 0.6121, 0.1517, 0.748, 0.6386] +2026-04-12 05:40:25.289053: Epoch time: 101.38 s +2026-04-12 05:40:26.474022: +2026-04-12 05:40:26.475932: Epoch 1512 +2026-04-12 05:40:26.478068: Current learning rate: 0.00652 +2026-04-12 05:42:07.878515: train_loss -0.3991 +2026-04-12 05:42:07.883501: val_loss -0.346 +2026-04-12 05:42:07.885903: Pseudo dice [0.0065, 0.3479, 0.8269, 0.798, 0.4186, 0.6265, 0.4881] +2026-04-12 05:42:07.887970: Epoch time: 101.41 s +2026-04-12 05:42:09.144430: +2026-04-12 05:42:09.146321: Epoch 1513 +2026-04-12 05:42:09.148445: Current learning rate: 0.00652 +2026-04-12 05:43:50.512543: train_loss -0.4053 +2026-04-12 05:43:50.518262: val_loss -0.3499 +2026-04-12 05:43:50.520911: Pseudo dice [0.2411, 0.0, 0.7728, 0.8239, 0.0831, 0.1164, 0.6554] +2026-04-12 05:43:50.522928: Epoch time: 101.37 s +2026-04-12 05:43:51.708604: +2026-04-12 05:43:51.710246: Epoch 1514 +2026-04-12 05:43:51.711900: Current learning rate: 0.00652 +2026-04-12 05:45:33.094393: train_loss -0.3728 +2026-04-12 05:45:33.099216: val_loss -0.3479 +2026-04-12 05:45:33.100936: Pseudo dice [0.0, 0.0, 0.6563, 0.7365, 0.3772, 0.818, 0.6733] +2026-04-12 05:45:33.102551: Epoch time: 101.39 s +2026-04-12 05:45:34.270225: +2026-04-12 05:45:34.272191: Epoch 1515 +2026-04-12 05:45:34.274023: Current learning rate: 0.00652 +2026-04-12 05:47:16.671368: train_loss -0.3966 +2026-04-12 05:47:16.677075: val_loss -0.326 +2026-04-12 05:47:16.678900: Pseudo dice [0.0, 0.0, 0.5682, 0.7677, 0.3409, 0.3214, 0.8039] +2026-04-12 05:47:16.680938: Epoch time: 102.4 s +2026-04-12 05:47:17.851129: +2026-04-12 05:47:17.852615: Epoch 1516 +2026-04-12 05:47:17.853852: Current learning rate: 0.00651 +2026-04-12 05:48:58.983457: train_loss -0.38 +2026-04-12 05:48:58.987918: val_loss -0.3298 +2026-04-12 05:48:58.989685: Pseudo dice [0.0, 0.0972, 0.6794, 0.7923, 0.3002, 0.6792, 0.544] +2026-04-12 05:48:58.991204: Epoch time: 101.14 s +2026-04-12 05:49:00.176968: +2026-04-12 05:49:00.178476: Epoch 1517 +2026-04-12 05:49:00.179948: Current learning rate: 0.00651 +2026-04-12 05:50:41.462002: train_loss -0.4047 +2026-04-12 05:50:41.467113: val_loss -0.3719 +2026-04-12 05:50:41.468818: Pseudo dice [0.4617, 0.4709, 0.5906, 0.7433, 0.3504, 0.6958, 0.7193] +2026-04-12 05:50:41.471270: Epoch time: 101.29 s +2026-04-12 05:50:42.647913: +2026-04-12 05:50:42.650037: Epoch 1518 +2026-04-12 05:50:42.651839: Current learning rate: 0.00651 +2026-04-12 05:52:23.903426: train_loss -0.3961 +2026-04-12 05:52:23.909386: val_loss -0.351 +2026-04-12 05:52:23.911500: Pseudo dice [0.4112, 0.368, 0.5142, 0.7159, 0.3837, 0.7652, 0.3282] +2026-04-12 05:52:23.913754: Epoch time: 101.26 s +2026-04-12 05:52:25.082865: +2026-04-12 05:52:25.084634: Epoch 1519 +2026-04-12 05:52:25.086186: Current learning rate: 0.00651 +2026-04-12 05:54:06.278059: train_loss -0.4146 +2026-04-12 05:54:06.285018: val_loss -0.3718 +2026-04-12 05:54:06.286613: Pseudo dice [0.6223, 0.3895, 0.7445, 0.134, 0.3288, 0.7931, 0.7468] +2026-04-12 05:54:06.288296: Epoch time: 101.2 s +2026-04-12 05:54:07.465031: +2026-04-12 05:54:07.466762: Epoch 1520 +2026-04-12 05:54:07.468274: Current learning rate: 0.0065 +2026-04-12 05:55:48.514406: train_loss -0.4096 +2026-04-12 05:55:48.520120: val_loss -0.3666 +2026-04-12 05:55:48.521497: Pseudo dice [0.1197, 0.1006, 0.7868, 0.7428, 0.5277, 0.4443, 0.7894] +2026-04-12 05:55:48.523392: Epoch time: 101.05 s +2026-04-12 05:55:49.716890: +2026-04-12 05:55:49.718473: Epoch 1521 +2026-04-12 05:55:49.719978: Current learning rate: 0.0065 +2026-04-12 05:57:31.022746: train_loss -0.4105 +2026-04-12 05:57:31.029502: val_loss -0.368 +2026-04-12 05:57:31.031348: Pseudo dice [0.1968, 0.0934, 0.6978, 0.8549, 0.101, 0.6239, 0.7729] +2026-04-12 05:57:31.033382: Epoch time: 101.31 s +2026-04-12 05:57:32.204437: +2026-04-12 05:57:32.206194: Epoch 1522 +2026-04-12 05:57:32.207876: Current learning rate: 0.0065 +2026-04-12 05:59:13.598558: train_loss -0.4229 +2026-04-12 05:59:13.604391: val_loss -0.3815 +2026-04-12 05:59:13.606077: Pseudo dice [0.4629, 0.161, 0.7126, 0.8266, 0.2944, 0.7042, 0.784] +2026-04-12 05:59:13.607875: Epoch time: 101.4 s +2026-04-12 05:59:15.023636: +2026-04-12 05:59:15.025988: Epoch 1523 +2026-04-12 05:59:15.027878: Current learning rate: 0.0065 +2026-04-12 06:00:56.543597: train_loss -0.416 +2026-04-12 06:00:56.550112: val_loss -0.3656 +2026-04-12 06:00:56.552459: Pseudo dice [0.7617, 0.1869, 0.6007, 0.67, 0.291, 0.5581, 0.7409] +2026-04-12 06:00:56.554837: Epoch time: 101.52 s +2026-04-12 06:00:57.739519: +2026-04-12 06:00:57.741418: Epoch 1524 +2026-04-12 06:00:57.743009: Current learning rate: 0.00649 +2026-04-12 06:02:39.014432: train_loss -0.4114 +2026-04-12 06:02:39.019525: val_loss -0.3304 +2026-04-12 06:02:39.020866: Pseudo dice [0.4736, 0.0745, 0.6535, 0.5996, 0.254, 0.603, 0.7622] +2026-04-12 06:02:39.022031: Epoch time: 101.28 s +2026-04-12 06:02:40.200073: +2026-04-12 06:02:40.201344: Epoch 1525 +2026-04-12 06:02:40.202647: Current learning rate: 0.00649 +2026-04-12 06:04:21.359165: train_loss -0.393 +2026-04-12 06:04:21.366512: val_loss -0.313 +2026-04-12 06:04:21.368779: Pseudo dice [0.3155, 0.1534, 0.0959, 0.026, 0.0584, 0.3756, 0.5264] +2026-04-12 06:04:21.370701: Epoch time: 101.16 s +2026-04-12 06:04:22.570428: +2026-04-12 06:04:22.572521: Epoch 1526 +2026-04-12 06:04:22.574587: Current learning rate: 0.00649 +2026-04-12 06:06:03.870763: train_loss -0.3659 +2026-04-12 06:06:03.876168: val_loss -0.342 +2026-04-12 06:06:03.877826: Pseudo dice [0.0, 0.261, 0.674, 0.7182, 0.2842, 0.7701, 0.8362] +2026-04-12 06:06:03.879371: Epoch time: 101.3 s +2026-04-12 06:06:05.081413: +2026-04-12 06:06:05.083194: Epoch 1527 +2026-04-12 06:06:05.084597: Current learning rate: 0.00649 +2026-04-12 06:07:46.397875: train_loss -0.3879 +2026-04-12 06:07:46.403391: val_loss -0.3718 +2026-04-12 06:07:46.405149: Pseudo dice [0.0, 0.5274, 0.8373, 0.8201, 0.5437, 0.6877, 0.854] +2026-04-12 06:07:46.406933: Epoch time: 101.32 s +2026-04-12 06:07:47.633466: +2026-04-12 06:07:47.635400: Epoch 1528 +2026-04-12 06:07:47.637363: Current learning rate: 0.00648 +2026-04-12 06:09:28.999088: train_loss -0.3449 +2026-04-12 06:09:29.004272: val_loss -0.3699 +2026-04-12 06:09:29.006061: Pseudo dice [0.0, 0.3616, 0.5376, 0.894, 0.4207, 0.6691, 0.7386] +2026-04-12 06:09:29.007516: Epoch time: 101.37 s +2026-04-12 06:09:30.214367: +2026-04-12 06:09:30.216501: Epoch 1529 +2026-04-12 06:09:30.218347: Current learning rate: 0.00648 +2026-04-12 06:11:11.621070: train_loss -0.3958 +2026-04-12 06:11:11.627028: val_loss -0.3465 +2026-04-12 06:11:11.628792: Pseudo dice [0.0, 0.3257, 0.7772, 0.8427, 0.3365, 0.7509, 0.8647] +2026-04-12 06:11:11.630559: Epoch time: 101.41 s +2026-04-12 06:11:12.814487: +2026-04-12 06:11:12.817351: Epoch 1530 +2026-04-12 06:11:12.823084: Current learning rate: 0.00648 +2026-04-12 06:12:53.930657: train_loss -0.39 +2026-04-12 06:12:53.935849: val_loss -0.3736 +2026-04-12 06:12:53.937776: Pseudo dice [0.039, 0.5026, 0.5653, 0.8087, 0.4474, 0.8285, 0.7938] +2026-04-12 06:12:53.939403: Epoch time: 101.12 s +2026-04-12 06:12:55.153446: +2026-04-12 06:12:55.155349: Epoch 1531 +2026-04-12 06:12:55.156954: Current learning rate: 0.00648 +2026-04-12 06:14:36.486860: train_loss -0.4132 +2026-04-12 06:14:36.493190: val_loss -0.3577 +2026-04-12 06:14:36.494958: Pseudo dice [0.4331, 0.2669, 0.6461, 0.4265, 0.3809, 0.769, 0.7349] +2026-04-12 06:14:36.496736: Epoch time: 101.34 s +2026-04-12 06:14:37.694450: +2026-04-12 06:14:37.696173: Epoch 1532 +2026-04-12 06:14:37.697815: Current learning rate: 0.00648 +2026-04-12 06:16:18.980792: train_loss -0.4151 +2026-04-12 06:16:18.985676: val_loss -0.3609 +2026-04-12 06:16:18.987779: Pseudo dice [0.4498, 0.0955, 0.5875, 0.8525, 0.2241, 0.6534, 0.8753] +2026-04-12 06:16:18.989385: Epoch time: 101.29 s +2026-04-12 06:16:20.184257: +2026-04-12 06:16:20.187202: Epoch 1533 +2026-04-12 06:16:20.189342: Current learning rate: 0.00647 +2026-04-12 06:18:01.470853: train_loss -0.3958 +2026-04-12 06:18:01.476014: val_loss -0.3346 +2026-04-12 06:18:01.477639: Pseudo dice [0.5241, 0.0152, 0.7206, 0.8666, 0.2272, 0.6694, 0.4883] +2026-04-12 06:18:01.479425: Epoch time: 101.29 s +2026-04-12 06:18:02.667412: +2026-04-12 06:18:02.669658: Epoch 1534 +2026-04-12 06:18:02.671125: Current learning rate: 0.00647 +2026-04-12 06:19:44.059067: train_loss -0.3894 +2026-04-12 06:19:44.064089: val_loss -0.3748 +2026-04-12 06:19:44.065424: Pseudo dice [0.408, 0.0, 0.7153, 0.8631, 0.4074, 0.578, 0.8817] +2026-04-12 06:19:44.066865: Epoch time: 101.39 s +2026-04-12 06:19:46.117812: +2026-04-12 06:19:46.119537: Epoch 1535 +2026-04-12 06:19:46.121206: Current learning rate: 0.00647 +2026-04-12 06:21:27.440367: train_loss -0.3901 +2026-04-12 06:21:27.445972: val_loss -0.3252 +2026-04-12 06:21:27.448118: Pseudo dice [0.684, 0.0, 0.6281, 0.5977, 0.2586, 0.756, 0.6736] +2026-04-12 06:21:27.453606: Epoch time: 101.33 s +2026-04-12 06:21:28.671423: +2026-04-12 06:21:28.673658: Epoch 1536 +2026-04-12 06:21:28.675401: Current learning rate: 0.00647 +2026-04-12 06:23:10.138846: train_loss -0.4046 +2026-04-12 06:23:10.143927: val_loss -0.3667 +2026-04-12 06:23:10.146087: Pseudo dice [0.2829, 0.0, 0.4431, 0.2445, 0.3737, 0.6403, 0.7425] +2026-04-12 06:23:10.147830: Epoch time: 101.47 s +2026-04-12 06:23:11.363658: +2026-04-12 06:23:11.365695: Epoch 1537 +2026-04-12 06:23:11.367208: Current learning rate: 0.00646 +2026-04-12 06:24:52.728008: train_loss -0.3929 +2026-04-12 06:24:52.733695: val_loss -0.3613 +2026-04-12 06:24:52.735451: Pseudo dice [0.2255, 0.0092, 0.6987, 0.8848, 0.1497, 0.7299, 0.4832] +2026-04-12 06:24:52.736892: Epoch time: 101.37 s +2026-04-12 06:24:53.931474: +2026-04-12 06:24:53.933354: Epoch 1538 +2026-04-12 06:24:53.934772: Current learning rate: 0.00646 +2026-04-12 06:26:35.233958: train_loss -0.3914 +2026-04-12 06:26:35.240153: val_loss -0.3613 +2026-04-12 06:26:35.242058: Pseudo dice [0.0292, 0.2128, 0.5493, 0.6314, 0.4251, 0.5972, 0.8092] +2026-04-12 06:26:35.244215: Epoch time: 101.31 s +2026-04-12 06:26:36.440610: +2026-04-12 06:26:36.442410: Epoch 1539 +2026-04-12 06:26:36.444041: Current learning rate: 0.00646 +2026-04-12 06:28:17.813251: train_loss -0.4138 +2026-04-12 06:28:17.818722: val_loss -0.3521 +2026-04-12 06:28:17.820520: Pseudo dice [0.2134, 0.1349, 0.6442, 0.5659, 0.4474, 0.7303, 0.518] +2026-04-12 06:28:17.822237: Epoch time: 101.38 s +2026-04-12 06:28:19.020307: +2026-04-12 06:28:19.022177: Epoch 1540 +2026-04-12 06:28:19.023766: Current learning rate: 0.00646 +2026-04-12 06:30:00.300125: train_loss -0.3849 +2026-04-12 06:30:00.306179: val_loss -0.3092 +2026-04-12 06:30:00.308155: Pseudo dice [0.2463, 0.3875, 0.6713, 0.6305, 0.0516, 0.1387, 0.6978] +2026-04-12 06:30:00.309818: Epoch time: 101.28 s +2026-04-12 06:30:01.514236: +2026-04-12 06:30:01.517041: Epoch 1541 +2026-04-12 06:30:01.518553: Current learning rate: 0.00645 +2026-04-12 06:31:42.697380: train_loss -0.3862 +2026-04-12 06:31:42.703512: val_loss -0.3606 +2026-04-12 06:31:42.705727: Pseudo dice [0.551, 0.0153, 0.735, 0.7051, 0.1807, 0.7707, 0.8257] +2026-04-12 06:31:42.707273: Epoch time: 101.19 s +2026-04-12 06:31:43.906697: +2026-04-12 06:31:43.908668: Epoch 1542 +2026-04-12 06:31:43.910313: Current learning rate: 0.00645 +2026-04-12 06:33:25.520079: train_loss -0.4094 +2026-04-12 06:33:25.526102: val_loss -0.3622 +2026-04-12 06:33:25.527791: Pseudo dice [0.4769, 0.1422, 0.7109, 0.7833, 0.1509, 0.4961, 0.8049] +2026-04-12 06:33:25.529571: Epoch time: 101.62 s +2026-04-12 06:33:26.748659: +2026-04-12 06:33:26.750224: Epoch 1543 +2026-04-12 06:33:26.751868: Current learning rate: 0.00645 +2026-04-12 06:35:08.021498: train_loss -0.3942 +2026-04-12 06:35:08.025978: val_loss -0.3324 +2026-04-12 06:35:08.027481: Pseudo dice [0.0233, 0.1664, 0.4925, 0.807, 0.2268, 0.7049, 0.7454] +2026-04-12 06:35:08.029186: Epoch time: 101.28 s +2026-04-12 06:35:09.223937: +2026-04-12 06:35:09.226006: Epoch 1544 +2026-04-12 06:35:09.227794: Current learning rate: 0.00645 +2026-04-12 06:36:50.515512: train_loss -0.3719 +2026-04-12 06:36:50.520133: val_loss -0.341 +2026-04-12 06:36:50.521966: Pseudo dice [0.218, 0.1116, 0.7384, 0.7946, 0.2473, 0.7489, 0.5718] +2026-04-12 06:36:50.524175: Epoch time: 101.3 s +2026-04-12 06:36:51.713862: +2026-04-12 06:36:51.715368: Epoch 1545 +2026-04-12 06:36:51.716676: Current learning rate: 0.00644 +2026-04-12 06:38:33.221296: train_loss -0.4165 +2026-04-12 06:38:33.226304: val_loss -0.3771 +2026-04-12 06:38:33.227666: Pseudo dice [0.1576, 0.5822, 0.5266, 0.8579, 0.2099, 0.378, 0.7953] +2026-04-12 06:38:33.229215: Epoch time: 101.51 s +2026-04-12 06:38:34.416173: +2026-04-12 06:38:34.417997: Epoch 1546 +2026-04-12 06:38:34.419595: Current learning rate: 0.00644 +2026-04-12 06:40:15.758915: train_loss -0.4156 +2026-04-12 06:40:15.764537: val_loss -0.3165 +2026-04-12 06:40:15.766097: Pseudo dice [0.1668, 0.6171, 0.2995, 0.7004, 0.2193, 0.5513, 0.4845] +2026-04-12 06:40:15.767777: Epoch time: 101.35 s +2026-04-12 06:40:16.967152: +2026-04-12 06:40:16.968461: Epoch 1547 +2026-04-12 06:40:16.969680: Current learning rate: 0.00644 +2026-04-12 06:41:58.394061: train_loss -0.387 +2026-04-12 06:41:58.398855: val_loss -0.3156 +2026-04-12 06:41:58.400707: Pseudo dice [0.0041, 0.0, 0.5781, 0.8041, 0.3078, 0.4648, 0.5842] +2026-04-12 06:41:58.402411: Epoch time: 101.43 s +2026-04-12 06:41:59.623601: +2026-04-12 06:41:59.625577: Epoch 1548 +2026-04-12 06:41:59.627351: Current learning rate: 0.00644 +2026-04-12 06:43:41.168953: train_loss -0.3756 +2026-04-12 06:43:41.173735: val_loss -0.2953 +2026-04-12 06:43:41.175452: Pseudo dice [0.0, 0.0051, 0.3539, 0.7794, 0.0722, 0.7543, 0.4611] +2026-04-12 06:43:41.178184: Epoch time: 101.55 s +2026-04-12 06:43:42.388831: +2026-04-12 06:43:42.390231: Epoch 1549 +2026-04-12 06:43:42.391535: Current learning rate: 0.00644 +2026-04-12 06:45:23.761131: train_loss -0.3747 +2026-04-12 06:45:23.767151: val_loss -0.2888 +2026-04-12 06:45:23.768714: Pseudo dice [0.0, 0.0388, 0.113, 0.4788, 0.0901, 0.7022, 0.3367] +2026-04-12 06:45:23.770299: Epoch time: 101.38 s +2026-04-12 06:45:26.650074: +2026-04-12 06:45:26.652303: Epoch 1550 +2026-04-12 06:45:26.653871: Current learning rate: 0.00643 +2026-04-12 06:47:07.882038: train_loss -0.3468 +2026-04-12 06:47:07.886930: val_loss -0.3143 +2026-04-12 06:47:07.891145: Pseudo dice [0.0, 0.7209, 0.1629, 0.6715, 0.1917, 0.2106, 0.7608] +2026-04-12 06:47:07.893037: Epoch time: 101.23 s +2026-04-12 06:47:09.093347: +2026-04-12 06:47:09.095061: Epoch 1551 +2026-04-12 06:47:09.096714: Current learning rate: 0.00643 +2026-04-12 06:48:50.388120: train_loss -0.3835 +2026-04-12 06:48:50.396515: val_loss -0.3645 +2026-04-12 06:48:50.398383: Pseudo dice [0.0304, 0.0219, 0.7082, 0.7013, 0.4082, 0.7038, 0.8593] +2026-04-12 06:48:50.399962: Epoch time: 101.3 s +2026-04-12 06:48:51.619866: +2026-04-12 06:48:51.621698: Epoch 1552 +2026-04-12 06:48:51.623271: Current learning rate: 0.00643 +2026-04-12 06:50:32.965357: train_loss -0.409 +2026-04-12 06:50:32.970735: val_loss -0.356 +2026-04-12 06:50:32.972710: Pseudo dice [0.0773, 0.2229, 0.7254, 0.7993, 0.4053, 0.6603, 0.6872] +2026-04-12 06:50:32.975648: Epoch time: 101.35 s +2026-04-12 06:50:34.232401: +2026-04-12 06:50:34.235690: Epoch 1553 +2026-04-12 06:50:34.237406: Current learning rate: 0.00643 +2026-04-12 06:52:15.376029: train_loss -0.406 +2026-04-12 06:52:15.382082: val_loss -0.3513 +2026-04-12 06:52:15.384045: Pseudo dice [0.5134, 0.0844, 0.7534, 0.7358, 0.1895, 0.7485, 0.3979] +2026-04-12 06:52:15.385504: Epoch time: 101.15 s +2026-04-12 06:52:17.571363: +2026-04-12 06:52:17.573384: Epoch 1554 +2026-04-12 06:52:17.575003: Current learning rate: 0.00642 +2026-04-12 06:53:58.844267: train_loss -0.3658 +2026-04-12 06:53:58.849227: val_loss -0.3087 +2026-04-12 06:53:58.850822: Pseudo dice [0.0, 0.1674, 0.2583, 0.1617, 0.5061, 0.3669, 0.6456] +2026-04-12 06:53:58.852336: Epoch time: 101.28 s +2026-04-12 06:54:00.056978: +2026-04-12 06:54:00.059043: Epoch 1555 +2026-04-12 06:54:00.060924: Current learning rate: 0.00642 +2026-04-12 06:55:41.344345: train_loss -0.3829 +2026-04-12 06:55:41.348932: val_loss -0.3483 +2026-04-12 06:55:41.350306: Pseudo dice [0.0, 0.5582, 0.7084, 0.7678, 0.3553, 0.59, 0.6578] +2026-04-12 06:55:41.351975: Epoch time: 101.29 s +2026-04-12 06:55:42.564747: +2026-04-12 06:55:42.566172: Epoch 1556 +2026-04-12 06:55:42.567405: Current learning rate: 0.00642 +2026-04-12 06:57:23.840245: train_loss -0.3261 +2026-04-12 06:57:23.846558: val_loss -0.2979 +2026-04-12 06:57:23.848654: Pseudo dice [0.0, 0.305, 0.5209, 0.7308, 0.2475, 0.5742, 0.5801] +2026-04-12 06:57:23.850709: Epoch time: 101.28 s +2026-04-12 06:57:25.025474: +2026-04-12 06:57:25.027634: Epoch 1557 +2026-04-12 06:57:25.029166: Current learning rate: 0.00642 +2026-04-12 06:59:06.299201: train_loss -0.3811 +2026-04-12 06:59:06.305516: val_loss -0.3343 +2026-04-12 06:59:06.307352: Pseudo dice [0.0, 0.1851, 0.5368, 0.8221, 0.2977, 0.4647, 0.6972] +2026-04-12 06:59:06.309522: Epoch time: 101.28 s +2026-04-12 06:59:07.537729: +2026-04-12 06:59:07.539245: Epoch 1558 +2026-04-12 06:59:07.540632: Current learning rate: 0.00641 +2026-04-12 07:00:48.875484: train_loss -0.415 +2026-04-12 07:00:48.879725: val_loss -0.3436 +2026-04-12 07:00:48.881706: Pseudo dice [0.0, 0.4214, 0.842, 0.7764, 0.3597, 0.5379, 0.8281] +2026-04-12 07:00:48.883449: Epoch time: 101.34 s +2026-04-12 07:00:50.089642: +2026-04-12 07:00:50.091418: Epoch 1559 +2026-04-12 07:00:50.092885: Current learning rate: 0.00641 +2026-04-12 07:02:31.245215: train_loss -0.3876 +2026-04-12 07:02:31.250104: val_loss -0.3603 +2026-04-12 07:02:31.252043: Pseudo dice [0.0, 0.6352, 0.4218, 0.6002, 0.4663, 0.6243, 0.731] +2026-04-12 07:02:31.254606: Epoch time: 101.16 s +2026-04-12 07:02:32.471567: +2026-04-12 07:02:32.473104: Epoch 1560 +2026-04-12 07:02:32.474308: Current learning rate: 0.00641 +2026-04-12 07:04:13.806591: train_loss -0.3837 +2026-04-12 07:04:13.810719: val_loss -0.3448 +2026-04-12 07:04:13.812287: Pseudo dice [0.0, 0.116, 0.7053, 0.451, 0.3425, 0.5445, 0.76] +2026-04-12 07:04:13.813584: Epoch time: 101.34 s +2026-04-12 07:04:15.033028: +2026-04-12 07:04:15.034400: Epoch 1561 +2026-04-12 07:04:15.035595: Current learning rate: 0.00641 +2026-04-12 07:05:56.203480: train_loss -0.4056 +2026-04-12 07:05:56.208332: val_loss -0.3541 +2026-04-12 07:05:56.209729: Pseudo dice [0.0, 0.0355, 0.6965, 0.3638, 0.3607, 0.6339, 0.7562] +2026-04-12 07:05:56.211363: Epoch time: 101.17 s +2026-04-12 07:05:57.415806: +2026-04-12 07:05:57.417987: Epoch 1562 +2026-04-12 07:05:57.419671: Current learning rate: 0.0064 +2026-04-12 07:07:38.755852: train_loss -0.3879 +2026-04-12 07:07:38.760166: val_loss -0.3245 +2026-04-12 07:07:38.761775: Pseudo dice [0.0, 0.2083, 0.4972, 0.0067, 0.1019, 0.6813, 0.6643] +2026-04-12 07:07:38.763258: Epoch time: 101.34 s +2026-04-12 07:07:39.978348: +2026-04-12 07:07:39.979983: Epoch 1563 +2026-04-12 07:07:39.981600: Current learning rate: 0.0064 +2026-04-12 07:09:21.443033: train_loss -0.4059 +2026-04-12 07:09:21.448435: val_loss -0.3051 +2026-04-12 07:09:21.450328: Pseudo dice [0.0, 0.0546, 0.7071, 0.7869, 0.04, 0.5517, 0.6343] +2026-04-12 07:09:21.451675: Epoch time: 101.47 s +2026-04-12 07:09:22.671639: +2026-04-12 07:09:22.673413: Epoch 1564 +2026-04-12 07:09:22.674982: Current learning rate: 0.0064 +2026-04-12 07:11:04.034676: train_loss -0.4004 +2026-04-12 07:11:04.039372: val_loss -0.3598 +2026-04-12 07:11:04.041471: Pseudo dice [0.0, 0.1541, 0.6156, 0.6932, 0.4532, 0.7031, 0.8722] +2026-04-12 07:11:04.043385: Epoch time: 101.37 s +2026-04-12 07:11:05.247153: +2026-04-12 07:11:05.251339: Epoch 1565 +2026-04-12 07:11:05.252753: Current learning rate: 0.0064 +2026-04-12 07:12:46.602363: train_loss -0.4074 +2026-04-12 07:12:46.607041: val_loss -0.3204 +2026-04-12 07:12:46.609052: Pseudo dice [0.0965, 0.1565, 0.7373, 0.5874, 0.194, 0.7002, 0.8648] +2026-04-12 07:12:46.610919: Epoch time: 101.36 s +2026-04-12 07:12:47.802277: +2026-04-12 07:12:47.804311: Epoch 1566 +2026-04-12 07:12:47.805879: Current learning rate: 0.00639 +2026-04-12 07:14:29.170758: train_loss -0.4259 +2026-04-12 07:14:29.174414: val_loss -0.3099 +2026-04-12 07:14:29.176083: Pseudo dice [0.1406, 0.1453, 0.4482, 0.5056, 0.1955, 0.4603, 0.6763] +2026-04-12 07:14:29.177845: Epoch time: 101.37 s +2026-04-12 07:14:30.370729: +2026-04-12 07:14:30.372158: Epoch 1567 +2026-04-12 07:14:30.373386: Current learning rate: 0.00639 +2026-04-12 07:16:11.585896: train_loss -0.402 +2026-04-12 07:16:11.589699: val_loss -0.2949 +2026-04-12 07:16:11.591023: Pseudo dice [0.0511, 0.558, 0.532, 0.6136, 0.3901, 0.1388, 0.6984] +2026-04-12 07:16:11.592191: Epoch time: 101.22 s +2026-04-12 07:16:12.772372: +2026-04-12 07:16:12.773797: Epoch 1568 +2026-04-12 07:16:12.775177: Current learning rate: 0.00639 +2026-04-12 07:17:54.191526: train_loss -0.3944 +2026-04-12 07:17:54.196263: val_loss -0.3176 +2026-04-12 07:17:54.198031: Pseudo dice [0.0673, 0.5092, 0.599, 0.5783, 0.3014, 0.7034, 0.559] +2026-04-12 07:17:54.199481: Epoch time: 101.42 s +2026-04-12 07:17:55.375611: +2026-04-12 07:17:55.377563: Epoch 1569 +2026-04-12 07:17:55.380822: Current learning rate: 0.00639 +2026-04-12 07:19:36.549651: train_loss -0.4112 +2026-04-12 07:19:36.555365: val_loss -0.3298 +2026-04-12 07:19:36.557837: Pseudo dice [0.1829, 0.0612, 0.5751, 0.16, 0.2779, 0.7477, 0.8031] +2026-04-12 07:19:36.559552: Epoch time: 101.18 s +2026-04-12 07:19:37.762587: +2026-04-12 07:19:37.764165: Epoch 1570 +2026-04-12 07:19:37.765631: Current learning rate: 0.00639 +2026-04-12 07:21:19.022344: train_loss -0.4027 +2026-04-12 07:21:19.026440: val_loss -0.3687 +2026-04-12 07:21:19.028569: Pseudo dice [0.4387, 0.4595, 0.7545, 0.7108, 0.3072, 0.2443, 0.8649] +2026-04-12 07:21:19.030118: Epoch time: 101.26 s +2026-04-12 07:21:20.239275: +2026-04-12 07:21:20.240936: Epoch 1571 +2026-04-12 07:21:20.242309: Current learning rate: 0.00638 +2026-04-12 07:23:01.489878: train_loss -0.4003 +2026-04-12 07:23:01.494619: val_loss -0.3223 +2026-04-12 07:23:01.496652: Pseudo dice [0.3333, 0.2105, 0.5221, 0.7576, 0.133, 0.595, 0.6257] +2026-04-12 07:23:01.498156: Epoch time: 101.25 s +2026-04-12 07:23:02.702241: +2026-04-12 07:23:02.707266: Epoch 1572 +2026-04-12 07:23:02.708838: Current learning rate: 0.00638 +2026-04-12 07:24:43.889017: train_loss -0.3967 +2026-04-12 07:24:43.895139: val_loss -0.3327 +2026-04-12 07:24:43.897062: Pseudo dice [0.1694, 0.2221, 0.7321, 0.8138, 0.1217, 0.504, 0.7716] +2026-04-12 07:24:43.900850: Epoch time: 101.19 s +2026-04-12 07:24:45.117455: +2026-04-12 07:24:45.119265: Epoch 1573 +2026-04-12 07:24:45.120946: Current learning rate: 0.00638 +2026-04-12 07:26:26.364808: train_loss -0.3972 +2026-04-12 07:26:26.368862: val_loss -0.3613 +2026-04-12 07:26:26.370242: Pseudo dice [0.232, 0.0556, 0.792, 0.6281, 0.3309, 0.8239, 0.7767] +2026-04-12 07:26:26.371753: Epoch time: 101.25 s +2026-04-12 07:26:28.607084: +2026-04-12 07:26:28.608362: Epoch 1574 +2026-04-12 07:26:28.609617: Current learning rate: 0.00638 +2026-04-12 07:28:09.931359: train_loss -0.396 +2026-04-12 07:28:09.935505: val_loss -0.349 +2026-04-12 07:28:09.936930: Pseudo dice [0.0, 0.395, 0.8155, 0.7932, 0.1136, 0.755, 0.5257] +2026-04-12 07:28:09.938164: Epoch time: 101.33 s +2026-04-12 07:28:11.125265: +2026-04-12 07:28:11.127812: Epoch 1575 +2026-04-12 07:28:11.129235: Current learning rate: 0.00637 +2026-04-12 07:29:52.546125: train_loss -0.3841 +2026-04-12 07:29:52.550420: val_loss -0.3748 +2026-04-12 07:29:52.551929: Pseudo dice [0.1758, 0.3502, 0.8276, 0.4274, 0.2282, 0.7386, 0.5747] +2026-04-12 07:29:52.553419: Epoch time: 101.42 s +2026-04-12 07:29:53.753660: +2026-04-12 07:29:53.755892: Epoch 1576 +2026-04-12 07:29:53.757926: Current learning rate: 0.00637 +2026-04-12 07:31:35.077573: train_loss -0.3999 +2026-04-12 07:31:35.083019: val_loss -0.3332 +2026-04-12 07:31:35.085443: Pseudo dice [0.0, 0.0711, 0.7912, 0.8168, 0.0144, 0.6738, 0.7899] +2026-04-12 07:31:35.089993: Epoch time: 101.33 s +2026-04-12 07:31:36.308857: +2026-04-12 07:31:36.310563: Epoch 1577 +2026-04-12 07:31:36.311892: Current learning rate: 0.00637 +2026-04-12 07:33:17.583786: train_loss -0.3757 +2026-04-12 07:33:17.588146: val_loss -0.3217 +2026-04-12 07:33:17.591043: Pseudo dice [0.0092, 0.106, 0.6326, 0.8183, 0.2031, 0.6504, 0.8263] +2026-04-12 07:33:17.592698: Epoch time: 101.28 s +2026-04-12 07:33:18.793632: +2026-04-12 07:33:18.795263: Epoch 1578 +2026-04-12 07:33:18.796634: Current learning rate: 0.00637 +2026-04-12 07:35:00.145099: train_loss -0.3894 +2026-04-12 07:35:00.149445: val_loss -0.3093 +2026-04-12 07:35:00.151378: Pseudo dice [0.1694, 0.1441, 0.5351, 0.695, 0.096, 0.6511, 0.5149] +2026-04-12 07:35:00.153237: Epoch time: 101.35 s +2026-04-12 07:35:01.371403: +2026-04-12 07:35:01.373171: Epoch 1579 +2026-04-12 07:35:01.374701: Current learning rate: 0.00636 +2026-04-12 07:36:42.584624: train_loss -0.3772 +2026-04-12 07:36:42.589765: val_loss -0.3155 +2026-04-12 07:36:42.591621: Pseudo dice [0.0, 0.3506, 0.6063, 0.4033, 0.0, 0.6198, 0.6377] +2026-04-12 07:36:42.593329: Epoch time: 101.22 s +2026-04-12 07:36:43.798217: +2026-04-12 07:36:43.799816: Epoch 1580 +2026-04-12 07:36:43.801515: Current learning rate: 0.00636 +2026-04-12 07:38:25.196998: train_loss -0.3643 +2026-04-12 07:38:25.202509: val_loss -0.3315 +2026-04-12 07:38:25.204164: Pseudo dice [0.0, 0.5047, 0.5822, 0.743, 0.1442, 0.7043, 0.7955] +2026-04-12 07:38:25.205947: Epoch time: 101.4 s +2026-04-12 07:38:26.405780: +2026-04-12 07:38:26.407361: Epoch 1581 +2026-04-12 07:38:26.408969: Current learning rate: 0.00636 +2026-04-12 07:40:07.687083: train_loss -0.3747 +2026-04-12 07:40:07.691906: val_loss -0.3633 +2026-04-12 07:40:07.693657: Pseudo dice [0.0, 0.0307, 0.8076, 0.8128, 0.3558, 0.7438, 0.8549] +2026-04-12 07:40:07.696229: Epoch time: 101.28 s +2026-04-12 07:40:08.881395: +2026-04-12 07:40:08.882995: Epoch 1582 +2026-04-12 07:40:08.884456: Current learning rate: 0.00636 +2026-04-12 07:41:50.130732: train_loss -0.4035 +2026-04-12 07:41:50.134741: val_loss -0.2982 +2026-04-12 07:41:50.136154: Pseudo dice [0.0, 0.1601, 0.6677, 0.8893, 0.1902, 0.5238, 0.4384] +2026-04-12 07:41:50.137860: Epoch time: 101.25 s +2026-04-12 07:41:51.326782: +2026-04-12 07:41:51.328449: Epoch 1583 +2026-04-12 07:41:51.329877: Current learning rate: 0.00635 +2026-04-12 07:43:32.648987: train_loss -0.4089 +2026-04-12 07:43:32.654652: val_loss -0.3429 +2026-04-12 07:43:32.657915: Pseudo dice [0.0, 0.5389, 0.5181, 0.7561, 0.3623, 0.7018, 0.7664] +2026-04-12 07:43:32.659388: Epoch time: 101.33 s +2026-04-12 07:43:33.850812: +2026-04-12 07:43:33.852287: Epoch 1584 +2026-04-12 07:43:33.853712: Current learning rate: 0.00635 +2026-04-12 07:45:15.275331: train_loss -0.4159 +2026-04-12 07:45:15.279156: val_loss -0.3816 +2026-04-12 07:45:15.280479: Pseudo dice [0.0, 0.5017, 0.805, 0.75, 0.5357, 0.6516, 0.7711] +2026-04-12 07:45:15.282055: Epoch time: 101.43 s +2026-04-12 07:45:16.486011: +2026-04-12 07:45:16.487669: Epoch 1585 +2026-04-12 07:45:16.489214: Current learning rate: 0.00635 +2026-04-12 07:46:57.784545: train_loss -0.4043 +2026-04-12 07:46:57.790576: val_loss -0.3874 +2026-04-12 07:46:57.792689: Pseudo dice [0.0, 0.0866, 0.8196, 0.9113, 0.4896, 0.8417, 0.5882] +2026-04-12 07:46:57.794628: Epoch time: 101.3 s +2026-04-12 07:46:59.005355: +2026-04-12 07:46:59.007280: Epoch 1586 +2026-04-12 07:46:59.008763: Current learning rate: 0.00635 +2026-04-12 07:48:40.519986: train_loss -0.4177 +2026-04-12 07:48:40.525269: val_loss -0.3411 +2026-04-12 07:48:40.526729: Pseudo dice [0.0, 0.6165, 0.6747, 0.6467, 0.458, 0.4855, 0.5274] +2026-04-12 07:48:40.528176: Epoch time: 101.52 s +2026-04-12 07:48:41.729360: +2026-04-12 07:48:41.731412: Epoch 1587 +2026-04-12 07:48:41.733356: Current learning rate: 0.00635 +2026-04-12 07:50:23.186710: train_loss -0.4025 +2026-04-12 07:50:23.191707: val_loss -0.3175 +2026-04-12 07:50:23.193479: Pseudo dice [0.0, 0.0752, 0.4934, 0.0364, 0.3215, 0.6563, 0.628] +2026-04-12 07:50:23.195477: Epoch time: 101.46 s +2026-04-12 07:50:24.383762: +2026-04-12 07:50:24.385132: Epoch 1588 +2026-04-12 07:50:24.386536: Current learning rate: 0.00634 +2026-04-12 07:52:05.837539: train_loss -0.4034 +2026-04-12 07:52:05.842441: val_loss -0.3708 +2026-04-12 07:52:05.844012: Pseudo dice [0.0, 0.5222, 0.7914, 0.6651, 0.2115, 0.6972, 0.7667] +2026-04-12 07:52:05.845579: Epoch time: 101.46 s +2026-04-12 07:52:07.056004: +2026-04-12 07:52:07.057834: Epoch 1589 +2026-04-12 07:52:07.060113: Current learning rate: 0.00634 +2026-04-12 07:53:48.496370: train_loss -0.4018 +2026-04-12 07:53:48.501677: val_loss -0.3477 +2026-04-12 07:53:48.502899: Pseudo dice [0.0, 0.1928, 0.6262, 0.671, 0.364, 0.2521, 0.6292] +2026-04-12 07:53:48.504610: Epoch time: 101.44 s +2026-04-12 07:53:49.691303: +2026-04-12 07:53:49.692659: Epoch 1590 +2026-04-12 07:53:49.694042: Current learning rate: 0.00634 +2026-04-12 07:55:31.071896: train_loss -0.3904 +2026-04-12 07:55:31.076574: val_loss -0.3564 +2026-04-12 07:55:31.078124: Pseudo dice [0.0, 0.4913, 0.5013, 0.7203, 0.1583, 0.7243, 0.8918] +2026-04-12 07:55:31.079772: Epoch time: 101.38 s +2026-04-12 07:55:32.293701: +2026-04-12 07:55:32.295666: Epoch 1591 +2026-04-12 07:55:32.297181: Current learning rate: 0.00634 +2026-04-12 07:57:13.818324: train_loss -0.4076 +2026-04-12 07:57:13.836118: val_loss -0.3305 +2026-04-12 07:57:13.838317: Pseudo dice [0.1692, 0.2354, 0.6224, 0.5084, 0.2412, 0.4905, 0.5918] +2026-04-12 07:57:13.840235: Epoch time: 101.53 s +2026-04-12 07:57:15.052442: +2026-04-12 07:57:15.054167: Epoch 1592 +2026-04-12 07:57:15.055525: Current learning rate: 0.00633 +2026-04-12 07:58:56.456611: train_loss -0.4112 +2026-04-12 07:58:56.460840: val_loss -0.3882 +2026-04-12 07:58:56.462624: Pseudo dice [0.2594, 0.6864, 0.731, 0.7276, 0.4423, 0.8086, 0.8371] +2026-04-12 07:58:56.464440: Epoch time: 101.41 s +2026-04-12 07:58:57.642144: +2026-04-12 07:58:57.643909: Epoch 1593 +2026-04-12 07:58:57.645189: Current learning rate: 0.00633 +2026-04-12 08:00:38.906730: train_loss -0.4035 +2026-04-12 08:00:38.912588: val_loss -0.3206 +2026-04-12 08:00:38.914808: Pseudo dice [0.1729, 0.1769, 0.4241, 0.5389, 0.2995, 0.5424, 0.7249] +2026-04-12 08:00:38.917337: Epoch time: 101.27 s +2026-04-12 08:00:41.115044: +2026-04-12 08:00:41.116791: Epoch 1594 +2026-04-12 08:00:41.118566: Current learning rate: 0.00633 +2026-04-12 08:02:22.439379: train_loss -0.3736 +2026-04-12 08:02:22.448495: val_loss -0.3238 +2026-04-12 08:02:22.450606: Pseudo dice [0.0, 0.3436, 0.3909, 0.8752, 0.3588, 0.8345, 0.5177] +2026-04-12 08:02:22.452358: Epoch time: 101.33 s +2026-04-12 08:02:23.670593: +2026-04-12 08:02:23.672317: Epoch 1595 +2026-04-12 08:02:23.674010: Current learning rate: 0.00633 +2026-04-12 08:04:05.020518: train_loss -0.3793 +2026-04-12 08:04:05.024863: val_loss -0.3864 +2026-04-12 08:04:05.026649: Pseudo dice [0.0, 0.4604, 0.6925, 0.844, 0.4192, 0.7567, 0.7942] +2026-04-12 08:04:05.027966: Epoch time: 101.35 s +2026-04-12 08:04:06.220727: +2026-04-12 08:04:06.222479: Epoch 1596 +2026-04-12 08:04:06.223845: Current learning rate: 0.00632 +2026-04-12 08:05:47.524073: train_loss -0.4075 +2026-04-12 08:05:47.528139: val_loss -0.319 +2026-04-12 08:05:47.529580: Pseudo dice [0.0214, 0.0592, 0.6307, 0.7849, 0.2898, 0.1636, 0.8959] +2026-04-12 08:05:47.530869: Epoch time: 101.31 s +2026-04-12 08:05:48.737883: +2026-04-12 08:05:48.739335: Epoch 1597 +2026-04-12 08:05:48.740556: Current learning rate: 0.00632 +2026-04-12 08:07:29.931354: train_loss -0.4008 +2026-04-12 08:07:29.939772: val_loss -0.356 +2026-04-12 08:07:29.941396: Pseudo dice [0.0277, 0.5063, 0.505, 0.6187, 0.3977, 0.8337, 0.8373] +2026-04-12 08:07:29.944074: Epoch time: 101.2 s +2026-04-12 08:07:31.150849: +2026-04-12 08:07:31.152488: Epoch 1598 +2026-04-12 08:07:31.154023: Current learning rate: 0.00632 +2026-04-12 08:09:12.512134: train_loss -0.3881 +2026-04-12 08:09:12.518027: val_loss -0.3313 +2026-04-12 08:09:12.519727: Pseudo dice [0.5046, 0.3198, 0.7418, 0.8331, 0.1834, 0.2675, 0.8304] +2026-04-12 08:09:12.521136: Epoch time: 101.36 s +2026-04-12 08:09:13.724378: +2026-04-12 08:09:13.725846: Epoch 1599 +2026-04-12 08:09:13.727430: Current learning rate: 0.00632 +2026-04-12 08:10:55.029473: train_loss -0.413 +2026-04-12 08:10:55.033620: val_loss -0.3274 +2026-04-12 08:10:55.034981: Pseudo dice [0.1976, 0.4625, 0.2105, 0.442, 0.3169, 0.6587, 0.5079] +2026-04-12 08:10:55.036098: Epoch time: 101.31 s +2026-04-12 08:10:57.890101: +2026-04-12 08:10:57.891787: Epoch 1600 +2026-04-12 08:10:57.893071: Current learning rate: 0.00631 +2026-04-12 08:12:39.326951: train_loss -0.3652 +2026-04-12 08:12:39.331165: val_loss -0.3095 +2026-04-12 08:12:39.332461: Pseudo dice [0.0139, 0.0896, 0.638, 0.341, 0.0936, 0.5024, 0.6432] +2026-04-12 08:12:39.333828: Epoch time: 101.44 s +2026-04-12 08:12:40.520301: +2026-04-12 08:12:40.521693: Epoch 1601 +2026-04-12 08:12:40.522846: Current learning rate: 0.00631 +2026-04-12 08:14:22.011642: train_loss -0.389 +2026-04-12 08:14:22.018190: val_loss -0.365 +2026-04-12 08:14:22.020278: Pseudo dice [0.2495, 0.0618, 0.6722, 0.4736, 0.3974, 0.5762, 0.7708] +2026-04-12 08:14:22.022450: Epoch time: 101.49 s +2026-04-12 08:14:23.235645: +2026-04-12 08:14:23.237995: Epoch 1602 +2026-04-12 08:14:23.239799: Current learning rate: 0.00631 +2026-04-12 08:16:04.586409: train_loss -0.3722 +2026-04-12 08:16:04.590543: val_loss -0.3423 +2026-04-12 08:16:04.592037: Pseudo dice [0.0208, 0.2362, 0.5722, 0.7928, 0.2984, 0.7138, 0.7958] +2026-04-12 08:16:04.593834: Epoch time: 101.35 s +2026-04-12 08:16:05.804486: +2026-04-12 08:16:05.805857: Epoch 1603 +2026-04-12 08:16:05.807224: Current learning rate: 0.00631 +2026-04-12 08:17:47.283141: train_loss -0.375 +2026-04-12 08:17:47.287579: val_loss -0.3263 +2026-04-12 08:17:47.288842: Pseudo dice [0.0567, 0.4209, 0.7296, 0.4871, 0.2719, 0.4262, 0.4809] +2026-04-12 08:17:47.290061: Epoch time: 101.48 s +2026-04-12 08:17:48.493278: +2026-04-12 08:17:48.494783: Epoch 1604 +2026-04-12 08:17:48.496286: Current learning rate: 0.0063 +2026-04-12 08:19:30.003461: train_loss -0.388 +2026-04-12 08:19:30.009357: val_loss -0.3417 +2026-04-12 08:19:30.012566: Pseudo dice [0.1428, 0.506, 0.2512, 0.6889, 0.4548, 0.5161, 0.7279] +2026-04-12 08:19:30.013855: Epoch time: 101.51 s +2026-04-12 08:19:31.229681: +2026-04-12 08:19:31.248287: Epoch 1605 +2026-04-12 08:19:31.250129: Current learning rate: 0.0063 +2026-04-12 08:21:12.638973: train_loss -0.3822 +2026-04-12 08:21:12.643236: val_loss -0.3696 +2026-04-12 08:21:12.647183: Pseudo dice [0.0354, 0.0947, 0.667, 0.8027, 0.5265, 0.5129, 0.7923] +2026-04-12 08:21:12.648659: Epoch time: 101.41 s +2026-04-12 08:21:13.861366: +2026-04-12 08:21:13.863167: Epoch 1606 +2026-04-12 08:21:13.864722: Current learning rate: 0.0063 +2026-04-12 08:22:55.455168: train_loss -0.414 +2026-04-12 08:22:55.459092: val_loss -0.3727 +2026-04-12 08:22:55.460682: Pseudo dice [0.4045, 0.1761, 0.7419, 0.7237, 0.3588, 0.6035, 0.8295] +2026-04-12 08:22:55.462104: Epoch time: 101.6 s +2026-04-12 08:22:56.646926: +2026-04-12 08:22:56.648896: Epoch 1607 +2026-04-12 08:22:56.650819: Current learning rate: 0.0063 +2026-04-12 08:24:38.066458: train_loss -0.4016 +2026-04-12 08:24:38.070649: val_loss -0.3599 +2026-04-12 08:24:38.072257: Pseudo dice [0.0813, 0.6863, 0.5548, 0.8112, 0.2709, 0.5729, 0.6167] +2026-04-12 08:24:38.073643: Epoch time: 101.42 s +2026-04-12 08:24:39.255520: +2026-04-12 08:24:39.257347: Epoch 1608 +2026-04-12 08:24:39.258609: Current learning rate: 0.0063 +2026-04-12 08:26:20.651390: train_loss -0.3637 +2026-04-12 08:26:20.657513: val_loss -0.3142 +2026-04-12 08:26:20.659063: Pseudo dice [0.0, 0.0, 0.6911, 0.7079, 0.1272, 0.692, 0.5218] +2026-04-12 08:26:20.660693: Epoch time: 101.4 s +2026-04-12 08:26:21.864300: +2026-04-12 08:26:21.865657: Epoch 1609 +2026-04-12 08:26:21.866921: Current learning rate: 0.00629 +2026-04-12 08:28:03.343744: train_loss -0.3876 +2026-04-12 08:28:03.348370: val_loss -0.3381 +2026-04-12 08:28:03.349894: Pseudo dice [0.0032, 0.0906, 0.6681, 0.5656, 0.3412, 0.4135, 0.7558] +2026-04-12 08:28:03.351502: Epoch time: 101.48 s +2026-04-12 08:28:04.554753: +2026-04-12 08:28:04.556865: Epoch 1610 +2026-04-12 08:28:04.559065: Current learning rate: 0.00629 +2026-04-12 08:29:45.989506: train_loss -0.3908 +2026-04-12 08:29:45.994046: val_loss -0.3425 +2026-04-12 08:29:45.995445: Pseudo dice [0.0138, 0.2535, 0.8249, 0.7377, 0.3203, 0.5919, 0.7545] +2026-04-12 08:29:45.996848: Epoch time: 101.44 s +2026-04-12 08:29:47.223785: +2026-04-12 08:29:47.225229: Epoch 1611 +2026-04-12 08:29:47.226384: Current learning rate: 0.00629 +2026-04-12 08:31:28.431152: train_loss -0.3799 +2026-04-12 08:31:28.435504: val_loss -0.3528 +2026-04-12 08:31:28.437095: Pseudo dice [0.6989, 0.0882, 0.7151, 0.5126, 0.2753, 0.7459, 0.6926] +2026-04-12 08:31:28.438689: Epoch time: 101.21 s +2026-04-12 08:31:29.668150: +2026-04-12 08:31:29.670092: Epoch 1612 +2026-04-12 08:31:29.671609: Current learning rate: 0.00629 +2026-04-12 08:33:11.026347: train_loss -0.399 +2026-04-12 08:33:11.031885: val_loss -0.386 +2026-04-12 08:33:11.033511: Pseudo dice [0.3619, 0.6975, 0.5808, 0.0061, 0.5979, 0.7683, 0.8206] +2026-04-12 08:33:11.035136: Epoch time: 101.36 s +2026-04-12 08:33:13.218037: +2026-04-12 08:33:13.219472: Epoch 1613 +2026-04-12 08:33:13.221120: Current learning rate: 0.00628 +2026-04-12 08:34:54.531360: train_loss -0.3906 +2026-04-12 08:34:54.536920: val_loss -0.365 +2026-04-12 08:34:54.538577: Pseudo dice [0.2713, 0.3531, 0.6237, 0.2803, 0.5001, 0.5292, 0.7105] +2026-04-12 08:34:54.541670: Epoch time: 101.32 s +2026-04-12 08:34:55.748244: +2026-04-12 08:34:55.750747: Epoch 1614 +2026-04-12 08:34:55.752382: Current learning rate: 0.00628 +2026-04-12 08:36:37.221822: train_loss -0.3906 +2026-04-12 08:36:37.226538: val_loss -0.3532 +2026-04-12 08:36:37.228131: Pseudo dice [0.4328, 0.1582, 0.4268, 0.4976, 0.5223, 0.5489, 0.4091] +2026-04-12 08:36:37.229755: Epoch time: 101.48 s +2026-04-12 08:36:38.419864: +2026-04-12 08:36:38.421432: Epoch 1615 +2026-04-12 08:36:38.422901: Current learning rate: 0.00628 +2026-04-12 08:38:19.819897: train_loss -0.3905 +2026-04-12 08:38:19.843528: val_loss -0.3407 +2026-04-12 08:38:19.844807: Pseudo dice [0.0906, 0.2196, 0.6707, 0.348, 0.1826, 0.8094, 0.3653] +2026-04-12 08:38:19.846134: Epoch time: 101.4 s +2026-04-12 08:38:21.025427: +2026-04-12 08:38:21.027169: Epoch 1616 +2026-04-12 08:38:21.028480: Current learning rate: 0.00628 +2026-04-12 08:40:02.378335: train_loss -0.3945 +2026-04-12 08:40:02.383405: val_loss -0.3136 +2026-04-12 08:40:02.385144: Pseudo dice [0.3482, 0.6123, 0.7452, 0.0015, 0.367, 0.4047, 0.3614] +2026-04-12 08:40:02.386564: Epoch time: 101.36 s +2026-04-12 08:40:03.599849: +2026-04-12 08:40:03.604313: Epoch 1617 +2026-04-12 08:40:03.606058: Current learning rate: 0.00627 +2026-04-12 08:41:45.000618: train_loss -0.3958 +2026-04-12 08:41:45.006956: val_loss -0.3472 +2026-04-12 08:41:45.008498: Pseudo dice [0.0555, 0.1178, 0.0921, 0.6576, 0.5685, 0.7576, 0.5601] +2026-04-12 08:41:45.010150: Epoch time: 101.4 s +2026-04-12 08:41:46.212835: +2026-04-12 08:41:46.214675: Epoch 1618 +2026-04-12 08:41:46.218755: Current learning rate: 0.00627 +2026-04-12 08:43:27.544846: train_loss -0.414 +2026-04-12 08:43:27.549105: val_loss -0.333 +2026-04-12 08:43:27.550599: Pseudo dice [0.6638, 0.032, 0.5124, 0.6908, 0.2692, 0.5161, 0.8307] +2026-04-12 08:43:27.551934: Epoch time: 101.34 s +2026-04-12 08:43:28.756382: +2026-04-12 08:43:28.758305: Epoch 1619 +2026-04-12 08:43:28.760419: Current learning rate: 0.00627 +2026-04-12 08:45:10.200882: train_loss -0.3815 +2026-04-12 08:45:10.205629: val_loss -0.3307 +2026-04-12 08:45:10.207611: Pseudo dice [0.0, 0.0, 0.6888, 0.7923, 0.256, 0.4846, 0.7625] +2026-04-12 08:45:10.209590: Epoch time: 101.45 s +2026-04-12 08:45:11.398722: +2026-04-12 08:45:11.401002: Epoch 1620 +2026-04-12 08:45:11.402773: Current learning rate: 0.00627 +2026-04-12 08:46:52.817039: train_loss -0.3655 +2026-04-12 08:46:52.822152: val_loss -0.3552 +2026-04-12 08:46:52.823870: Pseudo dice [0.0, 0.0, 0.7095, 0.7363, 0.3317, 0.7251, 0.8408] +2026-04-12 08:46:52.825513: Epoch time: 101.42 s +2026-04-12 08:46:54.023437: +2026-04-12 08:46:54.024925: Epoch 1621 +2026-04-12 08:46:54.026365: Current learning rate: 0.00626 +2026-04-12 08:48:35.497260: train_loss -0.3763 +2026-04-12 08:48:35.501604: val_loss -0.3172 +2026-04-12 08:48:35.503190: Pseudo dice [0.0, 0.0, 0.8007, 0.7296, 0.151, 0.8384, 0.4784] +2026-04-12 08:48:35.504726: Epoch time: 101.48 s +2026-04-12 08:48:36.721395: +2026-04-12 08:48:36.723000: Epoch 1622 +2026-04-12 08:48:36.724537: Current learning rate: 0.00626 +2026-04-12 08:50:18.112562: train_loss -0.3641 +2026-04-12 08:50:18.117217: val_loss -0.3256 +2026-04-12 08:50:18.119240: Pseudo dice [0.0, 0.0, 0.6143, 0.7668, 0.3739, 0.391, 0.4926] +2026-04-12 08:50:18.120971: Epoch time: 101.39 s +2026-04-12 08:50:19.318853: +2026-04-12 08:50:19.320650: Epoch 1623 +2026-04-12 08:50:19.322161: Current learning rate: 0.00626 +2026-04-12 08:52:00.669443: train_loss -0.4031 +2026-04-12 08:52:00.673762: val_loss -0.3561 +2026-04-12 08:52:00.675225: Pseudo dice [0.0, 0.3678, 0.8312, 0.6776, 0.316, 0.8086, 0.7486] +2026-04-12 08:52:00.677091: Epoch time: 101.35 s +2026-04-12 08:52:01.888517: +2026-04-12 08:52:01.890835: Epoch 1624 +2026-04-12 08:52:01.892601: Current learning rate: 0.00626 +2026-04-12 08:53:43.297939: train_loss -0.3963 +2026-04-12 08:53:43.302279: val_loss -0.365 +2026-04-12 08:53:43.304281: Pseudo dice [0.0, 0.092, 0.755, 0.6996, 0.2876, 0.706, 0.8375] +2026-04-12 08:53:43.305942: Epoch time: 101.41 s +2026-04-12 08:53:44.501559: +2026-04-12 08:53:44.504010: Epoch 1625 +2026-04-12 08:53:44.505819: Current learning rate: 0.00626 +2026-04-12 08:55:25.774770: train_loss -0.4027 +2026-04-12 08:55:25.780003: val_loss -0.3808 +2026-04-12 08:55:25.781899: Pseudo dice [0.0, 0.3678, 0.8094, 0.8063, 0.4293, 0.8706, 0.6331] +2026-04-12 08:55:25.783990: Epoch time: 101.28 s +2026-04-12 08:55:26.981976: +2026-04-12 08:55:26.983322: Epoch 1626 +2026-04-12 08:55:26.984978: Current learning rate: 0.00625 +2026-04-12 08:57:08.403305: train_loss -0.3961 +2026-04-12 08:57:08.408129: val_loss -0.3733 +2026-04-12 08:57:08.410075: Pseudo dice [0.0691, 0.4483, 0.6545, 0.7422, 0.4578, 0.854, 0.7928] +2026-04-12 08:57:08.411583: Epoch time: 101.42 s +2026-04-12 08:57:09.638845: +2026-04-12 08:57:09.640573: Epoch 1627 +2026-04-12 08:57:09.642154: Current learning rate: 0.00625 +2026-04-12 08:58:51.029956: train_loss -0.4103 +2026-04-12 08:58:51.034600: val_loss -0.3814 +2026-04-12 08:58:51.036314: Pseudo dice [0.5291, 0.2335, 0.7502, 0.9089, 0.2001, 0.7306, 0.9094] +2026-04-12 08:58:51.037886: Epoch time: 101.39 s +2026-04-12 08:58:52.228590: +2026-04-12 08:58:52.230547: Epoch 1628 +2026-04-12 08:58:52.232279: Current learning rate: 0.00625 +2026-04-12 09:00:33.690223: train_loss -0.4029 +2026-04-12 09:00:33.696308: val_loss -0.3634 +2026-04-12 09:00:33.698367: Pseudo dice [0.5093, 0.2505, 0.7451, 0.0045, 0.3006, 0.5748, 0.7668] +2026-04-12 09:00:33.699973: Epoch time: 101.46 s +2026-04-12 09:00:34.892511: +2026-04-12 09:00:34.894390: Epoch 1629 +2026-04-12 09:00:34.896142: Current learning rate: 0.00625 +2026-04-12 09:02:16.330487: train_loss -0.4024 +2026-04-12 09:02:16.336334: val_loss -0.3627 +2026-04-12 09:02:16.338269: Pseudo dice [0.0, 0.4824, 0.7149, 0.6628, 0.3705, 0.6809, 0.5638] +2026-04-12 09:02:16.340173: Epoch time: 101.44 s +2026-04-12 09:02:17.534833: +2026-04-12 09:02:17.536666: Epoch 1630 +2026-04-12 09:02:17.538026: Current learning rate: 0.00624 +2026-04-12 09:03:59.122582: train_loss -0.3946 +2026-04-12 09:03:59.127727: val_loss -0.3403 +2026-04-12 09:03:59.129562: Pseudo dice [0.0, 0.2404, 0.7916, 0.2091, 0.1975, 0.6811, 0.7654] +2026-04-12 09:03:59.131180: Epoch time: 101.59 s +2026-04-12 09:04:00.334957: +2026-04-12 09:04:00.337129: Epoch 1631 +2026-04-12 09:04:00.338886: Current learning rate: 0.00624 +2026-04-12 09:05:41.830673: train_loss -0.3895 +2026-04-12 09:05:41.836758: val_loss -0.3313 +2026-04-12 09:05:41.838778: Pseudo dice [0.0, 0.2555, 0.668, 0.6513, 0.374, 0.6775, 0.684] +2026-04-12 09:05:41.840211: Epoch time: 101.5 s +2026-04-12 09:05:43.054426: +2026-04-12 09:05:43.056795: Epoch 1632 +2026-04-12 09:05:43.058965: Current learning rate: 0.00624 +2026-04-12 09:07:24.666102: train_loss -0.4036 +2026-04-12 09:07:24.672555: val_loss -0.3747 +2026-04-12 09:07:24.674302: Pseudo dice [0.0, 0.4743, 0.6921, 0.6811, 0.5262, 0.7545, 0.7717] +2026-04-12 09:07:24.676119: Epoch time: 101.61 s +2026-04-12 09:07:26.928935: +2026-04-12 09:07:26.931048: Epoch 1633 +2026-04-12 09:07:26.932717: Current learning rate: 0.00624 +2026-04-12 09:09:08.495424: train_loss -0.4198 +2026-04-12 09:09:08.500771: val_loss -0.3117 +2026-04-12 09:09:08.502356: Pseudo dice [0.0217, 0.3332, 0.454, 0.3799, 0.296, 0.5889, 0.4624] +2026-04-12 09:09:08.504168: Epoch time: 101.57 s +2026-04-12 09:09:09.704313: +2026-04-12 09:09:09.705986: Epoch 1634 +2026-04-12 09:09:09.707644: Current learning rate: 0.00623 +2026-04-12 09:10:51.448794: train_loss -0.4123 +2026-04-12 09:10:51.454153: val_loss -0.388 +2026-04-12 09:10:51.455784: Pseudo dice [0.1994, 0.4891, 0.7823, 0.8459, 0.4869, 0.8432, 0.9006] +2026-04-12 09:10:51.457900: Epoch time: 101.75 s +2026-04-12 09:10:52.657617: +2026-04-12 09:10:52.659712: Epoch 1635 +2026-04-12 09:10:52.661470: Current learning rate: 0.00623 +2026-04-12 09:12:34.204555: train_loss -0.4038 +2026-04-12 09:12:34.209221: val_loss -0.3534 +2026-04-12 09:12:34.210716: Pseudo dice [0.5181, 0.4806, 0.699, 0.3951, 0.3427, 0.6436, 0.8106] +2026-04-12 09:12:34.212034: Epoch time: 101.55 s +2026-04-12 09:12:35.408749: +2026-04-12 09:12:35.411092: Epoch 1636 +2026-04-12 09:12:35.412590: Current learning rate: 0.00623 +2026-04-12 09:14:16.833859: train_loss -0.4093 +2026-04-12 09:14:16.838281: val_loss -0.3753 +2026-04-12 09:14:16.840646: Pseudo dice [0.235, 0.1959, 0.7668, 0.6753, 0.2806, 0.6656, 0.7936] +2026-04-12 09:14:16.842384: Epoch time: 101.43 s +2026-04-12 09:14:18.037046: +2026-04-12 09:14:18.038801: Epoch 1637 +2026-04-12 09:14:18.040412: Current learning rate: 0.00623 +2026-04-12 09:15:59.454965: train_loss -0.3974 +2026-04-12 09:15:59.460947: val_loss -0.3574 +2026-04-12 09:15:59.462839: Pseudo dice [0.3766, 0.2291, 0.6688, 0.6469, 0.1948, 0.3338, 0.8452] +2026-04-12 09:15:59.465086: Epoch time: 101.42 s +2026-04-12 09:16:00.642452: +2026-04-12 09:16:00.644026: Epoch 1638 +2026-04-12 09:16:00.645436: Current learning rate: 0.00622 +2026-04-12 09:17:42.254392: train_loss -0.4191 +2026-04-12 09:17:42.258780: val_loss -0.3377 +2026-04-12 09:17:42.260339: Pseudo dice [0.3407, 0.1615, 0.6287, 0.7353, 0.3067, 0.4627, 0.7513] +2026-04-12 09:17:42.261602: Epoch time: 101.61 s +2026-04-12 09:17:43.436728: +2026-04-12 09:17:43.438523: Epoch 1639 +2026-04-12 09:17:43.440190: Current learning rate: 0.00622 +2026-04-12 09:19:24.894816: train_loss -0.3933 +2026-04-12 09:19:24.899061: val_loss -0.3421 +2026-04-12 09:19:24.900492: Pseudo dice [0.0, 0.3152, 0.6903, 0.645, 0.3942, 0.6804, 0.7026] +2026-04-12 09:19:24.902189: Epoch time: 101.46 s +2026-04-12 09:19:26.054363: +2026-04-12 09:19:26.056285: Epoch 1640 +2026-04-12 09:19:26.057726: Current learning rate: 0.00622 +2026-04-12 09:21:07.302225: train_loss -0.4144 +2026-04-12 09:21:07.307282: val_loss -0.3649 +2026-04-12 09:21:07.309183: Pseudo dice [0.0, 0.2122, 0.6997, 0.5502, 0.2793, 0.6974, 0.607] +2026-04-12 09:21:07.310884: Epoch time: 101.25 s +2026-04-12 09:21:08.473008: +2026-04-12 09:21:08.474603: Epoch 1641 +2026-04-12 09:21:08.476151: Current learning rate: 0.00622 +2026-04-12 09:22:49.504684: train_loss -0.3844 +2026-04-12 09:22:49.509739: val_loss -0.3441 +2026-04-12 09:22:49.511493: Pseudo dice [0.2233, 0.0664, 0.5005, 0.8196, 0.3792, 0.6794, 0.7752] +2026-04-12 09:22:49.513530: Epoch time: 101.03 s +2026-04-12 09:22:50.680884: +2026-04-12 09:22:50.682576: Epoch 1642 +2026-04-12 09:22:50.683968: Current learning rate: 0.00621 +2026-04-12 09:24:31.845644: train_loss -0.3819 +2026-04-12 09:24:31.853492: val_loss -0.3348 +2026-04-12 09:24:31.855143: Pseudo dice [0.2202, 0.1764, 0.5827, 0.7561, 0.3685, 0.6057, 0.6777] +2026-04-12 09:24:31.860632: Epoch time: 101.17 s +2026-04-12 09:24:33.033705: +2026-04-12 09:24:33.035649: Epoch 1643 +2026-04-12 09:24:33.037339: Current learning rate: 0.00621 +2026-04-12 09:26:14.321066: train_loss -0.3904 +2026-04-12 09:26:14.326338: val_loss -0.3747 +2026-04-12 09:26:14.328105: Pseudo dice [0.3203, 0.0, 0.6281, 0.7499, 0.41, 0.5769, 0.5697] +2026-04-12 09:26:14.329491: Epoch time: 101.29 s +2026-04-12 09:26:15.484557: +2026-04-12 09:26:15.486292: Epoch 1644 +2026-04-12 09:26:15.487923: Current learning rate: 0.00621 +2026-04-12 09:27:56.891041: train_loss -0.362 +2026-04-12 09:27:56.895872: val_loss -0.3535 +2026-04-12 09:27:56.897609: Pseudo dice [0.0, 0.0621, 0.402, 0.8571, 0.1899, 0.7114, 0.8592] +2026-04-12 09:27:56.899098: Epoch time: 101.41 s +2026-04-12 09:27:58.072313: +2026-04-12 09:27:58.074074: Epoch 1645 +2026-04-12 09:27:58.076309: Current learning rate: 0.00621 +2026-04-12 09:29:39.472433: train_loss -0.3905 +2026-04-12 09:29:39.478677: val_loss -0.3072 +2026-04-12 09:29:39.480746: Pseudo dice [0.03, 0.5736, 0.5557, 0.6949, 0.1031, 0.572, 0.6147] +2026-04-12 09:29:39.482624: Epoch time: 101.4 s +2026-04-12 09:29:40.641151: +2026-04-12 09:29:40.654843: Epoch 1646 +2026-04-12 09:29:40.656422: Current learning rate: 0.00621 +2026-04-12 09:31:22.117548: train_loss -0.3837 +2026-04-12 09:31:22.123023: val_loss -0.34 +2026-04-12 09:31:22.125045: Pseudo dice [0.0, 0.0, 0.7881, 0.7907, 0.3696, 0.5359, 0.548] +2026-04-12 09:31:22.126634: Epoch time: 101.48 s +2026-04-12 09:31:23.278932: +2026-04-12 09:31:23.280933: Epoch 1647 +2026-04-12 09:31:23.282627: Current learning rate: 0.0062 +2026-04-12 09:33:04.669672: train_loss -0.382 +2026-04-12 09:33:04.673890: val_loss -0.3353 +2026-04-12 09:33:04.675465: Pseudo dice [0.0278, 0.3676, 0.5885, 0.7951, 0.4565, 0.7476, 0.767] +2026-04-12 09:33:04.676880: Epoch time: 101.39 s +2026-04-12 09:33:05.838523: +2026-04-12 09:33:05.839956: Epoch 1648 +2026-04-12 09:33:05.841373: Current learning rate: 0.0062 +2026-04-12 09:34:47.070763: train_loss -0.395 +2026-04-12 09:34:47.075848: val_loss -0.3093 +2026-04-12 09:34:47.077719: Pseudo dice [0.225, 0.1382, 0.6972, 0.6132, 0.094, 0.3602, 0.4417] +2026-04-12 09:34:47.080149: Epoch time: 101.24 s +2026-04-12 09:34:48.244757: +2026-04-12 09:34:48.246853: Epoch 1649 +2026-04-12 09:34:48.249110: Current learning rate: 0.0062 +2026-04-12 09:36:29.735551: train_loss -0.3607 +2026-04-12 09:36:29.740277: val_loss -0.3296 +2026-04-12 09:36:29.743056: Pseudo dice [0.0, 0.0, 0.727, 0.5621, 0.3005, 0.4182, 0.4293] +2026-04-12 09:36:29.744905: Epoch time: 101.49 s +2026-04-12 09:36:32.543222: +2026-04-12 09:36:32.544908: Epoch 1650 +2026-04-12 09:36:32.546658: Current learning rate: 0.0062 +2026-04-12 09:38:13.837006: train_loss -0.3719 +2026-04-12 09:38:13.842777: val_loss -0.3301 +2026-04-12 09:38:13.844835: Pseudo dice [0.0, 0.1182, 0.7959, 0.6112, 0.3175, 0.2959, 0.7874] +2026-04-12 09:38:13.846835: Epoch time: 101.3 s +2026-04-12 09:38:15.021825: +2026-04-12 09:38:15.023503: Epoch 1651 +2026-04-12 09:38:15.025805: Current learning rate: 0.00619 +2026-04-12 09:39:56.503385: train_loss -0.4005 +2026-04-12 09:39:56.507790: val_loss -0.3542 +2026-04-12 09:39:56.509677: Pseudo dice [0.0, 0.5318, 0.8421, 0.5717, 0.3626, 0.5748, 0.7273] +2026-04-12 09:39:56.511286: Epoch time: 101.48 s +2026-04-12 09:39:57.683756: +2026-04-12 09:39:57.685542: Epoch 1652 +2026-04-12 09:39:57.686991: Current learning rate: 0.00619 +2026-04-12 09:41:40.201393: train_loss -0.397 +2026-04-12 09:41:40.205289: val_loss -0.3634 +2026-04-12 09:41:40.207054: Pseudo dice [0.0, 0.1563, 0.7404, 0.0003, 0.001, 0.6185, 0.8033] +2026-04-12 09:41:40.208537: Epoch time: 102.52 s +2026-04-12 09:41:41.380204: +2026-04-12 09:41:41.381635: Epoch 1653 +2026-04-12 09:41:41.383081: Current learning rate: 0.00619 +2026-04-12 09:43:23.064172: train_loss -0.376 +2026-04-12 09:43:23.068629: val_loss -0.3822 +2026-04-12 09:43:23.070443: Pseudo dice [0.0, 0.203, 0.669, 0.8593, 0.1897, 0.7514, 0.8536] +2026-04-12 09:43:23.073221: Epoch time: 101.69 s +2026-04-12 09:43:24.238958: +2026-04-12 09:43:24.241132: Epoch 1654 +2026-04-12 09:43:24.242673: Current learning rate: 0.00619 +2026-04-12 09:45:05.756148: train_loss -0.3772 +2026-04-12 09:45:05.761090: val_loss -0.3219 +2026-04-12 09:45:05.762817: Pseudo dice [0.0, 0.4816, 0.7409, 0.3149, 0.1185, 0.7694, 0.4998] +2026-04-12 09:45:05.764493: Epoch time: 101.52 s +2026-04-12 09:45:06.927184: +2026-04-12 09:45:06.928964: Epoch 1655 +2026-04-12 09:45:06.930665: Current learning rate: 0.00618 +2026-04-12 09:46:48.155345: train_loss -0.3844 +2026-04-12 09:46:48.159879: val_loss -0.3567 +2026-04-12 09:46:48.161563: Pseudo dice [0.0637, 0.1131, 0.7047, 0.7803, 0.3454, 0.6324, 0.8594] +2026-04-12 09:46:48.163335: Epoch time: 101.23 s +2026-04-12 09:46:49.336043: +2026-04-12 09:46:49.337885: Epoch 1656 +2026-04-12 09:46:49.339282: Current learning rate: 0.00618 +2026-04-12 09:48:30.441546: train_loss -0.417 +2026-04-12 09:48:30.446063: val_loss -0.3261 +2026-04-12 09:48:30.447805: Pseudo dice [0.0, 0.6446, 0.6081, 0.8306, 0.3393, 0.2153, 0.8015] +2026-04-12 09:48:30.449590: Epoch time: 101.11 s +2026-04-12 09:48:31.610396: +2026-04-12 09:48:31.612224: Epoch 1657 +2026-04-12 09:48:31.613701: Current learning rate: 0.00618 +2026-04-12 09:50:12.780622: train_loss -0.3866 +2026-04-12 09:50:12.785556: val_loss -0.3844 +2026-04-12 09:50:12.787970: Pseudo dice [0.0, 0.2002, 0.7305, 0.9008, 0.3169, 0.8173, 0.8676] +2026-04-12 09:50:12.789460: Epoch time: 101.17 s +2026-04-12 09:50:13.962359: +2026-04-12 09:50:13.963877: Epoch 1658 +2026-04-12 09:50:13.965196: Current learning rate: 0.00618 +2026-04-12 09:51:55.235133: train_loss -0.3952 +2026-04-12 09:51:55.239646: val_loss -0.3074 +2026-04-12 09:51:55.241272: Pseudo dice [0.0, 0.1531, 0.5385, 0.5698, 0.1095, 0.2995, 0.8784] +2026-04-12 09:51:55.243428: Epoch time: 101.28 s +2026-04-12 09:51:56.402201: +2026-04-12 09:51:56.403815: Epoch 1659 +2026-04-12 09:51:56.405335: Current learning rate: 0.00617 +2026-04-12 09:53:37.753387: train_loss -0.406 +2026-04-12 09:53:37.758272: val_loss -0.3697 +2026-04-12 09:53:37.760107: Pseudo dice [0.0, 0.3284, 0.6874, 0.794, 0.2237, 0.7325, 0.5129] +2026-04-12 09:53:37.761842: Epoch time: 101.35 s +2026-04-12 09:53:38.932825: +2026-04-12 09:53:38.934703: Epoch 1660 +2026-04-12 09:53:38.936224: Current learning rate: 0.00617 +2026-04-12 09:55:20.144952: train_loss -0.3928 +2026-04-12 09:55:20.151763: val_loss -0.3642 +2026-04-12 09:55:20.153528: Pseudo dice [0.0, 0.3865, 0.7948, 0.5836, 0.3401, 0.5284, 0.7753] +2026-04-12 09:55:20.155240: Epoch time: 101.22 s +2026-04-12 09:55:21.320361: +2026-04-12 09:55:21.321851: Epoch 1661 +2026-04-12 09:55:21.323092: Current learning rate: 0.00617 +2026-04-12 09:57:02.397885: train_loss -0.3997 +2026-04-12 09:57:02.403339: val_loss -0.3369 +2026-04-12 09:57:02.405348: Pseudo dice [0.0, 0.0, 0.6604, 0.0709, 0.3704, 0.5493, 0.8834] +2026-04-12 09:57:02.407793: Epoch time: 101.08 s +2026-04-12 09:57:03.582765: +2026-04-12 09:57:03.584998: Epoch 1662 +2026-04-12 09:57:03.586581: Current learning rate: 0.00617 +2026-04-12 09:58:45.190708: train_loss -0.4021 +2026-04-12 09:58:45.195617: val_loss -0.2961 +2026-04-12 09:58:45.197613: Pseudo dice [0.0, 0.0395, 0.7555, 0.4962, 0.066, 0.4678, 0.7528] +2026-04-12 09:58:45.199396: Epoch time: 101.61 s +2026-04-12 09:58:46.367122: +2026-04-12 09:58:46.369270: Epoch 1663 +2026-04-12 09:58:46.371893: Current learning rate: 0.00617 +2026-04-12 10:00:27.689184: train_loss -0.4165 +2026-04-12 10:00:27.695593: val_loss -0.3423 +2026-04-12 10:00:27.697597: Pseudo dice [0.0, 0.2007, 0.6988, 0.5678, 0.3806, 0.6598, 0.7649] +2026-04-12 10:00:27.704875: Epoch time: 101.33 s +2026-04-12 10:00:28.873383: +2026-04-12 10:00:28.874971: Epoch 1664 +2026-04-12 10:00:28.876649: Current learning rate: 0.00616 +2026-04-12 10:02:10.285131: train_loss -0.4096 +2026-04-12 10:02:10.289249: val_loss -0.3324 +2026-04-12 10:02:10.290786: Pseudo dice [0.0, 0.2265, 0.2842, 0.5754, 0.2822, 0.1702, 0.7968] +2026-04-12 10:02:10.292438: Epoch time: 101.41 s +2026-04-12 10:02:11.471680: +2026-04-12 10:02:11.473611: Epoch 1665 +2026-04-12 10:02:11.475188: Current learning rate: 0.00616 +2026-04-12 10:03:52.686815: train_loss -0.3942 +2026-04-12 10:03:52.692766: val_loss -0.3445 +2026-04-12 10:03:52.694346: Pseudo dice [0.0, 0.3671, 0.6277, 0.7467, 0.2078, 0.6769, 0.5933] +2026-04-12 10:03:52.696179: Epoch time: 101.22 s +2026-04-12 10:03:53.884866: +2026-04-12 10:03:53.886514: Epoch 1666 +2026-04-12 10:03:53.888117: Current learning rate: 0.00616 +2026-04-12 10:05:34.958650: train_loss -0.3899 +2026-04-12 10:05:34.963072: val_loss -0.3438 +2026-04-12 10:05:34.964955: Pseudo dice [0.0, 0.3104, 0.7017, 0.6197, 0.1176, 0.6895, 0.4569] +2026-04-12 10:05:34.966694: Epoch time: 101.08 s +2026-04-12 10:05:36.243520: +2026-04-12 10:05:36.245182: Epoch 1667 +2026-04-12 10:05:36.246962: Current learning rate: 0.00616 +2026-04-12 10:07:17.615636: train_loss -0.4048 +2026-04-12 10:07:17.619432: val_loss -0.3321 +2026-04-12 10:07:17.620732: Pseudo dice [0.0, 0.0956, 0.69, 0.5058, 0.3084, 0.4943, 0.8449] +2026-04-12 10:07:17.622221: Epoch time: 101.38 s +2026-04-12 10:07:18.825400: +2026-04-12 10:07:18.826930: Epoch 1668 +2026-04-12 10:07:18.828669: Current learning rate: 0.00615 +2026-04-12 10:09:00.197757: train_loss -0.3902 +2026-04-12 10:09:00.202370: val_loss -0.3281 +2026-04-12 10:09:00.204268: Pseudo dice [0.0, 0.0196, 0.7825, 0.1142, 0.3093, 0.7667, 0.6516] +2026-04-12 10:09:00.206257: Epoch time: 101.38 s +2026-04-12 10:09:01.394753: +2026-04-12 10:09:01.396906: Epoch 1669 +2026-04-12 10:09:01.398685: Current learning rate: 0.00615 +2026-04-12 10:10:42.467140: train_loss -0.4116 +2026-04-12 10:10:42.472096: val_loss -0.3452 +2026-04-12 10:10:42.474655: Pseudo dice [0.0, 0.3184, 0.7308, 0.6052, 0.2439, 0.7599, 0.7333] +2026-04-12 10:10:42.476837: Epoch time: 101.08 s +2026-04-12 10:10:43.660243: +2026-04-12 10:10:43.662114: Epoch 1670 +2026-04-12 10:10:43.663513: Current learning rate: 0.00615 +2026-04-12 10:12:24.787461: train_loss -0.3993 +2026-04-12 10:12:24.792969: val_loss -0.3446 +2026-04-12 10:12:24.794849: Pseudo dice [0.0, 0.0, 0.628, 0.3261, 0.4359, 0.7959, 0.5536] +2026-04-12 10:12:24.796316: Epoch time: 101.13 s +2026-04-12 10:12:26.013659: +2026-04-12 10:12:26.015356: Epoch 1671 +2026-04-12 10:12:26.016658: Current learning rate: 0.00615 +2026-04-12 10:14:07.647076: train_loss -0.4147 +2026-04-12 10:14:07.651405: val_loss -0.3769 +2026-04-12 10:14:07.653440: Pseudo dice [0.0, 0.3264, 0.6726, 0.7434, 0.2712, 0.6822, 0.793] +2026-04-12 10:14:07.655067: Epoch time: 101.64 s +2026-04-12 10:14:08.835876: +2026-04-12 10:14:08.837787: Epoch 1672 +2026-04-12 10:14:08.839521: Current learning rate: 0.00614 +2026-04-12 10:15:51.445025: train_loss -0.4098 +2026-04-12 10:15:51.449482: val_loss -0.3774 +2026-04-12 10:15:51.451619: Pseudo dice [0.0, 0.3352, 0.6167, 0.6898, 0.1869, 0.625, 0.6488] +2026-04-12 10:15:51.453464: Epoch time: 102.61 s +2026-04-12 10:15:52.645574: +2026-04-12 10:15:52.647788: Epoch 1673 +2026-04-12 10:15:52.649808: Current learning rate: 0.00614 +2026-04-12 10:17:34.219892: train_loss -0.3735 +2026-04-12 10:17:34.224528: val_loss -0.3414 +2026-04-12 10:17:34.226549: Pseudo dice [0.0, 0.6413, 0.7334, 0.7765, 0.3188, 0.8347, 0.7733] +2026-04-12 10:17:34.228111: Epoch time: 101.58 s +2026-04-12 10:17:35.416081: +2026-04-12 10:17:35.418096: Epoch 1674 +2026-04-12 10:17:35.419915: Current learning rate: 0.00614 +2026-04-12 10:19:16.668407: train_loss -0.3999 +2026-04-12 10:19:16.675990: val_loss -0.3737 +2026-04-12 10:19:16.677315: Pseudo dice [0.0, 0.4177, 0.7595, 0.8175, 0.4059, 0.6929, 0.8153] +2026-04-12 10:19:16.678686: Epoch time: 101.26 s +2026-04-12 10:19:17.861541: +2026-04-12 10:19:17.863377: Epoch 1675 +2026-04-12 10:19:17.865165: Current learning rate: 0.00614 +2026-04-12 10:20:59.197257: train_loss -0.3996 +2026-04-12 10:20:59.202812: val_loss -0.356 +2026-04-12 10:20:59.204543: Pseudo dice [0.0, 0.0819, 0.5074, 0.9398, 0.2863, 0.6303, 0.8346] +2026-04-12 10:20:59.206315: Epoch time: 101.34 s +2026-04-12 10:21:00.388952: +2026-04-12 10:21:00.390968: Epoch 1676 +2026-04-12 10:21:00.393090: Current learning rate: 0.00613 +2026-04-12 10:22:41.484655: train_loss -0.4223 +2026-04-12 10:22:41.489098: val_loss -0.3761 +2026-04-12 10:22:41.490822: Pseudo dice [0.0, 0.0, 0.737, 0.8738, 0.4544, 0.7391, 0.6588] +2026-04-12 10:22:41.492327: Epoch time: 101.1 s +2026-04-12 10:22:42.672802: +2026-04-12 10:22:42.674690: Epoch 1677 +2026-04-12 10:22:42.676337: Current learning rate: 0.00613 +2026-04-12 10:24:23.687288: train_loss -0.3919 +2026-04-12 10:24:23.693662: val_loss -0.3436 +2026-04-12 10:24:23.697307: Pseudo dice [0.0, 0.0083, 0.676, 0.3978, 0.1447, 0.6026, 0.7954] +2026-04-12 10:24:23.698820: Epoch time: 101.02 s +2026-04-12 10:24:24.886749: +2026-04-12 10:24:24.888706: Epoch 1678 +2026-04-12 10:24:24.890374: Current learning rate: 0.00613 +2026-04-12 10:26:05.977694: train_loss -0.3967 +2026-04-12 10:26:05.982298: val_loss -0.3808 +2026-04-12 10:26:05.984201: Pseudo dice [0.0, 0.747, 0.5805, 0.6334, 0.2725, 0.8307, 0.8538] +2026-04-12 10:26:05.985763: Epoch time: 101.09 s +2026-04-12 10:26:07.187508: +2026-04-12 10:26:07.189043: Epoch 1679 +2026-04-12 10:26:07.190497: Current learning rate: 0.00613 +2026-04-12 10:27:48.457531: train_loss -0.3964 +2026-04-12 10:27:48.462998: val_loss -0.3856 +2026-04-12 10:27:48.464639: Pseudo dice [0.0, 0.0242, 0.7368, 0.8446, 0.2758, 0.8264, 0.8403] +2026-04-12 10:27:48.466477: Epoch time: 101.27 s +2026-04-12 10:27:49.662798: +2026-04-12 10:27:49.664536: Epoch 1680 +2026-04-12 10:27:49.666090: Current learning rate: 0.00612 +2026-04-12 10:29:31.118537: train_loss -0.3864 +2026-04-12 10:29:31.124196: val_loss -0.3523 +2026-04-12 10:29:31.125961: Pseudo dice [0.0, 0.6676, 0.7421, 0.8271, 0.127, 0.7899, 0.6511] +2026-04-12 10:29:31.127396: Epoch time: 101.46 s +2026-04-12 10:29:32.312811: +2026-04-12 10:29:32.314456: Epoch 1681 +2026-04-12 10:29:32.315853: Current learning rate: 0.00612 +2026-04-12 10:31:13.972832: train_loss -0.4084 +2026-04-12 10:31:13.978726: val_loss -0.3913 +2026-04-12 10:31:13.980979: Pseudo dice [0.0, 0.67, 0.7394, 0.8148, 0.4887, 0.7345, 0.7734] +2026-04-12 10:31:13.983266: Epoch time: 101.66 s +2026-04-12 10:31:15.191114: +2026-04-12 10:31:15.193380: Epoch 1682 +2026-04-12 10:31:15.195190: Current learning rate: 0.00612 +2026-04-12 10:32:56.684458: train_loss -0.4027 +2026-04-12 10:32:56.689088: val_loss -0.3758 +2026-04-12 10:32:56.691038: Pseudo dice [0.0, 0.3724, 0.6513, 0.4628, 0.4645, 0.6666, 0.7936] +2026-04-12 10:32:56.692651: Epoch time: 101.5 s +2026-04-12 10:32:57.886134: +2026-04-12 10:32:57.888010: Epoch 1683 +2026-04-12 10:32:57.889657: Current learning rate: 0.00612 +2026-04-12 10:34:39.221392: train_loss -0.3874 +2026-04-12 10:34:39.226672: val_loss -0.3155 +2026-04-12 10:34:39.228232: Pseudo dice [0.0, 0.1131, 0.7618, 0.4519, 0.255, 0.7375, 0.8608] +2026-04-12 10:34:39.229810: Epoch time: 101.34 s +2026-04-12 10:34:40.554893: +2026-04-12 10:34:40.556709: Epoch 1684 +2026-04-12 10:34:40.558254: Current learning rate: 0.00612 +2026-04-12 10:36:21.898498: train_loss -0.3875 +2026-04-12 10:36:21.909992: val_loss -0.3431 +2026-04-12 10:36:21.914020: Pseudo dice [0.0, 0.1197, 0.4761, 0.4674, 0.4718, 0.7087, 0.4104] +2026-04-12 10:36:21.918699: Epoch time: 101.35 s +2026-04-12 10:36:23.110845: +2026-04-12 10:36:23.113218: Epoch 1685 +2026-04-12 10:36:23.115047: Current learning rate: 0.00611 +2026-04-12 10:38:04.396128: train_loss -0.3915 +2026-04-12 10:38:04.402489: val_loss -0.3643 +2026-04-12 10:38:04.405213: Pseudo dice [0.0, 0.2426, 0.478, 0.4984, 0.4328, 0.7935, 0.8102] +2026-04-12 10:38:04.407110: Epoch time: 101.29 s +2026-04-12 10:38:05.589807: +2026-04-12 10:38:05.591493: Epoch 1686 +2026-04-12 10:38:05.592869: Current learning rate: 0.00611 +2026-04-12 10:39:46.593567: train_loss -0.3772 +2026-04-12 10:39:46.597995: val_loss -0.3624 +2026-04-12 10:39:46.599763: Pseudo dice [0.0, 0.0422, 0.6485, 0.719, 0.4742, 0.829, 0.6391] +2026-04-12 10:39:46.602267: Epoch time: 101.01 s +2026-04-12 10:39:47.792805: +2026-04-12 10:39:47.794766: Epoch 1687 +2026-04-12 10:39:47.796445: Current learning rate: 0.00611 +2026-04-12 10:41:28.953256: train_loss -0.4156 +2026-04-12 10:41:28.958380: val_loss -0.3668 +2026-04-12 10:41:28.960409: Pseudo dice [0.0, 0.8125, 0.8346, 0.811, 0.387, 0.8055, 0.2157] +2026-04-12 10:41:28.962244: Epoch time: 101.16 s +2026-04-12 10:41:30.144598: +2026-04-12 10:41:30.146499: Epoch 1688 +2026-04-12 10:41:30.148280: Current learning rate: 0.00611 +2026-04-12 10:43:11.296351: train_loss -0.3761 +2026-04-12 10:43:11.301842: val_loss -0.3446 +2026-04-12 10:43:11.304037: Pseudo dice [0.0, 0.1342, 0.4035, 0.5821, 0.2728, 0.6995, 0.5638] +2026-04-12 10:43:11.306172: Epoch time: 101.15 s +2026-04-12 10:43:12.521611: +2026-04-12 10:43:12.523454: Epoch 1689 +2026-04-12 10:43:12.526181: Current learning rate: 0.0061 +2026-04-12 10:44:53.732619: train_loss -0.3775 +2026-04-12 10:44:53.738373: val_loss -0.3765 +2026-04-12 10:44:53.740361: Pseudo dice [0.2272, 0.4074, 0.6799, 0.7781, 0.432, 0.651, 0.811] +2026-04-12 10:44:53.741752: Epoch time: 101.21 s +2026-04-12 10:44:54.956587: +2026-04-12 10:44:54.958611: Epoch 1690 +2026-04-12 10:44:54.960091: Current learning rate: 0.0061 +2026-04-12 10:46:35.862276: train_loss -0.3841 +2026-04-12 10:46:35.866656: val_loss -0.3563 +2026-04-12 10:46:35.868032: Pseudo dice [0.3347, 0.2283, 0.6937, 0.7982, 0.2131, 0.8282, 0.6587] +2026-04-12 10:46:35.869518: Epoch time: 100.91 s +2026-04-12 10:46:37.046114: +2026-04-12 10:46:37.047737: Epoch 1691 +2026-04-12 10:46:37.049400: Current learning rate: 0.0061 +2026-04-12 10:48:18.148443: train_loss -0.3685 +2026-04-12 10:48:18.153733: val_loss -0.2972 +2026-04-12 10:48:18.155633: Pseudo dice [0.0, 0.0, 0.6826, 0.567, 0.3662, 0.3618, 0.6426] +2026-04-12 10:48:18.157344: Epoch time: 101.11 s +2026-04-12 10:48:20.335898: +2026-04-12 10:48:20.338698: Epoch 1692 +2026-04-12 10:48:20.345386: Current learning rate: 0.0061 +2026-04-12 10:50:01.561297: train_loss -0.3422 +2026-04-12 10:50:01.566105: val_loss -0.2974 +2026-04-12 10:50:01.568129: Pseudo dice [0.0, 0.0, 0.5194, 0.7131, 0.3553, 0.578, 0.3389] +2026-04-12 10:50:01.569955: Epoch time: 101.23 s +2026-04-12 10:50:02.749431: +2026-04-12 10:50:02.751813: Epoch 1693 +2026-04-12 10:50:02.754987: Current learning rate: 0.00609 +2026-04-12 10:51:44.132266: train_loss -0.3763 +2026-04-12 10:51:44.136832: val_loss -0.3326 +2026-04-12 10:51:44.138724: Pseudo dice [0.0, 0.0, 0.4856, 0.711, 0.2738, 0.5568, 0.3999] +2026-04-12 10:51:44.140107: Epoch time: 101.39 s +2026-04-12 10:51:45.335527: +2026-04-12 10:51:45.337317: Epoch 1694 +2026-04-12 10:51:45.338971: Current learning rate: 0.00609 +2026-04-12 10:53:26.775586: train_loss -0.4047 +2026-04-12 10:53:26.779871: val_loss -0.3116 +2026-04-12 10:53:26.781595: Pseudo dice [0.0, 0.0, 0.7265, 0.7065, 0.1063, 0.6362, 0.5222] +2026-04-12 10:53:26.783406: Epoch time: 101.44 s +2026-04-12 10:53:27.973427: +2026-04-12 10:53:27.975022: Epoch 1695 +2026-04-12 10:53:27.976403: Current learning rate: 0.00609 +2026-04-12 10:55:09.311466: train_loss -0.3895 +2026-04-12 10:55:09.316437: val_loss -0.3437 +2026-04-12 10:55:09.318338: Pseudo dice [0.0, 0.0, 0.5965, 0.5903, 0.2157, 0.7158, 0.6608] +2026-04-12 10:55:09.320120: Epoch time: 101.34 s +2026-04-12 10:55:10.493273: +2026-04-12 10:55:10.495580: Epoch 1696 +2026-04-12 10:55:10.497298: Current learning rate: 0.00609 +2026-04-12 10:56:52.088125: train_loss -0.3814 +2026-04-12 10:56:52.093529: val_loss -0.3571 +2026-04-12 10:56:52.095153: Pseudo dice [0.1149, 0.0, 0.6687, 0.2038, 0.2116, 0.7841, 0.8155] +2026-04-12 10:56:52.097313: Epoch time: 101.6 s +2026-04-12 10:56:53.289209: +2026-04-12 10:56:53.291052: Epoch 1697 +2026-04-12 10:56:53.292762: Current learning rate: 0.00608 +2026-04-12 10:58:34.977809: train_loss -0.4092 +2026-04-12 10:58:34.981897: val_loss -0.311 +2026-04-12 10:58:34.983342: Pseudo dice [0.2566, 0.0, 0.5167, 0.6237, 0.1013, 0.5934, 0.8234] +2026-04-12 10:58:34.984616: Epoch time: 101.69 s +2026-04-12 10:58:36.193253: +2026-04-12 10:58:36.194905: Epoch 1698 +2026-04-12 10:58:36.196329: Current learning rate: 0.00608 +2026-04-12 11:00:17.790000: train_loss -0.3957 +2026-04-12 11:00:17.794765: val_loss -0.3673 +2026-04-12 11:00:17.796940: Pseudo dice [0.6699, 0.0, 0.6216, 0.6963, 0.443, 0.6971, 0.5565] +2026-04-12 11:00:17.798541: Epoch time: 101.6 s +2026-04-12 11:00:18.976434: +2026-04-12 11:00:18.978946: Epoch 1699 +2026-04-12 11:00:18.980707: Current learning rate: 0.00608 +2026-04-12 11:02:00.645181: train_loss -0.3979 +2026-04-12 11:02:00.649962: val_loss -0.3584 +2026-04-12 11:02:00.652419: Pseudo dice [0.2001, 0.0, 0.7078, 0.9011, 0.3995, 0.7257, 0.4539] +2026-04-12 11:02:00.654207: Epoch time: 101.67 s +2026-04-12 11:02:03.511928: +2026-04-12 11:02:03.513817: Epoch 1700 +2026-04-12 11:02:03.515564: Current learning rate: 0.00608 +2026-04-12 11:03:45.112551: train_loss -0.4063 +2026-04-12 11:03:45.117124: val_loss -0.3691 +2026-04-12 11:03:45.118410: Pseudo dice [0.2664, 0.0, 0.541, 0.6927, 0.41, 0.7216, 0.6595] +2026-04-12 11:03:45.119933: Epoch time: 101.6 s +2026-04-12 11:03:46.303076: +2026-04-12 11:03:46.304697: Epoch 1701 +2026-04-12 11:03:46.308795: Current learning rate: 0.00607 +2026-04-12 11:05:27.862329: train_loss -0.4269 +2026-04-12 11:05:27.867417: val_loss -0.3944 +2026-04-12 11:05:27.868968: Pseudo dice [0.5329, 0.0, 0.7789, 0.8847, 0.519, 0.7123, 0.8623] +2026-04-12 11:05:27.870521: Epoch time: 101.56 s +2026-04-12 11:05:29.060303: +2026-04-12 11:05:29.062113: Epoch 1702 +2026-04-12 11:05:29.063811: Current learning rate: 0.00607 +2026-04-12 11:07:10.678016: train_loss -0.38 +2026-04-12 11:07:10.682842: val_loss -0.3623 +2026-04-12 11:07:10.684290: Pseudo dice [0.5011, 0.0, 0.6526, 0.5453, 0.3139, 0.8305, 0.4935] +2026-04-12 11:07:10.686562: Epoch time: 101.62 s +2026-04-12 11:07:11.853869: +2026-04-12 11:07:11.855367: Epoch 1703 +2026-04-12 11:07:11.856960: Current learning rate: 0.00607 +2026-04-12 11:08:53.408692: train_loss -0.3863 +2026-04-12 11:08:53.413750: val_loss -0.2936 +2026-04-12 11:08:53.415447: Pseudo dice [0.0, 0.0, 0.637, 0.6585, 0.216, 0.6369, 0.5169] +2026-04-12 11:08:53.418461: Epoch time: 101.56 s +2026-04-12 11:08:54.620132: +2026-04-12 11:08:54.622323: Epoch 1704 +2026-04-12 11:08:54.624497: Current learning rate: 0.00607 +2026-04-12 11:10:36.237132: train_loss -0.3703 +2026-04-12 11:10:36.242999: val_loss -0.3145 +2026-04-12 11:10:36.244724: Pseudo dice [0.0, 0.0, 0.4946, 0.7346, 0.2084, 0.6469, 0.8157] +2026-04-12 11:10:36.249576: Epoch time: 101.62 s +2026-04-12 11:10:37.442046: +2026-04-12 11:10:37.443660: Epoch 1705 +2026-04-12 11:10:37.445318: Current learning rate: 0.00607 +2026-04-12 11:12:18.961304: train_loss -0.3983 +2026-04-12 11:12:18.966561: val_loss -0.3398 +2026-04-12 11:12:18.968354: Pseudo dice [0.3504, 0.0, 0.6177, 0.5532, 0.2329, 0.7517, 0.6919] +2026-04-12 11:12:18.969968: Epoch time: 101.52 s +2026-04-12 11:12:20.154748: +2026-04-12 11:12:20.156523: Epoch 1706 +2026-04-12 11:12:20.158102: Current learning rate: 0.00606 +2026-04-12 11:14:01.618623: train_loss -0.4011 +2026-04-12 11:14:01.623681: val_loss -0.2961 +2026-04-12 11:14:01.626221: Pseudo dice [0.0, 0.0, 0.8488, 0.7798, 0.2711, 0.5915, 0.7595] +2026-04-12 11:14:01.628283: Epoch time: 101.47 s +2026-04-12 11:14:02.812687: +2026-04-12 11:14:02.814781: Epoch 1707 +2026-04-12 11:14:02.816411: Current learning rate: 0.00606 +2026-04-12 11:15:44.420713: train_loss -0.3702 +2026-04-12 11:15:44.425804: val_loss -0.3477 +2026-04-12 11:15:44.427912: Pseudo dice [0.0718, 0.0, 0.681, 0.7105, 0.3857, 0.2642, 0.6685] +2026-04-12 11:15:44.429478: Epoch time: 101.61 s +2026-04-12 11:15:45.621073: +2026-04-12 11:15:45.622457: Epoch 1708 +2026-04-12 11:15:45.623875: Current learning rate: 0.00606 +2026-04-12 11:17:27.169705: train_loss -0.3888 +2026-04-12 11:17:27.175013: val_loss -0.3862 +2026-04-12 11:17:27.176693: Pseudo dice [0.681, 0.0, 0.7962, 0.685, 0.4881, 0.6606, 0.726] +2026-04-12 11:17:27.178383: Epoch time: 101.55 s +2026-04-12 11:17:28.367601: +2026-04-12 11:17:28.369343: Epoch 1709 +2026-04-12 11:17:28.371070: Current learning rate: 0.00606 +2026-04-12 11:19:10.092123: train_loss -0.3678 +2026-04-12 11:19:10.096836: val_loss -0.3807 +2026-04-12 11:19:10.098704: Pseudo dice [0.3403, 0.0, 0.6177, 0.8537, 0.4656, 0.8066, 0.603] +2026-04-12 11:19:10.100224: Epoch time: 101.73 s +2026-04-12 11:19:11.289580: +2026-04-12 11:19:11.292088: Epoch 1710 +2026-04-12 11:19:11.293658: Current learning rate: 0.00605 +2026-04-12 11:20:52.947991: train_loss -0.4029 +2026-04-12 11:20:52.952374: val_loss -0.3753 +2026-04-12 11:20:52.953766: Pseudo dice [0.444, 0.0, 0.7026, 0.8548, 0.3349, 0.6863, 0.8436] +2026-04-12 11:20:52.955097: Epoch time: 101.66 s +2026-04-12 11:20:54.143915: +2026-04-12 11:20:54.145934: Epoch 1711 +2026-04-12 11:20:54.147472: Current learning rate: 0.00605 +2026-04-12 11:22:35.751071: train_loss -0.3892 +2026-04-12 11:22:35.756338: val_loss -0.3567 +2026-04-12 11:22:35.758258: Pseudo dice [0.438, 0.0, 0.6175, 0.6847, 0.2793, 0.8112, 0.7957] +2026-04-12 11:22:35.778183: Epoch time: 101.61 s +2026-04-12 11:22:37.999389: +2026-04-12 11:22:38.001051: Epoch 1712 +2026-04-12 11:22:38.002597: Current learning rate: 0.00605 +2026-04-12 11:24:19.706698: train_loss -0.3766 +2026-04-12 11:24:19.711292: val_loss -0.3555 +2026-04-12 11:24:19.713095: Pseudo dice [0.481, 0.0, 0.7399, 0.8542, 0.0743, 0.2901, 0.7241] +2026-04-12 11:24:19.714749: Epoch time: 101.71 s +2026-04-12 11:24:20.901882: +2026-04-12 11:24:20.903444: Epoch 1713 +2026-04-12 11:24:20.904755: Current learning rate: 0.00605 +2026-04-12 11:26:02.475832: train_loss -0.3769 +2026-04-12 11:26:02.481820: val_loss -0.3588 +2026-04-12 11:26:02.483734: Pseudo dice [0.4546, 0.0, 0.5741, 0.6151, 0.265, 0.6567, 0.8262] +2026-04-12 11:26:02.485692: Epoch time: 101.58 s +2026-04-12 11:26:03.681770: +2026-04-12 11:26:03.683521: Epoch 1714 +2026-04-12 11:26:03.685207: Current learning rate: 0.00604 +2026-04-12 11:27:45.233852: train_loss -0.393 +2026-04-12 11:27:45.238715: val_loss -0.3325 +2026-04-12 11:27:45.240605: Pseudo dice [0.0177, 0.0, 0.5876, 0.2882, 0.2118, 0.7491, 0.5744] +2026-04-12 11:27:45.242290: Epoch time: 101.56 s +2026-04-12 11:27:46.437924: +2026-04-12 11:27:46.439607: Epoch 1715 +2026-04-12 11:27:46.441044: Current learning rate: 0.00604 +2026-04-12 11:29:27.972857: train_loss -0.3802 +2026-04-12 11:29:27.978178: val_loss -0.3395 +2026-04-12 11:29:27.979812: Pseudo dice [0.0, 0.0, 0.4086, 0.0081, 0.4509, 0.7137, 0.6824] +2026-04-12 11:29:27.981812: Epoch time: 101.54 s +2026-04-12 11:29:29.173239: +2026-04-12 11:29:29.175450: Epoch 1716 +2026-04-12 11:29:29.177337: Current learning rate: 0.00604 +2026-04-12 11:31:10.884784: train_loss -0.3886 +2026-04-12 11:31:10.888855: val_loss -0.3827 +2026-04-12 11:31:10.890332: Pseudo dice [0.0, 0.0, 0.7077, 0.4666, 0.439, 0.7109, 0.6868] +2026-04-12 11:31:10.891742: Epoch time: 101.71 s +2026-04-12 11:31:12.086455: +2026-04-12 11:31:12.087886: Epoch 1717 +2026-04-12 11:31:12.089118: Current learning rate: 0.00604 +2026-04-12 11:32:53.579197: train_loss -0.4192 +2026-04-12 11:32:53.584273: val_loss -0.3051 +2026-04-12 11:32:53.586202: Pseudo dice [0.0, 0.0, 0.75, 0.3272, 0.2295, 0.4092, 0.8634] +2026-04-12 11:32:53.587829: Epoch time: 101.5 s +2026-04-12 11:32:54.770706: +2026-04-12 11:32:54.772110: Epoch 1718 +2026-04-12 11:32:54.773672: Current learning rate: 0.00603 +2026-04-12 11:34:36.359114: train_loss -0.402 +2026-04-12 11:34:36.363755: val_loss -0.3492 +2026-04-12 11:34:36.365211: Pseudo dice [0.182, 0.0, 0.6368, 0.7824, 0.3368, 0.3734, 0.7981] +2026-04-12 11:34:36.366640: Epoch time: 101.59 s +2026-04-12 11:34:37.551991: +2026-04-12 11:34:37.553282: Epoch 1719 +2026-04-12 11:34:37.554631: Current learning rate: 0.00603 +2026-04-12 11:36:19.268745: train_loss -0.3937 +2026-04-12 11:36:19.274466: val_loss -0.3102 +2026-04-12 11:36:19.276469: Pseudo dice [0.2413, 0.0, 0.6459, 0.4279, 0.2311, 0.394, 0.7819] +2026-04-12 11:36:19.278269: Epoch time: 101.72 s +2026-04-12 11:36:20.481462: +2026-04-12 11:36:20.483313: Epoch 1720 +2026-04-12 11:36:20.484989: Current learning rate: 0.00603 +2026-04-12 11:38:02.073300: train_loss -0.3707 +2026-04-12 11:38:02.077861: val_loss -0.318 +2026-04-12 11:38:02.080113: Pseudo dice [0.0666, 0.0, 0.6646, 0.7115, 0.0147, 0.4114, 0.7552] +2026-04-12 11:38:02.081934: Epoch time: 101.59 s +2026-04-12 11:38:03.269352: +2026-04-12 11:38:03.271235: Epoch 1721 +2026-04-12 11:38:03.274072: Current learning rate: 0.00603 +2026-04-12 11:39:45.044741: train_loss -0.3717 +2026-04-12 11:39:45.049399: val_loss -0.314 +2026-04-12 11:39:45.051071: Pseudo dice [0.0, 0.0, 0.7175, 0.529, 0.2578, 0.4156, 0.806] +2026-04-12 11:39:45.052634: Epoch time: 101.78 s +2026-04-12 11:39:46.244566: +2026-04-12 11:39:46.246210: Epoch 1722 +2026-04-12 11:39:46.247742: Current learning rate: 0.00602 +2026-04-12 11:41:27.782092: train_loss -0.3642 +2026-04-12 11:41:27.786195: val_loss -0.3439 +2026-04-12 11:41:27.787647: Pseudo dice [0.0, 0.0, 0.7205, 0.4852, 0.3532, 0.7592, 0.5998] +2026-04-12 11:41:27.789627: Epoch time: 101.54 s +2026-04-12 11:41:28.978189: +2026-04-12 11:41:28.979722: Epoch 1723 +2026-04-12 11:41:28.981226: Current learning rate: 0.00602 +2026-04-12 11:43:10.545072: train_loss -0.3935 +2026-04-12 11:43:10.551592: val_loss -0.3146 +2026-04-12 11:43:10.554143: Pseudo dice [0.0, 0.0, 0.4425, 0.7715, 0.3249, 0.6426, 0.8527] +2026-04-12 11:43:10.556177: Epoch time: 101.57 s +2026-04-12 11:43:11.772928: +2026-04-12 11:43:11.774778: Epoch 1724 +2026-04-12 11:43:11.776799: Current learning rate: 0.00602 +2026-04-12 11:44:53.509564: train_loss -0.3933 +2026-04-12 11:44:53.514271: val_loss -0.3368 +2026-04-12 11:44:53.515937: Pseudo dice [0.0, 0.0, 0.6534, 0.3196, 0.2975, 0.6734, 0.8686] +2026-04-12 11:44:53.517347: Epoch time: 101.74 s +2026-04-12 11:44:54.690125: +2026-04-12 11:44:54.691908: Epoch 1725 +2026-04-12 11:44:54.693436: Current learning rate: 0.00602 +2026-04-12 11:46:36.539448: train_loss -0.3773 +2026-04-12 11:46:36.550728: val_loss -0.3009 +2026-04-12 11:46:36.554733: Pseudo dice [0.0, 0.0, 0.4367, 0.0353, 0.3006, 0.3257, 0.8488] +2026-04-12 11:46:36.557170: Epoch time: 101.85 s +2026-04-12 11:46:37.727704: +2026-04-12 11:46:37.729546: Epoch 1726 +2026-04-12 11:46:37.731068: Current learning rate: 0.00602 +2026-04-12 11:48:19.412648: train_loss -0.3706 +2026-04-12 11:48:19.417589: val_loss -0.361 +2026-04-12 11:48:19.419356: Pseudo dice [0.0, 0.0, 0.6789, 0.6352, 0.1585, 0.7815, 0.6049] +2026-04-12 11:48:19.421929: Epoch time: 101.69 s +2026-04-12 11:48:20.616159: +2026-04-12 11:48:20.618107: Epoch 1727 +2026-04-12 11:48:20.619740: Current learning rate: 0.00601 +2026-04-12 11:50:02.178686: train_loss -0.3805 +2026-04-12 11:50:02.183154: val_loss -0.3254 +2026-04-12 11:50:02.184550: Pseudo dice [0.0002, 0.0, 0.2208, 0.725, 0.0465, 0.7186, 0.7742] +2026-04-12 11:50:02.185953: Epoch time: 101.57 s +2026-04-12 11:50:03.377131: +2026-04-12 11:50:03.378647: Epoch 1728 +2026-04-12 11:50:03.380075: Current learning rate: 0.00601 +2026-04-12 11:51:45.013147: train_loss -0.3827 +2026-04-12 11:51:45.018195: val_loss -0.3399 +2026-04-12 11:51:45.019681: Pseudo dice [0.3166, 0.0, 0.8059, 0.8222, 0.3544, 0.4297, 0.6523] +2026-04-12 11:51:45.021842: Epoch time: 101.64 s +2026-04-12 11:51:46.222579: +2026-04-12 11:51:46.225300: Epoch 1729 +2026-04-12 11:51:46.228585: Current learning rate: 0.00601 +2026-04-12 11:53:27.796838: train_loss -0.4214 +2026-04-12 11:53:27.801849: val_loss -0.3712 +2026-04-12 11:53:27.803657: Pseudo dice [0.4597, 0.0, 0.7504, 0.917, 0.2063, 0.7044, 0.9233] +2026-04-12 11:53:27.805516: Epoch time: 101.58 s +2026-04-12 11:53:29.010159: +2026-04-12 11:53:29.011796: Epoch 1730 +2026-04-12 11:53:29.013257: Current learning rate: 0.00601 +2026-04-12 11:55:10.561820: train_loss -0.4292 +2026-04-12 11:55:10.566178: val_loss -0.3646 +2026-04-12 11:55:10.567947: Pseudo dice [0.3746, 0.0, 0.6243, 0.6714, 0.009, 0.8103, 0.8274] +2026-04-12 11:55:10.570424: Epoch time: 101.55 s +2026-04-12 11:55:11.756830: +2026-04-12 11:55:11.758683: Epoch 1731 +2026-04-12 11:55:11.760235: Current learning rate: 0.006 +2026-04-12 11:56:54.426302: train_loss -0.3788 +2026-04-12 11:56:54.431802: val_loss -0.4013 +2026-04-12 11:56:54.433671: Pseudo dice [0.4554, 0.0, 0.7862, 0.811, 0.4173, 0.7178, 0.8797] +2026-04-12 11:56:54.435633: Epoch time: 102.67 s +2026-04-12 11:56:55.632017: +2026-04-12 11:56:55.633851: Epoch 1732 +2026-04-12 11:56:55.635720: Current learning rate: 0.006 +2026-04-12 11:58:37.213501: train_loss -0.4104 +2026-04-12 11:58:37.221047: val_loss -0.3614 +2026-04-12 11:58:37.223237: Pseudo dice [0.2359, 0.0, 0.6399, 0.6041, 0.4202, 0.617, 0.7467] +2026-04-12 11:58:37.225168: Epoch time: 101.58 s +2026-04-12 11:58:38.416674: +2026-04-12 11:58:38.418381: Epoch 1733 +2026-04-12 11:58:38.419694: Current learning rate: 0.006 +2026-04-12 12:00:19.958107: train_loss -0.3911 +2026-04-12 12:00:19.962485: val_loss -0.349 +2026-04-12 12:00:19.964105: Pseudo dice [0.4398, 0.0, 0.5354, 0.5756, 0.4575, 0.3291, 0.6758] +2026-04-12 12:00:19.965519: Epoch time: 101.54 s +2026-04-12 12:00:21.166310: +2026-04-12 12:00:21.167996: Epoch 1734 +2026-04-12 12:00:21.169425: Current learning rate: 0.006 +2026-04-12 12:02:02.585974: train_loss -0.3879 +2026-04-12 12:02:02.590919: val_loss -0.3488 +2026-04-12 12:02:02.593305: Pseudo dice [0.0, 0.0, 0.7063, 0.8638, 0.4864, 0.8368, 0.754] +2026-04-12 12:02:02.594969: Epoch time: 101.42 s +2026-04-12 12:02:03.793919: +2026-04-12 12:02:03.796208: Epoch 1735 +2026-04-12 12:02:03.797751: Current learning rate: 0.00599 +2026-04-12 12:03:45.234490: train_loss -0.3816 +2026-04-12 12:03:45.239768: val_loss -0.343 +2026-04-12 12:03:45.241418: Pseudo dice [0.2192, 0.0, 0.5491, 0.846, 0.3814, 0.4791, 0.6681] +2026-04-12 12:03:45.243124: Epoch time: 101.44 s +2026-04-12 12:03:46.418869: +2026-04-12 12:03:46.420861: Epoch 1736 +2026-04-12 12:03:46.422429: Current learning rate: 0.00599 +2026-04-12 12:05:27.963922: train_loss -0.3686 +2026-04-12 12:05:27.968256: val_loss -0.3464 +2026-04-12 12:05:27.970170: Pseudo dice [0.0, 0.0, 0.7901, 0.6402, 0.1665, 0.7891, 0.855] +2026-04-12 12:05:27.971827: Epoch time: 101.55 s +2026-04-12 12:05:29.163128: +2026-04-12 12:05:29.165039: Epoch 1737 +2026-04-12 12:05:29.166626: Current learning rate: 0.00599 +2026-04-12 12:07:10.702389: train_loss -0.3854 +2026-04-12 12:07:10.707307: val_loss -0.3249 +2026-04-12 12:07:10.709322: Pseudo dice [0.3822, 0.0, 0.658, 0.8358, 0.0447, 0.7574, 0.642] +2026-04-12 12:07:10.710998: Epoch time: 101.54 s +2026-04-12 12:07:11.891803: +2026-04-12 12:07:11.893412: Epoch 1738 +2026-04-12 12:07:11.895328: Current learning rate: 0.00599 +2026-04-12 12:08:53.171017: train_loss -0.3824 +2026-04-12 12:08:53.175209: val_loss -0.3739 +2026-04-12 12:08:53.176859: Pseudo dice [0.5354, 0.0, 0.4819, 0.7757, 0.1537, 0.6345, 0.8058] +2026-04-12 12:08:53.178327: Epoch time: 101.28 s +2026-04-12 12:08:54.383637: +2026-04-12 12:08:54.384942: Epoch 1739 +2026-04-12 12:08:54.386368: Current learning rate: 0.00598 +2026-04-12 12:10:35.558631: train_loss -0.3804 +2026-04-12 12:10:35.563415: val_loss -0.3551 +2026-04-12 12:10:35.564957: Pseudo dice [0.2004, 0.0, 0.8479, 0.2962, 0.3522, 0.6268, 0.4722] +2026-04-12 12:10:35.566711: Epoch time: 101.18 s +2026-04-12 12:10:36.782756: +2026-04-12 12:10:36.785023: Epoch 1740 +2026-04-12 12:10:36.786864: Current learning rate: 0.00598 +2026-04-12 12:12:18.278305: train_loss -0.3923 +2026-04-12 12:12:18.282610: val_loss -0.3657 +2026-04-12 12:12:18.284020: Pseudo dice [0.1917, 0.0, 0.5121, 0.8078, 0.243, 0.7659, 0.6708] +2026-04-12 12:12:18.285455: Epoch time: 101.5 s +2026-04-12 12:12:19.680589: +2026-04-12 12:12:19.682087: Epoch 1741 +2026-04-12 12:12:19.683560: Current learning rate: 0.00598 +2026-04-12 12:14:01.126924: train_loss -0.4127 +2026-04-12 12:14:01.130917: val_loss -0.3555 +2026-04-12 12:14:01.132643: Pseudo dice [0.4659, 0.0, 0.7522, 0.817, 0.1694, 0.695, 0.6215] +2026-04-12 12:14:01.134081: Epoch time: 101.45 s +2026-04-12 12:14:02.317550: +2026-04-12 12:14:02.319253: Epoch 1742 +2026-04-12 12:14:02.320791: Current learning rate: 0.00598 +2026-04-12 12:15:43.699727: train_loss -0.405 +2026-04-12 12:15:43.704574: val_loss -0.2748 +2026-04-12 12:15:43.706290: Pseudo dice [0.0013, 0.0, 0.697, 0.2361, 0.3329, 0.6623, 0.0846] +2026-04-12 12:15:43.708616: Epoch time: 101.39 s +2026-04-12 12:15:44.899872: +2026-04-12 12:15:44.901947: Epoch 1743 +2026-04-12 12:15:44.903550: Current learning rate: 0.00597 +2026-04-12 12:17:26.250417: train_loss -0.3773 +2026-04-12 12:17:26.256232: val_loss -0.3714 +2026-04-12 12:17:26.259603: Pseudo dice [0.1087, 0.0, 0.6561, 0.7672, 0.3973, 0.5892, 0.8225] +2026-04-12 12:17:26.262049: Epoch time: 101.35 s +2026-04-12 12:17:27.466228: +2026-04-12 12:17:27.468342: Epoch 1744 +2026-04-12 12:17:27.470005: Current learning rate: 0.00597 +2026-04-12 12:19:08.826219: train_loss -0.4032 +2026-04-12 12:19:08.832409: val_loss -0.3877 +2026-04-12 12:19:08.834020: Pseudo dice [0.3468, 0.0, 0.6028, 0.7869, 0.5024, 0.71, 0.8654] +2026-04-12 12:19:08.836298: Epoch time: 101.36 s +2026-04-12 12:19:10.024575: +2026-04-12 12:19:10.025906: Epoch 1745 +2026-04-12 12:19:10.027201: Current learning rate: 0.00597 +2026-04-12 12:20:51.312665: train_loss -0.4055 +2026-04-12 12:20:51.318326: val_loss -0.3672 +2026-04-12 12:20:51.320031: Pseudo dice [0.3009, 0.0, 0.7289, 0.8565, 0.469, 0.7468, 0.7199] +2026-04-12 12:20:51.321645: Epoch time: 101.29 s +2026-04-12 12:20:52.509269: +2026-04-12 12:20:52.510720: Epoch 1746 +2026-04-12 12:20:52.512234: Current learning rate: 0.00597 +2026-04-12 12:22:33.645770: train_loss -0.3909 +2026-04-12 12:22:33.651184: val_loss -0.3514 +2026-04-12 12:22:33.652821: Pseudo dice [0.3718, 0.0, 0.7184, 0.6615, 0.4004, 0.3461, 0.3737] +2026-04-12 12:22:33.654319: Epoch time: 101.14 s +2026-04-12 12:22:34.819956: +2026-04-12 12:22:34.821765: Epoch 1747 +2026-04-12 12:22:34.823220: Current learning rate: 0.00597 +2026-04-12 12:24:16.339784: train_loss -0.3892 +2026-04-12 12:24:16.344377: val_loss -0.3532 +2026-04-12 12:24:16.345841: Pseudo dice [0.4736, 0.0, 0.524, 0.7338, 0.3247, 0.6268, 0.8295] +2026-04-12 12:24:16.347407: Epoch time: 101.52 s +2026-04-12 12:24:17.544408: +2026-04-12 12:24:17.545786: Epoch 1748 +2026-04-12 12:24:17.547024: Current learning rate: 0.00596 +2026-04-12 12:25:58.868288: train_loss -0.4108 +2026-04-12 12:25:58.873592: val_loss -0.3303 +2026-04-12 12:25:58.875709: Pseudo dice [0.2905, 0.0, 0.7797, 0.6451, 0.3478, 0.3954, 0.6436] +2026-04-12 12:25:58.878302: Epoch time: 101.33 s +2026-04-12 12:26:00.049630: +2026-04-12 12:26:00.051207: Epoch 1749 +2026-04-12 12:26:00.052561: Current learning rate: 0.00596 +2026-04-12 12:27:41.662467: train_loss -0.3705 +2026-04-12 12:27:41.667061: val_loss -0.3434 +2026-04-12 12:27:41.668689: Pseudo dice [0.1842, 0.0, 0.7083, 0.8322, 0.3512, 0.0378, 0.5295] +2026-04-12 12:27:41.669995: Epoch time: 101.62 s +2026-04-12 12:27:44.488822: +2026-04-12 12:27:44.490739: Epoch 1750 +2026-04-12 12:27:44.492453: Current learning rate: 0.00596 +2026-04-12 12:29:27.040738: train_loss -0.3972 +2026-04-12 12:29:27.047237: val_loss -0.3102 +2026-04-12 12:29:27.049469: Pseudo dice [0.2533, 0.0, 0.763, 0.7471, 0.025, 0.189, 0.6122] +2026-04-12 12:29:27.051242: Epoch time: 102.55 s +2026-04-12 12:29:28.236151: +2026-04-12 12:29:28.237797: Epoch 1751 +2026-04-12 12:29:28.239524: Current learning rate: 0.00596 +2026-04-12 12:31:09.407280: train_loss -0.4018 +2026-04-12 12:31:09.412334: val_loss -0.3672 +2026-04-12 12:31:09.413923: Pseudo dice [0.7061, 0.0, 0.758, 0.734, 0.2622, 0.6375, 0.5556] +2026-04-12 12:31:09.416442: Epoch time: 101.17 s +2026-04-12 12:31:10.587328: +2026-04-12 12:31:10.588979: Epoch 1752 +2026-04-12 12:31:10.590536: Current learning rate: 0.00595 +2026-04-12 12:32:51.682555: train_loss -0.4066 +2026-04-12 12:32:51.687299: val_loss -0.3958 +2026-04-12 12:32:51.689924: Pseudo dice [0.6905, 0.0, 0.7511, 0.7969, 0.4103, 0.8527, 0.7997] +2026-04-12 12:32:51.691433: Epoch time: 101.1 s +2026-04-12 12:32:52.878942: +2026-04-12 12:32:52.880996: Epoch 1753 +2026-04-12 12:32:52.883316: Current learning rate: 0.00595 +2026-04-12 12:34:34.059321: train_loss -0.391 +2026-04-12 12:34:34.063754: val_loss -0.2809 +2026-04-12 12:34:34.065488: Pseudo dice [0.0, 0.0, 0.1811, 0.7103, 0.2507, 0.4444, 0.5904] +2026-04-12 12:34:34.067046: Epoch time: 101.18 s +2026-04-12 12:34:35.257478: +2026-04-12 12:34:35.259145: Epoch 1754 +2026-04-12 12:34:35.260721: Current learning rate: 0.00595 +2026-04-12 12:36:16.403498: train_loss -0.3683 +2026-04-12 12:36:16.408808: val_loss -0.3548 +2026-04-12 12:36:16.410263: Pseudo dice [0.2282, 0.0, 0.684, 0.8029, 0.3344, 0.5466, 0.7494] +2026-04-12 12:36:16.411751: Epoch time: 101.15 s +2026-04-12 12:36:17.585998: +2026-04-12 12:36:17.587743: Epoch 1755 +2026-04-12 12:36:17.589174: Current learning rate: 0.00595 +2026-04-12 12:37:59.047721: train_loss -0.39 +2026-04-12 12:37:59.052648: val_loss -0.3564 +2026-04-12 12:37:59.054137: Pseudo dice [0.5317, 0.0, 0.6555, 0.0053, 0.2115, 0.7337, 0.8481] +2026-04-12 12:37:59.055461: Epoch time: 101.46 s +2026-04-12 12:38:00.251786: +2026-04-12 12:38:00.253682: Epoch 1756 +2026-04-12 12:38:00.255012: Current learning rate: 0.00594 +2026-04-12 12:39:41.796749: train_loss -0.3851 +2026-04-12 12:39:41.800958: val_loss -0.3096 +2026-04-12 12:39:41.803366: Pseudo dice [0.1278, 0.0, 0.7628, 0.5109, 0.071, 0.725, 0.2248] +2026-04-12 12:39:41.804848: Epoch time: 101.55 s +2026-04-12 12:39:42.998636: +2026-04-12 12:39:43.000503: Epoch 1757 +2026-04-12 12:39:43.002049: Current learning rate: 0.00594 +2026-04-12 12:41:24.518337: train_loss -0.379 +2026-04-12 12:41:24.522503: val_loss -0.3397 +2026-04-12 12:41:24.523739: Pseudo dice [0.3699, 0.0, 0.7239, 0.7777, 0.2052, 0.4742, 0.5186] +2026-04-12 12:41:24.525046: Epoch time: 101.52 s +2026-04-12 12:41:25.719373: +2026-04-12 12:41:25.720611: Epoch 1758 +2026-04-12 12:41:25.721815: Current learning rate: 0.00594 +2026-04-12 12:43:06.860637: train_loss -0.3846 +2026-04-12 12:43:06.865705: val_loss -0.3828 +2026-04-12 12:43:06.867434: Pseudo dice [0.6567, 0.0, 0.6848, 0.4807, 0.3495, 0.7039, 0.8179] +2026-04-12 12:43:06.869788: Epoch time: 101.14 s +2026-04-12 12:43:08.073403: +2026-04-12 12:43:08.074748: Epoch 1759 +2026-04-12 12:43:08.076132: Current learning rate: 0.00594 +2026-04-12 12:44:49.018180: train_loss -0.406 +2026-04-12 12:44:49.023393: val_loss -0.3848 +2026-04-12 12:44:49.025163: Pseudo dice [0.422, 0.0, 0.6398, 0.9018, 0.3449, 0.7917, 0.6345] +2026-04-12 12:44:49.027442: Epoch time: 100.95 s +2026-04-12 12:44:50.209069: +2026-04-12 12:44:50.210669: Epoch 1760 +2026-04-12 12:44:50.212151: Current learning rate: 0.00593 +2026-04-12 12:46:31.223905: train_loss -0.4077 +2026-04-12 12:46:31.227981: val_loss -0.3856 +2026-04-12 12:46:31.229147: Pseudo dice [0.7731, 0.0, 0.8171, 0.81, 0.4114, 0.477, 0.5554] +2026-04-12 12:46:31.230416: Epoch time: 101.02 s +2026-04-12 12:46:32.408131: +2026-04-12 12:46:32.409573: Epoch 1761 +2026-04-12 12:46:32.410774: Current learning rate: 0.00593 +2026-04-12 12:48:13.486173: train_loss -0.3895 +2026-04-12 12:48:13.490411: val_loss -0.3149 +2026-04-12 12:48:13.491973: Pseudo dice [0.0233, 0.0, 0.7151, 0.8391, 0.2249, 0.7161, 0.2601] +2026-04-12 12:48:13.493382: Epoch time: 101.08 s +2026-04-12 12:48:14.660958: +2026-04-12 12:48:14.662385: Epoch 1762 +2026-04-12 12:48:14.664430: Current learning rate: 0.00593 +2026-04-12 12:49:55.867255: train_loss -0.3948 +2026-04-12 12:49:55.873267: val_loss -0.3246 +2026-04-12 12:49:55.874936: Pseudo dice [0.0455, 0.0, 0.3586, 0.8234, 0.3892, 0.4981, 0.7396] +2026-04-12 12:49:55.876707: Epoch time: 101.21 s +2026-04-12 12:49:57.060334: +2026-04-12 12:49:57.062071: Epoch 1763 +2026-04-12 12:49:57.063810: Current learning rate: 0.00593 +2026-04-12 12:51:38.103073: train_loss -0.4017 +2026-04-12 12:51:38.107613: val_loss -0.3498 +2026-04-12 12:51:38.109070: Pseudo dice [0.0795, 0.0, 0.7728, 0.8242, 0.5272, 0.7404, 0.6999] +2026-04-12 12:51:38.110216: Epoch time: 101.05 s +2026-04-12 12:51:39.302221: +2026-04-12 12:51:39.303482: Epoch 1764 +2026-04-12 12:51:39.304912: Current learning rate: 0.00592 +2026-04-12 12:53:20.285585: train_loss -0.4055 +2026-04-12 12:53:20.290071: val_loss -0.328 +2026-04-12 12:53:20.291891: Pseudo dice [0.2345, 0.0, 0.8114, 0.3533, 0.4135, 0.5174, 0.7781] +2026-04-12 12:53:20.293654: Epoch time: 100.99 s +2026-04-12 12:53:21.489771: +2026-04-12 12:53:21.491713: Epoch 1765 +2026-04-12 12:53:21.493371: Current learning rate: 0.00592 +2026-04-12 12:55:02.539187: train_loss -0.3496 +2026-04-12 12:55:02.544812: val_loss -0.353 +2026-04-12 12:55:02.546797: Pseudo dice [0.5684, 0.0, 0.547, 0.7238, 0.3454, 0.3664, 0.7196] +2026-04-12 12:55:02.548499: Epoch time: 101.05 s +2026-04-12 12:55:03.755431: +2026-04-12 12:55:03.757243: Epoch 1766 +2026-04-12 12:55:03.758652: Current learning rate: 0.00592 +2026-04-12 12:56:44.729031: train_loss -0.3955 +2026-04-12 12:56:44.736281: val_loss -0.3568 +2026-04-12 12:56:44.738396: Pseudo dice [0.2156, 0.0, 0.6428, 0.8076, 0.2138, 0.6511, 0.7647] +2026-04-12 12:56:44.740177: Epoch time: 100.98 s +2026-04-12 12:56:45.936883: +2026-04-12 12:56:45.938469: Epoch 1767 +2026-04-12 12:56:45.940295: Current learning rate: 0.00592 +2026-04-12 12:58:27.005902: train_loss -0.4002 +2026-04-12 12:58:27.010472: val_loss -0.3848 +2026-04-12 12:58:27.012095: Pseudo dice [0.1322, 0.0, 0.613, 0.6956, 0.2721, 0.5017, 0.8162] +2026-04-12 12:58:27.013628: Epoch time: 101.07 s +2026-04-12 12:58:28.216192: +2026-04-12 12:58:28.218156: Epoch 1768 +2026-04-12 12:58:28.219773: Current learning rate: 0.00592 +2026-04-12 13:00:09.293669: train_loss -0.4054 +2026-04-12 13:00:09.299265: val_loss -0.3718 +2026-04-12 13:00:09.301068: Pseudo dice [0.2778, 0.0, 0.5324, 0.8955, 0.4315, 0.5524, 0.6358] +2026-04-12 13:00:09.302634: Epoch time: 101.08 s +2026-04-12 13:00:10.484992: +2026-04-12 13:00:10.486783: Epoch 1769 +2026-04-12 13:00:10.487996: Current learning rate: 0.00591 +2026-04-12 13:01:51.559421: train_loss -0.4043 +2026-04-12 13:01:51.565279: val_loss -0.3188 +2026-04-12 13:01:51.568189: Pseudo dice [0.1633, 0.0, 0.6006, 0.4882, 0.2954, 0.7781, 0.4906] +2026-04-12 13:01:51.570203: Epoch time: 101.08 s +2026-04-12 13:01:53.738829: +2026-04-12 13:01:53.740382: Epoch 1770 +2026-04-12 13:01:53.741815: Current learning rate: 0.00591 +2026-04-12 13:03:35.218215: train_loss -0.4101 +2026-04-12 13:03:35.222884: val_loss -0.3519 +2026-04-12 13:03:35.224568: Pseudo dice [0.0185, 0.0, 0.4679, 0.8802, 0.3599, 0.4335, 0.5587] +2026-04-12 13:03:35.226534: Epoch time: 101.48 s +2026-04-12 13:03:36.412696: +2026-04-12 13:03:36.414939: Epoch 1771 +2026-04-12 13:03:36.416489: Current learning rate: 0.00591 +2026-04-12 13:05:17.905915: train_loss -0.3961 +2026-04-12 13:05:17.910068: val_loss -0.3796 +2026-04-12 13:05:17.911498: Pseudo dice [0.2666, 0.0, 0.8206, 0.7666, 0.2816, 0.861, 0.7639] +2026-04-12 13:05:17.912897: Epoch time: 101.5 s +2026-04-12 13:05:19.086107: +2026-04-12 13:05:19.087891: Epoch 1772 +2026-04-12 13:05:19.089417: Current learning rate: 0.00591 +2026-04-12 13:07:00.629268: train_loss -0.4114 +2026-04-12 13:07:00.634189: val_loss -0.3822 +2026-04-12 13:07:00.635877: Pseudo dice [0.5985, 0.0, 0.6541, 0.4448, 0.4815, 0.6401, 0.7747] +2026-04-12 13:07:00.637303: Epoch time: 101.55 s +2026-04-12 13:07:01.824556: +2026-04-12 13:07:01.826209: Epoch 1773 +2026-04-12 13:07:01.827793: Current learning rate: 0.0059 +2026-04-12 13:08:43.205442: train_loss -0.4051 +2026-04-12 13:08:43.210263: val_loss -0.334 +2026-04-12 13:08:43.212165: Pseudo dice [0.0994, 0.0, 0.6934, 0.5542, 0.4136, 0.6031, 0.6671] +2026-04-12 13:08:43.213896: Epoch time: 101.38 s +2026-04-12 13:08:44.394977: +2026-04-12 13:08:44.396707: Epoch 1774 +2026-04-12 13:08:44.398332: Current learning rate: 0.0059 +2026-04-12 13:10:25.698408: train_loss -0.3787 +2026-04-12 13:10:25.704820: val_loss -0.3212 +2026-04-12 13:10:25.706823: Pseudo dice [0.0363, 0.0, 0.5712, 0.1286, 0.3916, 0.6066, 0.6078] +2026-04-12 13:10:25.708474: Epoch time: 101.31 s +2026-04-12 13:10:26.884744: +2026-04-12 13:10:26.886720: Epoch 1775 +2026-04-12 13:10:26.888166: Current learning rate: 0.0059 +2026-04-12 13:12:08.428931: train_loss -0.3897 +2026-04-12 13:12:08.434623: val_loss -0.3633 +2026-04-12 13:12:08.436193: Pseudo dice [0.2454, 0.0, 0.5526, 0.7698, 0.2015, 0.8098, 0.8981] +2026-04-12 13:12:08.437909: Epoch time: 101.55 s +2026-04-12 13:12:09.613982: +2026-04-12 13:12:09.616050: Epoch 1776 +2026-04-12 13:12:09.617707: Current learning rate: 0.0059 +2026-04-12 13:13:50.926680: train_loss -0.3937 +2026-04-12 13:13:50.931759: val_loss -0.3373 +2026-04-12 13:13:50.933431: Pseudo dice [0.3037, 0.0, 0.6969, 0.869, 0.3992, 0.3483, 0.1953] +2026-04-12 13:13:50.934852: Epoch time: 101.32 s +2026-04-12 13:13:52.116245: +2026-04-12 13:13:52.118008: Epoch 1777 +2026-04-12 13:13:52.119817: Current learning rate: 0.00589 +2026-04-12 13:15:33.574598: train_loss -0.3892 +2026-04-12 13:15:33.578996: val_loss -0.3525 +2026-04-12 13:15:33.580483: Pseudo dice [0.0, 0.0, 0.5954, 0.8295, 0.2255, 0.8352, 0.7108] +2026-04-12 13:15:33.581892: Epoch time: 101.46 s +2026-04-12 13:15:34.740338: +2026-04-12 13:15:34.742126: Epoch 1778 +2026-04-12 13:15:34.743728: Current learning rate: 0.00589 +2026-04-12 13:17:16.172776: train_loss -0.3913 +2026-04-12 13:17:16.177393: val_loss -0.3369 +2026-04-12 13:17:16.179599: Pseudo dice [0.1242, 0.0, 0.657, 0.5892, 0.1964, 0.6004, 0.79] +2026-04-12 13:17:16.181630: Epoch time: 101.44 s +2026-04-12 13:17:17.363015: +2026-04-12 13:17:17.364480: Epoch 1779 +2026-04-12 13:17:17.365900: Current learning rate: 0.00589 +2026-04-12 13:18:58.945221: train_loss -0.4159 +2026-04-12 13:18:58.950022: val_loss -0.3417 +2026-04-12 13:18:58.951473: Pseudo dice [0.2735, 0.0, 0.7227, 0.3151, 0.1347, 0.8092, 0.646] +2026-04-12 13:18:58.952993: Epoch time: 101.59 s +2026-04-12 13:19:00.129627: +2026-04-12 13:19:00.130813: Epoch 1780 +2026-04-12 13:19:00.132259: Current learning rate: 0.00589 +2026-04-12 13:20:41.615090: train_loss -0.4078 +2026-04-12 13:20:41.619042: val_loss -0.327 +2026-04-12 13:20:41.620483: Pseudo dice [0.5554, 0.0, 0.421, 0.5133, 0.2088, 0.7511, 0.9045] +2026-04-12 13:20:41.622188: Epoch time: 101.49 s +2026-04-12 13:20:42.814694: +2026-04-12 13:20:42.816871: Epoch 1781 +2026-04-12 13:20:42.818855: Current learning rate: 0.00588 +2026-04-12 13:22:24.062167: train_loss -0.3861 +2026-04-12 13:22:24.066551: val_loss -0.375 +2026-04-12 13:22:24.068043: Pseudo dice [0.288, 0.0, 0.8287, 0.5683, 0.3456, 0.7545, 0.7546] +2026-04-12 13:22:24.069574: Epoch time: 101.25 s +2026-04-12 13:22:25.229585: +2026-04-12 13:22:25.231250: Epoch 1782 +2026-04-12 13:22:25.232683: Current learning rate: 0.00588 +2026-04-12 13:24:06.323869: train_loss -0.3931 +2026-04-12 13:24:06.328195: val_loss -0.3148 +2026-04-12 13:24:06.329850: Pseudo dice [0.0505, 0.0, 0.3117, 0.7746, 0.3271, 0.7254, 0.7189] +2026-04-12 13:24:06.331258: Epoch time: 101.1 s +2026-04-12 13:24:07.517247: +2026-04-12 13:24:07.519058: Epoch 1783 +2026-04-12 13:24:07.520412: Current learning rate: 0.00588 +2026-04-12 13:25:48.618217: train_loss -0.4053 +2026-04-12 13:25:48.623863: val_loss -0.3529 +2026-04-12 13:25:48.625463: Pseudo dice [0.4257, 0.0, 0.75, 0.3193, 0.3527, 0.7649, 0.6739] +2026-04-12 13:25:48.627454: Epoch time: 101.1 s +2026-04-12 13:25:49.796032: +2026-04-12 13:25:49.797528: Epoch 1784 +2026-04-12 13:25:49.798892: Current learning rate: 0.00588 +2026-04-12 13:27:31.022843: train_loss -0.3854 +2026-04-12 13:27:31.029898: val_loss -0.3176 +2026-04-12 13:27:31.031584: Pseudo dice [0.368, 0.0, 0.7141, 0.0002, 0.2322, 0.5179, 0.4791] +2026-04-12 13:27:31.033824: Epoch time: 101.23 s +2026-04-12 13:27:32.210651: +2026-04-12 13:27:32.212728: Epoch 1785 +2026-04-12 13:27:32.214459: Current learning rate: 0.00587 +2026-04-12 13:29:13.705027: train_loss -0.3981 +2026-04-12 13:29:13.711304: val_loss -0.393 +2026-04-12 13:29:13.713418: Pseudo dice [0.3728, 0.0, 0.6536, 0.7869, 0.3787, 0.5559, 0.7497] +2026-04-12 13:29:13.715117: Epoch time: 101.5 s +2026-04-12 13:29:14.900315: +2026-04-12 13:29:14.902164: Epoch 1786 +2026-04-12 13:29:14.903865: Current learning rate: 0.00587 +2026-04-12 13:30:56.189002: train_loss -0.3852 +2026-04-12 13:30:56.193006: val_loss -0.3305 +2026-04-12 13:30:56.194384: Pseudo dice [0.4981, 0.0, 0.2947, 0.2658, 0.2149, 0.3516, 0.8546] +2026-04-12 13:30:56.195677: Epoch time: 101.29 s +2026-04-12 13:30:57.370410: +2026-04-12 13:30:57.372612: Epoch 1787 +2026-04-12 13:30:57.374126: Current learning rate: 0.00587 +2026-04-12 13:32:38.864881: train_loss -0.4115 +2026-04-12 13:32:38.870483: val_loss -0.3343 +2026-04-12 13:32:38.872456: Pseudo dice [0.0112, 0.0, 0.5181, 0.0049, 0.2851, 0.6881, 0.515] +2026-04-12 13:32:38.873824: Epoch time: 101.5 s +2026-04-12 13:32:40.043255: +2026-04-12 13:32:40.044939: Epoch 1788 +2026-04-12 13:32:40.046448: Current learning rate: 0.00587 +2026-04-12 13:34:21.555493: train_loss -0.3707 +2026-04-12 13:34:21.560424: val_loss -0.2871 +2026-04-12 13:34:21.562367: Pseudo dice [0.1425, 0.0, 0.539, 0.265, 0.1148, 0.3035, 0.2229] +2026-04-12 13:34:21.564173: Epoch time: 101.52 s +2026-04-12 13:34:22.741744: +2026-04-12 13:34:22.743677: Epoch 1789 +2026-04-12 13:34:22.745475: Current learning rate: 0.00587 +2026-04-12 13:36:04.118422: train_loss -0.3889 +2026-04-12 13:36:04.124880: val_loss -0.3463 +2026-04-12 13:36:04.126450: Pseudo dice [0.2736, 0.0, 0.6701, 0.7857, 0.354, 0.7697, 0.6037] +2026-04-12 13:36:04.128006: Epoch time: 101.38 s +2026-04-12 13:36:05.314830: +2026-04-12 13:36:05.317847: Epoch 1790 +2026-04-12 13:36:05.320477: Current learning rate: 0.00586 +2026-04-12 13:37:47.721247: train_loss -0.3939 +2026-04-12 13:37:47.727264: val_loss -0.2901 +2026-04-12 13:37:47.729506: Pseudo dice [0.3923, 0.0, 0.5391, 0.5369, 0.0769, 0.6626, 0.6677] +2026-04-12 13:37:47.732799: Epoch time: 102.41 s +2026-04-12 13:37:48.901514: +2026-04-12 13:37:48.903306: Epoch 1791 +2026-04-12 13:37:48.904921: Current learning rate: 0.00586 +2026-04-12 13:39:30.044606: train_loss -0.3753 +2026-04-12 13:39:30.050014: val_loss -0.3634 +2026-04-12 13:39:30.051944: Pseudo dice [0.5872, 0.0, 0.8211, 0.7472, 0.1395, 0.384, 0.8258] +2026-04-12 13:39:30.053660: Epoch time: 101.15 s +2026-04-12 13:39:31.240613: +2026-04-12 13:39:31.242951: Epoch 1792 +2026-04-12 13:39:31.244705: Current learning rate: 0.00586 +2026-04-12 13:41:12.647870: train_loss -0.3949 +2026-04-12 13:41:12.652206: val_loss -0.3562 +2026-04-12 13:41:12.653727: Pseudo dice [0.3496, 0.0, 0.7075, 0.7575, 0.3389, 0.7153, 0.799] +2026-04-12 13:41:12.655497: Epoch time: 101.41 s +2026-04-12 13:41:13.836073: +2026-04-12 13:41:13.837800: Epoch 1793 +2026-04-12 13:41:13.839304: Current learning rate: 0.00586 +2026-04-12 13:42:55.334997: train_loss -0.3997 +2026-04-12 13:42:55.340821: val_loss -0.3656 +2026-04-12 13:42:55.342511: Pseudo dice [0.6488, 0.0, 0.7421, 0.5461, 0.4649, 0.7469, 0.6353] +2026-04-12 13:42:55.344356: Epoch time: 101.5 s +2026-04-12 13:42:56.526881: +2026-04-12 13:42:56.528828: Epoch 1794 +2026-04-12 13:42:56.530388: Current learning rate: 0.00585 +2026-04-12 13:44:37.823727: train_loss -0.3867 +2026-04-12 13:44:37.828759: val_loss -0.3155 +2026-04-12 13:44:37.830297: Pseudo dice [0.4962, 0.0, 0.4681, 0.0, 0.3072, 0.3593, 0.3615] +2026-04-12 13:44:37.831651: Epoch time: 101.3 s +2026-04-12 13:44:39.009978: +2026-04-12 13:44:39.011578: Epoch 1795 +2026-04-12 13:44:39.013210: Current learning rate: 0.00585 +2026-04-12 13:46:20.540592: train_loss -0.3934 +2026-04-12 13:46:20.544755: val_loss -0.3576 +2026-04-12 13:46:20.546345: Pseudo dice [0.4244, 0.0, 0.727, 0.8697, 0.4675, 0.4046, 0.6656] +2026-04-12 13:46:20.547976: Epoch time: 101.53 s +2026-04-12 13:46:21.739066: +2026-04-12 13:46:21.746576: Epoch 1796 +2026-04-12 13:46:21.750963: Current learning rate: 0.00585 +2026-04-12 13:48:02.955742: train_loss -0.4068 +2026-04-12 13:48:02.961914: val_loss -0.3744 +2026-04-12 13:48:02.963779: Pseudo dice [0.2903, 0.0, 0.7439, 0.8368, 0.4422, 0.648, 0.7807] +2026-04-12 13:48:02.965605: Epoch time: 101.22 s +2026-04-12 13:48:04.144946: +2026-04-12 13:48:04.147199: Epoch 1797 +2026-04-12 13:48:04.148779: Current learning rate: 0.00585 +2026-04-12 13:49:45.077492: train_loss -0.3915 +2026-04-12 13:49:45.082098: val_loss -0.3405 +2026-04-12 13:49:45.083488: Pseudo dice [0.604, 0.0, 0.5298, 0.4281, 0.2109, 0.5182, 0.822] +2026-04-12 13:49:45.084781: Epoch time: 100.94 s +2026-04-12 13:49:46.279113: +2026-04-12 13:49:46.280732: Epoch 1798 +2026-04-12 13:49:46.282337: Current learning rate: 0.00584 +2026-04-12 13:51:27.283943: train_loss -0.3971 +2026-04-12 13:51:27.289104: val_loss -0.3495 +2026-04-12 13:51:27.290613: Pseudo dice [0.4329, 0.0, 0.8216, 0.5638, 0.291, 0.1771, 0.6297] +2026-04-12 13:51:27.292392: Epoch time: 101.01 s +2026-04-12 13:51:28.486954: +2026-04-12 13:51:28.488681: Epoch 1799 +2026-04-12 13:51:28.490179: Current learning rate: 0.00584 +2026-04-12 13:53:09.539263: train_loss -0.3838 +2026-04-12 13:53:09.543772: val_loss -0.3138 +2026-04-12 13:53:09.545408: Pseudo dice [0.0661, 0.0, 0.5681, 0.5149, 0.1515, 0.7944, 0.8133] +2026-04-12 13:53:09.547164: Epoch time: 101.06 s +2026-04-12 13:53:12.340619: +2026-04-12 13:53:12.342231: Epoch 1800 +2026-04-12 13:53:12.343612: Current learning rate: 0.00584 +2026-04-12 13:54:53.737481: train_loss -0.3724 +2026-04-12 13:54:53.742331: val_loss -0.3613 +2026-04-12 13:54:53.744627: Pseudo dice [0.5416, 0.0, 0.6372, 0.847, 0.4574, 0.295, 0.7199] +2026-04-12 13:54:53.746083: Epoch time: 101.4 s +2026-04-12 13:54:54.925912: +2026-04-12 13:54:54.927896: Epoch 1801 +2026-04-12 13:54:54.929563: Current learning rate: 0.00584 +2026-04-12 13:56:36.403007: train_loss -0.3507 +2026-04-12 13:56:36.408519: val_loss -0.3413 +2026-04-12 13:56:36.409997: Pseudo dice [0.0127, 0.0, 0.6695, 0.8017, 0.3917, 0.4232, 0.8214] +2026-04-12 13:56:36.411638: Epoch time: 101.48 s +2026-04-12 13:56:37.594322: +2026-04-12 13:56:37.596349: Epoch 1802 +2026-04-12 13:56:37.598294: Current learning rate: 0.00583 +2026-04-12 13:58:18.885637: train_loss -0.382 +2026-04-12 13:58:18.889922: val_loss -0.358 +2026-04-12 13:58:18.892143: Pseudo dice [0.4557, 0.0, 0.8105, 0.6867, 0.3655, 0.8045, 0.7014] +2026-04-12 13:58:18.893893: Epoch time: 101.29 s +2026-04-12 13:58:20.090740: +2026-04-12 13:58:20.092554: Epoch 1803 +2026-04-12 13:58:20.094233: Current learning rate: 0.00583 +2026-04-12 14:00:01.569140: train_loss -0.3949 +2026-04-12 14:00:01.574842: val_loss -0.3897 +2026-04-12 14:00:01.576766: Pseudo dice [0.4636, 0.0, 0.8249, 0.837, 0.3541, 0.8044, 0.6929] +2026-04-12 14:00:01.578318: Epoch time: 101.48 s +2026-04-12 14:00:02.742856: +2026-04-12 14:00:02.744945: Epoch 1804 +2026-04-12 14:00:02.746497: Current learning rate: 0.00583 +2026-04-12 14:01:43.921693: train_loss -0.3545 +2026-04-12 14:01:43.926780: val_loss -0.3421 +2026-04-12 14:01:43.930062: Pseudo dice [0.0, 0.0, 0.7517, 0.7855, 0.4007, 0.5589, 0.4816] +2026-04-12 14:01:43.932752: Epoch time: 101.18 s +2026-04-12 14:01:45.118797: +2026-04-12 14:01:45.121248: Epoch 1805 +2026-04-12 14:01:45.122919: Current learning rate: 0.00583 +2026-04-12 14:03:26.509162: train_loss -0.3627 +2026-04-12 14:03:26.513579: val_loss -0.341 +2026-04-12 14:03:26.515060: Pseudo dice [0.0, 0.0, 0.3876, 0.8002, 0.3604, 0.5966, 0.6328] +2026-04-12 14:03:26.517100: Epoch time: 101.39 s +2026-04-12 14:03:27.700343: +2026-04-12 14:03:27.702188: Epoch 1806 +2026-04-12 14:03:27.704024: Current learning rate: 0.00582 +2026-04-12 14:05:08.990895: train_loss -0.3851 +2026-04-12 14:05:08.996902: val_loss -0.3345 +2026-04-12 14:05:08.999208: Pseudo dice [0.0, 0.0, 0.8362, 0.6864, 0.26, 0.6723, 0.6575] +2026-04-12 14:05:09.001300: Epoch time: 101.29 s +2026-04-12 14:05:10.281255: +2026-04-12 14:05:10.283483: Epoch 1807 +2026-04-12 14:05:10.285437: Current learning rate: 0.00582 +2026-04-12 14:06:51.603773: train_loss -0.395 +2026-04-12 14:06:51.608057: val_loss -0.3607 +2026-04-12 14:06:51.609661: Pseudo dice [0.0, 0.0, 0.6297, 0.7463, 0.196, 0.654, 0.7876] +2026-04-12 14:06:51.610952: Epoch time: 101.33 s +2026-04-12 14:06:52.787576: +2026-04-12 14:06:52.789051: Epoch 1808 +2026-04-12 14:06:52.790598: Current learning rate: 0.00582 +2026-04-12 14:08:34.200449: train_loss -0.3978 +2026-04-12 14:08:34.205230: val_loss -0.3339 +2026-04-12 14:08:34.207228: Pseudo dice [0.0, 0.0, 0.5284, 0.6036, 0.2769, 0.8022, 0.7906] +2026-04-12 14:08:34.208774: Epoch time: 101.42 s +2026-04-12 14:08:35.423771: +2026-04-12 14:08:35.426100: Epoch 1809 +2026-04-12 14:08:35.427799: Current learning rate: 0.00582 +2026-04-12 14:10:16.683349: train_loss -0.4087 +2026-04-12 14:10:16.687884: val_loss -0.3499 +2026-04-12 14:10:16.689356: Pseudo dice [0.0, 0.0, 0.7386, 0.7124, 0.397, 0.7745, 0.5224] +2026-04-12 14:10:16.690701: Epoch time: 101.26 s +2026-04-12 14:10:18.855020: +2026-04-12 14:10:18.856641: Epoch 1810 +2026-04-12 14:10:18.858328: Current learning rate: 0.00581 +2026-04-12 14:12:00.379540: train_loss -0.3938 +2026-04-12 14:12:00.383765: val_loss -0.3608 +2026-04-12 14:12:00.385281: Pseudo dice [0.0, 0.0, 0.7392, 0.7072, 0.3576, 0.6707, 0.7997] +2026-04-12 14:12:00.386795: Epoch time: 101.53 s +2026-04-12 14:12:01.563725: +2026-04-12 14:12:01.565389: Epoch 1811 +2026-04-12 14:12:01.566617: Current learning rate: 0.00581 +2026-04-12 14:13:43.108439: train_loss -0.4004 +2026-04-12 14:13:43.113465: val_loss -0.3353 +2026-04-12 14:13:43.115811: Pseudo dice [0.0, 0.0, 0.4325, 0.7295, 0.1814, 0.7902, 0.7169] +2026-04-12 14:13:43.117571: Epoch time: 101.55 s +2026-04-12 14:13:44.309559: +2026-04-12 14:13:44.311076: Epoch 1812 +2026-04-12 14:13:44.312634: Current learning rate: 0.00581 +2026-04-12 14:15:25.417510: train_loss -0.4036 +2026-04-12 14:15:25.422120: val_loss -0.3771 +2026-04-12 14:15:25.423380: Pseudo dice [0.0, 0.0, 0.7477, 0.765, 0.5175, 0.7652, 0.7016] +2026-04-12 14:15:25.424933: Epoch time: 101.11 s +2026-04-12 14:15:26.620815: +2026-04-12 14:15:26.624198: Epoch 1813 +2026-04-12 14:15:26.625470: Current learning rate: 0.00581 +2026-04-12 14:17:07.804449: train_loss -0.4025 +2026-04-12 14:17:07.808561: val_loss -0.3235 +2026-04-12 14:17:07.810032: Pseudo dice [0.0, 0.0, 0.5309, 0.6644, 0.4364, 0.4861, 0.6837] +2026-04-12 14:17:07.811337: Epoch time: 101.19 s +2026-04-12 14:17:09.016240: +2026-04-12 14:17:09.017732: Epoch 1814 +2026-04-12 14:17:09.019181: Current learning rate: 0.00581 +2026-04-12 14:18:50.441054: train_loss -0.3876 +2026-04-12 14:18:50.446930: val_loss -0.3482 +2026-04-12 14:18:50.450717: Pseudo dice [0.0, 0.0, 0.7584, 0.802, 0.1548, 0.703, 0.5699] +2026-04-12 14:18:50.452735: Epoch time: 101.43 s +2026-04-12 14:18:51.612712: +2026-04-12 14:18:51.614716: Epoch 1815 +2026-04-12 14:18:51.616525: Current learning rate: 0.0058 +2026-04-12 14:20:33.001208: train_loss -0.396 +2026-04-12 14:20:33.006207: val_loss -0.362 +2026-04-12 14:20:33.008071: Pseudo dice [0.0, 0.0, 0.6429, 0.7849, 0.3422, 0.8073, 0.6879] +2026-04-12 14:20:33.009784: Epoch time: 101.39 s +2026-04-12 14:20:34.169489: +2026-04-12 14:20:34.172934: Epoch 1816 +2026-04-12 14:20:34.174502: Current learning rate: 0.0058 +2026-04-12 14:22:15.773631: train_loss -0.3842 +2026-04-12 14:22:15.778514: val_loss -0.3584 +2026-04-12 14:22:15.780267: Pseudo dice [0.0, 0.0, 0.6177, 0.8285, 0.5073, 0.6775, 0.815] +2026-04-12 14:22:15.785928: Epoch time: 101.61 s +2026-04-12 14:22:16.975616: +2026-04-12 14:22:16.977775: Epoch 1817 +2026-04-12 14:22:16.979357: Current learning rate: 0.0058 +2026-04-12 14:23:58.441671: train_loss -0.39 +2026-04-12 14:23:58.446420: val_loss -0.3449 +2026-04-12 14:23:58.448371: Pseudo dice [0.0, 0.0, 0.7829, 0.263, 0.4384, 0.685, 0.8779] +2026-04-12 14:23:58.449737: Epoch time: 101.47 s +2026-04-12 14:23:59.641349: +2026-04-12 14:23:59.643193: Epoch 1818 +2026-04-12 14:23:59.645087: Current learning rate: 0.0058 +2026-04-12 14:25:40.963358: train_loss -0.3971 +2026-04-12 14:25:40.967620: val_loss -0.348 +2026-04-12 14:25:40.968977: Pseudo dice [0.0, 0.0, 0.2621, 0.8884, 0.1173, 0.8144, 0.8771] +2026-04-12 14:25:40.970252: Epoch time: 101.33 s +2026-04-12 14:25:42.130428: +2026-04-12 14:25:42.131913: Epoch 1819 +2026-04-12 14:25:42.133419: Current learning rate: 0.00579 +2026-04-12 14:27:23.707128: train_loss -0.3716 +2026-04-12 14:27:23.711107: val_loss -0.3393 +2026-04-12 14:27:23.712366: Pseudo dice [0.0, 0.0, 0.566, 0.2982, 0.4461, 0.42, 0.4585] +2026-04-12 14:27:23.713552: Epoch time: 101.58 s +2026-04-12 14:27:24.902061: +2026-04-12 14:27:24.903806: Epoch 1820 +2026-04-12 14:27:24.905354: Current learning rate: 0.00579 +2026-04-12 14:29:06.316783: train_loss -0.3569 +2026-04-12 14:29:06.322377: val_loss -0.3328 +2026-04-12 14:29:06.325268: Pseudo dice [0.0, 0.0, 0.565, 0.6181, 0.2616, 0.4936, 0.4586] +2026-04-12 14:29:06.328603: Epoch time: 101.42 s +2026-04-12 14:29:07.501396: +2026-04-12 14:29:07.504077: Epoch 1821 +2026-04-12 14:29:07.505972: Current learning rate: 0.00579 +2026-04-12 14:30:48.973104: train_loss -0.3918 +2026-04-12 14:30:48.977937: val_loss -0.3493 +2026-04-12 14:30:48.979481: Pseudo dice [0.0, 0.0, 0.5981, 0.8197, 0.3289, 0.7026, 0.8757] +2026-04-12 14:30:48.980907: Epoch time: 101.47 s +2026-04-12 14:30:50.171230: +2026-04-12 14:30:50.172880: Epoch 1822 +2026-04-12 14:30:50.174482: Current learning rate: 0.00579 +2026-04-12 14:32:31.712378: train_loss -0.3698 +2026-04-12 14:32:31.717765: val_loss -0.3346 +2026-04-12 14:32:31.719865: Pseudo dice [0.0, 0.0, 0.6267, 0.6769, 0.1348, 0.8398, 0.8298] +2026-04-12 14:32:31.721728: Epoch time: 101.54 s +2026-04-12 14:32:32.893270: +2026-04-12 14:32:32.894639: Epoch 1823 +2026-04-12 14:32:32.896150: Current learning rate: 0.00578 +2026-04-12 14:34:14.429577: train_loss -0.3925 +2026-04-12 14:34:14.434511: val_loss -0.3211 +2026-04-12 14:34:14.437061: Pseudo dice [0.0, 0.0, 0.7306, 0.8448, 0.2668, 0.6807, 0.3547] +2026-04-12 14:34:14.439034: Epoch time: 101.54 s +2026-04-12 14:34:15.621008: +2026-04-12 14:34:15.631067: Epoch 1824 +2026-04-12 14:34:15.632579: Current learning rate: 0.00578 +2026-04-12 14:35:57.147377: train_loss -0.3942 +2026-04-12 14:35:57.152163: val_loss -0.3574 +2026-04-12 14:35:57.153818: Pseudo dice [0.0, 0.0, 0.7794, 0.8, 0.4595, 0.7972, 0.8275] +2026-04-12 14:35:57.155596: Epoch time: 101.53 s +2026-04-12 14:35:58.334311: +2026-04-12 14:35:58.336157: Epoch 1825 +2026-04-12 14:35:58.337825: Current learning rate: 0.00578 +2026-04-12 14:37:39.522280: train_loss -0.3989 +2026-04-12 14:37:39.526776: val_loss -0.3225 +2026-04-12 14:37:39.528897: Pseudo dice [0.0, 0.0, 0.6535, 0.4644, 0.2943, 0.6053, 0.7496] +2026-04-12 14:37:39.530780: Epoch time: 101.19 s +2026-04-12 14:37:40.703368: +2026-04-12 14:37:40.705333: Epoch 1826 +2026-04-12 14:37:40.707277: Current learning rate: 0.00578 +2026-04-12 14:39:21.780961: train_loss -0.3865 +2026-04-12 14:39:21.785346: val_loss -0.3394 +2026-04-12 14:39:21.786925: Pseudo dice [0.0, 0.0, 0.7162, 0.7802, 0.4664, 0.3784, 0.4249] +2026-04-12 14:39:21.788437: Epoch time: 101.08 s +2026-04-12 14:39:22.982476: +2026-04-12 14:39:22.984200: Epoch 1827 +2026-04-12 14:39:22.985623: Current learning rate: 0.00577 +2026-04-12 14:41:04.184733: train_loss -0.3911 +2026-04-12 14:41:04.191193: val_loss -0.3451 +2026-04-12 14:41:04.198693: Pseudo dice [0.0, 0.0, 0.4813, 0.7058, 0.2454, 0.7818, 0.826] +2026-04-12 14:41:04.200659: Epoch time: 101.21 s +2026-04-12 14:41:05.383321: +2026-04-12 14:41:05.385148: Epoch 1828 +2026-04-12 14:41:05.386980: Current learning rate: 0.00577 +2026-04-12 14:42:46.543298: train_loss -0.3893 +2026-04-12 14:42:46.547586: val_loss -0.3766 +2026-04-12 14:42:46.548999: Pseudo dice [0.0, 0.0, 0.675, 0.6166, 0.2844, 0.8352, 0.684] +2026-04-12 14:42:46.550480: Epoch time: 101.16 s +2026-04-12 14:42:47.721147: +2026-04-12 14:42:47.722622: Epoch 1829 +2026-04-12 14:42:47.723858: Current learning rate: 0.00577 +2026-04-12 14:44:28.941237: train_loss -0.4009 +2026-04-12 14:44:28.947043: val_loss -0.3305 +2026-04-12 14:44:28.948524: Pseudo dice [0.0, 0.0, 0.5346, 0.6905, 0.0823, 0.8579, 0.8036] +2026-04-12 14:44:28.950037: Epoch time: 101.22 s +2026-04-12 14:44:31.096582: +2026-04-12 14:44:31.098661: Epoch 1830 +2026-04-12 14:44:31.100169: Current learning rate: 0.00577 +2026-04-12 14:46:12.669574: train_loss -0.3935 +2026-04-12 14:46:12.675504: val_loss -0.3598 +2026-04-12 14:46:12.677570: Pseudo dice [0.0, 0.0, 0.6429, 0.8465, 0.226, 0.6039, 0.8505] +2026-04-12 14:46:12.680014: Epoch time: 101.58 s +2026-04-12 14:46:13.851806: +2026-04-12 14:46:13.853580: Epoch 1831 +2026-04-12 14:46:13.855134: Current learning rate: 0.00576 +2026-04-12 14:47:55.258235: train_loss -0.3728 +2026-04-12 14:47:55.263393: val_loss -0.3008 +2026-04-12 14:47:55.265461: Pseudo dice [0.0, 0.0, 0.4718, 0.3479, 0.2555, 0.8757, 0.5361] +2026-04-12 14:47:55.267736: Epoch time: 101.41 s +2026-04-12 14:47:56.548764: +2026-04-12 14:47:56.550194: Epoch 1832 +2026-04-12 14:47:56.551770: Current learning rate: 0.00576 +2026-04-12 14:49:38.147811: train_loss -0.3723 +2026-04-12 14:49:38.152793: val_loss -0.3357 +2026-04-12 14:49:38.154369: Pseudo dice [0.0005, 0.0, 0.341, 0.4271, 0.3943, 0.6267, 0.6334] +2026-04-12 14:49:38.156830: Epoch time: 101.6 s +2026-04-12 14:49:39.343920: +2026-04-12 14:49:39.345287: Epoch 1833 +2026-04-12 14:49:39.347114: Current learning rate: 0.00576 +2026-04-12 14:51:20.882678: train_loss -0.3787 +2026-04-12 14:51:20.887851: val_loss -0.354 +2026-04-12 14:51:20.890062: Pseudo dice [0.0, 0.0, 0.7136, 0.8048, 0.3619, 0.4666, 0.7881] +2026-04-12 14:51:20.891843: Epoch time: 101.54 s +2026-04-12 14:51:22.061508: +2026-04-12 14:51:22.064703: Epoch 1834 +2026-04-12 14:51:22.066979: Current learning rate: 0.00576 +2026-04-12 14:53:03.435765: train_loss -0.3807 +2026-04-12 14:53:03.441120: val_loss -0.3437 +2026-04-12 14:53:03.443043: Pseudo dice [0.0, 0.0, 0.5832, 0.7483, 0.3266, 0.549, 0.7965] +2026-04-12 14:53:03.445100: Epoch time: 101.38 s +2026-04-12 14:53:04.639381: +2026-04-12 14:53:04.641191: Epoch 1835 +2026-04-12 14:53:04.642706: Current learning rate: 0.00576 +2026-04-12 14:54:46.067740: train_loss -0.3979 +2026-04-12 14:54:46.072386: val_loss -0.3214 +2026-04-12 14:54:46.074664: Pseudo dice [0.0, 0.0, 0.6816, 0.7634, 0.1892, 0.7836, 0.7938] +2026-04-12 14:54:46.076316: Epoch time: 101.43 s +2026-04-12 14:54:47.256757: +2026-04-12 14:54:47.258470: Epoch 1836 +2026-04-12 14:54:47.259969: Current learning rate: 0.00575 +2026-04-12 14:56:28.554853: train_loss -0.4013 +2026-04-12 14:56:28.559630: val_loss -0.3371 +2026-04-12 14:56:28.562111: Pseudo dice [0.5124, 0.0, 0.7955, 0.7789, 0.239, 0.5388, 0.8421] +2026-04-12 14:56:28.564354: Epoch time: 101.3 s +2026-04-12 14:56:29.744661: +2026-04-12 14:56:29.746310: Epoch 1837 +2026-04-12 14:56:29.747735: Current learning rate: 0.00575 +2026-04-12 14:58:11.236580: train_loss -0.3855 +2026-04-12 14:58:11.240750: val_loss -0.2587 +2026-04-12 14:58:11.242167: Pseudo dice [0.0729, 0.0, 0.4256, 0.3, 0.1772, 0.7007, 0.5992] +2026-04-12 14:58:11.243813: Epoch time: 101.49 s +2026-04-12 14:58:12.422184: +2026-04-12 14:58:12.423679: Epoch 1838 +2026-04-12 14:58:12.425080: Current learning rate: 0.00575 +2026-04-12 14:59:53.783296: train_loss -0.3776 +2026-04-12 14:59:53.788762: val_loss -0.3452 +2026-04-12 14:59:53.791312: Pseudo dice [0.4881, 0.0, 0.8108, 0.8206, 0.3851, 0.1693, 0.7704] +2026-04-12 14:59:53.793544: Epoch time: 101.36 s +2026-04-12 14:59:54.999610: +2026-04-12 14:59:55.001643: Epoch 1839 +2026-04-12 14:59:55.003297: Current learning rate: 0.00575 +2026-04-12 15:01:36.559350: train_loss -0.398 +2026-04-12 15:01:36.565270: val_loss -0.3501 +2026-04-12 15:01:36.566893: Pseudo dice [0.4291, 0.0, 0.6048, 0.516, 0.425, 0.3226, 0.4049] +2026-04-12 15:01:36.568683: Epoch time: 101.56 s +2026-04-12 15:01:37.754212: +2026-04-12 15:01:37.756088: Epoch 1840 +2026-04-12 15:01:37.757900: Current learning rate: 0.00574 +2026-04-12 15:03:19.323909: train_loss -0.3924 +2026-04-12 15:03:19.330307: val_loss -0.337 +2026-04-12 15:03:19.332203: Pseudo dice [0.2288, 0.0, 0.7468, 0.854, 0.2648, 0.6277, 0.8603] +2026-04-12 15:03:19.334169: Epoch time: 101.57 s +2026-04-12 15:03:20.517552: +2026-04-12 15:03:20.519005: Epoch 1841 +2026-04-12 15:03:20.520558: Current learning rate: 0.00574 +2026-04-12 15:05:01.782711: train_loss -0.3975 +2026-04-12 15:05:01.788165: val_loss -0.3758 +2026-04-12 15:05:01.790007: Pseudo dice [0.1593, 0.0, 0.5893, 0.7913, 0.469, 0.4893, 0.8329] +2026-04-12 15:05:01.791338: Epoch time: 101.27 s +2026-04-12 15:05:02.969627: +2026-04-12 15:05:02.971084: Epoch 1842 +2026-04-12 15:05:02.972667: Current learning rate: 0.00574 +2026-04-12 15:06:44.056433: train_loss -0.4063 +2026-04-12 15:06:44.061669: val_loss -0.336 +2026-04-12 15:06:44.063487: Pseudo dice [0.3305, 0.0, 0.4371, 0.7443, 0.2537, 0.4591, 0.8132] +2026-04-12 15:06:44.064909: Epoch time: 101.09 s +2026-04-12 15:06:45.266687: +2026-04-12 15:06:45.268401: Epoch 1843 +2026-04-12 15:06:45.270002: Current learning rate: 0.00574 +2026-04-12 15:08:26.773957: train_loss -0.4069 +2026-04-12 15:08:26.778911: val_loss -0.3599 +2026-04-12 15:08:26.780666: Pseudo dice [0.2097, 0.0, 0.7081, 0.9136, 0.3107, 0.3506, 0.833] +2026-04-12 15:08:26.782336: Epoch time: 101.51 s +2026-04-12 15:08:27.974974: +2026-04-12 15:08:27.978211: Epoch 1844 +2026-04-12 15:08:27.984117: Current learning rate: 0.00573 +2026-04-12 15:10:09.568289: train_loss -0.3937 +2026-04-12 15:10:09.573934: val_loss -0.3721 +2026-04-12 15:10:09.575643: Pseudo dice [0.6544, 0.0, 0.4292, 0.0945, 0.4201, 0.6868, 0.7548] +2026-04-12 15:10:09.577568: Epoch time: 101.6 s +2026-04-12 15:10:10.764963: +2026-04-12 15:10:10.766671: Epoch 1845 +2026-04-12 15:10:10.768155: Current learning rate: 0.00573 +2026-04-12 15:11:52.216369: train_loss -0.3906 +2026-04-12 15:11:52.239439: val_loss -0.3248 +2026-04-12 15:11:52.241183: Pseudo dice [0.0176, 0.0, 0.5966, 0.8437, 0.2203, 0.679, 0.7492] +2026-04-12 15:11:52.246253: Epoch time: 101.45 s +2026-04-12 15:11:53.430613: +2026-04-12 15:11:53.432462: Epoch 1846 +2026-04-12 15:11:53.434346: Current learning rate: 0.00573 +2026-04-12 15:13:35.083512: train_loss -0.3798 +2026-04-12 15:13:35.088025: val_loss -0.4052 +2026-04-12 15:13:35.089231: Pseudo dice [0.6768, 0.0, 0.6422, 0.879, 0.3578, 0.6078, 0.8004] +2026-04-12 15:13:35.090674: Epoch time: 101.66 s +2026-04-12 15:13:36.264129: +2026-04-12 15:13:36.265934: Epoch 1847 +2026-04-12 15:13:36.267620: Current learning rate: 0.00573 +2026-04-12 15:15:17.897818: train_loss -0.3745 +2026-04-12 15:15:17.902285: val_loss -0.349 +2026-04-12 15:15:17.903646: Pseudo dice [0.1213, 0.0, 0.5999, 0.6661, 0.1916, 0.6236, 0.7642] +2026-04-12 15:15:17.905134: Epoch time: 101.64 s +2026-04-12 15:15:19.100010: +2026-04-12 15:15:19.102036: Epoch 1848 +2026-04-12 15:15:19.103907: Current learning rate: 0.00572 +2026-04-12 15:17:00.609870: train_loss -0.3774 +2026-04-12 15:17:00.615124: val_loss -0.3025 +2026-04-12 15:17:00.617290: Pseudo dice [0.0, 0.0, 0.4514, 0.4811, 0.2791, 0.5577, 0.7879] +2026-04-12 15:17:00.619230: Epoch time: 101.51 s +2026-04-12 15:17:01.818524: +2026-04-12 15:17:01.820116: Epoch 1849 +2026-04-12 15:17:01.821510: Current learning rate: 0.00572 +2026-04-12 15:18:43.577583: train_loss -0.393 +2026-04-12 15:18:43.584657: val_loss -0.3743 +2026-04-12 15:18:43.587286: Pseudo dice [0.0, 0.0, 0.7359, 0.7917, 0.3692, 0.6839, 0.7912] +2026-04-12 15:18:43.589818: Epoch time: 101.76 s +2026-04-12 15:18:47.375962: +2026-04-12 15:18:47.377556: Epoch 1850 +2026-04-12 15:18:47.379162: Current learning rate: 0.00572 +2026-04-12 15:20:29.017633: train_loss -0.4032 +2026-04-12 15:20:29.022265: val_loss -0.3224 +2026-04-12 15:20:29.023814: Pseudo dice [0.0, 0.0, 0.5797, 0.8459, 0.1368, 0.7563, 0.8666] +2026-04-12 15:20:29.025706: Epoch time: 101.65 s +2026-04-12 15:20:30.206573: +2026-04-12 15:20:30.208641: Epoch 1851 +2026-04-12 15:20:30.210074: Current learning rate: 0.00572 +2026-04-12 15:22:11.986125: train_loss -0.3913 +2026-04-12 15:22:11.990872: val_loss -0.3508 +2026-04-12 15:22:11.993175: Pseudo dice [0.0, 0.0, 0.7765, 0.5629, 0.3783, 0.7425, 0.8725] +2026-04-12 15:22:11.994992: Epoch time: 101.78 s +2026-04-12 15:22:13.171270: +2026-04-12 15:22:13.173059: Epoch 1852 +2026-04-12 15:22:13.174981: Current learning rate: 0.00571 +2026-04-12 15:23:54.808708: train_loss -0.4011 +2026-04-12 15:23:54.815521: val_loss -0.3591 +2026-04-12 15:23:54.817738: Pseudo dice [0.0, 0.0, 0.5875, 0.5209, 0.464, 0.7686, 0.6095] +2026-04-12 15:23:54.819901: Epoch time: 101.64 s +2026-04-12 15:23:56.011549: +2026-04-12 15:23:56.013028: Epoch 1853 +2026-04-12 15:23:56.014522: Current learning rate: 0.00571 +2026-04-12 15:25:37.658964: train_loss -0.3947 +2026-04-12 15:25:37.663903: val_loss -0.3772 +2026-04-12 15:25:37.665450: Pseudo dice [0.2603, 0.0, 0.4454, 0.8952, 0.3682, 0.7868, 0.8771] +2026-04-12 15:25:37.667354: Epoch time: 101.65 s +2026-04-12 15:25:38.863483: +2026-04-12 15:25:38.865240: Epoch 1854 +2026-04-12 15:25:38.866954: Current learning rate: 0.00571 +2026-04-12 15:27:20.722527: train_loss -0.3773 +2026-04-12 15:27:20.728731: val_loss -0.2995 +2026-04-12 15:27:20.730279: Pseudo dice [0.2651, 0.0, 0.6117, 0.3576, 0.0986, 0.4725, 0.5103] +2026-04-12 15:27:20.731644: Epoch time: 101.86 s +2026-04-12 15:27:21.916142: +2026-04-12 15:27:21.918036: Epoch 1855 +2026-04-12 15:27:21.919514: Current learning rate: 0.00571 +2026-04-12 15:29:03.493584: train_loss -0.3728 +2026-04-12 15:29:03.498991: val_loss -0.3317 +2026-04-12 15:29:03.500566: Pseudo dice [0.3703, 0.0, 0.644, 0.6836, 0.2102, 0.2631, 0.632] +2026-04-12 15:29:03.501947: Epoch time: 101.58 s +2026-04-12 15:29:04.670721: +2026-04-12 15:29:04.672524: Epoch 1856 +2026-04-12 15:29:04.674085: Current learning rate: 0.0057 +2026-04-12 15:30:46.430628: train_loss -0.3711 +2026-04-12 15:30:46.435506: val_loss -0.3284 +2026-04-12 15:30:46.437451: Pseudo dice [0.0, 0.0, 0.774, 0.7269, 0.228, 0.633, 0.7202] +2026-04-12 15:30:46.438831: Epoch time: 101.76 s +2026-04-12 15:30:47.633203: +2026-04-12 15:30:47.634873: Epoch 1857 +2026-04-12 15:30:47.636612: Current learning rate: 0.0057 +2026-04-12 15:32:29.390640: train_loss -0.4006 +2026-04-12 15:32:29.395078: val_loss -0.3277 +2026-04-12 15:32:29.396756: Pseudo dice [0.0, 0.0, 0.7424, 0.7206, 0.3195, 0.535, 0.8516] +2026-04-12 15:32:29.398304: Epoch time: 101.76 s +2026-04-12 15:32:30.597135: +2026-04-12 15:32:30.598630: Epoch 1858 +2026-04-12 15:32:30.600207: Current learning rate: 0.0057 +2026-04-12 15:34:12.173265: train_loss -0.3905 +2026-04-12 15:34:12.178176: val_loss -0.3878 +2026-04-12 15:34:12.179476: Pseudo dice [0.0, 0.0, 0.7171, 0.3984, 0.3198, 0.8247, 0.881] +2026-04-12 15:34:12.180957: Epoch time: 101.58 s +2026-04-12 15:34:13.389517: +2026-04-12 15:34:13.391813: Epoch 1859 +2026-04-12 15:34:13.393833: Current learning rate: 0.0057 +2026-04-12 15:35:55.001741: train_loss -0.3909 +2026-04-12 15:35:55.005802: val_loss -0.3647 +2026-04-12 15:35:55.007238: Pseudo dice [0.1348, 0.0, 0.4874, 0.4289, 0.1407, 0.6643, 0.8982] +2026-04-12 15:35:55.009029: Epoch time: 101.62 s +2026-04-12 15:35:56.187414: +2026-04-12 15:35:56.188922: Epoch 1860 +2026-04-12 15:35:56.190338: Current learning rate: 0.0057 +2026-04-12 15:37:37.860999: train_loss -0.4069 +2026-04-12 15:37:37.866458: val_loss -0.3289 +2026-04-12 15:37:37.868125: Pseudo dice [0.1815, 0.0, 0.5535, 0.5387, 0.2454, 0.469, 0.6209] +2026-04-12 15:37:37.869779: Epoch time: 101.68 s +2026-04-12 15:37:39.050651: +2026-04-12 15:37:39.052342: Epoch 1861 +2026-04-12 15:37:39.053807: Current learning rate: 0.00569 +2026-04-12 15:39:20.841928: train_loss -0.3765 +2026-04-12 15:39:20.847354: val_loss -0.2695 +2026-04-12 15:39:20.849189: Pseudo dice [0.0, 0.0, 0.2884, 0.3448, 0.0696, 0.0194, 0.8122] +2026-04-12 15:39:20.850577: Epoch time: 101.79 s +2026-04-12 15:39:22.033410: +2026-04-12 15:39:22.034859: Epoch 1862 +2026-04-12 15:39:22.036411: Current learning rate: 0.00569 +2026-04-12 15:41:03.930898: train_loss -0.3504 +2026-04-12 15:41:03.955824: val_loss -0.3451 +2026-04-12 15:41:03.957726: Pseudo dice [0.2547, 0.0, 0.7624, 0.8349, 0.3168, 0.2209, 0.7363] +2026-04-12 15:41:03.959523: Epoch time: 101.9 s +2026-04-12 15:41:05.170324: +2026-04-12 15:41:05.172110: Epoch 1863 +2026-04-12 15:41:05.173736: Current learning rate: 0.00569 +2026-04-12 15:42:47.007818: train_loss -0.3907 +2026-04-12 15:42:47.012356: val_loss -0.3708 +2026-04-12 15:42:47.013732: Pseudo dice [0.3751, 0.0, 0.6606, 0.5078, 0.2464, 0.7312, 0.461] +2026-04-12 15:42:47.015217: Epoch time: 101.84 s +2026-04-12 15:42:48.185999: +2026-04-12 15:42:48.187691: Epoch 1864 +2026-04-12 15:42:48.189145: Current learning rate: 0.00569 +2026-04-12 15:44:30.149314: train_loss -0.4077 +2026-04-12 15:44:30.154075: val_loss -0.3192 +2026-04-12 15:44:30.155556: Pseudo dice [0.0258, 0.0, 0.37, 0.3837, 0.3588, 0.7528, 0.7554] +2026-04-12 15:44:30.156828: Epoch time: 101.97 s +2026-04-12 15:44:32.059080: +2026-04-12 15:44:32.060651: Epoch 1865 +2026-04-12 15:44:32.061955: Current learning rate: 0.00568 +2026-04-12 15:46:13.945414: train_loss -0.3829 +2026-04-12 15:46:13.950429: val_loss -0.3329 +2026-04-12 15:46:13.952141: Pseudo dice [0.2029, 0.0, 0.3683, 0.8291, 0.2613, 0.665, 0.785] +2026-04-12 15:46:13.953593: Epoch time: 101.89 s +2026-04-12 15:46:15.139563: +2026-04-12 15:46:15.141463: Epoch 1866 +2026-04-12 15:46:15.143073: Current learning rate: 0.00568 +2026-04-12 15:47:57.169247: train_loss -0.4166 +2026-04-12 15:47:57.174530: val_loss -0.371 +2026-04-12 15:47:57.176248: Pseudo dice [0.3323, 0.0, 0.7735, 0.8609, 0.1869, 0.8181, 0.4487] +2026-04-12 15:47:57.178705: Epoch time: 102.03 s +2026-04-12 15:47:58.364005: +2026-04-12 15:47:58.365495: Epoch 1867 +2026-04-12 15:47:58.366894: Current learning rate: 0.00568 +2026-04-12 15:49:40.059006: train_loss -0.3954 +2026-04-12 15:49:40.063471: val_loss -0.3007 +2026-04-12 15:49:40.065121: Pseudo dice [0.0, 0.0, 0.59, 0.1116, 0.1486, 0.6431, 0.742] +2026-04-12 15:49:40.066357: Epoch time: 101.7 s +2026-04-12 15:49:41.302080: +2026-04-12 15:49:41.303509: Epoch 1868 +2026-04-12 15:49:41.305207: Current learning rate: 0.00568 +2026-04-12 15:51:23.069598: train_loss -0.3894 +2026-04-12 15:51:23.075714: val_loss -0.3519 +2026-04-12 15:51:23.086883: Pseudo dice [0.0, 0.0, 0.6778, 0.8119, 0.2965, 0.7926, 0.7807] +2026-04-12 15:51:23.091679: Epoch time: 101.77 s +2026-04-12 15:51:24.281323: +2026-04-12 15:51:24.283127: Epoch 1869 +2026-04-12 15:51:24.284700: Current learning rate: 0.00567 +2026-04-12 15:53:06.066985: train_loss -0.3945 +2026-04-12 15:53:06.074027: val_loss -0.3548 +2026-04-12 15:53:06.075968: Pseudo dice [0.2123, 0.0, 0.6504, 0.8576, 0.341, 0.7728, 0.2864] +2026-04-12 15:53:06.078312: Epoch time: 101.79 s +2026-04-12 15:53:08.336791: +2026-04-12 15:53:08.338964: Epoch 1870 +2026-04-12 15:53:08.340462: Current learning rate: 0.00567 +2026-04-12 15:54:50.216155: train_loss -0.3764 +2026-04-12 15:54:50.220816: val_loss -0.2959 +2026-04-12 15:54:50.222872: Pseudo dice [0.329, 0.0, 0.5305, 0.6641, 0.2639, 0.0578, 0.7559] +2026-04-12 15:54:50.224393: Epoch time: 101.88 s +2026-04-12 15:54:51.429275: +2026-04-12 15:54:51.431106: Epoch 1871 +2026-04-12 15:54:51.432760: Current learning rate: 0.00567 +2026-04-12 15:56:33.304640: train_loss -0.3808 +2026-04-12 15:56:33.309916: val_loss -0.3286 +2026-04-12 15:56:33.311398: Pseudo dice [0.1944, 0.0, 0.719, 0.18, 0.0715, 0.4702, 0.7636] +2026-04-12 15:56:33.313036: Epoch time: 101.88 s +2026-04-12 15:56:34.477674: +2026-04-12 15:56:34.479378: Epoch 1872 +2026-04-12 15:56:34.480869: Current learning rate: 0.00567 +2026-04-12 15:58:16.250001: train_loss -0.3722 +2026-04-12 15:58:16.254901: val_loss -0.3197 +2026-04-12 15:58:16.256656: Pseudo dice [0.1214, 0.0, 0.5776, 0.6474, 0.256, 0.1849, 0.7237] +2026-04-12 15:58:16.258605: Epoch time: 101.78 s +2026-04-12 15:58:17.437584: +2026-04-12 15:58:17.439454: Epoch 1873 +2026-04-12 15:58:17.440935: Current learning rate: 0.00566 +2026-04-12 15:59:59.382035: train_loss -0.3766 +2026-04-12 15:59:59.386413: val_loss -0.3577 +2026-04-12 15:59:59.388056: Pseudo dice [0.2238, 0.0, 0.6001, 0.8836, 0.364, 0.5908, 0.7188] +2026-04-12 15:59:59.389678: Epoch time: 101.95 s +2026-04-12 16:00:00.579017: +2026-04-12 16:00:00.580495: Epoch 1874 +2026-04-12 16:00:00.581800: Current learning rate: 0.00566 +2026-04-12 16:01:42.624956: train_loss -0.389 +2026-04-12 16:01:42.629631: val_loss -0.367 +2026-04-12 16:01:42.631295: Pseudo dice [0.1816, 0.0, 0.548, 0.7068, 0.3186, 0.6673, 0.741] +2026-04-12 16:01:42.633097: Epoch time: 102.05 s +2026-04-12 16:01:43.806850: +2026-04-12 16:01:43.808948: Epoch 1875 +2026-04-12 16:01:43.810751: Current learning rate: 0.00566 +2026-04-12 16:03:25.512064: train_loss -0.4097 +2026-04-12 16:03:25.516455: val_loss -0.356 +2026-04-12 16:03:25.518105: Pseudo dice [0.3367, 0.0, 0.4264, 0.7504, 0.2294, 0.6536, 0.6006] +2026-04-12 16:03:25.520217: Epoch time: 101.71 s +2026-04-12 16:03:26.698123: +2026-04-12 16:03:26.699710: Epoch 1876 +2026-04-12 16:03:26.701141: Current learning rate: 0.00566 +2026-04-12 16:05:08.423087: train_loss -0.4043 +2026-04-12 16:05:08.427793: val_loss -0.3302 +2026-04-12 16:05:08.429820: Pseudo dice [0.4612, 0.0, 0.6422, 0.6176, 0.2901, 0.6133, 0.7602] +2026-04-12 16:05:08.431444: Epoch time: 101.73 s +2026-04-12 16:05:09.622651: +2026-04-12 16:05:09.624118: Epoch 1877 +2026-04-12 16:05:09.625892: Current learning rate: 0.00565 +2026-04-12 16:06:51.237386: train_loss -0.412 +2026-04-12 16:06:51.241655: val_loss -0.3533 +2026-04-12 16:06:51.243427: Pseudo dice [0.3406, 0.0, 0.7494, 0.4601, 0.482, 0.5522, 0.7463] +2026-04-12 16:06:51.244885: Epoch time: 101.62 s +2026-04-12 16:06:52.416739: +2026-04-12 16:06:52.418287: Epoch 1878 +2026-04-12 16:06:52.419914: Current learning rate: 0.00565 +2026-04-12 16:08:34.145629: train_loss -0.4173 +2026-04-12 16:08:34.150414: val_loss -0.3616 +2026-04-12 16:08:34.152064: Pseudo dice [0.3898, 0.0, 0.7291, 0.8158, 0.2606, 0.7686, 0.9105] +2026-04-12 16:08:34.153378: Epoch time: 101.73 s +2026-04-12 16:08:35.333821: +2026-04-12 16:08:35.335528: Epoch 1879 +2026-04-12 16:08:35.337368: Current learning rate: 0.00565 +2026-04-12 16:10:17.164626: train_loss -0.4047 +2026-04-12 16:10:17.169386: val_loss -0.3224 +2026-04-12 16:10:17.171028: Pseudo dice [0.2581, 0.0, 0.6932, 0.7114, 0.1921, 0.0508, 0.4828] +2026-04-12 16:10:17.172822: Epoch time: 101.83 s +2026-04-12 16:10:18.363478: +2026-04-12 16:10:18.365169: Epoch 1880 +2026-04-12 16:10:18.367876: Current learning rate: 0.00565 +2026-04-12 16:12:00.072572: train_loss -0.3753 +2026-04-12 16:12:00.077371: val_loss -0.3673 +2026-04-12 16:12:00.078919: Pseudo dice [0.0, 0.0, 0.8747, 0.6295, 0.5045, 0.7829, 0.8848] +2026-04-12 16:12:00.080353: Epoch time: 101.71 s +2026-04-12 16:12:01.255906: +2026-04-12 16:12:01.257897: Epoch 1881 +2026-04-12 16:12:01.259373: Current learning rate: 0.00564 +2026-04-12 16:13:42.881507: train_loss -0.3973 +2026-04-12 16:13:42.886033: val_loss -0.3305 +2026-04-12 16:13:42.887871: Pseudo dice [0.0148, 0.0, 0.7698, 0.5154, 0.3504, 0.6503, 0.7458] +2026-04-12 16:13:42.890151: Epoch time: 101.63 s +2026-04-12 16:13:44.082767: +2026-04-12 16:13:44.085486: Epoch 1882 +2026-04-12 16:13:44.087068: Current learning rate: 0.00564 +2026-04-12 16:15:25.735023: train_loss -0.4034 +2026-04-12 16:15:25.740128: val_loss -0.3271 +2026-04-12 16:15:25.741766: Pseudo dice [0.2527, 0.0, 0.6243, 0.793, 0.2751, 0.5414, 0.694] +2026-04-12 16:15:25.744208: Epoch time: 101.66 s +2026-04-12 16:15:26.937617: +2026-04-12 16:15:26.939245: Epoch 1883 +2026-04-12 16:15:26.940604: Current learning rate: 0.00564 +2026-04-12 16:17:08.614619: train_loss -0.3927 +2026-04-12 16:17:08.619991: val_loss -0.3163 +2026-04-12 16:17:08.622016: Pseudo dice [0.0, 0.0, 0.6511, 0.5402, 0.128, 0.5187, 0.7317] +2026-04-12 16:17:08.624586: Epoch time: 101.68 s +2026-04-12 16:17:09.809047: +2026-04-12 16:17:09.811059: Epoch 1884 +2026-04-12 16:17:09.813249: Current learning rate: 0.00564 +2026-04-12 16:18:51.361545: train_loss -0.3681 +2026-04-12 16:18:51.366177: val_loss -0.3276 +2026-04-12 16:18:51.367491: Pseudo dice [0.0, 0.0, 0.7847, 0.0263, 0.3554, 0.4536, 0.7079] +2026-04-12 16:18:51.369173: Epoch time: 101.56 s +2026-04-12 16:18:52.641746: +2026-04-12 16:18:52.643755: Epoch 1885 +2026-04-12 16:18:52.645596: Current learning rate: 0.00564 +2026-04-12 16:20:34.203671: train_loss -0.3757 +2026-04-12 16:20:34.207969: val_loss -0.3363 +2026-04-12 16:20:34.209449: Pseudo dice [0.0, 0.0, 0.6885, 0.6593, 0.4598, 0.7169, 0.7137] +2026-04-12 16:20:34.210772: Epoch time: 101.57 s +2026-04-12 16:20:35.415078: +2026-04-12 16:20:35.416940: Epoch 1886 +2026-04-12 16:20:35.418435: Current learning rate: 0.00563 +2026-04-12 16:22:17.117034: train_loss -0.3685 +2026-04-12 16:22:17.122500: val_loss -0.2507 +2026-04-12 16:22:17.124197: Pseudo dice [0.0, 0.0, 0.2632, 0.7797, 0.1425, 0.0396, 0.4193] +2026-04-12 16:22:17.125821: Epoch time: 101.71 s +2026-04-12 16:22:18.301578: +2026-04-12 16:22:18.303380: Epoch 1887 +2026-04-12 16:22:18.304838: Current learning rate: 0.00563 +2026-04-12 16:24:00.152180: train_loss -0.3639 +2026-04-12 16:24:00.156828: val_loss -0.3053 +2026-04-12 16:24:00.158551: Pseudo dice [0.0, 0.0, 0.4898, 0.613, 0.148, 0.3401, 0.683] +2026-04-12 16:24:00.160200: Epoch time: 101.85 s +2026-04-12 16:24:02.071501: +2026-04-12 16:24:02.073029: Epoch 1888 +2026-04-12 16:24:02.074607: Current learning rate: 0.00563 +2026-04-12 16:25:43.947005: train_loss -0.3718 +2026-04-12 16:25:43.950823: val_loss -0.3328 +2026-04-12 16:25:43.952147: Pseudo dice [0.0, 0.0, 0.6597, 0.752, 0.3398, 0.4436, 0.5616] +2026-04-12 16:25:43.953491: Epoch time: 101.88 s +2026-04-12 16:25:45.134787: +2026-04-12 16:25:45.136182: Epoch 1889 +2026-04-12 16:25:45.137392: Current learning rate: 0.00563 +2026-04-12 16:27:26.909032: train_loss -0.3984 +2026-04-12 16:27:26.913327: val_loss -0.3647 +2026-04-12 16:27:26.914876: Pseudo dice [0.0, 0.0, 0.7966, 0.7762, 0.4368, 0.6455, 0.868] +2026-04-12 16:27:26.916361: Epoch time: 101.78 s +2026-04-12 16:27:29.050952: +2026-04-12 16:27:29.052386: Epoch 1890 +2026-04-12 16:27:29.053593: Current learning rate: 0.00562 +2026-04-12 16:29:10.996049: train_loss -0.4073 +2026-04-12 16:29:11.001696: val_loss -0.3687 +2026-04-12 16:29:11.003520: Pseudo dice [0.2986, 0.0, 0.5447, 0.4109, 0.4523, 0.8196, 0.6988] +2026-04-12 16:29:11.005548: Epoch time: 101.95 s +2026-04-12 16:29:12.195434: +2026-04-12 16:29:12.198029: Epoch 1891 +2026-04-12 16:29:12.199846: Current learning rate: 0.00562 +2026-04-12 16:30:53.906434: train_loss -0.4051 +2026-04-12 16:30:53.911422: val_loss -0.3443 +2026-04-12 16:30:53.913265: Pseudo dice [0.7443, 0.0, 0.7873, 0.3862, 0.3297, 0.7634, 0.8037] +2026-04-12 16:30:53.916503: Epoch time: 101.71 s +2026-04-12 16:30:55.118169: +2026-04-12 16:30:55.119964: Epoch 1892 +2026-04-12 16:30:55.121536: Current learning rate: 0.00562 +2026-04-12 16:32:36.908751: train_loss -0.394 +2026-04-12 16:32:36.913953: val_loss -0.3371 +2026-04-12 16:32:36.915553: Pseudo dice [0.0532, 0.0, 0.7236, 0.1241, 0.244, 0.7965, 0.7375] +2026-04-12 16:32:36.917869: Epoch time: 101.79 s +2026-04-12 16:32:38.833516: +2026-04-12 16:32:38.835716: Epoch 1893 +2026-04-12 16:32:38.837498: Current learning rate: 0.00562 +2026-04-12 16:34:20.555389: train_loss -0.3985 +2026-04-12 16:34:20.560482: val_loss -0.3563 +2026-04-12 16:34:20.562297: Pseudo dice [0.1144, 0.0, 0.2445, 0.8101, 0.3698, 0.7165, 0.7127] +2026-04-12 16:34:20.564252: Epoch time: 101.72 s +2026-04-12 16:34:21.759416: +2026-04-12 16:34:21.761374: Epoch 1894 +2026-04-12 16:34:21.762843: Current learning rate: 0.00561 +2026-04-12 16:36:03.467900: train_loss -0.416 +2026-04-12 16:36:03.472890: val_loss -0.3813 +2026-04-12 16:36:03.474555: Pseudo dice [0.1348, 0.0, 0.8595, 0.8297, 0.2979, 0.7963, 0.7981] +2026-04-12 16:36:03.476029: Epoch time: 101.71 s +2026-04-12 16:36:04.675646: +2026-04-12 16:36:04.678167: Epoch 1895 +2026-04-12 16:36:04.681198: Current learning rate: 0.00561 +2026-04-12 16:37:46.436342: train_loss -0.4056 +2026-04-12 16:37:46.441842: val_loss -0.3629 +2026-04-12 16:37:46.443512: Pseudo dice [0.2258, 0.0, 0.5953, 0.8896, 0.3215, 0.6381, 0.617] +2026-04-12 16:37:46.445615: Epoch time: 101.76 s +2026-04-12 16:37:47.700330: +2026-04-12 16:37:47.702247: Epoch 1896 +2026-04-12 16:37:47.703954: Current learning rate: 0.00561 +2026-04-12 16:39:29.560418: train_loss -0.4044 +2026-04-12 16:39:29.573493: val_loss -0.3523 +2026-04-12 16:39:29.576458: Pseudo dice [0.2063, 0.0, 0.5151, 0.499, 0.3916, 0.7364, 0.8602] +2026-04-12 16:39:29.579471: Epoch time: 101.86 s +2026-04-12 16:39:30.775165: +2026-04-12 16:39:30.777555: Epoch 1897 +2026-04-12 16:39:30.779236: Current learning rate: 0.00561 +2026-04-12 16:41:12.442223: train_loss -0.3954 +2026-04-12 16:41:12.447814: val_loss -0.3329 +2026-04-12 16:41:12.449648: Pseudo dice [0.3185, 0.0, 0.6246, 0.4878, 0.2974, 0.6016, 0.8595] +2026-04-12 16:41:12.451252: Epoch time: 101.67 s +2026-04-12 16:41:13.660816: +2026-04-12 16:41:13.662708: Epoch 1898 +2026-04-12 16:41:13.664105: Current learning rate: 0.0056 +2026-04-12 16:42:55.307390: train_loss -0.4037 +2026-04-12 16:42:55.312472: val_loss -0.3799 +2026-04-12 16:42:55.313884: Pseudo dice [0.2631, 0.0, 0.7639, 0.8444, 0.3916, 0.7105, 0.8449] +2026-04-12 16:42:55.315107: Epoch time: 101.65 s +2026-04-12 16:42:56.505643: +2026-04-12 16:42:56.507410: Epoch 1899 +2026-04-12 16:42:56.509165: Current learning rate: 0.0056 +2026-04-12 16:44:38.223521: train_loss -0.3921 +2026-04-12 16:44:38.240602: val_loss -0.3453 +2026-04-12 16:44:38.247095: Pseudo dice [0.2692, 0.0, 0.7044, 0.8649, 0.2615, 0.5193, 0.8512] +2026-04-12 16:44:38.249700: Epoch time: 101.72 s +2026-04-12 16:44:41.040214: +2026-04-12 16:44:41.042078: Epoch 1900 +2026-04-12 16:44:41.043734: Current learning rate: 0.0056 +2026-04-12 16:46:22.883944: train_loss -0.3778 +2026-04-12 16:46:22.888236: val_loss -0.3301 +2026-04-12 16:46:22.890245: Pseudo dice [0.0, 0.0, 0.717, 0.0146, 0.2936, 0.5844, 0.8483] +2026-04-12 16:46:22.891708: Epoch time: 101.85 s +2026-04-12 16:46:24.085823: +2026-04-12 16:46:24.087511: Epoch 1901 +2026-04-12 16:46:24.088776: Current learning rate: 0.0056 +2026-04-12 16:48:05.841255: train_loss -0.3516 +2026-04-12 16:48:05.852828: val_loss -0.3159 +2026-04-12 16:48:05.854639: Pseudo dice [0.0, 0.0, 0.5957, 0.3218, 0.3256, 0.6732, 0.4614] +2026-04-12 16:48:05.856331: Epoch time: 101.76 s +2026-04-12 16:48:07.046430: +2026-04-12 16:48:07.047933: Epoch 1902 +2026-04-12 16:48:07.049528: Current learning rate: 0.00559 +2026-04-12 16:49:48.679274: train_loss -0.383 +2026-04-12 16:49:48.685555: val_loss -0.3213 +2026-04-12 16:49:48.687924: Pseudo dice [0.0, 0.0, 0.6929, 0.4943, 0.0843, 0.2508, 0.7029] +2026-04-12 16:49:48.689454: Epoch time: 101.64 s +2026-04-12 16:49:49.863918: +2026-04-12 16:49:49.865994: Epoch 1903 +2026-04-12 16:49:49.867966: Current learning rate: 0.00559 +2026-04-12 16:51:31.752005: train_loss -0.3892 +2026-04-12 16:51:31.756890: val_loss -0.3377 +2026-04-12 16:51:31.758324: Pseudo dice [0.0, 0.0, 0.5625, 0.5355, 0.2166, 0.4655, 0.823] +2026-04-12 16:51:31.760104: Epoch time: 101.89 s +2026-04-12 16:51:32.943978: +2026-04-12 16:51:32.946378: Epoch 1904 +2026-04-12 16:51:32.948275: Current learning rate: 0.00559 +2026-04-12 16:53:14.641663: train_loss -0.3979 +2026-04-12 16:53:14.647313: val_loss -0.3141 +2026-04-12 16:53:14.649266: Pseudo dice [0.0, 0.0, 0.3563, 0.3817, 0.2144, 0.3248, 0.7276] +2026-04-12 16:53:14.654203: Epoch time: 101.7 s +2026-04-12 16:53:15.848685: +2026-04-12 16:53:15.850795: Epoch 1905 +2026-04-12 16:53:15.852857: Current learning rate: 0.00559 +2026-04-12 16:54:57.503244: train_loss -0.4049 +2026-04-12 16:54:57.508381: val_loss -0.3743 +2026-04-12 16:54:57.510935: Pseudo dice [0.0, 0.0, 0.39, 0.8298, 0.4028, 0.8712, 0.4287] +2026-04-12 16:54:57.513277: Epoch time: 101.66 s +2026-04-12 16:54:58.748394: +2026-04-12 16:54:58.750358: Epoch 1906 +2026-04-12 16:54:58.752288: Current learning rate: 0.00559 +2026-04-12 16:56:40.390240: train_loss -0.4206 +2026-04-12 16:56:40.394796: val_loss -0.3618 +2026-04-12 16:56:40.396318: Pseudo dice [0.0, 0.0, 0.6831, 0.529, 0.3816, 0.6606, 0.8595] +2026-04-12 16:56:40.397849: Epoch time: 101.64 s +2026-04-12 16:56:41.601872: +2026-04-12 16:56:41.603151: Epoch 1907 +2026-04-12 16:56:41.604802: Current learning rate: 0.00558 +2026-04-12 16:58:22.992495: train_loss -0.3943 +2026-04-12 16:58:22.999151: val_loss -0.4024 +2026-04-12 16:58:23.001927: Pseudo dice [0.0, 0.0, 0.7002, 0.8271, 0.4759, 0.8018, 0.7499] +2026-04-12 16:58:23.003561: Epoch time: 101.39 s +2026-04-12 16:58:24.223298: +2026-04-12 16:58:24.225080: Epoch 1908 +2026-04-12 16:58:24.226943: Current learning rate: 0.00558 +2026-04-12 17:00:05.958011: train_loss -0.3879 +2026-04-12 17:00:05.964653: val_loss -0.3668 +2026-04-12 17:00:05.966354: Pseudo dice [0.0, 0.0, 0.7279, 0.3885, 0.3646, 0.8147, 0.5534] +2026-04-12 17:00:05.967781: Epoch time: 101.74 s +2026-04-12 17:00:07.190262: +2026-04-12 17:00:07.192089: Epoch 1909 +2026-04-12 17:00:07.194308: Current learning rate: 0.00558 +2026-04-12 17:01:49.002354: train_loss -0.3878 +2026-04-12 17:01:49.007102: val_loss -0.3616 +2026-04-12 17:01:49.009326: Pseudo dice [0.0, 0.0, 0.6162, 0.6509, 0.3154, 0.4796, 0.7705] +2026-04-12 17:01:49.010822: Epoch time: 101.82 s +2026-04-12 17:01:51.215370: +2026-04-12 17:01:51.217147: Epoch 1910 +2026-04-12 17:01:51.219225: Current learning rate: 0.00558 +2026-04-12 17:03:33.261627: train_loss -0.4063 +2026-04-12 17:03:33.267246: val_loss -0.3302 +2026-04-12 17:03:33.268985: Pseudo dice [0.0, 0.0, 0.7687, 0.7626, 0.2805, 0.7182, 0.6707] +2026-04-12 17:03:33.270727: Epoch time: 102.05 s +2026-04-12 17:03:34.472032: +2026-04-12 17:03:34.473795: Epoch 1911 +2026-04-12 17:03:34.475646: Current learning rate: 0.00557 +2026-04-12 17:05:16.221698: train_loss -0.3924 +2026-04-12 17:05:16.227857: val_loss -0.3746 +2026-04-12 17:05:16.237374: Pseudo dice [0.0, 0.0, 0.7051, 0.6323, 0.4619, 0.675, 0.8538] +2026-04-12 17:05:16.239249: Epoch time: 101.75 s +2026-04-12 17:05:17.518615: +2026-04-12 17:05:17.520711: Epoch 1912 +2026-04-12 17:05:17.522899: Current learning rate: 0.00557 +2026-04-12 17:06:59.190634: train_loss -0.3999 +2026-04-12 17:06:59.197325: val_loss -0.3045 +2026-04-12 17:06:59.199116: Pseudo dice [0.0, 0.0, 0.6874, 0.1311, 0.284, 0.5447, 0.8301] +2026-04-12 17:06:59.200654: Epoch time: 101.68 s +2026-04-12 17:07:00.407381: +2026-04-12 17:07:00.409573: Epoch 1913 +2026-04-12 17:07:00.411510: Current learning rate: 0.00557 +2026-04-12 17:08:42.218487: train_loss -0.3842 +2026-04-12 17:08:42.225426: val_loss -0.3197 +2026-04-12 17:08:42.227019: Pseudo dice [0.0, 0.0, 0.5851, 0.8293, 0.3251, 0.2849, 0.8832] +2026-04-12 17:08:42.228466: Epoch time: 101.81 s +2026-04-12 17:08:43.422275: +2026-04-12 17:08:43.424314: Epoch 1914 +2026-04-12 17:08:43.426472: Current learning rate: 0.00557 +2026-04-12 17:10:25.231034: train_loss -0.388 +2026-04-12 17:10:25.236574: val_loss -0.3731 +2026-04-12 17:10:25.238300: Pseudo dice [0.0, 0.0, 0.6597, 0.746, 0.3214, 0.8699, 0.9222] +2026-04-12 17:10:25.240095: Epoch time: 101.81 s +2026-04-12 17:10:26.429091: +2026-04-12 17:10:26.431750: Epoch 1915 +2026-04-12 17:10:26.433669: Current learning rate: 0.00556 +2026-04-12 17:12:08.038884: train_loss -0.3962 +2026-04-12 17:12:08.043367: val_loss -0.3772 +2026-04-12 17:12:08.045247: Pseudo dice [0.0, 0.0, 0.6388, 0.732, 0.3874, 0.7042, 0.6961] +2026-04-12 17:12:08.046701: Epoch time: 101.61 s +2026-04-12 17:12:09.253307: +2026-04-12 17:12:09.254943: Epoch 1916 +2026-04-12 17:12:09.256759: Current learning rate: 0.00556 +2026-04-12 17:13:51.085995: train_loss -0.4025 +2026-04-12 17:13:51.091635: val_loss -0.3576 +2026-04-12 17:13:51.093284: Pseudo dice [0.0, 0.0, 0.3418, 0.8932, 0.2771, 0.6882, 0.7774] +2026-04-12 17:13:51.095124: Epoch time: 101.84 s +2026-04-12 17:13:52.314921: +2026-04-12 17:13:52.316742: Epoch 1917 +2026-04-12 17:13:52.318802: Current learning rate: 0.00556 +2026-04-12 17:15:33.955096: train_loss -0.4146 +2026-04-12 17:15:33.959859: val_loss -0.3933 +2026-04-12 17:15:33.962278: Pseudo dice [0.0, 0.0, 0.6705, 0.6561, 0.4557, 0.7728, 0.9383] +2026-04-12 17:15:33.964001: Epoch time: 101.64 s +2026-04-12 17:15:35.153889: +2026-04-12 17:15:35.155254: Epoch 1918 +2026-04-12 17:15:35.156732: Current learning rate: 0.00556 +2026-04-12 17:17:16.982910: train_loss -0.38 +2026-04-12 17:17:16.989007: val_loss -0.35 +2026-04-12 17:17:16.991182: Pseudo dice [0.0, 0.0, 0.5517, 0.8398, 0.3544, 0.7696, 0.6251] +2026-04-12 17:17:16.992842: Epoch time: 101.83 s +2026-04-12 17:17:18.214492: +2026-04-12 17:17:18.216363: Epoch 1919 +2026-04-12 17:17:18.218547: Current learning rate: 0.00555 +2026-04-12 17:18:59.607599: train_loss -0.3852 +2026-04-12 17:18:59.615968: val_loss -0.3478 +2026-04-12 17:18:59.617450: Pseudo dice [0.0, 0.0, 0.7681, 0.6957, 0.4461, 0.5875, 0.6219] +2026-04-12 17:18:59.618824: Epoch time: 101.4 s +2026-04-12 17:19:00.852451: +2026-04-12 17:19:00.853993: Epoch 1920 +2026-04-12 17:19:00.856244: Current learning rate: 0.00555 +2026-04-12 17:20:42.419367: train_loss -0.3935 +2026-04-12 17:20:42.426780: val_loss -0.3357 +2026-04-12 17:20:42.428613: Pseudo dice [0.0, 0.0, 0.6841, 0.8686, 0.3122, 0.6511, 0.8354] +2026-04-12 17:20:42.431144: Epoch time: 101.57 s +2026-04-12 17:20:43.643194: +2026-04-12 17:20:43.645263: Epoch 1921 +2026-04-12 17:20:43.647026: Current learning rate: 0.00555 +2026-04-12 17:22:25.160101: train_loss -0.4012 +2026-04-12 17:22:25.164960: val_loss -0.3516 +2026-04-12 17:22:25.166960: Pseudo dice [0.0, 0.0, 0.7325, 0.1185, 0.3375, 0.7323, 0.7451] +2026-04-12 17:22:25.168383: Epoch time: 101.52 s +2026-04-12 17:22:26.382970: +2026-04-12 17:22:26.384315: Epoch 1922 +2026-04-12 17:22:26.385964: Current learning rate: 0.00555 +2026-04-12 17:24:07.982373: train_loss -0.3997 +2026-04-12 17:24:07.987692: val_loss -0.3972 +2026-04-12 17:24:07.989519: Pseudo dice [0.0, 0.0, 0.7677, 0.8038, 0.4522, 0.7586, 0.6884] +2026-04-12 17:24:07.991202: Epoch time: 101.6 s +2026-04-12 17:24:09.219953: +2026-04-12 17:24:09.221885: Epoch 1923 +2026-04-12 17:24:09.223883: Current learning rate: 0.00554 +2026-04-12 17:25:50.873950: train_loss -0.3807 +2026-04-12 17:25:50.878641: val_loss -0.3341 +2026-04-12 17:25:50.880274: Pseudo dice [0.0, 0.0, 0.7727, 0.6183, 0.4966, 0.3715, 0.7571] +2026-04-12 17:25:50.882253: Epoch time: 101.66 s +2026-04-12 17:25:52.079493: +2026-04-12 17:25:52.081127: Epoch 1924 +2026-04-12 17:25:52.083062: Current learning rate: 0.00554 +2026-04-12 17:27:33.778268: train_loss -0.3982 +2026-04-12 17:27:33.783396: val_loss -0.3383 +2026-04-12 17:27:33.785063: Pseudo dice [0.0, 0.0, 0.5205, 0.5282, 0.2229, 0.7248, 0.9123] +2026-04-12 17:27:33.787392: Epoch time: 101.7 s +2026-04-12 17:27:34.998999: +2026-04-12 17:27:35.000700: Epoch 1925 +2026-04-12 17:27:35.002541: Current learning rate: 0.00554 +2026-04-12 17:29:16.733197: train_loss -0.3933 +2026-04-12 17:29:16.739368: val_loss -0.3293 +2026-04-12 17:29:16.742603: Pseudo dice [0.0, 0.0, 0.5524, 0.749, 0.2886, 0.5444, 0.6174] +2026-04-12 17:29:16.744520: Epoch time: 101.74 s +2026-04-12 17:29:17.959946: +2026-04-12 17:29:17.962167: Epoch 1926 +2026-04-12 17:29:17.964641: Current learning rate: 0.00554 +2026-04-12 17:30:59.666859: train_loss -0.3754 +2026-04-12 17:30:59.672180: val_loss -0.2503 +2026-04-12 17:30:59.673934: Pseudo dice [0.0, 0.0, 0.3716, 0.3708, 0.3365, 0.2952, 0.1534] +2026-04-12 17:30:59.675995: Epoch time: 101.71 s +2026-04-12 17:31:00.887831: +2026-04-12 17:31:00.889746: Epoch 1927 +2026-04-12 17:31:00.892197: Current learning rate: 0.00553 +2026-04-12 17:32:42.631701: train_loss -0.3825 +2026-04-12 17:32:42.638652: val_loss -0.3435 +2026-04-12 17:32:42.640404: Pseudo dice [0.1815, 0.0, 0.7691, 0.5733, 0.3083, 0.8374, 0.7135] +2026-04-12 17:32:42.641940: Epoch time: 101.75 s +2026-04-12 17:32:43.845364: +2026-04-12 17:32:43.846704: Epoch 1928 +2026-04-12 17:32:43.848362: Current learning rate: 0.00553 +2026-04-12 17:34:25.673263: train_loss -0.4075 +2026-04-12 17:34:25.679193: val_loss -0.3865 +2026-04-12 17:34:25.681137: Pseudo dice [0.4126, 0.0, 0.7502, 0.0963, 0.3059, 0.6239, 0.932] +2026-04-12 17:34:25.683344: Epoch time: 101.83 s +2026-04-12 17:34:26.877048: +2026-04-12 17:34:26.878612: Epoch 1929 +2026-04-12 17:34:26.880717: Current learning rate: 0.00553 +2026-04-12 17:36:08.675219: train_loss -0.4058 +2026-04-12 17:36:08.680642: val_loss -0.3466 +2026-04-12 17:36:08.682177: Pseudo dice [0.1209, 0.0, 0.4641, 0.8359, 0.2538, 0.4838, 0.8107] +2026-04-12 17:36:08.684395: Epoch time: 101.8 s +2026-04-12 17:36:10.897435: +2026-04-12 17:36:10.899203: Epoch 1930 +2026-04-12 17:36:10.901189: Current learning rate: 0.00553 +2026-04-12 17:37:52.679992: train_loss -0.4049 +2026-04-12 17:37:52.686545: val_loss -0.3274 +2026-04-12 17:37:52.688367: Pseudo dice [0.4757, 0.0, 0.7392, 0.4704, 0.1896, 0.1652, 0.7321] +2026-04-12 17:37:52.690156: Epoch time: 101.79 s +2026-04-12 17:37:53.896211: +2026-04-12 17:37:53.897559: Epoch 1931 +2026-04-12 17:37:53.899086: Current learning rate: 0.00552 +2026-04-12 17:39:35.589384: train_loss -0.4132 +2026-04-12 17:39:35.596467: val_loss -0.3688 +2026-04-12 17:39:35.609508: Pseudo dice [0.1454, 0.0, 0.761, 0.7201, 0.4417, 0.6813, 0.609] +2026-04-12 17:39:35.611013: Epoch time: 101.7 s +2026-04-12 17:39:36.817684: +2026-04-12 17:39:36.820045: Epoch 1932 +2026-04-12 17:39:36.822355: Current learning rate: 0.00552 +2026-04-12 17:41:18.647349: train_loss -0.4013 +2026-04-12 17:41:18.653131: val_loss -0.3878 +2026-04-12 17:41:18.654729: Pseudo dice [0.4155, 0.0, 0.7554, 0.7813, 0.574, 0.6967, 0.807] +2026-04-12 17:41:18.656946: Epoch time: 101.83 s +2026-04-12 17:41:19.876683: +2026-04-12 17:41:19.878376: Epoch 1933 +2026-04-12 17:41:19.881210: Current learning rate: 0.00552 +2026-04-12 17:43:01.931829: train_loss -0.4162 +2026-04-12 17:43:01.936286: val_loss -0.3414 +2026-04-12 17:43:01.938491: Pseudo dice [0.2339, 0.0, 0.5457, 0.6942, 0.2668, 0.6463, 0.8341] +2026-04-12 17:43:01.940067: Epoch time: 102.06 s +2026-04-12 17:43:03.149933: +2026-04-12 17:43:03.151865: Epoch 1934 +2026-04-12 17:43:03.153620: Current learning rate: 0.00552 +2026-04-12 17:44:45.025366: train_loss -0.4134 +2026-04-12 17:44:45.031427: val_loss -0.3622 +2026-04-12 17:44:45.033424: Pseudo dice [0.2982, 0.0, 0.6432, 0.824, 0.5336, 0.7456, 0.8638] +2026-04-12 17:44:45.035105: Epoch time: 101.88 s +2026-04-12 17:44:46.242512: +2026-04-12 17:44:46.244610: Epoch 1935 +2026-04-12 17:44:46.246440: Current learning rate: 0.00552 +2026-04-12 17:46:28.109512: train_loss -0.4158 +2026-04-12 17:46:28.113867: val_loss -0.3564 +2026-04-12 17:46:28.115736: Pseudo dice [0.1511, 0.0, 0.7026, 0.7522, 0.3328, 0.8442, 0.8628] +2026-04-12 17:46:28.117406: Epoch time: 101.87 s +2026-04-12 17:46:29.332773: +2026-04-12 17:46:29.334586: Epoch 1936 +2026-04-12 17:46:29.337202: Current learning rate: 0.00551 +2026-04-12 17:48:10.991312: train_loss -0.4063 +2026-04-12 17:48:10.997336: val_loss -0.3319 +2026-04-12 17:48:10.999142: Pseudo dice [0.0, 0.0, 0.6293, 0.6674, 0.3381, 0.7773, 0.601] +2026-04-12 17:48:11.000650: Epoch time: 101.66 s +2026-04-12 17:48:12.207960: +2026-04-12 17:48:12.210196: Epoch 1937 +2026-04-12 17:48:12.212829: Current learning rate: 0.00551 +2026-04-12 17:49:54.032280: train_loss -0.3975 +2026-04-12 17:49:54.036752: val_loss -0.3809 +2026-04-12 17:49:54.038433: Pseudo dice [0.0809, 0.0, 0.7338, 0.8542, 0.5089, 0.7077, 0.6838] +2026-04-12 17:49:54.039933: Epoch time: 101.83 s +2026-04-12 17:49:55.245460: +2026-04-12 17:49:55.246994: Epoch 1938 +2026-04-12 17:49:55.248885: Current learning rate: 0.00551 +2026-04-12 17:51:37.159883: train_loss -0.3901 +2026-04-12 17:51:37.174557: val_loss -0.3301 +2026-04-12 17:51:37.183239: Pseudo dice [0.2157, 0.0, 0.7893, 0.8739, 0.0982, 0.7811, 0.6976] +2026-04-12 17:51:37.185363: Epoch time: 101.92 s +2026-04-12 17:51:38.406718: +2026-04-12 17:51:38.408610: Epoch 1939 +2026-04-12 17:51:38.410350: Current learning rate: 0.00551 +2026-04-12 17:53:20.196541: train_loss -0.403 +2026-04-12 17:53:20.202431: val_loss -0.3483 +2026-04-12 17:53:20.203958: Pseudo dice [0.3855, 0.0, 0.4161, 0.0734, 0.2817, 0.2238, 0.8714] +2026-04-12 17:53:20.205454: Epoch time: 101.79 s +2026-04-12 17:53:21.425255: +2026-04-12 17:53:21.427111: Epoch 1940 +2026-04-12 17:53:21.429029: Current learning rate: 0.0055 +2026-04-12 17:55:02.989199: train_loss -0.3595 +2026-04-12 17:55:02.994302: val_loss -0.3538 +2026-04-12 17:55:02.995904: Pseudo dice [0.3953, 0.0, 0.4169, 0.5783, 0.3541, 0.6285, 0.782] +2026-04-12 17:55:02.997287: Epoch time: 101.57 s +2026-04-12 17:55:04.220595: +2026-04-12 17:55:04.223165: Epoch 1941 +2026-04-12 17:55:04.225479: Current learning rate: 0.0055 +2026-04-12 17:56:45.930323: train_loss -0.3732 +2026-04-12 17:56:45.935619: val_loss -0.3115 +2026-04-12 17:56:45.937263: Pseudo dice [0.2579, 0.0, 0.5904, 0.5906, 0.1434, 0.2533, 0.6999] +2026-04-12 17:56:45.938899: Epoch time: 101.71 s +2026-04-12 17:56:47.156244: +2026-04-12 17:56:47.158034: Epoch 1942 +2026-04-12 17:56:47.159945: Current learning rate: 0.0055 +2026-04-12 17:58:28.958373: train_loss -0.3868 +2026-04-12 17:58:28.964119: val_loss -0.3454 +2026-04-12 17:58:28.966589: Pseudo dice [0.2373, 0.0, 0.726, 0.864, 0.4028, 0.714, 0.8543] +2026-04-12 17:58:28.968318: Epoch time: 101.81 s +2026-04-12 17:58:30.181554: +2026-04-12 17:58:30.183160: Epoch 1943 +2026-04-12 17:58:30.185179: Current learning rate: 0.0055 +2026-04-12 18:00:11.760705: train_loss -0.3774 +2026-04-12 18:00:11.767091: val_loss -0.338 +2026-04-12 18:00:11.768991: Pseudo dice [0.0, 0.0, 0.7576, 0.4382, 0.216, 0.5969, 0.8146] +2026-04-12 18:00:11.771007: Epoch time: 101.58 s +2026-04-12 18:00:12.976968: +2026-04-12 18:00:12.979832: Epoch 1944 +2026-04-12 18:00:12.982590: Current learning rate: 0.00549 +2026-04-12 18:01:54.859739: train_loss -0.3862 +2026-04-12 18:01:54.864421: val_loss -0.3175 +2026-04-12 18:01:54.866684: Pseudo dice [0.4143, 0.0, 0.76, 0.2143, 0.0685, 0.6677, 0.1936] +2026-04-12 18:01:54.868863: Epoch time: 101.89 s +2026-04-12 18:01:56.068949: +2026-04-12 18:01:56.070902: Epoch 1945 +2026-04-12 18:01:56.073089: Current learning rate: 0.00549 +2026-04-12 18:03:38.067661: train_loss -0.3863 +2026-04-12 18:03:38.072887: val_loss -0.3844 +2026-04-12 18:03:38.075255: Pseudo dice [0.1225, 0.0, 0.6746, 0.7318, 0.4865, 0.7294, 0.7402] +2026-04-12 18:03:38.076737: Epoch time: 102.0 s +2026-04-12 18:03:39.275115: +2026-04-12 18:03:39.276979: Epoch 1946 +2026-04-12 18:03:39.278976: Current learning rate: 0.00549 +2026-04-12 18:05:21.131109: train_loss -0.4057 +2026-04-12 18:05:21.136307: val_loss -0.3796 +2026-04-12 18:05:21.138592: Pseudo dice [0.1807, 0.0, 0.6338, 0.701, 0.2649, 0.7484, 0.8104] +2026-04-12 18:05:21.140633: Epoch time: 101.86 s +2026-04-12 18:05:22.341300: +2026-04-12 18:05:22.344023: Epoch 1947 +2026-04-12 18:05:22.346069: Current learning rate: 0.00549 +2026-04-12 18:07:04.132187: train_loss -0.4063 +2026-04-12 18:07:04.137965: val_loss -0.3648 +2026-04-12 18:07:04.140542: Pseudo dice [0.1426, 0.0, 0.7859, 0.8195, 0.4296, 0.7213, 0.8809] +2026-04-12 18:07:04.142350: Epoch time: 101.79 s +2026-04-12 18:07:05.351105: +2026-04-12 18:07:05.353029: Epoch 1948 +2026-04-12 18:07:05.354900: Current learning rate: 0.00548 +2026-04-12 18:08:47.164344: train_loss -0.3906 +2026-04-12 18:08:47.169274: val_loss -0.3376 +2026-04-12 18:08:47.171286: Pseudo dice [0.2825, 0.0, 0.7527, 0.2124, 0.3849, 0.4765, 0.528] +2026-04-12 18:08:47.172914: Epoch time: 101.82 s +2026-04-12 18:08:48.397692: +2026-04-12 18:08:48.399349: Epoch 1949 +2026-04-12 18:08:48.401291: Current learning rate: 0.00548 +2026-04-12 18:10:31.328351: train_loss -0.3825 +2026-04-12 18:10:31.333852: val_loss -0.362 +2026-04-12 18:10:31.335545: Pseudo dice [0.4296, 0.0, 0.6043, 0.1464, 0.3444, 0.6518, 0.4897] +2026-04-12 18:10:31.337529: Epoch time: 102.93 s +2026-04-12 18:10:34.186653: +2026-04-12 18:10:34.188125: Epoch 1950 +2026-04-12 18:10:34.189837: Current learning rate: 0.00548 +2026-04-12 18:12:16.230239: train_loss -0.3788 +2026-04-12 18:12:16.235327: val_loss -0.3322 +2026-04-12 18:12:16.237364: Pseudo dice [0.5357, 0.0, 0.7257, 0.4802, 0.3064, 0.7405, 0.5542] +2026-04-12 18:12:16.239336: Epoch time: 102.05 s +2026-04-12 18:12:17.453836: +2026-04-12 18:12:17.455817: Epoch 1951 +2026-04-12 18:12:17.457680: Current learning rate: 0.00548 +2026-04-12 18:13:59.601368: train_loss -0.3952 +2026-04-12 18:13:59.606117: val_loss -0.3449 +2026-04-12 18:13:59.607671: Pseudo dice [0.6528, 0.0, 0.7385, 0.7398, 0.317, 0.4107, 0.5108] +2026-04-12 18:13:59.609825: Epoch time: 102.15 s +2026-04-12 18:14:00.830188: +2026-04-12 18:14:00.831862: Epoch 1952 +2026-04-12 18:14:00.834127: Current learning rate: 0.00547 +2026-04-12 18:15:42.517010: train_loss -0.3925 +2026-04-12 18:15:42.522980: val_loss -0.367 +2026-04-12 18:15:42.524793: Pseudo dice [0.7533, 0.0, 0.3654, 0.3891, 0.4016, 0.1235, 0.7345] +2026-04-12 18:15:42.526624: Epoch time: 101.69 s +2026-04-12 18:15:43.724575: +2026-04-12 18:15:43.726231: Epoch 1953 +2026-04-12 18:15:43.728305: Current learning rate: 0.00547 +2026-04-12 18:17:25.508008: train_loss -0.3892 +2026-04-12 18:17:25.513681: val_loss -0.3416 +2026-04-12 18:17:25.515907: Pseudo dice [0.309, 0.0, 0.6696, 0.7533, 0.3685, 0.3278, 0.372] +2026-04-12 18:17:25.518130: Epoch time: 101.79 s +2026-04-12 18:17:26.737642: +2026-04-12 18:17:26.739476: Epoch 1954 +2026-04-12 18:17:26.741436: Current learning rate: 0.00547 +2026-04-12 18:19:08.325655: train_loss -0.4113 +2026-04-12 18:19:08.330700: val_loss -0.3773 +2026-04-12 18:19:08.332871: Pseudo dice [0.2615, 0.0, 0.7329, 0.2055, 0.2266, 0.6998, 0.7319] +2026-04-12 18:19:08.335167: Epoch time: 101.59 s +2026-04-12 18:19:09.557136: +2026-04-12 18:19:09.559083: Epoch 1955 +2026-04-12 18:19:09.560961: Current learning rate: 0.00547 +2026-04-12 18:20:51.094947: train_loss -0.3893 +2026-04-12 18:20:51.099776: val_loss -0.3541 +2026-04-12 18:20:51.102252: Pseudo dice [0.4499, 0.0, 0.7208, 0.7583, 0.0563, 0.4013, 0.7109] +2026-04-12 18:20:51.104047: Epoch time: 101.54 s +2026-04-12 18:20:52.306372: +2026-04-12 18:20:52.308136: Epoch 1956 +2026-04-12 18:20:52.310105: Current learning rate: 0.00546 +2026-04-12 18:22:33.823472: train_loss -0.3862 +2026-04-12 18:22:33.828340: val_loss -0.35 +2026-04-12 18:22:33.830105: Pseudo dice [0.491, 0.0, 0.7318, 0.2705, 0.1711, 0.7234, 0.7437] +2026-04-12 18:22:33.831724: Epoch time: 101.52 s +2026-04-12 18:22:35.047805: +2026-04-12 18:22:35.049543: Epoch 1957 +2026-04-12 18:22:35.051364: Current learning rate: 0.00546 +2026-04-12 18:24:16.645061: train_loss -0.3759 +2026-04-12 18:24:16.649924: val_loss -0.3238 +2026-04-12 18:24:16.651616: Pseudo dice [0.0, 0.0, 0.777, 0.8748, 0.3129, 0.7718, 0.5131] +2026-04-12 18:24:16.653437: Epoch time: 101.6 s +2026-04-12 18:24:17.856136: +2026-04-12 18:24:17.857797: Epoch 1958 +2026-04-12 18:24:17.859792: Current learning rate: 0.00546 +2026-04-12 18:25:59.611143: train_loss -0.3864 +2026-04-12 18:25:59.616335: val_loss -0.3717 +2026-04-12 18:25:59.617956: Pseudo dice [0.0, 0.0, 0.674, 0.6868, 0.4212, 0.5448, 0.6393] +2026-04-12 18:25:59.619564: Epoch time: 101.76 s +2026-04-12 18:26:00.827518: +2026-04-12 18:26:00.829264: Epoch 1959 +2026-04-12 18:26:00.831142: Current learning rate: 0.00546 +2026-04-12 18:27:42.381749: train_loss -0.3619 +2026-04-12 18:27:42.387449: val_loss -0.3529 +2026-04-12 18:27:42.389020: Pseudo dice [0.0, 0.0, 0.6721, 0.5009, 0.5102, 0.8293, 0.5893] +2026-04-12 18:27:42.390719: Epoch time: 101.56 s +2026-04-12 18:27:43.599086: +2026-04-12 18:27:43.600404: Epoch 1960 +2026-04-12 18:27:43.602287: Current learning rate: 0.00546 +2026-04-12 18:29:25.064293: train_loss -0.3821 +2026-04-12 18:29:25.068730: val_loss -0.3636 +2026-04-12 18:29:25.070192: Pseudo dice [0.0, 0.0, 0.3889, 0.8167, 0.2979, 0.845, 0.7353] +2026-04-12 18:29:25.071758: Epoch time: 101.47 s +2026-04-12 18:29:26.289965: +2026-04-12 18:29:26.291462: Epoch 1961 +2026-04-12 18:29:26.293039: Current learning rate: 0.00545 +2026-04-12 18:31:07.977606: train_loss -0.3853 +2026-04-12 18:31:07.982505: val_loss -0.3647 +2026-04-12 18:31:07.984459: Pseudo dice [0.0, 0.0, 0.6459, 0.452, 0.2463, 0.6866, 0.7897] +2026-04-12 18:31:07.986494: Epoch time: 101.69 s +2026-04-12 18:31:09.200124: +2026-04-12 18:31:09.202056: Epoch 1962 +2026-04-12 18:31:09.203979: Current learning rate: 0.00545 +2026-04-12 18:32:50.848844: train_loss -0.3869 +2026-04-12 18:32:50.856010: val_loss -0.3053 +2026-04-12 18:32:50.858194: Pseudo dice [0.0009, 0.0, 0.4877, 0.5222, 0.0485, 0.6273, 0.713] +2026-04-12 18:32:50.860443: Epoch time: 101.65 s +2026-04-12 18:32:52.069484: +2026-04-12 18:32:52.071163: Epoch 1963 +2026-04-12 18:32:52.073924: Current learning rate: 0.00545 +2026-04-12 18:34:33.692958: train_loss -0.379 +2026-04-12 18:34:33.699186: val_loss -0.3394 +2026-04-12 18:34:33.700872: Pseudo dice [0.1266, 0.0, 0.6931, 0.8053, 0.3583, 0.6745, 0.5782] +2026-04-12 18:34:33.702438: Epoch time: 101.63 s +2026-04-12 18:34:34.896333: +2026-04-12 18:34:34.898163: Epoch 1964 +2026-04-12 18:34:34.900658: Current learning rate: 0.00545 +2026-04-12 18:36:16.545742: train_loss -0.412 +2026-04-12 18:36:16.552461: val_loss -0.3634 +2026-04-12 18:36:16.554484: Pseudo dice [0.5472, 0.0, 0.7878, 0.1192, 0.3824, 0.3428, 0.7904] +2026-04-12 18:36:16.556610: Epoch time: 101.65 s +2026-04-12 18:36:17.762308: +2026-04-12 18:36:17.764121: Epoch 1965 +2026-04-12 18:36:17.766725: Current learning rate: 0.00544 +2026-04-12 18:37:59.318271: train_loss -0.397 +2026-04-12 18:37:59.323494: val_loss -0.3627 +2026-04-12 18:37:59.325228: Pseudo dice [0.1195, 0.0, 0.7578, 0.4714, 0.3398, 0.8067, 0.3488] +2026-04-12 18:37:59.326795: Epoch time: 101.56 s +2026-04-12 18:38:00.542573: +2026-04-12 18:38:00.544105: Epoch 1966 +2026-04-12 18:38:00.546130: Current learning rate: 0.00544 +2026-04-12 18:39:42.343472: train_loss -0.3969 +2026-04-12 18:39:42.350872: val_loss -0.3451 +2026-04-12 18:39:42.352966: Pseudo dice [0.221, 0.0, 0.7769, 0.1876, 0.3661, 0.8092, 0.3618] +2026-04-12 18:39:42.354723: Epoch time: 101.8 s +2026-04-12 18:39:43.559394: +2026-04-12 18:39:43.560908: Epoch 1967 +2026-04-12 18:39:43.562688: Current learning rate: 0.00544 +2026-04-12 18:41:25.380877: train_loss -0.38 +2026-04-12 18:41:25.386767: val_loss -0.3593 +2026-04-12 18:41:25.388878: Pseudo dice [0.2919, 0.0, 0.6505, 0.5551, 0.192, 0.7143, 0.862] +2026-04-12 18:41:25.390726: Epoch time: 101.82 s +2026-04-12 18:41:26.592194: +2026-04-12 18:41:26.593994: Epoch 1968 +2026-04-12 18:41:26.595921: Current learning rate: 0.00544 +2026-04-12 18:43:08.242808: train_loss -0.3923 +2026-04-12 18:43:08.248404: val_loss -0.3508 +2026-04-12 18:43:08.250279: Pseudo dice [0.1841, 0.0, 0.2041, 0.6643, 0.4571, 0.4987, 0.7042] +2026-04-12 18:43:08.252371: Epoch time: 101.65 s +2026-04-12 18:43:10.431191: +2026-04-12 18:43:10.433191: Epoch 1969 +2026-04-12 18:43:10.435241: Current learning rate: 0.00543 +2026-04-12 18:44:51.937849: train_loss -0.3903 +2026-04-12 18:44:51.944795: val_loss -0.3594 +2026-04-12 18:44:51.946617: Pseudo dice [0.4484, 0.0, 0.8335, 0.6287, 0.3631, 0.7291, 0.6184] +2026-04-12 18:44:51.948395: Epoch time: 101.51 s +2026-04-12 18:44:53.151639: +2026-04-12 18:44:53.153297: Epoch 1970 +2026-04-12 18:44:53.155368: Current learning rate: 0.00543 +2026-04-12 18:46:35.005527: train_loss -0.3952 +2026-04-12 18:46:35.010013: val_loss -0.3827 +2026-04-12 18:46:35.011657: Pseudo dice [0.6669, 0.0, 0.6798, 0.3074, 0.4471, 0.7184, 0.5101] +2026-04-12 18:46:35.013047: Epoch time: 101.86 s +2026-04-12 18:46:36.226424: +2026-04-12 18:46:36.228294: Epoch 1971 +2026-04-12 18:46:36.230057: Current learning rate: 0.00543 +2026-04-12 18:48:17.902447: train_loss -0.4125 +2026-04-12 18:48:17.908720: val_loss -0.3893 +2026-04-12 18:48:17.910887: Pseudo dice [0.316, 0.0, 0.7736, 0.7796, 0.4431, 0.7927, 0.5681] +2026-04-12 18:48:17.912910: Epoch time: 101.68 s +2026-04-12 18:48:19.124891: +2026-04-12 18:48:19.127181: Epoch 1972 +2026-04-12 18:48:19.128740: Current learning rate: 0.00543 +2026-04-12 18:50:00.938193: train_loss -0.4227 +2026-04-12 18:50:00.944659: val_loss -0.4003 +2026-04-12 18:50:00.946701: Pseudo dice [0.6667, 0.0, 0.732, 0.7581, 0.3799, 0.4275, 0.7342] +2026-04-12 18:50:00.949129: Epoch time: 101.82 s +2026-04-12 18:50:02.185317: +2026-04-12 18:50:02.187332: Epoch 1973 +2026-04-12 18:50:02.189635: Current learning rate: 0.00542 +2026-04-12 18:51:43.897451: train_loss -0.4292 +2026-04-12 18:51:43.903223: val_loss -0.3465 +2026-04-12 18:51:43.905686: Pseudo dice [0.3491, 0.0, 0.386, 0.3953, 0.1841, 0.4849, 0.7573] +2026-04-12 18:51:43.907329: Epoch time: 101.72 s +2026-04-12 18:51:45.116266: +2026-04-12 18:51:45.118301: Epoch 1974 +2026-04-12 18:51:45.120476: Current learning rate: 0.00542 +2026-04-12 18:53:26.934684: train_loss -0.4078 +2026-04-12 18:53:26.942967: val_loss -0.3599 +2026-04-12 18:53:26.944774: Pseudo dice [0.5651, 0.0, 0.7227, 0.6349, 0.3673, 0.7271, 0.7793] +2026-04-12 18:53:26.946270: Epoch time: 101.82 s +2026-04-12 18:53:28.164348: +2026-04-12 18:53:28.166019: Epoch 1975 +2026-04-12 18:53:28.167778: Current learning rate: 0.00542 +2026-04-12 18:55:09.895831: train_loss -0.4072 +2026-04-12 18:55:09.903216: val_loss -0.3698 +2026-04-12 18:55:09.905855: Pseudo dice [0.3008, 0.0, 0.6375, 0.8931, 0.3824, 0.8284, 0.5783] +2026-04-12 18:55:09.908732: Epoch time: 101.73 s +2026-04-12 18:55:11.123767: +2026-04-12 18:55:11.125755: Epoch 1976 +2026-04-12 18:55:11.127666: Current learning rate: 0.00542 +2026-04-12 18:56:52.846494: train_loss -0.4143 +2026-04-12 18:56:52.851663: val_loss -0.3541 +2026-04-12 18:56:52.853828: Pseudo dice [0.6489, 0.0, 0.7086, 0.0565, 0.2161, 0.6974, 0.7513] +2026-04-12 18:56:52.855497: Epoch time: 101.73 s +2026-04-12 18:56:54.077487: +2026-04-12 18:56:54.079156: Epoch 1977 +2026-04-12 18:56:54.080834: Current learning rate: 0.00541 +2026-04-12 18:58:35.823930: train_loss -0.4041 +2026-04-12 18:58:35.840718: val_loss -0.3655 +2026-04-12 18:58:35.842266: Pseudo dice [0.2009, 0.0, 0.5855, 0.7198, 0.464, 0.4209, 0.7541] +2026-04-12 18:58:35.844603: Epoch time: 101.75 s +2026-04-12 18:58:37.065536: +2026-04-12 18:58:37.067269: Epoch 1978 +2026-04-12 18:58:37.069265: Current learning rate: 0.00541 +2026-04-12 19:00:18.668051: train_loss -0.3957 +2026-04-12 19:00:18.688316: val_loss -0.3385 +2026-04-12 19:00:18.690418: Pseudo dice [0.3937, 0.0, 0.6768, 0.7907, 0.1773, 0.1784, 0.5266] +2026-04-12 19:00:18.691947: Epoch time: 101.61 s +2026-04-12 19:00:19.894823: +2026-04-12 19:00:19.896865: Epoch 1979 +2026-04-12 19:00:19.898730: Current learning rate: 0.00541 +2026-04-12 19:02:01.479404: train_loss -0.3954 +2026-04-12 19:02:01.485732: val_loss -0.3622 +2026-04-12 19:02:01.488097: Pseudo dice [0.2716, 0.0, 0.4594, 0.4566, 0.2726, 0.7158, 0.7456] +2026-04-12 19:02:01.490576: Epoch time: 101.59 s +2026-04-12 19:02:02.701006: +2026-04-12 19:02:02.702687: Epoch 1980 +2026-04-12 19:02:02.704265: Current learning rate: 0.00541 +2026-04-12 19:03:44.364130: train_loss -0.3964 +2026-04-12 19:03:44.368656: val_loss -0.3571 +2026-04-12 19:03:44.370282: Pseudo dice [0.302, 0.0, 0.5806, 0.6741, 0.3375, 0.7309, 0.6528] +2026-04-12 19:03:44.371825: Epoch time: 101.67 s +2026-04-12 19:03:45.590639: +2026-04-12 19:03:45.592885: Epoch 1981 +2026-04-12 19:03:45.595183: Current learning rate: 0.0054 +2026-04-12 19:05:27.306967: train_loss -0.4101 +2026-04-12 19:05:27.311334: val_loss -0.3793 +2026-04-12 19:05:27.312785: Pseudo dice [0.4571, 0.0, 0.7005, 0.723, 0.4192, 0.4272, 0.8131] +2026-04-12 19:05:27.314208: Epoch time: 101.72 s +2026-04-12 19:05:28.537704: +2026-04-12 19:05:28.540545: Epoch 1982 +2026-04-12 19:05:28.542642: Current learning rate: 0.0054 +2026-04-12 19:07:10.114294: train_loss -0.3745 +2026-04-12 19:07:10.118496: val_loss -0.3055 +2026-04-12 19:07:10.121411: Pseudo dice [0.1957, 0.0, 0.7925, 0.5765, 0.0102, 0.3913, 0.6829] +2026-04-12 19:07:10.122912: Epoch time: 101.58 s +2026-04-12 19:07:11.327654: +2026-04-12 19:07:11.329197: Epoch 1983 +2026-04-12 19:07:11.330683: Current learning rate: 0.0054 +2026-04-12 19:08:52.887204: train_loss -0.39 +2026-04-12 19:08:52.891716: val_loss -0.3259 +2026-04-12 19:08:52.893605: Pseudo dice [0.4743, 0.0, 0.563, 0.7532, 0.2466, 0.4834, 0.6008] +2026-04-12 19:08:52.895428: Epoch time: 101.56 s +2026-04-12 19:08:54.102820: +2026-04-12 19:08:54.104403: Epoch 1984 +2026-04-12 19:08:54.106524: Current learning rate: 0.0054 +2026-04-12 19:10:35.829787: train_loss -0.3893 +2026-04-12 19:10:35.834461: val_loss -0.3698 +2026-04-12 19:10:35.836510: Pseudo dice [0.7339, 0.0, 0.6629, 0.3481, 0.2794, 0.7844, 0.9369] +2026-04-12 19:10:35.838117: Epoch time: 101.73 s +2026-04-12 19:10:37.042036: +2026-04-12 19:10:37.043694: Epoch 1985 +2026-04-12 19:10:37.045508: Current learning rate: 0.0054 +2026-04-12 19:12:18.901900: train_loss -0.3922 +2026-04-12 19:12:18.906424: val_loss -0.3414 +2026-04-12 19:12:18.908121: Pseudo dice [0.4023, 0.0, 0.6929, 0.7343, 0.3834, 0.4494, 0.7247] +2026-04-12 19:12:18.909598: Epoch time: 101.86 s +2026-04-12 19:12:20.133557: +2026-04-12 19:12:20.135183: Epoch 1986 +2026-04-12 19:12:20.136941: Current learning rate: 0.00539 +2026-04-12 19:14:01.926085: train_loss -0.3899 +2026-04-12 19:14:01.931850: val_loss -0.3679 +2026-04-12 19:14:01.933764: Pseudo dice [0.3867, 0.0, 0.7126, 0.8003, 0.3019, 0.686, 0.8041] +2026-04-12 19:14:01.935930: Epoch time: 101.8 s +2026-04-12 19:14:03.147378: +2026-04-12 19:14:03.149014: Epoch 1987 +2026-04-12 19:14:03.150675: Current learning rate: 0.00539 +2026-04-12 19:15:44.928330: train_loss -0.3931 +2026-04-12 19:15:44.933728: val_loss -0.3543 +2026-04-12 19:15:44.935253: Pseudo dice [0.1973, 0.0, 0.6815, 0.5429, 0.4282, 0.6584, 0.606] +2026-04-12 19:15:44.936745: Epoch time: 101.78 s +2026-04-12 19:15:46.142277: +2026-04-12 19:15:46.143768: Epoch 1988 +2026-04-12 19:15:46.145445: Current learning rate: 0.00539 +2026-04-12 19:17:28.952068: train_loss -0.4073 +2026-04-12 19:17:28.957027: val_loss -0.3579 +2026-04-12 19:17:28.958764: Pseudo dice [0.6523, 0.0, 0.7257, 0.5514, 0.3955, 0.7089, 0.7778] +2026-04-12 19:17:28.960832: Epoch time: 102.81 s +2026-04-12 19:17:30.175404: +2026-04-12 19:17:30.177032: Epoch 1989 +2026-04-12 19:17:30.178980: Current learning rate: 0.00539 +2026-04-12 19:19:11.871201: train_loss -0.4001 +2026-04-12 19:19:11.876425: val_loss -0.3531 +2026-04-12 19:19:11.878073: Pseudo dice [0.2848, 0.0, 0.7653, 0.8786, 0.4943, 0.543, 0.8825] +2026-04-12 19:19:11.879396: Epoch time: 101.7 s +2026-04-12 19:19:13.088752: +2026-04-12 19:19:13.090465: Epoch 1990 +2026-04-12 19:19:13.092494: Current learning rate: 0.00538 +2026-04-12 19:20:54.965926: train_loss -0.3892 +2026-04-12 19:20:54.970869: val_loss -0.3668 +2026-04-12 19:20:54.972597: Pseudo dice [0.0211, 0.0, 0.708, 0.6825, 0.526, 0.753, 0.8617] +2026-04-12 19:20:54.974231: Epoch time: 101.88 s +2026-04-12 19:20:56.185408: +2026-04-12 19:20:56.187102: Epoch 1991 +2026-04-12 19:20:56.188974: Current learning rate: 0.00538 +2026-04-12 19:22:37.963930: train_loss -0.4067 +2026-04-12 19:22:37.968602: val_loss -0.3451 +2026-04-12 19:22:37.970229: Pseudo dice [0.2121, 0.0, 0.6913, 0.6639, 0.1469, 0.6288, 0.8251] +2026-04-12 19:22:37.972244: Epoch time: 101.78 s +2026-04-12 19:22:39.152644: +2026-04-12 19:22:39.154451: Epoch 1992 +2026-04-12 19:22:39.157458: Current learning rate: 0.00538 +2026-04-12 19:24:20.935755: train_loss -0.4119 +2026-04-12 19:24:20.940580: val_loss -0.3352 +2026-04-12 19:24:20.942234: Pseudo dice [0.2497, 0.0, 0.6447, 0.7752, 0.251, 0.4221, 0.877] +2026-04-12 19:24:20.944247: Epoch time: 101.79 s +2026-04-12 19:24:22.154701: +2026-04-12 19:24:22.156253: Epoch 1993 +2026-04-12 19:24:22.158125: Current learning rate: 0.00538 +2026-04-12 19:26:04.049333: train_loss -0.3923 +2026-04-12 19:26:04.054126: val_loss -0.345 +2026-04-12 19:26:04.055722: Pseudo dice [0.5647, 0.0, 0.6265, 0.5415, 0.4243, 0.7154, 0.7021] +2026-04-12 19:26:04.057508: Epoch time: 101.9 s +2026-04-12 19:26:05.270235: +2026-04-12 19:26:05.272013: Epoch 1994 +2026-04-12 19:26:05.273841: Current learning rate: 0.00537 +2026-04-12 19:27:47.186611: train_loss -0.4086 +2026-04-12 19:27:47.192221: val_loss -0.3476 +2026-04-12 19:27:47.194094: Pseudo dice [0.0, 0.0, 0.5732, 0.7407, 0.3627, 0.7743, 0.7159] +2026-04-12 19:27:47.196985: Epoch time: 101.92 s +2026-04-12 19:27:48.412047: +2026-04-12 19:27:48.413759: Epoch 1995 +2026-04-12 19:27:48.415492: Current learning rate: 0.00537 +2026-04-12 19:29:30.202912: train_loss -0.3922 +2026-04-12 19:29:30.207878: val_loss -0.3369 +2026-04-12 19:29:30.209601: Pseudo dice [0.0, 0.0, 0.4862, 0.5288, 0.5239, 0.4856, 0.6812] +2026-04-12 19:29:30.211807: Epoch time: 101.79 s +2026-04-12 19:29:31.417295: +2026-04-12 19:29:31.419945: Epoch 1996 +2026-04-12 19:29:31.422215: Current learning rate: 0.00537 +2026-04-12 19:31:13.163638: train_loss -0.3902 +2026-04-12 19:31:13.168602: val_loss -0.3362 +2026-04-12 19:31:13.170542: Pseudo dice [0.0, 0.0, 0.6418, 0.6123, 0.3762, 0.6813, 0.8367] +2026-04-12 19:31:13.171923: Epoch time: 101.75 s +2026-04-12 19:31:14.378403: +2026-04-12 19:31:14.380410: Epoch 1997 +2026-04-12 19:31:14.382205: Current learning rate: 0.00537 +2026-04-12 19:32:56.275171: train_loss -0.3942 +2026-04-12 19:32:56.281044: val_loss -0.3129 +2026-04-12 19:32:56.283211: Pseudo dice [0.0, 0.0, 0.7772, 0.6568, 0.0, 0.5825, 0.4929] +2026-04-12 19:32:56.285142: Epoch time: 101.9 s +2026-04-12 19:32:57.506012: +2026-04-12 19:32:57.507739: Epoch 1998 +2026-04-12 19:32:57.509588: Current learning rate: 0.00536 +2026-04-12 19:34:39.358829: train_loss -0.3808 +2026-04-12 19:34:39.364491: val_loss -0.35 +2026-04-12 19:34:39.366292: Pseudo dice [0.475, 0.0, 0.7547, 0.753, 0.3268, 0.7978, 0.7594] +2026-04-12 19:34:39.367791: Epoch time: 101.86 s +2026-04-12 19:34:40.572798: +2026-04-12 19:34:40.574960: Epoch 1999 +2026-04-12 19:34:40.576954: Current learning rate: 0.00536 +2026-04-12 19:36:22.190878: train_loss -0.4193 +2026-04-12 19:36:22.195570: val_loss -0.3742 +2026-04-12 19:36:22.197464: Pseudo dice [0.5729, 0.0, 0.737, 0.5259, 0.4909, 0.5802, 0.6634] +2026-04-12 19:36:22.198925: Epoch time: 101.62 s +2026-04-12 19:36:25.048101: +2026-04-12 19:36:25.049946: Epoch 2000 +2026-04-12 19:36:25.051854: Current learning rate: 0.00536 +2026-04-12 19:38:06.626152: train_loss -0.4185 +2026-04-12 19:38:06.631544: val_loss -0.3846 +2026-04-12 19:38:06.633494: Pseudo dice [0.4162, 0.0, 0.7861, 0.8805, 0.5639, 0.7266, 0.7727] +2026-04-12 19:38:06.635626: Epoch time: 101.58 s +2026-04-12 19:38:07.839979: +2026-04-12 19:38:07.842084: Epoch 2001 +2026-04-12 19:38:07.843881: Current learning rate: 0.00536 +2026-04-12 19:39:49.337571: train_loss -0.4081 +2026-04-12 19:39:49.343447: val_loss -0.361 +2026-04-12 19:39:49.345154: Pseudo dice [0.6094, 0.0, 0.8318, 0.898, 0.4485, 0.6573, 0.6035] +2026-04-12 19:39:49.347014: Epoch time: 101.5 s +2026-04-12 19:39:50.557784: +2026-04-12 19:39:50.559531: Epoch 2002 +2026-04-12 19:39:50.561424: Current learning rate: 0.00535 +2026-04-12 19:41:32.057126: train_loss -0.4117 +2026-04-12 19:41:32.063221: val_loss -0.3528 +2026-04-12 19:41:32.065386: Pseudo dice [0.4527, 0.0, 0.7163, 0.6485, 0.2734, 0.6618, 0.8445] +2026-04-12 19:41:32.067146: Epoch time: 101.5 s +2026-04-12 19:41:33.282234: +2026-04-12 19:41:33.284114: Epoch 2003 +2026-04-12 19:41:33.286240: Current learning rate: 0.00535 +2026-04-12 19:43:14.847175: train_loss -0.421 +2026-04-12 19:43:14.852819: val_loss -0.3603 +2026-04-12 19:43:14.854363: Pseudo dice [0.4368, 0.0, 0.7856, 0.566, 0.3033, 0.812, 0.8841] +2026-04-12 19:43:14.855660: Epoch time: 101.57 s +2026-04-12 19:43:16.066734: +2026-04-12 19:43:16.068308: Epoch 2004 +2026-04-12 19:43:16.070094: Current learning rate: 0.00535 +2026-04-12 19:44:57.865096: train_loss -0.373 +2026-04-12 19:44:57.871214: val_loss -0.3472 +2026-04-12 19:44:57.874178: Pseudo dice [0.0, 0.0, 0.5765, 0.7923, 0.4342, 0.5702, 0.6691] +2026-04-12 19:44:57.876018: Epoch time: 101.8 s +2026-04-12 19:44:59.097832: +2026-04-12 19:44:59.099576: Epoch 2005 +2026-04-12 19:44:59.101705: Current learning rate: 0.00535 +2026-04-12 19:46:40.737594: train_loss -0.3993 +2026-04-12 19:46:40.742635: val_loss -0.3673 +2026-04-12 19:46:40.744232: Pseudo dice [0.0, 0.0, 0.6069, 0.8005, 0.3908, 0.6196, 0.5504] +2026-04-12 19:46:40.746137: Epoch time: 101.64 s +2026-04-12 19:46:41.954043: +2026-04-12 19:46:41.955528: Epoch 2006 +2026-04-12 19:46:41.957261: Current learning rate: 0.00534 +2026-04-12 19:48:23.450733: train_loss -0.4012 +2026-04-12 19:48:23.455594: val_loss -0.3565 +2026-04-12 19:48:23.457239: Pseudo dice [0.0, 0.0, 0.8176, 0.8938, 0.3769, 0.7631, 0.8761] +2026-04-12 19:48:23.459423: Epoch time: 101.5 s +2026-04-12 19:48:24.670899: +2026-04-12 19:48:24.672649: Epoch 2007 +2026-04-12 19:48:24.674418: Current learning rate: 0.00534 +2026-04-12 19:50:06.197221: train_loss -0.3969 +2026-04-12 19:50:06.201975: val_loss -0.348 +2026-04-12 19:50:06.203544: Pseudo dice [0.0, 0.0, 0.788, 0.7326, 0.6396, 0.4683, 0.5935] +2026-04-12 19:50:06.205805: Epoch time: 101.53 s +2026-04-12 19:50:08.368539: +2026-04-12 19:50:08.370426: Epoch 2008 +2026-04-12 19:50:08.372493: Current learning rate: 0.00534 +2026-04-12 19:51:49.877595: train_loss -0.3974 +2026-04-12 19:51:49.884487: val_loss -0.3333 +2026-04-12 19:51:49.886622: Pseudo dice [0.0, 0.0, 0.6064, 0.7311, 0.3973, 0.7947, 0.6982] +2026-04-12 19:51:49.888345: Epoch time: 101.51 s +2026-04-12 19:51:51.106908: +2026-04-12 19:51:51.108737: Epoch 2009 +2026-04-12 19:51:51.110492: Current learning rate: 0.00534 +2026-04-12 19:53:32.805053: train_loss -0.4036 +2026-04-12 19:53:32.809937: val_loss -0.3673 +2026-04-12 19:53:32.811653: Pseudo dice [0.0, 0.0, 0.7461, 0.7331, 0.4611, 0.599, 0.7786] +2026-04-12 19:53:32.813408: Epoch time: 101.7 s +2026-04-12 19:53:34.003690: +2026-04-12 19:53:34.005363: Epoch 2010 +2026-04-12 19:53:34.007498: Current learning rate: 0.00533 +2026-04-12 19:55:15.386224: train_loss -0.4001 +2026-04-12 19:55:15.391528: val_loss -0.3508 +2026-04-12 19:55:15.393350: Pseudo dice [0.0, 0.0, 0.6979, 0.708, 0.2391, 0.7425, 0.8919] +2026-04-12 19:55:15.395103: Epoch time: 101.39 s +2026-04-12 19:55:16.615500: +2026-04-12 19:55:16.617175: Epoch 2011 +2026-04-12 19:55:16.619006: Current learning rate: 0.00533 +2026-04-12 19:56:57.945011: train_loss -0.4023 +2026-04-12 19:56:57.950114: val_loss -0.355 +2026-04-12 19:56:57.958266: Pseudo dice [0.0, 0.0, 0.6784, 0.8619, 0.3826, 0.6025, 0.8334] +2026-04-12 19:56:57.961275: Epoch time: 101.33 s +2026-04-12 19:56:59.338502: +2026-04-12 19:56:59.340082: Epoch 2012 +2026-04-12 19:56:59.342041: Current learning rate: 0.00533 +2026-04-12 19:58:40.965115: train_loss -0.4046 +2026-04-12 19:58:40.970742: val_loss -0.3569 +2026-04-12 19:58:40.972759: Pseudo dice [0.0, 0.0, 0.4406, 0.7629, 0.4165, 0.5303, 0.8822] +2026-04-12 19:58:40.974591: Epoch time: 101.63 s +2026-04-12 19:58:42.162038: +2026-04-12 19:58:42.163975: Epoch 2013 +2026-04-12 19:58:42.166249: Current learning rate: 0.00533 +2026-04-12 20:00:23.887288: train_loss -0.3888 +2026-04-12 20:00:23.892687: val_loss -0.3576 +2026-04-12 20:00:23.894546: Pseudo dice [0.0, 0.0, 0.7201, 0.7224, 0.1702, 0.8116, 0.6191] +2026-04-12 20:00:23.896517: Epoch time: 101.73 s +2026-04-12 20:00:25.091584: +2026-04-12 20:00:25.093498: Epoch 2014 +2026-04-12 20:00:25.095410: Current learning rate: 0.00533 +2026-04-12 20:02:06.772170: train_loss -0.3933 +2026-04-12 20:02:06.778069: val_loss -0.3269 +2026-04-12 20:02:06.779783: Pseudo dice [0.0235, 0.0, 0.5635, 0.6756, 0.2319, 0.5754, 0.6816] +2026-04-12 20:02:06.781994: Epoch time: 101.68 s +2026-04-12 20:02:07.995583: +2026-04-12 20:02:07.997203: Epoch 2015 +2026-04-12 20:02:07.999054: Current learning rate: 0.00532 +2026-04-12 20:03:49.699313: train_loss -0.3976 +2026-04-12 20:03:49.704622: val_loss -0.3675 +2026-04-12 20:03:49.706391: Pseudo dice [0.1736, 0.0, 0.7377, 0.8335, 0.4186, 0.5101, 0.6423] +2026-04-12 20:03:49.708054: Epoch time: 101.71 s +2026-04-12 20:03:50.910268: +2026-04-12 20:03:50.912947: Epoch 2016 +2026-04-12 20:03:50.916004: Current learning rate: 0.00532 +2026-04-12 20:05:32.792872: train_loss -0.4165 +2026-04-12 20:05:32.797525: val_loss -0.3233 +2026-04-12 20:05:32.799196: Pseudo dice [0.2303, 0.0, 0.8173, 0.6272, 0.1963, 0.3643, 0.6447] +2026-04-12 20:05:32.800675: Epoch time: 101.89 s +2026-04-12 20:05:34.010890: +2026-04-12 20:05:34.012459: Epoch 2017 +2026-04-12 20:05:34.014192: Current learning rate: 0.00532 +2026-04-12 20:07:15.697027: train_loss -0.3868 +2026-04-12 20:07:15.702330: val_loss -0.3443 +2026-04-12 20:07:15.707901: Pseudo dice [0.184, 0.0, 0.6224, 0.5347, 0.3624, 0.4751, 0.8031] +2026-04-12 20:07:15.709996: Epoch time: 101.69 s +2026-04-12 20:07:16.921569: +2026-04-12 20:07:16.923271: Epoch 2018 +2026-04-12 20:07:16.925288: Current learning rate: 0.00532 +2026-04-12 20:08:58.522405: train_loss -0.3797 +2026-04-12 20:08:58.527153: val_loss -0.3389 +2026-04-12 20:08:58.529089: Pseudo dice [0.0, 0.0, 0.5753, 0.7513, 0.2726, 0.5316, 0.9122] +2026-04-12 20:08:58.530772: Epoch time: 101.6 s +2026-04-12 20:08:59.728092: +2026-04-12 20:08:59.730316: Epoch 2019 +2026-04-12 20:08:59.732435: Current learning rate: 0.00531 +2026-04-12 20:10:41.375376: train_loss -0.4014 +2026-04-12 20:10:41.380543: val_loss -0.3928 +2026-04-12 20:10:41.382567: Pseudo dice [0.0, 0.0, 0.4657, 0.8108, 0.3966, 0.8788, 0.8922] +2026-04-12 20:10:41.384582: Epoch time: 101.65 s +2026-04-12 20:10:42.580142: +2026-04-12 20:10:42.581826: Epoch 2020 +2026-04-12 20:10:42.583695: Current learning rate: 0.00531 +2026-04-12 20:12:24.314447: train_loss -0.4167 +2026-04-12 20:12:24.319729: val_loss -0.3454 +2026-04-12 20:12:24.321187: Pseudo dice [0.0, 0.0, 0.6664, 0.5151, 0.512, 0.6879, 0.6366] +2026-04-12 20:12:24.323759: Epoch time: 101.74 s +2026-04-12 20:12:25.542019: +2026-04-12 20:12:25.543536: Epoch 2021 +2026-04-12 20:12:25.545422: Current learning rate: 0.00531 +2026-04-12 20:14:07.291198: train_loss -0.4135 +2026-04-12 20:14:07.296118: val_loss -0.3361 +2026-04-12 20:14:07.297497: Pseudo dice [0.0, 0.0, 0.5085, 0.5396, 0.1832, 0.4174, 0.8459] +2026-04-12 20:14:07.300198: Epoch time: 101.75 s +2026-04-12 20:14:08.513790: +2026-04-12 20:14:08.515580: Epoch 2022 +2026-04-12 20:14:08.517611: Current learning rate: 0.00531 +2026-04-12 20:15:50.326754: train_loss -0.3952 +2026-04-12 20:15:50.331502: val_loss -0.3794 +2026-04-12 20:15:50.333270: Pseudo dice [0.0, 0.0, 0.7486, 0.8482, 0.4417, 0.8555, 0.8217] +2026-04-12 20:15:50.334583: Epoch time: 101.82 s +2026-04-12 20:15:51.548417: +2026-04-12 20:15:51.549862: Epoch 2023 +2026-04-12 20:15:51.551683: Current learning rate: 0.0053 +2026-04-12 20:17:33.328761: train_loss -0.4003 +2026-04-12 20:17:33.333274: val_loss -0.362 +2026-04-12 20:17:33.334636: Pseudo dice [0.0, 0.0, 0.7517, 0.2287, 0.2256, 0.7749, 0.8324] +2026-04-12 20:17:33.336084: Epoch time: 101.78 s +2026-04-12 20:17:34.571884: +2026-04-12 20:17:34.573331: Epoch 2024 +2026-04-12 20:17:34.575113: Current learning rate: 0.0053 +2026-04-12 20:19:16.308140: train_loss -0.4069 +2026-04-12 20:19:16.313155: val_loss -0.3775 +2026-04-12 20:19:16.314550: Pseudo dice [0.0, 0.0, 0.6661, 0.8849, 0.3841, 0.5238, 0.8671] +2026-04-12 20:19:16.315869: Epoch time: 101.74 s +2026-04-12 20:19:17.522017: +2026-04-12 20:19:17.523631: Epoch 2025 +2026-04-12 20:19:17.525611: Current learning rate: 0.0053 +2026-04-12 20:20:59.004443: train_loss -0.4036 +2026-04-12 20:20:59.009454: val_loss -0.303 +2026-04-12 20:20:59.010934: Pseudo dice [0.0, 0.0, 0.5262, 0.5887, 0.1833, 0.6728, 0.8404] +2026-04-12 20:20:59.012569: Epoch time: 101.49 s +2026-04-12 20:21:00.218347: +2026-04-12 20:21:00.220320: Epoch 2026 +2026-04-12 20:21:00.222061: Current learning rate: 0.0053 +2026-04-12 20:22:41.943660: train_loss -0.4056 +2026-04-12 20:22:41.949067: val_loss -0.3489 +2026-04-12 20:22:41.950607: Pseudo dice [0.0, 0.0, 0.7616, 0.8204, 0.2373, 0.3616, 0.8132] +2026-04-12 20:22:41.953135: Epoch time: 101.73 s +2026-04-12 20:22:43.157040: +2026-04-12 20:22:43.158665: Epoch 2027 +2026-04-12 20:22:43.160686: Current learning rate: 0.00529 +2026-04-12 20:24:25.702947: train_loss -0.4217 +2026-04-12 20:24:25.709882: val_loss -0.358 +2026-04-12 20:24:25.711846: Pseudo dice [0.0, 0.0, 0.7392, 0.9019, 0.3132, 0.8625, 0.6263] +2026-04-12 20:24:25.713616: Epoch time: 102.55 s +2026-04-12 20:24:26.923333: +2026-04-12 20:24:26.925050: Epoch 2028 +2026-04-12 20:24:26.927006: Current learning rate: 0.00529 +2026-04-12 20:26:08.793226: train_loss -0.3946 +2026-04-12 20:26:08.798639: val_loss -0.3462 +2026-04-12 20:26:08.800462: Pseudo dice [0.0, 0.0, 0.7061, 0.7972, 0.2021, 0.3877, 0.7864] +2026-04-12 20:26:08.802542: Epoch time: 101.87 s +2026-04-12 20:26:10.018743: +2026-04-12 20:26:10.020525: Epoch 2029 +2026-04-12 20:26:10.022583: Current learning rate: 0.00529 +2026-04-12 20:27:51.760265: train_loss -0.3892 +2026-04-12 20:27:51.764512: val_loss -0.37 +2026-04-12 20:27:51.765990: Pseudo dice [0.0, 0.0, 0.6582, 0.4763, 0.4435, 0.4622, 0.79] +2026-04-12 20:27:51.767613: Epoch time: 101.74 s +2026-04-12 20:27:52.984173: +2026-04-12 20:27:52.985576: Epoch 2030 +2026-04-12 20:27:52.987476: Current learning rate: 0.00529 +2026-04-12 20:29:34.715498: train_loss -0.3995 +2026-04-12 20:29:34.721032: val_loss -0.346 +2026-04-12 20:29:34.722639: Pseudo dice [0.0, 0.0, 0.7152, 0.8824, 0.2787, 0.4883, 0.5144] +2026-04-12 20:29:34.724280: Epoch time: 101.73 s +2026-04-12 20:29:35.928655: +2026-04-12 20:29:35.930528: Epoch 2031 +2026-04-12 20:29:35.932357: Current learning rate: 0.00528 +2026-04-12 20:31:17.791967: train_loss -0.3911 +2026-04-12 20:31:17.799543: val_loss -0.3494 +2026-04-12 20:31:17.801186: Pseudo dice [0.0, 0.0, 0.7275, 0.6132, 0.3459, 0.0783, 0.7719] +2026-04-12 20:31:17.802916: Epoch time: 101.87 s +2026-04-12 20:31:19.013026: +2026-04-12 20:31:19.014728: Epoch 2032 +2026-04-12 20:31:19.017665: Current learning rate: 0.00528 +2026-04-12 20:33:00.627353: train_loss -0.3912 +2026-04-12 20:33:00.633301: val_loss -0.3602 +2026-04-12 20:33:00.635010: Pseudo dice [0.0, 0.0, 0.7199, 0.8695, 0.3104, 0.7504, 0.8134] +2026-04-12 20:33:00.637256: Epoch time: 101.62 s +2026-04-12 20:33:01.849225: +2026-04-12 20:33:01.851021: Epoch 2033 +2026-04-12 20:33:01.852955: Current learning rate: 0.00528 +2026-04-12 20:34:43.663573: train_loss -0.3989 +2026-04-12 20:34:43.668896: val_loss -0.3803 +2026-04-12 20:34:43.671693: Pseudo dice [0.0, 0.0, 0.8289, 0.8257, 0.4544, 0.7761, 0.92] +2026-04-12 20:34:43.673789: Epoch time: 101.82 s +2026-04-12 20:34:44.864506: +2026-04-12 20:34:44.866418: Epoch 2034 +2026-04-12 20:34:44.869027: Current learning rate: 0.00528 +2026-04-12 20:36:26.628645: train_loss -0.3824 +2026-04-12 20:36:26.634179: val_loss -0.3736 +2026-04-12 20:36:26.636307: Pseudo dice [0.0, 0.0, 0.6178, 0.1798, 0.1806, 0.8467, 0.7972] +2026-04-12 20:36:26.638229: Epoch time: 101.77 s +2026-04-12 20:36:27.843203: +2026-04-12 20:36:27.844716: Epoch 2035 +2026-04-12 20:36:27.846174: Current learning rate: 0.00527 +2026-04-12 20:38:09.666645: train_loss -0.387 +2026-04-12 20:38:09.672567: val_loss -0.3484 +2026-04-12 20:38:09.674124: Pseudo dice [0.0, 0.0, 0.6811, 0.718, 0.3852, 0.725, 0.8917] +2026-04-12 20:38:09.675959: Epoch time: 101.83 s +2026-04-12 20:38:10.873582: +2026-04-12 20:38:10.875538: Epoch 2036 +2026-04-12 20:38:10.877930: Current learning rate: 0.00527 +2026-04-12 20:39:52.489687: train_loss -0.3911 +2026-04-12 20:39:52.494385: val_loss -0.3424 +2026-04-12 20:39:52.496037: Pseudo dice [0.0, 0.0, 0.8107, 0.6174, 0.3348, 0.75, 0.6706] +2026-04-12 20:39:52.497655: Epoch time: 101.62 s +2026-04-12 20:39:53.698463: +2026-04-12 20:39:53.700052: Epoch 2037 +2026-04-12 20:39:53.701934: Current learning rate: 0.00527 +2026-04-12 20:41:35.317424: train_loss -0.4135 +2026-04-12 20:41:35.323600: val_loss -0.3604 +2026-04-12 20:41:35.325184: Pseudo dice [0.0, 0.0, 0.7753, 0.2744, 0.5281, 0.6811, 0.8999] +2026-04-12 20:41:35.327038: Epoch time: 101.62 s +2026-04-12 20:41:36.550474: +2026-04-12 20:41:36.552277: Epoch 2038 +2026-04-12 20:41:36.554186: Current learning rate: 0.00527 +2026-04-12 20:43:18.248868: train_loss -0.4034 +2026-04-12 20:43:18.253588: val_loss -0.3794 +2026-04-12 20:43:18.255268: Pseudo dice [0.0, 0.0, 0.3785, 0.6894, 0.4232, 0.7691, 0.8451] +2026-04-12 20:43:18.256543: Epoch time: 101.7 s +2026-04-12 20:43:19.454084: +2026-04-12 20:43:19.455709: Epoch 2039 +2026-04-12 20:43:19.457330: Current learning rate: 0.00526 +2026-04-12 20:45:01.210554: train_loss -0.3874 +2026-04-12 20:45:01.217483: val_loss -0.3382 +2026-04-12 20:45:01.219402: Pseudo dice [0.0, 0.0, 0.7249, 0.4804, 0.2721, 0.7266, 0.3671] +2026-04-12 20:45:01.220819: Epoch time: 101.76 s +2026-04-12 20:45:02.417529: +2026-04-12 20:45:02.419435: Epoch 2040 +2026-04-12 20:45:02.421119: Current learning rate: 0.00526 +2026-04-12 20:46:43.952218: train_loss -0.3978 +2026-04-12 20:46:43.957950: val_loss -0.3449 +2026-04-12 20:46:43.960608: Pseudo dice [0.0, 0.0, 0.7714, 0.8827, 0.4461, 0.766, 0.6868] +2026-04-12 20:46:43.962867: Epoch time: 101.54 s +2026-04-12 20:46:45.164756: +2026-04-12 20:46:45.166954: Epoch 2041 +2026-04-12 20:46:45.169701: Current learning rate: 0.00526 +2026-04-12 20:48:26.848785: train_loss -0.4025 +2026-04-12 20:48:26.853167: val_loss -0.3326 +2026-04-12 20:48:26.854625: Pseudo dice [0.0, 0.0, 0.5327, 0.5367, 0.3786, 0.5363, 0.7551] +2026-04-12 20:48:26.856088: Epoch time: 101.69 s +2026-04-12 20:48:28.077922: +2026-04-12 20:48:28.079933: Epoch 2042 +2026-04-12 20:48:28.081760: Current learning rate: 0.00526 +2026-04-12 20:50:09.906558: train_loss -0.3983 +2026-04-12 20:50:09.912524: val_loss -0.3546 +2026-04-12 20:50:09.914626: Pseudo dice [0.3008, 0.0, 0.5263, 0.4727, 0.2756, 0.8678, 0.8368] +2026-04-12 20:50:09.916601: Epoch time: 101.83 s +2026-04-12 20:50:11.113744: +2026-04-12 20:50:11.115268: Epoch 2043 +2026-04-12 20:50:11.117394: Current learning rate: 0.00526 +2026-04-12 20:51:52.805440: train_loss -0.3981 +2026-04-12 20:51:52.810128: val_loss -0.3582 +2026-04-12 20:51:52.811574: Pseudo dice [0.2624, 0.0, 0.7375, 0.7446, 0.5363, 0.1915, 0.7409] +2026-04-12 20:51:52.813591: Epoch time: 101.69 s +2026-04-12 20:51:54.031110: +2026-04-12 20:51:54.033084: Epoch 2044 +2026-04-12 20:51:54.035054: Current learning rate: 0.00525 +2026-04-12 20:53:35.899498: train_loss -0.3824 +2026-04-12 20:53:35.905917: val_loss -0.3591 +2026-04-12 20:53:35.907425: Pseudo dice [0.6881, 0.0, 0.7189, 0.8347, 0.2028, 0.4017, 0.6036] +2026-04-12 20:53:35.908869: Epoch time: 101.87 s +2026-04-12 20:53:37.114895: +2026-04-12 20:53:37.117038: Epoch 2045 +2026-04-12 20:53:37.119315: Current learning rate: 0.00525 +2026-04-12 20:55:18.890208: train_loss -0.3987 +2026-04-12 20:55:18.896040: val_loss -0.381 +2026-04-12 20:55:18.898270: Pseudo dice [0.3152, 0.0, 0.6913, 0.6385, 0.3965, 0.5824, 0.8144] +2026-04-12 20:55:18.900928: Epoch time: 101.78 s +2026-04-12 20:55:20.120758: +2026-04-12 20:55:20.122556: Epoch 2046 +2026-04-12 20:55:20.124727: Current learning rate: 0.00525 +2026-04-12 20:57:01.790359: train_loss -0.4077 +2026-04-12 20:57:01.794843: val_loss -0.3453 +2026-04-12 20:57:01.798638: Pseudo dice [0.4989, 0.0, 0.6996, 0.1929, 0.2939, 0.6345, 0.7619] +2026-04-12 20:57:01.800142: Epoch time: 101.67 s +2026-04-12 20:57:03.898037: +2026-04-12 20:57:03.899791: Epoch 2047 +2026-04-12 20:57:03.901730: Current learning rate: 0.00525 +2026-04-12 20:58:45.461277: train_loss -0.396 +2026-04-12 20:58:45.467986: val_loss -0.3563 +2026-04-12 20:58:45.469928: Pseudo dice [0.6011, 0.0, 0.5435, 0.7673, 0.289, 0.7623, 0.5524] +2026-04-12 20:58:45.471456: Epoch time: 101.57 s +2026-04-12 20:58:46.615750: +2026-04-12 20:58:46.626740: Epoch 2048 +2026-04-12 20:58:46.628616: Current learning rate: 0.00524 +2026-04-12 21:00:28.197380: train_loss -0.4169 +2026-04-12 21:00:28.202358: val_loss -0.3108 +2026-04-12 21:00:28.204333: Pseudo dice [0.2748, 0.0, 0.7977, 0.6189, 0.3647, 0.7037, 0.4895] +2026-04-12 21:00:28.205890: Epoch time: 101.58 s +2026-04-12 21:00:29.362175: +2026-04-12 21:00:29.363816: Epoch 2049 +2026-04-12 21:00:29.365791: Current learning rate: 0.00524 +2026-04-12 21:02:10.833362: train_loss -0.4141 +2026-04-12 21:02:10.838950: val_loss -0.3389 +2026-04-12 21:02:10.840652: Pseudo dice [0.2627, 0.0, 0.8105, 0.8614, 0.338, 0.66, 0.5971] +2026-04-12 21:02:10.842132: Epoch time: 101.47 s +2026-04-12 21:02:13.639172: +2026-04-12 21:02:13.641200: Epoch 2050 +2026-04-12 21:02:13.643493: Current learning rate: 0.00524 +2026-04-12 21:03:55.455292: train_loss -0.4087 +2026-04-12 21:03:55.461624: val_loss -0.3984 +2026-04-12 21:03:55.463695: Pseudo dice [0.2817, 0.0, 0.7686, 0.8304, 0.3829, 0.7906, 0.9006] +2026-04-12 21:03:55.465956: Epoch time: 101.82 s +2026-04-12 21:03:56.595631: +2026-04-12 21:03:56.597544: Epoch 2051 +2026-04-12 21:03:56.599656: Current learning rate: 0.00524 +2026-04-12 21:05:38.321866: train_loss -0.4064 +2026-04-12 21:05:38.327077: val_loss -0.3795 +2026-04-12 21:05:38.329483: Pseudo dice [0.3206, 0.0, 0.5778, 0.5447, 0.3231, 0.679, 0.8584] +2026-04-12 21:05:38.331290: Epoch time: 101.73 s +2026-04-12 21:05:39.505498: +2026-04-12 21:05:39.507367: Epoch 2052 +2026-04-12 21:05:39.509330: Current learning rate: 0.00523 +2026-04-12 21:07:21.125556: train_loss -0.3669 +2026-04-12 21:07:21.130441: val_loss -0.344 +2026-04-12 21:07:21.132221: Pseudo dice [0.6046, 0.0, 0.5665, 0.2994, 0.1369, 0.3216, 0.9041] +2026-04-12 21:07:21.133670: Epoch time: 101.62 s +2026-04-12 21:07:22.287807: +2026-04-12 21:07:22.289904: Epoch 2053 +2026-04-12 21:07:22.292238: Current learning rate: 0.00523 +2026-04-12 21:09:03.964056: train_loss -0.3748 +2026-04-12 21:09:03.969239: val_loss -0.3096 +2026-04-12 21:09:03.970927: Pseudo dice [0.2379, 0.0, 0.7009, 0.1581, 0.0201, 0.7126, 0.6946] +2026-04-12 21:09:03.972447: Epoch time: 101.68 s +2026-04-12 21:09:05.123824: +2026-04-12 21:09:05.125639: Epoch 2054 +2026-04-12 21:09:05.127681: Current learning rate: 0.00523 +2026-04-12 21:10:46.800166: train_loss -0.3623 +2026-04-12 21:10:46.807664: val_loss -0.3398 +2026-04-12 21:10:46.809610: Pseudo dice [0.4543, 0.0, 0.7307, 0.3524, 0.0912, 0.777, 0.5332] +2026-04-12 21:10:46.811688: Epoch time: 101.68 s +2026-04-12 21:10:47.962916: +2026-04-12 21:10:47.964882: Epoch 2055 +2026-04-12 21:10:47.966707: Current learning rate: 0.00523 +2026-04-12 21:12:29.534060: train_loss -0.3783 +2026-04-12 21:12:29.540844: val_loss -0.3548 +2026-04-12 21:12:29.542567: Pseudo dice [0.1854, 0.0, 0.7109, 0.7429, 0.256, 0.6031, 0.8263] +2026-04-12 21:12:29.544488: Epoch time: 101.57 s +2026-04-12 21:12:30.688783: +2026-04-12 21:12:30.690308: Epoch 2056 +2026-04-12 21:12:30.691845: Current learning rate: 0.00522 +2026-04-12 21:14:12.403946: train_loss -0.3894 +2026-04-12 21:14:12.409041: val_loss -0.3584 +2026-04-12 21:14:12.411002: Pseudo dice [0.3636, 0.0, 0.6669, 0.5499, 0.356, 0.5911, 0.678] +2026-04-12 21:14:12.413276: Epoch time: 101.72 s +2026-04-12 21:14:13.556184: +2026-04-12 21:14:13.557827: Epoch 2057 +2026-04-12 21:14:13.559649: Current learning rate: 0.00522 +2026-04-12 21:15:55.082629: train_loss -0.3835 +2026-04-12 21:15:55.087934: val_loss -0.363 +2026-04-12 21:15:55.090030: Pseudo dice [0.6532, 0.0, 0.725, 0.25, 0.2443, 0.5479, 0.887] +2026-04-12 21:15:55.092077: Epoch time: 101.53 s +2026-04-12 21:15:56.244752: +2026-04-12 21:15:56.246583: Epoch 2058 +2026-04-12 21:15:56.248304: Current learning rate: 0.00522 +2026-04-12 21:17:37.724791: train_loss -0.3849 +2026-04-12 21:17:37.729779: val_loss -0.3687 +2026-04-12 21:17:37.731135: Pseudo dice [0.0077, 0.0, 0.5678, 0.6231, 0.1984, 0.7641, 0.8317] +2026-04-12 21:17:37.733022: Epoch time: 101.48 s +2026-04-12 21:17:38.884511: +2026-04-12 21:17:38.887017: Epoch 2059 +2026-04-12 21:17:38.889984: Current learning rate: 0.00522 +2026-04-12 21:19:20.557126: train_loss -0.3963 +2026-04-12 21:19:20.562673: val_loss -0.3889 +2026-04-12 21:19:20.564377: Pseudo dice [0.1354, 0.0, 0.8043, 0.7959, 0.2367, 0.8597, 0.7975] +2026-04-12 21:19:20.566086: Epoch time: 101.68 s +2026-04-12 21:19:21.703658: +2026-04-12 21:19:21.705506: Epoch 2060 +2026-04-12 21:19:21.707683: Current learning rate: 0.00521 +2026-04-12 21:21:03.317446: train_loss -0.3954 +2026-04-12 21:21:03.321793: val_loss -0.3517 +2026-04-12 21:21:03.323605: Pseudo dice [0.0, 0.0, 0.721, 0.2837, 0.2334, 0.8169, 0.6369] +2026-04-12 21:21:03.325898: Epoch time: 101.62 s +2026-04-12 21:21:04.479032: +2026-04-12 21:21:04.480810: Epoch 2061 +2026-04-12 21:21:04.482790: Current learning rate: 0.00521 +2026-04-12 21:22:46.129018: train_loss -0.4022 +2026-04-12 21:22:46.135700: val_loss -0.3475 +2026-04-12 21:22:46.138065: Pseudo dice [0.0013, 0.0, 0.7379, 0.7191, 0.2237, 0.3495, 0.6179] +2026-04-12 21:22:46.140647: Epoch time: 101.65 s +2026-04-12 21:22:47.286171: +2026-04-12 21:22:47.288001: Epoch 2062 +2026-04-12 21:22:47.290372: Current learning rate: 0.00521 +2026-04-12 21:24:28.833117: train_loss -0.4045 +2026-04-12 21:24:28.838315: val_loss -0.3279 +2026-04-12 21:24:28.840189: Pseudo dice [0.2399, 0.0, 0.6837, 0.4868, 0.4388, 0.7494, 0.3087] +2026-04-12 21:24:28.842586: Epoch time: 101.55 s +2026-04-12 21:24:29.982648: +2026-04-12 21:24:29.984482: Epoch 2063 +2026-04-12 21:24:29.986783: Current learning rate: 0.00521 +2026-04-12 21:26:11.524541: train_loss -0.4104 +2026-04-12 21:26:11.529480: val_loss -0.3822 +2026-04-12 21:26:11.531112: Pseudo dice [0.6515, 0.0, 0.4835, 0.5892, 0.391, 0.6996, 0.7405] +2026-04-12 21:26:11.533012: Epoch time: 101.54 s +2026-04-12 21:26:12.675428: +2026-04-12 21:26:12.677050: Epoch 2064 +2026-04-12 21:26:12.678717: Current learning rate: 0.0052 +2026-04-12 21:27:54.357172: train_loss -0.4127 +2026-04-12 21:27:54.363385: val_loss -0.3626 +2026-04-12 21:27:54.365245: Pseudo dice [0.3833, 0.0, 0.6344, 0.2649, 0.4335, 0.7621, 0.8697] +2026-04-12 21:27:54.366930: Epoch time: 101.68 s +2026-04-12 21:27:55.506912: +2026-04-12 21:27:55.508644: Epoch 2065 +2026-04-12 21:27:55.510734: Current learning rate: 0.0052 +2026-04-12 21:29:37.316478: train_loss -0.3964 +2026-04-12 21:29:37.324606: val_loss -0.3486 +2026-04-12 21:29:37.326563: Pseudo dice [0.482, 0.0, 0.6918, 0.5871, 0.3899, 0.7787, 0.5862] +2026-04-12 21:29:37.328845: Epoch time: 101.81 s +2026-04-12 21:29:38.489174: +2026-04-12 21:29:38.491389: Epoch 2066 +2026-04-12 21:29:38.494702: Current learning rate: 0.0052 +2026-04-12 21:31:20.223678: train_loss -0.4071 +2026-04-12 21:31:20.228495: val_loss -0.3637 +2026-04-12 21:31:20.230125: Pseudo dice [0.3015, 0.0, 0.7986, 0.5403, 0.2544, 0.7969, 0.6853] +2026-04-12 21:31:20.232117: Epoch time: 101.74 s +2026-04-12 21:31:21.367446: +2026-04-12 21:31:21.369661: Epoch 2067 +2026-04-12 21:31:21.371692: Current learning rate: 0.0052 +2026-04-12 21:33:03.135140: train_loss -0.4095 +2026-04-12 21:33:03.139753: val_loss -0.3441 +2026-04-12 21:33:03.141770: Pseudo dice [0.568, 0.0, 0.5502, 0.3335, 0.2529, 0.5355, 0.7519] +2026-04-12 21:33:03.143391: Epoch time: 101.77 s +2026-04-12 21:33:05.306994: +2026-04-12 21:33:05.308690: Epoch 2068 +2026-04-12 21:33:05.310327: Current learning rate: 0.00519 +2026-04-12 21:34:47.286904: train_loss -0.4 +2026-04-12 21:34:47.295918: val_loss -0.3588 +2026-04-12 21:34:47.297846: Pseudo dice [0.3553, 0.0, 0.7577, 0.7135, 0.2001, 0.7693, 0.8435] +2026-04-12 21:34:47.299903: Epoch time: 101.98 s +2026-04-12 21:34:48.447000: +2026-04-12 21:34:48.449141: Epoch 2069 +2026-04-12 21:34:48.451112: Current learning rate: 0.00519 +2026-04-12 21:36:30.122967: train_loss -0.4024 +2026-04-12 21:36:30.145017: val_loss -0.3266 +2026-04-12 21:36:30.149222: Pseudo dice [0.0397, 0.0, 0.3959, 0.7353, 0.4171, 0.7256, 0.5671] +2026-04-12 21:36:30.150911: Epoch time: 101.68 s +2026-04-12 21:36:31.277302: +2026-04-12 21:36:31.279878: Epoch 2070 +2026-04-12 21:36:31.282331: Current learning rate: 0.00519 +2026-04-12 21:38:13.188698: train_loss -0.3895 +2026-04-12 21:38:13.193030: val_loss -0.3623 +2026-04-12 21:38:13.194911: Pseudo dice [0.2388, 0.0, 0.7621, 0.8031, 0.1373, 0.1541, 0.9322] +2026-04-12 21:38:13.196411: Epoch time: 101.91 s +2026-04-12 21:38:14.347133: +2026-04-12 21:38:14.348659: Epoch 2071 +2026-04-12 21:38:14.350291: Current learning rate: 0.00519 +2026-04-12 21:39:56.219206: train_loss -0.4061 +2026-04-12 21:39:56.225167: val_loss -0.3537 +2026-04-12 21:39:56.227894: Pseudo dice [0.234, 0.0, 0.7009, 0.7967, 0.1156, 0.7325, 0.6299] +2026-04-12 21:39:56.234551: Epoch time: 101.88 s +2026-04-12 21:39:57.381686: +2026-04-12 21:39:57.383862: Epoch 2072 +2026-04-12 21:39:57.386272: Current learning rate: 0.00518 +2026-04-12 21:41:39.121762: train_loss -0.4106 +2026-04-12 21:41:39.126286: val_loss -0.3384 +2026-04-12 21:41:39.129159: Pseudo dice [0.2589, 0.0, 0.4773, 0.3839, 0.3874, 0.8182, 0.4437] +2026-04-12 21:41:39.130970: Epoch time: 101.74 s +2026-04-12 21:41:40.271332: +2026-04-12 21:41:40.272711: Epoch 2073 +2026-04-12 21:41:40.274267: Current learning rate: 0.00518 +2026-04-12 21:43:21.930313: train_loss -0.3839 +2026-04-12 21:43:21.934741: val_loss -0.3576 +2026-04-12 21:43:21.936631: Pseudo dice [0.4847, 0.0, 0.7191, 0.3585, 0.3058, 0.7265, 0.5146] +2026-04-12 21:43:21.938241: Epoch time: 101.66 s +2026-04-12 21:43:23.101251: +2026-04-12 21:43:23.103282: Epoch 2074 +2026-04-12 21:43:23.105080: Current learning rate: 0.00518 +2026-04-12 21:45:04.856766: train_loss -0.3986 +2026-04-12 21:45:04.861307: val_loss -0.3771 +2026-04-12 21:45:04.862967: Pseudo dice [0.1993, 0.0, 0.7602, 0.2362, 0.3533, 0.4991, 0.7207] +2026-04-12 21:45:04.864611: Epoch time: 101.76 s +2026-04-12 21:45:06.010152: +2026-04-12 21:45:06.011962: Epoch 2075 +2026-04-12 21:45:06.014052: Current learning rate: 0.00518 +2026-04-12 21:46:47.729248: train_loss -0.3931 +2026-04-12 21:46:47.734417: val_loss -0.3493 +2026-04-12 21:46:47.736349: Pseudo dice [0.3158, 0.0, 0.6305, 0.6463, 0.1903, 0.7374, 0.7609] +2026-04-12 21:46:47.737884: Epoch time: 101.72 s +2026-04-12 21:46:48.876979: +2026-04-12 21:46:48.878441: Epoch 2076 +2026-04-12 21:46:48.880084: Current learning rate: 0.00518 +2026-04-12 21:48:30.395400: train_loss -0.4163 +2026-04-12 21:48:30.400887: val_loss -0.372 +2026-04-12 21:48:30.402742: Pseudo dice [0.6818, 0.0, 0.7388, 0.3684, 0.3956, 0.6626, 0.6719] +2026-04-12 21:48:30.404514: Epoch time: 101.52 s +2026-04-12 21:48:31.547287: +2026-04-12 21:48:31.548919: Epoch 2077 +2026-04-12 21:48:31.550735: Current learning rate: 0.00517 +2026-04-12 21:50:13.051448: train_loss -0.4245 +2026-04-12 21:50:13.056557: val_loss -0.351 +2026-04-12 21:50:13.058317: Pseudo dice [0.1497, 0.0, 0.5269, 0.4028, 0.4607, 0.368, 0.8537] +2026-04-12 21:50:13.060052: Epoch time: 101.51 s +2026-04-12 21:50:14.207314: +2026-04-12 21:50:14.208982: Epoch 2078 +2026-04-12 21:50:14.211242: Current learning rate: 0.00517 +2026-04-12 21:51:55.817627: train_loss -0.3964 +2026-04-12 21:51:55.824382: val_loss -0.34 +2026-04-12 21:51:55.826396: Pseudo dice [0.52, 0.0, 0.6916, 0.6313, 0.2339, 0.7461, 0.4123] +2026-04-12 21:51:55.828116: Epoch time: 101.61 s +2026-04-12 21:51:56.983788: +2026-04-12 21:51:56.985502: Epoch 2079 +2026-04-12 21:51:56.987430: Current learning rate: 0.00517 +2026-04-12 21:53:38.798924: train_loss -0.4165 +2026-04-12 21:53:38.804142: val_loss -0.4132 +2026-04-12 21:53:38.806313: Pseudo dice [0.2165, 0.0, 0.7731, 0.8294, 0.301, 0.766, 0.8847] +2026-04-12 21:53:38.807768: Epoch time: 101.82 s +2026-04-12 21:53:39.959994: +2026-04-12 21:53:39.962101: Epoch 2080 +2026-04-12 21:53:39.964757: Current learning rate: 0.00517 +2026-04-12 21:55:21.759076: train_loss -0.4104 +2026-04-12 21:55:21.766718: val_loss -0.3723 +2026-04-12 21:55:21.776410: Pseudo dice [0.4787, 0.0, 0.7042, 0.6734, 0.3301, 0.8137, 0.4819] +2026-04-12 21:55:21.778813: Epoch time: 101.8 s +2026-04-12 21:55:22.911528: +2026-04-12 21:55:22.913298: Epoch 2081 +2026-04-12 21:55:22.915338: Current learning rate: 0.00516 +2026-04-12 21:57:04.504347: train_loss -0.397 +2026-04-12 21:57:04.509638: val_loss -0.3264 +2026-04-12 21:57:04.511270: Pseudo dice [0.294, 0.0, 0.5679, 0.7213, 0.3713, 0.4497, 0.2194] +2026-04-12 21:57:04.512668: Epoch time: 101.6 s +2026-04-12 21:57:05.648122: +2026-04-12 21:57:05.649816: Epoch 2082 +2026-04-12 21:57:05.651813: Current learning rate: 0.00516 +2026-04-12 21:58:47.251932: train_loss -0.4136 +2026-04-12 21:58:47.256508: val_loss -0.3444 +2026-04-12 21:58:47.258498: Pseudo dice [0.2235, 0.0, 0.731, 0.3915, 0.3897, 0.8063, 0.7843] +2026-04-12 21:58:47.260201: Epoch time: 101.61 s +2026-04-12 21:58:48.406268: +2026-04-12 21:58:48.408177: Epoch 2083 +2026-04-12 21:58:48.410969: Current learning rate: 0.00516 +2026-04-12 22:00:30.200907: train_loss -0.3912 +2026-04-12 22:00:30.205768: val_loss -0.3803 +2026-04-12 22:00:30.207803: Pseudo dice [0.3436, 0.0, 0.7792, 0.6716, 0.3346, 0.7061, 0.8876] +2026-04-12 22:00:30.210028: Epoch time: 101.8 s +2026-04-12 22:00:31.365412: +2026-04-12 22:00:31.367282: Epoch 2084 +2026-04-12 22:00:31.369227: Current learning rate: 0.00516 +2026-04-12 22:02:13.061104: train_loss -0.4061 +2026-04-12 22:02:13.068301: val_loss -0.3664 +2026-04-12 22:02:13.070309: Pseudo dice [0.0883, 0.0, 0.6885, 0.766, 0.5082, 0.1981, 0.3607] +2026-04-12 22:02:13.071975: Epoch time: 101.7 s +2026-04-12 22:02:14.217719: +2026-04-12 22:02:14.219387: Epoch 2085 +2026-04-12 22:02:14.221996: Current learning rate: 0.00515 +2026-04-12 22:03:55.922342: train_loss -0.4018 +2026-04-12 22:03:55.927573: val_loss -0.3613 +2026-04-12 22:03:55.929841: Pseudo dice [0.446, 0.0, 0.5532, 0.8601, 0.3423, 0.8228, 0.6152] +2026-04-12 22:03:55.931569: Epoch time: 101.71 s +2026-04-12 22:03:57.064421: +2026-04-12 22:03:57.066280: Epoch 2086 +2026-04-12 22:03:57.068306: Current learning rate: 0.00515 +2026-04-12 22:05:38.624580: train_loss -0.4127 +2026-04-12 22:05:38.634217: val_loss -0.35 +2026-04-12 22:05:38.636001: Pseudo dice [0.7383, 0.0, 0.1214, 0.4305, 0.3549, 0.4626, 0.7897] +2026-04-12 22:05:38.637797: Epoch time: 101.56 s +2026-04-12 22:05:39.777564: +2026-04-12 22:05:39.779718: Epoch 2087 +2026-04-12 22:05:39.782347: Current learning rate: 0.00515 +2026-04-12 22:07:21.367051: train_loss -0.4132 +2026-04-12 22:07:21.372931: val_loss -0.3552 +2026-04-12 22:07:21.374779: Pseudo dice [0.4841, 0.0, 0.7, 0.7108, 0.2008, 0.5367, 0.8492] +2026-04-12 22:07:21.376305: Epoch time: 101.59 s +2026-04-12 22:07:22.519755: +2026-04-12 22:07:22.521810: Epoch 2088 +2026-04-12 22:07:22.524114: Current learning rate: 0.00515 +2026-04-12 22:09:05.066415: train_loss -0.4061 +2026-04-12 22:09:05.071041: val_loss -0.3434 +2026-04-12 22:09:05.072869: Pseudo dice [0.4332, 0.0, 0.8266, 0.1164, 0.4146, 0.6673, 0.6855] +2026-04-12 22:09:05.075134: Epoch time: 102.55 s +2026-04-12 22:09:06.221449: +2026-04-12 22:09:06.223238: Epoch 2089 +2026-04-12 22:09:06.225323: Current learning rate: 0.00514 +2026-04-12 22:10:47.864820: train_loss -0.379 +2026-04-12 22:10:47.869826: val_loss -0.2976 +2026-04-12 22:10:47.873019: Pseudo dice [0.0, 0.0, 0.5171, 0.4855, 0.0128, 0.8551, 0.0938] +2026-04-12 22:10:47.875235: Epoch time: 101.65 s +2026-04-12 22:10:49.017653: +2026-04-12 22:10:49.019326: Epoch 2090 +2026-04-12 22:10:49.021137: Current learning rate: 0.00514 +2026-04-12 22:12:30.758160: train_loss -0.3489 +2026-04-12 22:12:30.763257: val_loss -0.3645 +2026-04-12 22:12:30.764997: Pseudo dice [0.0, 0.0, 0.7758, 0.7705, 0.3239, 0.6523, 0.6554] +2026-04-12 22:12:30.766882: Epoch time: 101.74 s +2026-04-12 22:12:31.911160: +2026-04-12 22:12:31.912691: Epoch 2091 +2026-04-12 22:12:31.915057: Current learning rate: 0.00514 +2026-04-12 22:14:13.556192: train_loss -0.4017 +2026-04-12 22:14:13.562008: val_loss -0.346 +2026-04-12 22:14:13.563860: Pseudo dice [0.1927, 0.0, 0.6171, 0.6756, 0.3794, 0.6393, 0.734] +2026-04-12 22:14:13.565581: Epoch time: 101.65 s +2026-04-12 22:14:14.701396: +2026-04-12 22:14:14.703197: Epoch 2092 +2026-04-12 22:14:14.705338: Current learning rate: 0.00514 +2026-04-12 22:15:56.560082: train_loss -0.3869 +2026-04-12 22:15:56.564808: val_loss -0.3706 +2026-04-12 22:15:56.566339: Pseudo dice [0.2349, 0.0, 0.5909, 0.5794, 0.4679, 0.7492, 0.8253] +2026-04-12 22:15:56.567721: Epoch time: 101.86 s +2026-04-12 22:15:57.703653: +2026-04-12 22:15:57.705371: Epoch 2093 +2026-04-12 22:15:57.706861: Current learning rate: 0.00513 +2026-04-12 22:17:39.324725: train_loss -0.4081 +2026-04-12 22:17:39.330404: val_loss -0.341 +2026-04-12 22:17:39.332017: Pseudo dice [0.4096, 0.0, 0.6436, 0.3958, 0.19, 0.6012, 0.4779] +2026-04-12 22:17:39.333762: Epoch time: 101.62 s +2026-04-12 22:17:40.459019: +2026-04-12 22:17:40.460835: Epoch 2094 +2026-04-12 22:17:40.462979: Current learning rate: 0.00513 +2026-04-12 22:19:22.245501: train_loss -0.4201 +2026-04-12 22:19:22.251029: val_loss -0.4006 +2026-04-12 22:19:22.252851: Pseudo dice [0.7798, 0.0, 0.6232, 0.7922, 0.4692, 0.7796, 0.8394] +2026-04-12 22:19:22.254485: Epoch time: 101.79 s +2026-04-12 22:19:23.481801: +2026-04-12 22:19:23.483539: Epoch 2095 +2026-04-12 22:19:23.485933: Current learning rate: 0.00513 +2026-04-12 22:21:05.237787: train_loss -0.4065 +2026-04-12 22:21:05.243102: val_loss -0.3848 +2026-04-12 22:21:05.244860: Pseudo dice [0.4046, 0.0, 0.7341, 0.6356, 0.294, 0.368, 0.9014] +2026-04-12 22:21:05.246873: Epoch time: 101.76 s +2026-04-12 22:21:06.387586: +2026-04-12 22:21:06.389340: Epoch 2096 +2026-04-12 22:21:06.391036: Current learning rate: 0.00513 +2026-04-12 22:22:47.829278: train_loss -0.4126 +2026-04-12 22:22:47.835410: val_loss -0.3159 +2026-04-12 22:22:47.837328: Pseudo dice [0.0778, 0.0, 0.5849, 0.539, 0.2271, 0.5507, 0.4186] +2026-04-12 22:22:47.839793: Epoch time: 101.44 s +2026-04-12 22:22:48.974356: +2026-04-12 22:22:48.976096: Epoch 2097 +2026-04-12 22:22:48.978109: Current learning rate: 0.00512 +2026-04-12 22:24:30.557131: train_loss -0.4047 +2026-04-12 22:24:30.563128: val_loss -0.3438 +2026-04-12 22:24:30.564600: Pseudo dice [0.1752, 0.0, 0.4891, 0.6341, 0.1514, 0.5587, 0.8281] +2026-04-12 22:24:30.566743: Epoch time: 101.59 s +2026-04-12 22:24:31.709864: +2026-04-12 22:24:31.711915: Epoch 2098 +2026-04-12 22:24:31.713771: Current learning rate: 0.00512 +2026-04-12 22:26:13.494186: train_loss -0.3967 +2026-04-12 22:26:13.515662: val_loss -0.3518 +2026-04-12 22:26:13.517351: Pseudo dice [0.4833, 0.0, 0.6452, 0.7092, 0.1425, 0.65, 0.7868] +2026-04-12 22:26:13.518998: Epoch time: 101.79 s +2026-04-12 22:26:14.663025: +2026-04-12 22:26:14.664911: Epoch 2099 +2026-04-12 22:26:14.666985: Current learning rate: 0.00512 +2026-04-12 22:27:56.273235: train_loss -0.4066 +2026-04-12 22:27:56.280455: val_loss -0.3083 +2026-04-12 22:27:56.282231: Pseudo dice [0.3344, 0.0, 0.6924, 0.1336, 0.1111, 0.6716, 0.4061] +2026-04-12 22:27:56.284068: Epoch time: 101.61 s +2026-04-12 22:27:59.058556: +2026-04-12 22:27:59.060262: Epoch 2100 +2026-04-12 22:27:59.062299: Current learning rate: 0.00512 +2026-04-12 22:29:40.544753: train_loss -0.4066 +2026-04-12 22:29:40.550074: val_loss -0.4104 +2026-04-12 22:29:40.551504: Pseudo dice [0.3028, 0.0, 0.8061, 0.8137, 0.5438, 0.6439, 0.8404] +2026-04-12 22:29:40.552981: Epoch time: 101.49 s +2026-04-12 22:29:41.704207: +2026-04-12 22:29:41.705734: Epoch 2101 +2026-04-12 22:29:41.707418: Current learning rate: 0.00511 +2026-04-12 22:31:23.211881: train_loss -0.3954 +2026-04-12 22:31:23.217141: val_loss -0.3648 +2026-04-12 22:31:23.219986: Pseudo dice [0.0, 0.0, 0.8111, 0.3535, 0.4842, 0.7721, 0.6792] +2026-04-12 22:31:23.221682: Epoch time: 101.51 s +2026-04-12 22:31:24.372806: +2026-04-12 22:31:24.374666: Epoch 2102 +2026-04-12 22:31:24.376894: Current learning rate: 0.00511 +2026-04-12 22:33:05.999901: train_loss -0.4018 +2026-04-12 22:33:06.004420: val_loss -0.37 +2026-04-12 22:33:06.006300: Pseudo dice [0.0, 0.0, 0.8232, 0.7818, 0.3641, 0.7473, 0.8477] +2026-04-12 22:33:06.008117: Epoch time: 101.63 s +2026-04-12 22:33:07.146396: +2026-04-12 22:33:07.148015: Epoch 2103 +2026-04-12 22:33:07.149860: Current learning rate: 0.00511 +2026-04-12 22:34:48.715145: train_loss -0.398 +2026-04-12 22:34:48.719839: val_loss -0.3413 +2026-04-12 22:34:48.721740: Pseudo dice [0.0, 0.0, 0.6537, 0.7883, 0.5244, 0.6378, 0.5803] +2026-04-12 22:34:48.724218: Epoch time: 101.57 s +2026-04-12 22:34:49.868812: +2026-04-12 22:34:49.870445: Epoch 2104 +2026-04-12 22:34:49.872162: Current learning rate: 0.00511 +2026-04-12 22:36:31.577481: train_loss -0.3961 +2026-04-12 22:36:31.582317: val_loss -0.348 +2026-04-12 22:36:31.583925: Pseudo dice [0.0, 0.0, 0.608, 0.6427, 0.3219, 0.7536, 0.5442] +2026-04-12 22:36:31.585376: Epoch time: 101.71 s +2026-04-12 22:36:32.730603: +2026-04-12 22:36:32.733206: Epoch 2105 +2026-04-12 22:36:32.735624: Current learning rate: 0.0051 +2026-04-12 22:38:14.368609: train_loss -0.4041 +2026-04-12 22:38:14.376071: val_loss -0.3824 +2026-04-12 22:38:14.377990: Pseudo dice [0.0556, 0.0, 0.6856, 0.7885, 0.2541, 0.777, 0.758] +2026-04-12 22:38:14.380179: Epoch time: 101.64 s +2026-04-12 22:38:15.539379: +2026-04-12 22:38:15.541094: Epoch 2106 +2026-04-12 22:38:15.543111: Current learning rate: 0.0051 +2026-04-12 22:39:57.240047: train_loss -0.4116 +2026-04-12 22:39:57.245854: val_loss -0.3532 +2026-04-12 22:39:57.247730: Pseudo dice [0.3173, 0.0, 0.6629, 0.7379, 0.2049, 0.4329, 0.7994] +2026-04-12 22:39:57.254123: Epoch time: 101.7 s +2026-04-12 22:39:58.387869: +2026-04-12 22:39:58.389473: Epoch 2107 +2026-04-12 22:39:58.391077: Current learning rate: 0.0051 +2026-04-12 22:41:39.890762: train_loss -0.4041 +2026-04-12 22:41:39.895776: val_loss -0.3205 +2026-04-12 22:41:39.897319: Pseudo dice [0.3747, 0.0, 0.5796, 0.1635, 0.1467, 0.4628, 0.6531] +2026-04-12 22:41:39.898850: Epoch time: 101.51 s +2026-04-12 22:41:41.054202: +2026-04-12 22:41:41.055881: Epoch 2108 +2026-04-12 22:41:41.057898: Current learning rate: 0.0051 +2026-04-12 22:43:22.716166: train_loss -0.3879 +2026-04-12 22:43:22.721414: val_loss -0.3984 +2026-04-12 22:43:22.723638: Pseudo dice [0.3677, 0.0, 0.8631, 0.7697, 0.2975, 0.756, 0.7971] +2026-04-12 22:43:22.725735: Epoch time: 101.67 s +2026-04-12 22:43:24.870531: +2026-04-12 22:43:24.872802: Epoch 2109 +2026-04-12 22:43:24.875229: Current learning rate: 0.0051 +2026-04-12 22:45:06.608566: train_loss -0.398 +2026-04-12 22:45:06.613301: val_loss -0.3456 +2026-04-12 22:45:06.614926: Pseudo dice [0.1734, 0.0, 0.6633, 0.7303, 0.355, 0.7441, 0.7494] +2026-04-12 22:45:06.616853: Epoch time: 101.74 s +2026-04-12 22:45:07.762051: +2026-04-12 22:45:07.764409: Epoch 2110 +2026-04-12 22:45:07.766006: Current learning rate: 0.00509 +2026-04-12 22:46:49.561935: train_loss -0.4112 +2026-04-12 22:46:49.566608: val_loss -0.3864 +2026-04-12 22:46:49.568406: Pseudo dice [0.5925, 0.0, 0.3094, 0.7819, 0.1402, 0.6732, 0.6864] +2026-04-12 22:46:49.570327: Epoch time: 101.8 s +2026-04-12 22:46:50.712141: +2026-04-12 22:46:50.714057: Epoch 2111 +2026-04-12 22:46:50.716140: Current learning rate: 0.00509 +2026-04-12 22:48:32.561753: train_loss -0.4089 +2026-04-12 22:48:32.566690: val_loss -0.3521 +2026-04-12 22:48:32.568342: Pseudo dice [0.6066, 0.0, 0.6356, 0.7871, 0.4811, 0.3807, 0.3652] +2026-04-12 22:48:32.570198: Epoch time: 101.85 s +2026-04-12 22:48:33.697480: +2026-04-12 22:48:33.699364: Epoch 2112 +2026-04-12 22:48:33.701430: Current learning rate: 0.00509 +2026-04-12 22:50:15.608001: train_loss -0.4105 +2026-04-12 22:50:15.612822: val_loss -0.3563 +2026-04-12 22:50:15.614818: Pseudo dice [0.2296, 0.0, 0.5003, 0.82, 0.3459, 0.7257, 0.8882] +2026-04-12 22:50:15.616318: Epoch time: 101.91 s +2026-04-12 22:50:16.762514: +2026-04-12 22:50:16.764183: Epoch 2113 +2026-04-12 22:50:16.766611: Current learning rate: 0.00509 +2026-04-12 22:51:58.363705: train_loss -0.4148 +2026-04-12 22:51:58.368336: val_loss -0.3939 +2026-04-12 22:51:58.370416: Pseudo dice [0.1934, 0.0, 0.7784, 0.8311, 0.3099, 0.292, 0.8623] +2026-04-12 22:51:58.372089: Epoch time: 101.6 s +2026-04-12 22:51:59.506027: +2026-04-12 22:51:59.507830: Epoch 2114 +2026-04-12 22:51:59.509874: Current learning rate: 0.00508 +2026-04-12 22:53:41.016704: train_loss -0.4021 +2026-04-12 22:53:41.022163: val_loss -0.3611 +2026-04-12 22:53:41.024131: Pseudo dice [0.6237, 0.0, 0.6317, 0.3799, 0.324, 0.6325, 0.7056] +2026-04-12 22:53:41.025776: Epoch time: 101.51 s +2026-04-12 22:53:42.169061: +2026-04-12 22:53:42.170890: Epoch 2115 +2026-04-12 22:53:42.172971: Current learning rate: 0.00508 +2026-04-12 22:55:23.865161: train_loss -0.3723 +2026-04-12 22:55:23.871412: val_loss -0.3105 +2026-04-12 22:55:23.873298: Pseudo dice [0.0, 0.0, 0.6594, 0.7235, 0.0499, 0.3638, 0.6614] +2026-04-12 22:55:23.875281: Epoch time: 101.7 s +2026-04-12 22:55:25.022966: +2026-04-12 22:55:25.024726: Epoch 2116 +2026-04-12 22:55:25.026751: Current learning rate: 0.00508 +2026-04-12 22:57:06.687292: train_loss -0.392 +2026-04-12 22:57:06.694423: val_loss -0.3459 +2026-04-12 22:57:06.696221: Pseudo dice [0.2234, 0.0, 0.6434, 0.7018, 0.2513, 0.7388, 0.6891] +2026-04-12 22:57:06.699491: Epoch time: 101.67 s +2026-04-12 22:57:07.845407: +2026-04-12 22:57:07.846861: Epoch 2117 +2026-04-12 22:57:07.848876: Current learning rate: 0.00508 +2026-04-12 22:58:49.650066: train_loss -0.4137 +2026-04-12 22:58:49.654866: val_loss -0.3497 +2026-04-12 22:58:49.656928: Pseudo dice [0.2042, 0.0, 0.7565, 0.8447, 0.435, 0.4032, 0.5593] +2026-04-12 22:58:49.658306: Epoch time: 101.81 s +2026-04-12 22:58:50.796578: +2026-04-12 22:58:50.798200: Epoch 2118 +2026-04-12 22:58:50.799952: Current learning rate: 0.00507 +2026-04-12 23:00:32.540109: train_loss -0.4161 +2026-04-12 23:00:32.545407: val_loss -0.3303 +2026-04-12 23:00:32.547191: Pseudo dice [0.4204, 0.0, 0.4775, 0.7062, 0.3435, 0.7514, 0.5969] +2026-04-12 23:00:32.548877: Epoch time: 101.75 s +2026-04-12 23:00:33.710346: +2026-04-12 23:00:33.713392: Epoch 2119 +2026-04-12 23:00:33.715713: Current learning rate: 0.00507 +2026-04-12 23:02:15.432586: train_loss -0.3863 +2026-04-12 23:02:15.437361: val_loss -0.3466 +2026-04-12 23:02:15.440440: Pseudo dice [0.2873, 0.0, 0.6076, 0.7778, 0.1647, 0.3574, 0.8647] +2026-04-12 23:02:15.442112: Epoch time: 101.73 s +2026-04-12 23:02:16.580741: +2026-04-12 23:02:16.582472: Epoch 2120 +2026-04-12 23:02:16.584275: Current learning rate: 0.00507 +2026-04-12 23:03:58.168599: train_loss -0.4002 +2026-04-12 23:03:58.174441: val_loss -0.344 +2026-04-12 23:03:58.176122: Pseudo dice [0.4138, 0.0, 0.6488, 0.7717, 0.3059, 0.2212, 0.6676] +2026-04-12 23:03:58.177961: Epoch time: 101.59 s +2026-04-12 23:03:59.331722: +2026-04-12 23:03:59.333546: Epoch 2121 +2026-04-12 23:03:59.335860: Current learning rate: 0.00507 +2026-04-12 23:05:41.105249: train_loss -0.3733 +2026-04-12 23:05:41.109943: val_loss -0.318 +2026-04-12 23:05:41.111831: Pseudo dice [0.1009, 0.0, 0.6839, 0.6674, 0.3224, 0.6279, 0.3591] +2026-04-12 23:05:41.113486: Epoch time: 101.78 s +2026-04-12 23:05:42.240973: +2026-04-12 23:05:42.242885: Epoch 2122 +2026-04-12 23:05:42.244971: Current learning rate: 0.00506 +2026-04-12 23:07:23.824632: train_loss -0.4007 +2026-04-12 23:07:23.833071: val_loss -0.397 +2026-04-12 23:07:23.835095: Pseudo dice [0.7167, 0.0, 0.6896, 0.5894, 0.4604, 0.5295, 0.7729] +2026-04-12 23:07:23.836743: Epoch time: 101.59 s +2026-04-12 23:07:24.989953: +2026-04-12 23:07:24.991748: Epoch 2123 +2026-04-12 23:07:24.993767: Current learning rate: 0.00506 +2026-04-12 23:09:06.849024: train_loss -0.4003 +2026-04-12 23:09:06.854943: val_loss -0.3736 +2026-04-12 23:09:06.856857: Pseudo dice [0.3181, 0.0, 0.7233, 0.7105, 0.3594, 0.7681, 0.9345] +2026-04-12 23:09:06.858571: Epoch time: 101.86 s +2026-04-12 23:09:08.012563: +2026-04-12 23:09:08.014507: Epoch 2124 +2026-04-12 23:09:08.016510: Current learning rate: 0.00506 +2026-04-12 23:10:49.725708: train_loss -0.414 +2026-04-12 23:10:49.735267: val_loss -0.3303 +2026-04-12 23:10:49.737309: Pseudo dice [0.4405, 0.0, 0.5563, 0.5071, 0.2939, 0.1402, 0.6953] +2026-04-12 23:10:49.739172: Epoch time: 101.72 s +2026-04-12 23:10:50.926467: +2026-04-12 23:10:50.928196: Epoch 2125 +2026-04-12 23:10:50.929657: Current learning rate: 0.00506 +2026-04-12 23:12:32.518120: train_loss -0.3989 +2026-04-12 23:12:32.523503: val_loss -0.3436 +2026-04-12 23:12:32.525586: Pseudo dice [0.0, 0.0, 0.5977, 0.3555, 0.4173, 0.4424, 0.6984] +2026-04-12 23:12:32.527662: Epoch time: 101.59 s +2026-04-12 23:12:33.664983: +2026-04-12 23:12:33.671286: Epoch 2126 +2026-04-12 23:12:33.674939: Current learning rate: 0.00505 +2026-04-12 23:14:15.341291: train_loss -0.3677 +2026-04-12 23:14:15.347434: val_loss -0.3461 +2026-04-12 23:14:15.349008: Pseudo dice [0.1849, 0.0, 0.559, 0.0453, 0.1594, 0.6918, 0.7347] +2026-04-12 23:14:15.350679: Epoch time: 101.68 s +2026-04-12 23:14:16.501780: +2026-04-12 23:14:16.503819: Epoch 2127 +2026-04-12 23:14:16.506073: Current learning rate: 0.00505 +2026-04-12 23:15:58.035902: train_loss -0.3909 +2026-04-12 23:15:58.041341: val_loss -0.3409 +2026-04-12 23:15:58.043156: Pseudo dice [0.0136, 0.0, 0.4136, 0.7629, 0.3116, 0.7813, 0.428] +2026-04-12 23:15:58.045043: Epoch time: 101.54 s +2026-04-12 23:15:59.196057: +2026-04-12 23:15:59.198074: Epoch 2128 +2026-04-12 23:15:59.200686: Current learning rate: 0.00505 +2026-04-12 23:17:40.829626: train_loss -0.3763 +2026-04-12 23:17:40.834876: val_loss -0.3088 +2026-04-12 23:17:40.836548: Pseudo dice [0.0, 0.0, 0.4896, 0.1921, 0.1845, 0.6968, 0.2449] +2026-04-12 23:17:40.838592: Epoch time: 101.64 s +2026-04-12 23:17:41.986187: +2026-04-12 23:17:41.989022: Epoch 2129 +2026-04-12 23:17:41.991152: Current learning rate: 0.00505 +2026-04-12 23:19:23.438823: train_loss -0.3843 +2026-04-12 23:19:23.448753: val_loss -0.3779 +2026-04-12 23:19:23.450393: Pseudo dice [0.4035, 0.0, 0.7809, 0.7751, 0.5281, 0.3139, 0.713] +2026-04-12 23:19:23.451937: Epoch time: 101.46 s +2026-04-12 23:19:25.552906: +2026-04-12 23:19:25.555723: Epoch 2130 +2026-04-12 23:19:25.557843: Current learning rate: 0.00504 +2026-04-12 23:21:06.975081: train_loss -0.4063 +2026-04-12 23:21:06.980683: val_loss -0.3053 +2026-04-12 23:21:06.982815: Pseudo dice [0.0, 0.0, 0.5, 0.7194, 0.2101, 0.4539, 0.6305] +2026-04-12 23:21:06.984247: Epoch time: 101.43 s +2026-04-12 23:21:08.128932: +2026-04-12 23:21:08.130561: Epoch 2131 +2026-04-12 23:21:08.132702: Current learning rate: 0.00504 +2026-04-12 23:22:49.656728: train_loss -0.3909 +2026-04-12 23:22:49.664241: val_loss -0.3519 +2026-04-12 23:22:49.665866: Pseudo dice [0.0916, 0.0, 0.6997, 0.7665, 0.4732, 0.4529, 0.5943] +2026-04-12 23:22:49.668787: Epoch time: 101.53 s +2026-04-12 23:22:50.809326: +2026-04-12 23:22:50.811267: Epoch 2132 +2026-04-12 23:22:50.812910: Current learning rate: 0.00504 +2026-04-12 23:24:32.310907: train_loss -0.396 +2026-04-12 23:24:32.316063: val_loss -0.342 +2026-04-12 23:24:32.318461: Pseudo dice [0.1951, 0.0, 0.7969, 0.8006, 0.5903, 0.619, 0.464] +2026-04-12 23:24:32.320460: Epoch time: 101.5 s +2026-04-12 23:24:33.477350: +2026-04-12 23:24:33.479551: Epoch 2133 +2026-04-12 23:24:33.481975: Current learning rate: 0.00504 +2026-04-12 23:26:15.171849: train_loss -0.383 +2026-04-12 23:26:15.180278: val_loss -0.3359 +2026-04-12 23:26:15.181970: Pseudo dice [0.2429, 0.0, 0.8043, 0.726, 0.0934, 0.2121, 0.8315] +2026-04-12 23:26:15.183773: Epoch time: 101.7 s +2026-04-12 23:26:16.332509: +2026-04-12 23:26:16.335165: Epoch 2134 +2026-04-12 23:26:16.337108: Current learning rate: 0.00503 +2026-04-12 23:27:57.829591: train_loss -0.4054 +2026-04-12 23:27:57.837927: val_loss -0.3956 +2026-04-12 23:27:57.840059: Pseudo dice [0.5328, 0.0, 0.7019, 0.8638, 0.5309, 0.8545, 0.4186] +2026-04-12 23:27:57.842902: Epoch time: 101.5 s +2026-04-12 23:27:58.995979: +2026-04-12 23:27:58.997856: Epoch 2135 +2026-04-12 23:27:58.999893: Current learning rate: 0.00503 +2026-04-12 23:29:40.721786: train_loss -0.4225 +2026-04-12 23:29:40.727219: val_loss -0.3659 +2026-04-12 23:29:40.728947: Pseudo dice [0.2344, 0.0, 0.766, 0.5977, 0.2231, 0.5497, 0.7985] +2026-04-12 23:29:40.730675: Epoch time: 101.73 s +2026-04-12 23:29:41.882132: +2026-04-12 23:29:41.883875: Epoch 2136 +2026-04-12 23:29:41.885992: Current learning rate: 0.00503 +2026-04-12 23:31:23.491656: train_loss -0.4234 +2026-04-12 23:31:23.498171: val_loss -0.3651 +2026-04-12 23:31:23.500961: Pseudo dice [0.4554, 0.0, 0.661, 0.7095, 0.3788, 0.6492, 0.7405] +2026-04-12 23:31:23.502912: Epoch time: 101.61 s +2026-04-12 23:31:24.653785: +2026-04-12 23:31:24.655683: Epoch 2137 +2026-04-12 23:31:24.657590: Current learning rate: 0.00503 +2026-04-12 23:33:06.262912: train_loss -0.4073 +2026-04-12 23:33:06.267636: val_loss -0.3551 +2026-04-12 23:33:06.269284: Pseudo dice [0.4203, 0.0, 0.7863, 0.171, 0.3662, 0.5386, 0.6395] +2026-04-12 23:33:06.271000: Epoch time: 101.61 s +2026-04-12 23:33:07.431264: +2026-04-12 23:33:07.433035: Epoch 2138 +2026-04-12 23:33:07.435129: Current learning rate: 0.00502 +2026-04-12 23:34:48.992255: train_loss -0.4094 +2026-04-12 23:34:48.997609: val_loss -0.2992 +2026-04-12 23:34:48.999277: Pseudo dice [0.0588, 0.0, 0.3579, 0.6896, 0.2241, 0.4179, 0.7392] +2026-04-12 23:34:49.000896: Epoch time: 101.56 s +2026-04-12 23:34:50.152481: +2026-04-12 23:34:50.153946: Epoch 2139 +2026-04-12 23:34:50.155581: Current learning rate: 0.00502 +2026-04-12 23:36:31.599465: train_loss -0.3971 +2026-04-12 23:36:31.603783: val_loss -0.3648 +2026-04-12 23:36:31.605252: Pseudo dice [0.4253, 0.0, 0.7235, 0.8992, 0.3204, 0.7072, 0.8459] +2026-04-12 23:36:31.607339: Epoch time: 101.45 s +2026-04-12 23:36:32.761282: +2026-04-12 23:36:32.762846: Epoch 2140 +2026-04-12 23:36:32.764752: Current learning rate: 0.00502 +2026-04-12 23:38:14.395631: train_loss -0.3958 +2026-04-12 23:38:14.400086: val_loss -0.32 +2026-04-12 23:38:14.401762: Pseudo dice [0.0, 0.0, 0.1532, 0.6101, 0.0588, 0.6184, 0.7709] +2026-04-12 23:38:14.403331: Epoch time: 101.64 s +2026-04-12 23:38:15.551491: +2026-04-12 23:38:15.553506: Epoch 2141 +2026-04-12 23:38:15.555560: Current learning rate: 0.00502 +2026-04-12 23:39:57.142143: train_loss -0.3764 +2026-04-12 23:39:57.146713: val_loss -0.349 +2026-04-12 23:39:57.148582: Pseudo dice [0.0467, 0.0, 0.7954, 0.8327, 0.3188, 0.7434, 0.7738] +2026-04-12 23:39:57.149985: Epoch time: 101.59 s +2026-04-12 23:39:58.311255: +2026-04-12 23:39:58.313801: Epoch 2142 +2026-04-12 23:39:58.317232: Current learning rate: 0.00502 +2026-04-12 23:41:39.992212: train_loss -0.4242 +2026-04-12 23:41:39.997769: val_loss -0.3724 +2026-04-12 23:41:40.000565: Pseudo dice [0.2736, 0.0, 0.8044, 0.4001, 0.2555, 0.6705, 0.6989] +2026-04-12 23:41:40.002171: Epoch time: 101.68 s +2026-04-12 23:41:41.167045: +2026-04-12 23:41:41.169152: Epoch 2143 +2026-04-12 23:41:41.171420: Current learning rate: 0.00501 +2026-04-12 23:43:22.999357: train_loss -0.4041 +2026-04-12 23:43:23.013966: val_loss -0.3554 +2026-04-12 23:43:23.015369: Pseudo dice [0.4773, 0.0, 0.6876, 0.7547, 0.1761, 0.5527, 0.7994] +2026-04-12 23:43:23.017046: Epoch time: 101.84 s +2026-04-12 23:43:24.177004: +2026-04-12 23:43:24.178598: Epoch 2144 +2026-04-12 23:43:24.180317: Current learning rate: 0.00501 +2026-04-12 23:45:05.799690: train_loss -0.4052 +2026-04-12 23:45:05.805584: val_loss -0.3618 +2026-04-12 23:45:05.807326: Pseudo dice [0.0067, 0.0, 0.7233, 0.8178, 0.3966, 0.5807, 0.8261] +2026-04-12 23:45:05.808773: Epoch time: 101.63 s +2026-04-12 23:45:06.947875: +2026-04-12 23:45:06.949655: Epoch 2145 +2026-04-12 23:45:06.951910: Current learning rate: 0.00501 +2026-04-12 23:46:48.793307: train_loss -0.3893 +2026-04-12 23:46:48.802844: val_loss -0.4036 +2026-04-12 23:46:48.804595: Pseudo dice [0.6502, 0.0, 0.8666, 0.8273, 0.4726, 0.7544, 0.8287] +2026-04-12 23:46:48.806401: Epoch time: 101.85 s +2026-04-12 23:46:50.007593: +2026-04-12 23:46:50.009044: Epoch 2146 +2026-04-12 23:46:50.010764: Current learning rate: 0.00501 +2026-04-12 23:48:31.929749: train_loss -0.404 +2026-04-12 23:48:31.935907: val_loss -0.3761 +2026-04-12 23:48:31.938695: Pseudo dice [0.3117, 0.0, 0.7558, 0.863, 0.335, 0.7942, 0.6848] +2026-04-12 23:48:31.940403: Epoch time: 101.93 s +2026-04-12 23:48:33.086939: +2026-04-12 23:48:33.088456: Epoch 2147 +2026-04-12 23:48:33.090333: Current learning rate: 0.005 +2026-04-12 23:50:14.688961: train_loss -0.4221 +2026-04-12 23:50:14.694381: val_loss -0.3633 +2026-04-12 23:50:14.697894: Pseudo dice [0.2171, 0.0, 0.7427, 0.8247, 0.2276, 0.7302, 0.8761] +2026-04-12 23:50:14.700095: Epoch time: 101.61 s +2026-04-12 23:50:15.844137: +2026-04-12 23:50:15.845860: Epoch 2148 +2026-04-12 23:50:15.847868: Current learning rate: 0.005 +2026-04-12 23:51:57.770372: train_loss -0.3934 +2026-04-12 23:51:57.774679: val_loss -0.3657 +2026-04-12 23:51:57.776546: Pseudo dice [0.5999, 0.0, 0.484, 0.704, 0.2276, 0.6296, 0.6207] +2026-04-12 23:51:57.778252: Epoch time: 101.93 s +2026-04-12 23:51:58.921171: +2026-04-12 23:51:58.922667: Epoch 2149 +2026-04-12 23:51:58.924686: Current learning rate: 0.005 +2026-04-12 23:53:40.680003: train_loss -0.3952 +2026-04-12 23:53:40.690202: val_loss -0.3467 +2026-04-12 23:53:40.691787: Pseudo dice [0.0, 0.0, 0.4865, 0.7997, 0.416, 0.5402, 0.7845] +2026-04-12 23:53:40.693209: Epoch time: 101.76 s +2026-04-12 23:53:43.511193: +2026-04-12 23:53:43.512760: Epoch 2150 +2026-04-12 23:53:43.514887: Current learning rate: 0.005 +2026-04-12 23:55:26.769795: train_loss -0.3824 +2026-04-12 23:55:26.775036: val_loss -0.3811 +2026-04-12 23:55:26.777485: Pseudo dice [0.3352, 0.0, 0.6121, 0.8422, 0.4897, 0.6135, 0.6512] +2026-04-12 23:55:26.779098: Epoch time: 103.26 s +2026-04-12 23:55:27.918331: +2026-04-12 23:55:27.920292: Epoch 2151 +2026-04-12 23:55:27.922351: Current learning rate: 0.00499 +2026-04-12 23:57:09.639154: train_loss -0.4074 +2026-04-12 23:57:09.646437: val_loss -0.3493 +2026-04-12 23:57:09.648400: Pseudo dice [0.6016, 0.0, 0.7082, 0.8643, 0.0598, 0.6302, 0.5732] +2026-04-12 23:57:09.650174: Epoch time: 101.72 s +2026-04-12 23:57:10.801944: +2026-04-12 23:57:10.803852: Epoch 2152 +2026-04-12 23:57:10.806057: Current learning rate: 0.00499 +2026-04-12 23:58:52.650118: train_loss -0.4037 +2026-04-12 23:58:52.655910: val_loss -0.3674 +2026-04-12 23:58:52.658054: Pseudo dice [0.6149, 0.0, 0.7579, 0.6431, 0.1506, 0.6272, 0.7881] +2026-04-12 23:58:52.659654: Epoch time: 101.85 s +2026-04-12 23:58:53.818475: +2026-04-12 23:58:53.820227: Epoch 2153 +2026-04-12 23:58:53.822262: Current learning rate: 0.00499 +2026-04-13 00:00:35.637767: train_loss -0.4087 +2026-04-13 00:00:35.644279: val_loss -0.3175 +2026-04-13 00:00:35.646383: Pseudo dice [0.2957, 0.0, 0.6875, 0.7351, 0.1042, 0.7772, 0.4304] +2026-04-13 00:00:35.649053: Epoch time: 101.82 s +2026-04-13 00:00:36.821902: +2026-04-13 00:00:36.823665: Epoch 2154 +2026-04-13 00:00:36.825439: Current learning rate: 0.00499 +2026-04-13 00:02:18.754635: train_loss -0.3889 +2026-04-13 00:02:18.760954: val_loss -0.3827 +2026-04-13 00:02:18.762395: Pseudo dice [0.0, 0.0, 0.5824, 0.7859, 0.4186, 0.4979, 0.9214] +2026-04-13 00:02:18.765486: Epoch time: 101.94 s +2026-04-13 00:02:19.909114: +2026-04-13 00:02:19.911055: Epoch 2155 +2026-04-13 00:02:19.913108: Current learning rate: 0.00498 +2026-04-13 00:04:01.676794: train_loss -0.384 +2026-04-13 00:04:01.682666: val_loss -0.3476 +2026-04-13 00:04:01.684567: Pseudo dice [0.2297, 0.0, 0.5502, 0.8223, 0.4126, 0.7809, 0.8056] +2026-04-13 00:04:01.686229: Epoch time: 101.77 s +2026-04-13 00:04:02.818751: +2026-04-13 00:04:02.822473: Epoch 2156 +2026-04-13 00:04:02.825276: Current learning rate: 0.00498 +2026-04-13 00:05:44.494185: train_loss -0.3997 +2026-04-13 00:05:44.499134: val_loss -0.3193 +2026-04-13 00:05:44.500757: Pseudo dice [0.2949, 0.0, 0.2828, 0.8784, 0.3359, 0.1769, 0.6975] +2026-04-13 00:05:44.502207: Epoch time: 101.68 s +2026-04-13 00:05:45.648236: +2026-04-13 00:05:45.649780: Epoch 2157 +2026-04-13 00:05:45.651700: Current learning rate: 0.00498 +2026-04-13 00:07:27.285067: train_loss -0.4197 +2026-04-13 00:07:27.290732: val_loss -0.3525 +2026-04-13 00:07:27.292323: Pseudo dice [0.6427, 0.0, 0.6635, 0.3159, 0.257, 0.6306, 0.4786] +2026-04-13 00:07:27.294260: Epoch time: 101.64 s +2026-04-13 00:07:28.438844: +2026-04-13 00:07:28.440454: Epoch 2158 +2026-04-13 00:07:28.442348: Current learning rate: 0.00498 +2026-04-13 00:09:10.113339: train_loss -0.4082 +2026-04-13 00:09:10.135145: val_loss -0.3493 +2026-04-13 00:09:10.143305: Pseudo dice [0.4951, 0.0, 0.5687, 0.813, 0.0654, 0.5001, 0.7552] +2026-04-13 00:09:10.145402: Epoch time: 101.68 s +2026-04-13 00:09:11.294677: +2026-04-13 00:09:11.296318: Epoch 2159 +2026-04-13 00:09:11.298156: Current learning rate: 0.00497 +2026-04-13 00:10:53.230720: train_loss -0.419 +2026-04-13 00:10:53.235634: val_loss -0.3783 +2026-04-13 00:10:53.237341: Pseudo dice [0.3872, 0.0, 0.7982, 0.8325, 0.2404, 0.7635, 0.9134] +2026-04-13 00:10:53.238805: Epoch time: 101.94 s +2026-04-13 00:10:54.397651: +2026-04-13 00:10:54.399190: Epoch 2160 +2026-04-13 00:10:54.400871: Current learning rate: 0.00497 +2026-04-13 00:12:35.915074: train_loss -0.4352 +2026-04-13 00:12:35.920546: val_loss -0.3804 +2026-04-13 00:12:35.922010: Pseudo dice [0.6543, 0.0, 0.8003, 0.607, 0.4742, 0.7556, 0.6767] +2026-04-13 00:12:35.924556: Epoch time: 101.52 s +2026-04-13 00:12:37.093904: +2026-04-13 00:12:37.095996: Epoch 2161 +2026-04-13 00:12:37.097967: Current learning rate: 0.00497 +2026-04-13 00:14:18.862789: train_loss -0.4043 +2026-04-13 00:14:18.868744: val_loss -0.3946 +2026-04-13 00:14:18.870639: Pseudo dice [0.8437, 0.0, 0.7081, 0.5545, 0.2876, 0.7417, 0.6702] +2026-04-13 00:14:18.872066: Epoch time: 101.77 s +2026-04-13 00:14:20.022965: +2026-04-13 00:14:20.024815: Epoch 2162 +2026-04-13 00:14:20.026930: Current learning rate: 0.00497 +2026-04-13 00:16:01.688233: train_loss -0.3937 +2026-04-13 00:16:01.696227: val_loss -0.4021 +2026-04-13 00:16:01.698330: Pseudo dice [0.2998, 0.0, 0.7769, 0.8125, 0.4736, 0.6051, 0.7556] +2026-04-13 00:16:01.700208: Epoch time: 101.67 s +2026-04-13 00:16:02.852548: +2026-04-13 00:16:02.855267: Epoch 2163 +2026-04-13 00:16:02.857323: Current learning rate: 0.00496 +2026-04-13 00:17:44.511775: train_loss -0.4273 +2026-04-13 00:17:44.517469: val_loss -0.3642 +2026-04-13 00:17:44.519223: Pseudo dice [0.3496, 0.0, 0.6027, 0.7121, 0.2901, 0.4897, 0.8808] +2026-04-13 00:17:44.520888: Epoch time: 101.66 s +2026-04-13 00:17:45.675414: +2026-04-13 00:17:45.677183: Epoch 2164 +2026-04-13 00:17:45.679119: Current learning rate: 0.00496 +2026-04-13 00:19:27.259252: train_loss -0.3851 +2026-04-13 00:19:27.265479: val_loss -0.2948 +2026-04-13 00:19:27.267853: Pseudo dice [0.0771, 0.0, 0.4893, 0.6761, 0.1784, 0.2222, 0.263] +2026-04-13 00:19:27.269434: Epoch time: 101.59 s +2026-04-13 00:19:28.412235: +2026-04-13 00:19:28.413909: Epoch 2165 +2026-04-13 00:19:28.415720: Current learning rate: 0.00496 +2026-04-13 00:21:10.138038: train_loss -0.3854 +2026-04-13 00:21:10.142938: val_loss -0.3432 +2026-04-13 00:21:10.144417: Pseudo dice [0.168, 0.0, 0.5818, 0.3722, 0.1511, 0.7205, 0.6466] +2026-04-13 00:21:10.145984: Epoch time: 101.73 s +2026-04-13 00:21:11.294939: +2026-04-13 00:21:11.296779: Epoch 2166 +2026-04-13 00:21:11.298747: Current learning rate: 0.00496 +2026-04-13 00:22:53.065297: train_loss -0.4169 +2026-04-13 00:22:53.070763: val_loss -0.3702 +2026-04-13 00:22:53.072751: Pseudo dice [0.4997, 0.0, 0.7057, 0.8949, 0.2756, 0.7452, 0.8212] +2026-04-13 00:22:53.074475: Epoch time: 101.77 s +2026-04-13 00:22:54.224166: +2026-04-13 00:22:54.225685: Epoch 2167 +2026-04-13 00:22:54.227540: Current learning rate: 0.00495 +2026-04-13 00:24:36.024214: train_loss -0.3884 +2026-04-13 00:24:36.028916: val_loss -0.3659 +2026-04-13 00:24:36.030375: Pseudo dice [0.5584, 0.0, 0.6245, 0.7624, 0.2854, 0.5908, 0.6923] +2026-04-13 00:24:36.032088: Epoch time: 101.8 s +2026-04-13 00:24:37.176468: +2026-04-13 00:24:37.177948: Epoch 2168 +2026-04-13 00:24:37.179644: Current learning rate: 0.00495 +2026-04-13 00:26:18.921891: train_loss -0.4051 +2026-04-13 00:26:18.927516: val_loss -0.3801 +2026-04-13 00:26:18.929149: Pseudo dice [0.119, 0.0, 0.692, 0.8833, 0.3671, 0.7117, 0.7516] +2026-04-13 00:26:18.931187: Epoch time: 101.75 s +2026-04-13 00:26:20.071973: +2026-04-13 00:26:20.074099: Epoch 2169 +2026-04-13 00:26:20.076565: Current learning rate: 0.00495 +2026-04-13 00:28:01.823483: train_loss -0.3995 +2026-04-13 00:28:01.827555: val_loss -0.3393 +2026-04-13 00:28:01.829199: Pseudo dice [0.0178, 0.0, 0.432, 0.6232, 0.2964, 0.6286, 0.5734] +2026-04-13 00:28:01.830642: Epoch time: 101.75 s +2026-04-13 00:28:02.976613: +2026-04-13 00:28:02.978329: Epoch 2170 +2026-04-13 00:28:02.980322: Current learning rate: 0.00495 +2026-04-13 00:29:44.629161: train_loss -0.4172 +2026-04-13 00:29:44.633312: val_loss -0.3767 +2026-04-13 00:29:44.634772: Pseudo dice [0.3384, 0.0, 0.8699, 0.7764, 0.587, 0.5704, 0.7645] +2026-04-13 00:29:44.636137: Epoch time: 101.66 s +2026-04-13 00:29:46.731467: +2026-04-13 00:29:46.733813: Epoch 2171 +2026-04-13 00:29:46.735904: Current learning rate: 0.00494 +2026-04-13 00:31:28.654394: train_loss -0.4149 +2026-04-13 00:31:28.660420: val_loss -0.3347 +2026-04-13 00:31:28.661915: Pseudo dice [0.5021, 0.0, 0.4663, 0.6719, 0.3302, 0.5007, 0.7936] +2026-04-13 00:31:28.663743: Epoch time: 101.93 s +2026-04-13 00:31:29.813433: +2026-04-13 00:31:29.815053: Epoch 2172 +2026-04-13 00:31:29.817162: Current learning rate: 0.00494 +2026-04-13 00:33:11.635450: train_loss -0.373 +2026-04-13 00:33:11.640638: val_loss -0.3646 +2026-04-13 00:33:11.642175: Pseudo dice [0.5225, 0.0, 0.7258, 0.4675, 0.3142, 0.7172, 0.7458] +2026-04-13 00:33:11.644236: Epoch time: 101.83 s +2026-04-13 00:33:12.792626: +2026-04-13 00:33:12.794989: Epoch 2173 +2026-04-13 00:33:12.797093: Current learning rate: 0.00494 +2026-04-13 00:34:54.572779: train_loss -0.4127 +2026-04-13 00:34:54.578761: val_loss -0.3723 +2026-04-13 00:34:54.581141: Pseudo dice [0.3255, 0.0, 0.7268, 0.817, 0.4841, 0.7117, 0.8542] +2026-04-13 00:34:54.583125: Epoch time: 101.78 s +2026-04-13 00:34:55.724383: +2026-04-13 00:34:55.726055: Epoch 2174 +2026-04-13 00:34:55.727361: Current learning rate: 0.00494 +2026-04-13 00:36:37.353872: train_loss -0.4036 +2026-04-13 00:36:37.358396: val_loss -0.3822 +2026-04-13 00:36:37.359683: Pseudo dice [0.5529, 0.0, 0.6929, 0.6916, 0.2267, 0.3256, 0.7869] +2026-04-13 00:36:37.361177: Epoch time: 101.63 s +2026-04-13 00:36:38.505838: +2026-04-13 00:36:38.507145: Epoch 2175 +2026-04-13 00:36:38.508447: Current learning rate: 0.00493 +2026-04-13 00:38:20.216438: train_loss -0.4063 +2026-04-13 00:38:20.220896: val_loss -0.3544 +2026-04-13 00:38:20.222287: Pseudo dice [0.3758, 0.0, 0.7925, 0.8979, 0.1005, 0.6456, 0.6693] +2026-04-13 00:38:20.223938: Epoch time: 101.71 s +2026-04-13 00:38:21.374483: +2026-04-13 00:38:21.375965: Epoch 2176 +2026-04-13 00:38:21.377330: Current learning rate: 0.00493 +2026-04-13 00:40:03.138433: train_loss -0.398 +2026-04-13 00:40:03.144315: val_loss -0.2901 +2026-04-13 00:40:03.145774: Pseudo dice [0.2899, 0.0, 0.3628, 0.5113, 0.2211, 0.2612, 0.5647] +2026-04-13 00:40:03.147135: Epoch time: 101.77 s +2026-04-13 00:40:04.299528: +2026-04-13 00:40:04.301416: Epoch 2177 +2026-04-13 00:40:04.303184: Current learning rate: 0.00493 +2026-04-13 00:41:45.848577: train_loss -0.4045 +2026-04-13 00:41:45.853792: val_loss -0.3371 +2026-04-13 00:41:45.855403: Pseudo dice [0.3345, 0.0, 0.4503, 0.4129, 0.1976, 0.4368, 0.5518] +2026-04-13 00:41:45.856805: Epoch time: 101.55 s +2026-04-13 00:41:47.004716: +2026-04-13 00:41:47.006442: Epoch 2178 +2026-04-13 00:41:47.007953: Current learning rate: 0.00493 +2026-04-13 00:43:28.710945: train_loss -0.4023 +2026-04-13 00:43:28.715682: val_loss -0.3412 +2026-04-13 00:43:28.717072: Pseudo dice [0.1576, 0.0, 0.3319, 0.691, 0.3515, 0.5017, 0.6216] +2026-04-13 00:43:28.718716: Epoch time: 101.71 s +2026-04-13 00:43:29.870867: +2026-04-13 00:43:29.872283: Epoch 2179 +2026-04-13 00:43:29.873528: Current learning rate: 0.00493 +2026-04-13 00:45:11.372071: train_loss -0.4057 +2026-04-13 00:45:11.384197: val_loss -0.3746 +2026-04-13 00:45:11.386710: Pseudo dice [0.3839, 0.0, 0.7276, 0.7349, 0.3244, 0.5752, 0.8479] +2026-04-13 00:45:11.388844: Epoch time: 101.5 s +2026-04-13 00:45:12.587202: +2026-04-13 00:45:12.588778: Epoch 2180 +2026-04-13 00:45:12.590753: Current learning rate: 0.00492 +2026-04-13 00:46:54.295859: train_loss -0.3518 +2026-04-13 00:46:54.301167: val_loss -0.3398 +2026-04-13 00:46:54.302717: Pseudo dice [0.22, 0.0, 0.4436, 0.0733, 0.3769, 0.7008, 0.806] +2026-04-13 00:46:54.304041: Epoch time: 101.71 s +2026-04-13 00:46:55.455352: +2026-04-13 00:46:55.457107: Epoch 2181 +2026-04-13 00:46:55.458560: Current learning rate: 0.00492 +2026-04-13 00:48:37.276272: train_loss -0.3831 +2026-04-13 00:48:37.280468: val_loss -0.409 +2026-04-13 00:48:37.281898: Pseudo dice [0.7439, 0.0, 0.7898, 0.8523, 0.399, 0.7895, 0.8655] +2026-04-13 00:48:37.283688: Epoch time: 101.82 s +2026-04-13 00:48:38.433682: +2026-04-13 00:48:38.435098: Epoch 2182 +2026-04-13 00:48:38.436329: Current learning rate: 0.00492 +2026-04-13 00:50:20.281424: train_loss -0.407 +2026-04-13 00:50:20.288687: val_loss -0.3755 +2026-04-13 00:50:20.290415: Pseudo dice [0.6619, 0.0, 0.842, 0.3285, 0.1808, 0.6777, 0.6933] +2026-04-13 00:50:20.292071: Epoch time: 101.85 s +2026-04-13 00:50:21.479952: +2026-04-13 00:50:21.482143: Epoch 2183 +2026-04-13 00:50:21.483765: Current learning rate: 0.00492 +2026-04-13 00:52:03.325563: train_loss -0.3649 +2026-04-13 00:52:03.331483: val_loss -0.3385 +2026-04-13 00:52:03.333123: Pseudo dice [0.1432, 0.0, 0.634, 0.6381, 0.273, 0.6118, 0.6191] +2026-04-13 00:52:03.335062: Epoch time: 101.85 s +2026-04-13 00:52:04.469531: +2026-04-13 00:52:04.471248: Epoch 2184 +2026-04-13 00:52:04.472692: Current learning rate: 0.00491 +2026-04-13 00:53:46.425860: train_loss -0.3638 +2026-04-13 00:53:46.430745: val_loss -0.3514 +2026-04-13 00:53:46.432283: Pseudo dice [0.337, 0.0, 0.6725, 0.6824, 0.3616, 0.61, 0.6171] +2026-04-13 00:53:46.433680: Epoch time: 101.96 s +2026-04-13 00:53:47.573550: +2026-04-13 00:53:47.575341: Epoch 2185 +2026-04-13 00:53:47.576865: Current learning rate: 0.00491 +2026-04-13 00:55:29.326283: train_loss -0.4026 +2026-04-13 00:55:29.331407: val_loss -0.3807 +2026-04-13 00:55:29.333776: Pseudo dice [0.3649, 0.0, 0.6756, 0.8345, 0.2537, 0.8033, 0.8314] +2026-04-13 00:55:29.335595: Epoch time: 101.76 s +2026-04-13 00:55:30.473327: +2026-04-13 00:55:30.475146: Epoch 2186 +2026-04-13 00:55:30.476702: Current learning rate: 0.00491 +2026-04-13 00:57:12.247971: train_loss -0.4155 +2026-04-13 00:57:12.253563: val_loss -0.3725 +2026-04-13 00:57:12.256393: Pseudo dice [0.8146, 0.0, 0.7516, 0.7381, 0.4489, 0.5913, 0.8343] +2026-04-13 00:57:12.258137: Epoch time: 101.78 s +2026-04-13 00:57:13.410946: +2026-04-13 00:57:13.412863: Epoch 2187 +2026-04-13 00:57:13.415179: Current learning rate: 0.00491 +2026-04-13 00:58:55.225271: train_loss -0.4118 +2026-04-13 00:58:55.229790: val_loss -0.3359 +2026-04-13 00:58:55.231337: Pseudo dice [0.6756, 0.0, 0.4214, 0.5707, 0.1287, 0.5349, 0.3786] +2026-04-13 00:58:55.232671: Epoch time: 101.82 s +2026-04-13 00:58:56.384936: +2026-04-13 00:58:56.386462: Epoch 2188 +2026-04-13 00:58:56.387679: Current learning rate: 0.0049 +2026-04-13 01:00:38.145195: train_loss -0.4088 +2026-04-13 01:00:38.149381: val_loss -0.3251 +2026-04-13 01:00:38.150962: Pseudo dice [0.461, 0.0, 0.5932, 0.5173, 0.2613, 0.4086, 0.8693] +2026-04-13 01:00:38.152473: Epoch time: 101.76 s +2026-04-13 01:00:39.296205: +2026-04-13 01:00:39.297861: Epoch 2189 +2026-04-13 01:00:39.299166: Current learning rate: 0.0049 +2026-04-13 01:02:21.165069: train_loss -0.414 +2026-04-13 01:02:21.169198: val_loss -0.3198 +2026-04-13 01:02:21.170664: Pseudo dice [0.2345, 0.0, 0.8763, 0.582, 0.0308, 0.7769, 0.8402] +2026-04-13 01:02:21.171983: Epoch time: 101.87 s +2026-04-13 01:02:22.356317: +2026-04-13 01:02:22.357703: Epoch 2190 +2026-04-13 01:02:22.358988: Current learning rate: 0.0049 +2026-04-13 01:04:03.865098: train_loss -0.4184 +2026-04-13 01:04:03.869874: val_loss -0.3605 +2026-04-13 01:04:03.871307: Pseudo dice [0.1844, 0.0, 0.6123, 0.563, 0.4258, 0.8047, 0.8153] +2026-04-13 01:04:03.873549: Epoch time: 101.51 s +2026-04-13 01:04:05.020656: +2026-04-13 01:04:05.022521: Epoch 2191 +2026-04-13 01:04:05.025092: Current learning rate: 0.0049 +2026-04-13 01:05:46.559576: train_loss -0.3918 +2026-04-13 01:05:46.565251: val_loss -0.3498 +2026-04-13 01:05:46.566891: Pseudo dice [0.553, 0.0, 0.5512, 0.6992, 0.1715, 0.6713, 0.6787] +2026-04-13 01:05:46.568698: Epoch time: 101.54 s +2026-04-13 01:05:48.693033: +2026-04-13 01:05:48.694554: Epoch 2192 +2026-04-13 01:05:48.695980: Current learning rate: 0.00489 +2026-04-13 01:07:30.436378: train_loss -0.3765 +2026-04-13 01:07:30.441499: val_loss -0.318 +2026-04-13 01:07:30.442982: Pseudo dice [0.2628, 0.0, 0.6661, 0.0451, 0.2803, 0.4795, 0.727] +2026-04-13 01:07:30.444419: Epoch time: 101.75 s +2026-04-13 01:07:31.592244: +2026-04-13 01:07:31.593810: Epoch 2193 +2026-04-13 01:07:31.595323: Current learning rate: 0.00489 +2026-04-13 01:09:13.411489: train_loss -0.3877 +2026-04-13 01:09:13.417468: val_loss -0.3343 +2026-04-13 01:09:13.419485: Pseudo dice [0.2216, 0.0, 0.5844, 0.8708, 0.2419, 0.7227, 0.9127] +2026-04-13 01:09:13.421068: Epoch time: 101.82 s +2026-04-13 01:09:14.589837: +2026-04-13 01:09:14.591616: Epoch 2194 +2026-04-13 01:09:14.593077: Current learning rate: 0.00489 +2026-04-13 01:10:56.304785: train_loss -0.408 +2026-04-13 01:10:56.310110: val_loss -0.3609 +2026-04-13 01:10:56.312085: Pseudo dice [0.6037, 0.0, 0.8203, 0.8604, 0.2157, 0.7354, 0.9068] +2026-04-13 01:10:56.314114: Epoch time: 101.72 s +2026-04-13 01:10:57.461413: +2026-04-13 01:10:57.463146: Epoch 2195 +2026-04-13 01:10:57.464811: Current learning rate: 0.00489 +2026-04-13 01:12:39.287011: train_loss -0.3911 +2026-04-13 01:12:39.292023: val_loss -0.3253 +2026-04-13 01:12:39.293685: Pseudo dice [0.3195, 0.0, 0.5613, 0.7978, 0.2876, 0.54, 0.5261] +2026-04-13 01:12:39.295390: Epoch time: 101.83 s +2026-04-13 01:12:40.439105: +2026-04-13 01:12:40.440730: Epoch 2196 +2026-04-13 01:12:40.442020: Current learning rate: 0.00488 +2026-04-13 01:14:22.105921: train_loss -0.3845 +2026-04-13 01:14:22.110345: val_loss -0.3164 +2026-04-13 01:14:22.111691: Pseudo dice [0.0358, 0.0, 0.4638, 0.7172, 0.3002, 0.3644, 0.7386] +2026-04-13 01:14:22.113310: Epoch time: 101.67 s +2026-04-13 01:14:23.242137: +2026-04-13 01:14:23.243554: Epoch 2197 +2026-04-13 01:14:23.245013: Current learning rate: 0.00488 +2026-04-13 01:16:04.852361: train_loss -0.3887 +2026-04-13 01:16:04.857913: val_loss -0.3317 +2026-04-13 01:16:04.859507: Pseudo dice [0.0126, 0.0, 0.7993, 0.2871, 0.1043, 0.5454, 0.6995] +2026-04-13 01:16:04.861208: Epoch time: 101.61 s +2026-04-13 01:16:06.026998: +2026-04-13 01:16:06.028735: Epoch 2198 +2026-04-13 01:16:06.030481: Current learning rate: 0.00488 +2026-04-13 01:17:47.547018: train_loss -0.4087 +2026-04-13 01:17:47.552678: val_loss -0.3681 +2026-04-13 01:17:47.555410: Pseudo dice [0.1799, 0.0, 0.7657, 0.7677, 0.3371, 0.8601, 0.8364] +2026-04-13 01:17:47.557149: Epoch time: 101.52 s +2026-04-13 01:17:48.714996: +2026-04-13 01:17:48.716713: Epoch 2199 +2026-04-13 01:17:48.718201: Current learning rate: 0.00488 +2026-04-13 01:19:30.397498: train_loss -0.4118 +2026-04-13 01:19:30.403152: val_loss -0.372 +2026-04-13 01:19:30.405091: Pseudo dice [0.2713, 0.0, 0.7451, 0.8809, 0.3317, 0.5001, 0.5608] +2026-04-13 01:19:30.406758: Epoch time: 101.69 s +2026-04-13 01:19:33.204915: +2026-04-13 01:19:33.206633: Epoch 2200 +2026-04-13 01:19:33.207919: Current learning rate: 0.00487 +2026-04-13 01:21:15.016014: train_loss -0.3829 +2026-04-13 01:21:15.020940: val_loss -0.2826 +2026-04-13 01:21:15.022398: Pseudo dice [0.4069, 0.0, 0.2755, 0.3903, 0.2295, 0.1681, 0.8897] +2026-04-13 01:21:15.023719: Epoch time: 101.81 s +2026-04-13 01:21:16.232874: +2026-04-13 01:21:16.234366: Epoch 2201 +2026-04-13 01:21:16.235743: Current learning rate: 0.00487 +2026-04-13 01:22:58.039878: train_loss -0.3869 +2026-04-13 01:22:58.044486: val_loss -0.2975 +2026-04-13 01:22:58.046120: Pseudo dice [0.186, 0.0, 0.437, 0.1955, 0.1461, 0.462, 0.587] +2026-04-13 01:22:58.047469: Epoch time: 101.81 s +2026-04-13 01:22:59.203691: +2026-04-13 01:22:59.205551: Epoch 2202 +2026-04-13 01:22:59.207361: Current learning rate: 0.00487 +2026-04-13 01:24:40.886367: train_loss -0.3958 +2026-04-13 01:24:40.891707: val_loss -0.3563 +2026-04-13 01:24:40.893244: Pseudo dice [0.1351, 0.0, 0.8477, 0.5536, 0.4513, 0.5733, 0.5993] +2026-04-13 01:24:40.895060: Epoch time: 101.69 s +2026-04-13 01:24:42.032899: +2026-04-13 01:24:42.034904: Epoch 2203 +2026-04-13 01:24:42.037192: Current learning rate: 0.00487 +2026-04-13 01:26:23.851827: train_loss -0.402 +2026-04-13 01:26:23.856785: val_loss -0.3632 +2026-04-13 01:26:23.858359: Pseudo dice [0.416, 0.0, 0.6661, 0.8139, 0.3995, 0.4128, 0.7133] +2026-04-13 01:26:23.859940: Epoch time: 101.82 s +2026-04-13 01:26:25.015936: +2026-04-13 01:26:25.017724: Epoch 2204 +2026-04-13 01:26:25.019223: Current learning rate: 0.00486 +2026-04-13 01:28:06.687171: train_loss -0.4067 +2026-04-13 01:28:06.693890: val_loss -0.3489 +2026-04-13 01:28:06.695881: Pseudo dice [0.2064, 0.0, 0.6814, 0.7241, 0.2636, 0.5577, 0.5776] +2026-04-13 01:28:06.698232: Epoch time: 101.67 s +2026-04-13 01:28:07.858093: +2026-04-13 01:28:07.860038: Epoch 2205 +2026-04-13 01:28:07.861687: Current learning rate: 0.00486 +2026-04-13 01:29:49.748795: train_loss -0.4049 +2026-04-13 01:29:49.754681: val_loss -0.3821 +2026-04-13 01:29:49.756610: Pseudo dice [0.3341, 0.0, 0.7294, 0.1684, 0.4567, 0.6339, 0.7999] +2026-04-13 01:29:49.758627: Epoch time: 101.89 s +2026-04-13 01:29:50.911759: +2026-04-13 01:29:50.915867: Epoch 2206 +2026-04-13 01:29:50.918889: Current learning rate: 0.00486 +2026-04-13 01:31:32.541006: train_loss -0.4151 +2026-04-13 01:31:32.545401: val_loss -0.348 +2026-04-13 01:31:32.547045: Pseudo dice [0.7747, 0.0, 0.6923, 0.4656, 0.4304, 0.3863, 0.8387] +2026-04-13 01:31:32.548909: Epoch time: 101.63 s +2026-04-13 01:31:33.684424: +2026-04-13 01:31:33.686101: Epoch 2207 +2026-04-13 01:31:33.687567: Current learning rate: 0.00486 +2026-04-13 01:33:15.372515: train_loss -0.4149 +2026-04-13 01:33:15.376920: val_loss -0.3599 +2026-04-13 01:33:15.378819: Pseudo dice [0.2556, 0.0, 0.753, 0.5484, 0.3939, 0.7643, 0.505] +2026-04-13 01:33:15.381607: Epoch time: 101.69 s +2026-04-13 01:33:16.551451: +2026-04-13 01:33:16.553392: Epoch 2208 +2026-04-13 01:33:16.555086: Current learning rate: 0.00485 +2026-04-13 01:34:58.234319: train_loss -0.3961 +2026-04-13 01:34:58.238776: val_loss -0.3449 +2026-04-13 01:34:58.240252: Pseudo dice [0.3431, 0.0, 0.7393, 0.8642, 0.1031, 0.2842, 0.6847] +2026-04-13 01:34:58.241682: Epoch time: 101.69 s +2026-04-13 01:34:59.378332: +2026-04-13 01:34:59.379841: Epoch 2209 +2026-04-13 01:34:59.381239: Current learning rate: 0.00485 +2026-04-13 01:36:41.019466: train_loss -0.409 +2026-04-13 01:36:41.024522: val_loss -0.3638 +2026-04-13 01:36:41.026606: Pseudo dice [0.7241, 0.0, 0.7478, 0.5348, 0.2314, 0.7419, 0.8426] +2026-04-13 01:36:41.028376: Epoch time: 101.64 s +2026-04-13 01:36:42.143427: +2026-04-13 01:36:42.145164: Epoch 2210 +2026-04-13 01:36:42.146690: Current learning rate: 0.00485 +2026-04-13 01:38:23.863561: train_loss -0.4222 +2026-04-13 01:38:23.868861: val_loss -0.3444 +2026-04-13 01:38:23.870528: Pseudo dice [0.4159, 0.0, 0.8179, 0.4239, 0.1765, 0.3283, 0.9166] +2026-04-13 01:38:23.872574: Epoch time: 101.72 s +2026-04-13 01:38:25.045113: +2026-04-13 01:38:25.046811: Epoch 2211 +2026-04-13 01:38:25.048583: Current learning rate: 0.00485 +2026-04-13 01:40:06.682338: train_loss -0.3973 +2026-04-13 01:40:06.688226: val_loss -0.3288 +2026-04-13 01:40:06.690060: Pseudo dice [0.3534, 0.0, 0.6009, 0.8437, 0.2366, 0.3631, 0.668] +2026-04-13 01:40:06.693599: Epoch time: 101.64 s +2026-04-13 01:40:07.853055: +2026-04-13 01:40:07.854667: Epoch 2212 +2026-04-13 01:40:07.856229: Current learning rate: 0.00484 +2026-04-13 01:41:50.618688: train_loss -0.3743 +2026-04-13 01:41:50.623242: val_loss -0.3653 +2026-04-13 01:41:50.624905: Pseudo dice [0.1815, 0.0, 0.5073, 0.8158, 0.3081, 0.6881, 0.6473] +2026-04-13 01:41:50.626452: Epoch time: 102.77 s +2026-04-13 01:41:51.784644: +2026-04-13 01:41:51.790148: Epoch 2213 +2026-04-13 01:41:51.791770: Current learning rate: 0.00484 +2026-04-13 01:43:33.335641: train_loss -0.3704 +2026-04-13 01:43:33.340497: val_loss -0.3241 +2026-04-13 01:43:33.342067: Pseudo dice [0.0, 0.0, 0.7549, 0.6751, 0.1723, 0.8634, 0.5221] +2026-04-13 01:43:33.343725: Epoch time: 101.55 s +2026-04-13 01:43:34.481790: +2026-04-13 01:43:34.483184: Epoch 2214 +2026-04-13 01:43:34.484596: Current learning rate: 0.00484 +2026-04-13 01:45:16.172127: train_loss -0.3931 +2026-04-13 01:45:16.177551: val_loss -0.3094 +2026-04-13 01:45:16.179357: Pseudo dice [0.0, 0.0, 0.4921, 0.8602, 0.3571, 0.3209, 0.6359] +2026-04-13 01:45:16.180714: Epoch time: 101.69 s +2026-04-13 01:45:17.334440: +2026-04-13 01:45:17.337252: Epoch 2215 +2026-04-13 01:45:17.340110: Current learning rate: 0.00484 +2026-04-13 01:46:58.779160: train_loss -0.4 +2026-04-13 01:46:58.785116: val_loss -0.3434 +2026-04-13 01:46:58.786823: Pseudo dice [0.0, 0.0, 0.7694, 0.7511, 0.3926, 0.5327, 0.8694] +2026-04-13 01:46:58.788419: Epoch time: 101.45 s +2026-04-13 01:46:59.927277: +2026-04-13 01:46:59.929481: Epoch 2216 +2026-04-13 01:46:59.931414: Current learning rate: 0.00484 +2026-04-13 01:48:41.553747: train_loss -0.3825 +2026-04-13 01:48:41.558721: val_loss -0.3595 +2026-04-13 01:48:41.560400: Pseudo dice [0.0, 0.0, 0.412, 0.8845, 0.1459, 0.7175, 0.9184] +2026-04-13 01:48:41.562290: Epoch time: 101.63 s +2026-04-13 01:48:42.713127: +2026-04-13 01:48:42.714922: Epoch 2217 +2026-04-13 01:48:42.716987: Current learning rate: 0.00483 +2026-04-13 01:50:24.302374: train_loss -0.3847 +2026-04-13 01:50:24.306493: val_loss -0.3424 +2026-04-13 01:50:24.308105: Pseudo dice [0.0, 0.0, 0.6338, 0.4905, 0.3158, 0.7815, 0.5154] +2026-04-13 01:50:24.309973: Epoch time: 101.59 s +2026-04-13 01:50:25.455367: +2026-04-13 01:50:25.457582: Epoch 2218 +2026-04-13 01:50:25.459210: Current learning rate: 0.00483 +2026-04-13 01:52:07.301446: train_loss -0.4279 +2026-04-13 01:52:07.306800: val_loss -0.3761 +2026-04-13 01:52:07.308812: Pseudo dice [0.0031, 0.0, 0.745, 0.7826, 0.5152, 0.8157, 0.8129] +2026-04-13 01:52:07.311302: Epoch time: 101.85 s +2026-04-13 01:52:08.453989: +2026-04-13 01:52:08.455851: Epoch 2219 +2026-04-13 01:52:08.457502: Current learning rate: 0.00483 +2026-04-13 01:53:49.957241: train_loss -0.4084 +2026-04-13 01:53:49.963052: val_loss -0.3816 +2026-04-13 01:53:49.965500: Pseudo dice [0.0376, 0.0, 0.8259, 0.5467, 0.3823, 0.6651, 0.7612] +2026-04-13 01:53:49.967422: Epoch time: 101.51 s +2026-04-13 01:53:51.128880: +2026-04-13 01:53:51.130548: Epoch 2220 +2026-04-13 01:53:51.132170: Current learning rate: 0.00483 +2026-04-13 01:55:32.743924: train_loss -0.3963 +2026-04-13 01:55:32.749782: val_loss -0.3202 +2026-04-13 01:55:32.752356: Pseudo dice [0.4146, 0.0, 0.7772, 0.0008, 0.0627, 0.8654, 0.2966] +2026-04-13 01:55:32.753759: Epoch time: 101.62 s +2026-04-13 01:55:33.892885: +2026-04-13 01:55:33.894344: Epoch 2221 +2026-04-13 01:55:33.895664: Current learning rate: 0.00482 +2026-04-13 01:57:15.637911: train_loss -0.4221 +2026-04-13 01:57:15.642441: val_loss -0.3588 +2026-04-13 01:57:15.643986: Pseudo dice [0.5353, 0.0, 0.5106, 0.4293, 0.4458, 0.6425, 0.2132] +2026-04-13 01:57:15.645933: Epoch time: 101.75 s +2026-04-13 01:57:16.784518: +2026-04-13 01:57:16.787328: Epoch 2222 +2026-04-13 01:57:16.790288: Current learning rate: 0.00482 +2026-04-13 01:58:58.460969: train_loss -0.4019 +2026-04-13 01:58:58.465371: val_loss -0.3813 +2026-04-13 01:58:58.467292: Pseudo dice [0.2604, 0.0, 0.7978, 0.7348, 0.4675, 0.752, 0.3926] +2026-04-13 01:58:58.468537: Epoch time: 101.68 s +2026-04-13 01:58:59.605200: +2026-04-13 01:58:59.606596: Epoch 2223 +2026-04-13 01:58:59.607986: Current learning rate: 0.00482 +2026-04-13 02:00:41.390659: train_loss -0.414 +2026-04-13 02:00:41.395640: val_loss -0.3725 +2026-04-13 02:00:41.397307: Pseudo dice [0.5316, 0.0, 0.7912, 0.7597, 0.2716, 0.7971, 0.5901] +2026-04-13 02:00:41.399402: Epoch time: 101.79 s +2026-04-13 02:00:42.540185: +2026-04-13 02:00:42.542121: Epoch 2224 +2026-04-13 02:00:42.543895: Current learning rate: 0.00482 +2026-04-13 02:02:24.260069: train_loss -0.4166 +2026-04-13 02:02:24.264796: val_loss -0.3249 +2026-04-13 02:02:24.266505: Pseudo dice [0.0, 0.0, 0.7207, 0.7889, 0.0485, 0.4954, 0.7869] +2026-04-13 02:02:24.268409: Epoch time: 101.72 s +2026-04-13 02:02:25.411739: +2026-04-13 02:02:25.413975: Epoch 2225 +2026-04-13 02:02:25.415477: Current learning rate: 0.00481 +2026-04-13 02:04:07.280109: train_loss -0.3864 +2026-04-13 02:04:07.285586: val_loss -0.3441 +2026-04-13 02:04:07.287102: Pseudo dice [0.0917, 0.0, 0.7061, 0.6986, 0.2011, 0.7825, 0.7683] +2026-04-13 02:04:07.288900: Epoch time: 101.87 s +2026-04-13 02:04:08.436488: +2026-04-13 02:04:08.438073: Epoch 2226 +2026-04-13 02:04:08.439611: Current learning rate: 0.00481 +2026-04-13 02:05:50.107026: train_loss -0.3951 +2026-04-13 02:05:50.112289: val_loss -0.3256 +2026-04-13 02:05:50.113745: Pseudo dice [0.3676, 0.0, 0.3358, 0.438, 0.4258, 0.4416, 0.7376] +2026-04-13 02:05:50.115401: Epoch time: 101.67 s +2026-04-13 02:05:51.271306: +2026-04-13 02:05:51.273111: Epoch 2227 +2026-04-13 02:05:51.274850: Current learning rate: 0.00481 +2026-04-13 02:07:33.076047: train_loss -0.4116 +2026-04-13 02:07:33.081479: val_loss -0.3714 +2026-04-13 02:07:33.083073: Pseudo dice [0.6852, 0.0, 0.5168, 0.8487, 0.4889, 0.8127, 0.4882] +2026-04-13 02:07:33.084565: Epoch time: 101.81 s +2026-04-13 02:07:34.230172: +2026-04-13 02:07:34.231749: Epoch 2228 +2026-04-13 02:07:34.233383: Current learning rate: 0.00481 +2026-04-13 02:09:15.898524: train_loss -0.4099 +2026-04-13 02:09:15.902967: val_loss -0.3725 +2026-04-13 02:09:15.904263: Pseudo dice [0.4785, 0.0, 0.7144, 0.2868, 0.2975, 0.8138, 0.8532] +2026-04-13 02:09:15.906874: Epoch time: 101.67 s +2026-04-13 02:09:17.065599: +2026-04-13 02:09:17.067039: Epoch 2229 +2026-04-13 02:09:17.068209: Current learning rate: 0.0048 +2026-04-13 02:10:58.816211: train_loss -0.4076 +2026-04-13 02:10:58.820477: val_loss -0.3511 +2026-04-13 02:10:58.822025: Pseudo dice [0.3518, 0.0, 0.7419, 0.4462, 0.1871, 0.7838, 0.5804] +2026-04-13 02:10:58.823643: Epoch time: 101.75 s +2026-04-13 02:10:59.977844: +2026-04-13 02:10:59.979557: Epoch 2230 +2026-04-13 02:10:59.981286: Current learning rate: 0.0048 +2026-04-13 02:12:41.615679: train_loss -0.4019 +2026-04-13 02:12:41.620048: val_loss -0.358 +2026-04-13 02:12:41.621787: Pseudo dice [0.4453, 0.0, 0.298, 0.7141, 0.2735, 0.666, 0.7588] +2026-04-13 02:12:41.623432: Epoch time: 101.64 s +2026-04-13 02:12:42.770808: +2026-04-13 02:12:42.772645: Epoch 2231 +2026-04-13 02:12:42.774181: Current learning rate: 0.0048 +2026-04-13 02:14:24.560089: train_loss -0.3983 +2026-04-13 02:14:24.565364: val_loss -0.364 +2026-04-13 02:14:24.567528: Pseudo dice [0.35, 0.0, 0.6822, 0.7725, 0.2565, 0.7821, 0.7529] +2026-04-13 02:14:24.569054: Epoch time: 101.79 s +2026-04-13 02:14:25.729294: +2026-04-13 02:14:25.731514: Epoch 2232 +2026-04-13 02:14:25.733152: Current learning rate: 0.0048 +2026-04-13 02:16:07.348406: train_loss -0.4028 +2026-04-13 02:16:07.353769: val_loss -0.3939 +2026-04-13 02:16:07.355339: Pseudo dice [0.0449, 0.0, 0.8415, 0.8228, 0.5144, 0.6984, 0.6336] +2026-04-13 02:16:07.357304: Epoch time: 101.62 s +2026-04-13 02:16:08.507938: +2026-04-13 02:16:08.509932: Epoch 2233 +2026-04-13 02:16:08.511894: Current learning rate: 0.00479 +2026-04-13 02:17:51.178207: train_loss -0.417 +2026-04-13 02:17:51.182239: val_loss -0.381 +2026-04-13 02:17:51.183588: Pseudo dice [0.4778, 0.0, 0.7708, 0.6438, 0.3338, 0.5459, 0.7227] +2026-04-13 02:17:51.185064: Epoch time: 102.67 s +2026-04-13 02:17:52.329235: +2026-04-13 02:17:52.330705: Epoch 2234 +2026-04-13 02:17:52.331868: Current learning rate: 0.00479 +2026-04-13 02:19:33.992083: train_loss -0.4225 +2026-04-13 02:19:33.997646: val_loss -0.323 +2026-04-13 02:19:33.999324: Pseudo dice [0.2094, 0.0, 0.4837, 0.7159, 0.2169, 0.5485, 0.7752] +2026-04-13 02:19:34.001030: Epoch time: 101.67 s +2026-04-13 02:19:35.143996: +2026-04-13 02:19:35.145672: Epoch 2235 +2026-04-13 02:19:35.147156: Current learning rate: 0.00479 +2026-04-13 02:21:16.698824: train_loss -0.3882 +2026-04-13 02:21:16.702660: val_loss -0.353 +2026-04-13 02:21:16.704103: Pseudo dice [0.0998, 0.0, 0.5152, 0.694, 0.2388, 0.8043, 0.5588] +2026-04-13 02:21:16.705826: Epoch time: 101.56 s +2026-04-13 02:21:17.864091: +2026-04-13 02:21:17.865443: Epoch 2236 +2026-04-13 02:21:17.866853: Current learning rate: 0.00479 +2026-04-13 02:22:59.552327: train_loss -0.4135 +2026-04-13 02:22:59.556498: val_loss -0.3666 +2026-04-13 02:22:59.557853: Pseudo dice [0.2501, 0.0, 0.7273, 0.7515, 0.417, 0.2461, 0.8587] +2026-04-13 02:22:59.559375: Epoch time: 101.69 s +2026-04-13 02:23:00.685066: +2026-04-13 02:23:00.686616: Epoch 2237 +2026-04-13 02:23:00.688170: Current learning rate: 0.00478 +2026-04-13 02:24:42.341928: train_loss -0.426 +2026-04-13 02:24:42.348157: val_loss -0.3432 +2026-04-13 02:24:42.350655: Pseudo dice [0.7655, 0.0, 0.7457, 0.8018, 0.3772, 0.7381, 0.3672] +2026-04-13 02:24:42.352662: Epoch time: 101.66 s +2026-04-13 02:24:43.495982: +2026-04-13 02:24:43.497765: Epoch 2238 +2026-04-13 02:24:43.499465: Current learning rate: 0.00478 +2026-04-13 02:26:25.131225: train_loss -0.4117 +2026-04-13 02:26:25.135296: val_loss -0.3825 +2026-04-13 02:26:25.136874: Pseudo dice [0.577, 0.0, 0.6741, 0.8554, 0.2904, 0.6727, 0.5951] +2026-04-13 02:26:25.138859: Epoch time: 101.64 s +2026-04-13 02:26:26.292451: +2026-04-13 02:26:26.293943: Epoch 2239 +2026-04-13 02:26:26.295157: Current learning rate: 0.00478 +2026-04-13 02:28:07.990970: train_loss -0.4062 +2026-04-13 02:28:07.994861: val_loss -0.3543 +2026-04-13 02:28:07.996184: Pseudo dice [0.2234, 0.0, 0.8149, 0.7461, 0.3753, 0.7977, 0.5563] +2026-04-13 02:28:07.997428: Epoch time: 101.7 s +2026-04-13 02:28:09.136813: +2026-04-13 02:28:09.138199: Epoch 2240 +2026-04-13 02:28:09.139585: Current learning rate: 0.00478 +2026-04-13 02:29:50.801786: train_loss -0.4046 +2026-04-13 02:29:50.807069: val_loss -0.3748 +2026-04-13 02:29:50.809503: Pseudo dice [0.255, 0.0, 0.856, 0.5149, 0.3912, 0.4338, 0.5804] +2026-04-13 02:29:50.811538: Epoch time: 101.67 s +2026-04-13 02:29:51.966259: +2026-04-13 02:29:51.969544: Epoch 2241 +2026-04-13 02:29:51.972084: Current learning rate: 0.00477 +2026-04-13 02:31:33.703712: train_loss -0.4186 +2026-04-13 02:31:33.709588: val_loss -0.3825 +2026-04-13 02:31:33.711546: Pseudo dice [0.4425, 0.0, 0.6425, 0.4698, 0.5061, 0.5639, 0.6955] +2026-04-13 02:31:33.713292: Epoch time: 101.74 s +2026-04-13 02:31:34.876294: +2026-04-13 02:31:34.878014: Epoch 2242 +2026-04-13 02:31:34.879554: Current learning rate: 0.00477 +2026-04-13 02:33:16.628806: train_loss -0.4145 +2026-04-13 02:33:16.634684: val_loss -0.3515 +2026-04-13 02:33:16.636318: Pseudo dice [0.3765, 0.0, 0.7396, 0.9002, 0.3408, 0.7237, 0.6157] +2026-04-13 02:33:16.637837: Epoch time: 101.75 s +2026-04-13 02:33:17.842489: +2026-04-13 02:33:17.844307: Epoch 2243 +2026-04-13 02:33:17.845856: Current learning rate: 0.00477 +2026-04-13 02:34:59.733243: train_loss -0.4205 +2026-04-13 02:34:59.740281: val_loss -0.3829 +2026-04-13 02:34:59.741610: Pseudo dice [0.5979, 0.0, 0.5046, 0.6912, 0.3003, 0.791, 0.8606] +2026-04-13 02:34:59.743264: Epoch time: 101.89 s +2026-04-13 02:35:00.898234: +2026-04-13 02:35:00.900181: Epoch 2244 +2026-04-13 02:35:00.901541: Current learning rate: 0.00477 +2026-04-13 02:36:42.697455: train_loss -0.4036 +2026-04-13 02:36:42.701973: val_loss -0.3523 +2026-04-13 02:36:42.703941: Pseudo dice [0.6365, 0.0, 0.8302, 0.6968, 0.0684, 0.4658, 0.6281] +2026-04-13 02:36:42.706313: Epoch time: 101.8 s +2026-04-13 02:36:43.860348: +2026-04-13 02:36:43.862026: Epoch 2245 +2026-04-13 02:36:43.863623: Current learning rate: 0.00476 +2026-04-13 02:38:25.618748: train_loss -0.3945 +2026-04-13 02:38:25.623660: val_loss -0.3456 +2026-04-13 02:38:25.625325: Pseudo dice [0.2572, 0.0, 0.7233, 0.7305, 0.2479, 0.7832, 0.6626] +2026-04-13 02:38:25.626915: Epoch time: 101.76 s +2026-04-13 02:38:26.788159: +2026-04-13 02:38:26.789917: Epoch 2246 +2026-04-13 02:38:26.791650: Current learning rate: 0.00476 +2026-04-13 02:40:08.633750: train_loss -0.3849 +2026-04-13 02:40:08.638284: val_loss -0.3138 +2026-04-13 02:40:08.639868: Pseudo dice [0.1319, 0.0, 0.109, 0.7742, 0.2388, 0.3061, 0.8507] +2026-04-13 02:40:08.641719: Epoch time: 101.85 s +2026-04-13 02:40:09.795927: +2026-04-13 02:40:09.797555: Epoch 2247 +2026-04-13 02:40:09.798945: Current learning rate: 0.00476 +2026-04-13 02:41:51.461407: train_loss -0.3963 +2026-04-13 02:41:51.465215: val_loss -0.3587 +2026-04-13 02:41:51.466560: Pseudo dice [0.8631, 0.0, 0.7692, 0.8377, 0.2905, 0.0836, 0.9031] +2026-04-13 02:41:51.467904: Epoch time: 101.67 s +2026-04-13 02:41:52.612255: +2026-04-13 02:41:52.613717: Epoch 2248 +2026-04-13 02:41:52.614936: Current learning rate: 0.00476 +2026-04-13 02:43:34.398643: train_loss -0.4163 +2026-04-13 02:43:34.405372: val_loss -0.3702 +2026-04-13 02:43:34.407079: Pseudo dice [0.2661, 0.0, 0.6562, 0.8149, 0.4342, 0.5212, 0.7623] +2026-04-13 02:43:34.408745: Epoch time: 101.79 s +2026-04-13 02:43:35.555314: +2026-04-13 02:43:35.557085: Epoch 2249 +2026-04-13 02:43:35.559259: Current learning rate: 0.00475 +2026-04-13 02:45:17.210905: train_loss -0.4062 +2026-04-13 02:45:17.215815: val_loss -0.3564 +2026-04-13 02:45:17.217574: Pseudo dice [0.2252, 0.0, 0.4981, 0.8053, 0.2091, 0.751, 0.8774] +2026-04-13 02:45:17.219395: Epoch time: 101.66 s +2026-04-13 02:45:19.954339: +2026-04-13 02:45:19.956523: Epoch 2250 +2026-04-13 02:45:19.958066: Current learning rate: 0.00475 +2026-04-13 02:47:01.684520: train_loss -0.386 +2026-04-13 02:47:01.688714: val_loss -0.3186 +2026-04-13 02:47:01.690343: Pseudo dice [0.0, 0.0, 0.5499, 0.8288, 0.3231, 0.475, 0.3866] +2026-04-13 02:47:01.692086: Epoch time: 101.73 s +2026-04-13 02:47:02.832848: +2026-04-13 02:47:02.834722: Epoch 2251 +2026-04-13 02:47:02.836281: Current learning rate: 0.00475 +2026-04-13 02:48:44.644629: train_loss -0.3967 +2026-04-13 02:48:44.648540: val_loss -0.3103 +2026-04-13 02:48:44.650113: Pseudo dice [0.0023, 0.0, 0.5323, 0.3805, 0.3552, 0.6382, 0.4202] +2026-04-13 02:48:44.651493: Epoch time: 101.81 s +2026-04-13 02:48:45.771595: +2026-04-13 02:48:45.773286: Epoch 2252 +2026-04-13 02:48:45.774673: Current learning rate: 0.00475 +2026-04-13 02:50:27.489262: train_loss -0.4027 +2026-04-13 02:50:27.493729: val_loss -0.3808 +2026-04-13 02:50:27.495503: Pseudo dice [0.3856, 0.0, 0.7437, 0.6428, 0.5109, 0.7262, 0.6002] +2026-04-13 02:50:27.497233: Epoch time: 101.72 s +2026-04-13 02:50:28.649981: +2026-04-13 02:50:28.651732: Epoch 2253 +2026-04-13 02:50:28.653361: Current learning rate: 0.00474 +2026-04-13 02:52:10.426615: train_loss -0.3899 +2026-04-13 02:52:10.432932: val_loss -0.3616 +2026-04-13 02:52:10.435023: Pseudo dice [0.257, 0.0, 0.7635, 0.2273, 0.4951, 0.7237, 0.8253] +2026-04-13 02:52:10.436644: Epoch time: 101.78 s +2026-04-13 02:52:12.551772: +2026-04-13 02:52:12.553701: Epoch 2254 +2026-04-13 02:52:12.555544: Current learning rate: 0.00474 +2026-04-13 02:53:54.203420: train_loss -0.4086 +2026-04-13 02:53:54.208914: val_loss -0.387 +2026-04-13 02:53:54.210412: Pseudo dice [0.3943, 0.0, 0.8176, 0.7121, 0.5, 0.7927, 0.8066] +2026-04-13 02:53:54.212124: Epoch time: 101.65 s +2026-04-13 02:53:55.360124: +2026-04-13 02:53:55.362077: Epoch 2255 +2026-04-13 02:53:55.364323: Current learning rate: 0.00474 +2026-04-13 02:55:37.009090: train_loss -0.4012 +2026-04-13 02:55:37.014915: val_loss -0.3484 +2026-04-13 02:55:37.016449: Pseudo dice [0.0409, 0.0, 0.4061, 0.611, 0.2539, 0.7606, 0.7187] +2026-04-13 02:55:37.017826: Epoch time: 101.65 s +2026-04-13 02:55:38.174288: +2026-04-13 02:55:38.176193: Epoch 2256 +2026-04-13 02:55:38.177680: Current learning rate: 0.00474 +2026-04-13 02:57:19.840575: train_loss -0.4104 +2026-04-13 02:57:19.845063: val_loss -0.3463 +2026-04-13 02:57:19.846365: Pseudo dice [0.3613, 0.0, 0.6866, 0.7792, 0.4643, 0.6465, 0.7551] +2026-04-13 02:57:19.847867: Epoch time: 101.67 s +2026-04-13 02:57:21.029519: +2026-04-13 02:57:21.030861: Epoch 2257 +2026-04-13 02:57:21.032078: Current learning rate: 0.00473 +2026-04-13 02:59:02.811192: train_loss -0.4114 +2026-04-13 02:59:02.815053: val_loss -0.3346 +2026-04-13 02:59:02.816291: Pseudo dice [0.0052, 0.0, 0.7637, 0.4061, 0.3521, 0.7432, 0.7199] +2026-04-13 02:59:02.817521: Epoch time: 101.78 s +2026-04-13 02:59:03.966528: +2026-04-13 02:59:03.968064: Epoch 2258 +2026-04-13 02:59:03.969321: Current learning rate: 0.00473 +2026-04-13 03:00:45.764407: train_loss -0.4073 +2026-04-13 03:00:45.768826: val_loss -0.3159 +2026-04-13 03:00:45.770211: Pseudo dice [0.0943, 0.0, 0.7122, 0.6468, 0.1702, 0.7746, 0.8099] +2026-04-13 03:00:45.771527: Epoch time: 101.8 s +2026-04-13 03:00:46.918826: +2026-04-13 03:00:46.920478: Epoch 2259 +2026-04-13 03:00:46.921881: Current learning rate: 0.00473 +2026-04-13 03:02:28.618495: train_loss -0.4058 +2026-04-13 03:02:28.622785: val_loss -0.38 +2026-04-13 03:02:28.624225: Pseudo dice [0.5263, 0.0, 0.8081, 0.7532, 0.3385, 0.8503, 0.6101] +2026-04-13 03:02:28.625751: Epoch time: 101.7 s +2026-04-13 03:02:29.772285: +2026-04-13 03:02:29.774461: Epoch 2260 +2026-04-13 03:02:29.776890: Current learning rate: 0.00473 +2026-04-13 03:04:11.230308: train_loss -0.4034 +2026-04-13 03:04:11.235014: val_loss -0.2978 +2026-04-13 03:04:11.236661: Pseudo dice [0.2982, 0.0, 0.6586, 0.7067, 0.0437, 0.6771, 0.3284] +2026-04-13 03:04:11.238035: Epoch time: 101.46 s +2026-04-13 03:04:12.390918: +2026-04-13 03:04:12.393082: Epoch 2261 +2026-04-13 03:04:12.395444: Current learning rate: 0.00473 +2026-04-13 03:05:53.998212: train_loss -0.3954 +2026-04-13 03:05:54.002863: val_loss -0.344 +2026-04-13 03:05:54.004215: Pseudo dice [0.3394, 0.0, 0.3819, 0.8619, 0.2666, 0.7469, 0.4633] +2026-04-13 03:05:54.005399: Epoch time: 101.61 s +2026-04-13 03:05:55.139432: +2026-04-13 03:05:55.140703: Epoch 2262 +2026-04-13 03:05:55.142002: Current learning rate: 0.00472 +2026-04-13 03:07:36.692240: train_loss -0.4025 +2026-04-13 03:07:36.701004: val_loss -0.3636 +2026-04-13 03:07:36.702583: Pseudo dice [0.1981, 0.0, 0.7681, 0.8029, 0.4401, 0.6974, 0.8317] +2026-04-13 03:07:36.704433: Epoch time: 101.56 s +2026-04-13 03:07:37.856102: +2026-04-13 03:07:37.857793: Epoch 2263 +2026-04-13 03:07:37.859295: Current learning rate: 0.00472 +2026-04-13 03:09:19.356569: train_loss -0.4068 +2026-04-13 03:09:19.362250: val_loss -0.3621 +2026-04-13 03:09:19.365307: Pseudo dice [0.1958, 0.0, 0.6123, 0.6972, 0.3443, 0.6008, 0.7845] +2026-04-13 03:09:19.366951: Epoch time: 101.5 s +2026-04-13 03:09:20.609741: +2026-04-13 03:09:20.611417: Epoch 2264 +2026-04-13 03:09:20.612895: Current learning rate: 0.00472 +2026-04-13 03:11:02.159961: train_loss -0.399 +2026-04-13 03:11:02.165474: val_loss -0.3769 +2026-04-13 03:11:02.167609: Pseudo dice [0.3927, 0.0, 0.756, 0.8503, 0.5285, 0.8157, 0.7454] +2026-04-13 03:11:02.169035: Epoch time: 101.55 s +2026-04-13 03:11:03.319419: +2026-04-13 03:11:03.321177: Epoch 2265 +2026-04-13 03:11:03.322425: Current learning rate: 0.00472 +2026-04-13 03:12:45.018677: train_loss -0.3985 +2026-04-13 03:12:45.022899: val_loss -0.3637 +2026-04-13 03:12:45.024275: Pseudo dice [0.0516, 0.0, 0.7447, 0.8623, 0.7153, 0.6521, 0.7849] +2026-04-13 03:12:45.025509: Epoch time: 101.7 s +2026-04-13 03:12:46.166034: +2026-04-13 03:12:46.167398: Epoch 2266 +2026-04-13 03:12:46.168655: Current learning rate: 0.00471 +2026-04-13 03:14:27.670725: train_loss -0.4111 +2026-04-13 03:14:27.674582: val_loss -0.3517 +2026-04-13 03:14:27.675862: Pseudo dice [0.6343, 0.0, 0.3544, 0.5825, 0.1727, 0.7814, 0.7017] +2026-04-13 03:14:27.677164: Epoch time: 101.51 s +2026-04-13 03:14:28.822655: +2026-04-13 03:14:28.823975: Epoch 2267 +2026-04-13 03:14:28.825252: Current learning rate: 0.00471 +2026-04-13 03:16:10.415631: train_loss -0.4056 +2026-04-13 03:16:10.422454: val_loss -0.4 +2026-04-13 03:16:10.424236: Pseudo dice [0.655, 0.0, 0.7698, 0.8761, 0.2358, 0.8192, 0.9052] +2026-04-13 03:16:10.425828: Epoch time: 101.6 s +2026-04-13 03:16:11.577228: +2026-04-13 03:16:11.579099: Epoch 2268 +2026-04-13 03:16:11.580617: Current learning rate: 0.00471 +2026-04-13 03:17:53.323103: train_loss -0.4097 +2026-04-13 03:17:53.327442: val_loss -0.3233 +2026-04-13 03:17:53.329046: Pseudo dice [0.0098, 0.0, 0.4971, 0.7785, 0.2869, 0.3414, 0.8421] +2026-04-13 03:17:53.330633: Epoch time: 101.75 s +2026-04-13 03:17:54.488399: +2026-04-13 03:17:54.489780: Epoch 2269 +2026-04-13 03:17:54.491019: Current learning rate: 0.00471 +2026-04-13 03:19:36.058080: train_loss -0.4079 +2026-04-13 03:19:36.063919: val_loss -0.3861 +2026-04-13 03:19:36.065487: Pseudo dice [0.3007, 0.0, 0.8241, 0.8771, 0.3766, 0.7604, 0.6051] +2026-04-13 03:19:36.067201: Epoch time: 101.57 s +2026-04-13 03:19:37.212626: +2026-04-13 03:19:37.214452: Epoch 2270 +2026-04-13 03:19:37.215898: Current learning rate: 0.0047 +2026-04-13 03:21:18.811529: train_loss -0.4194 +2026-04-13 03:21:18.816255: val_loss -0.3733 +2026-04-13 03:21:18.817882: Pseudo dice [0.5514, 0.0, 0.6632, 0.7135, 0.3583, 0.7677, 0.6559] +2026-04-13 03:21:18.820100: Epoch time: 101.6 s +2026-04-13 03:21:19.972885: +2026-04-13 03:21:19.974139: Epoch 2271 +2026-04-13 03:21:19.975586: Current learning rate: 0.0047 +2026-04-13 03:23:01.694883: train_loss -0.4 +2026-04-13 03:23:01.700658: val_loss -0.3422 +2026-04-13 03:23:01.702221: Pseudo dice [0.485, 0.0, 0.7239, 0.7974, 0.1703, 0.769, 0.7841] +2026-04-13 03:23:01.703791: Epoch time: 101.73 s +2026-04-13 03:23:02.850659: +2026-04-13 03:23:02.853545: Epoch 2272 +2026-04-13 03:23:02.855948: Current learning rate: 0.0047 +2026-04-13 03:24:44.479679: train_loss -0.3974 +2026-04-13 03:24:44.485723: val_loss -0.4043 +2026-04-13 03:24:44.487402: Pseudo dice [0.4907, 0.0, 0.6667, 0.7799, 0.5749, 0.82, 0.7969] +2026-04-13 03:24:44.488892: Epoch time: 101.63 s +2026-04-13 03:24:45.630228: +2026-04-13 03:24:45.631832: Epoch 2273 +2026-04-13 03:24:45.633497: Current learning rate: 0.0047 +2026-04-13 03:26:27.296790: train_loss -0.405 +2026-04-13 03:26:27.300935: val_loss -0.3806 +2026-04-13 03:26:27.302406: Pseudo dice [0.2868, 0.0, 0.7059, 0.8262, 0.4563, 0.7368, 0.7797] +2026-04-13 03:26:27.304123: Epoch time: 101.67 s +2026-04-13 03:26:28.457667: +2026-04-13 03:26:28.459056: Epoch 2274 +2026-04-13 03:26:28.460254: Current learning rate: 0.00469 +2026-04-13 03:28:11.018108: train_loss -0.4072 +2026-04-13 03:28:11.021930: val_loss -0.3522 +2026-04-13 03:28:11.023708: Pseudo dice [0.5927, 0.0, 0.714, 0.8362, 0.3071, 0.2486, 0.2236] +2026-04-13 03:28:11.025406: Epoch time: 102.56 s +2026-04-13 03:28:12.166643: +2026-04-13 03:28:12.168090: Epoch 2275 +2026-04-13 03:28:12.169305: Current learning rate: 0.00469 +2026-04-13 03:29:53.793467: train_loss -0.4146 +2026-04-13 03:29:53.797490: val_loss -0.3472 +2026-04-13 03:29:53.798892: Pseudo dice [0.3864, 0.0, 0.8031, 0.4972, 0.2709, 0.6622, 0.3755] +2026-04-13 03:29:53.800470: Epoch time: 101.63 s +2026-04-13 03:29:54.950550: +2026-04-13 03:29:54.952744: Epoch 2276 +2026-04-13 03:29:54.954478: Current learning rate: 0.00469 +2026-04-13 03:31:36.665735: train_loss -0.4033 +2026-04-13 03:31:36.671233: val_loss -0.3933 +2026-04-13 03:31:36.672905: Pseudo dice [0.059, 0.0, 0.7557, 0.786, 0.5814, 0.5345, 0.9262] +2026-04-13 03:31:36.674450: Epoch time: 101.72 s +2026-04-13 03:31:37.829582: +2026-04-13 03:31:37.831666: Epoch 2277 +2026-04-13 03:31:37.833389: Current learning rate: 0.00469 +2026-04-13 03:33:19.689407: train_loss -0.4051 +2026-04-13 03:33:19.693767: val_loss -0.3278 +2026-04-13 03:33:19.695822: Pseudo dice [0.1601, 0.0, 0.5816, 0.7661, 0.1432, 0.7476, 0.5348] +2026-04-13 03:33:19.697372: Epoch time: 101.86 s +2026-04-13 03:33:20.844051: +2026-04-13 03:33:20.845509: Epoch 2278 +2026-04-13 03:33:20.846894: Current learning rate: 0.00468 +2026-04-13 03:35:02.785604: train_loss -0.4179 +2026-04-13 03:35:02.789370: val_loss -0.3773 +2026-04-13 03:35:02.791027: Pseudo dice [0.4974, 0.0, 0.6446, 0.7978, 0.4638, 0.6063, 0.7356] +2026-04-13 03:35:02.792727: Epoch time: 101.94 s +2026-04-13 03:35:03.936361: +2026-04-13 03:35:03.937758: Epoch 2279 +2026-04-13 03:35:03.939355: Current learning rate: 0.00468 +2026-04-13 03:36:45.742123: train_loss -0.4205 +2026-04-13 03:36:45.747084: val_loss -0.3737 +2026-04-13 03:36:45.749799: Pseudo dice [0.3716, 0.0, 0.7659, 0.3537, 0.4949, 0.5367, 0.7381] +2026-04-13 03:36:45.752005: Epoch time: 101.81 s +2026-04-13 03:36:46.907219: +2026-04-13 03:36:46.909017: Epoch 2280 +2026-04-13 03:36:46.910707: Current learning rate: 0.00468 +2026-04-13 03:38:28.585212: train_loss -0.407 +2026-04-13 03:38:28.590892: val_loss -0.3866 +2026-04-13 03:38:28.593032: Pseudo dice [0.8287, 0.0, 0.6208, 0.3293, 0.4206, 0.7403, 0.7969] +2026-04-13 03:38:28.594888: Epoch time: 101.68 s +2026-04-13 03:38:29.742688: +2026-04-13 03:38:29.744703: Epoch 2281 +2026-04-13 03:38:29.746348: Current learning rate: 0.00468 +2026-04-13 03:40:11.355119: train_loss -0.4113 +2026-04-13 03:40:11.360494: val_loss -0.3365 +2026-04-13 03:40:11.362245: Pseudo dice [0.1515, 0.0, 0.673, 0.5711, 0.1921, 0.6844, 0.8563] +2026-04-13 03:40:11.364020: Epoch time: 101.62 s +2026-04-13 03:40:12.525334: +2026-04-13 03:40:12.526975: Epoch 2282 +2026-04-13 03:40:12.528479: Current learning rate: 0.00467 +2026-04-13 03:41:54.075962: train_loss -0.3941 +2026-04-13 03:41:54.080989: val_loss -0.3608 +2026-04-13 03:41:54.082894: Pseudo dice [0.0, 0.0, 0.7859, 0.7343, 0.3932, 0.7427, 0.5966] +2026-04-13 03:41:54.084395: Epoch time: 101.55 s +2026-04-13 03:41:55.227850: +2026-04-13 03:41:55.229679: Epoch 2283 +2026-04-13 03:41:55.231244: Current learning rate: 0.00467 +2026-04-13 03:43:36.827053: train_loss -0.3897 +2026-04-13 03:43:36.832488: val_loss -0.402 +2026-04-13 03:43:36.834421: Pseudo dice [0.0, 0.0, 0.7257, 0.8363, 0.5521, 0.803, 0.5681] +2026-04-13 03:43:36.836165: Epoch time: 101.6 s +2026-04-13 03:43:38.018548: +2026-04-13 03:43:38.020140: Epoch 2284 +2026-04-13 03:43:38.021573: Current learning rate: 0.00467 +2026-04-13 03:45:19.683646: train_loss -0.4045 +2026-04-13 03:45:19.688660: val_loss -0.325 +2026-04-13 03:45:19.690425: Pseudo dice [0.0, 0.0, 0.6165, 0.0608, 0.1865, 0.5444, 0.5065] +2026-04-13 03:45:19.692037: Epoch time: 101.67 s +2026-04-13 03:45:20.834523: +2026-04-13 03:45:20.836282: Epoch 2285 +2026-04-13 03:45:20.838105: Current learning rate: 0.00467 +2026-04-13 03:47:02.411822: train_loss -0.3887 +2026-04-13 03:47:02.416425: val_loss -0.3532 +2026-04-13 03:47:02.418331: Pseudo dice [0.0, 0.0, 0.7929, 0.2745, 0.3795, 0.4232, 0.4458] +2026-04-13 03:47:02.419846: Epoch time: 101.58 s +2026-04-13 03:47:03.548245: +2026-04-13 03:47:03.549824: Epoch 2286 +2026-04-13 03:47:03.551563: Current learning rate: 0.00466 +2026-04-13 03:48:45.316926: train_loss -0.3666 +2026-04-13 03:48:45.321292: val_loss -0.3351 +2026-04-13 03:48:45.323090: Pseudo dice [0.0, 0.0, 0.6566, 0.6764, 0.2051, 0.7949, 0.6972] +2026-04-13 03:48:45.324918: Epoch time: 101.77 s +2026-04-13 03:48:46.467490: +2026-04-13 03:48:46.470488: Epoch 2287 +2026-04-13 03:48:46.471902: Current learning rate: 0.00466 +2026-04-13 03:50:28.126645: train_loss -0.4026 +2026-04-13 03:50:28.131305: val_loss -0.3138 +2026-04-13 03:50:28.133044: Pseudo dice [0.0, 0.0, 0.6394, 0.0889, 0.2801, 0.7817, 0.7601] +2026-04-13 03:50:28.134680: Epoch time: 101.66 s +2026-04-13 03:50:29.282784: +2026-04-13 03:50:29.284971: Epoch 2288 +2026-04-13 03:50:29.286792: Current learning rate: 0.00466 +2026-04-13 03:52:11.060668: train_loss -0.4122 +2026-04-13 03:52:11.066296: val_loss -0.3268 +2026-04-13 03:52:11.067903: Pseudo dice [0.0, 0.0, 0.7564, 0.73, 0.4148, 0.828, 0.6326] +2026-04-13 03:52:11.069788: Epoch time: 101.78 s +2026-04-13 03:52:12.227561: +2026-04-13 03:52:12.229317: Epoch 2289 +2026-04-13 03:52:12.231183: Current learning rate: 0.00466 +2026-04-13 03:53:54.038037: train_loss -0.4135 +2026-04-13 03:53:54.042458: val_loss -0.3538 +2026-04-13 03:53:54.044479: Pseudo dice [0.0, 0.0, 0.6117, 0.673, 0.2286, 0.6006, 0.8211] +2026-04-13 03:53:54.046255: Epoch time: 101.81 s +2026-04-13 03:53:55.246836: +2026-04-13 03:53:55.248616: Epoch 2290 +2026-04-13 03:53:55.250175: Current learning rate: 0.00465 +2026-04-13 03:55:37.007261: train_loss -0.4 +2026-04-13 03:55:37.012449: val_loss -0.3409 +2026-04-13 03:55:37.014792: Pseudo dice [0.0, 0.0, 0.6329, 0.1791, 0.278, 0.4737, 0.6751] +2026-04-13 03:55:37.016331: Epoch time: 101.76 s +2026-04-13 03:55:38.157280: +2026-04-13 03:55:38.159668: Epoch 2291 +2026-04-13 03:55:38.162320: Current learning rate: 0.00465 +2026-04-13 03:57:19.822852: train_loss -0.4092 +2026-04-13 03:57:19.827193: val_loss -0.3521 +2026-04-13 03:57:19.829102: Pseudo dice [0.0, 0.0, 0.7689, 0.0964, 0.4505, 0.7156, 0.3938] +2026-04-13 03:57:19.830678: Epoch time: 101.67 s +2026-04-13 03:57:20.969908: +2026-04-13 03:57:20.972701: Epoch 2292 +2026-04-13 03:57:20.974200: Current learning rate: 0.00465 +2026-04-13 03:59:02.639182: train_loss -0.406 +2026-04-13 03:59:02.643895: val_loss -0.332 +2026-04-13 03:59:02.645914: Pseudo dice [0.0, 0.0, 0.7371, 0.4164, 0.4483, 0.4993, 0.1674] +2026-04-13 03:59:02.647514: Epoch time: 101.67 s +2026-04-13 03:59:03.791632: +2026-04-13 03:59:03.793260: Epoch 2293 +2026-04-13 03:59:03.794626: Current learning rate: 0.00465 +2026-04-13 04:00:45.475207: train_loss -0.4143 +2026-04-13 04:00:45.480174: val_loss -0.3378 +2026-04-13 04:00:45.482037: Pseudo dice [0.0, 0.0, 0.4249, 0.862, 0.2098, 0.6823, 0.28] +2026-04-13 04:00:45.483901: Epoch time: 101.69 s +2026-04-13 04:00:46.641448: +2026-04-13 04:00:46.643157: Epoch 2294 +2026-04-13 04:00:46.644555: Current learning rate: 0.00464 +2026-04-13 04:02:28.428196: train_loss -0.4071 +2026-04-13 04:02:28.432575: val_loss -0.3362 +2026-04-13 04:02:28.434328: Pseudo dice [0.0, 0.0, 0.676, 0.8334, 0.421, 0.6275, 0.7903] +2026-04-13 04:02:28.435797: Epoch time: 101.79 s +2026-04-13 04:02:29.573017: +2026-04-13 04:02:29.576310: Epoch 2295 +2026-04-13 04:02:29.578575: Current learning rate: 0.00464 +2026-04-13 04:04:12.156632: train_loss -0.3987 +2026-04-13 04:04:12.162608: val_loss -0.3279 +2026-04-13 04:04:12.164741: Pseudo dice [0.1705, 0.0, 0.1863, 0.2522, 0.2449, 0.4036, 0.905] +2026-04-13 04:04:12.166550: Epoch time: 102.59 s +2026-04-13 04:04:13.315045: +2026-04-13 04:04:13.317182: Epoch 2296 +2026-04-13 04:04:13.319066: Current learning rate: 0.00464 +2026-04-13 04:05:55.098514: train_loss -0.4184 +2026-04-13 04:05:55.103986: val_loss -0.3682 +2026-04-13 04:05:55.105587: Pseudo dice [0.2851, 0.0, 0.6449, 0.6373, 0.1545, 0.802, 0.7544] +2026-04-13 04:05:55.107036: Epoch time: 101.79 s +2026-04-13 04:05:56.251578: +2026-04-13 04:05:56.253587: Epoch 2297 +2026-04-13 04:05:56.255069: Current learning rate: 0.00464 +2026-04-13 04:07:38.108042: train_loss -0.4214 +2026-04-13 04:07:38.112484: val_loss -0.4095 +2026-04-13 04:07:38.114033: Pseudo dice [0.7382, 0.0, 0.8082, 0.6495, 0.6237, 0.8625, 0.5838] +2026-04-13 04:07:38.115505: Epoch time: 101.86 s +2026-04-13 04:07:39.248648: +2026-04-13 04:07:39.250867: Epoch 2298 +2026-04-13 04:07:39.252439: Current learning rate: 0.00463 +2026-04-13 04:09:20.758296: train_loss -0.4314 +2026-04-13 04:09:20.762638: val_loss -0.3915 +2026-04-13 04:09:20.764061: Pseudo dice [0.8119, 0.0, 0.6073, 0.5544, 0.1898, 0.7222, 0.8749] +2026-04-13 04:09:20.765484: Epoch time: 101.51 s +2026-04-13 04:09:21.922747: +2026-04-13 04:09:21.924354: Epoch 2299 +2026-04-13 04:09:21.925912: Current learning rate: 0.00463 +2026-04-13 04:11:03.594972: train_loss -0.4271 +2026-04-13 04:11:03.600127: val_loss -0.3438 +2026-04-13 04:11:03.601812: Pseudo dice [0.3679, 0.0, 0.7241, 0.67, 0.2206, 0.8126, 0.7909] +2026-04-13 04:11:03.603742: Epoch time: 101.68 s +2026-04-13 04:11:06.371498: +2026-04-13 04:11:06.373806: Epoch 2300 +2026-04-13 04:11:06.375858: Current learning rate: 0.00463 +2026-04-13 04:12:48.037709: train_loss -0.41 +2026-04-13 04:12:48.043450: val_loss -0.3599 +2026-04-13 04:12:48.045062: Pseudo dice [0.3531, 0.0, 0.6865, 0.7371, 0.3208, 0.6987, 0.7026] +2026-04-13 04:12:48.046522: Epoch time: 101.67 s +2026-04-13 04:12:49.193625: +2026-04-13 04:12:49.195132: Epoch 2301 +2026-04-13 04:12:49.196453: Current learning rate: 0.00463 +2026-04-13 04:14:30.772926: train_loss -0.411 +2026-04-13 04:14:30.777110: val_loss -0.3693 +2026-04-13 04:14:30.779646: Pseudo dice [0.6343, 0.0, 0.7941, 0.735, 0.0687, 0.7267, 0.804] +2026-04-13 04:14:30.781625: Epoch time: 101.58 s +2026-04-13 04:14:31.934185: +2026-04-13 04:14:31.935634: Epoch 2302 +2026-04-13 04:14:31.937103: Current learning rate: 0.00462 +2026-04-13 04:16:13.457738: train_loss -0.4098 +2026-04-13 04:16:13.462248: val_loss -0.3715 +2026-04-13 04:16:13.464079: Pseudo dice [0.5763, 0.0, 0.7976, 0.7114, 0.2858, 0.6274, 0.7681] +2026-04-13 04:16:13.465459: Epoch time: 101.53 s +2026-04-13 04:16:14.618914: +2026-04-13 04:16:14.621086: Epoch 2303 +2026-04-13 04:16:14.623087: Current learning rate: 0.00462 +2026-04-13 04:17:56.434099: train_loss -0.4112 +2026-04-13 04:17:56.438106: val_loss -0.3902 +2026-04-13 04:17:56.439622: Pseudo dice [0.0729, 0.0, 0.8687, 0.8762, 0.4455, 0.7333, 0.6716] +2026-04-13 04:17:56.440828: Epoch time: 101.82 s +2026-04-13 04:17:57.576772: +2026-04-13 04:17:57.578093: Epoch 2304 +2026-04-13 04:17:57.579297: Current learning rate: 0.00462 +2026-04-13 04:19:39.336008: train_loss -0.4086 +2026-04-13 04:19:39.341616: val_loss -0.3375 +2026-04-13 04:19:39.343378: Pseudo dice [0.3792, 0.0, 0.4886, 0.8301, 0.1253, 0.64, 0.8305] +2026-04-13 04:19:39.345014: Epoch time: 101.76 s +2026-04-13 04:19:40.501090: +2026-04-13 04:19:40.503519: Epoch 2305 +2026-04-13 04:19:40.505498: Current learning rate: 0.00462 +2026-04-13 04:21:22.162049: train_loss -0.4077 +2026-04-13 04:21:22.168283: val_loss -0.3702 +2026-04-13 04:21:22.171414: Pseudo dice [0.4402, 0.0, 0.7514, 0.854, 0.3522, 0.7786, 0.6215] +2026-04-13 04:21:22.173567: Epoch time: 101.66 s +2026-04-13 04:21:23.317641: +2026-04-13 04:21:23.319321: Epoch 2306 +2026-04-13 04:21:23.321168: Current learning rate: 0.00461 +2026-04-13 04:23:04.945430: train_loss -0.4235 +2026-04-13 04:23:04.949836: val_loss -0.3641 +2026-04-13 04:23:04.951298: Pseudo dice [0.5729, 0.0, 0.4184, 0.8174, 0.3258, 0.7464, 0.4122] +2026-04-13 04:23:04.953001: Epoch time: 101.63 s +2026-04-13 04:23:06.103214: +2026-04-13 04:23:06.104911: Epoch 2307 +2026-04-13 04:23:06.106395: Current learning rate: 0.00461 +2026-04-13 04:24:47.712574: train_loss -0.4071 +2026-04-13 04:24:47.716398: val_loss -0.378 +2026-04-13 04:24:47.718120: Pseudo dice [0.4847, 0.0, 0.779, 0.3569, 0.3568, 0.8087, 0.8426] +2026-04-13 04:24:47.719562: Epoch time: 101.61 s +2026-04-13 04:24:48.861846: +2026-04-13 04:24:48.863528: Epoch 2308 +2026-04-13 04:24:48.865059: Current learning rate: 0.00461 +2026-04-13 04:26:30.558003: train_loss -0.4054 +2026-04-13 04:26:30.562553: val_loss -0.3308 +2026-04-13 04:26:30.564151: Pseudo dice [0.337, 0.0, 0.4987, 0.7097, 0.3806, 0.6975, 0.6202] +2026-04-13 04:26:30.565798: Epoch time: 101.7 s +2026-04-13 04:26:31.723348: +2026-04-13 04:26:31.724983: Epoch 2309 +2026-04-13 04:26:31.726572: Current learning rate: 0.00461 +2026-04-13 04:28:13.364881: train_loss -0.4127 +2026-04-13 04:28:13.369906: val_loss -0.3592 +2026-04-13 04:28:13.371756: Pseudo dice [0.2278, 0.0, 0.4507, 0.6163, 0.3884, 0.7506, 0.8144] +2026-04-13 04:28:13.373381: Epoch time: 101.64 s +2026-04-13 04:28:14.520295: +2026-04-13 04:28:14.522277: Epoch 2310 +2026-04-13 04:28:14.523823: Current learning rate: 0.00461 +2026-04-13 04:29:56.307961: train_loss -0.3951 +2026-04-13 04:29:56.312954: val_loss -0.3505 +2026-04-13 04:29:56.314641: Pseudo dice [0.4667, 0.0, 0.7257, 0.5589, 0.2822, 0.7384, 0.8691] +2026-04-13 04:29:56.316503: Epoch time: 101.79 s +2026-04-13 04:29:57.457799: +2026-04-13 04:29:57.459610: Epoch 2311 +2026-04-13 04:29:57.461247: Current learning rate: 0.0046 +2026-04-13 04:31:39.044543: train_loss -0.4066 +2026-04-13 04:31:39.049027: val_loss -0.354 +2026-04-13 04:31:39.053921: Pseudo dice [0.3792, 0.0, 0.3267, 0.5862, 0.4156, 0.7898, 0.7211] +2026-04-13 04:31:39.056210: Epoch time: 101.59 s +2026-04-13 04:31:40.207245: +2026-04-13 04:31:40.208627: Epoch 2312 +2026-04-13 04:31:40.209898: Current learning rate: 0.0046 +2026-04-13 04:33:21.932020: train_loss -0.4069 +2026-04-13 04:33:21.936628: val_loss -0.3557 +2026-04-13 04:33:21.938495: Pseudo dice [0.3979, 0.0, 0.6572, 0.5607, 0.3005, 0.8449, 0.7955] +2026-04-13 04:33:21.940636: Epoch time: 101.73 s +2026-04-13 04:33:23.099201: +2026-04-13 04:33:23.100936: Epoch 2313 +2026-04-13 04:33:23.102850: Current learning rate: 0.0046 +2026-04-13 04:35:04.969835: train_loss -0.4163 +2026-04-13 04:35:04.975696: val_loss -0.3192 +2026-04-13 04:35:04.977638: Pseudo dice [0.5184, 0.0, 0.4767, 0.0431, 0.3318, 0.8332, 0.7239] +2026-04-13 04:35:04.979470: Epoch time: 101.87 s +2026-04-13 04:35:06.151811: +2026-04-13 04:35:06.153604: Epoch 2314 +2026-04-13 04:35:06.155134: Current learning rate: 0.0046 +2026-04-13 04:36:47.983006: train_loss -0.4152 +2026-04-13 04:36:47.987299: val_loss -0.2985 +2026-04-13 04:36:47.988831: Pseudo dice [0.1605, 0.0, 0.4624, 0.81, 0.0509, 0.3504, 0.8474] +2026-04-13 04:36:47.990134: Epoch time: 101.83 s +2026-04-13 04:36:49.136080: +2026-04-13 04:36:49.138314: Epoch 2315 +2026-04-13 04:36:49.139988: Current learning rate: 0.00459 +2026-04-13 04:38:30.948774: train_loss -0.4015 +2026-04-13 04:38:30.953377: val_loss -0.3934 +2026-04-13 04:38:30.954978: Pseudo dice [0.714, 0.0, 0.8364, 0.6654, 0.4617, 0.7403, 0.8196] +2026-04-13 04:38:30.956545: Epoch time: 101.82 s +2026-04-13 04:38:33.077822: +2026-04-13 04:38:33.079397: Epoch 2316 +2026-04-13 04:38:33.080719: Current learning rate: 0.00459 +2026-04-13 04:40:14.984928: train_loss -0.4013 +2026-04-13 04:40:14.989809: val_loss -0.3588 +2026-04-13 04:40:14.991355: Pseudo dice [0.598, 0.0, 0.6983, 0.7586, 0.2364, 0.7507, 0.9231] +2026-04-13 04:40:14.992862: Epoch time: 101.91 s +2026-04-13 04:40:16.124480: +2026-04-13 04:40:16.125892: Epoch 2317 +2026-04-13 04:40:16.127239: Current learning rate: 0.00459 +2026-04-13 04:41:57.867037: train_loss -0.3954 +2026-04-13 04:41:57.871887: val_loss -0.3621 +2026-04-13 04:41:57.873440: Pseudo dice [0.1506, 0.0, 0.6835, 0.7299, 0.3687, 0.725, 0.8346] +2026-04-13 04:41:57.874830: Epoch time: 101.75 s +2026-04-13 04:41:59.028193: +2026-04-13 04:41:59.029954: Epoch 2318 +2026-04-13 04:41:59.031373: Current learning rate: 0.00459 +2026-04-13 04:43:40.575435: train_loss -0.4135 +2026-04-13 04:43:40.579908: val_loss -0.3924 +2026-04-13 04:43:40.581226: Pseudo dice [0.345, 0.0, 0.7246, 0.0332, 0.516, 0.7545, 0.8666] +2026-04-13 04:43:40.583098: Epoch time: 101.55 s +2026-04-13 04:43:41.720355: +2026-04-13 04:43:41.721718: Epoch 2319 +2026-04-13 04:43:41.722936: Current learning rate: 0.00458 +2026-04-13 04:45:23.324676: train_loss -0.4024 +2026-04-13 04:45:23.329453: val_loss -0.3795 +2026-04-13 04:45:23.330753: Pseudo dice [0.483, 0.0, 0.714, 0.5222, 0.4813, 0.723, 0.8104] +2026-04-13 04:45:23.332200: Epoch time: 101.61 s +2026-04-13 04:45:24.462969: +2026-04-13 04:45:24.464349: Epoch 2320 +2026-04-13 04:45:24.465670: Current learning rate: 0.00458 +2026-04-13 04:47:06.479463: train_loss -0.3839 +2026-04-13 04:47:06.503361: val_loss -0.3328 +2026-04-13 04:47:06.504580: Pseudo dice [0.0151, 0.0, 0.6702, 0.5712, 0.181, 0.8083, 0.8068] +2026-04-13 04:47:06.505973: Epoch time: 102.02 s +2026-04-13 04:47:07.661547: +2026-04-13 04:47:07.663367: Epoch 2321 +2026-04-13 04:47:07.664948: Current learning rate: 0.00458 +2026-04-13 04:48:49.322140: train_loss -0.3956 +2026-04-13 04:48:49.325797: val_loss -0.3467 +2026-04-13 04:48:49.327230: Pseudo dice [0.017, 0.0, 0.7761, 0.767, 0.2326, 0.5, 0.4148] +2026-04-13 04:48:49.328535: Epoch time: 101.66 s +2026-04-13 04:48:50.479184: +2026-04-13 04:48:50.480951: Epoch 2322 +2026-04-13 04:48:50.482664: Current learning rate: 0.00458 +2026-04-13 04:50:32.094814: train_loss -0.3764 +2026-04-13 04:50:32.100243: val_loss -0.3739 +2026-04-13 04:50:32.102730: Pseudo dice [0.5509, 0.0, 0.4808, 0.8482, 0.3697, 0.4312, 0.5401] +2026-04-13 04:50:32.104230: Epoch time: 101.62 s +2026-04-13 04:50:33.237999: +2026-04-13 04:50:33.239966: Epoch 2323 +2026-04-13 04:50:33.241771: Current learning rate: 0.00457 +2026-04-13 04:52:14.826872: train_loss -0.3993 +2026-04-13 04:52:14.831758: val_loss -0.3551 +2026-04-13 04:52:14.833502: Pseudo dice [0.0992, 0.0, 0.6031, 0.0, 0.2342, 0.7775, 0.8439] +2026-04-13 04:52:14.835341: Epoch time: 101.59 s +2026-04-13 04:52:16.083164: +2026-04-13 04:52:16.084953: Epoch 2324 +2026-04-13 04:52:16.086495: Current learning rate: 0.00457 +2026-04-13 04:53:57.981524: train_loss -0.4025 +2026-04-13 04:53:57.986131: val_loss -0.3878 +2026-04-13 04:53:57.987667: Pseudo dice [0.5947, 0.0, 0.7306, 0.6216, 0.4544, 0.8155, 0.6988] +2026-04-13 04:53:57.989119: Epoch time: 101.9 s +2026-04-13 04:53:59.143942: +2026-04-13 04:53:59.145604: Epoch 2325 +2026-04-13 04:53:59.147059: Current learning rate: 0.00457 +2026-04-13 04:55:40.887502: train_loss -0.4144 +2026-04-13 04:55:40.892810: val_loss -0.3299 +2026-04-13 04:55:40.894571: Pseudo dice [0.3721, 0.0, 0.4328, 0.7465, 0.2105, 0.595, 0.5308] +2026-04-13 04:55:40.896134: Epoch time: 101.75 s +2026-04-13 04:55:42.047164: +2026-04-13 04:55:42.048820: Epoch 2326 +2026-04-13 04:55:42.050412: Current learning rate: 0.00457 +2026-04-13 04:57:23.873029: train_loss -0.3803 +2026-04-13 04:57:23.877157: val_loss -0.3315 +2026-04-13 04:57:23.878653: Pseudo dice [0.5058, 0.0, 0.5837, 0.366, 0.2102, 0.6984, 0.849] +2026-04-13 04:57:23.880911: Epoch time: 101.83 s +2026-04-13 04:57:25.021791: +2026-04-13 04:57:25.023427: Epoch 2327 +2026-04-13 04:57:25.024695: Current learning rate: 0.00456 +2026-04-13 04:59:06.838962: train_loss -0.408 +2026-04-13 04:59:06.843145: val_loss -0.3382 +2026-04-13 04:59:06.845057: Pseudo dice [0.1179, 0.0, 0.759, 0.4435, 0.4278, 0.67, 0.7538] +2026-04-13 04:59:06.847334: Epoch time: 101.82 s +2026-04-13 04:59:08.002046: +2026-04-13 04:59:08.003831: Epoch 2328 +2026-04-13 04:59:08.006017: Current learning rate: 0.00456 +2026-04-13 05:00:49.702675: train_loss -0.405 +2026-04-13 05:00:49.707006: val_loss -0.3855 +2026-04-13 05:00:49.708703: Pseudo dice [0.7571, 0.0, 0.5086, 0.5006, 0.3354, 0.5965, 0.81] +2026-04-13 05:00:49.710140: Epoch time: 101.7 s +2026-04-13 05:00:50.855196: +2026-04-13 05:00:50.856985: Epoch 2329 +2026-04-13 05:00:50.858312: Current learning rate: 0.00456 +2026-04-13 05:02:32.579153: train_loss -0.4078 +2026-04-13 05:02:32.584487: val_loss -0.3328 +2026-04-13 05:02:32.586186: Pseudo dice [0.2624, 0.0, 0.6413, 0.0562, 0.1426, 0.7645, 0.8906] +2026-04-13 05:02:32.588199: Epoch time: 101.73 s +2026-04-13 05:02:33.740392: +2026-04-13 05:02:33.743899: Epoch 2330 +2026-04-13 05:02:33.748258: Current learning rate: 0.00456 +2026-04-13 05:04:15.457673: train_loss -0.4051 +2026-04-13 05:04:15.461918: val_loss -0.3921 +2026-04-13 05:04:15.463923: Pseudo dice [0.6931, 0.0, 0.7549, 0.8934, 0.1603, 0.7493, 0.8941] +2026-04-13 05:04:15.465187: Epoch time: 101.72 s +2026-04-13 05:04:16.617017: +2026-04-13 05:04:16.618647: Epoch 2331 +2026-04-13 05:04:16.619930: Current learning rate: 0.00455 +2026-04-13 05:05:58.343525: train_loss -0.4242 +2026-04-13 05:05:58.347432: val_loss -0.3917 +2026-04-13 05:05:58.349007: Pseudo dice [0.6953, 0.0, 0.7454, 0.8892, 0.3614, 0.7962, 0.8319] +2026-04-13 05:05:58.352236: Epoch time: 101.73 s +2026-04-13 05:05:59.500485: +2026-04-13 05:05:59.502262: Epoch 2332 +2026-04-13 05:05:59.503568: Current learning rate: 0.00455 +2026-04-13 05:07:41.358036: train_loss -0.4175 +2026-04-13 05:07:41.362282: val_loss -0.3441 +2026-04-13 05:07:41.363991: Pseudo dice [0.2862, 0.0, 0.774, 0.3403, 0.3456, 0.7453, 0.6895] +2026-04-13 05:07:41.365242: Epoch time: 101.86 s +2026-04-13 05:07:42.511386: +2026-04-13 05:07:42.513017: Epoch 2333 +2026-04-13 05:07:42.514517: Current learning rate: 0.00455 +2026-04-13 05:09:24.264835: train_loss -0.392 +2026-04-13 05:09:24.270315: val_loss -0.3431 +2026-04-13 05:09:24.272343: Pseudo dice [0.4361, 0.0, 0.6339, 0.745, 0.1789, 0.311, 0.6632] +2026-04-13 05:09:24.275334: Epoch time: 101.76 s +2026-04-13 05:09:25.420352: +2026-04-13 05:09:25.421725: Epoch 2334 +2026-04-13 05:09:25.423204: Current learning rate: 0.00455 +2026-04-13 05:11:07.037597: train_loss -0.4155 +2026-04-13 05:11:07.042168: val_loss -0.3113 +2026-04-13 05:11:07.043949: Pseudo dice [0.2933, 0.0, 0.5497, 0.7637, 0.2083, 0.5241, 0.6801] +2026-04-13 05:11:07.045989: Epoch time: 101.62 s +2026-04-13 05:11:08.209966: +2026-04-13 05:11:08.211623: Epoch 2335 +2026-04-13 05:11:08.213102: Current learning rate: 0.00454 +2026-04-13 05:12:50.004537: train_loss -0.3684 +2026-04-13 05:12:50.008716: val_loss -0.2684 +2026-04-13 05:12:50.010310: Pseudo dice [0.2667, 0.0, 0.3189, 0.0186, 0.0253, 0.2637, 0.7428] +2026-04-13 05:12:50.011784: Epoch time: 101.8 s +2026-04-13 05:12:51.174598: +2026-04-13 05:12:51.175929: Epoch 2336 +2026-04-13 05:12:51.177239: Current learning rate: 0.00454 +2026-04-13 05:14:32.952219: train_loss -0.3917 +2026-04-13 05:14:32.956615: val_loss -0.3162 +2026-04-13 05:14:32.958077: Pseudo dice [0.3232, 0.0, 0.4889, 0.5476, 0.2936, 0.5494, 0.8576] +2026-04-13 05:14:32.959550: Epoch time: 101.78 s +2026-04-13 05:14:34.934460: +2026-04-13 05:14:34.936278: Epoch 2337 +2026-04-13 05:14:34.937793: Current learning rate: 0.00454 +2026-04-13 05:16:16.782768: train_loss -0.4033 +2026-04-13 05:16:16.787267: val_loss -0.3418 +2026-04-13 05:16:16.789017: Pseudo dice [0.0131, 0.0, 0.62, 0.4854, 0.2683, 0.67, 0.7568] +2026-04-13 05:16:16.790682: Epoch time: 101.85 s +2026-04-13 05:16:17.927258: +2026-04-13 05:16:17.928954: Epoch 2338 +2026-04-13 05:16:17.930539: Current learning rate: 0.00454 +2026-04-13 05:17:59.577919: train_loss -0.404 +2026-04-13 05:17:59.583013: val_loss -0.3583 +2026-04-13 05:17:59.584598: Pseudo dice [0.192, 0.0, 0.6842, 0.7644, 0.0621, 0.5892, 0.7286] +2026-04-13 05:17:59.586300: Epoch time: 101.65 s +2026-04-13 05:18:00.722332: +2026-04-13 05:18:00.724525: Epoch 2339 +2026-04-13 05:18:00.726138: Current learning rate: 0.00453 +2026-04-13 05:19:42.420983: train_loss -0.4055 +2026-04-13 05:19:42.425760: val_loss -0.3321 +2026-04-13 05:19:42.427572: Pseudo dice [0.2287, 0.0, 0.484, 0.7625, 0.2773, 0.7677, 0.6373] +2026-04-13 05:19:42.429063: Epoch time: 101.7 s +2026-04-13 05:19:43.563408: +2026-04-13 05:19:43.565039: Epoch 2340 +2026-04-13 05:19:43.566581: Current learning rate: 0.00453 +2026-04-13 05:21:25.546796: train_loss -0.3975 +2026-04-13 05:21:25.551319: val_loss -0.3585 +2026-04-13 05:21:25.553068: Pseudo dice [0.3062, 0.0, 0.5749, 0.7524, 0.2913, 0.3103, 0.7513] +2026-04-13 05:21:25.555428: Epoch time: 101.99 s +2026-04-13 05:21:26.698595: +2026-04-13 05:21:26.700389: Epoch 2341 +2026-04-13 05:21:26.702230: Current learning rate: 0.00453 +2026-04-13 05:23:08.459871: train_loss -0.416 +2026-04-13 05:23:08.464450: val_loss -0.3817 +2026-04-13 05:23:08.466573: Pseudo dice [0.5179, 0.0, 0.6272, 0.7022, 0.4987, 0.623, 0.6491] +2026-04-13 05:23:08.467834: Epoch time: 101.76 s +2026-04-13 05:23:09.633874: +2026-04-13 05:23:09.635583: Epoch 2342 +2026-04-13 05:23:09.637029: Current learning rate: 0.00453 +2026-04-13 05:24:51.272624: train_loss -0.4136 +2026-04-13 05:24:51.276913: val_loss -0.403 +2026-04-13 05:24:51.278538: Pseudo dice [0.4443, 0.0, 0.7881, 0.7601, 0.4382, 0.7994, 0.654] +2026-04-13 05:24:51.280065: Epoch time: 101.64 s +2026-04-13 05:24:52.444374: +2026-04-13 05:24:52.445778: Epoch 2343 +2026-04-13 05:24:52.447014: Current learning rate: 0.00452 +2026-04-13 05:26:34.280173: train_loss -0.3959 +2026-04-13 05:26:34.284564: val_loss -0.3207 +2026-04-13 05:26:34.286465: Pseudo dice [0.3234, 0.0, 0.7433, 0.5054, 0.4125, 0.484, 0.7804] +2026-04-13 05:26:34.289180: Epoch time: 101.84 s +2026-04-13 05:26:35.470458: +2026-04-13 05:26:35.472541: Epoch 2344 +2026-04-13 05:26:35.474237: Current learning rate: 0.00452 +2026-04-13 05:28:17.241935: train_loss -0.4073 +2026-04-13 05:28:17.245979: val_loss -0.3519 +2026-04-13 05:28:17.247461: Pseudo dice [0.39, 0.0, 0.7831, 0.3933, 0.0961, 0.8077, 0.8436] +2026-04-13 05:28:17.248940: Epoch time: 101.77 s +2026-04-13 05:28:18.412308: +2026-04-13 05:28:18.413759: Epoch 2345 +2026-04-13 05:28:18.414995: Current learning rate: 0.00452 +2026-04-13 05:30:00.175738: train_loss -0.4036 +2026-04-13 05:30:00.180607: val_loss -0.325 +2026-04-13 05:30:00.182785: Pseudo dice [0.0548, 0.0, 0.38, 0.257, 0.3033, 0.595, 0.8452] +2026-04-13 05:30:00.184566: Epoch time: 101.77 s +2026-04-13 05:30:01.351390: +2026-04-13 05:30:01.353134: Epoch 2346 +2026-04-13 05:30:01.354631: Current learning rate: 0.00452 +2026-04-13 05:31:43.110088: train_loss -0.4141 +2026-04-13 05:31:43.114339: val_loss -0.3716 +2026-04-13 05:31:43.116027: Pseudo dice [0.5978, 0.0, 0.168, 0.5197, 0.5667, 0.6526, 0.7461] +2026-04-13 05:31:43.117577: Epoch time: 101.76 s +2026-04-13 05:31:44.264594: +2026-04-13 05:31:44.266071: Epoch 2347 +2026-04-13 05:31:44.267653: Current learning rate: 0.00451 +2026-04-13 05:33:25.879520: train_loss -0.4174 +2026-04-13 05:33:25.883861: val_loss -0.3878 +2026-04-13 05:33:25.885477: Pseudo dice [0.768, 0.0, 0.7285, 0.7936, 0.4362, 0.7189, 0.3974] +2026-04-13 05:33:25.886755: Epoch time: 101.62 s +2026-04-13 05:33:27.123250: +2026-04-13 05:33:27.125062: Epoch 2348 +2026-04-13 05:33:27.126839: Current learning rate: 0.00451 +2026-04-13 05:35:08.623959: train_loss -0.4209 +2026-04-13 05:35:08.627660: val_loss -0.3352 +2026-04-13 05:35:08.629167: Pseudo dice [0.7268, 0.0, 0.7306, 0.6126, 0.312, 0.7435, 0.7097] +2026-04-13 05:35:08.630474: Epoch time: 101.5 s +2026-04-13 05:35:09.781047: +2026-04-13 05:35:09.782589: Epoch 2349 +2026-04-13 05:35:09.784061: Current learning rate: 0.00451 +2026-04-13 05:36:51.452559: train_loss -0.3901 +2026-04-13 05:36:51.457541: val_loss -0.3531 +2026-04-13 05:36:51.458877: Pseudo dice [0.3123, 0.0, 0.8288, 0.5684, 0.1816, 0.1943, 0.6775] +2026-04-13 05:36:51.460187: Epoch time: 101.67 s +2026-04-13 05:36:54.240010: +2026-04-13 05:36:54.241408: Epoch 2350 +2026-04-13 05:36:54.243551: Current learning rate: 0.00451 +2026-04-13 05:38:35.810165: train_loss -0.4076 +2026-04-13 05:38:35.815416: val_loss -0.3592 +2026-04-13 05:38:35.817050: Pseudo dice [0.5925, 0.0, 0.6814, 0.3079, 0.4179, 0.6706, 0.8915] +2026-04-13 05:38:35.818787: Epoch time: 101.57 s +2026-04-13 05:38:36.985535: +2026-04-13 05:38:36.987633: Epoch 2351 +2026-04-13 05:38:36.989518: Current learning rate: 0.0045 +2026-04-13 05:40:18.635623: train_loss -0.4201 +2026-04-13 05:40:18.640434: val_loss -0.3994 +2026-04-13 05:40:18.642303: Pseudo dice [0.7456, 0.0, 0.794, 0.8267, 0.3247, 0.8423, 0.7445] +2026-04-13 05:40:18.643913: Epoch time: 101.65 s +2026-04-13 05:40:19.798730: +2026-04-13 05:40:19.800444: Epoch 2352 +2026-04-13 05:40:19.802421: Current learning rate: 0.0045 +2026-04-13 05:42:01.442215: train_loss -0.4122 +2026-04-13 05:42:01.448406: val_loss -0.3606 +2026-04-13 05:42:01.450096: Pseudo dice [0.575, 0.0, 0.6615, 0.535, 0.2658, 0.7186, 0.8238] +2026-04-13 05:42:01.451619: Epoch time: 101.65 s +2026-04-13 05:42:02.615287: +2026-04-13 05:42:02.617657: Epoch 2353 +2026-04-13 05:42:02.619649: Current learning rate: 0.0045 +2026-04-13 05:43:44.303035: train_loss -0.4152 +2026-04-13 05:43:44.308991: val_loss -0.3445 +2026-04-13 05:43:44.310211: Pseudo dice [0.2374, 0.0, 0.7867, 0.8584, 0.2054, 0.8056, 0.3865] +2026-04-13 05:43:44.311804: Epoch time: 101.69 s +2026-04-13 05:43:45.443392: +2026-04-13 05:43:45.444726: Epoch 2354 +2026-04-13 05:43:45.445954: Current learning rate: 0.0045 +2026-04-13 05:45:27.070220: train_loss -0.4095 +2026-04-13 05:45:27.074484: val_loss -0.3533 +2026-04-13 05:45:27.076101: Pseudo dice [0.4849, 0.0, 0.7231, 0.7507, 0.0034, 0.801, 0.9121] +2026-04-13 05:45:27.077793: Epoch time: 101.63 s +2026-04-13 05:45:28.241127: +2026-04-13 05:45:28.243463: Epoch 2355 +2026-04-13 05:45:28.245190: Current learning rate: 0.00449 +2026-04-13 05:47:10.026467: train_loss -0.4021 +2026-04-13 05:47:10.030511: val_loss -0.3785 +2026-04-13 05:47:10.032234: Pseudo dice [0.7311, 0.0, 0.7454, 0.6926, 0.317, 0.7915, 0.7976] +2026-04-13 05:47:10.033576: Epoch time: 101.79 s +2026-04-13 05:47:11.181625: +2026-04-13 05:47:11.183279: Epoch 2356 +2026-04-13 05:47:11.184770: Current learning rate: 0.00449 +2026-04-13 05:48:52.917454: train_loss -0.4133 +2026-04-13 05:48:52.923864: val_loss -0.4201 +2026-04-13 05:48:52.926998: Pseudo dice [0.6269, 0.0, 0.8276, 0.8381, 0.6582, 0.8392, 0.8634] +2026-04-13 05:48:52.929510: Epoch time: 101.74 s +2026-04-13 05:48:54.078861: +2026-04-13 05:48:54.080290: Epoch 2357 +2026-04-13 05:48:54.081724: Current learning rate: 0.00449 +2026-04-13 05:50:37.228162: train_loss -0.4088 +2026-04-13 05:50:37.233293: val_loss -0.3561 +2026-04-13 05:50:37.235070: Pseudo dice [0.5159, 0.0, 0.7046, 0.7988, 0.082, 0.7391, 0.8471] +2026-04-13 05:50:37.236503: Epoch time: 103.15 s +2026-04-13 05:50:38.466531: +2026-04-13 05:50:38.468239: Epoch 2358 +2026-04-13 05:50:38.469859: Current learning rate: 0.00449 +2026-04-13 05:52:20.171589: train_loss -0.4071 +2026-04-13 05:52:20.175653: val_loss -0.3553 +2026-04-13 05:52:20.177347: Pseudo dice [0.2849, 0.0, 0.632, 0.7149, 0.2959, 0.811, 0.6783] +2026-04-13 05:52:20.179057: Epoch time: 101.71 s +2026-04-13 05:52:21.344041: +2026-04-13 05:52:21.345350: Epoch 2359 +2026-04-13 05:52:21.346587: Current learning rate: 0.00448 +2026-04-13 05:54:03.064154: train_loss -0.4194 +2026-04-13 05:54:03.068538: val_loss -0.4003 +2026-04-13 05:54:03.070222: Pseudo dice [0.7509, 0.0, 0.7765, 0.692, 0.3916, 0.8862, 0.7828] +2026-04-13 05:54:03.071786: Epoch time: 101.72 s +2026-04-13 05:54:04.233038: +2026-04-13 05:54:04.235115: Epoch 2360 +2026-04-13 05:54:04.236922: Current learning rate: 0.00448 +2026-04-13 05:55:45.985559: train_loss -0.3855 +2026-04-13 05:55:45.990075: val_loss -0.288 +2026-04-13 05:55:45.991786: Pseudo dice [0.3897, 0.0, 0.4637, 0.7782, 0.12, 0.7258, 0.5635] +2026-04-13 05:55:45.993856: Epoch time: 101.76 s +2026-04-13 05:55:47.154279: +2026-04-13 05:55:47.157279: Epoch 2361 +2026-04-13 05:55:47.159163: Current learning rate: 0.00448 +2026-04-13 05:57:28.886293: train_loss -0.4122 +2026-04-13 05:57:28.890891: val_loss -0.375 +2026-04-13 05:57:28.892503: Pseudo dice [0.5714, 0.0, 0.8521, 0.2678, 0.1568, 0.6218, 0.6541] +2026-04-13 05:57:28.894006: Epoch time: 101.74 s +2026-04-13 05:57:30.038959: +2026-04-13 05:57:30.040567: Epoch 2362 +2026-04-13 05:57:30.041965: Current learning rate: 0.00448 +2026-04-13 05:59:11.756571: train_loss -0.4236 +2026-04-13 05:59:11.761398: val_loss -0.3756 +2026-04-13 05:59:11.762782: Pseudo dice [0.5131, 0.0, 0.7388, 0.789, 0.5688, 0.2295, 0.8409] +2026-04-13 05:59:11.764352: Epoch time: 101.72 s +2026-04-13 05:59:12.915394: +2026-04-13 05:59:12.916930: Epoch 2363 +2026-04-13 05:59:12.918510: Current learning rate: 0.00447 +2026-04-13 06:00:54.567100: train_loss -0.4135 +2026-04-13 06:00:54.582503: val_loss -0.3888 +2026-04-13 06:00:54.584208: Pseudo dice [0.2176, 0.0, 0.8706, 0.882, 0.4489, 0.6389, 0.7086] +2026-04-13 06:00:54.588306: Epoch time: 101.65 s +2026-04-13 06:00:55.763100: +2026-04-13 06:00:55.765091: Epoch 2364 +2026-04-13 06:00:55.766953: Current learning rate: 0.00447 +2026-04-13 06:02:37.521127: train_loss -0.4065 +2026-04-13 06:02:37.526324: val_loss -0.3597 +2026-04-13 06:02:37.528382: Pseudo dice [0.5887, 0.0, 0.813, 0.5347, 0.5799, 0.6625, 0.625] +2026-04-13 06:02:37.530674: Epoch time: 101.76 s +2026-04-13 06:02:38.684341: +2026-04-13 06:02:38.685830: Epoch 2365 +2026-04-13 06:02:38.687307: Current learning rate: 0.00447 +2026-04-13 06:04:20.375519: train_loss -0.4166 +2026-04-13 06:04:20.380384: val_loss -0.3806 +2026-04-13 06:04:20.382115: Pseudo dice [0.2233, 0.0, 0.8077, 0.8451, 0.4184, 0.481, 0.8584] +2026-04-13 06:04:20.383903: Epoch time: 101.69 s +2026-04-13 06:04:21.544423: +2026-04-13 06:04:21.546557: Epoch 2366 +2026-04-13 06:04:21.548345: Current learning rate: 0.00447 +2026-04-13 06:06:03.284685: train_loss -0.398 +2026-04-13 06:06:03.289122: val_loss -0.3101 +2026-04-13 06:06:03.290807: Pseudo dice [0.3844, 0.0, 0.7301, 0.8094, 0.1389, 0.2201, 0.3107] +2026-04-13 06:06:03.292229: Epoch time: 101.74 s +2026-04-13 06:06:04.446513: +2026-04-13 06:06:04.448577: Epoch 2367 +2026-04-13 06:06:04.450062: Current learning rate: 0.00447 +2026-04-13 06:07:46.147755: train_loss -0.3775 +2026-04-13 06:07:46.153602: val_loss -0.3018 +2026-04-13 06:07:46.155363: Pseudo dice [0.3086, 0.0, 0.6375, 0.7121, 0.1813, 0.6882, 0.6804] +2026-04-13 06:07:46.156935: Epoch time: 101.7 s +2026-04-13 06:07:47.321220: +2026-04-13 06:07:47.323017: Epoch 2368 +2026-04-13 06:07:47.324439: Current learning rate: 0.00446 +2026-04-13 06:09:28.972917: train_loss -0.4095 +2026-04-13 06:09:28.977935: val_loss -0.3859 +2026-04-13 06:09:28.979643: Pseudo dice [0.8379, 0.0, 0.6466, 0.8509, 0.3375, 0.7095, 0.9121] +2026-04-13 06:09:28.981219: Epoch time: 101.65 s +2026-04-13 06:09:30.119506: +2026-04-13 06:09:30.121174: Epoch 2369 +2026-04-13 06:09:30.123001: Current learning rate: 0.00446 +2026-04-13 06:11:11.838959: train_loss -0.4207 +2026-04-13 06:11:11.843168: val_loss -0.3963 +2026-04-13 06:11:11.844707: Pseudo dice [0.1414, 0.0, 0.7818, 0.8542, 0.3979, 0.6277, 0.7542] +2026-04-13 06:11:11.846631: Epoch time: 101.72 s +2026-04-13 06:11:12.994022: +2026-04-13 06:11:12.996010: Epoch 2370 +2026-04-13 06:11:12.997485: Current learning rate: 0.00446 +2026-04-13 06:12:54.552401: train_loss -0.4005 +2026-04-13 06:12:54.557490: val_loss -0.3901 +2026-04-13 06:12:54.559216: Pseudo dice [0.5927, 0.0, 0.8004, 0.7748, 0.508, 0.4967, 0.8128] +2026-04-13 06:12:54.560845: Epoch time: 101.56 s +2026-04-13 06:12:55.695252: +2026-04-13 06:12:55.696614: Epoch 2371 +2026-04-13 06:12:55.697860: Current learning rate: 0.00446 +2026-04-13 06:14:37.619406: train_loss -0.419 +2026-04-13 06:14:37.623436: val_loss -0.3476 +2026-04-13 06:14:37.625228: Pseudo dice [0.0491, 0.0, 0.7309, 0.7984, 0.4378, 0.232, 0.8355] +2026-04-13 06:14:37.627497: Epoch time: 101.93 s +2026-04-13 06:14:38.771504: +2026-04-13 06:14:38.773148: Epoch 2372 +2026-04-13 06:14:38.774641: Current learning rate: 0.00445 +2026-04-13 06:16:20.351997: train_loss -0.4058 +2026-04-13 06:16:20.356476: val_loss -0.3392 +2026-04-13 06:16:20.358123: Pseudo dice [0.3617, 0.0, 0.3324, 0.5667, 0.2107, 0.6749, 0.555] +2026-04-13 06:16:20.360866: Epoch time: 101.58 s +2026-04-13 06:16:21.540141: +2026-04-13 06:16:21.541775: Epoch 2373 +2026-04-13 06:16:21.546248: Current learning rate: 0.00445 +2026-04-13 06:18:03.193486: train_loss -0.3805 +2026-04-13 06:18:03.198027: val_loss -0.3609 +2026-04-13 06:18:03.199790: Pseudo dice [0.3868, 0.0, 0.6911, 0.6422, 0.2214, 0.6251, 0.8594] +2026-04-13 06:18:03.201075: Epoch time: 101.66 s +2026-04-13 06:18:04.349438: +2026-04-13 06:18:04.351062: Epoch 2374 +2026-04-13 06:18:04.352317: Current learning rate: 0.00445 +2026-04-13 06:19:45.929714: train_loss -0.3961 +2026-04-13 06:19:45.937888: val_loss -0.338 +2026-04-13 06:19:45.939527: Pseudo dice [0.0031, 0.0, 0.609, 0.7252, 0.327, 0.603, 0.8037] +2026-04-13 06:19:45.941303: Epoch time: 101.58 s +2026-04-13 06:19:47.091053: +2026-04-13 06:19:47.092738: Epoch 2375 +2026-04-13 06:19:47.094306: Current learning rate: 0.00445 +2026-04-13 06:21:28.794910: train_loss -0.4063 +2026-04-13 06:21:28.800368: val_loss -0.3614 +2026-04-13 06:21:28.802095: Pseudo dice [0.2284, 0.0, 0.6805, 0.3033, 0.6412, 0.4899, 0.6088] +2026-04-13 06:21:28.804055: Epoch time: 101.71 s +2026-04-13 06:21:29.951834: +2026-04-13 06:21:29.954692: Epoch 2376 +2026-04-13 06:21:29.956473: Current learning rate: 0.00444 +2026-04-13 06:23:11.521802: train_loss -0.3973 +2026-04-13 06:23:11.527045: val_loss -0.3761 +2026-04-13 06:23:11.528848: Pseudo dice [0.4995, 0.0, 0.6337, 0.4824, 0.4581, 0.7784, 0.7886] +2026-04-13 06:23:11.530153: Epoch time: 101.57 s +2026-04-13 06:23:12.688550: +2026-04-13 06:23:12.690345: Epoch 2377 +2026-04-13 06:23:12.691878: Current learning rate: 0.00444 +2026-04-13 06:24:54.381628: train_loss -0.4164 +2026-04-13 06:24:54.385340: val_loss -0.3564 +2026-04-13 06:24:54.386600: Pseudo dice [0.7703, 0.0, 0.5007, 0.6235, 0.2499, 0.7382, 0.7908] +2026-04-13 06:24:54.387816: Epoch time: 101.7 s +2026-04-13 06:24:56.514944: +2026-04-13 06:24:56.516335: Epoch 2378 +2026-04-13 06:24:56.517560: Current learning rate: 0.00444 +2026-04-13 06:26:38.170822: train_loss -0.4094 +2026-04-13 06:26:38.174812: val_loss -0.3565 +2026-04-13 06:26:38.176271: Pseudo dice [0.6227, 0.0, 0.7074, 0.366, 0.1384, 0.5726, 0.794] +2026-04-13 06:26:38.177985: Epoch time: 101.66 s +2026-04-13 06:26:39.323738: +2026-04-13 06:26:39.325686: Epoch 2379 +2026-04-13 06:26:39.327415: Current learning rate: 0.00444 +2026-04-13 06:28:20.967559: train_loss -0.4021 +2026-04-13 06:28:20.972447: val_loss -0.3362 +2026-04-13 06:28:20.974200: Pseudo dice [0.2527, 0.0, 0.3401, 0.0822, 0.2763, 0.7732, 0.8491] +2026-04-13 06:28:20.976697: Epoch time: 101.65 s +2026-04-13 06:28:22.127673: +2026-04-13 06:28:22.129500: Epoch 2380 +2026-04-13 06:28:22.130882: Current learning rate: 0.00443 +2026-04-13 06:30:03.872235: train_loss -0.3976 +2026-04-13 06:30:03.876424: val_loss -0.3235 +2026-04-13 06:30:03.877755: Pseudo dice [0.0, 0.0, 0.6735, 0.3701, 0.1309, 0.7876, 0.2844] +2026-04-13 06:30:03.879117: Epoch time: 101.75 s +2026-04-13 06:30:05.031081: +2026-04-13 06:30:05.032657: Epoch 2381 +2026-04-13 06:30:05.033846: Current learning rate: 0.00443 +2026-04-13 06:31:46.562288: train_loss -0.3868 +2026-04-13 06:31:46.567411: val_loss -0.3516 +2026-04-13 06:31:46.568977: Pseudo dice [0.0, 0.0, 0.7859, 0.8674, 0.2692, 0.2748, 0.8397] +2026-04-13 06:31:46.570673: Epoch time: 101.53 s +2026-04-13 06:31:47.732002: +2026-04-13 06:31:47.734221: Epoch 2382 +2026-04-13 06:31:47.735967: Current learning rate: 0.00443 +2026-04-13 06:33:29.325736: train_loss -0.3906 +2026-04-13 06:33:29.330390: val_loss -0.3377 +2026-04-13 06:33:29.332034: Pseudo dice [0.0, 0.0, 0.707, 0.4822, 0.4901, 0.5492, 0.55] +2026-04-13 06:33:29.333439: Epoch time: 101.6 s +2026-04-13 06:33:30.504281: +2026-04-13 06:33:30.506758: Epoch 2383 +2026-04-13 06:33:30.508325: Current learning rate: 0.00443 +2026-04-13 06:35:12.120373: train_loss -0.3966 +2026-04-13 06:35:12.124615: val_loss -0.3521 +2026-04-13 06:35:12.126584: Pseudo dice [0.0, 0.0, 0.6803, 0.8577, 0.341, 0.7944, 0.5556] +2026-04-13 06:35:12.128028: Epoch time: 101.62 s +2026-04-13 06:35:13.294621: +2026-04-13 06:35:13.296202: Epoch 2384 +2026-04-13 06:35:13.297417: Current learning rate: 0.00442 +2026-04-13 06:36:54.974007: train_loss -0.3907 +2026-04-13 06:36:54.978611: val_loss -0.3668 +2026-04-13 06:36:54.980717: Pseudo dice [0.0, 0.0, 0.8093, 0.5128, 0.042, 0.7247, 0.914] +2026-04-13 06:36:54.982499: Epoch time: 101.68 s +2026-04-13 06:36:56.148048: +2026-04-13 06:36:56.149726: Epoch 2385 +2026-04-13 06:36:56.151274: Current learning rate: 0.00442 +2026-04-13 06:38:37.679600: train_loss -0.4154 +2026-04-13 06:38:37.685181: val_loss -0.349 +2026-04-13 06:38:37.687086: Pseudo dice [0.0, 0.0, 0.2186, 0.7868, 0.3152, 0.7688, 0.6169] +2026-04-13 06:38:37.689034: Epoch time: 101.53 s +2026-04-13 06:38:38.838320: +2026-04-13 06:38:38.840021: Epoch 2386 +2026-04-13 06:38:38.841571: Current learning rate: 0.00442 +2026-04-13 06:40:20.463429: train_loss -0.411 +2026-04-13 06:40:20.468160: val_loss -0.3519 +2026-04-13 06:40:20.470575: Pseudo dice [0.0, 0.0, 0.7908, 0.6405, 0.2716, 0.7516, 0.8639] +2026-04-13 06:40:20.472985: Epoch time: 101.63 s +2026-04-13 06:40:21.648102: +2026-04-13 06:40:21.654018: Epoch 2387 +2026-04-13 06:40:21.657690: Current learning rate: 0.00442 +2026-04-13 06:42:03.208703: train_loss -0.4106 +2026-04-13 06:42:03.213291: val_loss -0.3424 +2026-04-13 06:42:03.214988: Pseudo dice [0.0, 0.0, 0.6207, 0.3162, 0.5536, 0.8265, 0.4954] +2026-04-13 06:42:03.217250: Epoch time: 101.56 s +2026-04-13 06:42:04.387669: +2026-04-13 06:42:04.389251: Epoch 2388 +2026-04-13 06:42:04.390819: Current learning rate: 0.00441 +2026-04-13 06:43:46.032075: train_loss -0.4297 +2026-04-13 06:43:46.038595: val_loss -0.3768 +2026-04-13 06:43:46.040587: Pseudo dice [0.0, 0.0, 0.7759, 0.6763, 0.2484, 0.7325, 0.92] +2026-04-13 06:43:46.042376: Epoch time: 101.65 s +2026-04-13 06:43:47.283670: +2026-04-13 06:43:47.286136: Epoch 2389 +2026-04-13 06:43:47.288292: Current learning rate: 0.00441 +2026-04-13 06:45:28.975054: train_loss -0.4286 +2026-04-13 06:45:28.981584: val_loss -0.3698 +2026-04-13 06:45:28.983218: Pseudo dice [0.0, 0.0, 0.5544, 0.7524, 0.5303, 0.8475, 0.574] +2026-04-13 06:45:28.984793: Epoch time: 101.69 s +2026-04-13 06:45:30.149495: +2026-04-13 06:45:30.151196: Epoch 2390 +2026-04-13 06:45:30.152674: Current learning rate: 0.00441 +2026-04-13 06:47:11.897468: train_loss -0.4013 +2026-04-13 06:47:11.901988: val_loss -0.3571 +2026-04-13 06:47:11.903526: Pseudo dice [0.0, 0.0, 0.5812, 0.0735, 0.5373, 0.7908, 0.6122] +2026-04-13 06:47:11.905225: Epoch time: 101.75 s +2026-04-13 06:47:13.076378: +2026-04-13 06:47:13.077862: Epoch 2391 +2026-04-13 06:47:13.079090: Current learning rate: 0.00441 +2026-04-13 06:48:54.797129: train_loss -0.4126 +2026-04-13 06:48:54.801893: val_loss -0.3611 +2026-04-13 06:48:54.803392: Pseudo dice [0.3734, 0.0, 0.7092, 0.0, 0.3289, 0.5735, 0.6732] +2026-04-13 06:48:54.804927: Epoch time: 101.72 s +2026-04-13 06:48:56.000320: +2026-04-13 06:48:56.001808: Epoch 2392 +2026-04-13 06:48:56.003378: Current learning rate: 0.0044 +2026-04-13 06:50:37.926143: train_loss -0.4246 +2026-04-13 06:50:37.930708: val_loss -0.3872 +2026-04-13 06:50:37.932345: Pseudo dice [0.5217, 0.0, 0.7319, 0.7593, 0.4694, 0.7274, 0.9264] +2026-04-13 06:50:37.933947: Epoch time: 101.93 s +2026-04-13 06:50:39.104919: +2026-04-13 06:50:39.106627: Epoch 2393 +2026-04-13 06:50:39.108174: Current learning rate: 0.0044 +2026-04-13 06:52:20.720010: train_loss -0.422 +2026-04-13 06:52:20.725104: val_loss -0.3629 +2026-04-13 06:52:20.726627: Pseudo dice [0.5207, 0.0, 0.7641, 0.5328, 0.3474, 0.8269, 0.655] +2026-04-13 06:52:20.728404: Epoch time: 101.62 s +2026-04-13 06:52:21.921466: +2026-04-13 06:52:21.923635: Epoch 2394 +2026-04-13 06:52:21.925234: Current learning rate: 0.0044 +2026-04-13 06:54:03.618045: train_loss -0.4164 +2026-04-13 06:54:03.622361: val_loss -0.3588 +2026-04-13 06:54:03.624372: Pseudo dice [0.2579, 0.0, 0.7728, 0.7111, 0.5188, 0.3716, 0.8185] +2026-04-13 06:54:03.626068: Epoch time: 101.7 s +2026-04-13 06:54:04.814695: +2026-04-13 06:54:04.817116: Epoch 2395 +2026-04-13 06:54:04.819835: Current learning rate: 0.0044 +2026-04-13 06:55:46.646870: train_loss -0.4009 +2026-04-13 06:55:46.651241: val_loss -0.3487 +2026-04-13 06:55:46.652706: Pseudo dice [0.354, 0.0, 0.7984, 0.7899, 0.5034, 0.3517, 0.5609] +2026-04-13 06:55:46.654029: Epoch time: 101.84 s +2026-04-13 06:55:47.834646: +2026-04-13 06:55:47.836173: Epoch 2396 +2026-04-13 06:55:47.837578: Current learning rate: 0.00439 +2026-04-13 06:57:29.472054: train_loss -0.426 +2026-04-13 06:57:29.476538: val_loss -0.3743 +2026-04-13 06:57:29.478326: Pseudo dice [0.4601, 0.0, 0.6245, 0.868, 0.255, 0.5128, 0.9187] +2026-04-13 06:57:29.479784: Epoch time: 101.64 s +2026-04-13 06:57:30.636062: +2026-04-13 06:57:30.637636: Epoch 2397 +2026-04-13 06:57:30.638999: Current learning rate: 0.00439 +2026-04-13 06:59:12.094297: train_loss -0.3944 +2026-04-13 06:59:12.099300: val_loss -0.3777 +2026-04-13 06:59:12.101555: Pseudo dice [0.5237, 0.0, 0.868, 0.7544, 0.207, 0.7789, 0.6589] +2026-04-13 06:59:12.103476: Epoch time: 101.46 s +2026-04-13 06:59:13.276574: +2026-04-13 06:59:13.278099: Epoch 2398 +2026-04-13 06:59:13.279385: Current learning rate: 0.00439 +2026-04-13 07:00:55.004207: train_loss -0.4258 +2026-04-13 07:00:55.010877: val_loss -0.3514 +2026-04-13 07:00:55.012758: Pseudo dice [0.291, 0.0, 0.7254, 0.8235, 0.2945, 0.6241, 0.6128] +2026-04-13 07:00:55.014599: Epoch time: 101.73 s +2026-04-13 07:00:57.025126: +2026-04-13 07:00:57.027250: Epoch 2399 +2026-04-13 07:00:57.028764: Current learning rate: 0.00439 +2026-04-13 07:02:38.626803: train_loss -0.4193 +2026-04-13 07:02:38.630948: val_loss -0.3738 +2026-04-13 07:02:38.632343: Pseudo dice [0.6185, 0.0, 0.5951, 0.1144, 0.2118, 0.759, 0.8528] +2026-04-13 07:02:38.633712: Epoch time: 101.6 s +2026-04-13 07:02:41.472292: +2026-04-13 07:02:41.473909: Epoch 2400 +2026-04-13 07:02:41.475424: Current learning rate: 0.00438 +2026-04-13 07:04:23.088520: train_loss -0.4192 +2026-04-13 07:04:23.095183: val_loss -0.3738 +2026-04-13 07:04:23.097954: Pseudo dice [0.3667, 0.0, 0.7292, 0.8154, 0.4572, 0.7057, 0.8685] +2026-04-13 07:04:23.100007: Epoch time: 101.62 s +2026-04-13 07:04:24.266403: +2026-04-13 07:04:24.268403: Epoch 2401 +2026-04-13 07:04:24.270005: Current learning rate: 0.00438 +2026-04-13 07:06:05.837979: train_loss -0.416 +2026-04-13 07:06:05.841745: val_loss -0.3767 +2026-04-13 07:06:05.843001: Pseudo dice [0.365, 0.0, 0.7391, 0.7948, 0.3617, 0.672, 0.8774] +2026-04-13 07:06:05.844151: Epoch time: 101.57 s +2026-04-13 07:06:07.030821: +2026-04-13 07:06:07.032708: Epoch 2402 +2026-04-13 07:06:07.034429: Current learning rate: 0.00438 +2026-04-13 07:07:48.540894: train_loss -0.4241 +2026-04-13 07:07:48.544837: val_loss -0.3667 +2026-04-13 07:07:48.546548: Pseudo dice [0.7208, 0.0, 0.7515, 0.5793, 0.4057, 0.7943, 0.626] +2026-04-13 07:07:48.547687: Epoch time: 101.51 s +2026-04-13 07:07:49.754007: +2026-04-13 07:07:49.755358: Epoch 2403 +2026-04-13 07:07:49.756606: Current learning rate: 0.00438 +2026-04-13 07:09:31.153143: train_loss -0.4403 +2026-04-13 07:09:31.166984: val_loss -0.3696 +2026-04-13 07:09:31.169409: Pseudo dice [0.1514, 0.0, 0.3326, 0.921, 0.2023, 0.6609, 0.8457] +2026-04-13 07:09:31.171291: Epoch time: 101.4 s +2026-04-13 07:09:32.352875: +2026-04-13 07:09:32.354462: Epoch 2404 +2026-04-13 07:09:32.356119: Current learning rate: 0.00437 +2026-04-13 07:11:13.904024: train_loss -0.4194 +2026-04-13 07:11:13.909253: val_loss -0.3355 +2026-04-13 07:11:13.911002: Pseudo dice [0.4375, 0.0, 0.5801, 0.4013, 0.0638, 0.7188, 0.859] +2026-04-13 07:11:13.912601: Epoch time: 101.55 s +2026-04-13 07:11:15.084548: +2026-04-13 07:11:15.086292: Epoch 2405 +2026-04-13 07:11:15.088120: Current learning rate: 0.00437 +2026-04-13 07:12:56.600464: train_loss -0.4185 +2026-04-13 07:12:56.604754: val_loss -0.3566 +2026-04-13 07:12:56.606089: Pseudo dice [0.6315, 0.0, 0.6899, 0.8181, 0.2864, 0.7187, 0.5473] +2026-04-13 07:12:56.607350: Epoch time: 101.52 s +2026-04-13 07:12:57.767223: +2026-04-13 07:12:57.768589: Epoch 2406 +2026-04-13 07:12:57.769819: Current learning rate: 0.00437 +2026-04-13 07:14:39.411508: train_loss -0.4217 +2026-04-13 07:14:39.415837: val_loss -0.3809 +2026-04-13 07:14:39.417470: Pseudo dice [0.1068, 0.0, 0.6815, 0.84, 0.5105, 0.7793, 0.8294] +2026-04-13 07:14:39.419181: Epoch time: 101.65 s +2026-04-13 07:14:40.574909: +2026-04-13 07:14:40.576490: Epoch 2407 +2026-04-13 07:14:40.577764: Current learning rate: 0.00437 +2026-04-13 07:16:22.354948: train_loss -0.3906 +2026-04-13 07:16:22.361109: val_loss -0.3524 +2026-04-13 07:16:22.363352: Pseudo dice [0.1861, 0.0, 0.7059, 0.0, 0.2865, 0.8384, 0.8204] +2026-04-13 07:16:22.365433: Epoch time: 101.78 s +2026-04-13 07:16:23.545335: +2026-04-13 07:16:23.547230: Epoch 2408 +2026-04-13 07:16:23.548616: Current learning rate: 0.00436 +2026-04-13 07:18:04.997714: train_loss -0.3895 +2026-04-13 07:18:05.002639: val_loss -0.351 +2026-04-13 07:18:05.004209: Pseudo dice [0.3333, 0.0, 0.5547, 0.5142, 0.5212, 0.7818, 0.7993] +2026-04-13 07:18:05.005711: Epoch time: 101.46 s +2026-04-13 07:18:06.181871: +2026-04-13 07:18:06.183816: Epoch 2409 +2026-04-13 07:18:06.185473: Current learning rate: 0.00436 +2026-04-13 07:19:47.949823: train_loss -0.379 +2026-04-13 07:19:47.954810: val_loss -0.3256 +2026-04-13 07:19:47.956421: Pseudo dice [0.0, 0.0, 0.8428, 0.7271, 0.4316, 0.5585, 0.6835] +2026-04-13 07:19:47.958133: Epoch time: 101.77 s +2026-04-13 07:19:49.159627: +2026-04-13 07:19:49.160987: Epoch 2410 +2026-04-13 07:19:49.162091: Current learning rate: 0.00436 +2026-04-13 07:21:30.665365: train_loss -0.3891 +2026-04-13 07:21:30.669228: val_loss -0.3409 +2026-04-13 07:21:30.670661: Pseudo dice [0.1829, 0.0, 0.6977, 0.774, 0.2425, 0.302, 0.7213] +2026-04-13 07:21:30.672141: Epoch time: 101.51 s +2026-04-13 07:21:31.843662: +2026-04-13 07:21:31.844909: Epoch 2411 +2026-04-13 07:21:31.846185: Current learning rate: 0.00436 +2026-04-13 07:23:13.342819: train_loss -0.3894 +2026-04-13 07:23:13.349098: val_loss -0.3538 +2026-04-13 07:23:13.350859: Pseudo dice [0.5113, 0.0, 0.7913, 0.7298, 0.041, 0.6256, 0.7548] +2026-04-13 07:23:13.352612: Epoch time: 101.5 s +2026-04-13 07:23:14.543388: +2026-04-13 07:23:14.545181: Epoch 2412 +2026-04-13 07:23:14.546685: Current learning rate: 0.00435 +2026-04-13 07:24:56.096388: train_loss -0.3965 +2026-04-13 07:24:56.100259: val_loss -0.345 +2026-04-13 07:24:56.102077: Pseudo dice [0.1591, 0.0, 0.7092, 0.8001, 0.3692, 0.6971, 0.5275] +2026-04-13 07:24:56.103974: Epoch time: 101.56 s +2026-04-13 07:24:57.334830: +2026-04-13 07:24:57.336204: Epoch 2413 +2026-04-13 07:24:57.337410: Current learning rate: 0.00435 +2026-04-13 07:26:38.823460: train_loss -0.3958 +2026-04-13 07:26:38.827563: val_loss -0.3757 +2026-04-13 07:26:38.829185: Pseudo dice [0.4132, 0.0, 0.746, 0.7046, 0.5266, 0.8015, 0.8366] +2026-04-13 07:26:38.831176: Epoch time: 101.49 s +2026-04-13 07:26:40.004620: +2026-04-13 07:26:40.005957: Epoch 2414 +2026-04-13 07:26:40.007144: Current learning rate: 0.00435 +2026-04-13 07:28:21.442073: train_loss -0.3824 +2026-04-13 07:28:21.450711: val_loss -0.3824 +2026-04-13 07:28:21.452284: Pseudo dice [0.4865, 0.0, 0.5864, 0.8834, 0.5487, 0.6001, 0.368] +2026-04-13 07:28:21.454008: Epoch time: 101.44 s +2026-04-13 07:28:22.637947: +2026-04-13 07:28:22.640368: Epoch 2415 +2026-04-13 07:28:22.642140: Current learning rate: 0.00435 +2026-04-13 07:30:04.279260: train_loss -0.4115 +2026-04-13 07:30:04.283839: val_loss -0.342 +2026-04-13 07:30:04.285554: Pseudo dice [0.4826, 0.0, 0.5301, 0.708, 0.1368, 0.7364, 0.7763] +2026-04-13 07:30:04.287186: Epoch time: 101.64 s +2026-04-13 07:30:05.436965: +2026-04-13 07:30:05.439105: Epoch 2416 +2026-04-13 07:30:05.440785: Current learning rate: 0.00434 +2026-04-13 07:31:46.921810: train_loss -0.4044 +2026-04-13 07:31:46.928339: val_loss -0.3241 +2026-04-13 07:31:46.930680: Pseudo dice [0.5331, 0.0, 0.3712, 0.0889, 0.2949, 0.645, 0.8796] +2026-04-13 07:31:46.933642: Epoch time: 101.49 s +2026-04-13 07:31:48.107875: +2026-04-13 07:31:48.109530: Epoch 2417 +2026-04-13 07:31:48.110952: Current learning rate: 0.00434 +2026-04-13 07:33:29.640785: train_loss -0.3943 +2026-04-13 07:33:29.645546: val_loss -0.3757 +2026-04-13 07:33:29.648135: Pseudo dice [0.3373, 0.0, 0.6855, 0.7195, 0.2847, 0.7838, 0.7297] +2026-04-13 07:33:29.650021: Epoch time: 101.54 s +2026-04-13 07:33:30.811823: +2026-04-13 07:33:30.813671: Epoch 2418 +2026-04-13 07:33:30.815264: Current learning rate: 0.00434 +2026-04-13 07:35:12.397481: train_loss -0.421 +2026-04-13 07:35:12.402267: val_loss -0.3524 +2026-04-13 07:35:12.404504: Pseudo dice [0.3159, 0.0, 0.6451, 0.8618, 0.2921, 0.6553, 0.706] +2026-04-13 07:35:12.405976: Epoch time: 101.59 s +2026-04-13 07:35:14.566380: +2026-04-13 07:35:14.568568: Epoch 2419 +2026-04-13 07:35:14.570320: Current learning rate: 0.00434 +2026-04-13 07:36:56.215559: train_loss -0.4085 +2026-04-13 07:36:56.220386: val_loss -0.3469 +2026-04-13 07:36:56.221951: Pseudo dice [0.3272, 0.0, 0.7708, 0.7853, 0.2006, 0.4767, 0.7151] +2026-04-13 07:36:56.223372: Epoch time: 101.65 s +2026-04-13 07:36:57.397708: +2026-04-13 07:36:57.399284: Epoch 2420 +2026-04-13 07:36:57.400639: Current learning rate: 0.00433 +2026-04-13 07:38:39.018349: train_loss -0.4042 +2026-04-13 07:38:39.024007: val_loss -0.3352 +2026-04-13 07:38:39.025599: Pseudo dice [0.2102, 0.0, 0.6895, 0.8161, 0.171, 0.7105, 0.7753] +2026-04-13 07:38:39.027081: Epoch time: 101.62 s +2026-04-13 07:38:40.204691: +2026-04-13 07:38:40.206453: Epoch 2421 +2026-04-13 07:38:40.207649: Current learning rate: 0.00433 +2026-04-13 07:40:21.967036: train_loss -0.402 +2026-04-13 07:40:21.971561: val_loss -0.3815 +2026-04-13 07:40:21.972964: Pseudo dice [0.5308, 0.0, 0.7376, 0.9142, 0.4852, 0.3296, 0.7145] +2026-04-13 07:40:21.974231: Epoch time: 101.77 s +2026-04-13 07:40:23.174017: +2026-04-13 07:40:23.175489: Epoch 2422 +2026-04-13 07:40:23.176824: Current learning rate: 0.00433 +2026-04-13 07:42:04.967650: train_loss -0.412 +2026-04-13 07:42:04.972325: val_loss -0.351 +2026-04-13 07:42:04.974398: Pseudo dice [0.1484, 0.0, 0.6088, 0.7065, 0.2691, 0.7367, 0.6894] +2026-04-13 07:42:04.976279: Epoch time: 101.8 s +2026-04-13 07:42:06.184229: +2026-04-13 07:42:06.186046: Epoch 2423 +2026-04-13 07:42:06.188002: Current learning rate: 0.00433 +2026-04-13 07:43:47.965066: train_loss -0.4043 +2026-04-13 07:43:47.969535: val_loss -0.365 +2026-04-13 07:43:47.971197: Pseudo dice [0.2665, 0.0, 0.7335, 0.8204, 0.3002, 0.8505, 0.7659] +2026-04-13 07:43:47.972883: Epoch time: 101.78 s +2026-04-13 07:43:49.132238: +2026-04-13 07:43:49.134259: Epoch 2424 +2026-04-13 07:43:49.135684: Current learning rate: 0.00432 +2026-04-13 07:45:30.760182: train_loss -0.4143 +2026-04-13 07:45:30.764592: val_loss -0.3036 +2026-04-13 07:45:30.766335: Pseudo dice [0.1173, 0.0, 0.5343, 0.4561, 0.1655, 0.1124, 0.6573] +2026-04-13 07:45:30.767670: Epoch time: 101.63 s +2026-04-13 07:45:31.964757: +2026-04-13 07:45:31.966388: Epoch 2425 +2026-04-13 07:45:31.968474: Current learning rate: 0.00432 +2026-04-13 07:47:13.557140: train_loss -0.4192 +2026-04-13 07:47:13.560688: val_loss -0.4039 +2026-04-13 07:47:13.562033: Pseudo dice [0.2487, 0.0, 0.6964, 0.7104, 0.4215, 0.8046, 0.8757] +2026-04-13 07:47:13.563228: Epoch time: 101.6 s +2026-04-13 07:47:14.727823: +2026-04-13 07:47:14.729397: Epoch 2426 +2026-04-13 07:47:14.730588: Current learning rate: 0.00432 +2026-04-13 07:48:56.465368: train_loss -0.4154 +2026-04-13 07:48:56.470346: val_loss -0.375 +2026-04-13 07:48:56.471922: Pseudo dice [0.5271, 0.0, 0.4964, 0.8393, 0.4276, 0.6169, 0.6735] +2026-04-13 07:48:56.473483: Epoch time: 101.74 s +2026-04-13 07:48:57.645488: +2026-04-13 07:48:57.647614: Epoch 2427 +2026-04-13 07:48:57.649516: Current learning rate: 0.00432 +2026-04-13 07:50:39.423229: train_loss -0.4196 +2026-04-13 07:50:39.427562: val_loss -0.3749 +2026-04-13 07:50:39.429013: Pseudo dice [0.5028, 0.0, 0.7978, 0.0678, 0.3199, 0.4737, 0.8705] +2026-04-13 07:50:39.430527: Epoch time: 101.78 s +2026-04-13 07:50:40.622617: +2026-04-13 07:50:40.624051: Epoch 2428 +2026-04-13 07:50:40.625287: Current learning rate: 0.00431 +2026-04-13 07:52:22.201200: train_loss -0.4177 +2026-04-13 07:52:22.206520: val_loss -0.3516 +2026-04-13 07:52:22.208455: Pseudo dice [0.844, 0.0, 0.3526, 0.2054, 0.1113, 0.7818, 0.5589] +2026-04-13 07:52:22.210135: Epoch time: 101.58 s +2026-04-13 07:52:23.391121: +2026-04-13 07:52:23.392961: Epoch 2429 +2026-04-13 07:52:23.394665: Current learning rate: 0.00431 +2026-04-13 07:54:05.060775: train_loss -0.4229 +2026-04-13 07:54:05.067186: val_loss -0.3742 +2026-04-13 07:54:05.070194: Pseudo dice [0.3541, 0.0, 0.5977, 0.8051, 0.3915, 0.6897, 0.912] +2026-04-13 07:54:05.071942: Epoch time: 101.67 s +2026-04-13 07:54:06.256920: +2026-04-13 07:54:06.258989: Epoch 2430 +2026-04-13 07:54:06.260627: Current learning rate: 0.00431 +2026-04-13 07:55:47.882043: train_loss -0.4068 +2026-04-13 07:55:47.887558: val_loss -0.3485 +2026-04-13 07:55:47.889173: Pseudo dice [0.5945, 0.0, 0.6871, 0.0043, 0.3334, 0.4877, 0.6421] +2026-04-13 07:55:47.890721: Epoch time: 101.63 s +2026-04-13 07:55:49.094959: +2026-04-13 07:55:49.096920: Epoch 2431 +2026-04-13 07:55:49.098338: Current learning rate: 0.00431 +2026-04-13 07:57:30.508160: train_loss -0.4029 +2026-04-13 07:57:30.513262: val_loss -0.3895 +2026-04-13 07:57:30.514931: Pseudo dice [0.2372, 0.0, 0.6771, 0.7194, 0.4536, 0.9024, 0.7585] +2026-04-13 07:57:30.516732: Epoch time: 101.42 s +2026-04-13 07:57:31.720339: +2026-04-13 07:57:31.721830: Epoch 2432 +2026-04-13 07:57:31.723868: Current learning rate: 0.0043 +2026-04-13 07:59:13.274526: train_loss -0.4023 +2026-04-13 07:59:13.279809: val_loss -0.3645 +2026-04-13 07:59:13.281845: Pseudo dice [0.4224, 0.0, 0.7997, 0.9176, 0.4252, 0.5751, 0.7286] +2026-04-13 07:59:13.284646: Epoch time: 101.56 s +2026-04-13 07:59:14.496445: +2026-04-13 07:59:14.498005: Epoch 2433 +2026-04-13 07:59:14.499862: Current learning rate: 0.0043 +2026-04-13 08:00:56.100944: train_loss -0.4228 +2026-04-13 08:00:56.106248: val_loss -0.3522 +2026-04-13 08:00:56.107783: Pseudo dice [0.3686, 0.0, 0.513, 0.6956, 0.1847, 0.7406, 0.6876] +2026-04-13 08:00:56.110021: Epoch time: 101.61 s +2026-04-13 08:00:57.279514: +2026-04-13 08:00:57.281467: Epoch 2434 +2026-04-13 08:00:57.283915: Current learning rate: 0.0043 +2026-04-13 08:02:39.033469: train_loss -0.4098 +2026-04-13 08:02:39.038481: val_loss -0.3452 +2026-04-13 08:02:39.040324: Pseudo dice [0.6446, 0.0, 0.7996, 0.4269, 0.3097, 0.6165, 0.8243] +2026-04-13 08:02:39.042158: Epoch time: 101.76 s +2026-04-13 08:02:40.227155: +2026-04-13 08:02:40.229084: Epoch 2435 +2026-04-13 08:02:40.230887: Current learning rate: 0.0043 +2026-04-13 08:04:21.828778: train_loss -0.4123 +2026-04-13 08:04:21.834313: val_loss -0.3728 +2026-04-13 08:04:21.836089: Pseudo dice [0.7053, 0.0, 0.6432, 0.6149, 0.3664, 0.8014, 0.6102] +2026-04-13 08:04:21.837673: Epoch time: 101.6 s +2026-04-13 08:04:23.023494: +2026-04-13 08:04:23.025650: Epoch 2436 +2026-04-13 08:04:23.027546: Current learning rate: 0.00429 +2026-04-13 08:06:04.607343: train_loss -0.4116 +2026-04-13 08:06:04.612623: val_loss -0.3716 +2026-04-13 08:06:04.614542: Pseudo dice [0.679, 0.0, 0.7164, 0.5091, 0.4748, 0.5824, 0.733] +2026-04-13 08:06:04.615998: Epoch time: 101.59 s +2026-04-13 08:06:05.819145: +2026-04-13 08:06:05.821118: Epoch 2437 +2026-04-13 08:06:05.822963: Current learning rate: 0.00429 +2026-04-13 08:07:47.322103: train_loss -0.4067 +2026-04-13 08:07:47.327221: val_loss -0.3795 +2026-04-13 08:07:47.329171: Pseudo dice [0.3092, 0.0, 0.705, 0.7617, 0.4546, 0.6551, 0.625] +2026-04-13 08:07:47.330741: Epoch time: 101.51 s +2026-04-13 08:07:48.502646: +2026-04-13 08:07:48.504346: Epoch 2438 +2026-04-13 08:07:48.506273: Current learning rate: 0.00429 +2026-04-13 08:09:30.070092: train_loss -0.4179 +2026-04-13 08:09:30.074745: val_loss -0.3271 +2026-04-13 08:09:30.076893: Pseudo dice [0.3152, 0.0, 0.5324, 0.7361, 0.2201, 0.6468, 0.7674] +2026-04-13 08:09:30.078703: Epoch time: 101.57 s +2026-04-13 08:09:31.285874: +2026-04-13 08:09:31.287445: Epoch 2439 +2026-04-13 08:09:31.289227: Current learning rate: 0.00429 +2026-04-13 08:11:12.933169: train_loss -0.4115 +2026-04-13 08:11:12.938980: val_loss -0.3561 +2026-04-13 08:11:12.941076: Pseudo dice [0.5356, 0.0, 0.5195, 0.8718, 0.256, 0.7127, 0.6286] +2026-04-13 08:11:12.942747: Epoch time: 101.65 s +2026-04-13 08:11:15.113978: +2026-04-13 08:11:15.115824: Epoch 2440 +2026-04-13 08:11:15.118321: Current learning rate: 0.00429 +2026-04-13 08:12:56.799715: train_loss -0.422 +2026-04-13 08:12:56.805009: val_loss -0.3712 +2026-04-13 08:12:56.806913: Pseudo dice [0.5354, 0.0, 0.6496, 0.167, 0.2814, 0.6045, 0.8374] +2026-04-13 08:12:56.809482: Epoch time: 101.69 s +2026-04-13 08:12:57.992161: +2026-04-13 08:12:57.993853: Epoch 2441 +2026-04-13 08:12:57.995837: Current learning rate: 0.00428 +2026-04-13 08:14:39.800841: train_loss -0.4027 +2026-04-13 08:14:39.806431: val_loss -0.3682 +2026-04-13 08:14:39.808259: Pseudo dice [0.2476, 0.0, 0.369, 0.538, 0.1883, 0.6068, 0.7667] +2026-04-13 08:14:39.809632: Epoch time: 101.81 s +2026-04-13 08:14:40.995491: +2026-04-13 08:14:40.997441: Epoch 2442 +2026-04-13 08:14:40.999597: Current learning rate: 0.00428 +2026-04-13 08:16:22.647720: train_loss -0.4097 +2026-04-13 08:16:22.652825: val_loss -0.3747 +2026-04-13 08:16:22.654621: Pseudo dice [0.5416, 0.0, 0.447, 0.8321, 0.4368, 0.669, 0.8996] +2026-04-13 08:16:22.656121: Epoch time: 101.66 s +2026-04-13 08:16:23.823633: +2026-04-13 08:16:23.826030: Epoch 2443 +2026-04-13 08:16:23.827846: Current learning rate: 0.00428 +2026-04-13 08:18:05.384853: train_loss -0.4014 +2026-04-13 08:18:05.391514: val_loss -0.3554 +2026-04-13 08:18:05.393406: Pseudo dice [0.2484, 0.0, 0.6896, 0.0567, 0.4497, 0.8161, 0.7946] +2026-04-13 08:18:05.396080: Epoch time: 101.56 s +2026-04-13 08:18:06.569672: +2026-04-13 08:18:06.571306: Epoch 2444 +2026-04-13 08:18:06.573277: Current learning rate: 0.00428 +2026-04-13 08:19:48.391397: train_loss -0.4287 +2026-04-13 08:19:48.396263: val_loss -0.399 +2026-04-13 08:19:48.398079: Pseudo dice [0.7032, 0.0, 0.8222, 0.8761, 0.3202, 0.8179, 0.7728] +2026-04-13 08:19:48.400048: Epoch time: 101.82 s +2026-04-13 08:19:49.582370: +2026-04-13 08:19:49.584107: Epoch 2445 +2026-04-13 08:19:49.585863: Current learning rate: 0.00427 +2026-04-13 08:21:31.121153: train_loss -0.4229 +2026-04-13 08:21:31.126220: val_loss -0.3603 +2026-04-13 08:21:31.128327: Pseudo dice [0.3165, 0.0, 0.7626, 0.7531, 0.2538, 0.809, 0.4785] +2026-04-13 08:21:31.130205: Epoch time: 101.54 s +2026-04-13 08:21:32.334323: +2026-04-13 08:21:32.335981: Epoch 2446 +2026-04-13 08:21:32.337925: Current learning rate: 0.00427 +2026-04-13 08:23:13.886156: train_loss -0.39 +2026-04-13 08:23:13.894704: val_loss -0.3408 +2026-04-13 08:23:13.896073: Pseudo dice [0.4088, 0.0, 0.7687, 0.5309, 0.0, 0.7641, 0.7346] +2026-04-13 08:23:13.897721: Epoch time: 101.55 s +2026-04-13 08:23:15.072649: +2026-04-13 08:23:15.074479: Epoch 2447 +2026-04-13 08:23:15.076218: Current learning rate: 0.00427 +2026-04-13 08:24:56.605844: train_loss -0.3911 +2026-04-13 08:24:56.610479: val_loss -0.3616 +2026-04-13 08:24:56.612225: Pseudo dice [0.6809, 0.0, 0.7431, 0.5665, 0.0291, 0.5789, 0.8579] +2026-04-13 08:24:56.613738: Epoch time: 101.54 s +2026-04-13 08:24:57.799448: +2026-04-13 08:24:57.800909: Epoch 2448 +2026-04-13 08:24:57.802515: Current learning rate: 0.00427 +2026-04-13 08:26:39.407996: train_loss -0.4056 +2026-04-13 08:26:39.417695: val_loss -0.3044 +2026-04-13 08:26:39.419330: Pseudo dice [0.1971, 0.0, 0.2811, 0.5356, 0.068, 0.4707, 0.8013] +2026-04-13 08:26:39.421498: Epoch time: 101.61 s +2026-04-13 08:26:40.628996: +2026-04-13 08:26:40.630878: Epoch 2449 +2026-04-13 08:26:40.633035: Current learning rate: 0.00426 +2026-04-13 08:28:22.297108: train_loss -0.4121 +2026-04-13 08:28:22.302166: val_loss -0.384 +2026-04-13 08:28:22.304080: Pseudo dice [0.5445, 0.0, 0.6798, 0.3376, 0.497, 0.7666, 0.8934] +2026-04-13 08:28:22.305917: Epoch time: 101.67 s +2026-04-13 08:28:25.202213: +2026-04-13 08:28:25.203924: Epoch 2450 +2026-04-13 08:28:25.205742: Current learning rate: 0.00426 +2026-04-13 08:30:06.902023: train_loss -0.3933 +2026-04-13 08:30:06.908125: val_loss -0.3109 +2026-04-13 08:30:06.909710: Pseudo dice [0.3226, 0.0, 0.3724, 0.4236, 0.0689, 0.7577, 0.8869] +2026-04-13 08:30:06.911184: Epoch time: 101.7 s +2026-04-13 08:30:08.085592: +2026-04-13 08:30:08.087196: Epoch 2451 +2026-04-13 08:30:08.089225: Current learning rate: 0.00426 +2026-04-13 08:31:49.832454: train_loss -0.4064 +2026-04-13 08:31:49.839614: val_loss -0.3609 +2026-04-13 08:31:49.841436: Pseudo dice [0.3647, 0.0, 0.589, 0.851, 0.1625, 0.5611, 0.6589] +2026-04-13 08:31:49.843657: Epoch time: 101.75 s +2026-04-13 08:31:51.020890: +2026-04-13 08:31:51.023085: Epoch 2452 +2026-04-13 08:31:51.024958: Current learning rate: 0.00426 +2026-04-13 08:33:32.588724: train_loss -0.414 +2026-04-13 08:33:32.593586: val_loss -0.3593 +2026-04-13 08:33:32.595454: Pseudo dice [0.3153, 0.0, 0.7064, 0.5752, 0.3342, 0.7076, 0.5455] +2026-04-13 08:33:32.596963: Epoch time: 101.57 s +2026-04-13 08:33:33.778094: +2026-04-13 08:33:33.779851: Epoch 2453 +2026-04-13 08:33:33.782800: Current learning rate: 0.00425 +2026-04-13 08:35:15.477159: train_loss -0.4169 +2026-04-13 08:35:15.483284: val_loss -0.3358 +2026-04-13 08:35:15.485093: Pseudo dice [0.5946, 0.0, 0.7503, 0.8091, 0.202, 0.842, 0.9489] +2026-04-13 08:35:15.486741: Epoch time: 101.7 s +2026-04-13 08:35:16.680394: +2026-04-13 08:35:16.683524: Epoch 2454 +2026-04-13 08:35:16.685491: Current learning rate: 0.00425 +2026-04-13 08:36:58.276777: train_loss -0.4179 +2026-04-13 08:36:58.281257: val_loss -0.3765 +2026-04-13 08:36:58.282771: Pseudo dice [0.2046, 0.0, 0.6998, 0.4683, 0.4723, 0.5524, 0.8607] +2026-04-13 08:36:58.284252: Epoch time: 101.6 s +2026-04-13 08:36:59.458878: +2026-04-13 08:36:59.460350: Epoch 2455 +2026-04-13 08:36:59.462323: Current learning rate: 0.00425 +2026-04-13 08:38:40.985755: train_loss -0.4091 +2026-04-13 08:38:40.991962: val_loss -0.3753 +2026-04-13 08:38:40.994354: Pseudo dice [0.1311, 0.0, 0.6226, 0.0249, 0.5481, 0.5874, 0.8117] +2026-04-13 08:38:40.996528: Epoch time: 101.53 s +2026-04-13 08:38:42.185008: +2026-04-13 08:38:42.187455: Epoch 2456 +2026-04-13 08:38:42.190631: Current learning rate: 0.00425 +2026-04-13 08:40:23.773022: train_loss -0.4055 +2026-04-13 08:40:23.779150: val_loss -0.3455 +2026-04-13 08:40:23.780694: Pseudo dice [0.5139, 0.0, 0.4041, 0.4049, 0.3304, 0.8091, 0.5526] +2026-04-13 08:40:23.782045: Epoch time: 101.59 s +2026-04-13 08:40:24.968758: +2026-04-13 08:40:24.970593: Epoch 2457 +2026-04-13 08:40:24.972649: Current learning rate: 0.00424 +2026-04-13 08:42:06.597840: train_loss -0.4031 +2026-04-13 08:42:06.603064: val_loss -0.3635 +2026-04-13 08:42:06.605086: Pseudo dice [0.6572, 0.0, 0.6577, 0.5397, 0.4399, 0.6201, 0.7353] +2026-04-13 08:42:06.607417: Epoch time: 101.63 s +2026-04-13 08:42:07.796930: +2026-04-13 08:42:07.799638: Epoch 2458 +2026-04-13 08:42:07.803403: Current learning rate: 0.00424 +2026-04-13 08:43:49.543278: train_loss -0.4188 +2026-04-13 08:43:49.548226: val_loss -0.3639 +2026-04-13 08:43:49.550124: Pseudo dice [0.3862, 0.0, 0.7139, 0.6754, 0.3029, 0.7368, 0.8096] +2026-04-13 08:43:49.551502: Epoch time: 101.75 s +2026-04-13 08:43:50.727420: +2026-04-13 08:43:50.729009: Epoch 2459 +2026-04-13 08:43:50.730500: Current learning rate: 0.00424 +2026-04-13 08:45:32.227251: train_loss -0.412 +2026-04-13 08:45:32.233282: val_loss -0.3799 +2026-04-13 08:45:32.236067: Pseudo dice [0.1007, 0.0, 0.8181, 0.8069, 0.3382, 0.6601, 0.8574] +2026-04-13 08:45:32.238087: Epoch time: 101.5 s +2026-04-13 08:45:33.445669: +2026-04-13 08:45:33.447137: Epoch 2460 +2026-04-13 08:45:33.448920: Current learning rate: 0.00424 +2026-04-13 08:47:16.033243: train_loss -0.4346 +2026-04-13 08:47:16.039286: val_loss -0.3494 +2026-04-13 08:47:16.042819: Pseudo dice [0.6608, 0.0, 0.5359, 0.7667, 0.2742, 0.6471, 0.4323] +2026-04-13 08:47:16.048992: Epoch time: 102.59 s +2026-04-13 08:47:17.234090: +2026-04-13 08:47:17.235815: Epoch 2461 +2026-04-13 08:47:17.237820: Current learning rate: 0.00423 +2026-04-13 08:48:58.896182: train_loss -0.4222 +2026-04-13 08:48:58.901753: val_loss -0.3772 +2026-04-13 08:48:58.903698: Pseudo dice [0.4637, 0.0, 0.7757, 0.9137, 0.4461, 0.6856, 0.8407] +2026-04-13 08:48:58.905889: Epoch time: 101.67 s +2026-04-13 08:49:00.091904: +2026-04-13 08:49:00.093809: Epoch 2462 +2026-04-13 08:49:00.095978: Current learning rate: 0.00423 +2026-04-13 08:50:41.653267: train_loss -0.4061 +2026-04-13 08:50:41.658885: val_loss -0.3662 +2026-04-13 08:50:41.660778: Pseudo dice [0.6016, 0.0, 0.6233, 0.7778, 0.3956, 0.733, 0.3996] +2026-04-13 08:50:41.662328: Epoch time: 101.56 s +2026-04-13 08:50:42.840711: +2026-04-13 08:50:42.842415: Epoch 2463 +2026-04-13 08:50:42.844428: Current learning rate: 0.00423 +2026-04-13 08:52:24.625905: train_loss -0.4235 +2026-04-13 08:52:24.631898: val_loss -0.3671 +2026-04-13 08:52:24.634649: Pseudo dice [0.4724, 0.0, 0.7582, 0.577, 0.5061, 0.78, 0.7529] +2026-04-13 08:52:24.636501: Epoch time: 101.79 s +2026-04-13 08:52:25.815562: +2026-04-13 08:52:25.817681: Epoch 2464 +2026-04-13 08:52:25.820061: Current learning rate: 0.00423 +2026-04-13 08:54:07.545922: train_loss -0.432 +2026-04-13 08:54:07.551557: val_loss -0.3693 +2026-04-13 08:54:07.553645: Pseudo dice [0.349, 0.0, 0.807, 0.8632, 0.423, 0.8075, 0.803] +2026-04-13 08:54:07.555913: Epoch time: 101.73 s +2026-04-13 08:54:08.734962: +2026-04-13 08:54:08.736650: Epoch 2465 +2026-04-13 08:54:08.738580: Current learning rate: 0.00422 +2026-04-13 08:55:50.369413: train_loss -0.388 +2026-04-13 08:55:50.374088: val_loss -0.359 +2026-04-13 08:55:50.375742: Pseudo dice [0.0279, 0.0, 0.7622, 0.5954, 0.2598, 0.6637, 0.8762] +2026-04-13 08:55:50.377306: Epoch time: 101.64 s +2026-04-13 08:55:51.548928: +2026-04-13 08:55:51.550249: Epoch 2466 +2026-04-13 08:55:51.551682: Current learning rate: 0.00422 +2026-04-13 08:57:33.282196: train_loss -0.4141 +2026-04-13 08:57:33.287215: val_loss -0.3774 +2026-04-13 08:57:33.289054: Pseudo dice [0.3412, 0.0, 0.7259, 0.7844, 0.3059, 0.815, 0.8173] +2026-04-13 08:57:33.291283: Epoch time: 101.74 s +2026-04-13 08:57:34.470860: +2026-04-13 08:57:34.472356: Epoch 2467 +2026-04-13 08:57:34.474284: Current learning rate: 0.00422 +2026-04-13 08:59:16.085765: train_loss -0.4099 +2026-04-13 08:59:16.092001: val_loss -0.3115 +2026-04-13 08:59:16.093881: Pseudo dice [0.3594, 0.0, 0.6621, 0.6992, 0.4463, 0.653, 0.7025] +2026-04-13 08:59:16.095991: Epoch time: 101.62 s +2026-04-13 08:59:17.271257: +2026-04-13 08:59:17.272806: Epoch 2468 +2026-04-13 08:59:17.274563: Current learning rate: 0.00422 +2026-04-13 09:00:58.956933: train_loss -0.3913 +2026-04-13 09:00:58.962284: val_loss -0.3702 +2026-04-13 09:00:58.964102: Pseudo dice [0.3589, 0.0, 0.7475, 0.6133, 0.5171, 0.7205, 0.8602] +2026-04-13 09:00:58.965389: Epoch time: 101.69 s +2026-04-13 09:01:00.149745: +2026-04-13 09:01:00.151313: Epoch 2469 +2026-04-13 09:01:00.153740: Current learning rate: 0.00421 +2026-04-13 09:02:41.776460: train_loss -0.3795 +2026-04-13 09:02:41.781759: val_loss -0.3427 +2026-04-13 09:02:41.783111: Pseudo dice [0.6355, 0.0, 0.6152, 0.7652, 0.19, 0.6395, 0.4176] +2026-04-13 09:02:41.784675: Epoch time: 101.63 s +2026-04-13 09:02:43.003090: +2026-04-13 09:02:43.004425: Epoch 2470 +2026-04-13 09:02:43.005949: Current learning rate: 0.00421 +2026-04-13 09:04:24.705154: train_loss -0.3894 +2026-04-13 09:04:24.710692: val_loss -0.3152 +2026-04-13 09:04:24.713611: Pseudo dice [0.0409, 0.0, 0.4773, 0.0704, 0.0855, 0.7323, 0.6645] +2026-04-13 09:04:24.716396: Epoch time: 101.71 s +2026-04-13 09:04:25.896467: +2026-04-13 09:04:25.898031: Epoch 2471 +2026-04-13 09:04:25.900074: Current learning rate: 0.00421 +2026-04-13 09:06:07.378888: train_loss -0.4006 +2026-04-13 09:06:07.383966: val_loss -0.3883 +2026-04-13 09:06:07.385453: Pseudo dice [0.7042, 0.0, 0.6015, 0.7731, 0.4898, 0.6158, 0.7668] +2026-04-13 09:06:07.387395: Epoch time: 101.49 s +2026-04-13 09:06:08.577025: +2026-04-13 09:06:08.578640: Epoch 2472 +2026-04-13 09:06:08.580861: Current learning rate: 0.00421 +2026-04-13 09:07:50.108855: train_loss -0.4123 +2026-04-13 09:07:50.115328: val_loss -0.3583 +2026-04-13 09:07:50.117090: Pseudo dice [0.2436, 0.0, 0.7872, 0.3312, 0.343, 0.6009, 0.8262] +2026-04-13 09:07:50.118623: Epoch time: 101.53 s +2026-04-13 09:07:51.293982: +2026-04-13 09:07:51.295809: Epoch 2473 +2026-04-13 09:07:51.297989: Current learning rate: 0.0042 +2026-04-13 09:09:32.971795: train_loss -0.4238 +2026-04-13 09:09:32.976779: val_loss -0.3908 +2026-04-13 09:09:32.978154: Pseudo dice [0.317, 0.0, 0.7575, 0.5213, 0.509, 0.7824, 0.8781] +2026-04-13 09:09:32.979997: Epoch time: 101.68 s +2026-04-13 09:09:34.144006: +2026-04-13 09:09:34.145776: Epoch 2474 +2026-04-13 09:09:34.148057: Current learning rate: 0.0042 +2026-04-13 09:11:15.906451: train_loss -0.413 +2026-04-13 09:11:15.912906: val_loss -0.3427 +2026-04-13 09:11:15.914811: Pseudo dice [0.5238, 0.0, 0.775, 0.5049, 0.2004, 0.6562, 0.355] +2026-04-13 09:11:15.923759: Epoch time: 101.77 s +2026-04-13 09:11:17.089372: +2026-04-13 09:11:17.091024: Epoch 2475 +2026-04-13 09:11:17.092870: Current learning rate: 0.0042 +2026-04-13 09:12:58.866527: train_loss -0.4132 +2026-04-13 09:12:58.872646: val_loss -0.3734 +2026-04-13 09:12:58.874161: Pseudo dice [0.3663, 0.0, 0.7375, 0.7946, 0.5391, 0.6372, 0.7676] +2026-04-13 09:12:58.875857: Epoch time: 101.78 s +2026-04-13 09:13:00.056305: +2026-04-13 09:13:00.058172: Epoch 2476 +2026-04-13 09:13:00.060176: Current learning rate: 0.0042 +2026-04-13 09:14:41.606386: train_loss -0.4383 +2026-04-13 09:14:41.611286: val_loss -0.3668 +2026-04-13 09:14:41.612634: Pseudo dice [0.5289, 0.0, 0.7994, 0.7917, 0.22, 0.7835, 0.7156] +2026-04-13 09:14:41.614202: Epoch time: 101.55 s +2026-04-13 09:14:42.791664: +2026-04-13 09:14:42.793886: Epoch 2477 +2026-04-13 09:14:42.796638: Current learning rate: 0.00419 +2026-04-13 09:16:24.399481: train_loss -0.4329 +2026-04-13 09:16:24.407084: val_loss -0.3959 +2026-04-13 09:16:24.409144: Pseudo dice [0.6776, 0.0, 0.6467, 0.8962, 0.298, 0.7613, 0.921] +2026-04-13 09:16:24.411903: Epoch time: 101.61 s +2026-04-13 09:16:25.599472: +2026-04-13 09:16:25.601464: Epoch 2478 +2026-04-13 09:16:25.603676: Current learning rate: 0.00419 +2026-04-13 09:18:07.334527: train_loss -0.4276 +2026-04-13 09:18:07.339651: val_loss -0.3689 +2026-04-13 09:18:07.341254: Pseudo dice [0.5504, 0.0, 0.73, 0.0765, 0.3014, 0.7915, 0.5545] +2026-04-13 09:18:07.343036: Epoch time: 101.74 s +2026-04-13 09:18:08.522790: +2026-04-13 09:18:08.524392: Epoch 2479 +2026-04-13 09:18:08.526818: Current learning rate: 0.00419 +2026-04-13 09:19:50.195969: train_loss -0.4214 +2026-04-13 09:19:50.202418: val_loss -0.3787 +2026-04-13 09:19:50.204571: Pseudo dice [0.4093, 0.0, 0.6689, 0.579, 0.3744, 0.6719, 0.7307] +2026-04-13 09:19:50.206799: Epoch time: 101.68 s +2026-04-13 09:19:51.375371: +2026-04-13 09:19:51.377072: Epoch 2480 +2026-04-13 09:19:51.378856: Current learning rate: 0.00419 +2026-04-13 09:21:33.002981: train_loss -0.3997 +2026-04-13 09:21:33.010404: val_loss -0.3426 +2026-04-13 09:21:33.011962: Pseudo dice [0.3724, 0.0, 0.6369, 0.1025, 0.4192, 0.4624, 0.5169] +2026-04-13 09:21:33.013848: Epoch time: 101.63 s +2026-04-13 09:21:35.182813: +2026-04-13 09:21:35.185372: Epoch 2481 +2026-04-13 09:21:35.188027: Current learning rate: 0.00418 +2026-04-13 09:23:16.969236: train_loss -0.3958 +2026-04-13 09:23:16.975619: val_loss -0.3726 +2026-04-13 09:23:16.978096: Pseudo dice [0.632, 0.0, 0.7185, 0.811, 0.2642, 0.6615, 0.4235] +2026-04-13 09:23:16.979515: Epoch time: 101.79 s +2026-04-13 09:23:18.155477: +2026-04-13 09:23:18.157600: Epoch 2482 +2026-04-13 09:23:18.159658: Current learning rate: 0.00418 +2026-04-13 09:24:59.657497: train_loss -0.3924 +2026-04-13 09:24:59.661751: val_loss -0.3505 +2026-04-13 09:24:59.663314: Pseudo dice [0.4089, 0.0, 0.4639, 0.0464, 0.4733, 0.595, 0.6886] +2026-04-13 09:24:59.664720: Epoch time: 101.51 s +2026-04-13 09:25:00.845702: +2026-04-13 09:25:00.847665: Epoch 2483 +2026-04-13 09:25:00.849483: Current learning rate: 0.00418 +2026-04-13 09:26:42.548076: train_loss -0.3935 +2026-04-13 09:26:42.554569: val_loss -0.3674 +2026-04-13 09:26:42.556499: Pseudo dice [0.5031, 0.0, 0.7591, 0.4668, 0.2652, 0.7351, 0.5167] +2026-04-13 09:26:42.558228: Epoch time: 101.71 s +2026-04-13 09:26:43.747338: +2026-04-13 09:26:43.749159: Epoch 2484 +2026-04-13 09:26:43.751203: Current learning rate: 0.00418 +2026-04-13 09:28:25.266755: train_loss -0.3949 +2026-04-13 09:28:25.272968: val_loss -0.3673 +2026-04-13 09:28:25.274835: Pseudo dice [0.4184, 0.0, 0.6883, 0.2163, 0.3949, 0.7252, 0.8783] +2026-04-13 09:28:25.276491: Epoch time: 101.52 s +2026-04-13 09:28:26.449231: +2026-04-13 09:28:26.451512: Epoch 2485 +2026-04-13 09:28:26.453830: Current learning rate: 0.00417 +2026-04-13 09:30:08.107034: train_loss -0.3961 +2026-04-13 09:30:08.112604: val_loss -0.358 +2026-04-13 09:30:08.114520: Pseudo dice [0.3805, 0.0, 0.7749, 0.7626, 0.4677, 0.4394, 0.7198] +2026-04-13 09:30:08.117021: Epoch time: 101.66 s +2026-04-13 09:30:09.318343: +2026-04-13 09:30:09.320018: Epoch 2486 +2026-04-13 09:30:09.322150: Current learning rate: 0.00417 +2026-04-13 09:31:50.810684: train_loss -0.3923 +2026-04-13 09:31:50.815593: val_loss -0.3135 +2026-04-13 09:31:50.817185: Pseudo dice [0.0828, 0.0, 0.422, 0.7509, 0.3972, 0.4643, 0.2727] +2026-04-13 09:31:50.818803: Epoch time: 101.5 s +2026-04-13 09:31:51.985564: +2026-04-13 09:31:51.986925: Epoch 2487 +2026-04-13 09:31:51.988642: Current learning rate: 0.00417 +2026-04-13 09:33:33.639234: train_loss -0.4092 +2026-04-13 09:33:33.644337: val_loss -0.3766 +2026-04-13 09:33:33.645775: Pseudo dice [0.613, 0.0, 0.6461, 0.72, 0.4773, 0.4939, 0.5547] +2026-04-13 09:33:33.647557: Epoch time: 101.66 s +2026-04-13 09:33:34.830674: +2026-04-13 09:33:34.832115: Epoch 2488 +2026-04-13 09:33:34.833644: Current learning rate: 0.00417 +2026-04-13 09:35:16.700933: train_loss -0.4128 +2026-04-13 09:35:16.705870: val_loss -0.3626 +2026-04-13 09:35:16.709677: Pseudo dice [0.6745, 0.0, 0.7165, 0.5442, 0.28, 0.8498, 0.3594] +2026-04-13 09:35:16.711995: Epoch time: 101.87 s +2026-04-13 09:35:17.907403: +2026-04-13 09:35:17.909395: Epoch 2489 +2026-04-13 09:35:17.911847: Current learning rate: 0.00416 +2026-04-13 09:36:59.540731: train_loss -0.4085 +2026-04-13 09:36:59.545631: val_loss -0.338 +2026-04-13 09:36:59.547598: Pseudo dice [0.5523, 0.0, 0.2122, 0.5198, 0.321, 0.5142, 0.7985] +2026-04-13 09:36:59.549148: Epoch time: 101.64 s +2026-04-13 09:37:00.727044: +2026-04-13 09:37:00.728464: Epoch 2490 +2026-04-13 09:37:00.730231: Current learning rate: 0.00416 +2026-04-13 09:38:42.407847: train_loss -0.3838 +2026-04-13 09:38:42.414517: val_loss -0.3377 +2026-04-13 09:38:42.417086: Pseudo dice [0.5958, 0.0, 0.7283, 0.6645, 0.3537, 0.3481, 0.3812] +2026-04-13 09:38:42.418811: Epoch time: 101.68 s +2026-04-13 09:38:43.605176: +2026-04-13 09:38:43.606846: Epoch 2491 +2026-04-13 09:38:43.608913: Current learning rate: 0.00416 +2026-04-13 09:40:25.204208: train_loss -0.4031 +2026-04-13 09:40:25.210701: val_loss -0.3486 +2026-04-13 09:40:25.212838: Pseudo dice [0.5105, 0.0, 0.6467, 0.2596, 0.3934, 0.754, 0.7249] +2026-04-13 09:40:25.214624: Epoch time: 101.6 s +2026-04-13 09:40:26.405482: +2026-04-13 09:40:26.407078: Epoch 2492 +2026-04-13 09:40:26.408918: Current learning rate: 0.00416 +2026-04-13 09:42:07.968652: train_loss -0.3951 +2026-04-13 09:42:07.973886: val_loss -0.3741 +2026-04-13 09:42:07.975598: Pseudo dice [0.5833, 0.0, 0.7313, 0.8668, 0.3155, 0.7855, 0.6948] +2026-04-13 09:42:07.977389: Epoch time: 101.57 s +2026-04-13 09:42:09.149495: +2026-04-13 09:42:09.150999: Epoch 2493 +2026-04-13 09:42:09.152790: Current learning rate: 0.00415 +2026-04-13 09:43:50.615552: train_loss -0.4177 +2026-04-13 09:43:50.621277: val_loss -0.4062 +2026-04-13 09:43:50.623144: Pseudo dice [0.2423, 0.0, 0.6213, 0.9014, 0.5307, 0.725, 0.7855] +2026-04-13 09:43:50.625000: Epoch time: 101.47 s +2026-04-13 09:43:51.805254: +2026-04-13 09:43:51.806794: Epoch 2494 +2026-04-13 09:43:51.808484: Current learning rate: 0.00415 +2026-04-13 09:45:33.528856: train_loss -0.4004 +2026-04-13 09:45:33.534937: val_loss -0.3681 +2026-04-13 09:45:33.536989: Pseudo dice [0.1713, 0.0, 0.7972, 0.6171, 0.2675, 0.8138, 0.8826] +2026-04-13 09:45:33.538560: Epoch time: 101.73 s +2026-04-13 09:45:34.712667: +2026-04-13 09:45:34.714433: Epoch 2495 +2026-04-13 09:45:34.716200: Current learning rate: 0.00415 +2026-04-13 09:47:16.428196: train_loss -0.4124 +2026-04-13 09:47:16.433405: val_loss -0.3679 +2026-04-13 09:47:16.436013: Pseudo dice [0.3438, 0.0, 0.7726, 0.5844, 0.2775, 0.2959, 0.7319] +2026-04-13 09:47:16.438518: Epoch time: 101.72 s +2026-04-13 09:47:17.623821: +2026-04-13 09:47:17.626233: Epoch 2496 +2026-04-13 09:47:17.628343: Current learning rate: 0.00415 +2026-04-13 09:48:59.137026: train_loss -0.4093 +2026-04-13 09:48:59.142341: val_loss -0.3116 +2026-04-13 09:48:59.143886: Pseudo dice [0.0216, 0.0, 0.5865, 0.7866, 0.2726, 0.7, 0.2695] +2026-04-13 09:48:59.145730: Epoch time: 101.52 s +2026-04-13 09:49:00.331153: +2026-04-13 09:49:00.332773: Epoch 2497 +2026-04-13 09:49:00.335571: Current learning rate: 0.00414 +2026-04-13 09:50:41.974620: train_loss -0.4019 +2026-04-13 09:50:41.979094: val_loss -0.3404 +2026-04-13 09:50:41.980866: Pseudo dice [0.2836, 0.0, 0.7174, 0.6749, 0.3327, 0.8017, 0.4903] +2026-04-13 09:50:41.982862: Epoch time: 101.65 s +2026-04-13 09:50:43.180169: +2026-04-13 09:50:43.181813: Epoch 2498 +2026-04-13 09:50:43.183922: Current learning rate: 0.00414 +2026-04-13 09:52:24.801574: train_loss -0.4286 +2026-04-13 09:52:24.806650: val_loss -0.3746 +2026-04-13 09:52:24.808291: Pseudo dice [0.2373, 0.0, 0.6922, 0.8351, 0.4758, 0.6164, 0.6613] +2026-04-13 09:52:24.812297: Epoch time: 101.62 s +2026-04-13 09:52:26.000591: +2026-04-13 09:52:26.002469: Epoch 2499 +2026-04-13 09:52:26.004150: Current learning rate: 0.00414 +2026-04-13 09:54:07.661705: train_loss -0.4192 +2026-04-13 09:54:07.668625: val_loss -0.401 +2026-04-13 09:54:07.670722: Pseudo dice [0.6566, 0.0, 0.7489, 0.7932, 0.5824, 0.8075, 0.872] +2026-04-13 09:54:07.672866: Epoch time: 101.66 s +2026-04-13 09:54:10.598872: +2026-04-13 09:54:10.600376: Epoch 2500 +2026-04-13 09:54:10.602422: Current learning rate: 0.00414 +2026-04-13 09:55:52.147266: train_loss -0.419 +2026-04-13 09:55:52.151589: val_loss -0.3949 +2026-04-13 09:55:52.153773: Pseudo dice [0.6578, 0.0, 0.8382, 0.5503, 0.394, 0.7354, 0.8509] +2026-04-13 09:55:52.155848: Epoch time: 101.55 s +2026-04-13 09:55:54.312043: +2026-04-13 09:55:54.313693: Epoch 2501 +2026-04-13 09:55:54.315538: Current learning rate: 0.00413 +2026-04-13 09:57:35.872979: train_loss -0.4036 +2026-04-13 09:57:35.878246: val_loss -0.3586 +2026-04-13 09:57:35.880001: Pseudo dice [0.6161, 0.0, 0.6791, 0.5977, 0.2801, 0.5775, 0.7197] +2026-04-13 09:57:35.882038: Epoch time: 101.56 s +2026-04-13 09:57:37.072272: +2026-04-13 09:57:37.073895: Epoch 2502 +2026-04-13 09:57:37.075611: Current learning rate: 0.00413 +2026-04-13 09:59:18.724415: train_loss -0.42 +2026-04-13 09:59:18.729079: val_loss -0.3651 +2026-04-13 09:59:18.730937: Pseudo dice [0.4031, 0.0, 0.7709, 0.5928, 0.5864, 0.6441, 0.6434] +2026-04-13 09:59:18.733114: Epoch time: 101.66 s +2026-04-13 09:59:19.892706: +2026-04-13 09:59:19.894291: Epoch 2503 +2026-04-13 09:59:19.895938: Current learning rate: 0.00413 +2026-04-13 10:01:01.418618: train_loss -0.4274 +2026-04-13 10:01:01.451725: val_loss -0.3483 +2026-04-13 10:01:01.453488: Pseudo dice [0.371, 0.0, 0.5824, 0.823, 0.0441, 0.7602, 0.9022] +2026-04-13 10:01:01.455134: Epoch time: 101.53 s +2026-04-13 10:01:02.620467: +2026-04-13 10:01:02.622270: Epoch 2504 +2026-04-13 10:01:02.624082: Current learning rate: 0.00413 +2026-04-13 10:02:44.259330: train_loss -0.4109 +2026-04-13 10:02:44.265139: val_loss -0.3413 +2026-04-13 10:02:44.268283: Pseudo dice [0.173, 0.0, 0.3713, 0.631, 0.3922, 0.5015, 0.7893] +2026-04-13 10:02:44.271701: Epoch time: 101.64 s +2026-04-13 10:02:45.450507: +2026-04-13 10:02:45.452594: Epoch 2505 +2026-04-13 10:02:45.454635: Current learning rate: 0.00412 +2026-04-13 10:04:27.034017: train_loss -0.4205 +2026-04-13 10:04:27.039970: val_loss -0.3894 +2026-04-13 10:04:27.041995: Pseudo dice [0.6789, 0.0, 0.7152, 0.8708, 0.4109, 0.6879, 0.8882] +2026-04-13 10:04:27.043621: Epoch time: 101.59 s +2026-04-13 10:04:28.208453: +2026-04-13 10:04:28.210472: Epoch 2506 +2026-04-13 10:04:28.212525: Current learning rate: 0.00412 +2026-04-13 10:06:09.732428: train_loss -0.4105 +2026-04-13 10:06:09.736860: val_loss -0.3836 +2026-04-13 10:06:09.738998: Pseudo dice [0.5309, 0.0, 0.7265, 0.703, 0.2808, 0.7637, 0.6409] +2026-04-13 10:06:09.740688: Epoch time: 101.53 s +2026-04-13 10:06:10.907495: +2026-04-13 10:06:10.910038: Epoch 2507 +2026-04-13 10:06:10.912425: Current learning rate: 0.00412 +2026-04-13 10:07:52.689829: train_loss -0.4325 +2026-04-13 10:07:52.696811: val_loss -0.3907 +2026-04-13 10:07:52.699590: Pseudo dice [0.3687, 0.0, 0.778, 0.8076, 0.3637, 0.7692, 0.5843] +2026-04-13 10:07:52.702921: Epoch time: 101.79 s +2026-04-13 10:07:53.932073: +2026-04-13 10:07:53.934162: Epoch 2508 +2026-04-13 10:07:53.936954: Current learning rate: 0.00412 +2026-04-13 10:09:35.659343: train_loss -0.4338 +2026-04-13 10:09:35.665615: val_loss -0.3644 +2026-04-13 10:09:35.667640: Pseudo dice [0.5083, 0.0, 0.7933, 0.0343, 0.3688, 0.8513, 0.6753] +2026-04-13 10:09:35.669781: Epoch time: 101.73 s +2026-04-13 10:09:36.862228: +2026-04-13 10:09:36.864438: Epoch 2509 +2026-04-13 10:09:36.866338: Current learning rate: 0.00411 +2026-04-13 10:11:18.468462: train_loss -0.4243 +2026-04-13 10:11:18.473773: val_loss -0.3649 +2026-04-13 10:11:18.475518: Pseudo dice [0.447, 0.0, 0.6567, 0.7359, 0.322, 0.787, 0.6986] +2026-04-13 10:11:18.477053: Epoch time: 101.61 s +2026-04-13 10:11:19.662511: +2026-04-13 10:11:19.664397: Epoch 2510 +2026-04-13 10:11:19.666444: Current learning rate: 0.00411 +2026-04-13 10:13:01.216328: train_loss -0.4324 +2026-04-13 10:13:01.223611: val_loss -0.3968 +2026-04-13 10:13:01.226492: Pseudo dice [0.5108, 0.0, 0.6315, 0.764, 0.493, 0.7624, 0.6954] +2026-04-13 10:13:01.227987: Epoch time: 101.56 s +2026-04-13 10:13:02.417617: +2026-04-13 10:13:02.419345: Epoch 2511 +2026-04-13 10:13:02.421268: Current learning rate: 0.00411 +2026-04-13 10:14:43.864645: train_loss -0.4163 +2026-04-13 10:14:43.869835: val_loss -0.3547 +2026-04-13 10:14:43.871768: Pseudo dice [0.2627, 0.0, 0.5509, 0.775, 0.2128, 0.6934, 0.5525] +2026-04-13 10:14:43.873722: Epoch time: 101.45 s +2026-04-13 10:14:45.050744: +2026-04-13 10:14:45.053394: Epoch 2512 +2026-04-13 10:14:45.056755: Current learning rate: 0.00411 +2026-04-13 10:16:26.731740: train_loss -0.4157 +2026-04-13 10:16:26.737100: val_loss -0.3633 +2026-04-13 10:16:26.738879: Pseudo dice [0.184, 0.0, 0.6232, 0.7502, 0.1908, 0.7964, 0.8442] +2026-04-13 10:16:26.740984: Epoch time: 101.68 s +2026-04-13 10:16:27.915082: +2026-04-13 10:16:27.916528: Epoch 2513 +2026-04-13 10:16:27.918364: Current learning rate: 0.0041 +2026-04-13 10:18:09.445032: train_loss -0.4277 +2026-04-13 10:18:09.449380: val_loss -0.3674 +2026-04-13 10:18:09.450693: Pseudo dice [0.3739, 0.0, 0.704, 0.7111, 0.312, 0.542, 0.7408] +2026-04-13 10:18:09.452390: Epoch time: 101.53 s +2026-04-13 10:18:10.629928: +2026-04-13 10:18:10.631427: Epoch 2514 +2026-04-13 10:18:10.633302: Current learning rate: 0.0041 +2026-04-13 10:19:52.184372: train_loss -0.4164 +2026-04-13 10:19:52.190112: val_loss -0.3611 +2026-04-13 10:19:52.192601: Pseudo dice [0.3458, 0.0, 0.722, 0.6481, 0.3599, 0.4173, 0.8544] +2026-04-13 10:19:52.194442: Epoch time: 101.56 s +2026-04-13 10:19:53.369463: +2026-04-13 10:19:53.372085: Epoch 2515 +2026-04-13 10:19:53.375659: Current learning rate: 0.0041 +2026-04-13 10:21:34.884497: train_loss -0.408 +2026-04-13 10:21:34.889765: val_loss -0.3861 +2026-04-13 10:21:34.891444: Pseudo dice [0.6468, 0.0, 0.5843, 0.5948, 0.3895, 0.8319, 0.8231] +2026-04-13 10:21:34.893342: Epoch time: 101.52 s +2026-04-13 10:21:36.072295: +2026-04-13 10:21:36.074553: Epoch 2516 +2026-04-13 10:21:36.076745: Current learning rate: 0.0041 +2026-04-13 10:23:17.640297: train_loss -0.4164 +2026-04-13 10:23:17.645889: val_loss -0.3602 +2026-04-13 10:23:17.647867: Pseudo dice [0.362, 0.0, 0.5155, 0.7736, 0.3779, 0.4094, 0.8839] +2026-04-13 10:23:17.650395: Epoch time: 101.57 s +2026-04-13 10:23:18.844567: +2026-04-13 10:23:18.846295: Epoch 2517 +2026-04-13 10:23:18.848335: Current learning rate: 0.00409 +2026-04-13 10:25:00.241799: train_loss -0.4091 +2026-04-13 10:25:00.246648: val_loss -0.3542 +2026-04-13 10:25:00.248485: Pseudo dice [0.4076, 0.0, 0.7256, 0.691, 0.4419, 0.799, 0.8583] +2026-04-13 10:25:00.250694: Epoch time: 101.4 s +2026-04-13 10:25:01.429703: +2026-04-13 10:25:01.432267: Epoch 2518 +2026-04-13 10:25:01.434457: Current learning rate: 0.00409 +2026-04-13 10:26:43.025167: train_loss -0.4067 +2026-04-13 10:26:43.029812: val_loss -0.3772 +2026-04-13 10:26:43.031708: Pseudo dice [0.0015, 0.0, 0.7191, 0.8846, 0.4028, 0.5421, 0.8552] +2026-04-13 10:26:43.033484: Epoch time: 101.6 s +2026-04-13 10:26:44.204424: +2026-04-13 10:26:44.205717: Epoch 2519 +2026-04-13 10:26:44.207254: Current learning rate: 0.00409 +2026-04-13 10:28:25.548253: train_loss -0.4056 +2026-04-13 10:28:25.553898: val_loss -0.3424 +2026-04-13 10:28:25.555832: Pseudo dice [0.2022, 0.0, 0.5345, 0.5332, 0.3602, 0.2334, 0.6179] +2026-04-13 10:28:25.557634: Epoch time: 101.35 s +2026-04-13 10:28:26.766614: +2026-04-13 10:28:26.768265: Epoch 2520 +2026-04-13 10:28:26.770072: Current learning rate: 0.00409 +2026-04-13 10:30:08.334514: train_loss -0.4002 +2026-04-13 10:30:08.339616: val_loss -0.3632 +2026-04-13 10:30:08.341380: Pseudo dice [0.1564, 0.0, 0.7844, 0.782, 0.3578, 0.1208, 0.7943] +2026-04-13 10:30:08.343582: Epoch time: 101.57 s +2026-04-13 10:30:09.528194: +2026-04-13 10:30:09.529906: Epoch 2521 +2026-04-13 10:30:09.531706: Current learning rate: 0.00408 +2026-04-13 10:31:52.096695: train_loss -0.3909 +2026-04-13 10:31:52.104181: val_loss -0.3453 +2026-04-13 10:31:52.106505: Pseudo dice [0.2267, 0.0, 0.5496, 0.6646, 0.2546, 0.8015, 0.5682] +2026-04-13 10:31:52.108646: Epoch time: 102.57 s +2026-04-13 10:31:53.291041: +2026-04-13 10:31:53.292774: Epoch 2522 +2026-04-13 10:31:53.294354: Current learning rate: 0.00408 +2026-04-13 10:33:34.805846: train_loss -0.4281 +2026-04-13 10:33:34.810372: val_loss -0.3667 +2026-04-13 10:33:34.812230: Pseudo dice [0.5389, 0.0, 0.3457, 0.5882, 0.2454, 0.7995, 0.8219] +2026-04-13 10:33:34.813974: Epoch time: 101.52 s +2026-04-13 10:33:36.010166: +2026-04-13 10:33:36.011668: Epoch 2523 +2026-04-13 10:33:36.013642: Current learning rate: 0.00408 +2026-04-13 10:35:17.216430: train_loss -0.3968 +2026-04-13 10:35:17.221475: val_loss -0.3745 +2026-04-13 10:35:17.224185: Pseudo dice [0.2828, 0.0, 0.71, 0.7662, 0.3689, 0.488, 0.6605] +2026-04-13 10:35:17.226217: Epoch time: 101.21 s +2026-04-13 10:35:18.411521: +2026-04-13 10:35:18.413342: Epoch 2524 +2026-04-13 10:35:18.415327: Current learning rate: 0.00408 +2026-04-13 10:37:00.067582: train_loss -0.4338 +2026-04-13 10:37:00.072836: val_loss -0.3716 +2026-04-13 10:37:00.074507: Pseudo dice [0.3377, 0.0, 0.6673, 0.294, 0.3724, 0.6684, 0.9012] +2026-04-13 10:37:00.076363: Epoch time: 101.66 s +2026-04-13 10:37:01.256609: +2026-04-13 10:37:01.259036: Epoch 2525 +2026-04-13 10:37:01.261766: Current learning rate: 0.00407 +2026-04-13 10:38:42.947289: train_loss -0.4159 +2026-04-13 10:38:42.952769: val_loss -0.3663 +2026-04-13 10:38:42.954779: Pseudo dice [0.5767, 0.0, 0.7942, 0.7317, 0.3517, 0.7407, 0.4311] +2026-04-13 10:38:42.956606: Epoch time: 101.69 s +2026-04-13 10:38:44.138361: +2026-04-13 10:38:44.139876: Epoch 2526 +2026-04-13 10:38:44.141494: Current learning rate: 0.00407 +2026-04-13 10:40:25.785154: train_loss -0.4114 +2026-04-13 10:40:25.791394: val_loss -0.3619 +2026-04-13 10:40:25.793531: Pseudo dice [0.3773, 0.0, 0.4498, 0.8604, 0.4502, 0.6075, 0.5496] +2026-04-13 10:40:25.795370: Epoch time: 101.65 s +2026-04-13 10:40:26.967671: +2026-04-13 10:40:26.969486: Epoch 2527 +2026-04-13 10:40:26.971731: Current learning rate: 0.00407 +2026-04-13 10:42:08.484581: train_loss -0.4125 +2026-04-13 10:42:08.489296: val_loss -0.3827 +2026-04-13 10:42:08.491513: Pseudo dice [0.8098, 0.0, 0.655, 0.4926, 0.3664, 0.5067, 0.8026] +2026-04-13 10:42:08.493877: Epoch time: 101.52 s +2026-04-13 10:42:09.681487: +2026-04-13 10:42:09.683097: Epoch 2528 +2026-04-13 10:42:09.684979: Current learning rate: 0.00407 +2026-04-13 10:43:51.060149: train_loss -0.4214 +2026-04-13 10:43:51.065409: val_loss -0.3032 +2026-04-13 10:43:51.067377: Pseudo dice [0.2906, 0.0, 0.5817, 0.7272, 0.0825, 0.8035, 0.8971] +2026-04-13 10:43:51.069120: Epoch time: 101.38 s +2026-04-13 10:43:52.311978: +2026-04-13 10:43:52.313670: Epoch 2529 +2026-04-13 10:43:52.315433: Current learning rate: 0.00406 +2026-04-13 10:45:33.869838: train_loss -0.4064 +2026-04-13 10:45:33.875630: val_loss -0.3724 +2026-04-13 10:45:33.877700: Pseudo dice [0.5541, 0.0, 0.6247, 0.7877, 0.483, 0.6139, 0.8095] +2026-04-13 10:45:33.879504: Epoch time: 101.56 s +2026-04-13 10:45:35.062183: +2026-04-13 10:45:35.064275: Epoch 2530 +2026-04-13 10:45:35.066324: Current learning rate: 0.00406 +2026-04-13 10:47:16.511652: train_loss -0.4242 +2026-04-13 10:47:16.519285: val_loss -0.376 +2026-04-13 10:47:16.520875: Pseudo dice [0.6082, 0.0, 0.7778, 0.7164, 0.3535, 0.6727, 0.6961] +2026-04-13 10:47:16.522641: Epoch time: 101.45 s +2026-04-13 10:47:17.697010: +2026-04-13 10:47:17.698678: Epoch 2531 +2026-04-13 10:47:17.700683: Current learning rate: 0.00406 +2026-04-13 10:48:59.248412: train_loss -0.427 +2026-04-13 10:48:59.253108: val_loss -0.3796 +2026-04-13 10:48:59.254754: Pseudo dice [0.4229, 0.0, 0.8674, 0.8986, 0.4968, 0.69, 0.8574] +2026-04-13 10:48:59.257885: Epoch time: 101.55 s +2026-04-13 10:49:00.432388: +2026-04-13 10:49:00.434243: Epoch 2532 +2026-04-13 10:49:00.437275: Current learning rate: 0.00406 +2026-04-13 10:50:42.179736: train_loss -0.4259 +2026-04-13 10:50:42.184964: val_loss -0.3919 +2026-04-13 10:50:42.186829: Pseudo dice [0.5989, 0.0, 0.576, 0.9081, 0.3578, 0.7712, 0.7774] +2026-04-13 10:50:42.188805: Epoch time: 101.75 s +2026-04-13 10:50:43.384368: +2026-04-13 10:50:43.386042: Epoch 2533 +2026-04-13 10:50:43.391653: Current learning rate: 0.00405 +2026-04-13 10:52:24.999647: train_loss -0.4244 +2026-04-13 10:52:25.006008: val_loss -0.3795 +2026-04-13 10:52:25.008674: Pseudo dice [0.2, 0.0, 0.8443, 0.6704, 0.4141, 0.711, 0.7866] +2026-04-13 10:52:25.010307: Epoch time: 101.62 s +2026-04-13 10:52:26.210712: +2026-04-13 10:52:26.212766: Epoch 2534 +2026-04-13 10:52:26.215125: Current learning rate: 0.00405 +2026-04-13 10:54:07.898828: train_loss -0.4358 +2026-04-13 10:54:07.907411: val_loss -0.3718 +2026-04-13 10:54:07.909570: Pseudo dice [0.5194, 0.0, 0.4616, 0.6877, 0.4029, 0.6693, 0.7893] +2026-04-13 10:54:07.911617: Epoch time: 101.69 s +2026-04-13 10:54:09.113228: +2026-04-13 10:54:09.115063: Epoch 2535 +2026-04-13 10:54:09.116910: Current learning rate: 0.00405 +2026-04-13 10:55:50.650882: train_loss -0.4104 +2026-04-13 10:55:50.657361: val_loss -0.3666 +2026-04-13 10:55:50.659462: Pseudo dice [0.0, 0.0, 0.7341, 0.7994, 0.3335, 0.8193, 0.6815] +2026-04-13 10:55:50.665182: Epoch time: 101.54 s +2026-04-13 10:55:51.902611: +2026-04-13 10:55:51.904539: Epoch 2536 +2026-04-13 10:55:51.906646: Current learning rate: 0.00405 +2026-04-13 10:57:33.375034: train_loss -0.4183 +2026-04-13 10:57:33.381456: val_loss -0.3267 +2026-04-13 10:57:33.383269: Pseudo dice [0.0004, 0.0, 0.5448, 0.7543, 0.36, 0.7141, 0.5962] +2026-04-13 10:57:33.385478: Epoch time: 101.48 s +2026-04-13 10:57:34.577473: +2026-04-13 10:57:34.579180: Epoch 2537 +2026-04-13 10:57:34.581158: Current learning rate: 0.00404 +2026-04-13 10:59:15.826560: train_loss -0.396 +2026-04-13 10:59:15.833327: val_loss -0.3214 +2026-04-13 10:59:15.835601: Pseudo dice [0.0314, 0.0, 0.4456, 0.7911, 0.0974, 0.3523, 0.8003] +2026-04-13 10:59:15.837236: Epoch time: 101.25 s +2026-04-13 10:59:17.051727: +2026-04-13 10:59:17.053564: Epoch 2538 +2026-04-13 10:59:17.055475: Current learning rate: 0.00404 +2026-04-13 11:00:58.383683: train_loss -0.4092 +2026-04-13 11:00:58.389966: val_loss -0.368 +2026-04-13 11:00:58.391867: Pseudo dice [0.6169, 0.0, 0.6754, 0.8777, 0.4465, 0.3871, 0.5504] +2026-04-13 11:00:58.394079: Epoch time: 101.34 s +2026-04-13 11:00:59.573691: +2026-04-13 11:00:59.575790: Epoch 2539 +2026-04-13 11:00:59.578432: Current learning rate: 0.00404 +2026-04-13 11:02:41.125105: train_loss -0.3999 +2026-04-13 11:02:41.130090: val_loss -0.3634 +2026-04-13 11:02:41.131711: Pseudo dice [0.4216, 0.0, 0.7581, 0.862, 0.2082, 0.7151, 0.5986] +2026-04-13 11:02:41.133750: Epoch time: 101.55 s +2026-04-13 11:02:42.332423: +2026-04-13 11:02:42.333930: Epoch 2540 +2026-04-13 11:02:42.336214: Current learning rate: 0.00404 +2026-04-13 11:04:23.880414: train_loss -0.4087 +2026-04-13 11:04:23.885698: val_loss -0.3697 +2026-04-13 11:04:23.887632: Pseudo dice [0.2545, 0.0, 0.7395, 0.8126, 0.5045, 0.5131, 0.6616] +2026-04-13 11:04:23.889367: Epoch time: 101.55 s +2026-04-13 11:04:25.067776: +2026-04-13 11:04:25.069225: Epoch 2541 +2026-04-13 11:04:25.071220: Current learning rate: 0.00403 +2026-04-13 11:06:06.539034: train_loss -0.4023 +2026-04-13 11:06:06.546113: val_loss -0.3529 +2026-04-13 11:06:06.548893: Pseudo dice [0.26, 0.0, 0.6782, 0.5191, 0.2474, 0.8448, 0.7264] +2026-04-13 11:06:06.551543: Epoch time: 101.47 s +2026-04-13 11:06:08.787285: +2026-04-13 11:06:08.789041: Epoch 2542 +2026-04-13 11:06:08.791102: Current learning rate: 0.00403 +2026-04-13 11:07:50.488336: train_loss -0.4033 +2026-04-13 11:07:50.493681: val_loss -0.3425 +2026-04-13 11:07:50.495380: Pseudo dice [0.5926, 0.0, 0.6377, 0.832, 0.2232, 0.7721, 0.5303] +2026-04-13 11:07:50.496906: Epoch time: 101.7 s +2026-04-13 11:07:51.690958: +2026-04-13 11:07:51.692610: Epoch 2543 +2026-04-13 11:07:51.694674: Current learning rate: 0.00403 +2026-04-13 11:09:33.398839: train_loss -0.4187 +2026-04-13 11:09:33.404217: val_loss -0.3838 +2026-04-13 11:09:33.406100: Pseudo dice [0.6369, 0.0, 0.7006, 0.8711, 0.3774, 0.7557, 0.7129] +2026-04-13 11:09:33.407751: Epoch time: 101.71 s +2026-04-13 11:09:34.585099: +2026-04-13 11:09:34.587035: Epoch 2544 +2026-04-13 11:09:34.588905: Current learning rate: 0.00403 +2026-04-13 11:11:16.176146: train_loss -0.4067 +2026-04-13 11:11:16.181525: val_loss -0.3331 +2026-04-13 11:11:16.183495: Pseudo dice [0.0, 0.0, 0.5316, 0.5675, 0.1754, 0.4475, 0.8118] +2026-04-13 11:11:16.185132: Epoch time: 101.59 s +2026-04-13 11:11:17.371278: +2026-04-13 11:11:17.373312: Epoch 2545 +2026-04-13 11:11:17.378553: Current learning rate: 0.00402 +2026-04-13 11:12:59.266948: train_loss -0.3981 +2026-04-13 11:12:59.271950: val_loss -0.3454 +2026-04-13 11:12:59.274014: Pseudo dice [0.0, 0.0, 0.7167, 0.9099, 0.3008, 0.6089, 0.8766] +2026-04-13 11:12:59.275761: Epoch time: 101.9 s +2026-04-13 11:13:00.470181: +2026-04-13 11:13:00.473011: Epoch 2546 +2026-04-13 11:13:00.475081: Current learning rate: 0.00402 +2026-04-13 11:14:42.019320: train_loss -0.3983 +2026-04-13 11:14:42.025567: val_loss -0.3441 +2026-04-13 11:14:42.027459: Pseudo dice [0.0, 0.0, 0.7559, 0.7708, 0.5358, 0.7883, 0.4392] +2026-04-13 11:14:42.029570: Epoch time: 101.55 s +2026-04-13 11:14:43.215810: +2026-04-13 11:14:43.217480: Epoch 2547 +2026-04-13 11:14:43.219356: Current learning rate: 0.00402 +2026-04-13 11:16:24.646399: train_loss -0.4013 +2026-04-13 11:16:24.651728: val_loss -0.3317 +2026-04-13 11:16:24.653452: Pseudo dice [0.0, 0.0, 0.3438, 0.1013, 0.3235, 0.8148, 0.686] +2026-04-13 11:16:24.655121: Epoch time: 101.43 s +2026-04-13 11:16:25.841815: +2026-04-13 11:16:25.843878: Epoch 2548 +2026-04-13 11:16:25.845783: Current learning rate: 0.00402 +2026-04-13 11:18:07.676541: train_loss -0.4353 +2026-04-13 11:18:07.681596: val_loss -0.3147 +2026-04-13 11:18:07.684129: Pseudo dice [0.0042, 0.0, 0.6556, 0.7598, 0.2313, 0.7644, 0.4739] +2026-04-13 11:18:07.685821: Epoch time: 101.84 s +2026-04-13 11:18:08.873980: +2026-04-13 11:18:08.875633: Epoch 2549 +2026-04-13 11:18:08.877547: Current learning rate: 0.00401 +2026-04-13 11:19:50.296496: train_loss -0.4075 +2026-04-13 11:19:50.302612: val_loss -0.3655 +2026-04-13 11:19:50.304615: Pseudo dice [0.1101, 0.0, 0.8033, 0.7472, 0.2614, 0.7784, 0.8502] +2026-04-13 11:19:50.306629: Epoch time: 101.43 s +2026-04-13 11:19:53.339685: +2026-04-13 11:19:53.341941: Epoch 2550 +2026-04-13 11:19:53.344452: Current learning rate: 0.00401 +2026-04-13 11:21:35.538490: train_loss -0.4193 +2026-04-13 11:21:35.544502: val_loss -0.387 +2026-04-13 11:21:35.546081: Pseudo dice [0.3069, 0.0, 0.4152, 0.8757, 0.6041, 0.352, 0.7733] +2026-04-13 11:21:35.547669: Epoch time: 102.2 s +2026-04-13 11:21:36.736083: +2026-04-13 11:21:36.737711: Epoch 2551 +2026-04-13 11:21:36.740179: Current learning rate: 0.00401 +2026-04-13 11:23:18.590851: train_loss -0.4387 +2026-04-13 11:23:18.597255: val_loss -0.3612 +2026-04-13 11:23:18.599520: Pseudo dice [0.2241, 0.0, 0.775, 0.719, 0.2796, 0.7288, 0.505] +2026-04-13 11:23:18.601830: Epoch time: 101.86 s +2026-04-13 11:23:19.875653: +2026-04-13 11:23:19.877342: Epoch 2552 +2026-04-13 11:23:19.879195: Current learning rate: 0.00401 +2026-04-13 11:25:03.112098: train_loss -0.4297 +2026-04-13 11:25:03.119673: val_loss -0.3542 +2026-04-13 11:25:03.122165: Pseudo dice [0.37, 0.0, 0.7414, 0.7429, 0.3583, 0.5847, 0.4527] +2026-04-13 11:25:03.124084: Epoch time: 103.24 s +2026-04-13 11:25:04.316124: +2026-04-13 11:25:04.319262: Epoch 2553 +2026-04-13 11:25:04.322518: Current learning rate: 0.004 +2026-04-13 11:26:45.898021: train_loss -0.4167 +2026-04-13 11:26:45.910305: val_loss -0.3825 +2026-04-13 11:26:45.912258: Pseudo dice [0.7679, 0.0, 0.6108, 0.6935, 0.2488, 0.3337, 0.8321] +2026-04-13 11:26:45.913962: Epoch time: 101.58 s +2026-04-13 11:26:47.122530: +2026-04-13 11:26:47.124972: Epoch 2554 +2026-04-13 11:26:47.126992: Current learning rate: 0.004 +2026-04-13 11:28:28.836880: train_loss -0.4165 +2026-04-13 11:28:28.842484: val_loss -0.3804 +2026-04-13 11:28:28.844349: Pseudo dice [0.3853, 0.0, 0.8406, 0.7755, 0.6048, 0.4291, 0.8356] +2026-04-13 11:28:28.846140: Epoch time: 101.72 s +2026-04-13 11:28:30.029130: +2026-04-13 11:28:30.031472: Epoch 2555 +2026-04-13 11:28:30.033305: Current learning rate: 0.004 +2026-04-13 11:30:11.834634: train_loss -0.4235 +2026-04-13 11:30:11.845808: val_loss -0.3574 +2026-04-13 11:30:11.847888: Pseudo dice [0.393, 0.0, 0.7006, 0.6271, 0.2487, 0.7179, 0.7472] +2026-04-13 11:30:11.849574: Epoch time: 101.81 s +2026-04-13 11:30:13.020901: +2026-04-13 11:30:13.023510: Epoch 2556 +2026-04-13 11:30:13.025576: Current learning rate: 0.004 +2026-04-13 11:31:54.779171: train_loss -0.4242 +2026-04-13 11:31:54.787752: val_loss -0.3446 +2026-04-13 11:31:54.791255: Pseudo dice [0.6805, 0.0, 0.3116, 0.4839, 0.4402, 0.2273, 0.8362] +2026-04-13 11:31:54.793220: Epoch time: 101.76 s +2026-04-13 11:31:55.979346: +2026-04-13 11:31:55.982462: Epoch 2557 +2026-04-13 11:31:55.986993: Current learning rate: 0.00399 +2026-04-13 11:33:37.578292: train_loss -0.4147 +2026-04-13 11:33:37.583614: val_loss -0.339 +2026-04-13 11:33:37.585735: Pseudo dice [0.5364, 0.0, 0.4802, 0.7332, 0.1076, 0.8115, 0.5609] +2026-04-13 11:33:37.587467: Epoch time: 101.6 s +2026-04-13 11:33:38.822418: +2026-04-13 11:33:38.824227: Epoch 2558 +2026-04-13 11:33:38.826277: Current learning rate: 0.00399 +2026-04-13 11:35:20.511972: train_loss -0.4151 +2026-04-13 11:35:20.517157: val_loss -0.3462 +2026-04-13 11:35:20.519006: Pseudo dice [0.8033, 0.0, 0.601, 0.635, 0.2911, 0.4709, 0.6222] +2026-04-13 11:35:20.520732: Epoch time: 101.69 s +2026-04-13 11:35:21.714770: +2026-04-13 11:35:21.716738: Epoch 2559 +2026-04-13 11:35:21.718416: Current learning rate: 0.00399 +2026-04-13 11:37:03.566556: train_loss -0.3903 +2026-04-13 11:37:03.572630: val_loss -0.363 +2026-04-13 11:37:03.575104: Pseudo dice [0.741, 0.0, 0.7477, 0.1773, 0.378, 0.6843, 0.7448] +2026-04-13 11:37:03.576771: Epoch time: 101.85 s +2026-04-13 11:37:04.785908: +2026-04-13 11:37:04.793423: Epoch 2560 +2026-04-13 11:37:04.796898: Current learning rate: 0.00399 +2026-04-13 11:38:46.490850: train_loss -0.4108 +2026-04-13 11:38:46.497103: val_loss -0.3795 +2026-04-13 11:38:46.500578: Pseudo dice [0.6622, 0.0, 0.6384, 0.7638, 0.3253, 0.7624, 0.7627] +2026-04-13 11:38:46.502678: Epoch time: 101.71 s +2026-04-13 11:38:47.684665: +2026-04-13 11:38:47.686285: Epoch 2561 +2026-04-13 11:38:47.688981: Current learning rate: 0.00398 +2026-04-13 11:40:29.202558: train_loss -0.4151 +2026-04-13 11:40:29.208027: val_loss -0.3623 +2026-04-13 11:40:29.209888: Pseudo dice [0.3288, 0.0, 0.7202, 0.7684, 0.4122, 0.32, 0.6394] +2026-04-13 11:40:29.211612: Epoch time: 101.52 s +2026-04-13 11:40:31.393072: +2026-04-13 11:40:31.394738: Epoch 2562 +2026-04-13 11:40:31.397559: Current learning rate: 0.00398 +2026-04-13 11:42:13.173390: train_loss -0.4251 +2026-04-13 11:42:13.178992: val_loss -0.3211 +2026-04-13 11:42:13.181068: Pseudo dice [0.372, 0.0, 0.4905, 0.8553, 0.1702, 0.7179, 0.4595] +2026-04-13 11:42:13.182813: Epoch time: 101.78 s +2026-04-13 11:42:14.370472: +2026-04-13 11:42:14.372167: Epoch 2563 +2026-04-13 11:42:14.374001: Current learning rate: 0.00398 +2026-04-13 11:43:56.150995: train_loss -0.4064 +2026-04-13 11:43:56.156290: val_loss -0.3626 +2026-04-13 11:43:56.157869: Pseudo dice [0.265, 0.0, 0.6737, 0.8449, 0.1968, 0.7106, 0.6008] +2026-04-13 11:43:56.159519: Epoch time: 101.78 s +2026-04-13 11:43:57.355882: +2026-04-13 11:43:57.358076: Epoch 2564 +2026-04-13 11:43:57.360682: Current learning rate: 0.00398 +2026-04-13 11:45:39.005445: train_loss -0.4049 +2026-04-13 11:45:39.011453: val_loss -0.3634 +2026-04-13 11:45:39.013849: Pseudo dice [0.328, 0.0, 0.6436, 0.8825, 0.4001, 0.3503, 0.3681] +2026-04-13 11:45:39.015907: Epoch time: 101.65 s +2026-04-13 11:45:40.185873: +2026-04-13 11:45:40.187392: Epoch 2565 +2026-04-13 11:45:40.189209: Current learning rate: 0.00397 +2026-04-13 11:47:21.857019: train_loss -0.3924 +2026-04-13 11:47:21.863783: val_loss -0.3137 +2026-04-13 11:47:21.865506: Pseudo dice [0.4618, 0.0, 0.5714, 0.4109, 0.3061, 0.2021, 0.3047] +2026-04-13 11:47:21.867750: Epoch time: 101.67 s +2026-04-13 11:47:23.046817: +2026-04-13 11:47:23.049190: Epoch 2566 +2026-04-13 11:47:23.051326: Current learning rate: 0.00397 +2026-04-13 11:49:04.806014: train_loss -0.3886 +2026-04-13 11:49:04.810898: val_loss -0.3469 +2026-04-13 11:49:04.812809: Pseudo dice [0.0825, 0.0, 0.7243, 0.4462, 0.2576, 0.6448, 0.725] +2026-04-13 11:49:04.815004: Epoch time: 101.76 s +2026-04-13 11:49:06.002451: +2026-04-13 11:49:06.004485: Epoch 2567 +2026-04-13 11:49:06.006742: Current learning rate: 0.00397 +2026-04-13 11:50:47.696298: train_loss -0.394 +2026-04-13 11:50:47.703478: val_loss -0.3642 +2026-04-13 11:50:47.705239: Pseudo dice [0.1553, 0.0, 0.7812, 0.7002, 0.2719, 0.8055, 0.4398] +2026-04-13 11:50:47.713484: Epoch time: 101.7 s +2026-04-13 11:50:48.889226: +2026-04-13 11:50:48.891216: Epoch 2568 +2026-04-13 11:50:48.893231: Current learning rate: 0.00397 +2026-04-13 11:52:30.456035: train_loss -0.4233 +2026-04-13 11:52:30.461799: val_loss -0.4026 +2026-04-13 11:52:30.463477: Pseudo dice [0.7515, 0.0, 0.7626, 0.7839, 0.5692, 0.621, 0.8009] +2026-04-13 11:52:30.465452: Epoch time: 101.57 s +2026-04-13 11:52:31.643729: +2026-04-13 11:52:31.645409: Epoch 2569 +2026-04-13 11:52:31.647832: Current learning rate: 0.00396 +2026-04-13 11:54:12.999499: train_loss -0.4268 +2026-04-13 11:54:13.003938: val_loss -0.3593 +2026-04-13 11:54:13.006492: Pseudo dice [0.5155, 0.0, 0.5502, 0.8282, 0.4038, 0.733, 0.7208] +2026-04-13 11:54:13.008497: Epoch time: 101.36 s +2026-04-13 11:54:14.186826: +2026-04-13 11:54:14.189416: Epoch 2570 +2026-04-13 11:54:14.191727: Current learning rate: 0.00396 +2026-04-13 11:55:55.791048: train_loss -0.4217 +2026-04-13 11:55:55.796884: val_loss -0.3943 +2026-04-13 11:55:55.799356: Pseudo dice [0.5035, 0.0, 0.6752, 0.8386, 0.4744, 0.3289, 0.702] +2026-04-13 11:55:55.801038: Epoch time: 101.61 s +2026-04-13 11:55:56.968882: +2026-04-13 11:55:56.970535: Epoch 2571 +2026-04-13 11:55:56.972388: Current learning rate: 0.00396 +2026-04-13 11:57:38.347450: train_loss -0.4165 +2026-04-13 11:57:38.356968: val_loss -0.3758 +2026-04-13 11:57:38.359395: Pseudo dice [0.6501, 0.0, 0.5259, 0.2249, 0.511, 0.7389, 0.5901] +2026-04-13 11:57:38.361310: Epoch time: 101.38 s +2026-04-13 11:57:39.564355: +2026-04-13 11:57:39.567148: Epoch 2572 +2026-04-13 11:57:39.569112: Current learning rate: 0.00396 +2026-04-13 11:59:21.068530: train_loss -0.3968 +2026-04-13 11:59:21.075626: val_loss -0.3597 +2026-04-13 11:59:21.077668: Pseudo dice [0.1861, 0.0, 0.7506, 0.4956, 0.3166, 0.704, 0.5766] +2026-04-13 11:59:21.080027: Epoch time: 101.51 s +2026-04-13 11:59:22.264005: +2026-04-13 11:59:22.267629: Epoch 2573 +2026-04-13 11:59:22.272566: Current learning rate: 0.00395 +2026-04-13 12:01:03.810636: train_loss -0.3922 +2026-04-13 12:01:03.817784: val_loss -0.3197 +2026-04-13 12:01:03.819928: Pseudo dice [0.2474, 0.0, 0.5621, 0.3825, 0.0751, 0.4267, 0.7316] +2026-04-13 12:01:03.821911: Epoch time: 101.55 s +2026-04-13 12:01:05.005169: +2026-04-13 12:01:05.010873: Epoch 2574 +2026-04-13 12:01:05.014219: Current learning rate: 0.00395 +2026-04-13 12:02:46.450080: train_loss -0.4216 +2026-04-13 12:02:46.455131: val_loss -0.3666 +2026-04-13 12:02:46.456882: Pseudo dice [0.3717, 0.0, 0.7486, 0.8107, 0.3388, 0.8221, 0.8331] +2026-04-13 12:02:46.458634: Epoch time: 101.45 s +2026-04-13 12:02:47.646307: +2026-04-13 12:02:47.647762: Epoch 2575 +2026-04-13 12:02:47.649742: Current learning rate: 0.00395 +2026-04-13 12:04:28.838564: train_loss -0.4063 +2026-04-13 12:04:28.845862: val_loss -0.308 +2026-04-13 12:04:28.847779: Pseudo dice [0.0088, 0.0, 0.781, 0.5938, 0.1362, 0.4012, 0.4154] +2026-04-13 12:04:28.849490: Epoch time: 101.2 s +2026-04-13 12:04:30.041879: +2026-04-13 12:04:30.043649: Epoch 2576 +2026-04-13 12:04:30.049769: Current learning rate: 0.00395 +2026-04-13 12:06:11.408898: train_loss -0.4059 +2026-04-13 12:06:11.415094: val_loss -0.3671 +2026-04-13 12:06:11.417149: Pseudo dice [0.3966, 0.0, 0.7909, 0.7867, 0.3131, 0.6837, 0.5214] +2026-04-13 12:06:11.421917: Epoch time: 101.37 s +2026-04-13 12:06:12.613268: +2026-04-13 12:06:12.615838: Epoch 2577 +2026-04-13 12:06:12.618899: Current learning rate: 0.00394 +2026-04-13 12:07:54.138311: train_loss -0.4239 +2026-04-13 12:07:54.144047: val_loss -0.3513 +2026-04-13 12:07:54.147084: Pseudo dice [0.5933, 0.0, 0.457, 0.6374, 0.1063, 0.3617, 0.8727] +2026-04-13 12:07:54.149093: Epoch time: 101.53 s +2026-04-13 12:07:55.332286: +2026-04-13 12:07:55.333928: Epoch 2578 +2026-04-13 12:07:55.336370: Current learning rate: 0.00394 +2026-04-13 12:09:36.989794: train_loss -0.4168 +2026-04-13 12:09:36.995773: val_loss -0.3324 +2026-04-13 12:09:36.998109: Pseudo dice [0.0, 0.0, 0.6556, 0.5642, 0.4283, 0.6943, 0.8649] +2026-04-13 12:09:37.000381: Epoch time: 101.66 s +2026-04-13 12:09:38.188660: +2026-04-13 12:09:38.190259: Epoch 2579 +2026-04-13 12:09:38.192263: Current learning rate: 0.00394 +2026-04-13 12:11:19.800271: train_loss -0.4242 +2026-04-13 12:11:19.807302: val_loss -0.3649 +2026-04-13 12:11:19.825946: Pseudo dice [0.4615, 0.0, 0.7667, 0.6477, 0.3247, 0.7316, 0.754] +2026-04-13 12:11:19.828464: Epoch time: 101.61 s +2026-04-13 12:11:21.022065: +2026-04-13 12:11:21.024541: Epoch 2580 +2026-04-13 12:11:21.026882: Current learning rate: 0.00394 +2026-04-13 12:13:02.562175: train_loss -0.4411 +2026-04-13 12:13:02.568945: val_loss -0.37 +2026-04-13 12:13:02.571517: Pseudo dice [0.4525, 0.0, 0.6628, 0.5706, 0.5074, 0.8271, 0.8567] +2026-04-13 12:13:02.573933: Epoch time: 101.54 s +2026-04-13 12:13:03.750057: +2026-04-13 12:13:03.752665: Epoch 2581 +2026-04-13 12:13:03.754706: Current learning rate: 0.00393 +2026-04-13 12:14:45.345716: train_loss -0.4184 +2026-04-13 12:14:45.351115: val_loss -0.364 +2026-04-13 12:14:45.352768: Pseudo dice [0.149, 0.0, 0.7197, 0.3958, 0.0472, 0.4742, 0.8991] +2026-04-13 12:14:45.354426: Epoch time: 101.6 s +2026-04-13 12:14:46.547497: +2026-04-13 12:14:46.549439: Epoch 2582 +2026-04-13 12:14:46.551208: Current learning rate: 0.00393 +2026-04-13 12:16:28.940484: train_loss -0.4165 +2026-04-13 12:16:28.947409: val_loss -0.3389 +2026-04-13 12:16:28.949496: Pseudo dice [0.1762, 0.0, 0.7307, 0.7032, 0.164, 0.6998, 0.8523] +2026-04-13 12:16:28.951161: Epoch time: 102.4 s +2026-04-13 12:16:30.350763: +2026-04-13 12:16:30.352327: Epoch 2583 +2026-04-13 12:16:30.354245: Current learning rate: 0.00393 +2026-04-13 12:18:11.894729: train_loss -0.4255 +2026-04-13 12:18:11.901289: val_loss -0.355 +2026-04-13 12:18:11.906813: Pseudo dice [0.3755, 0.0, 0.6903, 0.3081, 0.2477, 0.8205, 0.7713] +2026-04-13 12:18:11.909575: Epoch time: 101.55 s +2026-04-13 12:18:13.098509: +2026-04-13 12:18:13.100387: Epoch 2584 +2026-04-13 12:18:13.102676: Current learning rate: 0.00393 +2026-04-13 12:19:54.633537: train_loss -0.4251 +2026-04-13 12:19:54.638926: val_loss -0.3984 +2026-04-13 12:19:54.640800: Pseudo dice [0.5012, 0.0, 0.7735, 0.144, 0.4665, 0.767, 0.8177] +2026-04-13 12:19:54.642372: Epoch time: 101.54 s +2026-04-13 12:19:55.812063: +2026-04-13 12:19:55.813871: Epoch 2585 +2026-04-13 12:19:55.816241: Current learning rate: 0.00392 +2026-04-13 12:21:37.659774: train_loss -0.435 +2026-04-13 12:21:37.665098: val_loss -0.3697 +2026-04-13 12:21:37.667381: Pseudo dice [0.4162, 0.0, 0.7842, 0.6466, 0.2049, 0.806, 0.9049] +2026-04-13 12:21:37.669800: Epoch time: 101.85 s +2026-04-13 12:21:38.865820: +2026-04-13 12:21:38.868845: Epoch 2586 +2026-04-13 12:21:38.871251: Current learning rate: 0.00392 +2026-04-13 12:23:20.506121: train_loss -0.4107 +2026-04-13 12:23:20.518785: val_loss -0.3274 +2026-04-13 12:23:20.521328: Pseudo dice [0.4015, 0.0, 0.6606, 0.8088, 0.2911, 0.6144, 0.5916] +2026-04-13 12:23:20.523653: Epoch time: 101.64 s +2026-04-13 12:23:21.721872: +2026-04-13 12:23:21.723605: Epoch 2587 +2026-04-13 12:23:21.725977: Current learning rate: 0.00392 +2026-04-13 12:25:03.171841: train_loss -0.419 +2026-04-13 12:25:03.179742: val_loss -0.3894 +2026-04-13 12:25:03.181490: Pseudo dice [0.5188, 0.0, 0.6597, 0.6873, 0.3457, 0.8714, 0.871] +2026-04-13 12:25:03.182906: Epoch time: 101.45 s +2026-04-13 12:25:04.380432: +2026-04-13 12:25:04.382509: Epoch 2588 +2026-04-13 12:25:04.384520: Current learning rate: 0.00392 +2026-04-13 12:26:45.871253: train_loss -0.4226 +2026-04-13 12:26:45.878305: val_loss -0.3273 +2026-04-13 12:26:45.880009: Pseudo dice [0.5082, 0.0, 0.323, 0.8798, 0.3088, 0.7513, 0.906] +2026-04-13 12:26:45.882648: Epoch time: 101.49 s +2026-04-13 12:26:47.083386: +2026-04-13 12:26:47.085783: Epoch 2589 +2026-04-13 12:26:47.087864: Current learning rate: 0.00391 +2026-04-13 12:28:28.483988: train_loss -0.4137 +2026-04-13 12:28:28.495409: val_loss -0.3689 +2026-04-13 12:28:28.497118: Pseudo dice [0.1039, 0.0, 0.794, 0.3224, 0.3215, 0.6704, 0.7837] +2026-04-13 12:28:28.498944: Epoch time: 101.4 s +2026-04-13 12:28:29.701038: +2026-04-13 12:28:29.708241: Epoch 2590 +2026-04-13 12:28:29.714097: Current learning rate: 0.00391 +2026-04-13 12:30:11.202923: train_loss -0.4163 +2026-04-13 12:30:11.208975: val_loss -0.3449 +2026-04-13 12:30:11.211123: Pseudo dice [0.7152, 0.0, 0.3766, 0.728, 0.4408, 0.6599, 0.6133] +2026-04-13 12:30:11.213364: Epoch time: 101.5 s +2026-04-13 12:30:12.392581: +2026-04-13 12:30:12.394214: Epoch 2591 +2026-04-13 12:30:12.396553: Current learning rate: 0.00391 +2026-04-13 12:31:54.636973: train_loss -0.4187 +2026-04-13 12:31:54.643799: val_loss -0.3517 +2026-04-13 12:31:54.650249: Pseudo dice [0.6253, 0.0, 0.564, 0.7919, 0.3097, 0.7757, 0.7654] +2026-04-13 12:31:54.656951: Epoch time: 102.25 s +2026-04-13 12:31:55.831888: +2026-04-13 12:31:55.833927: Epoch 2592 +2026-04-13 12:31:55.836053: Current learning rate: 0.00391 +2026-04-13 12:33:37.438946: train_loss -0.406 +2026-04-13 12:33:37.445580: val_loss -0.3561 +2026-04-13 12:33:37.448313: Pseudo dice [0.4688, 0.0, 0.7211, 0.6816, 0.4225, 0.3444, 0.7859] +2026-04-13 12:33:37.450484: Epoch time: 101.61 s +2026-04-13 12:33:38.629857: +2026-04-13 12:33:38.631810: Epoch 2593 +2026-04-13 12:33:38.633995: Current learning rate: 0.0039 +2026-04-13 12:35:20.529586: train_loss -0.4308 +2026-04-13 12:35:20.535320: val_loss -0.3632 +2026-04-13 12:35:20.537267: Pseudo dice [0.6936, 0.0, 0.6225, 0.7222, 0.114, 0.6391, 0.4587] +2026-04-13 12:35:20.539128: Epoch time: 101.9 s +2026-04-13 12:35:21.742721: +2026-04-13 12:35:21.744511: Epoch 2594 +2026-04-13 12:35:21.746899: Current learning rate: 0.0039 +2026-04-13 12:37:03.298377: train_loss -0.4033 +2026-04-13 12:37:03.304863: val_loss -0.3264 +2026-04-13 12:37:03.306885: Pseudo dice [0.3902, 0.0, 0.3967, 0.8199, 0.1252, 0.6496, 0.8483] +2026-04-13 12:37:03.309346: Epoch time: 101.56 s +2026-04-13 12:37:04.475043: +2026-04-13 12:37:04.476824: Epoch 2595 +2026-04-13 12:37:04.478623: Current learning rate: 0.0039 +2026-04-13 12:38:46.321977: train_loss -0.4134 +2026-04-13 12:38:46.328245: val_loss -0.3566 +2026-04-13 12:38:46.330566: Pseudo dice [0.2122, 0.0, 0.6027, 0.5751, 0.3628, 0.6003, 0.7829] +2026-04-13 12:38:46.332929: Epoch time: 101.85 s +2026-04-13 12:38:47.493294: +2026-04-13 12:38:47.495362: Epoch 2596 +2026-04-13 12:38:47.497485: Current learning rate: 0.0039 +2026-04-13 12:40:29.219007: train_loss -0.3845 +2026-04-13 12:40:29.224198: val_loss -0.3688 +2026-04-13 12:40:29.227139: Pseudo dice [0.2291, 0.0, 0.8237, 0.8326, 0.4103, 0.8378, 0.7582] +2026-04-13 12:40:29.229157: Epoch time: 101.73 s +2026-04-13 12:40:30.442467: +2026-04-13 12:40:30.444193: Epoch 2597 +2026-04-13 12:40:30.445954: Current learning rate: 0.00389 +2026-04-13 12:42:12.170598: train_loss -0.4305 +2026-04-13 12:42:12.176702: val_loss -0.3334 +2026-04-13 12:42:12.179643: Pseudo dice [0.2258, 0.0, 0.7518, 0.2812, 0.3502, 0.5424, 0.7758] +2026-04-13 12:42:12.181506: Epoch time: 101.73 s +2026-04-13 12:42:13.368847: +2026-04-13 12:42:13.370671: Epoch 2598 +2026-04-13 12:42:13.372509: Current learning rate: 0.00389 +2026-04-13 12:43:55.146732: train_loss -0.4241 +2026-04-13 12:43:55.152364: val_loss -0.3616 +2026-04-13 12:43:55.156155: Pseudo dice [0.4115, 0.0, 0.4821, 0.8346, 0.2431, 0.6406, 0.7719] +2026-04-13 12:43:55.159383: Epoch time: 101.78 s +2026-04-13 12:43:56.374058: +2026-04-13 12:43:56.376364: Epoch 2599 +2026-04-13 12:43:56.379460: Current learning rate: 0.00389 +2026-04-13 12:45:38.190628: train_loss -0.4303 +2026-04-13 12:45:38.196148: val_loss -0.3715 +2026-04-13 12:45:38.198057: Pseudo dice [0.527, 0.0, 0.3061, 0.8694, 0.2709, 0.7675, 0.848] +2026-04-13 12:45:38.199610: Epoch time: 101.82 s +2026-04-13 12:45:41.156579: +2026-04-13 12:45:41.158901: Epoch 2600 +2026-04-13 12:45:41.161074: Current learning rate: 0.00389 +2026-04-13 12:47:22.936806: train_loss -0.3981 +2026-04-13 12:47:22.941915: val_loss -0.3556 +2026-04-13 12:47:22.943891: Pseudo dice [0.3123, 0.0, 0.7017, 0.3574, 0.2036, 0.4094, 0.4791] +2026-04-13 12:47:22.945521: Epoch time: 101.78 s +2026-04-13 12:47:24.134002: +2026-04-13 12:47:24.135735: Epoch 2601 +2026-04-13 12:47:24.137809: Current learning rate: 0.00388 +2026-04-13 12:49:05.980430: train_loss -0.4175 +2026-04-13 12:49:05.990832: val_loss -0.3588 +2026-04-13 12:49:05.993778: Pseudo dice [0.6542, 0.0, 0.5673, 0.8432, 0.4505, 0.7106, 0.9148] +2026-04-13 12:49:05.996004: Epoch time: 101.85 s +2026-04-13 12:49:07.259797: +2026-04-13 12:49:07.261666: Epoch 2602 +2026-04-13 12:49:07.264253: Current learning rate: 0.00388 +2026-04-13 12:50:50.110731: train_loss -0.3898 +2026-04-13 12:50:50.116325: val_loss -0.366 +2026-04-13 12:50:50.118073: Pseudo dice [0.7403, 0.0, 0.6494, 0.6296, 0.2814, 0.8135, 0.7145] +2026-04-13 12:50:50.119703: Epoch time: 102.85 s +2026-04-13 12:50:51.328979: +2026-04-13 12:50:51.330767: Epoch 2603 +2026-04-13 12:50:51.333412: Current learning rate: 0.00388 +2026-04-13 12:52:32.921277: train_loss -0.4262 +2026-04-13 12:52:32.926654: val_loss -0.3749 +2026-04-13 12:52:32.928267: Pseudo dice [0.4106, 0.0, 0.6782, 0.4213, 0.4293, 0.5497, 0.9094] +2026-04-13 12:52:32.930491: Epoch time: 101.6 s +2026-04-13 12:52:34.147050: +2026-04-13 12:52:34.149710: Epoch 2604 +2026-04-13 12:52:34.152245: Current learning rate: 0.00388 +2026-04-13 12:54:15.681376: train_loss -0.4189 +2026-04-13 12:54:15.687617: val_loss -0.369 +2026-04-13 12:54:15.689279: Pseudo dice [0.3497, 0.0, 0.854, 0.6775, 0.3426, 0.7399, 0.7977] +2026-04-13 12:54:15.691453: Epoch time: 101.54 s +2026-04-13 12:54:16.857758: +2026-04-13 12:54:16.860929: Epoch 2605 +2026-04-13 12:54:16.863874: Current learning rate: 0.00387 +2026-04-13 12:55:58.546449: train_loss -0.4009 +2026-04-13 12:55:58.557737: val_loss -0.3777 +2026-04-13 12:55:58.559565: Pseudo dice [0.3762, 0.0, 0.7259, 0.7092, 0.1442, 0.8102, 0.5667] +2026-04-13 12:55:58.561148: Epoch time: 101.69 s +2026-04-13 12:55:59.770010: +2026-04-13 12:55:59.773345: Epoch 2606 +2026-04-13 12:55:59.776658: Current learning rate: 0.00387 +2026-04-13 12:57:41.346268: train_loss -0.4235 +2026-04-13 12:57:41.351577: val_loss -0.3511 +2026-04-13 12:57:41.353462: Pseudo dice [0.558, 0.0, 0.1445, 0.6875, 0.2758, 0.7756, 0.8612] +2026-04-13 12:57:41.355075: Epoch time: 101.58 s +2026-04-13 12:57:42.540236: +2026-04-13 12:57:42.542213: Epoch 2607 +2026-04-13 12:57:42.545399: Current learning rate: 0.00387 +2026-04-13 12:59:24.278038: train_loss -0.4274 +2026-04-13 12:59:24.283518: val_loss -0.3511 +2026-04-13 12:59:24.285308: Pseudo dice [0.463, 0.0, 0.4106, 0.8865, 0.2033, 0.5046, 0.6046] +2026-04-13 12:59:24.286774: Epoch time: 101.74 s +2026-04-13 12:59:25.465870: +2026-04-13 12:59:25.467151: Epoch 2608 +2026-04-13 12:59:25.468654: Current learning rate: 0.00387 +2026-04-13 13:01:07.216177: train_loss -0.4196 +2026-04-13 13:01:07.222625: val_loss -0.376 +2026-04-13 13:01:07.225096: Pseudo dice [0.3944, 0.0, 0.6632, 0.7881, 0.3114, 0.7998, 0.8408] +2026-04-13 13:01:07.228324: Epoch time: 101.75 s +2026-04-13 13:01:08.416771: +2026-04-13 13:01:08.418343: Epoch 2609 +2026-04-13 13:01:08.420762: Current learning rate: 0.00386 +2026-04-13 13:02:50.668983: train_loss -0.4317 +2026-04-13 13:02:50.674270: val_loss -0.3972 +2026-04-13 13:02:50.676575: Pseudo dice [0.4625, 0.0, 0.7685, 0.7401, 0.2753, 0.7329, 0.8777] +2026-04-13 13:02:50.678613: Epoch time: 102.26 s +2026-04-13 13:02:51.867667: +2026-04-13 13:02:51.870065: Epoch 2610 +2026-04-13 13:02:51.872183: Current learning rate: 0.00386 +2026-04-13 13:04:33.532218: train_loss -0.4253 +2026-04-13 13:04:33.537868: val_loss -0.3772 +2026-04-13 13:04:33.539850: Pseudo dice [0.4314, 0.0, 0.7749, 0.7994, 0.2763, 0.7175, 0.8137] +2026-04-13 13:04:33.541744: Epoch time: 101.67 s +2026-04-13 13:04:34.719753: +2026-04-13 13:04:34.721538: Epoch 2611 +2026-04-13 13:04:34.723630: Current learning rate: 0.00386 +2026-04-13 13:06:16.366268: train_loss -0.4204 +2026-04-13 13:06:16.371634: val_loss -0.3921 +2026-04-13 13:06:16.373321: Pseudo dice [0.0634, 0.0, 0.7499, 0.8874, 0.4287, 0.6243, 0.9134] +2026-04-13 13:06:16.374944: Epoch time: 101.65 s +2026-04-13 13:06:17.557272: +2026-04-13 13:06:17.558870: Epoch 2612 +2026-04-13 13:06:17.560709: Current learning rate: 0.00386 +2026-04-13 13:07:59.258687: train_loss -0.4216 +2026-04-13 13:07:59.302839: val_loss -0.3623 +2026-04-13 13:07:59.305337: Pseudo dice [0.5998, 0.0, 0.434, 0.7793, 0.3083, 0.7749, 0.8177] +2026-04-13 13:07:59.315181: Epoch time: 101.7 s +2026-04-13 13:08:00.534079: +2026-04-13 13:08:00.535952: Epoch 2613 +2026-04-13 13:08:00.537828: Current learning rate: 0.00385 +2026-04-13 13:09:42.244323: train_loss -0.414 +2026-04-13 13:09:42.249565: val_loss -0.373 +2026-04-13 13:09:42.251459: Pseudo dice [0.6273, 0.0, 0.6586, 0.3327, 0.2735, 0.5961, 0.8195] +2026-04-13 13:09:42.253457: Epoch time: 101.71 s +2026-04-13 13:09:43.446509: +2026-04-13 13:09:43.448237: Epoch 2614 +2026-04-13 13:09:43.450049: Current learning rate: 0.00385 +2026-04-13 13:11:25.090639: train_loss -0.4286 +2026-04-13 13:11:25.098404: val_loss -0.3632 +2026-04-13 13:11:25.100587: Pseudo dice [0.3145, 0.0, 0.7158, 0.6373, 0.5328, 0.7749, 0.5797] +2026-04-13 13:11:25.102616: Epoch time: 101.65 s +2026-04-13 13:11:26.288351: +2026-04-13 13:11:26.290153: Epoch 2615 +2026-04-13 13:11:26.292198: Current learning rate: 0.00385 +2026-04-13 13:13:07.877658: train_loss -0.4284 +2026-04-13 13:13:07.884348: val_loss -0.3986 +2026-04-13 13:13:07.886424: Pseudo dice [0.6307, 0.0, 0.7981, 0.8287, 0.47, 0.8225, 0.7635] +2026-04-13 13:13:07.889414: Epoch time: 101.59 s +2026-04-13 13:13:09.073165: +2026-04-13 13:13:09.075882: Epoch 2616 +2026-04-13 13:13:09.078180: Current learning rate: 0.00385 +2026-04-13 13:14:51.199311: train_loss -0.4281 +2026-04-13 13:14:51.204795: val_loss -0.3957 +2026-04-13 13:14:51.206671: Pseudo dice [0.2912, 0.0, 0.8313, 0.8415, 0.6194, 0.6617, 0.7737] +2026-04-13 13:14:51.208395: Epoch time: 102.13 s +2026-04-13 13:14:52.370626: +2026-04-13 13:14:52.379582: Epoch 2617 +2026-04-13 13:14:52.381637: Current learning rate: 0.00384 +2026-04-13 13:16:33.980615: train_loss -0.4403 +2026-04-13 13:16:33.986128: val_loss -0.3574 +2026-04-13 13:16:33.988249: Pseudo dice [0.2949, 0.0, 0.7753, 0.5806, 0.2106, 0.4396, 0.8842] +2026-04-13 13:16:33.989987: Epoch time: 101.61 s +2026-04-13 13:16:35.171129: +2026-04-13 13:16:35.172804: Epoch 2618 +2026-04-13 13:16:35.174778: Current learning rate: 0.00384 +2026-04-13 13:18:16.941828: train_loss -0.42 +2026-04-13 13:18:16.948167: val_loss -0.3697 +2026-04-13 13:18:16.951081: Pseudo dice [0.5971, 0.0, 0.6991, 0.7723, 0.2494, 0.5571, 0.8649] +2026-04-13 13:18:16.953324: Epoch time: 101.77 s +2026-04-13 13:18:18.120490: +2026-04-13 13:18:18.123474: Epoch 2619 +2026-04-13 13:18:18.126756: Current learning rate: 0.00384 +2026-04-13 13:20:01.021693: train_loss -0.4096 +2026-04-13 13:20:01.027983: val_loss -0.3566 +2026-04-13 13:20:01.030485: Pseudo dice [0.557, 0.0, 0.6709, 0.1015, 0.2413, 0.6276, 0.8913] +2026-04-13 13:20:01.032626: Epoch time: 102.9 s +2026-04-13 13:20:02.249286: +2026-04-13 13:20:02.251124: Epoch 2620 +2026-04-13 13:20:02.252948: Current learning rate: 0.00384 +2026-04-13 13:21:43.979100: train_loss -0.4132 +2026-04-13 13:21:43.984003: val_loss -0.3702 +2026-04-13 13:21:43.985924: Pseudo dice [0.786, 0.0, 0.7594, 0.6458, 0.2872, 0.8079, 0.7525] +2026-04-13 13:21:43.987766: Epoch time: 101.73 s +2026-04-13 13:21:45.175751: +2026-04-13 13:21:45.177633: Epoch 2621 +2026-04-13 13:21:45.180371: Current learning rate: 0.00383 +2026-04-13 13:23:27.190140: train_loss -0.4167 +2026-04-13 13:23:27.196407: val_loss -0.4047 +2026-04-13 13:23:27.198194: Pseudo dice [0.8506, 0.0, 0.8563, 0.9011, 0.3175, 0.7331, 0.6872] +2026-04-13 13:23:27.199930: Epoch time: 102.02 s +2026-04-13 13:23:28.371758: +2026-04-13 13:23:28.373443: Epoch 2622 +2026-04-13 13:23:28.375606: Current learning rate: 0.00383 +2026-04-13 13:25:10.633403: train_loss -0.4407 +2026-04-13 13:25:10.639041: val_loss -0.3929 +2026-04-13 13:25:10.640766: Pseudo dice [0.6772, 0.0, 0.742, 0.7153, 0.3741, 0.6384, 0.8726] +2026-04-13 13:25:10.642496: Epoch time: 102.26 s +2026-04-13 13:25:12.852017: +2026-04-13 13:25:12.854429: Epoch 2623 +2026-04-13 13:25:12.857362: Current learning rate: 0.00383 +2026-04-13 13:26:54.491596: train_loss -0.4372 +2026-04-13 13:26:54.496960: val_loss -0.3976 +2026-04-13 13:26:54.498588: Pseudo dice [0.317, 0.0, 0.656, 0.6928, 0.222, 0.8475, 0.6609] +2026-04-13 13:26:54.500844: Epoch time: 101.64 s +2026-04-13 13:26:55.681277: +2026-04-13 13:26:55.683795: Epoch 2624 +2026-04-13 13:26:55.685778: Current learning rate: 0.00383 +2026-04-13 13:28:37.129834: train_loss -0.4226 +2026-04-13 13:28:37.135947: val_loss -0.3711 +2026-04-13 13:28:37.138150: Pseudo dice [0.6432, 0.0, 0.7215, 0.8702, 0.3513, 0.8021, 0.4483] +2026-04-13 13:28:37.139984: Epoch time: 101.45 s +2026-04-13 13:28:38.322990: +2026-04-13 13:28:38.324987: Epoch 2625 +2026-04-13 13:28:38.327485: Current learning rate: 0.00382 +2026-04-13 13:30:19.909295: train_loss -0.4318 +2026-04-13 13:30:19.916170: val_loss -0.3161 +2026-04-13 13:30:19.918469: Pseudo dice [0.3392, 0.0, 0.3226, 0.3776, 0.3881, 0.6244, 0.4798] +2026-04-13 13:30:19.920805: Epoch time: 101.59 s +2026-04-13 13:30:21.105015: +2026-04-13 13:30:21.106959: Epoch 2626 +2026-04-13 13:30:21.109126: Current learning rate: 0.00382 +2026-04-13 13:32:03.688673: train_loss -0.4007 +2026-04-13 13:32:03.695331: val_loss -0.3479 +2026-04-13 13:32:03.697793: Pseudo dice [0.7199, 0.0, 0.5406, 0.4775, 0.1003, 0.7013, 0.8379] +2026-04-13 13:32:03.701154: Epoch time: 102.59 s +2026-04-13 13:32:04.906952: +2026-04-13 13:32:04.908678: Epoch 2627 +2026-04-13 13:32:04.911160: Current learning rate: 0.00382 +2026-04-13 13:33:46.656871: train_loss -0.4144 +2026-04-13 13:33:46.664596: val_loss -0.373 +2026-04-13 13:33:46.666737: Pseudo dice [0.5559, 0.0, 0.7769, 0.8232, 0.2182, 0.6352, 0.6637] +2026-04-13 13:33:46.668801: Epoch time: 101.75 s +2026-04-13 13:33:47.853754: +2026-04-13 13:33:47.856112: Epoch 2628 +2026-04-13 13:33:47.858931: Current learning rate: 0.00382 +2026-04-13 13:35:29.317712: train_loss -0.4032 +2026-04-13 13:35:29.323394: val_loss -0.3689 +2026-04-13 13:35:29.325120: Pseudo dice [0.0161, 0.0, 0.7238, 0.7888, 0.489, 0.6209, 0.6783] +2026-04-13 13:35:29.326671: Epoch time: 101.47 s +2026-04-13 13:35:30.510809: +2026-04-13 13:35:30.512417: Epoch 2629 +2026-04-13 13:35:30.514344: Current learning rate: 0.00381 +2026-04-13 13:37:12.228624: train_loss -0.3923 +2026-04-13 13:37:12.235634: val_loss -0.3625 +2026-04-13 13:37:12.238396: Pseudo dice [0.0468, 0.0, 0.7402, 0.7168, 0.384, 0.7598, 0.7342] +2026-04-13 13:37:12.240617: Epoch time: 101.72 s +2026-04-13 13:37:13.434500: +2026-04-13 13:37:13.437910: Epoch 2630 +2026-04-13 13:37:13.440995: Current learning rate: 0.00381 +2026-04-13 13:38:55.453105: train_loss -0.4203 +2026-04-13 13:38:55.458586: val_loss -0.3664 +2026-04-13 13:38:55.460827: Pseudo dice [0.6608, 0.0, 0.7303, 0.799, 0.4805, 0.6793, 0.7594] +2026-04-13 13:38:55.463093: Epoch time: 102.02 s +2026-04-13 13:38:56.656619: +2026-04-13 13:38:56.658371: Epoch 2631 +2026-04-13 13:38:56.660366: Current learning rate: 0.00381 +2026-04-13 13:40:38.100869: train_loss -0.4233 +2026-04-13 13:40:38.106775: val_loss -0.3928 +2026-04-13 13:40:38.109029: Pseudo dice [0.1446, 0.0, 0.7347, 0.7575, 0.3473, 0.7251, 0.8725] +2026-04-13 13:40:38.110698: Epoch time: 101.45 s +2026-04-13 13:40:39.301805: +2026-04-13 13:40:39.303692: Epoch 2632 +2026-04-13 13:40:39.305748: Current learning rate: 0.00381 +2026-04-13 13:42:21.003292: train_loss -0.4183 +2026-04-13 13:42:21.009113: val_loss -0.3998 +2026-04-13 13:42:21.012351: Pseudo dice [0.4301, 0.0, 0.8024, 0.7397, 0.3237, 0.8232, 0.8149] +2026-04-13 13:42:21.014654: Epoch time: 101.7 s +2026-04-13 13:42:22.245761: +2026-04-13 13:42:22.247788: Epoch 2633 +2026-04-13 13:42:22.250287: Current learning rate: 0.0038 +2026-04-13 13:44:04.358165: train_loss -0.4371 +2026-04-13 13:44:04.364004: val_loss -0.3824 +2026-04-13 13:44:04.366646: Pseudo dice [0.3346, 0.0, 0.8249, 0.7091, 0.5088, 0.7581, 0.6655] +2026-04-13 13:44:04.369217: Epoch time: 102.12 s +2026-04-13 13:44:05.578357: +2026-04-13 13:44:05.580270: Epoch 2634 +2026-04-13 13:44:05.582666: Current learning rate: 0.0038 +2026-04-13 13:45:46.997025: train_loss -0.4208 +2026-04-13 13:45:47.003877: val_loss -0.327 +2026-04-13 13:45:47.005841: Pseudo dice [0.3758, 0.0, 0.4741, 0.7851, 0.2627, 0.7376, 0.7476] +2026-04-13 13:45:47.007524: Epoch time: 101.42 s +2026-04-13 13:45:48.165426: +2026-04-13 13:45:48.167101: Epoch 2635 +2026-04-13 13:45:48.168886: Current learning rate: 0.0038 +2026-04-13 13:47:30.448355: train_loss -0.3995 +2026-04-13 13:47:30.454378: val_loss -0.3662 +2026-04-13 13:47:30.456251: Pseudo dice [0.2674, 0.0, 0.7669, 0.6418, 0.5225, 0.5574, 0.8287] +2026-04-13 13:47:30.458807: Epoch time: 102.28 s +2026-04-13 13:47:31.682646: +2026-04-13 13:47:31.684206: Epoch 2636 +2026-04-13 13:47:31.686017: Current learning rate: 0.0038 +2026-04-13 13:49:13.440311: train_loss -0.4053 +2026-04-13 13:49:13.446441: val_loss -0.3483 +2026-04-13 13:49:13.448767: Pseudo dice [0.2191, 0.0, 0.7937, 0.8363, 0.4186, 0.6548, 0.7193] +2026-04-13 13:49:13.450377: Epoch time: 101.76 s +2026-04-13 13:49:14.641544: +2026-04-13 13:49:14.644018: Epoch 2637 +2026-04-13 13:49:14.647178: Current learning rate: 0.00379 +2026-04-13 13:50:56.223177: train_loss -0.4136 +2026-04-13 13:50:56.248542: val_loss -0.3605 +2026-04-13 13:50:56.250377: Pseudo dice [0.3177, 0.0, 0.7582, 0.6981, 0.2002, 0.3998, 0.7431] +2026-04-13 13:50:56.252728: Epoch time: 101.58 s +2026-04-13 13:50:57.516037: +2026-04-13 13:50:57.517639: Epoch 2638 +2026-04-13 13:50:57.519382: Current learning rate: 0.00379 +2026-04-13 13:52:39.072895: train_loss -0.4128 +2026-04-13 13:52:39.078102: val_loss -0.3716 +2026-04-13 13:52:39.079530: Pseudo dice [0.4849, 0.0, 0.4001, 0.6443, 0.4559, 0.2676, 0.8903] +2026-04-13 13:52:39.081209: Epoch time: 101.56 s +2026-04-13 13:52:40.339557: +2026-04-13 13:52:40.341417: Epoch 2639 +2026-04-13 13:52:40.343314: Current learning rate: 0.00379 +2026-04-13 13:54:21.802658: train_loss -0.4356 +2026-04-13 13:54:21.807963: val_loss -0.4063 +2026-04-13 13:54:21.809980: Pseudo dice [0.3986, 0.0, 0.7372, 0.7647, 0.5211, 0.8153, 0.9128] +2026-04-13 13:54:21.812106: Epoch time: 101.47 s +2026-04-13 13:54:23.000506: +2026-04-13 13:54:23.002371: Epoch 2640 +2026-04-13 13:54:23.004374: Current learning rate: 0.00379 +2026-04-13 13:56:04.665605: train_loss -0.4369 +2026-04-13 13:56:04.672374: val_loss -0.3903 +2026-04-13 13:56:04.675595: Pseudo dice [0.3995, 0.0, 0.7112, 0.6013, 0.562, 0.7321, 0.8515] +2026-04-13 13:56:04.677934: Epoch time: 101.67 s +2026-04-13 13:56:05.856898: +2026-04-13 13:56:05.859241: Epoch 2641 +2026-04-13 13:56:05.861552: Current learning rate: 0.00378 +2026-04-13 13:57:47.348003: train_loss -0.431 +2026-04-13 13:57:47.353547: val_loss -0.376 +2026-04-13 13:57:47.355155: Pseudo dice [0.4856, 0.0, 0.6518, 0.8154, 0.4059, 0.705, 0.9262] +2026-04-13 13:57:47.357123: Epoch time: 101.49 s +2026-04-13 13:57:48.581564: +2026-04-13 13:57:48.583152: Epoch 2642 +2026-04-13 13:57:48.585130: Current learning rate: 0.00378 +2026-04-13 13:59:30.033406: train_loss -0.407 +2026-04-13 13:59:30.038576: val_loss -0.3936 +2026-04-13 13:59:30.040378: Pseudo dice [0.5788, 0.0, 0.509, 0.7119, 0.1707, 0.8185, 0.8801] +2026-04-13 13:59:30.042005: Epoch time: 101.45 s +2026-04-13 13:59:31.229224: +2026-04-13 13:59:31.231241: Epoch 2643 +2026-04-13 13:59:31.233034: Current learning rate: 0.00378 +2026-04-13 14:01:13.963690: train_loss -0.4392 +2026-04-13 14:01:13.971231: val_loss -0.3614 +2026-04-13 14:01:13.973580: Pseudo dice [0.2404, 0.0, 0.6841, 0.8675, 0.1799, 0.5783, 0.9173] +2026-04-13 14:01:13.976498: Epoch time: 102.74 s +2026-04-13 14:01:15.151136: +2026-04-13 14:01:15.152844: Epoch 2644 +2026-04-13 14:01:15.154672: Current learning rate: 0.00378 +2026-04-13 14:02:57.297322: train_loss -0.4223 +2026-04-13 14:02:57.314685: val_loss -0.3866 +2026-04-13 14:02:57.317433: Pseudo dice [0.4479, 0.0, 0.7712, 0.8364, 0.2056, 0.7929, 0.7555] +2026-04-13 14:02:57.319256: Epoch time: 102.15 s +2026-04-13 14:02:58.555784: +2026-04-13 14:02:58.557689: Epoch 2645 +2026-04-13 14:02:58.560135: Current learning rate: 0.00377 +2026-04-13 14:04:40.164938: train_loss -0.4225 +2026-04-13 14:04:40.170435: val_loss -0.3905 +2026-04-13 14:04:40.171952: Pseudo dice [0.5354, 0.0, 0.6873, 0.6749, 0.4933, 0.6951, 0.8524] +2026-04-13 14:04:40.173566: Epoch time: 101.61 s +2026-04-13 14:04:41.340892: +2026-04-13 14:04:41.342463: Epoch 2646 +2026-04-13 14:04:41.344155: Current learning rate: 0.00377 +2026-04-13 14:06:22.779892: train_loss -0.4207 +2026-04-13 14:06:22.785510: val_loss -0.3611 +2026-04-13 14:06:22.787394: Pseudo dice [0.3909, 0.0, 0.7286, 0.7332, 0.3792, 0.6637, 0.8178] +2026-04-13 14:06:22.789521: Epoch time: 101.44 s +2026-04-13 14:06:23.960387: +2026-04-13 14:06:23.962233: Epoch 2647 +2026-04-13 14:06:23.964241: Current learning rate: 0.00377 +2026-04-13 14:08:05.757222: train_loss -0.4179 +2026-04-13 14:08:05.763988: val_loss -0.3574 +2026-04-13 14:08:05.766269: Pseudo dice [0.6928, 0.0, 0.6918, 0.7018, 0.4266, 0.6439, 0.3229] +2026-04-13 14:08:05.768439: Epoch time: 101.8 s +2026-04-13 14:08:06.951159: +2026-04-13 14:08:06.972053: Epoch 2648 +2026-04-13 14:08:06.983225: Current learning rate: 0.00377 +2026-04-13 14:09:48.530562: train_loss -0.4288 +2026-04-13 14:09:48.536129: val_loss -0.3992 +2026-04-13 14:09:48.538315: Pseudo dice [0.7223, 0.0, 0.7127, 0.7916, 0.5732, 0.4617, 0.6284] +2026-04-13 14:09:48.539997: Epoch time: 101.58 s +2026-04-13 14:09:49.758258: +2026-04-13 14:09:49.759983: Epoch 2649 +2026-04-13 14:09:49.761931: Current learning rate: 0.00376 +2026-04-13 14:11:31.519956: train_loss -0.4192 +2026-04-13 14:11:31.525799: val_loss -0.3487 +2026-04-13 14:11:31.527984: Pseudo dice [0.4878, 0.0, 0.6604, 0.6887, 0.0672, 0.4816, 0.7167] +2026-04-13 14:11:31.530001: Epoch time: 101.77 s +2026-04-13 14:11:34.356178: +2026-04-13 14:11:34.361903: Epoch 2650 +2026-04-13 14:11:34.364692: Current learning rate: 0.00376 +2026-04-13 14:13:15.932950: train_loss -0.398 +2026-04-13 14:13:15.937466: val_loss -0.3403 +2026-04-13 14:13:15.939714: Pseudo dice [0.4542, 0.0, 0.426, 0.0002, 0.2598, 0.5478, 0.4522] +2026-04-13 14:13:15.941831: Epoch time: 101.58 s +2026-04-13 14:13:17.116969: +2026-04-13 14:13:17.118865: Epoch 2651 +2026-04-13 14:13:17.121245: Current learning rate: 0.00376 +2026-04-13 14:14:58.673942: train_loss -0.4201 +2026-04-13 14:14:58.679952: val_loss -0.3727 +2026-04-13 14:14:58.682880: Pseudo dice [0.2675, 0.0, 0.7261, 0.744, 0.3517, 0.6537, 0.7914] +2026-04-13 14:14:58.685839: Epoch time: 101.56 s +2026-04-13 14:14:59.874689: +2026-04-13 14:14:59.876789: Epoch 2652 +2026-04-13 14:14:59.878885: Current learning rate: 0.00376 +2026-04-13 14:16:41.414271: train_loss -0.4124 +2026-04-13 14:16:41.419029: val_loss -0.3599 +2026-04-13 14:16:41.421010: Pseudo dice [0.5457, 0.0, 0.7467, 0.6363, 0.3781, 0.508, 0.5945] +2026-04-13 14:16:41.422571: Epoch time: 101.54 s +2026-04-13 14:16:42.596534: +2026-04-13 14:16:42.598276: Epoch 2653 +2026-04-13 14:16:42.600124: Current learning rate: 0.00375 +2026-04-13 14:18:24.210327: train_loss -0.4235 +2026-04-13 14:18:24.215367: val_loss -0.3942 +2026-04-13 14:18:24.217100: Pseudo dice [0.289, 0.0, 0.7918, 0.6685, 0.5033, 0.7892, 0.8424] +2026-04-13 14:18:24.218764: Epoch time: 101.62 s +2026-04-13 14:18:25.404056: +2026-04-13 14:18:25.405807: Epoch 2654 +2026-04-13 14:18:25.407764: Current learning rate: 0.00375 +2026-04-13 14:20:06.719091: train_loss -0.4093 +2026-04-13 14:20:06.724721: val_loss -0.3443 +2026-04-13 14:20:06.726553: Pseudo dice [0.4557, 0.0, 0.6342, 0.7063, 0.269, 0.2731, 0.7779] +2026-04-13 14:20:06.728370: Epoch time: 101.32 s +2026-04-13 14:20:07.907864: +2026-04-13 14:20:07.910956: Epoch 2655 +2026-04-13 14:20:07.912811: Current learning rate: 0.00375 +2026-04-13 14:21:49.564299: train_loss -0.4132 +2026-04-13 14:21:49.582700: val_loss -0.3755 +2026-04-13 14:21:49.584954: Pseudo dice [0.5554, 0.0, 0.725, 0.5144, 0.4022, 0.5633, 0.638] +2026-04-13 14:21:49.587081: Epoch time: 101.66 s +2026-04-13 14:21:50.776227: +2026-04-13 14:21:50.778074: Epoch 2656 +2026-04-13 14:21:50.780132: Current learning rate: 0.00375 +2026-04-13 14:23:32.165195: train_loss -0.4051 +2026-04-13 14:23:32.173837: val_loss -0.3808 +2026-04-13 14:23:32.175591: Pseudo dice [0.49, 0.0, 0.6261, 0.7013, 0.5286, 0.725, 0.6856] +2026-04-13 14:23:32.177170: Epoch time: 101.39 s +2026-04-13 14:23:33.393888: +2026-04-13 14:23:33.395708: Epoch 2657 +2026-04-13 14:23:33.397550: Current learning rate: 0.00374 +2026-04-13 14:25:15.058438: train_loss -0.4166 +2026-04-13 14:25:15.064275: val_loss -0.3499 +2026-04-13 14:25:15.066407: Pseudo dice [0.2664, 0.0, 0.454, 0.6742, 0.2248, 0.8265, 0.3194] +2026-04-13 14:25:15.069358: Epoch time: 101.67 s +2026-04-13 14:25:16.261191: +2026-04-13 14:25:16.263026: Epoch 2658 +2026-04-13 14:25:16.265279: Current learning rate: 0.00374 +2026-04-13 14:26:57.848270: train_loss -0.3952 +2026-04-13 14:26:57.866416: val_loss -0.3228 +2026-04-13 14:26:57.873824: Pseudo dice [0.2002, 0.0, 0.7702, 0.5003, 0.3291, 0.4209, 0.5864] +2026-04-13 14:26:57.877626: Epoch time: 101.59 s +2026-04-13 14:26:59.096415: +2026-04-13 14:26:59.100081: Epoch 2659 +2026-04-13 14:26:59.130415: Current learning rate: 0.00374 +2026-04-13 14:28:40.791990: train_loss -0.4313 +2026-04-13 14:28:40.801297: val_loss -0.3604 +2026-04-13 14:28:40.803613: Pseudo dice [0.3341, 0.0, 0.6032, 0.7408, 0.6325, 0.6493, 0.7185] +2026-04-13 14:28:40.805814: Epoch time: 101.7 s +2026-04-13 14:28:42.000947: +2026-04-13 14:28:42.003253: Epoch 2660 +2026-04-13 14:28:42.005864: Current learning rate: 0.00374 +2026-04-13 14:30:23.606844: train_loss -0.428 +2026-04-13 14:30:23.614542: val_loss -0.381 +2026-04-13 14:30:23.616583: Pseudo dice [0.6365, 0.0, 0.7536, 0.8004, 0.5176, 0.7128, 0.7417] +2026-04-13 14:30:23.618804: Epoch time: 101.61 s +2026-04-13 14:30:24.830042: +2026-04-13 14:30:24.831850: Epoch 2661 +2026-04-13 14:30:24.833853: Current learning rate: 0.00373 +2026-04-13 14:32:06.272262: train_loss -0.4115 +2026-04-13 14:32:06.282081: val_loss -0.3796 +2026-04-13 14:32:06.283785: Pseudo dice [0.5375, 0.0, 0.6822, 0.8098, 0.4604, 0.8305, 0.4476] +2026-04-13 14:32:06.285695: Epoch time: 101.45 s +2026-04-13 14:32:07.464075: +2026-04-13 14:32:07.465962: Epoch 2662 +2026-04-13 14:32:07.467691: Current learning rate: 0.00373 +2026-04-13 14:33:48.991465: train_loss -0.4071 +2026-04-13 14:33:49.000909: val_loss -0.3969 +2026-04-13 14:33:49.003624: Pseudo dice [0.351, 0.0, 0.7181, 0.8632, 0.3192, 0.8074, 0.7818] +2026-04-13 14:33:49.005577: Epoch time: 101.53 s +2026-04-13 14:33:50.224409: +2026-04-13 14:33:50.226427: Epoch 2663 +2026-04-13 14:33:50.228629: Current learning rate: 0.00373 +2026-04-13 14:35:31.808356: train_loss -0.398 +2026-04-13 14:35:31.814451: val_loss -0.3836 +2026-04-13 14:35:31.816452: Pseudo dice [0.6744, 0.0, 0.7239, 0.8555, 0.4672, 0.703, 0.7469] +2026-04-13 14:35:31.818306: Epoch time: 101.59 s +2026-04-13 14:35:34.074218: +2026-04-13 14:35:34.076006: Epoch 2664 +2026-04-13 14:35:34.078103: Current learning rate: 0.00373 +2026-04-13 14:37:15.724857: train_loss -0.4119 +2026-04-13 14:37:15.729947: val_loss -0.3442 +2026-04-13 14:37:15.731661: Pseudo dice [0.3824, 0.0, 0.7535, 0.3478, 0.3952, 0.7235, 0.3695] +2026-04-13 14:37:15.733480: Epoch time: 101.65 s +2026-04-13 14:37:16.924372: +2026-04-13 14:37:16.926048: Epoch 2665 +2026-04-13 14:37:16.928116: Current learning rate: 0.00372 +2026-04-13 14:38:58.527033: train_loss -0.4111 +2026-04-13 14:38:58.532942: val_loss -0.3829 +2026-04-13 14:38:58.534410: Pseudo dice [0.3979, 0.0, 0.745, 0.7905, 0.3775, 0.7505, 0.6096] +2026-04-13 14:38:58.536389: Epoch time: 101.61 s +2026-04-13 14:38:59.743753: +2026-04-13 14:38:59.745554: Epoch 2666 +2026-04-13 14:38:59.747437: Current learning rate: 0.00372 +2026-04-13 14:40:41.230460: train_loss -0.3931 +2026-04-13 14:40:41.238889: val_loss -0.3696 +2026-04-13 14:40:41.241614: Pseudo dice [0.3752, 0.0, 0.3671, 0.76, 0.5193, 0.6506, 0.533] +2026-04-13 14:40:41.245021: Epoch time: 101.49 s +2026-04-13 14:40:42.452852: +2026-04-13 14:40:42.454848: Epoch 2667 +2026-04-13 14:40:42.456932: Current learning rate: 0.00372 +2026-04-13 14:42:24.088837: train_loss -0.4171 +2026-04-13 14:42:24.094292: val_loss -0.3472 +2026-04-13 14:42:24.096013: Pseudo dice [0.3684, 0.0, 0.7826, 0.5271, 0.3824, 0.7903, 0.6299] +2026-04-13 14:42:24.098033: Epoch time: 101.64 s +2026-04-13 14:42:25.287704: +2026-04-13 14:42:25.291344: Epoch 2668 +2026-04-13 14:42:25.293848: Current learning rate: 0.00372 +2026-04-13 14:44:06.799518: train_loss -0.4206 +2026-04-13 14:44:06.809790: val_loss -0.361 +2026-04-13 14:44:06.813590: Pseudo dice [0.4222, 0.0, 0.7755, 0.4458, 0.5243, 0.6023, 0.6975] +2026-04-13 14:44:06.816832: Epoch time: 101.51 s +2026-04-13 14:44:08.001125: +2026-04-13 14:44:08.003212: Epoch 2669 +2026-04-13 14:44:08.005576: Current learning rate: 0.00371 +2026-04-13 14:45:49.671280: train_loss -0.4276 +2026-04-13 14:45:49.681104: val_loss -0.3942 +2026-04-13 14:45:49.682851: Pseudo dice [0.4244, 0.0, 0.5643, 0.7733, 0.5333, 0.7464, 0.7478] +2026-04-13 14:45:49.685197: Epoch time: 101.67 s +2026-04-13 14:45:50.861026: +2026-04-13 14:45:50.862726: Epoch 2670 +2026-04-13 14:45:50.864980: Current learning rate: 0.00371 +2026-04-13 14:47:32.579804: train_loss -0.4203 +2026-04-13 14:47:32.586430: val_loss -0.3694 +2026-04-13 14:47:32.588432: Pseudo dice [0.5867, 0.0, 0.619, 0.4899, 0.4606, 0.7989, 0.5933] +2026-04-13 14:47:32.590325: Epoch time: 101.72 s +2026-04-13 14:47:33.822392: +2026-04-13 14:47:33.824089: Epoch 2671 +2026-04-13 14:47:33.826651: Current learning rate: 0.00371 +2026-04-13 14:49:15.478749: train_loss -0.4347 +2026-04-13 14:49:15.485582: val_loss -0.4008 +2026-04-13 14:49:15.487113: Pseudo dice [0.4685, 0.0, 0.8387, 0.4889, 0.6159, 0.7745, 0.8097] +2026-04-13 14:49:15.488477: Epoch time: 101.66 s +2026-04-13 14:49:16.667730: +2026-04-13 14:49:16.670205: Epoch 2672 +2026-04-13 14:49:16.672256: Current learning rate: 0.00371 +2026-04-13 14:50:58.221394: train_loss -0.4156 +2026-04-13 14:50:58.228480: val_loss -0.324 +2026-04-13 14:50:58.230378: Pseudo dice [0.0404, 0.0, 0.8025, 0.5825, 0.103, 0.7628, 0.893] +2026-04-13 14:50:58.232099: Epoch time: 101.56 s +2026-04-13 14:50:59.416309: +2026-04-13 14:50:59.418263: Epoch 2673 +2026-04-13 14:50:59.420138: Current learning rate: 0.0037 +2026-04-13 14:52:41.730981: train_loss -0.3733 +2026-04-13 14:52:41.737762: val_loss -0.354 +2026-04-13 14:52:41.740942: Pseudo dice [0.0, 0.0, 0.8363, 0.9073, 0.4343, 0.7813, 0.3924] +2026-04-13 14:52:41.744924: Epoch time: 102.32 s +2026-04-13 14:52:42.921087: +2026-04-13 14:52:42.922957: Epoch 2674 +2026-04-13 14:52:42.925737: Current learning rate: 0.0037 +2026-04-13 14:54:24.571586: train_loss -0.4222 +2026-04-13 14:54:24.576964: val_loss -0.4021 +2026-04-13 14:54:24.578809: Pseudo dice [0.4318, 0.0, 0.7631, 0.6179, 0.4055, 0.7621, 0.8458] +2026-04-13 14:54:24.580656: Epoch time: 101.65 s +2026-04-13 14:54:25.766505: +2026-04-13 14:54:25.768779: Epoch 2675 +2026-04-13 14:54:25.771218: Current learning rate: 0.0037 +2026-04-13 14:56:07.371182: train_loss -0.4063 +2026-04-13 14:56:07.377693: val_loss -0.3908 +2026-04-13 14:56:07.380030: Pseudo dice [0.5349, 0.0, 0.6189, 0.0648, 0.5392, 0.8661, 0.8442] +2026-04-13 14:56:07.382945: Epoch time: 101.61 s +2026-04-13 14:56:08.566712: +2026-04-13 14:56:08.569032: Epoch 2676 +2026-04-13 14:56:08.572574: Current learning rate: 0.0037 +2026-04-13 14:57:50.075629: train_loss -0.4039 +2026-04-13 14:57:50.081359: val_loss -0.3509 +2026-04-13 14:57:50.083335: Pseudo dice [0.3624, 0.0, 0.5401, 0.2282, 0.3064, 0.6601, 0.6643] +2026-04-13 14:57:50.085459: Epoch time: 101.51 s +2026-04-13 14:57:51.271469: +2026-04-13 14:57:51.273621: Epoch 2677 +2026-04-13 14:57:51.276331: Current learning rate: 0.00369 +2026-04-13 14:59:32.757483: train_loss -0.418 +2026-04-13 14:59:32.762964: val_loss -0.3803 +2026-04-13 14:59:32.764898: Pseudo dice [0.4691, 0.0, 0.7309, 0.7046, 0.429, 0.5896, 0.624] +2026-04-13 14:59:32.767236: Epoch time: 101.49 s +2026-04-13 14:59:33.950853: +2026-04-13 14:59:33.953180: Epoch 2678 +2026-04-13 14:59:33.955445: Current learning rate: 0.00369 +2026-04-13 15:01:15.671353: train_loss -0.4169 +2026-04-13 15:01:15.676176: val_loss -0.4059 +2026-04-13 15:01:15.678077: Pseudo dice [0.5529, 0.0, 0.8358, 0.7065, 0.5856, 0.607, 0.8006] +2026-04-13 15:01:15.680428: Epoch time: 101.72 s +2026-04-13 15:01:16.875871: +2026-04-13 15:01:16.877788: Epoch 2679 +2026-04-13 15:01:16.879609: Current learning rate: 0.00369 +2026-04-13 15:02:58.608366: train_loss -0.4176 +2026-04-13 15:02:58.613893: val_loss -0.3823 +2026-04-13 15:02:58.615542: Pseudo dice [0.7093, 0.0, 0.7631, 0.7207, 0.5403, 0.5123, 0.7501] +2026-04-13 15:02:58.617294: Epoch time: 101.74 s +2026-04-13 15:02:59.794626: +2026-04-13 15:02:59.796535: Epoch 2680 +2026-04-13 15:02:59.798461: Current learning rate: 0.00369 +2026-04-13 15:04:41.383946: train_loss -0.4346 +2026-04-13 15:04:41.388144: val_loss -0.3915 +2026-04-13 15:04:41.389541: Pseudo dice [0.5354, 0.0, 0.8001, 0.547, 0.728, 0.7556, 0.5557] +2026-04-13 15:04:41.391313: Epoch time: 101.59 s +2026-04-13 15:04:42.560512: +2026-04-13 15:04:42.562561: Epoch 2681 +2026-04-13 15:04:42.564609: Current learning rate: 0.00368 +2026-04-13 15:06:24.308859: train_loss -0.4067 +2026-04-13 15:06:24.314565: val_loss -0.3749 +2026-04-13 15:06:24.316761: Pseudo dice [0.7548, 0.0, 0.5837, 0.1864, 0.5319, 0.8289, 0.745] +2026-04-13 15:06:24.318884: Epoch time: 101.75 s +2026-04-13 15:06:25.505553: +2026-04-13 15:06:25.506949: Epoch 2682 +2026-04-13 15:06:25.508582: Current learning rate: 0.00368 +2026-04-13 15:08:07.089836: train_loss -0.4248 +2026-04-13 15:08:07.096868: val_loss -0.3489 +2026-04-13 15:08:07.099121: Pseudo dice [0.4348, 0.0, 0.5975, 0.4088, 0.4123, 0.7975, 0.7464] +2026-04-13 15:08:07.101301: Epoch time: 101.59 s +2026-04-13 15:08:08.282022: +2026-04-13 15:08:08.284495: Epoch 2683 +2026-04-13 15:08:08.286383: Current learning rate: 0.00368 +2026-04-13 15:09:49.668648: train_loss -0.4333 +2026-04-13 15:09:49.677489: val_loss -0.3881 +2026-04-13 15:09:49.680817: Pseudo dice [0.626, 0.0, 0.7883, 0.8461, 0.4581, 0.7979, 0.8064] +2026-04-13 15:09:49.684364: Epoch time: 101.39 s +2026-04-13 15:09:50.878398: +2026-04-13 15:09:50.880441: Epoch 2684 +2026-04-13 15:09:50.883883: Current learning rate: 0.00368 +2026-04-13 15:11:34.115278: train_loss -0.4151 +2026-04-13 15:11:34.120861: val_loss -0.3769 +2026-04-13 15:11:34.122594: Pseudo dice [0.3247, 0.0, 0.484, 0.8012, 0.3497, 0.5495, 0.8479] +2026-04-13 15:11:34.124624: Epoch time: 103.24 s +2026-04-13 15:11:35.335313: +2026-04-13 15:11:35.337820: Epoch 2685 +2026-04-13 15:11:35.340551: Current learning rate: 0.00367 +2026-04-13 15:13:16.771325: train_loss -0.3979 +2026-04-13 15:13:16.778030: val_loss -0.3421 +2026-04-13 15:13:16.780109: Pseudo dice [0.1719, 0.0, 0.4665, 0.6507, 0.2947, 0.808, 0.5161] +2026-04-13 15:13:16.781764: Epoch time: 101.44 s +2026-04-13 15:13:17.981283: +2026-04-13 15:13:17.983367: Epoch 2686 +2026-04-13 15:13:17.985439: Current learning rate: 0.00367 +2026-04-13 15:14:59.633855: train_loss -0.4114 +2026-04-13 15:14:59.638829: val_loss -0.3702 +2026-04-13 15:14:59.640644: Pseudo dice [0.3106, 0.0, 0.7538, 0.8637, 0.4491, 0.7026, 0.5865] +2026-04-13 15:14:59.642594: Epoch time: 101.66 s +2026-04-13 15:15:00.822777: +2026-04-13 15:15:00.824439: Epoch 2687 +2026-04-13 15:15:00.826437: Current learning rate: 0.00367 +2026-04-13 15:16:42.406793: train_loss -0.4376 +2026-04-13 15:16:42.413541: val_loss -0.3767 +2026-04-13 15:16:42.415240: Pseudo dice [0.3013, 0.0, 0.7456, 0.8925, 0.3171, 0.8585, 0.7121] +2026-04-13 15:16:42.417381: Epoch time: 101.59 s +2026-04-13 15:16:43.611672: +2026-04-13 15:16:43.613427: Epoch 2688 +2026-04-13 15:16:43.615759: Current learning rate: 0.00367 +2026-04-13 15:18:25.194597: train_loss -0.42 +2026-04-13 15:18:25.200865: val_loss -0.376 +2026-04-13 15:18:25.202708: Pseudo dice [0.3976, 0.0, 0.628, 0.6835, 0.5185, 0.4964, 0.8484] +2026-04-13 15:18:25.205037: Epoch time: 101.59 s +2026-04-13 15:18:26.380827: +2026-04-13 15:18:26.382586: Epoch 2689 +2026-04-13 15:18:26.384376: Current learning rate: 0.00366 +2026-04-13 15:20:09.840399: train_loss -0.422 +2026-04-13 15:20:09.846591: val_loss -0.3671 +2026-04-13 15:20:09.848893: Pseudo dice [0.4242, 0.0, 0.4471, 0.007, 0.4708, 0.4839, 0.5719] +2026-04-13 15:20:09.850977: Epoch time: 103.46 s +2026-04-13 15:20:11.028573: +2026-04-13 15:20:11.030299: Epoch 2690 +2026-04-13 15:20:11.032278: Current learning rate: 0.00366 +2026-04-13 15:21:52.730162: train_loss -0.3971 +2026-04-13 15:21:52.734439: val_loss -0.3822 +2026-04-13 15:21:52.736463: Pseudo dice [0.1786, 0.0, 0.7117, 0.7074, 0.3549, 0.7843, 0.8357] +2026-04-13 15:21:52.737785: Epoch time: 101.7 s +2026-04-13 15:21:53.927944: +2026-04-13 15:21:53.930184: Epoch 2691 +2026-04-13 15:21:53.932640: Current learning rate: 0.00366 +2026-04-13 15:23:35.731447: train_loss -0.4195 +2026-04-13 15:23:35.739264: val_loss -0.3911 +2026-04-13 15:23:35.741346: Pseudo dice [0.6592, 0.0, 0.5464, 0.2013, 0.4231, 0.7505, 0.7487] +2026-04-13 15:23:35.745588: Epoch time: 101.81 s +2026-04-13 15:23:36.991925: +2026-04-13 15:23:36.994275: Epoch 2692 +2026-04-13 15:23:36.996230: Current learning rate: 0.00366 +2026-04-13 15:25:18.591629: train_loss -0.4068 +2026-04-13 15:25:18.596905: val_loss -0.3589 +2026-04-13 15:25:18.598590: Pseudo dice [0.6032, 0.0, 0.343, 0.8517, 0.369, 0.5915, 0.7554] +2026-04-13 15:25:18.600188: Epoch time: 101.6 s +2026-04-13 15:25:19.798516: +2026-04-13 15:25:19.800108: Epoch 2693 +2026-04-13 15:25:19.801923: Current learning rate: 0.00365 +2026-04-13 15:27:01.643991: train_loss -0.4286 +2026-04-13 15:27:01.648961: val_loss -0.3953 +2026-04-13 15:27:01.650612: Pseudo dice [0.1983, 0.0, 0.3946, 0.8318, 0.5472, 0.8034, 0.6469] +2026-04-13 15:27:01.652186: Epoch time: 101.85 s +2026-04-13 15:27:02.839434: +2026-04-13 15:27:02.841204: Epoch 2694 +2026-04-13 15:27:02.842973: Current learning rate: 0.00365 +2026-04-13 15:28:44.585441: train_loss -0.4234 +2026-04-13 15:28:44.590924: val_loss -0.3998 +2026-04-13 15:28:44.592368: Pseudo dice [0.7499, 0.0, 0.7481, 0.9226, 0.5132, 0.7079, 0.8765] +2026-04-13 15:28:44.593905: Epoch time: 101.75 s +2026-04-13 15:28:45.773984: +2026-04-13 15:28:45.775402: Epoch 2695 +2026-04-13 15:28:45.776956: Current learning rate: 0.00365 +2026-04-13 15:30:27.316469: train_loss -0.4327 +2026-04-13 15:30:27.321857: val_loss -0.3503 +2026-04-13 15:30:27.323720: Pseudo dice [0.2614, 0.0, 0.6134, 0.5233, 0.3296, 0.7752, 0.7177] +2026-04-13 15:30:27.326723: Epoch time: 101.55 s +2026-04-13 15:30:28.508529: +2026-04-13 15:30:28.510355: Epoch 2696 +2026-04-13 15:30:28.512125: Current learning rate: 0.00365 +2026-04-13 15:32:10.244323: train_loss -0.4374 +2026-04-13 15:32:10.250869: val_loss -0.3772 +2026-04-13 15:32:10.253298: Pseudo dice [0.6268, 0.0, 0.7781, 0.8401, 0.3648, 0.7189, 0.6801] +2026-04-13 15:32:10.255298: Epoch time: 101.74 s +2026-04-13 15:32:11.426829: +2026-04-13 15:32:11.428634: Epoch 2697 +2026-04-13 15:32:11.430344: Current learning rate: 0.00364 +2026-04-13 15:33:53.131829: train_loss -0.4285 +2026-04-13 15:33:53.137086: val_loss -0.406 +2026-04-13 15:33:53.138906: Pseudo dice [0.4361, 0.0, 0.5583, 0.8801, 0.5703, 0.8418, 0.9294] +2026-04-13 15:33:53.140363: Epoch time: 101.71 s +2026-04-13 15:33:54.344879: +2026-04-13 15:33:54.346934: Epoch 2698 +2026-04-13 15:33:54.348856: Current learning rate: 0.00364 +2026-04-13 15:35:36.026417: train_loss -0.4244 +2026-04-13 15:35:36.032456: val_loss -0.3545 +2026-04-13 15:35:36.035135: Pseudo dice [0.2013, 0.0, 0.839, 0.7886, 0.5548, 0.5031, 0.8236] +2026-04-13 15:35:36.037156: Epoch time: 101.68 s +2026-04-13 15:35:37.228881: +2026-04-13 15:35:37.231039: Epoch 2699 +2026-04-13 15:35:37.233138: Current learning rate: 0.00364 +2026-04-13 15:37:18.777411: train_loss -0.4215 +2026-04-13 15:37:18.782043: val_loss -0.3696 +2026-04-13 15:37:18.783753: Pseudo dice [0.3065, 0.0, 0.8027, 0.8211, 0.5045, 0.5497, 0.4311] +2026-04-13 15:37:18.785456: Epoch time: 101.55 s +2026-04-13 15:37:21.683738: +2026-04-13 15:37:21.685773: Epoch 2700 +2026-04-13 15:37:21.688426: Current learning rate: 0.00364 +2026-04-13 15:39:03.239496: train_loss -0.4351 +2026-04-13 15:39:03.244978: val_loss -0.3608 +2026-04-13 15:39:03.246700: Pseudo dice [0.3655, 0.0, 0.7606, 0.474, 0.3902, 0.7117, 0.6343] +2026-04-13 15:39:03.248546: Epoch time: 101.56 s +2026-04-13 15:39:04.435313: +2026-04-13 15:39:04.437766: Epoch 2701 +2026-04-13 15:39:04.440009: Current learning rate: 0.00363 +2026-04-13 15:40:45.923088: train_loss -0.4271 +2026-04-13 15:40:45.928155: val_loss -0.3585 +2026-04-13 15:40:45.930325: Pseudo dice [0.2817, 0.0, 0.6708, 0.817, 0.3146, 0.7448, 0.6854] +2026-04-13 15:40:45.932676: Epoch time: 101.49 s +2026-04-13 15:40:47.119279: +2026-04-13 15:40:47.121236: Epoch 2702 +2026-04-13 15:40:47.123650: Current learning rate: 0.00363 +2026-04-13 15:42:28.560705: train_loss -0.4191 +2026-04-13 15:42:28.565545: val_loss -0.3679 +2026-04-13 15:42:28.567909: Pseudo dice [0.1365, 0.0, 0.7887, 0.7366, 0.4711, 0.7916, 0.7283] +2026-04-13 15:42:28.570081: Epoch time: 101.44 s +2026-04-13 15:42:29.755847: +2026-04-13 15:42:29.757972: Epoch 2703 +2026-04-13 15:42:29.760426: Current learning rate: 0.00363 +2026-04-13 15:44:11.297111: train_loss -0.4155 +2026-04-13 15:44:11.302220: val_loss -0.3662 +2026-04-13 15:44:11.303740: Pseudo dice [0.251, 0.0, 0.5797, 0.1716, 0.2943, 0.7957, 0.8615] +2026-04-13 15:44:11.305589: Epoch time: 101.54 s +2026-04-13 15:44:12.496083: +2026-04-13 15:44:12.497501: Epoch 2704 +2026-04-13 15:44:12.499069: Current learning rate: 0.00363 +2026-04-13 15:45:54.854557: train_loss -0.424 +2026-04-13 15:45:54.860162: val_loss -0.3552 +2026-04-13 15:45:54.861780: Pseudo dice [0.2275, 0.0, 0.6952, 0.5668, 0.4639, 0.7798, 0.7026] +2026-04-13 15:45:54.863981: Epoch time: 102.36 s +2026-04-13 15:45:56.049248: +2026-04-13 15:45:56.050808: Epoch 2705 +2026-04-13 15:45:56.052771: Current learning rate: 0.00362 +2026-04-13 15:47:37.519784: train_loss -0.4406 +2026-04-13 15:47:37.524123: val_loss -0.3643 +2026-04-13 15:47:37.526237: Pseudo dice [0.3775, 0.0, 0.7058, 0.5434, 0.5123, 0.7433, 0.8615] +2026-04-13 15:47:37.528096: Epoch time: 101.47 s +2026-04-13 15:47:38.706622: +2026-04-13 15:47:38.708413: Epoch 2706 +2026-04-13 15:47:38.710401: Current learning rate: 0.00362 +2026-04-13 15:49:20.461120: train_loss -0.4299 +2026-04-13 15:49:20.466834: val_loss -0.3962 +2026-04-13 15:49:20.468554: Pseudo dice [0.2916, 0.0, 0.7221, 0.447, 0.4461, 0.7928, 0.8485] +2026-04-13 15:49:20.470128: Epoch time: 101.76 s +2026-04-13 15:49:21.645918: +2026-04-13 15:49:21.647711: Epoch 2707 +2026-04-13 15:49:21.649738: Current learning rate: 0.00362 +2026-04-13 15:51:03.416453: train_loss -0.4286 +2026-04-13 15:51:03.421390: val_loss -0.2994 +2026-04-13 15:51:03.423120: Pseudo dice [0.2641, 0.0, 0.3415, 0.5206, 0.257, 0.2372, 0.3008] +2026-04-13 15:51:03.424820: Epoch time: 101.77 s +2026-04-13 15:51:04.609810: +2026-04-13 15:51:04.611513: Epoch 2708 +2026-04-13 15:51:04.613423: Current learning rate: 0.00362 +2026-04-13 15:52:46.302275: train_loss -0.4308 +2026-04-13 15:52:46.307196: val_loss -0.3762 +2026-04-13 15:52:46.309359: Pseudo dice [0.4822, 0.0, 0.6902, 0.768, 0.5798, 0.6217, 0.8073] +2026-04-13 15:52:46.310854: Epoch time: 101.7 s +2026-04-13 15:52:47.497674: +2026-04-13 15:52:47.500469: Epoch 2709 +2026-04-13 15:52:47.502724: Current learning rate: 0.00361 +2026-04-13 15:54:29.093615: train_loss -0.4177 +2026-04-13 15:54:29.099074: val_loss -0.3372 +2026-04-13 15:54:29.100856: Pseudo dice [0.3102, 0.0, 0.6775, 0.753, 0.3803, 0.4786, 0.8117] +2026-04-13 15:54:29.102575: Epoch time: 101.6 s +2026-04-13 15:54:30.301028: +2026-04-13 15:54:30.304225: Epoch 2710 +2026-04-13 15:54:30.306338: Current learning rate: 0.00361 +2026-04-13 15:56:11.843612: train_loss -0.4223 +2026-04-13 15:56:11.853168: val_loss -0.3794 +2026-04-13 15:56:11.855300: Pseudo dice [0.279, 0.0, 0.5369, 0.593, 0.2912, 0.5007, 0.8802] +2026-04-13 15:56:11.856990: Epoch time: 101.55 s +2026-04-13 15:56:13.049774: +2026-04-13 15:56:13.051580: Epoch 2711 +2026-04-13 15:56:13.053604: Current learning rate: 0.00361 +2026-04-13 15:57:54.633673: train_loss -0.4269 +2026-04-13 15:57:54.638335: val_loss -0.3137 +2026-04-13 15:57:54.640092: Pseudo dice [0.7334, 0.0, 0.7199, 0.7637, 0.2366, 0.5296, 0.7811] +2026-04-13 15:57:54.641734: Epoch time: 101.59 s +2026-04-13 15:57:55.835898: +2026-04-13 15:57:55.838084: Epoch 2712 +2026-04-13 15:57:55.840300: Current learning rate: 0.00361 +2026-04-13 15:59:37.525061: train_loss -0.4355 +2026-04-13 15:59:37.535638: val_loss -0.3399 +2026-04-13 15:59:37.537192: Pseudo dice [0.264, 0.0, 0.733, 0.811, 0.3086, 0.5709, 0.4123] +2026-04-13 15:59:37.538640: Epoch time: 101.69 s +2026-04-13 15:59:38.711336: +2026-04-13 15:59:38.713849: Epoch 2713 +2026-04-13 15:59:38.716305: Current learning rate: 0.0036 +2026-04-13 16:01:20.287883: train_loss -0.4163 +2026-04-13 16:01:20.293298: val_loss -0.3343 +2026-04-13 16:01:20.294993: Pseudo dice [0.3521, 0.0, 0.7078, 0.2462, 0.4285, 0.661, 0.2455] +2026-04-13 16:01:20.296849: Epoch time: 101.58 s +2026-04-13 16:01:21.515738: +2026-04-13 16:01:21.517346: Epoch 2714 +2026-04-13 16:01:21.519389: Current learning rate: 0.0036 +2026-04-13 16:03:03.350148: train_loss -0.4353 +2026-04-13 16:03:03.354659: val_loss -0.3755 +2026-04-13 16:03:03.356332: Pseudo dice [0.2788, 0.0, 0.365, 0.6566, 0.1817, 0.7547, 0.8488] +2026-04-13 16:03:03.357742: Epoch time: 101.84 s +2026-04-13 16:03:04.537870: +2026-04-13 16:03:04.539410: Epoch 2715 +2026-04-13 16:03:04.541052: Current learning rate: 0.0036 +2026-04-13 16:04:46.104166: train_loss -0.4265 +2026-04-13 16:04:46.109454: val_loss -0.3658 +2026-04-13 16:04:46.111445: Pseudo dice [0.6044, 0.0, 0.3429, 0.7849, 0.0506, 0.6763, 0.8828] +2026-04-13 16:04:46.113174: Epoch time: 101.57 s +2026-04-13 16:04:47.284549: +2026-04-13 16:04:47.286290: Epoch 2716 +2026-04-13 16:04:47.288126: Current learning rate: 0.0036 +2026-04-13 16:06:28.840830: train_loss -0.411 +2026-04-13 16:06:28.846348: val_loss -0.3897 +2026-04-13 16:06:28.848171: Pseudo dice [0.6879, 0.0, 0.7703, 0.5268, 0.3392, 0.7738, 0.8339] +2026-04-13 16:06:28.850526: Epoch time: 101.56 s +2026-04-13 16:06:30.051824: +2026-04-13 16:06:30.054950: Epoch 2717 +2026-04-13 16:06:30.057694: Current learning rate: 0.00359 +2026-04-13 16:08:11.878860: train_loss -0.4228 +2026-04-13 16:08:11.884864: val_loss -0.3412 +2026-04-13 16:08:11.886685: Pseudo dice [0.4139, 0.0, 0.519, 0.7327, 0.2777, 0.5787, 0.751] +2026-04-13 16:08:11.888390: Epoch time: 101.83 s +2026-04-13 16:08:13.075018: +2026-04-13 16:08:13.076685: Epoch 2718 +2026-04-13 16:08:13.078593: Current learning rate: 0.00359 +2026-04-13 16:09:54.766302: train_loss -0.4153 +2026-04-13 16:09:54.772380: val_loss -0.3704 +2026-04-13 16:09:54.774115: Pseudo dice [0.4496, 0.0, 0.6933, 0.5804, 0.1633, 0.8312, 0.7999] +2026-04-13 16:09:54.775745: Epoch time: 101.69 s +2026-04-13 16:09:55.973209: +2026-04-13 16:09:55.975391: Epoch 2719 +2026-04-13 16:09:55.977100: Current learning rate: 0.00359 +2026-04-13 16:11:37.952953: train_loss -0.424 +2026-04-13 16:11:37.958976: val_loss -0.379 +2026-04-13 16:11:37.960998: Pseudo dice [0.7329, 0.0, 0.6001, 0.6656, 0.4774, 0.7938, 0.5434] +2026-04-13 16:11:37.962802: Epoch time: 101.98 s +2026-04-13 16:11:39.166116: +2026-04-13 16:11:39.168911: Epoch 2720 +2026-04-13 16:11:39.172255: Current learning rate: 0.00359 +2026-04-13 16:13:20.882274: train_loss -0.4221 +2026-04-13 16:13:20.888550: val_loss -0.3582 +2026-04-13 16:13:20.890054: Pseudo dice [0.0, 0.0, 0.5022, 0.7392, 0.4272, 0.5426, 0.8373] +2026-04-13 16:13:20.892368: Epoch time: 101.72 s +2026-04-13 16:13:22.077568: +2026-04-13 16:13:22.080239: Epoch 2721 +2026-04-13 16:13:22.082598: Current learning rate: 0.00358 +2026-04-13 16:15:03.970179: train_loss -0.3957 +2026-04-13 16:15:03.976916: val_loss -0.38 +2026-04-13 16:15:03.978669: Pseudo dice [0.0781, 0.0, 0.6157, 0.6037, 0.56, 0.6209, 0.6963] +2026-04-13 16:15:03.980826: Epoch time: 101.9 s +2026-04-13 16:15:05.170534: +2026-04-13 16:15:05.172295: Epoch 2722 +2026-04-13 16:15:05.174017: Current learning rate: 0.00358 +2026-04-13 16:16:46.779037: train_loss -0.4209 +2026-04-13 16:16:46.784298: val_loss -0.4157 +2026-04-13 16:16:46.785996: Pseudo dice [0.5432, 0.0, 0.6949, 0.8438, 0.5803, 0.8708, 0.8906] +2026-04-13 16:16:46.787445: Epoch time: 101.61 s +2026-04-13 16:16:47.959932: +2026-04-13 16:16:47.961846: Epoch 2723 +2026-04-13 16:16:47.963465: Current learning rate: 0.00358 +2026-04-13 16:18:29.654423: train_loss -0.4097 +2026-04-13 16:18:29.660227: val_loss -0.39 +2026-04-13 16:18:29.664033: Pseudo dice [0.5036, 0.0, 0.504, 0.8889, 0.6193, 0.7319, 0.8188] +2026-04-13 16:18:29.666299: Epoch time: 101.7 s +2026-04-13 16:18:30.835050: +2026-04-13 16:18:30.837256: Epoch 2724 +2026-04-13 16:18:30.839425: Current learning rate: 0.00358 +2026-04-13 16:20:12.365963: train_loss -0.4177 +2026-04-13 16:20:12.371471: val_loss -0.3555 +2026-04-13 16:20:12.373346: Pseudo dice [0.1028, 0.0, 0.7148, 0.0014, 0.3621, 0.7372, 0.8433] +2026-04-13 16:20:12.376635: Epoch time: 101.53 s +2026-04-13 16:20:14.561357: +2026-04-13 16:20:14.564580: Epoch 2725 +2026-04-13 16:20:14.567703: Current learning rate: 0.00357 +2026-04-13 16:21:56.280463: train_loss -0.404 +2026-04-13 16:21:56.286904: val_loss -0.3298 +2026-04-13 16:21:56.288728: Pseudo dice [0.1512, 0.0, 0.5412, 0.8395, 0.3226, 0.5731, 0.405] +2026-04-13 16:21:56.290934: Epoch time: 101.72 s +2026-04-13 16:21:57.473916: +2026-04-13 16:21:57.475666: Epoch 2726 +2026-04-13 16:21:57.477672: Current learning rate: 0.00357 +2026-04-13 16:23:39.025645: train_loss -0.4085 +2026-04-13 16:23:39.031016: val_loss -0.3574 +2026-04-13 16:23:39.032803: Pseudo dice [0.2793, 0.0, 0.7552, 0.7913, 0.2522, 0.6011, 0.7326] +2026-04-13 16:23:39.034389: Epoch time: 101.55 s +2026-04-13 16:23:40.230042: +2026-04-13 16:23:40.232065: Epoch 2727 +2026-04-13 16:23:40.234497: Current learning rate: 0.00357 +2026-04-13 16:25:21.835668: train_loss -0.4157 +2026-04-13 16:25:21.841883: val_loss -0.3593 +2026-04-13 16:25:21.844052: Pseudo dice [0.1857, 0.0, 0.6439, 0.6873, 0.2287, 0.5442, 0.656] +2026-04-13 16:25:21.845980: Epoch time: 101.61 s +2026-04-13 16:25:23.038071: +2026-04-13 16:25:23.040816: Epoch 2728 +2026-04-13 16:25:23.042687: Current learning rate: 0.00357 +2026-04-13 16:27:04.704794: train_loss -0.4127 +2026-04-13 16:27:04.709432: val_loss -0.3619 +2026-04-13 16:27:04.711894: Pseudo dice [0.5467, 0.0, 0.866, 0.8867, 0.1748, 0.2008, 0.8696] +2026-04-13 16:27:04.714526: Epoch time: 101.67 s +2026-04-13 16:27:05.919104: +2026-04-13 16:27:05.921428: Epoch 2729 +2026-04-13 16:27:05.923838: Current learning rate: 0.00356 +2026-04-13 16:28:47.754796: train_loss -0.4357 +2026-04-13 16:28:47.759997: val_loss -0.4221 +2026-04-13 16:28:47.762263: Pseudo dice [0.7421, 0.0, 0.7618, 0.8714, 0.4715, 0.8392, 0.8967] +2026-04-13 16:28:47.763781: Epoch time: 101.84 s +2026-04-13 16:28:48.929736: +2026-04-13 16:28:48.931470: Epoch 2730 +2026-04-13 16:28:48.933455: Current learning rate: 0.00356 +2026-04-13 16:30:30.671963: train_loss -0.4158 +2026-04-13 16:30:30.678769: val_loss -0.4263 +2026-04-13 16:30:30.680440: Pseudo dice [0.0329, 0.0, 0.5112, 0.7912, 0.5405, 0.7654, 0.9188] +2026-04-13 16:30:30.682503: Epoch time: 101.75 s +2026-04-13 16:30:31.908258: +2026-04-13 16:30:31.910415: Epoch 2731 +2026-04-13 16:30:31.912258: Current learning rate: 0.00356 +2026-04-13 16:32:13.602003: train_loss -0.4302 +2026-04-13 16:32:13.606922: val_loss -0.3687 +2026-04-13 16:32:13.609350: Pseudo dice [0.597, 0.0, 0.7095, 0.9029, 0.275, 0.8402, 0.7751] +2026-04-13 16:32:13.612226: Epoch time: 101.7 s +2026-04-13 16:32:14.811188: +2026-04-13 16:32:14.812987: Epoch 2732 +2026-04-13 16:32:14.814993: Current learning rate: 0.00356 +2026-04-13 16:33:56.372150: train_loss -0.4358 +2026-04-13 16:33:56.378674: val_loss -0.3908 +2026-04-13 16:33:56.380651: Pseudo dice [0.4312, 0.0, 0.7751, 0.2156, 0.5924, 0.781, 0.8953] +2026-04-13 16:33:56.382436: Epoch time: 101.56 s +2026-04-13 16:33:57.568450: +2026-04-13 16:33:57.570151: Epoch 2733 +2026-04-13 16:33:57.572140: Current learning rate: 0.00355 +2026-04-13 16:35:39.212850: train_loss -0.445 +2026-04-13 16:35:39.218121: val_loss -0.3891 +2026-04-13 16:35:39.219714: Pseudo dice [0.4271, 0.0, 0.5179, 0.8349, 0.217, 0.6953, 0.7845] +2026-04-13 16:35:39.221008: Epoch time: 101.65 s +2026-04-13 16:35:40.398710: +2026-04-13 16:35:40.400219: Epoch 2734 +2026-04-13 16:35:40.402001: Current learning rate: 0.00355 +2026-04-13 16:37:22.218349: train_loss -0.4414 +2026-04-13 16:37:22.224179: val_loss -0.396 +2026-04-13 16:37:22.225895: Pseudo dice [0.6366, 0.0, 0.7919, 0.8839, 0.3352, 0.764, 0.8661] +2026-04-13 16:37:22.227670: Epoch time: 101.82 s +2026-04-13 16:37:23.439789: +2026-04-13 16:37:23.441379: Epoch 2735 +2026-04-13 16:37:23.444501: Current learning rate: 0.00355 +2026-04-13 16:39:05.054205: train_loss -0.4263 +2026-04-13 16:39:05.058479: val_loss -0.3665 +2026-04-13 16:39:05.060062: Pseudo dice [0.3255, 0.0, 0.6393, 0.825, 0.3485, 0.7596, 0.3002] +2026-04-13 16:39:05.061614: Epoch time: 101.62 s +2026-04-13 16:39:06.249725: +2026-04-13 16:39:06.251540: Epoch 2736 +2026-04-13 16:39:06.252762: Current learning rate: 0.00355 +2026-04-13 16:40:47.828346: train_loss -0.4201 +2026-04-13 16:40:47.846029: val_loss -0.4044 +2026-04-13 16:40:47.847563: Pseudo dice [0.3467, 0.0, 0.6297, 0.8295, 0.645, 0.7618, 0.8421] +2026-04-13 16:40:47.849774: Epoch time: 101.58 s +2026-04-13 16:40:49.038291: +2026-04-13 16:40:49.040093: Epoch 2737 +2026-04-13 16:40:49.041643: Current learning rate: 0.00354 +2026-04-13 16:42:30.732227: train_loss -0.4177 +2026-04-13 16:42:30.739531: val_loss -0.3914 +2026-04-13 16:42:30.742078: Pseudo dice [0.6693, 0.0, 0.8238, 0.5946, 0.5079, 0.7601, 0.8937] +2026-04-13 16:42:30.743708: Epoch time: 101.7 s +2026-04-13 16:42:31.917758: +2026-04-13 16:42:31.919737: Epoch 2738 +2026-04-13 16:42:31.921839: Current learning rate: 0.00354 +2026-04-13 16:44:13.555735: train_loss -0.4154 +2026-04-13 16:44:13.562193: val_loss -0.3423 +2026-04-13 16:44:13.564365: Pseudo dice [0.2754, 0.0, 0.5824, 0.3719, 0.3693, 0.2001, 0.4154] +2026-04-13 16:44:13.566330: Epoch time: 101.64 s +2026-04-13 16:44:14.754983: +2026-04-13 16:44:14.759216: Epoch 2739 +2026-04-13 16:44:14.762813: Current learning rate: 0.00354 +2026-04-13 16:45:56.301637: train_loss -0.4298 +2026-04-13 16:45:56.307351: val_loss -0.4026 +2026-04-13 16:45:56.309526: Pseudo dice [0.8011, 0.0, 0.7287, 0.6498, 0.645, 0.7658, 0.7875] +2026-04-13 16:45:56.311342: Epoch time: 101.55 s +2026-04-13 16:45:57.502724: +2026-04-13 16:45:57.504551: Epoch 2740 +2026-04-13 16:45:57.506703: Current learning rate: 0.00354 +2026-04-13 16:47:38.937664: train_loss -0.4318 +2026-04-13 16:47:38.943003: val_loss -0.331 +2026-04-13 16:47:38.946092: Pseudo dice [0.3106, 0.0, 0.6918, 0.4828, 0.3108, 0.5357, 0.6285] +2026-04-13 16:47:38.947762: Epoch time: 101.44 s +2026-04-13 16:47:40.122460: +2026-04-13 16:47:40.124588: Epoch 2741 +2026-04-13 16:47:40.127125: Current learning rate: 0.00353 +2026-04-13 16:49:21.590932: train_loss -0.4374 +2026-04-13 16:49:21.611874: val_loss -0.3851 +2026-04-13 16:49:21.614120: Pseudo dice [0.6426, 0.0, 0.6306, 0.7984, 0.4459, 0.7412, 0.8224] +2026-04-13 16:49:21.616004: Epoch time: 101.47 s +2026-04-13 16:49:22.792299: +2026-04-13 16:49:22.794322: Epoch 2742 +2026-04-13 16:49:22.796268: Current learning rate: 0.00353 +2026-04-13 16:51:04.289154: train_loss -0.4322 +2026-04-13 16:51:04.294929: val_loss -0.3854 +2026-04-13 16:51:04.296982: Pseudo dice [0.6222, 0.0, 0.7961, 0.8468, 0.3846, 0.635, 0.8352] +2026-04-13 16:51:04.299493: Epoch time: 101.5 s +2026-04-13 16:51:05.504236: +2026-04-13 16:51:05.505746: Epoch 2743 +2026-04-13 16:51:05.507307: Current learning rate: 0.00353 +2026-04-13 16:52:46.911378: train_loss -0.4267 +2026-04-13 16:52:46.916960: val_loss -0.3588 +2026-04-13 16:52:46.918675: Pseudo dice [0.4634, 0.0, 0.7998, 0.7953, 0.3879, 0.6402, 0.7497] +2026-04-13 16:52:46.920993: Epoch time: 101.41 s +2026-04-13 16:52:48.120826: +2026-04-13 16:52:48.122772: Epoch 2744 +2026-04-13 16:52:48.124582: Current learning rate: 0.00353 +2026-04-13 16:54:29.606898: train_loss -0.4329 +2026-04-13 16:54:29.613827: val_loss -0.3405 +2026-04-13 16:54:29.615801: Pseudo dice [0.0263, 0.0, 0.5262, 0.4283, 0.2388, 0.2118, 0.8727] +2026-04-13 16:54:29.617723: Epoch time: 101.49 s +2026-04-13 16:54:30.812892: +2026-04-13 16:54:30.814652: Epoch 2745 +2026-04-13 16:54:30.816374: Current learning rate: 0.00352 +2026-04-13 16:56:13.428378: train_loss -0.4162 +2026-04-13 16:56:13.435159: val_loss -0.3525 +2026-04-13 16:56:13.437318: Pseudo dice [0.2383, 0.0, 0.1776, 0.7685, 0.5165, 0.4741, 0.7933] +2026-04-13 16:56:13.438814: Epoch time: 102.62 s +2026-04-13 16:56:14.612864: +2026-04-13 16:56:14.615717: Epoch 2746 +2026-04-13 16:56:14.617769: Current learning rate: 0.00352 +2026-04-13 16:57:56.087108: train_loss -0.4052 +2026-04-13 16:57:56.092607: val_loss -0.3778 +2026-04-13 16:57:56.094483: Pseudo dice [0.528, 0.0, 0.8538, 0.5691, 0.4946, 0.5303, 0.3704] +2026-04-13 16:57:56.096288: Epoch time: 101.48 s +2026-04-13 16:57:57.324159: +2026-04-13 16:57:57.325948: Epoch 2747 +2026-04-13 16:57:57.327488: Current learning rate: 0.00352 +2026-04-13 16:59:38.811290: train_loss -0.4183 +2026-04-13 16:59:38.815680: val_loss -0.358 +2026-04-13 16:59:38.817305: Pseudo dice [0.151, 0.0, 0.6599, 0.4746, 0.4021, 0.3102, 0.8004] +2026-04-13 16:59:38.819009: Epoch time: 101.49 s +2026-04-13 16:59:40.001937: +2026-04-13 16:59:40.003820: Epoch 2748 +2026-04-13 16:59:40.005378: Current learning rate: 0.00352 +2026-04-13 17:01:21.446769: train_loss -0.4015 +2026-04-13 17:01:21.452384: val_loss -0.3275 +2026-04-13 17:01:21.454335: Pseudo dice [0.3447, 0.0, 0.5543, 0.6337, 0.2614, 0.4132, 0.8385] +2026-04-13 17:01:21.456066: Epoch time: 101.45 s +2026-04-13 17:01:22.642149: +2026-04-13 17:01:22.644283: Epoch 2749 +2026-04-13 17:01:22.645947: Current learning rate: 0.00351 +2026-04-13 17:03:04.195713: train_loss -0.3973 +2026-04-13 17:03:04.200155: val_loss -0.3677 +2026-04-13 17:03:04.202554: Pseudo dice [0.4534, 0.0, 0.4931, 0.8243, 0.4743, 0.7342, 0.7833] +2026-04-13 17:03:04.204510: Epoch time: 101.56 s +2026-04-13 17:03:07.127958: +2026-04-13 17:03:07.130119: Epoch 2750 +2026-04-13 17:03:07.131869: Current learning rate: 0.00351 +2026-04-13 17:04:48.539050: train_loss -0.3958 +2026-04-13 17:04:48.544556: val_loss -0.3583 +2026-04-13 17:04:48.547200: Pseudo dice [0.2655, 0.0, 0.4048, 0.8303, 0.3997, 0.7428, 0.7593] +2026-04-13 17:04:48.549632: Epoch time: 101.41 s +2026-04-13 17:04:49.753621: +2026-04-13 17:04:49.755424: Epoch 2751 +2026-04-13 17:04:49.756724: Current learning rate: 0.00351 +2026-04-13 17:06:31.392562: train_loss -0.4237 +2026-04-13 17:06:31.396774: val_loss -0.3977 +2026-04-13 17:06:31.398626: Pseudo dice [0.3569, 0.0, 0.4589, 0.4415, 0.5332, 0.8731, 0.5272] +2026-04-13 17:06:31.400230: Epoch time: 101.64 s +2026-04-13 17:06:32.587434: +2026-04-13 17:06:32.588757: Epoch 2752 +2026-04-13 17:06:32.590189: Current learning rate: 0.00351 +2026-04-13 17:08:13.889361: train_loss -0.4258 +2026-04-13 17:08:13.894929: val_loss -0.3145 +2026-04-13 17:08:13.896879: Pseudo dice [0.329, 0.0, 0.7605, 0.9026, 0.2891, 0.7749, 0.4661] +2026-04-13 17:08:13.898940: Epoch time: 101.3 s +2026-04-13 17:08:15.087264: +2026-04-13 17:08:15.089109: Epoch 2753 +2026-04-13 17:08:15.090667: Current learning rate: 0.0035 +2026-04-13 17:09:56.539487: train_loss -0.3994 +2026-04-13 17:09:56.545057: val_loss -0.3405 +2026-04-13 17:09:56.546491: Pseudo dice [0.3756, 0.0, 0.6168, 0.1072, 0.3074, 0.6712, 0.572] +2026-04-13 17:09:56.548887: Epoch time: 101.46 s +2026-04-13 17:09:57.728993: +2026-04-13 17:09:57.730658: Epoch 2754 +2026-04-13 17:09:57.732166: Current learning rate: 0.0035 +2026-04-13 17:11:39.103409: train_loss -0.4378 +2026-04-13 17:11:39.109399: val_loss -0.3879 +2026-04-13 17:11:39.111037: Pseudo dice [0.571, 0.0, 0.5452, 0.1751, 0.5106, 0.5145, 0.8786] +2026-04-13 17:11:39.112636: Epoch time: 101.38 s +2026-04-13 17:11:40.293530: +2026-04-13 17:11:40.295444: Epoch 2755 +2026-04-13 17:11:40.297120: Current learning rate: 0.0035 +2026-04-13 17:13:21.672220: train_loss -0.4323 +2026-04-13 17:13:21.684191: val_loss -0.367 +2026-04-13 17:13:21.686200: Pseudo dice [0.4608, 0.0, 0.6869, 0.6586, 0.4039, 0.4598, 0.6425] +2026-04-13 17:13:21.687575: Epoch time: 101.38 s +2026-04-13 17:13:22.864401: +2026-04-13 17:13:22.866366: Epoch 2756 +2026-04-13 17:13:22.868300: Current learning rate: 0.0035 +2026-04-13 17:15:04.310698: train_loss -0.4234 +2026-04-13 17:15:04.317847: val_loss -0.3402 +2026-04-13 17:15:04.320075: Pseudo dice [0.3403, 0.0, 0.7357, 0.7641, 0.1786, 0.6709, 0.7257] +2026-04-13 17:15:04.322053: Epoch time: 101.45 s +2026-04-13 17:15:05.515353: +2026-04-13 17:15:05.517243: Epoch 2757 +2026-04-13 17:15:05.518726: Current learning rate: 0.00349 +2026-04-13 17:16:46.991967: train_loss -0.4238 +2026-04-13 17:16:46.996958: val_loss -0.3442 +2026-04-13 17:16:46.998678: Pseudo dice [0.2344, 0.0, 0.6753, 0.8937, 0.1639, 0.7239, 0.8373] +2026-04-13 17:16:47.000251: Epoch time: 101.48 s +2026-04-13 17:16:48.186165: +2026-04-13 17:16:48.187934: Epoch 2758 +2026-04-13 17:16:48.189728: Current learning rate: 0.00349 +2026-04-13 17:18:29.717383: train_loss -0.4137 +2026-04-13 17:18:29.724459: val_loss -0.3496 +2026-04-13 17:18:29.726969: Pseudo dice [0.5828, 0.0, 0.6349, 0.0024, 0.3504, 0.5152, 0.6293] +2026-04-13 17:18:29.729098: Epoch time: 101.53 s +2026-04-13 17:18:30.910916: +2026-04-13 17:18:30.913418: Epoch 2759 +2026-04-13 17:18:30.915882: Current learning rate: 0.00349 +2026-04-13 17:20:12.637574: train_loss -0.4007 +2026-04-13 17:20:12.643072: val_loss -0.3473 +2026-04-13 17:20:12.644923: Pseudo dice [0.0622, 0.0, 0.3214, 0.6803, 0.5073, 0.5957, 0.7557] +2026-04-13 17:20:12.647010: Epoch time: 101.73 s +2026-04-13 17:20:13.831462: +2026-04-13 17:20:13.833354: Epoch 2760 +2026-04-13 17:20:13.835322: Current learning rate: 0.00349 +2026-04-13 17:21:55.230275: train_loss -0.4288 +2026-04-13 17:21:55.236695: val_loss -0.373 +2026-04-13 17:21:55.238501: Pseudo dice [0.4306, 0.0, 0.7066, 0.3885, 0.4709, 0.8328, 0.8741] +2026-04-13 17:21:55.240682: Epoch time: 101.4 s +2026-04-13 17:21:56.433552: +2026-04-13 17:21:56.435473: Epoch 2761 +2026-04-13 17:21:56.437527: Current learning rate: 0.00348 +2026-04-13 17:23:37.964796: train_loss -0.4211 +2026-04-13 17:23:37.969991: val_loss -0.3759 +2026-04-13 17:23:37.971690: Pseudo dice [0.3666, 0.0, 0.8001, 0.7247, 0.3693, 0.5781, 0.909] +2026-04-13 17:23:37.973899: Epoch time: 101.53 s +2026-04-13 17:23:39.144422: +2026-04-13 17:23:39.146086: Epoch 2762 +2026-04-13 17:23:39.147657: Current learning rate: 0.00348 +2026-04-13 17:25:20.843408: train_loss -0.4189 +2026-04-13 17:25:20.849819: val_loss -0.3936 +2026-04-13 17:25:20.852204: Pseudo dice [0.5814, 0.0, 0.4053, 0.7693, 0.5135, 0.7474, 0.7625] +2026-04-13 17:25:20.854909: Epoch time: 101.7 s +2026-04-13 17:25:22.040271: +2026-04-13 17:25:22.043220: Epoch 2763 +2026-04-13 17:25:22.045690: Current learning rate: 0.00348 +2026-04-13 17:27:03.460581: train_loss -0.4048 +2026-04-13 17:27:03.466497: val_loss -0.3795 +2026-04-13 17:27:03.468439: Pseudo dice [0.2817, 0.0, 0.6481, 0.3451, 0.5878, 0.6498, 0.7342] +2026-04-13 17:27:03.470574: Epoch time: 101.42 s +2026-04-13 17:27:04.657952: +2026-04-13 17:27:04.659737: Epoch 2764 +2026-04-13 17:27:04.661144: Current learning rate: 0.00348 +2026-04-13 17:28:45.997202: train_loss -0.3914 +2026-04-13 17:28:46.002511: val_loss -0.3756 +2026-04-13 17:28:46.004297: Pseudo dice [0.6672, 0.0, 0.4861, 0.3224, 0.4423, 0.5403, 0.7446] +2026-04-13 17:28:46.006152: Epoch time: 101.34 s +2026-04-13 17:28:47.194303: +2026-04-13 17:28:47.195882: Epoch 2765 +2026-04-13 17:28:47.197597: Current learning rate: 0.00347 +2026-04-13 17:30:29.622697: train_loss -0.4126 +2026-04-13 17:30:29.627392: val_loss -0.4005 +2026-04-13 17:30:29.629117: Pseudo dice [0.6401, 0.0, 0.4502, 0.861, 0.5195, 0.6861, 0.857] +2026-04-13 17:30:29.630432: Epoch time: 102.43 s +2026-04-13 17:30:30.816258: +2026-04-13 17:30:30.817705: Epoch 2766 +2026-04-13 17:30:30.818980: Current learning rate: 0.00347 +2026-04-13 17:32:12.362915: train_loss -0.4337 +2026-04-13 17:32:12.369151: val_loss -0.4012 +2026-04-13 17:32:12.371223: Pseudo dice [0.6366, 0.0, 0.6371, 0.5477, 0.5352, 0.836, 0.8434] +2026-04-13 17:32:12.372877: Epoch time: 101.55 s +2026-04-13 17:32:13.575859: +2026-04-13 17:32:13.577886: Epoch 2767 +2026-04-13 17:32:13.579574: Current learning rate: 0.00347 +2026-04-13 17:33:55.049949: train_loss -0.4345 +2026-04-13 17:33:55.054868: val_loss -0.344 +2026-04-13 17:33:55.056937: Pseudo dice [0.2969, 0.0, 0.7349, 0.8118, 0.2769, 0.3005, 0.6694] +2026-04-13 17:33:55.058653: Epoch time: 101.48 s +2026-04-13 17:33:56.254465: +2026-04-13 17:33:56.256948: Epoch 2768 +2026-04-13 17:33:56.259003: Current learning rate: 0.00346 +2026-04-13 17:35:37.822684: train_loss -0.3945 +2026-04-13 17:35:37.834607: val_loss -0.2948 +2026-04-13 17:35:37.836528: Pseudo dice [0.509, 0.0, 0.3117, 0.474, 0.0755, 0.293, 0.5947] +2026-04-13 17:35:37.839172: Epoch time: 101.57 s +2026-04-13 17:35:39.029797: +2026-04-13 17:35:39.031612: Epoch 2769 +2026-04-13 17:35:39.032952: Current learning rate: 0.00346 +2026-04-13 17:37:20.570539: train_loss -0.3938 +2026-04-13 17:37:20.576384: val_loss -0.3398 +2026-04-13 17:37:20.578078: Pseudo dice [0.4851, 0.0, 0.5095, 0.5704, 0.2907, 0.5064, 0.8268] +2026-04-13 17:37:20.580087: Epoch time: 101.54 s +2026-04-13 17:37:21.771094: +2026-04-13 17:37:21.772640: Epoch 2770 +2026-04-13 17:37:21.774227: Current learning rate: 0.00346 +2026-04-13 17:39:03.234502: train_loss -0.4193 +2026-04-13 17:39:03.240077: val_loss -0.3632 +2026-04-13 17:39:03.241673: Pseudo dice [0.3455, 0.0, 0.5679, 0.6941, 0.3451, 0.7504, 0.8837] +2026-04-13 17:39:03.243423: Epoch time: 101.47 s +2026-04-13 17:39:04.421702: +2026-04-13 17:39:04.423216: Epoch 2771 +2026-04-13 17:39:04.424615: Current learning rate: 0.00346 +2026-04-13 17:40:45.870516: train_loss -0.4172 +2026-04-13 17:40:45.874647: val_loss -0.3688 +2026-04-13 17:40:45.876677: Pseudo dice [0.4857, 0.0, 0.6843, 0.2778, 0.4932, 0.6596, 0.7741] +2026-04-13 17:40:45.878422: Epoch time: 101.45 s +2026-04-13 17:40:47.081564: +2026-04-13 17:40:47.083936: Epoch 2772 +2026-04-13 17:40:47.085945: Current learning rate: 0.00345 +2026-04-13 17:42:28.727580: train_loss -0.432 +2026-04-13 17:42:28.733378: val_loss -0.3772 +2026-04-13 17:42:28.736172: Pseudo dice [0.3801, 0.0, 0.5865, 0.888, 0.4316, 0.745, 0.8264] +2026-04-13 17:42:28.738196: Epoch time: 101.65 s +2026-04-13 17:42:29.931122: +2026-04-13 17:42:29.932843: Epoch 2773 +2026-04-13 17:42:29.934273: Current learning rate: 0.00345 +2026-04-13 17:44:11.353192: train_loss -0.4405 +2026-04-13 17:44:11.357907: val_loss -0.367 +2026-04-13 17:44:11.360330: Pseudo dice [0.3874, 0.0, 0.7183, 0.7552, 0.2887, 0.3952, 0.8766] +2026-04-13 17:44:11.362927: Epoch time: 101.43 s +2026-04-13 17:44:12.549887: +2026-04-13 17:44:12.552614: Epoch 2774 +2026-04-13 17:44:12.555003: Current learning rate: 0.00345 +2026-04-13 17:45:54.131526: train_loss -0.4225 +2026-04-13 17:45:54.136965: val_loss -0.3608 +2026-04-13 17:45:54.140234: Pseudo dice [0.4367, 0.0, 0.5462, 0.8838, 0.4152, 0.6389, 0.4261] +2026-04-13 17:45:54.142340: Epoch time: 101.58 s +2026-04-13 17:45:55.334703: +2026-04-13 17:45:55.336185: Epoch 2775 +2026-04-13 17:45:55.337561: Current learning rate: 0.00345 +2026-04-13 17:47:36.846442: train_loss -0.4239 +2026-04-13 17:47:36.851495: val_loss -0.318 +2026-04-13 17:47:36.853276: Pseudo dice [0.765, 0.0, 0.7038, 0.219, 0.2252, 0.6774, 0.6968] +2026-04-13 17:47:36.854601: Epoch time: 101.51 s +2026-04-13 17:47:38.020695: +2026-04-13 17:47:38.022247: Epoch 2776 +2026-04-13 17:47:38.024616: Current learning rate: 0.00344 +2026-04-13 17:49:19.435793: train_loss -0.4164 +2026-04-13 17:49:19.440474: val_loss -0.3754 +2026-04-13 17:49:19.442274: Pseudo dice [0.3176, 0.0, 0.5259, 0.7784, 0.3989, 0.3715, 0.7779] +2026-04-13 17:49:19.443577: Epoch time: 101.42 s +2026-04-13 17:49:20.637484: +2026-04-13 17:49:20.639637: Epoch 2777 +2026-04-13 17:49:20.641568: Current learning rate: 0.00344 +2026-04-13 17:51:02.115529: train_loss -0.4116 +2026-04-13 17:51:02.121965: val_loss -0.3698 +2026-04-13 17:51:02.123500: Pseudo dice [0.5835, 0.0, 0.2999, 0.861, 0.4267, 0.5414, 0.6338] +2026-04-13 17:51:02.125878: Epoch time: 101.48 s +2026-04-13 17:51:03.313963: +2026-04-13 17:51:03.316641: Epoch 2778 +2026-04-13 17:51:03.318413: Current learning rate: 0.00344 +2026-04-13 17:52:44.752418: train_loss -0.4209 +2026-04-13 17:52:44.777899: val_loss -0.3707 +2026-04-13 17:52:44.779487: Pseudo dice [0.1406, 0.0, 0.5391, 0.8076, 0.6427, 0.6571, 0.7803] +2026-04-13 17:52:44.780805: Epoch time: 101.44 s +2026-04-13 17:52:45.958668: +2026-04-13 17:52:45.960176: Epoch 2779 +2026-04-13 17:52:45.961416: Current learning rate: 0.00344 +2026-04-13 17:54:27.457551: train_loss -0.4144 +2026-04-13 17:54:27.462934: val_loss -0.3743 +2026-04-13 17:54:27.465235: Pseudo dice [0.3471, 0.0, 0.7163, 0.8803, 0.2593, 0.7517, 0.7792] +2026-04-13 17:54:27.467116: Epoch time: 101.5 s +2026-04-13 17:54:28.650016: +2026-04-13 17:54:28.651778: Epoch 2780 +2026-04-13 17:54:28.653500: Current learning rate: 0.00343 +2026-04-13 17:56:10.124803: train_loss -0.4383 +2026-04-13 17:56:10.129780: val_loss -0.401 +2026-04-13 17:56:10.131564: Pseudo dice [0.6147, 0.0, 0.7971, 0.8356, 0.6065, 0.5621, 0.8846] +2026-04-13 17:56:10.133330: Epoch time: 101.48 s +2026-04-13 17:56:11.315580: +2026-04-13 17:56:11.318433: Epoch 2781 +2026-04-13 17:56:11.320603: Current learning rate: 0.00343 +2026-04-13 17:57:52.932324: train_loss -0.4119 +2026-04-13 17:57:52.937587: val_loss -0.3554 +2026-04-13 17:57:52.939440: Pseudo dice [0.0456, 0.0, 0.5919, 0.7281, 0.3504, 0.7584, 0.3373] +2026-04-13 17:57:52.941190: Epoch time: 101.62 s +2026-04-13 17:57:54.126851: +2026-04-13 17:57:54.128472: Epoch 2782 +2026-04-13 17:57:54.130074: Current learning rate: 0.00343 +2026-04-13 17:59:35.548672: train_loss -0.4251 +2026-04-13 17:59:35.553635: val_loss -0.3984 +2026-04-13 17:59:35.555406: Pseudo dice [0.5601, 0.0, 0.7586, 0.5812, 0.3298, 0.8287, 0.6984] +2026-04-13 17:59:35.556818: Epoch time: 101.42 s +2026-04-13 17:59:36.754082: +2026-04-13 17:59:36.757296: Epoch 2783 +2026-04-13 17:59:36.759026: Current learning rate: 0.00343 +2026-04-13 18:01:18.145014: train_loss -0.4208 +2026-04-13 18:01:18.149013: val_loss -0.382 +2026-04-13 18:01:18.150540: Pseudo dice [0.3275, 0.0, 0.7289, 0.8553, 0.1595, 0.8173, 0.8985] +2026-04-13 18:01:18.151937: Epoch time: 101.39 s +2026-04-13 18:01:19.323956: +2026-04-13 18:01:19.325346: Epoch 2784 +2026-04-13 18:01:19.326589: Current learning rate: 0.00342 +2026-04-13 18:03:00.809604: train_loss -0.4165 +2026-04-13 18:03:00.815216: val_loss -0.3816 +2026-04-13 18:03:00.817181: Pseudo dice [0.2945, 0.0, 0.5304, 0.8726, 0.4534, 0.7491, 0.7253] +2026-04-13 18:03:00.820035: Epoch time: 101.49 s +2026-04-13 18:03:02.012722: +2026-04-13 18:03:02.015205: Epoch 2785 +2026-04-13 18:03:02.017356: Current learning rate: 0.00342 +2026-04-13 18:04:43.496996: train_loss -0.4334 +2026-04-13 18:04:43.505055: val_loss -0.3735 +2026-04-13 18:04:43.506932: Pseudo dice [0.6662, 0.0, 0.7062, 0.5437, 0.3038, 0.7141, 0.7323] +2026-04-13 18:04:43.508860: Epoch time: 101.49 s +2026-04-13 18:04:45.666816: +2026-04-13 18:04:45.669115: Epoch 2786 +2026-04-13 18:04:45.671011: Current learning rate: 0.00342 +2026-04-13 18:06:27.169389: train_loss -0.4247 +2026-04-13 18:06:27.175909: val_loss -0.361 +2026-04-13 18:06:27.177846: Pseudo dice [0.3643, 0.0, 0.5647, 0.3974, 0.5183, 0.5594, 0.5863] +2026-04-13 18:06:27.180540: Epoch time: 101.51 s +2026-04-13 18:06:28.375138: +2026-04-13 18:06:28.376957: Epoch 2787 +2026-04-13 18:06:28.379115: Current learning rate: 0.00342 +2026-04-13 18:08:09.783929: train_loss -0.4315 +2026-04-13 18:08:09.791011: val_loss -0.3753 +2026-04-13 18:08:09.793109: Pseudo dice [0.1194, 0.0, 0.7194, 0.7363, 0.3857, 0.6439, 0.763] +2026-04-13 18:08:09.795161: Epoch time: 101.41 s +2026-04-13 18:08:10.989450: +2026-04-13 18:08:10.993331: Epoch 2788 +2026-04-13 18:08:10.995589: Current learning rate: 0.00341 +2026-04-13 18:09:52.495270: train_loss -0.4198 +2026-04-13 18:09:52.500752: val_loss -0.3793 +2026-04-13 18:09:52.502748: Pseudo dice [0.7056, 0.0, 0.7786, 0.08, 0.3422, 0.782, 0.8871] +2026-04-13 18:09:52.504481: Epoch time: 101.51 s +2026-04-13 18:09:53.695287: +2026-04-13 18:09:53.697047: Epoch 2789 +2026-04-13 18:09:53.698657: Current learning rate: 0.00341 +2026-04-13 18:11:35.165787: train_loss -0.4138 +2026-04-13 18:11:35.172307: val_loss -0.3396 +2026-04-13 18:11:35.174621: Pseudo dice [0.2697, 0.0, 0.5714, 0.2895, 0.3205, 0.483, 0.8953] +2026-04-13 18:11:35.177181: Epoch time: 101.47 s +2026-04-13 18:11:36.369786: +2026-04-13 18:11:36.372462: Epoch 2790 +2026-04-13 18:11:36.374185: Current learning rate: 0.00341 +2026-04-13 18:13:17.842205: train_loss -0.3982 +2026-04-13 18:13:17.847716: val_loss -0.3747 +2026-04-13 18:13:17.849572: Pseudo dice [0.2335, 0.0, 0.7738, 0.8572, 0.3582, 0.5951, 0.7397] +2026-04-13 18:13:17.851578: Epoch time: 101.48 s +2026-04-13 18:13:19.034815: +2026-04-13 18:13:19.036924: Epoch 2791 +2026-04-13 18:13:19.038424: Current learning rate: 0.00341 +2026-04-13 18:15:00.341284: train_loss -0.4143 +2026-04-13 18:15:00.347119: val_loss -0.4146 +2026-04-13 18:15:00.348777: Pseudo dice [0.7753, 0.0, 0.8896, 0.8634, 0.4942, 0.6364, 0.8113] +2026-04-13 18:15:00.351145: Epoch time: 101.31 s +2026-04-13 18:15:01.545269: +2026-04-13 18:15:01.547757: Epoch 2792 +2026-04-13 18:15:01.549762: Current learning rate: 0.0034 +2026-04-13 18:16:42.823669: train_loss -0.4227 +2026-04-13 18:16:42.828193: val_loss -0.377 +2026-04-13 18:16:42.830039: Pseudo dice [0.5585, 0.0, 0.7169, 0.6484, 0.4637, 0.5373, 0.8625] +2026-04-13 18:16:42.831575: Epoch time: 101.28 s +2026-04-13 18:16:44.025774: +2026-04-13 18:16:44.027495: Epoch 2793 +2026-04-13 18:16:44.029476: Current learning rate: 0.0034 +2026-04-13 18:18:25.357365: train_loss -0.434 +2026-04-13 18:18:25.363949: val_loss -0.3583 +2026-04-13 18:18:25.365552: Pseudo dice [0.7909, 0.0, 0.3909, 0.8298, 0.3557, 0.7661, 0.403] +2026-04-13 18:18:25.369509: Epoch time: 101.33 s +2026-04-13 18:18:26.550297: +2026-04-13 18:18:26.552021: Epoch 2794 +2026-04-13 18:18:26.553613: Current learning rate: 0.0034 +2026-04-13 18:20:08.079273: train_loss -0.4335 +2026-04-13 18:20:08.090223: val_loss -0.331 +2026-04-13 18:20:08.092294: Pseudo dice [0.5233, 0.0, 0.7241, 0.8518, 0.2567, 0.7507, 0.2901] +2026-04-13 18:20:08.094043: Epoch time: 101.53 s +2026-04-13 18:20:09.290718: +2026-04-13 18:20:09.292633: Epoch 2795 +2026-04-13 18:20:09.294251: Current learning rate: 0.0034 +2026-04-13 18:21:50.676484: train_loss -0.4224 +2026-04-13 18:21:50.681037: val_loss -0.3889 +2026-04-13 18:21:50.683342: Pseudo dice [0.4597, 0.0, 0.6384, 0.8453, 0.3641, 0.8359, 0.765] +2026-04-13 18:21:50.684899: Epoch time: 101.39 s +2026-04-13 18:21:51.870528: +2026-04-13 18:21:51.872267: Epoch 2796 +2026-04-13 18:21:51.873727: Current learning rate: 0.00339 +2026-04-13 18:23:33.294882: train_loss -0.4224 +2026-04-13 18:23:33.299894: val_loss -0.394 +2026-04-13 18:23:33.302015: Pseudo dice [0.4919, 0.0, 0.7603, 0.8954, 0.4591, 0.656, 0.7558] +2026-04-13 18:23:33.304173: Epoch time: 101.43 s +2026-04-13 18:23:34.493697: +2026-04-13 18:23:34.495412: Epoch 2797 +2026-04-13 18:23:34.497056: Current learning rate: 0.00339 +2026-04-13 18:25:16.005732: train_loss -0.4168 +2026-04-13 18:25:16.010496: val_loss -0.3701 +2026-04-13 18:25:16.011977: Pseudo dice [0.6923, 0.0, 0.4386, 0.8435, 0.2698, 0.7616, 0.7542] +2026-04-13 18:25:16.013207: Epoch time: 101.52 s +2026-04-13 18:25:17.192039: +2026-04-13 18:25:17.194186: Epoch 2798 +2026-04-13 18:25:17.195799: Current learning rate: 0.00339 +2026-04-13 18:26:58.721926: train_loss -0.4136 +2026-04-13 18:26:58.727718: val_loss -0.3855 +2026-04-13 18:26:58.729436: Pseudo dice [0.6601, 0.0, 0.6478, 0.7449, 0.5357, 0.8557, 0.7133] +2026-04-13 18:26:58.731235: Epoch time: 101.53 s +2026-04-13 18:26:59.910010: +2026-04-13 18:26:59.912066: Epoch 2799 +2026-04-13 18:26:59.914250: Current learning rate: 0.00339 +2026-04-13 18:28:41.504064: train_loss -0.4131 +2026-04-13 18:28:41.508663: val_loss -0.3459 +2026-04-13 18:28:41.510494: Pseudo dice [0.4059, 0.0, 0.4309, 0.8056, 0.4007, 0.7175, 0.5039] +2026-04-13 18:28:41.512190: Epoch time: 101.6 s +2026-04-13 18:28:44.392225: +2026-04-13 18:28:44.393857: Epoch 2800 +2026-04-13 18:28:44.395382: Current learning rate: 0.00338 +2026-04-13 18:30:25.839666: train_loss -0.4243 +2026-04-13 18:30:25.844523: val_loss -0.3616 +2026-04-13 18:30:25.846378: Pseudo dice [0.6102, 0.0, 0.6562, 0.6099, 0.3945, 0.8005, 0.5221] +2026-04-13 18:30:25.847890: Epoch time: 101.45 s +2026-04-13 18:30:27.036444: +2026-04-13 18:30:27.038497: Epoch 2801 +2026-04-13 18:30:27.040099: Current learning rate: 0.00338 +2026-04-13 18:32:08.540535: train_loss -0.4176 +2026-04-13 18:32:08.544769: val_loss -0.4008 +2026-04-13 18:32:08.546101: Pseudo dice [0.6005, 0.0, 0.6939, 0.7997, 0.5452, 0.7126, 0.8197] +2026-04-13 18:32:08.547522: Epoch time: 101.51 s +2026-04-13 18:32:09.734613: +2026-04-13 18:32:09.736241: Epoch 2802 +2026-04-13 18:32:09.738369: Current learning rate: 0.00338 +2026-04-13 18:33:51.294337: train_loss -0.4245 +2026-04-13 18:33:51.299336: val_loss -0.3368 +2026-04-13 18:33:51.301951: Pseudo dice [0.5034, 0.0, 0.7851, 0.0741, 0.0886, 0.8192, 0.4584] +2026-04-13 18:33:51.303592: Epoch time: 101.56 s +2026-04-13 18:33:52.488637: +2026-04-13 18:33:52.490654: Epoch 2803 +2026-04-13 18:33:52.492441: Current learning rate: 0.00338 +2026-04-13 18:35:34.188725: train_loss -0.427 +2026-04-13 18:35:34.193493: val_loss -0.3921 +2026-04-13 18:35:34.195393: Pseudo dice [0.6655, 0.0, 0.6706, 0.893, 0.6031, 0.7701, 0.8296] +2026-04-13 18:35:34.197076: Epoch time: 101.7 s +2026-04-13 18:35:35.384983: +2026-04-13 18:35:35.392859: Epoch 2804 +2026-04-13 18:35:35.394392: Current learning rate: 0.00337 +2026-04-13 18:37:17.087276: train_loss -0.414 +2026-04-13 18:37:17.092736: val_loss -0.3718 +2026-04-13 18:37:17.094728: Pseudo dice [0.4451, 0.0, 0.6784, 0.6673, 0.1416, 0.7063, 0.6608] +2026-04-13 18:37:17.096349: Epoch time: 101.71 s +2026-04-13 18:37:18.280976: +2026-04-13 18:37:18.282487: Epoch 2805 +2026-04-13 18:37:18.283983: Current learning rate: 0.00337 +2026-04-13 18:38:59.935504: train_loss -0.4183 +2026-04-13 18:38:59.940803: val_loss -0.3465 +2026-04-13 18:38:59.942907: Pseudo dice [0.5109, 0.0, 0.7448, 0.3835, 0.1317, 0.7726, 0.8709] +2026-04-13 18:38:59.944850: Epoch time: 101.66 s +2026-04-13 18:39:02.129209: +2026-04-13 18:39:02.130797: Epoch 2806 +2026-04-13 18:39:02.132272: Current learning rate: 0.00337 +2026-04-13 18:40:43.900362: train_loss -0.4096 +2026-04-13 18:40:43.905911: val_loss -0.3605 +2026-04-13 18:40:43.907650: Pseudo dice [0.2861, 0.0, 0.7774, 0.2466, 0.2573, 0.654, 0.6144] +2026-04-13 18:40:43.909026: Epoch time: 101.77 s +2026-04-13 18:40:45.078229: +2026-04-13 18:40:45.080095: Epoch 2807 +2026-04-13 18:40:45.082062: Current learning rate: 0.00337 +2026-04-13 18:42:26.619605: train_loss -0.4222 +2026-04-13 18:42:26.624930: val_loss -0.3809 +2026-04-13 18:42:26.626609: Pseudo dice [0.7107, 0.0, 0.7402, 0.1718, 0.3013, 0.6031, 0.8193] +2026-04-13 18:42:26.628372: Epoch time: 101.54 s +2026-04-13 18:42:27.792812: +2026-04-13 18:42:27.794888: Epoch 2808 +2026-04-13 18:42:27.796545: Current learning rate: 0.00336 +2026-04-13 18:44:09.178964: train_loss -0.4338 +2026-04-13 18:44:09.184558: val_loss -0.3943 +2026-04-13 18:44:09.186215: Pseudo dice [0.5938, 0.0, 0.7292, 0.9043, 0.4136, 0.8166, 0.5766] +2026-04-13 18:44:09.187618: Epoch time: 101.39 s +2026-04-13 18:44:10.370819: +2026-04-13 18:44:10.375856: Epoch 2809 +2026-04-13 18:44:10.377410: Current learning rate: 0.00336 +2026-04-13 18:45:51.738510: train_loss -0.4209 +2026-04-13 18:45:51.743065: val_loss -0.3645 +2026-04-13 18:45:51.744799: Pseudo dice [0.3731, 0.0, 0.6162, 0.6564, 0.3833, 0.8216, 0.6732] +2026-04-13 18:45:51.746393: Epoch time: 101.37 s +2026-04-13 18:45:52.937136: +2026-04-13 18:45:52.939443: Epoch 2810 +2026-04-13 18:45:52.941321: Current learning rate: 0.00336 +2026-04-13 18:47:34.353723: train_loss -0.4101 +2026-04-13 18:47:34.358397: val_loss -0.3807 +2026-04-13 18:47:34.361289: Pseudo dice [0.3562, 0.0, 0.4442, 0.7587, 0.4718, 0.8297, 0.5665] +2026-04-13 18:47:34.363110: Epoch time: 101.42 s +2026-04-13 18:47:35.530081: +2026-04-13 18:47:35.532420: Epoch 2811 +2026-04-13 18:47:35.534951: Current learning rate: 0.00336 +2026-04-13 18:49:16.850757: train_loss -0.4167 +2026-04-13 18:49:16.856860: val_loss -0.3731 +2026-04-13 18:49:16.858879: Pseudo dice [0.6109, 0.0, 0.5351, 0.4849, 0.2812, 0.7642, 0.5749] +2026-04-13 18:49:16.860833: Epoch time: 101.32 s +2026-04-13 18:49:18.066500: +2026-04-13 18:49:18.068450: Epoch 2812 +2026-04-13 18:49:18.070252: Current learning rate: 0.00335 +2026-04-13 18:50:59.519436: train_loss -0.4121 +2026-04-13 18:50:59.523693: val_loss -0.3701 +2026-04-13 18:50:59.525637: Pseudo dice [0.3255, 0.0, 0.7057, 0.8545, 0.3826, 0.5812, 0.8689] +2026-04-13 18:50:59.527100: Epoch time: 101.46 s +2026-04-13 18:51:00.710819: +2026-04-13 18:51:00.713323: Epoch 2813 +2026-04-13 18:51:00.715142: Current learning rate: 0.00335 +2026-04-13 18:52:41.968737: train_loss -0.4338 +2026-04-13 18:52:41.974379: val_loss -0.3609 +2026-04-13 18:52:41.976356: Pseudo dice [0.3997, 0.0, 0.7254, 0.745, 0.4392, 0.3126, 0.8235] +2026-04-13 18:52:41.978269: Epoch time: 101.26 s +2026-04-13 18:52:43.165385: +2026-04-13 18:52:43.168561: Epoch 2814 +2026-04-13 18:52:43.170252: Current learning rate: 0.00335 +2026-04-13 18:54:24.662092: train_loss -0.4196 +2026-04-13 18:54:24.667027: val_loss -0.3843 +2026-04-13 18:54:24.670408: Pseudo dice [0.351, 0.0, 0.5342, 0.8289, 0.3348, 0.7705, 0.9137] +2026-04-13 18:54:24.672214: Epoch time: 101.5 s +2026-04-13 18:54:25.849225: +2026-04-13 18:54:25.850871: Epoch 2815 +2026-04-13 18:54:25.852614: Current learning rate: 0.00335 +2026-04-13 18:56:07.201362: train_loss -0.4245 +2026-04-13 18:56:07.207310: val_loss -0.4032 +2026-04-13 18:56:07.208857: Pseudo dice [0.4025, 0.0, 0.5874, 0.6999, 0.4623, 0.822, 0.8324] +2026-04-13 18:56:07.210930: Epoch time: 101.36 s +2026-04-13 18:56:08.430175: +2026-04-13 18:56:08.432652: Epoch 2816 +2026-04-13 18:56:08.434481: Current learning rate: 0.00334 +2026-04-13 18:57:49.803691: train_loss -0.4372 +2026-04-13 18:57:49.809067: val_loss -0.3529 +2026-04-13 18:57:49.810858: Pseudo dice [0.4757, 0.0, 0.5845, 0.822, 0.2803, 0.6317, 0.832] +2026-04-13 18:57:49.812552: Epoch time: 101.38 s +2026-04-13 18:57:51.004035: +2026-04-13 18:57:51.006280: Epoch 2817 +2026-04-13 18:57:51.008551: Current learning rate: 0.00334 +2026-04-13 18:59:32.517099: train_loss -0.4323 +2026-04-13 18:59:32.522819: val_loss -0.3836 +2026-04-13 18:59:32.524584: Pseudo dice [0.6529, 0.0, 0.6052, 0.7787, 0.5828, 0.5134, 0.6995] +2026-04-13 18:59:32.526849: Epoch time: 101.52 s +2026-04-13 18:59:33.723029: +2026-04-13 18:59:33.724466: Epoch 2818 +2026-04-13 18:59:33.725944: Current learning rate: 0.00334 +2026-04-13 19:01:15.189196: train_loss -0.4269 +2026-04-13 19:01:15.193894: val_loss -0.3718 +2026-04-13 19:01:15.195843: Pseudo dice [0.3442, 0.0, 0.7042, 0.1873, 0.3811, 0.7639, 0.8594] +2026-04-13 19:01:15.197289: Epoch time: 101.47 s +2026-04-13 19:01:16.365973: +2026-04-13 19:01:16.367826: Epoch 2819 +2026-04-13 19:01:16.369352: Current learning rate: 0.00334 +2026-04-13 19:02:57.779643: train_loss -0.4362 +2026-04-13 19:02:57.783710: val_loss -0.4056 +2026-04-13 19:02:57.785171: Pseudo dice [0.2398, 0.0, 0.7227, 0.7961, 0.5274, 0.6219, 0.8814] +2026-04-13 19:02:57.787275: Epoch time: 101.42 s +2026-04-13 19:02:58.954785: +2026-04-13 19:02:58.956321: Epoch 2820 +2026-04-13 19:02:58.957882: Current learning rate: 0.00333 +2026-04-13 19:04:40.560002: train_loss -0.4325 +2026-04-13 19:04:40.565688: val_loss -0.361 +2026-04-13 19:04:40.567428: Pseudo dice [0.2375, 0.0, 0.7484, 0.743, 0.2404, 0.4972, 0.7363] +2026-04-13 19:04:40.569977: Epoch time: 101.61 s +2026-04-13 19:04:41.746326: +2026-04-13 19:04:41.748707: Epoch 2821 +2026-04-13 19:04:41.750370: Current learning rate: 0.00333 +2026-04-13 19:06:23.047836: train_loss -0.4274 +2026-04-13 19:06:23.052304: val_loss -0.3891 +2026-04-13 19:06:23.054126: Pseudo dice [0.4797, 0.0, 0.8047, 0.8048, 0.5564, 0.7556, 0.8791] +2026-04-13 19:06:23.056504: Epoch time: 101.3 s +2026-04-13 19:06:24.238706: +2026-04-13 19:06:24.240862: Epoch 2822 +2026-04-13 19:06:24.242721: Current learning rate: 0.00333 +2026-04-13 19:08:05.766612: train_loss -0.4203 +2026-04-13 19:08:05.771841: val_loss -0.3994 +2026-04-13 19:08:05.773549: Pseudo dice [0.7511, 0.0, 0.7437, 0.8183, 0.4668, 0.718, 0.7322] +2026-04-13 19:08:05.774964: Epoch time: 101.53 s +2026-04-13 19:08:06.981668: +2026-04-13 19:08:06.983649: Epoch 2823 +2026-04-13 19:08:06.984931: Current learning rate: 0.00333 +2026-04-13 19:09:48.574607: train_loss -0.4158 +2026-04-13 19:09:48.590448: val_loss -0.3928 +2026-04-13 19:09:48.599945: Pseudo dice [0.3429, 0.0, 0.8491, 0.8829, 0.5289, 0.7704, 0.8781] +2026-04-13 19:09:48.602027: Epoch time: 101.6 s +2026-04-13 19:09:49.789203: +2026-04-13 19:09:49.790659: Epoch 2824 +2026-04-13 19:09:49.792053: Current learning rate: 0.00332 +2026-04-13 19:11:31.350336: train_loss -0.4329 +2026-04-13 19:11:31.354947: val_loss -0.3952 +2026-04-13 19:11:31.356506: Pseudo dice [0.7187, 0.0, 0.7128, 0.4321, 0.5844, 0.6961, 0.9118] +2026-04-13 19:11:31.358167: Epoch time: 101.56 s +2026-04-13 19:11:32.550030: +2026-04-13 19:11:32.551564: Epoch 2825 +2026-04-13 19:11:32.553216: Current learning rate: 0.00332 +2026-04-13 19:13:14.045752: train_loss -0.4442 +2026-04-13 19:13:14.050482: val_loss -0.3719 +2026-04-13 19:13:14.052073: Pseudo dice [0.3749, 0.0, 0.7745, 0.6013, 0.2573, 0.8253, 0.8925] +2026-04-13 19:13:14.053732: Epoch time: 101.5 s +2026-04-13 19:13:15.244003: +2026-04-13 19:13:15.253739: Epoch 2826 +2026-04-13 19:13:15.256828: Current learning rate: 0.00332 +2026-04-13 19:14:56.968177: train_loss -0.4268 +2026-04-13 19:14:56.973851: val_loss -0.4026 +2026-04-13 19:14:56.977756: Pseudo dice [0.6053, 0.0, 0.5512, 0.704, 0.5957, 0.7747, 0.8243] +2026-04-13 19:14:56.979549: Epoch time: 101.73 s +2026-04-13 19:14:59.202954: +2026-04-13 19:14:59.205410: Epoch 2827 +2026-04-13 19:14:59.207281: Current learning rate: 0.00332 +2026-04-13 19:16:40.634367: train_loss -0.4432 +2026-04-13 19:16:40.641175: val_loss -0.4131 +2026-04-13 19:16:40.644029: Pseudo dice [0.7362, 0.0, 0.8354, 0.7739, 0.5037, 0.8348, 0.8663] +2026-04-13 19:16:40.645880: Epoch time: 101.43 s +2026-04-13 19:16:40.648131: Yayy! New best EMA pseudo Dice: 0.5534 +2026-04-13 19:16:43.501495: +2026-04-13 19:16:43.503283: Epoch 2828 +2026-04-13 19:16:43.504841: Current learning rate: 0.00331 +2026-04-13 19:18:25.191630: train_loss -0.438 +2026-04-13 19:18:25.197268: val_loss -0.3911 +2026-04-13 19:18:25.199485: Pseudo dice [0.6255, 0.0, 0.6699, 0.8358, 0.3907, 0.4232, 0.7656] +2026-04-13 19:18:25.201560: Epoch time: 101.69 s +2026-04-13 19:18:26.378129: +2026-04-13 19:18:26.380032: Epoch 2829 +2026-04-13 19:18:26.381837: Current learning rate: 0.00331 +2026-04-13 19:20:07.793013: train_loss -0.4129 +2026-04-13 19:20:07.799287: val_loss -0.3849 +2026-04-13 19:20:07.800974: Pseudo dice [0.2525, 0.0, 0.8306, 0.7167, 0.4901, 0.6368, 0.5277] +2026-04-13 19:20:07.802915: Epoch time: 101.42 s +2026-04-13 19:20:08.983677: +2026-04-13 19:20:08.986239: Epoch 2830 +2026-04-13 19:20:08.987941: Current learning rate: 0.00331 +2026-04-13 19:21:50.447509: train_loss -0.4105 +2026-04-13 19:21:50.452477: val_loss -0.3826 +2026-04-13 19:21:50.454468: Pseudo dice [0.3098, 0.0, 0.6942, 0.8758, 0.2596, 0.8009, 0.8344] +2026-04-13 19:21:50.456030: Epoch time: 101.47 s +2026-04-13 19:21:51.659295: +2026-04-13 19:21:51.661500: Epoch 2831 +2026-04-13 19:21:51.663147: Current learning rate: 0.00331 +2026-04-13 19:23:33.135600: train_loss -0.426 +2026-04-13 19:23:33.141251: val_loss -0.3442 +2026-04-13 19:23:33.143362: Pseudo dice [0.6472, 0.0, 0.6483, 0.4272, 0.4568, 0.7834, 0.3853] +2026-04-13 19:23:33.145452: Epoch time: 101.48 s +2026-04-13 19:23:34.344744: +2026-04-13 19:23:34.346627: Epoch 2832 +2026-04-13 19:23:34.348105: Current learning rate: 0.0033 +2026-04-13 19:25:15.693144: train_loss -0.3958 +2026-04-13 19:25:15.699644: val_loss -0.3485 +2026-04-13 19:25:15.701810: Pseudo dice [0.6811, 0.0, 0.7013, 0.2633, 0.2564, 0.6484, 0.8308] +2026-04-13 19:25:15.703470: Epoch time: 101.35 s +2026-04-13 19:25:16.899296: +2026-04-13 19:25:16.901688: Epoch 2833 +2026-04-13 19:25:16.903959: Current learning rate: 0.0033 +2026-04-13 19:26:58.407090: train_loss -0.4134 +2026-04-13 19:26:58.412205: val_loss -0.3778 +2026-04-13 19:26:58.414129: Pseudo dice [0.6581, 0.0, 0.5325, 0.8957, 0.1082, 0.7065, 0.6329] +2026-04-13 19:26:58.415550: Epoch time: 101.51 s +2026-04-13 19:26:59.577973: +2026-04-13 19:26:59.579572: Epoch 2834 +2026-04-13 19:26:59.581176: Current learning rate: 0.0033 +2026-04-13 19:28:40.914458: train_loss -0.4258 +2026-04-13 19:28:40.920806: val_loss -0.3628 +2026-04-13 19:28:40.922720: Pseudo dice [0.299, 0.0, 0.674, 0.8262, 0.2457, 0.3025, 0.6324] +2026-04-13 19:28:40.924739: Epoch time: 101.34 s +2026-04-13 19:28:42.129305: +2026-04-13 19:28:42.130761: Epoch 2835 +2026-04-13 19:28:42.132359: Current learning rate: 0.00329 +2026-04-13 19:30:23.638886: train_loss -0.4178 +2026-04-13 19:30:23.643192: val_loss -0.3639 +2026-04-13 19:30:23.644859: Pseudo dice [0.3095, 0.0, 0.7768, 0.8498, 0.3857, 0.7754, 0.4354] +2026-04-13 19:30:23.646597: Epoch time: 101.51 s +2026-04-13 19:30:24.844007: +2026-04-13 19:30:24.846106: Epoch 2836 +2026-04-13 19:30:24.857028: Current learning rate: 0.00329 +2026-04-13 19:32:06.304109: train_loss -0.411 +2026-04-13 19:32:06.308782: val_loss -0.403 +2026-04-13 19:32:06.310134: Pseudo dice [0.6701, 0.0, 0.6838, 0.8956, 0.5255, 0.7541, 0.6055] +2026-04-13 19:32:06.311774: Epoch time: 101.46 s +2026-04-13 19:32:07.506821: +2026-04-13 19:32:07.508219: Epoch 2837 +2026-04-13 19:32:07.509585: Current learning rate: 0.00329 +2026-04-13 19:33:48.924528: train_loss -0.4148 +2026-04-13 19:33:48.930293: val_loss -0.3655 +2026-04-13 19:33:48.933187: Pseudo dice [0.1791, 0.0, 0.5988, 0.8203, 0.2267, 0.8243, 0.6094] +2026-04-13 19:33:48.934746: Epoch time: 101.42 s +2026-04-13 19:33:50.116308: +2026-04-13 19:33:50.118575: Epoch 2838 +2026-04-13 19:33:50.120334: Current learning rate: 0.00329 +2026-04-13 19:35:31.548721: train_loss -0.4236 +2026-04-13 19:35:31.557799: val_loss -0.4182 +2026-04-13 19:35:31.560066: Pseudo dice [0.6873, 0.0, 0.8203, 0.73, 0.3907, 0.7433, 0.77] +2026-04-13 19:35:31.562078: Epoch time: 101.44 s +2026-04-13 19:35:32.777191: +2026-04-13 19:35:32.779929: Epoch 2839 +2026-04-13 19:35:32.781981: Current learning rate: 0.00328 +2026-04-13 19:37:14.470228: train_loss -0.4458 +2026-04-13 19:37:14.475774: val_loss -0.4028 +2026-04-13 19:37:14.479718: Pseudo dice [0.6048, 0.0, 0.4241, 0.754, 0.4203, 0.7088, 0.8018] +2026-04-13 19:37:14.481404: Epoch time: 101.7 s +2026-04-13 19:37:15.724867: +2026-04-13 19:37:15.727350: Epoch 2840 +2026-04-13 19:37:15.729617: Current learning rate: 0.00328 +2026-04-13 19:38:57.266804: train_loss -0.4379 +2026-04-13 19:38:57.277846: val_loss -0.4038 +2026-04-13 19:38:57.279816: Pseudo dice [0.7866, 0.0, 0.7928, 0.7322, 0.5567, 0.8487, 0.8355] +2026-04-13 19:38:57.281967: Epoch time: 101.54 s +2026-04-13 19:38:59.373823: +2026-04-13 19:38:59.376180: Epoch 2841 +2026-04-13 19:38:59.378124: Current learning rate: 0.00328 +2026-04-13 19:40:40.968818: train_loss -0.4282 +2026-04-13 19:40:40.973041: val_loss -0.3854 +2026-04-13 19:40:40.974841: Pseudo dice [0.3575, 0.0, 0.7647, 0.6055, 0.4905, 0.6977, 0.8514] +2026-04-13 19:40:40.976453: Epoch time: 101.6 s +2026-04-13 19:40:42.155635: +2026-04-13 19:40:42.157363: Epoch 2842 +2026-04-13 19:40:42.158854: Current learning rate: 0.00328 +2026-04-13 19:42:23.658359: train_loss -0.4348 +2026-04-13 19:42:23.663369: val_loss -0.3488 +2026-04-13 19:42:23.665446: Pseudo dice [0.0369, 0.0, 0.8109, 0.4978, 0.3608, 0.6126, 0.8676] +2026-04-13 19:42:23.666978: Epoch time: 101.51 s +2026-04-13 19:42:24.858887: +2026-04-13 19:42:24.860512: Epoch 2843 +2026-04-13 19:42:24.861897: Current learning rate: 0.00327 +2026-04-13 19:44:06.303635: train_loss -0.4426 +2026-04-13 19:44:06.307748: val_loss -0.4076 +2026-04-13 19:44:06.309702: Pseudo dice [0.1451, 0.0, 0.8202, 0.8106, 0.4089, 0.7038, 0.9355] +2026-04-13 19:44:06.311265: Epoch time: 101.45 s +2026-04-13 19:44:07.499144: +2026-04-13 19:44:07.500785: Epoch 2844 +2026-04-13 19:44:07.502538: Current learning rate: 0.00327 +2026-04-13 19:45:48.766490: train_loss -0.4181 +2026-04-13 19:45:48.770825: val_loss -0.3619 +2026-04-13 19:45:48.772219: Pseudo dice [0.3224, 0.0, 0.5918, 0.8488, 0.2821, 0.6554, 0.9552] +2026-04-13 19:45:48.774017: Epoch time: 101.27 s +2026-04-13 19:45:49.965041: +2026-04-13 19:45:49.966858: Epoch 2845 +2026-04-13 19:45:49.968288: Current learning rate: 0.00327 +2026-04-13 19:47:31.535808: train_loss -0.4349 +2026-04-13 19:47:31.541492: val_loss -0.3834 +2026-04-13 19:47:31.543181: Pseudo dice [0.6958, 0.0, 0.7723, 0.7479, 0.492, 0.6865, 0.8812] +2026-04-13 19:47:31.545219: Epoch time: 101.57 s +2026-04-13 19:47:32.744153: +2026-04-13 19:47:32.745873: Epoch 2846 +2026-04-13 19:47:32.747860: Current learning rate: 0.00327 +2026-04-13 19:49:14.339870: train_loss -0.4328 +2026-04-13 19:49:14.345892: val_loss -0.3732 +2026-04-13 19:49:14.347970: Pseudo dice [0.3317, 0.0, 0.6955, 0.8256, 0.4598, 0.6186, 0.7022] +2026-04-13 19:49:14.349868: Epoch time: 101.6 s +2026-04-13 19:49:16.513640: +2026-04-13 19:49:16.515200: Epoch 2847 +2026-04-13 19:49:16.516814: Current learning rate: 0.00326 +2026-04-13 19:50:58.136899: train_loss -0.422 +2026-04-13 19:50:58.142039: val_loss -0.3912 +2026-04-13 19:50:58.143696: Pseudo dice [0.6835, 0.0, 0.6013, 0.7746, 0.584, 0.8326, 0.9352] +2026-04-13 19:50:58.145730: Epoch time: 101.63 s +2026-04-13 19:50:59.333307: +2026-04-13 19:50:59.335218: Epoch 2848 +2026-04-13 19:50:59.336924: Current learning rate: 0.00326 +2026-04-13 19:52:40.938415: train_loss -0.4302 +2026-04-13 19:52:40.943626: val_loss -0.3721 +2026-04-13 19:52:40.945582: Pseudo dice [0.5609, 0.0, 0.7204, 0.7593, 0.3877, 0.7122, 0.8495] +2026-04-13 19:52:40.947730: Epoch time: 101.61 s +2026-04-13 19:52:42.135332: +2026-04-13 19:52:42.137515: Epoch 2849 +2026-04-13 19:52:42.140174: Current learning rate: 0.00326 +2026-04-13 19:54:23.930638: train_loss -0.4214 +2026-04-13 19:54:23.935924: val_loss -0.3829 +2026-04-13 19:54:23.937907: Pseudo dice [0.6969, 0.0, 0.8103, 0.782, 0.4222, 0.7907, 0.8332] +2026-04-13 19:54:23.939355: Epoch time: 101.8 s +2026-04-13 19:54:25.650149: Yayy! New best EMA pseudo Dice: 0.5559 +2026-04-13 19:54:28.539207: +2026-04-13 19:54:28.541086: Epoch 2850 +2026-04-13 19:54:28.544715: Current learning rate: 0.00326 +2026-04-13 19:56:10.130219: train_loss -0.4312 +2026-04-13 19:56:10.135024: val_loss -0.3665 +2026-04-13 19:56:10.136933: Pseudo dice [0.7536, 0.0, 0.7583, 0.5163, 0.3786, 0.7094, 0.6836] +2026-04-13 19:56:10.138577: Epoch time: 101.59 s +2026-04-13 19:56:11.333134: +2026-04-13 19:56:11.334792: Epoch 2851 +2026-04-13 19:56:11.336313: Current learning rate: 0.00325 +2026-04-13 19:57:52.932043: train_loss -0.4226 +2026-04-13 19:57:52.938514: val_loss -0.3729 +2026-04-13 19:57:52.940063: Pseudo dice [0.462, 0.0, 0.6235, 0.6558, 0.4318, 0.4301, 0.8042] +2026-04-13 19:57:52.941836: Epoch time: 101.6 s +2026-04-13 19:57:54.130676: +2026-04-13 19:57:54.132583: Epoch 2852 +2026-04-13 19:57:54.134162: Current learning rate: 0.00325 +2026-04-13 19:59:35.601082: train_loss -0.4206 +2026-04-13 19:59:35.606663: val_loss -0.3763 +2026-04-13 19:59:35.608560: Pseudo dice [0.3464, 0.0, 0.7623, 0.6687, 0.5587, 0.7277, 0.8122] +2026-04-13 19:59:35.610317: Epoch time: 101.47 s +2026-04-13 19:59:36.809609: +2026-04-13 19:59:36.811786: Epoch 2853 +2026-04-13 19:59:36.814108: Current learning rate: 0.00325 +2026-04-13 20:01:18.355687: train_loss -0.4108 +2026-04-13 20:01:18.360759: val_loss -0.3562 +2026-04-13 20:01:18.362599: Pseudo dice [0.5886, 0.0, 0.5543, 0.7624, 0.4207, 0.8422, 0.6043] +2026-04-13 20:01:18.364763: Epoch time: 101.55 s +2026-04-13 20:01:19.561826: +2026-04-13 20:01:19.563542: Epoch 2854 +2026-04-13 20:01:19.564810: Current learning rate: 0.00325 +2026-04-13 20:03:01.258985: train_loss -0.4391 +2026-04-13 20:03:01.263163: val_loss -0.3741 +2026-04-13 20:03:01.264696: Pseudo dice [0.417, 0.0, 0.6603, 0.4306, 0.4035, 0.7577, 0.88] +2026-04-13 20:03:01.266166: Epoch time: 101.7 s +2026-04-13 20:03:02.457878: +2026-04-13 20:03:02.459728: Epoch 2855 +2026-04-13 20:03:02.461240: Current learning rate: 0.00324 +2026-04-13 20:04:43.942735: train_loss -0.4181 +2026-04-13 20:04:43.947463: val_loss -0.3615 +2026-04-13 20:04:43.949144: Pseudo dice [0.3862, 0.0, 0.7979, 0.826, 0.3403, 0.8255, 0.8176] +2026-04-13 20:04:43.950874: Epoch time: 101.49 s +2026-04-13 20:04:45.130579: +2026-04-13 20:04:45.132378: Epoch 2856 +2026-04-13 20:04:45.133891: Current learning rate: 0.00324 +2026-04-13 20:06:26.766750: train_loss -0.4011 +2026-04-13 20:06:26.779072: val_loss -0.4023 +2026-04-13 20:06:26.780765: Pseudo dice [0.8281, 0.0, 0.6417, 0.8186, 0.5446, 0.5838, 0.7836] +2026-04-13 20:06:26.782049: Epoch time: 101.64 s +2026-04-13 20:06:27.963700: +2026-04-13 20:06:27.965791: Epoch 2857 +2026-04-13 20:06:27.967927: Current learning rate: 0.00324 +2026-04-13 20:08:09.482833: train_loss -0.4376 +2026-04-13 20:08:09.487342: val_loss -0.382 +2026-04-13 20:08:09.489464: Pseudo dice [0.214, 0.0, 0.7839, 0.3705, 0.434, 0.7765, 0.7186] +2026-04-13 20:08:09.491258: Epoch time: 101.52 s +2026-04-13 20:08:10.688181: +2026-04-13 20:08:10.690440: Epoch 2858 +2026-04-13 20:08:10.692745: Current learning rate: 0.00324 +2026-04-13 20:09:52.413869: train_loss -0.4554 +2026-04-13 20:09:52.418159: val_loss -0.3748 +2026-04-13 20:09:52.419893: Pseudo dice [0.3532, 0.0, 0.8301, 0.8966, 0.3575, 0.6658, 0.7034] +2026-04-13 20:09:52.421512: Epoch time: 101.73 s +2026-04-13 20:09:53.628766: +2026-04-13 20:09:53.630310: Epoch 2859 +2026-04-13 20:09:53.643420: Current learning rate: 0.00323 +2026-04-13 20:11:35.343266: train_loss -0.4331 +2026-04-13 20:11:35.347431: val_loss -0.4182 +2026-04-13 20:11:35.349179: Pseudo dice [0.5073, 0.0, 0.8019, 0.7787, 0.6994, 0.7916, 0.8107] +2026-04-13 20:11:35.350601: Epoch time: 101.72 s +2026-04-13 20:11:36.557042: +2026-04-13 20:11:36.558641: Epoch 2860 +2026-04-13 20:11:36.560036: Current learning rate: 0.00323 +2026-04-13 20:13:18.126274: train_loss -0.435 +2026-04-13 20:13:18.134314: val_loss -0.3747 +2026-04-13 20:13:18.136817: Pseudo dice [0.7109, 0.0, 0.6008, 0.8706, 0.4054, 0.8336, 0.5208] +2026-04-13 20:13:18.138627: Epoch time: 101.57 s +2026-04-13 20:13:19.349941: +2026-04-13 20:13:19.352489: Epoch 2861 +2026-04-13 20:13:19.354136: Current learning rate: 0.00323 +2026-04-13 20:15:01.041491: train_loss -0.4374 +2026-04-13 20:15:01.046344: val_loss -0.4062 +2026-04-13 20:15:01.048074: Pseudo dice [0.4712, 0.0, 0.7135, 0.7526, 0.5927, 0.7343, 0.7837] +2026-04-13 20:15:01.049686: Epoch time: 101.69 s +2026-04-13 20:15:02.264755: +2026-04-13 20:15:02.267764: Epoch 2862 +2026-04-13 20:15:02.269650: Current learning rate: 0.00323 +2026-04-13 20:16:43.773981: train_loss -0.4334 +2026-04-13 20:16:43.779302: val_loss -0.4155 +2026-04-13 20:16:43.780926: Pseudo dice [0.7448, 0.0, 0.5415, 0.0056, 0.2971, 0.7974, 0.8962] +2026-04-13 20:16:43.783282: Epoch time: 101.51 s +2026-04-13 20:16:44.999614: +2026-04-13 20:16:45.002309: Epoch 2863 +2026-04-13 20:16:45.004497: Current learning rate: 0.00322 +2026-04-13 20:18:26.448544: train_loss -0.4424 +2026-04-13 20:18:26.453221: val_loss -0.4091 +2026-04-13 20:18:26.455307: Pseudo dice [0.7192, 0.0, 0.6296, 0.7648, 0.3297, 0.8234, 0.8163] +2026-04-13 20:18:26.456778: Epoch time: 101.45 s +2026-04-13 20:18:27.650939: +2026-04-13 20:18:27.652969: Epoch 2864 +2026-04-13 20:18:27.654551: Current learning rate: 0.00322 +2026-04-13 20:20:09.096976: train_loss -0.4487 +2026-04-13 20:20:09.102495: val_loss -0.4008 +2026-04-13 20:20:09.104322: Pseudo dice [0.4796, 0.0, 0.7489, 0.7816, 0.3886, 0.7233, 0.6876] +2026-04-13 20:20:09.105965: Epoch time: 101.45 s +2026-04-13 20:20:10.301975: +2026-04-13 20:20:10.304358: Epoch 2865 +2026-04-13 20:20:10.306259: Current learning rate: 0.00322 +2026-04-13 20:21:51.709340: train_loss -0.4274 +2026-04-13 20:21:51.713910: val_loss -0.3458 +2026-04-13 20:21:51.715683: Pseudo dice [0.2587, 0.0, 0.7204, 0.2512, 0.2287, 0.7726, 0.7071] +2026-04-13 20:21:51.717435: Epoch time: 101.41 s +2026-04-13 20:21:52.918005: +2026-04-13 20:21:52.919643: Epoch 2866 +2026-04-13 20:21:52.921208: Current learning rate: 0.00322 +2026-04-13 20:23:35.281243: train_loss -0.4405 +2026-04-13 20:23:35.285848: val_loss -0.3582 +2026-04-13 20:23:35.287591: Pseudo dice [0.2871, 0.0, 0.6319, 0.3658, 0.4476, 0.6944, 0.6694] +2026-04-13 20:23:35.289081: Epoch time: 102.37 s +2026-04-13 20:23:36.500647: +2026-04-13 20:23:36.502256: Epoch 2867 +2026-04-13 20:23:36.503537: Current learning rate: 0.00321 +2026-04-13 20:25:18.015658: train_loss -0.4185 +2026-04-13 20:25:18.020039: val_loss -0.3882 +2026-04-13 20:25:18.021489: Pseudo dice [0.5092, 0.0, 0.7377, 0.7865, 0.2822, 0.5209, 0.5081] +2026-04-13 20:25:18.022807: Epoch time: 101.52 s +2026-04-13 20:25:19.228493: +2026-04-13 20:25:19.230828: Epoch 2868 +2026-04-13 20:25:19.232092: Current learning rate: 0.00321 +2026-04-13 20:27:00.816954: train_loss -0.4281 +2026-04-13 20:27:00.823147: val_loss -0.3855 +2026-04-13 20:27:00.825099: Pseudo dice [0.5227, 0.0, 0.7168, 0.7924, 0.6084, 0.8627, 0.4512] +2026-04-13 20:27:00.826796: Epoch time: 101.59 s +2026-04-13 20:27:02.030230: +2026-04-13 20:27:02.031789: Epoch 2869 +2026-04-13 20:27:02.033369: Current learning rate: 0.00321 +2026-04-13 20:28:43.738917: train_loss -0.4116 +2026-04-13 20:28:43.744071: val_loss -0.3741 +2026-04-13 20:28:43.745992: Pseudo dice [0.3602, 0.0, 0.7167, 0.5962, 0.4539, 0.6614, 0.8413] +2026-04-13 20:28:43.747761: Epoch time: 101.71 s +2026-04-13 20:28:44.965238: +2026-04-13 20:28:44.967457: Epoch 2870 +2026-04-13 20:28:44.969032: Current learning rate: 0.00321 +2026-04-13 20:30:26.552681: train_loss -0.4226 +2026-04-13 20:30:26.556802: val_loss -0.3863 +2026-04-13 20:30:26.558429: Pseudo dice [0.2841, 0.0, 0.8188, 0.7119, 0.4165, 0.8157, 0.5305] +2026-04-13 20:30:26.560022: Epoch time: 101.59 s +2026-04-13 20:30:27.755067: +2026-04-13 20:30:27.756398: Epoch 2871 +2026-04-13 20:30:27.757639: Current learning rate: 0.0032 +2026-04-13 20:32:09.377595: train_loss -0.4382 +2026-04-13 20:32:09.382138: val_loss -0.4203 +2026-04-13 20:32:09.383913: Pseudo dice [0.6108, 0.0, 0.8327, 0.7401, 0.5818, 0.7782, 0.693] +2026-04-13 20:32:09.385955: Epoch time: 101.63 s +2026-04-13 20:32:10.626685: +2026-04-13 20:32:10.628175: Epoch 2872 +2026-04-13 20:32:10.629546: Current learning rate: 0.0032 +2026-04-13 20:33:52.170108: train_loss -0.422 +2026-04-13 20:33:52.174279: val_loss -0.381 +2026-04-13 20:33:52.175690: Pseudo dice [0.5148, 0.0, 0.7262, 0.5765, 0.4769, 0.6648, 0.738] +2026-04-13 20:33:52.177235: Epoch time: 101.55 s +2026-04-13 20:33:53.396880: +2026-04-13 20:33:53.400972: Epoch 2873 +2026-04-13 20:33:53.402959: Current learning rate: 0.0032 +2026-04-13 20:35:34.993278: train_loss -0.4324 +2026-04-13 20:35:34.998105: val_loss -0.4017 +2026-04-13 20:35:35.000473: Pseudo dice [0.2405, 0.0, 0.8348, 0.7722, 0.587, 0.8064, 0.8857] +2026-04-13 20:35:35.002305: Epoch time: 101.6 s +2026-04-13 20:35:36.203660: +2026-04-13 20:35:36.205169: Epoch 2874 +2026-04-13 20:35:36.206964: Current learning rate: 0.0032 +2026-04-13 20:37:17.862645: train_loss -0.4454 +2026-04-13 20:37:17.869451: val_loss -0.3835 +2026-04-13 20:37:17.871504: Pseudo dice [0.3947, 0.0, 0.741, 0.7605, 0.4431, 0.6073, 0.8586] +2026-04-13 20:37:17.873322: Epoch time: 101.66 s +2026-04-13 20:37:19.136039: +2026-04-13 20:37:19.137648: Epoch 2875 +2026-04-13 20:37:19.139030: Current learning rate: 0.00319 +2026-04-13 20:39:00.542329: train_loss -0.4399 +2026-04-13 20:39:00.547331: val_loss -0.3874 +2026-04-13 20:39:00.548980: Pseudo dice [0.4911, 0.0, 0.7597, 0.8968, 0.5129, 0.3156, 0.9336] +2026-04-13 20:39:00.550945: Epoch time: 101.41 s +2026-04-13 20:39:01.759837: +2026-04-13 20:39:01.761738: Epoch 2876 +2026-04-13 20:39:01.764014: Current learning rate: 0.00319 +2026-04-13 20:40:43.160467: train_loss -0.4349 +2026-04-13 20:40:43.166210: val_loss -0.3945 +2026-04-13 20:40:43.168709: Pseudo dice [0.7008, 0.0, 0.6587, 0.8659, 0.3521, 0.8012, 0.9006] +2026-04-13 20:40:43.171049: Epoch time: 101.4 s +2026-04-13 20:40:44.386988: +2026-04-13 20:40:44.388719: Epoch 2877 +2026-04-13 20:40:44.391599: Current learning rate: 0.00319 +2026-04-13 20:42:25.916019: train_loss -0.4473 +2026-04-13 20:42:25.921361: val_loss -0.3523 +2026-04-13 20:42:25.923336: Pseudo dice [0.6592, 0.0, 0.4825, 0.9037, 0.3721, 0.5428, 0.7843] +2026-04-13 20:42:25.925655: Epoch time: 101.53 s +2026-04-13 20:42:27.143934: +2026-04-13 20:42:27.145445: Epoch 2878 +2026-04-13 20:42:27.146954: Current learning rate: 0.00319 +2026-04-13 20:44:08.706094: train_loss -0.446 +2026-04-13 20:44:08.711860: val_loss -0.3749 +2026-04-13 20:44:08.713678: Pseudo dice [0.7416, 0.0, 0.6094, 0.6426, 0.3509, 0.7933, 0.7806] +2026-04-13 20:44:08.715304: Epoch time: 101.57 s +2026-04-13 20:44:09.944886: +2026-04-13 20:44:09.947576: Epoch 2879 +2026-04-13 20:44:09.949312: Current learning rate: 0.00318 +2026-04-13 20:45:51.457199: train_loss -0.4318 +2026-04-13 20:45:51.463664: val_loss -0.3551 +2026-04-13 20:45:51.465664: Pseudo dice [0.3419, 0.0, 0.6867, 0.4238, 0.2735, 0.8554, 0.8708] +2026-04-13 20:45:51.467474: Epoch time: 101.52 s +2026-04-13 20:45:52.669426: +2026-04-13 20:45:52.671307: Epoch 2880 +2026-04-13 20:45:52.676151: Current learning rate: 0.00318 +2026-04-13 20:47:34.115853: train_loss -0.4239 +2026-04-13 20:47:34.121520: val_loss -0.3965 +2026-04-13 20:47:34.123538: Pseudo dice [0.4896, 0.0, 0.7614, 0.9042, 0.5403, 0.8563, 0.8313] +2026-04-13 20:47:34.125404: Epoch time: 101.45 s +2026-04-13 20:47:35.342020: +2026-04-13 20:47:35.343856: Epoch 2881 +2026-04-13 20:47:35.345334: Current learning rate: 0.00318 +2026-04-13 20:49:16.792902: train_loss -0.3906 +2026-04-13 20:49:16.798069: val_loss -0.3603 +2026-04-13 20:49:16.800305: Pseudo dice [0.352, 0.0, 0.6475, 0.3416, 0.397, 0.8098, 0.6067] +2026-04-13 20:49:16.802456: Epoch time: 101.45 s +2026-04-13 20:49:18.011261: +2026-04-13 20:49:18.013021: Epoch 2882 +2026-04-13 20:49:18.015146: Current learning rate: 0.00317 +2026-04-13 20:50:59.506104: train_loss -0.4116 +2026-04-13 20:50:59.510564: val_loss -0.3698 +2026-04-13 20:50:59.512210: Pseudo dice [0.3598, 0.0, 0.7199, 0.3107, 0.4023, 0.6015, 0.826] +2026-04-13 20:50:59.513819: Epoch time: 101.5 s +2026-04-13 20:51:00.728631: +2026-04-13 20:51:00.730224: Epoch 2883 +2026-04-13 20:51:00.731675: Current learning rate: 0.00317 +2026-04-13 20:52:42.451835: train_loss -0.428 +2026-04-13 20:52:42.456743: val_loss -0.3689 +2026-04-13 20:52:42.459032: Pseudo dice [0.5118, 0.0, 0.4121, 0.3056, 0.3481, 0.7297, 0.8911] +2026-04-13 20:52:42.460879: Epoch time: 101.73 s +2026-04-13 20:52:43.656831: +2026-04-13 20:52:43.658199: Epoch 2884 +2026-04-13 20:52:43.660160: Current learning rate: 0.00317 +2026-04-13 20:54:25.196624: train_loss -0.439 +2026-04-13 20:54:25.200943: val_loss -0.3842 +2026-04-13 20:54:25.208446: Pseudo dice [0.3099, 0.0, 0.7095, 0.1968, 0.4317, 0.8186, 0.861] +2026-04-13 20:54:25.210617: Epoch time: 101.54 s +2026-04-13 20:54:26.413818: +2026-04-13 20:54:26.415499: Epoch 2885 +2026-04-13 20:54:26.416957: Current learning rate: 0.00317 +2026-04-13 20:56:07.854431: train_loss -0.428 +2026-04-13 20:56:07.859237: val_loss -0.3487 +2026-04-13 20:56:07.861182: Pseudo dice [0.1492, 0.0, 0.7791, 0.773, 0.4083, 0.7856, 0.7918] +2026-04-13 20:56:07.862933: Epoch time: 101.44 s +2026-04-13 20:56:09.060664: +2026-04-13 20:56:09.062052: Epoch 2886 +2026-04-13 20:56:09.063400: Current learning rate: 0.00316 +2026-04-13 20:57:51.412023: train_loss -0.431 +2026-04-13 20:57:51.418524: val_loss -0.4181 +2026-04-13 20:57:51.420395: Pseudo dice [0.6769, 0.0, 0.8212, 0.7945, 0.387, 0.7843, 0.6739] +2026-04-13 20:57:51.422760: Epoch time: 102.35 s +2026-04-13 20:57:52.635628: +2026-04-13 20:57:52.637108: Epoch 2887 +2026-04-13 20:57:52.638384: Current learning rate: 0.00316 +2026-04-13 20:59:34.205765: train_loss -0.4392 +2026-04-13 20:59:34.210062: val_loss -0.3435 +2026-04-13 20:59:34.211907: Pseudo dice [0.4969, 0.0, 0.5695, 0.8033, 0.4298, 0.74, 0.5216] +2026-04-13 20:59:34.213745: Epoch time: 101.57 s +2026-04-13 20:59:35.421007: +2026-04-13 20:59:35.422827: Epoch 2888 +2026-04-13 20:59:35.424817: Current learning rate: 0.00316 +2026-04-13 21:01:16.713703: train_loss -0.4208 +2026-04-13 21:01:16.719375: val_loss -0.3707 +2026-04-13 21:01:16.720915: Pseudo dice [0.2911, 0.0, 0.8109, 0.581, 0.3084, 0.8139, 0.8486] +2026-04-13 21:01:16.722675: Epoch time: 101.3 s +2026-04-13 21:01:17.971638: +2026-04-13 21:01:17.973575: Epoch 2889 +2026-04-13 21:01:17.975197: Current learning rate: 0.00316 +2026-04-13 21:02:59.468685: train_loss -0.4319 +2026-04-13 21:02:59.473769: val_loss -0.3958 +2026-04-13 21:02:59.475596: Pseudo dice [0.775, 0.0, 0.6023, 0.8237, 0.5244, 0.5392, 0.7961] +2026-04-13 21:02:59.477106: Epoch time: 101.5 s +2026-04-13 21:03:00.709272: +2026-04-13 21:03:00.710856: Epoch 2890 +2026-04-13 21:03:00.712381: Current learning rate: 0.00315 +2026-04-13 21:04:42.269303: train_loss -0.4315 +2026-04-13 21:04:42.273412: val_loss -0.3785 +2026-04-13 21:04:42.274858: Pseudo dice [0.3969, 0.0, 0.7502, 0.7189, 0.3784, 0.4864, 0.8948] +2026-04-13 21:04:42.276175: Epoch time: 101.56 s +2026-04-13 21:04:43.489364: +2026-04-13 21:04:43.490768: Epoch 2891 +2026-04-13 21:04:43.492019: Current learning rate: 0.00315 +2026-04-13 21:06:25.032154: train_loss -0.4288 +2026-04-13 21:06:25.036765: val_loss -0.3963 +2026-04-13 21:06:25.038655: Pseudo dice [0.5688, 0.0, 0.7328, 0.7936, 0.2545, 0.7459, 0.7819] +2026-04-13 21:06:25.040648: Epoch time: 101.55 s +2026-04-13 21:06:26.254491: +2026-04-13 21:06:26.256099: Epoch 2892 +2026-04-13 21:06:26.257475: Current learning rate: 0.00315 +2026-04-13 21:08:07.894648: train_loss -0.4229 +2026-04-13 21:08:07.899870: val_loss -0.389 +2026-04-13 21:08:07.902040: Pseudo dice [0.175, 0.0, 0.7687, 0.8487, 0.4299, 0.8044, 0.8235] +2026-04-13 21:08:07.903875: Epoch time: 101.64 s +2026-04-13 21:08:09.109586: +2026-04-13 21:08:09.111499: Epoch 2893 +2026-04-13 21:08:09.113308: Current learning rate: 0.00315 +2026-04-13 21:09:50.702166: train_loss -0.4345 +2026-04-13 21:09:50.708088: val_loss -0.4077 +2026-04-13 21:09:50.710051: Pseudo dice [0.4957, 0.0, 0.7418, 0.7226, 0.6367, 0.7947, 0.7632] +2026-04-13 21:09:50.712069: Epoch time: 101.6 s +2026-04-13 21:09:51.954433: +2026-04-13 21:09:51.955992: Epoch 2894 +2026-04-13 21:09:51.957382: Current learning rate: 0.00314 +2026-04-13 21:11:33.463894: train_loss -0.4376 +2026-04-13 21:11:33.470649: val_loss -0.3787 +2026-04-13 21:11:33.472637: Pseudo dice [0.3328, 0.0, 0.781, 0.3789, 0.6041, 0.7415, 0.4509] +2026-04-13 21:11:33.474578: Epoch time: 101.51 s +2026-04-13 21:11:34.677562: +2026-04-13 21:11:34.679199: Epoch 2895 +2026-04-13 21:11:34.680954: Current learning rate: 0.00314 +2026-04-13 21:13:16.068935: train_loss -0.4526 +2026-04-13 21:13:16.073684: val_loss -0.3925 +2026-04-13 21:13:16.075338: Pseudo dice [0.737, 0.0, 0.658, 0.8231, 0.403, 0.6634, 0.8301] +2026-04-13 21:13:16.078336: Epoch time: 101.39 s +2026-04-13 21:13:17.266141: +2026-04-13 21:13:17.268003: Epoch 2896 +2026-04-13 21:13:17.269569: Current learning rate: 0.00314 +2026-04-13 21:14:58.771592: train_loss -0.4193 +2026-04-13 21:14:58.776573: val_loss -0.3756 +2026-04-13 21:14:58.779057: Pseudo dice [0.3517, 0.0, 0.5701, 0.7005, 0.391, 0.6635, 0.7879] +2026-04-13 21:14:58.781524: Epoch time: 101.51 s +2026-04-13 21:14:59.989787: +2026-04-13 21:14:59.991818: Epoch 2897 +2026-04-13 21:14:59.993210: Current learning rate: 0.00314 +2026-04-13 21:16:41.451938: train_loss -0.4371 +2026-04-13 21:16:41.459069: val_loss -0.4034 +2026-04-13 21:16:41.460896: Pseudo dice [0.6076, 0.0, 0.808, 0.6832, 0.3017, 0.6731, 0.8119] +2026-04-13 21:16:41.462500: Epoch time: 101.47 s +2026-04-13 21:16:42.655363: +2026-04-13 21:16:42.657434: Epoch 2898 +2026-04-13 21:16:42.659595: Current learning rate: 0.00313 +2026-04-13 21:18:24.210727: train_loss -0.4333 +2026-04-13 21:18:24.215714: val_loss -0.4084 +2026-04-13 21:18:24.217826: Pseudo dice [0.5064, 0.0, 0.5483, 0.5804, 0.2864, 0.8228, 0.9207] +2026-04-13 21:18:24.219948: Epoch time: 101.56 s +2026-04-13 21:18:25.422090: +2026-04-13 21:18:25.423972: Epoch 2899 +2026-04-13 21:18:25.425413: Current learning rate: 0.00313 +2026-04-13 21:20:06.979219: train_loss -0.4467 +2026-04-13 21:20:06.983607: val_loss -0.372 +2026-04-13 21:20:06.985259: Pseudo dice [0.7006, 0.0, 0.7698, 0.6425, 0.5406, 0.6279, 0.4961] +2026-04-13 21:20:06.986635: Epoch time: 101.56 s +2026-04-13 21:20:09.986867: +2026-04-13 21:20:09.988874: Epoch 2900 +2026-04-13 21:20:09.990436: Current learning rate: 0.00313 +2026-04-13 21:21:51.577713: train_loss -0.4355 +2026-04-13 21:21:51.582285: val_loss -0.365 +2026-04-13 21:21:51.584181: Pseudo dice [0.6638, 0.0, 0.5399, 0.6587, 0.2742, 0.3905, 0.216] +2026-04-13 21:21:51.586227: Epoch time: 101.59 s +2026-04-13 21:21:52.781703: +2026-04-13 21:21:52.783581: Epoch 2901 +2026-04-13 21:21:52.785134: Current learning rate: 0.00313 +2026-04-13 21:23:34.375285: train_loss -0.4384 +2026-04-13 21:23:34.379911: val_loss -0.3955 +2026-04-13 21:23:34.382137: Pseudo dice [0.5934, 0.0, 0.8208, 0.8075, 0.3585, 0.6087, 0.856] +2026-04-13 21:23:34.384086: Epoch time: 101.6 s +2026-04-13 21:23:35.569252: +2026-04-13 21:23:35.571316: Epoch 2902 +2026-04-13 21:23:35.572814: Current learning rate: 0.00312 +2026-04-13 21:25:17.291441: train_loss -0.4402 +2026-04-13 21:25:17.296147: val_loss -0.3555 +2026-04-13 21:25:17.298040: Pseudo dice [0.6663, 0.0, 0.5712, 0.6585, 0.2383, 0.6574, 0.6577] +2026-04-13 21:25:17.300782: Epoch time: 101.73 s +2026-04-13 21:25:18.523276: +2026-04-13 21:25:18.525391: Epoch 2903 +2026-04-13 21:25:18.527359: Current learning rate: 0.00312 +2026-04-13 21:26:59.958409: train_loss -0.4263 +2026-04-13 21:26:59.964313: val_loss -0.3885 +2026-04-13 21:26:59.966085: Pseudo dice [0.7034, 0.0, 0.5885, 0.4215, 0.4615, 0.7631, 0.686] +2026-04-13 21:26:59.968073: Epoch time: 101.44 s +2026-04-13 21:27:01.198939: +2026-04-13 21:27:01.201843: Epoch 2904 +2026-04-13 21:27:01.204351: Current learning rate: 0.00312 +2026-04-13 21:28:42.715900: train_loss -0.4304 +2026-04-13 21:28:42.720896: val_loss -0.3646 +2026-04-13 21:28:42.722731: Pseudo dice [0.3981, 0.0, 0.7628, 0.3279, 0.4071, 0.5928, 0.6701] +2026-04-13 21:28:42.724517: Epoch time: 101.52 s +2026-04-13 21:28:43.961175: +2026-04-13 21:28:43.963031: Epoch 2905 +2026-04-13 21:28:43.965309: Current learning rate: 0.00312 +2026-04-13 21:30:25.525827: train_loss -0.438 +2026-04-13 21:30:25.530692: val_loss -0.3837 +2026-04-13 21:30:25.533810: Pseudo dice [0.4471, 0.0, 0.3375, 0.5315, 0.5672, 0.6048, 0.9076] +2026-04-13 21:30:25.535511: Epoch time: 101.57 s +2026-04-13 21:30:27.719587: +2026-04-13 21:30:27.721098: Epoch 2906 +2026-04-13 21:30:27.722876: Current learning rate: 0.00311 +2026-04-13 21:32:09.233345: train_loss -0.4212 +2026-04-13 21:32:09.237932: val_loss -0.3902 +2026-04-13 21:32:09.239841: Pseudo dice [0.3835, 0.0, 0.7415, 0.8625, 0.2665, 0.8377, 0.8491] +2026-04-13 21:32:09.241813: Epoch time: 101.52 s +2026-04-13 21:32:10.433191: +2026-04-13 21:32:10.435221: Epoch 2907 +2026-04-13 21:32:10.436879: Current learning rate: 0.00311 +2026-04-13 21:33:51.936568: train_loss -0.4007 +2026-04-13 21:33:51.940962: val_loss -0.3618 +2026-04-13 21:33:51.942872: Pseudo dice [0.3258, 0.0, 0.7165, 0.6304, 0.414, 0.6211, 0.7645] +2026-04-13 21:33:51.944806: Epoch time: 101.51 s +2026-04-13 21:33:53.147612: +2026-04-13 21:33:53.149438: Epoch 2908 +2026-04-13 21:33:53.150999: Current learning rate: 0.00311 +2026-04-13 21:35:34.829243: train_loss -0.4196 +2026-04-13 21:35:34.833313: val_loss -0.365 +2026-04-13 21:35:34.835079: Pseudo dice [0.3844, 0.0, 0.7495, 0.726, 0.4508, 0.5687, 0.7995] +2026-04-13 21:35:34.837317: Epoch time: 101.68 s +2026-04-13 21:35:36.029711: +2026-04-13 21:35:36.031190: Epoch 2909 +2026-04-13 21:35:36.032443: Current learning rate: 0.00311 +2026-04-13 21:37:17.581007: train_loss -0.409 +2026-04-13 21:37:17.586446: val_loss -0.3475 +2026-04-13 21:37:17.588917: Pseudo dice [0.2305, 0.0, 0.7844, 0.0555, 0.3303, 0.7665, 0.8169] +2026-04-13 21:37:17.590799: Epoch time: 101.55 s +2026-04-13 21:37:18.789984: +2026-04-13 21:37:18.791492: Epoch 2910 +2026-04-13 21:37:18.793087: Current learning rate: 0.0031 +2026-04-13 21:39:00.216295: train_loss -0.4326 +2026-04-13 21:39:00.222505: val_loss -0.386 +2026-04-13 21:39:00.224785: Pseudo dice [0.3691, 0.0, 0.5751, 0.5932, 0.3958, 0.811, 0.6206] +2026-04-13 21:39:00.226726: Epoch time: 101.43 s +2026-04-13 21:39:01.451361: +2026-04-13 21:39:01.454645: Epoch 2911 +2026-04-13 21:39:01.457229: Current learning rate: 0.0031 +2026-04-13 21:40:42.793527: train_loss -0.4367 +2026-04-13 21:40:42.802484: val_loss -0.3971 +2026-04-13 21:40:42.804415: Pseudo dice [0.722, 0.0, 0.4277, 0.6529, 0.4318, 0.8201, 0.7079] +2026-04-13 21:40:42.807967: Epoch time: 101.35 s +2026-04-13 21:40:44.004620: +2026-04-13 21:40:44.006268: Epoch 2912 +2026-04-13 21:40:44.007715: Current learning rate: 0.0031 +2026-04-13 21:42:25.249482: train_loss -0.4425 +2026-04-13 21:42:25.254346: val_loss -0.3713 +2026-04-13 21:42:25.256006: Pseudo dice [0.5841, 0.0, 0.6317, 0.3941, 0.4822, 0.6034, 0.8068] +2026-04-13 21:42:25.258027: Epoch time: 101.25 s +2026-04-13 21:42:26.465048: +2026-04-13 21:42:26.466968: Epoch 2913 +2026-04-13 21:42:26.468304: Current learning rate: 0.0031 +2026-04-13 21:44:07.886799: train_loss -0.4342 +2026-04-13 21:44:07.892079: val_loss -0.3705 +2026-04-13 21:44:07.893934: Pseudo dice [0.4652, 0.0, 0.5139, 0.7787, 0.4794, 0.6651, 0.7984] +2026-04-13 21:44:07.897458: Epoch time: 101.42 s +2026-04-13 21:44:09.104259: +2026-04-13 21:44:09.106637: Epoch 2914 +2026-04-13 21:44:09.108432: Current learning rate: 0.00309 +2026-04-13 21:45:50.673739: train_loss -0.4199 +2026-04-13 21:45:50.679438: val_loss -0.3846 +2026-04-13 21:45:50.681232: Pseudo dice [0.6025, 0.0, 0.8039, 0.574, 0.2935, 0.8414, 0.6678] +2026-04-13 21:45:50.684325: Epoch time: 101.57 s +2026-04-13 21:45:51.899613: +2026-04-13 21:45:51.901797: Epoch 2915 +2026-04-13 21:45:51.904459: Current learning rate: 0.00309 +2026-04-13 21:47:33.512320: train_loss -0.4306 +2026-04-13 21:47:33.517567: val_loss -0.3477 +2026-04-13 21:47:33.519839: Pseudo dice [0.4281, 0.0, 0.6476, 0.8364, 0.0804, 0.7758, 0.6758] +2026-04-13 21:47:33.521899: Epoch time: 101.62 s +2026-04-13 21:47:34.763215: +2026-04-13 21:47:34.765292: Epoch 2916 +2026-04-13 21:47:34.766873: Current learning rate: 0.00309 +2026-04-13 21:49:16.579215: train_loss -0.4159 +2026-04-13 21:49:16.585196: val_loss -0.3834 +2026-04-13 21:49:16.587103: Pseudo dice [0.7754, 0.0, 0.8185, 0.0755, 0.2847, 0.7907, 0.7395] +2026-04-13 21:49:16.590041: Epoch time: 101.82 s +2026-04-13 21:49:17.875177: +2026-04-13 21:49:17.876918: Epoch 2917 +2026-04-13 21:49:17.878812: Current learning rate: 0.00309 +2026-04-13 21:50:59.633719: train_loss -0.4386 +2026-04-13 21:50:59.641411: val_loss -0.3701 +2026-04-13 21:50:59.643760: Pseudo dice [0.2817, 0.0, 0.5932, 0.773, 0.3176, 0.6754, 0.8449] +2026-04-13 21:50:59.646418: Epoch time: 101.76 s +2026-04-13 21:51:00.865349: +2026-04-13 21:51:00.867247: Epoch 2918 +2026-04-13 21:51:00.868993: Current learning rate: 0.00308 +2026-04-13 21:52:42.427872: train_loss -0.4418 +2026-04-13 21:52:42.433873: val_loss -0.4011 +2026-04-13 21:52:42.435591: Pseudo dice [0.2961, 0.0, 0.853, 0.6988, 0.4242, 0.8323, 0.9196] +2026-04-13 21:52:42.437562: Epoch time: 101.57 s +2026-04-13 21:52:43.655079: +2026-04-13 21:52:43.656797: Epoch 2919 +2026-04-13 21:52:43.658265: Current learning rate: 0.00308 +2026-04-13 21:54:25.613076: train_loss -0.4347 +2026-04-13 21:54:25.638880: val_loss -0.348 +2026-04-13 21:54:25.641048: Pseudo dice [0.5119, 0.0, 0.7786, 0.6219, 0.1757, 0.534, 0.5809] +2026-04-13 21:54:25.643091: Epoch time: 101.96 s +2026-04-13 21:54:26.891140: +2026-04-13 21:54:26.893303: Epoch 2920 +2026-04-13 21:54:26.895354: Current learning rate: 0.00308 +2026-04-13 21:56:08.510470: train_loss -0.4486 +2026-04-13 21:56:08.514928: val_loss -0.3896 +2026-04-13 21:56:08.516651: Pseudo dice [0.4763, 0.0, 0.5553, 0.771, 0.48, 0.6915, 0.8112] +2026-04-13 21:56:08.519308: Epoch time: 101.62 s +2026-04-13 21:56:09.723095: +2026-04-13 21:56:09.724740: Epoch 2921 +2026-04-13 21:56:09.726181: Current learning rate: 0.00308 +2026-04-13 21:57:51.109700: train_loss -0.4032 +2026-04-13 21:57:51.115113: val_loss -0.3816 +2026-04-13 21:57:51.117094: Pseudo dice [0.2684, 0.0, 0.7736, 0.9274, 0.509, 0.8534, 0.7121] +2026-04-13 21:57:51.119155: Epoch time: 101.39 s +2026-04-13 21:57:52.343337: +2026-04-13 21:57:52.344980: Epoch 2922 +2026-04-13 21:57:52.346453: Current learning rate: 0.00307 +2026-04-13 21:59:33.840131: train_loss -0.4224 +2026-04-13 21:59:33.845606: val_loss -0.377 +2026-04-13 21:59:33.847401: Pseudo dice [0.5098, 0.0, 0.7845, 0.8303, 0.5479, 0.4553, 0.4925] +2026-04-13 21:59:33.849342: Epoch time: 101.5 s +2026-04-13 21:59:35.058015: +2026-04-13 21:59:35.061529: Epoch 2923 +2026-04-13 21:59:35.063670: Current learning rate: 0.00307 +2026-04-13 22:01:16.705720: train_loss -0.435 +2026-04-13 22:01:16.710852: val_loss -0.3909 +2026-04-13 22:01:16.712415: Pseudo dice [0.4261, 0.0, 0.6942, 0.8703, 0.4665, 0.5078, 0.76] +2026-04-13 22:01:16.714145: Epoch time: 101.65 s +2026-04-13 22:01:17.917754: +2026-04-13 22:01:17.919546: Epoch 2924 +2026-04-13 22:01:17.921085: Current learning rate: 0.00307 +2026-04-13 22:02:59.352204: train_loss -0.453 +2026-04-13 22:02:59.357957: val_loss -0.3584 +2026-04-13 22:02:59.360724: Pseudo dice [0.3587, 0.0, 0.7279, 0.8617, 0.3276, 0.8028, 0.6484] +2026-04-13 22:02:59.362664: Epoch time: 101.44 s +2026-04-13 22:03:00.581178: +2026-04-13 22:03:00.582878: Epoch 2925 +2026-04-13 22:03:00.584492: Current learning rate: 0.00306 +2026-04-13 22:04:42.065855: train_loss -0.4315 +2026-04-13 22:04:42.071191: val_loss -0.374 +2026-04-13 22:04:42.072926: Pseudo dice [0.6155, 0.0, 0.7106, 0.518, 0.6144, 0.7339, 0.6993] +2026-04-13 22:04:42.074457: Epoch time: 101.49 s +2026-04-13 22:04:44.330519: +2026-04-13 22:04:44.332821: Epoch 2926 +2026-04-13 22:04:44.335936: Current learning rate: 0.00306 +2026-04-13 22:06:25.817016: train_loss -0.4339 +2026-04-13 22:06:25.822204: val_loss -0.3599 +2026-04-13 22:06:25.825082: Pseudo dice [0.4121, 0.0, 0.729, 0.3031, 0.2652, 0.6106, 0.6992] +2026-04-13 22:06:25.827331: Epoch time: 101.49 s +2026-04-13 22:06:27.027766: +2026-04-13 22:06:27.030095: Epoch 2927 +2026-04-13 22:06:27.031907: Current learning rate: 0.00306 +2026-04-13 22:08:08.664097: train_loss -0.446 +2026-04-13 22:08:08.668964: val_loss -0.3755 +2026-04-13 22:08:08.670794: Pseudo dice [0.7269, 0.0, 0.5706, 0.8807, 0.5349, 0.7159, 0.7208] +2026-04-13 22:08:08.672654: Epoch time: 101.64 s +2026-04-13 22:08:09.880646: +2026-04-13 22:08:09.882887: Epoch 2928 +2026-04-13 22:08:09.884774: Current learning rate: 0.00306 +2026-04-13 22:09:51.523760: train_loss -0.4471 +2026-04-13 22:09:51.529483: val_loss -0.3674 +2026-04-13 22:09:51.531962: Pseudo dice [0.3551, 0.0, 0.7103, 0.5255, 0.5632, 0.8088, 0.6319] +2026-04-13 22:09:51.534285: Epoch time: 101.65 s +2026-04-13 22:09:52.722331: +2026-04-13 22:09:52.724062: Epoch 2929 +2026-04-13 22:09:52.725823: Current learning rate: 0.00305 +2026-04-13 22:11:34.050216: train_loss -0.4396 +2026-04-13 22:11:34.055213: val_loss -0.3859 +2026-04-13 22:11:34.057203: Pseudo dice [0.7045, 0.0, 0.7851, 0.2977, 0.4224, 0.662, 0.7609] +2026-04-13 22:11:34.059026: Epoch time: 101.33 s +2026-04-13 22:11:35.269722: +2026-04-13 22:11:35.271351: Epoch 2930 +2026-04-13 22:11:35.272854: Current learning rate: 0.00305 +2026-04-13 22:13:16.845605: train_loss -0.4518 +2026-04-13 22:13:16.850760: val_loss -0.3511 +2026-04-13 22:13:16.852392: Pseudo dice [0.3093, 0.0, 0.7423, 0.2775, 0.3549, 0.7898, 0.725] +2026-04-13 22:13:16.854209: Epoch time: 101.58 s +2026-04-13 22:13:18.059829: +2026-04-13 22:13:18.061540: Epoch 2931 +2026-04-13 22:13:18.063155: Current learning rate: 0.00305 +2026-04-13 22:14:59.455833: train_loss -0.439 +2026-04-13 22:14:59.461373: val_loss -0.3718 +2026-04-13 22:14:59.463468: Pseudo dice [0.7089, 0.0, 0.7626, 0.4517, 0.4691, 0.6741, 0.7931] +2026-04-13 22:14:59.465381: Epoch time: 101.4 s +2026-04-13 22:15:00.663514: +2026-04-13 22:15:00.665409: Epoch 2932 +2026-04-13 22:15:00.667220: Current learning rate: 0.00305 +2026-04-13 22:16:42.070429: train_loss -0.4315 +2026-04-13 22:16:42.079785: val_loss -0.4044 +2026-04-13 22:16:42.081610: Pseudo dice [0.5282, 0.0, 0.7164, 0.3717, 0.4853, 0.6779, 0.9232] +2026-04-13 22:16:42.083892: Epoch time: 101.41 s +2026-04-13 22:16:43.300523: +2026-04-13 22:16:43.303024: Epoch 2933 +2026-04-13 22:16:43.304486: Current learning rate: 0.00304 +2026-04-13 22:18:24.852303: train_loss -0.4537 +2026-04-13 22:18:24.857978: val_loss -0.3797 +2026-04-13 22:18:24.860407: Pseudo dice [0.3794, 0.0, 0.7881, 0.7726, 0.3724, 0.883, 0.8397] +2026-04-13 22:18:24.862345: Epoch time: 101.55 s +2026-04-13 22:18:26.169174: +2026-04-13 22:18:26.171508: Epoch 2934 +2026-04-13 22:18:26.173687: Current learning rate: 0.00304 +2026-04-13 22:20:07.661920: train_loss -0.4447 +2026-04-13 22:20:07.668251: val_loss -0.3797 +2026-04-13 22:20:07.670044: Pseudo dice [0.1089, 0.0, 0.8244, 0.7901, 0.6475, 0.5951, 0.7707] +2026-04-13 22:20:07.671594: Epoch time: 101.5 s +2026-04-13 22:20:08.875729: +2026-04-13 22:20:08.877872: Epoch 2935 +2026-04-13 22:20:08.879763: Current learning rate: 0.00304 +2026-04-13 22:21:50.348126: train_loss -0.4275 +2026-04-13 22:21:50.353326: val_loss -0.391 +2026-04-13 22:21:50.355114: Pseudo dice [0.2656, 0.0, 0.7852, 0.8811, 0.4534, 0.8218, 0.5514] +2026-04-13 22:21:50.356465: Epoch time: 101.48 s +2026-04-13 22:21:51.555437: +2026-04-13 22:21:51.557456: Epoch 2936 +2026-04-13 22:21:51.559464: Current learning rate: 0.00304 +2026-04-13 22:23:33.170431: train_loss -0.4331 +2026-04-13 22:23:33.175994: val_loss -0.3654 +2026-04-13 22:23:33.178047: Pseudo dice [0.468, 0.0, 0.8218, 0.3742, 0.3155, 0.718, 0.818] +2026-04-13 22:23:33.179845: Epoch time: 101.62 s +2026-04-13 22:23:34.415860: +2026-04-13 22:23:34.417643: Epoch 2937 +2026-04-13 22:23:34.419368: Current learning rate: 0.00303 +2026-04-13 22:25:15.890310: train_loss -0.432 +2026-04-13 22:25:15.912801: val_loss -0.3304 +2026-04-13 22:25:15.914620: Pseudo dice [0.3507, 0.0, 0.6567, 0.3551, 0.339, 0.7456, 0.2685] +2026-04-13 22:25:15.916946: Epoch time: 101.48 s +2026-04-13 22:25:17.131282: +2026-04-13 22:25:17.133060: Epoch 2938 +2026-04-13 22:25:17.135008: Current learning rate: 0.00303 +2026-04-13 22:26:58.726254: train_loss -0.4245 +2026-04-13 22:26:58.731237: val_loss -0.3206 +2026-04-13 22:26:58.733973: Pseudo dice [0.4752, 0.0, 0.6845, 0.6068, 0.3436, 0.6738, 0.3029] +2026-04-13 22:26:58.735919: Epoch time: 101.6 s +2026-04-13 22:26:59.932978: +2026-04-13 22:26:59.935256: Epoch 2939 +2026-04-13 22:26:59.936780: Current learning rate: 0.00303 +2026-04-13 22:28:41.682688: train_loss -0.3942 +2026-04-13 22:28:41.689301: val_loss -0.3345 +2026-04-13 22:28:41.694088: Pseudo dice [0.3644, 0.0, 0.6777, 0.2842, 0.1289, 0.4754, 0.5442] +2026-04-13 22:28:41.695741: Epoch time: 101.75 s +2026-04-13 22:28:42.914631: +2026-04-13 22:28:42.916892: Epoch 2940 +2026-04-13 22:28:42.919801: Current learning rate: 0.00303 +2026-04-13 22:30:24.519705: train_loss -0.4072 +2026-04-13 22:30:24.525167: val_loss -0.3692 +2026-04-13 22:30:24.527425: Pseudo dice [0.4538, 0.0, 0.4406, 0.7338, 0.5514, 0.7782, 0.8025] +2026-04-13 22:30:24.529136: Epoch time: 101.61 s +2026-04-13 22:30:25.728555: +2026-04-13 22:30:25.730803: Epoch 2941 +2026-04-13 22:30:25.732699: Current learning rate: 0.00302 +2026-04-13 22:32:07.284365: train_loss -0.4115 +2026-04-13 22:32:07.288456: val_loss -0.3899 +2026-04-13 22:32:07.290131: Pseudo dice [0.3559, 0.0, 0.791, 0.5424, 0.2379, 0.6503, 0.6873] +2026-04-13 22:32:07.291664: Epoch time: 101.56 s +2026-04-13 22:32:08.481669: +2026-04-13 22:32:08.483417: Epoch 2942 +2026-04-13 22:32:08.484905: Current learning rate: 0.00302 +2026-04-13 22:33:50.097579: train_loss -0.4177 +2026-04-13 22:33:50.102280: val_loss -0.367 +2026-04-13 22:33:50.105511: Pseudo dice [0.1818, 0.0, 0.8403, 0.8377, 0.3616, 0.8143, 0.7001] +2026-04-13 22:33:50.107224: Epoch time: 101.62 s +2026-04-13 22:33:51.314332: +2026-04-13 22:33:51.315970: Epoch 2943 +2026-04-13 22:33:51.317713: Current learning rate: 0.00302 +2026-04-13 22:35:32.688865: train_loss -0.4325 +2026-04-13 22:35:32.696832: val_loss -0.3824 +2026-04-13 22:35:32.698576: Pseudo dice [0.3551, 0.0, 0.6448, 0.7604, 0.1011, 0.8861, 0.7811] +2026-04-13 22:35:32.700084: Epoch time: 101.38 s +2026-04-13 22:35:33.907205: +2026-04-13 22:35:33.909477: Epoch 2944 +2026-04-13 22:35:33.911156: Current learning rate: 0.00302 +2026-04-13 22:37:15.398800: train_loss -0.4292 +2026-04-13 22:37:15.411294: val_loss -0.3686 +2026-04-13 22:37:15.413216: Pseudo dice [0.6629, 0.0, 0.4442, 0.583, 0.3729, 0.7461, 0.1235] +2026-04-13 22:37:15.415015: Epoch time: 101.49 s +2026-04-13 22:37:16.636938: +2026-04-13 22:37:16.638897: Epoch 2945 +2026-04-13 22:37:16.641230: Current learning rate: 0.00301 +2026-04-13 22:38:58.118509: train_loss -0.4314 +2026-04-13 22:38:58.123149: val_loss -0.3595 +2026-04-13 22:38:58.124873: Pseudo dice [0.52, 0.0, 0.4875, 0.8247, 0.3729, 0.7869, 0.9103] +2026-04-13 22:38:58.126271: Epoch time: 101.48 s +2026-04-13 22:38:59.325926: +2026-04-13 22:38:59.328211: Epoch 2946 +2026-04-13 22:38:59.330396: Current learning rate: 0.00301 +2026-04-13 22:40:41.856894: train_loss -0.4173 +2026-04-13 22:40:41.861861: val_loss -0.3978 +2026-04-13 22:40:41.863782: Pseudo dice [0.2236, 0.0, 0.7659, 0.6614, 0.2681, 0.7352, 0.7925] +2026-04-13 22:40:41.865307: Epoch time: 102.53 s +2026-04-13 22:40:43.076860: +2026-04-13 22:40:43.078346: Epoch 2947 +2026-04-13 22:40:43.080073: Current learning rate: 0.00301 +2026-04-13 22:42:24.715225: train_loss -0.4264 +2026-04-13 22:42:24.720962: val_loss -0.3514 +2026-04-13 22:42:24.722597: Pseudo dice [0.418, 0.0, 0.5345, 0.1019, 0.4257, 0.3956, 0.8634] +2026-04-13 22:42:24.724554: Epoch time: 101.64 s +2026-04-13 22:42:25.939678: +2026-04-13 22:42:25.942320: Epoch 2948 +2026-04-13 22:42:25.944292: Current learning rate: 0.00301 +2026-04-13 22:44:07.412699: train_loss -0.3976 +2026-04-13 22:44:07.417684: val_loss -0.385 +2026-04-13 22:44:07.419530: Pseudo dice [0.2894, 0.0, 0.7329, 0.3306, 0.4332, 0.6492, 0.8029] +2026-04-13 22:44:07.421185: Epoch time: 101.48 s +2026-04-13 22:44:08.628259: +2026-04-13 22:44:08.629997: Epoch 2949 +2026-04-13 22:44:08.631385: Current learning rate: 0.003 +2026-04-13 22:45:50.250213: train_loss -0.4197 +2026-04-13 22:45:50.255781: val_loss -0.365 +2026-04-13 22:45:50.257612: Pseudo dice [0.3202, 0.0, 0.8177, 0.8071, 0.2533, 0.8127, 0.6678] +2026-04-13 22:45:50.259287: Epoch time: 101.62 s +2026-04-13 22:45:53.251858: +2026-04-13 22:45:53.255777: Epoch 2950 +2026-04-13 22:45:53.257652: Current learning rate: 0.003 +2026-04-13 22:47:34.760221: train_loss -0.4347 +2026-04-13 22:47:34.765392: val_loss -0.3559 +2026-04-13 22:47:34.773772: Pseudo dice [0.6366, 0.0, 0.7636, 0.5219, 0.2383, 0.708, 0.4478] +2026-04-13 22:47:34.776178: Epoch time: 101.51 s +2026-04-13 22:47:35.993346: +2026-04-13 22:47:35.995408: Epoch 2951 +2026-04-13 22:47:35.997102: Current learning rate: 0.003 +2026-04-13 22:49:17.508199: train_loss -0.4204 +2026-04-13 22:49:17.514904: val_loss -0.367 +2026-04-13 22:49:17.516714: Pseudo dice [0.1938, 0.0, 0.8059, 0.3389, 0.2969, 0.7772, 0.8128] +2026-04-13 22:49:17.518207: Epoch time: 101.52 s +2026-04-13 22:49:18.728512: +2026-04-13 22:49:18.730358: Epoch 2952 +2026-04-13 22:49:18.732608: Current learning rate: 0.003 +2026-04-13 22:51:00.192138: train_loss -0.4288 +2026-04-13 22:51:00.196777: val_loss -0.3932 +2026-04-13 22:51:00.198123: Pseudo dice [0.6689, 0.0, 0.758, 0.8661, 0.3386, 0.8165, 0.8053] +2026-04-13 22:51:00.199826: Epoch time: 101.47 s +2026-04-13 22:51:01.413230: +2026-04-13 22:51:01.415006: Epoch 2953 +2026-04-13 22:51:01.416596: Current learning rate: 0.00299 +2026-04-13 22:52:42.971995: train_loss -0.4445 +2026-04-13 22:52:42.977438: val_loss -0.3699 +2026-04-13 22:52:42.979468: Pseudo dice [0.7406, 0.0, 0.5836, 0.6399, 0.3909, 0.6221, 0.5566] +2026-04-13 22:52:42.981433: Epoch time: 101.56 s +2026-04-13 22:52:44.187173: +2026-04-13 22:52:44.189202: Epoch 2954 +2026-04-13 22:52:44.191184: Current learning rate: 0.00299 +2026-04-13 22:54:25.705372: train_loss -0.4391 +2026-04-13 22:54:25.710980: val_loss -0.3847 +2026-04-13 22:54:25.712917: Pseudo dice [0.2344, 0.0, 0.7049, 0.7054, 0.4182, 0.6786, 0.7938] +2026-04-13 22:54:25.714651: Epoch time: 101.52 s +2026-04-13 22:54:26.919477: +2026-04-13 22:54:26.921131: Epoch 2955 +2026-04-13 22:54:26.922625: Current learning rate: 0.00299 +2026-04-13 22:56:08.248147: train_loss -0.4242 +2026-04-13 22:56:08.253227: val_loss -0.3785 +2026-04-13 22:56:08.254765: Pseudo dice [0.4261, 0.0, 0.6733, 0.6065, 0.5573, 0.7475, 0.8437] +2026-04-13 22:56:08.256288: Epoch time: 101.33 s +2026-04-13 22:56:09.461854: +2026-04-13 22:56:09.464126: Epoch 2956 +2026-04-13 22:56:09.466003: Current learning rate: 0.00299 +2026-04-13 22:57:51.071735: train_loss -0.4437 +2026-04-13 22:57:51.077628: val_loss -0.3656 +2026-04-13 22:57:51.079418: Pseudo dice [0.2828, 0.0, 0.7971, 0.7362, 0.3679, 0.7862, 0.6901] +2026-04-13 22:57:51.081042: Epoch time: 101.61 s +2026-04-13 22:57:52.293950: +2026-04-13 22:57:52.295842: Epoch 2957 +2026-04-13 22:57:52.297738: Current learning rate: 0.00298 +2026-04-13 22:59:33.865629: train_loss -0.4419 +2026-04-13 22:59:33.870383: val_loss -0.3703 +2026-04-13 22:59:33.871881: Pseudo dice [0.3323, 0.0, 0.7845, 0.8468, 0.506, 0.7989, 0.7722] +2026-04-13 22:59:33.874091: Epoch time: 101.57 s +2026-04-13 22:59:35.092751: +2026-04-13 22:59:35.094260: Epoch 2958 +2026-04-13 22:59:35.095597: Current learning rate: 0.00298 +2026-04-13 23:01:16.605720: train_loss -0.4503 +2026-04-13 23:01:16.610196: val_loss -0.3781 +2026-04-13 23:01:16.612117: Pseudo dice [0.1636, 0.0, 0.6879, 0.4058, 0.3493, 0.5898, 0.8758] +2026-04-13 23:01:16.613391: Epoch time: 101.52 s +2026-04-13 23:01:17.804527: +2026-04-13 23:01:17.806474: Epoch 2959 +2026-04-13 23:01:17.808372: Current learning rate: 0.00298 +2026-04-13 23:02:59.491751: train_loss -0.4165 +2026-04-13 23:02:59.496724: val_loss -0.3679 +2026-04-13 23:02:59.498607: Pseudo dice [0.5226, 0.0, 0.5975, 0.6754, 0.3583, 0.7299, 0.881] +2026-04-13 23:02:59.500722: Epoch time: 101.69 s +2026-04-13 23:03:00.702167: +2026-04-13 23:03:00.703951: Epoch 2960 +2026-04-13 23:03:00.705444: Current learning rate: 0.00297 +2026-04-13 23:04:42.235492: train_loss -0.4225 +2026-04-13 23:04:42.240524: val_loss -0.3495 +2026-04-13 23:04:42.241960: Pseudo dice [0.1994, 0.0, 0.6618, 0.599, 0.351, 0.7451, 0.6841] +2026-04-13 23:04:42.243378: Epoch time: 101.54 s +2026-04-13 23:04:43.450145: +2026-04-13 23:04:43.451830: Epoch 2961 +2026-04-13 23:04:43.453318: Current learning rate: 0.00297 +2026-04-13 23:06:24.949796: train_loss -0.4283 +2026-04-13 23:06:24.955017: val_loss -0.405 +2026-04-13 23:06:24.957250: Pseudo dice [0.7893, 0.0, 0.8052, 0.4756, 0.4588, 0.6803, 0.8598] +2026-04-13 23:06:24.960469: Epoch time: 101.5 s +2026-04-13 23:06:26.158756: +2026-04-13 23:06:26.160839: Epoch 2962 +2026-04-13 23:06:26.162535: Current learning rate: 0.00297 +2026-04-13 23:08:07.687010: train_loss -0.4327 +2026-04-13 23:08:07.692196: val_loss -0.4294 +2026-04-13 23:08:07.694644: Pseudo dice [0.5993, 0.0, 0.7796, 0.7806, 0.6217, 0.7583, 0.9145] +2026-04-13 23:08:07.696732: Epoch time: 101.53 s +2026-04-13 23:08:08.906145: +2026-04-13 23:08:08.908250: Epoch 2963 +2026-04-13 23:08:08.910388: Current learning rate: 0.00297 +2026-04-13 23:09:50.462629: train_loss -0.4581 +2026-04-13 23:09:50.467685: val_loss -0.4008 +2026-04-13 23:09:50.470300: Pseudo dice [0.3147, 0.0, 0.6418, 0.7928, 0.6162, 0.678, 0.863] +2026-04-13 23:09:50.472353: Epoch time: 101.56 s +2026-04-13 23:09:51.839609: +2026-04-13 23:09:51.841511: Epoch 2964 +2026-04-13 23:09:51.843365: Current learning rate: 0.00296 +2026-04-13 23:11:33.308875: train_loss -0.4353 +2026-04-13 23:11:33.314341: val_loss -0.4116 +2026-04-13 23:11:33.316874: Pseudo dice [0.4735, 0.0, 0.7397, 0.8249, 0.5226, 0.8563, 0.6457] +2026-04-13 23:11:33.318767: Epoch time: 101.47 s +2026-04-13 23:11:34.531180: +2026-04-13 23:11:34.532785: Epoch 2965 +2026-04-13 23:11:34.534648: Current learning rate: 0.00296 +2026-04-13 23:13:16.093838: train_loss -0.4091 +2026-04-13 23:13:16.098922: val_loss -0.3355 +2026-04-13 23:13:16.100706: Pseudo dice [0.0, 0.0, 0.6677, 0.2544, 0.131, 0.7001, 0.8179] +2026-04-13 23:13:16.102550: Epoch time: 101.57 s +2026-04-13 23:13:18.339540: +2026-04-13 23:13:18.341395: Epoch 2966 +2026-04-13 23:13:18.343019: Current learning rate: 0.00296 +2026-04-13 23:14:59.965936: train_loss -0.4091 +2026-04-13 23:14:59.971221: val_loss -0.3897 +2026-04-13 23:14:59.973258: Pseudo dice [0.1362, 0.0, 0.7305, 0.7973, 0.2845, 0.7898, 0.7083] +2026-04-13 23:14:59.975226: Epoch time: 101.63 s +2026-04-13 23:15:01.195374: +2026-04-13 23:15:01.197021: Epoch 2967 +2026-04-13 23:15:01.198408: Current learning rate: 0.00296 +2026-04-13 23:16:42.838930: train_loss -0.4185 +2026-04-13 23:16:42.849050: val_loss -0.3721 +2026-04-13 23:16:42.850750: Pseudo dice [0.4063, 0.0, 0.4492, 0.4177, 0.4856, 0.7776, 0.6111] +2026-04-13 23:16:42.852396: Epoch time: 101.65 s +2026-04-13 23:16:44.081999: +2026-04-13 23:16:44.083814: Epoch 2968 +2026-04-13 23:16:44.085626: Current learning rate: 0.00295 +2026-04-13 23:18:25.567948: train_loss -0.4248 +2026-04-13 23:18:25.574817: val_loss -0.3615 +2026-04-13 23:18:25.576928: Pseudo dice [0.4493, 0.0, 0.7072, 0.8332, 0.5069, 0.7634, 0.7595] +2026-04-13 23:18:25.578718: Epoch time: 101.49 s +2026-04-13 23:18:26.784854: +2026-04-13 23:18:26.786675: Epoch 2969 +2026-04-13 23:18:26.788437: Current learning rate: 0.00295 +2026-04-13 23:20:08.340248: train_loss -0.4338 +2026-04-13 23:20:08.345290: val_loss -0.3756 +2026-04-13 23:20:08.347025: Pseudo dice [0.6531, 0.0, 0.6643, 0.8745, 0.5265, 0.6574, 0.9277] +2026-04-13 23:20:08.348576: Epoch time: 101.56 s +2026-04-13 23:20:09.549856: +2026-04-13 23:20:09.552936: Epoch 2970 +2026-04-13 23:20:09.554667: Current learning rate: 0.00295 +2026-04-13 23:21:51.058882: train_loss -0.4475 +2026-04-13 23:21:51.063996: val_loss -0.3869 +2026-04-13 23:21:51.065857: Pseudo dice [0.335, 0.0, 0.684, 0.7494, 0.3202, 0.7526, 0.8694] +2026-04-13 23:21:51.068079: Epoch time: 101.51 s +2026-04-13 23:21:52.257681: +2026-04-13 23:21:52.259481: Epoch 2971 +2026-04-13 23:21:52.261383: Current learning rate: 0.00295 +2026-04-13 23:23:33.726082: train_loss -0.426 +2026-04-13 23:23:33.731000: val_loss -0.4156 +2026-04-13 23:23:33.733118: Pseudo dice [0.5387, 0.0, 0.6755, 0.7228, 0.5862, 0.8127, 0.8564] +2026-04-13 23:23:33.735084: Epoch time: 101.47 s +2026-04-13 23:23:34.945114: +2026-04-13 23:23:34.947605: Epoch 2972 +2026-04-13 23:23:34.949910: Current learning rate: 0.00294 +2026-04-13 23:25:16.679615: train_loss -0.4224 +2026-04-13 23:25:16.686773: val_loss -0.3488 +2026-04-13 23:25:16.689148: Pseudo dice [0.3166, 0.0, 0.5292, 0.7504, 0.5055, 0.7953, 0.6832] +2026-04-13 23:25:16.691186: Epoch time: 101.74 s +2026-04-13 23:25:17.906413: +2026-04-13 23:25:17.908090: Epoch 2973 +2026-04-13 23:25:17.909892: Current learning rate: 0.00294 +2026-04-13 23:26:59.466915: train_loss -0.4324 +2026-04-13 23:26:59.471850: val_loss -0.3806 +2026-04-13 23:26:59.473449: Pseudo dice [0.4211, 0.0, 0.6025, 0.841, 0.4878, 0.5417, 0.4924] +2026-04-13 23:26:59.474983: Epoch time: 101.56 s +2026-04-13 23:27:00.682008: +2026-04-13 23:27:00.683741: Epoch 2974 +2026-04-13 23:27:00.685634: Current learning rate: 0.00294 +2026-04-13 23:28:42.371090: train_loss -0.4376 +2026-04-13 23:28:42.375776: val_loss -0.3614 +2026-04-13 23:28:42.380112: Pseudo dice [0.2548, 0.0, 0.6216, 0.2606, 0.4312, 0.8051, 0.8126] +2026-04-13 23:28:42.382068: Epoch time: 101.69 s +2026-04-13 23:28:43.580438: +2026-04-13 23:28:43.581998: Epoch 2975 +2026-04-13 23:28:43.583598: Current learning rate: 0.00294 +2026-04-13 23:30:25.243868: train_loss -0.423 +2026-04-13 23:30:25.247830: val_loss -0.3928 +2026-04-13 23:30:25.249206: Pseudo dice [0.4723, 0.0, 0.7067, 0.9295, 0.4665, 0.4536, 0.8208] +2026-04-13 23:30:25.250872: Epoch time: 101.67 s +2026-04-13 23:30:26.449823: +2026-04-13 23:30:26.451641: Epoch 2976 +2026-04-13 23:30:26.453328: Current learning rate: 0.00293 +2026-04-13 23:32:08.044473: train_loss -0.4113 +2026-04-13 23:32:08.049325: val_loss -0.3504 +2026-04-13 23:32:08.050691: Pseudo dice [0.4388, 0.0, 0.4813, 0.5119, 0.3583, 0.4716, 0.7403] +2026-04-13 23:32:08.052955: Epoch time: 101.6 s +2026-04-13 23:32:09.242656: +2026-04-13 23:32:09.244285: Epoch 2977 +2026-04-13 23:32:09.246001: Current learning rate: 0.00293 +2026-04-13 23:33:50.807249: train_loss -0.4399 +2026-04-13 23:33:50.812435: val_loss -0.3567 +2026-04-13 23:33:50.814661: Pseudo dice [0.6862, 0.0, 0.6476, 0.1938, 0.3811, 0.5901, 0.7142] +2026-04-13 23:33:50.817554: Epoch time: 101.57 s +2026-04-13 23:33:52.032273: +2026-04-13 23:33:52.034261: Epoch 2978 +2026-04-13 23:33:52.036579: Current learning rate: 0.00293 +2026-04-13 23:35:33.709019: train_loss -0.4479 +2026-04-13 23:35:33.713331: val_loss -0.3903 +2026-04-13 23:35:33.714904: Pseudo dice [0.6225, 0.0, 0.6772, 0.3308, 0.4264, 0.7622, 0.6709] +2026-04-13 23:35:33.716630: Epoch time: 101.68 s +2026-04-13 23:35:34.921602: +2026-04-13 23:35:34.923750: Epoch 2979 +2026-04-13 23:35:34.925194: Current learning rate: 0.00293 +2026-04-13 23:37:16.480691: train_loss -0.4294 +2026-04-13 23:37:16.486132: val_loss -0.3577 +2026-04-13 23:37:16.487963: Pseudo dice [0.1434, 0.0, 0.6633, 0.6994, 0.1632, 0.7888, 0.8369] +2026-04-13 23:37:16.489847: Epoch time: 101.56 s +2026-04-13 23:37:17.702375: +2026-04-13 23:37:17.704186: Epoch 2980 +2026-04-13 23:37:17.705732: Current learning rate: 0.00292 +2026-04-13 23:38:59.378747: train_loss -0.4265 +2026-04-13 23:38:59.383269: val_loss -0.3285 +2026-04-13 23:38:59.385028: Pseudo dice [0.1156, 0.0, 0.4729, 0.7909, 0.2575, 0.5203, 0.8269] +2026-04-13 23:38:59.386781: Epoch time: 101.68 s +2026-04-13 23:39:00.601035: +2026-04-13 23:39:00.603174: Epoch 2981 +2026-04-13 23:39:00.604796: Current learning rate: 0.00292 +2026-04-13 23:40:42.195331: train_loss -0.4325 +2026-04-13 23:40:42.201235: val_loss -0.3834 +2026-04-13 23:40:42.203542: Pseudo dice [0.6343, 0.0, 0.5596, 0.9184, 0.2655, 0.6868, 0.7669] +2026-04-13 23:40:42.205426: Epoch time: 101.6 s +2026-04-13 23:40:43.400329: +2026-04-13 23:40:43.402463: Epoch 2982 +2026-04-13 23:40:43.404443: Current learning rate: 0.00292 +2026-04-13 23:42:24.992016: train_loss -0.429 +2026-04-13 23:42:24.997031: val_loss -0.3628 +2026-04-13 23:42:24.998920: Pseudo dice [0.7981, 0.0, 0.5124, 0.2619, 0.2781, 0.3297, 0.8964] +2026-04-13 23:42:25.000639: Epoch time: 101.59 s +2026-04-13 23:42:26.218299: +2026-04-13 23:42:26.220238: Epoch 2983 +2026-04-13 23:42:26.221939: Current learning rate: 0.00292 +2026-04-13 23:44:07.967648: train_loss -0.44 +2026-04-13 23:44:07.972764: val_loss -0.3384 +2026-04-13 23:44:07.974689: Pseudo dice [0.1926, 0.0, 0.8487, 0.784, 0.447, 0.764, 0.3442] +2026-04-13 23:44:07.976200: Epoch time: 101.75 s +2026-04-13 23:44:09.178560: +2026-04-13 23:44:09.180281: Epoch 2984 +2026-04-13 23:44:09.181717: Current learning rate: 0.00291 +2026-04-13 23:45:50.613988: train_loss -0.4382 +2026-04-13 23:45:50.618978: val_loss -0.3749 +2026-04-13 23:45:50.620810: Pseudo dice [0.3394, 0.0, 0.807, 0.8443, 0.4811, 0.6217, 0.8759] +2026-04-13 23:45:50.622293: Epoch time: 101.44 s +2026-04-13 23:45:51.819224: +2026-04-13 23:45:51.821453: Epoch 2985 +2026-04-13 23:45:51.823356: Current learning rate: 0.00291 +2026-04-13 23:47:33.372671: train_loss -0.4443 +2026-04-13 23:47:33.383733: val_loss -0.3708 +2026-04-13 23:47:33.385437: Pseudo dice [0.5839, 0.0, 0.5266, 0.7595, 0.3378, 0.624, 0.8882] +2026-04-13 23:47:33.387312: Epoch time: 101.56 s +2026-04-13 23:47:35.637232: +2026-04-13 23:47:35.638981: Epoch 2986 +2026-04-13 23:47:35.640402: Current learning rate: 0.00291 +2026-04-13 23:49:17.216751: train_loss -0.4458 +2026-04-13 23:49:17.221421: val_loss -0.3655 +2026-04-13 23:49:17.222905: Pseudo dice [0.6998, 0.0, 0.7603, 0.7651, 0.1832, 0.6418, 0.5793] +2026-04-13 23:49:17.225155: Epoch time: 101.58 s +2026-04-13 23:49:18.451621: +2026-04-13 23:49:18.453280: Epoch 2987 +2026-04-13 23:49:18.456578: Current learning rate: 0.00291 +2026-04-13 23:51:00.037067: train_loss -0.4538 +2026-04-13 23:51:00.041651: val_loss -0.3803 +2026-04-13 23:51:00.043523: Pseudo dice [0.4742, 0.0, 0.8524, 0.647, 0.2593, 0.7801, 0.8706] +2026-04-13 23:51:00.045834: Epoch time: 101.59 s +2026-04-13 23:51:01.238836: +2026-04-13 23:51:01.240619: Epoch 2988 +2026-04-13 23:51:01.242355: Current learning rate: 0.0029 +2026-04-13 23:52:42.967995: train_loss -0.451 +2026-04-13 23:52:42.973064: val_loss -0.3398 +2026-04-13 23:52:42.975480: Pseudo dice [0.3499, 0.0, 0.6037, 0.6145, 0.2081, 0.2707, 0.8266] +2026-04-13 23:52:42.977566: Epoch time: 101.73 s +2026-04-13 23:52:44.186871: +2026-04-13 23:52:44.189026: Epoch 2989 +2026-04-13 23:52:44.191317: Current learning rate: 0.0029 +2026-04-13 23:54:25.770049: train_loss -0.4378 +2026-04-13 23:54:25.774873: val_loss -0.3626 +2026-04-13 23:54:25.776440: Pseudo dice [0.7026, 0.0, 0.7106, 0.0032, 0.177, 0.7038, 0.7083] +2026-04-13 23:54:25.777746: Epoch time: 101.59 s +2026-04-13 23:54:26.983640: +2026-04-13 23:54:26.984967: Epoch 2990 +2026-04-13 23:54:26.986384: Current learning rate: 0.0029 +2026-04-13 23:56:08.639514: train_loss -0.437 +2026-04-13 23:56:08.643958: val_loss -0.4087 +2026-04-13 23:56:08.645743: Pseudo dice [0.7285, 0.0, 0.5831, 0.1697, 0.4378, 0.8239, 0.8546] +2026-04-13 23:56:08.647949: Epoch time: 101.66 s +2026-04-13 23:56:09.872994: +2026-04-13 23:56:09.875385: Epoch 2991 +2026-04-13 23:56:09.877874: Current learning rate: 0.00289 +2026-04-13 23:57:51.666398: train_loss -0.4392 +2026-04-13 23:57:51.671340: val_loss -0.3567 +2026-04-13 23:57:51.673067: Pseudo dice [0.3693, 0.0, 0.6669, 0.1552, 0.3955, 0.4796, 0.7163] +2026-04-13 23:57:51.674891: Epoch time: 101.8 s +2026-04-13 23:57:52.870619: +2026-04-13 23:57:52.872407: Epoch 2992 +2026-04-13 23:57:52.874113: Current learning rate: 0.00289 +2026-04-13 23:59:34.519968: train_loss -0.4343 +2026-04-13 23:59:34.524543: val_loss -0.384 +2026-04-13 23:59:34.526030: Pseudo dice [0.369, 0.0, 0.6694, 0.8092, 0.4702, 0.8553, 0.6294] +2026-04-13 23:59:34.527638: Epoch time: 101.65 s +2026-04-13 23:59:35.727918: +2026-04-13 23:59:35.729498: Epoch 2993 +2026-04-13 23:59:35.730970: Current learning rate: 0.00289 +2026-04-14 00:01:17.166014: train_loss -0.4267 +2026-04-14 00:01:17.172097: val_loss -0.3471 +2026-04-14 00:01:17.174272: Pseudo dice [0.2283, 0.0, 0.7822, 0.8682, 0.1922, 0.7653, 0.825] +2026-04-14 00:01:17.176212: Epoch time: 101.44 s +2026-04-14 00:01:18.397266: +2026-04-14 00:01:18.401333: Epoch 2994 +2026-04-14 00:01:18.403648: Current learning rate: 0.00289 +2026-04-14 00:02:59.848420: train_loss -0.4375 +2026-04-14 00:02:59.852870: val_loss -0.3977 +2026-04-14 00:02:59.854541: Pseudo dice [0.6864, 0.0, 0.551, 0.6063, 0.5016, 0.7378, 0.7414] +2026-04-14 00:02:59.856264: Epoch time: 101.45 s +2026-04-14 00:03:01.069264: +2026-04-14 00:03:01.070869: Epoch 2995 +2026-04-14 00:03:01.072336: Current learning rate: 0.00288 +2026-04-14 00:04:42.746890: train_loss -0.4206 +2026-04-14 00:04:42.751123: val_loss -0.361 +2026-04-14 00:04:42.753167: Pseudo dice [0.6972, 0.0, 0.4828, 0.822, 0.4045, 0.4232, 0.8103] +2026-04-14 00:04:42.754623: Epoch time: 101.68 s +2026-04-14 00:04:43.949775: +2026-04-14 00:04:43.951414: Epoch 2996 +2026-04-14 00:04:43.952860: Current learning rate: 0.00288 +2026-04-14 00:06:25.563016: train_loss -0.443 +2026-04-14 00:06:25.567868: val_loss -0.4063 +2026-04-14 00:06:25.569504: Pseudo dice [0.5767, 0.0, 0.8216, 0.8397, 0.5668, 0.7704, 0.8883] +2026-04-14 00:06:25.570966: Epoch time: 101.62 s +2026-04-14 00:06:26.782985: +2026-04-14 00:06:26.784614: Epoch 2997 +2026-04-14 00:06:26.785985: Current learning rate: 0.00288 +2026-04-14 00:08:08.196013: train_loss -0.4341 +2026-04-14 00:08:08.200777: val_loss -0.3979 +2026-04-14 00:08:08.202297: Pseudo dice [0.2277, 0.0, 0.7849, 0.8157, 0.3847, 0.8282, 0.8772] +2026-04-14 00:08:08.204019: Epoch time: 101.42 s +2026-04-14 00:08:09.448246: +2026-04-14 00:08:09.449946: Epoch 2998 +2026-04-14 00:08:09.451360: Current learning rate: 0.00288 +2026-04-14 00:09:50.909802: train_loss -0.4481 +2026-04-14 00:09:50.915169: val_loss -0.3792 +2026-04-14 00:09:50.916958: Pseudo dice [0.4639, 0.0, 0.7068, 0.8436, 0.5285, 0.247, 0.7491] +2026-04-14 00:09:50.918703: Epoch time: 101.46 s +2026-04-14 00:09:52.112803: +2026-04-14 00:09:52.114849: Epoch 2999 +2026-04-14 00:09:52.116385: Current learning rate: 0.00287 +2026-04-14 00:11:33.587239: train_loss -0.4453 +2026-04-14 00:11:33.591753: val_loss -0.3871 +2026-04-14 00:11:33.593437: Pseudo dice [0.6956, 0.0, 0.4907, 0.8245, 0.2587, 0.7422, 0.8329] +2026-04-14 00:11:33.594847: Epoch time: 101.48 s +2026-04-14 00:11:36.517683: +2026-04-14 00:11:36.519592: Epoch 3000 +2026-04-14 00:11:36.521125: Current learning rate: 0.00287 +2026-04-14 00:13:18.087146: train_loss -0.4448 +2026-04-14 00:13:18.092360: val_loss -0.3714 +2026-04-14 00:13:18.094132: Pseudo dice [0.2782, 0.0, 0.6911, 0.7668, 0.1491, 0.2394, 0.9065] +2026-04-14 00:13:18.095787: Epoch time: 101.57 s +2026-04-14 00:13:19.304388: +2026-04-14 00:13:19.306397: Epoch 3001 +2026-04-14 00:13:19.308168: Current learning rate: 0.00287 +2026-04-14 00:15:00.988070: train_loss -0.4382 +2026-04-14 00:15:00.992942: val_loss -0.3725 +2026-04-14 00:15:00.994763: Pseudo dice [0.4321, 0.0, 0.4916, 0.6786, 0.344, 0.4825, 0.605] +2026-04-14 00:15:00.996966: Epoch time: 101.69 s +2026-04-14 00:15:02.207707: +2026-04-14 00:15:02.209609: Epoch 3002 +2026-04-14 00:15:02.211088: Current learning rate: 0.00287 +2026-04-14 00:16:43.713283: train_loss -0.4239 +2026-04-14 00:16:43.718694: val_loss -0.4029 +2026-04-14 00:16:43.720505: Pseudo dice [0.7431, 0.0, 0.5974, 0.6244, 0.5942, 0.7266, 0.7825] +2026-04-14 00:16:43.721919: Epoch time: 101.51 s +2026-04-14 00:16:45.140442: +2026-04-14 00:16:45.142108: Epoch 3003 +2026-04-14 00:16:45.143526: Current learning rate: 0.00286 +2026-04-14 00:18:26.806875: train_loss -0.4405 +2026-04-14 00:18:26.812283: val_loss -0.3838 +2026-04-14 00:18:26.814180: Pseudo dice [0.3776, 0.0, 0.6728, 0.7504, 0.4294, 0.7328, 0.755] +2026-04-14 00:18:26.815997: Epoch time: 101.67 s +2026-04-14 00:18:28.045772: +2026-04-14 00:18:28.047408: Epoch 3004 +2026-04-14 00:18:28.049253: Current learning rate: 0.00286 +2026-04-14 00:20:09.618915: train_loss -0.4449 +2026-04-14 00:20:09.623790: val_loss -0.3921 +2026-04-14 00:20:09.625648: Pseudo dice [0.5165, 0.0, 0.7511, 0.8284, 0.4272, 0.7148, 0.854] +2026-04-14 00:20:09.627387: Epoch time: 101.58 s +2026-04-14 00:20:10.831468: +2026-04-14 00:20:10.833056: Epoch 3005 +2026-04-14 00:20:10.834502: Current learning rate: 0.00286 +2026-04-14 00:21:52.275324: train_loss -0.4544 +2026-04-14 00:21:52.280242: val_loss -0.3384 +2026-04-14 00:21:52.281962: Pseudo dice [0.1786, 0.0, 0.7967, 0.5303, 0.4706, 0.7275, 0.6895] +2026-04-14 00:21:52.283218: Epoch time: 101.45 s +2026-04-14 00:21:54.525300: +2026-04-14 00:21:54.527148: Epoch 3006 +2026-04-14 00:21:54.528849: Current learning rate: 0.00286 +2026-04-14 00:23:36.131444: train_loss -0.4407 +2026-04-14 00:23:36.136371: val_loss -0.3726 +2026-04-14 00:23:36.138060: Pseudo dice [0.2339, 0.0, 0.8836, 0.7438, 0.5596, 0.7313, 0.7249] +2026-04-14 00:23:36.139799: Epoch time: 101.61 s +2026-04-14 00:23:37.336850: +2026-04-14 00:23:37.338569: Epoch 3007 +2026-04-14 00:23:37.340427: Current learning rate: 0.00285 +2026-04-14 00:25:18.442611: train_loss -0.4186 +2026-04-14 00:25:18.447489: val_loss -0.3715 +2026-04-14 00:25:18.449936: Pseudo dice [0.5441, 0.0, 0.8013, 0.821, 0.6468, 0.7672, 0.645] +2026-04-14 00:25:18.452273: Epoch time: 101.11 s +2026-04-14 00:25:19.679960: +2026-04-14 00:25:19.681555: Epoch 3008 +2026-04-14 00:25:19.683341: Current learning rate: 0.00285 +2026-04-14 00:27:00.883453: train_loss -0.4276 +2026-04-14 00:27:00.888182: val_loss -0.3964 +2026-04-14 00:27:00.889943: Pseudo dice [0.6666, 0.0, 0.8993, 0.707, 0.2578, 0.8668, 0.7205] +2026-04-14 00:27:00.891416: Epoch time: 101.21 s +2026-04-14 00:27:02.075138: +2026-04-14 00:27:02.076998: Epoch 3009 +2026-04-14 00:27:02.078472: Current learning rate: 0.00285 +2026-04-14 00:28:43.579513: train_loss -0.4466 +2026-04-14 00:28:43.584166: val_loss -0.4163 +2026-04-14 00:28:43.587002: Pseudo dice [0.4834, 0.0, 0.8256, 0.7734, 0.4746, 0.7889, 0.8613] +2026-04-14 00:28:43.588574: Epoch time: 101.51 s +2026-04-14 00:28:44.808569: +2026-04-14 00:28:44.810682: Epoch 3010 +2026-04-14 00:28:44.812441: Current learning rate: 0.00285 +2026-04-14 00:30:26.227998: train_loss -0.4438 +2026-04-14 00:30:26.232723: val_loss -0.3565 +2026-04-14 00:30:26.234509: Pseudo dice [0.6657, 0.0, 0.6811, 0.7022, 0.2951, 0.7919, 0.6309] +2026-04-14 00:30:26.236029: Epoch time: 101.42 s +2026-04-14 00:30:27.432280: +2026-04-14 00:30:27.433811: Epoch 3011 +2026-04-14 00:30:27.435536: Current learning rate: 0.00284 +2026-04-14 00:32:08.655689: train_loss -0.435 +2026-04-14 00:32:08.660059: val_loss -0.388 +2026-04-14 00:32:08.661746: Pseudo dice [0.5328, 0.0, 0.7834, 0.883, 0.4103, 0.8176, 0.7577] +2026-04-14 00:32:08.663056: Epoch time: 101.23 s +2026-04-14 00:32:09.856432: +2026-04-14 00:32:09.858740: Epoch 3012 +2026-04-14 00:32:09.860205: Current learning rate: 0.00284 +2026-04-14 00:33:51.263249: train_loss -0.4392 +2026-04-14 00:33:51.267968: val_loss -0.3486 +2026-04-14 00:33:51.270084: Pseudo dice [0.3028, 0.0, 0.5729, 0.4336, 0.3136, 0.747, 0.8487] +2026-04-14 00:33:51.271657: Epoch time: 101.41 s +2026-04-14 00:33:52.496619: +2026-04-14 00:33:52.498451: Epoch 3013 +2026-04-14 00:33:52.500154: Current learning rate: 0.00284 +2026-04-14 00:35:33.662907: train_loss -0.4507 +2026-04-14 00:35:33.669617: val_loss -0.3804 +2026-04-14 00:35:33.674095: Pseudo dice [0.2286, 0.0, 0.7112, 0.6935, 0.5476, 0.7867, 0.7903] +2026-04-14 00:35:33.676767: Epoch time: 101.17 s +2026-04-14 00:35:34.871614: +2026-04-14 00:35:34.873538: Epoch 3014 +2026-04-14 00:35:34.875541: Current learning rate: 0.00284 +2026-04-14 00:37:16.173385: train_loss -0.4221 +2026-04-14 00:37:16.178428: val_loss -0.3847 +2026-04-14 00:37:16.180177: Pseudo dice [0.5857, 0.0, 0.7706, 0.8515, 0.2853, 0.7672, 0.826] +2026-04-14 00:37:16.181965: Epoch time: 101.3 s +2026-04-14 00:37:17.394961: +2026-04-14 00:37:17.397086: Epoch 3015 +2026-04-14 00:37:17.398971: Current learning rate: 0.00283 +2026-04-14 00:38:58.862296: train_loss -0.4171 +2026-04-14 00:38:58.867616: val_loss -0.3887 +2026-04-14 00:38:58.872097: Pseudo dice [0.3875, 0.0, 0.629, 0.834, 0.4332, 0.7653, 0.8051] +2026-04-14 00:38:58.873649: Epoch time: 101.47 s +2026-04-14 00:39:00.075338: +2026-04-14 00:39:00.077358: Epoch 3016 +2026-04-14 00:39:00.078764: Current learning rate: 0.00283 +2026-04-14 00:40:41.109572: train_loss -0.4434 +2026-04-14 00:40:41.115816: val_loss -0.3702 +2026-04-14 00:40:41.117861: Pseudo dice [0.6997, 0.0, 0.5465, 0.785, 0.4623, 0.7858, 0.812] +2026-04-14 00:40:41.119176: Epoch time: 101.04 s +2026-04-14 00:40:42.352402: +2026-04-14 00:40:42.353875: Epoch 3017 +2026-04-14 00:40:42.355093: Current learning rate: 0.00283 +2026-04-14 00:42:23.625181: train_loss -0.4288 +2026-04-14 00:42:23.630313: val_loss -0.3229 +2026-04-14 00:42:23.632420: Pseudo dice [0.4037, 0.0, 0.5732, 0.7626, 0.1302, 0.7476, 0.6798] +2026-04-14 00:42:23.634868: Epoch time: 101.28 s +2026-04-14 00:42:24.828271: +2026-04-14 00:42:24.829973: Epoch 3018 +2026-04-14 00:42:24.831628: Current learning rate: 0.00283 +2026-04-14 00:44:06.100743: train_loss -0.4279 +2026-04-14 00:44:06.104851: val_loss -0.4028 +2026-04-14 00:44:06.106732: Pseudo dice [0.6593, 0.0, 0.789, 0.4402, 0.3956, 0.639, 0.846] +2026-04-14 00:44:06.108526: Epoch time: 101.28 s +2026-04-14 00:44:07.290370: +2026-04-14 00:44:07.292190: Epoch 3019 +2026-04-14 00:44:07.293807: Current learning rate: 0.00282 +2026-04-14 00:45:48.452432: train_loss -0.4356 +2026-04-14 00:45:48.456826: val_loss -0.4007 +2026-04-14 00:45:48.458651: Pseudo dice [0.3522, 0.0, 0.7078, 0.103, 0.5108, 0.614, 0.7875] +2026-04-14 00:45:48.460133: Epoch time: 101.17 s +2026-04-14 00:45:49.659925: +2026-04-14 00:45:49.661748: Epoch 3020 +2026-04-14 00:45:49.663236: Current learning rate: 0.00282 +2026-04-14 00:47:30.894521: train_loss -0.4232 +2026-04-14 00:47:30.899417: val_loss -0.3648 +2026-04-14 00:47:30.900752: Pseudo dice [0.2433, 0.0, 0.6112, 0.027, 0.2351, 0.7675, 0.7712] +2026-04-14 00:47:30.902139: Epoch time: 101.24 s +2026-04-14 00:47:32.110008: +2026-04-14 00:47:32.111325: Epoch 3021 +2026-04-14 00:47:32.112603: Current learning rate: 0.00282 +2026-04-14 00:49:13.216074: train_loss -0.4236 +2026-04-14 00:49:13.220095: val_loss -0.3859 +2026-04-14 00:49:13.221564: Pseudo dice [0.494, 0.0, 0.8104, 0.3338, 0.4138, 0.7889, 0.8341] +2026-04-14 00:49:13.223179: Epoch time: 101.11 s +2026-04-14 00:49:14.508803: +2026-04-14 00:49:14.511045: Epoch 3022 +2026-04-14 00:49:14.513333: Current learning rate: 0.00281 +2026-04-14 00:50:55.317710: train_loss -0.4223 +2026-04-14 00:50:55.323554: val_loss -0.3505 +2026-04-14 00:50:55.325315: Pseudo dice [0.4362, 0.0, 0.6855, 0.0532, 0.2067, 0.7617, 0.6457] +2026-04-14 00:50:55.327341: Epoch time: 100.81 s +2026-04-14 00:50:56.507158: +2026-04-14 00:50:56.509184: Epoch 3023 +2026-04-14 00:50:56.510666: Current learning rate: 0.00281 +2026-04-14 00:52:37.349591: train_loss -0.4139 +2026-04-14 00:52:37.362540: val_loss -0.3572 +2026-04-14 00:52:37.364195: Pseudo dice [0.3153, 0.0, 0.6246, 0.6617, 0.4306, 0.5466, 0.7511] +2026-04-14 00:52:37.368119: Epoch time: 100.85 s +2026-04-14 00:52:38.566048: +2026-04-14 00:52:38.571515: Epoch 3024 +2026-04-14 00:52:38.576168: Current learning rate: 0.00281 +2026-04-14 00:54:19.690332: train_loss -0.4363 +2026-04-14 00:54:19.694732: val_loss -0.3608 +2026-04-14 00:54:19.696673: Pseudo dice [0.1715, 0.0, 0.6957, 0.6094, 0.4263, 0.3846, 0.7225] +2026-04-14 00:54:19.698701: Epoch time: 101.13 s +2026-04-14 00:54:20.895589: +2026-04-14 00:54:20.897401: Epoch 3025 +2026-04-14 00:54:20.898807: Current learning rate: 0.00281 +2026-04-14 00:56:02.162484: train_loss -0.4263 +2026-04-14 00:56:02.166904: val_loss -0.3548 +2026-04-14 00:56:02.169124: Pseudo dice [0.6194, 0.0, 0.6189, 0.5394, 0.305, 0.3842, 0.7395] +2026-04-14 00:56:02.171429: Epoch time: 101.27 s +2026-04-14 00:56:03.377110: +2026-04-14 00:56:03.378786: Epoch 3026 +2026-04-14 00:56:03.380234: Current learning rate: 0.0028 +2026-04-14 00:57:45.671476: train_loss -0.4232 +2026-04-14 00:57:45.675635: val_loss -0.4003 +2026-04-14 00:57:45.677448: Pseudo dice [0.5534, 0.0, 0.8398, 0.8122, 0.4825, 0.7849, 0.7242] +2026-04-14 00:57:45.679366: Epoch time: 102.3 s +2026-04-14 00:57:46.885321: +2026-04-14 00:57:46.886779: Epoch 3027 +2026-04-14 00:57:46.888286: Current learning rate: 0.0028 +2026-04-14 00:59:28.166236: train_loss -0.4433 +2026-04-14 00:59:28.171158: val_loss -0.3814 +2026-04-14 00:59:28.172626: Pseudo dice [0.1758, 0.0, 0.4963, 0.4004, 0.6159, 0.7019, 0.8554] +2026-04-14 00:59:28.174122: Epoch time: 101.28 s +2026-04-14 00:59:29.373946: +2026-04-14 00:59:29.375688: Epoch 3028 +2026-04-14 00:59:29.377100: Current learning rate: 0.0028 +2026-04-14 01:01:10.707409: train_loss -0.4225 +2026-04-14 01:01:10.713411: val_loss -0.3558 +2026-04-14 01:01:10.715420: Pseudo dice [0.5505, 0.0, 0.7813, 0.3858, 0.3807, 0.7432, 0.7659] +2026-04-14 01:01:10.718371: Epoch time: 101.34 s +2026-04-14 01:01:11.931565: +2026-04-14 01:01:11.933118: Epoch 3029 +2026-04-14 01:01:11.934443: Current learning rate: 0.0028 +2026-04-14 01:02:53.147518: train_loss -0.4305 +2026-04-14 01:02:53.152900: val_loss -0.398 +2026-04-14 01:02:53.154665: Pseudo dice [0.2832, 0.0, 0.7633, 0.6663, 0.5168, 0.5508, 0.8087] +2026-04-14 01:02:53.156261: Epoch time: 101.22 s +2026-04-14 01:02:54.355605: +2026-04-14 01:02:54.357229: Epoch 3030 +2026-04-14 01:02:54.359115: Current learning rate: 0.00279 +2026-04-14 01:04:35.769070: train_loss -0.4404 +2026-04-14 01:04:35.775820: val_loss -0.3827 +2026-04-14 01:04:35.777872: Pseudo dice [0.786, 0.0, 0.3476, 0.7791, 0.411, 0.7966, 0.7947] +2026-04-14 01:04:35.779390: Epoch time: 101.42 s +2026-04-14 01:04:36.992519: +2026-04-14 01:04:36.994298: Epoch 3031 +2026-04-14 01:04:36.996444: Current learning rate: 0.00279 +2026-04-14 01:06:18.255790: train_loss -0.4228 +2026-04-14 01:06:18.260951: val_loss -0.3544 +2026-04-14 01:06:18.262771: Pseudo dice [0.6172, 0.0, 0.6806, 0.8413, 0.3088, 0.7368, 0.1761] +2026-04-14 01:06:18.264538: Epoch time: 101.27 s +2026-04-14 01:06:19.445874: +2026-04-14 01:06:19.447682: Epoch 3032 +2026-04-14 01:06:19.449484: Current learning rate: 0.00279 +2026-04-14 01:08:00.761400: train_loss -0.4301 +2026-04-14 01:08:00.765818: val_loss -0.3862 +2026-04-14 01:08:00.767665: Pseudo dice [0.7379, 0.0, 0.6414, 0.2174, 0.4447, 0.7006, 0.8496] +2026-04-14 01:08:00.769744: Epoch time: 101.32 s +2026-04-14 01:08:01.971272: +2026-04-14 01:08:01.973109: Epoch 3033 +2026-04-14 01:08:01.974556: Current learning rate: 0.00279 +2026-04-14 01:09:43.384546: train_loss -0.4262 +2026-04-14 01:09:43.390858: val_loss -0.3783 +2026-04-14 01:09:43.392642: Pseudo dice [0.5939, 0.0, 0.7824, 0.8927, 0.4434, 0.3216, 0.8846] +2026-04-14 01:09:43.394177: Epoch time: 101.42 s +2026-04-14 01:09:44.594327: +2026-04-14 01:09:44.595939: Epoch 3034 +2026-04-14 01:09:44.597424: Current learning rate: 0.00278 +2026-04-14 01:11:25.956664: train_loss -0.4266 +2026-04-14 01:11:25.961392: val_loss -0.3826 +2026-04-14 01:11:25.963162: Pseudo dice [0.2847, 0.0, 0.7707, 0.5553, 0.2922, 0.8163, 0.7198] +2026-04-14 01:11:25.964894: Epoch time: 101.37 s +2026-04-14 01:11:27.170745: +2026-04-14 01:11:27.172507: Epoch 3035 +2026-04-14 01:11:27.173796: Current learning rate: 0.00278 +2026-04-14 01:13:08.480888: train_loss -0.4344 +2026-04-14 01:13:08.485801: val_loss -0.3867 +2026-04-14 01:13:08.488481: Pseudo dice [0.6684, 0.0, 0.781, 0.7517, 0.3417, 0.6841, 0.8112] +2026-04-14 01:13:08.491813: Epoch time: 101.31 s +2026-04-14 01:13:09.696492: +2026-04-14 01:13:09.698418: Epoch 3036 +2026-04-14 01:13:09.700695: Current learning rate: 0.00278 +2026-04-14 01:14:51.094785: train_loss -0.4514 +2026-04-14 01:14:51.099696: val_loss -0.3932 +2026-04-14 01:14:51.101750: Pseudo dice [0.5282, 0.0, 0.5956, 0.5901, 0.4085, 0.7884, 0.5493] +2026-04-14 01:14:51.103109: Epoch time: 101.4 s +2026-04-14 01:14:52.315851: +2026-04-14 01:14:52.318352: Epoch 3037 +2026-04-14 01:14:52.320382: Current learning rate: 0.00278 +2026-04-14 01:16:33.410661: train_loss -0.4396 +2026-04-14 01:16:33.415843: val_loss -0.3573 +2026-04-14 01:16:33.418349: Pseudo dice [0.3501, 0.0, 0.6017, 0.4389, 0.1274, 0.7335, 0.6834] +2026-04-14 01:16:33.420192: Epoch time: 101.1 s +2026-04-14 01:16:34.614805: +2026-04-14 01:16:34.616444: Epoch 3038 +2026-04-14 01:16:34.618020: Current learning rate: 0.00277 +2026-04-14 01:18:15.465940: train_loss -0.4425 +2026-04-14 01:18:15.471682: val_loss -0.3776 +2026-04-14 01:18:15.473557: Pseudo dice [0.646, 0.0, 0.8088, 0.139, 0.4693, 0.6999, 0.5508] +2026-04-14 01:18:15.476226: Epoch time: 100.85 s +2026-04-14 01:18:16.661944: +2026-04-14 01:18:16.663672: Epoch 3039 +2026-04-14 01:18:16.665165: Current learning rate: 0.00277 +2026-04-14 01:19:57.580583: train_loss -0.4368 +2026-04-14 01:19:57.585755: val_loss -0.3737 +2026-04-14 01:19:57.588134: Pseudo dice [0.3098, 0.0, 0.6302, 0.7517, 0.4841, 0.4445, 0.7762] +2026-04-14 01:19:57.589793: Epoch time: 100.92 s +2026-04-14 01:19:58.778280: +2026-04-14 01:19:58.779788: Epoch 3040 +2026-04-14 01:19:58.781651: Current learning rate: 0.00277 +2026-04-14 01:21:40.070306: train_loss -0.4517 +2026-04-14 01:21:40.074956: val_loss -0.4093 +2026-04-14 01:21:40.076991: Pseudo dice [0.5005, 0.0, 0.7962, 0.7701, 0.5895, 0.6158, 0.719] +2026-04-14 01:21:40.078890: Epoch time: 101.3 s +2026-04-14 01:21:41.274201: +2026-04-14 01:21:41.276115: Epoch 3041 +2026-04-14 01:21:41.277574: Current learning rate: 0.00277 +2026-04-14 01:23:22.549570: train_loss -0.4462 +2026-04-14 01:23:22.554199: val_loss -0.3632 +2026-04-14 01:23:22.555738: Pseudo dice [0.6841, 0.0, 0.7441, 0.0523, 0.384, 0.7407, 0.53] +2026-04-14 01:23:22.557389: Epoch time: 101.28 s +2026-04-14 01:23:23.759584: +2026-04-14 01:23:23.761092: Epoch 3042 +2026-04-14 01:23:23.762694: Current learning rate: 0.00276 +2026-04-14 01:25:05.093132: train_loss -0.4601 +2026-04-14 01:25:05.097244: val_loss -0.4051 +2026-04-14 01:25:05.099011: Pseudo dice [0.3137, 0.0, 0.7114, 0.8315, 0.5582, 0.3663, 0.9479] +2026-04-14 01:25:05.100745: Epoch time: 101.34 s +2026-04-14 01:25:06.307722: +2026-04-14 01:25:06.309540: Epoch 3043 +2026-04-14 01:25:06.310984: Current learning rate: 0.00276 +2026-04-14 01:26:47.668852: train_loss -0.4589 +2026-04-14 01:26:47.673440: val_loss -0.3726 +2026-04-14 01:26:47.675390: Pseudo dice [0.2713, 0.0, 0.7272, 0.3609, 0.5011, 0.806, 0.9128] +2026-04-14 01:26:47.677060: Epoch time: 101.36 s +2026-04-14 01:26:48.875573: +2026-04-14 01:26:48.883189: Epoch 3044 +2026-04-14 01:26:48.884866: Current learning rate: 0.00276 +2026-04-14 01:28:30.378349: train_loss -0.441 +2026-04-14 01:28:30.383571: val_loss -0.4003 +2026-04-14 01:28:30.385686: Pseudo dice [0.8717, 0.0, 0.5648, 0.8944, 0.1615, 0.5616, 0.8108] +2026-04-14 01:28:30.387426: Epoch time: 101.51 s +2026-04-14 01:28:31.588962: +2026-04-14 01:28:31.590726: Epoch 3045 +2026-04-14 01:28:31.592388: Current learning rate: 0.00276 +2026-04-14 01:30:12.978462: train_loss -0.4449 +2026-04-14 01:30:12.983547: val_loss -0.4073 +2026-04-14 01:30:12.987776: Pseudo dice [0.4705, 0.0, 0.8344, 0.8683, 0.2963, 0.8291, 0.8582] +2026-04-14 01:30:12.989307: Epoch time: 101.39 s +2026-04-14 01:30:14.180607: +2026-04-14 01:30:14.182683: Epoch 3046 +2026-04-14 01:30:14.184164: Current learning rate: 0.00275 +2026-04-14 01:31:56.366176: train_loss -0.4359 +2026-04-14 01:31:56.370841: val_loss -0.3638 +2026-04-14 01:31:56.372378: Pseudo dice [0.3242, 0.0, 0.7273, 0.2414, 0.2188, 0.7287, 0.839] +2026-04-14 01:31:56.373744: Epoch time: 102.19 s +2026-04-14 01:31:57.568668: +2026-04-14 01:31:57.570054: Epoch 3047 +2026-04-14 01:31:57.571376: Current learning rate: 0.00275 +2026-04-14 01:33:38.883748: train_loss -0.4125 +2026-04-14 01:33:38.889824: val_loss -0.3373 +2026-04-14 01:33:38.891962: Pseudo dice [0.7069, 0.0, 0.6517, 0.0532, 0.233, 0.5747, 0.4678] +2026-04-14 01:33:38.893639: Epoch time: 101.32 s +2026-04-14 01:33:40.097729: +2026-04-14 01:33:40.099446: Epoch 3048 +2026-04-14 01:33:40.101015: Current learning rate: 0.00275 +2026-04-14 01:35:21.508156: train_loss -0.4374 +2026-04-14 01:35:21.512836: val_loss -0.3884 +2026-04-14 01:35:21.514361: Pseudo dice [0.5205, 0.0, 0.5341, 0.7762, 0.359, 0.7511, 0.8618] +2026-04-14 01:35:21.516369: Epoch time: 101.41 s +2026-04-14 01:35:22.733691: +2026-04-14 01:35:22.735428: Epoch 3049 +2026-04-14 01:35:22.736895: Current learning rate: 0.00274 +2026-04-14 01:37:04.242520: train_loss -0.4298 +2026-04-14 01:37:04.246995: val_loss -0.389 +2026-04-14 01:37:04.248637: Pseudo dice [0.7043, 0.0, 0.7887, 0.8225, 0.2968, 0.6048, 0.5109] +2026-04-14 01:37:04.250143: Epoch time: 101.51 s +2026-04-14 01:37:07.163775: +2026-04-14 01:37:07.165479: Epoch 3050 +2026-04-14 01:37:07.166929: Current learning rate: 0.00274 +2026-04-14 01:38:48.548398: train_loss -0.4368 +2026-04-14 01:38:48.552761: val_loss -0.3673 +2026-04-14 01:38:48.554351: Pseudo dice [0.6292, 0.0, 0.7019, 0.8234, 0.3424, 0.5552, 0.7318] +2026-04-14 01:38:48.555971: Epoch time: 101.39 s +2026-04-14 01:38:49.764767: +2026-04-14 01:38:49.766278: Epoch 3051 +2026-04-14 01:38:49.767700: Current learning rate: 0.00274 +2026-04-14 01:40:31.070362: train_loss -0.4356 +2026-04-14 01:40:31.075115: val_loss -0.3765 +2026-04-14 01:40:31.076566: Pseudo dice [0.2314, 0.0, 0.7553, 0.8746, 0.4064, 0.7598, 0.6319] +2026-04-14 01:40:31.078121: Epoch time: 101.31 s +2026-04-14 01:40:32.285791: +2026-04-14 01:40:32.287668: Epoch 3052 +2026-04-14 01:40:32.289370: Current learning rate: 0.00274 +2026-04-14 01:42:13.698803: train_loss -0.4468 +2026-04-14 01:42:13.704871: val_loss -0.3875 +2026-04-14 01:42:13.707987: Pseudo dice [0.3432, 0.0, 0.6886, 0.9095, 0.5263, 0.8065, 0.7897] +2026-04-14 01:42:13.710115: Epoch time: 101.42 s +2026-04-14 01:42:14.906464: +2026-04-14 01:42:14.908391: Epoch 3053 +2026-04-14 01:42:14.910157: Current learning rate: 0.00273 +2026-04-14 01:43:56.247241: train_loss -0.4297 +2026-04-14 01:43:56.252685: val_loss -0.4086 +2026-04-14 01:43:56.254510: Pseudo dice [0.3372, 0.0, 0.786, 0.8453, 0.1862, 0.8941, 0.6451] +2026-04-14 01:43:56.256207: Epoch time: 101.34 s +2026-04-14 01:43:57.455315: +2026-04-14 01:43:57.457666: Epoch 3054 +2026-04-14 01:43:57.459634: Current learning rate: 0.00273 +2026-04-14 01:45:39.003249: train_loss -0.4406 +2026-04-14 01:45:39.007935: val_loss -0.3518 +2026-04-14 01:45:39.009403: Pseudo dice [0.8361, 0.0, 0.6355, 0.8257, 0.1295, 0.8306, 0.7025] +2026-04-14 01:45:39.010864: Epoch time: 101.55 s +2026-04-14 01:45:40.215762: +2026-04-14 01:45:40.217384: Epoch 3055 +2026-04-14 01:45:40.218906: Current learning rate: 0.00273 +2026-04-14 01:47:21.535812: train_loss -0.4106 +2026-04-14 01:47:21.542372: val_loss -0.3358 +2026-04-14 01:47:21.544899: Pseudo dice [0.2667, 0.0, 0.61, 0.8011, 0.1833, 0.651, 0.4953] +2026-04-14 01:47:21.546522: Epoch time: 101.32 s +2026-04-14 01:47:22.721115: +2026-04-14 01:47:22.722641: Epoch 3056 +2026-04-14 01:47:22.724995: Current learning rate: 0.00273 +2026-04-14 01:49:03.979049: train_loss -0.4413 +2026-04-14 01:49:03.983131: val_loss -0.4081 +2026-04-14 01:49:03.984757: Pseudo dice [0.4839, 0.0, 0.6873, 0.865, 0.433, 0.6839, 0.8729] +2026-04-14 01:49:03.986125: Epoch time: 101.26 s +2026-04-14 01:49:05.169107: +2026-04-14 01:49:05.170988: Epoch 3057 +2026-04-14 01:49:05.172541: Current learning rate: 0.00272 +2026-04-14 01:50:46.474933: train_loss -0.4305 +2026-04-14 01:50:46.480216: val_loss -0.3725 +2026-04-14 01:50:46.481981: Pseudo dice [0.3816, 0.0, 0.783, 0.2104, 0.381, 0.7265, 0.804] +2026-04-14 01:50:46.483660: Epoch time: 101.31 s +2026-04-14 01:50:47.664538: +2026-04-14 01:50:47.666563: Epoch 3058 +2026-04-14 01:50:47.668239: Current learning rate: 0.00272 +2026-04-14 01:52:29.031725: train_loss -0.4395 +2026-04-14 01:52:29.046158: val_loss -0.4137 +2026-04-14 01:52:29.048319: Pseudo dice [0.7156, 0.0, 0.7436, 0.8975, 0.307, 0.7804, 0.8099] +2026-04-14 01:52:29.049845: Epoch time: 101.37 s +2026-04-14 01:52:30.247499: +2026-04-14 01:52:30.249155: Epoch 3059 +2026-04-14 01:52:30.250806: Current learning rate: 0.00272 +2026-04-14 01:54:11.648041: train_loss -0.4355 +2026-04-14 01:54:11.652241: val_loss -0.3174 +2026-04-14 01:54:11.653773: Pseudo dice [0.3719, 0.0, 0.6562, 0.3797, 0.2198, 0.8114, 0.7721] +2026-04-14 01:54:11.655213: Epoch time: 101.4 s +2026-04-14 01:54:12.861254: +2026-04-14 01:54:12.862642: Epoch 3060 +2026-04-14 01:54:12.863910: Current learning rate: 0.00272 +2026-04-14 01:55:54.245799: train_loss -0.4189 +2026-04-14 01:55:54.250671: val_loss -0.3902 +2026-04-14 01:55:54.252319: Pseudo dice [0.3686, 0.0, 0.6958, 0.8613, 0.3436, 0.6155, 0.7223] +2026-04-14 01:55:54.254295: Epoch time: 101.39 s +2026-04-14 01:55:55.453877: +2026-04-14 01:55:55.455568: Epoch 3061 +2026-04-14 01:55:55.457240: Current learning rate: 0.00271 +2026-04-14 01:57:36.820889: train_loss -0.4164 +2026-04-14 01:57:36.826320: val_loss -0.339 +2026-04-14 01:57:36.828203: Pseudo dice [0.3342, 0.0, 0.6314, 0.7227, 0.1618, 0.8384, 0.6556] +2026-04-14 01:57:36.829892: Epoch time: 101.37 s +2026-04-14 01:57:38.012994: +2026-04-14 01:57:38.014930: Epoch 3062 +2026-04-14 01:57:38.016461: Current learning rate: 0.00271 +2026-04-14 01:59:19.411537: train_loss -0.4332 +2026-04-14 01:59:19.419644: val_loss -0.3947 +2026-04-14 01:59:19.427639: Pseudo dice [0.4386, 0.0, 0.7558, 0.7774, 0.5044, 0.7858, 0.6888] +2026-04-14 01:59:19.436683: Epoch time: 101.4 s +2026-04-14 01:59:20.637146: +2026-04-14 01:59:20.638721: Epoch 3063 +2026-04-14 01:59:20.640270: Current learning rate: 0.00271 +2026-04-14 02:01:01.925727: train_loss -0.4337 +2026-04-14 02:01:01.929955: val_loss -0.3904 +2026-04-14 02:01:01.931467: Pseudo dice [0.6151, 0.0, 0.6837, 0.6613, 0.385, 0.8109, 0.7695] +2026-04-14 02:01:01.932905: Epoch time: 101.29 s +2026-04-14 02:01:03.143946: +2026-04-14 02:01:03.145443: Epoch 3064 +2026-04-14 02:01:03.147122: Current learning rate: 0.00271 +2026-04-14 02:02:44.780198: train_loss -0.4189 +2026-04-14 02:02:44.784840: val_loss -0.3635 +2026-04-14 02:02:44.786542: Pseudo dice [0.1627, 0.0, 0.3197, 0.7782, 0.2549, 0.7369, 0.6459] +2026-04-14 02:02:44.788448: Epoch time: 101.64 s +2026-04-14 02:02:46.000910: +2026-04-14 02:02:46.002824: Epoch 3065 +2026-04-14 02:02:46.004697: Current learning rate: 0.0027 +2026-04-14 02:04:27.437229: train_loss -0.4123 +2026-04-14 02:04:27.444813: val_loss -0.364 +2026-04-14 02:04:27.447613: Pseudo dice [0.2765, 0.0, 0.6994, 0.5131, 0.3653, 0.7633, 0.5408] +2026-04-14 02:04:27.450180: Epoch time: 101.44 s +2026-04-14 02:04:29.719552: +2026-04-14 02:04:29.723021: Epoch 3066 +2026-04-14 02:04:29.725398: Current learning rate: 0.0027 +2026-04-14 02:06:11.179433: train_loss -0.4365 +2026-04-14 02:06:11.184927: val_loss -0.3894 +2026-04-14 02:06:11.187043: Pseudo dice [0.4401, 0.0, 0.6955, 0.502, 0.3567, 0.7396, 0.7503] +2026-04-14 02:06:11.188538: Epoch time: 101.46 s +2026-04-14 02:06:12.381903: +2026-04-14 02:06:12.383667: Epoch 3067 +2026-04-14 02:06:12.385460: Current learning rate: 0.0027 +2026-04-14 02:07:53.701601: train_loss -0.4438 +2026-04-14 02:07:53.708204: val_loss -0.3945 +2026-04-14 02:07:53.710240: Pseudo dice [0.4311, 0.0, 0.8486, 0.8442, 0.4422, 0.7946, 0.5393] +2026-04-14 02:07:53.712057: Epoch time: 101.32 s +2026-04-14 02:07:54.909072: +2026-04-14 02:07:54.910833: Epoch 3068 +2026-04-14 02:07:54.912559: Current learning rate: 0.0027 +2026-04-14 02:09:36.136440: train_loss -0.4473 +2026-04-14 02:09:36.142141: val_loss -0.4039 +2026-04-14 02:09:36.144660: Pseudo dice [0.253, 0.0, 0.5457, 0.484, 0.4821, 0.883, 0.7178] +2026-04-14 02:09:36.146664: Epoch time: 101.23 s +2026-04-14 02:09:37.341964: +2026-04-14 02:09:37.344083: Epoch 3069 +2026-04-14 02:09:37.345919: Current learning rate: 0.00269 +2026-04-14 02:11:18.712477: train_loss -0.4492 +2026-04-14 02:11:18.718769: val_loss -0.3995 +2026-04-14 02:11:18.721437: Pseudo dice [0.3155, 0.0, 0.6605, 0.6346, 0.5083, 0.7565, 0.7857] +2026-04-14 02:11:18.723601: Epoch time: 101.37 s +2026-04-14 02:11:19.923775: +2026-04-14 02:11:19.925224: Epoch 3070 +2026-04-14 02:11:19.926924: Current learning rate: 0.00269 +2026-04-14 02:13:01.465135: train_loss -0.4432 +2026-04-14 02:13:01.470495: val_loss -0.4065 +2026-04-14 02:13:01.472308: Pseudo dice [0.4848, 0.0, 0.7442, 0.7737, 0.3565, 0.69, 0.91] +2026-04-14 02:13:01.473967: Epoch time: 101.54 s +2026-04-14 02:13:02.661259: +2026-04-14 02:13:02.663459: Epoch 3071 +2026-04-14 02:13:02.666200: Current learning rate: 0.00269 +2026-04-14 02:14:43.916657: train_loss -0.446 +2026-04-14 02:14:43.921952: val_loss -0.4049 +2026-04-14 02:14:43.923840: Pseudo dice [0.5273, 0.0, 0.7005, 0.6504, 0.4256, 0.8643, 0.8387] +2026-04-14 02:14:43.925496: Epoch time: 101.26 s +2026-04-14 02:14:45.126015: +2026-04-14 02:14:45.128072: Epoch 3072 +2026-04-14 02:14:45.129887: Current learning rate: 0.00268 +2026-04-14 02:16:26.469847: train_loss -0.4529 +2026-04-14 02:16:26.476745: val_loss -0.4293 +2026-04-14 02:16:26.479017: Pseudo dice [0.5369, 0.0, 0.8019, 0.8086, 0.4952, 0.6925, 0.8499] +2026-04-14 02:16:26.480805: Epoch time: 101.35 s +2026-04-14 02:16:27.689738: +2026-04-14 02:16:27.692156: Epoch 3073 +2026-04-14 02:16:27.694879: Current learning rate: 0.00268 +2026-04-14 02:18:09.071921: train_loss -0.4542 +2026-04-14 02:18:09.076975: val_loss -0.4154 +2026-04-14 02:18:09.078640: Pseudo dice [0.5196, 0.0, 0.6255, 0.8773, 0.5756, 0.6973, 0.7548] +2026-04-14 02:18:09.080366: Epoch time: 101.39 s +2026-04-14 02:18:10.299536: +2026-04-14 02:18:10.301177: Epoch 3074 +2026-04-14 02:18:10.302727: Current learning rate: 0.00268 +2026-04-14 02:19:51.471717: train_loss -0.4415 +2026-04-14 02:19:51.477172: val_loss -0.3666 +2026-04-14 02:19:51.478805: Pseudo dice [0.4266, 0.0, 0.7685, 0.7555, 0.455, 0.6463, 0.5673] +2026-04-14 02:19:51.480320: Epoch time: 101.18 s +2026-04-14 02:19:52.675182: +2026-04-14 02:19:52.677276: Epoch 3075 +2026-04-14 02:19:52.679402: Current learning rate: 0.00268 +2026-04-14 02:21:34.172184: train_loss -0.4321 +2026-04-14 02:21:34.178191: val_loss -0.4159 +2026-04-14 02:21:34.180083: Pseudo dice [0.677, 0.0, 0.8118, 0.8155, 0.5968, 0.5682, 0.8093] +2026-04-14 02:21:34.183039: Epoch time: 101.5 s +2026-04-14 02:21:35.363353: +2026-04-14 02:21:35.365251: Epoch 3076 +2026-04-14 02:21:35.367812: Current learning rate: 0.00267 +2026-04-14 02:23:16.793568: train_loss -0.4527 +2026-04-14 02:23:16.798082: val_loss -0.3886 +2026-04-14 02:23:16.799726: Pseudo dice [0.3366, 0.0, 0.7909, 0.676, 0.319, 0.6821, 0.8749] +2026-04-14 02:23:16.801384: Epoch time: 101.43 s +2026-04-14 02:23:17.998282: +2026-04-14 02:23:18.000118: Epoch 3077 +2026-04-14 02:23:18.001914: Current learning rate: 0.00267 +2026-04-14 02:24:59.444296: train_loss -0.4299 +2026-04-14 02:24:59.449128: val_loss -0.3588 +2026-04-14 02:24:59.450659: Pseudo dice [0.4741, 0.0, 0.7688, 0.6128, 0.1519, 0.5071, 0.8415] +2026-04-14 02:24:59.452141: Epoch time: 101.45 s +2026-04-14 02:25:00.645552: +2026-04-14 02:25:00.647195: Epoch 3078 +2026-04-14 02:25:00.649446: Current learning rate: 0.00267 +2026-04-14 02:26:42.061486: train_loss -0.4293 +2026-04-14 02:26:42.067244: val_loss -0.3407 +2026-04-14 02:26:42.068848: Pseudo dice [0.3986, 0.0, 0.7364, 0.3104, 0.2873, 0.7566, 0.5689] +2026-04-14 02:26:42.071012: Epoch time: 101.42 s +2026-04-14 02:26:43.253488: +2026-04-14 02:26:43.255841: Epoch 3079 +2026-04-14 02:26:43.258632: Current learning rate: 0.00267 +2026-04-14 02:28:24.733139: train_loss -0.4295 +2026-04-14 02:28:24.738723: val_loss -0.388 +2026-04-14 02:28:24.740523: Pseudo dice [0.4579, 0.0, 0.812, 0.6511, 0.4647, 0.8107, 0.8069] +2026-04-14 02:28:24.743089: Epoch time: 101.48 s +2026-04-14 02:28:25.952712: +2026-04-14 02:28:25.954540: Epoch 3080 +2026-04-14 02:28:25.957157: Current learning rate: 0.00266 +2026-04-14 02:30:07.258386: train_loss -0.441 +2026-04-14 02:30:07.262868: val_loss -0.3834 +2026-04-14 02:30:07.264189: Pseudo dice [0.5307, 0.0, 0.6837, 0.6407, 0.4919, 0.3773, 0.8893] +2026-04-14 02:30:07.265756: Epoch time: 101.31 s +2026-04-14 02:30:08.471240: +2026-04-14 02:30:08.473053: Epoch 3081 +2026-04-14 02:30:08.474856: Current learning rate: 0.00266 +2026-04-14 02:31:49.775736: train_loss -0.4149 +2026-04-14 02:31:49.781290: val_loss -0.3699 +2026-04-14 02:31:49.782975: Pseudo dice [0.4055, 0.0, 0.4363, 0.5048, 0.404, 0.7749, 0.898] +2026-04-14 02:31:49.784586: Epoch time: 101.31 s +2026-04-14 02:31:50.977040: +2026-04-14 02:31:50.979214: Epoch 3082 +2026-04-14 02:31:50.981290: Current learning rate: 0.00266 +2026-04-14 02:33:32.270456: train_loss -0.4347 +2026-04-14 02:33:32.275270: val_loss -0.3761 +2026-04-14 02:33:32.276753: Pseudo dice [0.6139, 0.0, 0.7201, 0.8577, 0.3644, 0.7167, 0.6506] +2026-04-14 02:33:32.278481: Epoch time: 101.3 s +2026-04-14 02:33:33.483879: +2026-04-14 02:33:33.485784: Epoch 3083 +2026-04-14 02:33:33.488661: Current learning rate: 0.00266 +2026-04-14 02:35:14.899719: train_loss -0.4265 +2026-04-14 02:35:14.908591: val_loss -0.3491 +2026-04-14 02:35:14.914389: Pseudo dice [0.4139, 0.0, 0.6838, 0.8236, 0.4703, 0.8651, 0.6132] +2026-04-14 02:35:14.916796: Epoch time: 101.42 s +2026-04-14 02:35:16.124222: +2026-04-14 02:35:16.126230: Epoch 3084 +2026-04-14 02:35:16.128235: Current learning rate: 0.00265 +2026-04-14 02:36:57.506201: train_loss -0.423 +2026-04-14 02:36:57.512501: val_loss -0.3824 +2026-04-14 02:36:57.514185: Pseudo dice [0.763, 0.0, 0.4224, 0.1475, 0.5436, 0.756, 0.88] +2026-04-14 02:36:57.515550: Epoch time: 101.38 s +2026-04-14 02:36:58.720580: +2026-04-14 02:36:58.722348: Epoch 3085 +2026-04-14 02:36:58.724593: Current learning rate: 0.00265 +2026-04-14 02:38:40.074413: train_loss -0.4266 +2026-04-14 02:38:40.080555: val_loss -0.3852 +2026-04-14 02:38:40.082275: Pseudo dice [0.4072, 0.0, 0.4157, 0.5302, 0.5082, 0.7603, 0.7579] +2026-04-14 02:38:40.083929: Epoch time: 101.36 s +2026-04-14 02:38:42.427559: +2026-04-14 02:38:42.429862: Epoch 3086 +2026-04-14 02:38:42.432015: Current learning rate: 0.00265 +2026-04-14 02:40:23.718891: train_loss -0.4326 +2026-04-14 02:40:23.725193: val_loss -0.3641 +2026-04-14 02:40:23.727146: Pseudo dice [0.7034, 0.0, 0.6902, 0.8892, 0.3776, 0.6286, 0.7124] +2026-04-14 02:40:23.729861: Epoch time: 101.29 s +2026-04-14 02:40:24.929102: +2026-04-14 02:40:24.931342: Epoch 3087 +2026-04-14 02:40:24.933444: Current learning rate: 0.00265 +2026-04-14 02:42:06.082679: train_loss -0.4414 +2026-04-14 02:42:06.087704: val_loss -0.3528 +2026-04-14 02:42:06.089425: Pseudo dice [0.2127, 0.0, 0.6703, 0.6388, 0.4788, 0.6497, 0.5574] +2026-04-14 02:42:06.090763: Epoch time: 101.16 s +2026-04-14 02:42:07.296681: +2026-04-14 02:42:07.298222: Epoch 3088 +2026-04-14 02:42:07.300035: Current learning rate: 0.00264 +2026-04-14 02:43:48.587834: train_loss -0.4423 +2026-04-14 02:43:48.592539: val_loss -0.3768 +2026-04-14 02:43:48.594430: Pseudo dice [0.4409, 0.0, 0.7437, 0.7078, 0.2072, 0.6078, 0.7331] +2026-04-14 02:43:48.596491: Epoch time: 101.29 s +2026-04-14 02:43:49.778417: +2026-04-14 02:43:49.780351: Epoch 3089 +2026-04-14 02:43:49.782376: Current learning rate: 0.00264 +2026-04-14 02:45:31.179863: train_loss -0.4435 +2026-04-14 02:45:31.185340: val_loss -0.3819 +2026-04-14 02:45:31.186861: Pseudo dice [0.3685, 0.0, 0.8142, 0.7087, 0.3638, 0.6574, 0.8155] +2026-04-14 02:45:31.188387: Epoch time: 101.4 s +2026-04-14 02:45:32.367296: +2026-04-14 02:45:32.369052: Epoch 3090 +2026-04-14 02:45:32.370875: Current learning rate: 0.00264 +2026-04-14 02:47:13.929594: train_loss -0.4483 +2026-04-14 02:47:13.934474: val_loss -0.3682 +2026-04-14 02:47:13.936092: Pseudo dice [0.5097, 0.0, 0.697, 0.8987, 0.5495, 0.4895, 0.4865] +2026-04-14 02:47:13.937687: Epoch time: 101.57 s +2026-04-14 02:47:15.143471: +2026-04-14 02:47:15.145051: Epoch 3091 +2026-04-14 02:47:15.146874: Current learning rate: 0.00264 +2026-04-14 02:48:56.670460: train_loss -0.4335 +2026-04-14 02:48:56.677782: val_loss -0.3969 +2026-04-14 02:48:56.681541: Pseudo dice [0.7158, 0.0, 0.647, 0.7921, 0.3733, 0.7437, 0.8849] +2026-04-14 02:48:56.683499: Epoch time: 101.53 s +2026-04-14 02:48:57.903228: +2026-04-14 02:48:57.905073: Epoch 3092 +2026-04-14 02:48:57.907675: Current learning rate: 0.00263 +2026-04-14 02:50:39.385061: train_loss -0.4448 +2026-04-14 02:50:39.389771: val_loss -0.3782 +2026-04-14 02:50:39.391468: Pseudo dice [0.5475, 0.0, 0.7118, 0.8353, 0.3535, 0.7064, 0.7011] +2026-04-14 02:50:39.392928: Epoch time: 101.48 s +2026-04-14 02:50:40.685683: +2026-04-14 02:50:40.687400: Epoch 3093 +2026-04-14 02:50:40.689365: Current learning rate: 0.00263 +2026-04-14 02:52:22.100422: train_loss -0.4442 +2026-04-14 02:52:22.105491: val_loss -0.4025 +2026-04-14 02:52:22.107026: Pseudo dice [0.6898, 0.0, 0.6884, 0.1433, 0.4536, 0.6686, 0.8595] +2026-04-14 02:52:22.109326: Epoch time: 101.42 s +2026-04-14 02:52:23.313897: +2026-04-14 02:52:23.315484: Epoch 3094 +2026-04-14 02:52:23.317401: Current learning rate: 0.00263 +2026-04-14 02:54:04.745471: train_loss -0.4331 +2026-04-14 02:54:04.751126: val_loss -0.3944 +2026-04-14 02:54:04.753174: Pseudo dice [0.3786, 0.0, 0.8245, 0.7616, 0.6467, 0.7485, 0.8069] +2026-04-14 02:54:04.754979: Epoch time: 101.43 s +2026-04-14 02:54:05.950544: +2026-04-14 02:54:05.952099: Epoch 3095 +2026-04-14 02:54:05.954082: Current learning rate: 0.00263 +2026-04-14 02:55:47.445109: train_loss -0.4424 +2026-04-14 02:55:47.450612: val_loss -0.3772 +2026-04-14 02:55:47.452788: Pseudo dice [0.412, 0.0, 0.6458, 0.6785, 0.5045, 0.7025, 0.8258] +2026-04-14 02:55:47.454798: Epoch time: 101.5 s +2026-04-14 02:55:48.693583: +2026-04-14 02:55:48.695408: Epoch 3096 +2026-04-14 02:55:48.697599: Current learning rate: 0.00262 +2026-04-14 02:57:30.179927: train_loss -0.4401 +2026-04-14 02:57:30.187786: val_loss -0.3874 +2026-04-14 02:57:30.189477: Pseudo dice [0.3595, 0.0, 0.7676, 0.5592, 0.49, 0.8341, 0.8914] +2026-04-14 02:57:30.191600: Epoch time: 101.49 s +2026-04-14 02:57:31.437297: +2026-04-14 02:57:31.439581: Epoch 3097 +2026-04-14 02:57:31.442244: Current learning rate: 0.00262 +2026-04-14 02:59:12.898623: train_loss -0.4544 +2026-04-14 02:59:12.906368: val_loss -0.3705 +2026-04-14 02:59:12.908523: Pseudo dice [0.4351, 0.0, 0.7992, 0.7139, 0.5893, 0.7774, 0.7581] +2026-04-14 02:59:12.910085: Epoch time: 101.46 s +2026-04-14 02:59:14.108292: +2026-04-14 02:59:14.110112: Epoch 3098 +2026-04-14 02:59:14.114338: Current learning rate: 0.00262 +2026-04-14 03:00:55.611439: train_loss -0.4385 +2026-04-14 03:00:55.616345: val_loss -0.3789 +2026-04-14 03:00:55.617975: Pseudo dice [0.3537, 0.0, 0.5567, 0.5434, 0.5824, 0.855, 0.5458] +2026-04-14 03:00:55.619377: Epoch time: 101.51 s +2026-04-14 03:00:56.820959: +2026-04-14 03:00:56.822749: Epoch 3099 +2026-04-14 03:00:56.824653: Current learning rate: 0.00261 +2026-04-14 03:02:38.391432: train_loss -0.4265 +2026-04-14 03:02:38.396918: val_loss -0.401 +2026-04-14 03:02:38.398748: Pseudo dice [0.8552, 0.0, 0.7322, 0.7521, 0.562, 0.763, 0.7727] +2026-04-14 03:02:38.400870: Epoch time: 101.57 s +2026-04-14 03:02:41.358628: +2026-04-14 03:02:41.365900: Epoch 3100 +2026-04-14 03:02:41.368402: Current learning rate: 0.00261 +2026-04-14 03:04:22.815624: train_loss -0.434 +2026-04-14 03:04:22.821918: val_loss -0.4217 +2026-04-14 03:04:22.823835: Pseudo dice [0.4912, 0.0, 0.8547, 0.8031, 0.5964, 0.7471, 0.8534] +2026-04-14 03:04:22.826597: Epoch time: 101.46 s +2026-04-14 03:04:24.046922: +2026-04-14 03:04:24.049119: Epoch 3101 +2026-04-14 03:04:24.051749: Current learning rate: 0.00261 +2026-04-14 03:06:05.490307: train_loss -0.4383 +2026-04-14 03:06:05.499253: val_loss -0.3834 +2026-04-14 03:06:05.500959: Pseudo dice [0.4696, 0.0, 0.7189, 0.7427, 0.6361, 0.2988, 0.7987] +2026-04-14 03:06:05.503233: Epoch time: 101.45 s +2026-04-14 03:06:06.716738: +2026-04-14 03:06:06.718217: Epoch 3102 +2026-04-14 03:06:06.720076: Current learning rate: 0.00261 +2026-04-14 03:07:47.988661: train_loss -0.431 +2026-04-14 03:07:48.000227: val_loss -0.3565 +2026-04-14 03:07:48.006989: Pseudo dice [0.4778, 0.0, 0.5085, 0.0299, 0.389, 0.6849, 0.7498] +2026-04-14 03:07:48.009026: Epoch time: 101.28 s +2026-04-14 03:07:49.221184: +2026-04-14 03:07:49.223195: Epoch 3103 +2026-04-14 03:07:49.225101: Current learning rate: 0.0026 +2026-04-14 03:09:30.674801: train_loss -0.4365 +2026-04-14 03:09:30.679795: val_loss -0.3618 +2026-04-14 03:09:30.681555: Pseudo dice [0.2779, 0.0, 0.5218, 0.6925, 0.5796, 0.7164, 0.8416] +2026-04-14 03:09:30.683154: Epoch time: 101.46 s +2026-04-14 03:09:31.877372: +2026-04-14 03:09:31.879337: Epoch 3104 +2026-04-14 03:09:31.881330: Current learning rate: 0.0026 +2026-04-14 03:11:13.236204: train_loss -0.4412 +2026-04-14 03:11:13.241052: val_loss -0.3809 +2026-04-14 03:11:13.242433: Pseudo dice [0.4879, 0.0, 0.7451, 0.6383, 0.5024, 0.6254, 0.8936] +2026-04-14 03:11:13.243764: Epoch time: 101.36 s +2026-04-14 03:11:15.421124: +2026-04-14 03:11:15.422907: Epoch 3105 +2026-04-14 03:11:15.424776: Current learning rate: 0.0026 +2026-04-14 03:12:56.927609: train_loss -0.4114 +2026-04-14 03:12:56.932543: val_loss -0.3712 +2026-04-14 03:12:56.934899: Pseudo dice [0.3577, 0.0, 0.7988, 0.5037, 0.3569, 0.5529, 0.7841] +2026-04-14 03:12:56.936654: Epoch time: 101.51 s +2026-04-14 03:12:58.136394: +2026-04-14 03:12:58.138500: Epoch 3106 +2026-04-14 03:12:58.142247: Current learning rate: 0.0026 +2026-04-14 03:14:39.533329: train_loss -0.43 +2026-04-14 03:14:39.540752: val_loss -0.3825 +2026-04-14 03:14:39.542528: Pseudo dice [0.6673, 0.0, 0.8525, 0.6369, 0.4388, 0.7754, 0.7634] +2026-04-14 03:14:39.544418: Epoch time: 101.4 s +2026-04-14 03:14:40.721744: +2026-04-14 03:14:40.723648: Epoch 3107 +2026-04-14 03:14:40.725818: Current learning rate: 0.00259 +2026-04-14 03:16:22.198780: train_loss -0.4284 +2026-04-14 03:16:22.205256: val_loss -0.3799 +2026-04-14 03:16:22.207337: Pseudo dice [0.4289, 0.0, 0.785, 0.8743, 0.2737, 0.7628, 0.7959] +2026-04-14 03:16:22.209743: Epoch time: 101.48 s +2026-04-14 03:16:23.423622: +2026-04-14 03:16:23.425576: Epoch 3108 +2026-04-14 03:16:23.427602: Current learning rate: 0.00259 +2026-04-14 03:18:05.226244: train_loss -0.4223 +2026-04-14 03:18:05.232881: val_loss -0.3599 +2026-04-14 03:18:05.234851: Pseudo dice [0.5971, 0.0, 0.6554, 0.7773, 0.4124, 0.6526, 0.7434] +2026-04-14 03:18:05.238062: Epoch time: 101.81 s +2026-04-14 03:18:06.439866: +2026-04-14 03:18:06.442271: Epoch 3109 +2026-04-14 03:18:06.444434: Current learning rate: 0.00259 +2026-04-14 03:19:48.023289: train_loss -0.4298 +2026-04-14 03:19:48.029078: val_loss -0.3491 +2026-04-14 03:19:48.030784: Pseudo dice [0.3535, 0.0, 0.6325, 0.5463, 0.3914, 0.6877, 0.8498] +2026-04-14 03:19:48.032108: Epoch time: 101.59 s +2026-04-14 03:19:49.242959: +2026-04-14 03:19:49.244634: Epoch 3110 +2026-04-14 03:19:49.246285: Current learning rate: 0.00259 +2026-04-14 03:21:30.772685: train_loss -0.4349 +2026-04-14 03:21:30.777940: val_loss -0.3837 +2026-04-14 03:21:30.779731: Pseudo dice [0.8386, 0.0, 0.6196, 0.7671, 0.3746, 0.7934, 0.5395] +2026-04-14 03:21:30.781467: Epoch time: 101.53 s +2026-04-14 03:21:31.981507: +2026-04-14 03:21:31.983171: Epoch 3111 +2026-04-14 03:21:31.985228: Current learning rate: 0.00258 +2026-04-14 03:23:13.496366: train_loss -0.4287 +2026-04-14 03:23:13.501334: val_loss -0.3695 +2026-04-14 03:23:13.503101: Pseudo dice [0.3105, 0.0, 0.6721, 0.7627, 0.2689, 0.529, 0.6562] +2026-04-14 03:23:13.505637: Epoch time: 101.52 s +2026-04-14 03:23:14.703861: +2026-04-14 03:23:14.705960: Epoch 3112 +2026-04-14 03:23:14.707888: Current learning rate: 0.00258 +2026-04-14 03:24:56.268986: train_loss -0.4347 +2026-04-14 03:24:56.274263: val_loss -0.3568 +2026-04-14 03:24:56.276328: Pseudo dice [0.8107, 0.0, 0.4706, 0.6844, 0.3085, 0.7308, 0.7455] +2026-04-14 03:24:56.279152: Epoch time: 101.57 s +2026-04-14 03:24:57.470245: +2026-04-14 03:24:57.471893: Epoch 3113 +2026-04-14 03:24:57.474289: Current learning rate: 0.00258 +2026-04-14 03:26:38.662887: train_loss -0.423 +2026-04-14 03:26:38.668224: val_loss -0.4016 +2026-04-14 03:26:38.671059: Pseudo dice [0.7328, 0.0, 0.5862, 0.2581, 0.5823, 0.7045, 0.8147] +2026-04-14 03:26:38.673148: Epoch time: 101.2 s +2026-04-14 03:26:39.881852: +2026-04-14 03:26:39.884602: Epoch 3114 +2026-04-14 03:26:39.887131: Current learning rate: 0.00258 +2026-04-14 03:28:21.230540: train_loss -0.4414 +2026-04-14 03:28:21.237024: val_loss -0.3871 +2026-04-14 03:28:21.238848: Pseudo dice [0.5772, 0.0, 0.6106, 0.7965, 0.5071, 0.7593, 0.865] +2026-04-14 03:28:21.240860: Epoch time: 101.35 s +2026-04-14 03:28:22.427982: +2026-04-14 03:28:22.430010: Epoch 3115 +2026-04-14 03:28:22.432003: Current learning rate: 0.00257 +2026-04-14 03:30:03.694833: train_loss -0.4356 +2026-04-14 03:30:03.700497: val_loss -0.3684 +2026-04-14 03:30:03.702853: Pseudo dice [0.3001, 0.0, 0.8651, 0.6903, 0.3482, 0.6748, 0.831] +2026-04-14 03:30:03.704628: Epoch time: 101.27 s +2026-04-14 03:30:04.905329: +2026-04-14 03:30:04.907014: Epoch 3116 +2026-04-14 03:30:04.908807: Current learning rate: 0.00257 +2026-04-14 03:31:46.244815: train_loss -0.4471 +2026-04-14 03:31:46.249503: val_loss -0.3787 +2026-04-14 03:31:46.251037: Pseudo dice [0.4772, 0.0, 0.7656, 0.8494, 0.4708, 0.6623, 0.8483] +2026-04-14 03:31:46.252595: Epoch time: 101.34 s +2026-04-14 03:31:47.467696: +2026-04-14 03:31:47.470273: Epoch 3117 +2026-04-14 03:31:47.472873: Current learning rate: 0.00257 +2026-04-14 03:33:28.831455: train_loss -0.4321 +2026-04-14 03:33:28.835921: val_loss -0.3649 +2026-04-14 03:33:28.837693: Pseudo dice [0.61, 0.0, 0.7644, 0.7715, 0.2192, 0.8127, 0.7431] +2026-04-14 03:33:28.839402: Epoch time: 101.37 s +2026-04-14 03:33:30.032654: +2026-04-14 03:33:30.034468: Epoch 3118 +2026-04-14 03:33:30.036826: Current learning rate: 0.00256 +2026-04-14 03:35:11.531119: train_loss -0.4292 +2026-04-14 03:35:11.536586: val_loss -0.3935 +2026-04-14 03:35:11.538895: Pseudo dice [0.4367, 0.0, 0.8451, 0.6451, 0.4273, 0.61, 0.7581] +2026-04-14 03:35:11.540544: Epoch time: 101.5 s +2026-04-14 03:35:12.740557: +2026-04-14 03:35:12.742755: Epoch 3119 +2026-04-14 03:35:12.744931: Current learning rate: 0.00256 +2026-04-14 03:36:54.254927: train_loss -0.4524 +2026-04-14 03:36:54.259160: val_loss -0.3696 +2026-04-14 03:36:54.260649: Pseudo dice [0.7657, 0.0, 0.1998, 0.6866, 0.1977, 0.5962, 0.4477] +2026-04-14 03:36:54.262402: Epoch time: 101.52 s +2026-04-14 03:36:55.446509: +2026-04-14 03:36:55.449082: Epoch 3120 +2026-04-14 03:36:55.457904: Current learning rate: 0.00256 +2026-04-14 03:38:36.950984: train_loss -0.4072 +2026-04-14 03:38:36.957170: val_loss -0.3883 +2026-04-14 03:38:36.958654: Pseudo dice [0.2683, 0.0, 0.693, 0.7995, 0.4505, 0.8255, 0.8571] +2026-04-14 03:38:36.960304: Epoch time: 101.51 s +2026-04-14 03:38:38.155393: +2026-04-14 03:38:38.156917: Epoch 3121 +2026-04-14 03:38:38.158724: Current learning rate: 0.00256 +2026-04-14 03:40:19.613149: train_loss -0.426 +2026-04-14 03:40:19.619171: val_loss -0.3755 +2026-04-14 03:40:19.621372: Pseudo dice [0.8764, 0.0, 0.8194, 0.6222, 0.4653, 0.5411, 0.6392] +2026-04-14 03:40:19.623172: Epoch time: 101.46 s +2026-04-14 03:40:20.827118: +2026-04-14 03:40:20.828682: Epoch 3122 +2026-04-14 03:40:20.830409: Current learning rate: 0.00255 +2026-04-14 03:42:02.192871: train_loss -0.424 +2026-04-14 03:42:02.200554: val_loss -0.4052 +2026-04-14 03:42:02.202827: Pseudo dice [0.7385, 0.0, 0.7424, 0.8604, 0.5318, 0.8548, 0.883] +2026-04-14 03:42:02.205805: Epoch time: 101.37 s +2026-04-14 03:42:03.409770: +2026-04-14 03:42:03.412538: Epoch 3123 +2026-04-14 03:42:03.415638: Current learning rate: 0.00255 +2026-04-14 03:43:44.692348: train_loss -0.4305 +2026-04-14 03:43:44.698401: val_loss -0.4039 +2026-04-14 03:43:44.701020: Pseudo dice [0.3519, 0.0, 0.7707, 0.0211, 0.3299, 0.8599, 0.791] +2026-04-14 03:43:44.703076: Epoch time: 101.29 s +2026-04-14 03:43:45.903512: +2026-04-14 03:43:45.905710: Epoch 3124 +2026-04-14 03:43:45.907936: Current learning rate: 0.00255 +2026-04-14 03:45:27.373994: train_loss -0.4384 +2026-04-14 03:45:27.382390: val_loss -0.3755 +2026-04-14 03:45:27.385106: Pseudo dice [0.5333, 0.0, 0.7904, 0.3098, 0.4133, 0.6759, 0.6616] +2026-04-14 03:45:27.389569: Epoch time: 101.47 s +2026-04-14 03:45:28.589493: +2026-04-14 03:45:28.591062: Epoch 3125 +2026-04-14 03:45:28.592861: Current learning rate: 0.00255 +2026-04-14 03:47:11.042392: train_loss -0.4304 +2026-04-14 03:47:11.056557: val_loss -0.3926 +2026-04-14 03:47:11.059089: Pseudo dice [0.7986, 0.0, 0.7994, 0.1338, 0.2178, 0.6253, 0.4793] +2026-04-14 03:47:11.061440: Epoch time: 102.46 s +2026-04-14 03:47:12.263136: +2026-04-14 03:47:12.265094: Epoch 3126 +2026-04-14 03:47:12.267220: Current learning rate: 0.00254 +2026-04-14 03:48:53.835541: train_loss -0.4388 +2026-04-14 03:48:53.843535: val_loss -0.4132 +2026-04-14 03:48:53.846492: Pseudo dice [0.5007, 0.0, 0.7112, 0.8271, 0.5819, 0.8588, 0.7159] +2026-04-14 03:48:53.848433: Epoch time: 101.58 s +2026-04-14 03:48:55.093025: +2026-04-14 03:48:55.094788: Epoch 3127 +2026-04-14 03:48:55.097242: Current learning rate: 0.00254 +2026-04-14 03:50:36.585545: train_loss -0.4204 +2026-04-14 03:50:36.591496: val_loss -0.3834 +2026-04-14 03:50:36.593507: Pseudo dice [0.287, 0.0, 0.8347, 0.6983, 0.4767, 0.7696, 0.7272] +2026-04-14 03:50:36.595688: Epoch time: 101.5 s +2026-04-14 03:50:37.803057: +2026-04-14 03:50:37.804897: Epoch 3128 +2026-04-14 03:50:37.807045: Current learning rate: 0.00254 +2026-04-14 03:52:19.207542: train_loss -0.4388 +2026-04-14 03:52:19.215483: val_loss -0.3658 +2026-04-14 03:52:19.217556: Pseudo dice [0.3802, 0.0, 0.7117, 0.8573, 0.3125, 0.4692, 0.8273] +2026-04-14 03:52:19.220110: Epoch time: 101.41 s +2026-04-14 03:52:20.432386: +2026-04-14 03:52:20.434814: Epoch 3129 +2026-04-14 03:52:20.437278: Current learning rate: 0.00254 +2026-04-14 03:54:01.770573: train_loss -0.4393 +2026-04-14 03:54:01.776140: val_loss -0.4049 +2026-04-14 03:54:01.778073: Pseudo dice [0.7012, 0.0, 0.8266, 0.7508, 0.3633, 0.8157, 0.8831] +2026-04-14 03:54:01.780437: Epoch time: 101.34 s +2026-04-14 03:54:02.986045: +2026-04-14 03:54:02.988477: Epoch 3130 +2026-04-14 03:54:02.992063: Current learning rate: 0.00253 +2026-04-14 03:55:44.481573: train_loss -0.4336 +2026-04-14 03:55:44.487134: val_loss -0.4029 +2026-04-14 03:55:44.489298: Pseudo dice [0.443, 0.0, 0.7913, 0.7856, 0.5217, 0.799, 0.8736] +2026-04-14 03:55:44.496521: Epoch time: 101.5 s +2026-04-14 03:55:45.699105: +2026-04-14 03:55:45.711716: Epoch 3131 +2026-04-14 03:55:45.715870: Current learning rate: 0.00253 +2026-04-14 03:57:27.038856: train_loss -0.4513 +2026-04-14 03:57:27.043791: val_loss -0.3774 +2026-04-14 03:57:27.045374: Pseudo dice [0.3664, 0.0, 0.54, 0.7586, 0.3386, 0.7531, 0.7634] +2026-04-14 03:57:27.047246: Epoch time: 101.34 s +2026-04-14 03:57:28.236766: +2026-04-14 03:57:28.238457: Epoch 3132 +2026-04-14 03:57:28.240457: Current learning rate: 0.00253 +2026-04-14 03:59:09.967377: train_loss -0.4593 +2026-04-14 03:59:09.972853: val_loss -0.3973 +2026-04-14 03:59:09.974638: Pseudo dice [0.6643, 0.0, 0.7524, 0.9058, 0.2815, 0.7924, 0.594] +2026-04-14 03:59:09.976510: Epoch time: 101.73 s +2026-04-14 03:59:11.172710: +2026-04-14 03:59:11.174704: Epoch 3133 +2026-04-14 03:59:11.176603: Current learning rate: 0.00253 +2026-04-14 04:00:52.649795: train_loss -0.4489 +2026-04-14 04:00:52.654580: val_loss -0.3878 +2026-04-14 04:00:52.656639: Pseudo dice [0.6845, 0.0, 0.5794, 0.8623, 0.407, 0.7281, 0.8442] +2026-04-14 04:00:52.659067: Epoch time: 101.48 s +2026-04-14 04:00:53.844774: +2026-04-14 04:00:53.846709: Epoch 3134 +2026-04-14 04:00:53.848995: Current learning rate: 0.00252 +2026-04-14 04:02:35.097397: train_loss -0.4467 +2026-04-14 04:02:35.102929: val_loss -0.3767 +2026-04-14 04:02:35.105045: Pseudo dice [0.3224, 0.0, 0.6092, 0.5927, 0.5091, 0.7191, 0.5741] +2026-04-14 04:02:35.107438: Epoch time: 101.26 s +2026-04-14 04:02:36.316419: +2026-04-14 04:02:36.318217: Epoch 3135 +2026-04-14 04:02:36.320292: Current learning rate: 0.00252 +2026-04-14 04:04:17.774110: train_loss -0.4366 +2026-04-14 04:04:17.780915: val_loss -0.3545 +2026-04-14 04:04:17.784574: Pseudo dice [0.5543, 0.0, 0.6604, 0.3997, 0.0847, 0.6427, 0.7909] +2026-04-14 04:04:17.786802: Epoch time: 101.46 s +2026-04-14 04:04:18.978916: +2026-04-14 04:04:18.984124: Epoch 3136 +2026-04-14 04:04:18.989052: Current learning rate: 0.00252 +2026-04-14 04:06:00.249813: train_loss -0.4328 +2026-04-14 04:06:00.256695: val_loss -0.4046 +2026-04-14 04:06:00.258482: Pseudo dice [0.5522, 0.0, 0.647, 0.9148, 0.4495, 0.8452, 0.8725] +2026-04-14 04:06:00.260481: Epoch time: 101.27 s +2026-04-14 04:06:01.533670: +2026-04-14 04:06:01.535397: Epoch 3137 +2026-04-14 04:06:01.537379: Current learning rate: 0.00252 +2026-04-14 04:07:42.916850: train_loss -0.4388 +2026-04-14 04:07:42.933065: val_loss -0.3731 +2026-04-14 04:07:42.936032: Pseudo dice [0.712, 0.0, 0.6024, 0.3328, 0.1793, 0.7353, 0.8252] +2026-04-14 04:07:42.938770: Epoch time: 101.39 s +2026-04-14 04:07:44.400645: +2026-04-14 04:07:44.402673: Epoch 3138 +2026-04-14 04:07:44.404403: Current learning rate: 0.00251 +2026-04-14 04:09:25.853293: train_loss -0.4492 +2026-04-14 04:09:25.870419: val_loss -0.3833 +2026-04-14 04:09:25.882766: Pseudo dice [0.6843, 0.0, 0.7007, 0.8245, 0.3467, 0.7513, 0.8268] +2026-04-14 04:09:25.888015: Epoch time: 101.46 s +2026-04-14 04:09:27.097835: +2026-04-14 04:09:27.100074: Epoch 3139 +2026-04-14 04:09:27.102448: Current learning rate: 0.00251 +2026-04-14 04:11:08.515801: train_loss -0.44 +2026-04-14 04:11:08.523418: val_loss -0.3975 +2026-04-14 04:11:08.525136: Pseudo dice [0.5266, 0.0, 0.7804, 0.6924, 0.1073, 0.813, 0.8698] +2026-04-14 04:11:08.526759: Epoch time: 101.42 s +2026-04-14 04:11:09.726055: +2026-04-14 04:11:09.727930: Epoch 3140 +2026-04-14 04:11:09.729942: Current learning rate: 0.00251 +2026-04-14 04:12:51.786313: train_loss -0.4476 +2026-04-14 04:12:51.791882: val_loss -0.4055 +2026-04-14 04:12:51.794877: Pseudo dice [0.6978, 0.0, 0.8712, 0.908, 0.3557, 0.809, 0.8659] +2026-04-14 04:12:51.796778: Epoch time: 102.06 s +2026-04-14 04:12:52.989980: +2026-04-14 04:12:52.992100: Epoch 3141 +2026-04-14 04:12:52.995070: Current learning rate: 0.0025 +2026-04-14 04:14:34.263878: train_loss -0.4468 +2026-04-14 04:14:34.270516: val_loss -0.3735 +2026-04-14 04:14:34.272321: Pseudo dice [0.4879, 0.0, 0.7732, 0.5162, 0.2702, 0.6979, 0.3436] +2026-04-14 04:14:34.275500: Epoch time: 101.28 s +2026-04-14 04:14:35.489258: +2026-04-14 04:14:35.490685: Epoch 3142 +2026-04-14 04:14:35.492439: Current learning rate: 0.0025 +2026-04-14 04:16:16.893495: train_loss -0.4491 +2026-04-14 04:16:16.898964: val_loss -0.355 +2026-04-14 04:16:16.901107: Pseudo dice [0.5024, 0.0, 0.7593, 0.7854, 0.2738, 0.8106, 0.5406] +2026-04-14 04:16:16.904434: Epoch time: 101.41 s +2026-04-14 04:16:18.110893: +2026-04-14 04:16:18.113272: Epoch 3143 +2026-04-14 04:16:18.116227: Current learning rate: 0.0025 +2026-04-14 04:17:59.558208: train_loss -0.437 +2026-04-14 04:17:59.567578: val_loss -0.3899 +2026-04-14 04:17:59.569414: Pseudo dice [0.5681, 0.0, 0.7328, 0.8925, 0.1804, 0.8326, 0.8507] +2026-04-14 04:17:59.571144: Epoch time: 101.45 s +2026-04-14 04:18:00.777293: +2026-04-14 04:18:00.778899: Epoch 3144 +2026-04-14 04:18:00.780733: Current learning rate: 0.0025 +2026-04-14 04:19:42.450949: train_loss -0.4182 +2026-04-14 04:19:42.456758: val_loss -0.3672 +2026-04-14 04:19:42.459214: Pseudo dice [0.3148, 0.0, 0.7088, 0.2486, 0.3292, 0.3817, 0.6098] +2026-04-14 04:19:42.462511: Epoch time: 101.68 s +2026-04-14 04:19:43.647238: +2026-04-14 04:19:43.649400: Epoch 3145 +2026-04-14 04:19:43.651418: Current learning rate: 0.00249 +2026-04-14 04:21:26.157059: train_loss -0.4378 +2026-04-14 04:21:26.161743: val_loss -0.3756 +2026-04-14 04:21:26.164136: Pseudo dice [0.3103, 0.0, 0.7731, 0.7369, 0.397, 0.7527, 0.7443] +2026-04-14 04:21:26.165849: Epoch time: 102.51 s +2026-04-14 04:21:27.398460: +2026-04-14 04:21:27.400295: Epoch 3146 +2026-04-14 04:21:27.402109: Current learning rate: 0.00249 +2026-04-14 04:23:08.875890: train_loss -0.445 +2026-04-14 04:23:08.882583: val_loss -0.3937 +2026-04-14 04:23:08.885100: Pseudo dice [0.5869, 0.0, 0.8367, 0.5355, 0.3333, 0.5115, 0.8857] +2026-04-14 04:23:08.887402: Epoch time: 101.48 s +2026-04-14 04:23:10.153785: +2026-04-14 04:23:10.155731: Epoch 3147 +2026-04-14 04:23:10.157623: Current learning rate: 0.00249 +2026-04-14 04:24:51.660528: train_loss -0.442 +2026-04-14 04:24:51.675370: val_loss -0.4194 +2026-04-14 04:24:51.677135: Pseudo dice [0.8365, 0.0, 0.7641, 0.8589, 0.5517, 0.7802, 0.8261] +2026-04-14 04:24:51.678944: Epoch time: 101.51 s +2026-04-14 04:24:52.880910: +2026-04-14 04:24:52.882833: Epoch 3148 +2026-04-14 04:24:52.884774: Current learning rate: 0.00249 +2026-04-14 04:26:34.280806: train_loss -0.4351 +2026-04-14 04:26:34.286244: val_loss -0.3696 +2026-04-14 04:26:34.287799: Pseudo dice [0.3627, 0.0, 0.6673, 0.6648, 0.4672, 0.745, 0.8225] +2026-04-14 04:26:34.289475: Epoch time: 101.4 s +2026-04-14 04:26:35.495129: +2026-04-14 04:26:35.497562: Epoch 3149 +2026-04-14 04:26:35.499659: Current learning rate: 0.00248 +2026-04-14 04:28:16.736166: train_loss -0.449 +2026-04-14 04:28:16.743175: val_loss -0.4009 +2026-04-14 04:28:16.745134: Pseudo dice [0.6524, 0.0, 0.7171, 0.8855, 0.519, 0.7499, 0.7577] +2026-04-14 04:28:16.747551: Epoch time: 101.24 s +2026-04-14 04:28:19.642015: +2026-04-14 04:28:19.643804: Epoch 3150 +2026-04-14 04:28:19.646042: Current learning rate: 0.00248 +2026-04-14 04:30:00.906685: train_loss -0.4422 +2026-04-14 04:30:00.912912: val_loss -0.3855 +2026-04-14 04:30:00.915453: Pseudo dice [0.6211, 0.0, 0.6751, 0.8074, 0.5389, 0.8226, 0.6003] +2026-04-14 04:30:00.917273: Epoch time: 101.27 s +2026-04-14 04:30:02.119288: +2026-04-14 04:30:02.120959: Epoch 3151 +2026-04-14 04:30:02.123466: Current learning rate: 0.00248 +2026-04-14 04:31:43.418677: train_loss -0.4291 +2026-04-14 04:31:43.424388: val_loss -0.3877 +2026-04-14 04:31:43.426223: Pseudo dice [0.3719, 0.0, 0.7085, 0.5804, 0.4275, 0.7576, 0.6977] +2026-04-14 04:31:43.427940: Epoch time: 101.3 s +2026-04-14 04:31:44.638884: +2026-04-14 04:31:44.640595: Epoch 3152 +2026-04-14 04:31:44.642776: Current learning rate: 0.00248 +2026-04-14 04:33:25.912300: train_loss -0.4464 +2026-04-14 04:33:25.917467: val_loss -0.4003 +2026-04-14 04:33:25.919357: Pseudo dice [0.5404, 0.0, 0.7073, 0.6729, 0.257, 0.805, 0.7818] +2026-04-14 04:33:25.921091: Epoch time: 101.28 s +2026-04-14 04:33:27.117454: +2026-04-14 04:33:27.118978: Epoch 3153 +2026-04-14 04:33:27.121662: Current learning rate: 0.00247 +2026-04-14 04:35:08.504915: train_loss -0.4473 +2026-04-14 04:35:08.514693: val_loss -0.3894 +2026-04-14 04:35:08.518398: Pseudo dice [0.3824, 0.0, 0.7847, 0.7995, 0.4172, 0.8075, 0.662] +2026-04-14 04:35:08.520622: Epoch time: 101.39 s +2026-04-14 04:35:09.716013: +2026-04-14 04:35:09.717970: Epoch 3154 +2026-04-14 04:35:09.720736: Current learning rate: 0.00247 +2026-04-14 04:36:51.135970: train_loss -0.4388 +2026-04-14 04:36:51.140662: val_loss -0.4054 +2026-04-14 04:36:51.142176: Pseudo dice [0.5991, 0.0, 0.801, 0.7937, 0.6548, 0.6609, 0.8363] +2026-04-14 04:36:51.144251: Epoch time: 101.42 s +2026-04-14 04:36:52.327679: +2026-04-14 04:36:52.329254: Epoch 3155 +2026-04-14 04:36:52.331225: Current learning rate: 0.00247 +2026-04-14 04:38:33.836766: train_loss -0.4541 +2026-04-14 04:38:33.843092: val_loss -0.3842 +2026-04-14 04:38:33.844901: Pseudo dice [0.3501, 0.0, 0.7459, 0.7393, 0.4092, 0.8174, 0.8839] +2026-04-14 04:38:33.846576: Epoch time: 101.51 s +2026-04-14 04:38:35.051337: +2026-04-14 04:38:35.053008: Epoch 3156 +2026-04-14 04:38:35.055742: Current learning rate: 0.00247 +2026-04-14 04:40:16.640271: train_loss -0.4497 +2026-04-14 04:40:16.646192: val_loss -0.3915 +2026-04-14 04:40:16.648075: Pseudo dice [0.4009, 0.0, 0.8185, 0.7629, 0.4033, 0.7792, 0.9031] +2026-04-14 04:40:16.650520: Epoch time: 101.59 s +2026-04-14 04:40:16.652198: Yayy! New best EMA pseudo Dice: 0.5563 +2026-04-14 04:40:19.637658: +2026-04-14 04:40:19.640289: Epoch 3157 +2026-04-14 04:40:19.642279: Current learning rate: 0.00246 +2026-04-14 04:42:00.902671: train_loss -0.4583 +2026-04-14 04:42:00.908437: val_loss -0.3932 +2026-04-14 04:42:00.911128: Pseudo dice [0.2232, 0.0, 0.6326, 0.8375, 0.6023, 0.7538, 0.7956] +2026-04-14 04:42:00.912883: Epoch time: 101.27 s +2026-04-14 04:42:02.106339: +2026-04-14 04:42:02.108907: Epoch 3158 +2026-04-14 04:42:02.111306: Current learning rate: 0.00246 +2026-04-14 04:43:43.535153: train_loss -0.4396 +2026-04-14 04:43:43.540440: val_loss -0.3866 +2026-04-14 04:43:43.542080: Pseudo dice [0.6487, 0.0, 0.7902, 0.8886, 0.6249, 0.8451, 0.4871] +2026-04-14 04:43:43.543683: Epoch time: 101.43 s +2026-04-14 04:43:43.545172: Yayy! New best EMA pseudo Dice: 0.5612 +2026-04-14 04:43:46.494261: +2026-04-14 04:43:46.496687: Epoch 3159 +2026-04-14 04:43:46.498190: Current learning rate: 0.00246 +2026-04-14 04:45:27.836169: train_loss -0.4534 +2026-04-14 04:45:27.841480: val_loss -0.3926 +2026-04-14 04:45:27.842769: Pseudo dice [0.2057, 0.0, 0.6003, 0.846, 0.5251, 0.6675, 0.9273] +2026-04-14 04:45:27.844310: Epoch time: 101.34 s +2026-04-14 04:45:29.044684: +2026-04-14 04:45:29.046163: Epoch 3160 +2026-04-14 04:45:29.047742: Current learning rate: 0.00245 +2026-04-14 04:47:10.287734: train_loss -0.4427 +2026-04-14 04:47:10.293011: val_loss -0.3621 +2026-04-14 04:47:10.294559: Pseudo dice [0.3246, 0.0, 0.6524, 0.8273, 0.3162, 0.623, 0.9169] +2026-04-14 04:47:10.297037: Epoch time: 101.25 s +2026-04-14 04:47:11.506289: +2026-04-14 04:47:11.508068: Epoch 3161 +2026-04-14 04:47:11.510047: Current learning rate: 0.00245 +2026-04-14 04:48:52.851383: train_loss -0.4368 +2026-04-14 04:48:52.858958: val_loss -0.3901 +2026-04-14 04:48:52.860529: Pseudo dice [0.478, 0.0, 0.6914, 0.7322, 0.3075, 0.7275, 0.8658] +2026-04-14 04:48:52.863022: Epoch time: 101.35 s +2026-04-14 04:48:54.073760: +2026-04-14 04:48:54.075830: Epoch 3162 +2026-04-14 04:48:54.077698: Current learning rate: 0.00245 +2026-04-14 04:50:35.314648: train_loss -0.4083 +2026-04-14 04:50:35.319585: val_loss -0.3903 +2026-04-14 04:50:35.321042: Pseudo dice [0.3419, 0.0, 0.679, 0.4364, 0.6173, 0.3712, 0.9117] +2026-04-14 04:50:35.322550: Epoch time: 101.24 s +2026-04-14 04:50:36.529646: +2026-04-14 04:50:36.531259: Epoch 3163 +2026-04-14 04:50:36.533061: Current learning rate: 0.00245 +2026-04-14 04:52:17.960453: train_loss -0.4314 +2026-04-14 04:52:17.965359: val_loss -0.3613 +2026-04-14 04:52:17.967253: Pseudo dice [0.4353, 0.0, 0.6383, 0.739, 0.1526, 0.2337, 0.7693] +2026-04-14 04:52:17.968979: Epoch time: 101.43 s +2026-04-14 04:52:20.170429: +2026-04-14 04:52:20.172646: Epoch 3164 +2026-04-14 04:52:20.174785: Current learning rate: 0.00244 +2026-04-14 04:54:01.573639: train_loss -0.4501 +2026-04-14 04:54:01.589305: val_loss -0.387 +2026-04-14 04:54:01.591192: Pseudo dice [0.6468, 0.0, 0.4842, 0.6292, 0.4179, 0.7666, 0.8762] +2026-04-14 04:54:01.593092: Epoch time: 101.41 s +2026-04-14 04:54:02.784347: +2026-04-14 04:54:02.786440: Epoch 3165 +2026-04-14 04:54:02.788880: Current learning rate: 0.00244 +2026-04-14 04:55:44.173749: train_loss -0.4447 +2026-04-14 04:55:44.179709: val_loss -0.3959 +2026-04-14 04:55:44.181194: Pseudo dice [0.543, 0.0, 0.7767, 0.7425, 0.3791, 0.8058, 0.8822] +2026-04-14 04:55:44.185218: Epoch time: 101.39 s +2026-04-14 04:55:45.385063: +2026-04-14 04:55:45.386892: Epoch 3166 +2026-04-14 04:55:45.388881: Current learning rate: 0.00244 +2026-04-14 04:57:26.638773: train_loss -0.4276 +2026-04-14 04:57:26.645617: val_loss -0.3669 +2026-04-14 04:57:26.647830: Pseudo dice [0.2211, 0.0, 0.6364, 0.7578, 0.5476, 0.4577, 0.4349] +2026-04-14 04:57:26.650114: Epoch time: 101.26 s +2026-04-14 04:57:27.947468: +2026-04-14 04:57:27.949395: Epoch 3167 +2026-04-14 04:57:27.951301: Current learning rate: 0.00244 +2026-04-14 04:59:09.274146: train_loss -0.4287 +2026-04-14 04:59:09.279457: val_loss -0.3574 +2026-04-14 04:59:09.281060: Pseudo dice [0.3845, 0.0, 0.5053, 0.005, 0.3494, 0.8179, 0.4637] +2026-04-14 04:59:09.282520: Epoch time: 101.33 s +2026-04-14 04:59:10.482097: +2026-04-14 04:59:10.483802: Epoch 3168 +2026-04-14 04:59:10.485699: Current learning rate: 0.00243 +2026-04-14 05:00:52.109012: train_loss -0.4293 +2026-04-14 05:00:52.115260: val_loss -0.3852 +2026-04-14 05:00:52.117045: Pseudo dice [0.5455, 0.0, 0.6878, 0.5801, 0.2857, 0.7829, 0.9051] +2026-04-14 05:00:52.119252: Epoch time: 101.63 s +2026-04-14 05:00:53.314200: +2026-04-14 05:00:53.315889: Epoch 3169 +2026-04-14 05:00:53.318080: Current learning rate: 0.00243 +2026-04-14 05:02:34.551444: train_loss -0.4274 +2026-04-14 05:02:34.555957: val_loss -0.3839 +2026-04-14 05:02:34.557400: Pseudo dice [0.6028, 0.0, 0.565, 0.7661, 0.4685, 0.7966, 0.5152] +2026-04-14 05:02:34.559494: Epoch time: 101.24 s +2026-04-14 05:02:35.754596: +2026-04-14 05:02:35.756232: Epoch 3170 +2026-04-14 05:02:35.757895: Current learning rate: 0.00243 +2026-04-14 05:04:17.139323: train_loss -0.4322 +2026-04-14 05:04:17.144554: val_loss -0.3746 +2026-04-14 05:04:17.146316: Pseudo dice [0.7112, 0.0, 0.809, 0.5596, 0.5515, 0.7951, 0.7079] +2026-04-14 05:04:17.148322: Epoch time: 101.39 s +2026-04-14 05:04:18.357316: +2026-04-14 05:04:18.359473: Epoch 3171 +2026-04-14 05:04:18.361985: Current learning rate: 0.00243 +2026-04-14 05:05:59.725821: train_loss -0.4217 +2026-04-14 05:05:59.732640: val_loss -0.3526 +2026-04-14 05:05:59.734758: Pseudo dice [0.5709, 0.0, 0.7463, 0.6169, 0.0303, 0.6703, 0.7861] +2026-04-14 05:05:59.738463: Epoch time: 101.37 s +2026-04-14 05:06:00.965326: +2026-04-14 05:06:00.967329: Epoch 3172 +2026-04-14 05:06:00.969222: Current learning rate: 0.00242 +2026-04-14 05:07:42.262304: train_loss -0.4472 +2026-04-14 05:07:42.267606: val_loss -0.4029 +2026-04-14 05:07:42.269735: Pseudo dice [0.7628, 0.0, 0.7078, 0.7644, 0.4533, 0.5897, 0.8471] +2026-04-14 05:07:42.271545: Epoch time: 101.3 s +2026-04-14 05:07:43.475519: +2026-04-14 05:07:43.477394: Epoch 3173 +2026-04-14 05:07:43.479526: Current learning rate: 0.00242 +2026-04-14 05:09:24.582411: train_loss -0.4464 +2026-04-14 05:09:24.588104: val_loss -0.3973 +2026-04-14 05:09:24.590019: Pseudo dice [0.7302, 0.0, 0.6994, 0.3757, 0.4893, 0.6384, 0.811] +2026-04-14 05:09:24.591927: Epoch time: 101.11 s +2026-04-14 05:09:25.792648: +2026-04-14 05:09:25.794346: Epoch 3174 +2026-04-14 05:09:25.796394: Current learning rate: 0.00242 +2026-04-14 05:11:07.000214: train_loss -0.4454 +2026-04-14 05:11:07.005658: val_loss -0.3929 +2026-04-14 05:11:07.007346: Pseudo dice [0.6455, 0.0, 0.8519, 0.6231, 0.4239, 0.7143, 0.808] +2026-04-14 05:11:07.009051: Epoch time: 101.21 s +2026-04-14 05:11:08.229434: +2026-04-14 05:11:08.231520: Epoch 3175 +2026-04-14 05:11:08.233293: Current learning rate: 0.00242 +2026-04-14 05:12:49.559537: train_loss -0.4467 +2026-04-14 05:12:49.565133: val_loss -0.3887 +2026-04-14 05:12:49.567432: Pseudo dice [0.5051, 0.0, 0.766, 0.518, 0.2396, 0.7478, 0.8373] +2026-04-14 05:12:49.569373: Epoch time: 101.33 s +2026-04-14 05:12:50.799104: +2026-04-14 05:12:50.801580: Epoch 3176 +2026-04-14 05:12:50.804388: Current learning rate: 0.00241 +2026-04-14 05:14:32.156045: train_loss -0.4613 +2026-04-14 05:14:32.161914: val_loss -0.3672 +2026-04-14 05:14:32.164292: Pseudo dice [0.2578, 0.0, 0.6939, 0.8265, 0.2391, 0.4374, 0.8116] +2026-04-14 05:14:32.166365: Epoch time: 101.36 s +2026-04-14 05:14:33.371708: +2026-04-14 05:14:33.373660: Epoch 3177 +2026-04-14 05:14:33.376041: Current learning rate: 0.00241 +2026-04-14 05:16:14.848896: train_loss -0.4445 +2026-04-14 05:16:14.853898: val_loss -0.3646 +2026-04-14 05:16:14.855329: Pseudo dice [0.5441, 0.0, 0.8232, 0.9001, 0.1182, 0.7431, 0.8238] +2026-04-14 05:16:14.856623: Epoch time: 101.48 s +2026-04-14 05:16:16.045748: +2026-04-14 05:16:16.048168: Epoch 3178 +2026-04-14 05:16:16.050338: Current learning rate: 0.00241 +2026-04-14 05:17:57.546042: train_loss -0.4611 +2026-04-14 05:17:57.551486: val_loss -0.3774 +2026-04-14 05:17:57.553018: Pseudo dice [0.2337, 0.0, 0.8184, 0.6993, 0.1586, 0.7976, 0.8366] +2026-04-14 05:17:57.554643: Epoch time: 101.5 s +2026-04-14 05:17:58.757563: +2026-04-14 05:17:58.759550: Epoch 3179 +2026-04-14 05:17:58.761981: Current learning rate: 0.0024 +2026-04-14 05:19:40.163898: train_loss -0.4552 +2026-04-14 05:19:40.168453: val_loss -0.4106 +2026-04-14 05:19:40.169959: Pseudo dice [0.4659, 0.0, 0.7647, 0.7855, 0.7068, 0.6581, 0.8209] +2026-04-14 05:19:40.171440: Epoch time: 101.41 s +2026-04-14 05:19:41.369666: +2026-04-14 05:19:41.371235: Epoch 3180 +2026-04-14 05:19:41.372785: Current learning rate: 0.0024 +2026-04-14 05:21:23.044965: train_loss -0.4387 +2026-04-14 05:21:23.051662: val_loss -0.3601 +2026-04-14 05:21:23.053515: Pseudo dice [0.2956, 0.0, 0.6854, 0.6309, 0.5902, 0.8508, 0.5998] +2026-04-14 05:21:23.055285: Epoch time: 101.68 s +2026-04-14 05:21:24.243391: +2026-04-14 05:21:24.245123: Epoch 3181 +2026-04-14 05:21:24.246950: Current learning rate: 0.0024 +2026-04-14 05:23:05.607269: train_loss -0.443 +2026-04-14 05:23:05.613586: val_loss -0.3941 +2026-04-14 05:23:05.615333: Pseudo dice [0.3881, 0.0, 0.8501, 0.9015, 0.5012, 0.6282, 0.8227] +2026-04-14 05:23:05.618372: Epoch time: 101.37 s +2026-04-14 05:23:06.797480: +2026-04-14 05:23:06.799661: Epoch 3182 +2026-04-14 05:23:06.801559: Current learning rate: 0.0024 +2026-04-14 05:24:48.118425: train_loss -0.4455 +2026-04-14 05:24:48.124261: val_loss -0.404 +2026-04-14 05:24:48.126498: Pseudo dice [0.4878, 0.0, 0.7765, 0.92, 0.5109, 0.794, 0.772] +2026-04-14 05:24:48.128464: Epoch time: 101.32 s +2026-04-14 05:24:49.325839: +2026-04-14 05:24:49.327607: Epoch 3183 +2026-04-14 05:24:49.329625: Current learning rate: 0.00239 +2026-04-14 05:26:30.730412: train_loss -0.4452 +2026-04-14 05:26:30.735233: val_loss -0.335 +2026-04-14 05:26:30.737317: Pseudo dice [0.7093, 0.0, 0.774, 0.8665, 0.1744, 0.4743, 0.5585] +2026-04-14 05:26:30.738903: Epoch time: 101.41 s +2026-04-14 05:26:31.946064: +2026-04-14 05:26:31.952209: Epoch 3184 +2026-04-14 05:26:31.954684: Current learning rate: 0.00239 +2026-04-14 05:28:14.423991: train_loss -0.4498 +2026-04-14 05:28:14.428347: val_loss -0.3776 +2026-04-14 05:28:14.429924: Pseudo dice [0.8216, 0.0, 0.5119, 0.3695, 0.4065, 0.4855, 0.8306] +2026-04-14 05:28:14.431246: Epoch time: 102.48 s +2026-04-14 05:28:15.628682: +2026-04-14 05:28:15.630389: Epoch 3185 +2026-04-14 05:28:15.632247: Current learning rate: 0.00239 +2026-04-14 05:29:57.094805: train_loss -0.4483 +2026-04-14 05:29:57.100463: val_loss -0.3969 +2026-04-14 05:29:57.102772: Pseudo dice [0.6889, 0.0, 0.7558, 0.7689, 0.4905, 0.7956, 0.8542] +2026-04-14 05:29:57.106597: Epoch time: 101.47 s +2026-04-14 05:29:58.306976: +2026-04-14 05:29:58.309729: Epoch 3186 +2026-04-14 05:29:58.311661: Current learning rate: 0.00239 +2026-04-14 05:31:39.739834: train_loss -0.4555 +2026-04-14 05:31:39.746579: val_loss -0.3462 +2026-04-14 05:31:39.748671: Pseudo dice [0.4215, 0.0, 0.6695, 0.7401, 0.2609, 0.793, 0.4824] +2026-04-14 05:31:39.750410: Epoch time: 101.44 s +2026-04-14 05:31:40.969496: +2026-04-14 05:31:40.971189: Epoch 3187 +2026-04-14 05:31:40.972932: Current learning rate: 0.00238 +2026-04-14 05:33:22.615829: train_loss -0.4458 +2026-04-14 05:33:22.621333: val_loss -0.3926 +2026-04-14 05:33:22.622852: Pseudo dice [0.4424, 0.0, 0.6999, 0.7687, 0.6268, 0.7173, 0.8149] +2026-04-14 05:33:22.624285: Epoch time: 101.65 s +2026-04-14 05:33:23.837408: +2026-04-14 05:33:23.839056: Epoch 3188 +2026-04-14 05:33:23.840847: Current learning rate: 0.00238 +2026-04-14 05:35:05.313561: train_loss -0.4419 +2026-04-14 05:35:05.318435: val_loss -0.3985 +2026-04-14 05:35:05.319852: Pseudo dice [0.6817, 0.0, 0.686, 0.7495, 0.4729, 0.7949, 0.8523] +2026-04-14 05:35:05.321729: Epoch time: 101.48 s +2026-04-14 05:35:06.627378: +2026-04-14 05:35:06.629161: Epoch 3189 +2026-04-14 05:35:06.631294: Current learning rate: 0.00238 +2026-04-14 05:36:48.040082: train_loss -0.4439 +2026-04-14 05:36:48.045408: val_loss -0.3931 +2026-04-14 05:36:48.047379: Pseudo dice [0.4648, 0.0, 0.7863, 0.744, 0.3854, 0.7469, 0.6423] +2026-04-14 05:36:48.049133: Epoch time: 101.42 s +2026-04-14 05:36:49.265175: +2026-04-14 05:36:49.266924: Epoch 3190 +2026-04-14 05:36:49.268634: Current learning rate: 0.00238 +2026-04-14 05:38:30.672210: train_loss -0.4309 +2026-04-14 05:38:30.677077: val_loss -0.3958 +2026-04-14 05:38:30.678621: Pseudo dice [0.7311, 0.0, 0.8093, 0.7654, 0.3024, 0.8108, 0.7971] +2026-04-14 05:38:30.680249: Epoch time: 101.41 s +2026-04-14 05:38:31.892847: +2026-04-14 05:38:31.894493: Epoch 3191 +2026-04-14 05:38:31.896207: Current learning rate: 0.00237 +2026-04-14 05:40:13.323558: train_loss -0.4532 +2026-04-14 05:40:13.329248: val_loss -0.386 +2026-04-14 05:40:13.330917: Pseudo dice [0.6554, 0.0, 0.7192, 0.6813, 0.4588, 0.8497, 0.8476] +2026-04-14 05:40:13.332859: Epoch time: 101.43 s +2026-04-14 05:40:14.543981: +2026-04-14 05:40:14.545587: Epoch 3192 +2026-04-14 05:40:14.547461: Current learning rate: 0.00237 +2026-04-14 05:41:55.966822: train_loss -0.4536 +2026-04-14 05:41:55.972737: val_loss -0.4226 +2026-04-14 05:41:55.975135: Pseudo dice [0.4835, 0.0, 0.7712, 0.7549, 0.3344, 0.8386, 0.7652] +2026-04-14 05:41:55.976985: Epoch time: 101.43 s +2026-04-14 05:41:57.189437: +2026-04-14 05:41:57.191095: Epoch 3193 +2026-04-14 05:41:57.193096: Current learning rate: 0.00237 +2026-04-14 05:43:38.805619: train_loss -0.4482 +2026-04-14 05:43:38.810827: val_loss -0.3748 +2026-04-14 05:43:38.812658: Pseudo dice [0.6731, 0.0, 0.7752, 0.6844, 0.2473, 0.2319, 0.8112] +2026-04-14 05:43:38.814346: Epoch time: 101.62 s +2026-04-14 05:43:40.041560: +2026-04-14 05:43:40.043480: Epoch 3194 +2026-04-14 05:43:40.045823: Current learning rate: 0.00237 +2026-04-14 05:45:21.408694: train_loss -0.4393 +2026-04-14 05:45:21.415189: val_loss -0.3983 +2026-04-14 05:45:21.417116: Pseudo dice [0.4067, 0.0, 0.4256, 0.7709, 0.4489, 0.7092, 0.847] +2026-04-14 05:45:21.418808: Epoch time: 101.37 s +2026-04-14 05:45:22.612029: +2026-04-14 05:45:22.613696: Epoch 3195 +2026-04-14 05:45:22.615540: Current learning rate: 0.00236 +2026-04-14 05:47:04.017257: train_loss -0.449 +2026-04-14 05:47:04.022204: val_loss -0.4138 +2026-04-14 05:47:04.024109: Pseudo dice [0.6732, 0.0, 0.8027, 0.845, 0.5099, 0.6835, 0.7029] +2026-04-14 05:47:04.025759: Epoch time: 101.41 s +2026-04-14 05:47:05.238877: +2026-04-14 05:47:05.240556: Epoch 3196 +2026-04-14 05:47:05.242444: Current learning rate: 0.00236 +2026-04-14 05:48:46.605658: train_loss -0.4396 +2026-04-14 05:48:46.611825: val_loss -0.3737 +2026-04-14 05:48:46.613870: Pseudo dice [0.3016, 0.0, 0.75, 0.7221, 0.3026, 0.7451, 0.8425] +2026-04-14 05:48:46.615447: Epoch time: 101.37 s +2026-04-14 05:48:47.828572: +2026-04-14 05:48:47.830303: Epoch 3197 +2026-04-14 05:48:47.832010: Current learning rate: 0.00236 +2026-04-14 05:50:29.508322: train_loss -0.4431 +2026-04-14 05:50:29.513977: val_loss -0.3749 +2026-04-14 05:50:29.515727: Pseudo dice [0.4954, 0.0, 0.462, 0.3325, 0.4467, 0.7374, 0.7131] +2026-04-14 05:50:29.517643: Epoch time: 101.68 s +2026-04-14 05:50:30.730230: +2026-04-14 05:50:30.732274: Epoch 3198 +2026-04-14 05:50:30.734613: Current learning rate: 0.00235 +2026-04-14 05:52:12.171913: train_loss -0.4338 +2026-04-14 05:52:12.176833: val_loss -0.3855 +2026-04-14 05:52:12.178933: Pseudo dice [0.3344, 0.0, 0.7623, 0.8844, 0.3856, 0.651, 0.8008] +2026-04-14 05:52:12.180653: Epoch time: 101.44 s +2026-04-14 05:52:13.382695: +2026-04-14 05:52:13.384969: Epoch 3199 +2026-04-14 05:52:13.387046: Current learning rate: 0.00235 +2026-04-14 05:53:54.816954: train_loss -0.4318 +2026-04-14 05:53:54.827236: val_loss -0.3851 +2026-04-14 05:53:54.840127: Pseudo dice [0.5543, 0.0, 0.6232, 0.8566, 0.4521, 0.6798, 0.6512] +2026-04-14 05:53:54.842318: Epoch time: 101.44 s +2026-04-14 05:53:57.805177: +2026-04-14 05:53:57.807274: Epoch 3200 +2026-04-14 05:53:57.809542: Current learning rate: 0.00235 +2026-04-14 05:55:39.334701: train_loss -0.4473 +2026-04-14 05:55:39.339291: val_loss -0.3823 +2026-04-14 05:55:39.340774: Pseudo dice [0.747, 0.0, 0.7765, 0.8284, 0.3262, 0.6899, 0.803] +2026-04-14 05:55:39.342152: Epoch time: 101.53 s +2026-04-14 05:55:40.535240: +2026-04-14 05:55:40.537289: Epoch 3201 +2026-04-14 05:55:40.540274: Current learning rate: 0.00235 +2026-04-14 05:57:22.210358: train_loss -0.4412 +2026-04-14 05:57:22.216982: val_loss -0.3894 +2026-04-14 05:57:22.219136: Pseudo dice [0.6801, 0.0, 0.7584, 0.8219, 0.3691, 0.5224, 0.8961] +2026-04-14 05:57:22.221283: Epoch time: 101.68 s +2026-04-14 05:57:23.437834: +2026-04-14 05:57:23.439739: Epoch 3202 +2026-04-14 05:57:23.441656: Current learning rate: 0.00234 +2026-04-14 05:59:04.874128: train_loss -0.4375 +2026-04-14 05:59:04.878835: val_loss -0.3828 +2026-04-14 05:59:04.880570: Pseudo dice [0.7046, 0.0, 0.7374, 0.8068, 0.5987, 0.7614, 0.7332] +2026-04-14 05:59:04.882240: Epoch time: 101.44 s +2026-04-14 05:59:06.081633: +2026-04-14 05:59:06.083626: Epoch 3203 +2026-04-14 05:59:06.085804: Current learning rate: 0.00234 +2026-04-14 06:00:47.439885: train_loss -0.4435 +2026-04-14 06:00:47.452174: val_loss -0.3703 +2026-04-14 06:00:47.454699: Pseudo dice [0.3307, 0.0, 0.7204, 0.83, 0.3061, 0.5213, 0.6854] +2026-04-14 06:00:47.457455: Epoch time: 101.36 s +2026-04-14 06:00:49.687143: +2026-04-14 06:00:49.688926: Epoch 3204 +2026-04-14 06:00:49.690772: Current learning rate: 0.00234 +2026-04-14 06:02:31.061316: train_loss -0.4472 +2026-04-14 06:02:31.067716: val_loss -0.3437 +2026-04-14 06:02:31.069668: Pseudo dice [0.6195, 0.0, 0.6504, 0.7397, 0.2865, 0.2972, 0.6819] +2026-04-14 06:02:31.071178: Epoch time: 101.38 s +2026-04-14 06:02:32.285686: +2026-04-14 06:02:32.287243: Epoch 3205 +2026-04-14 06:02:32.289082: Current learning rate: 0.00234 +2026-04-14 06:04:13.752186: train_loss -0.4405 +2026-04-14 06:04:13.757468: val_loss -0.3875 +2026-04-14 06:04:13.759480: Pseudo dice [0.4091, 0.0, 0.7997, 0.9158, 0.5071, 0.5005, 0.7632] +2026-04-14 06:04:13.761111: Epoch time: 101.47 s +2026-04-14 06:04:14.942902: +2026-04-14 06:04:14.945461: Epoch 3206 +2026-04-14 06:04:14.948035: Current learning rate: 0.00233 +2026-04-14 06:05:56.636902: train_loss -0.4396 +2026-04-14 06:05:56.641464: val_loss -0.3673 +2026-04-14 06:05:56.642869: Pseudo dice [0.1055, 0.0, 0.8122, 0.6966, 0.5106, 0.7668, 0.7572] +2026-04-14 06:05:56.644346: Epoch time: 101.7 s +2026-04-14 06:05:57.851639: +2026-04-14 06:05:57.853771: Epoch 3207 +2026-04-14 06:05:57.855561: Current learning rate: 0.00233 +2026-04-14 06:07:39.300034: train_loss -0.4257 +2026-04-14 06:07:39.310914: val_loss -0.3413 +2026-04-14 06:07:39.313735: Pseudo dice [0.2446, 0.0, 0.6536, 0.0819, 0.2253, 0.8258, 0.7824] +2026-04-14 06:07:39.315499: Epoch time: 101.45 s +2026-04-14 06:07:40.532478: +2026-04-14 06:07:40.534915: Epoch 3208 +2026-04-14 06:07:40.537091: Current learning rate: 0.00233 +2026-04-14 06:09:21.982253: train_loss -0.4459 +2026-04-14 06:09:21.988713: val_loss -0.4168 +2026-04-14 06:09:21.990630: Pseudo dice [0.6342, 0.0, 0.836, 0.5595, 0.3615, 0.8415, 0.4945] +2026-04-14 06:09:21.992445: Epoch time: 101.45 s +2026-04-14 06:09:23.226317: +2026-04-14 06:09:23.228277: Epoch 3209 +2026-04-14 06:09:23.230563: Current learning rate: 0.00233 +2026-04-14 06:11:04.876115: train_loss -0.4495 +2026-04-14 06:11:04.883358: val_loss -0.3972 +2026-04-14 06:11:04.884862: Pseudo dice [0.6315, 0.0, 0.6843, 0.8334, 0.5455, 0.6246, 0.8426] +2026-04-14 06:11:04.887052: Epoch time: 101.65 s +2026-04-14 06:11:06.100770: +2026-04-14 06:11:06.103023: Epoch 3210 +2026-04-14 06:11:06.105398: Current learning rate: 0.00232 +2026-04-14 06:12:47.449212: train_loss -0.4561 +2026-04-14 06:12:47.453952: val_loss -0.3677 +2026-04-14 06:12:47.460555: Pseudo dice [0.7639, 0.0, 0.671, 0.8091, 0.2544, 0.8174, 0.7116] +2026-04-14 06:12:47.463348: Epoch time: 101.35 s +2026-04-14 06:12:48.677111: +2026-04-14 06:12:48.680069: Epoch 3211 +2026-04-14 06:12:48.682100: Current learning rate: 0.00232 +2026-04-14 06:14:30.106417: train_loss -0.4368 +2026-04-14 06:14:30.116225: val_loss -0.4042 +2026-04-14 06:14:30.118154: Pseudo dice [0.3677, 0.0, 0.6917, 0.8718, 0.5855, 0.8554, 0.6104] +2026-04-14 06:14:30.120090: Epoch time: 101.43 s +2026-04-14 06:14:31.341857: +2026-04-14 06:14:31.343435: Epoch 3212 +2026-04-14 06:14:31.345268: Current learning rate: 0.00232 +2026-04-14 06:16:12.660342: train_loss -0.4467 +2026-04-14 06:16:12.665358: val_loss -0.3951 +2026-04-14 06:16:12.667284: Pseudo dice [0.6164, 0.0, 0.7327, 0.406, 0.3773, 0.8477, 0.6873] +2026-04-14 06:16:12.669469: Epoch time: 101.32 s +2026-04-14 06:16:13.884075: +2026-04-14 06:16:13.886173: Epoch 3213 +2026-04-14 06:16:13.888546: Current learning rate: 0.00231 +2026-04-14 06:17:55.322399: train_loss -0.4334 +2026-04-14 06:17:55.329194: val_loss -0.3714 +2026-04-14 06:17:55.330984: Pseudo dice [0.2363, 0.0, 0.5359, 0.8068, 0.6132, 0.6836, 0.8166] +2026-04-14 06:17:55.332577: Epoch time: 101.44 s +2026-04-14 06:17:56.557510: +2026-04-14 06:17:56.559906: Epoch 3214 +2026-04-14 06:17:56.562096: Current learning rate: 0.00231 +2026-04-14 06:19:38.107961: train_loss -0.4431 +2026-04-14 06:19:38.114736: val_loss -0.4109 +2026-04-14 06:19:38.117065: Pseudo dice [0.4336, 0.0, 0.8393, 0.8028, 0.4646, 0.6584, 0.8157] +2026-04-14 06:19:38.119807: Epoch time: 101.55 s +2026-04-14 06:19:39.322563: +2026-04-14 06:19:39.324156: Epoch 3215 +2026-04-14 06:19:39.325880: Current learning rate: 0.00231 +2026-04-14 06:21:20.790138: train_loss -0.443 +2026-04-14 06:21:20.794699: val_loss -0.3794 +2026-04-14 06:21:20.796308: Pseudo dice [0.4616, 0.0, 0.8417, 0.0, 0.2717, 0.7368, 0.8798] +2026-04-14 06:21:20.797683: Epoch time: 101.47 s +2026-04-14 06:21:21.998886: +2026-04-14 06:21:22.000345: Epoch 3216 +2026-04-14 06:21:22.001934: Current learning rate: 0.00231 +2026-04-14 06:23:03.419127: train_loss -0.4334 +2026-04-14 06:23:03.424992: val_loss -0.3808 +2026-04-14 06:23:03.426677: Pseudo dice [0.2522, 0.0, 0.6453, 0.8863, 0.3333, 0.335, 0.9096] +2026-04-14 06:23:03.429027: Epoch time: 101.42 s +2026-04-14 06:23:04.629313: +2026-04-14 06:23:04.630944: Epoch 3217 +2026-04-14 06:23:04.632778: Current learning rate: 0.0023 +2026-04-14 06:24:46.160955: train_loss -0.4441 +2026-04-14 06:24:46.166016: val_loss -0.3857 +2026-04-14 06:24:46.167805: Pseudo dice [0.4286, 0.0, 0.673, 0.888, 0.4921, 0.7535, 0.7028] +2026-04-14 06:24:46.170685: Epoch time: 101.53 s +2026-04-14 06:24:47.363779: +2026-04-14 06:24:47.365402: Epoch 3218 +2026-04-14 06:24:47.367117: Current learning rate: 0.0023 +2026-04-14 06:26:28.861386: train_loss -0.4592 +2026-04-14 06:26:28.867417: val_loss -0.3926 +2026-04-14 06:26:28.871311: Pseudo dice [0.6353, 0.0, 0.4418, 0.1598, 0.4894, 0.7981, 0.9063] +2026-04-14 06:26:28.873107: Epoch time: 101.5 s +2026-04-14 06:26:30.069060: +2026-04-14 06:26:30.070580: Epoch 3219 +2026-04-14 06:26:30.072480: Current learning rate: 0.0023 +2026-04-14 06:28:11.421250: train_loss -0.4427 +2026-04-14 06:28:11.427647: val_loss -0.3701 +2026-04-14 06:28:11.430006: Pseudo dice [0.6587, 0.0, 0.7885, 0.6473, 0.2106, 0.8051, 0.8989] +2026-04-14 06:28:11.431840: Epoch time: 101.36 s +2026-04-14 06:28:12.634393: +2026-04-14 06:28:12.636005: Epoch 3220 +2026-04-14 06:28:12.637908: Current learning rate: 0.0023 +2026-04-14 06:29:53.933077: train_loss -0.4569 +2026-04-14 06:29:53.938569: val_loss -0.4276 +2026-04-14 06:29:53.942096: Pseudo dice [0.8262, 0.0, 0.6039, 0.8876, 0.644, 0.7095, 0.9089] +2026-04-14 06:29:53.944086: Epoch time: 101.3 s +2026-04-14 06:29:55.152723: +2026-04-14 06:29:55.154344: Epoch 3221 +2026-04-14 06:29:55.156168: Current learning rate: 0.00229 +2026-04-14 06:31:36.426423: train_loss -0.4408 +2026-04-14 06:31:36.431041: val_loss -0.3921 +2026-04-14 06:31:36.432880: Pseudo dice [0.7421, 0.0, 0.6735, 0.671, 0.2399, 0.6979, 0.892] +2026-04-14 06:31:36.434325: Epoch time: 101.28 s +2026-04-14 06:31:37.651066: +2026-04-14 06:31:37.652663: Epoch 3222 +2026-04-14 06:31:37.654611: Current learning rate: 0.00229 +2026-04-14 06:33:19.150444: train_loss -0.4388 +2026-04-14 06:33:19.154880: val_loss -0.389 +2026-04-14 06:33:19.156671: Pseudo dice [0.3438, 0.0, 0.707, 0.3778, 0.3466, 0.7087, 0.699] +2026-04-14 06:33:19.159051: Epoch time: 101.5 s +2026-04-14 06:33:20.380595: +2026-04-14 06:33:20.382761: Epoch 3223 +2026-04-14 06:33:20.384969: Current learning rate: 0.00229 +2026-04-14 06:35:01.756799: train_loss -0.4283 +2026-04-14 06:35:01.766519: val_loss -0.3578 +2026-04-14 06:35:01.775623: Pseudo dice [0.7403, 0.0, 0.8075, 0.8173, 0.4163, 0.8822, 0.1403] +2026-04-14 06:35:01.777436: Epoch time: 101.38 s +2026-04-14 06:35:04.022877: +2026-04-14 06:35:04.024613: Epoch 3224 +2026-04-14 06:35:04.026492: Current learning rate: 0.00229 +2026-04-14 06:36:45.291925: train_loss -0.4416 +2026-04-14 06:36:45.298063: val_loss -0.3853 +2026-04-14 06:36:45.299729: Pseudo dice [0.5807, 0.0, 0.66, 0.624, 0.2877, 0.3747, 0.8408] +2026-04-14 06:36:45.301406: Epoch time: 101.27 s +2026-04-14 06:36:46.508749: +2026-04-14 06:36:46.510615: Epoch 3225 +2026-04-14 06:36:46.512388: Current learning rate: 0.00228 +2026-04-14 06:38:28.049558: train_loss -0.4388 +2026-04-14 06:38:28.054488: val_loss -0.4056 +2026-04-14 06:38:28.056555: Pseudo dice [0.3991, 0.0, 0.6722, 0.7206, 0.4359, 0.8219, 0.7115] +2026-04-14 06:38:28.058026: Epoch time: 101.54 s +2026-04-14 06:38:29.250100: +2026-04-14 06:38:29.251786: Epoch 3226 +2026-04-14 06:38:29.253609: Current learning rate: 0.00228 +2026-04-14 06:40:10.653904: train_loss -0.4561 +2026-04-14 06:40:10.659102: val_loss -0.394 +2026-04-14 06:40:10.660856: Pseudo dice [0.5949, 0.0, 0.6792, 0.7639, 0.347, 0.6939, 0.8295] +2026-04-14 06:40:10.662725: Epoch time: 101.41 s +2026-04-14 06:40:11.878839: +2026-04-14 06:40:11.880819: Epoch 3227 +2026-04-14 06:40:11.882796: Current learning rate: 0.00228 +2026-04-14 06:41:53.399834: train_loss -0.4343 +2026-04-14 06:41:53.406191: val_loss -0.3852 +2026-04-14 06:41:53.408224: Pseudo dice [0.548, 0.0, 0.6693, 0.7186, 0.5343, 0.7266, 0.7872] +2026-04-14 06:41:53.411218: Epoch time: 101.52 s +2026-04-14 06:41:54.635761: +2026-04-14 06:41:54.637597: Epoch 3228 +2026-04-14 06:41:54.639682: Current learning rate: 0.00228 +2026-04-14 06:43:36.155277: train_loss -0.4452 +2026-04-14 06:43:36.160319: val_loss -0.3651 +2026-04-14 06:43:36.162456: Pseudo dice [0.4145, 0.0, 0.6468, 0.6865, 0.3687, 0.6365, 0.5527] +2026-04-14 06:43:36.164569: Epoch time: 101.52 s +2026-04-14 06:43:37.354492: +2026-04-14 06:43:37.356411: Epoch 3229 +2026-04-14 06:43:37.358376: Current learning rate: 0.00227 +2026-04-14 06:45:18.720747: train_loss -0.4489 +2026-04-14 06:45:18.725286: val_loss -0.4181 +2026-04-14 06:45:18.726659: Pseudo dice [0.7954, 0.0, 0.7433, 0.8784, 0.6673, 0.7817, 0.8576] +2026-04-14 06:45:18.728106: Epoch time: 101.37 s +2026-04-14 06:45:19.955478: +2026-04-14 06:45:19.956995: Epoch 3230 +2026-04-14 06:45:19.958669: Current learning rate: 0.00227 +2026-04-14 06:47:01.459470: train_loss -0.4461 +2026-04-14 06:47:01.465234: val_loss -0.3977 +2026-04-14 06:47:01.467032: Pseudo dice [0.8208, 0.0, 0.6212, 0.6829, 0.3446, 0.8452, 0.7795] +2026-04-14 06:47:01.468681: Epoch time: 101.51 s +2026-04-14 06:47:02.671240: +2026-04-14 06:47:02.674455: Epoch 3231 +2026-04-14 06:47:02.676798: Current learning rate: 0.00227 +2026-04-14 06:48:43.973588: train_loss -0.4517 +2026-04-14 06:48:43.980066: val_loss -0.4064 +2026-04-14 06:48:43.982670: Pseudo dice [0.5188, 0.0, 0.6414, 0.8714, 0.6666, 0.7057, 0.8671] +2026-04-14 06:48:43.984403: Epoch time: 101.31 s +2026-04-14 06:48:45.172132: +2026-04-14 06:48:45.173870: Epoch 3232 +2026-04-14 06:48:45.175774: Current learning rate: 0.00226 +2026-04-14 06:50:26.501240: train_loss -0.4478 +2026-04-14 06:50:26.505716: val_loss -0.3765 +2026-04-14 06:50:26.507800: Pseudo dice [0.3663, 0.0, 0.6611, 0.741, 0.2442, 0.3379, 0.8715] +2026-04-14 06:50:26.509756: Epoch time: 101.33 s +2026-04-14 06:50:27.723221: +2026-04-14 06:50:27.724663: Epoch 3233 +2026-04-14 06:50:27.726498: Current learning rate: 0.00226 +2026-04-14 06:52:09.189695: train_loss -0.4515 +2026-04-14 06:52:09.194501: val_loss -0.3829 +2026-04-14 06:52:09.196400: Pseudo dice [0.8219, 0.0, 0.7871, 0.2907, 0.1513, 0.8147, 0.8689] +2026-04-14 06:52:09.197886: Epoch time: 101.47 s +2026-04-14 06:52:10.390750: +2026-04-14 06:52:10.392398: Epoch 3234 +2026-04-14 06:52:10.394257: Current learning rate: 0.00226 +2026-04-14 06:53:52.004629: train_loss -0.4414 +2026-04-14 06:53:52.009849: val_loss -0.3594 +2026-04-14 06:53:52.011561: Pseudo dice [0.5627, 0.0, 0.833, 0.6874, 0.5676, 0.8036, 0.8401] +2026-04-14 06:53:52.013197: Epoch time: 101.62 s +2026-04-14 06:53:53.223192: +2026-04-14 06:53:53.225296: Epoch 3235 +2026-04-14 06:53:53.227572: Current learning rate: 0.00226 +2026-04-14 06:55:34.587064: train_loss -0.4508 +2026-04-14 06:55:34.594360: val_loss -0.4033 +2026-04-14 06:55:34.596570: Pseudo dice [0.5291, 0.0, 0.7747, 0.6769, 0.4636, 0.7462, 0.5641] +2026-04-14 06:55:34.598509: Epoch time: 101.37 s +2026-04-14 06:55:35.804318: +2026-04-14 06:55:35.806114: Epoch 3236 +2026-04-14 06:55:35.808197: Current learning rate: 0.00225 +2026-04-14 06:57:17.242810: train_loss -0.4538 +2026-04-14 06:57:17.247878: val_loss -0.3785 +2026-04-14 06:57:17.249847: Pseudo dice [0.443, 0.0, 0.7964, 0.7108, 0.1107, 0.701, 0.7506] +2026-04-14 06:57:17.251709: Epoch time: 101.44 s +2026-04-14 06:57:18.472720: +2026-04-14 06:57:18.474438: Epoch 3237 +2026-04-14 06:57:18.476143: Current learning rate: 0.00225 +2026-04-14 06:58:59.922595: train_loss -0.452 +2026-04-14 06:58:59.927723: val_loss -0.39 +2026-04-14 06:58:59.929202: Pseudo dice [0.4836, 0.0, 0.8382, 0.563, 0.4648, 0.3761, 0.8528] +2026-04-14 06:58:59.930738: Epoch time: 101.45 s +2026-04-14 06:59:01.139036: +2026-04-14 06:59:01.140579: Epoch 3238 +2026-04-14 06:59:01.142395: Current learning rate: 0.00225 +2026-04-14 07:00:42.562081: train_loss -0.4565 +2026-04-14 07:00:42.567775: val_loss -0.4024 +2026-04-14 07:00:42.569796: Pseudo dice [0.5587, 0.0, 0.8894, 0.5043, 0.3069, 0.8106, 0.8701] +2026-04-14 07:00:42.571455: Epoch time: 101.43 s +2026-04-14 07:00:43.789162: +2026-04-14 07:00:43.791108: Epoch 3239 +2026-04-14 07:00:43.793254: Current learning rate: 0.00225 +2026-04-14 07:02:25.317376: train_loss -0.4496 +2026-04-14 07:02:25.322702: val_loss -0.4042 +2026-04-14 07:02:25.324501: Pseudo dice [0.487, 0.0, 0.6994, 0.6233, 0.6245, 0.802, 0.8407] +2026-04-14 07:02:25.326527: Epoch time: 101.53 s +2026-04-14 07:02:26.574528: +2026-04-14 07:02:26.576145: Epoch 3240 +2026-04-14 07:02:26.577936: Current learning rate: 0.00224 +2026-04-14 07:04:07.981427: train_loss -0.4573 +2026-04-14 07:04:07.986769: val_loss -0.4012 +2026-04-14 07:04:07.988460: Pseudo dice [0.6815, 0.0, 0.7748, 0.764, 0.5188, 0.6916, 0.8266] +2026-04-14 07:04:07.989991: Epoch time: 101.41 s +2026-04-14 07:04:09.184657: +2026-04-14 07:04:09.186992: Epoch 3241 +2026-04-14 07:04:09.188922: Current learning rate: 0.00224 +2026-04-14 07:05:50.712759: train_loss -0.4568 +2026-04-14 07:05:50.719380: val_loss -0.3645 +2026-04-14 07:05:50.721463: Pseudo dice [0.5183, 0.0, 0.6599, 0.5331, 0.4392, 0.7701, 0.8851] +2026-04-14 07:05:50.723225: Epoch time: 101.53 s +2026-04-14 07:05:51.939991: +2026-04-14 07:05:51.941764: Epoch 3242 +2026-04-14 07:05:51.943543: Current learning rate: 0.00224 +2026-04-14 07:07:33.199946: train_loss -0.4555 +2026-04-14 07:07:33.207587: val_loss -0.4187 +2026-04-14 07:07:33.209380: Pseudo dice [0.5284, 0.0, 0.8351, 0.8269, 0.5396, 0.6152, 0.7976] +2026-04-14 07:07:33.211618: Epoch time: 101.26 s +2026-04-14 07:07:34.422789: +2026-04-14 07:07:34.424709: Epoch 3243 +2026-04-14 07:07:34.426946: Current learning rate: 0.00224 +2026-04-14 07:09:15.651320: train_loss -0.4612 +2026-04-14 07:09:15.656040: val_loss -0.4152 +2026-04-14 07:09:15.657604: Pseudo dice [0.6664, 0.0, 0.7933, 0.7073, 0.4356, 0.75, 0.8817] +2026-04-14 07:09:15.659074: Epoch time: 101.23 s +2026-04-14 07:09:15.660348: Yayy! New best EMA pseudo Dice: 0.5617 +2026-04-14 07:09:19.633749: +2026-04-14 07:09:19.635670: Epoch 3244 +2026-04-14 07:09:19.637156: Current learning rate: 0.00223 +2026-04-14 07:11:01.005086: train_loss -0.4564 +2026-04-14 07:11:01.010756: val_loss -0.4179 +2026-04-14 07:11:01.012294: Pseudo dice [0.3821, 0.0, 0.5516, 0.8167, 0.6011, 0.8201, 0.8547] +2026-04-14 07:11:01.013805: Epoch time: 101.37 s +2026-04-14 07:11:01.015752: Yayy! New best EMA pseudo Dice: 0.563 +2026-04-14 07:11:04.032812: +2026-04-14 07:11:04.034904: Epoch 3245 +2026-04-14 07:11:04.036535: Current learning rate: 0.00223 +2026-04-14 07:12:45.396632: train_loss -0.4481 +2026-04-14 07:12:45.404960: val_loss -0.3978 +2026-04-14 07:12:45.407714: Pseudo dice [0.6983, 0.0, 0.7746, 0.9242, 0.4039, 0.8239, 0.612] +2026-04-14 07:12:45.409670: Epoch time: 101.37 s +2026-04-14 07:12:45.414984: Yayy! New best EMA pseudo Dice: 0.5672 +2026-04-14 07:12:48.441843: +2026-04-14 07:12:48.444250: Epoch 3246 +2026-04-14 07:12:48.445966: Current learning rate: 0.00223 +2026-04-14 07:14:29.912735: train_loss -0.4522 +2026-04-14 07:14:29.919708: val_loss -0.4005 +2026-04-14 07:14:29.922344: Pseudo dice [0.8164, 0.0, 0.7029, 0.7546, 0.377, 0.6944, 0.8953] +2026-04-14 07:14:29.924176: Epoch time: 101.47 s +2026-04-14 07:14:29.925615: Yayy! New best EMA pseudo Dice: 0.5711 +2026-04-14 07:14:32.838730: +2026-04-14 07:14:32.844963: Epoch 3247 +2026-04-14 07:14:32.846870: Current learning rate: 0.00222 +2026-04-14 07:16:14.187893: train_loss -0.4418 +2026-04-14 07:16:14.192777: val_loss -0.3538 +2026-04-14 07:16:14.194199: Pseudo dice [0.3252, 0.0, 0.4921, 0.4161, 0.5156, 0.5781, 0.5702] +2026-04-14 07:16:14.195743: Epoch time: 101.35 s +2026-04-14 07:16:15.380979: +2026-04-14 07:16:15.382849: Epoch 3248 +2026-04-14 07:16:15.384768: Current learning rate: 0.00222 +2026-04-14 07:17:56.680674: train_loss -0.446 +2026-04-14 07:17:56.688736: val_loss -0.3931 +2026-04-14 07:17:56.690590: Pseudo dice [0.3545, 0.0, 0.796, 0.8327, 0.5231, 0.6135, 0.7867] +2026-04-14 07:17:56.692803: Epoch time: 101.3 s +2026-04-14 07:17:57.897885: +2026-04-14 07:17:57.900131: Epoch 3249 +2026-04-14 07:17:57.902944: Current learning rate: 0.00222 +2026-04-14 07:19:39.186632: train_loss -0.4408 +2026-04-14 07:19:39.193196: val_loss -0.3655 +2026-04-14 07:19:39.195914: Pseudo dice [0.7266, 0.0, 0.424, 0.2031, 0.357, 0.758, 0.9203] +2026-04-14 07:19:39.198719: Epoch time: 101.29 s +2026-04-14 07:19:41.985815: +2026-04-14 07:19:41.988346: Epoch 3250 +2026-04-14 07:19:41.990260: Current learning rate: 0.00222 +2026-04-14 07:21:23.244084: train_loss -0.4542 +2026-04-14 07:21:23.248897: val_loss -0.3889 +2026-04-14 07:21:23.250597: Pseudo dice [0.6461, 0.0, 0.7533, 0.4829, 0.5529, 0.8571, 0.7778] +2026-04-14 07:21:23.252072: Epoch time: 101.26 s +2026-04-14 07:21:24.463197: +2026-04-14 07:21:24.464765: Epoch 3251 +2026-04-14 07:21:24.466513: Current learning rate: 0.00221 +2026-04-14 07:23:05.840798: train_loss -0.4472 +2026-04-14 07:23:05.845403: val_loss -0.3935 +2026-04-14 07:23:05.847854: Pseudo dice [0.3343, 0.0, 0.627, 0.6258, 0.6017, 0.7546, 0.7421] +2026-04-14 07:23:05.849923: Epoch time: 101.38 s +2026-04-14 07:23:07.081640: +2026-04-14 07:23:07.083159: Epoch 3252 +2026-04-14 07:23:07.084902: Current learning rate: 0.00221 +2026-04-14 07:24:48.540590: train_loss -0.4625 +2026-04-14 07:24:48.545703: val_loss -0.3645 +2026-04-14 07:24:48.547358: Pseudo dice [0.4914, 0.0, 0.7907, 0.4729, 0.4092, 0.8087, 0.8332] +2026-04-14 07:24:48.548921: Epoch time: 101.46 s +2026-04-14 07:24:49.765502: +2026-04-14 07:24:49.767166: Epoch 3253 +2026-04-14 07:24:49.768966: Current learning rate: 0.00221 +2026-04-14 07:26:31.091419: train_loss -0.4577 +2026-04-14 07:26:31.097945: val_loss -0.3883 +2026-04-14 07:26:31.100342: Pseudo dice [0.2114, 0.0, 0.7008, 0.2141, 0.6018, 0.7904, 0.8726] +2026-04-14 07:26:31.102417: Epoch time: 101.33 s +2026-04-14 07:26:32.288126: +2026-04-14 07:26:32.289690: Epoch 3254 +2026-04-14 07:26:32.291347: Current learning rate: 0.00221 +2026-04-14 07:28:13.511034: train_loss -0.4483 +2026-04-14 07:28:13.515564: val_loss -0.4039 +2026-04-14 07:28:13.517364: Pseudo dice [0.529, 0.0, 0.6709, 0.7095, 0.4871, 0.8508, 0.8808] +2026-04-14 07:28:13.518710: Epoch time: 101.23 s +2026-04-14 07:28:14.723482: +2026-04-14 07:28:14.724989: Epoch 3255 +2026-04-14 07:28:14.726745: Current learning rate: 0.0022 +2026-04-14 07:29:56.161034: train_loss -0.4498 +2026-04-14 07:29:56.165624: val_loss -0.4237 +2026-04-14 07:29:56.167384: Pseudo dice [0.4519, 0.0, 0.5148, 0.7773, 0.3562, 0.7617, 0.9202] +2026-04-14 07:29:56.169727: Epoch time: 101.44 s +2026-04-14 07:29:57.361828: +2026-04-14 07:29:57.363553: Epoch 3256 +2026-04-14 07:29:57.365222: Current learning rate: 0.0022 +2026-04-14 07:31:38.713015: train_loss -0.4534 +2026-04-14 07:31:38.718311: val_loss -0.3629 +2026-04-14 07:31:38.719991: Pseudo dice [0.6497, 0.0, 0.6477, 0.8723, 0.0952, 0.5806, 0.8056] +2026-04-14 07:31:38.721990: Epoch time: 101.35 s +2026-04-14 07:31:39.920574: +2026-04-14 07:31:39.923352: Epoch 3257 +2026-04-14 07:31:39.925706: Current learning rate: 0.0022 +2026-04-14 07:33:21.324797: train_loss -0.4519 +2026-04-14 07:33:21.329830: val_loss -0.388 +2026-04-14 07:33:21.331303: Pseudo dice [0.4942, 0.0, 0.6153, 0.7999, 0.3484, 0.7144, 0.8787] +2026-04-14 07:33:21.333032: Epoch time: 101.41 s +2026-04-14 07:33:22.521934: +2026-04-14 07:33:22.523818: Epoch 3258 +2026-04-14 07:33:22.525699: Current learning rate: 0.0022 +2026-04-14 07:35:03.892595: train_loss -0.4476 +2026-04-14 07:35:03.897597: val_loss -0.3744 +2026-04-14 07:35:03.899371: Pseudo dice [0.6718, 0.0, 0.7583, 0.6624, 0.1401, 0.7238, 0.4531] +2026-04-14 07:35:03.901999: Epoch time: 101.37 s +2026-04-14 07:35:05.095907: +2026-04-14 07:35:05.097512: Epoch 3259 +2026-04-14 07:35:05.099288: Current learning rate: 0.00219 +2026-04-14 07:36:46.308635: train_loss -0.4472 +2026-04-14 07:36:46.314001: val_loss -0.4117 +2026-04-14 07:36:46.315793: Pseudo dice [0.3208, 0.0, 0.7983, 0.8123, 0.6282, 0.7561, 0.8266] +2026-04-14 07:36:46.317736: Epoch time: 101.22 s +2026-04-14 07:36:47.521731: +2026-04-14 07:36:47.523723: Epoch 3260 +2026-04-14 07:36:47.525912: Current learning rate: 0.00219 +2026-04-14 07:38:28.789256: train_loss -0.4511 +2026-04-14 07:38:28.793836: val_loss -0.3548 +2026-04-14 07:38:28.795966: Pseudo dice [0.5059, 0.0, 0.6434, 0.7745, 0.3447, 0.7323, 0.8064] +2026-04-14 07:38:28.799116: Epoch time: 101.27 s +2026-04-14 07:38:30.024090: +2026-04-14 07:38:30.025880: Epoch 3261 +2026-04-14 07:38:30.027503: Current learning rate: 0.00219 +2026-04-14 07:40:11.344263: train_loss -0.4379 +2026-04-14 07:40:11.348529: val_loss -0.4043 +2026-04-14 07:40:11.351858: Pseudo dice [0.4597, 0.0, 0.667, 0.8612, 0.6599, 0.6617, 0.9266] +2026-04-14 07:40:11.353639: Epoch time: 101.32 s +2026-04-14 07:40:13.531374: +2026-04-14 07:40:13.532838: Epoch 3262 +2026-04-14 07:40:13.534865: Current learning rate: 0.00218 +2026-04-14 07:41:54.888400: train_loss -0.438 +2026-04-14 07:41:54.893745: val_loss -0.3986 +2026-04-14 07:41:54.896290: Pseudo dice [0.275, 0.0, 0.8659, 0.8272, 0.5655, 0.7366, 0.8587] +2026-04-14 07:41:54.897730: Epoch time: 101.36 s +2026-04-14 07:41:56.109719: +2026-04-14 07:41:56.111095: Epoch 3263 +2026-04-14 07:41:56.113938: Current learning rate: 0.00218 +2026-04-14 07:43:37.427513: train_loss -0.4461 +2026-04-14 07:43:37.432442: val_loss -0.3782 +2026-04-14 07:43:37.434057: Pseudo dice [0.4308, 0.0, 0.4778, 0.598, 0.5885, 0.6939, 0.7563] +2026-04-14 07:43:37.435853: Epoch time: 101.32 s +2026-04-14 07:43:38.654444: +2026-04-14 07:43:38.656369: Epoch 3264 +2026-04-14 07:43:38.658793: Current learning rate: 0.00218 +2026-04-14 07:45:20.173306: train_loss -0.4577 +2026-04-14 07:45:20.177392: val_loss -0.3795 +2026-04-14 07:45:20.178973: Pseudo dice [0.7166, 0.0, 0.6409, 0.6428, 0.5041, 0.6276, 0.709] +2026-04-14 07:45:20.180617: Epoch time: 101.52 s +2026-04-14 07:45:21.387613: +2026-04-14 07:45:21.389559: Epoch 3265 +2026-04-14 07:45:21.391215: Current learning rate: 0.00218 +2026-04-14 07:47:02.981883: train_loss -0.4494 +2026-04-14 07:47:02.991304: val_loss -0.3677 +2026-04-14 07:47:02.993082: Pseudo dice [0.6081, 0.0, 0.5585, 0.7359, 0.2515, 0.4875, 0.6715] +2026-04-14 07:47:02.994704: Epoch time: 101.6 s +2026-04-14 07:47:04.190484: +2026-04-14 07:47:04.192108: Epoch 3266 +2026-04-14 07:47:04.193988: Current learning rate: 0.00217 +2026-04-14 07:48:45.766869: train_loss -0.4449 +2026-04-14 07:48:45.772778: val_loss -0.3969 +2026-04-14 07:48:45.774349: Pseudo dice [0.6984, 0.0, 0.609, 0.8845, 0.1574, 0.704, 0.7042] +2026-04-14 07:48:45.775993: Epoch time: 101.58 s +2026-04-14 07:48:46.980085: +2026-04-14 07:48:46.981859: Epoch 3267 +2026-04-14 07:48:46.983660: Current learning rate: 0.00217 +2026-04-14 07:50:28.470420: train_loss -0.439 +2026-04-14 07:50:28.475671: val_loss -0.3845 +2026-04-14 07:50:28.477642: Pseudo dice [0.31, 0.0, 0.6243, 0.889, 0.2189, 0.6694, 0.9112] +2026-04-14 07:50:28.479659: Epoch time: 101.49 s +2026-04-14 07:50:29.698422: +2026-04-14 07:50:29.700024: Epoch 3268 +2026-04-14 07:50:29.701733: Current learning rate: 0.00217 +2026-04-14 07:52:11.190942: train_loss -0.4461 +2026-04-14 07:52:11.196227: val_loss -0.3929 +2026-04-14 07:52:11.198817: Pseudo dice [0.7225, 0.0, 0.7021, 0.8597, 0.6162, 0.7977, 0.6325] +2026-04-14 07:52:11.200390: Epoch time: 101.5 s +2026-04-14 07:52:12.391504: +2026-04-14 07:52:12.393439: Epoch 3269 +2026-04-14 07:52:12.396103: Current learning rate: 0.00217 +2026-04-14 07:53:53.747486: train_loss -0.4407 +2026-04-14 07:53:53.751183: val_loss -0.3784 +2026-04-14 07:53:53.752460: Pseudo dice [0.2512, 0.0, 0.6562, 0.9073, 0.3023, 0.7145, 0.857] +2026-04-14 07:53:53.754077: Epoch time: 101.36 s +2026-04-14 07:53:54.951154: +2026-04-14 07:53:54.952754: Epoch 3270 +2026-04-14 07:53:54.954363: Current learning rate: 0.00216 +2026-04-14 07:55:36.312117: train_loss -0.4377 +2026-04-14 07:55:36.319680: val_loss -0.3982 +2026-04-14 07:55:36.321630: Pseudo dice [0.4303, 0.0, 0.5504, 0.7789, 0.5127, 0.7923, 0.8486] +2026-04-14 07:55:36.323362: Epoch time: 101.36 s +2026-04-14 07:55:37.548469: +2026-04-14 07:55:37.550117: Epoch 3271 +2026-04-14 07:55:37.551931: Current learning rate: 0.00216 +2026-04-14 07:57:18.991139: train_loss -0.4561 +2026-04-14 07:57:19.015426: val_loss -0.41 +2026-04-14 07:57:19.018302: Pseudo dice [0.3182, 0.0, 0.5034, 0.9272, 0.5611, 0.6942, 0.8104] +2026-04-14 07:57:19.019923: Epoch time: 101.45 s +2026-04-14 07:57:20.226758: +2026-04-14 07:57:20.228603: Epoch 3272 +2026-04-14 07:57:20.230510: Current learning rate: 0.00216 +2026-04-14 07:59:01.524642: train_loss -0.4527 +2026-04-14 07:59:01.531182: val_loss -0.3795 +2026-04-14 07:59:01.532964: Pseudo dice [0.334, 0.0, 0.7742, 0.873, 0.4345, 0.8516, 0.8183] +2026-04-14 07:59:01.534916: Epoch time: 101.3 s +2026-04-14 07:59:02.727122: +2026-04-14 07:59:02.728664: Epoch 3273 +2026-04-14 07:59:02.730419: Current learning rate: 0.00216 +2026-04-14 08:00:44.020300: train_loss -0.4433 +2026-04-14 08:00:44.025205: val_loss -0.3853 +2026-04-14 08:00:44.027108: Pseudo dice [0.3828, 0.0, 0.7551, 0.7728, 0.4832, 0.7374, 0.8495] +2026-04-14 08:00:44.028751: Epoch time: 101.3 s +2026-04-14 08:00:45.236670: +2026-04-14 08:00:45.238240: Epoch 3274 +2026-04-14 08:00:45.240053: Current learning rate: 0.00215 +2026-04-14 08:02:26.653804: train_loss -0.4541 +2026-04-14 08:02:26.660470: val_loss -0.4202 +2026-04-14 08:02:26.663640: Pseudo dice [0.4534, 0.0, 0.6587, 0.9292, 0.3816, 0.8138, 0.8667] +2026-04-14 08:02:26.665271: Epoch time: 101.42 s +2026-04-14 08:02:27.869997: +2026-04-14 08:02:27.871764: Epoch 3275 +2026-04-14 08:02:27.873605: Current learning rate: 0.00215 +2026-04-14 08:04:09.418734: train_loss -0.4674 +2026-04-14 08:04:09.423816: val_loss -0.4045 +2026-04-14 08:04:09.425616: Pseudo dice [0.7676, 0.0, 0.591, 0.8706, 0.486, 0.7756, 0.7818] +2026-04-14 08:04:09.427318: Epoch time: 101.55 s +2026-04-14 08:04:10.648907: +2026-04-14 08:04:10.650398: Epoch 3276 +2026-04-14 08:04:10.652156: Current learning rate: 0.00215 +2026-04-14 08:05:51.936457: train_loss -0.4597 +2026-04-14 08:05:51.944669: val_loss -0.402 +2026-04-14 08:05:51.946202: Pseudo dice [0.4105, 0.0, 0.7079, 0.7711, 0.6623, 0.7923, 0.9281] +2026-04-14 08:05:51.947784: Epoch time: 101.29 s +2026-04-14 08:05:53.145506: +2026-04-14 08:05:53.146996: Epoch 3277 +2026-04-14 08:05:53.148770: Current learning rate: 0.00214 +2026-04-14 08:07:34.563621: train_loss -0.457 +2026-04-14 08:07:34.571424: val_loss -0.3787 +2026-04-14 08:07:34.572961: Pseudo dice [0.4543, 0.0, 0.6229, 0.8924, 0.1792, 0.7685, 0.8168] +2026-04-14 08:07:34.574789: Epoch time: 101.42 s +2026-04-14 08:07:35.793602: +2026-04-14 08:07:35.795457: Epoch 3278 +2026-04-14 08:07:35.797535: Current learning rate: 0.00214 +2026-04-14 08:09:17.079981: train_loss -0.4552 +2026-04-14 08:09:17.084527: val_loss -0.4054 +2026-04-14 08:09:17.086564: Pseudo dice [0.0964, 0.0, 0.4798, 0.776, 0.5554, 0.8207, 0.8242] +2026-04-14 08:09:17.088171: Epoch time: 101.29 s +2026-04-14 08:09:18.279964: +2026-04-14 08:09:18.281697: Epoch 3279 +2026-04-14 08:09:18.284048: Current learning rate: 0.00214 +2026-04-14 08:10:59.663382: train_loss -0.4456 +2026-04-14 08:10:59.668386: val_loss -0.367 +2026-04-14 08:10:59.669991: Pseudo dice [0.4179, 0.0, 0.7726, 0.6438, 0.2643, 0.8528, 0.6371] +2026-04-14 08:10:59.671632: Epoch time: 101.39 s +2026-04-14 08:11:00.881691: +2026-04-14 08:11:00.883353: Epoch 3280 +2026-04-14 08:11:00.885122: Current learning rate: 0.00214 +2026-04-14 08:12:42.237156: train_loss -0.447 +2026-04-14 08:12:42.242754: val_loss -0.3798 +2026-04-14 08:12:42.244687: Pseudo dice [0.4391, 0.0, 0.8496, 0.9046, 0.3411, 0.6397, 0.8775] +2026-04-14 08:12:42.246403: Epoch time: 101.36 s +2026-04-14 08:12:43.442840: +2026-04-14 08:12:43.444456: Epoch 3281 +2026-04-14 08:12:43.446127: Current learning rate: 0.00213 +2026-04-14 08:14:24.902583: train_loss -0.4437 +2026-04-14 08:14:24.910772: val_loss -0.3947 +2026-04-14 08:14:24.912526: Pseudo dice [0.324, 0.0, 0.7112, 0.7417, 0.6374, 0.5802, 0.7922] +2026-04-14 08:14:24.914486: Epoch time: 101.46 s +2026-04-14 08:14:27.158657: +2026-04-14 08:14:27.160172: Epoch 3282 +2026-04-14 08:14:27.162105: Current learning rate: 0.00213 +2026-04-14 08:16:08.652503: train_loss -0.4422 +2026-04-14 08:16:08.657638: val_loss -0.405 +2026-04-14 08:16:08.659332: Pseudo dice [0.5658, 0.0, 0.7881, 0.6056, 0.5635, 0.3994, 0.8805] +2026-04-14 08:16:08.661112: Epoch time: 101.5 s +2026-04-14 08:16:09.864481: +2026-04-14 08:16:09.866055: Epoch 3283 +2026-04-14 08:16:09.867756: Current learning rate: 0.00213 +2026-04-14 08:17:51.444086: train_loss -0.4554 +2026-04-14 08:17:51.449207: val_loss -0.3912 +2026-04-14 08:17:51.451104: Pseudo dice [0.6781, 0.0, 0.4019, 0.5429, 0.5104, 0.8392, 0.7614] +2026-04-14 08:17:51.452657: Epoch time: 101.58 s +2026-04-14 08:17:52.660726: +2026-04-14 08:17:52.662241: Epoch 3284 +2026-04-14 08:17:52.664014: Current learning rate: 0.00213 +2026-04-14 08:19:34.529084: train_loss -0.4539 +2026-04-14 08:19:34.534883: val_loss -0.3902 +2026-04-14 08:19:34.536754: Pseudo dice [0.3289, 0.0, 0.6314, 0.802, 0.4207, 0.7735, 0.8622] +2026-04-14 08:19:34.538447: Epoch time: 101.87 s +2026-04-14 08:19:35.828244: +2026-04-14 08:19:35.829875: Epoch 3285 +2026-04-14 08:19:35.831421: Current learning rate: 0.00212 +2026-04-14 08:21:17.339818: train_loss -0.4546 +2026-04-14 08:21:17.344631: val_loss -0.4129 +2026-04-14 08:21:17.346790: Pseudo dice [0.359, 0.0, 0.744, 0.7974, 0.7115, 0.8177, 0.7812] +2026-04-14 08:21:17.348264: Epoch time: 101.51 s +2026-04-14 08:21:18.555802: +2026-04-14 08:21:18.557317: Epoch 3286 +2026-04-14 08:21:18.558791: Current learning rate: 0.00212 +2026-04-14 08:22:59.955161: train_loss -0.456 +2026-04-14 08:22:59.959943: val_loss -0.3904 +2026-04-14 08:22:59.961557: Pseudo dice [0.2136, 0.0, 0.646, 0.8191, 0.5098, 0.643, 0.913] +2026-04-14 08:22:59.963148: Epoch time: 101.4 s +2026-04-14 08:23:01.174971: +2026-04-14 08:23:01.176546: Epoch 3287 +2026-04-14 08:23:01.178172: Current learning rate: 0.00212 +2026-04-14 08:24:42.514773: train_loss -0.4604 +2026-04-14 08:24:42.519995: val_loss -0.4081 +2026-04-14 08:24:42.521558: Pseudo dice [0.5545, 0.0, 0.7827, 0.1576, 0.4471, 0.7455, 0.8145] +2026-04-14 08:24:42.523003: Epoch time: 101.34 s +2026-04-14 08:24:43.727224: +2026-04-14 08:24:43.728972: Epoch 3288 +2026-04-14 08:24:43.730737: Current learning rate: 0.00212 +2026-04-14 08:26:25.266379: train_loss -0.4604 +2026-04-14 08:26:25.271397: val_loss -0.3896 +2026-04-14 08:26:25.273321: Pseudo dice [0.1888, 0.0, 0.7754, 0.715, 0.5095, 0.7577, 0.8877] +2026-04-14 08:26:25.274750: Epoch time: 101.54 s +2026-04-14 08:26:26.479091: +2026-04-14 08:26:26.480574: Epoch 3289 +2026-04-14 08:26:26.482268: Current learning rate: 0.00211 +2026-04-14 08:28:07.786476: train_loss -0.4524 +2026-04-14 08:28:07.791831: val_loss -0.3823 +2026-04-14 08:28:07.794215: Pseudo dice [0.602, 0.0, 0.7467, 0.8242, 0.4735, 0.6303, 0.8197] +2026-04-14 08:28:07.796413: Epoch time: 101.31 s +2026-04-14 08:28:08.996584: +2026-04-14 08:28:08.998302: Epoch 3290 +2026-04-14 08:28:09.000637: Current learning rate: 0.00211 +2026-04-14 08:29:50.450270: train_loss -0.4463 +2026-04-14 08:29:50.454191: val_loss -0.3958 +2026-04-14 08:29:50.455506: Pseudo dice [0.7878, 0.0, 0.8622, 0.6069, 0.5221, 0.6558, 0.6721] +2026-04-14 08:29:50.457098: Epoch time: 101.46 s +2026-04-14 08:29:51.669996: +2026-04-14 08:29:51.671510: Epoch 3291 +2026-04-14 08:29:51.673262: Current learning rate: 0.00211 +2026-04-14 08:31:33.017764: train_loss -0.4569 +2026-04-14 08:31:33.023851: val_loss -0.383 +2026-04-14 08:31:33.025593: Pseudo dice [0.64, 0.0, 0.5595, 0.7446, 0.4761, 0.8561, 0.8011] +2026-04-14 08:31:33.027357: Epoch time: 101.35 s +2026-04-14 08:31:34.257975: +2026-04-14 08:31:34.259734: Epoch 3292 +2026-04-14 08:31:34.261955: Current learning rate: 0.0021 +2026-04-14 08:33:15.774686: train_loss -0.4515 +2026-04-14 08:33:15.781264: val_loss -0.3774 +2026-04-14 08:33:15.783226: Pseudo dice [0.4958, 0.0, 0.2785, 0.8861, 0.1171, 0.7012, 0.7725] +2026-04-14 08:33:15.784978: Epoch time: 101.52 s +2026-04-14 08:33:17.066733: +2026-04-14 08:33:17.068635: Epoch 3293 +2026-04-14 08:33:17.070671: Current learning rate: 0.0021 +2026-04-14 08:34:58.504219: train_loss -0.4458 +2026-04-14 08:34:58.508968: val_loss -0.3897 +2026-04-14 08:34:58.510856: Pseudo dice [0.7218, 0.0, 0.7057, 0.7225, 0.3806, 0.6858, 0.8034] +2026-04-14 08:34:58.512556: Epoch time: 101.44 s +2026-04-14 08:34:59.734010: +2026-04-14 08:34:59.735591: Epoch 3294 +2026-04-14 08:34:59.737440: Current learning rate: 0.0021 +2026-04-14 08:36:41.101577: train_loss -0.4563 +2026-04-14 08:36:41.106393: val_loss -0.3579 +2026-04-14 08:36:41.108636: Pseudo dice [0.7036, 0.0, 0.5678, 0.622, 0.0878, 0.7579, 0.517] +2026-04-14 08:36:41.110399: Epoch time: 101.37 s +2026-04-14 08:36:42.356536: +2026-04-14 08:36:42.358016: Epoch 3295 +2026-04-14 08:36:42.359852: Current learning rate: 0.0021 +2026-04-14 08:38:23.790880: train_loss -0.4386 +2026-04-14 08:38:23.795856: val_loss -0.384 +2026-04-14 08:38:23.797520: Pseudo dice [0.2178, 0.0, 0.6769, 0.4182, 0.4195, 0.7825, 0.6139] +2026-04-14 08:38:23.799223: Epoch time: 101.44 s +2026-04-14 08:38:24.998555: +2026-04-14 08:38:25.000045: Epoch 3296 +2026-04-14 08:38:25.001789: Current learning rate: 0.00209 +2026-04-14 08:40:06.312973: train_loss -0.4366 +2026-04-14 08:40:06.318624: val_loss -0.3811 +2026-04-14 08:40:06.320924: Pseudo dice [0.3435, 0.0, 0.7491, 0.491, 0.3849, 0.6278, 0.8625] +2026-04-14 08:40:06.323641: Epoch time: 101.32 s +2026-04-14 08:40:07.528834: +2026-04-14 08:40:07.530483: Epoch 3297 +2026-04-14 08:40:07.532293: Current learning rate: 0.00209 +2026-04-14 08:41:48.944636: train_loss -0.4494 +2026-04-14 08:41:48.951417: val_loss -0.3799 +2026-04-14 08:41:48.952975: Pseudo dice [0.4678, 0.0, 0.8266, 0.6492, 0.5259, 0.6546, 0.8018] +2026-04-14 08:41:48.954319: Epoch time: 101.42 s +2026-04-14 08:41:50.166229: +2026-04-14 08:41:50.167957: Epoch 3298 +2026-04-14 08:41:50.169743: Current learning rate: 0.00209 +2026-04-14 08:43:31.582879: train_loss -0.4592 +2026-04-14 08:43:31.587999: val_loss -0.3731 +2026-04-14 08:43:31.590707: Pseudo dice [0.6478, 0.0, 0.5835, 0.1651, 0.3977, 0.6532, 0.746] +2026-04-14 08:43:31.592802: Epoch time: 101.42 s +2026-04-14 08:43:32.808603: +2026-04-14 08:43:32.810163: Epoch 3299 +2026-04-14 08:43:32.811997: Current learning rate: 0.00209 +2026-04-14 08:45:14.217607: train_loss -0.4476 +2026-04-14 08:45:14.221821: val_loss -0.3737 +2026-04-14 08:45:14.223417: Pseudo dice [0.3445, 0.0, 0.8212, 0.7786, 0.4525, 0.2688, 0.5487] +2026-04-14 08:45:14.225356: Epoch time: 101.41 s +2026-04-14 08:45:17.142999: +2026-04-14 08:45:17.146653: Epoch 3300 +2026-04-14 08:45:17.149090: Current learning rate: 0.00208 +2026-04-14 08:46:58.743056: train_loss -0.4445 +2026-04-14 08:46:58.748688: val_loss -0.4304 +2026-04-14 08:46:58.750411: Pseudo dice [0.3707, 0.0, 0.8705, 0.854, 0.595, 0.8614, 0.7353] +2026-04-14 08:46:58.752186: Epoch time: 101.6 s +2026-04-14 08:46:59.959764: +2026-04-14 08:46:59.961496: Epoch 3301 +2026-04-14 08:46:59.963299: Current learning rate: 0.00208 +2026-04-14 08:48:41.278132: train_loss -0.4482 +2026-04-14 08:48:41.282689: val_loss -0.3678 +2026-04-14 08:48:41.284309: Pseudo dice [0.7109, 0.0, 0.7027, 0.889, 0.1507, 0.7575, 0.7992] +2026-04-14 08:48:41.285971: Epoch time: 101.32 s +2026-04-14 08:48:43.519455: +2026-04-14 08:48:43.521035: Epoch 3302 +2026-04-14 08:48:43.522748: Current learning rate: 0.00208 +2026-04-14 08:50:24.879422: train_loss -0.4509 +2026-04-14 08:50:24.885872: val_loss -0.3326 +2026-04-14 08:50:24.887817: Pseudo dice [0.321, 0.0, 0.861, 0.5441, 0.2884, 0.7793, 0.7008] +2026-04-14 08:50:24.889435: Epoch time: 101.36 s +2026-04-14 08:50:26.105267: +2026-04-14 08:50:26.107027: Epoch 3303 +2026-04-14 08:50:26.109194: Current learning rate: 0.00208 +2026-04-14 08:52:07.439108: train_loss -0.4523 +2026-04-14 08:52:07.444097: val_loss -0.4127 +2026-04-14 08:52:07.446045: Pseudo dice [0.5453, 0.0, 0.7403, 0.5437, 0.5429, 0.8474, 0.8762] +2026-04-14 08:52:07.447531: Epoch time: 101.34 s +2026-04-14 08:52:08.629838: +2026-04-14 08:52:08.631749: Epoch 3304 +2026-04-14 08:52:08.634818: Current learning rate: 0.00207 +2026-04-14 08:53:49.822354: train_loss -0.4606 +2026-04-14 08:53:49.826966: val_loss -0.4212 +2026-04-14 08:53:49.828629: Pseudo dice [0.7054, 0.0, 0.7412, 0.8671, 0.5031, 0.7697, 0.938] +2026-04-14 08:53:49.830078: Epoch time: 101.2 s +2026-04-14 08:53:51.027364: +2026-04-14 08:53:51.028833: Epoch 3305 +2026-04-14 08:53:51.030307: Current learning rate: 0.00207 +2026-04-14 08:55:32.269254: train_loss -0.4557 +2026-04-14 08:55:32.274161: val_loss -0.3845 +2026-04-14 08:55:32.276245: Pseudo dice [0.386, 0.0, 0.8225, 0.8028, 0.5751, 0.4985, 0.8349] +2026-04-14 08:55:32.279060: Epoch time: 101.25 s +2026-04-14 08:55:33.483512: +2026-04-14 08:55:33.485310: Epoch 3306 +2026-04-14 08:55:33.487354: Current learning rate: 0.00207 +2026-04-14 08:57:14.965821: train_loss -0.4604 +2026-04-14 08:57:14.971628: val_loss -0.4025 +2026-04-14 08:57:14.973454: Pseudo dice [0.7623, 0.0, 0.6758, 0.7045, 0.439, 0.8768, 0.8709] +2026-04-14 08:57:14.975170: Epoch time: 101.49 s +2026-04-14 08:57:16.202322: +2026-04-14 08:57:16.204416: Epoch 3307 +2026-04-14 08:57:16.206500: Current learning rate: 0.00206 +2026-04-14 08:58:57.575695: train_loss -0.4518 +2026-04-14 08:58:57.580868: val_loss -0.3867 +2026-04-14 08:58:57.583082: Pseudo dice [0.3054, 0.0, 0.7072, 0.8114, 0.4757, 0.687, 0.8425] +2026-04-14 08:58:57.584988: Epoch time: 101.38 s +2026-04-14 08:58:58.797215: +2026-04-14 08:58:58.798905: Epoch 3308 +2026-04-14 08:58:58.800617: Current learning rate: 0.00206 +2026-04-14 09:00:40.104012: train_loss -0.4488 +2026-04-14 09:00:40.109739: val_loss -0.4057 +2026-04-14 09:00:40.111603: Pseudo dice [0.4526, 0.0, 0.7423, 0.3159, 0.5425, 0.8709, 0.8418] +2026-04-14 09:00:40.113086: Epoch time: 101.31 s +2026-04-14 09:00:41.324794: +2026-04-14 09:00:41.327080: Epoch 3309 +2026-04-14 09:00:41.329448: Current learning rate: 0.00206 +2026-04-14 09:02:22.551465: train_loss -0.4462 +2026-04-14 09:02:22.556687: val_loss -0.3661 +2026-04-14 09:02:22.558672: Pseudo dice [0.6268, 0.0, 0.5837, 0.6918, 0.2105, 0.8191, 0.5956] +2026-04-14 09:02:22.560370: Epoch time: 101.23 s +2026-04-14 09:02:23.759010: +2026-04-14 09:02:23.760944: Epoch 3310 +2026-04-14 09:02:23.762762: Current learning rate: 0.00206 +2026-04-14 09:04:05.072263: train_loss -0.4458 +2026-04-14 09:04:05.077810: val_loss -0.3929 +2026-04-14 09:04:05.080015: Pseudo dice [0.2554, 0.0, 0.7816, 0.8184, 0.4121, 0.8292, 0.8292] +2026-04-14 09:04:05.081940: Epoch time: 101.32 s +2026-04-14 09:04:06.290238: +2026-04-14 09:04:06.292181: Epoch 3311 +2026-04-14 09:04:06.294561: Current learning rate: 0.00205 +2026-04-14 09:05:47.648787: train_loss -0.4536 +2026-04-14 09:05:47.653111: val_loss -0.3863 +2026-04-14 09:05:47.655115: Pseudo dice [0.4945, 0.0, 0.626, 0.5548, 0.1532, 0.4625, 0.7774] +2026-04-14 09:05:47.656730: Epoch time: 101.36 s +2026-04-14 09:05:48.855797: +2026-04-14 09:05:48.857325: Epoch 3312 +2026-04-14 09:05:48.858969: Current learning rate: 0.00205 +2026-04-14 09:07:30.142978: train_loss -0.4541 +2026-04-14 09:07:30.151043: val_loss -0.3393 +2026-04-14 09:07:30.153479: Pseudo dice [0.6361, 0.0, 0.647, 0.3617, 0.2583, 0.5721, 0.6785] +2026-04-14 09:07:30.155370: Epoch time: 101.29 s +2026-04-14 09:07:31.364232: +2026-04-14 09:07:31.365820: Epoch 3313 +2026-04-14 09:07:31.367676: Current learning rate: 0.00205 +2026-04-14 09:09:12.842287: train_loss -0.4533 +2026-04-14 09:09:12.848775: val_loss -0.3582 +2026-04-14 09:09:12.850645: Pseudo dice [0.8142, 0.0, 0.7373, 0.8472, 0.1447, 0.647, 0.8466] +2026-04-14 09:09:12.852560: Epoch time: 101.48 s +2026-04-14 09:09:14.047766: +2026-04-14 09:09:14.049824: Epoch 3314 +2026-04-14 09:09:14.052076: Current learning rate: 0.00205 +2026-04-14 09:10:55.732192: train_loss -0.4566 +2026-04-14 09:10:55.737113: val_loss -0.3752 +2026-04-14 09:10:55.738816: Pseudo dice [0.599, 0.0, 0.8066, 0.8447, 0.1954, 0.6811, 0.8249] +2026-04-14 09:10:55.740347: Epoch time: 101.69 s +2026-04-14 09:10:56.974874: +2026-04-14 09:10:56.976643: Epoch 3315 +2026-04-14 09:10:56.978461: Current learning rate: 0.00204 +2026-04-14 09:12:38.438442: train_loss -0.4435 +2026-04-14 09:12:38.443881: val_loss -0.3347 +2026-04-14 09:12:38.445645: Pseudo dice [0.3886, 0.0, 0.4969, 0.8018, 0.4606, 0.6778, 0.2896] +2026-04-14 09:12:38.447106: Epoch time: 101.47 s +2026-04-14 09:12:39.661522: +2026-04-14 09:12:39.663143: Epoch 3316 +2026-04-14 09:12:39.664948: Current learning rate: 0.00204 +2026-04-14 09:14:21.228433: train_loss -0.4289 +2026-04-14 09:14:21.233659: val_loss -0.3488 +2026-04-14 09:14:21.235877: Pseudo dice [0.2871, 0.0, 0.727, 0.8356, 0.3725, 0.4364, 0.7062] +2026-04-14 09:14:21.237696: Epoch time: 101.57 s +2026-04-14 09:14:22.437223: +2026-04-14 09:14:22.438704: Epoch 3317 +2026-04-14 09:14:22.440756: Current learning rate: 0.00204 +2026-04-14 09:16:04.258784: train_loss -0.4332 +2026-04-14 09:16:04.263880: val_loss -0.3729 +2026-04-14 09:16:04.265233: Pseudo dice [0.6723, 0.0, 0.6313, 0.2496, 0.4393, 0.502, 0.7635] +2026-04-14 09:16:04.266567: Epoch time: 101.82 s +2026-04-14 09:16:05.471608: +2026-04-14 09:16:05.473335: Epoch 3318 +2026-04-14 09:16:05.475260: Current learning rate: 0.00203 +2026-04-14 09:17:46.938561: train_loss -0.4378 +2026-04-14 09:17:46.943517: val_loss -0.3982 +2026-04-14 09:17:46.945188: Pseudo dice [0.3859, 0.0, 0.758, 0.8568, 0.5567, 0.7733, 0.8277] +2026-04-14 09:17:46.947458: Epoch time: 101.47 s +2026-04-14 09:17:48.153829: +2026-04-14 09:17:48.155522: Epoch 3319 +2026-04-14 09:17:48.157655: Current learning rate: 0.00203 +2026-04-14 09:19:29.816469: train_loss -0.444 +2026-04-14 09:19:29.822753: val_loss -0.387 +2026-04-14 09:19:29.824125: Pseudo dice [0.8397, 0.0, 0.7568, 0.341, 0.4299, 0.8381, 0.5187] +2026-04-14 09:19:29.825490: Epoch time: 101.67 s +2026-04-14 09:19:31.044659: +2026-04-14 09:19:31.046402: Epoch 3320 +2026-04-14 09:19:31.048235: Current learning rate: 0.00203 +2026-04-14 09:21:12.634999: train_loss -0.4547 +2026-04-14 09:21:12.640522: val_loss -0.379 +2026-04-14 09:21:12.642719: Pseudo dice [0.7512, 0.0, 0.464, 0.165, 0.2696, 0.6877, 0.8266] +2026-04-14 09:21:12.644384: Epoch time: 101.59 s +2026-04-14 09:21:13.850461: +2026-04-14 09:21:13.852185: Epoch 3321 +2026-04-14 09:21:13.854180: Current learning rate: 0.00203 +2026-04-14 09:22:56.689809: train_loss -0.4297 +2026-04-14 09:22:56.694473: val_loss -0.3789 +2026-04-14 09:22:56.696435: Pseudo dice [0.2109, 0.0, 0.7523, 0.7582, 0.4205, 0.564, 0.8303] +2026-04-14 09:22:56.697934: Epoch time: 102.84 s +2026-04-14 09:22:57.892216: +2026-04-14 09:22:57.894153: Epoch 3322 +2026-04-14 09:22:57.895948: Current learning rate: 0.00202 +2026-04-14 09:24:39.580925: train_loss -0.4471 +2026-04-14 09:24:39.585618: val_loss -0.3893 +2026-04-14 09:24:39.587198: Pseudo dice [0.5016, 0.0, 0.8532, 0.4325, 0.4974, 0.8333, 0.5273] +2026-04-14 09:24:39.588914: Epoch time: 101.69 s +2026-04-14 09:24:40.791953: +2026-04-14 09:24:40.793394: Epoch 3323 +2026-04-14 09:24:40.795140: Current learning rate: 0.00202 +2026-04-14 09:26:22.403838: train_loss -0.465 +2026-04-14 09:26:22.408915: val_loss -0.3925 +2026-04-14 09:26:22.410444: Pseudo dice [0.3926, 0.0, 0.2315, 0.8037, 0.5405, 0.7527, 0.8792] +2026-04-14 09:26:22.412409: Epoch time: 101.61 s +2026-04-14 09:26:23.619147: +2026-04-14 09:26:23.621602: Epoch 3324 +2026-04-14 09:26:23.623893: Current learning rate: 0.00202 +2026-04-14 09:28:05.403139: train_loss -0.4359 +2026-04-14 09:28:05.408181: val_loss -0.3906 +2026-04-14 09:28:05.411937: Pseudo dice [0.7885, 0.0, 0.5126, 0.5395, 0.3307, 0.653, 0.8324] +2026-04-14 09:28:05.413660: Epoch time: 101.79 s +2026-04-14 09:28:06.619949: +2026-04-14 09:28:06.621367: Epoch 3325 +2026-04-14 09:28:06.622896: Current learning rate: 0.00202 +2026-04-14 09:29:48.290076: train_loss -0.4482 +2026-04-14 09:29:48.296388: val_loss -0.392 +2026-04-14 09:29:48.300525: Pseudo dice [0.7296, 0.0, 0.7708, 0.8144, 0.392, 0.841, 0.7524] +2026-04-14 09:29:48.302170: Epoch time: 101.67 s +2026-04-14 09:29:49.518728: +2026-04-14 09:29:49.529973: Epoch 3326 +2026-04-14 09:29:49.533166: Current learning rate: 0.00201 +2026-04-14 09:31:31.266971: train_loss -0.4408 +2026-04-14 09:31:31.271780: val_loss -0.406 +2026-04-14 09:31:31.273299: Pseudo dice [0.7789, 0.0, 0.806, 0.7917, 0.6744, 0.7659, 0.5735] +2026-04-14 09:31:31.274589: Epoch time: 101.75 s +2026-04-14 09:31:32.484944: +2026-04-14 09:31:32.486424: Epoch 3327 +2026-04-14 09:31:32.488214: Current learning rate: 0.00201 +2026-04-14 09:33:14.233264: train_loss -0.4501 +2026-04-14 09:33:14.238221: val_loss -0.3932 +2026-04-14 09:33:14.239714: Pseudo dice [0.544, 0.0, 0.845, 0.8717, 0.6083, 0.7572, 0.8947] +2026-04-14 09:33:14.240971: Epoch time: 101.75 s +2026-04-14 09:33:15.438359: +2026-04-14 09:33:15.440089: Epoch 3328 +2026-04-14 09:33:15.442317: Current learning rate: 0.00201 +2026-04-14 09:34:57.095664: train_loss -0.4513 +2026-04-14 09:34:57.101413: val_loss -0.3885 +2026-04-14 09:34:57.102957: Pseudo dice [0.3483, 0.0, 0.7373, 0.6062, 0.5561, 0.2629, 0.7769] +2026-04-14 09:34:57.104340: Epoch time: 101.66 s +2026-04-14 09:34:58.306669: +2026-04-14 09:34:58.308423: Epoch 3329 +2026-04-14 09:34:58.310211: Current learning rate: 0.00201 +2026-04-14 09:36:40.054941: train_loss -0.4453 +2026-04-14 09:36:40.064629: val_loss -0.3951 +2026-04-14 09:36:40.067209: Pseudo dice [0.2768, 0.0, 0.7712, 0.8445, 0.6312, 0.4839, 0.8737] +2026-04-14 09:36:40.068972: Epoch time: 101.75 s +2026-04-14 09:36:41.284426: +2026-04-14 09:36:41.286775: Epoch 3330 +2026-04-14 09:36:41.288388: Current learning rate: 0.002 +2026-04-14 09:38:23.029264: train_loss -0.4514 +2026-04-14 09:38:23.033817: val_loss -0.338 +2026-04-14 09:38:23.035498: Pseudo dice [0.6823, 0.0, 0.2794, 0.8473, 0.2415, 0.7023, 0.8481] +2026-04-14 09:38:23.037044: Epoch time: 101.75 s +2026-04-14 09:38:24.240854: +2026-04-14 09:38:24.242985: Epoch 3331 +2026-04-14 09:38:24.244352: Current learning rate: 0.002 +2026-04-14 09:40:06.033122: train_loss -0.4419 +2026-04-14 09:40:06.037438: val_loss -0.3908 +2026-04-14 09:40:06.038834: Pseudo dice [0.7255, 0.0, 0.7414, 0.267, 0.2548, 0.5701, 0.5673] +2026-04-14 09:40:06.040047: Epoch time: 101.8 s +2026-04-14 09:40:07.231666: +2026-04-14 09:40:07.233066: Epoch 3332 +2026-04-14 09:40:07.234361: Current learning rate: 0.002 +2026-04-14 09:41:48.844047: train_loss -0.4313 +2026-04-14 09:41:48.848051: val_loss -0.3693 +2026-04-14 09:41:48.849927: Pseudo dice [0.4093, 0.0, 0.5628, 0.8846, 0.4794, 0.8104, 0.6923] +2026-04-14 09:41:48.851659: Epoch time: 101.62 s +2026-04-14 09:41:50.063027: +2026-04-14 09:41:50.064651: Epoch 3333 +2026-04-14 09:41:50.066085: Current learning rate: 0.00199 +2026-04-14 09:43:31.631407: train_loss -0.4506 +2026-04-14 09:43:31.636209: val_loss -0.3923 +2026-04-14 09:43:31.637820: Pseudo dice [0.3091, 0.0, 0.8004, 0.3187, 0.4742, 0.8648, 0.8772] +2026-04-14 09:43:31.639436: Epoch time: 101.57 s +2026-04-14 09:43:32.858333: +2026-04-14 09:43:32.860126: Epoch 3334 +2026-04-14 09:43:32.861731: Current learning rate: 0.00199 +2026-04-14 09:45:14.706323: train_loss -0.4537 +2026-04-14 09:45:14.710373: val_loss -0.4038 +2026-04-14 09:45:14.711819: Pseudo dice [0.2825, 0.0, 0.7695, 0.7763, 0.5885, 0.8168, 0.8258] +2026-04-14 09:45:14.713338: Epoch time: 101.85 s +2026-04-14 09:45:15.953942: +2026-04-14 09:45:15.955641: Epoch 3335 +2026-04-14 09:45:15.956965: Current learning rate: 0.00199 +2026-04-14 09:46:57.655242: train_loss -0.465 +2026-04-14 09:46:57.660910: val_loss -0.374 +2026-04-14 09:46:57.662861: Pseudo dice [0.308, 0.0, 0.7881, 0.9289, 0.3965, 0.7819, 0.5999] +2026-04-14 09:46:57.664686: Epoch time: 101.7 s +2026-04-14 09:46:58.878139: +2026-04-14 09:46:58.879614: Epoch 3336 +2026-04-14 09:46:58.880915: Current learning rate: 0.00199 +2026-04-14 09:48:40.740443: train_loss -0.4607 +2026-04-14 09:48:40.745826: val_loss -0.4132 +2026-04-14 09:48:40.747616: Pseudo dice [0.6553, 0.0, 0.776, 0.8372, 0.3468, 0.5455, 0.9352] +2026-04-14 09:48:40.750184: Epoch time: 101.87 s +2026-04-14 09:48:41.983775: +2026-04-14 09:48:41.985519: Epoch 3337 +2026-04-14 09:48:41.987041: Current learning rate: 0.00198 +2026-04-14 09:50:23.737296: train_loss -0.4553 +2026-04-14 09:50:23.741937: val_loss -0.3833 +2026-04-14 09:50:23.743423: Pseudo dice [0.3013, 0.0, 0.7022, 0.6647, 0.2112, 0.8217, 0.9186] +2026-04-14 09:50:23.744664: Epoch time: 101.76 s +2026-04-14 09:50:24.956890: +2026-04-14 09:50:24.958441: Epoch 3338 +2026-04-14 09:50:24.959953: Current learning rate: 0.00198 +2026-04-14 09:52:06.704744: train_loss -0.4565 +2026-04-14 09:52:06.708761: val_loss -0.388 +2026-04-14 09:52:06.710395: Pseudo dice [0.4084, 0.0, 0.5543, 0.2036, 0.3515, 0.8075, 0.7735] +2026-04-14 09:52:06.711675: Epoch time: 101.75 s +2026-04-14 09:52:07.931762: +2026-04-14 09:52:07.933128: Epoch 3339 +2026-04-14 09:52:07.934299: Current learning rate: 0.00198 +2026-04-14 09:53:49.593484: train_loss -0.4615 +2026-04-14 09:53:49.599642: val_loss -0.374 +2026-04-14 09:53:49.601381: Pseudo dice [0.4263, 0.0, 0.7463, 0.4197, 0.3531, 0.7325, 0.819] +2026-04-14 09:53:49.603124: Epoch time: 101.66 s +2026-04-14 09:53:50.823793: +2026-04-14 09:53:50.825313: Epoch 3340 +2026-04-14 09:53:50.826802: Current learning rate: 0.00198 +2026-04-14 09:55:32.503257: train_loss -0.4567 +2026-04-14 09:55:32.509390: val_loss -0.3778 +2026-04-14 09:55:32.510772: Pseudo dice [0.525, 0.0, 0.6294, 0.7596, 0.4896, 0.6858, 0.6103] +2026-04-14 09:55:32.512389: Epoch time: 101.68 s +2026-04-14 09:55:34.733376: +2026-04-14 09:55:34.735112: Epoch 3341 +2026-04-14 09:55:34.737422: Current learning rate: 0.00197 +2026-04-14 09:57:16.314682: train_loss -0.4553 +2026-04-14 09:57:16.319013: val_loss -0.3995 +2026-04-14 09:57:16.320803: Pseudo dice [0.7191, 0.0, 0.7809, 0.8887, 0.3314, 0.7632, 0.8159] +2026-04-14 09:57:16.322899: Epoch time: 101.58 s +2026-04-14 09:57:17.544439: +2026-04-14 09:57:17.545862: Epoch 3342 +2026-04-14 09:57:17.547241: Current learning rate: 0.00197 +2026-04-14 09:58:59.298594: train_loss -0.4612 +2026-04-14 09:58:59.304070: val_loss -0.3915 +2026-04-14 09:58:59.305862: Pseudo dice [0.4605, 0.0, 0.4683, 0.9018, 0.3419, 0.8573, 0.6776] +2026-04-14 09:58:59.307307: Epoch time: 101.76 s +2026-04-14 09:59:00.536840: +2026-04-14 09:59:00.538223: Epoch 3343 +2026-04-14 09:59:00.539480: Current learning rate: 0.00197 +2026-04-14 10:00:42.197308: train_loss -0.4645 +2026-04-14 10:00:42.202479: val_loss -0.3982 +2026-04-14 10:00:42.203872: Pseudo dice [0.5741, 0.0, 0.6832, 0.7622, 0.2596, 0.8391, 0.9112] +2026-04-14 10:00:42.205320: Epoch time: 101.66 s +2026-04-14 10:00:43.428627: +2026-04-14 10:00:43.430726: Epoch 3344 +2026-04-14 10:00:43.432498: Current learning rate: 0.00196 +2026-04-14 10:02:24.988551: train_loss -0.4659 +2026-04-14 10:02:24.992377: val_loss -0.3733 +2026-04-14 10:02:24.993910: Pseudo dice [0.1787, 0.0, 0.7213, 0.8432, 0.2348, 0.8593, 0.5537] +2026-04-14 10:02:24.995195: Epoch time: 101.56 s +2026-04-14 10:02:26.214585: +2026-04-14 10:02:26.215995: Epoch 3345 +2026-04-14 10:02:26.217274: Current learning rate: 0.00196 +2026-04-14 10:04:07.984683: train_loss -0.46 +2026-04-14 10:04:07.988790: val_loss -0.3771 +2026-04-14 10:04:07.990079: Pseudo dice [0.5529, 0.0, 0.6999, 0.7859, 0.317, 0.8176, 0.4828] +2026-04-14 10:04:07.992157: Epoch time: 101.77 s +2026-04-14 10:04:09.226513: +2026-04-14 10:04:09.227964: Epoch 3346 +2026-04-14 10:04:09.229227: Current learning rate: 0.00196 +2026-04-14 10:05:50.979652: train_loss -0.4611 +2026-04-14 10:05:50.984238: val_loss -0.3808 +2026-04-14 10:05:50.988675: Pseudo dice [0.3689, 0.0, 0.7085, 0.6054, 0.4165, 0.8159, 0.8403] +2026-04-14 10:05:50.990716: Epoch time: 101.76 s +2026-04-14 10:05:52.229053: +2026-04-14 10:05:52.230522: Epoch 3347 +2026-04-14 10:05:52.231942: Current learning rate: 0.00196 +2026-04-14 10:07:34.156106: train_loss -0.4292 +2026-04-14 10:07:34.167332: val_loss -0.3717 +2026-04-14 10:07:34.168612: Pseudo dice [0.6059, 0.0, 0.8448, 0.3769, 0.5962, 0.8901, 0.4045] +2026-04-14 10:07:34.169913: Epoch time: 101.93 s +2026-04-14 10:07:35.398979: +2026-04-14 10:07:35.400503: Epoch 3348 +2026-04-14 10:07:35.401794: Current learning rate: 0.00195 +2026-04-14 10:09:17.281306: train_loss -0.4415 +2026-04-14 10:09:17.287456: val_loss -0.3452 +2026-04-14 10:09:17.289017: Pseudo dice [0.4236, 0.0, 0.5076, 0.8156, 0.3972, 0.8656, 0.5884] +2026-04-14 10:09:17.290415: Epoch time: 101.89 s +2026-04-14 10:09:18.532662: +2026-04-14 10:09:18.534396: Epoch 3349 +2026-04-14 10:09:18.535605: Current learning rate: 0.00195 +2026-04-14 10:11:00.307685: train_loss -0.4587 +2026-04-14 10:11:00.312738: val_loss -0.393 +2026-04-14 10:11:00.315884: Pseudo dice [0.3078, 0.0, 0.6344, 0.8315, 0.5971, 0.5682, 0.7971] +2026-04-14 10:11:00.317564: Epoch time: 101.78 s +2026-04-14 10:11:03.335714: +2026-04-14 10:11:03.337558: Epoch 3350 +2026-04-14 10:11:03.339177: Current learning rate: 0.00195 +2026-04-14 10:12:45.027642: train_loss -0.4264 +2026-04-14 10:12:45.035468: val_loss -0.3504 +2026-04-14 10:12:45.037222: Pseudo dice [0.289, 0.0, 0.263, 0.1716, 0.4926, 0.6987, 0.8797] +2026-04-14 10:12:45.038870: Epoch time: 101.69 s +2026-04-14 10:12:46.303956: +2026-04-14 10:12:46.305824: Epoch 3351 +2026-04-14 10:12:46.307477: Current learning rate: 0.00195 +2026-04-14 10:14:27.972491: train_loss -0.4594 +2026-04-14 10:14:27.977807: val_loss -0.4008 +2026-04-14 10:14:27.979249: Pseudo dice [0.6702, 0.0, 0.6716, 0.6479, 0.3645, 0.7489, 0.8199] +2026-04-14 10:14:27.980497: Epoch time: 101.67 s +2026-04-14 10:14:29.206890: +2026-04-14 10:14:29.208216: Epoch 3352 +2026-04-14 10:14:29.209471: Current learning rate: 0.00194 +2026-04-14 10:16:10.934489: train_loss -0.4418 +2026-04-14 10:16:10.939668: val_loss -0.382 +2026-04-14 10:16:10.941160: Pseudo dice [0.2472, 0.0, 0.7139, 0.7577, 0.5138, 0.2471, 0.8792] +2026-04-14 10:16:10.942950: Epoch time: 101.73 s +2026-04-14 10:16:12.176570: +2026-04-14 10:16:12.179938: Epoch 3353 +2026-04-14 10:16:12.181186: Current learning rate: 0.00194 +2026-04-14 10:17:53.952796: train_loss -0.4498 +2026-04-14 10:17:53.957310: val_loss -0.3996 +2026-04-14 10:17:53.959253: Pseudo dice [0.4823, 0.0, 0.7048, 0.7794, 0.142, 0.76, 0.8263] +2026-04-14 10:17:53.960788: Epoch time: 101.78 s +2026-04-14 10:17:55.186889: +2026-04-14 10:17:55.188392: Epoch 3354 +2026-04-14 10:17:55.189804: Current learning rate: 0.00194 +2026-04-14 10:19:37.147915: train_loss -0.4325 +2026-04-14 10:19:37.153020: val_loss -0.3525 +2026-04-14 10:19:37.154498: Pseudo dice [0.7525, 0.0, 0.6532, 0.7399, 0.3371, 0.6854, 0.5336] +2026-04-14 10:19:37.156026: Epoch time: 101.96 s +2026-04-14 10:19:38.378668: +2026-04-14 10:19:38.380225: Epoch 3355 +2026-04-14 10:19:38.381513: Current learning rate: 0.00194 +2026-04-14 10:21:19.982371: train_loss -0.4329 +2026-04-14 10:21:19.987034: val_loss -0.359 +2026-04-14 10:21:19.988668: Pseudo dice [0.5476, 0.0, 0.7824, 0.6375, 0.3058, 0.7671, 0.6882] +2026-04-14 10:21:19.990651: Epoch time: 101.61 s +2026-04-14 10:21:21.218026: +2026-04-14 10:21:21.219596: Epoch 3356 +2026-04-14 10:21:21.221096: Current learning rate: 0.00193 +2026-04-14 10:23:02.876380: train_loss -0.4458 +2026-04-14 10:23:02.882043: val_loss -0.3877 +2026-04-14 10:23:02.885098: Pseudo dice [0.474, 0.0, 0.8287, 0.9189, 0.5267, 0.8255, 0.5399] +2026-04-14 10:23:02.886859: Epoch time: 101.66 s +2026-04-14 10:23:04.132244: +2026-04-14 10:23:04.133885: Epoch 3357 +2026-04-14 10:23:04.135251: Current learning rate: 0.00193 +2026-04-14 10:24:45.958842: train_loss -0.4343 +2026-04-14 10:24:45.964290: val_loss -0.3719 +2026-04-14 10:24:45.965778: Pseudo dice [0.4556, 0.0, 0.7368, 0.6155, 0.4221, 0.721, 0.8035] +2026-04-14 10:24:45.967864: Epoch time: 101.83 s +2026-04-14 10:24:47.207840: +2026-04-14 10:24:47.209738: Epoch 3358 +2026-04-14 10:24:47.211431: Current learning rate: 0.00193 +2026-04-14 10:26:29.060228: train_loss -0.4529 +2026-04-14 10:26:29.065191: val_loss -0.4024 +2026-04-14 10:26:29.066822: Pseudo dice [0.3529, 0.0, 0.7864, 0.7798, 0.5037, 0.8053, 0.8692] +2026-04-14 10:26:29.068317: Epoch time: 101.86 s +2026-04-14 10:26:30.288652: +2026-04-14 10:26:30.291088: Epoch 3359 +2026-04-14 10:26:30.292357: Current learning rate: 0.00192 +2026-04-14 10:28:12.038653: train_loss -0.455 +2026-04-14 10:28:12.043964: val_loss -0.4004 +2026-04-14 10:28:12.045552: Pseudo dice [0.6218, 0.0, 0.6335, 0.4482, 0.7141, 0.7675, 0.9188] +2026-04-14 10:28:12.047081: Epoch time: 101.75 s +2026-04-14 10:28:13.279923: +2026-04-14 10:28:13.281448: Epoch 3360 +2026-04-14 10:28:13.282806: Current learning rate: 0.00192 +2026-04-14 10:29:55.048058: train_loss -0.444 +2026-04-14 10:29:55.053165: val_loss -0.4049 +2026-04-14 10:29:55.054773: Pseudo dice [0.475, 0.0, 0.7593, 0.8322, 0.578, 0.7613, 0.7923] +2026-04-14 10:29:55.056274: Epoch time: 101.77 s +2026-04-14 10:29:57.339381: +2026-04-14 10:29:57.340777: Epoch 3361 +2026-04-14 10:29:57.342137: Current learning rate: 0.00192 +2026-04-14 10:31:39.009799: train_loss -0.4196 +2026-04-14 10:31:39.017465: val_loss -0.3426 +2026-04-14 10:31:39.019405: Pseudo dice [0.3952, 0.0, 0.7739, 0.3237, 0.2266, 0.4448, 0.7758] +2026-04-14 10:31:39.020902: Epoch time: 101.67 s +2026-04-14 10:31:40.253412: +2026-04-14 10:31:40.255097: Epoch 3362 +2026-04-14 10:31:40.256340: Current learning rate: 0.00192 +2026-04-14 10:33:21.897445: train_loss -0.4487 +2026-04-14 10:33:21.903572: val_loss -0.3902 +2026-04-14 10:33:21.905228: Pseudo dice [0.723, 0.0, 0.6479, 0.781, 0.4474, 0.7268, 0.9268] +2026-04-14 10:33:21.906784: Epoch time: 101.65 s +2026-04-14 10:33:23.125729: +2026-04-14 10:33:23.127250: Epoch 3363 +2026-04-14 10:33:23.128774: Current learning rate: 0.00191 +2026-04-14 10:35:04.853435: train_loss -0.4474 +2026-04-14 10:35:04.860041: val_loss -0.4107 +2026-04-14 10:35:04.861690: Pseudo dice [0.5186, 0.0, 0.6603, 0.8548, 0.5269, 0.6249, 0.874] +2026-04-14 10:35:04.863109: Epoch time: 101.73 s +2026-04-14 10:35:06.101649: +2026-04-14 10:35:06.103210: Epoch 3364 +2026-04-14 10:35:06.104716: Current learning rate: 0.00191 +2026-04-14 10:36:47.839456: train_loss -0.4471 +2026-04-14 10:36:47.844919: val_loss -0.3917 +2026-04-14 10:36:47.846289: Pseudo dice [0.4836, 0.0, 0.6593, 0.8, 0.2708, 0.7743, 0.653] +2026-04-14 10:36:47.847576: Epoch time: 101.74 s +2026-04-14 10:36:49.068788: +2026-04-14 10:36:49.070720: Epoch 3365 +2026-04-14 10:36:49.072096: Current learning rate: 0.00191 +2026-04-14 10:38:30.876917: train_loss -0.4445 +2026-04-14 10:38:30.881691: val_loss -0.3574 +2026-04-14 10:38:30.886943: Pseudo dice [0.3031, 0.0, 0.7385, 0.0608, 0.2002, 0.6313, 0.8398] +2026-04-14 10:38:30.888777: Epoch time: 101.81 s +2026-04-14 10:38:32.143666: +2026-04-14 10:38:32.145004: Epoch 3366 +2026-04-14 10:38:32.146196: Current learning rate: 0.00191 +2026-04-14 10:40:14.242784: train_loss -0.4513 +2026-04-14 10:40:14.249429: val_loss -0.4029 +2026-04-14 10:40:14.250861: Pseudo dice [0.4746, 0.0, 0.7766, 0.7375, 0.5001, 0.5988, 0.8699] +2026-04-14 10:40:14.252104: Epoch time: 102.1 s +2026-04-14 10:40:15.499054: +2026-04-14 10:40:15.500941: Epoch 3367 +2026-04-14 10:40:15.502430: Current learning rate: 0.0019 +2026-04-14 10:41:57.239245: train_loss -0.4291 +2026-04-14 10:41:57.246927: val_loss -0.406 +2026-04-14 10:41:57.250365: Pseudo dice [0.2826, 0.0, 0.8301, 0.8635, 0.525, 0.6792, 0.8388] +2026-04-14 10:41:57.251656: Epoch time: 101.74 s +2026-04-14 10:41:58.485431: +2026-04-14 10:41:58.486934: Epoch 3368 +2026-04-14 10:41:58.488111: Current learning rate: 0.0019 +2026-04-14 10:43:40.284465: train_loss -0.4585 +2026-04-14 10:43:40.291300: val_loss -0.3986 +2026-04-14 10:43:40.293236: Pseudo dice [0.4933, 0.0, 0.7406, 0.8451, 0.343, 0.7998, 0.8901] +2026-04-14 10:43:40.294858: Epoch time: 101.8 s +2026-04-14 10:43:41.535393: +2026-04-14 10:43:41.537653: Epoch 3369 +2026-04-14 10:43:41.539037: Current learning rate: 0.0019 +2026-04-14 10:45:23.241365: train_loss -0.4544 +2026-04-14 10:45:23.248850: val_loss -0.3402 +2026-04-14 10:45:23.250408: Pseudo dice [0.5599, 0.0, 0.675, 0.53, 0.0858, 0.2158, 0.89] +2026-04-14 10:45:23.251904: Epoch time: 101.71 s +2026-04-14 10:45:24.463838: +2026-04-14 10:45:24.465320: Epoch 3370 +2026-04-14 10:45:24.466698: Current learning rate: 0.00189 +2026-04-14 10:47:06.206518: train_loss -0.4446 +2026-04-14 10:47:06.211119: val_loss -0.3905 +2026-04-14 10:47:06.213046: Pseudo dice [0.5115, 0.0, 0.6618, 0.7714, 0.4148, 0.4632, 0.793] +2026-04-14 10:47:06.214381: Epoch time: 101.75 s +2026-04-14 10:47:07.442289: +2026-04-14 10:47:07.443968: Epoch 3371 +2026-04-14 10:47:07.445280: Current learning rate: 0.00189 +2026-04-14 10:48:49.140709: train_loss -0.4615 +2026-04-14 10:48:49.145394: val_loss -0.3473 +2026-04-14 10:48:49.147053: Pseudo dice [0.6451, 0.0, 0.6867, 0.3113, 0.4037, 0.7646, 0.8277] +2026-04-14 10:48:49.148480: Epoch time: 101.7 s +2026-04-14 10:48:50.378626: +2026-04-14 10:48:50.380218: Epoch 3372 +2026-04-14 10:48:50.381712: Current learning rate: 0.00189 +2026-04-14 10:50:32.088226: train_loss -0.4673 +2026-04-14 10:50:32.094241: val_loss -0.4076 +2026-04-14 10:50:32.096106: Pseudo dice [0.5871, 0.0, 0.5717, 0.751, 0.525, 0.4415, 0.86] +2026-04-14 10:50:32.097629: Epoch time: 101.71 s +2026-04-14 10:50:33.340095: +2026-04-14 10:50:33.342121: Epoch 3373 +2026-04-14 10:50:33.344106: Current learning rate: 0.00189 +2026-04-14 10:52:14.951577: train_loss -0.4556 +2026-04-14 10:52:14.962522: val_loss -0.3593 +2026-04-14 10:52:14.971820: Pseudo dice [0.8325, 0.0, 0.6972, 0.2742, 0.2081, 0.6964, 0.4494] +2026-04-14 10:52:14.973165: Epoch time: 101.61 s +2026-04-14 10:52:16.201873: +2026-04-14 10:52:16.203989: Epoch 3374 +2026-04-14 10:52:16.205360: Current learning rate: 0.00188 +2026-04-14 10:53:58.044799: train_loss -0.464 +2026-04-14 10:53:58.049271: val_loss -0.3804 +2026-04-14 10:53:58.050609: Pseudo dice [0.1993, 0.0, 0.705, 0.8448, 0.6316, 0.8233, 0.657] +2026-04-14 10:53:58.052023: Epoch time: 101.85 s +2026-04-14 10:53:59.274868: +2026-04-14 10:53:59.276253: Epoch 3375 +2026-04-14 10:53:59.277504: Current learning rate: 0.00188 +2026-04-14 10:55:40.998209: train_loss -0.4696 +2026-04-14 10:55:41.003431: val_loss -0.4097 +2026-04-14 10:55:41.005026: Pseudo dice [0.7058, 0.0, 0.8145, 0.7448, 0.337, 0.6391, 0.8281] +2026-04-14 10:55:41.006624: Epoch time: 101.73 s +2026-04-14 10:55:42.237391: +2026-04-14 10:55:42.238991: Epoch 3376 +2026-04-14 10:55:42.240378: Current learning rate: 0.00188 +2026-04-14 10:57:24.043624: train_loss -0.4593 +2026-04-14 10:57:24.049692: val_loss -0.376 +2026-04-14 10:57:24.051374: Pseudo dice [0.2298, 0.0, 0.7937, 0.8544, 0.4846, 0.7979, 0.8853] +2026-04-14 10:57:24.052953: Epoch time: 101.81 s +2026-04-14 10:57:25.269793: +2026-04-14 10:57:25.271300: Epoch 3377 +2026-04-14 10:57:25.272866: Current learning rate: 0.00188 +2026-04-14 10:59:07.155361: train_loss -0.455 +2026-04-14 10:59:07.160582: val_loss -0.4034 +2026-04-14 10:59:07.162281: Pseudo dice [0.62, 0.0, 0.7836, 0.852, 0.715, 0.7812, 0.7367] +2026-04-14 10:59:07.163803: Epoch time: 101.89 s +2026-04-14 10:59:08.393260: +2026-04-14 10:59:08.394704: Epoch 3378 +2026-04-14 10:59:08.396083: Current learning rate: 0.00187 +2026-04-14 11:00:50.249899: train_loss -0.4648 +2026-04-14 11:00:50.254644: val_loss -0.4195 +2026-04-14 11:00:50.256396: Pseudo dice [0.793, 0.0, 0.6708, 0.7481, 0.717, 0.8836, 0.8341] +2026-04-14 11:00:50.258135: Epoch time: 101.86 s +2026-04-14 11:00:51.478088: +2026-04-14 11:00:51.479321: Epoch 3379 +2026-04-14 11:00:51.480698: Current learning rate: 0.00187 +2026-04-14 11:02:33.316479: train_loss -0.4643 +2026-04-14 11:02:33.320363: val_loss -0.4041 +2026-04-14 11:02:33.322415: Pseudo dice [0.5476, 0.0, 0.7525, 0.1491, 0.4859, 0.6843, 0.8509] +2026-04-14 11:02:33.323753: Epoch time: 101.84 s +2026-04-14 11:02:35.413372: +2026-04-14 11:02:35.415866: Epoch 3380 +2026-04-14 11:02:35.417794: Current learning rate: 0.00187 +2026-04-14 11:04:17.051744: train_loss -0.4343 +2026-04-14 11:04:17.056111: val_loss -0.3754 +2026-04-14 11:04:17.057793: Pseudo dice [0.412, 0.0, 0.7237, 0.7431, 0.5652, 0.6871, 0.6494] +2026-04-14 11:04:17.059103: Epoch time: 101.64 s +2026-04-14 11:04:18.278876: +2026-04-14 11:04:18.280162: Epoch 3381 +2026-04-14 11:04:18.281390: Current learning rate: 0.00186 +2026-04-14 11:05:59.961951: train_loss -0.4597 +2026-04-14 11:05:59.966213: val_loss -0.4073 +2026-04-14 11:05:59.968059: Pseudo dice [0.4522, 0.0, 0.8171, 0.4435, 0.5889, 0.7991, 0.8573] +2026-04-14 11:05:59.970149: Epoch time: 101.69 s +2026-04-14 11:06:01.198593: +2026-04-14 11:06:01.200032: Epoch 3382 +2026-04-14 11:06:01.201126: Current learning rate: 0.00186 +2026-04-14 11:07:42.412925: train_loss -0.4643 +2026-04-14 11:07:42.417429: val_loss -0.3818 +2026-04-14 11:07:42.418879: Pseudo dice [0.5131, 0.0, 0.6386, 0.5425, 0.5867, 0.7713, 0.7133] +2026-04-14 11:07:42.420413: Epoch time: 101.22 s +2026-04-14 11:07:43.654435: +2026-04-14 11:07:43.655950: Epoch 3383 +2026-04-14 11:07:43.657216: Current learning rate: 0.00186 +2026-04-14 11:09:24.717943: train_loss -0.4593 +2026-04-14 11:09:24.723278: val_loss -0.3966 +2026-04-14 11:09:24.724877: Pseudo dice [0.5489, 0.0, 0.7482, 0.8364, 0.6319, 0.7163, 0.9003] +2026-04-14 11:09:24.727079: Epoch time: 101.07 s +2026-04-14 11:09:25.958643: +2026-04-14 11:09:25.961019: Epoch 3384 +2026-04-14 11:09:25.962683: Current learning rate: 0.00186 +2026-04-14 11:11:06.904716: train_loss -0.4661 +2026-04-14 11:11:06.910493: val_loss -0.3748 +2026-04-14 11:11:06.912493: Pseudo dice [0.3212, 0.0, 0.8104, 0.602, 0.4957, 0.7569, 0.644] +2026-04-14 11:11:06.914461: Epoch time: 100.95 s +2026-04-14 11:11:08.144834: +2026-04-14 11:11:08.146655: Epoch 3385 +2026-04-14 11:11:08.148062: Current learning rate: 0.00185 +2026-04-14 11:12:49.144103: train_loss -0.4534 +2026-04-14 11:12:49.151154: val_loss -0.3933 +2026-04-14 11:12:49.153676: Pseudo dice [0.5122, 0.0, 0.3387, 0.8549, 0.4548, 0.7735, 0.7807] +2026-04-14 11:12:49.155412: Epoch time: 101.0 s +2026-04-14 11:12:50.377332: +2026-04-14 11:12:50.378951: Epoch 3386 +2026-04-14 11:12:50.380604: Current learning rate: 0.00185 +2026-04-14 11:14:31.334205: train_loss -0.4402 +2026-04-14 11:14:31.338719: val_loss -0.3804 +2026-04-14 11:14:31.340528: Pseudo dice [0.4958, 0.0, 0.7948, 0.2796, 0.5169, 0.7912, 0.7317] +2026-04-14 11:14:31.342313: Epoch time: 100.96 s +2026-04-14 11:14:32.553869: +2026-04-14 11:14:32.555487: Epoch 3387 +2026-04-14 11:14:32.557023: Current learning rate: 0.00185 +2026-04-14 11:16:13.797379: train_loss -0.4577 +2026-04-14 11:16:13.802151: val_loss -0.4269 +2026-04-14 11:16:13.803450: Pseudo dice [0.4183, 0.0, 0.8482, 0.8514, 0.6845, 0.8323, 0.8563] +2026-04-14 11:16:13.804712: Epoch time: 101.25 s +2026-04-14 11:16:15.018603: +2026-04-14 11:16:15.020062: Epoch 3388 +2026-04-14 11:16:15.021293: Current learning rate: 0.00185 +2026-04-14 11:17:56.100240: train_loss -0.4446 +2026-04-14 11:17:56.104485: val_loss -0.3676 +2026-04-14 11:17:56.106135: Pseudo dice [0.4073, 0.0, 0.7213, 0.5988, 0.4856, 0.8141, 0.8473] +2026-04-14 11:17:56.107290: Epoch time: 101.08 s +2026-04-14 11:17:57.343403: +2026-04-14 11:17:57.344801: Epoch 3389 +2026-04-14 11:17:57.346376: Current learning rate: 0.00184 +2026-04-14 11:19:38.504807: train_loss -0.4682 +2026-04-14 11:19:38.508722: val_loss -0.374 +2026-04-14 11:19:38.510332: Pseudo dice [0.4676, 0.0, 0.7428, 0.0003, 0.4303, 0.7798, 0.4934] +2026-04-14 11:19:38.511733: Epoch time: 101.16 s +2026-04-14 11:19:39.743755: +2026-04-14 11:19:39.745145: Epoch 3390 +2026-04-14 11:19:39.746673: Current learning rate: 0.00184 +2026-04-14 11:21:20.794382: train_loss -0.4592 +2026-04-14 11:21:20.799224: val_loss -0.3788 +2026-04-14 11:21:20.801110: Pseudo dice [0.2362, 0.0, 0.7244, 0.5846, 0.6339, 0.8159, 0.6067] +2026-04-14 11:21:20.802442: Epoch time: 101.05 s +2026-04-14 11:21:22.022593: +2026-04-14 11:21:22.026867: Epoch 3391 +2026-04-14 11:21:22.028298: Current learning rate: 0.00184 +2026-04-14 11:23:03.032389: train_loss -0.4449 +2026-04-14 11:23:03.038779: val_loss -0.3844 +2026-04-14 11:23:03.042152: Pseudo dice [0.3901, 0.0, 0.6265, 0.446, 0.6762, 0.7038, 0.7239] +2026-04-14 11:23:03.044137: Epoch time: 101.01 s +2026-04-14 11:23:04.259728: +2026-04-14 11:23:04.261959: Epoch 3392 +2026-04-14 11:23:04.264345: Current learning rate: 0.00184 +2026-04-14 11:24:45.328822: train_loss -0.4643 +2026-04-14 11:24:45.333465: val_loss -0.4223 +2026-04-14 11:24:45.335190: Pseudo dice [0.706, 0.0, 0.8224, 0.7316, 0.6029, 0.7874, 0.8566] +2026-04-14 11:24:45.337008: Epoch time: 101.07 s +2026-04-14 11:24:46.551642: +2026-04-14 11:24:46.553668: Epoch 3393 +2026-04-14 11:24:46.555357: Current learning rate: 0.00183 +2026-04-14 11:26:27.596107: train_loss -0.4489 +2026-04-14 11:26:27.600402: val_loss -0.3667 +2026-04-14 11:26:27.602530: Pseudo dice [0.4558, 0.0, 0.7888, 0.6788, 0.2247, 0.7462, 0.8524] +2026-04-14 11:26:27.604055: Epoch time: 101.05 s +2026-04-14 11:26:28.829938: +2026-04-14 11:26:28.831662: Epoch 3394 +2026-04-14 11:26:28.833422: Current learning rate: 0.00183 +2026-04-14 11:28:09.946260: train_loss -0.461 +2026-04-14 11:28:09.950925: val_loss -0.3954 +2026-04-14 11:28:09.952501: Pseudo dice [0.6837, 0.0, 0.732, 0.7775, 0.38, 0.7312, 0.7851] +2026-04-14 11:28:09.953936: Epoch time: 101.12 s +2026-04-14 11:28:11.174305: +2026-04-14 11:28:11.176183: Epoch 3395 +2026-04-14 11:28:11.177620: Current learning rate: 0.00183 +2026-04-14 11:29:52.362521: train_loss -0.4436 +2026-04-14 11:29:52.367207: val_loss -0.4049 +2026-04-14 11:29:52.368799: Pseudo dice [0.5209, 0.0, 0.4441, 0.828, 0.5125, 0.7913, 0.8329] +2026-04-14 11:29:52.370119: Epoch time: 101.19 s +2026-04-14 11:29:53.574558: +2026-04-14 11:29:53.576059: Epoch 3396 +2026-04-14 11:29:53.577507: Current learning rate: 0.00182 +2026-04-14 11:31:34.878873: train_loss -0.4682 +2026-04-14 11:31:34.886173: val_loss -0.3916 +2026-04-14 11:31:34.887978: Pseudo dice [0.4534, 0.0, 0.7628, 0.7527, 0.4336, 0.7946, 0.8419] +2026-04-14 11:31:34.890144: Epoch time: 101.31 s +2026-04-14 11:31:36.096114: +2026-04-14 11:31:36.097901: Epoch 3397 +2026-04-14 11:31:36.099655: Current learning rate: 0.00182 +2026-04-14 11:33:17.374933: train_loss -0.4398 +2026-04-14 11:33:17.379226: val_loss -0.3498 +2026-04-14 11:33:17.380983: Pseudo dice [0.2755, 0.0, 0.4923, 0.6463, 0.3436, 0.797, 0.679] +2026-04-14 11:33:17.382995: Epoch time: 101.28 s +2026-04-14 11:33:18.609151: +2026-04-14 11:33:18.610906: Epoch 3398 +2026-04-14 11:33:18.612204: Current learning rate: 0.00182 +2026-04-14 11:34:59.961121: train_loss -0.4477 +2026-04-14 11:34:59.965146: val_loss -0.4297 +2026-04-14 11:34:59.966773: Pseudo dice [0.7353, 0.0, 0.7224, 0.5857, 0.4953, 0.8132, 0.8132] +2026-04-14 11:34:59.968117: Epoch time: 101.36 s +2026-04-14 11:35:01.203910: +2026-04-14 11:35:01.205278: Epoch 3399 +2026-04-14 11:35:01.206590: Current learning rate: 0.00182 +2026-04-14 11:36:42.295069: train_loss -0.4414 +2026-04-14 11:36:42.299297: val_loss -0.406 +2026-04-14 11:36:42.300637: Pseudo dice [0.4544, 0.0, 0.457, 0.4563, 0.659, 0.841, 0.7187] +2026-04-14 11:36:42.301929: Epoch time: 101.09 s +2026-04-14 11:36:46.224025: +2026-04-14 11:36:46.226794: Epoch 3400 +2026-04-14 11:36:46.228335: Current learning rate: 0.00181 +2026-04-14 11:38:27.442546: train_loss -0.4617 +2026-04-14 11:38:27.446798: val_loss -0.4047 +2026-04-14 11:38:27.448123: Pseudo dice [0.6284, 0.0, 0.6885, 0.4081, 0.532, 0.7739, 0.9442] +2026-04-14 11:38:27.449332: Epoch time: 101.22 s +2026-04-14 11:38:28.654225: +2026-04-14 11:38:28.655835: Epoch 3401 +2026-04-14 11:38:28.657367: Current learning rate: 0.00181 +2026-04-14 11:40:09.811453: train_loss -0.4391 +2026-04-14 11:40:09.815651: val_loss -0.3744 +2026-04-14 11:40:09.817201: Pseudo dice [0.2882, 0.0, 0.7214, 0.3467, 0.2131, 0.724, 0.8885] +2026-04-14 11:40:09.819203: Epoch time: 101.16 s +2026-04-14 11:40:11.033335: +2026-04-14 11:40:11.034699: Epoch 3402 +2026-04-14 11:40:11.036550: Current learning rate: 0.00181 +2026-04-14 11:41:52.305436: train_loss -0.4662 +2026-04-14 11:41:52.311661: val_loss -0.3726 +2026-04-14 11:41:52.313190: Pseudo dice [0.7219, 0.0, 0.5448, 0.2193, 0.4043, 0.7854, 0.9366] +2026-04-14 11:41:52.314681: Epoch time: 101.28 s +2026-04-14 11:41:53.530118: +2026-04-14 11:41:53.531874: Epoch 3403 +2026-04-14 11:41:53.533227: Current learning rate: 0.00181 +2026-04-14 11:43:34.740419: train_loss -0.4655 +2026-04-14 11:43:34.745522: val_loss -0.4124 +2026-04-14 11:43:34.747421: Pseudo dice [0.3359, 0.0, 0.8199, 0.7824, 0.3742, 0.792, 0.821] +2026-04-14 11:43:34.749049: Epoch time: 101.21 s +2026-04-14 11:43:35.976331: +2026-04-14 11:43:35.978082: Epoch 3404 +2026-04-14 11:43:35.979654: Current learning rate: 0.0018 +2026-04-14 11:45:17.100664: train_loss -0.4571 +2026-04-14 11:45:17.106087: val_loss -0.3489 +2026-04-14 11:45:17.107738: Pseudo dice [0.792, 0.0, 0.551, 0.4394, 0.2171, 0.7166, 0.8466] +2026-04-14 11:45:17.110090: Epoch time: 101.13 s +2026-04-14 11:45:18.337625: +2026-04-14 11:45:18.339397: Epoch 3405 +2026-04-14 11:45:18.341012: Current learning rate: 0.0018 +2026-04-14 11:46:59.404573: train_loss -0.4645 +2026-04-14 11:46:59.409049: val_loss -0.4355 +2026-04-14 11:46:59.410692: Pseudo dice [0.7291, 0.0, 0.827, 0.8464, 0.6057, 0.8391, 0.7987] +2026-04-14 11:46:59.412070: Epoch time: 101.07 s +2026-04-14 11:47:00.634159: +2026-04-14 11:47:00.635890: Epoch 3406 +2026-04-14 11:47:00.637599: Current learning rate: 0.0018 +2026-04-14 11:48:41.884118: train_loss -0.4555 +2026-04-14 11:48:41.888052: val_loss -0.3967 +2026-04-14 11:48:41.889517: Pseudo dice [0.7892, 0.0, 0.7583, 0.872, 0.3619, 0.7177, 0.9013] +2026-04-14 11:48:41.890899: Epoch time: 101.25 s +2026-04-14 11:48:43.205060: +2026-04-14 11:48:43.206426: Epoch 3407 +2026-04-14 11:48:43.207612: Current learning rate: 0.00179 +2026-04-14 11:50:24.407383: train_loss -0.4508 +2026-04-14 11:50:24.415264: val_loss -0.4011 +2026-04-14 11:50:24.417348: Pseudo dice [0.7451, 0.0, 0.6695, 0.045, 0.379, 0.8072, 0.831] +2026-04-14 11:50:24.419835: Epoch time: 101.21 s +2026-04-14 11:50:25.650255: +2026-04-14 11:50:25.652064: Epoch 3408 +2026-04-14 11:50:25.653978: Current learning rate: 0.00179 +2026-04-14 11:52:06.771774: train_loss -0.4526 +2026-04-14 11:52:06.778416: val_loss -0.4219 +2026-04-14 11:52:06.781640: Pseudo dice [0.7454, 0.0, 0.6706, 0.7889, 0.5168, 0.8254, 0.8813] +2026-04-14 11:52:06.784510: Epoch time: 101.12 s +2026-04-14 11:52:07.997514: +2026-04-14 11:52:07.999239: Epoch 3409 +2026-04-14 11:52:08.000609: Current learning rate: 0.00179 +2026-04-14 11:53:49.250025: train_loss -0.4593 +2026-04-14 11:53:49.254624: val_loss -0.3532 +2026-04-14 11:53:49.256366: Pseudo dice [0.4959, 0.0, 0.5468, 0.6775, 0.3118, 0.4384, 0.7081] +2026-04-14 11:53:49.258150: Epoch time: 101.26 s +2026-04-14 11:53:50.475845: +2026-04-14 11:53:50.477522: Epoch 3410 +2026-04-14 11:53:50.479555: Current learning rate: 0.00179 +2026-04-14 11:55:31.723582: train_loss -0.4481 +2026-04-14 11:55:31.729321: val_loss -0.388 +2026-04-14 11:55:31.730619: Pseudo dice [0.4185, 0.0, 0.8508, 0.8255, 0.6224, 0.8119, 0.8188] +2026-04-14 11:55:31.732341: Epoch time: 101.25 s +2026-04-14 11:55:32.947764: +2026-04-14 11:55:32.949617: Epoch 3411 +2026-04-14 11:55:32.950963: Current learning rate: 0.00178 +2026-04-14 11:57:14.208715: train_loss -0.4661 +2026-04-14 11:57:14.213670: val_loss -0.3904 +2026-04-14 11:57:14.215567: Pseudo dice [0.5892, 0.0, 0.5431, 0.7992, 0.5224, 0.766, 0.7378] +2026-04-14 11:57:14.217023: Epoch time: 101.26 s +2026-04-14 11:57:15.449776: +2026-04-14 11:57:15.451496: Epoch 3412 +2026-04-14 11:57:15.453109: Current learning rate: 0.00178 +2026-04-14 11:58:56.689076: train_loss -0.4678 +2026-04-14 11:58:56.694058: val_loss -0.3832 +2026-04-14 11:58:56.695807: Pseudo dice [0.3001, 0.0, 0.6719, 0.0156, 0.493, 0.741, 0.8469] +2026-04-14 11:58:56.697619: Epoch time: 101.24 s +2026-04-14 11:58:57.910558: +2026-04-14 11:58:57.912322: Epoch 3413 +2026-04-14 11:58:57.913776: Current learning rate: 0.00178 +2026-04-14 12:00:39.196005: train_loss -0.4635 +2026-04-14 12:00:39.200261: val_loss -0.4121 +2026-04-14 12:00:39.201784: Pseudo dice [0.7975, 0.0, 0.8743, 0.2083, 0.6061, 0.7388, 0.7219] +2026-04-14 12:00:39.203402: Epoch time: 101.29 s +2026-04-14 12:00:40.444905: +2026-04-14 12:00:40.446255: Epoch 3414 +2026-04-14 12:00:40.447536: Current learning rate: 0.00178 +2026-04-14 12:02:21.521534: train_loss -0.4593 +2026-04-14 12:02:21.526590: val_loss -0.391 +2026-04-14 12:02:21.528390: Pseudo dice [0.7451, 0.0, 0.6731, 0.451, 0.5419, 0.6467, 0.7633] +2026-04-14 12:02:21.529992: Epoch time: 101.08 s +2026-04-14 12:02:22.729307: +2026-04-14 12:02:22.741202: Epoch 3415 +2026-04-14 12:02:22.744016: Current learning rate: 0.00177 +2026-04-14 12:04:03.869117: train_loss -0.4584 +2026-04-14 12:04:03.874036: val_loss -0.3901 +2026-04-14 12:04:03.875546: Pseudo dice [0.4518, 0.0, 0.5207, 0.7582, 0.6464, 0.5951, 0.6152] +2026-04-14 12:04:03.877034: Epoch time: 101.14 s +2026-04-14 12:04:05.080394: +2026-04-14 12:04:05.082085: Epoch 3416 +2026-04-14 12:04:05.083652: Current learning rate: 0.00177 +2026-04-14 12:05:46.341751: train_loss -0.457 +2026-04-14 12:05:46.347844: val_loss -0.373 +2026-04-14 12:05:46.349827: Pseudo dice [0.4636, 0.0, 0.6163, 0.8973, 0.0492, 0.7699, 0.8255] +2026-04-14 12:05:46.351390: Epoch time: 101.26 s +2026-04-14 12:05:47.567017: +2026-04-14 12:05:47.568825: Epoch 3417 +2026-04-14 12:05:47.570927: Current learning rate: 0.00177 +2026-04-14 12:07:28.794703: train_loss -0.4558 +2026-04-14 12:07:28.800330: val_loss -0.4052 +2026-04-14 12:07:28.802113: Pseudo dice [0.7705, 0.0, 0.7049, 0.8428, 0.491, 0.8369, 0.8404] +2026-04-14 12:07:28.803778: Epoch time: 101.23 s +2026-04-14 12:07:30.020576: +2026-04-14 12:07:30.022170: Epoch 3418 +2026-04-14 12:07:30.023512: Current learning rate: 0.00176 +2026-04-14 12:09:11.174783: train_loss -0.4597 +2026-04-14 12:09:11.180331: val_loss -0.4145 +2026-04-14 12:09:11.181879: Pseudo dice [0.4634, 0.0, 0.7088, 0.8305, 0.378, 0.8451, 0.7734] +2026-04-14 12:09:11.183202: Epoch time: 101.16 s +2026-04-14 12:09:12.416412: +2026-04-14 12:09:12.418103: Epoch 3419 +2026-04-14 12:09:12.419613: Current learning rate: 0.00176 +2026-04-14 12:10:53.464499: train_loss -0.4617 +2026-04-14 12:10:53.470145: val_loss -0.3927 +2026-04-14 12:10:53.471653: Pseudo dice [0.4118, 0.0, 0.8655, 0.63, 0.4405, 0.8344, 0.659] +2026-04-14 12:10:53.473131: Epoch time: 101.05 s +2026-04-14 12:10:55.532761: +2026-04-14 12:10:55.535081: Epoch 3420 +2026-04-14 12:10:55.536659: Current learning rate: 0.00176 +2026-04-14 12:12:36.612158: train_loss -0.4597 +2026-04-14 12:12:36.617890: val_loss -0.3762 +2026-04-14 12:12:36.620615: Pseudo dice [0.2799, 0.0, 0.7315, 0.827, 0.2537, 0.8327, 0.6159] +2026-04-14 12:12:36.623050: Epoch time: 101.08 s +2026-04-14 12:12:37.854289: +2026-04-14 12:12:37.876662: Epoch 3421 +2026-04-14 12:12:37.883316: Current learning rate: 0.00176 +2026-04-14 12:14:18.848594: train_loss -0.4618 +2026-04-14 12:14:18.853076: val_loss -0.3634 +2026-04-14 12:14:18.854694: Pseudo dice [0.5932, 0.0, 0.6281, 0.3571, 0.503, 0.7786, 0.755] +2026-04-14 12:14:18.856162: Epoch time: 101.0 s +2026-04-14 12:14:20.072666: +2026-04-14 12:14:20.074017: Epoch 3422 +2026-04-14 12:14:20.075382: Current learning rate: 0.00175 +2026-04-14 12:16:01.278236: train_loss -0.4603 +2026-04-14 12:16:01.285905: val_loss -0.3945 +2026-04-14 12:16:01.288472: Pseudo dice [0.5364, 0.0, 0.8225, 0.8794, 0.6037, 0.849, 0.7461] +2026-04-14 12:16:01.290887: Epoch time: 101.21 s +2026-04-14 12:16:02.510227: +2026-04-14 12:16:02.512221: Epoch 3423 +2026-04-14 12:16:02.513796: Current learning rate: 0.00175 +2026-04-14 12:17:43.733271: train_loss -0.4668 +2026-04-14 12:17:43.738978: val_loss -0.3948 +2026-04-14 12:17:43.740464: Pseudo dice [0.2726, 0.0, 0.7779, 0.3309, 0.4595, 0.6852, 0.8877] +2026-04-14 12:17:43.742085: Epoch time: 101.23 s +2026-04-14 12:17:44.957623: +2026-04-14 12:17:44.959280: Epoch 3424 +2026-04-14 12:17:44.960753: Current learning rate: 0.00175 +2026-04-14 12:19:26.071304: train_loss -0.4525 +2026-04-14 12:19:26.076823: val_loss -0.4065 +2026-04-14 12:19:26.078596: Pseudo dice [0.6351, 0.0, 0.6739, 0.8832, 0.4868, 0.8089, 0.8455] +2026-04-14 12:19:26.080004: Epoch time: 101.12 s +2026-04-14 12:19:27.293052: +2026-04-14 12:19:27.294808: Epoch 3425 +2026-04-14 12:19:27.296260: Current learning rate: 0.00175 +2026-04-14 12:21:08.349267: train_loss -0.4501 +2026-04-14 12:21:08.354229: val_loss -0.4125 +2026-04-14 12:21:08.355902: Pseudo dice [0.4897, 0.0, 0.72, 0.9048, 0.4523, 0.8257, 0.7891] +2026-04-14 12:21:08.357442: Epoch time: 101.06 s +2026-04-14 12:21:09.578561: +2026-04-14 12:21:09.580072: Epoch 3426 +2026-04-14 12:21:09.581302: Current learning rate: 0.00174 +2026-04-14 12:22:50.731495: train_loss -0.451 +2026-04-14 12:22:50.736498: val_loss -0.3705 +2026-04-14 12:22:50.738413: Pseudo dice [0.4147, 0.0, 0.6276, 0.3444, 0.532, 0.6979, 0.7289] +2026-04-14 12:22:50.740202: Epoch time: 101.16 s +2026-04-14 12:22:51.937925: +2026-04-14 12:22:51.940144: Epoch 3427 +2026-04-14 12:22:51.943178: Current learning rate: 0.00174 +2026-04-14 12:24:33.287678: train_loss -0.4449 +2026-04-14 12:24:33.294172: val_loss -0.3732 +2026-04-14 12:24:33.295640: Pseudo dice [0.3785, 0.0, 0.7071, 0.8197, 0.0955, 0.7922, 0.6009] +2026-04-14 12:24:33.297287: Epoch time: 101.35 s +2026-04-14 12:24:34.540243: +2026-04-14 12:24:34.542166: Epoch 3428 +2026-04-14 12:24:34.543644: Current learning rate: 0.00174 +2026-04-14 12:26:15.866563: train_loss -0.4456 +2026-04-14 12:26:15.871650: val_loss -0.4136 +2026-04-14 12:26:15.873158: Pseudo dice [0.3248, 0.0, 0.769, 0.8877, 0.6952, 0.7582, 0.8965] +2026-04-14 12:26:15.874839: Epoch time: 101.33 s +2026-04-14 12:26:17.085324: +2026-04-14 12:26:17.086769: Epoch 3429 +2026-04-14 12:26:17.088179: Current learning rate: 0.00173 +2026-04-14 12:27:58.438970: train_loss -0.4566 +2026-04-14 12:27:58.443394: val_loss -0.3723 +2026-04-14 12:27:58.445546: Pseudo dice [0.3781, 0.0, 0.7123, 0.6538, 0.5307, 0.8115, 0.7247] +2026-04-14 12:27:58.447181: Epoch time: 101.36 s +2026-04-14 12:27:59.674502: +2026-04-14 12:27:59.676059: Epoch 3430 +2026-04-14 12:27:59.677463: Current learning rate: 0.00173 +2026-04-14 12:29:40.851729: train_loss -0.4394 +2026-04-14 12:29:40.856064: val_loss -0.391 +2026-04-14 12:29:40.857446: Pseudo dice [0.3245, 0.0, 0.7613, 0.0322, 0.5183, 0.6158, 0.7413] +2026-04-14 12:29:40.858969: Epoch time: 101.18 s +2026-04-14 12:29:42.087124: +2026-04-14 12:29:42.088670: Epoch 3431 +2026-04-14 12:29:42.089972: Current learning rate: 0.00173 +2026-04-14 12:31:23.205537: train_loss -0.4415 +2026-04-14 12:31:23.209634: val_loss -0.3866 +2026-04-14 12:31:23.211123: Pseudo dice [0.4166, 0.0, 0.7855, 0.81, 0.4054, 0.742, 0.9177] +2026-04-14 12:31:23.212695: Epoch time: 101.12 s +2026-04-14 12:31:24.428514: +2026-04-14 12:31:24.429964: Epoch 3432 +2026-04-14 12:31:24.431104: Current learning rate: 0.00173 +2026-04-14 12:33:05.539162: train_loss -0.4561 +2026-04-14 12:33:05.545136: val_loss -0.3994 +2026-04-14 12:33:05.546917: Pseudo dice [0.7643, 0.0, 0.8044, 0.8293, 0.4446, 0.8214, 0.5428] +2026-04-14 12:33:05.548318: Epoch time: 101.11 s +2026-04-14 12:33:06.776494: +2026-04-14 12:33:06.778568: Epoch 3433 +2026-04-14 12:33:06.780100: Current learning rate: 0.00172 +2026-04-14 12:34:47.959858: train_loss -0.4624 +2026-04-14 12:34:47.963596: val_loss -0.3854 +2026-04-14 12:34:47.965020: Pseudo dice [0.4178, 0.0, 0.667, 0.6417, 0.4299, 0.763, 0.8761] +2026-04-14 12:34:47.966518: Epoch time: 101.19 s +2026-04-14 12:34:49.176073: +2026-04-14 12:34:49.177591: Epoch 3434 +2026-04-14 12:34:49.178781: Current learning rate: 0.00172 +2026-04-14 12:36:30.295220: train_loss -0.4587 +2026-04-14 12:36:30.300135: val_loss -0.411 +2026-04-14 12:36:30.302461: Pseudo dice [0.4403, 0.0, 0.7457, 0.1006, 0.5933, 0.7997, 0.9052] +2026-04-14 12:36:30.304918: Epoch time: 101.12 s +2026-04-14 12:36:31.535482: +2026-04-14 12:36:31.538571: Epoch 3435 +2026-04-14 12:36:31.540156: Current learning rate: 0.00172 +2026-04-14 12:38:13.031018: train_loss -0.4512 +2026-04-14 12:38:13.035284: val_loss -0.3722 +2026-04-14 12:38:13.036747: Pseudo dice [0.6824, 0.0, 0.7615, 0.7924, 0.4112, 0.8545, 0.6906] +2026-04-14 12:38:13.038185: Epoch time: 101.5 s +2026-04-14 12:38:14.255525: +2026-04-14 12:38:14.256986: Epoch 3436 +2026-04-14 12:38:14.258335: Current learning rate: 0.00172 +2026-04-14 12:39:55.459156: train_loss -0.467 +2026-04-14 12:39:55.463884: val_loss -0.4078 +2026-04-14 12:39:55.465874: Pseudo dice [0.7754, 0.0, 0.8083, 0.8158, 0.4192, 0.8354, 0.9312] +2026-04-14 12:39:55.467691: Epoch time: 101.21 s +2026-04-14 12:39:56.682905: +2026-04-14 12:39:56.684423: Epoch 3437 +2026-04-14 12:39:56.685892: Current learning rate: 0.00171 +2026-04-14 12:41:38.029688: train_loss -0.4563 +2026-04-14 12:41:38.034397: val_loss -0.3673 +2026-04-14 12:41:38.036206: Pseudo dice [0.6338, 0.0, 0.3355, 0.8379, 0.1679, 0.8009, 0.3836] +2026-04-14 12:41:38.037809: Epoch time: 101.35 s +2026-04-14 12:41:39.271158: +2026-04-14 12:41:39.272885: Epoch 3438 +2026-04-14 12:41:39.274459: Current learning rate: 0.00171 +2026-04-14 12:43:20.545800: train_loss -0.4594 +2026-04-14 12:43:20.550728: val_loss -0.3577 +2026-04-14 12:43:20.553351: Pseudo dice [0.3479, 0.0, 0.8124, 0.0904, 0.4774, 0.7563, 0.7742] +2026-04-14 12:43:20.555055: Epoch time: 101.28 s +2026-04-14 12:43:21.773849: +2026-04-14 12:43:21.775298: Epoch 3439 +2026-04-14 12:43:21.776520: Current learning rate: 0.00171 +2026-04-14 12:45:03.064784: train_loss -0.4542 +2026-04-14 12:45:03.068960: val_loss -0.3795 +2026-04-14 12:45:03.071223: Pseudo dice [0.7598, 0.0, 0.7945, 0.2629, 0.2069, 0.7362, 0.791] +2026-04-14 12:45:03.072800: Epoch time: 101.29 s +2026-04-14 12:45:04.318110: +2026-04-14 12:45:04.319630: Epoch 3440 +2026-04-14 12:45:04.320868: Current learning rate: 0.0017 +2026-04-14 12:46:46.679234: train_loss -0.4501 +2026-04-14 12:46:46.683592: val_loss -0.3726 +2026-04-14 12:46:46.685531: Pseudo dice [0.2227, 0.0, 0.6772, 0.1356, 0.5887, 0.5608, 0.791] +2026-04-14 12:46:46.687155: Epoch time: 102.36 s +2026-04-14 12:46:47.907061: +2026-04-14 12:46:47.908909: Epoch 3441 +2026-04-14 12:46:47.910438: Current learning rate: 0.0017 +2026-04-14 12:48:29.255065: train_loss -0.4552 +2026-04-14 12:48:29.258770: val_loss -0.3806 +2026-04-14 12:48:29.259862: Pseudo dice [0.0591, 0.0, 0.6839, 0.8651, 0.3056, 0.8195, 0.6811] +2026-04-14 12:48:29.261080: Epoch time: 101.35 s +2026-04-14 12:48:30.465146: +2026-04-14 12:48:30.467029: Epoch 3442 +2026-04-14 12:48:30.468481: Current learning rate: 0.0017 +2026-04-14 12:50:11.848322: train_loss -0.4451 +2026-04-14 12:50:11.853500: val_loss -0.3977 +2026-04-14 12:50:11.855119: Pseudo dice [0.2521, 0.0, 0.8091, 0.9103, 0.4728, 0.8195, 0.8686] +2026-04-14 12:50:11.856804: Epoch time: 101.39 s +2026-04-14 12:50:13.072191: +2026-04-14 12:50:13.074086: Epoch 3443 +2026-04-14 12:50:13.075642: Current learning rate: 0.0017 +2026-04-14 12:51:54.195405: train_loss -0.4502 +2026-04-14 12:51:54.199722: val_loss -0.3845 +2026-04-14 12:51:54.201383: Pseudo dice [0.4843, 0.0, 0.8144, 0.6521, 0.55, 0.8612, 0.7438] +2026-04-14 12:51:54.202890: Epoch time: 101.13 s +2026-04-14 12:51:55.422620: +2026-04-14 12:51:55.424586: Epoch 3444 +2026-04-14 12:51:55.426074: Current learning rate: 0.00169 +2026-04-14 12:53:36.676240: train_loss -0.4532 +2026-04-14 12:53:36.680338: val_loss -0.4162 +2026-04-14 12:53:36.682119: Pseudo dice [0.2769, 0.0, 0.7371, 0.8073, 0.4724, 0.7113, 0.7634] +2026-04-14 12:53:36.683306: Epoch time: 101.26 s +2026-04-14 12:53:37.902119: +2026-04-14 12:53:37.903669: Epoch 3445 +2026-04-14 12:53:37.904910: Current learning rate: 0.00169 +2026-04-14 12:55:19.009691: train_loss -0.4472 +2026-04-14 12:55:19.014595: val_loss -0.3819 +2026-04-14 12:55:19.016481: Pseudo dice [0.6436, 0.0, 0.7702, 0.7556, 0.3728, 0.7779, 0.6817] +2026-04-14 12:55:19.017845: Epoch time: 101.11 s +2026-04-14 12:55:20.226552: +2026-04-14 12:55:20.228172: Epoch 3446 +2026-04-14 12:55:20.229749: Current learning rate: 0.00169 +2026-04-14 12:57:01.487772: train_loss -0.4548 +2026-04-14 12:57:01.493181: val_loss -0.3678 +2026-04-14 12:57:01.495037: Pseudo dice [0.2071, 0.0, 0.4537, 0.5799, 0.3165, 0.882, 0.7881] +2026-04-14 12:57:01.496660: Epoch time: 101.26 s +2026-04-14 12:57:02.727627: +2026-04-14 12:57:02.729382: Epoch 3447 +2026-04-14 12:57:02.730948: Current learning rate: 0.00168 +2026-04-14 12:58:43.754152: train_loss -0.4637 +2026-04-14 12:58:43.759257: val_loss -0.4216 +2026-04-14 12:58:43.761647: Pseudo dice [0.7441, 0.0, 0.6343, 0.3939, 0.4621, 0.7831, 0.9017] +2026-04-14 12:58:43.763259: Epoch time: 101.03 s +2026-04-14 12:58:44.983338: +2026-04-14 12:58:44.985571: Epoch 3448 +2026-04-14 12:58:44.987333: Current learning rate: 0.00168 +2026-04-14 13:00:25.999773: train_loss -0.4568 +2026-04-14 13:00:26.005507: val_loss -0.4214 +2026-04-14 13:00:26.007421: Pseudo dice [0.5055, 0.0, 0.831, 0.5707, 0.526, 0.5692, 0.8732] +2026-04-14 13:00:26.010540: Epoch time: 101.02 s +2026-04-14 13:00:27.230341: +2026-04-14 13:00:27.232623: Epoch 3449 +2026-04-14 13:00:27.234453: Current learning rate: 0.00168 +2026-04-14 13:02:08.366127: train_loss -0.4578 +2026-04-14 13:02:08.371794: val_loss -0.357 +2026-04-14 13:02:08.373312: Pseudo dice [0.5661, 0.0, 0.7557, 0.9143, 0.0884, 0.7435, 0.8408] +2026-04-14 13:02:08.374924: Epoch time: 101.14 s +2026-04-14 13:02:11.353269: +2026-04-14 13:02:11.354847: Epoch 3450 +2026-04-14 13:02:11.356263: Current learning rate: 0.00168 +2026-04-14 13:03:52.600276: train_loss -0.4677 +2026-04-14 13:03:52.604644: val_loss -0.3909 +2026-04-14 13:03:52.606432: Pseudo dice [0.3199, 0.0, 0.8678, 0.6841, 0.4609, 0.8116, 0.8232] +2026-04-14 13:03:52.608285: Epoch time: 101.25 s +2026-04-14 13:03:53.845320: +2026-04-14 13:03:53.846905: Epoch 3451 +2026-04-14 13:03:53.848359: Current learning rate: 0.00167 +2026-04-14 13:05:34.995179: train_loss -0.4564 +2026-04-14 13:05:35.000146: val_loss -0.3896 +2026-04-14 13:05:35.002900: Pseudo dice [0.518, 0.0, 0.745, 0.6819, 0.2557, 0.7218, 0.9442] +2026-04-14 13:05:35.005062: Epoch time: 101.15 s +2026-04-14 13:05:36.219280: +2026-04-14 13:05:36.221201: Epoch 3452 +2026-04-14 13:05:36.222836: Current learning rate: 0.00167 +2026-04-14 13:07:17.431832: train_loss -0.4694 +2026-04-14 13:07:17.436691: val_loss -0.3675 +2026-04-14 13:07:17.438678: Pseudo dice [0.6756, 0.0, 0.6886, 0.4859, 0.3899, 0.7501, 0.7107] +2026-04-14 13:07:17.440342: Epoch time: 101.22 s +2026-04-14 13:07:18.663201: +2026-04-14 13:07:18.664710: Epoch 3453 +2026-04-14 13:07:18.666046: Current learning rate: 0.00167 +2026-04-14 13:08:59.836082: train_loss -0.4613 +2026-04-14 13:08:59.840837: val_loss -0.361 +2026-04-14 13:08:59.842804: Pseudo dice [0.7397, 0.0, 0.6817, 0.6784, 0.3473, 0.6422, 0.688] +2026-04-14 13:08:59.844673: Epoch time: 101.18 s +2026-04-14 13:09:01.058604: +2026-04-14 13:09:01.061248: Epoch 3454 +2026-04-14 13:09:01.063398: Current learning rate: 0.00167 +2026-04-14 13:10:42.115671: train_loss -0.4608 +2026-04-14 13:10:42.122650: val_loss -0.4176 +2026-04-14 13:10:42.125007: Pseudo dice [0.7142, 0.0, 0.7698, 0.4697, 0.5411, 0.8114, 0.7634] +2026-04-14 13:10:42.126343: Epoch time: 101.06 s +2026-04-14 13:10:43.346253: +2026-04-14 13:10:43.347719: Epoch 3455 +2026-04-14 13:10:43.348893: Current learning rate: 0.00166 +2026-04-14 13:12:24.430932: train_loss -0.4611 +2026-04-14 13:12:24.435813: val_loss -0.3922 +2026-04-14 13:12:24.437541: Pseudo dice [0.583, 0.0, 0.8066, 0.154, 0.2852, 0.8591, 0.7991] +2026-04-14 13:12:24.438974: Epoch time: 101.09 s +2026-04-14 13:12:25.651427: +2026-04-14 13:12:25.652892: Epoch 3456 +2026-04-14 13:12:25.654315: Current learning rate: 0.00166 +2026-04-14 13:14:06.921392: train_loss -0.4701 +2026-04-14 13:14:06.926771: val_loss -0.4114 +2026-04-14 13:14:06.928457: Pseudo dice [0.6899, 0.0, 0.673, 0.6691, 0.5419, 0.8743, 0.779] +2026-04-14 13:14:06.930086: Epoch time: 101.27 s +2026-04-14 13:14:08.171125: +2026-04-14 13:14:08.174029: Epoch 3457 +2026-04-14 13:14:08.175402: Current learning rate: 0.00166 +2026-04-14 13:15:49.332608: train_loss -0.4659 +2026-04-14 13:15:49.337261: val_loss -0.3746 +2026-04-14 13:15:49.338847: Pseudo dice [0.36, 0.0, 0.6821, 0.843, 0.5027, 0.6906, 0.6271] +2026-04-14 13:15:49.340616: Epoch time: 101.16 s +2026-04-14 13:15:50.557731: +2026-04-14 13:15:50.560336: Epoch 3458 +2026-04-14 13:15:50.562053: Current learning rate: 0.00165 +2026-04-14 13:17:31.781428: train_loss -0.4514 +2026-04-14 13:17:31.787192: val_loss -0.4394 +2026-04-14 13:17:31.788851: Pseudo dice [0.7007, 0.0, 0.772, 0.8504, 0.6186, 0.7318, 0.7779] +2026-04-14 13:17:31.790621: Epoch time: 101.23 s +2026-04-14 13:17:33.017514: +2026-04-14 13:17:33.019078: Epoch 3459 +2026-04-14 13:17:33.020511: Current learning rate: 0.00165 +2026-04-14 13:19:14.232608: train_loss -0.4703 +2026-04-14 13:19:14.243830: val_loss -0.3915 +2026-04-14 13:19:14.246208: Pseudo dice [0.7393, 0.0, 0.6872, 0.4924, 0.4877, 0.8039, 0.4763] +2026-04-14 13:19:14.248001: Epoch time: 101.22 s +2026-04-14 13:19:16.495824: +2026-04-14 13:19:16.498161: Epoch 3460 +2026-04-14 13:19:16.499771: Current learning rate: 0.00165 +2026-04-14 13:20:57.889328: train_loss -0.4522 +2026-04-14 13:20:57.894656: val_loss -0.369 +2026-04-14 13:20:57.896743: Pseudo dice [0.2596, 0.0, 0.8471, 0.765, 0.3898, 0.8451, 0.5291] +2026-04-14 13:20:57.899065: Epoch time: 101.4 s +2026-04-14 13:20:59.130844: +2026-04-14 13:20:59.132752: Epoch 3461 +2026-04-14 13:20:59.134201: Current learning rate: 0.00165 +2026-04-14 13:22:40.491401: train_loss -0.4611 +2026-04-14 13:22:40.495759: val_loss -0.3819 +2026-04-14 13:22:40.497401: Pseudo dice [0.519, 0.0, 0.608, 0.8904, 0.2175, 0.5934, 0.8738] +2026-04-14 13:22:40.499023: Epoch time: 101.36 s +2026-04-14 13:22:41.715254: +2026-04-14 13:22:41.716929: Epoch 3462 +2026-04-14 13:22:41.718609: Current learning rate: 0.00164 +2026-04-14 13:24:22.963865: train_loss -0.4535 +2026-04-14 13:24:22.968749: val_loss -0.3866 +2026-04-14 13:24:22.970348: Pseudo dice [0.3735, 0.0, 0.7883, 0.8822, 0.4325, 0.6163, 0.7834] +2026-04-14 13:24:22.971723: Epoch time: 101.25 s +2026-04-14 13:24:24.184046: +2026-04-14 13:24:24.185701: Epoch 3463 +2026-04-14 13:24:24.187197: Current learning rate: 0.00164 +2026-04-14 13:26:05.296841: train_loss -0.4492 +2026-04-14 13:26:05.302551: val_loss -0.3945 +2026-04-14 13:26:05.304549: Pseudo dice [0.41, 0.0, 0.8516, 0.7946, 0.2797, 0.6673, 0.9114] +2026-04-14 13:26:05.306350: Epoch time: 101.12 s +2026-04-14 13:26:06.512827: +2026-04-14 13:26:06.515893: Epoch 3464 +2026-04-14 13:26:06.520361: Current learning rate: 0.00164 +2026-04-14 13:27:47.656349: train_loss -0.4641 +2026-04-14 13:27:47.661292: val_loss -0.3984 +2026-04-14 13:27:47.663140: Pseudo dice [0.3109, 0.0, 0.7554, 0.8547, 0.4673, 0.7782, 0.7443] +2026-04-14 13:27:47.664784: Epoch time: 101.15 s +2026-04-14 13:27:48.891050: +2026-04-14 13:27:48.893117: Epoch 3465 +2026-04-14 13:27:48.894874: Current learning rate: 0.00164 +2026-04-14 13:29:30.186644: train_loss -0.4538 +2026-04-14 13:29:30.191808: val_loss -0.3988 +2026-04-14 13:29:30.193575: Pseudo dice [0.4154, 0.0, 0.7426, 0.6357, 0.5846, 0.8395, 0.605] +2026-04-14 13:29:30.195517: Epoch time: 101.3 s +2026-04-14 13:29:31.390450: +2026-04-14 13:29:31.392607: Epoch 3466 +2026-04-14 13:29:31.393978: Current learning rate: 0.00163 +2026-04-14 13:31:12.424970: train_loss -0.4536 +2026-04-14 13:31:12.429519: val_loss -0.4228 +2026-04-14 13:31:12.431406: Pseudo dice [0.5519, 0.0, 0.7258, 0.8507, 0.5956, 0.8191, 0.8784] +2026-04-14 13:31:12.433239: Epoch time: 101.04 s +2026-04-14 13:31:13.645204: +2026-04-14 13:31:13.647032: Epoch 3467 +2026-04-14 13:31:13.648457: Current learning rate: 0.00163 +2026-04-14 13:32:54.832149: train_loss -0.4616 +2026-04-14 13:32:54.839075: val_loss -0.3984 +2026-04-14 13:32:54.840840: Pseudo dice [0.7414, 0.0, 0.669, 0.7474, 0.4734, 0.7684, 0.6863] +2026-04-14 13:32:54.842731: Epoch time: 101.19 s +2026-04-14 13:32:56.070149: +2026-04-14 13:32:56.072000: Epoch 3468 +2026-04-14 13:32:56.073955: Current learning rate: 0.00163 +2026-04-14 13:34:37.160721: train_loss -0.4599 +2026-04-14 13:34:37.166669: val_loss -0.4158 +2026-04-14 13:34:37.169265: Pseudo dice [0.8437, 0.0, 0.8355, 0.3059, 0.6002, 0.8138, 0.8649] +2026-04-14 13:34:37.171473: Epoch time: 101.09 s +2026-04-14 13:34:38.396626: +2026-04-14 13:34:38.398195: Epoch 3469 +2026-04-14 13:34:38.399737: Current learning rate: 0.00162 +2026-04-14 13:36:19.587752: train_loss -0.4724 +2026-04-14 13:36:19.592877: val_loss -0.4092 +2026-04-14 13:36:19.594280: Pseudo dice [0.4225, 0.0, 0.8526, 0.8815, 0.2957, 0.799, 0.918] +2026-04-14 13:36:19.596273: Epoch time: 101.19 s +2026-04-14 13:36:20.806764: +2026-04-14 13:36:20.808476: Epoch 3470 +2026-04-14 13:36:20.809850: Current learning rate: 0.00162 +2026-04-14 13:38:01.915322: train_loss -0.466 +2026-04-14 13:38:01.919810: val_loss -0.4072 +2026-04-14 13:38:01.921383: Pseudo dice [0.7726, 0.0, 0.707, 0.8941, 0.3694, 0.7097, 0.8754] +2026-04-14 13:38:01.922758: Epoch time: 101.11 s +2026-04-14 13:38:01.924442: Yayy! New best EMA pseudo Dice: 0.5729 +2026-04-14 13:38:04.828310: +2026-04-14 13:38:04.830513: Epoch 3471 +2026-04-14 13:38:04.832671: Current learning rate: 0.00162 +2026-04-14 13:39:46.030071: train_loss -0.4507 +2026-04-14 13:39:46.034830: val_loss -0.3909 +2026-04-14 13:39:46.036736: Pseudo dice [0.7447, 0.0, 0.6986, 0.4119, 0.3829, 0.8229, 0.7018] +2026-04-14 13:39:46.038371: Epoch time: 101.2 s +2026-04-14 13:39:47.251703: +2026-04-14 13:39:47.253348: Epoch 3472 +2026-04-14 13:39:47.255233: Current learning rate: 0.00162 +2026-04-14 13:41:28.328963: train_loss -0.457 +2026-04-14 13:41:28.333451: val_loss -0.3554 +2026-04-14 13:41:28.334951: Pseudo dice [0.6692, 0.0, 0.6016, 0.7561, 0.1422, 0.7013, 0.8604] +2026-04-14 13:41:28.336392: Epoch time: 101.08 s +2026-04-14 13:41:29.549168: +2026-04-14 13:41:29.550617: Epoch 3473 +2026-04-14 13:41:29.551811: Current learning rate: 0.00161 +2026-04-14 13:43:10.841315: train_loss -0.4621 +2026-04-14 13:43:10.845528: val_loss -0.4057 +2026-04-14 13:43:10.847216: Pseudo dice [0.3767, 0.0, 0.747, 0.7173, 0.4802, 0.7352, 0.8763] +2026-04-14 13:43:10.848569: Epoch time: 101.3 s +2026-04-14 13:43:12.068675: +2026-04-14 13:43:12.070082: Epoch 3474 +2026-04-14 13:43:12.071462: Current learning rate: 0.00161 +2026-04-14 13:44:53.288459: train_loss -0.4748 +2026-04-14 13:44:53.293984: val_loss -0.3851 +2026-04-14 13:44:53.295903: Pseudo dice [0.3635, 0.0, 0.7524, 0.4467, 0.4306, 0.6688, 0.8672] +2026-04-14 13:44:53.297396: Epoch time: 101.22 s +2026-04-14 13:44:54.554407: +2026-04-14 13:44:54.556391: Epoch 3475 +2026-04-14 13:44:54.557952: Current learning rate: 0.00161 +2026-04-14 13:46:35.767860: train_loss -0.4543 +2026-04-14 13:46:35.772887: val_loss -0.3917 +2026-04-14 13:46:35.774508: Pseudo dice [0.3115, 0.0, 0.7888, 0.8604, 0.5687, 0.8194, 0.8531] +2026-04-14 13:46:35.776373: Epoch time: 101.22 s +2026-04-14 13:46:36.993455: +2026-04-14 13:46:36.995814: Epoch 3476 +2026-04-14 13:46:36.997413: Current learning rate: 0.00161 +2026-04-14 13:48:18.086169: train_loss -0.4561 +2026-04-14 13:48:18.090561: val_loss -0.3955 +2026-04-14 13:48:18.092253: Pseudo dice [0.4111, 0.0, 0.8594, 0.8857, 0.2628, 0.801, 0.9067] +2026-04-14 13:48:18.094414: Epoch time: 101.1 s +2026-04-14 13:48:19.318653: +2026-04-14 13:48:19.320544: Epoch 3477 +2026-04-14 13:48:19.322076: Current learning rate: 0.0016 +2026-04-14 13:50:00.448828: train_loss -0.4649 +2026-04-14 13:50:00.453855: val_loss -0.4075 +2026-04-14 13:50:00.466052: Pseudo dice [0.3858, 0.0, 0.842, 0.9215, 0.5433, 0.5747, 0.2876] +2026-04-14 13:50:00.467650: Epoch time: 101.13 s +2026-04-14 13:50:01.687675: +2026-04-14 13:50:01.689728: Epoch 3478 +2026-04-14 13:50:01.691752: Current learning rate: 0.0016 +2026-04-14 13:51:43.052783: train_loss -0.4605 +2026-04-14 13:51:43.057458: val_loss -0.3722 +2026-04-14 13:51:43.058909: Pseudo dice [0.4381, 0.0, 0.7746, 0.3829, 0.2964, 0.7598, 0.6669] +2026-04-14 13:51:43.060452: Epoch time: 101.37 s +2026-04-14 13:51:44.288869: +2026-04-14 13:51:44.290370: Epoch 3479 +2026-04-14 13:51:44.291672: Current learning rate: 0.0016 +2026-04-14 13:53:25.748429: train_loss -0.4733 +2026-04-14 13:53:25.754466: val_loss -0.3981 +2026-04-14 13:53:25.756785: Pseudo dice [0.5103, 0.0, 0.7744, 0.8963, 0.4783, 0.8223, 0.9118] +2026-04-14 13:53:25.758221: Epoch time: 101.46 s +2026-04-14 13:53:28.033315: +2026-04-14 13:53:28.034817: Epoch 3480 +2026-04-14 13:53:28.036917: Current learning rate: 0.00159 +2026-04-14 13:55:09.359614: train_loss -0.459 +2026-04-14 13:55:09.364251: val_loss -0.3979 +2026-04-14 13:55:09.366323: Pseudo dice [0.5745, 0.0, 0.8811, 0.8115, 0.1905, 0.6759, 0.8353] +2026-04-14 13:55:09.367782: Epoch time: 101.33 s +2026-04-14 13:55:10.587460: +2026-04-14 13:55:10.589342: Epoch 3481 +2026-04-14 13:55:10.590812: Current learning rate: 0.00159 +2026-04-14 13:56:51.753305: train_loss -0.4595 +2026-04-14 13:56:51.758068: val_loss -0.417 +2026-04-14 13:56:51.759763: Pseudo dice [0.6738, 0.0, 0.7433, 0.192, 0.6246, 0.749, 0.7209] +2026-04-14 13:56:51.761333: Epoch time: 101.17 s +2026-04-14 13:56:52.988552: +2026-04-14 13:56:52.990288: Epoch 3482 +2026-04-14 13:56:52.991744: Current learning rate: 0.00159 +2026-04-14 13:58:34.283892: train_loss -0.4605 +2026-04-14 13:58:34.316103: val_loss -0.3766 +2026-04-14 13:58:34.317665: Pseudo dice [0.1979, 0.0, 0.4148, 0.8809, 0.4493, 0.689, 0.6233] +2026-04-14 13:58:34.319077: Epoch time: 101.3 s +2026-04-14 13:58:35.528618: +2026-04-14 13:58:35.530193: Epoch 3483 +2026-04-14 13:58:35.532731: Current learning rate: 0.00159 +2026-04-14 14:00:16.713414: train_loss -0.4672 +2026-04-14 14:00:16.717903: val_loss -0.3575 +2026-04-14 14:00:16.719786: Pseudo dice [0.5177, 0.0, 0.7531, 0.1805, 0.2724, 0.6369, 0.7209] +2026-04-14 14:00:16.721804: Epoch time: 101.19 s +2026-04-14 14:00:17.940382: +2026-04-14 14:00:17.942182: Epoch 3484 +2026-04-14 14:00:17.944195: Current learning rate: 0.00158 +2026-04-14 14:01:59.104222: train_loss -0.4607 +2026-04-14 14:01:59.109846: val_loss -0.3758 +2026-04-14 14:01:59.111794: Pseudo dice [0.79, 0.0, 0.7936, 0.016, 0.0559, 0.8011, 0.8125] +2026-04-14 14:01:59.113680: Epoch time: 101.17 s +2026-04-14 14:02:00.349005: +2026-04-14 14:02:00.351003: Epoch 3485 +2026-04-14 14:02:00.352928: Current learning rate: 0.00158 +2026-04-14 14:03:41.199824: train_loss -0.4717 +2026-04-14 14:03:41.204315: val_loss -0.39 +2026-04-14 14:03:41.206136: Pseudo dice [0.5957, 0.0, 0.746, 0.8597, 0.2239, 0.6205, 0.8468] +2026-04-14 14:03:41.207875: Epoch time: 100.85 s +2026-04-14 14:03:42.422526: +2026-04-14 14:03:42.424061: Epoch 3486 +2026-04-14 14:03:42.425577: Current learning rate: 0.00158 +2026-04-14 14:05:23.618637: train_loss -0.4536 +2026-04-14 14:05:23.623425: val_loss -0.4198 +2026-04-14 14:05:23.624820: Pseudo dice [0.8578, 0.0, 0.7879, 0.8299, 0.4597, 0.7892, 0.8314] +2026-04-14 14:05:23.625986: Epoch time: 101.2 s +2026-04-14 14:05:24.851503: +2026-04-14 14:05:24.853700: Epoch 3487 +2026-04-14 14:05:24.855592: Current learning rate: 0.00157 +2026-04-14 14:07:05.896229: train_loss -0.476 +2026-04-14 14:07:05.901460: val_loss -0.3882 +2026-04-14 14:07:05.903184: Pseudo dice [0.2465, 0.0, 0.4358, 0.8392, 0.241, 0.8967, 0.7174] +2026-04-14 14:07:05.905786: Epoch time: 101.05 s +2026-04-14 14:07:07.147385: +2026-04-14 14:07:07.148950: Epoch 3488 +2026-04-14 14:07:07.150325: Current learning rate: 0.00157 +2026-04-14 14:08:48.146327: train_loss -0.4586 +2026-04-14 14:08:48.152348: val_loss -0.4125 +2026-04-14 14:08:48.153857: Pseudo dice [0.4816, 0.0, 0.6256, 0.7621, 0.5544, 0.7937, 0.7334] +2026-04-14 14:08:48.155281: Epoch time: 101.0 s +2026-04-14 14:08:49.366938: +2026-04-14 14:08:49.369066: Epoch 3489 +2026-04-14 14:08:49.370724: Current learning rate: 0.00157 +2026-04-14 14:10:30.410849: train_loss -0.4542 +2026-04-14 14:10:30.415831: val_loss -0.3698 +2026-04-14 14:10:30.418050: Pseudo dice [0.3422, 0.0, 0.7771, 0.1761, 0.2486, 0.6597, 0.7655] +2026-04-14 14:10:30.419845: Epoch time: 101.05 s +2026-04-14 14:10:31.639138: +2026-04-14 14:10:31.640814: Epoch 3490 +2026-04-14 14:10:31.642160: Current learning rate: 0.00157 +2026-04-14 14:12:12.834917: train_loss -0.4762 +2026-04-14 14:12:12.846230: val_loss -0.4188 +2026-04-14 14:12:12.854807: Pseudo dice [0.6539, 0.0, 0.8206, 0.3003, 0.6956, 0.848, 0.776] +2026-04-14 14:12:12.856852: Epoch time: 101.2 s +2026-04-14 14:12:14.089682: +2026-04-14 14:12:14.091634: Epoch 3491 +2026-04-14 14:12:14.093465: Current learning rate: 0.00156 +2026-04-14 14:13:55.223874: train_loss -0.4658 +2026-04-14 14:13:55.230023: val_loss -0.3845 +2026-04-14 14:13:55.232012: Pseudo dice [0.3972, 0.0, 0.7173, 0.8757, 0.5204, 0.7922, 0.8511] +2026-04-14 14:13:55.233835: Epoch time: 101.14 s +2026-04-14 14:13:56.454969: +2026-04-14 14:13:56.456825: Epoch 3492 +2026-04-14 14:13:56.458960: Current learning rate: 0.00156 +2026-04-14 14:15:37.673340: train_loss -0.4758 +2026-04-14 14:15:37.679720: val_loss -0.3855 +2026-04-14 14:15:37.681454: Pseudo dice [0.3612, 0.0, 0.821, 0.6747, 0.4425, 0.7269, 0.8479] +2026-04-14 14:15:37.683135: Epoch time: 101.22 s +2026-04-14 14:15:38.902366: +2026-04-14 14:15:38.903927: Epoch 3493 +2026-04-14 14:15:38.905202: Current learning rate: 0.00156 +2026-04-14 14:17:20.136241: train_loss -0.4583 +2026-04-14 14:17:20.141016: val_loss -0.4116 +2026-04-14 14:17:20.142632: Pseudo dice [0.731, 0.0, 0.5776, 0.8528, 0.5164, 0.7454, 0.7447] +2026-04-14 14:17:20.144040: Epoch time: 101.24 s +2026-04-14 14:17:21.496757: +2026-04-14 14:17:21.498670: Epoch 3494 +2026-04-14 14:17:21.499992: Current learning rate: 0.00156 +2026-04-14 14:19:02.791229: train_loss -0.445 +2026-04-14 14:19:02.796103: val_loss -0.3466 +2026-04-14 14:19:02.798685: Pseudo dice [0.1772, 0.0, 0.5447, 0.7815, 0.3498, 0.333, 0.4961] +2026-04-14 14:19:02.800419: Epoch time: 101.3 s +2026-04-14 14:19:04.016608: +2026-04-14 14:19:04.018265: Epoch 3495 +2026-04-14 14:19:04.020053: Current learning rate: 0.00155 +2026-04-14 14:20:45.344001: train_loss -0.4548 +2026-04-14 14:20:45.349144: val_loss -0.3783 +2026-04-14 14:20:45.351006: Pseudo dice [0.1797, 0.0, 0.8483, 0.9326, 0.5186, 0.7432, 0.8057] +2026-04-14 14:20:45.352856: Epoch time: 101.33 s +2026-04-14 14:20:46.573163: +2026-04-14 14:20:46.574808: Epoch 3496 +2026-04-14 14:20:46.576273: Current learning rate: 0.00155 +2026-04-14 14:22:27.957557: train_loss -0.4692 +2026-04-14 14:22:27.962509: val_loss -0.398 +2026-04-14 14:22:27.964462: Pseudo dice [0.8435, 0.0, 0.7891, 0.9168, 0.5683, 0.7577, 0.6223] +2026-04-14 14:22:27.966479: Epoch time: 101.39 s +2026-04-14 14:22:29.179037: +2026-04-14 14:22:29.180560: Epoch 3497 +2026-04-14 14:22:29.182158: Current learning rate: 0.00155 +2026-04-14 14:24:10.508914: train_loss -0.4607 +2026-04-14 14:24:10.513544: val_loss -0.3845 +2026-04-14 14:24:10.515426: Pseudo dice [0.4999, 0.0, 0.6525, 0.7298, 0.28, 0.8331, 0.6917] +2026-04-14 14:24:10.516985: Epoch time: 101.33 s +2026-04-14 14:24:11.736328: +2026-04-14 14:24:11.737795: Epoch 3498 +2026-04-14 14:24:11.739259: Current learning rate: 0.00154 +2026-04-14 14:25:53.005696: train_loss -0.4721 +2026-04-14 14:25:53.010746: val_loss -0.3967 +2026-04-14 14:25:53.012309: Pseudo dice [0.718, 0.0, 0.6255, 0.6467, 0.539, 0.5637, 0.8799] +2026-04-14 14:25:53.013889: Epoch time: 101.27 s +2026-04-14 14:25:54.248117: +2026-04-14 14:25:54.249676: Epoch 3499 +2026-04-14 14:25:54.251019: Current learning rate: 0.00154 +2026-04-14 14:27:35.583289: train_loss -0.4648 +2026-04-14 14:27:35.591734: val_loss -0.4132 +2026-04-14 14:27:35.593980: Pseudo dice [0.419, 0.0, 0.7369, 0.9058, 0.5587, 0.7393, 0.9159] +2026-04-14 14:27:35.596197: Epoch time: 101.34 s +2026-04-14 14:27:39.460513: +2026-04-14 14:27:39.462227: Epoch 3500 +2026-04-14 14:27:39.463625: Current learning rate: 0.00154 +2026-04-14 14:29:20.706139: train_loss -0.4774 +2026-04-14 14:29:20.710611: val_loss -0.4197 +2026-04-14 14:29:20.712550: Pseudo dice [0.7776, 0.0, 0.8414, 0.88, 0.5258, 0.8853, 0.6413] +2026-04-14 14:29:20.714201: Epoch time: 101.25 s +2026-04-14 14:29:21.946167: +2026-04-14 14:29:21.948092: Epoch 3501 +2026-04-14 14:29:21.949791: Current learning rate: 0.00154 +2026-04-14 14:31:03.222288: train_loss -0.4519 +2026-04-14 14:31:03.227723: val_loss -0.3753 +2026-04-14 14:31:03.229288: Pseudo dice [0.7378, 0.0, 0.2925, 0.8697, 0.2411, 0.7927, 0.7735] +2026-04-14 14:31:03.231189: Epoch time: 101.28 s +2026-04-14 14:31:04.466107: +2026-04-14 14:31:04.467677: Epoch 3502 +2026-04-14 14:31:04.469031: Current learning rate: 0.00153 +2026-04-14 14:32:45.797729: train_loss -0.4614 +2026-04-14 14:32:45.803638: val_loss -0.3991 +2026-04-14 14:32:45.805930: Pseudo dice [0.7464, 0.0, 0.8394, 0.7511, 0.414, 0.6897, 0.7719] +2026-04-14 14:32:45.807558: Epoch time: 101.33 s +2026-04-14 14:32:47.042598: +2026-04-14 14:32:47.044132: Epoch 3503 +2026-04-14 14:32:47.045746: Current learning rate: 0.00153 +2026-04-14 14:34:28.250545: train_loss -0.4647 +2026-04-14 14:34:28.256728: val_loss -0.4052 +2026-04-14 14:34:28.259405: Pseudo dice [0.8261, 0.0, 0.7572, 0.058, 0.5976, 0.6944, 0.4734] +2026-04-14 14:34:28.261630: Epoch time: 101.21 s +2026-04-14 14:34:29.480930: +2026-04-14 14:34:29.483065: Epoch 3504 +2026-04-14 14:34:29.485265: Current learning rate: 0.00153 +2026-04-14 14:36:10.774687: train_loss -0.4525 +2026-04-14 14:36:10.780058: val_loss -0.4081 +2026-04-14 14:36:10.782995: Pseudo dice [0.4744, 0.0, 0.6298, 0.7198, 0.486, 0.6787, 0.7393] +2026-04-14 14:36:10.784667: Epoch time: 101.3 s +2026-04-14 14:36:12.013982: +2026-04-14 14:36:12.015558: Epoch 3505 +2026-04-14 14:36:12.016932: Current learning rate: 0.00153 +2026-04-14 14:37:53.350529: train_loss -0.4676 +2026-04-14 14:37:53.355860: val_loss -0.3842 +2026-04-14 14:37:53.358201: Pseudo dice [0.4881, 0.0, 0.6396, 0.4046, 0.3508, 0.7941, 0.491] +2026-04-14 14:37:53.360375: Epoch time: 101.34 s +2026-04-14 14:37:54.578731: +2026-04-14 14:37:54.580778: Epoch 3506 +2026-04-14 14:37:54.582482: Current learning rate: 0.00152 +2026-04-14 14:39:35.693643: train_loss -0.4657 +2026-04-14 14:39:35.697843: val_loss -0.3901 +2026-04-14 14:39:35.699177: Pseudo dice [0.4431, 0.0, 0.8112, 0.6976, 0.3169, 0.855, 0.7161] +2026-04-14 14:39:35.700871: Epoch time: 101.12 s +2026-04-14 14:39:36.916087: +2026-04-14 14:39:36.917833: Epoch 3507 +2026-04-14 14:39:36.920009: Current learning rate: 0.00152 +2026-04-14 14:41:18.069223: train_loss -0.4691 +2026-04-14 14:41:18.073830: val_loss -0.3679 +2026-04-14 14:41:18.075412: Pseudo dice [0.3417, 0.0, 0.4574, 0.9296, 0.4879, 0.6619, 0.9058] +2026-04-14 14:41:18.077337: Epoch time: 101.16 s +2026-04-14 14:41:19.317274: +2026-04-14 14:41:19.320080: Epoch 3508 +2026-04-14 14:41:19.321394: Current learning rate: 0.00152 +2026-04-14 14:43:00.554396: train_loss -0.4681 +2026-04-14 14:43:00.559447: val_loss -0.4121 +2026-04-14 14:43:00.561106: Pseudo dice [0.5364, 0.0, 0.7465, 0.9196, 0.6628, 0.8753, 0.8317] +2026-04-14 14:43:00.563831: Epoch time: 101.24 s +2026-04-14 14:43:01.777577: +2026-04-14 14:43:01.779997: Epoch 3509 +2026-04-14 14:43:01.781566: Current learning rate: 0.00151 +2026-04-14 14:44:43.158061: train_loss -0.4636 +2026-04-14 14:44:43.162315: val_loss -0.4033 +2026-04-14 14:44:43.163812: Pseudo dice [0.5039, 0.0, 0.7276, 0.7794, 0.4847, 0.8901, 0.6322] +2026-04-14 14:44:43.165156: Epoch time: 101.38 s +2026-04-14 14:44:44.404035: +2026-04-14 14:44:44.405527: Epoch 3510 +2026-04-14 14:44:44.406847: Current learning rate: 0.00151 +2026-04-14 14:46:25.771870: train_loss -0.4625 +2026-04-14 14:46:25.776787: val_loss -0.3988 +2026-04-14 14:46:25.778230: Pseudo dice [0.2959, 0.0, 0.5644, 0.8854, 0.5979, 0.8222, 0.8114] +2026-04-14 14:46:25.779694: Epoch time: 101.37 s +2026-04-14 14:46:27.006430: +2026-04-14 14:46:27.008037: Epoch 3511 +2026-04-14 14:46:27.009271: Current learning rate: 0.00151 +2026-04-14 14:48:08.460082: train_loss -0.4609 +2026-04-14 14:48:08.466588: val_loss -0.352 +2026-04-14 14:48:08.468393: Pseudo dice [0.5294, 0.0, 0.7134, 0.517, 0.1908, 0.7696, 0.8186] +2026-04-14 14:48:08.470611: Epoch time: 101.46 s +2026-04-14 14:48:09.704267: +2026-04-14 14:48:09.705925: Epoch 3512 +2026-04-14 14:48:09.707377: Current learning rate: 0.00151 +2026-04-14 14:49:50.928299: train_loss -0.4689 +2026-04-14 14:49:50.943929: val_loss -0.4126 +2026-04-14 14:49:50.945150: Pseudo dice [0.4369, 0.0, 0.6725, 0.817, 0.5739, 0.4843, 0.8276] +2026-04-14 14:49:50.946972: Epoch time: 101.23 s +2026-04-14 14:49:52.170832: +2026-04-14 14:49:52.172270: Epoch 3513 +2026-04-14 14:49:52.173762: Current learning rate: 0.0015 +2026-04-14 14:51:33.285122: train_loss -0.4737 +2026-04-14 14:51:33.290238: val_loss -0.3629 +2026-04-14 14:51:33.291745: Pseudo dice [0.3779, 0.0, 0.753, 0.0685, 0.5661, 0.7347, 0.8484] +2026-04-14 14:51:33.293449: Epoch time: 101.12 s +2026-04-14 14:51:34.518085: +2026-04-14 14:51:34.519809: Epoch 3514 +2026-04-14 14:51:34.521245: Current learning rate: 0.0015 +2026-04-14 14:53:15.730164: train_loss -0.4692 +2026-04-14 14:53:15.735165: val_loss -0.3729 +2026-04-14 14:53:15.737388: Pseudo dice [0.4904, 0.0, 0.6536, 0.2153, 0.1206, 0.8722, 0.8539] +2026-04-14 14:53:15.739590: Epoch time: 101.22 s +2026-04-14 14:53:17.064645: +2026-04-14 14:53:17.066279: Epoch 3515 +2026-04-14 14:53:17.067713: Current learning rate: 0.0015 +2026-04-14 14:54:58.347136: train_loss -0.475 +2026-04-14 14:54:58.352239: val_loss -0.4098 +2026-04-14 14:54:58.354236: Pseudo dice [0.7596, 0.0, 0.7737, 0.8727, 0.6373, 0.7582, 0.8764] +2026-04-14 14:54:58.355808: Epoch time: 101.29 s +2026-04-14 14:54:59.555724: +2026-04-14 14:54:59.557584: Epoch 3516 +2026-04-14 14:54:59.559206: Current learning rate: 0.00149 +2026-04-14 14:56:40.795000: train_loss -0.4583 +2026-04-14 14:56:40.800589: val_loss -0.3753 +2026-04-14 14:56:40.802610: Pseudo dice [0.3371, 0.0, 0.5321, 0.8219, 0.3329, 0.8495, 0.7564] +2026-04-14 14:56:40.804326: Epoch time: 101.24 s +2026-04-14 14:56:42.026749: +2026-04-14 14:56:42.028522: Epoch 3517 +2026-04-14 14:56:42.030076: Current learning rate: 0.00149 +2026-04-14 14:58:23.211859: train_loss -0.4751 +2026-04-14 14:58:23.217042: val_loss -0.4047 +2026-04-14 14:58:23.218750: Pseudo dice [0.577, 0.0, 0.747, 0.3739, 0.6052, 0.7834, 0.8735] +2026-04-14 14:58:23.220440: Epoch time: 101.19 s +2026-04-14 14:58:24.475050: +2026-04-14 14:58:24.477414: Epoch 3518 +2026-04-14 14:58:24.479631: Current learning rate: 0.00149 +2026-04-14 15:00:05.844696: train_loss -0.4675 +2026-04-14 15:00:05.853060: val_loss -0.4018 +2026-04-14 15:00:05.854671: Pseudo dice [0.3987, 0.0, 0.7557, 0.9419, 0.3811, 0.7987, 0.7261] +2026-04-14 15:00:05.856231: Epoch time: 101.37 s +2026-04-14 15:00:07.086947: +2026-04-14 15:00:07.088944: Epoch 3519 +2026-04-14 15:00:07.090902: Current learning rate: 0.00149 +2026-04-14 15:01:48.369453: train_loss -0.4668 +2026-04-14 15:01:48.373845: val_loss -0.3933 +2026-04-14 15:01:48.375276: Pseudo dice [0.4409, 0.0, 0.7657, 0.8527, 0.4226, 0.747, 0.8013] +2026-04-14 15:01:48.376513: Epoch time: 101.29 s +2026-04-14 15:01:50.623559: +2026-04-14 15:01:50.625519: Epoch 3520 +2026-04-14 15:01:50.627015: Current learning rate: 0.00148 +2026-04-14 15:03:31.781631: train_loss -0.4762 +2026-04-14 15:03:31.786001: val_loss -0.3929 +2026-04-14 15:03:31.787509: Pseudo dice [0.4498, 0.0, 0.8269, 0.8325, 0.6362, 0.8199, 0.8332] +2026-04-14 15:03:31.788726: Epoch time: 101.16 s +2026-04-14 15:03:33.024228: +2026-04-14 15:03:33.025833: Epoch 3521 +2026-04-14 15:03:33.027387: Current learning rate: 0.00148 +2026-04-14 15:05:14.174883: train_loss -0.4613 +2026-04-14 15:05:14.180529: val_loss -0.3845 +2026-04-14 15:05:14.182080: Pseudo dice [0.585, 0.0, 0.5981, 0.8821, 0.4366, 0.6795, 0.883] +2026-04-14 15:05:14.183947: Epoch time: 101.15 s +2026-04-14 15:05:15.397206: +2026-04-14 15:05:15.398936: Epoch 3522 +2026-04-14 15:05:15.400542: Current learning rate: 0.00148 +2026-04-14 15:06:56.641807: train_loss -0.4597 +2026-04-14 15:06:56.647973: val_loss -0.4118 +2026-04-14 15:06:56.650007: Pseudo dice [0.8385, 0.0, 0.6983, 0.672, 0.4641, 0.6416, 0.8681] +2026-04-14 15:06:56.651813: Epoch time: 101.25 s +2026-04-14 15:06:57.879292: +2026-04-14 15:06:57.881184: Epoch 3523 +2026-04-14 15:06:57.882657: Current learning rate: 0.00148 +2026-04-14 15:08:39.050458: train_loss -0.4632 +2026-04-14 15:08:39.056859: val_loss -0.4293 +2026-04-14 15:08:39.058983: Pseudo dice [0.7585, 0.0, 0.8172, 0.8701, 0.5139, 0.8016, 0.7049] +2026-04-14 15:08:39.060981: Epoch time: 101.17 s +2026-04-14 15:08:39.063356: Yayy! New best EMA pseudo Dice: 0.5731 +2026-04-14 15:08:42.095215: +2026-04-14 15:08:42.097868: Epoch 3524 +2026-04-14 15:08:42.099485: Current learning rate: 0.00147 +2026-04-14 15:10:23.406729: train_loss -0.4482 +2026-04-14 15:10:23.412519: val_loss -0.3475 +2026-04-14 15:10:23.414317: Pseudo dice [0.7221, 0.0, 0.5791, 0.4488, 0.2836, 0.7655, 0.6786] +2026-04-14 15:10:23.416101: Epoch time: 101.31 s +2026-04-14 15:10:24.644829: +2026-04-14 15:10:24.647578: Epoch 3525 +2026-04-14 15:10:24.649403: Current learning rate: 0.00147 +2026-04-14 15:12:05.978347: train_loss -0.457 +2026-04-14 15:12:05.982811: val_loss -0.3822 +2026-04-14 15:12:05.984381: Pseudo dice [0.7637, 0.0, 0.6429, 0.9061, 0.3097, 0.6533, 0.8808] +2026-04-14 15:12:05.985895: Epoch time: 101.34 s +2026-04-14 15:12:07.207708: +2026-04-14 15:12:07.209320: Epoch 3526 +2026-04-14 15:12:07.210524: Current learning rate: 0.00147 +2026-04-14 15:13:48.649492: train_loss -0.4568 +2026-04-14 15:13:48.654417: val_loss -0.3701 +2026-04-14 15:13:48.656036: Pseudo dice [0.5462, 0.0, 0.732, 0.7486, 0.42, 0.7806, 0.619] +2026-04-14 15:13:48.657390: Epoch time: 101.44 s +2026-04-14 15:13:49.879821: +2026-04-14 15:13:49.882116: Epoch 3527 +2026-04-14 15:13:49.884186: Current learning rate: 0.00146 +2026-04-14 15:15:31.115294: train_loss -0.4501 +2026-04-14 15:15:31.120865: val_loss -0.3858 +2026-04-14 15:15:31.123172: Pseudo dice [0.7678, 0.0, 0.6099, 0.7509, 0.4027, 0.7614, 0.6938] +2026-04-14 15:15:31.125148: Epoch time: 101.24 s +2026-04-14 15:15:32.356462: +2026-04-14 15:15:32.358174: Epoch 3528 +2026-04-14 15:15:32.360392: Current learning rate: 0.00146 +2026-04-14 15:17:13.720855: train_loss -0.4638 +2026-04-14 15:17:13.726588: val_loss -0.3673 +2026-04-14 15:17:13.728281: Pseudo dice [0.3594, 0.0, 0.4652, 0.8448, 0.4855, 0.7094, 0.8986] +2026-04-14 15:17:13.729925: Epoch time: 101.37 s +2026-04-14 15:17:14.944365: +2026-04-14 15:17:14.946517: Epoch 3529 +2026-04-14 15:17:14.948589: Current learning rate: 0.00146 +2026-04-14 15:18:56.240239: train_loss -0.4556 +2026-04-14 15:18:56.246498: val_loss -0.4078 +2026-04-14 15:18:56.248625: Pseudo dice [0.433, 0.0, 0.7402, 0.7408, 0.5325, 0.7339, 0.804] +2026-04-14 15:18:56.250309: Epoch time: 101.3 s +2026-04-14 15:18:57.472900: +2026-04-14 15:18:57.474624: Epoch 3530 +2026-04-14 15:18:57.475986: Current learning rate: 0.00146 +2026-04-14 15:20:38.757114: train_loss -0.4518 +2026-04-14 15:20:38.762233: val_loss -0.4128 +2026-04-14 15:20:38.764055: Pseudo dice [0.859, 0.0, 0.6793, 0.6491, 0.4006, 0.8267, 0.5974] +2026-04-14 15:20:38.765945: Epoch time: 101.29 s +2026-04-14 15:20:40.006971: +2026-04-14 15:20:40.008338: Epoch 3531 +2026-04-14 15:20:40.009739: Current learning rate: 0.00145 +2026-04-14 15:22:21.110596: train_loss -0.4625 +2026-04-14 15:22:21.117759: val_loss -0.3835 +2026-04-14 15:22:21.119398: Pseudo dice [0.5049, 0.0, 0.794, 0.7807, 0.4019, 0.7875, 0.7188] +2026-04-14 15:22:21.121398: Epoch time: 101.11 s +2026-04-14 15:22:22.380143: +2026-04-14 15:22:22.381853: Epoch 3532 +2026-04-14 15:22:22.383444: Current learning rate: 0.00145 +2026-04-14 15:24:03.577551: train_loss -0.4583 +2026-04-14 15:24:03.583922: val_loss -0.4096 +2026-04-14 15:24:03.585731: Pseudo dice [0.5418, 0.0, 0.787, 0.6193, 0.4991, 0.7627, 0.8711] +2026-04-14 15:24:03.587627: Epoch time: 101.2 s +2026-04-14 15:24:04.799866: +2026-04-14 15:24:04.801377: Epoch 3533 +2026-04-14 15:24:04.802837: Current learning rate: 0.00145 +2026-04-14 15:25:46.149897: train_loss -0.47 +2026-04-14 15:25:46.155474: val_loss -0.4051 +2026-04-14 15:25:46.157499: Pseudo dice [0.407, 0.0, 0.6001, 0.8093, 0.7461, 0.7396, 0.8519] +2026-04-14 15:25:46.159159: Epoch time: 101.35 s +2026-04-14 15:25:47.397709: +2026-04-14 15:25:47.399217: Epoch 3534 +2026-04-14 15:25:47.400759: Current learning rate: 0.00144 +2026-04-14 15:27:28.624378: train_loss -0.4719 +2026-04-14 15:27:28.642788: val_loss -0.3891 +2026-04-14 15:27:28.644310: Pseudo dice [0.3764, 0.0, 0.7434, 0.6377, 0.5301, 0.6894, 0.8056] +2026-04-14 15:27:28.646208: Epoch time: 101.23 s +2026-04-14 15:27:29.885634: +2026-04-14 15:27:29.887327: Epoch 3535 +2026-04-14 15:27:29.898248: Current learning rate: 0.00144 +2026-04-14 15:29:11.299056: train_loss -0.4557 +2026-04-14 15:29:11.305840: val_loss -0.3992 +2026-04-14 15:29:11.308057: Pseudo dice [0.2782, 0.0, 0.714, 0.563, 0.3243, 0.4897, 0.8843] +2026-04-14 15:29:11.309567: Epoch time: 101.42 s +2026-04-14 15:29:12.531179: +2026-04-14 15:29:12.532731: Epoch 3536 +2026-04-14 15:29:12.534146: Current learning rate: 0.00144 +2026-04-14 15:30:53.693637: train_loss -0.4626 +2026-04-14 15:30:53.697731: val_loss -0.3904 +2026-04-14 15:30:53.699341: Pseudo dice [0.3841, 0.0, 0.6535, 0.8241, 0.3428, 0.6248, 0.7862] +2026-04-14 15:30:53.701097: Epoch time: 101.17 s +2026-04-14 15:30:54.930131: +2026-04-14 15:30:54.931475: Epoch 3537 +2026-04-14 15:30:54.932792: Current learning rate: 0.00144 +2026-04-14 15:32:36.281741: train_loss -0.4618 +2026-04-14 15:32:36.286267: val_loss -0.383 +2026-04-14 15:32:36.287908: Pseudo dice [0.4284, 0.0, 0.813, 0.8731, 0.3471, 0.8971, 0.8383] +2026-04-14 15:32:36.289419: Epoch time: 101.35 s +2026-04-14 15:32:37.531032: +2026-04-14 15:32:37.532447: Epoch 3538 +2026-04-14 15:32:37.533728: Current learning rate: 0.00143 +2026-04-14 15:34:18.705572: train_loss -0.453 +2026-04-14 15:34:18.710142: val_loss -0.3795 +2026-04-14 15:34:18.711602: Pseudo dice [0.4482, 0.0, 0.7895, 0.8369, 0.0877, 0.7713, 0.7869] +2026-04-14 15:34:18.713182: Epoch time: 101.18 s +2026-04-14 15:34:19.938067: +2026-04-14 15:34:19.940015: Epoch 3539 +2026-04-14 15:34:19.941490: Current learning rate: 0.00143 +2026-04-14 15:36:02.311571: train_loss -0.4594 +2026-04-14 15:36:02.316220: val_loss -0.4096 +2026-04-14 15:36:02.318177: Pseudo dice [0.5214, 0.0, 0.799, 0.7944, 0.4883, 0.7472, 0.8053] +2026-04-14 15:36:02.319722: Epoch time: 102.38 s +2026-04-14 15:36:03.529348: +2026-04-14 15:36:03.530947: Epoch 3540 +2026-04-14 15:36:03.532549: Current learning rate: 0.00143 +2026-04-14 15:37:44.836741: train_loss -0.4648 +2026-04-14 15:37:44.841428: val_loss -0.4046 +2026-04-14 15:37:44.843926: Pseudo dice [0.4659, 0.0, 0.8082, 0.6916, 0.396, 0.8082, 0.494] +2026-04-14 15:37:44.845895: Epoch time: 101.31 s +2026-04-14 15:37:46.060884: +2026-04-14 15:37:46.062733: Epoch 3541 +2026-04-14 15:37:46.064412: Current learning rate: 0.00142 +2026-04-14 15:39:27.288627: train_loss -0.4501 +2026-04-14 15:39:27.294363: val_loss -0.4078 +2026-04-14 15:39:27.300523: Pseudo dice [0.747, 0.0, 0.6323, 0.5867, 0.5747, 0.8485, 0.6942] +2026-04-14 15:39:27.302307: Epoch time: 101.23 s +2026-04-14 15:39:28.546726: +2026-04-14 15:39:28.548821: Epoch 3542 +2026-04-14 15:39:28.550386: Current learning rate: 0.00142 +2026-04-14 15:41:09.753035: train_loss -0.4628 +2026-04-14 15:41:09.760458: val_loss -0.389 +2026-04-14 15:41:09.762504: Pseudo dice [0.4233, 0.0, 0.7179, 0.6862, 0.5289, 0.798, 0.7676] +2026-04-14 15:41:09.764343: Epoch time: 101.21 s +2026-04-14 15:41:10.973257: +2026-04-14 15:41:10.974890: Epoch 3543 +2026-04-14 15:41:10.976416: Current learning rate: 0.00142 +2026-04-14 15:42:52.292981: train_loss -0.4633 +2026-04-14 15:42:52.298425: val_loss -0.4178 +2026-04-14 15:42:52.300189: Pseudo dice [0.5857, 0.0, 0.7586, 0.93, 0.3982, 0.8207, 0.8786] +2026-04-14 15:42:52.302166: Epoch time: 101.32 s +2026-04-14 15:42:53.529125: +2026-04-14 15:42:53.530656: Epoch 3544 +2026-04-14 15:42:53.532165: Current learning rate: 0.00142 +2026-04-14 15:44:34.959935: train_loss -0.4666 +2026-04-14 15:44:34.966488: val_loss -0.4186 +2026-04-14 15:44:34.968843: Pseudo dice [0.6897, 0.0, 0.8141, 0.709, 0.6133, 0.8375, 0.8434] +2026-04-14 15:44:34.971296: Epoch time: 101.43 s +2026-04-14 15:44:36.193568: +2026-04-14 15:44:36.194866: Epoch 3545 +2026-04-14 15:44:36.196662: Current learning rate: 0.00141 +2026-04-14 15:46:17.402528: train_loss -0.4689 +2026-04-14 15:46:17.411113: val_loss -0.4042 +2026-04-14 15:46:17.412632: Pseudo dice [0.485, 0.0, 0.7066, 0.7691, 0.415, 0.8261, 0.9277] +2026-04-14 15:46:17.414520: Epoch time: 101.21 s +2026-04-14 15:46:17.418280: Yayy! New best EMA pseudo Dice: 0.5745 +2026-04-14 15:46:20.409104: +2026-04-14 15:46:20.411117: Epoch 3546 +2026-04-14 15:46:20.412507: Current learning rate: 0.00141 +2026-04-14 15:48:01.713122: train_loss -0.4639 +2026-04-14 15:48:01.717491: val_loss -0.4261 +2026-04-14 15:48:01.719423: Pseudo dice [0.8005, 0.0, 0.7084, 0.4563, 0.691, 0.821, 0.8331] +2026-04-14 15:48:01.721304: Epoch time: 101.31 s +2026-04-14 15:48:01.722778: Yayy! New best EMA pseudo Dice: 0.5786 +2026-04-14 15:48:04.631041: +2026-04-14 15:48:04.632488: Epoch 3547 +2026-04-14 15:48:04.633928: Current learning rate: 0.00141 +2026-04-14 15:49:46.064163: train_loss -0.4664 +2026-04-14 15:49:46.069444: val_loss -0.4098 +2026-04-14 15:49:46.071052: Pseudo dice [0.7988, 0.0, 0.7298, 0.8483, 0.6246, 0.726, 0.774] +2026-04-14 15:49:46.072468: Epoch time: 101.44 s +2026-04-14 15:49:46.074015: Yayy! New best EMA pseudo Dice: 0.5851 +2026-04-14 15:49:49.083949: +2026-04-14 15:49:49.085394: Epoch 3548 +2026-04-14 15:49:49.086912: Current learning rate: 0.00141 +2026-04-14 15:51:30.426274: train_loss -0.4475 +2026-04-14 15:51:30.431979: val_loss -0.3787 +2026-04-14 15:51:30.433529: Pseudo dice [0.4581, 0.0, 0.8344, 0.7287, 0.4266, 0.8153, 0.6626] +2026-04-14 15:51:30.435082: Epoch time: 101.35 s +2026-04-14 15:51:31.655802: +2026-04-14 15:51:31.657744: Epoch 3549 +2026-04-14 15:51:31.660166: Current learning rate: 0.0014 +2026-04-14 15:53:12.913220: train_loss -0.4721 +2026-04-14 15:53:12.924622: val_loss -0.4002 +2026-04-14 15:53:12.926705: Pseudo dice [0.6961, 0.0, 0.6867, 0.8275, 0.5622, 0.7251, 0.7528] +2026-04-14 15:53:12.931306: Epoch time: 101.26 s +2026-04-14 15:53:14.728441: Yayy! New best EMA pseudo Dice: 0.5851 +2026-04-14 15:53:17.569165: +2026-04-14 15:53:17.570810: Epoch 3550 +2026-04-14 15:53:17.572155: Current learning rate: 0.0014 +2026-04-14 15:54:59.003538: train_loss -0.4492 +2026-04-14 15:54:59.008005: val_loss -0.4049 +2026-04-14 15:54:59.010355: Pseudo dice [0.8042, 0.0, 0.8319, 0.5608, 0.3607, 0.7417, 0.9258] +2026-04-14 15:54:59.011875: Epoch time: 101.44 s +2026-04-14 15:54:59.014292: Yayy! New best EMA pseudo Dice: 0.5869 +2026-04-14 15:55:02.017431: +2026-04-14 15:55:02.019833: Epoch 3551 +2026-04-14 15:55:02.021785: Current learning rate: 0.0014 +2026-04-14 15:56:43.246767: train_loss -0.4571 +2026-04-14 15:56:43.251626: val_loss -0.4073 +2026-04-14 15:56:43.253432: Pseudo dice [0.8133, 0.0, 0.7335, 0.7577, 0.6041, 0.8352, 0.7328] +2026-04-14 15:56:43.255117: Epoch time: 101.23 s +2026-04-14 15:56:43.256679: Yayy! New best EMA pseudo Dice: 0.5922 +2026-04-14 15:56:46.277559: +2026-04-14 15:56:46.279181: Epoch 3552 +2026-04-14 15:56:46.280455: Current learning rate: 0.00139 +2026-04-14 15:58:27.649780: train_loss -0.4692 +2026-04-14 15:58:27.655021: val_loss -0.3882 +2026-04-14 15:58:27.656820: Pseudo dice [0.509, 0.0, 0.6615, 0.8786, 0.5295, 0.7813, 0.8797] +2026-04-14 15:58:27.658551: Epoch time: 101.38 s +2026-04-14 15:58:27.660447: Yayy! New best EMA pseudo Dice: 0.5935 +2026-04-14 15:58:30.753485: +2026-04-14 15:58:30.755484: Epoch 3553 +2026-04-14 15:58:30.756991: Current learning rate: 0.00139 +2026-04-14 16:00:12.251605: train_loss -0.4783 +2026-04-14 16:00:12.256603: val_loss -0.3927 +2026-04-14 16:00:12.260255: Pseudo dice [0.5564, 0.0, 0.7671, 0.596, 0.5318, 0.5922, 0.7691] +2026-04-14 16:00:12.262170: Epoch time: 101.5 s +2026-04-14 16:00:13.525186: +2026-04-14 16:00:13.527057: Epoch 3554 +2026-04-14 16:00:13.528726: Current learning rate: 0.00139 +2026-04-14 16:01:54.845174: train_loss -0.4713 +2026-04-14 16:01:54.849410: val_loss -0.3996 +2026-04-14 16:01:54.851098: Pseudo dice [0.3301, 0.0, 0.7131, 0.8149, 0.4001, 0.7682, 0.8417] +2026-04-14 16:01:54.852357: Epoch time: 101.32 s +2026-04-14 16:01:56.071758: +2026-04-14 16:01:56.073094: Epoch 3555 +2026-04-14 16:01:56.074463: Current learning rate: 0.00139 +2026-04-14 16:03:37.292237: train_loss -0.464 +2026-04-14 16:03:37.298100: val_loss -0.3927 +2026-04-14 16:03:37.299823: Pseudo dice [0.4266, 0.0, 0.7245, 0.2217, 0.4962, 0.8135, 0.7148] +2026-04-14 16:03:37.304081: Epoch time: 101.22 s +2026-04-14 16:03:38.535345: +2026-04-14 16:03:38.536667: Epoch 3556 +2026-04-14 16:03:38.537993: Current learning rate: 0.00138 +2026-04-14 16:05:20.908421: train_loss -0.4649 +2026-04-14 16:05:20.913205: val_loss -0.406 +2026-04-14 16:05:20.914689: Pseudo dice [0.7589, 0.0, 0.7028, 0.396, 0.481, 0.8222, 0.6106] +2026-04-14 16:05:20.916479: Epoch time: 102.38 s +2026-04-14 16:05:22.142313: +2026-04-14 16:05:22.146046: Epoch 3557 +2026-04-14 16:05:22.148000: Current learning rate: 0.00138 +2026-04-14 16:07:03.503596: train_loss -0.477 +2026-04-14 16:07:03.509190: val_loss -0.3939 +2026-04-14 16:07:03.525020: Pseudo dice [0.5674, 0.0, 0.7995, 0.8216, 0.4724, 0.8188, 0.7541] +2026-04-14 16:07:03.526931: Epoch time: 101.36 s +2026-04-14 16:07:04.757445: +2026-04-14 16:07:04.759840: Epoch 3558 +2026-04-14 16:07:04.761973: Current learning rate: 0.00138 +2026-04-14 16:08:46.107881: train_loss -0.4769 +2026-04-14 16:08:46.113307: val_loss -0.3805 +2026-04-14 16:08:46.118050: Pseudo dice [0.8023, 0.0, 0.7415, 0.8561, 0.3914, 0.7867, 0.5242] +2026-04-14 16:08:46.119896: Epoch time: 101.35 s +2026-04-14 16:08:47.343672: +2026-04-14 16:08:47.345497: Epoch 3559 +2026-04-14 16:08:47.346929: Current learning rate: 0.00137 +2026-04-14 16:10:28.733232: train_loss -0.4715 +2026-04-14 16:10:28.739261: val_loss -0.412 +2026-04-14 16:10:28.741206: Pseudo dice [0.7375, 0.0, 0.7622, 0.7243, 0.583, 0.8302, 0.8801] +2026-04-14 16:10:28.743066: Epoch time: 101.39 s +2026-04-14 16:10:29.990388: +2026-04-14 16:10:29.992333: Epoch 3560 +2026-04-14 16:10:29.994165: Current learning rate: 0.00137 +2026-04-14 16:12:11.278118: train_loss -0.4705 +2026-04-14 16:12:11.282838: val_loss -0.4038 +2026-04-14 16:12:11.285724: Pseudo dice [0.5838, 0.0, 0.8508, 0.826, 0.4734, 0.8635, 0.5969] +2026-04-14 16:12:11.290210: Epoch time: 101.29 s +2026-04-14 16:12:12.509898: +2026-04-14 16:12:12.511243: Epoch 3561 +2026-04-14 16:12:12.512832: Current learning rate: 0.00137 +2026-04-14 16:13:53.874000: train_loss -0.4767 +2026-04-14 16:13:53.879287: val_loss -0.4341 +2026-04-14 16:13:53.881087: Pseudo dice [0.7555, 0.0, 0.8912, 0.7888, 0.7423, 0.7961, 0.8922] +2026-04-14 16:13:53.882436: Epoch time: 101.37 s +2026-04-14 16:13:53.884099: Yayy! New best EMA pseudo Dice: 0.5956 +2026-04-14 16:13:56.890602: +2026-04-14 16:13:56.892157: Epoch 3562 +2026-04-14 16:13:56.894459: Current learning rate: 0.00137 +2026-04-14 16:15:38.408303: train_loss -0.4632 +2026-04-14 16:15:38.412857: val_loss -0.3997 +2026-04-14 16:15:38.414433: Pseudo dice [0.8135, 0.0, 0.7937, 0.2863, 0.4687, 0.7189, 0.8998] +2026-04-14 16:15:38.416511: Epoch time: 101.52 s +2026-04-14 16:15:39.620221: +2026-04-14 16:15:39.622851: Epoch 3563 +2026-04-14 16:15:39.624337: Current learning rate: 0.00136 +2026-04-14 16:17:20.860677: train_loss -0.4657 +2026-04-14 16:17:20.865878: val_loss -0.4019 +2026-04-14 16:17:20.868307: Pseudo dice [0.369, 0.0, 0.8183, 0.8099, 0.8222, 0.8169, 0.6193] +2026-04-14 16:17:20.870185: Epoch time: 101.24 s +2026-04-14 16:17:22.085063: +2026-04-14 16:17:22.087549: Epoch 3564 +2026-04-14 16:17:22.089710: Current learning rate: 0.00136 +2026-04-14 16:19:03.450526: train_loss -0.4744 +2026-04-14 16:19:03.457307: val_loss -0.396 +2026-04-14 16:19:03.458897: Pseudo dice [0.5396, 0.0, 0.7923, 0.874, 0.4582, 0.7694, 0.8623] +2026-04-14 16:19:03.461015: Epoch time: 101.37 s +2026-04-14 16:19:03.463183: Yayy! New best EMA pseudo Dice: 0.5963 +2026-04-14 16:19:06.431908: +2026-04-14 16:19:06.433666: Epoch 3565 +2026-04-14 16:19:06.435596: Current learning rate: 0.00136 +2026-04-14 16:20:47.785163: train_loss -0.4739 +2026-04-14 16:20:47.789643: val_loss -0.4044 +2026-04-14 16:20:47.791151: Pseudo dice [0.6047, 0.0, 0.6487, 0.8526, 0.5214, 0.8326, 0.8466] +2026-04-14 16:20:47.792899: Epoch time: 101.36 s +2026-04-14 16:20:47.794470: Yayy! New best EMA pseudo Dice: 0.5982 +2026-04-14 16:20:50.809423: +2026-04-14 16:20:50.811171: Epoch 3566 +2026-04-14 16:20:50.813253: Current learning rate: 0.00135 +2026-04-14 16:22:32.738733: train_loss -0.4688 +2026-04-14 16:22:32.744108: val_loss -0.4273 +2026-04-14 16:22:32.745731: Pseudo dice [0.2938, 0.0, 0.8021, 0.48, 0.4472, 0.7865, 0.8876] +2026-04-14 16:22:32.747527: Epoch time: 101.93 s +2026-04-14 16:22:33.998478: +2026-04-14 16:22:34.000276: Epoch 3567 +2026-04-14 16:22:34.002374: Current learning rate: 0.00135 +2026-04-14 16:24:15.366943: train_loss -0.4786 +2026-04-14 16:24:15.373404: val_loss -0.3758 +2026-04-14 16:24:15.375742: Pseudo dice [0.3762, 0.0, 0.7057, 0.3751, 0.5025, 0.7569, 0.9024] +2026-04-14 16:24:15.377550: Epoch time: 101.37 s +2026-04-14 16:24:16.614285: +2026-04-14 16:24:16.616034: Epoch 3568 +2026-04-14 16:24:16.617393: Current learning rate: 0.00135 +2026-04-14 16:25:58.157388: train_loss -0.4769 +2026-04-14 16:25:58.162287: val_loss -0.385 +2026-04-14 16:25:58.163997: Pseudo dice [0.5696, 0.0, 0.7998, 0.3796, 0.3765, 0.8475, 0.7672] +2026-04-14 16:25:58.165986: Epoch time: 101.55 s +2026-04-14 16:25:59.391170: +2026-04-14 16:25:59.393075: Epoch 3569 +2026-04-14 16:25:59.394609: Current learning rate: 0.00135 +2026-04-14 16:27:40.626648: train_loss -0.4786 +2026-04-14 16:27:40.631134: val_loss -0.4468 +2026-04-14 16:27:40.632865: Pseudo dice [0.8886, 0.0, 0.7088, 0.8928, 0.7071, 0.7352, 0.903] +2026-04-14 16:27:40.634736: Epoch time: 101.24 s +2026-04-14 16:27:41.846879: +2026-04-14 16:27:41.848558: Epoch 3570 +2026-04-14 16:27:41.849983: Current learning rate: 0.00134 +2026-04-14 16:29:23.157112: train_loss -0.464 +2026-04-14 16:29:23.162340: val_loss -0.4132 +2026-04-14 16:29:23.164073: Pseudo dice [0.7351, 0.0, 0.7126, 0.8845, 0.5597, 0.8321, 0.8035] +2026-04-14 16:29:23.165545: Epoch time: 101.31 s +2026-04-14 16:29:24.389034: +2026-04-14 16:29:24.390796: Epoch 3571 +2026-04-14 16:29:24.392197: Current learning rate: 0.00134 +2026-04-14 16:31:05.735496: train_loss -0.4615 +2026-04-14 16:31:05.739974: val_loss -0.4015 +2026-04-14 16:31:05.742059: Pseudo dice [0.3771, 0.0, 0.5992, 0.1446, 0.5526, 0.7889, 0.7078] +2026-04-14 16:31:05.744252: Epoch time: 101.35 s +2026-04-14 16:31:06.955522: +2026-04-14 16:31:06.957556: Epoch 3572 +2026-04-14 16:31:06.959927: Current learning rate: 0.00134 +2026-04-14 16:32:48.268985: train_loss -0.4756 +2026-04-14 16:32:48.274033: val_loss -0.3907 +2026-04-14 16:32:48.275968: Pseudo dice [0.388, 0.0, 0.649, 0.8557, 0.5917, 0.5207, 0.6637] +2026-04-14 16:32:48.277822: Epoch time: 101.32 s +2026-04-14 16:32:49.504354: +2026-04-14 16:32:49.506126: Epoch 3573 +2026-04-14 16:32:49.507664: Current learning rate: 0.00134 +2026-04-14 16:34:30.891115: train_loss -0.4602 +2026-04-14 16:34:30.900079: val_loss -0.4132 +2026-04-14 16:34:30.902053: Pseudo dice [0.3466, 0.0, 0.5362, 0.6844, 0.5817, 0.7858, 0.9355] +2026-04-14 16:34:30.904152: Epoch time: 101.39 s +2026-04-14 16:34:32.126557: +2026-04-14 16:34:32.129457: Epoch 3574 +2026-04-14 16:34:32.131496: Current learning rate: 0.00133 +2026-04-14 16:36:13.504307: train_loss -0.4687 +2026-04-14 16:36:13.508843: val_loss -0.4236 +2026-04-14 16:36:13.510845: Pseudo dice [0.4138, 0.0, 0.687, 0.6347, 0.5108, 0.8704, 0.8577] +2026-04-14 16:36:13.512610: Epoch time: 101.38 s +2026-04-14 16:36:15.780347: +2026-04-14 16:36:15.781716: Epoch 3575 +2026-04-14 16:36:15.783068: Current learning rate: 0.00133 +2026-04-14 16:37:57.162054: train_loss -0.4721 +2026-04-14 16:37:57.167348: val_loss -0.423 +2026-04-14 16:37:57.169802: Pseudo dice [0.6498, 0.0, 0.794, 0.9306, 0.5444, 0.7014, 0.8103] +2026-04-14 16:37:57.172097: Epoch time: 101.38 s +2026-04-14 16:37:58.390082: +2026-04-14 16:37:58.391702: Epoch 3576 +2026-04-14 16:37:58.393395: Current learning rate: 0.00133 +2026-04-14 16:39:39.600626: train_loss -0.4746 +2026-04-14 16:39:39.606704: val_loss -0.4043 +2026-04-14 16:39:39.608479: Pseudo dice [0.4193, 0.0, 0.4052, 0.8757, 0.4736, 0.7972, 0.8793] +2026-04-14 16:39:39.610351: Epoch time: 101.21 s +2026-04-14 16:39:40.844866: +2026-04-14 16:39:40.859847: Epoch 3577 +2026-04-14 16:39:40.861280: Current learning rate: 0.00132 +2026-04-14 16:41:22.105401: train_loss -0.4605 +2026-04-14 16:41:22.109510: val_loss -0.4265 +2026-04-14 16:41:22.111697: Pseudo dice [0.8542, 0.0, 0.8937, 0.913, 0.6584, 0.8501, 0.9443] +2026-04-14 16:41:22.113407: Epoch time: 101.26 s +2026-04-14 16:41:23.361747: +2026-04-14 16:41:23.363332: Epoch 3578 +2026-04-14 16:41:23.364804: Current learning rate: 0.00132 +2026-04-14 16:43:04.707266: train_loss -0.4719 +2026-04-14 16:43:04.711806: val_loss -0.4187 +2026-04-14 16:43:04.713742: Pseudo dice [0.7119, 0.0, 0.8485, 0.5173, 0.4483, 0.8512, 0.9368] +2026-04-14 16:43:04.716058: Epoch time: 101.35 s +2026-04-14 16:43:05.946235: +2026-04-14 16:43:05.947913: Epoch 3579 +2026-04-14 16:43:05.949392: Current learning rate: 0.00132 +2026-04-14 16:44:47.318748: train_loss -0.4572 +2026-04-14 16:44:47.323503: val_loss -0.3928 +2026-04-14 16:44:47.325275: Pseudo dice [0.6854, 0.0, 0.6434, 0.5318, 0.369, 0.4936, 0.7469] +2026-04-14 16:44:47.326949: Epoch time: 101.38 s +2026-04-14 16:44:48.534729: +2026-04-14 16:44:48.536320: Epoch 3580 +2026-04-14 16:44:48.537799: Current learning rate: 0.00132 +2026-04-14 16:46:30.020929: train_loss -0.4484 +2026-04-14 16:46:30.043139: val_loss -0.367 +2026-04-14 16:46:30.045515: Pseudo dice [0.1488, 0.0, 0.788, 0.7351, 0.5284, 0.796, 0.3] +2026-04-14 16:46:30.047353: Epoch time: 101.49 s +2026-04-14 16:46:31.257957: +2026-04-14 16:46:31.259809: Epoch 3581 +2026-04-14 16:46:31.261356: Current learning rate: 0.00131 +2026-04-14 16:48:12.692817: train_loss -0.4548 +2026-04-14 16:48:12.697761: val_loss -0.4005 +2026-04-14 16:48:12.699683: Pseudo dice [0.2549, 0.0, 0.8111, 0.6606, 0.6346, 0.8384, 0.8584] +2026-04-14 16:48:12.701438: Epoch time: 101.44 s +2026-04-14 16:48:13.904635: +2026-04-14 16:48:13.906687: Epoch 3582 +2026-04-14 16:48:13.908163: Current learning rate: 0.00131 +2026-04-14 16:49:55.278897: train_loss -0.4556 +2026-04-14 16:49:55.291734: val_loss -0.42 +2026-04-14 16:49:55.301825: Pseudo dice [0.4859, 0.0, 0.6585, 0.8483, 0.6152, 0.7431, 0.7843] +2026-04-14 16:49:55.303667: Epoch time: 101.38 s +2026-04-14 16:49:56.538831: +2026-04-14 16:49:56.540582: Epoch 3583 +2026-04-14 16:49:56.544853: Current learning rate: 0.00131 +2026-04-14 16:51:37.901314: train_loss -0.4638 +2026-04-14 16:51:37.907133: val_loss -0.415 +2026-04-14 16:51:37.908615: Pseudo dice [0.5718, 0.0, 0.8871, 0.5421, 0.5958, 0.8197, 0.9419] +2026-04-14 16:51:37.923374: Epoch time: 101.37 s +2026-04-14 16:51:39.156216: +2026-04-14 16:51:39.159927: Epoch 3584 +2026-04-14 16:51:39.161199: Current learning rate: 0.0013 +2026-04-14 16:53:20.567211: train_loss -0.4636 +2026-04-14 16:53:20.574306: val_loss -0.4031 +2026-04-14 16:53:20.576246: Pseudo dice [0.4223, 0.0, 0.7273, 0.7816, 0.4564, 0.8383, 0.4417] +2026-04-14 16:53:20.578072: Epoch time: 101.41 s +2026-04-14 16:53:21.798290: +2026-04-14 16:53:21.799875: Epoch 3585 +2026-04-14 16:53:21.801598: Current learning rate: 0.0013 +2026-04-14 16:55:03.147850: train_loss -0.4715 +2026-04-14 16:55:03.152443: val_loss -0.4301 +2026-04-14 16:55:03.154214: Pseudo dice [0.7658, 0.0, 0.775, 0.8263, 0.6063, 0.7756, 0.9441] +2026-04-14 16:55:03.156084: Epoch time: 101.35 s +2026-04-14 16:55:04.388409: +2026-04-14 16:55:04.395066: Epoch 3586 +2026-04-14 16:55:04.409379: Current learning rate: 0.0013 +2026-04-14 16:56:45.841067: train_loss -0.4743 +2026-04-14 16:56:45.845810: val_loss -0.3624 +2026-04-14 16:56:45.847634: Pseudo dice [0.3369, 0.0, 0.6648, 0.7635, 0.3858, 0.8396, 0.7523] +2026-04-14 16:56:45.849117: Epoch time: 101.46 s +2026-04-14 16:56:47.066279: +2026-04-14 16:56:47.068217: Epoch 3587 +2026-04-14 16:56:47.069879: Current learning rate: 0.0013 +2026-04-14 16:58:28.250593: train_loss -0.471 +2026-04-14 16:58:28.257693: val_loss -0.3936 +2026-04-14 16:58:28.263350: Pseudo dice [0.4471, 0.0, 0.7068, 0.9008, 0.6496, 0.8421, 0.6668] +2026-04-14 16:58:28.264719: Epoch time: 101.19 s +2026-04-14 16:58:29.475297: +2026-04-14 16:58:29.476787: Epoch 3588 +2026-04-14 16:58:29.477953: Current learning rate: 0.00129 +2026-04-14 17:00:10.833324: train_loss -0.4667 +2026-04-14 17:00:10.841084: val_loss -0.3991 +2026-04-14 17:00:10.842751: Pseudo dice [0.3902, 0.0, 0.8445, 0.7534, 0.5222, 0.7979, 0.5859] +2026-04-14 17:00:10.844412: Epoch time: 101.36 s +2026-04-14 17:00:12.077765: +2026-04-14 17:00:12.079441: Epoch 3589 +2026-04-14 17:00:12.080697: Current learning rate: 0.00129 +2026-04-14 17:01:53.489243: train_loss -0.4662 +2026-04-14 17:01:53.497203: val_loss -0.4336 +2026-04-14 17:01:53.498955: Pseudo dice [0.5405, 0.0, 0.6986, 0.8934, 0.6919, 0.7878, 0.8517] +2026-04-14 17:01:53.507731: Epoch time: 101.41 s +2026-04-14 17:01:54.744975: +2026-04-14 17:01:54.746878: Epoch 3590 +2026-04-14 17:01:54.748497: Current learning rate: 0.00129 +2026-04-14 17:03:36.139577: train_loss -0.4662 +2026-04-14 17:03:36.144049: val_loss -0.4003 +2026-04-14 17:03:36.145591: Pseudo dice [0.7238, 0.0, 0.81, 0.8608, 0.185, 0.8441, 0.7297] +2026-04-14 17:03:36.146872: Epoch time: 101.4 s +2026-04-14 17:03:37.467467: +2026-04-14 17:03:37.469283: Epoch 3591 +2026-04-14 17:03:37.470865: Current learning rate: 0.00128 +2026-04-14 17:05:19.097508: train_loss -0.464 +2026-04-14 17:05:19.103023: val_loss -0.43 +2026-04-14 17:05:19.104893: Pseudo dice [0.7446, 0.0, 0.8026, 0.3857, 0.6064, 0.822, 0.8303] +2026-04-14 17:05:19.106758: Epoch time: 101.63 s +2026-04-14 17:05:20.344097: +2026-04-14 17:05:20.345623: Epoch 3592 +2026-04-14 17:05:20.347028: Current learning rate: 0.00128 +2026-04-14 17:07:01.992085: train_loss -0.4672 +2026-04-14 17:07:01.998744: val_loss -0.3939 +2026-04-14 17:07:02.000528: Pseudo dice [0.3458, 0.0, 0.7416, 0.8533, 0.4722, 0.7739, 0.7827] +2026-04-14 17:07:02.002280: Epoch time: 101.65 s +2026-04-14 17:07:03.226475: +2026-04-14 17:07:03.228431: Epoch 3593 +2026-04-14 17:07:03.230333: Current learning rate: 0.00128 +2026-04-14 17:08:44.736608: train_loss -0.4813 +2026-04-14 17:08:44.742282: val_loss -0.3626 +2026-04-14 17:08:44.743929: Pseudo dice [0.7469, 0.0, 0.5555, 0.6666, 0.3626, 0.6886, 0.7362] +2026-04-14 17:08:44.745694: Epoch time: 101.51 s +2026-04-14 17:08:45.952713: +2026-04-14 17:08:45.954399: Epoch 3594 +2026-04-14 17:08:45.956040: Current learning rate: 0.00128 +2026-04-14 17:10:27.562859: train_loss -0.4717 +2026-04-14 17:10:27.567728: val_loss -0.4143 +2026-04-14 17:10:27.569584: Pseudo dice [0.4931, 0.0, 0.8728, 0.8471, 0.6452, 0.8575, 0.7175] +2026-04-14 17:10:27.571767: Epoch time: 101.61 s +2026-04-14 17:10:28.785165: +2026-04-14 17:10:28.786762: Epoch 3595 +2026-04-14 17:10:28.788306: Current learning rate: 0.00127 +2026-04-14 17:12:11.344507: train_loss -0.4765 +2026-04-14 17:12:11.349425: val_loss -0.4233 +2026-04-14 17:12:11.351213: Pseudo dice [0.5028, 0.0, 0.8853, 0.8223, 0.455, 0.6401, 0.8501] +2026-04-14 17:12:11.352914: Epoch time: 102.56 s +2026-04-14 17:12:12.570165: +2026-04-14 17:12:12.572117: Epoch 3596 +2026-04-14 17:12:12.573581: Current learning rate: 0.00127 +2026-04-14 17:13:54.259087: train_loss -0.463 +2026-04-14 17:13:54.264088: val_loss -0.3927 +2026-04-14 17:13:54.265745: Pseudo dice [0.6263, 0.0, 0.8106, 0.8884, 0.5818, 0.6034, 0.4168] +2026-04-14 17:13:54.267516: Epoch time: 101.69 s +2026-04-14 17:13:55.481808: +2026-04-14 17:13:55.483417: Epoch 3597 +2026-04-14 17:13:55.484889: Current learning rate: 0.00127 +2026-04-14 17:15:36.904589: train_loss -0.47 +2026-04-14 17:15:36.917453: val_loss -0.3645 +2026-04-14 17:15:36.918932: Pseudo dice [0.5211, 0.0, 0.6713, 0.8699, 0.2932, 0.7128, 0.4068] +2026-04-14 17:15:36.921707: Epoch time: 101.43 s +2026-04-14 17:15:38.133068: +2026-04-14 17:15:38.134853: Epoch 3598 +2026-04-14 17:15:38.136203: Current learning rate: 0.00126 +2026-04-14 17:17:19.625556: train_loss -0.4673 +2026-04-14 17:17:19.629847: val_loss -0.3707 +2026-04-14 17:17:19.631469: Pseudo dice [0.4291, 0.0, 0.3857, 0.5364, 0.5385, 0.8494, 0.4682] +2026-04-14 17:17:19.633021: Epoch time: 101.5 s +2026-04-14 17:17:20.858934: +2026-04-14 17:17:20.860497: Epoch 3599 +2026-04-14 17:17:20.861942: Current learning rate: 0.00126 +2026-04-14 17:19:02.390120: train_loss -0.45 +2026-04-14 17:19:02.394585: val_loss -0.3837 +2026-04-14 17:19:02.396126: Pseudo dice [0.7007, 0.0, 0.6209, 0.6158, 0.4839, 0.7667, 0.7257] +2026-04-14 17:19:02.397878: Epoch time: 101.53 s +2026-04-14 17:19:05.371821: +2026-04-14 17:19:05.373662: Epoch 3600 +2026-04-14 17:19:05.375209: Current learning rate: 0.00126 +2026-04-14 17:20:46.730416: train_loss -0.4663 +2026-04-14 17:20:46.736257: val_loss -0.4068 +2026-04-14 17:20:46.737853: Pseudo dice [0.2375, 0.0, 0.7892, 0.6536, 0.6457, 0.8778, 0.834] +2026-04-14 17:20:46.739875: Epoch time: 101.36 s +2026-04-14 17:20:47.964650: +2026-04-14 17:20:47.966467: Epoch 3601 +2026-04-14 17:20:47.968715: Current learning rate: 0.00126 +2026-04-14 17:22:29.453457: train_loss -0.4584 +2026-04-14 17:22:29.457676: val_loss -0.4034 +2026-04-14 17:22:29.459354: Pseudo dice [0.7681, 0.0, 0.7198, 0.9024, 0.5938, 0.6628, 0.515] +2026-04-14 17:22:29.460903: Epoch time: 101.49 s +2026-04-14 17:22:30.676425: +2026-04-14 17:22:30.678038: Epoch 3602 +2026-04-14 17:22:30.679407: Current learning rate: 0.00125 +2026-04-14 17:24:11.947848: train_loss -0.4704 +2026-04-14 17:24:11.952392: val_loss -0.3891 +2026-04-14 17:24:11.954404: Pseudo dice [0.5702, 0.0, 0.8299, 0.7818, 0.3548, 0.7309, 0.9092] +2026-04-14 17:24:11.956500: Epoch time: 101.27 s +2026-04-14 17:24:13.197740: +2026-04-14 17:24:13.199473: Epoch 3603 +2026-04-14 17:24:13.200892: Current learning rate: 0.00125 +2026-04-14 17:25:54.570467: train_loss -0.4797 +2026-04-14 17:25:54.575251: val_loss -0.4186 +2026-04-14 17:25:54.577549: Pseudo dice [0.4758, 0.0, 0.648, 0.9388, 0.5494, 0.8044, 0.882] +2026-04-14 17:25:54.579330: Epoch time: 101.38 s +2026-04-14 17:25:55.791473: +2026-04-14 17:25:55.795087: Epoch 3604 +2026-04-14 17:25:55.796522: Current learning rate: 0.00125 +2026-04-14 17:27:37.158181: train_loss -0.4619 +2026-04-14 17:27:37.164253: val_loss -0.3926 +2026-04-14 17:27:37.166393: Pseudo dice [0.7845, 0.0, 0.6246, 0.574, 0.5366, 0.8485, 0.6911] +2026-04-14 17:27:37.169906: Epoch time: 101.37 s +2026-04-14 17:27:38.398237: +2026-04-14 17:27:38.400681: Epoch 3605 +2026-04-14 17:27:38.403408: Current learning rate: 0.00124 +2026-04-14 17:29:19.623205: train_loss -0.4754 +2026-04-14 17:29:19.629656: val_loss -0.4009 +2026-04-14 17:29:19.631983: Pseudo dice [0.4077, 0.0, 0.8023, 0.7317, 0.5996, 0.8203, 0.473] +2026-04-14 17:29:19.635516: Epoch time: 101.23 s +2026-04-14 17:29:20.859241: +2026-04-14 17:29:20.860780: Epoch 3606 +2026-04-14 17:29:20.862255: Current learning rate: 0.00124 +2026-04-14 17:31:02.092013: train_loss -0.4683 +2026-04-14 17:31:02.105136: val_loss -0.3942 +2026-04-14 17:31:02.111655: Pseudo dice [0.3344, 0.0, 0.8474, 0.8842, 0.5604, 0.8416, 0.8641] +2026-04-14 17:31:02.113767: Epoch time: 101.24 s +2026-04-14 17:31:03.315024: +2026-04-14 17:31:03.317544: Epoch 3607 +2026-04-14 17:31:03.319148: Current learning rate: 0.00124 +2026-04-14 17:32:44.541506: train_loss -0.4687 +2026-04-14 17:32:44.545795: val_loss -0.3766 +2026-04-14 17:32:44.547791: Pseudo dice [0.5192, 0.0, 0.6701, 0.6956, 0.5107, 0.687, 0.9233] +2026-04-14 17:32:44.549264: Epoch time: 101.23 s +2026-04-14 17:32:45.748748: +2026-04-14 17:32:45.750263: Epoch 3608 +2026-04-14 17:32:45.751557: Current learning rate: 0.00124 +2026-04-14 17:34:27.429306: train_loss -0.476 +2026-04-14 17:34:27.435386: val_loss -0.4294 +2026-04-14 17:34:27.437253: Pseudo dice [0.8698, 0.0, 0.849, 0.865, 0.7106, 0.7104, 0.9262] +2026-04-14 17:34:27.438854: Epoch time: 101.68 s +2026-04-14 17:34:28.656937: +2026-04-14 17:34:28.658459: Epoch 3609 +2026-04-14 17:34:28.659702: Current learning rate: 0.00123 +2026-04-14 17:36:10.208907: train_loss -0.4586 +2026-04-14 17:36:10.213449: val_loss -0.3882 +2026-04-14 17:36:10.216625: Pseudo dice [0.7812, 0.0, 0.5358, 0.8232, 0.5574, 0.7514, 0.9087] +2026-04-14 17:36:10.218113: Epoch time: 101.56 s +2026-04-14 17:36:11.441411: +2026-04-14 17:36:11.443378: Epoch 3610 +2026-04-14 17:36:11.444919: Current learning rate: 0.00123 +2026-04-14 17:37:53.116611: train_loss -0.4771 +2026-04-14 17:37:53.121281: val_loss -0.412 +2026-04-14 17:37:53.122928: Pseudo dice [0.7311, 0.0, 0.7376, 0.8958, 0.5459, 0.6261, 0.627] +2026-04-14 17:37:53.124256: Epoch time: 101.68 s +2026-04-14 17:37:54.345421: +2026-04-14 17:37:54.347892: Epoch 3611 +2026-04-14 17:37:54.349538: Current learning rate: 0.00123 +2026-04-14 17:39:35.909142: train_loss -0.4599 +2026-04-14 17:39:35.919988: val_loss -0.4203 +2026-04-14 17:39:35.922103: Pseudo dice [0.4395, 0.0, 0.7279, 0.8526, 0.6809, 0.6945, 0.9349] +2026-04-14 17:39:35.924189: Epoch time: 101.57 s +2026-04-14 17:39:37.151694: +2026-04-14 17:39:37.153647: Epoch 3612 +2026-04-14 17:39:37.155297: Current learning rate: 0.00122 +2026-04-14 17:41:18.725687: train_loss -0.4625 +2026-04-14 17:41:18.730617: val_loss -0.3915 +2026-04-14 17:41:18.732303: Pseudo dice [0.3981, 0.0, 0.7123, 0.8046, 0.3846, 0.6077, 0.8941] +2026-04-14 17:41:18.734441: Epoch time: 101.58 s +2026-04-14 17:41:19.962671: +2026-04-14 17:41:19.964775: Epoch 3613 +2026-04-14 17:41:19.966547: Current learning rate: 0.00122 +2026-04-14 17:43:01.687174: train_loss -0.4714 +2026-04-14 17:43:01.694545: val_loss -0.4097 +2026-04-14 17:43:01.701616: Pseudo dice [0.5057, 0.0, 0.6453, 0.8841, 0.6152, 0.822, 0.8756] +2026-04-14 17:43:01.703494: Epoch time: 101.73 s +2026-04-14 17:43:02.923753: +2026-04-14 17:43:02.925386: Epoch 3614 +2026-04-14 17:43:02.927003: Current learning rate: 0.00122 +2026-04-14 17:44:44.680626: train_loss -0.4815 +2026-04-14 17:44:44.684664: val_loss -0.3796 +2026-04-14 17:44:44.686243: Pseudo dice [0.1479, 0.0, 0.5614, 0.8741, 0.4832, 0.6496, 0.6043] +2026-04-14 17:44:44.688432: Epoch time: 101.76 s +2026-04-14 17:44:46.964841: +2026-04-14 17:44:46.974744: Epoch 3615 +2026-04-14 17:44:46.977328: Current learning rate: 0.00122 +2026-04-14 17:46:28.767195: train_loss -0.4568 +2026-04-14 17:46:28.771419: val_loss -0.4025 +2026-04-14 17:46:28.772918: Pseudo dice [0.7824, 0.0, 0.5334, 0.8362, 0.4801, 0.6103, 0.8848] +2026-04-14 17:46:28.774295: Epoch time: 101.81 s +2026-04-14 17:46:29.990742: +2026-04-14 17:46:29.992328: Epoch 3616 +2026-04-14 17:46:29.993741: Current learning rate: 0.00121 +2026-04-14 17:48:12.006865: train_loss -0.4681 +2026-04-14 17:48:12.013538: val_loss -0.4165 +2026-04-14 17:48:12.015623: Pseudo dice [0.753, 0.0, 0.8147, 0.8707, 0.6293, 0.7845, 0.8284] +2026-04-14 17:48:12.017495: Epoch time: 102.02 s +2026-04-14 17:48:13.221564: +2026-04-14 17:48:13.223956: Epoch 3617 +2026-04-14 17:48:13.225288: Current learning rate: 0.00121 +2026-04-14 17:49:55.123614: train_loss -0.4776 +2026-04-14 17:49:55.128117: val_loss -0.3433 +2026-04-14 17:49:55.129687: Pseudo dice [0.3495, 0.0, 0.6486, 0.4733, 0.3, 0.828, 0.5936] +2026-04-14 17:49:55.131805: Epoch time: 101.91 s +2026-04-14 17:49:56.339545: +2026-04-14 17:49:56.344508: Epoch 3618 +2026-04-14 17:49:56.346092: Current learning rate: 0.00121 +2026-04-14 17:51:38.518059: train_loss -0.468 +2026-04-14 17:51:38.527451: val_loss -0.3723 +2026-04-14 17:51:38.529116: Pseudo dice [0.3951, 0.0, 0.6051, 0.8617, 0.4992, 0.7969, 0.8462] +2026-04-14 17:51:38.531301: Epoch time: 102.18 s +2026-04-14 17:51:39.770285: +2026-04-14 17:51:39.772014: Epoch 3619 +2026-04-14 17:51:39.773591: Current learning rate: 0.0012 +2026-04-14 17:53:21.508955: train_loss -0.4667 +2026-04-14 17:53:21.513391: val_loss -0.4277 +2026-04-14 17:53:21.514833: Pseudo dice [0.8817, 0.0, 0.8398, 0.7837, 0.582, 0.8376, 0.7978] +2026-04-14 17:53:21.516182: Epoch time: 101.74 s +2026-04-14 17:53:22.735453: +2026-04-14 17:53:22.737460: Epoch 3620 +2026-04-14 17:53:22.739437: Current learning rate: 0.0012 +2026-04-14 17:55:04.363019: train_loss -0.4657 +2026-04-14 17:55:04.367733: val_loss -0.4199 +2026-04-14 17:55:04.369448: Pseudo dice [0.6196, 0.0, 0.666, 0.8385, 0.4254, 0.8937, 0.8957] +2026-04-14 17:55:04.371230: Epoch time: 101.63 s +2026-04-14 17:55:05.585191: +2026-04-14 17:55:05.586831: Epoch 3621 +2026-04-14 17:55:05.588497: Current learning rate: 0.0012 +2026-04-14 17:56:47.348388: train_loss -0.4708 +2026-04-14 17:56:47.353546: val_loss -0.3938 +2026-04-14 17:56:47.355337: Pseudo dice [0.3203, 0.0, 0.7442, 0.7189, 0.568, 0.8152, 0.7378] +2026-04-14 17:56:47.357063: Epoch time: 101.77 s +2026-04-14 17:56:48.568512: +2026-04-14 17:56:48.570194: Epoch 3622 +2026-04-14 17:56:48.571788: Current learning rate: 0.0012 +2026-04-14 17:58:30.270947: train_loss -0.4725 +2026-04-14 17:58:30.277509: val_loss -0.3958 +2026-04-14 17:58:30.279197: Pseudo dice [0.6315, 0.0, 0.7386, 0.7089, 0.5844, 0.8299, 0.8815] +2026-04-14 17:58:30.281410: Epoch time: 101.71 s +2026-04-14 17:58:31.498302: +2026-04-14 17:58:31.499997: Epoch 3623 +2026-04-14 17:58:31.501466: Current learning rate: 0.00119 +2026-04-14 18:00:13.195057: train_loss -0.4691 +2026-04-14 18:00:13.201127: val_loss -0.3982 +2026-04-14 18:00:13.202965: Pseudo dice [0.7175, 0.0, 0.7824, 0.8348, 0.4665, 0.7571, 0.889] +2026-04-14 18:00:13.204430: Epoch time: 101.7 s +2026-04-14 18:00:14.423373: +2026-04-14 18:00:14.425549: Epoch 3624 +2026-04-14 18:00:14.427575: Current learning rate: 0.00119 +2026-04-14 18:01:56.164604: train_loss -0.4754 +2026-04-14 18:01:56.189705: val_loss -0.4049 +2026-04-14 18:01:56.191439: Pseudo dice [0.4745, 0.0, 0.7555, 0.8929, 0.483, 0.8353, 0.8476] +2026-04-14 18:01:56.192832: Epoch time: 101.74 s +2026-04-14 18:01:57.407318: +2026-04-14 18:01:57.409187: Epoch 3625 +2026-04-14 18:01:57.410678: Current learning rate: 0.00119 +2026-04-14 18:03:39.119200: train_loss -0.4809 +2026-04-14 18:03:39.123587: val_loss -0.3933 +2026-04-14 18:03:39.125135: Pseudo dice [0.7032, 0.0, 0.7964, 0.8608, 0.6524, 0.7798, 0.6843] +2026-04-14 18:03:39.126462: Epoch time: 101.72 s +2026-04-14 18:03:39.128453: Yayy! New best EMA pseudo Dice: 0.6011 +2026-04-14 18:03:42.189040: +2026-04-14 18:03:42.190933: Epoch 3626 +2026-04-14 18:03:42.192808: Current learning rate: 0.00119 +2026-04-14 18:05:23.919944: train_loss -0.4693 +2026-04-14 18:05:23.926179: val_loss -0.3952 +2026-04-14 18:05:23.927972: Pseudo dice [0.7118, 0.0, 0.8313, 0.609, 0.4995, 0.7852, 0.738] +2026-04-14 18:05:23.929662: Epoch time: 101.73 s +2026-04-14 18:05:25.135063: +2026-04-14 18:05:25.137094: Epoch 3627 +2026-04-14 18:05:25.139085: Current learning rate: 0.00118 +2026-04-14 18:07:06.977516: train_loss -0.4684 +2026-04-14 18:07:06.981903: val_loss -0.3982 +2026-04-14 18:07:06.983509: Pseudo dice [0.3221, 0.0, 0.6496, 0.8766, 0.6318, 0.8218, 0.8414] +2026-04-14 18:07:06.985755: Epoch time: 101.85 s +2026-04-14 18:07:08.220725: +2026-04-14 18:07:08.222296: Epoch 3628 +2026-04-14 18:07:08.223934: Current learning rate: 0.00118 +2026-04-14 18:08:50.054573: train_loss -0.4675 +2026-04-14 18:08:50.061243: val_loss -0.4091 +2026-04-14 18:08:50.062644: Pseudo dice [0.521, 0.0, 0.7305, 0.7653, 0.6367, 0.8483, 0.6436] +2026-04-14 18:08:50.064388: Epoch time: 101.84 s +2026-04-14 18:08:51.286682: +2026-04-14 18:08:51.289107: Epoch 3629 +2026-04-14 18:08:51.290976: Current learning rate: 0.00118 +2026-04-14 18:10:33.163791: train_loss -0.475 +2026-04-14 18:10:33.169757: val_loss -0.4231 +2026-04-14 18:10:33.171488: Pseudo dice [0.7283, 0.0, 0.6573, 0.707, 0.52, 0.6691, 0.9291] +2026-04-14 18:10:33.173387: Epoch time: 101.88 s +2026-04-14 18:10:34.396052: +2026-04-14 18:10:34.397579: Epoch 3630 +2026-04-14 18:10:34.399244: Current learning rate: 0.00117 +2026-04-14 18:12:16.174690: train_loss -0.4745 +2026-04-14 18:12:16.179012: val_loss -0.4156 +2026-04-14 18:12:16.180619: Pseudo dice [0.2806, 0.0, 0.7634, 0.8427, 0.4623, 0.8363, 0.8304] +2026-04-14 18:12:16.182237: Epoch time: 101.78 s +2026-04-14 18:12:17.392294: +2026-04-14 18:12:17.393963: Epoch 3631 +2026-04-14 18:12:17.395657: Current learning rate: 0.00117 +2026-04-14 18:13:59.199345: train_loss -0.4788 +2026-04-14 18:13:59.205219: val_loss -0.3942 +2026-04-14 18:13:59.206826: Pseudo dice [0.5088, 0.0, 0.6213, 0.8878, 0.5269, 0.703, 0.9263] +2026-04-14 18:13:59.208317: Epoch time: 101.81 s +2026-04-14 18:14:00.423607: +2026-04-14 18:14:00.426339: Epoch 3632 +2026-04-14 18:14:00.428012: Current learning rate: 0.00117 +2026-04-14 18:15:42.064634: train_loss -0.481 +2026-04-14 18:15:42.078613: val_loss -0.411 +2026-04-14 18:15:42.079968: Pseudo dice [0.4565, 0.0, 0.6486, 0.7913, 0.5697, 0.7203, 0.8748] +2026-04-14 18:15:42.082291: Epoch time: 101.64 s +2026-04-14 18:15:43.319377: +2026-04-14 18:15:43.321133: Epoch 3633 +2026-04-14 18:15:43.322514: Current learning rate: 0.00117 +2026-04-14 18:17:25.073486: train_loss -0.4784 +2026-04-14 18:17:25.079013: val_loss -0.3969 +2026-04-14 18:17:25.080552: Pseudo dice [0.3696, 0.0, 0.6455, 0.9342, 0.3724, 0.6982, 0.6596] +2026-04-14 18:17:25.082407: Epoch time: 101.76 s +2026-04-14 18:17:26.340119: +2026-04-14 18:17:26.341614: Epoch 3634 +2026-04-14 18:17:26.343105: Current learning rate: 0.00116 +2026-04-14 18:19:08.117003: train_loss -0.4748 +2026-04-14 18:19:08.121974: val_loss -0.4238 +2026-04-14 18:19:08.123266: Pseudo dice [0.3893, 0.0, 0.6808, 0.9127, 0.5684, 0.8726, 0.7392] +2026-04-14 18:19:08.125127: Epoch time: 101.78 s +2026-04-14 18:19:10.347009: +2026-04-14 18:19:10.348815: Epoch 3635 +2026-04-14 18:19:10.350226: Current learning rate: 0.00116 +2026-04-14 18:20:52.114674: train_loss -0.4578 +2026-04-14 18:20:52.119980: val_loss -0.3744 +2026-04-14 18:20:52.121727: Pseudo dice [0.5157, 0.0, 0.7492, 0.7347, 0.3582, 0.8388, 0.8496] +2026-04-14 18:20:52.123325: Epoch time: 101.77 s +2026-04-14 18:20:53.363545: +2026-04-14 18:20:53.365455: Epoch 3636 +2026-04-14 18:20:53.367223: Current learning rate: 0.00116 +2026-04-14 18:22:35.235237: train_loss -0.4729 +2026-04-14 18:22:35.239291: val_loss -0.4144 +2026-04-14 18:22:35.240562: Pseudo dice [0.4601, 0.0, 0.8542, 0.8395, 0.5271, 0.8622, 0.9151] +2026-04-14 18:22:35.241938: Epoch time: 101.87 s +2026-04-14 18:22:36.464028: +2026-04-14 18:22:36.465577: Epoch 3637 +2026-04-14 18:22:36.466956: Current learning rate: 0.00115 +2026-04-14 18:24:18.049629: train_loss -0.4689 +2026-04-14 18:24:18.053905: val_loss -0.4197 +2026-04-14 18:24:18.055209: Pseudo dice [0.7737, 0.0, 0.7354, 0.8627, 0.5343, 0.7629, 0.8966] +2026-04-14 18:24:18.056593: Epoch time: 101.59 s +2026-04-14 18:24:19.262695: +2026-04-14 18:24:19.264051: Epoch 3638 +2026-04-14 18:24:19.265290: Current learning rate: 0.00115 +2026-04-14 18:26:01.184478: train_loss -0.4757 +2026-04-14 18:26:01.190166: val_loss -0.3893 +2026-04-14 18:26:01.195022: Pseudo dice [0.6913, 0.0, 0.5479, 0.8061, 0.362, 0.7087, 0.9294] +2026-04-14 18:26:01.200932: Epoch time: 101.93 s +2026-04-14 18:26:02.442904: +2026-04-14 18:26:02.444548: Epoch 3639 +2026-04-14 18:26:02.445859: Current learning rate: 0.00115 +2026-04-14 18:27:44.195806: train_loss -0.4684 +2026-04-14 18:27:44.201707: val_loss -0.3906 +2026-04-14 18:27:44.205878: Pseudo dice [0.5847, 0.0, 0.7057, 0.6278, 0.398, 0.7107, 0.7691] +2026-04-14 18:27:44.207315: Epoch time: 101.76 s +2026-04-14 18:27:45.423502: +2026-04-14 18:27:45.425173: Epoch 3640 +2026-04-14 18:27:45.426360: Current learning rate: 0.00115 +2026-04-14 18:29:27.200761: train_loss -0.4734 +2026-04-14 18:29:27.205964: val_loss -0.4004 +2026-04-14 18:29:27.207517: Pseudo dice [0.451, 0.0, 0.7485, 0.8727, 0.4977, 0.8574, 0.8973] +2026-04-14 18:29:27.209472: Epoch time: 101.78 s +2026-04-14 18:29:28.415178: +2026-04-14 18:29:28.418838: Epoch 3641 +2026-04-14 18:29:28.422835: Current learning rate: 0.00114 +2026-04-14 18:31:10.116816: train_loss -0.4738 +2026-04-14 18:31:10.126020: val_loss -0.4273 +2026-04-14 18:31:10.128993: Pseudo dice [0.7111, 0.0, 0.7732, 0.4572, 0.5317, 0.708, 0.9021] +2026-04-14 18:31:10.130747: Epoch time: 101.7 s +2026-04-14 18:31:11.348633: +2026-04-14 18:31:11.350076: Epoch 3642 +2026-04-14 18:31:11.351358: Current learning rate: 0.00114 +2026-04-14 18:32:53.159833: train_loss -0.4709 +2026-04-14 18:32:53.167043: val_loss -0.4287 +2026-04-14 18:32:53.168599: Pseudo dice [0.7, 0.0, 0.8194, 0.3898, 0.3629, 0.7839, 0.9252] +2026-04-14 18:32:53.170241: Epoch time: 101.81 s +2026-04-14 18:32:54.377146: +2026-04-14 18:32:54.379053: Epoch 3643 +2026-04-14 18:32:54.380620: Current learning rate: 0.00114 +2026-04-14 18:34:36.140439: train_loss -0.4654 +2026-04-14 18:34:36.144953: val_loss -0.4112 +2026-04-14 18:34:36.146523: Pseudo dice [0.433, 0.0, 0.8101, 0.7531, 0.7763, 0.837, 0.9016] +2026-04-14 18:34:36.148271: Epoch time: 101.77 s +2026-04-14 18:34:37.376783: +2026-04-14 18:34:37.378407: Epoch 3644 +2026-04-14 18:34:37.379886: Current learning rate: 0.00113 +2026-04-14 18:36:19.036161: train_loss -0.4737 +2026-04-14 18:36:19.041593: val_loss -0.3898 +2026-04-14 18:36:19.043645: Pseudo dice [0.3538, 0.0, 0.6558, 0.8935, 0.5627, 0.6279, 0.804] +2026-04-14 18:36:19.046456: Epoch time: 101.66 s +2026-04-14 18:36:20.262879: +2026-04-14 18:36:20.265570: Epoch 3645 +2026-04-14 18:36:20.267763: Current learning rate: 0.00113 +2026-04-14 18:38:02.114448: train_loss -0.4645 +2026-04-14 18:38:02.119163: val_loss -0.3839 +2026-04-14 18:38:02.121196: Pseudo dice [0.5204, 0.0, 0.7546, 0.7929, 0.626, 0.8686, 0.7458] +2026-04-14 18:38:02.123256: Epoch time: 101.85 s +2026-04-14 18:38:03.330925: +2026-04-14 18:38:03.332574: Epoch 3646 +2026-04-14 18:38:03.334555: Current learning rate: 0.00113 +2026-04-14 18:39:45.030442: train_loss -0.4704 +2026-04-14 18:39:45.035256: val_loss -0.3948 +2026-04-14 18:39:45.037569: Pseudo dice [0.6336, 0.0, 0.8429, 0.6337, 0.498, 0.6524, 0.8681] +2026-04-14 18:39:45.039155: Epoch time: 101.7 s +2026-04-14 18:39:46.275522: +2026-04-14 18:39:46.277192: Epoch 3647 +2026-04-14 18:39:46.278674: Current learning rate: 0.00112 +2026-04-14 18:41:28.023286: train_loss -0.4909 +2026-04-14 18:41:28.030777: val_loss -0.3931 +2026-04-14 18:41:28.034061: Pseudo dice [0.4718, 0.0, 0.7441, 0.9124, 0.2131, 0.861, 0.8048] +2026-04-14 18:41:28.036091: Epoch time: 101.75 s +2026-04-14 18:41:29.255412: +2026-04-14 18:41:29.256870: Epoch 3648 +2026-04-14 18:41:29.258275: Current learning rate: 0.00112 +2026-04-14 18:43:11.118036: train_loss -0.4852 +2026-04-14 18:43:11.123845: val_loss -0.4213 +2026-04-14 18:43:11.125834: Pseudo dice [0.7358, 0.0, 0.7102, 0.7637, 0.5951, 0.7897, 0.8216] +2026-04-14 18:43:11.127293: Epoch time: 101.87 s +2026-04-14 18:43:12.343160: +2026-04-14 18:43:12.344535: Epoch 3649 +2026-04-14 18:43:12.345992: Current learning rate: 0.00112 +2026-04-14 18:44:54.010972: train_loss -0.4631 +2026-04-14 18:44:54.015162: val_loss -0.4036 +2026-04-14 18:44:54.017203: Pseudo dice [0.2726, 0.0, 0.5599, 0.8874, 0.4556, 0.8381, 0.8951] +2026-04-14 18:44:54.018835: Epoch time: 101.67 s +2026-04-14 18:44:56.950696: +2026-04-14 18:44:56.952474: Epoch 3650 +2026-04-14 18:44:56.954150: Current learning rate: 0.00112 +2026-04-14 18:46:38.772271: train_loss -0.4683 +2026-04-14 18:46:38.777386: val_loss -0.389 +2026-04-14 18:46:38.779159: Pseudo dice [0.5712, 0.0, 0.678, 0.8135, 0.249, 0.6932, 0.7538] +2026-04-14 18:46:38.780745: Epoch time: 101.82 s +2026-04-14 18:46:39.998409: +2026-04-14 18:46:40.000033: Epoch 3651 +2026-04-14 18:46:40.001515: Current learning rate: 0.00111 +2026-04-14 18:48:21.587254: train_loss -0.4702 +2026-04-14 18:48:21.593290: val_loss -0.3978 +2026-04-14 18:48:21.595109: Pseudo dice [0.3694, 0.0, 0.6192, 0.6907, 0.5165, 0.8209, 0.8249] +2026-04-14 18:48:21.597316: Epoch time: 101.59 s +2026-04-14 18:48:22.815398: +2026-04-14 18:48:22.817091: Epoch 3652 +2026-04-14 18:48:22.818914: Current learning rate: 0.00111 +2026-04-14 18:50:05.349651: train_loss -0.4656 +2026-04-14 18:50:05.354099: val_loss -0.3741 +2026-04-14 18:50:05.355831: Pseudo dice [0.6554, 0.0, 0.7271, 0.8236, 0.4879, 0.787, 0.9118] +2026-04-14 18:50:05.357485: Epoch time: 102.54 s +2026-04-14 18:50:06.567631: +2026-04-14 18:50:06.569179: Epoch 3653 +2026-04-14 18:50:06.570833: Current learning rate: 0.00111 +2026-04-14 18:51:48.198383: train_loss -0.4777 +2026-04-14 18:51:48.202592: val_loss -0.4455 +2026-04-14 18:51:48.204326: Pseudo dice [0.8137, 0.0, 0.8017, 0.733, 0.5292, 0.8622, 0.8754] +2026-04-14 18:51:48.205727: Epoch time: 101.63 s +2026-04-14 18:51:49.416144: +2026-04-14 18:51:49.417657: Epoch 3654 +2026-04-14 18:51:49.419421: Current learning rate: 0.0011 +2026-04-14 18:53:31.951912: train_loss -0.4618 +2026-04-14 18:53:31.957443: val_loss -0.3974 +2026-04-14 18:53:31.959906: Pseudo dice [0.8504, 0.0, 0.7903, 0.3449, 0.3086, 0.7035, 0.7456] +2026-04-14 18:53:31.961416: Epoch time: 102.54 s +2026-04-14 18:53:33.151546: +2026-04-14 18:53:33.153337: Epoch 3655 +2026-04-14 18:53:33.155363: Current learning rate: 0.0011 +2026-04-14 18:55:14.698827: train_loss -0.4648 +2026-04-14 18:55:14.703823: val_loss -0.4154 +2026-04-14 18:55:14.705673: Pseudo dice [0.7384, 0.0, 0.5009, 0.7942, 0.7026, 0.8791, 0.8353] +2026-04-14 18:55:14.707552: Epoch time: 101.55 s +2026-04-14 18:55:15.917467: +2026-04-14 18:55:15.919317: Epoch 3656 +2026-04-14 18:55:15.920994: Current learning rate: 0.0011 +2026-04-14 18:56:57.339179: train_loss -0.4721 +2026-04-14 18:56:57.344234: val_loss -0.3502 +2026-04-14 18:56:57.345881: Pseudo dice [0.2352, 0.0, 0.7464, 0.3789, 0.464, 0.5296, 0.615] +2026-04-14 18:56:57.347075: Epoch time: 101.42 s +2026-04-14 18:56:58.757613: +2026-04-14 18:56:58.766760: Epoch 3657 +2026-04-14 18:56:58.768502: Current learning rate: 0.0011 +2026-04-14 18:58:40.242720: train_loss -0.4695 +2026-04-14 18:58:40.248405: val_loss -0.4101 +2026-04-14 18:58:40.250238: Pseudo dice [0.7381, 0.0, 0.7742, 0.416, 0.6999, 0.7496, 0.8249] +2026-04-14 18:58:40.251924: Epoch time: 101.49 s +2026-04-14 18:58:41.443969: +2026-04-14 18:58:41.445667: Epoch 3658 +2026-04-14 18:58:41.447657: Current learning rate: 0.00109 +2026-04-14 19:00:23.073695: train_loss -0.48 +2026-04-14 19:00:23.081763: val_loss -0.3531 +2026-04-14 19:00:23.083920: Pseudo dice [0.4346, 0.0, 0.7057, 0.2223, 0.2992, 0.8138, 0.5809] +2026-04-14 19:00:23.085723: Epoch time: 101.63 s +2026-04-14 19:00:24.305879: +2026-04-14 19:00:24.308514: Epoch 3659 +2026-04-14 19:00:24.310785: Current learning rate: 0.00109 +2026-04-14 19:02:05.983667: train_loss -0.4703 +2026-04-14 19:02:05.988729: val_loss -0.3959 +2026-04-14 19:02:05.990507: Pseudo dice [0.7524, 0.0, 0.8018, 0.7077, 0.4235, 0.7174, 0.6964] +2026-04-14 19:02:05.992815: Epoch time: 101.68 s +2026-04-14 19:02:07.210448: +2026-04-14 19:02:07.212264: Epoch 3660 +2026-04-14 19:02:07.214187: Current learning rate: 0.00109 +2026-04-14 19:03:48.838066: train_loss -0.4765 +2026-04-14 19:03:48.843247: val_loss -0.3905 +2026-04-14 19:03:48.845706: Pseudo dice [0.7243, 0.0, 0.719, 0.7505, 0.5039, 0.7525, 0.7284] +2026-04-14 19:03:48.847260: Epoch time: 101.63 s +2026-04-14 19:03:50.066412: +2026-04-14 19:03:50.067923: Epoch 3661 +2026-04-14 19:03:50.069901: Current learning rate: 0.00108 +2026-04-14 19:05:31.609298: train_loss -0.4662 +2026-04-14 19:05:31.614190: val_loss -0.4179 +2026-04-14 19:05:31.616254: Pseudo dice [0.4118, 0.0, 0.6387, 0.8156, 0.6069, 0.8291, 0.8618] +2026-04-14 19:05:31.617893: Epoch time: 101.55 s +2026-04-14 19:05:32.839192: +2026-04-14 19:05:32.840662: Epoch 3662 +2026-04-14 19:05:32.842301: Current learning rate: 0.00108 +2026-04-14 19:07:14.438369: train_loss -0.4708 +2026-04-14 19:07:14.444005: val_loss -0.4069 +2026-04-14 19:07:14.445814: Pseudo dice [0.5089, 0.0, 0.784, 0.9448, 0.5428, 0.8165, 0.4678] +2026-04-14 19:07:14.447509: Epoch time: 101.6 s +2026-04-14 19:07:15.661723: +2026-04-14 19:07:15.663333: Epoch 3663 +2026-04-14 19:07:15.665677: Current learning rate: 0.00108 +2026-04-14 19:08:57.300936: train_loss -0.4885 +2026-04-14 19:08:57.305731: val_loss -0.3996 +2026-04-14 19:08:57.307622: Pseudo dice [0.4678, 0.0, 0.7822, 0.8147, 0.5347, 0.8707, 0.9006] +2026-04-14 19:08:57.309426: Epoch time: 101.64 s +2026-04-14 19:08:58.527923: +2026-04-14 19:08:58.529466: Epoch 3664 +2026-04-14 19:08:58.531468: Current learning rate: 0.00108 +2026-04-14 19:10:40.338760: train_loss -0.4753 +2026-04-14 19:10:40.343454: val_loss -0.4018 +2026-04-14 19:10:40.345157: Pseudo dice [0.7317, 0.0, 0.7584, 0.7334, 0.6481, 0.4161, 0.6214] +2026-04-14 19:10:40.347026: Epoch time: 101.81 s +2026-04-14 19:10:41.553837: +2026-04-14 19:10:41.555459: Epoch 3665 +2026-04-14 19:10:41.557797: Current learning rate: 0.00107 +2026-04-14 19:12:23.103440: train_loss -0.4739 +2026-04-14 19:12:23.109737: val_loss -0.406 +2026-04-14 19:12:23.111142: Pseudo dice [0.7454, 0.0, 0.6257, 0.7361, 0.6675, 0.8192, 0.6653] +2026-04-14 19:12:23.113070: Epoch time: 101.55 s +2026-04-14 19:12:24.344768: +2026-04-14 19:12:24.346901: Epoch 3666 +2026-04-14 19:12:24.348941: Current learning rate: 0.00107 +2026-04-14 19:14:06.052969: train_loss -0.4795 +2026-04-14 19:14:06.058784: val_loss -0.4136 +2026-04-14 19:14:06.060524: Pseudo dice [0.2256, 0.0, 0.7797, 0.9142, 0.5471, 0.8318, 0.8913] +2026-04-14 19:14:06.062428: Epoch time: 101.71 s +2026-04-14 19:14:07.278321: +2026-04-14 19:14:07.280368: Epoch 3667 +2026-04-14 19:14:07.283313: Current learning rate: 0.00107 +2026-04-14 19:15:48.786309: train_loss -0.468 +2026-04-14 19:15:48.791463: val_loss -0.3867 +2026-04-14 19:15:48.792863: Pseudo dice [0.3996, 0.0, 0.7786, 0.8087, 0.4415, 0.7187, 0.8631] +2026-04-14 19:15:48.794868: Epoch time: 101.51 s +2026-04-14 19:15:50.012002: +2026-04-14 19:15:50.013712: Epoch 3668 +2026-04-14 19:15:50.015542: Current learning rate: 0.00106 +2026-04-14 19:17:31.505026: train_loss -0.4821 +2026-04-14 19:17:31.509823: val_loss -0.4036 +2026-04-14 19:17:31.511535: Pseudo dice [0.2506, 0.0, 0.6939, 0.9002, 0.6923, 0.5561, 0.7588] +2026-04-14 19:17:31.513366: Epoch time: 101.5 s +2026-04-14 19:17:32.721680: +2026-04-14 19:17:32.723243: Epoch 3669 +2026-04-14 19:17:32.725379: Current learning rate: 0.00106 +2026-04-14 19:19:14.465135: train_loss -0.4782 +2026-04-14 19:19:14.470641: val_loss -0.354 +2026-04-14 19:19:14.472763: Pseudo dice [0.7746, 0.0, 0.6915, 0.4745, 0.2201, 0.7255, 0.5438] +2026-04-14 19:19:14.474724: Epoch time: 101.75 s +2026-04-14 19:19:15.726271: +2026-04-14 19:19:15.728539: Epoch 3670 +2026-04-14 19:19:15.730995: Current learning rate: 0.00106 +2026-04-14 19:20:57.413559: train_loss -0.4799 +2026-04-14 19:20:57.418543: val_loss -0.4047 +2026-04-14 19:20:57.420699: Pseudo dice [0.7692, 0.0, 0.7414, 0.9276, 0.3368, 0.6869, 0.7604] +2026-04-14 19:20:57.423171: Epoch time: 101.69 s +2026-04-14 19:20:58.657322: +2026-04-14 19:20:58.659194: Epoch 3671 +2026-04-14 19:20:58.661091: Current learning rate: 0.00106 +2026-04-14 19:22:40.396287: train_loss -0.4676 +2026-04-14 19:22:40.402265: val_loss -0.3985 +2026-04-14 19:22:40.404661: Pseudo dice [0.5636, 0.0, 0.6945, 0.5744, 0.5565, 0.6116, 0.8194] +2026-04-14 19:22:40.406738: Epoch time: 101.74 s +2026-04-14 19:22:41.633058: +2026-04-14 19:22:41.634638: Epoch 3672 +2026-04-14 19:22:41.636432: Current learning rate: 0.00105 +2026-04-14 19:24:23.475578: train_loss -0.4728 +2026-04-14 19:24:23.480598: val_loss -0.3977 +2026-04-14 19:24:23.483167: Pseudo dice [0.7047, 0.0, 0.7483, 0.7983, 0.6421, 0.1744, 0.9045] +2026-04-14 19:24:23.484990: Epoch time: 101.85 s +2026-04-14 19:24:24.682568: +2026-04-14 19:24:24.684246: Epoch 3673 +2026-04-14 19:24:24.685807: Current learning rate: 0.00105 +2026-04-14 19:26:06.333833: train_loss -0.476 +2026-04-14 19:26:06.339398: val_loss -0.4189 +2026-04-14 19:26:06.341162: Pseudo dice [0.6719, 0.0, 0.3905, 0.8895, 0.7309, 0.7337, 0.8918] +2026-04-14 19:26:06.342817: Epoch time: 101.65 s +2026-04-14 19:26:07.565553: +2026-04-14 19:26:07.567337: Epoch 3674 +2026-04-14 19:26:07.569200: Current learning rate: 0.00105 +2026-04-14 19:27:50.309220: train_loss -0.4602 +2026-04-14 19:27:50.313459: val_loss -0.4251 +2026-04-14 19:27:50.314916: Pseudo dice [0.5799, 0.0, 0.5145, 0.9098, 0.6678, 0.7126, 0.9196] +2026-04-14 19:27:50.316800: Epoch time: 102.75 s +2026-04-14 19:27:51.535341: +2026-04-14 19:27:51.536954: Epoch 3675 +2026-04-14 19:27:51.538892: Current learning rate: 0.00104 +2026-04-14 19:29:33.292782: train_loss -0.4732 +2026-04-14 19:29:33.297572: val_loss -0.4259 +2026-04-14 19:29:33.299297: Pseudo dice [0.8227, 0.0, 0.8162, 0.6864, 0.5268, 0.8261, 0.9505] +2026-04-14 19:29:33.300945: Epoch time: 101.76 s +2026-04-14 19:29:34.527914: +2026-04-14 19:29:34.529668: Epoch 3676 +2026-04-14 19:29:34.531521: Current learning rate: 0.00104 +2026-04-14 19:31:16.406285: train_loss -0.4845 +2026-04-14 19:31:16.411198: val_loss -0.3997 +2026-04-14 19:31:16.418375: Pseudo dice [0.3415, 0.0, 0.6238, 0.6348, 0.5682, 0.8911, 0.8453] +2026-04-14 19:31:16.420122: Epoch time: 101.88 s +2026-04-14 19:31:17.666899: +2026-04-14 19:31:17.668870: Epoch 3677 +2026-04-14 19:31:17.671095: Current learning rate: 0.00104 +2026-04-14 19:32:59.625392: train_loss -0.4727 +2026-04-14 19:32:59.631566: val_loss -0.408 +2026-04-14 19:32:59.633206: Pseudo dice [0.647, 0.0, 0.8174, 0.8787, 0.6858, 0.8071, 0.8748] +2026-04-14 19:32:59.634933: Epoch time: 101.96 s +2026-04-14 19:33:00.855342: +2026-04-14 19:33:00.857029: Epoch 3678 +2026-04-14 19:33:00.858803: Current learning rate: 0.00104 +2026-04-14 19:34:42.578894: train_loss -0.4745 +2026-04-14 19:34:42.585345: val_loss -0.4196 +2026-04-14 19:34:42.587561: Pseudo dice [0.7918, 0.0, 0.7298, 0.6358, 0.6616, 0.8141, 0.6929] +2026-04-14 19:34:42.589128: Epoch time: 101.73 s +2026-04-14 19:34:43.824062: +2026-04-14 19:34:43.825541: Epoch 3679 +2026-04-14 19:34:43.827157: Current learning rate: 0.00103 +2026-04-14 19:36:25.599530: train_loss -0.4718 +2026-04-14 19:36:25.604196: val_loss -0.4089 +2026-04-14 19:36:25.605742: Pseudo dice [0.3508, 0.0, 0.8632, 0.6571, 0.5903, 0.7481, 0.8672] +2026-04-14 19:36:25.607373: Epoch time: 101.78 s +2026-04-14 19:36:26.832829: +2026-04-14 19:36:26.835243: Epoch 3680 +2026-04-14 19:36:26.837373: Current learning rate: 0.00103 +2026-04-14 19:38:08.767519: train_loss -0.4756 +2026-04-14 19:38:08.772048: val_loss -0.4079 +2026-04-14 19:38:08.773846: Pseudo dice [0.4967, 0.0, 0.8273, 0.8743, 0.656, 0.4984, 0.8544] +2026-04-14 19:38:08.775498: Epoch time: 101.94 s +2026-04-14 19:38:10.010674: +2026-04-14 19:38:10.012470: Epoch 3681 +2026-04-14 19:38:10.014320: Current learning rate: 0.00103 +2026-04-14 19:39:51.606936: train_loss -0.4757 +2026-04-14 19:39:51.611710: val_loss -0.3839 +2026-04-14 19:39:51.613440: Pseudo dice [0.3635, 0.0, 0.7458, 0.9073, 0.539, 0.7697, 0.8939] +2026-04-14 19:39:51.615760: Epoch time: 101.6 s +2026-04-14 19:39:52.839955: +2026-04-14 19:39:52.841911: Epoch 3682 +2026-04-14 19:39:52.843821: Current learning rate: 0.00102 +2026-04-14 19:41:34.675289: train_loss -0.4677 +2026-04-14 19:41:34.680448: val_loss -0.3656 +2026-04-14 19:41:34.682601: Pseudo dice [0.5564, 0.0, 0.7933, 0.839, 0.6266, 0.7907, 0.4995] +2026-04-14 19:41:34.684260: Epoch time: 101.84 s +2026-04-14 19:41:35.965407: +2026-04-14 19:41:35.967545: Epoch 3683 +2026-04-14 19:41:35.969524: Current learning rate: 0.00102 +2026-04-14 19:43:17.807461: train_loss -0.4782 +2026-04-14 19:43:17.812424: val_loss -0.3963 +2026-04-14 19:43:17.815184: Pseudo dice [0.5383, 0.0, 0.8452, 0.7778, 0.6483, 0.849, 0.872] +2026-04-14 19:43:17.817412: Epoch time: 101.85 s +2026-04-14 19:43:19.040810: +2026-04-14 19:43:19.042444: Epoch 3684 +2026-04-14 19:43:19.044170: Current learning rate: 0.00102 +2026-04-14 19:45:00.676202: train_loss -0.4759 +2026-04-14 19:45:00.681206: val_loss -0.409 +2026-04-14 19:45:00.685825: Pseudo dice [0.8093, 0.0, 0.4124, 0.8554, 0.5113, 0.8618, 0.7323] +2026-04-14 19:45:00.687415: Epoch time: 101.64 s +2026-04-14 19:45:01.904080: +2026-04-14 19:45:01.905790: Epoch 3685 +2026-04-14 19:45:01.907625: Current learning rate: 0.00102 +2026-04-14 19:46:43.502063: train_loss -0.4775 +2026-04-14 19:46:43.507767: val_loss -0.3844 +2026-04-14 19:46:43.510657: Pseudo dice [0.7372, 0.0, 0.6264, 0.7276, 0.5669, 0.3146, 0.951] +2026-04-14 19:46:43.512375: Epoch time: 101.6 s +2026-04-14 19:46:44.710416: +2026-04-14 19:46:44.712046: Epoch 3686 +2026-04-14 19:46:44.713819: Current learning rate: 0.00101 +2026-04-14 19:48:26.284754: train_loss -0.4765 +2026-04-14 19:48:26.291105: val_loss -0.4272 +2026-04-14 19:48:26.293027: Pseudo dice [0.6234, 0.0, 0.727, 0.792, 0.6333, 0.8671, 0.9118] +2026-04-14 19:48:26.295010: Epoch time: 101.58 s +2026-04-14 19:48:27.536353: +2026-04-14 19:48:27.538147: Epoch 3687 +2026-04-14 19:48:27.539797: Current learning rate: 0.00101 +2026-04-14 19:50:09.293930: train_loss -0.4783 +2026-04-14 19:50:09.298772: val_loss -0.3953 +2026-04-14 19:50:09.301085: Pseudo dice [0.5173, 0.0, 0.8422, 0.8867, 0.4386, 0.744, 0.9092] +2026-04-14 19:50:09.302858: Epoch time: 101.76 s +2026-04-14 19:50:09.304842: Yayy! New best EMA pseudo Dice: 0.6029 +2026-04-14 19:50:12.353618: +2026-04-14 19:50:12.355484: Epoch 3688 +2026-04-14 19:50:12.357307: Current learning rate: 0.00101 +2026-04-14 19:51:53.954325: train_loss -0.4873 +2026-04-14 19:51:53.959172: val_loss -0.4058 +2026-04-14 19:51:53.960579: Pseudo dice [0.5374, 0.0, 0.8345, 0.8469, 0.4929, 0.7567, 0.7461] +2026-04-14 19:51:53.962861: Epoch time: 101.6 s +2026-04-14 19:51:55.163824: +2026-04-14 19:51:55.165632: Epoch 3689 +2026-04-14 19:51:55.167409: Current learning rate: 0.001 +2026-04-14 19:53:36.868825: train_loss -0.4777 +2026-04-14 19:53:36.873851: val_loss -0.3859 +2026-04-14 19:53:36.877357: Pseudo dice [0.267, 0.0, 0.7526, 0.8905, 0.5653, 0.7841, 0.6973] +2026-04-14 19:53:36.879330: Epoch time: 101.71 s +2026-04-14 19:53:38.107018: +2026-04-14 19:53:38.109518: Epoch 3690 +2026-04-14 19:53:38.111749: Current learning rate: 0.001 +2026-04-14 19:55:19.838296: train_loss -0.4759 +2026-04-14 19:55:19.843121: val_loss -0.4104 +2026-04-14 19:55:19.844816: Pseudo dice [0.7905, 0.0, 0.822, 0.7966, 0.614, 0.8373, 0.8876] +2026-04-14 19:55:19.846750: Epoch time: 101.73 s +2026-04-14 19:55:19.848170: Yayy! New best EMA pseudo Dice: 0.607 +2026-04-14 19:55:22.844732: +2026-04-14 19:55:22.846993: Epoch 3691 +2026-04-14 19:55:22.848971: Current learning rate: 0.001 +2026-04-14 19:57:04.575911: train_loss -0.4757 +2026-04-14 19:57:04.581599: val_loss -0.3933 +2026-04-14 19:57:04.583226: Pseudo dice [0.3793, 0.0, 0.7225, 0.9173, 0.4776, 0.5852, 0.8568] +2026-04-14 19:57:04.584785: Epoch time: 101.73 s +2026-04-14 19:57:05.815365: +2026-04-14 19:57:05.817049: Epoch 3692 +2026-04-14 19:57:05.819079: Current learning rate: 0.001 +2026-04-14 19:58:47.511325: train_loss -0.4908 +2026-04-14 19:58:47.517388: val_loss -0.3938 +2026-04-14 19:58:47.518916: Pseudo dice [0.5037, 0.0, 0.7784, 0.8915, 0.4177, 0.6661, 0.7401] +2026-04-14 19:58:47.520907: Epoch time: 101.7 s +2026-04-14 19:58:48.781107: +2026-04-14 19:58:48.785290: Epoch 3693 +2026-04-14 19:58:48.788213: Current learning rate: 0.00099 +2026-04-14 20:00:32.040745: train_loss -0.4757 +2026-04-14 20:00:32.046706: val_loss -0.4038 +2026-04-14 20:00:32.048331: Pseudo dice [0.6949, 0.0, 0.6822, 0.6403, 0.6181, 0.8437, 0.6316] +2026-04-14 20:00:32.050326: Epoch time: 103.26 s +2026-04-14 20:00:33.289179: +2026-04-14 20:00:33.290838: Epoch 3694 +2026-04-14 20:00:33.292740: Current learning rate: 0.00099 +2026-04-14 20:02:15.027013: train_loss -0.4789 +2026-04-14 20:02:15.031232: val_loss -0.3939 +2026-04-14 20:02:15.032818: Pseudo dice [0.457, 0.0, 0.8317, 0.8538, 0.4774, 0.8374, 0.6705] +2026-04-14 20:02:15.034373: Epoch time: 101.74 s +2026-04-14 20:02:16.238293: +2026-04-14 20:02:16.239844: Epoch 3695 +2026-04-14 20:02:16.241859: Current learning rate: 0.00099 +2026-04-14 20:04:00.317838: train_loss -0.4857 +2026-04-14 20:04:00.322798: val_loss -0.3895 +2026-04-14 20:04:00.324598: Pseudo dice [0.3904, 0.0, 0.7497, 0.7968, 0.5441, 0.8662, 0.7073] +2026-04-14 20:04:00.326405: Epoch time: 104.08 s +2026-04-14 20:04:01.545629: +2026-04-14 20:04:01.547431: Epoch 3696 +2026-04-14 20:04:01.549218: Current learning rate: 0.00098 +2026-04-14 20:05:43.267147: train_loss -0.4658 +2026-04-14 20:05:43.272651: val_loss -0.438 +2026-04-14 20:05:43.274466: Pseudo dice [0.7804, 0.0, 0.9081, 0.9039, 0.3979, 0.8273, 0.9398] +2026-04-14 20:05:43.276083: Epoch time: 101.72 s +2026-04-14 20:05:44.488431: +2026-04-14 20:05:44.490287: Epoch 3697 +2026-04-14 20:05:44.492266: Current learning rate: 0.00098 +2026-04-14 20:07:26.311622: train_loss -0.4841 +2026-04-14 20:07:26.316713: val_loss -0.4296 +2026-04-14 20:07:26.318247: Pseudo dice [0.7014, 0.0, 0.7704, 0.9085, 0.6598, 0.8451, 0.7953] +2026-04-14 20:07:26.319826: Epoch time: 101.83 s +2026-04-14 20:07:26.321468: Yayy! New best EMA pseudo Dice: 0.6104 +2026-04-14 20:07:29.315068: +2026-04-14 20:07:29.317322: Epoch 3698 +2026-04-14 20:07:29.319332: Current learning rate: 0.00098 +2026-04-14 20:09:11.086304: train_loss -0.4706 +2026-04-14 20:09:11.091851: val_loss -0.3931 +2026-04-14 20:09:11.093687: Pseudo dice [0.2716, 0.0, 0.7984, 0.8878, 0.5038, 0.7915, 0.803] +2026-04-14 20:09:11.095466: Epoch time: 101.77 s +2026-04-14 20:09:12.309139: +2026-04-14 20:09:12.310650: Epoch 3699 +2026-04-14 20:09:12.312460: Current learning rate: 0.00097 +2026-04-14 20:10:53.981476: train_loss -0.4866 +2026-04-14 20:10:53.988609: val_loss -0.3853 +2026-04-14 20:10:53.990450: Pseudo dice [0.6008, 0.0, 0.7208, 0.8784, 0.454, 0.8149, 0.6399] +2026-04-14 20:10:53.993721: Epoch time: 101.68 s +2026-04-14 20:10:57.083106: +2026-04-14 20:10:57.084836: Epoch 3700 +2026-04-14 20:10:57.086969: Current learning rate: 0.00097 +2026-04-14 20:12:38.857269: train_loss -0.4826 +2026-04-14 20:12:38.861781: val_loss -0.3955 +2026-04-14 20:12:38.863471: Pseudo dice [0.4768, 0.0, 0.7288, 0.6421, 0.3046, 0.4567, 0.6166] +2026-04-14 20:12:38.864970: Epoch time: 101.78 s +2026-04-14 20:12:40.070104: +2026-04-14 20:12:40.072333: Epoch 3701 +2026-04-14 20:12:40.074184: Current learning rate: 0.00097 +2026-04-14 20:14:21.943095: train_loss -0.4885 +2026-04-14 20:14:21.946950: val_loss -0.4394 +2026-04-14 20:14:21.948891: Pseudo dice [0.5466, 0.0, 0.7572, 0.8595, 0.7097, 0.722, 0.832] +2026-04-14 20:14:21.950351: Epoch time: 101.88 s +2026-04-14 20:14:23.167161: +2026-04-14 20:14:23.168689: Epoch 3702 +2026-04-14 20:14:23.170583: Current learning rate: 0.00097 +2026-04-14 20:16:04.899377: train_loss -0.4726 +2026-04-14 20:16:04.904040: val_loss -0.39 +2026-04-14 20:16:04.906698: Pseudo dice [0.5042, 0.0, 0.7216, 0.3863, 0.3184, 0.714, 0.7657] +2026-04-14 20:16:04.908447: Epoch time: 101.74 s +2026-04-14 20:16:06.116918: +2026-04-14 20:16:06.118674: Epoch 3703 +2026-04-14 20:16:06.120667: Current learning rate: 0.00096 +2026-04-14 20:17:48.033221: train_loss -0.4751 +2026-04-14 20:17:48.038848: val_loss -0.4083 +2026-04-14 20:17:48.041752: Pseudo dice [0.5744, 0.0, 0.7625, 0.5122, 0.3797, 0.5035, 0.9026] +2026-04-14 20:17:48.044137: Epoch time: 101.92 s +2026-04-14 20:17:49.264476: +2026-04-14 20:17:49.266724: Epoch 3704 +2026-04-14 20:17:49.268784: Current learning rate: 0.00096 +2026-04-14 20:19:31.102788: train_loss -0.4675 +2026-04-14 20:19:31.108919: val_loss -0.4466 +2026-04-14 20:19:31.110634: Pseudo dice [0.7384, 0.0, 0.7993, 0.9268, 0.5943, 0.8453, 0.8683] +2026-04-14 20:19:31.112590: Epoch time: 101.84 s +2026-04-14 20:19:32.333855: +2026-04-14 20:19:32.335402: Epoch 3705 +2026-04-14 20:19:32.337161: Current learning rate: 0.00096 +2026-04-14 20:21:14.058412: train_loss -0.4784 +2026-04-14 20:21:14.063914: val_loss -0.4112 +2026-04-14 20:21:14.065684: Pseudo dice [0.1676, 0.0, 0.8092, 0.6699, 0.6541, 0.6916, 0.8896] +2026-04-14 20:21:14.067752: Epoch time: 101.73 s +2026-04-14 20:21:15.287032: +2026-04-14 20:21:15.289395: Epoch 3706 +2026-04-14 20:21:15.291757: Current learning rate: 0.00095 +2026-04-14 20:22:57.161614: train_loss -0.4737 +2026-04-14 20:22:57.166849: val_loss -0.3788 +2026-04-14 20:22:57.168826: Pseudo dice [0.4477, 0.0, 0.7217, 0.9248, 0.242, 0.4331, 0.8276] +2026-04-14 20:22:57.171085: Epoch time: 101.88 s +2026-04-14 20:22:58.388174: +2026-04-14 20:22:58.390560: Epoch 3707 +2026-04-14 20:22:58.392637: Current learning rate: 0.00095 +2026-04-14 20:24:40.077754: train_loss -0.4709 +2026-04-14 20:24:40.082905: val_loss -0.4034 +2026-04-14 20:24:40.084623: Pseudo dice [0.8558, 0.0, 0.8063, 0.9135, 0.6371, 0.6232, 0.843] +2026-04-14 20:24:40.086470: Epoch time: 101.69 s +2026-04-14 20:24:41.299269: +2026-04-14 20:24:41.301087: Epoch 3708 +2026-04-14 20:24:41.302976: Current learning rate: 0.00095 +2026-04-14 20:26:23.181803: train_loss -0.4725 +2026-04-14 20:26:23.187680: val_loss -0.374 +2026-04-14 20:26:23.190440: Pseudo dice [0.3504, 0.0, 0.6334, 0.6453, 0.3832, 0.6876, 0.6185] +2026-04-14 20:26:23.191896: Epoch time: 101.89 s +2026-04-14 20:26:24.415471: +2026-04-14 20:26:24.416970: Epoch 3709 +2026-04-14 20:26:24.418808: Current learning rate: 0.00095 +2026-04-14 20:28:06.194260: train_loss -0.4777 +2026-04-14 20:28:06.199039: val_loss -0.418 +2026-04-14 20:28:06.200600: Pseudo dice [0.4938, 0.0, 0.7998, 0.8442, 0.7295, 0.8202, 0.9449] +2026-04-14 20:28:06.202139: Epoch time: 101.78 s +2026-04-14 20:28:07.404874: +2026-04-14 20:28:07.406419: Epoch 3710 +2026-04-14 20:28:07.408097: Current learning rate: 0.00094 +2026-04-14 20:29:49.374686: train_loss -0.4862 +2026-04-14 20:29:49.380129: val_loss -0.426 +2026-04-14 20:29:49.382016: Pseudo dice [0.5122, 0.0, 0.7024, 0.7963, 0.5989, 0.6733, 0.9028] +2026-04-14 20:29:49.383668: Epoch time: 101.97 s +2026-04-14 20:29:50.619057: +2026-04-14 20:29:50.621803: Epoch 3711 +2026-04-14 20:29:50.623678: Current learning rate: 0.00094 +2026-04-14 20:31:32.481602: train_loss -0.4741 +2026-04-14 20:31:32.486667: val_loss -0.4287 +2026-04-14 20:31:32.489079: Pseudo dice [0.5311, 0.0, 0.8078, 0.3702, 0.6906, 0.816, 0.7884] +2026-04-14 20:31:32.491487: Epoch time: 101.87 s +2026-04-14 20:31:33.707424: +2026-04-14 20:31:33.709520: Epoch 3712 +2026-04-14 20:31:33.711719: Current learning rate: 0.00094 +2026-04-14 20:33:16.651399: train_loss -0.4703 +2026-04-14 20:33:16.656630: val_loss -0.4085 +2026-04-14 20:33:16.658436: Pseudo dice [0.4607, 0.0, 0.7415, 0.8778, 0.4736, 0.8557, 0.803] +2026-04-14 20:33:16.660055: Epoch time: 102.95 s +2026-04-14 20:33:17.868314: +2026-04-14 20:33:17.870700: Epoch 3713 +2026-04-14 20:33:17.872430: Current learning rate: 0.00093 +2026-04-14 20:34:59.806639: train_loss -0.4843 +2026-04-14 20:34:59.811855: val_loss -0.413 +2026-04-14 20:34:59.814611: Pseudo dice [0.8877, 0.0, 0.8029, 0.9004, 0.5059, 0.8551, 0.8817] +2026-04-14 20:34:59.816662: Epoch time: 101.94 s +2026-04-14 20:35:01.079699: +2026-04-14 20:35:01.081358: Epoch 3714 +2026-04-14 20:35:01.082983: Current learning rate: 0.00093 +2026-04-14 20:36:42.852784: train_loss -0.4762 +2026-04-14 20:36:42.857922: val_loss -0.4052 +2026-04-14 20:36:42.859889: Pseudo dice [0.8353, 0.0, 0.8214, 0.8702, 0.3968, 0.7881, 0.9402] +2026-04-14 20:36:42.861269: Epoch time: 101.78 s +2026-04-14 20:36:44.096337: +2026-04-14 20:36:44.098062: Epoch 3715 +2026-04-14 20:36:44.099799: Current learning rate: 0.00093 +2026-04-14 20:38:26.112671: train_loss -0.4714 +2026-04-14 20:38:26.117592: val_loss -0.4191 +2026-04-14 20:38:26.119089: Pseudo dice [0.8318, 0.0, 0.8689, 0.8699, 0.5332, 0.7862, 0.9412] +2026-04-14 20:38:26.120569: Epoch time: 102.02 s +2026-04-14 20:38:26.122261: Yayy! New best EMA pseudo Dice: 0.6119 +2026-04-14 20:38:29.154196: +2026-04-14 20:38:29.156178: Epoch 3716 +2026-04-14 20:38:29.157781: Current learning rate: 0.00092 +2026-04-14 20:40:11.102683: train_loss -0.4772 +2026-04-14 20:40:11.107140: val_loss -0.3993 +2026-04-14 20:40:11.108974: Pseudo dice [0.6562, 0.0, 0.7503, 0.6622, 0.3466, 0.8436, 0.8556] +2026-04-14 20:40:11.110671: Epoch time: 101.95 s +2026-04-14 20:40:12.348505: +2026-04-14 20:40:12.349889: Epoch 3717 +2026-04-14 20:40:12.351290: Current learning rate: 0.00092 +2026-04-14 20:41:54.311312: train_loss -0.4677 +2026-04-14 20:41:54.316966: val_loss -0.4109 +2026-04-14 20:41:54.318366: Pseudo dice [0.5085, 0.0, 0.7937, 0.8266, 0.7022, 0.743, 0.8977] +2026-04-14 20:41:54.319870: Epoch time: 101.97 s +2026-04-14 20:41:54.321403: Yayy! New best EMA pseudo Dice: 0.6124 +2026-04-14 20:41:57.349873: +2026-04-14 20:41:57.352272: Epoch 3718 +2026-04-14 20:41:57.353842: Current learning rate: 0.00092 +2026-04-14 20:43:39.138324: train_loss -0.4834 +2026-04-14 20:43:39.150466: val_loss -0.4163 +2026-04-14 20:43:39.152332: Pseudo dice [0.6787, 0.0, 0.7165, 0.7308, 0.4726, 0.8245, 0.8194] +2026-04-14 20:43:39.153909: Epoch time: 101.79 s +2026-04-14 20:43:40.380949: +2026-04-14 20:43:40.382924: Epoch 3719 +2026-04-14 20:43:40.384945: Current learning rate: 0.00092 +2026-04-14 20:45:22.250985: train_loss -0.4743 +2026-04-14 20:45:22.259260: val_loss -0.4243 +2026-04-14 20:45:22.261040: Pseudo dice [0.7694, 0.0, 0.7026, 0.8247, 0.5971, 0.8956, 0.8923] +2026-04-14 20:45:22.262566: Epoch time: 101.87 s +2026-04-14 20:45:22.264750: Yayy! New best EMA pseudo Dice: 0.6175 +2026-04-14 20:45:25.327737: +2026-04-14 20:45:25.330124: Epoch 3720 +2026-04-14 20:45:25.335876: Current learning rate: 0.00091 +2026-04-14 20:47:07.035075: train_loss -0.4674 +2026-04-14 20:47:07.040888: val_loss -0.408 +2026-04-14 20:47:07.043119: Pseudo dice [0.8329, 0.0, 0.7331, 0.8553, 0.6289, 0.6539, 0.9015] +2026-04-14 20:47:07.044701: Epoch time: 101.71 s +2026-04-14 20:47:07.046437: Yayy! New best EMA pseudo Dice: 0.6215 +2026-04-14 20:47:10.030431: +2026-04-14 20:47:10.032679: Epoch 3721 +2026-04-14 20:47:10.034497: Current learning rate: 0.00091 +2026-04-14 20:48:51.695369: train_loss -0.4774 +2026-04-14 20:48:51.700848: val_loss -0.4224 +2026-04-14 20:48:51.702701: Pseudo dice [0.8452, 0.0, 0.79, 0.9252, 0.3276, 0.8427, 0.7043] +2026-04-14 20:48:51.704182: Epoch time: 101.67 s +2026-04-14 20:48:51.705847: Yayy! New best EMA pseudo Dice: 0.6227 +2026-04-14 20:48:54.611704: +2026-04-14 20:48:54.613934: Epoch 3722 +2026-04-14 20:48:54.615586: Current learning rate: 0.00091 +2026-04-14 20:50:36.182348: train_loss -0.4785 +2026-04-14 20:50:36.189469: val_loss -0.4183 +2026-04-14 20:50:36.191430: Pseudo dice [0.3982, 0.0, 0.7177, 0.655, 0.6745, 0.7848, 0.9187] +2026-04-14 20:50:36.193286: Epoch time: 101.57 s +2026-04-14 20:50:37.435103: +2026-04-14 20:50:37.437442: Epoch 3723 +2026-04-14 20:50:37.440114: Current learning rate: 0.0009 +2026-04-14 20:52:19.081829: train_loss -0.4817 +2026-04-14 20:52:19.086470: val_loss -0.4031 +2026-04-14 20:52:19.088757: Pseudo dice [0.5712, 0.0, 0.6637, 0.8321, 0.4732, 0.7926, 0.9079] +2026-04-14 20:52:19.090364: Epoch time: 101.65 s +2026-04-14 20:52:20.291959: +2026-04-14 20:52:20.293993: Epoch 3724 +2026-04-14 20:52:20.296103: Current learning rate: 0.0009 +2026-04-14 20:54:01.940459: train_loss -0.474 +2026-04-14 20:54:01.946447: val_loss -0.4136 +2026-04-14 20:54:01.948093: Pseudo dice [0.5305, 0.0, 0.7008, 0.7211, 0.5334, 0.8255, 0.8759] +2026-04-14 20:54:01.950242: Epoch time: 101.65 s +2026-04-14 20:54:03.142936: +2026-04-14 20:54:03.144583: Epoch 3725 +2026-04-14 20:54:03.146428: Current learning rate: 0.0009 +2026-04-14 20:55:44.797308: train_loss -0.4758 +2026-04-14 20:55:44.802164: val_loss -0.4028 +2026-04-14 20:55:44.803812: Pseudo dice [0.5447, 0.0, 0.8178, 0.7414, 0.613, 0.6946, 0.7545] +2026-04-14 20:55:44.805663: Epoch time: 101.66 s +2026-04-14 20:55:46.022269: +2026-04-14 20:55:46.024181: Epoch 3726 +2026-04-14 20:55:46.026221: Current learning rate: 0.0009 +2026-04-14 20:57:27.679108: train_loss -0.4747 +2026-04-14 20:57:27.685555: val_loss -0.4091 +2026-04-14 20:57:27.687205: Pseudo dice [0.5052, 0.0, 0.8237, 0.8359, 0.5242, 0.8085, 0.8407] +2026-04-14 20:57:27.689236: Epoch time: 101.66 s +2026-04-14 20:57:28.904608: +2026-04-14 20:57:28.906659: Epoch 3727 +2026-04-14 20:57:28.909186: Current learning rate: 0.00089 +2026-04-14 20:59:10.687559: train_loss -0.4628 +2026-04-14 20:59:10.692422: val_loss -0.3902 +2026-04-14 20:59:10.694105: Pseudo dice [0.5106, 0.0, 0.7879, 0.7741, 0.5253, 0.6089, 0.7637] +2026-04-14 20:59:10.696183: Epoch time: 101.79 s +2026-04-14 20:59:11.929154: +2026-04-14 20:59:11.930859: Epoch 3728 +2026-04-14 20:59:11.932642: Current learning rate: 0.00089 +2026-04-14 21:00:53.568500: train_loss -0.4765 +2026-04-14 21:00:53.573348: val_loss -0.4315 +2026-04-14 21:00:53.575280: Pseudo dice [0.5979, 0.0, 0.6734, 0.5927, 0.5518, 0.8583, 0.8288] +2026-04-14 21:00:53.576982: Epoch time: 101.64 s +2026-04-14 21:00:54.797465: +2026-04-14 21:00:54.799397: Epoch 3729 +2026-04-14 21:00:54.801124: Current learning rate: 0.00089 +2026-04-14 21:02:36.479372: train_loss -0.4642 +2026-04-14 21:02:36.484617: val_loss -0.4015 +2026-04-14 21:02:36.486994: Pseudo dice [0.3385, 0.0, 0.7206, 0.8086, 0.5459, 0.7979, 0.8365] +2026-04-14 21:02:36.488835: Epoch time: 101.69 s +2026-04-14 21:02:37.705520: +2026-04-14 21:02:37.707515: Epoch 3730 +2026-04-14 21:02:37.709918: Current learning rate: 0.00088 +2026-04-14 21:04:20.377839: train_loss -0.4702 +2026-04-14 21:04:20.402820: val_loss -0.4032 +2026-04-14 21:04:20.404550: Pseudo dice [0.43, 0.0, 0.7638, 0.7608, 0.4716, 0.6968, 0.8999] +2026-04-14 21:04:20.406080: Epoch time: 102.68 s +2026-04-14 21:04:21.631557: +2026-04-14 21:04:21.634151: Epoch 3731 +2026-04-14 21:04:21.635933: Current learning rate: 0.00088 +2026-04-14 21:06:03.378468: train_loss -0.4948 +2026-04-14 21:06:03.384368: val_loss -0.3827 +2026-04-14 21:06:03.385873: Pseudo dice [0.7854, 0.0, 0.6256, 0.6578, 0.3933, 0.619, 0.9401] +2026-04-14 21:06:03.387709: Epoch time: 101.75 s +2026-04-14 21:06:04.624122: +2026-04-14 21:06:04.625748: Epoch 3732 +2026-04-14 21:06:04.627511: Current learning rate: 0.00088 +2026-04-14 21:07:46.405992: train_loss -0.466 +2026-04-14 21:07:46.412711: val_loss -0.378 +2026-04-14 21:07:46.415883: Pseudo dice [0.4068, 0.0, 0.7043, 0.8999, 0.5127, 0.8834, 0.8251] +2026-04-14 21:07:46.417420: Epoch time: 101.78 s +2026-04-14 21:07:47.637805: +2026-04-14 21:07:47.639446: Epoch 3733 +2026-04-14 21:07:47.641946: Current learning rate: 0.00087 +2026-04-14 21:09:29.396452: train_loss -0.4851 +2026-04-14 21:09:29.407478: val_loss -0.407 +2026-04-14 21:09:29.409297: Pseudo dice [0.7311, 0.0, 0.8586, 0.7841, 0.5058, 0.7458, 0.9205] +2026-04-14 21:09:29.411395: Epoch time: 101.76 s +2026-04-14 21:09:30.631263: +2026-04-14 21:09:30.640233: Epoch 3734 +2026-04-14 21:09:30.642340: Current learning rate: 0.00087 +2026-04-14 21:11:12.454140: train_loss -0.4842 +2026-04-14 21:11:12.460594: val_loss -0.4235 +2026-04-14 21:11:12.462112: Pseudo dice [0.8432, 0.0, 0.7446, 0.8457, 0.6524, 0.721, 0.865] +2026-04-14 21:11:12.464175: Epoch time: 101.83 s +2026-04-14 21:11:13.687115: +2026-04-14 21:11:13.690098: Epoch 3735 +2026-04-14 21:11:13.694328: Current learning rate: 0.00087 +2026-04-14 21:12:55.492678: train_loss -0.4737 +2026-04-14 21:12:55.497671: val_loss -0.3861 +2026-04-14 21:12:55.500280: Pseudo dice [0.4824, 0.0, 0.7055, 0.7206, 0.0199, 0.8418, 0.8062] +2026-04-14 21:12:55.502184: Epoch time: 101.81 s +2026-04-14 21:12:56.717005: +2026-04-14 21:12:56.718642: Epoch 3736 +2026-04-14 21:12:56.720691: Current learning rate: 0.00087 +2026-04-14 21:14:38.338611: train_loss -0.4698 +2026-04-14 21:14:38.343746: val_loss -0.3939 +2026-04-14 21:14:38.345589: Pseudo dice [0.7949, 0.0, 0.6466, 0.9162, 0.3673, 0.7969, 0.8823] +2026-04-14 21:14:38.347378: Epoch time: 101.62 s +2026-04-14 21:14:39.568987: +2026-04-14 21:14:39.570979: Epoch 3737 +2026-04-14 21:14:39.573184: Current learning rate: 0.00086 +2026-04-14 21:16:21.472579: train_loss -0.4769 +2026-04-14 21:16:21.477815: val_loss -0.4125 +2026-04-14 21:16:21.479692: Pseudo dice [0.5619, 0.0, 0.7402, 0.7946, 0.4392, 0.8178, 0.8907] +2026-04-14 21:16:21.481035: Epoch time: 101.91 s +2026-04-14 21:16:22.701837: +2026-04-14 21:16:22.704299: Epoch 3738 +2026-04-14 21:16:22.706145: Current learning rate: 0.00086 +2026-04-14 21:18:04.620417: train_loss -0.4732 +2026-04-14 21:18:04.626841: val_loss -0.4277 +2026-04-14 21:18:04.629831: Pseudo dice [0.4264, 0.0, 0.7971, 0.9047, 0.721, 0.7694, 0.8921] +2026-04-14 21:18:04.631793: Epoch time: 101.92 s +2026-04-14 21:18:05.858013: +2026-04-14 21:18:05.860000: Epoch 3739 +2026-04-14 21:18:05.861982: Current learning rate: 0.00086 +2026-04-14 21:19:47.561437: train_loss -0.4813 +2026-04-14 21:19:47.567979: val_loss -0.4167 +2026-04-14 21:19:47.570515: Pseudo dice [0.8891, 0.0, 0.7032, 0.4687, 0.6654, 0.6228, 0.8466] +2026-04-14 21:19:47.572289: Epoch time: 101.71 s +2026-04-14 21:19:48.878052: +2026-04-14 21:19:48.879542: Epoch 3740 +2026-04-14 21:19:48.881362: Current learning rate: 0.00085 +2026-04-14 21:21:30.643058: train_loss -0.4793 +2026-04-14 21:21:30.649680: val_loss -0.4181 +2026-04-14 21:21:30.651556: Pseudo dice [0.458, 0.0, 0.7508, 0.8756, 0.4629, 0.9008, 0.8378] +2026-04-14 21:21:30.654582: Epoch time: 101.77 s +2026-04-14 21:21:31.874880: +2026-04-14 21:21:31.876566: Epoch 3741 +2026-04-14 21:21:31.878253: Current learning rate: 0.00085 +2026-04-14 21:23:13.486398: train_loss -0.4778 +2026-04-14 21:23:13.491134: val_loss -0.4241 +2026-04-14 21:23:13.492663: Pseudo dice [0.8463, 0.0, 0.8083, 0.8179, 0.5519, 0.7708, 0.7493] +2026-04-14 21:23:13.494481: Epoch time: 101.61 s +2026-04-14 21:23:14.729389: +2026-04-14 21:23:14.731163: Epoch 3742 +2026-04-14 21:23:14.736537: Current learning rate: 0.00085 +2026-04-14 21:24:56.344829: train_loss -0.4676 +2026-04-14 21:24:56.350355: val_loss -0.4313 +2026-04-14 21:24:56.352756: Pseudo dice [0.5459, 0.0, 0.8053, 0.7375, 0.616, 0.8283, 0.7722] +2026-04-14 21:24:56.355325: Epoch time: 101.62 s +2026-04-14 21:24:57.570552: +2026-04-14 21:24:57.572128: Epoch 3743 +2026-04-14 21:24:57.574036: Current learning rate: 0.00085 +2026-04-14 21:26:39.260677: train_loss -0.4836 +2026-04-14 21:26:39.266278: val_loss -0.3931 +2026-04-14 21:26:39.267885: Pseudo dice [0.3144, 0.0, 0.7058, 0.6405, 0.6189, 0.8059, 0.5421] +2026-04-14 21:26:39.269622: Epoch time: 101.69 s +2026-04-14 21:26:40.475773: +2026-04-14 21:26:40.477751: Epoch 3744 +2026-04-14 21:26:40.479890: Current learning rate: 0.00084 +2026-04-14 21:28:22.206104: train_loss -0.4789 +2026-04-14 21:28:22.212317: val_loss -0.407 +2026-04-14 21:28:22.214668: Pseudo dice [0.4184, 0.0, 0.8066, 0.868, 0.4215, 0.8053, 0.8703] +2026-04-14 21:28:22.216539: Epoch time: 101.73 s +2026-04-14 21:28:23.443868: +2026-04-14 21:28:23.445538: Epoch 3745 +2026-04-14 21:28:23.447484: Current learning rate: 0.00084 +2026-04-14 21:30:05.132068: train_loss -0.4716 +2026-04-14 21:30:05.137318: val_loss -0.4038 +2026-04-14 21:30:05.138981: Pseudo dice [0.506, 0.0, 0.7986, 0.3285, 0.4109, 0.8352, 0.8901] +2026-04-14 21:30:05.140701: Epoch time: 101.69 s +2026-04-14 21:30:06.349601: +2026-04-14 21:30:06.351830: Epoch 3746 +2026-04-14 21:30:06.354059: Current learning rate: 0.00084 +2026-04-14 21:31:48.199355: train_loss -0.4826 +2026-04-14 21:31:48.204951: val_loss -0.4124 +2026-04-14 21:31:48.207447: Pseudo dice [0.5776, 0.0, 0.6856, 0.9106, 0.4216, 0.7931, 0.8934] +2026-04-14 21:31:48.209439: Epoch time: 101.85 s +2026-04-14 21:31:49.430181: +2026-04-14 21:31:49.431672: Epoch 3747 +2026-04-14 21:31:49.433381: Current learning rate: 0.00083 +2026-04-14 21:33:31.153598: train_loss -0.479 +2026-04-14 21:33:31.158273: val_loss -0.4179 +2026-04-14 21:33:31.159823: Pseudo dice [0.7671, 0.0, 0.7506, 0.8422, 0.6766, 0.8531, 0.5911] +2026-04-14 21:33:31.162055: Epoch time: 101.73 s +2026-04-14 21:33:32.380200: +2026-04-14 21:33:32.381758: Epoch 3748 +2026-04-14 21:33:32.383641: Current learning rate: 0.00083 +2026-04-14 21:35:14.020713: train_loss -0.4835 +2026-04-14 21:35:14.027236: val_loss -0.4149 +2026-04-14 21:35:14.029255: Pseudo dice [0.5534, 0.0, 0.8568, 0.8173, 0.6005, 0.7885, 0.6571] +2026-04-14 21:35:14.031498: Epoch time: 101.64 s +2026-04-14 21:35:15.222207: +2026-04-14 21:35:15.223911: Epoch 3749 +2026-04-14 21:35:15.226082: Current learning rate: 0.00083 +2026-04-14 21:36:57.092873: train_loss -0.4798 +2026-04-14 21:36:57.098006: val_loss -0.3907 +2026-04-14 21:36:57.099739: Pseudo dice [0.8147, 0.0, 0.779, 0.9103, 0.4573, 0.7936, 0.851] +2026-04-14 21:36:57.101818: Epoch time: 101.87 s +2026-04-14 21:37:01.076570: +2026-04-14 21:37:01.078215: Epoch 3750 +2026-04-14 21:37:01.080135: Current learning rate: 0.00082 +2026-04-14 21:38:42.897734: train_loss -0.474 +2026-04-14 21:38:42.912307: val_loss -0.3455 +2026-04-14 21:38:42.914247: Pseudo dice [0.3715, 0.0, 0.7668, 0.7544, 0.3432, 0.7407, 0.9378] +2026-04-14 21:38:42.916028: Epoch time: 101.82 s +2026-04-14 21:38:44.120608: +2026-04-14 21:38:44.122433: Epoch 3751 +2026-04-14 21:38:44.124510: Current learning rate: 0.00082 +2026-04-14 21:40:26.062468: train_loss -0.4716 +2026-04-14 21:40:26.068220: val_loss -0.3804 +2026-04-14 21:40:26.070478: Pseudo dice [0.3571, 0.0, 0.65, 0.7924, 0.472, 0.779, 0.5873] +2026-04-14 21:40:26.072425: Epoch time: 101.94 s +2026-04-14 21:40:27.276503: +2026-04-14 21:40:27.278265: Epoch 3752 +2026-04-14 21:40:27.280177: Current learning rate: 0.00082 +2026-04-14 21:42:09.022115: train_loss -0.4701 +2026-04-14 21:42:09.027365: val_loss -0.4231 +2026-04-14 21:42:09.029702: Pseudo dice [0.5919, 0.0, 0.8299, 0.8537, 0.5618, 0.7943, 0.7018] +2026-04-14 21:42:09.031702: Epoch time: 101.75 s +2026-04-14 21:42:10.267159: +2026-04-14 21:42:10.268766: Epoch 3753 +2026-04-14 21:42:10.271554: Current learning rate: 0.00082 +2026-04-14 21:43:51.950967: train_loss -0.4823 +2026-04-14 21:43:51.960438: val_loss -0.4244 +2026-04-14 21:43:51.962380: Pseudo dice [0.5391, 0.0, 0.692, 0.694, 0.5726, 0.8518, 0.7914] +2026-04-14 21:43:51.964046: Epoch time: 101.69 s +2026-04-14 21:43:53.166424: +2026-04-14 21:43:53.168046: Epoch 3754 +2026-04-14 21:43:53.169851: Current learning rate: 0.00081 +2026-04-14 21:45:34.969607: train_loss -0.4811 +2026-04-14 21:45:34.973948: val_loss -0.3862 +2026-04-14 21:45:34.975331: Pseudo dice [0.6229, 0.0, 0.6912, 0.5848, 0.2575, 0.7081, 0.9136] +2026-04-14 21:45:34.976519: Epoch time: 101.81 s +2026-04-14 21:45:36.170985: +2026-04-14 21:45:36.172981: Epoch 3755 +2026-04-14 21:45:36.175065: Current learning rate: 0.00081 +2026-04-14 21:47:17.901574: train_loss -0.4727 +2026-04-14 21:47:17.907440: val_loss -0.4136 +2026-04-14 21:47:17.909266: Pseudo dice [0.4877, 0.0, 0.8176, 0.7724, 0.5442, 0.7791, 0.7985] +2026-04-14 21:47:17.910916: Epoch time: 101.73 s +2026-04-14 21:47:19.119133: +2026-04-14 21:47:19.121005: Epoch 3756 +2026-04-14 21:47:19.123216: Current learning rate: 0.00081 +2026-04-14 21:49:00.992538: train_loss -0.4729 +2026-04-14 21:49:00.997388: val_loss -0.4108 +2026-04-14 21:49:00.998979: Pseudo dice [0.6422, 0.0, 0.7383, 0.8449, 0.4604, 0.8229, 0.8955] +2026-04-14 21:49:01.000449: Epoch time: 101.88 s +2026-04-14 21:49:02.223853: +2026-04-14 21:49:02.225515: Epoch 3757 +2026-04-14 21:49:02.227328: Current learning rate: 0.0008 +2026-04-14 21:50:44.109573: train_loss -0.4777 +2026-04-14 21:50:44.115812: val_loss -0.4234 +2026-04-14 21:50:44.119205: Pseudo dice [0.6505, 0.0, 0.7482, 0.9076, 0.6736, 0.8797, 0.5808] +2026-04-14 21:50:44.121124: Epoch time: 101.89 s +2026-04-14 21:50:45.326319: +2026-04-14 21:50:45.328229: Epoch 3758 +2026-04-14 21:50:45.330145: Current learning rate: 0.0008 +2026-04-14 21:52:27.135544: train_loss -0.4785 +2026-04-14 21:52:27.140676: val_loss -0.4198 +2026-04-14 21:52:27.142716: Pseudo dice [0.4538, 0.0, 0.7331, 0.5558, 0.6435, 0.8149, 0.7982] +2026-04-14 21:52:27.144593: Epoch time: 101.81 s +2026-04-14 21:52:28.342116: +2026-04-14 21:52:28.343927: Epoch 3759 +2026-04-14 21:52:28.345951: Current learning rate: 0.0008 +2026-04-14 21:54:10.127282: train_loss -0.4751 +2026-04-14 21:54:10.132427: val_loss -0.4158 +2026-04-14 21:54:10.134566: Pseudo dice [0.74, 0.0, 0.778, 0.5698, 0.6063, 0.7579, 0.8353] +2026-04-14 21:54:10.136430: Epoch time: 101.79 s +2026-04-14 21:54:11.355648: +2026-04-14 21:54:11.357237: Epoch 3760 +2026-04-14 21:54:11.359010: Current learning rate: 0.00079 +2026-04-14 21:55:53.110168: train_loss -0.4746 +2026-04-14 21:55:53.115915: val_loss -0.4078 +2026-04-14 21:55:53.117498: Pseudo dice [0.8017, 0.0, 0.6843, 0.8454, 0.3577, 0.6781, 0.8797] +2026-04-14 21:55:53.119155: Epoch time: 101.76 s +2026-04-14 21:55:54.333214: +2026-04-14 21:55:54.335084: Epoch 3761 +2026-04-14 21:55:54.336786: Current learning rate: 0.00079 +2026-04-14 21:57:36.267298: train_loss -0.4884 +2026-04-14 21:57:36.272797: val_loss -0.4182 +2026-04-14 21:57:36.275152: Pseudo dice [0.7972, 0.0, 0.7797, 0.8516, 0.5555, 0.7945, 0.555] +2026-04-14 21:57:36.277028: Epoch time: 101.94 s +2026-04-14 21:57:37.476156: +2026-04-14 21:57:37.478178: Epoch 3762 +2026-04-14 21:57:37.479923: Current learning rate: 0.00079 +2026-04-14 21:59:19.264918: train_loss -0.4945 +2026-04-14 21:59:19.270387: val_loss -0.4087 +2026-04-14 21:59:19.272114: Pseudo dice [0.518, 0.0, 0.5812, 0.9, 0.5041, 0.8214, 0.6994] +2026-04-14 21:59:19.273921: Epoch time: 101.79 s +2026-04-14 21:59:20.478146: +2026-04-14 21:59:20.480930: Epoch 3763 +2026-04-14 21:59:20.484224: Current learning rate: 0.00079 +2026-04-14 22:01:02.282136: train_loss -0.4814 +2026-04-14 22:01:02.286950: val_loss -0.3819 +2026-04-14 22:01:02.288623: Pseudo dice [0.8372, 0.0, 0.8082, 0.8257, 0.5369, 0.729, 0.6773] +2026-04-14 22:01:02.290419: Epoch time: 101.81 s +2026-04-14 22:01:03.504084: +2026-04-14 22:01:03.506343: Epoch 3764 +2026-04-14 22:01:03.508709: Current learning rate: 0.00078 +2026-04-14 22:02:45.341967: train_loss -0.4735 +2026-04-14 22:02:45.348446: val_loss -0.3892 +2026-04-14 22:02:45.350704: Pseudo dice [0.6415, 0.0, 0.8021, 0.7772, 0.1043, 0.8016, 0.7628] +2026-04-14 22:02:45.352462: Epoch time: 101.84 s +2026-04-14 22:02:46.558702: +2026-04-14 22:02:46.560555: Epoch 3765 +2026-04-14 22:02:46.562614: Current learning rate: 0.00078 +2026-04-14 22:04:28.318460: train_loss -0.4785 +2026-04-14 22:04:28.323643: val_loss -0.4146 +2026-04-14 22:04:28.325225: Pseudo dice [0.3602, 0.0, 0.744, 0.9105, 0.5601, 0.7777, 0.8534] +2026-04-14 22:04:28.326833: Epoch time: 101.76 s +2026-04-14 22:04:29.545674: +2026-04-14 22:04:29.547396: Epoch 3766 +2026-04-14 22:04:29.548970: Current learning rate: 0.00078 +2026-04-14 22:06:11.173008: train_loss -0.4837 +2026-04-14 22:06:11.177649: val_loss -0.4039 +2026-04-14 22:06:11.179444: Pseudo dice [0.4577, 0.0, 0.7621, 0.8151, 0.407, 0.8195, 0.8447] +2026-04-14 22:06:11.181173: Epoch time: 101.63 s +2026-04-14 22:06:12.394843: +2026-04-14 22:06:12.396685: Epoch 3767 +2026-04-14 22:06:12.399070: Current learning rate: 0.00077 +2026-04-14 22:07:54.085351: train_loss -0.4841 +2026-04-14 22:07:54.090209: val_loss -0.3988 +2026-04-14 22:07:54.091901: Pseudo dice [0.6459, 0.0, 0.7258, 0.8912, 0.5157, 0.73, 0.8949] +2026-04-14 22:07:54.093485: Epoch time: 101.69 s +2026-04-14 22:07:55.307397: +2026-04-14 22:07:55.308972: Epoch 3768 +2026-04-14 22:07:55.311248: Current learning rate: 0.00077 +2026-04-14 22:09:36.969710: train_loss -0.4785 +2026-04-14 22:09:36.975194: val_loss -0.403 +2026-04-14 22:09:36.976939: Pseudo dice [0.7159, 0.0, 0.7175, 0.5472, 0.5122, 0.8561, 0.8763] +2026-04-14 22:09:36.978448: Epoch time: 101.67 s +2026-04-14 22:09:38.204382: +2026-04-14 22:09:38.206318: Epoch 3769 +2026-04-14 22:09:38.208554: Current learning rate: 0.00077 +2026-04-14 22:11:19.844594: train_loss -0.4863 +2026-04-14 22:11:19.850376: val_loss -0.4059 +2026-04-14 22:11:19.852380: Pseudo dice [0.2131, 0.0, 0.7326, 0.9372, 0.5202, 0.7704, 0.7934] +2026-04-14 22:11:19.854735: Epoch time: 101.64 s +2026-04-14 22:11:22.147638: +2026-04-14 22:11:22.149785: Epoch 3770 +2026-04-14 22:11:22.151958: Current learning rate: 0.00077 +2026-04-14 22:13:03.945288: train_loss -0.4814 +2026-04-14 22:13:03.949881: val_loss -0.4063 +2026-04-14 22:13:03.951361: Pseudo dice [0.2349, 0.0, 0.6785, 0.595, 0.557, 0.8429, 0.8627] +2026-04-14 22:13:03.953420: Epoch time: 101.8 s +2026-04-14 22:13:05.159688: +2026-04-14 22:13:05.161152: Epoch 3771 +2026-04-14 22:13:05.162728: Current learning rate: 0.00076 +2026-04-14 22:14:46.990695: train_loss -0.4796 +2026-04-14 22:14:46.996740: val_loss -0.4033 +2026-04-14 22:14:46.998066: Pseudo dice [0.4836, 0.0, 0.6743, 0.8359, 0.3239, 0.814, 0.8634] +2026-04-14 22:14:46.999206: Epoch time: 101.83 s +2026-04-14 22:14:48.195476: +2026-04-14 22:14:48.203242: Epoch 3772 +2026-04-14 22:14:48.204877: Current learning rate: 0.00076 +2026-04-14 22:16:29.829638: train_loss -0.4765 +2026-04-14 22:16:29.835424: val_loss -0.4072 +2026-04-14 22:16:29.836856: Pseudo dice [0.8471, 0.0, 0.7162, 0.312, 0.6797, 0.804, 0.6423] +2026-04-14 22:16:29.838689: Epoch time: 101.64 s +2026-04-14 22:16:31.061621: +2026-04-14 22:16:31.063580: Epoch 3773 +2026-04-14 22:16:31.065320: Current learning rate: 0.00076 +2026-04-14 22:18:12.763469: train_loss -0.4825 +2026-04-14 22:18:12.768266: val_loss -0.4281 +2026-04-14 22:18:12.770714: Pseudo dice [0.8069, 0.0, 0.7751, 0.9335, 0.5141, 0.8557, 0.8677] +2026-04-14 22:18:12.772938: Epoch time: 101.7 s +2026-04-14 22:18:13.984365: +2026-04-14 22:18:13.985963: Epoch 3774 +2026-04-14 22:18:13.988286: Current learning rate: 0.00075 +2026-04-14 22:19:55.748806: train_loss -0.4803 +2026-04-14 22:19:55.755020: val_loss -0.419 +2026-04-14 22:19:55.756827: Pseudo dice [0.4282, 0.0, 0.5955, 0.9069, 0.5711, 0.692, 0.9506] +2026-04-14 22:19:55.759316: Epoch time: 101.77 s +2026-04-14 22:19:56.978913: +2026-04-14 22:19:56.980615: Epoch 3775 +2026-04-14 22:19:56.982646: Current learning rate: 0.00075 +2026-04-14 22:21:38.845095: train_loss -0.4939 +2026-04-14 22:21:38.850958: val_loss -0.4373 +2026-04-14 22:21:38.852698: Pseudo dice [0.7737, 0.0, 0.8559, 0.6701, 0.7597, 0.8616, 0.8585] +2026-04-14 22:21:38.854237: Epoch time: 101.87 s +2026-04-14 22:21:40.061964: +2026-04-14 22:21:40.064170: Epoch 3776 +2026-04-14 22:21:40.066468: Current learning rate: 0.00075 +2026-04-14 22:23:21.728649: train_loss -0.4817 +2026-04-14 22:23:21.733921: val_loss -0.4093 +2026-04-14 22:23:21.735824: Pseudo dice [0.5945, 0.0, 0.822, 0.8831, 0.6508, 0.872, 0.9351] +2026-04-14 22:23:21.737872: Epoch time: 101.67 s +2026-04-14 22:23:22.956252: +2026-04-14 22:23:22.958530: Epoch 3777 +2026-04-14 22:23:22.961253: Current learning rate: 0.00074 +2026-04-14 22:25:04.745028: train_loss -0.4827 +2026-04-14 22:25:04.750127: val_loss -0.4146 +2026-04-14 22:25:04.751769: Pseudo dice [0.3857, 0.0, 0.8386, 0.8249, 0.7018, 0.8817, 0.9206] +2026-04-14 22:25:04.753342: Epoch time: 101.79 s +2026-04-14 22:25:05.970242: +2026-04-14 22:25:05.972377: Epoch 3778 +2026-04-14 22:25:05.974754: Current learning rate: 0.00074 +2026-04-14 22:26:47.887838: train_loss -0.4868 +2026-04-14 22:26:47.893425: val_loss -0.3993 +2026-04-14 22:26:47.895742: Pseudo dice [0.3741, 0.0, 0.5998, 0.9263, 0.6868, 0.8147, 0.8361] +2026-04-14 22:26:47.898295: Epoch time: 101.92 s +2026-04-14 22:26:49.131437: +2026-04-14 22:26:49.133429: Epoch 3779 +2026-04-14 22:26:49.135402: Current learning rate: 0.00074 +2026-04-14 22:28:31.064269: train_loss -0.4921 +2026-04-14 22:28:31.069490: val_loss -0.3943 +2026-04-14 22:28:31.072261: Pseudo dice [0.5456, 0.0, 0.736, 0.7636, 0.3766, 0.7642, 0.8898] +2026-04-14 22:28:31.074261: Epoch time: 101.94 s +2026-04-14 22:28:32.291546: +2026-04-14 22:28:32.293717: Epoch 3780 +2026-04-14 22:28:32.296473: Current learning rate: 0.00074 +2026-04-14 22:30:14.240454: train_loss -0.4845 +2026-04-14 22:30:14.245486: val_loss -0.4206 +2026-04-14 22:30:14.246945: Pseudo dice [0.54, 0.0, 0.6801, 0.8578, 0.5483, 0.8148, 0.8565] +2026-04-14 22:30:14.248585: Epoch time: 101.95 s +2026-04-14 22:30:15.486826: +2026-04-14 22:30:15.488401: Epoch 3781 +2026-04-14 22:30:15.490332: Current learning rate: 0.00073 +2026-04-14 22:31:57.354292: train_loss -0.4972 +2026-04-14 22:31:57.359684: val_loss -0.3928 +2026-04-14 22:31:57.361209: Pseudo dice [0.4136, 0.0, 0.698, 0.7994, 0.4554, 0.7315, 0.798] +2026-04-14 22:31:57.362958: Epoch time: 101.87 s +2026-04-14 22:31:58.568884: +2026-04-14 22:31:58.570349: Epoch 3782 +2026-04-14 22:31:58.572200: Current learning rate: 0.00073 +2026-04-14 22:33:40.289154: train_loss -0.4759 +2026-04-14 22:33:40.294297: val_loss -0.4238 +2026-04-14 22:33:40.296268: Pseudo dice [0.5207, 0.0, 0.8897, 0.8809, 0.7303, 0.8443, 0.9389] +2026-04-14 22:33:40.298476: Epoch time: 101.72 s +2026-04-14 22:33:41.540491: +2026-04-14 22:33:41.541984: Epoch 3783 +2026-04-14 22:33:41.544307: Current learning rate: 0.00073 +2026-04-14 22:35:23.397126: train_loss -0.4915 +2026-04-14 22:35:23.401494: val_loss -0.4078 +2026-04-14 22:35:23.403168: Pseudo dice [0.6962, 0.0, 0.6159, 0.9152, 0.6476, 0.8111, 0.8534] +2026-04-14 22:35:23.405051: Epoch time: 101.86 s +2026-04-14 22:35:24.619316: +2026-04-14 22:35:24.621087: Epoch 3784 +2026-04-14 22:35:24.623183: Current learning rate: 0.00072 +2026-04-14 22:37:06.293054: train_loss -0.4791 +2026-04-14 22:37:06.297913: val_loss -0.379 +2026-04-14 22:37:06.300570: Pseudo dice [0.6581, 0.0, 0.7067, 0.1916, 0.405, 0.7445, 0.8712] +2026-04-14 22:37:06.302627: Epoch time: 101.68 s +2026-04-14 22:37:07.524197: +2026-04-14 22:37:07.526436: Epoch 3785 +2026-04-14 22:37:07.528816: Current learning rate: 0.00072 +2026-04-14 22:38:49.279008: train_loss -0.4829 +2026-04-14 22:38:49.283387: val_loss -0.4223 +2026-04-14 22:38:49.285034: Pseudo dice [0.6659, 0.0, 0.8446, 0.553, 0.4347, 0.8857, 0.8589] +2026-04-14 22:38:49.286375: Epoch time: 101.76 s +2026-04-14 22:38:50.523668: +2026-04-14 22:38:50.525428: Epoch 3786 +2026-04-14 22:38:50.527323: Current learning rate: 0.00072 +2026-04-14 22:40:32.389423: train_loss -0.4815 +2026-04-14 22:40:32.394227: val_loss -0.3835 +2026-04-14 22:40:32.395778: Pseudo dice [0.6795, 0.0, 0.7179, 0.5236, 0.4682, 0.7883, 0.7579] +2026-04-14 22:40:32.397350: Epoch time: 101.87 s +2026-04-14 22:40:33.605273: +2026-04-14 22:40:33.606895: Epoch 3787 +2026-04-14 22:40:33.608670: Current learning rate: 0.00071 +2026-04-14 22:42:15.417499: train_loss -0.4795 +2026-04-14 22:42:15.423325: val_loss -0.3979 +2026-04-14 22:42:15.424953: Pseudo dice [0.3302, 0.0, 0.8705, 0.7289, 0.3377, 0.7575, 0.8526] +2026-04-14 22:42:15.426861: Epoch time: 101.82 s +2026-04-14 22:42:16.654427: +2026-04-14 22:42:16.656699: Epoch 3788 +2026-04-14 22:42:16.658363: Current learning rate: 0.00071 +2026-04-14 22:43:58.383889: train_loss -0.4752 +2026-04-14 22:43:58.388641: val_loss -0.4022 +2026-04-14 22:43:58.390474: Pseudo dice [0.7215, 0.0, 0.6917, 0.244, 0.461, 0.8125, 0.8309] +2026-04-14 22:43:58.392019: Epoch time: 101.73 s +2026-04-14 22:43:59.592595: +2026-04-14 22:43:59.594370: Epoch 3789 +2026-04-14 22:43:59.596177: Current learning rate: 0.00071 +2026-04-14 22:45:41.323922: train_loss -0.4725 +2026-04-14 22:45:41.328430: val_loss -0.3802 +2026-04-14 22:45:41.330158: Pseudo dice [0.739, 0.0, 0.4988, 0.8929, 0.3886, 0.6981, 0.9251] +2026-04-14 22:45:41.332198: Epoch time: 101.73 s +2026-04-14 22:45:42.546573: +2026-04-14 22:45:42.548039: Epoch 3790 +2026-04-14 22:45:42.549810: Current learning rate: 0.0007 +2026-04-14 22:47:25.448172: train_loss -0.4745 +2026-04-14 22:47:25.457063: val_loss -0.4139 +2026-04-14 22:47:25.459415: Pseudo dice [0.5763, 0.0, 0.5404, 0.8226, 0.5604, 0.7892, 0.8342] +2026-04-14 22:47:25.461702: Epoch time: 102.9 s +2026-04-14 22:47:26.661723: +2026-04-14 22:47:26.664046: Epoch 3791 +2026-04-14 22:47:26.666566: Current learning rate: 0.0007 +2026-04-14 22:49:08.452328: train_loss -0.4755 +2026-04-14 22:49:08.459656: val_loss -0.3866 +2026-04-14 22:49:08.461417: Pseudo dice [0.6993, 0.0, 0.4212, 0.7941, 0.4861, 0.7359, 0.8599] +2026-04-14 22:49:08.463704: Epoch time: 101.79 s +2026-04-14 22:49:09.679895: +2026-04-14 22:49:09.681579: Epoch 3792 +2026-04-14 22:49:09.683377: Current learning rate: 0.0007 +2026-04-14 22:50:51.639065: train_loss -0.4772 +2026-04-14 22:50:51.645118: val_loss -0.394 +2026-04-14 22:50:51.647601: Pseudo dice [0.4477, 0.0, 0.6637, 0.8469, 0.3189, 0.794, 0.8917] +2026-04-14 22:50:51.649944: Epoch time: 101.96 s +2026-04-14 22:50:52.853244: +2026-04-14 22:50:52.854968: Epoch 3793 +2026-04-14 22:50:52.856817: Current learning rate: 0.0007 +2026-04-14 22:52:34.745111: train_loss -0.4818 +2026-04-14 22:52:34.750364: val_loss -0.3884 +2026-04-14 22:52:34.752548: Pseudo dice [0.3028, 0.0, 0.8318, 0.8097, 0.5571, 0.8276, 0.8124] +2026-04-14 22:52:34.754257: Epoch time: 101.89 s +2026-04-14 22:52:35.975897: +2026-04-14 22:52:35.977394: Epoch 3794 +2026-04-14 22:52:35.979084: Current learning rate: 0.00069 +2026-04-14 22:54:17.812109: train_loss -0.4913 +2026-04-14 22:54:17.820662: val_loss -0.4198 +2026-04-14 22:54:17.822260: Pseudo dice [0.585, 0.0, 0.8209, 0.7954, 0.5048, 0.8363, 0.8909] +2026-04-14 22:54:17.823972: Epoch time: 101.84 s +2026-04-14 22:54:19.045010: +2026-04-14 22:54:19.046664: Epoch 3795 +2026-04-14 22:54:19.048636: Current learning rate: 0.00069 +2026-04-14 22:56:00.851977: train_loss -0.4794 +2026-04-14 22:56:00.856929: val_loss -0.3884 +2026-04-14 22:56:00.859324: Pseudo dice [0.7133, 0.0, 0.868, 0.6887, 0.4386, 0.8532, 0.6985] +2026-04-14 22:56:00.861027: Epoch time: 101.81 s +2026-04-14 22:56:02.070314: +2026-04-14 22:56:02.077869: Epoch 3796 +2026-04-14 22:56:02.079820: Current learning rate: 0.00069 +2026-04-14 22:57:43.716811: train_loss -0.4863 +2026-04-14 22:57:43.722180: val_loss -0.3995 +2026-04-14 22:57:43.724108: Pseudo dice [0.4567, 0.0, 0.6637, 0.8417, 0.3519, 0.8735, 0.8387] +2026-04-14 22:57:43.734760: Epoch time: 101.65 s +2026-04-14 22:57:44.954947: +2026-04-14 22:57:44.960937: Epoch 3797 +2026-04-14 22:57:44.965461: Current learning rate: 0.00068 +2026-04-14 22:59:26.761952: train_loss -0.4838 +2026-04-14 22:59:26.766934: val_loss -0.4133 +2026-04-14 22:59:26.768765: Pseudo dice [0.5076, 0.0, 0.8265, 0.6617, 0.6468, 0.8595, 0.7998] +2026-04-14 22:59:26.770557: Epoch time: 101.81 s +2026-04-14 22:59:27.980071: +2026-04-14 22:59:27.981556: Epoch 3798 +2026-04-14 22:59:27.983215: Current learning rate: 0.00068 +2026-04-14 23:01:09.685830: train_loss -0.4856 +2026-04-14 23:01:09.691321: val_loss -0.3878 +2026-04-14 23:01:09.693442: Pseudo dice [0.7719, 0.0, 0.6692, 0.854, 0.3031, 0.8228, 0.9152] +2026-04-14 23:01:09.695102: Epoch time: 101.71 s +2026-04-14 23:01:10.921568: +2026-04-14 23:01:10.923771: Epoch 3799 +2026-04-14 23:01:10.925614: Current learning rate: 0.00068 +2026-04-14 23:02:52.685956: train_loss -0.4809 +2026-04-14 23:02:52.691092: val_loss -0.3526 +2026-04-14 23:02:52.693168: Pseudo dice [0.6841, 0.0, 0.7672, 0.6521, 0.4027, 0.7909, 0.798] +2026-04-14 23:02:52.695338: Epoch time: 101.77 s +2026-04-14 23:02:55.698509: +2026-04-14 23:02:55.700315: Epoch 3800 +2026-04-14 23:02:55.702302: Current learning rate: 0.00067 +2026-04-14 23:04:37.557608: train_loss -0.4813 +2026-04-14 23:04:37.563273: val_loss -0.4265 +2026-04-14 23:04:37.565130: Pseudo dice [0.372, 0.0, 0.8312, 0.9387, 0.6455, 0.8223, 0.8793] +2026-04-14 23:04:37.566658: Epoch time: 101.86 s +2026-04-14 23:04:38.769157: +2026-04-14 23:04:38.770749: Epoch 3801 +2026-04-14 23:04:38.772552: Current learning rate: 0.00067 +2026-04-14 23:06:20.558697: train_loss -0.4847 +2026-04-14 23:06:20.563527: val_loss -0.4058 +2026-04-14 23:06:20.565518: Pseudo dice [0.5386, 0.0, 0.8315, 0.8708, 0.4323, 0.7945, 0.7943] +2026-04-14 23:06:20.567671: Epoch time: 101.79 s +2026-04-14 23:06:21.770653: +2026-04-14 23:06:21.772390: Epoch 3802 +2026-04-14 23:06:21.774223: Current learning rate: 0.00067 +2026-04-14 23:08:03.550008: train_loss -0.4822 +2026-04-14 23:08:03.555779: val_loss -0.3533 +2026-04-14 23:08:03.557640: Pseudo dice [0.3468, 0.0, 0.5091, 0.8359, 0.36, 0.8058, 0.8587] +2026-04-14 23:08:03.559276: Epoch time: 101.78 s +2026-04-14 23:08:04.786341: +2026-04-14 23:08:04.788065: Epoch 3803 +2026-04-14 23:08:04.789851: Current learning rate: 0.00067 +2026-04-14 23:09:46.702649: train_loss -0.4784 +2026-04-14 23:09:46.708934: val_loss -0.4176 +2026-04-14 23:09:46.711301: Pseudo dice [0.7394, 0.0, 0.5356, 0.8361, 0.6613, 0.8639, 0.6227] +2026-04-14 23:09:46.713625: Epoch time: 101.92 s +2026-04-14 23:09:47.930611: +2026-04-14 23:09:47.932282: Epoch 3804 +2026-04-14 23:09:47.934547: Current learning rate: 0.00066 +2026-04-14 23:11:29.753411: train_loss -0.4843 +2026-04-14 23:11:29.759176: val_loss -0.3904 +2026-04-14 23:11:29.761039: Pseudo dice [0.4688, 0.0, 0.6281, 0.7322, 0.267, 0.8347, 0.9448] +2026-04-14 23:11:29.762811: Epoch time: 101.83 s +2026-04-14 23:11:30.982120: +2026-04-14 23:11:30.983600: Epoch 3805 +2026-04-14 23:11:30.985366: Current learning rate: 0.00066 +2026-04-14 23:13:12.806068: train_loss -0.4823 +2026-04-14 23:13:12.810236: val_loss -0.4145 +2026-04-14 23:13:12.811926: Pseudo dice [0.4878, 0.0, 0.8565, 0.5175, 0.6197, 0.6885, 0.8161] +2026-04-14 23:13:12.813628: Epoch time: 101.83 s +2026-04-14 23:13:14.032177: +2026-04-14 23:13:14.033974: Epoch 3806 +2026-04-14 23:13:14.035650: Current learning rate: 0.00066 +2026-04-14 23:14:55.798548: train_loss -0.4955 +2026-04-14 23:14:55.803598: val_loss -0.3995 +2026-04-14 23:14:55.805009: Pseudo dice [0.4523, 0.0, 0.8686, 0.1202, 0.4561, 0.8237, 0.6378] +2026-04-14 23:14:55.807152: Epoch time: 101.77 s +2026-04-14 23:14:57.008159: +2026-04-14 23:14:57.009829: Epoch 3807 +2026-04-14 23:14:57.012301: Current learning rate: 0.00065 +2026-04-14 23:16:38.677633: train_loss -0.488 +2026-04-14 23:16:38.683198: val_loss -0.3907 +2026-04-14 23:16:38.685517: Pseudo dice [0.3211, 0.0, 0.5718, 0.7869, 0.5648, 0.7853, 0.9155] +2026-04-14 23:16:38.687195: Epoch time: 101.67 s +2026-04-14 23:16:39.899939: +2026-04-14 23:16:39.901918: Epoch 3808 +2026-04-14 23:16:39.903637: Current learning rate: 0.00065 +2026-04-14 23:18:21.604349: train_loss -0.4898 +2026-04-14 23:18:21.608921: val_loss -0.4185 +2026-04-14 23:18:21.610620: Pseudo dice [0.6268, 0.0, 0.7948, 0.8776, 0.6894, 0.8346, 0.788] +2026-04-14 23:18:21.612919: Epoch time: 101.71 s +2026-04-14 23:18:22.832612: +2026-04-14 23:18:22.834899: Epoch 3809 +2026-04-14 23:18:22.837721: Current learning rate: 0.00065 +2026-04-14 23:20:04.505955: train_loss -0.4781 +2026-04-14 23:20:04.514159: val_loss -0.3962 +2026-04-14 23:20:04.516006: Pseudo dice [0.5826, 0.0, 0.6588, 0.8714, 0.4213, 0.8451, 0.6504] +2026-04-14 23:20:04.518183: Epoch time: 101.68 s +2026-04-14 23:20:06.791741: +2026-04-14 23:20:06.793596: Epoch 3810 +2026-04-14 23:20:06.795541: Current learning rate: 0.00064 +2026-04-14 23:21:48.751613: train_loss -0.4911 +2026-04-14 23:21:48.756398: val_loss -0.3306 +2026-04-14 23:21:48.758265: Pseudo dice [0.7694, 0.0, 0.5571, 0.4188, 0.0798, 0.6306, 0.7011] +2026-04-14 23:21:48.759938: Epoch time: 101.96 s +2026-04-14 23:21:50.002441: +2026-04-14 23:21:50.004157: Epoch 3811 +2026-04-14 23:21:50.006180: Current learning rate: 0.00064 +2026-04-14 23:23:31.746123: train_loss -0.4776 +2026-04-14 23:23:31.751255: val_loss -0.3994 +2026-04-14 23:23:31.753348: Pseudo dice [0.4367, 0.0, 0.6555, 0.6833, 0.0662, 0.8193, 0.8903] +2026-04-14 23:23:31.755211: Epoch time: 101.75 s +2026-04-14 23:23:33.148088: +2026-04-14 23:23:33.149866: Epoch 3812 +2026-04-14 23:23:33.151958: Current learning rate: 0.00064 +2026-04-14 23:25:14.957687: train_loss -0.4747 +2026-04-14 23:25:14.962147: val_loss -0.4071 +2026-04-14 23:25:14.964287: Pseudo dice [0.4984, 0.0, 0.7943, 0.8387, 0.5048, 0.7535, 0.8456] +2026-04-14 23:25:14.965602: Epoch time: 101.81 s +2026-04-14 23:25:16.191452: +2026-04-14 23:25:16.193216: Epoch 3813 +2026-04-14 23:25:16.195048: Current learning rate: 0.00064 +2026-04-14 23:26:57.851350: train_loss -0.4886 +2026-04-14 23:26:57.856068: val_loss -0.3927 +2026-04-14 23:26:57.857602: Pseudo dice [0.6057, 0.0, 0.8093, 0.6585, 0.5398, 0.768, 0.7898] +2026-04-14 23:26:57.859773: Epoch time: 101.66 s +2026-04-14 23:26:59.100004: +2026-04-14 23:26:59.102189: Epoch 3814 +2026-04-14 23:26:59.104396: Current learning rate: 0.00063 +2026-04-14 23:28:41.063671: train_loss -0.4779 +2026-04-14 23:28:41.069107: val_loss -0.3745 +2026-04-14 23:28:41.070786: Pseudo dice [0.8403, 0.0, 0.6388, 0.7768, 0.4391, 0.857, 0.7782] +2026-04-14 23:28:41.072388: Epoch time: 101.97 s +2026-04-14 23:28:42.284657: +2026-04-14 23:28:42.286606: Epoch 3815 +2026-04-14 23:28:42.288837: Current learning rate: 0.00063 +2026-04-14 23:30:24.292372: train_loss -0.4767 +2026-04-14 23:30:24.297926: val_loss -0.3903 +2026-04-14 23:30:24.299663: Pseudo dice [0.4524, 0.0, 0.7635, 0.862, 0.59, 0.8315, 0.4457] +2026-04-14 23:30:24.301874: Epoch time: 102.01 s +2026-04-14 23:30:25.535467: +2026-04-14 23:30:25.537132: Epoch 3816 +2026-04-14 23:30:25.540426: Current learning rate: 0.00063 +2026-04-14 23:32:07.391107: train_loss -0.4884 +2026-04-14 23:32:07.396977: val_loss -0.4036 +2026-04-14 23:32:07.399070: Pseudo dice [0.7008, 0.0, 0.6393, 0.8386, 0.4805, 0.732, 0.4158] +2026-04-14 23:32:07.401630: Epoch time: 101.86 s +2026-04-14 23:32:08.638478: +2026-04-14 23:32:08.640736: Epoch 3817 +2026-04-14 23:32:08.643057: Current learning rate: 0.00062 +2026-04-14 23:33:50.393216: train_loss -0.5098 +2026-04-14 23:33:50.398547: val_loss -0.4355 +2026-04-14 23:33:50.401885: Pseudo dice [0.8185, 0.0, 0.6458, 0.4472, 0.6218, 0.8774, 0.8084] +2026-04-14 23:33:50.404463: Epoch time: 101.76 s +2026-04-14 23:33:51.672478: +2026-04-14 23:33:51.674270: Epoch 3818 +2026-04-14 23:33:51.676287: Current learning rate: 0.00062 +2026-04-14 23:35:33.588564: train_loss -0.5172 +2026-04-14 23:35:33.593339: val_loss -0.4838 +2026-04-14 23:35:33.595286: Pseudo dice [0.7703, 0.0, 0.8807, 0.87, 0.7816, 0.8192, 0.7962] +2026-04-14 23:35:33.596782: Epoch time: 101.92 s +2026-04-14 23:35:34.829930: +2026-04-14 23:35:34.832340: Epoch 3819 +2026-04-14 23:35:34.834146: Current learning rate: 0.00062 +2026-04-14 23:37:16.680915: train_loss -0.542 +2026-04-14 23:37:16.686769: val_loss -0.427 +2026-04-14 23:37:16.688747: Pseudo dice [0.3979, 0.0, 0.8755, 0.683, 0.4285, 0.8065, 0.8135] +2026-04-14 23:37:16.690598: Epoch time: 101.85 s +2026-04-14 23:37:17.926589: +2026-04-14 23:37:17.928854: Epoch 3820 +2026-04-14 23:37:17.930729: Current learning rate: 0.00061 +2026-04-14 23:38:59.807306: train_loss -0.5539 +2026-04-14 23:38:59.812715: val_loss -0.4471 +2026-04-14 23:38:59.815042: Pseudo dice [0.7472, 0.0, 0.8451, 0.7789, 0.4223, 0.8125, 0.68] +2026-04-14 23:38:59.817716: Epoch time: 101.88 s +2026-04-14 23:39:01.065371: +2026-04-14 23:39:01.068183: Epoch 3821 +2026-04-14 23:39:01.070459: Current learning rate: 0.00061 +2026-04-14 23:40:42.796440: train_loss -0.5617 +2026-04-14 23:40:42.801229: val_loss -0.4907 +2026-04-14 23:40:42.803041: Pseudo dice [0.2566, 0.0, 0.8504, 0.8723, 0.4586, 0.7737, 0.9149] +2026-04-14 23:40:42.805050: Epoch time: 101.73 s +2026-04-14 23:40:44.038763: +2026-04-14 23:40:44.040878: Epoch 3822 +2026-04-14 23:40:44.043253: Current learning rate: 0.00061 +2026-04-14 23:42:25.873338: train_loss -0.5557 +2026-04-14 23:42:25.880206: val_loss -0.5004 +2026-04-14 23:42:25.882837: Pseudo dice [0.8307, 0.0, 0.8018, 0.8401, 0.2265, 0.7512, 0.8955] +2026-04-14 23:42:25.884665: Epoch time: 101.84 s +2026-04-14 23:42:27.112489: +2026-04-14 23:42:27.115115: Epoch 3823 +2026-04-14 23:42:27.118678: Current learning rate: 0.0006 +2026-04-14 23:44:08.779318: train_loss -0.5633 +2026-04-14 23:44:08.784214: val_loss -0.4664 +2026-04-14 23:44:08.785658: Pseudo dice [0.3277, 0.0, 0.7306, 0.6557, 0.4943, 0.7978, 0.7321] +2026-04-14 23:44:08.787200: Epoch time: 101.67 s +2026-04-14 23:44:10.028353: +2026-04-14 23:44:10.030425: Epoch 3824 +2026-04-14 23:44:10.032190: Current learning rate: 0.0006 +2026-04-14 23:45:51.878889: train_loss -0.5583 +2026-04-14 23:45:51.883901: val_loss -0.4761 +2026-04-14 23:45:51.885663: Pseudo dice [0.4245, 0.0, 0.7426, 0.8751, 0.755, 0.8302, 0.802] +2026-04-14 23:45:51.887227: Epoch time: 101.85 s +2026-04-14 23:45:53.115559: +2026-04-14 23:45:53.118088: Epoch 3825 +2026-04-14 23:45:53.119874: Current learning rate: 0.0006 +2026-04-14 23:47:34.821222: train_loss -0.5544 +2026-04-14 23:47:34.827610: val_loss -0.4836 +2026-04-14 23:47:34.829495: Pseudo dice [0.5782, 0.0, 0.8264, 0.8517, 0.2169, 0.8282, 0.8845] +2026-04-14 23:47:34.831278: Epoch time: 101.71 s +2026-04-14 23:47:36.069505: +2026-04-14 23:47:36.071174: Epoch 3826 +2026-04-14 23:47:36.073030: Current learning rate: 0.0006 +2026-04-14 23:49:17.988087: train_loss -0.5603 +2026-04-14 23:49:17.993083: val_loss -0.4758 +2026-04-14 23:49:17.994880: Pseudo dice [0.5713, 0.0, 0.7415, 0.882, 0.5884, 0.7601, 0.9124] +2026-04-14 23:49:17.996427: Epoch time: 101.92 s +2026-04-14 23:49:19.220951: +2026-04-14 23:49:19.222520: Epoch 3827 +2026-04-14 23:49:19.224235: Current learning rate: 0.00059 +2026-04-14 23:51:01.065931: train_loss -0.5683 +2026-04-14 23:51:01.073056: val_loss -0.4788 +2026-04-14 23:51:01.075225: Pseudo dice [0.6627, 0.0, 0.5924, 0.6395, 0.5753, 0.7405, 0.9027] +2026-04-14 23:51:01.077450: Epoch time: 101.85 s +2026-04-14 23:51:02.312102: +2026-04-14 23:51:02.314111: Epoch 3828 +2026-04-14 23:51:02.316380: Current learning rate: 0.00059 +2026-04-14 23:52:44.294777: train_loss -0.5694 +2026-04-14 23:52:44.300064: val_loss -0.5124 +2026-04-14 23:52:44.301765: Pseudo dice [0.7929, 0.0, 0.8183, 0.8136, 0.7259, 0.7246, 0.9191] +2026-04-14 23:52:44.303861: Epoch time: 101.99 s +2026-04-14 23:52:45.570010: +2026-04-14 23:52:45.571674: Epoch 3829 +2026-04-14 23:52:45.573512: Current learning rate: 0.00059 +2026-04-14 23:54:28.773625: train_loss -0.5566 +2026-04-14 23:54:28.778606: val_loss -0.5127 +2026-04-14 23:54:28.781239: Pseudo dice [0.75, 0.0, 0.7779, 0.7998, 0.6229, 0.8712, 0.886] +2026-04-14 23:54:28.783764: Epoch time: 103.21 s +2026-04-14 23:54:30.021722: +2026-04-14 23:54:30.023495: Epoch 3830 +2026-04-14 23:54:30.025369: Current learning rate: 0.00058 +2026-04-14 23:56:11.807913: train_loss -0.5593 +2026-04-14 23:56:11.814236: val_loss -0.4711 +2026-04-14 23:56:11.815915: Pseudo dice [0.5609, 0.0, 0.6018, 0.5131, 0.4232, 0.7237, 0.9269] +2026-04-14 23:56:11.817452: Epoch time: 101.79 s +2026-04-14 23:56:13.067577: +2026-04-14 23:56:13.069362: Epoch 3831 +2026-04-14 23:56:13.071556: Current learning rate: 0.00058 +2026-04-14 23:57:54.935330: train_loss -0.55 +2026-04-14 23:57:54.942830: val_loss -0.4731 +2026-04-14 23:57:54.944840: Pseudo dice [0.4389, 0.0, 0.6916, 0.6393, 0.3067, 0.8533, 0.8163] +2026-04-14 23:57:54.946393: Epoch time: 101.87 s +2026-04-14 23:57:56.180412: +2026-04-14 23:57:56.182013: Epoch 3832 +2026-04-14 23:57:56.183755: Current learning rate: 0.00058 +2026-04-14 23:59:38.027215: train_loss -0.5461 +2026-04-14 23:59:38.032799: val_loss -0.4878 +2026-04-14 23:59:38.035122: Pseudo dice [0.6801, 0.0, 0.7119, 0.5237, 0.4462, 0.831, 0.8813] +2026-04-14 23:59:38.036885: Epoch time: 101.85 s +2026-04-14 23:59:39.270862: +2026-04-14 23:59:39.273375: Epoch 3833 +2026-04-14 23:59:39.275709: Current learning rate: 0.00057 +2026-04-15 00:01:21.022867: train_loss -0.5717 +2026-04-15 00:01:21.028297: val_loss -0.4616 +2026-04-15 00:01:21.030017: Pseudo dice [0.3432, 0.0, 0.7165, 0.7392, 0.4165, 0.7564, 0.8323] +2026-04-15 00:01:21.031830: Epoch time: 101.76 s +2026-04-15 00:01:22.268126: +2026-04-15 00:01:22.270084: Epoch 3834 +2026-04-15 00:01:22.272879: Current learning rate: 0.00057 +2026-04-15 00:03:04.126547: train_loss -0.5704 +2026-04-15 00:03:04.132256: val_loss -0.4836 +2026-04-15 00:03:04.134082: Pseudo dice [0.3219, 0.0, 0.8791, 0.768, 0.535, 0.716, 0.9272] +2026-04-15 00:03:04.135813: Epoch time: 101.86 s +2026-04-15 00:03:05.367229: +2026-04-15 00:03:05.369011: Epoch 3835 +2026-04-15 00:03:05.371105: Current learning rate: 0.00057 +2026-04-15 00:04:47.024309: train_loss -0.5619 +2026-04-15 00:04:47.030749: val_loss -0.518 +2026-04-15 00:04:47.032713: Pseudo dice [0.8632, 0.0, 0.8141, 0.9143, 0.6173, 0.8368, 0.7306] +2026-04-15 00:04:47.034628: Epoch time: 101.66 s +2026-04-15 00:04:48.266663: +2026-04-15 00:04:48.268661: Epoch 3836 +2026-04-15 00:04:48.270609: Current learning rate: 0.00056 +2026-04-15 00:06:29.841041: train_loss -0.5746 +2026-04-15 00:06:29.846453: val_loss -0.5195 +2026-04-15 00:06:29.848093: Pseudo dice [0.3964, 0.0, 0.7535, 0.7433, 0.6208, 0.7593, 0.8846] +2026-04-15 00:06:29.849909: Epoch time: 101.58 s +2026-04-15 00:06:31.112252: +2026-04-15 00:06:31.113832: Epoch 3837 +2026-04-15 00:06:31.115324: Current learning rate: 0.00056 +2026-04-15 00:08:13.039872: train_loss -0.5673 +2026-04-15 00:08:13.045265: val_loss -0.48 +2026-04-15 00:08:13.047040: Pseudo dice [0.2342, 0.0, 0.7336, 0.8674, 0.6224, 0.5966, 0.7034] +2026-04-15 00:08:13.049448: Epoch time: 101.93 s +2026-04-15 00:08:14.283398: +2026-04-15 00:08:14.284938: Epoch 3838 +2026-04-15 00:08:14.286736: Current learning rate: 0.00056 +2026-04-15 00:09:55.899855: train_loss -0.5596 +2026-04-15 00:09:55.906495: val_loss -0.5129 +2026-04-15 00:09:55.908003: Pseudo dice [0.5364, 0.0, 0.8334, 0.9104, 0.5436, 0.8829, 0.9317] +2026-04-15 00:09:55.911148: Epoch time: 101.62 s +2026-04-15 00:09:57.159683: +2026-04-15 00:09:57.161255: Epoch 3839 +2026-04-15 00:09:57.163014: Current learning rate: 0.00055 +2026-04-15 00:11:38.902543: train_loss -0.5703 +2026-04-15 00:11:38.907135: val_loss -0.4999 +2026-04-15 00:11:38.909346: Pseudo dice [0.4632, 0.0, 0.8093, 0.8613, 0.4694, 0.8903, 0.8433] +2026-04-15 00:11:38.910932: Epoch time: 101.75 s +2026-04-15 00:11:40.138177: +2026-04-15 00:11:40.139846: Epoch 3840 +2026-04-15 00:11:40.141747: Current learning rate: 0.00055 +2026-04-15 00:13:21.994418: train_loss -0.5613 +2026-04-15 00:13:22.000388: val_loss -0.4676 +2026-04-15 00:13:22.002643: Pseudo dice [0.6421, 0.0, 0.7239, 0.6883, 0.3431, 0.7412, 0.9319] +2026-04-15 00:13:22.004784: Epoch time: 101.86 s +2026-04-15 00:13:23.229328: +2026-04-15 00:13:23.231232: Epoch 3841 +2026-04-15 00:13:23.233243: Current learning rate: 0.00055 +2026-04-15 00:15:04.989371: train_loss -0.5578 +2026-04-15 00:15:04.995010: val_loss -0.4687 +2026-04-15 00:15:04.996846: Pseudo dice [0.3908, 0.0, 0.5938, 0.7215, 0.575, 0.7281, 0.8443] +2026-04-15 00:15:04.999359: Epoch time: 101.76 s +2026-04-15 00:15:06.257586: +2026-04-15 00:15:06.259316: Epoch 3842 +2026-04-15 00:15:06.261187: Current learning rate: 0.00055 +2026-04-15 00:16:48.161536: train_loss -0.5645 +2026-04-15 00:16:48.166541: val_loss -0.5308 +2026-04-15 00:16:48.168704: Pseudo dice [0.5549, 0.0, 0.8008, 0.8928, 0.6043, 0.8315, 0.9087] +2026-04-15 00:16:48.170510: Epoch time: 101.91 s +2026-04-15 00:16:49.411018: +2026-04-15 00:16:49.412659: Epoch 3843 +2026-04-15 00:16:49.414481: Current learning rate: 0.00054 +2026-04-15 00:18:31.261635: train_loss -0.5692 +2026-04-15 00:18:31.265908: val_loss -0.4571 +2026-04-15 00:18:31.267631: Pseudo dice [0.7918, 0.0, 0.265, 0.8098, 0.2969, 0.792, 0.8874] +2026-04-15 00:18:31.269111: Epoch time: 101.85 s +2026-04-15 00:18:32.494977: +2026-04-15 00:18:32.496692: Epoch 3844 +2026-04-15 00:18:32.498497: Current learning rate: 0.00054 +2026-04-15 00:20:14.244259: train_loss -0.5514 +2026-04-15 00:20:14.251545: val_loss -0.425 +2026-04-15 00:20:14.254734: Pseudo dice [0.5189, 0.0, 0.7393, 0.5816, 0.2474, 0.7008, 0.8267] +2026-04-15 00:20:14.256671: Epoch time: 101.75 s +2026-04-15 00:20:15.502696: +2026-04-15 00:20:15.504316: Epoch 3845 +2026-04-15 00:20:15.506446: Current learning rate: 0.00054 +2026-04-15 00:21:57.256598: train_loss -0.5623 +2026-04-15 00:21:57.262656: val_loss -0.5072 +2026-04-15 00:21:57.264388: Pseudo dice [0.4998, 0.0, 0.6547, 0.8721, 0.6122, 0.8056, 0.9396] +2026-04-15 00:21:57.265970: Epoch time: 101.76 s +2026-04-15 00:21:58.490285: +2026-04-15 00:21:58.492123: Epoch 3846 +2026-04-15 00:21:58.494148: Current learning rate: 0.00053 +2026-04-15 00:23:40.191073: train_loss -0.5632 +2026-04-15 00:23:40.196743: val_loss -0.457 +2026-04-15 00:23:40.198837: Pseudo dice [0.6858, 0.0, 0.7522, 0.8977, 0.3965, 0.791, 0.9167] +2026-04-15 00:23:40.201339: Epoch time: 101.7 s +2026-04-15 00:23:41.446413: +2026-04-15 00:23:41.448695: Epoch 3847 +2026-04-15 00:23:41.451121: Current learning rate: 0.00053 +2026-04-15 00:25:23.411398: train_loss -0.5644 +2026-04-15 00:25:23.417603: val_loss -0.493 +2026-04-15 00:25:23.419518: Pseudo dice [0.7198, 0.0, 0.7165, 0.8398, 0.316, 0.8446, 0.8491] +2026-04-15 00:25:23.421905: Epoch time: 101.97 s +2026-04-15 00:25:24.654077: +2026-04-15 00:25:24.656255: Epoch 3848 +2026-04-15 00:25:24.658447: Current learning rate: 0.00053 +2026-04-15 00:27:06.477826: train_loss -0.5636 +2026-04-15 00:27:06.482426: val_loss -0.4619 +2026-04-15 00:27:06.484159: Pseudo dice [0.5791, 0.0, 0.5927, 0.8695, 0.2999, 0.7811, 0.8655] +2026-04-15 00:27:06.485864: Epoch time: 101.83 s +2026-04-15 00:27:08.758454: +2026-04-15 00:27:08.760161: Epoch 3849 +2026-04-15 00:27:08.761967: Current learning rate: 0.00052 +2026-04-15 00:28:50.440327: train_loss -0.5589 +2026-04-15 00:28:50.445176: val_loss -0.5176 +2026-04-15 00:28:50.446892: Pseudo dice [0.7756, 0.0, 0.7603, 0.6995, 0.4613, 0.8894, 0.898] +2026-04-15 00:28:50.448568: Epoch time: 101.69 s +2026-04-15 00:28:53.449097: +2026-04-15 00:28:53.450815: Epoch 3850 +2026-04-15 00:28:53.452653: Current learning rate: 0.00052 +2026-04-15 00:30:35.319481: train_loss -0.5632 +2026-04-15 00:30:35.324384: val_loss -0.5105 +2026-04-15 00:30:35.325884: Pseudo dice [0.799, 0.0, 0.8335, 0.6569, 0.4375, 0.8035, 0.926] +2026-04-15 00:30:35.327379: Epoch time: 101.87 s +2026-04-15 00:30:36.629701: +2026-04-15 00:30:36.631469: Epoch 3851 +2026-04-15 00:30:36.633139: Current learning rate: 0.00052 +2026-04-15 00:32:18.638731: train_loss -0.5601 +2026-04-15 00:32:18.645911: val_loss -0.4966 +2026-04-15 00:32:18.648645: Pseudo dice [0.528, 0.0, 0.7925, 0.8201, 0.6231, 0.8719, 0.9173] +2026-04-15 00:32:18.649961: Epoch time: 102.01 s +2026-04-15 00:32:19.879039: +2026-04-15 00:32:19.881620: Epoch 3852 +2026-04-15 00:32:19.884300: Current learning rate: 0.00051 +2026-04-15 00:34:01.554250: train_loss -0.5579 +2026-04-15 00:34:01.562304: val_loss -0.5241 +2026-04-15 00:34:01.564089: Pseudo dice [0.6502, 0.0, 0.7521, 0.8278, 0.6766, 0.9013, 0.936] +2026-04-15 00:34:01.566508: Epoch time: 101.68 s +2026-04-15 00:34:02.816782: +2026-04-15 00:34:02.818595: Epoch 3853 +2026-04-15 00:34:02.820649: Current learning rate: 0.00051 +2026-04-15 00:35:44.617825: train_loss -0.564 +2026-04-15 00:35:44.627441: val_loss -0.4269 +2026-04-15 00:35:44.629405: Pseudo dice [0.3307, 0.0, 0.5013, 0.7071, 0.4109, 0.8139, 0.4779] +2026-04-15 00:35:44.631513: Epoch time: 101.8 s +2026-04-15 00:35:45.880238: +2026-04-15 00:35:45.882799: Epoch 3854 +2026-04-15 00:35:45.885093: Current learning rate: 0.00051 +2026-04-15 00:37:27.956081: train_loss -0.56 +2026-04-15 00:37:27.960813: val_loss -0.4745 +2026-04-15 00:37:27.962616: Pseudo dice [0.7437, 0.0, 0.7102, 0.6736, 0.59, 0.8241, 0.5799] +2026-04-15 00:37:27.964434: Epoch time: 102.08 s +2026-04-15 00:37:29.202590: +2026-04-15 00:37:29.204234: Epoch 3855 +2026-04-15 00:37:29.205990: Current learning rate: 0.00051 +2026-04-15 00:39:10.989353: train_loss -0.5702 +2026-04-15 00:39:10.995044: val_loss -0.483 +2026-04-15 00:39:10.996721: Pseudo dice [0.6182, 0.0, 0.7517, 0.8137, 0.4471, 0.7929, 0.6268] +2026-04-15 00:39:10.998138: Epoch time: 101.79 s +2026-04-15 00:39:12.227956: +2026-04-15 00:39:12.229830: Epoch 3856 +2026-04-15 00:39:12.231694: Current learning rate: 0.0005 +2026-04-15 00:40:53.923014: train_loss -0.563 +2026-04-15 00:40:53.927746: val_loss -0.4922 +2026-04-15 00:40:53.929254: Pseudo dice [0.5331, 0.0, 0.8091, 0.8139, 0.3353, 0.8711, 0.9208] +2026-04-15 00:40:53.931363: Epoch time: 101.7 s +2026-04-15 00:40:55.151032: +2026-04-15 00:40:55.152570: Epoch 3857 +2026-04-15 00:40:55.154274: Current learning rate: 0.0005 +2026-04-15 00:42:37.093544: train_loss -0.5637 +2026-04-15 00:42:37.099688: val_loss -0.4869 +2026-04-15 00:42:37.101673: Pseudo dice [0.3812, 0.0, 0.7613, 0.8989, 0.5111, 0.8063, 0.8988] +2026-04-15 00:42:37.103417: Epoch time: 101.95 s +2026-04-15 00:42:38.330652: +2026-04-15 00:42:38.332139: Epoch 3858 +2026-04-15 00:42:38.334050: Current learning rate: 0.0005 +2026-04-15 00:44:20.233162: train_loss -0.5731 +2026-04-15 00:44:20.238100: val_loss -0.4931 +2026-04-15 00:44:20.239625: Pseudo dice [0.7841, 0.0, 0.8069, 0.7561, 0.6246, 0.8791, 0.9181] +2026-04-15 00:44:20.241225: Epoch time: 101.91 s +2026-04-15 00:44:21.461728: +2026-04-15 00:44:21.463892: Epoch 3859 +2026-04-15 00:44:21.466129: Current learning rate: 0.00049 +2026-04-15 00:46:03.305405: train_loss -0.5728 +2026-04-15 00:46:03.309894: val_loss -0.4844 +2026-04-15 00:46:03.312113: Pseudo dice [0.5023, 0.0, 0.7648, 0.6749, 0.5381, 0.7504, 0.8938] +2026-04-15 00:46:03.313616: Epoch time: 101.85 s +2026-04-15 00:46:04.544904: +2026-04-15 00:46:04.546607: Epoch 3860 +2026-04-15 00:46:04.549405: Current learning rate: 0.00049 +2026-04-15 00:47:46.319668: train_loss -0.5578 +2026-04-15 00:47:46.324823: val_loss -0.4762 +2026-04-15 00:47:46.327062: Pseudo dice [0.7038, 0.0, 0.823, 0.8496, 0.2903, 0.6977, 0.8201] +2026-04-15 00:47:46.328812: Epoch time: 101.78 s +2026-04-15 00:47:47.577409: +2026-04-15 00:47:47.579356: Epoch 3861 +2026-04-15 00:47:47.581395: Current learning rate: 0.00049 +2026-04-15 00:49:29.442325: train_loss -0.558 +2026-04-15 00:49:29.447229: val_loss -0.5242 +2026-04-15 00:49:29.449026: Pseudo dice [0.4284, 0.0, 0.8723, 0.7806, 0.7554, 0.893, 0.8206] +2026-04-15 00:49:29.450479: Epoch time: 101.87 s +2026-04-15 00:49:30.678209: +2026-04-15 00:49:30.679450: Epoch 3862 +2026-04-15 00:49:30.681213: Current learning rate: 0.00048 +2026-04-15 00:51:12.476716: train_loss -0.5717 +2026-04-15 00:51:12.481785: val_loss -0.4697 +2026-04-15 00:51:12.484405: Pseudo dice [0.2817, 0.0, 0.7785, 0.6324, 0.5295, 0.8209, 0.8781] +2026-04-15 00:51:12.486259: Epoch time: 101.8 s +2026-04-15 00:51:13.729146: +2026-04-15 00:51:13.730900: Epoch 3863 +2026-04-15 00:51:13.733178: Current learning rate: 0.00048 +2026-04-15 00:52:55.619118: train_loss -0.5548 +2026-04-15 00:52:55.623971: val_loss -0.426 +2026-04-15 00:52:55.627758: Pseudo dice [0.4908, 0.0, 0.5943, 0.6225, 0.3811, 0.8075, 0.8484] +2026-04-15 00:52:55.635862: Epoch time: 101.89 s +2026-04-15 00:52:56.858190: +2026-04-15 00:52:56.859745: Epoch 3864 +2026-04-15 00:52:56.861676: Current learning rate: 0.00048 +2026-04-15 00:54:38.792823: train_loss -0.5575 +2026-04-15 00:54:38.797810: val_loss -0.4728 +2026-04-15 00:54:38.800578: Pseudo dice [0.1982, 0.0, 0.6553, 0.5456, 0.5506, 0.8426, 0.8365] +2026-04-15 00:54:38.802536: Epoch time: 101.94 s +2026-04-15 00:54:40.022027: +2026-04-15 00:54:40.023556: Epoch 3865 +2026-04-15 00:54:40.025291: Current learning rate: 0.00047 +2026-04-15 00:56:21.937130: train_loss -0.5635 +2026-04-15 00:56:21.941456: val_loss -0.498 +2026-04-15 00:56:21.943039: Pseudo dice [0.5198, 0.0, 0.7581, 0.6316, 0.6642, 0.7882, 0.9143] +2026-04-15 00:56:21.945175: Epoch time: 101.92 s +2026-04-15 00:56:23.176985: +2026-04-15 00:56:23.178514: Epoch 3866 +2026-04-15 00:56:23.180064: Current learning rate: 0.00047 +2026-04-15 00:58:04.942678: train_loss -0.5633 +2026-04-15 00:58:04.948026: val_loss -0.488 +2026-04-15 00:58:04.949403: Pseudo dice [0.7355, 0.0, 0.7525, 0.8479, 0.4845, 0.8569, 0.9437] +2026-04-15 00:58:04.951149: Epoch time: 101.77 s +2026-04-15 00:58:06.182940: +2026-04-15 00:58:06.184590: Epoch 3867 +2026-04-15 00:58:06.186376: Current learning rate: 0.00047 +2026-04-15 00:59:47.989216: train_loss -0.5718 +2026-04-15 00:59:47.994241: val_loss -0.4854 +2026-04-15 00:59:47.996568: Pseudo dice [0.4792, 0.0, 0.8206, 0.8394, 0.519, 0.7625, 0.7179] +2026-04-15 00:59:47.998088: Epoch time: 101.81 s +2026-04-15 00:59:49.219924: +2026-04-15 00:59:49.221694: Epoch 3868 +2026-04-15 00:59:49.223356: Current learning rate: 0.00046 +2026-04-15 01:01:32.210866: train_loss -0.5757 +2026-04-15 01:01:32.216036: val_loss -0.4441 +2026-04-15 01:01:32.218561: Pseudo dice [0.3349, 0.0, 0.7545, 0.8497, 0.4793, 0.6776, 0.7721] +2026-04-15 01:01:32.220397: Epoch time: 102.99 s +2026-04-15 01:01:33.456969: +2026-04-15 01:01:33.458468: Epoch 3869 +2026-04-15 01:01:33.460271: Current learning rate: 0.00046 +2026-04-15 01:03:15.236135: train_loss -0.5616 +2026-04-15 01:03:15.241517: val_loss -0.4773 +2026-04-15 01:03:15.243145: Pseudo dice [0.343, 0.0, 0.8461, 0.9268, 0.5245, 0.8227, 0.8203] +2026-04-15 01:03:15.244683: Epoch time: 101.78 s +2026-04-15 01:03:16.500988: +2026-04-15 01:03:16.502759: Epoch 3870 +2026-04-15 01:03:16.504632: Current learning rate: 0.00046 +2026-04-15 01:04:58.326416: train_loss -0.5633 +2026-04-15 01:04:58.331962: val_loss -0.4411 +2026-04-15 01:04:58.333705: Pseudo dice [0.3005, 0.0, 0.6338, 0.8622, 0.2726, 0.7927, 0.8478] +2026-04-15 01:04:58.336448: Epoch time: 101.83 s +2026-04-15 01:04:59.581252: +2026-04-15 01:04:59.583611: Epoch 3871 +2026-04-15 01:04:59.585170: Current learning rate: 0.00045 +2026-04-15 01:06:41.438526: train_loss -0.5624 +2026-04-15 01:06:41.462729: val_loss -0.4836 +2026-04-15 01:06:41.464288: Pseudo dice [0.8635, 0.0, 0.8646, 0.9056, 0.4367, 0.8948, 0.8909] +2026-04-15 01:06:41.465661: Epoch time: 101.86 s +2026-04-15 01:06:42.707856: +2026-04-15 01:06:42.709718: Epoch 3872 +2026-04-15 01:06:42.711353: Current learning rate: 0.00045 +2026-04-15 01:08:24.488489: train_loss -0.5695 +2026-04-15 01:08:24.493748: val_loss -0.5046 +2026-04-15 01:08:24.495444: Pseudo dice [0.5843, 0.0, 0.7953, 0.8722, 0.6248, 0.7846, 0.7014] +2026-04-15 01:08:24.497055: Epoch time: 101.78 s +2026-04-15 01:08:25.744773: +2026-04-15 01:08:25.746449: Epoch 3873 +2026-04-15 01:08:25.748558: Current learning rate: 0.00045 +2026-04-15 01:10:07.616394: train_loss -0.5784 +2026-04-15 01:10:07.621150: val_loss -0.4785 +2026-04-15 01:10:07.623326: Pseudo dice [0.7637, 0.0, 0.7518, 0.2172, 0.6735, 0.7878, 0.9142] +2026-04-15 01:10:07.624978: Epoch time: 101.87 s +2026-04-15 01:10:08.863371: +2026-04-15 01:10:08.865352: Epoch 3874 +2026-04-15 01:10:08.867268: Current learning rate: 0.00045 +2026-04-15 01:11:50.663352: train_loss -0.5572 +2026-04-15 01:11:50.669691: val_loss -0.4989 +2026-04-15 01:11:50.671417: Pseudo dice [0.6762, 0.0, 0.7838, 0.8373, 0.5354, 0.8915, 0.7359] +2026-04-15 01:11:50.673642: Epoch time: 101.8 s +2026-04-15 01:11:51.914793: +2026-04-15 01:11:51.916627: Epoch 3875 +2026-04-15 01:11:51.918714: Current learning rate: 0.00044 +2026-04-15 01:13:33.868859: train_loss -0.5656 +2026-04-15 01:13:33.873255: val_loss -0.4849 +2026-04-15 01:13:33.874924: Pseudo dice [0.3438, 0.0, 0.7234, 0.8297, 0.7814, 0.8552, 0.8499] +2026-04-15 01:13:33.877385: Epoch time: 101.96 s +2026-04-15 01:13:35.116015: +2026-04-15 01:13:35.117667: Epoch 3876 +2026-04-15 01:13:35.119588: Current learning rate: 0.00044 +2026-04-15 01:15:16.859005: train_loss -0.5632 +2026-04-15 01:15:16.864068: val_loss -0.4571 +2026-04-15 01:15:16.865845: Pseudo dice [0.3468, 0.0, 0.5127, 0.9057, 0.5711, 0.7377, 0.7376] +2026-04-15 01:15:16.867503: Epoch time: 101.75 s +2026-04-15 01:15:18.093016: +2026-04-15 01:15:18.094496: Epoch 3877 +2026-04-15 01:15:18.095965: Current learning rate: 0.00044 +2026-04-15 01:16:59.804823: train_loss -0.5643 +2026-04-15 01:16:59.810639: val_loss -0.4874 +2026-04-15 01:16:59.812673: Pseudo dice [0.7894, 0.0, 0.8248, 0.833, 0.5968, 0.8188, 0.7894] +2026-04-15 01:16:59.814073: Epoch time: 101.71 s +2026-04-15 01:17:01.051423: +2026-04-15 01:17:01.053626: Epoch 3878 +2026-04-15 01:17:01.055737: Current learning rate: 0.00043 +2026-04-15 01:18:42.941278: train_loss -0.5679 +2026-04-15 01:18:42.946409: val_loss -0.5043 +2026-04-15 01:18:42.948025: Pseudo dice [0.6997, 0.0, 0.8421, 0.5123, 0.6089, 0.7715, 0.8432] +2026-04-15 01:18:42.949734: Epoch time: 101.89 s +2026-04-15 01:18:44.198701: +2026-04-15 01:18:44.201617: Epoch 3879 +2026-04-15 01:18:44.204927: Current learning rate: 0.00043 +2026-04-15 01:20:25.946062: train_loss -0.5659 +2026-04-15 01:20:25.951541: val_loss -0.4873 +2026-04-15 01:20:25.953346: Pseudo dice [0.8255, 0.0, 0.745, 0.8999, 0.523, 0.7847, 0.9018] +2026-04-15 01:20:25.955096: Epoch time: 101.75 s +2026-04-15 01:20:27.182237: +2026-04-15 01:20:27.184313: Epoch 3880 +2026-04-15 01:20:27.185898: Current learning rate: 0.00043 +2026-04-15 01:22:08.844074: train_loss -0.579 +2026-04-15 01:22:08.849424: val_loss -0.4953 +2026-04-15 01:22:08.851130: Pseudo dice [0.3356, 0.0, 0.6652, 0.8707, 0.5132, 0.7759, 0.8236] +2026-04-15 01:22:08.852847: Epoch time: 101.66 s +2026-04-15 01:22:10.104689: +2026-04-15 01:22:10.106211: Epoch 3881 +2026-04-15 01:22:10.107961: Current learning rate: 0.00042 +2026-04-15 01:23:51.888502: train_loss -0.5609 +2026-04-15 01:23:51.894015: val_loss -0.4866 +2026-04-15 01:23:51.895648: Pseudo dice [0.802, 0.0, 0.7288, 0.5863, 0.7017, 0.7777, 0.6591] +2026-04-15 01:23:51.897826: Epoch time: 101.79 s +2026-04-15 01:23:53.115517: +2026-04-15 01:23:53.117470: Epoch 3882 +2026-04-15 01:23:53.119452: Current learning rate: 0.00042 +2026-04-15 01:25:34.865387: train_loss -0.5668 +2026-04-15 01:25:34.870463: val_loss -0.49 +2026-04-15 01:25:34.872040: Pseudo dice [0.8503, 0.0, 0.5933, 0.0204, 0.6497, 0.7849, 0.8098] +2026-04-15 01:25:34.873620: Epoch time: 101.75 s +2026-04-15 01:25:36.112840: +2026-04-15 01:25:36.114490: Epoch 3883 +2026-04-15 01:25:36.116152: Current learning rate: 0.00042 +2026-04-15 01:27:18.203388: train_loss -0.5751 +2026-04-15 01:27:18.208594: val_loss -0.4742 +2026-04-15 01:27:18.210346: Pseudo dice [0.5037, 0.0, 0.7346, 0.2497, 0.6321, 0.8235, 0.9102] +2026-04-15 01:27:18.212047: Epoch time: 102.09 s +2026-04-15 01:27:19.467283: +2026-04-15 01:27:19.469325: Epoch 3884 +2026-04-15 01:27:19.471041: Current learning rate: 0.00041 +2026-04-15 01:29:01.253996: train_loss -0.5666 +2026-04-15 01:29:01.259619: val_loss -0.4815 +2026-04-15 01:29:01.261895: Pseudo dice [0.5665, 0.0, 0.7506, 0.9234, 0.4953, 0.7176, 0.9216] +2026-04-15 01:29:01.263962: Epoch time: 101.79 s +2026-04-15 01:29:02.500764: +2026-04-15 01:29:02.502426: Epoch 3885 +2026-04-15 01:29:02.504036: Current learning rate: 0.00041 +2026-04-15 01:30:44.334762: train_loss -0.5709 +2026-04-15 01:30:44.342757: val_loss -0.4757 +2026-04-15 01:30:44.344442: Pseudo dice [0.4756, 0.0, 0.7975, 0.9038, 0.4518, 0.7639, 0.7385] +2026-04-15 01:30:44.345973: Epoch time: 101.84 s +2026-04-15 01:30:45.581316: +2026-04-15 01:30:45.583153: Epoch 3886 +2026-04-15 01:30:45.585197: Current learning rate: 0.00041 +2026-04-15 01:32:27.306305: train_loss -0.5697 +2026-04-15 01:32:27.311197: val_loss -0.4631 +2026-04-15 01:32:27.313435: Pseudo dice [0.7802, 0.0, 0.7941, 0.861, 0.2713, 0.7377, 0.6454] +2026-04-15 01:32:27.315412: Epoch time: 101.73 s +2026-04-15 01:32:28.531696: +2026-04-15 01:32:28.533328: Epoch 3887 +2026-04-15 01:32:28.535029: Current learning rate: 0.0004 +2026-04-15 01:34:09.847765: train_loss -0.577 +2026-04-15 01:34:09.852121: val_loss -0.5255 +2026-04-15 01:34:09.853343: Pseudo dice [0.8021, 0.0, 0.7828, 0.7986, 0.4621, 0.6036, 0.7452] +2026-04-15 01:34:09.854952: Epoch time: 101.32 s +2026-04-15 01:34:12.122610: +2026-04-15 01:34:12.124451: Epoch 3888 +2026-04-15 01:34:12.125851: Current learning rate: 0.0004 +2026-04-15 01:35:53.492940: train_loss -0.58 +2026-04-15 01:35:53.503564: val_loss -0.4694 +2026-04-15 01:35:53.505659: Pseudo dice [0.2855, 0.0, 0.6475, 0.8862, 0.3539, 0.7796, 0.4888] +2026-04-15 01:35:53.507267: Epoch time: 101.37 s +2026-04-15 01:35:54.772632: +2026-04-15 01:35:54.774642: Epoch 3889 +2026-04-15 01:35:54.776739: Current learning rate: 0.0004 +2026-04-15 01:37:36.188415: train_loss -0.5688 +2026-04-15 01:37:36.194177: val_loss -0.4697 +2026-04-15 01:37:36.196022: Pseudo dice [0.8165, 0.0, 0.8424, 0.7204, 0.4888, 0.7257, 0.8219] +2026-04-15 01:37:36.197618: Epoch time: 101.42 s +2026-04-15 01:37:37.445366: +2026-04-15 01:37:37.447140: Epoch 3890 +2026-04-15 01:37:37.448951: Current learning rate: 0.00039 +2026-04-15 01:39:19.004955: train_loss -0.5719 +2026-04-15 01:39:19.010044: val_loss -0.4908 +2026-04-15 01:39:19.012408: Pseudo dice [0.3109, 0.0, 0.6276, 0.8713, 0.5478, 0.8249, 0.9443] +2026-04-15 01:39:19.014167: Epoch time: 101.56 s +2026-04-15 01:39:20.282383: +2026-04-15 01:39:20.283706: Epoch 3891 +2026-04-15 01:39:20.285308: Current learning rate: 0.00039 +2026-04-15 01:41:02.068117: train_loss -0.5808 +2026-04-15 01:41:02.077079: val_loss -0.4624 +2026-04-15 01:41:02.090328: Pseudo dice [0.5755, 0.0, 0.6781, 0.9179, 0.2632, 0.83, 0.6869] +2026-04-15 01:41:02.094873: Epoch time: 101.79 s +2026-04-15 01:41:03.350632: +2026-04-15 01:41:03.352899: Epoch 3892 +2026-04-15 01:41:03.356040: Current learning rate: 0.00039 +2026-04-15 01:42:44.825141: train_loss -0.5693 +2026-04-15 01:42:44.829777: val_loss -0.4588 +2026-04-15 01:42:44.831319: Pseudo dice [0.4523, 0.0, 0.6207, 0.8981, 0.4274, 0.7984, 0.6896] +2026-04-15 01:42:44.833004: Epoch time: 101.48 s +2026-04-15 01:42:46.071425: +2026-04-15 01:42:46.073429: Epoch 3893 +2026-04-15 01:42:46.075536: Current learning rate: 0.00038 +2026-04-15 01:44:27.688291: train_loss -0.5837 +2026-04-15 01:44:27.693572: val_loss -0.4686 +2026-04-15 01:44:27.695242: Pseudo dice [0.6389, 0.0, 0.7041, 0.8647, 0.2905, 0.8135, 0.828] +2026-04-15 01:44:27.697508: Epoch time: 101.62 s +2026-04-15 01:44:28.930031: +2026-04-15 01:44:28.931769: Epoch 3894 +2026-04-15 01:44:28.934088: Current learning rate: 0.00038 +2026-04-15 01:46:10.541198: train_loss -0.5734 +2026-04-15 01:46:10.545511: val_loss -0.4876 +2026-04-15 01:46:10.547236: Pseudo dice [0.3059, 0.0, 0.6145, 0.9097, 0.6121, 0.8078, 0.7066] +2026-04-15 01:46:10.550378: Epoch time: 101.61 s +2026-04-15 01:46:11.813451: +2026-04-15 01:46:11.814952: Epoch 3895 +2026-04-15 01:46:11.816592: Current learning rate: 0.00038 +2026-04-15 01:47:53.467987: train_loss -0.5594 +2026-04-15 01:47:53.472581: val_loss -0.4778 +2026-04-15 01:47:53.474199: Pseudo dice [0.5059, 0.0, 0.6879, 0.9007, 0.4235, 0.7676, 0.9268] +2026-04-15 01:47:53.475788: Epoch time: 101.66 s +2026-04-15 01:47:54.736358: +2026-04-15 01:47:54.738167: Epoch 3896 +2026-04-15 01:47:54.739991: Current learning rate: 0.00037 +2026-04-15 01:49:36.463201: train_loss -0.5693 +2026-04-15 01:49:36.468773: val_loss -0.4842 +2026-04-15 01:49:36.471119: Pseudo dice [0.4879, 0.0, 0.8018, 0.8768, 0.4934, 0.8574, 0.8791] +2026-04-15 01:49:36.473309: Epoch time: 101.73 s +2026-04-15 01:49:37.724009: +2026-04-15 01:49:37.725662: Epoch 3897 +2026-04-15 01:49:37.727492: Current learning rate: 0.00037 +2026-04-15 01:51:19.301549: train_loss -0.5719 +2026-04-15 01:51:19.306381: val_loss -0.4951 +2026-04-15 01:51:19.307774: Pseudo dice [0.3835, 0.0, 0.8752, 0.9139, 0.6597, 0.6913, 0.855] +2026-04-15 01:51:19.309404: Epoch time: 101.58 s +2026-04-15 01:51:20.568712: +2026-04-15 01:51:20.570043: Epoch 3898 +2026-04-15 01:51:20.571497: Current learning rate: 0.00037 +2026-04-15 01:53:02.363432: train_loss -0.5754 +2026-04-15 01:53:02.368064: val_loss -0.4891 +2026-04-15 01:53:02.369984: Pseudo dice [0.7211, 0.0, 0.8097, 0.7135, 0.3851, 0.8282, 0.7061] +2026-04-15 01:53:02.371593: Epoch time: 101.8 s +2026-04-15 01:53:03.645491: +2026-04-15 01:53:03.647310: Epoch 3899 +2026-04-15 01:53:03.649162: Current learning rate: 0.00036 +2026-04-15 01:54:45.314091: train_loss -0.5772 +2026-04-15 01:54:45.320086: val_loss -0.471 +2026-04-15 01:54:45.322325: Pseudo dice [0.6612, 0.0, 0.7738, 0.7612, 0.2687, 0.8346, 0.9518] +2026-04-15 01:54:45.324581: Epoch time: 101.67 s +2026-04-15 01:54:48.403544: +2026-04-15 01:54:48.405419: Epoch 3900 +2026-04-15 01:54:48.407069: Current learning rate: 0.00036 +2026-04-15 01:56:29.925833: train_loss -0.5789 +2026-04-15 01:56:29.930662: val_loss -0.4887 +2026-04-15 01:56:29.932740: Pseudo dice [0.6443, 0.0, 0.8173, 0.8974, 0.4964, 0.866, 0.9581] +2026-04-15 01:56:29.934724: Epoch time: 101.53 s +2026-04-15 01:56:31.183303: +2026-04-15 01:56:31.185359: Epoch 3901 +2026-04-15 01:56:31.187643: Current learning rate: 0.00036 +2026-04-15 01:58:12.591477: train_loss -0.5696 +2026-04-15 01:58:12.596285: val_loss -0.5081 +2026-04-15 01:58:12.598133: Pseudo dice [0.8286, 0.0, 0.8137, 0.8658, 0.5299, 0.8196, 0.9412] +2026-04-15 01:58:12.599850: Epoch time: 101.41 s +2026-04-15 01:58:13.842659: +2026-04-15 01:58:13.844362: Epoch 3902 +2026-04-15 01:58:13.846324: Current learning rate: 0.00036 +2026-04-15 01:59:55.543453: train_loss -0.5778 +2026-04-15 01:59:55.549064: val_loss -0.4588 +2026-04-15 01:59:55.550936: Pseudo dice [0.4079, 0.0, 0.7246, 0.9122, 0.4601, 0.7561, 0.7608] +2026-04-15 01:59:55.553133: Epoch time: 101.7 s +2026-04-15 01:59:56.785717: +2026-04-15 01:59:56.787156: Epoch 3903 +2026-04-15 01:59:56.788918: Current learning rate: 0.00035 +2026-04-15 02:01:38.785580: train_loss -0.5824 +2026-04-15 02:01:38.791276: val_loss -0.5122 +2026-04-15 02:01:38.792737: Pseudo dice [0.7143, 0.0, 0.7964, 0.7297, 0.4755, 0.7806, 0.9088] +2026-04-15 02:01:38.794487: Epoch time: 102.0 s +2026-04-15 02:01:40.024236: +2026-04-15 02:01:40.025940: Epoch 3904 +2026-04-15 02:01:40.027727: Current learning rate: 0.00035 +2026-04-15 02:03:22.044585: train_loss -0.5768 +2026-04-15 02:03:22.050217: val_loss -0.5079 +2026-04-15 02:03:22.052916: Pseudo dice [0.4873, 0.0, 0.7929, 0.855, 0.391, 0.8822, 0.843] +2026-04-15 02:03:22.055293: Epoch time: 102.02 s +2026-04-15 02:03:23.297339: +2026-04-15 02:03:23.299441: Epoch 3905 +2026-04-15 02:03:23.301641: Current learning rate: 0.00035 +2026-04-15 02:05:04.978792: train_loss -0.5714 +2026-04-15 02:05:04.985696: val_loss -0.4942 +2026-04-15 02:05:04.987563: Pseudo dice [0.5825, 0.0, 0.7617, 0.8027, 0.6609, 0.7641, 0.8232] +2026-04-15 02:05:04.990004: Epoch time: 101.68 s +2026-04-15 02:05:06.242335: +2026-04-15 02:05:06.243699: Epoch 3906 +2026-04-15 02:05:06.245212: Current learning rate: 0.00034 +2026-04-15 02:06:48.172512: train_loss -0.5768 +2026-04-15 02:06:48.176695: val_loss -0.4887 +2026-04-15 02:06:48.178544: Pseudo dice [0.5927, 0.0, 0.7531, 0.2392, 0.3874, 0.8629, 0.9279] +2026-04-15 02:06:48.180237: Epoch time: 101.93 s +2026-04-15 02:06:50.412116: +2026-04-15 02:06:50.413529: Epoch 3907 +2026-04-15 02:06:50.415036: Current learning rate: 0.00034 +2026-04-15 02:08:32.610138: train_loss -0.5812 +2026-04-15 02:08:32.615243: val_loss -0.4955 +2026-04-15 02:08:32.616809: Pseudo dice [0.4148, 0.0, 0.8361, 0.7004, 0.4902, 0.8312, 0.9434] +2026-04-15 02:08:32.618366: Epoch time: 102.2 s +2026-04-15 02:08:33.863156: +2026-04-15 02:08:33.864711: Epoch 3908 +2026-04-15 02:08:33.866351: Current learning rate: 0.00034 +2026-04-15 02:10:15.590208: train_loss -0.572 +2026-04-15 02:10:15.599466: val_loss -0.4724 +2026-04-15 02:10:15.602266: Pseudo dice [0.7784, 0.0, 0.8251, 0.8733, 0.289, 0.8034, 0.9474] +2026-04-15 02:10:15.604766: Epoch time: 101.73 s +2026-04-15 02:10:16.885960: +2026-04-15 02:10:16.887387: Epoch 3909 +2026-04-15 02:10:16.889627: Current learning rate: 0.00033 +2026-04-15 02:11:58.897560: train_loss -0.5788 +2026-04-15 02:11:58.902877: val_loss -0.4875 +2026-04-15 02:11:58.905062: Pseudo dice [0.5042, 0.0, 0.8282, 0.7656, 0.4611, 0.8147, 0.8493] +2026-04-15 02:11:58.906885: Epoch time: 102.01 s +2026-04-15 02:12:00.142935: +2026-04-15 02:12:00.146665: Epoch 3910 +2026-04-15 02:12:00.148710: Current learning rate: 0.00033 +2026-04-15 02:13:42.050977: train_loss -0.56 +2026-04-15 02:13:42.055557: val_loss -0.5086 +2026-04-15 02:13:42.057284: Pseudo dice [0.7946, 0.0, 0.542, 0.8828, 0.5859, 0.8499, 0.892] +2026-04-15 02:13:42.058920: Epoch time: 101.91 s +2026-04-15 02:13:43.275317: +2026-04-15 02:13:43.276998: Epoch 3911 +2026-04-15 02:13:43.278578: Current learning rate: 0.00033 +2026-04-15 02:15:25.300515: train_loss -0.5757 +2026-04-15 02:15:25.305144: val_loss -0.4832 +2026-04-15 02:15:25.306878: Pseudo dice [0.1651, 0.0, 0.6569, 0.8419, 0.4867, 0.7839, 0.8356] +2026-04-15 02:15:25.308728: Epoch time: 102.03 s +2026-04-15 02:15:26.560910: +2026-04-15 02:15:26.562888: Epoch 3912 +2026-04-15 02:15:26.565060: Current learning rate: 0.00032 +2026-04-15 02:17:08.392906: train_loss -0.5725 +2026-04-15 02:17:08.398357: val_loss -0.5181 +2026-04-15 02:17:08.399702: Pseudo dice [0.7887, 0.0, 0.8388, 0.8712, 0.5213, 0.7779, 0.9401] +2026-04-15 02:17:08.401571: Epoch time: 101.83 s +2026-04-15 02:17:09.619519: +2026-04-15 02:17:09.628951: Epoch 3913 +2026-04-15 02:17:09.630712: Current learning rate: 0.00032 +2026-04-15 02:18:51.443642: train_loss -0.5812 +2026-04-15 02:18:51.447814: val_loss -0.4836 +2026-04-15 02:18:51.449288: Pseudo dice [0.2229, 0.3574, 0.6588, 0.9159, 0.7003, 0.8462, 0.6629] +2026-04-15 02:18:51.451215: Epoch time: 101.83 s +2026-04-15 02:18:52.690512: +2026-04-15 02:18:52.692604: Epoch 3914 +2026-04-15 02:18:52.694725: Current learning rate: 0.00032 +2026-04-15 02:20:34.399519: train_loss -0.5767 +2026-04-15 02:20:34.418546: val_loss -0.5035 +2026-04-15 02:20:34.422077: Pseudo dice [0.8217, 0.0, 0.7882, 0.9088, 0.5915, 0.7295, 0.7516] +2026-04-15 02:20:34.428821: Epoch time: 101.71 s +2026-04-15 02:20:35.666336: +2026-04-15 02:20:35.668628: Epoch 3915 +2026-04-15 02:20:35.670757: Current learning rate: 0.00031 +2026-04-15 02:22:17.443767: train_loss -0.5809 +2026-04-15 02:22:17.449332: val_loss -0.4931 +2026-04-15 02:22:17.450885: Pseudo dice [0.2407, 0.731, 0.768, 0.8657, 0.5515, 0.8534, 0.6558] +2026-04-15 02:22:17.452447: Epoch time: 101.78 s +2026-04-15 02:22:18.678446: +2026-04-15 02:22:18.680324: Epoch 3916 +2026-04-15 02:22:18.682097: Current learning rate: 0.00031 +2026-04-15 02:24:00.394503: train_loss -0.5892 +2026-04-15 02:24:00.398934: val_loss -0.4937 +2026-04-15 02:24:00.400694: Pseudo dice [0.7534, 0.0204, 0.7072, 0.9212, 0.6819, 0.7995, 0.7539] +2026-04-15 02:24:00.402297: Epoch time: 101.72 s +2026-04-15 02:24:00.403925: Yayy! New best EMA pseudo Dice: 0.6259 +2026-04-15 02:24:03.421611: +2026-04-15 02:24:03.423917: Epoch 3917 +2026-04-15 02:24:03.425719: Current learning rate: 0.00031 +2026-04-15 02:25:45.014666: train_loss -0.5797 +2026-04-15 02:25:45.019583: val_loss -0.4755 +2026-04-15 02:25:45.021274: Pseudo dice [0.6697, 0.2421, 0.7312, 0.7891, 0.2008, 0.816, 0.7251] +2026-04-15 02:25:45.023148: Epoch time: 101.6 s +2026-04-15 02:25:46.246839: +2026-04-15 02:25:46.248423: Epoch 3918 +2026-04-15 02:25:46.250581: Current learning rate: 0.0003 +2026-04-15 02:27:27.560297: train_loss -0.5868 +2026-04-15 02:27:27.565011: val_loss -0.4568 +2026-04-15 02:27:27.566552: Pseudo dice [0.4648, 0.2558, 0.6388, 0.8773, 0.2889, 0.8243, 0.8548] +2026-04-15 02:27:27.568262: Epoch time: 101.32 s +2026-04-15 02:27:28.799764: +2026-04-15 02:27:28.801949: Epoch 3919 +2026-04-15 02:27:28.804734: Current learning rate: 0.0003 +2026-04-15 02:29:10.097795: train_loss -0.5828 +2026-04-15 02:29:10.103390: val_loss -0.5209 +2026-04-15 02:29:10.105449: Pseudo dice [0.695, 0.4749, 0.7763, 0.8891, 0.566, 0.8578, 0.5229] +2026-04-15 02:29:10.107223: Epoch time: 101.3 s +2026-04-15 02:29:10.108732: Yayy! New best EMA pseudo Dice: 0.627 +2026-04-15 02:29:13.112115: +2026-04-15 02:29:13.114270: Epoch 3920 +2026-04-15 02:29:13.115962: Current learning rate: 0.0003 +2026-04-15 02:30:54.489347: train_loss -0.5819 +2026-04-15 02:30:54.495087: val_loss -0.5056 +2026-04-15 02:30:54.496915: Pseudo dice [0.3709, 0.1195, 0.8153, 0.8645, 0.5734, 0.8264, 0.937] +2026-04-15 02:30:54.499241: Epoch time: 101.38 s +2026-04-15 02:30:54.501862: Yayy! New best EMA pseudo Dice: 0.6287 +2026-04-15 02:30:57.491978: +2026-04-15 02:30:57.494579: Epoch 3921 +2026-04-15 02:30:57.496590: Current learning rate: 0.00029 +2026-04-15 02:32:39.352142: train_loss -0.5806 +2026-04-15 02:32:39.357047: val_loss -0.4879 +2026-04-15 02:32:39.358560: Pseudo dice [0.8166, 0.1933, 0.693, 0.8581, 0.4452, 0.7943, 0.7734] +2026-04-15 02:32:39.360297: Epoch time: 101.86 s +2026-04-15 02:32:39.361868: Yayy! New best EMA pseudo Dice: 0.6311 +2026-04-15 02:32:42.316227: +2026-04-15 02:32:42.318377: Epoch 3922 +2026-04-15 02:32:42.320215: Current learning rate: 0.00029 +2026-04-15 02:34:24.153149: train_loss -0.5791 +2026-04-15 02:34:24.158084: val_loss -0.5171 +2026-04-15 02:34:24.159643: Pseudo dice [0.5112, 0.6088, 0.6933, 0.8147, 0.7098, 0.8915, 0.9504] +2026-04-15 02:34:24.161290: Epoch time: 101.84 s +2026-04-15 02:34:24.162951: Yayy! New best EMA pseudo Dice: 0.642 +2026-04-15 02:34:27.187193: +2026-04-15 02:34:27.189502: Epoch 3923 +2026-04-15 02:34:27.191471: Current learning rate: 0.00029 +2026-04-15 02:36:08.966119: train_loss -0.5917 +2026-04-15 02:36:08.971467: val_loss -0.4608 +2026-04-15 02:36:08.973382: Pseudo dice [0.4893, 0.2015, 0.8318, 0.8352, 0.4846, 0.717, 0.8368] +2026-04-15 02:36:08.975001: Epoch time: 101.78 s +2026-04-15 02:36:10.213017: +2026-04-15 02:36:10.214955: Epoch 3924 +2026-04-15 02:36:10.217170: Current learning rate: 0.00028 +2026-04-15 02:37:52.271562: train_loss -0.5862 +2026-04-15 02:37:52.276796: val_loss -0.48 +2026-04-15 02:37:52.279908: Pseudo dice [0.5657, 0.4751, 0.7698, 0.8089, 0.6062, 0.7987, 0.7758] +2026-04-15 02:37:52.281473: Epoch time: 102.06 s +2026-04-15 02:37:52.283112: Yayy! New best EMA pseudo Dice: 0.6451 +2026-04-15 02:37:56.072660: +2026-04-15 02:37:56.075207: Epoch 3925 +2026-04-15 02:37:56.077274: Current learning rate: 0.00028 +2026-04-15 02:39:37.946150: train_loss -0.5942 +2026-04-15 02:39:37.950643: val_loss -0.4816 +2026-04-15 02:39:37.952651: Pseudo dice [0.6169, 0.2879, 0.6651, 0.6667, 0.6097, 0.8417, 0.8999] +2026-04-15 02:39:37.954082: Epoch time: 101.88 s +2026-04-15 02:39:37.955431: Yayy! New best EMA pseudo Dice: 0.6462 +2026-04-15 02:39:40.853384: +2026-04-15 02:39:40.856289: Epoch 3926 +2026-04-15 02:39:40.858220: Current learning rate: 0.00028 +2026-04-15 02:41:22.490771: train_loss -0.5825 +2026-04-15 02:41:22.495878: val_loss -0.5289 +2026-04-15 02:41:22.497400: Pseudo dice [0.8597, 0.5787, 0.8295, 0.8871, 0.7023, 0.9089, 0.8464] +2026-04-15 02:41:22.499669: Epoch time: 101.64 s +2026-04-15 02:41:22.501610: Yayy! New best EMA pseudo Dice: 0.6617 +2026-04-15 02:41:25.435239: +2026-04-15 02:41:25.437626: Epoch 3927 +2026-04-15 02:41:25.439164: Current learning rate: 0.00027 +2026-04-15 02:43:07.299133: train_loss -0.5777 +2026-04-15 02:43:07.304283: val_loss -0.4478 +2026-04-15 02:43:07.306161: Pseudo dice [0.3273, 0.3994, 0.7547, 0.5906, 0.3023, 0.8643, 0.8648] +2026-04-15 02:43:07.307961: Epoch time: 101.87 s +2026-04-15 02:43:08.517847: +2026-04-15 02:43:08.519561: Epoch 3928 +2026-04-15 02:43:08.521037: Current learning rate: 0.00027 +2026-04-15 02:44:50.375123: train_loss -0.5848 +2026-04-15 02:44:50.379962: val_loss -0.4743 +2026-04-15 02:44:50.381317: Pseudo dice [0.4148, 0.8213, 0.8413, 0.9402, 0.36, 0.8486, 0.8599] +2026-04-15 02:44:50.382864: Epoch time: 101.86 s +2026-04-15 02:44:51.605994: +2026-04-15 02:44:51.607933: Epoch 3929 +2026-04-15 02:44:51.609759: Current learning rate: 0.00027 +2026-04-15 02:46:33.338608: train_loss -0.5787 +2026-04-15 02:46:33.343549: val_loss -0.4644 +2026-04-15 02:46:33.345086: Pseudo dice [0.4461, 0.1958, 0.7663, 0.6466, 0.6502, 0.8364, 0.6301] +2026-04-15 02:46:33.346693: Epoch time: 101.74 s +2026-04-15 02:46:34.563520: +2026-04-15 02:46:34.565091: Epoch 3930 +2026-04-15 02:46:34.566548: Current learning rate: 0.00026 +2026-04-15 02:48:16.361549: train_loss -0.5809 +2026-04-15 02:48:16.366414: val_loss -0.4934 +2026-04-15 02:48:16.369060: Pseudo dice [0.3714, 0.4725, 0.8677, 0.1862, 0.6224, 0.8586, 0.7612] +2026-04-15 02:48:16.372379: Epoch time: 101.8 s +2026-04-15 02:48:17.605022: +2026-04-15 02:48:17.606664: Epoch 3931 +2026-04-15 02:48:17.608459: Current learning rate: 0.00026 +2026-04-15 02:49:59.027654: train_loss -0.5826 +2026-04-15 02:49:59.033081: val_loss -0.4785 +2026-04-15 02:49:59.034865: Pseudo dice [0.741, 0.1642, 0.8008, 0.8973, 0.5352, 0.8452, 0.5512] +2026-04-15 02:49:59.036582: Epoch time: 101.43 s +2026-04-15 02:50:00.258690: +2026-04-15 02:50:00.260947: Epoch 3932 +2026-04-15 02:50:00.262527: Current learning rate: 0.00026 +2026-04-15 02:51:42.008065: train_loss -0.5833 +2026-04-15 02:51:42.012606: val_loss -0.4494 +2026-04-15 02:51:42.013870: Pseudo dice [0.6842, 0.0909, 0.6749, 0.4729, 0.2582, 0.7908, 0.8974] +2026-04-15 02:51:42.015512: Epoch time: 101.75 s +2026-04-15 02:51:43.244951: +2026-04-15 02:51:43.246765: Epoch 3933 +2026-04-15 02:51:43.248255: Current learning rate: 0.00025 +2026-04-15 02:53:24.939723: train_loss -0.5736 +2026-04-15 02:53:24.944812: val_loss -0.5212 +2026-04-15 02:53:24.946737: Pseudo dice [0.6972, 0.8266, 0.7247, 0.6961, 0.6785, 0.7539, 0.866] +2026-04-15 02:53:24.948406: Epoch time: 101.7 s +2026-04-15 02:53:26.202472: +2026-04-15 02:53:26.204317: Epoch 3934 +2026-04-15 02:53:26.205783: Current learning rate: 0.00025 +2026-04-15 02:55:07.668391: train_loss -0.5854 +2026-04-15 02:55:07.673465: val_loss -0.439 +2026-04-15 02:55:07.674985: Pseudo dice [0.4422, 0.2645, 0.6699, 0.8251, 0.2381, 0.8426, 0.8367] +2026-04-15 02:55:07.676607: Epoch time: 101.47 s +2026-04-15 02:55:08.900219: +2026-04-15 02:55:08.901758: Epoch 3935 +2026-04-15 02:55:08.903143: Current learning rate: 0.00025 +2026-04-15 02:56:50.240736: train_loss -0.5776 +2026-04-15 02:56:50.245338: val_loss -0.4746 +2026-04-15 02:56:50.246878: Pseudo dice [0.3163, 0.1516, 0.7098, 0.7575, 0.4296, 0.768, 0.9198] +2026-04-15 02:56:50.248189: Epoch time: 101.34 s +2026-04-15 02:56:51.466988: +2026-04-15 02:56:51.468941: Epoch 3936 +2026-04-15 02:56:51.470616: Current learning rate: 0.00024 +2026-04-15 02:58:32.727184: train_loss -0.5937 +2026-04-15 02:58:32.732835: val_loss -0.5004 +2026-04-15 02:58:32.734716: Pseudo dice [0.7844, 0.5553, 0.8554, 0.8031, 0.5547, 0.813, 0.9345] +2026-04-15 02:58:32.736493: Epoch time: 101.26 s +2026-04-15 02:58:33.975993: +2026-04-15 02:58:33.977842: Epoch 3937 +2026-04-15 02:58:33.979485: Current learning rate: 0.00024 +2026-04-15 03:00:15.625407: train_loss -0.5858 +2026-04-15 03:00:15.629492: val_loss -0.4868 +2026-04-15 03:00:15.631029: Pseudo dice [0.2831, 0.0768, 0.8559, 0.3956, 0.652, 0.8423, 0.8873] +2026-04-15 03:00:15.632425: Epoch time: 101.65 s +2026-04-15 03:00:16.863730: +2026-04-15 03:00:16.865226: Epoch 3938 +2026-04-15 03:00:16.866439: Current learning rate: 0.00024 +2026-04-15 03:01:58.519614: train_loss -0.5762 +2026-04-15 03:01:58.524065: val_loss -0.4742 +2026-04-15 03:01:58.526063: Pseudo dice [0.3348, 0.5709, 0.6248, 0.5543, 0.5842, 0.8151, 0.9043] +2026-04-15 03:01:58.527752: Epoch time: 101.66 s +2026-04-15 03:01:59.752612: +2026-04-15 03:01:59.754559: Epoch 3939 +2026-04-15 03:01:59.756212: Current learning rate: 0.00023 +2026-04-15 03:03:41.407430: train_loss -0.5889 +2026-04-15 03:03:41.413899: val_loss -0.4652 +2026-04-15 03:03:41.415950: Pseudo dice [0.402, 0.4259, 0.7549, 0.7213, 0.4651, 0.8155, 0.4582] +2026-04-15 03:03:41.417642: Epoch time: 101.66 s +2026-04-15 03:03:42.640928: +2026-04-15 03:03:42.642938: Epoch 3940 +2026-04-15 03:03:42.644543: Current learning rate: 0.00023 +2026-04-15 03:05:24.534880: train_loss -0.588 +2026-04-15 03:05:24.539856: val_loss -0.4559 +2026-04-15 03:05:24.541767: Pseudo dice [0.5613, 0.4066, 0.5562, 0.4476, 0.6614, 0.8228, 0.8558] +2026-04-15 03:05:24.543610: Epoch time: 101.9 s +2026-04-15 03:05:25.769794: +2026-04-15 03:05:25.771500: Epoch 3941 +2026-04-15 03:05:25.773005: Current learning rate: 0.00022 +2026-04-15 03:07:07.469437: train_loss -0.5775 +2026-04-15 03:07:07.474416: val_loss -0.4574 +2026-04-15 03:07:07.476282: Pseudo dice [0.4866, 0.1432, 0.7643, 0.818, 0.5623, 0.8854, 0.7503] +2026-04-15 03:07:07.478293: Epoch time: 101.7 s +2026-04-15 03:07:08.728260: +2026-04-15 03:07:08.730005: Epoch 3942 +2026-04-15 03:07:08.731433: Current learning rate: 0.00022 +2026-04-15 03:08:50.367691: train_loss -0.5957 +2026-04-15 03:08:50.372794: val_loss -0.4716 +2026-04-15 03:08:50.374098: Pseudo dice [0.7329, 0.2292, 0.7424, 0.6847, 0.3878, 0.8518, 0.4946] +2026-04-15 03:08:50.375584: Epoch time: 101.64 s +2026-04-15 03:08:51.588407: +2026-04-15 03:08:51.590234: Epoch 3943 +2026-04-15 03:08:51.591485: Current learning rate: 0.00022 +2026-04-15 03:10:37.718940: train_loss -0.5857 +2026-04-15 03:10:37.723815: val_loss -0.5072 +2026-04-15 03:10:37.726225: Pseudo dice [0.529, 0.3063, 0.7555, 0.8925, 0.4949, 0.8012, 0.8964] +2026-04-15 03:10:37.727367: Epoch time: 106.13 s +2026-04-15 03:10:38.941945: +2026-04-15 03:10:38.943537: Epoch 3944 +2026-04-15 03:10:38.944769: Current learning rate: 0.00021 +2026-04-15 03:12:20.681309: train_loss -0.5786 +2026-04-15 03:12:20.686064: val_loss -0.5044 +2026-04-15 03:12:20.687578: Pseudo dice [0.3685, 0.3904, 0.7927, 0.6339, 0.6203, 0.8453, 0.8008] +2026-04-15 03:12:20.689208: Epoch time: 101.74 s +2026-04-15 03:12:21.917985: +2026-04-15 03:12:21.919563: Epoch 3945 +2026-04-15 03:12:21.920824: Current learning rate: 0.00021 +2026-04-15 03:14:03.703233: train_loss -0.5899 +2026-04-15 03:14:03.708370: val_loss -0.4999 +2026-04-15 03:14:03.710113: Pseudo dice [0.7493, 0.1311, 0.7644, 0.8924, 0.4689, 0.8235, 0.8863] +2026-04-15 03:14:03.711968: Epoch time: 101.79 s +2026-04-15 03:14:04.953469: +2026-04-15 03:14:04.955114: Epoch 3946 +2026-04-15 03:14:04.956501: Current learning rate: 0.00021 +2026-04-15 03:15:46.850573: train_loss -0.5918 +2026-04-15 03:15:46.856037: val_loss -0.4917 +2026-04-15 03:15:46.857594: Pseudo dice [0.4402, 0.6256, 0.6234, 0.5985, 0.4979, 0.8292, 0.9112] +2026-04-15 03:15:46.859009: Epoch time: 101.9 s +2026-04-15 03:15:48.072850: +2026-04-15 03:15:48.074126: Epoch 3947 +2026-04-15 03:15:48.075438: Current learning rate: 0.0002 +2026-04-15 03:17:30.016231: train_loss -0.5883 +2026-04-15 03:17:30.022557: val_loss -0.4807 +2026-04-15 03:17:30.024750: Pseudo dice [0.6342, 0.3614, 0.8045, 0.6135, 0.2993, 0.8177, 0.8914] +2026-04-15 03:17:30.026264: Epoch time: 101.95 s +2026-04-15 03:17:31.236480: +2026-04-15 03:17:31.238142: Epoch 3948 +2026-04-15 03:17:31.239667: Current learning rate: 0.0002 +2026-04-15 03:19:13.142834: train_loss -0.5867 +2026-04-15 03:19:13.148833: val_loss -0.514 +2026-04-15 03:19:13.150810: Pseudo dice [0.5556, 0.233, 0.8319, 0.6875, 0.6257, 0.6188, 0.9076] +2026-04-15 03:19:13.152288: Epoch time: 101.91 s +2026-04-15 03:19:14.374513: +2026-04-15 03:19:14.376030: Epoch 3949 +2026-04-15 03:19:14.377394: Current learning rate: 0.0002 +2026-04-15 03:20:56.172228: train_loss -0.5886 +2026-04-15 03:20:56.177796: val_loss -0.497 +2026-04-15 03:20:56.179725: Pseudo dice [0.8328, 0.2732, 0.8778, 0.8491, 0.531, 0.8122, 0.8306] +2026-04-15 03:20:56.181062: Epoch time: 101.8 s +2026-04-15 03:20:59.155168: +2026-04-15 03:20:59.156656: Epoch 3950 +2026-04-15 03:20:59.158176: Current learning rate: 0.00019 +2026-04-15 03:22:40.950744: train_loss -0.5798 +2026-04-15 03:22:40.956203: val_loss -0.5094 +2026-04-15 03:22:40.957778: Pseudo dice [0.8425, 0.5065, 0.7351, 0.5103, 0.706, 0.7907, 0.7994] +2026-04-15 03:22:40.959472: Epoch time: 101.8 s +2026-04-15 03:22:42.188848: +2026-04-15 03:22:42.190832: Epoch 3951 +2026-04-15 03:22:42.192453: Current learning rate: 0.00019 +2026-04-15 03:24:23.595898: train_loss -0.5789 +2026-04-15 03:24:23.603630: val_loss -0.46 +2026-04-15 03:24:23.605329: Pseudo dice [0.3193, 0.4435, 0.7088, 0.4242, 0.498, 0.8187, 0.7514] +2026-04-15 03:24:23.607000: Epoch time: 101.41 s +2026-04-15 03:24:24.831963: +2026-04-15 03:24:24.833565: Epoch 3952 +2026-04-15 03:24:24.835174: Current learning rate: 0.00019 +2026-04-15 03:26:06.164971: train_loss -0.5827 +2026-04-15 03:26:06.171222: val_loss -0.4606 +2026-04-15 03:26:06.172714: Pseudo dice [0.4465, 0.5055, 0.7503, 0.5314, 0.609, 0.7572, 0.5875] +2026-04-15 03:26:06.174294: Epoch time: 101.34 s +2026-04-15 03:26:07.423910: +2026-04-15 03:26:07.425863: Epoch 3953 +2026-04-15 03:26:07.427506: Current learning rate: 0.00018 +2026-04-15 03:27:49.070468: train_loss -0.5864 +2026-04-15 03:27:49.076238: val_loss -0.4668 +2026-04-15 03:27:49.078165: Pseudo dice [0.8601, 0.2012, 0.8079, 0.8103, 0.5439, 0.8557, 0.3447] +2026-04-15 03:27:49.079592: Epoch time: 101.65 s +2026-04-15 03:27:50.308702: +2026-04-15 03:27:50.310591: Epoch 3954 +2026-04-15 03:27:50.311793: Current learning rate: 0.00018 +2026-04-15 03:29:31.959615: train_loss -0.5809 +2026-04-15 03:29:31.966834: val_loss -0.4922 +2026-04-15 03:29:31.969996: Pseudo dice [0.5679, 0.6293, 0.6125, 0.8319, 0.4878, 0.8798, 0.8349] +2026-04-15 03:29:31.971965: Epoch time: 101.65 s +2026-04-15 03:29:33.195493: +2026-04-15 03:29:33.197093: Epoch 3955 +2026-04-15 03:29:33.198443: Current learning rate: 0.00018 +2026-04-15 03:31:15.097802: train_loss -0.5786 +2026-04-15 03:31:15.103218: val_loss -0.4925 +2026-04-15 03:31:15.106494: Pseudo dice [0.5652, 0.4238, 0.8138, 0.8919, 0.4861, 0.8145, 0.8817] +2026-04-15 03:31:15.108311: Epoch time: 101.91 s +2026-04-15 03:31:16.326836: +2026-04-15 03:31:16.335150: Epoch 3956 +2026-04-15 03:31:16.339136: Current learning rate: 0.00017 +2026-04-15 03:32:57.950862: train_loss -0.581 +2026-04-15 03:32:57.959210: val_loss -0.4956 +2026-04-15 03:32:57.961225: Pseudo dice [0.7199, 0.8085, 0.5431, 0.6212, 0.6377, 0.8641, 0.8438] +2026-04-15 03:32:57.963027: Epoch time: 101.63 s +2026-04-15 03:32:59.179136: +2026-04-15 03:32:59.180954: Epoch 3957 +2026-04-15 03:32:59.182253: Current learning rate: 0.00017 +2026-04-15 03:34:40.935915: train_loss -0.5899 +2026-04-15 03:34:40.941167: val_loss -0.4708 +2026-04-15 03:34:40.942677: Pseudo dice [0.5202, 0.3382, 0.7907, 0.8037, 0.421, 0.794, 0.5584] +2026-04-15 03:34:40.944086: Epoch time: 101.76 s +2026-04-15 03:34:42.169506: +2026-04-15 03:34:42.171580: Epoch 3958 +2026-04-15 03:34:42.173406: Current learning rate: 0.00017 +2026-04-15 03:36:23.973304: train_loss -0.5948 +2026-04-15 03:36:23.979553: val_loss -0.4713 +2026-04-15 03:36:23.981356: Pseudo dice [0.7148, 0.1396, 0.8003, 0.8494, 0.3956, 0.8086, 0.6242] +2026-04-15 03:36:23.983563: Epoch time: 101.81 s +2026-04-15 03:36:25.216866: +2026-04-15 03:36:25.221875: Epoch 3959 +2026-04-15 03:36:25.225894: Current learning rate: 0.00016 +2026-04-15 03:38:06.933334: train_loss -0.5861 +2026-04-15 03:38:06.939391: val_loss -0.4918 +2026-04-15 03:38:06.941212: Pseudo dice [0.8169, 0.2408, 0.8499, 0.8702, 0.4368, 0.7896, 0.7902] +2026-04-15 03:38:06.943213: Epoch time: 101.72 s +2026-04-15 03:38:08.160950: +2026-04-15 03:38:08.162851: Epoch 3960 +2026-04-15 03:38:08.164553: Current learning rate: 0.00016 +2026-04-15 03:39:50.034328: train_loss -0.5767 +2026-04-15 03:39:50.042120: val_loss -0.4675 +2026-04-15 03:39:50.043839: Pseudo dice [0.4213, 0.3513, 0.878, 0.8407, 0.3453, 0.7826, 0.8803] +2026-04-15 03:39:50.045623: Epoch time: 101.88 s +2026-04-15 03:39:51.266543: +2026-04-15 03:39:51.268232: Epoch 3961 +2026-04-15 03:39:51.269591: Current learning rate: 0.00015 +2026-04-15 03:41:33.045101: train_loss -0.5826 +2026-04-15 03:41:33.050967: val_loss -0.4519 +2026-04-15 03:41:33.052450: Pseudo dice [0.6066, 0.245, 0.6323, 0.8027, 0.3886, 0.8415, 0.7895] +2026-04-15 03:41:33.054183: Epoch time: 101.78 s +2026-04-15 03:41:34.262156: +2026-04-15 03:41:34.263848: Epoch 3962 +2026-04-15 03:41:34.265418: Current learning rate: 0.00015 +2026-04-15 03:43:17.098507: train_loss -0.5896 +2026-04-15 03:43:17.103445: val_loss -0.4952 +2026-04-15 03:43:17.104971: Pseudo dice [0.4735, 0.2658, 0.8328, 0.7102, 0.5499, 0.7887, 0.9] +2026-04-15 03:43:17.106813: Epoch time: 102.84 s +2026-04-15 03:43:18.327435: +2026-04-15 03:43:18.328991: Epoch 3963 +2026-04-15 03:43:18.330283: Current learning rate: 0.00015 +2026-04-15 03:45:00.119452: train_loss -0.5849 +2026-04-15 03:45:00.124629: val_loss -0.4792 +2026-04-15 03:45:00.126453: Pseudo dice [0.6417, 0.2514, 0.6213, 0.8584, 0.6533, 0.8177, 0.8588] +2026-04-15 03:45:00.128453: Epoch time: 101.8 s +2026-04-15 03:45:01.332237: +2026-04-15 03:45:01.334280: Epoch 3964 +2026-04-15 03:45:01.335701: Current learning rate: 0.00014 +2026-04-15 03:46:43.045396: train_loss -0.5834 +2026-04-15 03:46:43.049846: val_loss -0.4583 +2026-04-15 03:46:43.051543: Pseudo dice [0.7705, 0.2735, 0.6224, 0.9284, 0.3011, 0.7325, 0.864] +2026-04-15 03:46:43.053323: Epoch time: 101.72 s +2026-04-15 03:46:44.265243: +2026-04-15 03:46:44.267578: Epoch 3965 +2026-04-15 03:46:44.269297: Current learning rate: 0.00014 +2026-04-15 03:48:26.046364: train_loss -0.5879 +2026-04-15 03:48:26.052786: val_loss -0.5145 +2026-04-15 03:48:26.055759: Pseudo dice [0.4424, 0.0124, 0.7219, 0.841, 0.7478, 0.8409, 0.9027] +2026-04-15 03:48:26.057553: Epoch time: 101.78 s +2026-04-15 03:48:27.277125: +2026-04-15 03:48:27.278784: Epoch 3966 +2026-04-15 03:48:27.280318: Current learning rate: 0.00014 +2026-04-15 03:50:09.218649: train_loss -0.5914 +2026-04-15 03:50:09.222885: val_loss -0.5165 +2026-04-15 03:50:09.224616: Pseudo dice [0.616, 0.5515, 0.6891, 0.7838, 0.6266, 0.8027, 0.9454] +2026-04-15 03:50:09.225982: Epoch time: 101.94 s +2026-04-15 03:50:10.436728: +2026-04-15 03:50:10.438114: Epoch 3967 +2026-04-15 03:50:10.439686: Current learning rate: 0.00013 +2026-04-15 03:51:52.244819: train_loss -0.5835 +2026-04-15 03:51:52.250320: val_loss -0.5252 +2026-04-15 03:51:52.258562: Pseudo dice [0.8347, 0.6908, 0.8454, 0.7282, 0.6299, 0.865, 0.9175] +2026-04-15 03:51:52.260374: Epoch time: 101.81 s +2026-04-15 03:51:52.261856: Yayy! New best EMA pseudo Dice: 0.6679 +2026-04-15 03:51:55.214830: +2026-04-15 03:51:55.216640: Epoch 3968 +2026-04-15 03:51:55.218694: Current learning rate: 0.00013 +2026-04-15 03:53:36.873043: train_loss -0.5854 +2026-04-15 03:53:36.877697: val_loss -0.452 +2026-04-15 03:53:36.879617: Pseudo dice [0.3966, 0.3844, 0.8487, 0.7296, 0.382, 0.8175, 0.936] +2026-04-15 03:53:36.881246: Epoch time: 101.66 s +2026-04-15 03:53:38.097020: +2026-04-15 03:53:38.098620: Epoch 3969 +2026-04-15 03:53:38.099985: Current learning rate: 0.00013 +2026-04-15 03:55:19.463140: train_loss -0.5776 +2026-04-15 03:55:19.468015: val_loss -0.4864 +2026-04-15 03:55:19.469726: Pseudo dice [0.7422, 0.614, 0.635, 0.7211, 0.6971, 0.8081, 0.5854] +2026-04-15 03:55:19.471575: Epoch time: 101.37 s +2026-04-15 03:55:20.684317: +2026-04-15 03:55:20.685892: Epoch 3970 +2026-04-15 03:55:20.687431: Current learning rate: 0.00012 +2026-04-15 03:57:02.332883: train_loss -0.5915 +2026-04-15 03:57:02.337117: val_loss -0.4954 +2026-04-15 03:57:02.339677: Pseudo dice [0.8035, 0.5432, 0.8234, 0.8148, 0.4737, 0.8471, 0.7752] +2026-04-15 03:57:02.341432: Epoch time: 101.65 s +2026-04-15 03:57:02.343142: Yayy! New best EMA pseudo Dice: 0.6732 +2026-04-15 03:57:05.340462: +2026-04-15 03:57:05.342327: Epoch 3971 +2026-04-15 03:57:05.343776: Current learning rate: 0.00012 +2026-04-15 03:58:47.081383: train_loss -0.577 +2026-04-15 03:58:47.086387: val_loss -0.5004 +2026-04-15 03:58:47.088718: Pseudo dice [0.5391, 0.2199, 0.756, 0.9169, 0.6076, 0.769, 0.936] +2026-04-15 03:58:47.090721: Epoch time: 101.74 s +2026-04-15 03:58:47.093029: Yayy! New best EMA pseudo Dice: 0.6737 +2026-04-15 03:58:50.028052: +2026-04-15 03:58:50.029545: Epoch 3972 +2026-04-15 03:58:50.031028: Current learning rate: 0.00011 +2026-04-15 04:00:31.633873: train_loss -0.5914 +2026-04-15 04:00:31.649959: val_loss -0.4584 +2026-04-15 04:00:31.652039: Pseudo dice [0.3807, 0.1404, 0.5711, 0.8972, 0.395, 0.7861, 0.7724] +2026-04-15 04:00:31.654168: Epoch time: 101.61 s +2026-04-15 04:00:32.898816: +2026-04-15 04:00:32.900893: Epoch 3973 +2026-04-15 04:00:32.902669: Current learning rate: 0.00011 +2026-04-15 04:02:14.687430: train_loss -0.5785 +2026-04-15 04:02:14.695235: val_loss -0.4919 +2026-04-15 04:02:14.696834: Pseudo dice [0.8087, 0.4375, 0.8189, 0.6095, 0.5351, 0.7511, 0.5379] +2026-04-15 04:02:14.698569: Epoch time: 101.79 s +2026-04-15 04:02:15.923243: +2026-04-15 04:02:15.925159: Epoch 3974 +2026-04-15 04:02:15.926567: Current learning rate: 0.00011 +2026-04-15 04:03:57.860929: train_loss -0.586 +2026-04-15 04:03:57.865983: val_loss -0.4824 +2026-04-15 04:03:57.868078: Pseudo dice [0.8069, 0.0265, 0.7232, 0.8556, 0.3584, 0.8456, 0.8696] +2026-04-15 04:03:57.869797: Epoch time: 101.94 s +2026-04-15 04:03:59.091947: +2026-04-15 04:03:59.093699: Epoch 3975 +2026-04-15 04:03:59.095225: Current learning rate: 0.0001 +2026-04-15 04:05:40.670110: train_loss -0.5824 +2026-04-15 04:05:40.675441: val_loss -0.4793 +2026-04-15 04:05:40.677099: Pseudo dice [0.5319, 0.356, 0.7698, 0.5292, 0.5296, 0.8368, 0.8975] +2026-04-15 04:05:40.678601: Epoch time: 101.58 s +2026-04-15 04:05:41.901366: +2026-04-15 04:05:41.903864: Epoch 3976 +2026-04-15 04:05:41.905727: Current learning rate: 0.0001 +2026-04-15 04:07:23.878723: train_loss -0.6003 +2026-04-15 04:07:23.883494: val_loss -0.4913 +2026-04-15 04:07:23.884955: Pseudo dice [0.8197, 0.3299, 0.8327, 0.8561, 0.4949, 0.7261, 0.9307] +2026-04-15 04:07:23.886288: Epoch time: 101.98 s +2026-04-15 04:07:25.100303: +2026-04-15 04:07:25.102023: Epoch 3977 +2026-04-15 04:07:25.103458: Current learning rate: 0.0001 +2026-04-15 04:09:07.030608: train_loss -0.5961 +2026-04-15 04:09:07.039248: val_loss -0.4824 +2026-04-15 04:09:07.042104: Pseudo dice [0.4596, 0.2633, 0.7429, 0.7221, 0.5346, 0.7715, 0.8618] +2026-04-15 04:09:07.043901: Epoch time: 101.93 s +2026-04-15 04:09:08.272697: +2026-04-15 04:09:08.274157: Epoch 3978 +2026-04-15 04:09:08.275591: Current learning rate: 9e-05 +2026-04-15 04:10:49.968915: train_loss -0.5808 +2026-04-15 04:10:49.973174: val_loss -0.481 +2026-04-15 04:10:49.974668: Pseudo dice [0.3109, 0.2922, 0.8404, 0.8478, 0.5965, 0.8406, 0.8947] +2026-04-15 04:10:49.976357: Epoch time: 101.7 s +2026-04-15 04:10:51.175410: +2026-04-15 04:10:51.177024: Epoch 3979 +2026-04-15 04:10:51.178484: Current learning rate: 9e-05 +2026-04-15 04:12:32.949368: train_loss -0.589 +2026-04-15 04:12:32.954416: val_loss -0.4851 +2026-04-15 04:12:32.955818: Pseudo dice [0.496, 0.3133, 0.8328, 0.689, 0.4104, 0.7471, 0.7453] +2026-04-15 04:12:32.957481: Epoch time: 101.78 s +2026-04-15 04:12:34.177724: +2026-04-15 04:12:34.179433: Epoch 3980 +2026-04-15 04:12:34.180866: Current learning rate: 8e-05 +2026-04-15 04:14:16.996400: train_loss -0.5894 +2026-04-15 04:14:17.001014: val_loss -0.5288 +2026-04-15 04:14:17.002774: Pseudo dice [0.3875, 0.549, 0.8313, 0.8789, 0.6486, 0.8471, 0.9381] +2026-04-15 04:14:17.004455: Epoch time: 102.82 s +2026-04-15 04:14:18.205039: +2026-04-15 04:14:18.207158: Epoch 3981 +2026-04-15 04:14:18.208308: Current learning rate: 8e-05 +2026-04-15 04:15:59.732845: train_loss -0.5814 +2026-04-15 04:15:59.738416: val_loss -0.4835 +2026-04-15 04:15:59.741539: Pseudo dice [0.7707, 0.5209, 0.8548, 0.3475, 0.6846, 0.7391, 0.8258] +2026-04-15 04:15:59.743419: Epoch time: 101.53 s +2026-04-15 04:16:00.965091: +2026-04-15 04:16:00.967030: Epoch 3982 +2026-04-15 04:16:00.968958: Current learning rate: 8e-05 +2026-04-15 04:17:42.772594: train_loss -0.5829 +2026-04-15 04:17:42.776871: val_loss -0.4558 +2026-04-15 04:17:42.779342: Pseudo dice [0.6988, 0.1323, 0.8074, 0.7187, 0.2549, 0.867, 0.9342] +2026-04-15 04:17:42.786821: Epoch time: 101.81 s +2026-04-15 04:17:44.084997: +2026-04-15 04:17:44.086651: Epoch 3983 +2026-04-15 04:17:44.087996: Current learning rate: 7e-05 +2026-04-15 04:19:26.149007: train_loss -0.5808 +2026-04-15 04:19:26.154049: val_loss -0.4757 +2026-04-15 04:19:26.156029: Pseudo dice [0.5385, 0.3039, 0.796, 0.8874, 0.2544, 0.8263, 0.8654] +2026-04-15 04:19:26.157632: Epoch time: 102.07 s +2026-04-15 04:19:27.388520: +2026-04-15 04:19:27.390699: Epoch 3984 +2026-04-15 04:19:27.392524: Current learning rate: 7e-05 +2026-04-15 04:21:08.949839: train_loss -0.5839 +2026-04-15 04:21:08.955683: val_loss -0.5041 +2026-04-15 04:21:08.957464: Pseudo dice [0.8097, 0.6249, 0.7596, 0.8201, 0.496, 0.8189, 0.906] +2026-04-15 04:21:08.959238: Epoch time: 101.56 s +2026-04-15 04:21:10.188575: +2026-04-15 04:21:10.190608: Epoch 3985 +2026-04-15 04:21:10.192208: Current learning rate: 7e-05 +2026-04-15 04:22:51.582353: train_loss -0.5948 +2026-04-15 04:22:51.586958: val_loss -0.4574 +2026-04-15 04:22:51.588302: Pseudo dice [0.8335, 0.3312, 0.6639, 0.9257, 0.3191, 0.7755, 0.534] +2026-04-15 04:22:51.590046: Epoch time: 101.4 s +2026-04-15 04:22:52.799091: +2026-04-15 04:22:52.802248: Epoch 3986 +2026-04-15 04:22:52.803964: Current learning rate: 6e-05 +2026-04-15 04:24:34.325876: train_loss -0.5887 +2026-04-15 04:24:34.331176: val_loss -0.4734 +2026-04-15 04:24:34.332923: Pseudo dice [0.4233, 0.3369, 0.7784, 0.8914, 0.463, 0.8526, 0.6425] +2026-04-15 04:24:34.335056: Epoch time: 101.53 s +2026-04-15 04:24:35.546724: +2026-04-15 04:24:35.548679: Epoch 3987 +2026-04-15 04:24:35.550847: Current learning rate: 6e-05 +2026-04-15 04:26:16.914335: train_loss -0.5916 +2026-04-15 04:26:16.918976: val_loss -0.5249 +2026-04-15 04:26:16.920587: Pseudo dice [0.5729, 0.7184, 0.7969, 0.8619, 0.5966, 0.8662, 0.9004] +2026-04-15 04:26:16.922138: Epoch time: 101.37 s +2026-04-15 04:26:18.128249: +2026-04-15 04:26:18.130000: Epoch 3988 +2026-04-15 04:26:18.131394: Current learning rate: 5e-05 +2026-04-15 04:27:59.814695: train_loss -0.5821 +2026-04-15 04:27:59.819632: val_loss -0.5082 +2026-04-15 04:27:59.821020: Pseudo dice [0.7898, 0.4851, 0.7414, 0.0923, 0.5647, 0.7071, 0.8316] +2026-04-15 04:27:59.823011: Epoch time: 101.69 s +2026-04-15 04:28:01.062514: +2026-04-15 04:28:01.064097: Epoch 3989 +2026-04-15 04:28:01.065867: Current learning rate: 5e-05 +2026-04-15 04:29:42.878858: train_loss -0.5879 +2026-04-15 04:29:42.883821: val_loss -0.5147 +2026-04-15 04:29:42.886403: Pseudo dice [0.7068, 0.4769, 0.7699, 0.8952, 0.6224, 0.7874, 0.9448] +2026-04-15 04:29:42.887735: Epoch time: 101.82 s +2026-04-15 04:29:44.091605: +2026-04-15 04:29:44.093349: Epoch 3990 +2026-04-15 04:29:44.094801: Current learning rate: 5e-05 +2026-04-15 04:31:26.084899: train_loss -0.5812 +2026-04-15 04:31:26.089736: val_loss -0.4939 +2026-04-15 04:31:26.092037: Pseudo dice [0.8098, 0.4118, 0.7373, 0.6673, 0.4776, 0.8243, 0.9149] +2026-04-15 04:31:26.093408: Epoch time: 102.0 s +2026-04-15 04:31:27.328203: +2026-04-15 04:31:27.329979: Epoch 3991 +2026-04-15 04:31:27.331671: Current learning rate: 4e-05 +2026-04-15 04:33:09.097045: train_loss -0.5904 +2026-04-15 04:33:09.101329: val_loss -0.4966 +2026-04-15 04:33:09.103166: Pseudo dice [0.4738, 0.5009, 0.7796, 0.6785, 0.4529, 0.8398, 0.8389] +2026-04-15 04:33:09.104712: Epoch time: 101.77 s +2026-04-15 04:33:10.321544: +2026-04-15 04:33:10.323052: Epoch 3992 +2026-04-15 04:33:10.324862: Current learning rate: 4e-05 +2026-04-15 04:34:52.172538: train_loss -0.5863 +2026-04-15 04:34:52.179129: val_loss -0.4952 +2026-04-15 04:34:52.182399: Pseudo dice [0.8433, 0.209, 0.8205, 0.8446, 0.5143, 0.7858, 0.7315] +2026-04-15 04:34:52.184165: Epoch time: 101.85 s +2026-04-15 04:34:53.426327: +2026-04-15 04:34:53.427741: Epoch 3993 +2026-04-15 04:34:53.428918: Current learning rate: 3e-05 +2026-04-15 04:36:35.429278: train_loss -0.5927 +2026-04-15 04:36:35.436156: val_loss -0.4695 +2026-04-15 04:36:35.437793: Pseudo dice [0.4526, 0.1778, 0.7291, 0.8271, 0.5016, 0.7148, 0.6689] +2026-04-15 04:36:35.439362: Epoch time: 102.01 s +2026-04-15 04:36:36.674017: +2026-04-15 04:36:36.675967: Epoch 3994 +2026-04-15 04:36:36.677513: Current learning rate: 3e-05 +2026-04-15 04:38:18.558501: train_loss -0.5859 +2026-04-15 04:38:18.564693: val_loss -0.4712 +2026-04-15 04:38:18.566655: Pseudo dice [0.3531, 0.496, 0.7011, 0.8089, 0.4635, 0.828, 0.8713] +2026-04-15 04:38:18.568369: Epoch time: 101.89 s +2026-04-15 04:38:19.799641: +2026-04-15 04:38:19.801301: Epoch 3995 +2026-04-15 04:38:19.802916: Current learning rate: 2e-05 +2026-04-15 04:40:01.926538: train_loss -0.5887 +2026-04-15 04:40:01.931517: val_loss -0.5089 +2026-04-15 04:40:01.933152: Pseudo dice [0.3439, 0.0796, 0.8182, 0.8363, 0.5926, 0.7177, 0.8364] +2026-04-15 04:40:01.934688: Epoch time: 102.13 s +2026-04-15 04:40:03.141171: +2026-04-15 04:40:03.143298: Epoch 3996 +2026-04-15 04:40:03.145130: Current learning rate: 2e-05 +2026-04-15 04:41:44.979755: train_loss -0.5764 +2026-04-15 04:41:44.984068: val_loss -0.4879 +2026-04-15 04:41:44.985750: Pseudo dice [0.8392, 0.5264, 0.7131, 0.8673, 0.4661, 0.8202, 0.8387] +2026-04-15 04:41:44.987262: Epoch time: 101.84 s +2026-04-15 04:41:46.198678: +2026-04-15 04:41:46.200087: Epoch 3997 +2026-04-15 04:41:46.201369: Current learning rate: 2e-05 +2026-04-15 04:43:28.118234: train_loss -0.5866 +2026-04-15 04:43:28.122816: val_loss -0.4934 +2026-04-15 04:43:28.124575: Pseudo dice [0.8121, 0.349, 0.8053, 0.8255, 0.207, 0.8361, 0.8448] +2026-04-15 04:43:28.126839: Epoch time: 101.92 s +2026-04-15 04:43:29.351395: +2026-04-15 04:43:29.352862: Epoch 3998 +2026-04-15 04:43:29.354683: Current learning rate: 1e-05 +2026-04-15 04:45:11.506339: train_loss -0.5948 +2026-04-15 04:45:11.510091: val_loss -0.4778 +2026-04-15 04:45:11.511968: Pseudo dice [0.3278, 0.2944, 0.6692, 0.5946, 0.5647, 0.8533, 0.6749] +2026-04-15 04:45:11.516671: Epoch time: 102.16 s +2026-04-15 04:45:12.742056: +2026-04-15 04:45:12.744012: Epoch 3999 +2026-04-15 04:45:12.745368: Current learning rate: 1e-05 +2026-04-15 04:46:54.503483: train_loss -0.5835 +2026-04-15 04:46:54.509017: val_loss -0.4479 +2026-04-15 04:46:54.510633: Pseudo dice [0.7044, 0.1907, 0.6854, 0.8883, 0.3602, 0.8762, 0.8151] +2026-04-15 04:46:54.512386: Epoch time: 101.76 s +2026-04-15 04:46:58.328694: Training done. +2026-04-15 04:46:58.550650: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json +2026-04-15 04:46:58.556787: The split file contains 5 splits. +2026-04-15 04:46:58.558005: Desired fold for training: 3 +2026-04-15 04:46:58.561305: This split has 387 training and 97 validation cases. +2026-04-15 04:46:58.565834: predicting MSWAL_0002 +2026-04-15 04:46:58.579714: MSWAL_0002, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 04:47:50.267734: predicting MSWAL_0003 +2026-04-15 04:47:50.281928: MSWAL_0003, shape torch.Size([1, 381, 603, 603]), rank 0 +2026-04-15 04:48:34.495785: predicting MSWAL_0013 +2026-04-15 04:48:34.521099: MSWAL_0013, shape torch.Size([1, 165, 520, 520]), rank 0 +2026-04-15 04:48:49.495918: predicting MSWAL_0037 +2026-04-15 04:48:49.507074: MSWAL_0037, shape torch.Size([1, 168, 507, 507]), rank 0 +2026-04-15 04:48:58.252249: predicting MSWAL_0038 +2026-04-15 04:48:58.266021: MSWAL_0038, shape torch.Size([1, 293, 528, 528]), rank 0 +2026-04-15 04:49:35.110971: predicting MSWAL_0049 +2026-04-15 04:49:35.127675: MSWAL_0049, shape torch.Size([1, 185, 507, 507]), rank 0 +2026-04-15 04:49:47.927121: predicting MSWAL_0055 +2026-04-15 04:49:47.941467: MSWAL_0055, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 04:50:00.877347: predicting MSWAL_0093 +2026-04-15 04:50:00.890933: MSWAL_0093, shape torch.Size([1, 265, 507, 507]), rank 0 +2026-04-15 04:50:17.771556: predicting MSWAL_0098 +2026-04-15 04:50:17.786829: MSWAL_0098, shape torch.Size([1, 159, 429, 429]), rank 0 +2026-04-15 04:50:26.166482: predicting MSWAL_0103 +2026-04-15 04:50:26.177036: MSWAL_0103, shape torch.Size([1, 172, 472, 472]), rank 0 +2026-04-15 04:50:39.053212: predicting MSWAL_0113 +2026-04-15 04:50:39.074183: MSWAL_0113, shape torch.Size([1, 277, 543, 543]), rank 0 +2026-04-15 04:51:08.615391: predicting MSWAL_0114 +2026-04-15 04:51:08.656667: MSWAL_0114, shape torch.Size([1, 333, 507, 507]), rank 0 +2026-04-15 04:51:29.764410: predicting MSWAL_0126 +2026-04-15 04:51:29.784905: MSWAL_0126, shape torch.Size([1, 169, 507, 507]), rank 0 +2026-04-15 04:51:42.412041: predicting MSWAL_0133 +2026-04-15 04:51:42.424678: MSWAL_0133, shape torch.Size([1, 288, 540, 540]), rank 0 +2026-04-15 04:52:19.173174: predicting MSWAL_0134 +2026-04-15 04:52:19.190012: MSWAL_0134, shape torch.Size([1, 166, 480, 480]), rank 0 +2026-04-15 04:52:27.622457: predicting MSWAL_0136 +2026-04-15 04:52:27.634892: MSWAL_0136, shape torch.Size([1, 258, 515, 515]), rank 0 +2026-04-15 04:52:57.239034: predicting MSWAL_0139 +2026-04-15 04:52:57.253851: MSWAL_0139, shape torch.Size([1, 451, 601, 601]), rank 0 +2026-04-15 04:53:55.871032: predicting MSWAL_0141 +2026-04-15 04:53:55.903836: MSWAL_0141, shape torch.Size([1, 414, 545, 545]), rank 0 +2026-04-15 04:54:47.035148: predicting MSWAL_0147 +2026-04-15 04:54:47.059172: MSWAL_0147, shape torch.Size([1, 310, 480, 480]), rank 0 +2026-04-15 04:55:08.000459: predicting MSWAL_0159 +2026-04-15 04:55:08.014982: MSWAL_0159, shape torch.Size([1, 294, 532, 532]), rank 0 +2026-04-15 04:55:44.694517: predicting MSWAL_0163 +2026-04-15 04:55:44.710307: MSWAL_0163, shape torch.Size([1, 310, 480, 480]), rank 0 +2026-04-15 04:56:05.586825: predicting MSWAL_0169 +2026-04-15 04:56:05.607452: MSWAL_0169, shape torch.Size([1, 274, 565, 565]), rank 0 +2026-04-15 04:56:35.329195: predicting MSWAL_0174 +2026-04-15 04:56:35.345455: MSWAL_0174, shape torch.Size([1, 434, 607, 607]), rank 0 +2026-04-15 04:57:26.919428: predicting MSWAL_0178 +2026-04-15 04:57:26.947744: MSWAL_0178, shape torch.Size([1, 310, 573, 573]), rank 0 +2026-04-15 04:58:04.312142: predicting MSWAL_0179 +2026-04-15 04:58:04.334723: MSWAL_0179, shape torch.Size([1, 322, 509, 509]), rank 0 +2026-04-15 04:58:25.449516: predicting MSWAL_0185 +2026-04-15 04:58:25.474588: MSWAL_0185, shape torch.Size([1, 190, 545, 545]), rank 0 +2026-04-15 04:58:47.823368: predicting MSWAL_0199 +2026-04-15 04:58:47.849955: MSWAL_0199, shape torch.Size([1, 182, 504, 504]), rank 0 +2026-04-15 04:59:00.603178: predicting MSWAL_0220 +2026-04-15 04:59:00.621880: MSWAL_0220, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 04:59:13.178405: predicting MSWAL_0222 +2026-04-15 04:59:13.193722: MSWAL_0222, shape torch.Size([1, 265, 491, 491]), rank 0 +2026-04-15 04:59:30.173359: predicting MSWAL_0243 +2026-04-15 04:59:30.189579: MSWAL_0243, shape torch.Size([1, 264, 557, 557]), rank 0 +2026-04-15 04:59:59.694956: predicting MSWAL_0255 +2026-04-15 04:59:59.722094: MSWAL_0255, shape torch.Size([1, 305, 527, 527]), rank 0 +2026-04-15 05:00:36.805181: predicting MSWAL_0260 +2026-04-15 05:00:36.840138: MSWAL_0260, shape torch.Size([1, 142, 525, 525]), rank 0 +2026-04-15 05:00:51.758465: predicting MSWAL_0274 +2026-04-15 05:00:51.776571: MSWAL_0274, shape torch.Size([1, 334, 480, 480]), rank 0 +2026-04-15 05:01:12.654147: predicting MSWAL_0275 +2026-04-15 05:01:12.677478: MSWAL_0275, shape torch.Size([1, 320, 521, 521]), rank 0 +2026-04-15 05:01:49.553506: predicting MSWAL_0276 +2026-04-15 05:01:49.585603: MSWAL_0276, shape torch.Size([1, 284, 516, 516]), rank 0 +2026-04-15 05:02:26.229399: predicting MSWAL_0281 +2026-04-15 05:02:26.245744: MSWAL_0281, shape torch.Size([1, 357, 557, 557]), rank 0 +2026-04-15 05:03:10.455900: predicting MSWAL_0282 +2026-04-15 05:03:10.486844: MSWAL_0282, shape torch.Size([1, 233, 543, 543]), rank 0 +2026-04-15 05:03:40.203526: predicting MSWAL_0283 +2026-04-15 05:03:40.229337: MSWAL_0283, shape torch.Size([1, 217, 507, 507]), rank 0 +2026-04-15 05:03:53.029479: predicting MSWAL_0290 +2026-04-15 05:03:53.042675: MSWAL_0290, shape torch.Size([1, 193, 507, 507]), rank 0 +2026-04-15 05:04:05.639618: predicting MSWAL_0303 +2026-04-15 05:04:05.658281: MSWAL_0303, shape torch.Size([1, 269, 507, 507]), rank 0 +2026-04-15 05:04:22.952542: predicting MSWAL_0306 +2026-04-15 05:04:22.970339: MSWAL_0306, shape torch.Size([1, 205, 507, 507]), rank 0 +2026-04-15 05:04:36.013372: predicting MSWAL_0308 +2026-04-15 05:04:36.027228: MSWAL_0308, shape torch.Size([1, 229, 507, 507]), rank 0 +2026-04-15 05:04:52.896342: predicting MSWAL_0324 +2026-04-15 05:04:52.916569: MSWAL_0324, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 05:05:05.612716: predicting MSWAL_0326 +2026-04-15 05:05:05.631727: MSWAL_0326, shape torch.Size([1, 197, 507, 507]), rank 0 +2026-04-15 05:05:18.328383: predicting MSWAL_0330 +2026-04-15 05:05:18.351904: MSWAL_0330, shape torch.Size([1, 189, 507, 507]), rank 0 +2026-04-15 05:05:31.023135: predicting MSWAL_0334 +2026-04-15 05:05:31.035896: MSWAL_0334, shape torch.Size([1, 197, 507, 507]), rank 0 +2026-04-15 05:05:44.070368: predicting MSWAL_0335 +2026-04-15 05:05:44.082388: MSWAL_0335, shape torch.Size([1, 361, 591, 591]), rank 0 +2026-04-15 05:06:28.385815: predicting MSWAL_0338 +2026-04-15 05:06:28.415492: MSWAL_0338, shape torch.Size([1, 317, 617, 617]), rank 0 +2026-04-15 05:07:05.263792: predicting MSWAL_0342 +2026-04-15 05:07:05.293398: MSWAL_0342, shape torch.Size([1, 309, 493, 493]), rank 0 +2026-04-15 05:07:26.295917: predicting MSWAL_0346 +2026-04-15 05:07:26.311095: MSWAL_0346, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 05:07:35.150289: predicting MSWAL_0354 +2026-04-15 05:07:35.165709: MSWAL_0354, shape torch.Size([1, 283, 519, 519]), rank 0 +2026-04-15 05:08:11.943247: predicting MSWAL_0375 +2026-04-15 05:08:11.959744: MSWAL_0375, shape torch.Size([1, 277, 568, 568]), rank 0 +2026-04-15 05:08:41.551449: predicting MSWAL_0376 +2026-04-15 05:08:41.577618: MSWAL_0376, shape torch.Size([1, 305, 537, 537]), rank 0 +2026-04-15 05:09:18.616124: predicting MSWAL_0381 +2026-04-15 05:09:18.632797: MSWAL_0381, shape torch.Size([1, 325, 541, 541]), rank 0 +2026-04-15 05:09:55.692340: predicting MSWAL_0389 +2026-04-15 05:09:55.719726: MSWAL_0389, shape torch.Size([1, 217, 667, 667]), rank 0 +2026-04-15 05:10:30.273409: predicting MSWAL_0390 +2026-04-15 05:10:30.291587: MSWAL_0390, shape torch.Size([1, 273, 560, 560]), rank 0 +2026-04-15 05:11:00.036522: predicting MSWAL_0402 +2026-04-15 05:11:00.054008: MSWAL_0402, shape torch.Size([1, 149, 536, 536]), rank 0 +2026-04-15 05:11:15.039298: predicting MSWAL_0409 +2026-04-15 05:11:15.059315: MSWAL_0409, shape torch.Size([1, 415, 559, 559]), rank 0 +2026-04-15 05:12:06.567746: predicting MSWAL_0414 +2026-04-15 05:12:06.588442: MSWAL_0414, shape torch.Size([1, 305, 521, 521]), rank 0 +2026-04-15 05:12:43.643632: predicting MSWAL_0423 +2026-04-15 05:12:43.665734: MSWAL_0423, shape torch.Size([1, 337, 529, 529]), rank 0 +2026-04-15 05:13:28.074454: predicting MSWAL_0432 +2026-04-15 05:13:28.093300: MSWAL_0432, shape torch.Size([1, 357, 507, 507]), rank 0 +2026-04-15 05:13:53.476356: predicting MSWAL_0439 +2026-04-15 05:13:53.506640: MSWAL_0439, shape torch.Size([1, 337, 559, 559]), rank 0 +2026-04-15 05:14:37.941640: predicting MSWAL_0460 +2026-04-15 05:14:37.963266: MSWAL_0460, shape torch.Size([1, 197, 595, 595]), rank 0 +2026-04-15 05:15:00.997759: predicting MSWAL_0463 +2026-04-15 05:15:01.018143: MSWAL_0463, shape torch.Size([1, 197, 507, 507]), rank 0 +2026-04-15 05:15:13.829036: predicting MSWAL_0466 +2026-04-15 05:15:13.848051: MSWAL_0466, shape torch.Size([1, 317, 584, 584]), rank 0 +2026-04-15 05:15:50.937248: predicting MSWAL_0468 +2026-04-15 05:15:50.962450: MSWAL_0468, shape torch.Size([1, 377, 615, 615]), rank 0 +2026-04-15 05:16:35.475245: predicting MSWAL_0473 +2026-04-15 05:16:35.531435: MSWAL_0473, shape torch.Size([1, 177, 579, 579]), rank 0 +2026-04-15 05:16:58.055309: predicting MSWAL_0483 +2026-04-15 05:16:58.096315: MSWAL_0483, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 05:17:11.102100: predicting MSWAL_0486 +2026-04-15 05:17:11.114113: MSWAL_0486, shape torch.Size([1, 201, 507, 507]), rank 0 +2026-04-15 05:17:24.079123: predicting MSWAL_0497 +2026-04-15 05:17:24.100595: MSWAL_0497, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 05:17:36.726815: predicting MSWAL_0498 +2026-04-15 05:17:36.748680: MSWAL_0498, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 05:17:49.402527: predicting MSWAL_0521 +2026-04-15 05:17:49.420661: MSWAL_0521, shape torch.Size([1, 261, 517, 517]), rank 0 +2026-04-15 05:18:19.195043: predicting MSWAL_0526 +2026-04-15 05:18:19.212958: MSWAL_0526, shape torch.Size([1, 397, 507, 507]), rank 0 +2026-04-15 05:18:48.493625: predicting MSWAL_0531 +2026-04-15 05:18:48.525010: MSWAL_0531, shape torch.Size([1, 145, 531, 531]), rank 0 +2026-04-15 05:19:03.538347: predicting MSWAL_0539 +2026-04-15 05:19:03.557110: MSWAL_0539, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 05:19:16.314480: predicting MSWAL_0549 +2026-04-15 05:19:16.333922: MSWAL_0549, shape torch.Size([1, 321, 507, 507]), rank 0 +2026-04-15 05:19:37.370458: predicting MSWAL_0550 +2026-04-15 05:19:37.416783: MSWAL_0550, shape torch.Size([1, 269, 652, 652]), rank 0 +2026-04-15 05:20:23.467571: predicting MSWAL_0553 +2026-04-15 05:20:23.492160: MSWAL_0553, shape torch.Size([1, 201, 507, 507]), rank 0 +2026-04-15 05:20:36.386854: predicting MSWAL_0557 +2026-04-15 05:20:36.408171: MSWAL_0557, shape torch.Size([1, 412, 553, 553]), rank 0 +2026-04-15 05:21:27.873094: predicting MSWAL_0561 +2026-04-15 05:21:27.895404: MSWAL_0561, shape torch.Size([1, 289, 507, 507]), rank 0 +2026-04-15 05:21:49.317563: predicting MSWAL_0574 +2026-04-15 05:21:49.336028: MSWAL_0574, shape torch.Size([1, 166, 480, 480]), rank 0 +2026-04-15 05:21:57.871453: predicting MSWAL_0579 +2026-04-15 05:21:57.879748: MSWAL_0579, shape torch.Size([1, 302, 480, 480]), rank 0 +2026-04-15 05:22:19.033591: predicting MSWAL_0581 +2026-04-15 05:22:19.052798: MSWAL_0581, shape torch.Size([1, 205, 572, 572]), rank 0 +2026-04-15 05:22:41.128221: predicting MSWAL_0591 +2026-04-15 05:22:41.149131: MSWAL_0591, shape torch.Size([1, 166, 544, 544]), rank 0 +2026-04-15 05:22:56.053712: predicting MSWAL_0593 +2026-04-15 05:22:56.069560: MSWAL_0593, shape torch.Size([1, 158, 480, 480]), rank 0 +2026-04-15 05:23:04.543620: predicting MSWAL_0598 +2026-04-15 05:23:04.560031: MSWAL_0598, shape torch.Size([1, 174, 480, 480]), rank 0 +2026-04-15 05:23:17.007823: predicting MSWAL_0601 +2026-04-15 05:23:17.030120: MSWAL_0601, shape torch.Size([1, 154, 480, 480]), rank 0 +2026-04-15 05:23:25.605471: predicting MSWAL_0602 +2026-04-15 05:23:25.623578: MSWAL_0602, shape torch.Size([1, 338, 568, 568]), rank 0 +2026-04-15 05:24:09.564070: predicting MSWAL_0604 +2026-04-15 05:24:09.584736: MSWAL_0604, shape torch.Size([1, 346, 480, 480]), rank 0 +2026-04-15 05:24:34.648417: predicting MSWAL_0605 +2026-04-15 05:24:34.665925: MSWAL_0605, shape torch.Size([1, 314, 537, 537]), rank 0 +2026-04-15 05:25:11.782661: predicting MSWAL_0615 +2026-04-15 05:25:11.806749: MSWAL_0615, shape torch.Size([1, 366, 597, 597]), rank 0 +2026-04-15 05:25:56.081149: predicting MSWAL_0630 +2026-04-15 05:25:56.110873: MSWAL_0630, shape torch.Size([1, 266, 475, 475]), rank 0 +2026-04-15 05:26:12.949023: predicting MSWAL_0656 +2026-04-15 05:26:12.965482: MSWAL_0656, shape torch.Size([1, 137, 420, 420]), rank 0 +2026-04-15 05:26:21.325978: predicting MSWAL_0661 +2026-04-15 05:26:21.334697: MSWAL_0661, shape torch.Size([1, 157, 469, 469]), rank 0 +2026-04-15 05:26:29.816513: predicting MSWAL_0669 +2026-04-15 05:26:29.826874: MSWAL_0669, shape torch.Size([1, 321, 507, 507]), rank 0 +2026-04-15 05:26:50.877363: predicting MSWAL_0671 +2026-04-15 05:26:50.900606: MSWAL_0671, shape torch.Size([1, 317, 507, 507]), rank 0 +2026-04-15 05:27:11.954707: predicting MSWAL_0680 +2026-04-15 05:27:11.971303: MSWAL_0680, shape torch.Size([1, 328, 585, 585]), rank 0 +2026-04-15 05:29:16.563924: Validation complete +2026-04-15 05:29:16.566048: Mean Validation Dice: 0.48599401374240436 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_best.pth b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_best.pth new file mode 100644 index 0000000000000000000000000000000000000000..780f810fe0a86dc46d6ef2aef8e60cdb8d6507a9 --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_best.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab30ba7f58d2c7e62ee79397a872e05040ba6f10513c4fb9ba42c12a27c744d4 +size 1132099218 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_final.pth 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"configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}", + "configuration_name": "3d_fullres", + "cudnn_version": 90100, + "current_epoch": "0", + "dataloader_train": "", + "dataloader_train.generator": "", + "dataloader_train.num_processes": "12", + "dataloader_train.transform": "None", + "dataloader_val": "", + "dataloader_val.generator": "", + "dataloader_val.num_processes": "6", + "dataloader_val.transform": "None", + "dataset_json": "{'name': 'MSWAL', 'description': ' 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset', 'licence': 'CC BY-NC 4.0', 'relase': 'July 8, 2025', 'tensorImageSize': '3D', 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'gallstone': 1, 'kidney stone': 2, 'liver tumor': 3, 'kidney tumor': 4, 'pancreatic cancer': 5, 'liver cyst': 6, 'kidney cyst': 7}, 'numTraining': 484, 'numTest': 210, 'training': [{'image': './imagesTr/MSWAL_0001_0000.nii.gz', 'label': './labelsTr/MSWAL_0001.nii.gz'}, {'image': './imagesTr/MSWAL_0002_0000.nii.gz', 'label': './labelsTr/MSWAL_0002.nii.gz'}, {'image': './imagesTr/MSWAL_0003_0000.nii.gz', 'label': './labelsTr/MSWAL_0003.nii.gz'}, {'image': './imagesTr/MSWAL_0008_0000.nii.gz', 'label': './labelsTr/MSWAL_0008.nii.gz'}, {'image': './imagesTr/MSWAL_0009_0000.nii.gz', 'label': './labelsTr/MSWAL_0009.nii.gz'}, {'image': './imagesTr/MSWAL_0011_0000.nii.gz', 'label': './labelsTr/MSWAL_0011.nii.gz'}, {'image': './imagesTr/MSWAL_0013_0000.nii.gz', 'label': './labelsTr/MSWAL_0013.nii.gz'}, {'image': './imagesTr/MSWAL_0014_0000.nii.gz', 'label': './labelsTr/MSWAL_0014.nii.gz'}, {'image': './imagesTr/MSWAL_0015_0000.nii.gz', 'label': './labelsTr/MSWAL_0015.nii.gz'}, {'image': './imagesTr/MSWAL_0017_0000.nii.gz', 'label': './labelsTr/MSWAL_0017.nii.gz'}, {'image': './imagesTr/MSWAL_0018_0000.nii.gz', 'label': './labelsTr/MSWAL_0018.nii.gz'}, {'image': './imagesTr/MSWAL_0020_0000.nii.gz', 'label': './labelsTr/MSWAL_0020.nii.gz'}, {'image': './imagesTr/MSWAL_0021_0000.nii.gz', 'label': './labelsTr/MSWAL_0021.nii.gz'}, {'image': './imagesTr/MSWAL_0022_0000.nii.gz', 'label': './labelsTr/MSWAL_0022.nii.gz'}, {'image': './imagesTr/MSWAL_0024_0000.nii.gz', 'label': './labelsTr/MSWAL_0024.nii.gz'}, {'image': './imagesTr/MSWAL_0026_0000.nii.gz', 'label': './labelsTr/MSWAL_0026.nii.gz'}, {'image': './imagesTr/MSWAL_0027_0000.nii.gz', 'label': 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'./imagesTs/MSWAL_0664_0000.nii.gz', 'label': './labelsTs/MSWAL_0664.nii.gz'}, {'image': './imagesTs/MSWAL_0665_0000.nii.gz', 'label': './labelsTs/MSWAL_0665.nii.gz'}, {'image': './imagesTs/MSWAL_0672_0000.nii.gz', 'label': './labelsTs/MSWAL_0672.nii.gz'}, {'image': './imagesTs/MSWAL_0678_0000.nii.gz', 'label': './labelsTs/MSWAL_0678.nii.gz'}, {'image': './imagesTs/MSWAL_0683_0000.nii.gz', 'label': './labelsTs/MSWAL_0683.nii.gz'}, {'image': './imagesTs/MSWAL_0684_0000.nii.gz', 'label': './labelsTs/MSWAL_0684.nii.gz'}, {'image': './imagesTs/MSWAL_0689_0000.nii.gz', 'label': './labelsTs/MSWAL_0689.nii.gz'}, {'image': './imagesTs/MSWAL_0691_0000.nii.gz', 'label': './labelsTs/MSWAL_0691.nii.gz'}]}, 'unpack_dataset': True, 'device': device(type='cuda')}", + "network": "OptimizedModule", + "num_epochs": "4000", + "num_input_channels": "1", + "num_iterations_per_epoch": "250", + "num_val_iterations_per_epoch": "50", + "optimizer": "SGD (\nParameter Group 0\n dampening: 0\n differentiable: False\n foreach: None\n fused: None\n initial_lr: 0.01\n lr: 0.01\n maximize: False\n momentum: 0.99\n nesterov: True\n weight_decay: 3e-05\n)", + "output_folder": "/data/houbb/nnunetv2/nnUNet_results/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4", + "output_folder_base": "/data/houbb/nnunetv2/nnUNet_results/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres", + "oversample_foreground_percent": "0.33", + "plans_manager": "{'dataset_name': 'Dataset201_MSWAL', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.25, 0.75, 0.75], 'original_median_shape_after_transp': [261, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 35, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [190, 381, 381], 'spacing': [1.6798954741801528, 1.0079372845080916, 1.0079372845080916], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 71.96339416503906, 'median': 45.0, 'min': -932.0, 'percentile_00_5': -93.0, 'percentile_99_5': 1052.0, 'std': 141.6230926513672}}}", + "preprocessed_dataset_folder": "/data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/nnUNetPlans_3d_fullres", + "preprocessed_dataset_folder_base": "/data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL", + "save_every": "50", + "torch_version": "2.5.0+cu121", + "unpack_dataset": "True", + "was_initialized": "True", + "weight_decay": "3e-05" +} \ No newline at end of file diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4/progress.png b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4/progress.png new file mode 100644 index 0000000000000000000000000000000000000000..eff26ee1e71e41bc30bc9b0c53d35f5e6689f4b8 --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4/progress.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:231875e9029ea5fee77fa2f42d587660e1e1283c40628e028a2141f5f0e749bf +size 1402644 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4/training_log_2026_4_10_10_10_48.txt b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4/training_log_2026_4_10_10_10_48.txt new file mode 100644 index 0000000000000000000000000000000000000000..ecb45981b7198f0afe562c1c76e30ce2fbd0bb1c --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/fold_4/training_log_2026_4_10_10_10_48.txt @@ -0,0 +1,28395 @@ + +####################################################################### +Please cite the following paper when using nnU-Net: +Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. +####################################################################### + +2026-04-10 10:10:48.360561: do_dummy_2d_data_aug: False +2026-04-10 10:10:48.408766: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json +2026-04-10 10:10:48.411814: The split file contains 5 splits. +2026-04-10 10:10:48.413111: Desired fold for training: 4 +2026-04-10 10:10:48.414394: This split has 388 training and 96 validation cases. +2026-04-10 10:10:56.052041: Using torch.compile... + +This is the configuration used by this training: +Configuration name: 3d_fullres + {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 256, 256], 'median_image_size_in_voxels': [255.5, 512.0, 512.0], 'spacing': [1.25, 0.75, 0.75], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True} + +These are the global plan.json settings: + {'dataset_name': 'Dataset201_MSWAL', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.25, 0.75, 0.75], 'original_median_shape_after_transp': [261, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 71.96339416503906, 'median': 45.0, 'min': -932.0, 'percentile_00_5': -93.0, 'percentile_99_5': 1052.0, 'std': 141.6230926513672}}} + +2026-04-10 10:10:57.505968: unpacking dataset... +2026-04-10 10:11:05.732554: unpacking done... +2026-04-10 10:11:05.758355: Unable to plot network architecture: nnUNet_compile is enabled! +2026-04-10 10:11:05.805157: +2026-04-10 10:11:05.807601: Epoch 0 +2026-04-10 10:11:05.811559: Current learning rate: 0.01 +2026-04-10 10:15:13.482853: train_loss 0.1811 +2026-04-10 10:15:13.495654: val_loss 0.1392 +2026-04-10 10:15:13.499454: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:15:13.502491: Epoch time: 247.68 s +2026-04-10 10:15:13.507033: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 10:15:16.019818: +2026-04-10 10:15:16.021423: Epoch 1 +2026-04-10 10:15:16.022857: Current learning rate: 0.01 +2026-04-10 10:17:00.544644: train_loss 0.051 +2026-04-10 10:17:00.550859: val_loss 0.0608 +2026-04-10 10:17:00.553382: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:17:00.555652: Epoch time: 104.53 s +2026-04-10 10:17:01.616414: +2026-04-10 10:17:01.618300: Epoch 2 +2026-04-10 10:17:01.620096: Current learning rate: 0.01 +2026-04-10 10:18:43.339949: train_loss 0.0617 +2026-04-10 10:18:43.349623: val_loss 0.0871 +2026-04-10 10:18:43.354762: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:18:43.356729: Epoch time: 101.73 s +2026-04-10 10:18:44.458872: +2026-04-10 10:18:44.460599: Epoch 3 +2026-04-10 10:18:44.462054: Current learning rate: 0.00999 +2026-04-10 10:20:26.461704: train_loss 0.0598 +2026-04-10 10:20:26.467606: val_loss 0.0738 +2026-04-10 10:20:26.469443: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:20:26.471937: Epoch time: 102.01 s +2026-04-10 10:20:27.516400: +2026-04-10 10:20:27.517939: Epoch 4 +2026-04-10 10:20:27.519544: Current learning rate: 0.00999 +2026-04-10 10:22:09.531562: train_loss 0.0517 +2026-04-10 10:22:09.537153: val_loss 0.049 +2026-04-10 10:22:09.539451: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:22:09.542381: Epoch time: 102.02 s +2026-04-10 10:22:10.627309: +2026-04-10 10:22:10.628864: Epoch 5 +2026-04-10 10:22:10.630256: Current learning rate: 0.00999 +2026-04-10 10:23:52.271397: train_loss 0.0554 +2026-04-10 10:23:52.277606: val_loss 0.052 +2026-04-10 10:23:52.279106: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:23:52.281139: Epoch time: 101.65 s +2026-04-10 10:23:53.331113: +2026-04-10 10:23:53.333103: Epoch 6 +2026-04-10 10:23:53.334866: Current learning rate: 0.00999 +2026-04-10 10:25:43.837695: train_loss 0.0557 +2026-04-10 10:25:43.842627: val_loss 0.0641 +2026-04-10 10:25:43.844528: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:25:43.846678: Epoch time: 110.51 s +2026-04-10 10:25:44.918801: +2026-04-10 10:25:44.920785: Epoch 7 +2026-04-10 10:25:44.923018: Current learning rate: 0.00998 +2026-04-10 10:27:26.600968: train_loss 0.0559 +2026-04-10 10:27:26.606523: val_loss 0.0359 +2026-04-10 10:27:26.608415: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:27:26.611165: Epoch time: 101.69 s +2026-04-10 10:27:27.673381: +2026-04-10 10:27:27.675709: Epoch 8 +2026-04-10 10:27:27.677143: Current learning rate: 0.00998 +2026-04-10 10:29:09.355307: train_loss 0.0461 +2026-04-10 10:29:09.369324: val_loss 0.0645 +2026-04-10 10:29:09.371111: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:29:09.373062: Epoch time: 101.69 s +2026-04-10 10:29:10.455898: +2026-04-10 10:29:10.457347: Epoch 9 +2026-04-10 10:29:10.458540: Current learning rate: 0.00998 +2026-04-10 10:30:52.785375: train_loss 0.0511 +2026-04-10 10:30:52.790516: val_loss 0.0879 +2026-04-10 10:30:52.792161: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:30:52.794083: Epoch time: 102.33 s +2026-04-10 10:30:53.796271: +2026-04-10 10:30:53.798077: Epoch 10 +2026-04-10 10:30:53.800010: Current learning rate: 0.00998 +2026-04-10 10:32:36.120663: train_loss 0.0541 +2026-04-10 10:32:36.126439: val_loss 0.043 +2026-04-10 10:32:36.128423: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:32:36.130714: Epoch time: 102.33 s +2026-04-10 10:32:37.188910: +2026-04-10 10:32:37.190536: Epoch 11 +2026-04-10 10:32:37.192481: Current learning rate: 0.00998 +2026-04-10 10:34:19.645983: train_loss 0.0385 +2026-04-10 10:34:19.651228: val_loss 0.0612 +2026-04-10 10:34:19.653538: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:34:19.655774: Epoch time: 102.46 s +2026-04-10 10:34:20.707061: +2026-04-10 10:34:20.708663: Epoch 12 +2026-04-10 10:34:20.709987: Current learning rate: 0.00997 +2026-04-10 10:36:02.498358: train_loss 0.0501 +2026-04-10 10:36:02.503972: val_loss 0.0591 +2026-04-10 10:36:02.505828: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:36:02.508051: Epoch time: 101.79 s +2026-04-10 10:36:03.631447: +2026-04-10 10:36:03.633533: Epoch 13 +2026-04-10 10:36:03.635452: Current learning rate: 0.00997 +2026-04-10 10:37:45.863216: train_loss 0.0467 +2026-04-10 10:37:45.869740: val_loss 0.0719 +2026-04-10 10:37:45.873732: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:37:45.877003: Epoch time: 102.24 s +2026-04-10 10:37:46.942997: +2026-04-10 10:37:46.944752: Epoch 14 +2026-04-10 10:37:46.946186: Current learning rate: 0.00997 +2026-04-10 10:39:28.898988: train_loss 0.0462 +2026-04-10 10:39:28.904730: val_loss 0.0571 +2026-04-10 10:39:28.906694: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:39:28.908732: Epoch time: 101.96 s +2026-04-10 10:39:30.001662: +2026-04-10 10:39:30.003084: Epoch 15 +2026-04-10 10:39:30.004533: Current learning rate: 0.00997 +2026-04-10 10:41:13.963193: train_loss 0.0548 +2026-04-10 10:41:13.970909: val_loss 0.0512 +2026-04-10 10:41:13.972906: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:41:13.975012: Epoch time: 103.96 s +2026-04-10 10:41:15.038803: +2026-04-10 10:41:15.040795: Epoch 16 +2026-04-10 10:41:15.042661: Current learning rate: 0.00996 +2026-04-10 10:42:57.197349: train_loss 0.0501 +2026-04-10 10:42:57.203689: val_loss 0.0747 +2026-04-10 10:42:57.205931: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:42:57.208309: Epoch time: 102.16 s +2026-04-10 10:42:58.341767: +2026-04-10 10:42:58.344222: Epoch 17 +2026-04-10 10:42:58.346420: Current learning rate: 0.00996 +2026-04-10 10:44:40.022446: train_loss 0.0468 +2026-04-10 10:44:40.028488: val_loss 0.0435 +2026-04-10 10:44:40.030450: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:44:40.032396: Epoch time: 101.68 s +2026-04-10 10:44:42.346550: +2026-04-10 10:44:42.347942: Epoch 18 +2026-04-10 10:44:42.349446: Current learning rate: 0.00996 +2026-04-10 10:46:25.925652: train_loss 0.0397 +2026-04-10 10:46:25.932204: val_loss 0.0498 +2026-04-10 10:46:25.934366: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:46:25.936799: Epoch time: 103.58 s +2026-04-10 10:46:27.049275: +2026-04-10 10:46:27.051101: Epoch 19 +2026-04-10 10:46:27.052477: Current learning rate: 0.00996 +2026-04-10 10:48:09.982685: train_loss 0.0496 +2026-04-10 10:48:10.000365: val_loss 0.0733 +2026-04-10 10:48:10.006397: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:48:10.010335: Epoch time: 102.94 s +2026-04-10 10:48:11.103524: +2026-04-10 10:48:11.106146: Epoch 20 +2026-04-10 10:48:11.109382: Current learning rate: 0.00995 +2026-04-10 10:49:54.666500: train_loss 0.0569 +2026-04-10 10:49:54.692093: val_loss 0.0656 +2026-04-10 10:49:54.694396: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:49:54.696672: Epoch time: 103.57 s +2026-04-10 10:49:55.790706: +2026-04-10 10:49:55.799797: Epoch 21 +2026-04-10 10:49:55.802460: Current learning rate: 0.00995 +2026-04-10 10:51:38.558336: train_loss 0.0381 +2026-04-10 10:51:38.571904: val_loss 0.0321 +2026-04-10 10:51:38.574090: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:51:38.576604: Epoch time: 102.77 s +2026-04-10 10:51:39.596985: +2026-04-10 10:51:39.600033: Epoch 22 +2026-04-10 10:51:39.602013: Current learning rate: 0.00995 +2026-04-10 10:53:23.321661: train_loss 0.0356 +2026-04-10 10:53:23.331054: val_loss 0.0273 +2026-04-10 10:53:23.333226: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:53:23.338852: Epoch time: 103.72 s +2026-04-10 10:53:24.358492: +2026-04-10 10:53:24.363961: Epoch 23 +2026-04-10 10:53:24.365948: Current learning rate: 0.00995 +2026-04-10 10:55:06.352813: train_loss 0.0306 +2026-04-10 10:55:06.358891: val_loss 0.0604 +2026-04-10 10:55:06.361064: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:55:06.362873: Epoch time: 102.0 s +2026-04-10 10:55:07.390872: +2026-04-10 10:55:07.392342: Epoch 24 +2026-04-10 10:55:07.393862: Current learning rate: 0.00995 +2026-04-10 10:56:51.430312: train_loss 0.0529 +2026-04-10 10:56:51.449226: val_loss 0.0492 +2026-04-10 10:56:51.451007: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:56:51.453477: Epoch time: 104.04 s +2026-04-10 10:56:52.515313: +2026-04-10 10:56:52.521592: Epoch 25 +2026-04-10 10:56:52.525559: Current learning rate: 0.00994 +2026-04-10 10:58:35.757708: train_loss 0.04 +2026-04-10 10:58:35.764118: val_loss 0.0608 +2026-04-10 10:58:35.766017: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 10:58:35.768599: Epoch time: 103.24 s +2026-04-10 10:58:36.834778: +2026-04-10 10:58:36.840340: Epoch 26 +2026-04-10 10:58:36.847934: Current learning rate: 0.00994 +2026-04-10 11:00:21.195050: train_loss 0.037 +2026-04-10 11:00:21.207903: val_loss 0.0267 +2026-04-10 11:00:21.211898: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:00:21.216701: Epoch time: 104.36 s +2026-04-10 11:00:22.308554: +2026-04-10 11:00:22.311136: Epoch 27 +2026-04-10 11:00:22.313807: Current learning rate: 0.00994 +2026-04-10 11:02:13.809865: train_loss 0.0418 +2026-04-10 11:02:13.818237: val_loss 0.048 +2026-04-10 11:02:13.820875: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:02:13.823476: Epoch time: 111.5 s +2026-04-10 11:02:14.876439: +2026-04-10 11:02:14.878754: Epoch 28 +2026-04-10 11:02:14.880680: Current learning rate: 0.00994 +2026-04-10 11:03:56.721295: train_loss 0.0342 +2026-04-10 11:03:56.727570: val_loss 0.0493 +2026-04-10 11:03:56.730310: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:03:56.732131: Epoch time: 101.85 s +2026-04-10 11:03:57.807041: +2026-04-10 11:03:57.808430: Epoch 29 +2026-04-10 11:03:57.809698: Current learning rate: 0.00993 +2026-04-10 11:05:40.246538: train_loss 0.0476 +2026-04-10 11:05:40.255103: val_loss 0.0438 +2026-04-10 11:05:40.258319: Pseudo dice [0.0, 0.0, 0.0009, 0.0, 0.0, 0.0, 0.0] +2026-04-10 11:05:40.260223: Epoch time: 102.44 s +2026-04-10 11:05:40.264013: Yayy! New best EMA pseudo Dice: 0.0 +2026-04-10 11:05:42.955712: +2026-04-10 11:05:42.957043: Epoch 30 +2026-04-10 11:05:42.958296: Current learning rate: 0.00993 +2026-04-10 11:07:25.531519: train_loss 0.0343 +2026-04-10 11:07:25.538924: val_loss 0.0692 +2026-04-10 11:07:25.540858: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0145] +2026-04-10 11:07:25.542757: Epoch time: 102.58 s +2026-04-10 11:07:25.544307: Yayy! New best EMA pseudo Dice: 0.0002 +2026-04-10 11:07:28.385528: +2026-04-10 11:07:28.387807: Epoch 31 +2026-04-10 11:07:28.389305: Current learning rate: 0.00993 +2026-04-10 11:09:10.896757: train_loss 0.0407 +2026-04-10 11:09:10.903840: val_loss 0.0375 +2026-04-10 11:09:10.905701: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0395] +2026-04-10 11:09:10.907803: Epoch time: 102.51 s +2026-04-10 11:09:10.909340: Yayy! New best EMA pseudo Dice: 0.0008 +2026-04-10 11:09:13.571311: +2026-04-10 11:09:13.572806: Epoch 32 +2026-04-10 11:09:13.574121: Current learning rate: 0.00993 +2026-04-10 11:10:58.896994: train_loss 0.0312 +2026-04-10 11:10:58.904274: val_loss 0.0583 +2026-04-10 11:10:58.906093: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1485] +2026-04-10 11:10:58.909451: Epoch time: 105.33 s +2026-04-10 11:10:58.916713: Yayy! New best EMA pseudo Dice: 0.0028 +2026-04-10 11:11:01.795602: +2026-04-10 11:11:01.797059: Epoch 33 +2026-04-10 11:11:01.798476: Current learning rate: 0.00993 +2026-04-10 11:12:43.448759: train_loss 0.0417 +2026-04-10 11:12:43.456289: val_loss 0.0454 +2026-04-10 11:12:43.459275: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0771] +2026-04-10 11:12:43.462033: Epoch time: 101.66 s +2026-04-10 11:12:43.464437: Yayy! New best EMA pseudo Dice: 0.0036 +2026-04-10 11:12:46.158387: +2026-04-10 11:12:46.159820: Epoch 34 +2026-04-10 11:12:46.161171: Current learning rate: 0.00992 +2026-04-10 11:14:28.985381: train_loss 0.0418 +2026-04-10 11:14:28.991293: val_loss 0.0434 +2026-04-10 11:14:28.993286: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1215] +2026-04-10 11:14:28.995119: Epoch time: 102.83 s +2026-04-10 11:14:28.997166: Yayy! New best EMA pseudo Dice: 0.005 +2026-04-10 11:14:31.659427: +2026-04-10 11:14:31.661517: Epoch 35 +2026-04-10 11:14:31.663048: Current learning rate: 0.00992 +2026-04-10 11:16:15.273940: train_loss 0.0373 +2026-04-10 11:16:15.281101: val_loss 0.0811 +2026-04-10 11:16:15.283357: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1082] +2026-04-10 11:16:15.285921: Epoch time: 103.62 s +2026-04-10 11:16:15.287792: Yayy! New best EMA pseudo Dice: 0.006 +2026-04-10 11:16:18.099650: +2026-04-10 11:16:18.101051: Epoch 36 +2026-04-10 11:16:18.102267: Current learning rate: 0.00992 +2026-04-10 11:18:03.588171: train_loss 0.0288 +2026-04-10 11:18:03.594655: val_loss 0.034 +2026-04-10 11:18:03.596899: Pseudo dice [0.0, 0.0, 0.0001, 0.0, 0.0, 0.0, 0.1042] +2026-04-10 11:18:03.598904: Epoch time: 105.49 s +2026-04-10 11:18:03.601104: Yayy! New best EMA pseudo Dice: 0.0069 +2026-04-10 11:18:06.342755: +2026-04-10 11:18:06.344768: Epoch 37 +2026-04-10 11:18:06.346167: Current learning rate: 0.00992 +2026-04-10 11:19:48.114870: train_loss 0.0254 +2026-04-10 11:19:48.120858: val_loss 0.0467 +2026-04-10 11:19:48.123082: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1267] +2026-04-10 11:19:48.125525: Epoch time: 101.77 s +2026-04-10 11:19:48.127094: Yayy! New best EMA pseudo Dice: 0.008 +2026-04-10 11:19:50.812093: +2026-04-10 11:19:50.814201: Epoch 38 +2026-04-10 11:19:50.815726: Current learning rate: 0.00991 +2026-04-10 11:21:33.025582: train_loss 0.0263 +2026-04-10 11:21:33.034829: val_loss 0.0391 +2026-04-10 11:21:33.036884: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1372] +2026-04-10 11:21:33.039177: Epoch time: 102.22 s +2026-04-10 11:21:33.040796: Yayy! New best EMA pseudo Dice: 0.0092 +2026-04-10 11:21:35.758034: +2026-04-10 11:21:35.759399: Epoch 39 +2026-04-10 11:21:35.760859: Current learning rate: 0.00991 +2026-04-10 11:23:18.570616: train_loss 0.0228 +2026-04-10 11:23:18.576222: val_loss 0.0494 +2026-04-10 11:23:18.578569: Pseudo dice [0.0, 0.0, 0.0005, 0.0, 0.0, 0.0, 0.1905] +2026-04-10 11:23:18.580600: Epoch time: 102.81 s +2026-04-10 11:23:18.582478: Yayy! New best EMA pseudo Dice: 0.011 +2026-04-10 11:23:21.370025: +2026-04-10 11:23:21.371445: Epoch 40 +2026-04-10 11:23:21.372737: Current learning rate: 0.00991 +2026-04-10 11:25:03.450552: train_loss 0.0422 +2026-04-10 11:25:03.458231: val_loss 0.0512 +2026-04-10 11:25:03.460063: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1831] +2026-04-10 11:25:03.462308: Epoch time: 102.08 s +2026-04-10 11:25:03.464345: Yayy! New best EMA pseudo Dice: 0.0125 +2026-04-10 11:25:06.187932: +2026-04-10 11:25:06.189617: Epoch 41 +2026-04-10 11:25:06.190797: Current learning rate: 0.00991 +2026-04-10 11:26:48.416239: train_loss 0.0236 +2026-04-10 11:26:48.423347: val_loss 0.0579 +2026-04-10 11:26:48.425991: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2504] +2026-04-10 11:26:48.428767: Epoch time: 102.23 s +2026-04-10 11:26:48.430514: Yayy! New best EMA pseudo Dice: 0.0149 +2026-04-10 11:26:51.058156: +2026-04-10 11:26:51.059896: Epoch 42 +2026-04-10 11:26:51.061152: Current learning rate: 0.00991 +2026-04-10 11:28:33.185229: train_loss 0.0255 +2026-04-10 11:28:33.190778: val_loss 0.0218 +2026-04-10 11:28:33.192693: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1956] +2026-04-10 11:28:33.194939: Epoch time: 102.13 s +2026-04-10 11:28:33.196590: Yayy! New best EMA pseudo Dice: 0.0162 +2026-04-10 11:28:35.719411: +2026-04-10 11:28:35.720882: Epoch 43 +2026-04-10 11:28:35.722125: Current learning rate: 0.0099 +2026-04-10 11:30:17.933460: train_loss 0.0292 +2026-04-10 11:30:17.939785: val_loss 0.0378 +2026-04-10 11:30:17.941477: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1047] +2026-04-10 11:30:17.943595: Epoch time: 102.22 s +2026-04-10 11:30:18.999465: +2026-04-10 11:30:19.002739: Epoch 44 +2026-04-10 11:30:19.005033: Current learning rate: 0.0099 +2026-04-10 11:32:00.878521: train_loss 0.0207 +2026-04-10 11:32:00.885440: val_loss 0.0174 +2026-04-10 11:32:00.887163: Pseudo dice [0.0, 0.0, 0.0001, 0.0, 0.0, 0.0, 0.3286] +2026-04-10 11:32:00.890143: Epoch time: 101.88 s +2026-04-10 11:32:00.892128: Yayy! New best EMA pseudo Dice: 0.0191 +2026-04-10 11:32:03.549410: +2026-04-10 11:32:03.551217: Epoch 45 +2026-04-10 11:32:03.552526: Current learning rate: 0.0099 +2026-04-10 11:33:45.475679: train_loss 0.0177 +2026-04-10 11:33:45.482250: val_loss 0.0262 +2026-04-10 11:33:45.484355: Pseudo dice [0.0, 0.0, 0.0006, 0.0, 0.0, 0.0, 0.499] +2026-04-10 11:33:45.486271: Epoch time: 101.93 s +2026-04-10 11:33:45.488103: Yayy! New best EMA pseudo Dice: 0.0244 +2026-04-10 11:33:48.272377: +2026-04-10 11:33:48.274379: Epoch 46 +2026-04-10 11:33:48.275756: Current learning rate: 0.0099 +2026-04-10 11:35:30.539009: train_loss 0.0042 +2026-04-10 11:35:30.547651: val_loss 0.0084 +2026-04-10 11:35:30.549375: Pseudo dice [0.0, 0.0, 0.0464, 0.0, 0.0, 0.0, 0.2298] +2026-04-10 11:35:30.551785: Epoch time: 102.27 s +2026-04-10 11:35:30.554095: Yayy! New best EMA pseudo Dice: 0.0259 +2026-04-10 11:35:33.300189: +2026-04-10 11:35:33.302051: Epoch 47 +2026-04-10 11:35:33.303294: Current learning rate: 0.00989 +2026-04-10 11:37:15.235949: train_loss 0.0111 +2026-04-10 11:37:15.245374: val_loss 0.0472 +2026-04-10 11:37:15.247762: Pseudo dice [0.0, 0.0, 0.0543, 0.0, 0.0, 0.0, 0.3042] +2026-04-10 11:37:15.249909: Epoch time: 101.94 s +2026-04-10 11:37:15.252018: Yayy! New best EMA pseudo Dice: 0.0284 +2026-04-10 11:37:18.069617: +2026-04-10 11:37:18.071419: Epoch 48 +2026-04-10 11:37:18.072787: Current learning rate: 0.00989 +2026-04-10 11:39:00.498114: train_loss 0.0225 +2026-04-10 11:39:00.504729: val_loss 0.0092 +2026-04-10 11:39:00.506932: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5254] +2026-04-10 11:39:00.508943: Epoch time: 102.43 s +2026-04-10 11:39:00.510647: Yayy! New best EMA pseudo Dice: 0.0331 +2026-04-10 11:39:03.203879: +2026-04-10 11:39:03.205213: Epoch 49 +2026-04-10 11:39:03.206454: Current learning rate: 0.00989 +2026-04-10 11:40:46.171682: train_loss 0.013 +2026-04-10 11:40:46.176859: val_loss 0.0279 +2026-04-10 11:40:46.179316: Pseudo dice [0.0, 0.0, 0.0343, 0.0, 0.0, 0.0, 0.3242] +2026-04-10 11:40:46.182359: Epoch time: 102.97 s +2026-04-10 11:40:47.804645: Yayy! New best EMA pseudo Dice: 0.0349 +2026-04-10 11:40:50.448418: +2026-04-10 11:40:50.450456: Epoch 50 +2026-04-10 11:40:50.451734: Current learning rate: 0.00989 +2026-04-10 11:42:32.283137: train_loss 0.0042 +2026-04-10 11:42:32.289376: val_loss 0.0304 +2026-04-10 11:42:32.291044: Pseudo dice [0.0, 0.0, 0.1294, 0.0, 0.0, 0.0, 0.2955] +2026-04-10 11:42:32.292760: Epoch time: 101.84 s +2026-04-10 11:42:32.294644: Yayy! New best EMA pseudo Dice: 0.0375 +2026-04-10 11:42:34.995456: +2026-04-10 11:42:34.997002: Epoch 51 +2026-04-10 11:42:34.998371: Current learning rate: 0.00989 +2026-04-10 11:44:16.725544: train_loss 0.0111 +2026-04-10 11:44:16.731436: val_loss 0.0374 +2026-04-10 11:44:16.733415: Pseudo dice [0.0, 0.0, 0.1781, 0.0, 0.0, 0.0, 0.3769] +2026-04-10 11:44:16.735273: Epoch time: 101.73 s +2026-04-10 11:44:16.736949: Yayy! New best EMA pseudo Dice: 0.0416 +2026-04-10 11:44:19.584631: +2026-04-10 11:44:19.586803: Epoch 52 +2026-04-10 11:44:19.588205: Current learning rate: 0.00988 +2026-04-10 11:46:01.584542: train_loss 0.0047 +2026-04-10 11:46:01.590157: val_loss 0.0318 +2026-04-10 11:46:01.591962: Pseudo dice [0.0, 0.0, 0.2108, 0.0, 0.0, 0.0, 0.2983] +2026-04-10 11:46:01.594409: Epoch time: 102.0 s +2026-04-10 11:46:01.596214: Yayy! New best EMA pseudo Dice: 0.0448 +2026-04-10 11:46:05.429602: +2026-04-10 11:46:05.430979: Epoch 53 +2026-04-10 11:46:05.432381: Current learning rate: 0.00988 +2026-04-10 11:47:47.296852: train_loss 0.0164 +2026-04-10 11:47:47.309994: val_loss 0.0363 +2026-04-10 11:47:47.311837: Pseudo dice [0.0, 0.0, 0.0086, 0.0, 0.0, 0.0, 0.3025] +2026-04-10 11:47:47.314054: Epoch time: 101.87 s +2026-04-10 11:47:48.396817: +2026-04-10 11:47:48.398566: Epoch 54 +2026-04-10 11:47:48.399831: Current learning rate: 0.00988 +2026-04-10 11:49:30.617007: train_loss 0.0043 +2026-04-10 11:49:30.623176: val_loss 0.0054 +2026-04-10 11:49:30.624631: Pseudo dice [0.0, 0.0, 0.2111, 0.0, 0.0, 0.0, 0.2537] +2026-04-10 11:49:30.627730: Epoch time: 102.22 s +2026-04-10 11:49:30.630123: Yayy! New best EMA pseudo Dice: 0.0469 +2026-04-10 11:49:33.084384: +2026-04-10 11:49:33.086316: Epoch 55 +2026-04-10 11:49:33.087696: Current learning rate: 0.00988 +2026-04-10 11:51:15.027168: train_loss -0.0031 +2026-04-10 11:51:15.034820: val_loss 0.011 +2026-04-10 11:51:15.036687: Pseudo dice [0.0, 0.0, 0.2336, 0.0, 0.0, 0.0, 0.3735] +2026-04-10 11:51:15.038800: Epoch time: 101.95 s +2026-04-10 11:51:15.040749: Yayy! New best EMA pseudo Dice: 0.0509 +2026-04-10 11:51:17.737293: +2026-04-10 11:51:17.739320: Epoch 56 +2026-04-10 11:51:17.740661: Current learning rate: 0.00987 +2026-04-10 11:52:59.550953: train_loss -0.0025 +2026-04-10 11:52:59.557917: val_loss 0.0149 +2026-04-10 11:52:59.560071: Pseudo dice [0.0, 0.0, 0.2147, 0.0, 0.0, 0.0039, 0.2636] +2026-04-10 11:52:59.562644: Epoch time: 101.82 s +2026-04-10 11:52:59.564852: Yayy! New best EMA pseudo Dice: 0.0527 +2026-04-10 11:53:02.065292: +2026-04-10 11:53:02.068374: Epoch 57 +2026-04-10 11:53:02.069671: Current learning rate: 0.00987 +2026-04-10 11:54:44.671516: train_loss 0.0043 +2026-04-10 11:54:44.679067: val_loss 0.0082 +2026-04-10 11:54:44.681321: Pseudo dice [0.0, 0.0, 0.1557, 0.0, 0.0, 0.0198, 0.2706] +2026-04-10 11:54:44.683671: Epoch time: 102.61 s +2026-04-10 11:54:44.685467: Yayy! New best EMA pseudo Dice: 0.0538 +2026-04-10 11:54:47.433733: +2026-04-10 11:54:47.435548: Epoch 58 +2026-04-10 11:54:47.436902: Current learning rate: 0.00987 +2026-04-10 11:56:30.230681: train_loss 0.0012 +2026-04-10 11:56:30.236453: val_loss 0.0163 +2026-04-10 11:56:30.238337: Pseudo dice [0.0, 0.0, 0.2341, 0.0, 0.0, 0.0014, 0.3665] +2026-04-10 11:56:30.240537: Epoch time: 102.8 s +2026-04-10 11:56:30.242780: Yayy! New best EMA pseudo Dice: 0.057 +2026-04-10 11:56:32.802691: +2026-04-10 11:56:32.804067: Epoch 59 +2026-04-10 11:56:32.805470: Current learning rate: 0.00987 +2026-04-10 11:58:14.615730: train_loss 0.0009 +2026-04-10 11:58:14.622617: val_loss 0.0169 +2026-04-10 11:58:14.624584: Pseudo dice [0.0, 0.0, 0.0922, 0.0, 0.0, 0.1505, 0.1851] +2026-04-10 11:58:14.627234: Epoch time: 101.82 s +2026-04-10 11:58:14.628846: Yayy! New best EMA pseudo Dice: 0.0574 +2026-04-10 11:58:17.171965: +2026-04-10 11:58:17.174026: Epoch 60 +2026-04-10 11:58:17.175529: Current learning rate: 0.00986 +2026-04-10 11:59:59.457979: train_loss -0.0113 +2026-04-10 11:59:59.470169: val_loss -0.029 +2026-04-10 11:59:59.471964: Pseudo dice [0.0, 0.0, 0.2497, 0.0, 0.0, 0.4739, 0.466] +2026-04-10 11:59:59.475884: Epoch time: 102.29 s +2026-04-10 11:59:59.477959: Yayy! New best EMA pseudo Dice: 0.0687 +2026-04-10 12:00:01.960431: +2026-04-10 12:00:01.962277: Epoch 61 +2026-04-10 12:00:01.963508: Current learning rate: 0.00986 +2026-04-10 12:01:44.245857: train_loss -0.0148 +2026-04-10 12:01:44.251028: val_loss 0.001 +2026-04-10 12:01:44.252691: Pseudo dice [0.0, 0.0, 0.3806, 0.0, 0.0, 0.1309, 0.3826] +2026-04-10 12:01:44.254993: Epoch time: 102.29 s +2026-04-10 12:01:44.256708: Yayy! New best EMA pseudo Dice: 0.0746 +2026-04-10 12:01:47.006565: +2026-04-10 12:01:47.008489: Epoch 62 +2026-04-10 12:01:47.009836: Current learning rate: 0.00986 +2026-04-10 12:03:29.174414: train_loss -0.002 +2026-04-10 12:03:29.180122: val_loss 0.0124 +2026-04-10 12:03:29.181866: Pseudo dice [0.0, 0.0, 0.3166, 0.0, 0.0, 0.1076, 0.6152] +2026-04-10 12:03:29.184690: Epoch time: 102.17 s +2026-04-10 12:03:29.187061: Yayy! New best EMA pseudo Dice: 0.082 +2026-04-10 12:03:31.720744: +2026-04-10 12:03:31.723144: Epoch 63 +2026-04-10 12:03:31.724514: Current learning rate: 0.00986 +2026-04-10 12:05:13.513855: train_loss -0.0235 +2026-04-10 12:05:13.522038: val_loss 0.0067 +2026-04-10 12:05:13.523584: Pseudo dice [0.0, 0.0, 0.2635, 0.0, 0.0, 0.228, 0.2647] +2026-04-10 12:05:13.526650: Epoch time: 101.8 s +2026-04-10 12:05:13.528255: Yayy! New best EMA pseudo Dice: 0.0846 +2026-04-10 12:05:16.290679: +2026-04-10 12:05:16.292416: Epoch 64 +2026-04-10 12:05:16.293843: Current learning rate: 0.00986 +2026-04-10 12:06:59.026404: train_loss -0.0112 +2026-04-10 12:06:59.033236: val_loss 0.0477 +2026-04-10 12:06:59.034894: Pseudo dice [0.0, 0.0, 0.067, 0.0, 0.0, 0.0827, 0.4902] +2026-04-10 12:06:59.039057: Epoch time: 102.74 s +2026-04-10 12:06:59.040525: Yayy! New best EMA pseudo Dice: 0.0853 +2026-04-10 12:07:01.730196: +2026-04-10 12:07:01.732166: Epoch 65 +2026-04-10 12:07:01.733532: Current learning rate: 0.00985 +2026-04-10 12:08:44.312191: train_loss -0.0244 +2026-04-10 12:08:44.328942: val_loss -0.0291 +2026-04-10 12:08:44.330598: Pseudo dice [0.0, 0.0, 0.2293, 0.0, 0.0, 0.3745, 0.6106] +2026-04-10 12:08:44.335468: Epoch time: 102.59 s +2026-04-10 12:08:44.337723: Yayy! New best EMA pseudo Dice: 0.0941 +2026-04-10 12:08:46.837963: +2026-04-10 12:08:46.839786: Epoch 66 +2026-04-10 12:08:46.841112: Current learning rate: 0.00985 +2026-04-10 12:10:28.588063: train_loss -0.0247 +2026-04-10 12:10:28.593760: val_loss 0.0265 +2026-04-10 12:10:28.595833: Pseudo dice [0.0, 0.0, 0.2864, 0.0, 0.0, 0.1271, 0.3363] +2026-04-10 12:10:28.598552: Epoch time: 101.75 s +2026-04-10 12:10:28.600984: Yayy! New best EMA pseudo Dice: 0.0954 +2026-04-10 12:10:31.159987: +2026-04-10 12:10:31.161454: Epoch 67 +2026-04-10 12:10:31.162841: Current learning rate: 0.00985 +2026-04-10 12:12:13.173521: train_loss -0.0224 +2026-04-10 12:12:13.179327: val_loss -0.0063 +2026-04-10 12:12:13.181442: Pseudo dice [0.0, 0.0, 0.3384, 0.0, 0.0, 0.2898, 0.4273] +2026-04-10 12:12:13.183775: Epoch time: 102.02 s +2026-04-10 12:12:13.185855: Yayy! New best EMA pseudo Dice: 0.1009 +2026-04-10 12:12:15.927570: +2026-04-10 12:12:15.929543: Epoch 68 +2026-04-10 12:12:15.930959: Current learning rate: 0.00985 +2026-04-10 12:13:57.476667: train_loss -0.0171 +2026-04-10 12:13:57.485284: val_loss 0.0018 +2026-04-10 12:13:57.487031: Pseudo dice [0.0, 0.0, 0.1885, 0.0, 0.0, 0.1392, 0.2612] +2026-04-10 12:13:57.488921: Epoch time: 101.55 s +2026-04-10 12:13:59.662167: +2026-04-10 12:13:59.663803: Epoch 69 +2026-04-10 12:13:59.665015: Current learning rate: 0.00984 +2026-04-10 12:15:41.503460: train_loss -0.0137 +2026-04-10 12:15:41.508894: val_loss 0.0608 +2026-04-10 12:15:41.510857: Pseudo dice [0.0, 0.0, 0.2761, 0.0, 0.0, 0.3616, 0.3036] +2026-04-10 12:15:41.512819: Epoch time: 101.84 s +2026-04-10 12:15:41.514553: Yayy! New best EMA pseudo Dice: 0.1028 +2026-04-10 12:15:44.258647: +2026-04-10 12:15:44.260332: Epoch 70 +2026-04-10 12:15:44.261594: Current learning rate: 0.00984 +2026-04-10 12:17:26.095928: train_loss -0.0165 +2026-04-10 12:17:26.102263: val_loss -0.0339 +2026-04-10 12:17:26.104439: Pseudo dice [0.0, 0.0, 0.4392, 0.0, 0.0, 0.2786, 0.3463] +2026-04-10 12:17:26.106626: Epoch time: 101.84 s +2026-04-10 12:17:26.108541: Yayy! New best EMA pseudo Dice: 0.1077 +2026-04-10 12:17:28.812154: +2026-04-10 12:17:28.814033: Epoch 71 +2026-04-10 12:17:28.815395: Current learning rate: 0.00984 +2026-04-10 12:19:10.946629: train_loss -0.028 +2026-04-10 12:19:10.952267: val_loss 0.0051 +2026-04-10 12:19:10.954940: Pseudo dice [0.0, 0.0, 0.4464, 0.0, 0.0, 0.0733, 0.3573] +2026-04-10 12:19:10.957901: Epoch time: 102.14 s +2026-04-10 12:19:10.960063: Yayy! New best EMA pseudo Dice: 0.1094 +2026-04-10 12:19:13.722075: +2026-04-10 12:19:13.723642: Epoch 72 +2026-04-10 12:19:13.724881: Current learning rate: 0.00984 +2026-04-10 12:20:55.655220: train_loss -0.0198 +2026-04-10 12:20:55.661921: val_loss -0.0168 +2026-04-10 12:20:55.663839: Pseudo dice [0.0, 0.0, 0.2375, 0.0, 0.0, 0.2198, 0.2729] +2026-04-10 12:20:55.666063: Epoch time: 101.94 s +2026-04-10 12:20:56.770428: +2026-04-10 12:20:56.772982: Epoch 73 +2026-04-10 12:20:56.778551: Current learning rate: 0.00984 +2026-04-10 12:22:38.622392: train_loss -0.0243 +2026-04-10 12:22:38.628340: val_loss -0.0115 +2026-04-10 12:22:38.630244: Pseudo dice [0.0, 0.0, 0.3692, 0.0, 0.0, 0.1405, 0.5439] +2026-04-10 12:22:38.632709: Epoch time: 101.86 s +2026-04-10 12:22:38.635019: Yayy! New best EMA pseudo Dice: 0.1131 +2026-04-10 12:22:41.441172: +2026-04-10 12:22:41.451676: Epoch 74 +2026-04-10 12:22:41.453036: Current learning rate: 0.00983 +2026-04-10 12:24:23.215022: train_loss -0.0211 +2026-04-10 12:24:23.221128: val_loss -0.0069 +2026-04-10 12:24:23.222948: Pseudo dice [0.0, 0.0, 0.293, 0.0, 0.0, 0.2098, 0.4361] +2026-04-10 12:24:23.225136: Epoch time: 101.78 s +2026-04-10 12:24:23.228128: Yayy! New best EMA pseudo Dice: 0.1152 +2026-04-10 12:24:26.031323: +2026-04-10 12:24:26.033463: Epoch 75 +2026-04-10 12:24:26.034952: Current learning rate: 0.00983 +2026-04-10 12:26:08.110735: train_loss -0.0398 +2026-04-10 12:26:08.116786: val_loss -0.0132 +2026-04-10 12:26:08.118240: Pseudo dice [0.0, 0.0, 0.4331, 0.0, 0.0, 0.2696, 0.7171] +2026-04-10 12:26:08.120302: Epoch time: 102.08 s +2026-04-10 12:26:08.121716: Yayy! New best EMA pseudo Dice: 0.124 +2026-04-10 12:26:10.775758: +2026-04-10 12:26:10.777605: Epoch 76 +2026-04-10 12:26:10.778902: Current learning rate: 0.00983 +2026-04-10 12:27:52.785425: train_loss -0.0296 +2026-04-10 12:27:52.791210: val_loss 0.0055 +2026-04-10 12:27:52.792929: Pseudo dice [0.0, 0.0, 0.1762, 0.0, 0.0, 0.1317, 0.2538] +2026-04-10 12:27:52.795181: Epoch time: 102.01 s +2026-04-10 12:27:53.929608: +2026-04-10 12:27:53.931396: Epoch 77 +2026-04-10 12:27:53.933005: Current learning rate: 0.00983 +2026-04-10 12:29:36.020771: train_loss -0.0375 +2026-04-10 12:29:36.028596: val_loss 0.0073 +2026-04-10 12:29:36.030026: Pseudo dice [0.0, 0.0, 0.318, 0.0, 0.0, 0.075, 0.3823] +2026-04-10 12:29:36.032165: Epoch time: 102.09 s +2026-04-10 12:29:37.155610: +2026-04-10 12:29:37.157271: Epoch 78 +2026-04-10 12:29:37.158580: Current learning rate: 0.00982 +2026-04-10 12:31:19.443370: train_loss -0.0547 +2026-04-10 12:31:19.455996: val_loss 0.0301 +2026-04-10 12:31:19.463027: Pseudo dice [0.0, 0.0, 0.0104, 0.0, 0.0, 0.0589, 0.27] +2026-04-10 12:31:19.468190: Epoch time: 102.29 s +2026-04-10 12:31:20.585784: +2026-04-10 12:31:20.588439: Epoch 79 +2026-04-10 12:31:20.590675: Current learning rate: 0.00982 +2026-04-10 12:33:02.886845: train_loss -0.0266 +2026-04-10 12:33:02.892247: val_loss -0.0252 +2026-04-10 12:33:02.894114: Pseudo dice [0.0, 0.0, 0.5163, 0.0, 0.0, 0.1512, 0.3075] +2026-04-10 12:33:02.896043: Epoch time: 102.3 s +2026-04-10 12:33:04.018774: +2026-04-10 12:33:04.020504: Epoch 80 +2026-04-10 12:33:04.022701: Current learning rate: 0.00982 +2026-04-10 12:34:46.365757: train_loss -0.0259 +2026-04-10 12:34:46.373312: val_loss -0.033 +2026-04-10 12:34:46.375204: Pseudo dice [0.0, 0.0, 0.127, 0.0, 0.0, 0.4105, 0.6102] +2026-04-10 12:34:46.378554: Epoch time: 102.35 s +2026-04-10 12:34:47.517364: +2026-04-10 12:34:47.519461: Epoch 81 +2026-04-10 12:34:47.521208: Current learning rate: 0.00982 +2026-04-10 12:36:29.411841: train_loss -0.0403 +2026-04-10 12:36:29.419456: val_loss 0.0244 +2026-04-10 12:36:29.422075: Pseudo dice [0.0, 0.0, 0.1255, 0.0, 0.0, 0.1357, 0.296] +2026-04-10 12:36:29.424462: Epoch time: 101.9 s +2026-04-10 12:36:30.551188: +2026-04-10 12:36:30.552926: Epoch 82 +2026-04-10 12:36:30.554449: Current learning rate: 0.00982 +2026-04-10 12:38:12.330895: train_loss -0.0457 +2026-04-10 12:38:12.336878: val_loss -0.0392 +2026-04-10 12:38:12.338732: Pseudo dice [0.0, 0.0, 0.3857, 0.0, 0.0, 0.4571, 0.6954] +2026-04-10 12:38:12.341051: Epoch time: 101.78 s +2026-04-10 12:38:12.342599: Yayy! New best EMA pseudo Dice: 0.1258 +2026-04-10 12:38:14.862833: +2026-04-10 12:38:14.865160: Epoch 83 +2026-04-10 12:38:14.866396: Current learning rate: 0.00981 +2026-04-10 12:39:56.762601: train_loss -0.053 +2026-04-10 12:39:56.767388: val_loss -0.0252 +2026-04-10 12:39:56.769238: Pseudo dice [0.0, 0.0, 0.2206, 0.0, 0.0, 0.1214, 0.482] +2026-04-10 12:39:56.771064: Epoch time: 101.9 s +2026-04-10 12:39:57.815376: +2026-04-10 12:39:57.817524: Epoch 84 +2026-04-10 12:39:57.819554: Current learning rate: 0.00981 +2026-04-10 12:41:39.943946: train_loss -0.0291 +2026-04-10 12:41:39.950521: val_loss -0.0447 +2026-04-10 12:41:39.952251: Pseudo dice [0.0, 0.0, 0.3583, 0.0, 0.0, 0.3787, 0.5221] +2026-04-10 12:41:39.955125: Epoch time: 102.13 s +2026-04-10 12:41:39.957301: Yayy! New best EMA pseudo Dice: 0.1305 +2026-04-10 12:41:42.562167: +2026-04-10 12:41:42.564011: Epoch 85 +2026-04-10 12:41:42.565270: Current learning rate: 0.00981 +2026-04-10 12:43:24.791391: train_loss -0.0513 +2026-04-10 12:43:24.797443: val_loss -0.0439 +2026-04-10 12:43:24.799205: Pseudo dice [0.0, 0.1179, 0.526, 0.0, 0.0, 0.3448, 0.5187] +2026-04-10 12:43:24.802566: Epoch time: 102.23 s +2026-04-10 12:43:24.804279: Yayy! New best EMA pseudo Dice: 0.139 +2026-04-10 12:43:27.601002: +2026-04-10 12:43:27.602674: Epoch 86 +2026-04-10 12:43:27.603969: Current learning rate: 0.00981 +2026-04-10 12:45:09.324139: train_loss -0.0452 +2026-04-10 12:45:09.330103: val_loss -0.0233 +2026-04-10 12:45:09.331872: Pseudo dice [0.0, 0.2155, 0.569, 0.0, 0.0, 0.1058, 0.4863] +2026-04-10 12:45:09.334274: Epoch time: 101.73 s +2026-04-10 12:45:09.336336: Yayy! New best EMA pseudo Dice: 0.1448 +2026-04-10 12:45:13.097454: +2026-04-10 12:45:13.099145: Epoch 87 +2026-04-10 12:45:13.100398: Current learning rate: 0.0098 +2026-04-10 12:46:55.586651: train_loss -0.0645 +2026-04-10 12:46:55.592456: val_loss -0.043 +2026-04-10 12:46:55.594013: Pseudo dice [0.0556, 0.2205, 0.5193, 0.0, 0.0, 0.1748, 0.5672] +2026-04-10 12:46:55.596153: Epoch time: 102.49 s +2026-04-10 12:46:55.598847: Yayy! New best EMA pseudo Dice: 0.1523 +2026-04-10 12:46:58.355752: +2026-04-10 12:46:58.357435: Epoch 88 +2026-04-10 12:46:58.359839: Current learning rate: 0.0098 +2026-04-10 12:48:40.546870: train_loss -0.0559 +2026-04-10 12:48:40.552793: val_loss -0.0521 +2026-04-10 12:48:40.554710: Pseudo dice [0.3371, 0.4792, 0.3902, 0.0, 0.0, 0.2304, 0.5999] +2026-04-10 12:48:40.556522: Epoch time: 102.19 s +2026-04-10 12:48:40.558465: Yayy! New best EMA pseudo Dice: 0.1661 +2026-04-10 12:48:43.294074: +2026-04-10 12:48:43.295928: Epoch 89 +2026-04-10 12:48:43.297416: Current learning rate: 0.0098 +2026-04-10 12:50:25.318600: train_loss -0.0451 +2026-04-10 12:50:25.325577: val_loss 0.0381 +2026-04-10 12:50:25.327446: Pseudo dice [0.0943, 0.243, 0.2335, 0.0, 0.0, 0.0743, 0.3937] +2026-04-10 12:50:25.330161: Epoch time: 102.03 s +2026-04-10 12:50:26.379822: +2026-04-10 12:50:26.381293: Epoch 90 +2026-04-10 12:50:26.382679: Current learning rate: 0.0098 +2026-04-10 12:52:08.605551: train_loss -0.0501 +2026-04-10 12:52:08.612088: val_loss -0.0324 +2026-04-10 12:52:08.613758: Pseudo dice [0.1036, 0.8311, 0.4679, 0.0, 0.0, 0.1293, 0.3029] +2026-04-10 12:52:08.615913: Epoch time: 102.23 s +2026-04-10 12:52:08.618275: Yayy! New best EMA pseudo Dice: 0.1741 +2026-04-10 12:52:11.378495: +2026-04-10 12:52:11.383401: Epoch 91 +2026-04-10 12:52:11.384639: Current learning rate: 0.0098 +2026-04-10 12:53:53.174030: train_loss -0.0509 +2026-04-10 12:53:53.180182: val_loss -0.0465 +2026-04-10 12:53:53.181972: Pseudo dice [0.281, 0.7251, 0.4238, 0.0, 0.0, 0.1729, 0.6041] +2026-04-10 12:53:53.184414: Epoch time: 101.8 s +2026-04-10 12:53:53.186019: Yayy! New best EMA pseudo Dice: 0.1882 +2026-04-10 12:53:55.537701: +2026-04-10 12:53:55.539565: Epoch 92 +2026-04-10 12:53:55.540855: Current learning rate: 0.00979 +2026-04-10 12:55:37.218265: train_loss -0.0557 +2026-04-10 12:55:37.223964: val_loss -0.069 +2026-04-10 12:55:37.225541: Pseudo dice [0.2698, 0.1615, 0.3894, 0.0, 0.0, 0.1675, 0.5721] +2026-04-10 12:55:37.227494: Epoch time: 101.68 s +2026-04-10 12:55:37.231019: Yayy! New best EMA pseudo Dice: 0.1917 +2026-04-10 12:55:39.821884: +2026-04-10 12:55:39.823796: Epoch 93 +2026-04-10 12:55:39.825150: Current learning rate: 0.00979 +2026-04-10 12:57:21.938874: train_loss -0.0598 +2026-04-10 12:57:21.944699: val_loss -0.0223 +2026-04-10 12:57:21.946395: Pseudo dice [0.2126, 0.6981, 0.4912, 0.0, 0.0, 0.0921, 0.4862] +2026-04-10 12:57:21.949598: Epoch time: 102.12 s +2026-04-10 12:57:21.952628: Yayy! New best EMA pseudo Dice: 0.2008 +2026-04-10 12:57:24.388597: +2026-04-10 12:57:24.390587: Epoch 94 +2026-04-10 12:57:24.391916: Current learning rate: 0.00979 +2026-04-10 12:59:06.305110: train_loss -0.0575 +2026-04-10 12:59:06.312000: val_loss -0.0368 +2026-04-10 12:59:06.313910: Pseudo dice [0.286, 0.5341, 0.3394, 0.0, 0.0, 0.2127, 0.4683] +2026-04-10 12:59:06.315856: Epoch time: 101.92 s +2026-04-10 12:59:06.317671: Yayy! New best EMA pseudo Dice: 0.207 +2026-04-10 12:59:09.060389: +2026-04-10 12:59:09.062494: Epoch 95 +2026-04-10 12:59:09.063889: Current learning rate: 0.00979 +2026-04-10 13:00:50.737779: train_loss -0.0732 +2026-04-10 13:00:50.747180: val_loss 0.0079 +2026-04-10 13:00:50.750468: Pseudo dice [0.1067, 0.1282, 0.4246, 0.0, 0.0, 0.0727, 0.5654] +2026-04-10 13:00:50.753107: Epoch time: 101.68 s +2026-04-10 13:00:51.804928: +2026-04-10 13:00:51.806351: Epoch 96 +2026-04-10 13:00:51.807624: Current learning rate: 0.00978 +2026-04-10 13:02:33.369110: train_loss -0.0575 +2026-04-10 13:02:33.375371: val_loss -0.0525 +2026-04-10 13:02:33.377050: Pseudo dice [0.151, 0.7623, 0.6179, 0.0, 0.0, 0.1049, 0.4648] +2026-04-10 13:02:33.379689: Epoch time: 101.57 s +2026-04-10 13:02:33.383580: Yayy! New best EMA pseudo Dice: 0.2144 +2026-04-10 13:02:36.109645: +2026-04-10 13:02:36.112126: Epoch 97 +2026-04-10 13:02:36.113658: Current learning rate: 0.00978 +2026-04-10 13:04:18.025042: train_loss -0.0521 +2026-04-10 13:04:18.031175: val_loss -0.0575 +2026-04-10 13:04:18.032623: Pseudo dice [0.1881, 0.5705, 0.2844, 0.0, 0.0, 0.1515, 0.6535] +2026-04-10 13:04:18.034536: Epoch time: 101.92 s +2026-04-10 13:04:18.036927: Yayy! New best EMA pseudo Dice: 0.2194 +2026-04-10 13:04:20.429909: +2026-04-10 13:04:20.431822: Epoch 98 +2026-04-10 13:04:20.433124: Current learning rate: 0.00978 +2026-04-10 13:06:02.963840: train_loss -0.0629 +2026-04-10 13:06:02.970209: val_loss -0.0402 +2026-04-10 13:06:02.972474: Pseudo dice [0.1551, 0.7278, 0.2392, 0.0, 0.0, 0.1238, 0.5869] +2026-04-10 13:06:02.975111: Epoch time: 102.54 s +2026-04-10 13:06:02.977393: Yayy! New best EMA pseudo Dice: 0.2236 +2026-04-10 13:06:05.682253: +2026-04-10 13:06:05.684144: Epoch 99 +2026-04-10 13:06:05.685485: Current learning rate: 0.00978 +2026-04-10 13:07:47.439422: train_loss -0.0629 +2026-04-10 13:07:47.447977: val_loss -0.0338 +2026-04-10 13:07:47.450458: Pseudo dice [0.0766, 0.4418, 0.4434, 0.0, 0.0, 0.2793, 0.3327] +2026-04-10 13:07:47.453429: Epoch time: 101.76 s +2026-04-10 13:07:49.099954: Yayy! New best EMA pseudo Dice: 0.2237 +2026-04-10 13:07:51.815492: +2026-04-10 13:07:51.817031: Epoch 100 +2026-04-10 13:07:51.818557: Current learning rate: 0.00977 +2026-04-10 13:09:33.998009: train_loss -0.0687 +2026-04-10 13:09:34.004325: val_loss -0.0422 +2026-04-10 13:09:34.006377: Pseudo dice [0.1504, 0.8624, 0.5678, 0.0, 0.0, 0.2594, 0.5915] +2026-04-10 13:09:34.009229: Epoch time: 102.19 s +2026-04-10 13:09:34.011953: Yayy! New best EMA pseudo Dice: 0.2361 +2026-04-10 13:09:36.741906: +2026-04-10 13:09:36.743722: Epoch 101 +2026-04-10 13:09:36.745119: Current learning rate: 0.00977 +2026-04-10 13:11:18.747984: train_loss -0.0686 +2026-04-10 13:11:18.753419: val_loss -0.064 +2026-04-10 13:11:18.756275: Pseudo dice [0.3104, 0.7243, 0.6015, 0.0, 0.0, 0.1966, 0.3579] +2026-04-10 13:11:18.758688: Epoch time: 102.01 s +2026-04-10 13:11:18.760944: Yayy! New best EMA pseudo Dice: 0.2438 +2026-04-10 13:11:21.358984: +2026-04-10 13:11:21.361665: Epoch 102 +2026-04-10 13:11:21.364453: Current learning rate: 0.00977 +2026-04-10 13:13:03.521755: train_loss -0.0635 +2026-04-10 13:13:03.527174: val_loss -0.0544 +2026-04-10 13:13:03.528811: Pseudo dice [0.0861, 0.5099, 0.5755, 0.0, 0.0, 0.1506, 0.5069] +2026-04-10 13:13:03.531431: Epoch time: 102.17 s +2026-04-10 13:13:03.533053: Yayy! New best EMA pseudo Dice: 0.2455 +2026-04-10 13:13:06.097689: +2026-04-10 13:13:06.099280: Epoch 103 +2026-04-10 13:13:06.100604: Current learning rate: 0.00977 +2026-04-10 13:14:48.462598: train_loss -0.078 +2026-04-10 13:14:48.468207: val_loss -0.0818 +2026-04-10 13:14:48.470192: Pseudo dice [0.4979, 0.5622, 0.6245, 0.0, 0.0, 0.3136, 0.4284] +2026-04-10 13:14:48.472410: Epoch time: 102.37 s +2026-04-10 13:14:48.474165: Yayy! New best EMA pseudo Dice: 0.2556 +2026-04-10 13:14:52.231591: +2026-04-10 13:14:52.233004: Epoch 104 +2026-04-10 13:14:52.234370: Current learning rate: 0.00977 +2026-04-10 13:16:34.387156: train_loss -0.0603 +2026-04-10 13:16:34.406412: val_loss -0.0401 +2026-04-10 13:16:34.408559: Pseudo dice [0.3489, 0.2486, 0.6006, 0.0, 0.0, 0.145, 0.5434] +2026-04-10 13:16:34.411710: Epoch time: 102.16 s +2026-04-10 13:16:34.414196: Yayy! New best EMA pseudo Dice: 0.257 +2026-04-10 13:16:36.860454: +2026-04-10 13:16:36.862445: Epoch 105 +2026-04-10 13:16:36.863741: Current learning rate: 0.00976 +2026-04-10 13:18:19.226142: train_loss -0.0772 +2026-04-10 13:18:19.232669: val_loss -0.0601 +2026-04-10 13:18:19.234685: Pseudo dice [0.2034, 0.1046, 0.5423, 0.0, 0.0, 0.3363, 0.6984] +2026-04-10 13:18:19.237063: Epoch time: 102.37 s +2026-04-10 13:18:19.238676: Yayy! New best EMA pseudo Dice: 0.2583 +2026-04-10 13:18:21.965967: +2026-04-10 13:18:21.967600: Epoch 106 +2026-04-10 13:18:21.968969: Current learning rate: 0.00976 +2026-04-10 13:20:04.411149: train_loss -0.0826 +2026-04-10 13:20:04.417058: val_loss -0.0597 +2026-04-10 13:20:04.419911: Pseudo dice [0.1362, 0.277, 0.5022, 0.0, 0.0, 0.1626, 0.502] +2026-04-10 13:20:04.423141: Epoch time: 102.45 s +2026-04-10 13:20:05.508495: +2026-04-10 13:20:05.510076: Epoch 107 +2026-04-10 13:20:05.511385: Current learning rate: 0.00976 +2026-04-10 13:21:47.401453: train_loss -0.0831 +2026-04-10 13:21:47.407253: val_loss -0.0438 +2026-04-10 13:21:47.409055: Pseudo dice [0.2125, 0.4807, 0.3894, 0.0, 0.0, 0.1445, 0.7279] +2026-04-10 13:21:47.411486: Epoch time: 101.9 s +2026-04-10 13:21:48.507297: +2026-04-10 13:21:48.509112: Epoch 108 +2026-04-10 13:21:48.510496: Current learning rate: 0.00976 +2026-04-10 13:23:30.690874: train_loss -0.0711 +2026-04-10 13:23:30.696106: val_loss -0.0715 +2026-04-10 13:23:30.699316: Pseudo dice [0.4938, 0.6785, 0.4638, 0.0, 0.0, 0.155, 0.3283] +2026-04-10 13:23:30.701740: Epoch time: 102.19 s +2026-04-10 13:23:30.703961: Yayy! New best EMA pseudo Dice: 0.262 +2026-04-10 13:23:33.147246: +2026-04-10 13:23:33.148670: Epoch 109 +2026-04-10 13:23:33.149890: Current learning rate: 0.00975 +2026-04-10 13:25:15.271392: train_loss -0.0727 +2026-04-10 13:25:15.277977: val_loss -0.0526 +2026-04-10 13:25:15.280381: Pseudo dice [0.0983, 0.152, 0.5484, 0.0, 0.0, 0.1099, 0.4258] +2026-04-10 13:25:15.282257: Epoch time: 102.13 s +2026-04-10 13:25:16.341464: +2026-04-10 13:25:16.343952: Epoch 110 +2026-04-10 13:25:16.345266: Current learning rate: 0.00975 +2026-04-10 13:26:58.400969: train_loss -0.084 +2026-04-10 13:26:58.406313: val_loss -0.0491 +2026-04-10 13:26:58.408459: Pseudo dice [0.4241, 0.6729, 0.5433, 0.0, 0.0, 0.2548, 0.5837] +2026-04-10 13:26:58.411559: Epoch time: 102.06 s +2026-04-10 13:26:58.413484: Yayy! New best EMA pseudo Dice: 0.2648 +2026-04-10 13:27:00.883153: +2026-04-10 13:27:00.885334: Epoch 111 +2026-04-10 13:27:00.886711: Current learning rate: 0.00975 +2026-04-10 13:28:43.939814: train_loss -0.0733 +2026-04-10 13:28:43.947474: val_loss -0.0424 +2026-04-10 13:28:43.950209: Pseudo dice [0.0641, 0.0455, 0.5575, 0.0, 0.0, 0.2673, 0.3679] +2026-04-10 13:28:43.952232: Epoch time: 103.06 s +2026-04-10 13:28:45.014220: +2026-04-10 13:28:45.016043: Epoch 112 +2026-04-10 13:28:45.017786: Current learning rate: 0.00975 +2026-04-10 13:30:26.643363: train_loss -0.0814 +2026-04-10 13:30:26.649500: val_loss -0.0665 +2026-04-10 13:30:26.651068: Pseudo dice [0.1336, 0.4788, 0.5409, 0.0, 0.0, 0.1037, 0.4651] +2026-04-10 13:30:26.653115: Epoch time: 101.63 s +2026-04-10 13:30:27.743920: +2026-04-10 13:30:27.746332: Epoch 113 +2026-04-10 13:30:27.748021: Current learning rate: 0.00975 +2026-04-10 13:32:10.153766: train_loss -0.0735 +2026-04-10 13:32:10.159717: val_loss -0.0186 +2026-04-10 13:32:10.161601: Pseudo dice [0.1711, 0.2486, 0.5051, 0.0, 0.0, 0.5024, 0.2738] +2026-04-10 13:32:10.163369: Epoch time: 102.41 s +2026-04-10 13:32:11.288528: +2026-04-10 13:32:11.290088: Epoch 114 +2026-04-10 13:32:11.291405: Current learning rate: 0.00974 +2026-04-10 13:33:53.442771: train_loss -0.0892 +2026-04-10 13:33:53.449821: val_loss -0.0536 +2026-04-10 13:33:53.452042: Pseudo dice [0.3054, 0.3404, 0.4763, 0.0, 0.0, 0.2035, 0.435] +2026-04-10 13:33:53.454530: Epoch time: 102.16 s +2026-04-10 13:33:54.512512: +2026-04-10 13:33:54.514153: Epoch 115 +2026-04-10 13:33:54.516402: Current learning rate: 0.00974 +2026-04-10 13:35:36.879408: train_loss -0.079 +2026-04-10 13:35:36.885484: val_loss -0.0114 +2026-04-10 13:35:36.887450: Pseudo dice [0.1617, 0.2917, 0.4533, 0.0, 0.0, 0.3248, 0.226] +2026-04-10 13:35:36.889489: Epoch time: 102.37 s +2026-04-10 13:35:37.960738: +2026-04-10 13:35:37.963220: Epoch 116 +2026-04-10 13:35:37.964777: Current learning rate: 0.00974 +2026-04-10 13:37:21.309862: train_loss -0.0852 +2026-04-10 13:37:21.362030: val_loss -0.0361 +2026-04-10 13:37:21.370976: Pseudo dice [0.231, 0.1141, 0.3516, 0.0, 0.0, 0.3099, 0.6212] +2026-04-10 13:37:21.377873: Epoch time: 103.35 s +2026-04-10 13:37:22.539917: +2026-04-10 13:37:22.557324: Epoch 117 +2026-04-10 13:37:22.563861: Current learning rate: 0.00974 +2026-04-10 13:39:04.120330: train_loss -0.0877 +2026-04-10 13:39:04.126181: val_loss -0.0321 +2026-04-10 13:39:04.127827: Pseudo dice [0.2702, 0.3021, 0.5461, 0.0, 0.0, 0.1146, 0.5687] +2026-04-10 13:39:04.130090: Epoch time: 101.58 s +2026-04-10 13:39:05.195518: +2026-04-10 13:39:05.201409: Epoch 118 +2026-04-10 13:39:05.206887: Current learning rate: 0.00973 +2026-04-10 13:40:46.985332: train_loss -0.0857 +2026-04-10 13:40:46.992653: val_loss -0.0746 +2026-04-10 13:40:46.995543: Pseudo dice [0.2882, 0.055, 0.6221, 0.0, 0.0, 0.1046, 0.4687] +2026-04-10 13:40:46.998195: Epoch time: 101.79 s +2026-04-10 13:40:48.069895: +2026-04-10 13:40:48.072718: Epoch 119 +2026-04-10 13:40:48.074757: Current learning rate: 0.00973 +2026-04-10 13:42:30.233850: train_loss -0.086 +2026-04-10 13:42:30.241039: val_loss -0.0633 +2026-04-10 13:42:30.242756: Pseudo dice [0.1998, 0.1798, 0.539, 0.0, 0.0, 0.2841, 0.5942] +2026-04-10 13:42:30.245121: Epoch time: 102.17 s +2026-04-10 13:42:31.346352: +2026-04-10 13:42:31.348521: Epoch 120 +2026-04-10 13:42:31.350500: Current learning rate: 0.00973 +2026-04-10 13:44:13.502266: train_loss -0.0768 +2026-04-10 13:44:13.507750: val_loss -0.0331 +2026-04-10 13:44:13.509233: Pseudo dice [0.2711, 0.7933, 0.4619, 0.0189, 0.0, 0.2044, 0.4263] +2026-04-10 13:44:13.511042: Epoch time: 102.16 s +2026-04-10 13:44:14.575643: +2026-04-10 13:44:14.577014: Epoch 121 +2026-04-10 13:44:14.578264: Current learning rate: 0.00973 +2026-04-10 13:45:56.651276: train_loss -0.0713 +2026-04-10 13:45:56.657744: val_loss -0.0319 +2026-04-10 13:45:56.659473: Pseudo dice [0.2167, 0.7607, 0.4033, 0.0, 0.0, 0.5678, 0.3446] +2026-04-10 13:45:56.661482: Epoch time: 102.08 s +2026-04-10 13:45:57.735414: +2026-04-10 13:45:57.737110: Epoch 122 +2026-04-10 13:45:57.738523: Current learning rate: 0.00973 +2026-04-10 13:47:39.456746: train_loss -0.0695 +2026-04-10 13:47:39.472893: val_loss -0.0608 +2026-04-10 13:47:39.474544: Pseudo dice [0.2292, 0.6168, 0.5871, 0.0, 0.0, 0.1065, 0.6385] +2026-04-10 13:47:39.478475: Epoch time: 101.72 s +2026-04-10 13:47:39.480150: Yayy! New best EMA pseudo Dice: 0.2659 +2026-04-10 13:47:43.170231: +2026-04-10 13:47:43.172141: Epoch 123 +2026-04-10 13:47:43.173672: Current learning rate: 0.00972 +2026-04-10 13:49:24.992071: train_loss -0.0969 +2026-04-10 13:49:24.998182: val_loss -0.0441 +2026-04-10 13:49:24.999995: Pseudo dice [0.2204, 0.2373, 0.5467, 0.0, 0.0, 0.2282, 0.5391] +2026-04-10 13:49:25.002482: Epoch time: 101.83 s +2026-04-10 13:49:26.074697: +2026-04-10 13:49:26.076337: Epoch 124 +2026-04-10 13:49:26.077726: Current learning rate: 0.00972 +2026-04-10 13:51:08.514581: train_loss -0.0951 +2026-04-10 13:51:08.520213: val_loss -0.0717 +2026-04-10 13:51:08.522507: Pseudo dice [0.4272, 0.814, 0.5647, 0.0, 0.0, 0.2087, 0.6423] +2026-04-10 13:51:08.524724: Epoch time: 102.44 s +2026-04-10 13:51:08.526489: Yayy! New best EMA pseudo Dice: 0.2761 +2026-04-10 13:51:11.280119: +2026-04-10 13:51:11.282084: Epoch 125 +2026-04-10 13:51:11.283863: Current learning rate: 0.00972 +2026-04-10 13:52:53.616546: train_loss -0.0934 +2026-04-10 13:52:53.622693: val_loss -0.0664 +2026-04-10 13:52:53.624601: Pseudo dice [0.3672, 0.3242, 0.5619, 0.0, 0.0, 0.3667, 0.5207] +2026-04-10 13:52:53.627279: Epoch time: 102.34 s +2026-04-10 13:52:53.629030: Yayy! New best EMA pseudo Dice: 0.2791 +2026-04-10 13:52:56.097638: +2026-04-10 13:52:56.099746: Epoch 126 +2026-04-10 13:52:56.101422: Current learning rate: 0.00972 +2026-04-10 13:54:38.045830: train_loss -0.0924 +2026-04-10 13:54:38.053200: val_loss -0.0913 +2026-04-10 13:54:38.055944: Pseudo dice [0.3957, 0.2539, 0.5734, 0.0008, 0.0, 0.2351, 0.6103] +2026-04-10 13:54:38.058525: Epoch time: 101.95 s +2026-04-10 13:54:38.060803: Yayy! New best EMA pseudo Dice: 0.2807 +2026-04-10 13:54:40.879781: +2026-04-10 13:54:40.882164: Epoch 127 +2026-04-10 13:54:40.883566: Current learning rate: 0.00971 +2026-04-10 13:56:22.718309: train_loss -0.081 +2026-04-10 13:56:22.725085: val_loss -0.0442 +2026-04-10 13:56:22.726582: Pseudo dice [0.1487, 0.5667, 0.5024, 0.0, 0.0, 0.1908, 0.4054] +2026-04-10 13:56:22.728556: Epoch time: 101.84 s +2026-04-10 13:56:24.059937: +2026-04-10 13:56:24.061551: Epoch 128 +2026-04-10 13:56:24.062805: Current learning rate: 0.00971 +2026-04-10 13:58:05.760248: train_loss -0.0651 +2026-04-10 13:58:05.766006: val_loss -0.0809 +2026-04-10 13:58:05.768227: Pseudo dice [0.2794, 0.2148, 0.6184, 0.0191, 0.0, 0.2504, 0.3844] +2026-04-10 13:58:05.770508: Epoch time: 101.7 s +2026-04-10 13:58:06.839148: +2026-04-10 13:58:06.841325: Epoch 129 +2026-04-10 13:58:06.842697: Current learning rate: 0.00971 +2026-04-10 13:59:49.164255: train_loss -0.0823 +2026-04-10 13:59:49.174320: val_loss -0.0718 +2026-04-10 13:59:49.176355: Pseudo dice [0.4172, 0.7252, 0.598, 0.0029, 0.0, 0.278, 0.5054] +2026-04-10 13:59:49.178896: Epoch time: 102.33 s +2026-04-10 13:59:49.180656: Yayy! New best EMA pseudo Dice: 0.2844 +2026-04-10 13:59:51.837101: +2026-04-10 13:59:51.838809: Epoch 130 +2026-04-10 13:59:51.840254: Current learning rate: 0.00971 +2026-04-10 14:01:33.786164: train_loss -0.0914 +2026-04-10 14:01:33.791707: val_loss -0.0105 +2026-04-10 14:01:33.793569: Pseudo dice [0.6339, 0.5709, 0.4202, 0.0001, 0.0, 0.1063, 0.6373] +2026-04-10 14:01:33.797654: Epoch time: 101.95 s +2026-04-10 14:01:33.799167: Yayy! New best EMA pseudo Dice: 0.2898 +2026-04-10 14:01:36.216398: +2026-04-10 14:01:36.218194: Epoch 131 +2026-04-10 14:01:36.219476: Current learning rate: 0.0097 +2026-04-10 14:03:18.324378: train_loss -0.0806 +2026-04-10 14:03:18.329934: val_loss -0.0675 +2026-04-10 14:03:18.332313: Pseudo dice [0.5338, 0.4482, 0.7112, 0.0064, 0.0, 0.1724, 0.5985] +2026-04-10 14:03:18.334615: Epoch time: 102.11 s +2026-04-10 14:03:18.337049: Yayy! New best EMA pseudo Dice: 0.2961 +2026-04-10 14:03:21.126302: +2026-04-10 14:03:21.128239: Epoch 132 +2026-04-10 14:03:21.129566: Current learning rate: 0.0097 +2026-04-10 14:05:02.633498: train_loss -0.0856 +2026-04-10 14:05:02.638971: val_loss -0.067 +2026-04-10 14:05:02.640857: Pseudo dice [0.108, 0.5101, 0.5957, 0.0032, 0.0, 0.0827, 0.4957] +2026-04-10 14:05:02.643465: Epoch time: 101.51 s +2026-04-10 14:05:03.723768: +2026-04-10 14:05:03.726217: Epoch 133 +2026-04-10 14:05:03.727816: Current learning rate: 0.0097 +2026-04-10 14:06:46.207816: train_loss -0.0852 +2026-04-10 14:06:46.214731: val_loss -0.0741 +2026-04-10 14:06:46.216364: Pseudo dice [0.0842, 0.3341, 0.6664, 0.0019, 0.0, 0.432, 0.3329] +2026-04-10 14:06:46.219592: Epoch time: 102.49 s +2026-04-10 14:06:47.300432: +2026-04-10 14:06:47.303089: Epoch 134 +2026-04-10 14:06:47.304806: Current learning rate: 0.0097 +2026-04-10 14:08:29.432439: train_loss -0.0905 +2026-04-10 14:08:29.442772: val_loss -0.0792 +2026-04-10 14:08:29.444889: Pseudo dice [0.2079, 0.0291, 0.6464, 0.1281, 0.0, 0.2611, 0.7052] +2026-04-10 14:08:29.450793: Epoch time: 102.14 s +2026-04-10 14:08:30.534622: +2026-04-10 14:08:30.536315: Epoch 135 +2026-04-10 14:08:30.537641: Current learning rate: 0.0097 +2026-04-10 14:10:13.072410: train_loss -0.1133 +2026-04-10 14:10:13.077542: val_loss -0.081 +2026-04-10 14:10:13.079669: Pseudo dice [0.3416, 0.7856, 0.5884, 0.0099, 0.0014, 0.2911, 0.55] +2026-04-10 14:10:13.082010: Epoch time: 102.54 s +2026-04-10 14:10:13.083883: Yayy! New best EMA pseudo Dice: 0.2965 +2026-04-10 14:10:15.931592: +2026-04-10 14:10:15.934348: Epoch 136 +2026-04-10 14:10:15.935651: Current learning rate: 0.00969 +2026-04-10 14:11:58.768178: train_loss -0.101 +2026-04-10 14:11:58.774679: val_loss -0.1068 +2026-04-10 14:11:58.776742: Pseudo dice [0.2214, 0.5494, 0.6758, 0.1274, 0.0087, 0.188, 0.6894] +2026-04-10 14:11:58.779114: Epoch time: 102.84 s +2026-04-10 14:11:58.781355: Yayy! New best EMA pseudo Dice: 0.302 +2026-04-10 14:12:01.313281: +2026-04-10 14:12:01.316201: Epoch 137 +2026-04-10 14:12:01.317832: Current learning rate: 0.00969 +2026-04-10 14:13:43.262854: train_loss -0.0836 +2026-04-10 14:13:43.268934: val_loss -0.0673 +2026-04-10 14:13:43.271352: Pseudo dice [0.2349, 0.4219, 0.6344, 0.2647, 0.0386, 0.4229, 0.4612] +2026-04-10 14:13:43.273449: Epoch time: 101.95 s +2026-04-10 14:13:43.275519: Yayy! New best EMA pseudo Dice: 0.3072 +2026-04-10 14:13:46.066105: +2026-04-10 14:13:46.069041: Epoch 138 +2026-04-10 14:13:46.070397: Current learning rate: 0.00969 +2026-04-10 14:15:27.760570: train_loss -0.0851 +2026-04-10 14:15:27.765901: val_loss -0.1018 +2026-04-10 14:15:27.767585: Pseudo dice [0.2074, 0.2478, 0.6287, 0.0, 0.1149, 0.4171, 0.5437] +2026-04-10 14:15:27.769307: Epoch time: 101.7 s +2026-04-10 14:15:27.771271: Yayy! New best EMA pseudo Dice: 0.3074 +2026-04-10 14:15:30.253050: +2026-04-10 14:15:30.254943: Epoch 139 +2026-04-10 14:15:30.256340: Current learning rate: 0.00969 +2026-04-10 14:17:12.750593: train_loss -0.0936 +2026-04-10 14:17:12.757421: val_loss -0.0883 +2026-04-10 14:17:12.759358: Pseudo dice [0.4928, 0.1405, 0.6819, 0.0654, 0.3142, 0.7917, 0.4698] +2026-04-10 14:17:12.761068: Epoch time: 102.5 s +2026-04-10 14:17:12.762679: Yayy! New best EMA pseudo Dice: 0.3189 +2026-04-10 14:17:16.690685: +2026-04-10 14:17:16.692877: Epoch 140 +2026-04-10 14:17:16.694154: Current learning rate: 0.00968 +2026-04-10 14:18:58.934372: train_loss -0.1005 +2026-04-10 14:18:58.940080: val_loss -0.0468 +2026-04-10 14:18:58.941918: Pseudo dice [0.3129, 0.3451, 0.5972, 0.0775, 0.2147, 0.2723, 0.6674] +2026-04-10 14:18:58.944313: Epoch time: 102.25 s +2026-04-10 14:18:58.946398: Yayy! New best EMA pseudo Dice: 0.3225 +2026-04-10 14:19:01.619267: +2026-04-10 14:19:01.621245: Epoch 141 +2026-04-10 14:19:01.623050: Current learning rate: 0.00968 +2026-04-10 14:20:43.781796: train_loss -0.1083 +2026-04-10 14:20:43.791120: val_loss -0.0638 +2026-04-10 14:20:43.792812: Pseudo dice [0.3097, 0.0736, 0.4977, 0.1093, 0.1983, 0.1432, 0.5264] +2026-04-10 14:20:43.795218: Epoch time: 102.17 s +2026-04-10 14:20:44.894541: +2026-04-10 14:20:44.896288: Epoch 142 +2026-04-10 14:20:44.898041: Current learning rate: 0.00968 +2026-04-10 14:22:38.515075: train_loss -0.1021 +2026-04-10 14:22:38.522809: val_loss -0.0632 +2026-04-10 14:22:38.525280: Pseudo dice [0.6115, 0.1386, 0.5682, 0.0567, 0.2339, 0.0973, 0.4639] +2026-04-10 14:22:38.528288: Epoch time: 113.62 s +2026-04-10 14:22:39.657411: +2026-04-10 14:22:39.658915: Epoch 143 +2026-04-10 14:22:39.660303: Current learning rate: 0.00968 +2026-04-10 14:24:21.373962: train_loss -0.1037 +2026-04-10 14:24:21.380227: val_loss -0.0514 +2026-04-10 14:24:21.382876: Pseudo dice [0.2215, 0.46, 0.5883, 0.1056, 0.063, 0.24, 0.459] +2026-04-10 14:24:21.384908: Epoch time: 101.72 s +2026-04-10 14:24:22.493899: +2026-04-10 14:24:22.495396: Epoch 144 +2026-04-10 14:24:22.496687: Current learning rate: 0.00968 +2026-04-10 14:26:04.893028: train_loss -0.1018 +2026-04-10 14:26:04.898200: val_loss -0.1072 +2026-04-10 14:26:04.899904: Pseudo dice [0.3055, 0.7906, 0.4402, 0.0015, 0.2672, 0.4028, 0.6656] +2026-04-10 14:26:04.901881: Epoch time: 102.4 s +2026-04-10 14:26:04.903475: Yayy! New best EMA pseudo Dice: 0.3246 +2026-04-10 14:26:07.557532: +2026-04-10 14:26:07.559402: Epoch 145 +2026-04-10 14:26:07.560853: Current learning rate: 0.00967 +2026-04-10 14:27:49.723737: train_loss -0.0874 +2026-04-10 14:27:49.729923: val_loss -0.0679 +2026-04-10 14:27:49.732720: Pseudo dice [0.5335, 0.373, 0.4773, 0.0051, 0.2917, 0.2197, 0.4687] +2026-04-10 14:27:49.734864: Epoch time: 102.17 s +2026-04-10 14:27:49.736917: Yayy! New best EMA pseudo Dice: 0.326 +2026-04-10 14:27:52.216398: +2026-04-10 14:27:52.218332: Epoch 146 +2026-04-10 14:27:52.219788: Current learning rate: 0.00967 +2026-04-10 14:29:34.208707: train_loss -0.1138 +2026-04-10 14:29:34.215207: val_loss -0.0849 +2026-04-10 14:29:34.217158: Pseudo dice [0.1896, 0.3717, 0.6596, 0.0529, 0.2557, 0.207, 0.6726] +2026-04-10 14:29:34.220073: Epoch time: 102.0 s +2026-04-10 14:29:34.222238: Yayy! New best EMA pseudo Dice: 0.3278 +2026-04-10 14:29:37.000298: +2026-04-10 14:29:37.002847: Epoch 147 +2026-04-10 14:29:37.004201: Current learning rate: 0.00967 +2026-04-10 14:31:18.754139: train_loss -0.1082 +2026-04-10 14:31:18.760441: val_loss -0.0841 +2026-04-10 14:31:18.762382: Pseudo dice [0.3531, 0.4377, 0.4951, 0.2648, 0.1504, 0.1499, 0.3066] +2026-04-10 14:31:18.764861: Epoch time: 101.76 s +2026-04-10 14:31:19.878916: +2026-04-10 14:31:19.881068: Epoch 148 +2026-04-10 14:31:19.882534: Current learning rate: 0.00967 +2026-04-10 14:33:01.644904: train_loss -0.1031 +2026-04-10 14:33:01.651427: val_loss -0.0498 +2026-04-10 14:33:01.653083: Pseudo dice [0.2928, 0.5262, 0.5953, 0.0338, 0.3531, 0.1224, 0.5092] +2026-04-10 14:33:01.655655: Epoch time: 101.77 s +2026-04-10 14:33:01.657365: Yayy! New best EMA pseudo Dice: 0.328 +2026-04-10 14:33:04.466280: +2026-04-10 14:33:04.468548: Epoch 149 +2026-04-10 14:33:04.470017: Current learning rate: 0.00966 +2026-04-10 14:34:47.041761: train_loss -0.1025 +2026-04-10 14:34:47.048820: val_loss -0.0783 +2026-04-10 14:34:47.051392: Pseudo dice [0.4181, 0.6037, 0.6166, 0.1166, 0.2523, 0.3317, 0.4171] +2026-04-10 14:34:47.053755: Epoch time: 102.58 s +2026-04-10 14:34:48.421336: Yayy! New best EMA pseudo Dice: 0.3346 +2026-04-10 14:34:51.127486: +2026-04-10 14:34:51.128835: Epoch 150 +2026-04-10 14:34:51.130075: Current learning rate: 0.00966 +2026-04-10 14:36:35.304685: train_loss -0.0939 +2026-04-10 14:36:35.311316: val_loss -0.0685 +2026-04-10 14:36:35.313244: Pseudo dice [0.2252, 0.3077, 0.4815, 0.0424, 0.2407, 0.1804, 0.6397] +2026-04-10 14:36:35.315468: Epoch time: 104.18 s +2026-04-10 14:36:36.408577: +2026-04-10 14:36:36.410349: Epoch 151 +2026-04-10 14:36:36.412229: Current learning rate: 0.00966 +2026-04-10 14:38:19.005694: train_loss -0.1056 +2026-04-10 14:38:19.011297: val_loss -0.0926 +2026-04-10 14:38:19.013479: Pseudo dice [0.3899, 0.4086, 0.747, 0.4327, 0.1577, 0.4353, 0.5047] +2026-04-10 14:38:19.015785: Epoch time: 102.6 s +2026-04-10 14:38:19.017421: Yayy! New best EMA pseudo Dice: 0.3422 +2026-04-10 14:38:21.735981: +2026-04-10 14:38:21.737698: Epoch 152 +2026-04-10 14:38:21.739071: Current learning rate: 0.00966 +2026-04-10 14:40:03.784269: train_loss -0.1059 +2026-04-10 14:40:03.790080: val_loss -0.0848 +2026-04-10 14:40:03.792603: Pseudo dice [0.4429, 0.5053, 0.7, 0.0713, 0.2589, 0.2659, 0.7008] +2026-04-10 14:40:03.794647: Epoch time: 102.05 s +2026-04-10 14:40:03.796421: Yayy! New best EMA pseudo Dice: 0.35 +2026-04-10 14:40:06.262924: +2026-04-10 14:40:06.264866: Epoch 153 +2026-04-10 14:40:06.266192: Current learning rate: 0.00966 +2026-04-10 14:41:48.646060: train_loss -0.0905 +2026-04-10 14:41:48.651056: val_loss -0.0717 +2026-04-10 14:41:48.652965: Pseudo dice [0.2118, 0.4184, 0.6064, 0.5558, 0.2504, 0.2726, 0.3095] +2026-04-10 14:41:48.654846: Epoch time: 102.39 s +2026-04-10 14:41:48.657157: Yayy! New best EMA pseudo Dice: 0.3525 +2026-04-10 14:41:51.168864: +2026-04-10 14:41:51.170791: Epoch 154 +2026-04-10 14:41:51.172031: Current learning rate: 0.00965 +2026-04-10 14:43:33.432480: train_loss -0.1086 +2026-04-10 14:43:33.437984: val_loss -0.0802 +2026-04-10 14:43:33.439762: Pseudo dice [0.3348, 0.1667, 0.6058, 0.0185, 0.2623, 0.5441, 0.5926] +2026-04-10 14:43:33.442063: Epoch time: 102.27 s +2026-04-10 14:43:33.444083: Yayy! New best EMA pseudo Dice: 0.3533 +2026-04-10 14:43:36.192120: +2026-04-10 14:43:36.194068: Epoch 155 +2026-04-10 14:43:36.195482: Current learning rate: 0.00965 +2026-04-10 14:45:19.953288: train_loss -0.1195 +2026-04-10 14:45:19.958560: val_loss -0.1067 +2026-04-10 14:45:19.960558: Pseudo dice [0.4093, 0.2964, 0.7303, 0.0005, 0.3599, 0.3495, 0.6188] +2026-04-10 14:45:19.962817: Epoch time: 103.76 s +2026-04-10 14:45:19.964450: Yayy! New best EMA pseudo Dice: 0.3575 +2026-04-10 14:45:23.604805: +2026-04-10 14:45:23.606796: Epoch 156 +2026-04-10 14:45:23.608115: Current learning rate: 0.00965 +2026-04-10 14:47:22.840639: train_loss -0.1202 +2026-04-10 14:47:22.846177: val_loss -0.0722 +2026-04-10 14:47:22.848035: Pseudo dice [0.1838, 0.5619, 0.4995, 0.0, 0.4188, 0.4143, 0.6154] +2026-04-10 14:47:22.850348: Epoch time: 119.24 s +2026-04-10 14:47:22.852223: Yayy! New best EMA pseudo Dice: 0.3602 +2026-04-10 14:47:25.453241: +2026-04-10 14:47:25.455043: Epoch 157 +2026-04-10 14:47:25.456453: Current learning rate: 0.00965 +2026-04-10 14:49:07.507273: train_loss -0.1003 +2026-04-10 14:49:07.513285: val_loss -0.1127 +2026-04-10 14:49:07.515167: Pseudo dice [0.4573, 0.798, 0.6784, 0.0579, 0.1065, 0.1297, 0.653] +2026-04-10 14:49:07.517628: Epoch time: 102.06 s +2026-04-10 14:49:07.519339: Yayy! New best EMA pseudo Dice: 0.3654 +2026-04-10 14:49:10.402061: +2026-04-10 14:49:10.404097: Epoch 158 +2026-04-10 14:49:10.405330: Current learning rate: 0.00964 +2026-04-10 14:50:52.323900: train_loss -0.1066 +2026-04-10 14:50:52.329379: val_loss -0.1043 +2026-04-10 14:50:52.331261: Pseudo dice [0.0714, 0.1712, 0.7022, 0.058, 0.3721, 0.4321, 0.6219] +2026-04-10 14:50:52.333206: Epoch time: 101.93 s +2026-04-10 14:50:53.450964: +2026-04-10 14:50:53.452432: Epoch 159 +2026-04-10 14:50:53.453709: Current learning rate: 0.00964 +2026-04-10 14:52:35.612611: train_loss -0.1267 +2026-04-10 14:52:35.620632: val_loss -0.0926 +2026-04-10 14:52:35.623375: Pseudo dice [0.2954, 0.4416, 0.6856, 0.1027, 0.2152, 0.6142, 0.7051] +2026-04-10 14:52:35.626234: Epoch time: 102.17 s +2026-04-10 14:52:35.628210: Yayy! New best EMA pseudo Dice: 0.3709 +2026-04-10 14:52:38.409990: +2026-04-10 14:52:38.412057: Epoch 160 +2026-04-10 14:52:38.413363: Current learning rate: 0.00964 +2026-04-10 14:54:21.516758: train_loss -0.1183 +2026-04-10 14:54:21.522816: val_loss -0.1035 +2026-04-10 14:54:21.524679: Pseudo dice [0.3639, 0.82, 0.6805, 0.0346, 0.206, 0.3672, 0.7325] +2026-04-10 14:54:21.526977: Epoch time: 103.11 s +2026-04-10 14:54:21.529870: Yayy! New best EMA pseudo Dice: 0.3796 +2026-04-10 14:54:24.223339: +2026-04-10 14:54:24.225243: Epoch 161 +2026-04-10 14:54:24.226646: Current learning rate: 0.00964 +2026-04-10 14:56:05.997738: train_loss -0.1174 +2026-04-10 14:56:06.004040: val_loss -0.0812 +2026-04-10 14:56:06.006025: Pseudo dice [0.5999, 0.6037, 0.7052, 0.1158, 0.0573, 0.3371, 0.5981] +2026-04-10 14:56:06.008735: Epoch time: 101.78 s +2026-04-10 14:56:06.010772: Yayy! New best EMA pseudo Dice: 0.3847 +2026-04-10 14:56:08.777784: +2026-04-10 14:56:08.781533: Epoch 162 +2026-04-10 14:56:08.784492: Current learning rate: 0.00963 +2026-04-10 14:57:50.586602: train_loss -0.1103 +2026-04-10 14:57:50.593831: val_loss -0.1134 +2026-04-10 14:57:50.596192: Pseudo dice [0.4148, 0.214, 0.4954, 0.2378, 0.3689, 0.3135, 0.8067] +2026-04-10 14:57:50.598893: Epoch time: 101.81 s +2026-04-10 14:57:50.600730: Yayy! New best EMA pseudo Dice: 0.387 +2026-04-10 14:57:53.344454: +2026-04-10 14:57:53.346620: Epoch 163 +2026-04-10 14:57:53.348068: Current learning rate: 0.00963 +2026-04-10 14:59:35.935595: train_loss -0.112 +2026-04-10 14:59:35.942836: val_loss -0.097 +2026-04-10 14:59:35.944641: Pseudo dice [0.4718, 0.8865, 0.619, 0.0043, 0.3192, 0.4164, 0.3202] +2026-04-10 14:59:35.946448: Epoch time: 102.59 s +2026-04-10 14:59:35.948195: Yayy! New best EMA pseudo Dice: 0.3917 +2026-04-10 14:59:38.742391: +2026-04-10 14:59:38.744213: Epoch 164 +2026-04-10 14:59:38.745460: Current learning rate: 0.00963 +2026-04-10 15:01:20.810977: train_loss -0.1279 +2026-04-10 15:01:20.817317: val_loss -0.1034 +2026-04-10 15:01:20.819258: Pseudo dice [0.5862, 0.1213, 0.8127, 0.2397, 0.1549, 0.2533, 0.348] +2026-04-10 15:01:20.821120: Epoch time: 102.07 s +2026-04-10 15:01:21.900043: +2026-04-10 15:01:21.902091: Epoch 165 +2026-04-10 15:01:21.903763: Current learning rate: 0.00963 +2026-04-10 15:03:03.566280: train_loss -0.1178 +2026-04-10 15:03:03.573031: val_loss -0.0853 +2026-04-10 15:03:03.574806: Pseudo dice [0.3402, 0.1107, 0.5565, 0.0806, 0.0811, 0.2955, 0.5991] +2026-04-10 15:03:03.577313: Epoch time: 101.67 s +2026-04-10 15:03:04.663625: +2026-04-10 15:03:04.665183: Epoch 166 +2026-04-10 15:03:04.666558: Current learning rate: 0.00963 +2026-04-10 15:04:46.252120: train_loss -0.1147 +2026-04-10 15:04:46.258094: val_loss -0.1046 +2026-04-10 15:04:46.259953: Pseudo dice [0.372, 0.7341, 0.6356, 0.1006, 0.2514, 0.1182, 0.8323] +2026-04-10 15:04:46.262011: Epoch time: 101.59 s +2026-04-10 15:04:47.350057: +2026-04-10 15:04:47.351575: Epoch 167 +2026-04-10 15:04:47.353705: Current learning rate: 0.00962 +2026-04-10 15:06:29.410784: train_loss -0.1243 +2026-04-10 15:06:29.430548: val_loss -0.088 +2026-04-10 15:06:29.432898: Pseudo dice [0.2384, 0.7091, 0.6821, 0.1482, 0.1985, 0.1906, 0.712] +2026-04-10 15:06:29.435419: Epoch time: 102.06 s +2026-04-10 15:06:30.523444: +2026-04-10 15:06:30.524817: Epoch 168 +2026-04-10 15:06:30.526044: Current learning rate: 0.00962 +2026-04-10 15:08:12.808472: train_loss -0.1137 +2026-04-10 15:08:12.815058: val_loss -0.0876 +2026-04-10 15:08:12.816944: Pseudo dice [0.4617, 0.2923, 0.6297, 0.2347, 0.2641, 0.3871, 0.5728] +2026-04-10 15:08:12.820320: Epoch time: 102.29 s +2026-04-10 15:08:13.932730: +2026-04-10 15:08:13.935978: Epoch 169 +2026-04-10 15:08:13.938157: Current learning rate: 0.00962 +2026-04-10 15:09:55.985508: train_loss -0.115 +2026-04-10 15:09:55.992929: val_loss -0.0717 +2026-04-10 15:09:55.995220: Pseudo dice [0.6287, 0.1813, 0.48, 0.1928, 0.25, 0.6672, 0.5041] +2026-04-10 15:09:55.997445: Epoch time: 102.06 s +2026-04-10 15:09:55.999885: Yayy! New best EMA pseudo Dice: 0.3918 +2026-04-10 15:09:58.834932: +2026-04-10 15:09:58.837507: Epoch 170 +2026-04-10 15:09:58.838902: Current learning rate: 0.00962 +2026-04-10 15:11:40.934242: train_loss -0.1122 +2026-04-10 15:11:40.940566: val_loss -0.1059 +2026-04-10 15:11:40.942075: Pseudo dice [0.3018, 0.3013, 0.6395, 0.0645, 0.3797, 0.4228, 0.2381] +2026-04-10 15:11:40.944263: Epoch time: 102.1 s +2026-04-10 15:11:42.019536: +2026-04-10 15:11:42.021218: Epoch 171 +2026-04-10 15:11:42.022792: Current learning rate: 0.00961 +2026-04-10 15:13:24.336862: train_loss -0.1145 +2026-04-10 15:13:24.343839: val_loss -0.0996 +2026-04-10 15:13:24.345904: Pseudo dice [0.294, 0.4607, 0.6339, 0.0004, 0.2484, 0.278, 0.5063] +2026-04-10 15:13:24.348281: Epoch time: 102.32 s +2026-04-10 15:13:25.434009: +2026-04-10 15:13:25.435784: Epoch 172 +2026-04-10 15:13:25.437348: Current learning rate: 0.00961 +2026-04-10 15:15:07.929932: train_loss -0.1239 +2026-04-10 15:15:07.936005: val_loss -0.0701 +2026-04-10 15:15:07.939204: Pseudo dice [0.3104, 0.4273, 0.4588, 0.0965, 0.1816, 0.3201, 0.5053] +2026-04-10 15:15:07.941651: Epoch time: 102.5 s +2026-04-10 15:15:09.057034: +2026-04-10 15:15:09.059559: Epoch 173 +2026-04-10 15:15:09.061495: Current learning rate: 0.00961 +2026-04-10 15:16:52.278090: train_loss -0.1077 +2026-04-10 15:16:52.286733: val_loss -0.107 +2026-04-10 15:16:52.292215: Pseudo dice [0.1607, 0.3789, 0.6587, 0.0029, 0.4798, 0.1989, 0.7242] +2026-04-10 15:16:52.297982: Epoch time: 103.22 s +2026-04-10 15:16:53.385570: +2026-04-10 15:16:53.387612: Epoch 174 +2026-04-10 15:16:53.390488: Current learning rate: 0.00961 +2026-04-10 15:18:39.702427: train_loss -0.1265 +2026-04-10 15:18:39.714485: val_loss -0.1074 +2026-04-10 15:18:39.716612: Pseudo dice [0.2499, 0.1398, 0.7447, 0.2001, 0.3229, 0.1857, 0.605] +2026-04-10 15:18:39.719373: Epoch time: 106.32 s +2026-04-10 15:18:40.821465: +2026-04-10 15:18:40.823371: Epoch 175 +2026-04-10 15:18:40.825010: Current learning rate: 0.00961 +2026-04-10 15:20:23.158240: train_loss -0.125 +2026-04-10 15:20:23.164338: val_loss -0.0866 +2026-04-10 15:20:23.167010: Pseudo dice [0.1659, 0.1675, 0.6321, 0.112, 0.3236, 0.3446, 0.378] +2026-04-10 15:20:23.169311: Epoch time: 102.34 s +2026-04-10 15:20:24.276767: +2026-04-10 15:20:24.278832: Epoch 176 +2026-04-10 15:20:24.280714: Current learning rate: 0.0096 +2026-04-10 15:22:06.395742: train_loss -0.1237 +2026-04-10 15:22:06.401973: val_loss -0.0668 +2026-04-10 15:22:06.404287: Pseudo dice [0.3643, 0.8822, 0.4867, 0.0296, 0.2291, 0.1663, 0.594] +2026-04-10 15:22:06.406468: Epoch time: 102.12 s +2026-04-10 15:22:07.516617: +2026-04-10 15:22:07.518605: Epoch 177 +2026-04-10 15:22:07.521132: Current learning rate: 0.0096 +2026-04-10 15:23:49.745526: train_loss -0.1327 +2026-04-10 15:23:49.750978: val_loss -0.0786 +2026-04-10 15:23:49.753220: Pseudo dice [0.2588, 0.4643, 0.3721, 0.2164, 0.1651, 0.1306, 0.6024] +2026-04-10 15:23:49.756424: Epoch time: 102.23 s +2026-04-10 15:23:50.884100: +2026-04-10 15:23:50.885922: Epoch 178 +2026-04-10 15:23:50.887279: Current learning rate: 0.0096 +2026-04-10 15:25:33.113940: train_loss -0.1098 +2026-04-10 15:25:33.126394: val_loss -0.0603 +2026-04-10 15:25:33.128175: Pseudo dice [0.6551, 0.7656, 0.5343, 0.382, 0.1461, 0.1857, 0.5248] +2026-04-10 15:25:33.130368: Epoch time: 102.23 s +2026-04-10 15:25:34.218705: +2026-04-10 15:25:34.220737: Epoch 179 +2026-04-10 15:25:34.222721: Current learning rate: 0.0096 +2026-04-10 15:27:16.731901: train_loss -0.133 +2026-04-10 15:27:16.738070: val_loss -0.0905 +2026-04-10 15:27:16.741010: Pseudo dice [0.113, 0.7718, 0.7071, 0.2536, 0.103, 0.1886, 0.553] +2026-04-10 15:27:16.743746: Epoch time: 102.52 s +2026-04-10 15:27:17.844153: +2026-04-10 15:27:17.846364: Epoch 180 +2026-04-10 15:27:17.848485: Current learning rate: 0.00959 +2026-04-10 15:29:00.063085: train_loss -0.1251 +2026-04-10 15:29:00.068923: val_loss -0.1036 +2026-04-10 15:29:00.070753: Pseudo dice [0.5983, 0.3449, 0.4244, 0.2053, 0.315, 0.2771, 0.4655] +2026-04-10 15:29:00.074357: Epoch time: 102.22 s +2026-04-10 15:29:01.175144: +2026-04-10 15:29:01.176794: Epoch 181 +2026-04-10 15:29:01.178632: Current learning rate: 0.00959 +2026-04-10 15:30:43.843302: train_loss -0.1232 +2026-04-10 15:30:43.849925: val_loss -0.0795 +2026-04-10 15:30:43.853276: Pseudo dice [0.4005, 0.2054, 0.6956, 0.2628, 0.3333, 0.3475, 0.5873] +2026-04-10 15:30:43.855625: Epoch time: 102.67 s +2026-04-10 15:30:44.968125: +2026-04-10 15:30:44.970006: Epoch 182 +2026-04-10 15:30:44.971517: Current learning rate: 0.00959 +2026-04-10 15:32:27.248640: train_loss -0.0996 +2026-04-10 15:32:27.254859: val_loss -0.0855 +2026-04-10 15:32:27.256980: Pseudo dice [0.6847, 0.7765, 0.5268, 0.2397, 0.2185, 0.0832, 0.6853] +2026-04-10 15:32:27.259495: Epoch time: 102.28 s +2026-04-10 15:32:28.346743: +2026-04-10 15:32:28.348577: Epoch 183 +2026-04-10 15:32:28.349879: Current learning rate: 0.00959 +2026-04-10 15:34:10.952632: train_loss -0.1096 +2026-04-10 15:34:10.959899: val_loss -0.0909 +2026-04-10 15:34:10.961602: Pseudo dice [0.1895, 0.4574, 0.6221, 0.2359, 0.3524, 0.249, 0.4518] +2026-04-10 15:34:10.967508: Epoch time: 102.61 s +2026-04-10 15:34:12.079764: +2026-04-10 15:34:12.081355: Epoch 184 +2026-04-10 15:34:12.082731: Current learning rate: 0.00959 +2026-04-10 15:35:54.752522: train_loss -0.1042 +2026-04-10 15:35:54.758460: val_loss -0.0645 +2026-04-10 15:35:54.760551: Pseudo dice [0.0736, 0.246, 0.3879, 0.3678, 0.3832, 0.1789, 0.4681] +2026-04-10 15:35:54.763155: Epoch time: 102.68 s +2026-04-10 15:35:55.873553: +2026-04-10 15:35:55.875284: Epoch 185 +2026-04-10 15:35:55.876884: Current learning rate: 0.00958 +2026-04-10 15:37:38.339174: train_loss -0.117 +2026-04-10 15:37:38.348410: val_loss -0.0758 +2026-04-10 15:37:38.350444: Pseudo dice [0.5602, 0.0268, 0.6349, 0.3442, 0.2336, 0.2238, 0.5861] +2026-04-10 15:37:38.352517: Epoch time: 102.47 s +2026-04-10 15:37:39.469327: +2026-04-10 15:37:39.471612: Epoch 186 +2026-04-10 15:37:39.474534: Current learning rate: 0.00958 +2026-04-10 15:39:21.869954: train_loss -0.1154 +2026-04-10 15:39:21.879761: val_loss -0.1235 +2026-04-10 15:39:21.882175: Pseudo dice [0.3588, 0.6606, 0.617, 0.2124, 0.604, 0.4204, 0.8047] +2026-04-10 15:39:21.884021: Epoch time: 102.4 s +2026-04-10 15:39:22.971949: +2026-04-10 15:39:22.973891: Epoch 187 +2026-04-10 15:39:22.976261: Current learning rate: 0.00958 +2026-04-10 15:41:04.783773: train_loss -0.1361 +2026-04-10 15:41:04.793057: val_loss -0.1018 +2026-04-10 15:41:04.795316: Pseudo dice [0.3544, 0.2579, 0.5094, 0.0, 0.4501, 0.5478, 0.7634] +2026-04-10 15:41:04.798423: Epoch time: 101.81 s +2026-04-10 15:41:04.801208: Yayy! New best EMA pseudo Dice: 0.3923 +2026-04-10 15:41:07.669141: +2026-04-10 15:41:07.671347: Epoch 188 +2026-04-10 15:41:07.672936: Current learning rate: 0.00958 +2026-04-10 15:42:49.482107: train_loss -0.1232 +2026-04-10 15:42:49.494382: val_loss -0.0784 +2026-04-10 15:42:49.496282: Pseudo dice [0.4061, 0.6705, 0.6706, 0.1101, 0.4153, 0.432, 0.1854] +2026-04-10 15:42:49.498478: Epoch time: 101.82 s +2026-04-10 15:42:49.500618: Yayy! New best EMA pseudo Dice: 0.3943 +2026-04-10 15:42:52.085657: +2026-04-10 15:42:52.088475: Epoch 189 +2026-04-10 15:42:52.089939: Current learning rate: 0.00957 +2026-04-10 15:44:34.660969: train_loss -0.1104 +2026-04-10 15:44:34.667048: val_loss -0.0739 +2026-04-10 15:44:34.669014: Pseudo dice [0.3629, 0.838, 0.6326, 0.0745, 0.3503, 0.696, 0.4805] +2026-04-10 15:44:34.671850: Epoch time: 102.58 s +2026-04-10 15:44:34.673534: Yayy! New best EMA pseudo Dice: 0.404 +2026-04-10 15:44:37.501424: +2026-04-10 15:44:37.503374: Epoch 190 +2026-04-10 15:44:37.504740: Current learning rate: 0.00957 +2026-04-10 15:46:20.281043: train_loss -0.1183 +2026-04-10 15:46:20.287975: val_loss -0.0984 +2026-04-10 15:46:20.290570: Pseudo dice [0.7446, 0.1385, 0.5551, 0.2013, 0.3243, 0.7364, 0.4732] +2026-04-10 15:46:20.294083: Epoch time: 102.78 s +2026-04-10 15:46:20.296237: Yayy! New best EMA pseudo Dice: 0.4089 +2026-04-10 15:46:23.012000: +2026-04-10 15:46:23.015064: Epoch 191 +2026-04-10 15:46:23.016462: Current learning rate: 0.00957 +2026-04-10 15:48:09.078465: train_loss -0.1215 +2026-04-10 15:48:09.084169: val_loss -0.0962 +2026-04-10 15:48:09.086018: Pseudo dice [0.4327, 0.5897, 0.6965, 0.0265, 0.3634, 0.1831, 0.4547] +2026-04-10 15:48:09.088266: Epoch time: 106.07 s +2026-04-10 15:48:10.174874: +2026-04-10 15:48:10.176756: Epoch 192 +2026-04-10 15:48:10.178330: Current learning rate: 0.00957 +2026-04-10 15:49:53.124061: train_loss -0.1303 +2026-04-10 15:49:53.131810: val_loss -0.0859 +2026-04-10 15:49:53.134233: Pseudo dice [0.646, 0.5116, 0.4468, 0.0355, 0.0525, 0.2963, 0.7284] +2026-04-10 15:49:53.136956: Epoch time: 102.95 s +2026-04-10 15:49:54.240878: +2026-04-10 15:49:54.242904: Epoch 193 +2026-04-10 15:49:54.244834: Current learning rate: 0.00956 +2026-04-10 15:51:36.454277: train_loss -0.1214 +2026-04-10 15:51:36.460158: val_loss -0.0861 +2026-04-10 15:51:36.462386: Pseudo dice [0.6326, 0.126, 0.6021, 0.322, 0.3196, 0.358, 0.286] +2026-04-10 15:51:36.466146: Epoch time: 102.22 s +2026-04-10 15:51:37.587668: +2026-04-10 15:51:37.589408: Epoch 194 +2026-04-10 15:51:37.590878: Current learning rate: 0.00956 +2026-04-10 15:53:22.084521: train_loss -0.1266 +2026-04-10 15:53:22.091522: val_loss -0.1076 +2026-04-10 15:53:22.094059: Pseudo dice [0.5388, 0.768, 0.6478, 0.135, 0.3152, 0.1852, 0.7144] +2026-04-10 15:53:22.097015: Epoch time: 104.5 s +2026-04-10 15:53:22.099112: Yayy! New best EMA pseudo Dice: 0.4096 +2026-04-10 15:53:25.062166: +2026-04-10 15:53:25.063976: Epoch 195 +2026-04-10 15:53:25.065430: Current learning rate: 0.00956 +2026-04-10 15:55:07.155672: train_loss -0.1228 +2026-04-10 15:55:07.162695: val_loss -0.0929 +2026-04-10 15:55:07.164654: Pseudo dice [0.6349, 0.3288, 0.5275, 0.4365, 0.2635, 0.5313, 0.5513] +2026-04-10 15:55:07.166961: Epoch time: 102.1 s +2026-04-10 15:55:07.168853: Yayy! New best EMA pseudo Dice: 0.4154 +2026-04-10 15:55:09.754136: +2026-04-10 15:55:09.756098: Epoch 196 +2026-04-10 15:55:09.757459: Current learning rate: 0.00956 +2026-04-10 15:56:51.450572: train_loss -0.1145 +2026-04-10 15:56:51.456850: val_loss -0.1198 +2026-04-10 15:56:51.458483: Pseudo dice [0.5641, 0.8666, 0.7421, 0.4254, 0.4968, 0.2307, 0.6577] +2026-04-10 15:56:51.460928: Epoch time: 101.7 s +2026-04-10 15:56:51.462657: Yayy! New best EMA pseudo Dice: 0.4307 +2026-04-10 15:56:54.252136: +2026-04-10 15:56:54.254193: Epoch 197 +2026-04-10 15:56:54.255588: Current learning rate: 0.00956 +2026-04-10 15:58:36.958708: train_loss -0.129 +2026-04-10 15:58:36.964627: val_loss -0.0915 +2026-04-10 15:58:36.967323: Pseudo dice [0.5479, 0.3811, 0.6121, 0.1442, 0.4019, 0.2652, 0.7325] +2026-04-10 15:58:36.969552: Epoch time: 102.71 s +2026-04-10 15:58:36.971289: Yayy! New best EMA pseudo Dice: 0.4317 +2026-04-10 15:58:39.739022: +2026-04-10 15:58:39.740740: Epoch 198 +2026-04-10 15:58:39.742014: Current learning rate: 0.00955 +2026-04-10 16:00:23.111223: train_loss -0.1279 +2026-04-10 16:00:23.117335: val_loss -0.0858 +2026-04-10 16:00:23.119780: Pseudo dice [0.1054, 0.8191, 0.6784, 0.1934, 0.5995, 0.4143, 0.4576] +2026-04-10 16:00:23.122162: Epoch time: 103.38 s +2026-04-10 16:00:23.124434: Yayy! New best EMA pseudo Dice: 0.4352 +2026-04-10 16:00:25.949760: +2026-04-10 16:00:25.951221: Epoch 199 +2026-04-10 16:00:25.952660: Current learning rate: 0.00955 +2026-04-10 16:02:08.127821: train_loss -0.1221 +2026-04-10 16:02:08.134219: val_loss -0.0876 +2026-04-10 16:02:08.136146: Pseudo dice [0.3782, 0.6683, 0.4997, 0.1259, 0.3684, 0.7525, 0.6109] +2026-04-10 16:02:08.138716: Epoch time: 102.18 s +2026-04-10 16:02:09.669639: Yayy! New best EMA pseudo Dice: 0.4403 +2026-04-10 16:02:12.534010: +2026-04-10 16:02:12.535470: Epoch 200 +2026-04-10 16:02:12.537468: Current learning rate: 0.00955 +2026-04-10 16:03:54.226024: train_loss -0.1243 +2026-04-10 16:03:54.233439: val_loss -0.0814 +2026-04-10 16:03:54.237015: Pseudo dice [0.3693, 0.6951, 0.433, 0.2903, 0.4953, 0.1951, 0.2645] +2026-04-10 16:03:54.239742: Epoch time: 101.7 s +2026-04-10 16:03:55.344215: +2026-04-10 16:03:55.345984: Epoch 201 +2026-04-10 16:03:55.347886: Current learning rate: 0.00955 +2026-04-10 16:05:37.666588: train_loss -0.1405 +2026-04-10 16:05:37.671993: val_loss -0.1145 +2026-04-10 16:05:37.674028: Pseudo dice [0.5237, 0.4096, 0.6412, 0.1799, 0.3502, 0.2162, 0.6825] +2026-04-10 16:05:37.676528: Epoch time: 102.33 s +2026-04-10 16:05:38.780429: +2026-04-10 16:05:38.782218: Epoch 202 +2026-04-10 16:05:38.783698: Current learning rate: 0.00954 +2026-04-10 16:07:21.449184: train_loss -0.132 +2026-04-10 16:07:21.455071: val_loss -0.0985 +2026-04-10 16:07:21.457943: Pseudo dice [0.336, 0.8874, 0.5674, 0.3743, 0.4041, 0.3426, 0.6196] +2026-04-10 16:07:21.460515: Epoch time: 102.67 s +2026-04-10 16:07:21.462367: Yayy! New best EMA pseudo Dice: 0.4418 +2026-04-10 16:07:24.303420: +2026-04-10 16:07:24.305276: Epoch 203 +2026-04-10 16:07:24.306787: Current learning rate: 0.00954 +2026-04-10 16:09:06.736856: train_loss -0.1359 +2026-04-10 16:09:06.742817: val_loss -0.0975 +2026-04-10 16:09:06.744815: Pseudo dice [0.7191, 0.5691, 0.604, 0.2516, 0.5035, 0.1563, 0.4584] +2026-04-10 16:09:06.747288: Epoch time: 102.44 s +2026-04-10 16:09:06.748746: Yayy! New best EMA pseudo Dice: 0.4442 +2026-04-10 16:09:09.566649: +2026-04-10 16:09:09.568839: Epoch 204 +2026-04-10 16:09:09.570269: Current learning rate: 0.00954 +2026-04-10 16:10:51.612812: train_loss -0.1302 +2026-04-10 16:10:51.617609: val_loss -0.0811 +2026-04-10 16:10:51.619355: Pseudo dice [0.4798, 0.562, 0.4873, 0.2587, 0.2333, 0.5017, 0.4535] +2026-04-10 16:10:51.621222: Epoch time: 102.05 s +2026-04-10 16:10:52.715317: +2026-04-10 16:10:52.717379: Epoch 205 +2026-04-10 16:10:52.718897: Current learning rate: 0.00954 +2026-04-10 16:12:35.323608: train_loss -0.1299 +2026-04-10 16:12:35.328903: val_loss -0.0639 +2026-04-10 16:12:35.330565: Pseudo dice [0.5524, 0.5636, 0.4751, 0.2728, 0.2873, 0.5281, 0.6467] +2026-04-10 16:12:35.332805: Epoch time: 102.61 s +2026-04-10 16:12:35.334249: Yayy! New best EMA pseudo Dice: 0.4456 +2026-04-10 16:12:38.199679: +2026-04-10 16:12:38.201227: Epoch 206 +2026-04-10 16:12:38.202633: Current learning rate: 0.00954 +2026-04-10 16:14:20.639581: train_loss -0.1356 +2026-04-10 16:14:20.645702: val_loss -0.1001 +2026-04-10 16:14:20.647959: Pseudo dice [0.6057, 0.6471, 0.5853, 0.2627, 0.2583, 0.2532, 0.609] +2026-04-10 16:14:20.650911: Epoch time: 102.44 s +2026-04-10 16:14:20.653013: Yayy! New best EMA pseudo Dice: 0.4471 +2026-04-10 16:14:23.454372: +2026-04-10 16:14:23.456317: Epoch 207 +2026-04-10 16:14:23.457600: Current learning rate: 0.00953 +2026-04-10 16:16:05.789138: train_loss -0.1458 +2026-04-10 16:16:05.798164: val_loss -0.0946 +2026-04-10 16:16:05.801491: Pseudo dice [0.4887, 0.6253, 0.6008, 0.1028, 0.3044, 0.3632, 0.603] +2026-04-10 16:16:05.805413: Epoch time: 102.34 s +2026-04-10 16:16:08.024616: +2026-04-10 16:16:08.026579: Epoch 208 +2026-04-10 16:16:08.027873: Current learning rate: 0.00953 +2026-04-10 16:17:50.740646: train_loss -0.1289 +2026-04-10 16:17:50.747049: val_loss -0.1411 +2026-04-10 16:17:50.749266: Pseudo dice [0.4373, 0.6841, 0.7609, 0.5084, 0.4539, 0.6556, 0.7051] +2026-04-10 16:17:50.752321: Epoch time: 102.72 s +2026-04-10 16:17:50.754132: Yayy! New best EMA pseudo Dice: 0.4619 +2026-04-10 16:17:53.361806: +2026-04-10 16:17:53.363458: Epoch 209 +2026-04-10 16:17:53.364856: Current learning rate: 0.00953 +2026-04-10 16:19:35.225547: train_loss -0.1343 +2026-04-10 16:19:35.251064: val_loss -0.113 +2026-04-10 16:19:35.252689: Pseudo dice [0.4716, 0.2623, 0.6956, 0.1348, 0.3369, 0.169, 0.7318] +2026-04-10 16:19:35.255345: Epoch time: 101.87 s +2026-04-10 16:19:36.304961: +2026-04-10 16:19:36.307580: Epoch 210 +2026-04-10 16:19:36.309263: Current learning rate: 0.00953 +2026-04-10 16:21:19.512762: train_loss -0.1216 +2026-04-10 16:21:19.537331: val_loss -0.1131 +2026-04-10 16:21:19.538895: Pseudo dice [0.487, 0.9, 0.4151, 0.3524, 0.3367, 0.4696, 0.8332] +2026-04-10 16:21:19.544243: Epoch time: 103.21 s +2026-04-10 16:21:19.546316: Yayy! New best EMA pseudo Dice: 0.4644 +2026-04-10 16:21:21.938234: +2026-04-10 16:21:21.940241: Epoch 211 +2026-04-10 16:21:21.941672: Current learning rate: 0.00952 +2026-04-10 16:23:04.014117: train_loss -0.1493 +2026-04-10 16:23:04.019571: val_loss -0.1322 +2026-04-10 16:23:04.021772: Pseudo dice [0.5881, 0.5956, 0.6386, 0.4455, 0.647, 0.5351, 0.7791] +2026-04-10 16:23:04.023740: Epoch time: 102.08 s +2026-04-10 16:23:04.025239: Yayy! New best EMA pseudo Dice: 0.4783 +2026-04-10 16:23:06.451152: +2026-04-10 16:23:06.452999: Epoch 212 +2026-04-10 16:23:06.455534: Current learning rate: 0.00952 +2026-04-10 16:24:49.019538: train_loss -0.1367 +2026-04-10 16:24:49.025277: val_loss -0.0794 +2026-04-10 16:24:49.027258: Pseudo dice [0.5246, 0.8156, 0.6539, 0.1835, 0.1888, 0.2993, 0.3199] +2026-04-10 16:24:49.029741: Epoch time: 102.57 s +2026-04-10 16:24:50.072417: +2026-04-10 16:24:50.073912: Epoch 213 +2026-04-10 16:24:50.075238: Current learning rate: 0.00952 +2026-04-10 16:26:32.907255: train_loss -0.1549 +2026-04-10 16:26:32.913496: val_loss -0.1282 +2026-04-10 16:26:32.915693: Pseudo dice [0.3841, 0.1216, 0.7375, 0.3122, 0.2624, 0.4144, 0.6051] +2026-04-10 16:26:32.918071: Epoch time: 102.84 s +2026-04-10 16:26:33.981619: +2026-04-10 16:26:33.983743: Epoch 214 +2026-04-10 16:26:33.985607: Current learning rate: 0.00952 +2026-04-10 16:28:16.450933: train_loss -0.1448 +2026-04-10 16:28:16.456481: val_loss -0.1 +2026-04-10 16:28:16.458596: Pseudo dice [0.2352, 0.2303, 0.7129, 0.3529, 0.2836, 0.3381, 0.5978] +2026-04-10 16:28:16.460775: Epoch time: 102.47 s +2026-04-10 16:28:17.494192: +2026-04-10 16:28:17.496799: Epoch 215 +2026-04-10 16:28:17.499150: Current learning rate: 0.00951 +2026-04-10 16:29:59.644926: train_loss -0.1303 +2026-04-10 16:29:59.652467: val_loss -0.0792 +2026-04-10 16:29:59.654457: Pseudo dice [0.4436, 0.4499, 0.3215, 0.4707, 0.358, 0.4816, 0.4864] +2026-04-10 16:29:59.657784: Epoch time: 102.15 s +2026-04-10 16:30:00.716342: +2026-04-10 16:30:00.719007: Epoch 216 +2026-04-10 16:30:00.721690: Current learning rate: 0.00951 +2026-04-10 16:31:43.056044: train_loss -0.1135 +2026-04-10 16:31:43.062037: val_loss -0.1064 +2026-04-10 16:31:43.063880: Pseudo dice [0.49, 0.6672, 0.6245, 0.1902, 0.3604, 0.236, 0.6979] +2026-04-10 16:31:43.066617: Epoch time: 102.34 s +2026-04-10 16:31:44.103506: +2026-04-10 16:31:44.105336: Epoch 217 +2026-04-10 16:31:44.106776: Current learning rate: 0.00951 +2026-04-10 16:33:26.810261: train_loss -0.1424 +2026-04-10 16:33:26.816240: val_loss -0.096 +2026-04-10 16:33:26.817806: Pseudo dice [0.2081, 0.7938, 0.6468, 0.3834, 0.3833, 0.6041, 0.2535] +2026-04-10 16:33:26.820265: Epoch time: 102.71 s +2026-04-10 16:33:27.875982: +2026-04-10 16:33:27.877895: Epoch 218 +2026-04-10 16:33:27.881238: Current learning rate: 0.00951 +2026-04-10 16:35:10.103609: train_loss -0.1491 +2026-04-10 16:35:10.109751: val_loss -0.1204 +2026-04-10 16:35:10.111504: Pseudo dice [0.1471, 0.8181, 0.6867, 0.3024, 0.3894, 0.4066, 0.5957] +2026-04-10 16:35:10.114079: Epoch time: 102.23 s +2026-04-10 16:35:11.169820: +2026-04-10 16:35:11.173196: Epoch 219 +2026-04-10 16:35:11.175080: Current learning rate: 0.00951 +2026-04-10 16:36:52.982139: train_loss -0.1441 +2026-04-10 16:36:52.988345: val_loss -0.0162 +2026-04-10 16:36:52.990317: Pseudo dice [0.5586, 0.5102, 0.2641, 0.3978, 0.2761, 0.6432, 0.4784] +2026-04-10 16:36:52.992291: Epoch time: 101.82 s +2026-04-10 16:36:54.023629: +2026-04-10 16:36:54.025081: Epoch 220 +2026-04-10 16:36:54.026482: Current learning rate: 0.0095 +2026-04-10 16:38:35.976306: train_loss -0.133 +2026-04-10 16:38:35.981745: val_loss -0.0859 +2026-04-10 16:38:35.983924: Pseudo dice [0.3408, 0.8439, 0.3797, 0.2755, 0.2704, 0.1152, 0.4836] +2026-04-10 16:38:35.986550: Epoch time: 101.96 s +2026-04-10 16:38:37.038480: +2026-04-10 16:38:37.042176: Epoch 221 +2026-04-10 16:38:37.043878: Current learning rate: 0.0095 +2026-04-10 16:40:19.708958: train_loss -0.1207 +2026-04-10 16:40:19.715218: val_loss -0.1085 +2026-04-10 16:40:19.717490: Pseudo dice [0.2984, 0.1534, 0.5824, 0.4772, 0.4989, 0.4675, 0.4562] +2026-04-10 16:40:19.719734: Epoch time: 102.67 s +2026-04-10 16:40:20.755219: +2026-04-10 16:40:20.757831: Epoch 222 +2026-04-10 16:40:20.759571: Current learning rate: 0.0095 +2026-04-10 16:42:04.518852: train_loss -0.1262 +2026-04-10 16:42:04.524940: val_loss -0.0913 +2026-04-10 16:42:04.526770: Pseudo dice [0.5652, 0.7492, 0.6101, 0.5605, 0.4021, 0.1504, 0.4018] +2026-04-10 16:42:04.528979: Epoch time: 103.77 s +2026-04-10 16:42:05.583956: +2026-04-10 16:42:05.585526: Epoch 223 +2026-04-10 16:42:05.586866: Current learning rate: 0.0095 +2026-04-10 16:43:47.975860: train_loss -0.1328 +2026-04-10 16:43:47.981285: val_loss -0.1214 +2026-04-10 16:43:47.983543: Pseudo dice [0.3782, 0.433, 0.537, 0.5044, 0.5418, 0.2576, 0.5565] +2026-04-10 16:43:47.985593: Epoch time: 102.4 s +2026-04-10 16:43:49.082228: +2026-04-10 16:43:49.084610: Epoch 224 +2026-04-10 16:43:49.086398: Current learning rate: 0.00949 +2026-04-10 16:45:33.937731: train_loss -0.125 +2026-04-10 16:45:33.943561: val_loss -0.1087 +2026-04-10 16:45:33.945674: Pseudo dice [0.3234, 0.6443, 0.5868, 0.1956, 0.188, 0.3593, 0.6726] +2026-04-10 16:45:33.947639: Epoch time: 104.86 s +2026-04-10 16:45:34.978462: +2026-04-10 16:45:34.980445: Epoch 225 +2026-04-10 16:45:34.982246: Current learning rate: 0.00949 +2026-04-10 16:47:17.622903: train_loss -0.1493 +2026-04-10 16:47:17.630083: val_loss -0.0952 +2026-04-10 16:47:17.632107: Pseudo dice [0.5035, 0.8156, 0.6403, 0.0306, 0.4374, 0.1473, 0.3599] +2026-04-10 16:47:17.635012: Epoch time: 102.65 s +2026-04-10 16:47:18.677411: +2026-04-10 16:47:18.679136: Epoch 226 +2026-04-10 16:47:18.681351: Current learning rate: 0.00949 +2026-04-10 16:49:01.103454: train_loss -0.1088 +2026-04-10 16:49:01.112220: val_loss -0.0624 +2026-04-10 16:49:01.114284: Pseudo dice [0.2444, 0.0621, 0.4377, 0.2879, 0.2756, 0.42, 0.6872] +2026-04-10 16:49:01.117565: Epoch time: 102.43 s +2026-04-10 16:49:02.144898: +2026-04-10 16:49:02.147823: Epoch 227 +2026-04-10 16:49:02.149373: Current learning rate: 0.00949 +2026-04-10 16:50:44.485944: train_loss -0.1292 +2026-04-10 16:50:44.492730: val_loss -0.0802 +2026-04-10 16:50:44.497751: Pseudo dice [0.3332, 0.452, 0.555, 0.2073, 0.4099, 0.5021, 0.3855] +2026-04-10 16:50:44.500190: Epoch time: 102.34 s +2026-04-10 16:50:46.760364: +2026-04-10 16:50:46.762035: Epoch 228 +2026-04-10 16:50:46.763498: Current learning rate: 0.00949 +2026-04-10 16:52:30.809780: train_loss -0.1218 +2026-04-10 16:52:30.816133: val_loss -0.1037 +2026-04-10 16:52:30.818190: Pseudo dice [0.2155, 0.6074, 0.5979, 0.2576, 0.4217, 0.1467, 0.7061] +2026-04-10 16:52:30.821043: Epoch time: 104.05 s +2026-04-10 16:52:31.868815: +2026-04-10 16:52:31.870975: Epoch 229 +2026-04-10 16:52:31.872921: Current learning rate: 0.00948 +2026-04-10 16:54:14.800420: train_loss -0.1449 +2026-04-10 16:54:14.807169: val_loss -0.0859 +2026-04-10 16:54:14.809758: Pseudo dice [0.1847, 0.6688, 0.6667, 0.0939, 0.1951, 0.4016, 0.6051] +2026-04-10 16:54:14.812281: Epoch time: 102.93 s +2026-04-10 16:54:15.872954: +2026-04-10 16:54:15.874774: Epoch 230 +2026-04-10 16:54:15.876853: Current learning rate: 0.00948 +2026-04-10 16:55:58.801992: train_loss -0.1312 +2026-04-10 16:55:58.808059: val_loss -0.1171 +2026-04-10 16:55:58.813988: Pseudo dice [0.2657, 0.5802, 0.6919, 0.213, 0.3583, 0.4947, 0.5751] +2026-04-10 16:55:58.817730: Epoch time: 102.93 s +2026-04-10 16:55:59.881584: +2026-04-10 16:55:59.884198: Epoch 231 +2026-04-10 16:55:59.885940: Current learning rate: 0.00948 +2026-04-10 16:57:41.913937: train_loss -0.1466 +2026-04-10 16:57:41.921827: val_loss -0.119 +2026-04-10 16:57:41.923934: Pseudo dice [0.5533, 0.1403, 0.4567, 0.5505, 0.3906, 0.4651, 0.7126] +2026-04-10 16:57:41.926669: Epoch time: 102.04 s +2026-04-10 16:57:42.968199: +2026-04-10 16:57:42.970231: Epoch 232 +2026-04-10 16:57:42.971998: Current learning rate: 0.00948 +2026-04-10 16:59:25.778888: train_loss -0.1413 +2026-04-10 16:59:25.787829: val_loss -0.1007 +2026-04-10 16:59:25.791067: Pseudo dice [0.6031, 0.8363, 0.3941, 0.1307, 0.5174, 0.6001, 0.4344] +2026-04-10 16:59:25.794164: Epoch time: 102.81 s +2026-04-10 16:59:26.844952: +2026-04-10 16:59:26.846719: Epoch 233 +2026-04-10 16:59:26.848976: Current learning rate: 0.00947 +2026-04-10 17:01:09.448484: train_loss -0.1589 +2026-04-10 17:01:09.454532: val_loss -0.0952 +2026-04-10 17:01:09.456687: Pseudo dice [0.3596, 0.8554, 0.6096, 0.153, 0.4618, 0.3104, 0.6665] +2026-04-10 17:01:09.459466: Epoch time: 102.61 s +2026-04-10 17:01:10.501033: +2026-04-10 17:01:10.502568: Epoch 234 +2026-04-10 17:01:10.503934: Current learning rate: 0.00947 +2026-04-10 17:02:53.006053: train_loss -0.1332 +2026-04-10 17:02:53.013925: val_loss -0.1066 +2026-04-10 17:02:53.016698: Pseudo dice [0.1116, 0.7679, 0.7426, 0.1298, 0.4421, 0.4686, 0.7879] +2026-04-10 17:02:53.018828: Epoch time: 102.51 s +2026-04-10 17:02:54.076760: +2026-04-10 17:02:54.078757: Epoch 235 +2026-04-10 17:02:54.081055: Current learning rate: 0.00947 +2026-04-10 17:04:36.763592: train_loss -0.1315 +2026-04-10 17:04:36.771519: val_loss -0.0797 +2026-04-10 17:04:36.773529: Pseudo dice [0.736, 0.6301, 0.4522, 0.2139, 0.3018, 0.2848, 0.5331] +2026-04-10 17:04:36.776710: Epoch time: 102.69 s +2026-04-10 17:04:37.846011: +2026-04-10 17:04:37.848412: Epoch 236 +2026-04-10 17:04:37.850599: Current learning rate: 0.00947 +2026-04-10 17:06:20.598282: train_loss -0.1371 +2026-04-10 17:06:20.604620: val_loss -0.1227 +2026-04-10 17:06:20.606530: Pseudo dice [0.4701, 0.6028, 0.646, 0.0416, 0.4114, 0.4639, 0.8196] +2026-04-10 17:06:20.608720: Epoch time: 102.76 s +2026-04-10 17:06:21.647758: +2026-04-10 17:06:21.649952: Epoch 237 +2026-04-10 17:06:21.652004: Current learning rate: 0.00947 +2026-04-10 17:08:04.000684: train_loss -0.1259 +2026-04-10 17:08:04.006969: val_loss -0.0756 +2026-04-10 17:08:04.015114: Pseudo dice [0.3376, 0.4719, 0.1423, 0.0035, 0.1023, 0.4009, 0.1812] +2026-04-10 17:08:04.017782: Epoch time: 102.36 s +2026-04-10 17:08:05.066945: +2026-04-10 17:08:05.069531: Epoch 238 +2026-04-10 17:08:05.071288: Current learning rate: 0.00946 +2026-04-10 17:09:47.624205: train_loss -0.1318 +2026-04-10 17:09:47.629479: val_loss -0.1184 +2026-04-10 17:09:47.631363: Pseudo dice [0.4751, 0.1645, 0.6249, 0.3243, 0.45, 0.636, 0.73] +2026-04-10 17:09:47.636458: Epoch time: 102.56 s +2026-04-10 17:09:48.684121: +2026-04-10 17:09:48.685878: Epoch 239 +2026-04-10 17:09:48.687298: Current learning rate: 0.00946 +2026-04-10 17:11:31.053690: train_loss -0.1353 +2026-04-10 17:11:31.060260: val_loss -0.0916 +2026-04-10 17:11:31.062797: Pseudo dice [0.5186, 0.3661, 0.3164, 0.2013, 0.461, 0.7003, 0.6303] +2026-04-10 17:11:31.065058: Epoch time: 102.37 s +2026-04-10 17:11:32.143574: +2026-04-10 17:11:32.145185: Epoch 240 +2026-04-10 17:11:32.146881: Current learning rate: 0.00946 +2026-04-10 17:13:14.861781: train_loss -0.1299 +2026-04-10 17:13:14.871497: val_loss -0.1149 +2026-04-10 17:13:14.873626: Pseudo dice [0.3613, 0.2266, 0.7316, 0.2493, 0.3863, 0.7034, 0.6267] +2026-04-10 17:13:14.876302: Epoch time: 102.72 s +2026-04-10 17:13:16.171971: +2026-04-10 17:13:16.174197: Epoch 241 +2026-04-10 17:13:16.176021: Current learning rate: 0.00946 +2026-04-10 17:14:58.367234: train_loss -0.1452 +2026-04-10 17:14:58.376331: val_loss -0.1236 +2026-04-10 17:14:58.378463: Pseudo dice [0.577, 0.4066, 0.6845, 0.2832, 0.1048, 0.6025, 0.5883] +2026-04-10 17:14:58.380870: Epoch time: 102.2 s +2026-04-10 17:14:59.474901: +2026-04-10 17:14:59.476858: Epoch 242 +2026-04-10 17:14:59.479016: Current learning rate: 0.00945 +2026-04-10 17:16:41.876005: train_loss -0.1337 +2026-04-10 17:16:41.881877: val_loss -0.0857 +2026-04-10 17:16:41.884014: Pseudo dice [0.4698, 0.451, 0.6056, 0.1145, 0.4315, 0.2252, 0.5971] +2026-04-10 17:16:41.885951: Epoch time: 102.4 s +2026-04-10 17:16:42.970714: +2026-04-10 17:16:42.972718: Epoch 243 +2026-04-10 17:16:42.974917: Current learning rate: 0.00945 +2026-04-10 17:18:24.655001: train_loss -0.1272 +2026-04-10 17:18:24.660554: val_loss -0.1038 +2026-04-10 17:18:24.662795: Pseudo dice [0.3191, 0.3195, 0.628, 0.2224, 0.3289, 0.4688, 0.6582] +2026-04-10 17:18:24.664865: Epoch time: 101.69 s +2026-04-10 17:18:25.739812: +2026-04-10 17:18:25.743351: Epoch 244 +2026-04-10 17:18:25.745613: Current learning rate: 0.00945 +2026-04-10 17:20:08.705212: train_loss -0.1571 +2026-04-10 17:20:08.712216: val_loss -0.1126 +2026-04-10 17:20:08.714504: Pseudo dice [0.4047, 0.8727, 0.7258, 0.634, 0.4147, 0.4172, 0.6644] +2026-04-10 17:20:08.716904: Epoch time: 102.97 s +2026-04-10 17:20:09.851363: +2026-04-10 17:20:09.853429: Epoch 245 +2026-04-10 17:20:09.855045: Current learning rate: 0.00945 +2026-04-10 17:21:52.399798: train_loss -0.143 +2026-04-10 17:21:52.405893: val_loss -0.0999 +2026-04-10 17:21:52.408318: Pseudo dice [0.4121, 0.8219, 0.5929, 0.4099, 0.2797, 0.3495, 0.7349] +2026-04-10 17:21:52.410583: Epoch time: 102.55 s +2026-04-10 17:21:53.476679: +2026-04-10 17:21:53.478603: Epoch 246 +2026-04-10 17:21:53.480578: Current learning rate: 0.00944 +2026-04-10 17:23:35.331990: train_loss -0.133 +2026-04-10 17:23:35.338047: val_loss -0.1167 +2026-04-10 17:23:35.340221: Pseudo dice [0.763, 0.7137, 0.6434, 0.5644, 0.2836, 0.4202, 0.5865] +2026-04-10 17:23:35.342551: Epoch time: 101.86 s +2026-04-10 17:23:36.419199: +2026-04-10 17:23:36.421099: Epoch 247 +2026-04-10 17:23:36.422958: Current learning rate: 0.00944 +2026-04-10 17:25:18.608362: train_loss -0.1473 +2026-04-10 17:25:18.616098: val_loss -0.1252 +2026-04-10 17:25:18.618095: Pseudo dice [0.5292, 0.3285, 0.3973, 0.3012, 0.3284, 0.6033, 0.6436] +2026-04-10 17:25:18.620345: Epoch time: 102.19 s +2026-04-10 17:25:19.684712: +2026-04-10 17:25:19.686673: Epoch 248 +2026-04-10 17:25:19.688842: Current learning rate: 0.00944 +2026-04-10 17:27:02.287271: train_loss -0.1461 +2026-04-10 17:27:02.293463: val_loss -0.1139 +2026-04-10 17:27:02.296254: Pseudo dice [0.5794, 0.5714, 0.7294, 0.2698, 0.4317, 0.4894, 0.5429] +2026-04-10 17:27:02.298975: Epoch time: 102.61 s +2026-04-10 17:27:04.582524: +2026-04-10 17:27:04.584126: Epoch 249 +2026-04-10 17:27:04.585445: Current learning rate: 0.00944 +2026-04-10 17:28:46.513495: train_loss -0.1387 +2026-04-10 17:28:46.519561: val_loss -0.1254 +2026-04-10 17:28:46.521316: Pseudo dice [0.1705, 0.1476, 0.6981, 0.1214, 0.3796, 0.3354, 0.6837] +2026-04-10 17:28:46.524272: Epoch time: 101.93 s +2026-04-10 17:28:49.204496: +2026-04-10 17:28:49.206665: Epoch 250 +2026-04-10 17:28:49.208071: Current learning rate: 0.00944 +2026-04-10 17:30:31.630695: train_loss -0.1291 +2026-04-10 17:30:31.637071: val_loss -0.1025 +2026-04-10 17:30:31.640726: Pseudo dice [0.1855, 0.805, 0.5618, 0.0156, 0.4424, 0.2853, 0.5228] +2026-04-10 17:30:31.643126: Epoch time: 102.43 s +2026-04-10 17:30:32.717836: +2026-04-10 17:30:32.719706: Epoch 251 +2026-04-10 17:30:32.721152: Current learning rate: 0.00943 +2026-04-10 17:32:14.898250: train_loss -0.1407 +2026-04-10 17:32:14.905817: val_loss -0.1373 +2026-04-10 17:32:14.908944: Pseudo dice [0.7023, 0.1935, 0.7426, 0.2216, 0.3941, 0.2334, 0.7388] +2026-04-10 17:32:14.911584: Epoch time: 102.18 s +2026-04-10 17:32:15.970307: +2026-04-10 17:32:15.972068: Epoch 252 +2026-04-10 17:32:15.973607: Current learning rate: 0.00943 +2026-04-10 17:33:58.777135: train_loss -0.1384 +2026-04-10 17:33:58.801657: val_loss -0.096 +2026-04-10 17:33:58.805462: Pseudo dice [0.5786, 0.4316, 0.7217, 0.2269, 0.2932, 0.472, 0.4338] +2026-04-10 17:33:58.808685: Epoch time: 102.81 s +2026-04-10 17:33:59.874802: +2026-04-10 17:33:59.877119: Epoch 253 +2026-04-10 17:33:59.878994: Current learning rate: 0.00943 +2026-04-10 17:35:42.737877: train_loss -0.1374 +2026-04-10 17:35:42.746644: val_loss -0.1059 +2026-04-10 17:35:42.749302: Pseudo dice [0.521, 0.7144, 0.7559, 0.4717, 0.4033, 0.1743, 0.6136] +2026-04-10 17:35:42.752318: Epoch time: 102.87 s +2026-04-10 17:35:43.840070: +2026-04-10 17:35:43.842165: Epoch 254 +2026-04-10 17:35:43.845101: Current learning rate: 0.00943 +2026-04-10 17:37:27.111745: train_loss -0.1339 +2026-04-10 17:37:27.119732: val_loss -0.1229 +2026-04-10 17:37:27.128751: Pseudo dice [0.5096, 0.6652, 0.7825, 0.2865, 0.3557, 0.2691, 0.7402] +2026-04-10 17:37:27.132225: Epoch time: 103.27 s +2026-04-10 17:37:28.282818: +2026-04-10 17:37:28.285446: Epoch 255 +2026-04-10 17:37:28.287406: Current learning rate: 0.00942 +2026-04-10 17:39:11.700435: train_loss -0.1439 +2026-04-10 17:39:11.708085: val_loss -0.1272 +2026-04-10 17:39:11.710513: Pseudo dice [0.0993, 0.6087, 0.7002, 0.1062, 0.2866, 0.6613, 0.6728] +2026-04-10 17:39:11.712731: Epoch time: 103.42 s +2026-04-10 17:39:12.785580: +2026-04-10 17:39:12.788578: Epoch 256 +2026-04-10 17:39:12.790683: Current learning rate: 0.00942 +2026-04-10 17:41:02.173666: train_loss -0.1524 +2026-04-10 17:41:02.181239: val_loss -0.1146 +2026-04-10 17:41:02.183485: Pseudo dice [0.3465, 0.5472, 0.6636, 0.1817, 0.4364, 0.5769, 0.7727] +2026-04-10 17:41:02.186177: Epoch time: 109.39 s +2026-04-10 17:41:03.249307: +2026-04-10 17:41:03.250932: Epoch 257 +2026-04-10 17:41:03.252687: Current learning rate: 0.00942 +2026-04-10 17:42:45.189323: train_loss -0.1484 +2026-04-10 17:42:45.197140: val_loss -0.1218 +2026-04-10 17:42:45.199022: Pseudo dice [0.3285, 0.2998, 0.6167, 0.3839, 0.5073, 0.4452, 0.7523] +2026-04-10 17:42:45.202808: Epoch time: 101.94 s +2026-04-10 17:42:46.279631: +2026-04-10 17:42:46.281582: Epoch 258 +2026-04-10 17:42:46.286093: Current learning rate: 0.00942 +2026-04-10 17:44:28.817582: train_loss -0.1341 +2026-04-10 17:44:28.824336: val_loss -0.1236 +2026-04-10 17:44:28.826721: Pseudo dice [0.1156, 0.6759, 0.7016, 0.2585, 0.2436, 0.744, 0.5967] +2026-04-10 17:44:28.829420: Epoch time: 102.54 s +2026-04-10 17:44:29.885166: +2026-04-10 17:44:29.887868: Epoch 259 +2026-04-10 17:44:29.890658: Current learning rate: 0.00942 +2026-04-10 17:46:12.513537: train_loss -0.1512 +2026-04-10 17:46:12.519849: val_loss -0.115 +2026-04-10 17:46:12.522276: Pseudo dice [0.3896, 0.2368, 0.6886, 0.5106, 0.5277, 0.1903, 0.7864] +2026-04-10 17:46:12.524396: Epoch time: 102.63 s +2026-04-10 17:46:13.577389: +2026-04-10 17:46:13.579693: Epoch 260 +2026-04-10 17:46:13.581328: Current learning rate: 0.00941 +2026-04-10 17:47:56.789901: train_loss -0.144 +2026-04-10 17:47:56.796794: val_loss -0.0791 +2026-04-10 17:47:56.799276: Pseudo dice [0.5356, 0.1913, 0.4174, 0.1311, 0.337, 0.2515, 0.7063] +2026-04-10 17:47:56.801881: Epoch time: 103.22 s +2026-04-10 17:47:57.867247: +2026-04-10 17:47:57.870332: Epoch 261 +2026-04-10 17:47:57.872344: Current learning rate: 0.00941 +2026-04-10 17:49:41.138412: train_loss -0.1639 +2026-04-10 17:49:41.145473: val_loss -0.1214 +2026-04-10 17:49:41.148321: Pseudo dice [0.4814, 0.1695, 0.7853, 0.2427, 0.2158, 0.6302, 0.754] +2026-04-10 17:49:41.151224: Epoch time: 103.27 s +2026-04-10 17:49:42.253795: +2026-04-10 17:49:42.255793: Epoch 262 +2026-04-10 17:49:42.258348: Current learning rate: 0.00941 +2026-04-10 17:51:24.443358: train_loss -0.1523 +2026-04-10 17:51:24.449541: val_loss -0.1026 +2026-04-10 17:51:24.451503: Pseudo dice [0.4794, 0.1883, 0.7351, 0.3472, 0.3898, 0.6289, 0.3903] +2026-04-10 17:51:24.453938: Epoch time: 102.19 s +2026-04-10 17:51:25.525596: +2026-04-10 17:51:25.527690: Epoch 263 +2026-04-10 17:51:25.529088: Current learning rate: 0.00941 +2026-04-10 17:53:11.831458: train_loss -0.1579 +2026-04-10 17:53:11.839558: val_loss -0.1008 +2026-04-10 17:53:11.841733: Pseudo dice [0.6907, 0.2175, 0.4573, 0.1334, 0.3173, 0.329, 0.6961] +2026-04-10 17:53:11.846294: Epoch time: 106.31 s +2026-04-10 17:53:12.927905: +2026-04-10 17:53:12.930235: Epoch 264 +2026-04-10 17:53:12.932035: Current learning rate: 0.0094 +2026-04-10 17:54:56.035444: train_loss -0.1637 +2026-04-10 17:54:56.041840: val_loss -0.1113 +2026-04-10 17:54:56.043762: Pseudo dice [0.2518, 0.6481, 0.7204, 0.231, 0.3211, 0.4983, 0.6378] +2026-04-10 17:54:56.047768: Epoch time: 103.11 s +2026-04-10 17:54:57.134007: +2026-04-10 17:54:57.136169: Epoch 265 +2026-04-10 17:54:57.138197: Current learning rate: 0.0094 +2026-04-10 17:56:40.340641: train_loss -0.1507 +2026-04-10 17:56:40.347848: val_loss -0.0945 +2026-04-10 17:56:40.350256: Pseudo dice [0.5511, 0.1189, 0.6166, 0.6025, 0.4553, 0.5715, 0.441] +2026-04-10 17:56:40.352551: Epoch time: 103.21 s +2026-04-10 17:56:41.432497: +2026-04-10 17:56:41.434623: Epoch 266 +2026-04-10 17:56:41.436457: Current learning rate: 0.0094 +2026-04-10 17:58:25.309295: train_loss -0.138 +2026-04-10 17:58:25.318614: val_loss -0.1557 +2026-04-10 17:58:25.321912: Pseudo dice [0.4937, 0.6422, 0.7027, 0.4114, 0.4817, 0.5304, 0.8358] +2026-04-10 17:58:25.325343: Epoch time: 103.88 s +2026-04-10 17:58:26.376287: +2026-04-10 17:58:26.378500: Epoch 267 +2026-04-10 17:58:26.380919: Current learning rate: 0.0094 +2026-04-10 18:00:09.676647: train_loss -0.1523 +2026-04-10 18:00:09.693993: val_loss -0.1197 +2026-04-10 18:00:09.695852: Pseudo dice [0.665, 0.1141, 0.6959, 0.455, 0.2214, 0.5615, 0.6703] +2026-04-10 18:00:09.698487: Epoch time: 103.3 s +2026-04-10 18:00:10.761504: +2026-04-10 18:00:10.766612: Epoch 268 +2026-04-10 18:00:10.777540: Current learning rate: 0.00939 +2026-04-10 18:01:59.487329: train_loss -0.1503 +2026-04-10 18:01:59.499685: val_loss -0.1062 +2026-04-10 18:01:59.509242: Pseudo dice [0.0669, 0.1004, 0.7576, 0.1074, 0.2037, 0.607, 0.777] +2026-04-10 18:01:59.520774: Epoch time: 108.73 s +2026-04-10 18:02:01.946324: +2026-04-10 18:02:01.948931: Epoch 269 +2026-04-10 18:02:01.950739: Current learning rate: 0.00939 +2026-04-10 18:03:45.564946: train_loss -0.1493 +2026-04-10 18:03:45.575301: val_loss -0.0915 +2026-04-10 18:03:45.577694: Pseudo dice [0.4312, 0.6297, 0.7197, 0.3509, 0.2808, 0.358, 0.7429] +2026-04-10 18:03:45.581547: Epoch time: 103.62 s +2026-04-10 18:03:46.669246: +2026-04-10 18:03:46.672321: Epoch 270 +2026-04-10 18:03:46.674935: Current learning rate: 0.00939 +2026-04-10 18:05:30.717226: train_loss -0.1241 +2026-04-10 18:05:30.725157: val_loss -0.1287 +2026-04-10 18:05:30.727772: Pseudo dice [0.4965, 0.6106, 0.8072, 0.3099, 0.4189, 0.3278, 0.5599] +2026-04-10 18:05:30.731408: Epoch time: 104.05 s +2026-04-10 18:05:31.841737: +2026-04-10 18:05:31.845697: Epoch 271 +2026-04-10 18:05:31.848456: Current learning rate: 0.00939 +2026-04-10 18:07:15.665552: train_loss -0.1579 +2026-04-10 18:07:15.674654: val_loss -0.1413 +2026-04-10 18:07:15.678184: Pseudo dice [0.5624, 0.4907, 0.6636, 0.0371, 0.4836, 0.2304, 0.6967] +2026-04-10 18:07:15.681147: Epoch time: 103.83 s +2026-04-10 18:07:16.771999: +2026-04-10 18:07:16.775102: Epoch 272 +2026-04-10 18:07:16.777588: Current learning rate: 0.00939 +2026-04-10 18:09:00.308089: train_loss -0.1389 +2026-04-10 18:09:00.314762: val_loss -0.0922 +2026-04-10 18:09:00.317250: Pseudo dice [0.4619, 0.4594, 0.3328, 0.359, 0.5153, 0.1618, 0.6469] +2026-04-10 18:09:00.319916: Epoch time: 103.54 s +2026-04-10 18:09:01.402458: +2026-04-10 18:09:01.405345: Epoch 273 +2026-04-10 18:09:01.407403: Current learning rate: 0.00938 +2026-04-10 18:10:48.283243: train_loss -0.1472 +2026-04-10 18:10:48.289914: val_loss -0.1396 +2026-04-10 18:10:48.292257: Pseudo dice [0.6304, 0.6941, 0.665, 0.3342, 0.3813, 0.2943, 0.6988] +2026-04-10 18:10:48.294858: Epoch time: 106.88 s +2026-04-10 18:10:49.359151: +2026-04-10 18:10:49.362038: Epoch 274 +2026-04-10 18:10:49.363939: Current learning rate: 0.00938 +2026-04-10 18:12:32.108851: train_loss -0.142 +2026-04-10 18:12:32.114723: val_loss -0.1281 +2026-04-10 18:12:32.117211: Pseudo dice [0.3632, 0.1803, 0.7159, 0.0701, 0.2863, 0.6971, 0.749] +2026-04-10 18:12:32.120375: Epoch time: 102.75 s +2026-04-10 18:12:33.219388: +2026-04-10 18:12:33.221202: Epoch 275 +2026-04-10 18:12:33.223781: Current learning rate: 0.00938 +2026-04-10 18:14:16.604452: train_loss -0.1533 +2026-04-10 18:14:16.616232: val_loss -0.0165 +2026-04-10 18:14:16.618394: Pseudo dice [0.4723, 0.8615, 0.2389, 0.1233, 0.2173, 0.1555, 0.4149] +2026-04-10 18:14:16.621109: Epoch time: 103.39 s +2026-04-10 18:14:17.694594: +2026-04-10 18:14:17.697569: Epoch 276 +2026-04-10 18:14:17.700351: Current learning rate: 0.00938 +2026-04-10 18:16:00.779822: train_loss -0.1438 +2026-04-10 18:16:00.786274: val_loss -0.1443 +2026-04-10 18:16:00.796883: Pseudo dice [0.6295, 0.5375, 0.6732, 0.471, 0.4466, 0.2172, 0.7942] +2026-04-10 18:16:00.800189: Epoch time: 103.09 s +2026-04-10 18:16:01.899515: +2026-04-10 18:16:01.901823: Epoch 277 +2026-04-10 18:16:01.903666: Current learning rate: 0.00937 +2026-04-10 18:17:46.259202: train_loss -0.159 +2026-04-10 18:17:46.266392: val_loss -0.1373 +2026-04-10 18:17:46.271550: Pseudo dice [0.387, 0.6121, 0.5997, 0.26, 0.4389, 0.7611, 0.7059] +2026-04-10 18:17:46.274403: Epoch time: 104.36 s +2026-04-10 18:17:47.397903: +2026-04-10 18:17:47.399682: Epoch 278 +2026-04-10 18:17:47.401163: Current learning rate: 0.00937 +2026-04-10 18:19:30.429031: train_loss -0.1461 +2026-04-10 18:19:30.436324: val_loss -0.1159 +2026-04-10 18:19:30.438586: Pseudo dice [0.4164, 0.072, 0.6687, 0.3359, 0.4458, 0.4888, 0.1702] +2026-04-10 18:19:30.441144: Epoch time: 103.03 s +2026-04-10 18:19:31.508610: +2026-04-10 18:19:31.510610: Epoch 279 +2026-04-10 18:19:31.513211: Current learning rate: 0.00937 +2026-04-10 18:21:14.515917: train_loss -0.153 +2026-04-10 18:21:14.522909: val_loss -0.1239 +2026-04-10 18:21:14.525970: Pseudo dice [0.2268, 0.6141, 0.6465, 0.4403, 0.35, 0.8046, 0.7395] +2026-04-10 18:21:14.529105: Epoch time: 103.01 s +2026-04-10 18:21:15.617161: +2026-04-10 18:21:15.620801: Epoch 280 +2026-04-10 18:21:15.623806: Current learning rate: 0.00937 +2026-04-10 18:22:59.247061: train_loss -0.1522 +2026-04-10 18:22:59.254083: val_loss -0.1038 +2026-04-10 18:22:59.256685: Pseudo dice [0.2576, 0.8905, 0.654, 0.1264, 0.0, 0.6108, 0.5408] +2026-04-10 18:22:59.259258: Epoch time: 103.63 s +2026-04-10 18:23:00.352968: +2026-04-10 18:23:00.355517: Epoch 281 +2026-04-10 18:23:00.357132: Current learning rate: 0.00937 +2026-04-10 18:24:43.694399: train_loss -0.159 +2026-04-10 18:24:43.701433: val_loss -0.1176 +2026-04-10 18:24:43.703711: Pseudo dice [0.3623, 0.8146, 0.5965, 0.3874, 0.3213, 0.204, 0.6861] +2026-04-10 18:24:43.706049: Epoch time: 103.34 s +2026-04-10 18:24:44.799975: +2026-04-10 18:24:44.802333: Epoch 282 +2026-04-10 18:24:44.804156: Current learning rate: 0.00936 +2026-04-10 18:26:27.784265: train_loss -0.1414 +2026-04-10 18:26:27.790772: val_loss -0.1104 +2026-04-10 18:26:27.792997: Pseudo dice [0.3636, 0.4655, 0.6876, 0.3085, 0.4409, 0.7226, 0.5787] +2026-04-10 18:26:27.795868: Epoch time: 102.99 s +2026-04-10 18:26:28.890409: +2026-04-10 18:26:28.892247: Epoch 283 +2026-04-10 18:26:28.893857: Current learning rate: 0.00936 +2026-04-10 18:28:12.218002: train_loss -0.1466 +2026-04-10 18:28:12.225804: val_loss -0.1124 +2026-04-10 18:28:12.228728: Pseudo dice [0.5625, 0.1095, 0.6176, 0.2974, 0.3997, 0.6459, 0.7245] +2026-04-10 18:28:12.231193: Epoch time: 103.33 s +2026-04-10 18:28:13.299592: +2026-04-10 18:28:13.301674: Epoch 284 +2026-04-10 18:28:13.303610: Current learning rate: 0.00936 +2026-04-10 18:29:57.574471: train_loss -0.1491 +2026-04-10 18:29:57.581158: val_loss -0.0997 +2026-04-10 18:29:57.583204: Pseudo dice [0.3617, 0.1918, 0.6724, 0.4193, 0.009, 0.2686, 0.7909] +2026-04-10 18:29:57.585894: Epoch time: 104.28 s +2026-04-10 18:29:58.693988: +2026-04-10 18:29:58.696345: Epoch 285 +2026-04-10 18:29:58.700299: Current learning rate: 0.00936 +2026-04-10 18:31:41.970698: train_loss -0.1517 +2026-04-10 18:31:41.976927: val_loss -0.0906 +2026-04-10 18:31:41.979096: Pseudo dice [0.2089, 0.5169, 0.6563, 0.4251, 0.349, 0.4337, 0.6718] +2026-04-10 18:31:41.981460: Epoch time: 103.28 s +2026-04-10 18:31:43.098429: +2026-04-10 18:31:43.101674: Epoch 286 +2026-04-10 18:31:43.103575: Current learning rate: 0.00935 +2026-04-10 18:33:25.661989: train_loss -0.1439 +2026-04-10 18:33:25.667813: val_loss -0.1254 +2026-04-10 18:33:25.670194: Pseudo dice [0.4131, 0.0144, 0.8168, 0.4279, 0.4111, 0.1605, 0.7114] +2026-04-10 18:33:25.672363: Epoch time: 102.57 s +2026-04-10 18:33:26.790479: +2026-04-10 18:33:26.793603: Epoch 287 +2026-04-10 18:33:26.796793: Current learning rate: 0.00935 +2026-04-10 18:35:10.252921: train_loss -0.1451 +2026-04-10 18:35:10.259493: val_loss -0.1018 +2026-04-10 18:35:10.261680: Pseudo dice [0.5872, 0.3708, 0.7716, 0.481, 0.3653, 0.4009, 0.549] +2026-04-10 18:35:10.264173: Epoch time: 103.47 s +2026-04-10 18:35:11.354394: +2026-04-10 18:35:11.357189: Epoch 288 +2026-04-10 18:35:11.359126: Current learning rate: 0.00935 +2026-04-10 18:36:54.929507: train_loss -0.1438 +2026-04-10 18:36:54.936841: val_loss -0.0849 +2026-04-10 18:36:54.940422: Pseudo dice [0.2121, 0.783, 0.7179, 0.1521, 0.4955, 0.3169, 0.1509] +2026-04-10 18:36:54.943198: Epoch time: 103.58 s +2026-04-10 18:36:56.081323: +2026-04-10 18:36:56.083221: Epoch 289 +2026-04-10 18:36:56.085076: Current learning rate: 0.00935 +2026-04-10 18:38:40.403510: train_loss -0.1447 +2026-04-10 18:38:40.411979: val_loss -0.121 +2026-04-10 18:38:40.414242: Pseudo dice [0.2432, 0.2491, 0.684, 0.0679, 0.5608, 0.285, 0.7196] +2026-04-10 18:38:40.418048: Epoch time: 104.33 s +2026-04-10 18:38:41.534145: +2026-04-10 18:38:41.536377: Epoch 290 +2026-04-10 18:38:41.537974: Current learning rate: 0.00935 +2026-04-10 18:40:24.477722: train_loss -0.1451 +2026-04-10 18:40:24.485489: val_loss -0.1448 +2026-04-10 18:40:24.487817: Pseudo dice [0.3506, 0.3051, 0.7036, 0.576, 0.3401, 0.6021, 0.6774] +2026-04-10 18:40:24.490283: Epoch time: 102.95 s +2026-04-10 18:40:25.584984: +2026-04-10 18:40:25.586972: Epoch 291 +2026-04-10 18:40:25.589017: Current learning rate: 0.00934 +2026-04-10 18:42:12.430596: train_loss -0.1615 +2026-04-10 18:42:12.439899: val_loss -0.1095 +2026-04-10 18:42:12.442282: Pseudo dice [0.4479, 0.5308, 0.6628, 0.4525, 0.4736, 0.7656, 0.6839] +2026-04-10 18:42:12.446027: Epoch time: 106.85 s +2026-04-10 18:42:13.554617: +2026-04-10 18:42:13.557355: Epoch 292 +2026-04-10 18:42:13.559838: Current learning rate: 0.00934 +2026-04-10 18:43:57.270904: train_loss -0.1605 +2026-04-10 18:43:57.278380: val_loss -0.0933 +2026-04-10 18:43:57.280877: Pseudo dice [0.3595, 0.3516, 0.6758, 0.2863, 0.2896, 0.3455, 0.554] +2026-04-10 18:43:57.283473: Epoch time: 103.72 s +2026-04-10 18:43:58.373528: +2026-04-10 18:43:58.375102: Epoch 293 +2026-04-10 18:43:58.376536: Current learning rate: 0.00934 +2026-04-10 18:45:41.144675: train_loss -0.1533 +2026-04-10 18:45:41.150946: val_loss -0.102 +2026-04-10 18:45:41.153079: Pseudo dice [0.3417, 0.1694, 0.7232, 0.2635, 0.3939, 0.3413, 0.6718] +2026-04-10 18:45:41.155029: Epoch time: 102.77 s +2026-04-10 18:45:42.251788: +2026-04-10 18:45:42.253675: Epoch 294 +2026-04-10 18:45:42.255257: Current learning rate: 0.00934 +2026-04-10 18:47:24.618946: train_loss -0.1424 +2026-04-10 18:47:24.624689: val_loss -0.1153 +2026-04-10 18:47:24.626837: Pseudo dice [0.7078, 0.691, 0.7254, 0.3006, 0.1193, 0.0798, 0.7441] +2026-04-10 18:47:24.629869: Epoch time: 102.37 s +2026-04-10 18:47:25.770688: +2026-04-10 18:47:25.773162: Epoch 295 +2026-04-10 18:47:25.775857: Current learning rate: 0.00933 +2026-04-10 18:49:08.671401: train_loss -0.1557 +2026-04-10 18:49:08.680692: val_loss -0.0864 +2026-04-10 18:49:08.682998: Pseudo dice [0.6294, 0.6417, 0.6253, 0.2068, 0.2868, 0.2912, 0.3753] +2026-04-10 18:49:08.685551: Epoch time: 102.9 s +2026-04-10 18:49:09.775737: +2026-04-10 18:49:09.777993: Epoch 296 +2026-04-10 18:49:09.779896: Current learning rate: 0.00933 +2026-04-10 18:50:52.485532: train_loss -0.1488 +2026-04-10 18:50:52.492272: val_loss -0.1174 +2026-04-10 18:50:52.494226: Pseudo dice [0.033, 0.5745, 0.6688, 0.3979, 0.5398, 0.224, 0.5085] +2026-04-10 18:50:52.496997: Epoch time: 102.71 s +2026-04-10 18:50:53.612960: +2026-04-10 18:50:53.615252: Epoch 297 +2026-04-10 18:50:53.618149: Current learning rate: 0.00933 +2026-04-10 18:52:36.329080: train_loss -0.1579 +2026-04-10 18:52:36.335603: val_loss -0.1199 +2026-04-10 18:52:36.337943: Pseudo dice [0.6193, 0.3427, 0.7825, 0.2192, 0.2603, 0.6488, 0.6157] +2026-04-10 18:52:36.340855: Epoch time: 102.72 s +2026-04-10 18:52:37.429958: +2026-04-10 18:52:37.433116: Epoch 298 +2026-04-10 18:52:37.436026: Current learning rate: 0.00933 +2026-04-10 18:54:20.525234: train_loss -0.1532 +2026-04-10 18:54:20.531622: val_loss -0.0927 +2026-04-10 18:54:20.534226: Pseudo dice [0.2006, 0.8865, 0.5571, 0.5242, 0.3926, 0.3609, 0.8392] +2026-04-10 18:54:20.536506: Epoch time: 103.1 s +2026-04-10 18:54:21.654229: +2026-04-10 18:54:21.656353: Epoch 299 +2026-04-10 18:54:21.658387: Current learning rate: 0.00932 +2026-04-10 18:56:04.413274: train_loss -0.1385 +2026-04-10 18:56:04.422135: val_loss -0.1327 +2026-04-10 18:56:04.426041: Pseudo dice [0.1137, 0.2821, 0.3874, 0.3869, 0.2498, 0.4902, 0.755] +2026-04-10 18:56:04.428334: Epoch time: 102.76 s +2026-04-10 18:56:07.206923: +2026-04-10 18:56:07.210738: Epoch 300 +2026-04-10 18:56:07.212581: Current learning rate: 0.00932 +2026-04-10 18:57:49.994022: train_loss -0.1389 +2026-04-10 18:57:50.015385: val_loss -0.0838 +2026-04-10 18:57:50.018051: Pseudo dice [0.3428, 0.2096, 0.6203, 0.2202, 0.1479, 0.6155, 0.3143] +2026-04-10 18:57:50.029782: Epoch time: 102.79 s +2026-04-10 18:57:51.125901: +2026-04-10 18:57:51.129153: Epoch 301 +2026-04-10 18:57:51.131955: Current learning rate: 0.00932 +2026-04-10 18:59:33.925294: train_loss -0.1675 +2026-04-10 18:59:33.932169: val_loss -0.122 +2026-04-10 18:59:33.934169: Pseudo dice [0.5365, 0.4043, 0.4093, 0.4238, 0.1631, 0.7058, 0.5835] +2026-04-10 18:59:33.936581: Epoch time: 102.8 s +2026-04-10 18:59:35.031685: +2026-04-10 18:59:35.034415: Epoch 302 +2026-04-10 18:59:35.036378: Current learning rate: 0.00932 +2026-04-10 19:01:18.318117: train_loss -0.145 +2026-04-10 19:01:18.326654: val_loss -0.1117 +2026-04-10 19:01:18.330231: Pseudo dice [0.5671, 0.506, 0.6424, 0.2799, 0.3389, 0.484, 0.6873] +2026-04-10 19:01:18.333118: Epoch time: 103.29 s +2026-04-10 19:01:19.434119: +2026-04-10 19:01:19.436033: Epoch 303 +2026-04-10 19:01:19.438084: Current learning rate: 0.00932 +2026-04-10 19:03:03.008857: train_loss -0.1681 +2026-04-10 19:03:03.016901: val_loss -0.1102 +2026-04-10 19:03:03.019216: Pseudo dice [0.2683, 0.8989, 0.4921, 0.4161, 0.2799, 0.158, 0.6768] +2026-04-10 19:03:03.021894: Epoch time: 103.58 s +2026-04-10 19:03:04.355442: +2026-04-10 19:03:04.357836: Epoch 304 +2026-04-10 19:03:04.361041: Current learning rate: 0.00931 +2026-04-10 19:04:47.501659: train_loss -0.1427 +2026-04-10 19:04:47.509303: val_loss -0.1143 +2026-04-10 19:04:47.512910: Pseudo dice [0.662, 0.3371, 0.6325, 0.2093, 0.3051, 0.364, 0.6205] +2026-04-10 19:04:47.515587: Epoch time: 103.15 s +2026-04-10 19:04:48.623398: +2026-04-10 19:04:48.625130: Epoch 305 +2026-04-10 19:04:48.626750: Current learning rate: 0.00931 +2026-04-10 19:06:31.604147: train_loss -0.1537 +2026-04-10 19:06:31.611616: val_loss -0.1191 +2026-04-10 19:06:31.613596: Pseudo dice [0.3341, 0.8966, 0.753, 0.0923, 0.2904, 0.5742, 0.4106] +2026-04-10 19:06:31.615995: Epoch time: 102.98 s +2026-04-10 19:06:32.720330: +2026-04-10 19:06:32.722184: Epoch 306 +2026-04-10 19:06:32.724151: Current learning rate: 0.00931 +2026-04-10 19:08:15.986036: train_loss -0.1397 +2026-04-10 19:08:15.992318: val_loss -0.1386 +2026-04-10 19:08:15.994631: Pseudo dice [0.5744, 0.3816, 0.6148, 0.5489, 0.3979, 0.7549, 0.7754] +2026-04-10 19:08:15.997642: Epoch time: 103.27 s +2026-04-10 19:08:17.104360: +2026-04-10 19:08:17.106745: Epoch 307 +2026-04-10 19:08:17.108554: Current learning rate: 0.00931 +2026-04-10 19:10:00.812421: train_loss -0.1658 +2026-04-10 19:10:00.819540: val_loss -0.1358 +2026-04-10 19:10:00.821920: Pseudo dice [0.4745, 0.1724, 0.8236, 0.3719, 0.4902, 0.5711, 0.6183] +2026-04-10 19:10:00.824479: Epoch time: 103.71 s +2026-04-10 19:10:01.942447: +2026-04-10 19:10:01.945257: Epoch 308 +2026-04-10 19:10:01.947823: Current learning rate: 0.0093 +2026-04-10 19:11:45.512748: train_loss -0.1475 +2026-04-10 19:11:45.520598: val_loss -0.114 +2026-04-10 19:11:45.523789: Pseudo dice [0.5399, 0.2799, 0.7187, 0.6904, 0.2879, 0.4263, 0.6519] +2026-04-10 19:11:45.526664: Epoch time: 103.57 s +2026-04-10 19:11:46.638256: +2026-04-10 19:11:46.640785: Epoch 309 +2026-04-10 19:11:46.642679: Current learning rate: 0.0093 +2026-04-10 19:13:31.221500: train_loss -0.1531 +2026-04-10 19:13:31.229993: val_loss -0.1054 +2026-04-10 19:13:31.232864: Pseudo dice [0.2838, 0.6223, 0.5311, 0.4734, 0.3143, 0.3119, 0.5962] +2026-04-10 19:13:31.235404: Epoch time: 104.59 s +2026-04-10 19:13:32.339024: +2026-04-10 19:13:32.340951: Epoch 310 +2026-04-10 19:13:32.342565: Current learning rate: 0.0093 +2026-04-10 19:15:16.307695: train_loss -0.1429 +2026-04-10 19:15:16.315848: val_loss -0.1155 +2026-04-10 19:15:16.319622: Pseudo dice [0.6192, 0.5882, 0.7795, 0.3632, 0.4693, 0.5402, 0.6331] +2026-04-10 19:15:16.322761: Epoch time: 103.97 s +2026-04-10 19:15:16.325360: Yayy! New best EMA pseudo Dice: 0.483 +2026-04-10 19:15:19.183343: +2026-04-10 19:15:19.186091: Epoch 311 +2026-04-10 19:15:19.187530: Current learning rate: 0.0093 +2026-04-10 19:17:09.088181: train_loss -0.1421 +2026-04-10 19:17:09.094551: val_loss -0.1077 +2026-04-10 19:17:09.096448: Pseudo dice [0.4252, 0.3032, 0.745, 0.2664, 0.4258, 0.3426, 0.4889] +2026-04-10 19:17:09.099116: Epoch time: 109.91 s +2026-04-10 19:17:10.220837: +2026-04-10 19:17:10.223114: Epoch 312 +2026-04-10 19:17:10.225573: Current learning rate: 0.0093 +2026-04-10 19:18:52.420084: train_loss -0.1615 +2026-04-10 19:18:52.426767: val_loss -0.1189 +2026-04-10 19:18:52.429837: Pseudo dice [0.6351, 0.2417, 0.5758, 0.4234, 0.4054, 0.5647, 0.6674] +2026-04-10 19:18:52.432050: Epoch time: 102.2 s +2026-04-10 19:18:53.547989: +2026-04-10 19:18:53.551412: Epoch 313 +2026-04-10 19:18:53.553667: Current learning rate: 0.00929 +2026-04-10 19:20:36.272756: train_loss -0.153 +2026-04-10 19:20:36.299767: val_loss -0.1305 +2026-04-10 19:20:36.302806: Pseudo dice [0.0985, 0.8256, 0.7348, 0.5636, 0.4204, 0.559, 0.4987] +2026-04-10 19:20:36.305717: Epoch time: 102.73 s +2026-04-10 19:20:36.308156: Yayy! New best EMA pseudo Dice: 0.4848 +2026-04-10 19:20:39.025987: +2026-04-10 19:20:39.028303: Epoch 314 +2026-04-10 19:20:39.029831: Current learning rate: 0.00929 +2026-04-10 19:22:21.385207: train_loss -0.1688 +2026-04-10 19:22:21.391077: val_loss -0.1174 +2026-04-10 19:22:21.393362: Pseudo dice [0.1117, 0.6234, 0.6624, 0.2318, 0.4285, 0.5783, 0.7579] +2026-04-10 19:22:21.395911: Epoch time: 102.36 s +2026-04-10 19:22:21.398332: Yayy! New best EMA pseudo Dice: 0.4848 +2026-04-10 19:22:23.973520: +2026-04-10 19:22:23.976114: Epoch 315 +2026-04-10 19:22:23.977571: Current learning rate: 0.00929 +2026-04-10 19:24:06.792377: train_loss -0.1642 +2026-04-10 19:24:06.799147: val_loss -0.1135 +2026-04-10 19:24:06.801795: Pseudo dice [0.7568, 0.5673, 0.6836, 0.4273, 0.3522, 0.6962, 0.4888] +2026-04-10 19:24:06.803993: Epoch time: 102.82 s +2026-04-10 19:24:06.806037: Yayy! New best EMA pseudo Dice: 0.4931 +2026-04-10 19:24:09.656758: +2026-04-10 19:24:09.658702: Epoch 316 +2026-04-10 19:24:09.660153: Current learning rate: 0.00929 +2026-04-10 19:25:56.427184: train_loss -0.1621 +2026-04-10 19:25:56.434980: val_loss -0.1364 +2026-04-10 19:25:56.437230: Pseudo dice [0.3724, 0.3898, 0.7693, 0.5705, 0.3536, 0.2499, 0.6486] +2026-04-10 19:25:56.439945: Epoch time: 106.77 s +2026-04-10 19:25:57.536501: +2026-04-10 19:25:57.539191: Epoch 317 +2026-04-10 19:25:57.541988: Current learning rate: 0.00928 +2026-04-10 19:27:39.503851: train_loss -0.1191 +2026-04-10 19:27:39.511921: val_loss -0.0765 +2026-04-10 19:27:39.514878: Pseudo dice [0.2919, 0.6842, 0.3538, 0.2514, 0.2545, 0.361, 0.4043] +2026-04-10 19:27:39.517242: Epoch time: 101.97 s +2026-04-10 19:27:40.606540: +2026-04-10 19:27:40.608456: Epoch 318 +2026-04-10 19:27:40.611701: Current learning rate: 0.00928 +2026-04-10 19:29:23.921405: train_loss -0.1362 +2026-04-10 19:29:23.927456: val_loss -0.1255 +2026-04-10 19:29:23.929543: Pseudo dice [0.325, 0.2438, 0.6501, 0.1606, 0.433, 0.468, 0.6431] +2026-04-10 19:29:23.933193: Epoch time: 103.32 s +2026-04-10 19:29:25.039701: +2026-04-10 19:29:25.042308: Epoch 319 +2026-04-10 19:29:25.044293: Current learning rate: 0.00928 +2026-04-10 19:31:07.996907: train_loss -0.1448 +2026-04-10 19:31:08.003339: val_loss -0.1238 +2026-04-10 19:31:08.005075: Pseudo dice [0.7091, 0.4122, 0.7373, 0.4516, 0.2551, 0.4293, 0.7016] +2026-04-10 19:31:08.007119: Epoch time: 102.96 s +2026-04-10 19:31:09.126325: +2026-04-10 19:31:09.128498: Epoch 320 +2026-04-10 19:31:09.130346: Current learning rate: 0.00928 +2026-04-10 19:32:51.186539: train_loss -0.137 +2026-04-10 19:32:51.197195: val_loss -0.1021 +2026-04-10 19:32:51.200288: Pseudo dice [0.3518, 0.5048, 0.6842, 0.1783, 0.4628, 0.3803, 0.7648] +2026-04-10 19:32:51.203561: Epoch time: 102.06 s +2026-04-10 19:32:52.323492: +2026-04-10 19:32:52.335531: Epoch 321 +2026-04-10 19:32:52.337708: Current learning rate: 0.00927 +2026-04-10 19:34:35.564947: train_loss -0.1648 +2026-04-10 19:34:35.573997: val_loss -0.1331 +2026-04-10 19:34:35.576884: Pseudo dice [0.4866, 0.5583, 0.7736, 0.519, 0.2506, 0.3793, 0.668] +2026-04-10 19:34:35.579514: Epoch time: 103.24 s +2026-04-10 19:34:36.664303: +2026-04-10 19:34:36.666002: Epoch 322 +2026-04-10 19:34:36.667528: Current learning rate: 0.00927 +2026-04-10 19:36:20.151762: train_loss -0.1613 +2026-04-10 19:36:20.160019: val_loss -0.1513 +2026-04-10 19:36:20.163227: Pseudo dice [0.5648, 0.8436, 0.8129, 0.1945, 0.3638, 0.1792, 0.6753] +2026-04-10 19:36:20.166998: Epoch time: 103.49 s +2026-04-10 19:36:21.271807: +2026-04-10 19:36:21.273691: Epoch 323 +2026-04-10 19:36:21.275791: Current learning rate: 0.00927 +2026-04-10 19:38:05.665853: train_loss -0.1575 +2026-04-10 19:38:05.674589: val_loss -0.1225 +2026-04-10 19:38:05.678076: Pseudo dice [0.5006, 0.2213, 0.7927, 0.4781, 0.3609, 0.6689, 0.7913] +2026-04-10 19:38:05.686130: Epoch time: 104.4 s +2026-04-10 19:38:06.794059: +2026-04-10 19:38:06.796836: Epoch 324 +2026-04-10 19:38:06.800509: Current learning rate: 0.00927 +2026-04-10 19:39:49.959542: train_loss -0.1563 +2026-04-10 19:39:49.968156: val_loss -0.1326 +2026-04-10 19:39:49.971661: Pseudo dice [0.5208, 0.7612, 0.7421, 0.3029, 0.5518, 0.2202, 0.7554] +2026-04-10 19:39:49.975379: Epoch time: 103.17 s +2026-04-10 19:39:49.977562: Yayy! New best EMA pseudo Dice: 0.498 +2026-04-10 19:39:52.491069: +2026-04-10 19:39:52.493754: Epoch 325 +2026-04-10 19:39:52.495607: Current learning rate: 0.00927 +2026-04-10 19:41:35.150569: train_loss -0.1626 +2026-04-10 19:41:35.157468: val_loss -0.1407 +2026-04-10 19:41:35.159326: Pseudo dice [0.7105, 0.2428, 0.7455, 0.2687, 0.5912, 0.3974, 0.8189] +2026-04-10 19:41:35.161840: Epoch time: 102.66 s +2026-04-10 19:41:35.163872: Yayy! New best EMA pseudo Dice: 0.5021 +2026-04-10 19:41:37.770380: +2026-04-10 19:41:37.772406: Epoch 326 +2026-04-10 19:41:37.773804: Current learning rate: 0.00926 +2026-04-10 19:43:21.064613: train_loss -0.1479 +2026-04-10 19:43:21.071838: val_loss -0.1199 +2026-04-10 19:43:21.074100: Pseudo dice [0.1909, 0.819, 0.7375, 0.4139, 0.1644, 0.4722, 0.68] +2026-04-10 19:43:21.078177: Epoch time: 103.3 s +2026-04-10 19:43:23.413810: +2026-04-10 19:43:23.415935: Epoch 327 +2026-04-10 19:43:23.417646: Current learning rate: 0.00926 +2026-04-10 19:45:06.773389: train_loss -0.1585 +2026-04-10 19:45:06.780246: val_loss -0.1592 +2026-04-10 19:45:06.784426: Pseudo dice [0.6034, 0.6772, 0.704, 0.4286, 0.6254, 0.519, 0.4836] +2026-04-10 19:45:06.787543: Epoch time: 103.36 s +2026-04-10 19:45:06.789749: Yayy! New best EMA pseudo Dice: 0.5092 +2026-04-10 19:45:09.802603: +2026-04-10 19:45:09.804635: Epoch 328 +2026-04-10 19:45:09.805974: Current learning rate: 0.00926 +2026-04-10 19:46:53.697032: train_loss -0.1579 +2026-04-10 19:46:53.703809: val_loss -0.0787 +2026-04-10 19:46:53.705964: Pseudo dice [0.391, 0.7195, 0.5358, 0.3962, 0.4981, 0.2405, 0.6942] +2026-04-10 19:46:53.708612: Epoch time: 103.9 s +2026-04-10 19:46:54.851115: +2026-04-10 19:46:54.853135: Epoch 329 +2026-04-10 19:46:54.854841: Current learning rate: 0.00926 +2026-04-10 19:48:37.402685: train_loss -0.1597 +2026-04-10 19:48:37.410773: val_loss -0.1252 +2026-04-10 19:48:37.413000: Pseudo dice [0.5401, 0.7171, 0.5052, 0.3027, 0.2884, 0.863, 0.67] +2026-04-10 19:48:37.416158: Epoch time: 102.55 s +2026-04-10 19:48:37.418766: Yayy! New best EMA pseudo Dice: 0.5126 +2026-04-10 19:48:39.943113: +2026-04-10 19:48:39.945158: Epoch 330 +2026-04-10 19:48:39.946529: Current learning rate: 0.00925 +2026-04-10 19:50:22.399754: train_loss -0.1646 +2026-04-10 19:50:22.405310: val_loss -0.1162 +2026-04-10 19:50:22.407710: Pseudo dice [0.7304, 0.7711, 0.6987, 0.0944, 0.2808, 0.2463, 0.7354] +2026-04-10 19:50:22.409923: Epoch time: 102.46 s +2026-04-10 19:50:23.522766: +2026-04-10 19:50:23.524961: Epoch 331 +2026-04-10 19:50:23.526344: Current learning rate: 0.00925 +2026-04-10 19:52:07.094091: train_loss -0.1495 +2026-04-10 19:52:07.102087: val_loss -0.125 +2026-04-10 19:52:07.105062: Pseudo dice [0.4567, 0.2259, 0.5734, 0.2035, 0.1776, 0.4222, 0.7573] +2026-04-10 19:52:07.107761: Epoch time: 103.57 s +2026-04-10 19:52:08.226667: +2026-04-10 19:52:08.229020: Epoch 332 +2026-04-10 19:52:08.231075: Current learning rate: 0.00925 +2026-04-10 19:53:51.066620: train_loss -0.1554 +2026-04-10 19:53:51.072379: val_loss -0.1308 +2026-04-10 19:53:51.074455: Pseudo dice [0.4108, 0.5961, 0.7404, 0.4841, 0.3879, 0.1619, 0.6363] +2026-04-10 19:53:51.076846: Epoch time: 102.84 s +2026-04-10 19:53:52.185048: +2026-04-10 19:53:52.187012: Epoch 333 +2026-04-10 19:53:52.190388: Current learning rate: 0.00925 +2026-04-10 19:55:35.117877: train_loss -0.1564 +2026-04-10 19:55:35.125538: val_loss -0.1167 +2026-04-10 19:55:35.127965: Pseudo dice [0.6313, 0.8933, 0.7526, 0.1959, 0.2425, 0.2771, 0.6545] +2026-04-10 19:55:35.131696: Epoch time: 102.94 s +2026-04-10 19:55:36.235413: +2026-04-10 19:55:36.238638: Epoch 334 +2026-04-10 19:55:36.240487: Current learning rate: 0.00925 +2026-04-10 19:57:19.464941: train_loss -0.163 +2026-04-10 19:57:19.471054: val_loss -0.1464 +2026-04-10 19:57:19.473267: Pseudo dice [0.5294, 0.8645, 0.5692, 0.4907, 0.4772, 0.5455, 0.767] +2026-04-10 19:57:19.475972: Epoch time: 103.23 s +2026-04-10 19:57:20.594939: +2026-04-10 19:57:20.596804: Epoch 335 +2026-04-10 19:57:20.598262: Current learning rate: 0.00924 +2026-04-10 19:59:03.136616: train_loss -0.1619 +2026-04-10 19:59:03.143139: val_loss -0.1283 +2026-04-10 19:59:03.145042: Pseudo dice [0.6044, 0.3292, 0.6688, 0.1906, 0.4276, 0.3129, 0.8144] +2026-04-10 19:59:03.147935: Epoch time: 102.54 s +2026-04-10 19:59:04.306217: +2026-04-10 19:59:04.314774: Epoch 336 +2026-04-10 19:59:04.327860: Current learning rate: 0.00924 +2026-04-10 20:00:47.198283: train_loss -0.156 +2026-04-10 20:00:47.210296: val_loss -0.1221 +2026-04-10 20:00:47.217668: Pseudo dice [0.5441, 0.3236, 0.7915, 0.3751, 0.3958, 0.4556, 0.6485] +2026-04-10 20:00:47.220579: Epoch time: 102.9 s +2026-04-10 20:00:48.411102: +2026-04-10 20:00:48.413150: Epoch 337 +2026-04-10 20:00:48.415459: Current learning rate: 0.00924 +2026-04-10 20:02:32.648303: train_loss -0.1593 +2026-04-10 20:02:32.655281: val_loss -0.1421 +2026-04-10 20:02:32.658073: Pseudo dice [0.3017, 0.6462, 0.7415, 0.4532, 0.5064, 0.715, 0.4264] +2026-04-10 20:02:32.661460: Epoch time: 104.24 s +2026-04-10 20:02:33.898834: +2026-04-10 20:02:33.900935: Epoch 338 +2026-04-10 20:02:33.903333: Current learning rate: 0.00924 +2026-04-10 20:04:17.757783: train_loss -0.1394 +2026-04-10 20:04:17.766610: val_loss -0.1018 +2026-04-10 20:04:17.768347: Pseudo dice [0.1928, 0.25, 0.5637, 0.3652, 0.3721, 0.2778, 0.6195] +2026-04-10 20:04:17.770971: Epoch time: 103.86 s +2026-04-10 20:04:18.968927: +2026-04-10 20:04:18.970729: Epoch 339 +2026-04-10 20:04:18.972966: Current learning rate: 0.00923 +2026-04-10 20:06:03.182663: train_loss -0.1583 +2026-04-10 20:06:03.188841: val_loss -0.139 +2026-04-10 20:06:03.192176: Pseudo dice [0.5985, 0.769, 0.7458, 0.1315, 0.4435, 0.7565, 0.3297] +2026-04-10 20:06:03.195922: Epoch time: 104.22 s +2026-04-10 20:06:04.353644: +2026-04-10 20:06:04.356094: Epoch 340 +2026-04-10 20:06:04.358104: Current learning rate: 0.00923 +2026-04-10 20:07:48.202349: train_loss -0.1654 +2026-04-10 20:07:48.209662: val_loss -0.0991 +2026-04-10 20:07:48.211792: Pseudo dice [0.3623, 0.5448, 0.5818, 0.1516, 0.4394, 0.2978, 0.6819] +2026-04-10 20:07:48.215285: Epoch time: 103.85 s +2026-04-10 20:07:49.434719: +2026-04-10 20:07:49.436595: Epoch 341 +2026-04-10 20:07:49.438476: Current learning rate: 0.00923 +2026-04-10 20:09:33.784784: train_loss -0.1642 +2026-04-10 20:09:33.791110: val_loss -0.1424 +2026-04-10 20:09:33.793775: Pseudo dice [0.2473, 0.8566, 0.7793, 0.1938, 0.4444, 0.3699, 0.6899] +2026-04-10 20:09:33.797719: Epoch time: 104.35 s +2026-04-10 20:09:34.960049: +2026-04-10 20:09:34.961674: Epoch 342 +2026-04-10 20:09:34.963122: Current learning rate: 0.00923 +2026-04-10 20:11:18.938573: train_loss -0.1711 +2026-04-10 20:11:18.946051: val_loss -0.1548 +2026-04-10 20:11:18.948076: Pseudo dice [0.409, 0.2343, 0.6216, 0.3024, 0.5373, 0.7212, 0.6984] +2026-04-10 20:11:18.951249: Epoch time: 103.98 s +2026-04-10 20:11:20.127519: +2026-04-10 20:11:20.129830: Epoch 343 +2026-04-10 20:11:20.132229: Current learning rate: 0.00922 +2026-04-10 20:13:06.632300: train_loss -0.1615 +2026-04-10 20:13:06.638649: val_loss -0.1282 +2026-04-10 20:13:06.641870: Pseudo dice [0.2065, 0.5998, 0.734, 0.0705, 0.3413, 0.1074, 0.7942] +2026-04-10 20:13:06.644546: Epoch time: 106.51 s +2026-04-10 20:13:07.793429: +2026-04-10 20:13:07.795511: Epoch 344 +2026-04-10 20:13:07.797987: Current learning rate: 0.00922 +2026-04-10 20:14:51.296541: train_loss -0.164 +2026-04-10 20:14:51.304275: val_loss -0.1153 +2026-04-10 20:14:51.309082: Pseudo dice [0.5633, 0.9165, 0.7044, 0.4778, 0.2063, 0.7135, 0.3638] +2026-04-10 20:14:51.311647: Epoch time: 103.51 s +2026-04-10 20:14:52.465541: +2026-04-10 20:14:52.468373: Epoch 345 +2026-04-10 20:14:52.470315: Current learning rate: 0.00922 +2026-04-10 20:16:36.091429: train_loss -0.1676 +2026-04-10 20:16:36.098484: val_loss -0.1349 +2026-04-10 20:16:36.101228: Pseudo dice [0.7101, 0.826, 0.7316, 0.4897, 0.5045, 0.1391, 0.7486] +2026-04-10 20:16:36.104206: Epoch time: 103.63 s +2026-04-10 20:16:37.208562: +2026-04-10 20:16:37.210323: Epoch 346 +2026-04-10 20:16:37.212594: Current learning rate: 0.00922 +2026-04-10 20:18:22.121480: train_loss -0.1407 +2026-04-10 20:18:22.129608: val_loss -0.1342 +2026-04-10 20:18:22.131762: Pseudo dice [0.171, 0.5505, 0.7625, 0.2639, 0.3531, 0.6721, 0.7266] +2026-04-10 20:18:22.134256: Epoch time: 104.92 s +2026-04-10 20:18:23.265787: +2026-04-10 20:18:23.269423: Epoch 347 +2026-04-10 20:18:23.273685: Current learning rate: 0.00922 +2026-04-10 20:20:07.526007: train_loss -0.1411 +2026-04-10 20:20:07.532836: val_loss -0.0917 +2026-04-10 20:20:07.535864: Pseudo dice [0.3362, 0.2705, 0.6352, 0.1588, 0.374, 0.6937, 0.615] +2026-04-10 20:20:07.538529: Epoch time: 104.26 s +2026-04-10 20:20:08.686616: +2026-04-10 20:20:08.689804: Epoch 348 +2026-04-10 20:20:08.691467: Current learning rate: 0.00921 +2026-04-10 20:21:52.693045: train_loss -0.1555 +2026-04-10 20:21:52.699765: val_loss -0.0868 +2026-04-10 20:21:52.702243: Pseudo dice [0.5923, 0.881, 0.5538, 0.2796, 0.4203, 0.4444, 0.7569] +2026-04-10 20:21:52.704683: Epoch time: 104.01 s +2026-04-10 20:21:53.844452: +2026-04-10 20:21:53.846914: Epoch 349 +2026-04-10 20:21:53.848550: Current learning rate: 0.00921 +2026-04-10 20:23:37.909721: train_loss -0.1608 +2026-04-10 20:23:37.918234: val_loss -0.1099 +2026-04-10 20:23:37.921093: Pseudo dice [0.7621, 0.6145, 0.5765, 0.4717, 0.2155, 0.3236, 0.5737] +2026-04-10 20:23:37.924793: Epoch time: 104.07 s +2026-04-10 20:23:40.867845: +2026-04-10 20:23:40.870512: Epoch 350 +2026-04-10 20:23:40.871892: Current learning rate: 0.00921 +2026-04-10 20:25:24.701535: train_loss -0.1418 +2026-04-10 20:25:24.708166: val_loss -0.105 +2026-04-10 20:25:24.710171: Pseudo dice [0.7065, 0.2538, 0.6315, 0.7169, 0.5706, 0.2553, 0.6548] +2026-04-10 20:25:24.713558: Epoch time: 103.84 s +2026-04-10 20:25:25.820734: +2026-04-10 20:25:25.823289: Epoch 351 +2026-04-10 20:25:25.825119: Current learning rate: 0.00921 +2026-04-10 20:27:09.160750: train_loss -0.1444 +2026-04-10 20:27:09.168543: val_loss -0.1037 +2026-04-10 20:27:09.170876: Pseudo dice [0.358, 0.8633, 0.731, 0.2464, 0.4068, 0.2556, 0.7331] +2026-04-10 20:27:09.175231: Epoch time: 103.34 s +2026-04-10 20:27:10.298261: +2026-04-10 20:27:10.300646: Epoch 352 +2026-04-10 20:27:10.302381: Current learning rate: 0.0092 +2026-04-10 20:28:53.638388: train_loss -0.1583 +2026-04-10 20:28:53.646871: val_loss -0.1578 +2026-04-10 20:28:53.650587: Pseudo dice [0.6384, 0.2181, 0.7289, 0.5054, 0.2815, 0.5849, 0.4653] +2026-04-10 20:28:53.655200: Epoch time: 103.34 s +2026-04-10 20:28:54.782941: +2026-04-10 20:28:54.785331: Epoch 353 +2026-04-10 20:28:54.787323: Current learning rate: 0.0092 +2026-04-10 20:30:37.377752: train_loss -0.171 +2026-04-10 20:30:37.384473: val_loss -0.1211 +2026-04-10 20:30:37.386590: Pseudo dice [0.5662, 0.1475, 0.6837, 0.4941, 0.2716, 0.358, 0.5741] +2026-04-10 20:30:37.388423: Epoch time: 102.6 s +2026-04-10 20:30:38.486446: +2026-04-10 20:30:38.491307: Epoch 354 +2026-04-10 20:30:38.494734: Current learning rate: 0.0092 +2026-04-10 20:32:21.829681: train_loss -0.1557 +2026-04-10 20:32:21.836884: val_loss -0.1231 +2026-04-10 20:32:21.838778: Pseudo dice [0.3296, 0.6344, 0.5261, 0.7194, 0.3298, 0.4313, 0.7012] +2026-04-10 20:32:21.842110: Epoch time: 103.35 s +2026-04-10 20:32:22.988908: +2026-04-10 20:32:22.991061: Epoch 355 +2026-04-10 20:32:22.992873: Current learning rate: 0.0092 +2026-04-10 20:34:05.490641: train_loss -0.1506 +2026-04-10 20:34:05.497745: val_loss -0.1241 +2026-04-10 20:34:05.499467: Pseudo dice [0.4825, 0.1898, 0.5033, 0.5264, 0.5945, 0.4553, 0.401] +2026-04-10 20:34:05.501680: Epoch time: 102.5 s +2026-04-10 20:34:06.616302: +2026-04-10 20:34:06.618240: Epoch 356 +2026-04-10 20:34:06.620029: Current learning rate: 0.0092 +2026-04-10 20:35:50.244454: train_loss -0.1615 +2026-04-10 20:35:50.251583: val_loss -0.1303 +2026-04-10 20:35:50.255084: Pseudo dice [0.6304, 0.2933, 0.7352, 0.6897, 0.3907, 0.7337, 0.6963] +2026-04-10 20:35:50.258440: Epoch time: 103.63 s +2026-04-10 20:35:51.372842: +2026-04-10 20:35:51.376370: Epoch 357 +2026-04-10 20:35:51.378166: Current learning rate: 0.00919 +2026-04-10 20:37:34.833456: train_loss -0.1722 +2026-04-10 20:37:34.838929: val_loss -0.095 +2026-04-10 20:37:34.840964: Pseudo dice [0.6491, 0.268, 0.3328, 0.3481, 0.5184, 0.7022, 0.6375] +2026-04-10 20:37:34.842953: Epoch time: 103.46 s +2026-04-10 20:37:35.962497: +2026-04-10 20:37:35.965200: Epoch 358 +2026-04-10 20:37:35.967632: Current learning rate: 0.00919 +2026-04-10 20:39:20.413088: train_loss -0.156 +2026-04-10 20:39:20.420050: val_loss -0.1061 +2026-04-10 20:39:20.422924: Pseudo dice [0.6071, 0.8074, 0.5292, 0.3381, 0.1529, 0.5176, 0.7217] +2026-04-10 20:39:20.426714: Epoch time: 104.45 s +2026-04-10 20:39:21.606748: +2026-04-10 20:39:21.609233: Epoch 359 +2026-04-10 20:39:21.611249: Current learning rate: 0.00919 +2026-04-10 20:41:05.107794: train_loss -0.1495 +2026-04-10 20:41:05.117200: val_loss -0.0719 +2026-04-10 20:41:05.120116: Pseudo dice [0.3108, 0.5185, 0.5056, 0.0116, 0.513, 0.4596, 0.6758] +2026-04-10 20:41:05.122695: Epoch time: 103.5 s +2026-04-10 20:41:06.248353: +2026-04-10 20:41:06.250458: Epoch 360 +2026-04-10 20:41:06.252638: Current learning rate: 0.00919 +2026-04-10 20:42:49.391523: train_loss -0.1619 +2026-04-10 20:42:49.399360: val_loss -0.1459 +2026-04-10 20:42:49.402666: Pseudo dice [0.3138, 0.4721, 0.7068, 0.797, 0.3289, 0.5731, 0.7076] +2026-04-10 20:42:49.405962: Epoch time: 103.15 s +2026-04-10 20:42:50.515298: +2026-04-10 20:42:50.516813: Epoch 361 +2026-04-10 20:42:50.518267: Current learning rate: 0.00918 +2026-04-10 20:44:34.211782: train_loss -0.1422 +2026-04-10 20:44:34.218729: val_loss -0.1053 +2026-04-10 20:44:34.220858: Pseudo dice [0.5709, 0.4268, 0.5751, 0.2464, 0.2147, 0.3882, 0.5566] +2026-04-10 20:44:34.223255: Epoch time: 103.7 s +2026-04-10 20:44:35.362726: +2026-04-10 20:44:35.365230: Epoch 362 +2026-04-10 20:44:35.367103: Current learning rate: 0.00918 +2026-04-10 20:46:18.731305: train_loss -0.1632 +2026-04-10 20:46:18.737017: val_loss -0.1118 +2026-04-10 20:46:18.739955: Pseudo dice [0.5615, 0.089, 0.642, 0.3432, 0.2625, 0.7926, 0.5218] +2026-04-10 20:46:18.742233: Epoch time: 103.37 s +2026-04-10 20:46:19.844630: +2026-04-10 20:46:19.846533: Epoch 363 +2026-04-10 20:46:19.848812: Current learning rate: 0.00918 +2026-04-10 20:48:02.393242: train_loss -0.1413 +2026-04-10 20:48:02.400223: val_loss -0.137 +2026-04-10 20:48:02.402170: Pseudo dice [0.5878, 0.4209, 0.6691, 0.7154, 0.3372, 0.4598, 0.7826] +2026-04-10 20:48:02.404800: Epoch time: 102.55 s +2026-04-10 20:48:03.528024: +2026-04-10 20:48:03.529922: Epoch 364 +2026-04-10 20:48:03.531732: Current learning rate: 0.00918 +2026-04-10 20:49:46.149405: train_loss -0.1477 +2026-04-10 20:49:46.156141: val_loss -0.1319 +2026-04-10 20:49:46.158682: Pseudo dice [0.2488, 0.5711, 0.6246, 0.556, 0.4236, 0.4374, 0.7855] +2026-04-10 20:49:46.161975: Epoch time: 102.62 s +2026-04-10 20:49:47.261305: +2026-04-10 20:49:47.263284: Epoch 365 +2026-04-10 20:49:47.265130: Current learning rate: 0.00917 +2026-04-10 20:51:30.974306: train_loss -0.1397 +2026-04-10 20:51:30.981692: val_loss -0.1328 +2026-04-10 20:51:30.983791: Pseudo dice [0.5886, 0.6887, 0.7854, 0.2595, 0.3975, 0.3567, 0.1496] +2026-04-10 20:51:30.988123: Epoch time: 103.72 s +2026-04-10 20:51:32.068497: +2026-04-10 20:51:32.070968: Epoch 366 +2026-04-10 20:51:32.072762: Current learning rate: 0.00917 +2026-04-10 20:53:15.171662: train_loss -0.1625 +2026-04-10 20:53:15.179612: val_loss -0.1083 +2026-04-10 20:53:15.181980: Pseudo dice [0.1235, 0.2843, 0.7719, 0.505, 0.3474, 0.3069, 0.7171] +2026-04-10 20:53:15.184543: Epoch time: 103.11 s +2026-04-10 20:53:16.314290: +2026-04-10 20:53:16.316649: Epoch 367 +2026-04-10 20:53:16.318574: Current learning rate: 0.00917 +2026-04-10 20:54:59.627676: train_loss -0.1601 +2026-04-10 20:54:59.634887: val_loss -0.1382 +2026-04-10 20:54:59.637062: Pseudo dice [0.5935, 0.88, 0.6596, 0.3437, 0.5641, 0.2794, 0.8069] +2026-04-10 20:54:59.640551: Epoch time: 103.32 s +2026-04-10 20:55:00.748912: +2026-04-10 20:55:00.751070: Epoch 368 +2026-04-10 20:55:00.752578: Current learning rate: 0.00917 +2026-04-10 20:56:48.064949: train_loss -0.1797 +2026-04-10 20:56:48.073028: val_loss -0.1483 +2026-04-10 20:56:48.074890: Pseudo dice [0.6548, 0.3618, 0.7238, 0.5317, 0.5117, 0.711, 0.4453] +2026-04-10 20:56:48.078115: Epoch time: 107.32 s +2026-04-10 20:56:49.207854: +2026-04-10 20:56:49.211080: Epoch 369 +2026-04-10 20:56:49.213071: Current learning rate: 0.00917 +2026-04-10 20:58:32.320609: train_loss -0.1701 +2026-04-10 20:58:32.329998: val_loss -0.1282 +2026-04-10 20:58:32.332234: Pseudo dice [0.4996, 0.7838, 0.6957, 0.4301, 0.3313, 0.3952, 0.6903] +2026-04-10 20:58:32.335802: Epoch time: 103.12 s +2026-04-10 20:58:33.458738: +2026-04-10 20:58:33.463115: Epoch 370 +2026-04-10 20:58:33.467201: Current learning rate: 0.00916 +2026-04-10 21:00:17.313591: train_loss -0.1611 +2026-04-10 21:00:17.320618: val_loss -0.1262 +2026-04-10 21:00:17.322669: Pseudo dice [0.1442, 0.8934, 0.7608, 0.5026, 0.369, 0.3228, 0.7609] +2026-04-10 21:00:17.325518: Epoch time: 103.86 s +2026-04-10 21:00:17.328593: Yayy! New best EMA pseudo Dice: 0.5147 +2026-04-10 21:00:19.786185: +2026-04-10 21:00:19.788778: Epoch 371 +2026-04-10 21:00:19.790206: Current learning rate: 0.00916 +2026-04-10 21:02:02.712902: train_loss -0.159 +2026-04-10 21:02:02.719723: val_loss -0.1447 +2026-04-10 21:02:02.722335: Pseudo dice [0.4327, 0.2549, 0.7829, 0.4835, 0.3805, 0.1364, 0.7992] +2026-04-10 21:02:02.726493: Epoch time: 102.93 s +2026-04-10 21:02:03.830185: +2026-04-10 21:02:03.832437: Epoch 372 +2026-04-10 21:02:03.834491: Current learning rate: 0.00916 +2026-04-10 21:03:46.549927: train_loss -0.163 +2026-04-10 21:03:46.557849: val_loss -0.1221 +2026-04-10 21:03:46.560499: Pseudo dice [0.5282, 0.2433, 0.7532, 0.1878, 0.3143, 0.1397, 0.5502] +2026-04-10 21:03:46.563183: Epoch time: 102.72 s +2026-04-10 21:03:47.676759: +2026-04-10 21:03:47.678866: Epoch 373 +2026-04-10 21:03:47.681022: Current learning rate: 0.00916 +2026-04-10 21:05:31.538996: train_loss -0.1643 +2026-04-10 21:05:31.546627: val_loss -0.0909 +2026-04-10 21:05:31.549726: Pseudo dice [0.4797, 0.2824, 0.5586, 0.5595, 0.4814, 0.6311, 0.5365] +2026-04-10 21:05:31.552647: Epoch time: 103.87 s +2026-04-10 21:05:32.646739: +2026-04-10 21:05:32.649504: Epoch 374 +2026-04-10 21:05:32.651243: Current learning rate: 0.00915 +2026-04-10 21:07:16.656001: train_loss -0.1601 +2026-04-10 21:07:16.667923: val_loss -0.1128 +2026-04-10 21:07:16.672383: Pseudo dice [0.0786, 0.535, 0.7327, 0.7843, 0.4311, 0.6453, 0.7372] +2026-04-10 21:07:16.674778: Epoch time: 104.01 s +2026-04-10 21:07:17.822876: +2026-04-10 21:07:17.828971: Epoch 375 +2026-04-10 21:07:17.834611: Current learning rate: 0.00915 +2026-04-10 21:09:00.982028: train_loss -0.1469 +2026-04-10 21:09:00.992786: val_loss -0.135 +2026-04-10 21:09:00.996233: Pseudo dice [0.2108, 0.7757, 0.6316, 0.3512, 0.5302, 0.313, 0.8008] +2026-04-10 21:09:01.000671: Epoch time: 103.16 s +2026-04-10 21:09:02.115288: +2026-04-10 21:09:02.118950: Epoch 376 +2026-04-10 21:09:02.121768: Current learning rate: 0.00915 +2026-04-10 21:10:45.635845: train_loss -0.1616 +2026-04-10 21:10:45.643202: val_loss -0.1263 +2026-04-10 21:10:45.646462: Pseudo dice [0.6081, 0.3432, 0.741, 0.4663, 0.2725, 0.4831, 0.7384] +2026-04-10 21:10:45.649319: Epoch time: 103.52 s +2026-04-10 21:10:46.767604: +2026-04-10 21:10:46.769514: Epoch 377 +2026-04-10 21:10:46.771276: Current learning rate: 0.00915 +2026-04-10 21:12:30.306652: train_loss -0.1509 +2026-04-10 21:12:30.317262: val_loss -0.1396 +2026-04-10 21:12:30.319294: Pseudo dice [0.1491, 0.4562, 0.7184, 0.0755, 0.4643, 0.5776, 0.7087] +2026-04-10 21:12:30.323705: Epoch time: 103.54 s +2026-04-10 21:12:31.438412: +2026-04-10 21:12:31.440943: Epoch 378 +2026-04-10 21:12:31.443177: Current learning rate: 0.00915 +2026-04-10 21:14:14.694309: train_loss -0.1648 +2026-04-10 21:14:14.701034: val_loss -0.1445 +2026-04-10 21:14:14.703788: Pseudo dice [0.4853, 0.1124, 0.5996, 0.3753, 0.6042, 0.713, 0.5402] +2026-04-10 21:14:14.707029: Epoch time: 103.26 s +2026-04-10 21:14:15.815732: +2026-04-10 21:14:15.817626: Epoch 379 +2026-04-10 21:14:15.819602: Current learning rate: 0.00914 +2026-04-10 21:15:59.044265: train_loss -0.1641 +2026-04-10 21:15:59.051584: val_loss -0.0994 +2026-04-10 21:15:59.054426: Pseudo dice [0.2897, 0.4626, 0.7263, 0.2208, 0.3634, 0.3985, 0.4854] +2026-04-10 21:15:59.056903: Epoch time: 103.23 s +2026-04-10 21:16:00.175328: +2026-04-10 21:16:00.178874: Epoch 380 +2026-04-10 21:16:00.181748: Current learning rate: 0.00914 +2026-04-10 21:17:42.743423: train_loss -0.1543 +2026-04-10 21:17:42.749574: val_loss -0.1346 +2026-04-10 21:17:42.752347: Pseudo dice [0.2053, 0.5411, 0.632, 0.3474, 0.3998, 0.6914, 0.7199] +2026-04-10 21:17:42.755583: Epoch time: 102.57 s +2026-04-10 21:17:43.854733: +2026-04-10 21:17:43.856767: Epoch 381 +2026-04-10 21:17:43.859612: Current learning rate: 0.00914 +2026-04-10 21:19:26.929280: train_loss -0.1664 +2026-04-10 21:19:26.935766: val_loss -0.1115 +2026-04-10 21:19:26.937748: Pseudo dice [0.4726, 0.1905, 0.611, 0.3053, 0.4863, 0.7736, 0.4487] +2026-04-10 21:19:26.940550: Epoch time: 103.08 s +2026-04-10 21:19:28.080702: +2026-04-10 21:19:28.082466: Epoch 382 +2026-04-10 21:19:28.085226: Current learning rate: 0.00914 +2026-04-10 21:21:11.170011: train_loss -0.161 +2026-04-10 21:21:11.178221: val_loss -0.105 +2026-04-10 21:21:11.182121: Pseudo dice [0.411, 0.8437, 0.7057, 0.1663, 0.4737, 0.6597, 0.5432] +2026-04-10 21:21:11.186548: Epoch time: 103.09 s +2026-04-10 21:21:12.313096: +2026-04-10 21:21:12.314712: Epoch 383 +2026-04-10 21:21:12.316147: Current learning rate: 0.00913 +2026-04-10 21:22:55.597622: train_loss -0.162 +2026-04-10 21:22:55.605998: val_loss -0.1241 +2026-04-10 21:22:55.608410: Pseudo dice [0.6322, 0.0779, 0.6129, 0.1677, 0.4823, 0.4833, 0.7282] +2026-04-10 21:22:55.611539: Epoch time: 103.29 s +2026-04-10 21:22:56.765325: +2026-04-10 21:22:56.768865: Epoch 384 +2026-04-10 21:22:56.770844: Current learning rate: 0.00913 +2026-04-10 21:24:40.137981: train_loss -0.1546 +2026-04-10 21:24:40.144866: val_loss -0.1413 +2026-04-10 21:24:40.148243: Pseudo dice [0.4742, 0.8616, 0.765, 0.4691, 0.2815, 0.3801, 0.7558] +2026-04-10 21:24:40.151823: Epoch time: 103.38 s +2026-04-10 21:24:42.146813: +2026-04-10 21:24:42.148786: Epoch 385 +2026-04-10 21:24:42.151532: Current learning rate: 0.00913 +2026-04-10 21:26:25.071898: train_loss -0.164 +2026-04-10 21:26:25.078538: val_loss -0.1335 +2026-04-10 21:26:25.080308: Pseudo dice [0.5748, 0.8245, 0.5552, 0.402, 0.2594, 0.2149, 0.6928] +2026-04-10 21:26:25.082317: Epoch time: 102.93 s +2026-04-10 21:26:26.210052: +2026-04-10 21:26:26.211830: Epoch 386 +2026-04-10 21:26:26.215330: Current learning rate: 0.00913 +2026-04-10 21:28:09.282682: train_loss -0.1777 +2026-04-10 21:28:09.289332: val_loss -0.1426 +2026-04-10 21:28:09.292079: Pseudo dice [0.535, 0.6667, 0.7539, 0.5968, 0.18, 0.7344, 0.7592] +2026-04-10 21:28:09.295049: Epoch time: 103.08 s +2026-04-10 21:28:10.419830: +2026-04-10 21:28:10.422226: Epoch 387 +2026-04-10 21:28:10.424095: Current learning rate: 0.00912 +2026-04-10 21:29:53.518958: train_loss -0.1575 +2026-04-10 21:29:53.537127: val_loss -0.1278 +2026-04-10 21:29:53.539330: Pseudo dice [0.6156, 0.264, 0.652, 0.2603, 0.2365, 0.7714, 0.7109] +2026-04-10 21:29:53.546794: Epoch time: 103.1 s +2026-04-10 21:29:54.738555: +2026-04-10 21:29:54.740726: Epoch 388 +2026-04-10 21:29:54.742546: Current learning rate: 0.00912 +2026-04-10 21:31:37.703479: train_loss -0.1671 +2026-04-10 21:31:37.710642: val_loss -0.1057 +2026-04-10 21:31:37.713312: Pseudo dice [0.6865, 0.084, 0.6688, 0.2669, 0.1983, 0.3405, 0.6595] +2026-04-10 21:31:37.719762: Epoch time: 102.97 s +2026-04-10 21:31:38.859225: +2026-04-10 21:31:38.861803: Epoch 389 +2026-04-10 21:31:38.864093: Current learning rate: 0.00912 +2026-04-10 21:33:22.394302: train_loss -0.1669 +2026-04-10 21:33:22.404881: val_loss -0.1667 +2026-04-10 21:33:22.409226: Pseudo dice [0.6991, 0.8173, 0.6552, 0.7134, 0.576, 0.6624, 0.6221] +2026-04-10 21:33:22.412929: Epoch time: 103.54 s +2026-04-10 21:33:22.417843: Yayy! New best EMA pseudo Dice: 0.5182 +2026-04-10 21:33:25.435487: +2026-04-10 21:33:25.437245: Epoch 390 +2026-04-10 21:33:25.438809: Current learning rate: 0.00912 +2026-04-10 21:35:08.118676: train_loss -0.1707 +2026-04-10 21:35:08.124961: val_loss -0.1189 +2026-04-10 21:35:08.127302: Pseudo dice [0.5902, 0.3137, 0.633, 0.0841, 0.3643, 0.7677, 0.7704] +2026-04-10 21:35:08.130140: Epoch time: 102.69 s +2026-04-10 21:35:09.298653: +2026-04-10 21:35:09.301357: Epoch 391 +2026-04-10 21:35:09.303752: Current learning rate: 0.00912 +2026-04-10 21:36:52.279534: train_loss -0.1695 +2026-04-10 21:36:52.286907: val_loss -0.1696 +2026-04-10 21:36:52.292769: Pseudo dice [0.4553, 0.9067, 0.7523, 0.7631, 0.5261, 0.696, 0.7266] +2026-04-10 21:36:52.295320: Epoch time: 102.98 s +2026-04-10 21:36:52.298682: Yayy! New best EMA pseudo Dice: 0.534 +2026-04-10 21:36:55.182325: +2026-04-10 21:36:55.183962: Epoch 392 +2026-04-10 21:36:55.185331: Current learning rate: 0.00911 +2026-04-10 21:38:38.642030: train_loss -0.1529 +2026-04-10 21:38:38.647971: val_loss -0.0881 +2026-04-10 21:38:38.650372: Pseudo dice [0.2961, 0.3015, 0.5862, 0.2279, 0.3197, 0.4133, 0.6439] +2026-04-10 21:38:38.652838: Epoch time: 103.46 s +2026-04-10 21:38:39.856136: +2026-04-10 21:38:39.860327: Epoch 393 +2026-04-10 21:38:39.863668: Current learning rate: 0.00911 +2026-04-10 21:40:23.659575: train_loss -0.1461 +2026-04-10 21:40:23.667579: val_loss -0.1077 +2026-04-10 21:40:23.669763: Pseudo dice [0.3007, 0.314, 0.7132, 0.6189, 0.3585, 0.2666, 0.6849] +2026-04-10 21:40:23.672618: Epoch time: 103.81 s +2026-04-10 21:40:24.808138: +2026-04-10 21:40:24.811060: Epoch 394 +2026-04-10 21:40:24.813069: Current learning rate: 0.00911 +2026-04-10 21:42:08.448734: train_loss -0.1582 +2026-04-10 21:42:08.456246: val_loss -0.0276 +2026-04-10 21:42:08.458525: Pseudo dice [0.5601, 0.2048, 0.4018, 0.3819, 0.2631, 0.1638, 0.5053] +2026-04-10 21:42:08.460917: Epoch time: 103.64 s +2026-04-10 21:42:09.661577: +2026-04-10 21:42:09.663493: Epoch 395 +2026-04-10 21:42:09.665471: Current learning rate: 0.00911 +2026-04-10 21:43:52.761390: train_loss -0.1623 +2026-04-10 21:43:52.768619: val_loss -0.1222 +2026-04-10 21:43:52.772513: Pseudo dice [0.4271, 0.3129, 0.7756, 0.5091, 0.3692, 0.288, 0.6007] +2026-04-10 21:43:52.775591: Epoch time: 103.1 s +2026-04-10 21:43:54.000645: +2026-04-10 21:43:54.002845: Epoch 396 +2026-04-10 21:43:54.005347: Current learning rate: 0.0091 +2026-04-10 21:45:37.184317: train_loss -0.1528 +2026-04-10 21:45:37.193733: val_loss -0.1359 +2026-04-10 21:45:37.195656: Pseudo dice [0.5365, 0.9025, 0.7415, 0.5434, 0.3771, 0.5392, 0.5942] +2026-04-10 21:45:37.198523: Epoch time: 103.19 s +2026-04-10 21:45:38.413753: +2026-04-10 21:45:38.416161: Epoch 397 +2026-04-10 21:45:38.418360: Current learning rate: 0.0091 +2026-04-10 21:47:21.827466: train_loss -0.1658 +2026-04-10 21:47:21.836564: val_loss -0.1127 +2026-04-10 21:47:21.839415: Pseudo dice [0.5944, 0.868, 0.6926, 0.1198, 0.3049, 0.3033, 0.7635] +2026-04-10 21:47:21.842749: Epoch time: 103.42 s +2026-04-10 21:47:22.999257: +2026-04-10 21:47:23.001437: Epoch 398 +2026-04-10 21:47:23.003021: Current learning rate: 0.0091 +2026-04-10 21:49:06.292723: train_loss -0.1528 +2026-04-10 21:49:06.300378: val_loss -0.1486 +2026-04-10 21:49:06.302882: Pseudo dice [0.3087, 0.663, 0.7307, 0.5003, 0.1966, 0.2597, 0.633] +2026-04-10 21:49:06.306101: Epoch time: 103.3 s +2026-04-10 21:49:07.482780: +2026-04-10 21:49:07.487251: Epoch 399 +2026-04-10 21:49:07.491991: Current learning rate: 0.0091 +2026-04-10 21:50:51.076248: train_loss -0.1457 +2026-04-10 21:50:51.082853: val_loss -0.1247 +2026-04-10 21:50:51.085034: Pseudo dice [0.5444, 0.733, 0.7149, 0.1028, 0.4226, 0.4212, 0.4715] +2026-04-10 21:50:51.088194: Epoch time: 103.6 s +2026-04-10 21:50:53.970071: +2026-04-10 21:50:53.972310: Epoch 400 +2026-04-10 21:50:53.973798: Current learning rate: 0.0091 +2026-04-10 21:52:37.159857: train_loss -0.156 +2026-04-10 21:52:37.168302: val_loss -0.1477 +2026-04-10 21:52:37.170505: Pseudo dice [0.6365, 0.3048, 0.6774, 0.551, 0.319, 0.7825, 0.7175] +2026-04-10 21:52:37.173565: Epoch time: 103.19 s +2026-04-10 21:52:38.360190: +2026-04-10 21:52:38.362547: Epoch 401 +2026-04-10 21:52:38.365436: Current learning rate: 0.00909 +2026-04-10 21:54:22.299434: train_loss -0.168 +2026-04-10 21:54:22.306125: val_loss -0.1146 +2026-04-10 21:54:22.309757: Pseudo dice [0.5517, 0.509, 0.6768, 0.2902, 0.585, 0.5816, 0.6505] +2026-04-10 21:54:22.312739: Epoch time: 103.94 s +2026-04-10 21:54:23.469577: +2026-04-10 21:54:23.471375: Epoch 402 +2026-04-10 21:54:23.473451: Current learning rate: 0.00909 +2026-04-10 21:56:06.327317: train_loss -0.161 +2026-04-10 21:56:06.334023: val_loss -0.1355 +2026-04-10 21:56:06.337269: Pseudo dice [0.5667, 0.7396, 0.7276, 0.6234, 0.4837, 0.2684, 0.5292] +2026-04-10 21:56:06.341387: Epoch time: 102.86 s +2026-04-10 21:56:07.535888: +2026-04-10 21:56:07.537945: Epoch 403 +2026-04-10 21:56:07.540117: Current learning rate: 0.00909 +2026-04-10 21:57:50.867246: train_loss -0.1635 +2026-04-10 21:57:50.874839: val_loss -0.1445 +2026-04-10 21:57:50.877248: Pseudo dice [0.6594, 0.8288, 0.6085, 0.364, 0.6103, 0.1958, 0.5138] +2026-04-10 21:57:50.880401: Epoch time: 103.33 s +2026-04-10 21:57:53.129196: +2026-04-10 21:57:53.131811: Epoch 404 +2026-04-10 21:57:53.133398: Current learning rate: 0.00909 +2026-04-10 21:59:35.300447: train_loss -0.1605 +2026-04-10 21:59:35.306341: val_loss -0.1197 +2026-04-10 21:59:35.308080: Pseudo dice [0.4997, 0.3065, 0.7309, 0.0547, 0.4152, 0.3103, 0.8045] +2026-04-10 21:59:35.310434: Epoch time: 102.17 s +2026-04-10 21:59:36.459536: +2026-04-10 21:59:36.461356: Epoch 405 +2026-04-10 21:59:36.463331: Current learning rate: 0.00908 +2026-04-10 22:01:19.916736: train_loss -0.1637 +2026-04-10 22:01:19.924471: val_loss -0.1311 +2026-04-10 22:01:19.926295: Pseudo dice [0.6751, 0.189, 0.5475, 0.4783, 0.3938, 0.8425, 0.7452] +2026-04-10 22:01:19.928520: Epoch time: 103.46 s +2026-04-10 22:01:21.063541: +2026-04-10 22:01:21.065831: Epoch 406 +2026-04-10 22:01:21.067665: Current learning rate: 0.00908 +2026-04-10 22:03:04.753923: train_loss -0.1458 +2026-04-10 22:03:04.761371: val_loss -0.1264 +2026-04-10 22:03:04.791075: Pseudo dice [0.3764, 0.2284, 0.6428, 0.6534, 0.199, 0.5629, 0.611] +2026-04-10 22:03:04.794014: Epoch time: 103.69 s +2026-04-10 22:03:05.918617: +2026-04-10 22:03:05.921526: Epoch 407 +2026-04-10 22:03:05.923579: Current learning rate: 0.00908 +2026-04-10 22:04:49.594016: train_loss -0.1596 +2026-04-10 22:04:49.602520: val_loss -0.1067 +2026-04-10 22:04:49.605227: Pseudo dice [0.4647, 0.8076, 0.618, 0.3292, 0.198, 0.382, 0.6793] +2026-04-10 22:04:49.608854: Epoch time: 103.68 s +2026-04-10 22:04:50.742694: +2026-04-10 22:04:50.748434: Epoch 408 +2026-04-10 22:04:50.755029: Current learning rate: 0.00908 +2026-04-10 22:06:34.980240: train_loss -0.1591 +2026-04-10 22:06:34.986989: val_loss -0.1204 +2026-04-10 22:06:34.989377: Pseudo dice [0.3502, 0.7601, 0.6783, 0.3424, 0.4786, 0.5063, 0.8162] +2026-04-10 22:06:34.992774: Epoch time: 104.24 s +2026-04-10 22:06:36.137311: +2026-04-10 22:06:36.140043: Epoch 409 +2026-04-10 22:06:36.141997: Current learning rate: 0.00907 +2026-04-10 22:08:20.458664: train_loss -0.1507 +2026-04-10 22:08:20.466394: val_loss -0.109 +2026-04-10 22:08:20.468450: Pseudo dice [0.7838, 0.4922, 0.603, 0.1812, 0.3424, 0.6374, 0.6381] +2026-04-10 22:08:20.471662: Epoch time: 104.32 s +2026-04-10 22:08:21.600436: +2026-04-10 22:08:21.602994: Epoch 410 +2026-04-10 22:08:21.605597: Current learning rate: 0.00907 +2026-04-10 22:10:05.627936: train_loss -0.1592 +2026-04-10 22:10:05.636064: val_loss -0.1592 +2026-04-10 22:10:05.639040: Pseudo dice [0.6393, 0.8736, 0.7944, 0.5426, 0.4616, 0.4428, 0.6797] +2026-04-10 22:10:05.642432: Epoch time: 104.03 s +2026-04-10 22:10:06.728299: +2026-04-10 22:10:06.730974: Epoch 411 +2026-04-10 22:10:06.732994: Current learning rate: 0.00907 +2026-04-10 22:11:50.648049: train_loss -0.17 +2026-04-10 22:11:50.656766: val_loss -0.132 +2026-04-10 22:11:50.658851: Pseudo dice [0.5956, 0.2641, 0.614, 0.3385, 0.4354, 0.6444, 0.7469] +2026-04-10 22:11:50.661786: Epoch time: 103.92 s +2026-04-10 22:11:51.740739: +2026-04-10 22:11:51.742853: Epoch 412 +2026-04-10 22:11:51.744611: Current learning rate: 0.00907 +2026-04-10 22:13:35.844452: train_loss -0.1547 +2026-04-10 22:13:35.853004: val_loss -0.1212 +2026-04-10 22:13:35.855984: Pseudo dice [0.6823, 0.811, 0.7001, 0.3453, 0.4746, 0.1523, 0.5897] +2026-04-10 22:13:35.858706: Epoch time: 104.11 s +2026-04-10 22:13:36.919339: +2026-04-10 22:13:36.921932: Epoch 413 +2026-04-10 22:13:36.924124: Current learning rate: 0.00907 +2026-04-10 22:15:21.172409: train_loss -0.1373 +2026-04-10 22:15:21.180308: val_loss -0.1034 +2026-04-10 22:15:21.183748: Pseudo dice [0.4526, 0.2868, 0.6323, 0.0717, 0.2837, 0.526, 0.6897] +2026-04-10 22:15:21.192411: Epoch time: 104.26 s +2026-04-10 22:15:22.250692: +2026-04-10 22:15:22.252825: Epoch 414 +2026-04-10 22:15:22.255233: Current learning rate: 0.00906 +2026-04-10 22:17:05.450047: train_loss -0.1176 +2026-04-10 22:17:05.460084: val_loss -0.086 +2026-04-10 22:17:05.463115: Pseudo dice [0.2351, 0.5234, 0.5356, 0.2714, 0.3872, 0.7621, 0.3186] +2026-04-10 22:17:05.466775: Epoch time: 103.2 s +2026-04-10 22:17:06.540687: +2026-04-10 22:17:06.543505: Epoch 415 +2026-04-10 22:17:06.546543: Current learning rate: 0.00906 +2026-04-10 22:18:50.384881: train_loss -0.1646 +2026-04-10 22:18:50.391228: val_loss -0.0857 +2026-04-10 22:18:50.393653: Pseudo dice [0.2038, 0.8674, 0.565, 0.426, 0.1657, 0.434, 0.674] +2026-04-10 22:18:50.397060: Epoch time: 103.85 s +2026-04-10 22:18:51.487227: +2026-04-10 22:18:51.489039: Epoch 416 +2026-04-10 22:18:51.490907: Current learning rate: 0.00906 +2026-04-10 22:20:34.429085: train_loss -0.1556 +2026-04-10 22:20:34.435720: val_loss -0.1442 +2026-04-10 22:20:34.437875: Pseudo dice [0.2738, 0.7817, 0.8221, 0.2575, 0.4857, 0.7529, 0.6264] +2026-04-10 22:20:34.440298: Epoch time: 102.95 s +2026-04-10 22:20:35.547055: +2026-04-10 22:20:35.548934: Epoch 417 +2026-04-10 22:20:35.550373: Current learning rate: 0.00906 +2026-04-10 22:22:18.254963: train_loss -0.1634 +2026-04-10 22:22:18.261373: val_loss -0.092 +2026-04-10 22:22:18.263761: Pseudo dice [0.5762, 0.8736, 0.5669, 0.3689, 0.419, 0.1991, 0.4967] +2026-04-10 22:22:18.266412: Epoch time: 102.71 s +2026-04-10 22:22:19.343724: +2026-04-10 22:22:19.345914: Epoch 418 +2026-04-10 22:22:19.347469: Current learning rate: 0.00905 +2026-04-10 22:24:02.666800: train_loss -0.161 +2026-04-10 22:24:02.673779: val_loss -0.1227 +2026-04-10 22:24:02.676673: Pseudo dice [0.8439, 0.8347, 0.6335, 0.2302, 0.2946, 0.5651, 0.1819] +2026-04-10 22:24:02.679127: Epoch time: 103.33 s +2026-04-10 22:24:03.818998: +2026-04-10 22:24:03.821177: Epoch 419 +2026-04-10 22:24:03.822920: Current learning rate: 0.00905 +2026-04-10 22:25:46.905153: train_loss -0.1637 +2026-04-10 22:25:46.913183: val_loss -0.1386 +2026-04-10 22:25:46.916312: Pseudo dice [0.5735, 0.6933, 0.6591, 0.5004, 0.4293, 0.5362, 0.8336] +2026-04-10 22:25:46.918696: Epoch time: 103.09 s +2026-04-10 22:25:47.974659: +2026-04-10 22:25:47.978005: Epoch 420 +2026-04-10 22:25:47.981321: Current learning rate: 0.00905 +2026-04-10 22:27:30.633513: train_loss -0.1755 +2026-04-10 22:27:30.640198: val_loss -0.1275 +2026-04-10 22:27:30.642946: Pseudo dice [0.6397, 0.7656, 0.734, 0.6446, 0.2859, 0.511, 0.7419] +2026-04-10 22:27:30.645649: Epoch time: 102.66 s +2026-04-10 22:27:31.717493: +2026-04-10 22:27:31.720002: Epoch 421 +2026-04-10 22:27:31.723483: Current learning rate: 0.00905 +2026-04-10 22:29:15.647731: train_loss -0.1651 +2026-04-10 22:29:15.665056: val_loss -0.0931 +2026-04-10 22:29:15.673761: Pseudo dice [0.408, 0.2217, 0.6513, 0.1488, 0.324, 0.5023, 0.6297] +2026-04-10 22:29:15.676997: Epoch time: 103.93 s +2026-04-10 22:29:16.715178: +2026-04-10 22:29:16.716802: Epoch 422 +2026-04-10 22:29:16.718185: Current learning rate: 0.00905 +2026-04-10 22:31:04.076099: train_loss -0.1615 +2026-04-10 22:31:04.086119: val_loss -0.1251 +2026-04-10 22:31:04.089741: Pseudo dice [0.5746, 0.4225, 0.6125, 0.1846, 0.2958, 0.5633, 0.509] +2026-04-10 22:31:04.092672: Epoch time: 107.36 s +2026-04-10 22:31:05.150011: +2026-04-10 22:31:05.152247: Epoch 423 +2026-04-10 22:31:05.155654: Current learning rate: 0.00904 +2026-04-10 22:32:50.087570: train_loss -0.1443 +2026-04-10 22:32:50.097318: val_loss -0.1157 +2026-04-10 22:32:50.101126: Pseudo dice [0.7133, 0.7362, 0.6559, 0.5066, 0.2262, 0.7435, 0.6898] +2026-04-10 22:32:50.104556: Epoch time: 104.94 s +2026-04-10 22:32:52.291852: +2026-04-10 22:32:52.293811: Epoch 424 +2026-04-10 22:32:52.295452: Current learning rate: 0.00904 +2026-04-10 22:34:36.683353: train_loss -0.1559 +2026-04-10 22:34:36.690248: val_loss -0.128 +2026-04-10 22:34:36.692680: Pseudo dice [0.2172, 0.335, 0.7438, 0.3446, 0.3158, 0.5204, 0.7474] +2026-04-10 22:34:36.695593: Epoch time: 104.39 s +2026-04-10 22:34:37.776572: +2026-04-10 22:34:37.780824: Epoch 425 +2026-04-10 22:34:37.782800: Current learning rate: 0.00904 +2026-04-10 22:36:20.750190: train_loss -0.1575 +2026-04-10 22:36:20.757348: val_loss -0.1273 +2026-04-10 22:36:20.759748: Pseudo dice [0.446, 0.3, 0.7197, 0.4211, 0.3944, 0.5615, 0.7785] +2026-04-10 22:36:20.762409: Epoch time: 102.98 s +2026-04-10 22:36:21.830820: +2026-04-10 22:36:21.833329: Epoch 426 +2026-04-10 22:36:21.836438: Current learning rate: 0.00904 +2026-04-10 22:38:06.390347: train_loss -0.143 +2026-04-10 22:38:06.397116: val_loss -0.1153 +2026-04-10 22:38:06.402318: Pseudo dice [0.4375, 0.177, 0.7298, 0.4123, 0.4683, 0.5972, 0.5924] +2026-04-10 22:38:06.405305: Epoch time: 104.56 s +2026-04-10 22:38:07.507522: +2026-04-10 22:38:07.510032: Epoch 427 +2026-04-10 22:38:07.511764: Current learning rate: 0.00903 +2026-04-10 22:39:52.052432: train_loss -0.1629 +2026-04-10 22:39:52.061072: val_loss -0.1275 +2026-04-10 22:39:52.064436: Pseudo dice [0.6057, 0.6208, 0.5872, 0.3149, 0.4266, 0.5795, 0.6275] +2026-04-10 22:39:52.069191: Epoch time: 104.55 s +2026-04-10 22:39:53.132208: +2026-04-10 22:39:53.135466: Epoch 428 +2026-04-10 22:39:53.138476: Current learning rate: 0.00903 +2026-04-10 22:41:37.018878: train_loss -0.1568 +2026-04-10 22:41:37.027193: val_loss -0.1251 +2026-04-10 22:41:37.030605: Pseudo dice [0.4246, 0.8195, 0.6518, 0.4621, 0.4272, 0.5553, 0.6274] +2026-04-10 22:41:37.034273: Epoch time: 103.89 s +2026-04-10 22:41:38.102276: +2026-04-10 22:41:38.104576: Epoch 429 +2026-04-10 22:41:38.106889: Current learning rate: 0.00903 +2026-04-10 22:43:23.086959: train_loss -0.1663 +2026-04-10 22:43:23.094787: val_loss -0.128 +2026-04-10 22:43:23.098082: Pseudo dice [0.665, 0.6316, 0.6311, 0.169, 0.3554, 0.7077, 0.6916] +2026-04-10 22:43:23.110088: Epoch time: 104.99 s +2026-04-10 22:43:24.279922: +2026-04-10 22:43:24.284883: Epoch 430 +2026-04-10 22:43:24.287593: Current learning rate: 0.00903 +2026-04-10 22:45:08.072461: train_loss -0.1811 +2026-04-10 22:45:08.081714: val_loss -0.1056 +2026-04-10 22:45:08.085079: Pseudo dice [0.5988, 0.5477, 0.614, 0.123, 0.2204, 0.6334, 0.3851] +2026-04-10 22:45:08.087448: Epoch time: 103.8 s +2026-04-10 22:45:09.145973: +2026-04-10 22:45:09.147872: Epoch 431 +2026-04-10 22:45:09.149808: Current learning rate: 0.00902 +2026-04-10 22:46:53.093887: train_loss -0.1324 +2026-04-10 22:46:53.113264: val_loss -0.0558 +2026-04-10 22:46:53.116006: Pseudo dice [0.2357, 0.164, 0.3896, 0.3066, 0.3368, 0.1413, 0.7945] +2026-04-10 22:46:53.124859: Epoch time: 103.95 s +2026-04-10 22:46:54.189238: +2026-04-10 22:46:54.190860: Epoch 432 +2026-04-10 22:46:54.194065: Current learning rate: 0.00902 +2026-04-10 22:48:37.802720: train_loss -0.1611 +2026-04-10 22:48:37.810772: val_loss -0.1034 +2026-04-10 22:48:37.813384: Pseudo dice [0.6573, 0.8095, 0.6662, 0.0792, 0.1812, 0.4513, 0.3587] +2026-04-10 22:48:37.816276: Epoch time: 103.62 s +2026-04-10 22:48:38.885917: +2026-04-10 22:48:38.887693: Epoch 433 +2026-04-10 22:48:38.889477: Current learning rate: 0.00902 +2026-04-10 22:50:21.973352: train_loss -0.1595 +2026-04-10 22:50:21.981317: val_loss -0.1356 +2026-04-10 22:50:21.983585: Pseudo dice [0.4169, 0.2098, 0.7798, 0.3575, 0.461, 0.5534, 0.7335] +2026-04-10 22:50:21.985699: Epoch time: 103.09 s +2026-04-10 22:50:23.044935: +2026-04-10 22:50:23.047493: Epoch 434 +2026-04-10 22:50:23.049320: Current learning rate: 0.00902 +2026-04-10 22:52:06.586697: train_loss -0.1625 +2026-04-10 22:52:06.597855: val_loss -0.1166 +2026-04-10 22:52:06.601928: Pseudo dice [0.2755, 0.3138, 0.7554, 0.2002, 0.2723, 0.5948, 0.8149] +2026-04-10 22:52:06.610219: Epoch time: 103.55 s +2026-04-10 22:52:07.691644: +2026-04-10 22:52:07.693985: Epoch 435 +2026-04-10 22:52:07.696004: Current learning rate: 0.00902 +2026-04-10 22:53:50.270867: train_loss -0.1671 +2026-04-10 22:53:50.277737: val_loss -0.1245 +2026-04-10 22:53:50.279371: Pseudo dice [0.5906, 0.4122, 0.5438, 0.5285, 0.4552, 0.2902, 0.8668] +2026-04-10 22:53:50.281913: Epoch time: 102.58 s +2026-04-10 22:53:51.516659: +2026-04-10 22:53:51.518957: Epoch 436 +2026-04-10 22:53:51.521710: Current learning rate: 0.00901 +2026-04-10 22:55:34.686081: train_loss -0.1817 +2026-04-10 22:55:34.693154: val_loss -0.0994 +2026-04-10 22:55:34.695512: Pseudo dice [0.1304, 0.0837, 0.7649, 0.2627, 0.3851, 0.2923, 0.6222] +2026-04-10 22:55:34.698533: Epoch time: 103.17 s +2026-04-10 22:55:35.769877: +2026-04-10 22:55:35.772670: Epoch 437 +2026-04-10 22:55:35.775192: Current learning rate: 0.00901 +2026-04-10 22:57:19.123133: train_loss -0.1613 +2026-04-10 22:57:19.131711: val_loss -0.0759 +2026-04-10 22:57:19.134380: Pseudo dice [0.576, 0.6272, 0.4211, 0.4273, 0.0678, 0.5673, 0.6374] +2026-04-10 22:57:19.137261: Epoch time: 103.36 s +2026-04-10 22:57:20.226290: +2026-04-10 22:57:20.227909: Epoch 438 +2026-04-10 22:57:20.229841: Current learning rate: 0.00901 +2026-04-10 22:59:03.229005: train_loss -0.1448 +2026-04-10 22:59:03.235452: val_loss -0.136 +2026-04-10 22:59:03.238049: Pseudo dice [0.3156, 0.254, 0.7238, 0.4207, 0.5539, 0.7922, 0.7932] +2026-04-10 22:59:03.241031: Epoch time: 103.01 s +2026-04-10 22:59:04.324588: +2026-04-10 22:59:04.327798: Epoch 439 +2026-04-10 22:59:04.330800: Current learning rate: 0.00901 +2026-04-10 23:00:48.118721: train_loss -0.1753 +2026-04-10 23:00:48.127502: val_loss -0.1005 +2026-04-10 23:00:48.129520: Pseudo dice [0.5715, 0.8667, 0.6585, 0.3052, 0.6236, 0.5256, 0.5711] +2026-04-10 23:00:48.133492: Epoch time: 103.8 s +2026-04-10 23:00:49.217353: +2026-04-10 23:00:49.219243: Epoch 440 +2026-04-10 23:00:49.221064: Current learning rate: 0.009 +2026-04-10 23:02:32.402421: train_loss -0.1673 +2026-04-10 23:02:32.410355: val_loss -0.1525 +2026-04-10 23:02:32.413071: Pseudo dice [0.4366, 0.8533, 0.7613, 0.5362, 0.2381, 0.452, 0.2812] +2026-04-10 23:02:32.416805: Epoch time: 103.19 s +2026-04-10 23:02:33.465013: +2026-04-10 23:02:33.467007: Epoch 441 +2026-04-10 23:02:33.468360: Current learning rate: 0.009 +2026-04-10 23:04:17.117450: train_loss -0.1589 +2026-04-10 23:04:17.124877: val_loss -0.1226 +2026-04-10 23:04:17.127768: Pseudo dice [0.5088, 0.4637, 0.7585, 0.7513, 0.3146, 0.6037, 0.7898] +2026-04-10 23:04:17.131021: Epoch time: 103.66 s +2026-04-10 23:04:18.231815: +2026-04-10 23:04:18.236037: Epoch 442 +2026-04-10 23:04:18.237667: Current learning rate: 0.009 +2026-04-10 23:06:00.964726: train_loss -0.1506 +2026-04-10 23:06:00.971035: val_loss -0.1376 +2026-04-10 23:06:00.973223: Pseudo dice [0.7385, 0.7574, 0.6907, 0.2673, 0.4638, 0.5792, 0.7276] +2026-04-10 23:06:00.975731: Epoch time: 102.74 s +2026-04-10 23:06:02.065223: +2026-04-10 23:06:02.067728: Epoch 443 +2026-04-10 23:06:02.069744: Current learning rate: 0.009 +2026-04-10 23:07:44.877175: train_loss -0.1578 +2026-04-10 23:07:44.885780: val_loss -0.1475 +2026-04-10 23:07:44.889046: Pseudo dice [0.4955, 0.6683, 0.7424, 0.4281, 0.5032, 0.8307, 0.7248] +2026-04-10 23:07:44.891900: Epoch time: 102.82 s +2026-04-10 23:07:45.963168: +2026-04-10 23:07:45.966491: Epoch 444 +2026-04-10 23:07:45.968688: Current learning rate: 0.009 +2026-04-10 23:09:29.146978: train_loss -0.1526 +2026-04-10 23:09:29.153256: val_loss -0.1375 +2026-04-10 23:09:29.155392: Pseudo dice [0.6577, 0.1979, 0.7185, 0.5211, 0.4964, 0.1207, 0.6803] +2026-04-10 23:09:29.157958: Epoch time: 103.19 s +2026-04-10 23:09:31.416335: +2026-04-10 23:09:31.418180: Epoch 445 +2026-04-10 23:09:31.419791: Current learning rate: 0.00899 +2026-04-10 23:11:15.478031: train_loss -0.1667 +2026-04-10 23:11:15.486621: val_loss -0.1383 +2026-04-10 23:11:15.488852: Pseudo dice [0.2964, 0.6285, 0.7227, 0.2957, 0.5138, 0.0816, 0.641] +2026-04-10 23:11:15.491772: Epoch time: 104.06 s +2026-04-10 23:11:16.557125: +2026-04-10 23:11:16.558775: Epoch 446 +2026-04-10 23:11:16.561305: Current learning rate: 0.00899 +2026-04-10 23:12:59.824053: train_loss -0.1633 +2026-04-10 23:12:59.830023: val_loss -0.1578 +2026-04-10 23:12:59.832985: Pseudo dice [0.3933, 0.5666, 0.6486, 0.3288, 0.5059, 0.8405, 0.8106] +2026-04-10 23:12:59.835818: Epoch time: 103.27 s +2026-04-10 23:13:00.920698: +2026-04-10 23:13:00.922873: Epoch 447 +2026-04-10 23:13:00.924255: Current learning rate: 0.00899 +2026-04-10 23:14:44.418174: train_loss -0.1727 +2026-04-10 23:14:44.426507: val_loss -0.143 +2026-04-10 23:14:44.428503: Pseudo dice [0.7094, 0.3398, 0.8155, 0.4334, 0.2161, 0.3869, 0.5802] +2026-04-10 23:14:44.431123: Epoch time: 103.5 s +2026-04-10 23:14:45.514221: +2026-04-10 23:14:45.515996: Epoch 448 +2026-04-10 23:14:45.517838: Current learning rate: 0.00899 +2026-04-10 23:16:28.635097: train_loss -0.167 +2026-04-10 23:16:28.642804: val_loss -0.1646 +2026-04-10 23:16:28.644877: Pseudo dice [0.6472, 0.8268, 0.67, 0.6714, 0.5222, 0.8773, 0.7679] +2026-04-10 23:16:28.647415: Epoch time: 103.12 s +2026-04-10 23:16:28.649855: Yayy! New best EMA pseudo Dice: 0.5408 +2026-04-10 23:16:31.323663: +2026-04-10 23:16:31.327311: Epoch 449 +2026-04-10 23:16:31.329689: Current learning rate: 0.00898 +2026-04-10 23:18:13.870664: train_loss -0.1663 +2026-04-10 23:18:13.876625: val_loss -0.1405 +2026-04-10 23:18:13.878796: Pseudo dice [0.0614, 0.5684, 0.7629, 0.4668, 0.5781, 0.273, 0.7833] +2026-04-10 23:18:13.880911: Epoch time: 102.55 s +2026-04-10 23:18:16.497072: +2026-04-10 23:18:16.500055: Epoch 450 +2026-04-10 23:18:16.501513: Current learning rate: 0.00898 +2026-04-10 23:19:59.884469: train_loss -0.1516 +2026-04-10 23:19:59.893385: val_loss -0.0809 +2026-04-10 23:19:59.895981: Pseudo dice [0.0836, 0.6831, 0.643, 0.3647, 0.4049, 0.4351, 0.5891] +2026-04-10 23:19:59.898570: Epoch time: 103.39 s +2026-04-10 23:20:00.960729: +2026-04-10 23:20:00.962662: Epoch 451 +2026-04-10 23:20:00.964709: Current learning rate: 0.00898 +2026-04-10 23:21:44.412315: train_loss -0.1751 +2026-04-10 23:21:44.417626: val_loss -0.1409 +2026-04-10 23:21:44.419827: Pseudo dice [0.3214, 0.1384, 0.5361, 0.7273, 0.3308, 0.5092, 0.7895] +2026-04-10 23:21:44.421918: Epoch time: 103.45 s +2026-04-10 23:21:45.479206: +2026-04-10 23:21:45.481036: Epoch 452 +2026-04-10 23:21:45.482785: Current learning rate: 0.00898 +2026-04-10 23:23:28.686079: train_loss -0.1602 +2026-04-10 23:23:28.717236: val_loss -0.105 +2026-04-10 23:23:28.721865: Pseudo dice [0.5761, 0.2275, 0.5114, 0.2692, 0.492, 0.5226, 0.6826] +2026-04-10 23:23:28.724722: Epoch time: 103.21 s +2026-04-10 23:23:29.806290: +2026-04-10 23:23:29.807877: Epoch 453 +2026-04-10 23:23:29.809497: Current learning rate: 0.00897 +2026-04-10 23:25:13.496376: train_loss -0.1567 +2026-04-10 23:25:13.503919: val_loss -0.1007 +2026-04-10 23:25:13.508010: Pseudo dice [0.6019, 0.2627, 0.4548, 0.2851, 0.4585, 0.7342, 0.5485] +2026-04-10 23:25:13.510167: Epoch time: 103.69 s +2026-04-10 23:25:14.619258: +2026-04-10 23:25:14.620775: Epoch 454 +2026-04-10 23:25:14.622216: Current learning rate: 0.00897 +2026-04-10 23:26:57.383727: train_loss -0.1656 +2026-04-10 23:26:57.394433: val_loss -0.0951 +2026-04-10 23:26:57.396078: Pseudo dice [0.2934, 0.3169, 0.5301, 0.1039, 0.3167, 0.3478, 0.6641] +2026-04-10 23:26:57.398705: Epoch time: 102.77 s +2026-04-10 23:26:58.471258: +2026-04-10 23:26:58.473104: Epoch 455 +2026-04-10 23:26:58.476563: Current learning rate: 0.00897 +2026-04-10 23:28:41.656505: train_loss -0.1761 +2026-04-10 23:28:41.665114: val_loss -0.1186 +2026-04-10 23:28:41.668995: Pseudo dice [0.0529, 0.6315, 0.7063, 0.1545, 0.2633, 0.5888, 0.6896] +2026-04-10 23:28:41.671090: Epoch time: 103.19 s +2026-04-10 23:28:42.774896: +2026-04-10 23:28:42.776984: Epoch 456 +2026-04-10 23:28:42.778765: Current learning rate: 0.00897 +2026-04-10 23:30:25.819666: train_loss -0.1639 +2026-04-10 23:30:25.828707: val_loss -0.149 +2026-04-10 23:30:25.831475: Pseudo dice [0.3833, 0.188, 0.7668, 0.5726, 0.4639, 0.7503, 0.8171] +2026-04-10 23:30:25.835807: Epoch time: 103.05 s +2026-04-10 23:30:26.932085: +2026-04-10 23:30:26.934015: Epoch 457 +2026-04-10 23:30:26.936688: Current learning rate: 0.00897 +2026-04-10 23:32:09.196052: train_loss -0.1653 +2026-04-10 23:32:09.203617: val_loss -0.1588 +2026-04-10 23:32:09.205804: Pseudo dice [0.533, 0.5177, 0.7793, 0.6559, 0.4445, 0.8643, 0.869] +2026-04-10 23:32:09.208962: Epoch time: 102.27 s +2026-04-10 23:32:10.337544: +2026-04-10 23:32:10.339700: Epoch 458 +2026-04-10 23:32:10.341941: Current learning rate: 0.00896 +2026-04-10 23:33:54.828706: train_loss -0.1688 +2026-04-10 23:33:54.835767: val_loss -0.164 +2026-04-10 23:33:54.839754: Pseudo dice [0.6003, 0.614, 0.8174, 0.5679, 0.3543, 0.598, 0.6456] +2026-04-10 23:33:54.843918: Epoch time: 104.49 s +2026-04-10 23:33:55.911991: +2026-04-10 23:33:55.914179: Epoch 459 +2026-04-10 23:33:55.916364: Current learning rate: 0.00896 +2026-04-10 23:35:38.264904: train_loss -0.1796 +2026-04-10 23:35:38.270841: val_loss -0.1334 +2026-04-10 23:35:38.273978: Pseudo dice [0.4755, 0.3215, 0.6739, 0.3676, 0.4329, 0.3022, 0.8195] +2026-04-10 23:35:38.276694: Epoch time: 102.36 s +2026-04-10 23:35:39.324445: +2026-04-10 23:35:39.326153: Epoch 460 +2026-04-10 23:35:39.328531: Current learning rate: 0.00896 +2026-04-10 23:37:21.882650: train_loss -0.1696 +2026-04-10 23:37:21.888696: val_loss -0.1293 +2026-04-10 23:37:21.891004: Pseudo dice [0.6384, 0.5357, 0.5917, 0.4026, 0.4506, 0.2677, 0.7611] +2026-04-10 23:37:21.894301: Epoch time: 102.56 s +2026-04-10 23:37:22.955024: +2026-04-10 23:37:22.957087: Epoch 461 +2026-04-10 23:37:22.958694: Current learning rate: 0.00896 +2026-04-10 23:39:05.334461: train_loss -0.1681 +2026-04-10 23:39:05.339735: val_loss -0.1065 +2026-04-10 23:39:05.341573: Pseudo dice [0.5685, 0.824, 0.5932, 0.1626, 0.3674, 0.6781, 0.6328] +2026-04-10 23:39:05.343546: Epoch time: 102.38 s +2026-04-10 23:39:06.427315: +2026-04-10 23:39:06.429529: Epoch 462 +2026-04-10 23:39:06.431231: Current learning rate: 0.00895 +2026-04-10 23:40:48.769715: train_loss -0.1761 +2026-04-10 23:40:48.782429: val_loss -0.1506 +2026-04-10 23:40:48.786144: Pseudo dice [0.5963, 0.7731, 0.6969, 0.2831, 0.2405, 0.7317, 0.5096] +2026-04-10 23:40:48.788701: Epoch time: 102.35 s +2026-04-10 23:40:49.851117: +2026-04-10 23:40:49.853773: Epoch 463 +2026-04-10 23:40:49.856450: Current learning rate: 0.00895 +2026-04-10 23:42:32.555308: train_loss -0.1644 +2026-04-10 23:42:32.561592: val_loss -0.1657 +2026-04-10 23:42:32.563831: Pseudo dice [0.688, 0.3923, 0.6497, 0.3686, 0.4319, 0.4864, 0.7842] +2026-04-10 23:42:32.566736: Epoch time: 102.71 s +2026-04-10 23:42:33.617623: +2026-04-10 23:42:33.620656: Epoch 464 +2026-04-10 23:42:33.623057: Current learning rate: 0.00895 +2026-04-10 23:44:16.399597: train_loss -0.1693 +2026-04-10 23:44:16.406066: val_loss -0.1229 +2026-04-10 23:44:16.408402: Pseudo dice [0.4887, 0.6824, 0.6826, 0.3103, 0.5098, 0.7643, 0.7087] +2026-04-10 23:44:16.411016: Epoch time: 102.79 s +2026-04-10 23:44:17.467320: +2026-04-10 23:44:17.469823: Epoch 465 +2026-04-10 23:44:17.471882: Current learning rate: 0.00895 +2026-04-10 23:46:01.343030: train_loss -0.1492 +2026-04-10 23:46:01.349396: val_loss -0.132 +2026-04-10 23:46:01.351618: Pseudo dice [0.416, 0.9005, 0.7286, 0.2425, 0.5306, 0.615, 0.6313] +2026-04-10 23:46:01.354537: Epoch time: 103.88 s +2026-04-10 23:46:02.453627: +2026-04-10 23:46:02.456143: Epoch 466 +2026-04-10 23:46:02.458180: Current learning rate: 0.00895 +2026-04-10 23:47:45.744721: train_loss -0.129 +2026-04-10 23:47:45.751948: val_loss -0.1448 +2026-04-10 23:47:45.754579: Pseudo dice [0.2271, 0.8535, 0.6589, 0.5194, 0.4489, 0.18, 0.7033] +2026-04-10 23:47:45.757013: Epoch time: 103.29 s +2026-04-10 23:47:46.830542: +2026-04-10 23:47:46.840731: Epoch 467 +2026-04-10 23:47:46.845472: Current learning rate: 0.00894 +2026-04-10 23:49:29.659540: train_loss -0.1599 +2026-04-10 23:49:29.665916: val_loss -0.1427 +2026-04-10 23:49:29.668145: Pseudo dice [0.6906, 0.4439, 0.6787, 0.1885, 0.3676, 0.6485, 0.741] +2026-04-10 23:49:29.671034: Epoch time: 102.83 s +2026-04-10 23:49:30.732701: +2026-04-10 23:49:30.735256: Epoch 468 +2026-04-10 23:49:30.737212: Current learning rate: 0.00894 +2026-04-10 23:51:13.565980: train_loss -0.175 +2026-04-10 23:51:13.572484: val_loss -0.1622 +2026-04-10 23:51:13.574925: Pseudo dice [0.5766, 0.5272, 0.6152, 0.2862, 0.4703, 0.3987, 0.7633] +2026-04-10 23:51:13.577656: Epoch time: 102.84 s +2026-04-10 23:51:14.640804: +2026-04-10 23:51:14.642524: Epoch 469 +2026-04-10 23:51:14.644939: Current learning rate: 0.00894 +2026-04-10 23:52:57.189522: train_loss -0.1644 +2026-04-10 23:52:57.197152: val_loss -0.1564 +2026-04-10 23:52:57.200114: Pseudo dice [0.3466, 0.2078, 0.7748, 0.6625, 0.4507, 0.3442, 0.7659] +2026-04-10 23:52:57.203398: Epoch time: 102.55 s +2026-04-10 23:52:58.282166: +2026-04-10 23:52:58.284375: Epoch 470 +2026-04-10 23:52:58.286619: Current learning rate: 0.00894 +2026-04-10 23:54:41.376934: train_loss -0.1606 +2026-04-10 23:54:41.382800: val_loss -0.1152 +2026-04-10 23:54:41.384465: Pseudo dice [0.535, 0.0317, 0.5446, 0.4158, 0.5878, 0.5022, 0.4554] +2026-04-10 23:54:41.387035: Epoch time: 103.1 s +2026-04-10 23:54:42.443283: +2026-04-10 23:54:42.445085: Epoch 471 +2026-04-10 23:54:42.447653: Current learning rate: 0.00893 +2026-04-10 23:56:25.010107: train_loss -0.1708 +2026-04-10 23:56:25.016812: val_loss -0.111 +2026-04-10 23:56:25.018773: Pseudo dice [0.71, 0.9057, 0.5779, 0.6912, 0.3293, 0.2449, 0.7683] +2026-04-10 23:56:25.021208: Epoch time: 102.57 s +2026-04-10 23:56:26.088742: +2026-04-10 23:56:26.090550: Epoch 472 +2026-04-10 23:56:26.091898: Current learning rate: 0.00893 +2026-04-10 23:58:08.435603: train_loss -0.1813 +2026-04-10 23:58:08.442988: val_loss -0.1308 +2026-04-10 23:58:08.444958: Pseudo dice [0.6638, 0.8673, 0.5675, 0.3465, 0.4139, 0.3733, 0.7492] +2026-04-10 23:58:08.448164: Epoch time: 102.35 s +2026-04-10 23:58:09.512077: +2026-04-10 23:58:09.515314: Epoch 473 +2026-04-10 23:58:09.517185: Current learning rate: 0.00893 +2026-04-10 23:59:52.526763: train_loss -0.1594 +2026-04-10 23:59:52.534404: val_loss -0.1238 +2026-04-10 23:59:52.537030: Pseudo dice [0.3206, 0.8451, 0.6948, 0.3735, 0.3489, 0.1038, 0.5841] +2026-04-10 23:59:52.539567: Epoch time: 103.02 s +2026-04-10 23:59:53.588445: +2026-04-10 23:59:53.590766: Epoch 474 +2026-04-10 23:59:53.592461: Current learning rate: 0.00893 +2026-04-11 00:01:37.809522: train_loss -0.1789 +2026-04-11 00:01:37.817527: val_loss -0.1731 +2026-04-11 00:01:37.822362: Pseudo dice [0.6708, 0.9236, 0.7696, 0.4144, 0.6096, 0.6514, 0.6561] +2026-04-11 00:01:37.826318: Epoch time: 104.22 s +2026-04-11 00:01:37.829076: Yayy! New best EMA pseudo Dice: 0.5422 +2026-04-11 00:01:40.749643: +2026-04-11 00:01:40.753659: Epoch 475 +2026-04-11 00:01:40.755784: Current learning rate: 0.00892 +2026-04-11 00:03:23.557930: train_loss -0.1847 +2026-04-11 00:03:23.565917: val_loss -0.1453 +2026-04-11 00:03:23.568936: Pseudo dice [0.7775, 0.8585, 0.7578, 0.5194, 0.5712, 0.5507, 0.7759] +2026-04-11 00:03:23.571522: Epoch time: 102.81 s +2026-04-11 00:03:23.574849: Yayy! New best EMA pseudo Dice: 0.5567 +2026-04-11 00:03:26.530133: +2026-04-11 00:03:26.533092: Epoch 476 +2026-04-11 00:03:26.534891: Current learning rate: 0.00892 +2026-04-11 00:05:10.636243: train_loss -0.178 +2026-04-11 00:05:10.650147: val_loss -0.1335 +2026-04-11 00:05:10.652834: Pseudo dice [0.1031, 0.3885, 0.6699, 0.4727, 0.4105, 0.3612, 0.7611] +2026-04-11 00:05:10.658313: Epoch time: 104.11 s +2026-04-11 00:05:11.766453: +2026-04-11 00:05:11.768256: Epoch 477 +2026-04-11 00:05:11.769922: Current learning rate: 0.00892 +2026-04-11 00:06:54.191647: train_loss -0.155 +2026-04-11 00:06:54.197170: val_loss -0.1443 +2026-04-11 00:06:54.199022: Pseudo dice [0.6334, 0.2038, 0.7059, 0.2222, 0.3942, 0.6049, 0.5525] +2026-04-11 00:06:54.201936: Epoch time: 102.43 s +2026-04-11 00:06:55.286274: +2026-04-11 00:06:55.290042: Epoch 478 +2026-04-11 00:06:55.291652: Current learning rate: 0.00892 +2026-04-11 00:08:38.180409: train_loss -0.1696 +2026-04-11 00:08:38.187869: val_loss -0.0835 +2026-04-11 00:08:38.190024: Pseudo dice [0.3676, 0.2673, 0.6936, 0.0505, 0.2098, 0.2761, 0.6097] +2026-04-11 00:08:38.194019: Epoch time: 102.9 s +2026-04-11 00:08:39.544312: +2026-04-11 00:08:39.547115: Epoch 479 +2026-04-11 00:08:39.550307: Current learning rate: 0.00892 +2026-04-11 00:10:21.881046: train_loss -0.1516 +2026-04-11 00:10:21.887625: val_loss -0.1322 +2026-04-11 00:10:21.889923: Pseudo dice [0.5791, 0.783, 0.7257, 0.7836, 0.4588, 0.5537, 0.7355] +2026-04-11 00:10:21.892361: Epoch time: 102.34 s +2026-04-11 00:10:23.023079: +2026-04-11 00:10:23.025314: Epoch 480 +2026-04-11 00:10:23.027481: Current learning rate: 0.00891 +2026-04-11 00:12:06.083804: train_loss -0.1747 +2026-04-11 00:12:06.097309: val_loss -0.1175 +2026-04-11 00:12:06.099742: Pseudo dice [0.5618, 0.8098, 0.6, 0.223, 0.4926, 0.1181, 0.7969] +2026-04-11 00:12:06.102077: Epoch time: 103.06 s +2026-04-11 00:12:07.251686: +2026-04-11 00:12:07.253254: Epoch 481 +2026-04-11 00:12:07.254596: Current learning rate: 0.00891 +2026-04-11 00:13:49.194105: train_loss -0.1635 +2026-04-11 00:13:49.200840: val_loss -0.1345 +2026-04-11 00:13:49.203164: Pseudo dice [0.1885, 0.3786, 0.7772, 0.0894, 0.268, 0.5834, 0.7941] +2026-04-11 00:13:49.205801: Epoch time: 101.95 s +2026-04-11 00:13:50.288244: +2026-04-11 00:13:50.289712: Epoch 482 +2026-04-11 00:13:50.291203: Current learning rate: 0.00891 +2026-04-11 00:15:32.648547: train_loss -0.1604 +2026-04-11 00:15:32.655064: val_loss -0.1354 +2026-04-11 00:15:32.657398: Pseudo dice [0.5447, 0.8114, 0.6161, 0.7493, 0.5258, 0.1963, 0.5576] +2026-04-11 00:15:32.661296: Epoch time: 102.36 s +2026-04-11 00:15:33.772703: +2026-04-11 00:15:33.775581: Epoch 483 +2026-04-11 00:15:33.777063: Current learning rate: 0.00891 +2026-04-11 00:17:16.313145: train_loss -0.1366 +2026-04-11 00:17:16.319384: val_loss -0.1238 +2026-04-11 00:17:16.321003: Pseudo dice [0.4361, 0.7122, 0.4483, 0.4383, 0.3361, 0.6321, 0.673] +2026-04-11 00:17:16.323030: Epoch time: 102.54 s +2026-04-11 00:17:17.437948: +2026-04-11 00:17:17.439654: Epoch 484 +2026-04-11 00:17:17.441534: Current learning rate: 0.0089 +2026-04-11 00:19:00.555105: train_loss -0.1412 +2026-04-11 00:19:00.561343: val_loss -0.1259 +2026-04-11 00:19:00.562824: Pseudo dice [0.3263, 0.8465, 0.7166, 0.4662, 0.3627, 0.09, 0.7202] +2026-04-11 00:19:00.564843: Epoch time: 103.12 s +2026-04-11 00:19:02.823803: +2026-04-11 00:19:02.826512: Epoch 485 +2026-04-11 00:19:02.828907: Current learning rate: 0.0089 +2026-04-11 00:20:45.552343: train_loss -0.1721 +2026-04-11 00:20:45.558850: val_loss -0.1555 +2026-04-11 00:20:45.561381: Pseudo dice [0.7559, 0.8804, 0.6411, 0.5641, 0.3993, 0.8137, 0.6558] +2026-04-11 00:20:45.563773: Epoch time: 102.73 s +2026-04-11 00:20:46.641974: +2026-04-11 00:20:46.646345: Epoch 486 +2026-04-11 00:20:46.648538: Current learning rate: 0.0089 +2026-04-11 00:22:29.351192: train_loss -0.1875 +2026-04-11 00:22:29.358107: val_loss -0.1427 +2026-04-11 00:22:29.359915: Pseudo dice [0.5645, 0.3869, 0.6756, 0.5457, 0.3923, 0.7949, 0.7316] +2026-04-11 00:22:29.361956: Epoch time: 102.71 s +2026-04-11 00:22:30.453978: +2026-04-11 00:22:30.455415: Epoch 487 +2026-04-11 00:22:30.456828: Current learning rate: 0.0089 +2026-04-11 00:24:13.531511: train_loss -0.17 +2026-04-11 00:24:13.538095: val_loss -0.1299 +2026-04-11 00:24:13.540064: Pseudo dice [0.4057, 0.6608, 0.5691, 0.4378, 0.2637, 0.7577, 0.5551] +2026-04-11 00:24:13.542563: Epoch time: 103.08 s +2026-04-11 00:24:14.632547: +2026-04-11 00:24:14.638897: Epoch 488 +2026-04-11 00:24:14.640646: Current learning rate: 0.00889 +2026-04-11 00:25:58.381086: train_loss -0.1426 +2026-04-11 00:25:58.388441: val_loss -0.1267 +2026-04-11 00:25:58.390759: Pseudo dice [0.5255, 0.1233, 0.6379, 0.383, 0.1431, 0.4789, 0.7756] +2026-04-11 00:25:58.392765: Epoch time: 103.75 s +2026-04-11 00:25:59.493755: +2026-04-11 00:25:59.496372: Epoch 489 +2026-04-11 00:25:59.497941: Current learning rate: 0.00889 +2026-04-11 00:27:41.811353: train_loss -0.1758 +2026-04-11 00:27:41.820075: val_loss -0.1421 +2026-04-11 00:27:41.822305: Pseudo dice [0.7228, 0.6621, 0.7214, 0.3442, 0.4334, 0.6788, 0.6812] +2026-04-11 00:27:41.824782: Epoch time: 102.32 s +2026-04-11 00:27:42.914197: +2026-04-11 00:27:42.916365: Epoch 490 +2026-04-11 00:27:42.918204: Current learning rate: 0.00889 +2026-04-11 00:29:25.765064: train_loss -0.1727 +2026-04-11 00:29:25.771754: val_loss -0.131 +2026-04-11 00:29:25.773998: Pseudo dice [0.7716, 0.5458, 0.6653, 0.2507, 0.5008, 0.4394, 0.4883] +2026-04-11 00:29:25.776820: Epoch time: 102.85 s +2026-04-11 00:29:26.878059: +2026-04-11 00:29:26.880560: Epoch 491 +2026-04-11 00:29:26.882690: Current learning rate: 0.00889 +2026-04-11 00:31:10.112073: train_loss -0.1688 +2026-04-11 00:31:10.120875: val_loss -0.134 +2026-04-11 00:31:10.122955: Pseudo dice [0.5503, 0.4318, 0.7843, 0.6184, 0.3843, 0.5141, 0.7124] +2026-04-11 00:31:10.125085: Epoch time: 103.24 s +2026-04-11 00:31:11.271776: +2026-04-11 00:31:11.274379: Epoch 492 +2026-04-11 00:31:11.275954: Current learning rate: 0.00889 +2026-04-11 00:32:54.440037: train_loss -0.1787 +2026-04-11 00:32:54.446429: val_loss -0.176 +2026-04-11 00:32:54.448352: Pseudo dice [0.4394, 0.7836, 0.7959, 0.7063, 0.5894, 0.1757, 0.8035] +2026-04-11 00:32:54.450683: Epoch time: 103.17 s +2026-04-11 00:32:55.537367: +2026-04-11 00:32:55.539282: Epoch 493 +2026-04-11 00:32:55.545057: Current learning rate: 0.00888 +2026-04-11 00:34:38.098774: train_loss -0.1659 +2026-04-11 00:34:38.107582: val_loss -0.1617 +2026-04-11 00:34:38.109976: Pseudo dice [0.8524, 0.5228, 0.785, 0.5232, 0.4911, 0.8054, 0.8272] +2026-04-11 00:34:38.112974: Epoch time: 102.56 s +2026-04-11 00:34:38.115305: Yayy! New best EMA pseudo Dice: 0.5621 +2026-04-11 00:34:40.921313: +2026-04-11 00:34:40.923495: Epoch 494 +2026-04-11 00:34:40.924851: Current learning rate: 0.00888 +2026-04-11 00:36:23.395600: train_loss -0.1597 +2026-04-11 00:36:23.402686: val_loss -0.1272 +2026-04-11 00:36:23.405244: Pseudo dice [0.5775, 0.0306, 0.7319, 0.821, 0.2711, 0.3951, 0.7267] +2026-04-11 00:36:23.408629: Epoch time: 102.48 s +2026-04-11 00:36:24.537028: +2026-04-11 00:36:24.538806: Epoch 495 +2026-04-11 00:36:24.540306: Current learning rate: 0.00888 +2026-04-11 00:38:07.245267: train_loss -0.1563 +2026-04-11 00:38:07.252270: val_loss -0.1167 +2026-04-11 00:38:07.254495: Pseudo dice [0.7643, 0.1424, 0.7223, 0.4406, 0.1808, 0.472, 0.7418] +2026-04-11 00:38:07.256980: Epoch time: 102.71 s +2026-04-11 00:38:08.352899: +2026-04-11 00:38:08.354802: Epoch 496 +2026-04-11 00:38:08.357082: Current learning rate: 0.00888 +2026-04-11 00:39:51.059225: train_loss -0.1816 +2026-04-11 00:39:51.068430: val_loss -0.1304 +2026-04-11 00:39:51.070560: Pseudo dice [0.7091, 0.8926, 0.6119, 0.4327, 0.5391, 0.5536, 0.6955] +2026-04-11 00:39:51.073485: Epoch time: 102.71 s +2026-04-11 00:39:52.174090: +2026-04-11 00:39:52.176574: Epoch 497 +2026-04-11 00:39:52.178435: Current learning rate: 0.00887 +2026-04-11 00:41:35.780774: train_loss -0.1573 +2026-04-11 00:41:35.788165: val_loss -0.1309 +2026-04-11 00:41:35.790061: Pseudo dice [0.2863, 0.5144, 0.709, 0.2657, 0.5378, 0.3239, 0.3663] +2026-04-11 00:41:35.792661: Epoch time: 103.61 s +2026-04-11 00:41:36.934867: +2026-04-11 00:41:36.936891: Epoch 498 +2026-04-11 00:41:36.939628: Current learning rate: 0.00887 +2026-04-11 00:43:19.804844: train_loss -0.1529 +2026-04-11 00:43:19.811468: val_loss -0.1283 +2026-04-11 00:43:19.813942: Pseudo dice [0.4712, 0.7843, 0.6149, 0.003, 0.4224, 0.23, 0.4852] +2026-04-11 00:43:19.816936: Epoch time: 102.87 s +2026-04-11 00:43:20.904892: +2026-04-11 00:43:20.907039: Epoch 499 +2026-04-11 00:43:20.908667: Current learning rate: 0.00887 +2026-04-11 00:45:07.777330: train_loss -0.1589 +2026-04-11 00:45:07.783722: val_loss -0.1011 +2026-04-11 00:45:07.785724: Pseudo dice [0.7229, 0.7862, 0.6909, 0.0959, 0.431, 0.2444, 0.8673] +2026-04-11 00:45:07.788559: Epoch time: 106.88 s +2026-04-11 00:45:10.495238: +2026-04-11 00:45:10.497664: Epoch 500 +2026-04-11 00:45:10.499337: Current learning rate: 0.00887 +2026-04-11 00:46:52.674555: train_loss -0.1559 +2026-04-11 00:46:52.684595: val_loss -0.135 +2026-04-11 00:46:52.687380: Pseudo dice [0.5782, 0.3419, 0.7268, 0.3668, 0.3776, 0.7603, 0.4719] +2026-04-11 00:46:52.690949: Epoch time: 102.18 s +2026-04-11 00:46:53.798646: +2026-04-11 00:46:53.801157: Epoch 501 +2026-04-11 00:46:53.803482: Current learning rate: 0.00887 +2026-04-11 00:48:37.495601: train_loss -0.1656 +2026-04-11 00:48:37.503375: val_loss -0.1397 +2026-04-11 00:48:37.505707: Pseudo dice [0.4585, 0.5843, 0.7587, 0.365, 0.3802, 0.206, 0.6074] +2026-04-11 00:48:37.508347: Epoch time: 103.7 s +2026-04-11 00:48:38.592034: +2026-04-11 00:48:38.593719: Epoch 502 +2026-04-11 00:48:38.595967: Current learning rate: 0.00886 +2026-04-11 00:50:21.610572: train_loss -0.1603 +2026-04-11 00:50:21.616811: val_loss -0.1159 +2026-04-11 00:50:21.619093: Pseudo dice [0.3604, 0.8616, 0.5079, 0.6533, 0.2437, 0.2499, 0.278] +2026-04-11 00:50:21.621663: Epoch time: 103.02 s +2026-04-11 00:50:22.727012: +2026-04-11 00:50:22.728982: Epoch 503 +2026-04-11 00:50:22.730579: Current learning rate: 0.00886 +2026-04-11 00:52:06.083082: train_loss -0.1468 +2026-04-11 00:52:06.090503: val_loss -0.1195 +2026-04-11 00:52:06.092358: Pseudo dice [0.2869, 0.3115, 0.5821, 0.3046, 0.2423, 0.493, 0.6726] +2026-04-11 00:52:06.095537: Epoch time: 103.36 s +2026-04-11 00:52:07.190589: +2026-04-11 00:52:07.192526: Epoch 504 +2026-04-11 00:52:07.194088: Current learning rate: 0.00886 +2026-04-11 00:53:50.920386: train_loss -0.1448 +2026-04-11 00:53:50.927061: val_loss -0.1222 +2026-04-11 00:53:50.930682: Pseudo dice [0.2831, 0.4801, 0.604, 0.4105, 0.4753, 0.7529, 0.6818] +2026-04-11 00:53:50.932859: Epoch time: 103.73 s +2026-04-11 00:53:52.020730: +2026-04-11 00:53:52.023753: Epoch 505 +2026-04-11 00:53:52.026148: Current learning rate: 0.00886 +2026-04-11 00:55:36.875296: train_loss -0.1498 +2026-04-11 00:55:36.882394: val_loss -0.136 +2026-04-11 00:55:36.884477: Pseudo dice [0.8346, 0.8535, 0.6382, 0.3834, 0.6768, 0.7569, 0.4949] +2026-04-11 00:55:36.887167: Epoch time: 104.86 s +2026-04-11 00:55:37.978568: +2026-04-11 00:55:37.980420: Epoch 506 +2026-04-11 00:55:37.982689: Current learning rate: 0.00885 +2026-04-11 00:57:20.472932: train_loss -0.1703 +2026-04-11 00:57:20.479362: val_loss -0.1628 +2026-04-11 00:57:20.481394: Pseudo dice [0.5725, 0.7592, 0.7299, 0.3377, 0.6078, 0.1208, 0.7378] +2026-04-11 00:57:20.483209: Epoch time: 102.5 s +2026-04-11 00:57:21.571014: +2026-04-11 00:57:21.572817: Epoch 507 +2026-04-11 00:57:21.574150: Current learning rate: 0.00885 +2026-04-11 00:59:04.861865: train_loss -0.1675 +2026-04-11 00:59:04.868164: val_loss -0.1251 +2026-04-11 00:59:04.875064: Pseudo dice [0.5737, 0.2455, 0.5254, 0.6209, 0.4702, 0.3952, 0.7991] +2026-04-11 00:59:04.877215: Epoch time: 103.29 s +2026-04-11 00:59:05.974971: +2026-04-11 00:59:05.976697: Epoch 508 +2026-04-11 00:59:05.978400: Current learning rate: 0.00885 +2026-04-11 01:00:48.374236: train_loss -0.1727 +2026-04-11 01:00:48.381669: val_loss -0.1363 +2026-04-11 01:00:48.384670: Pseudo dice [0.3147, 0.8654, 0.6419, 0.439, 0.5923, 0.7154, 0.5451] +2026-04-11 01:00:48.387257: Epoch time: 102.4 s +2026-04-11 01:00:49.484495: +2026-04-11 01:00:49.486396: Epoch 509 +2026-04-11 01:00:49.488301: Current learning rate: 0.00885 +2026-04-11 01:02:32.212423: train_loss -0.1768 +2026-04-11 01:02:32.221508: val_loss -0.155 +2026-04-11 01:02:32.223566: Pseudo dice [0.5999, 0.2952, 0.5501, 0.6843, 0.3458, 0.7865, 0.8379] +2026-04-11 01:02:32.226334: Epoch time: 102.73 s +2026-04-11 01:02:33.321874: +2026-04-11 01:02:33.323419: Epoch 510 +2026-04-11 01:02:33.324764: Current learning rate: 0.00884 +2026-04-11 01:04:16.053988: train_loss -0.1696 +2026-04-11 01:04:16.061586: val_loss -0.1151 +2026-04-11 01:04:16.063603: Pseudo dice [0.372, 0.1874, 0.5698, 0.2538, 0.3771, 0.8103, 0.6028] +2026-04-11 01:04:16.067918: Epoch time: 102.74 s +2026-04-11 01:04:17.155206: +2026-04-11 01:04:17.157655: Epoch 511 +2026-04-11 01:04:17.159420: Current learning rate: 0.00884 +2026-04-11 01:06:00.206875: train_loss -0.1594 +2026-04-11 01:06:00.212792: val_loss -0.1366 +2026-04-11 01:06:00.214614: Pseudo dice [0.562, 0.7357, 0.3922, 0.5761, 0.5247, 0.487, 0.8553] +2026-04-11 01:06:00.217052: Epoch time: 103.05 s +2026-04-11 01:06:01.312632: +2026-04-11 01:06:01.315445: Epoch 512 +2026-04-11 01:06:01.317184: Current learning rate: 0.00884 +2026-04-11 01:07:44.498940: train_loss -0.1635 +2026-04-11 01:07:44.505749: val_loss -0.1443 +2026-04-11 01:07:44.508024: Pseudo dice [0.2938, 0.7329, 0.6731, 0.5699, 0.4087, 0.6737, 0.7936] +2026-04-11 01:07:44.510877: Epoch time: 103.19 s +2026-04-11 01:07:45.632159: +2026-04-11 01:07:45.634195: Epoch 513 +2026-04-11 01:07:45.636508: Current learning rate: 0.00884 +2026-04-11 01:09:28.278526: train_loss -0.1556 +2026-04-11 01:09:28.284113: val_loss -0.1085 +2026-04-11 01:09:28.286497: Pseudo dice [0.4573, 0.4025, 0.6344, 0.4893, 0.4412, 0.7039, 0.6928] +2026-04-11 01:09:28.289352: Epoch time: 102.65 s +2026-04-11 01:09:29.369625: +2026-04-11 01:09:29.371182: Epoch 514 +2026-04-11 01:09:29.372553: Current learning rate: 0.00884 +2026-04-11 01:11:11.610654: train_loss -0.1402 +2026-04-11 01:11:11.618999: val_loss -0.1119 +2026-04-11 01:11:11.621166: Pseudo dice [0.1966, 0.8804, 0.7416, 0.3749, 0.4974, 0.373, 0.2672] +2026-04-11 01:11:11.623940: Epoch time: 102.24 s +2026-04-11 01:11:12.732455: +2026-04-11 01:11:12.734350: Epoch 515 +2026-04-11 01:11:12.736124: Current learning rate: 0.00883 +2026-04-11 01:12:55.438752: train_loss -0.1651 +2026-04-11 01:12:55.454957: val_loss -0.1203 +2026-04-11 01:12:55.463669: Pseudo dice [0.5708, 0.6077, 0.6554, 0.3679, 0.3659, 0.7566, 0.5869] +2026-04-11 01:12:55.469585: Epoch time: 102.71 s +2026-04-11 01:12:56.578209: +2026-04-11 01:12:56.579663: Epoch 516 +2026-04-11 01:12:56.581062: Current learning rate: 0.00883 +2026-04-11 01:14:38.930273: train_loss -0.1598 +2026-04-11 01:14:38.935900: val_loss -0.1122 +2026-04-11 01:14:38.937527: Pseudo dice [0.6966, 0.432, 0.511, 0.2199, 0.454, 0.3617, 0.7293] +2026-04-11 01:14:38.940430: Epoch time: 102.36 s +2026-04-11 01:14:40.012169: +2026-04-11 01:14:40.013594: Epoch 517 +2026-04-11 01:14:40.014865: Current learning rate: 0.00883 +2026-04-11 01:16:22.226053: train_loss -0.1711 +2026-04-11 01:16:22.232071: val_loss -0.1822 +2026-04-11 01:16:22.233841: Pseudo dice [0.1689, 0.3406, 0.6868, 0.5713, 0.6116, 0.5799, 0.8396] +2026-04-11 01:16:22.236372: Epoch time: 102.22 s +2026-04-11 01:16:23.341295: +2026-04-11 01:16:23.342988: Epoch 518 +2026-04-11 01:16:23.344671: Current learning rate: 0.00883 +2026-04-11 01:18:05.872927: train_loss -0.173 +2026-04-11 01:18:05.878350: val_loss -0.1553 +2026-04-11 01:18:05.880900: Pseudo dice [0.6289, 0.8433, 0.6699, 0.5729, 0.421, 0.1354, 0.6557] +2026-04-11 01:18:05.883114: Epoch time: 102.53 s +2026-04-11 01:18:06.974794: +2026-04-11 01:18:06.976792: Epoch 519 +2026-04-11 01:18:06.978361: Current learning rate: 0.00882 +2026-04-11 01:19:49.801882: train_loss -0.1696 +2026-04-11 01:19:49.807458: val_loss -0.1072 +2026-04-11 01:19:49.809162: Pseudo dice [0.6315, 0.8126, 0.7137, 0.365, 0.6456, 0.3955, 0.6842] +2026-04-11 01:19:49.811307: Epoch time: 102.83 s +2026-04-11 01:19:50.907910: +2026-04-11 01:19:50.909421: Epoch 520 +2026-04-11 01:19:50.910922: Current learning rate: 0.00882 +2026-04-11 01:21:33.319669: train_loss -0.1723 +2026-04-11 01:21:33.325529: val_loss -0.1507 +2026-04-11 01:21:33.327703: Pseudo dice [0.5763, 0.5129, 0.6314, 0.5005, 0.5561, 0.7207, 0.842] +2026-04-11 01:21:33.330286: Epoch time: 102.41 s +2026-04-11 01:21:34.402275: +2026-04-11 01:21:34.404304: Epoch 521 +2026-04-11 01:21:34.405714: Current learning rate: 0.00882 +2026-04-11 01:23:17.337489: train_loss -0.1814 +2026-04-11 01:23:17.343581: val_loss -0.1456 +2026-04-11 01:23:17.345261: Pseudo dice [0.4506, 0.1908, 0.6516, 0.4084, 0.4484, 0.4373, 0.5113] +2026-04-11 01:23:17.348812: Epoch time: 102.94 s +2026-04-11 01:23:18.472434: +2026-04-11 01:23:18.474968: Epoch 522 +2026-04-11 01:23:18.476609: Current learning rate: 0.00882 +2026-04-11 01:25:00.994892: train_loss -0.153 +2026-04-11 01:25:01.002962: val_loss -0.1272 +2026-04-11 01:25:01.005279: Pseudo dice [0.7295, 0.7828, 0.5454, 0.618, 0.4108, 0.4366, 0.623] +2026-04-11 01:25:01.007629: Epoch time: 102.53 s +2026-04-11 01:25:02.103015: +2026-04-11 01:25:02.104762: Epoch 523 +2026-04-11 01:25:02.106321: Current learning rate: 0.00882 +2026-04-11 01:26:44.381614: train_loss -0.172 +2026-04-11 01:26:44.387399: val_loss -0.1366 +2026-04-11 01:26:44.389260: Pseudo dice [0.4467, 0.8862, 0.7379, 0.3017, 0.5358, 0.3041, 0.7426] +2026-04-11 01:26:44.391988: Epoch time: 102.28 s +2026-04-11 01:26:45.488610: +2026-04-11 01:26:45.490103: Epoch 524 +2026-04-11 01:26:45.491516: Current learning rate: 0.00881 +2026-04-11 01:28:28.048124: train_loss -0.1818 +2026-04-11 01:28:28.057650: val_loss -0.113 +2026-04-11 01:28:28.060684: Pseudo dice [0.7371, 0.847, 0.455, 0.2722, 0.4005, 0.7555, 0.3047] +2026-04-11 01:28:28.065342: Epoch time: 102.56 s +2026-04-11 01:28:30.293576: +2026-04-11 01:28:30.295086: Epoch 525 +2026-04-11 01:28:30.296582: Current learning rate: 0.00881 +2026-04-11 01:30:12.908469: train_loss -0.1814 +2026-04-11 01:30:12.913911: val_loss -0.1118 +2026-04-11 01:30:12.915998: Pseudo dice [0.7112, 0.5655, 0.6298, 0.3623, 0.3218, 0.6333, 0.8734] +2026-04-11 01:30:12.919357: Epoch time: 102.62 s +2026-04-11 01:30:14.002165: +2026-04-11 01:30:14.003510: Epoch 526 +2026-04-11 01:30:14.004777: Current learning rate: 0.00881 +2026-04-11 01:31:56.478078: train_loss -0.1567 +2026-04-11 01:31:56.484434: val_loss -0.1281 +2026-04-11 01:31:56.486779: Pseudo dice [0.4525, 0.2587, 0.7167, 0.1268, 0.3577, 0.7342, 0.3555] +2026-04-11 01:31:56.489119: Epoch time: 102.48 s +2026-04-11 01:31:57.595898: +2026-04-11 01:31:57.597681: Epoch 527 +2026-04-11 01:31:57.599062: Current learning rate: 0.00881 +2026-04-11 01:33:39.991031: train_loss -0.1621 +2026-04-11 01:33:39.996931: val_loss -0.1138 +2026-04-11 01:33:39.999914: Pseudo dice [0.5829, 0.6735, 0.7183, 0.4857, 0.3847, 0.8611, 0.4572] +2026-04-11 01:33:40.001990: Epoch time: 102.4 s +2026-04-11 01:33:41.076814: +2026-04-11 01:33:41.078753: Epoch 528 +2026-04-11 01:33:41.080171: Current learning rate: 0.0088 +2026-04-11 01:35:23.511647: train_loss -0.1777 +2026-04-11 01:35:23.518467: val_loss -0.1531 +2026-04-11 01:35:23.521086: Pseudo dice [0.3864, 0.3852, 0.7546, 0.6468, 0.4311, 0.4145, 0.6532] +2026-04-11 01:35:23.523464: Epoch time: 102.44 s +2026-04-11 01:35:24.673713: +2026-04-11 01:35:24.675431: Epoch 529 +2026-04-11 01:35:24.676930: Current learning rate: 0.0088 +2026-04-11 01:37:06.781896: train_loss -0.1341 +2026-04-11 01:37:06.787652: val_loss -0.1272 +2026-04-11 01:37:06.789439: Pseudo dice [0.641, 0.5881, 0.5415, 0.3946, 0.3629, 0.0815, 0.7425] +2026-04-11 01:37:06.791500: Epoch time: 102.11 s +2026-04-11 01:37:07.873040: +2026-04-11 01:37:07.874680: Epoch 530 +2026-04-11 01:37:07.876361: Current learning rate: 0.0088 +2026-04-11 01:38:50.439734: train_loss -0.1612 +2026-04-11 01:38:50.453201: val_loss -0.1211 +2026-04-11 01:38:50.455536: Pseudo dice [0.5735, 0.7233, 0.7564, 0.302, 0.5579, 0.0811, 0.5484] +2026-04-11 01:38:50.457741: Epoch time: 102.57 s +2026-04-11 01:38:51.575743: +2026-04-11 01:38:51.578069: Epoch 531 +2026-04-11 01:38:51.579580: Current learning rate: 0.0088 +2026-04-11 01:40:33.965154: train_loss -0.163 +2026-04-11 01:40:33.971494: val_loss -0.1467 +2026-04-11 01:40:33.974311: Pseudo dice [0.5884, 0.7874, 0.795, 0.5501, 0.3766, 0.2121, 0.4684] +2026-04-11 01:40:33.977039: Epoch time: 102.39 s +2026-04-11 01:40:35.070681: +2026-04-11 01:40:35.072457: Epoch 532 +2026-04-11 01:40:35.073888: Current learning rate: 0.00879 +2026-04-11 01:42:17.194347: train_loss -0.1684 +2026-04-11 01:42:17.201187: val_loss -0.1604 +2026-04-11 01:42:17.203813: Pseudo dice [0.2822, 0.8759, 0.7796, 0.4141, 0.4069, 0.7865, 0.7272] +2026-04-11 01:42:17.206075: Epoch time: 102.13 s +2026-04-11 01:42:18.305631: +2026-04-11 01:42:18.308155: Epoch 533 +2026-04-11 01:42:18.310791: Current learning rate: 0.00879 +2026-04-11 01:44:01.012973: train_loss -0.1699 +2026-04-11 01:44:01.019923: val_loss -0.1188 +2026-04-11 01:44:01.021869: Pseudo dice [0.5873, 0.2527, 0.6864, 0.5051, 0.5819, 0.7697, 0.708] +2026-04-11 01:44:01.024569: Epoch time: 102.71 s +2026-04-11 01:44:02.158995: +2026-04-11 01:44:02.160914: Epoch 534 +2026-04-11 01:44:02.163242: Current learning rate: 0.00879 +2026-04-11 01:45:44.586832: train_loss -0.1521 +2026-04-11 01:45:44.594743: val_loss -0.1371 +2026-04-11 01:45:44.597460: Pseudo dice [0.0885, 0.7055, 0.746, 0.196, 0.2593, 0.4582, 0.6913] +2026-04-11 01:45:44.600486: Epoch time: 102.43 s +2026-04-11 01:45:45.712385: +2026-04-11 01:45:45.714406: Epoch 535 +2026-04-11 01:45:45.716181: Current learning rate: 0.00879 +2026-04-11 01:47:29.152544: train_loss -0.1463 +2026-04-11 01:47:29.158388: val_loss -0.1433 +2026-04-11 01:47:29.161453: Pseudo dice [0.6288, 0.3624, 0.6853, 0.4743, 0.5921, 0.5489, 0.8409] +2026-04-11 01:47:29.164695: Epoch time: 103.44 s +2026-04-11 01:47:30.324797: +2026-04-11 01:47:30.328053: Epoch 536 +2026-04-11 01:47:30.329618: Current learning rate: 0.00879 +2026-04-11 01:49:12.637944: train_loss -0.1661 +2026-04-11 01:49:12.644384: val_loss -0.1115 +2026-04-11 01:49:12.647011: Pseudo dice [0.6715, 0.7151, 0.6673, 0.6327, 0.4007, 0.4782, 0.412] +2026-04-11 01:49:12.649663: Epoch time: 102.32 s +2026-04-11 01:49:13.835339: +2026-04-11 01:49:13.836801: Epoch 537 +2026-04-11 01:49:13.838326: Current learning rate: 0.00878 +2026-04-11 01:50:56.049787: train_loss -0.1792 +2026-04-11 01:50:56.055106: val_loss -0.1792 +2026-04-11 01:50:56.057441: Pseudo dice [0.4893, 0.7271, 0.7871, 0.4954, 0.5765, 0.5811, 0.7405] +2026-04-11 01:50:56.059409: Epoch time: 102.22 s +2026-04-11 01:50:57.173818: +2026-04-11 01:50:57.175682: Epoch 538 +2026-04-11 01:50:57.177245: Current learning rate: 0.00878 +2026-04-11 01:52:39.312946: train_loss -0.1633 +2026-04-11 01:52:39.318362: val_loss -0.1352 +2026-04-11 01:52:39.320663: Pseudo dice [0.6123, 0.7939, 0.4363, 0.4689, 0.2783, 0.7686, 0.7797] +2026-04-11 01:52:39.323028: Epoch time: 102.14 s +2026-04-11 01:52:40.416705: +2026-04-11 01:52:40.418304: Epoch 539 +2026-04-11 01:52:40.419657: Current learning rate: 0.00878 +2026-04-11 01:54:22.636582: train_loss -0.1787 +2026-04-11 01:54:22.643527: val_loss -0.1724 +2026-04-11 01:54:22.645404: Pseudo dice [0.451, 0.8443, 0.6722, 0.5509, 0.4607, 0.6264, 0.8351] +2026-04-11 01:54:22.648854: Epoch time: 102.22 s +2026-04-11 01:54:22.650950: Yayy! New best EMA pseudo Dice: 0.5641 +2026-04-11 01:54:25.442962: +2026-04-11 01:54:25.445155: Epoch 540 +2026-04-11 01:54:25.446599: Current learning rate: 0.00878 +2026-04-11 01:56:07.786033: train_loss -0.1564 +2026-04-11 01:56:07.792161: val_loss -0.1564 +2026-04-11 01:56:07.794348: Pseudo dice [0.5248, 0.2912, 0.708, 0.6086, 0.2829, 0.7576, 0.6945] +2026-04-11 01:56:07.796494: Epoch time: 102.35 s +2026-04-11 01:56:09.136552: +2026-04-11 01:56:09.138094: Epoch 541 +2026-04-11 01:56:09.139374: Current learning rate: 0.00877 +2026-04-11 01:57:51.849082: train_loss -0.1931 +2026-04-11 01:57:51.855821: val_loss -0.129 +2026-04-11 01:57:51.857595: Pseudo dice [0.6066, 0.805, 0.5601, 0.4752, 0.4577, 0.497, 0.5471] +2026-04-11 01:57:51.860386: Epoch time: 102.72 s +2026-04-11 01:57:52.958966: +2026-04-11 01:57:52.960782: Epoch 542 +2026-04-11 01:57:52.962209: Current learning rate: 0.00877 +2026-04-11 01:59:35.476951: train_loss -0.1675 +2026-04-11 01:59:35.482365: val_loss -0.1417 +2026-04-11 01:59:35.484237: Pseudo dice [0.5337, 0.7734, 0.7436, 0.5711, 0.381, 0.5518, 0.8268] +2026-04-11 01:59:35.486502: Epoch time: 102.52 s +2026-04-11 01:59:35.488382: Yayy! New best EMA pseudo Dice: 0.5693 +2026-04-11 01:59:38.267755: +2026-04-11 01:59:38.269462: Epoch 543 +2026-04-11 01:59:38.270953: Current learning rate: 0.00877 +2026-04-11 02:01:24.500909: train_loss -0.1819 +2026-04-11 02:01:24.509369: val_loss -0.1665 +2026-04-11 02:01:24.511510: Pseudo dice [0.4954, 0.7703, 0.6968, 0.4504, 0.6113, 0.7069, 0.8244] +2026-04-11 02:01:24.514289: Epoch time: 106.24 s +2026-04-11 02:01:24.515878: Yayy! New best EMA pseudo Dice: 0.5775 +2026-04-11 02:01:28.340109: +2026-04-11 02:01:28.341589: Epoch 544 +2026-04-11 02:01:28.342880: Current learning rate: 0.00877 +2026-04-11 02:03:10.516443: train_loss -0.1798 +2026-04-11 02:03:10.522284: val_loss -0.1336 +2026-04-11 02:03:10.523975: Pseudo dice [0.5114, 0.3705, 0.6552, 0.5706, 0.5317, 0.4892, 0.7494] +2026-04-11 02:03:10.526090: Epoch time: 102.18 s +2026-04-11 02:03:11.640404: +2026-04-11 02:03:11.642078: Epoch 545 +2026-04-11 02:03:11.644081: Current learning rate: 0.00876 +2026-04-11 02:04:53.674169: train_loss -0.1649 +2026-04-11 02:04:53.681803: val_loss -0.1485 +2026-04-11 02:04:53.683579: Pseudo dice [0.4158, 0.8414, 0.7283, 0.5379, 0.438, 0.3249, 0.6896] +2026-04-11 02:04:53.685492: Epoch time: 102.04 s +2026-04-11 02:04:54.782761: +2026-04-11 02:04:54.784161: Epoch 546 +2026-04-11 02:04:54.785579: Current learning rate: 0.00876 +2026-04-11 02:06:37.394515: train_loss -0.1765 +2026-04-11 02:06:37.400995: val_loss -0.13 +2026-04-11 02:06:37.403088: Pseudo dice [0.8547, 0.6052, 0.5936, 0.5362, 0.5052, 0.5474, 0.7365] +2026-04-11 02:06:37.405856: Epoch time: 102.62 s +2026-04-11 02:06:37.407851: Yayy! New best EMA pseudo Dice: 0.5795 +2026-04-11 02:06:40.131773: +2026-04-11 02:06:40.133224: Epoch 547 +2026-04-11 02:06:40.134604: Current learning rate: 0.00876 +2026-04-11 02:08:22.474626: train_loss -0.1824 +2026-04-11 02:08:22.480191: val_loss -0.1349 +2026-04-11 02:08:22.481916: Pseudo dice [0.616, 0.8357, 0.7607, 0.2787, 0.7046, 0.2365, 0.6037] +2026-04-11 02:08:22.484208: Epoch time: 102.35 s +2026-04-11 02:08:23.576515: +2026-04-11 02:08:23.578870: Epoch 548 +2026-04-11 02:08:23.580583: Current learning rate: 0.00876 +2026-04-11 02:10:06.182752: train_loss -0.1723 +2026-04-11 02:10:06.188705: val_loss -0.1476 +2026-04-11 02:10:06.190318: Pseudo dice [0.5837, 0.5839, 0.7588, 0.4025, 0.5029, 0.8494, 0.7528] +2026-04-11 02:10:06.192508: Epoch time: 102.61 s +2026-04-11 02:10:06.194314: Yayy! New best EMA pseudo Dice: 0.5847 +2026-04-11 02:10:08.929519: +2026-04-11 02:10:08.931014: Epoch 549 +2026-04-11 02:10:08.932319: Current learning rate: 0.00876 +2026-04-11 02:11:51.170398: train_loss -0.1745 +2026-04-11 02:11:51.176531: val_loss -0.1513 +2026-04-11 02:11:51.177974: Pseudo dice [0.4803, 0.7284, 0.7097, 0.1888, 0.4599, 0.5965, 0.8147] +2026-04-11 02:11:51.180686: Epoch time: 102.24 s +2026-04-11 02:11:53.936345: +2026-04-11 02:11:53.938658: Epoch 550 +2026-04-11 02:11:53.939960: Current learning rate: 0.00875 +2026-04-11 02:13:36.220548: train_loss -0.1693 +2026-04-11 02:13:36.226562: val_loss -0.1234 +2026-04-11 02:13:36.228339: Pseudo dice [0.2596, 0.8133, 0.7656, 0.4716, 0.3359, 0.6025, 0.7982] +2026-04-11 02:13:36.230816: Epoch time: 102.29 s +2026-04-11 02:13:37.521819: +2026-04-11 02:13:37.523813: Epoch 551 +2026-04-11 02:13:37.525630: Current learning rate: 0.00875 +2026-04-11 02:15:20.156873: train_loss -0.1624 +2026-04-11 02:15:20.162965: val_loss -0.1353 +2026-04-11 02:15:20.165330: Pseudo dice [0.2493, 0.594, 0.6937, 0.5974, 0.2478, 0.4094, 0.7846] +2026-04-11 02:15:20.167335: Epoch time: 102.64 s +2026-04-11 02:15:21.265410: +2026-04-11 02:15:21.267088: Epoch 552 +2026-04-11 02:15:21.268718: Current learning rate: 0.00875 +2026-04-11 02:17:03.678216: train_loss -0.1742 +2026-04-11 02:17:03.685009: val_loss -0.1644 +2026-04-11 02:17:03.686950: Pseudo dice [0.7764, 0.9041, 0.6899, 0.425, 0.5161, 0.6617, 0.7526] +2026-04-11 02:17:03.689613: Epoch time: 102.42 s +2026-04-11 02:17:03.691703: Yayy! New best EMA pseudo Dice: 0.5853 +2026-04-11 02:17:06.544869: +2026-04-11 02:17:06.546173: Epoch 553 +2026-04-11 02:17:06.547458: Current learning rate: 0.00875 +2026-04-11 02:18:48.808087: train_loss -0.1746 +2026-04-11 02:18:48.814584: val_loss -0.1732 +2026-04-11 02:18:48.816395: Pseudo dice [0.5444, 0.3518, 0.7346, 0.7944, 0.5266, 0.7463, 0.8379] +2026-04-11 02:18:48.818896: Epoch time: 102.27 s +2026-04-11 02:18:48.820812: Yayy! New best EMA pseudo Dice: 0.5916 +2026-04-11 02:18:51.594411: +2026-04-11 02:18:51.595894: Epoch 554 +2026-04-11 02:18:51.597176: Current learning rate: 0.00874 +2026-04-11 02:20:34.362997: train_loss -0.1762 +2026-04-11 02:20:34.368529: val_loss -0.1302 +2026-04-11 02:20:34.370186: Pseudo dice [0.441, 0.469, 0.7839, 0.125, 0.3788, 0.7752, 0.6262] +2026-04-11 02:20:34.372028: Epoch time: 102.77 s +2026-04-11 02:20:35.512273: +2026-04-11 02:20:35.513919: Epoch 555 +2026-04-11 02:20:35.515181: Current learning rate: 0.00874 +2026-04-11 02:22:18.072051: train_loss -0.1654 +2026-04-11 02:22:18.079007: val_loss -0.1231 +2026-04-11 02:22:18.080761: Pseudo dice [0.6372, 0.3336, 0.5566, 0.2611, 0.5708, 0.5482, 0.692] +2026-04-11 02:22:18.082861: Epoch time: 102.56 s +2026-04-11 02:22:19.221960: +2026-04-11 02:22:19.223575: Epoch 556 +2026-04-11 02:22:19.225083: Current learning rate: 0.00874 +2026-04-11 02:24:01.946123: train_loss -0.1575 +2026-04-11 02:24:01.951876: val_loss -0.1415 +2026-04-11 02:24:01.953870: Pseudo dice [0.1831, 0.5802, 0.6675, 0.4833, 0.4749, 0.2554, 0.664] +2026-04-11 02:24:01.955931: Epoch time: 102.73 s +2026-04-11 02:24:03.080133: +2026-04-11 02:24:03.082400: Epoch 557 +2026-04-11 02:24:03.085228: Current learning rate: 0.00874 +2026-04-11 02:25:45.461419: train_loss -0.1809 +2026-04-11 02:25:45.467373: val_loss -0.0973 +2026-04-11 02:25:45.469388: Pseudo dice [0.1477, 0.8221, 0.5071, 0.1366, 0.498, 0.4233, 0.6117] +2026-04-11 02:25:45.471352: Epoch time: 102.38 s +2026-04-11 02:25:46.552041: +2026-04-11 02:25:46.553585: Epoch 558 +2026-04-11 02:25:46.555135: Current learning rate: 0.00874 +2026-04-11 02:27:28.647161: train_loss -0.1796 +2026-04-11 02:27:28.654794: val_loss -0.1535 +2026-04-11 02:27:28.656738: Pseudo dice [0.6366, 0.5362, 0.7733, 0.5269, 0.2957, 0.5452, 0.6461] +2026-04-11 02:27:28.658727: Epoch time: 102.1 s +2026-04-11 02:27:29.768911: +2026-04-11 02:27:29.770801: Epoch 559 +2026-04-11 02:27:29.772358: Current learning rate: 0.00873 +2026-04-11 02:29:12.431523: train_loss -0.1722 +2026-04-11 02:29:12.436707: val_loss -0.1621 +2026-04-11 02:29:12.438280: Pseudo dice [0.4582, 0.4451, 0.7038, 0.4529, 0.4759, 0.679, 0.8162] +2026-04-11 02:29:12.440542: Epoch time: 102.67 s +2026-04-11 02:29:13.600340: +2026-04-11 02:29:13.601763: Epoch 560 +2026-04-11 02:29:13.603050: Current learning rate: 0.00873 +2026-04-11 02:30:55.990540: train_loss -0.1654 +2026-04-11 02:30:55.995583: val_loss -0.1385 +2026-04-11 02:30:55.997641: Pseudo dice [0.4771, 0.2798, 0.5538, 0.2971, 0.3786, 0.5248, 0.7127] +2026-04-11 02:30:55.999793: Epoch time: 102.39 s +2026-04-11 02:30:57.093516: +2026-04-11 02:30:57.094924: Epoch 561 +2026-04-11 02:30:57.096395: Current learning rate: 0.00873 +2026-04-11 02:32:39.479797: train_loss -0.1569 +2026-04-11 02:32:39.484650: val_loss -0.1307 +2026-04-11 02:32:39.486385: Pseudo dice [0.5759, 0.2668, 0.7139, 0.2123, 0.467, 0.3615, 0.7675] +2026-04-11 02:32:39.488227: Epoch time: 102.39 s +2026-04-11 02:32:41.694281: +2026-04-11 02:32:41.695654: Epoch 562 +2026-04-11 02:32:41.697665: Current learning rate: 0.00873 +2026-04-11 02:34:24.015454: train_loss -0.1578 +2026-04-11 02:34:24.021077: val_loss -0.1197 +2026-04-11 02:34:24.023053: Pseudo dice [0.4379, 0.0311, 0.6889, 0.3666, 0.5449, 0.6728, 0.789] +2026-04-11 02:34:24.025087: Epoch time: 102.32 s +2026-04-11 02:34:25.115794: +2026-04-11 02:34:25.119742: Epoch 563 +2026-04-11 02:34:25.121370: Current learning rate: 0.00872 +2026-04-11 02:36:07.656280: train_loss -0.1639 +2026-04-11 02:36:07.662372: val_loss -0.1149 +2026-04-11 02:36:07.665294: Pseudo dice [0.3061, 0.6, 0.5099, 0.5438, 0.4811, 0.6964, 0.7266] +2026-04-11 02:36:07.667232: Epoch time: 102.54 s +2026-04-11 02:36:08.769591: +2026-04-11 02:36:08.771003: Epoch 564 +2026-04-11 02:36:08.772396: Current learning rate: 0.00872 +2026-04-11 02:37:51.115513: train_loss -0.1738 +2026-04-11 02:37:51.121211: val_loss -0.1507 +2026-04-11 02:37:51.123364: Pseudo dice [0.478, 0.3427, 0.7295, 0.3372, 0.551, 0.8864, 0.7244] +2026-04-11 02:37:51.126436: Epoch time: 102.35 s +2026-04-11 02:37:52.272809: +2026-04-11 02:37:52.274204: Epoch 565 +2026-04-11 02:37:52.276390: Current learning rate: 0.00872 +2026-04-11 02:39:34.473504: train_loss -0.1607 +2026-04-11 02:39:34.479677: val_loss -0.1117 +2026-04-11 02:39:34.481415: Pseudo dice [0.5044, 0.8763, 0.6633, 0.2625, 0.4282, 0.6013, 0.7962] +2026-04-11 02:39:34.483957: Epoch time: 102.2 s +2026-04-11 02:39:35.621316: +2026-04-11 02:39:35.622803: Epoch 566 +2026-04-11 02:39:35.624193: Current learning rate: 0.00872 +2026-04-11 02:41:17.888733: train_loss -0.1619 +2026-04-11 02:41:17.895151: val_loss -0.138 +2026-04-11 02:41:17.897016: Pseudo dice [0.5043, 0.1218, 0.7027, 0.2095, 0.3451, 0.7872, 0.5688] +2026-04-11 02:41:17.899053: Epoch time: 102.27 s +2026-04-11 02:41:19.066753: +2026-04-11 02:41:19.068828: Epoch 567 +2026-04-11 02:41:19.071308: Current learning rate: 0.00871 +2026-04-11 02:43:01.684999: train_loss -0.1544 +2026-04-11 02:43:01.690878: val_loss -0.1056 +2026-04-11 02:43:01.693742: Pseudo dice [0.5844, 0.8741, 0.6184, 0.458, 0.4638, 0.5718, 0.6368] +2026-04-11 02:43:01.696290: Epoch time: 102.62 s +2026-04-11 02:43:02.881809: +2026-04-11 02:43:02.883432: Epoch 568 +2026-04-11 02:43:02.884827: Current learning rate: 0.00871 +2026-04-11 02:44:45.411359: train_loss -0.1395 +2026-04-11 02:44:45.417625: val_loss -0.1475 +2026-04-11 02:44:45.419794: Pseudo dice [0.6804, 0.8797, 0.684, 0.5033, 0.3988, 0.1292, 0.6953] +2026-04-11 02:44:45.422728: Epoch time: 102.53 s +2026-04-11 02:44:46.514762: +2026-04-11 02:44:46.516425: Epoch 569 +2026-04-11 02:44:46.517877: Current learning rate: 0.00871 +2026-04-11 02:46:28.396726: train_loss -0.1621 +2026-04-11 02:46:28.403647: val_loss -0.0855 +2026-04-11 02:46:28.406264: Pseudo dice [0.2613, 0.2089, 0.245, 0.6799, 0.3018, 0.3352, 0.7392] +2026-04-11 02:46:28.408605: Epoch time: 101.89 s +2026-04-11 02:46:29.515240: +2026-04-11 02:46:29.516900: Epoch 570 +2026-04-11 02:46:29.518245: Current learning rate: 0.00871 +2026-04-11 02:48:11.726228: train_loss -0.1755 +2026-04-11 02:48:11.731933: val_loss -0.1055 +2026-04-11 02:48:11.734032: Pseudo dice [0.7174, 0.8835, 0.6129, 0.2813, 0.6172, 0.3478, 0.7373] +2026-04-11 02:48:11.736505: Epoch time: 102.21 s +2026-04-11 02:48:12.840023: +2026-04-11 02:48:12.841568: Epoch 571 +2026-04-11 02:48:12.842980: Current learning rate: 0.00871 +2026-04-11 02:49:55.219610: train_loss -0.1743 +2026-04-11 02:49:55.225697: val_loss -0.154 +2026-04-11 02:49:55.227589: Pseudo dice [0.7711, 0.865, 0.7068, 0.5653, 0.5588, 0.4671, 0.527] +2026-04-11 02:49:55.229686: Epoch time: 102.38 s +2026-04-11 02:49:56.342146: +2026-04-11 02:49:56.343729: Epoch 572 +2026-04-11 02:49:56.345275: Current learning rate: 0.0087 +2026-04-11 02:51:38.692552: train_loss -0.1656 +2026-04-11 02:51:38.700217: val_loss -0.1615 +2026-04-11 02:51:38.702508: Pseudo dice [0.6059, 0.3029, 0.5608, 0.3482, 0.5512, 0.6092, 0.8577] +2026-04-11 02:51:38.704541: Epoch time: 102.35 s +2026-04-11 02:51:39.805786: +2026-04-11 02:51:39.807343: Epoch 573 +2026-04-11 02:51:39.808664: Current learning rate: 0.0087 +2026-04-11 02:53:22.194483: train_loss -0.1815 +2026-04-11 02:53:22.200594: val_loss -0.1539 +2026-04-11 02:53:22.202724: Pseudo dice [0.7711, 0.5211, 0.6805, 0.2672, 0.3971, 0.6748, 0.5776] +2026-04-11 02:53:22.204963: Epoch time: 102.39 s +2026-04-11 02:53:23.386274: +2026-04-11 02:53:23.387809: Epoch 574 +2026-04-11 02:53:23.389167: Current learning rate: 0.0087 +2026-04-11 02:55:05.547524: train_loss -0.1816 +2026-04-11 02:55:05.553857: val_loss -0.1659 +2026-04-11 02:55:05.555704: Pseudo dice [0.6155, 0.86, 0.7291, 0.5868, 0.4521, 0.6601, 0.8592] +2026-04-11 02:55:05.557780: Epoch time: 102.16 s +2026-04-11 02:55:06.720189: +2026-04-11 02:55:06.725811: Epoch 575 +2026-04-11 02:55:06.729455: Current learning rate: 0.0087 +2026-04-11 02:56:49.162752: train_loss -0.1552 +2026-04-11 02:56:49.168678: val_loss -0.1221 +2026-04-11 02:56:49.171018: Pseudo dice [0.5012, 0.258, 0.5712, 0.4507, 0.3606, 0.5882, 0.4353] +2026-04-11 02:56:49.173127: Epoch time: 102.45 s +2026-04-11 02:56:50.389282: +2026-04-11 02:56:50.390847: Epoch 576 +2026-04-11 02:56:50.392336: Current learning rate: 0.00869 +2026-04-11 02:58:32.892694: train_loss -0.1783 +2026-04-11 02:58:32.898311: val_loss -0.1526 +2026-04-11 02:58:32.900187: Pseudo dice [0.4253, 0.8264, 0.7574, 0.5716, 0.2747, 0.751, 0.7646] +2026-04-11 02:58:32.902070: Epoch time: 102.51 s +2026-04-11 02:58:34.014269: +2026-04-11 02:58:34.015713: Epoch 577 +2026-04-11 02:58:34.017446: Current learning rate: 0.00869 +2026-04-11 03:00:16.250596: train_loss -0.1672 +2026-04-11 03:00:16.257017: val_loss -0.1094 +2026-04-11 03:00:16.259215: Pseudo dice [0.5073, 0.5876, 0.5928, 0.6287, 0.279, 0.8105, 0.685] +2026-04-11 03:00:16.262218: Epoch time: 102.24 s +2026-04-11 03:00:17.410847: +2026-04-11 03:00:17.412432: Epoch 578 +2026-04-11 03:00:17.413984: Current learning rate: 0.00869 +2026-04-11 03:01:59.565555: train_loss -0.1749 +2026-04-11 03:01:59.571213: val_loss -0.1184 +2026-04-11 03:01:59.574101: Pseudo dice [0.5817, 0.5854, 0.6434, 0.5815, 0.479, 0.1729, 0.8087] +2026-04-11 03:01:59.576791: Epoch time: 102.16 s +2026-04-11 03:02:00.732134: +2026-04-11 03:02:00.733557: Epoch 579 +2026-04-11 03:02:00.735001: Current learning rate: 0.00869 +2026-04-11 03:03:42.783745: train_loss -0.1667 +2026-04-11 03:03:42.790411: val_loss -0.1184 +2026-04-11 03:03:42.792566: Pseudo dice [0.5503, 0.8522, 0.5693, 0.4503, 0.5713, 0.0619, 0.773] +2026-04-11 03:03:42.794723: Epoch time: 102.05 s +2026-04-11 03:03:43.958118: +2026-04-11 03:03:43.959692: Epoch 580 +2026-04-11 03:03:43.961282: Current learning rate: 0.00868 +2026-04-11 03:05:26.348817: train_loss -0.1727 +2026-04-11 03:05:26.358444: val_loss -0.1396 +2026-04-11 03:05:26.360987: Pseudo dice [0.5278, 0.8827, 0.6494, 0.296, 0.4866, 0.661, 0.7056] +2026-04-11 03:05:26.369307: Epoch time: 102.39 s +2026-04-11 03:05:27.530424: +2026-04-11 03:05:27.532055: Epoch 581 +2026-04-11 03:05:27.533573: Current learning rate: 0.00868 +2026-04-11 03:07:09.819568: train_loss -0.1674 +2026-04-11 03:07:09.825677: val_loss -0.1226 +2026-04-11 03:07:09.827684: Pseudo dice [0.5887, 0.8812, 0.7165, 0.5531, 0.3317, 0.4756, 0.6989] +2026-04-11 03:07:09.829901: Epoch time: 102.29 s +2026-04-11 03:07:12.078115: +2026-04-11 03:07:12.080130: Epoch 582 +2026-04-11 03:07:12.081641: Current learning rate: 0.00868 +2026-04-11 03:08:54.548780: train_loss -0.1847 +2026-04-11 03:08:54.560859: val_loss -0.1049 +2026-04-11 03:08:54.564345: Pseudo dice [0.7655, 0.7751, 0.6212, 0.0981, 0.402, 0.6228, 0.1653] +2026-04-11 03:08:54.571686: Epoch time: 102.47 s +2026-04-11 03:08:55.795421: +2026-04-11 03:08:55.797216: Epoch 583 +2026-04-11 03:08:55.798774: Current learning rate: 0.00868 +2026-04-11 03:10:37.832797: train_loss -0.1777 +2026-04-11 03:10:37.838665: val_loss -0.1206 +2026-04-11 03:10:37.840709: Pseudo dice [0.5415, 0.515, 0.6361, 0.4307, 0.4076, 0.3818, 0.7978] +2026-04-11 03:10:37.843511: Epoch time: 102.04 s +2026-04-11 03:10:38.957330: +2026-04-11 03:10:38.958857: Epoch 584 +2026-04-11 03:10:38.960114: Current learning rate: 0.00868 +2026-04-11 03:12:21.428826: train_loss -0.1751 +2026-04-11 03:12:21.447386: val_loss -0.1395 +2026-04-11 03:12:21.449158: Pseudo dice [0.6205, 0.7673, 0.7893, 0.0972, 0.2504, 0.8357, 0.7626] +2026-04-11 03:12:21.451028: Epoch time: 102.47 s +2026-04-11 03:12:22.588994: +2026-04-11 03:12:22.590723: Epoch 585 +2026-04-11 03:12:22.592119: Current learning rate: 0.00867 +2026-04-11 03:14:04.812088: train_loss -0.1735 +2026-04-11 03:14:04.817455: val_loss -0.0455 +2026-04-11 03:14:04.819443: Pseudo dice [0.3504, 0.3561, 0.1371, 0.312, 0.5253, 0.1239, 0.7639] +2026-04-11 03:14:04.821790: Epoch time: 102.23 s +2026-04-11 03:14:05.956969: +2026-04-11 03:14:05.959092: Epoch 586 +2026-04-11 03:14:05.961364: Current learning rate: 0.00867 +2026-04-11 03:15:48.840145: train_loss -0.1477 +2026-04-11 03:15:48.846364: val_loss -0.1009 +2026-04-11 03:15:48.848163: Pseudo dice [0.3614, 0.0791, 0.6516, 0.3904, 0.4289, 0.6227, 0.7642] +2026-04-11 03:15:48.850869: Epoch time: 102.89 s +2026-04-11 03:15:50.021801: +2026-04-11 03:15:50.025374: Epoch 587 +2026-04-11 03:15:50.026947: Current learning rate: 0.00867 +2026-04-11 03:17:32.415895: train_loss -0.1462 +2026-04-11 03:17:32.422056: val_loss -0.117 +2026-04-11 03:17:32.424032: Pseudo dice [0.25, 0.8602, 0.4244, 0.2609, 0.1409, 0.6059, 0.4007] +2026-04-11 03:17:32.426152: Epoch time: 102.4 s +2026-04-11 03:17:33.602589: +2026-04-11 03:17:33.604142: Epoch 588 +2026-04-11 03:17:33.605515: Current learning rate: 0.00867 +2026-04-11 03:19:15.536799: train_loss -0.1627 +2026-04-11 03:19:15.543624: val_loss -0.127 +2026-04-11 03:19:15.545621: Pseudo dice [0.1162, 0.6285, 0.7619, 0.4606, 0.1852, 0.5757, 0.3519] +2026-04-11 03:19:15.547685: Epoch time: 101.94 s +2026-04-11 03:19:16.699338: +2026-04-11 03:19:16.701019: Epoch 589 +2026-04-11 03:19:16.702792: Current learning rate: 0.00866 +2026-04-11 03:20:58.951536: train_loss -0.1539 +2026-04-11 03:20:58.956901: val_loss -0.1165 +2026-04-11 03:20:58.960119: Pseudo dice [0.5886, 0.2859, 0.6354, 0.1485, 0.4893, 0.6266, 0.8204] +2026-04-11 03:20:58.962445: Epoch time: 102.26 s +2026-04-11 03:21:00.135696: +2026-04-11 03:21:00.137893: Epoch 590 +2026-04-11 03:21:00.139766: Current learning rate: 0.00866 +2026-04-11 03:22:43.002737: train_loss -0.1691 +2026-04-11 03:22:43.009693: val_loss -0.1533 +2026-04-11 03:22:43.011909: Pseudo dice [0.4392, 0.7153, 0.6843, 0.6006, 0.3379, 0.7362, 0.5032] +2026-04-11 03:22:43.014608: Epoch time: 102.87 s +2026-04-11 03:22:44.176325: +2026-04-11 03:22:44.178389: Epoch 591 +2026-04-11 03:22:44.180959: Current learning rate: 0.00866 +2026-04-11 03:24:26.709080: train_loss -0.1736 +2026-04-11 03:24:26.718757: val_loss -0.0912 +2026-04-11 03:24:26.720942: Pseudo dice [0.168, 0.893, 0.598, 0.3576, 0.5543, 0.5001, 0.8138] +2026-04-11 03:24:26.723243: Epoch time: 102.54 s +2026-04-11 03:24:27.910936: +2026-04-11 03:24:27.913065: Epoch 592 +2026-04-11 03:24:27.915008: Current learning rate: 0.00866 +2026-04-11 03:26:10.247329: train_loss -0.1957 +2026-04-11 03:26:10.255062: val_loss -0.1545 +2026-04-11 03:26:10.257458: Pseudo dice [0.3149, 0.734, 0.8087, 0.1607, 0.3401, 0.6915, 0.8627] +2026-04-11 03:26:10.260098: Epoch time: 102.34 s +2026-04-11 03:26:11.418955: +2026-04-11 03:26:11.420603: Epoch 593 +2026-04-11 03:26:11.422046: Current learning rate: 0.00866 +2026-04-11 03:27:53.873683: train_loss -0.1565 +2026-04-11 03:27:53.879950: val_loss -0.1252 +2026-04-11 03:27:53.882089: Pseudo dice [0.5189, 0.7366, 0.6151, 0.6147, 0.3681, 0.2176, 0.525] +2026-04-11 03:27:53.883802: Epoch time: 102.46 s +2026-04-11 03:27:55.048505: +2026-04-11 03:27:55.050383: Epoch 594 +2026-04-11 03:27:55.052150: Current learning rate: 0.00865 +2026-04-11 03:29:37.331560: train_loss -0.1607 +2026-04-11 03:29:37.337409: val_loss -0.1432 +2026-04-11 03:29:37.339259: Pseudo dice [0.4865, 0.2771, 0.6594, 0.441, 0.3722, 0.7768, 0.7842] +2026-04-11 03:29:37.341610: Epoch time: 102.29 s +2026-04-11 03:29:38.478491: +2026-04-11 03:29:38.480189: Epoch 595 +2026-04-11 03:29:38.481523: Current learning rate: 0.00865 +2026-04-11 03:31:20.752210: train_loss -0.1696 +2026-04-11 03:31:20.757908: val_loss -0.1396 +2026-04-11 03:31:20.759658: Pseudo dice [0.3788, 0.0176, 0.6764, 0.4036, 0.3816, 0.7692, 0.7291] +2026-04-11 03:31:20.762200: Epoch time: 102.28 s +2026-04-11 03:31:21.938751: +2026-04-11 03:31:21.940336: Epoch 596 +2026-04-11 03:31:21.941922: Current learning rate: 0.00865 +2026-04-11 03:33:04.411610: train_loss -0.1755 +2026-04-11 03:33:04.419010: val_loss -0.1542 +2026-04-11 03:33:04.420917: Pseudo dice [0.4577, 0.8579, 0.746, 0.525, 0.6861, 0.5159, 0.747] +2026-04-11 03:33:04.423241: Epoch time: 102.48 s +2026-04-11 03:33:05.581015: +2026-04-11 03:33:05.583177: Epoch 597 +2026-04-11 03:33:05.584814: Current learning rate: 0.00865 +2026-04-11 03:34:47.590218: train_loss -0.1631 +2026-04-11 03:34:47.599079: val_loss -0.1582 +2026-04-11 03:34:47.601350: Pseudo dice [0.6996, 0.7065, 0.6802, 0.5125, 0.3104, 0.6034, 0.5528] +2026-04-11 03:34:47.604485: Epoch time: 102.01 s +2026-04-11 03:34:48.768935: +2026-04-11 03:34:48.770517: Epoch 598 +2026-04-11 03:34:48.771843: Current learning rate: 0.00864 +2026-04-11 03:36:30.787614: train_loss -0.1776 +2026-04-11 03:36:30.796960: val_loss -0.1402 +2026-04-11 03:36:30.798973: Pseudo dice [0.4387, 0.6017, 0.5788, 0.4435, 0.4242, 0.6811, 0.7743] +2026-04-11 03:36:30.801415: Epoch time: 102.02 s +2026-04-11 03:36:31.939530: +2026-04-11 03:36:31.941265: Epoch 599 +2026-04-11 03:36:31.943051: Current learning rate: 0.00864 +2026-04-11 03:38:14.197171: train_loss -0.1674 +2026-04-11 03:38:14.203114: val_loss -0.1224 +2026-04-11 03:38:14.204769: Pseudo dice [0.4892, 0.4738, 0.5684, 0.4097, 0.576, 0.3873, 0.5782] +2026-04-11 03:38:14.206631: Epoch time: 102.26 s +2026-04-11 03:38:16.942250: +2026-04-11 03:38:16.943691: Epoch 600 +2026-04-11 03:38:16.944968: Current learning rate: 0.00864 +2026-04-11 03:39:59.536943: train_loss -0.182 +2026-04-11 03:39:59.542676: val_loss -0.1565 +2026-04-11 03:39:59.544310: Pseudo dice [0.4906, 0.6467, 0.8071, 0.5787, 0.5745, 0.7028, 0.7107] +2026-04-11 03:39:59.547024: Epoch time: 102.6 s +2026-04-11 03:40:00.724922: +2026-04-11 03:40:00.726728: Epoch 601 +2026-04-11 03:40:00.728573: Current learning rate: 0.00864 +2026-04-11 03:41:43.096473: train_loss -0.1954 +2026-04-11 03:41:43.103954: val_loss -0.1489 +2026-04-11 03:41:43.106019: Pseudo dice [0.6991, 0.8126, 0.6818, 0.3153, 0.3689, 0.492, 0.8718] +2026-04-11 03:41:43.108680: Epoch time: 102.37 s +2026-04-11 03:41:45.364785: +2026-04-11 03:41:45.366109: Epoch 602 +2026-04-11 03:41:45.367282: Current learning rate: 0.00863 +2026-04-11 03:43:28.314735: train_loss -0.1727 +2026-04-11 03:43:28.322978: val_loss -0.1105 +2026-04-11 03:43:28.325047: Pseudo dice [0.31, 0.8024, 0.7639, 0.287, 0.3041, 0.3709, 0.1796] +2026-04-11 03:43:28.329196: Epoch time: 102.95 s +2026-04-11 03:43:29.521603: +2026-04-11 03:43:29.523709: Epoch 603 +2026-04-11 03:43:29.526145: Current learning rate: 0.00863 +2026-04-11 03:45:11.501267: train_loss -0.1735 +2026-04-11 03:45:11.507282: val_loss -0.13 +2026-04-11 03:45:11.509450: Pseudo dice [0.6356, 0.6204, 0.6462, 0.4656, 0.4702, 0.5708, 0.798] +2026-04-11 03:45:11.512012: Epoch time: 101.98 s +2026-04-11 03:45:12.642497: +2026-04-11 03:45:12.645252: Epoch 604 +2026-04-11 03:45:12.646957: Current learning rate: 0.00863 +2026-04-11 03:46:54.917783: train_loss -0.1605 +2026-04-11 03:46:54.924176: val_loss -0.0876 +2026-04-11 03:46:54.926253: Pseudo dice [0.6137, 0.8754, 0.3542, 0.1829, 0.3221, 0.22, 0.7082] +2026-04-11 03:46:54.928470: Epoch time: 102.28 s +2026-04-11 03:46:56.147506: +2026-04-11 03:46:56.149624: Epoch 605 +2026-04-11 03:46:56.151098: Current learning rate: 0.00863 +2026-04-11 03:48:38.259915: train_loss -0.1488 +2026-04-11 03:48:38.265870: val_loss -0.1326 +2026-04-11 03:48:38.267977: Pseudo dice [0.4907, 0.8283, 0.4634, 0.4046, 0.401, 0.9026, 0.6109] +2026-04-11 03:48:38.270391: Epoch time: 102.12 s +2026-04-11 03:48:39.413350: +2026-04-11 03:48:39.415159: Epoch 606 +2026-04-11 03:48:39.416545: Current learning rate: 0.00863 +2026-04-11 03:50:21.745063: train_loss -0.1549 +2026-04-11 03:50:21.751447: val_loss -0.1431 +2026-04-11 03:50:21.753453: Pseudo dice [0.6243, 0.7974, 0.7004, 0.1679, 0.592, 0.5183, 0.7458] +2026-04-11 03:50:21.755789: Epoch time: 102.33 s +2026-04-11 03:50:22.893118: +2026-04-11 03:50:22.894559: Epoch 607 +2026-04-11 03:50:22.896312: Current learning rate: 0.00862 +2026-04-11 03:52:05.430068: train_loss -0.1867 +2026-04-11 03:52:05.436362: val_loss -0.1507 +2026-04-11 03:52:05.438153: Pseudo dice [0.5056, 0.7036, 0.7552, 0.2894, 0.4622, 0.6226, 0.5674] +2026-04-11 03:52:05.440873: Epoch time: 102.54 s +2026-04-11 03:52:06.571372: +2026-04-11 03:52:06.573411: Epoch 608 +2026-04-11 03:52:06.575695: Current learning rate: 0.00862 +2026-04-11 03:53:48.915886: train_loss -0.1657 +2026-04-11 03:53:48.921671: val_loss -0.1445 +2026-04-11 03:53:48.924539: Pseudo dice [0.2907, 0.1971, 0.7993, 0.4137, 0.3206, 0.6951, 0.7739] +2026-04-11 03:53:48.927184: Epoch time: 102.35 s +2026-04-11 03:53:50.077999: +2026-04-11 03:53:50.079867: Epoch 609 +2026-04-11 03:53:50.081258: Current learning rate: 0.00862 +2026-04-11 03:55:32.688359: train_loss -0.1706 +2026-04-11 03:55:32.695487: val_loss -0.1114 +2026-04-11 03:55:32.697673: Pseudo dice [0.2896, 0.6981, 0.7349, 0.2697, 0.3208, 0.5366, 0.4963] +2026-04-11 03:55:32.700675: Epoch time: 102.61 s +2026-04-11 03:55:33.853535: +2026-04-11 03:55:33.854863: Epoch 610 +2026-04-11 03:55:33.856221: Current learning rate: 0.00862 +2026-04-11 03:57:15.994267: train_loss -0.1657 +2026-04-11 03:57:16.000671: val_loss -0.0996 +2026-04-11 03:57:16.002336: Pseudo dice [0.5559, 0.8982, 0.7236, 0.5662, 0.33, 0.3025, 0.1945] +2026-04-11 03:57:16.004538: Epoch time: 102.14 s +2026-04-11 03:57:17.171417: +2026-04-11 03:57:17.173654: Epoch 611 +2026-04-11 03:57:17.175290: Current learning rate: 0.00861 +2026-04-11 03:58:59.495903: train_loss -0.1769 +2026-04-11 03:58:59.501972: val_loss -0.1207 +2026-04-11 03:58:59.504105: Pseudo dice [0.602, 0.3657, 0.6346, 0.3801, 0.3405, 0.729, 0.8166] +2026-04-11 03:58:59.506376: Epoch time: 102.33 s +2026-04-11 03:59:00.727593: +2026-04-11 03:59:00.729097: Epoch 612 +2026-04-11 03:59:00.730372: Current learning rate: 0.00861 +2026-04-11 04:00:43.108825: train_loss -0.1716 +2026-04-11 04:00:43.114089: val_loss -0.1449 +2026-04-11 04:00:43.115971: Pseudo dice [0.5372, 0.8497, 0.6266, 0.4156, 0.4363, 0.8665, 0.7177] +2026-04-11 04:00:43.118313: Epoch time: 102.38 s +2026-04-11 04:00:44.235575: +2026-04-11 04:00:44.236971: Epoch 613 +2026-04-11 04:00:44.238358: Current learning rate: 0.00861 +2026-04-11 04:02:26.684133: train_loss -0.187 +2026-04-11 04:02:26.694052: val_loss -0.1436 +2026-04-11 04:02:26.696296: Pseudo dice [0.6171, 0.1347, 0.7258, 0.6766, 0.4026, 0.5305, 0.7602] +2026-04-11 04:02:26.698376: Epoch time: 102.45 s +2026-04-11 04:02:27.860040: +2026-04-11 04:02:27.862237: Epoch 614 +2026-04-11 04:02:27.864043: Current learning rate: 0.00861 +2026-04-11 04:04:09.826804: train_loss -0.194 +2026-04-11 04:04:09.835644: val_loss -0.1322 +2026-04-11 04:04:09.837428: Pseudo dice [0.8289, 0.5805, 0.6239, 0.5384, 0.3451, 0.816, 0.65] +2026-04-11 04:04:09.839583: Epoch time: 101.97 s +2026-04-11 04:04:10.970020: +2026-04-11 04:04:10.971851: Epoch 615 +2026-04-11 04:04:10.973452: Current learning rate: 0.0086 +2026-04-11 04:05:53.586444: train_loss -0.1585 +2026-04-11 04:05:53.593078: val_loss -0.1547 +2026-04-11 04:05:53.595954: Pseudo dice [0.5327, 0.5118, 0.7339, 0.3875, 0.3923, 0.6886, 0.4949] +2026-04-11 04:05:53.598213: Epoch time: 102.62 s +2026-04-11 04:05:54.754661: +2026-04-11 04:05:54.756293: Epoch 616 +2026-04-11 04:05:54.757742: Current learning rate: 0.0086 +2026-04-11 04:07:36.652444: train_loss -0.1756 +2026-04-11 04:07:36.660522: val_loss -0.0938 +2026-04-11 04:07:36.662547: Pseudo dice [0.6112, 0.0789, 0.7237, 0.3998, 0.3658, 0.7167, 0.5954] +2026-04-11 04:07:36.669539: Epoch time: 101.9 s +2026-04-11 04:07:37.863537: +2026-04-11 04:07:37.866073: Epoch 617 +2026-04-11 04:07:37.867664: Current learning rate: 0.0086 +2026-04-11 04:09:19.837007: train_loss -0.1713 +2026-04-11 04:09:19.842690: val_loss -0.1504 +2026-04-11 04:09:19.844845: Pseudo dice [0.7852, 0.3156, 0.7101, 0.3955, 0.4104, 0.8453, 0.4959] +2026-04-11 04:09:19.846982: Epoch time: 101.98 s +2026-04-11 04:09:20.989651: +2026-04-11 04:09:20.991205: Epoch 618 +2026-04-11 04:09:20.992714: Current learning rate: 0.0086 +2026-04-11 04:11:03.153799: train_loss -0.1693 +2026-04-11 04:11:03.160220: val_loss -0.122 +2026-04-11 04:11:03.162067: Pseudo dice [0.5647, 0.7315, 0.6468, 0.1292, 0.0853, 0.6306, 0.42] +2026-04-11 04:11:03.164352: Epoch time: 102.17 s +2026-04-11 04:11:04.344428: +2026-04-11 04:11:04.345847: Epoch 619 +2026-04-11 04:11:04.351173: Current learning rate: 0.0086 +2026-04-11 04:12:46.784385: train_loss -0.1477 +2026-04-11 04:12:46.791037: val_loss -0.1319 +2026-04-11 04:12:46.793911: Pseudo dice [0.6887, 0.8917, 0.7153, 0.0374, 0.2931, 0.1035, 0.449] +2026-04-11 04:12:46.796083: Epoch time: 102.44 s +2026-04-11 04:12:47.953932: +2026-04-11 04:12:47.955977: Epoch 620 +2026-04-11 04:12:47.957599: Current learning rate: 0.00859 +2026-04-11 04:14:30.591181: train_loss -0.164 +2026-04-11 04:14:30.600165: val_loss -0.1145 +2026-04-11 04:14:30.608201: Pseudo dice [0.2768, 0.3464, 0.7081, 0.5773, 0.4442, 0.6645, 0.7904] +2026-04-11 04:14:30.614024: Epoch time: 102.64 s +2026-04-11 04:14:31.747944: +2026-04-11 04:14:31.749542: Epoch 621 +2026-04-11 04:14:31.751016: Current learning rate: 0.00859 +2026-04-11 04:16:14.222025: train_loss -0.1608 +2026-04-11 04:16:14.228733: val_loss -0.1233 +2026-04-11 04:16:14.230369: Pseudo dice [0.3656, 0.8923, 0.6655, 0.4767, 0.2707, 0.1086, 0.5173] +2026-04-11 04:16:14.232616: Epoch time: 102.48 s +2026-04-11 04:16:16.538265: +2026-04-11 04:16:16.539791: Epoch 622 +2026-04-11 04:16:16.541168: Current learning rate: 0.00859 +2026-04-11 04:17:58.659250: train_loss -0.1722 +2026-04-11 04:17:58.665062: val_loss -0.1149 +2026-04-11 04:17:58.666900: Pseudo dice [0.311, 0.7617, 0.6991, 0.5499, 0.4127, 0.6226, 0.1292] +2026-04-11 04:17:58.669382: Epoch time: 102.12 s +2026-04-11 04:17:59.849097: +2026-04-11 04:17:59.850589: Epoch 623 +2026-04-11 04:17:59.852029: Current learning rate: 0.00859 +2026-04-11 04:19:42.401934: train_loss -0.1634 +2026-04-11 04:19:42.408892: val_loss -0.1323 +2026-04-11 04:19:42.410884: Pseudo dice [0.3148, 0.807, 0.6257, 0.2384, 0.318, 0.3216, 0.3625] +2026-04-11 04:19:42.412895: Epoch time: 102.56 s +2026-04-11 04:19:43.565310: +2026-04-11 04:19:43.566783: Epoch 624 +2026-04-11 04:19:43.568197: Current learning rate: 0.00858 +2026-04-11 04:21:26.254005: train_loss -0.1488 +2026-04-11 04:21:26.260870: val_loss -0.141 +2026-04-11 04:21:26.262823: Pseudo dice [0.2018, 0.883, 0.805, 0.1699, 0.4238, 0.1165, 0.8313] +2026-04-11 04:21:26.267450: Epoch time: 102.69 s +2026-04-11 04:21:27.439322: +2026-04-11 04:21:27.441170: Epoch 625 +2026-04-11 04:21:27.443088: Current learning rate: 0.00858 +2026-04-11 04:23:09.689067: train_loss -0.1614 +2026-04-11 04:23:09.697626: val_loss -0.0822 +2026-04-11 04:23:09.699301: Pseudo dice [0.3308, 0.9218, 0.5288, 0.357, 0.2997, 0.401, 0.8196] +2026-04-11 04:23:09.701838: Epoch time: 102.25 s +2026-04-11 04:23:10.903090: +2026-04-11 04:23:10.905041: Epoch 626 +2026-04-11 04:23:10.906569: Current learning rate: 0.00858 +2026-04-11 04:24:53.521053: train_loss -0.1626 +2026-04-11 04:24:53.528582: val_loss -0.116 +2026-04-11 04:24:53.530843: Pseudo dice [0.5409, 0.9004, 0.3665, 0.3904, 0.4764, 0.6499, 0.7669] +2026-04-11 04:24:53.533878: Epoch time: 102.62 s +2026-04-11 04:24:54.728106: +2026-04-11 04:24:54.730349: Epoch 627 +2026-04-11 04:24:54.732383: Current learning rate: 0.00858 +2026-04-11 04:26:37.242896: train_loss -0.1771 +2026-04-11 04:26:37.248876: val_loss -0.1488 +2026-04-11 04:26:37.250828: Pseudo dice [0.3033, 0.7091, 0.7334, 0.6421, 0.41, 0.8799, 0.783] +2026-04-11 04:26:37.253160: Epoch time: 102.52 s +2026-04-11 04:26:38.434892: +2026-04-11 04:26:38.437616: Epoch 628 +2026-04-11 04:26:38.439118: Current learning rate: 0.00858 +2026-04-11 04:28:20.597900: train_loss -0.1752 +2026-04-11 04:28:20.604985: val_loss -0.1247 +2026-04-11 04:28:20.607004: Pseudo dice [0.2346, 0.7467, 0.6509, 0.6305, 0.5491, 0.5404, 0.5966] +2026-04-11 04:28:20.609427: Epoch time: 102.17 s +2026-04-11 04:28:21.756133: +2026-04-11 04:28:21.758156: Epoch 629 +2026-04-11 04:28:21.759952: Current learning rate: 0.00857 +2026-04-11 04:30:03.824733: train_loss -0.1761 +2026-04-11 04:30:03.836012: val_loss -0.1416 +2026-04-11 04:30:03.839475: Pseudo dice [0.5882, 0.6958, 0.7591, 0.625, 0.3464, 0.3332, 0.6581] +2026-04-11 04:30:03.842731: Epoch time: 102.07 s +2026-04-11 04:30:04.961209: +2026-04-11 04:30:04.965739: Epoch 630 +2026-04-11 04:30:04.968879: Current learning rate: 0.00857 +2026-04-11 04:31:47.566652: train_loss -0.1729 +2026-04-11 04:31:47.572251: val_loss -0.1272 +2026-04-11 04:31:47.574101: Pseudo dice [0.5556, 0.6542, 0.5874, 0.6015, 0.5189, 0.6873, 0.5865] +2026-04-11 04:31:47.576531: Epoch time: 102.61 s +2026-04-11 04:31:48.725304: +2026-04-11 04:31:48.727381: Epoch 631 +2026-04-11 04:31:48.728805: Current learning rate: 0.00857 +2026-04-11 04:33:31.089433: train_loss -0.1655 +2026-04-11 04:33:31.095694: val_loss -0.1458 +2026-04-11 04:33:31.098327: Pseudo dice [0.3753, 0.8924, 0.7401, 0.6673, 0.5763, 0.3321, 0.7504] +2026-04-11 04:33:31.100292: Epoch time: 102.37 s +2026-04-11 04:33:32.254798: +2026-04-11 04:33:32.256298: Epoch 632 +2026-04-11 04:33:32.257632: Current learning rate: 0.00857 +2026-04-11 04:35:15.069959: train_loss -0.1813 +2026-04-11 04:35:15.075131: val_loss -0.1451 +2026-04-11 04:35:15.077207: Pseudo dice [0.0585, 0.5848, 0.6196, 0.7782, 0.6362, 0.2135, 0.6729] +2026-04-11 04:35:15.079093: Epoch time: 102.82 s +2026-04-11 04:35:16.267154: +2026-04-11 04:35:16.269023: Epoch 633 +2026-04-11 04:35:16.270488: Current learning rate: 0.00856 +2026-04-11 04:36:58.635419: train_loss -0.1786 +2026-04-11 04:36:58.641115: val_loss -0.1132 +2026-04-11 04:36:58.643955: Pseudo dice [0.6217, 0.2996, 0.6286, 0.4414, 0.3861, 0.6933, 0.6552] +2026-04-11 04:36:58.646667: Epoch time: 102.37 s +2026-04-11 04:36:59.801440: +2026-04-11 04:36:59.803248: Epoch 634 +2026-04-11 04:36:59.805097: Current learning rate: 0.00856 +2026-04-11 04:38:42.066014: train_loss -0.1741 +2026-04-11 04:38:42.071597: val_loss -0.1667 +2026-04-11 04:38:42.073780: Pseudo dice [0.4936, 0.7826, 0.7379, 0.2348, 0.2881, 0.7272, 0.7476] +2026-04-11 04:38:42.075819: Epoch time: 102.27 s +2026-04-11 04:38:43.250843: +2026-04-11 04:38:43.252361: Epoch 635 +2026-04-11 04:38:43.253734: Current learning rate: 0.00856 +2026-04-11 04:40:25.573854: train_loss -0.1705 +2026-04-11 04:40:25.579563: val_loss -0.1443 +2026-04-11 04:40:25.581141: Pseudo dice [0.6634, 0.8689, 0.7956, 0.396, 0.2706, 0.6788, 0.654] +2026-04-11 04:40:25.583379: Epoch time: 102.33 s +2026-04-11 04:40:26.727065: +2026-04-11 04:40:26.728569: Epoch 636 +2026-04-11 04:40:26.729981: Current learning rate: 0.00856 +2026-04-11 04:42:09.310525: train_loss -0.1696 +2026-04-11 04:42:09.315980: val_loss -0.1484 +2026-04-11 04:42:09.317839: Pseudo dice [0.217, 0.6617, 0.8012, 0.2463, 0.4238, 0.7682, 0.7098] +2026-04-11 04:42:09.320215: Epoch time: 102.59 s +2026-04-11 04:42:10.514808: +2026-04-11 04:42:10.516477: Epoch 637 +2026-04-11 04:42:10.517859: Current learning rate: 0.00855 +2026-04-11 04:43:52.811803: train_loss -0.1822 +2026-04-11 04:43:52.818176: val_loss -0.1343 +2026-04-11 04:43:52.819835: Pseudo dice [0.6336, 0.3285, 0.6668, 0.5939, 0.2722, 0.5704, 0.5345] +2026-04-11 04:43:52.821930: Epoch time: 102.3 s +2026-04-11 04:43:53.951399: +2026-04-11 04:43:53.953407: Epoch 638 +2026-04-11 04:43:53.955301: Current learning rate: 0.00855 +2026-04-11 04:45:36.497628: train_loss -0.1773 +2026-04-11 04:45:36.504316: val_loss -0.1586 +2026-04-11 04:45:36.506130: Pseudo dice [0.5073, 0.7819, 0.6721, 0.5144, 0.1839, 0.8078, 0.5699] +2026-04-11 04:45:36.508128: Epoch time: 102.55 s +2026-04-11 04:45:37.682829: +2026-04-11 04:45:37.685896: Epoch 639 +2026-04-11 04:45:37.687919: Current learning rate: 0.00855 +2026-04-11 04:47:20.866372: train_loss -0.1729 +2026-04-11 04:47:20.872540: val_loss -0.1594 +2026-04-11 04:47:20.874540: Pseudo dice [0.8597, 0.6069, 0.6688, 0.5221, 0.3467, 0.8131, 0.4832] +2026-04-11 04:47:20.876814: Epoch time: 103.19 s +2026-04-11 04:47:22.024292: +2026-04-11 04:47:22.026091: Epoch 640 +2026-04-11 04:47:22.027681: Current learning rate: 0.00855 +2026-04-11 04:49:04.348886: train_loss -0.1781 +2026-04-11 04:49:04.354999: val_loss -0.1611 +2026-04-11 04:49:04.357557: Pseudo dice [0.5745, 0.5035, 0.7082, 0.4788, 0.4195, 0.5809, 0.6451] +2026-04-11 04:49:04.360527: Epoch time: 102.33 s +2026-04-11 04:49:05.506009: +2026-04-11 04:49:05.519034: Epoch 641 +2026-04-11 04:49:05.520598: Current learning rate: 0.00855 +2026-04-11 04:50:47.807436: train_loss -0.1809 +2026-04-11 04:50:47.813258: val_loss -0.1474 +2026-04-11 04:50:47.815189: Pseudo dice [0.4267, 0.4529, 0.7779, 0.5913, 0.3031, 0.7629, 0.4607] +2026-04-11 04:50:47.817474: Epoch time: 102.3 s +2026-04-11 04:50:50.074957: +2026-04-11 04:50:50.076592: Epoch 642 +2026-04-11 04:50:50.077896: Current learning rate: 0.00854 +2026-04-11 04:52:32.849814: train_loss -0.1779 +2026-04-11 04:52:32.855780: val_loss -0.1657 +2026-04-11 04:52:32.857736: Pseudo dice [0.462, 0.8176, 0.7503, 0.4089, 0.4458, 0.7115, 0.7896] +2026-04-11 04:52:32.860063: Epoch time: 102.78 s +2026-04-11 04:52:33.990978: +2026-04-11 04:52:33.993083: Epoch 643 +2026-04-11 04:52:33.995193: Current learning rate: 0.00854 +2026-04-11 04:54:16.097295: train_loss -0.1889 +2026-04-11 04:54:16.102841: val_loss -0.1624 +2026-04-11 04:54:16.104698: Pseudo dice [0.7752, 0.6443, 0.7791, 0.7294, 0.3124, 0.736, 0.7319] +2026-04-11 04:54:16.106434: Epoch time: 102.11 s +2026-04-11 04:54:17.230243: +2026-04-11 04:54:17.232415: Epoch 644 +2026-04-11 04:54:17.234535: Current learning rate: 0.00854 +2026-04-11 04:55:59.513766: train_loss -0.1762 +2026-04-11 04:55:59.519887: val_loss -0.1321 +2026-04-11 04:55:59.521392: Pseudo dice [0.324, 0.5528, 0.6662, 0.2829, 0.5914, 0.165, 0.7154] +2026-04-11 04:55:59.524081: Epoch time: 102.29 s +2026-04-11 04:56:00.650977: +2026-04-11 04:56:00.652601: Epoch 645 +2026-04-11 04:56:00.654042: Current learning rate: 0.00854 +2026-04-11 04:57:42.827823: train_loss -0.1734 +2026-04-11 04:57:42.833866: val_loss -0.1015 +2026-04-11 04:57:42.836020: Pseudo dice [0.1852, 0.3834, 0.7072, 0.7399, 0.3098, 0.4937, 0.71] +2026-04-11 04:57:42.838027: Epoch time: 102.18 s +2026-04-11 04:57:44.034430: +2026-04-11 04:57:44.036243: Epoch 646 +2026-04-11 04:57:44.037773: Current learning rate: 0.00853 +2026-04-11 04:59:26.583266: train_loss -0.1889 +2026-04-11 04:59:26.588399: val_loss -0.1057 +2026-04-11 04:59:26.590252: Pseudo dice [0.4585, 0.7812, 0.7046, 0.7582, 0.2516, 0.1384, 0.6638] +2026-04-11 04:59:26.592409: Epoch time: 102.55 s +2026-04-11 04:59:27.736755: +2026-04-11 04:59:27.738300: Epoch 647 +2026-04-11 04:59:27.741233: Current learning rate: 0.00853 +2026-04-11 05:01:10.039738: train_loss -0.1628 +2026-04-11 05:01:10.045417: val_loss -0.0832 +2026-04-11 05:01:10.047464: Pseudo dice [0.2546, 0.0263, 0.4938, 0.6183, 0.3127, 0.374, 0.7864] +2026-04-11 05:01:10.049721: Epoch time: 102.31 s +2026-04-11 05:01:11.195484: +2026-04-11 05:01:11.196999: Epoch 648 +2026-04-11 05:01:11.198365: Current learning rate: 0.00853 +2026-04-11 05:02:53.315254: train_loss -0.1614 +2026-04-11 05:02:53.322180: val_loss -0.1512 +2026-04-11 05:02:53.324564: Pseudo dice [0.1575, 0.201, 0.7461, 0.4983, 0.4978, 0.9023, 0.7679] +2026-04-11 05:02:53.326841: Epoch time: 102.12 s +2026-04-11 05:02:54.546760: +2026-04-11 05:02:54.548470: Epoch 649 +2026-04-11 05:02:54.549901: Current learning rate: 0.00853 +2026-04-11 05:04:37.011458: train_loss -0.1783 +2026-04-11 05:04:37.017338: val_loss -0.1115 +2026-04-11 05:04:37.019114: Pseudo dice [0.6, 0.8097, 0.7106, 0.2862, 0.5007, 0.5447, 0.608] +2026-04-11 05:04:37.020828: Epoch time: 102.47 s +2026-04-11 05:04:39.933917: +2026-04-11 05:04:39.935322: Epoch 650 +2026-04-11 05:04:39.936674: Current learning rate: 0.00852 +2026-04-11 05:06:22.086023: train_loss -0.1686 +2026-04-11 05:06:22.092677: val_loss -0.1134 +2026-04-11 05:06:22.095203: Pseudo dice [0.526, 0.4138, 0.4544, 0.3437, 0.5488, 0.4073, 0.5466] +2026-04-11 05:06:22.097198: Epoch time: 102.16 s +2026-04-11 05:06:23.272990: +2026-04-11 05:06:23.274457: Epoch 651 +2026-04-11 05:06:23.275974: Current learning rate: 0.00852 +2026-04-11 05:08:05.483600: train_loss -0.1716 +2026-04-11 05:08:05.503818: val_loss -0.1484 +2026-04-11 05:08:05.506088: Pseudo dice [0.465, 0.5097, 0.6304, 0.4978, 0.4161, 0.4043, 0.815] +2026-04-11 05:08:05.508296: Epoch time: 102.21 s +2026-04-11 05:08:06.680181: +2026-04-11 05:08:06.682566: Epoch 652 +2026-04-11 05:08:06.684374: Current learning rate: 0.00852 +2026-04-11 05:09:51.932842: train_loss -0.1675 +2026-04-11 05:09:51.938739: val_loss -0.1182 +2026-04-11 05:09:51.940324: Pseudo dice [0.7331, 0.7043, 0.5782, 0.4667, 0.307, 0.7419, 0.8279] +2026-04-11 05:09:51.942229: Epoch time: 105.26 s +2026-04-11 05:09:53.109975: +2026-04-11 05:09:53.112152: Epoch 653 +2026-04-11 05:09:53.113837: Current learning rate: 0.00852 +2026-04-11 05:11:35.034154: train_loss -0.1914 +2026-04-11 05:11:35.042969: val_loss -0.1046 +2026-04-11 05:11:35.044810: Pseudo dice [0.7161, 0.7729, 0.6657, 0.4886, 0.4659, 0.7074, 0.6511] +2026-04-11 05:11:35.047020: Epoch time: 101.93 s +2026-04-11 05:11:36.188378: +2026-04-11 05:11:36.189826: Epoch 654 +2026-04-11 05:11:36.191172: Current learning rate: 0.00852 +2026-04-11 05:13:18.247340: train_loss -0.1759 +2026-04-11 05:13:18.254671: val_loss -0.1418 +2026-04-11 05:13:18.258566: Pseudo dice [0.4051, 0.4258, 0.7581, 0.4229, 0.0897, 0.7881, 0.7004] +2026-04-11 05:13:18.261229: Epoch time: 102.06 s +2026-04-11 05:13:19.406848: +2026-04-11 05:13:19.409008: Epoch 655 +2026-04-11 05:13:19.410654: Current learning rate: 0.00851 +2026-04-11 05:15:02.188705: train_loss -0.1774 +2026-04-11 05:15:02.194839: val_loss -0.1451 +2026-04-11 05:15:02.197374: Pseudo dice [0.3828, 0.7399, 0.6872, 0.6039, 0.4056, 0.6511, 0.6619] +2026-04-11 05:15:02.200165: Epoch time: 102.79 s +2026-04-11 05:15:03.377522: +2026-04-11 05:15:03.379364: Epoch 656 +2026-04-11 05:15:03.380698: Current learning rate: 0.00851 +2026-04-11 05:16:45.428179: train_loss -0.1881 +2026-04-11 05:16:45.433965: val_loss -0.1334 +2026-04-11 05:16:45.435587: Pseudo dice [0.306, 0.828, 0.602, 0.3603, 0.6028, 0.7959, 0.8082] +2026-04-11 05:16:45.437727: Epoch time: 102.05 s +2026-04-11 05:16:46.856255: +2026-04-11 05:16:46.857906: Epoch 657 +2026-04-11 05:16:46.859413: Current learning rate: 0.00851 +2026-04-11 05:18:29.304968: train_loss -0.1728 +2026-04-11 05:18:29.312760: val_loss -0.1265 +2026-04-11 05:18:29.314578: Pseudo dice [0.5731, 0.8872, 0.6095, 0.0, 0.4453, 0.3375, 0.5131] +2026-04-11 05:18:29.317201: Epoch time: 102.45 s +2026-04-11 05:18:30.546357: +2026-04-11 05:18:30.548021: Epoch 658 +2026-04-11 05:18:30.549544: Current learning rate: 0.00851 +2026-04-11 05:20:12.878927: train_loss -0.1756 +2026-04-11 05:20:12.885098: val_loss -0.1539 +2026-04-11 05:20:12.887002: Pseudo dice [0.1813, 0.7987, 0.7679, 0.6355, 0.5193, 0.5917, 0.8173] +2026-04-11 05:20:12.889220: Epoch time: 102.34 s +2026-04-11 05:20:14.042432: +2026-04-11 05:20:14.043828: Epoch 659 +2026-04-11 05:20:14.045145: Current learning rate: 0.0085 +2026-04-11 05:21:56.499367: train_loss -0.1905 +2026-04-11 05:21:56.506152: val_loss -0.1326 +2026-04-11 05:21:56.509003: Pseudo dice [0.8274, 0.9197, 0.5831, 0.4035, 0.2417, 0.1935, 0.7491] +2026-04-11 05:21:56.511505: Epoch time: 102.46 s +2026-04-11 05:21:57.653550: +2026-04-11 05:21:57.655176: Epoch 660 +2026-04-11 05:21:57.656542: Current learning rate: 0.0085 +2026-04-11 05:23:39.889605: train_loss -0.1796 +2026-04-11 05:23:39.895846: val_loss -0.117 +2026-04-11 05:23:39.898359: Pseudo dice [0.2303, 0.8349, 0.6651, 0.5798, 0.4146, 0.4654, 0.7964] +2026-04-11 05:23:39.900624: Epoch time: 102.24 s +2026-04-11 05:23:41.019997: +2026-04-11 05:23:41.022217: Epoch 661 +2026-04-11 05:23:41.023928: Current learning rate: 0.0085 +2026-04-11 05:25:23.054544: train_loss -0.1787 +2026-04-11 05:25:23.063099: val_loss -0.114 +2026-04-11 05:25:23.066136: Pseudo dice [0.7333, 0.8501, 0.7401, 0.223, 0.2944, 0.6063, 0.5782] +2026-04-11 05:25:23.068147: Epoch time: 102.04 s +2026-04-11 05:25:25.288259: +2026-04-11 05:25:25.289918: Epoch 662 +2026-04-11 05:25:25.291576: Current learning rate: 0.0085 +2026-04-11 05:27:07.525723: train_loss -0.1884 +2026-04-11 05:27:07.555075: val_loss -0.1157 +2026-04-11 05:27:07.556523: Pseudo dice [0.5084, 0.9062, 0.6824, 0.3007, 0.5161, 0.1226, 0.5307] +2026-04-11 05:27:07.560859: Epoch time: 102.24 s +2026-04-11 05:27:08.695637: +2026-04-11 05:27:08.704660: Epoch 663 +2026-04-11 05:27:08.706783: Current learning rate: 0.0085 +2026-04-11 05:28:51.110695: train_loss -0.1633 +2026-04-11 05:28:51.116785: val_loss -0.1368 +2026-04-11 05:28:51.119119: Pseudo dice [0.3083, 0.7392, 0.7503, 0.3956, 0.7286, 0.7429, 0.8225] +2026-04-11 05:28:51.121095: Epoch time: 102.42 s +2026-04-11 05:28:52.279525: +2026-04-11 05:28:52.280903: Epoch 664 +2026-04-11 05:28:52.282395: Current learning rate: 0.00849 +2026-04-11 05:30:34.534643: train_loss -0.169 +2026-04-11 05:30:34.540388: val_loss -0.1286 +2026-04-11 05:30:34.542417: Pseudo dice [0.2006, 0.9114, 0.675, 0.2913, 0.4304, 0.4633, 0.6642] +2026-04-11 05:30:34.545089: Epoch time: 102.26 s +2026-04-11 05:30:35.690693: +2026-04-11 05:30:35.692254: Epoch 665 +2026-04-11 05:30:35.693715: Current learning rate: 0.00849 +2026-04-11 05:32:17.746140: train_loss -0.1602 +2026-04-11 05:32:17.752848: val_loss -0.1314 +2026-04-11 05:32:17.754852: Pseudo dice [0.0922, 0.5033, 0.5877, 0.2053, 0.3969, 0.7805, 0.6876] +2026-04-11 05:32:17.757486: Epoch time: 102.06 s +2026-04-11 05:32:18.906727: +2026-04-11 05:32:18.913203: Epoch 666 +2026-04-11 05:32:18.914631: Current learning rate: 0.00849 +2026-04-11 05:34:01.721323: train_loss -0.1782 +2026-04-11 05:34:01.727500: val_loss -0.119 +2026-04-11 05:34:01.729569: Pseudo dice [0.328, 0.4326, 0.699, 0.4435, 0.2376, 0.4442, 0.7419] +2026-04-11 05:34:01.732491: Epoch time: 102.82 s +2026-04-11 05:34:02.887252: +2026-04-11 05:34:02.889913: Epoch 667 +2026-04-11 05:34:02.892695: Current learning rate: 0.00849 +2026-04-11 05:35:44.758194: train_loss -0.1698 +2026-04-11 05:35:44.765338: val_loss -0.1288 +2026-04-11 05:35:44.767359: Pseudo dice [0.2906, 0.7877, 0.7321, 0.4727, 0.404, 0.7713, 0.7847] +2026-04-11 05:35:44.769292: Epoch time: 101.87 s +2026-04-11 05:35:45.968080: +2026-04-11 05:35:45.969738: Epoch 668 +2026-04-11 05:35:45.971303: Current learning rate: 0.00848 +2026-04-11 05:37:28.130768: train_loss -0.1861 +2026-04-11 05:37:28.136319: val_loss -0.1251 +2026-04-11 05:37:28.138047: Pseudo dice [0.4187, 0.4873, 0.672, 0.1446, 0.2348, 0.865, 0.791] +2026-04-11 05:37:28.140116: Epoch time: 102.17 s +2026-04-11 05:37:29.298593: +2026-04-11 05:37:29.300792: Epoch 669 +2026-04-11 05:37:29.302334: Current learning rate: 0.00848 +2026-04-11 05:39:11.758477: train_loss -0.1756 +2026-04-11 05:39:11.764598: val_loss -0.1393 +2026-04-11 05:39:11.766181: Pseudo dice [0.1686, 0.257, 0.8081, 0.6115, 0.2065, 0.8625, 0.8316] +2026-04-11 05:39:11.780025: Epoch time: 102.46 s +2026-04-11 05:39:12.953864: +2026-04-11 05:39:12.955639: Epoch 670 +2026-04-11 05:39:12.957172: Current learning rate: 0.00848 +2026-04-11 05:40:55.156234: train_loss -0.1719 +2026-04-11 05:40:55.163265: val_loss -0.1301 +2026-04-11 05:40:55.164804: Pseudo dice [0.4064, 0.4694, 0.668, 0.5371, 0.3884, 0.6917, 0.6221] +2026-04-11 05:40:55.167503: Epoch time: 102.21 s +2026-04-11 05:40:56.344678: +2026-04-11 05:40:56.346332: Epoch 671 +2026-04-11 05:40:56.347795: Current learning rate: 0.00848 +2026-04-11 05:42:38.215786: train_loss -0.1781 +2026-04-11 05:42:38.222393: val_loss -0.1546 +2026-04-11 05:42:38.225753: Pseudo dice [0.4588, 0.6542, 0.7174, 0.4316, 0.5677, 0.8993, 0.8244] +2026-04-11 05:42:38.228232: Epoch time: 101.87 s +2026-04-11 05:42:39.472472: +2026-04-11 05:42:39.474678: Epoch 672 +2026-04-11 05:42:39.476528: Current learning rate: 0.00847 +2026-04-11 05:44:22.137692: train_loss -0.181 +2026-04-11 05:44:22.142769: val_loss -0.1536 +2026-04-11 05:44:22.144375: Pseudo dice [0.5469, 0.891, 0.8041, 0.0323, 0.4695, 0.4924, 0.8391] +2026-04-11 05:44:22.146449: Epoch time: 102.67 s +2026-04-11 05:44:23.314065: +2026-04-11 05:44:23.316794: Epoch 673 +2026-04-11 05:44:23.318710: Current learning rate: 0.00847 +2026-04-11 05:46:06.168526: train_loss -0.1695 +2026-04-11 05:46:06.174581: val_loss -0.1021 +2026-04-11 05:46:06.176415: Pseudo dice [0.5207, 0.3529, 0.3448, 0.4795, 0.354, 0.4058, 0.6501] +2026-04-11 05:46:06.178977: Epoch time: 102.86 s +2026-04-11 05:46:07.334896: +2026-04-11 05:46:07.336535: Epoch 674 +2026-04-11 05:46:07.338069: Current learning rate: 0.00847 +2026-04-11 05:47:49.892557: train_loss -0.1711 +2026-04-11 05:47:49.900527: val_loss -0.127 +2026-04-11 05:47:49.902658: Pseudo dice [0.5327, 0.223, 0.4258, 0.5083, 0.4723, 0.4434, 0.7203] +2026-04-11 05:47:49.904729: Epoch time: 102.56 s +2026-04-11 05:47:51.062370: +2026-04-11 05:47:51.063856: Epoch 675 +2026-04-11 05:47:51.065279: Current learning rate: 0.00847 +2026-04-11 05:49:33.440755: train_loss -0.1709 +2026-04-11 05:49:33.447172: val_loss -0.1254 +2026-04-11 05:49:33.448686: Pseudo dice [0.0847, 0.2593, 0.7155, 0.3811, 0.3927, 0.4466, 0.774] +2026-04-11 05:49:33.450630: Epoch time: 102.38 s +2026-04-11 05:49:34.602537: +2026-04-11 05:49:34.604311: Epoch 676 +2026-04-11 05:49:34.605681: Current learning rate: 0.00847 +2026-04-11 05:51:16.949818: train_loss -0.1836 +2026-04-11 05:51:16.956012: val_loss -0.1432 +2026-04-11 05:51:16.957623: Pseudo dice [0.8134, 0.1929, 0.7456, 0.4439, 0.2184, 0.8718, 0.775] +2026-04-11 05:51:16.959712: Epoch time: 102.35 s +2026-04-11 05:51:18.100290: +2026-04-11 05:51:18.101803: Epoch 677 +2026-04-11 05:51:18.103058: Current learning rate: 0.00846 +2026-04-11 05:53:00.411268: train_loss -0.1771 +2026-04-11 05:53:00.419176: val_loss -0.1279 +2026-04-11 05:53:00.421177: Pseudo dice [0.3306, 0.1957, 0.7178, 0.1794, 0.2907, 0.4952, 0.2664] +2026-04-11 05:53:00.423905: Epoch time: 102.31 s +2026-04-11 05:53:01.569826: +2026-04-11 05:53:01.571892: Epoch 678 +2026-04-11 05:53:01.574092: Current learning rate: 0.00846 +2026-04-11 05:54:44.244954: train_loss -0.1784 +2026-04-11 05:54:44.251226: val_loss -0.1352 +2026-04-11 05:54:44.252953: Pseudo dice [0.6076, 0.8966, 0.751, 0.4727, 0.3382, 0.4458, 0.5017] +2026-04-11 05:54:44.255121: Epoch time: 102.68 s +2026-04-11 05:54:45.392248: +2026-04-11 05:54:45.393890: Epoch 679 +2026-04-11 05:54:45.395359: Current learning rate: 0.00846 +2026-04-11 05:56:28.481404: train_loss -0.1683 +2026-04-11 05:56:28.487953: val_loss -0.1398 +2026-04-11 05:56:28.490724: Pseudo dice [0.6854, 0.4728, 0.6322, 0.3011, 0.6398, 0.1914, 0.6471] +2026-04-11 05:56:28.493075: Epoch time: 103.09 s +2026-04-11 05:56:29.676534: +2026-04-11 05:56:29.678396: Epoch 680 +2026-04-11 05:56:29.680833: Current learning rate: 0.00846 +2026-04-11 05:58:11.822201: train_loss -0.1742 +2026-04-11 05:58:11.828631: val_loss -0.0974 +2026-04-11 05:58:11.830753: Pseudo dice [0.6065, 0.9095, 0.493, 0.2048, 0.2843, 0.1274, 0.7351] +2026-04-11 05:58:11.832963: Epoch time: 102.15 s +2026-04-11 05:58:12.978974: +2026-04-11 05:58:12.980496: Epoch 681 +2026-04-11 05:58:12.981944: Current learning rate: 0.00845 +2026-04-11 05:59:56.607478: train_loss -0.1662 +2026-04-11 05:59:56.614900: val_loss -0.1227 +2026-04-11 05:59:56.616546: Pseudo dice [0.573, 0.8023, 0.261, 0.2148, 0.5225, 0.3922, 0.7055] +2026-04-11 05:59:56.619411: Epoch time: 103.63 s +2026-04-11 05:59:57.755712: +2026-04-11 05:59:57.757817: Epoch 682 +2026-04-11 05:59:57.759248: Current learning rate: 0.00845 +2026-04-11 06:01:40.858067: train_loss -0.1725 +2026-04-11 06:01:40.866993: val_loss -0.1245 +2026-04-11 06:01:40.868988: Pseudo dice [0.4053, 0.2421, 0.4921, 0.1391, 0.3533, 0.5359, 0.7035] +2026-04-11 06:01:40.871249: Epoch time: 103.11 s +2026-04-11 06:01:42.038805: +2026-04-11 06:01:42.040707: Epoch 683 +2026-04-11 06:01:42.042476: Current learning rate: 0.00845 +2026-04-11 06:03:24.851436: train_loss -0.1712 +2026-04-11 06:03:24.857155: val_loss -0.1652 +2026-04-11 06:03:24.859223: Pseudo dice [0.5267, 0.1742, 0.6936, 0.674, 0.5886, 0.8825, 0.7372] +2026-04-11 06:03:24.861613: Epoch time: 102.82 s +2026-04-11 06:03:26.015512: +2026-04-11 06:03:26.017298: Epoch 684 +2026-04-11 06:03:26.018776: Current learning rate: 0.00845 +2026-04-11 06:05:08.833488: train_loss -0.1815 +2026-04-11 06:05:08.838975: val_loss -0.1288 +2026-04-11 06:05:08.840878: Pseudo dice [0.5624, 0.8486, 0.5121, 0.5369, 0.3653, 0.2804, 0.5517] +2026-04-11 06:05:08.844184: Epoch time: 102.82 s +2026-04-11 06:05:10.054288: +2026-04-11 06:05:10.055879: Epoch 685 +2026-04-11 06:05:10.057391: Current learning rate: 0.00844 +2026-04-11 06:06:52.479205: train_loss -0.1711 +2026-04-11 06:06:52.485736: val_loss -0.1219 +2026-04-11 06:06:52.487725: Pseudo dice [0.3595, 0.7485, 0.7425, 0.4504, 0.4454, 0.5974, 0.7184] +2026-04-11 06:06:52.489871: Epoch time: 102.43 s +2026-04-11 06:06:53.632282: +2026-04-11 06:06:53.634589: Epoch 686 +2026-04-11 06:06:53.636348: Current learning rate: 0.00844 +2026-04-11 06:08:36.082400: train_loss -0.1974 +2026-04-11 06:08:36.089614: val_loss -0.1387 +2026-04-11 06:08:36.091862: Pseudo dice [0.0731, 0.7942, 0.6929, 0.6248, 0.4504, 0.8186, 0.7514] +2026-04-11 06:08:36.093797: Epoch time: 102.45 s +2026-04-11 06:08:37.271746: +2026-04-11 06:08:37.273969: Epoch 687 +2026-04-11 06:08:37.275471: Current learning rate: 0.00844 +2026-04-11 06:10:19.322868: train_loss -0.1826 +2026-04-11 06:10:19.328982: val_loss -0.1052 +2026-04-11 06:10:19.330942: Pseudo dice [0.5433, 0.5645, 0.7541, 0.4747, 0.5454, 0.6144, 0.3973] +2026-04-11 06:10:19.333114: Epoch time: 102.05 s +2026-04-11 06:10:20.507027: +2026-04-11 06:10:20.512652: Epoch 688 +2026-04-11 06:10:20.514051: Current learning rate: 0.00844 +2026-04-11 06:12:02.719978: train_loss -0.1834 +2026-04-11 06:12:02.726176: val_loss -0.1304 +2026-04-11 06:12:02.728445: Pseudo dice [0.3922, 0.8859, 0.7642, 0.2978, 0.4166, 0.7489, 0.6823] +2026-04-11 06:12:02.740219: Epoch time: 102.22 s +2026-04-11 06:12:03.957959: +2026-04-11 06:12:03.959330: Epoch 689 +2026-04-11 06:12:03.960571: Current learning rate: 0.00844 +2026-04-11 06:13:46.327989: train_loss -0.167 +2026-04-11 06:13:46.335782: val_loss -0.143 +2026-04-11 06:13:46.337441: Pseudo dice [0.3134, 0.2152, 0.8274, 0.2909, 0.4677, 0.866, 0.6986] +2026-04-11 06:13:46.341433: Epoch time: 102.37 s +2026-04-11 06:13:47.502164: +2026-04-11 06:13:47.503884: Epoch 690 +2026-04-11 06:13:47.514145: Current learning rate: 0.00843 +2026-04-11 06:15:29.563880: train_loss -0.1775 +2026-04-11 06:15:29.571619: val_loss -0.1624 +2026-04-11 06:15:29.573862: Pseudo dice [0.8167, 0.617, 0.7219, 0.3199, 0.4216, 0.5293, 0.4997] +2026-04-11 06:15:29.576967: Epoch time: 102.06 s +2026-04-11 06:15:30.736444: +2026-04-11 06:15:30.738897: Epoch 691 +2026-04-11 06:15:30.740181: Current learning rate: 0.00843 +2026-04-11 06:17:13.056834: train_loss -0.1708 +2026-04-11 06:17:13.062446: val_loss -0.0964 +2026-04-11 06:17:13.064093: Pseudo dice [0.4072, 0.1031, 0.6331, 0.4219, 0.36, 0.2352, 0.6498] +2026-04-11 06:17:13.066384: Epoch time: 102.32 s +2026-04-11 06:17:14.223775: +2026-04-11 06:17:14.225325: Epoch 692 +2026-04-11 06:17:14.226698: Current learning rate: 0.00843 +2026-04-11 06:18:56.377509: train_loss -0.1702 +2026-04-11 06:18:56.383629: val_loss -0.1559 +2026-04-11 06:18:56.385312: Pseudo dice [0.4563, 0.8714, 0.81, 0.63, 0.5867, 0.6954, 0.5959] +2026-04-11 06:18:56.387135: Epoch time: 102.16 s +2026-04-11 06:18:57.554992: +2026-04-11 06:18:57.556422: Epoch 693 +2026-04-11 06:18:57.558395: Current learning rate: 0.00843 +2026-04-11 06:20:39.858244: train_loss -0.1862 +2026-04-11 06:20:39.863714: val_loss -0.1318 +2026-04-11 06:20:39.865880: Pseudo dice [0.6963, 0.3661, 0.69, 0.3765, 0.5273, 0.5787, 0.8367] +2026-04-11 06:20:39.868341: Epoch time: 102.31 s +2026-04-11 06:20:41.013993: +2026-04-11 06:20:41.015666: Epoch 694 +2026-04-11 06:20:41.017230: Current learning rate: 0.00842 +2026-04-11 06:22:23.211867: train_loss -0.1642 +2026-04-11 06:22:23.217668: val_loss -0.1375 +2026-04-11 06:22:23.219354: Pseudo dice [0.5307, 0.8049, 0.7676, 0.3054, 0.3082, 0.2892, 0.6184] +2026-04-11 06:22:23.221069: Epoch time: 102.2 s +2026-04-11 06:22:24.375599: +2026-04-11 06:22:24.376987: Epoch 695 +2026-04-11 06:22:24.378242: Current learning rate: 0.00842 +2026-04-11 06:24:05.710804: train_loss -0.1708 +2026-04-11 06:24:05.716192: val_loss -0.133 +2026-04-11 06:24:05.718096: Pseudo dice [0.4523, 0.1195, 0.7379, 0.2068, 0.4673, 0.5818, 0.6224] +2026-04-11 06:24:05.720057: Epoch time: 101.34 s +2026-04-11 06:24:06.939706: +2026-04-11 06:24:06.941277: Epoch 696 +2026-04-11 06:24:06.942969: Current learning rate: 0.00842 +2026-04-11 06:25:48.726512: train_loss -0.1773 +2026-04-11 06:25:48.732159: val_loss -0.1413 +2026-04-11 06:25:48.734893: Pseudo dice [0.5958, 0.7197, 0.7313, 0.1694, 0.4105, 0.327, 0.6864] +2026-04-11 06:25:48.737259: Epoch time: 101.79 s +2026-04-11 06:25:49.882457: +2026-04-11 06:25:49.884114: Epoch 697 +2026-04-11 06:25:49.885580: Current learning rate: 0.00842 +2026-04-11 06:27:31.445056: train_loss -0.1623 +2026-04-11 06:27:31.451195: val_loss -0.1435 +2026-04-11 06:27:31.453184: Pseudo dice [0.5808, 0.8043, 0.7342, 0.6256, 0.5327, 0.4806, 0.4354] +2026-04-11 06:27:31.455594: Epoch time: 101.57 s +2026-04-11 06:27:32.624276: +2026-04-11 06:27:32.626216: Epoch 698 +2026-04-11 06:27:32.628036: Current learning rate: 0.00841 +2026-04-11 06:29:13.933365: train_loss -0.1833 +2026-04-11 06:29:13.939436: val_loss -0.1089 +2026-04-11 06:29:13.941477: Pseudo dice [0.6258, 0.7394, 0.6899, 0.6342, 0.2233, 0.737, 0.7569] +2026-04-11 06:29:13.943141: Epoch time: 101.31 s +2026-04-11 06:29:15.105242: +2026-04-11 06:29:15.106882: Epoch 699 +2026-04-11 06:29:15.111364: Current learning rate: 0.00841 +2026-04-11 06:30:56.673778: train_loss -0.1758 +2026-04-11 06:30:56.679385: val_loss -0.1282 +2026-04-11 06:30:56.681132: Pseudo dice [0.4591, 0.8633, 0.6065, 0.2606, 0.4344, 0.688, 0.6309] +2026-04-11 06:30:56.683359: Epoch time: 101.57 s +2026-04-11 06:30:59.592787: +2026-04-11 06:30:59.594709: Epoch 700 +2026-04-11 06:30:59.596201: Current learning rate: 0.00841 +2026-04-11 06:32:42.081000: train_loss -0.1609 +2026-04-11 06:32:42.087884: val_loss -0.1525 +2026-04-11 06:32:42.089975: Pseudo dice [0.3819, 0.683, 0.6871, 0.5086, 0.6394, 0.7675, 0.7509] +2026-04-11 06:32:42.092383: Epoch time: 102.49 s +2026-04-11 06:32:43.320643: +2026-04-11 06:32:43.323600: Epoch 701 +2026-04-11 06:32:43.325947: Current learning rate: 0.00841 +2026-04-11 06:34:25.015647: train_loss -0.1806 +2026-04-11 06:34:25.022888: val_loss -0.1023 +2026-04-11 06:34:25.025779: Pseudo dice [0.0876, 0.2522, 0.4062, 0.5104, 0.3376, 0.5949, 0.2972] +2026-04-11 06:34:25.028604: Epoch time: 101.7 s +2026-04-11 06:34:26.264374: +2026-04-11 06:34:26.266668: Epoch 702 +2026-04-11 06:34:26.268131: Current learning rate: 0.00841 +2026-04-11 06:36:07.451900: train_loss -0.1819 +2026-04-11 06:36:07.458835: val_loss -0.147 +2026-04-11 06:36:07.460959: Pseudo dice [0.6825, 0.0682, 0.6934, 0.5374, 0.3658, 0.6649, 0.4454] +2026-04-11 06:36:07.463056: Epoch time: 101.19 s +2026-04-11 06:36:08.612782: +2026-04-11 06:36:08.615146: Epoch 703 +2026-04-11 06:36:08.616706: Current learning rate: 0.0084 +2026-04-11 06:37:50.230012: train_loss -0.1755 +2026-04-11 06:37:50.235901: val_loss -0.1651 +2026-04-11 06:37:50.237796: Pseudo dice [0.4514, 0.5521, 0.738, 0.7056, 0.4599, 0.3781, 0.8483] +2026-04-11 06:37:50.239916: Epoch time: 101.62 s +2026-04-11 06:37:51.390724: +2026-04-11 06:37:51.392788: Epoch 704 +2026-04-11 06:37:51.394284: Current learning rate: 0.0084 +2026-04-11 06:39:32.892105: train_loss -0.1877 +2026-04-11 06:39:32.898006: val_loss -0.1397 +2026-04-11 06:39:32.899515: Pseudo dice [0.3497, 0.8939, 0.6207, 0.5713, 0.5694, 0.6068, 0.7877] +2026-04-11 06:39:32.901645: Epoch time: 101.5 s +2026-04-11 06:39:34.100733: +2026-04-11 06:39:34.102236: Epoch 705 +2026-04-11 06:39:34.103652: Current learning rate: 0.0084 +2026-04-11 06:41:15.782926: train_loss -0.1692 +2026-04-11 06:41:15.789509: val_loss -0.1007 +2026-04-11 06:41:15.791424: Pseudo dice [0.4361, 0.6672, 0.6559, 0.4893, 0.2426, 0.7123, 0.2675] +2026-04-11 06:41:15.794312: Epoch time: 101.69 s +2026-04-11 06:41:16.991088: +2026-04-11 06:41:16.992913: Epoch 706 +2026-04-11 06:41:16.994591: Current learning rate: 0.0084 +2026-04-11 06:42:58.473043: train_loss -0.1751 +2026-04-11 06:42:58.480783: val_loss -0.1367 +2026-04-11 06:42:58.482862: Pseudo dice [0.1831, 0.8168, 0.695, 0.5601, 0.2327, 0.4718, 0.7575] +2026-04-11 06:42:58.485798: Epoch time: 101.49 s +2026-04-11 06:42:59.697122: +2026-04-11 06:42:59.698515: Epoch 707 +2026-04-11 06:42:59.699873: Current learning rate: 0.00839 +2026-04-11 06:44:41.234730: train_loss -0.1839 +2026-04-11 06:44:41.240781: val_loss -0.1376 +2026-04-11 06:44:41.242877: Pseudo dice [0.7067, 0.6914, 0.7273, 0.0652, 0.4419, 0.3427, 0.7728] +2026-04-11 06:44:41.245318: Epoch time: 101.54 s +2026-04-11 06:44:42.432790: +2026-04-11 06:44:42.434922: Epoch 708 +2026-04-11 06:44:42.436752: Current learning rate: 0.00839 +2026-04-11 06:46:23.997413: train_loss -0.1703 +2026-04-11 06:46:24.003449: val_loss -0.0968 +2026-04-11 06:46:24.005382: Pseudo dice [0.4489, 0.6382, 0.7058, 0.4329, 0.3255, 0.382, 0.5249] +2026-04-11 06:46:24.007856: Epoch time: 101.57 s +2026-04-11 06:46:25.182781: +2026-04-11 06:46:25.184323: Epoch 709 +2026-04-11 06:46:25.187362: Current learning rate: 0.00839 +2026-04-11 06:48:06.805083: train_loss -0.1687 +2026-04-11 06:48:06.811110: val_loss -0.1474 +2026-04-11 06:48:06.812886: Pseudo dice [0.6199, 0.695, 0.6684, 0.3467, 0.4532, 0.7897, 0.7496] +2026-04-11 06:48:06.814902: Epoch time: 101.63 s +2026-04-11 06:48:08.013143: +2026-04-11 06:48:08.014796: Epoch 710 +2026-04-11 06:48:08.016242: Current learning rate: 0.00839 +2026-04-11 06:49:49.547560: train_loss -0.1704 +2026-04-11 06:49:49.554309: val_loss -0.0959 +2026-04-11 06:49:49.556026: Pseudo dice [0.6853, 0.7357, 0.3152, 0.3787, 0.4011, 0.3701, 0.7503] +2026-04-11 06:49:49.559047: Epoch time: 101.54 s +2026-04-11 06:49:50.746361: +2026-04-11 06:49:50.747834: Epoch 711 +2026-04-11 06:49:50.749095: Current learning rate: 0.00839 +2026-04-11 06:51:32.253752: train_loss -0.1759 +2026-04-11 06:51:32.260073: val_loss -0.1396 +2026-04-11 06:51:32.262691: Pseudo dice [0.602, 0.4368, 0.7138, 0.0091, 0.3989, 0.8333, 0.644] +2026-04-11 06:51:32.264778: Epoch time: 101.51 s +2026-04-11 06:51:33.490668: +2026-04-11 06:51:33.492331: Epoch 712 +2026-04-11 06:51:33.493788: Current learning rate: 0.00838 +2026-04-11 06:53:14.878866: train_loss -0.1741 +2026-04-11 06:53:14.885179: val_loss -0.1694 +2026-04-11 06:53:14.887209: Pseudo dice [0.7066, 0.848, 0.5331, 0.7169, 0.5482, 0.8296, 0.7775] +2026-04-11 06:53:14.889649: Epoch time: 101.39 s +2026-04-11 06:53:16.074726: +2026-04-11 06:53:16.076099: Epoch 713 +2026-04-11 06:53:16.077346: Current learning rate: 0.00838 +2026-04-11 06:54:57.461366: train_loss -0.1719 +2026-04-11 06:54:57.467673: val_loss -0.1276 +2026-04-11 06:54:57.469691: Pseudo dice [0.4862, 0.7456, 0.6392, 0.365, 0.6422, 0.5615, 0.7718] +2026-04-11 06:54:57.471982: Epoch time: 101.39 s +2026-04-11 06:54:58.635318: +2026-04-11 06:54:58.637677: Epoch 714 +2026-04-11 06:54:58.640095: Current learning rate: 0.00838 +2026-04-11 06:56:39.988817: train_loss -0.1811 +2026-04-11 06:56:39.995081: val_loss -0.148 +2026-04-11 06:56:39.996862: Pseudo dice [0.5286, 0.6431, 0.7191, 0.13, 0.4476, 0.6593, 0.8443] +2026-04-11 06:56:39.999307: Epoch time: 101.36 s +2026-04-11 06:56:41.140103: +2026-04-11 06:56:41.141856: Epoch 715 +2026-04-11 06:56:41.143563: Current learning rate: 0.00838 +2026-04-11 06:58:22.716463: train_loss -0.1831 +2026-04-11 06:58:22.722640: val_loss -0.108 +2026-04-11 06:58:22.724402: Pseudo dice [0.6833, 0.5121, 0.6429, 0.4323, 0.5152, 0.8412, 0.6791] +2026-04-11 06:58:22.726532: Epoch time: 101.58 s +2026-04-11 06:58:23.954798: +2026-04-11 06:58:23.956342: Epoch 716 +2026-04-11 06:58:23.957666: Current learning rate: 0.00837 +2026-04-11 07:00:05.582611: train_loss -0.1751 +2026-04-11 07:00:05.589118: val_loss -0.1256 +2026-04-11 07:00:05.591598: Pseudo dice [0.595, 0.3926, 0.7738, 0.4088, 0.378, 0.84, 0.838] +2026-04-11 07:00:05.595380: Epoch time: 101.63 s +2026-04-11 07:00:06.807824: +2026-04-11 07:00:06.809513: Epoch 717 +2026-04-11 07:00:06.811053: Current learning rate: 0.00837 +2026-04-11 07:01:48.604393: train_loss -0.1617 +2026-04-11 07:01:48.610754: val_loss -0.1022 +2026-04-11 07:01:48.613054: Pseudo dice [0.4597, 0.846, 0.5651, 0.3289, 0.257, 0.3486, 0.4092] +2026-04-11 07:01:48.615299: Epoch time: 101.8 s +2026-04-11 07:01:49.805117: +2026-04-11 07:01:49.806612: Epoch 718 +2026-04-11 07:01:49.807933: Current learning rate: 0.00837 +2026-04-11 07:03:31.443702: train_loss -0.1706 +2026-04-11 07:03:31.449639: val_loss -0.128 +2026-04-11 07:03:31.451687: Pseudo dice [0.6774, 0.576, 0.6362, 0.4086, 0.7225, 0.6518, 0.7792] +2026-04-11 07:03:31.454301: Epoch time: 101.64 s +2026-04-11 07:03:32.662596: +2026-04-11 07:03:32.663996: Epoch 719 +2026-04-11 07:03:32.665303: Current learning rate: 0.00837 +2026-04-11 07:05:13.844259: train_loss -0.1697 +2026-04-11 07:05:13.850497: val_loss -0.1533 +2026-04-11 07:05:13.852509: Pseudo dice [0.6009, 0.7573, 0.7113, 0.5235, 0.5279, 0.7514, 0.8336] +2026-04-11 07:05:13.855041: Epoch time: 101.18 s +2026-04-11 07:05:16.099658: +2026-04-11 07:05:16.101289: Epoch 720 +2026-04-11 07:05:16.102655: Current learning rate: 0.00836 +2026-04-11 07:06:57.745722: train_loss -0.1801 +2026-04-11 07:06:57.751894: val_loss -0.1481 +2026-04-11 07:06:57.753365: Pseudo dice [0.2308, 0.1843, 0.7609, 0.5483, 0.4331, 0.6772, 0.7381] +2026-04-11 07:06:57.755277: Epoch time: 101.65 s +2026-04-11 07:06:58.921727: +2026-04-11 07:06:58.923493: Epoch 721 +2026-04-11 07:06:58.925031: Current learning rate: 0.00836 +2026-04-11 07:08:40.460818: train_loss -0.1658 +2026-04-11 07:08:40.466612: val_loss -0.1193 +2026-04-11 07:08:40.468590: Pseudo dice [0.7719, 0.801, 0.5509, 0.2715, 0.394, 0.3201, 0.7664] +2026-04-11 07:08:40.470749: Epoch time: 101.54 s +2026-04-11 07:08:41.652576: +2026-04-11 07:08:41.654149: Epoch 722 +2026-04-11 07:08:41.656132: Current learning rate: 0.00836 +2026-04-11 07:10:23.168409: train_loss -0.1823 +2026-04-11 07:10:23.174086: val_loss -0.1544 +2026-04-11 07:10:23.175817: Pseudo dice [0.3284, 0.6918, 0.7048, 0.5047, 0.572, 0.8655, 0.7931] +2026-04-11 07:10:23.178958: Epoch time: 101.52 s +2026-04-11 07:10:24.367751: +2026-04-11 07:10:24.369800: Epoch 723 +2026-04-11 07:10:24.371190: Current learning rate: 0.00836 +2026-04-11 07:12:06.064373: train_loss -0.1814 +2026-04-11 07:12:06.071257: val_loss -0.1311 +2026-04-11 07:12:06.074154: Pseudo dice [0.8128, 0.3272, 0.7221, 0.5077, 0.4774, 0.8955, 0.8404] +2026-04-11 07:12:06.076937: Epoch time: 101.7 s +2026-04-11 07:12:07.294313: +2026-04-11 07:12:07.296320: Epoch 724 +2026-04-11 07:12:07.297569: Current learning rate: 0.00836 +2026-04-11 07:13:48.907956: train_loss -0.1794 +2026-04-11 07:13:48.913612: val_loss -0.1142 +2026-04-11 07:13:48.915702: Pseudo dice [0.4383, 0.2331, 0.5565, 0.5657, 0.5215, 0.2627, 0.631] +2026-04-11 07:13:48.917715: Epoch time: 101.62 s +2026-04-11 07:13:50.103161: +2026-04-11 07:13:50.104656: Epoch 725 +2026-04-11 07:13:50.106067: Current learning rate: 0.00835 +2026-04-11 07:15:31.733618: train_loss -0.1833 +2026-04-11 07:15:31.739413: val_loss -0.1309 +2026-04-11 07:15:31.741697: Pseudo dice [0.6939, 0.7245, 0.7948, 0.7256, 0.1853, 0.6526, 0.1816] +2026-04-11 07:15:31.743799: Epoch time: 101.63 s +2026-04-11 07:15:32.926945: +2026-04-11 07:15:32.928793: Epoch 726 +2026-04-11 07:15:32.930323: Current learning rate: 0.00835 +2026-04-11 07:17:14.459458: train_loss -0.175 +2026-04-11 07:17:14.465383: val_loss -0.1395 +2026-04-11 07:17:14.469737: Pseudo dice [0.5185, 0.7942, 0.7928, 0.1584, 0.2429, 0.6888, 0.4937] +2026-04-11 07:17:14.471982: Epoch time: 101.54 s +2026-04-11 07:17:15.659028: +2026-04-11 07:17:15.660457: Epoch 727 +2026-04-11 07:17:15.661834: Current learning rate: 0.00835 +2026-04-11 07:18:57.549095: train_loss -0.1753 +2026-04-11 07:18:57.555402: val_loss -0.1171 +2026-04-11 07:18:57.557409: Pseudo dice [0.4795, 0.525, 0.6472, 0.2421, 0.3891, 0.6065, 0.8073] +2026-04-11 07:18:57.559363: Epoch time: 101.89 s +2026-04-11 07:18:58.748431: +2026-04-11 07:18:58.750959: Epoch 728 +2026-04-11 07:18:58.752505: Current learning rate: 0.00835 +2026-04-11 07:20:40.621110: train_loss -0.1563 +2026-04-11 07:20:40.626721: val_loss -0.0973 +2026-04-11 07:20:40.630051: Pseudo dice [0.3857, 0.617, 0.6921, 0.3655, 0.3654, 0.3342, 0.5458] +2026-04-11 07:20:40.632244: Epoch time: 101.88 s +2026-04-11 07:20:41.810203: +2026-04-11 07:20:41.811591: Epoch 729 +2026-04-11 07:20:41.812958: Current learning rate: 0.00834 +2026-04-11 07:22:23.318694: train_loss -0.1686 +2026-04-11 07:22:23.323968: val_loss -0.1615 +2026-04-11 07:22:23.326043: Pseudo dice [0.6088, 0.4569, 0.715, 0.7405, 0.4165, 0.7181, 0.5851] +2026-04-11 07:22:23.328425: Epoch time: 101.51 s +2026-04-11 07:22:24.534411: +2026-04-11 07:22:24.536568: Epoch 730 +2026-04-11 07:22:24.538872: Current learning rate: 0.00834 +2026-04-11 07:24:05.996767: train_loss -0.1612 +2026-04-11 07:24:06.002590: val_loss -0.0926 +2026-04-11 07:24:06.004148: Pseudo dice [0.2205, 0.2878, 0.6807, 0.4336, 0.1664, 0.1667, 0.4518] +2026-04-11 07:24:06.006366: Epoch time: 101.47 s +2026-04-11 07:24:07.141946: +2026-04-11 07:24:07.143461: Epoch 731 +2026-04-11 07:24:07.144766: Current learning rate: 0.00834 +2026-04-11 07:25:48.499726: train_loss -0.1728 +2026-04-11 07:25:48.505521: val_loss -0.1306 +2026-04-11 07:25:48.507369: Pseudo dice [0.3974, 0.7894, 0.7463, 0.6065, 0.3986, 0.7019, 0.7977] +2026-04-11 07:25:48.509240: Epoch time: 101.36 s +2026-04-11 07:25:49.690280: +2026-04-11 07:25:49.691828: Epoch 732 +2026-04-11 07:25:49.693260: Current learning rate: 0.00834 +2026-04-11 07:27:31.066894: train_loss -0.1774 +2026-04-11 07:27:31.092102: val_loss -0.1098 +2026-04-11 07:27:31.093988: Pseudo dice [0.2599, 0.9053, 0.6298, 0.801, 0.5883, 0.7028, 0.8313] +2026-04-11 07:27:31.096281: Epoch time: 101.38 s +2026-04-11 07:27:32.272218: +2026-04-11 07:27:32.273702: Epoch 733 +2026-04-11 07:27:32.274989: Current learning rate: 0.00833 +2026-04-11 07:29:13.661196: train_loss -0.1681 +2026-04-11 07:29:13.668709: val_loss -0.1083 +2026-04-11 07:29:13.672602: Pseudo dice [0.3818, 0.895, 0.6629, 0.1793, 0.4593, 0.1704, 0.3164] +2026-04-11 07:29:13.677046: Epoch time: 101.39 s +2026-04-11 07:29:14.857295: +2026-04-11 07:29:14.859070: Epoch 734 +2026-04-11 07:29:14.861211: Current learning rate: 0.00833 +2026-04-11 07:30:56.442852: train_loss -0.1685 +2026-04-11 07:30:56.450422: val_loss -0.1577 +2026-04-11 07:30:56.453575: Pseudo dice [0.4442, 0.466, 0.6747, 0.7349, 0.4127, 0.7193, 0.8252] +2026-04-11 07:30:56.455931: Epoch time: 101.59 s +2026-04-11 07:30:57.651318: +2026-04-11 07:30:57.653291: Epoch 735 +2026-04-11 07:30:57.654627: Current learning rate: 0.00833 +2026-04-11 07:32:38.967210: train_loss -0.1765 +2026-04-11 07:32:38.973368: val_loss -0.1244 +2026-04-11 07:32:38.975203: Pseudo dice [0.2272, 0.7048, 0.5955, 0.4464, 0.3167, 0.7769, 0.7182] +2026-04-11 07:32:38.978734: Epoch time: 101.32 s +2026-04-11 07:32:40.145130: +2026-04-11 07:32:40.147344: Epoch 736 +2026-04-11 07:32:40.148670: Current learning rate: 0.00833 +2026-04-11 07:34:21.679350: train_loss -0.1907 +2026-04-11 07:34:21.685044: val_loss -0.1443 +2026-04-11 07:34:21.686905: Pseudo dice [0.5967, 0.1728, 0.7858, 0.5688, 0.5224, 0.5937, 0.6489] +2026-04-11 07:34:21.689274: Epoch time: 101.54 s +2026-04-11 07:34:22.867578: +2026-04-11 07:34:22.869415: Epoch 737 +2026-04-11 07:34:22.870928: Current learning rate: 0.00833 +2026-04-11 07:36:04.726134: train_loss -0.1768 +2026-04-11 07:36:04.731968: val_loss -0.1414 +2026-04-11 07:36:04.733836: Pseudo dice [0.4586, 0.8006, 0.5451, 0.5657, 0.6906, 0.8972, 0.6106] +2026-04-11 07:36:04.736439: Epoch time: 101.86 s +2026-04-11 07:36:05.924288: +2026-04-11 07:36:05.926747: Epoch 738 +2026-04-11 07:36:05.929342: Current learning rate: 0.00832 +2026-04-11 07:37:47.812353: train_loss -0.1757 +2026-04-11 07:37:47.821717: val_loss -0.148 +2026-04-11 07:37:47.824312: Pseudo dice [0.2378, 0.393, 0.7548, 0.4722, 0.4322, 0.5112, 0.715] +2026-04-11 07:37:47.826972: Epoch time: 101.89 s +2026-04-11 07:37:49.035409: +2026-04-11 07:37:49.037660: Epoch 739 +2026-04-11 07:37:49.039604: Current learning rate: 0.00832 +2026-04-11 07:39:32.338936: train_loss -0.1836 +2026-04-11 07:39:32.346353: val_loss -0.1289 +2026-04-11 07:39:32.348695: Pseudo dice [0.5681, 0.253, 0.7423, 0.4632, 0.5405, 0.891, 0.8358] +2026-04-11 07:39:32.351082: Epoch time: 103.31 s +2026-04-11 07:39:33.584307: +2026-04-11 07:39:33.585727: Epoch 740 +2026-04-11 07:39:33.587103: Current learning rate: 0.00832 +2026-04-11 07:41:15.201355: train_loss -0.1676 +2026-04-11 07:41:15.210842: val_loss -0.0633 +2026-04-11 07:41:15.212994: Pseudo dice [0.4728, 0.309, 0.4215, 0.2643, 0.4072, 0.5147, 0.7896] +2026-04-11 07:41:15.215553: Epoch time: 101.62 s +2026-04-11 07:41:16.485270: +2026-04-11 07:41:16.487267: Epoch 741 +2026-04-11 07:41:16.488715: Current learning rate: 0.00832 +2026-04-11 07:42:58.276604: train_loss -0.172 +2026-04-11 07:42:58.282881: val_loss -0.1296 +2026-04-11 07:42:58.285889: Pseudo dice [0.416, 0.8725, 0.6071, 0.2011, 0.407, 0.4712, 0.8094] +2026-04-11 07:42:58.288514: Epoch time: 101.79 s +2026-04-11 07:42:59.577756: +2026-04-11 07:42:59.579406: Epoch 742 +2026-04-11 07:42:59.581234: Current learning rate: 0.00831 +2026-04-11 07:44:41.673791: train_loss -0.1842 +2026-04-11 07:44:41.680666: val_loss -0.1226 +2026-04-11 07:44:41.682818: Pseudo dice [0.7933, 0.8404, 0.5732, 0.4425, 0.3534, 0.2667, 0.726] +2026-04-11 07:44:41.685285: Epoch time: 102.1 s +2026-04-11 07:44:42.885884: +2026-04-11 07:44:42.887778: Epoch 743 +2026-04-11 07:44:42.889343: Current learning rate: 0.00831 +2026-04-11 07:46:24.380594: train_loss -0.1705 +2026-04-11 07:46:24.386750: val_loss -0.1675 +2026-04-11 07:46:24.388887: Pseudo dice [0.4709, 0.4249, 0.8159, 0.5062, 0.4815, 0.9094, 0.744] +2026-04-11 07:46:24.391057: Epoch time: 101.5 s +2026-04-11 07:46:25.596256: +2026-04-11 07:46:25.598047: Epoch 744 +2026-04-11 07:46:25.599565: Current learning rate: 0.00831 +2026-04-11 07:48:07.557983: train_loss -0.1889 +2026-04-11 07:48:07.564932: val_loss -0.1407 +2026-04-11 07:48:07.567364: Pseudo dice [0.2709, 0.6025, 0.587, 0.5697, 0.3282, 0.9001, 0.744] +2026-04-11 07:48:07.569442: Epoch time: 101.96 s +2026-04-11 07:48:08.744126: +2026-04-11 07:48:08.746820: Epoch 745 +2026-04-11 07:48:08.748410: Current learning rate: 0.00831 +2026-04-11 07:49:50.668113: train_loss -0.1791 +2026-04-11 07:49:50.674913: val_loss -0.14 +2026-04-11 07:49:50.678812: Pseudo dice [0.4337, 0.5769, 0.7707, 0.3384, 0.5401, 0.852, 0.6709] +2026-04-11 07:49:50.681141: Epoch time: 101.93 s +2026-04-11 07:49:51.848783: +2026-04-11 07:49:51.851087: Epoch 746 +2026-04-11 07:49:51.852604: Current learning rate: 0.0083 +2026-04-11 07:51:33.391089: train_loss -0.1758 +2026-04-11 07:51:33.397191: val_loss -0.1263 +2026-04-11 07:51:33.399069: Pseudo dice [0.251, 0.5312, 0.5507, 0.4197, 0.3864, 0.6059, 0.7744] +2026-04-11 07:51:33.401030: Epoch time: 101.55 s +2026-04-11 07:51:34.586585: +2026-04-11 07:51:34.601098: Epoch 747 +2026-04-11 07:51:34.611711: Current learning rate: 0.0083 +2026-04-11 07:53:16.353989: train_loss -0.1541 +2026-04-11 07:53:16.359643: val_loss -0.1587 +2026-04-11 07:53:16.361527: Pseudo dice [0.6548, 0.2855, 0.7823, 0.6073, 0.4039, 0.8454, 0.6558] +2026-04-11 07:53:16.363508: Epoch time: 101.77 s +2026-04-11 07:53:17.537828: +2026-04-11 07:53:17.539281: Epoch 748 +2026-04-11 07:53:17.540601: Current learning rate: 0.0083 +2026-04-11 07:54:59.197707: train_loss -0.1735 +2026-04-11 07:54:59.204823: val_loss -0.1205 +2026-04-11 07:54:59.206900: Pseudo dice [0.5127, 0.1384, 0.6198, 0.6197, 0.3842, 0.8111, 0.4933] +2026-04-11 07:54:59.209419: Epoch time: 101.66 s +2026-04-11 07:55:00.370388: +2026-04-11 07:55:00.371887: Epoch 749 +2026-04-11 07:55:00.373259: Current learning rate: 0.0083 +2026-04-11 07:56:42.250316: train_loss -0.1827 +2026-04-11 07:56:42.256124: val_loss -0.141 +2026-04-11 07:56:42.258461: Pseudo dice [0.6727, 0.4462, 0.723, 0.7421, 0.5887, 0.7648, 0.7176] +2026-04-11 07:56:42.260878: Epoch time: 101.88 s +2026-04-11 07:56:45.165136: +2026-04-11 07:56:45.166694: Epoch 750 +2026-04-11 07:56:45.168332: Current learning rate: 0.0083 +2026-04-11 07:58:26.836052: train_loss -0.1782 +2026-04-11 07:58:26.844843: val_loss -0.1516 +2026-04-11 07:58:26.846848: Pseudo dice [0.6298, 0.8345, 0.6947, 0.6267, 0.5318, 0.0783, 0.874] +2026-04-11 07:58:26.849177: Epoch time: 101.67 s +2026-04-11 07:58:28.092116: +2026-04-11 07:58:28.094186: Epoch 751 +2026-04-11 07:58:28.096255: Current learning rate: 0.00829 +2026-04-11 08:00:09.938704: train_loss -0.1754 +2026-04-11 08:00:09.946695: val_loss -0.132 +2026-04-11 08:00:09.949298: Pseudo dice [0.084, 0.4339, 0.7372, 0.5809, 0.4392, 0.3779, 0.4137] +2026-04-11 08:00:09.951911: Epoch time: 101.85 s +2026-04-11 08:00:11.168296: +2026-04-11 08:00:11.170277: Epoch 752 +2026-04-11 08:00:11.172275: Current learning rate: 0.00829 +2026-04-11 08:01:52.805038: train_loss -0.1822 +2026-04-11 08:01:52.810667: val_loss -0.1508 +2026-04-11 08:01:52.812846: Pseudo dice [0.5057, 0.8781, 0.6954, 0.4882, 0.5327, 0.5429, 0.8655] +2026-04-11 08:01:52.815302: Epoch time: 101.64 s +2026-04-11 08:01:53.979743: +2026-04-11 08:01:53.981417: Epoch 753 +2026-04-11 08:01:53.982868: Current learning rate: 0.00829 +2026-04-11 08:03:35.711403: train_loss -0.1709 +2026-04-11 08:03:35.718546: val_loss -0.1129 +2026-04-11 08:03:35.720324: Pseudo dice [0.7145, 0.8082, 0.6974, 0.3839, 0.1877, 0.5293, 0.3581] +2026-04-11 08:03:35.722700: Epoch time: 101.73 s +2026-04-11 08:03:36.876119: +2026-04-11 08:03:36.878160: Epoch 754 +2026-04-11 08:03:36.880713: Current learning rate: 0.00829 +2026-04-11 08:05:18.240131: train_loss -0.1773 +2026-04-11 08:05:18.246870: val_loss -0.1343 +2026-04-11 08:05:18.248849: Pseudo dice [0.2378, 0.8038, 0.6315, 0.0352, 0.3367, 0.0933, 0.7771] +2026-04-11 08:05:18.251388: Epoch time: 101.37 s +2026-04-11 08:05:19.436401: +2026-04-11 08:05:19.438330: Epoch 755 +2026-04-11 08:05:19.439915: Current learning rate: 0.00828 +2026-04-11 08:07:01.172007: train_loss -0.1873 +2026-04-11 08:07:01.178424: val_loss -0.1717 +2026-04-11 08:07:01.180970: Pseudo dice [0.7429, 0.2059, 0.7439, 0.448, 0.3812, 0.3411, 0.7809] +2026-04-11 08:07:01.183338: Epoch time: 101.74 s +2026-04-11 08:07:02.404694: +2026-04-11 08:07:02.406510: Epoch 756 +2026-04-11 08:07:02.407953: Current learning rate: 0.00828 +2026-04-11 08:08:43.978174: train_loss -0.1775 +2026-04-11 08:08:43.984482: val_loss -0.1239 +2026-04-11 08:08:43.986761: Pseudo dice [0.1113, 0.6215, 0.7767, 0.3499, 0.449, 0.217, 0.7871] +2026-04-11 08:08:43.989830: Epoch time: 101.58 s +2026-04-11 08:08:45.175280: +2026-04-11 08:08:45.177044: Epoch 757 +2026-04-11 08:08:45.178562: Current learning rate: 0.00828 +2026-04-11 08:10:26.839324: train_loss -0.1789 +2026-04-11 08:10:26.851017: val_loss -0.1201 +2026-04-11 08:10:26.852654: Pseudo dice [0.4086, 0.8045, 0.6224, 0.2519, 0.3722, 0.5756, 0.5672] +2026-04-11 08:10:26.855352: Epoch time: 101.67 s +2026-04-11 08:10:28.048587: +2026-04-11 08:10:28.050486: Epoch 758 +2026-04-11 08:10:28.052150: Current learning rate: 0.00828 +2026-04-11 08:12:11.340898: train_loss -0.1824 +2026-04-11 08:12:11.346982: val_loss -0.1433 +2026-04-11 08:12:11.349711: Pseudo dice [0.5763, 0.585, 0.7546, 0.3194, 0.391, 0.592, 0.7228] +2026-04-11 08:12:11.352469: Epoch time: 103.3 s +2026-04-11 08:12:12.587310: +2026-04-11 08:12:12.589411: Epoch 759 +2026-04-11 08:12:12.591451: Current learning rate: 0.00827 +2026-04-11 08:13:54.361511: train_loss -0.1791 +2026-04-11 08:13:54.367528: val_loss -0.1319 +2026-04-11 08:13:54.369805: Pseudo dice [0.1673, 0.4415, 0.724, 0.33, 0.4021, 0.4611, 0.6885] +2026-04-11 08:13:54.374079: Epoch time: 101.78 s +2026-04-11 08:13:55.554236: +2026-04-11 08:13:55.556909: Epoch 760 +2026-04-11 08:13:55.558740: Current learning rate: 0.00827 +2026-04-11 08:15:37.782332: train_loss -0.1787 +2026-04-11 08:15:37.788468: val_loss -0.1548 +2026-04-11 08:15:37.793077: Pseudo dice [0.4342, 0.4698, 0.851, 0.2203, 0.4197, 0.8317, 0.587] +2026-04-11 08:15:37.796199: Epoch time: 102.23 s +2026-04-11 08:15:38.980409: +2026-04-11 08:15:38.982252: Epoch 761 +2026-04-11 08:15:38.983958: Current learning rate: 0.00827 +2026-04-11 08:17:21.025943: train_loss -0.1812 +2026-04-11 08:17:21.031533: val_loss -0.1445 +2026-04-11 08:17:21.033257: Pseudo dice [0.6133, 0.1891, 0.6929, 0.3489, 0.5071, 0.6946, 0.8618] +2026-04-11 08:17:21.035550: Epoch time: 102.05 s +2026-04-11 08:17:22.274153: +2026-04-11 08:17:22.275975: Epoch 762 +2026-04-11 08:17:22.278162: Current learning rate: 0.00827 +2026-04-11 08:19:03.733227: train_loss -0.1906 +2026-04-11 08:19:03.739457: val_loss -0.135 +2026-04-11 08:19:03.741604: Pseudo dice [0.4742, 0.7004, 0.6638, 0.2089, 0.4028, 0.6133, 0.6651] +2026-04-11 08:19:03.744163: Epoch time: 101.46 s +2026-04-11 08:19:04.909002: +2026-04-11 08:19:04.911228: Epoch 763 +2026-04-11 08:19:04.913163: Current learning rate: 0.00827 +2026-04-11 08:20:46.632871: train_loss -0.1764 +2026-04-11 08:20:46.640063: val_loss -0.1428 +2026-04-11 08:20:46.642697: Pseudo dice [0.7669, 0.8529, 0.6153, 0.6584, 0.2903, 0.7864, 0.6026] +2026-04-11 08:20:46.645123: Epoch time: 101.73 s +2026-04-11 08:20:47.872870: +2026-04-11 08:20:47.875085: Epoch 764 +2026-04-11 08:20:47.876839: Current learning rate: 0.00826 +2026-04-11 08:22:29.774138: train_loss -0.1798 +2026-04-11 08:22:29.779786: val_loss -0.1592 +2026-04-11 08:22:29.781753: Pseudo dice [0.3229, 0.5254, 0.8128, 0.4123, 0.22, 0.734, 0.2186] +2026-04-11 08:22:29.784148: Epoch time: 101.9 s +2026-04-11 08:22:30.989959: +2026-04-11 08:22:30.991976: Epoch 765 +2026-04-11 08:22:30.994933: Current learning rate: 0.00826 +2026-04-11 08:24:13.106247: train_loss -0.1774 +2026-04-11 08:24:13.113217: val_loss -0.1197 +2026-04-11 08:24:13.115608: Pseudo dice [0.7395, 0.455, 0.6826, 0.7991, 0.4688, 0.77, 0.7837] +2026-04-11 08:24:13.118040: Epoch time: 102.12 s +2026-04-11 08:24:14.341024: +2026-04-11 08:24:14.342925: Epoch 766 +2026-04-11 08:24:14.344810: Current learning rate: 0.00826 +2026-04-11 08:25:56.060323: train_loss -0.188 +2026-04-11 08:25:56.067254: val_loss -0.1186 +2026-04-11 08:25:56.069054: Pseudo dice [0.5951, 0.7805, 0.6827, 0.4532, 0.2383, 0.8676, 0.6997] +2026-04-11 08:25:56.071647: Epoch time: 101.72 s +2026-04-11 08:25:57.290712: +2026-04-11 08:25:57.292789: Epoch 767 +2026-04-11 08:25:57.294986: Current learning rate: 0.00826 +2026-04-11 08:27:39.672950: train_loss -0.1887 +2026-04-11 08:27:39.681302: val_loss -0.1403 +2026-04-11 08:27:39.683705: Pseudo dice [0.7033, 0.4113, 0.6106, 0.3827, 0.2884, 0.7436, 0.8285] +2026-04-11 08:27:39.686517: Epoch time: 102.39 s +2026-04-11 08:27:40.928903: +2026-04-11 08:27:40.930984: Epoch 768 +2026-04-11 08:27:40.932888: Current learning rate: 0.00825 +2026-04-11 08:29:23.466185: train_loss -0.1866 +2026-04-11 08:29:23.473557: val_loss -0.157 +2026-04-11 08:29:23.475396: Pseudo dice [0.6432, 0.7125, 0.772, 0.5306, 0.5457, 0.3752, 0.6541] +2026-04-11 08:29:23.478072: Epoch time: 102.54 s +2026-04-11 08:29:24.704427: +2026-04-11 08:29:24.706092: Epoch 769 +2026-04-11 08:29:24.707976: Current learning rate: 0.00825 +2026-04-11 08:31:08.510423: train_loss -0.17 +2026-04-11 08:31:08.516984: val_loss -0.1603 +2026-04-11 08:31:08.522371: Pseudo dice [0.3419, 0.5342, 0.7694, 0.6138, 0.4718, 0.7206, 0.8225] +2026-04-11 08:31:08.525163: Epoch time: 103.81 s +2026-04-11 08:31:09.741666: +2026-04-11 08:31:09.744040: Epoch 770 +2026-04-11 08:31:09.746038: Current learning rate: 0.00825 +2026-04-11 08:32:51.461433: train_loss -0.1845 +2026-04-11 08:32:51.468107: val_loss -0.1616 +2026-04-11 08:32:51.470159: Pseudo dice [0.4505, 0.8969, 0.6442, 0.5613, 0.4989, 0.1469, 0.7007] +2026-04-11 08:32:51.472421: Epoch time: 101.72 s +2026-04-11 08:32:52.677705: +2026-04-11 08:32:52.679796: Epoch 771 +2026-04-11 08:32:52.682399: Current learning rate: 0.00825 +2026-04-11 08:34:35.794066: train_loss -0.1773 +2026-04-11 08:34:35.801329: val_loss -0.1334 +2026-04-11 08:34:35.803498: Pseudo dice [0.3128, 0.3228, 0.6601, 0.0237, 0.5213, 0.2489, 0.7484] +2026-04-11 08:34:35.805850: Epoch time: 103.12 s +2026-04-11 08:34:37.024937: +2026-04-11 08:34:37.032742: Epoch 772 +2026-04-11 08:34:37.040014: Current learning rate: 0.00824 +2026-04-11 08:36:19.058017: train_loss -0.1801 +2026-04-11 08:36:19.066421: val_loss -0.1624 +2026-04-11 08:36:19.068506: Pseudo dice [0.491, 0.8996, 0.668, 0.4193, 0.3168, 0.8194, 0.5147] +2026-04-11 08:36:19.071786: Epoch time: 102.04 s +2026-04-11 08:36:20.274380: +2026-04-11 08:36:20.276414: Epoch 773 +2026-04-11 08:36:20.278327: Current learning rate: 0.00824 +2026-04-11 08:38:02.979710: train_loss -0.1785 +2026-04-11 08:38:02.987698: val_loss -0.1434 +2026-04-11 08:38:02.989866: Pseudo dice [0.332, 0.2485, 0.7098, 0.5066, 0.4693, 0.8573, 0.7917] +2026-04-11 08:38:02.992995: Epoch time: 102.71 s +2026-04-11 08:38:04.184585: +2026-04-11 08:38:04.188087: Epoch 774 +2026-04-11 08:38:04.191158: Current learning rate: 0.00824 +2026-04-11 08:39:46.879931: train_loss -0.1911 +2026-04-11 08:39:46.888000: val_loss -0.1501 +2026-04-11 08:39:46.890440: Pseudo dice [0.5707, 0.3275, 0.709, 0.3606, 0.5119, 0.8889, 0.7031] +2026-04-11 08:39:46.893063: Epoch time: 102.7 s +2026-04-11 08:39:48.058584: +2026-04-11 08:39:48.060669: Epoch 775 +2026-04-11 08:39:48.062758: Current learning rate: 0.00824 +2026-04-11 08:41:30.655774: train_loss -0.1891 +2026-04-11 08:41:30.662541: val_loss -0.1525 +2026-04-11 08:41:30.664726: Pseudo dice [0.3767, 0.8286, 0.7306, 0.2776, 0.5119, 0.7759, 0.7803] +2026-04-11 08:41:30.667013: Epoch time: 102.6 s +2026-04-11 08:41:31.898939: +2026-04-11 08:41:31.900758: Epoch 776 +2026-04-11 08:41:31.902787: Current learning rate: 0.00824 +2026-04-11 08:43:14.337764: train_loss -0.1891 +2026-04-11 08:43:14.344274: val_loss -0.1701 +2026-04-11 08:43:14.346670: Pseudo dice [0.592, 0.124, 0.752, 0.3454, 0.5597, 0.6664, 0.8199] +2026-04-11 08:43:14.349948: Epoch time: 102.44 s +2026-04-11 08:43:15.559498: +2026-04-11 08:43:15.561891: Epoch 777 +2026-04-11 08:43:15.563841: Current learning rate: 0.00823 +2026-04-11 08:44:58.761111: train_loss -0.1801 +2026-04-11 08:44:58.768869: val_loss -0.1469 +2026-04-11 08:44:58.780802: Pseudo dice [0.5347, 0.8312, 0.7468, 0.4528, 0.4172, 0.3278, 0.8645] +2026-04-11 08:44:58.783306: Epoch time: 103.2 s +2026-04-11 08:45:01.120183: +2026-04-11 08:45:01.121907: Epoch 778 +2026-04-11 08:45:01.123987: Current learning rate: 0.00823 +2026-04-11 08:46:43.538620: train_loss -0.1659 +2026-04-11 08:46:43.544806: val_loss -0.1161 +2026-04-11 08:46:43.547054: Pseudo dice [0.8283, 0.3603, 0.3421, 0.2244, 0.3126, 0.7958, 0.7708] +2026-04-11 08:46:43.549619: Epoch time: 102.42 s +2026-04-11 08:46:44.764244: +2026-04-11 08:46:44.768364: Epoch 779 +2026-04-11 08:46:44.771098: Current learning rate: 0.00823 +2026-04-11 08:48:27.057064: train_loss -0.1843 +2026-04-11 08:48:27.062748: val_loss -0.177 +2026-04-11 08:48:27.064948: Pseudo dice [0.3718, 0.799, 0.8044, 0.5671, 0.6231, 0.885, 0.7857] +2026-04-11 08:48:27.067497: Epoch time: 102.3 s +2026-04-11 08:48:28.259659: +2026-04-11 08:48:28.261461: Epoch 780 +2026-04-11 08:48:28.263287: Current learning rate: 0.00823 +2026-04-11 08:50:10.499037: train_loss -0.1821 +2026-04-11 08:50:10.506092: val_loss -0.1443 +2026-04-11 08:50:10.508022: Pseudo dice [0.6937, 0.3738, 0.5997, 0.7668, 0.573, 0.3574, 0.7313] +2026-04-11 08:50:10.510348: Epoch time: 102.24 s +2026-04-11 08:50:11.686113: +2026-04-11 08:50:11.687918: Epoch 781 +2026-04-11 08:50:11.689939: Current learning rate: 0.00822 +2026-04-11 08:51:53.997052: train_loss -0.1864 +2026-04-11 08:51:54.003810: val_loss -0.1331 +2026-04-11 08:51:54.005809: Pseudo dice [0.2112, 0.901, 0.7152, 0.3108, 0.6, 0.5677, 0.6011] +2026-04-11 08:51:54.008343: Epoch time: 102.31 s +2026-04-11 08:51:55.186586: +2026-04-11 08:51:55.188329: Epoch 782 +2026-04-11 08:51:55.190171: Current learning rate: 0.00822 +2026-04-11 08:53:37.621876: train_loss -0.1762 +2026-04-11 08:53:37.629073: val_loss -0.0675 +2026-04-11 08:53:37.631028: Pseudo dice [0.699, 0.366, 0.25, 0.6245, 0.4454, 0.4033, 0.707] +2026-04-11 08:53:37.633787: Epoch time: 102.44 s +2026-04-11 08:53:38.872600: +2026-04-11 08:53:38.874682: Epoch 783 +2026-04-11 08:53:38.877542: Current learning rate: 0.00822 +2026-04-11 08:55:21.237717: train_loss -0.1497 +2026-04-11 08:55:21.246640: val_loss -0.1252 +2026-04-11 08:55:21.249436: Pseudo dice [0.6011, 0.4643, 0.7526, 0.6544, 0.5887, 0.4908, 0.5615] +2026-04-11 08:55:21.252933: Epoch time: 102.37 s +2026-04-11 08:55:22.480950: +2026-04-11 08:55:22.482603: Epoch 784 +2026-04-11 08:55:22.484457: Current learning rate: 0.00822 +2026-04-11 08:57:04.612423: train_loss -0.1696 +2026-04-11 08:57:04.622426: val_loss -0.1501 +2026-04-11 08:57:04.625942: Pseudo dice [0.3131, 0.1101, 0.7428, 0.4575, 0.6191, 0.6918, 0.68] +2026-04-11 08:57:04.628690: Epoch time: 102.13 s +2026-04-11 08:57:05.816896: +2026-04-11 08:57:05.819132: Epoch 785 +2026-04-11 08:57:05.821601: Current learning rate: 0.00822 +2026-04-11 08:58:48.147840: train_loss -0.1707 +2026-04-11 08:58:48.155256: val_loss -0.1341 +2026-04-11 08:58:48.157405: Pseudo dice [0.7495, 0.6379, 0.7623, 0.305, 0.4088, 0.1708, 0.7532] +2026-04-11 08:58:48.159781: Epoch time: 102.33 s +2026-04-11 08:58:49.349255: +2026-04-11 08:58:49.350890: Epoch 786 +2026-04-11 08:58:49.352713: Current learning rate: 0.00821 +2026-04-11 09:00:31.834338: train_loss -0.1858 +2026-04-11 09:00:31.841508: val_loss -0.0721 +2026-04-11 09:00:31.843466: Pseudo dice [0.3275, 0.3275, 0.5207, 0.3928, 0.5775, 0.3539, 0.7923] +2026-04-11 09:00:31.846076: Epoch time: 102.49 s +2026-04-11 09:00:33.063982: +2026-04-11 09:00:33.066065: Epoch 787 +2026-04-11 09:00:33.068022: Current learning rate: 0.00821 +2026-04-11 09:02:15.291157: train_loss -0.1759 +2026-04-11 09:02:15.300311: val_loss -0.1283 +2026-04-11 09:02:15.303743: Pseudo dice [0.3483, 0.1323, 0.7606, 0.2664, 0.4434, 0.3855, 0.8166] +2026-04-11 09:02:15.306172: Epoch time: 102.23 s +2026-04-11 09:02:16.496875: +2026-04-11 09:02:16.498765: Epoch 788 +2026-04-11 09:02:16.500498: Current learning rate: 0.00821 +2026-04-11 09:03:58.833862: train_loss -0.1814 +2026-04-11 09:03:58.842094: val_loss -0.1717 +2026-04-11 09:03:58.844536: Pseudo dice [0.5141, 0.7801, 0.6763, 0.669, 0.649, 0.7413, 0.7884] +2026-04-11 09:03:58.846663: Epoch time: 102.34 s +2026-04-11 09:04:00.018148: +2026-04-11 09:04:00.019935: Epoch 789 +2026-04-11 09:04:00.021565: Current learning rate: 0.00821 +2026-04-11 09:05:42.751582: train_loss -0.1796 +2026-04-11 09:05:42.760994: val_loss -0.1584 +2026-04-11 09:05:42.763170: Pseudo dice [0.556, 0.7099, 0.6479, 0.5054, 0.3301, 0.8371, 0.7519] +2026-04-11 09:05:42.765651: Epoch time: 102.74 s +2026-04-11 09:05:43.971858: +2026-04-11 09:05:43.973542: Epoch 790 +2026-04-11 09:05:43.975376: Current learning rate: 0.0082 +2026-04-11 09:07:26.533844: train_loss -0.1834 +2026-04-11 09:07:26.541317: val_loss -0.1412 +2026-04-11 09:07:26.543569: Pseudo dice [0.4911, 0.8257, 0.8095, 0.6433, 0.4905, 0.6416, 0.6937] +2026-04-11 09:07:26.546421: Epoch time: 102.57 s +2026-04-11 09:07:27.772494: +2026-04-11 09:07:27.774055: Epoch 791 +2026-04-11 09:07:27.775896: Current learning rate: 0.0082 +2026-04-11 09:09:10.315103: train_loss -0.1853 +2026-04-11 09:09:10.322309: val_loss -0.1435 +2026-04-11 09:09:10.324454: Pseudo dice [0.5133, 0.7179, 0.7299, 0.2718, 0.5389, 0.1972, 0.7471] +2026-04-11 09:09:10.326902: Epoch time: 102.55 s +2026-04-11 09:09:11.585486: +2026-04-11 09:09:11.590666: Epoch 792 +2026-04-11 09:09:11.593002: Current learning rate: 0.0082 +2026-04-11 09:10:54.281669: train_loss -0.1665 +2026-04-11 09:10:54.289587: val_loss -0.1604 +2026-04-11 09:10:54.292194: Pseudo dice [0.2395, 0.7357, 0.7344, 0.5, 0.3801, 0.5311, 0.64] +2026-04-11 09:10:54.294741: Epoch time: 102.7 s +2026-04-11 09:10:55.490538: +2026-04-11 09:10:55.492557: Epoch 793 +2026-04-11 09:10:55.494307: Current learning rate: 0.0082 +2026-04-11 09:12:38.609184: train_loss -0.1784 +2026-04-11 09:12:38.617038: val_loss -0.1406 +2026-04-11 09:12:38.620040: Pseudo dice [0.55, 0.7367, 0.5766, 0.0825, 0.6007, 0.2996, 0.8555] +2026-04-11 09:12:38.622679: Epoch time: 103.12 s +2026-04-11 09:12:39.801759: +2026-04-11 09:12:39.803467: Epoch 794 +2026-04-11 09:12:39.805326: Current learning rate: 0.00819 +2026-04-11 09:14:22.822921: train_loss -0.1708 +2026-04-11 09:14:22.829929: val_loss -0.1343 +2026-04-11 09:14:22.831940: Pseudo dice [0.7088, 0.4017, 0.8142, 0.2746, 0.3676, 0.6688, 0.3999] +2026-04-11 09:14:22.834324: Epoch time: 103.02 s +2026-04-11 09:14:24.039888: +2026-04-11 09:14:24.041889: Epoch 795 +2026-04-11 09:14:24.044010: Current learning rate: 0.00819 +2026-04-11 09:16:07.136077: train_loss -0.1839 +2026-04-11 09:16:07.141990: val_loss -0.1409 +2026-04-11 09:16:07.144035: Pseudo dice [0.5436, 0.1366, 0.493, 0.4941, 0.4383, 0.8835, 0.7067] +2026-04-11 09:16:07.146388: Epoch time: 103.1 s +2026-04-11 09:16:08.352552: +2026-04-11 09:16:08.354398: Epoch 796 +2026-04-11 09:16:08.357946: Current learning rate: 0.00819 +2026-04-11 09:17:51.487624: train_loss -0.1807 +2026-04-11 09:17:51.496281: val_loss -0.141 +2026-04-11 09:17:51.498814: Pseudo dice [0.6004, 0.2449, 0.7633, 0.3457, 0.3782, 0.8314, 0.6119] +2026-04-11 09:17:51.501387: Epoch time: 103.14 s +2026-04-11 09:17:53.915837: +2026-04-11 09:17:53.917464: Epoch 797 +2026-04-11 09:17:53.919293: Current learning rate: 0.00819 +2026-04-11 09:19:36.990768: train_loss -0.162 +2026-04-11 09:19:37.000124: val_loss -0.1397 +2026-04-11 09:19:37.004190: Pseudo dice [0.7525, 0.6687, 0.7152, 0.1182, 0.5747, 0.8249, 0.6557] +2026-04-11 09:19:37.007848: Epoch time: 103.08 s +2026-04-11 09:19:38.218466: +2026-04-11 09:19:38.220688: Epoch 798 +2026-04-11 09:19:38.222920: Current learning rate: 0.00819 +2026-04-11 09:21:21.316346: train_loss -0.1839 +2026-04-11 09:21:21.323972: val_loss -0.1484 +2026-04-11 09:21:21.326969: Pseudo dice [0.3938, 0.8532, 0.8185, 0.167, 0.4361, 0.7429, 0.7227] +2026-04-11 09:21:21.329908: Epoch time: 103.1 s +2026-04-11 09:21:22.563243: +2026-04-11 09:21:22.565404: Epoch 799 +2026-04-11 09:21:22.567459: Current learning rate: 0.00818 +2026-04-11 09:23:05.146331: train_loss -0.1781 +2026-04-11 09:23:05.153518: val_loss -0.1324 +2026-04-11 09:23:05.156813: Pseudo dice [0.6474, 0.6571, 0.6681, 0.1841, 0.3884, 0.5628, 0.8334] +2026-04-11 09:23:05.159269: Epoch time: 102.59 s +2026-04-11 09:23:08.267214: +2026-04-11 09:23:08.268850: Epoch 800 +2026-04-11 09:23:08.270932: Current learning rate: 0.00818 +2026-04-11 09:24:51.399683: train_loss -0.1803 +2026-04-11 09:24:51.413177: val_loss -0.1534 +2026-04-11 09:24:51.416010: Pseudo dice [0.0964, 0.5319, 0.7389, 0.5386, 0.5227, 0.609, 0.7931] +2026-04-11 09:24:51.418644: Epoch time: 103.14 s +2026-04-11 09:24:52.651104: +2026-04-11 09:24:52.653094: Epoch 801 +2026-04-11 09:24:52.655401: Current learning rate: 0.00818 +2026-04-11 09:26:35.382794: train_loss -0.187 +2026-04-11 09:26:35.392202: val_loss -0.1373 +2026-04-11 09:26:35.394278: Pseudo dice [0.4883, 0.404, 0.8137, 0.5212, 0.5648, 0.6193, 0.7187] +2026-04-11 09:26:35.396409: Epoch time: 102.74 s +2026-04-11 09:26:36.664991: +2026-04-11 09:26:36.667101: Epoch 802 +2026-04-11 09:26:36.670217: Current learning rate: 0.00818 +2026-04-11 09:28:19.804541: train_loss -0.1979 +2026-04-11 09:28:19.810888: val_loss -0.128 +2026-04-11 09:28:19.813065: Pseudo dice [0.759, 0.7041, 0.7783, 0.2062, 0.2477, 0.7545, 0.6747] +2026-04-11 09:28:19.815115: Epoch time: 103.14 s +2026-04-11 09:28:21.020538: +2026-04-11 09:28:21.023573: Epoch 803 +2026-04-11 09:28:21.025722: Current learning rate: 0.00817 +2026-04-11 09:30:04.617567: train_loss -0.1706 +2026-04-11 09:30:04.625010: val_loss -0.1203 +2026-04-11 09:30:04.627448: Pseudo dice [0.758, 0.6857, 0.7358, 0.0884, 0.4976, 0.6349, 0.7025] +2026-04-11 09:30:04.629872: Epoch time: 103.6 s +2026-04-11 09:30:05.865588: +2026-04-11 09:30:05.867351: Epoch 804 +2026-04-11 09:30:05.869184: Current learning rate: 0.00817 +2026-04-11 09:31:48.965188: train_loss -0.1792 +2026-04-11 09:31:48.973860: val_loss -0.1329 +2026-04-11 09:31:48.976118: Pseudo dice [0.3918, 0.3963, 0.7543, 0.3916, 0.1348, 0.5374, 0.6998] +2026-04-11 09:31:48.978305: Epoch time: 103.1 s +2026-04-11 09:31:50.224528: +2026-04-11 09:31:50.226461: Epoch 805 +2026-04-11 09:31:50.228683: Current learning rate: 0.00817 +2026-04-11 09:33:31.966862: train_loss -0.1856 +2026-04-11 09:33:31.974604: val_loss -0.1574 +2026-04-11 09:33:31.976643: Pseudo dice [0.7556, 0.4617, 0.7266, 0.3654, 0.2252, 0.5043, 0.7511] +2026-04-11 09:33:31.979562: Epoch time: 101.75 s +2026-04-11 09:33:33.132305: +2026-04-11 09:33:33.134121: Epoch 806 +2026-04-11 09:33:33.135878: Current learning rate: 0.00817 +2026-04-11 09:35:14.512973: train_loss -0.1783 +2026-04-11 09:35:14.524972: val_loss -0.1378 +2026-04-11 09:35:14.527708: Pseudo dice [0.5527, 0.3613, 0.7596, 0.5938, 0.7449, 0.6845, 0.6595] +2026-04-11 09:35:14.530354: Epoch time: 101.38 s +2026-04-11 09:35:15.745030: +2026-04-11 09:35:15.747129: Epoch 807 +2026-04-11 09:35:15.748824: Current learning rate: 0.00816 +2026-04-11 09:36:57.419785: train_loss -0.1858 +2026-04-11 09:36:57.427970: val_loss -0.1346 +2026-04-11 09:36:57.430230: Pseudo dice [0.2716, 0.8372, 0.7765, 0.7715, 0.3262, 0.5016, 0.7632] +2026-04-11 09:36:57.433493: Epoch time: 101.68 s +2026-04-11 09:36:58.637159: +2026-04-11 09:36:58.638773: Epoch 808 +2026-04-11 09:36:58.640644: Current learning rate: 0.00816 +2026-04-11 09:38:40.440834: train_loss -0.182 +2026-04-11 09:38:40.449503: val_loss -0.1177 +2026-04-11 09:38:40.451436: Pseudo dice [0.3443, 0.1875, 0.4901, 0.1993, 0.4291, 0.4161, 0.7228] +2026-04-11 09:38:40.454043: Epoch time: 101.81 s +2026-04-11 09:38:41.689170: +2026-04-11 09:38:41.691366: Epoch 809 +2026-04-11 09:38:41.693705: Current learning rate: 0.00816 +2026-04-11 09:40:23.495887: train_loss -0.1655 +2026-04-11 09:40:23.502740: val_loss -0.1203 +2026-04-11 09:40:23.504857: Pseudo dice [0.3887, 0.0437, 0.7578, 0.5374, 0.4204, 0.5165, 0.341] +2026-04-11 09:40:23.507420: Epoch time: 101.81 s +2026-04-11 09:40:24.745956: +2026-04-11 09:40:24.747883: Epoch 810 +2026-04-11 09:40:24.749868: Current learning rate: 0.00816 +2026-04-11 09:42:07.539888: train_loss -0.1786 +2026-04-11 09:42:07.547220: val_loss -0.1434 +2026-04-11 09:42:07.549333: Pseudo dice [0.6233, 0.6218, 0.6909, 0.7674, 0.5175, 0.3534, 0.8094] +2026-04-11 09:42:07.552943: Epoch time: 102.8 s +2026-04-11 09:42:08.784201: +2026-04-11 09:42:08.786804: Epoch 811 +2026-04-11 09:42:08.794140: Current learning rate: 0.00816 +2026-04-11 09:43:50.393978: train_loss -0.158 +2026-04-11 09:43:50.400957: val_loss -0.0876 +2026-04-11 09:43:50.403373: Pseudo dice [0.3465, 0.8456, 0.7197, 0.0779, 0.3535, 0.4168, 0.5596] +2026-04-11 09:43:50.405807: Epoch time: 101.61 s +2026-04-11 09:43:51.585248: +2026-04-11 09:43:51.587648: Epoch 812 +2026-04-11 09:43:51.589880: Current learning rate: 0.00815 +2026-04-11 09:45:33.715736: train_loss -0.1608 +2026-04-11 09:45:33.723324: val_loss -0.1354 +2026-04-11 09:45:33.725234: Pseudo dice [0.7865, 0.826, 0.6623, 0.1168, 0.3973, 0.4659, 0.6647] +2026-04-11 09:45:33.727360: Epoch time: 102.13 s +2026-04-11 09:45:34.937733: +2026-04-11 09:45:34.947877: Epoch 813 +2026-04-11 09:45:34.954269: Current learning rate: 0.00815 +2026-04-11 09:47:16.517564: train_loss -0.1583 +2026-04-11 09:47:16.527543: val_loss -0.1486 +2026-04-11 09:47:16.529690: Pseudo dice [0.6595, 0.0468, 0.7281, 0.3696, 0.1681, 0.8614, 0.7681] +2026-04-11 09:47:16.531776: Epoch time: 101.58 s +2026-04-11 09:47:17.744993: +2026-04-11 09:47:17.746665: Epoch 814 +2026-04-11 09:47:17.749211: Current learning rate: 0.00815 +2026-04-11 09:48:59.517386: train_loss -0.1708 +2026-04-11 09:48:59.527114: val_loss -0.1215 +2026-04-11 09:48:59.529794: Pseudo dice [0.5322, 0.8043, 0.5441, 0.4936, 0.578, 0.1341, 0.777] +2026-04-11 09:48:59.532151: Epoch time: 101.78 s +2026-04-11 09:49:00.736145: +2026-04-11 09:49:00.737777: Epoch 815 +2026-04-11 09:49:00.739654: Current learning rate: 0.00815 +2026-04-11 09:50:42.053197: train_loss -0.1761 +2026-04-11 09:50:42.060481: val_loss -0.1565 +2026-04-11 09:50:42.062661: Pseudo dice [0.4805, 0.1683, 0.7933, 0.5717, 0.3722, 0.7557, 0.8389] +2026-04-11 09:50:42.065872: Epoch time: 101.32 s +2026-04-11 09:50:43.310431: +2026-04-11 09:50:43.312551: Epoch 816 +2026-04-11 09:50:43.314444: Current learning rate: 0.00814 +2026-04-11 09:52:26.597799: train_loss -0.1856 +2026-04-11 09:52:26.604157: val_loss -0.1613 +2026-04-11 09:52:26.606451: Pseudo dice [0.733, 0.7371, 0.8195, 0.6052, 0.4359, 0.7092, 0.8289] +2026-04-11 09:52:26.609277: Epoch time: 103.29 s +2026-04-11 09:52:27.866468: +2026-04-11 09:52:27.868990: Epoch 817 +2026-04-11 09:52:27.871056: Current learning rate: 0.00814 +2026-04-11 09:54:09.303017: train_loss -0.1904 +2026-04-11 09:54:09.309772: val_loss -0.1543 +2026-04-11 09:54:09.312070: Pseudo dice [0.6163, 0.6186, 0.7462, 0.5073, 0.5424, 0.2704, 0.8479] +2026-04-11 09:54:09.314077: Epoch time: 101.44 s +2026-04-11 09:54:10.525410: +2026-04-11 09:54:10.527730: Epoch 818 +2026-04-11 09:54:10.529790: Current learning rate: 0.00814 +2026-04-11 09:55:52.369417: train_loss -0.174 +2026-04-11 09:55:52.376247: val_loss -0.1133 +2026-04-11 09:55:52.378751: Pseudo dice [0.8269, 0.8107, 0.7098, 0.2497, 0.3964, 0.6021, 0.5233] +2026-04-11 09:55:52.380766: Epoch time: 101.85 s +2026-04-11 09:55:53.656011: +2026-04-11 09:55:53.657973: Epoch 819 +2026-04-11 09:55:53.660016: Current learning rate: 0.00814 +2026-04-11 09:57:35.123045: train_loss -0.1802 +2026-04-11 09:57:35.132727: val_loss -0.1352 +2026-04-11 09:57:35.136028: Pseudo dice [0.2065, 0.1898, 0.7822, 0.453, 0.5887, 0.6184, 0.8396] +2026-04-11 09:57:35.139304: Epoch time: 101.47 s +2026-04-11 09:57:36.244826: +2026-04-11 09:57:36.246554: Epoch 820 +2026-04-11 09:57:36.248885: Current learning rate: 0.00813 +2026-04-11 09:59:17.552294: train_loss -0.1805 +2026-04-11 09:59:17.559818: val_loss -0.1342 +2026-04-11 09:59:17.562125: Pseudo dice [0.3834, 0.1769, 0.7356, 0.4009, 0.4919, 0.5691, 0.6765] +2026-04-11 09:59:17.564705: Epoch time: 101.31 s +2026-04-11 09:59:18.705142: +2026-04-11 09:59:18.707499: Epoch 821 +2026-04-11 09:59:18.709475: Current learning rate: 0.00813 +2026-04-11 10:01:00.283919: train_loss -0.1681 +2026-04-11 10:01:00.290145: val_loss -0.1433 +2026-04-11 10:01:00.291936: Pseudo dice [0.6187, 0.6182, 0.7737, 0.108, 0.419, 0.8278, 0.5737] +2026-04-11 10:01:00.294733: Epoch time: 101.58 s +2026-04-11 10:01:01.437157: +2026-04-11 10:01:01.439323: Epoch 822 +2026-04-11 10:01:01.441612: Current learning rate: 0.00813 +2026-04-11 10:02:43.268541: train_loss -0.1772 +2026-04-11 10:02:43.276070: val_loss -0.1466 +2026-04-11 10:02:43.279009: Pseudo dice [0.6109, 0.4867, 0.7735, 0.5368, 0.3331, 0.4357, 0.3749] +2026-04-11 10:02:43.280945: Epoch time: 101.83 s +2026-04-11 10:02:44.421554: +2026-04-11 10:02:44.424066: Epoch 823 +2026-04-11 10:02:44.426492: Current learning rate: 0.00813 +2026-04-11 10:04:25.930686: train_loss -0.1848 +2026-04-11 10:04:25.937397: val_loss -0.1315 +2026-04-11 10:04:25.939091: Pseudo dice [0.2814, 0.4169, 0.6074, 0.4573, 0.2799, 0.5545, 0.7202] +2026-04-11 10:04:25.941527: Epoch time: 101.51 s +2026-04-11 10:04:27.161652: +2026-04-11 10:04:27.163282: Epoch 824 +2026-04-11 10:04:27.165216: Current learning rate: 0.00813 +2026-04-11 10:06:08.529313: train_loss -0.1784 +2026-04-11 10:06:08.536504: val_loss -0.1605 +2026-04-11 10:06:08.538878: Pseudo dice [0.4499, 0.7741, 0.7522, 0.8067, 0.3831, 0.785, 0.7461] +2026-04-11 10:06:08.541507: Epoch time: 101.37 s +2026-04-11 10:06:09.690659: +2026-04-11 10:06:09.692671: Epoch 825 +2026-04-11 10:06:09.694629: Current learning rate: 0.00812 +2026-04-11 10:07:51.176229: train_loss -0.1723 +2026-04-11 10:07:51.183225: val_loss -0.1112 +2026-04-11 10:07:51.185491: Pseudo dice [0.5551, 0.3652, 0.7335, 0.1775, 0.5055, 0.2802, 0.2335] +2026-04-11 10:07:51.188160: Epoch time: 101.49 s +2026-04-11 10:07:52.337376: +2026-04-11 10:07:52.339332: Epoch 826 +2026-04-11 10:07:52.341843: Current learning rate: 0.00812 +2026-04-11 10:09:33.763054: train_loss -0.177 +2026-04-11 10:09:33.780859: val_loss -0.1291 +2026-04-11 10:09:33.787409: Pseudo dice [0.1547, 0.5482, 0.7368, 0.5343, 0.4607, 0.4278, 0.6304] +2026-04-11 10:09:33.791775: Epoch time: 101.43 s +2026-04-11 10:09:34.980408: +2026-04-11 10:09:34.982544: Epoch 827 +2026-04-11 10:09:34.984352: Current learning rate: 0.00812 +2026-04-11 10:11:16.715860: train_loss -0.1913 +2026-04-11 10:11:16.723114: val_loss -0.1196 +2026-04-11 10:11:16.725835: Pseudo dice [0.5906, 0.3884, 0.7774, 0.43, 0.4136, 0.6375, 0.7891] +2026-04-11 10:11:16.728517: Epoch time: 101.74 s +2026-04-11 10:11:17.911567: +2026-04-11 10:11:17.914256: Epoch 828 +2026-04-11 10:11:17.916562: Current learning rate: 0.00812 +2026-04-11 10:12:59.225689: train_loss -0.1834 +2026-04-11 10:12:59.232134: val_loss -0.1113 +2026-04-11 10:12:59.234512: Pseudo dice [0.8296, 0.3871, 0.6501, 0.4181, 0.2706, 0.8977, 0.4892] +2026-04-11 10:12:59.237290: Epoch time: 101.32 s +2026-04-11 10:13:00.408322: +2026-04-11 10:13:00.409931: Epoch 829 +2026-04-11 10:13:00.411847: Current learning rate: 0.00811 +2026-04-11 10:14:42.219272: train_loss -0.1625 +2026-04-11 10:14:42.227062: val_loss -0.1258 +2026-04-11 10:14:42.229769: Pseudo dice [0.5421, 0.304, 0.5558, 0.6968, 0.3714, 0.6437, 0.8083] +2026-04-11 10:14:42.232628: Epoch time: 101.81 s +2026-04-11 10:14:43.371111: +2026-04-11 10:14:43.373304: Epoch 830 +2026-04-11 10:14:43.376248: Current learning rate: 0.00811 +2026-04-11 10:16:25.153604: train_loss -0.1669 +2026-04-11 10:16:25.161814: val_loss -0.1641 +2026-04-11 10:16:25.164004: Pseudo dice [0.2238, 0.5125, 0.7079, 0.6119, 0.3899, 0.8386, 0.7381] +2026-04-11 10:16:25.166827: Epoch time: 101.79 s +2026-04-11 10:16:26.324883: +2026-04-11 10:16:26.326864: Epoch 831 +2026-04-11 10:16:26.328736: Current learning rate: 0.00811 +2026-04-11 10:18:07.997255: train_loss -0.1473 +2026-04-11 10:18:08.004152: val_loss -0.1078 +2026-04-11 10:18:08.007955: Pseudo dice [0.6053, 0.0931, 0.6264, 0.1881, 0.3357, 0.7285, 0.5032] +2026-04-11 10:18:08.010860: Epoch time: 101.68 s +2026-04-11 10:18:09.173574: +2026-04-11 10:18:09.175651: Epoch 832 +2026-04-11 10:18:09.177866: Current learning rate: 0.00811 +2026-04-11 10:19:50.381354: train_loss -0.163 +2026-04-11 10:19:50.388809: val_loss -0.1684 +2026-04-11 10:19:50.391799: Pseudo dice [0.6099, 0.476, 0.7319, 0.4801, 0.3008, 0.617, 0.8456] +2026-04-11 10:19:50.394269: Epoch time: 101.21 s +2026-04-11 10:19:51.546718: +2026-04-11 10:19:51.548409: Epoch 833 +2026-04-11 10:19:51.550421: Current learning rate: 0.0081 +2026-04-11 10:21:33.668237: train_loss -0.1707 +2026-04-11 10:21:33.675580: val_loss -0.129 +2026-04-11 10:21:33.677939: Pseudo dice [0.3814, 0.1228, 0.5922, 0.5511, 0.3803, 0.6223, 0.2759] +2026-04-11 10:21:33.680485: Epoch time: 102.12 s +2026-04-11 10:21:34.862445: +2026-04-11 10:21:34.864496: Epoch 834 +2026-04-11 10:21:34.866658: Current learning rate: 0.0081 +2026-04-11 10:23:16.580387: train_loss -0.1994 +2026-04-11 10:23:16.587751: val_loss -0.1201 +2026-04-11 10:23:16.590352: Pseudo dice [0.4946, 0.8447, 0.6559, 0.6154, 0.4931, 0.3863, 0.5385] +2026-04-11 10:23:16.593269: Epoch time: 101.72 s +2026-04-11 10:23:17.736918: +2026-04-11 10:23:17.739116: Epoch 835 +2026-04-11 10:23:17.741777: Current learning rate: 0.0081 +2026-04-11 10:25:00.121182: train_loss -0.186 +2026-04-11 10:25:00.127127: val_loss -0.1716 +2026-04-11 10:25:00.129847: Pseudo dice [0.496, 0.752, 0.8275, 0.6842, 0.1424, 0.7809, 0.7212] +2026-04-11 10:25:00.133215: Epoch time: 102.39 s +2026-04-11 10:25:01.322366: +2026-04-11 10:25:01.324537: Epoch 836 +2026-04-11 10:25:01.326996: Current learning rate: 0.0081 +2026-04-11 10:26:44.368782: train_loss -0.2178 +2026-04-11 10:26:44.375628: val_loss -0.1997 +2026-04-11 10:26:44.377919: Pseudo dice [0.6599, 0.2237, 0.722, 0.468, 0.4777, 0.3196, 0.7625] +2026-04-11 10:26:44.380572: Epoch time: 103.05 s +2026-04-11 10:26:45.536467: +2026-04-11 10:26:45.538687: Epoch 837 +2026-04-11 10:26:45.540601: Current learning rate: 0.0081 +2026-04-11 10:28:26.904580: train_loss -0.2319 +2026-04-11 10:28:26.932850: val_loss -0.198 +2026-04-11 10:28:26.936390: Pseudo dice [0.5711, 0.8925, 0.6927, 0.6462, 0.337, 0.6428, 0.8301] +2026-04-11 10:28:26.939682: Epoch time: 101.37 s +2026-04-11 10:28:28.020445: +2026-04-11 10:28:28.022124: Epoch 838 +2026-04-11 10:28:28.023933: Current learning rate: 0.00809 +2026-04-11 10:30:09.554408: train_loss -0.2305 +2026-04-11 10:30:09.560925: val_loss -0.1954 +2026-04-11 10:30:09.563464: Pseudo dice [0.557, 0.1563, 0.768, 0.5361, 0.0607, 0.5089, 0.6573] +2026-04-11 10:30:09.565663: Epoch time: 101.54 s +2026-04-11 10:30:10.740817: +2026-04-11 10:30:10.742584: Epoch 839 +2026-04-11 10:30:10.744745: Current learning rate: 0.00809 +2026-04-11 10:31:52.434072: train_loss -0.2411 +2026-04-11 10:31:52.440498: val_loss -0.166 +2026-04-11 10:31:52.442711: Pseudo dice [0.3017, 0.1352, 0.6238, 0.1014, 0.5729, 0.8155, 0.3732] +2026-04-11 10:31:52.444734: Epoch time: 101.7 s +2026-04-11 10:31:53.578602: +2026-04-11 10:31:53.581747: Epoch 840 +2026-04-11 10:31:53.583884: Current learning rate: 0.00809 +2026-04-11 10:33:34.923358: train_loss -0.2449 +2026-04-11 10:33:34.929522: val_loss -0.2168 +2026-04-11 10:33:34.931260: Pseudo dice [0.2545, 0.7879, 0.8244, 0.731, 0.6302, 0.7304, 0.7606] +2026-04-11 10:33:34.933325: Epoch time: 101.35 s +2026-04-11 10:33:36.052035: +2026-04-11 10:33:36.053546: Epoch 841 +2026-04-11 10:33:36.055296: Current learning rate: 0.00809 +2026-04-11 10:35:17.586795: train_loss -0.2375 +2026-04-11 10:35:17.593513: val_loss -0.1976 +2026-04-11 10:35:17.595275: Pseudo dice [0.5015, 0.4819, 0.7174, 0.6772, 0.2199, 0.755, 0.8036] +2026-04-11 10:35:17.597768: Epoch time: 101.54 s +2026-04-11 10:35:18.780746: +2026-04-11 10:35:18.783512: Epoch 842 +2026-04-11 10:35:18.785679: Current learning rate: 0.00808 +2026-04-11 10:37:00.262956: train_loss -0.2302 +2026-04-11 10:37:00.271167: val_loss -0.2012 +2026-04-11 10:37:00.274553: Pseudo dice [0.4794, 0.3293, 0.6642, 0.5485, 0.4763, 0.8024, 0.6885] +2026-04-11 10:37:00.277496: Epoch time: 101.49 s +2026-04-11 10:37:01.452307: +2026-04-11 10:37:01.454102: Epoch 843 +2026-04-11 10:37:01.456647: Current learning rate: 0.00808 +2026-04-11 10:38:43.470138: train_loss -0.2175 +2026-04-11 10:38:43.476690: val_loss -0.1539 +2026-04-11 10:38:43.479760: Pseudo dice [0.5114, 0.7471, 0.5088, 0.4177, 0.4916, 0.6337, 0.3883] +2026-04-11 10:38:43.482954: Epoch time: 102.02 s +2026-04-11 10:38:44.641625: +2026-04-11 10:38:44.646822: Epoch 844 +2026-04-11 10:38:44.648992: Current learning rate: 0.00808 +2026-04-11 10:40:26.841118: train_loss -0.2364 +2026-04-11 10:40:26.848176: val_loss -0.18 +2026-04-11 10:40:26.850670: Pseudo dice [0.4288, 0.8158, 0.7889, 0.4305, 0.3298, 0.1144, 0.7755] +2026-04-11 10:40:26.854587: Epoch time: 102.2 s +2026-04-11 10:40:27.978533: +2026-04-11 10:40:27.980309: Epoch 845 +2026-04-11 10:40:27.982216: Current learning rate: 0.00808 +2026-04-11 10:42:09.334797: train_loss -0.2407 +2026-04-11 10:42:09.341617: val_loss -0.1837 +2026-04-11 10:42:09.343772: Pseudo dice [0.7563, 0.5104, 0.6893, 0.3191, 0.3067, 0.3629, 0.7665] +2026-04-11 10:42:09.346152: Epoch time: 101.36 s +2026-04-11 10:42:10.445837: +2026-04-11 10:42:10.447812: Epoch 846 +2026-04-11 10:42:10.449932: Current learning rate: 0.00807 +2026-04-11 10:43:51.981847: train_loss -0.2501 +2026-04-11 10:43:51.988358: val_loss -0.2022 +2026-04-11 10:43:51.990294: Pseudo dice [0.3468, 0.7496, 0.8396, 0.4405, 0.4572, 0.2946, 0.5891] +2026-04-11 10:43:51.992895: Epoch time: 101.54 s +2026-04-11 10:43:53.130833: +2026-04-11 10:43:53.133631: Epoch 847 +2026-04-11 10:43:53.135571: Current learning rate: 0.00807 +2026-04-11 10:45:34.819948: train_loss -0.236 +2026-04-11 10:45:34.826019: val_loss -0.1857 +2026-04-11 10:45:34.828284: Pseudo dice [0.6248, 0.6226, 0.7303, 0.1334, 0.3625, 0.7157, 0.7334] +2026-04-11 10:45:34.830772: Epoch time: 101.69 s +2026-04-11 10:45:35.943632: +2026-04-11 10:45:35.945677: Epoch 848 +2026-04-11 10:45:35.947670: Current learning rate: 0.00807 +2026-04-11 10:47:17.576381: train_loss -0.2458 +2026-04-11 10:47:17.583800: val_loss -0.2053 +2026-04-11 10:47:17.586753: Pseudo dice [0.4063, 0.6402, 0.603, 0.6418, 0.5444, 0.6964, 0.724] +2026-04-11 10:47:17.589462: Epoch time: 101.64 s +2026-04-11 10:47:18.745213: +2026-04-11 10:47:18.747292: Epoch 849 +2026-04-11 10:47:18.749071: Current learning rate: 0.00807 +2026-04-11 10:49:00.540104: train_loss -0.2468 +2026-04-11 10:49:00.547345: val_loss -0.2323 +2026-04-11 10:49:00.549294: Pseudo dice [0.359, 0.6248, 0.7085, 0.4644, 0.5729, 0.5715, 0.672] +2026-04-11 10:49:00.551714: Epoch time: 101.8 s +2026-04-11 10:49:03.414726: +2026-04-11 10:49:03.416208: Epoch 850 +2026-04-11 10:49:03.417953: Current learning rate: 0.00807 +2026-04-11 10:50:45.090091: train_loss -0.2524 +2026-04-11 10:50:45.096985: val_loss -0.2071 +2026-04-11 10:50:45.099249: Pseudo dice [0.4491, 0.6732, 0.7028, 0.2991, 0.3156, 0.4668, 0.7632] +2026-04-11 10:50:45.101712: Epoch time: 101.68 s +2026-04-11 10:50:46.339736: +2026-04-11 10:50:46.341533: Epoch 851 +2026-04-11 10:50:46.346500: Current learning rate: 0.00806 +2026-04-11 10:52:27.926540: train_loss -0.2388 +2026-04-11 10:52:27.933260: val_loss -0.2253 +2026-04-11 10:52:27.935789: Pseudo dice [0.5275, 0.6113, 0.729, 0.1106, 0.4988, 0.8016, 0.7317] +2026-04-11 10:52:27.938486: Epoch time: 101.59 s +2026-04-11 10:52:29.080621: +2026-04-11 10:52:29.082324: Epoch 852 +2026-04-11 10:52:29.084162: Current learning rate: 0.00806 +2026-04-11 10:54:11.003318: train_loss -0.2487 +2026-04-11 10:54:11.012732: val_loss -0.233 +2026-04-11 10:54:11.014745: Pseudo dice [0.4203, 0.8412, 0.7834, 0.5047, 0.3637, 0.8333, 0.668] +2026-04-11 10:54:11.017504: Epoch time: 101.93 s +2026-04-11 10:54:12.133168: +2026-04-11 10:54:12.134937: Epoch 853 +2026-04-11 10:54:12.136822: Current learning rate: 0.00806 +2026-04-11 10:55:53.852574: train_loss -0.2495 +2026-04-11 10:55:53.859023: val_loss -0.1793 +2026-04-11 10:55:53.861100: Pseudo dice [0.4105, 0.4983, 0.6283, 0.0314, 0.1944, 0.3075, 0.8212] +2026-04-11 10:55:53.863059: Epoch time: 101.72 s +2026-04-11 10:55:55.026979: +2026-04-11 10:55:55.029298: Epoch 854 +2026-04-11 10:55:55.031867: Current learning rate: 0.00806 +2026-04-11 10:57:37.146076: train_loss -0.2187 +2026-04-11 10:57:37.152398: val_loss -0.1903 +2026-04-11 10:57:37.154669: Pseudo dice [0.653, 0.6031, 0.7165, 0.508, 0.4262, 0.512, 0.2227] +2026-04-11 10:57:37.157349: Epoch time: 102.12 s +2026-04-11 10:57:38.289778: +2026-04-11 10:57:38.292387: Epoch 855 +2026-04-11 10:57:38.295306: Current learning rate: 0.00805 +2026-04-11 10:59:19.867187: train_loss -0.2424 +2026-04-11 10:59:19.875568: val_loss -0.2119 +2026-04-11 10:59:19.877718: Pseudo dice [0.8647, 0.454, 0.7347, 0.5762, 0.3057, 0.7843, 0.7327] +2026-04-11 10:59:19.881550: Epoch time: 101.58 s +2026-04-11 10:59:21.011884: +2026-04-11 10:59:21.014850: Epoch 856 +2026-04-11 10:59:21.016914: Current learning rate: 0.00805 +2026-04-11 11:01:03.372458: train_loss -0.2386 +2026-04-11 11:01:03.388850: val_loss -0.2245 +2026-04-11 11:01:03.390871: Pseudo dice [0.4627, 0.1765, 0.7817, 0.4594, 0.3592, 0.4746, 0.8497] +2026-04-11 11:01:03.397120: Epoch time: 102.36 s +2026-04-11 11:01:04.564352: +2026-04-11 11:01:04.566278: Epoch 857 +2026-04-11 11:01:04.568838: Current learning rate: 0.00805 +2026-04-11 11:02:46.027968: train_loss -0.2517 +2026-04-11 11:02:46.033626: val_loss -0.2213 +2026-04-11 11:02:46.035681: Pseudo dice [0.2003, 0.5668, 0.8139, 0.2892, 0.2634, 0.4537, 0.7275] +2026-04-11 11:02:46.037762: Epoch time: 101.47 s +2026-04-11 11:02:47.203335: +2026-04-11 11:02:47.206075: Epoch 858 +2026-04-11 11:02:47.208126: Current learning rate: 0.00805 +2026-04-11 11:04:29.632615: train_loss -0.2128 +2026-04-11 11:04:29.639662: val_loss -0.1736 +2026-04-11 11:04:29.641735: Pseudo dice [0.5354, 0.5549, 0.677, 0.0552, 0.3131, 0.5077, 0.464] +2026-04-11 11:04:29.644176: Epoch time: 102.43 s +2026-04-11 11:04:30.766600: +2026-04-11 11:04:30.768659: Epoch 859 +2026-04-11 11:04:30.771329: Current learning rate: 0.00804 +2026-04-11 11:06:12.960449: train_loss -0.2228 +2026-04-11 11:06:12.971010: val_loss -0.1099 +2026-04-11 11:06:12.973501: Pseudo dice [0.3421, 0.7267, 0.3059, 0.1515, 0.3414, 0.7057, 0.3436] +2026-04-11 11:06:12.976664: Epoch time: 102.2 s +2026-04-11 11:06:14.110487: +2026-04-11 11:06:14.113573: Epoch 860 +2026-04-11 11:06:14.115649: Current learning rate: 0.00804 +2026-04-11 11:07:55.928303: train_loss -0.2359 +2026-04-11 11:07:55.939179: val_loss -0.1909 +2026-04-11 11:07:55.942820: Pseudo dice [0.2727, 0.6758, 0.8163, 0.259, 0.3085, 0.4553, 0.6714] +2026-04-11 11:07:55.945374: Epoch time: 101.82 s +2026-04-11 11:07:57.085020: +2026-04-11 11:07:57.086751: Epoch 861 +2026-04-11 11:07:57.089012: Current learning rate: 0.00804 +2026-04-11 11:09:38.820052: train_loss -0.2394 +2026-04-11 11:09:38.826224: val_loss -0.2088 +2026-04-11 11:09:38.828392: Pseudo dice [0.13, 0.8647, 0.7447, 0.503, 0.4013, 0.133, 0.7959] +2026-04-11 11:09:38.830616: Epoch time: 101.74 s +2026-04-11 11:09:39.941839: +2026-04-11 11:09:39.943408: Epoch 862 +2026-04-11 11:09:39.945278: Current learning rate: 0.00804 +2026-04-11 11:11:21.258161: train_loss -0.2387 +2026-04-11 11:11:21.264588: val_loss -0.2192 +2026-04-11 11:11:21.266926: Pseudo dice [0.8772, 0.7436, 0.7312, 0.6251, 0.4127, 0.5693, 0.7934] +2026-04-11 11:11:21.269134: Epoch time: 101.32 s +2026-04-11 11:11:22.451294: +2026-04-11 11:11:22.453469: Epoch 863 +2026-04-11 11:11:22.455938: Current learning rate: 0.00804 +2026-04-11 11:13:04.595038: train_loss -0.2498 +2026-04-11 11:13:04.601812: val_loss -0.192 +2026-04-11 11:13:04.603565: Pseudo dice [0.8632, 0.7669, 0.6485, 0.2953, 0.5048, 0.6778, 0.7973] +2026-04-11 11:13:04.606473: Epoch time: 102.15 s +2026-04-11 11:13:05.737631: +2026-04-11 11:13:05.739580: Epoch 864 +2026-04-11 11:13:05.741657: Current learning rate: 0.00803 +2026-04-11 11:14:47.279007: train_loss -0.2333 +2026-04-11 11:14:47.286664: val_loss -0.1902 +2026-04-11 11:14:47.291420: Pseudo dice [0.6484, 0.8881, 0.7318, 0.2845, 0.2692, 0.4197, 0.7199] +2026-04-11 11:14:47.294342: Epoch time: 101.54 s +2026-04-11 11:14:48.437912: +2026-04-11 11:14:48.440790: Epoch 865 +2026-04-11 11:14:48.442918: Current learning rate: 0.00803 +2026-04-11 11:16:30.615229: train_loss -0.2483 +2026-04-11 11:16:30.622879: val_loss -0.1726 +2026-04-11 11:16:30.626059: Pseudo dice [0.5477, 0.6073, 0.4903, 0.024, 0.3281, 0.8732, 0.7956] +2026-04-11 11:16:30.628200: Epoch time: 102.18 s +2026-04-11 11:16:31.796110: +2026-04-11 11:16:31.797919: Epoch 866 +2026-04-11 11:16:31.799880: Current learning rate: 0.00803 +2026-04-11 11:18:13.361939: train_loss -0.2272 +2026-04-11 11:18:13.369078: val_loss -0.212 +2026-04-11 11:18:13.372239: Pseudo dice [0.1577, 0.8562, 0.7107, 0.4245, 0.2637, 0.7894, 0.8322] +2026-04-11 11:18:13.374671: Epoch time: 101.57 s +2026-04-11 11:18:14.511742: +2026-04-11 11:18:14.513328: Epoch 867 +2026-04-11 11:18:14.515174: Current learning rate: 0.00803 +2026-04-11 11:19:56.190479: train_loss -0.2328 +2026-04-11 11:19:56.195969: val_loss -0.2176 +2026-04-11 11:19:56.197995: Pseudo dice [0.3665, 0.3379, 0.7775, 0.5158, 0.4104, 0.4567, 0.7578] +2026-04-11 11:19:56.200419: Epoch time: 101.68 s +2026-04-11 11:19:57.353275: +2026-04-11 11:19:57.354853: Epoch 868 +2026-04-11 11:19:57.356749: Current learning rate: 0.00802 +2026-04-11 11:21:39.364951: train_loss -0.2167 +2026-04-11 11:21:39.374924: val_loss -0.1704 +2026-04-11 11:21:39.376971: Pseudo dice [0.4821, 0.8745, 0.6185, 0.2645, 0.2414, 0.678, 0.6168] +2026-04-11 11:21:39.379199: Epoch time: 102.02 s +2026-04-11 11:21:40.509382: +2026-04-11 11:21:40.511396: Epoch 869 +2026-04-11 11:21:40.514918: Current learning rate: 0.00802 +2026-04-11 11:23:22.495408: train_loss -0.2257 +2026-04-11 11:23:22.503087: val_loss -0.1773 +2026-04-11 11:23:22.506057: Pseudo dice [0.249, 0.707, 0.8113, 0.2853, 0.1946, 0.3674, 0.5834] +2026-04-11 11:23:22.508643: Epoch time: 101.99 s +2026-04-11 11:23:23.649193: +2026-04-11 11:23:23.651215: Epoch 870 +2026-04-11 11:23:23.653333: Current learning rate: 0.00802 +2026-04-11 11:25:05.836951: train_loss -0.2579 +2026-04-11 11:25:05.844048: val_loss -0.2214 +2026-04-11 11:25:05.853943: Pseudo dice [0.3504, 0.1679, 0.7539, 0.5059, 0.6526, 0.6941, 0.8207] +2026-04-11 11:25:05.867340: Epoch time: 102.19 s +2026-04-11 11:25:07.011032: +2026-04-11 11:25:07.012778: Epoch 871 +2026-04-11 11:25:07.014747: Current learning rate: 0.00802 +2026-04-11 11:26:48.704308: train_loss -0.2479 +2026-04-11 11:26:48.711761: val_loss -0.1416 +2026-04-11 11:26:48.714676: Pseudo dice [0.1621, 0.8747, 0.1869, 0.6105, 0.5022, 0.2041, 0.7776] +2026-04-11 11:26:48.717355: Epoch time: 101.7 s +2026-04-11 11:26:49.835248: +2026-04-11 11:26:49.837280: Epoch 872 +2026-04-11 11:26:49.839175: Current learning rate: 0.00801 +2026-04-11 11:28:31.769539: train_loss -0.2471 +2026-04-11 11:28:31.796325: val_loss -0.1356 +2026-04-11 11:28:31.798518: Pseudo dice [0.8223, 0.6758, 0.5326, 0.7014, 0.5144, 0.2895, 0.75] +2026-04-11 11:28:31.801430: Epoch time: 101.94 s +2026-04-11 11:28:32.942858: +2026-04-11 11:28:32.944831: Epoch 873 +2026-04-11 11:28:32.947330: Current learning rate: 0.00801 +2026-04-11 11:30:14.670276: train_loss -0.2312 +2026-04-11 11:30:14.697923: val_loss -0.2016 +2026-04-11 11:30:14.706040: Pseudo dice [0.5166, 0.8643, 0.5895, 0.1948, 0.4597, 0.751, 0.5334] +2026-04-11 11:30:14.708921: Epoch time: 101.73 s +2026-04-11 11:30:15.884381: +2026-04-11 11:30:15.886983: Epoch 874 +2026-04-11 11:30:15.889782: Current learning rate: 0.00801 +2026-04-11 11:31:58.259806: train_loss -0.2513 +2026-04-11 11:31:58.266195: val_loss -0.2004 +2026-04-11 11:31:58.268167: Pseudo dice [0.5753, 0.6804, 0.6836, 0.712, 0.4449, 0.2983, 0.6629] +2026-04-11 11:31:58.270503: Epoch time: 102.38 s +2026-04-11 11:31:59.407617: +2026-04-11 11:31:59.409387: Epoch 875 +2026-04-11 11:31:59.411520: Current learning rate: 0.00801 +2026-04-11 11:33:41.240630: train_loss -0.2502 +2026-04-11 11:33:41.255268: val_loss -0.1958 +2026-04-11 11:33:41.263500: Pseudo dice [0.6474, 0.7349, 0.7356, 0.1702, 0.493, 0.1407, 0.6169] +2026-04-11 11:33:41.269491: Epoch time: 101.84 s +2026-04-11 11:33:42.393287: +2026-04-11 11:33:42.395354: Epoch 876 +2026-04-11 11:33:42.397391: Current learning rate: 0.00801 +2026-04-11 11:35:24.539944: train_loss -0.246 +2026-04-11 11:35:24.546068: val_loss -0.2252 +2026-04-11 11:35:24.549204: Pseudo dice [0.4224, 0.2551, 0.7176, 0.6373, 0.3046, 0.6758, 0.7662] +2026-04-11 11:35:24.551721: Epoch time: 102.15 s +2026-04-11 11:35:25.687156: +2026-04-11 11:35:25.688960: Epoch 877 +2026-04-11 11:35:25.691044: Current learning rate: 0.008 +2026-04-11 11:37:08.532555: train_loss -0.2529 +2026-04-11 11:37:08.541327: val_loss -0.209 +2026-04-11 11:37:08.543342: Pseudo dice [0.0907, 0.6056, 0.7355, 0.6741, 0.3874, 0.1237, 0.4556] +2026-04-11 11:37:08.545980: Epoch time: 102.85 s +2026-04-11 11:37:09.652269: +2026-04-11 11:37:09.654110: Epoch 878 +2026-04-11 11:37:09.655905: Current learning rate: 0.008 +2026-04-11 11:38:51.320515: train_loss -0.2584 +2026-04-11 11:38:51.328309: val_loss -0.1872 +2026-04-11 11:38:51.330616: Pseudo dice [0.4915, 0.0873, 0.7359, 0.2501, 0.5126, 0.6861, 0.6479] +2026-04-11 11:38:51.332996: Epoch time: 101.67 s +2026-04-11 11:38:52.484529: +2026-04-11 11:38:52.486198: Epoch 879 +2026-04-11 11:38:52.487709: Current learning rate: 0.008 +2026-04-11 11:40:34.462731: train_loss -0.2543 +2026-04-11 11:40:34.469723: val_loss -0.2177 +2026-04-11 11:40:34.474062: Pseudo dice [0.3966, 0.7672, 0.7741, 0.2201, 0.4115, 0.7959, 0.6731] +2026-04-11 11:40:34.476573: Epoch time: 101.98 s +2026-04-11 11:40:35.569983: +2026-04-11 11:40:35.572545: Epoch 880 +2026-04-11 11:40:35.575660: Current learning rate: 0.008 +2026-04-11 11:42:18.113879: train_loss -0.2497 +2026-04-11 11:42:18.121102: val_loss -0.1932 +2026-04-11 11:42:18.124018: Pseudo dice [0.6226, 0.7597, 0.6745, 0.2721, 0.4015, 0.4226, 0.5069] +2026-04-11 11:42:18.127240: Epoch time: 102.55 s +2026-04-11 11:42:19.275571: +2026-04-11 11:42:19.278317: Epoch 881 +2026-04-11 11:42:19.282465: Current learning rate: 0.00799 +2026-04-11 11:44:01.120262: train_loss -0.2606 +2026-04-11 11:44:01.127752: val_loss -0.227 +2026-04-11 11:44:01.130226: Pseudo dice [0.5846, 0.3669, 0.7479, 0.5201, 0.4807, 0.8732, 0.7811] +2026-04-11 11:44:01.132453: Epoch time: 101.85 s +2026-04-11 11:44:02.327777: +2026-04-11 11:44:02.330658: Epoch 882 +2026-04-11 11:44:02.333833: Current learning rate: 0.00799 +2026-04-11 11:45:44.038419: train_loss -0.2604 +2026-04-11 11:45:44.045392: val_loss -0.2467 +2026-04-11 11:45:44.048007: Pseudo dice [0.7421, 0.309, 0.7507, 0.3285, 0.3787, 0.8231, 0.7973] +2026-04-11 11:45:44.051154: Epoch time: 101.71 s +2026-04-11 11:45:45.134599: +2026-04-11 11:45:45.136679: Epoch 883 +2026-04-11 11:45:45.139082: Current learning rate: 0.00799 +2026-04-11 11:47:27.436513: train_loss -0.2613 +2026-04-11 11:47:27.447344: val_loss -0.2149 +2026-04-11 11:47:27.449564: Pseudo dice [0.6305, 0.3372, 0.7637, 0.6456, 0.63, 0.744, 0.7749] +2026-04-11 11:47:27.451975: Epoch time: 102.31 s +2026-04-11 11:47:28.591116: +2026-04-11 11:47:28.593063: Epoch 884 +2026-04-11 11:47:28.595374: Current learning rate: 0.00799 +2026-04-11 11:49:10.659903: train_loss -0.2342 +2026-04-11 11:49:10.667069: val_loss -0.232 +2026-04-11 11:49:10.669247: Pseudo dice [0.7442, 0.8503, 0.7915, 0.5364, 0.5488, 0.2116, 0.1447] +2026-04-11 11:49:10.671273: Epoch time: 102.07 s +2026-04-11 11:49:11.824372: +2026-04-11 11:49:11.826283: Epoch 885 +2026-04-11 11:49:11.828257: Current learning rate: 0.00798 +2026-04-11 11:50:53.654889: train_loss -0.2527 +2026-04-11 11:50:53.661803: val_loss -0.2257 +2026-04-11 11:50:53.663834: Pseudo dice [0.501, 0.0761, 0.7849, 0.576, 0.483, 0.7793, 0.823] +2026-04-11 11:50:53.666060: Epoch time: 101.83 s +2026-04-11 11:50:54.792341: +2026-04-11 11:50:54.794605: Epoch 886 +2026-04-11 11:50:54.803658: Current learning rate: 0.00798 +2026-04-11 11:52:36.622986: train_loss -0.2351 +2026-04-11 11:52:36.630120: val_loss -0.219 +2026-04-11 11:52:36.634280: Pseudo dice [0.5133, 0.7532, 0.7791, 0.5021, 0.3953, 0.756, 0.4444] +2026-04-11 11:52:36.637525: Epoch time: 101.83 s +2026-04-11 11:52:37.786597: +2026-04-11 11:52:37.790241: Epoch 887 +2026-04-11 11:52:37.792930: Current learning rate: 0.00798 +2026-04-11 11:54:19.504566: train_loss -0.2248 +2026-04-11 11:54:19.510855: val_loss -0.2161 +2026-04-11 11:54:19.512887: Pseudo dice [0.3126, 0.7881, 0.7958, 0.4118, 0.3763, 0.8743, 0.7438] +2026-04-11 11:54:19.515501: Epoch time: 101.72 s +2026-04-11 11:54:20.628375: +2026-04-11 11:54:20.630255: Epoch 888 +2026-04-11 11:54:20.632313: Current learning rate: 0.00798 +2026-04-11 11:56:02.724436: train_loss -0.2266 +2026-04-11 11:56:02.730878: val_loss -0.1835 +2026-04-11 11:56:02.732958: Pseudo dice [0.5119, 0.6815, 0.7109, 0.1328, 0.581, 0.6522, 0.6609] +2026-04-11 11:56:02.735278: Epoch time: 102.1 s +2026-04-11 11:56:03.871628: +2026-04-11 11:56:03.873556: Epoch 889 +2026-04-11 11:56:03.876044: Current learning rate: 0.00798 +2026-04-11 11:57:46.148189: train_loss -0.2337 +2026-04-11 11:57:46.154691: val_loss -0.2151 +2026-04-11 11:57:46.156717: Pseudo dice [0.3121, 0.1898, 0.6472, 0.384, 0.3002, 0.7269, 0.7119] +2026-04-11 11:57:46.159033: Epoch time: 102.28 s +2026-04-11 11:57:47.274102: +2026-04-11 11:57:47.275757: Epoch 890 +2026-04-11 11:57:47.277645: Current learning rate: 0.00797 +2026-04-11 11:59:29.145976: train_loss -0.2335 +2026-04-11 11:59:29.151738: val_loss -0.2261 +2026-04-11 11:59:29.153658: Pseudo dice [0.5825, 0.8254, 0.725, 0.5458, 0.4523, 0.7114, 0.6935] +2026-04-11 11:59:29.156189: Epoch time: 101.88 s +2026-04-11 11:59:30.261458: +2026-04-11 11:59:30.263113: Epoch 891 +2026-04-11 11:59:30.265112: Current learning rate: 0.00797 +2026-04-11 12:01:12.280586: train_loss -0.2611 +2026-04-11 12:01:12.287138: val_loss -0.1894 +2026-04-11 12:01:12.289010: Pseudo dice [0.8443, 0.6973, 0.6237, 0.4352, 0.3838, 0.5937, 0.5823] +2026-04-11 12:01:12.291664: Epoch time: 102.02 s +2026-04-11 12:01:13.408914: +2026-04-11 12:01:13.410644: Epoch 892 +2026-04-11 12:01:13.412446: Current learning rate: 0.00797 +2026-04-11 12:02:55.167615: train_loss -0.2702 +2026-04-11 12:02:55.174817: val_loss -0.2069 +2026-04-11 12:02:55.176711: Pseudo dice [0.8465, 0.4526, 0.6135, 0.1538, 0.5123, 0.4146, 0.8221] +2026-04-11 12:02:55.180372: Epoch time: 101.76 s +2026-04-11 12:02:56.314751: +2026-04-11 12:02:56.316809: Epoch 893 +2026-04-11 12:02:56.318684: Current learning rate: 0.00797 +2026-04-11 12:04:37.801934: train_loss -0.267 +2026-04-11 12:04:37.809129: val_loss -0.2461 +2026-04-11 12:04:37.811165: Pseudo dice [0.5244, 0.2816, 0.7425, 0.578, 0.3527, 0.4044, 0.8563] +2026-04-11 12:04:37.814931: Epoch time: 101.49 s +2026-04-11 12:04:38.919871: +2026-04-11 12:04:38.922057: Epoch 894 +2026-04-11 12:04:38.924271: Current learning rate: 0.00796 +2026-04-11 12:06:21.063411: train_loss -0.261 +2026-04-11 12:06:21.070351: val_loss -0.1907 +2026-04-11 12:06:21.073111: Pseudo dice [0.6241, 0.9183, 0.6699, 0.4618, 0.5757, 0.1289, 0.7877] +2026-04-11 12:06:21.075393: Epoch time: 102.15 s +2026-04-11 12:06:22.240659: +2026-04-11 12:06:22.242570: Epoch 895 +2026-04-11 12:06:22.244506: Current learning rate: 0.00796 +2026-04-11 12:08:04.586904: train_loss -0.2366 +2026-04-11 12:08:04.593789: val_loss -0.2054 +2026-04-11 12:08:04.596730: Pseudo dice [0.7458, 0.7338, 0.7885, 0.2622, 0.4838, 0.5717, 0.4306] +2026-04-11 12:08:04.600011: Epoch time: 102.35 s +2026-04-11 12:08:05.728428: +2026-04-11 12:08:05.730780: Epoch 896 +2026-04-11 12:08:05.733384: Current learning rate: 0.00796 +2026-04-11 12:09:47.656796: train_loss -0.2447 +2026-04-11 12:09:47.662455: val_loss -0.214 +2026-04-11 12:09:47.664322: Pseudo dice [0.5652, 0.8726, 0.7683, 0.4022, 0.2122, 0.6742, 0.4744] +2026-04-11 12:09:47.666945: Epoch time: 101.93 s +2026-04-11 12:09:48.764134: +2026-04-11 12:09:48.765896: Epoch 897 +2026-04-11 12:09:48.768676: Current learning rate: 0.00796 +2026-04-11 12:11:30.595339: train_loss -0.2512 +2026-04-11 12:11:30.602325: val_loss -0.2279 +2026-04-11 12:11:30.604371: Pseudo dice [0.5505, 0.4642, 0.7108, 0.8181, 0.3767, 0.7741, 0.7805] +2026-04-11 12:11:30.607290: Epoch time: 101.83 s +2026-04-11 12:11:32.849376: +2026-04-11 12:11:32.851449: Epoch 898 +2026-04-11 12:11:32.853563: Current learning rate: 0.00795 +2026-04-11 12:13:14.525564: train_loss -0.2566 +2026-04-11 12:13:14.534240: val_loss -0.1906 +2026-04-11 12:13:14.537062: Pseudo dice [0.6346, 0.2994, 0.6176, 0.6217, 0.2659, 0.8624, 0.5669] +2026-04-11 12:13:14.540888: Epoch time: 101.68 s +2026-04-11 12:13:15.694019: +2026-04-11 12:13:15.695951: Epoch 899 +2026-04-11 12:13:15.698072: Current learning rate: 0.00795 +2026-04-11 12:14:57.621833: train_loss -0.257 +2026-04-11 12:14:57.628578: val_loss -0.229 +2026-04-11 12:14:57.631089: Pseudo dice [0.5514, 0.3297, 0.765, 0.5121, 0.4406, 0.303, 0.8797] +2026-04-11 12:14:57.633420: Epoch time: 101.93 s +2026-04-11 12:15:00.529870: +2026-04-11 12:15:00.531806: Epoch 900 +2026-04-11 12:15:00.534363: Current learning rate: 0.00795 +2026-04-11 12:16:42.626503: train_loss -0.267 +2026-04-11 12:16:42.635329: val_loss -0.2179 +2026-04-11 12:16:42.637417: Pseudo dice [0.3968, 0.4288, 0.6136, 0.4382, 0.4533, 0.9169, 0.8621] +2026-04-11 12:16:42.641041: Epoch time: 102.1 s +2026-04-11 12:16:43.798903: +2026-04-11 12:16:43.800718: Epoch 901 +2026-04-11 12:16:43.803637: Current learning rate: 0.00795 +2026-04-11 12:18:25.565318: train_loss -0.2619 +2026-04-11 12:18:25.571971: val_loss -0.1975 +2026-04-11 12:18:25.574153: Pseudo dice [0.8408, 0.4784, 0.7155, 0.451, 0.5658, 0.0423, 0.7736] +2026-04-11 12:18:25.576684: Epoch time: 101.77 s +2026-04-11 12:18:26.699898: +2026-04-11 12:18:26.701538: Epoch 902 +2026-04-11 12:18:26.703705: Current learning rate: 0.00795 +2026-04-11 12:20:08.167717: train_loss -0.2544 +2026-04-11 12:20:08.174895: val_loss -0.2001 +2026-04-11 12:20:08.176964: Pseudo dice [0.4581, 0.8666, 0.6999, 0.7312, 0.377, 0.849, 0.732] +2026-04-11 12:20:08.179832: Epoch time: 101.47 s +2026-04-11 12:20:09.303066: +2026-04-11 12:20:09.304720: Epoch 903 +2026-04-11 12:20:09.306644: Current learning rate: 0.00794 +2026-04-11 12:21:50.738976: train_loss -0.2653 +2026-04-11 12:21:50.744982: val_loss -0.2222 +2026-04-11 12:21:50.747051: Pseudo dice [0.4473, 0.8723, 0.7199, 0.8032, 0.3227, 0.1155, 0.6876] +2026-04-11 12:21:50.749525: Epoch time: 101.44 s +2026-04-11 12:21:51.883669: +2026-04-11 12:21:51.885516: Epoch 904 +2026-04-11 12:21:51.887465: Current learning rate: 0.00794 +2026-04-11 12:23:33.194641: train_loss -0.2543 +2026-04-11 12:23:33.200498: val_loss -0.1703 +2026-04-11 12:23:33.203475: Pseudo dice [0.539, 0.2161, 0.6252, 0.3708, 0.578, 0.4577, 0.4686] +2026-04-11 12:23:33.205770: Epoch time: 101.31 s +2026-04-11 12:23:34.425890: +2026-04-11 12:23:34.427682: Epoch 905 +2026-04-11 12:23:34.430363: Current learning rate: 0.00794 +2026-04-11 12:25:16.470707: train_loss -0.2552 +2026-04-11 12:25:16.477762: val_loss -0.1956 +2026-04-11 12:25:16.480119: Pseudo dice [0.5571, 0.928, 0.783, 0.3182, 0.4898, 0.3593, 0.7763] +2026-04-11 12:25:16.482504: Epoch time: 102.05 s +2026-04-11 12:25:17.581960: +2026-04-11 12:25:17.583846: Epoch 906 +2026-04-11 12:25:17.586703: Current learning rate: 0.00794 +2026-04-11 12:26:59.500906: train_loss -0.2406 +2026-04-11 12:26:59.508678: val_loss -0.185 +2026-04-11 12:26:59.511134: Pseudo dice [0.466, 0.4156, 0.7724, 0.3682, 0.3942, 0.7454, 0.4953] +2026-04-11 12:26:59.513686: Epoch time: 101.92 s +2026-04-11 12:27:00.639447: +2026-04-11 12:27:00.641784: Epoch 907 +2026-04-11 12:27:00.644022: Current learning rate: 0.00793 +2026-04-11 12:28:42.696842: train_loss -0.2501 +2026-04-11 12:28:42.723998: val_loss -0.1996 +2026-04-11 12:28:42.726039: Pseudo dice [0.2772, 0.8435, 0.763, 0.637, 0.5506, 0.595, 0.7462] +2026-04-11 12:28:42.728262: Epoch time: 102.06 s +2026-04-11 12:28:43.846354: +2026-04-11 12:28:43.848465: Epoch 908 +2026-04-11 12:28:43.850728: Current learning rate: 0.00793 +2026-04-11 12:30:25.434793: train_loss -0.2412 +2026-04-11 12:30:25.442969: val_loss -0.2122 +2026-04-11 12:30:25.445791: Pseudo dice [0.5313, 0.7811, 0.7306, 0.711, 0.4254, 0.3851, 0.6579] +2026-04-11 12:30:25.448834: Epoch time: 101.59 s +2026-04-11 12:30:26.562309: +2026-04-11 12:30:26.564276: Epoch 909 +2026-04-11 12:30:26.566630: Current learning rate: 0.00793 +2026-04-11 12:32:08.604972: train_loss -0.2442 +2026-04-11 12:32:08.611990: val_loss -0.1344 +2026-04-11 12:32:08.614718: Pseudo dice [0.6585, 0.4707, 0.5031, 0.3972, 0.4205, 0.1102, 0.6334] +2026-04-11 12:32:08.617011: Epoch time: 102.05 s +2026-04-11 12:32:09.778300: +2026-04-11 12:32:09.780129: Epoch 910 +2026-04-11 12:32:09.782733: Current learning rate: 0.00793 +2026-04-11 12:33:51.476546: train_loss -0.2447 +2026-04-11 12:33:51.484156: val_loss -0.2405 +2026-04-11 12:33:51.488652: Pseudo dice [0.5985, 0.2965, 0.7924, 0.5643, 0.2778, 0.7444, 0.7003] +2026-04-11 12:33:51.492262: Epoch time: 101.7 s +2026-04-11 12:33:52.616156: +2026-04-11 12:33:52.618197: Epoch 911 +2026-04-11 12:33:52.621690: Current learning rate: 0.00792 +2026-04-11 12:35:34.139633: train_loss -0.2687 +2026-04-11 12:35:34.147001: val_loss -0.2517 +2026-04-11 12:35:34.149832: Pseudo dice [0.1903, 0.7805, 0.8109, 0.4292, 0.4565, 0.8758, 0.663] +2026-04-11 12:35:34.152792: Epoch time: 101.53 s +2026-04-11 12:35:35.287403: +2026-04-11 12:35:35.289411: Epoch 912 +2026-04-11 12:35:35.291911: Current learning rate: 0.00792 +2026-04-11 12:37:16.830323: train_loss -0.2593 +2026-04-11 12:37:16.838210: val_loss -0.229 +2026-04-11 12:37:16.840669: Pseudo dice [0.5216, 0.8957, 0.7905, 0.7565, 0.347, 0.6186, 0.5163] +2026-04-11 12:37:16.844070: Epoch time: 101.55 s +2026-04-11 12:37:17.979306: +2026-04-11 12:37:17.981421: Epoch 913 +2026-04-11 12:37:17.983467: Current learning rate: 0.00792 +2026-04-11 12:38:59.668509: train_loss -0.2667 +2026-04-11 12:38:59.674000: val_loss -0.2474 +2026-04-11 12:38:59.675991: Pseudo dice [0.5781, 0.5601, 0.7937, 0.6724, 0.508, 0.7219, 0.7584] +2026-04-11 12:38:59.678595: Epoch time: 101.69 s +2026-04-11 12:39:00.820097: +2026-04-11 12:39:00.822046: Epoch 914 +2026-04-11 12:39:00.825155: Current learning rate: 0.00792 +2026-04-11 12:40:42.334314: train_loss -0.2707 +2026-04-11 12:40:42.341361: val_loss -0.209 +2026-04-11 12:40:42.343535: Pseudo dice [0.3197, 0.597, 0.76, 0.8421, 0.4456, 0.4359, 0.3502] +2026-04-11 12:40:42.346911: Epoch time: 101.52 s +2026-04-11 12:40:43.463388: +2026-04-11 12:40:43.465059: Epoch 915 +2026-04-11 12:40:43.467087: Current learning rate: 0.00792 +2026-04-11 12:42:25.123128: train_loss -0.2498 +2026-04-11 12:42:25.130558: val_loss -0.1556 +2026-04-11 12:42:25.136019: Pseudo dice [0.4749, 0.4987, 0.229, 0.4159, 0.3548, 0.2893, 0.8047] +2026-04-11 12:42:25.139435: Epoch time: 101.66 s +2026-04-11 12:42:26.274177: +2026-04-11 12:42:26.276901: Epoch 916 +2026-04-11 12:42:26.279211: Current learning rate: 0.00791 +2026-04-11 12:44:08.538028: train_loss -0.2357 +2026-04-11 12:44:08.551554: val_loss -0.215 +2026-04-11 12:44:08.554319: Pseudo dice [0.1723, 0.7538, 0.7599, 0.7977, 0.455, 0.1215, 0.7833] +2026-04-11 12:44:08.557556: Epoch time: 102.27 s +2026-04-11 12:44:09.722534: +2026-04-11 12:44:09.724269: Epoch 917 +2026-04-11 12:44:09.726038: Current learning rate: 0.00791 +2026-04-11 12:45:51.577138: train_loss -0.2353 +2026-04-11 12:45:51.584359: val_loss -0.1727 +2026-04-11 12:45:51.586917: Pseudo dice [0.3802, 0.7815, 0.3945, 0.5003, 0.5901, 0.1754, 0.8173] +2026-04-11 12:45:51.589323: Epoch time: 101.86 s +2026-04-11 12:45:52.722545: +2026-04-11 12:45:52.724817: Epoch 918 +2026-04-11 12:45:52.726835: Current learning rate: 0.00791 +2026-04-11 12:47:34.322933: train_loss -0.2422 +2026-04-11 12:47:34.330740: val_loss -0.1137 +2026-04-11 12:47:34.333094: Pseudo dice [0.6481, 0.8523, 0.1984, 0.072, 0.4202, 0.191, 0.3122] +2026-04-11 12:47:34.335790: Epoch time: 101.6 s +2026-04-11 12:47:36.650373: +2026-04-11 12:47:36.652040: Epoch 919 +2026-04-11 12:47:36.654976: Current learning rate: 0.00791 +2026-04-11 12:49:18.804205: train_loss -0.2251 +2026-04-11 12:49:18.810570: val_loss -0.1773 +2026-04-11 12:49:18.812778: Pseudo dice [0.1748, 0.7574, 0.6471, 0.3643, 0.4011, 0.7931, 0.7345] +2026-04-11 12:49:18.815020: Epoch time: 102.16 s +2026-04-11 12:49:19.980353: +2026-04-11 12:49:19.982328: Epoch 920 +2026-04-11 12:49:19.984613: Current learning rate: 0.0079 +2026-04-11 12:51:01.774392: train_loss -0.2452 +2026-04-11 12:51:01.781740: val_loss -0.2058 +2026-04-11 12:51:01.784371: Pseudo dice [0.5041, 0.5955, 0.782, 0.6556, 0.2821, 0.8695, 0.6489] +2026-04-11 12:51:01.786825: Epoch time: 101.8 s +2026-04-11 12:51:02.926701: +2026-04-11 12:51:02.928657: Epoch 921 +2026-04-11 12:51:02.931079: Current learning rate: 0.0079 +2026-04-11 12:52:45.068093: train_loss -0.2381 +2026-04-11 12:52:45.074048: val_loss -0.2045 +2026-04-11 12:52:45.076021: Pseudo dice [0.1217, 0.5138, 0.6278, 0.4888, 0.5846, 0.8643, 0.4702] +2026-04-11 12:52:45.078214: Epoch time: 102.14 s +2026-04-11 12:52:46.214790: +2026-04-11 12:52:46.216495: Epoch 922 +2026-04-11 12:52:46.218446: Current learning rate: 0.0079 +2026-04-11 12:54:28.550343: train_loss -0.2442 +2026-04-11 12:54:28.557425: val_loss -0.2277 +2026-04-11 12:54:28.559273: Pseudo dice [0.3357, 0.3955, 0.552, 0.5654, 0.5302, 0.8453, 0.6533] +2026-04-11 12:54:28.568619: Epoch time: 102.34 s +2026-04-11 12:54:29.662058: +2026-04-11 12:54:29.664357: Epoch 923 +2026-04-11 12:54:29.666614: Current learning rate: 0.0079 +2026-04-11 12:56:11.515823: train_loss -0.2353 +2026-04-11 12:56:11.522783: val_loss -0.1761 +2026-04-11 12:56:11.525084: Pseudo dice [0.6915, 0.7963, 0.4769, 0.3749, 0.3482, 0.3642, 0.8086] +2026-04-11 12:56:11.527237: Epoch time: 101.86 s +2026-04-11 12:56:12.633395: +2026-04-11 12:56:12.635179: Epoch 924 +2026-04-11 12:56:12.637017: Current learning rate: 0.00789 +2026-04-11 12:57:54.863290: train_loss -0.2413 +2026-04-11 12:57:54.869625: val_loss -0.2347 +2026-04-11 12:57:54.871767: Pseudo dice [0.5451, 0.7643, 0.5304, 0.5373, 0.3971, 0.8003, 0.8264] +2026-04-11 12:57:54.874760: Epoch time: 102.23 s +2026-04-11 12:57:55.969590: +2026-04-11 12:57:55.971938: Epoch 925 +2026-04-11 12:57:55.974049: Current learning rate: 0.00789 +2026-04-11 12:59:38.095736: train_loss -0.2439 +2026-04-11 12:59:38.102494: val_loss -0.1303 +2026-04-11 12:59:38.104601: Pseudo dice [0.7742, 0.6253, 0.4322, 0.0924, 0.2469, 0.681, 0.3312] +2026-04-11 12:59:38.106701: Epoch time: 102.13 s +2026-04-11 12:59:39.198412: +2026-04-11 12:59:39.200284: Epoch 926 +2026-04-11 12:59:39.202518: Current learning rate: 0.00789 +2026-04-11 13:01:21.382328: train_loss -0.2214 +2026-04-11 13:01:21.388621: val_loss -0.2203 +2026-04-11 13:01:21.391060: Pseudo dice [0.776, 0.8452, 0.7704, 0.5517, 0.5024, 0.5977, 0.8929] +2026-04-11 13:01:21.393477: Epoch time: 102.19 s +2026-04-11 13:01:22.480196: +2026-04-11 13:01:22.482252: Epoch 927 +2026-04-11 13:01:22.484249: Current learning rate: 0.00789 +2026-04-11 13:03:04.377398: train_loss -0.2424 +2026-04-11 13:03:04.384644: val_loss -0.2144 +2026-04-11 13:03:04.386604: Pseudo dice [0.2349, 0.1793, 0.6672, 0.4648, 0.464, 0.4379, 0.7144] +2026-04-11 13:03:04.389162: Epoch time: 101.9 s +2026-04-11 13:03:05.498124: +2026-04-11 13:03:05.500464: Epoch 928 +2026-04-11 13:03:05.502471: Current learning rate: 0.00789 +2026-04-11 13:04:47.937910: train_loss -0.2587 +2026-04-11 13:04:47.943922: val_loss -0.2097 +2026-04-11 13:04:47.945804: Pseudo dice [0.71, 0.642, 0.7186, 0.484, 0.5081, 0.6966, 0.5884] +2026-04-11 13:04:47.948293: Epoch time: 102.44 s +2026-04-11 13:04:49.081869: +2026-04-11 13:04:49.083611: Epoch 929 +2026-04-11 13:04:49.085595: Current learning rate: 0.00788 +2026-04-11 13:06:31.596860: train_loss -0.2534 +2026-04-11 13:06:31.603176: val_loss -0.1939 +2026-04-11 13:06:31.605421: Pseudo dice [0.3957, 0.2053, 0.7074, 0.315, 0.5037, 0.899, 0.6568] +2026-04-11 13:06:31.607503: Epoch time: 102.52 s +2026-04-11 13:06:32.756306: +2026-04-11 13:06:32.758034: Epoch 930 +2026-04-11 13:06:32.760167: Current learning rate: 0.00788 +2026-04-11 13:08:15.150986: train_loss -0.2486 +2026-04-11 13:08:15.160825: val_loss -0.2001 +2026-04-11 13:08:15.167686: Pseudo dice [0.6423, 0.3447, 0.6991, 0.4565, 0.3732, 0.644, 0.5174] +2026-04-11 13:08:15.169957: Epoch time: 102.4 s +2026-04-11 13:08:16.248951: +2026-04-11 13:08:16.251463: Epoch 931 +2026-04-11 13:08:16.254416: Current learning rate: 0.00788 +2026-04-11 13:09:57.914450: train_loss -0.2541 +2026-04-11 13:09:57.921689: val_loss -0.198 +2026-04-11 13:09:57.925036: Pseudo dice [0.1729, 0.346, 0.643, 0.4406, 0.294, 0.1618, 0.73] +2026-04-11 13:09:57.927397: Epoch time: 101.67 s +2026-04-11 13:09:59.011600: +2026-04-11 13:09:59.014180: Epoch 932 +2026-04-11 13:09:59.016098: Current learning rate: 0.00788 +2026-04-11 13:11:41.004934: train_loss -0.2621 +2026-04-11 13:11:41.011227: val_loss -0.2127 +2026-04-11 13:11:41.013375: Pseudo dice [0.4542, 0.6241, 0.5928, 0.5394, 0.4548, 0.407, 0.5894] +2026-04-11 13:11:41.016563: Epoch time: 102.0 s +2026-04-11 13:11:42.112536: +2026-04-11 13:11:42.115791: Epoch 933 +2026-04-11 13:11:42.118318: Current learning rate: 0.00787 +2026-04-11 13:13:24.801771: train_loss -0.2568 +2026-04-11 13:13:24.808692: val_loss -0.2256 +2026-04-11 13:13:24.811030: Pseudo dice [0.5482, 0.8587, 0.6604, 0.4431, 0.2159, 0.1513, 0.7013] +2026-04-11 13:13:24.813215: Epoch time: 102.69 s +2026-04-11 13:13:25.897040: +2026-04-11 13:13:25.899045: Epoch 934 +2026-04-11 13:13:25.901084: Current learning rate: 0.00787 +2026-04-11 13:15:07.744874: train_loss -0.2599 +2026-04-11 13:15:07.752068: val_loss -0.2363 +2026-04-11 13:15:07.754014: Pseudo dice [0.4191, 0.4109, 0.8007, 0.649, 0.4059, 0.4316, 0.8707] +2026-04-11 13:15:07.756374: Epoch time: 101.85 s +2026-04-11 13:15:08.844577: +2026-04-11 13:15:08.846331: Epoch 935 +2026-04-11 13:15:08.849087: Current learning rate: 0.00787 +2026-04-11 13:16:50.757195: train_loss -0.2397 +2026-04-11 13:16:50.765146: val_loss -0.1941 +2026-04-11 13:16:50.768816: Pseudo dice [0.4401, 0.0307, 0.7182, 0.5395, 0.4146, 0.5596, 0.727] +2026-04-11 13:16:50.772994: Epoch time: 101.92 s +2026-04-11 13:16:51.874498: +2026-04-11 13:16:51.876970: Epoch 936 +2026-04-11 13:16:51.879646: Current learning rate: 0.00787 +2026-04-11 13:18:33.790287: train_loss -0.234 +2026-04-11 13:18:33.796279: val_loss -0.1888 +2026-04-11 13:18:33.798094: Pseudo dice [0.7486, 0.3437, 0.7888, 0.378, 0.42, 0.8435, 0.7015] +2026-04-11 13:18:33.800683: Epoch time: 101.92 s +2026-04-11 13:18:34.893879: +2026-04-11 13:18:34.895845: Epoch 937 +2026-04-11 13:18:34.898022: Current learning rate: 0.00786 +2026-04-11 13:20:16.985481: train_loss -0.2544 +2026-04-11 13:20:16.992450: val_loss -0.2045 +2026-04-11 13:20:16.995987: Pseudo dice [0.3182, 0.3823, 0.5998, 0.3681, 0.5066, 0.531, 0.7937] +2026-04-11 13:20:16.999254: Epoch time: 102.09 s +2026-04-11 13:20:18.106381: +2026-04-11 13:20:18.108407: Epoch 938 +2026-04-11 13:20:18.111469: Current learning rate: 0.00786 +2026-04-11 13:22:00.170173: train_loss -0.2648 +2026-04-11 13:22:00.177095: val_loss -0.2009 +2026-04-11 13:22:00.179883: Pseudo dice [0.6077, 0.891, 0.6132, 0.5007, 0.3872, 0.8024, 0.6286] +2026-04-11 13:22:00.182518: Epoch time: 102.07 s +2026-04-11 13:22:01.280423: +2026-04-11 13:22:01.282351: Epoch 939 +2026-04-11 13:22:01.284682: Current learning rate: 0.00786 +2026-04-11 13:23:43.229351: train_loss -0.2619 +2026-04-11 13:23:43.248055: val_loss -0.2266 +2026-04-11 13:23:43.254484: Pseudo dice [0.6593, 0.6409, 0.7803, 0.3298, 0.3458, 0.8511, 0.7949] +2026-04-11 13:23:43.259853: Epoch time: 101.95 s +2026-04-11 13:23:44.373028: +2026-04-11 13:23:44.377253: Epoch 940 +2026-04-11 13:23:44.380339: Current learning rate: 0.00786 +2026-04-11 13:25:27.467498: train_loss -0.2527 +2026-04-11 13:25:27.473146: val_loss -0.2135 +2026-04-11 13:25:27.475262: Pseudo dice [0.5521, 0.8638, 0.7974, 0.4545, 0.3985, 0.091, 0.7647] +2026-04-11 13:25:27.477710: Epoch time: 103.1 s +2026-04-11 13:25:28.568122: +2026-04-11 13:25:28.569695: Epoch 941 +2026-04-11 13:25:28.571501: Current learning rate: 0.00786 +2026-04-11 13:27:10.942979: train_loss -0.2553 +2026-04-11 13:27:10.949541: val_loss -0.1934 +2026-04-11 13:27:10.951962: Pseudo dice [0.7005, 0.8984, 0.7426, 0.4355, 0.1852, 0.6983, 0.5952] +2026-04-11 13:27:10.954365: Epoch time: 102.38 s +2026-04-11 13:27:12.044020: +2026-04-11 13:27:12.046234: Epoch 942 +2026-04-11 13:27:12.048196: Current learning rate: 0.00785 +2026-04-11 13:28:54.079657: train_loss -0.2498 +2026-04-11 13:28:54.099266: val_loss -0.1985 +2026-04-11 13:28:54.101312: Pseudo dice [0.7006, 0.4289, 0.7742, 0.2482, 0.2196, 0.8779, 0.8231] +2026-04-11 13:28:54.103956: Epoch time: 102.04 s +2026-04-11 13:28:55.216547: +2026-04-11 13:28:55.219218: Epoch 943 +2026-04-11 13:28:55.221109: Current learning rate: 0.00785 +2026-04-11 13:30:37.717412: train_loss -0.2524 +2026-04-11 13:30:37.725719: val_loss -0.2275 +2026-04-11 13:30:37.728078: Pseudo dice [0.6386, 0.8976, 0.8068, 0.715, 0.4901, 0.3813, 0.5785] +2026-04-11 13:30:37.730561: Epoch time: 102.5 s +2026-04-11 13:30:38.830052: +2026-04-11 13:30:38.832491: Epoch 944 +2026-04-11 13:30:38.834865: Current learning rate: 0.00785 +2026-04-11 13:32:20.579963: train_loss -0.2529 +2026-04-11 13:32:20.587170: val_loss -0.2369 +2026-04-11 13:32:20.589570: Pseudo dice [0.625, 0.658, 0.8013, 0.6357, 0.4534, 0.7661, 0.8197] +2026-04-11 13:32:20.591747: Epoch time: 101.75 s +2026-04-11 13:32:21.715395: +2026-04-11 13:32:21.717557: Epoch 945 +2026-04-11 13:32:21.720460: Current learning rate: 0.00785 +2026-04-11 13:34:03.597032: train_loss -0.2367 +2026-04-11 13:34:03.604959: val_loss -0.1642 +2026-04-11 13:34:03.607021: Pseudo dice [0.3388, 0.8762, 0.5797, 0.2901, 0.3842, 0.4091, 0.6712] +2026-04-11 13:34:03.609194: Epoch time: 101.88 s +2026-04-11 13:34:04.689172: +2026-04-11 13:34:04.691074: Epoch 946 +2026-04-11 13:34:04.693037: Current learning rate: 0.00784 +2026-04-11 13:35:46.712440: train_loss -0.26 +2026-04-11 13:35:46.718961: val_loss -0.2177 +2026-04-11 13:35:46.720865: Pseudo dice [0.7442, 0.9089, 0.7572, 0.7182, 0.5605, 0.2244, 0.7708] +2026-04-11 13:35:46.723243: Epoch time: 102.03 s +2026-04-11 13:35:47.801826: +2026-04-11 13:35:47.803961: Epoch 947 +2026-04-11 13:35:47.806024: Current learning rate: 0.00784 +2026-04-11 13:37:30.122374: train_loss -0.2485 +2026-04-11 13:37:30.128881: val_loss -0.1655 +2026-04-11 13:37:30.131603: Pseudo dice [0.4581, 0.6198, 0.5798, 0.2942, 0.3162, 0.2308, 0.5577] +2026-04-11 13:37:30.134091: Epoch time: 102.32 s +2026-04-11 13:37:31.245017: +2026-04-11 13:37:31.246864: Epoch 948 +2026-04-11 13:37:31.249846: Current learning rate: 0.00784 +2026-04-11 13:39:13.124591: train_loss -0.257 +2026-04-11 13:39:13.133158: val_loss -0.227 +2026-04-11 13:39:13.135359: Pseudo dice [0.6079, 0.3813, 0.7669, 0.8095, 0.2835, 0.6291, 0.7522] +2026-04-11 13:39:13.137941: Epoch time: 101.88 s +2026-04-11 13:39:14.256686: +2026-04-11 13:39:14.258966: Epoch 949 +2026-04-11 13:39:14.261076: Current learning rate: 0.00784 +2026-04-11 13:40:56.228645: train_loss -0.2723 +2026-04-11 13:40:56.235160: val_loss -0.2164 +2026-04-11 13:40:56.237672: Pseudo dice [0.3554, 0.1575, 0.8204, 0.3699, 0.4321, 0.76, 0.857] +2026-04-11 13:40:56.241187: Epoch time: 101.98 s +2026-04-11 13:40:58.977187: +2026-04-11 13:40:58.979603: Epoch 950 +2026-04-11 13:40:58.981138: Current learning rate: 0.00783 +2026-04-11 13:42:40.770128: train_loss -0.242 +2026-04-11 13:42:40.776373: val_loss -0.2054 +2026-04-11 13:42:40.778790: Pseudo dice [0.7528, 0.3784, 0.6695, 0.3883, 0.2345, 0.3287, 0.788] +2026-04-11 13:42:40.781862: Epoch time: 101.8 s +2026-04-11 13:42:41.892936: +2026-04-11 13:42:41.894864: Epoch 951 +2026-04-11 13:42:41.897107: Current learning rate: 0.00783 +2026-04-11 13:44:23.542651: train_loss -0.2364 +2026-04-11 13:44:23.551108: val_loss -0.1549 +2026-04-11 13:44:23.553861: Pseudo dice [0.3691, 0.5912, 0.6206, 0.074, 0.3799, 0.6538, 0.3395] +2026-04-11 13:44:23.556860: Epoch time: 101.65 s +2026-04-11 13:44:24.719818: +2026-04-11 13:44:24.722080: Epoch 952 +2026-04-11 13:44:24.724066: Current learning rate: 0.00783 +2026-04-11 13:46:06.965231: train_loss -0.2519 +2026-04-11 13:46:06.972972: val_loss -0.2072 +2026-04-11 13:46:06.976419: Pseudo dice [0.7073, 0.8447, 0.5638, 0.5021, 0.2667, 0.7346, 0.7368] +2026-04-11 13:46:06.980042: Epoch time: 102.25 s +2026-04-11 13:46:08.092278: +2026-04-11 13:46:08.094760: Epoch 953 +2026-04-11 13:46:08.096816: Current learning rate: 0.00783 +2026-04-11 13:47:50.504952: train_loss -0.2555 +2026-04-11 13:47:50.511319: val_loss -0.1994 +2026-04-11 13:47:50.513585: Pseudo dice [0.7316, 0.7308, 0.7429, 0.2899, 0.203, 0.2469, 0.4649] +2026-04-11 13:47:50.516993: Epoch time: 102.42 s +2026-04-11 13:47:51.638632: +2026-04-11 13:47:51.640256: Epoch 954 +2026-04-11 13:47:51.642127: Current learning rate: 0.00783 +2026-04-11 13:49:33.642244: train_loss -0.2388 +2026-04-11 13:49:33.648639: val_loss -0.2005 +2026-04-11 13:49:33.650629: Pseudo dice [0.035, 0.7987, 0.7781, 0.4964, 0.1872, 0.1733, 0.8406] +2026-04-11 13:49:33.653158: Epoch time: 102.01 s +2026-04-11 13:49:34.773461: +2026-04-11 13:49:34.775406: Epoch 955 +2026-04-11 13:49:34.777871: Current learning rate: 0.00782 +2026-04-11 13:51:16.300866: train_loss -0.2501 +2026-04-11 13:51:16.310528: val_loss -0.1641 +2026-04-11 13:51:16.312487: Pseudo dice [0.2886, 0.552, 0.6109, 0.8375, 0.3814, 0.2068, 0.7587] +2026-04-11 13:51:16.314997: Epoch time: 101.53 s +2026-04-11 13:51:17.430749: +2026-04-11 13:51:17.433468: Epoch 956 +2026-04-11 13:51:17.435775: Current learning rate: 0.00782 +2026-04-11 13:52:59.217292: train_loss -0.252 +2026-04-11 13:52:59.224202: val_loss -0.1683 +2026-04-11 13:52:59.226804: Pseudo dice [0.5533, 0.8434, 0.6163, 0.319, 0.4471, 0.3316, 0.5558] +2026-04-11 13:52:59.230183: Epoch time: 101.79 s +2026-04-11 13:53:00.344627: +2026-04-11 13:53:00.348161: Epoch 957 +2026-04-11 13:53:00.350215: Current learning rate: 0.00782 +2026-04-11 13:54:42.847143: train_loss -0.2409 +2026-04-11 13:54:42.854625: val_loss -0.1611 +2026-04-11 13:54:42.856427: Pseudo dice [0.5124, 0.9201, 0.5727, 0.3159, 0.4229, 0.4331, 0.3032] +2026-04-11 13:54:42.858702: Epoch time: 102.51 s +2026-04-11 13:54:43.974155: +2026-04-11 13:54:43.975784: Epoch 958 +2026-04-11 13:54:43.977566: Current learning rate: 0.00782 +2026-04-11 13:56:25.491169: train_loss -0.239 +2026-04-11 13:56:25.497508: val_loss -0.2079 +2026-04-11 13:56:25.499648: Pseudo dice [0.4551, 0.7881, 0.7447, 0.2957, 0.5509, 0.5618, 0.7375] +2026-04-11 13:56:25.501838: Epoch time: 101.52 s +2026-04-11 13:56:26.606783: +2026-04-11 13:56:26.608460: Epoch 959 +2026-04-11 13:56:26.610259: Current learning rate: 0.00781 +2026-04-11 13:58:08.860881: train_loss -0.2637 +2026-04-11 13:58:08.868374: val_loss -0.244 +2026-04-11 13:58:08.870308: Pseudo dice [0.7201, 0.376, 0.8106, 0.4716, 0.6418, 0.803, 0.8189] +2026-04-11 13:58:08.872722: Epoch time: 102.26 s +2026-04-11 13:58:09.996684: +2026-04-11 13:58:09.998768: Epoch 960 +2026-04-11 13:58:10.000530: Current learning rate: 0.00781 +2026-04-11 13:59:51.951912: train_loss -0.2602 +2026-04-11 13:59:51.958061: val_loss -0.214 +2026-04-11 13:59:51.959979: Pseudo dice [0.5518, 0.9096, 0.7872, 0.4152, 0.5689, 0.57, 0.2334] +2026-04-11 13:59:51.962351: Epoch time: 101.96 s +2026-04-11 13:59:54.242281: +2026-04-11 13:59:54.244447: Epoch 961 +2026-04-11 13:59:54.246644: Current learning rate: 0.00781 +2026-04-11 14:01:36.234950: train_loss -0.258 +2026-04-11 14:01:36.241184: val_loss -0.1968 +2026-04-11 14:01:36.243300: Pseudo dice [0.7502, 0.8161, 0.6523, 0.4064, 0.4979, 0.6981, 0.4382] +2026-04-11 14:01:36.245727: Epoch time: 102.0 s +2026-04-11 14:01:37.362300: +2026-04-11 14:01:37.364480: Epoch 962 +2026-04-11 14:01:37.368415: Current learning rate: 0.00781 +2026-04-11 14:03:19.927153: train_loss -0.265 +2026-04-11 14:03:19.937223: val_loss -0.1456 +2026-04-11 14:03:19.939322: Pseudo dice [0.3307, 0.8887, 0.6337, 0.3693, 0.5232, 0.4099, 0.6244] +2026-04-11 14:03:19.941692: Epoch time: 102.57 s +2026-04-11 14:03:21.044106: +2026-04-11 14:03:21.045933: Epoch 963 +2026-04-11 14:03:21.048135: Current learning rate: 0.0078 +2026-04-11 14:05:03.212870: train_loss -0.2542 +2026-04-11 14:05:03.219299: val_loss -0.2092 +2026-04-11 14:05:03.221317: Pseudo dice [0.6999, 0.55, 0.7603, 0.6007, 0.5946, 0.4762, 0.7557] +2026-04-11 14:05:03.223580: Epoch time: 102.17 s +2026-04-11 14:05:04.354158: +2026-04-11 14:05:04.357179: Epoch 964 +2026-04-11 14:05:04.359371: Current learning rate: 0.0078 +2026-04-11 14:06:46.306238: train_loss -0.2585 +2026-04-11 14:06:46.313707: val_loss -0.2047 +2026-04-11 14:06:46.315909: Pseudo dice [0.2231, 0.8665, 0.7113, 0.5514, 0.2201, 0.5663, 0.655] +2026-04-11 14:06:46.318548: Epoch time: 101.96 s +2026-04-11 14:06:47.436773: +2026-04-11 14:06:47.438761: Epoch 965 +2026-04-11 14:06:47.440702: Current learning rate: 0.0078 +2026-04-11 14:08:29.668330: train_loss -0.2376 +2026-04-11 14:08:29.674782: val_loss -0.2 +2026-04-11 14:08:29.677171: Pseudo dice [0.6487, 0.6218, 0.6634, 0.0748, 0.3629, 0.7758, 0.4633] +2026-04-11 14:08:29.679500: Epoch time: 102.23 s +2026-04-11 14:08:30.830353: +2026-04-11 14:08:30.832528: Epoch 966 +2026-04-11 14:08:30.835042: Current learning rate: 0.0078 +2026-04-11 14:10:12.473174: train_loss -0.2483 +2026-04-11 14:10:12.479168: val_loss -0.1947 +2026-04-11 14:10:12.481202: Pseudo dice [0.6733, 0.3041, 0.7697, 0.306, 0.3621, 0.3083, 0.8152] +2026-04-11 14:10:12.483652: Epoch time: 101.65 s +2026-04-11 14:10:13.595835: +2026-04-11 14:10:13.597406: Epoch 967 +2026-04-11 14:10:13.599231: Current learning rate: 0.0078 +2026-04-11 14:11:55.417026: train_loss -0.2502 +2026-04-11 14:11:55.424588: val_loss -0.1695 +2026-04-11 14:11:55.426889: Pseudo dice [0.58, 0.1898, 0.5148, 0.6651, 0.362, 0.481, 0.7137] +2026-04-11 14:11:55.429331: Epoch time: 101.82 s +2026-04-11 14:11:56.555613: +2026-04-11 14:11:56.558025: Epoch 968 +2026-04-11 14:11:56.560050: Current learning rate: 0.00779 +2026-04-11 14:13:38.791879: train_loss -0.2532 +2026-04-11 14:13:38.798858: val_loss -0.2157 +2026-04-11 14:13:38.801190: Pseudo dice [0.2558, 0.7735, 0.7352, 0.4468, 0.4142, 0.5631, 0.7651] +2026-04-11 14:13:38.803586: Epoch time: 102.24 s +2026-04-11 14:13:39.925752: +2026-04-11 14:13:39.927968: Epoch 969 +2026-04-11 14:13:39.930063: Current learning rate: 0.00779 +2026-04-11 14:15:22.112788: train_loss -0.2435 +2026-04-11 14:15:22.119775: val_loss -0.207 +2026-04-11 14:15:22.121763: Pseudo dice [0.4484, 0.5522, 0.7104, 0.5419, 0.3667, 0.5045, 0.7162] +2026-04-11 14:15:22.126178: Epoch time: 102.19 s +2026-04-11 14:15:23.317348: +2026-04-11 14:15:23.319556: Epoch 970 +2026-04-11 14:15:23.321753: Current learning rate: 0.00779 +2026-04-11 14:17:05.294586: train_loss -0.227 +2026-04-11 14:17:05.300419: val_loss -0.2265 +2026-04-11 14:17:05.302371: Pseudo dice [0.3117, 0.7874, 0.6743, 0.269, 0.3537, 0.8602, 0.8205] +2026-04-11 14:17:05.304569: Epoch time: 101.98 s +2026-04-11 14:17:06.418717: +2026-04-11 14:17:06.420640: Epoch 971 +2026-04-11 14:17:06.422619: Current learning rate: 0.00779 +2026-04-11 14:18:48.277707: train_loss -0.26 +2026-04-11 14:18:48.285561: val_loss -0.2374 +2026-04-11 14:18:48.287990: Pseudo dice [0.7717, 0.6328, 0.7834, 0.5102, 0.3031, 0.8911, 0.6894] +2026-04-11 14:18:48.290383: Epoch time: 101.86 s +2026-04-11 14:18:49.386697: +2026-04-11 14:18:49.391245: Epoch 972 +2026-04-11 14:18:49.393375: Current learning rate: 0.00778 +2026-04-11 14:20:31.648148: train_loss -0.25 +2026-04-11 14:20:31.654149: val_loss -0.1527 +2026-04-11 14:20:31.656074: Pseudo dice [0.4162, 0.816, 0.171, 0.2198, 0.2628, 0.2914, 0.5863] +2026-04-11 14:20:31.659495: Epoch time: 102.26 s +2026-04-11 14:20:32.767323: +2026-04-11 14:20:32.768844: Epoch 973 +2026-04-11 14:20:32.770657: Current learning rate: 0.00778 +2026-04-11 14:22:15.019743: train_loss -0.2529 +2026-04-11 14:22:15.027406: val_loss -0.2093 +2026-04-11 14:22:15.030192: Pseudo dice [0.3578, 0.5638, 0.7168, 0.5139, 0.4806, 0.4261, 0.6903] +2026-04-11 14:22:15.033656: Epoch time: 102.26 s +2026-04-11 14:22:16.154974: +2026-04-11 14:22:16.156769: Epoch 974 +2026-04-11 14:22:16.158598: Current learning rate: 0.00778 +2026-04-11 14:23:58.119929: train_loss -0.2521 +2026-04-11 14:23:58.126930: val_loss -0.2222 +2026-04-11 14:23:58.129611: Pseudo dice [0.5042, 0.8584, 0.6432, 0.3673, 0.57, 0.3852, 0.7582] +2026-04-11 14:23:58.132790: Epoch time: 101.97 s +2026-04-11 14:23:59.247509: +2026-04-11 14:23:59.249445: Epoch 975 +2026-04-11 14:23:59.251941: Current learning rate: 0.00778 +2026-04-11 14:25:40.968877: train_loss -0.2737 +2026-04-11 14:25:40.977911: val_loss -0.227 +2026-04-11 14:25:40.981985: Pseudo dice [0.5331, 0.394, 0.5828, 0.8323, 0.4909, 0.813, 0.8114] +2026-04-11 14:25:40.984471: Epoch time: 101.72 s +2026-04-11 14:25:42.097126: +2026-04-11 14:25:42.098831: Epoch 976 +2026-04-11 14:25:42.101101: Current learning rate: 0.00777 +2026-04-11 14:27:24.105174: train_loss -0.2602 +2026-04-11 14:27:24.111827: val_loss -0.2118 +2026-04-11 14:27:24.114076: Pseudo dice [0.6999, 0.7483, 0.7985, 0.0883, 0.3394, 0.3801, 0.3028] +2026-04-11 14:27:24.116514: Epoch time: 102.01 s +2026-04-11 14:27:25.217763: +2026-04-11 14:27:25.219368: Epoch 977 +2026-04-11 14:27:25.221271: Current learning rate: 0.00777 +2026-04-11 14:29:07.203946: train_loss -0.2533 +2026-04-11 14:29:07.221101: val_loss -0.2008 +2026-04-11 14:29:07.223542: Pseudo dice [0.735, 0.8417, 0.7446, 0.5322, 0.3364, 0.596, 0.3064] +2026-04-11 14:29:07.226163: Epoch time: 101.99 s +2026-04-11 14:29:08.325050: +2026-04-11 14:29:08.329574: Epoch 978 +2026-04-11 14:29:08.331456: Current learning rate: 0.00777 +2026-04-11 14:30:50.473264: train_loss -0.2606 +2026-04-11 14:30:50.480083: val_loss -0.2119 +2026-04-11 14:30:50.482825: Pseudo dice [0.6638, 0.8191, 0.8336, 0.6137, 0.4793, 0.7385, 0.4852] +2026-04-11 14:30:50.485924: Epoch time: 102.15 s +2026-04-11 14:30:51.602308: +2026-04-11 14:30:51.604029: Epoch 979 +2026-04-11 14:30:51.606598: Current learning rate: 0.00777 +2026-04-11 14:32:33.389160: train_loss -0.2612 +2026-04-11 14:32:33.396278: val_loss -0.2116 +2026-04-11 14:32:33.398517: Pseudo dice [0.7609, 0.8982, 0.7587, 0.737, 0.4437, 0.3542, 0.6831] +2026-04-11 14:32:33.401651: Epoch time: 101.79 s +2026-04-11 14:32:34.541116: +2026-04-11 14:32:34.543327: Epoch 980 +2026-04-11 14:32:34.545547: Current learning rate: 0.00777 +2026-04-11 14:34:16.125488: train_loss -0.2522 +2026-04-11 14:34:16.134765: val_loss -0.2395 +2026-04-11 14:34:16.139364: Pseudo dice [0.6462, 0.4538, 0.665, 0.7718, 0.6485, 0.9217, 0.7176] +2026-04-11 14:34:16.142704: Epoch time: 101.59 s +2026-04-11 14:34:17.247930: +2026-04-11 14:34:17.249825: Epoch 981 +2026-04-11 14:34:17.251839: Current learning rate: 0.00776 +2026-04-11 14:35:59.184340: train_loss -0.2531 +2026-04-11 14:35:59.191978: val_loss -0.2376 +2026-04-11 14:35:59.194487: Pseudo dice [0.7148, 0.8216, 0.7163, 0.7267, 0.3974, 0.6086, 0.7509] +2026-04-11 14:35:59.197473: Epoch time: 101.94 s +2026-04-11 14:35:59.199486: Yayy! New best EMA pseudo Dice: 0.5947 +2026-04-11 14:36:02.970559: +2026-04-11 14:36:02.972963: Epoch 982 +2026-04-11 14:36:02.974515: Current learning rate: 0.00776 +2026-04-11 14:37:44.982689: train_loss -0.253 +2026-04-11 14:37:44.987698: val_loss -0.2132 +2026-04-11 14:37:44.989471: Pseudo dice [0.8187, 0.3834, 0.8166, 0.387, 0.6246, 0.8262, 0.6082] +2026-04-11 14:37:44.991487: Epoch time: 102.02 s +2026-04-11 14:37:44.993255: Yayy! New best EMA pseudo Dice: 0.599 +2026-04-11 14:37:47.794177: +2026-04-11 14:37:47.797126: Epoch 983 +2026-04-11 14:37:47.798661: Current learning rate: 0.00776 +2026-04-11 14:39:29.912483: train_loss -0.2608 +2026-04-11 14:39:29.919533: val_loss -0.2388 +2026-04-11 14:39:29.922183: Pseudo dice [0.6288, 0.6704, 0.7606, 0.5676, 0.5365, 0.9153, 0.599] +2026-04-11 14:39:29.924466: Epoch time: 102.12 s +2026-04-11 14:39:29.926875: Yayy! New best EMA pseudo Dice: 0.6059 +2026-04-11 14:39:32.831284: +2026-04-11 14:39:32.833673: Epoch 984 +2026-04-11 14:39:32.835274: Current learning rate: 0.00776 +2026-04-11 14:41:14.819506: train_loss -0.2582 +2026-04-11 14:41:14.826426: val_loss -0.2222 +2026-04-11 14:41:14.828585: Pseudo dice [0.1928, 0.7636, 0.5824, 0.2927, 0.3917, 0.6922, 0.6834] +2026-04-11 14:41:14.830798: Epoch time: 101.99 s +2026-04-11 14:41:15.953367: +2026-04-11 14:41:15.955680: Epoch 985 +2026-04-11 14:41:15.957501: Current learning rate: 0.00775 +2026-04-11 14:42:58.253997: train_loss -0.2533 +2026-04-11 14:42:58.262224: val_loss -0.2041 +2026-04-11 14:42:58.264266: Pseudo dice [0.1615, 0.6096, 0.6743, 0.6322, 0.407, 0.7385, 0.2974] +2026-04-11 14:42:58.266733: Epoch time: 102.3 s +2026-04-11 14:42:59.449327: +2026-04-11 14:42:59.450931: Epoch 986 +2026-04-11 14:42:59.453031: Current learning rate: 0.00775 +2026-04-11 14:44:41.881072: train_loss -0.2635 +2026-04-11 14:44:41.888333: val_loss -0.2111 +2026-04-11 14:44:41.890583: Pseudo dice [0.35, 0.8512, 0.697, 0.3604, 0.3322, 0.2897, 0.8562] +2026-04-11 14:44:41.893479: Epoch time: 102.43 s +2026-04-11 14:44:43.006237: +2026-04-11 14:44:43.008473: Epoch 987 +2026-04-11 14:44:43.011470: Current learning rate: 0.00775 +2026-04-11 14:46:24.821207: train_loss -0.2605 +2026-04-11 14:46:24.830285: val_loss -0.2164 +2026-04-11 14:46:24.832848: Pseudo dice [0.2045, 0.5149, 0.7485, 0.3285, 0.5548, 0.6645, 0.6834] +2026-04-11 14:46:24.835519: Epoch time: 101.82 s +2026-04-11 14:46:25.974136: +2026-04-11 14:46:25.976164: Epoch 988 +2026-04-11 14:46:25.978776: Current learning rate: 0.00775 +2026-04-11 14:48:08.336520: train_loss -0.263 +2026-04-11 14:48:08.344795: val_loss -0.2375 +2026-04-11 14:48:08.347128: Pseudo dice [0.4412, 0.2824, 0.736, 0.5059, 0.5246, 0.8379, 0.6742] +2026-04-11 14:48:08.349590: Epoch time: 102.37 s +2026-04-11 14:48:09.478665: +2026-04-11 14:48:09.481048: Epoch 989 +2026-04-11 14:48:09.484506: Current learning rate: 0.00774 +2026-04-11 14:49:51.430630: train_loss -0.2671 +2026-04-11 14:49:51.437522: val_loss -0.2234 +2026-04-11 14:49:51.439660: Pseudo dice [0.6951, 0.4821, 0.7618, 0.4531, 0.7013, 0.9022, 0.7597] +2026-04-11 14:49:51.442057: Epoch time: 101.96 s +2026-04-11 14:49:52.579000: +2026-04-11 14:49:52.581697: Epoch 990 +2026-04-11 14:49:52.584066: Current learning rate: 0.00774 +2026-04-11 14:51:34.430481: train_loss -0.2646 +2026-04-11 14:51:34.436914: val_loss -0.235 +2026-04-11 14:51:34.439725: Pseudo dice [0.7201, 0.8048, 0.7432, 0.5983, 0.5242, 0.4571, 0.5466] +2026-04-11 14:51:34.442604: Epoch time: 101.85 s +2026-04-11 14:51:35.543900: +2026-04-11 14:51:35.545974: Epoch 991 +2026-04-11 14:51:35.548353: Current learning rate: 0.00774 +2026-04-11 14:53:17.556040: train_loss -0.2483 +2026-04-11 14:53:17.563241: val_loss -0.2323 +2026-04-11 14:53:17.564934: Pseudo dice [0.4099, 0.7259, 0.5706, 0.6533, 0.5237, 0.6355, 0.7881] +2026-04-11 14:53:17.567175: Epoch time: 102.02 s +2026-04-11 14:53:18.692063: +2026-04-11 14:53:18.694574: Epoch 992 +2026-04-11 14:53:18.696378: Current learning rate: 0.00774 +2026-04-11 14:55:00.949858: train_loss -0.2304 +2026-04-11 14:55:00.956108: val_loss -0.1797 +2026-04-11 14:55:00.958675: Pseudo dice [0.3727, 0.4408, 0.7402, 0.4011, 0.4555, 0.5337, 0.5708] +2026-04-11 14:55:00.961376: Epoch time: 102.26 s +2026-04-11 14:55:02.061937: +2026-04-11 14:55:02.064293: Epoch 993 +2026-04-11 14:55:02.066501: Current learning rate: 0.00774 +2026-04-11 14:56:44.089833: train_loss -0.2449 +2026-04-11 14:56:44.097730: val_loss -0.1849 +2026-04-11 14:56:44.100010: Pseudo dice [0.4304, 0.3698, 0.3477, 0.4025, 0.5635, 0.3652, 0.8008] +2026-04-11 14:56:44.102668: Epoch time: 102.03 s +2026-04-11 14:56:45.229378: +2026-04-11 14:56:45.231302: Epoch 994 +2026-04-11 14:56:45.233228: Current learning rate: 0.00773 +2026-04-11 14:58:27.333525: train_loss -0.2613 +2026-04-11 14:58:27.339187: val_loss -0.1719 +2026-04-11 14:58:27.340999: Pseudo dice [0.0484, 0.7159, 0.5575, 0.3618, 0.1732, 0.3605, 0.8074] +2026-04-11 14:58:27.343379: Epoch time: 102.11 s +2026-04-11 14:58:28.450028: +2026-04-11 14:58:28.452192: Epoch 995 +2026-04-11 14:58:28.454077: Current learning rate: 0.00773 +2026-04-11 15:00:10.675280: train_loss -0.259 +2026-04-11 15:00:10.682586: val_loss -0.2003 +2026-04-11 15:00:10.684588: Pseudo dice [0.3162, 0.8238, 0.812, 0.584, 0.5593, 0.8379, 0.7914] +2026-04-11 15:00:10.687492: Epoch time: 102.23 s +2026-04-11 15:00:11.832565: +2026-04-11 15:00:11.834483: Epoch 996 +2026-04-11 15:00:11.836460: Current learning rate: 0.00773 +2026-04-11 15:01:55.958531: train_loss -0.2656 +2026-04-11 15:01:55.964842: val_loss -0.1515 +2026-04-11 15:01:55.966681: Pseudo dice [0.711, 0.3698, 0.5845, 0.4029, 0.1549, 0.7326, 0.5394] +2026-04-11 15:01:55.968894: Epoch time: 104.13 s +2026-04-11 15:01:57.088326: +2026-04-11 15:01:57.090221: Epoch 997 +2026-04-11 15:01:57.092178: Current learning rate: 0.00773 +2026-04-11 15:03:39.085106: train_loss -0.2682 +2026-04-11 15:03:39.093063: val_loss -0.2198 +2026-04-11 15:03:39.094934: Pseudo dice [0.4906, 0.3624, 0.7761, 0.2791, 0.5693, 0.8025, 0.593] +2026-04-11 15:03:39.098334: Epoch time: 102.0 s +2026-04-11 15:03:40.211415: +2026-04-11 15:03:40.213651: Epoch 998 +2026-04-11 15:03:40.215608: Current learning rate: 0.00772 +2026-04-11 15:05:22.105217: train_loss -0.2594 +2026-04-11 15:05:22.111746: val_loss -0.2367 +2026-04-11 15:05:22.113734: Pseudo dice [0.177, 0.2494, 0.75, 0.5229, 0.3589, 0.6798, 0.4917] +2026-04-11 15:05:22.116459: Epoch time: 101.9 s +2026-04-11 15:05:23.221219: +2026-04-11 15:05:23.223201: Epoch 999 +2026-04-11 15:05:23.224900: Current learning rate: 0.00772 +2026-04-11 15:07:05.499215: train_loss -0.2499 +2026-04-11 15:07:05.506352: val_loss -0.232 +2026-04-11 15:07:05.508296: Pseudo dice [0.5132, 0.3311, 0.5762, 0.8568, 0.6138, 0.8756, 0.6578] +2026-04-11 15:07:05.510886: Epoch time: 102.28 s +2026-04-11 15:07:08.431395: +2026-04-11 15:07:08.434083: Epoch 1000 +2026-04-11 15:07:08.435713: Current learning rate: 0.00772 +2026-04-11 15:08:50.297393: train_loss -0.2479 +2026-04-11 15:08:50.304571: val_loss -0.2282 +2026-04-11 15:08:50.306609: Pseudo dice [0.3687, 0.5617, 0.757, 0.5362, 0.5015, 0.1555, 0.6235] +2026-04-11 15:08:50.309335: Epoch time: 101.87 s +2026-04-11 15:08:52.499750: +2026-04-11 15:08:52.501555: Epoch 1001 +2026-04-11 15:08:52.503286: Current learning rate: 0.00772 +2026-04-11 15:10:34.880408: train_loss -0.2269 +2026-04-11 15:10:34.888441: val_loss -0.2196 +2026-04-11 15:10:34.890718: Pseudo dice [0.509, 0.5392, 0.7801, 0.2434, 0.3412, 0.4494, 0.3845] +2026-04-11 15:10:34.893861: Epoch time: 102.38 s +2026-04-11 15:10:36.042821: +2026-04-11 15:10:36.046133: Epoch 1002 +2026-04-11 15:10:36.048400: Current learning rate: 0.00771 +2026-04-11 15:12:17.801888: train_loss -0.2471 +2026-04-11 15:12:17.807832: val_loss -0.2007 +2026-04-11 15:12:17.810062: Pseudo dice [0.4622, 0.1121, 0.6548, 0.5674, 0.5346, 0.8558, 0.7189] +2026-04-11 15:12:17.812448: Epoch time: 101.76 s +2026-04-11 15:12:18.936901: +2026-04-11 15:12:18.938865: Epoch 1003 +2026-04-11 15:12:18.940680: Current learning rate: 0.00771 +2026-04-11 15:14:00.598314: train_loss -0.2221 +2026-04-11 15:14:00.604734: val_loss -0.1912 +2026-04-11 15:14:00.607061: Pseudo dice [0.7575, 0.4856, 0.6723, 0.1326, 0.4001, 0.8864, 0.7629] +2026-04-11 15:14:00.609468: Epoch time: 101.66 s +2026-04-11 15:14:01.731229: +2026-04-11 15:14:01.733372: Epoch 1004 +2026-04-11 15:14:01.734952: Current learning rate: 0.00771 +2026-04-11 15:15:43.774775: train_loss -0.2409 +2026-04-11 15:15:43.781084: val_loss -0.2121 +2026-04-11 15:15:43.783001: Pseudo dice [0.4149, 0.7032, 0.6256, 0.4758, 0.4773, 0.7347, 0.7579] +2026-04-11 15:15:43.786797: Epoch time: 102.05 s +2026-04-11 15:15:44.907851: +2026-04-11 15:15:44.909752: Epoch 1005 +2026-04-11 15:15:44.911547: Current learning rate: 0.00771 +2026-04-11 15:17:26.608078: train_loss -0.2503 +2026-04-11 15:17:26.614220: val_loss -0.2128 +2026-04-11 15:17:26.617213: Pseudo dice [0.5218, 0.8511, 0.6231, 0.1411, 0.3355, 0.6035, 0.8378] +2026-04-11 15:17:26.619697: Epoch time: 101.7 s +2026-04-11 15:17:27.752935: +2026-04-11 15:17:27.754865: Epoch 1006 +2026-04-11 15:17:27.756744: Current learning rate: 0.0077 +2026-04-11 15:19:09.706703: train_loss -0.2387 +2026-04-11 15:19:09.712586: val_loss -0.2309 +2026-04-11 15:19:09.714427: Pseudo dice [0.5888, 0.7316, 0.8088, 0.2735, 0.469, 0.7207, 0.6715] +2026-04-11 15:19:09.716853: Epoch time: 101.96 s +2026-04-11 15:19:10.833433: +2026-04-11 15:19:10.835740: Epoch 1007 +2026-04-11 15:19:10.837570: Current learning rate: 0.0077 +2026-04-11 15:20:52.456568: train_loss -0.2434 +2026-04-11 15:20:52.463257: val_loss -0.2524 +2026-04-11 15:20:52.465340: Pseudo dice [0.493, 0.3578, 0.7206, 0.4169, 0.5172, 0.896, 0.8552] +2026-04-11 15:20:52.467999: Epoch time: 101.63 s +2026-04-11 15:20:53.607819: +2026-04-11 15:20:53.609875: Epoch 1008 +2026-04-11 15:20:53.611429: Current learning rate: 0.0077 +2026-04-11 15:22:35.310785: train_loss -0.244 +2026-04-11 15:22:35.316642: val_loss -0.2141 +2026-04-11 15:22:35.318796: Pseudo dice [0.6298, 0.735, 0.7243, 0.861, 0.0021, 0.3176, 0.732] +2026-04-11 15:22:35.320970: Epoch time: 101.71 s +2026-04-11 15:22:36.431154: +2026-04-11 15:22:36.433852: Epoch 1009 +2026-04-11 15:22:36.435863: Current learning rate: 0.0077 +2026-04-11 15:24:18.521952: train_loss -0.2464 +2026-04-11 15:24:18.529167: val_loss -0.1534 +2026-04-11 15:24:18.531790: Pseudo dice [0.3817, 0.6824, 0.4382, 0.465, 0.5815, 0.2368, 0.7012] +2026-04-11 15:24:18.534195: Epoch time: 102.09 s +2026-04-11 15:24:19.669291: +2026-04-11 15:24:19.671200: Epoch 1010 +2026-04-11 15:24:19.673484: Current learning rate: 0.0077 +2026-04-11 15:26:02.198678: train_loss -0.2571 +2026-04-11 15:26:02.204622: val_loss -0.2277 +2026-04-11 15:26:02.206426: Pseudo dice [0.1938, 0.2297, 0.7461, 0.7425, 0.5278, 0.5623, 0.7137] +2026-04-11 15:26:02.209022: Epoch time: 102.53 s +2026-04-11 15:26:03.325895: +2026-04-11 15:26:03.328868: Epoch 1011 +2026-04-11 15:26:03.330661: Current learning rate: 0.00769 +2026-04-11 15:27:45.836258: train_loss -0.2565 +2026-04-11 15:27:45.845129: val_loss -0.1888 +2026-04-11 15:27:45.847795: Pseudo dice [0.2856, 0.8701, 0.682, 0.2473, 0.4828, 0.6245, 0.5389] +2026-04-11 15:27:45.852168: Epoch time: 102.51 s +2026-04-11 15:27:47.023373: +2026-04-11 15:27:47.025243: Epoch 1012 +2026-04-11 15:27:47.027107: Current learning rate: 0.00769 +2026-04-11 15:29:28.664921: train_loss -0.2391 +2026-04-11 15:29:28.672380: val_loss -0.1952 +2026-04-11 15:29:28.674641: Pseudo dice [0.4299, 0.1763, 0.7112, 0.4952, 0.3953, 0.8246, 0.819] +2026-04-11 15:29:28.676718: Epoch time: 101.64 s +2026-04-11 15:29:29.777597: +2026-04-11 15:29:29.780321: Epoch 1013 +2026-04-11 15:29:29.782881: Current learning rate: 0.00769 +2026-04-11 15:31:11.520708: train_loss -0.2459 +2026-04-11 15:31:11.526568: val_loss -0.1861 +2026-04-11 15:31:11.528694: Pseudo dice [0.3516, 0.6966, 0.7428, 0.4422, 0.4534, 0.6543, 0.7683] +2026-04-11 15:31:11.530753: Epoch time: 101.75 s +2026-04-11 15:31:12.650453: +2026-04-11 15:31:12.651928: Epoch 1014 +2026-04-11 15:31:12.653342: Current learning rate: 0.00769 +2026-04-11 15:32:54.240649: train_loss -0.2513 +2026-04-11 15:32:54.251748: val_loss -0.1943 +2026-04-11 15:32:54.253466: Pseudo dice [0.6016, 0.376, 0.7028, 0.2702, 0.5529, 0.4706, 0.7948] +2026-04-11 15:32:54.257391: Epoch time: 101.59 s +2026-04-11 15:32:55.364203: +2026-04-11 15:32:55.365840: Epoch 1015 +2026-04-11 15:32:55.367388: Current learning rate: 0.00768 +2026-04-11 15:34:37.525546: train_loss -0.2625 +2026-04-11 15:34:37.533594: val_loss -0.1558 +2026-04-11 15:34:37.535784: Pseudo dice [0.2288, 0.8238, 0.6977, 0.2511, 0.2418, 0.5384, 0.415] +2026-04-11 15:34:37.539006: Epoch time: 102.16 s +2026-04-11 15:34:38.685999: +2026-04-11 15:34:38.688069: Epoch 1016 +2026-04-11 15:34:38.689592: Current learning rate: 0.00768 +2026-04-11 15:36:20.320911: train_loss -0.2592 +2026-04-11 15:36:20.328376: val_loss -0.1948 +2026-04-11 15:36:20.331151: Pseudo dice [0.4874, 0.0262, 0.6829, 0.4792, 0.5272, 0.6223, 0.7214] +2026-04-11 15:36:20.333247: Epoch time: 101.64 s +2026-04-11 15:36:21.437411: +2026-04-11 15:36:21.438913: Epoch 1017 +2026-04-11 15:36:21.440335: Current learning rate: 0.00768 +2026-04-11 15:38:03.212078: train_loss -0.2573 +2026-04-11 15:38:03.220458: val_loss -0.2223 +2026-04-11 15:38:03.223402: Pseudo dice [0.4444, 0.8388, 0.7804, 0.3715, 0.5689, 0.6291, 0.7583] +2026-04-11 15:38:03.226749: Epoch time: 101.78 s +2026-04-11 15:38:04.335984: +2026-04-11 15:38:04.337906: Epoch 1018 +2026-04-11 15:38:04.339458: Current learning rate: 0.00768 +2026-04-11 15:39:45.913285: train_loss -0.2659 +2026-04-11 15:39:45.919020: val_loss -0.2048 +2026-04-11 15:39:45.921022: Pseudo dice [0.579, 0.7999, 0.6768, 0.7231, 0.5919, 0.6143, 0.7383] +2026-04-11 15:39:45.923108: Epoch time: 101.58 s +2026-04-11 15:39:47.087754: +2026-04-11 15:39:47.090664: Epoch 1019 +2026-04-11 15:39:47.099366: Current learning rate: 0.00767 +2026-04-11 15:41:28.721333: train_loss -0.267 +2026-04-11 15:41:28.728726: val_loss -0.2198 +2026-04-11 15:41:28.730714: Pseudo dice [0.3705, 0.7334, 0.7142, 0.6008, 0.5183, 0.8128, 0.763] +2026-04-11 15:41:28.734103: Epoch time: 101.64 s +2026-04-11 15:41:29.875427: +2026-04-11 15:41:29.877180: Epoch 1020 +2026-04-11 15:41:29.879013: Current learning rate: 0.00767 +2026-04-11 15:43:11.452902: train_loss -0.2541 +2026-04-11 15:43:11.459600: val_loss -0.186 +2026-04-11 15:43:11.461871: Pseudo dice [0.6473, 0.1685, 0.5972, 0.0339, 0.4822, 0.2181, 0.8338] +2026-04-11 15:43:11.465540: Epoch time: 101.58 s +2026-04-11 15:43:12.611527: +2026-04-11 15:43:12.613086: Epoch 1021 +2026-04-11 15:43:12.614702: Current learning rate: 0.00767 +2026-04-11 15:44:54.258789: train_loss -0.2444 +2026-04-11 15:44:54.264816: val_loss -0.1942 +2026-04-11 15:44:54.267477: Pseudo dice [0.8425, 0.8053, 0.6515, 0.6065, 0.4903, 0.0694, 0.6484] +2026-04-11 15:44:54.269818: Epoch time: 101.65 s +2026-04-11 15:44:56.606553: +2026-04-11 15:44:56.608722: Epoch 1022 +2026-04-11 15:44:56.610844: Current learning rate: 0.00767 +2026-04-11 15:46:38.187712: train_loss -0.2358 +2026-04-11 15:46:38.194679: val_loss -0.2257 +2026-04-11 15:46:38.197226: Pseudo dice [0.2612, 0.4951, 0.835, 0.1698, 0.6204, 0.91, 0.4633] +2026-04-11 15:46:38.200081: Epoch time: 101.58 s +2026-04-11 15:46:39.358600: +2026-04-11 15:46:39.360645: Epoch 1023 +2026-04-11 15:46:39.362771: Current learning rate: 0.00767 +2026-04-11 15:48:21.120896: train_loss -0.2564 +2026-04-11 15:48:21.126812: val_loss -0.2073 +2026-04-11 15:48:21.128824: Pseudo dice [0.8299, 0.7847, 0.6429, 0.685, 0.2525, 0.3654, 0.664] +2026-04-11 15:48:21.131198: Epoch time: 101.77 s +2026-04-11 15:48:22.252262: +2026-04-11 15:48:22.255199: Epoch 1024 +2026-04-11 15:48:22.257792: Current learning rate: 0.00766 +2026-04-11 15:50:03.917088: train_loss -0.2599 +2026-04-11 15:50:03.923625: val_loss -0.2191 +2026-04-11 15:50:03.925808: Pseudo dice [0.3079, 0.3552, 0.8002, 0.7525, 0.5014, 0.8737, 0.687] +2026-04-11 15:50:03.928044: Epoch time: 101.67 s +2026-04-11 15:50:05.117216: +2026-04-11 15:50:05.119701: Epoch 1025 +2026-04-11 15:50:05.121861: Current learning rate: 0.00766 +2026-04-11 15:51:47.006280: train_loss -0.261 +2026-04-11 15:51:47.012605: val_loss -0.2275 +2026-04-11 15:51:47.014659: Pseudo dice [0.6005, 0.5931, 0.815, 0.5086, 0.711, 0.6014, 0.6703] +2026-04-11 15:51:47.017006: Epoch time: 101.89 s +2026-04-11 15:51:48.153418: +2026-04-11 15:51:48.155011: Epoch 1026 +2026-04-11 15:51:48.156630: Current learning rate: 0.00766 +2026-04-11 15:53:29.812531: train_loss -0.2525 +2026-04-11 15:53:29.817873: val_loss -0.1993 +2026-04-11 15:53:29.819706: Pseudo dice [0.5762, 0.62, 0.6864, 0.3921, 0.4739, 0.413, 0.694] +2026-04-11 15:53:29.821792: Epoch time: 101.66 s +2026-04-11 15:53:30.970237: +2026-04-11 15:53:30.971821: Epoch 1027 +2026-04-11 15:53:30.973248: Current learning rate: 0.00766 +2026-04-11 15:55:12.960624: train_loss -0.2565 +2026-04-11 15:55:12.967874: val_loss -0.1739 +2026-04-11 15:55:12.969922: Pseudo dice [0.6076, 0.2925, 0.4589, 0.6938, 0.2152, 0.3785, 0.6825] +2026-04-11 15:55:12.972503: Epoch time: 101.99 s +2026-04-11 15:55:14.176130: +2026-04-11 15:55:14.178025: Epoch 1028 +2026-04-11 15:55:14.179693: Current learning rate: 0.00765 +2026-04-11 15:56:56.156141: train_loss -0.2228 +2026-04-11 15:56:56.162600: val_loss -0.1971 +2026-04-11 15:56:56.164431: Pseudo dice [0.1839, 0.6551, 0.7775, 0.4233, 0.2844, 0.8246, 0.4953] +2026-04-11 15:56:56.166730: Epoch time: 101.98 s +2026-04-11 15:56:57.259102: +2026-04-11 15:56:57.261016: Epoch 1029 +2026-04-11 15:56:57.262728: Current learning rate: 0.00765 +2026-04-11 15:58:39.545239: train_loss -0.2559 +2026-04-11 15:58:39.551925: val_loss -0.2018 +2026-04-11 15:58:39.554112: Pseudo dice [0.6298, 0.9043, 0.7067, 0.4044, 0.3985, 0.7263, 0.4142] +2026-04-11 15:58:39.556627: Epoch time: 102.29 s +2026-04-11 15:58:40.687831: +2026-04-11 15:58:40.689479: Epoch 1030 +2026-04-11 15:58:40.690819: Current learning rate: 0.00765 +2026-04-11 16:00:22.653772: train_loss -0.2564 +2026-04-11 16:00:22.659764: val_loss -0.2379 +2026-04-11 16:00:22.670591: Pseudo dice [0.5406, 0.6252, 0.7256, 0.6524, 0.4663, 0.764, 0.6489] +2026-04-11 16:00:22.673062: Epoch time: 101.97 s +2026-04-11 16:00:23.775352: +2026-04-11 16:00:23.777078: Epoch 1031 +2026-04-11 16:00:23.778482: Current learning rate: 0.00765 +2026-04-11 16:02:05.658708: train_loss -0.2592 +2026-04-11 16:02:05.665372: val_loss -0.2082 +2026-04-11 16:02:05.667201: Pseudo dice [0.2495, 0.8621, 0.825, 0.8628, 0.6542, 0.1845, 0.3529] +2026-04-11 16:02:05.669319: Epoch time: 101.89 s +2026-04-11 16:02:06.781448: +2026-04-11 16:02:06.783050: Epoch 1032 +2026-04-11 16:02:06.784742: Current learning rate: 0.00764 +2026-04-11 16:03:48.794520: train_loss -0.2635 +2026-04-11 16:03:48.802386: val_loss -0.2137 +2026-04-11 16:03:48.804668: Pseudo dice [0.6877, 0.8634, 0.7257, 0.7214, 0.6017, 0.6722, 0.8239] +2026-04-11 16:03:48.807092: Epoch time: 102.02 s +2026-04-11 16:03:49.923271: +2026-04-11 16:03:49.925241: Epoch 1033 +2026-04-11 16:03:49.927652: Current learning rate: 0.00764 +2026-04-11 16:05:31.862000: train_loss -0.2501 +2026-04-11 16:05:31.868723: val_loss -0.2149 +2026-04-11 16:05:31.871249: Pseudo dice [0.2362, 0.1795, 0.7132, 0.4435, 0.3484, 0.7319, 0.582] +2026-04-11 16:05:31.873667: Epoch time: 101.94 s +2026-04-11 16:05:32.984048: +2026-04-11 16:05:32.985816: Epoch 1034 +2026-04-11 16:05:32.987358: Current learning rate: 0.00764 +2026-04-11 16:07:15.292363: train_loss -0.2468 +2026-04-11 16:07:15.298692: val_loss -0.2259 +2026-04-11 16:07:15.301927: Pseudo dice [0.3525, 0.9071, 0.7545, 0.4694, 0.4949, 0.7834, 0.5831] +2026-04-11 16:07:15.304334: Epoch time: 102.31 s +2026-04-11 16:07:16.403257: +2026-04-11 16:07:16.405258: Epoch 1035 +2026-04-11 16:07:16.407204: Current learning rate: 0.00764 +2026-04-11 16:08:58.493943: train_loss -0.2531 +2026-04-11 16:08:58.500148: val_loss -0.1993 +2026-04-11 16:08:58.503078: Pseudo dice [0.4464, 0.7983, 0.629, 0.5989, 0.3668, 0.7028, 0.6796] +2026-04-11 16:08:58.505546: Epoch time: 102.09 s +2026-04-11 16:08:59.616645: +2026-04-11 16:08:59.618353: Epoch 1036 +2026-04-11 16:08:59.619900: Current learning rate: 0.00764 +2026-04-11 16:10:41.345746: train_loss -0.2608 +2026-04-11 16:10:41.352161: val_loss -0.215 +2026-04-11 16:10:41.354213: Pseudo dice [0.6206, 0.5885, 0.6689, 0.6886, 0.2915, 0.4397, 0.7812] +2026-04-11 16:10:41.356472: Epoch time: 101.73 s +2026-04-11 16:10:42.478434: +2026-04-11 16:10:42.480946: Epoch 1037 +2026-04-11 16:10:42.482578: Current learning rate: 0.00763 +2026-04-11 16:12:24.186488: train_loss -0.2629 +2026-04-11 16:12:24.192266: val_loss -0.228 +2026-04-11 16:12:24.194811: Pseudo dice [0.5233, 0.3843, 0.6551, 0.5825, 0.5793, 0.9153, 0.6823] +2026-04-11 16:12:24.197958: Epoch time: 101.71 s +2026-04-11 16:12:25.318254: +2026-04-11 16:12:25.320320: Epoch 1038 +2026-04-11 16:12:25.322463: Current learning rate: 0.00763 +2026-04-11 16:14:06.878303: train_loss -0.2618 +2026-04-11 16:14:06.885371: val_loss -0.2335 +2026-04-11 16:14:06.887562: Pseudo dice [0.4526, 0.1815, 0.7479, 0.3447, 0.3464, 0.8683, 0.7244] +2026-04-11 16:14:06.890130: Epoch time: 101.56 s +2026-04-11 16:14:08.008004: +2026-04-11 16:14:08.009939: Epoch 1039 +2026-04-11 16:14:08.012133: Current learning rate: 0.00763 +2026-04-11 16:15:49.900150: train_loss -0.2477 +2026-04-11 16:15:49.906779: val_loss -0.2298 +2026-04-11 16:15:49.909050: Pseudo dice [0.3401, 0.4209, 0.6469, 0.1286, 0.4997, 0.654, 0.7375] +2026-04-11 16:15:49.911396: Epoch time: 101.9 s +2026-04-11 16:15:51.027496: +2026-04-11 16:15:51.028919: Epoch 1040 +2026-04-11 16:15:51.030230: Current learning rate: 0.00763 +2026-04-11 16:17:32.842470: train_loss -0.2568 +2026-04-11 16:17:32.849412: val_loss -0.2071 +2026-04-11 16:17:32.851282: Pseudo dice [0.3355, 0.8106, 0.6316, 0.2436, 0.4058, 0.6199, 0.7985] +2026-04-11 16:17:32.853853: Epoch time: 101.82 s +2026-04-11 16:17:33.985863: +2026-04-11 16:17:33.987327: Epoch 1041 +2026-04-11 16:17:33.988779: Current learning rate: 0.00762 +2026-04-11 16:19:16.129873: train_loss -0.2358 +2026-04-11 16:19:16.137366: val_loss -0.2144 +2026-04-11 16:19:16.139340: Pseudo dice [0.5255, 0.687, 0.6687, 0.7493, 0.5427, 0.7996, 0.4639] +2026-04-11 16:19:16.141669: Epoch time: 102.15 s +2026-04-11 16:19:17.274611: +2026-04-11 16:19:17.276530: Epoch 1042 +2026-04-11 16:19:17.278344: Current learning rate: 0.00762 +2026-04-11 16:20:59.025668: train_loss -0.2433 +2026-04-11 16:20:59.032111: val_loss -0.2191 +2026-04-11 16:20:59.033863: Pseudo dice [0.5879, 0.2765, 0.7804, 0.6062, 0.2307, 0.5767, 0.708] +2026-04-11 16:20:59.035700: Epoch time: 101.75 s +2026-04-11 16:21:01.210905: +2026-04-11 16:21:01.212431: Epoch 1043 +2026-04-11 16:21:01.213895: Current learning rate: 0.00762 +2026-04-11 16:22:43.338858: train_loss -0.2348 +2026-04-11 16:22:43.345650: val_loss -0.1786 +2026-04-11 16:22:43.348042: Pseudo dice [0.4362, 0.5631, 0.4776, 0.6645, 0.2963, 0.4677, 0.5073] +2026-04-11 16:22:43.350739: Epoch time: 102.13 s +2026-04-11 16:22:44.483909: +2026-04-11 16:22:44.488930: Epoch 1044 +2026-04-11 16:22:44.493493: Current learning rate: 0.00762 +2026-04-11 16:24:26.672224: train_loss -0.2397 +2026-04-11 16:24:26.679741: val_loss -0.2205 +2026-04-11 16:24:26.681800: Pseudo dice [0.4961, 0.664, 0.6379, 0.5744, 0.4294, 0.8666, 0.5581] +2026-04-11 16:24:26.683952: Epoch time: 102.19 s +2026-04-11 16:24:27.823986: +2026-04-11 16:24:27.826024: Epoch 1045 +2026-04-11 16:24:27.828657: Current learning rate: 0.00761 +2026-04-11 16:26:09.452514: train_loss -0.244 +2026-04-11 16:26:09.458372: val_loss -0.2104 +2026-04-11 16:26:09.461737: Pseudo dice [0.7325, 0.8945, 0.72, 0.558, 0.4281, 0.5263, 0.4136] +2026-04-11 16:26:09.464041: Epoch time: 101.63 s +2026-04-11 16:26:10.569900: +2026-04-11 16:26:10.571488: Epoch 1046 +2026-04-11 16:26:10.572857: Current learning rate: 0.00761 +2026-04-11 16:27:52.233961: train_loss -0.2541 +2026-04-11 16:27:52.242687: val_loss -0.2364 +2026-04-11 16:27:52.244360: Pseudo dice [0.6344, 0.7455, 0.7317, 0.4068, 0.4577, 0.8194, 0.8466] +2026-04-11 16:27:52.246591: Epoch time: 101.67 s +2026-04-11 16:27:53.358545: +2026-04-11 16:27:53.360810: Epoch 1047 +2026-04-11 16:27:53.362674: Current learning rate: 0.00761 +2026-04-11 16:29:35.472741: train_loss -0.2573 +2026-04-11 16:29:35.500768: val_loss -0.2259 +2026-04-11 16:29:35.502879: Pseudo dice [0.5403, 0.761, 0.7019, 0.5423, 0.7386, 0.3995, 0.7595] +2026-04-11 16:29:35.505270: Epoch time: 102.12 s +2026-04-11 16:29:36.632094: +2026-04-11 16:29:36.633674: Epoch 1048 +2026-04-11 16:29:36.635085: Current learning rate: 0.00761 +2026-04-11 16:31:18.591673: train_loss -0.252 +2026-04-11 16:31:18.597627: val_loss -0.2339 +2026-04-11 16:31:18.599576: Pseudo dice [0.3193, 0.7056, 0.7247, 0.3878, 0.5113, 0.6017, 0.8289] +2026-04-11 16:31:18.601657: Epoch time: 101.96 s +2026-04-11 16:31:19.730028: +2026-04-11 16:31:19.732145: Epoch 1049 +2026-04-11 16:31:19.733923: Current learning rate: 0.00761 +2026-04-11 16:33:01.391349: train_loss -0.2485 +2026-04-11 16:33:01.396539: val_loss -0.2278 +2026-04-11 16:33:01.398626: Pseudo dice [0.5327, 0.306, 0.7155, 0.3299, 0.5866, 0.8328, 0.775] +2026-04-11 16:33:01.401122: Epoch time: 101.66 s +2026-04-11 16:33:04.208517: +2026-04-11 16:33:04.210773: Epoch 1050 +2026-04-11 16:33:04.212194: Current learning rate: 0.0076 +2026-04-11 16:34:46.038420: train_loss -0.256 +2026-04-11 16:34:46.045061: val_loss -0.2105 +2026-04-11 16:34:46.046946: Pseudo dice [0.157, 0.3841, 0.7005, 0.5075, 0.6561, 0.1949, 0.7308] +2026-04-11 16:34:46.049116: Epoch time: 101.83 s +2026-04-11 16:34:47.216696: +2026-04-11 16:34:47.218929: Epoch 1051 +2026-04-11 16:34:47.220442: Current learning rate: 0.0076 +2026-04-11 16:36:29.295790: train_loss -0.2529 +2026-04-11 16:36:29.305902: val_loss -0.1967 +2026-04-11 16:36:29.308122: Pseudo dice [0.4307, 0.2894, 0.7423, 0.5432, 0.2822, 0.6466, 0.3062] +2026-04-11 16:36:29.312231: Epoch time: 102.08 s +2026-04-11 16:36:30.440375: +2026-04-11 16:36:30.442020: Epoch 1052 +2026-04-11 16:36:30.443534: Current learning rate: 0.0076 +2026-04-11 16:38:12.533125: train_loss -0.2588 +2026-04-11 16:38:12.544549: val_loss -0.2046 +2026-04-11 16:38:12.546170: Pseudo dice [0.8567, 0.9118, 0.5048, 0.3958, 0.6175, 0.6206, 0.7276] +2026-04-11 16:38:12.548792: Epoch time: 102.1 s +2026-04-11 16:38:13.673272: +2026-04-11 16:38:13.676499: Epoch 1053 +2026-04-11 16:38:13.678021: Current learning rate: 0.0076 +2026-04-11 16:39:55.593102: train_loss -0.2728 +2026-04-11 16:39:55.599743: val_loss -0.2455 +2026-04-11 16:39:55.601805: Pseudo dice [0.5612, 0.6135, 0.7014, 0.6435, 0.6123, 0.7292, 0.7771] +2026-04-11 16:39:55.604445: Epoch time: 101.92 s +2026-04-11 16:39:56.786542: +2026-04-11 16:39:56.789490: Epoch 1054 +2026-04-11 16:39:56.792522: Current learning rate: 0.00759 +2026-04-11 16:41:38.883122: train_loss -0.243 +2026-04-11 16:41:38.889455: val_loss -0.1811 +2026-04-11 16:41:38.891780: Pseudo dice [0.7458, 0.831, 0.6214, 0.6594, 0.1375, 0.3623, 0.7455] +2026-04-11 16:41:38.894290: Epoch time: 102.1 s +2026-04-11 16:41:40.009713: +2026-04-11 16:41:40.011917: Epoch 1055 +2026-04-11 16:41:40.013491: Current learning rate: 0.00759 +2026-04-11 16:43:22.362945: train_loss -0.2474 +2026-04-11 16:43:22.370511: val_loss -0.2393 +2026-04-11 16:43:22.373277: Pseudo dice [0.525, 0.2199, 0.7564, 0.8311, 0.4071, 0.8634, 0.7336] +2026-04-11 16:43:22.375404: Epoch time: 102.36 s +2026-04-11 16:43:23.485161: +2026-04-11 16:43:23.486578: Epoch 1056 +2026-04-11 16:43:23.487902: Current learning rate: 0.00759 +2026-04-11 16:45:05.515304: train_loss -0.2416 +2026-04-11 16:45:05.521178: val_loss -0.2117 +2026-04-11 16:45:05.523134: Pseudo dice [0.5669, 0.493, 0.7355, 0.3182, 0.3877, 0.7806, 0.7699] +2026-04-11 16:45:05.525154: Epoch time: 102.03 s +2026-04-11 16:45:06.656405: +2026-04-11 16:45:06.658517: Epoch 1057 +2026-04-11 16:45:06.660285: Current learning rate: 0.00759 +2026-04-11 16:46:48.924501: train_loss -0.2363 +2026-04-11 16:46:48.930645: val_loss -0.2002 +2026-04-11 16:46:48.932519: Pseudo dice [0.6079, 0.1632, 0.7824, 0.4722, 0.551, 0.7341, 0.8307] +2026-04-11 16:46:48.934477: Epoch time: 102.27 s +2026-04-11 16:46:50.075027: +2026-04-11 16:46:50.076582: Epoch 1058 +2026-04-11 16:46:50.078129: Current learning rate: 0.00758 +2026-04-11 16:48:32.205157: train_loss -0.258 +2026-04-11 16:48:32.211595: val_loss -0.2048 +2026-04-11 16:48:32.213498: Pseudo dice [0.5878, 0.6167, 0.6575, 0.649, 0.5431, 0.9085, 0.3287] +2026-04-11 16:48:32.216253: Epoch time: 102.13 s +2026-04-11 16:48:33.313225: +2026-04-11 16:48:33.315436: Epoch 1059 +2026-04-11 16:48:33.317136: Current learning rate: 0.00758 +2026-04-11 16:50:14.973004: train_loss -0.2458 +2026-04-11 16:50:14.978647: val_loss -0.2412 +2026-04-11 16:50:14.980595: Pseudo dice [0.6612, 0.9074, 0.7574, 0.6591, 0.4431, 0.816, 0.5118] +2026-04-11 16:50:14.982572: Epoch time: 101.66 s +2026-04-11 16:50:16.080751: +2026-04-11 16:50:16.082791: Epoch 1060 +2026-04-11 16:50:16.084855: Current learning rate: 0.00758 +2026-04-11 16:51:57.796910: train_loss -0.236 +2026-04-11 16:51:57.804718: val_loss -0.2167 +2026-04-11 16:51:57.807216: Pseudo dice [0.5365, 0.8518, 0.7287, 0.7706, 0.5017, 0.7298, 0.8192] +2026-04-11 16:51:57.809373: Epoch time: 101.72 s +2026-04-11 16:51:57.811342: Yayy! New best EMA pseudo Dice: 0.6084 +2026-04-11 16:52:00.725830: +2026-04-11 16:52:00.728120: Epoch 1061 +2026-04-11 16:52:00.729909: Current learning rate: 0.00758 +2026-04-11 16:53:42.609856: train_loss -0.2418 +2026-04-11 16:53:42.617593: val_loss -0.2278 +2026-04-11 16:53:42.619515: Pseudo dice [0.3857, 0.4173, 0.7144, 0.597, 0.4324, 0.5606, 0.7254] +2026-04-11 16:53:42.622241: Epoch time: 101.89 s +2026-04-11 16:53:43.740933: +2026-04-11 16:53:43.743014: Epoch 1062 +2026-04-11 16:53:43.745090: Current learning rate: 0.00758 +2026-04-11 16:55:25.917000: train_loss -0.206 +2026-04-11 16:55:25.924329: val_loss -0.163 +2026-04-11 16:55:25.926869: Pseudo dice [0.1244, 0.121, 0.557, 0.3529, 0.4821, 0.8508, 0.622] +2026-04-11 16:55:25.929644: Epoch time: 102.18 s +2026-04-11 16:55:28.152863: +2026-04-11 16:55:28.154542: Epoch 1063 +2026-04-11 16:55:28.155909: Current learning rate: 0.00757 +2026-04-11 16:57:10.127073: train_loss -0.2282 +2026-04-11 16:57:10.134681: val_loss -0.1885 +2026-04-11 16:57:10.137649: Pseudo dice [0.4816, 0.8203, 0.6793, 0.5779, 0.4293, 0.1071, 0.2639] +2026-04-11 16:57:10.140137: Epoch time: 101.98 s +2026-04-11 16:57:11.289215: +2026-04-11 16:57:11.290890: Epoch 1064 +2026-04-11 16:57:11.292308: Current learning rate: 0.00757 +2026-04-11 16:58:53.653506: train_loss -0.2443 +2026-04-11 16:58:53.660220: val_loss -0.1995 +2026-04-11 16:58:53.662116: Pseudo dice [0.5201, 0.6812, 0.7377, 0.6273, 0.3743, 0.7988, 0.8105] +2026-04-11 16:58:53.664332: Epoch time: 102.37 s +2026-04-11 16:58:54.785436: +2026-04-11 16:58:54.787328: Epoch 1065 +2026-04-11 16:58:54.789327: Current learning rate: 0.00757 +2026-04-11 17:00:37.235849: train_loss -0.2502 +2026-04-11 17:00:37.252403: val_loss -0.217 +2026-04-11 17:00:37.255363: Pseudo dice [0.6071, 0.6908, 0.7166, 0.7019, 0.5529, 0.8626, 0.6576] +2026-04-11 17:00:37.263877: Epoch time: 102.45 s +2026-04-11 17:00:38.385556: +2026-04-11 17:00:38.387355: Epoch 1066 +2026-04-11 17:00:38.388886: Current learning rate: 0.00757 +2026-04-11 17:02:20.515618: train_loss -0.245 +2026-04-11 17:02:20.523792: val_loss -0.1735 +2026-04-11 17:02:20.525923: Pseudo dice [0.5456, 0.4771, 0.5193, 0.5023, 0.2762, 0.643, 0.7005] +2026-04-11 17:02:20.528123: Epoch time: 102.13 s +2026-04-11 17:02:21.685711: +2026-04-11 17:02:21.687778: Epoch 1067 +2026-04-11 17:02:21.689612: Current learning rate: 0.00756 +2026-04-11 17:04:03.633304: train_loss -0.2545 +2026-04-11 17:04:03.639393: val_loss -0.2089 +2026-04-11 17:04:03.641801: Pseudo dice [0.4114, 0.6525, 0.7645, 0.58, 0.5865, 0.6524, 0.7937] +2026-04-11 17:04:03.644311: Epoch time: 101.95 s +2026-04-11 17:04:04.756234: +2026-04-11 17:04:04.758621: Epoch 1068 +2026-04-11 17:04:04.760028: Current learning rate: 0.00756 +2026-04-11 17:05:46.611246: train_loss -0.2531 +2026-04-11 17:05:46.617773: val_loss -0.1898 +2026-04-11 17:05:46.619409: Pseudo dice [0.2138, 0.8367, 0.6729, 0.3268, 0.4951, 0.7412, 0.5071] +2026-04-11 17:05:46.622051: Epoch time: 101.86 s +2026-04-11 17:05:47.731785: +2026-04-11 17:05:47.733806: Epoch 1069 +2026-04-11 17:05:47.735355: Current learning rate: 0.00756 +2026-04-11 17:07:30.274930: train_loss -0.2586 +2026-04-11 17:07:30.282076: val_loss -0.1881 +2026-04-11 17:07:30.284176: Pseudo dice [0.4287, 0.4297, 0.7674, 0.1195, 0.5231, 0.5327, 0.3715] +2026-04-11 17:07:30.286530: Epoch time: 102.55 s +2026-04-11 17:07:31.410043: +2026-04-11 17:07:31.412000: Epoch 1070 +2026-04-11 17:07:31.413925: Current learning rate: 0.00756 +2026-04-11 17:09:13.352628: train_loss -0.2522 +2026-04-11 17:09:13.360106: val_loss -0.1982 +2026-04-11 17:09:13.362813: Pseudo dice [0.1288, 0.7506, 0.7431, 0.5124, 0.4104, 0.2252, 0.7885] +2026-04-11 17:09:13.364868: Epoch time: 101.95 s +2026-04-11 17:09:14.473297: +2026-04-11 17:09:14.474993: Epoch 1071 +2026-04-11 17:09:14.476716: Current learning rate: 0.00755 +2026-04-11 17:10:56.656766: train_loss -0.2571 +2026-04-11 17:10:56.664095: val_loss -0.2053 +2026-04-11 17:10:56.666067: Pseudo dice [0.3447, 0.6845, 0.7071, 0.4632, 0.4982, 0.6667, 0.6777] +2026-04-11 17:10:56.669060: Epoch time: 102.19 s +2026-04-11 17:10:58.034364: +2026-04-11 17:10:58.036015: Epoch 1072 +2026-04-11 17:10:58.037450: Current learning rate: 0.00755 +2026-04-11 17:12:39.899224: train_loss -0.2571 +2026-04-11 17:12:39.904629: val_loss -0.2135 +2026-04-11 17:12:39.906354: Pseudo dice [0.4555, 0.7527, 0.8369, 0.3006, 0.3657, 0.5233, 0.6109] +2026-04-11 17:12:39.908843: Epoch time: 101.87 s +2026-04-11 17:12:41.017509: +2026-04-11 17:12:41.019191: Epoch 1073 +2026-04-11 17:12:41.021691: Current learning rate: 0.00755 +2026-04-11 17:14:22.859629: train_loss -0.2697 +2026-04-11 17:14:22.866652: val_loss -0.2173 +2026-04-11 17:14:22.869291: Pseudo dice [0.6623, 0.5785, 0.7376, 0.7877, 0.6077, 0.8139, 0.7773] +2026-04-11 17:14:22.872247: Epoch time: 101.85 s +2026-04-11 17:14:24.016740: +2026-04-11 17:14:24.018533: Epoch 1074 +2026-04-11 17:14:24.020576: Current learning rate: 0.00755 +2026-04-11 17:16:06.083525: train_loss -0.2225 +2026-04-11 17:16:06.089493: val_loss -0.2 +2026-04-11 17:16:06.091545: Pseudo dice [0.4484, 0.6961, 0.5361, 0.5567, 0.5512, 0.3078, 0.8333] +2026-04-11 17:16:06.094253: Epoch time: 102.07 s +2026-04-11 17:16:07.209712: +2026-04-11 17:16:07.211518: Epoch 1075 +2026-04-11 17:16:07.213683: Current learning rate: 0.00755 +2026-04-11 17:17:49.095456: train_loss -0.2479 +2026-04-11 17:17:49.102017: val_loss -0.2067 +2026-04-11 17:17:49.103771: Pseudo dice [0.3196, 0.3481, 0.6964, 0.5799, 0.4979, 0.7191, 0.5841] +2026-04-11 17:17:49.105914: Epoch time: 101.89 s +2026-04-11 17:17:50.238216: +2026-04-11 17:17:50.239919: Epoch 1076 +2026-04-11 17:17:50.241341: Current learning rate: 0.00754 +2026-04-11 17:19:32.102208: train_loss -0.2633 +2026-04-11 17:19:32.108380: val_loss -0.2033 +2026-04-11 17:19:32.111274: Pseudo dice [0.5132, 0.3211, 0.6471, 0.3193, 0.3293, 0.7355, 0.7527] +2026-04-11 17:19:32.113631: Epoch time: 101.87 s +2026-04-11 17:19:33.212498: +2026-04-11 17:19:33.214527: Epoch 1077 +2026-04-11 17:19:33.216539: Current learning rate: 0.00754 +2026-04-11 17:21:15.290002: train_loss -0.2615 +2026-04-11 17:21:15.303155: val_loss -0.2041 +2026-04-11 17:21:15.306216: Pseudo dice [0.4719, 0.2171, 0.7785, 0.7973, 0.422, 0.8037, 0.6569] +2026-04-11 17:21:15.308100: Epoch time: 102.08 s +2026-04-11 17:21:16.442606: +2026-04-11 17:21:16.444146: Epoch 1078 +2026-04-11 17:21:16.445465: Current learning rate: 0.00754 +2026-04-11 17:22:58.270365: train_loss -0.2588 +2026-04-11 17:22:58.277113: val_loss -0.2131 +2026-04-11 17:22:58.279853: Pseudo dice [0.1568, 0.4988, 0.7452, 0.2543, 0.5491, 0.7411, 0.6251] +2026-04-11 17:22:58.282490: Epoch time: 101.83 s +2026-04-11 17:22:59.434140: +2026-04-11 17:22:59.435725: Epoch 1079 +2026-04-11 17:22:59.437089: Current learning rate: 0.00754 +2026-04-11 17:24:41.418660: train_loss -0.2577 +2026-04-11 17:24:41.424195: val_loss -0.2056 +2026-04-11 17:24:41.425922: Pseudo dice [0.6642, 0.8658, 0.7977, 0.3552, 0.5043, 0.6692, 0.4411] +2026-04-11 17:24:41.427949: Epoch time: 101.99 s +2026-04-11 17:24:42.567113: +2026-04-11 17:24:42.568918: Epoch 1080 +2026-04-11 17:24:42.570828: Current learning rate: 0.00753 +2026-04-11 17:26:24.866966: train_loss -0.261 +2026-04-11 17:26:24.873660: val_loss -0.1915 +2026-04-11 17:26:24.875649: Pseudo dice [0.3421, 0.8851, 0.7844, 0.7569, 0.3614, 0.6654, 0.6531] +2026-04-11 17:26:24.878378: Epoch time: 102.3 s +2026-04-11 17:26:26.009580: +2026-04-11 17:26:26.011182: Epoch 1081 +2026-04-11 17:26:26.012582: Current learning rate: 0.00753 +2026-04-11 17:28:07.975483: train_loss -0.2628 +2026-04-11 17:28:07.981242: val_loss -0.1858 +2026-04-11 17:28:07.982933: Pseudo dice [0.6062, 0.6866, 0.5947, 0.3755, 0.3942, 0.628, 0.7325] +2026-04-11 17:28:07.986304: Epoch time: 101.97 s +2026-04-11 17:28:09.095731: +2026-04-11 17:28:09.097172: Epoch 1082 +2026-04-11 17:28:09.098694: Current learning rate: 0.00753 +2026-04-11 17:29:51.000616: train_loss -0.2604 +2026-04-11 17:29:51.027725: val_loss -0.2484 +2026-04-11 17:29:51.030432: Pseudo dice [0.795, 0.4579, 0.7763, 0.706, 0.4878, 0.8019, 0.8434] +2026-04-11 17:29:51.033399: Epoch time: 101.91 s +2026-04-11 17:29:52.155044: +2026-04-11 17:29:52.157072: Epoch 1083 +2026-04-11 17:29:52.159258: Current learning rate: 0.00753 +2026-04-11 17:31:34.253870: train_loss -0.254 +2026-04-11 17:31:34.271997: val_loss -0.1915 +2026-04-11 17:31:34.274120: Pseudo dice [0.7937, 0.8146, 0.6294, 0.2415, 0.2393, 0.8685, 0.6166] +2026-04-11 17:31:34.278311: Epoch time: 102.1 s +2026-04-11 17:31:36.514714: +2026-04-11 17:31:36.516155: Epoch 1084 +2026-04-11 17:31:36.517552: Current learning rate: 0.00752 +2026-04-11 17:33:18.533060: train_loss -0.2361 +2026-04-11 17:33:18.539958: val_loss -0.1402 +2026-04-11 17:33:18.542315: Pseudo dice [0.3134, 0.8841, 0.2772, 0.0146, 0.3962, 0.5094, 0.8058] +2026-04-11 17:33:18.544509: Epoch time: 102.02 s +2026-04-11 17:33:19.667502: +2026-04-11 17:33:19.669102: Epoch 1085 +2026-04-11 17:33:19.670600: Current learning rate: 0.00752 +2026-04-11 17:35:01.542985: train_loss -0.2554 +2026-04-11 17:35:01.552571: val_loss -0.1629 +2026-04-11 17:35:01.555213: Pseudo dice [0.3808, 0.6828, 0.7212, 0.4857, 0.5438, 0.544, 0.6965] +2026-04-11 17:35:01.560541: Epoch time: 101.88 s +2026-04-11 17:35:02.673462: +2026-04-11 17:35:02.675894: Epoch 1086 +2026-04-11 17:35:02.677562: Current learning rate: 0.00752 +2026-04-11 17:36:44.447025: train_loss -0.2464 +2026-04-11 17:36:44.453864: val_loss -0.2045 +2026-04-11 17:36:44.455856: Pseudo dice [0.7292, 0.3305, 0.6088, 0.3977, 0.5098, 0.7682, 0.7443] +2026-04-11 17:36:44.458165: Epoch time: 101.78 s +2026-04-11 17:36:45.561063: +2026-04-11 17:36:45.562855: Epoch 1087 +2026-04-11 17:36:45.564438: Current learning rate: 0.00752 +2026-04-11 17:38:27.458325: train_loss -0.2598 +2026-04-11 17:38:27.465027: val_loss -0.1768 +2026-04-11 17:38:27.467012: Pseudo dice [0.4031, 0.8844, 0.7136, 0.5193, 0.4328, 0.299, 0.4978] +2026-04-11 17:38:27.469241: Epoch time: 101.9 s +2026-04-11 17:38:28.587898: +2026-04-11 17:38:28.589985: Epoch 1088 +2026-04-11 17:38:28.591544: Current learning rate: 0.00751 +2026-04-11 17:40:10.347373: train_loss -0.2422 +2026-04-11 17:40:10.354906: val_loss -0.2099 +2026-04-11 17:40:10.357798: Pseudo dice [0.4993, 0.8831, 0.699, 0.521, 0.5643, 0.8155, 0.7093] +2026-04-11 17:40:10.359776: Epoch time: 101.76 s +2026-04-11 17:40:11.479737: +2026-04-11 17:40:11.481925: Epoch 1089 +2026-04-11 17:40:11.483660: Current learning rate: 0.00751 +2026-04-11 17:41:53.693829: train_loss -0.2718 +2026-04-11 17:41:53.699682: val_loss -0.1918 +2026-04-11 17:41:53.701731: Pseudo dice [0.5464, 0.8987, 0.7667, 0.1782, 0.2583, 0.6022, 0.7613] +2026-04-11 17:41:53.705409: Epoch time: 102.22 s +2026-04-11 17:41:54.840176: +2026-04-11 17:41:54.841872: Epoch 1090 +2026-04-11 17:41:54.843277: Current learning rate: 0.00751 +2026-04-11 17:43:36.760978: train_loss -0.2561 +2026-04-11 17:43:36.767592: val_loss -0.2009 +2026-04-11 17:43:36.769408: Pseudo dice [0.4784, 0.6353, 0.7206, 0.5501, 0.4349, 0.7138, 0.7282] +2026-04-11 17:43:36.772779: Epoch time: 101.92 s +2026-04-11 17:43:37.934709: +2026-04-11 17:43:37.936814: Epoch 1091 +2026-04-11 17:43:37.938319: Current learning rate: 0.00751 +2026-04-11 17:45:19.845700: train_loss -0.2443 +2026-04-11 17:45:19.851494: val_loss -0.1821 +2026-04-11 17:45:19.853785: Pseudo dice [0.2703, 0.9059, 0.7143, 0.4015, 0.3956, 0.6924, 0.7428] +2026-04-11 17:45:19.856234: Epoch time: 101.91 s +2026-04-11 17:45:20.986084: +2026-04-11 17:45:20.987927: Epoch 1092 +2026-04-11 17:45:20.989645: Current learning rate: 0.00751 +2026-04-11 17:47:03.145236: train_loss -0.2542 +2026-04-11 17:47:03.151410: val_loss -0.1618 +2026-04-11 17:47:03.153299: Pseudo dice [0.5683, 0.057, 0.5858, 0.6618, 0.2323, 0.5582, 0.7841] +2026-04-11 17:47:03.155165: Epoch time: 102.16 s +2026-04-11 17:47:04.273613: +2026-04-11 17:47:04.275270: Epoch 1093 +2026-04-11 17:47:04.276687: Current learning rate: 0.0075 +2026-04-11 17:48:46.289826: train_loss -0.2532 +2026-04-11 17:48:46.295460: val_loss -0.2286 +2026-04-11 17:48:46.297560: Pseudo dice [0.4338, 0.8999, 0.4799, 0.8267, 0.5115, 0.7784, 0.7602] +2026-04-11 17:48:46.299690: Epoch time: 102.02 s +2026-04-11 17:48:47.436513: +2026-04-11 17:48:47.438245: Epoch 1094 +2026-04-11 17:48:47.440071: Current learning rate: 0.0075 +2026-04-11 17:50:29.313271: train_loss -0.2603 +2026-04-11 17:50:29.320027: val_loss -0.2321 +2026-04-11 17:50:29.322540: Pseudo dice [0.5451, 0.1995, 0.7396, 0.7006, 0.5064, 0.5243, 0.7155] +2026-04-11 17:50:29.324921: Epoch time: 101.88 s +2026-04-11 17:50:30.443161: +2026-04-11 17:50:30.445975: Epoch 1095 +2026-04-11 17:50:30.449667: Current learning rate: 0.0075 +2026-04-11 17:52:12.275268: train_loss -0.2519 +2026-04-11 17:52:12.280667: val_loss -0.2218 +2026-04-11 17:52:12.282631: Pseudo dice [0.4316, 0.3748, 0.6955, 0.2759, 0.5637, 0.7363, 0.711] +2026-04-11 17:52:12.285200: Epoch time: 101.84 s +2026-04-11 17:52:13.417202: +2026-04-11 17:52:13.418733: Epoch 1096 +2026-04-11 17:52:13.420099: Current learning rate: 0.0075 +2026-04-11 17:53:55.548331: train_loss -0.261 +2026-04-11 17:53:55.554685: val_loss -0.2099 +2026-04-11 17:53:55.556430: Pseudo dice [0.4083, 0.3703, 0.7539, 0.4116, 0.4945, 0.2255, 0.4827] +2026-04-11 17:53:55.558563: Epoch time: 102.13 s +2026-04-11 17:53:56.716787: +2026-04-11 17:53:56.719046: Epoch 1097 +2026-04-11 17:53:56.720652: Current learning rate: 0.00749 +2026-04-11 17:55:38.911444: train_loss -0.2634 +2026-04-11 17:55:38.918900: val_loss -0.2399 +2026-04-11 17:55:38.920991: Pseudo dice [0.494, 0.3023, 0.7385, 0.0847, 0.4795, 0.6506, 0.8901] +2026-04-11 17:55:38.923520: Epoch time: 102.2 s +2026-04-11 17:55:40.036739: +2026-04-11 17:55:40.038618: Epoch 1098 +2026-04-11 17:55:40.040134: Current learning rate: 0.00749 +2026-04-11 17:57:22.082490: train_loss -0.2636 +2026-04-11 17:57:22.090057: val_loss -0.1944 +2026-04-11 17:57:22.092246: Pseudo dice [0.8402, 0.7863, 0.6746, 0.4888, 0.3431, 0.7114, 0.8635] +2026-04-11 17:57:22.094997: Epoch time: 102.05 s +2026-04-11 17:57:23.214741: +2026-04-11 17:57:23.216927: Epoch 1099 +2026-04-11 17:57:23.218603: Current learning rate: 0.00749 +2026-04-11 17:59:05.046706: train_loss -0.2581 +2026-04-11 17:59:05.052264: val_loss -0.2061 +2026-04-11 17:59:05.053824: Pseudo dice [0.5608, 0.3068, 0.6807, 0.2382, 0.5757, 0.7802, 0.7307] +2026-04-11 17:59:05.056058: Epoch time: 101.84 s +2026-04-11 17:59:07.725431: +2026-04-11 17:59:07.727494: Epoch 1100 +2026-04-11 17:59:07.728812: Current learning rate: 0.00749 +2026-04-11 18:00:49.531425: train_loss -0.266 +2026-04-11 18:00:49.537303: val_loss -0.2113 +2026-04-11 18:00:49.539187: Pseudo dice [0.2172, 0.2811, 0.6589, 0.5752, 0.4287, 0.9175, 0.8574] +2026-04-11 18:00:49.541214: Epoch time: 101.81 s +2026-04-11 18:00:50.648912: +2026-04-11 18:00:50.650354: Epoch 1101 +2026-04-11 18:00:50.651710: Current learning rate: 0.00748 +2026-04-11 18:02:32.150544: train_loss -0.2489 +2026-04-11 18:02:32.156400: val_loss -0.2109 +2026-04-11 18:02:32.158654: Pseudo dice [0.3427, 0.5249, 0.7444, 0.5645, 0.5656, 0.4675, 0.8005] +2026-04-11 18:02:32.160548: Epoch time: 101.5 s +2026-04-11 18:02:33.287431: +2026-04-11 18:02:33.289895: Epoch 1102 +2026-04-11 18:02:33.291597: Current learning rate: 0.00748 +2026-04-11 18:04:15.105698: train_loss -0.2463 +2026-04-11 18:04:15.113402: val_loss -0.2307 +2026-04-11 18:04:15.115319: Pseudo dice [0.3907, 0.545, 0.77, 0.1804, 0.4069, 0.3894, 0.7336] +2026-04-11 18:04:15.118915: Epoch time: 101.82 s +2026-04-11 18:04:16.279697: +2026-04-11 18:04:16.281666: Epoch 1103 +2026-04-11 18:04:16.283592: Current learning rate: 0.00748 +2026-04-11 18:05:57.956730: train_loss -0.2502 +2026-04-11 18:05:57.962868: val_loss -0.197 +2026-04-11 18:05:57.964920: Pseudo dice [0.5328, 0.8904, 0.4787, 0.2589, 0.5793, 0.5507, 0.4633] +2026-04-11 18:05:57.967602: Epoch time: 101.68 s +2026-04-11 18:05:59.073565: +2026-04-11 18:05:59.075242: Epoch 1104 +2026-04-11 18:05:59.076677: Current learning rate: 0.00748 +2026-04-11 18:07:41.829455: train_loss -0.2615 +2026-04-11 18:07:41.838725: val_loss -0.1659 +2026-04-11 18:07:41.840633: Pseudo dice [0.6421, 0.6367, 0.3957, 0.388, 0.5406, 0.3648, 0.6256] +2026-04-11 18:07:41.842631: Epoch time: 102.76 s +2026-04-11 18:07:42.969592: +2026-04-11 18:07:42.971439: Epoch 1105 +2026-04-11 18:07:42.972908: Current learning rate: 0.00748 +2026-04-11 18:09:25.183005: train_loss -0.2476 +2026-04-11 18:09:25.190044: val_loss -0.241 +2026-04-11 18:09:25.192000: Pseudo dice [0.5567, 0.6474, 0.8329, 0.6149, 0.3794, 0.4505, 0.8415] +2026-04-11 18:09:25.194621: Epoch time: 102.22 s +2026-04-11 18:09:26.318585: +2026-04-11 18:09:26.320241: Epoch 1106 +2026-04-11 18:09:26.321616: Current learning rate: 0.00747 +2026-04-11 18:11:08.072619: train_loss -0.2611 +2026-04-11 18:11:08.079354: val_loss -0.2101 +2026-04-11 18:11:08.081210: Pseudo dice [0.3741, 0.7596, 0.3515, 0.4483, 0.3384, 0.758, 0.4349] +2026-04-11 18:11:08.083366: Epoch time: 101.76 s +2026-04-11 18:11:09.209309: +2026-04-11 18:11:09.210837: Epoch 1107 +2026-04-11 18:11:09.212165: Current learning rate: 0.00747 +2026-04-11 18:12:51.012150: train_loss -0.2618 +2026-04-11 18:12:51.018432: val_loss -0.2194 +2026-04-11 18:12:51.020554: Pseudo dice [0.8245, 0.7896, 0.7386, 0.4219, 0.6479, 0.6355, 0.677] +2026-04-11 18:12:51.022875: Epoch time: 101.81 s +2026-04-11 18:12:52.134505: +2026-04-11 18:12:52.136019: Epoch 1108 +2026-04-11 18:12:52.138227: Current learning rate: 0.00747 +2026-04-11 18:14:33.896411: train_loss -0.2631 +2026-04-11 18:14:33.903765: val_loss -0.2117 +2026-04-11 18:14:33.905475: Pseudo dice [0.6061, 0.6098, 0.7566, 0.2178, 0.2725, 0.6222, 0.8156] +2026-04-11 18:14:33.907919: Epoch time: 101.76 s +2026-04-11 18:14:35.035044: +2026-04-11 18:14:35.037441: Epoch 1109 +2026-04-11 18:14:35.039151: Current learning rate: 0.00747 +2026-04-11 18:16:16.934600: train_loss -0.2547 +2026-04-11 18:16:16.943515: val_loss -0.1864 +2026-04-11 18:16:16.946234: Pseudo dice [0.4154, 0.6718, 0.725, 0.0746, 0.3218, 0.641, 0.4606] +2026-04-11 18:16:16.949209: Epoch time: 101.9 s +2026-04-11 18:16:18.067437: +2026-04-11 18:16:18.069627: Epoch 1110 +2026-04-11 18:16:18.071107: Current learning rate: 0.00746 +2026-04-11 18:17:59.892524: train_loss -0.2523 +2026-04-11 18:17:59.899461: val_loss -0.1658 +2026-04-11 18:17:59.901797: Pseudo dice [0.4351, 0.8079, 0.6375, 0.5551, 0.4421, 0.6216, 0.6911] +2026-04-11 18:17:59.904546: Epoch time: 101.83 s +2026-04-11 18:18:01.033684: +2026-04-11 18:18:01.035173: Epoch 1111 +2026-04-11 18:18:01.036741: Current learning rate: 0.00746 +2026-04-11 18:19:43.141664: train_loss -0.259 +2026-04-11 18:19:43.147599: val_loss -0.2146 +2026-04-11 18:19:43.149866: Pseudo dice [0.3914, 0.4673, 0.6899, 0.2459, 0.5495, 0.5937, 0.6515] +2026-04-11 18:19:43.152121: Epoch time: 102.11 s +2026-04-11 18:19:44.256948: +2026-04-11 18:19:44.258629: Epoch 1112 +2026-04-11 18:19:44.260268: Current learning rate: 0.00746 +2026-04-11 18:21:26.193782: train_loss -0.249 +2026-04-11 18:21:26.200473: val_loss -0.2399 +2026-04-11 18:21:26.202994: Pseudo dice [0.5224, 0.4715, 0.8082, 0.3294, 0.5956, 0.6824, 0.6432] +2026-04-11 18:21:26.205493: Epoch time: 101.94 s +2026-04-11 18:21:27.332105: +2026-04-11 18:21:27.333877: Epoch 1113 +2026-04-11 18:21:27.335882: Current learning rate: 0.00746 +2026-04-11 18:23:09.103230: train_loss -0.264 +2026-04-11 18:23:09.110454: val_loss -0.2119 +2026-04-11 18:23:09.113252: Pseudo dice [0.721, 0.7657, 0.8047, 0.766, 0.5625, 0.7211, 0.815] +2026-04-11 18:23:09.115777: Epoch time: 101.77 s +2026-04-11 18:23:10.233708: +2026-04-11 18:23:10.235292: Epoch 1114 +2026-04-11 18:23:10.236748: Current learning rate: 0.00745 +2026-04-11 18:24:51.974146: train_loss -0.2493 +2026-04-11 18:24:51.982245: val_loss -0.2384 +2026-04-11 18:24:51.984290: Pseudo dice [0.3967, 0.8398, 0.6511, 0.5588, 0.3513, 0.2245, 0.8382] +2026-04-11 18:24:51.986750: Epoch time: 101.74 s +2026-04-11 18:24:53.163604: +2026-04-11 18:24:53.165394: Epoch 1115 +2026-04-11 18:24:53.166781: Current learning rate: 0.00745 +2026-04-11 18:26:34.792303: train_loss -0.2509 +2026-04-11 18:26:34.798393: val_loss -0.1875 +2026-04-11 18:26:34.800570: Pseudo dice [0.491, 0.7431, 0.6547, 0.0107, 0.4373, 0.6979, 0.3074] +2026-04-11 18:26:34.802995: Epoch time: 101.63 s +2026-04-11 18:26:35.925082: +2026-04-11 18:26:35.927335: Epoch 1116 +2026-04-11 18:26:35.928969: Current learning rate: 0.00745 +2026-04-11 18:28:17.610177: train_loss -0.2589 +2026-04-11 18:28:17.616044: val_loss -0.2251 +2026-04-11 18:28:17.617939: Pseudo dice [0.3282, 0.8672, 0.812, 0.5442, 0.5047, 0.7757, 0.6938] +2026-04-11 18:28:17.619925: Epoch time: 101.69 s +2026-04-11 18:28:18.737584: +2026-04-11 18:28:18.739146: Epoch 1117 +2026-04-11 18:28:18.740652: Current learning rate: 0.00745 +2026-04-11 18:30:00.633268: train_loss -0.2369 +2026-04-11 18:30:00.639200: val_loss -0.1727 +2026-04-11 18:30:00.641793: Pseudo dice [0.5638, 0.113, 0.7196, 0.4551, 0.4335, 0.4087, 0.8151] +2026-04-11 18:30:00.643809: Epoch time: 101.9 s +2026-04-11 18:30:01.757045: +2026-04-11 18:30:01.758735: Epoch 1118 +2026-04-11 18:30:01.761273: Current learning rate: 0.00745 +2026-04-11 18:31:43.937638: train_loss -0.2284 +2026-04-11 18:31:43.943912: val_loss -0.2132 +2026-04-11 18:31:43.945467: Pseudo dice [0.2505, 0.3591, 0.7009, 0.3158, 0.186, 0.843, 0.839] +2026-04-11 18:31:43.947626: Epoch time: 102.18 s +2026-04-11 18:31:45.064709: +2026-04-11 18:31:45.066570: Epoch 1119 +2026-04-11 18:31:45.068352: Current learning rate: 0.00744 +2026-04-11 18:33:27.269808: train_loss -0.2439 +2026-04-11 18:33:27.276125: val_loss -0.2098 +2026-04-11 18:33:27.279547: Pseudo dice [0.4107, 0.3123, 0.6337, 0.3123, 0.5395, 0.6328, 0.8395] +2026-04-11 18:33:27.282228: Epoch time: 102.21 s +2026-04-11 18:33:28.407357: +2026-04-11 18:33:28.409725: Epoch 1120 +2026-04-11 18:33:28.411413: Current learning rate: 0.00744 +2026-04-11 18:35:10.636121: train_loss -0.249 +2026-04-11 18:35:10.646035: val_loss -0.184 +2026-04-11 18:35:10.651144: Pseudo dice [0.3507, 0.4677, 0.684, 0.1003, 0.3139, 0.1519, 0.7328] +2026-04-11 18:35:10.653842: Epoch time: 102.23 s +2026-04-11 18:35:11.788829: +2026-04-11 18:35:11.790617: Epoch 1121 +2026-04-11 18:35:11.792141: Current learning rate: 0.00744 +2026-04-11 18:36:54.000434: train_loss -0.2503 +2026-04-11 18:36:54.006289: val_loss -0.2034 +2026-04-11 18:36:54.008245: Pseudo dice [0.4208, 0.8448, 0.6998, 0.1259, 0.3663, 0.331, 0.6014] +2026-04-11 18:36:54.010173: Epoch time: 102.21 s +2026-04-11 18:36:55.138785: +2026-04-11 18:36:55.140249: Epoch 1122 +2026-04-11 18:36:55.141747: Current learning rate: 0.00744 +2026-04-11 18:38:37.339339: train_loss -0.2317 +2026-04-11 18:38:37.345057: val_loss -0.1875 +2026-04-11 18:38:37.346934: Pseudo dice [0.6832, 0.6537, 0.7508, 0.2245, 0.3039, 0.467, 0.4695] +2026-04-11 18:38:37.349231: Epoch time: 102.2 s +2026-04-11 18:38:38.485176: +2026-04-11 18:38:38.486739: Epoch 1123 +2026-04-11 18:38:38.488100: Current learning rate: 0.00743 +2026-04-11 18:40:20.304003: train_loss -0.2475 +2026-04-11 18:40:20.309896: val_loss -0.2055 +2026-04-11 18:40:20.312218: Pseudo dice [0.5098, 0.2408, 0.5345, 0.1087, 0.5317, 0.6773, 0.7258] +2026-04-11 18:40:20.314326: Epoch time: 101.82 s +2026-04-11 18:40:21.461638: +2026-04-11 18:40:21.463392: Epoch 1124 +2026-04-11 18:40:21.464930: Current learning rate: 0.00743 +2026-04-11 18:42:03.772866: train_loss -0.265 +2026-04-11 18:42:03.782140: val_loss -0.2056 +2026-04-11 18:42:03.785002: Pseudo dice [0.758, 0.782, 0.5068, 0.6227, 0.3582, 0.6859, 0.8502] +2026-04-11 18:42:03.787916: Epoch time: 102.31 s +2026-04-11 18:42:06.091050: +2026-04-11 18:42:06.092647: Epoch 1125 +2026-04-11 18:42:06.094023: Current learning rate: 0.00743 +2026-04-11 18:43:48.022967: train_loss -0.2742 +2026-04-11 18:43:48.028709: val_loss -0.2469 +2026-04-11 18:43:48.030716: Pseudo dice [0.5155, 0.7485, 0.8415, 0.4994, 0.408, 0.7406, 0.7444] +2026-04-11 18:43:48.033050: Epoch time: 101.93 s +2026-04-11 18:43:49.200399: +2026-04-11 18:43:49.202521: Epoch 1126 +2026-04-11 18:43:49.204437: Current learning rate: 0.00743 +2026-04-11 18:45:31.106344: train_loss -0.2402 +2026-04-11 18:45:31.112713: val_loss -0.1937 +2026-04-11 18:45:31.115069: Pseudo dice [0.5777, 0.8463, 0.6899, 0.2711, 0.4655, 0.7605, 0.6914] +2026-04-11 18:45:31.117550: Epoch time: 101.91 s +2026-04-11 18:45:32.237413: +2026-04-11 18:45:32.238996: Epoch 1127 +2026-04-11 18:45:32.240416: Current learning rate: 0.00742 +2026-04-11 18:47:14.017148: train_loss -0.2535 +2026-04-11 18:47:14.023035: val_loss -0.1266 +2026-04-11 18:47:14.025265: Pseudo dice [0.6575, 0.6559, 0.4676, 0.0079, 0.2241, 0.3339, 0.8006] +2026-04-11 18:47:14.028952: Epoch time: 101.78 s +2026-04-11 18:47:15.139642: +2026-04-11 18:47:15.141465: Epoch 1128 +2026-04-11 18:47:15.143157: Current learning rate: 0.00742 +2026-04-11 18:48:57.145328: train_loss -0.2719 +2026-04-11 18:48:57.151168: val_loss -0.2046 +2026-04-11 18:48:57.153738: Pseudo dice [0.2664, 0.6633, 0.7577, 0.5526, 0.4191, 0.4732, 0.6233] +2026-04-11 18:48:57.155827: Epoch time: 102.01 s +2026-04-11 18:48:58.291880: +2026-04-11 18:48:58.293628: Epoch 1129 +2026-04-11 18:48:58.294999: Current learning rate: 0.00742 +2026-04-11 18:50:39.941056: train_loss -0.2459 +2026-04-11 18:50:39.947885: val_loss -0.2142 +2026-04-11 18:50:39.950428: Pseudo dice [0.7175, 0.1334, 0.6264, 0.3434, 0.4271, 0.2121, 0.6771] +2026-04-11 18:50:39.954147: Epoch time: 101.65 s +2026-04-11 18:50:41.068768: +2026-04-11 18:50:41.070354: Epoch 1130 +2026-04-11 18:50:41.072009: Current learning rate: 0.00742 +2026-04-11 18:52:22.744805: train_loss -0.2633 +2026-04-11 18:52:22.751350: val_loss -0.2188 +2026-04-11 18:52:22.753285: Pseudo dice [0.835, 0.1096, 0.6628, 0.3845, 0.456, 0.8823, 0.7569] +2026-04-11 18:52:22.755935: Epoch time: 101.68 s +2026-04-11 18:52:23.928233: +2026-04-11 18:52:23.929913: Epoch 1131 +2026-04-11 18:52:23.931291: Current learning rate: 0.00741 +2026-04-11 18:54:05.699174: train_loss -0.2686 +2026-04-11 18:54:05.704217: val_loss -0.2372 +2026-04-11 18:54:05.706623: Pseudo dice [0.6175, 0.8819, 0.6944, 0.6366, 0.5413, 0.192, 0.4052] +2026-04-11 18:54:05.708642: Epoch time: 101.77 s +2026-04-11 18:54:06.820923: +2026-04-11 18:54:06.822439: Epoch 1132 +2026-04-11 18:54:06.823848: Current learning rate: 0.00741 +2026-04-11 18:55:48.806247: train_loss -0.2344 +2026-04-11 18:55:48.813341: val_loss -0.2213 +2026-04-11 18:55:48.815518: Pseudo dice [0.2585, 0.3298, 0.7399, 0.4247, 0.3441, 0.657, 0.7755] +2026-04-11 18:55:48.818218: Epoch time: 101.99 s +2026-04-11 18:55:49.941824: +2026-04-11 18:55:49.943432: Epoch 1133 +2026-04-11 18:55:49.944806: Current learning rate: 0.00741 +2026-04-11 18:57:31.924879: train_loss -0.2431 +2026-04-11 18:57:31.930652: val_loss -0.2213 +2026-04-11 18:57:31.932599: Pseudo dice [0.7856, 0.4671, 0.5725, 0.5858, 0.5887, 0.8516, 0.6095] +2026-04-11 18:57:31.935555: Epoch time: 101.99 s +2026-04-11 18:57:33.050756: +2026-04-11 18:57:33.052384: Epoch 1134 +2026-04-11 18:57:33.054418: Current learning rate: 0.00741 +2026-04-11 18:59:15.602752: train_loss -0.2564 +2026-04-11 18:59:15.608496: val_loss -0.2085 +2026-04-11 18:59:15.610750: Pseudo dice [0.4743, 0.8638, 0.5784, 0.2916, 0.4127, 0.2323, 0.7136] +2026-04-11 18:59:15.612629: Epoch time: 102.56 s +2026-04-11 18:59:16.757324: +2026-04-11 18:59:16.759170: Epoch 1135 +2026-04-11 18:59:16.761063: Current learning rate: 0.00741 +2026-04-11 19:00:59.045386: train_loss -0.265 +2026-04-11 19:00:59.050869: val_loss -0.2014 +2026-04-11 19:00:59.052684: Pseudo dice [0.265, 0.5539, 0.7399, 0.5274, 0.4202, 0.3589, 0.7913] +2026-04-11 19:00:59.054997: Epoch time: 102.29 s +2026-04-11 19:01:00.182438: +2026-04-11 19:01:00.184217: Epoch 1136 +2026-04-11 19:01:00.185848: Current learning rate: 0.0074 +2026-04-11 19:02:41.905667: train_loss -0.2456 +2026-04-11 19:02:41.911982: val_loss -0.221 +2026-04-11 19:02:41.916134: Pseudo dice [0.6484, 0.6603, 0.7946, 0.5537, 0.6173, 0.8715, 0.8142] +2026-04-11 19:02:41.920277: Epoch time: 101.73 s +2026-04-11 19:02:43.044214: +2026-04-11 19:02:43.046255: Epoch 1137 +2026-04-11 19:02:43.047755: Current learning rate: 0.0074 +2026-04-11 19:04:24.930643: train_loss -0.2549 +2026-04-11 19:04:24.937771: val_loss -0.1212 +2026-04-11 19:04:24.941296: Pseudo dice [0.1724, 0.8407, 0.2739, 0.0898, 0.3468, 0.6916, 0.791] +2026-04-11 19:04:24.943767: Epoch time: 101.89 s +2026-04-11 19:04:26.071625: +2026-04-11 19:04:26.073351: Epoch 1138 +2026-04-11 19:04:26.074830: Current learning rate: 0.0074 +2026-04-11 19:06:08.153914: train_loss -0.2466 +2026-04-11 19:06:08.159693: val_loss -0.184 +2026-04-11 19:06:08.161989: Pseudo dice [0.3022, 0.0971, 0.6216, 0.6545, 0.2505, 0.8222, 0.7407] +2026-04-11 19:06:08.164444: Epoch time: 102.09 s +2026-04-11 19:06:09.294856: +2026-04-11 19:06:09.297174: Epoch 1139 +2026-04-11 19:06:09.298921: Current learning rate: 0.0074 +2026-04-11 19:07:51.401037: train_loss -0.2492 +2026-04-11 19:07:51.408147: val_loss -0.199 +2026-04-11 19:07:51.410334: Pseudo dice [0.5131, 0.8582, 0.7253, 0.3614, 0.5952, 0.8045, 0.5518] +2026-04-11 19:07:51.412387: Epoch time: 102.11 s +2026-04-11 19:07:52.561315: +2026-04-11 19:07:52.570431: Epoch 1140 +2026-04-11 19:07:52.571897: Current learning rate: 0.00739 +2026-04-11 19:09:34.753089: train_loss -0.2589 +2026-04-11 19:09:34.759081: val_loss -0.207 +2026-04-11 19:09:34.761021: Pseudo dice [0.1845, 0.799, 0.7382, 0.6609, 0.4625, 0.1279, 0.7459] +2026-04-11 19:09:34.763369: Epoch time: 102.19 s +2026-04-11 19:09:35.890486: +2026-04-11 19:09:35.892364: Epoch 1141 +2026-04-11 19:09:35.894124: Current learning rate: 0.00739 +2026-04-11 19:11:17.729939: train_loss -0.268 +2026-04-11 19:11:17.736390: val_loss -0.1943 +2026-04-11 19:11:17.738262: Pseudo dice [0.5023, 0.7884, 0.7708, 0.2109, 0.6042, 0.2529, 0.2408] +2026-04-11 19:11:17.740536: Epoch time: 101.84 s +2026-04-11 19:11:18.860234: +2026-04-11 19:11:18.862133: Epoch 1142 +2026-04-11 19:11:18.863574: Current learning rate: 0.00739 +2026-04-11 19:13:00.712949: train_loss -0.2771 +2026-04-11 19:13:00.720343: val_loss -0.239 +2026-04-11 19:13:00.722160: Pseudo dice [0.2321, 0.2186, 0.7563, 0.4949, 0.4783, 0.9115, 0.8093] +2026-04-11 19:13:00.724488: Epoch time: 101.86 s +2026-04-11 19:13:01.878893: +2026-04-11 19:13:01.881131: Epoch 1143 +2026-04-11 19:13:01.882826: Current learning rate: 0.00739 +2026-04-11 19:14:44.235249: train_loss -0.2524 +2026-04-11 19:14:44.241375: val_loss -0.202 +2026-04-11 19:14:44.243173: Pseudo dice [0.8466, 0.732, 0.7305, 0.6509, 0.5105, 0.3219, 0.8175] +2026-04-11 19:14:44.245224: Epoch time: 102.36 s +2026-04-11 19:14:45.383814: +2026-04-11 19:14:45.385197: Epoch 1144 +2026-04-11 19:14:45.386608: Current learning rate: 0.00738 +2026-04-11 19:16:27.237421: train_loss -0.2428 +2026-04-11 19:16:27.243802: val_loss -0.2307 +2026-04-11 19:16:27.246175: Pseudo dice [0.8151, 0.748, 0.7826, 0.7738, 0.5301, 0.6793, 0.7495] +2026-04-11 19:16:27.248621: Epoch time: 101.86 s +2026-04-11 19:16:28.379155: +2026-04-11 19:16:28.380694: Epoch 1145 +2026-04-11 19:16:28.382066: Current learning rate: 0.00738 +2026-04-11 19:18:10.153328: train_loss -0.2677 +2026-04-11 19:18:10.159734: val_loss -0.2199 +2026-04-11 19:18:10.161803: Pseudo dice [0.5339, 0.3817, 0.8151, 0.6153, 0.6187, 0.7638, 0.8404] +2026-04-11 19:18:10.164544: Epoch time: 101.78 s +2026-04-11 19:18:12.384591: +2026-04-11 19:18:12.386466: Epoch 1146 +2026-04-11 19:18:12.388121: Current learning rate: 0.00738 +2026-04-11 19:19:54.129601: train_loss -0.2557 +2026-04-11 19:19:54.141471: val_loss -0.2101 +2026-04-11 19:19:54.143273: Pseudo dice [0.0448, 0.6119, 0.5885, 0.4932, 0.5079, 0.8764, 0.8829] +2026-04-11 19:19:54.145264: Epoch time: 101.75 s +2026-04-11 19:19:55.278169: +2026-04-11 19:19:55.279762: Epoch 1147 +2026-04-11 19:19:55.281260: Current learning rate: 0.00738 +2026-04-11 19:21:37.402888: train_loss -0.2527 +2026-04-11 19:21:37.408463: val_loss -0.1844 +2026-04-11 19:21:37.410440: Pseudo dice [0.3958, 0.6055, 0.6183, 0.4745, 0.5815, 0.7671, 0.7728] +2026-04-11 19:21:37.412638: Epoch time: 102.13 s +2026-04-11 19:21:38.547362: +2026-04-11 19:21:38.548851: Epoch 1148 +2026-04-11 19:21:38.550397: Current learning rate: 0.00738 +2026-04-11 19:23:20.379972: train_loss -0.2453 +2026-04-11 19:23:20.385863: val_loss -0.2111 +2026-04-11 19:23:20.388161: Pseudo dice [0.6721, 0.818, 0.7154, 0.5972, 0.3994, 0.3774, 0.2102] +2026-04-11 19:23:20.390510: Epoch time: 101.84 s +2026-04-11 19:23:21.529934: +2026-04-11 19:23:21.531380: Epoch 1149 +2026-04-11 19:23:21.532803: Current learning rate: 0.00737 +2026-04-11 19:25:03.527739: train_loss -0.2476 +2026-04-11 19:25:03.533871: val_loss -0.2162 +2026-04-11 19:25:03.535788: Pseudo dice [0.278, 0.5079, 0.7417, 0.2479, 0.3376, 0.6817, 0.7295] +2026-04-11 19:25:03.538194: Epoch time: 102.0 s +2026-04-11 19:25:06.400406: +2026-04-11 19:25:06.402280: Epoch 1150 +2026-04-11 19:25:06.403966: Current learning rate: 0.00737 +2026-04-11 19:26:48.577875: train_loss -0.2581 +2026-04-11 19:26:48.583592: val_loss -0.2382 +2026-04-11 19:26:48.585940: Pseudo dice [0.387, 0.8625, 0.7846, 0.5157, 0.6846, 0.6769, 0.8072] +2026-04-11 19:26:48.588731: Epoch time: 102.18 s +2026-04-11 19:26:49.750538: +2026-04-11 19:26:49.752040: Epoch 1151 +2026-04-11 19:26:49.753473: Current learning rate: 0.00737 +2026-04-11 19:28:31.573853: train_loss -0.2699 +2026-04-11 19:28:31.578696: val_loss -0.2296 +2026-04-11 19:28:31.580424: Pseudo dice [0.4474, 0.6384, 0.7859, 0.8125, 0.5608, 0.7173, 0.7701] +2026-04-11 19:28:31.582290: Epoch time: 101.83 s +2026-04-11 19:28:32.717328: +2026-04-11 19:28:32.718880: Epoch 1152 +2026-04-11 19:28:32.720321: Current learning rate: 0.00737 +2026-04-11 19:30:14.843522: train_loss -0.2383 +2026-04-11 19:30:14.871812: val_loss -0.1527 +2026-04-11 19:30:14.874242: Pseudo dice [0.56, 0.7712, 0.4421, 0.2557, 0.5056, 0.4115, 0.6169] +2026-04-11 19:30:14.877127: Epoch time: 102.13 s +2026-04-11 19:30:16.023483: +2026-04-11 19:30:16.025109: Epoch 1153 +2026-04-11 19:30:16.026550: Current learning rate: 0.00736 +2026-04-11 19:31:57.854144: train_loss -0.2368 +2026-04-11 19:31:57.860848: val_loss -0.1861 +2026-04-11 19:31:57.862810: Pseudo dice [0.3287, 0.4517, 0.4114, 0.2197, 0.4668, 0.8631, 0.5453] +2026-04-11 19:31:57.865065: Epoch time: 101.83 s +2026-04-11 19:31:59.013036: +2026-04-11 19:31:59.014910: Epoch 1154 +2026-04-11 19:31:59.016491: Current learning rate: 0.00736 +2026-04-11 19:33:40.786579: train_loss -0.2656 +2026-04-11 19:33:40.794427: val_loss -0.2164 +2026-04-11 19:33:40.796446: Pseudo dice [0.824, 0.8457, 0.7145, 0.8426, 0.4729, 0.7582, 0.8279] +2026-04-11 19:33:40.799761: Epoch time: 101.78 s +2026-04-11 19:33:41.934811: +2026-04-11 19:33:41.936200: Epoch 1155 +2026-04-11 19:33:41.937497: Current learning rate: 0.00736 +2026-04-11 19:35:23.831407: train_loss -0.2689 +2026-04-11 19:35:23.839404: val_loss -0.2091 +2026-04-11 19:35:23.841351: Pseudo dice [0.3052, 0.8495, 0.6851, 0.2095, 0.5594, 0.6935, 0.6392] +2026-04-11 19:35:23.844609: Epoch time: 101.9 s +2026-04-11 19:35:24.971950: +2026-04-11 19:35:24.974200: Epoch 1156 +2026-04-11 19:35:24.975795: Current learning rate: 0.00736 +2026-04-11 19:37:06.609041: train_loss -0.2525 +2026-04-11 19:37:06.614748: val_loss -0.204 +2026-04-11 19:37:06.616581: Pseudo dice [0.4079, 0.8833, 0.5482, 0.6768, 0.5392, 0.5963, 0.8816] +2026-04-11 19:37:06.618975: Epoch time: 101.64 s +2026-04-11 19:37:07.791808: +2026-04-11 19:37:07.793532: Epoch 1157 +2026-04-11 19:37:07.794918: Current learning rate: 0.00735 +2026-04-11 19:38:50.337452: train_loss -0.243 +2026-04-11 19:38:50.342957: val_loss -0.1897 +2026-04-11 19:38:50.344708: Pseudo dice [0.5052, 0.8895, 0.7492, 0.1932, 0.4503, 0.2856, 0.4605] +2026-04-11 19:38:50.346878: Epoch time: 102.55 s +2026-04-11 19:38:51.498809: +2026-04-11 19:38:51.500576: Epoch 1158 +2026-04-11 19:38:51.502504: Current learning rate: 0.00735 +2026-04-11 19:40:33.319331: train_loss -0.2367 +2026-04-11 19:40:33.324173: val_loss -0.1681 +2026-04-11 19:40:33.325699: Pseudo dice [0.4562, 0.7584, 0.7077, 0.1564, 0.4505, 0.0442, 0.5927] +2026-04-11 19:40:33.327882: Epoch time: 101.82 s +2026-04-11 19:40:34.467520: +2026-04-11 19:40:34.469056: Epoch 1159 +2026-04-11 19:40:34.470421: Current learning rate: 0.00735 +2026-04-11 19:42:16.235943: train_loss -0.2447 +2026-04-11 19:42:16.252748: val_loss -0.2555 +2026-04-11 19:42:16.254912: Pseudo dice [0.5843, 0.8679, 0.7065, 0.2222, 0.4143, 0.6457, 0.8348] +2026-04-11 19:42:16.259117: Epoch time: 101.77 s +2026-04-11 19:42:17.417470: +2026-04-11 19:42:17.420385: Epoch 1160 +2026-04-11 19:42:17.422243: Current learning rate: 0.00735 +2026-04-11 19:43:59.098640: train_loss -0.2656 +2026-04-11 19:43:59.106430: val_loss -0.2343 +2026-04-11 19:43:59.108294: Pseudo dice [0.5041, 0.6638, 0.7458, 0.4982, 0.4951, 0.8977, 0.7317] +2026-04-11 19:43:59.111010: Epoch time: 101.68 s +2026-04-11 19:44:00.247530: +2026-04-11 19:44:00.249418: Epoch 1161 +2026-04-11 19:44:00.251059: Current learning rate: 0.00735 +2026-04-11 19:45:42.306748: train_loss -0.2495 +2026-04-11 19:45:42.312749: val_loss -0.1952 +2026-04-11 19:45:42.314579: Pseudo dice [0.6106, 0.7031, 0.8143, 0.0817, 0.5891, 0.3052, 0.2292] +2026-04-11 19:45:42.316981: Epoch time: 102.06 s +2026-04-11 19:45:43.465034: +2026-04-11 19:45:43.466704: Epoch 1162 +2026-04-11 19:45:43.468017: Current learning rate: 0.00734 +2026-04-11 19:47:25.633099: train_loss -0.2574 +2026-04-11 19:47:25.638152: val_loss -0.2036 +2026-04-11 19:47:25.640373: Pseudo dice [0.4864, 0.7425, 0.6131, 0.3087, 0.4304, 0.6926, 0.7029] +2026-04-11 19:47:25.642557: Epoch time: 102.17 s +2026-04-11 19:47:26.788801: +2026-04-11 19:47:26.790578: Epoch 1163 +2026-04-11 19:47:26.792176: Current learning rate: 0.00734 +2026-04-11 19:49:09.042740: train_loss -0.2631 +2026-04-11 19:49:09.048698: val_loss -0.2229 +2026-04-11 19:49:09.050397: Pseudo dice [0.5082, 0.4712, 0.7193, 0.6498, 0.3325, 0.5795, 0.4532] +2026-04-11 19:49:09.053256: Epoch time: 102.26 s +2026-04-11 19:49:10.199466: +2026-04-11 19:49:10.201179: Epoch 1164 +2026-04-11 19:49:10.202737: Current learning rate: 0.00734 +2026-04-11 19:50:52.268430: train_loss -0.2596 +2026-04-11 19:50:52.275685: val_loss -0.2184 +2026-04-11 19:50:52.277954: Pseudo dice [0.5512, 0.8108, 0.4343, 0.6326, 0.4287, 0.5746, 0.2265] +2026-04-11 19:50:52.280826: Epoch time: 102.07 s +2026-04-11 19:50:53.429318: +2026-04-11 19:50:53.431006: Epoch 1165 +2026-04-11 19:50:53.432526: Current learning rate: 0.00734 +2026-04-11 19:52:35.558884: train_loss -0.2661 +2026-04-11 19:52:35.564359: val_loss -0.1263 +2026-04-11 19:52:35.566254: Pseudo dice [0.6615, 0.8779, 0.5632, 0.7332, 0.2017, 0.3498, 0.5222] +2026-04-11 19:52:35.568980: Epoch time: 102.13 s +2026-04-11 19:52:37.770922: +2026-04-11 19:52:37.774519: Epoch 1166 +2026-04-11 19:52:37.775851: Current learning rate: 0.00733 +2026-04-11 19:54:19.857477: train_loss -0.2672 +2026-04-11 19:54:19.864012: val_loss -0.2187 +2026-04-11 19:54:19.866922: Pseudo dice [0.4345, 0.1174, 0.7759, 0.5268, 0.4921, 0.7594, 0.5357] +2026-04-11 19:54:19.869614: Epoch time: 102.09 s +2026-04-11 19:54:21.042055: +2026-04-11 19:54:21.043903: Epoch 1167 +2026-04-11 19:54:21.047250: Current learning rate: 0.00733 +2026-04-11 19:56:02.765611: train_loss -0.2606 +2026-04-11 19:56:02.770538: val_loss -0.2097 +2026-04-11 19:56:02.772354: Pseudo dice [0.5292, 0.6281, 0.7018, 0.3907, 0.4727, 0.8544, 0.6872] +2026-04-11 19:56:02.774507: Epoch time: 101.73 s +2026-04-11 19:56:03.896543: +2026-04-11 19:56:03.898159: Epoch 1168 +2026-04-11 19:56:03.899598: Current learning rate: 0.00733 +2026-04-11 19:57:46.158014: train_loss -0.2683 +2026-04-11 19:57:46.163097: val_loss -0.2032 +2026-04-11 19:57:46.166612: Pseudo dice [0.4654, 0.8526, 0.7219, 0.3253, 0.3754, 0.2562, 0.6631] +2026-04-11 19:57:46.170946: Epoch time: 102.26 s +2026-04-11 19:57:47.315034: +2026-04-11 19:57:47.317354: Epoch 1169 +2026-04-11 19:57:47.319481: Current learning rate: 0.00733 +2026-04-11 19:59:29.276801: train_loss -0.267 +2026-04-11 19:59:29.282041: val_loss -0.1878 +2026-04-11 19:59:29.283866: Pseudo dice [0.6383, 0.8576, 0.5721, 0.5183, 0.5033, 0.5551, 0.5945] +2026-04-11 19:59:29.285724: Epoch time: 101.96 s +2026-04-11 19:59:30.444784: +2026-04-11 19:59:30.446255: Epoch 1170 +2026-04-11 19:59:30.447667: Current learning rate: 0.00732 +2026-04-11 20:01:12.222692: train_loss -0.267 +2026-04-11 20:01:12.239731: val_loss -0.2145 +2026-04-11 20:01:12.241505: Pseudo dice [0.8954, 0.8543, 0.8374, 0.5692, 0.3181, 0.6631, 0.66] +2026-04-11 20:01:12.250864: Epoch time: 101.78 s +2026-04-11 20:01:13.394776: +2026-04-11 20:01:13.397458: Epoch 1171 +2026-04-11 20:01:13.400077: Current learning rate: 0.00732 +2026-04-11 20:02:55.325575: train_loss -0.2512 +2026-04-11 20:02:55.331555: val_loss -0.214 +2026-04-11 20:02:55.333871: Pseudo dice [0.3091, 0.4758, 0.7289, 0.5108, 0.4366, 0.8086, 0.757] +2026-04-11 20:02:55.336154: Epoch time: 101.93 s +2026-04-11 20:02:56.509078: +2026-04-11 20:02:56.511512: Epoch 1172 +2026-04-11 20:02:56.513589: Current learning rate: 0.00732 +2026-04-11 20:04:38.420908: train_loss -0.2464 +2026-04-11 20:04:38.428861: val_loss -0.195 +2026-04-11 20:04:38.430872: Pseudo dice [0.5132, 0.9066, 0.8079, 0.3465, 0.6552, 0.5428, 0.7931] +2026-04-11 20:04:38.432977: Epoch time: 101.91 s +2026-04-11 20:04:39.599603: +2026-04-11 20:04:39.601589: Epoch 1173 +2026-04-11 20:04:39.603299: Current learning rate: 0.00732 +2026-04-11 20:06:21.845479: train_loss -0.2461 +2026-04-11 20:06:21.851040: val_loss -0.241 +2026-04-11 20:06:21.852777: Pseudo dice [0.6645, 0.0609, 0.7441, 0.4509, 0.3923, 0.7186, 0.8251] +2026-04-11 20:06:21.854492: Epoch time: 102.25 s +2026-04-11 20:06:23.045332: +2026-04-11 20:06:23.047803: Epoch 1174 +2026-04-11 20:06:23.049397: Current learning rate: 0.00731 +2026-04-11 20:08:04.665259: train_loss -0.2576 +2026-04-11 20:08:04.670426: val_loss -0.2318 +2026-04-11 20:08:04.671937: Pseudo dice [0.6668, 0.6553, 0.6882, 0.6154, 0.5586, 0.2358, 0.5971] +2026-04-11 20:08:04.674363: Epoch time: 101.62 s +2026-04-11 20:08:05.821781: +2026-04-11 20:08:05.823210: Epoch 1175 +2026-04-11 20:08:05.825236: Current learning rate: 0.00731 +2026-04-11 20:09:47.428426: train_loss -0.2535 +2026-04-11 20:09:47.434790: val_loss -0.2385 +2026-04-11 20:09:47.437764: Pseudo dice [0.3919, 0.3661, 0.802, 0.7051, 0.5682, 0.7665, 0.654] +2026-04-11 20:09:47.440034: Epoch time: 101.61 s +2026-04-11 20:09:48.563916: +2026-04-11 20:09:48.565383: Epoch 1176 +2026-04-11 20:09:48.566891: Current learning rate: 0.00731 +2026-04-11 20:11:30.268536: train_loss -0.2554 +2026-04-11 20:11:30.274238: val_loss -0.2346 +2026-04-11 20:11:30.276285: Pseudo dice [0.5756, 0.5979, 0.7681, 0.6116, 0.4296, 0.4341, 0.62] +2026-04-11 20:11:30.278565: Epoch time: 101.71 s +2026-04-11 20:11:31.408029: +2026-04-11 20:11:31.409895: Epoch 1177 +2026-04-11 20:11:31.411508: Current learning rate: 0.00731 +2026-04-11 20:13:13.211202: train_loss -0.2548 +2026-04-11 20:13:13.217334: val_loss -0.1973 +2026-04-11 20:13:13.219774: Pseudo dice [0.6807, 0.7818, 0.8584, 0.4819, 0.4258, 0.6566, 0.7037] +2026-04-11 20:13:13.221939: Epoch time: 101.81 s +2026-04-11 20:13:14.373951: +2026-04-11 20:13:14.375611: Epoch 1178 +2026-04-11 20:13:14.377133: Current learning rate: 0.00731 +2026-04-11 20:14:56.531919: train_loss -0.2599 +2026-04-11 20:14:56.537765: val_loss -0.2267 +2026-04-11 20:14:56.539557: Pseudo dice [0.7114, 0.8274, 0.7125, 0.4192, 0.4297, 0.2124, 0.852] +2026-04-11 20:14:56.541465: Epoch time: 102.16 s +2026-04-11 20:14:57.704830: +2026-04-11 20:14:57.707615: Epoch 1179 +2026-04-11 20:14:57.709854: Current learning rate: 0.0073 +2026-04-11 20:16:39.662978: train_loss -0.2411 +2026-04-11 20:16:39.669754: val_loss -0.2233 +2026-04-11 20:16:39.672940: Pseudo dice [0.3878, 0.64, 0.7421, 0.5806, 0.4773, 0.6059, 0.3406] +2026-04-11 20:16:39.675836: Epoch time: 101.96 s +2026-04-11 20:16:40.823647: +2026-04-11 20:16:40.825653: Epoch 1180 +2026-04-11 20:16:40.827095: Current learning rate: 0.0073 +2026-04-11 20:18:23.121424: train_loss -0.2512 +2026-04-11 20:18:23.127314: val_loss -0.1709 +2026-04-11 20:18:23.128796: Pseudo dice [0.5049, 0.3923, 0.7599, 0.3651, 0.5029, 0.5056, 0.5491] +2026-04-11 20:18:23.130926: Epoch time: 102.3 s +2026-04-11 20:18:24.281846: +2026-04-11 20:18:24.283290: Epoch 1181 +2026-04-11 20:18:24.284639: Current learning rate: 0.0073 +2026-04-11 20:20:06.132062: train_loss -0.2514 +2026-04-11 20:20:06.137577: val_loss -0.2169 +2026-04-11 20:20:06.139509: Pseudo dice [0.1425, 0.791, 0.794, 0.3483, 0.4574, 0.5594, 0.7801] +2026-04-11 20:20:06.141648: Epoch time: 101.85 s +2026-04-11 20:20:07.283772: +2026-04-11 20:20:07.285299: Epoch 1182 +2026-04-11 20:20:07.286626: Current learning rate: 0.0073 +2026-04-11 20:21:49.404078: train_loss -0.2607 +2026-04-11 20:21:49.408994: val_loss -0.1529 +2026-04-11 20:21:49.410697: Pseudo dice [0.4836, 0.866, 0.6543, 0.133, 0.3551, 0.3103, 0.1777] +2026-04-11 20:21:49.412926: Epoch time: 102.12 s +2026-04-11 20:21:50.609202: +2026-04-11 20:21:50.610659: Epoch 1183 +2026-04-11 20:21:50.612065: Current learning rate: 0.00729 +2026-04-11 20:23:32.247085: train_loss -0.2467 +2026-04-11 20:23:32.253385: val_loss -0.2192 +2026-04-11 20:23:32.255288: Pseudo dice [0.2858, 0.6028, 0.4276, 0.3652, 0.2888, 0.6091, 0.8309] +2026-04-11 20:23:32.257226: Epoch time: 101.64 s +2026-04-11 20:23:33.403170: +2026-04-11 20:23:33.405423: Epoch 1184 +2026-04-11 20:23:33.407186: Current learning rate: 0.00729 +2026-04-11 20:25:15.286875: train_loss -0.2498 +2026-04-11 20:25:15.294740: val_loss -0.1973 +2026-04-11 20:25:15.299835: Pseudo dice [0.0706, 0.7509, 0.6925, 0.4943, 0.0802, 0.8113, 0.7792] +2026-04-11 20:25:15.304860: Epoch time: 101.89 s +2026-04-11 20:25:16.440669: +2026-04-11 20:25:16.443005: Epoch 1185 +2026-04-11 20:25:16.448310: Current learning rate: 0.00729 +2026-04-11 20:26:58.235049: train_loss -0.2213 +2026-04-11 20:26:58.240844: val_loss -0.2043 +2026-04-11 20:26:58.242910: Pseudo dice [0.7103, 0.3543, 0.7199, 0.6052, 0.5827, 0.7339, 0.6181] +2026-04-11 20:26:58.244940: Epoch time: 101.8 s +2026-04-11 20:27:00.603574: +2026-04-11 20:27:00.605132: Epoch 1186 +2026-04-11 20:27:00.606561: Current learning rate: 0.00729 +2026-04-11 20:28:42.935010: train_loss -0.2263 +2026-04-11 20:28:42.940303: val_loss -0.1953 +2026-04-11 20:28:42.942515: Pseudo dice [0.5808, 0.1232, 0.6821, 0.0217, 0.6421, 0.2902, 0.8197] +2026-04-11 20:28:42.944724: Epoch time: 102.33 s +2026-04-11 20:28:44.096059: +2026-04-11 20:28:44.098377: Epoch 1187 +2026-04-11 20:28:44.100433: Current learning rate: 0.00728 +2026-04-11 20:30:26.192492: train_loss -0.2238 +2026-04-11 20:30:26.197876: val_loss -0.2006 +2026-04-11 20:30:26.199686: Pseudo dice [0.6187, 0.2291, 0.7542, 0.3377, 0.5347, 0.6654, 0.489] +2026-04-11 20:30:26.201713: Epoch time: 102.1 s +2026-04-11 20:30:27.347650: +2026-04-11 20:30:27.349290: Epoch 1188 +2026-04-11 20:30:27.350745: Current learning rate: 0.00728 +2026-04-11 20:32:09.172607: train_loss -0.2359 +2026-04-11 20:32:09.178046: val_loss -0.2163 +2026-04-11 20:32:09.179987: Pseudo dice [0.5081, 0.3041, 0.7514, 0.6171, 0.5976, 0.5505, 0.8141] +2026-04-11 20:32:09.182060: Epoch time: 101.83 s +2026-04-11 20:32:10.323256: +2026-04-11 20:32:10.324723: Epoch 1189 +2026-04-11 20:32:10.326194: Current learning rate: 0.00728 +2026-04-11 20:33:51.954994: train_loss -0.2557 +2026-04-11 20:33:51.961543: val_loss -0.2252 +2026-04-11 20:33:51.963425: Pseudo dice [0.5216, 0.5716, 0.7358, 0.3225, 0.3605, 0.6656, 0.8619] +2026-04-11 20:33:51.965544: Epoch time: 101.63 s +2026-04-11 20:33:53.112704: +2026-04-11 20:33:53.114680: Epoch 1190 +2026-04-11 20:33:53.116075: Current learning rate: 0.00728 +2026-04-11 20:35:34.867272: train_loss -0.2493 +2026-04-11 20:35:34.879706: val_loss -0.2008 +2026-04-11 20:35:34.881756: Pseudo dice [0.6709, 0.1822, 0.7342, 0.6926, 0.4423, 0.6852, 0.1162] +2026-04-11 20:35:34.884290: Epoch time: 101.76 s +2026-04-11 20:35:36.067464: +2026-04-11 20:35:36.069012: Epoch 1191 +2026-04-11 20:35:36.070550: Current learning rate: 0.00728 +2026-04-11 20:37:18.492021: train_loss -0.2125 +2026-04-11 20:37:18.505471: val_loss -0.186 +2026-04-11 20:37:18.507480: Pseudo dice [0.5321, 0.7553, 0.6318, 0.0594, 0.6465, 0.3121, 0.165] +2026-04-11 20:37:18.510189: Epoch time: 102.43 s +2026-04-11 20:37:19.662927: +2026-04-11 20:37:19.664504: Epoch 1192 +2026-04-11 20:37:19.666712: Current learning rate: 0.00727 +2026-04-11 20:39:01.640528: train_loss -0.2521 +2026-04-11 20:39:01.645718: val_loss -0.2141 +2026-04-11 20:39:01.647352: Pseudo dice [0.3416, 0.2365, 0.736, 0.5345, 0.3935, 0.1787, 0.8242] +2026-04-11 20:39:01.650550: Epoch time: 101.98 s +2026-04-11 20:39:02.792547: +2026-04-11 20:39:02.794049: Epoch 1193 +2026-04-11 20:39:02.795432: Current learning rate: 0.00727 +2026-04-11 20:40:44.678749: train_loss -0.2716 +2026-04-11 20:40:44.685725: val_loss -0.2207 +2026-04-11 20:40:44.687780: Pseudo dice [0.4647, 0.8575, 0.8647, 0.5913, 0.4901, 0.4723, 0.8232] +2026-04-11 20:40:44.690193: Epoch time: 101.89 s +2026-04-11 20:40:45.882109: +2026-04-11 20:40:45.883806: Epoch 1194 +2026-04-11 20:40:45.885504: Current learning rate: 0.00727 +2026-04-11 20:42:27.988355: train_loss -0.27 +2026-04-11 20:42:27.994993: val_loss -0.2165 +2026-04-11 20:42:27.997244: Pseudo dice [0.2326, 0.5784, 0.7251, 0.6287, 0.4649, 0.7421, 0.6652] +2026-04-11 20:42:28.000246: Epoch time: 102.11 s +2026-04-11 20:42:29.163629: +2026-04-11 20:42:29.165585: Epoch 1195 +2026-04-11 20:42:29.167139: Current learning rate: 0.00727 +2026-04-11 20:44:11.076039: train_loss -0.2537 +2026-04-11 20:44:11.082957: val_loss -0.1944 +2026-04-11 20:44:11.085501: Pseudo dice [0.5511, 0.2599, 0.5792, 0.5963, 0.4245, 0.534, 0.5979] +2026-04-11 20:44:11.088245: Epoch time: 101.92 s +2026-04-11 20:44:12.231354: +2026-04-11 20:44:12.233301: Epoch 1196 +2026-04-11 20:44:12.235755: Current learning rate: 0.00726 +2026-04-11 20:45:54.179233: train_loss -0.2662 +2026-04-11 20:45:54.185191: val_loss -0.2296 +2026-04-11 20:45:54.187440: Pseudo dice [0.5021, 0.4237, 0.6875, 0.3389, 0.5177, 0.5704, 0.823] +2026-04-11 20:45:54.189694: Epoch time: 101.95 s +2026-04-11 20:45:55.364624: +2026-04-11 20:45:55.366388: Epoch 1197 +2026-04-11 20:45:55.367913: Current learning rate: 0.00726 +2026-04-11 20:47:37.408215: train_loss -0.2688 +2026-04-11 20:47:37.414438: val_loss -0.2143 +2026-04-11 20:47:37.416325: Pseudo dice [0.6127, 0.8686, 0.6544, 0.5602, 0.3873, 0.8357, 0.6902] +2026-04-11 20:47:37.419135: Epoch time: 102.05 s +2026-04-11 20:47:38.576771: +2026-04-11 20:47:38.578549: Epoch 1198 +2026-04-11 20:47:38.580112: Current learning rate: 0.00726 +2026-04-11 20:49:20.542852: train_loss -0.2648 +2026-04-11 20:49:20.549423: val_loss -0.2178 +2026-04-11 20:49:20.551212: Pseudo dice [0.5745, 0.1473, 0.7915, 0.5665, 0.531, 0.3331, 0.8678] +2026-04-11 20:49:20.553978: Epoch time: 101.97 s +2026-04-11 20:49:21.699547: +2026-04-11 20:49:21.701115: Epoch 1199 +2026-04-11 20:49:21.702439: Current learning rate: 0.00726 +2026-04-11 20:51:03.417271: train_loss -0.2748 +2026-04-11 20:51:03.423438: val_loss -0.2077 +2026-04-11 20:51:03.426823: Pseudo dice [0.4322, 0.3648, 0.7487, 0.2086, 0.258, 0.1014, 0.7879] +2026-04-11 20:51:03.429089: Epoch time: 101.72 s +2026-04-11 20:51:06.346459: +2026-04-11 20:51:06.348755: Epoch 1200 +2026-04-11 20:51:06.350205: Current learning rate: 0.00725 +2026-04-11 20:52:47.928720: train_loss -0.2539 +2026-04-11 20:52:47.934197: val_loss -0.2525 +2026-04-11 20:52:47.935897: Pseudo dice [0.5319, 0.7453, 0.7538, 0.5558, 0.7086, 0.7403, 0.5736] +2026-04-11 20:52:47.938122: Epoch time: 101.59 s +2026-04-11 20:52:49.074081: +2026-04-11 20:52:49.075642: Epoch 1201 +2026-04-11 20:52:49.077178: Current learning rate: 0.00725 +2026-04-11 20:54:30.613883: train_loss -0.273 +2026-04-11 20:54:30.619660: val_loss -0.2334 +2026-04-11 20:54:30.621595: Pseudo dice [0.3183, 0.6861, 0.7936, 0.3953, 0.4036, 0.8884, 0.8067] +2026-04-11 20:54:30.624102: Epoch time: 101.54 s +2026-04-11 20:54:31.781020: +2026-04-11 20:54:31.789180: Epoch 1202 +2026-04-11 20:54:31.790683: Current learning rate: 0.00725 +2026-04-11 20:56:13.580295: train_loss -0.2796 +2026-04-11 20:56:13.586393: val_loss -0.2308 +2026-04-11 20:56:13.588896: Pseudo dice [0.0967, 0.7805, 0.7122, 0.4363, 0.363, 0.8318, 0.5983] +2026-04-11 20:56:13.591374: Epoch time: 101.8 s +2026-04-11 20:56:14.733012: +2026-04-11 20:56:14.734869: Epoch 1203 +2026-04-11 20:56:14.736397: Current learning rate: 0.00725 +2026-04-11 20:57:56.537454: train_loss -0.2543 +2026-04-11 20:57:56.544513: val_loss -0.2253 +2026-04-11 20:57:56.546487: Pseudo dice [0.8291, 0.1025, 0.7652, 0.2, 0.481, 0.8036, 0.7311] +2026-04-11 20:57:56.549503: Epoch time: 101.81 s +2026-04-11 20:57:57.693068: +2026-04-11 20:57:57.694775: Epoch 1204 +2026-04-11 20:57:57.696164: Current learning rate: 0.00724 +2026-04-11 20:59:39.438982: train_loss -0.2646 +2026-04-11 20:59:39.444369: val_loss -0.2102 +2026-04-11 20:59:39.447284: Pseudo dice [0.5379, 0.8705, 0.6188, 0.6738, 0.5333, 0.5777, 0.4444] +2026-04-11 20:59:39.449397: Epoch time: 101.75 s +2026-04-11 20:59:40.595985: +2026-04-11 20:59:40.598375: Epoch 1205 +2026-04-11 20:59:40.600033: Current learning rate: 0.00724 +2026-04-11 21:01:22.329036: train_loss -0.2436 +2026-04-11 21:01:22.335615: val_loss -0.1592 +2026-04-11 21:01:22.337341: Pseudo dice [0.3181, 0.7182, 0.4178, 0.3641, 0.4849, 0.3321, 0.6067] +2026-04-11 21:01:22.340270: Epoch time: 101.74 s +2026-04-11 21:01:24.837889: +2026-04-11 21:01:24.839712: Epoch 1206 +2026-04-11 21:01:24.841232: Current learning rate: 0.00724 +2026-04-11 21:03:07.086174: train_loss -0.2418 +2026-04-11 21:03:07.092638: val_loss -0.2053 +2026-04-11 21:03:07.094429: Pseudo dice [0.5361, 0.8262, 0.7319, 0.1007, 0.4705, 0.5911, 0.524] +2026-04-11 21:03:07.096847: Epoch time: 102.25 s +2026-04-11 21:03:08.251123: +2026-04-11 21:03:08.252692: Epoch 1207 +2026-04-11 21:03:08.254186: Current learning rate: 0.00724 +2026-04-11 21:04:50.072736: train_loss -0.2385 +2026-04-11 21:04:50.079221: val_loss -0.192 +2026-04-11 21:04:50.082428: Pseudo dice [0.67, 0.8555, 0.6927, 0.298, 0.5832, 0.1979, 0.7354] +2026-04-11 21:04:50.085228: Epoch time: 101.83 s +2026-04-11 21:04:51.221246: +2026-04-11 21:04:51.223420: Epoch 1208 +2026-04-11 21:04:51.225324: Current learning rate: 0.00724 +2026-04-11 21:06:33.269298: train_loss -0.2448 +2026-04-11 21:06:33.275976: val_loss -0.2007 +2026-04-11 21:06:33.278139: Pseudo dice [0.4476, 0.9056, 0.7094, 0.559, 0.3516, 0.4822, 0.4669] +2026-04-11 21:06:33.280268: Epoch time: 102.05 s +2026-04-11 21:06:34.453713: +2026-04-11 21:06:34.455565: Epoch 1209 +2026-04-11 21:06:34.457636: Current learning rate: 0.00723 +2026-04-11 21:08:16.038273: train_loss -0.2249 +2026-04-11 21:08:16.052726: val_loss -0.1163 +2026-04-11 21:08:16.055119: Pseudo dice [0.2429, 0.5957, 0.2938, 0.0714, 0.3918, 0.3853, 0.6543] +2026-04-11 21:08:16.057633: Epoch time: 101.59 s +2026-04-11 21:08:17.208530: +2026-04-11 21:08:17.210687: Epoch 1210 +2026-04-11 21:08:17.212039: Current learning rate: 0.00723 +2026-04-11 21:09:59.428004: train_loss -0.2312 +2026-04-11 21:09:59.434076: val_loss -0.192 +2026-04-11 21:09:59.436289: Pseudo dice [0.6471, 0.8419, 0.6672, 0.3792, 0.3989, 0.6895, 0.7408] +2026-04-11 21:09:59.438836: Epoch time: 102.22 s +2026-04-11 21:10:00.619019: +2026-04-11 21:10:00.622032: Epoch 1211 +2026-04-11 21:10:00.623792: Current learning rate: 0.00723 +2026-04-11 21:11:42.193548: train_loss -0.2457 +2026-04-11 21:11:42.199059: val_loss -0.2016 +2026-04-11 21:11:42.200893: Pseudo dice [0.323, 0.9169, 0.5983, 0.3091, 0.6347, 0.4448, 0.7248] +2026-04-11 21:11:42.203362: Epoch time: 101.58 s +2026-04-11 21:11:43.342275: +2026-04-11 21:11:43.343925: Epoch 1212 +2026-04-11 21:11:43.347352: Current learning rate: 0.00723 +2026-04-11 21:13:26.657055: train_loss -0.2423 +2026-04-11 21:13:26.663236: val_loss -0.1733 +2026-04-11 21:13:26.665255: Pseudo dice [0.4082, 0.6616, 0.3854, 0.0902, 0.6398, 0.1021, 0.5864] +2026-04-11 21:13:26.667953: Epoch time: 103.32 s +2026-04-11 21:13:27.843214: +2026-04-11 21:13:27.845571: Epoch 1213 +2026-04-11 21:13:27.847533: Current learning rate: 0.00722 +2026-04-11 21:15:09.805119: train_loss -0.2376 +2026-04-11 21:15:09.810280: val_loss -0.1874 +2026-04-11 21:15:09.812427: Pseudo dice [0.4947, 0.2921, 0.688, 0.1309, 0.4726, 0.5316, 0.4688] +2026-04-11 21:15:09.814626: Epoch time: 101.97 s +2026-04-11 21:15:10.947427: +2026-04-11 21:15:10.949064: Epoch 1214 +2026-04-11 21:15:10.950814: Current learning rate: 0.00722 +2026-04-11 21:16:52.640900: train_loss -0.2604 +2026-04-11 21:16:52.647353: val_loss -0.2094 +2026-04-11 21:16:52.649537: Pseudo dice [0.5184, 0.5102, 0.5909, 0.2846, 0.5439, 0.7852, 0.4524] +2026-04-11 21:16:52.652009: Epoch time: 101.7 s +2026-04-11 21:16:53.793743: +2026-04-11 21:16:53.795260: Epoch 1215 +2026-04-11 21:16:53.796632: Current learning rate: 0.00722 +2026-04-11 21:18:35.675894: train_loss -0.2539 +2026-04-11 21:18:35.681043: val_loss -0.2484 +2026-04-11 21:18:35.683141: Pseudo dice [0.5386, 0.3335, 0.7507, 0.6429, 0.4387, 0.7333, 0.7609] +2026-04-11 21:18:35.685797: Epoch time: 101.89 s +2026-04-11 21:18:36.841314: +2026-04-11 21:18:36.843423: Epoch 1216 +2026-04-11 21:18:36.845089: Current learning rate: 0.00722 +2026-04-11 21:20:18.771657: train_loss -0.2499 +2026-04-11 21:20:18.777879: val_loss -0.1883 +2026-04-11 21:20:18.780429: Pseudo dice [0.4658, 0.7827, 0.7369, 0.1215, 0.4044, 0.0949, 0.6925] +2026-04-11 21:20:18.782718: Epoch time: 101.93 s +2026-04-11 21:20:19.951777: +2026-04-11 21:20:19.953436: Epoch 1217 +2026-04-11 21:20:19.956273: Current learning rate: 0.00721 +2026-04-11 21:22:01.698355: train_loss -0.2527 +2026-04-11 21:22:01.703944: val_loss -0.2014 +2026-04-11 21:22:01.706147: Pseudo dice [0.6911, 0.6447, 0.7663, 0.3744, 0.4361, 0.7254, 0.3663] +2026-04-11 21:22:01.708004: Epoch time: 101.75 s +2026-04-11 21:22:02.864082: +2026-04-11 21:22:02.865903: Epoch 1218 +2026-04-11 21:22:02.867861: Current learning rate: 0.00721 +2026-04-11 21:23:44.788525: train_loss -0.2669 +2026-04-11 21:23:44.795288: val_loss -0.2682 +2026-04-11 21:23:44.797278: Pseudo dice [0.8211, 0.8774, 0.8327, 0.4554, 0.5894, 0.8682, 0.6103] +2026-04-11 21:23:44.799345: Epoch time: 101.93 s +2026-04-11 21:23:45.955237: +2026-04-11 21:23:45.956893: Epoch 1219 +2026-04-11 21:23:45.958295: Current learning rate: 0.00721 +2026-04-11 21:25:27.793346: train_loss -0.2723 +2026-04-11 21:25:27.798754: val_loss -0.232 +2026-04-11 21:25:27.800551: Pseudo dice [0.5743, 0.425, 0.7003, 0.124, 0.6779, 0.8798, 0.8314] +2026-04-11 21:25:27.802741: Epoch time: 101.84 s +2026-04-11 21:25:28.966131: +2026-04-11 21:25:28.967738: Epoch 1220 +2026-04-11 21:25:28.969296: Current learning rate: 0.00721 +2026-04-11 21:27:11.200769: train_loss -0.2673 +2026-04-11 21:27:11.207709: val_loss -0.2003 +2026-04-11 21:27:11.209605: Pseudo dice [0.7255, 0.8913, 0.5565, 0.5211, 0.3926, 0.8883, 0.7109] +2026-04-11 21:27:11.213069: Epoch time: 102.24 s +2026-04-11 21:27:12.380154: +2026-04-11 21:27:12.382898: Epoch 1221 +2026-04-11 21:27:12.384543: Current learning rate: 0.00721 +2026-04-11 21:28:54.208873: train_loss -0.2694 +2026-04-11 21:28:54.215086: val_loss -0.2112 +2026-04-11 21:28:54.217021: Pseudo dice [0.5128, 0.7767, 0.782, 0.5431, 0.4167, 0.7014, 0.669] +2026-04-11 21:28:54.219855: Epoch time: 101.83 s +2026-04-11 21:28:55.375710: +2026-04-11 21:28:55.377439: Epoch 1222 +2026-04-11 21:28:55.378867: Current learning rate: 0.0072 +2026-04-11 21:30:36.853334: train_loss -0.2704 +2026-04-11 21:30:36.858622: val_loss -0.2204 +2026-04-11 21:30:36.860857: Pseudo dice [0.6326, 0.8331, 0.6951, 0.6296, 0.472, 0.8758, 0.6193] +2026-04-11 21:30:36.863123: Epoch time: 101.48 s +2026-04-11 21:30:38.016470: +2026-04-11 21:30:38.018580: Epoch 1223 +2026-04-11 21:30:38.020206: Current learning rate: 0.0072 +2026-04-11 21:32:19.729972: train_loss -0.2533 +2026-04-11 21:32:19.736073: val_loss -0.2177 +2026-04-11 21:32:19.738119: Pseudo dice [0.3083, 0.4759, 0.6963, 0.3267, 0.4766, 0.8597, 0.5727] +2026-04-11 21:32:19.740291: Epoch time: 101.72 s +2026-04-11 21:32:20.867831: +2026-04-11 21:32:20.869496: Epoch 1224 +2026-04-11 21:32:20.870881: Current learning rate: 0.0072 +2026-04-11 21:34:02.672111: train_loss -0.2527 +2026-04-11 21:34:02.677565: val_loss -0.212 +2026-04-11 21:34:02.679328: Pseudo dice [0.6872, 0.5567, 0.7521, 0.1232, 0.5058, 0.8321, 0.605] +2026-04-11 21:34:02.681545: Epoch time: 101.81 s +2026-04-11 21:34:03.827511: +2026-04-11 21:34:03.829386: Epoch 1225 +2026-04-11 21:34:03.830913: Current learning rate: 0.0072 +2026-04-11 21:35:45.624198: train_loss -0.2592 +2026-04-11 21:35:45.630247: val_loss -0.1947 +2026-04-11 21:35:45.632242: Pseudo dice [0.809, 0.9106, 0.7629, 0.5461, 0.3971, 0.1854, 0.7097] +2026-04-11 21:35:45.634659: Epoch time: 101.8 s +2026-04-11 21:35:46.774694: +2026-04-11 21:35:46.776639: Epoch 1226 +2026-04-11 21:35:46.778945: Current learning rate: 0.00719 +2026-04-11 21:37:29.607227: train_loss -0.2537 +2026-04-11 21:37:29.614252: val_loss -0.186 +2026-04-11 21:37:29.616450: Pseudo dice [0.5892, 0.7337, 0.7217, 0.2038, 0.4569, 0.762, 0.5489] +2026-04-11 21:37:29.618876: Epoch time: 102.84 s +2026-04-11 21:37:30.757349: +2026-04-11 21:37:30.759026: Epoch 1227 +2026-04-11 21:37:30.760415: Current learning rate: 0.00719 +2026-04-11 21:39:12.597200: train_loss -0.2383 +2026-04-11 21:39:12.603041: val_loss -0.1779 +2026-04-11 21:39:12.604971: Pseudo dice [0.659, 0.4442, 0.4968, 0.5825, 0.359, 0.5248, 0.6712] +2026-04-11 21:39:12.607183: Epoch time: 101.84 s +2026-04-11 21:39:13.761230: +2026-04-11 21:39:13.763267: Epoch 1228 +2026-04-11 21:39:13.765318: Current learning rate: 0.00719 +2026-04-11 21:40:55.421910: train_loss -0.2599 +2026-04-11 21:40:55.427475: val_loss -0.2361 +2026-04-11 21:40:55.429726: Pseudo dice [0.6523, 0.8563, 0.7443, 0.5453, 0.4263, 0.6476, 0.7617] +2026-04-11 21:40:55.431684: Epoch time: 101.66 s +2026-04-11 21:40:56.589527: +2026-04-11 21:40:56.591363: Epoch 1229 +2026-04-11 21:40:56.592823: Current learning rate: 0.00719 +2026-04-11 21:42:38.357592: train_loss -0.2465 +2026-04-11 21:42:38.363431: val_loss -0.2166 +2026-04-11 21:42:38.365687: Pseudo dice [0.7188, 0.3576, 0.735, 0.3149, 0.5734, 0.8374, 0.7177] +2026-04-11 21:42:38.367783: Epoch time: 101.77 s +2026-04-11 21:42:39.517520: +2026-04-11 21:42:39.520133: Epoch 1230 +2026-04-11 21:42:39.522087: Current learning rate: 0.00718 +2026-04-11 21:44:21.486920: train_loss -0.2593 +2026-04-11 21:44:21.493081: val_loss -0.2171 +2026-04-11 21:44:21.495353: Pseudo dice [0.5035, 0.8759, 0.6535, 0.4973, 0.4957, 0.1093, 0.3477] +2026-04-11 21:44:21.497555: Epoch time: 101.97 s +2026-04-11 21:44:22.671457: +2026-04-11 21:44:22.673098: Epoch 1231 +2026-04-11 21:44:22.674543: Current learning rate: 0.00718 +2026-04-11 21:46:04.492512: train_loss -0.2684 +2026-04-11 21:46:04.498450: val_loss -0.225 +2026-04-11 21:46:04.500680: Pseudo dice [0.5331, 0.839, 0.6545, 0.2342, 0.65, 0.5505, 0.74] +2026-04-11 21:46:04.502479: Epoch time: 101.82 s +2026-04-11 21:46:05.655307: +2026-04-11 21:46:05.656946: Epoch 1232 +2026-04-11 21:46:05.658379: Current learning rate: 0.00718 +2026-04-11 21:47:47.939468: train_loss -0.2745 +2026-04-11 21:47:47.945978: val_loss -0.2397 +2026-04-11 21:47:47.947930: Pseudo dice [0.6927, 0.6793, 0.7447, 0.4713, 0.7629, 0.895, 0.7166] +2026-04-11 21:47:47.949918: Epoch time: 102.29 s +2026-04-11 21:47:49.137506: +2026-04-11 21:47:49.139353: Epoch 1233 +2026-04-11 21:47:49.141195: Current learning rate: 0.00718 +2026-04-11 21:49:31.527874: train_loss -0.2777 +2026-04-11 21:49:31.534007: val_loss -0.1947 +2026-04-11 21:49:31.535992: Pseudo dice [0.2964, 0.8886, 0.6345, 0.4997, 0.4666, 0.1183, 0.3637] +2026-04-11 21:49:31.538372: Epoch time: 102.39 s +2026-04-11 21:49:32.694371: +2026-04-11 21:49:32.697376: Epoch 1234 +2026-04-11 21:49:32.699201: Current learning rate: 0.00717 +2026-04-11 21:51:14.541965: train_loss -0.2688 +2026-04-11 21:51:14.549146: val_loss -0.186 +2026-04-11 21:51:14.552203: Pseudo dice [0.5278, 0.7087, 0.6469, 0.514, 0.4287, 0.5917, 0.6041] +2026-04-11 21:51:14.554940: Epoch time: 101.85 s +2026-04-11 21:51:15.723520: +2026-04-11 21:51:15.725427: Epoch 1235 +2026-04-11 21:51:15.727384: Current learning rate: 0.00717 +2026-04-11 21:52:57.368366: train_loss -0.2709 +2026-04-11 21:52:57.374056: val_loss -0.2461 +2026-04-11 21:52:57.375861: Pseudo dice [0.6686, 0.5742, 0.8683, 0.5794, 0.4688, 0.8093, 0.7909] +2026-04-11 21:52:57.378737: Epoch time: 101.65 s +2026-04-11 21:52:58.519683: +2026-04-11 21:52:58.521202: Epoch 1236 +2026-04-11 21:52:58.523099: Current learning rate: 0.00717 +2026-04-11 21:54:40.562766: train_loss -0.2662 +2026-04-11 21:54:40.569726: val_loss -0.216 +2026-04-11 21:54:40.571551: Pseudo dice [0.4145, 0.8305, 0.7312, 0.5329, 0.569, 0.6063, 0.7575] +2026-04-11 21:54:40.573841: Epoch time: 102.05 s +2026-04-11 21:54:41.741986: +2026-04-11 21:54:41.743591: Epoch 1237 +2026-04-11 21:54:41.747175: Current learning rate: 0.00717 +2026-04-11 21:56:24.004237: train_loss -0.2776 +2026-04-11 21:56:24.010827: val_loss -0.198 +2026-04-11 21:56:24.012928: Pseudo dice [0.818, 0.6182, 0.7143, 0.2908, 0.5822, 0.7939, 0.6704] +2026-04-11 21:56:24.016708: Epoch time: 102.27 s +2026-04-11 21:56:25.197789: +2026-04-11 21:56:25.200218: Epoch 1238 +2026-04-11 21:56:25.202336: Current learning rate: 0.00717 +2026-04-11 21:58:07.233959: train_loss -0.2804 +2026-04-11 21:58:07.241113: val_loss -0.2434 +2026-04-11 21:58:07.243237: Pseudo dice [0.8522, 0.8857, 0.7374, 0.6168, 0.5299, 0.9077, 0.6611] +2026-04-11 21:58:07.246279: Epoch time: 102.04 s +2026-04-11 21:58:07.248222: Yayy! New best EMA pseudo Dice: 0.6134 +2026-04-11 21:58:09.929895: +2026-04-11 21:58:09.932038: Epoch 1239 +2026-04-11 21:58:09.933529: Current learning rate: 0.00716 +2026-04-11 21:59:51.778056: train_loss -0.2614 +2026-04-11 21:59:51.784891: val_loss -0.2385 +2026-04-11 21:59:51.787037: Pseudo dice [0.5858, 0.4349, 0.8769, 0.4688, 0.4156, 0.8445, 0.8257] +2026-04-11 21:59:51.789435: Epoch time: 101.85 s +2026-04-11 21:59:51.791584: Yayy! New best EMA pseudo Dice: 0.6157 +2026-04-11 21:59:54.562769: +2026-04-11 21:59:54.565022: Epoch 1240 +2026-04-11 21:59:54.566421: Current learning rate: 0.00716 +2026-04-11 22:01:36.451349: train_loss -0.267 +2026-04-11 22:01:36.458542: val_loss -0.2253 +2026-04-11 22:01:36.460701: Pseudo dice [0.7052, 0.7796, 0.8172, 0.3268, 0.6337, 0.621, 0.783] +2026-04-11 22:01:36.463039: Epoch time: 101.89 s +2026-04-11 22:01:36.464674: Yayy! New best EMA pseudo Dice: 0.6208 +2026-04-11 22:01:39.071347: +2026-04-11 22:01:39.074224: Epoch 1241 +2026-04-11 22:01:39.075763: Current learning rate: 0.00716 +2026-04-11 22:03:21.140904: train_loss -0.2636 +2026-04-11 22:03:21.148022: val_loss -0.2028 +2026-04-11 22:03:21.150331: Pseudo dice [0.5615, 0.3071, 0.7362, 0.5396, 0.3104, 0.7709, 0.8329] +2026-04-11 22:03:21.152765: Epoch time: 102.07 s +2026-04-11 22:03:22.291327: +2026-04-11 22:03:22.293146: Epoch 1242 +2026-04-11 22:03:22.294968: Current learning rate: 0.00716 +2026-04-11 22:05:03.946373: train_loss -0.2576 +2026-04-11 22:05:03.952755: val_loss -0.1824 +2026-04-11 22:05:03.954712: Pseudo dice [0.3059, 0.8184, 0.5891, 0.1692, 0.3912, 0.4116, 0.5929] +2026-04-11 22:05:03.957367: Epoch time: 101.66 s +2026-04-11 22:05:05.099383: +2026-04-11 22:05:05.101498: Epoch 1243 +2026-04-11 22:05:05.104219: Current learning rate: 0.00715 +2026-04-11 22:06:46.647570: train_loss -0.2656 +2026-04-11 22:06:46.653815: val_loss -0.163 +2026-04-11 22:06:46.656465: Pseudo dice [0.6433, 0.3493, 0.5398, 0.1405, 0.3124, 0.5706, 0.6697] +2026-04-11 22:06:46.658495: Epoch time: 101.55 s +2026-04-11 22:06:47.799742: +2026-04-11 22:06:47.801610: Epoch 1244 +2026-04-11 22:06:47.803714: Current learning rate: 0.00715 +2026-04-11 22:08:29.963011: train_loss -0.2477 +2026-04-11 22:08:29.971769: val_loss -0.1807 +2026-04-11 22:08:29.974318: Pseudo dice [0.5433, 0.4571, 0.6015, 0.7244, 0.1391, 0.2041, 0.7536] +2026-04-11 22:08:29.977696: Epoch time: 102.17 s +2026-04-11 22:08:31.164932: +2026-04-11 22:08:31.166486: Epoch 1245 +2026-04-11 22:08:31.168341: Current learning rate: 0.00715 +2026-04-11 22:10:13.990650: train_loss -0.2271 +2026-04-11 22:10:13.999587: val_loss -0.2108 +2026-04-11 22:10:14.001979: Pseudo dice [0.5325, 0.6246, 0.6786, 0.3327, 0.4196, 0.6965, 0.6566] +2026-04-11 22:10:14.004986: Epoch time: 102.83 s +2026-04-11 22:10:15.134316: +2026-04-11 22:10:15.136352: Epoch 1246 +2026-04-11 22:10:15.138230: Current learning rate: 0.00715 +2026-04-11 22:11:56.803426: train_loss -0.2591 +2026-04-11 22:11:56.810931: val_loss -0.2232 +2026-04-11 22:11:56.812873: Pseudo dice [0.5203, 0.7508, 0.6729, 0.3947, 0.4462, 0.7258, 0.7812] +2026-04-11 22:11:56.815336: Epoch time: 101.67 s +2026-04-11 22:11:57.980489: +2026-04-11 22:11:57.982457: Epoch 1247 +2026-04-11 22:11:57.984407: Current learning rate: 0.00714 +2026-04-11 22:13:39.791818: train_loss -0.2528 +2026-04-11 22:13:39.798247: val_loss -0.2174 +2026-04-11 22:13:39.800320: Pseudo dice [0.7054, 0.7246, 0.7701, 0.169, 0.5097, 0.7591, 0.7887] +2026-04-11 22:13:39.802811: Epoch time: 101.81 s +2026-04-11 22:13:41.008698: +2026-04-11 22:13:41.019688: Epoch 1248 +2026-04-11 22:13:41.023916: Current learning rate: 0.00714 +2026-04-11 22:15:23.377792: train_loss -0.2627 +2026-04-11 22:15:23.385963: val_loss -0.2219 +2026-04-11 22:15:23.388688: Pseudo dice [0.625, 0.6117, 0.7447, 0.3286, 0.4337, 0.7034, 0.7269] +2026-04-11 22:15:23.391136: Epoch time: 102.37 s +2026-04-11 22:15:24.559590: +2026-04-11 22:15:24.561815: Epoch 1249 +2026-04-11 22:15:24.563283: Current learning rate: 0.00714 +2026-04-11 22:17:07.116538: train_loss -0.2657 +2026-04-11 22:17:07.124328: val_loss -0.217 +2026-04-11 22:17:07.127057: Pseudo dice [0.7564, 0.6265, 0.6565, 0.6066, 0.6283, 0.8648, 0.7928] +2026-04-11 22:17:07.129104: Epoch time: 102.56 s +2026-04-11 22:17:09.767920: +2026-04-11 22:17:09.770936: Epoch 1250 +2026-04-11 22:17:09.772463: Current learning rate: 0.00714 +2026-04-11 22:18:52.072936: train_loss -0.2758 +2026-04-11 22:18:52.078790: val_loss -0.2485 +2026-04-11 22:18:52.080732: Pseudo dice [0.6576, 0.5723, 0.6861, 0.2829, 0.4752, 0.5165, 0.6636] +2026-04-11 22:18:52.083178: Epoch time: 102.31 s +2026-04-11 22:18:53.269586: +2026-04-11 22:18:53.271831: Epoch 1251 +2026-04-11 22:18:53.273401: Current learning rate: 0.00714 +2026-04-11 22:20:35.670403: train_loss -0.2471 +2026-04-11 22:20:35.676408: val_loss -0.2261 +2026-04-11 22:20:35.678837: Pseudo dice [0.5942, 0.4636, 0.7704, 0.7895, 0.3059, 0.747, 0.6635] +2026-04-11 22:20:35.681475: Epoch time: 102.4 s +2026-04-11 22:20:36.826463: +2026-04-11 22:20:36.829026: Epoch 1252 +2026-04-11 22:20:36.830707: Current learning rate: 0.00713 +2026-04-11 22:22:19.050969: train_loss -0.2596 +2026-04-11 22:22:19.058220: val_loss -0.2331 +2026-04-11 22:22:19.060467: Pseudo dice [0.5756, 0.4535, 0.7808, 0.1085, 0.4654, 0.4107, 0.8285] +2026-04-11 22:22:19.063003: Epoch time: 102.23 s +2026-04-11 22:22:20.219376: +2026-04-11 22:22:20.221128: Epoch 1253 +2026-04-11 22:22:20.223208: Current learning rate: 0.00713 +2026-04-11 22:24:02.899526: train_loss -0.2566 +2026-04-11 22:24:02.905310: val_loss -0.1876 +2026-04-11 22:24:02.908583: Pseudo dice [0.396, 0.6338, 0.6791, 0.2599, 0.4082, 0.838, 0.6148] +2026-04-11 22:24:02.910852: Epoch time: 102.68 s +2026-04-11 22:24:04.070214: +2026-04-11 22:24:04.072433: Epoch 1254 +2026-04-11 22:24:04.074017: Current learning rate: 0.00713 +2026-04-11 22:25:46.309934: train_loss -0.2501 +2026-04-11 22:25:46.316975: val_loss -0.1479 +2026-04-11 22:25:46.319094: Pseudo dice [0.5897, 0.3368, 0.3961, 0.4894, 0.4195, 0.8189, 0.8379] +2026-04-11 22:25:46.322260: Epoch time: 102.24 s +2026-04-11 22:25:47.459296: +2026-04-11 22:25:47.462121: Epoch 1255 +2026-04-11 22:25:47.463722: Current learning rate: 0.00713 +2026-04-11 22:27:29.506257: train_loss -0.2646 +2026-04-11 22:27:29.512767: val_loss -0.2309 +2026-04-11 22:27:29.514279: Pseudo dice [0.6906, 0.3975, 0.7381, 0.5328, 0.5933, 0.5304, 0.7348] +2026-04-11 22:27:29.516210: Epoch time: 102.05 s +2026-04-11 22:27:30.677064: +2026-04-11 22:27:30.679690: Epoch 1256 +2026-04-11 22:27:30.681343: Current learning rate: 0.00712 +2026-04-11 22:29:14.341404: train_loss -0.2627 +2026-04-11 22:29:14.349366: val_loss -0.2117 +2026-04-11 22:29:14.354181: Pseudo dice [0.5856, 0.6936, 0.6904, 0.5673, 0.562, 0.6737, 0.7299] +2026-04-11 22:29:14.356995: Epoch time: 103.67 s +2026-04-11 22:29:15.542043: +2026-04-11 22:29:15.543729: Epoch 1257 +2026-04-11 22:29:15.545879: Current learning rate: 0.00712 +2026-04-11 22:30:58.315009: train_loss -0.2753 +2026-04-11 22:30:58.321223: val_loss -0.2331 +2026-04-11 22:30:58.322956: Pseudo dice [0.5754, 0.8142, 0.7068, 0.5777, 0.6134, 0.7388, 0.6651] +2026-04-11 22:30:58.325576: Epoch time: 102.78 s +2026-04-11 22:30:59.476711: +2026-04-11 22:30:59.479793: Epoch 1258 +2026-04-11 22:30:59.482846: Current learning rate: 0.00712 +2026-04-11 22:32:42.113351: train_loss -0.2798 +2026-04-11 22:32:42.139539: val_loss -0.1935 +2026-04-11 22:32:42.143018: Pseudo dice [0.6258, 0.7106, 0.6269, 0.6249, 0.3237, 0.7876, 0.7841] +2026-04-11 22:32:42.147197: Epoch time: 102.64 s +2026-04-11 22:32:43.306938: +2026-04-11 22:32:43.309292: Epoch 1259 +2026-04-11 22:32:43.310942: Current learning rate: 0.00712 +2026-04-11 22:34:25.795987: train_loss -0.2389 +2026-04-11 22:34:25.802604: val_loss -0.1971 +2026-04-11 22:34:25.814212: Pseudo dice [0.2739, 0.3295, 0.6013, 0.2967, 0.3517, 0.8572, 0.6306] +2026-04-11 22:34:25.816683: Epoch time: 102.49 s +2026-04-11 22:34:26.945786: +2026-04-11 22:34:26.948498: Epoch 1260 +2026-04-11 22:34:26.950116: Current learning rate: 0.00711 +2026-04-11 22:36:09.776503: train_loss -0.2531 +2026-04-11 22:36:09.782600: val_loss -0.2337 +2026-04-11 22:36:09.785825: Pseudo dice [0.7271, 0.4134, 0.7904, 0.5974, 0.2857, 0.8452, 0.5533] +2026-04-11 22:36:09.788027: Epoch time: 102.83 s +2026-04-11 22:36:10.946984: +2026-04-11 22:36:10.949597: Epoch 1261 +2026-04-11 22:36:10.951392: Current learning rate: 0.00711 +2026-04-11 22:37:53.661720: train_loss -0.2519 +2026-04-11 22:37:53.668497: val_loss -0.2211 +2026-04-11 22:37:53.670896: Pseudo dice [0.6137, 0.3802, 0.748, 0.1606, 0.4352, 0.7967, 0.6768] +2026-04-11 22:37:53.674131: Epoch time: 102.72 s +2026-04-11 22:37:54.826756: +2026-04-11 22:37:54.829294: Epoch 1262 +2026-04-11 22:37:54.832143: Current learning rate: 0.00711 +2026-04-11 22:39:37.280599: train_loss -0.25 +2026-04-11 22:39:37.287914: val_loss -0.1828 +2026-04-11 22:39:37.290450: Pseudo dice [0.2532, 0.8406, 0.7406, 0.0669, 0.5408, 0.081, 0.3703] +2026-04-11 22:39:37.293092: Epoch time: 102.46 s +2026-04-11 22:39:38.471366: +2026-04-11 22:39:38.473151: Epoch 1263 +2026-04-11 22:39:38.475184: Current learning rate: 0.00711 +2026-04-11 22:41:20.305321: train_loss -0.2589 +2026-04-11 22:41:20.311824: val_loss -0.2256 +2026-04-11 22:41:20.314186: Pseudo dice [0.1385, 0.8453, 0.7366, 0.5608, 0.5394, 0.6963, 0.3865] +2026-04-11 22:41:20.317544: Epoch time: 101.84 s +2026-04-11 22:41:21.463252: +2026-04-11 22:41:21.464949: Epoch 1264 +2026-04-11 22:41:21.466852: Current learning rate: 0.0071 +2026-04-11 22:43:03.550605: train_loss -0.2666 +2026-04-11 22:43:03.558274: val_loss -0.2062 +2026-04-11 22:43:03.560458: Pseudo dice [0.7362, 0.3009, 0.6959, 0.6284, 0.3424, 0.3529, 0.842] +2026-04-11 22:43:03.564039: Epoch time: 102.09 s +2026-04-11 22:43:05.868264: +2026-04-11 22:43:05.870263: Epoch 1265 +2026-04-11 22:43:05.872598: Current learning rate: 0.0071 +2026-04-11 22:44:47.756327: train_loss -0.2465 +2026-04-11 22:44:47.762349: val_loss -0.1871 +2026-04-11 22:44:47.764288: Pseudo dice [0.341, 0.6224, 0.3241, 0.5896, 0.3002, 0.4728, 0.8272] +2026-04-11 22:44:47.767053: Epoch time: 101.89 s +2026-04-11 22:44:48.933781: +2026-04-11 22:44:48.935807: Epoch 1266 +2026-04-11 22:44:48.938057: Current learning rate: 0.0071 +2026-04-11 22:46:30.667780: train_loss -0.2595 +2026-04-11 22:46:30.673578: val_loss -0.1778 +2026-04-11 22:46:30.675478: Pseudo dice [0.6366, 0.8528, 0.3713, 0.2626, 0.4748, 0.407, 0.7249] +2026-04-11 22:46:30.677458: Epoch time: 101.74 s +2026-04-11 22:46:31.830184: +2026-04-11 22:46:31.831654: Epoch 1267 +2026-04-11 22:46:31.833260: Current learning rate: 0.0071 +2026-04-11 22:48:13.497848: train_loss -0.2553 +2026-04-11 22:48:13.504685: val_loss -0.2432 +2026-04-11 22:48:13.510176: Pseudo dice [0.8636, 0.8784, 0.8102, 0.659, 0.5396, 0.7171, 0.8325] +2026-04-11 22:48:13.512252: Epoch time: 101.67 s +2026-04-11 22:48:14.675444: +2026-04-11 22:48:14.677839: Epoch 1268 +2026-04-11 22:48:14.681097: Current learning rate: 0.0071 +2026-04-11 22:49:56.891627: train_loss -0.2618 +2026-04-11 22:49:56.897567: val_loss -0.2361 +2026-04-11 22:49:56.899506: Pseudo dice [0.6469, 0.5682, 0.7934, 0.3957, 0.4245, 0.7827, 0.8238] +2026-04-11 22:49:56.901814: Epoch time: 102.22 s +2026-04-11 22:49:58.073107: +2026-04-11 22:49:58.075081: Epoch 1269 +2026-04-11 22:49:58.077059: Current learning rate: 0.00709 +2026-04-11 22:51:40.277515: train_loss -0.2585 +2026-04-11 22:51:40.284077: val_loss -0.2003 +2026-04-11 22:51:40.286105: Pseudo dice [0.3567, 0.7548, 0.7177, 0.2444, 0.5034, 0.6296, 0.5688] +2026-04-11 22:51:40.288605: Epoch time: 102.21 s +2026-04-11 22:51:41.456077: +2026-04-11 22:51:41.457486: Epoch 1270 +2026-04-11 22:51:41.459138: Current learning rate: 0.00709 +2026-04-11 22:53:23.350587: train_loss -0.2587 +2026-04-11 22:53:23.357167: val_loss -0.2103 +2026-04-11 22:53:23.360263: Pseudo dice [0.3742, 0.6646, 0.6464, 0.4732, 0.4193, 0.5734, 0.6116] +2026-04-11 22:53:23.362643: Epoch time: 101.9 s +2026-04-11 22:53:24.512321: +2026-04-11 22:53:24.514412: Epoch 1271 +2026-04-11 22:53:24.517053: Current learning rate: 0.00709 +2026-04-11 22:55:06.646462: train_loss -0.2662 +2026-04-11 22:55:06.653391: val_loss -0.1653 +2026-04-11 22:55:06.655841: Pseudo dice [0.6291, 0.8726, 0.4741, 0.784, 0.3524, 0.6322, 0.8642] +2026-04-11 22:55:06.658146: Epoch time: 102.14 s +2026-04-11 22:55:07.826573: +2026-04-11 22:55:07.828491: Epoch 1272 +2026-04-11 22:55:07.830750: Current learning rate: 0.00709 +2026-04-11 22:56:49.448442: train_loss -0.2531 +2026-04-11 22:56:49.454833: val_loss -0.2353 +2026-04-11 22:56:49.456558: Pseudo dice [0.6311, 0.856, 0.8172, 0.6762, 0.6365, 0.171, 0.8652] +2026-04-11 22:56:49.459168: Epoch time: 101.63 s +2026-04-11 22:56:50.613510: +2026-04-11 22:56:50.615503: Epoch 1273 +2026-04-11 22:56:50.617500: Current learning rate: 0.00708 +2026-04-11 22:58:32.383477: train_loss -0.2588 +2026-04-11 22:58:32.389832: val_loss -0.1881 +2026-04-11 22:58:32.391809: Pseudo dice [0.6526, 0.6871, 0.6019, 0.2915, 0.491, 0.6946, 0.6077] +2026-04-11 22:58:32.394453: Epoch time: 101.77 s +2026-04-11 22:58:33.546922: +2026-04-11 22:58:33.548844: Epoch 1274 +2026-04-11 22:58:33.550571: Current learning rate: 0.00708 +2026-04-11 23:00:15.861356: train_loss -0.2461 +2026-04-11 23:00:15.868300: val_loss -0.193 +2026-04-11 23:00:15.871346: Pseudo dice [0.551, 0.615, 0.6137, 0.532, 0.4279, 0.7475, 0.8113] +2026-04-11 23:00:15.873595: Epoch time: 102.32 s +2026-04-11 23:00:17.074762: +2026-04-11 23:00:17.076304: Epoch 1275 +2026-04-11 23:00:17.078042: Current learning rate: 0.00708 +2026-04-11 23:01:58.696365: train_loss -0.2645 +2026-04-11 23:01:58.704472: val_loss -0.2032 +2026-04-11 23:01:58.708151: Pseudo dice [0.7339, 0.5382, 0.6119, 0.4755, 0.4425, 0.7037, 0.5633] +2026-04-11 23:01:58.710968: Epoch time: 101.62 s +2026-04-11 23:01:59.867529: +2026-04-11 23:01:59.869062: Epoch 1276 +2026-04-11 23:01:59.870887: Current learning rate: 0.00708 +2026-04-11 23:03:41.680973: train_loss -0.2665 +2026-04-11 23:03:41.693106: val_loss -0.1758 +2026-04-11 23:03:41.703696: Pseudo dice [0.873, 0.8023, 0.4325, 0.5822, 0.6025, 0.5293, 0.8049] +2026-04-11 23:03:41.706193: Epoch time: 101.82 s +2026-04-11 23:03:42.874230: +2026-04-11 23:03:42.877412: Epoch 1277 +2026-04-11 23:03:42.879497: Current learning rate: 0.00707 +2026-04-11 23:05:24.781966: train_loss -0.2684 +2026-04-11 23:05:24.788752: val_loss -0.2224 +2026-04-11 23:05:24.790565: Pseudo dice [0.5803, 0.3373, 0.8138, 0.2473, 0.7039, 0.789, 0.7305] +2026-04-11 23:05:24.792998: Epoch time: 101.91 s +2026-04-11 23:05:25.951192: +2026-04-11 23:05:25.953212: Epoch 1278 +2026-04-11 23:05:25.955473: Current learning rate: 0.00707 +2026-04-11 23:07:07.798656: train_loss -0.2744 +2026-04-11 23:07:07.807353: val_loss -0.2087 +2026-04-11 23:07:07.809811: Pseudo dice [0.4593, 0.8206, 0.67, 0.2919, 0.2717, 0.6493, 0.7885] +2026-04-11 23:07:07.812711: Epoch time: 101.85 s +2026-04-11 23:07:08.985291: +2026-04-11 23:07:08.987462: Epoch 1279 +2026-04-11 23:07:08.989369: Current learning rate: 0.00707 +2026-04-11 23:08:51.148424: train_loss -0.2657 +2026-04-11 23:08:51.154050: val_loss -0.2224 +2026-04-11 23:08:51.156020: Pseudo dice [0.3587, 0.8476, 0.6657, 0.7809, 0.2574, 0.4797, 0.6397] +2026-04-11 23:08:51.158556: Epoch time: 102.17 s +2026-04-11 23:08:52.311784: +2026-04-11 23:08:52.313555: Epoch 1280 +2026-04-11 23:08:52.315840: Current learning rate: 0.00707 +2026-04-11 23:10:34.671332: train_loss -0.27 +2026-04-11 23:10:34.678977: val_loss -0.2311 +2026-04-11 23:10:34.681358: Pseudo dice [0.236, 0.8391, 0.7087, 0.2399, 0.5107, 0.2747, 0.7058] +2026-04-11 23:10:34.684191: Epoch time: 102.36 s +2026-04-11 23:10:35.842301: +2026-04-11 23:10:35.844182: Epoch 1281 +2026-04-11 23:10:35.846443: Current learning rate: 0.00707 +2026-04-11 23:12:17.564043: train_loss -0.2513 +2026-04-11 23:12:17.570213: val_loss -0.204 +2026-04-11 23:12:17.572927: Pseudo dice [0.4515, 0.6484, 0.8268, 0.4407, 0.4361, 0.5759, 0.5339] +2026-04-11 23:12:17.576067: Epoch time: 101.72 s +2026-04-11 23:12:18.726026: +2026-04-11 23:12:18.728033: Epoch 1282 +2026-04-11 23:12:18.730049: Current learning rate: 0.00706 +2026-04-11 23:14:00.783163: train_loss -0.2623 +2026-04-11 23:14:00.790857: val_loss -0.2147 +2026-04-11 23:14:00.793087: Pseudo dice [0.3045, 0.8379, 0.6488, 0.4483, 0.5305, 0.7128, 0.7245] +2026-04-11 23:14:00.795555: Epoch time: 102.06 s +2026-04-11 23:14:01.989280: +2026-04-11 23:14:01.990939: Epoch 1283 +2026-04-11 23:14:01.993140: Current learning rate: 0.00706 +2026-04-11 23:15:43.921618: train_loss -0.2574 +2026-04-11 23:15:43.928846: val_loss -0.2381 +2026-04-11 23:15:43.930943: Pseudo dice [0.5135, 0.6916, 0.8038, 0.831, 0.4715, 0.926, 0.8645] +2026-04-11 23:15:43.933058: Epoch time: 101.94 s +2026-04-11 23:15:45.087490: +2026-04-11 23:15:45.091589: Epoch 1284 +2026-04-11 23:15:45.094290: Current learning rate: 0.00706 +2026-04-11 23:17:27.009091: train_loss -0.2765 +2026-04-11 23:17:27.014803: val_loss -0.225 +2026-04-11 23:17:27.017032: Pseudo dice [0.5755, 0.8544, 0.7788, 0.3225, 0.5854, 0.8292, 0.3921] +2026-04-11 23:17:27.019117: Epoch time: 101.92 s +2026-04-11 23:17:28.178388: +2026-04-11 23:17:28.181081: Epoch 1285 +2026-04-11 23:17:28.183189: Current learning rate: 0.00706 +2026-04-11 23:19:11.367215: train_loss -0.2655 +2026-04-11 23:19:11.375144: val_loss -0.2196 +2026-04-11 23:19:11.377451: Pseudo dice [0.4021, 0.1421, 0.6051, 0.522, 0.4356, 0.3952, 0.8153] +2026-04-11 23:19:11.380178: Epoch time: 103.19 s +2026-04-11 23:19:12.525699: +2026-04-11 23:19:12.528461: Epoch 1286 +2026-04-11 23:19:12.530693: Current learning rate: 0.00705 +2026-04-11 23:20:54.821676: train_loss -0.2624 +2026-04-11 23:20:54.828743: val_loss -0.2114 +2026-04-11 23:20:54.830851: Pseudo dice [0.1962, 0.4075, 0.6189, 0.6797, 0.5501, 0.8782, 0.5682] +2026-04-11 23:20:54.833073: Epoch time: 102.3 s +2026-04-11 23:20:55.989943: +2026-04-11 23:20:55.992169: Epoch 1287 +2026-04-11 23:20:55.994123: Current learning rate: 0.00705 +2026-04-11 23:22:37.934294: train_loss -0.2664 +2026-04-11 23:22:37.940547: val_loss -0.243 +2026-04-11 23:22:37.942540: Pseudo dice [0.3518, 0.4765, 0.809, 0.6147, 0.5481, 0.9137, 0.8397] +2026-04-11 23:22:37.945248: Epoch time: 101.95 s +2026-04-11 23:22:39.130049: +2026-04-11 23:22:39.132329: Epoch 1288 +2026-04-11 23:22:39.134441: Current learning rate: 0.00705 +2026-04-11 23:24:20.885542: train_loss -0.2692 +2026-04-11 23:24:20.892828: val_loss -0.2294 +2026-04-11 23:24:20.895701: Pseudo dice [0.1378, 0.5406, 0.7139, 0.2497, 0.4492, 0.8406, 0.7611] +2026-04-11 23:24:20.899618: Epoch time: 101.76 s +2026-04-11 23:24:22.063321: +2026-04-11 23:24:22.065068: Epoch 1289 +2026-04-11 23:24:22.066759: Current learning rate: 0.00705 +2026-04-11 23:26:04.354359: train_loss -0.252 +2026-04-11 23:26:04.361023: val_loss -0.2188 +2026-04-11 23:26:04.363414: Pseudo dice [0.7806, 0.8038, 0.6572, 0.5912, 0.2355, 0.7539, 0.6806] +2026-04-11 23:26:04.365908: Epoch time: 102.29 s +2026-04-11 23:26:05.525874: +2026-04-11 23:26:05.527488: Epoch 1290 +2026-04-11 23:26:05.529268: Current learning rate: 0.00704 +2026-04-11 23:27:47.367142: train_loss -0.2704 +2026-04-11 23:27:47.373197: val_loss -0.2417 +2026-04-11 23:27:47.375400: Pseudo dice [0.5955, 0.1736, 0.7262, 0.1514, 0.6693, 0.4935, 0.7747] +2026-04-11 23:27:47.377696: Epoch time: 101.84 s +2026-04-11 23:27:48.545007: +2026-04-11 23:27:48.547997: Epoch 1291 +2026-04-11 23:27:48.549972: Current learning rate: 0.00704 +2026-04-11 23:29:30.387911: train_loss -0.275 +2026-04-11 23:29:30.394396: val_loss -0.2326 +2026-04-11 23:29:30.396435: Pseudo dice [0.5101, 0.6337, 0.7352, 0.5853, 0.5374, 0.9137, 0.8052] +2026-04-11 23:29:30.398940: Epoch time: 101.85 s +2026-04-11 23:29:31.559648: +2026-04-11 23:29:31.561242: Epoch 1292 +2026-04-11 23:29:31.563578: Current learning rate: 0.00704 +2026-04-11 23:31:13.041408: train_loss -0.2447 +2026-04-11 23:31:13.047362: val_loss -0.2066 +2026-04-11 23:31:13.049194: Pseudo dice [0.2208, 0.6466, 0.747, 0.1182, 0.4357, 0.8878, 0.7007] +2026-04-11 23:31:13.051216: Epoch time: 101.48 s +2026-04-11 23:31:14.198879: +2026-04-11 23:31:14.200975: Epoch 1293 +2026-04-11 23:31:14.203281: Current learning rate: 0.00704 +2026-04-11 23:32:56.032878: train_loss -0.2637 +2026-04-11 23:32:56.058715: val_loss -0.2161 +2026-04-11 23:32:56.060961: Pseudo dice [0.095, 0.7734, 0.7746, 0.2501, 0.2689, 0.9298, 0.8164] +2026-04-11 23:32:56.063017: Epoch time: 101.84 s +2026-04-11 23:32:57.212221: +2026-04-11 23:32:57.213766: Epoch 1294 +2026-04-11 23:32:57.215557: Current learning rate: 0.00703 +2026-04-11 23:34:38.920778: train_loss -0.2604 +2026-04-11 23:34:38.927742: val_loss -0.2311 +2026-04-11 23:34:38.930058: Pseudo dice [0.634, 0.6935, 0.798, 0.6557, 0.142, 0.7901, 0.6766] +2026-04-11 23:34:38.931984: Epoch time: 101.71 s +2026-04-11 23:34:40.100901: +2026-04-11 23:34:40.102922: Epoch 1295 +2026-04-11 23:34:40.104803: Current learning rate: 0.00703 +2026-04-11 23:36:21.691404: train_loss -0.2614 +2026-04-11 23:36:21.698903: val_loss -0.2313 +2026-04-11 23:36:21.701107: Pseudo dice [0.4796, 0.887, 0.7397, 0.3414, 0.5181, 0.302, 0.751] +2026-04-11 23:36:21.703573: Epoch time: 101.59 s +2026-04-11 23:36:22.855983: +2026-04-11 23:36:22.858242: Epoch 1296 +2026-04-11 23:36:22.860299: Current learning rate: 0.00703 +2026-04-11 23:38:04.911304: train_loss -0.2597 +2026-04-11 23:38:04.917823: val_loss -0.2081 +2026-04-11 23:38:04.920999: Pseudo dice [0.7082, 0.8813, 0.7276, 0.5559, 0.4825, 0.5757, 0.8459] +2026-04-11 23:38:04.923571: Epoch time: 102.06 s +2026-04-11 23:38:06.075614: +2026-04-11 23:38:06.077822: Epoch 1297 +2026-04-11 23:38:06.079648: Current learning rate: 0.00703 +2026-04-11 23:39:48.045779: train_loss -0.2584 +2026-04-11 23:39:48.052351: val_loss -0.217 +2026-04-11 23:39:48.054100: Pseudo dice [0.4073, 0.7467, 0.764, 0.4925, 0.3185, 0.8737, 0.7479] +2026-04-11 23:39:48.056521: Epoch time: 101.97 s +2026-04-11 23:39:49.248412: +2026-04-11 23:39:49.250008: Epoch 1298 +2026-04-11 23:39:49.252134: Current learning rate: 0.00703 +2026-04-11 23:41:31.350062: train_loss -0.266 +2026-04-11 23:41:31.356045: val_loss -0.1649 +2026-04-11 23:41:31.358071: Pseudo dice [0.3666, 0.7394, 0.7162, 0.2873, 0.4705, 0.7026, 0.5213] +2026-04-11 23:41:31.361516: Epoch time: 102.1 s +2026-04-11 23:41:32.498300: +2026-04-11 23:41:32.500121: Epoch 1299 +2026-04-11 23:41:32.501947: Current learning rate: 0.00702 +2026-04-11 23:43:13.933825: train_loss -0.2632 +2026-04-11 23:43:13.941656: val_loss -0.2487 +2026-04-11 23:43:13.943685: Pseudo dice [0.6489, 0.7871, 0.7401, 0.7788, 0.4017, 0.8423, 0.8074] +2026-04-11 23:43:13.946017: Epoch time: 101.44 s +2026-04-11 23:43:16.776898: +2026-04-11 23:43:16.779485: Epoch 1300 +2026-04-11 23:43:16.781043: Current learning rate: 0.00702 +2026-04-11 23:44:58.951425: train_loss -0.2628 +2026-04-11 23:44:58.958234: val_loss -0.2024 +2026-04-11 23:44:58.960395: Pseudo dice [0.6197, 0.2393, 0.6835, 0.486, 0.2314, 0.9156, 0.6584] +2026-04-11 23:44:58.963123: Epoch time: 102.18 s +2026-04-11 23:45:00.143001: +2026-04-11 23:45:00.145545: Epoch 1301 +2026-04-11 23:45:00.147701: Current learning rate: 0.00702 +2026-04-11 23:46:41.630052: train_loss -0.2627 +2026-04-11 23:46:41.638266: val_loss -0.2352 +2026-04-11 23:46:41.640670: Pseudo dice [0.7891, 0.689, 0.7655, 0.3493, 0.389, 0.8904, 0.8043] +2026-04-11 23:46:41.643836: Epoch time: 101.49 s +2026-04-11 23:46:42.776169: +2026-04-11 23:46:42.777871: Epoch 1302 +2026-04-11 23:46:42.779579: Current learning rate: 0.00702 +2026-04-11 23:48:24.435024: train_loss -0.2743 +2026-04-11 23:48:24.441457: val_loss -0.2363 +2026-04-11 23:48:24.443563: Pseudo dice [0.4993, 0.5084, 0.4012, 0.6929, 0.5441, 0.4246, 0.8149] +2026-04-11 23:48:24.445755: Epoch time: 101.66 s +2026-04-11 23:48:25.610716: +2026-04-11 23:48:25.612620: Epoch 1303 +2026-04-11 23:48:25.614444: Current learning rate: 0.00701 +2026-04-11 23:50:07.471349: train_loss -0.2551 +2026-04-11 23:50:07.477067: val_loss -0.2264 +2026-04-11 23:50:07.478899: Pseudo dice [0.0996, 0.6936, 0.6914, 0.3607, 0.5993, 0.6734, 0.7635] +2026-04-11 23:50:07.482065: Epoch time: 101.86 s +2026-04-11 23:50:08.624071: +2026-04-11 23:50:08.626207: Epoch 1304 +2026-04-11 23:50:08.628537: Current learning rate: 0.00701 +2026-04-11 23:51:50.027596: train_loss -0.2599 +2026-04-11 23:51:50.033905: val_loss -0.2324 +2026-04-11 23:51:50.035680: Pseudo dice [0.7042, 0.3141, 0.7669, 0.1519, 0.4673, 0.759, 0.7239] +2026-04-11 23:51:50.038105: Epoch time: 101.41 s +2026-04-11 23:51:51.189220: +2026-04-11 23:51:51.192148: Epoch 1305 +2026-04-11 23:51:51.193964: Current learning rate: 0.00701 +2026-04-11 23:53:33.868644: train_loss -0.2555 +2026-04-11 23:53:33.874772: val_loss -0.2087 +2026-04-11 23:53:33.876805: Pseudo dice [0.6634, 0.5171, 0.4806, 0.2766, 0.3576, 0.6962, 0.8225] +2026-04-11 23:53:33.878790: Epoch time: 102.68 s +2026-04-11 23:53:35.024402: +2026-04-11 23:53:35.026223: Epoch 1306 +2026-04-11 23:53:35.029768: Current learning rate: 0.00701 +2026-04-11 23:55:17.385520: train_loss -0.2622 +2026-04-11 23:55:17.392005: val_loss -0.2616 +2026-04-11 23:55:17.393844: Pseudo dice [0.6224, 0.4136, 0.7564, 0.7254, 0.5064, 0.8664, 0.8724] +2026-04-11 23:55:17.397427: Epoch time: 102.36 s +2026-04-11 23:55:18.552704: +2026-04-11 23:55:18.554493: Epoch 1307 +2026-04-11 23:55:18.556512: Current learning rate: 0.007 +2026-04-11 23:57:00.865283: train_loss -0.266 +2026-04-11 23:57:00.872401: val_loss -0.1841 +2026-04-11 23:57:00.874579: Pseudo dice [0.7815, 0.7362, 0.5425, 0.7916, 0.2248, 0.6801, 0.7856] +2026-04-11 23:57:00.877921: Epoch time: 102.32 s +2026-04-11 23:57:02.025150: +2026-04-11 23:57:02.026956: Epoch 1308 +2026-04-11 23:57:02.028721: Current learning rate: 0.007 +2026-04-11 23:58:44.429124: train_loss -0.2535 +2026-04-11 23:58:44.437973: val_loss -0.2177 +2026-04-11 23:58:44.440553: Pseudo dice [0.676, 0.6301, 0.7377, 0.2216, 0.4635, 0.9026, 0.5958] +2026-04-11 23:58:44.443890: Epoch time: 102.41 s +2026-04-11 23:58:45.593045: +2026-04-11 23:58:45.594522: Epoch 1309 +2026-04-11 23:58:45.596154: Current learning rate: 0.007 +2026-04-12 00:00:28.965499: train_loss -0.2731 +2026-04-12 00:00:28.973785: val_loss -0.2112 +2026-04-12 00:00:28.976305: Pseudo dice [0.5949, 0.2647, 0.7289, 0.5385, 0.2879, 0.4428, 0.71] +2026-04-12 00:00:28.980093: Epoch time: 103.38 s +2026-04-12 00:00:30.157347: +2026-04-12 00:00:30.159521: Epoch 1310 +2026-04-12 00:00:30.161650: Current learning rate: 0.007 +2026-04-12 00:02:13.691005: train_loss -0.263 +2026-04-12 00:02:13.699329: val_loss -0.2378 +2026-04-12 00:02:13.701425: Pseudo dice [0.6639, 0.5473, 0.5363, 0.3206, 0.5347, 0.8649, 0.7392] +2026-04-12 00:02:13.704694: Epoch time: 103.54 s +2026-04-12 00:02:14.923085: +2026-04-12 00:02:14.926379: Epoch 1311 +2026-04-12 00:02:14.928664: Current learning rate: 0.00699 +2026-04-12 00:03:57.080716: train_loss -0.2679 +2026-04-12 00:03:57.088908: val_loss -0.2356 +2026-04-12 00:03:57.091941: Pseudo dice [0.6272, 0.4075, 0.8012, 0.4545, 0.5374, 0.6996, 0.7702] +2026-04-12 00:03:57.094723: Epoch time: 102.16 s +2026-04-12 00:03:58.296867: +2026-04-12 00:03:58.299365: Epoch 1312 +2026-04-12 00:03:58.301711: Current learning rate: 0.00699 +2026-04-12 00:05:40.239049: train_loss -0.2465 +2026-04-12 00:05:40.245545: val_loss -0.2209 +2026-04-12 00:05:40.247972: Pseudo dice [0.7969, 0.2678, 0.7985, 0.1655, 0.6461, 0.8375, 0.6018] +2026-04-12 00:05:40.250491: Epoch time: 101.95 s +2026-04-12 00:05:41.417127: +2026-04-12 00:05:41.419032: Epoch 1313 +2026-04-12 00:05:41.421126: Current learning rate: 0.00699 +2026-04-12 00:07:23.458438: train_loss -0.2617 +2026-04-12 00:07:23.465724: val_loss -0.216 +2026-04-12 00:07:23.467636: Pseudo dice [0.4695, 0.7375, 0.7552, 0.4995, 0.4617, 0.7858, 0.8442] +2026-04-12 00:07:23.470370: Epoch time: 102.04 s +2026-04-12 00:07:24.633729: +2026-04-12 00:07:24.635514: Epoch 1314 +2026-04-12 00:07:24.637379: Current learning rate: 0.00699 +2026-04-12 00:09:06.865562: train_loss -0.2579 +2026-04-12 00:09:06.873443: val_loss -0.1955 +2026-04-12 00:09:06.876461: Pseudo dice [0.4637, 0.6889, 0.3069, 0.4072, 0.292, 0.7557, 0.746] +2026-04-12 00:09:06.880339: Epoch time: 102.23 s +2026-04-12 00:09:08.069576: +2026-04-12 00:09:08.071196: Epoch 1315 +2026-04-12 00:09:08.074011: Current learning rate: 0.00699 +2026-04-12 00:10:49.749922: train_loss -0.2478 +2026-04-12 00:10:49.755911: val_loss -0.1979 +2026-04-12 00:10:49.758098: Pseudo dice [0.8728, 0.8686, 0.7797, 0.3562, 0.3884, 0.6657, 0.6583] +2026-04-12 00:10:49.760153: Epoch time: 101.68 s +2026-04-12 00:10:50.906231: +2026-04-12 00:10:50.907986: Epoch 1316 +2026-04-12 00:10:50.909895: Current learning rate: 0.00698 +2026-04-12 00:12:32.527124: train_loss -0.2486 +2026-04-12 00:12:32.533382: val_loss -0.193 +2026-04-12 00:12:32.535901: Pseudo dice [0.7627, 0.8633, 0.6138, 0.7943, 0.4943, 0.1065, 0.3926] +2026-04-12 00:12:32.538583: Epoch time: 101.62 s +2026-04-12 00:12:33.743050: +2026-04-12 00:12:33.744647: Epoch 1317 +2026-04-12 00:12:33.746538: Current learning rate: 0.00698 +2026-04-12 00:14:15.451880: train_loss -0.2605 +2026-04-12 00:14:15.457772: val_loss -0.204 +2026-04-12 00:14:15.459405: Pseudo dice [0.4796, 0.8857, 0.7799, 0.1399, 0.4664, 0.4142, 0.8469] +2026-04-12 00:14:15.461747: Epoch time: 101.71 s +2026-04-12 00:14:16.616830: +2026-04-12 00:14:16.618816: Epoch 1318 +2026-04-12 00:14:16.621242: Current learning rate: 0.00698 +2026-04-12 00:15:58.330914: train_loss -0.2466 +2026-04-12 00:15:58.339831: val_loss -0.2097 +2026-04-12 00:15:58.341846: Pseudo dice [0.587, 0.1976, 0.6912, 0.3863, 0.3639, 0.7373, 0.8204] +2026-04-12 00:15:58.344120: Epoch time: 101.72 s +2026-04-12 00:15:59.510854: +2026-04-12 00:15:59.512406: Epoch 1319 +2026-04-12 00:15:59.514237: Current learning rate: 0.00698 +2026-04-12 00:17:41.324988: train_loss -0.2462 +2026-04-12 00:17:41.331731: val_loss -0.221 +2026-04-12 00:17:41.337063: Pseudo dice [0.5749, 0.1122, 0.7105, 0.573, 0.5296, 0.6744, 0.6584] +2026-04-12 00:17:41.339287: Epoch time: 101.82 s +2026-04-12 00:17:42.505869: +2026-04-12 00:17:42.507902: Epoch 1320 +2026-04-12 00:17:42.510071: Current learning rate: 0.00697 +2026-04-12 00:19:24.495923: train_loss -0.2576 +2026-04-12 00:19:24.507349: val_loss -0.227 +2026-04-12 00:19:24.515846: Pseudo dice [0.4941, 0.7074, 0.6474, 0.8141, 0.3104, 0.446, 0.4168] +2026-04-12 00:19:24.518091: Epoch time: 101.99 s +2026-04-12 00:19:25.673438: +2026-04-12 00:19:25.675914: Epoch 1321 +2026-04-12 00:19:25.677790: Current learning rate: 0.00697 +2026-04-12 00:21:07.574199: train_loss -0.2649 +2026-04-12 00:21:07.582963: val_loss -0.2118 +2026-04-12 00:21:07.584617: Pseudo dice [0.8287, 0.8639, 0.727, 0.6742, 0.5088, 0.0819, 0.8127] +2026-04-12 00:21:07.586956: Epoch time: 101.9 s +2026-04-12 00:21:08.754973: +2026-04-12 00:21:08.756905: Epoch 1322 +2026-04-12 00:21:08.758749: Current learning rate: 0.00697 +2026-04-12 00:22:50.596849: train_loss -0.2606 +2026-04-12 00:22:50.602658: val_loss -0.2099 +2026-04-12 00:22:50.605183: Pseudo dice [0.5619, 0.8718, 0.7673, 0.6506, 0.5879, 0.587, 0.3612] +2026-04-12 00:22:50.607445: Epoch time: 101.85 s +2026-04-12 00:22:51.768161: +2026-04-12 00:22:51.769766: Epoch 1323 +2026-04-12 00:22:51.771569: Current learning rate: 0.00697 +2026-04-12 00:24:33.336923: train_loss -0.2612 +2026-04-12 00:24:33.343566: val_loss -0.1981 +2026-04-12 00:24:33.345360: Pseudo dice [0.4733, 0.6553, 0.7421, 0.3554, 0.3273, 0.6379, 0.8442] +2026-04-12 00:24:33.347532: Epoch time: 101.57 s +2026-04-12 00:24:34.544279: +2026-04-12 00:24:34.545801: Epoch 1324 +2026-04-12 00:24:34.547472: Current learning rate: 0.00696 +2026-04-12 00:26:16.357508: train_loss -0.2647 +2026-04-12 00:26:16.364042: val_loss -0.2592 +2026-04-12 00:26:16.366964: Pseudo dice [0.6198, 0.7406, 0.7626, 0.637, 0.5349, 0.7097, 0.75] +2026-04-12 00:26:16.369318: Epoch time: 101.82 s +2026-04-12 00:26:17.554654: +2026-04-12 00:26:17.556613: Epoch 1325 +2026-04-12 00:26:17.558374: Current learning rate: 0.00696 +2026-04-12 00:28:00.676520: train_loss -0.262 +2026-04-12 00:28:00.683416: val_loss -0.2127 +2026-04-12 00:28:00.686694: Pseudo dice [0.7246, 0.8591, 0.6215, 0.3208, 0.5769, 0.6501, 0.7456] +2026-04-12 00:28:00.689913: Epoch time: 103.13 s +2026-04-12 00:28:01.863523: +2026-04-12 00:28:01.865562: Epoch 1326 +2026-04-12 00:28:01.867488: Current learning rate: 0.00696 +2026-04-12 00:29:43.990520: train_loss -0.2701 +2026-04-12 00:29:43.996789: val_loss -0.2405 +2026-04-12 00:29:43.998930: Pseudo dice [0.6892, 0.9122, 0.8287, 0.8312, 0.5613, 0.2568, 0.1788] +2026-04-12 00:29:44.001122: Epoch time: 102.13 s +2026-04-12 00:29:45.179948: +2026-04-12 00:29:45.181824: Epoch 1327 +2026-04-12 00:29:45.184030: Current learning rate: 0.00696 +2026-04-12 00:31:27.699843: train_loss -0.2571 +2026-04-12 00:31:27.711110: val_loss -0.2309 +2026-04-12 00:31:27.713352: Pseudo dice [0.1679, 0.5499, 0.8235, 0.5645, 0.3524, 0.8807, 0.7822] +2026-04-12 00:31:27.715972: Epoch time: 102.52 s +2026-04-12 00:31:28.951817: +2026-04-12 00:31:28.954107: Epoch 1328 +2026-04-12 00:31:28.956221: Current learning rate: 0.00696 +2026-04-12 00:33:10.485230: train_loss -0.2722 +2026-04-12 00:33:10.513740: val_loss -0.2392 +2026-04-12 00:33:10.515754: Pseudo dice [0.6573, 0.7843, 0.7398, 0.3573, 0.3345, 0.876, 0.6921] +2026-04-12 00:33:10.518254: Epoch time: 101.54 s +2026-04-12 00:33:11.743181: +2026-04-12 00:33:11.745051: Epoch 1329 +2026-04-12 00:33:11.746762: Current learning rate: 0.00695 +2026-04-12 00:34:53.688553: train_loss -0.2655 +2026-04-12 00:34:53.695034: val_loss -0.2064 +2026-04-12 00:34:53.697051: Pseudo dice [0.3712, 0.7056, 0.695, 0.4744, 0.2793, 0.368, 0.7518] +2026-04-12 00:34:53.699955: Epoch time: 101.95 s +2026-04-12 00:34:54.912257: +2026-04-12 00:34:54.915180: Epoch 1330 +2026-04-12 00:34:54.918147: Current learning rate: 0.00695 +2026-04-12 00:36:37.271791: train_loss -0.2601 +2026-04-12 00:36:37.279092: val_loss -0.1492 +2026-04-12 00:36:37.281074: Pseudo dice [0.3601, 0.7604, 0.2509, 0.6807, 0.2918, 0.1406, 0.7949] +2026-04-12 00:36:37.283578: Epoch time: 102.36 s +2026-04-12 00:36:38.551094: +2026-04-12 00:36:38.552670: Epoch 1331 +2026-04-12 00:36:38.554378: Current learning rate: 0.00695 +2026-04-12 00:38:20.166848: train_loss -0.2584 +2026-04-12 00:38:20.173873: val_loss -0.1954 +2026-04-12 00:38:20.176182: Pseudo dice [0.263, 0.5925, 0.5981, 0.478, 0.4906, 0.5676, 0.6609] +2026-04-12 00:38:20.179021: Epoch time: 101.62 s +2026-04-12 00:38:21.367762: +2026-04-12 00:38:21.369359: Epoch 1332 +2026-04-12 00:38:21.371215: Current learning rate: 0.00695 +2026-04-12 00:40:02.856920: train_loss -0.2755 +2026-04-12 00:40:02.864806: val_loss -0.2215 +2026-04-12 00:40:02.867018: Pseudo dice [0.6808, 0.6716, 0.698, 0.7599, 0.2775, 0.9179, 0.7824] +2026-04-12 00:40:02.869673: Epoch time: 101.49 s +2026-04-12 00:40:04.073125: +2026-04-12 00:40:04.075375: Epoch 1333 +2026-04-12 00:40:04.077764: Current learning rate: 0.00694 +2026-04-12 00:41:45.871038: train_loss -0.2784 +2026-04-12 00:41:45.878682: val_loss -0.2203 +2026-04-12 00:41:45.880713: Pseudo dice [0.5869, 0.3211, 0.7534, 0.7255, 0.3289, 0.798, 0.8041] +2026-04-12 00:41:45.883447: Epoch time: 101.8 s +2026-04-12 00:41:47.086095: +2026-04-12 00:41:47.087927: Epoch 1334 +2026-04-12 00:41:47.089970: Current learning rate: 0.00694 +2026-04-12 00:43:28.757336: train_loss -0.2836 +2026-04-12 00:43:28.764758: val_loss -0.2148 +2026-04-12 00:43:28.766574: Pseudo dice [0.5213, 0.5299, 0.662, 0.6544, 0.4404, 0.3771, 0.478] +2026-04-12 00:43:28.769223: Epoch time: 101.67 s +2026-04-12 00:43:29.969527: +2026-04-12 00:43:29.973094: Epoch 1335 +2026-04-12 00:43:29.977445: Current learning rate: 0.00694 +2026-04-12 00:45:11.984632: train_loss -0.2628 +2026-04-12 00:45:11.992651: val_loss -0.207 +2026-04-12 00:45:11.995707: Pseudo dice [0.5002, 0.908, 0.7334, 0.3675, 0.6424, 0.6107, 0.1874] +2026-04-12 00:45:11.999360: Epoch time: 102.02 s +2026-04-12 00:45:13.212139: +2026-04-12 00:45:13.214109: Epoch 1336 +2026-04-12 00:45:13.215921: Current learning rate: 0.00694 +2026-04-12 00:46:54.831994: train_loss -0.2584 +2026-04-12 00:46:54.838544: val_loss -0.2047 +2026-04-12 00:46:54.840514: Pseudo dice [0.7627, 0.8804, 0.7614, 0.3768, 0.2675, 0.1416, 0.5972] +2026-04-12 00:46:54.843247: Epoch time: 101.62 s +2026-04-12 00:46:56.065867: +2026-04-12 00:46:56.067667: Epoch 1337 +2026-04-12 00:46:56.070078: Current learning rate: 0.00693 +2026-04-12 00:48:38.092249: train_loss -0.2578 +2026-04-12 00:48:38.105008: val_loss -0.2053 +2026-04-12 00:48:38.107347: Pseudo dice [0.7676, 0.4907, 0.692, 0.3594, 0.3239, 0.6962, 0.6999] +2026-04-12 00:48:38.109734: Epoch time: 102.03 s +2026-04-12 00:48:39.289832: +2026-04-12 00:48:39.292188: Epoch 1338 +2026-04-12 00:48:39.294377: Current learning rate: 0.00693 +2026-04-12 00:50:20.921123: train_loss -0.2618 +2026-04-12 00:50:20.929078: val_loss -0.1701 +2026-04-12 00:50:20.931271: Pseudo dice [0.3118, 0.3009, 0.5549, 0.7223, 0.5215, 0.473, 0.589] +2026-04-12 00:50:20.934055: Epoch time: 101.63 s +2026-04-12 00:50:22.129897: +2026-04-12 00:50:22.131877: Epoch 1339 +2026-04-12 00:50:22.133869: Current learning rate: 0.00693 +2026-04-12 00:52:03.646153: train_loss -0.2617 +2026-04-12 00:52:03.652917: val_loss -0.2267 +2026-04-12 00:52:03.655125: Pseudo dice [0.6898, 0.8369, 0.6832, 0.4719, 0.3556, 0.2657, 0.8552] +2026-04-12 00:52:03.657783: Epoch time: 101.52 s +2026-04-12 00:52:04.825598: +2026-04-12 00:52:04.827385: Epoch 1340 +2026-04-12 00:52:04.829359: Current learning rate: 0.00693 +2026-04-12 00:53:46.991174: train_loss -0.269 +2026-04-12 00:53:47.006135: val_loss -0.2046 +2026-04-12 00:53:47.008322: Pseudo dice [0.5438, 0.1639, 0.7451, 0.4657, 0.3648, 0.7071, 0.7245] +2026-04-12 00:53:47.010795: Epoch time: 102.17 s +2026-04-12 00:53:48.197143: +2026-04-12 00:53:48.199243: Epoch 1341 +2026-04-12 00:53:48.202524: Current learning rate: 0.00692 +2026-04-12 00:55:30.115618: train_loss -0.2594 +2026-04-12 00:55:30.123218: val_loss -0.2161 +2026-04-12 00:55:30.125852: Pseudo dice [0.5589, 0.2623, 0.8105, 0.7888, 0.3837, 0.8896, 0.7292] +2026-04-12 00:55:30.128374: Epoch time: 101.92 s +2026-04-12 00:55:31.333347: +2026-04-12 00:55:31.335355: Epoch 1342 +2026-04-12 00:55:31.337468: Current learning rate: 0.00692 +2026-04-12 00:57:13.146267: train_loss -0.2699 +2026-04-12 00:57:13.155232: val_loss -0.2348 +2026-04-12 00:57:13.157623: Pseudo dice [0.5512, 0.8172, 0.7352, 0.3419, 0.6408, 0.4973, 0.8213] +2026-04-12 00:57:13.159828: Epoch time: 101.82 s +2026-04-12 00:57:14.358755: +2026-04-12 00:57:14.361459: Epoch 1343 +2026-04-12 00:57:14.363936: Current learning rate: 0.00692 +2026-04-12 00:58:56.410145: train_loss -0.2736 +2026-04-12 00:58:56.416190: val_loss -0.231 +2026-04-12 00:58:56.418382: Pseudo dice [0.5015, 0.6648, 0.7643, 0.6163, 0.4165, 0.615, 0.3495] +2026-04-12 00:58:56.421492: Epoch time: 102.05 s +2026-04-12 00:58:57.613824: +2026-04-12 00:58:57.615868: Epoch 1344 +2026-04-12 00:58:57.617921: Current learning rate: 0.00692 +2026-04-12 01:00:41.077663: train_loss -0.2649 +2026-04-12 01:00:41.085789: val_loss -0.1697 +2026-04-12 01:00:41.088647: Pseudo dice [0.7444, 0.7643, 0.637, 0.0237, 0.4184, 0.4437, 0.3498] +2026-04-12 01:00:41.092089: Epoch time: 103.47 s +2026-04-12 01:00:42.263687: +2026-04-12 01:00:42.266039: Epoch 1345 +2026-04-12 01:00:42.270012: Current learning rate: 0.00692 +2026-04-12 01:02:26.804567: train_loss -0.2641 +2026-04-12 01:02:26.814485: val_loss -0.199 +2026-04-12 01:02:26.820205: Pseudo dice [0.3501, 0.7161, 0.6567, 0.6178, 0.5615, 0.7777, 0.5088] +2026-04-12 01:02:26.826005: Epoch time: 104.54 s +2026-04-12 01:02:28.026079: +2026-04-12 01:02:28.034610: Epoch 1346 +2026-04-12 01:02:28.041294: Current learning rate: 0.00691 +2026-04-12 01:04:09.714761: train_loss -0.26 +2026-04-12 01:04:09.722695: val_loss -0.2351 +2026-04-12 01:04:09.725981: Pseudo dice [0.4653, 0.2023, 0.794, 0.4952, 0.5671, 0.9328, 0.7546] +2026-04-12 01:04:09.731894: Epoch time: 101.69 s +2026-04-12 01:04:10.917898: +2026-04-12 01:04:10.922198: Epoch 1347 +2026-04-12 01:04:10.925028: Current learning rate: 0.00691 +2026-04-12 01:05:53.779485: train_loss -0.2554 +2026-04-12 01:05:53.789325: val_loss -0.2125 +2026-04-12 01:05:53.794439: Pseudo dice [0.6813, 0.7494, 0.7347, 0.1211, 0.5905, 0.7601, 0.5896] +2026-04-12 01:05:53.798097: Epoch time: 102.86 s +2026-04-12 01:05:54.995547: +2026-04-12 01:05:54.998698: Epoch 1348 +2026-04-12 01:05:55.001832: Current learning rate: 0.00691 +2026-04-12 01:07:38.811801: train_loss -0.2472 +2026-04-12 01:07:38.821485: val_loss -0.1929 +2026-04-12 01:07:38.825706: Pseudo dice [0.5725, 0.7578, 0.5788, 0.8146, 0.4985, 0.6126, 0.6442] +2026-04-12 01:07:38.834225: Epoch time: 103.82 s +2026-04-12 01:07:40.032022: +2026-04-12 01:07:40.035378: Epoch 1349 +2026-04-12 01:07:40.037977: Current learning rate: 0.00691 +2026-04-12 01:09:22.888660: train_loss -0.2524 +2026-04-12 01:09:22.897151: val_loss -0.2249 +2026-04-12 01:09:22.909059: Pseudo dice [0.7974, 0.8932, 0.7142, 0.5498, 0.3552, 0.1585, 0.761] +2026-04-12 01:09:22.911895: Epoch time: 102.86 s +2026-04-12 01:09:26.129321: +2026-04-12 01:09:26.132112: Epoch 1350 +2026-04-12 01:09:26.134027: Current learning rate: 0.0069 +2026-04-12 01:11:07.926334: train_loss -0.2598 +2026-04-12 01:11:07.936356: val_loss -0.2351 +2026-04-12 01:11:07.940578: Pseudo dice [0.4653, 0.1235, 0.7616, 0.639, 0.551, 0.6945, 0.7809] +2026-04-12 01:11:07.943624: Epoch time: 101.8 s +2026-04-12 01:11:09.146362: +2026-04-12 01:11:09.153931: Epoch 1351 +2026-04-12 01:11:09.163000: Current learning rate: 0.0069 +2026-04-12 01:12:51.947704: train_loss -0.272 +2026-04-12 01:12:51.954191: val_loss -0.2381 +2026-04-12 01:12:51.956200: Pseudo dice [0.5063, 0.4131, 0.7621, 0.5919, 0.5344, 0.3703, 0.6503] +2026-04-12 01:12:51.958744: Epoch time: 102.8 s +2026-04-12 01:12:53.177046: +2026-04-12 01:12:53.179258: Epoch 1352 +2026-04-12 01:12:53.181840: Current learning rate: 0.0069 +2026-04-12 01:14:36.593443: train_loss -0.2622 +2026-04-12 01:14:36.604486: val_loss -0.1803 +2026-04-12 01:14:36.608761: Pseudo dice [0.581, 0.4191, 0.5733, 0.2884, 0.3541, 0.6836, 0.823] +2026-04-12 01:14:36.613295: Epoch time: 103.42 s +2026-04-12 01:14:37.799112: +2026-04-12 01:14:37.801252: Epoch 1353 +2026-04-12 01:14:37.803814: Current learning rate: 0.0069 +2026-04-12 01:16:21.298845: train_loss -0.2539 +2026-04-12 01:16:21.307707: val_loss -0.2373 +2026-04-12 01:16:21.311893: Pseudo dice [0.5703, 0.5956, 0.697, 0.8425, 0.4566, 0.4605, 0.8209] +2026-04-12 01:16:21.317048: Epoch time: 103.5 s +2026-04-12 01:16:22.550078: +2026-04-12 01:16:22.553102: Epoch 1354 +2026-04-12 01:16:22.555751: Current learning rate: 0.00689 +2026-04-12 01:18:05.161858: train_loss -0.2594 +2026-04-12 01:18:05.172676: val_loss -0.2219 +2026-04-12 01:18:05.175959: Pseudo dice [0.5419, 0.3708, 0.7968, 0.5821, 0.5656, 0.8892, 0.7772] +2026-04-12 01:18:05.179065: Epoch time: 102.61 s +2026-04-12 01:18:06.388971: +2026-04-12 01:18:06.391527: Epoch 1355 +2026-04-12 01:18:06.394468: Current learning rate: 0.00689 +2026-04-12 01:19:49.067106: train_loss -0.2722 +2026-04-12 01:19:49.075660: val_loss -0.2344 +2026-04-12 01:19:49.078279: Pseudo dice [0.667, 0.6709, 0.8064, 0.5348, 0.2325, 0.3446, 0.4934] +2026-04-12 01:19:49.081165: Epoch time: 102.68 s +2026-04-12 01:19:50.284466: +2026-04-12 01:19:50.286507: Epoch 1356 +2026-04-12 01:19:50.289026: Current learning rate: 0.00689 +2026-04-12 01:21:31.832129: train_loss -0.2826 +2026-04-12 01:21:31.840216: val_loss -0.229 +2026-04-12 01:21:31.842662: Pseudo dice [0.6163, 0.6799, 0.7715, 0.6193, 0.4683, 0.7209, 0.6026] +2026-04-12 01:21:31.845773: Epoch time: 101.55 s +2026-04-12 01:21:33.029981: +2026-04-12 01:21:33.032264: Epoch 1357 +2026-04-12 01:21:33.034754: Current learning rate: 0.00689 +2026-04-12 01:23:15.335675: train_loss -0.2695 +2026-04-12 01:23:15.345670: val_loss -0.2112 +2026-04-12 01:23:15.347849: Pseudo dice [0.6244, 0.7849, 0.513, 0.7217, 0.5182, 0.292, 0.6587] +2026-04-12 01:23:15.351892: Epoch time: 102.31 s +2026-04-12 01:23:16.533563: +2026-04-12 01:23:16.536068: Epoch 1358 +2026-04-12 01:23:16.540958: Current learning rate: 0.00688 +2026-04-12 01:24:58.980541: train_loss -0.2504 +2026-04-12 01:24:58.988941: val_loss -0.2105 +2026-04-12 01:24:58.992467: Pseudo dice [0.7205, 0.8873, 0.4461, 0.3467, 0.5299, 0.5293, 0.8078] +2026-04-12 01:24:58.995665: Epoch time: 102.45 s +2026-04-12 01:25:00.169851: +2026-04-12 01:25:00.172540: Epoch 1359 +2026-04-12 01:25:00.175264: Current learning rate: 0.00688 +2026-04-12 01:26:43.249730: train_loss -0.2454 +2026-04-12 01:26:43.258958: val_loss -0.2125 +2026-04-12 01:26:43.263402: Pseudo dice [0.4528, 0.5922, 0.7857, 0.3181, 0.5339, 0.5889, 0.8003] +2026-04-12 01:26:43.267126: Epoch time: 103.08 s +2026-04-12 01:26:44.467974: +2026-04-12 01:26:44.470946: Epoch 1360 +2026-04-12 01:26:44.475565: Current learning rate: 0.00688 +2026-04-12 01:28:26.972908: train_loss -0.26 +2026-04-12 01:28:26.980803: val_loss -0.2067 +2026-04-12 01:28:26.982985: Pseudo dice [0.6965, 0.8794, 0.7395, 0.7842, 0.5581, 0.8095, 0.7924] +2026-04-12 01:28:26.985311: Epoch time: 102.51 s +2026-04-12 01:28:28.180833: +2026-04-12 01:28:28.185111: Epoch 1361 +2026-04-12 01:28:28.187721: Current learning rate: 0.00688 +2026-04-12 01:30:11.448138: train_loss -0.2783 +2026-04-12 01:30:11.456883: val_loss -0.216 +2026-04-12 01:30:11.460000: Pseudo dice [0.7256, 0.4702, 0.7478, 0.6582, 0.433, 0.2003, 0.7337] +2026-04-12 01:30:11.463021: Epoch time: 103.27 s +2026-04-12 01:30:12.718004: +2026-04-12 01:30:12.720198: Epoch 1362 +2026-04-12 01:30:12.722816: Current learning rate: 0.00688 +2026-04-12 01:31:55.170206: train_loss -0.2775 +2026-04-12 01:31:55.181176: val_loss -0.2548 +2026-04-12 01:31:55.184389: Pseudo dice [0.2085, 0.6061, 0.7025, 0.57, 0.6771, 0.5005, 0.6802] +2026-04-12 01:31:55.186874: Epoch time: 102.46 s +2026-04-12 01:31:56.386168: +2026-04-12 01:31:56.390834: Epoch 1363 +2026-04-12 01:31:56.395391: Current learning rate: 0.00687 +2026-04-12 01:33:39.277869: train_loss -0.2603 +2026-04-12 01:33:39.307510: val_loss -0.2278 +2026-04-12 01:33:39.310422: Pseudo dice [0.5809, 0.8557, 0.7053, 0.2902, 0.4383, 0.6309, 0.5755] +2026-04-12 01:33:39.313436: Epoch time: 102.89 s +2026-04-12 01:33:40.517911: +2026-04-12 01:33:40.520891: Epoch 1364 +2026-04-12 01:33:40.524958: Current learning rate: 0.00687 +2026-04-12 01:35:24.408301: train_loss -0.2675 +2026-04-12 01:35:24.417572: val_loss -0.2423 +2026-04-12 01:35:24.420575: Pseudo dice [0.5084, 0.7934, 0.7002, 0.5034, 0.3181, 0.7089, 0.7154] +2026-04-12 01:35:24.423966: Epoch time: 103.89 s +2026-04-12 01:35:26.769322: +2026-04-12 01:35:26.771668: Epoch 1365 +2026-04-12 01:35:26.774228: Current learning rate: 0.00687 +2026-04-12 01:37:09.552064: train_loss -0.2759 +2026-04-12 01:37:09.560848: val_loss -0.2355 +2026-04-12 01:37:09.563631: Pseudo dice [0.7337, 0.8865, 0.8295, 0.5242, 0.3806, 0.759, 0.7694] +2026-04-12 01:37:09.571202: Epoch time: 102.79 s +2026-04-12 01:37:10.739348: +2026-04-12 01:37:10.743614: Epoch 1366 +2026-04-12 01:37:10.747002: Current learning rate: 0.00687 +2026-04-12 01:38:53.688392: train_loss -0.2701 +2026-04-12 01:38:53.701088: val_loss -0.2248 +2026-04-12 01:38:53.703975: Pseudo dice [0.7706, 0.5066, 0.763, 0.5813, 0.3798, 0.8855, 0.7159] +2026-04-12 01:38:53.707488: Epoch time: 102.95 s +2026-04-12 01:38:54.970605: +2026-04-12 01:38:54.973072: Epoch 1367 +2026-04-12 01:38:54.976096: Current learning rate: 0.00686 +2026-04-12 01:40:38.716834: train_loss -0.2641 +2026-04-12 01:40:38.725199: val_loss -0.205 +2026-04-12 01:40:38.728708: Pseudo dice [0.5541, 0.3286, 0.6891, 0.3872, 0.4924, 0.8632, 0.6681] +2026-04-12 01:40:38.731642: Epoch time: 103.75 s +2026-04-12 01:40:39.930457: +2026-04-12 01:40:39.938251: Epoch 1368 +2026-04-12 01:40:39.941237: Current learning rate: 0.00686 +2026-04-12 01:42:23.465847: train_loss -0.256 +2026-04-12 01:42:23.475375: val_loss -0.2626 +2026-04-12 01:42:23.477590: Pseudo dice [0.5713, 0.684, 0.7255, 0.8472, 0.4878, 0.8327, 0.7499] +2026-04-12 01:42:23.480452: Epoch time: 103.54 s +2026-04-12 01:42:24.666631: +2026-04-12 01:42:24.668793: Epoch 1369 +2026-04-12 01:42:24.672718: Current learning rate: 0.00686 +2026-04-12 01:44:07.305661: train_loss -0.2684 +2026-04-12 01:44:07.314261: val_loss -0.2331 +2026-04-12 01:44:07.316957: Pseudo dice [0.5385, 0.8417, 0.7861, 0.1553, 0.6616, 0.6788, 0.2704] +2026-04-12 01:44:07.320177: Epoch time: 102.64 s +2026-04-12 01:44:08.492719: +2026-04-12 01:44:08.495407: Epoch 1370 +2026-04-12 01:44:08.498197: Current learning rate: 0.00686 +2026-04-12 01:45:52.624820: train_loss -0.2744 +2026-04-12 01:45:52.637575: val_loss -0.2 +2026-04-12 01:45:52.640557: Pseudo dice [0.2213, 0.325, 0.6741, 0.2261, 0.521, 0.6341, 0.7543] +2026-04-12 01:45:52.643404: Epoch time: 104.14 s +2026-04-12 01:45:53.820600: +2026-04-12 01:45:53.824102: Epoch 1371 +2026-04-12 01:45:53.826756: Current learning rate: 0.00685 +2026-04-12 01:47:37.496252: train_loss -0.2583 +2026-04-12 01:47:37.509572: val_loss -0.1736 +2026-04-12 01:47:37.513156: Pseudo dice [0.1793, 0.5179, 0.7553, 0.3907, 0.2372, 0.4842, 0.3748] +2026-04-12 01:47:37.517351: Epoch time: 103.68 s +2026-04-12 01:47:38.774118: +2026-04-12 01:47:38.779271: Epoch 1372 +2026-04-12 01:47:38.784979: Current learning rate: 0.00685 +2026-04-12 01:49:23.028591: train_loss -0.2771 +2026-04-12 01:49:23.038791: val_loss -0.2369 +2026-04-12 01:49:23.042976: Pseudo dice [0.6216, 0.8788, 0.7527, 0.4633, 0.4692, 0.8733, 0.4627] +2026-04-12 01:49:23.046770: Epoch time: 104.26 s +2026-04-12 01:49:24.261314: +2026-04-12 01:49:24.266021: Epoch 1373 +2026-04-12 01:49:24.269854: Current learning rate: 0.00685 +2026-04-12 01:51:07.746661: train_loss -0.2742 +2026-04-12 01:51:07.754583: val_loss -0.2136 +2026-04-12 01:51:07.757851: Pseudo dice [0.608, 0.8573, 0.638, 0.3323, 0.462, 0.8069, 0.7551] +2026-04-12 01:51:07.766457: Epoch time: 103.49 s +2026-04-12 01:51:08.980179: +2026-04-12 01:51:08.982928: Epoch 1374 +2026-04-12 01:51:08.986709: Current learning rate: 0.00685 +2026-04-12 01:52:51.727752: train_loss -0.2628 +2026-04-12 01:52:51.736443: val_loss -0.2516 +2026-04-12 01:52:51.740164: Pseudo dice [0.5225, 0.4294, 0.6753, 0.1468, 0.6307, 0.6285, 0.824] +2026-04-12 01:52:51.744361: Epoch time: 102.75 s +2026-04-12 01:52:52.928993: +2026-04-12 01:52:52.940835: Epoch 1375 +2026-04-12 01:52:52.944113: Current learning rate: 0.00684 +2026-04-12 01:54:37.935966: train_loss -0.2452 +2026-04-12 01:54:37.946245: val_loss -0.2359 +2026-04-12 01:54:37.963004: Pseudo dice [0.2257, 0.4317, 0.675, 0.6258, 0.487, 0.8298, 0.6517] +2026-04-12 01:54:37.969511: Epoch time: 105.01 s +2026-04-12 01:54:39.195315: +2026-04-12 01:54:39.199297: Epoch 1376 +2026-04-12 01:54:39.206038: Current learning rate: 0.00684 +2026-04-12 01:56:22.759632: train_loss -0.2644 +2026-04-12 01:56:22.769950: val_loss -0.2007 +2026-04-12 01:56:22.772889: Pseudo dice [0.7798, 0.8475, 0.7285, 0.2219, 0.2076, 0.9165, 0.6584] +2026-04-12 01:56:22.775361: Epoch time: 103.57 s +2026-04-12 01:56:24.010240: +2026-04-12 01:56:24.012952: Epoch 1377 +2026-04-12 01:56:24.015724: Current learning rate: 0.00684 +2026-04-12 01:58:08.056570: train_loss -0.267 +2026-04-12 01:58:08.076966: val_loss -0.2534 +2026-04-12 01:58:08.094209: Pseudo dice [0.7731, 0.5177, 0.8146, 0.5943, 0.3242, 0.9133, 0.7557] +2026-04-12 01:58:08.100800: Epoch time: 104.05 s +2026-04-12 01:58:09.322146: +2026-04-12 01:58:09.324842: Epoch 1378 +2026-04-12 01:58:09.327994: Current learning rate: 0.00684 +2026-04-12 01:59:53.210292: train_loss -0.2512 +2026-04-12 01:59:53.236997: val_loss -0.2263 +2026-04-12 01:59:53.240160: Pseudo dice [0.2888, 0.6662, 0.7232, 0.1566, 0.5516, 0.8038, 0.7437] +2026-04-12 01:59:53.243948: Epoch time: 103.89 s +2026-04-12 01:59:54.460403: +2026-04-12 01:59:54.462729: Epoch 1379 +2026-04-12 01:59:54.466114: Current learning rate: 0.00684 +2026-04-12 02:01:37.987412: train_loss -0.2624 +2026-04-12 02:01:37.996605: val_loss -0.1913 +2026-04-12 02:01:38.001005: Pseudo dice [0.5742, 0.2711, 0.7351, 0.3606, 0.3312, 0.4582, 0.3892] +2026-04-12 02:01:38.015854: Epoch time: 103.53 s +2026-04-12 02:01:39.237485: +2026-04-12 02:01:39.242826: Epoch 1380 +2026-04-12 02:01:39.248456: Current learning rate: 0.00683 +2026-04-12 02:03:23.743786: train_loss -0.2628 +2026-04-12 02:03:23.757470: val_loss -0.2255 +2026-04-12 02:03:23.762215: Pseudo dice [0.8269, 0.7397, 0.7794, 0.2666, 0.2886, 0.522, 0.6499] +2026-04-12 02:03:23.770568: Epoch time: 104.51 s +2026-04-12 02:03:25.040523: +2026-04-12 02:03:25.043166: Epoch 1381 +2026-04-12 02:03:25.045959: Current learning rate: 0.00683 +2026-04-12 02:05:08.723707: train_loss -0.2722 +2026-04-12 02:05:08.732407: val_loss -0.2278 +2026-04-12 02:05:08.734825: Pseudo dice [0.422, 0.7789, 0.7823, 0.4147, 0.4701, 0.6866, 0.4055] +2026-04-12 02:05:08.737479: Epoch time: 103.69 s +2026-04-12 02:05:09.965700: +2026-04-12 02:05:09.967815: Epoch 1382 +2026-04-12 02:05:09.970460: Current learning rate: 0.00683 +2026-04-12 02:06:53.521867: train_loss -0.2573 +2026-04-12 02:06:53.530658: val_loss -0.2084 +2026-04-12 02:06:53.533812: Pseudo dice [0.3764, 0.8575, 0.4749, 0.6042, 0.4239, 0.1968, 0.5688] +2026-04-12 02:06:53.536819: Epoch time: 103.56 s +2026-04-12 02:06:54.786483: +2026-04-12 02:06:54.789810: Epoch 1383 +2026-04-12 02:06:54.792239: Current learning rate: 0.00683 +2026-04-12 02:08:38.954096: train_loss -0.2672 +2026-04-12 02:08:38.965587: val_loss -0.2379 +2026-04-12 02:08:38.971124: Pseudo dice [0.7798, 0.5091, 0.8101, 0.6951, 0.528, 0.8556, 0.7999] +2026-04-12 02:08:38.974788: Epoch time: 104.17 s +2026-04-12 02:08:40.196903: +2026-04-12 02:08:40.201625: Epoch 1384 +2026-04-12 02:08:40.205338: Current learning rate: 0.00682 +2026-04-12 02:10:24.443442: train_loss -0.2666 +2026-04-12 02:10:24.454288: val_loss -0.1845 +2026-04-12 02:10:24.458286: Pseudo dice [0.8132, 0.8591, 0.64, 0.6317, 0.3027, 0.3445, 0.8279] +2026-04-12 02:10:24.461300: Epoch time: 104.25 s +2026-04-12 02:10:26.884292: +2026-04-12 02:10:26.886459: Epoch 1385 +2026-04-12 02:10:26.890932: Current learning rate: 0.00682 +2026-04-12 02:12:11.101072: train_loss -0.271 +2026-04-12 02:12:11.115712: val_loss -0.2132 +2026-04-12 02:12:11.119249: Pseudo dice [0.273, 0.3753, 0.781, 0.4116, 0.2703, 0.6985, 0.242] +2026-04-12 02:12:11.123569: Epoch time: 104.22 s +2026-04-12 02:12:12.304127: +2026-04-12 02:12:12.306523: Epoch 1386 +2026-04-12 02:12:12.312817: Current learning rate: 0.00682 +2026-04-12 02:13:56.344247: train_loss -0.2782 +2026-04-12 02:13:56.355181: val_loss -0.231 +2026-04-12 02:13:56.359592: Pseudo dice [0.655, 0.6902, 0.7947, 0.4953, 0.4401, 0.8385, 0.73] +2026-04-12 02:13:56.363589: Epoch time: 104.04 s +2026-04-12 02:13:57.656456: +2026-04-12 02:13:57.660251: Epoch 1387 +2026-04-12 02:13:57.663407: Current learning rate: 0.00682 +2026-04-12 02:15:41.300914: train_loss -0.2583 +2026-04-12 02:15:41.310467: val_loss -0.2206 +2026-04-12 02:15:41.312574: Pseudo dice [0.6165, 0.5215, 0.7339, 0.5231, 0.4437, 0.4962, 0.6141] +2026-04-12 02:15:41.317739: Epoch time: 103.65 s +2026-04-12 02:15:42.540134: +2026-04-12 02:15:42.542472: Epoch 1388 +2026-04-12 02:15:42.548147: Current learning rate: 0.00681 +2026-04-12 02:17:25.351712: train_loss -0.2418 +2026-04-12 02:17:25.360447: val_loss -0.1121 +2026-04-12 02:17:25.363081: Pseudo dice [0.6572, 0.8395, 0.2837, 0.2943, 0.3869, 0.2074, 0.7822] +2026-04-12 02:17:25.367683: Epoch time: 102.81 s +2026-04-12 02:17:26.562012: +2026-04-12 02:17:26.564332: Epoch 1389 +2026-04-12 02:17:26.566841: Current learning rate: 0.00681 +2026-04-12 02:19:09.455506: train_loss -0.2207 +2026-04-12 02:19:09.467257: val_loss -0.1851 +2026-04-12 02:19:09.471390: Pseudo dice [0.646, 0.4419, 0.6607, 0.3633, 0.3827, 0.4613, 0.6819] +2026-04-12 02:19:09.476050: Epoch time: 102.9 s +2026-04-12 02:19:10.680354: +2026-04-12 02:19:10.683581: Epoch 1390 +2026-04-12 02:19:10.687285: Current learning rate: 0.00681 +2026-04-12 02:20:54.853335: train_loss -0.2536 +2026-04-12 02:20:54.862420: val_loss -0.2219 +2026-04-12 02:20:54.865568: Pseudo dice [0.6143, 0.8673, 0.608, 0.269, 0.6186, 0.6942, 0.7351] +2026-04-12 02:20:54.868613: Epoch time: 104.18 s +2026-04-12 02:20:56.067066: +2026-04-12 02:20:56.069269: Epoch 1391 +2026-04-12 02:20:56.071934: Current learning rate: 0.00681 +2026-04-12 02:22:38.721274: train_loss -0.2243 +2026-04-12 02:22:38.734744: val_loss -0.1863 +2026-04-12 02:22:38.737823: Pseudo dice [0.8043, 0.4588, 0.6626, 0.197, 0.556, 0.2287, 0.697] +2026-04-12 02:22:38.741216: Epoch time: 102.66 s +2026-04-12 02:22:39.971598: +2026-04-12 02:22:39.973890: Epoch 1392 +2026-04-12 02:22:39.977021: Current learning rate: 0.0068 +2026-04-12 02:24:22.388598: train_loss -0.2226 +2026-04-12 02:24:22.396758: val_loss -0.2232 +2026-04-12 02:24:22.399736: Pseudo dice [0.7505, 0.3532, 0.7336, 0.5656, 0.4265, 0.7223, 0.5784] +2026-04-12 02:24:22.403220: Epoch time: 102.42 s +2026-04-12 02:24:23.600154: +2026-04-12 02:24:23.602963: Epoch 1393 +2026-04-12 02:24:23.605588: Current learning rate: 0.0068 +2026-04-12 02:26:07.269961: train_loss -0.2438 +2026-04-12 02:26:07.278095: val_loss -0.1994 +2026-04-12 02:26:07.281564: Pseudo dice [0.3645, 0.3805, 0.7357, 0.2107, 0.4583, 0.8208, 0.6149] +2026-04-12 02:26:07.285203: Epoch time: 103.67 s +2026-04-12 02:26:08.497232: +2026-04-12 02:26:08.500171: Epoch 1394 +2026-04-12 02:26:08.502990: Current learning rate: 0.0068 +2026-04-12 02:27:52.155026: train_loss -0.2509 +2026-04-12 02:27:52.163849: val_loss -0.2124 +2026-04-12 02:27:52.166125: Pseudo dice [0.8432, 0.5818, 0.7061, 0.1898, 0.5566, 0.6044, 0.6691] +2026-04-12 02:27:52.169862: Epoch time: 103.66 s +2026-04-12 02:27:53.384303: +2026-04-12 02:27:53.386643: Epoch 1395 +2026-04-12 02:27:53.389503: Current learning rate: 0.0068 +2026-04-12 02:29:35.705060: train_loss -0.27 +2026-04-12 02:29:35.713007: val_loss -0.2431 +2026-04-12 02:29:35.715515: Pseudo dice [0.643, 0.6037, 0.7864, 0.1373, 0.7057, 0.877, 0.8143] +2026-04-12 02:29:35.719132: Epoch time: 102.32 s +2026-04-12 02:29:36.929203: +2026-04-12 02:29:36.931759: Epoch 1396 +2026-04-12 02:29:36.935131: Current learning rate: 0.0068 +2026-04-12 02:31:20.006738: train_loss -0.2722 +2026-04-12 02:31:20.015059: val_loss -0.1915 +2026-04-12 02:31:20.017532: Pseudo dice [0.6871, 0.7287, 0.6243, 0.5108, 0.4484, 0.7935, 0.7185] +2026-04-12 02:31:20.020797: Epoch time: 103.08 s +2026-04-12 02:31:21.208339: +2026-04-12 02:31:21.211195: Epoch 1397 +2026-04-12 02:31:21.217203: Current learning rate: 0.00679 +2026-04-12 02:33:04.116172: train_loss -0.266 +2026-04-12 02:33:04.126585: val_loss -0.2481 +2026-04-12 02:33:04.131435: Pseudo dice [0.4896, 0.8305, 0.8396, 0.7389, 0.4127, 0.1564, 0.8389] +2026-04-12 02:33:04.134227: Epoch time: 102.91 s +2026-04-12 02:33:05.328880: +2026-04-12 02:33:05.330863: Epoch 1398 +2026-04-12 02:33:05.333154: Current learning rate: 0.00679 +2026-04-12 02:34:47.812094: train_loss -0.2534 +2026-04-12 02:34:47.820315: val_loss -0.2161 +2026-04-12 02:34:47.822613: Pseudo dice [0.5923, 0.8221, 0.6694, 0.4294, 0.312, 0.7313, 0.7973] +2026-04-12 02:34:47.825122: Epoch time: 102.49 s +2026-04-12 02:34:49.055248: +2026-04-12 02:34:49.057325: Epoch 1399 +2026-04-12 02:34:49.059698: Current learning rate: 0.00679 +2026-04-12 02:36:31.601256: train_loss -0.2609 +2026-04-12 02:36:31.607463: val_loss -0.218 +2026-04-12 02:36:31.609621: Pseudo dice [0.3925, 0.6383, 0.7701, 0.4625, 0.4506, 0.3428, 0.7615] +2026-04-12 02:36:31.612255: Epoch time: 102.55 s +2026-04-12 02:36:34.712220: +2026-04-12 02:36:34.715348: Epoch 1400 +2026-04-12 02:36:34.717256: Current learning rate: 0.00679 +2026-04-12 02:38:17.488088: train_loss -0.2534 +2026-04-12 02:38:17.497126: val_loss -0.175 +2026-04-12 02:38:17.499516: Pseudo dice [0.2077, 0.7678, 0.6666, 0.367, 0.3853, 0.5915, 0.4161] +2026-04-12 02:38:17.502113: Epoch time: 102.78 s +2026-04-12 02:38:18.697754: +2026-04-12 02:38:18.703868: Epoch 1401 +2026-04-12 02:38:18.706350: Current learning rate: 0.00678 +2026-04-12 02:40:00.930200: train_loss -0.2458 +2026-04-12 02:40:00.938183: val_loss -0.2168 +2026-04-12 02:40:00.940345: Pseudo dice [0.4446, 0.4575, 0.7986, 0.6421, 0.4972, 0.2299, 0.5891] +2026-04-12 02:40:00.943494: Epoch time: 102.24 s +2026-04-12 02:40:02.186214: +2026-04-12 02:40:02.188938: Epoch 1402 +2026-04-12 02:40:02.191321: Current learning rate: 0.00678 +2026-04-12 02:41:44.651212: train_loss -0.259 +2026-04-12 02:41:44.658335: val_loss -0.2213 +2026-04-12 02:41:44.660569: Pseudo dice [0.526, 0.5578, 0.6921, 0.4917, 0.4478, 0.8471, 0.7337] +2026-04-12 02:41:44.664828: Epoch time: 102.47 s +2026-04-12 02:41:45.879618: +2026-04-12 02:41:45.882191: Epoch 1403 +2026-04-12 02:41:45.884892: Current learning rate: 0.00678 +2026-04-12 02:43:28.390472: train_loss -0.2683 +2026-04-12 02:43:28.399447: val_loss -0.2351 +2026-04-12 02:43:28.401850: Pseudo dice [0.3983, 0.6707, 0.7599, 0.5585, 0.224, 0.9142, 0.7487] +2026-04-12 02:43:28.404512: Epoch time: 102.51 s +2026-04-12 02:43:29.602934: +2026-04-12 02:43:29.604802: Epoch 1404 +2026-04-12 02:43:29.606948: Current learning rate: 0.00678 +2026-04-12 02:45:12.501881: train_loss -0.258 +2026-04-12 02:45:12.509265: val_loss -0.2478 +2026-04-12 02:45:12.513301: Pseudo dice [0.5068, 0.3453, 0.8218, 0.5014, 0.5153, 0.7038, 0.8048] +2026-04-12 02:45:12.516021: Epoch time: 102.9 s +2026-04-12 02:45:14.802601: +2026-04-12 02:45:14.806130: Epoch 1405 +2026-04-12 02:45:14.808669: Current learning rate: 0.00677 +2026-04-12 02:46:56.761718: train_loss -0.2591 +2026-04-12 02:46:56.769385: val_loss -0.2106 +2026-04-12 02:46:56.772059: Pseudo dice [0.5642, 0.6568, 0.5798, 0.5369, 0.2814, 0.2826, 0.7865] +2026-04-12 02:46:56.774648: Epoch time: 101.96 s +2026-04-12 02:46:57.946200: +2026-04-12 02:46:57.949425: Epoch 1406 +2026-04-12 02:46:57.952760: Current learning rate: 0.00677 +2026-04-12 02:48:40.401989: train_loss -0.2654 +2026-04-12 02:48:40.411543: val_loss -0.2325 +2026-04-12 02:48:40.418015: Pseudo dice [0.6221, 0.5715, 0.7861, 0.7156, 0.4975, 0.8598, 0.8209] +2026-04-12 02:48:40.420944: Epoch time: 102.46 s +2026-04-12 02:48:41.592644: +2026-04-12 02:48:41.596375: Epoch 1407 +2026-04-12 02:48:41.599663: Current learning rate: 0.00677 +2026-04-12 02:50:23.911888: train_loss -0.2683 +2026-04-12 02:50:23.918332: val_loss -0.242 +2026-04-12 02:50:23.920837: Pseudo dice [0.713, 0.8655, 0.8122, 0.2431, 0.458, 0.6457, 0.745] +2026-04-12 02:50:23.923458: Epoch time: 102.32 s +2026-04-12 02:50:25.148241: +2026-04-12 02:50:25.150746: Epoch 1408 +2026-04-12 02:50:25.155462: Current learning rate: 0.00677 +2026-04-12 02:52:06.862759: train_loss -0.2738 +2026-04-12 02:52:06.871169: val_loss -0.2397 +2026-04-12 02:52:06.873560: Pseudo dice [0.7813, 0.2655, 0.7243, 0.5704, 0.5076, 0.543, 0.7479] +2026-04-12 02:52:06.876239: Epoch time: 101.72 s +2026-04-12 02:52:08.076283: +2026-04-12 02:52:08.078860: Epoch 1409 +2026-04-12 02:52:08.081243: Current learning rate: 0.00676 +2026-04-12 02:53:50.553066: train_loss -0.2619 +2026-04-12 02:53:50.561384: val_loss -0.207 +2026-04-12 02:53:50.564018: Pseudo dice [0.6162, 0.7335, 0.68, 0.3459, 0.5226, 0.8473, 0.6827] +2026-04-12 02:53:50.566662: Epoch time: 102.48 s +2026-04-12 02:53:51.757945: +2026-04-12 02:53:51.760031: Epoch 1410 +2026-04-12 02:53:51.762346: Current learning rate: 0.00676 +2026-04-12 02:55:34.374674: train_loss -0.2785 +2026-04-12 02:55:34.381388: val_loss -0.2196 +2026-04-12 02:55:34.383983: Pseudo dice [0.5664, 0.8508, 0.7581, 0.5288, 0.6392, 0.6725, 0.7834] +2026-04-12 02:55:34.387081: Epoch time: 102.62 s +2026-04-12 02:55:35.603090: +2026-04-12 02:55:35.605276: Epoch 1411 +2026-04-12 02:55:35.607321: Current learning rate: 0.00676 +2026-04-12 02:57:18.157969: train_loss -0.2745 +2026-04-12 02:57:18.165574: val_loss -0.2424 +2026-04-12 02:57:18.167575: Pseudo dice [0.6361, 0.7956, 0.7583, 0.5726, 0.5521, 0.6286, 0.6947] +2026-04-12 02:57:18.170100: Epoch time: 102.56 s +2026-04-12 02:57:19.361798: +2026-04-12 02:57:19.364383: Epoch 1412 +2026-04-12 02:57:19.366921: Current learning rate: 0.00676 +2026-04-12 02:59:01.649116: train_loss -0.2387 +2026-04-12 02:59:01.656527: val_loss -0.211 +2026-04-12 02:59:01.658345: Pseudo dice [0.2383, 0.7167, 0.7028, 0.4499, 0.527, 0.3034, 0.7641] +2026-04-12 02:59:01.662446: Epoch time: 102.29 s +2026-04-12 02:59:02.856414: +2026-04-12 02:59:02.859659: Epoch 1413 +2026-04-12 02:59:02.861458: Current learning rate: 0.00676 +2026-04-12 03:00:45.536103: train_loss -0.2621 +2026-04-12 03:00:45.544481: val_loss -0.2321 +2026-04-12 03:00:45.546737: Pseudo dice [0.6224, 0.8038, 0.6751, 0.8018, 0.4714, 0.9019, 0.7651] +2026-04-12 03:00:45.549478: Epoch time: 102.68 s +2026-04-12 03:00:46.735498: +2026-04-12 03:00:46.738143: Epoch 1414 +2026-04-12 03:00:46.741464: Current learning rate: 0.00675 +2026-04-12 03:02:29.043854: train_loss -0.2714 +2026-04-12 03:02:29.050359: val_loss -0.247 +2026-04-12 03:02:29.052797: Pseudo dice [0.6892, 0.546, 0.6254, 0.8789, 0.4754, 0.8693, 0.7978] +2026-04-12 03:02:29.056324: Epoch time: 102.31 s +2026-04-12 03:02:29.058842: Yayy! New best EMA pseudo Dice: 0.6227 +2026-04-12 03:02:31.694544: +2026-04-12 03:02:31.697082: Epoch 1415 +2026-04-12 03:02:31.698781: Current learning rate: 0.00675 +2026-04-12 03:04:14.817984: train_loss -0.2625 +2026-04-12 03:04:14.826996: val_loss -0.2065 +2026-04-12 03:04:14.829657: Pseudo dice [0.8809, 0.8947, 0.6678, 0.5503, 0.5092, 0.5645, 0.7179] +2026-04-12 03:04:14.832879: Epoch time: 103.13 s +2026-04-12 03:04:14.835493: Yayy! New best EMA pseudo Dice: 0.6288 +2026-04-12 03:04:17.887479: +2026-04-12 03:04:17.890750: Epoch 1416 +2026-04-12 03:04:17.892844: Current learning rate: 0.00675 +2026-04-12 03:06:00.390604: train_loss -0.2555 +2026-04-12 03:06:00.397802: val_loss -0.2169 +2026-04-12 03:06:00.400705: Pseudo dice [0.6519, 0.3599, 0.7114, 0.3331, 0.4473, 0.6412, 0.8067] +2026-04-12 03:06:00.405627: Epoch time: 102.51 s +2026-04-12 03:06:01.578472: +2026-04-12 03:06:01.581744: Epoch 1417 +2026-04-12 03:06:01.584370: Current learning rate: 0.00675 +2026-04-12 03:07:44.403230: train_loss -0.2692 +2026-04-12 03:07:44.413359: val_loss -0.2619 +2026-04-12 03:07:44.417009: Pseudo dice [0.7193, 0.4573, 0.8256, 0.9227, 0.4149, 0.6548, 0.5429] +2026-04-12 03:07:44.419688: Epoch time: 102.83 s +2026-04-12 03:07:45.663978: +2026-04-12 03:07:45.665858: Epoch 1418 +2026-04-12 03:07:45.668938: Current learning rate: 0.00674 +2026-04-12 03:09:27.987282: train_loss -0.2602 +2026-04-12 03:09:27.994516: val_loss -0.2021 +2026-04-12 03:09:27.997236: Pseudo dice [0.4423, 0.6082, 0.7451, 0.2978, 0.6369, 0.8914, 0.7801] +2026-04-12 03:09:28.002098: Epoch time: 102.33 s +2026-04-12 03:09:29.174166: +2026-04-12 03:09:29.178240: Epoch 1419 +2026-04-12 03:09:29.180535: Current learning rate: 0.00674 +2026-04-12 03:11:12.718775: train_loss -0.2655 +2026-04-12 03:11:12.726305: val_loss -0.2246 +2026-04-12 03:11:12.728864: Pseudo dice [0.6538, 0.817, 0.7532, 0.6386, 0.5002, 0.9047, 0.6971] +2026-04-12 03:11:12.732867: Epoch time: 103.55 s +2026-04-12 03:11:12.735369: Yayy! New best EMA pseudo Dice: 0.6337 +2026-04-12 03:11:15.756969: +2026-04-12 03:11:15.759964: Epoch 1420 +2026-04-12 03:11:15.761653: Current learning rate: 0.00674 +2026-04-12 03:12:58.271021: train_loss -0.2723 +2026-04-12 03:12:58.278188: val_loss -0.2197 +2026-04-12 03:12:58.280028: Pseudo dice [0.6195, 0.2398, 0.7773, 0.6481, 0.5729, 0.7592, 0.8361] +2026-04-12 03:12:58.282259: Epoch time: 102.52 s +2026-04-12 03:12:58.285087: Yayy! New best EMA pseudo Dice: 0.634 +2026-04-12 03:13:01.018850: +2026-04-12 03:13:01.021856: Epoch 1421 +2026-04-12 03:13:01.023483: Current learning rate: 0.00674 +2026-04-12 03:14:44.945745: train_loss -0.2703 +2026-04-12 03:14:44.955494: val_loss -0.2037 +2026-04-12 03:14:44.958013: Pseudo dice [0.7417, 0.7175, 0.6152, 0.2202, 0.5353, 0.6523, 0.4463] +2026-04-12 03:14:44.961004: Epoch time: 103.93 s +2026-04-12 03:14:46.151851: +2026-04-12 03:14:46.155859: Epoch 1422 +2026-04-12 03:14:46.158419: Current learning rate: 0.00673 +2026-04-12 03:16:28.430942: train_loss -0.2752 +2026-04-12 03:16:28.437852: val_loss -0.2394 +2026-04-12 03:16:28.441392: Pseudo dice [0.6863, 0.8929, 0.8323, 0.5637, 0.4716, 0.6907, 0.6424] +2026-04-12 03:16:28.444299: Epoch time: 102.28 s +2026-04-12 03:16:30.685361: +2026-04-12 03:16:30.687031: Epoch 1423 +2026-04-12 03:16:30.688858: Current learning rate: 0.00673 +2026-04-12 03:18:13.639752: train_loss -0.2682 +2026-04-12 03:18:13.645848: val_loss -0.1709 +2026-04-12 03:18:13.648776: Pseudo dice [0.6493, 0.1308, 0.667, 0.7334, 0.3274, 0.6328, 0.7655] +2026-04-12 03:18:13.652114: Epoch time: 102.96 s +2026-04-12 03:18:14.828872: +2026-04-12 03:18:14.831279: Epoch 1424 +2026-04-12 03:18:14.837531: Current learning rate: 0.00673 +2026-04-12 03:19:57.857792: train_loss -0.2703 +2026-04-12 03:19:57.865658: val_loss -0.256 +2026-04-12 03:19:57.867901: Pseudo dice [0.5552, 0.9089, 0.7133, 0.6211, 0.5776, 0.1625, 0.7333] +2026-04-12 03:19:57.870528: Epoch time: 103.03 s +2026-04-12 03:19:59.059716: +2026-04-12 03:19:59.064231: Epoch 1425 +2026-04-12 03:19:59.067318: Current learning rate: 0.00673 +2026-04-12 03:21:42.357300: train_loss -0.2542 +2026-04-12 03:21:42.364599: val_loss -0.2191 +2026-04-12 03:21:42.366978: Pseudo dice [0.2242, 0.8189, 0.7286, 0.37, 0.421, 0.5246, 0.8079] +2026-04-12 03:21:42.369965: Epoch time: 103.3 s +2026-04-12 03:21:43.557244: +2026-04-12 03:21:43.559386: Epoch 1426 +2026-04-12 03:21:43.562900: Current learning rate: 0.00673 +2026-04-12 03:23:25.406333: train_loss -0.2658 +2026-04-12 03:23:25.413877: val_loss -0.2316 +2026-04-12 03:23:25.416437: Pseudo dice [0.504, 0.7784, 0.7418, 0.6821, 0.5985, 0.5919, 0.4474] +2026-04-12 03:23:25.418803: Epoch time: 101.85 s +2026-04-12 03:23:26.646394: +2026-04-12 03:23:26.648867: Epoch 1427 +2026-04-12 03:23:26.652285: Current learning rate: 0.00672 +2026-04-12 03:25:09.030586: train_loss -0.2398 +2026-04-12 03:25:09.038236: val_loss -0.205 +2026-04-12 03:25:09.041047: Pseudo dice [0.4969, 0.8315, 0.7602, 0.2547, 0.4588, 0.1292, 0.7846] +2026-04-12 03:25:09.045141: Epoch time: 102.39 s +2026-04-12 03:25:10.238359: +2026-04-12 03:25:10.241128: Epoch 1428 +2026-04-12 03:25:10.243212: Current learning rate: 0.00672 +2026-04-12 03:26:55.302514: train_loss -0.253 +2026-04-12 03:26:55.316333: val_loss -0.2092 +2026-04-12 03:26:55.318856: Pseudo dice [0.5719, 0.5597, 0.562, 0.7384, 0.5262, 0.7216, 0.7459] +2026-04-12 03:26:55.321452: Epoch time: 105.07 s +2026-04-12 03:26:56.488995: +2026-04-12 03:26:56.492560: Epoch 1429 +2026-04-12 03:26:56.495231: Current learning rate: 0.00672 +2026-04-12 03:28:39.066533: train_loss -0.2682 +2026-04-12 03:28:39.075022: val_loss -0.1998 +2026-04-12 03:28:39.077722: Pseudo dice [0.5587, 0.7821, 0.7715, 0.8085, 0.514, 0.6457, 0.7349] +2026-04-12 03:28:39.080719: Epoch time: 102.58 s +2026-04-12 03:28:40.275651: +2026-04-12 03:28:40.278143: Epoch 1430 +2026-04-12 03:28:40.281264: Current learning rate: 0.00672 +2026-04-12 03:30:23.217246: train_loss -0.2766 +2026-04-12 03:30:23.226559: val_loss -0.211 +2026-04-12 03:30:23.229156: Pseudo dice [0.8042, 0.8065, 0.7695, 0.5203, 0.6355, 0.7645, 0.714] +2026-04-12 03:30:23.231725: Epoch time: 102.94 s +2026-04-12 03:30:24.453706: +2026-04-12 03:30:24.456190: Epoch 1431 +2026-04-12 03:30:24.458553: Current learning rate: 0.00671 +2026-04-12 03:32:08.644306: train_loss -0.2744 +2026-04-12 03:32:08.651041: val_loss -0.1701 +2026-04-12 03:32:08.653016: Pseudo dice [0.4641, 0.8964, 0.6522, 0.3874, 0.567, 0.1493, 0.5828] +2026-04-12 03:32:08.658128: Epoch time: 104.19 s +2026-04-12 03:32:09.824151: +2026-04-12 03:32:09.826643: Epoch 1432 +2026-04-12 03:32:09.828904: Current learning rate: 0.00671 +2026-04-12 03:33:52.639913: train_loss -0.2573 +2026-04-12 03:33:52.668424: val_loss -0.1959 +2026-04-12 03:33:52.670678: Pseudo dice [0.5705, 0.4756, 0.681, 0.5979, 0.4484, 0.361, 0.7598] +2026-04-12 03:33:52.673117: Epoch time: 102.82 s +2026-04-12 03:33:53.861990: +2026-04-12 03:33:53.864268: Epoch 1433 +2026-04-12 03:33:53.866504: Current learning rate: 0.00671 +2026-04-12 03:35:36.611059: train_loss -0.261 +2026-04-12 03:35:36.618963: val_loss -0.1449 +2026-04-12 03:35:36.621382: Pseudo dice [0.2211, 0.8882, 0.3963, 0.6213, 0.5939, 0.4525, 0.7127] +2026-04-12 03:35:36.623779: Epoch time: 102.75 s +2026-04-12 03:35:37.850027: +2026-04-12 03:35:37.852064: Epoch 1434 +2026-04-12 03:35:37.854384: Current learning rate: 0.00671 +2026-04-12 03:37:21.376930: train_loss -0.2515 +2026-04-12 03:37:21.384429: val_loss -0.2404 +2026-04-12 03:37:21.387138: Pseudo dice [0.3702, 0.6156, 0.7619, 0.4844, 0.4922, 0.4261, 0.6908] +2026-04-12 03:37:21.392080: Epoch time: 103.53 s +2026-04-12 03:37:22.589865: +2026-04-12 03:37:22.592339: Epoch 1435 +2026-04-12 03:37:22.596751: Current learning rate: 0.0067 +2026-04-12 03:39:04.527052: train_loss -0.2584 +2026-04-12 03:39:04.533856: val_loss -0.2329 +2026-04-12 03:39:04.535870: Pseudo dice [0.7856, 0.8641, 0.573, 0.5545, 0.2357, 0.8254, 0.8323] +2026-04-12 03:39:04.538237: Epoch time: 101.94 s +2026-04-12 03:39:05.742563: +2026-04-12 03:39:05.744423: Epoch 1436 +2026-04-12 03:39:05.746927: Current learning rate: 0.0067 +2026-04-12 03:40:48.181330: train_loss -0.2436 +2026-04-12 03:40:48.189904: val_loss -0.2094 +2026-04-12 03:40:48.191842: Pseudo dice [0.5418, 0.8384, 0.6307, 0.495, 0.3739, 0.715, 0.5017] +2026-04-12 03:40:48.195366: Epoch time: 102.44 s +2026-04-12 03:40:49.395461: +2026-04-12 03:40:49.397957: Epoch 1437 +2026-04-12 03:40:49.400172: Current learning rate: 0.0067 +2026-04-12 03:42:31.999282: train_loss -0.2491 +2026-04-12 03:42:32.006763: val_loss -0.2136 +2026-04-12 03:42:32.010096: Pseudo dice [0.4557, 0.3174, 0.6534, 0.7344, 0.4576, 0.4361, 0.8077] +2026-04-12 03:42:32.015655: Epoch time: 102.61 s +2026-04-12 03:42:33.184504: +2026-04-12 03:42:33.186708: Epoch 1438 +2026-04-12 03:42:33.190340: Current learning rate: 0.0067 +2026-04-12 03:44:15.067991: train_loss -0.2674 +2026-04-12 03:44:15.074444: val_loss -0.23 +2026-04-12 03:44:15.076516: Pseudo dice [0.2498, 0.3291, 0.6978, 0.559, 0.6353, 0.7707, 0.6263] +2026-04-12 03:44:15.078713: Epoch time: 101.89 s +2026-04-12 03:44:16.293040: +2026-04-12 03:44:16.294852: Epoch 1439 +2026-04-12 03:44:16.296764: Current learning rate: 0.00669 +2026-04-12 03:45:58.304564: train_loss -0.2628 +2026-04-12 03:45:58.312073: val_loss -0.2146 +2026-04-12 03:45:58.314593: Pseudo dice [0.5343, 0.3476, 0.682, 0.7596, 0.504, 0.7263, 0.5672] +2026-04-12 03:45:58.316667: Epoch time: 102.01 s +2026-04-12 03:45:59.508924: +2026-04-12 03:45:59.510839: Epoch 1440 +2026-04-12 03:45:59.512856: Current learning rate: 0.00669 +2026-04-12 03:47:42.930482: train_loss -0.2718 +2026-04-12 03:47:42.937755: val_loss -0.2303 +2026-04-12 03:47:42.940137: Pseudo dice [0.6645, 0.8091, 0.7291, 0.6149, 0.5291, 0.2257, 0.786] +2026-04-12 03:47:42.943648: Epoch time: 103.42 s +2026-04-12 03:47:44.192583: +2026-04-12 03:47:44.195060: Epoch 1441 +2026-04-12 03:47:44.197686: Current learning rate: 0.00669 +2026-04-12 03:49:26.556243: train_loss -0.2666 +2026-04-12 03:49:26.563564: val_loss -0.2345 +2026-04-12 03:49:26.565920: Pseudo dice [0.6554, 0.5534, 0.761, 0.3686, 0.5639, 0.8194, 0.7456] +2026-04-12 03:49:26.568759: Epoch time: 102.37 s +2026-04-12 03:49:27.778544: +2026-04-12 03:49:27.781056: Epoch 1442 +2026-04-12 03:49:27.784579: Current learning rate: 0.00669 +2026-04-12 03:51:10.097611: train_loss -0.2819 +2026-04-12 03:51:10.104284: val_loss -0.1917 +2026-04-12 03:51:10.106120: Pseudo dice [0.1268, 0.1243, 0.5872, 0.7031, 0.3728, 0.6695, 0.7523] +2026-04-12 03:51:10.108266: Epoch time: 102.32 s +2026-04-12 03:51:12.389712: +2026-04-12 03:51:12.391364: Epoch 1443 +2026-04-12 03:51:12.393196: Current learning rate: 0.00669 +2026-04-12 03:52:55.340906: train_loss -0.266 +2026-04-12 03:52:55.351162: val_loss -0.225 +2026-04-12 03:52:55.354563: Pseudo dice [0.6447, 0.8441, 0.7377, 0.4711, 0.1503, 0.8165, 0.5246] +2026-04-12 03:52:55.358737: Epoch time: 102.95 s +2026-04-12 03:52:56.532693: +2026-04-12 03:52:56.534599: Epoch 1444 +2026-04-12 03:52:56.536561: Current learning rate: 0.00668 +2026-04-12 03:54:38.154981: train_loss -0.2707 +2026-04-12 03:54:38.161268: val_loss -0.224 +2026-04-12 03:54:38.163331: Pseudo dice [0.4581, 0.6852, 0.795, 0.2714, 0.601, 0.7132, 0.7162] +2026-04-12 03:54:38.165574: Epoch time: 101.63 s +2026-04-12 03:54:39.343415: +2026-04-12 03:54:39.345210: Epoch 1445 +2026-04-12 03:54:39.347139: Current learning rate: 0.00668 +2026-04-12 03:56:22.281153: train_loss -0.2624 +2026-04-12 03:56:22.286922: val_loss -0.2461 +2026-04-12 03:56:22.288843: Pseudo dice [0.4303, 0.6984, 0.7035, 0.4865, 0.4636, 0.887, 0.721] +2026-04-12 03:56:22.290981: Epoch time: 102.94 s +2026-04-12 03:56:23.512897: +2026-04-12 03:56:23.514482: Epoch 1446 +2026-04-12 03:56:23.516348: Current learning rate: 0.00668 +2026-04-12 03:58:05.412145: train_loss -0.2533 +2026-04-12 03:58:05.418888: val_loss -0.2101 +2026-04-12 03:58:05.420981: Pseudo dice [0.3034, 0.3086, 0.7166, 0.7338, 0.5065, 0.6535, 0.7794] +2026-04-12 03:58:05.424684: Epoch time: 101.9 s +2026-04-12 03:58:06.605719: +2026-04-12 03:58:06.607785: Epoch 1447 +2026-04-12 03:58:06.610058: Current learning rate: 0.00668 +2026-04-12 03:59:49.179296: train_loss -0.2447 +2026-04-12 03:59:49.185333: val_loss -0.2 +2026-04-12 03:59:49.187346: Pseudo dice [0.2636, 0.3347, 0.5697, 0.5599, 0.4072, 0.8791, 0.5877] +2026-04-12 03:59:49.189643: Epoch time: 102.58 s +2026-04-12 03:59:50.381101: +2026-04-12 03:59:50.383411: Epoch 1448 +2026-04-12 03:59:50.385738: Current learning rate: 0.00667 +2026-04-12 04:01:32.574411: train_loss -0.2634 +2026-04-12 04:01:32.583364: val_loss -0.2294 +2026-04-12 04:01:32.586315: Pseudo dice [0.7977, 0.5868, 0.778, 0.3341, 0.3941, 0.8915, 0.7486] +2026-04-12 04:01:32.589175: Epoch time: 102.2 s +2026-04-12 04:01:33.817111: +2026-04-12 04:01:33.818761: Epoch 1449 +2026-04-12 04:01:33.820904: Current learning rate: 0.00667 +2026-04-12 04:03:16.458738: train_loss -0.2634 +2026-04-12 04:03:16.466379: val_loss -0.2063 +2026-04-12 04:03:16.469356: Pseudo dice [0.3855, 0.7407, 0.7424, 0.6482, 0.4469, 0.4417, 0.7537] +2026-04-12 04:03:16.472875: Epoch time: 102.64 s +2026-04-12 04:03:19.633778: +2026-04-12 04:03:19.636569: Epoch 1450 +2026-04-12 04:03:19.638199: Current learning rate: 0.00667 +2026-04-12 04:05:01.172302: train_loss -0.2728 +2026-04-12 04:05:01.179549: val_loss -0.2274 +2026-04-12 04:05:01.181828: Pseudo dice [0.4094, 0.33, 0.7664, 0.7643, 0.5792, 0.7803, 0.7653] +2026-04-12 04:05:01.185062: Epoch time: 101.54 s +2026-04-12 04:05:02.398769: +2026-04-12 04:05:02.401314: Epoch 1451 +2026-04-12 04:05:02.403918: Current learning rate: 0.00667 +2026-04-12 04:06:46.256217: train_loss -0.2694 +2026-04-12 04:06:46.264263: val_loss -0.2193 +2026-04-12 04:06:46.266605: Pseudo dice [0.3316, 0.4621, 0.7708, 0.4359, 0.301, 0.3443, 0.8529] +2026-04-12 04:06:46.269185: Epoch time: 103.86 s +2026-04-12 04:06:47.458654: +2026-04-12 04:06:47.460813: Epoch 1452 +2026-04-12 04:06:47.463196: Current learning rate: 0.00666 +2026-04-12 04:08:30.130557: train_loss -0.2489 +2026-04-12 04:08:30.138956: val_loss -0.2218 +2026-04-12 04:08:30.141326: Pseudo dice [0.3332, 0.7164, 0.7129, 0.3161, 0.5177, 0.6558, 0.7431] +2026-04-12 04:08:30.143636: Epoch time: 102.68 s +2026-04-12 04:08:31.368413: +2026-04-12 04:08:31.370223: Epoch 1453 +2026-04-12 04:08:31.372082: Current learning rate: 0.00666 +2026-04-12 04:10:13.697619: train_loss -0.2586 +2026-04-12 04:10:13.704136: val_loss -0.2011 +2026-04-12 04:10:13.707666: Pseudo dice [0.2215, 0.1174, 0.7103, 0.5465, 0.5988, 0.6337, 0.654] +2026-04-12 04:10:13.709987: Epoch time: 102.33 s +2026-04-12 04:10:14.934270: +2026-04-12 04:10:14.936661: Epoch 1454 +2026-04-12 04:10:14.939352: Current learning rate: 0.00666 +2026-04-12 04:11:57.263745: train_loss -0.2793 +2026-04-12 04:11:57.270845: val_loss -0.2471 +2026-04-12 04:11:57.273084: Pseudo dice [0.6908, 0.6324, 0.7982, 0.5404, 0.701, 0.5595, 0.5686] +2026-04-12 04:11:57.275995: Epoch time: 102.33 s +2026-04-12 04:11:58.461452: +2026-04-12 04:11:58.463310: Epoch 1455 +2026-04-12 04:11:58.465899: Current learning rate: 0.00666 +2026-04-12 04:13:41.602207: train_loss -0.2692 +2026-04-12 04:13:41.609949: val_loss -0.1825 +2026-04-12 04:13:41.612755: Pseudo dice [0.8033, 0.8405, 0.742, 0.117, 0.515, 0.4248, 0.4481] +2026-04-12 04:13:41.618741: Epoch time: 103.14 s +2026-04-12 04:13:42.828773: +2026-04-12 04:13:42.830708: Epoch 1456 +2026-04-12 04:13:42.833302: Current learning rate: 0.00665 +2026-04-12 04:15:25.111493: train_loss -0.2722 +2026-04-12 04:15:25.119701: val_loss -0.2513 +2026-04-12 04:15:25.121971: Pseudo dice [0.6169, 0.1605, 0.8411, 0.5122, 0.5224, 0.8488, 0.7978] +2026-04-12 04:15:25.131195: Epoch time: 102.29 s +2026-04-12 04:15:26.314026: +2026-04-12 04:15:26.316668: Epoch 1457 +2026-04-12 04:15:26.318828: Current learning rate: 0.00665 +2026-04-12 04:17:08.523216: train_loss -0.2493 +2026-04-12 04:17:08.530813: val_loss -0.2209 +2026-04-12 04:17:08.532660: Pseudo dice [0.4012, 0.4265, 0.6204, 0.3039, 0.4822, 0.7229, 0.815] +2026-04-12 04:17:08.535295: Epoch time: 102.21 s +2026-04-12 04:17:09.728658: +2026-04-12 04:17:09.730662: Epoch 1458 +2026-04-12 04:17:09.732652: Current learning rate: 0.00665 +2026-04-12 04:18:52.295926: train_loss -0.2564 +2026-04-12 04:18:52.302853: val_loss -0.1689 +2026-04-12 04:18:52.305741: Pseudo dice [0.5608, 0.8701, 0.7464, 0.2971, 0.3178, 0.6687, 0.2079] +2026-04-12 04:18:52.308220: Epoch time: 102.57 s +2026-04-12 04:18:53.496013: +2026-04-12 04:18:53.497893: Epoch 1459 +2026-04-12 04:18:53.500203: Current learning rate: 0.00665 +2026-04-12 04:20:35.715419: train_loss -0.2588 +2026-04-12 04:20:35.721663: val_loss -0.2168 +2026-04-12 04:20:35.723380: Pseudo dice [0.6944, 0.8723, 0.5699, 0.6408, 0.3484, 0.3228, 0.8387] +2026-04-12 04:20:35.726273: Epoch time: 102.22 s +2026-04-12 04:20:36.906763: +2026-04-12 04:20:36.908826: Epoch 1460 +2026-04-12 04:20:36.911514: Current learning rate: 0.00665 +2026-04-12 04:22:19.367664: train_loss -0.2659 +2026-04-12 04:22:19.375677: val_loss -0.2309 +2026-04-12 04:22:19.378300: Pseudo dice [0.6029, 0.8343, 0.7669, 0.6707, 0.613, 0.6816, 0.6981] +2026-04-12 04:22:19.381195: Epoch time: 102.46 s +2026-04-12 04:22:20.592338: +2026-04-12 04:22:20.595815: Epoch 1461 +2026-04-12 04:22:20.599178: Current learning rate: 0.00664 +2026-04-12 04:24:02.336511: train_loss -0.2465 +2026-04-12 04:24:02.342904: val_loss -0.198 +2026-04-12 04:24:02.345329: Pseudo dice [0.5299, 0.1507, 0.5949, 0.3259, 0.3147, 0.289, 0.5828] +2026-04-12 04:24:02.347971: Epoch time: 101.75 s +2026-04-12 04:24:03.533932: +2026-04-12 04:24:03.535845: Epoch 1462 +2026-04-12 04:24:03.538005: Current learning rate: 0.00664 +2026-04-12 04:25:45.527004: train_loss -0.261 +2026-04-12 04:25:45.535631: val_loss -0.208 +2026-04-12 04:25:45.539275: Pseudo dice [0.7268, 0.4852, 0.6752, 0.0867, 0.5409, 0.6837, 0.5855] +2026-04-12 04:25:45.541584: Epoch time: 102.0 s +2026-04-12 04:25:47.839513: +2026-04-12 04:25:47.840942: Epoch 1463 +2026-04-12 04:25:47.842573: Current learning rate: 0.00664 +2026-04-12 04:27:30.425119: train_loss -0.2698 +2026-04-12 04:27:30.431007: val_loss -0.2405 +2026-04-12 04:27:30.433175: Pseudo dice [0.6541, 0.1811, 0.6725, 0.8314, 0.4601, 0.3326, 0.8286] +2026-04-12 04:27:30.435515: Epoch time: 102.59 s +2026-04-12 04:27:31.690809: +2026-04-12 04:27:31.692741: Epoch 1464 +2026-04-12 04:27:31.694711: Current learning rate: 0.00664 +2026-04-12 04:29:13.454978: train_loss -0.2716 +2026-04-12 04:29:13.461660: val_loss -0.2202 +2026-04-12 04:29:13.464184: Pseudo dice [0.5223, 0.4137, 0.785, 0.3415, 0.4143, 0.8473, 0.6688] +2026-04-12 04:29:13.466877: Epoch time: 101.77 s +2026-04-12 04:29:14.659240: +2026-04-12 04:29:14.660922: Epoch 1465 +2026-04-12 04:29:14.664169: Current learning rate: 0.00663 +2026-04-12 04:30:56.674885: train_loss -0.2451 +2026-04-12 04:30:56.681819: val_loss -0.2081 +2026-04-12 04:30:56.683916: Pseudo dice [0.5262, 0.7452, 0.5766, 0.6675, 0.7194, 0.22, 0.8426] +2026-04-12 04:30:56.686128: Epoch time: 102.02 s +2026-04-12 04:30:57.850850: +2026-04-12 04:30:57.853627: Epoch 1466 +2026-04-12 04:30:57.857023: Current learning rate: 0.00663 +2026-04-12 04:32:41.158231: train_loss -0.2526 +2026-04-12 04:32:41.166331: val_loss -0.1961 +2026-04-12 04:32:41.168766: Pseudo dice [0.4931, 0.8627, 0.8146, 0.3943, 0.4434, 0.8272, 0.4093] +2026-04-12 04:32:41.171035: Epoch time: 103.31 s +2026-04-12 04:32:42.357957: +2026-04-12 04:32:42.360898: Epoch 1467 +2026-04-12 04:32:42.364704: Current learning rate: 0.00663 +2026-04-12 04:34:24.946994: train_loss -0.2426 +2026-04-12 04:34:24.979316: val_loss -0.2299 +2026-04-12 04:34:24.981559: Pseudo dice [0.5479, 0.4797, 0.7022, 0.5055, 0.6673, 0.7782, 0.7967] +2026-04-12 04:34:24.984164: Epoch time: 102.59 s +2026-04-12 04:34:26.162694: +2026-04-12 04:34:26.164704: Epoch 1468 +2026-04-12 04:34:26.166818: Current learning rate: 0.00663 +2026-04-12 04:36:08.780395: train_loss -0.2667 +2026-04-12 04:36:08.786945: val_loss -0.2162 +2026-04-12 04:36:08.789049: Pseudo dice [0.1585, 0.8887, 0.6909, 0.4579, 0.4729, 0.1599, 0.8241] +2026-04-12 04:36:08.791332: Epoch time: 102.62 s +2026-04-12 04:36:09.973910: +2026-04-12 04:36:09.977620: Epoch 1469 +2026-04-12 04:36:09.979947: Current learning rate: 0.00662 +2026-04-12 04:37:53.034286: train_loss -0.2766 +2026-04-12 04:37:53.042668: val_loss -0.2144 +2026-04-12 04:37:53.044885: Pseudo dice [0.4993, 0.5827, 0.6019, 0.6451, 0.5296, 0.6746, 0.7336] +2026-04-12 04:37:53.048339: Epoch time: 103.06 s +2026-04-12 04:37:54.253227: +2026-04-12 04:37:54.255192: Epoch 1470 +2026-04-12 04:37:54.257309: Current learning rate: 0.00662 +2026-04-12 04:39:36.129944: train_loss -0.263 +2026-04-12 04:39:36.136513: val_loss -0.2086 +2026-04-12 04:39:36.138906: Pseudo dice [0.541, 0.5158, 0.6784, 0.3847, 0.5361, 0.9428, 0.8379] +2026-04-12 04:39:36.141149: Epoch time: 101.88 s +2026-04-12 04:39:37.329986: +2026-04-12 04:39:37.331883: Epoch 1471 +2026-04-12 04:39:37.335292: Current learning rate: 0.00662 +2026-04-12 04:41:19.844484: train_loss -0.2676 +2026-04-12 04:41:19.854356: val_loss -0.2212 +2026-04-12 04:41:19.857720: Pseudo dice [0.5709, 0.8362, 0.728, 0.8604, 0.146, 0.775, 0.7872] +2026-04-12 04:41:19.860604: Epoch time: 102.52 s +2026-04-12 04:41:21.079974: +2026-04-12 04:41:21.082861: Epoch 1472 +2026-04-12 04:41:21.086886: Current learning rate: 0.00662 +2026-04-12 04:43:03.783135: train_loss -0.2484 +2026-04-12 04:43:03.790717: val_loss -0.1579 +2026-04-12 04:43:03.793387: Pseudo dice [0.4759, 0.9027, 0.6382, 0.1229, 0.435, 0.0857, 0.6254] +2026-04-12 04:43:03.796478: Epoch time: 102.71 s +2026-04-12 04:43:04.999495: +2026-04-12 04:43:05.001570: Epoch 1473 +2026-04-12 04:43:05.003711: Current learning rate: 0.00661 +2026-04-12 04:44:47.160367: train_loss -0.2611 +2026-04-12 04:44:47.168884: val_loss -0.2377 +2026-04-12 04:44:47.171032: Pseudo dice [0.3493, 0.5735, 0.7887, 0.5616, 0.5528, 0.5639, 0.6373] +2026-04-12 04:44:47.173624: Epoch time: 102.16 s +2026-04-12 04:44:48.343642: +2026-04-12 04:44:48.345178: Epoch 1474 +2026-04-12 04:44:48.347046: Current learning rate: 0.00661 +2026-04-12 04:46:31.540019: train_loss -0.2601 +2026-04-12 04:46:31.548878: val_loss -0.1794 +2026-04-12 04:46:31.551891: Pseudo dice [0.5455, 0.8784, 0.3834, 0.2416, 0.4456, 0.3404, 0.6858] +2026-04-12 04:46:31.554224: Epoch time: 103.2 s +2026-04-12 04:46:32.728094: +2026-04-12 04:46:32.730731: Epoch 1475 +2026-04-12 04:46:32.733256: Current learning rate: 0.00661 +2026-04-12 04:48:15.450259: train_loss -0.2608 +2026-04-12 04:48:15.456831: val_loss -0.2272 +2026-04-12 04:48:15.458924: Pseudo dice [0.0872, 0.657, 0.7717, 0.2764, 0.4394, 0.852, 0.7083] +2026-04-12 04:48:15.462502: Epoch time: 102.73 s +2026-04-12 04:48:16.642164: +2026-04-12 04:48:16.645031: Epoch 1476 +2026-04-12 04:48:16.646751: Current learning rate: 0.00661 +2026-04-12 04:49:58.692786: train_loss -0.2648 +2026-04-12 04:49:58.699347: val_loss -0.2305 +2026-04-12 04:49:58.701501: Pseudo dice [0.579, 0.3637, 0.756, 0.3004, 0.5265, 0.8431, 0.7478] +2026-04-12 04:49:58.703584: Epoch time: 102.05 s +2026-04-12 04:49:59.890937: +2026-04-12 04:49:59.893840: Epoch 1477 +2026-04-12 04:49:59.897215: Current learning rate: 0.0066 +2026-04-12 04:51:43.145515: train_loss -0.2547 +2026-04-12 04:51:43.151486: val_loss -0.2114 +2026-04-12 04:51:43.153519: Pseudo dice [0.2871, 0.8668, 0.6787, 0.454, 0.2674, 0.8442, 0.5209] +2026-04-12 04:51:43.155675: Epoch time: 103.26 s +2026-04-12 04:51:44.332489: +2026-04-12 04:51:44.333997: Epoch 1478 +2026-04-12 04:51:44.335946: Current learning rate: 0.0066 +2026-04-12 04:53:27.183060: train_loss -0.2634 +2026-04-12 04:53:27.191119: val_loss -0.2078 +2026-04-12 04:53:27.193591: Pseudo dice [0.6065, 0.8676, 0.783, 0.058, 0.4918, 0.7788, 0.6364] +2026-04-12 04:53:27.198362: Epoch time: 102.85 s +2026-04-12 04:53:28.400037: +2026-04-12 04:53:28.402650: Epoch 1479 +2026-04-12 04:53:28.404667: Current learning rate: 0.0066 +2026-04-12 04:55:10.721494: train_loss -0.284 +2026-04-12 04:55:10.728199: val_loss -0.2368 +2026-04-12 04:55:10.729973: Pseudo dice [0.6983, 0.2744, 0.7801, 0.5965, 0.6436, 0.4364, 0.699] +2026-04-12 04:55:10.732403: Epoch time: 102.32 s +2026-04-12 04:55:11.921768: +2026-04-12 04:55:11.925114: Epoch 1480 +2026-04-12 04:55:11.928256: Current learning rate: 0.0066 +2026-04-12 04:56:54.215757: train_loss -0.2861 +2026-04-12 04:56:54.221929: val_loss -0.2441 +2026-04-12 04:56:54.223889: Pseudo dice [0.7584, 0.4292, 0.7355, 0.6017, 0.4136, 0.8159, 0.7886] +2026-04-12 04:56:54.226396: Epoch time: 102.3 s +2026-04-12 04:56:55.413982: +2026-04-12 04:56:55.416371: Epoch 1481 +2026-04-12 04:56:55.418594: Current learning rate: 0.0066 +2026-04-12 04:58:37.797344: train_loss -0.2781 +2026-04-12 04:58:37.806299: val_loss -0.22 +2026-04-12 04:58:37.808856: Pseudo dice [0.3559, 0.8836, 0.7373, 0.5575, 0.4601, 0.3182, 0.8272] +2026-04-12 04:58:37.812574: Epoch time: 102.39 s +2026-04-12 04:58:39.047950: +2026-04-12 04:58:39.050261: Epoch 1482 +2026-04-12 04:58:39.052816: Current learning rate: 0.00659 +2026-04-12 05:00:20.968981: train_loss -0.2698 +2026-04-12 05:00:20.974936: val_loss -0.2121 +2026-04-12 05:00:20.976699: Pseudo dice [0.3035, 0.7731, 0.8247, 0.3696, 0.5481, 0.2106, 0.7659] +2026-04-12 05:00:20.979255: Epoch time: 101.92 s +2026-04-12 05:00:23.233233: +2026-04-12 05:00:23.235110: Epoch 1483 +2026-04-12 05:00:23.237059: Current learning rate: 0.00659 +2026-04-12 05:02:05.649721: train_loss -0.2765 +2026-04-12 05:02:05.656439: val_loss -0.2175 +2026-04-12 05:02:05.658588: Pseudo dice [0.3777, 0.8801, 0.675, 0.5121, 0.6223, 0.1265, 0.7735] +2026-04-12 05:02:05.660821: Epoch time: 102.42 s +2026-04-12 05:02:06.860566: +2026-04-12 05:02:06.863187: Epoch 1484 +2026-04-12 05:02:06.868978: Current learning rate: 0.00659 +2026-04-12 05:03:49.411165: train_loss -0.2721 +2026-04-12 05:03:49.419732: val_loss -0.1916 +2026-04-12 05:03:49.421708: Pseudo dice [0.6303, 0.8858, 0.6589, 0.0545, 0.598, 0.3974, 0.1138] +2026-04-12 05:03:49.424466: Epoch time: 102.55 s +2026-04-12 05:03:50.627963: +2026-04-12 05:03:50.629884: Epoch 1485 +2026-04-12 05:03:50.631962: Current learning rate: 0.00659 +2026-04-12 05:05:33.257799: train_loss -0.2699 +2026-04-12 05:05:33.264406: val_loss -0.2083 +2026-04-12 05:05:33.266199: Pseudo dice [0.8335, 0.8714, 0.765, 0.2025, 0.3729, 0.7351, 0.6394] +2026-04-12 05:05:33.268739: Epoch time: 102.63 s +2026-04-12 05:05:34.430115: +2026-04-12 05:05:34.431583: Epoch 1486 +2026-04-12 05:05:34.433265: Current learning rate: 0.00658 +2026-04-12 05:07:16.954029: train_loss -0.2684 +2026-04-12 05:07:16.961474: val_loss -0.2556 +2026-04-12 05:07:16.963274: Pseudo dice [0.827, 0.5935, 0.8551, 0.5155, 0.5389, 0.8805, 0.8366] +2026-04-12 05:07:16.966030: Epoch time: 102.53 s +2026-04-12 05:07:18.142992: +2026-04-12 05:07:18.145499: Epoch 1487 +2026-04-12 05:07:18.147726: Current learning rate: 0.00658 +2026-04-12 05:09:00.774632: train_loss -0.2698 +2026-04-12 05:09:00.780239: val_loss -0.2016 +2026-04-12 05:09:00.782601: Pseudo dice [0.0792, 0.2798, 0.7869, 0.2015, 0.2537, 0.2115, 0.5499] +2026-04-12 05:09:00.784815: Epoch time: 102.63 s +2026-04-12 05:09:01.966006: +2026-04-12 05:09:01.967667: Epoch 1488 +2026-04-12 05:09:01.970158: Current learning rate: 0.00658 +2026-04-12 05:10:44.177145: train_loss -0.2632 +2026-04-12 05:10:44.185010: val_loss -0.1693 +2026-04-12 05:10:44.187550: Pseudo dice [0.5656, 0.6159, 0.528, 0.6204, 0.5058, 0.8816, 0.7925] +2026-04-12 05:10:44.190099: Epoch time: 102.21 s +2026-04-12 05:10:45.372054: +2026-04-12 05:10:45.373798: Epoch 1489 +2026-04-12 05:10:45.375813: Current learning rate: 0.00658 +2026-04-12 05:12:27.452152: train_loss -0.2583 +2026-04-12 05:12:27.459390: val_loss -0.2124 +2026-04-12 05:12:27.461462: Pseudo dice [0.8311, 0.6585, 0.6467, 0.5369, 0.3162, 0.8679, 0.7703] +2026-04-12 05:12:27.464271: Epoch time: 102.08 s +2026-04-12 05:12:28.729977: +2026-04-12 05:12:28.733469: Epoch 1490 +2026-04-12 05:12:28.735748: Current learning rate: 0.00657 +2026-04-12 05:14:10.988642: train_loss -0.2596 +2026-04-12 05:14:10.994588: val_loss -0.1811 +2026-04-12 05:14:10.996932: Pseudo dice [0.5848, 0.7245, 0.5268, 0.6181, 0.3595, 0.7034, 0.7438] +2026-04-12 05:14:10.999162: Epoch time: 102.26 s +2026-04-12 05:14:12.218767: +2026-04-12 05:14:12.220723: Epoch 1491 +2026-04-12 05:14:12.222939: Current learning rate: 0.00657 +2026-04-12 05:15:54.201070: train_loss -0.2529 +2026-04-12 05:15:54.208544: val_loss -0.2404 +2026-04-12 05:15:54.210703: Pseudo dice [0.4917, 0.3749, 0.8682, 0.0912, 0.423, 0.6053, 0.7712] +2026-04-12 05:15:54.213630: Epoch time: 101.99 s +2026-04-12 05:15:55.415793: +2026-04-12 05:15:55.418720: Epoch 1492 +2026-04-12 05:15:55.421966: Current learning rate: 0.00657 +2026-04-12 05:17:37.874906: train_loss -0.2669 +2026-04-12 05:17:37.882953: val_loss -0.2237 +2026-04-12 05:17:37.885073: Pseudo dice [0.6484, 0.2676, 0.796, 0.1, 0.5609, 0.8972, 0.6702] +2026-04-12 05:17:37.887204: Epoch time: 102.46 s +2026-04-12 05:17:39.061352: +2026-04-12 05:17:39.063505: Epoch 1493 +2026-04-12 05:17:39.065976: Current learning rate: 0.00657 +2026-04-12 05:19:21.259711: train_loss -0.2638 +2026-04-12 05:19:21.267370: val_loss -0.1543 +2026-04-12 05:19:21.269565: Pseudo dice [0.5194, 0.8511, 0.1802, 0.5204, 0.2535, 0.7506, 0.7812] +2026-04-12 05:19:21.272924: Epoch time: 102.2 s +2026-04-12 05:19:22.466052: +2026-04-12 05:19:22.468270: Epoch 1494 +2026-04-12 05:19:22.470446: Current learning rate: 0.00656 +2026-04-12 05:21:04.416882: train_loss -0.2526 +2026-04-12 05:21:04.424354: val_loss -0.2503 +2026-04-12 05:21:04.426338: Pseudo dice [0.6636, 0.7105, 0.7916, 0.2271, 0.3738, 0.9436, 0.7037] +2026-04-12 05:21:04.428682: Epoch time: 101.95 s +2026-04-12 05:21:05.630693: +2026-04-12 05:21:05.632467: Epoch 1495 +2026-04-12 05:21:05.634748: Current learning rate: 0.00656 +2026-04-12 05:22:47.856572: train_loss -0.2539 +2026-04-12 05:22:47.862508: val_loss -0.2111 +2026-04-12 05:22:47.865890: Pseudo dice [0.5371, 0.8651, 0.6835, 0.3236, 0.5295, 0.3173, 0.7585] +2026-04-12 05:22:47.868796: Epoch time: 102.23 s +2026-04-12 05:22:49.073375: +2026-04-12 05:22:49.075679: Epoch 1496 +2026-04-12 05:22:49.077144: Current learning rate: 0.00656 +2026-04-12 05:24:30.866202: train_loss -0.2507 +2026-04-12 05:24:30.873990: val_loss -0.2351 +2026-04-12 05:24:30.876376: Pseudo dice [0.5151, 0.8966, 0.7523, 0.4554, 0.575, 0.7114, 0.7327] +2026-04-12 05:24:30.879138: Epoch time: 101.8 s +2026-04-12 05:24:32.095730: +2026-04-12 05:24:32.101648: Epoch 1497 +2026-04-12 05:24:32.107554: Current learning rate: 0.00656 +2026-04-12 05:26:13.722372: train_loss -0.2719 +2026-04-12 05:26:13.729001: val_loss -0.2498 +2026-04-12 05:26:13.731329: Pseudo dice [0.4422, 0.8937, 0.7731, 0.4218, 0.3781, 0.4327, 0.7863] +2026-04-12 05:26:13.733560: Epoch time: 101.63 s +2026-04-12 05:26:14.918698: +2026-04-12 05:26:14.920274: Epoch 1498 +2026-04-12 05:26:14.922132: Current learning rate: 0.00656 +2026-04-12 05:27:57.171362: train_loss -0.275 +2026-04-12 05:27:57.177706: val_loss -0.218 +2026-04-12 05:27:57.179616: Pseudo dice [0.7481, 0.8215, 0.5855, 0.4792, 0.4492, 0.2879, 0.6665] +2026-04-12 05:27:57.181817: Epoch time: 102.26 s +2026-04-12 05:27:58.367844: +2026-04-12 05:27:58.369518: Epoch 1499 +2026-04-12 05:27:58.371387: Current learning rate: 0.00655 +2026-04-12 05:29:40.544274: train_loss -0.278 +2026-04-12 05:29:40.551868: val_loss -0.1786 +2026-04-12 05:29:40.554157: Pseudo dice [0.3325, 0.894, 0.6001, 0.6561, 0.5072, 0.0841, 0.8441] +2026-04-12 05:29:40.556496: Epoch time: 102.18 s +2026-04-12 05:29:43.515785: +2026-04-12 05:29:43.518008: Epoch 1500 +2026-04-12 05:29:43.519593: Current learning rate: 0.00655 +2026-04-12 05:31:25.445282: train_loss -0.2703 +2026-04-12 05:31:25.452835: val_loss -0.2177 +2026-04-12 05:31:25.455402: Pseudo dice [0.699, 0.6945, 0.7735, 0.6231, 0.618, 0.8161, 0.5678] +2026-04-12 05:31:25.457937: Epoch time: 101.93 s +2026-04-12 05:31:26.701264: +2026-04-12 05:31:26.703363: Epoch 1501 +2026-04-12 05:31:26.705448: Current learning rate: 0.00655 +2026-04-12 05:33:08.566893: train_loss -0.2598 +2026-04-12 05:33:08.574650: val_loss -0.1588 +2026-04-12 05:33:08.576893: Pseudo dice [0.4038, 0.8469, 0.6687, 0.2334, 0.38, 0.244, 0.2917] +2026-04-12 05:33:08.579894: Epoch time: 101.87 s +2026-04-12 05:33:09.764669: +2026-04-12 05:33:09.766451: Epoch 1502 +2026-04-12 05:33:09.768304: Current learning rate: 0.00655 +2026-04-12 05:34:52.818760: train_loss -0.2557 +2026-04-12 05:34:52.847095: val_loss -0.1723 +2026-04-12 05:34:52.849962: Pseudo dice [0.543, 0.735, 0.6555, 0.4774, 0.6158, 0.3284, 0.3349] +2026-04-12 05:34:52.852559: Epoch time: 103.06 s +2026-04-12 05:34:55.111538: +2026-04-12 05:34:55.113358: Epoch 1503 +2026-04-12 05:34:55.115215: Current learning rate: 0.00654 +2026-04-12 05:36:36.867916: train_loss -0.244 +2026-04-12 05:36:36.885664: val_loss -0.2174 +2026-04-12 05:36:36.887951: Pseudo dice [0.4147, 0.1804, 0.6436, 0.8888, 0.6089, 0.4503, 0.8533] +2026-04-12 05:36:36.890208: Epoch time: 101.76 s +2026-04-12 05:36:38.088570: +2026-04-12 05:36:38.090789: Epoch 1504 +2026-04-12 05:36:38.093521: Current learning rate: 0.00654 +2026-04-12 05:38:19.981930: train_loss -0.2617 +2026-04-12 05:38:19.989447: val_loss -0.2248 +2026-04-12 05:38:19.992086: Pseudo dice [0.2897, 0.8269, 0.7413, 0.8876, 0.5181, 0.6098, 0.3249] +2026-04-12 05:38:19.994982: Epoch time: 101.9 s +2026-04-12 05:38:21.182422: +2026-04-12 05:38:21.184664: Epoch 1505 +2026-04-12 05:38:21.191215: Current learning rate: 0.00654 +2026-04-12 05:40:03.493723: train_loss -0.2474 +2026-04-12 05:40:03.500511: val_loss -0.1977 +2026-04-12 05:40:03.504395: Pseudo dice [0.4167, 0.5913, 0.6638, 0.5027, 0.4176, 0.8737, 0.6775] +2026-04-12 05:40:03.506721: Epoch time: 102.31 s +2026-04-12 05:40:04.689620: +2026-04-12 05:40:04.691667: Epoch 1506 +2026-04-12 05:40:04.694721: Current learning rate: 0.00654 +2026-04-12 05:41:46.517552: train_loss -0.2397 +2026-04-12 05:41:46.524912: val_loss -0.185 +2026-04-12 05:41:46.527049: Pseudo dice [0.5873, 0.7873, 0.552, 0.5539, 0.2961, 0.5882, 0.8263] +2026-04-12 05:41:46.529986: Epoch time: 101.83 s +2026-04-12 05:41:47.704891: +2026-04-12 05:41:47.706904: Epoch 1507 +2026-04-12 05:41:47.708679: Current learning rate: 0.00653 +2026-04-12 05:43:30.271024: train_loss -0.2577 +2026-04-12 05:43:30.277349: val_loss -0.2179 +2026-04-12 05:43:30.279736: Pseudo dice [0.3063, 0.2462, 0.8073, 0.3601, 0.4934, 0.5105, 0.7601] +2026-04-12 05:43:30.282513: Epoch time: 102.57 s +2026-04-12 05:43:31.481026: +2026-04-12 05:43:31.483125: Epoch 1508 +2026-04-12 05:43:31.485571: Current learning rate: 0.00653 +2026-04-12 05:45:13.793108: train_loss -0.2625 +2026-04-12 05:45:13.802058: val_loss -0.18 +2026-04-12 05:45:13.804425: Pseudo dice [0.6666, 0.8594, 0.6924, 0.3899, 0.1191, 0.0648, 0.3338] +2026-04-12 05:45:13.808721: Epoch time: 102.32 s +2026-04-12 05:45:14.980969: +2026-04-12 05:45:14.984150: Epoch 1509 +2026-04-12 05:45:14.989275: Current learning rate: 0.00653 +2026-04-12 05:46:57.048054: train_loss -0.2495 +2026-04-12 05:46:57.055583: val_loss -0.1744 +2026-04-12 05:46:57.058157: Pseudo dice [0.25, 0.732, 0.4475, 0.1056, 0.3517, 0.7544, 0.7898] +2026-04-12 05:46:57.060878: Epoch time: 102.07 s +2026-04-12 05:46:58.245323: +2026-04-12 05:46:58.247849: Epoch 1510 +2026-04-12 05:46:58.250448: Current learning rate: 0.00653 +2026-04-12 05:48:40.209669: train_loss -0.231 +2026-04-12 05:48:40.228739: val_loss -0.1802 +2026-04-12 05:48:40.232109: Pseudo dice [0.3366, 0.2283, 0.695, 0.1745, 0.5001, 0.5991, 0.4174] +2026-04-12 05:48:40.234779: Epoch time: 101.97 s +2026-04-12 05:48:41.442321: +2026-04-12 05:48:41.444707: Epoch 1511 +2026-04-12 05:48:41.446681: Current learning rate: 0.00652 +2026-04-12 05:50:23.506707: train_loss -0.2587 +2026-04-12 05:50:23.513257: val_loss -0.2254 +2026-04-12 05:50:23.515333: Pseudo dice [0.5066, 0.6402, 0.6974, 0.4936, 0.5479, 0.7046, 0.838] +2026-04-12 05:50:23.518296: Epoch time: 102.07 s +2026-04-12 05:50:24.713826: +2026-04-12 05:50:24.715892: Epoch 1512 +2026-04-12 05:50:24.720513: Current learning rate: 0.00652 +2026-04-12 05:52:06.936133: train_loss -0.2694 +2026-04-12 05:52:06.943411: val_loss -0.226 +2026-04-12 05:52:06.945774: Pseudo dice [0.4396, 0.5766, 0.8233, 0.4088, 0.5861, 0.8884, 0.7177] +2026-04-12 05:52:06.947765: Epoch time: 102.23 s +2026-04-12 05:52:08.149002: +2026-04-12 05:52:08.150850: Epoch 1513 +2026-04-12 05:52:08.153914: Current learning rate: 0.00652 +2026-04-12 05:53:50.487600: train_loss -0.2731 +2026-04-12 05:53:50.495167: val_loss -0.2289 +2026-04-12 05:53:50.497823: Pseudo dice [0.5468, 0.7028, 0.7462, 0.4244, 0.5739, 0.4899, 0.4382] +2026-04-12 05:53:50.500607: Epoch time: 102.34 s +2026-04-12 05:53:51.700479: +2026-04-12 05:53:51.708107: Epoch 1514 +2026-04-12 05:53:51.723191: Current learning rate: 0.00652 +2026-04-12 05:55:34.786707: train_loss -0.2502 +2026-04-12 05:55:34.795042: val_loss -0.2196 +2026-04-12 05:55:34.797374: Pseudo dice [0.6807, 0.7669, 0.7408, 0.615, 0.1913, 0.6124, 0.8194] +2026-04-12 05:55:34.799813: Epoch time: 103.09 s +2026-04-12 05:55:35.989526: +2026-04-12 05:55:35.991349: Epoch 1515 +2026-04-12 05:55:35.993995: Current learning rate: 0.00652 +2026-04-12 05:57:17.601626: train_loss -0.2679 +2026-04-12 05:57:17.611805: val_loss -0.2441 +2026-04-12 05:57:17.614226: Pseudo dice [0.273, 0.8601, 0.8573, 0.6597, 0.3011, 0.6991, 0.502] +2026-04-12 05:57:17.616845: Epoch time: 101.62 s +2026-04-12 05:57:18.789496: +2026-04-12 05:57:18.791257: Epoch 1516 +2026-04-12 05:57:18.793177: Current learning rate: 0.00651 +2026-04-12 05:59:01.239199: train_loss -0.2625 +2026-04-12 05:59:01.245918: val_loss -0.2266 +2026-04-12 05:59:01.248193: Pseudo dice [0.5621, 0.2208, 0.7115, 0.4804, 0.4937, 0.8176, 0.7958] +2026-04-12 05:59:01.250837: Epoch time: 102.45 s +2026-04-12 05:59:02.440766: +2026-04-12 05:59:02.442584: Epoch 1517 +2026-04-12 05:59:02.445118: Current learning rate: 0.00651 +2026-04-12 06:00:44.303261: train_loss -0.2668 +2026-04-12 06:00:44.311055: val_loss -0.2264 +2026-04-12 06:00:44.313257: Pseudo dice [0.5632, 0.6423, 0.642, 0.6769, 0.5179, 0.1701, 0.6684] +2026-04-12 06:00:44.315972: Epoch time: 101.87 s +2026-04-12 06:00:45.536078: +2026-04-12 06:00:45.537784: Epoch 1518 +2026-04-12 06:00:45.539567: Current learning rate: 0.00651 +2026-04-12 06:02:28.106434: train_loss -0.2791 +2026-04-12 06:02:28.119057: val_loss -0.2144 +2026-04-12 06:02:28.120905: Pseudo dice [0.7055, 0.8898, 0.7349, 0.2474, 0.4494, 0.6205, 0.8676] +2026-04-12 06:02:28.124604: Epoch time: 102.57 s +2026-04-12 06:02:29.321158: +2026-04-12 06:02:29.323325: Epoch 1519 +2026-04-12 06:02:29.325776: Current learning rate: 0.00651 +2026-04-12 06:04:12.377816: train_loss -0.2622 +2026-04-12 06:04:12.386482: val_loss -0.2287 +2026-04-12 06:04:12.388910: Pseudo dice [0.6136, 0.8636, 0.8103, 0.3083, 0.2719, 0.1771, 0.7347] +2026-04-12 06:04:12.391643: Epoch time: 103.06 s +2026-04-12 06:04:13.589067: +2026-04-12 06:04:13.592597: Epoch 1520 +2026-04-12 06:04:13.594835: Current learning rate: 0.0065 +2026-04-12 06:05:55.771622: train_loss -0.2553 +2026-04-12 06:05:55.781202: val_loss -0.2289 +2026-04-12 06:05:55.782936: Pseudo dice [0.694, 0.5361, 0.7419, 0.2915, 0.4737, 0.3548, 0.5355] +2026-04-12 06:05:55.785806: Epoch time: 102.19 s +2026-04-12 06:05:57.021892: +2026-04-12 06:05:57.024375: Epoch 1521 +2026-04-12 06:05:57.026453: Current learning rate: 0.0065 +2026-04-12 06:07:38.895485: train_loss -0.2623 +2026-04-12 06:07:38.905024: val_loss -0.2145 +2026-04-12 06:07:38.908710: Pseudo dice [0.5972, 0.5221, 0.6166, 0.5174, 0.4559, 0.7766, 0.6241] +2026-04-12 06:07:38.911317: Epoch time: 101.88 s +2026-04-12 06:07:40.147893: +2026-04-12 06:07:40.150224: Epoch 1522 +2026-04-12 06:07:40.152701: Current learning rate: 0.0065 +2026-04-12 06:09:22.427198: train_loss -0.2646 +2026-04-12 06:09:22.442410: val_loss -0.2235 +2026-04-12 06:09:22.444594: Pseudo dice [0.7312, 0.8907, 0.835, 0.5876, 0.6096, 0.7749, 0.7612] +2026-04-12 06:09:22.448366: Epoch time: 102.28 s +2026-04-12 06:09:24.718571: +2026-04-12 06:09:24.720263: Epoch 1523 +2026-04-12 06:09:24.722101: Current learning rate: 0.0065 +2026-04-12 06:11:07.502714: train_loss -0.2689 +2026-04-12 06:11:07.510096: val_loss -0.2065 +2026-04-12 06:11:07.512645: Pseudo dice [0.4969, 0.5043, 0.5871, 0.4057, 0.5127, 0.4718, 0.8288] +2026-04-12 06:11:07.515191: Epoch time: 102.79 s +2026-04-12 06:11:08.713413: +2026-04-12 06:11:08.715410: Epoch 1524 +2026-04-12 06:11:08.717500: Current learning rate: 0.00649 +2026-04-12 06:12:50.761263: train_loss -0.2453 +2026-04-12 06:12:50.769102: val_loss -0.2065 +2026-04-12 06:12:50.771389: Pseudo dice [0.237, 0.5181, 0.8044, 0.2682, 0.2621, 0.8176, 0.5486] +2026-04-12 06:12:50.775777: Epoch time: 102.05 s +2026-04-12 06:12:52.013967: +2026-04-12 06:12:52.016863: Epoch 1525 +2026-04-12 06:12:52.019862: Current learning rate: 0.00649 +2026-04-12 06:14:34.117477: train_loss -0.2572 +2026-04-12 06:14:34.125765: val_loss -0.2331 +2026-04-12 06:14:34.127958: Pseudo dice [0.8147, 0.2831, 0.7774, 0.7126, 0.4718, 0.8944, 0.6764] +2026-04-12 06:14:34.131597: Epoch time: 102.11 s +2026-04-12 06:14:35.358956: +2026-04-12 06:14:35.361081: Epoch 1526 +2026-04-12 06:14:35.363657: Current learning rate: 0.00649 +2026-04-12 06:16:17.555008: train_loss -0.2543 +2026-04-12 06:16:17.565710: val_loss -0.2137 +2026-04-12 06:16:17.568556: Pseudo dice [0.7674, 0.7157, 0.7753, 0.4644, 0.2959, 0.7661, 0.779] +2026-04-12 06:16:17.574164: Epoch time: 102.2 s +2026-04-12 06:16:18.784770: +2026-04-12 06:16:18.787718: Epoch 1527 +2026-04-12 06:16:18.790302: Current learning rate: 0.00649 +2026-04-12 06:18:00.789447: train_loss -0.2651 +2026-04-12 06:18:00.800946: val_loss -0.2235 +2026-04-12 06:18:00.804149: Pseudo dice [0.6358, 0.4592, 0.7738, 0.6306, 0.5272, 0.8175, 0.7447] +2026-04-12 06:18:00.806861: Epoch time: 102.01 s +2026-04-12 06:18:02.008887: +2026-04-12 06:18:02.010856: Epoch 1528 +2026-04-12 06:18:02.014332: Current learning rate: 0.00648 +2026-04-12 06:19:43.719903: train_loss -0.2678 +2026-04-12 06:19:43.726834: val_loss -0.2443 +2026-04-12 06:19:43.728990: Pseudo dice [0.7331, 0.6805, 0.8117, 0.5709, 0.4542, 0.4936, 0.6161] +2026-04-12 06:19:43.731521: Epoch time: 101.71 s +2026-04-12 06:19:44.970072: +2026-04-12 06:19:44.972545: Epoch 1529 +2026-04-12 06:19:44.975502: Current learning rate: 0.00648 +2026-04-12 06:21:26.957771: train_loss -0.2596 +2026-04-12 06:21:26.967657: val_loss -0.2412 +2026-04-12 06:21:26.970739: Pseudo dice [0.6594, 0.1737, 0.8138, 0.5804, 0.3407, 0.7611, 0.744] +2026-04-12 06:21:26.974436: Epoch time: 101.99 s +2026-04-12 06:21:28.185900: +2026-04-12 06:21:28.187895: Epoch 1530 +2026-04-12 06:21:28.190192: Current learning rate: 0.00648 +2026-04-12 06:23:10.558972: train_loss -0.2752 +2026-04-12 06:23:10.566116: val_loss -0.2461 +2026-04-12 06:23:10.570335: Pseudo dice [0.2442, 0.6794, 0.61, 0.6117, 0.5292, 0.7178, 0.7304] +2026-04-12 06:23:10.573571: Epoch time: 102.38 s +2026-04-12 06:23:11.818549: +2026-04-12 06:23:11.821013: Epoch 1531 +2026-04-12 06:23:11.823529: Current learning rate: 0.00648 +2026-04-12 06:24:53.985138: train_loss -0.2542 +2026-04-12 06:24:53.992743: val_loss -0.2308 +2026-04-12 06:24:53.994831: Pseudo dice [0.6282, 0.5523, 0.7497, 0.5311, 0.5715, 0.6183, 0.7397] +2026-04-12 06:24:53.997560: Epoch time: 102.17 s +2026-04-12 06:24:55.202183: +2026-04-12 06:24:55.204192: Epoch 1532 +2026-04-12 06:24:55.206557: Current learning rate: 0.00648 +2026-04-12 06:26:37.046426: train_loss -0.2538 +2026-04-12 06:26:37.054250: val_loss -0.2215 +2026-04-12 06:26:37.056809: Pseudo dice [0.4077, 0.6142, 0.6607, 0.4784, 0.4858, 0.8848, 0.7722] +2026-04-12 06:26:37.059892: Epoch time: 101.85 s +2026-04-12 06:26:38.297903: +2026-04-12 06:26:38.300396: Epoch 1533 +2026-04-12 06:26:38.302917: Current learning rate: 0.00647 +2026-04-12 06:28:20.762352: train_loss -0.2407 +2026-04-12 06:28:20.769746: val_loss -0.1567 +2026-04-12 06:28:20.772458: Pseudo dice [0.3833, 0.6271, 0.7132, 0.3738, 0.4615, 0.4178, 0.7037] +2026-04-12 06:28:20.774943: Epoch time: 102.47 s +2026-04-12 06:28:21.981187: +2026-04-12 06:28:21.983908: Epoch 1534 +2026-04-12 06:28:21.986891: Current learning rate: 0.00647 +2026-04-12 06:30:03.920802: train_loss -0.2675 +2026-04-12 06:30:03.927161: val_loss -0.2303 +2026-04-12 06:30:03.929329: Pseudo dice [0.4311, 0.4858, 0.7604, 0.5527, 0.3559, 0.7344, 0.5898] +2026-04-12 06:30:03.931613: Epoch time: 101.94 s +2026-04-12 06:30:05.152263: +2026-04-12 06:30:05.154580: Epoch 1535 +2026-04-12 06:30:05.156370: Current learning rate: 0.00647 +2026-04-12 06:31:46.866556: train_loss -0.265 +2026-04-12 06:31:46.872461: val_loss -0.2377 +2026-04-12 06:31:46.874425: Pseudo dice [0.6981, 0.6167, 0.8091, 0.5239, 0.5388, 0.8871, 0.7367] +2026-04-12 06:31:46.876675: Epoch time: 101.72 s +2026-04-12 06:31:48.314121: +2026-04-12 06:31:48.316009: Epoch 1536 +2026-04-12 06:31:48.317753: Current learning rate: 0.00647 +2026-04-12 06:33:30.759851: train_loss -0.2704 +2026-04-12 06:33:30.767302: val_loss -0.1902 +2026-04-12 06:33:30.769835: Pseudo dice [0.4729, 0.8356, 0.5764, 0.4437, 0.6511, 0.2798, 0.6526] +2026-04-12 06:33:30.773279: Epoch time: 102.45 s +2026-04-12 06:33:31.989508: +2026-04-12 06:33:31.991629: Epoch 1537 +2026-04-12 06:33:31.993585: Current learning rate: 0.00646 +2026-04-12 06:35:14.300855: train_loss -0.2667 +2026-04-12 06:35:14.309550: val_loss -0.2416 +2026-04-12 06:35:14.313031: Pseudo dice [0.6596, 0.1112, 0.8139, 0.3955, 0.5196, 0.9059, 0.7892] +2026-04-12 06:35:14.315868: Epoch time: 102.31 s +2026-04-12 06:35:15.541571: +2026-04-12 06:35:15.543534: Epoch 1538 +2026-04-12 06:35:15.545620: Current learning rate: 0.00646 +2026-04-12 06:36:58.208189: train_loss -0.2772 +2026-04-12 06:36:58.214720: val_loss -0.2329 +2026-04-12 06:36:58.217783: Pseudo dice [0.6073, 0.5404, 0.748, 0.3069, 0.4577, 0.8704, 0.7308] +2026-04-12 06:36:58.220341: Epoch time: 102.67 s +2026-04-12 06:36:59.438316: +2026-04-12 06:36:59.440964: Epoch 1539 +2026-04-12 06:36:59.442950: Current learning rate: 0.00646 +2026-04-12 06:38:41.158774: train_loss -0.2817 +2026-04-12 06:38:41.166267: val_loss -0.224 +2026-04-12 06:38:41.168273: Pseudo dice [0.6186, 0.4221, 0.7546, 0.3803, 0.5425, 0.7459, 0.8556] +2026-04-12 06:38:41.170398: Epoch time: 101.72 s +2026-04-12 06:38:42.418758: +2026-04-12 06:38:42.424912: Epoch 1540 +2026-04-12 06:38:42.432964: Current learning rate: 0.00646 +2026-04-12 06:40:24.914910: train_loss -0.2764 +2026-04-12 06:40:24.921383: val_loss -0.2095 +2026-04-12 06:40:24.923726: Pseudo dice [0.3182, 0.5254, 0.7973, 0.5302, 0.4349, 0.75, 0.7611] +2026-04-12 06:40:24.926047: Epoch time: 102.5 s +2026-04-12 06:40:26.124608: +2026-04-12 06:40:26.126693: Epoch 1541 +2026-04-12 06:40:26.128547: Current learning rate: 0.00645 +2026-04-12 06:42:09.049884: train_loss -0.2689 +2026-04-12 06:42:09.057164: val_loss -0.1848 +2026-04-12 06:42:09.059321: Pseudo dice [0.7485, 0.6365, 0.5867, 0.5674, 0.5217, 0.8155, 0.3667] +2026-04-12 06:42:09.061616: Epoch time: 102.93 s +2026-04-12 06:42:10.275092: +2026-04-12 06:42:10.277378: Epoch 1542 +2026-04-12 06:42:10.279335: Current learning rate: 0.00645 +2026-04-12 06:43:53.377064: train_loss -0.2807 +2026-04-12 06:43:53.389132: val_loss -0.2101 +2026-04-12 06:43:53.391290: Pseudo dice [0.4613, 0.2269, 0.7053, 0.2823, 0.54, 0.4506, 0.8499] +2026-04-12 06:43:53.393485: Epoch time: 103.11 s +2026-04-12 06:43:54.624757: +2026-04-12 06:43:54.626802: Epoch 1543 +2026-04-12 06:43:54.628703: Current learning rate: 0.00645 +2026-04-12 06:45:36.578038: train_loss -0.2682 +2026-04-12 06:45:36.585972: val_loss -0.2281 +2026-04-12 06:45:36.588245: Pseudo dice [0.6383, 0.6433, 0.6632, 0.7545, 0.4052, 0.7643, 0.8332] +2026-04-12 06:45:36.590769: Epoch time: 101.96 s +2026-04-12 06:45:37.801907: +2026-04-12 06:45:37.804239: Epoch 1544 +2026-04-12 06:45:37.806463: Current learning rate: 0.00645 +2026-04-12 06:47:19.869327: train_loss -0.2561 +2026-04-12 06:47:19.879693: val_loss -0.258 +2026-04-12 06:47:19.883717: Pseudo dice [0.4444, 0.64, 0.7575, 0.6068, 0.4935, 0.8056, 0.8594] +2026-04-12 06:47:19.886808: Epoch time: 102.07 s +2026-04-12 06:47:21.127728: +2026-04-12 06:47:21.129846: Epoch 1545 +2026-04-12 06:47:21.131732: Current learning rate: 0.00644 +2026-04-12 06:49:03.335186: train_loss -0.2717 +2026-04-12 06:49:03.343143: val_loss -0.2253 +2026-04-12 06:49:03.345695: Pseudo dice [0.751, 0.8264, 0.7515, 0.448, 0.2998, 0.8577, 0.8311] +2026-04-12 06:49:03.349026: Epoch time: 102.21 s +2026-04-12 06:49:04.575979: +2026-04-12 06:49:04.577731: Epoch 1546 +2026-04-12 06:49:04.580113: Current learning rate: 0.00644 +2026-04-12 06:50:46.685161: train_loss -0.2719 +2026-04-12 06:50:46.692213: val_loss -0.2391 +2026-04-12 06:50:46.694132: Pseudo dice [0.5707, 0.6057, 0.7512, 0.8618, 0.5556, 0.8525, 0.845] +2026-04-12 06:50:46.697092: Epoch time: 102.11 s +2026-04-12 06:50:47.906600: +2026-04-12 06:50:47.914028: Epoch 1547 +2026-04-12 06:50:47.919762: Current learning rate: 0.00644 +2026-04-12 06:52:29.941802: train_loss -0.2626 +2026-04-12 06:52:29.949500: val_loss -0.2296 +2026-04-12 06:52:29.951580: Pseudo dice [0.5478, 0.6486, 0.6504, 0.5001, 0.1881, 0.6314, 0.7072] +2026-04-12 06:52:29.954314: Epoch time: 102.04 s +2026-04-12 06:52:31.177580: +2026-04-12 06:52:31.179552: Epoch 1548 +2026-04-12 06:52:31.182118: Current learning rate: 0.00644 +2026-04-12 06:54:13.012344: train_loss -0.2571 +2026-04-12 06:54:13.019728: val_loss -0.2103 +2026-04-12 06:54:13.023073: Pseudo dice [0.1975, 0.6939, 0.6456, 0.4158, 0.2741, 0.2012, 0.8189] +2026-04-12 06:54:13.027856: Epoch time: 101.84 s +2026-04-12 06:54:14.330669: +2026-04-12 06:54:14.332696: Epoch 1549 +2026-04-12 06:54:14.334396: Current learning rate: 0.00644 +2026-04-12 06:55:56.285973: train_loss -0.254 +2026-04-12 06:55:56.293512: val_loss -0.2302 +2026-04-12 06:55:56.295908: Pseudo dice [0.6624, 0.6774, 0.7834, 0.4581, 0.4577, 0.5986, 0.838] +2026-04-12 06:55:56.298489: Epoch time: 101.96 s +2026-04-12 06:55:59.188057: +2026-04-12 06:55:59.190339: Epoch 1550 +2026-04-12 06:55:59.191895: Current learning rate: 0.00643 +2026-04-12 06:57:40.861027: train_loss -0.2656 +2026-04-12 06:57:40.866882: val_loss -0.226 +2026-04-12 06:57:40.868936: Pseudo dice [0.6088, 0.1886, 0.7932, 0.4899, 0.4985, 0.7732, 0.846] +2026-04-12 06:57:40.871464: Epoch time: 101.68 s +2026-04-12 06:57:42.059019: +2026-04-12 06:57:42.060580: Epoch 1551 +2026-04-12 06:57:42.062132: Current learning rate: 0.00643 +2026-04-12 06:59:23.718347: train_loss -0.2608 +2026-04-12 06:59:23.725154: val_loss -0.2188 +2026-04-12 06:59:23.727532: Pseudo dice [0.41, 0.6149, 0.7175, 0.1632, 0.3493, 0.8717, 0.5236] +2026-04-12 06:59:23.729995: Epoch time: 101.66 s +2026-04-12 06:59:24.946553: +2026-04-12 06:59:24.948386: Epoch 1552 +2026-04-12 06:59:24.950948: Current learning rate: 0.00643 +2026-04-12 07:01:07.122859: train_loss -0.2618 +2026-04-12 07:01:07.129553: val_loss -0.2252 +2026-04-12 07:01:07.131549: Pseudo dice [0.8665, 0.777, 0.5889, 0.6468, 0.3302, 0.7385, 0.8465] +2026-04-12 07:01:07.133691: Epoch time: 102.18 s +2026-04-12 07:01:08.336378: +2026-04-12 07:01:08.342440: Epoch 1553 +2026-04-12 07:01:08.345055: Current learning rate: 0.00643 +2026-04-12 07:02:50.694787: train_loss -0.2743 +2026-04-12 07:02:50.702583: val_loss -0.2015 +2026-04-12 07:02:50.705596: Pseudo dice [0.8678, 0.4375, 0.7574, 0.3467, 0.5632, 0.6864, 0.6048] +2026-04-12 07:02:50.708142: Epoch time: 102.36 s +2026-04-12 07:02:51.908962: +2026-04-12 07:02:51.910582: Epoch 1554 +2026-04-12 07:02:51.912420: Current learning rate: 0.00642 +2026-04-12 07:04:34.105797: train_loss -0.2647 +2026-04-12 07:04:34.112266: val_loss -0.2352 +2026-04-12 07:04:34.114130: Pseudo dice [0.6113, 0.7375, 0.8318, 0.1798, 0.3228, 0.743, 0.7144] +2026-04-12 07:04:34.117488: Epoch time: 102.2 s +2026-04-12 07:04:35.313288: +2026-04-12 07:04:35.315206: Epoch 1555 +2026-04-12 07:04:35.316722: Current learning rate: 0.00642 +2026-04-12 07:06:17.236820: train_loss -0.265 +2026-04-12 07:06:17.244826: val_loss -0.1999 +2026-04-12 07:06:17.247199: Pseudo dice [0.2589, 0.2402, 0.7426, 0.2375, 0.3585, 0.8324, 0.8342] +2026-04-12 07:06:17.249696: Epoch time: 101.93 s +2026-04-12 07:06:18.438668: +2026-04-12 07:06:18.440637: Epoch 1556 +2026-04-12 07:06:18.442830: Current learning rate: 0.00642 +2026-04-12 07:08:00.903038: train_loss -0.2754 +2026-04-12 07:08:00.911986: val_loss -0.23 +2026-04-12 07:08:00.914301: Pseudo dice [0.4913, 0.7683, 0.6244, 0.32, 0.4027, 0.6768, 0.804] +2026-04-12 07:08:00.917003: Epoch time: 102.47 s +2026-04-12 07:08:02.150826: +2026-04-12 07:08:02.152392: Epoch 1557 +2026-04-12 07:08:02.153920: Current learning rate: 0.00642 +2026-04-12 07:09:44.205746: train_loss -0.2619 +2026-04-12 07:09:44.212529: val_loss -0.2391 +2026-04-12 07:09:44.215019: Pseudo dice [0.3123, 0.5101, 0.7432, 0.6974, 0.4512, 0.8965, 0.6725] +2026-04-12 07:09:44.222997: Epoch time: 102.06 s +2026-04-12 07:09:45.425302: +2026-04-12 07:09:45.426812: Epoch 1558 +2026-04-12 07:09:45.428288: Current learning rate: 0.00641 +2026-04-12 07:11:27.885503: train_loss -0.2616 +2026-04-12 07:11:27.892425: val_loss -0.2432 +2026-04-12 07:11:27.894740: Pseudo dice [0.6308, 0.6414, 0.7588, 0.7713, 0.4007, 0.8916, 0.6065] +2026-04-12 07:11:27.896960: Epoch time: 102.46 s +2026-04-12 07:11:29.092865: +2026-04-12 07:11:29.094571: Epoch 1559 +2026-04-12 07:11:29.096164: Current learning rate: 0.00641 +2026-04-12 07:13:10.835347: train_loss -0.2597 +2026-04-12 07:13:10.842011: val_loss -0.2143 +2026-04-12 07:13:10.844138: Pseudo dice [0.4838, 0.4314, 0.7142, 0.261, 0.3585, 0.9171, 0.5648] +2026-04-12 07:13:10.847122: Epoch time: 101.75 s +2026-04-12 07:13:12.047135: +2026-04-12 07:13:12.048778: Epoch 1560 +2026-04-12 07:13:12.050234: Current learning rate: 0.00641 +2026-04-12 07:14:53.950845: train_loss -0.2665 +2026-04-12 07:14:53.956519: val_loss -0.2192 +2026-04-12 07:14:53.958678: Pseudo dice [0.3993, 0.056, 0.637, 0.5648, 0.452, 0.7647, 0.6584] +2026-04-12 07:14:53.961761: Epoch time: 101.91 s +2026-04-12 07:14:55.171590: +2026-04-12 07:14:55.174270: Epoch 1561 +2026-04-12 07:14:55.176642: Current learning rate: 0.00641 +2026-04-12 07:16:37.919754: train_loss -0.2694 +2026-04-12 07:16:37.928329: val_loss -0.1939 +2026-04-12 07:16:37.930697: Pseudo dice [0.5341, 0.401, 0.7928, 0.4158, 0.2656, 0.6924, 0.7462] +2026-04-12 07:16:37.933008: Epoch time: 102.75 s +2026-04-12 07:16:39.144703: +2026-04-12 07:16:39.146339: Epoch 1562 +2026-04-12 07:16:39.147851: Current learning rate: 0.0064 +2026-04-12 07:18:21.281417: train_loss -0.2665 +2026-04-12 07:18:21.290734: val_loss -0.2221 +2026-04-12 07:18:21.294116: Pseudo dice [0.6635, 0.665, 0.705, 0.2065, 0.5356, 0.6556, 0.7471] +2026-04-12 07:18:21.296591: Epoch time: 102.14 s +2026-04-12 07:18:22.487232: +2026-04-12 07:18:22.489012: Epoch 1563 +2026-04-12 07:18:22.490475: Current learning rate: 0.0064 +2026-04-12 07:20:04.261678: train_loss -0.2615 +2026-04-12 07:20:04.268466: val_loss -0.2181 +2026-04-12 07:20:04.271053: Pseudo dice [0.8506, 0.4678, 0.6681, 0.3909, 0.5975, 0.822, 0.734] +2026-04-12 07:20:04.273404: Epoch time: 101.78 s +2026-04-12 07:20:05.563193: +2026-04-12 07:20:05.565242: Epoch 1564 +2026-04-12 07:20:05.568450: Current learning rate: 0.0064 +2026-04-12 07:21:47.683491: train_loss -0.2683 +2026-04-12 07:21:47.691225: val_loss -0.2253 +2026-04-12 07:21:47.693243: Pseudo dice [0.5047, 0.8248, 0.6906, 0.4373, 0.5603, 0.7442, 0.6427] +2026-04-12 07:21:47.696167: Epoch time: 102.12 s +2026-04-12 07:21:48.909312: +2026-04-12 07:21:48.910854: Epoch 1565 +2026-04-12 07:21:48.912483: Current learning rate: 0.0064 +2026-04-12 07:23:30.815693: train_loss -0.2721 +2026-04-12 07:23:30.823311: val_loss -0.2396 +2026-04-12 07:23:30.825970: Pseudo dice [0.6686, 0.6376, 0.7622, 0.1993, 0.5706, 0.743, 0.7591] +2026-04-12 07:23:30.828615: Epoch time: 101.91 s +2026-04-12 07:23:32.042986: +2026-04-12 07:23:32.045335: Epoch 1566 +2026-04-12 07:23:32.047129: Current learning rate: 0.00639 +2026-04-12 07:25:14.108707: train_loss -0.2719 +2026-04-12 07:25:14.115436: val_loss -0.2455 +2026-04-12 07:25:14.117963: Pseudo dice [0.4992, 0.7396, 0.8052, 0.6848, 0.4193, 0.8578, 0.8188] +2026-04-12 07:25:14.120829: Epoch time: 102.07 s +2026-04-12 07:25:15.317527: +2026-04-12 07:25:15.319970: Epoch 1567 +2026-04-12 07:25:15.321953: Current learning rate: 0.00639 +2026-04-12 07:26:57.632303: train_loss -0.2761 +2026-04-12 07:26:57.639470: val_loss -0.1966 +2026-04-12 07:26:57.641632: Pseudo dice [0.4366, 0.4258, 0.5614, 0.6117, 0.5723, 0.6959, 0.8837] +2026-04-12 07:26:57.643877: Epoch time: 102.32 s +2026-04-12 07:26:58.849568: +2026-04-12 07:26:58.852160: Epoch 1568 +2026-04-12 07:26:58.854181: Current learning rate: 0.00639 +2026-04-12 07:28:40.870204: train_loss -0.272 +2026-04-12 07:28:40.877378: val_loss -0.2151 +2026-04-12 07:28:40.879610: Pseudo dice [0.7996, 0.9043, 0.8153, 0.2522, 0.6233, 0.5741, 0.778] +2026-04-12 07:28:40.882458: Epoch time: 102.02 s +2026-04-12 07:28:42.101864: +2026-04-12 07:28:42.103793: Epoch 1569 +2026-04-12 07:28:42.105500: Current learning rate: 0.00639 +2026-04-12 07:30:23.692751: train_loss -0.2703 +2026-04-12 07:30:23.698691: val_loss -0.2127 +2026-04-12 07:30:23.701087: Pseudo dice [0.5066, 0.8281, 0.8426, 0.4587, 0.4314, 0.513, 0.7995] +2026-04-12 07:30:23.703756: Epoch time: 101.59 s +2026-04-12 07:30:24.948165: +2026-04-12 07:30:24.950275: Epoch 1570 +2026-04-12 07:30:24.952418: Current learning rate: 0.00639 +2026-04-12 07:32:06.956408: train_loss -0.2757 +2026-04-12 07:32:06.963254: val_loss -0.2123 +2026-04-12 07:32:06.965456: Pseudo dice [0.6037, 0.6579, 0.778, 0.4065, 0.4173, 0.8108, 0.7557] +2026-04-12 07:32:06.968319: Epoch time: 102.01 s +2026-04-12 07:32:08.189507: +2026-04-12 07:32:08.191426: Epoch 1571 +2026-04-12 07:32:08.193338: Current learning rate: 0.00638 +2026-04-12 07:33:50.011863: train_loss -0.2657 +2026-04-12 07:33:50.018277: val_loss -0.2272 +2026-04-12 07:33:50.020712: Pseudo dice [0.4396, 0.8314, 0.7179, 0.5614, 0.2831, 0.7707, 0.7905] +2026-04-12 07:33:50.023866: Epoch time: 101.83 s +2026-04-12 07:33:51.209874: +2026-04-12 07:33:51.212121: Epoch 1572 +2026-04-12 07:33:51.214430: Current learning rate: 0.00638 +2026-04-12 07:35:33.028957: train_loss -0.2655 +2026-04-12 07:35:33.038332: val_loss -0.2156 +2026-04-12 07:35:33.041464: Pseudo dice [0.6463, 0.4735, 0.7841, 0.5471, 0.2197, 0.4342, 0.8342] +2026-04-12 07:35:33.044084: Epoch time: 101.82 s +2026-04-12 07:35:34.233937: +2026-04-12 07:35:34.235837: Epoch 1573 +2026-04-12 07:35:34.238108: Current learning rate: 0.00638 +2026-04-12 07:37:16.320701: train_loss -0.2826 +2026-04-12 07:37:16.326713: val_loss -0.2445 +2026-04-12 07:37:16.328906: Pseudo dice [0.4211, 0.8768, 0.7507, 0.4573, 0.2784, 0.2552, 0.7285] +2026-04-12 07:37:16.331383: Epoch time: 102.09 s +2026-04-12 07:37:17.526088: +2026-04-12 07:37:17.528272: Epoch 1574 +2026-04-12 07:37:17.530539: Current learning rate: 0.00638 +2026-04-12 07:38:59.338342: train_loss -0.2744 +2026-04-12 07:38:59.344703: val_loss -0.2408 +2026-04-12 07:38:59.346537: Pseudo dice [0.7299, 0.8241, 0.8196, 0.4882, 0.306, 0.6009, 0.6072] +2026-04-12 07:38:59.349213: Epoch time: 101.82 s +2026-04-12 07:39:00.533284: +2026-04-12 07:39:00.535850: Epoch 1575 +2026-04-12 07:39:00.537835: Current learning rate: 0.00637 +2026-04-12 07:40:42.860271: train_loss -0.2744 +2026-04-12 07:40:42.866156: val_loss -0.2365 +2026-04-12 07:40:42.868062: Pseudo dice [0.614, 0.5937, 0.8175, 0.7127, 0.4275, 0.5248, 0.8403] +2026-04-12 07:40:42.870345: Epoch time: 102.33 s +2026-04-12 07:40:44.084798: +2026-04-12 07:40:44.086723: Epoch 1576 +2026-04-12 07:40:44.088343: Current learning rate: 0.00637 +2026-04-12 07:42:26.227413: train_loss -0.2839 +2026-04-12 07:42:26.234795: val_loss -0.202 +2026-04-12 07:42:26.237092: Pseudo dice [0.7578, 0.2644, 0.7624, 0.6137, 0.3053, 0.833, 0.7457] +2026-04-12 07:42:26.239763: Epoch time: 102.15 s +2026-04-12 07:42:27.442312: +2026-04-12 07:42:27.444224: Epoch 1577 +2026-04-12 07:42:27.446137: Current learning rate: 0.00637 +2026-04-12 07:44:09.336251: train_loss -0.2845 +2026-04-12 07:44:09.341746: val_loss -0.2426 +2026-04-12 07:44:09.344494: Pseudo dice [0.6947, 0.3313, 0.8267, 0.8438, 0.2976, 0.4476, 0.7895] +2026-04-12 07:44:09.347063: Epoch time: 101.9 s +2026-04-12 07:44:10.571451: +2026-04-12 07:44:10.573423: Epoch 1578 +2026-04-12 07:44:10.575436: Current learning rate: 0.00637 +2026-04-12 07:45:52.948840: train_loss -0.273 +2026-04-12 07:45:52.960183: val_loss -0.2271 +2026-04-12 07:45:52.962847: Pseudo dice [0.6394, 0.8595, 0.7429, 0.6653, 0.5692, 0.813, 0.582] +2026-04-12 07:45:52.964938: Epoch time: 102.38 s +2026-04-12 07:45:54.174885: +2026-04-12 07:45:54.176670: Epoch 1579 +2026-04-12 07:45:54.178196: Current learning rate: 0.00636 +2026-04-12 07:47:36.683576: train_loss -0.2491 +2026-04-12 07:47:36.690754: val_loss -0.1803 +2026-04-12 07:47:36.692805: Pseudo dice [0.4945, 0.8581, 0.6947, 0.3905, 0.3929, 0.677, 0.4964] +2026-04-12 07:47:36.695163: Epoch time: 102.51 s +2026-04-12 07:47:37.916750: +2026-04-12 07:47:37.919204: Epoch 1580 +2026-04-12 07:47:37.921017: Current learning rate: 0.00636 +2026-04-12 07:49:20.231744: train_loss -0.2519 +2026-04-12 07:49:20.238322: val_loss -0.2216 +2026-04-12 07:49:20.240281: Pseudo dice [0.5395, 0.6131, 0.7611, 0.2026, 0.5655, 0.6278, 0.7663] +2026-04-12 07:49:20.242790: Epoch time: 102.32 s +2026-04-12 07:49:22.518490: +2026-04-12 07:49:22.520249: Epoch 1581 +2026-04-12 07:49:22.521871: Current learning rate: 0.00636 +2026-04-12 07:51:04.867872: train_loss -0.2505 +2026-04-12 07:51:04.876054: val_loss -0.2206 +2026-04-12 07:51:04.878805: Pseudo dice [0.6255, 0.6177, 0.764, 0.8488, 0.3368, 0.6974, 0.745] +2026-04-12 07:51:04.881653: Epoch time: 102.35 s +2026-04-12 07:51:06.101458: +2026-04-12 07:51:06.103459: Epoch 1582 +2026-04-12 07:51:06.105018: Current learning rate: 0.00636 +2026-04-12 07:52:47.936878: train_loss -0.269 +2026-04-12 07:52:47.943732: val_loss -0.2279 +2026-04-12 07:52:47.947150: Pseudo dice [0.4069, 0.7366, 0.5154, 0.1786, 0.4948, 0.4542, 0.8039] +2026-04-12 07:52:47.949644: Epoch time: 101.84 s +2026-04-12 07:52:49.159052: +2026-04-12 07:52:49.160840: Epoch 1583 +2026-04-12 07:52:49.162419: Current learning rate: 0.00635 +2026-04-12 07:54:30.928936: train_loss -0.269 +2026-04-12 07:54:30.935172: val_loss -0.2214 +2026-04-12 07:54:30.938034: Pseudo dice [0.5889, 0.5953, 0.7103, 0.5286, 0.3444, 0.4969, 0.8083] +2026-04-12 07:54:30.941755: Epoch time: 101.77 s +2026-04-12 07:54:32.154871: +2026-04-12 07:54:32.156831: Epoch 1584 +2026-04-12 07:54:32.159308: Current learning rate: 0.00635 +2026-04-12 07:56:14.138392: train_loss -0.261 +2026-04-12 07:56:14.144978: val_loss -0.2261 +2026-04-12 07:56:14.147326: Pseudo dice [0.5822, 0.8457, 0.6724, 0.0527, 0.4878, 0.7634, 0.7728] +2026-04-12 07:56:14.150011: Epoch time: 101.99 s +2026-04-12 07:56:15.353108: +2026-04-12 07:56:15.354923: Epoch 1585 +2026-04-12 07:56:15.356626: Current learning rate: 0.00635 +2026-04-12 07:57:57.330134: train_loss -0.253 +2026-04-12 07:57:57.335930: val_loss -0.2065 +2026-04-12 07:57:57.337537: Pseudo dice [0.4762, 0.43, 0.6158, 0.103, 0.4452, 0.1786, 0.794] +2026-04-12 07:57:57.340203: Epoch time: 101.98 s +2026-04-12 07:57:58.573396: +2026-04-12 07:57:58.575306: Epoch 1586 +2026-04-12 07:57:58.577072: Current learning rate: 0.00635 +2026-04-12 07:59:40.194197: train_loss -0.266 +2026-04-12 07:59:40.200221: val_loss -0.2293 +2026-04-12 07:59:40.202268: Pseudo dice [0.8349, 0.5591, 0.7295, 0.3898, 0.4629, 0.7753, 0.6563] +2026-04-12 07:59:40.204518: Epoch time: 101.62 s +2026-04-12 07:59:41.427465: +2026-04-12 07:59:41.429084: Epoch 1587 +2026-04-12 07:59:41.430621: Current learning rate: 0.00635 +2026-04-12 08:01:23.442921: train_loss -0.274 +2026-04-12 08:01:23.451505: val_loss -0.2185 +2026-04-12 08:01:23.457254: Pseudo dice [0.7545, 0.1886, 0.6389, 0.2777, 0.2832, 0.7541, 0.6592] +2026-04-12 08:01:23.460261: Epoch time: 102.02 s +2026-04-12 08:01:24.702643: +2026-04-12 08:01:24.704356: Epoch 1588 +2026-04-12 08:01:24.706268: Current learning rate: 0.00634 +2026-04-12 08:03:06.429099: train_loss -0.2647 +2026-04-12 08:03:06.436319: val_loss -0.2111 +2026-04-12 08:03:06.439063: Pseudo dice [0.6133, 0.8114, 0.6912, 0.5369, 0.521, 0.2687, 0.8267] +2026-04-12 08:03:06.442180: Epoch time: 101.73 s +2026-04-12 08:03:07.668869: +2026-04-12 08:03:07.670734: Epoch 1589 +2026-04-12 08:03:07.672281: Current learning rate: 0.00634 +2026-04-12 08:04:49.356177: train_loss -0.2711 +2026-04-12 08:04:49.362930: val_loss -0.2465 +2026-04-12 08:04:49.366749: Pseudo dice [0.3836, 0.6649, 0.8032, 0.7668, 0.4716, 0.4914, 0.6973] +2026-04-12 08:04:49.368849: Epoch time: 101.69 s +2026-04-12 08:04:50.566243: +2026-04-12 08:04:50.567834: Epoch 1590 +2026-04-12 08:04:50.569351: Current learning rate: 0.00634 +2026-04-12 08:06:32.324418: train_loss -0.2615 +2026-04-12 08:06:32.330753: val_loss -0.2145 +2026-04-12 08:06:32.332770: Pseudo dice [0.2007, 0.8705, 0.8468, 0.4421, 0.5173, 0.7534, 0.3147] +2026-04-12 08:06:32.335035: Epoch time: 101.76 s +2026-04-12 08:06:33.549809: +2026-04-12 08:06:33.551560: Epoch 1591 +2026-04-12 08:06:33.553409: Current learning rate: 0.00634 +2026-04-12 08:08:15.889706: train_loss -0.2552 +2026-04-12 08:08:15.895954: val_loss -0.1914 +2026-04-12 08:08:15.899797: Pseudo dice [0.3813, 0.3834, 0.4459, 0.4083, 0.2359, 0.5471, 0.3476] +2026-04-12 08:08:15.902754: Epoch time: 102.34 s +2026-04-12 08:08:17.122175: +2026-04-12 08:08:17.124511: Epoch 1592 +2026-04-12 08:08:17.126346: Current learning rate: 0.00633 +2026-04-12 08:09:59.868679: train_loss -0.2485 +2026-04-12 08:09:59.874775: val_loss -0.1978 +2026-04-12 08:09:59.877101: Pseudo dice [0.7846, 0.877, 0.6495, 0.1033, 0.2935, 0.5797, 0.8285] +2026-04-12 08:09:59.881410: Epoch time: 102.75 s +2026-04-12 08:10:01.086737: +2026-04-12 08:10:01.088475: Epoch 1593 +2026-04-12 08:10:01.090268: Current learning rate: 0.00633 +2026-04-12 08:11:42.601545: train_loss -0.2667 +2026-04-12 08:11:42.608240: val_loss -0.1799 +2026-04-12 08:11:42.610250: Pseudo dice [0.5287, 0.8247, 0.735, 0.2953, 0.1926, 0.2823, 0.4717] +2026-04-12 08:11:42.613412: Epoch time: 101.52 s +2026-04-12 08:11:43.838077: +2026-04-12 08:11:43.841055: Epoch 1594 +2026-04-12 08:11:43.843390: Current learning rate: 0.00633 +2026-04-12 08:13:25.906999: train_loss -0.258 +2026-04-12 08:13:25.912330: val_loss -0.2269 +2026-04-12 08:13:25.915251: Pseudo dice [0.0552, 0.6573, 0.7892, 0.4518, 0.3684, 0.8904, 0.657] +2026-04-12 08:13:25.918988: Epoch time: 102.07 s +2026-04-12 08:13:27.131457: +2026-04-12 08:13:27.133495: Epoch 1595 +2026-04-12 08:13:27.135178: Current learning rate: 0.00633 +2026-04-12 08:15:09.125556: train_loss -0.2655 +2026-04-12 08:15:09.132671: val_loss -0.2315 +2026-04-12 08:15:09.134783: Pseudo dice [0.6562, 0.9047, 0.6899, 0.5337, 0.3819, 0.4433, 0.5005] +2026-04-12 08:15:09.137259: Epoch time: 102.0 s +2026-04-12 08:15:10.332450: +2026-04-12 08:15:10.334435: Epoch 1596 +2026-04-12 08:15:10.337028: Current learning rate: 0.00632 +2026-04-12 08:16:52.252495: train_loss -0.2626 +2026-04-12 08:16:52.261702: val_loss -0.1722 +2026-04-12 08:16:52.264234: Pseudo dice [0.7471, 0.8226, 0.4377, 0.4681, 0.2309, 0.2257, 0.6282] +2026-04-12 08:16:52.266908: Epoch time: 101.92 s +2026-04-12 08:16:53.455769: +2026-04-12 08:16:53.457325: Epoch 1597 +2026-04-12 08:16:53.458879: Current learning rate: 0.00632 +2026-04-12 08:18:35.311739: train_loss -0.271 +2026-04-12 08:18:35.319046: val_loss -0.204 +2026-04-12 08:18:35.322306: Pseudo dice [0.6229, 0.8773, 0.4514, 0.3078, 0.5843, 0.1848, 0.7772] +2026-04-12 08:18:35.324740: Epoch time: 101.86 s +2026-04-12 08:18:36.541480: +2026-04-12 08:18:36.543498: Epoch 1598 +2026-04-12 08:18:36.545542: Current learning rate: 0.00632 +2026-04-12 08:20:19.875553: train_loss -0.2785 +2026-04-12 08:20:19.882649: val_loss -0.216 +2026-04-12 08:20:19.885031: Pseudo dice [0.5033, 0.8718, 0.6266, 0.5108, 0.607, 0.6976, 0.7502] +2026-04-12 08:20:19.888478: Epoch time: 103.34 s +2026-04-12 08:20:21.107888: +2026-04-12 08:20:21.111619: Epoch 1599 +2026-04-12 08:20:21.113654: Current learning rate: 0.00632 +2026-04-12 08:22:03.253664: train_loss -0.2537 +2026-04-12 08:22:03.262899: val_loss -0.2107 +2026-04-12 08:22:03.264620: Pseudo dice [0.6655, 0.547, 0.6349, 0.4267, 0.4524, 0.6721, 0.4689] +2026-04-12 08:22:03.267034: Epoch time: 102.15 s +2026-04-12 08:22:07.016580: +2026-04-12 08:22:07.018776: Epoch 1600 +2026-04-12 08:22:07.020269: Current learning rate: 0.00631 +2026-04-12 08:23:48.796456: train_loss -0.2653 +2026-04-12 08:23:48.803333: val_loss -0.2004 +2026-04-12 08:23:48.805928: Pseudo dice [0.5551, 0.8851, 0.7555, 0.356, 0.3365, 0.4671, 0.4685] +2026-04-12 08:23:48.808686: Epoch time: 101.78 s +2026-04-12 08:23:50.103789: +2026-04-12 08:23:50.105825: Epoch 1601 +2026-04-12 08:23:50.107865: Current learning rate: 0.00631 +2026-04-12 08:25:31.997886: train_loss -0.2466 +2026-04-12 08:25:32.005481: val_loss -0.204 +2026-04-12 08:25:32.008018: Pseudo dice [0.8012, 0.8532, 0.425, 0.6358, 0.3441, 0.5974, 0.7187] +2026-04-12 08:25:32.013161: Epoch time: 101.9 s +2026-04-12 08:25:33.232497: +2026-04-12 08:25:33.234461: Epoch 1602 +2026-04-12 08:25:33.236152: Current learning rate: 0.00631 +2026-04-12 08:27:15.657912: train_loss -0.2594 +2026-04-12 08:27:15.665114: val_loss -0.228 +2026-04-12 08:27:15.667955: Pseudo dice [0.5165, 0.8012, 0.8322, 0.5867, 0.3628, 0.8128, 0.8711] +2026-04-12 08:27:15.670964: Epoch time: 102.43 s +2026-04-12 08:27:16.888714: +2026-04-12 08:27:16.890932: Epoch 1603 +2026-04-12 08:27:16.894001: Current learning rate: 0.00631 +2026-04-12 08:28:58.741060: train_loss -0.2652 +2026-04-12 08:28:58.748667: val_loss -0.2412 +2026-04-12 08:28:58.750671: Pseudo dice [0.3372, 0.7072, 0.7747, 0.786, 0.4175, 0.8783, 0.7753] +2026-04-12 08:28:58.752799: Epoch time: 101.86 s +2026-04-12 08:28:59.969995: +2026-04-12 08:28:59.971709: Epoch 1604 +2026-04-12 08:28:59.973308: Current learning rate: 0.0063 +2026-04-12 08:30:41.933776: train_loss -0.2713 +2026-04-12 08:30:41.941069: val_loss -0.2272 +2026-04-12 08:30:41.943378: Pseudo dice [0.6098, 0.3769, 0.7556, 0.4906, 0.4434, 0.9264, 0.7599] +2026-04-12 08:30:41.945465: Epoch time: 101.97 s +2026-04-12 08:30:43.158852: +2026-04-12 08:30:43.163295: Epoch 1605 +2026-04-12 08:30:43.165222: Current learning rate: 0.0063 +2026-04-12 08:32:24.945222: train_loss -0.2773 +2026-04-12 08:32:24.952177: val_loss -0.2654 +2026-04-12 08:32:24.954703: Pseudo dice [0.7208, 0.8256, 0.6597, 0.7409, 0.6308, 0.7294, 0.839] +2026-04-12 08:32:24.957137: Epoch time: 101.79 s +2026-04-12 08:32:26.199203: +2026-04-12 08:32:26.200871: Epoch 1606 +2026-04-12 08:32:26.202324: Current learning rate: 0.0063 +2026-04-12 08:34:08.030777: train_loss -0.2427 +2026-04-12 08:34:08.036202: val_loss -0.217 +2026-04-12 08:34:08.037826: Pseudo dice [0.4861, 0.7467, 0.718, 0.6659, 0.3821, 0.0671, 0.7121] +2026-04-12 08:34:08.039839: Epoch time: 101.83 s +2026-04-12 08:34:09.256866: +2026-04-12 08:34:09.258712: Epoch 1607 +2026-04-12 08:34:09.260458: Current learning rate: 0.0063 +2026-04-12 08:35:51.221848: train_loss -0.2529 +2026-04-12 08:35:51.228362: val_loss -0.2238 +2026-04-12 08:35:51.230524: Pseudo dice [0.4434, 0.7861, 0.7736, 0.5787, 0.453, 0.8369, 0.4115] +2026-04-12 08:35:51.233401: Epoch time: 101.97 s +2026-04-12 08:35:52.482678: +2026-04-12 08:35:52.484478: Epoch 1608 +2026-04-12 08:35:52.486176: Current learning rate: 0.0063 +2026-04-12 08:37:34.096105: train_loss -0.2674 +2026-04-12 08:37:34.104076: val_loss -0.2183 +2026-04-12 08:37:34.106193: Pseudo dice [0.5497, 0.8717, 0.7605, 0.6442, 0.3849, 0.6783, 0.4709] +2026-04-12 08:37:34.108526: Epoch time: 101.62 s +2026-04-12 08:37:35.317596: +2026-04-12 08:37:35.320219: Epoch 1609 +2026-04-12 08:37:35.322040: Current learning rate: 0.00629 +2026-04-12 08:39:17.259510: train_loss -0.2644 +2026-04-12 08:39:17.269645: val_loss -0.1581 +2026-04-12 08:39:17.271565: Pseudo dice [0.4179, 0.8805, 0.7464, 0.3678, 0.3444, 0.1077, 0.2936] +2026-04-12 08:39:17.274022: Epoch time: 101.95 s +2026-04-12 08:39:18.515586: +2026-04-12 08:39:18.517545: Epoch 1610 +2026-04-12 08:39:18.519619: Current learning rate: 0.00629 +2026-04-12 08:41:00.377769: train_loss -0.2574 +2026-04-12 08:41:00.384284: val_loss -0.1984 +2026-04-12 08:41:00.386434: Pseudo dice [0.6204, 0.7129, 0.5771, 0.38, 0.6604, 0.8759, 0.7356] +2026-04-12 08:41:00.388565: Epoch time: 101.87 s +2026-04-12 08:41:01.570761: +2026-04-12 08:41:01.572266: Epoch 1611 +2026-04-12 08:41:01.573767: Current learning rate: 0.00629 +2026-04-12 08:42:43.746846: train_loss -0.2481 +2026-04-12 08:42:43.752615: val_loss -0.2252 +2026-04-12 08:42:43.754498: Pseudo dice [0.5048, 0.6337, 0.6633, 0.6329, 0.3569, 0.9058, 0.8677] +2026-04-12 08:42:43.756625: Epoch time: 102.18 s +2026-04-12 08:42:44.957212: +2026-04-12 08:42:44.959170: Epoch 1612 +2026-04-12 08:42:44.960857: Current learning rate: 0.00629 +2026-04-12 08:44:26.962939: train_loss -0.2639 +2026-04-12 08:44:26.970679: val_loss -0.192 +2026-04-12 08:44:26.972875: Pseudo dice [0.7263, 0.3591, 0.6285, 0.4497, 0.6332, 0.7346, 0.7925] +2026-04-12 08:44:26.975313: Epoch time: 102.01 s +2026-04-12 08:44:28.201336: +2026-04-12 08:44:28.203403: Epoch 1613 +2026-04-12 08:44:28.205371: Current learning rate: 0.00628 +2026-04-12 08:46:09.750410: train_loss -0.2705 +2026-04-12 08:46:09.757261: val_loss -0.2322 +2026-04-12 08:46:09.759247: Pseudo dice [0.3404, 0.8036, 0.7847, 0.4942, 0.3234, 0.8033, 0.8797] +2026-04-12 08:46:09.761642: Epoch time: 101.55 s +2026-04-12 08:46:10.983589: +2026-04-12 08:46:10.985498: Epoch 1614 +2026-04-12 08:46:10.987392: Current learning rate: 0.00628 +2026-04-12 08:47:53.294080: train_loss -0.2631 +2026-04-12 08:47:53.307563: val_loss -0.0869 +2026-04-12 08:47:53.309992: Pseudo dice [0.3949, 0.5204, 0.1351, 0.0852, 0.357, 0.1116, 0.6655] +2026-04-12 08:47:53.312668: Epoch time: 102.31 s +2026-04-12 08:47:54.555863: +2026-04-12 08:47:54.557513: Epoch 1615 +2026-04-12 08:47:54.559089: Current learning rate: 0.00628 +2026-04-12 08:49:36.638030: train_loss -0.2479 +2026-04-12 08:49:36.643835: val_loss -0.2301 +2026-04-12 08:49:36.645494: Pseudo dice [0.1403, 0.847, 0.7139, 0.4221, 0.4607, 0.8519, 0.7628] +2026-04-12 08:49:36.648138: Epoch time: 102.09 s +2026-04-12 08:49:37.850422: +2026-04-12 08:49:37.852612: Epoch 1616 +2026-04-12 08:49:37.854594: Current learning rate: 0.00628 +2026-04-12 08:51:19.342126: train_loss -0.2621 +2026-04-12 08:51:19.351136: val_loss -0.2198 +2026-04-12 08:51:19.354220: Pseudo dice [0.4399, 0.6298, 0.7855, 0.4266, 0.5438, 0.8346, 0.6747] +2026-04-12 08:51:19.358594: Epoch time: 101.49 s +2026-04-12 08:51:20.562885: +2026-04-12 08:51:20.565221: Epoch 1617 +2026-04-12 08:51:20.567077: Current learning rate: 0.00627 +2026-04-12 08:53:02.484320: train_loss -0.2774 +2026-04-12 08:53:02.490727: val_loss -0.2545 +2026-04-12 08:53:02.493199: Pseudo dice [0.4332, 0.5605, 0.8139, 0.5301, 0.4316, 0.7502, 0.8275] +2026-04-12 08:53:02.496651: Epoch time: 101.92 s +2026-04-12 08:53:03.712334: +2026-04-12 08:53:03.715157: Epoch 1618 +2026-04-12 08:53:03.716952: Current learning rate: 0.00627 +2026-04-12 08:54:46.142651: train_loss -0.2578 +2026-04-12 08:54:46.155758: val_loss -0.2275 +2026-04-12 08:54:46.157842: Pseudo dice [0.614, 0.6618, 0.7355, 0.4372, 0.3194, 0.8677, 0.7588] +2026-04-12 08:54:46.160594: Epoch time: 102.43 s +2026-04-12 08:54:47.384543: +2026-04-12 08:54:47.387263: Epoch 1619 +2026-04-12 08:54:47.389300: Current learning rate: 0.00627 +2026-04-12 08:56:29.779705: train_loss -0.2656 +2026-04-12 08:56:29.787679: val_loss -0.2447 +2026-04-12 08:56:29.790826: Pseudo dice [0.821, 0.8187, 0.6732, 0.6913, 0.7399, 0.8178, 0.7275] +2026-04-12 08:56:29.793961: Epoch time: 102.4 s +2026-04-12 08:56:32.182992: +2026-04-12 08:56:32.184865: Epoch 1620 +2026-04-12 08:56:32.186622: Current learning rate: 0.00627 +2026-04-12 08:58:14.414273: train_loss -0.2759 +2026-04-12 08:58:14.421692: val_loss -0.1746 +2026-04-12 08:58:14.424112: Pseudo dice [0.6927, 0.8181, 0.6113, 0.5274, 0.499, 0.4623, 0.4278] +2026-04-12 08:58:14.426279: Epoch time: 102.23 s +2026-04-12 08:58:15.650990: +2026-04-12 08:58:15.653038: Epoch 1621 +2026-04-12 08:58:15.654605: Current learning rate: 0.00626 +2026-04-12 08:59:57.991939: train_loss -0.278 +2026-04-12 08:59:57.998432: val_loss -0.2128 +2026-04-12 08:59:58.000527: Pseudo dice [0.7735, 0.898, 0.7213, 0.6926, 0.3028, 0.0764, 0.553] +2026-04-12 08:59:58.002929: Epoch time: 102.34 s +2026-04-12 08:59:59.225888: +2026-04-12 08:59:59.228530: Epoch 1622 +2026-04-12 08:59:59.230200: Current learning rate: 0.00626 +2026-04-12 09:01:41.371935: train_loss -0.2798 +2026-04-12 09:01:41.377884: val_loss -0.2121 +2026-04-12 09:01:41.379912: Pseudo dice [0.8169, 0.3258, 0.6677, 0.4537, 0.1783, 0.6622, 0.8413] +2026-04-12 09:01:41.382817: Epoch time: 102.15 s +2026-04-12 09:01:42.623745: +2026-04-12 09:01:42.626523: Epoch 1623 +2026-04-12 09:01:42.629698: Current learning rate: 0.00626 +2026-04-12 09:03:25.200474: train_loss -0.2636 +2026-04-12 09:03:25.207255: val_loss -0.1615 +2026-04-12 09:03:25.209398: Pseudo dice [0.1871, 0.9036, 0.6728, 0.7462, 0.5004, 0.0958, 0.6753] +2026-04-12 09:03:25.216265: Epoch time: 102.58 s +2026-04-12 09:03:26.443342: +2026-04-12 09:03:26.446061: Epoch 1624 +2026-04-12 09:03:26.447951: Current learning rate: 0.00626 +2026-04-12 09:05:08.612386: train_loss -0.2687 +2026-04-12 09:05:08.647271: val_loss -0.2129 +2026-04-12 09:05:08.650428: Pseudo dice [0.5939, 0.5041, 0.7478, 0.7976, 0.4358, 0.4488, 0.8403] +2026-04-12 09:05:08.652822: Epoch time: 102.17 s +2026-04-12 09:05:09.860154: +2026-04-12 09:05:09.862731: Epoch 1625 +2026-04-12 09:05:09.865016: Current learning rate: 0.00626 +2026-04-12 09:06:52.671081: train_loss -0.2579 +2026-04-12 09:06:52.678736: val_loss -0.212 +2026-04-12 09:06:52.682712: Pseudo dice [0.386, 0.8144, 0.7529, 0.4079, 0.6647, 0.6813, 0.5822] +2026-04-12 09:06:52.685225: Epoch time: 102.81 s +2026-04-12 09:06:53.896107: +2026-04-12 09:06:53.898794: Epoch 1626 +2026-04-12 09:06:53.904329: Current learning rate: 0.00625 +2026-04-12 09:08:35.673991: train_loss -0.2755 +2026-04-12 09:08:35.682243: val_loss -0.2086 +2026-04-12 09:08:35.685401: Pseudo dice [0.4266, 0.2671, 0.7448, 0.5435, 0.5357, 0.7256, 0.7079] +2026-04-12 09:08:35.690349: Epoch time: 101.78 s +2026-04-12 09:08:36.914146: +2026-04-12 09:08:36.919868: Epoch 1627 +2026-04-12 09:08:36.921904: Current learning rate: 0.00625 +2026-04-12 09:10:19.342255: train_loss -0.276 +2026-04-12 09:10:19.348943: val_loss -0.2097 +2026-04-12 09:10:19.351595: Pseudo dice [0.4554, 0.3544, 0.6687, 0.4138, 0.5941, 0.242, 0.6885] +2026-04-12 09:10:19.354479: Epoch time: 102.43 s +2026-04-12 09:10:20.573687: +2026-04-12 09:10:20.576303: Epoch 1628 +2026-04-12 09:10:20.577806: Current learning rate: 0.00625 +2026-04-12 09:12:02.579092: train_loss -0.2657 +2026-04-12 09:12:02.586983: val_loss -0.2014 +2026-04-12 09:12:02.589655: Pseudo dice [0.1963, 0.3773, 0.7547, 0.2833, 0.3418, 0.8013, 0.4699] +2026-04-12 09:12:02.593323: Epoch time: 102.01 s +2026-04-12 09:12:03.830800: +2026-04-12 09:12:03.832489: Epoch 1629 +2026-04-12 09:12:03.834580: Current learning rate: 0.00625 +2026-04-12 09:13:45.660311: train_loss -0.2685 +2026-04-12 09:13:45.667784: val_loss -0.2147 +2026-04-12 09:13:45.673201: Pseudo dice [0.681, 0.6605, 0.7555, 0.5304, 0.4076, 0.8441, 0.6392] +2026-04-12 09:13:45.678050: Epoch time: 101.83 s +2026-04-12 09:13:46.915649: +2026-04-12 09:13:46.917908: Epoch 1630 +2026-04-12 09:13:46.920042: Current learning rate: 0.00624 +2026-04-12 09:15:29.494809: train_loss -0.2728 +2026-04-12 09:15:29.502244: val_loss -0.1826 +2026-04-12 09:15:29.504265: Pseudo dice [0.5067, 0.8572, 0.7814, 0.4263, 0.5024, 0.3732, 0.2767] +2026-04-12 09:15:29.506781: Epoch time: 102.58 s +2026-04-12 09:15:30.804697: +2026-04-12 09:15:30.806557: Epoch 1631 +2026-04-12 09:15:30.808035: Current learning rate: 0.00624 +2026-04-12 09:17:12.661132: train_loss -0.278 +2026-04-12 09:17:12.668371: val_loss -0.2162 +2026-04-12 09:17:12.670858: Pseudo dice [0.6678, 0.5683, 0.711, 0.6567, 0.6564, 0.5231, 0.8516] +2026-04-12 09:17:12.673251: Epoch time: 101.86 s +2026-04-12 09:17:13.884004: +2026-04-12 09:17:13.885647: Epoch 1632 +2026-04-12 09:17:13.887124: Current learning rate: 0.00624 +2026-04-12 09:18:55.894862: train_loss -0.2723 +2026-04-12 09:18:55.901363: val_loss -0.1761 +2026-04-12 09:18:55.903253: Pseudo dice [0.4032, 0.3778, 0.6127, 0.7677, 0.5216, 0.2985, 0.6198] +2026-04-12 09:18:55.905302: Epoch time: 102.01 s +2026-04-12 09:18:57.112607: +2026-04-12 09:18:57.114236: Epoch 1633 +2026-04-12 09:18:57.115828: Current learning rate: 0.00624 +2026-04-12 09:20:39.458123: train_loss -0.2856 +2026-04-12 09:20:39.464127: val_loss -0.2361 +2026-04-12 09:20:39.466217: Pseudo dice [0.4445, 0.5887, 0.763, 0.4767, 0.4534, 0.5751, 0.4384] +2026-04-12 09:20:39.468618: Epoch time: 102.35 s +2026-04-12 09:20:40.659454: +2026-04-12 09:20:40.662403: Epoch 1634 +2026-04-12 09:20:40.665007: Current learning rate: 0.00623 +2026-04-12 09:22:22.726584: train_loss -0.2755 +2026-04-12 09:22:22.732257: val_loss -0.2223 +2026-04-12 09:22:22.734429: Pseudo dice [0.6934, 0.8757, 0.8717, 0.1317, 0.5041, 0.7839, 0.5934] +2026-04-12 09:22:22.737051: Epoch time: 102.07 s +2026-04-12 09:22:24.190010: +2026-04-12 09:22:24.191653: Epoch 1635 +2026-04-12 09:22:24.194224: Current learning rate: 0.00623 +2026-04-12 09:24:06.661939: train_loss -0.2775 +2026-04-12 09:24:06.667988: val_loss -0.2331 +2026-04-12 09:24:06.670003: Pseudo dice [0.3515, 0.7801, 0.7258, 0.5103, 0.6064, 0.594, 0.6343] +2026-04-12 09:24:06.672260: Epoch time: 102.48 s +2026-04-12 09:24:07.863311: +2026-04-12 09:24:07.865564: Epoch 1636 +2026-04-12 09:24:07.868007: Current learning rate: 0.00623 +2026-04-12 09:25:50.036453: train_loss -0.2668 +2026-04-12 09:25:50.042330: val_loss -0.2129 +2026-04-12 09:25:50.044656: Pseudo dice [0.4386, 0.8967, 0.7777, 0.3188, 0.4814, 0.7164, 0.6999] +2026-04-12 09:25:50.047070: Epoch time: 102.18 s +2026-04-12 09:25:51.280413: +2026-04-12 09:25:51.282357: Epoch 1637 +2026-04-12 09:25:51.284149: Current learning rate: 0.00623 +2026-04-12 09:27:33.557181: train_loss -0.2707 +2026-04-12 09:27:33.563368: val_loss -0.1989 +2026-04-12 09:27:33.565346: Pseudo dice [0.19, 0.203, 0.6514, 0.4543, 0.5838, 0.8169, 0.8331] +2026-04-12 09:27:33.567575: Epoch time: 102.28 s +2026-04-12 09:27:34.755658: +2026-04-12 09:27:34.757897: Epoch 1638 +2026-04-12 09:27:34.759685: Current learning rate: 0.00622 +2026-04-12 09:29:16.423257: train_loss -0.26 +2026-04-12 09:29:16.430324: val_loss -0.2182 +2026-04-12 09:29:16.432860: Pseudo dice [0.4981, 0.6289, 0.6355, 0.7287, 0.4163, 0.4818, 0.7773] +2026-04-12 09:29:16.435586: Epoch time: 101.67 s +2026-04-12 09:29:17.618753: +2026-04-12 09:29:17.620623: Epoch 1639 +2026-04-12 09:29:17.622201: Current learning rate: 0.00622 +2026-04-12 09:31:00.010395: train_loss -0.2696 +2026-04-12 09:31:00.016721: val_loss -0.2403 +2026-04-12 09:31:00.019017: Pseudo dice [0.7414, 0.5097, 0.6545, 0.7897, 0.6211, 0.8344, 0.8814] +2026-04-12 09:31:00.021498: Epoch time: 102.39 s +2026-04-12 09:31:01.205102: +2026-04-12 09:31:01.206976: Epoch 1640 +2026-04-12 09:31:01.208499: Current learning rate: 0.00622 +2026-04-12 09:32:43.134499: train_loss -0.277 +2026-04-12 09:32:43.140422: val_loss -0.2216 +2026-04-12 09:32:43.143039: Pseudo dice [0.5631, 0.3575, 0.5958, 0.6983, 0.4456, 0.7333, 0.367] +2026-04-12 09:32:43.149584: Epoch time: 101.93 s +2026-04-12 09:32:44.323454: +2026-04-12 09:32:44.325420: Epoch 1641 +2026-04-12 09:32:44.327093: Current learning rate: 0.00622 +2026-04-12 09:34:26.562130: train_loss -0.2633 +2026-04-12 09:34:26.570051: val_loss -0.2052 +2026-04-12 09:34:26.572329: Pseudo dice [0.5754, 0.2609, 0.7587, 0.1158, 0.3578, 0.7549, 0.7231] +2026-04-12 09:34:26.574924: Epoch time: 102.24 s +2026-04-12 09:34:27.727889: +2026-04-12 09:34:27.729733: Epoch 1642 +2026-04-12 09:34:27.731560: Current learning rate: 0.00621 +2026-04-12 09:36:09.565212: train_loss -0.273 +2026-04-12 09:36:09.592674: val_loss -0.2601 +2026-04-12 09:36:09.594467: Pseudo dice [0.6478, 0.3939, 0.8146, 0.3719, 0.55, 0.9291, 0.8218] +2026-04-12 09:36:09.597126: Epoch time: 101.84 s +2026-04-12 09:36:10.789489: +2026-04-12 09:36:10.791524: Epoch 1643 +2026-04-12 09:36:10.793382: Current learning rate: 0.00621 +2026-04-12 09:37:53.268152: train_loss -0.2822 +2026-04-12 09:37:53.274590: val_loss -0.2229 +2026-04-12 09:37:53.276597: Pseudo dice [0.4775, 0.6865, 0.7672, 0.2992, 0.522, 0.5865, 0.7477] +2026-04-12 09:37:53.279094: Epoch time: 102.48 s +2026-04-12 09:37:54.487542: +2026-04-12 09:37:54.489211: Epoch 1644 +2026-04-12 09:37:54.490876: Current learning rate: 0.00621 +2026-04-12 09:39:35.995755: train_loss -0.2626 +2026-04-12 09:39:36.002785: val_loss -0.1987 +2026-04-12 09:39:36.004850: Pseudo dice [0.7466, 0.1531, 0.6361, 0.2121, 0.4295, 0.3317, 0.487] +2026-04-12 09:39:36.007624: Epoch time: 101.51 s +2026-04-12 09:39:37.189130: +2026-04-12 09:39:37.192478: Epoch 1645 +2026-04-12 09:39:37.194225: Current learning rate: 0.00621 +2026-04-12 09:41:19.145560: train_loss -0.2572 +2026-04-12 09:41:19.151473: val_loss -0.2372 +2026-04-12 09:41:19.153486: Pseudo dice [0.7762, 0.6246, 0.8004, 0.5995, 0.6358, 0.3051, 0.6768] +2026-04-12 09:41:19.156235: Epoch time: 101.96 s +2026-04-12 09:41:20.352747: +2026-04-12 09:41:20.356938: Epoch 1646 +2026-04-12 09:41:20.358811: Current learning rate: 0.00621 +2026-04-12 09:43:02.251772: train_loss -0.2575 +2026-04-12 09:43:02.257327: val_loss -0.1837 +2026-04-12 09:43:02.259250: Pseudo dice [0.5942, 0.621, 0.73, 0.2167, 0.1376, 0.7398, 0.6371] +2026-04-12 09:43:02.261972: Epoch time: 101.9 s +2026-04-12 09:43:03.447087: +2026-04-12 09:43:03.449617: Epoch 1647 +2026-04-12 09:43:03.452285: Current learning rate: 0.0062 +2026-04-12 09:44:45.238274: train_loss -0.252 +2026-04-12 09:44:45.244817: val_loss -0.0655 +2026-04-12 09:44:45.247262: Pseudo dice [0.0829, 0.8492, 0.2527, 0.0576, 0.5254, 0.1792, 0.583] +2026-04-12 09:44:45.249789: Epoch time: 101.79 s +2026-04-12 09:44:46.467891: +2026-04-12 09:44:46.470006: Epoch 1648 +2026-04-12 09:44:46.472216: Current learning rate: 0.0062 +2026-04-12 09:46:28.004266: train_loss -0.2629 +2026-04-12 09:46:28.010675: val_loss -0.228 +2026-04-12 09:46:28.012935: Pseudo dice [0.5125, 0.4653, 0.7923, 0.505, 0.5334, 0.6504, 0.7106] +2026-04-12 09:46:28.015469: Epoch time: 101.54 s +2026-04-12 09:46:29.196087: +2026-04-12 09:46:29.197809: Epoch 1649 +2026-04-12 09:46:29.199640: Current learning rate: 0.0062 +2026-04-12 09:48:10.503485: train_loss -0.2804 +2026-04-12 09:48:10.510004: val_loss -0.1913 +2026-04-12 09:48:10.512179: Pseudo dice [0.2499, 0.1953, 0.7492, 0.1608, 0.5487, 0.8549, 0.6452] +2026-04-12 09:48:10.514835: Epoch time: 101.31 s +2026-04-12 09:48:13.390298: +2026-04-12 09:48:13.392739: Epoch 1650 +2026-04-12 09:48:13.394390: Current learning rate: 0.0062 +2026-04-12 09:49:55.917376: train_loss -0.2771 +2026-04-12 09:49:55.931936: val_loss -0.2202 +2026-04-12 09:49:55.934058: Pseudo dice [0.5963, 0.137, 0.7563, 0.2056, 0.5687, 0.815, 0.8508] +2026-04-12 09:49:55.936719: Epoch time: 102.53 s +2026-04-12 09:49:57.121593: +2026-04-12 09:49:57.124041: Epoch 1651 +2026-04-12 09:49:57.126157: Current learning rate: 0.00619 +2026-04-12 09:51:39.002615: train_loss -0.2807 +2026-04-12 09:51:39.009203: val_loss -0.1669 +2026-04-12 09:51:39.011251: Pseudo dice [0.2039, 0.0873, 0.7566, 0.4504, 0.1297, 0.403, 0.579] +2026-04-12 09:51:39.013326: Epoch time: 101.88 s +2026-04-12 09:51:40.222446: +2026-04-12 09:51:40.224208: Epoch 1652 +2026-04-12 09:51:40.226606: Current learning rate: 0.00619 +2026-04-12 09:53:22.083598: train_loss -0.2669 +2026-04-12 09:53:22.092211: val_loss -0.2133 +2026-04-12 09:53:22.094276: Pseudo dice [0.4956, 0.8202, 0.8326, 0.5648, 0.4602, 0.6432, 0.838] +2026-04-12 09:53:22.097529: Epoch time: 101.86 s +2026-04-12 09:53:23.301978: +2026-04-12 09:53:23.305006: Epoch 1653 +2026-04-12 09:53:23.307083: Current learning rate: 0.00619 +2026-04-12 09:55:05.535754: train_loss -0.2611 +2026-04-12 09:55:05.548216: val_loss -0.1911 +2026-04-12 09:55:05.552576: Pseudo dice [0.5864, 0.2872, 0.7412, 0.1256, 0.3507, 0.8519, 0.7122] +2026-04-12 09:55:05.556566: Epoch time: 102.24 s +2026-04-12 09:55:06.726652: +2026-04-12 09:55:06.729715: Epoch 1654 +2026-04-12 09:55:06.732995: Current learning rate: 0.00619 +2026-04-12 09:56:48.711701: train_loss -0.2637 +2026-04-12 09:56:48.717668: val_loss -0.1268 +2026-04-12 09:56:48.719615: Pseudo dice [0.4838, 0.7631, 0.3744, 0.2845, 0.3529, 0.8356, 0.8166] +2026-04-12 09:56:48.721847: Epoch time: 101.99 s +2026-04-12 09:56:49.893534: +2026-04-12 09:56:49.895415: Epoch 1655 +2026-04-12 09:56:49.897165: Current learning rate: 0.00618 +2026-04-12 09:58:31.873077: train_loss -0.2543 +2026-04-12 09:58:31.881171: val_loss -0.2152 +2026-04-12 09:58:31.891941: Pseudo dice [0.6352, 0.7542, 0.7468, 0.4146, 0.4081, 0.6851, 0.514] +2026-04-12 09:58:31.895082: Epoch time: 101.98 s +2026-04-12 09:58:33.086859: +2026-04-12 09:58:33.090156: Epoch 1656 +2026-04-12 09:58:33.092159: Current learning rate: 0.00618 +2026-04-12 10:00:14.622901: train_loss -0.2563 +2026-04-12 10:00:14.630157: val_loss -0.2264 +2026-04-12 10:00:14.632535: Pseudo dice [0.6423, 0.2729, 0.7551, 0.5633, 0.4484, 0.7616, 0.7217] +2026-04-12 10:00:14.634737: Epoch time: 101.54 s +2026-04-12 10:00:15.814547: +2026-04-12 10:00:15.816085: Epoch 1657 +2026-04-12 10:00:15.817654: Current learning rate: 0.00618 +2026-04-12 10:01:58.109120: train_loss -0.2705 +2026-04-12 10:01:58.120389: val_loss -0.2011 +2026-04-12 10:01:58.122891: Pseudo dice [0.6419, 0.9049, 0.7163, 0.5217, 0.477, 0.263, 0.1892] +2026-04-12 10:01:58.126796: Epoch time: 102.3 s +2026-04-12 10:01:59.347594: +2026-04-12 10:01:59.349805: Epoch 1658 +2026-04-12 10:01:59.352000: Current learning rate: 0.00618 +2026-04-12 10:03:41.115775: train_loss -0.2694 +2026-04-12 10:03:41.121609: val_loss -0.2244 +2026-04-12 10:03:41.123510: Pseudo dice [0.6497, 0.6043, 0.7791, 0.1994, 0.5257, 0.7544, 0.3462] +2026-04-12 10:03:41.125844: Epoch time: 101.77 s +2026-04-12 10:03:43.336351: +2026-04-12 10:03:43.338145: Epoch 1659 +2026-04-12 10:03:43.339647: Current learning rate: 0.00617 +2026-04-12 10:05:25.296151: train_loss -0.2669 +2026-04-12 10:05:25.303947: val_loss -0.205 +2026-04-12 10:05:25.307318: Pseudo dice [0.5248, 0.9068, 0.7019, 0.4777, 0.3933, 0.4528, 0.5673] +2026-04-12 10:05:25.310777: Epoch time: 101.96 s +2026-04-12 10:05:26.495088: +2026-04-12 10:05:26.498151: Epoch 1660 +2026-04-12 10:05:26.500521: Current learning rate: 0.00617 +2026-04-12 10:07:08.235337: train_loss -0.2693 +2026-04-12 10:07:08.240997: val_loss -0.2485 +2026-04-12 10:07:08.244046: Pseudo dice [0.6384, 0.2542, 0.7919, 0.7231, 0.6389, 0.8644, 0.8091] +2026-04-12 10:07:08.246945: Epoch time: 101.74 s +2026-04-12 10:07:09.424009: +2026-04-12 10:07:09.425597: Epoch 1661 +2026-04-12 10:07:09.427187: Current learning rate: 0.00617 +2026-04-12 10:08:50.890594: train_loss -0.2808 +2026-04-12 10:08:50.896836: val_loss -0.2261 +2026-04-12 10:08:50.899084: Pseudo dice [0.8067, 0.7607, 0.7554, 0.3373, 0.4926, 0.4503, 0.709] +2026-04-12 10:08:50.901542: Epoch time: 101.47 s +2026-04-12 10:08:52.102380: +2026-04-12 10:08:52.104074: Epoch 1662 +2026-04-12 10:08:52.105493: Current learning rate: 0.00617 +2026-04-12 10:10:34.856308: train_loss -0.2631 +2026-04-12 10:10:34.878356: val_loss -0.2129 +2026-04-12 10:10:34.884596: Pseudo dice [0.7164, 0.5151, 0.7475, 0.4547, 0.6396, 0.895, 0.5041] +2026-04-12 10:10:34.887215: Epoch time: 102.76 s +2026-04-12 10:10:36.068040: +2026-04-12 10:10:36.070432: Epoch 1663 +2026-04-12 10:10:36.072038: Current learning rate: 0.00617 +2026-04-12 10:12:17.683326: train_loss -0.2545 +2026-04-12 10:12:17.690533: val_loss -0.225 +2026-04-12 10:12:17.693187: Pseudo dice [0.4556, 0.6064, 0.7967, 0.001, 0.3715, 0.8653, 0.833] +2026-04-12 10:12:17.696405: Epoch time: 101.62 s +2026-04-12 10:12:18.897610: +2026-04-12 10:12:18.899307: Epoch 1664 +2026-04-12 10:12:18.901013: Current learning rate: 0.00616 +2026-04-12 10:14:01.098238: train_loss -0.2707 +2026-04-12 10:14:01.103672: val_loss -0.2354 +2026-04-12 10:14:01.106117: Pseudo dice [0.4401, 0.7404, 0.7746, 0.5015, 0.521, 0.5092, 0.7162] +2026-04-12 10:14:01.108500: Epoch time: 102.2 s +2026-04-12 10:14:02.319263: +2026-04-12 10:14:02.321457: Epoch 1665 +2026-04-12 10:14:02.323172: Current learning rate: 0.00616 +2026-04-12 10:15:44.114621: train_loss -0.2709 +2026-04-12 10:15:44.120568: val_loss -0.22 +2026-04-12 10:15:44.122910: Pseudo dice [0.6312, 0.655, 0.8261, 0.1825, 0.4401, 0.6115, 0.8304] +2026-04-12 10:15:44.125656: Epoch time: 101.8 s +2026-04-12 10:15:45.306885: +2026-04-12 10:15:45.309163: Epoch 1666 +2026-04-12 10:15:45.311311: Current learning rate: 0.00616 +2026-04-12 10:17:27.111217: train_loss -0.2465 +2026-04-12 10:17:27.117577: val_loss -0.1817 +2026-04-12 10:17:27.119948: Pseudo dice [0.7031, 0.8532, 0.4973, 0.8213, 0.1949, 0.6775, 0.8717] +2026-04-12 10:17:27.122751: Epoch time: 101.81 s +2026-04-12 10:17:28.318854: +2026-04-12 10:17:28.321089: Epoch 1667 +2026-04-12 10:17:28.323393: Current learning rate: 0.00616 +2026-04-12 10:19:10.346503: train_loss -0.2625 +2026-04-12 10:19:10.354605: val_loss -0.2326 +2026-04-12 10:19:10.357030: Pseudo dice [0.4795, 0.6537, 0.8402, 0.1757, 0.511, 0.8348, 0.7054] +2026-04-12 10:19:10.359541: Epoch time: 102.03 s +2026-04-12 10:19:11.584363: +2026-04-12 10:19:11.586039: Epoch 1668 +2026-04-12 10:19:11.587622: Current learning rate: 0.00615 +2026-04-12 10:20:53.729963: train_loss -0.2683 +2026-04-12 10:20:53.737806: val_loss -0.1661 +2026-04-12 10:20:53.741917: Pseudo dice [0.6931, 0.4795, 0.5433, 0.0889, 0.2475, 0.8744, 0.6665] +2026-04-12 10:20:53.745070: Epoch time: 102.15 s +2026-04-12 10:20:54.979337: +2026-04-12 10:20:54.981146: Epoch 1669 +2026-04-12 10:20:54.982891: Current learning rate: 0.00615 +2026-04-12 10:22:36.977689: train_loss -0.268 +2026-04-12 10:22:36.985749: val_loss -0.1978 +2026-04-12 10:22:36.987988: Pseudo dice [0.536, 0.853, 0.6898, 0.24, 0.3004, 0.1276, 0.7316] +2026-04-12 10:22:36.991404: Epoch time: 102.0 s +2026-04-12 10:22:38.208051: +2026-04-12 10:22:38.210645: Epoch 1670 +2026-04-12 10:22:38.212662: Current learning rate: 0.00615 +2026-04-12 10:24:20.257314: train_loss -0.2576 +2026-04-12 10:24:20.264249: val_loss -0.2115 +2026-04-12 10:24:20.267616: Pseudo dice [0.5411, 0.8927, 0.7544, 0.653, 0.489, 0.1021, 0.6361] +2026-04-12 10:24:20.270381: Epoch time: 102.05 s +2026-04-12 10:24:21.520497: +2026-04-12 10:24:21.522137: Epoch 1671 +2026-04-12 10:24:21.523677: Current learning rate: 0.00615 +2026-04-12 10:26:02.865035: train_loss -0.2703 +2026-04-12 10:26:02.871331: val_loss -0.2527 +2026-04-12 10:26:02.873670: Pseudo dice [0.206, 0.805, 0.7961, 0.6171, 0.5761, 0.8581, 0.6897] +2026-04-12 10:26:02.876667: Epoch time: 101.35 s +2026-04-12 10:26:04.115448: +2026-04-12 10:26:04.117503: Epoch 1672 +2026-04-12 10:26:04.119867: Current learning rate: 0.00614 +2026-04-12 10:27:45.705241: train_loss -0.2596 +2026-04-12 10:27:45.711767: val_loss -0.2104 +2026-04-12 10:27:45.714449: Pseudo dice [0.8271, 0.0456, 0.6988, 0.2569, 0.4354, 0.796, 0.7947] +2026-04-12 10:27:45.717104: Epoch time: 101.59 s +2026-04-12 10:27:46.929181: +2026-04-12 10:27:46.931163: Epoch 1673 +2026-04-12 10:27:46.932847: Current learning rate: 0.00614 +2026-04-12 10:29:29.117417: train_loss -0.268 +2026-04-12 10:29:29.123840: val_loss -0.2331 +2026-04-12 10:29:29.126225: Pseudo dice [0.7491, 0.5513, 0.6955, 0.38, 0.3668, 0.7765, 0.5807] +2026-04-12 10:29:29.129279: Epoch time: 102.19 s +2026-04-12 10:29:30.300994: +2026-04-12 10:29:30.303080: Epoch 1674 +2026-04-12 10:29:30.304658: Current learning rate: 0.00614 +2026-04-12 10:31:12.543421: train_loss -0.2726 +2026-04-12 10:31:12.553280: val_loss -0.2415 +2026-04-12 10:31:12.556138: Pseudo dice [0.2977, 0.819, 0.843, 0.6942, 0.4176, 0.3108, 0.7003] +2026-04-12 10:31:12.560436: Epoch time: 102.25 s +2026-04-12 10:31:13.750147: +2026-04-12 10:31:13.752336: Epoch 1675 +2026-04-12 10:31:13.754442: Current learning rate: 0.00614 +2026-04-12 10:32:55.219534: train_loss -0.2574 +2026-04-12 10:32:55.227186: val_loss -0.2382 +2026-04-12 10:32:55.229147: Pseudo dice [0.5788, 0.7545, 0.6992, 0.2298, 0.419, 0.6071, 0.6598] +2026-04-12 10:32:55.231521: Epoch time: 101.47 s +2026-04-12 10:32:56.468540: +2026-04-12 10:32:56.470598: Epoch 1676 +2026-04-12 10:32:56.472403: Current learning rate: 0.00613 +2026-04-12 10:34:38.076738: train_loss -0.2628 +2026-04-12 10:34:38.084925: val_loss -0.2146 +2026-04-12 10:34:38.087104: Pseudo dice [0.7077, 0.2264, 0.7386, 0.1023, 0.3774, 0.8761, 0.8423] +2026-04-12 10:34:38.091923: Epoch time: 101.61 s +2026-04-12 10:34:39.313775: +2026-04-12 10:34:39.318066: Epoch 1677 +2026-04-12 10:34:39.320300: Current learning rate: 0.00613 +2026-04-12 10:36:21.173029: train_loss -0.2746 +2026-04-12 10:36:21.200392: val_loss -0.2264 +2026-04-12 10:36:21.203600: Pseudo dice [0.6846, 0.2882, 0.7815, 0.6496, 0.4738, 0.4294, 0.6824] +2026-04-12 10:36:21.207733: Epoch time: 101.86 s +2026-04-12 10:36:22.426727: +2026-04-12 10:36:22.428476: Epoch 1678 +2026-04-12 10:36:22.430242: Current learning rate: 0.00613 +2026-04-12 10:38:04.465281: train_loss -0.2638 +2026-04-12 10:38:04.472702: val_loss -0.2445 +2026-04-12 10:38:04.474785: Pseudo dice [0.3879, 0.7424, 0.5814, 0.6473, 0.3979, 0.7103, 0.8223] +2026-04-12 10:38:04.477239: Epoch time: 102.04 s +2026-04-12 10:38:06.806395: +2026-04-12 10:38:06.808162: Epoch 1679 +2026-04-12 10:38:06.809648: Current learning rate: 0.00613 +2026-04-12 10:39:48.429708: train_loss -0.2613 +2026-04-12 10:39:48.435891: val_loss -0.2463 +2026-04-12 10:39:48.438577: Pseudo dice [0.6654, 0.0334, 0.7754, 0.751, 0.5395, 0.7847, 0.8374] +2026-04-12 10:39:48.440826: Epoch time: 101.63 s +2026-04-12 10:39:49.639734: +2026-04-12 10:39:49.642012: Epoch 1680 +2026-04-12 10:39:49.644222: Current learning rate: 0.00612 +2026-04-12 10:41:31.271735: train_loss -0.2765 +2026-04-12 10:41:31.279231: val_loss -0.2292 +2026-04-12 10:41:31.281386: Pseudo dice [0.6896, 0.8886, 0.7676, 0.6, 0.3464, 0.1246, 0.6643] +2026-04-12 10:41:31.284442: Epoch time: 101.64 s +2026-04-12 10:41:32.484010: +2026-04-12 10:41:32.486469: Epoch 1681 +2026-04-12 10:41:32.488554: Current learning rate: 0.00612 +2026-04-12 10:43:14.540487: train_loss -0.2791 +2026-04-12 10:43:14.546577: val_loss -0.2222 +2026-04-12 10:43:14.548317: Pseudo dice [0.0702, 0.8936, 0.7187, 0.3578, 0.4869, 0.7466, 0.8564] +2026-04-12 10:43:14.550647: Epoch time: 102.06 s +2026-04-12 10:43:15.733891: +2026-04-12 10:43:15.735602: Epoch 1682 +2026-04-12 10:43:15.737598: Current learning rate: 0.00612 +2026-04-12 10:44:58.688761: train_loss -0.264 +2026-04-12 10:44:58.695261: val_loss -0.1926 +2026-04-12 10:44:58.697533: Pseudo dice [0.5117, 0.4005, 0.7271, 0.3325, 0.2265, 0.2371, 0.5808] +2026-04-12 10:44:58.699844: Epoch time: 102.96 s +2026-04-12 10:44:59.971353: +2026-04-12 10:44:59.973846: Epoch 1683 +2026-04-12 10:44:59.976152: Current learning rate: 0.00612 +2026-04-12 10:46:41.697006: train_loss -0.2487 +2026-04-12 10:46:41.705421: val_loss -0.2183 +2026-04-12 10:46:41.708344: Pseudo dice [0.5354, 0.4014, 0.7299, 0.8253, 0.3538, 0.6401, 0.6176] +2026-04-12 10:46:41.711596: Epoch time: 101.73 s +2026-04-12 10:46:42.920565: +2026-04-12 10:46:42.923150: Epoch 1684 +2026-04-12 10:46:42.925043: Current learning rate: 0.00612 +2026-04-12 10:48:24.508550: train_loss -0.2532 +2026-04-12 10:48:24.515230: val_loss -0.2247 +2026-04-12 10:48:24.517198: Pseudo dice [0.4358, 0.2047, 0.6855, 0.1765, 0.5657, 0.756, 0.8729] +2026-04-12 10:48:24.520132: Epoch time: 101.59 s +2026-04-12 10:48:25.747668: +2026-04-12 10:48:25.749878: Epoch 1685 +2026-04-12 10:48:25.751735: Current learning rate: 0.00611 +2026-04-12 10:50:08.367528: train_loss -0.2551 +2026-04-12 10:50:08.374977: val_loss -0.1703 +2026-04-12 10:50:08.377189: Pseudo dice [0.4459, 0.8061, 0.7502, 0.2762, 0.2748, 0.6651, 0.1877] +2026-04-12 10:50:08.379808: Epoch time: 102.62 s +2026-04-12 10:50:09.644254: +2026-04-12 10:50:09.647219: Epoch 1686 +2026-04-12 10:50:09.649448: Current learning rate: 0.00611 +2026-04-12 10:51:52.109966: train_loss -0.2671 +2026-04-12 10:51:52.116611: val_loss -0.2313 +2026-04-12 10:51:52.118642: Pseudo dice [0.5436, 0.4236, 0.7303, 0.8154, 0.4992, 0.2647, 0.618] +2026-04-12 10:51:52.120577: Epoch time: 102.47 s +2026-04-12 10:51:53.355450: +2026-04-12 10:51:53.358176: Epoch 1687 +2026-04-12 10:51:53.360126: Current learning rate: 0.00611 +2026-04-12 10:53:35.161205: train_loss -0.2716 +2026-04-12 10:53:35.167200: val_loss -0.2185 +2026-04-12 10:53:35.169310: Pseudo dice [0.0712, 0.3278, 0.6624, 0.3953, 0.3415, 0.6352, 0.8131] +2026-04-12 10:53:35.172189: Epoch time: 101.81 s +2026-04-12 10:53:36.375096: +2026-04-12 10:53:36.377081: Epoch 1688 +2026-04-12 10:53:36.378917: Current learning rate: 0.00611 +2026-04-12 10:55:18.436218: train_loss -0.2581 +2026-04-12 10:55:18.446106: val_loss -0.221 +2026-04-12 10:55:18.447906: Pseudo dice [0.566, 0.2349, 0.7193, 0.2288, 0.3783, 0.8846, 0.8496] +2026-04-12 10:55:18.450370: Epoch time: 102.06 s +2026-04-12 10:55:19.688871: +2026-04-12 10:55:19.691172: Epoch 1689 +2026-04-12 10:55:19.692899: Current learning rate: 0.0061 +2026-04-12 10:57:01.718025: train_loss -0.2615 +2026-04-12 10:57:01.723693: val_loss -0.2094 +2026-04-12 10:57:01.725784: Pseudo dice [0.7859, 0.4357, 0.6506, 0.5572, 0.3975, 0.8822, 0.7845] +2026-04-12 10:57:01.728142: Epoch time: 102.03 s +2026-04-12 10:57:02.967798: +2026-04-12 10:57:02.970014: Epoch 1690 +2026-04-12 10:57:02.971404: Current learning rate: 0.0061 +2026-04-12 10:58:44.477727: train_loss -0.2801 +2026-04-12 10:58:44.485353: val_loss -0.2362 +2026-04-12 10:58:44.488179: Pseudo dice [0.7173, 0.6509, 0.5754, 0.3959, 0.1441, 0.897, 0.8486] +2026-04-12 10:58:44.492102: Epoch time: 101.51 s +2026-04-12 10:58:45.687960: +2026-04-12 10:58:45.689853: Epoch 1691 +2026-04-12 10:58:45.691542: Current learning rate: 0.0061 +2026-04-12 11:00:27.846053: train_loss -0.2832 +2026-04-12 11:00:27.852448: val_loss -0.2342 +2026-04-12 11:00:27.854464: Pseudo dice [0.774, 0.8205, 0.8196, 0.7305, 0.3059, 0.6858, 0.7828] +2026-04-12 11:00:27.856572: Epoch time: 102.16 s +2026-04-12 11:00:29.058369: +2026-04-12 11:00:29.060484: Epoch 1692 +2026-04-12 11:00:29.062004: Current learning rate: 0.0061 +2026-04-12 11:02:10.916363: train_loss -0.2801 +2026-04-12 11:02:10.922230: val_loss -0.2275 +2026-04-12 11:02:10.924674: Pseudo dice [0.413, 0.3939, 0.7746, 0.6546, 0.4135, 0.7021, 0.7993] +2026-04-12 11:02:10.926928: Epoch time: 101.86 s +2026-04-12 11:02:12.134407: +2026-04-12 11:02:12.136077: Epoch 1693 +2026-04-12 11:02:12.137566: Current learning rate: 0.00609 +2026-04-12 11:03:54.113464: train_loss -0.2823 +2026-04-12 11:03:54.120751: val_loss -0.2267 +2026-04-12 11:03:54.122958: Pseudo dice [0.1535, 0.8421, 0.7346, 0.4802, 0.6425, 0.7646, 0.6342] +2026-04-12 11:03:54.125373: Epoch time: 101.98 s +2026-04-12 11:03:55.346582: +2026-04-12 11:03:55.348681: Epoch 1694 +2026-04-12 11:03:55.350695: Current learning rate: 0.00609 +2026-04-12 11:05:37.550495: train_loss -0.2771 +2026-04-12 11:05:37.556488: val_loss -0.2331 +2026-04-12 11:05:37.558331: Pseudo dice [0.4121, 0.2142, 0.7723, 0.3747, 0.5235, 0.7067, 0.8379] +2026-04-12 11:05:37.561095: Epoch time: 102.21 s +2026-04-12 11:05:38.770458: +2026-04-12 11:05:38.771929: Epoch 1695 +2026-04-12 11:05:38.773330: Current learning rate: 0.00609 +2026-04-12 11:07:20.776485: train_loss -0.2825 +2026-04-12 11:07:20.781738: val_loss -0.2312 +2026-04-12 11:07:20.783586: Pseudo dice [0.7695, 0.5569, 0.8097, 0.367, 0.5732, 0.8487, 0.8063] +2026-04-12 11:07:20.785790: Epoch time: 102.01 s +2026-04-12 11:07:22.004168: +2026-04-12 11:07:22.005920: Epoch 1696 +2026-04-12 11:07:22.007709: Current learning rate: 0.00609 +2026-04-12 11:09:04.474567: train_loss -0.2847 +2026-04-12 11:09:04.480970: val_loss -0.2412 +2026-04-12 11:09:04.483191: Pseudo dice [0.5978, 0.2493, 0.7713, 0.8136, 0.5836, 0.6235, 0.802] +2026-04-12 11:09:04.485697: Epoch time: 102.47 s +2026-04-12 11:09:05.662258: +2026-04-12 11:09:05.664541: Epoch 1697 +2026-04-12 11:09:05.666789: Current learning rate: 0.00608 +2026-04-12 11:10:48.284976: train_loss -0.2678 +2026-04-12 11:10:48.293197: val_loss -0.1942 +2026-04-12 11:10:48.296303: Pseudo dice [0.5684, 0.8912, 0.7104, 0.707, 0.3112, 0.1804, 0.2451] +2026-04-12 11:10:48.298993: Epoch time: 102.63 s +2026-04-12 11:10:49.522239: +2026-04-12 11:10:49.524045: Epoch 1698 +2026-04-12 11:10:49.525686: Current learning rate: 0.00608 +2026-04-12 11:12:33.096770: train_loss -0.2582 +2026-04-12 11:12:33.103256: val_loss -0.2129 +2026-04-12 11:12:33.105405: Pseudo dice [0.1578, 0.4831, 0.7551, 0.7057, 0.5391, 0.7266, 0.7067] +2026-04-12 11:12:33.107597: Epoch time: 103.58 s +2026-04-12 11:12:34.327630: +2026-04-12 11:12:34.329649: Epoch 1699 +2026-04-12 11:12:34.331683: Current learning rate: 0.00608 +2026-04-12 11:14:17.339190: train_loss -0.2645 +2026-04-12 11:14:17.346188: val_loss -0.2245 +2026-04-12 11:14:17.348500: Pseudo dice [0.4353, 0.7592, 0.5416, 0.6341, 0.266, 0.4359, 0.8123] +2026-04-12 11:14:17.350840: Epoch time: 103.01 s +2026-04-12 11:14:19.977725: +2026-04-12 11:14:19.979797: Epoch 1700 +2026-04-12 11:14:19.981322: Current learning rate: 0.00608 +2026-04-12 11:16:02.899332: train_loss -0.2657 +2026-04-12 11:16:02.907499: val_loss -0.2477 +2026-04-12 11:16:02.910736: Pseudo dice [0.6252, 0.6322, 0.7146, 0.3218, 0.5799, 0.7689, 0.8561] +2026-04-12 11:16:02.914183: Epoch time: 102.92 s +2026-04-12 11:16:04.155761: +2026-04-12 11:16:04.160133: Epoch 1701 +2026-04-12 11:16:04.162195: Current learning rate: 0.00607 +2026-04-12 11:17:46.414406: train_loss -0.2727 +2026-04-12 11:17:46.419957: val_loss -0.2009 +2026-04-12 11:17:46.422825: Pseudo dice [0.7256, 0.8723, 0.7636, 0.4224, 0.4709, 0.2489, 0.4394] +2026-04-12 11:17:46.425310: Epoch time: 102.26 s +2026-04-12 11:17:47.616128: +2026-04-12 11:17:47.618800: Epoch 1702 +2026-04-12 11:17:47.620561: Current learning rate: 0.00607 +2026-04-12 11:19:30.499743: train_loss -0.276 +2026-04-12 11:19:30.506376: val_loss -0.2175 +2026-04-12 11:19:30.508587: Pseudo dice [0.6396, 0.8698, 0.7852, 0.6647, 0.5995, 0.3262, 0.5782] +2026-04-12 11:19:30.511390: Epoch time: 102.89 s +2026-04-12 11:19:31.773170: +2026-04-12 11:19:31.775290: Epoch 1703 +2026-04-12 11:19:31.777009: Current learning rate: 0.00607 +2026-04-12 11:21:14.634358: train_loss -0.2779 +2026-04-12 11:21:14.640561: val_loss -0.2102 +2026-04-12 11:21:14.642263: Pseudo dice [0.6633, 0.8913, 0.8013, 0.6133, 0.3665, 0.4511, 0.2084] +2026-04-12 11:21:14.645140: Epoch time: 102.86 s +2026-04-12 11:21:15.853320: +2026-04-12 11:21:15.855572: Epoch 1704 +2026-04-12 11:21:15.857073: Current learning rate: 0.00607 +2026-04-12 11:22:58.436783: train_loss -0.2884 +2026-04-12 11:22:58.444617: val_loss -0.2131 +2026-04-12 11:22:58.447021: Pseudo dice [0.7388, 0.891, 0.7865, 0.5784, 0.5221, 0.0837, 0.7999] +2026-04-12 11:22:58.451868: Epoch time: 102.59 s +2026-04-12 11:22:59.672706: +2026-04-12 11:22:59.675598: Epoch 1705 +2026-04-12 11:22:59.677598: Current learning rate: 0.00607 +2026-04-12 11:24:41.790065: train_loss -0.2783 +2026-04-12 11:24:41.795987: val_loss -0.2258 +2026-04-12 11:24:41.798730: Pseudo dice [0.6158, 0.3009, 0.6455, 0.3944, 0.4219, 0.7844, 0.797] +2026-04-12 11:24:41.800810: Epoch time: 102.12 s +2026-04-12 11:24:42.981912: +2026-04-12 11:24:42.983799: Epoch 1706 +2026-04-12 11:24:42.985734: Current learning rate: 0.00606 +2026-04-12 11:26:25.135075: train_loss -0.2615 +2026-04-12 11:26:25.140946: val_loss -0.2136 +2026-04-12 11:26:25.143497: Pseudo dice [0.6522, 0.6395, 0.706, 0.3241, 0.287, 0.2828, 0.7781] +2026-04-12 11:26:25.146000: Epoch time: 102.16 s +2026-04-12 11:26:26.344197: +2026-04-12 11:26:26.346073: Epoch 1707 +2026-04-12 11:26:26.348146: Current learning rate: 0.00606 +2026-04-12 11:28:08.171314: train_loss -0.2554 +2026-04-12 11:28:08.177949: val_loss -0.1928 +2026-04-12 11:28:08.180109: Pseudo dice [0.6197, 0.873, 0.6963, 0.4364, 0.4526, 0.1463, 0.4812] +2026-04-12 11:28:08.182793: Epoch time: 101.83 s +2026-04-12 11:28:09.368343: +2026-04-12 11:28:09.371275: Epoch 1708 +2026-04-12 11:28:09.373143: Current learning rate: 0.00606 +2026-04-12 11:29:51.093551: train_loss -0.2604 +2026-04-12 11:29:51.100402: val_loss -0.2072 +2026-04-12 11:29:51.102779: Pseudo dice [0.7491, 0.2756, 0.7581, 0.5534, 0.1724, 0.8776, 0.7769] +2026-04-12 11:29:51.105084: Epoch time: 101.73 s +2026-04-12 11:29:52.308748: +2026-04-12 11:29:52.310274: Epoch 1709 +2026-04-12 11:29:52.311756: Current learning rate: 0.00606 +2026-04-12 11:31:34.192443: train_loss -0.2665 +2026-04-12 11:31:34.199095: val_loss -0.2477 +2026-04-12 11:31:34.204740: Pseudo dice [0.5122, 0.4353, 0.7777, 0.6794, 0.4736, 0.8481, 0.7363] +2026-04-12 11:31:34.207421: Epoch time: 101.89 s +2026-04-12 11:31:35.428716: +2026-04-12 11:31:35.430910: Epoch 1710 +2026-04-12 11:31:35.433455: Current learning rate: 0.00605 +2026-04-12 11:33:17.380676: train_loss -0.2363 +2026-04-12 11:33:17.388645: val_loss -0.1873 +2026-04-12 11:33:17.390718: Pseudo dice [0.2697, 0.8464, 0.6603, 0.3368, 0.3311, 0.3056, 0.765] +2026-04-12 11:33:17.394011: Epoch time: 101.95 s +2026-04-12 11:33:18.600201: +2026-04-12 11:33:18.602768: Epoch 1711 +2026-04-12 11:33:18.605758: Current learning rate: 0.00605 +2026-04-12 11:35:00.572447: train_loss -0.2612 +2026-04-12 11:35:00.579755: val_loss -0.2018 +2026-04-12 11:35:00.582362: Pseudo dice [0.7315, 0.6523, 0.7501, 0.7508, 0.3979, 0.5452, 0.6912] +2026-04-12 11:35:00.584790: Epoch time: 101.98 s +2026-04-12 11:35:01.837269: +2026-04-12 11:35:01.839211: Epoch 1712 +2026-04-12 11:35:01.841498: Current learning rate: 0.00605 +2026-04-12 11:36:44.036323: train_loss -0.2673 +2026-04-12 11:36:44.062542: val_loss -0.202 +2026-04-12 11:36:44.064449: Pseudo dice [0.6529, 0.543, 0.6318, 0.3796, 0.3349, 0.687, 0.5974] +2026-04-12 11:36:44.066542: Epoch time: 102.2 s +2026-04-12 11:36:45.274205: +2026-04-12 11:36:45.275905: Epoch 1713 +2026-04-12 11:36:45.277601: Current learning rate: 0.00605 +2026-04-12 11:38:26.730927: train_loss -0.2657 +2026-04-12 11:38:26.737030: val_loss -0.2283 +2026-04-12 11:38:26.739212: Pseudo dice [0.8338, 0.7949, 0.716, 0.4543, 0.6831, 0.4545, 0.7019] +2026-04-12 11:38:26.741479: Epoch time: 101.46 s +2026-04-12 11:38:27.940368: +2026-04-12 11:38:27.943488: Epoch 1714 +2026-04-12 11:38:27.945658: Current learning rate: 0.00604 +2026-04-12 11:40:10.649066: train_loss -0.2728 +2026-04-12 11:40:10.655929: val_loss -0.208 +2026-04-12 11:40:10.657818: Pseudo dice [0.7106, 0.8415, 0.7771, 0.201, 0.504, 0.4437, 0.6261] +2026-04-12 11:40:10.660079: Epoch time: 102.71 s +2026-04-12 11:40:11.861027: +2026-04-12 11:40:11.863344: Epoch 1715 +2026-04-12 11:40:11.865461: Current learning rate: 0.00604 +2026-04-12 11:41:53.924526: train_loss -0.2674 +2026-04-12 11:41:53.931583: val_loss -0.2159 +2026-04-12 11:41:53.933670: Pseudo dice [0.584, 0.8829, 0.7548, 0.311, 0.309, 0.3902, 0.3273] +2026-04-12 11:41:53.937132: Epoch time: 102.07 s +2026-04-12 11:41:55.117656: +2026-04-12 11:41:55.119405: Epoch 1716 +2026-04-12 11:41:55.121099: Current learning rate: 0.00604 +2026-04-12 11:43:36.843136: train_loss -0.2661 +2026-04-12 11:43:36.856888: val_loss -0.2233 +2026-04-12 11:43:36.860269: Pseudo dice [0.2161, 0.5851, 0.8032, 0.6814, 0.4589, 0.3999, 0.6243] +2026-04-12 11:43:36.863620: Epoch time: 101.73 s +2026-04-12 11:43:38.062176: +2026-04-12 11:43:38.065493: Epoch 1717 +2026-04-12 11:43:38.068918: Current learning rate: 0.00604 +2026-04-12 11:45:19.502088: train_loss -0.2785 +2026-04-12 11:45:19.508918: val_loss -0.2213 +2026-04-12 11:45:19.511404: Pseudo dice [0.3928, 0.7806, 0.7733, 0.5044, 0.3857, 0.5826, 0.7244] +2026-04-12 11:45:19.513939: Epoch time: 101.44 s +2026-04-12 11:45:21.588600: +2026-04-12 11:45:21.590667: Epoch 1718 +2026-04-12 11:45:21.592210: Current learning rate: 0.00603 +2026-04-12 11:47:03.129565: train_loss -0.2691 +2026-04-12 11:47:03.135719: val_loss -0.2304 +2026-04-12 11:47:03.137880: Pseudo dice [0.4916, 0.8845, 0.6821, 0.3854, 0.4264, 0.3787, 0.7949] +2026-04-12 11:47:03.142022: Epoch time: 101.54 s +2026-04-12 11:47:04.337998: +2026-04-12 11:47:04.339644: Epoch 1719 +2026-04-12 11:47:04.341117: Current learning rate: 0.00603 +2026-04-12 11:48:45.974664: train_loss -0.2794 +2026-04-12 11:48:45.980653: val_loss -0.2572 +2026-04-12 11:48:45.982975: Pseudo dice [0.5874, 0.8571, 0.749, 0.4415, 0.3889, 0.7822, 0.7047] +2026-04-12 11:48:45.985496: Epoch time: 101.64 s +2026-04-12 11:48:47.191924: +2026-04-12 11:48:47.194149: Epoch 1720 +2026-04-12 11:48:47.196275: Current learning rate: 0.00603 +2026-04-12 11:50:28.835686: train_loss -0.2771 +2026-04-12 11:50:28.841943: val_loss -0.2543 +2026-04-12 11:50:28.845222: Pseudo dice [0.8007, 0.6013, 0.7817, 0.8873, 0.3469, 0.6432, 0.8612] +2026-04-12 11:50:28.847907: Epoch time: 101.65 s +2026-04-12 11:50:30.067987: +2026-04-12 11:50:30.069709: Epoch 1721 +2026-04-12 11:50:30.071713: Current learning rate: 0.00603 +2026-04-12 11:52:11.828552: train_loss -0.2664 +2026-04-12 11:52:11.835582: val_loss -0.2055 +2026-04-12 11:52:11.837790: Pseudo dice [0.1918, 0.8343, 0.7235, 0.589, 0.5176, 0.7595, 0.7872] +2026-04-12 11:52:11.840109: Epoch time: 101.76 s +2026-04-12 11:52:13.073318: +2026-04-12 11:52:13.076599: Epoch 1722 +2026-04-12 11:52:13.079350: Current learning rate: 0.00602 +2026-04-12 11:53:55.158214: train_loss -0.2858 +2026-04-12 11:53:55.164332: val_loss -0.2135 +2026-04-12 11:53:55.166473: Pseudo dice [0.8408, 0.8519, 0.8015, 0.3416, 0.2361, 0.6364, 0.2801] +2026-04-12 11:53:55.168451: Epoch time: 102.09 s +2026-04-12 11:53:56.375019: +2026-04-12 11:53:56.377009: Epoch 1723 +2026-04-12 11:53:56.378541: Current learning rate: 0.00602 +2026-04-12 11:55:38.249947: train_loss -0.2758 +2026-04-12 11:55:38.256389: val_loss -0.246 +2026-04-12 11:55:38.258556: Pseudo dice [0.8029, 0.9019, 0.7625, 0.4833, 0.4985, 0.6909, 0.5509] +2026-04-12 11:55:38.261341: Epoch time: 101.88 s +2026-04-12 11:55:39.469611: +2026-04-12 11:55:39.472791: Epoch 1724 +2026-04-12 11:55:39.477462: Current learning rate: 0.00602 +2026-04-12 11:57:21.428011: train_loss -0.276 +2026-04-12 11:57:21.437798: val_loss -0.2152 +2026-04-12 11:57:21.439782: Pseudo dice [0.5759, 0.6805, 0.6087, 0.8174, 0.4822, 0.5232, 0.8281] +2026-04-12 11:57:21.441991: Epoch time: 101.96 s +2026-04-12 11:57:22.654512: +2026-04-12 11:57:22.656603: Epoch 1725 +2026-04-12 11:57:22.658335: Current learning rate: 0.00602 +2026-04-12 11:59:04.294466: train_loss -0.2738 +2026-04-12 11:59:04.300852: val_loss -0.2396 +2026-04-12 11:59:04.302668: Pseudo dice [0.2553, 0.8664, 0.8188, 0.8079, 0.2972, 0.8828, 0.7103] +2026-04-12 11:59:04.305691: Epoch time: 101.64 s +2026-04-12 11:59:05.496562: +2026-04-12 11:59:05.508675: Epoch 1726 +2026-04-12 11:59:05.510484: Current learning rate: 0.00602 +2026-04-12 12:00:47.287019: train_loss -0.2678 +2026-04-12 12:00:47.293048: val_loss -0.1927 +2026-04-12 12:00:47.295445: Pseudo dice [0.4549, 0.4786, 0.7504, 0.7527, 0.4468, 0.2678, 0.8332] +2026-04-12 12:00:47.297504: Epoch time: 101.79 s +2026-04-12 12:00:48.517073: +2026-04-12 12:00:48.519165: Epoch 1727 +2026-04-12 12:00:48.521047: Current learning rate: 0.00601 +2026-04-12 12:02:29.957582: train_loss -0.2501 +2026-04-12 12:02:29.970537: val_loss -0.2216 +2026-04-12 12:02:29.973443: Pseudo dice [0.6518, 0.754, 0.6093, 0.2049, 0.4237, 0.8627, 0.7687] +2026-04-12 12:02:29.976169: Epoch time: 101.44 s +2026-04-12 12:02:31.169864: +2026-04-12 12:02:31.171361: Epoch 1728 +2026-04-12 12:02:31.172670: Current learning rate: 0.00601 +2026-04-12 12:04:12.629990: train_loss -0.2553 +2026-04-12 12:04:12.636444: val_loss -0.2439 +2026-04-12 12:04:12.638217: Pseudo dice [0.5916, 0.7617, 0.8405, 0.8611, 0.3873, 0.6916, 0.788] +2026-04-12 12:04:12.640607: Epoch time: 101.46 s +2026-04-12 12:04:13.859470: +2026-04-12 12:04:13.861270: Epoch 1729 +2026-04-12 12:04:13.862843: Current learning rate: 0.00601 +2026-04-12 12:05:55.826053: train_loss -0.2701 +2026-04-12 12:05:55.832526: val_loss -0.1757 +2026-04-12 12:05:55.834562: Pseudo dice [0.0967, 0.865, 0.6986, 0.0397, 0.4357, 0.5871, 0.634] +2026-04-12 12:05:55.836612: Epoch time: 101.97 s +2026-04-12 12:05:57.032747: +2026-04-12 12:05:57.035039: Epoch 1730 +2026-04-12 12:05:57.036674: Current learning rate: 0.00601 +2026-04-12 12:07:39.295244: train_loss -0.2691 +2026-04-12 12:07:39.301263: val_loss -0.23 +2026-04-12 12:07:39.304229: Pseudo dice [0.3343, 0.8151, 0.7498, 0.1567, 0.514, 0.8481, 0.7366] +2026-04-12 12:07:39.307842: Epoch time: 102.27 s +2026-04-12 12:07:40.485944: +2026-04-12 12:07:40.488019: Epoch 1731 +2026-04-12 12:07:40.490673: Current learning rate: 0.006 +2026-04-12 12:09:22.423546: train_loss -0.2654 +2026-04-12 12:09:22.430306: val_loss -0.2119 +2026-04-12 12:09:22.432207: Pseudo dice [0.6421, 0.8732, 0.6695, 0.561, 0.327, 0.8044, 0.6203] +2026-04-12 12:09:22.434606: Epoch time: 101.94 s +2026-04-12 12:09:23.648318: +2026-04-12 12:09:23.650854: Epoch 1732 +2026-04-12 12:09:23.652478: Current learning rate: 0.006 +2026-04-12 12:11:05.701104: train_loss -0.2632 +2026-04-12 12:11:05.707724: val_loss -0.2332 +2026-04-12 12:11:05.709371: Pseudo dice [0.6211, 0.8105, 0.8086, 0.8299, 0.6269, 0.6539, 0.8601] +2026-04-12 12:11:05.711871: Epoch time: 102.06 s +2026-04-12 12:11:06.918369: +2026-04-12 12:11:06.919889: Epoch 1733 +2026-04-12 12:11:06.921291: Current learning rate: 0.006 +2026-04-12 12:12:48.250978: train_loss -0.2802 +2026-04-12 12:12:48.256896: val_loss -0.2395 +2026-04-12 12:12:48.259113: Pseudo dice [0.5899, 0.3212, 0.8013, 0.696, 0.5118, 0.5185, 0.7879] +2026-04-12 12:12:48.261607: Epoch time: 101.34 s +2026-04-12 12:12:49.463964: +2026-04-12 12:12:49.465829: Epoch 1734 +2026-04-12 12:12:49.467753: Current learning rate: 0.006 +2026-04-12 12:14:31.293159: train_loss -0.2804 +2026-04-12 12:14:31.299312: val_loss -0.1905 +2026-04-12 12:14:31.302300: Pseudo dice [0.4981, 0.8193, 0.606, 0.3197, 0.5283, 0.2361, 0.7728] +2026-04-12 12:14:31.305062: Epoch time: 101.83 s +2026-04-12 12:14:32.534382: +2026-04-12 12:14:32.536360: Epoch 1735 +2026-04-12 12:14:32.538021: Current learning rate: 0.00599 +2026-04-12 12:16:14.688763: train_loss -0.263 +2026-04-12 12:16:14.695981: val_loss -0.2458 +2026-04-12 12:16:14.698372: Pseudo dice [0.6317, 0.481, 0.7221, 0.2848, 0.4641, 0.8397, 0.8559] +2026-04-12 12:16:14.700702: Epoch time: 102.16 s +2026-04-12 12:16:15.914244: +2026-04-12 12:16:15.916183: Epoch 1736 +2026-04-12 12:16:15.917786: Current learning rate: 0.00599 +2026-04-12 12:17:57.808032: train_loss -0.2851 +2026-04-12 12:17:57.814014: val_loss -0.2403 +2026-04-12 12:17:57.816301: Pseudo dice [0.6668, 0.7026, 0.7697, 0.1789, 0.4778, 0.8263, 0.7767] +2026-04-12 12:17:57.818372: Epoch time: 101.9 s +2026-04-12 12:17:59.009945: +2026-04-12 12:17:59.011446: Epoch 1737 +2026-04-12 12:17:59.012884: Current learning rate: 0.00599 +2026-04-12 12:19:42.038462: train_loss -0.2696 +2026-04-12 12:19:42.045007: val_loss -0.2564 +2026-04-12 12:19:42.047043: Pseudo dice [0.6167, 0.5071, 0.8551, 0.8304, 0.498, 0.8306, 0.849] +2026-04-12 12:19:42.049384: Epoch time: 103.03 s +2026-04-12 12:19:43.253931: +2026-04-12 12:19:43.255796: Epoch 1738 +2026-04-12 12:19:43.257528: Current learning rate: 0.00599 +2026-04-12 12:21:24.979951: train_loss -0.2669 +2026-04-12 12:21:24.985642: val_loss -0.2281 +2026-04-12 12:21:24.987617: Pseudo dice [0.32, 0.8907, 0.6812, 0.6063, 0.5449, 0.7781, 0.74] +2026-04-12 12:21:24.989838: Epoch time: 101.73 s +2026-04-12 12:21:26.199464: +2026-04-12 12:21:26.201100: Epoch 1739 +2026-04-12 12:21:26.202574: Current learning rate: 0.00598 +2026-04-12 12:23:08.104119: train_loss -0.2602 +2026-04-12 12:23:08.110047: val_loss -0.2592 +2026-04-12 12:23:08.112860: Pseudo dice [0.7735, 0.913, 0.8118, 0.5914, 0.4453, 0.2896, 0.8604] +2026-04-12 12:23:08.115227: Epoch time: 101.91 s +2026-04-12 12:23:09.332036: +2026-04-12 12:23:09.334303: Epoch 1740 +2026-04-12 12:23:09.336160: Current learning rate: 0.00598 +2026-04-12 12:24:50.753353: train_loss -0.2795 +2026-04-12 12:24:50.759572: val_loss -0.1989 +2026-04-12 12:24:50.761587: Pseudo dice [0.6941, 0.5272, 0.6688, 0.4191, 0.5386, 0.6813, 0.809] +2026-04-12 12:24:50.764045: Epoch time: 101.42 s +2026-04-12 12:24:51.951832: +2026-04-12 12:24:51.953369: Epoch 1741 +2026-04-12 12:24:51.954817: Current learning rate: 0.00598 +2026-04-12 12:26:33.426138: train_loss -0.2754 +2026-04-12 12:26:33.434387: val_loss -0.2477 +2026-04-12 12:26:33.436855: Pseudo dice [0.6808, 0.8503, 0.7378, 0.5295, 0.6218, 0.8371, 0.638] +2026-04-12 12:26:33.438956: Epoch time: 101.48 s +2026-04-12 12:26:33.441166: Yayy! New best EMA pseudo Dice: 0.6362 +2026-04-12 12:26:36.415209: +2026-04-12 12:26:36.417789: Epoch 1742 +2026-04-12 12:26:36.419227: Current learning rate: 0.00598 +2026-04-12 12:28:18.523784: train_loss -0.2869 +2026-04-12 12:28:18.530579: val_loss -0.2435 +2026-04-12 12:28:18.533249: Pseudo dice [0.4338, 0.9106, 0.7994, 0.5299, 0.4639, 0.4067, 0.8012] +2026-04-12 12:28:18.536575: Epoch time: 102.11 s +2026-04-12 12:28:19.754268: +2026-04-12 12:28:19.755996: Epoch 1743 +2026-04-12 12:28:19.757656: Current learning rate: 0.00597 +2026-04-12 12:30:01.745185: train_loss -0.2838 +2026-04-12 12:30:01.751669: val_loss -0.259 +2026-04-12 12:30:01.753856: Pseudo dice [0.6001, 0.7551, 0.7744, 0.505, 0.3818, 0.9113, 0.8051] +2026-04-12 12:30:01.755980: Epoch time: 101.99 s +2026-04-12 12:30:01.758106: Yayy! New best EMA pseudo Dice: 0.6388 +2026-04-12 12:30:04.747034: +2026-04-12 12:30:04.749257: Epoch 1744 +2026-04-12 12:30:04.750693: Current learning rate: 0.00597 +2026-04-12 12:31:46.392642: train_loss -0.296 +2026-04-12 12:31:46.399835: val_loss -0.2181 +2026-04-12 12:31:46.402633: Pseudo dice [0.4069, 0.8402, 0.7301, 0.4159, 0.4326, 0.6519, 0.4809] +2026-04-12 12:31:46.405025: Epoch time: 101.65 s +2026-04-12 12:31:47.603704: +2026-04-12 12:31:47.606194: Epoch 1745 +2026-04-12 12:31:47.607875: Current learning rate: 0.00597 +2026-04-12 12:33:28.915823: train_loss -0.2511 +2026-04-12 12:33:28.921978: val_loss -0.1239 +2026-04-12 12:33:28.924617: Pseudo dice [0.837, 0.421, 0.1667, 0.2479, 0.5336, 0.5225, 0.3855] +2026-04-12 12:33:28.927127: Epoch time: 101.32 s +2026-04-12 12:33:30.111777: +2026-04-12 12:33:30.114104: Epoch 1746 +2026-04-12 12:33:30.116014: Current learning rate: 0.00597 +2026-04-12 12:35:11.912238: train_loss -0.2358 +2026-04-12 12:35:11.918065: val_loss -0.1954 +2026-04-12 12:35:11.919753: Pseudo dice [0.663, 0.8727, 0.7617, 0.1778, 0.2848, 0.4538, 0.7034] +2026-04-12 12:35:11.921835: Epoch time: 101.8 s +2026-04-12 12:35:13.129907: +2026-04-12 12:35:13.131823: Epoch 1747 +2026-04-12 12:35:13.133373: Current learning rate: 0.00597 +2026-04-12 12:36:54.771173: train_loss -0.2672 +2026-04-12 12:36:54.797763: val_loss -0.2191 +2026-04-12 12:36:54.800021: Pseudo dice [0.7753, 0.7707, 0.7052, 0.6743, 0.5294, 0.714, 0.8541] +2026-04-12 12:36:54.802915: Epoch time: 101.64 s +2026-04-12 12:36:56.024076: +2026-04-12 12:36:56.026001: Epoch 1748 +2026-04-12 12:36:56.027853: Current learning rate: 0.00596 +2026-04-12 12:38:37.820604: train_loss -0.2583 +2026-04-12 12:38:37.836076: val_loss -0.2166 +2026-04-12 12:38:37.838623: Pseudo dice [0.4278, 0.6717, 0.7529, 0.4385, 0.3524, 0.6208, 0.8286] +2026-04-12 12:38:37.841738: Epoch time: 101.8 s +2026-04-12 12:38:39.085446: +2026-04-12 12:38:39.087645: Epoch 1749 +2026-04-12 12:38:39.089528: Current learning rate: 0.00596 +2026-04-12 12:40:21.176648: train_loss -0.269 +2026-04-12 12:40:21.182709: val_loss -0.2263 +2026-04-12 12:40:21.184547: Pseudo dice [0.8382, 0.2291, 0.8043, 0.375, 0.4516, 0.8454, 0.3367] +2026-04-12 12:40:21.187004: Epoch time: 102.09 s +2026-04-12 12:40:23.782284: +2026-04-12 12:40:23.784702: Epoch 1750 +2026-04-12 12:40:23.786229: Current learning rate: 0.00596 +2026-04-12 12:42:05.436469: train_loss -0.2691 +2026-04-12 12:42:05.441993: val_loss -0.2342 +2026-04-12 12:42:05.443895: Pseudo dice [0.7343, 0.6332, 0.8082, 0.4699, 0.2983, 0.7786, 0.5548] +2026-04-12 12:42:05.446458: Epoch time: 101.66 s +2026-04-12 12:42:06.650797: +2026-04-12 12:42:06.652355: Epoch 1751 +2026-04-12 12:42:06.653739: Current learning rate: 0.00596 +2026-04-12 12:43:48.066931: train_loss -0.2654 +2026-04-12 12:43:48.073654: val_loss -0.25 +2026-04-12 12:43:48.076169: Pseudo dice [0.6117, 0.717, 0.8308, 0.5564, 0.575, 0.8812, 0.6313] +2026-04-12 12:43:48.079854: Epoch time: 101.42 s +2026-04-12 12:43:49.334176: +2026-04-12 12:43:49.335788: Epoch 1752 +2026-04-12 12:43:49.337196: Current learning rate: 0.00595 +2026-04-12 12:45:31.423913: train_loss -0.2761 +2026-04-12 12:45:31.430274: val_loss -0.2163 +2026-04-12 12:45:31.431959: Pseudo dice [0.2268, 0.6929, 0.6849, 0.2435, 0.7196, 0.2245, 0.5372] +2026-04-12 12:45:31.434065: Epoch time: 102.09 s +2026-04-12 12:45:32.613235: +2026-04-12 12:45:32.614967: Epoch 1753 +2026-04-12 12:45:32.616449: Current learning rate: 0.00595 +2026-04-12 12:47:14.333955: train_loss -0.2876 +2026-04-12 12:47:14.340546: val_loss -0.2388 +2026-04-12 12:47:14.342532: Pseudo dice [0.204, 0.7185, 0.7872, 0.7584, 0.4483, 0.8665, 0.7711] +2026-04-12 12:47:14.345425: Epoch time: 101.72 s +2026-04-12 12:47:15.542071: +2026-04-12 12:47:15.544382: Epoch 1754 +2026-04-12 12:47:15.546576: Current learning rate: 0.00595 +2026-04-12 12:48:57.939883: train_loss -0.2817 +2026-04-12 12:48:57.946976: val_loss -0.2134 +2026-04-12 12:48:57.950509: Pseudo dice [0.5689, 0.5449, 0.6684, 0.6486, 0.4767, 0.8533, 0.6946] +2026-04-12 12:48:57.953037: Epoch time: 102.4 s +2026-04-12 12:48:59.192971: +2026-04-12 12:48:59.196013: Epoch 1755 +2026-04-12 12:48:59.202578: Current learning rate: 0.00595 +2026-04-12 12:50:41.156168: train_loss -0.2686 +2026-04-12 12:50:41.162593: val_loss -0.1636 +2026-04-12 12:50:41.165838: Pseudo dice [0.6124, 0.8251, 0.6506, 0.4269, 0.2987, 0.1201, 0.2177] +2026-04-12 12:50:41.168508: Epoch time: 101.97 s +2026-04-12 12:50:43.423048: +2026-04-12 12:50:43.424823: Epoch 1756 +2026-04-12 12:50:43.426367: Current learning rate: 0.00594 +2026-04-12 12:52:25.503628: train_loss -0.2421 +2026-04-12 12:52:25.509925: val_loss -0.2097 +2026-04-12 12:52:25.512183: Pseudo dice [0.6758, 0.877, 0.7727, 0.017, 0.4142, 0.5654, 0.7371] +2026-04-12 12:52:25.514933: Epoch time: 102.08 s +2026-04-12 12:52:26.713692: +2026-04-12 12:52:26.715379: Epoch 1757 +2026-04-12 12:52:26.716958: Current learning rate: 0.00594 +2026-04-12 12:54:08.414109: train_loss -0.269 +2026-04-12 12:54:08.419881: val_loss -0.2171 +2026-04-12 12:54:08.421971: Pseudo dice [0.6371, 0.8093, 0.8099, 0.739, 0.4435, 0.3304, 0.8236] +2026-04-12 12:54:08.424277: Epoch time: 101.7 s +2026-04-12 12:54:09.631472: +2026-04-12 12:54:09.633546: Epoch 1758 +2026-04-12 12:54:09.635445: Current learning rate: 0.00594 +2026-04-12 12:55:51.277185: train_loss -0.2696 +2026-04-12 12:55:51.283163: val_loss -0.1716 +2026-04-12 12:55:51.285757: Pseudo dice [0.1286, 0.7385, 0.6333, 0.1918, 0.2817, 0.5703, 0.6807] +2026-04-12 12:55:51.288155: Epoch time: 101.65 s +2026-04-12 12:55:52.522179: +2026-04-12 12:55:52.524122: Epoch 1759 +2026-04-12 12:55:52.525652: Current learning rate: 0.00594 +2026-04-12 12:57:34.738146: train_loss -0.2653 +2026-04-12 12:57:34.744586: val_loss -0.2427 +2026-04-12 12:57:34.746793: Pseudo dice [0.6442, 0.1585, 0.7655, 0.5748, 0.5554, 0.7439, 0.7993] +2026-04-12 12:57:34.749532: Epoch time: 102.22 s +2026-04-12 12:57:35.976287: +2026-04-12 12:57:35.977914: Epoch 1760 +2026-04-12 12:57:35.979354: Current learning rate: 0.00593 +2026-04-12 12:59:17.405357: train_loss -0.2717 +2026-04-12 12:59:17.411719: val_loss -0.1819 +2026-04-12 12:59:17.414440: Pseudo dice [0.4403, 0.5421, 0.7476, 0.115, 0.2178, 0.6641, 0.1645] +2026-04-12 12:59:17.417544: Epoch time: 101.43 s +2026-04-12 12:59:18.599966: +2026-04-12 12:59:18.601839: Epoch 1761 +2026-04-12 12:59:18.603418: Current learning rate: 0.00593 +2026-04-12 13:01:00.457035: train_loss -0.2689 +2026-04-12 13:01:00.462595: val_loss -0.2326 +2026-04-12 13:01:00.464231: Pseudo dice [0.6046, 0.7462, 0.6224, 0.6514, 0.6152, 0.8745, 0.7897] +2026-04-12 13:01:00.467049: Epoch time: 101.86 s +2026-04-12 13:01:01.656821: +2026-04-12 13:01:01.659270: Epoch 1762 +2026-04-12 13:01:01.661427: Current learning rate: 0.00593 +2026-04-12 13:02:43.765978: train_loss -0.2734 +2026-04-12 13:02:43.772372: val_loss -0.2383 +2026-04-12 13:02:43.774891: Pseudo dice [0.4745, 0.306, 0.8005, 0.5417, 0.5968, 0.733, 0.7647] +2026-04-12 13:02:43.777185: Epoch time: 102.11 s +2026-04-12 13:02:45.009434: +2026-04-12 13:02:45.011420: Epoch 1763 +2026-04-12 13:02:45.013412: Current learning rate: 0.00593 +2026-04-12 13:04:26.850184: train_loss -0.2774 +2026-04-12 13:04:26.856542: val_loss -0.1893 +2026-04-12 13:04:26.858211: Pseudo dice [0.5111, 0.8432, 0.7925, 0.0641, 0.4318, 0.5248, 0.2232] +2026-04-12 13:04:26.860777: Epoch time: 101.84 s +2026-04-12 13:04:28.053504: +2026-04-12 13:04:28.055052: Epoch 1764 +2026-04-12 13:04:28.056398: Current learning rate: 0.00592 +2026-04-12 13:06:09.815750: train_loss -0.2501 +2026-04-12 13:06:09.821819: val_loss -0.2098 +2026-04-12 13:06:09.825429: Pseudo dice [0.7279, 0.9084, 0.6185, 0.6421, 0.2536, 0.1956, 0.7376] +2026-04-12 13:06:09.828767: Epoch time: 101.77 s +2026-04-12 13:06:11.027182: +2026-04-12 13:06:11.029184: Epoch 1765 +2026-04-12 13:06:11.031946: Current learning rate: 0.00592 +2026-04-12 13:07:53.122543: train_loss -0.2596 +2026-04-12 13:07:53.128201: val_loss -0.236 +2026-04-12 13:07:53.130089: Pseudo dice [0.4664, 0.5676, 0.7796, 0.752, 0.434, 0.6091, 0.8901] +2026-04-12 13:07:53.133081: Epoch time: 102.1 s +2026-04-12 13:07:54.338838: +2026-04-12 13:07:54.340698: Epoch 1766 +2026-04-12 13:07:54.342435: Current learning rate: 0.00592 +2026-04-12 13:09:35.981759: train_loss -0.2741 +2026-04-12 13:09:35.987748: val_loss -0.2316 +2026-04-12 13:09:35.989799: Pseudo dice [0.6881, 0.6004, 0.7747, 0.3576, 0.3626, 0.7399, 0.7899] +2026-04-12 13:09:35.993661: Epoch time: 101.65 s +2026-04-12 13:09:37.210478: +2026-04-12 13:09:37.212224: Epoch 1767 +2026-04-12 13:09:37.214130: Current learning rate: 0.00592 +2026-04-12 13:11:19.197949: train_loss -0.2727 +2026-04-12 13:11:19.204518: val_loss -0.1415 +2026-04-12 13:11:19.206722: Pseudo dice [0.6852, 0.5179, 0.5575, 0.754, 0.4466, 0.4537, 0.5622] +2026-04-12 13:11:19.208965: Epoch time: 101.99 s +2026-04-12 13:11:20.503906: +2026-04-12 13:11:20.505654: Epoch 1768 +2026-04-12 13:11:20.507210: Current learning rate: 0.00592 +2026-04-12 13:13:02.151441: train_loss -0.2795 +2026-04-12 13:13:02.157027: val_loss -0.233 +2026-04-12 13:13:02.159450: Pseudo dice [0.8351, 0.877, 0.7218, 0.1722, 0.6107, 0.141, 0.6493] +2026-04-12 13:13:02.161876: Epoch time: 101.65 s +2026-04-12 13:13:03.381047: +2026-04-12 13:13:03.383026: Epoch 1769 +2026-04-12 13:13:03.385016: Current learning rate: 0.00591 +2026-04-12 13:14:45.085962: train_loss -0.2781 +2026-04-12 13:14:45.095195: val_loss -0.2325 +2026-04-12 13:14:45.101265: Pseudo dice [0.5854, 0.2492, 0.6961, 0.4195, 0.4414, 0.7729, 0.5855] +2026-04-12 13:14:45.107876: Epoch time: 101.71 s +2026-04-12 13:14:46.317772: +2026-04-12 13:14:46.319328: Epoch 1770 +2026-04-12 13:14:46.320752: Current learning rate: 0.00591 +2026-04-12 13:16:28.699494: train_loss -0.2683 +2026-04-12 13:16:28.706093: val_loss -0.2369 +2026-04-12 13:16:28.709873: Pseudo dice [0.5025, 0.8164, 0.7117, 0.5092, 0.3883, 0.8735, 0.8082] +2026-04-12 13:16:28.712234: Epoch time: 102.38 s +2026-04-12 13:16:29.901979: +2026-04-12 13:16:29.903909: Epoch 1771 +2026-04-12 13:16:29.906315: Current learning rate: 0.00591 +2026-04-12 13:18:11.548152: train_loss -0.2726 +2026-04-12 13:18:11.554265: val_loss -0.2319 +2026-04-12 13:18:11.556784: Pseudo dice [0.6863, 0.4374, 0.7044, 0.7788, 0.3503, 0.5616, 0.878] +2026-04-12 13:18:11.560390: Epoch time: 101.65 s +2026-04-12 13:18:12.795416: +2026-04-12 13:18:12.797196: Epoch 1772 +2026-04-12 13:18:12.798739: Current learning rate: 0.00591 +2026-04-12 13:19:54.873989: train_loss -0.2697 +2026-04-12 13:19:54.880262: val_loss -0.1934 +2026-04-12 13:19:54.882160: Pseudo dice [0.5218, 0.8686, 0.6696, 0.1586, 0.5389, 0.3281, 0.1926] +2026-04-12 13:19:54.884644: Epoch time: 102.08 s +2026-04-12 13:19:56.069062: +2026-04-12 13:19:56.070646: Epoch 1773 +2026-04-12 13:19:56.072038: Current learning rate: 0.0059 +2026-04-12 13:21:38.089835: train_loss -0.2608 +2026-04-12 13:21:38.099475: val_loss -0.2068 +2026-04-12 13:21:38.102255: Pseudo dice [0.229, 0.5904, 0.6807, 0.5959, 0.6049, 0.7467, 0.7978] +2026-04-12 13:21:38.105848: Epoch time: 102.02 s +2026-04-12 13:21:39.312169: +2026-04-12 13:21:39.313768: Epoch 1774 +2026-04-12 13:21:39.315299: Current learning rate: 0.0059 +2026-04-12 13:23:21.799641: train_loss -0.2629 +2026-04-12 13:23:21.807229: val_loss -0.2217 +2026-04-12 13:23:21.809401: Pseudo dice [0.4284, 0.287, 0.7345, 0.3221, 0.1852, 0.9192, 0.7871] +2026-04-12 13:23:21.811894: Epoch time: 102.49 s +2026-04-12 13:23:23.032593: +2026-04-12 13:23:23.034636: Epoch 1775 +2026-04-12 13:23:23.036140: Current learning rate: 0.0059 +2026-04-12 13:25:04.537530: train_loss -0.2838 +2026-04-12 13:25:04.543430: val_loss -0.2225 +2026-04-12 13:25:04.545450: Pseudo dice [0.6209, 0.6882, 0.7099, 0.5128, 0.4917, 0.6118, 0.3855] +2026-04-12 13:25:04.547711: Epoch time: 101.51 s +2026-04-12 13:25:06.802661: +2026-04-12 13:25:06.804781: Epoch 1776 +2026-04-12 13:25:06.806517: Current learning rate: 0.0059 +2026-04-12 13:26:48.411075: train_loss -0.2794 +2026-04-12 13:26:48.420617: val_loss -0.1959 +2026-04-12 13:26:48.424756: Pseudo dice [0.4842, 0.8238, 0.6518, 0.2611, 0.2688, 0.2785, 0.8076] +2026-04-12 13:26:48.427352: Epoch time: 101.61 s +2026-04-12 13:26:49.607365: +2026-04-12 13:26:49.610144: Epoch 1777 +2026-04-12 13:26:49.612202: Current learning rate: 0.00589 +2026-04-12 13:28:31.481102: train_loss -0.2682 +2026-04-12 13:28:31.487547: val_loss -0.2341 +2026-04-12 13:28:31.489863: Pseudo dice [0.6923, 0.8544, 0.7854, 0.534, 0.4737, 0.1436, 0.6699] +2026-04-12 13:28:31.492567: Epoch time: 101.88 s +2026-04-12 13:28:32.735031: +2026-04-12 13:28:32.736697: Epoch 1778 +2026-04-12 13:28:32.738085: Current learning rate: 0.00589 +2026-04-12 13:30:14.147847: train_loss -0.2665 +2026-04-12 13:30:14.154418: val_loss -0.2345 +2026-04-12 13:30:14.156151: Pseudo dice [0.2991, 0.7207, 0.7722, 0.786, 0.5008, 0.7489, 0.8109] +2026-04-12 13:30:14.158258: Epoch time: 101.42 s +2026-04-12 13:30:15.348619: +2026-04-12 13:30:15.350212: Epoch 1779 +2026-04-12 13:30:15.351595: Current learning rate: 0.00589 +2026-04-12 13:31:56.587562: train_loss -0.2754 +2026-04-12 13:31:56.594410: val_loss -0.2478 +2026-04-12 13:31:56.597142: Pseudo dice [0.8664, 0.5665, 0.8152, 0.5241, 0.4261, 0.8859, 0.6901] +2026-04-12 13:31:56.600152: Epoch time: 101.24 s +2026-04-12 13:31:57.824092: +2026-04-12 13:31:57.825654: Epoch 1780 +2026-04-12 13:31:57.827013: Current learning rate: 0.00589 +2026-04-12 13:33:39.197605: train_loss -0.2644 +2026-04-12 13:33:39.205423: val_loss -0.2224 +2026-04-12 13:33:39.210864: Pseudo dice [0.6994, 0.5582, 0.5156, 0.2653, 0.3791, 0.4347, 0.781] +2026-04-12 13:33:39.213528: Epoch time: 101.38 s +2026-04-12 13:33:40.408968: +2026-04-12 13:33:40.410636: Epoch 1781 +2026-04-12 13:33:40.412395: Current learning rate: 0.00588 +2026-04-12 13:35:21.643804: train_loss -0.252 +2026-04-12 13:35:21.650402: val_loss -0.2296 +2026-04-12 13:35:21.652399: Pseudo dice [0.6474, 0.4233, 0.7645, 0.5884, 0.4216, 0.6055, 0.7446] +2026-04-12 13:35:21.655383: Epoch time: 101.24 s +2026-04-12 13:35:22.871088: +2026-04-12 13:35:22.873213: Epoch 1782 +2026-04-12 13:35:22.874977: Current learning rate: 0.00588 +2026-04-12 13:37:04.538167: train_loss -0.2808 +2026-04-12 13:37:04.564850: val_loss -0.2599 +2026-04-12 13:37:04.567648: Pseudo dice [0.5557, 0.4767, 0.77, 0.8971, 0.3847, 0.8989, 0.8479] +2026-04-12 13:37:04.570247: Epoch time: 101.67 s +2026-04-12 13:37:05.777973: +2026-04-12 13:37:05.779494: Epoch 1783 +2026-04-12 13:37:05.781062: Current learning rate: 0.00588 +2026-04-12 13:38:46.939385: train_loss -0.2891 +2026-04-12 13:38:46.945861: val_loss -0.207 +2026-04-12 13:38:46.948051: Pseudo dice [0.7775, 0.8359, 0.7659, 0.0781, 0.496, 0.6583, 0.5222] +2026-04-12 13:38:46.950865: Epoch time: 101.16 s +2026-04-12 13:38:48.154832: +2026-04-12 13:38:48.156534: Epoch 1784 +2026-04-12 13:38:48.157939: Current learning rate: 0.00588 +2026-04-12 13:40:29.550135: train_loss -0.2649 +2026-04-12 13:40:29.556033: val_loss -0.2413 +2026-04-12 13:40:29.558312: Pseudo dice [0.7541, 0.683, 0.7223, 0.5797, 0.517, 0.6129, 0.7517] +2026-04-12 13:40:29.560841: Epoch time: 101.4 s +2026-04-12 13:40:30.765429: +2026-04-12 13:40:30.767078: Epoch 1785 +2026-04-12 13:40:30.768584: Current learning rate: 0.00587 +2026-04-12 13:42:12.263609: train_loss -0.2572 +2026-04-12 13:42:12.269685: val_loss -0.2159 +2026-04-12 13:42:12.271793: Pseudo dice [0.6284, 0.2049, 0.7665, 0.765, 0.3352, 0.5944, 0.8376] +2026-04-12 13:42:12.274146: Epoch time: 101.5 s +2026-04-12 13:42:13.480242: +2026-04-12 13:42:13.482873: Epoch 1786 +2026-04-12 13:42:13.485038: Current learning rate: 0.00587 +2026-04-12 13:43:55.437253: train_loss -0.2734 +2026-04-12 13:43:55.443627: val_loss -0.2409 +2026-04-12 13:43:55.445755: Pseudo dice [0.7886, 0.6643, 0.7477, 0.1783, 0.3182, 0.8009, 0.7231] +2026-04-12 13:43:55.447778: Epoch time: 101.96 s +2026-04-12 13:43:56.653234: +2026-04-12 13:43:56.655051: Epoch 1787 +2026-04-12 13:43:56.656502: Current learning rate: 0.00587 +2026-04-12 13:45:38.320675: train_loss -0.2621 +2026-04-12 13:45:38.327132: val_loss -0.2273 +2026-04-12 13:45:38.329729: Pseudo dice [0.2534, 0.3475, 0.5351, 0.509, 0.6008, 0.8489, 0.8245] +2026-04-12 13:45:38.332098: Epoch time: 101.67 s +2026-04-12 13:45:39.617015: +2026-04-12 13:45:39.619654: Epoch 1788 +2026-04-12 13:45:39.621973: Current learning rate: 0.00587 +2026-04-12 13:47:21.355591: train_loss -0.2645 +2026-04-12 13:47:21.362401: val_loss -0.2435 +2026-04-12 13:47:21.364229: Pseudo dice [0.6826, 0.7963, 0.8213, 0.5995, 0.5107, 0.6462, 0.6739] +2026-04-12 13:47:21.367042: Epoch time: 101.74 s +2026-04-12 13:47:22.669601: +2026-04-12 13:47:22.671383: Epoch 1789 +2026-04-12 13:47:22.673142: Current learning rate: 0.00587 +2026-04-12 13:49:04.780069: train_loss -0.274 +2026-04-12 13:49:04.786054: val_loss -0.226 +2026-04-12 13:49:04.788079: Pseudo dice [0.842, 0.7282, 0.7877, 0.6073, 0.5202, 0.4468, 0.7736] +2026-04-12 13:49:04.790385: Epoch time: 102.11 s +2026-04-12 13:49:06.040758: +2026-04-12 13:49:06.042948: Epoch 1790 +2026-04-12 13:49:06.045409: Current learning rate: 0.00586 +2026-04-12 13:50:47.193643: train_loss -0.2788 +2026-04-12 13:50:47.209580: val_loss -0.2128 +2026-04-12 13:50:47.224148: Pseudo dice [0.7675, 0.3392, 0.6898, 0.5679, 0.3608, 0.8106, 0.7838] +2026-04-12 13:50:47.228880: Epoch time: 101.16 s +2026-04-12 13:50:48.491017: +2026-04-12 13:50:48.493039: Epoch 1791 +2026-04-12 13:50:48.494617: Current learning rate: 0.00586 +2026-04-12 13:52:30.199134: train_loss -0.2643 +2026-04-12 13:52:30.205970: val_loss -0.2381 +2026-04-12 13:52:30.208575: Pseudo dice [0.7281, 0.3889, 0.7744, 0.6741, 0.4075, 0.8545, 0.8451] +2026-04-12 13:52:30.210665: Epoch time: 101.71 s +2026-04-12 13:52:31.451584: +2026-04-12 13:52:31.454349: Epoch 1792 +2026-04-12 13:52:31.456380: Current learning rate: 0.00586 +2026-04-12 13:54:13.379721: train_loss -0.2793 +2026-04-12 13:54:13.385939: val_loss -0.2293 +2026-04-12 13:54:13.387782: Pseudo dice [0.7099, 0.3255, 0.6426, 0.396, 0.5351, 0.7899, 0.6631] +2026-04-12 13:54:13.390201: Epoch time: 101.93 s +2026-04-12 13:54:14.636129: +2026-04-12 13:54:14.638232: Epoch 1793 +2026-04-12 13:54:14.639809: Current learning rate: 0.00586 +2026-04-12 13:55:56.192319: train_loss -0.2659 +2026-04-12 13:55:56.198670: val_loss -0.1806 +2026-04-12 13:55:56.201307: Pseudo dice [0.6839, 0.1798, 0.6935, 0.5102, 0.4705, 0.5148, 0.6863] +2026-04-12 13:55:56.203288: Epoch time: 101.56 s +2026-04-12 13:55:57.415628: +2026-04-12 13:55:57.417800: Epoch 1794 +2026-04-12 13:55:57.419359: Current learning rate: 0.00585 +2026-04-12 13:57:38.810156: train_loss -0.2693 +2026-04-12 13:57:38.817312: val_loss -0.2322 +2026-04-12 13:57:38.819839: Pseudo dice [0.725, 0.5969, 0.7643, 0.3631, 0.3796, 0.8104, 0.427] +2026-04-12 13:57:38.822545: Epoch time: 101.4 s +2026-04-12 13:57:40.010642: +2026-04-12 13:57:40.012503: Epoch 1795 +2026-04-12 13:57:40.014028: Current learning rate: 0.00585 +2026-04-12 13:59:21.274354: train_loss -0.2698 +2026-04-12 13:59:21.280573: val_loss -0.1954 +2026-04-12 13:59:21.282890: Pseudo dice [0.6209, 0.3397, 0.3824, 0.0744, 0.2727, 0.783, 0.6524] +2026-04-12 13:59:21.286645: Epoch time: 101.27 s +2026-04-12 13:59:23.692509: +2026-04-12 13:59:23.694415: Epoch 1796 +2026-04-12 13:59:23.696016: Current learning rate: 0.00585 +2026-04-12 14:01:05.702288: train_loss -0.2571 +2026-04-12 14:01:05.710400: val_loss -0.168 +2026-04-12 14:01:05.713185: Pseudo dice [0.634, 0.6109, 0.5638, 0.1959, 0.4979, 0.3996, 0.5688] +2026-04-12 14:01:05.716465: Epoch time: 102.01 s +2026-04-12 14:01:06.991126: +2026-04-12 14:01:06.993156: Epoch 1797 +2026-04-12 14:01:06.995801: Current learning rate: 0.00585 +2026-04-12 14:02:49.999979: train_loss -0.2631 +2026-04-12 14:02:50.005716: val_loss -0.1856 +2026-04-12 14:02:50.007625: Pseudo dice [0.5807, 0.8938, 0.7001, 0.2614, 0.4686, 0.3252, 0.7315] +2026-04-12 14:02:50.010042: Epoch time: 103.01 s +2026-04-12 14:02:51.190010: +2026-04-12 14:02:51.191984: Epoch 1798 +2026-04-12 14:02:51.193973: Current learning rate: 0.00584 +2026-04-12 14:04:32.978764: train_loss -0.2711 +2026-04-12 14:04:32.996511: val_loss -0.2214 +2026-04-12 14:04:33.004889: Pseudo dice [0.7525, 0.9168, 0.7269, 0.6787, 0.4846, 0.5278, 0.8182] +2026-04-12 14:04:33.007770: Epoch time: 101.79 s +2026-04-12 14:04:34.238643: +2026-04-12 14:04:34.240747: Epoch 1799 +2026-04-12 14:04:34.242629: Current learning rate: 0.00584 +2026-04-12 14:06:15.643183: train_loss -0.2736 +2026-04-12 14:06:15.649499: val_loss -0.2435 +2026-04-12 14:06:15.654949: Pseudo dice [0.6617, 0.7928, 0.6582, 0.6132, 0.5056, 0.8574, 0.8639] +2026-04-12 14:06:15.657507: Epoch time: 101.41 s +2026-04-12 14:06:18.696416: +2026-04-12 14:06:18.699119: Epoch 1800 +2026-04-12 14:06:18.700805: Current learning rate: 0.00584 +2026-04-12 14:08:00.124945: train_loss -0.2822 +2026-04-12 14:08:00.131933: val_loss -0.2199 +2026-04-12 14:08:00.133883: Pseudo dice [0.4695, 0.826, 0.7486, 0.5936, 0.6078, 0.6511, 0.8349] +2026-04-12 14:08:00.136079: Epoch time: 101.43 s +2026-04-12 14:08:01.346766: +2026-04-12 14:08:01.348540: Epoch 1801 +2026-04-12 14:08:01.350417: Current learning rate: 0.00584 +2026-04-12 14:09:43.668006: train_loss -0.2622 +2026-04-12 14:09:43.678466: val_loss -0.1917 +2026-04-12 14:09:43.681191: Pseudo dice [0.3355, 0.1594, 0.6207, 0.5623, 0.3944, 0.6392, 0.6623] +2026-04-12 14:09:43.684588: Epoch time: 102.32 s +2026-04-12 14:09:44.866547: +2026-04-12 14:09:44.868337: Epoch 1802 +2026-04-12 14:09:44.869964: Current learning rate: 0.00583 +2026-04-12 14:11:27.016599: train_loss -0.2642 +2026-04-12 14:11:27.025588: val_loss -0.2078 +2026-04-12 14:11:27.028611: Pseudo dice [0.5588, 0.7846, 0.7682, 0.4382, 0.56, 0.0824, 0.8198] +2026-04-12 14:11:27.031176: Epoch time: 102.15 s +2026-04-12 14:11:28.255764: +2026-04-12 14:11:28.258278: Epoch 1803 +2026-04-12 14:11:28.260709: Current learning rate: 0.00583 +2026-04-12 14:13:10.472132: train_loss -0.2738 +2026-04-12 14:13:10.482574: val_loss -0.2002 +2026-04-12 14:13:10.484604: Pseudo dice [0.5384, 0.3922, 0.7399, 0.4117, 0.3457, 0.8026, 0.5609] +2026-04-12 14:13:10.486767: Epoch time: 102.22 s +2026-04-12 14:13:11.705135: +2026-04-12 14:13:11.706885: Epoch 1804 +2026-04-12 14:13:11.708429: Current learning rate: 0.00583 +2026-04-12 14:14:53.684837: train_loss -0.2729 +2026-04-12 14:14:53.693426: val_loss -0.229 +2026-04-12 14:14:53.695402: Pseudo dice [0.4288, 0.6546, 0.7933, 0.2008, 0.4574, 0.8432, 0.6718] +2026-04-12 14:14:53.698116: Epoch time: 101.98 s +2026-04-12 14:14:54.887028: +2026-04-12 14:14:54.888594: Epoch 1805 +2026-04-12 14:14:54.889951: Current learning rate: 0.00583 +2026-04-12 14:16:36.504316: train_loss -0.2755 +2026-04-12 14:16:36.510424: val_loss -0.2525 +2026-04-12 14:16:36.512960: Pseudo dice [0.6041, 0.7784, 0.8219, 0.3179, 0.5047, 0.707, 0.4465] +2026-04-12 14:16:36.515778: Epoch time: 101.62 s +2026-04-12 14:16:37.728991: +2026-04-12 14:16:37.731079: Epoch 1806 +2026-04-12 14:16:37.733021: Current learning rate: 0.00582 +2026-04-12 14:18:19.056541: train_loss -0.2768 +2026-04-12 14:18:19.062149: val_loss -0.225 +2026-04-12 14:18:19.064054: Pseudo dice [0.8022, 0.5041, 0.8291, 0.3187, 0.4136, 0.7292, 0.7164] +2026-04-12 14:18:19.066052: Epoch time: 101.33 s +2026-04-12 14:18:20.263244: +2026-04-12 14:18:20.265088: Epoch 1807 +2026-04-12 14:18:20.266583: Current learning rate: 0.00582 +2026-04-12 14:20:01.790688: train_loss -0.2868 +2026-04-12 14:20:01.797678: val_loss -0.2112 +2026-04-12 14:20:01.799772: Pseudo dice [0.4415, 0.6131, 0.743, 0.1967, 0.5925, 0.4111, 0.8422] +2026-04-12 14:20:01.802281: Epoch time: 101.53 s +2026-04-12 14:20:02.989824: +2026-04-12 14:20:02.991354: Epoch 1808 +2026-04-12 14:20:02.993007: Current learning rate: 0.00582 +2026-04-12 14:21:44.619780: train_loss -0.2819 +2026-04-12 14:21:44.625804: val_loss -0.1971 +2026-04-12 14:21:44.627722: Pseudo dice [0.7385, 0.1937, 0.7177, 0.3848, 0.5741, 0.1744, 0.8565] +2026-04-12 14:21:44.630406: Epoch time: 101.63 s +2026-04-12 14:21:45.821754: +2026-04-12 14:21:45.823815: Epoch 1809 +2026-04-12 14:21:45.825890: Current learning rate: 0.00582 +2026-04-12 14:23:28.155953: train_loss -0.2623 +2026-04-12 14:23:28.162173: val_loss -0.2239 +2026-04-12 14:23:28.163947: Pseudo dice [0.5903, 0.8556, 0.7273, 0.2953, 0.3958, 0.8242, 0.6116] +2026-04-12 14:23:28.166286: Epoch time: 102.34 s +2026-04-12 14:23:29.394592: +2026-04-12 14:23:29.396537: Epoch 1810 +2026-04-12 14:23:29.398336: Current learning rate: 0.00581 +2026-04-12 14:25:11.283872: train_loss -0.2529 +2026-04-12 14:25:11.291383: val_loss -0.2203 +2026-04-12 14:25:11.293709: Pseudo dice [0.6316, 0.8596, 0.7946, 0.4664, 0.4866, 0.2927, 0.7964] +2026-04-12 14:25:11.296067: Epoch time: 101.89 s +2026-04-12 14:25:12.493757: +2026-04-12 14:25:12.495286: Epoch 1811 +2026-04-12 14:25:12.496774: Current learning rate: 0.00581 +2026-04-12 14:26:55.338586: train_loss -0.2749 +2026-04-12 14:26:55.346088: val_loss -0.2361 +2026-04-12 14:26:55.348500: Pseudo dice [0.6131, 0.5938, 0.8276, 0.4148, 0.4759, 0.8782, 0.7066] +2026-04-12 14:26:55.351437: Epoch time: 102.85 s +2026-04-12 14:26:56.556530: +2026-04-12 14:26:56.558592: Epoch 1812 +2026-04-12 14:26:56.560696: Current learning rate: 0.00581 +2026-04-12 14:28:38.789816: train_loss -0.252 +2026-04-12 14:28:38.796120: val_loss -0.2022 +2026-04-12 14:28:38.798350: Pseudo dice [0.7125, 0.8323, 0.7224, 0.6985, 0.5459, 0.7904, 0.6913] +2026-04-12 14:28:38.800799: Epoch time: 102.24 s +2026-04-12 14:28:40.016191: +2026-04-12 14:28:40.018516: Epoch 1813 +2026-04-12 14:28:40.020147: Current learning rate: 0.00581 +2026-04-12 14:30:21.873675: train_loss -0.2655 +2026-04-12 14:30:21.879365: val_loss -0.1968 +2026-04-12 14:30:21.881271: Pseudo dice [0.1141, 0.7614, 0.7686, 0.6124, 0.3597, 0.2706, 0.8376] +2026-04-12 14:30:21.884167: Epoch time: 101.86 s +2026-04-12 14:30:23.071011: +2026-04-12 14:30:23.073970: Epoch 1814 +2026-04-12 14:30:23.076108: Current learning rate: 0.00581 +2026-04-12 14:32:04.794851: train_loss -0.2689 +2026-04-12 14:32:04.802330: val_loss -0.1981 +2026-04-12 14:32:04.804616: Pseudo dice [0.797, 0.8809, 0.612, 0.3688, 0.4093, 0.3284, 0.7387] +2026-04-12 14:32:04.806725: Epoch time: 101.73 s +2026-04-12 14:32:05.992555: +2026-04-12 14:32:05.994425: Epoch 1815 +2026-04-12 14:32:05.996074: Current learning rate: 0.0058 +2026-04-12 14:33:47.987124: train_loss -0.2844 +2026-04-12 14:33:47.995566: val_loss -0.2143 +2026-04-12 14:33:47.997898: Pseudo dice [0.3084, 0.8809, 0.7551, 0.3841, 0.4974, 0.2397, 0.8307] +2026-04-12 14:33:48.000774: Epoch time: 102.0 s +2026-04-12 14:33:50.377984: +2026-04-12 14:33:50.379632: Epoch 1816 +2026-04-12 14:33:50.381430: Current learning rate: 0.0058 +2026-04-12 14:35:33.382653: train_loss -0.2724 +2026-04-12 14:35:33.388849: val_loss -0.2431 +2026-04-12 14:35:33.390816: Pseudo dice [0.8607, 0.4637, 0.8224, 0.4079, 0.5145, 0.8739, 0.7472] +2026-04-12 14:35:33.392792: Epoch time: 103.01 s +2026-04-12 14:35:34.597086: +2026-04-12 14:35:34.599575: Epoch 1817 +2026-04-12 14:35:34.601582: Current learning rate: 0.0058 +2026-04-12 14:37:16.987108: train_loss -0.2628 +2026-04-12 14:37:17.012961: val_loss -0.2188 +2026-04-12 14:37:17.015532: Pseudo dice [0.6002, 0.9079, 0.713, 0.4511, 0.5132, 0.7484, 0.7119] +2026-04-12 14:37:17.018645: Epoch time: 102.39 s +2026-04-12 14:37:18.251099: +2026-04-12 14:37:18.252812: Epoch 1818 +2026-04-12 14:37:18.254151: Current learning rate: 0.0058 +2026-04-12 14:38:59.876361: train_loss -0.2549 +2026-04-12 14:38:59.882123: val_loss -0.211 +2026-04-12 14:38:59.885852: Pseudo dice [0.6837, 0.3411, 0.6498, 0.7493, 0.5504, 0.4778, 0.8481] +2026-04-12 14:38:59.888815: Epoch time: 101.63 s +2026-04-12 14:39:01.093124: +2026-04-12 14:39:01.095455: Epoch 1819 +2026-04-12 14:39:01.097068: Current learning rate: 0.00579 +2026-04-12 14:40:42.791599: train_loss -0.2681 +2026-04-12 14:40:42.797340: val_loss -0.2204 +2026-04-12 14:40:42.799037: Pseudo dice [0.813, 0.5244, 0.6318, 0.2061, 0.612, 0.7344, 0.7703] +2026-04-12 14:40:42.801157: Epoch time: 101.7 s +2026-04-12 14:40:44.016948: +2026-04-12 14:40:44.018486: Epoch 1820 +2026-04-12 14:40:44.020018: Current learning rate: 0.00579 +2026-04-12 14:42:26.041037: train_loss -0.2775 +2026-04-12 14:42:26.049655: val_loss -0.2277 +2026-04-12 14:42:26.052029: Pseudo dice [0.6545, 0.4135, 0.6826, 0.3697, 0.4864, 0.8245, 0.3397] +2026-04-12 14:42:26.054249: Epoch time: 102.03 s +2026-04-12 14:42:27.244575: +2026-04-12 14:42:27.246518: Epoch 1821 +2026-04-12 14:42:27.248221: Current learning rate: 0.00579 +2026-04-12 14:44:08.541840: train_loss -0.2736 +2026-04-12 14:44:08.547755: val_loss -0.2714 +2026-04-12 14:44:08.549753: Pseudo dice [0.8761, 0.5114, 0.8253, 0.5229, 0.655, 0.9009, 0.8545] +2026-04-12 14:44:08.552003: Epoch time: 101.3 s +2026-04-12 14:44:09.735796: +2026-04-12 14:44:09.737560: Epoch 1822 +2026-04-12 14:44:09.738970: Current learning rate: 0.00579 +2026-04-12 14:45:51.578552: train_loss -0.2681 +2026-04-12 14:45:51.586243: val_loss -0.2132 +2026-04-12 14:45:51.589063: Pseudo dice [0.2614, 0.8732, 0.7473, 0.4854, 0.5839, 0.8646, 0.3458] +2026-04-12 14:45:51.591405: Epoch time: 101.85 s +2026-04-12 14:45:52.811584: +2026-04-12 14:45:52.813673: Epoch 1823 +2026-04-12 14:45:52.817426: Current learning rate: 0.00578 +2026-04-12 14:47:34.768714: train_loss -0.2633 +2026-04-12 14:47:34.785107: val_loss -0.2179 +2026-04-12 14:47:34.787140: Pseudo dice [0.3933, 0.8968, 0.7924, 0.6398, 0.511, 0.5913, 0.7359] +2026-04-12 14:47:34.790161: Epoch time: 101.96 s +2026-04-12 14:47:35.992832: +2026-04-12 14:47:35.995571: Epoch 1824 +2026-04-12 14:47:35.997111: Current learning rate: 0.00578 +2026-04-12 14:49:17.581431: train_loss -0.2733 +2026-04-12 14:49:17.587056: val_loss -0.2229 +2026-04-12 14:49:17.589104: Pseudo dice [0.6371, 0.8834, 0.562, 0.1495, 0.5692, 0.6995, 0.6254] +2026-04-12 14:49:17.591284: Epoch time: 101.59 s +2026-04-12 14:49:18.775347: +2026-04-12 14:49:18.776965: Epoch 1825 +2026-04-12 14:49:18.778438: Current learning rate: 0.00578 +2026-04-12 14:51:00.210387: train_loss -0.2834 +2026-04-12 14:51:00.216179: val_loss -0.2221 +2026-04-12 14:51:00.218012: Pseudo dice [0.7638, 0.2297, 0.8051, 0.3704, 0.3929, 0.8775, 0.6414] +2026-04-12 14:51:00.219903: Epoch time: 101.44 s +2026-04-12 14:51:01.452282: +2026-04-12 14:51:01.453939: Epoch 1826 +2026-04-12 14:51:01.455645: Current learning rate: 0.00578 +2026-04-12 14:52:43.361563: train_loss -0.2804 +2026-04-12 14:52:43.369652: val_loss -0.2299 +2026-04-12 14:52:43.371704: Pseudo dice [0.5966, 0.3709, 0.7466, 0.2527, 0.407, 0.8852, 0.7494] +2026-04-12 14:52:43.374071: Epoch time: 101.91 s +2026-04-12 14:52:44.553244: +2026-04-12 14:52:44.555088: Epoch 1827 +2026-04-12 14:52:44.557299: Current learning rate: 0.00577 +2026-04-12 14:54:26.303278: train_loss -0.262 +2026-04-12 14:54:26.309544: val_loss -0.2172 +2026-04-12 14:54:26.311948: Pseudo dice [0.2628, 0.3603, 0.7205, 0.5996, 0.4728, 0.896, 0.7692] +2026-04-12 14:54:26.314327: Epoch time: 101.75 s +2026-04-12 14:54:27.525527: +2026-04-12 14:54:27.527430: Epoch 1828 +2026-04-12 14:54:27.529253: Current learning rate: 0.00577 +2026-04-12 14:56:09.294799: train_loss -0.263 +2026-04-12 14:56:09.303128: val_loss -0.2478 +2026-04-12 14:56:09.305325: Pseudo dice [0.6081, 0.7579, 0.7807, 0.5874, 0.4473, 0.8441, 0.8366] +2026-04-12 14:56:09.310079: Epoch time: 101.77 s +2026-04-12 14:56:10.532122: +2026-04-12 14:56:10.534508: Epoch 1829 +2026-04-12 14:56:10.536530: Current learning rate: 0.00577 +2026-04-12 14:57:52.142590: train_loss -0.2796 +2026-04-12 14:57:52.149294: val_loss -0.2319 +2026-04-12 14:57:52.151143: Pseudo dice [0.804, 0.7032, 0.664, 0.5609, 0.6212, 0.3436, 0.7956] +2026-04-12 14:57:52.153641: Epoch time: 101.61 s +2026-04-12 14:57:53.346913: +2026-04-12 14:57:53.348703: Epoch 1830 +2026-04-12 14:57:53.350272: Current learning rate: 0.00577 +2026-04-12 14:59:34.747999: train_loss -0.2813 +2026-04-12 14:59:34.753241: val_loss -0.2655 +2026-04-12 14:59:34.754758: Pseudo dice [0.7756, 0.7369, 0.7813, 0.6055, 0.671, 0.3226, 0.7662] +2026-04-12 14:59:34.757174: Epoch time: 101.4 s +2026-04-12 14:59:36.005989: +2026-04-12 14:59:36.007517: Epoch 1831 +2026-04-12 14:59:36.008965: Current learning rate: 0.00576 +2026-04-12 15:01:17.916331: train_loss -0.2746 +2026-04-12 15:01:17.923656: val_loss -0.2292 +2026-04-12 15:01:17.925796: Pseudo dice [0.7274, 0.7718, 0.7184, 0.6188, 0.6252, 0.7262, 0.8371] +2026-04-12 15:01:17.928403: Epoch time: 101.91 s +2026-04-12 15:01:19.140397: +2026-04-12 15:01:19.143754: Epoch 1832 +2026-04-12 15:01:19.146409: Current learning rate: 0.00576 +2026-04-12 15:03:01.176167: train_loss -0.2694 +2026-04-12 15:03:01.182477: val_loss -0.2262 +2026-04-12 15:03:01.184554: Pseudo dice [0.8316, 0.8172, 0.7288, 0.4799, 0.2524, 0.683, 0.5421] +2026-04-12 15:03:01.186803: Epoch time: 102.04 s +2026-04-12 15:03:02.357544: +2026-04-12 15:03:02.359252: Epoch 1833 +2026-04-12 15:03:02.360781: Current learning rate: 0.00576 +2026-04-12 15:04:44.249295: train_loss -0.2587 +2026-04-12 15:04:44.255209: val_loss -0.214 +2026-04-12 15:04:44.257373: Pseudo dice [0.5495, 0.3859, 0.5586, 0.2097, 0.5607, 0.895, 0.863] +2026-04-12 15:04:44.259800: Epoch time: 101.89 s +2026-04-12 15:04:45.438528: +2026-04-12 15:04:45.440267: Epoch 1834 +2026-04-12 15:04:45.441832: Current learning rate: 0.00576 +2026-04-12 15:06:27.475146: train_loss -0.2638 +2026-04-12 15:06:27.485740: val_loss -0.237 +2026-04-12 15:06:27.490075: Pseudo dice [0.5759, 0.1634, 0.7597, 0.4868, 0.4946, 0.7063, 0.7969] +2026-04-12 15:06:27.494478: Epoch time: 102.04 s +2026-04-12 15:06:28.698665: +2026-04-12 15:06:28.701970: Epoch 1835 +2026-04-12 15:06:28.703742: Current learning rate: 0.00576 +2026-04-12 15:08:10.358927: train_loss -0.2655 +2026-04-12 15:08:10.366597: val_loss -0.1926 +2026-04-12 15:08:10.368371: Pseudo dice [0.6116, 0.2025, 0.746, 0.3086, 0.4153, 0.1232, 0.3489] +2026-04-12 15:08:10.370537: Epoch time: 101.66 s +2026-04-12 15:08:11.559232: +2026-04-12 15:08:11.560689: Epoch 1836 +2026-04-12 15:08:11.562120: Current learning rate: 0.00575 +2026-04-12 15:09:54.368435: train_loss -0.2793 +2026-04-12 15:09:54.374293: val_loss -0.222 +2026-04-12 15:09:54.376158: Pseudo dice [0.4862, 0.6948, 0.6772, 0.1445, 0.5691, 0.7597, 0.5579] +2026-04-12 15:09:54.378441: Epoch time: 102.81 s +2026-04-12 15:09:55.572187: +2026-04-12 15:09:55.575386: Epoch 1837 +2026-04-12 15:09:55.577201: Current learning rate: 0.00575 +2026-04-12 15:11:37.295336: train_loss -0.2731 +2026-04-12 15:11:37.302226: val_loss -0.2375 +2026-04-12 15:11:37.304254: Pseudo dice [0.7778, 0.6265, 0.7995, 0.5901, 0.599, 0.474, 0.8376] +2026-04-12 15:11:37.307791: Epoch time: 101.73 s +2026-04-12 15:11:38.504545: +2026-04-12 15:11:38.506588: Epoch 1838 +2026-04-12 15:11:38.508279: Current learning rate: 0.00575 +2026-04-12 15:13:19.974300: train_loss -0.2716 +2026-04-12 15:13:19.980946: val_loss -0.2377 +2026-04-12 15:13:19.983173: Pseudo dice [0.6219, 0.2144, 0.7891, 0.4072, 0.4502, 0.7455, 0.8118] +2026-04-12 15:13:19.985489: Epoch time: 101.47 s +2026-04-12 15:13:21.184678: +2026-04-12 15:13:21.186308: Epoch 1839 +2026-04-12 15:13:21.187683: Current learning rate: 0.00575 +2026-04-12 15:15:03.373055: train_loss -0.2721 +2026-04-12 15:15:03.379372: val_loss -0.2268 +2026-04-12 15:15:03.381018: Pseudo dice [0.8059, 0.5886, 0.7922, 0.6216, 0.3606, 0.7238, 0.6615] +2026-04-12 15:15:03.383438: Epoch time: 102.19 s +2026-04-12 15:15:04.564896: +2026-04-12 15:15:04.566485: Epoch 1840 +2026-04-12 15:15:04.567898: Current learning rate: 0.00574 +2026-04-12 15:16:46.065409: train_loss -0.2627 +2026-04-12 15:16:46.072605: val_loss -0.2154 +2026-04-12 15:16:46.074916: Pseudo dice [0.4405, 0.8842, 0.7633, 0.6458, 0.6077, 0.4343, 0.4751] +2026-04-12 15:16:46.077259: Epoch time: 101.5 s +2026-04-12 15:16:47.265942: +2026-04-12 15:16:47.267914: Epoch 1841 +2026-04-12 15:16:47.269711: Current learning rate: 0.00574 +2026-04-12 15:18:28.883266: train_loss -0.2816 +2026-04-12 15:18:28.889820: val_loss -0.235 +2026-04-12 15:18:28.891972: Pseudo dice [0.1864, 0.5172, 0.7911, 0.5869, 0.3054, 0.8878, 0.7986] +2026-04-12 15:18:28.895592: Epoch time: 101.62 s +2026-04-12 15:18:30.078352: +2026-04-12 15:18:30.079941: Epoch 1842 +2026-04-12 15:18:30.081493: Current learning rate: 0.00574 +2026-04-12 15:20:11.527804: train_loss -0.2622 +2026-04-12 15:20:11.533971: val_loss -0.2203 +2026-04-12 15:20:11.535927: Pseudo dice [0.7961, 0.3021, 0.7047, 0.5967, 0.3884, 0.8181, 0.7593] +2026-04-12 15:20:11.538990: Epoch time: 101.45 s +2026-04-12 15:20:12.738289: +2026-04-12 15:20:12.740152: Epoch 1843 +2026-04-12 15:20:12.741941: Current learning rate: 0.00574 +2026-04-12 15:21:54.651597: train_loss -0.2866 +2026-04-12 15:21:54.658645: val_loss -0.2186 +2026-04-12 15:21:54.660824: Pseudo dice [0.5325, 0.277, 0.7037, 0.3924, 0.5991, 0.9175, 0.7201] +2026-04-12 15:21:54.663257: Epoch time: 101.92 s +2026-04-12 15:21:55.869802: +2026-04-12 15:21:55.871404: Epoch 1844 +2026-04-12 15:21:55.872892: Current learning rate: 0.00573 +2026-04-12 15:23:37.827091: train_loss -0.2766 +2026-04-12 15:23:37.833731: val_loss -0.2011 +2026-04-12 15:23:37.836242: Pseudo dice [0.4734, 0.9086, 0.7479, 0.3067, 0.2084, 0.1563, 0.1575] +2026-04-12 15:23:37.839353: Epoch time: 101.96 s +2026-04-12 15:23:39.031495: +2026-04-12 15:23:39.033308: Epoch 1845 +2026-04-12 15:23:39.035295: Current learning rate: 0.00573 +2026-04-12 15:25:20.608800: train_loss -0.258 +2026-04-12 15:25:20.614471: val_loss -0.2208 +2026-04-12 15:25:20.616138: Pseudo dice [0.2093, 0.877, 0.5106, 0.5489, 0.28, 0.1916, 0.7864] +2026-04-12 15:25:20.618241: Epoch time: 101.58 s +2026-04-12 15:25:21.815028: +2026-04-12 15:25:21.816570: Epoch 1846 +2026-04-12 15:25:21.817940: Current learning rate: 0.00573 +2026-04-12 15:27:03.701432: train_loss -0.2497 +2026-04-12 15:27:03.707253: val_loss -0.227 +2026-04-12 15:27:03.708915: Pseudo dice [0.5878, 0.5482, 0.6892, 0.5783, 0.3232, 0.4234, 0.5031] +2026-04-12 15:27:03.712253: Epoch time: 101.89 s +2026-04-12 15:27:04.921088: +2026-04-12 15:27:04.923528: Epoch 1847 +2026-04-12 15:27:04.925472: Current learning rate: 0.00573 +2026-04-12 15:28:46.492756: train_loss -0.2583 +2026-04-12 15:28:46.508475: val_loss -0.2061 +2026-04-12 15:28:46.510487: Pseudo dice [0.5466, 0.3673, 0.7987, 0.517, 0.4726, 0.6616, 0.7054] +2026-04-12 15:28:46.512584: Epoch time: 101.57 s +2026-04-12 15:28:47.721495: +2026-04-12 15:28:47.723676: Epoch 1848 +2026-04-12 15:28:47.725730: Current learning rate: 0.00572 +2026-04-12 15:30:29.875106: train_loss -0.2679 +2026-04-12 15:30:29.880919: val_loss -0.2115 +2026-04-12 15:30:29.883438: Pseudo dice [0.536, 0.5933, 0.613, 0.1363, 0.4528, 0.6941, 0.4078] +2026-04-12 15:30:29.886057: Epoch time: 102.16 s +2026-04-12 15:30:31.068453: +2026-04-12 15:30:31.070261: Epoch 1849 +2026-04-12 15:30:31.071750: Current learning rate: 0.00572 +2026-04-12 15:32:13.390105: train_loss -0.2597 +2026-04-12 15:32:13.396287: val_loss -0.2412 +2026-04-12 15:32:13.398481: Pseudo dice [0.6827, 0.7069, 0.7021, 0.4479, 0.3196, 0.8119, 0.65] +2026-04-12 15:32:13.401928: Epoch time: 102.32 s +2026-04-12 15:32:16.270871: +2026-04-12 15:32:16.272853: Epoch 1850 +2026-04-12 15:32:16.274399: Current learning rate: 0.00572 +2026-04-12 15:33:57.596407: train_loss -0.2557 +2026-04-12 15:33:57.603404: val_loss -0.1302 +2026-04-12 15:33:57.605556: Pseudo dice [0.5582, 0.3813, 0.648, 0.0284, 0.2262, 0.5345, 0.0984] +2026-04-12 15:33:57.607980: Epoch time: 101.33 s +2026-04-12 15:33:58.871707: +2026-04-12 15:33:58.874696: Epoch 1851 +2026-04-12 15:33:58.878664: Current learning rate: 0.00572 +2026-04-12 15:35:40.664341: train_loss -0.2664 +2026-04-12 15:35:40.671151: val_loss -0.2404 +2026-04-12 15:35:40.674352: Pseudo dice [0.585, 0.6719, 0.7826, 0.369, 0.5273, 0.8935, 0.6937] +2026-04-12 15:35:40.677052: Epoch time: 101.8 s +2026-04-12 15:35:41.865694: +2026-04-12 15:35:41.867515: Epoch 1852 +2026-04-12 15:35:41.868993: Current learning rate: 0.00571 +2026-04-12 15:37:23.645150: train_loss -0.2772 +2026-04-12 15:37:23.672460: val_loss -0.2439 +2026-04-12 15:37:23.674720: Pseudo dice [0.4817, 0.4798, 0.8192, 0.6532, 0.6075, 0.4336, 0.8411] +2026-04-12 15:37:23.676997: Epoch time: 101.78 s +2026-04-12 15:37:24.861069: +2026-04-12 15:37:24.862568: Epoch 1853 +2026-04-12 15:37:24.863966: Current learning rate: 0.00571 +2026-04-12 15:39:06.359751: train_loss -0.2781 +2026-04-12 15:39:06.365267: val_loss -0.2044 +2026-04-12 15:39:06.367847: Pseudo dice [0.7213, 0.2892, 0.7032, 0.3852, 0.4213, 0.2413, 0.7516] +2026-04-12 15:39:06.371577: Epoch time: 101.5 s +2026-04-12 15:39:07.567456: +2026-04-12 15:39:07.569566: Epoch 1854 +2026-04-12 15:39:07.571372: Current learning rate: 0.00571 +2026-04-12 15:40:48.949721: train_loss -0.2548 +2026-04-12 15:40:48.956389: val_loss -0.2224 +2026-04-12 15:40:48.960154: Pseudo dice [0.866, 0.794, 0.6694, 0.3867, 0.3298, 0.899, 0.7132] +2026-04-12 15:40:48.962568: Epoch time: 101.39 s +2026-04-12 15:40:50.169120: +2026-04-12 15:40:50.171574: Epoch 1855 +2026-04-12 15:40:50.173570: Current learning rate: 0.00571 +2026-04-12 15:42:32.661531: train_loss -0.2615 +2026-04-12 15:42:32.671201: val_loss -0.2254 +2026-04-12 15:42:32.673494: Pseudo dice [0.6154, 0.7051, 0.7383, 0.5498, 0.5584, 0.6485, 0.8333] +2026-04-12 15:42:32.675788: Epoch time: 102.5 s +2026-04-12 15:42:34.938373: +2026-04-12 15:42:34.940108: Epoch 1856 +2026-04-12 15:42:34.941551: Current learning rate: 0.0057 +2026-04-12 15:44:16.488259: train_loss -0.2723 +2026-04-12 15:44:16.494596: val_loss -0.2214 +2026-04-12 15:44:16.496738: Pseudo dice [0.7124, 0.7624, 0.714, 0.644, 0.3314, 0.5522, 0.6509] +2026-04-12 15:44:16.499843: Epoch time: 101.55 s +2026-04-12 15:44:17.712983: +2026-04-12 15:44:17.714836: Epoch 1857 +2026-04-12 15:44:17.716480: Current learning rate: 0.0057 +2026-04-12 15:45:59.678252: train_loss -0.2695 +2026-04-12 15:45:59.684130: val_loss -0.212 +2026-04-12 15:45:59.685843: Pseudo dice [0.4234, 0.8886, 0.7948, 0.4332, 0.3419, 0.3035, 0.7045] +2026-04-12 15:45:59.688410: Epoch time: 101.97 s +2026-04-12 15:46:00.889591: +2026-04-12 15:46:00.891331: Epoch 1858 +2026-04-12 15:46:00.893450: Current learning rate: 0.0057 +2026-04-12 15:47:42.426448: train_loss -0.2814 +2026-04-12 15:47:42.435160: val_loss -0.2098 +2026-04-12 15:47:42.437692: Pseudo dice [0.4921, 0.7957, 0.7789, 0.5402, 0.1692, 0.1008, 0.7596] +2026-04-12 15:47:42.451554: Epoch time: 101.54 s +2026-04-12 15:47:43.671824: +2026-04-12 15:47:43.673847: Epoch 1859 +2026-04-12 15:47:43.675388: Current learning rate: 0.0057 +2026-04-12 15:49:25.577414: train_loss -0.2529 +2026-04-12 15:49:25.583152: val_loss -0.1955 +2026-04-12 15:49:25.584822: Pseudo dice [0.5295, 0.8485, 0.7139, 0.3186, 0.277, 0.5767, 0.7824] +2026-04-12 15:49:25.587407: Epoch time: 101.91 s +2026-04-12 15:49:26.808329: +2026-04-12 15:49:26.810434: Epoch 1860 +2026-04-12 15:49:26.812228: Current learning rate: 0.0057 +2026-04-12 15:51:09.578460: train_loss -0.2652 +2026-04-12 15:51:09.585070: val_loss -0.2093 +2026-04-12 15:51:09.586780: Pseudo dice [0.5774, 0.6956, 0.7399, 0.2706, 0.4216, 0.1579, 0.2713] +2026-04-12 15:51:09.588869: Epoch time: 102.77 s +2026-04-12 15:51:11.008246: +2026-04-12 15:51:11.009866: Epoch 1861 +2026-04-12 15:51:11.011235: Current learning rate: 0.00569 +2026-04-12 15:52:53.489401: train_loss -0.2612 +2026-04-12 15:52:53.496886: val_loss -0.2144 +2026-04-12 15:52:53.498772: Pseudo dice [0.6121, 0.8706, 0.7233, 0.3933, 0.302, 0.7954, 0.2352] +2026-04-12 15:52:53.501149: Epoch time: 102.48 s +2026-04-12 15:52:54.714725: +2026-04-12 15:52:54.716441: Epoch 1862 +2026-04-12 15:52:54.718085: Current learning rate: 0.00569 +2026-04-12 15:54:36.707434: train_loss -0.2682 +2026-04-12 15:54:36.714106: val_loss -0.2061 +2026-04-12 15:54:36.716851: Pseudo dice [0.5532, 0.7209, 0.7159, 0.2105, 0.5588, 0.5935, 0.4341] +2026-04-12 15:54:36.719408: Epoch time: 102.0 s +2026-04-12 15:54:37.934732: +2026-04-12 15:54:37.936552: Epoch 1863 +2026-04-12 15:54:37.938383: Current learning rate: 0.00569 +2026-04-12 15:56:19.392651: train_loss -0.2678 +2026-04-12 15:56:19.398993: val_loss -0.2233 +2026-04-12 15:56:19.400982: Pseudo dice [0.2477, 0.5733, 0.8142, 0.5102, 0.5417, 0.5059, 0.5431] +2026-04-12 15:56:19.403721: Epoch time: 101.46 s +2026-04-12 15:56:20.590053: +2026-04-12 15:56:20.592150: Epoch 1864 +2026-04-12 15:56:20.593820: Current learning rate: 0.00569 +2026-04-12 15:58:01.887560: train_loss -0.2614 +2026-04-12 15:58:01.893865: val_loss -0.2156 +2026-04-12 15:58:01.897628: Pseudo dice [0.5811, 0.3853, 0.7507, 0.4937, 0.5562, 0.2411, 0.7306] +2026-04-12 15:58:01.899865: Epoch time: 101.3 s +2026-04-12 15:58:03.098691: +2026-04-12 15:58:03.101091: Epoch 1865 +2026-04-12 15:58:03.102914: Current learning rate: 0.00568 +2026-04-12 15:59:44.765139: train_loss -0.2615 +2026-04-12 15:59:44.772109: val_loss -0.2155 +2026-04-12 15:59:44.775006: Pseudo dice [0.6282, 0.5303, 0.7317, 0.5015, 0.2198, 0.6277, 0.6348] +2026-04-12 15:59:44.777517: Epoch time: 101.67 s +2026-04-12 15:59:46.012872: +2026-04-12 15:59:46.015313: Epoch 1866 +2026-04-12 15:59:46.018210: Current learning rate: 0.00568 +2026-04-12 16:01:27.952113: train_loss -0.2799 +2026-04-12 16:01:27.958933: val_loss -0.2094 +2026-04-12 16:01:27.961616: Pseudo dice [0.538, 0.4677, 0.6741, 0.1868, 0.4782, 0.8965, 0.8661] +2026-04-12 16:01:27.964308: Epoch time: 101.94 s +2026-04-12 16:01:29.193603: +2026-04-12 16:01:29.197224: Epoch 1867 +2026-04-12 16:01:29.200383: Current learning rate: 0.00568 +2026-04-12 16:03:10.731421: train_loss -0.2774 +2026-04-12 16:03:10.737432: val_loss -0.2166 +2026-04-12 16:03:10.739376: Pseudo dice [0.3096, 0.7756, 0.7784, 0.0611, 0.3917, 0.5693, 0.557] +2026-04-12 16:03:10.741784: Epoch time: 101.54 s +2026-04-12 16:03:11.930177: +2026-04-12 16:03:11.932140: Epoch 1868 +2026-04-12 16:03:11.933826: Current learning rate: 0.00568 +2026-04-12 16:04:53.362348: train_loss -0.2764 +2026-04-12 16:04:53.369656: val_loss -0.2489 +2026-04-12 16:04:53.371650: Pseudo dice [0.7457, 0.4892, 0.7603, 0.386, 0.6802, 0.9324, 0.7464] +2026-04-12 16:04:53.374829: Epoch time: 101.44 s +2026-04-12 16:04:54.547345: +2026-04-12 16:04:54.548981: Epoch 1869 +2026-04-12 16:04:54.550340: Current learning rate: 0.00567 +2026-04-12 16:06:36.423202: train_loss -0.2725 +2026-04-12 16:06:36.432794: val_loss -0.2414 +2026-04-12 16:06:36.436591: Pseudo dice [0.8538, 0.6552, 0.7186, 0.0768, 0.7029, 0.822, 0.815] +2026-04-12 16:06:36.439109: Epoch time: 101.88 s +2026-04-12 16:06:37.619803: +2026-04-12 16:06:37.621979: Epoch 1870 +2026-04-12 16:06:37.623555: Current learning rate: 0.00567 +2026-04-12 16:08:19.568292: train_loss -0.279 +2026-04-12 16:08:19.574789: val_loss -0.2325 +2026-04-12 16:08:19.577008: Pseudo dice [0.5877, 0.4582, 0.7763, 0.2413, 0.6562, 0.7106, 0.8292] +2026-04-12 16:08:19.579516: Epoch time: 101.95 s +2026-04-12 16:08:20.784676: +2026-04-12 16:08:20.786495: Epoch 1871 +2026-04-12 16:08:20.788614: Current learning rate: 0.00567 +2026-04-12 16:10:02.934384: train_loss -0.2889 +2026-04-12 16:10:02.940847: val_loss -0.2603 +2026-04-12 16:10:02.942796: Pseudo dice [0.3924, 0.9077, 0.7947, 0.6823, 0.5599, 0.2758, 0.8351] +2026-04-12 16:10:02.946207: Epoch time: 102.15 s +2026-04-12 16:10:04.167466: +2026-04-12 16:10:04.170150: Epoch 1872 +2026-04-12 16:10:04.172018: Current learning rate: 0.00567 +2026-04-12 16:11:46.707954: train_loss -0.2928 +2026-04-12 16:11:46.716589: val_loss -0.2348 +2026-04-12 16:11:46.718654: Pseudo dice [0.7025, 0.8524, 0.6464, 0.5401, 0.4927, 0.1512, 0.76] +2026-04-12 16:11:46.720889: Epoch time: 102.54 s +2026-04-12 16:11:47.942318: +2026-04-12 16:11:47.945539: Epoch 1873 +2026-04-12 16:11:47.947368: Current learning rate: 0.00566 +2026-04-12 16:13:30.306326: train_loss -0.2911 +2026-04-12 16:13:30.313299: val_loss -0.2627 +2026-04-12 16:13:30.315010: Pseudo dice [0.5892, 0.4747, 0.7969, 0.4456, 0.5047, 0.893, 0.7356] +2026-04-12 16:13:30.318038: Epoch time: 102.37 s +2026-04-12 16:13:31.532948: +2026-04-12 16:13:31.534507: Epoch 1874 +2026-04-12 16:13:31.536021: Current learning rate: 0.00566 +2026-04-12 16:15:13.248826: train_loss -0.2776 +2026-04-12 16:15:13.256429: val_loss -0.2322 +2026-04-12 16:15:13.258360: Pseudo dice [0.5031, 0.7731, 0.6266, 0.6432, 0.6428, 0.3691, 0.8901] +2026-04-12 16:15:13.260626: Epoch time: 101.72 s +2026-04-12 16:15:14.473318: +2026-04-12 16:15:14.475073: Epoch 1875 +2026-04-12 16:15:14.477364: Current learning rate: 0.00566 +2026-04-12 16:16:56.421162: train_loss -0.28 +2026-04-12 16:16:56.426713: val_loss -0.269 +2026-04-12 16:16:56.429055: Pseudo dice [0.6742, 0.5869, 0.6908, 0.8806, 0.4272, 0.9029, 0.7693] +2026-04-12 16:16:56.431444: Epoch time: 101.95 s +2026-04-12 16:16:57.625382: +2026-04-12 16:16:57.629575: Epoch 1876 +2026-04-12 16:16:57.631058: Current learning rate: 0.00566 +2026-04-12 16:18:40.810628: train_loss -0.277 +2026-04-12 16:18:40.816695: val_loss -0.2419 +2026-04-12 16:18:40.819334: Pseudo dice [0.6277, 0.4725, 0.7444, 0.4435, 0.4263, 0.9081, 0.7758] +2026-04-12 16:18:40.821651: Epoch time: 103.19 s +2026-04-12 16:18:42.042828: +2026-04-12 16:18:42.044532: Epoch 1877 +2026-04-12 16:18:42.046318: Current learning rate: 0.00565 +2026-04-12 16:20:24.727283: train_loss -0.2662 +2026-04-12 16:20:24.756716: val_loss -0.2313 +2026-04-12 16:20:24.758650: Pseudo dice [0.5777, 0.2077, 0.7113, 0.3217, 0.3953, 0.4537, 0.8078] +2026-04-12 16:20:24.760669: Epoch time: 102.69 s +2026-04-12 16:20:25.956779: +2026-04-12 16:20:25.958389: Epoch 1878 +2026-04-12 16:20:25.959802: Current learning rate: 0.00565 +2026-04-12 16:22:07.579772: train_loss -0.2884 +2026-04-12 16:22:07.584673: val_loss -0.2276 +2026-04-12 16:22:07.586803: Pseudo dice [0.5274, 0.876, 0.7215, 0.2505, 0.6418, 0.8129, 0.5713] +2026-04-12 16:22:07.588779: Epoch time: 101.63 s +2026-04-12 16:22:08.789068: +2026-04-12 16:22:08.790719: Epoch 1879 +2026-04-12 16:22:08.792170: Current learning rate: 0.00565 +2026-04-12 16:23:51.016572: train_loss -0.2816 +2026-04-12 16:23:51.023421: val_loss -0.2298 +2026-04-12 16:23:51.025393: Pseudo dice [0.6343, 0.5417, 0.7542, 0.3079, 0.4921, 0.6665, 0.8818] +2026-04-12 16:23:51.028031: Epoch time: 102.23 s +2026-04-12 16:23:52.267819: +2026-04-12 16:23:52.277748: Epoch 1880 +2026-04-12 16:23:52.294142: Current learning rate: 0.00565 +2026-04-12 16:25:34.201567: train_loss -0.2883 +2026-04-12 16:25:34.208427: val_loss -0.2444 +2026-04-12 16:25:34.210221: Pseudo dice [0.8399, 0.6778, 0.8314, 0.2478, 0.4022, 0.8722, 0.6621] +2026-04-12 16:25:34.212584: Epoch time: 101.94 s +2026-04-12 16:25:35.411651: +2026-04-12 16:25:35.413260: Epoch 1881 +2026-04-12 16:25:35.414793: Current learning rate: 0.00564 +2026-04-12 16:27:17.413501: train_loss -0.2718 +2026-04-12 16:27:17.418812: val_loss -0.2227 +2026-04-12 16:27:17.421320: Pseudo dice [0.8784, 0.8934, 0.7351, 0.5879, 0.2865, 0.6667, 0.7787] +2026-04-12 16:27:17.423628: Epoch time: 102.0 s +2026-04-12 16:27:18.668432: +2026-04-12 16:27:18.670433: Epoch 1882 +2026-04-12 16:27:18.672106: Current learning rate: 0.00564 +2026-04-12 16:29:00.368105: train_loss -0.2762 +2026-04-12 16:29:00.373963: val_loss -0.2207 +2026-04-12 16:29:00.375920: Pseudo dice [0.3555, 0.8811, 0.7632, 0.4937, 0.5876, 0.1747, 0.8085] +2026-04-12 16:29:00.378314: Epoch time: 101.7 s +2026-04-12 16:29:01.584671: +2026-04-12 16:29:01.586703: Epoch 1883 +2026-04-12 16:29:01.588331: Current learning rate: 0.00564 +2026-04-12 16:30:43.333210: train_loss -0.2821 +2026-04-12 16:30:43.339118: val_loss -0.2239 +2026-04-12 16:30:43.341363: Pseudo dice [0.5981, 0.5786, 0.8504, 0.2973, 0.3246, 0.2122, 0.7585] +2026-04-12 16:30:43.343421: Epoch time: 101.75 s +2026-04-12 16:30:44.553482: +2026-04-12 16:30:44.560290: Epoch 1884 +2026-04-12 16:30:44.562092: Current learning rate: 0.00564 +2026-04-12 16:32:26.050313: train_loss -0.2793 +2026-04-12 16:32:26.056369: val_loss -0.199 +2026-04-12 16:32:26.058419: Pseudo dice [0.2441, 0.303, 0.7063, 0.3102, 0.5116, 0.5786, 0.8513] +2026-04-12 16:32:26.061511: Epoch time: 101.5 s +2026-04-12 16:32:27.244325: +2026-04-12 16:32:27.246529: Epoch 1885 +2026-04-12 16:32:27.248257: Current learning rate: 0.00564 +2026-04-12 16:34:08.499695: train_loss -0.2772 +2026-04-12 16:34:08.505863: val_loss -0.2281 +2026-04-12 16:34:08.507945: Pseudo dice [0.7962, 0.3643, 0.7779, 0.2993, 0.5719, 0.4093, 0.5799] +2026-04-12 16:34:08.511384: Epoch time: 101.26 s +2026-04-12 16:34:09.688247: +2026-04-12 16:34:09.689790: Epoch 1886 +2026-04-12 16:34:09.691120: Current learning rate: 0.00563 +2026-04-12 16:35:52.097101: train_loss -0.2613 +2026-04-12 16:35:52.103529: val_loss -0.2054 +2026-04-12 16:35:52.105567: Pseudo dice [0.6592, 0.9074, 0.5894, 0.4166, 0.5597, 0.3976, 0.598] +2026-04-12 16:35:52.108642: Epoch time: 102.41 s +2026-04-12 16:35:53.333596: +2026-04-12 16:35:53.335347: Epoch 1887 +2026-04-12 16:35:53.337782: Current learning rate: 0.00563 +2026-04-12 16:37:35.006232: train_loss -0.2826 +2026-04-12 16:37:35.013112: val_loss -0.2519 +2026-04-12 16:37:35.015218: Pseudo dice [0.862, 0.7222, 0.7959, 0.4537, 0.4351, 0.4446, 0.8204] +2026-04-12 16:37:35.018621: Epoch time: 101.68 s +2026-04-12 16:37:36.480959: +2026-04-12 16:37:36.482901: Epoch 1888 +2026-04-12 16:37:36.484545: Current learning rate: 0.00563 +2026-04-12 16:39:18.421837: train_loss -0.2646 +2026-04-12 16:39:18.427584: val_loss -0.2035 +2026-04-12 16:39:18.429641: Pseudo dice [0.6184, 0.6665, 0.5305, 0.7117, 0.5138, 0.0266, 0.8518] +2026-04-12 16:39:18.432151: Epoch time: 101.94 s +2026-04-12 16:39:19.616935: +2026-04-12 16:39:19.618676: Epoch 1889 +2026-04-12 16:39:19.620271: Current learning rate: 0.00563 +2026-04-12 16:41:01.491624: train_loss -0.2354 +2026-04-12 16:41:01.499781: val_loss -0.1849 +2026-04-12 16:41:01.503348: Pseudo dice [0.4205, 0.895, 0.5306, 0.4508, 0.4764, 0.3748, 0.7452] +2026-04-12 16:41:01.506953: Epoch time: 101.88 s +2026-04-12 16:41:02.799268: +2026-04-12 16:41:02.801083: Epoch 1890 +2026-04-12 16:41:02.802817: Current learning rate: 0.00562 +2026-04-12 16:42:44.461344: train_loss -0.2533 +2026-04-12 16:42:44.471253: val_loss -0.2054 +2026-04-12 16:42:44.475164: Pseudo dice [0.6329, 0.7077, 0.7697, 0.4115, 0.4072, 0.307, 0.8236] +2026-04-12 16:42:44.478158: Epoch time: 101.67 s +2026-04-12 16:42:45.671228: +2026-04-12 16:42:45.673749: Epoch 1891 +2026-04-12 16:42:45.675762: Current learning rate: 0.00562 +2026-04-12 16:44:27.604076: train_loss -0.2761 +2026-04-12 16:44:27.617754: val_loss -0.2287 +2026-04-12 16:44:27.619861: Pseudo dice [0.6554, 0.5776, 0.7646, 0.4153, 0.1634, 0.7637, 0.7781] +2026-04-12 16:44:27.622283: Epoch time: 101.94 s +2026-04-12 16:44:28.838042: +2026-04-12 16:44:28.839693: Epoch 1892 +2026-04-12 16:44:28.841881: Current learning rate: 0.00562 +2026-04-12 16:46:11.244760: train_loss -0.2654 +2026-04-12 16:46:11.253731: val_loss -0.2265 +2026-04-12 16:46:11.255720: Pseudo dice [0.6413, 0.1528, 0.7891, 0.4743, 0.3592, 0.8012, 0.8056] +2026-04-12 16:46:11.259262: Epoch time: 102.41 s +2026-04-12 16:46:12.465384: +2026-04-12 16:46:12.467686: Epoch 1893 +2026-04-12 16:46:12.470052: Current learning rate: 0.00562 +2026-04-12 16:47:54.796978: train_loss -0.2707 +2026-04-12 16:47:54.803250: val_loss -0.2125 +2026-04-12 16:47:54.807555: Pseudo dice [0.2586, 0.2662, 0.6307, 0.4205, 0.3364, 0.6334, 0.4063] +2026-04-12 16:47:54.810223: Epoch time: 102.33 s +2026-04-12 16:47:56.025944: +2026-04-12 16:47:56.028443: Epoch 1894 +2026-04-12 16:47:56.030242: Current learning rate: 0.00561 +2026-04-12 16:49:37.947179: train_loss -0.2745 +2026-04-12 16:49:37.952653: val_loss -0.1569 +2026-04-12 16:49:37.954421: Pseudo dice [0.6957, 0.8455, 0.5203, 0.301, 0.4626, 0.3717, 0.443] +2026-04-12 16:49:37.957017: Epoch time: 101.92 s +2026-04-12 16:49:39.158731: +2026-04-12 16:49:39.160475: Epoch 1895 +2026-04-12 16:49:39.161970: Current learning rate: 0.00561 +2026-04-12 16:51:20.540884: train_loss -0.2812 +2026-04-12 16:51:20.546817: val_loss -0.219 +2026-04-12 16:51:20.548859: Pseudo dice [0.6487, 0.9074, 0.6954, 0.7585, 0.6492, 0.5133, 0.6971] +2026-04-12 16:51:20.551596: Epoch time: 101.39 s +2026-04-12 16:51:21.766805: +2026-04-12 16:51:21.768506: Epoch 1896 +2026-04-12 16:51:21.770044: Current learning rate: 0.00561 +2026-04-12 16:53:04.608193: train_loss -0.2804 +2026-04-12 16:53:04.614385: val_loss -0.2176 +2026-04-12 16:53:04.616219: Pseudo dice [0.5021, 0.8684, 0.704, 0.6945, 0.2275, 0.4949, 0.8378] +2026-04-12 16:53:04.618453: Epoch time: 102.84 s +2026-04-12 16:53:05.812587: +2026-04-12 16:53:05.814213: Epoch 1897 +2026-04-12 16:53:05.815770: Current learning rate: 0.00561 +2026-04-12 16:54:47.305408: train_loss -0.2792 +2026-04-12 16:54:47.311869: val_loss -0.2243 +2026-04-12 16:54:47.315118: Pseudo dice [0.4152, 0.3601, 0.6159, 0.5799, 0.596, 0.6997, 0.6376] +2026-04-12 16:54:47.317954: Epoch time: 101.5 s +2026-04-12 16:54:48.615845: +2026-04-12 16:54:48.617678: Epoch 1898 +2026-04-12 16:54:48.619269: Current learning rate: 0.0056 +2026-04-12 16:56:30.216121: train_loss -0.2825 +2026-04-12 16:56:30.222297: val_loss -0.2563 +2026-04-12 16:56:30.224843: Pseudo dice [0.6585, 0.5244, 0.7842, 0.6495, 0.5591, 0.9344, 0.8402] +2026-04-12 16:56:30.229002: Epoch time: 101.6 s +2026-04-12 16:56:31.449576: +2026-04-12 16:56:31.451581: Epoch 1899 +2026-04-12 16:56:31.453686: Current learning rate: 0.0056 +2026-04-12 16:58:13.755203: train_loss -0.2686 +2026-04-12 16:58:13.763164: val_loss -0.1967 +2026-04-12 16:58:13.765251: Pseudo dice [0.5655, 0.8652, 0.7198, 0.1398, 0.4664, 0.6792, 0.2173] +2026-04-12 16:58:13.767820: Epoch time: 102.31 s +2026-04-12 16:58:16.872915: +2026-04-12 16:58:16.875599: Epoch 1900 +2026-04-12 16:58:16.877426: Current learning rate: 0.0056 +2026-04-12 16:59:59.084286: train_loss -0.272 +2026-04-12 16:59:59.092099: val_loss -0.2282 +2026-04-12 16:59:59.096037: Pseudo dice [0.8479, 0.9053, 0.7915, 0.3959, 0.6319, 0.1828, 0.4543] +2026-04-12 16:59:59.098197: Epoch time: 102.22 s +2026-04-12 17:00:00.305902: +2026-04-12 17:00:00.308314: Epoch 1901 +2026-04-12 17:00:00.310800: Current learning rate: 0.0056 +2026-04-12 17:01:41.951551: train_loss -0.2744 +2026-04-12 17:01:41.958418: val_loss -0.2049 +2026-04-12 17:01:41.961004: Pseudo dice [0.2275, 0.2982, 0.7093, 0.5426, 0.4225, 0.5764, 0.7848] +2026-04-12 17:01:41.963462: Epoch time: 101.65 s +2026-04-12 17:01:43.187895: +2026-04-12 17:01:43.189898: Epoch 1902 +2026-04-12 17:01:43.191838: Current learning rate: 0.00559 +2026-04-12 17:03:24.922501: train_loss -0.2244 +2026-04-12 17:03:24.928420: val_loss -0.2077 +2026-04-12 17:03:24.932781: Pseudo dice [0.5222, 0.2605, 0.5739, 0.3628, 0.4588, 0.7807, 0.6132] +2026-04-12 17:03:24.935506: Epoch time: 101.74 s +2026-04-12 17:03:26.156033: +2026-04-12 17:03:26.157980: Epoch 1903 +2026-04-12 17:03:26.160135: Current learning rate: 0.00559 +2026-04-12 17:05:08.462442: train_loss -0.2442 +2026-04-12 17:05:08.469322: val_loss -0.2167 +2026-04-12 17:05:08.471578: Pseudo dice [0.7245, 0.843, 0.7849, 0.6807, 0.4906, 0.6826, 0.4145] +2026-04-12 17:05:08.475694: Epoch time: 102.31 s +2026-04-12 17:05:09.767008: +2026-04-12 17:05:09.769970: Epoch 1904 +2026-04-12 17:05:09.773486: Current learning rate: 0.00559 +2026-04-12 17:06:52.398030: train_loss -0.2613 +2026-04-12 17:06:52.404122: val_loss -0.1877 +2026-04-12 17:06:52.406654: Pseudo dice [0.5755, 0.7573, 0.2722, 0.3635, 0.5482, 0.2961, 0.7318] +2026-04-12 17:06:52.409604: Epoch time: 102.63 s +2026-04-12 17:06:53.614822: +2026-04-12 17:06:53.617291: Epoch 1905 +2026-04-12 17:06:53.619906: Current learning rate: 0.00559 +2026-04-12 17:08:35.477921: train_loss -0.2689 +2026-04-12 17:08:35.484266: val_loss -0.2427 +2026-04-12 17:08:35.486211: Pseudo dice [0.7099, 0.8975, 0.7504, 0.2289, 0.4553, 0.7554, 0.4148] +2026-04-12 17:08:35.488402: Epoch time: 101.87 s +2026-04-12 17:08:36.706826: +2026-04-12 17:08:36.708735: Epoch 1906 +2026-04-12 17:08:36.710736: Current learning rate: 0.00559 +2026-04-12 17:10:18.528207: train_loss -0.2617 +2026-04-12 17:10:18.534666: val_loss -0.1639 +2026-04-12 17:10:18.536895: Pseudo dice [0.6271, 0.6026, 0.4279, 0.1611, 0.5437, 0.4969, 0.247] +2026-04-12 17:10:18.539147: Epoch time: 101.82 s +2026-04-12 17:10:19.776029: +2026-04-12 17:10:19.777876: Epoch 1907 +2026-04-12 17:10:19.779944: Current learning rate: 0.00558 +2026-04-12 17:12:01.494648: train_loss -0.2624 +2026-04-12 17:12:01.502440: val_loss -0.1657 +2026-04-12 17:12:01.505218: Pseudo dice [0.3646, 0.3949, 0.6364, 0.4224, 0.4283, 0.4795, 0.5801] +2026-04-12 17:12:01.508009: Epoch time: 101.72 s +2026-04-12 17:12:02.753349: +2026-04-12 17:12:02.755967: Epoch 1908 +2026-04-12 17:12:02.758267: Current learning rate: 0.00558 +2026-04-12 17:13:44.742681: train_loss -0.2613 +2026-04-12 17:13:44.750179: val_loss -0.2279 +2026-04-12 17:13:44.753030: Pseudo dice [0.6086, 0.527, 0.7733, 0.7464, 0.2474, 0.5347, 0.3574] +2026-04-12 17:13:44.755970: Epoch time: 101.99 s +2026-04-12 17:13:45.985705: +2026-04-12 17:13:45.987794: Epoch 1909 +2026-04-12 17:13:45.989752: Current learning rate: 0.00558 +2026-04-12 17:15:28.572376: train_loss -0.2549 +2026-04-12 17:15:28.578841: val_loss -0.171 +2026-04-12 17:15:28.581196: Pseudo dice [0.6865, 0.8734, 0.3788, 0.0389, 0.493, 0.3855, 0.2919] +2026-04-12 17:15:28.583532: Epoch time: 102.59 s +2026-04-12 17:15:29.832697: +2026-04-12 17:15:29.834586: Epoch 1910 +2026-04-12 17:15:29.836615: Current learning rate: 0.00558 +2026-04-12 17:17:11.401205: train_loss -0.2779 +2026-04-12 17:17:11.408028: val_loss -0.2368 +2026-04-12 17:17:11.411246: Pseudo dice [0.6268, 0.8847, 0.7587, 0.6915, 0.5273, 0.7426, 0.7633] +2026-04-12 17:17:11.413626: Epoch time: 101.57 s +2026-04-12 17:17:12.615606: +2026-04-12 17:17:12.617817: Epoch 1911 +2026-04-12 17:17:12.620059: Current learning rate: 0.00557 +2026-04-12 17:18:54.809575: train_loss -0.2822 +2026-04-12 17:18:54.815963: val_loss -0.241 +2026-04-12 17:18:54.818936: Pseudo dice [0.5375, 0.7309, 0.8312, 0.828, 0.3518, 0.8365, 0.7465] +2026-04-12 17:18:54.821398: Epoch time: 102.2 s +2026-04-12 17:18:56.065510: +2026-04-12 17:18:56.068356: Epoch 1912 +2026-04-12 17:18:56.072236: Current learning rate: 0.00557 +2026-04-12 17:20:37.957462: train_loss -0.2774 +2026-04-12 17:20:37.966246: val_loss -0.1952 +2026-04-12 17:20:37.968339: Pseudo dice [0.3271, 0.8649, 0.5724, 0.4473, 0.5077, 0.6217, 0.7491] +2026-04-12 17:20:37.970658: Epoch time: 101.9 s +2026-04-12 17:20:39.178651: +2026-04-12 17:20:39.180303: Epoch 1913 +2026-04-12 17:20:39.182112: Current learning rate: 0.00557 +2026-04-12 17:22:21.635735: train_loss -0.2806 +2026-04-12 17:22:21.644605: val_loss -0.2306 +2026-04-12 17:22:21.646705: Pseudo dice [0.1789, 0.4009, 0.6671, 0.5513, 0.3632, 0.8999, 0.8255] +2026-04-12 17:22:21.649146: Epoch time: 102.46 s +2026-04-12 17:22:22.882787: +2026-04-12 17:22:22.884813: Epoch 1914 +2026-04-12 17:22:22.887011: Current learning rate: 0.00557 +2026-04-12 17:24:05.645421: train_loss -0.289 +2026-04-12 17:24:05.652765: val_loss -0.1974 +2026-04-12 17:24:05.655345: Pseudo dice [0.095, 0.8819, 0.6688, 0.6147, 0.4778, 0.544, 0.3229] +2026-04-12 17:24:05.657835: Epoch time: 102.77 s +2026-04-12 17:24:06.908939: +2026-04-12 17:24:06.912297: Epoch 1915 +2026-04-12 17:24:06.914714: Current learning rate: 0.00556 +2026-04-12 17:25:49.020943: train_loss -0.2542 +2026-04-12 17:25:49.027065: val_loss -0.1962 +2026-04-12 17:25:49.029662: Pseudo dice [0.5989, 0.9219, 0.5019, 0.3668, 0.2889, 0.4042, 0.7287] +2026-04-12 17:25:49.032225: Epoch time: 102.12 s +2026-04-12 17:25:51.435325: +2026-04-12 17:25:51.437950: Epoch 1916 +2026-04-12 17:25:51.440492: Current learning rate: 0.00556 +2026-04-12 17:27:33.847174: train_loss -0.2706 +2026-04-12 17:27:33.854820: val_loss -0.2285 +2026-04-12 17:27:33.857585: Pseudo dice [0.4084, 0.8821, 0.7482, 0.6095, 0.5405, 0.1769, 0.802] +2026-04-12 17:27:33.860218: Epoch time: 102.41 s +2026-04-12 17:27:35.114983: +2026-04-12 17:27:35.118502: Epoch 1917 +2026-04-12 17:27:35.123328: Current learning rate: 0.00556 +2026-04-12 17:29:16.999459: train_loss -0.2656 +2026-04-12 17:29:17.008297: val_loss -0.212 +2026-04-12 17:29:17.012264: Pseudo dice [0.6305, 0.5247, 0.6855, 0.1533, 0.5313, 0.8973, 0.7241] +2026-04-12 17:29:17.014894: Epoch time: 101.89 s +2026-04-12 17:29:18.220571: +2026-04-12 17:29:18.222341: Epoch 1918 +2026-04-12 17:29:18.224266: Current learning rate: 0.00556 +2026-04-12 17:31:00.115468: train_loss -0.2675 +2026-04-12 17:31:00.122168: val_loss -0.2197 +2026-04-12 17:31:00.124200: Pseudo dice [0.763, 0.7841, 0.6959, 0.4118, 0.2144, 0.6845, 0.8381] +2026-04-12 17:31:00.126354: Epoch time: 101.9 s +2026-04-12 17:31:01.349616: +2026-04-12 17:31:01.351394: Epoch 1919 +2026-04-12 17:31:01.353403: Current learning rate: 0.00555 +2026-04-12 17:32:43.245331: train_loss -0.2628 +2026-04-12 17:32:43.253010: val_loss -0.171 +2026-04-12 17:32:43.255147: Pseudo dice [0.4084, 0.7188, 0.5503, 0.2429, 0.186, 0.4485, 0.6203] +2026-04-12 17:32:43.258069: Epoch time: 101.9 s +2026-04-12 17:32:44.461070: +2026-04-12 17:32:44.463084: Epoch 1920 +2026-04-12 17:32:44.465360: Current learning rate: 0.00555 +2026-04-12 17:34:26.856452: train_loss -0.2652 +2026-04-12 17:34:26.864769: val_loss -0.1838 +2026-04-12 17:34:26.868266: Pseudo dice [0.5136, 0.9036, 0.6184, 0.5147, 0.4918, 0.2056, 0.6757] +2026-04-12 17:34:26.871292: Epoch time: 102.4 s +2026-04-12 17:34:28.127106: +2026-04-12 17:34:28.130500: Epoch 1921 +2026-04-12 17:34:28.132933: Current learning rate: 0.00555 +2026-04-12 17:36:10.294059: train_loss -0.2621 +2026-04-12 17:36:10.300441: val_loss -0.1671 +2026-04-12 17:36:10.303446: Pseudo dice [0.5573, 0.8078, 0.7023, 0.36, 0.466, 0.8198, 0.6991] +2026-04-12 17:36:10.305464: Epoch time: 102.17 s +2026-04-12 17:36:11.527578: +2026-04-12 17:36:11.529438: Epoch 1922 +2026-04-12 17:36:11.531397: Current learning rate: 0.00555 +2026-04-12 17:37:53.378883: train_loss -0.2566 +2026-04-12 17:37:53.385720: val_loss -0.1986 +2026-04-12 17:37:53.388570: Pseudo dice [0.6022, 0.8556, 0.7694, 0.2621, 0.4168, 0.1137, 0.7007] +2026-04-12 17:37:53.391135: Epoch time: 101.85 s +2026-04-12 17:37:54.628649: +2026-04-12 17:37:54.631074: Epoch 1923 +2026-04-12 17:37:54.633559: Current learning rate: 0.00554 +2026-04-12 17:39:36.092530: train_loss -0.2683 +2026-04-12 17:39:36.099293: val_loss -0.2259 +2026-04-12 17:39:36.101385: Pseudo dice [0.5225, 0.4171, 0.7912, 0.486, 0.6389, 0.893, 0.6412] +2026-04-12 17:39:36.104348: Epoch time: 101.47 s +2026-04-12 17:39:37.334672: +2026-04-12 17:39:37.337101: Epoch 1924 +2026-04-12 17:39:37.339624: Current learning rate: 0.00554 +2026-04-12 17:41:19.516400: train_loss -0.2521 +2026-04-12 17:41:19.524238: val_loss -0.2303 +2026-04-12 17:41:19.535482: Pseudo dice [0.3735, 0.7173, 0.7531, 0.0945, 0.348, 0.5059, 0.5951] +2026-04-12 17:41:19.538092: Epoch time: 102.18 s +2026-04-12 17:41:20.766730: +2026-04-12 17:41:20.768718: Epoch 1925 +2026-04-12 17:41:20.771521: Current learning rate: 0.00554 +2026-04-12 17:43:03.659596: train_loss -0.273 +2026-04-12 17:43:03.666592: val_loss -0.2263 +2026-04-12 17:43:03.668466: Pseudo dice [0.7878, 0.4983, 0.7105, 0.4209, 0.6395, 0.5962, 0.6931] +2026-04-12 17:43:03.671464: Epoch time: 102.9 s +2026-04-12 17:43:04.899776: +2026-04-12 17:43:04.902363: Epoch 1926 +2026-04-12 17:43:04.905699: Current learning rate: 0.00554 +2026-04-12 17:44:46.599619: train_loss -0.2778 +2026-04-12 17:44:46.607955: val_loss -0.2449 +2026-04-12 17:44:46.609823: Pseudo dice [0.6531, 0.5549, 0.7631, 0.6806, 0.6205, 0.397, 0.7497] +2026-04-12 17:44:46.612110: Epoch time: 101.7 s +2026-04-12 17:44:47.860556: +2026-04-12 17:44:47.862915: Epoch 1927 +2026-04-12 17:44:47.864953: Current learning rate: 0.00553 +2026-04-12 17:46:29.873593: train_loss -0.2608 +2026-04-12 17:46:29.879920: val_loss -0.1996 +2026-04-12 17:46:29.881909: Pseudo dice [0.656, 0.8379, 0.6461, 0.3653, 0.5604, 0.8054, 0.1962] +2026-04-12 17:46:29.884222: Epoch time: 102.02 s +2026-04-12 17:46:31.116766: +2026-04-12 17:46:31.120074: Epoch 1928 +2026-04-12 17:46:31.121828: Current learning rate: 0.00553 +2026-04-12 17:48:13.705128: train_loss -0.2853 +2026-04-12 17:48:13.711694: val_loss -0.2188 +2026-04-12 17:48:13.714333: Pseudo dice [0.7749, 0.4961, 0.8074, 0.2444, 0.5456, 0.7471, 0.3309] +2026-04-12 17:48:13.716945: Epoch time: 102.59 s +2026-04-12 17:48:14.979043: +2026-04-12 17:48:14.980491: Epoch 1929 +2026-04-12 17:48:14.982311: Current learning rate: 0.00553 +2026-04-12 17:49:57.689389: train_loss -0.272 +2026-04-12 17:49:57.698823: val_loss -0.2443 +2026-04-12 17:49:57.701924: Pseudo dice [0.6296, 0.8756, 0.7458, 0.1608, 0.4201, 0.2619, 0.5409] +2026-04-12 17:49:57.704943: Epoch time: 102.71 s +2026-04-12 17:49:58.942914: +2026-04-12 17:49:58.944726: Epoch 1930 +2026-04-12 17:49:58.946480: Current learning rate: 0.00553 +2026-04-12 17:51:40.967226: train_loss -0.2864 +2026-04-12 17:51:40.974287: val_loss -0.2114 +2026-04-12 17:51:40.976564: Pseudo dice [0.0802, 0.3904, 0.7758, 0.5546, 0.4509, 0.2485, 0.728] +2026-04-12 17:51:40.979466: Epoch time: 102.03 s +2026-04-12 17:51:42.198818: +2026-04-12 17:51:42.202320: Epoch 1931 +2026-04-12 17:51:42.204283: Current learning rate: 0.00552 +2026-04-12 17:53:24.712775: train_loss -0.2551 +2026-04-12 17:53:24.721836: val_loss -0.199 +2026-04-12 17:53:24.724398: Pseudo dice [0.5666, 0.4213, 0.5696, 0.4694, 0.5275, 0.4413, 0.7748] +2026-04-12 17:53:24.727143: Epoch time: 102.52 s +2026-04-12 17:53:25.955698: +2026-04-12 17:53:25.957619: Epoch 1932 +2026-04-12 17:53:25.959744: Current learning rate: 0.00552 +2026-04-12 17:55:08.251759: train_loss -0.2591 +2026-04-12 17:55:08.257953: val_loss -0.2118 +2026-04-12 17:55:08.259657: Pseudo dice [0.4116, 0.8962, 0.7361, 0.3125, 0.3346, 0.6027, 0.6839] +2026-04-12 17:55:08.261493: Epoch time: 102.3 s +2026-04-12 17:55:09.525858: +2026-04-12 17:55:09.527800: Epoch 1933 +2026-04-12 17:55:09.531583: Current learning rate: 0.00552 +2026-04-12 17:56:51.307083: train_loss -0.278 +2026-04-12 17:56:51.335083: val_loss -0.2411 +2026-04-12 17:56:51.338010: Pseudo dice [0.8356, 0.6952, 0.7177, 0.7587, 0.335, 0.9064, 0.831] +2026-04-12 17:56:51.341938: Epoch time: 101.78 s +2026-04-12 17:56:52.545655: +2026-04-12 17:56:52.547999: Epoch 1934 +2026-04-12 17:56:52.550852: Current learning rate: 0.00552 +2026-04-12 17:58:34.810073: train_loss -0.2777 +2026-04-12 17:58:34.817033: val_loss -0.2492 +2026-04-12 17:58:34.819390: Pseudo dice [0.6825, 0.506, 0.6115, 0.6803, 0.5692, 0.2707, 0.808] +2026-04-12 17:58:34.822333: Epoch time: 102.27 s +2026-04-12 17:58:36.048088: +2026-04-12 17:58:36.050667: Epoch 1935 +2026-04-12 17:58:36.053312: Current learning rate: 0.00552 +2026-04-12 18:00:18.271273: train_loss -0.2819 +2026-04-12 18:00:18.277322: val_loss -0.2109 +2026-04-12 18:00:18.279239: Pseudo dice [0.458, 0.3888, 0.7909, 0.2168, 0.5841, 0.6057, 0.7243] +2026-04-12 18:00:18.281689: Epoch time: 102.23 s +2026-04-12 18:00:20.583819: +2026-04-12 18:00:20.593021: Epoch 1936 +2026-04-12 18:00:20.598274: Current learning rate: 0.00551 +2026-04-12 18:02:02.106068: train_loss -0.2797 +2026-04-12 18:02:02.113986: val_loss -0.2462 +2026-04-12 18:02:02.117274: Pseudo dice [0.4378, 0.4166, 0.8418, 0.3817, 0.7078, 0.7846, 0.6753] +2026-04-12 18:02:02.120339: Epoch time: 101.53 s +2026-04-12 18:02:03.332312: +2026-04-12 18:02:03.333962: Epoch 1937 +2026-04-12 18:02:03.335771: Current learning rate: 0.00551 +2026-04-12 18:03:45.445782: train_loss -0.2797 +2026-04-12 18:03:45.451901: val_loss -0.2321 +2026-04-12 18:03:45.454979: Pseudo dice [0.732, 0.864, 0.8272, 0.2942, 0.5516, 0.2895, 0.8042] +2026-04-12 18:03:45.457128: Epoch time: 102.12 s +2026-04-12 18:03:46.672371: +2026-04-12 18:03:46.675673: Epoch 1938 +2026-04-12 18:03:46.678155: Current learning rate: 0.00551 +2026-04-12 18:05:29.416940: train_loss -0.2836 +2026-04-12 18:05:29.422859: val_loss -0.2048 +2026-04-12 18:05:29.425616: Pseudo dice [0.7892, 0.8478, 0.7487, 0.2568, 0.4943, 0.5604, 0.285] +2026-04-12 18:05:29.428835: Epoch time: 102.75 s +2026-04-12 18:05:30.658511: +2026-04-12 18:05:30.660200: Epoch 1939 +2026-04-12 18:05:30.662225: Current learning rate: 0.00551 +2026-04-12 18:07:13.190423: train_loss -0.271 +2026-04-12 18:07:13.208409: val_loss -0.2297 +2026-04-12 18:07:13.211654: Pseudo dice [0.5321, 0.8793, 0.7633, 0.6032, 0.4098, 0.3018, 0.8233] +2026-04-12 18:07:13.216927: Epoch time: 102.54 s +2026-04-12 18:07:14.461268: +2026-04-12 18:07:14.463912: Epoch 1940 +2026-04-12 18:07:14.466007: Current learning rate: 0.0055 +2026-04-12 18:08:56.368615: train_loss -0.2718 +2026-04-12 18:08:56.377022: val_loss -0.2372 +2026-04-12 18:08:56.380391: Pseudo dice [0.6533, 0.4906, 0.7829, 0.5203, 0.4311, 0.2056, 0.8492] +2026-04-12 18:08:56.383294: Epoch time: 101.91 s +2026-04-12 18:08:57.589469: +2026-04-12 18:08:57.592870: Epoch 1941 +2026-04-12 18:08:57.595707: Current learning rate: 0.0055 +2026-04-12 18:10:39.257869: train_loss -0.281 +2026-04-12 18:10:39.264840: val_loss -0.2176 +2026-04-12 18:10:39.266878: Pseudo dice [0.4786, 0.1837, 0.6396, 0.5059, 0.5524, 0.769, 0.696] +2026-04-12 18:10:39.269580: Epoch time: 101.67 s +2026-04-12 18:10:40.499607: +2026-04-12 18:10:40.501640: Epoch 1942 +2026-04-12 18:10:40.503639: Current learning rate: 0.0055 +2026-04-12 18:12:22.995165: train_loss -0.2668 +2026-04-12 18:12:23.003726: val_loss -0.2473 +2026-04-12 18:12:23.006947: Pseudo dice [0.8248, 0.6639, 0.8179, 0.7496, 0.279, 0.1368, 0.6558] +2026-04-12 18:12:23.010415: Epoch time: 102.5 s +2026-04-12 18:12:24.241144: +2026-04-12 18:12:24.243640: Epoch 1943 +2026-04-12 18:12:24.245916: Current learning rate: 0.0055 +2026-04-12 18:14:06.392326: train_loss -0.2773 +2026-04-12 18:14:06.399459: val_loss -0.2371 +2026-04-12 18:14:06.401985: Pseudo dice [0.6521, 0.5911, 0.6952, 0.3377, 0.4929, 0.8651, 0.7466] +2026-04-12 18:14:06.404492: Epoch time: 102.15 s +2026-04-12 18:14:07.618119: +2026-04-12 18:14:07.620147: Epoch 1944 +2026-04-12 18:14:07.622570: Current learning rate: 0.00549 +2026-04-12 18:15:49.482838: train_loss -0.287 +2026-04-12 18:15:49.490908: val_loss -0.2213 +2026-04-12 18:15:49.495112: Pseudo dice [0.6253, 0.8505, 0.8039, 0.4098, 0.5232, 0.5687, 0.7034] +2026-04-12 18:15:49.499001: Epoch time: 101.87 s +2026-04-12 18:15:50.748773: +2026-04-12 18:15:50.751902: Epoch 1945 +2026-04-12 18:15:50.754324: Current learning rate: 0.00549 +2026-04-12 18:17:33.475723: train_loss -0.2884 +2026-04-12 18:17:33.483234: val_loss -0.2017 +2026-04-12 18:17:33.485542: Pseudo dice [0.7328, 0.7969, 0.5955, 0.6977, 0.3174, 0.8747, 0.7859] +2026-04-12 18:17:33.488177: Epoch time: 102.73 s +2026-04-12 18:17:34.728361: +2026-04-12 18:17:34.730155: Epoch 1946 +2026-04-12 18:17:34.734799: Current learning rate: 0.00549 +2026-04-12 18:19:16.553530: train_loss -0.2756 +2026-04-12 18:19:16.559481: val_loss -0.1997 +2026-04-12 18:19:16.561035: Pseudo dice [0.6159, 0.8933, 0.7898, 0.8598, 0.6276, 0.2069, 0.8263] +2026-04-12 18:19:16.563280: Epoch time: 101.83 s +2026-04-12 18:19:17.792674: +2026-04-12 18:19:17.794233: Epoch 1947 +2026-04-12 18:19:17.796207: Current learning rate: 0.00549 +2026-04-12 18:20:59.514570: train_loss -0.2593 +2026-04-12 18:20:59.522398: val_loss -0.2252 +2026-04-12 18:20:59.524233: Pseudo dice [0.2857, 0.8647, 0.7868, 0.5977, 0.3062, 0.425, 0.5353] +2026-04-12 18:20:59.527456: Epoch time: 101.73 s +2026-04-12 18:21:00.742329: +2026-04-12 18:21:00.745699: Epoch 1948 +2026-04-12 18:21:00.748813: Current learning rate: 0.00548 +2026-04-12 18:22:42.473851: train_loss -0.2833 +2026-04-12 18:22:42.481487: val_loss -0.2691 +2026-04-12 18:22:42.483240: Pseudo dice [0.2716, 0.8116, 0.7864, 0.4443, 0.4453, 0.6133, 0.8208] +2026-04-12 18:22:42.485429: Epoch time: 101.73 s +2026-04-12 18:22:43.709158: +2026-04-12 18:22:43.711203: Epoch 1949 +2026-04-12 18:22:43.713489: Current learning rate: 0.00548 +2026-04-12 18:24:26.254516: train_loss -0.2941 +2026-04-12 18:24:26.262048: val_loss -0.2552 +2026-04-12 18:24:26.264081: Pseudo dice [0.315, 0.8053, 0.6245, 0.439, 0.3462, 0.6351, 0.7192] +2026-04-12 18:24:26.266393: Epoch time: 102.55 s +2026-04-12 18:24:29.324324: +2026-04-12 18:24:29.326765: Epoch 1950 +2026-04-12 18:24:29.328373: Current learning rate: 0.00548 +2026-04-12 18:26:11.978009: train_loss -0.3827 +2026-04-12 18:26:11.985475: val_loss -0.3307 +2026-04-12 18:26:11.989454: Pseudo dice [0.7191, 0.7297, 0.7219, 0.7046, 0.3672, 0.6067, 0.4294] +2026-04-12 18:26:11.993012: Epoch time: 102.66 s +2026-04-12 18:26:13.213724: +2026-04-12 18:26:13.216030: Epoch 1951 +2026-04-12 18:26:13.218161: Current learning rate: 0.00548 +2026-04-12 18:27:54.869132: train_loss -0.3756 +2026-04-12 18:27:54.875617: val_loss -0.2903 +2026-04-12 18:27:54.886432: Pseudo dice [0.3539, 0.1067, 0.6295, 0.176, 0.3952, 0.7578, 0.7033] +2026-04-12 18:27:54.888644: Epoch time: 101.66 s +2026-04-12 18:27:56.121529: +2026-04-12 18:27:56.123196: Epoch 1952 +2026-04-12 18:27:56.124985: Current learning rate: 0.00547 +2026-04-12 18:29:38.542780: train_loss -0.3402 +2026-04-12 18:29:38.551788: val_loss -0.2458 +2026-04-12 18:29:38.555307: Pseudo dice [0.5665, 0.8993, 0.7079, 0.1182, 0.3813, 0.335, 0.5629] +2026-04-12 18:29:38.559679: Epoch time: 102.42 s +2026-04-12 18:29:39.776148: +2026-04-12 18:29:39.778441: Epoch 1953 +2026-04-12 18:29:39.780509: Current learning rate: 0.00547 +2026-04-12 18:31:22.727724: train_loss -0.3868 +2026-04-12 18:31:22.733870: val_loss -0.3563 +2026-04-12 18:31:22.736372: Pseudo dice [0.0616, 0.8153, 0.7404, 0.455, 0.2026, 0.8133, 0.6822] +2026-04-12 18:31:22.739918: Epoch time: 102.95 s +2026-04-12 18:31:24.008949: +2026-04-12 18:31:24.010955: Epoch 1954 +2026-04-12 18:31:24.014098: Current learning rate: 0.00547 +2026-04-12 18:33:06.383461: train_loss -0.3715 +2026-04-12 18:33:06.392801: val_loss -0.2955 +2026-04-12 18:33:06.396180: Pseudo dice [0.7322, 0.618, 0.6806, 0.1648, 0.452, 0.6262, 0.5717] +2026-04-12 18:33:06.398849: Epoch time: 102.38 s +2026-04-12 18:33:07.643811: +2026-04-12 18:33:07.645784: Epoch 1955 +2026-04-12 18:33:07.647570: Current learning rate: 0.00547 +2026-04-12 18:34:51.564654: train_loss -0.3588 +2026-04-12 18:34:51.571936: val_loss -0.3228 +2026-04-12 18:34:51.573614: Pseudo dice [0.6772, 0.6697, 0.7235, 0.4788, 0.335, 0.3536, 0.7398] +2026-04-12 18:34:51.576602: Epoch time: 103.92 s +2026-04-12 18:34:52.821975: +2026-04-12 18:34:52.823496: Epoch 1956 +2026-04-12 18:34:52.825320: Current learning rate: 0.00546 +2026-04-12 18:36:34.967331: train_loss -0.3545 +2026-04-12 18:36:34.974547: val_loss -0.3006 +2026-04-12 18:36:34.977998: Pseudo dice [0.4536, 0.7589, 0.6432, 0.7366, 0.4417, 0.5788, 0.3369] +2026-04-12 18:36:34.983257: Epoch time: 102.15 s +2026-04-12 18:36:36.191031: +2026-04-12 18:36:36.192945: Epoch 1957 +2026-04-12 18:36:36.195242: Current learning rate: 0.00546 +2026-04-12 18:38:18.363466: train_loss -0.3572 +2026-04-12 18:38:18.370713: val_loss -0.3413 +2026-04-12 18:38:18.372849: Pseudo dice [0.2565, 0.5465, 0.7451, 0.7488, 0.4253, 0.4101, 0.7566] +2026-04-12 18:38:18.375608: Epoch time: 102.18 s +2026-04-12 18:38:19.591542: +2026-04-12 18:38:19.594075: Epoch 1958 +2026-04-12 18:38:19.596816: Current learning rate: 0.00546 +2026-04-12 18:40:01.519968: train_loss -0.3646 +2026-04-12 18:40:01.557756: val_loss -0.3485 +2026-04-12 18:40:01.561225: Pseudo dice [0.0976, 0.2728, 0.6917, 0.3821, 0.5054, 0.6227, 0.6392] +2026-04-12 18:40:01.565128: Epoch time: 101.93 s +2026-04-12 18:40:02.790102: +2026-04-12 18:40:02.792037: Epoch 1959 +2026-04-12 18:40:02.794259: Current learning rate: 0.00546 +2026-04-12 18:41:45.455952: train_loss -0.377 +2026-04-12 18:41:45.466736: val_loss -0.2666 +2026-04-12 18:41:45.469131: Pseudo dice [0.1946, 0.5915, 0.5709, 0.6529, 0.3858, 0.3442, 0.7653] +2026-04-12 18:41:45.471447: Epoch time: 102.67 s +2026-04-12 18:41:46.673734: +2026-04-12 18:41:46.675943: Epoch 1960 +2026-04-12 18:41:46.677878: Current learning rate: 0.00546 +2026-04-12 18:43:29.653566: train_loss -0.3799 +2026-04-12 18:43:29.660427: val_loss -0.3226 +2026-04-12 18:43:29.662713: Pseudo dice [0.6085, 0.8605, 0.7651, 0.6081, 0.4653, 0.1757, 0.7965] +2026-04-12 18:43:29.665602: Epoch time: 102.98 s +2026-04-12 18:43:30.859465: +2026-04-12 18:43:30.861045: Epoch 1961 +2026-04-12 18:43:30.862839: Current learning rate: 0.00545 +2026-04-12 18:45:13.320127: train_loss -0.3764 +2026-04-12 18:45:13.330324: val_loss -0.2885 +2026-04-12 18:45:13.333066: Pseudo dice [0.283, 0.6583, 0.429, 0.2333, 0.3611, 0.6511, 0.7871] +2026-04-12 18:45:13.338281: Epoch time: 102.46 s +2026-04-12 18:45:14.608812: +2026-04-12 18:45:14.611159: Epoch 1962 +2026-04-12 18:45:14.614229: Current learning rate: 0.00545 +2026-04-12 18:46:56.810398: train_loss -0.3174 +2026-04-12 18:46:56.817902: val_loss -0.2655 +2026-04-12 18:46:56.819676: Pseudo dice [0.3766, 0.7005, 0.0481, 0.4798, 0.5262, 0.6946, 0.8259] +2026-04-12 18:46:56.821648: Epoch time: 102.2 s +2026-04-12 18:46:58.055890: +2026-04-12 18:46:58.057965: Epoch 1963 +2026-04-12 18:46:58.059913: Current learning rate: 0.00545 +2026-04-12 18:48:41.025102: train_loss -0.3566 +2026-04-12 18:48:41.033077: val_loss -0.3584 +2026-04-12 18:48:41.034898: Pseudo dice [0.5749, 0.3039, 0.7253, 0.4575, 0.4285, 0.5041, 0.8575] +2026-04-12 18:48:41.037409: Epoch time: 102.97 s +2026-04-12 18:48:42.240243: +2026-04-12 18:48:42.243098: Epoch 1964 +2026-04-12 18:48:42.246258: Current learning rate: 0.00545 +2026-04-12 18:50:24.508868: train_loss -0.3523 +2026-04-12 18:50:24.515941: val_loss -0.3007 +2026-04-12 18:50:24.519120: Pseudo dice [0.7462, 0.655, 0.6919, 0.0405, 0.2592, 0.8668, 0.6541] +2026-04-12 18:50:24.522000: Epoch time: 102.27 s +2026-04-12 18:50:25.793303: +2026-04-12 18:50:25.795423: Epoch 1965 +2026-04-12 18:50:25.797766: Current learning rate: 0.00544 +2026-04-12 18:52:07.998253: train_loss -0.3654 +2026-04-12 18:52:08.005914: val_loss -0.2977 +2026-04-12 18:52:08.008898: Pseudo dice [0.6168, 0.7474, 0.6058, 0.3565, 0.368, 0.584, 0.5446] +2026-04-12 18:52:08.011891: Epoch time: 102.21 s +2026-04-12 18:52:09.221948: +2026-04-12 18:52:09.223648: Epoch 1966 +2026-04-12 18:52:09.225687: Current learning rate: 0.00544 +2026-04-12 18:53:52.085172: train_loss -0.354 +2026-04-12 18:53:52.091654: val_loss -0.2726 +2026-04-12 18:53:52.093954: Pseudo dice [0.7576, 0.2358, 0.5459, 0.4679, 0.1423, 0.17, 0.7877] +2026-04-12 18:53:52.096288: Epoch time: 102.87 s +2026-04-12 18:53:53.321838: +2026-04-12 18:53:53.325727: Epoch 1967 +2026-04-12 18:53:53.328500: Current learning rate: 0.00544 +2026-04-12 18:55:35.387371: train_loss -0.3414 +2026-04-12 18:55:35.394787: val_loss -0.2832 +2026-04-12 18:55:35.396909: Pseudo dice [0.1285, 0.7419, 0.7271, 0.0801, 0.4203, 0.7139, 0.3884] +2026-04-12 18:55:35.402045: Epoch time: 102.07 s +2026-04-12 18:55:36.631718: +2026-04-12 18:55:36.633542: Epoch 1968 +2026-04-12 18:55:36.635730: Current learning rate: 0.00544 +2026-04-12 18:57:18.919230: train_loss -0.3636 +2026-04-12 18:57:18.929353: val_loss -0.3505 +2026-04-12 18:57:18.932211: Pseudo dice [0.4315, 0.7951, 0.6652, 0.356, 0.4771, 0.8685, 0.8081] +2026-04-12 18:57:18.934926: Epoch time: 102.29 s +2026-04-12 18:57:20.149698: +2026-04-12 18:57:20.152649: Epoch 1969 +2026-04-12 18:57:20.155683: Current learning rate: 0.00543 +2026-04-12 18:59:01.970597: train_loss -0.3745 +2026-04-12 18:59:01.977481: val_loss -0.2781 +2026-04-12 18:59:01.979809: Pseudo dice [0.8452, 0.4099, 0.4623, 0.0339, 0.3027, 0.359, 0.7381] +2026-04-12 18:59:01.982776: Epoch time: 101.82 s +2026-04-12 18:59:03.211235: +2026-04-12 18:59:03.212957: Epoch 1970 +2026-04-12 18:59:03.215419: Current learning rate: 0.00543 +2026-04-12 19:00:45.111653: train_loss -0.3465 +2026-04-12 19:00:45.119184: val_loss -0.3595 +2026-04-12 19:00:45.121516: Pseudo dice [0.8466, 0.797, 0.7093, 0.0615, 0.4548, 0.2829, 0.6385] +2026-04-12 19:00:45.124504: Epoch time: 101.9 s +2026-04-12 19:00:46.369614: +2026-04-12 19:00:46.372487: Epoch 1971 +2026-04-12 19:00:46.374471: Current learning rate: 0.00543 +2026-04-12 19:02:28.562838: train_loss -0.3473 +2026-04-12 19:02:28.568807: val_loss -0.3006 +2026-04-12 19:02:28.571751: Pseudo dice [0.274, 0.8683, 0.7022, 0.6988, 0.5262, 0.761, 0.5487] +2026-04-12 19:02:28.574143: Epoch time: 102.2 s +2026-04-12 19:02:29.804538: +2026-04-12 19:02:29.806698: Epoch 1972 +2026-04-12 19:02:29.808745: Current learning rate: 0.00543 +2026-04-12 19:04:11.880167: train_loss -0.3742 +2026-04-12 19:04:11.886991: val_loss -0.3125 +2026-04-12 19:04:11.889265: Pseudo dice [0.4884, 0.1976, 0.6488, 0.3389, 0.1131, 0.6702, 0.6735] +2026-04-12 19:04:11.892687: Epoch time: 102.08 s +2026-04-12 19:04:13.124921: +2026-04-12 19:04:13.126993: Epoch 1973 +2026-04-12 19:04:13.129001: Current learning rate: 0.00542 +2026-04-12 19:05:55.761435: train_loss -0.3521 +2026-04-12 19:05:55.773331: val_loss -0.2545 +2026-04-12 19:05:55.776261: Pseudo dice [0.0317, 0.4912, 0.4453, 0.4796, 0.57, 0.7254, 0.6027] +2026-04-12 19:05:55.780731: Epoch time: 102.64 s +2026-04-12 19:05:56.998407: +2026-04-12 19:05:57.001297: Epoch 1974 +2026-04-12 19:05:57.004046: Current learning rate: 0.00542 +2026-04-12 19:07:39.165284: train_loss -0.3745 +2026-04-12 19:07:39.171797: val_loss -0.3308 +2026-04-12 19:07:39.173757: Pseudo dice [0.5031, 0.7514, 0.7027, 0.838, 0.3556, 0.7105, 0.7106] +2026-04-12 19:07:39.177011: Epoch time: 102.17 s +2026-04-12 19:07:41.487761: +2026-04-12 19:07:41.489273: Epoch 1975 +2026-04-12 19:07:41.491210: Current learning rate: 0.00542 +2026-04-12 19:09:23.604872: train_loss -0.3524 +2026-04-12 19:09:23.612700: val_loss -0.3004 +2026-04-12 19:09:23.614797: Pseudo dice [0.704, 0.6357, 0.637, 0.2052, 0.3171, 0.0568, 0.8014] +2026-04-12 19:09:23.617168: Epoch time: 102.12 s +2026-04-12 19:09:24.838665: +2026-04-12 19:09:24.840727: Epoch 1976 +2026-04-12 19:09:24.842869: Current learning rate: 0.00542 +2026-04-12 19:11:06.704353: train_loss -0.3697 +2026-04-12 19:11:06.711555: val_loss -0.3475 +2026-04-12 19:11:06.714254: Pseudo dice [0.2766, 0.4718, 0.5464, 0.8037, 0.414, 0.7245, 0.3122] +2026-04-12 19:11:06.717305: Epoch time: 101.87 s +2026-04-12 19:11:07.959230: +2026-04-12 19:11:07.961950: Epoch 1977 +2026-04-12 19:11:07.963820: Current learning rate: 0.00541 +2026-04-12 19:12:50.244486: train_loss -0.3574 +2026-04-12 19:12:50.251661: val_loss -0.3233 +2026-04-12 19:12:50.255690: Pseudo dice [0.4718, 0.8732, 0.4843, 0.2149, 0.3055, 0.1103, 0.2793] +2026-04-12 19:12:50.259239: Epoch time: 102.29 s +2026-04-12 19:12:51.462274: +2026-04-12 19:12:51.464585: Epoch 1978 +2026-04-12 19:12:51.466742: Current learning rate: 0.00541 +2026-04-12 19:14:33.635721: train_loss -0.3906 +2026-04-12 19:14:33.642100: val_loss -0.3345 +2026-04-12 19:14:33.644176: Pseudo dice [0.5236, 0.6492, 0.7249, 0.3977, 0.5128, 0.8046, 0.6798] +2026-04-12 19:14:33.646421: Epoch time: 102.18 s +2026-04-12 19:14:34.867311: +2026-04-12 19:14:34.869913: Epoch 1979 +2026-04-12 19:14:34.872138: Current learning rate: 0.00541 +2026-04-12 19:16:16.874352: train_loss -0.3752 +2026-04-12 19:16:16.882529: val_loss -0.3676 +2026-04-12 19:16:16.884770: Pseudo dice [0.6147, 0.783, 0.8019, 0.175, 0.4837, 0.4392, 0.6739] +2026-04-12 19:16:16.887369: Epoch time: 102.01 s +2026-04-12 19:16:18.113263: +2026-04-12 19:16:18.115146: Epoch 1980 +2026-04-12 19:16:18.117030: Current learning rate: 0.00541 +2026-04-12 19:17:59.908383: train_loss -0.3898 +2026-04-12 19:17:59.915494: val_loss -0.3249 +2026-04-12 19:17:59.917482: Pseudo dice [0.4956, 0.7123, 0.6419, 0.2461, 0.4654, 0.7182, 0.5246] +2026-04-12 19:17:59.920900: Epoch time: 101.8 s +2026-04-12 19:18:01.142154: +2026-04-12 19:18:01.143918: Epoch 1981 +2026-04-12 19:18:01.145990: Current learning rate: 0.0054 +2026-04-12 19:19:43.774111: train_loss -0.3491 +2026-04-12 19:19:43.780405: val_loss -0.3665 +2026-04-12 19:19:43.783750: Pseudo dice [0.7386, 0.8785, 0.7596, 0.6133, 0.3926, 0.2065, 0.8735] +2026-04-12 19:19:43.785689: Epoch time: 102.64 s +2026-04-12 19:19:45.056068: +2026-04-12 19:19:45.057755: Epoch 1982 +2026-04-12 19:19:45.059553: Current learning rate: 0.0054 +2026-04-12 19:21:29.724056: train_loss -0.3817 +2026-04-12 19:21:29.730585: val_loss -0.3538 +2026-04-12 19:21:29.732799: Pseudo dice [0.372, 0.615, 0.7687, 0.3813, 0.4364, 0.872, 0.7497] +2026-04-12 19:21:29.736795: Epoch time: 104.67 s +2026-04-12 19:21:30.961173: +2026-04-12 19:21:30.964350: Epoch 1983 +2026-04-12 19:21:30.967090: Current learning rate: 0.0054 +2026-04-12 19:23:12.831182: train_loss -0.4007 +2026-04-12 19:23:12.838035: val_loss -0.3404 +2026-04-12 19:23:12.840518: Pseudo dice [0.6815, 0.756, 0.8202, 0.3286, 0.3032, 0.4583, 0.296] +2026-04-12 19:23:12.843624: Epoch time: 101.87 s +2026-04-12 19:23:14.077691: +2026-04-12 19:23:14.080196: Epoch 1984 +2026-04-12 19:23:14.082687: Current learning rate: 0.0054 +2026-04-12 19:24:55.887875: train_loss -0.3805 +2026-04-12 19:24:55.894273: val_loss -0.315 +2026-04-12 19:24:55.896082: Pseudo dice [0.1351, 0.0801, 0.6808, 0.175, 0.4289, 0.3605, 0.7144] +2026-04-12 19:24:55.898131: Epoch time: 101.81 s +2026-04-12 19:24:57.114654: +2026-04-12 19:24:57.117643: Epoch 1985 +2026-04-12 19:24:57.121853: Current learning rate: 0.0054 +2026-04-12 19:26:39.483577: train_loss -0.3753 +2026-04-12 19:26:39.491291: val_loss -0.3461 +2026-04-12 19:26:39.493848: Pseudo dice [0.5472, 0.7955, 0.8005, 0.395, 0.4423, 0.3141, 0.7753] +2026-04-12 19:26:39.496707: Epoch time: 102.37 s +2026-04-12 19:26:40.715796: +2026-04-12 19:26:40.718477: Epoch 1986 +2026-04-12 19:26:40.720894: Current learning rate: 0.00539 +2026-04-12 19:28:23.480147: train_loss -0.32 +2026-04-12 19:28:23.487033: val_loss -0.1993 +2026-04-12 19:28:23.489326: Pseudo dice [0.469, 0.4524, 0.542, 0.0444, 0.5201, 0.7274, 0.4933] +2026-04-12 19:28:23.491928: Epoch time: 102.77 s +2026-04-12 19:28:24.722255: +2026-04-12 19:28:24.724689: Epoch 1987 +2026-04-12 19:28:24.727297: Current learning rate: 0.00539 +2026-04-12 19:30:07.508477: train_loss -0.318 +2026-04-12 19:30:07.515459: val_loss -0.2508 +2026-04-12 19:30:07.518120: Pseudo dice [0.3139, 0.8296, 0.5369, 0.5905, 0.4302, 0.3788, 0.6946] +2026-04-12 19:30:07.520467: Epoch time: 102.79 s +2026-04-12 19:30:08.741802: +2026-04-12 19:30:08.743545: Epoch 1988 +2026-04-12 19:30:08.745559: Current learning rate: 0.00539 +2026-04-12 19:31:51.511740: train_loss -0.3577 +2026-04-12 19:31:51.519494: val_loss -0.3454 +2026-04-12 19:31:51.521855: Pseudo dice [0.4848, 0.8798, 0.7709, 0.7247, 0.3598, 0.4266, 0.7564] +2026-04-12 19:31:51.525300: Epoch time: 102.77 s +2026-04-12 19:31:52.743194: +2026-04-12 19:31:52.745699: Epoch 1989 +2026-04-12 19:31:52.748162: Current learning rate: 0.00539 +2026-04-12 19:33:34.946324: train_loss -0.3624 +2026-04-12 19:33:34.954294: val_loss -0.3171 +2026-04-12 19:33:34.956271: Pseudo dice [0.8372, 0.8433, 0.688, 0.1126, 0.5953, 0.6079, 0.5847] +2026-04-12 19:33:34.959748: Epoch time: 102.21 s +2026-04-12 19:33:36.186325: +2026-04-12 19:33:36.188174: Epoch 1990 +2026-04-12 19:33:36.189927: Current learning rate: 0.00538 +2026-04-12 19:35:18.015256: train_loss -0.3747 +2026-04-12 19:35:18.022096: val_loss -0.2655 +2026-04-12 19:35:18.024277: Pseudo dice [0.6218, 0.8216, 0.5073, 0.6413, 0.1115, 0.3955, 0.8285] +2026-04-12 19:35:18.026603: Epoch time: 101.83 s +2026-04-12 19:35:19.233106: +2026-04-12 19:35:19.234951: Epoch 1991 +2026-04-12 19:35:19.237294: Current learning rate: 0.00538 +2026-04-12 19:37:01.746343: train_loss -0.3761 +2026-04-12 19:37:01.757813: val_loss -0.3227 +2026-04-12 19:37:01.761794: Pseudo dice [0.4555, 0.5336, 0.6072, 0.2797, 0.4056, 0.623, 0.6517] +2026-04-12 19:37:01.764766: Epoch time: 102.52 s +2026-04-12 19:37:02.994132: +2026-04-12 19:37:02.996141: Epoch 1992 +2026-04-12 19:37:02.998214: Current learning rate: 0.00538 +2026-04-12 19:38:45.072589: train_loss -0.3627 +2026-04-12 19:38:45.083370: val_loss -0.2975 +2026-04-12 19:38:45.090654: Pseudo dice [0.2182, 0.2899, 0.4773, 0.1508, 0.3077, 0.669, 0.8659] +2026-04-12 19:38:45.102697: Epoch time: 102.08 s +2026-04-12 19:38:46.294330: +2026-04-12 19:38:46.296459: Epoch 1993 +2026-04-12 19:38:46.298429: Current learning rate: 0.00538 +2026-04-12 19:40:28.624021: train_loss -0.3626 +2026-04-12 19:40:28.651428: val_loss -0.3337 +2026-04-12 19:40:28.653456: Pseudo dice [0.402, 0.8831, 0.7444, 0.6597, 0.3998, 0.6724, 0.5441] +2026-04-12 19:40:28.656041: Epoch time: 102.33 s +2026-04-12 19:40:29.880985: +2026-04-12 19:40:29.882619: Epoch 1994 +2026-04-12 19:40:29.884547: Current learning rate: 0.00537 +2026-04-12 19:42:12.198203: train_loss -0.3816 +2026-04-12 19:42:12.224355: val_loss -0.3856 +2026-04-12 19:42:12.231245: Pseudo dice [0.6114, 0.5793, 0.6159, 0.6951, 0.483, 0.3665, 0.7552] +2026-04-12 19:42:12.244718: Epoch time: 102.32 s +2026-04-12 19:42:14.552144: +2026-04-12 19:42:14.554005: Epoch 1995 +2026-04-12 19:42:14.555764: Current learning rate: 0.00537 +2026-04-12 19:43:55.969252: train_loss -0.3872 +2026-04-12 19:43:55.975370: val_loss -0.3282 +2026-04-12 19:43:55.977146: Pseudo dice [0.3182, 0.8617, 0.6674, 0.4957, 0.2418, 0.7442, 0.7953] +2026-04-12 19:43:55.979173: Epoch time: 101.42 s +2026-04-12 19:43:57.186671: +2026-04-12 19:43:57.189475: Epoch 1996 +2026-04-12 19:43:57.192016: Current learning rate: 0.00537 +2026-04-12 19:45:38.747601: train_loss -0.3789 +2026-04-12 19:45:38.757684: val_loss -0.2365 +2026-04-12 19:45:38.768228: Pseudo dice [0.5983, 0.8039, 0.292, 0.3692, 0.565, 0.7216, 0.5971] +2026-04-12 19:45:38.772891: Epoch time: 101.56 s +2026-04-12 19:45:39.975860: +2026-04-12 19:45:39.977596: Epoch 1997 +2026-04-12 19:45:39.979486: Current learning rate: 0.00537 +2026-04-12 19:47:22.035914: train_loss -0.3703 +2026-04-12 19:47:22.041483: val_loss -0.2579 +2026-04-12 19:47:22.043815: Pseudo dice [0.2117, 0.8489, 0.4265, 0.6759, 0.4907, 0.1893, 0.8132] +2026-04-12 19:47:22.046368: Epoch time: 102.06 s +2026-04-12 19:47:23.263415: +2026-04-12 19:47:23.265600: Epoch 1998 +2026-04-12 19:47:23.267655: Current learning rate: 0.00536 +2026-04-12 19:49:05.363533: train_loss -0.3756 +2026-04-12 19:49:05.370629: val_loss -0.3382 +2026-04-12 19:49:05.372596: Pseudo dice [0.0644, 0.0901, 0.5826, 0.3856, 0.4769, 0.7079, 0.5899] +2026-04-12 19:49:05.375089: Epoch time: 102.1 s +2026-04-12 19:49:06.600541: +2026-04-12 19:49:06.607868: Epoch 1999 +2026-04-12 19:49:06.610631: Current learning rate: 0.00536 +2026-04-12 19:50:49.316526: train_loss -0.3813 +2026-04-12 19:50:49.322940: val_loss -0.284 +2026-04-12 19:50:49.324826: Pseudo dice [0.1645, 0.5482, 0.3823, 0.2556, 0.25, 0.7568, 0.4359] +2026-04-12 19:50:49.327004: Epoch time: 102.72 s +2026-04-12 19:50:52.342331: +2026-04-12 19:50:52.349432: Epoch 2000 +2026-04-12 19:50:52.351012: Current learning rate: 0.00536 +2026-04-12 19:52:34.885283: train_loss -0.3551 +2026-04-12 19:52:34.893100: val_loss -0.2917 +2026-04-12 19:52:34.895293: Pseudo dice [0.5113, 0.8842, 0.7705, 0.0213, 0.4301, 0.5444, 0.2738] +2026-04-12 19:52:34.898028: Epoch time: 102.55 s +2026-04-12 19:52:36.110357: +2026-04-12 19:52:36.112960: Epoch 2001 +2026-04-12 19:52:36.117671: Current learning rate: 0.00536 +2026-04-12 19:54:18.722928: train_loss -0.376 +2026-04-12 19:54:18.729001: val_loss -0.3119 +2026-04-12 19:54:18.731061: Pseudo dice [0.1335, 0.5607, 0.4473, 0.3569, 0.3964, 0.6244, 0.7231] +2026-04-12 19:54:18.733093: Epoch time: 102.62 s +2026-04-12 19:54:19.938526: +2026-04-12 19:54:19.940158: Epoch 2002 +2026-04-12 19:54:19.942334: Current learning rate: 0.00535 +2026-04-12 19:56:02.112000: train_loss -0.3776 +2026-04-12 19:56:02.119495: val_loss -0.3297 +2026-04-12 19:56:02.122672: Pseudo dice [0.4583, 0.8353, 0.5947, 0.4283, 0.5514, 0.585, 0.7561] +2026-04-12 19:56:02.126252: Epoch time: 102.18 s +2026-04-12 19:56:03.351552: +2026-04-12 19:56:03.354035: Epoch 2003 +2026-04-12 19:56:03.356332: Current learning rate: 0.00535 +2026-04-12 19:57:45.680811: train_loss -0.3814 +2026-04-12 19:57:45.690194: val_loss -0.3379 +2026-04-12 19:57:45.692655: Pseudo dice [0.7254, 0.8699, 0.726, 0.2158, 0.1592, 0.6499, 0.5532] +2026-04-12 19:57:45.695653: Epoch time: 102.33 s +2026-04-12 19:57:46.902936: +2026-04-12 19:57:46.904921: Epoch 2004 +2026-04-12 19:57:46.908268: Current learning rate: 0.00535 +2026-04-12 19:59:29.039329: train_loss -0.3534 +2026-04-12 19:59:29.047583: val_loss -0.3344 +2026-04-12 19:59:29.050157: Pseudo dice [0.6557, 0.3614, 0.7981, 0.4478, 0.3643, 0.8002, 0.7795] +2026-04-12 19:59:29.053148: Epoch time: 102.14 s +2026-04-12 19:59:30.333914: +2026-04-12 19:59:30.336202: Epoch 2005 +2026-04-12 19:59:30.338843: Current learning rate: 0.00535 +2026-04-12 20:01:12.574194: train_loss -0.3677 +2026-04-12 20:01:12.582432: val_loss -0.3723 +2026-04-12 20:01:12.584990: Pseudo dice [0.6877, 0.3286, 0.8053, 0.6551, 0.5128, 0.6197, 0.7304] +2026-04-12 20:01:12.587834: Epoch time: 102.24 s +2026-04-12 20:01:13.836641: +2026-04-12 20:01:13.839802: Epoch 2006 +2026-04-12 20:01:13.841552: Current learning rate: 0.00534 +2026-04-12 20:02:55.374262: train_loss -0.4062 +2026-04-12 20:02:55.381609: val_loss -0.3584 +2026-04-12 20:02:55.383456: Pseudo dice [0.3441, 0.3886, 0.7533, 0.5642, 0.5204, 0.9444, 0.7578] +2026-04-12 20:02:55.385866: Epoch time: 101.54 s +2026-04-12 20:02:56.620364: +2026-04-12 20:02:56.623971: Epoch 2007 +2026-04-12 20:02:56.626087: Current learning rate: 0.00534 +2026-04-12 20:04:38.390540: train_loss -0.397 +2026-04-12 20:04:38.397901: val_loss -0.3389 +2026-04-12 20:04:38.400784: Pseudo dice [0.8619, 0.1879, 0.7295, 0.7379, 0.1805, 0.8327, 0.7775] +2026-04-12 20:04:38.403494: Epoch time: 101.77 s +2026-04-12 20:04:39.628698: +2026-04-12 20:04:39.633818: Epoch 2008 +2026-04-12 20:04:39.636989: Current learning rate: 0.00534 +2026-04-12 20:06:22.388194: train_loss -0.3979 +2026-04-12 20:06:22.394824: val_loss -0.3308 +2026-04-12 20:06:22.396696: Pseudo dice [0.8278, 0.7003, 0.6811, 0.3318, 0.3996, 0.8506, 0.8737] +2026-04-12 20:06:22.398901: Epoch time: 102.76 s +2026-04-12 20:06:23.590904: +2026-04-12 20:06:23.592479: Epoch 2009 +2026-04-12 20:06:23.594352: Current learning rate: 0.00534 +2026-04-12 20:08:05.772042: train_loss -0.3927 +2026-04-12 20:08:05.779600: val_loss -0.3692 +2026-04-12 20:08:05.781953: Pseudo dice [0.3799, 0.3982, 0.7734, 0.783, 0.5675, 0.714, 0.7799] +2026-04-12 20:08:05.785383: Epoch time: 102.18 s +2026-04-12 20:08:06.971501: +2026-04-12 20:08:06.973257: Epoch 2010 +2026-04-12 20:08:06.975198: Current learning rate: 0.00533 +2026-04-12 20:09:49.182375: train_loss -0.3841 +2026-04-12 20:09:49.197025: val_loss -0.3131 +2026-04-12 20:09:49.199622: Pseudo dice [0.3302, 0.7503, 0.7912, 0.1685, 0.2598, 0.568, 0.6085] +2026-04-12 20:09:49.201911: Epoch time: 102.21 s +2026-04-12 20:09:50.399338: +2026-04-12 20:09:50.402668: Epoch 2011 +2026-04-12 20:09:50.405293: Current learning rate: 0.00533 +2026-04-12 20:11:32.564265: train_loss -0.3933 +2026-04-12 20:11:32.571106: val_loss -0.3408 +2026-04-12 20:11:32.573317: Pseudo dice [0.3346, 0.8397, 0.8302, 0.5527, 0.3498, 0.1285, 0.5532] +2026-04-12 20:11:32.576920: Epoch time: 102.17 s +2026-04-12 20:11:33.798028: +2026-04-12 20:11:33.800354: Epoch 2012 +2026-04-12 20:11:33.802669: Current learning rate: 0.00533 +2026-04-12 20:13:16.269135: train_loss -0.3945 +2026-04-12 20:13:16.278747: val_loss -0.3509 +2026-04-12 20:13:16.281487: Pseudo dice [0.0962, 0.448, 0.7712, 0.417, 0.4447, 0.5703, 0.8241] +2026-04-12 20:13:16.288058: Epoch time: 102.47 s +2026-04-12 20:13:17.594172: +2026-04-12 20:13:17.597166: Epoch 2013 +2026-04-12 20:13:17.599722: Current learning rate: 0.00533 +2026-04-12 20:14:59.187295: train_loss -0.3841 +2026-04-12 20:14:59.194163: val_loss -0.3668 +2026-04-12 20:14:59.196349: Pseudo dice [0.3905, 0.6843, 0.7434, 0.8503, 0.4139, 0.5325, 0.622] +2026-04-12 20:14:59.199528: Epoch time: 101.6 s +2026-04-12 20:15:01.588494: +2026-04-12 20:15:01.590196: Epoch 2014 +2026-04-12 20:15:01.592011: Current learning rate: 0.00533 +2026-04-12 20:16:44.091250: train_loss -0.3898 +2026-04-12 20:16:44.100946: val_loss -0.364 +2026-04-12 20:16:44.103730: Pseudo dice [0.3737, 0.1468, 0.7862, 0.5238, 0.3837, 0.9016, 0.8302] +2026-04-12 20:16:44.106696: Epoch time: 102.51 s +2026-04-12 20:16:45.367796: +2026-04-12 20:16:45.369421: Epoch 2015 +2026-04-12 20:16:45.371479: Current learning rate: 0.00532 +2026-04-12 20:18:27.558651: train_loss -0.4015 +2026-04-12 20:18:27.567153: val_loss -0.3347 +2026-04-12 20:18:27.569501: Pseudo dice [0.2985, 0.2037, 0.6939, 0.1292, 0.4608, 0.7181, 0.7855] +2026-04-12 20:18:27.572569: Epoch time: 102.19 s +2026-04-12 20:18:28.876138: +2026-04-12 20:18:28.877860: Epoch 2016 +2026-04-12 20:18:28.879701: Current learning rate: 0.00532 +2026-04-12 20:20:10.781996: train_loss -0.4125 +2026-04-12 20:20:10.788337: val_loss -0.3731 +2026-04-12 20:20:10.790391: Pseudo dice [0.7946, 0.5123, 0.7014, 0.5624, 0.4832, 0.7152, 0.8035] +2026-04-12 20:20:10.793022: Epoch time: 101.91 s +2026-04-12 20:20:12.015673: +2026-04-12 20:20:12.019214: Epoch 2017 +2026-04-12 20:20:12.021797: Current learning rate: 0.00532 +2026-04-12 20:21:54.007171: train_loss -0.4007 +2026-04-12 20:21:54.013038: val_loss -0.3951 +2026-04-12 20:21:54.015031: Pseudo dice [0.4746, 0.7269, 0.7737, 0.7115, 0.3899, 0.6918, 0.7233] +2026-04-12 20:21:54.017883: Epoch time: 101.99 s +2026-04-12 20:21:55.258908: +2026-04-12 20:21:55.261834: Epoch 2018 +2026-04-12 20:21:55.264814: Current learning rate: 0.00532 +2026-04-12 20:23:37.826234: train_loss -0.4031 +2026-04-12 20:23:37.833462: val_loss -0.336 +2026-04-12 20:23:37.835513: Pseudo dice [0.5273, 0.8863, 0.7944, 0.4242, 0.4005, 0.6915, 0.426] +2026-04-12 20:23:37.838533: Epoch time: 102.57 s +2026-04-12 20:23:39.090851: +2026-04-12 20:23:39.093216: Epoch 2019 +2026-04-12 20:23:39.096315: Current learning rate: 0.00531 +2026-04-12 20:25:20.851612: train_loss -0.4159 +2026-04-12 20:25:20.857656: val_loss -0.2991 +2026-04-12 20:25:20.859831: Pseudo dice [0.5127, 0.8619, 0.5915, 0.3727, 0.2631, 0.5244, 0.4229] +2026-04-12 20:25:20.861767: Epoch time: 101.76 s +2026-04-12 20:25:22.123669: +2026-04-12 20:25:22.126466: Epoch 2020 +2026-04-12 20:25:22.145005: Current learning rate: 0.00531 +2026-04-12 20:27:04.171134: train_loss -0.387 +2026-04-12 20:27:04.180031: val_loss -0.3271 +2026-04-12 20:27:04.182217: Pseudo dice [0.6714, 0.1775, 0.6529, 0.5057, 0.3744, 0.686, 0.736] +2026-04-12 20:27:04.184602: Epoch time: 102.05 s +2026-04-12 20:27:05.476852: +2026-04-12 20:27:05.478838: Epoch 2021 +2026-04-12 20:27:05.480752: Current learning rate: 0.00531 +2026-04-12 20:28:48.115107: train_loss -0.3811 +2026-04-12 20:28:48.122094: val_loss -0.3158 +2026-04-12 20:28:48.124438: Pseudo dice [0.6822, 0.6224, 0.7437, 0.3252, 0.3863, 0.3779, 0.3489] +2026-04-12 20:28:48.127026: Epoch time: 102.64 s +2026-04-12 20:28:49.342484: +2026-04-12 20:28:49.344435: Epoch 2022 +2026-04-12 20:28:49.346375: Current learning rate: 0.00531 +2026-04-12 20:30:31.940406: train_loss -0.3859 +2026-04-12 20:30:31.946981: val_loss -0.3009 +2026-04-12 20:30:31.949068: Pseudo dice [0.3981, 0.643, 0.6162, 0.5459, 0.4038, 0.5782, 0.7435] +2026-04-12 20:30:31.951145: Epoch time: 102.6 s +2026-04-12 20:30:33.171902: +2026-04-12 20:30:33.173658: Epoch 2023 +2026-04-12 20:30:33.175676: Current learning rate: 0.0053 +2026-04-12 20:32:15.071931: train_loss -0.3833 +2026-04-12 20:32:15.080057: val_loss -0.3237 +2026-04-12 20:32:15.083842: Pseudo dice [0.2097, 0.6115, 0.71, 0.4707, 0.3737, 0.577, 0.7791] +2026-04-12 20:32:15.086680: Epoch time: 101.9 s +2026-04-12 20:32:16.295020: +2026-04-12 20:32:16.300462: Epoch 2024 +2026-04-12 20:32:16.305347: Current learning rate: 0.0053 +2026-04-12 20:33:57.960462: train_loss -0.366 +2026-04-12 20:33:57.968669: val_loss -0.3519 +2026-04-12 20:33:57.970737: Pseudo dice [0.5245, 0.7465, 0.7779, 0.4969, 0.4271, 0.4301, 0.8026] +2026-04-12 20:33:57.974061: Epoch time: 101.67 s +2026-04-12 20:33:59.226707: +2026-04-12 20:33:59.229446: Epoch 2025 +2026-04-12 20:33:59.231695: Current learning rate: 0.0053 +2026-04-12 20:35:41.771879: train_loss -0.3469 +2026-04-12 20:35:41.780468: val_loss -0.2402 +2026-04-12 20:35:41.782666: Pseudo dice [0.6317, 0.8781, 0.6314, 0.0558, 0.2813, 0.0873, 0.4192] +2026-04-12 20:35:41.786282: Epoch time: 102.55 s +2026-04-12 20:35:43.050609: +2026-04-12 20:35:43.054018: Epoch 2026 +2026-04-12 20:35:43.056746: Current learning rate: 0.0053 +2026-04-12 20:37:24.766837: train_loss -0.3932 +2026-04-12 20:37:24.773405: val_loss -0.3453 +2026-04-12 20:37:24.775785: Pseudo dice [0.5231, 0.8154, 0.7145, 0.4775, 0.4864, 0.7403, 0.6921] +2026-04-12 20:37:24.778081: Epoch time: 101.72 s +2026-04-12 20:37:26.006073: +2026-04-12 20:37:26.007925: Epoch 2027 +2026-04-12 20:37:26.010081: Current learning rate: 0.00529 +2026-04-12 20:39:07.647247: train_loss -0.3878 +2026-04-12 20:39:07.654777: val_loss -0.3214 +2026-04-12 20:39:07.657213: Pseudo dice [0.1701, 0.2488, 0.6601, 0.4682, 0.5679, 0.7109, 0.8607] +2026-04-12 20:39:07.659786: Epoch time: 101.64 s +2026-04-12 20:39:08.903545: +2026-04-12 20:39:08.905441: Epoch 2028 +2026-04-12 20:39:08.907218: Current learning rate: 0.00529 +2026-04-12 20:40:51.400203: train_loss -0.3868 +2026-04-12 20:40:51.426786: val_loss -0.3834 +2026-04-12 20:40:51.428968: Pseudo dice [0.5417, 0.2691, 0.7268, 0.5634, 0.5416, 0.7926, 0.8463] +2026-04-12 20:40:51.431580: Epoch time: 102.5 s +2026-04-12 20:40:52.670321: +2026-04-12 20:40:52.672359: Epoch 2029 +2026-04-12 20:40:52.674797: Current learning rate: 0.00529 +2026-04-12 20:42:35.192212: train_loss -0.3744 +2026-04-12 20:42:35.199134: val_loss -0.364 +2026-04-12 20:42:35.200824: Pseudo dice [0.6858, 0.5952, 0.7619, 0.5103, 0.3943, 0.3584, 0.8137] +2026-04-12 20:42:35.202697: Epoch time: 102.52 s +2026-04-12 20:42:36.420960: +2026-04-12 20:42:36.422455: Epoch 2030 +2026-04-12 20:42:36.425304: Current learning rate: 0.00529 +2026-04-12 20:44:18.152012: train_loss -0.3765 +2026-04-12 20:44:18.159641: val_loss -0.3422 +2026-04-12 20:44:18.164073: Pseudo dice [0.6653, 0.5481, 0.681, 0.2865, 0.5019, 0.8611, 0.6963] +2026-04-12 20:44:18.166596: Epoch time: 101.73 s +2026-04-12 20:44:19.452574: +2026-04-12 20:44:19.454556: Epoch 2031 +2026-04-12 20:44:19.456505: Current learning rate: 0.00528 +2026-04-12 20:46:01.479571: train_loss -0.3857 +2026-04-12 20:46:01.487984: val_loss -0.3347 +2026-04-12 20:46:01.494037: Pseudo dice [0.0995, 0.5386, 0.6317, 0.2611, 0.4073, 0.6545, 0.5082] +2026-04-12 20:46:01.502218: Epoch time: 102.03 s +2026-04-12 20:46:02.693789: +2026-04-12 20:46:02.695542: Epoch 2032 +2026-04-12 20:46:02.697524: Current learning rate: 0.00528 +2026-04-12 20:47:44.969229: train_loss -0.37 +2026-04-12 20:47:44.976125: val_loss -0.3024 +2026-04-12 20:47:44.978754: Pseudo dice [0.6662, 0.8687, 0.8485, 0.1141, 0.3959, 0.3991, 0.4915] +2026-04-12 20:47:44.981201: Epoch time: 102.28 s +2026-04-12 20:47:46.227766: +2026-04-12 20:47:46.230736: Epoch 2033 +2026-04-12 20:47:46.232921: Current learning rate: 0.00528 +2026-04-12 20:49:28.358743: train_loss -0.3545 +2026-04-12 20:49:28.365423: val_loss -0.359 +2026-04-12 20:49:28.367401: Pseudo dice [0.5963, 0.4139, 0.7343, 0.1717, 0.6128, 0.6537, 0.2662] +2026-04-12 20:49:28.369620: Epoch time: 102.13 s +2026-04-12 20:49:30.739057: +2026-04-12 20:49:30.740758: Epoch 2034 +2026-04-12 20:49:30.742570: Current learning rate: 0.00528 +2026-04-12 20:51:13.260116: train_loss -0.4113 +2026-04-12 20:51:13.268130: val_loss -0.3566 +2026-04-12 20:51:13.270055: Pseudo dice [0.6901, 0.6373, 0.7385, 0.4602, 0.5106, 0.6674, 0.4776] +2026-04-12 20:51:13.272701: Epoch time: 102.52 s +2026-04-12 20:51:14.495835: +2026-04-12 20:51:14.497521: Epoch 2035 +2026-04-12 20:51:14.499577: Current learning rate: 0.00527 +2026-04-12 20:52:56.900994: train_loss -0.3858 +2026-04-12 20:52:56.908615: val_loss -0.3606 +2026-04-12 20:52:56.911127: Pseudo dice [0.5616, 0.3576, 0.5599, 0.2917, 0.5361, 0.5687, 0.5172] +2026-04-12 20:52:56.913717: Epoch time: 102.41 s +2026-04-12 20:52:58.170444: +2026-04-12 20:52:58.171995: Epoch 2036 +2026-04-12 20:52:58.173886: Current learning rate: 0.00527 +2026-04-12 20:54:40.108259: train_loss -0.3971 +2026-04-12 20:54:40.114656: val_loss -0.343 +2026-04-12 20:54:40.117222: Pseudo dice [0.4608, 0.8427, 0.6875, 0.5298, 0.4664, 0.5433, 0.6676] +2026-04-12 20:54:40.119689: Epoch time: 101.94 s +2026-04-12 20:54:41.369473: +2026-04-12 20:54:41.371662: Epoch 2037 +2026-04-12 20:54:41.374069: Current learning rate: 0.00527 +2026-04-12 20:56:23.293411: train_loss -0.3737 +2026-04-12 20:56:23.302223: val_loss -0.3151 +2026-04-12 20:56:23.305283: Pseudo dice [0.4293, 0.8607, 0.6631, 0.398, 0.3551, 0.2188, 0.7686] +2026-04-12 20:56:23.307843: Epoch time: 101.93 s +2026-04-12 20:56:24.570673: +2026-04-12 20:56:24.572825: Epoch 2038 +2026-04-12 20:56:24.575184: Current learning rate: 0.00527 +2026-04-12 20:58:06.911924: train_loss -0.3706 +2026-04-12 20:58:06.918159: val_loss -0.3488 +2026-04-12 20:58:06.920267: Pseudo dice [0.6764, 0.7586, 0.6602, 0.3432, 0.4179, 0.5857, 0.7796] +2026-04-12 20:58:06.922955: Epoch time: 102.34 s +2026-04-12 20:58:08.134347: +2026-04-12 20:58:08.136139: Epoch 2039 +2026-04-12 20:58:08.138176: Current learning rate: 0.00526 +2026-04-12 20:59:50.637151: train_loss -0.3739 +2026-04-12 20:59:50.643501: val_loss -0.3272 +2026-04-12 20:59:50.646203: Pseudo dice [0.3214, 0.6568, 0.6018, 0.392, 0.401, 0.489, 0.6385] +2026-04-12 20:59:50.648875: Epoch time: 102.51 s +2026-04-12 20:59:51.858400: +2026-04-12 20:59:51.861567: Epoch 2040 +2026-04-12 20:59:51.863678: Current learning rate: 0.00526 +2026-04-12 21:01:33.908209: train_loss -0.3711 +2026-04-12 21:01:33.916198: val_loss -0.3569 +2026-04-12 21:01:33.918147: Pseudo dice [0.8172, 0.6911, 0.6695, 0.7564, 0.6255, 0.4093, 0.8108] +2026-04-12 21:01:33.920733: Epoch time: 102.05 s +2026-04-12 21:01:35.180707: +2026-04-12 21:01:35.184045: Epoch 2041 +2026-04-12 21:01:35.185977: Current learning rate: 0.00526 +2026-04-12 21:03:17.059857: train_loss -0.3894 +2026-04-12 21:03:17.066766: val_loss -0.3647 +2026-04-12 21:03:17.068927: Pseudo dice [0.8062, 0.6127, 0.6728, 0.398, 0.3911, 0.87, 0.7316] +2026-04-12 21:03:17.071070: Epoch time: 101.88 s +2026-04-12 21:03:18.286956: +2026-04-12 21:03:18.289073: Epoch 2042 +2026-04-12 21:03:18.292188: Current learning rate: 0.00526 +2026-04-12 21:05:00.476663: train_loss -0.3937 +2026-04-12 21:05:00.482696: val_loss -0.3627 +2026-04-12 21:05:00.484559: Pseudo dice [0.3425, 0.4531, 0.8122, 0.359, 0.5679, 0.8521, 0.586] +2026-04-12 21:05:00.487624: Epoch time: 102.19 s +2026-04-12 21:05:01.713319: +2026-04-12 21:05:01.715160: Epoch 2043 +2026-04-12 21:05:01.717433: Current learning rate: 0.00526 +2026-04-12 21:06:43.689317: train_loss -0.411 +2026-04-12 21:06:43.695958: val_loss -0.353 +2026-04-12 21:06:43.701531: Pseudo dice [0.7471, 0.7192, 0.8105, 0.6311, 0.6076, 0.6469, 0.8047] +2026-04-12 21:06:43.705190: Epoch time: 101.98 s +2026-04-12 21:06:44.931878: +2026-04-12 21:06:44.933566: Epoch 2044 +2026-04-12 21:06:44.935327: Current learning rate: 0.00525 +2026-04-12 21:08:27.066962: train_loss -0.4139 +2026-04-12 21:08:27.074333: val_loss -0.363 +2026-04-12 21:08:27.076748: Pseudo dice [0.1902, 0.6442, 0.7232, 0.4916, 0.6049, 0.8759, 0.7253] +2026-04-12 21:08:27.080336: Epoch time: 102.14 s +2026-04-12 21:08:28.324000: +2026-04-12 21:08:28.325882: Epoch 2045 +2026-04-12 21:08:28.329778: Current learning rate: 0.00525 +2026-04-12 21:10:10.798007: train_loss -0.3933 +2026-04-12 21:10:10.804211: val_loss -0.3698 +2026-04-12 21:10:10.806602: Pseudo dice [0.7406, 0.5629, 0.7411, 0.7667, 0.6229, 0.7604, 0.8098] +2026-04-12 21:10:10.809429: Epoch time: 102.48 s +2026-04-12 21:10:12.027895: +2026-04-12 21:10:12.029704: Epoch 2046 +2026-04-12 21:10:12.031672: Current learning rate: 0.00525 +2026-04-12 21:11:53.786876: train_loss -0.3744 +2026-04-12 21:11:53.794572: val_loss -0.3466 +2026-04-12 21:11:53.797436: Pseudo dice [0.5736, 0.4398, 0.6775, 0.5293, 0.3911, 0.6629, 0.5913] +2026-04-12 21:11:53.801355: Epoch time: 101.76 s +2026-04-12 21:11:54.959174: +2026-04-12 21:11:54.961233: Epoch 2047 +2026-04-12 21:11:54.963212: Current learning rate: 0.00525 +2026-04-12 21:13:37.135391: train_loss -0.3839 +2026-04-12 21:13:37.144904: val_loss -0.3204 +2026-04-12 21:13:37.148282: Pseudo dice [0.8678, 0.3017, 0.7325, 0.3235, 0.2302, 0.3835, 0.7122] +2026-04-12 21:13:37.151122: Epoch time: 102.18 s +2026-04-12 21:13:38.305289: +2026-04-12 21:13:38.309150: Epoch 2048 +2026-04-12 21:13:38.311483: Current learning rate: 0.00524 +2026-04-12 21:15:20.078296: train_loss -0.3938 +2026-04-12 21:15:20.087895: val_loss -0.3684 +2026-04-12 21:15:20.090048: Pseudo dice [0.5159, 0.2916, 0.8026, 0.2536, 0.5884, 0.921, 0.7064] +2026-04-12 21:15:20.092848: Epoch time: 101.78 s +2026-04-12 21:15:21.266447: +2026-04-12 21:15:21.268430: Epoch 2049 +2026-04-12 21:15:21.270805: Current learning rate: 0.00524 +2026-04-12 21:17:02.811471: train_loss -0.3878 +2026-04-12 21:17:02.817503: val_loss -0.3656 +2026-04-12 21:17:02.819855: Pseudo dice [0.1916, 0.6461, 0.665, 0.8733, 0.3679, 0.9183, 0.7477] +2026-04-12 21:17:02.821986: Epoch time: 101.55 s +2026-04-12 21:17:05.488808: +2026-04-12 21:17:05.491232: Epoch 2050 +2026-04-12 21:17:05.492944: Current learning rate: 0.00524 +2026-04-12 21:18:49.022905: train_loss -0.3921 +2026-04-12 21:18:49.030598: val_loss -0.3747 +2026-04-12 21:18:49.033200: Pseudo dice [0.5304, 0.6839, 0.7486, 0.3309, 0.5557, 0.8674, 0.7502] +2026-04-12 21:18:49.036229: Epoch time: 103.54 s +2026-04-12 21:18:50.197009: +2026-04-12 21:18:50.198647: Epoch 2051 +2026-04-12 21:18:50.200624: Current learning rate: 0.00524 +2026-04-12 21:20:32.416367: train_loss -0.4072 +2026-04-12 21:20:32.424773: val_loss -0.3213 +2026-04-12 21:20:32.426567: Pseudo dice [0.4379, 0.8114, 0.7341, 0.2388, 0.3771, 0.5525, 0.7788] +2026-04-12 21:20:32.429116: Epoch time: 102.22 s +2026-04-12 21:20:33.631806: +2026-04-12 21:20:33.633863: Epoch 2052 +2026-04-12 21:20:33.635829: Current learning rate: 0.00523 +2026-04-12 21:22:15.701068: train_loss -0.3727 +2026-04-12 21:22:15.710094: val_loss -0.3125 +2026-04-12 21:22:15.712148: Pseudo dice [0.7841, 0.9109, 0.7915, 0.2976, 0.4262, 0.1138, 0.5548] +2026-04-12 21:22:15.714674: Epoch time: 102.07 s +2026-04-12 21:22:16.861934: +2026-04-12 21:22:16.864433: Epoch 2053 +2026-04-12 21:22:16.866399: Current learning rate: 0.00523 +2026-04-12 21:23:59.412500: train_loss -0.3937 +2026-04-12 21:23:59.421958: val_loss -0.3568 +2026-04-12 21:23:59.427098: Pseudo dice [0.6281, 0.7386, 0.6206, 0.7744, 0.6506, 0.8071, 0.7127] +2026-04-12 21:23:59.431731: Epoch time: 102.55 s +2026-04-12 21:24:01.686421: +2026-04-12 21:24:01.688196: Epoch 2054 +2026-04-12 21:24:01.690043: Current learning rate: 0.00523 +2026-04-12 21:25:43.374521: train_loss -0.3975 +2026-04-12 21:25:43.381438: val_loss -0.3619 +2026-04-12 21:25:43.384109: Pseudo dice [0.7525, 0.7158, 0.7716, 0.4862, 0.4463, 0.7054, 0.7846] +2026-04-12 21:25:43.386121: Epoch time: 101.69 s +2026-04-12 21:25:44.557068: +2026-04-12 21:25:44.559997: Epoch 2055 +2026-04-12 21:25:44.561911: Current learning rate: 0.00523 +2026-04-12 21:27:26.941413: train_loss -0.4033 +2026-04-12 21:27:26.949075: val_loss -0.3696 +2026-04-12 21:27:26.950982: Pseudo dice [0.5985, 0.4298, 0.6044, 0.4398, 0.4549, 0.712, 0.8681] +2026-04-12 21:27:26.953300: Epoch time: 102.39 s +2026-04-12 21:27:28.137415: +2026-04-12 21:27:28.139342: Epoch 2056 +2026-04-12 21:27:28.141476: Current learning rate: 0.00522 +2026-04-12 21:29:10.028345: train_loss -0.3864 +2026-04-12 21:29:10.035897: val_loss -0.3634 +2026-04-12 21:29:10.037977: Pseudo dice [0.6297, 0.6155, 0.6634, 0.4843, 0.4661, 0.6943, 0.7512] +2026-04-12 21:29:10.041086: Epoch time: 101.89 s +2026-04-12 21:29:11.179757: +2026-04-12 21:29:11.181722: Epoch 2057 +2026-04-12 21:29:11.183870: Current learning rate: 0.00522 +2026-04-12 21:30:53.107950: train_loss -0.3911 +2026-04-12 21:30:53.115264: val_loss -0.3382 +2026-04-12 21:30:53.117371: Pseudo dice [0.9002, 0.2651, 0.7531, 0.4792, 0.5975, 0.613, 0.7821] +2026-04-12 21:30:53.119883: Epoch time: 101.93 s +2026-04-12 21:30:54.276765: +2026-04-12 21:30:54.278962: Epoch 2058 +2026-04-12 21:30:54.281286: Current learning rate: 0.00522 +2026-04-12 21:32:36.304919: train_loss -0.392 +2026-04-12 21:32:36.311918: val_loss -0.351 +2026-04-12 21:32:36.315595: Pseudo dice [0.704, 0.2463, 0.7407, 0.1301, 0.281, 0.6077, 0.7523] +2026-04-12 21:32:36.317909: Epoch time: 102.03 s +2026-04-12 21:32:37.453322: +2026-04-12 21:32:37.454800: Epoch 2059 +2026-04-12 21:32:37.456470: Current learning rate: 0.00522 +2026-04-12 21:34:19.660298: train_loss -0.3753 +2026-04-12 21:34:19.667184: val_loss -0.3544 +2026-04-12 21:34:19.669113: Pseudo dice [0.5893, 0.354, 0.7208, 0.044, 0.5279, 0.5275, 0.796] +2026-04-12 21:34:19.671551: Epoch time: 102.21 s +2026-04-12 21:34:20.823813: +2026-04-12 21:34:20.825490: Epoch 2060 +2026-04-12 21:34:20.827318: Current learning rate: 0.00521 +2026-04-12 21:36:03.504037: train_loss -0.3836 +2026-04-12 21:36:03.511981: val_loss -0.3111 +2026-04-12 21:36:03.514150: Pseudo dice [0.6237, 0.9034, 0.5833, 0.5339, 0.3048, 0.1109, 0.7358] +2026-04-12 21:36:03.518210: Epoch time: 102.68 s +2026-04-12 21:36:04.661846: +2026-04-12 21:36:04.664546: Epoch 2061 +2026-04-12 21:36:04.666824: Current learning rate: 0.00521 +2026-04-12 21:37:46.429151: train_loss -0.3698 +2026-04-12 21:37:46.436047: val_loss -0.3404 +2026-04-12 21:37:46.439440: Pseudo dice [0.6965, 0.7811, 0.6371, 0.1415, 0.3364, 0.8412, 0.7925] +2026-04-12 21:37:46.441678: Epoch time: 101.77 s +2026-04-12 21:37:47.615631: +2026-04-12 21:37:47.617416: Epoch 2062 +2026-04-12 21:37:47.619478: Current learning rate: 0.00521 +2026-04-12 21:39:29.429603: train_loss -0.3664 +2026-04-12 21:39:29.436053: val_loss -0.3492 +2026-04-12 21:39:29.438951: Pseudo dice [0.8094, 0.7784, 0.7621, 0.4192, 0.4119, 0.8847, 0.6956] +2026-04-12 21:39:29.441028: Epoch time: 101.82 s +2026-04-12 21:39:30.610021: +2026-04-12 21:39:30.611688: Epoch 2063 +2026-04-12 21:39:30.613594: Current learning rate: 0.00521 +2026-04-12 21:41:12.853292: train_loss -0.3668 +2026-04-12 21:41:12.862297: val_loss -0.2917 +2026-04-12 21:41:12.865749: Pseudo dice [0.5666, 0.5128, 0.6122, 0.0918, 0.4821, 0.7315, 0.6987] +2026-04-12 21:41:12.868345: Epoch time: 102.25 s +2026-04-12 21:41:14.059947: +2026-04-12 21:41:14.062324: Epoch 2064 +2026-04-12 21:41:14.064913: Current learning rate: 0.0052 +2026-04-12 21:42:56.163794: train_loss -0.3526 +2026-04-12 21:42:56.173269: val_loss -0.3564 +2026-04-12 21:42:56.175840: Pseudo dice [0.817, 0.4047, 0.7919, 0.5833, 0.238, 0.7778, 0.7755] +2026-04-12 21:42:56.179286: Epoch time: 102.11 s +2026-04-12 21:42:57.337641: +2026-04-12 21:42:57.339599: Epoch 2065 +2026-04-12 21:42:57.341527: Current learning rate: 0.0052 +2026-04-12 21:44:39.711896: train_loss -0.395 +2026-04-12 21:44:39.721600: val_loss -0.3652 +2026-04-12 21:44:39.723723: Pseudo dice [0.5453, 0.8914, 0.8061, 0.6972, 0.2336, 0.8896, 0.8294] +2026-04-12 21:44:39.726218: Epoch time: 102.38 s +2026-04-12 21:44:40.865532: +2026-04-12 21:44:40.868442: Epoch 2066 +2026-04-12 21:44:40.871010: Current learning rate: 0.0052 +2026-04-12 21:46:22.323288: train_loss -0.3937 +2026-04-12 21:46:22.332007: val_loss -0.3603 +2026-04-12 21:46:22.339788: Pseudo dice [0.6351, 0.3474, 0.8093, 0.7214, 0.5191, 0.9244, 0.7284] +2026-04-12 21:46:22.348644: Epoch time: 101.46 s +2026-04-12 21:46:23.486760: +2026-04-12 21:46:23.488323: Epoch 2067 +2026-04-12 21:46:23.489979: Current learning rate: 0.0052 +2026-04-12 21:48:05.916576: train_loss -0.3814 +2026-04-12 21:48:05.924473: val_loss -0.3234 +2026-04-12 21:48:05.926982: Pseudo dice [0.5759, 0.4928, 0.7217, 0.1031, 0.4347, 0.9161, 0.41] +2026-04-12 21:48:05.930650: Epoch time: 102.43 s +2026-04-12 21:48:07.081331: +2026-04-12 21:48:07.084116: Epoch 2068 +2026-04-12 21:48:07.085873: Current learning rate: 0.00519 +2026-04-12 21:49:49.695164: train_loss -0.3964 +2026-04-12 21:49:49.701909: val_loss -0.2781 +2026-04-12 21:49:49.703791: Pseudo dice [0.5031, 0.8168, 0.5959, 0.2358, 0.2348, 0.1254, 0.5891] +2026-04-12 21:49:49.706001: Epoch time: 102.62 s +2026-04-12 21:49:50.851680: +2026-04-12 21:49:50.853268: Epoch 2069 +2026-04-12 21:49:50.855018: Current learning rate: 0.00519 +2026-04-12 21:51:32.954278: train_loss -0.3736 +2026-04-12 21:51:32.960998: val_loss -0.341 +2026-04-12 21:51:32.963201: Pseudo dice [0.4147, 0.8438, 0.6728, 0.4534, 0.4499, 0.4423, 0.5183] +2026-04-12 21:51:32.966143: Epoch time: 102.11 s +2026-04-12 21:51:34.123571: +2026-04-12 21:51:34.127423: Epoch 2070 +2026-04-12 21:51:34.130156: Current learning rate: 0.00519 +2026-04-12 21:53:16.556327: train_loss -0.3864 +2026-04-12 21:53:16.563711: val_loss -0.3133 +2026-04-12 21:53:16.566197: Pseudo dice [0.7644, 0.8916, 0.6327, 0.4845, 0.3702, 0.4989, 0.8083] +2026-04-12 21:53:16.569006: Epoch time: 102.44 s +2026-04-12 21:53:17.724409: +2026-04-12 21:53:17.726771: Epoch 2071 +2026-04-12 21:53:17.729810: Current learning rate: 0.00519 +2026-04-12 21:55:00.176223: train_loss -0.3634 +2026-04-12 21:55:00.184737: val_loss -0.3415 +2026-04-12 21:55:00.186850: Pseudo dice [0.6479, 0.3198, 0.7734, 0.4508, 0.3551, 0.6158, 0.6717] +2026-04-12 21:55:00.189177: Epoch time: 102.45 s +2026-04-12 21:55:01.343647: +2026-04-12 21:55:01.345625: Epoch 2072 +2026-04-12 21:55:01.347666: Current learning rate: 0.00518 +2026-04-12 21:56:43.080573: train_loss -0.3773 +2026-04-12 21:56:43.086968: val_loss -0.3183 +2026-04-12 21:56:43.089244: Pseudo dice [0.4183, 0.5522, 0.4185, 0.779, 0.2805, 0.7364, 0.8117] +2026-04-12 21:56:43.091415: Epoch time: 101.74 s +2026-04-12 21:56:44.239612: +2026-04-12 21:56:44.241190: Epoch 2073 +2026-04-12 21:56:44.242960: Current learning rate: 0.00518 +2026-04-12 21:58:26.884696: train_loss -0.3737 +2026-04-12 21:58:26.890346: val_loss -0.349 +2026-04-12 21:58:26.892003: Pseudo dice [0.4443, 0.6346, 0.7545, 0.3526, 0.5365, 0.7167, 0.3713] +2026-04-12 21:58:26.893857: Epoch time: 102.65 s +2026-04-12 21:58:28.063903: +2026-04-12 21:58:28.065836: Epoch 2074 +2026-04-12 21:58:28.068663: Current learning rate: 0.00518 +2026-04-12 22:00:10.234843: train_loss -0.3984 +2026-04-12 22:00:10.242921: val_loss -0.3784 +2026-04-12 22:00:10.245729: Pseudo dice [0.4621, 0.6146, 0.8184, 0.6795, 0.5816, 0.4128, 0.8864] +2026-04-12 22:00:10.250639: Epoch time: 102.17 s +2026-04-12 22:00:12.470416: +2026-04-12 22:00:12.472033: Epoch 2075 +2026-04-12 22:00:12.474050: Current learning rate: 0.00518 +2026-04-12 22:01:54.416647: train_loss -0.3996 +2026-04-12 22:01:54.423867: val_loss -0.3787 +2026-04-12 22:01:54.430005: Pseudo dice [0.7639, 0.8284, 0.619, 0.5715, 0.566, 0.7635, 0.6987] +2026-04-12 22:01:54.433374: Epoch time: 101.95 s +2026-04-12 22:01:55.613420: +2026-04-12 22:01:55.616381: Epoch 2076 +2026-04-12 22:01:55.618860: Current learning rate: 0.00518 +2026-04-12 22:03:38.604625: train_loss -0.4048 +2026-04-12 22:03:38.616471: val_loss -0.3354 +2026-04-12 22:03:38.618529: Pseudo dice [0.4503, 0.6579, 0.762, 0.2677, 0.7001, 0.8182, 0.5021] +2026-04-12 22:03:38.621092: Epoch time: 102.99 s +2026-04-12 22:03:39.773829: +2026-04-12 22:03:39.775738: Epoch 2077 +2026-04-12 22:03:39.779138: Current learning rate: 0.00517 +2026-04-12 22:05:21.837294: train_loss -0.3856 +2026-04-12 22:05:21.845023: val_loss -0.3327 +2026-04-12 22:05:21.848531: Pseudo dice [0.5898, 0.8274, 0.6787, 0.2059, 0.4905, 0.7381, 0.685] +2026-04-12 22:05:21.851221: Epoch time: 102.07 s +2026-04-12 22:05:23.021059: +2026-04-12 22:05:23.022815: Epoch 2078 +2026-04-12 22:05:23.024679: Current learning rate: 0.00517 +2026-04-12 22:07:05.742673: train_loss -0.4019 +2026-04-12 22:07:05.749084: val_loss -0.3265 +2026-04-12 22:07:05.751186: Pseudo dice [0.4795, 0.2008, 0.5628, 0.3578, 0.443, 0.3487, 0.5701] +2026-04-12 22:07:05.753617: Epoch time: 102.72 s +2026-04-12 22:07:06.919413: +2026-04-12 22:07:06.921429: Epoch 2079 +2026-04-12 22:07:06.923439: Current learning rate: 0.00517 +2026-04-12 22:08:48.702238: train_loss -0.3858 +2026-04-12 22:08:48.707957: val_loss -0.3263 +2026-04-12 22:08:48.710249: Pseudo dice [0.5954, 0.8678, 0.7588, 0.1813, 0.5549, 0.1085, 0.4728] +2026-04-12 22:08:48.712718: Epoch time: 101.79 s +2026-04-12 22:08:49.871921: +2026-04-12 22:08:49.873811: Epoch 2080 +2026-04-12 22:08:49.875688: Current learning rate: 0.00517 +2026-04-12 22:10:31.643528: train_loss -0.3942 +2026-04-12 22:10:31.651051: val_loss -0.3765 +2026-04-12 22:10:31.654019: Pseudo dice [0.261, 0.7005, 0.7845, 0.7586, 0.5947, 0.8005, 0.7716] +2026-04-12 22:10:31.656898: Epoch time: 101.77 s +2026-04-12 22:10:32.794977: +2026-04-12 22:10:32.796446: Epoch 2081 +2026-04-12 22:10:32.798061: Current learning rate: 0.00516 +2026-04-12 22:12:15.317330: train_loss -0.3899 +2026-04-12 22:12:15.325265: val_loss -0.3642 +2026-04-12 22:12:15.327470: Pseudo dice [0.7044, 0.7449, 0.6918, 0.5573, 0.4372, 0.6862, 0.7882] +2026-04-12 22:12:15.330302: Epoch time: 102.53 s +2026-04-12 22:12:16.499726: +2026-04-12 22:12:16.501788: Epoch 2082 +2026-04-12 22:12:16.503769: Current learning rate: 0.00516 +2026-04-12 22:13:58.535987: train_loss -0.4103 +2026-04-12 22:13:58.544762: val_loss -0.353 +2026-04-12 22:13:58.547461: Pseudo dice [0.5798, 0.791, 0.752, 0.4006, 0.4683, 0.0796, 0.7351] +2026-04-12 22:13:58.549767: Epoch time: 102.04 s +2026-04-12 22:13:59.700592: +2026-04-12 22:13:59.703104: Epoch 2083 +2026-04-12 22:13:59.705520: Current learning rate: 0.00516 +2026-04-12 22:15:40.922737: train_loss -0.4089 +2026-04-12 22:15:40.929582: val_loss -0.3386 +2026-04-12 22:15:40.931676: Pseudo dice [0.429, 0.9128, 0.7386, 0.3928, 0.3467, 0.424, 0.7479] +2026-04-12 22:15:40.935344: Epoch time: 101.23 s +2026-04-12 22:15:42.095035: +2026-04-12 22:15:42.096931: Epoch 2084 +2026-04-12 22:15:42.099248: Current learning rate: 0.00516 +2026-04-12 22:17:24.015044: train_loss -0.3787 +2026-04-12 22:17:24.021667: val_loss -0.3529 +2026-04-12 22:17:24.023628: Pseudo dice [0.5664, 0.6104, 0.7606, 0.2384, 0.6296, 0.7156, 0.8469] +2026-04-12 22:17:24.026351: Epoch time: 101.92 s +2026-04-12 22:17:25.199997: +2026-04-12 22:17:25.202266: Epoch 2085 +2026-04-12 22:17:25.204329: Current learning rate: 0.00515 +2026-04-12 22:19:06.873610: train_loss -0.4056 +2026-04-12 22:19:06.880263: val_loss -0.3526 +2026-04-12 22:19:06.882254: Pseudo dice [0.1415, 0.6613, 0.6979, 0.2906, 0.4321, 0.7498, 0.7917] +2026-04-12 22:19:06.884539: Epoch time: 101.68 s +2026-04-12 22:19:08.047814: +2026-04-12 22:19:08.050012: Epoch 2086 +2026-04-12 22:19:08.052256: Current learning rate: 0.00515 +2026-04-12 22:20:50.275181: train_loss -0.4088 +2026-04-12 22:20:50.281761: val_loss -0.344 +2026-04-12 22:20:50.283925: Pseudo dice [0.3348, 0.8916, 0.6638, 0.3869, 0.5494, 0.3267, 0.7959] +2026-04-12 22:20:50.286712: Epoch time: 102.23 s +2026-04-12 22:20:51.468221: +2026-04-12 22:20:51.470499: Epoch 2087 +2026-04-12 22:20:51.472723: Current learning rate: 0.00515 +2026-04-12 22:22:33.921084: train_loss -0.3963 +2026-04-12 22:22:33.929216: val_loss -0.3359 +2026-04-12 22:22:33.931175: Pseudo dice [0.1664, 0.8538, 0.7789, 0.5457, 0.2241, 0.2132, 0.8095] +2026-04-12 22:22:33.934301: Epoch time: 102.46 s +2026-04-12 22:22:35.083591: +2026-04-12 22:22:35.086406: Epoch 2088 +2026-04-12 22:22:35.088505: Current learning rate: 0.00515 +2026-04-12 22:24:16.807464: train_loss -0.3811 +2026-04-12 22:24:16.814161: val_loss -0.3363 +2026-04-12 22:24:16.816859: Pseudo dice [0.4504, 0.1687, 0.743, 0.4348, 0.5832, 0.4202, 0.6295] +2026-04-12 22:24:16.819705: Epoch time: 101.73 s +2026-04-12 22:24:17.997791: +2026-04-12 22:24:17.999912: Epoch 2089 +2026-04-12 22:24:18.002096: Current learning rate: 0.00514 +2026-04-12 22:26:00.302104: train_loss -0.38 +2026-04-12 22:26:00.309823: val_loss -0.3471 +2026-04-12 22:26:00.311876: Pseudo dice [0.3988, 0.4656, 0.7594, 0.4738, 0.4642, 0.7382, 0.6136] +2026-04-12 22:26:00.314060: Epoch time: 102.31 s +2026-04-12 22:26:01.483076: +2026-04-12 22:26:01.486856: Epoch 2090 +2026-04-12 22:26:01.489745: Current learning rate: 0.00514 +2026-04-12 22:27:43.502132: train_loss -0.3711 +2026-04-12 22:27:43.508994: val_loss -0.3269 +2026-04-12 22:27:43.510965: Pseudo dice [0.738, 0.1815, 0.5963, 0.5465, 0.153, 0.7706, 0.8255] +2026-04-12 22:27:43.513641: Epoch time: 102.02 s +2026-04-12 22:27:44.695867: +2026-04-12 22:27:44.697892: Epoch 2091 +2026-04-12 22:27:44.700165: Current learning rate: 0.00514 +2026-04-12 22:29:26.740211: train_loss -0.3722 +2026-04-12 22:29:26.747280: val_loss -0.3655 +2026-04-12 22:29:26.749506: Pseudo dice [0.7659, 0.5228, 0.786, 0.4366, 0.428, 0.893, 0.7094] +2026-04-12 22:29:26.752009: Epoch time: 102.05 s +2026-04-12 22:29:27.941426: +2026-04-12 22:29:27.943182: Epoch 2092 +2026-04-12 22:29:27.945281: Current learning rate: 0.00514 +2026-04-12 22:31:09.570479: train_loss -0.4026 +2026-04-12 22:31:09.578524: val_loss -0.367 +2026-04-12 22:31:09.580557: Pseudo dice [0.4863, 0.4175, 0.7537, 0.4832, 0.4614, 0.8371, 0.8476] +2026-04-12 22:31:09.584456: Epoch time: 101.63 s +2026-04-12 22:31:10.725461: +2026-04-12 22:31:10.727299: Epoch 2093 +2026-04-12 22:31:10.730062: Current learning rate: 0.00513 +2026-04-12 22:32:52.848927: train_loss -0.4108 +2026-04-12 22:32:52.855112: val_loss -0.3632 +2026-04-12 22:32:52.857077: Pseudo dice [0.5551, 0.7059, 0.7167, 0.363, 0.4619, 0.8379, 0.8534] +2026-04-12 22:32:52.860151: Epoch time: 102.13 s +2026-04-12 22:32:54.035808: +2026-04-12 22:32:54.038004: Epoch 2094 +2026-04-12 22:32:54.040076: Current learning rate: 0.00513 +2026-04-12 22:34:36.754842: train_loss -0.4077 +2026-04-12 22:34:36.761588: val_loss -0.3386 +2026-04-12 22:34:36.763836: Pseudo dice [0.8081, 0.3283, 0.716, 0.1269, 0.2402, 0.5325, 0.7778] +2026-04-12 22:34:36.766534: Epoch time: 102.72 s +2026-04-12 22:34:37.903366: +2026-04-12 22:34:37.905383: Epoch 2095 +2026-04-12 22:34:37.908827: Current learning rate: 0.00513 +2026-04-12 22:36:20.299968: train_loss -0.3908 +2026-04-12 22:36:20.309415: val_loss -0.3103 +2026-04-12 22:36:20.311522: Pseudo dice [0.646, 0.3602, 0.7254, 0.0934, 0.2546, 0.7905, 0.397] +2026-04-12 22:36:20.313782: Epoch time: 102.4 s +2026-04-12 22:36:22.532336: +2026-04-12 22:36:22.534038: Epoch 2096 +2026-04-12 22:36:22.535800: Current learning rate: 0.00513 +2026-04-12 22:38:06.031467: train_loss -0.3713 +2026-04-12 22:38:06.040007: val_loss -0.3295 +2026-04-12 22:38:06.043329: Pseudo dice [0.739, 0.814, 0.617, 0.472, 0.3525, 0.2193, 0.8328] +2026-04-12 22:38:06.046113: Epoch time: 103.5 s +2026-04-12 22:38:07.211759: +2026-04-12 22:38:07.213718: Epoch 2097 +2026-04-12 22:38:07.216669: Current learning rate: 0.00512 +2026-04-12 22:39:49.561420: train_loss -0.3632 +2026-04-12 22:39:49.568031: val_loss -0.3387 +2026-04-12 22:39:49.569758: Pseudo dice [0.5385, 0.2822, 0.7289, 0.1197, 0.3026, 0.8603, 0.8287] +2026-04-12 22:39:49.572369: Epoch time: 102.35 s +2026-04-12 22:39:50.798056: +2026-04-12 22:39:50.800141: Epoch 2098 +2026-04-12 22:39:50.801986: Current learning rate: 0.00512 +2026-04-12 22:41:32.638960: train_loss -0.3864 +2026-04-12 22:41:32.645522: val_loss -0.3648 +2026-04-12 22:41:32.647292: Pseudo dice [0.5918, 0.3719, 0.6247, 0.7616, 0.6277, 0.5447, 0.8136] +2026-04-12 22:41:32.649674: Epoch time: 101.84 s +2026-04-12 22:41:33.815808: +2026-04-12 22:41:33.819152: Epoch 2099 +2026-04-12 22:41:33.821631: Current learning rate: 0.00512 +2026-04-12 22:43:15.923888: train_loss -0.3732 +2026-04-12 22:43:15.932015: val_loss -0.3302 +2026-04-12 22:43:15.934149: Pseudo dice [0.2133, 0.73, 0.7171, 0.2163, 0.5392, 0.8026, 0.2836] +2026-04-12 22:43:15.936471: Epoch time: 102.11 s +2026-04-12 22:43:18.791668: +2026-04-12 22:43:18.794784: Epoch 2100 +2026-04-12 22:43:18.796323: Current learning rate: 0.00512 +2026-04-12 22:45:00.785318: train_loss -0.3936 +2026-04-12 22:45:00.792625: val_loss -0.3643 +2026-04-12 22:45:00.794944: Pseudo dice [0.5129, 0.8467, 0.7959, 0.2981, 0.3413, 0.249, 0.8469] +2026-04-12 22:45:00.797088: Epoch time: 102.0 s +2026-04-12 22:45:02.010563: +2026-04-12 22:45:02.012226: Epoch 2101 +2026-04-12 22:45:02.014306: Current learning rate: 0.00511 +2026-04-12 22:46:44.393883: train_loss -0.3918 +2026-04-12 22:46:44.400884: val_loss -0.3005 +2026-04-12 22:46:44.402749: Pseudo dice [0.7737, 0.4319, 0.5377, 0.037, 0.4219, 0.7329, 0.6353] +2026-04-12 22:46:44.405216: Epoch time: 102.39 s +2026-04-12 22:46:45.557063: +2026-04-12 22:46:45.558632: Epoch 2102 +2026-04-12 22:46:45.561079: Current learning rate: 0.00511 +2026-04-12 22:48:28.460549: train_loss -0.4002 +2026-04-12 22:48:28.467620: val_loss -0.3086 +2026-04-12 22:48:28.469959: Pseudo dice [0.5384, 0.8575, 0.7832, 0.0283, 0.1624, 0.2051, 0.6989] +2026-04-12 22:48:28.471827: Epoch time: 102.91 s +2026-04-12 22:48:29.711924: +2026-04-12 22:48:29.714490: Epoch 2103 +2026-04-12 22:48:29.717038: Current learning rate: 0.00511 +2026-04-12 22:50:11.891075: train_loss -0.3981 +2026-04-12 22:50:11.897191: val_loss -0.3467 +2026-04-12 22:50:11.899608: Pseudo dice [0.4222, 0.8835, 0.7631, 0.2671, 0.6099, 0.4364, 0.7879] +2026-04-12 22:50:11.902241: Epoch time: 102.18 s +2026-04-12 22:50:13.090918: +2026-04-12 22:50:13.102522: Epoch 2104 +2026-04-12 22:50:13.112881: Current learning rate: 0.00511 +2026-04-12 22:51:55.532656: train_loss -0.4017 +2026-04-12 22:51:55.539493: val_loss -0.3807 +2026-04-12 22:51:55.541546: Pseudo dice [0.5263, 0.5597, 0.6131, 0.5476, 0.6404, 0.2982, 0.4782] +2026-04-12 22:51:55.543934: Epoch time: 102.44 s +2026-04-12 22:51:56.710505: +2026-04-12 22:51:56.712155: Epoch 2105 +2026-04-12 22:51:56.714020: Current learning rate: 0.0051 +2026-04-12 22:53:39.030021: train_loss -0.3764 +2026-04-12 22:53:39.037880: val_loss -0.3611 +2026-04-12 22:53:39.039798: Pseudo dice [0.7835, 0.8735, 0.7339, 0.2097, 0.5763, 0.2411, 0.628] +2026-04-12 22:53:39.042155: Epoch time: 102.32 s +2026-04-12 22:53:40.191960: +2026-04-12 22:53:40.201212: Epoch 2106 +2026-04-12 22:53:40.203659: Current learning rate: 0.0051 +2026-04-12 22:55:22.214109: train_loss -0.406 +2026-04-12 22:55:22.221310: val_loss -0.2846 +2026-04-12 22:55:22.223233: Pseudo dice [0.6269, 0.7536, 0.6313, 0.2017, 0.39, 0.1768, 0.5619] +2026-04-12 22:55:22.225568: Epoch time: 102.03 s +2026-04-12 22:55:23.379841: +2026-04-12 22:55:23.382695: Epoch 2107 +2026-04-12 22:55:23.385312: Current learning rate: 0.0051 +2026-04-12 22:57:05.226780: train_loss -0.3773 +2026-04-12 22:57:05.238982: val_loss -0.3535 +2026-04-12 22:57:05.241122: Pseudo dice [0.5647, 0.6169, 0.7586, 0.8221, 0.3124, 0.7192, 0.7916] +2026-04-12 22:57:05.243604: Epoch time: 101.85 s +2026-04-12 22:57:06.383400: +2026-04-12 22:57:06.385242: Epoch 2108 +2026-04-12 22:57:06.388827: Current learning rate: 0.0051 +2026-04-12 22:58:48.638131: train_loss -0.3789 +2026-04-12 22:58:48.646919: val_loss -0.364 +2026-04-12 22:58:48.649173: Pseudo dice [0.8414, 0.6089, 0.7159, 0.4909, 0.261, 0.9034, 0.7549] +2026-04-12 22:58:48.651672: Epoch time: 102.26 s +2026-04-12 22:58:49.813862: +2026-04-12 22:58:49.815525: Epoch 2109 +2026-04-12 22:58:49.817351: Current learning rate: 0.0051 +2026-04-12 23:00:32.154095: train_loss -0.4001 +2026-04-12 23:00:32.169978: val_loss -0.3607 +2026-04-12 23:00:32.172258: Pseudo dice [0.3804, 0.84, 0.7365, 0.7528, 0.4702, 0.6002, 0.7189] +2026-04-12 23:00:32.174726: Epoch time: 102.34 s +2026-04-12 23:00:33.330722: +2026-04-12 23:00:33.332902: Epoch 2110 +2026-04-12 23:00:33.335353: Current learning rate: 0.00509 +2026-04-12 23:02:15.245994: train_loss -0.4036 +2026-04-12 23:02:15.253530: val_loss -0.3932 +2026-04-12 23:02:15.255391: Pseudo dice [0.5843, 0.7841, 0.7944, 0.8405, 0.6308, 0.5457, 0.6917] +2026-04-12 23:02:15.258180: Epoch time: 101.92 s +2026-04-12 23:02:16.429199: +2026-04-12 23:02:16.430943: Epoch 2111 +2026-04-12 23:02:16.433243: Current learning rate: 0.00509 +2026-04-12 23:03:58.778846: train_loss -0.378 +2026-04-12 23:03:58.784972: val_loss -0.3493 +2026-04-12 23:03:58.787357: Pseudo dice [0.3611, 0.8767, 0.7247, 0.5713, 0.3244, 0.7244, 0.8039] +2026-04-12 23:03:58.789588: Epoch time: 102.35 s +2026-04-12 23:03:59.965519: +2026-04-12 23:03:59.967548: Epoch 2112 +2026-04-12 23:03:59.969871: Current learning rate: 0.00509 +2026-04-12 23:05:42.400123: train_loss -0.405 +2026-04-12 23:05:42.408718: val_loss -0.3449 +2026-04-12 23:05:42.410992: Pseudo dice [0.4141, 0.6883, 0.667, 0.4433, 0.4253, 0.772, 0.5711] +2026-04-12 23:05:42.414742: Epoch time: 102.44 s +2026-04-12 23:05:43.549195: +2026-04-12 23:05:43.551273: Epoch 2113 +2026-04-12 23:05:43.553727: Current learning rate: 0.00509 +2026-04-12 23:07:26.227762: train_loss -0.3709 +2026-04-12 23:07:26.235170: val_loss -0.3315 +2026-04-12 23:07:26.237304: Pseudo dice [0.5716, 0.8915, 0.7654, 0.7031, 0.4883, 0.7044, 0.7649] +2026-04-12 23:07:26.240077: Epoch time: 102.68 s +2026-04-12 23:07:27.388788: +2026-04-12 23:07:27.390564: Epoch 2114 +2026-04-12 23:07:27.392613: Current learning rate: 0.00508 +2026-04-12 23:09:09.511224: train_loss -0.3825 +2026-04-12 23:09:09.517658: val_loss -0.3565 +2026-04-12 23:09:09.520246: Pseudo dice [0.4999, 0.4841, 0.7515, 0.5135, 0.4338, 0.8366, 0.8241] +2026-04-12 23:09:09.522559: Epoch time: 102.13 s +2026-04-12 23:09:10.677914: +2026-04-12 23:09:10.680744: Epoch 2115 +2026-04-12 23:09:10.682833: Current learning rate: 0.00508 +2026-04-12 23:10:52.884868: train_loss -0.4073 +2026-04-12 23:10:52.890355: val_loss -0.3314 +2026-04-12 23:10:52.892013: Pseudo dice [0.647, 0.5905, 0.7168, 0.4316, 0.4783, 0.7077, 0.7276] +2026-04-12 23:10:52.893883: Epoch time: 102.21 s +2026-04-12 23:10:54.052079: +2026-04-12 23:10:54.054348: Epoch 2116 +2026-04-12 23:10:54.056690: Current learning rate: 0.00508 +2026-04-12 23:12:36.771421: train_loss -0.4069 +2026-04-12 23:12:36.787468: val_loss -0.3537 +2026-04-12 23:12:36.793070: Pseudo dice [0.3978, 0.8554, 0.7613, 0.1411, 0.5762, 0.8005, 0.8338] +2026-04-12 23:12:36.795491: Epoch time: 102.72 s +2026-04-12 23:12:37.969806: +2026-04-12 23:12:37.971694: Epoch 2117 +2026-04-12 23:12:37.973639: Current learning rate: 0.00508 +2026-04-12 23:14:20.326962: train_loss -0.4028 +2026-04-12 23:14:20.333361: val_loss -0.3168 +2026-04-12 23:14:20.335778: Pseudo dice [0.463, 0.6915, 0.5143, 0.2571, 0.4458, 0.2825, 0.7236] +2026-04-12 23:14:20.338988: Epoch time: 102.36 s +2026-04-12 23:14:21.507029: +2026-04-12 23:14:21.509101: Epoch 2118 +2026-04-12 23:14:21.511572: Current learning rate: 0.00507 +2026-04-12 23:16:03.148992: train_loss -0.3634 +2026-04-12 23:16:03.157570: val_loss -0.3394 +2026-04-12 23:16:03.160078: Pseudo dice [0.3842, 0.6078, 0.7817, 0.2429, 0.52, 0.553, 0.7771] +2026-04-12 23:16:03.162508: Epoch time: 101.65 s +2026-04-12 23:16:04.317492: +2026-04-12 23:16:04.324289: Epoch 2119 +2026-04-12 23:16:04.329783: Current learning rate: 0.00507 +2026-04-12 23:17:46.289421: train_loss -0.3966 +2026-04-12 23:17:46.295616: val_loss -0.339 +2026-04-12 23:17:46.297479: Pseudo dice [0.3747, 0.6227, 0.7372, 0.2562, 0.5157, 0.8557, 0.7944] +2026-04-12 23:17:46.299666: Epoch time: 101.98 s +2026-04-12 23:17:47.461202: +2026-04-12 23:17:47.463512: Epoch 2120 +2026-04-12 23:17:47.465714: Current learning rate: 0.00507 +2026-04-12 23:19:30.138281: train_loss -0.4061 +2026-04-12 23:19:30.145267: val_loss -0.3261 +2026-04-12 23:19:30.147108: Pseudo dice [0.3322, 0.8853, 0.7392, 0.3826, 0.2786, 0.7069, 0.8154] +2026-04-12 23:19:30.149765: Epoch time: 102.68 s +2026-04-12 23:19:31.317093: +2026-04-12 23:19:31.319263: Epoch 2121 +2026-04-12 23:19:31.321359: Current learning rate: 0.00507 +2026-04-12 23:21:12.973393: train_loss -0.4005 +2026-04-12 23:21:12.979633: val_loss -0.3446 +2026-04-12 23:21:12.982419: Pseudo dice [0.6876, 0.5216, 0.7611, 0.2047, 0.4165, 0.8185, 0.6012] +2026-04-12 23:21:12.985061: Epoch time: 101.66 s +2026-04-12 23:21:14.166267: +2026-04-12 23:21:14.168143: Epoch 2122 +2026-04-12 23:21:14.170071: Current learning rate: 0.00506 +2026-04-12 23:22:56.229367: train_loss -0.3962 +2026-04-12 23:22:56.236740: val_loss -0.3661 +2026-04-12 23:22:56.239365: Pseudo dice [0.5144, 0.8819, 0.7651, 0.8292, 0.3352, 0.7644, 0.8205] +2026-04-12 23:22:56.242117: Epoch time: 102.07 s +2026-04-12 23:22:57.401359: +2026-04-12 23:22:57.403347: Epoch 2123 +2026-04-12 23:22:57.407260: Current learning rate: 0.00506 +2026-04-12 23:24:38.939729: train_loss -0.4101 +2026-04-12 23:24:38.946830: val_loss -0.3468 +2026-04-12 23:24:38.948799: Pseudo dice [0.7803, 0.5634, 0.6941, 0.2866, 0.596, 0.8556, 0.7487] +2026-04-12 23:24:38.950956: Epoch time: 101.54 s +2026-04-12 23:24:40.137052: +2026-04-12 23:24:40.139150: Epoch 2124 +2026-04-12 23:24:40.141491: Current learning rate: 0.00506 +2026-04-12 23:26:22.651023: train_loss -0.3998 +2026-04-12 23:26:22.657740: val_loss -0.3635 +2026-04-12 23:26:22.660086: Pseudo dice [0.9119, 0.6043, 0.7722, 0.7976, 0.547, 0.9206, 0.7316] +2026-04-12 23:26:22.662702: Epoch time: 102.52 s +2026-04-12 23:26:23.801040: +2026-04-12 23:26:23.802876: Epoch 2125 +2026-04-12 23:26:23.804745: Current learning rate: 0.00506 +2026-04-12 23:28:06.130076: train_loss -0.3922 +2026-04-12 23:28:06.136281: val_loss -0.3475 +2026-04-12 23:28:06.138068: Pseudo dice [0.5241, 0.4631, 0.7316, 0.4075, 0.5198, 0.9396, 0.8275] +2026-04-12 23:28:06.140349: Epoch time: 102.33 s +2026-04-12 23:28:07.306487: +2026-04-12 23:28:07.309251: Epoch 2126 +2026-04-12 23:28:07.312168: Current learning rate: 0.00505 +2026-04-12 23:29:49.278661: train_loss -0.3876 +2026-04-12 23:29:49.310755: val_loss -0.3568 +2026-04-12 23:29:49.313200: Pseudo dice [0.3031, 0.2944, 0.7185, 0.4698, 0.5206, 0.4983, 0.8024] +2026-04-12 23:29:49.316169: Epoch time: 101.98 s +2026-04-12 23:29:50.478142: +2026-04-12 23:29:50.482690: Epoch 2127 +2026-04-12 23:29:50.486005: Current learning rate: 0.00505 +2026-04-12 23:31:32.591233: train_loss -0.3879 +2026-04-12 23:31:32.597808: val_loss -0.3748 +2026-04-12 23:31:32.600847: Pseudo dice [0.6181, 0.5803, 0.7631, 0.099, 0.5532, 0.8327, 0.8404] +2026-04-12 23:31:32.603153: Epoch time: 102.12 s +2026-04-12 23:31:33.788134: +2026-04-12 23:31:33.789938: Epoch 2128 +2026-04-12 23:31:33.792667: Current learning rate: 0.00505 +2026-04-12 23:33:15.805052: train_loss -0.3855 +2026-04-12 23:33:15.812302: val_loss -0.343 +2026-04-12 23:33:15.815028: Pseudo dice [0.3287, 0.6103, 0.5519, 0.3178, 0.4596, 0.5494, 0.618] +2026-04-12 23:33:15.817826: Epoch time: 102.02 s +2026-04-12 23:33:16.970516: +2026-04-12 23:33:16.972916: Epoch 2129 +2026-04-12 23:33:16.975465: Current learning rate: 0.00505 +2026-04-12 23:34:59.331169: train_loss -0.3753 +2026-04-12 23:34:59.340266: val_loss -0.3069 +2026-04-12 23:34:59.343127: Pseudo dice [0.5182, 0.5371, 0.6768, 0.0009, 0.2358, 0.8013, 0.4419] +2026-04-12 23:34:59.345717: Epoch time: 102.36 s +2026-04-12 23:35:00.537629: +2026-04-12 23:35:00.539740: Epoch 2130 +2026-04-12 23:35:00.541667: Current learning rate: 0.00504 +2026-04-12 23:36:42.776231: train_loss -0.3676 +2026-04-12 23:36:42.786197: val_loss -0.2554 +2026-04-12 23:36:42.789708: Pseudo dice [0.7395, 0.8205, 0.7996, 0.0694, 0.2042, 0.3502, 0.0856] +2026-04-12 23:36:42.792142: Epoch time: 102.24 s +2026-04-12 23:36:43.956551: +2026-04-12 23:36:43.958395: Epoch 2131 +2026-04-12 23:36:43.960673: Current learning rate: 0.00504 +2026-04-12 23:38:26.131169: train_loss -0.3633 +2026-04-12 23:38:26.139290: val_loss -0.3427 +2026-04-12 23:38:26.141105: Pseudo dice [0.3821, 0.5438, 0.7219, 0.6551, 0.2598, 0.5866, 0.7735] +2026-04-12 23:38:26.143914: Epoch time: 102.18 s +2026-04-12 23:38:27.290526: +2026-04-12 23:38:27.292293: Epoch 2132 +2026-04-12 23:38:27.294496: Current learning rate: 0.00504 +2026-04-12 23:40:09.589475: train_loss -0.341 +2026-04-12 23:40:09.597316: val_loss -0.2965 +2026-04-12 23:40:09.599330: Pseudo dice [0.6142, 0.894, 0.6377, 0.3292, 0.2802, 0.0747, 0.7291] +2026-04-12 23:40:09.601152: Epoch time: 102.3 s +2026-04-12 23:40:10.765900: +2026-04-12 23:40:10.767978: Epoch 2133 +2026-04-12 23:40:10.770085: Current learning rate: 0.00504 +2026-04-12 23:41:53.756151: train_loss -0.3805 +2026-04-12 23:41:53.763460: val_loss -0.3336 +2026-04-12 23:41:53.766144: Pseudo dice [0.4581, 0.787, 0.6917, 0.7685, 0.4043, 0.743, 0.6214] +2026-04-12 23:41:53.768338: Epoch time: 102.99 s +2026-04-12 23:41:54.912162: +2026-04-12 23:41:54.914832: Epoch 2134 +2026-04-12 23:41:54.917170: Current learning rate: 0.00503 +2026-04-12 23:43:37.687715: train_loss -0.3833 +2026-04-12 23:43:37.693894: val_loss -0.3188 +2026-04-12 23:43:37.696092: Pseudo dice [0.6346, 0.7866, 0.5584, 0.5464, 0.5298, 0.0748, 0.6599] +2026-04-12 23:43:37.698256: Epoch time: 102.78 s +2026-04-12 23:43:38.848549: +2026-04-12 23:43:38.850880: Epoch 2135 +2026-04-12 23:43:38.852971: Current learning rate: 0.00503 +2026-04-12 23:45:20.808847: train_loss -0.39 +2026-04-12 23:45:20.816661: val_loss -0.3515 +2026-04-12 23:45:20.819994: Pseudo dice [0.4791, 0.1617, 0.6903, 0.4916, 0.479, 0.4667, 0.8415] +2026-04-12 23:45:20.822553: Epoch time: 101.96 s +2026-04-12 23:45:21.980779: +2026-04-12 23:45:21.982770: Epoch 2136 +2026-04-12 23:45:21.984871: Current learning rate: 0.00503 +2026-04-12 23:47:03.667694: train_loss -0.3791 +2026-04-12 23:47:03.674664: val_loss -0.3306 +2026-04-12 23:47:03.676938: Pseudo dice [0.8055, 0.2344, 0.6644, 0.3306, 0.5858, 0.1391, 0.5402] +2026-04-12 23:47:03.679501: Epoch time: 101.69 s +2026-04-12 23:47:05.892539: +2026-04-12 23:47:05.894251: Epoch 2137 +2026-04-12 23:47:05.896244: Current learning rate: 0.00503 +2026-04-12 23:48:49.473413: train_loss -0.3702 +2026-04-12 23:48:49.479556: val_loss -0.3247 +2026-04-12 23:48:49.481775: Pseudo dice [0.4458, 0.6835, 0.5343, 0.3744, 0.354, 0.7387, 0.8105] +2026-04-12 23:48:49.484012: Epoch time: 103.58 s +2026-04-12 23:48:50.619621: +2026-04-12 23:48:50.621892: Epoch 2138 +2026-04-12 23:48:50.624184: Current learning rate: 0.00502 +2026-04-12 23:50:32.931878: train_loss -0.357 +2026-04-12 23:50:32.938743: val_loss -0.3411 +2026-04-12 23:50:32.941048: Pseudo dice [0.5804, 0.5561, 0.7824, 0.7252, 0.1505, 0.8987, 0.7942] +2026-04-12 23:50:32.943779: Epoch time: 102.32 s +2026-04-12 23:50:34.107489: +2026-04-12 23:50:34.111419: Epoch 2139 +2026-04-12 23:50:34.114342: Current learning rate: 0.00502 +2026-04-12 23:52:16.669187: train_loss -0.3847 +2026-04-12 23:52:16.675753: val_loss -0.332 +2026-04-12 23:52:16.678481: Pseudo dice [0.3722, 0.7655, 0.5587, 0.4713, 0.3555, 0.5615, 0.6504] +2026-04-12 23:52:16.680875: Epoch time: 102.56 s +2026-04-12 23:52:17.857632: +2026-04-12 23:52:17.861531: Epoch 2140 +2026-04-12 23:52:17.863961: Current learning rate: 0.00502 +2026-04-12 23:54:00.195314: train_loss -0.3836 +2026-04-12 23:54:00.203168: val_loss -0.335 +2026-04-12 23:54:00.205402: Pseudo dice [0.3957, 0.6478, 0.6997, 0.2833, 0.4219, 0.8669, 0.6365] +2026-04-12 23:54:00.207707: Epoch time: 102.34 s +2026-04-12 23:54:01.372215: +2026-04-12 23:54:01.374024: Epoch 2141 +2026-04-12 23:54:01.375985: Current learning rate: 0.00502 +2026-04-12 23:55:43.485577: train_loss -0.3809 +2026-04-12 23:55:43.493828: val_loss -0.3331 +2026-04-12 23:55:43.495996: Pseudo dice [0.3102, 0.6336, 0.7444, 0.4771, 0.3725, 0.5594, 0.4381] +2026-04-12 23:55:43.498513: Epoch time: 102.12 s +2026-04-12 23:55:44.629979: +2026-04-12 23:55:44.633148: Epoch 2142 +2026-04-12 23:55:44.636340: Current learning rate: 0.00502 +2026-04-12 23:57:26.666121: train_loss -0.394 +2026-04-12 23:57:26.673334: val_loss -0.3654 +2026-04-12 23:57:26.675496: Pseudo dice [0.6307, 0.5014, 0.747, 0.1487, 0.4912, 0.5578, 0.7824] +2026-04-12 23:57:26.677774: Epoch time: 102.04 s +2026-04-12 23:57:27.843559: +2026-04-12 23:57:27.845570: Epoch 2143 +2026-04-12 23:57:27.847888: Current learning rate: 0.00501 +2026-04-12 23:59:10.527888: train_loss -0.3995 +2026-04-12 23:59:10.533461: val_loss -0.3312 +2026-04-12 23:59:10.541044: Pseudo dice [0.6786, 0.7573, 0.7098, 0.3308, 0.4311, 0.6329, 0.4784] +2026-04-12 23:59:10.543683: Epoch time: 102.69 s +2026-04-12 23:59:11.724545: +2026-04-12 23:59:11.729659: Epoch 2144 +2026-04-12 23:59:11.731552: Current learning rate: 0.00501 +2026-04-13 00:00:55.673937: train_loss -0.4046 +2026-04-13 00:00:55.689302: val_loss -0.3597 +2026-04-13 00:00:55.692078: Pseudo dice [0.7662, 0.3553, 0.758, 0.5124, 0.2955, 0.6995, 0.8297] +2026-04-13 00:00:55.699003: Epoch time: 103.95 s +2026-04-13 00:00:56.861719: +2026-04-13 00:00:56.863783: Epoch 2145 +2026-04-13 00:00:56.866125: Current learning rate: 0.00501 +2026-04-13 00:02:40.163794: train_loss -0.3704 +2026-04-13 00:02:40.171339: val_loss -0.324 +2026-04-13 00:02:40.174438: Pseudo dice [0.7714, 0.5593, 0.7086, 0.0507, 0.3696, 0.5035, 0.7558] +2026-04-13 00:02:40.177216: Epoch time: 103.31 s +2026-04-13 00:02:41.351287: +2026-04-13 00:02:41.354481: Epoch 2146 +2026-04-13 00:02:41.356388: Current learning rate: 0.00501 +2026-04-13 00:04:25.126631: train_loss -0.3968 +2026-04-13 00:04:25.135686: val_loss -0.3691 +2026-04-13 00:04:25.137694: Pseudo dice [0.5569, 0.6742, 0.8228, 0.4688, 0.3646, 0.478, 0.658] +2026-04-13 00:04:25.143509: Epoch time: 103.78 s +2026-04-13 00:04:26.346999: +2026-04-13 00:04:26.350534: Epoch 2147 +2026-04-13 00:04:26.353736: Current learning rate: 0.005 +2026-04-13 00:06:08.923434: train_loss -0.3995 +2026-04-13 00:06:08.928892: val_loss -0.3684 +2026-04-13 00:06:08.931525: Pseudo dice [0.6252, 0.4527, 0.6905, 0.4376, 0.1782, 0.6092, 0.7067] +2026-04-13 00:06:08.934872: Epoch time: 102.58 s +2026-04-13 00:06:10.119353: +2026-04-13 00:06:10.121352: Epoch 2148 +2026-04-13 00:06:10.123610: Current learning rate: 0.005 +2026-04-13 00:07:53.252050: train_loss -0.4028 +2026-04-13 00:07:53.258969: val_loss -0.3337 +2026-04-13 00:07:53.260865: Pseudo dice [0.6696, 0.6956, 0.6927, 0.7023, 0.514, 0.2679, 0.3675] +2026-04-13 00:07:53.264915: Epoch time: 103.14 s +2026-04-13 00:07:54.431960: +2026-04-13 00:07:54.436019: Epoch 2149 +2026-04-13 00:07:54.438133: Current learning rate: 0.005 +2026-04-13 00:09:37.474242: train_loss -0.3841 +2026-04-13 00:09:37.484705: val_loss -0.3392 +2026-04-13 00:09:37.486748: Pseudo dice [0.3003, 0.6541, 0.7994, 0.5876, 0.1585, 0.7651, 0.7244] +2026-04-13 00:09:37.489218: Epoch time: 103.05 s +2026-04-13 00:09:40.422493: +2026-04-13 00:09:40.425446: Epoch 2150 +2026-04-13 00:09:40.427099: Current learning rate: 0.005 +2026-04-13 00:11:23.207675: train_loss -0.3963 +2026-04-13 00:11:23.220217: val_loss -0.3518 +2026-04-13 00:11:23.222129: Pseudo dice [0.6082, 0.8046, 0.6938, 0.4481, 0.601, 0.9235, 0.7352] +2026-04-13 00:11:23.228202: Epoch time: 102.79 s +2026-04-13 00:11:24.413110: +2026-04-13 00:11:24.415921: Epoch 2151 +2026-04-13 00:11:24.417516: Current learning rate: 0.00499 +2026-04-13 00:13:07.866186: train_loss -0.412 +2026-04-13 00:13:07.873312: val_loss -0.3839 +2026-04-13 00:13:07.875913: Pseudo dice [0.8119, 0.7931, 0.7954, 0.7996, 0.4432, 0.2453, 0.807] +2026-04-13 00:13:07.878335: Epoch time: 103.46 s +2026-04-13 00:13:09.017795: +2026-04-13 00:13:09.020764: Epoch 2152 +2026-04-13 00:13:09.022490: Current learning rate: 0.00499 +2026-04-13 00:14:52.106589: train_loss -0.4117 +2026-04-13 00:14:52.113938: val_loss -0.3599 +2026-04-13 00:14:52.115809: Pseudo dice [0.8923, 0.4569, 0.7139, 0.0616, 0.6159, 0.9178, 0.7184] +2026-04-13 00:14:52.118618: Epoch time: 103.09 s +2026-04-13 00:14:53.262176: +2026-04-13 00:14:53.264094: Epoch 2153 +2026-04-13 00:14:53.266381: Current learning rate: 0.00499 +2026-04-13 00:16:36.153896: train_loss -0.405 +2026-04-13 00:16:36.159905: val_loss -0.3483 +2026-04-13 00:16:36.162065: Pseudo dice [0.4999, 0.8656, 0.7603, 0.8186, 0.0791, 0.1175, 0.747] +2026-04-13 00:16:36.164444: Epoch time: 102.89 s +2026-04-13 00:16:37.339135: +2026-04-13 00:16:37.342292: Epoch 2154 +2026-04-13 00:16:37.344715: Current learning rate: 0.00499 +2026-04-13 00:18:19.831994: train_loss -0.4051 +2026-04-13 00:18:19.840696: val_loss -0.3653 +2026-04-13 00:18:19.843989: Pseudo dice [0.371, 0.8218, 0.7725, 0.841, 0.188, 0.8161, 0.7862] +2026-04-13 00:18:19.847907: Epoch time: 102.5 s +2026-04-13 00:18:21.012318: +2026-04-13 00:18:21.014857: Epoch 2155 +2026-04-13 00:18:21.016442: Current learning rate: 0.00498 +2026-04-13 00:20:03.143397: train_loss -0.4094 +2026-04-13 00:20:03.149555: val_loss -0.3805 +2026-04-13 00:20:03.151813: Pseudo dice [0.6065, 0.4433, 0.7945, 0.5137, 0.4352, 0.7471, 0.8328] +2026-04-13 00:20:03.154068: Epoch time: 102.13 s +2026-04-13 00:20:04.306335: +2026-04-13 00:20:04.308416: Epoch 2156 +2026-04-13 00:20:04.311788: Current learning rate: 0.00498 +2026-04-13 00:21:47.403473: train_loss -0.3981 +2026-04-13 00:21:47.409737: val_loss -0.3178 +2026-04-13 00:21:47.411578: Pseudo dice [0.6198, 0.3226, 0.769, 0.3893, 0.2953, 0.7763, 0.5816] +2026-04-13 00:21:47.413658: Epoch time: 103.1 s +2026-04-13 00:21:48.550881: +2026-04-13 00:21:48.554284: Epoch 2157 +2026-04-13 00:21:48.556132: Current learning rate: 0.00498 +2026-04-13 00:23:32.450661: train_loss -0.4036 +2026-04-13 00:23:32.457160: val_loss -0.3599 +2026-04-13 00:23:32.459260: Pseudo dice [0.4755, 0.136, 0.7581, 0.6063, 0.1804, 0.9281, 0.7736] +2026-04-13 00:23:32.461665: Epoch time: 103.9 s +2026-04-13 00:23:33.644329: +2026-04-13 00:23:33.646540: Epoch 2158 +2026-04-13 00:23:33.649483: Current learning rate: 0.00498 +2026-04-13 00:25:15.968734: train_loss -0.4051 +2026-04-13 00:25:15.974669: val_loss -0.3556 +2026-04-13 00:25:15.976799: Pseudo dice [0.8106, 0.8149, 0.7959, 0.26, 0.3804, 0.4338, 0.5842] +2026-04-13 00:25:15.979001: Epoch time: 102.33 s +2026-04-13 00:25:17.131156: +2026-04-13 00:25:17.132965: Epoch 2159 +2026-04-13 00:25:17.134724: Current learning rate: 0.00497 +2026-04-13 00:26:59.070679: train_loss -0.3906 +2026-04-13 00:26:59.079249: val_loss -0.3583 +2026-04-13 00:26:59.082258: Pseudo dice [0.2846, 0.4712, 0.7603, 0.1343, 0.5275, 0.7655, 0.7263] +2026-04-13 00:26:59.085851: Epoch time: 101.94 s +2026-04-13 00:27:00.258414: +2026-04-13 00:27:00.260220: Epoch 2160 +2026-04-13 00:27:00.262156: Current learning rate: 0.00497 +2026-04-13 00:28:41.954040: train_loss -0.3998 +2026-04-13 00:28:41.961043: val_loss -0.3143 +2026-04-13 00:28:41.964395: Pseudo dice [0.2301, 0.202, 0.6303, 0.4083, 0.5634, 0.7139, 0.8584] +2026-04-13 00:28:41.967526: Epoch time: 101.7 s +2026-04-13 00:28:43.130350: +2026-04-13 00:28:43.134115: Epoch 2161 +2026-04-13 00:28:43.136496: Current learning rate: 0.00497 +2026-04-13 00:30:25.049788: train_loss -0.3857 +2026-04-13 00:30:25.056831: val_loss -0.2992 +2026-04-13 00:30:25.059042: Pseudo dice [0.5576, 0.8791, 0.5397, 0.2644, 0.5102, 0.2277, 0.7163] +2026-04-13 00:30:25.062405: Epoch time: 101.92 s +2026-04-13 00:30:26.251948: +2026-04-13 00:30:26.253500: Epoch 2162 +2026-04-13 00:30:26.255141: Current learning rate: 0.00497 +2026-04-13 00:32:08.111888: train_loss -0.3899 +2026-04-13 00:32:08.118194: val_loss -0.3525 +2026-04-13 00:32:08.120360: Pseudo dice [0.7462, 0.7041, 0.8181, 0.4385, 0.1972, 0.8179, 0.4608] +2026-04-13 00:32:08.123186: Epoch time: 101.86 s +2026-04-13 00:32:09.283939: +2026-04-13 00:32:09.285693: Epoch 2163 +2026-04-13 00:32:09.287499: Current learning rate: 0.00496 +2026-04-13 00:33:50.629640: train_loss -0.4064 +2026-04-13 00:33:50.635798: val_loss -0.3673 +2026-04-13 00:33:50.637777: Pseudo dice [0.2632, 0.7349, 0.6914, 0.6827, 0.6137, 0.9167, 0.7663] +2026-04-13 00:33:50.640834: Epoch time: 101.35 s +2026-04-13 00:33:51.793400: +2026-04-13 00:33:51.795648: Epoch 2164 +2026-04-13 00:33:51.797474: Current learning rate: 0.00496 +2026-04-13 00:35:33.311066: train_loss -0.3821 +2026-04-13 00:35:33.317046: val_loss -0.3254 +2026-04-13 00:35:33.319606: Pseudo dice [0.4732, 0.8656, 0.63, 0.3201, 0.4196, 0.6256, 0.802] +2026-04-13 00:35:33.321715: Epoch time: 101.52 s +2026-04-13 00:35:34.470071: +2026-04-13 00:35:34.472241: Epoch 2165 +2026-04-13 00:35:34.474905: Current learning rate: 0.00496 +2026-04-13 00:37:15.696604: train_loss -0.4051 +2026-04-13 00:37:15.702623: val_loss -0.3703 +2026-04-13 00:37:15.704379: Pseudo dice [0.5858, 0.5695, 0.767, 0.8445, 0.5517, 0.8152, 0.6778] +2026-04-13 00:37:15.706349: Epoch time: 101.23 s +2026-04-13 00:37:16.867087: +2026-04-13 00:37:16.868649: Epoch 2166 +2026-04-13 00:37:16.870486: Current learning rate: 0.00496 +2026-04-13 00:38:59.037593: train_loss -0.408 +2026-04-13 00:38:59.044725: val_loss -0.3595 +2026-04-13 00:38:59.046707: Pseudo dice [0.5196, 0.8234, 0.806, 0.5492, 0.4817, 0.6885, 0.7407] +2026-04-13 00:38:59.050784: Epoch time: 102.17 s +2026-04-13 00:39:00.275687: +2026-04-13 00:39:00.277665: Epoch 2167 +2026-04-13 00:39:00.280149: Current learning rate: 0.00495 +2026-04-13 00:40:41.886768: train_loss -0.4019 +2026-04-13 00:40:41.893288: val_loss -0.3758 +2026-04-13 00:40:41.895453: Pseudo dice [0.7489, 0.5114, 0.7199, 0.5379, 0.5145, 0.7811, 0.7036] +2026-04-13 00:40:41.897668: Epoch time: 101.61 s +2026-04-13 00:40:43.049397: +2026-04-13 00:40:43.052626: Epoch 2168 +2026-04-13 00:40:43.056911: Current learning rate: 0.00495 +2026-04-13 00:42:24.476838: train_loss -0.3992 +2026-04-13 00:42:24.483722: val_loss -0.3746 +2026-04-13 00:42:24.485355: Pseudo dice [0.759, 0.2144, 0.6088, 0.4687, 0.7456, 0.8251, 0.6358] +2026-04-13 00:42:24.487662: Epoch time: 101.43 s +2026-04-13 00:42:25.649756: +2026-04-13 00:42:25.651474: Epoch 2169 +2026-04-13 00:42:25.653538: Current learning rate: 0.00495 +2026-04-13 00:44:07.059849: train_loss -0.3965 +2026-04-13 00:44:07.065943: val_loss -0.3539 +2026-04-13 00:44:07.068067: Pseudo dice [0.7242, 0.8615, 0.6765, 0.6753, 0.5079, 0.6526, 0.6661] +2026-04-13 00:44:07.070163: Epoch time: 101.41 s +2026-04-13 00:44:08.228352: +2026-04-13 00:44:08.229938: Epoch 2170 +2026-04-13 00:44:08.231678: Current learning rate: 0.00495 +2026-04-13 00:45:50.249081: train_loss -0.395 +2026-04-13 00:45:50.257468: val_loss -0.3644 +2026-04-13 00:45:50.259784: Pseudo dice [0.4941, 0.5583, 0.8136, 0.4431, 0.6087, 0.4338, 0.6164] +2026-04-13 00:45:50.262533: Epoch time: 102.02 s +2026-04-13 00:45:51.418141: +2026-04-13 00:45:51.421085: Epoch 2171 +2026-04-13 00:45:51.422814: Current learning rate: 0.00494 +2026-04-13 00:47:33.020090: train_loss -0.3959 +2026-04-13 00:47:33.028850: val_loss -0.3849 +2026-04-13 00:47:33.031349: Pseudo dice [0.2898, 0.5307, 0.7028, 0.5577, 0.533, 0.7593, 0.7254] +2026-04-13 00:47:33.033421: Epoch time: 101.6 s +2026-04-13 00:47:34.179788: +2026-04-13 00:47:34.182207: Epoch 2172 +2026-04-13 00:47:34.184207: Current learning rate: 0.00494 +2026-04-13 00:49:15.883496: train_loss -0.3975 +2026-04-13 00:49:15.889250: val_loss -0.3463 +2026-04-13 00:49:15.891845: Pseudo dice [0.4466, 0.4377, 0.7923, 0.5463, 0.543, 0.6778, 0.6292] +2026-04-13 00:49:15.894329: Epoch time: 101.71 s +2026-04-13 00:49:17.063083: +2026-04-13 00:49:17.064862: Epoch 2173 +2026-04-13 00:49:17.066654: Current learning rate: 0.00494 +2026-04-13 00:51:00.174027: train_loss -0.3982 +2026-04-13 00:51:00.183283: val_loss -0.3707 +2026-04-13 00:51:00.188125: Pseudo dice [0.4447, 0.6363, 0.8093, 0.7737, 0.2773, 0.8064, 0.7369] +2026-04-13 00:51:00.190605: Epoch time: 103.11 s +2026-04-13 00:51:01.336536: +2026-04-13 00:51:01.342049: Epoch 2174 +2026-04-13 00:51:01.344603: Current learning rate: 0.00494 +2026-04-13 00:52:43.016082: train_loss -0.364 +2026-04-13 00:52:43.021773: val_loss -0.3315 +2026-04-13 00:52:43.024005: Pseudo dice [0.4428, 0.4617, 0.6356, 0.1112, 0.2834, 0.705, 0.7676] +2026-04-13 00:52:43.026161: Epoch time: 101.68 s +2026-04-13 00:52:44.174134: +2026-04-13 00:52:44.175833: Epoch 2175 +2026-04-13 00:52:44.177531: Current learning rate: 0.00493 +2026-04-13 00:54:25.923416: train_loss -0.3757 +2026-04-13 00:54:25.929204: val_loss -0.3566 +2026-04-13 00:54:25.931365: Pseudo dice [0.5124, 0.7484, 0.8418, 0.6289, 0.2501, 0.7812, 0.198] +2026-04-13 00:54:25.933464: Epoch time: 101.75 s +2026-04-13 00:54:27.081637: +2026-04-13 00:54:27.084025: Epoch 2176 +2026-04-13 00:54:27.085695: Current learning rate: 0.00493 +2026-04-13 00:56:08.728132: train_loss -0.4028 +2026-04-13 00:56:08.732891: val_loss -0.3515 +2026-04-13 00:56:08.735521: Pseudo dice [0.5825, 0.6167, 0.7204, 0.0679, 0.2645, 0.6663, 0.588] +2026-04-13 00:56:08.737695: Epoch time: 101.65 s +2026-04-13 00:56:09.894928: +2026-04-13 00:56:09.896538: Epoch 2177 +2026-04-13 00:56:09.898008: Current learning rate: 0.00493 +2026-04-13 00:57:52.067809: train_loss -0.383 +2026-04-13 00:57:52.074959: val_loss -0.3568 +2026-04-13 00:57:52.077123: Pseudo dice [0.4092, 0.2585, 0.7645, 0.2588, 0.4069, 0.7916, 0.6552] +2026-04-13 00:57:52.079904: Epoch time: 102.18 s +2026-04-13 00:57:53.231347: +2026-04-13 00:57:53.233294: Epoch 2178 +2026-04-13 00:57:53.234721: Current learning rate: 0.00493 +2026-04-13 00:59:36.139010: train_loss -0.3934 +2026-04-13 00:59:36.145342: val_loss -0.362 +2026-04-13 00:59:36.147389: Pseudo dice [0.3832, 0.5433, 0.7146, 0.2978, 0.4867, 0.8992, 0.7572] +2026-04-13 00:59:36.150476: Epoch time: 102.91 s +2026-04-13 00:59:37.320089: +2026-04-13 00:59:37.322079: Epoch 2179 +2026-04-13 00:59:37.323811: Current learning rate: 0.00493 +2026-04-13 01:01:19.083301: train_loss -0.4105 +2026-04-13 01:01:19.088979: val_loss -0.3819 +2026-04-13 01:01:19.091242: Pseudo dice [0.339, 0.3849, 0.7427, 0.532, 0.624, 0.935, 0.7532] +2026-04-13 01:01:19.094275: Epoch time: 101.77 s +2026-04-13 01:01:20.236866: +2026-04-13 01:01:20.238554: Epoch 2180 +2026-04-13 01:01:20.239948: Current learning rate: 0.00492 +2026-04-13 01:03:02.541621: train_loss -0.3998 +2026-04-13 01:03:02.548228: val_loss -0.378 +2026-04-13 01:03:02.550246: Pseudo dice [0.3902, 0.717, 0.715, 0.8634, 0.4624, 0.6745, 0.4091] +2026-04-13 01:03:02.553624: Epoch time: 102.31 s +2026-04-13 01:03:03.696468: +2026-04-13 01:03:03.698210: Epoch 2181 +2026-04-13 01:03:03.699553: Current learning rate: 0.00492 +2026-04-13 01:04:45.695474: train_loss -0.4191 +2026-04-13 01:04:45.701671: val_loss -0.354 +2026-04-13 01:04:45.703742: Pseudo dice [0.6338, 0.8884, 0.7505, 0.3676, 0.4341, 0.3181, 0.4561] +2026-04-13 01:04:45.706260: Epoch time: 102.0 s +2026-04-13 01:04:46.908072: +2026-04-13 01:04:46.910264: Epoch 2182 +2026-04-13 01:04:46.911996: Current learning rate: 0.00492 +2026-04-13 01:06:28.769455: train_loss -0.4266 +2026-04-13 01:06:28.777335: val_loss -0.3419 +2026-04-13 01:06:28.779761: Pseudo dice [0.5472, 0.1763, 0.6412, 0.4448, 0.5935, 0.5347, 0.6038] +2026-04-13 01:06:28.783440: Epoch time: 101.86 s +2026-04-13 01:06:29.957779: +2026-04-13 01:06:29.959702: Epoch 2183 +2026-04-13 01:06:29.961206: Current learning rate: 0.00492 +2026-04-13 01:08:11.924880: train_loss -0.371 +2026-04-13 01:08:11.930530: val_loss -0.3516 +2026-04-13 01:08:11.932802: Pseudo dice [0.7152, 0.2208, 0.811, 0.2263, 0.318, 0.5608, 0.6636] +2026-04-13 01:08:11.935549: Epoch time: 101.97 s +2026-04-13 01:08:13.083436: +2026-04-13 01:08:13.086720: Epoch 2184 +2026-04-13 01:08:13.089296: Current learning rate: 0.00491 +2026-04-13 01:09:54.899611: train_loss -0.3942 +2026-04-13 01:09:54.907230: val_loss -0.3264 +2026-04-13 01:09:54.909389: Pseudo dice [0.7765, 0.6161, 0.5717, 0.6776, 0.6862, 0.2661, 0.7825] +2026-04-13 01:09:54.911552: Epoch time: 101.82 s +2026-04-13 01:09:56.083530: +2026-04-13 01:09:56.085778: Epoch 2185 +2026-04-13 01:09:56.087802: Current learning rate: 0.00491 +2026-04-13 01:11:38.705291: train_loss -0.3843 +2026-04-13 01:11:38.713086: val_loss -0.3395 +2026-04-13 01:11:38.715691: Pseudo dice [0.6071, 0.5296, 0.6727, 0.4419, 0.6203, 0.5125, 0.2988] +2026-04-13 01:11:38.718699: Epoch time: 102.62 s +2026-04-13 01:11:39.896331: +2026-04-13 01:11:39.900478: Epoch 2186 +2026-04-13 01:11:39.905239: Current learning rate: 0.00491 +2026-04-13 01:13:21.459397: train_loss -0.3924 +2026-04-13 01:13:21.469307: val_loss -0.3605 +2026-04-13 01:13:21.474274: Pseudo dice [0.6743, 0.2562, 0.7424, 0.1413, 0.3565, 0.7265, 0.7018] +2026-04-13 01:13:21.477589: Epoch time: 101.57 s +2026-04-13 01:13:22.635989: +2026-04-13 01:13:22.638161: Epoch 2187 +2026-04-13 01:13:22.640212: Current learning rate: 0.00491 +2026-04-13 01:15:04.101020: train_loss -0.416 +2026-04-13 01:15:04.109642: val_loss -0.3021 +2026-04-13 01:15:04.112171: Pseudo dice [0.3079, 0.7295, 0.5378, 0.1247, 0.3687, 0.3868, 0.5673] +2026-04-13 01:15:04.115576: Epoch time: 101.47 s +2026-04-13 01:15:05.277949: +2026-04-13 01:15:05.279893: Epoch 2188 +2026-04-13 01:15:05.281470: Current learning rate: 0.0049 +2026-04-13 01:16:47.300144: train_loss -0.4049 +2026-04-13 01:16:47.323265: val_loss -0.333 +2026-04-13 01:16:47.325329: Pseudo dice [0.7881, 0.6294, 0.7593, 0.1738, 0.2135, 0.7223, 0.4836] +2026-04-13 01:16:47.331895: Epoch time: 102.03 s +2026-04-13 01:16:48.483329: +2026-04-13 01:16:48.485057: Epoch 2189 +2026-04-13 01:16:48.487052: Current learning rate: 0.0049 +2026-04-13 01:18:30.388617: train_loss -0.4048 +2026-04-13 01:18:30.395583: val_loss -0.3556 +2026-04-13 01:18:30.398844: Pseudo dice [0.5676, 0.21, 0.7453, 0.5238, 0.2867, 0.6578, 0.7221] +2026-04-13 01:18:30.401010: Epoch time: 101.91 s +2026-04-13 01:18:31.576546: +2026-04-13 01:18:31.578291: Epoch 2190 +2026-04-13 01:18:31.579743: Current learning rate: 0.0049 +2026-04-13 01:20:13.956949: train_loss -0.4013 +2026-04-13 01:20:13.963641: val_loss -0.3445 +2026-04-13 01:20:13.965638: Pseudo dice [0.6674, 0.8842, 0.7735, 0.1911, 0.5048, 0.7251, 0.7441] +2026-04-13 01:20:13.967950: Epoch time: 102.38 s +2026-04-13 01:20:15.143287: +2026-04-13 01:20:15.144825: Epoch 2191 +2026-04-13 01:20:15.146347: Current learning rate: 0.0049 +2026-04-13 01:21:57.145876: train_loss -0.4067 +2026-04-13 01:21:57.153140: val_loss -0.3276 +2026-04-13 01:21:57.154822: Pseudo dice [0.6633, 0.7346, 0.7402, 0.1376, 0.4444, 0.9022, 0.6926] +2026-04-13 01:21:57.157465: Epoch time: 102.01 s +2026-04-13 01:21:58.313090: +2026-04-13 01:21:58.315180: Epoch 2192 +2026-04-13 01:21:58.316794: Current learning rate: 0.00489 +2026-04-13 01:23:40.391716: train_loss -0.4057 +2026-04-13 01:23:40.399829: val_loss -0.356 +2026-04-13 01:23:40.402369: Pseudo dice [0.4763, 0.3604, 0.6613, 0.3245, 0.4068, 0.3592, 0.4663] +2026-04-13 01:23:40.404903: Epoch time: 102.08 s +2026-04-13 01:23:41.559134: +2026-04-13 01:23:41.561307: Epoch 2193 +2026-04-13 01:23:41.563035: Current learning rate: 0.00489 +2026-04-13 01:25:23.269630: train_loss -0.4026 +2026-04-13 01:25:23.275527: val_loss -0.338 +2026-04-13 01:25:23.277731: Pseudo dice [0.6823, 0.5686, 0.7256, 0.2051, 0.3871, 0.7799, 0.3046] +2026-04-13 01:25:23.279827: Epoch time: 101.71 s +2026-04-13 01:25:24.423781: +2026-04-13 01:25:24.425746: Epoch 2194 +2026-04-13 01:25:24.427278: Current learning rate: 0.00489 +2026-04-13 01:27:06.511067: train_loss -0.392 +2026-04-13 01:27:06.518618: val_loss -0.3318 +2026-04-13 01:27:06.521247: Pseudo dice [0.8043, 0.5189, 0.7408, 0.025, 0.2481, 0.8565, 0.5612] +2026-04-13 01:27:06.524370: Epoch time: 102.09 s +2026-04-13 01:27:07.691057: +2026-04-13 01:27:07.693616: Epoch 2195 +2026-04-13 01:27:07.695542: Current learning rate: 0.00489 +2026-04-13 01:28:49.699863: train_loss -0.3838 +2026-04-13 01:28:49.705684: val_loss -0.3647 +2026-04-13 01:28:49.707816: Pseudo dice [0.5161, 0.7826, 0.724, 0.2706, 0.4993, 0.8442, 0.7863] +2026-04-13 01:28:49.709818: Epoch time: 102.01 s +2026-04-13 01:28:50.862618: +2026-04-13 01:28:50.864520: Epoch 2196 +2026-04-13 01:28:50.867366: Current learning rate: 0.00488 +2026-04-13 01:30:32.749205: train_loss -0.397 +2026-04-13 01:30:32.755301: val_loss -0.3584 +2026-04-13 01:30:32.757625: Pseudo dice [0.7967, 0.7939, 0.7482, 0.7101, 0.511, 0.5325, 0.3227] +2026-04-13 01:30:32.759782: Epoch time: 101.89 s +2026-04-13 01:30:33.921728: +2026-04-13 01:30:33.923804: Epoch 2197 +2026-04-13 01:30:33.925910: Current learning rate: 0.00488 +2026-04-13 01:32:15.509475: train_loss -0.4072 +2026-04-13 01:32:15.516652: val_loss -0.3473 +2026-04-13 01:32:15.518805: Pseudo dice [0.426, 0.8095, 0.6939, 0.464, 0.4449, 0.68, 0.8058] +2026-04-13 01:32:15.521084: Epoch time: 101.59 s +2026-04-13 01:32:16.688444: +2026-04-13 01:32:16.690449: Epoch 2198 +2026-04-13 01:32:16.692036: Current learning rate: 0.00488 +2026-04-13 01:33:58.768693: train_loss -0.3988 +2026-04-13 01:33:58.774604: val_loss -0.3188 +2026-04-13 01:33:58.777279: Pseudo dice [0.8527, 0.8966, 0.6581, 0.5319, 0.5316, 0.6462, 0.768] +2026-04-13 01:33:58.779818: Epoch time: 102.08 s +2026-04-13 01:34:00.970295: +2026-04-13 01:34:00.971961: Epoch 2199 +2026-04-13 01:34:00.973438: Current learning rate: 0.00488 +2026-04-13 01:35:43.090723: train_loss -0.4073 +2026-04-13 01:35:43.097307: val_loss -0.367 +2026-04-13 01:35:43.099564: Pseudo dice [0.5739, 0.4699, 0.8066, 0.6336, 0.5056, 0.5499, 0.3526] +2026-04-13 01:35:43.102022: Epoch time: 102.12 s +2026-04-13 01:35:45.965086: +2026-04-13 01:35:45.967763: Epoch 2200 +2026-04-13 01:35:45.969123: Current learning rate: 0.00487 +2026-04-13 01:37:28.154390: train_loss -0.3336 +2026-04-13 01:37:28.160137: val_loss -0.2878 +2026-04-13 01:37:28.162167: Pseudo dice [0.6299, 0.8702, 0.5262, 0.0121, 0.3666, 0.2674, 0.5976] +2026-04-13 01:37:28.164968: Epoch time: 102.19 s +2026-04-13 01:37:29.322417: +2026-04-13 01:37:29.324999: Epoch 2201 +2026-04-13 01:37:29.328601: Current learning rate: 0.00487 +2026-04-13 01:39:11.211319: train_loss -0.3599 +2026-04-13 01:39:11.216951: val_loss -0.3729 +2026-04-13 01:39:11.219491: Pseudo dice [0.7523, 0.258, 0.6368, 0.5544, 0.4803, 0.5865, 0.7673] +2026-04-13 01:39:11.221413: Epoch time: 101.89 s +2026-04-13 01:39:12.374034: +2026-04-13 01:39:12.376079: Epoch 2202 +2026-04-13 01:39:12.378737: Current learning rate: 0.00487 +2026-04-13 01:40:54.350674: train_loss -0.3699 +2026-04-13 01:40:54.357661: val_loss -0.3209 +2026-04-13 01:40:54.360833: Pseudo dice [0.4046, 0.3178, 0.666, 0.5184, 0.3568, 0.5749, 0.6519] +2026-04-13 01:40:54.370878: Epoch time: 101.98 s +2026-04-13 01:40:55.536379: +2026-04-13 01:40:55.539098: Epoch 2203 +2026-04-13 01:40:55.541342: Current learning rate: 0.00487 +2026-04-13 01:42:37.184310: train_loss -0.3924 +2026-04-13 01:42:37.210270: val_loss -0.3568 +2026-04-13 01:42:37.213022: Pseudo dice [0.677, 0.7653, 0.8207, 0.3639, 0.3614, 0.6159, 0.2938] +2026-04-13 01:42:37.215061: Epoch time: 101.65 s +2026-04-13 01:42:38.371691: +2026-04-13 01:42:38.373428: Epoch 2204 +2026-04-13 01:42:38.375451: Current learning rate: 0.00486 +2026-04-13 01:44:20.827766: train_loss -0.3805 +2026-04-13 01:44:20.833228: val_loss -0.2966 +2026-04-13 01:44:20.835296: Pseudo dice [0.2946, 0.7958, 0.4644, 0.5025, 0.3186, 0.6071, 0.3999] +2026-04-13 01:44:20.837165: Epoch time: 102.46 s +2026-04-13 01:44:22.020891: +2026-04-13 01:44:22.023178: Epoch 2205 +2026-04-13 01:44:22.024861: Current learning rate: 0.00486 +2026-04-13 01:46:03.778112: train_loss -0.3996 +2026-04-13 01:46:03.784377: val_loss -0.378 +2026-04-13 01:46:03.786366: Pseudo dice [0.4555, 0.6763, 0.6926, 0.5363, 0.4394, 0.8371, 0.7669] +2026-04-13 01:46:03.788295: Epoch time: 101.76 s +2026-04-13 01:46:04.995768: +2026-04-13 01:46:04.998232: Epoch 2206 +2026-04-13 01:46:05.001375: Current learning rate: 0.00486 +2026-04-13 01:47:47.166369: train_loss -0.3932 +2026-04-13 01:47:47.173430: val_loss -0.351 +2026-04-13 01:47:47.175654: Pseudo dice [0.6283, 0.2938, 0.7882, 0.2085, 0.6358, 0.7886, 0.7485] +2026-04-13 01:47:47.179057: Epoch time: 102.17 s +2026-04-13 01:47:48.327508: +2026-04-13 01:47:48.329241: Epoch 2207 +2026-04-13 01:47:48.331345: Current learning rate: 0.00486 +2026-04-13 01:49:30.445757: train_loss -0.3809 +2026-04-13 01:49:30.454093: val_loss -0.3241 +2026-04-13 01:49:30.456625: Pseudo dice [0.4837, 0.6613, 0.6317, 0.1728, 0.5089, 0.687, 0.4338] +2026-04-13 01:49:30.459033: Epoch time: 102.12 s +2026-04-13 01:49:31.621964: +2026-04-13 01:49:31.624125: Epoch 2208 +2026-04-13 01:49:31.625668: Current learning rate: 0.00485 +2026-04-13 01:51:13.396869: train_loss -0.3945 +2026-04-13 01:51:13.404567: val_loss -0.3368 +2026-04-13 01:51:13.407998: Pseudo dice [0.3862, 0.4568, 0.5589, 0.4354, 0.442, 0.9175, 0.782] +2026-04-13 01:51:13.412627: Epoch time: 101.78 s +2026-04-13 01:51:14.574574: +2026-04-13 01:51:14.576018: Epoch 2209 +2026-04-13 01:51:14.577367: Current learning rate: 0.00485 +2026-04-13 01:52:56.425408: train_loss -0.385 +2026-04-13 01:52:56.432206: val_loss -0.3607 +2026-04-13 01:52:56.434234: Pseudo dice [0.5323, 0.8869, 0.788, 0.2967, 0.4989, 0.7135, 0.7872] +2026-04-13 01:52:56.437251: Epoch time: 101.85 s +2026-04-13 01:52:57.592717: +2026-04-13 01:52:57.595096: Epoch 2210 +2026-04-13 01:52:57.596673: Current learning rate: 0.00485 +2026-04-13 01:54:39.253396: train_loss -0.3796 +2026-04-13 01:54:39.260105: val_loss -0.3284 +2026-04-13 01:54:39.262645: Pseudo dice [0.2566, 0.4405, 0.6729, 0.2367, 0.4207, 0.8117, 0.4862] +2026-04-13 01:54:39.264704: Epoch time: 101.66 s +2026-04-13 01:54:40.435147: +2026-04-13 01:54:40.437581: Epoch 2211 +2026-04-13 01:54:40.439189: Current learning rate: 0.00485 +2026-04-13 01:56:22.529969: train_loss -0.3861 +2026-04-13 01:56:22.535696: val_loss -0.3705 +2026-04-13 01:56:22.537802: Pseudo dice [0.6626, 0.5858, 0.7232, 0.2336, 0.4193, 0.6771, 0.4539] +2026-04-13 01:56:22.539766: Epoch time: 102.1 s +2026-04-13 01:56:23.674248: +2026-04-13 01:56:23.675853: Epoch 2212 +2026-04-13 01:56:23.677286: Current learning rate: 0.00484 +2026-04-13 01:58:05.608458: train_loss -0.3913 +2026-04-13 01:58:05.615238: val_loss -0.3317 +2026-04-13 01:58:05.617101: Pseudo dice [0.452, 0.624, 0.5866, 0.2793, 0.2186, 0.6894, 0.7923] +2026-04-13 01:58:05.620233: Epoch time: 101.94 s +2026-04-13 01:58:06.777612: +2026-04-13 01:58:06.779536: Epoch 2213 +2026-04-13 01:58:06.781214: Current learning rate: 0.00484 +2026-04-13 01:59:48.860297: train_loss -0.3975 +2026-04-13 01:59:48.868775: val_loss -0.3003 +2026-04-13 01:59:48.871304: Pseudo dice [0.2535, 0.1619, 0.5447, 0.3099, 0.3939, 0.2477, 0.6592] +2026-04-13 01:59:48.874167: Epoch time: 102.09 s +2026-04-13 01:59:50.047527: +2026-04-13 01:59:50.049290: Epoch 2214 +2026-04-13 01:59:50.050885: Current learning rate: 0.00484 +2026-04-13 02:01:31.536900: train_loss -0.3868 +2026-04-13 02:01:31.544074: val_loss -0.3496 +2026-04-13 02:01:31.545980: Pseudo dice [0.6609, 0.7971, 0.7367, 0.3459, 0.4777, 0.6762, 0.8052] +2026-04-13 02:01:31.548664: Epoch time: 101.49 s +2026-04-13 02:01:32.726795: +2026-04-13 02:01:32.729846: Epoch 2215 +2026-04-13 02:01:32.732101: Current learning rate: 0.00484 +2026-04-13 02:03:14.405861: train_loss -0.3927 +2026-04-13 02:03:14.415259: val_loss -0.3444 +2026-04-13 02:03:14.417539: Pseudo dice [0.3242, 0.2356, 0.7306, 0.0057, 0.4705, 0.9099, 0.662] +2026-04-13 02:03:14.422237: Epoch time: 101.68 s +2026-04-13 02:03:15.589167: +2026-04-13 02:03:15.590838: Epoch 2216 +2026-04-13 02:03:15.592329: Current learning rate: 0.00484 +2026-04-13 02:04:57.006506: train_loss -0.3834 +2026-04-13 02:04:57.014993: val_loss -0.3597 +2026-04-13 02:04:57.028301: Pseudo dice [0.514, 0.7057, 0.6554, 0.1591, 0.6085, 0.8529, 0.6149] +2026-04-13 02:04:57.030487: Epoch time: 101.42 s +2026-04-13 02:04:58.177497: +2026-04-13 02:04:58.179438: Epoch 2217 +2026-04-13 02:04:58.183418: Current learning rate: 0.00483 +2026-04-13 02:06:40.025122: train_loss -0.4146 +2026-04-13 02:06:40.031035: val_loss -0.3828 +2026-04-13 02:06:40.032865: Pseudo dice [0.722, 0.8878, 0.8184, 0.551, 0.5497, 0.8457, 0.8603] +2026-04-13 02:06:40.035335: Epoch time: 101.85 s +2026-04-13 02:06:41.231341: +2026-04-13 02:06:41.232872: Epoch 2218 +2026-04-13 02:06:41.234292: Current learning rate: 0.00483 +2026-04-13 02:08:22.769056: train_loss -0.4208 +2026-04-13 02:08:22.775568: val_loss -0.3705 +2026-04-13 02:08:22.777667: Pseudo dice [0.7933, 0.7865, 0.7998, 0.0979, 0.6063, 0.8798, 0.6395] +2026-04-13 02:08:22.780804: Epoch time: 101.54 s +2026-04-13 02:08:23.935532: +2026-04-13 02:08:23.937531: Epoch 2219 +2026-04-13 02:08:23.939010: Current learning rate: 0.00483 +2026-04-13 02:10:05.910597: train_loss -0.4083 +2026-04-13 02:10:05.918581: val_loss -0.2577 +2026-04-13 02:10:05.920720: Pseudo dice [0.4889, 0.7775, 0.4658, 0.1673, 0.5901, 0.6208, 0.6586] +2026-04-13 02:10:05.922950: Epoch time: 101.98 s +2026-04-13 02:10:08.189886: +2026-04-13 02:10:08.191646: Epoch 2220 +2026-04-13 02:10:08.193232: Current learning rate: 0.00483 +2026-04-13 02:11:49.745759: train_loss -0.3836 +2026-04-13 02:11:49.752374: val_loss -0.3076 +2026-04-13 02:11:49.754466: Pseudo dice [0.553, 0.8834, 0.4182, 0.5556, 0.4019, 0.679, 0.7909] +2026-04-13 02:11:49.756822: Epoch time: 101.56 s +2026-04-13 02:11:50.929066: +2026-04-13 02:11:50.932839: Epoch 2221 +2026-04-13 02:11:50.935173: Current learning rate: 0.00482 +2026-04-13 02:13:32.664821: train_loss -0.3697 +2026-04-13 02:13:32.670499: val_loss -0.2839 +2026-04-13 02:13:32.672986: Pseudo dice [0.5603, 0.6583, 0.4835, 0.1202, 0.4018, 0.8259, 0.6311] +2026-04-13 02:13:32.675604: Epoch time: 101.74 s +2026-04-13 02:13:33.860821: +2026-04-13 02:13:33.862772: Epoch 2222 +2026-04-13 02:13:33.864344: Current learning rate: 0.00482 +2026-04-13 02:15:16.072305: train_loss -0.3722 +2026-04-13 02:15:16.078722: val_loss -0.3371 +2026-04-13 02:15:16.080541: Pseudo dice [0.6798, 0.5762, 0.7048, 0.717, 0.3175, 0.7746, 0.7862] +2026-04-13 02:15:16.087111: Epoch time: 102.21 s +2026-04-13 02:15:17.252624: +2026-04-13 02:15:17.254432: Epoch 2223 +2026-04-13 02:15:17.256088: Current learning rate: 0.00482 +2026-04-13 02:16:58.823503: train_loss -0.3958 +2026-04-13 02:16:58.830574: val_loss -0.3544 +2026-04-13 02:16:58.833305: Pseudo dice [0.7101, 0.8851, 0.6855, 0.6909, 0.4988, 0.6231, 0.4253] +2026-04-13 02:16:58.836878: Epoch time: 101.57 s +2026-04-13 02:16:59.981597: +2026-04-13 02:16:59.983326: Epoch 2224 +2026-04-13 02:16:59.984798: Current learning rate: 0.00482 +2026-04-13 02:18:42.625780: train_loss -0.4058 +2026-04-13 02:18:42.632597: val_loss -0.3387 +2026-04-13 02:18:42.635323: Pseudo dice [0.7449, 0.8917, 0.7933, 0.0489, 0.3526, 0.7756, 0.6749] +2026-04-13 02:18:42.637477: Epoch time: 102.65 s +2026-04-13 02:18:43.801737: +2026-04-13 02:18:43.803960: Epoch 2225 +2026-04-13 02:18:43.806898: Current learning rate: 0.00481 +2026-04-13 02:20:25.463929: train_loss -0.4045 +2026-04-13 02:20:25.470136: val_loss -0.3719 +2026-04-13 02:20:25.472667: Pseudo dice [0.7333, 0.9019, 0.7755, 0.3639, 0.5279, 0.3079, 0.6758] +2026-04-13 02:20:25.475807: Epoch time: 101.67 s +2026-04-13 02:20:26.630324: +2026-04-13 02:20:26.631985: Epoch 2226 +2026-04-13 02:20:26.633909: Current learning rate: 0.00481 +2026-04-13 02:22:08.156391: train_loss -0.3979 +2026-04-13 02:22:08.163639: val_loss -0.3471 +2026-04-13 02:22:08.166494: Pseudo dice [0.5527, 0.561, 0.6813, 0.757, 0.5464, 0.829, 0.8482] +2026-04-13 02:22:08.168745: Epoch time: 101.53 s +2026-04-13 02:22:09.321754: +2026-04-13 02:22:09.323255: Epoch 2227 +2026-04-13 02:22:09.324710: Current learning rate: 0.00481 +2026-04-13 02:23:50.872293: train_loss -0.3732 +2026-04-13 02:23:50.881136: val_loss -0.3668 +2026-04-13 02:23:50.884845: Pseudo dice [0.6372, 0.3052, 0.7983, 0.5494, 0.422, 0.8447, 0.6738] +2026-04-13 02:23:50.887616: Epoch time: 101.55 s +2026-04-13 02:23:52.063116: +2026-04-13 02:23:52.064822: Epoch 2228 +2026-04-13 02:23:52.066908: Current learning rate: 0.00481 +2026-04-13 02:25:34.064556: train_loss -0.3986 +2026-04-13 02:25:34.071531: val_loss -0.3821 +2026-04-13 02:25:34.074008: Pseudo dice [0.7367, 0.5758, 0.7207, 0.7119, 0.3724, 0.9084, 0.8344] +2026-04-13 02:25:34.076551: Epoch time: 102.0 s +2026-04-13 02:25:35.236079: +2026-04-13 02:25:35.238115: Epoch 2229 +2026-04-13 02:25:35.239666: Current learning rate: 0.0048 +2026-04-13 02:27:17.047981: train_loss -0.4072 +2026-04-13 02:27:17.054479: val_loss -0.3721 +2026-04-13 02:27:17.056525: Pseudo dice [0.7752, 0.4853, 0.7102, 0.4768, 0.4112, 0.7031, 0.5685] +2026-04-13 02:27:17.058802: Epoch time: 101.81 s +2026-04-13 02:27:18.252556: +2026-04-13 02:27:18.254610: Epoch 2230 +2026-04-13 02:27:18.256275: Current learning rate: 0.0048 +2026-04-13 02:29:00.289228: train_loss -0.4025 +2026-04-13 02:29:00.295433: val_loss -0.388 +2026-04-13 02:29:00.297411: Pseudo dice [0.6557, 0.4045, 0.7714, 0.6554, 0.4794, 0.8897, 0.67] +2026-04-13 02:29:00.299665: Epoch time: 102.04 s +2026-04-13 02:29:01.508022: +2026-04-13 02:29:01.509725: Epoch 2231 +2026-04-13 02:29:01.511144: Current learning rate: 0.0048 +2026-04-13 02:30:42.986352: train_loss -0.4135 +2026-04-13 02:30:42.992381: val_loss -0.3309 +2026-04-13 02:30:42.994771: Pseudo dice [0.3556, 0.3128, 0.8049, 0.6899, 0.3576, 0.823, 0.783] +2026-04-13 02:30:42.996840: Epoch time: 101.48 s +2026-04-13 02:30:44.157592: +2026-04-13 02:30:44.159368: Epoch 2232 +2026-04-13 02:30:44.160948: Current learning rate: 0.0048 +2026-04-13 02:32:25.769804: train_loss -0.3833 +2026-04-13 02:32:25.777800: val_loss -0.339 +2026-04-13 02:32:25.779858: Pseudo dice [0.5199, 0.4462, 0.7095, 0.4704, 0.6474, 0.4932, 0.7359] +2026-04-13 02:32:25.782311: Epoch time: 101.62 s +2026-04-13 02:32:26.938959: +2026-04-13 02:32:26.940530: Epoch 2233 +2026-04-13 02:32:26.941907: Current learning rate: 0.00479 +2026-04-13 02:34:08.917937: train_loss -0.4018 +2026-04-13 02:34:08.924904: val_loss -0.3505 +2026-04-13 02:34:08.927108: Pseudo dice [0.5442, 0.9152, 0.7198, 0.3568, 0.5732, 0.1436, 0.7529] +2026-04-13 02:34:08.929106: Epoch time: 101.98 s +2026-04-13 02:34:10.088807: +2026-04-13 02:34:10.093794: Epoch 2234 +2026-04-13 02:34:10.095367: Current learning rate: 0.00479 +2026-04-13 02:35:51.438005: train_loss -0.385 +2026-04-13 02:35:51.446098: val_loss -0.3606 +2026-04-13 02:35:51.448261: Pseudo dice [0.4101, 0.9027, 0.7069, 0.5259, 0.5126, 0.458, 0.8365] +2026-04-13 02:35:51.451872: Epoch time: 101.35 s +2026-04-13 02:35:52.613667: +2026-04-13 02:35:52.615228: Epoch 2235 +2026-04-13 02:35:52.616607: Current learning rate: 0.00479 +2026-04-13 02:37:34.211888: train_loss -0.4047 +2026-04-13 02:37:34.218166: val_loss -0.34 +2026-04-13 02:37:34.220305: Pseudo dice [0.6366, 0.3584, 0.6999, 0.2454, 0.5545, 0.5208, 0.6785] +2026-04-13 02:37:34.222341: Epoch time: 101.6 s +2026-04-13 02:37:35.402278: +2026-04-13 02:37:35.404037: Epoch 2236 +2026-04-13 02:37:35.405582: Current learning rate: 0.00479 +2026-04-13 02:39:17.191951: train_loss -0.3968 +2026-04-13 02:39:17.199079: val_loss -0.3753 +2026-04-13 02:39:17.202482: Pseudo dice [0.585, 0.6468, 0.7708, 0.4835, 0.4768, 0.4592, 0.8346] +2026-04-13 02:39:17.204860: Epoch time: 101.79 s +2026-04-13 02:39:18.369224: +2026-04-13 02:39:18.371673: Epoch 2237 +2026-04-13 02:39:18.374012: Current learning rate: 0.00478 +2026-04-13 02:41:00.125746: train_loss -0.4144 +2026-04-13 02:41:00.139067: val_loss -0.3571 +2026-04-13 02:41:00.146431: Pseudo dice [0.8085, 0.8872, 0.8435, 0.7478, 0.54, 0.3735, 0.6471] +2026-04-13 02:41:00.148890: Epoch time: 101.76 s +2026-04-13 02:41:01.319583: +2026-04-13 02:41:01.321074: Epoch 2238 +2026-04-13 02:41:01.322539: Current learning rate: 0.00478 +2026-04-13 02:42:43.299917: train_loss -0.3999 +2026-04-13 02:42:43.326429: val_loss -0.3488 +2026-04-13 02:42:43.329198: Pseudo dice [0.793, 0.3441, 0.7288, 0.764, 0.3737, 0.8045, 0.7275] +2026-04-13 02:42:43.331652: Epoch time: 101.98 s +2026-04-13 02:42:44.490154: +2026-04-13 02:42:44.492103: Epoch 2239 +2026-04-13 02:42:44.495144: Current learning rate: 0.00478 +2026-04-13 02:44:26.278409: train_loss -0.3899 +2026-04-13 02:44:26.284129: val_loss -0.351 +2026-04-13 02:44:26.285991: Pseudo dice [0.6716, 0.8754, 0.4902, 0.3578, 0.5778, 0.836, 0.5711] +2026-04-13 02:44:26.287881: Epoch time: 101.79 s +2026-04-13 02:44:27.423425: +2026-04-13 02:44:27.425102: Epoch 2240 +2026-04-13 02:44:27.426621: Current learning rate: 0.00478 +2026-04-13 02:46:10.352612: train_loss -0.389 +2026-04-13 02:46:10.358677: val_loss -0.3283 +2026-04-13 02:46:10.361518: Pseudo dice [0.2104, 0.0386, 0.7516, 0.0927, 0.5073, 0.9152, 0.6685] +2026-04-13 02:46:10.364029: Epoch time: 102.93 s +2026-04-13 02:46:11.511875: +2026-04-13 02:46:11.513767: Epoch 2241 +2026-04-13 02:46:11.516493: Current learning rate: 0.00477 +2026-04-13 02:47:53.175838: train_loss -0.3698 +2026-04-13 02:47:53.182380: val_loss -0.334 +2026-04-13 02:47:53.184357: Pseudo dice [0.2226, 0.7363, 0.7752, 0.1782, 0.4035, 0.8532, 0.3191] +2026-04-13 02:47:53.187883: Epoch time: 101.67 s +2026-04-13 02:47:54.344528: +2026-04-13 02:47:54.346869: Epoch 2242 +2026-04-13 02:47:54.348413: Current learning rate: 0.00477 +2026-04-13 02:49:36.700206: train_loss -0.3829 +2026-04-13 02:49:36.706173: val_loss -0.3405 +2026-04-13 02:49:36.707908: Pseudo dice [0.7609, 0.614, 0.7, 0.0766, 0.3206, 0.2762, 0.7644] +2026-04-13 02:49:36.710643: Epoch time: 102.36 s +2026-04-13 02:49:37.907278: +2026-04-13 02:49:37.909336: Epoch 2243 +2026-04-13 02:49:37.911124: Current learning rate: 0.00477 +2026-04-13 02:51:19.518768: train_loss -0.386 +2026-04-13 02:51:19.524395: val_loss -0.3512 +2026-04-13 02:51:19.526392: Pseudo dice [0.3857, 0.4157, 0.7263, 0.6389, 0.4385, 0.5169, 0.6005] +2026-04-13 02:51:19.528616: Epoch time: 101.61 s +2026-04-13 02:51:20.704313: +2026-04-13 02:51:20.706284: Epoch 2244 +2026-04-13 02:51:20.708497: Current learning rate: 0.00477 +2026-04-13 02:53:02.423620: train_loss -0.3921 +2026-04-13 02:53:02.432151: val_loss -0.3579 +2026-04-13 02:53:02.434230: Pseudo dice [0.4552, 0.8925, 0.6674, 0.4999, 0.3912, 0.6836, 0.7023] +2026-04-13 02:53:02.436881: Epoch time: 101.72 s +2026-04-13 02:53:03.574964: +2026-04-13 02:53:03.576805: Epoch 2245 +2026-04-13 02:53:03.578287: Current learning rate: 0.00476 +2026-04-13 02:54:45.294421: train_loss -0.4013 +2026-04-13 02:54:45.300541: val_loss -0.3842 +2026-04-13 02:54:45.302391: Pseudo dice [0.6809, 0.6426, 0.7324, 0.6007, 0.2395, 0.4831, 0.7912] +2026-04-13 02:54:45.304567: Epoch time: 101.72 s +2026-04-13 02:54:46.461357: +2026-04-13 02:54:46.463619: Epoch 2246 +2026-04-13 02:54:46.465045: Current learning rate: 0.00476 +2026-04-13 02:56:29.088339: train_loss -0.408 +2026-04-13 02:56:29.094074: val_loss -0.3746 +2026-04-13 02:56:29.095910: Pseudo dice [0.6265, 0.6434, 0.7478, 0.4347, 0.4365, 0.6676, 0.6368] +2026-04-13 02:56:29.098134: Epoch time: 102.63 s +2026-04-13 02:56:30.274271: +2026-04-13 02:56:30.276086: Epoch 2247 +2026-04-13 02:56:30.277636: Current learning rate: 0.00476 +2026-04-13 02:58:12.307580: train_loss -0.3895 +2026-04-13 02:58:12.315048: val_loss -0.3669 +2026-04-13 02:58:12.317219: Pseudo dice [0.8687, 0.7079, 0.7168, 0.5626, 0.5304, 0.5418, 0.7935] +2026-04-13 02:58:12.320798: Epoch time: 102.04 s +2026-04-13 02:58:13.488512: +2026-04-13 02:58:13.490290: Epoch 2248 +2026-04-13 02:58:13.491886: Current learning rate: 0.00476 +2026-04-13 02:59:55.041552: train_loss -0.3946 +2026-04-13 02:59:55.046944: val_loss -0.3626 +2026-04-13 02:59:55.048518: Pseudo dice [0.6245, 0.7103, 0.6378, 0.6733, 0.4694, 0.8032, 0.7848] +2026-04-13 02:59:55.050336: Epoch time: 101.56 s +2026-04-13 02:59:56.211245: +2026-04-13 02:59:56.212804: Epoch 2249 +2026-04-13 02:59:56.214279: Current learning rate: 0.00475 +2026-04-13 03:01:38.093816: train_loss -0.4068 +2026-04-13 03:01:38.100586: val_loss -0.338 +2026-04-13 03:01:38.102222: Pseudo dice [0.8159, 0.877, 0.7369, 0.5009, 0.4981, 0.7108, 0.6916] +2026-04-13 03:01:38.104457: Epoch time: 101.89 s +2026-04-13 03:01:40.641330: +2026-04-13 03:01:40.643376: Epoch 2250 +2026-04-13 03:01:40.644932: Current learning rate: 0.00475 +2026-04-13 03:03:22.201458: train_loss -0.413 +2026-04-13 03:03:22.207168: val_loss -0.337 +2026-04-13 03:03:22.208747: Pseudo dice [0.7361, 0.7353, 0.7649, 0.3581, 0.4658, 0.2284, 0.6044] +2026-04-13 03:03:22.210810: Epoch time: 101.56 s +2026-04-13 03:03:23.364768: +2026-04-13 03:03:23.366701: Epoch 2251 +2026-04-13 03:03:23.368401: Current learning rate: 0.00475 +2026-04-13 03:05:04.913013: train_loss -0.4029 +2026-04-13 03:05:04.920609: val_loss -0.3915 +2026-04-13 03:05:04.922365: Pseudo dice [0.6546, 0.6537, 0.7661, 0.3949, 0.682, 0.8289, 0.8412] +2026-04-13 03:05:04.925048: Epoch time: 101.55 s +2026-04-13 03:05:06.070456: +2026-04-13 03:05:06.071976: Epoch 2252 +2026-04-13 03:05:06.073578: Current learning rate: 0.00475 +2026-04-13 03:06:47.699044: train_loss -0.4142 +2026-04-13 03:06:47.707658: val_loss -0.3417 +2026-04-13 03:06:47.709531: Pseudo dice [0.3502, 0.8915, 0.6969, 0.1479, 0.4344, 0.6554, 0.7898] +2026-04-13 03:06:47.711977: Epoch time: 101.63 s +2026-04-13 03:06:48.854824: +2026-04-13 03:06:48.857128: Epoch 2253 +2026-04-13 03:06:48.858727: Current learning rate: 0.00474 +2026-04-13 03:08:30.763024: train_loss -0.3986 +2026-04-13 03:08:30.769538: val_loss -0.3589 +2026-04-13 03:08:30.771488: Pseudo dice [0.6696, 0.4271, 0.7753, 0.2516, 0.5561, 0.5765, 0.816] +2026-04-13 03:08:30.773507: Epoch time: 101.91 s +2026-04-13 03:08:31.932755: +2026-04-13 03:08:31.934388: Epoch 2254 +2026-04-13 03:08:31.935925: Current learning rate: 0.00474 +2026-04-13 03:10:13.565881: train_loss -0.425 +2026-04-13 03:10:13.571376: val_loss -0.3852 +2026-04-13 03:10:13.573227: Pseudo dice [0.6785, 0.8183, 0.7778, 0.774, 0.5905, 0.7025, 0.7574] +2026-04-13 03:10:13.575778: Epoch time: 101.64 s +2026-04-13 03:10:14.725714: +2026-04-13 03:10:14.727473: Epoch 2255 +2026-04-13 03:10:14.729592: Current learning rate: 0.00474 +2026-04-13 03:11:56.223666: train_loss -0.4202 +2026-04-13 03:11:56.230283: val_loss -0.385 +2026-04-13 03:11:56.232227: Pseudo dice [0.6504, 0.8902, 0.695, 0.4008, 0.6236, 0.7944, 0.6597] +2026-04-13 03:11:56.234308: Epoch time: 101.5 s +2026-04-13 03:11:57.397731: +2026-04-13 03:11:57.399326: Epoch 2256 +2026-04-13 03:11:57.400776: Current learning rate: 0.00474 +2026-04-13 03:13:39.729736: train_loss -0.402 +2026-04-13 03:13:39.735539: val_loss -0.33 +2026-04-13 03:13:39.738264: Pseudo dice [0.5492, 0.8419, 0.7547, 0.0877, 0.4239, 0.7037, 0.3126] +2026-04-13 03:13:39.741132: Epoch time: 102.34 s +2026-04-13 03:13:40.914003: +2026-04-13 03:13:40.916486: Epoch 2257 +2026-04-13 03:13:40.918399: Current learning rate: 0.00473 +2026-04-13 03:15:22.647002: train_loss -0.4093 +2026-04-13 03:15:22.654206: val_loss -0.3745 +2026-04-13 03:15:22.656107: Pseudo dice [0.5981, 0.5817, 0.7129, 0.5513, 0.5092, 0.899, 0.8021] +2026-04-13 03:15:22.659245: Epoch time: 101.74 s +2026-04-13 03:15:23.837188: +2026-04-13 03:15:23.838824: Epoch 2258 +2026-04-13 03:15:23.840366: Current learning rate: 0.00473 +2026-04-13 03:17:05.251992: train_loss -0.3934 +2026-04-13 03:17:05.259526: val_loss -0.3283 +2026-04-13 03:17:05.262073: Pseudo dice [0.5693, 0.7182, 0.5555, 0.2947, 0.3873, 0.2536, 0.6309] +2026-04-13 03:17:05.264556: Epoch time: 101.42 s +2026-04-13 03:17:06.425790: +2026-04-13 03:17:06.427308: Epoch 2259 +2026-04-13 03:17:06.428761: Current learning rate: 0.00473 +2026-04-13 03:18:48.233156: train_loss -0.3764 +2026-04-13 03:18:48.239064: val_loss -0.3497 +2026-04-13 03:18:48.241715: Pseudo dice [0.7844, 0.3913, 0.7681, 0.5805, 0.3101, 0.4116, 0.6488] +2026-04-13 03:18:48.246536: Epoch time: 101.81 s +2026-04-13 03:18:49.402398: +2026-04-13 03:18:49.404431: Epoch 2260 +2026-04-13 03:18:49.405987: Current learning rate: 0.00473 +2026-04-13 03:20:31.169564: train_loss -0.3633 +2026-04-13 03:20:31.175020: val_loss -0.3433 +2026-04-13 03:20:31.176903: Pseudo dice [0.5339, 0.6317, 0.6855, 0.2665, 0.1776, 0.8674, 0.7818] +2026-04-13 03:20:31.179795: Epoch time: 101.77 s +2026-04-13 03:20:33.389854: +2026-04-13 03:20:33.391351: Epoch 2261 +2026-04-13 03:20:33.392679: Current learning rate: 0.00473 +2026-04-13 03:22:14.872156: train_loss -0.3653 +2026-04-13 03:22:14.878658: val_loss -0.3276 +2026-04-13 03:22:14.881583: Pseudo dice [0.7062, 0.893, 0.6383, 0.3809, 0.4549, 0.1193, 0.5661] +2026-04-13 03:22:14.887132: Epoch time: 101.49 s +2026-04-13 03:22:16.031645: +2026-04-13 03:22:16.033360: Epoch 2262 +2026-04-13 03:22:16.035074: Current learning rate: 0.00472 +2026-04-13 03:23:57.797281: train_loss -0.3919 +2026-04-13 03:23:57.802757: val_loss -0.3399 +2026-04-13 03:23:57.805243: Pseudo dice [0.4715, 0.7536, 0.701, 0.1248, 0.3513, 0.678, 0.7971] +2026-04-13 03:23:57.807524: Epoch time: 101.77 s +2026-04-13 03:23:58.953800: +2026-04-13 03:23:58.955673: Epoch 2263 +2026-04-13 03:23:58.958915: Current learning rate: 0.00472 +2026-04-13 03:25:41.026714: train_loss -0.3946 +2026-04-13 03:25:41.036516: val_loss -0.3669 +2026-04-13 03:25:41.038677: Pseudo dice [0.6364, 0.3686, 0.7647, 0.5512, 0.4571, 0.5265, 0.8164] +2026-04-13 03:25:41.041022: Epoch time: 102.08 s +2026-04-13 03:25:42.217699: +2026-04-13 03:25:42.220989: Epoch 2264 +2026-04-13 03:25:42.224509: Current learning rate: 0.00472 +2026-04-13 03:27:24.381446: train_loss -0.4061 +2026-04-13 03:27:24.388484: val_loss -0.3603 +2026-04-13 03:27:24.390474: Pseudo dice [0.8833, 0.8831, 0.6656, 0.1187, 0.6468, 0.686, 0.6536] +2026-04-13 03:27:24.392739: Epoch time: 102.17 s +2026-04-13 03:27:25.569318: +2026-04-13 03:27:25.571290: Epoch 2265 +2026-04-13 03:27:25.572963: Current learning rate: 0.00472 +2026-04-13 03:29:07.585342: train_loss -0.3866 +2026-04-13 03:29:07.591632: val_loss -0.3472 +2026-04-13 03:29:07.594447: Pseudo dice [0.5365, 0.7827, 0.6138, 0.4003, 0.6487, 0.1897, 0.6468] +2026-04-13 03:29:07.597280: Epoch time: 102.02 s +2026-04-13 03:29:08.754317: +2026-04-13 03:29:08.756165: Epoch 2266 +2026-04-13 03:29:08.757856: Current learning rate: 0.00471 +2026-04-13 03:30:50.559258: train_loss -0.4086 +2026-04-13 03:30:50.565087: val_loss -0.3378 +2026-04-13 03:30:50.566851: Pseudo dice [0.6437, 0.8737, 0.6709, 0.2085, 0.463, 0.707, 0.3337] +2026-04-13 03:30:50.569047: Epoch time: 101.81 s +2026-04-13 03:30:51.724896: +2026-04-13 03:30:51.726876: Epoch 2267 +2026-04-13 03:30:51.728498: Current learning rate: 0.00471 +2026-04-13 03:32:33.508855: train_loss -0.3903 +2026-04-13 03:32:33.515038: val_loss -0.3751 +2026-04-13 03:32:33.518023: Pseudo dice [0.6993, 0.886, 0.7814, 0.1563, 0.3949, 0.4049, 0.8136] +2026-04-13 03:32:33.521115: Epoch time: 101.79 s +2026-04-13 03:32:34.673939: +2026-04-13 03:32:34.675900: Epoch 2268 +2026-04-13 03:32:34.677756: Current learning rate: 0.00471 +2026-04-13 03:34:16.312149: train_loss -0.3972 +2026-04-13 03:34:16.317990: val_loss -0.3096 +2026-04-13 03:34:16.320110: Pseudo dice [0.6277, 0.1983, 0.5695, 0.1732, 0.5093, 0.3776, 0.8259] +2026-04-13 03:34:16.322391: Epoch time: 101.64 s +2026-04-13 03:34:17.473193: +2026-04-13 03:34:17.474664: Epoch 2269 +2026-04-13 03:34:17.476060: Current learning rate: 0.00471 +2026-04-13 03:35:58.990267: train_loss -0.3968 +2026-04-13 03:35:58.997882: val_loss -0.3294 +2026-04-13 03:35:58.999890: Pseudo dice [0.2769, 0.532, 0.7553, 0.1351, 0.453, 0.8062, 0.6792] +2026-04-13 03:35:59.001990: Epoch time: 101.52 s +2026-04-13 03:36:00.148704: +2026-04-13 03:36:00.150731: Epoch 2270 +2026-04-13 03:36:00.152986: Current learning rate: 0.0047 +2026-04-13 03:37:41.789936: train_loss -0.4062 +2026-04-13 03:37:41.797024: val_loss -0.3405 +2026-04-13 03:37:41.798632: Pseudo dice [0.4915, 0.2297, 0.7537, 0.0666, 0.4555, 0.3497, 0.5022] +2026-04-13 03:37:41.800858: Epoch time: 101.64 s +2026-04-13 03:37:42.940976: +2026-04-13 03:37:42.942473: Epoch 2271 +2026-04-13 03:37:42.943928: Current learning rate: 0.0047 +2026-04-13 03:39:24.415107: train_loss -0.4135 +2026-04-13 03:39:24.421129: val_loss -0.3489 +2026-04-13 03:39:24.422803: Pseudo dice [0.3544, 0.8574, 0.7961, 0.3017, 0.5273, 0.9123, 0.7128] +2026-04-13 03:39:24.424776: Epoch time: 101.48 s +2026-04-13 03:39:25.571134: +2026-04-13 03:39:25.572913: Epoch 2272 +2026-04-13 03:39:25.574324: Current learning rate: 0.0047 +2026-04-13 03:41:06.962597: train_loss -0.39 +2026-04-13 03:41:06.969572: val_loss -0.3013 +2026-04-13 03:41:06.971144: Pseudo dice [0.623, 0.8785, 0.4721, 0.1423, 0.4801, 0.3326, 0.4513] +2026-04-13 03:41:06.973108: Epoch time: 101.39 s +2026-04-13 03:41:08.130780: +2026-04-13 03:41:08.132256: Epoch 2273 +2026-04-13 03:41:08.133697: Current learning rate: 0.0047 +2026-04-13 03:42:50.222431: train_loss -0.4085 +2026-04-13 03:42:50.233246: val_loss -0.351 +2026-04-13 03:42:50.235240: Pseudo dice [0.1242, 0.2513, 0.7171, 0.4768, 0.4537, 0.726, 0.6244] +2026-04-13 03:42:50.237400: Epoch time: 102.09 s +2026-04-13 03:42:51.401803: +2026-04-13 03:42:51.403981: Epoch 2274 +2026-04-13 03:42:51.406253: Current learning rate: 0.00469 +2026-04-13 03:44:32.853251: train_loss -0.3943 +2026-04-13 03:44:32.859095: val_loss -0.3177 +2026-04-13 03:44:32.861913: Pseudo dice [0.5547, 0.2989, 0.682, 0.0326, 0.4738, 0.6909, 0.3625] +2026-04-13 03:44:32.864339: Epoch time: 101.45 s +2026-04-13 03:44:34.035710: +2026-04-13 03:44:34.038195: Epoch 2275 +2026-04-13 03:44:34.040849: Current learning rate: 0.00469 +2026-04-13 03:46:15.711082: train_loss -0.39 +2026-04-13 03:46:15.717015: val_loss -0.3203 +2026-04-13 03:46:15.719001: Pseudo dice [0.8261, 0.5992, 0.7232, 0.0996, 0.5294, 0.7865, 0.3262] +2026-04-13 03:46:15.722840: Epoch time: 101.68 s +2026-04-13 03:46:16.878654: +2026-04-13 03:46:16.880176: Epoch 2276 +2026-04-13 03:46:16.881738: Current learning rate: 0.00469 +2026-04-13 03:47:58.351877: train_loss -0.3872 +2026-04-13 03:47:58.358662: val_loss -0.3724 +2026-04-13 03:47:58.360753: Pseudo dice [0.6495, 0.8584, 0.7535, 0.6285, 0.5316, 0.6067, 0.2942] +2026-04-13 03:47:58.368156: Epoch time: 101.48 s +2026-04-13 03:47:59.557714: +2026-04-13 03:47:59.559440: Epoch 2277 +2026-04-13 03:47:59.560863: Current learning rate: 0.00469 +2026-04-13 03:49:41.394932: train_loss -0.3792 +2026-04-13 03:49:41.402862: val_loss -0.3722 +2026-04-13 03:49:41.404822: Pseudo dice [0.7439, 0.6341, 0.6778, 0.6539, 0.3406, 0.862, 0.7881] +2026-04-13 03:49:41.407714: Epoch time: 101.84 s +2026-04-13 03:49:42.570073: +2026-04-13 03:49:42.571978: Epoch 2278 +2026-04-13 03:49:42.573532: Current learning rate: 0.00468 +2026-04-13 03:51:24.477418: train_loss -0.3698 +2026-04-13 03:51:24.484436: val_loss -0.3578 +2026-04-13 03:51:24.486475: Pseudo dice [0.8137, 0.8389, 0.8056, 0.3854, 0.6134, 0.5137, 0.7812] +2026-04-13 03:51:24.488845: Epoch time: 101.91 s +2026-04-13 03:51:25.638602: +2026-04-13 03:51:25.641526: Epoch 2279 +2026-04-13 03:51:25.643499: Current learning rate: 0.00468 +2026-04-13 03:53:07.095371: train_loss -0.3941 +2026-04-13 03:53:07.100755: val_loss -0.3708 +2026-04-13 03:53:07.102584: Pseudo dice [0.7412, 0.2736, 0.7742, 0.3925, 0.5434, 0.3236, 0.7925] +2026-04-13 03:53:07.104892: Epoch time: 101.46 s +2026-04-13 03:53:08.262323: +2026-04-13 03:53:08.263680: Epoch 2280 +2026-04-13 03:53:08.265115: Current learning rate: 0.00468 +2026-04-13 03:54:50.599452: train_loss -0.373 +2026-04-13 03:54:50.608279: val_loss -0.2961 +2026-04-13 03:54:50.612451: Pseudo dice [0.4219, 0.6212, 0.7197, 0.4026, 0.3875, 0.4667, 0.285] +2026-04-13 03:54:50.615452: Epoch time: 102.34 s +2026-04-13 03:54:51.773502: +2026-04-13 03:54:51.775714: Epoch 2281 +2026-04-13 03:54:51.777658: Current learning rate: 0.00468 +2026-04-13 03:56:34.211650: train_loss -0.3839 +2026-04-13 03:56:34.219038: val_loss -0.3292 +2026-04-13 03:56:34.221249: Pseudo dice [0.7284, 0.8653, 0.7253, 0.4298, 0.1271, 0.7005, 0.6765] +2026-04-13 03:56:34.223052: Epoch time: 102.44 s +2026-04-13 03:56:36.384945: +2026-04-13 03:56:36.386536: Epoch 2282 +2026-04-13 03:56:36.387940: Current learning rate: 0.00467 +2026-04-13 03:58:18.219965: train_loss -0.3791 +2026-04-13 03:58:18.226254: val_loss -0.31 +2026-04-13 03:58:18.228389: Pseudo dice [0.1501, 0.913, 0.6583, 0.5397, 0.3842, 0.5131, 0.817] +2026-04-13 03:58:18.231312: Epoch time: 101.84 s +2026-04-13 03:58:19.373937: +2026-04-13 03:58:19.375875: Epoch 2283 +2026-04-13 03:58:19.377530: Current learning rate: 0.00467 +2026-04-13 04:00:01.225667: train_loss -0.399 +2026-04-13 04:00:01.231498: val_loss -0.3474 +2026-04-13 04:00:01.234171: Pseudo dice [0.4938, 0.8955, 0.7772, 0.357, 0.2631, 0.801, 0.7454] +2026-04-13 04:00:01.236727: Epoch time: 101.85 s +2026-04-13 04:00:02.690157: +2026-04-13 04:00:02.691874: Epoch 2284 +2026-04-13 04:00:02.693403: Current learning rate: 0.00467 +2026-04-13 04:01:44.278027: train_loss -0.4131 +2026-04-13 04:01:44.284356: val_loss -0.3375 +2026-04-13 04:01:44.286095: Pseudo dice [0.2809, 0.8771, 0.5975, 0.691, 0.481, 0.7688, 0.6576] +2026-04-13 04:01:44.288473: Epoch time: 101.59 s +2026-04-13 04:01:45.444349: +2026-04-13 04:01:45.447805: Epoch 2285 +2026-04-13 04:01:45.450347: Current learning rate: 0.00467 +2026-04-13 04:03:27.578391: train_loss -0.4031 +2026-04-13 04:03:27.584787: val_loss -0.3322 +2026-04-13 04:03:27.586436: Pseudo dice [0.3715, 0.4349, 0.7536, 0.3827, 0.3961, 0.7329, 0.6917] +2026-04-13 04:03:27.588294: Epoch time: 102.14 s +2026-04-13 04:03:28.743093: +2026-04-13 04:03:28.745145: Epoch 2286 +2026-04-13 04:03:28.746694: Current learning rate: 0.00466 +2026-04-13 04:05:10.296031: train_loss -0.3897 +2026-04-13 04:05:10.305471: val_loss -0.39 +2026-04-13 04:05:10.308099: Pseudo dice [0.3652, 0.6404, 0.7644, 0.7612, 0.4642, 0.8863, 0.7279] +2026-04-13 04:05:10.313449: Epoch time: 101.56 s +2026-04-13 04:05:11.501883: +2026-04-13 04:05:11.504010: Epoch 2287 +2026-04-13 04:05:11.505512: Current learning rate: 0.00466 +2026-04-13 04:06:53.139273: train_loss -0.3899 +2026-04-13 04:06:53.147561: val_loss -0.3486 +2026-04-13 04:06:53.149597: Pseudo dice [0.5918, 0.7343, 0.781, 0.299, 0.4415, 0.8152, 0.5237] +2026-04-13 04:06:53.151934: Epoch time: 101.64 s +2026-04-13 04:06:54.336960: +2026-04-13 04:06:54.338835: Epoch 2288 +2026-04-13 04:06:54.341343: Current learning rate: 0.00466 +2026-04-13 04:08:36.745646: train_loss -0.3803 +2026-04-13 04:08:36.752072: val_loss -0.3423 +2026-04-13 04:08:36.754363: Pseudo dice [0.4296, 0.6036, 0.6832, 0.4913, 0.4555, 0.9207, 0.5962] +2026-04-13 04:08:36.756438: Epoch time: 102.41 s +2026-04-13 04:08:37.894791: +2026-04-13 04:08:37.896399: Epoch 2289 +2026-04-13 04:08:37.897791: Current learning rate: 0.00466 +2026-04-13 04:10:19.346587: train_loss -0.3921 +2026-04-13 04:10:19.353203: val_loss -0.3173 +2026-04-13 04:10:19.355130: Pseudo dice [0.3265, 0.1278, 0.7908, 0.1858, 0.3769, 0.758, 0.6604] +2026-04-13 04:10:19.357742: Epoch time: 101.45 s +2026-04-13 04:10:20.514196: +2026-04-13 04:10:20.516786: Epoch 2290 +2026-04-13 04:10:20.518873: Current learning rate: 0.00465 +2026-04-13 04:12:02.492598: train_loss -0.3761 +2026-04-13 04:12:02.498937: val_loss -0.3085 +2026-04-13 04:12:02.500609: Pseudo dice [0.396, 0.6894, 0.6, 0.6322, 0.3224, 0.6286, 0.6088] +2026-04-13 04:12:02.503250: Epoch time: 101.98 s +2026-04-13 04:12:03.647667: +2026-04-13 04:12:03.649024: Epoch 2291 +2026-04-13 04:12:03.650267: Current learning rate: 0.00465 +2026-04-13 04:13:45.674751: train_loss -0.3882 +2026-04-13 04:13:45.681088: val_loss -0.3559 +2026-04-13 04:13:45.683002: Pseudo dice [0.6029, 0.8759, 0.7694, 0.1601, 0.4384, 0.642, 0.6579] +2026-04-13 04:13:45.688434: Epoch time: 102.03 s +2026-04-13 04:13:46.881685: +2026-04-13 04:13:46.883309: Epoch 2292 +2026-04-13 04:13:46.884671: Current learning rate: 0.00465 +2026-04-13 04:15:28.620721: train_loss -0.3933 +2026-04-13 04:15:28.627208: val_loss -0.3385 +2026-04-13 04:15:28.629365: Pseudo dice [0.5815, 0.6555, 0.716, 0.0926, 0.5067, 0.7832, 0.7482] +2026-04-13 04:15:28.631525: Epoch time: 101.74 s +2026-04-13 04:15:29.812715: +2026-04-13 04:15:29.815450: Epoch 2293 +2026-04-13 04:15:29.817360: Current learning rate: 0.00465 +2026-04-13 04:17:11.397702: train_loss -0.4006 +2026-04-13 04:17:11.402938: val_loss -0.3217 +2026-04-13 04:17:11.404499: Pseudo dice [0.664, 0.8423, 0.5532, 0.4975, 0.6068, 0.5483, 0.5713] +2026-04-13 04:17:11.406777: Epoch time: 101.59 s +2026-04-13 04:17:12.556774: +2026-04-13 04:17:12.559125: Epoch 2294 +2026-04-13 04:17:12.560682: Current learning rate: 0.00464 +2026-04-13 04:18:54.847870: train_loss -0.4039 +2026-04-13 04:18:54.855066: val_loss -0.3825 +2026-04-13 04:18:54.857332: Pseudo dice [0.5668, 0.2172, 0.7276, 0.6244, 0.5104, 0.5524, 0.7533] +2026-04-13 04:18:54.860388: Epoch time: 102.29 s +2026-04-13 04:18:55.995761: +2026-04-13 04:18:55.999318: Epoch 2295 +2026-04-13 04:18:56.001132: Current learning rate: 0.00464 +2026-04-13 04:20:37.718347: train_loss -0.4109 +2026-04-13 04:20:37.725590: val_loss -0.3704 +2026-04-13 04:20:37.727856: Pseudo dice [0.6304, 0.436, 0.798, 0.4437, 0.5992, 0.1001, 0.7452] +2026-04-13 04:20:37.730549: Epoch time: 101.73 s +2026-04-13 04:20:38.891075: +2026-04-13 04:20:38.892665: Epoch 2296 +2026-04-13 04:20:38.894218: Current learning rate: 0.00464 +2026-04-13 04:22:20.515467: train_loss -0.4158 +2026-04-13 04:22:20.521295: val_loss -0.3533 +2026-04-13 04:22:20.523094: Pseudo dice [0.0735, 0.4251, 0.8083, 0.4098, 0.3467, 0.8474, 0.5975] +2026-04-13 04:22:20.526126: Epoch time: 101.63 s +2026-04-13 04:22:21.673574: +2026-04-13 04:22:21.675831: Epoch 2297 +2026-04-13 04:22:21.677443: Current learning rate: 0.00464 +2026-04-13 04:24:03.392255: train_loss -0.4106 +2026-04-13 04:24:03.398732: val_loss -0.2933 +2026-04-13 04:24:03.400757: Pseudo dice [0.5783, 0.0766, 0.5348, 0.0247, 0.3021, 0.5843, 0.7398] +2026-04-13 04:24:03.403166: Epoch time: 101.72 s +2026-04-13 04:24:04.563085: +2026-04-13 04:24:04.564598: Epoch 2298 +2026-04-13 04:24:04.565924: Current learning rate: 0.00463 +2026-04-13 04:25:46.284863: train_loss -0.403 +2026-04-13 04:25:46.291570: val_loss -0.3951 +2026-04-13 04:25:46.293612: Pseudo dice [0.6793, 0.6325, 0.8183, 0.6101, 0.5778, 0.8844, 0.8376] +2026-04-13 04:25:46.295492: Epoch time: 101.73 s +2026-04-13 04:25:47.438587: +2026-04-13 04:25:47.440102: Epoch 2299 +2026-04-13 04:25:47.443176: Current learning rate: 0.00463 +2026-04-13 04:27:29.095054: train_loss -0.4069 +2026-04-13 04:27:29.101469: val_loss -0.3319 +2026-04-13 04:27:29.104034: Pseudo dice [0.2931, 0.8988, 0.7226, 0.7246, 0.1928, 0.7238, 0.7495] +2026-04-13 04:27:29.107222: Epoch time: 101.66 s +2026-04-13 04:27:31.760015: +2026-04-13 04:27:31.762201: Epoch 2300 +2026-04-13 04:27:31.763646: Current learning rate: 0.00463 +2026-04-13 04:29:13.796735: train_loss -0.408 +2026-04-13 04:29:13.802247: val_loss -0.3813 +2026-04-13 04:29:13.804227: Pseudo dice [0.1351, 0.2539, 0.7037, 0.3134, 0.5144, 0.7563, 0.7477] +2026-04-13 04:29:13.806798: Epoch time: 102.04 s +2026-04-13 04:29:14.974955: +2026-04-13 04:29:14.977110: Epoch 2301 +2026-04-13 04:29:14.978753: Current learning rate: 0.00463 +2026-04-13 04:30:57.301638: train_loss -0.4205 +2026-04-13 04:30:57.307892: val_loss -0.3589 +2026-04-13 04:30:57.310525: Pseudo dice [0.6457, 0.645, 0.8025, 0.1841, 0.6451, 0.918, 0.6381] +2026-04-13 04:30:57.312629: Epoch time: 102.33 s +2026-04-13 04:30:59.548182: +2026-04-13 04:30:59.549714: Epoch 2302 +2026-04-13 04:30:59.551112: Current learning rate: 0.00462 +2026-04-13 04:32:42.199851: train_loss -0.4175 +2026-04-13 04:32:42.205193: val_loss -0.3463 +2026-04-13 04:32:42.206882: Pseudo dice [0.5221, 0.6656, 0.7819, 0.3486, 0.3359, 0.5606, 0.7339] +2026-04-13 04:32:42.208911: Epoch time: 102.65 s +2026-04-13 04:32:43.362985: +2026-04-13 04:32:43.364642: Epoch 2303 +2026-04-13 04:32:43.366434: Current learning rate: 0.00462 +2026-04-13 04:34:25.172004: train_loss -0.4089 +2026-04-13 04:34:25.179023: val_loss -0.3515 +2026-04-13 04:34:25.180577: Pseudo dice [0.3188, 0.4751, 0.7086, 0.101, 0.3106, 0.9254, 0.8132] +2026-04-13 04:34:25.183188: Epoch time: 101.81 s +2026-04-13 04:34:26.340552: +2026-04-13 04:34:26.342233: Epoch 2304 +2026-04-13 04:34:26.343903: Current learning rate: 0.00462 +2026-04-13 04:36:08.164428: train_loss -0.4047 +2026-04-13 04:36:08.170497: val_loss -0.3546 +2026-04-13 04:36:08.173050: Pseudo dice [0.4503, 0.3437, 0.8196, 0.1766, 0.516, 0.8617, 0.663] +2026-04-13 04:36:08.175700: Epoch time: 101.83 s +2026-04-13 04:36:09.327404: +2026-04-13 04:36:09.328910: Epoch 2305 +2026-04-13 04:36:09.330356: Current learning rate: 0.00462 +2026-04-13 04:37:51.161165: train_loss -0.4049 +2026-04-13 04:37:51.167753: val_loss -0.3318 +2026-04-13 04:37:51.170345: Pseudo dice [0.4483, 0.8922, 0.7387, 0.2527, 0.1412, 0.7171, 0.5218] +2026-04-13 04:37:51.172686: Epoch time: 101.84 s +2026-04-13 04:37:52.345095: +2026-04-13 04:37:52.346906: Epoch 2306 +2026-04-13 04:37:52.349486: Current learning rate: 0.00461 +2026-04-13 04:39:34.164486: train_loss -0.3722 +2026-04-13 04:39:34.170583: val_loss -0.3071 +2026-04-13 04:39:34.172762: Pseudo dice [0.5729, 0.345, 0.6953, 0.1675, 0.2441, 0.824, 0.7635] +2026-04-13 04:39:34.175205: Epoch time: 101.82 s +2026-04-13 04:39:35.361703: +2026-04-13 04:39:35.363714: Epoch 2307 +2026-04-13 04:39:35.365793: Current learning rate: 0.00461 +2026-04-13 04:41:17.476706: train_loss -0.3844 +2026-04-13 04:41:17.483808: val_loss -0.3542 +2026-04-13 04:41:17.485882: Pseudo dice [0.1965, 0.1267, 0.6683, 0.5914, 0.4127, 0.809, 0.6893] +2026-04-13 04:41:17.488281: Epoch time: 102.12 s +2026-04-13 04:41:18.649473: +2026-04-13 04:41:18.652034: Epoch 2308 +2026-04-13 04:41:18.653811: Current learning rate: 0.00461 +2026-04-13 04:43:00.602839: train_loss -0.397 +2026-04-13 04:43:00.608513: val_loss -0.3344 +2026-04-13 04:43:00.610465: Pseudo dice [0.5475, 0.3659, 0.6603, 0.1185, 0.3578, 0.9151, 0.6383] +2026-04-13 04:43:00.612649: Epoch time: 101.96 s +2026-04-13 04:43:01.770368: +2026-04-13 04:43:01.772219: Epoch 2309 +2026-04-13 04:43:01.773710: Current learning rate: 0.00461 +2026-04-13 04:44:43.242845: train_loss -0.4037 +2026-04-13 04:44:43.249092: val_loss -0.3619 +2026-04-13 04:44:43.252145: Pseudo dice [0.6386, 0.8715, 0.7674, 0.5899, 0.4728, 0.8094, 0.8072] +2026-04-13 04:44:43.254085: Epoch time: 101.48 s +2026-04-13 04:44:44.419082: +2026-04-13 04:44:44.420987: Epoch 2310 +2026-04-13 04:44:44.422708: Current learning rate: 0.00461 +2026-04-13 04:46:26.770196: train_loss -0.4037 +2026-04-13 04:46:26.778612: val_loss -0.3633 +2026-04-13 04:46:26.780654: Pseudo dice [0.7705, 0.5841, 0.7947, 0.2788, 0.4329, 0.8867, 0.8146] +2026-04-13 04:46:26.783056: Epoch time: 102.35 s +2026-04-13 04:46:27.933805: +2026-04-13 04:46:27.935559: Epoch 2311 +2026-04-13 04:46:27.937261: Current learning rate: 0.0046 +2026-04-13 04:48:09.837332: train_loss -0.3821 +2026-04-13 04:48:09.843388: val_loss -0.2912 +2026-04-13 04:48:09.845159: Pseudo dice [0.4548, 0.7775, 0.5171, 0.2136, 0.4561, 0.5647, 0.6285] +2026-04-13 04:48:09.847327: Epoch time: 101.91 s +2026-04-13 04:48:11.005261: +2026-04-13 04:48:11.006886: Epoch 2312 +2026-04-13 04:48:11.009268: Current learning rate: 0.0046 +2026-04-13 04:49:53.563013: train_loss -0.36 +2026-04-13 04:49:53.571279: val_loss -0.3269 +2026-04-13 04:49:53.573581: Pseudo dice [0.6788, 0.6447, 0.698, 0.0314, 0.2865, 0.4747, 0.7079] +2026-04-13 04:49:53.576348: Epoch time: 102.56 s +2026-04-13 04:49:54.769219: +2026-04-13 04:49:54.771327: Epoch 2313 +2026-04-13 04:49:54.772952: Current learning rate: 0.0046 +2026-04-13 04:51:36.415803: train_loss -0.3565 +2026-04-13 04:51:36.422232: val_loss -0.3318 +2026-04-13 04:51:36.424341: Pseudo dice [0.4943, 0.3216, 0.7585, 0.305, 0.5492, 0.5447, 0.7317] +2026-04-13 04:51:36.426802: Epoch time: 101.65 s +2026-04-13 04:51:37.579145: +2026-04-13 04:51:37.580883: Epoch 2314 +2026-04-13 04:51:37.582368: Current learning rate: 0.0046 +2026-04-13 04:53:19.283006: train_loss -0.3892 +2026-04-13 04:53:19.289387: val_loss -0.3595 +2026-04-13 04:53:19.291404: Pseudo dice [0.6986, 0.4818, 0.7368, 0.2259, 0.3335, 0.727, 0.7613] +2026-04-13 04:53:19.293811: Epoch time: 101.71 s +2026-04-13 04:53:20.477355: +2026-04-13 04:53:20.480084: Epoch 2315 +2026-04-13 04:53:20.481930: Current learning rate: 0.00459 +2026-04-13 04:55:02.266628: train_loss -0.405 +2026-04-13 04:55:02.273951: val_loss -0.3276 +2026-04-13 04:55:02.277626: Pseudo dice [0.6922, 0.6607, 0.6967, 0.5709, 0.4328, 0.4735, 0.731] +2026-04-13 04:55:02.280424: Epoch time: 101.79 s +2026-04-13 04:55:03.448663: +2026-04-13 04:55:03.450448: Epoch 2316 +2026-04-13 04:55:03.452740: Current learning rate: 0.00459 +2026-04-13 04:56:45.096685: train_loss -0.4039 +2026-04-13 04:56:45.103395: val_loss -0.3566 +2026-04-13 04:56:45.105762: Pseudo dice [0.8201, 0.8724, 0.7084, 0.6023, 0.5421, 0.1317, 0.7581] +2026-04-13 04:56:45.108601: Epoch time: 101.65 s +2026-04-13 04:56:46.264356: +2026-04-13 04:56:46.266217: Epoch 2317 +2026-04-13 04:56:46.267617: Current learning rate: 0.00459 +2026-04-13 04:58:28.026994: train_loss -0.4174 +2026-04-13 04:58:28.034102: val_loss -0.3727 +2026-04-13 04:58:28.036897: Pseudo dice [0.5729, 0.6984, 0.7879, 0.4548, 0.67, 0.824, 0.656] +2026-04-13 04:58:28.039455: Epoch time: 101.77 s +2026-04-13 04:58:29.179330: +2026-04-13 04:58:29.181935: Epoch 2318 +2026-04-13 04:58:29.183733: Current learning rate: 0.00459 +2026-04-13 05:00:10.701623: train_loss -0.4076 +2026-04-13 05:00:10.708432: val_loss -0.37 +2026-04-13 05:00:10.710241: Pseudo dice [0.4884, 0.5983, 0.7678, 0.3788, 0.5215, 0.8257, 0.8551] +2026-04-13 05:00:10.712407: Epoch time: 101.53 s +2026-04-13 05:00:11.852711: +2026-04-13 05:00:11.854255: Epoch 2319 +2026-04-13 05:00:11.855648: Current learning rate: 0.00458 +2026-04-13 05:01:53.601847: train_loss -0.3946 +2026-04-13 05:01:53.610803: val_loss -0.3689 +2026-04-13 05:01:53.613039: Pseudo dice [0.4095, 0.6445, 0.827, 0.3274, 0.4998, 0.8705, 0.761] +2026-04-13 05:01:53.615110: Epoch time: 101.75 s +2026-04-13 05:01:54.772487: +2026-04-13 05:01:54.774030: Epoch 2320 +2026-04-13 05:01:54.775571: Current learning rate: 0.00458 +2026-04-13 05:03:36.369902: train_loss -0.4088 +2026-04-13 05:03:36.376915: val_loss -0.3818 +2026-04-13 05:03:36.378837: Pseudo dice [0.4552, 0.6896, 0.7688, 0.5338, 0.3472, 0.7847, 0.8035] +2026-04-13 05:03:36.381195: Epoch time: 101.6 s +2026-04-13 05:03:37.528185: +2026-04-13 05:03:37.530839: Epoch 2321 +2026-04-13 05:03:37.532400: Current learning rate: 0.00458 +2026-04-13 05:05:19.051668: train_loss -0.4164 +2026-04-13 05:05:19.056725: val_loss -0.3803 +2026-04-13 05:05:19.058318: Pseudo dice [0.6686, 0.4552, 0.7244, 0.2417, 0.3117, 0.7761, 0.7256] +2026-04-13 05:05:19.060333: Epoch time: 101.53 s +2026-04-13 05:05:20.240837: +2026-04-13 05:05:20.243941: Epoch 2322 +2026-04-13 05:05:20.245583: Current learning rate: 0.00458 +2026-04-13 05:07:01.974965: train_loss -0.4108 +2026-04-13 05:07:01.981205: val_loss -0.3227 +2026-04-13 05:07:01.982874: Pseudo dice [0.3084, 0.8408, 0.7558, 0.1139, 0.4376, 0.3301, 0.8677] +2026-04-13 05:07:01.986096: Epoch time: 101.74 s +2026-04-13 05:07:04.239908: +2026-04-13 05:07:04.256002: Epoch 2323 +2026-04-13 05:07:04.257856: Current learning rate: 0.00457 +2026-04-13 05:08:46.247839: train_loss -0.4041 +2026-04-13 05:08:46.254530: val_loss -0.3661 +2026-04-13 05:08:46.256551: Pseudo dice [0.6735, 0.648, 0.5649, 0.1476, 0.4815, 0.8028, 0.5575] +2026-04-13 05:08:46.258730: Epoch time: 102.01 s +2026-04-13 05:08:47.437169: +2026-04-13 05:08:47.439890: Epoch 2324 +2026-04-13 05:08:47.442230: Current learning rate: 0.00457 +2026-04-13 05:10:29.109049: train_loss -0.3998 +2026-04-13 05:10:29.115117: val_loss -0.3781 +2026-04-13 05:10:29.116951: Pseudo dice [0.6001, 0.5255, 0.7965, 0.1996, 0.3313, 0.908, 0.8377] +2026-04-13 05:10:29.119702: Epoch time: 101.67 s +2026-04-13 05:10:30.341661: +2026-04-13 05:10:30.343245: Epoch 2325 +2026-04-13 05:10:30.344680: Current learning rate: 0.00457 +2026-04-13 05:12:12.144571: train_loss -0.4098 +2026-04-13 05:12:12.151000: val_loss -0.3198 +2026-04-13 05:12:12.154313: Pseudo dice [0.5373, 0.7369, 0.769, 0.5675, 0.4535, 0.135, 0.5011] +2026-04-13 05:12:12.156641: Epoch time: 101.81 s +2026-04-13 05:12:13.303946: +2026-04-13 05:12:13.306435: Epoch 2326 +2026-04-13 05:12:13.307997: Current learning rate: 0.00457 +2026-04-13 05:13:55.220925: train_loss -0.393 +2026-04-13 05:13:55.231015: val_loss -0.3351 +2026-04-13 05:13:55.232902: Pseudo dice [0.1967, 0.5824, 0.5632, 0.3062, 0.2178, 0.1147, 0.7627] +2026-04-13 05:13:55.241825: Epoch time: 101.92 s +2026-04-13 05:13:56.417399: +2026-04-13 05:13:56.420660: Epoch 2327 +2026-04-13 05:13:56.423373: Current learning rate: 0.00456 +2026-04-13 05:15:38.410412: train_loss -0.3759 +2026-04-13 05:15:38.417402: val_loss -0.3663 +2026-04-13 05:15:38.419096: Pseudo dice [0.6413, 0.8135, 0.7231, 0.4865, 0.4713, 0.8115, 0.7591] +2026-04-13 05:15:38.421090: Epoch time: 102.0 s +2026-04-13 05:15:39.578544: +2026-04-13 05:15:39.580537: Epoch 2328 +2026-04-13 05:15:39.582343: Current learning rate: 0.00456 +2026-04-13 05:17:21.627033: train_loss -0.3968 +2026-04-13 05:17:21.632632: val_loss -0.3745 +2026-04-13 05:17:21.634658: Pseudo dice [0.7426, 0.781, 0.7678, 0.4772, 0.3268, 0.8023, 0.8082] +2026-04-13 05:17:21.637455: Epoch time: 102.05 s +2026-04-13 05:17:22.798900: +2026-04-13 05:17:22.800613: Epoch 2329 +2026-04-13 05:17:22.802094: Current learning rate: 0.00456 +2026-04-13 05:19:04.426278: train_loss -0.3868 +2026-04-13 05:19:04.431866: val_loss -0.3241 +2026-04-13 05:19:04.433756: Pseudo dice [0.5681, 0.3635, 0.6371, 0.2375, 0.3627, 0.822, 0.4795] +2026-04-13 05:19:04.435864: Epoch time: 101.63 s +2026-04-13 05:19:05.591496: +2026-04-13 05:19:05.593616: Epoch 2330 +2026-04-13 05:19:05.595759: Current learning rate: 0.00456 +2026-04-13 05:20:47.349834: train_loss -0.4011 +2026-04-13 05:20:47.355601: val_loss -0.3562 +2026-04-13 05:20:47.357630: Pseudo dice [0.4455, 0.6528, 0.7517, 0.3535, 0.2037, 0.5346, 0.6663] +2026-04-13 05:20:47.360010: Epoch time: 101.76 s +2026-04-13 05:20:48.509081: +2026-04-13 05:20:48.510815: Epoch 2331 +2026-04-13 05:20:48.512527: Current learning rate: 0.00455 +2026-04-13 05:22:30.066019: train_loss -0.3659 +2026-04-13 05:22:30.072026: val_loss -0.303 +2026-04-13 05:22:30.073959: Pseudo dice [0.3731, 0.5, 0.6631, 0.123, 0.6094, 0.458, 0.5571] +2026-04-13 05:22:30.077503: Epoch time: 101.56 s +2026-04-13 05:22:31.234271: +2026-04-13 05:22:31.236147: Epoch 2332 +2026-04-13 05:22:31.237865: Current learning rate: 0.00455 +2026-04-13 05:24:12.755623: train_loss -0.3927 +2026-04-13 05:24:12.761874: val_loss -0.3654 +2026-04-13 05:24:12.765339: Pseudo dice [0.7413, 0.6744, 0.7841, 0.537, 0.4832, 0.8901, 0.7474] +2026-04-13 05:24:12.768121: Epoch time: 101.52 s +2026-04-13 05:24:13.935181: +2026-04-13 05:24:13.936724: Epoch 2333 +2026-04-13 05:24:13.938127: Current learning rate: 0.00455 +2026-04-13 05:25:55.440489: train_loss -0.3903 +2026-04-13 05:25:55.447177: val_loss -0.3566 +2026-04-13 05:25:55.451113: Pseudo dice [0.368, 0.8257, 0.8341, 0.403, 0.6756, 0.5736, 0.465] +2026-04-13 05:25:55.455158: Epoch time: 101.51 s +2026-04-13 05:25:56.617165: +2026-04-13 05:25:56.622832: Epoch 2334 +2026-04-13 05:25:56.625010: Current learning rate: 0.00455 +2026-04-13 05:27:38.382149: train_loss -0.4012 +2026-04-13 05:27:38.388459: val_loss -0.3208 +2026-04-13 05:27:38.391709: Pseudo dice [0.5756, 0.5727, 0.5639, 0.4959, 0.5688, 0.3776, 0.6744] +2026-04-13 05:27:38.394011: Epoch time: 101.77 s +2026-04-13 05:27:39.562963: +2026-04-13 05:27:39.565235: Epoch 2335 +2026-04-13 05:27:39.567655: Current learning rate: 0.00454 +2026-04-13 05:29:21.078443: train_loss -0.409 +2026-04-13 05:29:21.084608: val_loss -0.3757 +2026-04-13 05:29:21.086484: Pseudo dice [0.458, 0.5099, 0.7911, 0.4603, 0.6122, 0.5221, 0.6011] +2026-04-13 05:29:21.089042: Epoch time: 101.52 s +2026-04-13 05:29:22.258128: +2026-04-13 05:29:22.260075: Epoch 2336 +2026-04-13 05:29:22.261563: Current learning rate: 0.00454 +2026-04-13 05:31:04.074991: train_loss -0.4045 +2026-04-13 05:31:04.080611: val_loss -0.3286 +2026-04-13 05:31:04.082787: Pseudo dice [0.334, 0.4883, 0.73, 0.231, 0.2313, 0.8435, 0.7731] +2026-04-13 05:31:04.085340: Epoch time: 101.82 s +2026-04-13 05:31:05.266552: +2026-04-13 05:31:05.268102: Epoch 2337 +2026-04-13 05:31:05.269460: Current learning rate: 0.00454 +2026-04-13 05:32:47.005342: train_loss -0.4023 +2026-04-13 05:32:47.013851: val_loss -0.3448 +2026-04-13 05:32:47.016753: Pseudo dice [0.7302, 0.1027, 0.6651, 0.7024, 0.4716, 0.3192, 0.8131] +2026-04-13 05:32:47.019270: Epoch time: 101.74 s +2026-04-13 05:32:48.194533: +2026-04-13 05:32:48.196594: Epoch 2338 +2026-04-13 05:32:48.198436: Current learning rate: 0.00454 +2026-04-13 05:34:29.731286: train_loss -0.4077 +2026-04-13 05:34:29.737382: val_loss -0.2752 +2026-04-13 05:34:29.739404: Pseudo dice [0.5527, 0.8667, 0.532, 0.4907, 0.1836, 0.1374, 0.7568] +2026-04-13 05:34:29.742033: Epoch time: 101.54 s +2026-04-13 05:34:30.902765: +2026-04-13 05:34:30.904757: Epoch 2339 +2026-04-13 05:34:30.906497: Current learning rate: 0.00453 +2026-04-13 05:36:12.464479: train_loss -0.3988 +2026-04-13 05:36:12.470462: val_loss -0.3557 +2026-04-13 05:36:12.472490: Pseudo dice [0.4982, 0.4241, 0.7041, 0.6654, 0.4453, 0.2716, 0.6118] +2026-04-13 05:36:12.475138: Epoch time: 101.56 s +2026-04-13 05:36:13.627702: +2026-04-13 05:36:13.629258: Epoch 2340 +2026-04-13 05:36:13.630654: Current learning rate: 0.00453 +2026-04-13 05:37:55.134473: train_loss -0.4102 +2026-04-13 05:37:55.140462: val_loss -0.3536 +2026-04-13 05:37:55.142301: Pseudo dice [0.6092, 0.5883, 0.7419, 0.7715, 0.0636, 0.0576, 0.7574] +2026-04-13 05:37:55.144838: Epoch time: 101.51 s +2026-04-13 05:37:56.293243: +2026-04-13 05:37:56.294952: Epoch 2341 +2026-04-13 05:37:56.297102: Current learning rate: 0.00453 +2026-04-13 05:39:38.311606: train_loss -0.3762 +2026-04-13 05:39:38.318362: val_loss -0.3414 +2026-04-13 05:39:38.320409: Pseudo dice [0.6354, 0.6701, 0.7526, 0.3815, 0.376, 0.7121, 0.7983] +2026-04-13 05:39:38.323113: Epoch time: 102.02 s +2026-04-13 05:39:39.481243: +2026-04-13 05:39:39.482977: Epoch 2342 +2026-04-13 05:39:39.484560: Current learning rate: 0.00453 +2026-04-13 05:41:21.138751: train_loss -0.3908 +2026-04-13 05:41:21.145139: val_loss -0.3415 +2026-04-13 05:41:21.147482: Pseudo dice [0.5308, 0.2055, 0.7435, 0.1539, 0.3578, 0.8405, 0.7436] +2026-04-13 05:41:21.150076: Epoch time: 101.66 s +2026-04-13 05:41:22.321018: +2026-04-13 05:41:22.323068: Epoch 2343 +2026-04-13 05:41:22.324735: Current learning rate: 0.00452 +2026-04-13 05:43:03.742151: train_loss -0.3761 +2026-04-13 05:43:03.768366: val_loss -0.3365 +2026-04-13 05:43:03.770128: Pseudo dice [0.4647, 0.7394, 0.5121, 0.2161, 0.4274, 0.1348, 0.6528] +2026-04-13 05:43:03.772713: Epoch time: 101.42 s +2026-04-13 05:43:06.012384: +2026-04-13 05:43:06.013947: Epoch 2344 +2026-04-13 05:43:06.015451: Current learning rate: 0.00452 +2026-04-13 05:44:47.878282: train_loss -0.4034 +2026-04-13 05:44:47.885348: val_loss -0.3649 +2026-04-13 05:44:47.888105: Pseudo dice [0.4634, 0.8781, 0.6396, 0.4182, 0.5419, 0.8126, 0.7911] +2026-04-13 05:44:47.890059: Epoch time: 101.87 s +2026-04-13 05:44:49.043080: +2026-04-13 05:44:49.044914: Epoch 2345 +2026-04-13 05:44:49.047870: Current learning rate: 0.00452 +2026-04-13 05:46:30.750802: train_loss -0.4054 +2026-04-13 05:46:30.759180: val_loss -0.3635 +2026-04-13 05:46:30.761160: Pseudo dice [0.5522, 0.3969, 0.7221, 0.3585, 0.485, 0.9035, 0.7149] +2026-04-13 05:46:30.763207: Epoch time: 101.71 s +2026-04-13 05:46:31.924947: +2026-04-13 05:46:31.927257: Epoch 2346 +2026-04-13 05:46:31.929235: Current learning rate: 0.00452 +2026-04-13 05:48:13.425110: train_loss -0.3967 +2026-04-13 05:48:13.430702: val_loss -0.3842 +2026-04-13 05:48:13.432529: Pseudo dice [0.6266, 0.7311, 0.8438, 0.4621, 0.3823, 0.9079, 0.87] +2026-04-13 05:48:13.434736: Epoch time: 101.5 s +2026-04-13 05:48:14.593748: +2026-04-13 05:48:14.595313: Epoch 2347 +2026-04-13 05:48:14.596736: Current learning rate: 0.00451 +2026-04-13 05:49:56.042680: train_loss -0.4083 +2026-04-13 05:49:56.048711: val_loss -0.3075 +2026-04-13 05:49:56.050740: Pseudo dice [0.413, 0.5926, 0.5756, 0.2297, 0.3548, 0.3206, 0.7576] +2026-04-13 05:49:56.054215: Epoch time: 101.45 s +2026-04-13 05:49:57.193143: +2026-04-13 05:49:57.194654: Epoch 2348 +2026-04-13 05:49:57.196123: Current learning rate: 0.00451 +2026-04-13 05:51:39.443958: train_loss -0.4063 +2026-04-13 05:51:39.450382: val_loss -0.3511 +2026-04-13 05:51:39.452356: Pseudo dice [0.6826, 0.907, 0.7115, 0.438, 0.3082, 0.2303, 0.796] +2026-04-13 05:51:39.454341: Epoch time: 102.25 s +2026-04-13 05:51:40.626803: +2026-04-13 05:51:40.628740: Epoch 2349 +2026-04-13 05:51:40.630211: Current learning rate: 0.00451 +2026-04-13 05:53:22.396939: train_loss -0.3995 +2026-04-13 05:53:22.404019: val_loss -0.3744 +2026-04-13 05:53:22.406146: Pseudo dice [0.7471, 0.4085, 0.724, 0.7799, 0.4267, 0.8734, 0.6882] +2026-04-13 05:53:22.408245: Epoch time: 101.77 s +2026-04-13 05:53:25.157521: +2026-04-13 05:53:25.159703: Epoch 2350 +2026-04-13 05:53:25.161096: Current learning rate: 0.00451 +2026-04-13 05:55:06.927397: train_loss -0.4003 +2026-04-13 05:55:06.933102: val_loss -0.2896 +2026-04-13 05:55:06.935496: Pseudo dice [0.3265, 0.8682, 0.4731, 0.3055, 0.2679, 0.6477, 0.5614] +2026-04-13 05:55:06.937696: Epoch time: 101.77 s +2026-04-13 05:55:08.082675: +2026-04-13 05:55:08.084502: Epoch 2351 +2026-04-13 05:55:08.086021: Current learning rate: 0.0045 +2026-04-13 05:56:50.175646: train_loss -0.399 +2026-04-13 05:56:50.181710: val_loss -0.3642 +2026-04-13 05:56:50.184772: Pseudo dice [0.3331, 0.7929, 0.6608, 0.3812, 0.5131, 0.8352, 0.8691] +2026-04-13 05:56:50.188119: Epoch time: 102.1 s +2026-04-13 05:56:51.325321: +2026-04-13 05:56:51.327864: Epoch 2352 +2026-04-13 05:56:51.329538: Current learning rate: 0.0045 +2026-04-13 05:58:33.281367: train_loss -0.3989 +2026-04-13 05:58:33.286877: val_loss -0.3185 +2026-04-13 05:58:33.289896: Pseudo dice [0.4828, 0.6028, 0.3715, 0.0294, 0.5913, 0.4516, 0.5079] +2026-04-13 05:58:33.293200: Epoch time: 101.96 s +2026-04-13 05:58:34.448253: +2026-04-13 05:58:34.449914: Epoch 2353 +2026-04-13 05:58:34.451365: Current learning rate: 0.0045 +2026-04-13 06:00:15.998032: train_loss -0.3846 +2026-04-13 06:00:16.004403: val_loss -0.2721 +2026-04-13 06:00:16.006216: Pseudo dice [0.5321, 0.7639, 0.475, 0.3966, 0.6081, 0.0914, 0.8158] +2026-04-13 06:00:16.008276: Epoch time: 101.55 s +2026-04-13 06:00:17.163101: +2026-04-13 06:00:17.165006: Epoch 2354 +2026-04-13 06:00:17.166558: Current learning rate: 0.0045 +2026-04-13 06:01:59.447659: train_loss -0.4022 +2026-04-13 06:01:59.455126: val_loss -0.346 +2026-04-13 06:01:59.456899: Pseudo dice [0.3331, 0.8957, 0.6889, 0.7213, 0.5071, 0.4508, 0.6398] +2026-04-13 06:01:59.460667: Epoch time: 102.29 s +2026-04-13 06:02:00.635052: +2026-04-13 06:02:00.636944: Epoch 2355 +2026-04-13 06:02:00.648037: Current learning rate: 0.00449 +2026-04-13 06:03:42.533928: train_loss -0.3919 +2026-04-13 06:03:42.541252: val_loss -0.3468 +2026-04-13 06:03:42.543692: Pseudo dice [0.6553, 0.2462, 0.7711, 0.228, 0.4059, 0.7316, 0.6999] +2026-04-13 06:03:42.545954: Epoch time: 101.9 s +2026-04-13 06:03:43.703340: +2026-04-13 06:03:43.705003: Epoch 2356 +2026-04-13 06:03:43.706704: Current learning rate: 0.00449 +2026-04-13 06:05:25.124777: train_loss -0.3918 +2026-04-13 06:05:25.135284: val_loss -0.345 +2026-04-13 06:05:25.140052: Pseudo dice [0.2333, 0.8727, 0.6257, 0.4565, 0.2557, 0.1533, 0.8421] +2026-04-13 06:05:25.144063: Epoch time: 101.42 s +2026-04-13 06:05:26.298204: +2026-04-13 06:05:26.300580: Epoch 2357 +2026-04-13 06:05:26.302667: Current learning rate: 0.00449 +2026-04-13 06:07:07.849581: train_loss -0.3772 +2026-04-13 06:07:07.856873: val_loss -0.3253 +2026-04-13 06:07:07.858919: Pseudo dice [0.5444, 0.5609, 0.6913, 0.5499, 0.6351, 0.8627, 0.5841] +2026-04-13 06:07:07.862384: Epoch time: 101.55 s +2026-04-13 06:07:09.015624: +2026-04-13 06:07:09.017867: Epoch 2358 +2026-04-13 06:07:09.019465: Current learning rate: 0.00449 +2026-04-13 06:08:50.760186: train_loss -0.3889 +2026-04-13 06:08:50.765993: val_loss -0.3338 +2026-04-13 06:08:50.768002: Pseudo dice [0.3141, 0.7929, 0.7476, 0.5053, 0.4147, 0.7146, 0.7193] +2026-04-13 06:08:50.770091: Epoch time: 101.75 s +2026-04-13 06:08:51.937115: +2026-04-13 06:08:51.939053: Epoch 2359 +2026-04-13 06:08:51.940933: Current learning rate: 0.00448 +2026-04-13 06:10:34.128311: train_loss -0.3949 +2026-04-13 06:10:34.138955: val_loss -0.3379 +2026-04-13 06:10:34.140949: Pseudo dice [0.6451, 0.7832, 0.7536, 0.4547, 0.2967, 0.703, 0.5141] +2026-04-13 06:10:34.145326: Epoch time: 102.19 s +2026-04-13 06:10:35.297122: +2026-04-13 06:10:35.298886: Epoch 2360 +2026-04-13 06:10:35.300942: Current learning rate: 0.00448 +2026-04-13 06:12:17.268900: train_loss -0.3796 +2026-04-13 06:12:17.277171: val_loss -0.313 +2026-04-13 06:12:17.278934: Pseudo dice [0.4247, 0.5127, 0.5037, 0.0358, 0.4628, 0.9335, 0.7832] +2026-04-13 06:12:17.281400: Epoch time: 101.97 s +2026-04-13 06:12:18.451102: +2026-04-13 06:12:18.453420: Epoch 2361 +2026-04-13 06:12:18.455020: Current learning rate: 0.00448 +2026-04-13 06:14:00.627910: train_loss -0.3886 +2026-04-13 06:14:00.634209: val_loss -0.3851 +2026-04-13 06:14:00.635914: Pseudo dice [0.4123, 0.6624, 0.6641, 0.6876, 0.3378, 0.7032, 0.8443] +2026-04-13 06:14:00.638439: Epoch time: 102.18 s +2026-04-13 06:14:01.790016: +2026-04-13 06:14:01.791883: Epoch 2362 +2026-04-13 06:14:01.793409: Current learning rate: 0.00448 +2026-04-13 06:15:43.714992: train_loss -0.4035 +2026-04-13 06:15:43.720567: val_loss -0.3639 +2026-04-13 06:15:43.722425: Pseudo dice [0.4106, 0.5862, 0.7421, 0.3452, 0.5055, 0.5595, 0.7848] +2026-04-13 06:15:43.725707: Epoch time: 101.93 s +2026-04-13 06:15:44.887186: +2026-04-13 06:15:44.889132: Epoch 2363 +2026-04-13 06:15:44.891025: Current learning rate: 0.00447 +2026-04-13 06:17:26.340684: train_loss -0.4001 +2026-04-13 06:17:26.347422: val_loss -0.3209 +2026-04-13 06:17:26.350041: Pseudo dice [0.3485, 0.8643, 0.7479, 0.3422, 0.2262, 0.7123, 0.3945] +2026-04-13 06:17:26.352252: Epoch time: 101.46 s +2026-04-13 06:17:27.512889: +2026-04-13 06:17:27.525757: Epoch 2364 +2026-04-13 06:17:27.531017: Current learning rate: 0.00447 +2026-04-13 06:19:10.435506: train_loss -0.3662 +2026-04-13 06:19:10.440639: val_loss -0.3459 +2026-04-13 06:19:10.442462: Pseudo dice [0.2362, 0.7873, 0.7338, 0.548, 0.4059, 0.7523, 0.7102] +2026-04-13 06:19:10.444626: Epoch time: 102.93 s +2026-04-13 06:19:11.676169: +2026-04-13 06:19:11.678151: Epoch 2365 +2026-04-13 06:19:11.679945: Current learning rate: 0.00447 +2026-04-13 06:20:53.687850: train_loss -0.406 +2026-04-13 06:20:53.694589: val_loss -0.3476 +2026-04-13 06:20:53.696672: Pseudo dice [0.6607, 0.527, 0.753, 0.3961, 0.4456, 0.8952, 0.7595] +2026-04-13 06:20:53.699596: Epoch time: 102.01 s +2026-04-13 06:20:54.847708: +2026-04-13 06:20:54.851301: Epoch 2366 +2026-04-13 06:20:54.853030: Current learning rate: 0.00447 +2026-04-13 06:22:36.774113: train_loss -0.4156 +2026-04-13 06:22:36.781202: val_loss -0.3018 +2026-04-13 06:22:36.783660: Pseudo dice [0.4492, 0.548, 0.5429, 0.2247, 0.5185, 0.3998, 0.6227] +2026-04-13 06:22:36.786443: Epoch time: 101.93 s +2026-04-13 06:22:37.951758: +2026-04-13 06:22:37.953645: Epoch 2367 +2026-04-13 06:22:37.955230: Current learning rate: 0.00447 +2026-04-13 06:24:19.439068: train_loss -0.3961 +2026-04-13 06:24:19.445927: val_loss -0.337 +2026-04-13 06:24:19.447951: Pseudo dice [0.5888, 0.3233, 0.6167, 0.1564, 0.3397, 0.9258, 0.8066] +2026-04-13 06:24:19.450301: Epoch time: 101.49 s +2026-04-13 06:24:20.589614: +2026-04-13 06:24:20.591274: Epoch 2368 +2026-04-13 06:24:20.593053: Current learning rate: 0.00446 +2026-04-13 06:26:02.409625: train_loss -0.3862 +2026-04-13 06:26:02.415451: val_loss -0.3322 +2026-04-13 06:26:02.417742: Pseudo dice [0.6832, 0.1751, 0.7584, 0.3507, 0.3551, 0.3256, 0.6408] +2026-04-13 06:26:02.420119: Epoch time: 101.82 s +2026-04-13 06:26:03.585288: +2026-04-13 06:26:03.587241: Epoch 2369 +2026-04-13 06:26:03.589643: Current learning rate: 0.00446 +2026-04-13 06:27:45.300727: train_loss -0.4154 +2026-04-13 06:27:45.307353: val_loss -0.3455 +2026-04-13 06:27:45.309983: Pseudo dice [0.5094, 0.1255, 0.6997, 0.4397, 0.4375, 0.645, 0.7846] +2026-04-13 06:27:45.312413: Epoch time: 101.72 s +2026-04-13 06:27:46.453928: +2026-04-13 06:27:46.455518: Epoch 2370 +2026-04-13 06:27:46.457416: Current learning rate: 0.00446 +2026-04-13 06:29:27.900874: train_loss -0.4143 +2026-04-13 06:29:27.906857: val_loss -0.3369 +2026-04-13 06:29:27.908629: Pseudo dice [0.5854, 0.5168, 0.7068, 0.4565, 0.451, 0.7159, 0.6481] +2026-04-13 06:29:27.911447: Epoch time: 101.45 s +2026-04-13 06:29:29.055389: +2026-04-13 06:29:29.056992: Epoch 2371 +2026-04-13 06:29:29.058440: Current learning rate: 0.00446 +2026-04-13 06:31:10.547703: train_loss -0.3809 +2026-04-13 06:31:10.553363: val_loss -0.3512 +2026-04-13 06:31:10.555334: Pseudo dice [0.5633, 0.6917, 0.7582, 0.5551, 0.3582, 0.5383, 0.5557] +2026-04-13 06:31:10.557293: Epoch time: 101.5 s +2026-04-13 06:31:11.706487: +2026-04-13 06:31:11.708046: Epoch 2372 +2026-04-13 06:31:11.709405: Current learning rate: 0.00445 +2026-04-13 06:32:53.234972: train_loss -0.3845 +2026-04-13 06:32:53.242629: val_loss -0.331 +2026-04-13 06:32:53.244582: Pseudo dice [0.1995, 0.3358, 0.6911, 0.1742, 0.4482, 0.839, 0.5393] +2026-04-13 06:32:53.246912: Epoch time: 101.53 s +2026-04-13 06:32:54.380825: +2026-04-13 06:32:54.383508: Epoch 2373 +2026-04-13 06:32:54.385100: Current learning rate: 0.00445 +2026-04-13 06:34:36.178880: train_loss -0.4086 +2026-04-13 06:34:36.184699: val_loss -0.3743 +2026-04-13 06:34:36.186848: Pseudo dice [0.6209, 0.6326, 0.7612, 0.4148, 0.523, 0.8243, 0.7354] +2026-04-13 06:34:36.189399: Epoch time: 101.8 s +2026-04-13 06:34:37.360123: +2026-04-13 06:34:37.362064: Epoch 2374 +2026-04-13 06:34:37.363644: Current learning rate: 0.00445 +2026-04-13 06:36:18.797036: train_loss -0.4101 +2026-04-13 06:36:18.803170: val_loss -0.3369 +2026-04-13 06:36:18.805871: Pseudo dice [0.6353, 0.6734, 0.6472, 0.5177, 0.1906, 0.5795, 0.7356] +2026-04-13 06:36:18.808328: Epoch time: 101.44 s +2026-04-13 06:36:19.999358: +2026-04-13 06:36:20.000982: Epoch 2375 +2026-04-13 06:36:20.002440: Current learning rate: 0.00445 +2026-04-13 06:38:01.572777: train_loss -0.4054 +2026-04-13 06:38:01.579271: val_loss -0.3144 +2026-04-13 06:38:01.581495: Pseudo dice [0.7693, 0.4877, 0.6538, 0.4605, 0.4927, 0.6954, 0.5536] +2026-04-13 06:38:01.583855: Epoch time: 101.58 s +2026-04-13 06:38:02.741185: +2026-04-13 06:38:02.742732: Epoch 2376 +2026-04-13 06:38:02.744191: Current learning rate: 0.00444 +2026-04-13 06:39:45.016968: train_loss -0.3687 +2026-04-13 06:39:45.023561: val_loss -0.332 +2026-04-13 06:39:45.025841: Pseudo dice [0.3545, 0.3235, 0.6175, 0.1895, 0.1744, 0.7069, 0.8064] +2026-04-13 06:39:45.028515: Epoch time: 102.28 s +2026-04-13 06:39:46.187973: +2026-04-13 06:39:46.189979: Epoch 2377 +2026-04-13 06:39:46.191707: Current learning rate: 0.00444 +2026-04-13 06:41:27.903526: train_loss -0.3836 +2026-04-13 06:41:27.909311: val_loss -0.3362 +2026-04-13 06:41:27.911372: Pseudo dice [0.4464, 0.7685, 0.6617, 0.6568, 0.4142, 0.2516, 0.8277] +2026-04-13 06:41:27.913759: Epoch time: 101.72 s +2026-04-13 06:41:29.092630: +2026-04-13 06:41:29.094252: Epoch 2378 +2026-04-13 06:41:29.095773: Current learning rate: 0.00444 +2026-04-13 06:43:10.688918: train_loss -0.4021 +2026-04-13 06:43:10.694779: val_loss -0.3478 +2026-04-13 06:43:10.697034: Pseudo dice [0.3826, 0.5578, 0.7629, 0.349, 0.6115, 0.8929, 0.7468] +2026-04-13 06:43:10.698902: Epoch time: 101.6 s +2026-04-13 06:43:11.846312: +2026-04-13 06:43:11.847946: Epoch 2379 +2026-04-13 06:43:11.849326: Current learning rate: 0.00444 +2026-04-13 06:44:53.556469: train_loss -0.4041 +2026-04-13 06:44:53.562487: val_loss -0.3579 +2026-04-13 06:44:53.564270: Pseudo dice [0.6901, 0.5716, 0.7258, 0.3415, 0.3068, 0.3978, 0.6338] +2026-04-13 06:44:53.566676: Epoch time: 101.71 s +2026-04-13 06:44:54.723972: +2026-04-13 06:44:54.725780: Epoch 2380 +2026-04-13 06:44:54.727270: Current learning rate: 0.00443 +2026-04-13 06:46:37.075645: train_loss -0.3809 +2026-04-13 06:46:37.081661: val_loss -0.3292 +2026-04-13 06:46:37.083575: Pseudo dice [0.2566, 0.5616, 0.6679, 0.1496, 0.4284, 0.8854, 0.6334] +2026-04-13 06:46:37.085966: Epoch time: 102.35 s +2026-04-13 06:46:38.275090: +2026-04-13 06:46:38.277786: Epoch 2381 +2026-04-13 06:46:38.279910: Current learning rate: 0.00443 +2026-04-13 06:48:20.365929: train_loss -0.3955 +2026-04-13 06:48:20.372787: val_loss -0.3654 +2026-04-13 06:48:20.374350: Pseudo dice [0.2084, 0.6917, 0.8029, 0.1445, 0.3738, 0.8783, 0.7958] +2026-04-13 06:48:20.376919: Epoch time: 102.09 s +2026-04-13 06:48:21.564331: +2026-04-13 06:48:21.566255: Epoch 2382 +2026-04-13 06:48:21.568035: Current learning rate: 0.00443 +2026-04-13 06:50:03.012018: train_loss -0.383 +2026-04-13 06:50:03.018413: val_loss -0.3521 +2026-04-13 06:50:03.020886: Pseudo dice [0.5085, 0.2057, 0.6414, 0.1628, 0.4753, 0.8592, 0.8294] +2026-04-13 06:50:03.023174: Epoch time: 101.45 s +2026-04-13 06:50:04.211504: +2026-04-13 06:50:04.213055: Epoch 2383 +2026-04-13 06:50:04.214419: Current learning rate: 0.00443 +2026-04-13 06:51:46.528721: train_loss -0.3951 +2026-04-13 06:51:46.536602: val_loss -0.3472 +2026-04-13 06:51:46.539638: Pseudo dice [0.7426, 0.5613, 0.6574, 0.4751, 0.6459, 0.7461, 0.434] +2026-04-13 06:51:46.543236: Epoch time: 102.32 s +2026-04-13 06:51:47.740694: +2026-04-13 06:51:47.744408: Epoch 2384 +2026-04-13 06:51:47.748119: Current learning rate: 0.00442 +2026-04-13 06:53:30.293653: train_loss -0.39 +2026-04-13 06:53:30.299655: val_loss -0.3538 +2026-04-13 06:53:30.301414: Pseudo dice [0.672, 0.7817, 0.8564, 0.2524, 0.3159, 0.3405, 0.4582] +2026-04-13 06:53:30.303604: Epoch time: 102.56 s +2026-04-13 06:53:32.595706: +2026-04-13 06:53:32.597320: Epoch 2385 +2026-04-13 06:53:32.598930: Current learning rate: 0.00442 +2026-04-13 06:55:14.278960: train_loss -0.3992 +2026-04-13 06:55:14.286245: val_loss -0.3309 +2026-04-13 06:55:14.289892: Pseudo dice [0.3352, 0.7849, 0.6056, 0.3765, 0.4665, 0.2093, 0.7502] +2026-04-13 06:55:14.292449: Epoch time: 101.69 s +2026-04-13 06:55:15.493367: +2026-04-13 06:55:15.496123: Epoch 2386 +2026-04-13 06:55:15.497933: Current learning rate: 0.00442 +2026-04-13 06:56:57.329160: train_loss -0.4154 +2026-04-13 06:56:57.335307: val_loss -0.3413 +2026-04-13 06:56:57.337011: Pseudo dice [0.6986, 0.8424, 0.7599, 0.4511, 0.2345, 0.224, 0.7483] +2026-04-13 06:56:57.339038: Epoch time: 101.84 s +2026-04-13 06:56:58.517029: +2026-04-13 06:56:58.519949: Epoch 2387 +2026-04-13 06:56:58.521632: Current learning rate: 0.00442 +2026-04-13 06:58:41.139724: train_loss -0.4222 +2026-04-13 06:58:41.146162: val_loss -0.3639 +2026-04-13 06:58:41.148007: Pseudo dice [0.5241, 0.772, 0.7771, 0.3848, 0.5458, 0.665, 0.773] +2026-04-13 06:58:41.150247: Epoch time: 102.63 s +2026-04-13 06:58:42.354342: +2026-04-13 06:58:42.357269: Epoch 2388 +2026-04-13 06:58:42.358837: Current learning rate: 0.00441 +2026-04-13 07:00:24.107778: train_loss -0.4143 +2026-04-13 07:00:24.114199: val_loss -0.373 +2026-04-13 07:00:24.117528: Pseudo dice [0.425, 0.4346, 0.7757, 0.524, 0.1977, 0.8703, 0.8345] +2026-04-13 07:00:24.120225: Epoch time: 101.76 s +2026-04-13 07:00:25.314559: +2026-04-13 07:00:25.316520: Epoch 2389 +2026-04-13 07:00:25.317987: Current learning rate: 0.00441 +2026-04-13 07:02:07.420212: train_loss -0.4143 +2026-04-13 07:02:07.426552: val_loss -0.3669 +2026-04-13 07:02:07.430344: Pseudo dice [0.6027, 0.2275, 0.6743, 0.106, 0.5501, 0.6301, 0.7194] +2026-04-13 07:02:07.433517: Epoch time: 102.11 s +2026-04-13 07:02:08.616782: +2026-04-13 07:02:08.618838: Epoch 2390 +2026-04-13 07:02:08.620779: Current learning rate: 0.00441 +2026-04-13 07:03:51.554311: train_loss -0.4181 +2026-04-13 07:03:51.560193: val_loss -0.3654 +2026-04-13 07:03:51.563367: Pseudo dice [0.6228, 0.803, 0.8492, 0.458, 0.6727, 0.6419, 0.6071] +2026-04-13 07:03:51.565665: Epoch time: 102.94 s +2026-04-13 07:03:52.760690: +2026-04-13 07:03:52.763504: Epoch 2391 +2026-04-13 07:03:52.765266: Current learning rate: 0.00441 +2026-04-13 07:05:34.463204: train_loss -0.394 +2026-04-13 07:05:34.469230: val_loss -0.3749 +2026-04-13 07:05:34.471209: Pseudo dice [0.4104, 0.5751, 0.8097, 0.7679, 0.3918, 0.5463, 0.6605] +2026-04-13 07:05:34.473279: Epoch time: 101.71 s +2026-04-13 07:05:35.664358: +2026-04-13 07:05:35.666561: Epoch 2392 +2026-04-13 07:05:35.668617: Current learning rate: 0.0044 +2026-04-13 07:07:17.461343: train_loss -0.3732 +2026-04-13 07:07:17.467558: val_loss -0.334 +2026-04-13 07:07:17.469835: Pseudo dice [0.479, 0.8488, 0.6057, 0.7609, 0.4109, 0.1891, 0.7548] +2026-04-13 07:07:17.472632: Epoch time: 101.8 s +2026-04-13 07:07:18.652261: +2026-04-13 07:07:18.654428: Epoch 2393 +2026-04-13 07:07:18.656328: Current learning rate: 0.0044 +2026-04-13 07:09:00.101299: train_loss -0.3957 +2026-04-13 07:09:00.107696: val_loss -0.3504 +2026-04-13 07:09:00.110239: Pseudo dice [0.2015, 0.8889, 0.7536, 0.418, 0.4782, 0.7001, 0.563] +2026-04-13 07:09:00.114215: Epoch time: 101.45 s +2026-04-13 07:09:01.280912: +2026-04-13 07:09:01.282554: Epoch 2394 +2026-04-13 07:09:01.284122: Current learning rate: 0.0044 +2026-04-13 07:10:43.061932: train_loss -0.4013 +2026-04-13 07:10:43.067526: val_loss -0.304 +2026-04-13 07:10:43.069731: Pseudo dice [0.0358, 0.9202, 0.5951, 0.2922, 0.1583, 0.3116, 0.6467] +2026-04-13 07:10:43.072212: Epoch time: 101.78 s +2026-04-13 07:10:44.267790: +2026-04-13 07:10:44.269294: Epoch 2395 +2026-04-13 07:10:44.270730: Current learning rate: 0.0044 +2026-04-13 07:12:25.882887: train_loss -0.4011 +2026-04-13 07:12:25.889284: val_loss -0.3261 +2026-04-13 07:12:25.891013: Pseudo dice [0.5663, 0.6231, 0.6173, 0.4062, 0.4112, 0.8814, 0.7707] +2026-04-13 07:12:25.893197: Epoch time: 101.62 s +2026-04-13 07:12:27.059012: +2026-04-13 07:12:27.060864: Epoch 2396 +2026-04-13 07:12:27.062768: Current learning rate: 0.00439 +2026-04-13 07:14:08.954058: train_loss -0.392 +2026-04-13 07:14:08.960721: val_loss -0.3663 +2026-04-13 07:14:08.963441: Pseudo dice [0.4321, 0.0232, 0.7616, 0.4724, 0.5159, 0.8994, 0.815] +2026-04-13 07:14:08.965834: Epoch time: 101.9 s +2026-04-13 07:14:10.146416: +2026-04-13 07:14:10.148055: Epoch 2397 +2026-04-13 07:14:10.149599: Current learning rate: 0.00439 +2026-04-13 07:15:51.765710: train_loss -0.4038 +2026-04-13 07:15:51.773574: val_loss -0.3626 +2026-04-13 07:15:51.777033: Pseudo dice [0.4383, 0.7967, 0.7737, 0.6479, 0.4294, 0.7623, 0.6847] +2026-04-13 07:15:51.779760: Epoch time: 101.62 s +2026-04-13 07:15:52.989984: +2026-04-13 07:15:52.991797: Epoch 2398 +2026-04-13 07:15:52.993377: Current learning rate: 0.00439 +2026-04-13 07:17:35.345406: train_loss -0.4107 +2026-04-13 07:17:35.350990: val_loss -0.364 +2026-04-13 07:17:35.353368: Pseudo dice [0.6265, 0.5503, 0.8049, 0.4849, 0.5107, 0.8041, 0.7681] +2026-04-13 07:17:35.355604: Epoch time: 102.36 s +2026-04-13 07:17:36.540290: +2026-04-13 07:17:36.542845: Epoch 2399 +2026-04-13 07:17:36.544362: Current learning rate: 0.00439 +2026-04-13 07:19:18.173018: train_loss -0.4066 +2026-04-13 07:19:18.181062: val_loss -0.3443 +2026-04-13 07:19:18.183062: Pseudo dice [0.6963, 0.892, 0.6512, 0.1677, 0.5475, 0.1984, 0.778] +2026-04-13 07:19:18.186367: Epoch time: 101.64 s +2026-04-13 07:19:20.840441: +2026-04-13 07:19:20.842616: Epoch 2400 +2026-04-13 07:19:20.844187: Current learning rate: 0.00438 +2026-04-13 07:21:02.647087: train_loss -0.4137 +2026-04-13 07:21:02.654663: val_loss -0.3339 +2026-04-13 07:21:02.656628: Pseudo dice [0.2073, 0.6177, 0.7134, 0.3558, 0.4291, 0.054, 0.5812] +2026-04-13 07:21:02.659105: Epoch time: 101.81 s +2026-04-13 07:21:03.831841: +2026-04-13 07:21:03.833701: Epoch 2401 +2026-04-13 07:21:03.835222: Current learning rate: 0.00438 +2026-04-13 07:22:45.863637: train_loss -0.4179 +2026-04-13 07:22:45.869899: val_loss -0.3509 +2026-04-13 07:22:45.872148: Pseudo dice [0.582, 0.3453, 0.8227, 0.3198, 0.3005, 0.823, 0.726] +2026-04-13 07:22:45.875694: Epoch time: 102.04 s +2026-04-13 07:22:47.049902: +2026-04-13 07:22:47.051659: Epoch 2402 +2026-04-13 07:22:47.053198: Current learning rate: 0.00438 +2026-04-13 07:24:28.739169: train_loss -0.3809 +2026-04-13 07:24:28.746030: val_loss -0.2787 +2026-04-13 07:24:28.747869: Pseudo dice [0.4659, 0.7136, 0.519, 0.4466, 0.1579, 0.3044, 0.8038] +2026-04-13 07:24:28.750241: Epoch time: 101.69 s +2026-04-13 07:24:29.928915: +2026-04-13 07:24:29.930843: Epoch 2403 +2026-04-13 07:24:29.932816: Current learning rate: 0.00438 +2026-04-13 07:26:11.898559: train_loss -0.3753 +2026-04-13 07:26:11.904730: val_loss -0.3441 +2026-04-13 07:26:11.906777: Pseudo dice [0.7725, 0.8745, 0.7019, 0.378, 0.4219, 0.4717, 0.7632] +2026-04-13 07:26:11.910218: Epoch time: 101.97 s +2026-04-13 07:26:13.109076: +2026-04-13 07:26:13.110924: Epoch 2404 +2026-04-13 07:26:13.112555: Current learning rate: 0.00437 +2026-04-13 07:27:54.935542: train_loss -0.4083 +2026-04-13 07:27:54.943347: val_loss -0.3349 +2026-04-13 07:27:54.948163: Pseudo dice [0.3827, 0.7562, 0.6892, 0.1608, 0.5133, 0.7716, 0.555] +2026-04-13 07:27:54.950566: Epoch time: 101.83 s +2026-04-13 07:27:56.125969: +2026-04-13 07:27:56.127999: Epoch 2405 +2026-04-13 07:27:56.132910: Current learning rate: 0.00437 +2026-04-13 07:29:38.722291: train_loss -0.3978 +2026-04-13 07:29:38.727571: val_loss -0.3565 +2026-04-13 07:29:38.729447: Pseudo dice [0.5807, 0.5594, 0.8069, 0.6907, 0.3917, 0.2804, 0.7921] +2026-04-13 07:29:38.731838: Epoch time: 102.6 s +2026-04-13 07:29:39.909934: +2026-04-13 07:29:39.912242: Epoch 2406 +2026-04-13 07:29:39.913867: Current learning rate: 0.00437 +2026-04-13 07:31:21.167955: train_loss -0.4118 +2026-04-13 07:31:21.173792: val_loss -0.3275 +2026-04-13 07:31:21.175892: Pseudo dice [0.8453, 0.9174, 0.5803, 0.4957, 0.5506, 0.6368, 0.7129] +2026-04-13 07:31:21.178511: Epoch time: 101.26 s +2026-04-13 07:31:22.362186: +2026-04-13 07:31:22.363793: Epoch 2407 +2026-04-13 07:31:22.365327: Current learning rate: 0.00437 +2026-04-13 07:33:04.283590: train_loss -0.4019 +2026-04-13 07:33:04.289345: val_loss -0.3366 +2026-04-13 07:33:04.291466: Pseudo dice [0.7602, 0.8288, 0.8315, 0.2561, 0.6576, 0.2718, 0.2452] +2026-04-13 07:33:04.294465: Epoch time: 101.92 s +2026-04-13 07:33:05.475286: +2026-04-13 07:33:05.477035: Epoch 2408 +2026-04-13 07:33:05.478476: Current learning rate: 0.00436 +2026-04-13 07:34:47.675470: train_loss -0.4105 +2026-04-13 07:34:47.681468: val_loss -0.3859 +2026-04-13 07:34:47.683937: Pseudo dice [0.7789, 0.3934, 0.7302, 0.6375, 0.3487, 0.8919, 0.6139] +2026-04-13 07:34:47.687144: Epoch time: 102.2 s +2026-04-13 07:34:48.877239: +2026-04-13 07:34:48.878929: Epoch 2409 +2026-04-13 07:34:48.880713: Current learning rate: 0.00436 +2026-04-13 07:36:30.636325: train_loss -0.4108 +2026-04-13 07:36:30.641390: val_loss -0.3765 +2026-04-13 07:36:30.643025: Pseudo dice [0.5312, 0.627, 0.4135, 0.7441, 0.7133, 0.5917, 0.6395] +2026-04-13 07:36:30.644899: Epoch time: 101.76 s +2026-04-13 07:36:31.849458: +2026-04-13 07:36:31.851158: Epoch 2410 +2026-04-13 07:36:31.852756: Current learning rate: 0.00436 +2026-04-13 07:38:13.624877: train_loss -0.4042 +2026-04-13 07:38:13.630969: val_loss -0.376 +2026-04-13 07:38:13.632805: Pseudo dice [0.5401, 0.8962, 0.7792, 0.521, 0.5271, 0.2887, 0.5583] +2026-04-13 07:38:13.635273: Epoch time: 101.78 s +2026-04-13 07:38:14.808098: +2026-04-13 07:38:14.809646: Epoch 2411 +2026-04-13 07:38:14.811135: Current learning rate: 0.00436 +2026-04-13 07:39:56.380473: train_loss -0.3894 +2026-04-13 07:39:56.387955: val_loss -0.3621 +2026-04-13 07:39:56.389854: Pseudo dice [0.619, 0.7811, 0.8061, 0.3143, 0.3738, 0.8246, 0.7439] +2026-04-13 07:39:56.391954: Epoch time: 101.58 s +2026-04-13 07:39:57.590488: +2026-04-13 07:39:57.592381: Epoch 2412 +2026-04-13 07:39:57.594303: Current learning rate: 0.00435 +2026-04-13 07:41:39.209062: train_loss -0.3641 +2026-04-13 07:41:39.215936: val_loss -0.3598 +2026-04-13 07:41:39.218238: Pseudo dice [0.6623, 0.8944, 0.6585, 0.2979, 0.3492, 0.2688, 0.7462] +2026-04-13 07:41:39.221016: Epoch time: 101.62 s +2026-04-13 07:41:40.412502: +2026-04-13 07:41:40.414208: Epoch 2413 +2026-04-13 07:41:40.415758: Current learning rate: 0.00435 +2026-04-13 07:43:21.910471: train_loss -0.3931 +2026-04-13 07:43:21.915657: val_loss -0.3451 +2026-04-13 07:43:21.918036: Pseudo dice [0.5031, 0.0271, 0.7957, 0.4024, 0.2927, 0.5793, 0.8271] +2026-04-13 07:43:21.920048: Epoch time: 101.5 s +2026-04-13 07:43:23.091466: +2026-04-13 07:43:23.093260: Epoch 2414 +2026-04-13 07:43:23.094855: Current learning rate: 0.00435 +2026-04-13 07:45:05.058354: train_loss -0.386 +2026-04-13 07:45:05.064121: val_loss -0.3043 +2026-04-13 07:45:05.066069: Pseudo dice [0.4818, 0.4203, 0.6511, 0.1337, 0.3109, 0.7899, 0.8454] +2026-04-13 07:45:05.068449: Epoch time: 101.97 s +2026-04-13 07:45:06.246857: +2026-04-13 07:45:06.248589: Epoch 2415 +2026-04-13 07:45:06.250482: Current learning rate: 0.00435 +2026-04-13 07:46:48.145504: train_loss -0.3989 +2026-04-13 07:46:48.151364: val_loss -0.3685 +2026-04-13 07:46:48.153591: Pseudo dice [0.6661, 0.8947, 0.7581, 0.0118, 0.5034, 0.3118, 0.8493] +2026-04-13 07:46:48.155904: Epoch time: 101.9 s +2026-04-13 07:46:49.378909: +2026-04-13 07:46:49.381012: Epoch 2416 +2026-04-13 07:46:49.382694: Current learning rate: 0.00434 +2026-04-13 07:48:31.435922: train_loss -0.3963 +2026-04-13 07:48:31.442324: val_loss -0.3425 +2026-04-13 07:48:31.444711: Pseudo dice [0.5899, 0.8956, 0.7321, 0.2466, 0.37, 0.2372, 0.7382] +2026-04-13 07:48:31.446985: Epoch time: 102.06 s +2026-04-13 07:48:32.613913: +2026-04-13 07:48:32.616193: Epoch 2417 +2026-04-13 07:48:32.618109: Current learning rate: 0.00434 +2026-04-13 07:50:14.246082: train_loss -0.3978 +2026-04-13 07:50:14.252681: val_loss -0.3516 +2026-04-13 07:50:14.255028: Pseudo dice [0.4455, 0.7434, 0.677, 0.5689, 0.4108, 0.681, 0.708] +2026-04-13 07:50:14.257428: Epoch time: 101.64 s +2026-04-13 07:50:15.433190: +2026-04-13 07:50:15.436942: Epoch 2418 +2026-04-13 07:50:15.438931: Current learning rate: 0.00434 +2026-04-13 07:51:57.036525: train_loss -0.402 +2026-04-13 07:51:57.041811: val_loss -0.3305 +2026-04-13 07:51:57.044279: Pseudo dice [0.6111, 0.8509, 0.628, 0.323, 0.2622, 0.7755, 0.6792] +2026-04-13 07:51:57.046640: Epoch time: 101.61 s +2026-04-13 07:51:58.208743: +2026-04-13 07:51:58.210263: Epoch 2419 +2026-04-13 07:51:58.211720: Current learning rate: 0.00434 +2026-04-13 07:53:39.903368: train_loss -0.3945 +2026-04-13 07:53:39.909818: val_loss -0.368 +2026-04-13 07:53:39.911972: Pseudo dice [0.189, 0.555, 0.7559, 0.7144, 0.6174, 0.8626, 0.5277] +2026-04-13 07:53:39.914117: Epoch time: 101.7 s +2026-04-13 07:53:41.097145: +2026-04-13 07:53:41.098654: Epoch 2420 +2026-04-13 07:53:41.100061: Current learning rate: 0.00433 +2026-04-13 07:55:23.302907: train_loss -0.4205 +2026-04-13 07:55:23.309053: val_loss -0.3585 +2026-04-13 07:55:23.311756: Pseudo dice [0.6763, 0.6705, 0.7405, 0.1891, 0.524, 0.4061, 0.6905] +2026-04-13 07:55:23.314118: Epoch time: 102.21 s +2026-04-13 07:55:24.504131: +2026-04-13 07:55:24.505781: Epoch 2421 +2026-04-13 07:55:24.510402: Current learning rate: 0.00433 +2026-04-13 07:57:06.165442: train_loss -0.4095 +2026-04-13 07:57:06.171474: val_loss -0.3568 +2026-04-13 07:57:06.173127: Pseudo dice [0.4805, 0.8753, 0.7572, 0.2671, 0.415, 0.4328, 0.8059] +2026-04-13 07:57:06.175243: Epoch time: 101.66 s +2026-04-13 07:57:07.382444: +2026-04-13 07:57:07.384584: Epoch 2422 +2026-04-13 07:57:07.386183: Current learning rate: 0.00433 +2026-04-13 07:58:48.964437: train_loss -0.4093 +2026-04-13 07:58:48.970703: val_loss -0.3665 +2026-04-13 07:58:48.973320: Pseudo dice [0.7914, 0.6155, 0.6712, 0.369, 0.1653, 0.7658, 0.8368] +2026-04-13 07:58:48.976154: Epoch time: 101.59 s +2026-04-13 07:58:50.152171: +2026-04-13 07:58:50.153975: Epoch 2423 +2026-04-13 07:58:50.155703: Current learning rate: 0.00433 +2026-04-13 08:00:32.061889: train_loss -0.3862 +2026-04-13 08:00:32.068719: val_loss -0.3316 +2026-04-13 08:00:32.070369: Pseudo dice [0.5761, 0.8986, 0.7497, 0.0364, 0.0006, 0.6209, 0.7235] +2026-04-13 08:00:32.073149: Epoch time: 101.91 s +2026-04-13 08:00:33.232752: +2026-04-13 08:00:33.234417: Epoch 2424 +2026-04-13 08:00:33.236160: Current learning rate: 0.00432 +2026-04-13 08:02:15.199283: train_loss -0.3768 +2026-04-13 08:02:15.205447: val_loss -0.3576 +2026-04-13 08:02:15.207247: Pseudo dice [0.6497, 0.8294, 0.693, 0.1664, 0.4241, 0.7314, 0.3932] +2026-04-13 08:02:15.209607: Epoch time: 101.97 s +2026-04-13 08:02:16.380091: +2026-04-13 08:02:16.381782: Epoch 2425 +2026-04-13 08:02:16.383992: Current learning rate: 0.00432 +2026-04-13 08:03:58.571375: train_loss -0.3848 +2026-04-13 08:03:58.577789: val_loss -0.3198 +2026-04-13 08:03:58.580030: Pseudo dice [0.6821, 0.9212, 0.757, 0.2973, 0.2979, 0.5046, 0.7755] +2026-04-13 08:03:58.582665: Epoch time: 102.19 s +2026-04-13 08:04:00.855407: +2026-04-13 08:04:00.857081: Epoch 2426 +2026-04-13 08:04:00.858968: Current learning rate: 0.00432 +2026-04-13 08:05:42.560620: train_loss -0.3815 +2026-04-13 08:05:42.566955: val_loss -0.3412 +2026-04-13 08:05:42.569808: Pseudo dice [0.5241, 0.7817, 0.6623, 0.344, 0.3325, 0.9001, 0.8063] +2026-04-13 08:05:42.572170: Epoch time: 101.71 s +2026-04-13 08:05:43.736121: +2026-04-13 08:05:43.738308: Epoch 2427 +2026-04-13 08:05:43.740432: Current learning rate: 0.00432 +2026-04-13 08:07:26.032752: train_loss -0.4019 +2026-04-13 08:07:26.040131: val_loss -0.3582 +2026-04-13 08:07:26.043278: Pseudo dice [0.6604, 0.4078, 0.74, 0.3057, 0.2526, 0.8471, 0.7837] +2026-04-13 08:07:26.046079: Epoch time: 102.3 s +2026-04-13 08:07:27.228895: +2026-04-13 08:07:27.230831: Epoch 2428 +2026-04-13 08:07:27.233364: Current learning rate: 0.00431 +2026-04-13 08:09:08.955980: train_loss -0.4083 +2026-04-13 08:09:08.961494: val_loss -0.3457 +2026-04-13 08:09:08.963974: Pseudo dice [0.6113, 0.1141, 0.6631, 0.3559, 0.5409, 0.4323, 0.5555] +2026-04-13 08:09:08.966574: Epoch time: 101.73 s +2026-04-13 08:09:10.145050: +2026-04-13 08:09:10.147936: Epoch 2429 +2026-04-13 08:09:10.149602: Current learning rate: 0.00431 +2026-04-13 08:10:52.294025: train_loss -0.4193 +2026-04-13 08:10:52.300440: val_loss -0.3391 +2026-04-13 08:10:52.304499: Pseudo dice [0.7939, 0.5712, 0.722, 0.5479, 0.3659, 0.6645, 0.5965] +2026-04-13 08:10:52.306952: Epoch time: 102.15 s +2026-04-13 08:10:53.498520: +2026-04-13 08:10:53.500554: Epoch 2430 +2026-04-13 08:10:53.503066: Current learning rate: 0.00431 +2026-04-13 08:12:35.484941: train_loss -0.412 +2026-04-13 08:12:35.491786: val_loss -0.2806 +2026-04-13 08:12:35.494800: Pseudo dice [0.3909, 0.836, 0.6655, 0.0969, 0.5067, 0.5168, 0.3463] +2026-04-13 08:12:35.497133: Epoch time: 101.99 s +2026-04-13 08:12:36.665343: +2026-04-13 08:12:36.667205: Epoch 2431 +2026-04-13 08:12:36.669595: Current learning rate: 0.00431 +2026-04-13 08:14:19.116419: train_loss -0.4006 +2026-04-13 08:14:19.122535: val_loss -0.3579 +2026-04-13 08:14:19.124499: Pseudo dice [0.5233, 0.6681, 0.7112, 0.0024, 0.4408, 0.7904, 0.8211] +2026-04-13 08:14:19.126872: Epoch time: 102.45 s +2026-04-13 08:14:20.308661: +2026-04-13 08:14:20.310485: Epoch 2432 +2026-04-13 08:14:20.312457: Current learning rate: 0.0043 +2026-04-13 08:16:02.310348: train_loss -0.3921 +2026-04-13 08:16:02.317545: val_loss -0.3432 +2026-04-13 08:16:02.319587: Pseudo dice [0.7236, 0.8043, 0.6858, 0.2458, 0.4044, 0.6725, 0.3537] +2026-04-13 08:16:02.321563: Epoch time: 102.0 s +2026-04-13 08:16:03.516998: +2026-04-13 08:16:03.519158: Epoch 2433 +2026-04-13 08:16:03.521141: Current learning rate: 0.0043 +2026-04-13 08:17:45.305529: train_loss -0.3849 +2026-04-13 08:17:45.311934: val_loss -0.35 +2026-04-13 08:17:45.315204: Pseudo dice [0.6185, 0.7097, 0.7734, 0.3708, 0.4782, 0.4162, 0.8202] +2026-04-13 08:17:45.317884: Epoch time: 101.79 s +2026-04-13 08:17:46.510776: +2026-04-13 08:17:46.512546: Epoch 2434 +2026-04-13 08:17:46.514919: Current learning rate: 0.0043 +2026-04-13 08:19:28.389616: train_loss -0.3949 +2026-04-13 08:19:28.395526: val_loss -0.3521 +2026-04-13 08:19:28.397838: Pseudo dice [0.4642, 0.8287, 0.7685, 0.3848, 0.3731, 0.7256, 0.8226] +2026-04-13 08:19:28.400850: Epoch time: 101.88 s +2026-04-13 08:19:29.605837: +2026-04-13 08:19:29.607888: Epoch 2435 +2026-04-13 08:19:29.609923: Current learning rate: 0.0043 +2026-04-13 08:21:11.662941: train_loss -0.4017 +2026-04-13 08:21:11.670373: val_loss -0.3733 +2026-04-13 08:21:11.673618: Pseudo dice [0.291, 0.8583, 0.7917, 0.6406, 0.4873, 0.7172, 0.6885] +2026-04-13 08:21:11.676152: Epoch time: 102.06 s +2026-04-13 08:21:12.841804: +2026-04-13 08:21:12.843620: Epoch 2436 +2026-04-13 08:21:12.845823: Current learning rate: 0.00429 +2026-04-13 08:22:54.888957: train_loss -0.4197 +2026-04-13 08:22:54.895330: val_loss -0.3342 +2026-04-13 08:22:54.897870: Pseudo dice [0.6673, 0.7138, 0.6271, 0.3963, 0.1217, 0.1225, 0.6666] +2026-04-13 08:22:54.900057: Epoch time: 102.05 s +2026-04-13 08:22:56.066800: +2026-04-13 08:22:56.068794: Epoch 2437 +2026-04-13 08:22:56.071118: Current learning rate: 0.00429 +2026-04-13 08:24:37.721360: train_loss -0.3984 +2026-04-13 08:24:37.728079: val_loss -0.3506 +2026-04-13 08:24:37.730833: Pseudo dice [0.224, 0.5299, 0.7447, 0.7477, 0.6473, 0.1379, 0.2948] +2026-04-13 08:24:37.733210: Epoch time: 101.66 s +2026-04-13 08:24:38.925040: +2026-04-13 08:24:38.927022: Epoch 2438 +2026-04-13 08:24:38.928873: Current learning rate: 0.00429 +2026-04-13 08:26:21.182449: train_loss -0.3837 +2026-04-13 08:26:21.189781: val_loss -0.3525 +2026-04-13 08:26:21.192785: Pseudo dice [0.7478, 0.5106, 0.6688, 0.6977, 0.3583, 0.3578, 0.776] +2026-04-13 08:26:21.195772: Epoch time: 102.26 s +2026-04-13 08:26:22.427739: +2026-04-13 08:26:22.429629: Epoch 2439 +2026-04-13 08:26:22.432104: Current learning rate: 0.00429 +2026-04-13 08:28:04.264460: train_loss -0.4057 +2026-04-13 08:28:04.270708: val_loss -0.3473 +2026-04-13 08:28:04.272743: Pseudo dice [0.7036, 0.4084, 0.7619, 0.3359, 0.2808, 0.9089, 0.7565] +2026-04-13 08:28:04.274631: Epoch time: 101.84 s +2026-04-13 08:28:05.436523: +2026-04-13 08:28:05.438117: Epoch 2440 +2026-04-13 08:28:05.440132: Current learning rate: 0.00429 +2026-04-13 08:29:47.503640: train_loss -0.4176 +2026-04-13 08:29:47.511551: val_loss -0.3745 +2026-04-13 08:29:47.514381: Pseudo dice [0.6652, 0.7355, 0.8173, 0.4771, 0.4471, 0.771, 0.8251] +2026-04-13 08:29:47.517139: Epoch time: 102.07 s +2026-04-13 08:29:48.688886: +2026-04-13 08:29:48.691511: Epoch 2441 +2026-04-13 08:29:48.693186: Current learning rate: 0.00428 +2026-04-13 08:31:31.195362: train_loss -0.4179 +2026-04-13 08:31:31.201279: val_loss -0.3829 +2026-04-13 08:31:31.203556: Pseudo dice [0.6468, 0.2675, 0.7873, 0.5631, 0.6756, 0.8584, 0.4875] +2026-04-13 08:31:31.205986: Epoch time: 102.51 s +2026-04-13 08:31:32.387209: +2026-04-13 08:31:32.389315: Epoch 2442 +2026-04-13 08:31:32.391316: Current learning rate: 0.00428 +2026-04-13 08:33:14.283563: train_loss -0.418 +2026-04-13 08:33:14.290946: val_loss -0.3778 +2026-04-13 08:33:14.297771: Pseudo dice [0.369, 0.4069, 0.7376, 0.7437, 0.5324, 0.9176, 0.8378] +2026-04-13 08:33:14.300757: Epoch time: 101.9 s +2026-04-13 08:33:15.454153: +2026-04-13 08:33:15.456877: Epoch 2443 +2026-04-13 08:33:15.458667: Current learning rate: 0.00428 +2026-04-13 08:34:57.872306: train_loss -0.4193 +2026-04-13 08:34:57.879355: val_loss -0.3751 +2026-04-13 08:34:57.881399: Pseudo dice [0.6231, 0.5189, 0.7457, 0.5512, 0.4619, 0.3202, 0.8529] +2026-04-13 08:34:57.883703: Epoch time: 102.42 s +2026-04-13 08:34:59.069464: +2026-04-13 08:34:59.071209: Epoch 2444 +2026-04-13 08:34:59.073121: Current learning rate: 0.00428 +2026-04-13 08:36:41.129687: train_loss -0.4253 +2026-04-13 08:36:41.136296: val_loss -0.3528 +2026-04-13 08:36:41.138362: Pseudo dice [0.6609, 0.9148, 0.6619, 0.5896, 0.4936, 0.7367, 0.731] +2026-04-13 08:36:41.141557: Epoch time: 102.06 s +2026-04-13 08:36:42.328342: +2026-04-13 08:36:42.330734: Epoch 2445 +2026-04-13 08:36:42.332826: Current learning rate: 0.00427 +2026-04-13 08:38:24.354013: train_loss -0.422 +2026-04-13 08:38:24.359554: val_loss -0.3489 +2026-04-13 08:38:24.361315: Pseudo dice [0.5945, 0.1819, 0.6833, 0.438, 0.4435, 0.6207, 0.8532] +2026-04-13 08:38:24.363303: Epoch time: 102.03 s +2026-04-13 08:38:25.537338: +2026-04-13 08:38:25.538928: Epoch 2446 +2026-04-13 08:38:25.540721: Current learning rate: 0.00427 +2026-04-13 08:40:08.022417: train_loss -0.4184 +2026-04-13 08:40:08.028787: val_loss -0.351 +2026-04-13 08:40:08.031036: Pseudo dice [0.7932, 0.8898, 0.7157, 0.5318, 0.5907, 0.852, 0.7537] +2026-04-13 08:40:08.033487: Epoch time: 102.49 s +2026-04-13 08:40:09.227792: +2026-04-13 08:40:09.229871: Epoch 2447 +2026-04-13 08:40:09.231701: Current learning rate: 0.00427 +2026-04-13 08:41:51.256085: train_loss -0.4045 +2026-04-13 08:41:51.263369: val_loss -0.3774 +2026-04-13 08:41:51.265442: Pseudo dice [0.338, 0.8181, 0.783, 0.5684, 0.3863, 0.6515, 0.7468] +2026-04-13 08:41:51.268232: Epoch time: 102.03 s +2026-04-13 08:41:52.444576: +2026-04-13 08:41:52.446404: Epoch 2448 +2026-04-13 08:41:52.448845: Current learning rate: 0.00427 +2026-04-13 08:43:33.807695: train_loss -0.3758 +2026-04-13 08:43:33.814214: val_loss -0.3469 +2026-04-13 08:43:33.817217: Pseudo dice [0.4133, 0.6714, 0.7195, 0.361, 0.5096, 0.6392, 0.5873] +2026-04-13 08:43:33.819625: Epoch time: 101.37 s +2026-04-13 08:43:35.014317: +2026-04-13 08:43:35.015951: Epoch 2449 +2026-04-13 08:43:35.017792: Current learning rate: 0.00426 +2026-04-13 08:45:16.610464: train_loss -0.3795 +2026-04-13 08:45:16.629809: val_loss -0.3617 +2026-04-13 08:45:16.632246: Pseudo dice [0.5661, 0.5135, 0.7718, 0.6553, 0.3193, 0.7555, 0.5748] +2026-04-13 08:45:16.634871: Epoch time: 101.6 s +2026-04-13 08:45:19.614870: +2026-04-13 08:45:19.617995: Epoch 2450 +2026-04-13 08:45:19.619600: Current learning rate: 0.00426 +2026-04-13 08:47:01.376690: train_loss -0.4052 +2026-04-13 08:47:01.384597: val_loss -0.3315 +2026-04-13 08:47:01.386943: Pseudo dice [0.618, 0.8119, 0.7637, 0.3329, 0.3277, 0.714, 0.35] +2026-04-13 08:47:01.389699: Epoch time: 101.76 s +2026-04-13 08:47:02.588845: +2026-04-13 08:47:02.590779: Epoch 2451 +2026-04-13 08:47:02.592584: Current learning rate: 0.00426 +2026-04-13 08:48:44.410936: train_loss -0.378 +2026-04-13 08:48:44.417495: val_loss -0.2592 +2026-04-13 08:48:44.419774: Pseudo dice [0.4087, 0.4025, 0.3149, 0.0684, 0.3495, 0.3562, 0.3826] +2026-04-13 08:48:44.422550: Epoch time: 101.83 s +2026-04-13 08:48:45.656576: +2026-04-13 08:48:45.658427: Epoch 2452 +2026-04-13 08:48:45.660521: Current learning rate: 0.00426 +2026-04-13 08:50:27.241632: train_loss -0.3825 +2026-04-13 08:50:27.248345: val_loss -0.3648 +2026-04-13 08:50:27.250661: Pseudo dice [0.5862, 0.2863, 0.7909, 0.3743, 0.4653, 0.7989, 0.6604] +2026-04-13 08:50:27.253073: Epoch time: 101.59 s +2026-04-13 08:50:28.431242: +2026-04-13 08:50:28.433015: Epoch 2453 +2026-04-13 08:50:28.435062: Current learning rate: 0.00425 +2026-04-13 08:52:10.272688: train_loss -0.3882 +2026-04-13 08:52:10.279585: val_loss -0.3438 +2026-04-13 08:52:10.281994: Pseudo dice [0.6253, 0.7027, 0.7138, 0.3367, 0.2979, 0.2998, 0.5817] +2026-04-13 08:52:10.284905: Epoch time: 101.84 s +2026-04-13 08:52:11.477190: +2026-04-13 08:52:11.479053: Epoch 2454 +2026-04-13 08:52:11.481163: Current learning rate: 0.00425 +2026-04-13 08:53:53.089687: train_loss -0.396 +2026-04-13 08:53:53.096365: val_loss -0.3061 +2026-04-13 08:53:53.098671: Pseudo dice [0.6877, 0.7148, 0.6327, 0.1646, 0.2684, 0.5251, 0.8071] +2026-04-13 08:53:53.101396: Epoch time: 101.62 s +2026-04-13 08:53:54.325278: +2026-04-13 08:53:54.327368: Epoch 2455 +2026-04-13 08:53:54.330382: Current learning rate: 0.00425 +2026-04-13 08:55:35.711884: train_loss -0.3929 +2026-04-13 08:55:35.718405: val_loss -0.3325 +2026-04-13 08:55:35.720433: Pseudo dice [0.6817, 0.3001, 0.7158, 0.2124, 0.2212, 0.9395, 0.4856] +2026-04-13 08:55:35.722853: Epoch time: 101.39 s +2026-04-13 08:55:36.918380: +2026-04-13 08:55:36.920823: Epoch 2456 +2026-04-13 08:55:36.923236: Current learning rate: 0.00425 +2026-04-13 08:57:19.559762: train_loss -0.3807 +2026-04-13 08:57:19.565793: val_loss -0.2926 +2026-04-13 08:57:19.568372: Pseudo dice [0.2653, 0.9152, 0.6357, 0.6726, 0.3865, 0.1713, 0.8207] +2026-04-13 08:57:19.570935: Epoch time: 102.64 s +2026-04-13 08:57:20.755613: +2026-04-13 08:57:20.758042: Epoch 2457 +2026-04-13 08:57:20.760225: Current learning rate: 0.00424 +2026-04-13 08:59:02.872297: train_loss -0.3923 +2026-04-13 08:59:02.879772: val_loss -0.3423 +2026-04-13 08:59:02.882025: Pseudo dice [0.5241, 0.8742, 0.7731, 0.1837, 0.5153, 0.838, 0.3106] +2026-04-13 08:59:02.884106: Epoch time: 102.12 s +2026-04-13 08:59:04.105845: +2026-04-13 08:59:04.109650: Epoch 2458 +2026-04-13 08:59:04.113041: Current learning rate: 0.00424 +2026-04-13 09:00:46.028846: train_loss -0.3847 +2026-04-13 09:00:46.038722: val_loss -0.3446 +2026-04-13 09:00:46.048638: Pseudo dice [0.1339, 0.6807, 0.8006, 0.2695, 0.6334, 0.759, 0.6044] +2026-04-13 09:00:46.061125: Epoch time: 101.93 s +2026-04-13 09:00:47.249982: +2026-04-13 09:00:47.252630: Epoch 2459 +2026-04-13 09:00:47.254435: Current learning rate: 0.00424 +2026-04-13 09:02:29.127385: train_loss -0.3873 +2026-04-13 09:02:29.133757: val_loss -0.3199 +2026-04-13 09:02:29.135737: Pseudo dice [0.7352, 0.8022, 0.6594, 0.3156, 0.3114, 0.2374, 0.4911] +2026-04-13 09:02:29.137844: Epoch time: 101.88 s +2026-04-13 09:02:30.334946: +2026-04-13 09:02:30.336866: Epoch 2460 +2026-04-13 09:02:30.338938: Current learning rate: 0.00424 +2026-04-13 09:04:13.474970: train_loss -0.3922 +2026-04-13 09:04:13.483423: val_loss -0.3489 +2026-04-13 09:04:13.485358: Pseudo dice [0.6372, 0.7829, 0.7392, 0.4088, 0.5798, 0.8514, 0.766] +2026-04-13 09:04:13.490566: Epoch time: 103.14 s +2026-04-13 09:04:14.702031: +2026-04-13 09:04:14.704379: Epoch 2461 +2026-04-13 09:04:14.707009: Current learning rate: 0.00423 +2026-04-13 09:05:56.517893: train_loss -0.3895 +2026-04-13 09:05:56.525241: val_loss -0.3629 +2026-04-13 09:05:56.527485: Pseudo dice [0.3883, 0.7716, 0.5903, 0.1539, 0.6113, 0.4179, 0.7866] +2026-04-13 09:05:56.530353: Epoch time: 101.82 s +2026-04-13 09:05:57.729698: +2026-04-13 09:05:57.731291: Epoch 2462 +2026-04-13 09:05:57.733232: Current learning rate: 0.00423 +2026-04-13 09:07:39.566912: train_loss -0.3904 +2026-04-13 09:07:39.573508: val_loss -0.3418 +2026-04-13 09:07:39.575540: Pseudo dice [0.0591, 0.7352, 0.7418, 0.1756, 0.4405, 0.8002, 0.8156] +2026-04-13 09:07:39.577871: Epoch time: 101.84 s +2026-04-13 09:07:40.742301: +2026-04-13 09:07:40.744619: Epoch 2463 +2026-04-13 09:07:40.747181: Current learning rate: 0.00423 +2026-04-13 09:09:22.617388: train_loss -0.4182 +2026-04-13 09:09:22.624624: val_loss -0.3511 +2026-04-13 09:09:22.626862: Pseudo dice [0.5784, 0.8324, 0.7744, 0.2028, 0.4429, 0.1894, 0.5116] +2026-04-13 09:09:22.629063: Epoch time: 101.88 s +2026-04-13 09:09:23.849862: +2026-04-13 09:09:23.851806: Epoch 2464 +2026-04-13 09:09:23.855269: Current learning rate: 0.00423 +2026-04-13 09:11:06.262986: train_loss -0.4143 +2026-04-13 09:11:06.270376: val_loss -0.3224 +2026-04-13 09:11:06.273462: Pseudo dice [0.6076, 0.8575, 0.7112, 0.1599, 0.431, 0.6552, 0.7805] +2026-04-13 09:11:06.276382: Epoch time: 102.42 s +2026-04-13 09:11:07.499437: +2026-04-13 09:11:07.501313: Epoch 2465 +2026-04-13 09:11:07.503622: Current learning rate: 0.00422 +2026-04-13 09:12:49.339288: train_loss -0.4008 +2026-04-13 09:12:49.346500: val_loss -0.3253 +2026-04-13 09:12:49.349131: Pseudo dice [0.139, 0.6967, 0.7521, 0.5098, 0.4132, 0.8032, 0.6761] +2026-04-13 09:12:49.351475: Epoch time: 101.84 s +2026-04-13 09:12:51.629952: +2026-04-13 09:12:51.631628: Epoch 2466 +2026-04-13 09:12:51.633460: Current learning rate: 0.00422 +2026-04-13 09:14:34.055730: train_loss -0.4064 +2026-04-13 09:14:34.065581: val_loss -0.3392 +2026-04-13 09:14:34.067842: Pseudo dice [0.4433, 0.7301, 0.7163, 0.6405, 0.3107, 0.7974, 0.7769] +2026-04-13 09:14:34.070199: Epoch time: 102.43 s +2026-04-13 09:14:35.260435: +2026-04-13 09:14:35.262509: Epoch 2467 +2026-04-13 09:14:35.264683: Current learning rate: 0.00422 +2026-04-13 09:16:17.676929: train_loss -0.417 +2026-04-13 09:16:17.683905: val_loss -0.3541 +2026-04-13 09:16:17.685804: Pseudo dice [0.4799, 0.8401, 0.6601, 0.5125, 0.7708, 0.7948, 0.6843] +2026-04-13 09:16:17.688479: Epoch time: 102.42 s +2026-04-13 09:16:18.865236: +2026-04-13 09:16:18.867085: Epoch 2468 +2026-04-13 09:16:18.869140: Current learning rate: 0.00422 +2026-04-13 09:18:00.485932: train_loss -0.4006 +2026-04-13 09:18:00.491737: val_loss -0.3615 +2026-04-13 09:18:00.493983: Pseudo dice [0.4774, 0.4703, 0.7836, 0.6838, 0.4574, 0.7562, 0.6123] +2026-04-13 09:18:00.496431: Epoch time: 101.62 s +2026-04-13 09:18:01.708016: +2026-04-13 09:18:01.710651: Epoch 2469 +2026-04-13 09:18:01.712711: Current learning rate: 0.00421 +2026-04-13 09:19:43.617784: train_loss -0.3977 +2026-04-13 09:19:43.624655: val_loss -0.3887 +2026-04-13 09:19:43.626852: Pseudo dice [0.7618, 0.482, 0.788, 0.5599, 0.4388, 0.5812, 0.657] +2026-04-13 09:19:43.629954: Epoch time: 101.91 s +2026-04-13 09:19:44.852547: +2026-04-13 09:19:44.854305: Epoch 2470 +2026-04-13 09:19:44.856293: Current learning rate: 0.00421 +2026-04-13 09:21:26.605454: train_loss -0.4272 +2026-04-13 09:21:26.613579: val_loss -0.3394 +2026-04-13 09:21:26.615889: Pseudo dice [0.4118, 0.6058, 0.7284, 0.8138, 0.2539, 0.8983, 0.8125] +2026-04-13 09:21:26.618213: Epoch time: 101.76 s +2026-04-13 09:21:27.822704: +2026-04-13 09:21:27.824971: Epoch 2471 +2026-04-13 09:21:27.826956: Current learning rate: 0.00421 +2026-04-13 09:23:09.908321: train_loss -0.4079 +2026-04-13 09:23:09.914439: val_loss -0.3716 +2026-04-13 09:23:09.916619: Pseudo dice [0.4427, 0.8169, 0.8086, 0.5392, 0.3791, 0.667, 0.7202] +2026-04-13 09:23:09.919126: Epoch time: 102.09 s +2026-04-13 09:23:11.125313: +2026-04-13 09:23:11.127252: Epoch 2472 +2026-04-13 09:23:11.129075: Current learning rate: 0.00421 +2026-04-13 09:24:53.796707: train_loss -0.4125 +2026-04-13 09:24:53.806077: val_loss -0.3493 +2026-04-13 09:24:53.809197: Pseudo dice [0.7942, 0.2257, 0.4674, 0.5556, 0.5213, 0.5648, 0.7408] +2026-04-13 09:24:53.812692: Epoch time: 102.67 s +2026-04-13 09:24:54.989837: +2026-04-13 09:24:54.993349: Epoch 2473 +2026-04-13 09:24:54.995438: Current learning rate: 0.0042 +2026-04-13 09:26:37.033475: train_loss -0.4142 +2026-04-13 09:26:37.041150: val_loss -0.3707 +2026-04-13 09:26:37.043694: Pseudo dice [0.4662, 0.3932, 0.622, 0.3362, 0.1432, 0.6845, 0.7258] +2026-04-13 09:26:37.046868: Epoch time: 102.05 s +2026-04-13 09:26:38.231036: +2026-04-13 09:26:38.234684: Epoch 2474 +2026-04-13 09:26:38.238150: Current learning rate: 0.0042 +2026-04-13 09:28:21.015414: train_loss -0.3814 +2026-04-13 09:28:21.022479: val_loss -0.3236 +2026-04-13 09:28:21.024642: Pseudo dice [0.6054, 0.4623, 0.7592, 0.0802, 0.3852, 0.6821, 0.6891] +2026-04-13 09:28:21.026843: Epoch time: 102.79 s +2026-04-13 09:28:22.208090: +2026-04-13 09:28:22.212216: Epoch 2475 +2026-04-13 09:28:22.214585: Current learning rate: 0.0042 +2026-04-13 09:30:03.823490: train_loss -0.3934 +2026-04-13 09:30:03.830570: val_loss -0.358 +2026-04-13 09:30:03.832458: Pseudo dice [0.4895, 0.6878, 0.7916, 0.2935, 0.5801, 0.8109, 0.6553] +2026-04-13 09:30:03.837620: Epoch time: 101.62 s +2026-04-13 09:30:05.032394: +2026-04-13 09:30:05.033957: Epoch 2476 +2026-04-13 09:30:05.036074: Current learning rate: 0.0042 +2026-04-13 09:31:47.305710: train_loss -0.3888 +2026-04-13 09:31:47.314372: val_loss -0.3508 +2026-04-13 09:31:47.316754: Pseudo dice [0.3401, 0.7127, 0.748, 0.4774, 0.4279, 0.774, 0.8085] +2026-04-13 09:31:47.318797: Epoch time: 102.28 s +2026-04-13 09:31:48.510721: +2026-04-13 09:31:48.512479: Epoch 2477 +2026-04-13 09:31:48.514333: Current learning rate: 0.00419 +2026-04-13 09:33:30.771927: train_loss -0.3885 +2026-04-13 09:33:30.779032: val_loss -0.3567 +2026-04-13 09:33:30.781246: Pseudo dice [0.4686, 0.5416, 0.7655, 0.5924, 0.4965, 0.9141, 0.4011] +2026-04-13 09:33:30.783680: Epoch time: 102.26 s +2026-04-13 09:33:31.965723: +2026-04-13 09:33:31.967795: Epoch 2478 +2026-04-13 09:33:31.969980: Current learning rate: 0.00419 +2026-04-13 09:35:13.625536: train_loss -0.4051 +2026-04-13 09:35:13.639461: val_loss -0.3536 +2026-04-13 09:35:13.642016: Pseudo dice [0.6889, 0.5561, 0.765, 0.3695, 0.7204, 0.8913, 0.3474] +2026-04-13 09:35:13.645206: Epoch time: 101.66 s +2026-04-13 09:35:14.829599: +2026-04-13 09:35:14.831379: Epoch 2479 +2026-04-13 09:35:14.833178: Current learning rate: 0.00419 +2026-04-13 09:36:56.935684: train_loss -0.4065 +2026-04-13 09:36:56.945439: val_loss -0.3515 +2026-04-13 09:36:56.947795: Pseudo dice [0.64, 0.7371, 0.5754, 0.324, 0.4876, 0.8681, 0.8124] +2026-04-13 09:36:56.949884: Epoch time: 102.11 s +2026-04-13 09:36:58.207136: +2026-04-13 09:36:58.209322: Epoch 2480 +2026-04-13 09:36:58.211478: Current learning rate: 0.00419 +2026-04-13 09:38:40.711125: train_loss -0.4177 +2026-04-13 09:38:40.722313: val_loss -0.3342 +2026-04-13 09:38:40.725236: Pseudo dice [0.5796, 0.6334, 0.7402, 0.1979, 0.3245, 0.1011, 0.6888] +2026-04-13 09:38:40.730309: Epoch time: 102.51 s +2026-04-13 09:38:41.928594: +2026-04-13 09:38:41.930826: Epoch 2481 +2026-04-13 09:38:41.933082: Current learning rate: 0.00418 +2026-04-13 09:40:24.185525: train_loss -0.4194 +2026-04-13 09:40:24.191491: val_loss -0.3838 +2026-04-13 09:40:24.194361: Pseudo dice [0.4829, 0.7551, 0.854, 0.4371, 0.4536, 0.9272, 0.7392] +2026-04-13 09:40:24.199107: Epoch time: 102.26 s +2026-04-13 09:40:25.408009: +2026-04-13 09:40:25.410084: Epoch 2482 +2026-04-13 09:40:25.412659: Current learning rate: 0.00418 +2026-04-13 09:42:07.688596: train_loss -0.4231 +2026-04-13 09:42:07.696918: val_loss -0.3684 +2026-04-13 09:42:07.699386: Pseudo dice [0.7066, 0.4428, 0.7479, 0.7261, 0.4199, 0.9455, 0.6728] +2026-04-13 09:42:07.703139: Epoch time: 102.28 s +2026-04-13 09:42:08.913097: +2026-04-13 09:42:08.915755: Epoch 2483 +2026-04-13 09:42:08.918496: Current learning rate: 0.00418 +2026-04-13 09:43:51.357887: train_loss -0.4198 +2026-04-13 09:43:51.365540: val_loss -0.327 +2026-04-13 09:43:51.367661: Pseudo dice [0.2262, 0.6124, 0.7231, 0.4147, 0.3691, 0.8542, 0.3019] +2026-04-13 09:43:51.371471: Epoch time: 102.45 s +2026-04-13 09:43:52.576708: +2026-04-13 09:43:52.578388: Epoch 2484 +2026-04-13 09:43:52.580258: Current learning rate: 0.00418 +2026-04-13 09:45:34.712772: train_loss -0.3977 +2026-04-13 09:45:34.719622: val_loss -0.2959 +2026-04-13 09:45:34.721974: Pseudo dice [0.7228, 0.535, 0.6775, 0.2168, 0.1125, 0.1218, 0.7354] +2026-04-13 09:45:34.724395: Epoch time: 102.14 s +2026-04-13 09:45:35.874496: +2026-04-13 09:45:35.878659: Epoch 2485 +2026-04-13 09:45:35.881925: Current learning rate: 0.00417 +2026-04-13 09:47:18.301951: train_loss -0.3823 +2026-04-13 09:47:18.310236: val_loss -0.3219 +2026-04-13 09:47:18.312324: Pseudo dice [0.738, 0.8901, 0.7775, 0.134, 0.3207, 0.5738, 0.6185] +2026-04-13 09:47:18.314853: Epoch time: 102.43 s +2026-04-13 09:47:19.504587: +2026-04-13 09:47:19.506389: Epoch 2486 +2026-04-13 09:47:19.508522: Current learning rate: 0.00417 +2026-04-13 09:49:01.772189: train_loss -0.3968 +2026-04-13 09:49:01.778950: val_loss -0.3654 +2026-04-13 09:49:01.782262: Pseudo dice [0.5604, 0.9124, 0.7937, 0.5121, 0.4641, 0.7075, 0.7749] +2026-04-13 09:49:01.785015: Epoch time: 102.27 s +2026-04-13 09:49:04.036682: +2026-04-13 09:49:04.038316: Epoch 2487 +2026-04-13 09:49:04.040121: Current learning rate: 0.00417 +2026-04-13 09:50:45.751310: train_loss -0.4171 +2026-04-13 09:50:45.759564: val_loss -0.3578 +2026-04-13 09:50:45.762568: Pseudo dice [0.5473, 0.8887, 0.7613, 0.3076, 0.5016, 0.571, 0.8457] +2026-04-13 09:50:45.765944: Epoch time: 101.72 s +2026-04-13 09:50:46.977642: +2026-04-13 09:50:46.979904: Epoch 2488 +2026-04-13 09:50:46.981899: Current learning rate: 0.00417 +2026-04-13 09:52:28.935777: train_loss -0.4155 +2026-04-13 09:52:28.941539: val_loss -0.3654 +2026-04-13 09:52:28.943449: Pseudo dice [0.6729, 0.5517, 0.7238, 0.1692, 0.5648, 0.8968, 0.3724] +2026-04-13 09:52:28.946162: Epoch time: 101.96 s +2026-04-13 09:52:30.162603: +2026-04-13 09:52:30.164558: Epoch 2489 +2026-04-13 09:52:30.166667: Current learning rate: 0.00416 +2026-04-13 09:54:12.163831: train_loss -0.4072 +2026-04-13 09:54:12.171722: val_loss -0.3372 +2026-04-13 09:54:12.176741: Pseudo dice [0.5696, 0.7194, 0.7635, 0.1609, 0.49, 0.6255, 0.6603] +2026-04-13 09:54:12.179690: Epoch time: 102.0 s +2026-04-13 09:54:13.370384: +2026-04-13 09:54:13.372129: Epoch 2490 +2026-04-13 09:54:13.374140: Current learning rate: 0.00416 +2026-04-13 09:55:55.443974: train_loss -0.4176 +2026-04-13 09:55:55.450791: val_loss -0.3727 +2026-04-13 09:55:55.453720: Pseudo dice [0.5562, 0.4213, 0.7311, 0.3951, 0.5339, 0.8758, 0.8415] +2026-04-13 09:55:55.456651: Epoch time: 102.08 s +2026-04-13 09:55:56.637636: +2026-04-13 09:55:56.639617: Epoch 2491 +2026-04-13 09:55:56.641425: Current learning rate: 0.00416 +2026-04-13 09:57:39.055825: train_loss -0.4095 +2026-04-13 09:57:39.062735: val_loss -0.3628 +2026-04-13 09:57:39.064855: Pseudo dice [0.8311, 0.3608, 0.7974, 0.22, 0.2797, 0.6199, 0.8591] +2026-04-13 09:57:39.067269: Epoch time: 102.42 s +2026-04-13 09:57:40.245130: +2026-04-13 09:57:40.247867: Epoch 2492 +2026-04-13 09:57:40.250133: Current learning rate: 0.00416 +2026-04-13 09:59:23.053446: train_loss -0.414 +2026-04-13 09:59:23.061128: val_loss -0.3286 +2026-04-13 09:59:23.063461: Pseudo dice [0.4498, 0.8582, 0.7449, 0.6376, 0.5675, 0.5053, 0.6362] +2026-04-13 09:59:23.065612: Epoch time: 102.81 s +2026-04-13 09:59:24.266648: +2026-04-13 09:59:24.269235: Epoch 2493 +2026-04-13 09:59:24.271884: Current learning rate: 0.00415 +2026-04-13 10:01:06.359826: train_loss -0.4222 +2026-04-13 10:01:06.370630: val_loss -0.3979 +2026-04-13 10:01:06.374099: Pseudo dice [0.5229, 0.5892, 0.7736, 0.8537, 0.6938, 0.775, 0.8203] +2026-04-13 10:01:06.379489: Epoch time: 102.1 s +2026-04-13 10:01:07.546402: +2026-04-13 10:01:07.548442: Epoch 2494 +2026-04-13 10:01:07.550557: Current learning rate: 0.00415 +2026-04-13 10:02:49.782205: train_loss -0.4233 +2026-04-13 10:02:49.790154: val_loss -0.3558 +2026-04-13 10:02:49.792681: Pseudo dice [0.6448, 0.7912, 0.8197, 0.2921, 0.5371, 0.4007, 0.3452] +2026-04-13 10:02:49.795484: Epoch time: 102.24 s +2026-04-13 10:02:50.991308: +2026-04-13 10:02:50.994022: Epoch 2495 +2026-04-13 10:02:50.997531: Current learning rate: 0.00415 +2026-04-13 10:04:32.947455: train_loss -0.4008 +2026-04-13 10:04:32.953479: val_loss -0.2735 +2026-04-13 10:04:32.956413: Pseudo dice [0.721, 0.913, 0.7407, 0.3682, 0.5026, 0.2678, 0.5774] +2026-04-13 10:04:32.959012: Epoch time: 101.96 s +2026-04-13 10:04:34.163761: +2026-04-13 10:04:34.169822: Epoch 2496 +2026-04-13 10:04:34.180264: Current learning rate: 0.00415 +2026-04-13 10:06:16.302229: train_loss -0.3895 +2026-04-13 10:06:16.310403: val_loss -0.3427 +2026-04-13 10:06:16.316224: Pseudo dice [0.7978, 0.823, 0.7956, 0.3447, 0.1414, 0.8187, 0.5853] +2026-04-13 10:06:16.320086: Epoch time: 102.14 s +2026-04-13 10:06:17.496231: +2026-04-13 10:06:17.506442: Epoch 2497 +2026-04-13 10:06:17.508444: Current learning rate: 0.00414 +2026-04-13 10:07:59.925608: train_loss -0.4143 +2026-04-13 10:07:59.932808: val_loss -0.3409 +2026-04-13 10:07:59.935971: Pseudo dice [0.5032, 0.7771, 0.6394, 0.3182, 0.2906, 0.8521, 0.732] +2026-04-13 10:07:59.938809: Epoch time: 102.43 s +2026-04-13 10:08:01.124251: +2026-04-13 10:08:01.127574: Epoch 2498 +2026-04-13 10:08:01.130007: Current learning rate: 0.00414 +2026-04-13 10:09:43.173924: train_loss -0.4158 +2026-04-13 10:09:43.182397: val_loss -0.3393 +2026-04-13 10:09:43.184836: Pseudo dice [0.4664, 0.8935, 0.7831, 0.3127, 0.3626, 0.4711, 0.3097] +2026-04-13 10:09:43.188911: Epoch time: 102.05 s +2026-04-13 10:09:44.372068: +2026-04-13 10:09:44.375777: Epoch 2499 +2026-04-13 10:09:44.377928: Current learning rate: 0.00414 +2026-04-13 10:11:25.913394: train_loss -0.3927 +2026-04-13 10:11:25.921134: val_loss -0.3475 +2026-04-13 10:11:25.923844: Pseudo dice [0.3807, 0.5888, 0.6778, 0.5476, 0.5499, 0.6113, 0.7055] +2026-04-13 10:11:25.926937: Epoch time: 101.54 s +2026-04-13 10:11:28.947335: +2026-04-13 10:11:28.949917: Epoch 2500 +2026-04-13 10:11:28.951693: Current learning rate: 0.00414 +2026-04-13 10:13:11.344891: train_loss -0.3907 +2026-04-13 10:13:11.352910: val_loss -0.3568 +2026-04-13 10:13:11.355254: Pseudo dice [0.2396, 0.7163, 0.7461, 0.2385, 0.5825, 0.3183, 0.8363] +2026-04-13 10:13:11.357811: Epoch time: 102.4 s +2026-04-13 10:13:12.549790: +2026-04-13 10:13:12.551531: Epoch 2501 +2026-04-13 10:13:12.553413: Current learning rate: 0.00413 +2026-04-13 10:14:55.123368: train_loss -0.4007 +2026-04-13 10:14:55.129256: val_loss -0.3524 +2026-04-13 10:14:55.131223: Pseudo dice [0.3702, 0.7418, 0.7149, 0.6803, 0.4679, 0.845, 0.7221] +2026-04-13 10:14:55.133639: Epoch time: 102.58 s +2026-04-13 10:14:56.305334: +2026-04-13 10:14:56.307020: Epoch 2502 +2026-04-13 10:14:56.311051: Current learning rate: 0.00413 +2026-04-13 10:16:38.496347: train_loss -0.4073 +2026-04-13 10:16:38.502753: val_loss -0.3411 +2026-04-13 10:16:38.504871: Pseudo dice [0.7249, 0.4042, 0.777, 0.3337, 0.3083, 0.1728, 0.7338] +2026-04-13 10:16:38.507010: Epoch time: 102.19 s +2026-04-13 10:16:39.698771: +2026-04-13 10:16:39.700834: Epoch 2503 +2026-04-13 10:16:39.702903: Current learning rate: 0.00413 +2026-04-13 10:18:21.745742: train_loss -0.3996 +2026-04-13 10:18:21.752945: val_loss -0.3344 +2026-04-13 10:18:21.754817: Pseudo dice [0.5434, 0.8366, 0.6899, 0.3151, 0.2303, 0.7199, 0.7491] +2026-04-13 10:18:21.757297: Epoch time: 102.05 s +2026-04-13 10:18:22.916972: +2026-04-13 10:18:22.918817: Epoch 2504 +2026-04-13 10:18:22.920845: Current learning rate: 0.00413 +2026-04-13 10:20:04.990820: train_loss -0.3963 +2026-04-13 10:20:05.001797: val_loss -0.3415 +2026-04-13 10:20:05.004005: Pseudo dice [0.3521, 0.5595, 0.705, 0.3611, 0.5589, 0.9036, 0.8076] +2026-04-13 10:20:05.006899: Epoch time: 102.08 s +2026-04-13 10:20:06.184139: +2026-04-13 10:20:06.186599: Epoch 2505 +2026-04-13 10:20:06.188855: Current learning rate: 0.00412 +2026-04-13 10:21:47.986192: train_loss -0.404 +2026-04-13 10:21:47.993958: val_loss -0.3394 +2026-04-13 10:21:47.996347: Pseudo dice [0.3841, 0.8392, 0.6911, 0.4839, 0.5657, 0.6043, 0.7952] +2026-04-13 10:21:47.998780: Epoch time: 101.81 s +2026-04-13 10:21:49.171530: +2026-04-13 10:21:49.175491: Epoch 2506 +2026-04-13 10:21:49.177778: Current learning rate: 0.00412 +2026-04-13 10:23:31.507244: train_loss -0.4104 +2026-04-13 10:23:31.516168: val_loss -0.3812 +2026-04-13 10:23:31.527171: Pseudo dice [0.8532, 0.7612, 0.8187, 0.875, 0.6046, 0.8707, 0.8058] +2026-04-13 10:23:31.530120: Epoch time: 102.34 s +2026-04-13 10:23:33.757890: +2026-04-13 10:23:33.759433: Epoch 2507 +2026-04-13 10:23:33.761178: Current learning rate: 0.00412 +2026-04-13 10:25:15.330538: train_loss -0.4026 +2026-04-13 10:25:15.339765: val_loss -0.3273 +2026-04-13 10:25:15.342264: Pseudo dice [0.6622, 0.6917, 0.7662, 0.626, 0.504, 0.5446, 0.7166] +2026-04-13 10:25:15.344379: Epoch time: 101.58 s +2026-04-13 10:25:16.530118: +2026-04-13 10:25:16.533750: Epoch 2508 +2026-04-13 10:25:16.535999: Current learning rate: 0.00412 +2026-04-13 10:26:58.638159: train_loss -0.4142 +2026-04-13 10:26:58.644619: val_loss -0.3387 +2026-04-13 10:26:58.646785: Pseudo dice [0.6711, 0.9056, 0.7896, 0.6132, 0.5589, 0.5221, 0.3016] +2026-04-13 10:26:58.649063: Epoch time: 102.11 s +2026-04-13 10:26:59.835627: +2026-04-13 10:26:59.838177: Epoch 2509 +2026-04-13 10:26:59.840205: Current learning rate: 0.00411 +2026-04-13 10:28:41.566294: train_loss -0.4149 +2026-04-13 10:28:41.574770: val_loss -0.3454 +2026-04-13 10:28:41.576820: Pseudo dice [0.6188, 0.6726, 0.7461, 0.701, 0.58, 0.3202, 0.3301] +2026-04-13 10:28:41.579433: Epoch time: 101.73 s +2026-04-13 10:28:42.772955: +2026-04-13 10:28:42.774629: Epoch 2510 +2026-04-13 10:28:42.776695: Current learning rate: 0.00411 +2026-04-13 10:30:25.237402: train_loss -0.4159 +2026-04-13 10:30:25.244802: val_loss -0.3461 +2026-04-13 10:30:25.246859: Pseudo dice [0.6097, 0.8196, 0.7404, 0.0092, 0.5062, 0.8341, 0.7023] +2026-04-13 10:30:25.252225: Epoch time: 102.47 s +2026-04-13 10:30:26.434953: +2026-04-13 10:30:26.436705: Epoch 2511 +2026-04-13 10:30:26.438981: Current learning rate: 0.00411 +2026-04-13 10:32:08.162441: train_loss -0.4128 +2026-04-13 10:32:08.169073: val_loss -0.3874 +2026-04-13 10:32:08.172273: Pseudo dice [0.7775, 0.8769, 0.8068, 0.8464, 0.2578, 0.8233, 0.817] +2026-04-13 10:32:08.175285: Epoch time: 101.73 s +2026-04-13 10:32:09.361136: +2026-04-13 10:32:09.364504: Epoch 2512 +2026-04-13 10:32:09.366966: Current learning rate: 0.00411 +2026-04-13 10:33:51.751359: train_loss -0.4052 +2026-04-13 10:33:51.761450: val_loss -0.4017 +2026-04-13 10:33:51.763938: Pseudo dice [0.6845, 0.6036, 0.8175, 0.5713, 0.5468, 0.6578, 0.8343] +2026-04-13 10:33:51.767111: Epoch time: 102.39 s +2026-04-13 10:33:52.953879: +2026-04-13 10:33:52.956068: Epoch 2513 +2026-04-13 10:33:52.958559: Current learning rate: 0.0041 +2026-04-13 10:35:34.894411: train_loss -0.4283 +2026-04-13 10:35:34.901184: val_loss -0.3478 +2026-04-13 10:35:34.903179: Pseudo dice [0.7723, 0.7804, 0.7916, 0.3558, 0.3659, 0.669, 0.4689] +2026-04-13 10:35:34.905781: Epoch time: 101.94 s +2026-04-13 10:35:36.084713: +2026-04-13 10:35:36.086847: Epoch 2514 +2026-04-13 10:35:36.089126: Current learning rate: 0.0041 +2026-04-13 10:37:18.168354: train_loss -0.4199 +2026-04-13 10:37:18.177307: val_loss -0.3525 +2026-04-13 10:37:18.179739: Pseudo dice [0.5891, 0.7147, 0.7315, 0.0897, 0.4615, 0.8836, 0.7863] +2026-04-13 10:37:18.183038: Epoch time: 102.09 s +2026-04-13 10:37:19.373772: +2026-04-13 10:37:19.375934: Epoch 2515 +2026-04-13 10:37:19.377825: Current learning rate: 0.0041 +2026-04-13 10:39:01.683330: train_loss -0.4205 +2026-04-13 10:39:01.689493: val_loss -0.3766 +2026-04-13 10:39:01.692048: Pseudo dice [0.817, 0.7996, 0.7496, 0.2647, 0.6768, 0.805, 0.514] +2026-04-13 10:39:01.694963: Epoch time: 102.31 s +2026-04-13 10:39:02.910192: +2026-04-13 10:39:02.913565: Epoch 2516 +2026-04-13 10:39:02.916542: Current learning rate: 0.0041 +2026-04-13 10:40:44.766142: train_loss -0.4116 +2026-04-13 10:40:44.772924: val_loss -0.3037 +2026-04-13 10:40:44.775177: Pseudo dice [0.1833, 0.5945, 0.7396, 0.1825, 0.4343, 0.685, 0.2362] +2026-04-13 10:40:44.777787: Epoch time: 101.86 s +2026-04-13 10:40:45.953952: +2026-04-13 10:40:45.957004: Epoch 2517 +2026-04-13 10:40:45.958733: Current learning rate: 0.00409 +2026-04-13 10:42:27.594930: train_loss -0.3944 +2026-04-13 10:42:27.602129: val_loss -0.3711 +2026-04-13 10:42:27.606753: Pseudo dice [0.7202, 0.6987, 0.7937, 0.4772, 0.5189, 0.8349, 0.8386] +2026-04-13 10:42:27.613117: Epoch time: 101.64 s +2026-04-13 10:42:28.799298: +2026-04-13 10:42:28.801559: Epoch 2518 +2026-04-13 10:42:28.803408: Current learning rate: 0.00409 +2026-04-13 10:44:11.932897: train_loss -0.4089 +2026-04-13 10:44:11.942831: val_loss -0.3574 +2026-04-13 10:44:11.945543: Pseudo dice [0.3945, 0.6206, 0.7877, 0.5111, 0.5466, 0.3212, 0.5222] +2026-04-13 10:44:11.949580: Epoch time: 103.14 s +2026-04-13 10:44:13.154829: +2026-04-13 10:44:13.157213: Epoch 2519 +2026-04-13 10:44:13.159558: Current learning rate: 0.00409 +2026-04-13 10:45:55.351562: train_loss -0.4214 +2026-04-13 10:45:55.358149: val_loss -0.3767 +2026-04-13 10:45:55.360307: Pseudo dice [0.4367, 0.3696, 0.7745, 0.4872, 0.4382, 0.8367, 0.849] +2026-04-13 10:45:55.362469: Epoch time: 102.2 s +2026-04-13 10:45:56.529832: +2026-04-13 10:45:56.532327: Epoch 2520 +2026-04-13 10:45:56.535021: Current learning rate: 0.00409 +2026-04-13 10:47:38.563179: train_loss -0.4256 +2026-04-13 10:47:38.569882: val_loss -0.3694 +2026-04-13 10:47:38.571791: Pseudo dice [0.6, 0.3767, 0.7657, 0.509, 0.4468, 0.9254, 0.8166] +2026-04-13 10:47:38.574764: Epoch time: 102.04 s +2026-04-13 10:47:39.779300: +2026-04-13 10:47:39.781002: Epoch 2521 +2026-04-13 10:47:39.782964: Current learning rate: 0.00408 +2026-04-13 10:49:22.353253: train_loss -0.4138 +2026-04-13 10:49:22.360144: val_loss -0.3345 +2026-04-13 10:49:22.362188: Pseudo dice [0.7116, 0.8251, 0.6941, 0.2498, 0.5118, 0.8296, 0.6315] +2026-04-13 10:49:22.364706: Epoch time: 102.58 s +2026-04-13 10:49:23.562192: +2026-04-13 10:49:23.564303: Epoch 2522 +2026-04-13 10:49:23.567127: Current learning rate: 0.00408 +2026-04-13 10:51:05.386497: train_loss -0.4 +2026-04-13 10:51:05.392888: val_loss -0.3596 +2026-04-13 10:51:05.394972: Pseudo dice [0.4716, 0.7929, 0.8039, 0.1994, 0.245, 0.8943, 0.7586] +2026-04-13 10:51:05.397429: Epoch time: 101.83 s +2026-04-13 10:51:06.589524: +2026-04-13 10:51:06.591716: Epoch 2523 +2026-04-13 10:51:06.594191: Current learning rate: 0.00408 +2026-04-13 10:52:48.562206: train_loss -0.4149 +2026-04-13 10:52:48.569552: val_loss -0.362 +2026-04-13 10:52:48.571820: Pseudo dice [0.2742, 0.7917, 0.7268, 0.3175, 0.3722, 0.4981, 0.7462] +2026-04-13 10:52:48.574944: Epoch time: 101.98 s +2026-04-13 10:52:49.762083: +2026-04-13 10:52:49.764174: Epoch 2524 +2026-04-13 10:52:49.766476: Current learning rate: 0.00408 +2026-04-13 10:54:31.552935: train_loss -0.4091 +2026-04-13 10:54:31.559228: val_loss -0.3659 +2026-04-13 10:54:31.561064: Pseudo dice [0.6492, 0.7329, 0.7306, 0.0835, 0.4598, 0.8297, 0.5978] +2026-04-13 10:54:31.563644: Epoch time: 101.79 s +2026-04-13 10:54:32.734860: +2026-04-13 10:54:32.737020: Epoch 2525 +2026-04-13 10:54:32.739697: Current learning rate: 0.00407 +2026-04-13 10:56:14.898310: train_loss -0.4064 +2026-04-13 10:56:14.908218: val_loss -0.3658 +2026-04-13 10:56:14.911156: Pseudo dice [0.831, 0.2434, 0.8079, 0.6774, 0.3824, 0.834, 0.8603] +2026-04-13 10:56:14.917612: Epoch time: 102.17 s +2026-04-13 10:56:16.090076: +2026-04-13 10:56:16.093814: Epoch 2526 +2026-04-13 10:56:16.096138: Current learning rate: 0.00407 +2026-04-13 10:57:58.693997: train_loss -0.4221 +2026-04-13 10:57:58.701875: val_loss -0.3956 +2026-04-13 10:57:58.704369: Pseudo dice [0.592, 0.8831, 0.8446, 0.5741, 0.5386, 0.7886, 0.6329] +2026-04-13 10:57:58.706885: Epoch time: 102.61 s +2026-04-13 10:57:59.912520: +2026-04-13 10:57:59.914475: Epoch 2527 +2026-04-13 10:57:59.916968: Current learning rate: 0.00407 +2026-04-13 10:59:42.726184: train_loss -0.406 +2026-04-13 10:59:42.733048: val_loss -0.3351 +2026-04-13 10:59:42.735098: Pseudo dice [0.4401, 0.7509, 0.8325, 0.1609, 0.46, 0.4319, 0.2712] +2026-04-13 10:59:42.740538: Epoch time: 102.82 s +2026-04-13 10:59:43.931865: +2026-04-13 10:59:43.934278: Epoch 2528 +2026-04-13 10:59:43.937156: Current learning rate: 0.00407 +2026-04-13 11:01:25.807560: train_loss -0.3916 +2026-04-13 11:01:25.815187: val_loss -0.3336 +2026-04-13 11:01:25.817642: Pseudo dice [0.5035, 0.9014, 0.6949, 0.5435, 0.4309, 0.5354, 0.5402] +2026-04-13 11:01:25.820196: Epoch time: 101.88 s +2026-04-13 11:01:27.005800: +2026-04-13 11:01:27.007447: Epoch 2529 +2026-04-13 11:01:27.009274: Current learning rate: 0.00406 +2026-04-13 11:03:09.233743: train_loss -0.3774 +2026-04-13 11:03:09.240898: val_loss -0.3116 +2026-04-13 11:03:09.243770: Pseudo dice [0.6313, 0.3746, 0.6603, 0.0577, 0.3883, 0.4427, 0.5184] +2026-04-13 11:03:09.246506: Epoch time: 102.23 s +2026-04-13 11:03:10.407787: +2026-04-13 11:03:10.409562: Epoch 2530 +2026-04-13 11:03:10.411471: Current learning rate: 0.00406 +2026-04-13 11:04:52.564484: train_loss -0.3819 +2026-04-13 11:04:52.571852: val_loss -0.312 +2026-04-13 11:04:52.574163: Pseudo dice [0.5283, 0.8281, 0.7016, 0.0937, 0.2343, 0.8597, 0.5523] +2026-04-13 11:04:52.576385: Epoch time: 102.16 s +2026-04-13 11:04:53.747552: +2026-04-13 11:04:53.749551: Epoch 2531 +2026-04-13 11:04:53.754064: Current learning rate: 0.00406 +2026-04-13 11:06:35.735736: train_loss -0.3913 +2026-04-13 11:06:35.743402: val_loss -0.3473 +2026-04-13 11:06:35.747991: Pseudo dice [0.2247, 0.7593, 0.7932, 0.3594, 0.3831, 0.5089, 0.742] +2026-04-13 11:06:35.752797: Epoch time: 101.99 s +2026-04-13 11:06:36.949878: +2026-04-13 11:06:36.951961: Epoch 2532 +2026-04-13 11:06:36.954451: Current learning rate: 0.00406 +2026-04-13 11:08:18.975223: train_loss -0.4169 +2026-04-13 11:08:18.987442: val_loss -0.3836 +2026-04-13 11:08:18.992546: Pseudo dice [0.7598, 0.7518, 0.7717, 0.179, 0.5396, 0.931, 0.6621] +2026-04-13 11:08:18.995780: Epoch time: 102.03 s +2026-04-13 11:08:20.181663: +2026-04-13 11:08:20.183619: Epoch 2533 +2026-04-13 11:08:20.185721: Current learning rate: 0.00405 +2026-04-13 11:10:02.748596: train_loss -0.4134 +2026-04-13 11:10:02.757409: val_loss -0.3573 +2026-04-13 11:10:02.759991: Pseudo dice [0.8478, 0.8669, 0.7548, 0.3019, 0.5071, 0.789, 0.8237] +2026-04-13 11:10:02.762594: Epoch time: 102.57 s +2026-04-13 11:10:03.952998: +2026-04-13 11:10:03.954619: Epoch 2534 +2026-04-13 11:10:03.957810: Current learning rate: 0.00405 +2026-04-13 11:11:46.004410: train_loss -0.421 +2026-04-13 11:11:46.010984: val_loss -0.3485 +2026-04-13 11:11:46.013240: Pseudo dice [0.8222, 0.5435, 0.7219, 0.163, 0.4683, 0.8088, 0.702] +2026-04-13 11:11:46.015900: Epoch time: 102.05 s +2026-04-13 11:11:47.193604: +2026-04-13 11:11:47.195211: Epoch 2535 +2026-04-13 11:11:47.198584: Current learning rate: 0.00405 +2026-04-13 11:13:29.091798: train_loss -0.4023 +2026-04-13 11:13:29.102603: val_loss -0.3177 +2026-04-13 11:13:29.104710: Pseudo dice [0.4273, 0.3834, 0.7503, 0.1692, 0.3286, 0.5854, 0.485] +2026-04-13 11:13:29.107260: Epoch time: 101.9 s +2026-04-13 11:13:30.290659: +2026-04-13 11:13:30.292675: Epoch 2536 +2026-04-13 11:13:30.294664: Current learning rate: 0.00405 +2026-04-13 11:15:13.047508: train_loss -0.3843 +2026-04-13 11:15:13.056205: val_loss -0.3485 +2026-04-13 11:15:13.058651: Pseudo dice [0.6201, 0.2516, 0.8002, 0.1507, 0.2853, 0.8421, 0.6548] +2026-04-13 11:15:13.061286: Epoch time: 102.76 s +2026-04-13 11:15:14.279664: +2026-04-13 11:15:14.282163: Epoch 2537 +2026-04-13 11:15:14.284045: Current learning rate: 0.00404 +2026-04-13 11:16:56.905324: train_loss -0.403 +2026-04-13 11:16:56.913670: val_loss -0.3361 +2026-04-13 11:16:56.915994: Pseudo dice [0.5717, 0.8732, 0.7059, 0.1776, 0.3892, 0.7741, 0.3462] +2026-04-13 11:16:56.918910: Epoch time: 102.63 s +2026-04-13 11:16:58.091580: +2026-04-13 11:16:58.093112: Epoch 2538 +2026-04-13 11:16:58.094845: Current learning rate: 0.00404 +2026-04-13 11:18:40.398141: train_loss -0.3932 +2026-04-13 11:18:40.405339: val_loss -0.3437 +2026-04-13 11:18:40.407292: Pseudo dice [0.5059, 0.3232, 0.793, 0.2994, 0.5081, 0.7017, 0.8366] +2026-04-13 11:18:40.409326: Epoch time: 102.31 s +2026-04-13 11:18:41.593705: +2026-04-13 11:18:41.596241: Epoch 2539 +2026-04-13 11:18:41.597981: Current learning rate: 0.00404 +2026-04-13 11:20:23.878159: train_loss -0.3922 +2026-04-13 11:20:23.885935: val_loss -0.3547 +2026-04-13 11:20:23.888270: Pseudo dice [0.3765, 0.6533, 0.7801, 0.2931, 0.4691, 0.7386, 0.6937] +2026-04-13 11:20:23.891406: Epoch time: 102.29 s +2026-04-13 11:20:25.123986: +2026-04-13 11:20:25.126205: Epoch 2540 +2026-04-13 11:20:25.128630: Current learning rate: 0.00404 +2026-04-13 11:22:08.403373: train_loss -0.4017 +2026-04-13 11:22:08.411512: val_loss -0.3608 +2026-04-13 11:22:08.417444: Pseudo dice [0.6609, 0.1924, 0.7503, 0.4203, 0.5499, 0.751, 0.6286] +2026-04-13 11:22:08.421296: Epoch time: 103.28 s +2026-04-13 11:22:09.625448: +2026-04-13 11:22:09.628693: Epoch 2541 +2026-04-13 11:22:09.632292: Current learning rate: 0.00403 +2026-04-13 11:23:52.395534: train_loss -0.3996 +2026-04-13 11:23:52.411242: val_loss -0.3705 +2026-04-13 11:23:52.418505: Pseudo dice [0.2226, 0.1749, 0.6252, 0.2229, 0.4418, 0.8936, 0.7893] +2026-04-13 11:23:52.423417: Epoch time: 102.77 s +2026-04-13 11:23:53.613406: +2026-04-13 11:23:53.615184: Epoch 2542 +2026-04-13 11:23:53.620880: Current learning rate: 0.00403 +2026-04-13 11:25:36.762654: train_loss -0.3838 +2026-04-13 11:25:36.771792: val_loss -0.3522 +2026-04-13 11:25:36.775398: Pseudo dice [0.0929, 0.6865, 0.7483, 0.4437, 0.3046, 0.6609, 0.7681] +2026-04-13 11:25:36.779106: Epoch time: 103.15 s +2026-04-13 11:25:38.000020: +2026-04-13 11:25:38.004360: Epoch 2543 +2026-04-13 11:25:38.007470: Current learning rate: 0.00403 +2026-04-13 11:27:21.062663: train_loss -0.3921 +2026-04-13 11:27:21.083002: val_loss -0.3693 +2026-04-13 11:27:21.085187: Pseudo dice [0.7701, 0.6799, 0.8401, 0.4311, 0.5942, 0.8143, 0.7804] +2026-04-13 11:27:21.087809: Epoch time: 103.07 s +2026-04-13 11:27:22.282576: +2026-04-13 11:27:22.284818: Epoch 2544 +2026-04-13 11:27:22.287713: Current learning rate: 0.00403 +2026-04-13 11:29:04.029955: train_loss -0.4248 +2026-04-13 11:29:04.038266: val_loss -0.353 +2026-04-13 11:29:04.040379: Pseudo dice [0.7259, 0.8934, 0.7597, 0.343, 0.6171, 0.3048, 0.647] +2026-04-13 11:29:04.042822: Epoch time: 101.75 s +2026-04-13 11:29:05.197993: +2026-04-13 11:29:05.199471: Epoch 2545 +2026-04-13 11:29:05.201167: Current learning rate: 0.00402 +2026-04-13 11:30:47.585185: train_loss -0.4252 +2026-04-13 11:30:47.594725: val_loss -0.3474 +2026-04-13 11:30:47.598498: Pseudo dice [0.3572, 0.5713, 0.7653, 0.2375, 0.4664, 0.9049, 0.7466] +2026-04-13 11:30:47.617041: Epoch time: 102.39 s +2026-04-13 11:30:48.802496: +2026-04-13 11:30:48.804554: Epoch 2546 +2026-04-13 11:30:48.806319: Current learning rate: 0.00402 +2026-04-13 11:32:30.614815: train_loss -0.406 +2026-04-13 11:32:30.628008: val_loss -0.3444 +2026-04-13 11:32:30.630319: Pseudo dice [0.8724, 0.5799, 0.61, 0.3408, 0.2457, 0.8248, 0.5689] +2026-04-13 11:32:30.638172: Epoch time: 101.82 s +2026-04-13 11:32:31.830157: +2026-04-13 11:32:31.832304: Epoch 2547 +2026-04-13 11:32:31.834424: Current learning rate: 0.00402 +2026-04-13 11:34:14.975690: train_loss -0.3955 +2026-04-13 11:34:14.983340: val_loss -0.2812 +2026-04-13 11:34:14.985543: Pseudo dice [0.504, 0.9087, 0.4919, 0.1872, 0.2964, 0.3, 0.7599] +2026-04-13 11:34:14.988519: Epoch time: 103.15 s +2026-04-13 11:34:16.163239: +2026-04-13 11:34:16.166051: Epoch 2548 +2026-04-13 11:34:16.168103: Current learning rate: 0.00402 +2026-04-13 11:35:58.009566: train_loss -0.3934 +2026-04-13 11:35:58.017983: val_loss -0.3513 +2026-04-13 11:35:58.020368: Pseudo dice [0.3553, 0.6198, 0.6915, 0.2519, 0.3883, 0.868, 0.7603] +2026-04-13 11:35:58.023327: Epoch time: 101.85 s +2026-04-13 11:35:59.245089: +2026-04-13 11:35:59.247015: Epoch 2549 +2026-04-13 11:35:59.249086: Current learning rate: 0.00401 +2026-04-13 11:37:41.478537: train_loss -0.3881 +2026-04-13 11:37:41.489272: val_loss -0.3285 +2026-04-13 11:37:41.491883: Pseudo dice [0.6793, 0.8129, 0.7633, 0.6859, 0.2484, 0.7125, 0.7532] +2026-04-13 11:37:41.494576: Epoch time: 102.24 s +2026-04-13 11:37:44.559190: +2026-04-13 11:37:44.562122: Epoch 2550 +2026-04-13 11:37:44.563795: Current learning rate: 0.00401 +2026-04-13 11:39:26.868148: train_loss -0.377 +2026-04-13 11:39:26.875453: val_loss -0.3172 +2026-04-13 11:39:26.878529: Pseudo dice [0.874, 0.9076, 0.5576, 0.0487, 0.4927, 0.56, 0.5183] +2026-04-13 11:39:26.881820: Epoch time: 102.31 s +2026-04-13 11:39:28.076518: +2026-04-13 11:39:28.080876: Epoch 2551 +2026-04-13 11:39:28.083309: Current learning rate: 0.00401 +2026-04-13 11:41:10.510960: train_loss -0.4153 +2026-04-13 11:41:10.517493: val_loss -0.3612 +2026-04-13 11:41:10.519788: Pseudo dice [0.3281, 0.6794, 0.7131, 0.2415, 0.1498, 0.6969, 0.8232] +2026-04-13 11:41:10.521840: Epoch time: 102.44 s +2026-04-13 11:41:11.700504: +2026-04-13 11:41:11.702563: Epoch 2552 +2026-04-13 11:41:11.705088: Current learning rate: 0.00401 +2026-04-13 11:42:54.135875: train_loss -0.403 +2026-04-13 11:42:54.143580: val_loss -0.3332 +2026-04-13 11:42:54.146966: Pseudo dice [0.6559, 0.737, 0.6665, 0.0752, 0.0203, 0.635, 0.8855] +2026-04-13 11:42:54.149856: Epoch time: 102.44 s +2026-04-13 11:42:55.340206: +2026-04-13 11:42:55.342704: Epoch 2553 +2026-04-13 11:42:55.345320: Current learning rate: 0.004 +2026-04-13 11:44:37.983016: train_loss -0.38 +2026-04-13 11:44:37.990678: val_loss -0.3138 +2026-04-13 11:44:37.994431: Pseudo dice [0.7076, 0.9103, 0.7138, 0.0787, 0.3616, 0.2341, 0.8377] +2026-04-13 11:44:37.997226: Epoch time: 102.65 s +2026-04-13 11:44:39.191895: +2026-04-13 11:44:39.193488: Epoch 2554 +2026-04-13 11:44:39.195282: Current learning rate: 0.004 +2026-04-13 11:46:21.661951: train_loss -0.4074 +2026-04-13 11:46:21.669066: val_loss -0.3564 +2026-04-13 11:46:21.671298: Pseudo dice [0.3791, 0.4017, 0.7371, 0.2091, 0.6023, 0.5741, 0.8023] +2026-04-13 11:46:21.674147: Epoch time: 102.47 s +2026-04-13 11:46:22.943008: +2026-04-13 11:46:22.945359: Epoch 2555 +2026-04-13 11:46:22.947743: Current learning rate: 0.004 +2026-04-13 11:48:05.008962: train_loss -0.4065 +2026-04-13 11:48:05.015844: val_loss -0.3685 +2026-04-13 11:48:05.017637: Pseudo dice [0.5931, 0.6803, 0.7635, 0.0337, 0.5466, 0.8514, 0.4772] +2026-04-13 11:48:05.020361: Epoch time: 102.07 s +2026-04-13 11:48:06.202804: +2026-04-13 11:48:06.206412: Epoch 2556 +2026-04-13 11:48:06.213467: Current learning rate: 0.004 +2026-04-13 11:49:49.170435: train_loss -0.395 +2026-04-13 11:49:49.181158: val_loss -0.3156 +2026-04-13 11:49:49.183300: Pseudo dice [0.6237, 0.6595, 0.745, 0.5659, 0.2533, 0.2974, 0.7229] +2026-04-13 11:49:49.186532: Epoch time: 102.97 s +2026-04-13 11:49:50.358594: +2026-04-13 11:49:50.360702: Epoch 2557 +2026-04-13 11:49:50.362763: Current learning rate: 0.00399 +2026-04-13 11:51:32.432325: train_loss -0.3986 +2026-04-13 11:51:32.440897: val_loss -0.3048 +2026-04-13 11:51:32.443767: Pseudo dice [0.5174, 0.1638, 0.6926, 0.0906, 0.0844, 0.8171, 0.659] +2026-04-13 11:51:32.451402: Epoch time: 102.08 s +2026-04-13 11:51:33.614407: +2026-04-13 11:51:33.619941: Epoch 2558 +2026-04-13 11:51:33.622208: Current learning rate: 0.00399 +2026-04-13 11:53:16.378097: train_loss -0.3872 +2026-04-13 11:53:16.386532: val_loss -0.3489 +2026-04-13 11:53:16.389022: Pseudo dice [0.7562, 0.762, 0.7611, 0.3932, 0.3727, 0.2902, 0.706] +2026-04-13 11:53:16.391870: Epoch time: 102.77 s +2026-04-13 11:53:17.578758: +2026-04-13 11:53:17.581471: Epoch 2559 +2026-04-13 11:53:17.584128: Current learning rate: 0.00399 +2026-04-13 11:54:59.881004: train_loss -0.4031 +2026-04-13 11:54:59.891388: val_loss -0.3086 +2026-04-13 11:54:59.893340: Pseudo dice [0.6173, 0.3071, 0.7457, 0.1899, 0.2348, 0.625, 0.7712] +2026-04-13 11:54:59.895996: Epoch time: 102.31 s +2026-04-13 11:55:01.101064: +2026-04-13 11:55:01.104468: Epoch 2560 +2026-04-13 11:55:01.109494: Current learning rate: 0.00399 +2026-04-13 11:56:43.396507: train_loss -0.3957 +2026-04-13 11:56:43.403604: val_loss -0.3831 +2026-04-13 11:56:43.406466: Pseudo dice [0.5826, 0.7393, 0.8047, 0.3258, 0.4952, 0.8417, 0.6599] +2026-04-13 11:56:43.409086: Epoch time: 102.3 s +2026-04-13 11:56:44.620938: +2026-04-13 11:56:44.622841: Epoch 2561 +2026-04-13 11:56:44.624955: Current learning rate: 0.00398 +2026-04-13 11:58:27.164893: train_loss -0.4029 +2026-04-13 11:58:27.171078: val_loss -0.3182 +2026-04-13 11:58:27.173324: Pseudo dice [0.5819, 0.2512, 0.7422, 0.1721, 0.3119, 0.7342, 0.3437] +2026-04-13 11:58:27.176062: Epoch time: 102.55 s +2026-04-13 11:58:28.365011: +2026-04-13 11:58:28.367719: Epoch 2562 +2026-04-13 11:58:28.372264: Current learning rate: 0.00398 +2026-04-13 12:00:10.980153: train_loss -0.4166 +2026-04-13 12:00:10.988096: val_loss -0.3567 +2026-04-13 12:00:10.990227: Pseudo dice [0.7472, 0.4, 0.824, 0.3812, 0.3587, 0.8343, 0.6472] +2026-04-13 12:00:10.994373: Epoch time: 102.62 s +2026-04-13 12:00:12.179216: +2026-04-13 12:00:12.181411: Epoch 2563 +2026-04-13 12:00:12.183737: Current learning rate: 0.00398 +2026-04-13 12:01:54.576429: train_loss -0.4052 +2026-04-13 12:01:54.584005: val_loss -0.3522 +2026-04-13 12:01:54.588403: Pseudo dice [0.393, 0.5603, 0.7598, 0.2387, 0.4801, 0.5599, 0.7754] +2026-04-13 12:01:54.590899: Epoch time: 102.4 s +2026-04-13 12:01:55.768395: +2026-04-13 12:01:55.771249: Epoch 2564 +2026-04-13 12:01:55.773013: Current learning rate: 0.00398 +2026-04-13 12:03:37.452986: train_loss -0.4178 +2026-04-13 12:03:37.460179: val_loss -0.3771 +2026-04-13 12:03:37.462837: Pseudo dice [0.4543, 0.4677, 0.8016, 0.5675, 0.253, 0.795, 0.7563] +2026-04-13 12:03:37.465582: Epoch time: 101.69 s +2026-04-13 12:03:38.655928: +2026-04-13 12:03:38.658974: Epoch 2565 +2026-04-13 12:03:38.662140: Current learning rate: 0.00397 +2026-04-13 12:05:21.193006: train_loss -0.3984 +2026-04-13 12:05:21.199523: val_loss -0.3335 +2026-04-13 12:05:21.202042: Pseudo dice [0.4042, 0.8217, 0.5555, 0.0372, 0.3349, 0.5012, 0.7234] +2026-04-13 12:05:21.205061: Epoch time: 102.54 s +2026-04-13 12:05:22.394765: +2026-04-13 12:05:22.396963: Epoch 2566 +2026-04-13 12:05:22.399172: Current learning rate: 0.00397 +2026-04-13 12:07:05.116304: train_loss -0.4073 +2026-04-13 12:07:05.122484: val_loss -0.381 +2026-04-13 12:07:05.125401: Pseudo dice [0.2659, 0.6649, 0.7845, 0.5308, 0.6622, 0.6972, 0.7901] +2026-04-13 12:07:05.128206: Epoch time: 102.72 s +2026-04-13 12:07:07.406209: +2026-04-13 12:07:07.408576: Epoch 2567 +2026-04-13 12:07:07.410364: Current learning rate: 0.00397 +2026-04-13 12:08:49.591873: train_loss -0.4266 +2026-04-13 12:08:49.600574: val_loss -0.3905 +2026-04-13 12:08:49.603484: Pseudo dice [0.4278, 0.7436, 0.8502, 0.2963, 0.7056, 0.8354, 0.503] +2026-04-13 12:08:49.607713: Epoch time: 102.19 s +2026-04-13 12:08:50.810216: +2026-04-13 12:08:50.812326: Epoch 2568 +2026-04-13 12:08:50.815815: Current learning rate: 0.00397 +2026-04-13 12:10:32.946116: train_loss -0.4083 +2026-04-13 12:10:32.970855: val_loss -0.3858 +2026-04-13 12:10:32.973066: Pseudo dice [0.6366, 0.8267, 0.7257, 0.7105, 0.5478, 0.3159, 0.8746] +2026-04-13 12:10:32.975981: Epoch time: 102.14 s +2026-04-13 12:10:34.187943: +2026-04-13 12:10:34.192206: Epoch 2569 +2026-04-13 12:10:34.194691: Current learning rate: 0.00396 +2026-04-13 12:12:17.067532: train_loss -0.4254 +2026-04-13 12:12:17.076782: val_loss -0.3533 +2026-04-13 12:12:17.079257: Pseudo dice [0.5992, 0.8337, 0.7299, 0.6401, 0.4363, 0.517, 0.6198] +2026-04-13 12:12:17.082647: Epoch time: 102.88 s +2026-04-13 12:12:18.290949: +2026-04-13 12:12:18.293078: Epoch 2570 +2026-04-13 12:12:18.296480: Current learning rate: 0.00396 +2026-04-13 12:14:00.714131: train_loss -0.4219 +2026-04-13 12:14:00.721011: val_loss -0.3717 +2026-04-13 12:14:00.723987: Pseudo dice [0.5389, 0.9059, 0.7392, 0.3608, 0.3695, 0.5883, 0.7967] +2026-04-13 12:14:00.728408: Epoch time: 102.43 s +2026-04-13 12:14:01.936302: +2026-04-13 12:14:01.938595: Epoch 2571 +2026-04-13 12:14:01.941259: Current learning rate: 0.00396 +2026-04-13 12:15:45.009202: train_loss -0.4144 +2026-04-13 12:15:45.018023: val_loss -0.3601 +2026-04-13 12:15:45.020662: Pseudo dice [0.6923, 0.8046, 0.6757, 0.3525, 0.5143, 0.6887, 0.498] +2026-04-13 12:15:45.023709: Epoch time: 103.08 s +2026-04-13 12:15:46.859561: +2026-04-13 12:15:46.861581: Epoch 2572 +2026-04-13 12:15:46.864085: Current learning rate: 0.00396 +2026-04-13 12:17:30.653938: train_loss -0.4037 +2026-04-13 12:17:30.672038: val_loss -0.3591 +2026-04-13 12:17:30.682109: Pseudo dice [0.0904, 0.6095, 0.7038, 0.7813, 0.4164, 0.5549, 0.8359] +2026-04-13 12:17:30.690482: Epoch time: 103.8 s +2026-04-13 12:17:31.876259: +2026-04-13 12:17:31.879855: Epoch 2573 +2026-04-13 12:17:31.881989: Current learning rate: 0.00395 +2026-04-13 12:19:14.490477: train_loss -0.4107 +2026-04-13 12:19:14.502743: val_loss -0.3519 +2026-04-13 12:19:14.505839: Pseudo dice [0.6672, 0.639, 0.6507, 0.547, 0.3915, 0.8137, 0.6936] +2026-04-13 12:19:14.507937: Epoch time: 102.62 s +2026-04-13 12:19:15.728373: +2026-04-13 12:19:15.731625: Epoch 2574 +2026-04-13 12:19:15.745886: Current learning rate: 0.00395 +2026-04-13 12:20:58.365988: train_loss -0.4116 +2026-04-13 12:20:58.373537: val_loss -0.3402 +2026-04-13 12:20:58.375948: Pseudo dice [0.6035, 0.6889, 0.6948, 0.5633, 0.2481, 0.295, 0.7318] +2026-04-13 12:20:58.378576: Epoch time: 102.64 s +2026-04-13 12:20:59.587145: +2026-04-13 12:20:59.589442: Epoch 2575 +2026-04-13 12:20:59.591323: Current learning rate: 0.00395 +2026-04-13 12:22:43.602487: train_loss -0.4197 +2026-04-13 12:22:43.612258: val_loss -0.3767 +2026-04-13 12:22:43.614637: Pseudo dice [0.5455, 0.5533, 0.7238, 0.7828, 0.3742, 0.6514, 0.8621] +2026-04-13 12:22:43.621408: Epoch time: 104.02 s +2026-04-13 12:22:44.790884: +2026-04-13 12:22:44.793500: Epoch 2576 +2026-04-13 12:22:44.798728: Current learning rate: 0.00395 +2026-04-13 12:24:26.855506: train_loss -0.4217 +2026-04-13 12:24:26.863508: val_loss -0.3999 +2026-04-13 12:24:26.866620: Pseudo dice [0.2256, 0.5957, 0.7656, 0.7695, 0.6544, 0.8729, 0.8379] +2026-04-13 12:24:26.871625: Epoch time: 102.07 s +2026-04-13 12:24:28.056020: +2026-04-13 12:24:28.058212: Epoch 2577 +2026-04-13 12:24:28.060309: Current learning rate: 0.00394 +2026-04-13 12:26:10.462644: train_loss -0.4239 +2026-04-13 12:26:10.470672: val_loss -0.3547 +2026-04-13 12:26:10.472959: Pseudo dice [0.5623, 0.6244, 0.7814, 0.1813, 0.3276, 0.522, 0.8297] +2026-04-13 12:26:10.478475: Epoch time: 102.41 s +2026-04-13 12:26:11.687637: +2026-04-13 12:26:11.698222: Epoch 2578 +2026-04-13 12:26:11.701849: Current learning rate: 0.00394 +2026-04-13 12:27:54.058515: train_loss -0.4238 +2026-04-13 12:27:54.078935: val_loss -0.3874 +2026-04-13 12:27:54.083537: Pseudo dice [0.5753, 0.6519, 0.691, 0.7409, 0.3953, 0.7751, 0.8404] +2026-04-13 12:27:54.087988: Epoch time: 102.37 s +2026-04-13 12:27:55.288656: +2026-04-13 12:27:55.291646: Epoch 2579 +2026-04-13 12:27:55.297365: Current learning rate: 0.00394 +2026-04-13 12:29:38.282458: train_loss -0.4234 +2026-04-13 12:29:38.292175: val_loss -0.3591 +2026-04-13 12:29:38.295657: Pseudo dice [0.6043, 0.7397, 0.8127, 0.5006, 0.4016, 0.1373, 0.8176] +2026-04-13 12:29:38.298207: Epoch time: 103.0 s +2026-04-13 12:29:39.479422: +2026-04-13 12:29:39.483333: Epoch 2580 +2026-04-13 12:29:39.485508: Current learning rate: 0.00394 +2026-04-13 12:31:22.039261: train_loss -0.4313 +2026-04-13 12:31:22.048939: val_loss -0.3818 +2026-04-13 12:31:22.051208: Pseudo dice [0.7542, 0.4312, 0.8447, 0.725, 0.5537, 0.7617, 0.8417] +2026-04-13 12:31:22.053730: Epoch time: 102.56 s +2026-04-13 12:31:23.311021: +2026-04-13 12:31:23.313401: Epoch 2581 +2026-04-13 12:31:23.315775: Current learning rate: 0.00393 +2026-04-13 12:33:05.573254: train_loss -0.4164 +2026-04-13 12:33:05.580627: val_loss -0.3464 +2026-04-13 12:33:05.582778: Pseudo dice [0.3359, 0.3743, 0.7397, 0.6524, 0.2697, 0.8453, 0.671] +2026-04-13 12:33:05.586409: Epoch time: 102.27 s +2026-04-13 12:33:06.766475: +2026-04-13 12:33:06.770682: Epoch 2582 +2026-04-13 12:33:06.776312: Current learning rate: 0.00393 +2026-04-13 12:34:50.147863: train_loss -0.4205 +2026-04-13 12:34:50.155340: val_loss -0.3463 +2026-04-13 12:34:50.157640: Pseudo dice [0.7226, 0.3679, 0.8169, 0.2519, 0.3247, 0.6159, 0.754] +2026-04-13 12:34:50.159755: Epoch time: 103.38 s +2026-04-13 12:34:51.331332: +2026-04-13 12:34:51.333521: Epoch 2583 +2026-04-13 12:34:51.336254: Current learning rate: 0.00393 +2026-04-13 12:36:33.461141: train_loss -0.4308 +2026-04-13 12:36:33.468368: val_loss -0.3564 +2026-04-13 12:36:33.471780: Pseudo dice [0.8467, 0.5375, 0.7612, 0.6236, 0.4202, 0.8837, 0.7646] +2026-04-13 12:36:33.474362: Epoch time: 102.13 s +2026-04-13 12:36:34.720738: +2026-04-13 12:36:34.724911: Epoch 2584 +2026-04-13 12:36:34.730503: Current learning rate: 0.00393 +2026-04-13 12:38:16.879203: train_loss -0.4254 +2026-04-13 12:38:16.886986: val_loss -0.4019 +2026-04-13 12:38:16.891220: Pseudo dice [0.6396, 0.2444, 0.738, 0.5896, 0.5859, 0.7085, 0.7946] +2026-04-13 12:38:16.893919: Epoch time: 102.16 s +2026-04-13 12:38:18.079690: +2026-04-13 12:38:18.081703: Epoch 2585 +2026-04-13 12:38:18.084542: Current learning rate: 0.00392 +2026-04-13 12:40:00.950054: train_loss -0.4288 +2026-04-13 12:40:00.959183: val_loss -0.3625 +2026-04-13 12:40:00.963350: Pseudo dice [0.2154, 0.7857, 0.8025, 0.1509, 0.3504, 0.7945, 0.8333] +2026-04-13 12:40:00.968835: Epoch time: 102.87 s +2026-04-13 12:40:02.162516: +2026-04-13 12:40:02.165294: Epoch 2586 +2026-04-13 12:40:02.168921: Current learning rate: 0.00392 +2026-04-13 12:41:44.399098: train_loss -0.4181 +2026-04-13 12:41:44.405925: val_loss -0.326 +2026-04-13 12:41:44.408346: Pseudo dice [0.4857, 0.0963, 0.7962, 0.5429, 0.234, 0.1266, 0.4513] +2026-04-13 12:41:44.411260: Epoch time: 102.24 s +2026-04-13 12:41:45.589760: +2026-04-13 12:41:45.591769: Epoch 2587 +2026-04-13 12:41:45.594732: Current learning rate: 0.00392 +2026-04-13 12:43:29.022613: train_loss -0.3929 +2026-04-13 12:43:29.030030: val_loss -0.354 +2026-04-13 12:43:29.032171: Pseudo dice [0.3332, 0.5633, 0.8095, 0.637, 0.6167, 0.8778, 0.3676] +2026-04-13 12:43:29.035180: Epoch time: 103.44 s +2026-04-13 12:43:30.223329: +2026-04-13 12:43:30.225296: Epoch 2588 +2026-04-13 12:43:30.227587: Current learning rate: 0.00392 +2026-04-13 12:45:12.868517: train_loss -0.3805 +2026-04-13 12:45:12.878192: val_loss -0.3387 +2026-04-13 12:45:12.881081: Pseudo dice [0.6003, 0.8575, 0.7232, 0.1884, 0.5427, 0.7871, 0.5995] +2026-04-13 12:45:12.883858: Epoch time: 102.65 s +2026-04-13 12:45:14.059444: +2026-04-13 12:45:14.062166: Epoch 2589 +2026-04-13 12:45:14.067174: Current learning rate: 0.00391 +2026-04-13 12:46:56.449327: train_loss -0.4081 +2026-04-13 12:46:56.456398: val_loss -0.3185 +2026-04-13 12:46:56.458760: Pseudo dice [0.4443, 0.315, 0.6367, 0.1303, 0.4801, 0.695, 0.7432] +2026-04-13 12:46:56.461346: Epoch time: 102.39 s +2026-04-13 12:46:57.661225: +2026-04-13 12:46:57.663364: Epoch 2590 +2026-04-13 12:46:57.665353: Current learning rate: 0.00391 +2026-04-13 12:48:40.429099: train_loss -0.3818 +2026-04-13 12:48:40.436344: val_loss -0.3738 +2026-04-13 12:48:40.438955: Pseudo dice [0.6617, 0.844, 0.7932, 0.4504, 0.3881, 0.6724, 0.8017] +2026-04-13 12:48:40.442176: Epoch time: 102.77 s +2026-04-13 12:48:41.639744: +2026-04-13 12:48:41.641653: Epoch 2591 +2026-04-13 12:48:41.644144: Current learning rate: 0.00391 +2026-04-13 12:50:25.140177: train_loss -0.4043 +2026-04-13 12:50:25.148693: val_loss -0.297 +2026-04-13 12:50:25.153010: Pseudo dice [0.3127, 0.8901, 0.7247, 0.2651, 0.2976, 0.1146, 0.6812] +2026-04-13 12:50:25.156999: Epoch time: 103.5 s +2026-04-13 12:50:26.369443: +2026-04-13 12:50:26.372024: Epoch 2592 +2026-04-13 12:50:26.374993: Current learning rate: 0.00391 +2026-04-13 12:52:09.604832: train_loss -0.3918 +2026-04-13 12:52:09.611488: val_loss -0.3577 +2026-04-13 12:52:09.615104: Pseudo dice [0.6367, 0.5503, 0.7767, 0.1896, 0.4407, 0.8589, 0.7996] +2026-04-13 12:52:09.618485: Epoch time: 103.24 s +2026-04-13 12:52:10.819916: +2026-04-13 12:52:10.822273: Epoch 2593 +2026-04-13 12:52:10.824537: Current learning rate: 0.0039 +2026-04-13 12:53:54.221253: train_loss -0.3909 +2026-04-13 12:53:54.228673: val_loss -0.32 +2026-04-13 12:53:54.232783: Pseudo dice [0.6822, 0.846, 0.4698, 0.3859, 0.3518, 0.319, 0.7452] +2026-04-13 12:53:54.236052: Epoch time: 103.4 s +2026-04-13 12:53:55.443376: +2026-04-13 12:53:55.446052: Epoch 2594 +2026-04-13 12:53:55.448609: Current learning rate: 0.0039 +2026-04-13 12:55:37.763768: train_loss -0.3844 +2026-04-13 12:55:37.773613: val_loss -0.3725 +2026-04-13 12:55:37.776134: Pseudo dice [0.5313, 0.4197, 0.7851, 0.3719, 0.5716, 0.8608, 0.6411] +2026-04-13 12:55:37.779007: Epoch time: 102.32 s +2026-04-13 12:55:38.968910: +2026-04-13 12:55:38.971936: Epoch 2595 +2026-04-13 12:55:38.973685: Current learning rate: 0.0039 +2026-04-13 12:57:22.098118: train_loss -0.3908 +2026-04-13 12:57:22.104666: val_loss -0.3258 +2026-04-13 12:57:22.106920: Pseudo dice [0.8045, 0.4327, 0.7796, 0.221, 0.2739, 0.6283, 0.3326] +2026-04-13 12:57:22.109391: Epoch time: 103.13 s +2026-04-13 12:57:23.286009: +2026-04-13 12:57:23.288738: Epoch 2596 +2026-04-13 12:57:23.290467: Current learning rate: 0.0039 +2026-04-13 12:59:06.332726: train_loss -0.3998 +2026-04-13 12:59:06.340541: val_loss -0.3557 +2026-04-13 12:59:06.343003: Pseudo dice [0.6711, 0.6898, 0.6314, 0.525, 0.4217, 0.3838, 0.5658] +2026-04-13 12:59:06.346035: Epoch time: 103.05 s +2026-04-13 12:59:07.551124: +2026-04-13 12:59:07.553900: Epoch 2597 +2026-04-13 12:59:07.555520: Current learning rate: 0.00389 +2026-04-13 13:00:51.497540: train_loss -0.3872 +2026-04-13 13:00:51.510962: val_loss -0.3578 +2026-04-13 13:00:51.514693: Pseudo dice [0.5698, 0.4736, 0.7885, 0.7398, 0.4154, 0.9211, 0.6141] +2026-04-13 13:00:51.519786: Epoch time: 103.95 s +2026-04-13 13:00:52.699445: +2026-04-13 13:00:52.703073: Epoch 2598 +2026-04-13 13:00:52.706037: Current learning rate: 0.00389 +2026-04-13 13:02:36.875511: train_loss -0.3912 +2026-04-13 13:02:36.884486: val_loss -0.3003 +2026-04-13 13:02:36.886712: Pseudo dice [0.6578, 0.8029, 0.7441, 0.3759, 0.391, 0.5165, 0.6933] +2026-04-13 13:02:36.889857: Epoch time: 104.18 s +2026-04-13 13:02:38.131795: +2026-04-13 13:02:38.134853: Epoch 2599 +2026-04-13 13:02:38.136592: Current learning rate: 0.00389 +2026-04-13 13:04:21.433994: train_loss -0.3846 +2026-04-13 13:04:21.442399: val_loss -0.3515 +2026-04-13 13:04:21.444595: Pseudo dice [0.6561, 0.357, 0.7554, 0.3266, 0.4898, 0.8059, 0.6009] +2026-04-13 13:04:21.447139: Epoch time: 103.31 s +2026-04-13 13:04:24.407529: +2026-04-13 13:04:24.411644: Epoch 2600 +2026-04-13 13:04:24.413323: Current learning rate: 0.00389 +2026-04-13 13:06:09.212280: train_loss -0.4097 +2026-04-13 13:06:09.220323: val_loss -0.3203 +2026-04-13 13:06:09.222678: Pseudo dice [0.6838, 0.8141, 0.7851, 0.3563, 0.3654, 0.4264, 0.5232] +2026-04-13 13:06:09.227291: Epoch time: 104.81 s +2026-04-13 13:06:10.446528: +2026-04-13 13:06:10.463743: Epoch 2601 +2026-04-13 13:06:10.477668: Current learning rate: 0.00388 +2026-04-13 13:07:54.015644: train_loss -0.4051 +2026-04-13 13:07:54.022542: val_loss -0.314 +2026-04-13 13:07:54.024705: Pseudo dice [0.3548, 0.8783, 0.6561, 0.543, 0.5619, 0.5994, 0.6525] +2026-04-13 13:07:54.027682: Epoch time: 103.57 s +2026-04-13 13:07:55.215255: +2026-04-13 13:07:55.217701: Epoch 2602 +2026-04-13 13:07:55.219543: Current learning rate: 0.00388 +2026-04-13 13:09:37.985556: train_loss -0.4122 +2026-04-13 13:09:37.993704: val_loss -0.3431 +2026-04-13 13:09:37.995962: Pseudo dice [0.8613, 0.8373, 0.783, 0.5672, 0.471, 0.0728, 0.8262] +2026-04-13 13:09:37.998365: Epoch time: 102.77 s +2026-04-13 13:09:39.215009: +2026-04-13 13:09:39.217525: Epoch 2603 +2026-04-13 13:09:39.219550: Current learning rate: 0.00388 +2026-04-13 13:11:23.255656: train_loss -0.4061 +2026-04-13 13:11:23.270844: val_loss -0.3567 +2026-04-13 13:11:23.273505: Pseudo dice [0.7941, 0.3085, 0.7736, 0.518, 0.256, 0.7184, 0.6663] +2026-04-13 13:11:23.276742: Epoch time: 104.04 s +2026-04-13 13:11:24.467586: +2026-04-13 13:11:24.471640: Epoch 2604 +2026-04-13 13:11:24.474560: Current learning rate: 0.00388 +2026-04-13 13:13:07.089675: train_loss -0.3881 +2026-04-13 13:13:07.097490: val_loss -0.3557 +2026-04-13 13:13:07.099899: Pseudo dice [0.7225, 0.5874, 0.7766, 0.3633, 0.4722, 0.8789, 0.686] +2026-04-13 13:13:07.102726: Epoch time: 102.63 s +2026-04-13 13:13:08.292744: +2026-04-13 13:13:08.295765: Epoch 2605 +2026-04-13 13:13:08.297508: Current learning rate: 0.00387 +2026-04-13 13:14:50.781587: train_loss -0.401 +2026-04-13 13:14:50.787897: val_loss -0.3506 +2026-04-13 13:14:50.789797: Pseudo dice [0.3885, 0.6816, 0.7661, 0.5544, 0.4143, 0.8789, 0.7293] +2026-04-13 13:14:50.792032: Epoch time: 102.49 s +2026-04-13 13:14:52.132605: +2026-04-13 13:14:52.135402: Epoch 2606 +2026-04-13 13:14:52.137159: Current learning rate: 0.00387 +2026-04-13 13:16:35.829267: train_loss -0.3844 +2026-04-13 13:16:35.837965: val_loss -0.3119 +2026-04-13 13:16:35.840566: Pseudo dice [0.4998, 0.8712, 0.6481, 0.1191, 0.5367, 0.1816, 0.5652] +2026-04-13 13:16:35.843169: Epoch time: 103.7 s +2026-04-13 13:16:37.019919: +2026-04-13 13:16:37.023683: Epoch 2607 +2026-04-13 13:16:37.026809: Current learning rate: 0.00387 +2026-04-13 13:18:20.906937: train_loss -0.4034 +2026-04-13 13:18:20.914854: val_loss -0.3537 +2026-04-13 13:18:20.918387: Pseudo dice [0.4377, 0.5307, 0.7574, 0.3395, 0.4684, 0.4686, 0.7608] +2026-04-13 13:18:20.921209: Epoch time: 103.89 s +2026-04-13 13:18:22.140511: +2026-04-13 13:18:22.142753: Epoch 2608 +2026-04-13 13:18:22.145489: Current learning rate: 0.00387 +2026-04-13 13:20:04.344092: train_loss -0.3884 +2026-04-13 13:20:04.353496: val_loss -0.3173 +2026-04-13 13:20:04.356329: Pseudo dice [0.6174, 0.7645, 0.7011, 0.2461, 0.3719, 0.2445, 0.5423] +2026-04-13 13:20:04.359588: Epoch time: 102.21 s +2026-04-13 13:20:05.616021: +2026-04-13 13:20:05.618751: Epoch 2609 +2026-04-13 13:20:05.620930: Current learning rate: 0.00386 +2026-04-13 13:21:49.222566: train_loss -0.4123 +2026-04-13 13:21:49.230491: val_loss -0.3716 +2026-04-13 13:21:49.234644: Pseudo dice [0.8686, 0.9061, 0.8018, 0.5552, 0.6154, 0.6472, 0.8002] +2026-04-13 13:21:49.237806: Epoch time: 103.61 s +2026-04-13 13:21:50.507871: +2026-04-13 13:21:50.510949: Epoch 2610 +2026-04-13 13:21:50.515985: Current learning rate: 0.00386 +2026-04-13 13:23:33.346769: train_loss -0.4163 +2026-04-13 13:23:33.353333: val_loss -0.3528 +2026-04-13 13:23:33.356036: Pseudo dice [0.6196, 0.5702, 0.7093, 0.3987, 0.3108, 0.8823, 0.6813] +2026-04-13 13:23:33.359488: Epoch time: 102.84 s +2026-04-13 13:23:34.519679: +2026-04-13 13:23:34.521678: Epoch 2611 +2026-04-13 13:23:34.523920: Current learning rate: 0.00386 +2026-04-13 13:25:16.820462: train_loss -0.4183 +2026-04-13 13:25:16.829383: val_loss -0.356 +2026-04-13 13:25:16.833044: Pseudo dice [0.8006, 0.6498, 0.8107, 0.4849, 0.0336, 0.7534, 0.6937] +2026-04-13 13:25:16.835703: Epoch time: 102.3 s +2026-04-13 13:25:18.031529: +2026-04-13 13:25:18.034366: Epoch 2612 +2026-04-13 13:25:18.036862: Current learning rate: 0.00386 +2026-04-13 13:26:59.502246: train_loss -0.4119 +2026-04-13 13:26:59.509079: val_loss -0.3532 +2026-04-13 13:26:59.514053: Pseudo dice [0.4421, 0.7954, 0.7437, 0.4656, 0.5423, 0.7644, 0.7614] +2026-04-13 13:26:59.519854: Epoch time: 101.47 s +2026-04-13 13:27:00.695143: +2026-04-13 13:27:00.698351: Epoch 2613 +2026-04-13 13:27:00.702071: Current learning rate: 0.00385 +2026-04-13 13:28:42.715336: train_loss -0.3991 +2026-04-13 13:28:42.722080: val_loss -0.3301 +2026-04-13 13:28:42.724957: Pseudo dice [0.8442, 0.5295, 0.7297, 0.2294, 0.3593, 0.8076, 0.689] +2026-04-13 13:28:42.727152: Epoch time: 102.02 s +2026-04-13 13:28:43.905536: +2026-04-13 13:28:43.907401: Epoch 2614 +2026-04-13 13:28:43.910766: Current learning rate: 0.00385 +2026-04-13 13:30:26.137845: train_loss -0.423 +2026-04-13 13:30:26.144567: val_loss -0.3454 +2026-04-13 13:30:26.147470: Pseudo dice [0.733, 0.8471, 0.741, 0.3226, 0.3973, 0.761, 0.2076] +2026-04-13 13:30:26.150126: Epoch time: 102.24 s +2026-04-13 13:30:27.350093: +2026-04-13 13:30:27.351909: Epoch 2615 +2026-04-13 13:30:27.353976: Current learning rate: 0.00385 +2026-04-13 13:32:09.621584: train_loss -0.4244 +2026-04-13 13:32:09.628756: val_loss -0.3637 +2026-04-13 13:32:09.631132: Pseudo dice [0.8175, 0.2549, 0.7915, 0.2526, 0.3052, 0.7262, 0.7346] +2026-04-13 13:32:09.633856: Epoch time: 102.27 s +2026-04-13 13:32:10.817755: +2026-04-13 13:32:10.819658: Epoch 2616 +2026-04-13 13:32:10.822091: Current learning rate: 0.00385 +2026-04-13 13:33:54.097788: train_loss -0.3883 +2026-04-13 13:33:54.108211: val_loss -0.3818 +2026-04-13 13:33:54.111544: Pseudo dice [0.7341, 0.6307, 0.5332, 0.4855, 0.5604, 0.8591, 0.7872] +2026-04-13 13:33:54.114450: Epoch time: 103.28 s +2026-04-13 13:33:55.279917: +2026-04-13 13:33:55.282425: Epoch 2617 +2026-04-13 13:33:55.284907: Current learning rate: 0.00384 +2026-04-13 13:35:37.507485: train_loss -0.4084 +2026-04-13 13:35:37.514917: val_loss -0.361 +2026-04-13 13:35:37.516939: Pseudo dice [0.7023, 0.3433, 0.7555, 0.6059, 0.5919, 0.8461, 0.5435] +2026-04-13 13:35:37.519332: Epoch time: 102.23 s +2026-04-13 13:35:38.684154: +2026-04-13 13:35:38.685863: Epoch 2618 +2026-04-13 13:35:38.688017: Current learning rate: 0.00384 +2026-04-13 13:37:20.649818: train_loss -0.3927 +2026-04-13 13:37:20.655891: val_loss -0.3537 +2026-04-13 13:37:20.659077: Pseudo dice [0.5172, 0.7066, 0.7619, 0.1825, 0.4579, 0.3904, 0.7967] +2026-04-13 13:37:20.662441: Epoch time: 101.97 s +2026-04-13 13:37:21.855950: +2026-04-13 13:37:21.857737: Epoch 2619 +2026-04-13 13:37:21.859674: Current learning rate: 0.00384 +2026-04-13 13:39:05.953049: train_loss -0.4017 +2026-04-13 13:39:05.986541: val_loss -0.3203 +2026-04-13 13:39:05.990564: Pseudo dice [0.5548, 0.7988, 0.732, 0.1506, 0.538, 0.3878, 0.6153] +2026-04-13 13:39:05.994710: Epoch time: 104.1 s +2026-04-13 13:39:07.172190: +2026-04-13 13:39:07.174451: Epoch 2620 +2026-04-13 13:39:07.177422: Current learning rate: 0.00384 +2026-04-13 13:40:49.343444: train_loss -0.4153 +2026-04-13 13:40:49.349683: val_loss -0.3585 +2026-04-13 13:40:49.351958: Pseudo dice [0.4669, 0.8718, 0.5282, 0.0021, 0.4265, 0.3123, 0.553] +2026-04-13 13:40:49.354487: Epoch time: 102.17 s +2026-04-13 13:40:50.536162: +2026-04-13 13:40:50.538276: Epoch 2621 +2026-04-13 13:40:50.540985: Current learning rate: 0.00383 +2026-04-13 13:42:32.496769: train_loss -0.4037 +2026-04-13 13:42:32.504321: val_loss -0.3455 +2026-04-13 13:42:32.508284: Pseudo dice [0.702, 0.7043, 0.6611, 0.389, 0.4466, 0.798, 0.7536] +2026-04-13 13:42:32.511720: Epoch time: 101.96 s +2026-04-13 13:42:33.695758: +2026-04-13 13:42:33.697846: Epoch 2622 +2026-04-13 13:42:33.700038: Current learning rate: 0.00383 +2026-04-13 13:44:15.872807: train_loss -0.4033 +2026-04-13 13:44:15.880873: val_loss -0.3691 +2026-04-13 13:44:15.883504: Pseudo dice [0.594, 0.3137, 0.7064, 0.3722, 0.6094, 0.8082, 0.7226] +2026-04-13 13:44:15.887227: Epoch time: 102.18 s +2026-04-13 13:44:17.079597: +2026-04-13 13:44:17.081656: Epoch 2623 +2026-04-13 13:44:17.084601: Current learning rate: 0.00383 +2026-04-13 13:45:59.966190: train_loss -0.4059 +2026-04-13 13:45:59.976561: val_loss -0.3648 +2026-04-13 13:45:59.978922: Pseudo dice [0.5132, 0.3165, 0.7113, 0.5476, 0.5258, 0.8784, 0.8782] +2026-04-13 13:45:59.981728: Epoch time: 102.89 s +2026-04-13 13:46:01.169435: +2026-04-13 13:46:01.171244: Epoch 2624 +2026-04-13 13:46:01.173377: Current learning rate: 0.00383 +2026-04-13 13:47:42.639605: train_loss -0.3864 +2026-04-13 13:47:42.657203: val_loss -0.3581 +2026-04-13 13:47:42.659574: Pseudo dice [0.6394, 0.4683, 0.8003, 0.5185, 0.5236, 0.9247, 0.7962] +2026-04-13 13:47:42.662128: Epoch time: 101.47 s +2026-04-13 13:47:43.862086: +2026-04-13 13:47:43.863929: Epoch 2625 +2026-04-13 13:47:43.866247: Current learning rate: 0.00382 +2026-04-13 13:49:25.367597: train_loss -0.3789 +2026-04-13 13:49:25.374732: val_loss -0.3402 +2026-04-13 13:49:25.377306: Pseudo dice [0.5819, 0.8521, 0.7479, 0.3692, 0.1243, 0.7416, 0.7283] +2026-04-13 13:49:25.380391: Epoch time: 101.51 s +2026-04-13 13:49:26.596645: +2026-04-13 13:49:26.598643: Epoch 2626 +2026-04-13 13:49:26.600533: Current learning rate: 0.00382 +2026-04-13 13:51:10.125462: train_loss -0.3971 +2026-04-13 13:51:10.135332: val_loss -0.3752 +2026-04-13 13:51:10.140963: Pseudo dice [0.6092, 0.5548, 0.8044, 0.5327, 0.534, 0.7015, 0.7045] +2026-04-13 13:51:10.145616: Epoch time: 103.53 s +2026-04-13 13:51:11.344083: +2026-04-13 13:51:11.348194: Epoch 2627 +2026-04-13 13:51:11.356996: Current learning rate: 0.00382 +2026-04-13 13:52:54.296938: train_loss -0.4114 +2026-04-13 13:52:54.303278: val_loss -0.3195 +2026-04-13 13:52:54.305585: Pseudo dice [0.6028, 0.5885, 0.742, 0.4193, 0.3314, 0.4746, 0.6895] +2026-04-13 13:52:54.308588: Epoch time: 102.96 s +2026-04-13 13:52:55.545523: +2026-04-13 13:52:55.547321: Epoch 2628 +2026-04-13 13:52:55.549141: Current learning rate: 0.00382 +2026-04-13 13:54:37.524282: train_loss -0.4121 +2026-04-13 13:54:37.533296: val_loss -0.3839 +2026-04-13 13:54:37.535652: Pseudo dice [0.3089, 0.117, 0.8139, 0.3438, 0.5705, 0.8796, 0.7309] +2026-04-13 13:54:37.538793: Epoch time: 101.98 s +2026-04-13 13:54:38.768911: +2026-04-13 13:54:38.771391: Epoch 2629 +2026-04-13 13:54:38.773871: Current learning rate: 0.00381 +2026-04-13 13:56:21.166532: train_loss -0.4005 +2026-04-13 13:56:21.174536: val_loss -0.3783 +2026-04-13 13:56:21.178263: Pseudo dice [0.3508, 0.6331, 0.7438, 0.4614, 0.5614, 0.8357, 0.324] +2026-04-13 13:56:21.181160: Epoch time: 102.4 s +2026-04-13 13:56:22.381048: +2026-04-13 13:56:22.383154: Epoch 2630 +2026-04-13 13:56:22.385602: Current learning rate: 0.00381 +2026-04-13 13:58:04.946356: train_loss -0.4128 +2026-04-13 13:58:04.954973: val_loss -0.3645 +2026-04-13 13:58:04.956975: Pseudo dice [0.5402, 0.6713, 0.7058, 0.5126, 0.4844, 0.4262, 0.6716] +2026-04-13 13:58:04.960209: Epoch time: 102.57 s +2026-04-13 13:58:06.182901: +2026-04-13 13:58:06.185334: Epoch 2631 +2026-04-13 13:58:06.187137: Current learning rate: 0.00381 +2026-04-13 13:59:48.023318: train_loss -0.3924 +2026-04-13 13:59:48.030781: val_loss -0.3196 +2026-04-13 13:59:48.034434: Pseudo dice [0.5719, 0.6696, 0.5629, 0.0015, 0.3975, 0.7088, 0.5475] +2026-04-13 13:59:48.037906: Epoch time: 101.84 s +2026-04-13 13:59:49.233615: +2026-04-13 13:59:49.235514: Epoch 2632 +2026-04-13 13:59:49.237361: Current learning rate: 0.00381 +2026-04-13 14:01:30.820917: train_loss -0.3979 +2026-04-13 14:01:30.833046: val_loss -0.3789 +2026-04-13 14:01:30.835018: Pseudo dice [0.7042, 0.7805, 0.7236, 0.6116, 0.4342, 0.5957, 0.4381] +2026-04-13 14:01:30.838995: Epoch time: 101.59 s +2026-04-13 14:01:32.038981: +2026-04-13 14:01:32.040596: Epoch 2633 +2026-04-13 14:01:32.042475: Current learning rate: 0.0038 +2026-04-13 14:03:14.704168: train_loss -0.4138 +2026-04-13 14:03:14.714113: val_loss -0.3843 +2026-04-13 14:03:14.717024: Pseudo dice [0.6351, 0.5007, 0.7861, 0.4852, 0.548, 0.8447, 0.8082] +2026-04-13 14:03:14.720603: Epoch time: 102.67 s +2026-04-13 14:03:15.924538: +2026-04-13 14:03:15.927347: Epoch 2634 +2026-04-13 14:03:15.929837: Current learning rate: 0.0038 +2026-04-13 14:04:58.113202: train_loss -0.3874 +2026-04-13 14:04:58.120860: val_loss -0.3608 +2026-04-13 14:04:58.123516: Pseudo dice [0.2924, 0.591, 0.6207, 0.445, 0.3874, 0.2105, 0.8109] +2026-04-13 14:04:58.126727: Epoch time: 102.19 s +2026-04-13 14:04:59.285268: +2026-04-13 14:04:59.288128: Epoch 2635 +2026-04-13 14:04:59.291092: Current learning rate: 0.0038 +2026-04-13 14:06:41.622863: train_loss -0.4041 +2026-04-13 14:06:41.631465: val_loss -0.3671 +2026-04-13 14:06:41.634448: Pseudo dice [0.6976, 0.4705, 0.7899, 0.5102, 0.4305, 0.8419, 0.8294] +2026-04-13 14:06:41.637982: Epoch time: 102.34 s +2026-04-13 14:06:42.848622: +2026-04-13 14:06:42.851043: Epoch 2636 +2026-04-13 14:06:42.853247: Current learning rate: 0.0038 +2026-04-13 14:08:26.149983: train_loss -0.4279 +2026-04-13 14:08:26.163663: val_loss -0.3704 +2026-04-13 14:08:26.167924: Pseudo dice [0.625, 0.8606, 0.7564, 0.5981, 0.3641, 0.8356, 0.6135] +2026-04-13 14:08:26.171550: Epoch time: 103.3 s +2026-04-13 14:08:27.381239: +2026-04-13 14:08:27.383291: Epoch 2637 +2026-04-13 14:08:27.385700: Current learning rate: 0.00379 +2026-04-13 14:10:09.533390: train_loss -0.4203 +2026-04-13 14:10:09.542693: val_loss -0.3749 +2026-04-13 14:10:09.545438: Pseudo dice [0.8557, 0.3994, 0.7367, 0.4623, 0.5038, 0.938, 0.6385] +2026-04-13 14:10:09.548178: Epoch time: 102.16 s +2026-04-13 14:10:10.960007: +2026-04-13 14:10:10.962235: Epoch 2638 +2026-04-13 14:10:10.963976: Current learning rate: 0.00379 +2026-04-13 14:11:52.530420: train_loss -0.4122 +2026-04-13 14:11:52.537450: val_loss -0.3521 +2026-04-13 14:11:52.539742: Pseudo dice [0.7609, 0.6458, 0.6857, 0.509, 0.2571, 0.5215, 0.8018] +2026-04-13 14:11:52.541985: Epoch time: 101.57 s +2026-04-13 14:11:53.706218: +2026-04-13 14:11:53.708735: Epoch 2639 +2026-04-13 14:11:53.711164: Current learning rate: 0.00379 +2026-04-13 14:13:35.421973: train_loss -0.415 +2026-04-13 14:13:35.430536: val_loss -0.3609 +2026-04-13 14:13:35.432492: Pseudo dice [0.6609, 0.7997, 0.6756, 0.1785, 0.6606, 0.352, 0.4359] +2026-04-13 14:13:35.435097: Epoch time: 101.72 s +2026-04-13 14:13:36.614243: +2026-04-13 14:13:36.616252: Epoch 2640 +2026-04-13 14:13:36.618270: Current learning rate: 0.00379 +2026-04-13 14:15:20.126265: train_loss -0.4245 +2026-04-13 14:15:20.133957: val_loss -0.3841 +2026-04-13 14:15:20.136362: Pseudo dice [0.758, 0.0713, 0.7753, 0.466, 0.5709, 0.9157, 0.7839] +2026-04-13 14:15:20.138878: Epoch time: 103.52 s +2026-04-13 14:15:21.336135: +2026-04-13 14:15:21.337975: Epoch 2641 +2026-04-13 14:15:21.340566: Current learning rate: 0.00378 +2026-04-13 14:17:02.975091: train_loss -0.41 +2026-04-13 14:17:02.984006: val_loss -0.3923 +2026-04-13 14:17:02.988353: Pseudo dice [0.8089, 0.8977, 0.8187, 0.4377, 0.356, 0.7173, 0.7003] +2026-04-13 14:17:02.991240: Epoch time: 101.64 s +2026-04-13 14:17:04.162697: +2026-04-13 14:17:04.164957: Epoch 2642 +2026-04-13 14:17:04.167588: Current learning rate: 0.00378 +2026-04-13 14:18:47.229080: train_loss -0.4157 +2026-04-13 14:18:47.236758: val_loss -0.3716 +2026-04-13 14:18:47.238674: Pseudo dice [0.6291, 0.6237, 0.8164, 0.4017, 0.536, 0.9006, 0.7304] +2026-04-13 14:18:47.241098: Epoch time: 103.07 s +2026-04-13 14:18:48.453798: +2026-04-13 14:18:48.455423: Epoch 2643 +2026-04-13 14:18:48.457499: Current learning rate: 0.00378 +2026-04-13 14:20:32.399061: train_loss -0.4177 +2026-04-13 14:20:32.409993: val_loss -0.3393 +2026-04-13 14:20:32.413351: Pseudo dice [0.6361, 0.7254, 0.7165, 0.5706, 0.3041, 0.8677, 0.8079] +2026-04-13 14:20:32.416676: Epoch time: 103.95 s +2026-04-13 14:20:33.604026: +2026-04-13 14:20:33.607588: Epoch 2644 +2026-04-13 14:20:33.611516: Current learning rate: 0.00378 +2026-04-13 14:22:16.321119: train_loss -0.4052 +2026-04-13 14:22:16.329743: val_loss -0.3538 +2026-04-13 14:22:16.332283: Pseudo dice [0.486, 0.8986, 0.6203, 0.435, 0.6564, 0.6763, 0.8872] +2026-04-13 14:22:16.334908: Epoch time: 102.72 s +2026-04-13 14:22:17.545264: +2026-04-13 14:22:17.546919: Epoch 2645 +2026-04-13 14:22:17.548666: Current learning rate: 0.00377 +2026-04-13 14:24:00.753729: train_loss -0.4222 +2026-04-13 14:24:00.761662: val_loss -0.396 +2026-04-13 14:24:00.764140: Pseudo dice [0.5743, 0.8982, 0.8153, 0.843, 0.6259, 0.6471, 0.814] +2026-04-13 14:24:00.766323: Epoch time: 103.21 s +2026-04-13 14:24:01.960190: +2026-04-13 14:24:01.962094: Epoch 2646 +2026-04-13 14:24:01.964271: Current learning rate: 0.00377 +2026-04-13 14:25:45.455483: train_loss -0.4334 +2026-04-13 14:25:45.464236: val_loss -0.3535 +2026-04-13 14:25:45.471066: Pseudo dice [0.6481, 0.8179, 0.7693, 0.5763, 0.6213, 0.793, 0.654] +2026-04-13 14:25:45.475974: Epoch time: 103.5 s +2026-04-13 14:25:46.663988: +2026-04-13 14:25:46.666979: Epoch 2647 +2026-04-13 14:25:46.672736: Current learning rate: 0.00377 +2026-04-13 14:27:29.123135: train_loss -0.4267 +2026-04-13 14:27:29.130178: val_loss -0.3474 +2026-04-13 14:27:29.131853: Pseudo dice [0.2356, 0.9185, 0.6872, 0.4956, 0.5922, 0.4539, 0.8529] +2026-04-13 14:27:29.134588: Epoch time: 102.46 s +2026-04-13 14:27:31.392666: +2026-04-13 14:27:31.395817: Epoch 2648 +2026-04-13 14:27:31.397830: Current learning rate: 0.00377 +2026-04-13 14:29:13.781640: train_loss -0.4187 +2026-04-13 14:29:13.790087: val_loss -0.3595 +2026-04-13 14:29:13.793056: Pseudo dice [0.5421, 0.8732, 0.6616, 0.4889, 0.4451, 0.2134, 0.4014] +2026-04-13 14:29:13.796760: Epoch time: 102.39 s +2026-04-13 14:29:14.997617: +2026-04-13 14:29:15.000339: Epoch 2649 +2026-04-13 14:29:15.002803: Current learning rate: 0.00376 +2026-04-13 14:30:58.666243: train_loss -0.4258 +2026-04-13 14:30:58.677522: val_loss -0.3684 +2026-04-13 14:30:58.681041: Pseudo dice [0.4696, 0.7763, 0.7687, 0.4581, 0.5389, 0.9115, 0.7814] +2026-04-13 14:30:58.685996: Epoch time: 103.67 s +2026-04-13 14:31:01.670190: +2026-04-13 14:31:01.673109: Epoch 2650 +2026-04-13 14:31:01.674747: Current learning rate: 0.00376 +2026-04-13 14:32:43.732869: train_loss -0.4059 +2026-04-13 14:32:43.764732: val_loss -0.332 +2026-04-13 14:32:43.767555: Pseudo dice [0.1873, 0.5135, 0.5444, 0.0734, 0.6395, 0.8194, 0.7751] +2026-04-13 14:32:43.818827: Epoch time: 102.07 s +2026-04-13 14:32:45.116259: +2026-04-13 14:32:45.118931: Epoch 2651 +2026-04-13 14:32:45.121214: Current learning rate: 0.00376 +2026-04-13 14:34:27.183834: train_loss -0.4182 +2026-04-13 14:34:27.193174: val_loss -0.3784 +2026-04-13 14:34:27.196221: Pseudo dice [0.7375, 0.8599, 0.7406, 0.7024, 0.4796, 0.7392, 0.7873] +2026-04-13 14:34:27.198785: Epoch time: 102.07 s +2026-04-13 14:34:28.384152: +2026-04-13 14:34:28.387049: Epoch 2652 +2026-04-13 14:34:28.390031: Current learning rate: 0.00376 +2026-04-13 14:36:10.488553: train_loss -0.4162 +2026-04-13 14:36:10.498652: val_loss -0.3265 +2026-04-13 14:36:10.503471: Pseudo dice [0.4951, 0.8694, 0.6331, 0.1619, 0.2502, 0.6348, 0.2629] +2026-04-13 14:36:10.508256: Epoch time: 102.11 s +2026-04-13 14:36:11.749048: +2026-04-13 14:36:11.752717: Epoch 2653 +2026-04-13 14:36:11.754941: Current learning rate: 0.00375 +2026-04-13 14:37:54.562414: train_loss -0.4164 +2026-04-13 14:37:54.569912: val_loss -0.3473 +2026-04-13 14:37:54.573938: Pseudo dice [0.3712, 0.7307, 0.8071, 0.3814, 0.3627, 0.6233, 0.5062] +2026-04-13 14:37:54.576721: Epoch time: 102.82 s +2026-04-13 14:37:55.806804: +2026-04-13 14:37:55.808745: Epoch 2654 +2026-04-13 14:37:55.812845: Current learning rate: 0.00375 +2026-04-13 14:39:37.647780: train_loss -0.4155 +2026-04-13 14:39:37.654971: val_loss -0.3615 +2026-04-13 14:39:37.657487: Pseudo dice [0.6146, 0.911, 0.6441, 0.7953, 0.5798, 0.7033, 0.8214] +2026-04-13 14:39:37.659916: Epoch time: 101.84 s +2026-04-13 14:39:38.859349: +2026-04-13 14:39:38.862332: Epoch 2655 +2026-04-13 14:39:38.865777: Current learning rate: 0.00375 +2026-04-13 14:41:21.216313: train_loss -0.4184 +2026-04-13 14:41:21.222813: val_loss -0.3652 +2026-04-13 14:41:21.224661: Pseudo dice [0.8412, 0.8687, 0.7348, 0.6215, 0.5916, 0.4791, 0.8508] +2026-04-13 14:41:21.227325: Epoch time: 102.36 s +2026-04-13 14:41:22.422668: +2026-04-13 14:41:22.424451: Epoch 2656 +2026-04-13 14:41:22.426438: Current learning rate: 0.00375 +2026-04-13 14:43:05.113648: train_loss -0.4121 +2026-04-13 14:43:05.125288: val_loss -0.3575 +2026-04-13 14:43:05.127355: Pseudo dice [0.1772, 0.3317, 0.7012, 0.5216, 0.5444, 0.5693, 0.8148] +2026-04-13 14:43:05.129814: Epoch time: 102.69 s +2026-04-13 14:43:06.317762: +2026-04-13 14:43:06.321124: Epoch 2657 +2026-04-13 14:43:06.323553: Current learning rate: 0.00374 +2026-04-13 14:44:48.581557: train_loss -0.4081 +2026-04-13 14:44:48.588598: val_loss -0.357 +2026-04-13 14:44:48.590605: Pseudo dice [0.4237, 0.3777, 0.7897, 0.3588, 0.5469, 0.762, 0.8155] +2026-04-13 14:44:48.592853: Epoch time: 102.27 s +2026-04-13 14:44:49.776469: +2026-04-13 14:44:49.778612: Epoch 2658 +2026-04-13 14:44:49.780875: Current learning rate: 0.00374 +2026-04-13 14:46:32.965240: train_loss -0.4103 +2026-04-13 14:46:32.982953: val_loss -0.3177 +2026-04-13 14:46:32.986535: Pseudo dice [0.3622, 0.794, 0.684, 0.3267, 0.3915, 0.6559, 0.589] +2026-04-13 14:46:32.991312: Epoch time: 103.19 s +2026-04-13 14:46:34.173552: +2026-04-13 14:46:34.187815: Epoch 2659 +2026-04-13 14:46:34.202942: Current learning rate: 0.00374 +2026-04-13 14:48:16.549566: train_loss -0.4006 +2026-04-13 14:48:16.557382: val_loss -0.3629 +2026-04-13 14:48:16.559221: Pseudo dice [0.7948, 0.5831, 0.6261, 0.245, 0.488, 0.8989, 0.5775] +2026-04-13 14:48:16.562935: Epoch time: 102.38 s +2026-04-13 14:48:17.750152: +2026-04-13 14:48:17.752481: Epoch 2660 +2026-04-13 14:48:17.755654: Current learning rate: 0.00374 +2026-04-13 14:50:00.816703: train_loss -0.4099 +2026-04-13 14:50:00.825499: val_loss -0.3502 +2026-04-13 14:50:00.828290: Pseudo dice [0.7578, 0.4665, 0.7268, 0.4222, 0.2149, 0.8037, 0.8201] +2026-04-13 14:50:00.831420: Epoch time: 103.07 s +2026-04-13 14:50:02.076669: +2026-04-13 14:50:02.079016: Epoch 2661 +2026-04-13 14:50:02.081562: Current learning rate: 0.00373 +2026-04-13 14:51:44.106354: train_loss -0.4102 +2026-04-13 14:51:44.113221: val_loss -0.3518 +2026-04-13 14:51:44.115390: Pseudo dice [0.7735, 0.7109, 0.7927, 0.7319, 0.3107, 0.1823, 0.394] +2026-04-13 14:51:44.117972: Epoch time: 102.03 s +2026-04-13 14:51:45.295591: +2026-04-13 14:51:45.298046: Epoch 2662 +2026-04-13 14:51:45.300135: Current learning rate: 0.00373 +2026-04-13 14:53:27.283336: train_loss -0.4054 +2026-04-13 14:53:27.290331: val_loss -0.3227 +2026-04-13 14:53:27.293167: Pseudo dice [0.7736, 0.8921, 0.7496, 0.654, 0.5203, 0.5444, 0.8798] +2026-04-13 14:53:27.295813: Epoch time: 101.99 s +2026-04-13 14:53:28.495825: +2026-04-13 14:53:28.497949: Epoch 2663 +2026-04-13 14:53:28.500005: Current learning rate: 0.00373 +2026-04-13 14:55:10.441596: train_loss -0.3874 +2026-04-13 14:55:10.457605: val_loss -0.285 +2026-04-13 14:55:10.460691: Pseudo dice [0.5908, 0.4864, 0.7226, 0.2989, 0.3419, 0.5656, 0.577] +2026-04-13 14:55:10.465961: Epoch time: 101.95 s +2026-04-13 14:55:11.653938: +2026-04-13 14:55:11.656089: Epoch 2664 +2026-04-13 14:55:11.660889: Current learning rate: 0.00373 +2026-04-13 14:56:53.585121: train_loss -0.3651 +2026-04-13 14:56:53.593193: val_loss -0.3683 +2026-04-13 14:56:53.595804: Pseudo dice [0.6787, 0.0532, 0.7335, 0.0612, 0.5136, 0.4631, 0.6077] +2026-04-13 14:56:53.598887: Epoch time: 101.93 s +2026-04-13 14:56:54.823329: +2026-04-13 14:56:54.825402: Epoch 2665 +2026-04-13 14:56:54.827943: Current learning rate: 0.00372 +2026-04-13 14:58:37.467278: train_loss -0.3612 +2026-04-13 14:58:37.475527: val_loss -0.2308 +2026-04-13 14:58:37.477814: Pseudo dice [0.0926, 0.9103, 0.1366, 0.5778, 0.4124, 0.304, 0.7096] +2026-04-13 14:58:37.480889: Epoch time: 102.65 s +2026-04-13 14:58:38.694477: +2026-04-13 14:58:38.697132: Epoch 2666 +2026-04-13 14:58:38.699185: Current learning rate: 0.00372 +2026-04-13 15:00:20.696917: train_loss -0.3962 +2026-04-13 15:00:20.704704: val_loss -0.333 +2026-04-13 15:00:20.706530: Pseudo dice [0.4938, 0.8567, 0.7687, 0.2846, 0.3737, 0.2558, 0.6571] +2026-04-13 15:00:20.708850: Epoch time: 102.01 s +2026-04-13 15:00:21.903069: +2026-04-13 15:00:21.906202: Epoch 2667 +2026-04-13 15:00:21.908749: Current learning rate: 0.00372 +2026-04-13 15:02:03.788412: train_loss -0.4113 +2026-04-13 15:02:03.795599: val_loss -0.3892 +2026-04-13 15:02:03.797606: Pseudo dice [0.6551, 0.2636, 0.826, 0.457, 0.4583, 0.9095, 0.6516] +2026-04-13 15:02:03.800366: Epoch time: 101.89 s +2026-04-13 15:02:06.095782: +2026-04-13 15:02:06.097877: Epoch 2668 +2026-04-13 15:02:06.100322: Current learning rate: 0.00372 +2026-04-13 15:03:48.057198: train_loss -0.418 +2026-04-13 15:03:48.065515: val_loss -0.3151 +2026-04-13 15:03:48.067690: Pseudo dice [0.6494, 0.1285, 0.7219, 0.275, 0.6821, 0.5523, 0.1698] +2026-04-13 15:03:48.070004: Epoch time: 101.96 s +2026-04-13 15:03:49.243809: +2026-04-13 15:03:49.247404: Epoch 2669 +2026-04-13 15:03:49.249600: Current learning rate: 0.00371 +2026-04-13 15:05:31.506241: train_loss -0.4126 +2026-04-13 15:05:31.513216: val_loss -0.3772 +2026-04-13 15:05:31.515632: Pseudo dice [0.6498, 0.3969, 0.6872, 0.5, 0.6788, 0.8869, 0.6651] +2026-04-13 15:05:31.518682: Epoch time: 102.27 s +2026-04-13 15:05:32.707180: +2026-04-13 15:05:32.710853: Epoch 2670 +2026-04-13 15:05:32.713995: Current learning rate: 0.00371 +2026-04-13 15:07:14.564301: train_loss -0.418 +2026-04-13 15:07:14.570438: val_loss -0.3724 +2026-04-13 15:07:14.574065: Pseudo dice [0.8, 0.5288, 0.8346, 0.4559, 0.5237, 0.4951, 0.8091] +2026-04-13 15:07:14.577423: Epoch time: 101.86 s +2026-04-13 15:07:15.770996: +2026-04-13 15:07:15.772552: Epoch 2671 +2026-04-13 15:07:15.774320: Current learning rate: 0.00371 +2026-04-13 15:08:58.454639: train_loss -0.4008 +2026-04-13 15:08:58.461562: val_loss -0.3645 +2026-04-13 15:08:58.464324: Pseudo dice [0.6401, 0.6699, 0.8151, 0.5541, 0.4567, 0.7033, 0.5458] +2026-04-13 15:08:58.466864: Epoch time: 102.69 s +2026-04-13 15:08:59.660811: +2026-04-13 15:08:59.662585: Epoch 2672 +2026-04-13 15:08:59.664603: Current learning rate: 0.00371 +2026-04-13 15:10:41.260428: train_loss -0.4177 +2026-04-13 15:10:41.267771: val_loss -0.3964 +2026-04-13 15:10:41.269414: Pseudo dice [0.7045, 0.8052, 0.8362, 0.7798, 0.5638, 0.8497, 0.7541] +2026-04-13 15:10:41.272064: Epoch time: 101.6 s +2026-04-13 15:10:42.456136: +2026-04-13 15:10:42.457792: Epoch 2673 +2026-04-13 15:10:42.459612: Current learning rate: 0.0037 +2026-04-13 15:12:23.985349: train_loss -0.423 +2026-04-13 15:12:23.997762: val_loss -0.3972 +2026-04-13 15:12:23.999993: Pseudo dice [0.7251, 0.5495, 0.8689, 0.594, 0.6483, 0.8007, 0.45] +2026-04-13 15:12:24.011305: Epoch time: 101.53 s +2026-04-13 15:12:25.200413: +2026-04-13 15:12:25.202254: Epoch 2674 +2026-04-13 15:12:25.204233: Current learning rate: 0.0037 +2026-04-13 15:14:07.739358: train_loss -0.4 +2026-04-13 15:14:07.746356: val_loss -0.3526 +2026-04-13 15:14:07.749112: Pseudo dice [0.8134, 0.4252, 0.6463, 0.4278, 0.3571, 0.71, 0.6486] +2026-04-13 15:14:07.753619: Epoch time: 102.54 s +2026-04-13 15:14:08.952982: +2026-04-13 15:14:08.954903: Epoch 2675 +2026-04-13 15:14:08.957664: Current learning rate: 0.0037 +2026-04-13 15:15:51.373257: train_loss -0.3976 +2026-04-13 15:15:51.384588: val_loss -0.3776 +2026-04-13 15:15:51.388329: Pseudo dice [0.3628, 0.3734, 0.7671, 0.6559, 0.4873, 0.2538, 0.8408] +2026-04-13 15:15:51.393803: Epoch time: 102.42 s +2026-04-13 15:15:52.600391: +2026-04-13 15:15:52.602463: Epoch 2676 +2026-04-13 15:15:52.605714: Current learning rate: 0.0037 +2026-04-13 15:17:34.401296: train_loss -0.4026 +2026-04-13 15:17:34.407336: val_loss -0.3022 +2026-04-13 15:17:34.409230: Pseudo dice [0.4156, 0.8968, 0.3712, 0.6867, 0.3664, 0.0491, 0.706] +2026-04-13 15:17:34.411658: Epoch time: 101.8 s +2026-04-13 15:17:35.610942: +2026-04-13 15:17:35.613435: Epoch 2677 +2026-04-13 15:17:35.616848: Current learning rate: 0.00369 +2026-04-13 15:19:17.726913: train_loss -0.4092 +2026-04-13 15:19:17.732655: val_loss -0.3468 +2026-04-13 15:19:17.734629: Pseudo dice [0.4422, 0.7827, 0.8282, 0.0009, 0.5926, 0.8327, 0.6943] +2026-04-13 15:19:17.736370: Epoch time: 102.12 s +2026-04-13 15:19:18.916543: +2026-04-13 15:19:18.919039: Epoch 2678 +2026-04-13 15:19:18.921911: Current learning rate: 0.00369 +2026-04-13 15:21:00.408783: train_loss -0.413 +2026-04-13 15:21:00.416996: val_loss -0.3504 +2026-04-13 15:21:00.419118: Pseudo dice [0.3176, 0.6189, 0.7658, 0.4221, 0.2254, 0.6418, 0.621] +2026-04-13 15:21:00.422499: Epoch time: 101.5 s +2026-04-13 15:21:01.604973: +2026-04-13 15:21:01.606872: Epoch 2679 +2026-04-13 15:21:01.609364: Current learning rate: 0.00369 +2026-04-13 15:22:43.538219: train_loss -0.4024 +2026-04-13 15:22:43.547675: val_loss -0.3682 +2026-04-13 15:22:43.550238: Pseudo dice [0.7205, 0.7756, 0.7883, 0.513, 0.3482, 0.7456, 0.757] +2026-04-13 15:22:43.552605: Epoch time: 101.94 s +2026-04-13 15:22:44.754012: +2026-04-13 15:22:44.756444: Epoch 2680 +2026-04-13 15:22:44.759310: Current learning rate: 0.00369 +2026-04-13 15:24:26.483120: train_loss -0.4053 +2026-04-13 15:24:26.490657: val_loss -0.3388 +2026-04-13 15:24:26.492385: Pseudo dice [0.4638, 0.6596, 0.7568, 0.3264, 0.4413, 0.5494, 0.8099] +2026-04-13 15:24:26.495498: Epoch time: 101.73 s +2026-04-13 15:24:27.671219: +2026-04-13 15:24:27.673440: Epoch 2681 +2026-04-13 15:24:27.675574: Current learning rate: 0.00368 +2026-04-13 15:26:10.303120: train_loss -0.4075 +2026-04-13 15:26:10.309339: val_loss -0.3675 +2026-04-13 15:26:10.311425: Pseudo dice [0.5626, 0.2551, 0.8083, 0.498, 0.2988, 0.9226, 0.8042] +2026-04-13 15:26:10.313420: Epoch time: 102.64 s +2026-04-13 15:26:11.510421: +2026-04-13 15:26:11.512324: Epoch 2682 +2026-04-13 15:26:11.514275: Current learning rate: 0.00368 +2026-04-13 15:27:53.395188: train_loss -0.3935 +2026-04-13 15:27:53.401041: val_loss -0.286 +2026-04-13 15:27:53.402908: Pseudo dice [0.4869, 0.8748, 0.4691, 0.0555, 0.5614, 0.7794, 0.766] +2026-04-13 15:27:53.405457: Epoch time: 101.89 s +2026-04-13 15:27:54.618472: +2026-04-13 15:27:54.620706: Epoch 2683 +2026-04-13 15:27:54.622687: Current learning rate: 0.00368 +2026-04-13 15:29:35.921867: train_loss -0.4098 +2026-04-13 15:29:35.929166: val_loss -0.3708 +2026-04-13 15:29:35.933084: Pseudo dice [0.7356, 0.9081, 0.8242, 0.5175, 0.4429, 0.1236, 0.8333] +2026-04-13 15:29:35.935386: Epoch time: 101.31 s +2026-04-13 15:29:37.143753: +2026-04-13 15:29:37.145299: Epoch 2684 +2026-04-13 15:29:37.146963: Current learning rate: 0.00368 +2026-04-13 15:31:18.678726: train_loss -0.4225 +2026-04-13 15:31:18.685400: val_loss -0.3785 +2026-04-13 15:31:18.687405: Pseudo dice [0.8011, 0.6137, 0.8056, 0.1549, 0.458, 0.6947, 0.8392] +2026-04-13 15:31:18.689722: Epoch time: 101.54 s +2026-04-13 15:31:19.884927: +2026-04-13 15:31:19.887422: Epoch 2685 +2026-04-13 15:31:19.892449: Current learning rate: 0.00367 +2026-04-13 15:33:01.949394: train_loss -0.4146 +2026-04-13 15:33:01.956465: val_loss -0.3633 +2026-04-13 15:33:01.958659: Pseudo dice [0.5927, 0.5196, 0.7796, 0.4644, 0.2807, 0.8011, 0.8303] +2026-04-13 15:33:01.961637: Epoch time: 102.07 s +2026-04-13 15:33:03.133237: +2026-04-13 15:33:03.135227: Epoch 2686 +2026-04-13 15:33:03.138696: Current learning rate: 0.00367 +2026-04-13 15:34:45.162944: train_loss -0.402 +2026-04-13 15:34:45.170190: val_loss -0.3601 +2026-04-13 15:34:45.172524: Pseudo dice [0.3558, 0.8924, 0.7722, 0.6717, 0.4831, 0.8247, 0.7852] +2026-04-13 15:34:45.174840: Epoch time: 102.03 s +2026-04-13 15:34:46.336987: +2026-04-13 15:34:46.340993: Epoch 2687 +2026-04-13 15:34:46.343784: Current learning rate: 0.00367 +2026-04-13 15:36:28.880020: train_loss -0.4043 +2026-04-13 15:36:28.886025: val_loss -0.3535 +2026-04-13 15:36:28.888468: Pseudo dice [0.6992, 0.8404, 0.8063, 0.6259, 0.5809, 0.4448, 0.7975] +2026-04-13 15:36:28.892624: Epoch time: 102.55 s +2026-04-13 15:36:30.122551: +2026-04-13 15:36:30.125155: Epoch 2688 +2026-04-13 15:36:30.127937: Current learning rate: 0.00367 +2026-04-13 15:38:12.829043: train_loss -0.4214 +2026-04-13 15:38:12.836607: val_loss -0.3673 +2026-04-13 15:38:12.839039: Pseudo dice [0.5952, 0.4958, 0.7929, 0.671, 0.5846, 0.3442, 0.8533] +2026-04-13 15:38:12.843379: Epoch time: 102.71 s +2026-04-13 15:38:14.012220: +2026-04-13 15:38:14.014096: Epoch 2689 +2026-04-13 15:38:14.015934: Current learning rate: 0.00366 +2026-04-13 15:39:55.942214: train_loss -0.4235 +2026-04-13 15:39:55.949092: val_loss -0.3207 +2026-04-13 15:39:55.951386: Pseudo dice [0.6175, 0.8526, 0.7186, 0.3196, 0.2315, 0.7344, 0.6316] +2026-04-13 15:39:55.954286: Epoch time: 101.93 s +2026-04-13 15:39:57.140478: +2026-04-13 15:39:57.142304: Epoch 2690 +2026-04-13 15:39:57.144553: Current learning rate: 0.00366 +2026-04-13 15:41:38.929327: train_loss -0.4283 +2026-04-13 15:41:38.935901: val_loss -0.3799 +2026-04-13 15:41:38.937942: Pseudo dice [0.6217, 0.3885, 0.7646, 0.6444, 0.32, 0.7103, 0.7738] +2026-04-13 15:41:38.940168: Epoch time: 101.79 s +2026-04-13 15:41:40.151383: +2026-04-13 15:41:40.153417: Epoch 2691 +2026-04-13 15:41:40.156385: Current learning rate: 0.00366 +2026-04-13 15:43:21.902514: train_loss -0.4149 +2026-04-13 15:43:21.908514: val_loss -0.3424 +2026-04-13 15:43:21.910986: Pseudo dice [0.472, 0.8411, 0.7191, 0.6608, 0.2711, 0.6579, 0.8194] +2026-04-13 15:43:21.914064: Epoch time: 101.75 s +2026-04-13 15:43:23.139066: +2026-04-13 15:43:23.141386: Epoch 2692 +2026-04-13 15:43:23.143501: Current learning rate: 0.00366 +2026-04-13 15:45:05.099639: train_loss -0.4105 +2026-04-13 15:45:05.106975: val_loss -0.3898 +2026-04-13 15:45:05.110996: Pseudo dice [0.4103, 0.7513, 0.7625, 0.2706, 0.6585, 0.7326, 0.6763] +2026-04-13 15:45:05.113288: Epoch time: 101.96 s +2026-04-13 15:45:06.280071: +2026-04-13 15:45:06.282027: Epoch 2693 +2026-04-13 15:45:06.285970: Current learning rate: 0.00365 +2026-04-13 15:46:48.060433: train_loss -0.4239 +2026-04-13 15:46:48.068007: val_loss -0.3668 +2026-04-13 15:46:48.070657: Pseudo dice [0.6204, 0.8223, 0.8421, 0.2051, 0.5008, 0.782, 0.7358] +2026-04-13 15:46:48.073247: Epoch time: 101.78 s +2026-04-13 15:46:49.280499: +2026-04-13 15:46:49.282361: Epoch 2694 +2026-04-13 15:46:49.284632: Current learning rate: 0.00365 +2026-04-13 15:48:31.297122: train_loss -0.3965 +2026-04-13 15:48:31.303317: val_loss -0.2995 +2026-04-13 15:48:31.305245: Pseudo dice [0.6001, 0.763, 0.4533, 0.0492, 0.397, 0.6807, 0.5572] +2026-04-13 15:48:31.307648: Epoch time: 102.02 s +2026-04-13 15:48:32.506017: +2026-04-13 15:48:32.508956: Epoch 2695 +2026-04-13 15:48:32.510986: Current learning rate: 0.00365 +2026-04-13 15:50:14.324459: train_loss -0.4063 +2026-04-13 15:50:14.331752: val_loss -0.392 +2026-04-13 15:50:14.333918: Pseudo dice [0.896, 0.1935, 0.8066, 0.4045, 0.5817, 0.8916, 0.8388] +2026-04-13 15:50:14.336852: Epoch time: 101.82 s +2026-04-13 15:50:15.521239: +2026-04-13 15:50:15.522840: Epoch 2696 +2026-04-13 15:50:15.524744: Current learning rate: 0.00365 +2026-04-13 15:51:57.651450: train_loss -0.4248 +2026-04-13 15:51:57.658784: val_loss -0.3211 +2026-04-13 15:51:57.660930: Pseudo dice [0.6029, 0.6588, 0.6763, 0.3777, 0.4744, 0.6917, 0.7795] +2026-04-13 15:51:57.663368: Epoch time: 102.13 s +2026-04-13 15:51:58.862867: +2026-04-13 15:51:58.864557: Epoch 2697 +2026-04-13 15:51:58.866578: Current learning rate: 0.00364 +2026-04-13 15:53:41.061836: train_loss -0.4194 +2026-04-13 15:53:41.068790: val_loss -0.3527 +2026-04-13 15:53:41.070557: Pseudo dice [0.651, 0.7639, 0.6986, 0.4196, 0.3983, 0.635, 0.6693] +2026-04-13 15:53:41.073134: Epoch time: 102.2 s +2026-04-13 15:53:42.262525: +2026-04-13 15:53:42.264106: Epoch 2698 +2026-04-13 15:53:42.265906: Current learning rate: 0.00364 +2026-04-13 15:55:24.227716: train_loss -0.4219 +2026-04-13 15:55:24.234486: val_loss -0.3629 +2026-04-13 15:55:24.240234: Pseudo dice [0.6838, 0.5656, 0.7268, 0.3897, 0.4885, 0.8393, 0.594] +2026-04-13 15:55:24.243964: Epoch time: 101.97 s +2026-04-13 15:55:25.448283: +2026-04-13 15:55:25.450208: Epoch 2699 +2026-04-13 15:55:25.452596: Current learning rate: 0.00364 +2026-04-13 15:57:07.499670: train_loss -0.4328 +2026-04-13 15:57:07.506273: val_loss -0.3773 +2026-04-13 15:57:07.509501: Pseudo dice [0.2556, 0.6851, 0.7407, 0.5336, 0.5786, 0.7931, 0.6601] +2026-04-13 15:57:07.512311: Epoch time: 102.05 s +2026-04-13 15:57:10.508009: +2026-04-13 15:57:10.510171: Epoch 2700 +2026-04-13 15:57:10.511792: Current learning rate: 0.00364 +2026-04-13 15:58:52.592159: train_loss -0.4067 +2026-04-13 15:58:52.598453: val_loss -0.3579 +2026-04-13 15:58:52.600179: Pseudo dice [0.472, 0.8503, 0.7516, 0.4875, 0.5078, 0.6751, 0.8238] +2026-04-13 15:58:52.602400: Epoch time: 102.09 s +2026-04-13 15:58:53.789279: +2026-04-13 15:58:53.791005: Epoch 2701 +2026-04-13 15:58:53.793020: Current learning rate: 0.00363 +2026-04-13 16:00:35.942785: train_loss -0.4195 +2026-04-13 16:00:35.948708: val_loss -0.3811 +2026-04-13 16:00:35.951067: Pseudo dice [0.3858, 0.5514, 0.8352, 0.384, 0.5304, 0.7892, 0.7844] +2026-04-13 16:00:35.953383: Epoch time: 102.16 s +2026-04-13 16:00:37.128269: +2026-04-13 16:00:37.132544: Epoch 2702 +2026-04-13 16:00:37.135774: Current learning rate: 0.00363 +2026-04-13 16:02:19.753057: train_loss -0.4111 +2026-04-13 16:02:19.762631: val_loss -0.3747 +2026-04-13 16:02:19.765637: Pseudo dice [0.7747, 0.8698, 0.6911, 0.5851, 0.6105, 0.7607, 0.8758] +2026-04-13 16:02:19.770961: Epoch time: 102.63 s +2026-04-13 16:02:20.980685: +2026-04-13 16:02:20.982278: Epoch 2703 +2026-04-13 16:02:20.984273: Current learning rate: 0.00363 +2026-04-13 16:04:02.632720: train_loss -0.4168 +2026-04-13 16:04:02.639764: val_loss -0.3478 +2026-04-13 16:04:02.642046: Pseudo dice [0.6286, 0.9127, 0.7992, 0.2543, 0.6744, 0.597, 0.5344] +2026-04-13 16:04:02.645979: Epoch time: 101.66 s +2026-04-13 16:04:04.104950: +2026-04-13 16:04:04.109004: Epoch 2704 +2026-04-13 16:04:04.110853: Current learning rate: 0.00363 +2026-04-13 16:05:45.847723: train_loss -0.4026 +2026-04-13 16:05:45.854100: val_loss -0.3122 +2026-04-13 16:05:45.856702: Pseudo dice [0.4758, 0.9058, 0.7525, 0.2167, 0.4259, 0.1968, 0.8103] +2026-04-13 16:05:45.858929: Epoch time: 101.75 s +2026-04-13 16:05:47.049532: +2026-04-13 16:05:47.051885: Epoch 2705 +2026-04-13 16:05:47.054300: Current learning rate: 0.00362 +2026-04-13 16:07:28.921830: train_loss -0.4106 +2026-04-13 16:07:28.928630: val_loss -0.3654 +2026-04-13 16:07:28.930825: Pseudo dice [0.7402, 0.8163, 0.7791, 0.2816, 0.3899, 0.8239, 0.7126] +2026-04-13 16:07:28.933528: Epoch time: 101.88 s +2026-04-13 16:07:30.111396: +2026-04-13 16:07:30.113021: Epoch 2706 +2026-04-13 16:07:30.114852: Current learning rate: 0.00362 +2026-04-13 16:09:12.845913: train_loss -0.4203 +2026-04-13 16:09:12.853544: val_loss -0.3701 +2026-04-13 16:09:12.855647: Pseudo dice [0.4609, 0.5084, 0.7613, 0.5283, 0.3745, 0.8229, 0.8066] +2026-04-13 16:09:12.863541: Epoch time: 102.74 s +2026-04-13 16:09:14.059072: +2026-04-13 16:09:14.061291: Epoch 2707 +2026-04-13 16:09:14.065638: Current learning rate: 0.00362 +2026-04-13 16:10:57.291479: train_loss -0.4029 +2026-04-13 16:10:57.297806: val_loss -0.3245 +2026-04-13 16:10:57.299941: Pseudo dice [0.6581, 0.8839, 0.6915, 0.1999, 0.3693, 0.2298, 0.5634] +2026-04-13 16:10:57.301929: Epoch time: 103.24 s +2026-04-13 16:10:59.563109: +2026-04-13 16:10:59.564863: Epoch 2708 +2026-04-13 16:10:59.566566: Current learning rate: 0.00362 +2026-04-13 16:12:41.682813: train_loss -0.4218 +2026-04-13 16:12:41.708294: val_loss -0.3386 +2026-04-13 16:12:41.711224: Pseudo dice [0.5967, 0.6901, 0.6456, 0.2672, 0.6444, 0.4048, 0.7553] +2026-04-13 16:12:41.713836: Epoch time: 102.12 s +2026-04-13 16:12:42.936774: +2026-04-13 16:12:42.939507: Epoch 2709 +2026-04-13 16:12:42.942711: Current learning rate: 0.00361 +2026-04-13 16:14:26.268653: train_loss -0.4268 +2026-04-13 16:14:26.275321: val_loss -0.3681 +2026-04-13 16:14:26.278097: Pseudo dice [0.3364, 0.7734, 0.6446, 0.3619, 0.4663, 0.8725, 0.7682] +2026-04-13 16:14:26.280852: Epoch time: 103.33 s +2026-04-13 16:14:27.477816: +2026-04-13 16:14:27.480735: Epoch 2710 +2026-04-13 16:14:27.482879: Current learning rate: 0.00361 +2026-04-13 16:16:09.684124: train_loss -0.4188 +2026-04-13 16:16:09.692514: val_loss -0.3437 +2026-04-13 16:16:09.694434: Pseudo dice [0.3698, 0.7514, 0.6564, 0.6988, 0.4278, 0.6997, 0.7293] +2026-04-13 16:16:09.697047: Epoch time: 102.21 s +2026-04-13 16:16:10.860645: +2026-04-13 16:16:10.863991: Epoch 2711 +2026-04-13 16:16:10.866593: Current learning rate: 0.00361 +2026-04-13 16:17:52.762416: train_loss -0.4212 +2026-04-13 16:17:52.768813: val_loss -0.3629 +2026-04-13 16:17:52.771265: Pseudo dice [0.4145, 0.6566, 0.7896, 0.5074, 0.4134, 0.721, 0.667] +2026-04-13 16:17:52.773515: Epoch time: 101.91 s +2026-04-13 16:17:53.959150: +2026-04-13 16:17:53.961937: Epoch 2712 +2026-04-13 16:17:53.964076: Current learning rate: 0.00361 +2026-04-13 16:19:36.646958: train_loss -0.4073 +2026-04-13 16:19:36.653717: val_loss -0.3591 +2026-04-13 16:19:36.656818: Pseudo dice [0.6694, 0.8144, 0.7881, 0.4667, 0.4159, 0.7011, 0.4149] +2026-04-13 16:19:36.659914: Epoch time: 102.69 s +2026-04-13 16:19:37.840024: +2026-04-13 16:19:37.842340: Epoch 2713 +2026-04-13 16:19:37.845366: Current learning rate: 0.0036 +2026-04-13 16:21:19.516720: train_loss -0.4051 +2026-04-13 16:21:19.522708: val_loss -0.3746 +2026-04-13 16:21:19.525542: Pseudo dice [0.6086, 0.4781, 0.6402, 0.5954, 0.5212, 0.9112, 0.7633] +2026-04-13 16:21:19.527886: Epoch time: 101.68 s +2026-04-13 16:21:20.709826: +2026-04-13 16:21:20.711965: Epoch 2714 +2026-04-13 16:21:20.713932: Current learning rate: 0.0036 +2026-04-13 16:23:02.920299: train_loss -0.4127 +2026-04-13 16:23:02.927482: val_loss -0.3349 +2026-04-13 16:23:02.930176: Pseudo dice [0.3911, 0.9095, 0.8019, 0.6026, 0.536, 0.079, 0.3368] +2026-04-13 16:23:02.932701: Epoch time: 102.21 s +2026-04-13 16:23:04.168112: +2026-04-13 16:23:04.170451: Epoch 2715 +2026-04-13 16:23:04.172137: Current learning rate: 0.0036 +2026-04-13 16:24:46.629277: train_loss -0.4011 +2026-04-13 16:24:46.637352: val_loss -0.3313 +2026-04-13 16:24:46.639775: Pseudo dice [0.2986, 0.6738, 0.744, 0.2177, 0.4081, 0.6424, 0.8744] +2026-04-13 16:24:46.642125: Epoch time: 102.46 s +2026-04-13 16:24:47.831277: +2026-04-13 16:24:47.834044: Epoch 2716 +2026-04-13 16:24:47.836535: Current learning rate: 0.0036 +2026-04-13 16:26:30.361485: train_loss -0.4117 +2026-04-13 16:26:30.368689: val_loss -0.3573 +2026-04-13 16:26:30.372618: Pseudo dice [0.6538, 0.8864, 0.7632, 0.3168, 0.5609, 0.5915, 0.5196] +2026-04-13 16:26:30.377385: Epoch time: 102.53 s +2026-04-13 16:26:31.567507: +2026-04-13 16:26:31.569116: Epoch 2717 +2026-04-13 16:26:31.571324: Current learning rate: 0.00359 +2026-04-13 16:28:13.592779: train_loss -0.4192 +2026-04-13 16:28:13.598803: val_loss -0.3495 +2026-04-13 16:28:13.600949: Pseudo dice [0.6102, 0.8953, 0.705, 0.6221, 0.5161, 0.1711, 0.8184] +2026-04-13 16:28:13.603479: Epoch time: 102.03 s +2026-04-13 16:28:14.795469: +2026-04-13 16:28:14.796978: Epoch 2718 +2026-04-13 16:28:14.799266: Current learning rate: 0.00359 +2026-04-13 16:29:56.601762: train_loss -0.4281 +2026-04-13 16:29:56.608619: val_loss -0.3864 +2026-04-13 16:29:56.610677: Pseudo dice [0.659, 0.5586, 0.8053, 0.7689, 0.5598, 0.8548, 0.8678] +2026-04-13 16:29:56.613571: Epoch time: 101.81 s +2026-04-13 16:29:57.816796: +2026-04-13 16:29:57.819910: Epoch 2719 +2026-04-13 16:29:57.823904: Current learning rate: 0.00359 +2026-04-13 16:31:40.592424: train_loss -0.4155 +2026-04-13 16:31:40.599308: val_loss -0.3762 +2026-04-13 16:31:40.601319: Pseudo dice [0.5266, 0.7465, 0.781, 0.3421, 0.3578, 0.8301, 0.7156] +2026-04-13 16:31:40.604212: Epoch time: 102.78 s +2026-04-13 16:31:41.784488: +2026-04-13 16:31:41.786203: Epoch 2720 +2026-04-13 16:31:41.787984: Current learning rate: 0.00359 +2026-04-13 16:33:24.299872: train_loss -0.4008 +2026-04-13 16:33:24.307437: val_loss -0.3478 +2026-04-13 16:33:24.311545: Pseudo dice [0.4899, 0.6226, 0.6992, 0.4888, 0.6083, 0.5994, 0.8739] +2026-04-13 16:33:24.314273: Epoch time: 102.52 s +2026-04-13 16:33:25.490588: +2026-04-13 16:33:25.492824: Epoch 2721 +2026-04-13 16:33:25.494985: Current learning rate: 0.00358 +2026-04-13 16:35:07.562925: train_loss -0.4139 +2026-04-13 16:35:07.569838: val_loss -0.3663 +2026-04-13 16:35:07.585873: Pseudo dice [0.7907, 0.691, 0.8014, 0.4731, 0.1778, 0.7265, 0.7212] +2026-04-13 16:35:07.588654: Epoch time: 102.08 s +2026-04-13 16:35:08.771411: +2026-04-13 16:35:08.772942: Epoch 2722 +2026-04-13 16:35:08.774772: Current learning rate: 0.00358 +2026-04-13 16:36:51.449621: train_loss -0.4006 +2026-04-13 16:36:51.459422: val_loss -0.3411 +2026-04-13 16:36:51.462064: Pseudo dice [0.5098, 0.6393, 0.7939, 0.7816, 0.4115, 0.7117, 0.5278] +2026-04-13 16:36:51.465205: Epoch time: 102.68 s +2026-04-13 16:36:52.657240: +2026-04-13 16:36:52.661464: Epoch 2723 +2026-04-13 16:36:52.663507: Current learning rate: 0.00358 +2026-04-13 16:38:35.186712: train_loss -0.4204 +2026-04-13 16:38:35.199990: val_loss -0.3927 +2026-04-13 16:38:35.202354: Pseudo dice [0.3427, 0.6394, 0.7217, 0.393, 0.5302, 0.9146, 0.7897] +2026-04-13 16:38:35.205797: Epoch time: 102.53 s +2026-04-13 16:38:36.381026: +2026-04-13 16:38:36.384734: Epoch 2724 +2026-04-13 16:38:36.396465: Current learning rate: 0.00358 +2026-04-13 16:40:18.304911: train_loss -0.4174 +2026-04-13 16:40:18.314276: val_loss -0.3435 +2026-04-13 16:40:18.317104: Pseudo dice [0.5668, 0.4895, 0.6823, 0.6513, 0.4392, 0.5465, 0.3052] +2026-04-13 16:40:18.321102: Epoch time: 101.93 s +2026-04-13 16:40:19.511322: +2026-04-13 16:40:19.515100: Epoch 2725 +2026-04-13 16:40:19.523072: Current learning rate: 0.00357 +2026-04-13 16:42:02.077558: train_loss -0.4048 +2026-04-13 16:42:02.087733: val_loss -0.3448 +2026-04-13 16:42:02.089966: Pseudo dice [0.4258, 0.914, 0.7788, 0.1943, 0.5045, 0.1598, 0.6174] +2026-04-13 16:42:02.093271: Epoch time: 102.57 s +2026-04-13 16:42:03.277582: +2026-04-13 16:42:03.279984: Epoch 2726 +2026-04-13 16:42:03.282256: Current learning rate: 0.00357 +2026-04-13 16:43:45.079103: train_loss -0.405 +2026-04-13 16:43:45.085925: val_loss -0.314 +2026-04-13 16:43:45.088109: Pseudo dice [0.628, 0.5083, 0.6974, 0.4609, 0.4793, 0.7778, 0.4433] +2026-04-13 16:43:45.090661: Epoch time: 101.8 s +2026-04-13 16:43:46.312360: +2026-04-13 16:43:46.314015: Epoch 2727 +2026-04-13 16:43:46.315547: Current learning rate: 0.00357 +2026-04-13 16:45:28.643959: train_loss -0.4038 +2026-04-13 16:45:28.650609: val_loss -0.3309 +2026-04-13 16:45:28.668331: Pseudo dice [0.7197, 0.7659, 0.5923, 0.1196, 0.4256, 0.1899, 0.6194] +2026-04-13 16:45:28.671044: Epoch time: 102.33 s +2026-04-13 16:45:29.851800: +2026-04-13 16:45:29.853991: Epoch 2728 +2026-04-13 16:45:29.856026: Current learning rate: 0.00357 +2026-04-13 16:47:12.560450: train_loss -0.4135 +2026-04-13 16:47:12.567494: val_loss -0.3578 +2026-04-13 16:47:12.570892: Pseudo dice [0.7866, 0.7543, 0.6412, 0.4986, 0.1774, 0.8985, 0.7475] +2026-04-13 16:47:12.573747: Epoch time: 102.71 s +2026-04-13 16:47:13.767708: +2026-04-13 16:47:13.770473: Epoch 2729 +2026-04-13 16:47:13.772633: Current learning rate: 0.00356 +2026-04-13 16:48:56.482843: train_loss -0.4195 +2026-04-13 16:48:56.490179: val_loss -0.3527 +2026-04-13 16:48:56.492419: Pseudo dice [0.7177, 0.4945, 0.6971, 0.1648, 0.3788, 0.3121, 0.8355] +2026-04-13 16:48:56.494946: Epoch time: 102.72 s +2026-04-13 16:48:57.677468: +2026-04-13 16:48:57.679370: Epoch 2730 +2026-04-13 16:48:57.681137: Current learning rate: 0.00356 +2026-04-13 16:50:39.873992: train_loss -0.4212 +2026-04-13 16:50:39.881748: val_loss -0.3557 +2026-04-13 16:50:39.884260: Pseudo dice [0.2557, 0.7112, 0.7783, 0.3233, 0.5, 0.5657, 0.6798] +2026-04-13 16:50:39.886578: Epoch time: 102.2 s +2026-04-13 16:50:41.322444: +2026-04-13 16:50:41.325180: Epoch 2731 +2026-04-13 16:50:41.327035: Current learning rate: 0.00356 +2026-04-13 16:52:23.267571: train_loss -0.386 +2026-04-13 16:52:23.275747: val_loss -0.3477 +2026-04-13 16:52:23.278248: Pseudo dice [0.6122, 0.7346, 0.7602, 0.5596, 0.4491, 0.8529, 0.8046] +2026-04-13 16:52:23.280969: Epoch time: 101.95 s +2026-04-13 16:52:24.480544: +2026-04-13 16:52:24.483135: Epoch 2732 +2026-04-13 16:52:24.485952: Current learning rate: 0.00356 +2026-04-13 16:54:05.998078: train_loss -0.4188 +2026-04-13 16:54:06.006111: val_loss -0.3855 +2026-04-13 16:54:06.009739: Pseudo dice [0.6823, 0.6611, 0.8142, 0.6389, 0.5169, 0.9042, 0.7995] +2026-04-13 16:54:06.012461: Epoch time: 101.52 s +2026-04-13 16:54:07.230286: +2026-04-13 16:54:07.232435: Epoch 2733 +2026-04-13 16:54:07.234718: Current learning rate: 0.00355 +2026-04-13 16:55:49.281713: train_loss -0.393 +2026-04-13 16:55:49.288415: val_loss -0.335 +2026-04-13 16:55:49.290884: Pseudo dice [0.3863, 0.9022, 0.7575, 0.0248, 0.5654, 0.7611, 0.7252] +2026-04-13 16:55:49.292989: Epoch time: 102.05 s +2026-04-13 16:55:50.485104: +2026-04-13 16:55:50.487961: Epoch 2734 +2026-04-13 16:55:50.489819: Current learning rate: 0.00355 +2026-04-13 16:57:32.660385: train_loss -0.4045 +2026-04-13 16:57:32.669343: val_loss -0.2765 +2026-04-13 16:57:32.672138: Pseudo dice [0.4118, 0.7868, 0.6789, 0.3768, 0.312, 0.1413, 0.7974] +2026-04-13 16:57:32.675531: Epoch time: 102.18 s +2026-04-13 16:57:33.846533: +2026-04-13 16:57:33.848547: Epoch 2735 +2026-04-13 16:57:33.850182: Current learning rate: 0.00355 +2026-04-13 16:59:15.883913: train_loss -0.4228 +2026-04-13 16:59:15.892341: val_loss -0.3057 +2026-04-13 16:59:15.894736: Pseudo dice [0.3659, 0.5995, 0.4936, 0.2245, 0.597, 0.8064, 0.5636] +2026-04-13 16:59:15.904237: Epoch time: 102.04 s +2026-04-13 16:59:17.105158: +2026-04-13 16:59:17.109127: Epoch 2736 +2026-04-13 16:59:17.111053: Current learning rate: 0.00355 +2026-04-13 17:00:59.487225: train_loss -0.4131 +2026-04-13 17:00:59.494797: val_loss -0.3334 +2026-04-13 17:00:59.497929: Pseudo dice [0.4455, 0.7025, 0.5928, 0.4914, 0.6805, 0.8872, 0.7377] +2026-04-13 17:00:59.500506: Epoch time: 102.39 s +2026-04-13 17:01:00.683527: +2026-04-13 17:01:00.686736: Epoch 2737 +2026-04-13 17:01:00.690314: Current learning rate: 0.00354 +2026-04-13 17:02:42.580575: train_loss -0.4088 +2026-04-13 17:02:42.587083: val_loss -0.3551 +2026-04-13 17:02:42.589069: Pseudo dice [0.5778, 0.8754, 0.7013, 0.4847, 0.3945, 0.8677, 0.7747] +2026-04-13 17:02:42.591648: Epoch time: 101.9 s +2026-04-13 17:02:43.772701: +2026-04-13 17:02:43.774449: Epoch 2738 +2026-04-13 17:02:43.776143: Current learning rate: 0.00354 +2026-04-13 17:04:25.958375: train_loss -0.3981 +2026-04-13 17:04:25.964773: val_loss -0.3753 +2026-04-13 17:04:25.966920: Pseudo dice [0.1496, 0.3392, 0.8332, 0.2266, 0.4292, 0.8373, 0.4479] +2026-04-13 17:04:25.969522: Epoch time: 102.19 s +2026-04-13 17:04:27.183752: +2026-04-13 17:04:27.185616: Epoch 2739 +2026-04-13 17:04:27.187483: Current learning rate: 0.00354 +2026-04-13 17:06:08.950857: train_loss -0.4136 +2026-04-13 17:06:08.957546: val_loss -0.356 +2026-04-13 17:06:08.959845: Pseudo dice [0.1442, 0.8547, 0.6801, 0.5061, 0.4747, 0.4099, 0.3976] +2026-04-13 17:06:08.962756: Epoch time: 101.77 s +2026-04-13 17:06:10.178138: +2026-04-13 17:06:10.179873: Epoch 2740 +2026-04-13 17:06:10.181566: Current learning rate: 0.00354 +2026-04-13 17:07:52.853487: train_loss -0.4273 +2026-04-13 17:07:52.859553: val_loss -0.3628 +2026-04-13 17:07:52.862152: Pseudo dice [0.7129, 0.7421, 0.7245, 0.4743, 0.6766, 0.6127, 0.8347] +2026-04-13 17:07:52.865093: Epoch time: 102.68 s +2026-04-13 17:07:54.058728: +2026-04-13 17:07:54.060268: Epoch 2741 +2026-04-13 17:07:54.061816: Current learning rate: 0.00353 +2026-04-13 17:09:35.852492: train_loss -0.4176 +2026-04-13 17:09:35.859354: val_loss -0.3803 +2026-04-13 17:09:35.861732: Pseudo dice [0.5066, 0.4919, 0.7665, 0.1735, 0.6046, 0.9129, 0.8151] +2026-04-13 17:09:35.864259: Epoch time: 101.8 s +2026-04-13 17:09:37.079743: +2026-04-13 17:09:37.082371: Epoch 2742 +2026-04-13 17:09:37.084742: Current learning rate: 0.00353 +2026-04-13 17:11:18.721180: train_loss -0.4113 +2026-04-13 17:11:18.727913: val_loss -0.3837 +2026-04-13 17:11:18.730472: Pseudo dice [0.0739, 0.4268, 0.7212, 0.6114, 0.5344, 0.7186, 0.8067] +2026-04-13 17:11:18.734478: Epoch time: 101.64 s +2026-04-13 17:11:19.930709: +2026-04-13 17:11:19.933332: Epoch 2743 +2026-04-13 17:11:19.935159: Current learning rate: 0.00353 +2026-04-13 17:13:01.933581: train_loss -0.4173 +2026-04-13 17:13:01.940404: val_loss -0.3418 +2026-04-13 17:13:01.942527: Pseudo dice [0.5777, 0.7164, 0.7691, 0.3235, 0.3689, 0.5939, 0.7106] +2026-04-13 17:13:01.944952: Epoch time: 102.01 s +2026-04-13 17:13:03.139994: +2026-04-13 17:13:03.141989: Epoch 2744 +2026-04-13 17:13:03.144127: Current learning rate: 0.00353 +2026-04-13 17:14:44.929519: train_loss -0.4196 +2026-04-13 17:14:44.936725: val_loss -0.3464 +2026-04-13 17:14:44.941365: Pseudo dice [0.0122, 0.2935, 0.8467, 0.5677, 0.4344, 0.4746, 0.8402] +2026-04-13 17:14:44.944232: Epoch time: 101.79 s +2026-04-13 17:14:46.099800: +2026-04-13 17:14:46.101516: Epoch 2745 +2026-04-13 17:14:46.103245: Current learning rate: 0.00352 +2026-04-13 17:16:28.162652: train_loss -0.4183 +2026-04-13 17:16:28.171165: val_loss -0.4015 +2026-04-13 17:16:28.173244: Pseudo dice [0.754, 0.1786, 0.8353, 0.6476, 0.473, 0.8254, 0.8429] +2026-04-13 17:16:28.177119: Epoch time: 102.07 s +2026-04-13 17:16:29.362352: +2026-04-13 17:16:29.364938: Epoch 2746 +2026-04-13 17:16:29.366542: Current learning rate: 0.00352 +2026-04-13 17:18:12.063472: train_loss -0.4264 +2026-04-13 17:18:12.069537: val_loss -0.3461 +2026-04-13 17:18:12.072373: Pseudo dice [0.457, 0.6323, 0.6059, 0.5271, 0.409, 0.9096, 0.7151] +2026-04-13 17:18:12.076205: Epoch time: 102.7 s +2026-04-13 17:18:13.290217: +2026-04-13 17:18:13.292448: Epoch 2747 +2026-04-13 17:18:13.294063: Current learning rate: 0.00352 +2026-04-13 17:19:55.940032: train_loss -0.4099 +2026-04-13 17:19:55.945695: val_loss -0.3228 +2026-04-13 17:19:55.948088: Pseudo dice [0.6155, 0.2874, 0.7801, 0.227, 0.161, 0.5906, 0.316] +2026-04-13 17:19:55.952135: Epoch time: 102.65 s +2026-04-13 17:19:57.144749: +2026-04-13 17:19:57.146414: Epoch 2748 +2026-04-13 17:19:57.147897: Current learning rate: 0.00352 +2026-04-13 17:21:39.332867: train_loss -0.4019 +2026-04-13 17:21:39.359873: val_loss -0.3563 +2026-04-13 17:21:39.362640: Pseudo dice [0.6573, 0.651, 0.8041, 0.1482, 0.6812, 0.8971, 0.543] +2026-04-13 17:21:39.365297: Epoch time: 102.19 s +2026-04-13 17:21:41.683380: +2026-04-13 17:21:41.685572: Epoch 2749 +2026-04-13 17:21:41.687141: Current learning rate: 0.00351 +2026-04-13 17:23:23.687926: train_loss -0.4068 +2026-04-13 17:23:23.694518: val_loss -0.3842 +2026-04-13 17:23:23.696874: Pseudo dice [0.743, 0.2129, 0.8379, 0.0, 0.4787, 0.7654, 0.6351] +2026-04-13 17:23:23.699495: Epoch time: 102.01 s +2026-04-13 17:23:26.761620: +2026-04-13 17:23:26.764169: Epoch 2750 +2026-04-13 17:23:26.765629: Current learning rate: 0.00351 +2026-04-13 17:25:08.781934: train_loss -0.4237 +2026-04-13 17:25:08.788652: val_loss -0.3954 +2026-04-13 17:25:08.791364: Pseudo dice [0.8405, 0.5504, 0.815, 0.4922, 0.5388, 0.8868, 0.8503] +2026-04-13 17:25:08.793818: Epoch time: 102.02 s +2026-04-13 17:25:09.981648: +2026-04-13 17:25:09.983970: Epoch 2751 +2026-04-13 17:25:09.986042: Current learning rate: 0.00351 +2026-04-13 17:26:52.424093: train_loss -0.4223 +2026-04-13 17:26:52.431463: val_loss -0.3631 +2026-04-13 17:26:52.433584: Pseudo dice [0.3034, 0.7815, 0.7982, 0.3276, 0.4217, 0.8004, 0.8202] +2026-04-13 17:26:52.436987: Epoch time: 102.45 s +2026-04-13 17:26:53.682365: +2026-04-13 17:26:53.684791: Epoch 2752 +2026-04-13 17:26:53.686545: Current learning rate: 0.00351 +2026-04-13 17:28:35.683311: train_loss -0.4359 +2026-04-13 17:28:35.691096: val_loss -0.3383 +2026-04-13 17:28:35.694560: Pseudo dice [0.5501, 0.8228, 0.7606, 0.0161, 0.3982, 0.7151, 0.4738] +2026-04-13 17:28:35.697244: Epoch time: 102.0 s +2026-04-13 17:28:36.868578: +2026-04-13 17:28:36.870692: Epoch 2753 +2026-04-13 17:28:36.872445: Current learning rate: 0.0035 +2026-04-13 17:30:18.999215: train_loss -0.4317 +2026-04-13 17:30:19.007668: val_loss -0.3297 +2026-04-13 17:30:19.009609: Pseudo dice [0.5795, 0.8704, 0.7805, 0.6799, 0.23, 0.51, 0.2133] +2026-04-13 17:30:19.013190: Epoch time: 102.13 s +2026-04-13 17:30:20.209925: +2026-04-13 17:30:20.211926: Epoch 2754 +2026-04-13 17:30:20.214469: Current learning rate: 0.0035 +2026-04-13 17:32:02.601386: train_loss -0.4141 +2026-04-13 17:32:02.608614: val_loss -0.3537 +2026-04-13 17:32:02.610409: Pseudo dice [0.5717, 0.7642, 0.7369, 0.4094, 0.54, 0.1114, 0.7151] +2026-04-13 17:32:02.612595: Epoch time: 102.39 s +2026-04-13 17:32:03.903494: +2026-04-13 17:32:03.905685: Epoch 2755 +2026-04-13 17:32:03.907691: Current learning rate: 0.0035 +2026-04-13 17:33:45.964014: train_loss -0.4254 +2026-04-13 17:33:45.971423: val_loss -0.3666 +2026-04-13 17:33:45.973936: Pseudo dice [0.8125, 0.8867, 0.7714, 0.6281, 0.6259, 0.8043, 0.7804] +2026-04-13 17:33:45.976314: Epoch time: 102.06 s +2026-04-13 17:33:47.172440: +2026-04-13 17:33:47.174167: Epoch 2756 +2026-04-13 17:33:47.175796: Current learning rate: 0.0035 +2026-04-13 17:35:28.775923: train_loss -0.4076 +2026-04-13 17:35:28.783646: val_loss -0.3205 +2026-04-13 17:35:28.786047: Pseudo dice [0.4401, 0.3787, 0.5986, 0.1394, 0.5611, 0.6902, 0.7155] +2026-04-13 17:35:28.789314: Epoch time: 101.61 s +2026-04-13 17:35:29.957765: +2026-04-13 17:35:29.959629: Epoch 2757 +2026-04-13 17:35:29.961888: Current learning rate: 0.00349 +2026-04-13 17:37:12.141718: train_loss -0.4322 +2026-04-13 17:37:12.147535: val_loss -0.388 +2026-04-13 17:37:12.149344: Pseudo dice [0.668, 0.7587, 0.8043, 0.601, 0.4461, 0.6709, 0.8438] +2026-04-13 17:37:12.151715: Epoch time: 102.19 s +2026-04-13 17:37:13.366170: +2026-04-13 17:37:13.368031: Epoch 2758 +2026-04-13 17:37:13.369398: Current learning rate: 0.00349 +2026-04-13 17:38:55.325515: train_loss -0.4238 +2026-04-13 17:38:55.338396: val_loss -0.3501 +2026-04-13 17:38:55.342803: Pseudo dice [0.8027, 0.832, 0.7683, 0.637, 0.3089, 0.6986, 0.6812] +2026-04-13 17:38:55.345130: Epoch time: 101.96 s +2026-04-13 17:38:56.515878: +2026-04-13 17:38:56.519422: Epoch 2759 +2026-04-13 17:38:56.521064: Current learning rate: 0.00349 +2026-04-13 17:40:38.883174: train_loss -0.4258 +2026-04-13 17:40:38.889237: val_loss -0.3804 +2026-04-13 17:40:38.891952: Pseudo dice [0.4957, 0.4046, 0.6696, 0.532, 0.5708, 0.8688, 0.7092] +2026-04-13 17:40:38.894523: Epoch time: 102.37 s +2026-04-13 17:40:40.155618: +2026-04-13 17:40:40.157980: Epoch 2760 +2026-04-13 17:40:40.160054: Current learning rate: 0.00349 +2026-04-13 17:42:22.802478: train_loss -0.4262 +2026-04-13 17:42:22.807957: val_loss -0.3428 +2026-04-13 17:42:22.809662: Pseudo dice [0.1909, 0.8688, 0.7134, 0.5481, 0.5142, 0.4784, 0.1095] +2026-04-13 17:42:22.811882: Epoch time: 102.65 s +2026-04-13 17:42:24.020447: +2026-04-13 17:42:24.022295: Epoch 2761 +2026-04-13 17:42:24.024004: Current learning rate: 0.00348 +2026-04-13 17:44:05.703891: train_loss -0.4119 +2026-04-13 17:44:05.710804: val_loss -0.3244 +2026-04-13 17:44:05.712667: Pseudo dice [0.7425, 0.2065, 0.6051, 0.1487, 0.7123, 0.135, 0.7615] +2026-04-13 17:44:05.714783: Epoch time: 101.69 s +2026-04-13 17:44:07.178611: +2026-04-13 17:44:07.180455: Epoch 2762 +2026-04-13 17:44:07.182996: Current learning rate: 0.00348 +2026-04-13 17:45:49.429301: train_loss -0.4047 +2026-04-13 17:45:49.435885: val_loss -0.333 +2026-04-13 17:45:49.437623: Pseudo dice [0.269, 0.8159, 0.6747, 0.3407, 0.3484, 0.518, 0.3927] +2026-04-13 17:45:49.440271: Epoch time: 102.25 s +2026-04-13 17:45:50.600593: +2026-04-13 17:45:50.602926: Epoch 2763 +2026-04-13 17:45:50.604518: Current learning rate: 0.00348 +2026-04-13 17:47:32.063908: train_loss -0.4146 +2026-04-13 17:47:32.071428: val_loss -0.3583 +2026-04-13 17:47:32.073448: Pseudo dice [0.4721, 0.7917, 0.671, 0.2426, 0.5277, 0.8173, 0.7776] +2026-04-13 17:47:32.075510: Epoch time: 101.47 s +2026-04-13 17:47:33.282142: +2026-04-13 17:47:33.284667: Epoch 2764 +2026-04-13 17:47:33.286572: Current learning rate: 0.00348 +2026-04-13 17:49:16.220420: train_loss -0.407 +2026-04-13 17:49:16.230019: val_loss -0.3561 +2026-04-13 17:49:16.235873: Pseudo dice [0.6875, 0.8289, 0.7353, 0.4488, 0.5797, 0.7096, 0.7571] +2026-04-13 17:49:16.239420: Epoch time: 102.94 s +2026-04-13 17:49:17.437702: +2026-04-13 17:49:17.439896: Epoch 2765 +2026-04-13 17:49:17.443247: Current learning rate: 0.00347 +2026-04-13 17:50:59.828796: train_loss -0.4088 +2026-04-13 17:50:59.835995: val_loss -0.3394 +2026-04-13 17:50:59.838331: Pseudo dice [0.4524, 0.4872, 0.6877, 0.485, 0.308, 0.6123, 0.5253] +2026-04-13 17:50:59.842125: Epoch time: 102.39 s +2026-04-13 17:51:01.037110: +2026-04-13 17:51:01.039318: Epoch 2766 +2026-04-13 17:51:01.041413: Current learning rate: 0.00347 +2026-04-13 17:52:43.088849: train_loss -0.4094 +2026-04-13 17:52:43.099416: val_loss -0.3663 +2026-04-13 17:52:43.101518: Pseudo dice [0.4357, 0.2225, 0.7898, 0.1753, 0.4916, 0.4322, 0.85] +2026-04-13 17:52:43.103482: Epoch time: 102.05 s +2026-04-13 17:52:44.314598: +2026-04-13 17:52:44.316967: Epoch 2767 +2026-04-13 17:52:44.319464: Current learning rate: 0.00347 +2026-04-13 17:54:26.212965: train_loss -0.4219 +2026-04-13 17:54:26.220102: val_loss -0.3196 +2026-04-13 17:54:26.222562: Pseudo dice [0.5798, 0.9036, 0.7667, 0.1843, 0.5895, 0.4545, 0.7382] +2026-04-13 17:54:26.224804: Epoch time: 101.9 s +2026-04-13 17:54:27.421935: +2026-04-13 17:54:27.423534: Epoch 2768 +2026-04-13 17:54:27.427750: Current learning rate: 0.00346 +2026-04-13 17:56:09.013384: train_loss -0.417 +2026-04-13 17:56:09.021146: val_loss -0.3631 +2026-04-13 17:56:09.023281: Pseudo dice [0.4879, 0.7132, 0.7314, 0.3199, 0.56, 0.7909, 0.7175] +2026-04-13 17:56:09.025941: Epoch time: 101.59 s +2026-04-13 17:56:11.238243: +2026-04-13 17:56:11.239830: Epoch 2769 +2026-04-13 17:56:11.241653: Current learning rate: 0.00346 +2026-04-13 17:57:53.136914: train_loss -0.3986 +2026-04-13 17:57:53.144702: val_loss -0.3285 +2026-04-13 17:57:53.147886: Pseudo dice [0.5099, 0.476, 0.5838, 0.2169, 0.3874, 0.845, 0.8011] +2026-04-13 17:57:53.151253: Epoch time: 101.9 s +2026-04-13 17:57:54.321738: +2026-04-13 17:57:54.323772: Epoch 2770 +2026-04-13 17:57:54.326316: Current learning rate: 0.00346 +2026-04-13 17:59:36.422657: train_loss -0.4206 +2026-04-13 17:59:36.433032: val_loss -0.3293 +2026-04-13 17:59:36.436797: Pseudo dice [0.7869, 0.8821, 0.7755, 0.8415, 0.2182, 0.129, 0.3742] +2026-04-13 17:59:36.440897: Epoch time: 102.1 s +2026-04-13 17:59:37.651867: +2026-04-13 17:59:37.654853: Epoch 2771 +2026-04-13 17:59:37.656879: Current learning rate: 0.00346 +2026-04-13 18:01:19.655010: train_loss -0.4219 +2026-04-13 18:01:19.661398: val_loss -0.3728 +2026-04-13 18:01:19.663842: Pseudo dice [0.7968, 0.1043, 0.768, 0.3272, 0.6345, 0.9137, 0.7667] +2026-04-13 18:01:19.666110: Epoch time: 102.01 s +2026-04-13 18:01:21.100555: +2026-04-13 18:01:21.102341: Epoch 2772 +2026-04-13 18:01:21.103920: Current learning rate: 0.00345 +2026-04-13 18:03:03.560798: train_loss -0.416 +2026-04-13 18:03:03.567689: val_loss -0.3811 +2026-04-13 18:03:03.569958: Pseudo dice [0.4987, 0.8975, 0.7806, 0.5955, 0.6274, 0.7702, 0.428] +2026-04-13 18:03:03.572718: Epoch time: 102.46 s +2026-04-13 18:03:04.757899: +2026-04-13 18:03:04.760586: Epoch 2773 +2026-04-13 18:03:04.762653: Current learning rate: 0.00345 +2026-04-13 18:04:46.414960: train_loss -0.4247 +2026-04-13 18:04:46.428207: val_loss -0.37 +2026-04-13 18:04:46.430354: Pseudo dice [0.3527, 0.4539, 0.8156, 0.3713, 0.6322, 0.3705, 0.8093] +2026-04-13 18:04:46.432513: Epoch time: 101.66 s +2026-04-13 18:04:47.645995: +2026-04-13 18:04:47.647772: Epoch 2774 +2026-04-13 18:04:47.649390: Current learning rate: 0.00345 +2026-04-13 18:06:29.836740: train_loss -0.415 +2026-04-13 18:06:29.842456: val_loss -0.3608 +2026-04-13 18:06:29.845079: Pseudo dice [0.8891, 0.6872, 0.7974, 0.0864, 0.5179, 0.6475, 0.8616] +2026-04-13 18:06:29.847650: Epoch time: 102.19 s +2026-04-13 18:06:31.066654: +2026-04-13 18:06:31.068481: Epoch 2775 +2026-04-13 18:06:31.070021: Current learning rate: 0.00345 +2026-04-13 18:08:13.281096: train_loss -0.4124 +2026-04-13 18:08:13.287452: val_loss -0.316 +2026-04-13 18:08:13.289948: Pseudo dice [0.544, 0.8772, 0.7189, 0.3163, 0.2165, 0.2722, 0.3994] +2026-04-13 18:08:13.292821: Epoch time: 102.22 s +2026-04-13 18:08:14.477578: +2026-04-13 18:08:14.479959: Epoch 2776 +2026-04-13 18:08:14.481823: Current learning rate: 0.00344 +2026-04-13 18:09:56.673269: train_loss -0.4102 +2026-04-13 18:09:56.686296: val_loss -0.367 +2026-04-13 18:09:56.689708: Pseudo dice [0.7673, 0.8999, 0.8292, 0.1819, 0.6438, 0.0791, 0.5279] +2026-04-13 18:09:56.692994: Epoch time: 102.2 s +2026-04-13 18:09:57.873379: +2026-04-13 18:09:57.875572: Epoch 2777 +2026-04-13 18:09:57.877167: Current learning rate: 0.00344 +2026-04-13 18:11:40.234908: train_loss -0.3967 +2026-04-13 18:11:40.241420: val_loss -0.3605 +2026-04-13 18:11:40.243367: Pseudo dice [0.3708, 0.8691, 0.7043, 0.5553, 0.5725, 0.1922, 0.7576] +2026-04-13 18:11:40.246480: Epoch time: 102.36 s +2026-04-13 18:11:41.424741: +2026-04-13 18:11:41.428197: Epoch 2778 +2026-04-13 18:11:41.429832: Current learning rate: 0.00344 +2026-04-13 18:13:23.669254: train_loss -0.3947 +2026-04-13 18:13:23.677917: val_loss -0.305 +2026-04-13 18:13:23.680419: Pseudo dice [0.6534, 0.7976, 0.3461, 0.3491, 0.2802, 0.6336, 0.6093] +2026-04-13 18:13:23.682696: Epoch time: 102.25 s +2026-04-13 18:13:24.859562: +2026-04-13 18:13:24.862928: Epoch 2779 +2026-04-13 18:13:24.864551: Current learning rate: 0.00344 +2026-04-13 18:15:06.761547: train_loss -0.4164 +2026-04-13 18:15:06.768090: val_loss -0.3786 +2026-04-13 18:15:06.771008: Pseudo dice [0.5706, 0.4852, 0.8237, 0.4649, 0.3774, 0.9108, 0.7184] +2026-04-13 18:15:06.774466: Epoch time: 101.91 s +2026-04-13 18:15:07.968336: +2026-04-13 18:15:07.973122: Epoch 2780 +2026-04-13 18:15:07.976258: Current learning rate: 0.00343 +2026-04-13 18:16:50.415982: train_loss -0.4283 +2026-04-13 18:16:50.422284: val_loss -0.3712 +2026-04-13 18:16:50.424137: Pseudo dice [0.6071, 0.69, 0.7161, 0.5034, 0.4771, 0.8722, 0.7177] +2026-04-13 18:16:50.426465: Epoch time: 102.45 s +2026-04-13 18:16:51.599123: +2026-04-13 18:16:51.601203: Epoch 2781 +2026-04-13 18:16:51.603091: Current learning rate: 0.00343 +2026-04-13 18:18:33.782844: train_loss -0.4187 +2026-04-13 18:18:33.790099: val_loss -0.332 +2026-04-13 18:18:33.792249: Pseudo dice [0.5477, 0.8859, 0.8031, 0.3866, 0.486, 0.7029, 0.7545] +2026-04-13 18:18:33.795180: Epoch time: 102.19 s +2026-04-13 18:18:35.322377: +2026-04-13 18:18:35.324047: Epoch 2782 +2026-04-13 18:18:35.325493: Current learning rate: 0.00343 +2026-04-13 18:20:17.226091: train_loss -0.4099 +2026-04-13 18:20:17.233865: val_loss -0.3752 +2026-04-13 18:20:17.235950: Pseudo dice [0.4829, 0.2631, 0.765, 0.3916, 0.4064, 0.9197, 0.794] +2026-04-13 18:20:17.237920: Epoch time: 101.91 s +2026-04-13 18:20:18.429651: +2026-04-13 18:20:18.431875: Epoch 2783 +2026-04-13 18:20:18.433503: Current learning rate: 0.00343 +2026-04-13 18:22:00.855055: train_loss -0.4008 +2026-04-13 18:22:00.861475: val_loss -0.3486 +2026-04-13 18:22:00.863575: Pseudo dice [0.6221, 0.8554, 0.692, 0.3338, 0.4469, 0.5856, 0.8297] +2026-04-13 18:22:00.865596: Epoch time: 102.43 s +2026-04-13 18:22:02.059367: +2026-04-13 18:22:02.061702: Epoch 2784 +2026-04-13 18:22:02.063700: Current learning rate: 0.00342 +2026-04-13 18:23:44.134805: train_loss -0.3821 +2026-04-13 18:23:44.141446: val_loss -0.3352 +2026-04-13 18:23:44.143498: Pseudo dice [0.7357, 0.7957, 0.661, 0.4388, 0.3049, 0.4871, 0.7559] +2026-04-13 18:23:44.145882: Epoch time: 102.08 s +2026-04-13 18:23:45.306468: +2026-04-13 18:23:45.309032: Epoch 2785 +2026-04-13 18:23:45.310819: Current learning rate: 0.00342 +2026-04-13 18:25:27.428195: train_loss -0.3981 +2026-04-13 18:25:27.435282: val_loss -0.3613 +2026-04-13 18:25:27.437489: Pseudo dice [0.4938, 0.6984, 0.632, 0.618, 0.2162, 0.6875, 0.8286] +2026-04-13 18:25:27.439580: Epoch time: 102.12 s +2026-04-13 18:25:28.684350: +2026-04-13 18:25:28.686257: Epoch 2786 +2026-04-13 18:25:28.688463: Current learning rate: 0.00342 +2026-04-13 18:27:10.430752: train_loss -0.4241 +2026-04-13 18:27:10.438361: val_loss -0.3437 +2026-04-13 18:27:10.440941: Pseudo dice [0.6965, 0.5859, 0.7961, 0.1622, 0.2045, 0.5916, 0.7969] +2026-04-13 18:27:10.443282: Epoch time: 101.75 s +2026-04-13 18:27:12.021044: +2026-04-13 18:27:12.022700: Epoch 2787 +2026-04-13 18:27:12.024843: Current learning rate: 0.00342 +2026-04-13 18:28:54.377940: train_loss -0.413 +2026-04-13 18:28:54.383477: val_loss -0.3607 +2026-04-13 18:28:54.387679: Pseudo dice [0.4859, 0.6952, 0.748, 0.597, 0.1769, 0.6659, 0.6609] +2026-04-13 18:28:54.391181: Epoch time: 102.36 s +2026-04-13 18:28:55.570734: +2026-04-13 18:28:55.573038: Epoch 2788 +2026-04-13 18:28:55.574903: Current learning rate: 0.00341 +2026-04-13 18:30:37.062407: train_loss -0.3893 +2026-04-13 18:30:37.069237: val_loss -0.355 +2026-04-13 18:30:37.072065: Pseudo dice [0.7161, 0.4938, 0.6219, 0.5519, 0.4665, 0.8232, 0.6963] +2026-04-13 18:30:37.075435: Epoch time: 101.49 s +2026-04-13 18:30:38.337477: +2026-04-13 18:30:38.339550: Epoch 2789 +2026-04-13 18:30:38.341795: Current learning rate: 0.00341 +2026-04-13 18:32:20.814825: train_loss -0.3816 +2026-04-13 18:32:20.822485: val_loss -0.3269 +2026-04-13 18:32:20.825022: Pseudo dice [0.2524, 0.7968, 0.6157, 0.3611, 0.4676, 0.5574, 0.8011] +2026-04-13 18:32:20.828048: Epoch time: 102.48 s +2026-04-13 18:32:22.056875: +2026-04-13 18:32:22.058412: Epoch 2790 +2026-04-13 18:32:22.060098: Current learning rate: 0.00341 +2026-04-13 18:34:04.363384: train_loss -0.3627 +2026-04-13 18:34:04.371491: val_loss -0.3468 +2026-04-13 18:34:04.375207: Pseudo dice [0.5384, 0.4775, 0.7569, 0.3809, 0.381, 0.7348, 0.5238] +2026-04-13 18:34:04.378328: Epoch time: 102.31 s +2026-04-13 18:34:05.570601: +2026-04-13 18:34:05.572534: Epoch 2791 +2026-04-13 18:34:05.574600: Current learning rate: 0.00341 +2026-04-13 18:35:47.152364: train_loss -0.403 +2026-04-13 18:35:47.160988: val_loss -0.282 +2026-04-13 18:35:47.163751: Pseudo dice [0.6896, 0.8665, 0.352, 0.3898, 0.3449, 0.2261, 0.4509] +2026-04-13 18:35:47.167364: Epoch time: 101.58 s +2026-04-13 18:35:48.367785: +2026-04-13 18:35:48.369468: Epoch 2792 +2026-04-13 18:35:48.371880: Current learning rate: 0.0034 +2026-04-13 18:37:30.746810: train_loss -0.409 +2026-04-13 18:37:30.753351: val_loss -0.3516 +2026-04-13 18:37:30.755622: Pseudo dice [0.3653, 0.2811, 0.7156, 0.4003, 0.4903, 0.586, 0.8483] +2026-04-13 18:37:30.758284: Epoch time: 102.38 s +2026-04-13 18:37:31.993688: +2026-04-13 18:37:31.995535: Epoch 2793 +2026-04-13 18:37:31.998784: Current learning rate: 0.0034 +2026-04-13 18:39:13.971152: train_loss -0.4198 +2026-04-13 18:39:13.978779: val_loss -0.356 +2026-04-13 18:39:13.984077: Pseudo dice [0.6698, 0.125, 0.6722, 0.3318, 0.495, 0.8617, 0.848] +2026-04-13 18:39:13.986575: Epoch time: 101.98 s +2026-04-13 18:39:15.265946: +2026-04-13 18:39:15.268404: Epoch 2794 +2026-04-13 18:39:15.270181: Current learning rate: 0.0034 +2026-04-13 18:40:56.728572: train_loss -0.4132 +2026-04-13 18:40:56.735893: val_loss -0.3287 +2026-04-13 18:40:56.737984: Pseudo dice [0.5858, 0.5397, 0.651, 0.5544, 0.4969, 0.5376, 0.8017] +2026-04-13 18:40:56.740396: Epoch time: 101.47 s +2026-04-13 18:40:57.940650: +2026-04-13 18:40:57.942624: Epoch 2795 +2026-04-13 18:40:57.945345: Current learning rate: 0.0034 +2026-04-13 18:42:39.962651: train_loss -0.4183 +2026-04-13 18:42:39.968579: val_loss -0.3596 +2026-04-13 18:42:39.970454: Pseudo dice [0.7203, 0.7436, 0.6939, 0.2245, 0.54, 0.7008, 0.8737] +2026-04-13 18:42:39.976590: Epoch time: 102.03 s +2026-04-13 18:42:41.172157: +2026-04-13 18:42:41.174800: Epoch 2796 +2026-04-13 18:42:41.176233: Current learning rate: 0.00339 +2026-04-13 18:44:23.321843: train_loss -0.3921 +2026-04-13 18:44:23.328442: val_loss -0.3589 +2026-04-13 18:44:23.331351: Pseudo dice [0.3906, 0.8619, 0.7501, 0.7759, 0.2652, 0.53, 0.6593] +2026-04-13 18:44:23.334149: Epoch time: 102.15 s +2026-04-13 18:44:24.545709: +2026-04-13 18:44:24.547750: Epoch 2797 +2026-04-13 18:44:24.549723: Current learning rate: 0.00339 +2026-04-13 18:46:05.839364: train_loss -0.4012 +2026-04-13 18:46:05.845731: val_loss -0.316 +2026-04-13 18:46:05.848157: Pseudo dice [0.3227, 0.4513, 0.6985, 0.393, 0.3986, 0.3755, 0.7276] +2026-04-13 18:46:05.851220: Epoch time: 101.3 s +2026-04-13 18:46:07.040491: +2026-04-13 18:46:07.043211: Epoch 2798 +2026-04-13 18:46:07.045013: Current learning rate: 0.00339 +2026-04-13 18:47:49.436011: train_loss -0.4177 +2026-04-13 18:47:49.442383: val_loss -0.3609 +2026-04-13 18:47:49.445043: Pseudo dice [0.5397, 0.7931, 0.7509, 0.8429, 0.4923, 0.6222, 0.8265] +2026-04-13 18:47:49.447442: Epoch time: 102.4 s +2026-04-13 18:47:50.623849: +2026-04-13 18:47:50.626266: Epoch 2799 +2026-04-13 18:47:50.627671: Current learning rate: 0.00339 +2026-04-13 18:49:32.341104: train_loss -0.4141 +2026-04-13 18:49:32.347364: val_loss -0.395 +2026-04-13 18:49:32.349157: Pseudo dice [0.4637, 0.4165, 0.7085, 0.7356, 0.6179, 0.8586, 0.8313] +2026-04-13 18:49:32.352049: Epoch time: 101.72 s +2026-04-13 18:49:35.353306: +2026-04-13 18:49:35.355957: Epoch 2800 +2026-04-13 18:49:35.357356: Current learning rate: 0.00338 +2026-04-13 18:51:16.958339: train_loss -0.4245 +2026-04-13 18:51:16.966861: val_loss -0.3784 +2026-04-13 18:51:16.969638: Pseudo dice [0.4635, 0.7589, 0.8209, 0.7333, 0.5488, 0.8581, 0.7143] +2026-04-13 18:51:16.972274: Epoch time: 101.61 s +2026-04-13 18:51:18.193724: +2026-04-13 18:51:18.195537: Epoch 2801 +2026-04-13 18:51:18.198827: Current learning rate: 0.00338 +2026-04-13 18:53:00.341666: train_loss -0.4116 +2026-04-13 18:53:00.347038: val_loss -0.3392 +2026-04-13 18:53:00.349057: Pseudo dice [0.5987, 0.5591, 0.7569, 0.7598, 0.5105, 0.7992, 0.7646] +2026-04-13 18:53:00.351480: Epoch time: 102.15 s +2026-04-13 18:53:01.625781: +2026-04-13 18:53:01.627609: Epoch 2802 +2026-04-13 18:53:01.629984: Current learning rate: 0.00338 +2026-04-13 18:54:42.986117: train_loss -0.4144 +2026-04-13 18:54:42.991880: val_loss -0.3646 +2026-04-13 18:54:42.994155: Pseudo dice [0.6535, 0.507, 0.7374, 0.7474, 0.3533, 0.7033, 0.5778] +2026-04-13 18:54:42.997133: Epoch time: 101.36 s +2026-04-13 18:54:44.188731: +2026-04-13 18:54:44.191786: Epoch 2803 +2026-04-13 18:54:44.194117: Current learning rate: 0.00338 +2026-04-13 18:56:25.874555: train_loss -0.422 +2026-04-13 18:56:25.881466: val_loss -0.3648 +2026-04-13 18:56:25.884060: Pseudo dice [0.5783, 0.8595, 0.8339, 0.6171, 0.3836, 0.7324, 0.5377] +2026-04-13 18:56:25.887871: Epoch time: 101.69 s +2026-04-13 18:56:27.134442: +2026-04-13 18:56:27.136514: Epoch 2804 +2026-04-13 18:56:27.138645: Current learning rate: 0.00337 +2026-04-13 18:58:10.038900: train_loss -0.3899 +2026-04-13 18:58:10.045491: val_loss -0.3255 +2026-04-13 18:58:10.047349: Pseudo dice [0.7675, 0.8008, 0.6096, 0.3842, 0.5118, 0.8799, 0.4606] +2026-04-13 18:58:10.049430: Epoch time: 102.91 s +2026-04-13 18:58:11.254746: +2026-04-13 18:58:11.257700: Epoch 2805 +2026-04-13 18:58:11.259471: Current learning rate: 0.00337 +2026-04-13 18:59:53.354094: train_loss -0.4028 +2026-04-13 18:59:53.360387: val_loss -0.3437 +2026-04-13 18:59:53.362843: Pseudo dice [0.576, 0.1678, 0.7873, 0.4841, 0.5997, 0.622, 0.5794] +2026-04-13 18:59:53.365145: Epoch time: 102.1 s +2026-04-13 18:59:54.563946: +2026-04-13 18:59:54.565679: Epoch 2806 +2026-04-13 18:59:54.567529: Current learning rate: 0.00337 +2026-04-13 19:01:37.295812: train_loss -0.4157 +2026-04-13 19:01:37.304841: val_loss -0.3602 +2026-04-13 19:01:37.307393: Pseudo dice [0.4563, 0.9063, 0.7316, 0.1411, 0.4538, 0.714, 0.3748] +2026-04-13 19:01:37.309373: Epoch time: 102.74 s +2026-04-13 19:01:38.482485: +2026-04-13 19:01:38.485488: Epoch 2807 +2026-04-13 19:01:38.487016: Current learning rate: 0.00337 +2026-04-13 19:03:20.353903: train_loss -0.4228 +2026-04-13 19:03:20.359777: val_loss -0.3658 +2026-04-13 19:03:20.362317: Pseudo dice [0.6502, 0.9044, 0.8474, 0.4828, 0.4396, 0.8315, 0.6503] +2026-04-13 19:03:20.364308: Epoch time: 101.87 s +2026-04-13 19:03:21.544332: +2026-04-13 19:03:21.546809: Epoch 2808 +2026-04-13 19:03:21.548748: Current learning rate: 0.00336 +2026-04-13 19:05:03.635329: train_loss -0.4236 +2026-04-13 19:05:03.643999: val_loss -0.3615 +2026-04-13 19:05:03.646396: Pseudo dice [0.757, 0.8195, 0.74, 0.3416, 0.3617, 0.7321, 0.4491] +2026-04-13 19:05:03.648571: Epoch time: 102.09 s +2026-04-13 19:05:04.840455: +2026-04-13 19:05:04.842359: Epoch 2809 +2026-04-13 19:05:04.843913: Current learning rate: 0.00336 +2026-04-13 19:06:48.008006: train_loss -0.4177 +2026-04-13 19:06:48.014462: val_loss -0.3631 +2026-04-13 19:06:48.017162: Pseudo dice [0.5707, 0.4816, 0.6428, 0.1254, 0.3936, 0.8963, 0.7966] +2026-04-13 19:06:48.019181: Epoch time: 103.17 s +2026-04-13 19:06:49.221087: +2026-04-13 19:06:49.223129: Epoch 2810 +2026-04-13 19:06:49.225315: Current learning rate: 0.00336 +2026-04-13 19:08:31.911227: train_loss -0.4116 +2026-04-13 19:08:31.917240: val_loss -0.3497 +2026-04-13 19:08:31.919394: Pseudo dice [0.4476, 0.8853, 0.7838, 0.5228, 0.4499, 0.7401, 0.746] +2026-04-13 19:08:31.921457: Epoch time: 102.69 s +2026-04-13 19:08:33.106378: +2026-04-13 19:08:33.107890: Epoch 2811 +2026-04-13 19:08:33.109333: Current learning rate: 0.00336 +2026-04-13 19:10:15.025543: train_loss -0.406 +2026-04-13 19:10:15.032808: val_loss -0.2872 +2026-04-13 19:10:15.037841: Pseudo dice [0.6069, 0.6607, 0.5943, 0.4772, 0.417, 0.0229, 0.6904] +2026-04-13 19:10:15.040866: Epoch time: 101.92 s +2026-04-13 19:10:16.247339: +2026-04-13 19:10:16.250269: Epoch 2812 +2026-04-13 19:10:16.252202: Current learning rate: 0.00335 +2026-04-13 19:11:57.811582: train_loss -0.4172 +2026-04-13 19:11:57.818830: val_loss -0.3417 +2026-04-13 19:11:57.820613: Pseudo dice [0.4708, 0.714, 0.7798, 0.8138, 0.4286, 0.3964, 0.7057] +2026-04-13 19:11:57.823136: Epoch time: 101.57 s +2026-04-13 19:11:58.990300: +2026-04-13 19:11:58.991891: Epoch 2813 +2026-04-13 19:11:58.993376: Current learning rate: 0.00335 +2026-04-13 19:13:41.048330: train_loss -0.4133 +2026-04-13 19:13:41.054111: val_loss -0.3254 +2026-04-13 19:13:41.055842: Pseudo dice [0.4144, 0.8984, 0.7791, 0.2906, 0.601, 0.103, 0.3087] +2026-04-13 19:13:41.059419: Epoch time: 102.06 s +2026-04-13 19:13:42.253221: +2026-04-13 19:13:42.255016: Epoch 2814 +2026-04-13 19:13:42.256958: Current learning rate: 0.00335 +2026-04-13 19:15:23.974912: train_loss -0.4004 +2026-04-13 19:15:23.982789: val_loss -0.3401 +2026-04-13 19:15:23.985247: Pseudo dice [0.3681, 0.7216, 0.8093, 0.3973, 0.3333, 0.4682, 0.7208] +2026-04-13 19:15:23.987779: Epoch time: 101.73 s +2026-04-13 19:15:25.217406: +2026-04-13 19:15:25.220970: Epoch 2815 +2026-04-13 19:15:25.224558: Current learning rate: 0.00335 +2026-04-13 19:17:07.396422: train_loss -0.4101 +2026-04-13 19:17:07.403794: val_loss -0.3165 +2026-04-13 19:17:07.405994: Pseudo dice [0.3411, 0.4483, 0.641, 0.7079, 0.4891, 0.5528, 0.7079] +2026-04-13 19:17:07.408629: Epoch time: 102.18 s +2026-04-13 19:17:08.607994: +2026-04-13 19:17:08.619225: Epoch 2816 +2026-04-13 19:17:08.620886: Current learning rate: 0.00334 +2026-04-13 19:18:50.581543: train_loss -0.4163 +2026-04-13 19:18:50.588147: val_loss -0.3766 +2026-04-13 19:18:50.590373: Pseudo dice [0.427, 0.511, 0.7188, 0.3494, 0.3553, 0.87, 0.8497] +2026-04-13 19:18:50.592822: Epoch time: 101.98 s +2026-04-13 19:18:51.768422: +2026-04-13 19:18:51.770458: Epoch 2817 +2026-04-13 19:18:51.772365: Current learning rate: 0.00334 +2026-04-13 19:20:33.973794: train_loss -0.4157 +2026-04-13 19:20:33.981805: val_loss -0.3917 +2026-04-13 19:20:33.983937: Pseudo dice [0.5024, 0.7079, 0.7731, 0.2997, 0.6193, 0.8635, 0.8441] +2026-04-13 19:20:33.986139: Epoch time: 102.21 s +2026-04-13 19:20:35.165763: +2026-04-13 19:20:35.169606: Epoch 2818 +2026-04-13 19:20:35.173971: Current learning rate: 0.00334 +2026-04-13 19:22:16.982081: train_loss -0.4137 +2026-04-13 19:22:16.989581: val_loss -0.3774 +2026-04-13 19:22:16.991606: Pseudo dice [0.4268, 0.4585, 0.7683, 0.5086, 0.4007, 0.6657, 0.6712] +2026-04-13 19:22:16.993650: Epoch time: 101.82 s +2026-04-13 19:22:18.187421: +2026-04-13 19:22:18.189392: Epoch 2819 +2026-04-13 19:22:18.191521: Current learning rate: 0.00334 +2026-04-13 19:24:00.817552: train_loss -0.417 +2026-04-13 19:24:00.824062: val_loss -0.3824 +2026-04-13 19:24:00.826118: Pseudo dice [0.4043, 0.6408, 0.8588, 0.2612, 0.5452, 0.8245, 0.3449] +2026-04-13 19:24:00.828327: Epoch time: 102.63 s +2026-04-13 19:24:02.020794: +2026-04-13 19:24:02.023689: Epoch 2820 +2026-04-13 19:24:02.026192: Current learning rate: 0.00333 +2026-04-13 19:25:43.516837: train_loss -0.4196 +2026-04-13 19:25:43.523274: val_loss -0.3761 +2026-04-13 19:25:43.525229: Pseudo dice [0.0894, 0.4177, 0.7171, 0.4616, 0.6068, 0.8916, 0.8403] +2026-04-13 19:25:43.527421: Epoch time: 101.5 s +2026-04-13 19:25:44.696710: +2026-04-13 19:25:44.699792: Epoch 2821 +2026-04-13 19:25:44.701369: Current learning rate: 0.00333 +2026-04-13 19:27:27.369006: train_loss -0.4179 +2026-04-13 19:27:27.376830: val_loss -0.352 +2026-04-13 19:27:27.379127: Pseudo dice [0.8745, 0.796, 0.7509, 0.1411, 0.2818, 0.811, 0.629] +2026-04-13 19:27:27.382653: Epoch time: 102.68 s +2026-04-13 19:27:28.565294: +2026-04-13 19:27:28.567078: Epoch 2822 +2026-04-13 19:27:28.568703: Current learning rate: 0.00333 +2026-04-13 19:29:10.101565: train_loss -0.4127 +2026-04-13 19:29:10.108155: val_loss -0.3765 +2026-04-13 19:29:10.110078: Pseudo dice [0.758, 0.64, 0.8047, 0.5851, 0.372, 0.8053, 0.8179] +2026-04-13 19:29:10.113626: Epoch time: 101.54 s +2026-04-13 19:29:11.311671: +2026-04-13 19:29:11.313432: Epoch 2823 +2026-04-13 19:29:11.315530: Current learning rate: 0.00333 +2026-04-13 19:30:53.309916: train_loss -0.4119 +2026-04-13 19:30:53.316296: val_loss -0.3646 +2026-04-13 19:30:53.318398: Pseudo dice [0.0441, 0.8263, 0.7418, 0.0467, 0.66, 0.4032, 0.7534] +2026-04-13 19:30:53.321192: Epoch time: 102.0 s +2026-04-13 19:30:54.535518: +2026-04-13 19:30:54.537264: Epoch 2824 +2026-04-13 19:30:54.538792: Current learning rate: 0.00332 +2026-04-13 19:32:36.618839: train_loss -0.4016 +2026-04-13 19:32:36.633857: val_loss -0.3513 +2026-04-13 19:32:36.636046: Pseudo dice [0.8509, 0.6147, 0.7545, 0.3469, 0.4634, 0.6648, 0.7028] +2026-04-13 19:32:36.638678: Epoch time: 102.09 s +2026-04-13 19:32:37.828810: +2026-04-13 19:32:37.830677: Epoch 2825 +2026-04-13 19:32:37.832255: Current learning rate: 0.00332 +2026-04-13 19:34:19.448703: train_loss -0.403 +2026-04-13 19:34:19.454731: val_loss -0.357 +2026-04-13 19:34:19.457342: Pseudo dice [0.4939, 0.8727, 0.7495, 0.318, 0.5907, 0.5982, 0.5771] +2026-04-13 19:34:19.459391: Epoch time: 101.62 s +2026-04-13 19:34:20.643374: +2026-04-13 19:34:20.645380: Epoch 2826 +2026-04-13 19:34:20.646790: Current learning rate: 0.00332 +2026-04-13 19:36:02.171829: train_loss -0.4195 +2026-04-13 19:36:02.177138: val_loss -0.3622 +2026-04-13 19:36:02.179141: Pseudo dice [0.547, 0.1576, 0.7094, 0.5803, 0.6257, 0.7739, 0.859] +2026-04-13 19:36:02.181144: Epoch time: 101.53 s +2026-04-13 19:36:03.387670: +2026-04-13 19:36:03.389576: Epoch 2827 +2026-04-13 19:36:03.391230: Current learning rate: 0.00332 +2026-04-13 19:37:45.017660: train_loss -0.4104 +2026-04-13 19:37:45.024675: val_loss -0.3588 +2026-04-13 19:37:45.027181: Pseudo dice [0.5561, 0.8055, 0.5575, 0.4677, 0.5459, 0.675, 0.7434] +2026-04-13 19:37:45.030203: Epoch time: 101.63 s +2026-04-13 19:37:46.239543: +2026-04-13 19:37:46.241329: Epoch 2828 +2026-04-13 19:37:46.242784: Current learning rate: 0.00331 +2026-04-13 19:39:28.135460: train_loss -0.4147 +2026-04-13 19:39:28.141458: val_loss -0.3543 +2026-04-13 19:39:28.143414: Pseudo dice [0.7647, 0.7101, 0.7242, 0.729, 0.3188, 0.904, 0.7841] +2026-04-13 19:39:28.146189: Epoch time: 101.9 s +2026-04-13 19:39:29.326321: +2026-04-13 19:39:29.327864: Epoch 2829 +2026-04-13 19:39:29.329293: Current learning rate: 0.00331 +2026-04-13 19:41:12.030616: train_loss -0.4195 +2026-04-13 19:41:12.037573: val_loss -0.3626 +2026-04-13 19:41:12.039667: Pseudo dice [0.5369, 0.3139, 0.818, 0.5397, 0.4578, 0.7456, 0.5931] +2026-04-13 19:41:12.042065: Epoch time: 102.71 s +2026-04-13 19:41:13.240287: +2026-04-13 19:41:13.242294: Epoch 2830 +2026-04-13 19:41:13.243909: Current learning rate: 0.00331 +2026-04-13 19:42:55.198975: train_loss -0.4235 +2026-04-13 19:42:55.205991: val_loss -0.383 +2026-04-13 19:42:55.207705: Pseudo dice [0.5811, 0.5744, 0.775, 0.8082, 0.3857, 0.8331, 0.7563] +2026-04-13 19:42:55.209837: Epoch time: 101.96 s +2026-04-13 19:42:56.404344: +2026-04-13 19:42:56.406562: Epoch 2831 +2026-04-13 19:42:56.408298: Current learning rate: 0.00331 +2026-04-13 19:44:38.346460: train_loss -0.3987 +2026-04-13 19:44:38.352799: val_loss -0.3394 +2026-04-13 19:44:38.355247: Pseudo dice [0.5547, 0.4974, 0.6297, 0.3098, 0.3292, 0.5316, 0.2582] +2026-04-13 19:44:38.357444: Epoch time: 101.95 s +2026-04-13 19:44:39.543927: +2026-04-13 19:44:39.545850: Epoch 2832 +2026-04-13 19:44:39.547713: Current learning rate: 0.0033 +2026-04-13 19:46:21.650317: train_loss -0.4003 +2026-04-13 19:46:21.656716: val_loss -0.3815 +2026-04-13 19:46:21.659578: Pseudo dice [0.3638, 0.9148, 0.7971, 0.7198, 0.6507, 0.6525, 0.5154] +2026-04-13 19:46:21.661983: Epoch time: 102.11 s +2026-04-13 19:46:22.860461: +2026-04-13 19:46:22.862529: Epoch 2833 +2026-04-13 19:46:22.865366: Current learning rate: 0.0033 +2026-04-13 19:48:04.452485: train_loss -0.4226 +2026-04-13 19:48:04.458618: val_loss -0.3644 +2026-04-13 19:48:04.460322: Pseudo dice [0.492, 0.4724, 0.7791, 0.8003, 0.3695, 0.5894, 0.8931] +2026-04-13 19:48:04.462895: Epoch time: 101.6 s +2026-04-13 19:48:05.660145: +2026-04-13 19:48:05.661765: Epoch 2834 +2026-04-13 19:48:05.663296: Current learning rate: 0.0033 +2026-04-13 19:49:47.582043: train_loss -0.3721 +2026-04-13 19:49:47.588282: val_loss -0.3438 +2026-04-13 19:49:47.590016: Pseudo dice [0.5698, 0.8842, 0.7384, 0.5251, 0.2986, 0.652, 0.7315] +2026-04-13 19:49:47.593715: Epoch time: 101.92 s +2026-04-13 19:49:48.802394: +2026-04-13 19:49:48.804615: Epoch 2835 +2026-04-13 19:49:48.806384: Current learning rate: 0.00329 +2026-04-13 19:51:30.475639: train_loss -0.4161 +2026-04-13 19:51:30.482647: val_loss -0.3674 +2026-04-13 19:51:30.487335: Pseudo dice [0.5894, 0.8252, 0.7557, 0.225, 0.5919, 0.6999, 0.4935] +2026-04-13 19:51:30.500247: Epoch time: 101.68 s +2026-04-13 19:51:31.722161: +2026-04-13 19:51:31.735363: Epoch 2836 +2026-04-13 19:51:31.737559: Current learning rate: 0.00329 +2026-04-13 19:53:13.434857: train_loss -0.4142 +2026-04-13 19:53:13.442035: val_loss -0.3781 +2026-04-13 19:53:13.444676: Pseudo dice [0.6604, 0.7017, 0.8778, 0.4191, 0.39, 0.6706, 0.8589] +2026-04-13 19:53:13.448163: Epoch time: 101.72 s +2026-04-13 19:53:14.641526: +2026-04-13 19:53:14.643360: Epoch 2837 +2026-04-13 19:53:14.645015: Current learning rate: 0.00329 +2026-04-13 19:54:55.799836: train_loss -0.4272 +2026-04-13 19:54:55.810234: val_loss -0.3679 +2026-04-13 19:54:55.812657: Pseudo dice [0.5009, 0.5658, 0.7111, 0.4641, 0.3446, 0.9209, 0.8564] +2026-04-13 19:54:55.814999: Epoch time: 101.16 s +2026-04-13 19:54:57.012403: +2026-04-13 19:54:57.015929: Epoch 2838 +2026-04-13 19:54:57.018993: Current learning rate: 0.00329 +2026-04-13 19:56:38.935575: train_loss -0.4167 +2026-04-13 19:56:38.944156: val_loss -0.3577 +2026-04-13 19:56:38.950156: Pseudo dice [0.827, 0.8381, 0.7943, 0.4578, 0.5209, 0.6603, 0.8927] +2026-04-13 19:56:38.952533: Epoch time: 101.93 s +2026-04-13 19:56:40.143774: +2026-04-13 19:56:40.145755: Epoch 2839 +2026-04-13 19:56:40.147290: Current learning rate: 0.00328 +2026-04-13 19:58:21.820427: train_loss -0.381 +2026-04-13 19:58:21.826626: val_loss -0.3573 +2026-04-13 19:58:21.828293: Pseudo dice [0.6315, 0.8173, 0.7485, 0.4518, 0.4586, 0.6565, 0.7964] +2026-04-13 19:58:21.830710: Epoch time: 101.68 s +2026-04-13 19:58:23.028884: +2026-04-13 19:58:23.030398: Epoch 2840 +2026-04-13 19:58:23.031814: Current learning rate: 0.00328 +2026-04-13 20:00:04.249144: train_loss -0.4055 +2026-04-13 20:00:04.256542: val_loss -0.3682 +2026-04-13 20:00:04.258701: Pseudo dice [0.6755, 0.5738, 0.7071, 0.2585, 0.3018, 0.5749, 0.657] +2026-04-13 20:00:04.262052: Epoch time: 101.22 s +2026-04-13 20:00:05.469121: +2026-04-13 20:00:05.470950: Epoch 2841 +2026-04-13 20:00:05.472614: Current learning rate: 0.00328 +2026-04-13 20:01:47.502867: train_loss -0.4158 +2026-04-13 20:01:47.508906: val_loss -0.3536 +2026-04-13 20:01:47.511089: Pseudo dice [0.3016, 0.4429, 0.6751, 0.2659, 0.6001, 0.8625, 0.6536] +2026-04-13 20:01:47.513688: Epoch time: 102.04 s +2026-04-13 20:01:48.708110: +2026-04-13 20:01:48.710793: Epoch 2842 +2026-04-13 20:01:48.712449: Current learning rate: 0.00328 +2026-04-13 20:03:30.325876: train_loss -0.4286 +2026-04-13 20:03:30.332941: val_loss -0.3418 +2026-04-13 20:03:30.334975: Pseudo dice [0.4934, 0.5062, 0.7614, 0.2852, 0.6883, 0.6264, 0.4424] +2026-04-13 20:03:30.337389: Epoch time: 101.62 s +2026-04-13 20:03:31.535097: +2026-04-13 20:03:31.538479: Epoch 2843 +2026-04-13 20:03:31.540257: Current learning rate: 0.00327 +2026-04-13 20:05:14.081834: train_loss -0.4237 +2026-04-13 20:05:14.089294: val_loss -0.3729 +2026-04-13 20:05:14.092270: Pseudo dice [0.8322, 0.8919, 0.7869, 0.3496, 0.5539, 0.756, 0.5195] +2026-04-13 20:05:14.094662: Epoch time: 102.55 s +2026-04-13 20:05:15.296956: +2026-04-13 20:05:15.299583: Epoch 2844 +2026-04-13 20:05:15.301694: Current learning rate: 0.00327 +2026-04-13 20:06:57.249246: train_loss -0.4201 +2026-04-13 20:06:57.263175: val_loss -0.3609 +2026-04-13 20:06:57.277749: Pseudo dice [0.6203, 0.8955, 0.8541, 0.4547, 0.4733, 0.1919, 0.6296] +2026-04-13 20:06:57.284106: Epoch time: 101.96 s +2026-04-13 20:06:58.477098: +2026-04-13 20:06:58.479623: Epoch 2845 +2026-04-13 20:06:58.481783: Current learning rate: 0.00327 +2026-04-13 20:08:40.208447: train_loss -0.4104 +2026-04-13 20:08:40.215850: val_loss -0.3538 +2026-04-13 20:08:40.217659: Pseudo dice [0.1287, 0.7765, 0.7301, 0.4695, 0.3231, 0.7795, 0.7098] +2026-04-13 20:08:40.219690: Epoch time: 101.73 s +2026-04-13 20:08:41.418322: +2026-04-13 20:08:41.420321: Epoch 2846 +2026-04-13 20:08:41.421814: Current learning rate: 0.00327 +2026-04-13 20:10:23.012897: train_loss -0.4165 +2026-04-13 20:10:23.020050: val_loss -0.3668 +2026-04-13 20:10:23.021818: Pseudo dice [0.4884, 0.4934, 0.7737, 0.6016, 0.4843, 0.9129, 0.6294] +2026-04-13 20:10:23.024173: Epoch time: 101.6 s +2026-04-13 20:10:24.239069: +2026-04-13 20:10:24.240685: Epoch 2847 +2026-04-13 20:10:24.242227: Current learning rate: 0.00326 +2026-04-13 20:12:05.877489: train_loss -0.4405 +2026-04-13 20:12:05.885151: val_loss -0.3868 +2026-04-13 20:12:05.887352: Pseudo dice [0.3638, 0.649, 0.6643, 0.5249, 0.2218, 0.5744, 0.8146] +2026-04-13 20:12:05.890095: Epoch time: 101.64 s +2026-04-13 20:12:07.120198: +2026-04-13 20:12:07.122324: Epoch 2848 +2026-04-13 20:12:07.124380: Current learning rate: 0.00326 +2026-04-13 20:13:48.837529: train_loss -0.4332 +2026-04-13 20:13:48.846278: val_loss -0.3738 +2026-04-13 20:13:48.848319: Pseudo dice [0.6116, 0.7127, 0.7515, 0.8175, 0.4015, 0.8704, 0.8563] +2026-04-13 20:13:48.850184: Epoch time: 101.72 s +2026-04-13 20:13:50.049803: +2026-04-13 20:13:50.052422: Epoch 2849 +2026-04-13 20:13:50.054936: Current learning rate: 0.00326 +2026-04-13 20:15:33.378083: train_loss -0.4305 +2026-04-13 20:15:33.387492: val_loss -0.355 +2026-04-13 20:15:33.390057: Pseudo dice [0.8923, 0.6258, 0.7323, 0.1396, 0.5226, 0.8724, 0.8477] +2026-04-13 20:15:33.393815: Epoch time: 103.33 s +2026-04-13 20:15:36.126098: +2026-04-13 20:15:36.128156: Epoch 2850 +2026-04-13 20:15:36.129538: Current learning rate: 0.00326 +2026-04-13 20:17:17.628963: train_loss -0.436 +2026-04-13 20:17:17.635435: val_loss -0.3637 +2026-04-13 20:17:17.637776: Pseudo dice [0.6653, 0.8829, 0.7263, 0.4869, 0.4794, 0.2495, 0.7485] +2026-04-13 20:17:17.641181: Epoch time: 101.51 s +2026-04-13 20:17:18.834105: +2026-04-13 20:17:18.835985: Epoch 2851 +2026-04-13 20:17:18.837720: Current learning rate: 0.00325 +2026-04-13 20:19:00.491144: train_loss -0.4259 +2026-04-13 20:19:00.498120: val_loss -0.3732 +2026-04-13 20:19:00.499790: Pseudo dice [0.7401, 0.6778, 0.7137, 0.7901, 0.5026, 0.465, 0.8463] +2026-04-13 20:19:00.502606: Epoch time: 101.66 s +2026-04-13 20:19:01.700108: +2026-04-13 20:19:01.701662: Epoch 2852 +2026-04-13 20:19:01.703687: Current learning rate: 0.00325 +2026-04-13 20:20:43.459733: train_loss -0.43 +2026-04-13 20:20:43.468176: val_loss -0.3917 +2026-04-13 20:20:43.470397: Pseudo dice [0.1989, 0.6661, 0.7733, 0.5804, 0.5212, 0.9105, 0.7507] +2026-04-13 20:20:43.473132: Epoch time: 101.76 s +2026-04-13 20:20:44.675411: +2026-04-13 20:20:44.677126: Epoch 2853 +2026-04-13 20:20:44.678589: Current learning rate: 0.00325 +2026-04-13 20:22:26.830114: train_loss -0.4255 +2026-04-13 20:22:26.836925: val_loss -0.3593 +2026-04-13 20:22:26.838904: Pseudo dice [0.7784, 0.8828, 0.759, 0.7715, 0.5439, 0.0405, 0.7188] +2026-04-13 20:22:26.841799: Epoch time: 102.16 s +2026-04-13 20:22:28.037827: +2026-04-13 20:22:28.039580: Epoch 2854 +2026-04-13 20:22:28.041052: Current learning rate: 0.00325 +2026-04-13 20:24:10.899977: train_loss -0.4237 +2026-04-13 20:24:10.909232: val_loss -0.3757 +2026-04-13 20:24:10.911055: Pseudo dice [0.7438, 0.6391, 0.7764, 0.7929, 0.5305, 0.7564, 0.6] +2026-04-13 20:24:10.913499: Epoch time: 102.87 s +2026-04-13 20:24:12.168752: +2026-04-13 20:24:12.170421: Epoch 2855 +2026-04-13 20:24:12.172000: Current learning rate: 0.00324 +2026-04-13 20:25:54.248192: train_loss -0.4271 +2026-04-13 20:25:54.257712: val_loss -0.2967 +2026-04-13 20:25:54.259827: Pseudo dice [0.7381, 0.8213, 0.6106, 0.5259, 0.676, 0.1889, 0.4441] +2026-04-13 20:25:54.261985: Epoch time: 102.08 s +2026-04-13 20:25:55.469374: +2026-04-13 20:25:55.471480: Epoch 2856 +2026-04-13 20:25:55.473588: Current learning rate: 0.00324 +2026-04-13 20:27:37.219031: train_loss -0.435 +2026-04-13 20:27:37.225397: val_loss -0.3732 +2026-04-13 20:27:37.227131: Pseudo dice [0.4935, 0.3348, 0.6743, 0.3056, 0.5133, 0.6982, 0.7486] +2026-04-13 20:27:37.229262: Epoch time: 101.75 s +2026-04-13 20:27:38.409240: +2026-04-13 20:27:38.411929: Epoch 2857 +2026-04-13 20:27:38.413584: Current learning rate: 0.00324 +2026-04-13 20:29:20.027598: train_loss -0.4375 +2026-04-13 20:29:20.034178: val_loss -0.3465 +2026-04-13 20:29:20.035913: Pseudo dice [0.8563, 0.8602, 0.7262, 0.3967, 0.4873, 0.4837, 0.7367] +2026-04-13 20:29:20.038706: Epoch time: 101.62 s +2026-04-13 20:29:21.231495: +2026-04-13 20:29:21.233161: Epoch 2858 +2026-04-13 20:29:21.234695: Current learning rate: 0.00324 +2026-04-13 20:31:02.923930: train_loss -0.4133 +2026-04-13 20:31:02.931253: val_loss -0.3621 +2026-04-13 20:31:02.933392: Pseudo dice [0.8575, 0.8972, 0.8733, 0.4263, 0.661, 0.1666, 0.7348] +2026-04-13 20:31:02.936136: Epoch time: 101.7 s +2026-04-13 20:31:04.145931: +2026-04-13 20:31:04.147808: Epoch 2859 +2026-04-13 20:31:04.149380: Current learning rate: 0.00323 +2026-04-13 20:32:45.638599: train_loss -0.4042 +2026-04-13 20:32:45.644222: val_loss -0.3493 +2026-04-13 20:32:45.646212: Pseudo dice [0.6536, 0.8876, 0.6948, 0.3667, 0.2754, 0.388, 0.6495] +2026-04-13 20:32:45.648428: Epoch time: 101.5 s +2026-04-13 20:32:46.920814: +2026-04-13 20:32:46.922533: Epoch 2860 +2026-04-13 20:32:46.924795: Current learning rate: 0.00323 +2026-04-13 20:34:29.127974: train_loss -0.4139 +2026-04-13 20:34:29.133909: val_loss -0.3461 +2026-04-13 20:34:29.135662: Pseudo dice [0.3706, 0.891, 0.6772, 0.2754, 0.3896, 0.3839, 0.826] +2026-04-13 20:34:29.138273: Epoch time: 102.21 s +2026-04-13 20:34:30.355408: +2026-04-13 20:34:30.357264: Epoch 2861 +2026-04-13 20:34:30.359035: Current learning rate: 0.00323 +2026-04-13 20:36:11.939637: train_loss -0.4124 +2026-04-13 20:36:11.946999: val_loss -0.3601 +2026-04-13 20:36:11.949102: Pseudo dice [0.4138, 0.886, 0.7843, 0.6032, 0.3661, 0.831, 0.6785] +2026-04-13 20:36:11.951231: Epoch time: 101.59 s +2026-04-13 20:36:13.152664: +2026-04-13 20:36:13.154254: Epoch 2862 +2026-04-13 20:36:13.155771: Current learning rate: 0.00323 +2026-04-13 20:37:55.003472: train_loss -0.3761 +2026-04-13 20:37:55.011116: val_loss -0.3025 +2026-04-13 20:37:55.013522: Pseudo dice [0.5949, 0.8726, 0.7089, 0.5429, 0.4663, 0.2857, 0.3663] +2026-04-13 20:37:55.016295: Epoch time: 101.85 s +2026-04-13 20:37:56.210527: +2026-04-13 20:37:56.212827: Epoch 2863 +2026-04-13 20:37:56.214560: Current learning rate: 0.00322 +2026-04-13 20:39:37.900532: train_loss -0.4142 +2026-04-13 20:39:37.906461: val_loss -0.3672 +2026-04-13 20:39:37.908622: Pseudo dice [0.6708, 0.666, 0.7071, 0.6821, 0.3975, 0.8482, 0.8209] +2026-04-13 20:39:37.911436: Epoch time: 101.69 s +2026-04-13 20:39:39.116729: +2026-04-13 20:39:39.119679: Epoch 2864 +2026-04-13 20:39:39.121916: Current learning rate: 0.00322 +2026-04-13 20:41:22.385606: train_loss -0.4201 +2026-04-13 20:41:22.394585: val_loss -0.3804 +2026-04-13 20:41:22.397637: Pseudo dice [0.8684, 0.8219, 0.7423, 0.4248, 0.5122, 0.5887, 0.8471] +2026-04-13 20:41:22.400478: Epoch time: 103.27 s +2026-04-13 20:41:23.605824: +2026-04-13 20:41:23.608520: Epoch 2865 +2026-04-13 20:41:23.610427: Current learning rate: 0.00322 +2026-04-13 20:43:05.940018: train_loss -0.4043 +2026-04-13 20:43:05.962242: val_loss -0.3288 +2026-04-13 20:43:05.971701: Pseudo dice [0.6808, 0.6716, 0.6178, 0.6753, 0.4876, 0.2603, 0.8486] +2026-04-13 20:43:05.974167: Epoch time: 102.34 s +2026-04-13 20:43:07.176270: +2026-04-13 20:43:07.178453: Epoch 2866 +2026-04-13 20:43:07.182869: Current learning rate: 0.00322 +2026-04-13 20:44:49.775360: train_loss -0.3939 +2026-04-13 20:44:49.781894: val_loss -0.3249 +2026-04-13 20:44:49.783551: Pseudo dice [0.8211, 0.8657, 0.6767, 0.3783, 0.3597, 0.5236, 0.7703] +2026-04-13 20:44:49.786356: Epoch time: 102.6 s +2026-04-13 20:44:50.986179: +2026-04-13 20:44:50.988686: Epoch 2867 +2026-04-13 20:44:50.990399: Current learning rate: 0.00321 +2026-04-13 20:46:33.156626: train_loss -0.4058 +2026-04-13 20:46:33.164771: val_loss -0.3577 +2026-04-13 20:46:33.167130: Pseudo dice [0.688, 0.6466, 0.7633, 0.4368, 0.1784, 0.407, 0.7413] +2026-04-13 20:46:33.170196: Epoch time: 102.17 s +2026-04-13 20:46:34.667640: +2026-04-13 20:46:34.686003: Epoch 2868 +2026-04-13 20:46:34.687818: Current learning rate: 0.00321 +2026-04-13 20:48:16.099418: train_loss -0.4239 +2026-04-13 20:48:16.105922: val_loss -0.383 +2026-04-13 20:48:16.108414: Pseudo dice [0.8098, 0.8087, 0.6155, 0.7638, 0.6016, 0.8712, 0.8516] +2026-04-13 20:48:16.111612: Epoch time: 101.44 s +2026-04-13 20:48:17.337837: +2026-04-13 20:48:17.340394: Epoch 2869 +2026-04-13 20:48:17.342352: Current learning rate: 0.00321 +2026-04-13 20:49:59.838668: train_loss -0.4156 +2026-04-13 20:49:59.845149: val_loss -0.3271 +2026-04-13 20:49:59.847712: Pseudo dice [0.7466, 0.8732, 0.687, 0.1263, 0.4141, 0.6728, 0.7373] +2026-04-13 20:49:59.850229: Epoch time: 102.5 s +2026-04-13 20:50:01.042400: +2026-04-13 20:50:01.051462: Epoch 2870 +2026-04-13 20:50:01.062516: Current learning rate: 0.00321 +2026-04-13 20:51:42.586953: train_loss -0.4184 +2026-04-13 20:51:42.593693: val_loss -0.3833 +2026-04-13 20:51:42.595923: Pseudo dice [0.7957, 0.3738, 0.7745, 0.5378, 0.4368, 0.5931, 0.8534] +2026-04-13 20:51:42.598169: Epoch time: 101.55 s +2026-04-13 20:51:43.810442: +2026-04-13 20:51:43.812141: Epoch 2871 +2026-04-13 20:51:43.813677: Current learning rate: 0.0032 +2026-04-13 20:53:25.642491: train_loss -0.4205 +2026-04-13 20:53:25.649898: val_loss -0.3693 +2026-04-13 20:53:25.651652: Pseudo dice [0.7136, 0.8644, 0.8276, 0.0662, 0.6829, 0.6475, 0.7699] +2026-04-13 20:53:25.653730: Epoch time: 101.84 s +2026-04-13 20:53:26.899351: +2026-04-13 20:53:26.900919: Epoch 2872 +2026-04-13 20:53:26.902372: Current learning rate: 0.0032 +2026-04-13 20:55:08.591451: train_loss -0.4247 +2026-04-13 20:55:08.597729: val_loss -0.3693 +2026-04-13 20:55:08.599467: Pseudo dice [0.8256, 0.8989, 0.85, 0.8051, 0.3917, 0.7307, 0.8118] +2026-04-13 20:55:08.601630: Epoch time: 101.7 s +2026-04-13 20:55:08.603567: Yayy! New best EMA pseudo Dice: 0.6422 +2026-04-13 20:55:11.616764: +2026-04-13 20:55:11.618397: Epoch 2873 +2026-04-13 20:55:11.619949: Current learning rate: 0.0032 +2026-04-13 20:56:53.323982: train_loss -0.4133 +2026-04-13 20:56:53.331883: val_loss -0.3932 +2026-04-13 20:56:53.334454: Pseudo dice [0.6278, 0.6019, 0.8136, 0.1729, 0.6335, 0.7302, 0.602] +2026-04-13 20:56:53.337554: Epoch time: 101.71 s +2026-04-13 20:56:54.666070: +2026-04-13 20:56:54.668242: Epoch 2874 +2026-04-13 20:56:54.670198: Current learning rate: 0.0032 +2026-04-13 20:58:36.555957: train_loss -0.4314 +2026-04-13 20:58:36.562627: val_loss -0.3534 +2026-04-13 20:58:36.564696: Pseudo dice [0.4819, 0.7632, 0.7858, 0.536, 0.3551, 0.6835, 0.8281] +2026-04-13 20:58:36.566974: Epoch time: 101.89 s +2026-04-13 20:58:37.794851: +2026-04-13 20:58:37.796562: Epoch 2875 +2026-04-13 20:58:37.798084: Current learning rate: 0.00319 +2026-04-13 21:00:20.205534: train_loss -0.4209 +2026-04-13 21:00:20.212464: val_loss -0.3743 +2026-04-13 21:00:20.215075: Pseudo dice [0.6393, 0.7897, 0.7805, 0.5943, 0.5662, 0.3211, 0.8696] +2026-04-13 21:00:20.217584: Epoch time: 102.41 s +2026-04-13 21:00:21.411453: +2026-04-13 21:00:21.414067: Epoch 2876 +2026-04-13 21:00:21.416544: Current learning rate: 0.00319 +2026-04-13 21:02:04.035466: train_loss -0.4385 +2026-04-13 21:02:04.044504: val_loss -0.3509 +2026-04-13 21:02:04.049676: Pseudo dice [0.2913, 0.9033, 0.7886, 0.1836, 0.3343, 0.1977, 0.6359] +2026-04-13 21:02:04.052791: Epoch time: 102.63 s +2026-04-13 21:02:05.261056: +2026-04-13 21:02:05.263586: Epoch 2877 +2026-04-13 21:02:05.267101: Current learning rate: 0.00319 +2026-04-13 21:03:47.546377: train_loss -0.4377 +2026-04-13 21:03:47.552779: val_loss -0.3434 +2026-04-13 21:03:47.554667: Pseudo dice [0.5514, 0.9261, 0.781, 0.2129, 0.5575, 0.7261, 0.7103] +2026-04-13 21:03:47.557590: Epoch time: 102.29 s +2026-04-13 21:03:48.757788: +2026-04-13 21:03:48.760459: Epoch 2878 +2026-04-13 21:03:48.762607: Current learning rate: 0.00319 +2026-04-13 21:05:30.564905: train_loss -0.4144 +2026-04-13 21:05:30.572604: val_loss -0.3448 +2026-04-13 21:05:30.574646: Pseudo dice [0.86, 0.6636, 0.3996, 0.054, 0.3974, 0.9125, 0.7518] +2026-04-13 21:05:30.577065: Epoch time: 101.81 s +2026-04-13 21:05:31.856538: +2026-04-13 21:05:31.858526: Epoch 2879 +2026-04-13 21:05:31.860254: Current learning rate: 0.00318 +2026-04-13 21:07:13.802427: train_loss -0.4211 +2026-04-13 21:07:13.809117: val_loss -0.3783 +2026-04-13 21:07:13.811347: Pseudo dice [0.1642, 0.5898, 0.8475, 0.7676, 0.4233, 0.3724, 0.8532] +2026-04-13 21:07:13.814714: Epoch time: 101.95 s +2026-04-13 21:07:15.004131: +2026-04-13 21:07:15.005585: Epoch 2880 +2026-04-13 21:07:15.006937: Current learning rate: 0.00318 +2026-04-13 21:08:56.555506: train_loss -0.4154 +2026-04-13 21:08:56.563248: val_loss -0.3194 +2026-04-13 21:08:56.565395: Pseudo dice [0.5042, 0.9253, 0.7925, 0.4369, 0.2377, 0.7081, 0.7728] +2026-04-13 21:08:56.567563: Epoch time: 101.55 s +2026-04-13 21:08:57.821182: +2026-04-13 21:08:57.823416: Epoch 2881 +2026-04-13 21:08:57.825097: Current learning rate: 0.00318 +2026-04-13 21:10:39.617650: train_loss -0.4193 +2026-04-13 21:10:39.623570: val_loss -0.3386 +2026-04-13 21:10:39.625354: Pseudo dice [0.7115, 0.6121, 0.7547, 0.6862, 0.4698, 0.6166, 0.5858] +2026-04-13 21:10:39.627496: Epoch time: 101.8 s +2026-04-13 21:10:40.819173: +2026-04-13 21:10:40.821102: Epoch 2882 +2026-04-13 21:10:40.823178: Current learning rate: 0.00317 +2026-04-13 21:12:22.214481: train_loss -0.4182 +2026-04-13 21:12:22.221482: val_loss -0.3706 +2026-04-13 21:12:22.224318: Pseudo dice [0.4694, 0.7005, 0.7968, 0.6185, 0.3221, 0.7586, 0.8355] +2026-04-13 21:12:22.226793: Epoch time: 101.4 s +2026-04-13 21:12:23.433805: +2026-04-13 21:12:23.437085: Epoch 2883 +2026-04-13 21:12:23.438821: Current learning rate: 0.00317 +2026-04-13 21:14:05.507333: train_loss -0.4322 +2026-04-13 21:14:05.513410: val_loss -0.3873 +2026-04-13 21:14:05.515530: Pseudo dice [0.3991, 0.9197, 0.7873, 0.7141, 0.556, 0.9147, 0.7025] +2026-04-13 21:14:05.518913: Epoch time: 102.08 s +2026-04-13 21:14:06.734614: +2026-04-13 21:14:06.737631: Epoch 2884 +2026-04-13 21:14:06.739778: Current learning rate: 0.00317 +2026-04-13 21:15:48.143736: train_loss -0.4375 +2026-04-13 21:15:48.149904: val_loss -0.3023 +2026-04-13 21:15:48.151946: Pseudo dice [0.3834, 0.9177, 0.7125, 0.0665, 0.3439, 0.4087, 0.4387] +2026-04-13 21:15:48.153939: Epoch time: 101.41 s +2026-04-13 21:15:49.433961: +2026-04-13 21:15:49.435580: Epoch 2885 +2026-04-13 21:15:49.437139: Current learning rate: 0.00317 +2026-04-13 21:17:31.372901: train_loss -0.4206 +2026-04-13 21:17:31.379322: val_loss -0.3534 +2026-04-13 21:17:31.383163: Pseudo dice [0.2367, 0.4939, 0.6795, 0.3016, 0.4171, 0.8915, 0.8504] +2026-04-13 21:17:31.385839: Epoch time: 101.94 s +2026-04-13 21:17:32.603077: +2026-04-13 21:17:32.605778: Epoch 2886 +2026-04-13 21:17:32.608868: Current learning rate: 0.00316 +2026-04-13 21:19:14.747865: train_loss -0.4028 +2026-04-13 21:19:14.760292: val_loss -0.3277 +2026-04-13 21:19:14.765017: Pseudo dice [0.7733, 0.652, 0.7483, 0.3189, 0.3023, 0.6098, 0.7396] +2026-04-13 21:19:14.767557: Epoch time: 102.15 s +2026-04-13 21:19:15.969545: +2026-04-13 21:19:15.971287: Epoch 2887 +2026-04-13 21:19:15.972867: Current learning rate: 0.00316 +2026-04-13 21:20:57.729638: train_loss -0.4143 +2026-04-13 21:20:57.740669: val_loss -0.3625 +2026-04-13 21:20:57.742996: Pseudo dice [0.5216, 0.7458, 0.7582, 0.3059, 0.5554, 0.8249, 0.6509] +2026-04-13 21:20:57.745650: Epoch time: 101.76 s +2026-04-13 21:20:58.953276: +2026-04-13 21:20:58.955053: Epoch 2888 +2026-04-13 21:20:58.957001: Current learning rate: 0.00316 +2026-04-13 21:22:40.625001: train_loss -0.4087 +2026-04-13 21:22:40.630936: val_loss -0.3718 +2026-04-13 21:22:40.633114: Pseudo dice [0.7129, 0.7141, 0.7659, 0.7863, 0.2198, 0.6425, 0.3983] +2026-04-13 21:22:40.636193: Epoch time: 101.67 s +2026-04-13 21:22:42.941272: +2026-04-13 21:22:42.942889: Epoch 2889 +2026-04-13 21:22:42.944508: Current learning rate: 0.00316 +2026-04-13 21:24:24.502180: train_loss -0.4031 +2026-04-13 21:24:24.508772: val_loss -0.3484 +2026-04-13 21:24:24.510833: Pseudo dice [0.509, 0.5895, 0.5718, 0.275, 0.647, 0.8513, 0.6716] +2026-04-13 21:24:24.515120: Epoch time: 101.56 s +2026-04-13 21:24:25.753613: +2026-04-13 21:24:25.755350: Epoch 2890 +2026-04-13 21:24:25.756986: Current learning rate: 0.00315 +2026-04-13 21:26:07.674196: train_loss -0.4072 +2026-04-13 21:26:07.680845: val_loss -0.3765 +2026-04-13 21:26:07.683903: Pseudo dice [0.6444, 0.7911, 0.7376, 0.1445, 0.3995, 0.7779, 0.8752] +2026-04-13 21:26:07.686892: Epoch time: 101.92 s +2026-04-13 21:26:08.909696: +2026-04-13 21:26:08.911757: Epoch 2891 +2026-04-13 21:26:08.913196: Current learning rate: 0.00315 +2026-04-13 21:27:51.479575: train_loss -0.42 +2026-04-13 21:27:51.522700: val_loss -0.3769 +2026-04-13 21:27:51.525017: Pseudo dice [0.863, 0.8568, 0.7063, 0.6968, 0.493, 0.7121, 0.7273] +2026-04-13 21:27:51.533383: Epoch time: 102.57 s +2026-04-13 21:27:52.790024: +2026-04-13 21:27:52.794969: Epoch 2892 +2026-04-13 21:27:52.797378: Current learning rate: 0.00315 +2026-04-13 21:29:34.408707: train_loss -0.3951 +2026-04-13 21:29:34.415262: val_loss -0.3502 +2026-04-13 21:29:34.417448: Pseudo dice [0.4703, 0.9188, 0.6852, 0.7653, 0.36, 0.4797, 0.8306] +2026-04-13 21:29:34.419865: Epoch time: 101.62 s +2026-04-13 21:29:35.648108: +2026-04-13 21:29:35.650089: Epoch 2893 +2026-04-13 21:29:35.651835: Current learning rate: 0.00315 +2026-04-13 21:31:17.207329: train_loss -0.4136 +2026-04-13 21:31:17.214984: val_loss -0.3753 +2026-04-13 21:31:17.216747: Pseudo dice [0.4229, 0.7237, 0.6963, 0.7947, 0.5339, 0.8572, 0.6923] +2026-04-13 21:31:17.218951: Epoch time: 101.56 s +2026-04-13 21:31:18.431163: +2026-04-13 21:31:18.459970: Epoch 2894 +2026-04-13 21:31:18.462101: Current learning rate: 0.00314 +2026-04-13 21:33:01.543729: train_loss -0.4214 +2026-04-13 21:33:01.550099: val_loss -0.3664 +2026-04-13 21:33:01.554085: Pseudo dice [0.1013, 0.9, 0.7474, 0.5012, 0.5913, 0.7534, 0.7968] +2026-04-13 21:33:01.558056: Epoch time: 103.12 s +2026-04-13 21:33:02.798217: +2026-04-13 21:33:02.800699: Epoch 2895 +2026-04-13 21:33:02.804946: Current learning rate: 0.00314 +2026-04-13 21:34:45.311384: train_loss -0.3959 +2026-04-13 21:34:45.317289: val_loss -0.3499 +2026-04-13 21:34:45.319550: Pseudo dice [0.6751, 0.6091, 0.6454, 0.63, 0.4655, 0.7973, 0.7205] +2026-04-13 21:34:45.321612: Epoch time: 102.52 s +2026-04-13 21:34:46.527178: +2026-04-13 21:34:46.529397: Epoch 2896 +2026-04-13 21:34:46.535807: Current learning rate: 0.00314 +2026-04-13 21:36:28.715652: train_loss -0.4062 +2026-04-13 21:36:28.722969: val_loss -0.3873 +2026-04-13 21:36:28.727288: Pseudo dice [0.3125, 0.5493, 0.8129, 0.2583, 0.4621, 0.7834, 0.863] +2026-04-13 21:36:28.730824: Epoch time: 102.19 s +2026-04-13 21:36:29.946112: +2026-04-13 21:36:29.949565: Epoch 2897 +2026-04-13 21:36:29.952222: Current learning rate: 0.00314 +2026-04-13 21:38:11.658530: train_loss -0.4115 +2026-04-13 21:38:11.668157: val_loss -0.3773 +2026-04-13 21:38:11.670264: Pseudo dice [0.7424, 0.6474, 0.7983, 0.4527, 0.2522, 0.8805, 0.5609] +2026-04-13 21:38:11.672594: Epoch time: 101.72 s +2026-04-13 21:38:12.885146: +2026-04-13 21:38:12.887429: Epoch 2898 +2026-04-13 21:38:12.889667: Current learning rate: 0.00313 +2026-04-13 21:39:54.538821: train_loss -0.4206 +2026-04-13 21:39:54.545033: val_loss -0.3824 +2026-04-13 21:39:54.546704: Pseudo dice [0.6076, 0.7153, 0.7995, 0.8034, 0.3841, 0.5934, 0.7038] +2026-04-13 21:39:54.549124: Epoch time: 101.66 s +2026-04-13 21:39:55.754413: +2026-04-13 21:39:55.756251: Epoch 2899 +2026-04-13 21:39:55.757787: Current learning rate: 0.00313 +2026-04-13 21:41:38.647474: train_loss -0.4253 +2026-04-13 21:41:38.653765: val_loss -0.383 +2026-04-13 21:41:38.655733: Pseudo dice [0.6659, 0.6512, 0.769, 0.7911, 0.5565, 0.9065, 0.8467] +2026-04-13 21:41:38.657931: Epoch time: 102.9 s +2026-04-13 21:41:41.725270: +2026-04-13 21:41:41.729516: Epoch 2900 +2026-04-13 21:41:41.731055: Current learning rate: 0.00313 +2026-04-13 21:43:23.796679: train_loss -0.4191 +2026-04-13 21:43:23.802641: val_loss -0.3753 +2026-04-13 21:43:23.804447: Pseudo dice [0.601, 0.5467, 0.8776, 0.5954, 0.5574, 0.7222, 0.7674] +2026-04-13 21:43:23.807019: Epoch time: 102.07 s +2026-04-13 21:43:25.007650: +2026-04-13 21:43:25.009402: Epoch 2901 +2026-04-13 21:43:25.010776: Current learning rate: 0.00313 +2026-04-13 21:45:06.973145: train_loss -0.4286 +2026-04-13 21:45:06.980925: val_loss -0.3652 +2026-04-13 21:45:06.982871: Pseudo dice [0.7277, 0.7627, 0.78, 0.3932, 0.4878, 0.2241, 0.7836] +2026-04-13 21:45:06.985868: Epoch time: 101.97 s +2026-04-13 21:45:08.200982: +2026-04-13 21:45:08.203278: Epoch 2902 +2026-04-13 21:45:08.205082: Current learning rate: 0.00312 +2026-04-13 21:46:50.189052: train_loss -0.4293 +2026-04-13 21:46:50.195599: val_loss -0.3589 +2026-04-13 21:46:50.197564: Pseudo dice [0.7409, 0.2303, 0.6263, 0.4644, 0.4205, 0.2593, 0.7747] +2026-04-13 21:46:50.200580: Epoch time: 101.99 s +2026-04-13 21:46:51.395703: +2026-04-13 21:46:51.397292: Epoch 2903 +2026-04-13 21:46:51.398884: Current learning rate: 0.00312 +2026-04-13 21:48:33.215385: train_loss -0.4298 +2026-04-13 21:48:33.243771: val_loss -0.406 +2026-04-13 21:48:33.247592: Pseudo dice [0.7275, 0.4117, 0.8322, 0.2367, 0.5764, 0.9029, 0.8291] +2026-04-13 21:48:33.250716: Epoch time: 101.82 s +2026-04-13 21:48:34.457633: +2026-04-13 21:48:34.459307: Epoch 2904 +2026-04-13 21:48:34.460822: Current learning rate: 0.00312 +2026-04-13 21:50:16.585209: train_loss -0.4285 +2026-04-13 21:50:16.593202: val_loss -0.4085 +2026-04-13 21:50:16.595101: Pseudo dice [0.7606, 0.8622, 0.7857, 0.5665, 0.4823, 0.6993, 0.7688] +2026-04-13 21:50:16.597672: Epoch time: 102.13 s +2026-04-13 21:50:17.799319: +2026-04-13 21:50:17.801239: Epoch 2905 +2026-04-13 21:50:17.802773: Current learning rate: 0.00312 +2026-04-13 21:51:59.344794: train_loss -0.4359 +2026-04-13 21:51:59.352511: val_loss -0.3729 +2026-04-13 21:51:59.354523: Pseudo dice [0.727, 0.549, 0.7953, 0.0945, 0.2189, 0.7138, 0.8128] +2026-04-13 21:51:59.357252: Epoch time: 101.55 s +2026-04-13 21:52:00.562020: +2026-04-13 21:52:00.563533: Epoch 2906 +2026-04-13 21:52:00.565001: Current learning rate: 0.00311 +2026-04-13 21:53:42.125056: train_loss -0.4331 +2026-04-13 21:53:42.132050: val_loss -0.3789 +2026-04-13 21:53:42.134425: Pseudo dice [0.7738, 0.6615, 0.7578, 0.37, 0.4766, 0.8496, 0.8468] +2026-04-13 21:53:42.136917: Epoch time: 101.57 s +2026-04-13 21:53:43.336847: +2026-04-13 21:53:43.338828: Epoch 2907 +2026-04-13 21:53:43.340289: Current learning rate: 0.00311 +2026-04-13 21:55:24.856899: train_loss -0.4188 +2026-04-13 21:55:24.869826: val_loss -0.3324 +2026-04-13 21:55:24.874394: Pseudo dice [0.726, 0.8957, 0.7086, 0.7218, 0.4052, 0.4919, 0.6972] +2026-04-13 21:55:24.879076: Epoch time: 101.52 s +2026-04-13 21:55:26.120608: +2026-04-13 21:55:26.123203: Epoch 2908 +2026-04-13 21:55:26.133624: Current learning rate: 0.00311 +2026-04-13 21:57:08.993743: train_loss -0.4326 +2026-04-13 21:57:09.000692: val_loss -0.3792 +2026-04-13 21:57:09.003307: Pseudo dice [0.5944, 0.5201, 0.7652, 0.5366, 0.2242, 0.8923, 0.7737] +2026-04-13 21:57:09.006543: Epoch time: 102.88 s +2026-04-13 21:57:10.291234: +2026-04-13 21:57:10.293935: Epoch 2909 +2026-04-13 21:57:10.296149: Current learning rate: 0.00311 +2026-04-13 21:58:52.121537: train_loss -0.4304 +2026-04-13 21:58:52.128614: val_loss -0.376 +2026-04-13 21:58:52.130820: Pseudo dice [0.618, 0.6956, 0.7195, 0.5527, 0.4364, 0.8375, 0.8335] +2026-04-13 21:58:52.133306: Epoch time: 101.83 s +2026-04-13 21:58:53.363032: +2026-04-13 21:58:53.364717: Epoch 2910 +2026-04-13 21:58:53.366901: Current learning rate: 0.0031 +2026-04-13 22:00:35.430157: train_loss -0.4223 +2026-04-13 22:00:35.436471: val_loss -0.3618 +2026-04-13 22:00:35.438563: Pseudo dice [0.4664, 0.6602, 0.8233, 0.3823, 0.4558, 0.899, 0.5644] +2026-04-13 22:00:35.440664: Epoch time: 102.07 s +2026-04-13 22:00:36.631305: +2026-04-13 22:00:36.632887: Epoch 2911 +2026-04-13 22:00:36.634375: Current learning rate: 0.0031 +2026-04-13 22:02:19.253441: train_loss -0.423 +2026-04-13 22:02:19.259809: val_loss -0.3319 +2026-04-13 22:02:19.264166: Pseudo dice [0.2855, 0.8808, 0.8097, 0.2985, 0.2801, 0.7986, 0.5994] +2026-04-13 22:02:19.266912: Epoch time: 102.63 s +2026-04-13 22:02:20.484227: +2026-04-13 22:02:20.486394: Epoch 2912 +2026-04-13 22:02:20.488534: Current learning rate: 0.0031 +2026-04-13 22:04:02.112778: train_loss -0.4185 +2026-04-13 22:04:02.120762: val_loss -0.3697 +2026-04-13 22:04:02.124399: Pseudo dice [0.4528, 0.4963, 0.7408, 0.6466, 0.4633, 0.7519, 0.6602] +2026-04-13 22:04:02.126729: Epoch time: 101.63 s +2026-04-13 22:04:03.330115: +2026-04-13 22:04:03.332043: Epoch 2913 +2026-04-13 22:04:03.334055: Current learning rate: 0.0031 +2026-04-13 22:05:45.201433: train_loss -0.4169 +2026-04-13 22:05:45.207313: val_loss -0.346 +2026-04-13 22:05:45.209033: Pseudo dice [0.7261, 0.5908, 0.6504, 0.5132, 0.5009, 0.6317, 0.781] +2026-04-13 22:05:45.211465: Epoch time: 101.87 s +2026-04-13 22:05:46.444622: +2026-04-13 22:05:46.447028: Epoch 2914 +2026-04-13 22:05:46.449456: Current learning rate: 0.00309 +2026-04-13 22:07:28.270970: train_loss -0.4239 +2026-04-13 22:07:28.277763: val_loss -0.3818 +2026-04-13 22:07:28.279908: Pseudo dice [0.644, 0.6209, 0.8004, 0.5336, 0.5151, 0.8959, 0.7985] +2026-04-13 22:07:28.282284: Epoch time: 101.83 s +2026-04-13 22:07:29.523962: +2026-04-13 22:07:29.526024: Epoch 2915 +2026-04-13 22:07:29.527602: Current learning rate: 0.00309 +2026-04-13 22:09:10.873987: train_loss -0.4155 +2026-04-13 22:09:10.880629: val_loss -0.3809 +2026-04-13 22:09:10.883334: Pseudo dice [0.1923, 0.4874, 0.7333, 0.7419, 0.3033, 0.8497, 0.7404] +2026-04-13 22:09:10.885467: Epoch time: 101.35 s +2026-04-13 22:09:12.118582: +2026-04-13 22:09:12.120797: Epoch 2916 +2026-04-13 22:09:12.122697: Current learning rate: 0.00309 +2026-04-13 22:10:53.919931: train_loss -0.4248 +2026-04-13 22:10:53.928687: val_loss -0.3912 +2026-04-13 22:10:53.931287: Pseudo dice [0.6966, 0.8475, 0.8154, 0.6677, 0.6479, 0.7072, 0.775] +2026-04-13 22:10:53.934991: Epoch time: 101.8 s +2026-04-13 22:10:55.211854: +2026-04-13 22:10:55.214100: Epoch 2917 +2026-04-13 22:10:55.215611: Current learning rate: 0.00309 +2026-04-13 22:12:37.113793: train_loss -0.4035 +2026-04-13 22:12:37.119894: val_loss -0.3423 +2026-04-13 22:12:37.122093: Pseudo dice [0.2508, 0.8435, 0.8139, 0.4861, 0.526, 0.4249, 0.3276] +2026-04-13 22:12:37.124377: Epoch time: 101.91 s +2026-04-13 22:12:38.345839: +2026-04-13 22:12:38.348109: Epoch 2918 +2026-04-13 22:12:38.349627: Current learning rate: 0.00308 +2026-04-13 22:14:20.068880: train_loss -0.4111 +2026-04-13 22:14:20.074610: val_loss -0.379 +2026-04-13 22:14:20.076805: Pseudo dice [0.7317, 0.5628, 0.8123, 0.4465, 0.5613, 0.7979, 0.7672] +2026-04-13 22:14:20.080605: Epoch time: 101.73 s +2026-04-13 22:14:21.291598: +2026-04-13 22:14:21.293442: Epoch 2919 +2026-04-13 22:14:21.294914: Current learning rate: 0.00308 +2026-04-13 22:16:03.505075: train_loss -0.4122 +2026-04-13 22:16:03.512767: val_loss -0.3374 +2026-04-13 22:16:03.514802: Pseudo dice [0.6843, 0.5586, 0.7981, 0.3858, 0.3715, 0.7235, 0.6468] +2026-04-13 22:16:03.519035: Epoch time: 102.22 s +2026-04-13 22:16:04.965722: +2026-04-13 22:16:04.967690: Epoch 2920 +2026-04-13 22:16:04.969860: Current learning rate: 0.00308 +2026-04-13 22:17:47.139834: train_loss -0.4098 +2026-04-13 22:17:47.146935: val_loss -0.3671 +2026-04-13 22:17:47.149265: Pseudo dice [0.3095, 0.6566, 0.697, 0.8066, 0.4173, 0.9021, 0.8536] +2026-04-13 22:17:47.151908: Epoch time: 102.18 s +2026-04-13 22:17:48.514403: +2026-04-13 22:17:48.516061: Epoch 2921 +2026-04-13 22:17:48.517832: Current learning rate: 0.00308 +2026-04-13 22:19:30.643903: train_loss -0.422 +2026-04-13 22:19:30.651699: val_loss -0.3666 +2026-04-13 22:19:30.653830: Pseudo dice [0.3454, 0.6731, 0.8093, 0.0862, 0.5306, 0.7248, 0.869] +2026-04-13 22:19:30.656272: Epoch time: 102.13 s +2026-04-13 22:19:31.899593: +2026-04-13 22:19:31.901931: Epoch 2922 +2026-04-13 22:19:31.904029: Current learning rate: 0.00307 +2026-04-13 22:21:13.989317: train_loss -0.4308 +2026-04-13 22:21:13.996018: val_loss -0.3705 +2026-04-13 22:21:13.997959: Pseudo dice [0.3656, 0.9029, 0.7463, 0.468, 0.5007, 0.6786, 0.8372] +2026-04-13 22:21:14.000324: Epoch time: 102.09 s +2026-04-13 22:21:15.205687: +2026-04-13 22:21:15.207676: Epoch 2923 +2026-04-13 22:21:15.209612: Current learning rate: 0.00307 +2026-04-13 22:22:57.428197: train_loss -0.433 +2026-04-13 22:22:57.434752: val_loss -0.3637 +2026-04-13 22:22:57.436838: Pseudo dice [0.6079, 0.7936, 0.7987, 0.3616, 0.4799, 0.4676, 0.8042] +2026-04-13 22:22:57.439676: Epoch time: 102.23 s +2026-04-13 22:22:58.638755: +2026-04-13 22:22:58.640637: Epoch 2924 +2026-04-13 22:22:58.642098: Current learning rate: 0.00307 +2026-04-13 22:24:41.258085: train_loss -0.4205 +2026-04-13 22:24:41.264759: val_loss -0.3613 +2026-04-13 22:24:41.266533: Pseudo dice [0.741, 0.8719, 0.767, 0.1864, 0.2492, 0.5676, 0.7331] +2026-04-13 22:24:41.269489: Epoch time: 102.62 s +2026-04-13 22:24:42.472846: +2026-04-13 22:24:42.474437: Epoch 2925 +2026-04-13 22:24:42.475773: Current learning rate: 0.00306 +2026-04-13 22:26:24.561289: train_loss -0.4269 +2026-04-13 22:26:24.567748: val_loss -0.3645 +2026-04-13 22:26:24.569958: Pseudo dice [0.8224, 0.2354, 0.82, 0.3602, 0.4491, 0.5317, 0.7851] +2026-04-13 22:26:24.572811: Epoch time: 102.09 s +2026-04-13 22:26:25.770097: +2026-04-13 22:26:25.771813: Epoch 2926 +2026-04-13 22:26:25.773612: Current learning rate: 0.00306 +2026-04-13 22:28:07.939842: train_loss -0.4109 +2026-04-13 22:28:07.948701: val_loss -0.3075 +2026-04-13 22:28:07.951426: Pseudo dice [0.3634, 0.7969, 0.5357, 0.1655, 0.4679, 0.2606, 0.7553] +2026-04-13 22:28:07.953859: Epoch time: 102.17 s +2026-04-13 22:28:09.170219: +2026-04-13 22:28:09.172351: Epoch 2927 +2026-04-13 22:28:09.174179: Current learning rate: 0.00306 +2026-04-13 22:29:51.160776: train_loss -0.4019 +2026-04-13 22:29:51.168268: val_loss -0.3668 +2026-04-13 22:29:51.170974: Pseudo dice [0.2854, 0.6629, 0.723, 0.6504, 0.4874, 0.58, 0.8716] +2026-04-13 22:29:51.174379: Epoch time: 101.99 s +2026-04-13 22:29:53.490536: +2026-04-13 22:29:53.492146: Epoch 2928 +2026-04-13 22:29:53.493628: Current learning rate: 0.00306 +2026-04-13 22:31:35.942965: train_loss -0.3998 +2026-04-13 22:31:35.950161: val_loss -0.3834 +2026-04-13 22:31:35.953925: Pseudo dice [0.8149, 0.5945, 0.6816, 0.3845, 0.3771, 0.8686, 0.7836] +2026-04-13 22:31:35.956273: Epoch time: 102.46 s +2026-04-13 22:31:37.172339: +2026-04-13 22:31:37.174863: Epoch 2929 +2026-04-13 22:31:37.176245: Current learning rate: 0.00305 +2026-04-13 22:33:19.051790: train_loss -0.3984 +2026-04-13 22:33:19.058727: val_loss -0.3129 +2026-04-13 22:33:19.061200: Pseudo dice [0.462, 0.8538, 0.7271, 0.2753, 0.3191, 0.6775, 0.5548] +2026-04-13 22:33:19.063332: Epoch time: 101.88 s +2026-04-13 22:33:20.266388: +2026-04-13 22:33:20.268043: Epoch 2930 +2026-04-13 22:33:20.269916: Current learning rate: 0.00305 +2026-04-13 22:35:03.430669: train_loss -0.4014 +2026-04-13 22:35:03.436662: val_loss -0.3754 +2026-04-13 22:35:03.454650: Pseudo dice [0.7907, 0.8811, 0.7833, 0.4424, 0.2073, 0.2711, 0.809] +2026-04-13 22:35:03.457127: Epoch time: 103.17 s +2026-04-13 22:35:04.640930: +2026-04-13 22:35:04.644496: Epoch 2931 +2026-04-13 22:35:04.646480: Current learning rate: 0.00305 +2026-04-13 22:36:46.817791: train_loss -0.4217 +2026-04-13 22:36:46.823597: val_loss -0.3782 +2026-04-13 22:36:46.825054: Pseudo dice [0.7675, 0.848, 0.7561, 0.441, 0.4485, 0.7844, 0.689] +2026-04-13 22:36:46.827296: Epoch time: 102.18 s +2026-04-13 22:36:48.012914: +2026-04-13 22:36:48.014511: Epoch 2932 +2026-04-13 22:36:48.015982: Current learning rate: 0.00305 +2026-04-13 22:38:29.900103: train_loss -0.4059 +2026-04-13 22:38:29.908268: val_loss -0.3562 +2026-04-13 22:38:29.910288: Pseudo dice [0.8666, 0.6822, 0.76, 0.186, 0.4487, 0.2074, 0.8117] +2026-04-13 22:38:29.912220: Epoch time: 101.89 s +2026-04-13 22:38:31.119122: +2026-04-13 22:38:31.121483: Epoch 2933 +2026-04-13 22:38:31.123499: Current learning rate: 0.00304 +2026-04-13 22:40:13.238408: train_loss -0.396 +2026-04-13 22:40:13.244978: val_loss -0.4011 +2026-04-13 22:40:13.246995: Pseudo dice [0.6397, 0.6774, 0.8286, 0.612, 0.6122, 0.9057, 0.7844] +2026-04-13 22:40:13.249281: Epoch time: 102.12 s +2026-04-13 22:40:14.484228: +2026-04-13 22:40:14.487958: Epoch 2934 +2026-04-13 22:40:14.489836: Current learning rate: 0.00304 +2026-04-13 22:41:56.876880: train_loss -0.4084 +2026-04-13 22:41:56.884282: val_loss -0.3662 +2026-04-13 22:41:56.886167: Pseudo dice [0.6985, 0.7225, 0.7392, 0.5411, 0.574, 0.9084, 0.7233] +2026-04-13 22:41:56.888279: Epoch time: 102.4 s +2026-04-13 22:41:58.091792: +2026-04-13 22:41:58.093715: Epoch 2935 +2026-04-13 22:41:58.096306: Current learning rate: 0.00304 +2026-04-13 22:43:39.830551: train_loss -0.4099 +2026-04-13 22:43:39.836998: val_loss -0.3732 +2026-04-13 22:43:39.839063: Pseudo dice [0.6336, 0.8193, 0.7356, 0.7296, 0.5957, 0.9153, 0.6537] +2026-04-13 22:43:39.841739: Epoch time: 101.74 s +2026-04-13 22:43:41.059119: +2026-04-13 22:43:41.062030: Epoch 2936 +2026-04-13 22:43:41.063845: Current learning rate: 0.00304 +2026-04-13 22:45:23.104234: train_loss -0.421 +2026-04-13 22:45:23.110852: val_loss -0.3377 +2026-04-13 22:45:23.112726: Pseudo dice [0.7313, 0.7068, 0.7209, 0.3892, 0.3689, 0.8744, 0.7462] +2026-04-13 22:45:23.115128: Epoch time: 102.05 s +2026-04-13 22:45:24.331195: +2026-04-13 22:45:24.333343: Epoch 2937 +2026-04-13 22:45:24.335042: Current learning rate: 0.00303 +2026-04-13 22:47:06.673328: train_loss -0.4207 +2026-04-13 22:47:06.679160: val_loss -0.3809 +2026-04-13 22:47:06.681413: Pseudo dice [0.7416, 0.7297, 0.828, 0.5035, 0.4627, 0.5791, 0.7805] +2026-04-13 22:47:06.684938: Epoch time: 102.35 s +2026-04-13 22:47:07.909444: +2026-04-13 22:47:07.911638: Epoch 2938 +2026-04-13 22:47:07.913367: Current learning rate: 0.00303 +2026-04-13 22:48:50.719883: train_loss -0.4265 +2026-04-13 22:48:50.730169: val_loss -0.3261 +2026-04-13 22:48:50.733810: Pseudo dice [0.3277, 0.866, 0.7135, 0.424, 0.3012, 0.6186, 0.8057] +2026-04-13 22:48:50.736715: Epoch time: 102.81 s +2026-04-13 22:48:51.950622: +2026-04-13 22:48:51.952829: Epoch 2939 +2026-04-13 22:48:51.954739: Current learning rate: 0.00303 +2026-04-13 22:50:34.776540: train_loss -0.4261 +2026-04-13 22:50:34.782845: val_loss -0.3896 +2026-04-13 22:50:34.784657: Pseudo dice [0.6261, 0.4945, 0.8369, 0.6817, 0.547, 0.923, 0.7692] +2026-04-13 22:50:34.786696: Epoch time: 102.83 s +2026-04-13 22:50:35.991277: +2026-04-13 22:50:35.995180: Epoch 2940 +2026-04-13 22:50:35.997439: Current learning rate: 0.00303 +2026-04-13 22:52:18.594818: train_loss -0.4183 +2026-04-13 22:52:18.601099: val_loss -0.3577 +2026-04-13 22:52:18.602892: Pseudo dice [0.4572, 0.8694, 0.801, 0.4263, 0.3007, 0.7321, 0.7828] +2026-04-13 22:52:18.605200: Epoch time: 102.61 s +2026-04-13 22:52:19.816801: +2026-04-13 22:52:19.818483: Epoch 2941 +2026-04-13 22:52:19.819998: Current learning rate: 0.00302 +2026-04-13 22:54:01.932460: train_loss -0.4117 +2026-04-13 22:54:01.940004: val_loss -0.3652 +2026-04-13 22:54:01.957157: Pseudo dice [0.7872, 0.6076, 0.7477, 0.5641, 0.3999, 0.3717, 0.8503] +2026-04-13 22:54:01.961675: Epoch time: 102.12 s +2026-04-13 22:54:03.157293: +2026-04-13 22:54:03.159447: Epoch 2942 +2026-04-13 22:54:03.161769: Current learning rate: 0.00302 +2026-04-13 22:55:45.702200: train_loss -0.4087 +2026-04-13 22:55:45.707860: val_loss -0.3471 +2026-04-13 22:55:45.713022: Pseudo dice [0.5776, 0.8257, 0.7755, 0.5655, 0.3082, 0.753, 0.7648] +2026-04-13 22:55:45.716198: Epoch time: 102.55 s +2026-04-13 22:55:46.943035: +2026-04-13 22:55:46.945100: Epoch 2943 +2026-04-13 22:55:46.947804: Current learning rate: 0.00302 +2026-04-13 22:57:29.400989: train_loss -0.4203 +2026-04-13 22:57:29.407166: val_loss -0.3591 +2026-04-13 22:57:29.409823: Pseudo dice [0.4801, 0.6913, 0.7763, 0.2127, 0.548, 0.93, 0.5365] +2026-04-13 22:57:29.412143: Epoch time: 102.46 s +2026-04-13 22:57:30.618828: +2026-04-13 22:57:30.621658: Epoch 2944 +2026-04-13 22:57:30.624641: Current learning rate: 0.00302 +2026-04-13 22:59:12.799246: train_loss -0.4282 +2026-04-13 22:59:12.806325: val_loss -0.3493 +2026-04-13 22:59:12.810172: Pseudo dice [0.8489, 0.8908, 0.7205, 0.2769, 0.6707, 0.7989, 0.8433] +2026-04-13 22:59:12.813400: Epoch time: 102.18 s +2026-04-13 22:59:12.816638: Yayy! New best EMA pseudo Dice: 0.6422 +2026-04-13 22:59:16.077592: +2026-04-13 22:59:16.081053: Epoch 2945 +2026-04-13 22:59:16.083387: Current learning rate: 0.00301 +2026-04-13 23:00:58.193029: train_loss -0.4211 +2026-04-13 23:00:58.204117: val_loss -0.3949 +2026-04-13 23:00:58.205841: Pseudo dice [0.4846, 0.7179, 0.7185, 0.415, 0.4835, 0.9285, 0.799] +2026-04-13 23:00:58.212925: Epoch time: 102.12 s +2026-04-13 23:00:58.215724: Yayy! New best EMA pseudo Dice: 0.643 +2026-04-13 23:01:00.936328: +2026-04-13 23:01:00.938330: Epoch 2946 +2026-04-13 23:01:00.939712: Current learning rate: 0.00301 +2026-04-13 23:02:43.190651: train_loss -0.4214 +2026-04-13 23:02:43.196798: val_loss -0.3712 +2026-04-13 23:02:43.199059: Pseudo dice [0.5936, 0.9174, 0.7696, 0.3576, 0.4095, 0.5979, 0.7253] +2026-04-13 23:02:43.201428: Epoch time: 102.26 s +2026-04-13 23:02:44.425810: +2026-04-13 23:02:44.428053: Epoch 2947 +2026-04-13 23:02:44.429586: Current learning rate: 0.00301 +2026-04-13 23:04:27.481913: train_loss -0.4224 +2026-04-13 23:04:27.487711: val_loss -0.3588 +2026-04-13 23:04:27.489511: Pseudo dice [0.7438, 0.4517, 0.8096, 0.3343, 0.3527, 0.9109, 0.6518] +2026-04-13 23:04:27.492037: Epoch time: 103.06 s +2026-04-13 23:04:28.731323: +2026-04-13 23:04:28.734546: Epoch 2948 +2026-04-13 23:04:28.736570: Current learning rate: 0.00301 +2026-04-13 23:06:10.981714: train_loss -0.4204 +2026-04-13 23:06:10.989177: val_loss -0.3155 +2026-04-13 23:06:10.991283: Pseudo dice [0.69, 0.1473, 0.4818, 0.631, 0.6334, 0.6776, 0.718] +2026-04-13 23:06:10.993790: Epoch time: 102.25 s +2026-04-13 23:06:12.208388: +2026-04-13 23:06:12.210599: Epoch 2949 +2026-04-13 23:06:12.212252: Current learning rate: 0.003 +2026-04-13 23:07:54.212774: train_loss -0.4304 +2026-04-13 23:07:54.219782: val_loss -0.363 +2026-04-13 23:07:54.221992: Pseudo dice [0.702, 0.4714, 0.8518, 0.5518, 0.5424, 0.6495, 0.5727] +2026-04-13 23:07:54.225612: Epoch time: 102.01 s +2026-04-13 23:07:57.256528: +2026-04-13 23:07:57.258713: Epoch 2950 +2026-04-13 23:07:57.260316: Current learning rate: 0.003 +2026-04-13 23:09:39.248936: train_loss -0.4297 +2026-04-13 23:09:39.258019: val_loss -0.3892 +2026-04-13 23:09:39.260482: Pseudo dice [0.5933, 0.4634, 0.7931, 0.6182, 0.5694, 0.9277, 0.7486] +2026-04-13 23:09:39.263131: Epoch time: 102.0 s +2026-04-13 23:09:40.490666: +2026-04-13 23:09:40.492686: Epoch 2951 +2026-04-13 23:09:40.494985: Current learning rate: 0.003 +2026-04-13 23:11:22.546724: train_loss -0.4214 +2026-04-13 23:11:22.553689: val_loss -0.3351 +2026-04-13 23:11:22.555838: Pseudo dice [0.6102, 0.8822, 0.7293, 0.31, 0.6202, 0.4093, 0.8562] +2026-04-13 23:11:22.558745: Epoch time: 102.06 s +2026-04-13 23:11:23.746802: +2026-04-13 23:11:23.748884: Epoch 2952 +2026-04-13 23:11:23.751008: Current learning rate: 0.003 +2026-04-13 23:13:06.268776: train_loss -0.411 +2026-04-13 23:13:06.274862: val_loss -0.3301 +2026-04-13 23:13:06.276901: Pseudo dice [0.3906, 0.8561, 0.6851, 0.0922, 0.4651, 0.1637, 0.6739] +2026-04-13 23:13:06.279308: Epoch time: 102.53 s +2026-04-13 23:13:07.509454: +2026-04-13 23:13:07.513005: Epoch 2953 +2026-04-13 23:13:07.515996: Current learning rate: 0.00299 +2026-04-13 23:14:49.530934: train_loss -0.4285 +2026-04-13 23:14:49.537893: val_loss -0.3666 +2026-04-13 23:14:49.539726: Pseudo dice [0.7408, 0.6301, 0.7628, 0.6386, 0.5492, 0.6924, 0.8159] +2026-04-13 23:14:49.542565: Epoch time: 102.02 s +2026-04-13 23:14:50.744631: +2026-04-13 23:14:50.746163: Epoch 2954 +2026-04-13 23:14:50.747694: Current learning rate: 0.00299 +2026-04-13 23:16:33.064393: train_loss -0.4045 +2026-04-13 23:16:33.070043: val_loss -0.3008 +2026-04-13 23:16:33.072973: Pseudo dice [0.4683, 0.8321, 0.5069, 0.1174, 0.4734, 0.5616, 0.8663] +2026-04-13 23:16:33.075532: Epoch time: 102.32 s +2026-04-13 23:16:34.293465: +2026-04-13 23:16:34.299343: Epoch 2955 +2026-04-13 23:16:34.302627: Current learning rate: 0.00299 +2026-04-13 23:18:16.625834: train_loss -0.4191 +2026-04-13 23:18:16.632571: val_loss -0.391 +2026-04-13 23:18:16.635022: Pseudo dice [0.7838, 0.8419, 0.8293, 0.5064, 0.6176, 0.4949, 0.622] +2026-04-13 23:18:16.637385: Epoch time: 102.34 s +2026-04-13 23:18:17.852492: +2026-04-13 23:18:17.856502: Epoch 2956 +2026-04-13 23:18:17.858804: Current learning rate: 0.00299 +2026-04-13 23:20:00.783450: train_loss -0.4195 +2026-04-13 23:20:00.789562: val_loss -0.3789 +2026-04-13 23:20:00.791742: Pseudo dice [0.7205, 0.8296, 0.8887, 0.114, 0.5005, 0.2014, 0.4731] +2026-04-13 23:20:00.793941: Epoch time: 102.93 s +2026-04-13 23:20:01.993282: +2026-04-13 23:20:01.995035: Epoch 2957 +2026-04-13 23:20:01.996524: Current learning rate: 0.00298 +2026-04-13 23:21:43.971385: train_loss -0.4299 +2026-04-13 23:21:43.977897: val_loss -0.3639 +2026-04-13 23:21:43.980546: Pseudo dice [0.7734, 0.2264, 0.6487, 0.1008, 0.6508, 0.537, 0.8604] +2026-04-13 23:21:43.983925: Epoch time: 101.98 s +2026-04-13 23:21:45.196163: +2026-04-13 23:21:45.198159: Epoch 2958 +2026-04-13 23:21:45.200099: Current learning rate: 0.00298 +2026-04-13 23:23:27.665911: train_loss -0.4336 +2026-04-13 23:23:27.672108: val_loss -0.3804 +2026-04-13 23:23:27.673974: Pseudo dice [0.5842, 0.8783, 0.7927, 0.4752, 0.5132, 0.842, 0.6556] +2026-04-13 23:23:27.676507: Epoch time: 102.47 s +2026-04-13 23:23:28.922795: +2026-04-13 23:23:28.924974: Epoch 2959 +2026-04-13 23:23:28.927060: Current learning rate: 0.00298 +2026-04-13 23:25:11.737580: train_loss -0.429 +2026-04-13 23:25:11.748918: val_loss -0.313 +2026-04-13 23:25:11.750997: Pseudo dice [0.4946, 0.1714, 0.7172, 0.2619, 0.4764, 0.479, 0.681] +2026-04-13 23:25:11.753352: Epoch time: 102.82 s +2026-04-13 23:25:12.962978: +2026-04-13 23:25:12.966250: Epoch 2960 +2026-04-13 23:25:12.968243: Current learning rate: 0.00297 +2026-04-13 23:26:55.300941: train_loss -0.4296 +2026-04-13 23:26:55.308334: val_loss -0.371 +2026-04-13 23:26:55.311455: Pseudo dice [0.6032, 0.6964, 0.8147, 0.1926, 0.3407, 0.8996, 0.7266] +2026-04-13 23:26:55.313792: Epoch time: 102.34 s +2026-04-13 23:26:56.520154: +2026-04-13 23:26:56.522001: Epoch 2961 +2026-04-13 23:26:56.523611: Current learning rate: 0.00297 +2026-04-13 23:28:38.533785: train_loss -0.4331 +2026-04-13 23:28:38.540492: val_loss -0.3991 +2026-04-13 23:28:38.542965: Pseudo dice [0.4419, 0.5372, 0.7891, 0.7744, 0.6491, 0.7178, 0.5344] +2026-04-13 23:28:38.545216: Epoch time: 102.02 s +2026-04-13 23:28:39.743274: +2026-04-13 23:28:39.745280: Epoch 2962 +2026-04-13 23:28:39.747515: Current learning rate: 0.00297 +2026-04-13 23:30:22.525972: train_loss -0.4417 +2026-04-13 23:30:22.531840: val_loss -0.3563 +2026-04-13 23:30:22.534400: Pseudo dice [0.4751, 0.4558, 0.6988, 0.2938, 0.5148, 0.9207, 0.7265] +2026-04-13 23:30:22.537408: Epoch time: 102.79 s +2026-04-13 23:30:23.757781: +2026-04-13 23:30:23.759975: Epoch 2963 +2026-04-13 23:30:23.761539: Current learning rate: 0.00297 +2026-04-13 23:32:06.145298: train_loss -0.4274 +2026-04-13 23:32:06.151731: val_loss -0.3806 +2026-04-13 23:32:06.153852: Pseudo dice [0.6439, 0.7309, 0.7917, 0.4756, 0.5578, 0.8586, 0.8258] +2026-04-13 23:32:06.156072: Epoch time: 102.39 s +2026-04-13 23:32:07.347744: +2026-04-13 23:32:07.350254: Epoch 2964 +2026-04-13 23:32:07.352351: Current learning rate: 0.00296 +2026-04-13 23:33:49.350922: train_loss -0.4352 +2026-04-13 23:33:49.357544: val_loss -0.3876 +2026-04-13 23:33:49.359917: Pseudo dice [0.7707, 0.7895, 0.8044, 0.4519, 0.5616, 0.9164, 0.5914] +2026-04-13 23:33:49.362438: Epoch time: 102.01 s +2026-04-13 23:33:50.563852: +2026-04-13 23:33:50.565384: Epoch 2965 +2026-04-13 23:33:50.566950: Current learning rate: 0.00296 +2026-04-13 23:35:33.047704: train_loss -0.4365 +2026-04-13 23:35:33.059129: val_loss -0.3792 +2026-04-13 23:35:33.061571: Pseudo dice [0.7145, 0.7973, 0.7255, 0.742, 0.2154, 0.8478, 0.3666] +2026-04-13 23:35:33.063723: Epoch time: 102.49 s +2026-04-13 23:35:34.254708: +2026-04-13 23:35:34.256725: Epoch 2966 +2026-04-13 23:35:34.258414: Current learning rate: 0.00296 +2026-04-13 23:37:16.656023: train_loss -0.4271 +2026-04-13 23:37:16.662652: val_loss -0.3439 +2026-04-13 23:37:16.664791: Pseudo dice [0.2266, 0.4198, 0.7463, 0.463, 0.5049, 0.1734, 0.634] +2026-04-13 23:37:16.667610: Epoch time: 102.4 s +2026-04-13 23:37:18.944468: +2026-04-13 23:37:18.946671: Epoch 2967 +2026-04-13 23:37:18.948191: Current learning rate: 0.00296 +2026-04-13 23:39:01.035599: train_loss -0.4243 +2026-04-13 23:39:01.041950: val_loss -0.3794 +2026-04-13 23:39:01.043898: Pseudo dice [0.6305, 0.8459, 0.8211, 0.3922, 0.4038, 0.7504, 0.7981] +2026-04-13 23:39:01.046140: Epoch time: 102.09 s +2026-04-13 23:39:02.284699: +2026-04-13 23:39:02.288128: Epoch 2968 +2026-04-13 23:39:02.291854: Current learning rate: 0.00295 +2026-04-13 23:40:44.537987: train_loss -0.4254 +2026-04-13 23:40:44.544327: val_loss -0.3446 +2026-04-13 23:40:44.546787: Pseudo dice [0.6099, 0.6873, 0.7595, 0.1284, 0.4445, 0.7688, 0.6699] +2026-04-13 23:40:44.549166: Epoch time: 102.26 s +2026-04-13 23:40:45.753439: +2026-04-13 23:40:45.755352: Epoch 2969 +2026-04-13 23:40:45.757517: Current learning rate: 0.00295 +2026-04-13 23:42:27.971708: train_loss -0.425 +2026-04-13 23:42:27.978674: val_loss -0.3601 +2026-04-13 23:42:27.981350: Pseudo dice [0.7564, 0.8195, 0.7098, 0.2929, 0.5107, 0.7757, 0.6279] +2026-04-13 23:42:27.984685: Epoch time: 102.22 s +2026-04-13 23:42:29.201934: +2026-04-13 23:42:29.203647: Epoch 2970 +2026-04-13 23:42:29.205114: Current learning rate: 0.00295 +2026-04-13 23:44:11.202106: train_loss -0.4338 +2026-04-13 23:44:11.209168: val_loss -0.4055 +2026-04-13 23:44:11.211270: Pseudo dice [0.6935, 0.5306, 0.7937, 0.6847, 0.6172, 0.5705, 0.8555] +2026-04-13 23:44:11.213480: Epoch time: 102.0 s +2026-04-13 23:44:12.432637: +2026-04-13 23:44:12.434859: Epoch 2971 +2026-04-13 23:44:12.436869: Current learning rate: 0.00295 +2026-04-13 23:45:55.291565: train_loss -0.4278 +2026-04-13 23:45:55.299337: val_loss -0.3627 +2026-04-13 23:45:55.301524: Pseudo dice [0.6105, 0.8773, 0.799, 0.277, 0.3167, 0.6948, 0.8421] +2026-04-13 23:45:55.303769: Epoch time: 102.86 s +2026-04-13 23:45:56.515369: +2026-04-13 23:45:56.517293: Epoch 2972 +2026-04-13 23:45:56.519091: Current learning rate: 0.00294 +2026-04-13 23:47:39.223848: train_loss -0.4187 +2026-04-13 23:47:39.235491: val_loss -0.3725 +2026-04-13 23:47:39.246970: Pseudo dice [0.6724, 0.6537, 0.7395, 0.6029, 0.4246, 0.7305, 0.7736] +2026-04-13 23:47:39.249596: Epoch time: 102.71 s +2026-04-13 23:47:40.468140: +2026-04-13 23:47:40.470682: Epoch 2973 +2026-04-13 23:47:40.472526: Current learning rate: 0.00294 +2026-04-13 23:49:22.734902: train_loss -0.4184 +2026-04-13 23:49:22.744876: val_loss -0.3483 +2026-04-13 23:49:22.748693: Pseudo dice [0.631, 0.8588, 0.7517, 0.4385, 0.5524, 0.7106, 0.519] +2026-04-13 23:49:22.753748: Epoch time: 102.27 s +2026-04-13 23:49:23.981831: +2026-04-13 23:49:23.985221: Epoch 2974 +2026-04-13 23:49:23.988406: Current learning rate: 0.00294 +2026-04-13 23:51:06.212830: train_loss -0.4324 +2026-04-13 23:51:06.219615: val_loss -0.3648 +2026-04-13 23:51:06.222802: Pseudo dice [0.241, 0.5288, 0.7795, 0.6048, 0.4784, 0.8997, 0.6459] +2026-04-13 23:51:06.225837: Epoch time: 102.23 s +2026-04-13 23:51:07.429912: +2026-04-13 23:51:07.431891: Epoch 2975 +2026-04-13 23:51:07.433530: Current learning rate: 0.00294 +2026-04-13 23:52:50.022568: train_loss -0.4146 +2026-04-13 23:52:50.052089: val_loss -0.3269 +2026-04-13 23:52:50.054316: Pseudo dice [0.678, 0.4549, 0.6695, 0.1572, 0.5295, 0.5771, 0.48] +2026-04-13 23:52:50.056450: Epoch time: 102.6 s +2026-04-13 23:52:51.273336: +2026-04-13 23:52:51.275372: Epoch 2976 +2026-04-13 23:52:51.277084: Current learning rate: 0.00293 +2026-04-13 23:54:33.055797: train_loss -0.4244 +2026-04-13 23:54:33.062767: val_loss -0.3182 +2026-04-13 23:54:33.065418: Pseudo dice [0.8539, 0.8007, 0.7022, 0.1619, 0.5454, 0.569, 0.638] +2026-04-13 23:54:33.067930: Epoch time: 101.79 s +2026-04-13 23:54:34.300686: +2026-04-13 23:54:34.303350: Epoch 2977 +2026-04-13 23:54:34.305020: Current learning rate: 0.00293 +2026-04-13 23:56:16.697889: train_loss -0.4177 +2026-04-13 23:56:16.704854: val_loss -0.3329 +2026-04-13 23:56:16.707350: Pseudo dice [0.8281, 0.8665, 0.4937, 0.4936, 0.4115, 0.6073, 0.7815] +2026-04-13 23:56:16.710523: Epoch time: 102.4 s +2026-04-13 23:56:17.926738: +2026-04-13 23:56:17.930683: Epoch 2978 +2026-04-13 23:56:17.933103: Current learning rate: 0.00293 +2026-04-13 23:58:00.113642: train_loss -0.4216 +2026-04-13 23:58:00.121498: val_loss -0.3748 +2026-04-13 23:58:00.123102: Pseudo dice [0.7992, 0.5168, 0.7913, 0.5614, 0.42, 0.9208, 0.7765] +2026-04-13 23:58:00.125131: Epoch time: 102.19 s +2026-04-13 23:58:01.327593: +2026-04-13 23:58:01.330371: Epoch 2979 +2026-04-13 23:58:01.331765: Current learning rate: 0.00293 +2026-04-13 23:59:43.083012: train_loss -0.4255 +2026-04-13 23:59:43.089369: val_loss -0.3965 +2026-04-13 23:59:43.091311: Pseudo dice [0.7797, 0.5906, 0.706, 0.7731, 0.3451, 0.9348, 0.8705] +2026-04-13 23:59:43.093940: Epoch time: 101.76 s +2026-04-13 23:59:44.280559: +2026-04-13 23:59:44.282016: Epoch 2980 +2026-04-13 23:59:44.283475: Current learning rate: 0.00292 +2026-04-14 00:01:26.973818: train_loss -0.4284 +2026-04-14 00:01:26.981030: val_loss -0.3771 +2026-04-14 00:01:26.984331: Pseudo dice [0.7491, 0.8773, 0.7034, 0.5207, 0.6184, 0.1273, 0.8639] +2026-04-14 00:01:26.987020: Epoch time: 102.7 s +2026-04-14 00:01:28.286998: +2026-04-14 00:01:28.289531: Epoch 2981 +2026-04-14 00:01:28.291699: Current learning rate: 0.00292 +2026-04-14 00:03:10.868679: train_loss -0.4288 +2026-04-14 00:03:10.875248: val_loss -0.3625 +2026-04-14 00:03:10.877669: Pseudo dice [0.1562, 0.4816, 0.8249, 0.3115, 0.5817, 0.9265, 0.811] +2026-04-14 00:03:10.884469: Epoch time: 102.58 s +2026-04-14 00:03:12.140933: +2026-04-14 00:03:12.143859: Epoch 2982 +2026-04-14 00:03:12.145705: Current learning rate: 0.00292 +2026-04-14 00:04:54.514693: train_loss -0.4235 +2026-04-14 00:04:54.521346: val_loss -0.3758 +2026-04-14 00:04:54.523689: Pseudo dice [0.7955, 0.7471, 0.8271, 0.3872, 0.4769, 0.6235, 0.7418] +2026-04-14 00:04:54.526493: Epoch time: 102.38 s +2026-04-14 00:04:55.760357: +2026-04-14 00:04:55.763833: Epoch 2983 +2026-04-14 00:04:55.769413: Current learning rate: 0.00292 +2026-04-14 00:06:38.413954: train_loss -0.4177 +2026-04-14 00:06:38.420024: val_loss -0.3712 +2026-04-14 00:06:38.421883: Pseudo dice [0.7948, 0.672, 0.7839, 0.5703, 0.443, 0.8279, 0.7952] +2026-04-14 00:06:38.424214: Epoch time: 102.66 s +2026-04-14 00:06:39.653547: +2026-04-14 00:06:39.655263: Epoch 2984 +2026-04-14 00:06:39.656910: Current learning rate: 0.00291 +2026-04-14 00:08:21.408078: train_loss -0.4128 +2026-04-14 00:08:21.422698: val_loss -0.3432 +2026-04-14 00:08:21.424953: Pseudo dice [0.8262, 0.6845, 0.7124, 0.2845, 0.1742, 0.8721, 0.1814] +2026-04-14 00:08:21.431668: Epoch time: 101.76 s +2026-04-14 00:08:22.630763: +2026-04-14 00:08:22.632885: Epoch 2985 +2026-04-14 00:08:22.634879: Current learning rate: 0.00291 +2026-04-14 00:10:04.292310: train_loss -0.4144 +2026-04-14 00:10:04.298806: val_loss -0.3945 +2026-04-14 00:10:04.301423: Pseudo dice [0.6136, 0.9069, 0.6495, 0.638, 0.6321, 0.8647, 0.7816] +2026-04-14 00:10:04.304110: Epoch time: 101.66 s +2026-04-14 00:10:05.493296: +2026-04-14 00:10:05.495272: Epoch 2986 +2026-04-14 00:10:05.499330: Current learning rate: 0.00291 +2026-04-14 00:12:00.610565: train_loss -0.4276 +2026-04-14 00:12:00.617633: val_loss -0.3518 +2026-04-14 00:12:00.620213: Pseudo dice [0.4009, 0.6477, 0.7982, 0.1837, 0.5125, 0.5999, 0.7877] +2026-04-14 00:12:00.622658: Epoch time: 115.12 s +2026-04-14 00:12:02.842804: +2026-04-14 00:12:02.844707: Epoch 2987 +2026-04-14 00:12:02.846302: Current learning rate: 0.00291 +2026-04-14 00:13:44.657790: train_loss -0.4039 +2026-04-14 00:13:44.664263: val_loss -0.3811 +2026-04-14 00:13:44.666338: Pseudo dice [0.5333, 0.6685, 0.7795, 0.4014, 0.5463, 0.6119, 0.8229] +2026-04-14 00:13:44.668694: Epoch time: 101.82 s +2026-04-14 00:13:45.895426: +2026-04-14 00:13:45.897476: Epoch 2988 +2026-04-14 00:13:45.899159: Current learning rate: 0.0029 +2026-04-14 00:15:28.120644: train_loss -0.4269 +2026-04-14 00:15:28.126847: val_loss -0.3117 +2026-04-14 00:15:28.129337: Pseudo dice [0.6309, 0.8835, 0.6346, 0.481, 0.3745, 0.2216, 0.2631] +2026-04-14 00:15:28.131753: Epoch time: 102.23 s +2026-04-14 00:15:29.377013: +2026-04-14 00:15:29.379252: Epoch 2989 +2026-04-14 00:15:29.380949: Current learning rate: 0.0029 +2026-04-14 00:17:11.976946: train_loss -0.4161 +2026-04-14 00:17:11.988837: val_loss -0.3489 +2026-04-14 00:17:11.993269: Pseudo dice [0.7777, 0.8861, 0.7163, 0.3576, 0.3976, 0.2802, 0.5681] +2026-04-14 00:17:11.998215: Epoch time: 102.6 s +2026-04-14 00:17:13.240299: +2026-04-14 00:17:13.242252: Epoch 2990 +2026-04-14 00:17:13.243986: Current learning rate: 0.0029 +2026-04-14 00:18:54.903510: train_loss -0.4125 +2026-04-14 00:18:54.910170: val_loss -0.3739 +2026-04-14 00:18:54.912652: Pseudo dice [0.6755, 0.6203, 0.732, 0.4311, 0.5062, 0.9099, 0.8107] +2026-04-14 00:18:54.914881: Epoch time: 101.67 s +2026-04-14 00:18:56.140165: +2026-04-14 00:18:56.142286: Epoch 2991 +2026-04-14 00:18:56.143924: Current learning rate: 0.00289 +2026-04-14 00:20:37.844735: train_loss -0.408 +2026-04-14 00:20:37.851876: val_loss -0.3934 +2026-04-14 00:20:37.854800: Pseudo dice [0.7097, 0.6816, 0.6941, 0.4266, 0.5047, 0.7834, 0.7657] +2026-04-14 00:20:37.859828: Epoch time: 101.71 s +2026-04-14 00:20:39.099936: +2026-04-14 00:20:39.101456: Epoch 2992 +2026-04-14 00:20:39.102974: Current learning rate: 0.00289 +2026-04-14 00:22:21.032667: train_loss -0.4122 +2026-04-14 00:22:21.038167: val_loss -0.3297 +2026-04-14 00:22:21.039965: Pseudo dice [0.647, 0.8164, 0.658, 0.2745, 0.6569, 0.6912, 0.7829] +2026-04-14 00:22:21.042034: Epoch time: 101.94 s +2026-04-14 00:22:22.257391: +2026-04-14 00:22:22.259083: Epoch 2993 +2026-04-14 00:22:22.260430: Current learning rate: 0.00289 +2026-04-14 00:24:03.813693: train_loss -0.427 +2026-04-14 00:24:03.820761: val_loss -0.3431 +2026-04-14 00:24:03.823020: Pseudo dice [0.2952, 0.5465, 0.6644, 0.34, 0.4635, 0.8664, 0.7336] +2026-04-14 00:24:03.825277: Epoch time: 101.56 s +2026-04-14 00:24:05.099132: +2026-04-14 00:24:05.102113: Epoch 2994 +2026-04-14 00:24:05.105352: Current learning rate: 0.00289 +2026-04-14 00:25:46.666220: train_loss -0.4238 +2026-04-14 00:25:46.674318: val_loss -0.2847 +2026-04-14 00:25:46.676198: Pseudo dice [0.1354, 0.9116, 0.5452, 0.2327, 0.2166, 0.0317, 0.4964] +2026-04-14 00:25:46.678707: Epoch time: 101.57 s +2026-04-14 00:25:47.903450: +2026-04-14 00:25:47.906240: Epoch 2995 +2026-04-14 00:25:47.908435: Current learning rate: 0.00288 +2026-04-14 00:27:29.463518: train_loss -0.4095 +2026-04-14 00:27:29.471898: val_loss -0.3524 +2026-04-14 00:27:29.473806: Pseudo dice [0.3367, 0.6915, 0.7541, 0.1961, 0.4655, 0.6601, 0.7513] +2026-04-14 00:27:29.476188: Epoch time: 101.56 s +2026-04-14 00:27:30.682823: +2026-04-14 00:27:30.684772: Epoch 2996 +2026-04-14 00:27:30.687773: Current learning rate: 0.00288 +2026-04-14 00:29:12.318401: train_loss -0.4244 +2026-04-14 00:29:12.330761: val_loss -0.3603 +2026-04-14 00:29:12.333143: Pseudo dice [0.7152, 0.611, 0.636, 0.1246, 0.5061, 0.9113, 0.8651] +2026-04-14 00:29:12.335736: Epoch time: 101.64 s +2026-04-14 00:29:13.562711: +2026-04-14 00:29:13.564346: Epoch 2997 +2026-04-14 00:29:13.566221: Current learning rate: 0.00288 +2026-04-14 00:30:55.512693: train_loss -0.4276 +2026-04-14 00:30:55.518497: val_loss -0.3155 +2026-04-14 00:30:55.520244: Pseudo dice [0.8713, 0.9041, 0.6444, 0.062, 0.3114, 0.1573, 0.7001] +2026-04-14 00:30:55.522836: Epoch time: 101.95 s +2026-04-14 00:30:56.746196: +2026-04-14 00:30:56.749153: Epoch 2998 +2026-04-14 00:30:56.751465: Current learning rate: 0.00288 +2026-04-14 00:32:38.594576: train_loss -0.4175 +2026-04-14 00:32:38.602183: val_loss -0.3807 +2026-04-14 00:32:38.605118: Pseudo dice [0.7616, 0.3284, 0.7226, 0.4383, 0.4512, 0.8688, 0.6738] +2026-04-14 00:32:38.608052: Epoch time: 101.85 s +2026-04-14 00:32:39.833878: +2026-04-14 00:32:39.835434: Epoch 2999 +2026-04-14 00:32:39.837041: Current learning rate: 0.00287 +2026-04-14 00:34:21.291559: train_loss -0.4263 +2026-04-14 00:34:21.298918: val_loss -0.3936 +2026-04-14 00:34:21.300692: Pseudo dice [0.8683, 0.4578, 0.707, 0.3453, 0.5941, 0.7211, 0.7897] +2026-04-14 00:34:21.303654: Epoch time: 101.46 s +2026-04-14 00:34:24.168787: +2026-04-14 00:34:24.171468: Epoch 3000 +2026-04-14 00:34:24.172876: Current learning rate: 0.00287 +2026-04-14 00:36:05.994591: train_loss -0.4297 +2026-04-14 00:36:06.001316: val_loss -0.3346 +2026-04-14 00:36:06.003759: Pseudo dice [0.5893, 0.5908, 0.7334, 0.2999, 0.4487, 0.744, 0.4092] +2026-04-14 00:36:06.006075: Epoch time: 101.83 s +2026-04-14 00:36:07.266116: +2026-04-14 00:36:07.268230: Epoch 3001 +2026-04-14 00:36:07.269726: Current learning rate: 0.00287 +2026-04-14 00:37:48.834426: train_loss -0.4201 +2026-04-14 00:37:48.840094: val_loss -0.41 +2026-04-14 00:37:48.842327: Pseudo dice [0.6625, 0.1408, 0.7628, 0.874, 0.6373, 0.8071, 0.829] +2026-04-14 00:37:48.845359: Epoch time: 101.57 s +2026-04-14 00:37:50.063089: +2026-04-14 00:37:50.064963: Epoch 3002 +2026-04-14 00:37:50.066480: Current learning rate: 0.00287 +2026-04-14 00:39:32.251657: train_loss -0.3989 +2026-04-14 00:39:32.258884: val_loss -0.3367 +2026-04-14 00:39:32.261479: Pseudo dice [0.7415, 0.714, 0.6657, 0.0188, 0.5462, 0.7739, 0.6988] +2026-04-14 00:39:32.263614: Epoch time: 102.19 s +2026-04-14 00:39:33.470038: +2026-04-14 00:39:33.471917: Epoch 3003 +2026-04-14 00:39:33.474349: Current learning rate: 0.00286 +2026-04-14 00:41:15.282892: train_loss -0.4142 +2026-04-14 00:41:15.289005: val_loss -0.3709 +2026-04-14 00:41:15.291354: Pseudo dice [0.7707, 0.6822, 0.7253, 0.3682, 0.6378, 0.7815, 0.859] +2026-04-14 00:41:15.293803: Epoch time: 101.82 s +2026-04-14 00:41:16.514106: +2026-04-14 00:41:16.516196: Epoch 3004 +2026-04-14 00:41:16.517819: Current learning rate: 0.00286 +2026-04-14 00:42:58.619508: train_loss -0.4074 +2026-04-14 00:42:58.625511: val_loss -0.3386 +2026-04-14 00:42:58.628883: Pseudo dice [0.6694, 0.348, 0.7334, 0.3839, 0.1958, 0.6619, 0.7102] +2026-04-14 00:42:58.632003: Epoch time: 102.11 s +2026-04-14 00:42:59.856955: +2026-04-14 00:42:59.859374: Epoch 3005 +2026-04-14 00:42:59.861049: Current learning rate: 0.00286 +2026-04-14 00:44:41.950273: train_loss -0.421 +2026-04-14 00:44:41.957698: val_loss -0.3729 +2026-04-14 00:44:41.960199: Pseudo dice [0.7255, 0.2633, 0.798, 0.7088, 0.4806, 0.8555, 0.5497] +2026-04-14 00:44:41.969336: Epoch time: 102.1 s +2026-04-14 00:44:43.177378: +2026-04-14 00:44:43.179200: Epoch 3006 +2026-04-14 00:44:43.180670: Current learning rate: 0.00286 +2026-04-14 00:46:26.219948: train_loss -0.431 +2026-04-14 00:46:26.226522: val_loss -0.3732 +2026-04-14 00:46:26.228782: Pseudo dice [0.5132, 0.6175, 0.7094, 0.1414, 0.4259, 0.6219, 0.8268] +2026-04-14 00:46:26.230861: Epoch time: 103.05 s +2026-04-14 00:46:27.464958: +2026-04-14 00:46:27.466531: Epoch 3007 +2026-04-14 00:46:27.472184: Current learning rate: 0.00285 +2026-04-14 00:48:09.371516: train_loss -0.4065 +2026-04-14 00:48:09.379365: val_loss -0.353 +2026-04-14 00:48:09.381473: Pseudo dice [0.8016, 0.7009, 0.5806, 0.3836, 0.2467, 0.835, 0.279] +2026-04-14 00:48:09.383895: Epoch time: 101.91 s +2026-04-14 00:48:10.596330: +2026-04-14 00:48:10.598150: Epoch 3008 +2026-04-14 00:48:10.599767: Current learning rate: 0.00285 +2026-04-14 00:49:52.465063: train_loss -0.4326 +2026-04-14 00:49:52.493599: val_loss -0.3317 +2026-04-14 00:49:52.495966: Pseudo dice [0.6049, 0.4508, 0.56, 0.1259, 0.4415, 0.3891, 0.8106] +2026-04-14 00:49:52.498825: Epoch time: 101.87 s +2026-04-14 00:49:53.716053: +2026-04-14 00:49:53.718427: Epoch 3009 +2026-04-14 00:49:53.720269: Current learning rate: 0.00285 +2026-04-14 00:51:35.341850: train_loss -0.414 +2026-04-14 00:51:35.348466: val_loss -0.3428 +2026-04-14 00:51:35.351072: Pseudo dice [0.5258, 0.8978, 0.694, 0.193, 0.5045, 0.8111, 0.3868] +2026-04-14 00:51:35.354297: Epoch time: 101.63 s +2026-04-14 00:51:36.596573: +2026-04-14 00:51:36.598152: Epoch 3010 +2026-04-14 00:51:36.599647: Current learning rate: 0.00285 +2026-04-14 00:53:18.308088: train_loss -0.4212 +2026-04-14 00:53:18.314583: val_loss -0.3781 +2026-04-14 00:53:18.316894: Pseudo dice [0.3038, 0.6325, 0.7748, 0.7867, 0.5509, 0.4948, 0.7059] +2026-04-14 00:53:18.319216: Epoch time: 101.71 s +2026-04-14 00:53:19.547073: +2026-04-14 00:53:19.549068: Epoch 3011 +2026-04-14 00:53:19.551170: Current learning rate: 0.00284 +2026-04-14 00:55:01.316207: train_loss -0.3986 +2026-04-14 00:55:01.324878: val_loss -0.3821 +2026-04-14 00:55:01.327777: Pseudo dice [0.6405, 0.4972, 0.7603, 0.6111, 0.5064, 0.8131, 0.8023] +2026-04-14 00:55:01.331752: Epoch time: 101.77 s +2026-04-14 00:55:02.583584: +2026-04-14 00:55:02.585203: Epoch 3012 +2026-04-14 00:55:02.586733: Current learning rate: 0.00284 +2026-04-14 00:56:44.375023: train_loss -0.425 +2026-04-14 00:56:44.382122: val_loss -0.3745 +2026-04-14 00:56:44.384748: Pseudo dice [0.6574, 0.5265, 0.7316, 0.8417, 0.3646, 0.9244, 0.8271] +2026-04-14 00:56:44.387321: Epoch time: 101.79 s +2026-04-14 00:56:45.599621: +2026-04-14 00:56:45.601871: Epoch 3013 +2026-04-14 00:56:45.603845: Current learning rate: 0.00284 +2026-04-14 00:58:27.799799: train_loss -0.4295 +2026-04-14 00:58:27.805879: val_loss -0.364 +2026-04-14 00:58:27.807885: Pseudo dice [0.7251, 0.89, 0.6777, 0.399, 0.4487, 0.8402, 0.7652] +2026-04-14 00:58:27.810047: Epoch time: 102.2 s +2026-04-14 00:58:29.019727: +2026-04-14 00:58:29.022011: Epoch 3014 +2026-04-14 00:58:29.023582: Current learning rate: 0.00284 +2026-04-14 01:00:10.468878: train_loss -0.4178 +2026-04-14 01:00:10.476484: val_loss -0.337 +2026-04-14 01:00:10.479071: Pseudo dice [0.6685, 0.0336, 0.7146, 0.3664, 0.383, 0.784, 0.6286] +2026-04-14 01:00:10.481464: Epoch time: 101.45 s +2026-04-14 01:00:11.714778: +2026-04-14 01:00:11.716321: Epoch 3015 +2026-04-14 01:00:11.717639: Current learning rate: 0.00283 +2026-04-14 01:01:53.268032: train_loss -0.4094 +2026-04-14 01:01:53.276202: val_loss -0.3303 +2026-04-14 01:01:53.278430: Pseudo dice [0.8548, 0.7907, 0.6597, 0.0065, 0.3663, 0.54, 0.8211] +2026-04-14 01:01:53.281335: Epoch time: 101.56 s +2026-04-14 01:01:54.536767: +2026-04-14 01:01:54.538803: Epoch 3016 +2026-04-14 01:01:54.540402: Current learning rate: 0.00283 +2026-04-14 01:03:36.248838: train_loss -0.4142 +2026-04-14 01:03:36.257069: val_loss -0.3764 +2026-04-14 01:03:36.259131: Pseudo dice [0.2542, 0.6069, 0.7398, 0.5081, 0.6859, 0.9195, 0.7809] +2026-04-14 01:03:36.261998: Epoch time: 101.72 s +2026-04-14 01:03:37.580632: +2026-04-14 01:03:37.582417: Epoch 3017 +2026-04-14 01:03:37.583930: Current learning rate: 0.00283 +2026-04-14 01:05:19.386977: train_loss -0.4178 +2026-04-14 01:05:19.394113: val_loss -0.3605 +2026-04-14 01:05:19.395734: Pseudo dice [0.4821, 0.8781, 0.7699, 0.3282, 0.4436, 0.7352, 0.7739] +2026-04-14 01:05:19.404638: Epoch time: 101.81 s +2026-04-14 01:05:20.631767: +2026-04-14 01:05:20.634515: Epoch 3018 +2026-04-14 01:05:20.636899: Current learning rate: 0.00283 +2026-04-14 01:07:03.259380: train_loss -0.4188 +2026-04-14 01:07:03.277828: val_loss -0.3639 +2026-04-14 01:07:03.288966: Pseudo dice [0.6374, 0.42, 0.7178, 0.5874, 0.5771, 0.7882, 0.5587] +2026-04-14 01:07:03.293705: Epoch time: 102.63 s +2026-04-14 01:07:04.731085: +2026-04-14 01:07:04.732740: Epoch 3019 +2026-04-14 01:07:04.734603: Current learning rate: 0.00282 +2026-04-14 01:08:46.837224: train_loss -0.4223 +2026-04-14 01:08:46.844103: val_loss -0.377 +2026-04-14 01:08:46.846317: Pseudo dice [0.5021, 0.8533, 0.7241, 0.1307, 0.4471, 0.7827, 0.6855] +2026-04-14 01:08:46.848669: Epoch time: 102.11 s +2026-04-14 01:08:48.071171: +2026-04-14 01:08:48.073409: Epoch 3020 +2026-04-14 01:08:48.075016: Current learning rate: 0.00282 +2026-04-14 01:10:29.543155: train_loss -0.4292 +2026-04-14 01:10:29.549602: val_loss -0.3406 +2026-04-14 01:10:29.551738: Pseudo dice [0.5861, 0.771, 0.8154, 0.4736, 0.4741, 0.5485, 0.3113] +2026-04-14 01:10:29.554044: Epoch time: 101.48 s +2026-04-14 01:10:30.786413: +2026-04-14 01:10:30.788440: Epoch 3021 +2026-04-14 01:10:30.790351: Current learning rate: 0.00282 +2026-04-14 01:12:12.525655: train_loss -0.4224 +2026-04-14 01:12:12.532709: val_loss -0.382 +2026-04-14 01:12:12.534976: Pseudo dice [0.7422, 0.7528, 0.781, 0.7911, 0.4645, 0.7086, 0.7883] +2026-04-14 01:12:12.537302: Epoch time: 101.74 s +2026-04-14 01:12:13.756860: +2026-04-14 01:12:13.761135: Epoch 3022 +2026-04-14 01:12:13.763892: Current learning rate: 0.00281 +2026-04-14 01:13:55.349225: train_loss -0.4112 +2026-04-14 01:13:55.356644: val_loss -0.355 +2026-04-14 01:13:55.358464: Pseudo dice [0.4691, 0.8525, 0.8039, 0.4496, 0.6, 0.8966, 0.4248] +2026-04-14 01:13:55.360839: Epoch time: 101.6 s +2026-04-14 01:13:56.607765: +2026-04-14 01:13:56.609444: Epoch 3023 +2026-04-14 01:13:56.611206: Current learning rate: 0.00281 +2026-04-14 01:15:38.677026: train_loss -0.4299 +2026-04-14 01:15:38.687152: val_loss -0.3488 +2026-04-14 01:15:38.690396: Pseudo dice [0.0479, 0.4408, 0.7615, 0.3519, 0.6385, 0.5563, 0.8232] +2026-04-14 01:15:38.693319: Epoch time: 102.07 s +2026-04-14 01:15:39.905977: +2026-04-14 01:15:39.907840: Epoch 3024 +2026-04-14 01:15:39.909990: Current learning rate: 0.00281 +2026-04-14 01:17:21.934424: train_loss -0.4191 +2026-04-14 01:17:21.940199: val_loss -0.3695 +2026-04-14 01:17:21.942663: Pseudo dice [0.5941, 0.5691, 0.7986, 0.3065, 0.5904, 0.473, 0.4998] +2026-04-14 01:17:21.945918: Epoch time: 102.03 s +2026-04-14 01:17:23.169450: +2026-04-14 01:17:23.171300: Epoch 3025 +2026-04-14 01:17:23.174088: Current learning rate: 0.00281 +2026-04-14 01:19:05.028649: train_loss -0.4238 +2026-04-14 01:19:05.034422: val_loss -0.3794 +2026-04-14 01:19:05.036309: Pseudo dice [0.8588, 0.8712, 0.679, 0.1937, 0.6262, 0.7514, 0.6987] +2026-04-14 01:19:05.038699: Epoch time: 101.86 s +2026-04-14 01:19:06.238765: +2026-04-14 01:19:06.240492: Epoch 3026 +2026-04-14 01:19:06.242100: Current learning rate: 0.0028 +2026-04-14 01:20:48.892040: train_loss -0.4382 +2026-04-14 01:20:48.899035: val_loss -0.3923 +2026-04-14 01:20:48.901418: Pseudo dice [0.6229, 0.5362, 0.782, 0.3149, 0.6341, 0.7862, 0.5721] +2026-04-14 01:20:48.904290: Epoch time: 102.66 s +2026-04-14 01:20:50.113708: +2026-04-14 01:20:50.115727: Epoch 3027 +2026-04-14 01:20:50.117368: Current learning rate: 0.0028 +2026-04-14 01:22:31.759004: train_loss -0.4218 +2026-04-14 01:22:31.765629: val_loss -0.3617 +2026-04-14 01:22:31.767928: Pseudo dice [0.6728, 0.7006, 0.6819, 0.3596, 0.5546, 0.7937, 0.8636] +2026-04-14 01:22:31.770279: Epoch time: 101.65 s +2026-04-14 01:22:33.007152: +2026-04-14 01:22:33.008859: Epoch 3028 +2026-04-14 01:22:33.010265: Current learning rate: 0.0028 +2026-04-14 01:24:14.457105: train_loss -0.406 +2026-04-14 01:24:14.466253: val_loss -0.335 +2026-04-14 01:24:14.468848: Pseudo dice [0.1825, 0.8139, 0.8052, 0.1909, 0.3847, 0.2496, 0.6069] +2026-04-14 01:24:14.471494: Epoch time: 101.45 s +2026-04-14 01:24:15.683228: +2026-04-14 01:24:15.684886: Epoch 3029 +2026-04-14 01:24:15.686319: Current learning rate: 0.0028 +2026-04-14 01:25:57.522598: train_loss -0.3839 +2026-04-14 01:25:57.529262: val_loss -0.3532 +2026-04-14 01:25:57.531737: Pseudo dice [0.2685, 0.5843, 0.6996, 0.4734, 0.368, 0.8257, 0.7693] +2026-04-14 01:25:57.534174: Epoch time: 101.84 s +2026-04-14 01:25:58.751554: +2026-04-14 01:25:58.754630: Epoch 3030 +2026-04-14 01:25:58.756365: Current learning rate: 0.00279 +2026-04-14 01:27:40.264254: train_loss -0.3948 +2026-04-14 01:27:40.271320: val_loss -0.3243 +2026-04-14 01:27:40.273222: Pseudo dice [0.2548, 0.7979, 0.7285, 0.0612, 0.4546, 0.1531, 0.4545] +2026-04-14 01:27:40.275321: Epoch time: 101.52 s +2026-04-14 01:27:41.494421: +2026-04-14 01:27:41.496265: Epoch 3031 +2026-04-14 01:27:41.497907: Current learning rate: 0.00279 +2026-04-14 01:29:23.226181: train_loss -0.3957 +2026-04-14 01:29:23.233632: val_loss -0.3423 +2026-04-14 01:29:23.235771: Pseudo dice [0.8069, 0.8977, 0.6761, 0.6082, 0.6514, 0.1479, 0.6293] +2026-04-14 01:29:23.238195: Epoch time: 101.73 s +2026-04-14 01:29:24.479641: +2026-04-14 01:29:24.481854: Epoch 3032 +2026-04-14 01:29:24.483382: Current learning rate: 0.00279 +2026-04-14 01:31:06.143046: train_loss -0.4058 +2026-04-14 01:31:06.151171: val_loss -0.309 +2026-04-14 01:31:06.152975: Pseudo dice [0.5945, 0.9037, 0.7393, 0.3708, 0.185, 0.6098, 0.2155] +2026-04-14 01:31:06.155169: Epoch time: 101.67 s +2026-04-14 01:31:07.384425: +2026-04-14 01:31:07.386397: Epoch 3033 +2026-04-14 01:31:07.387974: Current learning rate: 0.00279 +2026-04-14 01:32:49.195190: train_loss -0.4029 +2026-04-14 01:32:49.201856: val_loss -0.3153 +2026-04-14 01:32:49.204804: Pseudo dice [0.1739, 0.4741, 0.5707, 0.4814, 0.1875, 0.2489, 0.7368] +2026-04-14 01:32:49.207234: Epoch time: 101.81 s +2026-04-14 01:32:50.439893: +2026-04-14 01:32:50.441613: Epoch 3034 +2026-04-14 01:32:50.443392: Current learning rate: 0.00278 +2026-04-14 01:34:31.848799: train_loss -0.4199 +2026-04-14 01:34:31.855613: val_loss -0.3439 +2026-04-14 01:34:31.857523: Pseudo dice [0.837, 0.404, 0.765, 0.5623, 0.507, 0.9264, 0.8275] +2026-04-14 01:34:31.859822: Epoch time: 101.41 s +2026-04-14 01:34:33.091693: +2026-04-14 01:34:33.093200: Epoch 3035 +2026-04-14 01:34:33.094577: Current learning rate: 0.00278 +2026-04-14 01:36:14.771965: train_loss -0.4201 +2026-04-14 01:36:14.777909: val_loss -0.3573 +2026-04-14 01:36:14.779552: Pseudo dice [0.1619, 0.5052, 0.7667, 0.2108, 0.6839, 0.8604, 0.6354] +2026-04-14 01:36:14.782095: Epoch time: 101.68 s +2026-04-14 01:36:16.037664: +2026-04-14 01:36:16.039251: Epoch 3036 +2026-04-14 01:36:16.040721: Current learning rate: 0.00278 +2026-04-14 01:37:58.304136: train_loss -0.4272 +2026-04-14 01:37:58.311712: val_loss -0.3614 +2026-04-14 01:37:58.314039: Pseudo dice [0.7889, 0.8382, 0.7875, 0.4833, 0.5216, 0.0644, 0.7172] +2026-04-14 01:37:58.317622: Epoch time: 102.27 s +2026-04-14 01:37:59.886003: +2026-04-14 01:37:59.888105: Epoch 3037 +2026-04-14 01:37:59.889724: Current learning rate: 0.00278 +2026-04-14 01:39:42.469263: train_loss -0.4208 +2026-04-14 01:39:42.474319: val_loss -0.3481 +2026-04-14 01:39:42.475847: Pseudo dice [0.7115, 0.6313, 0.7885, 0.2602, 0.5138, 0.706, 0.6687] +2026-04-14 01:39:42.478230: Epoch time: 102.59 s +2026-04-14 01:39:43.698940: +2026-04-14 01:39:43.700848: Epoch 3038 +2026-04-14 01:39:43.703066: Current learning rate: 0.00277 +2026-04-14 01:41:26.961225: train_loss -0.4112 +2026-04-14 01:41:26.967116: val_loss -0.3536 +2026-04-14 01:41:26.974169: Pseudo dice [0.7033, 0.5022, 0.7427, 0.0914, 0.3702, 0.8429, 0.59] +2026-04-14 01:41:26.976383: Epoch time: 103.27 s +2026-04-14 01:41:28.215566: +2026-04-14 01:41:28.217302: Epoch 3039 +2026-04-14 01:41:28.218858: Current learning rate: 0.00277 +2026-04-14 01:43:10.191835: train_loss -0.418 +2026-04-14 01:43:10.200045: val_loss -0.3679 +2026-04-14 01:43:10.204736: Pseudo dice [0.6271, 0.4116, 0.6, 0.4034, 0.6326, 0.5136, 0.6759] +2026-04-14 01:43:10.209805: Epoch time: 101.98 s +2026-04-14 01:43:11.428365: +2026-04-14 01:43:11.430300: Epoch 3040 +2026-04-14 01:43:11.431904: Current learning rate: 0.00277 +2026-04-14 01:44:53.013096: train_loss -0.4168 +2026-04-14 01:44:53.019336: val_loss -0.3628 +2026-04-14 01:44:53.021473: Pseudo dice [0.7092, 0.7976, 0.6053, 0.5137, 0.5644, 0.9219, 0.6948] +2026-04-14 01:44:53.023800: Epoch time: 101.59 s +2026-04-14 01:44:54.254138: +2026-04-14 01:44:54.256530: Epoch 3041 +2026-04-14 01:44:54.258403: Current learning rate: 0.00277 +2026-04-14 01:46:36.621301: train_loss -0.4313 +2026-04-14 01:46:36.627437: val_loss -0.3868 +2026-04-14 01:46:36.629676: Pseudo dice [0.7727, 0.5841, 0.6901, 0.6867, 0.5915, 0.8976, 0.5773] +2026-04-14 01:46:36.631649: Epoch time: 102.37 s +2026-04-14 01:46:37.866078: +2026-04-14 01:46:37.870015: Epoch 3042 +2026-04-14 01:46:37.871884: Current learning rate: 0.00276 +2026-04-14 01:48:23.548300: train_loss -0.4195 +2026-04-14 01:48:23.555020: val_loss -0.3639 +2026-04-14 01:48:23.556770: Pseudo dice [0.7675, 0.8959, 0.7649, 0.7861, 0.5938, 0.5794, 0.2746] +2026-04-14 01:48:23.559390: Epoch time: 105.69 s +2026-04-14 01:48:24.797880: +2026-04-14 01:48:24.801566: Epoch 3043 +2026-04-14 01:48:24.803344: Current learning rate: 0.00276 +2026-04-14 01:50:14.216512: train_loss -0.4079 +2026-04-14 01:50:14.242802: val_loss -0.3928 +2026-04-14 01:50:14.245513: Pseudo dice [0.8008, 0.7154, 0.8154, 0.2313, 0.5009, 0.9238, 0.8397] +2026-04-14 01:50:14.247697: Epoch time: 109.42 s +2026-04-14 01:50:15.456457: +2026-04-14 01:50:15.458027: Epoch 3044 +2026-04-14 01:50:15.459755: Current learning rate: 0.00276 +2026-04-14 01:52:02.051090: train_loss -0.4256 +2026-04-14 01:52:02.059289: val_loss -0.3608 +2026-04-14 01:52:02.061827: Pseudo dice [0.5862, 0.3675, 0.7953, 0.3246, 0.5399, 0.7322, 0.4923] +2026-04-14 01:52:02.064203: Epoch time: 106.6 s +2026-04-14 01:52:03.288785: +2026-04-14 01:52:03.290731: Epoch 3045 +2026-04-14 01:52:03.292338: Current learning rate: 0.00276 +2026-04-14 01:53:52.911541: train_loss -0.421 +2026-04-14 01:53:52.917787: val_loss -0.3484 +2026-04-14 01:53:52.919656: Pseudo dice [0.6733, 0.8843, 0.8343, 0.395, 0.2828, 0.8025, 0.538] +2026-04-14 01:53:52.923082: Epoch time: 109.63 s +2026-04-14 01:53:55.645879: +2026-04-14 01:53:55.648077: Epoch 3046 +2026-04-14 01:53:55.649884: Current learning rate: 0.00275 +2026-04-14 01:55:41.771672: train_loss -0.412 +2026-04-14 01:55:41.779476: val_loss -0.3785 +2026-04-14 01:55:41.781481: Pseudo dice [0.22, 0.211, 0.7713, 0.5211, 0.4149, 0.1686, 0.7533] +2026-04-14 01:55:41.783325: Epoch time: 106.13 s +2026-04-14 01:55:43.015061: +2026-04-14 01:55:43.016840: Epoch 3047 +2026-04-14 01:55:43.018764: Current learning rate: 0.00275 +2026-04-14 01:57:25.162243: train_loss -0.4329 +2026-04-14 01:57:25.168386: val_loss -0.3875 +2026-04-14 01:57:25.171434: Pseudo dice [0.8292, 0.8495, 0.6659, 0.3416, 0.5429, 0.2782, 0.6828] +2026-04-14 01:57:25.174523: Epoch time: 102.15 s +2026-04-14 01:57:26.387125: +2026-04-14 01:57:26.389433: Epoch 3048 +2026-04-14 01:57:26.391465: Current learning rate: 0.00275 +2026-04-14 01:59:34.118778: train_loss -0.4195 +2026-04-14 01:59:34.124211: val_loss -0.3657 +2026-04-14 01:59:34.127400: Pseudo dice [0.6333, 0.5729, 0.6375, 0.3992, 0.4867, 0.5692, 0.5169] +2026-04-14 01:59:34.129716: Epoch time: 127.73 s +2026-04-14 01:59:35.345850: +2026-04-14 01:59:35.347644: Epoch 3049 +2026-04-14 01:59:35.349034: Current learning rate: 0.00274 +2026-04-14 02:01:33.415833: train_loss -0.4086 +2026-04-14 02:01:33.422327: val_loss -0.3621 +2026-04-14 02:01:33.424582: Pseudo dice [0.7911, 0.86, 0.7413, 0.4659, 0.6416, 0.7326, 0.6984] +2026-04-14 02:01:33.426897: Epoch time: 118.07 s +2026-04-14 02:01:36.402529: +2026-04-14 02:01:36.404165: Epoch 3050 +2026-04-14 02:01:36.405610: Current learning rate: 0.00274 +2026-04-14 02:03:22.292191: train_loss -0.3989 +2026-04-14 02:03:22.305992: val_loss -0.3948 +2026-04-14 02:03:22.309378: Pseudo dice [0.6009, 0.3266, 0.6932, 0.5273, 0.6649, 0.9122, 0.8466] +2026-04-14 02:03:22.312857: Epoch time: 105.89 s +2026-04-14 02:03:23.559556: +2026-04-14 02:03:23.561411: Epoch 3051 +2026-04-14 02:03:23.563732: Current learning rate: 0.00274 +2026-04-14 02:05:06.995892: train_loss -0.4246 +2026-04-14 02:05:07.001788: val_loss -0.2889 +2026-04-14 02:05:07.003655: Pseudo dice [0.0992, 0.8948, 0.7267, 0.0796, 0.583, 0.4034, 0.208] +2026-04-14 02:05:07.006478: Epoch time: 103.44 s +2026-04-14 02:05:08.225487: +2026-04-14 02:05:08.227740: Epoch 3052 +2026-04-14 02:05:08.230005: Current learning rate: 0.00274 +2026-04-14 02:08:26.536386: train_loss -0.4197 +2026-04-14 02:08:26.545447: val_loss -0.3802 +2026-04-14 02:08:26.548493: Pseudo dice [0.5777, 0.6293, 0.7374, 0.8062, 0.354, 0.8341, 0.8381] +2026-04-14 02:08:26.551424: Epoch time: 198.31 s +2026-04-14 02:08:28.054344: +2026-04-14 02:08:28.056961: Epoch 3053 +2026-04-14 02:08:28.060092: Current learning rate: 0.00273 +2026-04-14 02:10:35.838219: train_loss -0.4223 +2026-04-14 02:10:35.845102: val_loss -0.3269 +2026-04-14 02:10:35.847679: Pseudo dice [0.6479, 0.865, 0.7874, 0.1838, 0.4585, 0.5688, 0.6282] +2026-04-14 02:10:35.850097: Epoch time: 127.79 s +2026-04-14 02:10:37.138400: +2026-04-14 02:10:37.140894: Epoch 3054 +2026-04-14 02:10:37.144458: Current learning rate: 0.00273 +2026-04-14 02:13:12.160938: train_loss -0.4259 +2026-04-14 02:13:12.169073: val_loss -0.4102 +2026-04-14 02:13:12.171269: Pseudo dice [0.7098, 0.0966, 0.7371, 0.5277, 0.3804, 0.8934, 0.791] +2026-04-14 02:13:12.174197: Epoch time: 155.03 s +2026-04-14 02:13:13.389786: +2026-04-14 02:13:13.391472: Epoch 3055 +2026-04-14 02:13:13.393229: Current learning rate: 0.00273 +2026-04-14 02:14:56.442847: train_loss -0.4245 +2026-04-14 02:14:56.449912: val_loss -0.343 +2026-04-14 02:14:56.452014: Pseudo dice [0.7425, 0.8843, 0.8223, 0.3458, 0.5157, 0.1826, 0.5227] +2026-04-14 02:14:56.455123: Epoch time: 103.06 s +2026-04-14 02:14:57.671371: +2026-04-14 02:14:57.673256: Epoch 3056 +2026-04-14 02:14:57.675213: Current learning rate: 0.00273 +2026-04-14 02:16:46.559710: train_loss -0.3985 +2026-04-14 02:16:46.568230: val_loss -0.2928 +2026-04-14 02:16:46.570451: Pseudo dice [0.4289, 0.8596, 0.4052, 0.3153, 0.5262, 0.4976, 0.3654] +2026-04-14 02:16:46.572977: Epoch time: 108.89 s +2026-04-14 02:16:47.806286: +2026-04-14 02:16:47.808004: Epoch 3057 +2026-04-14 02:16:47.809880: Current learning rate: 0.00272 +2026-04-14 02:18:54.709221: train_loss -0.4041 +2026-04-14 02:18:54.716064: val_loss -0.3767 +2026-04-14 02:18:54.718664: Pseudo dice [0.4451, 0.5329, 0.8124, 0.2265, 0.4865, 0.8417, 0.8045] +2026-04-14 02:18:54.722311: Epoch time: 126.91 s +2026-04-14 02:18:55.965479: +2026-04-14 02:18:55.967664: Epoch 3058 +2026-04-14 02:18:55.970667: Current learning rate: 0.00272 +2026-04-14 02:20:55.396980: train_loss -0.4121 +2026-04-14 02:20:55.405182: val_loss -0.3665 +2026-04-14 02:20:55.407403: Pseudo dice [0.0902, 0.4878, 0.795, 0.4522, 0.5391, 0.9096, 0.7913] +2026-04-14 02:20:55.414235: Epoch time: 119.43 s +2026-04-14 02:20:56.641863: +2026-04-14 02:20:56.644427: Epoch 3059 +2026-04-14 02:20:56.647398: Current learning rate: 0.00272 +2026-04-14 02:22:50.312550: train_loss -0.417 +2026-04-14 02:22:50.319703: val_loss -0.3842 +2026-04-14 02:22:50.323929: Pseudo dice [0.5593, 0.4967, 0.8777, 0.5562, 0.4127, 0.8418, 0.7837] +2026-04-14 02:22:50.325988: Epoch time: 113.67 s +2026-04-14 02:22:51.608001: +2026-04-14 02:22:51.610460: Epoch 3060 +2026-04-14 02:22:51.612939: Current learning rate: 0.00272 +2026-04-14 02:24:54.294016: train_loss -0.4235 +2026-04-14 02:24:54.301620: val_loss -0.3624 +2026-04-14 02:24:54.303782: Pseudo dice [0.3565, 0.6327, 0.7216, 0.4127, 0.3079, 0.8351, 0.815] +2026-04-14 02:24:54.306951: Epoch time: 122.69 s +2026-04-14 02:24:55.535736: +2026-04-14 02:24:55.537776: Epoch 3061 +2026-04-14 02:24:55.539934: Current learning rate: 0.00271 +2026-04-14 02:26:49.946754: train_loss -0.4304 +2026-04-14 02:26:49.956301: val_loss -0.3601 +2026-04-14 02:26:49.959475: Pseudo dice [0.6874, 0.8718, 0.7589, 0.3714, 0.6312, 0.3064, 0.8734] +2026-04-14 02:26:49.962782: Epoch time: 114.41 s +2026-04-14 02:26:51.256998: +2026-04-14 02:26:51.259260: Epoch 3062 +2026-04-14 02:26:51.261945: Current learning rate: 0.00271 +2026-04-14 02:29:06.698740: train_loss -0.4347 +2026-04-14 02:29:06.706028: val_loss -0.3846 +2026-04-14 02:29:06.712246: Pseudo dice [0.7693, 0.5693, 0.8621, 0.4642, 0.492, 0.8168, 0.7822] +2026-04-14 02:29:06.715086: Epoch time: 135.44 s +2026-04-14 02:29:08.200910: +2026-04-14 02:29:08.203214: Epoch 3063 +2026-04-14 02:29:08.205642: Current learning rate: 0.00271 +2026-04-14 02:30:59.567153: train_loss -0.4257 +2026-04-14 02:30:59.583640: val_loss -0.3785 +2026-04-14 02:30:59.592701: Pseudo dice [0.8027, 0.3281, 0.7972, 0.5212, 0.6524, 0.7123, 0.8476] +2026-04-14 02:30:59.603021: Epoch time: 111.37 s +2026-04-14 02:31:00.837556: +2026-04-14 02:31:00.854433: Epoch 3064 +2026-04-14 02:31:00.856580: Current learning rate: 0.00271 +2026-04-14 02:32:54.503344: train_loss -0.4264 +2026-04-14 02:32:54.510258: val_loss -0.3844 +2026-04-14 02:32:54.512062: Pseudo dice [0.6303, 0.7555, 0.7904, 0.6406, 0.5553, 0.8132, 0.7582] +2026-04-14 02:32:54.514474: Epoch time: 113.67 s +2026-04-14 02:32:55.750886: +2026-04-14 02:32:55.753353: Epoch 3065 +2026-04-14 02:32:55.755335: Current learning rate: 0.0027 +2026-04-14 02:34:39.313551: train_loss -0.4329 +2026-04-14 02:34:39.321650: val_loss -0.3665 +2026-04-14 02:34:39.324262: Pseudo dice [0.7709, 0.7525, 0.7837, 0.1803, 0.3439, 0.8825, 0.6018] +2026-04-14 02:34:39.327328: Epoch time: 103.57 s +2026-04-14 02:34:40.581126: +2026-04-14 02:34:40.583443: Epoch 3066 +2026-04-14 02:34:40.585664: Current learning rate: 0.0027 +2026-04-14 02:36:22.392650: train_loss -0.4192 +2026-04-14 02:36:22.398925: val_loss -0.3589 +2026-04-14 02:36:22.401428: Pseudo dice [0.6405, 0.7203, 0.6952, 0.5824, 0.5995, 0.8848, 0.6395] +2026-04-14 02:36:22.403652: Epoch time: 101.81 s +2026-04-14 02:36:23.636102: +2026-04-14 02:36:23.639393: Epoch 3067 +2026-04-14 02:36:23.642623: Current learning rate: 0.0027 +2026-04-14 02:38:05.381609: train_loss -0.4317 +2026-04-14 02:38:05.389565: val_loss -0.3983 +2026-04-14 02:38:05.391606: Pseudo dice [0.8572, 0.5881, 0.7027, 0.1776, 0.5628, 0.9029, 0.876] +2026-04-14 02:38:05.394177: Epoch time: 101.75 s +2026-04-14 02:38:06.626120: +2026-04-14 02:38:06.628302: Epoch 3068 +2026-04-14 02:38:06.630394: Current learning rate: 0.0027 +2026-04-14 02:39:48.752999: train_loss -0.4417 +2026-04-14 02:39:48.761800: val_loss -0.3534 +2026-04-14 02:39:48.764576: Pseudo dice [0.736, 0.6403, 0.7317, 0.1345, 0.5232, 0.584, 0.7881] +2026-04-14 02:39:48.767382: Epoch time: 102.13 s +2026-04-14 02:39:50.030327: +2026-04-14 02:39:50.032487: Epoch 3069 +2026-04-14 02:39:50.035034: Current learning rate: 0.00269 +2026-04-14 02:41:31.695494: train_loss -0.4282 +2026-04-14 02:41:31.701517: val_loss -0.3875 +2026-04-14 02:41:31.703884: Pseudo dice [0.3475, 0.5719, 0.8354, 0.6253, 0.3478, 0.7135, 0.8331] +2026-04-14 02:41:31.707112: Epoch time: 101.67 s +2026-04-14 02:41:32.935765: +2026-04-14 02:41:32.938032: Epoch 3070 +2026-04-14 02:41:32.940270: Current learning rate: 0.00269 +2026-04-14 02:43:14.708495: train_loss -0.4252 +2026-04-14 02:43:14.717065: val_loss -0.3713 +2026-04-14 02:43:14.719441: Pseudo dice [0.4112, 0.3103, 0.7428, 0.7754, 0.2331, 0.8061, 0.8635] +2026-04-14 02:43:14.722199: Epoch time: 101.78 s +2026-04-14 02:43:15.956848: +2026-04-14 02:43:15.959550: Epoch 3071 +2026-04-14 02:43:15.962035: Current learning rate: 0.00269 +2026-04-14 02:44:57.715271: train_loss -0.4228 +2026-04-14 02:44:57.721558: val_loss -0.353 +2026-04-14 02:44:57.724153: Pseudo dice [0.405, 0.8918, 0.6834, 0.4042, 0.6227, 0.6808, 0.4089] +2026-04-14 02:44:57.726804: Epoch time: 101.76 s +2026-04-14 02:44:58.966964: +2026-04-14 02:44:58.968889: Epoch 3072 +2026-04-14 02:44:58.972026: Current learning rate: 0.00268 +2026-04-14 02:46:40.959728: train_loss -0.4256 +2026-04-14 02:46:40.966010: val_loss -0.3655 +2026-04-14 02:46:40.968490: Pseudo dice [0.8545, 0.4519, 0.7194, 0.5718, 0.4169, 0.7515, 0.8666] +2026-04-14 02:46:40.970909: Epoch time: 102.0 s +2026-04-14 02:46:42.203098: +2026-04-14 02:46:42.204897: Epoch 3073 +2026-04-14 02:46:42.206801: Current learning rate: 0.00268 +2026-04-14 02:48:25.771411: train_loss -0.4131 +2026-04-14 02:48:25.780295: val_loss -0.3661 +2026-04-14 02:48:25.782419: Pseudo dice [0.3721, 0.902, 0.6831, 0.6781, 0.5631, 0.3815, 0.6194] +2026-04-14 02:48:25.784598: Epoch time: 103.57 s +2026-04-14 02:48:27.027884: +2026-04-14 02:48:27.030728: Epoch 3074 +2026-04-14 02:48:27.032857: Current learning rate: 0.00268 +2026-04-14 02:50:16.193374: train_loss -0.4209 +2026-04-14 02:50:16.199786: val_loss -0.4003 +2026-04-14 02:50:16.201968: Pseudo dice [0.7695, 0.1913, 0.7922, 0.6333, 0.3629, 0.7765, 0.8348] +2026-04-14 02:50:16.204461: Epoch time: 109.17 s +2026-04-14 02:50:17.434557: +2026-04-14 02:50:17.436172: Epoch 3075 +2026-04-14 02:50:17.438303: Current learning rate: 0.00268 +2026-04-14 02:51:59.222558: train_loss -0.416 +2026-04-14 02:51:59.229753: val_loss -0.3298 +2026-04-14 02:51:59.233977: Pseudo dice [0.6668, 0.5778, 0.7013, 0.23, 0.4295, 0.3778, 0.7413] +2026-04-14 02:51:59.236714: Epoch time: 101.79 s +2026-04-14 02:52:00.470884: +2026-04-14 02:52:00.472984: Epoch 3076 +2026-04-14 02:52:00.475461: Current learning rate: 0.00267 +2026-04-14 02:53:57.513944: train_loss -0.4268 +2026-04-14 02:53:57.521666: val_loss -0.3653 +2026-04-14 02:53:57.524127: Pseudo dice [0.5998, 0.8629, 0.7439, 0.5079, 0.5029, 0.5602, 0.7985] +2026-04-14 02:53:57.527266: Epoch time: 117.05 s +2026-04-14 02:53:58.772256: +2026-04-14 02:53:58.774068: Epoch 3077 +2026-04-14 02:53:58.776047: Current learning rate: 0.00267 +2026-04-14 02:55:46.743680: train_loss -0.4234 +2026-04-14 02:55:46.750144: val_loss -0.3501 +2026-04-14 02:55:46.752167: Pseudo dice [0.8515, 0.8986, 0.791, 0.345, 0.223, 0.678, 0.4263] +2026-04-14 02:55:46.754405: Epoch time: 107.97 s +2026-04-14 02:55:47.982316: +2026-04-14 02:55:47.984551: Epoch 3078 +2026-04-14 02:55:47.986724: Current learning rate: 0.00267 +2026-04-14 02:57:29.960944: train_loss -0.4188 +2026-04-14 02:57:29.967331: val_loss -0.3751 +2026-04-14 02:57:29.969566: Pseudo dice [0.8343, 0.6163, 0.7828, 0.2563, 0.0612, 0.8896, 0.6588] +2026-04-14 02:57:29.972481: Epoch time: 101.98 s +2026-04-14 02:57:31.218199: +2026-04-14 02:57:31.219987: Epoch 3079 +2026-04-14 02:57:31.221943: Current learning rate: 0.00267 +2026-04-14 02:59:14.063837: train_loss -0.4265 +2026-04-14 02:59:14.074678: val_loss -0.3912 +2026-04-14 02:59:14.076926: Pseudo dice [0.7733, 0.7153, 0.8174, 0.5345, 0.6139, 0.905, 0.7309] +2026-04-14 02:59:14.080911: Epoch time: 102.85 s +2026-04-14 02:59:15.309386: +2026-04-14 02:59:15.311212: Epoch 3080 +2026-04-14 02:59:15.313549: Current learning rate: 0.00266 +2026-04-14 03:00:58.673305: train_loss -0.4262 +2026-04-14 03:00:58.697968: val_loss -0.3668 +2026-04-14 03:00:58.699822: Pseudo dice [0.592, 0.7162, 0.6898, 0.1351, 0.4851, 0.6793, 0.4499] +2026-04-14 03:00:58.702238: Epoch time: 103.37 s +2026-04-14 03:00:59.929230: +2026-04-14 03:00:59.932119: Epoch 3081 +2026-04-14 03:00:59.935802: Current learning rate: 0.00266 +2026-04-14 03:02:53.858222: train_loss -0.4288 +2026-04-14 03:02:53.865532: val_loss -0.3773 +2026-04-14 03:02:53.868593: Pseudo dice [0.8654, 0.5228, 0.8447, 0.4763, 0.5422, 0.7747, 0.2685] +2026-04-14 03:02:53.871395: Epoch time: 113.93 s +2026-04-14 03:02:55.119519: +2026-04-14 03:02:55.124569: Epoch 3082 +2026-04-14 03:02:55.127815: Current learning rate: 0.00266 +2026-04-14 03:04:37.860455: train_loss -0.4112 +2026-04-14 03:04:37.867179: val_loss -0.308 +2026-04-14 03:04:37.869688: Pseudo dice [0.6802, 0.6085, 0.6693, 0.2316, 0.3684, 0.4482, 0.3124] +2026-04-14 03:04:37.872266: Epoch time: 102.74 s +2026-04-14 03:04:39.085908: +2026-04-14 03:04:39.087954: Epoch 3083 +2026-04-14 03:04:39.089955: Current learning rate: 0.00266 +2026-04-14 03:06:23.705455: train_loss -0.4209 +2026-04-14 03:06:23.715239: val_loss -0.3741 +2026-04-14 03:06:23.718130: Pseudo dice [0.6141, 0.632, 0.7793, 0.1538, 0.5744, 0.8096, 0.6778] +2026-04-14 03:06:23.721131: Epoch time: 104.62 s +2026-04-14 03:06:24.932219: +2026-04-14 03:06:24.934753: Epoch 3084 +2026-04-14 03:06:24.937346: Current learning rate: 0.00265 +2026-04-14 03:08:19.105887: train_loss -0.4368 +2026-04-14 03:08:19.116036: val_loss -0.3503 +2026-04-14 03:08:19.118510: Pseudo dice [0.6803, 0.9027, 0.7796, 0.4596, 0.5259, 0.7626, 0.7574] +2026-04-14 03:08:19.121954: Epoch time: 114.18 s +2026-04-14 03:08:20.356869: +2026-04-14 03:08:20.360056: Epoch 3085 +2026-04-14 03:08:20.362290: Current learning rate: 0.00265 +2026-04-14 03:10:15.321078: train_loss -0.4302 +2026-04-14 03:10:15.328105: val_loss -0.3686 +2026-04-14 03:10:15.331059: Pseudo dice [0.1914, 0.5609, 0.8, 0.3318, 0.3726, 0.819, 0.7202] +2026-04-14 03:10:15.333661: Epoch time: 114.97 s +2026-04-14 03:10:16.568020: +2026-04-14 03:10:16.569867: Epoch 3086 +2026-04-14 03:10:16.572051: Current learning rate: 0.00265 +2026-04-14 03:11:58.655602: train_loss -0.4124 +2026-04-14 03:11:58.663231: val_loss -0.403 +2026-04-14 03:11:58.665736: Pseudo dice [0.6693, 0.7616, 0.7955, 0.7772, 0.3846, 0.8748, 0.8872] +2026-04-14 03:11:58.668049: Epoch time: 102.09 s +2026-04-14 03:11:59.893997: +2026-04-14 03:11:59.896014: Epoch 3087 +2026-04-14 03:11:59.898585: Current learning rate: 0.00265 +2026-04-14 03:13:42.343797: train_loss -0.4299 +2026-04-14 03:13:42.354673: val_loss -0.3718 +2026-04-14 03:13:42.357541: Pseudo dice [0.6412, 0.8474, 0.7698, 0.6855, 0.6074, 0.3243, 0.6557] +2026-04-14 03:13:42.361013: Epoch time: 102.45 s +2026-04-14 03:13:43.592812: +2026-04-14 03:13:43.595072: Epoch 3088 +2026-04-14 03:13:43.597071: Current learning rate: 0.00264 +2026-04-14 03:15:25.667972: train_loss -0.4348 +2026-04-14 03:15:25.675845: val_loss -0.3268 +2026-04-14 03:15:25.677911: Pseudo dice [0.8496, 0.8844, 0.7508, 0.1815, 0.4032, 0.6325, 0.2276] +2026-04-14 03:15:25.681591: Epoch time: 102.08 s +2026-04-14 03:15:26.902889: +2026-04-14 03:15:26.905586: Epoch 3089 +2026-04-14 03:15:26.908452: Current learning rate: 0.00264 +2026-04-14 03:17:09.194111: train_loss -0.4271 +2026-04-14 03:17:09.200405: val_loss -0.363 +2026-04-14 03:17:09.203132: Pseudo dice [0.2552, 0.8273, 0.7507, 0.3865, 0.5267, 0.7874, 0.8079] +2026-04-14 03:17:09.205644: Epoch time: 102.29 s +2026-04-14 03:17:10.436223: +2026-04-14 03:17:10.438967: Epoch 3090 +2026-04-14 03:17:10.441035: Current learning rate: 0.00264 +2026-04-14 03:18:52.299891: train_loss -0.4345 +2026-04-14 03:18:52.307188: val_loss -0.3843 +2026-04-14 03:18:52.309147: Pseudo dice [0.5503, 0.5017, 0.7448, 0.2376, 0.3711, 0.7689, 0.8845] +2026-04-14 03:18:52.312086: Epoch time: 101.87 s +2026-04-14 03:18:53.538545: +2026-04-14 03:18:53.540361: Epoch 3091 +2026-04-14 03:18:53.542166: Current learning rate: 0.00264 +2026-04-14 03:20:36.931857: train_loss -0.4341 +2026-04-14 03:20:36.939011: val_loss -0.3721 +2026-04-14 03:20:36.941087: Pseudo dice [0.8696, 0.6917, 0.7703, 0.2315, 0.5454, 0.7002, 0.8291] +2026-04-14 03:20:36.943105: Epoch time: 103.4 s +2026-04-14 03:20:38.180488: +2026-04-14 03:20:38.182788: Epoch 3092 +2026-04-14 03:20:38.184936: Current learning rate: 0.00263 +2026-04-14 03:22:20.335360: train_loss -0.4341 +2026-04-14 03:22:20.343426: val_loss -0.3941 +2026-04-14 03:22:20.347100: Pseudo dice [0.5879, 0.6693, 0.8522, 0.5548, 0.5571, 0.9202, 0.7465] +2026-04-14 03:22:20.350572: Epoch time: 102.16 s +2026-04-14 03:22:21.606636: +2026-04-14 03:22:21.608761: Epoch 3093 +2026-04-14 03:22:21.611244: Current learning rate: 0.00263 +2026-04-14 03:24:04.419137: train_loss -0.4251 +2026-04-14 03:24:04.425991: val_loss -0.3721 +2026-04-14 03:24:04.429853: Pseudo dice [0.4552, 0.215, 0.8229, 0.7115, 0.4523, 0.5667, 0.8208] +2026-04-14 03:24:04.433079: Epoch time: 102.82 s +2026-04-14 03:24:05.668786: +2026-04-14 03:24:05.671084: Epoch 3094 +2026-04-14 03:24:05.676828: Current learning rate: 0.00263 +2026-04-14 03:25:47.600023: train_loss -0.4398 +2026-04-14 03:25:47.606627: val_loss -0.3694 +2026-04-14 03:25:47.608914: Pseudo dice [0.4066, 0.6754, 0.7906, 0.5559, 0.608, 0.9167, 0.7246] +2026-04-14 03:25:47.611589: Epoch time: 101.93 s +2026-04-14 03:25:48.837111: +2026-04-14 03:25:48.838916: Epoch 3095 +2026-04-14 03:25:48.841124: Current learning rate: 0.00263 +2026-04-14 03:27:31.857902: train_loss -0.4227 +2026-04-14 03:27:31.866128: val_loss -0.3723 +2026-04-14 03:27:31.869326: Pseudo dice [0.6364, 0.6379, 0.7627, 0.3515, 0.4959, 0.9376, 0.7468] +2026-04-14 03:27:31.872830: Epoch time: 103.02 s +2026-04-14 03:27:33.101491: +2026-04-14 03:27:33.103382: Epoch 3096 +2026-04-14 03:27:33.105270: Current learning rate: 0.00262 +2026-04-14 03:29:15.251744: train_loss -0.4363 +2026-04-14 03:29:15.260564: val_loss -0.3501 +2026-04-14 03:29:15.263100: Pseudo dice [0.8074, 0.6217, 0.6646, 0.3903, 0.488, 0.6134, 0.8498] +2026-04-14 03:29:15.265682: Epoch time: 102.15 s +2026-04-14 03:29:16.504077: +2026-04-14 03:29:16.506399: Epoch 3097 +2026-04-14 03:29:16.508661: Current learning rate: 0.00262 +2026-04-14 03:30:58.562066: train_loss -0.419 +2026-04-14 03:30:58.569062: val_loss -0.3167 +2026-04-14 03:30:58.572085: Pseudo dice [0.4742, 0.9125, 0.8026, 0.3713, 0.3883, 0.5244, 0.4174] +2026-04-14 03:30:58.575246: Epoch time: 102.06 s +2026-04-14 03:30:59.809240: +2026-04-14 03:30:59.811840: Epoch 3098 +2026-04-14 03:30:59.814167: Current learning rate: 0.00262 +2026-04-14 03:32:42.065077: train_loss -0.4111 +2026-04-14 03:32:42.072378: val_loss -0.3375 +2026-04-14 03:32:42.074674: Pseudo dice [0.3409, 0.9033, 0.6306, 0.2023, 0.1754, 0.764, 0.7527] +2026-04-14 03:32:42.077495: Epoch time: 102.26 s +2026-04-14 03:32:43.327577: +2026-04-14 03:32:43.330007: Epoch 3099 +2026-04-14 03:32:43.333045: Current learning rate: 0.00261 +2026-04-14 03:34:25.745449: train_loss -0.4104 +2026-04-14 03:34:25.752295: val_loss -0.3599 +2026-04-14 03:34:25.754152: Pseudo dice [0.6418, 0.6163, 0.8231, 0.2788, 0.6174, 0.6054, 0.6707] +2026-04-14 03:34:25.756693: Epoch time: 102.42 s +2026-04-14 03:34:28.628402: +2026-04-14 03:34:28.631544: Epoch 3100 +2026-04-14 03:34:28.633229: Current learning rate: 0.00261 +2026-04-14 03:36:10.968437: train_loss -0.4287 +2026-04-14 03:36:10.977998: val_loss -0.3885 +2026-04-14 03:36:10.980118: Pseudo dice [0.6599, 0.7544, 0.7367, 0.5742, 0.4254, 0.8039, 0.8275] +2026-04-14 03:36:10.982437: Epoch time: 102.34 s +2026-04-14 03:36:12.257826: +2026-04-14 03:36:12.260063: Epoch 3101 +2026-04-14 03:36:12.262856: Current learning rate: 0.00261 +2026-04-14 03:37:54.994998: train_loss -0.4377 +2026-04-14 03:37:55.002185: val_loss -0.3836 +2026-04-14 03:37:55.004632: Pseudo dice [0.5769, 0.8558, 0.7232, 0.4791, 0.5393, 0.723, 0.8264] +2026-04-14 03:37:55.007153: Epoch time: 102.74 s +2026-04-14 03:37:56.241348: +2026-04-14 03:37:56.243297: Epoch 3102 +2026-04-14 03:37:56.245357: Current learning rate: 0.00261 +2026-04-14 03:39:37.861625: train_loss -0.4322 +2026-04-14 03:39:37.870449: val_loss -0.3469 +2026-04-14 03:39:37.872589: Pseudo dice [0.4555, 0.8724, 0.6022, 0.3717, 0.3522, 0.2728, 0.6567] +2026-04-14 03:39:37.875602: Epoch time: 101.62 s +2026-04-14 03:39:39.105940: +2026-04-14 03:39:39.108094: Epoch 3103 +2026-04-14 03:39:39.110178: Current learning rate: 0.0026 +2026-04-14 03:41:21.597534: train_loss -0.4326 +2026-04-14 03:41:21.604368: val_loss -0.4008 +2026-04-14 03:41:21.607211: Pseudo dice [0.662, 0.708, 0.8414, 0.5971, 0.4535, 0.5554, 0.724] +2026-04-14 03:41:21.609878: Epoch time: 102.49 s +2026-04-14 03:41:24.010299: +2026-04-14 03:41:24.012035: Epoch 3104 +2026-04-14 03:41:24.013927: Current learning rate: 0.0026 +2026-04-14 03:43:06.505911: train_loss -0.4414 +2026-04-14 03:43:06.513967: val_loss -0.3789 +2026-04-14 03:43:06.517628: Pseudo dice [0.8175, 0.7983, 0.7249, 0.6369, 0.2856, 0.8758, 0.6811] +2026-04-14 03:43:06.520935: Epoch time: 102.5 s +2026-04-14 03:43:07.764476: +2026-04-14 03:43:07.766729: Epoch 3105 +2026-04-14 03:43:07.769134: Current learning rate: 0.0026 +2026-04-14 03:44:49.393730: train_loss -0.4367 +2026-04-14 03:44:49.400321: val_loss -0.367 +2026-04-14 03:44:49.402279: Pseudo dice [0.8511, 0.8688, 0.7255, 0.4567, 0.306, 0.6557, 0.4589] +2026-04-14 03:44:49.405299: Epoch time: 101.63 s +2026-04-14 03:44:50.634813: +2026-04-14 03:44:50.638246: Epoch 3106 +2026-04-14 03:44:50.639978: Current learning rate: 0.0026 +2026-04-14 03:46:33.005179: train_loss -0.4269 +2026-04-14 03:46:33.021039: val_loss -0.372 +2026-04-14 03:46:33.023474: Pseudo dice [0.6967, 0.6185, 0.7025, 0.8072, 0.4411, 0.7121, 0.8002] +2026-04-14 03:46:33.026522: Epoch time: 102.37 s +2026-04-14 03:46:34.244864: +2026-04-14 03:46:34.247319: Epoch 3107 +2026-04-14 03:46:34.249863: Current learning rate: 0.00259 +2026-04-14 03:48:15.681304: train_loss -0.4365 +2026-04-14 03:48:15.690528: val_loss -0.3384 +2026-04-14 03:48:15.693295: Pseudo dice [0.7229, 0.9057, 0.8036, 0.0813, 0.4439, 0.734, 0.7002] +2026-04-14 03:48:15.696559: Epoch time: 101.44 s +2026-04-14 03:48:16.949201: +2026-04-14 03:48:16.952233: Epoch 3108 +2026-04-14 03:48:16.956428: Current learning rate: 0.00259 +2026-04-14 03:49:59.397569: train_loss -0.4302 +2026-04-14 03:49:59.404092: val_loss -0.4001 +2026-04-14 03:49:59.407990: Pseudo dice [0.6623, 0.6684, 0.7331, 0.592, 0.5653, 0.7427, 0.796] +2026-04-14 03:49:59.411767: Epoch time: 102.45 s +2026-04-14 03:50:00.640891: +2026-04-14 03:50:00.642851: Epoch 3109 +2026-04-14 03:50:00.644814: Current learning rate: 0.00259 +2026-04-14 03:51:42.833570: train_loss -0.4053 +2026-04-14 03:51:42.840656: val_loss -0.341 +2026-04-14 03:51:42.842361: Pseudo dice [0.6812, 0.5656, 0.7723, 0.7122, 0.412, 0.7406, 0.5028] +2026-04-14 03:51:42.844827: Epoch time: 102.2 s +2026-04-14 03:51:44.065061: +2026-04-14 03:51:44.067165: Epoch 3110 +2026-04-14 03:51:44.069339: Current learning rate: 0.00259 +2026-04-14 03:53:26.001359: train_loss -0.422 +2026-04-14 03:53:26.011251: val_loss -0.3897 +2026-04-14 03:53:26.014968: Pseudo dice [0.6729, 0.7768, 0.8062, 0.7941, 0.3844, 0.9038, 0.7102] +2026-04-14 03:53:26.017636: Epoch time: 101.94 s +2026-04-14 03:53:27.257051: +2026-04-14 03:53:27.269543: Epoch 3111 +2026-04-14 03:53:27.272292: Current learning rate: 0.00258 +2026-04-14 03:55:10.122545: train_loss -0.428 +2026-04-14 03:55:10.130300: val_loss -0.3846 +2026-04-14 03:55:10.134074: Pseudo dice [0.5765, 0.9052, 0.8247, 0.6872, 0.6287, 0.756, 0.8181] +2026-04-14 03:55:10.137214: Epoch time: 102.87 s +2026-04-14 03:55:10.140317: Yayy! New best EMA pseudo Dice: 0.6523 +2026-04-14 03:55:12.963389: +2026-04-14 03:55:12.965828: Epoch 3112 +2026-04-14 03:55:12.967520: Current learning rate: 0.00258 +2026-04-14 03:56:55.508491: train_loss -0.4273 +2026-04-14 03:56:55.515343: val_loss -0.3857 +2026-04-14 03:56:55.517005: Pseudo dice [0.6327, 0.9045, 0.6277, 0.3802, 0.5834, 0.4141, 0.7395] +2026-04-14 03:56:55.519064: Epoch time: 102.55 s +2026-04-14 03:56:56.742421: +2026-04-14 03:56:56.744484: Epoch 3113 +2026-04-14 03:56:56.747918: Current learning rate: 0.00258 +2026-04-14 03:58:38.310051: train_loss -0.4407 +2026-04-14 03:58:38.316798: val_loss -0.361 +2026-04-14 03:58:38.319493: Pseudo dice [0.3255, 0.7702, 0.7887, 0.7326, 0.5278, 0.8697, 0.8421] +2026-04-14 03:58:38.322031: Epoch time: 101.57 s +2026-04-14 03:58:38.324432: Yayy! New best EMA pseudo Dice: 0.6528 +2026-04-14 03:58:41.596054: +2026-04-14 03:58:41.599567: Epoch 3114 +2026-04-14 03:58:41.602928: Current learning rate: 0.00258 +2026-04-14 04:00:24.396350: train_loss -0.4351 +2026-04-14 04:00:24.402700: val_loss -0.384 +2026-04-14 04:00:24.404729: Pseudo dice [0.6095, 0.73, 0.7344, 0.692, 0.5149, 0.9329, 0.818] +2026-04-14 04:00:24.406665: Epoch time: 102.8 s +2026-04-14 04:00:24.409159: Yayy! New best EMA pseudo Dice: 0.6594 +2026-04-14 04:00:27.325515: +2026-04-14 04:00:27.327906: Epoch 3115 +2026-04-14 04:00:27.329583: Current learning rate: 0.00257 +2026-04-14 04:02:09.260936: train_loss -0.4414 +2026-04-14 04:02:09.267035: val_loss -0.3983 +2026-04-14 04:02:09.269589: Pseudo dice [0.4344, 0.7418, 0.754, 0.5422, 0.5985, 0.7086, 0.6804] +2026-04-14 04:02:09.272382: Epoch time: 101.94 s +2026-04-14 04:02:10.494885: +2026-04-14 04:02:10.498584: Epoch 3116 +2026-04-14 04:02:10.501324: Current learning rate: 0.00257 +2026-04-14 04:03:53.165454: train_loss -0.4317 +2026-04-14 04:03:53.171234: val_loss -0.3625 +2026-04-14 04:03:53.173270: Pseudo dice [0.6944, 0.5931, 0.632, 0.0489, 0.5018, 0.6938, 0.5993] +2026-04-14 04:03:53.175704: Epoch time: 102.67 s +2026-04-14 04:03:54.413384: +2026-04-14 04:03:54.416867: Epoch 3117 +2026-04-14 04:03:54.420194: Current learning rate: 0.00257 +2026-04-14 04:05:36.828269: train_loss -0.4438 +2026-04-14 04:05:36.836150: val_loss -0.3715 +2026-04-14 04:05:36.838953: Pseudo dice [0.7257, 0.8908, 0.8093, 0.302, 0.5632, 0.4767, 0.589] +2026-04-14 04:05:36.843643: Epoch time: 102.42 s +2026-04-14 04:05:38.078537: +2026-04-14 04:05:38.080598: Epoch 3118 +2026-04-14 04:05:38.082630: Current learning rate: 0.00256 +2026-04-14 04:07:19.607156: train_loss -0.4392 +2026-04-14 04:07:19.614924: val_loss -0.3597 +2026-04-14 04:07:19.616967: Pseudo dice [0.6431, 0.863, 0.7672, 0.5378, 0.5813, 0.642, 0.3312] +2026-04-14 04:07:19.619765: Epoch time: 101.53 s +2026-04-14 04:07:20.843503: +2026-04-14 04:07:20.845494: Epoch 3119 +2026-04-14 04:07:20.847960: Current learning rate: 0.00256 +2026-04-14 04:09:03.242733: train_loss -0.4476 +2026-04-14 04:09:03.250250: val_loss -0.3568 +2026-04-14 04:09:03.258028: Pseudo dice [0.6026, 0.8247, 0.7656, 0.3997, 0.6345, 0.7075, 0.3474] +2026-04-14 04:09:03.260761: Epoch time: 102.4 s +2026-04-14 04:09:04.521207: +2026-04-14 04:09:04.524046: Epoch 3120 +2026-04-14 04:09:04.526602: Current learning rate: 0.00256 +2026-04-14 04:10:47.134970: train_loss -0.4518 +2026-04-14 04:10:47.141116: val_loss -0.3865 +2026-04-14 04:10:47.143425: Pseudo dice [0.8788, 0.6521, 0.7671, 0.2187, 0.582, 0.8579, 0.5632] +2026-04-14 04:10:47.145828: Epoch time: 102.62 s +2026-04-14 04:10:48.394590: +2026-04-14 04:10:48.396638: Epoch 3121 +2026-04-14 04:10:48.400113: Current learning rate: 0.00256 +2026-04-14 04:12:31.824724: train_loss -0.4366 +2026-04-14 04:12:31.830466: val_loss -0.3733 +2026-04-14 04:12:31.832299: Pseudo dice [0.5196, 0.8832, 0.6891, 0.4214, 0.5466, 0.8658, 0.7153] +2026-04-14 04:12:31.835040: Epoch time: 103.43 s +2026-04-14 04:12:34.205976: +2026-04-14 04:12:34.207640: Epoch 3122 +2026-04-14 04:12:34.209488: Current learning rate: 0.00255 +2026-04-14 04:14:16.391985: train_loss -0.4374 +2026-04-14 04:14:16.398963: val_loss -0.3764 +2026-04-14 04:14:16.401368: Pseudo dice [0.6348, 0.6679, 0.7553, 0.4398, 0.2754, 0.9269, 0.7557] +2026-04-14 04:14:16.403500: Epoch time: 102.19 s +2026-04-14 04:14:17.620834: +2026-04-14 04:14:17.622684: Epoch 3123 +2026-04-14 04:14:17.624813: Current learning rate: 0.00255 +2026-04-14 04:15:59.008546: train_loss -0.4344 +2026-04-14 04:15:59.014878: val_loss -0.3896 +2026-04-14 04:15:59.017907: Pseudo dice [0.6169, 0.7212, 0.7735, 0.8695, 0.4105, 0.9162, 0.7778] +2026-04-14 04:15:59.020159: Epoch time: 101.39 s +2026-04-14 04:16:00.246863: +2026-04-14 04:16:00.249791: Epoch 3124 +2026-04-14 04:16:00.252705: Current learning rate: 0.00255 +2026-04-14 04:17:43.121932: train_loss -0.4324 +2026-04-14 04:17:43.130258: val_loss -0.3667 +2026-04-14 04:17:43.134066: Pseudo dice [0.6126, 0.5907, 0.808, 0.1431, 0.5857, 0.8876, 0.5724] +2026-04-14 04:17:43.136953: Epoch time: 102.88 s +2026-04-14 04:17:44.391309: +2026-04-14 04:17:44.393286: Epoch 3125 +2026-04-14 04:17:44.395483: Current learning rate: 0.00255 +2026-04-14 04:19:26.266981: train_loss -0.4295 +2026-04-14 04:19:26.275284: val_loss -0.3809 +2026-04-14 04:19:26.277687: Pseudo dice [0.6911, 0.8646, 0.8685, 0.5353, 0.3162, 0.2489, 0.8405] +2026-04-14 04:19:26.280119: Epoch time: 101.88 s +2026-04-14 04:19:27.549068: +2026-04-14 04:19:27.553068: Epoch 3126 +2026-04-14 04:19:27.557241: Current learning rate: 0.00254 +2026-04-14 04:21:10.033382: train_loss -0.4492 +2026-04-14 04:21:10.042482: val_loss -0.3549 +2026-04-14 04:21:10.044672: Pseudo dice [0.7333, 0.5841, 0.7532, 0.4996, 0.1486, 0.7766, 0.6621] +2026-04-14 04:21:10.046930: Epoch time: 102.49 s +2026-04-14 04:21:11.255154: +2026-04-14 04:21:11.257560: Epoch 3127 +2026-04-14 04:21:11.259723: Current learning rate: 0.00254 +2026-04-14 04:22:53.135492: train_loss -0.4393 +2026-04-14 04:22:53.143054: val_loss -0.3896 +2026-04-14 04:22:53.144778: Pseudo dice [0.7011, 0.7036, 0.6793, 0.6263, 0.5763, 0.8165, 0.853] +2026-04-14 04:22:53.146791: Epoch time: 101.88 s +2026-04-14 04:22:54.371779: +2026-04-14 04:22:54.374139: Epoch 3128 +2026-04-14 04:22:54.376484: Current learning rate: 0.00254 +2026-04-14 04:24:36.428344: train_loss -0.4312 +2026-04-14 04:24:36.439046: val_loss -0.3749 +2026-04-14 04:24:36.443968: Pseudo dice [0.8661, 0.8478, 0.8066, 0.2068, 0.5286, 0.8618, 0.7688] +2026-04-14 04:24:36.447515: Epoch time: 102.06 s +2026-04-14 04:24:37.683829: +2026-04-14 04:24:37.686022: Epoch 3129 +2026-04-14 04:24:37.687958: Current learning rate: 0.00254 +2026-04-14 04:26:19.388956: train_loss -0.4341 +2026-04-14 04:26:19.396333: val_loss -0.4005 +2026-04-14 04:26:19.398623: Pseudo dice [0.6776, 0.7819, 0.7959, 0.5201, 0.5993, 0.8605, 0.5865] +2026-04-14 04:26:19.401366: Epoch time: 101.71 s +2026-04-14 04:26:20.627971: +2026-04-14 04:26:20.629494: Epoch 3130 +2026-04-14 04:26:20.631334: Current learning rate: 0.00253 +2026-04-14 04:28:02.094512: train_loss -0.4423 +2026-04-14 04:28:02.101530: val_loss -0.3909 +2026-04-14 04:28:02.103692: Pseudo dice [0.5159, 0.8701, 0.735, 0.8051, 0.4869, 0.8659, 0.8472] +2026-04-14 04:28:02.105769: Epoch time: 101.47 s +2026-04-14 04:28:02.108185: Yayy! New best EMA pseudo Dice: 0.6616 +2026-04-14 04:28:05.206239: +2026-04-14 04:28:05.208808: Epoch 3131 +2026-04-14 04:28:05.210332: Current learning rate: 0.00253 +2026-04-14 04:29:47.103595: train_loss -0.4373 +2026-04-14 04:29:47.109678: val_loss -0.3393 +2026-04-14 04:29:47.111507: Pseudo dice [0.415, 0.4342, 0.7225, 0.4713, 0.483, 0.7131, 0.5233] +2026-04-14 04:29:47.113872: Epoch time: 101.9 s +2026-04-14 04:29:48.352767: +2026-04-14 04:29:48.355319: Epoch 3132 +2026-04-14 04:29:48.358360: Current learning rate: 0.00253 +2026-04-14 04:31:30.011168: train_loss -0.4215 +2026-04-14 04:31:30.018125: val_loss -0.379 +2026-04-14 04:31:30.020334: Pseudo dice [0.791, 0.7808, 0.8125, 0.6413, 0.47, 0.9129, 0.6969] +2026-04-14 04:31:30.032173: Epoch time: 101.66 s +2026-04-14 04:31:31.251600: +2026-04-14 04:31:31.253936: Epoch 3133 +2026-04-14 04:31:31.256332: Current learning rate: 0.00253 +2026-04-14 04:33:13.292880: train_loss -0.4378 +2026-04-14 04:33:13.299096: val_loss -0.3609 +2026-04-14 04:33:13.301037: Pseudo dice [0.4393, 0.9019, 0.7477, 0.321, 0.4054, 0.7659, 0.6474] +2026-04-14 04:33:13.303239: Epoch time: 102.04 s +2026-04-14 04:33:14.558545: +2026-04-14 04:33:14.560576: Epoch 3134 +2026-04-14 04:33:14.562653: Current learning rate: 0.00252 +2026-04-14 04:34:56.365237: train_loss -0.4384 +2026-04-14 04:34:56.372261: val_loss -0.3686 +2026-04-14 04:34:56.374216: Pseudo dice [0.8079, 0.6575, 0.7899, 0.3617, 0.1906, 0.7768, 0.7066] +2026-04-14 04:34:56.378987: Epoch time: 101.81 s +2026-04-14 04:34:57.641798: +2026-04-14 04:34:57.643926: Epoch 3135 +2026-04-14 04:34:57.646075: Current learning rate: 0.00252 +2026-04-14 04:36:39.748780: train_loss -0.4373 +2026-04-14 04:36:39.755507: val_loss -0.3582 +2026-04-14 04:36:39.758142: Pseudo dice [0.7712, 0.9174, 0.6323, 0.7105, 0.2154, 0.6845, 0.7081] +2026-04-14 04:36:39.762843: Epoch time: 102.11 s +2026-04-14 04:36:40.992352: +2026-04-14 04:36:40.995491: Epoch 3136 +2026-04-14 04:36:41.013309: Current learning rate: 0.00252 +2026-04-14 04:38:23.777989: train_loss -0.4402 +2026-04-14 04:38:23.787588: val_loss -0.3653 +2026-04-14 04:38:23.789893: Pseudo dice [0.6919, 0.0547, 0.8024, 0.1699, 0.5457, 0.9222, 0.7752] +2026-04-14 04:38:23.793609: Epoch time: 102.79 s +2026-04-14 04:38:25.030836: +2026-04-14 04:38:25.032737: Epoch 3137 +2026-04-14 04:38:25.034770: Current learning rate: 0.00252 +2026-04-14 04:40:07.029762: train_loss -0.4387 +2026-04-14 04:40:07.039479: val_loss -0.3911 +2026-04-14 04:40:07.041671: Pseudo dice [0.8362, 0.7251, 0.7795, 0.5657, 0.4471, 0.6699, 0.7923] +2026-04-14 04:40:07.044501: Epoch time: 102.0 s +2026-04-14 04:40:08.265850: +2026-04-14 04:40:08.267653: Epoch 3138 +2026-04-14 04:40:08.269919: Current learning rate: 0.00251 +2026-04-14 04:41:50.316114: train_loss -0.4143 +2026-04-14 04:41:50.324187: val_loss -0.3803 +2026-04-14 04:41:50.326558: Pseudo dice [0.5947, 0.7788, 0.7731, 0.2885, 0.4051, 0.7533, 0.7835] +2026-04-14 04:41:50.329332: Epoch time: 102.05 s +2026-04-14 04:41:51.556011: +2026-04-14 04:41:51.559103: Epoch 3139 +2026-04-14 04:41:51.561265: Current learning rate: 0.00251 +2026-04-14 04:43:33.157090: train_loss -0.422 +2026-04-14 04:43:33.164397: val_loss -0.3524 +2026-04-14 04:43:33.167306: Pseudo dice [0.6844, 0.4765, 0.7772, 0.7179, 0.2052, 0.8291, 0.8257] +2026-04-14 04:43:33.170639: Epoch time: 101.6 s +2026-04-14 04:43:34.413210: +2026-04-14 04:43:34.415251: Epoch 3140 +2026-04-14 04:43:34.417408: Current learning rate: 0.00251 +2026-04-14 04:45:16.610295: train_loss -0.4323 +2026-04-14 04:45:16.617594: val_loss -0.3674 +2026-04-14 04:45:16.619857: Pseudo dice [0.5244, 0.5835, 0.7917, 0.6967, 0.6303, 0.6749, 0.6486] +2026-04-14 04:45:16.622154: Epoch time: 102.2 s +2026-04-14 04:45:17.887947: +2026-04-14 04:45:17.889749: Epoch 3141 +2026-04-14 04:45:17.891451: Current learning rate: 0.0025 +2026-04-14 04:47:00.496564: train_loss -0.4201 +2026-04-14 04:47:00.503454: val_loss -0.3978 +2026-04-14 04:47:00.505685: Pseudo dice [0.5734, 0.564, 0.7673, 0.6562, 0.5262, 0.7992, 0.7975] +2026-04-14 04:47:00.509202: Epoch time: 102.61 s +2026-04-14 04:47:02.842541: +2026-04-14 04:47:02.844318: Epoch 3142 +2026-04-14 04:47:02.846192: Current learning rate: 0.0025 +2026-04-14 04:48:45.175149: train_loss -0.4311 +2026-04-14 04:48:45.181858: val_loss -0.39 +2026-04-14 04:48:45.184409: Pseudo dice [0.5615, 0.34, 0.7311, 0.7988, 0.4435, 0.6893, 0.7881] +2026-04-14 04:48:45.186495: Epoch time: 102.34 s +2026-04-14 04:48:46.398992: +2026-04-14 04:48:46.402717: Epoch 3143 +2026-04-14 04:48:46.404960: Current learning rate: 0.0025 +2026-04-14 04:50:28.690878: train_loss -0.4301 +2026-04-14 04:50:28.697412: val_loss -0.4004 +2026-04-14 04:50:28.700122: Pseudo dice [0.6612, 0.6735, 0.7922, 0.3463, 0.5253, 0.8712, 0.8589] +2026-04-14 04:50:28.702074: Epoch time: 102.29 s +2026-04-14 04:50:29.936006: +2026-04-14 04:50:29.938353: Epoch 3144 +2026-04-14 04:50:29.941276: Current learning rate: 0.0025 +2026-04-14 04:52:12.725632: train_loss -0.4222 +2026-04-14 04:52:12.732382: val_loss -0.3709 +2026-04-14 04:52:12.734951: Pseudo dice [0.7794, 0.509, 0.7867, 0.737, 0.5073, 0.9421, 0.7283] +2026-04-14 04:52:12.738170: Epoch time: 102.79 s +2026-04-14 04:52:13.984759: +2026-04-14 04:52:13.988805: Epoch 3145 +2026-04-14 04:52:13.991723: Current learning rate: 0.00249 +2026-04-14 04:53:55.791845: train_loss -0.43 +2026-04-14 04:53:55.799322: val_loss -0.3603 +2026-04-14 04:53:55.801924: Pseudo dice [0.6724, 0.2163, 0.7159, 0.3841, 0.5144, 0.8436, 0.8523] +2026-04-14 04:53:55.804705: Epoch time: 101.81 s +2026-04-14 04:53:57.031037: +2026-04-14 04:53:57.033247: Epoch 3146 +2026-04-14 04:53:57.035140: Current learning rate: 0.00249 +2026-04-14 04:55:38.827085: train_loss -0.4246 +2026-04-14 04:55:38.835182: val_loss -0.3663 +2026-04-14 04:55:38.837260: Pseudo dice [0.4986, 0.5538, 0.7862, 0.4233, 0.5759, 0.2072, 0.6656] +2026-04-14 04:55:38.839822: Epoch time: 101.8 s +2026-04-14 04:55:40.097600: +2026-04-14 04:55:40.100531: Epoch 3147 +2026-04-14 04:55:40.102890: Current learning rate: 0.00249 +2026-04-14 04:57:22.259177: train_loss -0.4242 +2026-04-14 04:57:22.266311: val_loss -0.3528 +2026-04-14 04:57:22.280386: Pseudo dice [0.7628, 0.2892, 0.5496, 0.449, 0.4569, 0.522, 0.3384] +2026-04-14 04:57:22.283926: Epoch time: 102.16 s +2026-04-14 04:57:23.533581: +2026-04-14 04:57:23.536935: Epoch 3148 +2026-04-14 04:57:23.539518: Current learning rate: 0.00249 +2026-04-14 04:59:05.449887: train_loss -0.4006 +2026-04-14 04:59:05.458789: val_loss -0.3194 +2026-04-14 04:59:05.461039: Pseudo dice [0.5981, 0.8705, 0.722, 0.25, 0.4853, 0.1282, 0.744] +2026-04-14 04:59:05.464468: Epoch time: 101.92 s +2026-04-14 04:59:06.679832: +2026-04-14 04:59:06.681784: Epoch 3149 +2026-04-14 04:59:06.684199: Current learning rate: 0.00248 +2026-04-14 05:00:48.093632: train_loss -0.4335 +2026-04-14 05:00:48.100332: val_loss -0.385 +2026-04-14 05:00:48.102494: Pseudo dice [0.6714, 0.4353, 0.674, 0.4724, 0.4524, 0.9283, 0.7183] +2026-04-14 05:00:48.105734: Epoch time: 101.42 s +2026-04-14 05:00:51.073626: +2026-04-14 05:00:51.076315: Epoch 3150 +2026-04-14 05:00:51.077976: Current learning rate: 0.00248 +2026-04-14 05:02:32.534357: train_loss -0.4397 +2026-04-14 05:02:32.541640: val_loss -0.3976 +2026-04-14 05:02:32.543787: Pseudo dice [0.8225, 0.8963, 0.7639, 0.8169, 0.6119, 0.8366, 0.8339] +2026-04-14 05:02:32.547344: Epoch time: 101.46 s +2026-04-14 05:02:33.767185: +2026-04-14 05:02:33.769223: Epoch 3151 +2026-04-14 05:02:33.773897: Current learning rate: 0.00248 +2026-04-14 05:04:15.792311: train_loss -0.4328 +2026-04-14 05:04:15.805024: val_loss -0.3567 +2026-04-14 05:04:15.807864: Pseudo dice [0.8505, 0.9135, 0.7597, 0.3577, 0.4658, 0.1488, 0.837] +2026-04-14 05:04:15.812196: Epoch time: 102.03 s +2026-04-14 05:04:17.063186: +2026-04-14 05:04:17.065084: Epoch 3152 +2026-04-14 05:04:17.067165: Current learning rate: 0.00248 +2026-04-14 05:05:59.321006: train_loss -0.4379 +2026-04-14 05:05:59.343330: val_loss -0.3878 +2026-04-14 05:05:59.345410: Pseudo dice [0.8118, 0.7543, 0.7588, 0.7637, 0.5423, 0.4779, 0.7938] +2026-04-14 05:05:59.350917: Epoch time: 102.26 s +2026-04-14 05:06:00.547876: +2026-04-14 05:06:00.549502: Epoch 3153 +2026-04-14 05:06:00.551367: Current learning rate: 0.00247 +2026-04-14 05:07:42.445931: train_loss -0.4351 +2026-04-14 05:07:42.463453: val_loss -0.3579 +2026-04-14 05:07:42.466177: Pseudo dice [0.3223, 0.7108, 0.6611, 0.4178, 0.5663, 0.7985, 0.7198] +2026-04-14 05:07:42.468789: Epoch time: 101.9 s +2026-04-14 05:07:43.697543: +2026-04-14 05:07:43.699399: Epoch 3154 +2026-04-14 05:07:43.701343: Current learning rate: 0.00247 +2026-04-14 05:09:26.080363: train_loss -0.4272 +2026-04-14 05:09:26.088376: val_loss -0.3766 +2026-04-14 05:09:26.092623: Pseudo dice [0.7094, 0.5177, 0.7951, 0.1274, 0.3584, 0.6896, 0.8399] +2026-04-14 05:09:26.095108: Epoch time: 102.39 s +2026-04-14 05:09:27.322287: +2026-04-14 05:09:27.324261: Epoch 3155 +2026-04-14 05:09:27.326205: Current learning rate: 0.00247 +2026-04-14 05:11:09.088726: train_loss -0.4311 +2026-04-14 05:11:09.097628: val_loss -0.3627 +2026-04-14 05:11:09.100180: Pseudo dice [0.611, 0.9216, 0.8257, 0.4993, 0.4374, 0.6545, 0.5344] +2026-04-14 05:11:09.103267: Epoch time: 101.77 s +2026-04-14 05:11:10.334533: +2026-04-14 05:11:10.336660: Epoch 3156 +2026-04-14 05:11:10.339477: Current learning rate: 0.00247 +2026-04-14 05:12:53.030383: train_loss -0.4327 +2026-04-14 05:12:53.036950: val_loss -0.3425 +2026-04-14 05:12:53.038547: Pseudo dice [0.6936, 0.8764, 0.8205, 0.4853, 0.4767, 0.4338, 0.2898] +2026-04-14 05:12:53.040532: Epoch time: 102.7 s +2026-04-14 05:12:54.262242: +2026-04-14 05:12:54.264251: Epoch 3157 +2026-04-14 05:12:54.266100: Current learning rate: 0.00246 +2026-04-14 05:14:36.231838: train_loss -0.4318 +2026-04-14 05:14:36.239041: val_loss -0.372 +2026-04-14 05:14:36.241335: Pseudo dice [0.3072, 0.8593, 0.7911, 0.2305, 0.6044, 0.4762, 0.8124] +2026-04-14 05:14:36.244465: Epoch time: 101.97 s +2026-04-14 05:14:37.486684: +2026-04-14 05:14:37.488570: Epoch 3158 +2026-04-14 05:14:37.491072: Current learning rate: 0.00246 +2026-04-14 05:16:19.007837: train_loss -0.437 +2026-04-14 05:16:19.017038: val_loss -0.3617 +2026-04-14 05:16:19.019868: Pseudo dice [0.7051, 0.9002, 0.8251, 0.6145, 0.4685, 0.5312, 0.7949] +2026-04-14 05:16:19.023870: Epoch time: 101.52 s +2026-04-14 05:16:20.231027: +2026-04-14 05:16:20.247867: Epoch 3159 +2026-04-14 05:16:20.252102: Current learning rate: 0.00246 +2026-04-14 05:18:02.124005: train_loss -0.4466 +2026-04-14 05:18:02.132070: val_loss -0.3927 +2026-04-14 05:18:02.134065: Pseudo dice [0.784, 0.8733, 0.7479, 0.6488, 0.3084, 0.8525, 0.8342] +2026-04-14 05:18:02.148564: Epoch time: 101.9 s +2026-04-14 05:18:03.376668: +2026-04-14 05:18:03.379786: Epoch 3160 +2026-04-14 05:18:03.381821: Current learning rate: 0.00245 +2026-04-14 05:19:46.024411: train_loss -0.4359 +2026-04-14 05:19:46.031348: val_loss -0.3665 +2026-04-14 05:19:46.034427: Pseudo dice [0.6998, 0.8275, 0.7364, 0.2672, 0.4599, 0.1524, 0.802] +2026-04-14 05:19:46.037058: Epoch time: 102.65 s +2026-04-14 05:19:47.268536: +2026-04-14 05:19:47.271214: Epoch 3161 +2026-04-14 05:19:47.274112: Current learning rate: 0.00245 +2026-04-14 05:21:30.862526: train_loss -0.4266 +2026-04-14 05:21:30.874364: val_loss -0.3829 +2026-04-14 05:21:30.876447: Pseudo dice [0.2916, 0.6801, 0.783, 0.8357, 0.5008, 0.8789, 0.7817] +2026-04-14 05:21:30.879106: Epoch time: 103.6 s +2026-04-14 05:21:32.132018: +2026-04-14 05:21:32.134041: Epoch 3162 +2026-04-14 05:21:32.136054: Current learning rate: 0.00245 +2026-04-14 05:23:13.440326: train_loss -0.4263 +2026-04-14 05:23:13.451047: val_loss -0.3499 +2026-04-14 05:23:13.453338: Pseudo dice [0.0567, 0.8825, 0.794, 0.3439, 0.6104, 0.6134, 0.326] +2026-04-14 05:23:13.456943: Epoch time: 101.31 s +2026-04-14 05:23:14.697917: +2026-04-14 05:23:14.699840: Epoch 3163 +2026-04-14 05:23:14.704882: Current learning rate: 0.00245 +2026-04-14 05:24:57.082773: train_loss -0.4196 +2026-04-14 05:24:57.092368: val_loss -0.3633 +2026-04-14 05:24:57.094824: Pseudo dice [0.6161, 0.3769, 0.7237, 0.4114, 0.4188, 0.8246, 0.7922] +2026-04-14 05:24:57.097369: Epoch time: 102.39 s +2026-04-14 05:24:58.320372: +2026-04-14 05:24:58.322569: Epoch 3164 +2026-04-14 05:24:58.325042: Current learning rate: 0.00244 +2026-04-14 05:26:40.708728: train_loss -0.4294 +2026-04-14 05:26:40.716059: val_loss -0.3776 +2026-04-14 05:26:40.719203: Pseudo dice [0.4864, 0.2989, 0.7388, 0.2881, 0.4379, 0.8986, 0.8675] +2026-04-14 05:26:40.722115: Epoch time: 102.39 s +2026-04-14 05:26:41.973832: +2026-04-14 05:26:41.975891: Epoch 3165 +2026-04-14 05:26:41.978167: Current learning rate: 0.00244 +2026-04-14 05:28:23.567691: train_loss -0.4334 +2026-04-14 05:28:23.574596: val_loss -0.3996 +2026-04-14 05:28:23.577300: Pseudo dice [0.8435, 0.6197, 0.769, 0.4659, 0.5233, 0.9188, 0.8516] +2026-04-14 05:28:23.579812: Epoch time: 101.6 s +2026-04-14 05:28:24.826959: +2026-04-14 05:28:24.829265: Epoch 3166 +2026-04-14 05:28:24.832589: Current learning rate: 0.00244 +2026-04-14 05:30:06.513476: train_loss -0.4389 +2026-04-14 05:30:06.519155: val_loss -0.4042 +2026-04-14 05:30:06.520971: Pseudo dice [0.754, 0.6296, 0.7686, 0.5961, 0.6142, 0.8892, 0.8334] +2026-04-14 05:30:06.523533: Epoch time: 101.69 s +2026-04-14 05:30:07.784605: +2026-04-14 05:30:07.786651: Epoch 3167 +2026-04-14 05:30:07.789917: Current learning rate: 0.00244 +2026-04-14 05:31:50.241143: train_loss -0.4511 +2026-04-14 05:31:50.247807: val_loss -0.3637 +2026-04-14 05:31:50.250315: Pseudo dice [0.7147, 0.9118, 0.8373, 0.2425, 0.5511, 0.1445, 0.61] +2026-04-14 05:31:50.253119: Epoch time: 102.46 s +2026-04-14 05:31:51.477145: +2026-04-14 05:31:51.479075: Epoch 3168 +2026-04-14 05:31:51.480936: Current learning rate: 0.00243 +2026-04-14 05:33:33.363790: train_loss -0.4403 +2026-04-14 05:33:33.372742: val_loss -0.3862 +2026-04-14 05:33:33.375274: Pseudo dice [0.4795, 0.7817, 0.7769, 0.6912, 0.5235, 0.7315, 0.6651] +2026-04-14 05:33:33.378196: Epoch time: 101.89 s +2026-04-14 05:33:34.647490: +2026-04-14 05:33:34.649656: Epoch 3169 +2026-04-14 05:33:34.651873: Current learning rate: 0.00243 +2026-04-14 05:35:15.990721: train_loss -0.4063 +2026-04-14 05:35:15.999596: val_loss -0.3545 +2026-04-14 05:35:16.002873: Pseudo dice [0.8018, 0.6427, 0.6957, 0.2937, 0.5226, 0.8796, 0.3484] +2026-04-14 05:35:16.006601: Epoch time: 101.35 s +2026-04-14 05:35:17.218120: +2026-04-14 05:35:17.219782: Epoch 3170 +2026-04-14 05:35:17.221920: Current learning rate: 0.00243 +2026-04-14 05:36:59.556346: train_loss -0.4083 +2026-04-14 05:36:59.563614: val_loss -0.3391 +2026-04-14 05:36:59.566167: Pseudo dice [0.812, 0.6766, 0.7965, 0.1003, 0.6218, 0.8121, 0.7129] +2026-04-14 05:36:59.569544: Epoch time: 102.34 s +2026-04-14 05:37:00.800130: +2026-04-14 05:37:00.801793: Epoch 3171 +2026-04-14 05:37:00.803591: Current learning rate: 0.00243 +2026-04-14 05:38:42.522220: train_loss -0.4246 +2026-04-14 05:38:42.530474: val_loss -0.4143 +2026-04-14 05:38:42.533884: Pseudo dice [0.7571, 0.7398, 0.7638, 0.8117, 0.5861, 0.7629, 0.7651] +2026-04-14 05:38:42.539257: Epoch time: 101.73 s +2026-04-14 05:38:43.759758: +2026-04-14 05:38:43.761988: Epoch 3172 +2026-04-14 05:38:43.764927: Current learning rate: 0.00242 +2026-04-14 05:40:25.830482: train_loss -0.4176 +2026-04-14 05:40:25.839362: val_loss -0.3568 +2026-04-14 05:40:25.841745: Pseudo dice [0.3906, 0.8477, 0.7516, 0.4732, 0.5087, 0.5264, 0.8455] +2026-04-14 05:40:25.845309: Epoch time: 102.07 s +2026-04-14 05:40:27.085454: +2026-04-14 05:40:27.087613: Epoch 3173 +2026-04-14 05:40:27.091173: Current learning rate: 0.00242 +2026-04-14 05:42:08.927757: train_loss -0.4232 +2026-04-14 05:42:08.934357: val_loss -0.348 +2026-04-14 05:42:08.936582: Pseudo dice [0.4048, 0.715, 0.7785, 0.1092, 0.5953, 0.3683, 0.5223] +2026-04-14 05:42:08.938775: Epoch time: 101.85 s +2026-04-14 05:42:10.192495: +2026-04-14 05:42:10.194363: Epoch 3174 +2026-04-14 05:42:10.196726: Current learning rate: 0.00242 +2026-04-14 05:43:52.038412: train_loss -0.4187 +2026-04-14 05:43:52.048925: val_loss -0.3747 +2026-04-14 05:43:52.051765: Pseudo dice [0.8113, 0.5064, 0.6691, 0.8801, 0.2104, 0.5016, 0.8595] +2026-04-14 05:43:52.054455: Epoch time: 101.85 s +2026-04-14 05:43:53.296666: +2026-04-14 05:43:53.299153: Epoch 3175 +2026-04-14 05:43:53.301310: Current learning rate: 0.00242 +2026-04-14 05:45:35.167219: train_loss -0.4249 +2026-04-14 05:45:35.186593: val_loss -0.3723 +2026-04-14 05:45:35.188999: Pseudo dice [0.8482, 0.763, 0.7392, 0.4681, 0.5982, 0.8565, 0.4912] +2026-04-14 05:45:35.191571: Epoch time: 101.87 s +2026-04-14 05:45:36.411638: +2026-04-14 05:45:36.416993: Epoch 3176 +2026-04-14 05:45:36.420338: Current learning rate: 0.00241 +2026-04-14 05:47:17.871717: train_loss -0.436 +2026-04-14 05:47:17.879186: val_loss -0.3648 +2026-04-14 05:47:17.881595: Pseudo dice [0.6832, 0.5942, 0.8384, 0.2102, 0.5926, 0.7039, 0.8085] +2026-04-14 05:47:17.884430: Epoch time: 101.46 s +2026-04-14 05:47:19.121520: +2026-04-14 05:47:19.124143: Epoch 3177 +2026-04-14 05:47:19.126382: Current learning rate: 0.00241 +2026-04-14 05:49:01.340593: train_loss -0.4282 +2026-04-14 05:49:01.350697: val_loss -0.3682 +2026-04-14 05:49:01.352898: Pseudo dice [0.6466, 0.9131, 0.8428, 0.6755, 0.522, 0.1215, 0.7903] +2026-04-14 05:49:01.355623: Epoch time: 102.22 s +2026-04-14 05:49:02.582477: +2026-04-14 05:49:02.584823: Epoch 3178 +2026-04-14 05:49:02.587663: Current learning rate: 0.00241 +2026-04-14 05:50:44.924097: train_loss -0.4415 +2026-04-14 05:50:44.930810: val_loss -0.3856 +2026-04-14 05:50:44.933547: Pseudo dice [0.8533, 0.8834, 0.8032, 0.6416, 0.4425, 0.5716, 0.8137] +2026-04-14 05:50:44.936782: Epoch time: 102.34 s +2026-04-14 05:50:46.160223: +2026-04-14 05:50:46.162390: Epoch 3179 +2026-04-14 05:50:46.164975: Current learning rate: 0.0024 +2026-04-14 05:52:28.164984: train_loss -0.4361 +2026-04-14 05:52:28.192338: val_loss -0.3923 +2026-04-14 05:52:28.194893: Pseudo dice [0.8405, 0.6268, 0.8464, 0.2633, 0.5343, 0.5928, 0.8687] +2026-04-14 05:52:28.198895: Epoch time: 102.01 s +2026-04-14 05:52:29.410629: +2026-04-14 05:52:29.412705: Epoch 3180 +2026-04-14 05:52:29.415087: Current learning rate: 0.0024 +2026-04-14 05:54:11.258860: train_loss -0.4179 +2026-04-14 05:54:11.267497: val_loss -0.3621 +2026-04-14 05:54:11.269758: Pseudo dice [0.3124, 0.5148, 0.7453, 0.0329, 0.4179, 0.1721, 0.507] +2026-04-14 05:54:11.271761: Epoch time: 101.85 s +2026-04-14 05:54:12.508752: +2026-04-14 05:54:12.511646: Epoch 3181 +2026-04-14 05:54:12.513943: Current learning rate: 0.0024 +2026-04-14 05:55:55.375098: train_loss -0.4326 +2026-04-14 05:55:55.393716: val_loss -0.3546 +2026-04-14 05:55:55.396352: Pseudo dice [0.7703, 0.3613, 0.7955, 0.1125, 0.5383, 0.6736, 0.5946] +2026-04-14 05:55:55.399019: Epoch time: 102.87 s +2026-04-14 05:55:56.619753: +2026-04-14 05:55:56.621480: Epoch 3182 +2026-04-14 05:55:56.623448: Current learning rate: 0.0024 +2026-04-14 05:57:38.685809: train_loss -0.4283 +2026-04-14 05:57:38.693271: val_loss -0.3843 +2026-04-14 05:57:38.695319: Pseudo dice [0.3469, 0.786, 0.7106, 0.4877, 0.4487, 0.8155, 0.763] +2026-04-14 05:57:38.699197: Epoch time: 102.07 s +2026-04-14 05:57:39.930003: +2026-04-14 05:57:39.932563: Epoch 3183 +2026-04-14 05:57:39.934512: Current learning rate: 0.00239 +2026-04-14 05:59:33.208228: train_loss -0.4205 +2026-04-14 05:59:33.215443: val_loss -0.295 +2026-04-14 05:59:33.217499: Pseudo dice [0.6552, 0.8286, 0.5393, 0.1416, 0.6276, 0.4897, 0.7953] +2026-04-14 05:59:33.221532: Epoch time: 113.28 s +2026-04-14 05:59:34.432930: +2026-04-14 05:59:34.435112: Epoch 3184 +2026-04-14 05:59:34.437119: Current learning rate: 0.00239 +2026-04-14 06:01:21.866484: train_loss -0.4362 +2026-04-14 06:01:21.877484: val_loss -0.3626 +2026-04-14 06:01:21.881290: Pseudo dice [0.3001, 0.7003, 0.7797, 0.3366, 0.5804, 0.8338, 0.6828] +2026-04-14 06:01:21.891903: Epoch time: 107.44 s +2026-04-14 06:01:23.450297: +2026-04-14 06:01:23.452832: Epoch 3185 +2026-04-14 06:01:23.455492: Current learning rate: 0.00239 +2026-04-14 06:04:42.228380: train_loss -0.439 +2026-04-14 06:04:42.237414: val_loss -0.3648 +2026-04-14 06:04:42.240155: Pseudo dice [0.7397, 0.8976, 0.8089, 0.3981, 0.4568, 0.7518, 0.8838] +2026-04-14 06:04:42.243091: Epoch time: 198.78 s +2026-04-14 06:04:43.476471: +2026-04-14 06:04:43.478737: Epoch 3186 +2026-04-14 06:04:43.481526: Current learning rate: 0.00239 +2026-04-14 06:17:02.144829: train_loss -0.4122 +2026-04-14 06:17:02.152291: val_loss -0.3698 +2026-04-14 06:17:02.154754: Pseudo dice [0.7647, 0.75, 0.7978, 0.5523, 0.3416, 0.4874, 0.8352] +2026-04-14 06:17:02.157107: Epoch time: 738.67 s +2026-04-14 06:17:03.718395: +2026-04-14 06:17:03.721232: Epoch 3187 +2026-04-14 06:17:03.723824: Current learning rate: 0.00238 +2026-04-14 06:28:04.689567: train_loss -0.4352 +2026-04-14 06:28:04.695695: val_loss -0.3988 +2026-04-14 06:28:04.698127: Pseudo dice [0.6218, 0.5585, 0.83, 0.5619, 0.5741, 0.8824, 0.7613] +2026-04-14 06:28:04.702007: Epoch time: 660.98 s +2026-04-14 06:28:06.059232: +2026-04-14 06:28:06.061302: Epoch 3188 +2026-04-14 06:28:06.063237: Current learning rate: 0.00238 +2026-04-14 06:30:05.977004: train_loss -0.4386 +2026-04-14 06:30:05.984487: val_loss -0.3548 +2026-04-14 06:30:05.986397: Pseudo dice [0.5958, 0.7573, 0.7622, 0.2236, 0.4398, 0.8091, 0.6021] +2026-04-14 06:30:05.989462: Epoch time: 119.92 s +2026-04-14 06:30:07.279506: +2026-04-14 06:30:07.281391: Epoch 3189 +2026-04-14 06:30:07.283334: Current learning rate: 0.00238 +2026-04-14 06:31:53.387152: train_loss -0.4459 +2026-04-14 06:31:53.396046: val_loss -0.3887 +2026-04-14 06:31:53.398405: Pseudo dice [0.678, 0.7896, 0.6969, 0.3324, 0.5645, 0.8585, 0.8793] +2026-04-14 06:31:53.401252: Epoch time: 106.11 s +2026-04-14 06:31:54.641808: +2026-04-14 06:31:54.643995: Epoch 3190 +2026-04-14 06:31:54.645935: Current learning rate: 0.00238 +2026-04-14 06:33:36.843108: train_loss -0.4395 +2026-04-14 06:33:36.849130: val_loss -0.3507 +2026-04-14 06:33:36.853946: Pseudo dice [0.6941, 0.8644, 0.7828, 0.4087, 0.388, 0.6699, 0.3153] +2026-04-14 06:33:36.856223: Epoch time: 102.2 s +2026-04-14 06:33:38.104039: +2026-04-14 06:33:38.106455: Epoch 3191 +2026-04-14 06:33:38.109194: Current learning rate: 0.00237 +2026-04-14 06:35:20.253692: train_loss -0.432 +2026-04-14 06:35:20.260677: val_loss -0.3913 +2026-04-14 06:35:20.262741: Pseudo dice [0.6348, 0.7568, 0.8002, 0.2671, 0.6444, 0.9347, 0.772] +2026-04-14 06:35:20.265475: Epoch time: 102.15 s +2026-04-14 06:35:21.526186: +2026-04-14 06:35:21.528787: Epoch 3192 +2026-04-14 06:35:21.531755: Current learning rate: 0.00237 +2026-04-14 06:37:03.585851: train_loss -0.4408 +2026-04-14 06:37:03.592278: val_loss -0.3908 +2026-04-14 06:37:03.596166: Pseudo dice [0.4199, 0.8331, 0.7782, 0.5137, 0.6633, 0.88, 0.8336] +2026-04-14 06:37:03.599910: Epoch time: 102.06 s +2026-04-14 06:37:04.860367: +2026-04-14 06:37:04.862311: Epoch 3193 +2026-04-14 06:37:04.864192: Current learning rate: 0.00237 +2026-04-14 06:38:46.913857: train_loss -0.4435 +2026-04-14 06:38:46.919614: val_loss -0.3481 +2026-04-14 06:38:46.921282: Pseudo dice [0.6737, 0.3131, 0.7247, 0.1655, 0.2846, 0.7217, 0.2703] +2026-04-14 06:38:46.923532: Epoch time: 102.06 s +2026-04-14 06:38:48.166714: +2026-04-14 06:38:48.168722: Epoch 3194 +2026-04-14 06:38:48.171231: Current learning rate: 0.00237 +2026-04-14 06:40:30.479268: train_loss -0.4393 +2026-04-14 06:40:30.485552: val_loss -0.3965 +2026-04-14 06:40:30.487944: Pseudo dice [0.4902, 0.411, 0.8641, 0.4426, 0.3397, 0.5506, 0.8169] +2026-04-14 06:40:30.490520: Epoch time: 102.32 s +2026-04-14 06:40:31.728909: +2026-04-14 06:40:31.731407: Epoch 3195 +2026-04-14 06:40:31.733977: Current learning rate: 0.00236 +2026-04-14 06:42:14.157189: train_loss -0.4308 +2026-04-14 06:42:14.165993: val_loss -0.3904 +2026-04-14 06:42:14.168157: Pseudo dice [0.4909, 0.7164, 0.7998, 0.4181, 0.5167, 0.8235, 0.6925] +2026-04-14 06:42:14.170852: Epoch time: 102.43 s +2026-04-14 06:42:15.401087: +2026-04-14 06:42:15.402780: Epoch 3196 +2026-04-14 06:42:15.404909: Current learning rate: 0.00236 +2026-04-14 06:43:57.240818: train_loss -0.4426 +2026-04-14 06:43:57.248612: val_loss -0.3329 +2026-04-14 06:43:57.251584: Pseudo dice [0.6178, 0.143, 0.7494, 0.1591, 0.7047, 0.7579, 0.6667] +2026-04-14 06:43:57.254452: Epoch time: 101.84 s +2026-04-14 06:43:58.485929: +2026-04-14 06:43:58.488372: Epoch 3197 +2026-04-14 06:43:58.490775: Current learning rate: 0.00236 +2026-04-14 06:45:40.916793: train_loss -0.4355 +2026-04-14 06:45:40.924872: val_loss -0.3629 +2026-04-14 06:45:40.928156: Pseudo dice [0.8752, 0.8492, 0.6636, 0.2883, 0.3822, 0.6409, 0.7764] +2026-04-14 06:45:40.931795: Epoch time: 102.43 s +2026-04-14 06:45:42.164566: +2026-04-14 06:45:42.166861: Epoch 3198 +2026-04-14 06:45:42.169236: Current learning rate: 0.00235 +2026-04-14 06:47:24.233128: train_loss -0.4551 +2026-04-14 06:47:24.239670: val_loss -0.3805 +2026-04-14 06:47:24.241598: Pseudo dice [0.6426, 0.4901, 0.7031, 0.5217, 0.6724, 0.2031, 0.8332] +2026-04-14 06:47:24.243913: Epoch time: 102.07 s +2026-04-14 06:47:25.488564: +2026-04-14 06:47:25.490276: Epoch 3199 +2026-04-14 06:47:25.491899: Current learning rate: 0.00235 +2026-04-14 06:49:07.690861: train_loss -0.4308 +2026-04-14 06:49:07.697991: val_loss -0.3659 +2026-04-14 06:49:07.700060: Pseudo dice [0.5961, 0.54, 0.7917, 0.3803, 0.3996, 0.7376, 0.7267] +2026-04-14 06:49:07.702567: Epoch time: 102.21 s +2026-04-14 06:49:10.605783: +2026-04-14 06:49:10.608111: Epoch 3200 +2026-04-14 06:49:10.609704: Current learning rate: 0.00235 +2026-04-14 06:50:53.652749: train_loss -0.4254 +2026-04-14 06:50:53.659315: val_loss -0.2099 +2026-04-14 06:50:53.661937: Pseudo dice [0.6752, 0.8871, 0.4496, 0.1066, 0.2257, 0.1366, 0.8027] +2026-04-14 06:50:53.664997: Epoch time: 103.05 s +2026-04-14 06:50:54.899909: +2026-04-14 06:50:54.901535: Epoch 3201 +2026-04-14 06:50:54.903649: Current learning rate: 0.00235 +2026-04-14 06:52:36.728690: train_loss -0.4276 +2026-04-14 06:52:36.740860: val_loss -0.3748 +2026-04-14 06:52:36.742800: Pseudo dice [0.6688, 0.5259, 0.7899, 0.18, 0.5527, 0.9212, 0.7616] +2026-04-14 06:52:36.745200: Epoch time: 101.83 s +2026-04-14 06:52:38.000225: +2026-04-14 06:52:38.002134: Epoch 3202 +2026-04-14 06:52:38.004000: Current learning rate: 0.00234 +2026-04-14 06:54:29.997948: train_loss -0.448 +2026-04-14 06:54:30.003728: val_loss -0.3771 +2026-04-14 06:54:30.007701: Pseudo dice [0.6793, 0.6428, 0.7037, 0.3793, 0.5032, 0.7869, 0.7102] +2026-04-14 06:54:30.010585: Epoch time: 112.0 s +2026-04-14 06:54:31.583193: +2026-04-14 06:54:31.585206: Epoch 3203 +2026-04-14 06:54:31.587189: Current learning rate: 0.00234 +2026-04-14 06:56:13.648175: train_loss -0.4355 +2026-04-14 06:56:13.656680: val_loss -0.3591 +2026-04-14 06:56:13.658771: Pseudo dice [0.8698, 0.4416, 0.6904, 0.2973, 0.4952, 0.9382, 0.7343] +2026-04-14 06:56:13.664845: Epoch time: 102.07 s +2026-04-14 06:56:14.921858: +2026-04-14 06:56:14.923704: Epoch 3204 +2026-04-14 06:56:14.926225: Current learning rate: 0.00234 +2026-04-14 07:00:14.504051: train_loss -0.4412 +2026-04-14 07:00:14.512216: val_loss -0.34 +2026-04-14 07:00:14.514358: Pseudo dice [0.2921, 0.8794, 0.6419, 0.6591, 0.4466, 0.7818, 0.7822] +2026-04-14 07:00:14.518264: Epoch time: 239.59 s +2026-04-14 07:00:16.235668: +2026-04-14 07:00:16.237643: Epoch 3205 +2026-04-14 07:00:16.239543: Current learning rate: 0.00234 +2026-04-14 07:17:14.749144: train_loss -0.445 +2026-04-14 07:17:14.756202: val_loss -0.3679 +2026-04-14 07:17:14.758475: Pseudo dice [0.5345, 0.7628, 0.776, 0.241, 0.4993, 0.8782, 0.8325] +2026-04-14 07:17:14.760969: Epoch time: 1018.52 s +2026-04-14 07:17:16.553110: +2026-04-14 07:17:16.554798: Epoch 3206 +2026-04-14 07:17:16.556703: Current learning rate: 0.00233 +2026-04-14 07:23:11.779321: train_loss -0.4253 +2026-04-14 07:23:11.788301: val_loss -0.3781 +2026-04-14 07:23:11.790472: Pseudo dice [0.4395, 0.3285, 0.8098, 0.3296, 0.4973, 0.7404, 0.8509] +2026-04-14 07:23:11.793100: Epoch time: 355.23 s +2026-04-14 07:23:13.066520: +2026-04-14 07:23:13.068558: Epoch 3207 +2026-04-14 07:23:13.070907: Current learning rate: 0.00233 +2026-04-14 07:27:54.438282: train_loss -0.4324 +2026-04-14 07:27:54.447094: val_loss -0.3848 +2026-04-14 07:27:54.449717: Pseudo dice [0.7916, 0.8895, 0.8276, 0.4847, 0.4005, 0.6318, 0.402] +2026-04-14 07:27:54.452586: Epoch time: 281.37 s +2026-04-14 07:27:55.735157: +2026-04-14 07:27:55.736676: Epoch 3208 +2026-04-14 07:27:55.739170: Current learning rate: 0.00233 +2026-04-14 07:29:37.716021: train_loss -0.4384 +2026-04-14 07:29:37.721974: val_loss -0.3802 +2026-04-14 07:29:37.724188: Pseudo dice [0.2272, 0.7355, 0.8111, 0.7115, 0.5221, 0.8764, 0.8279] +2026-04-14 07:29:37.726156: Epoch time: 101.98 s +2026-04-14 07:29:39.002817: +2026-04-14 07:29:39.009486: Epoch 3209 +2026-04-14 07:29:39.013288: Current learning rate: 0.00233 +2026-04-14 07:31:21.030057: train_loss -0.4342 +2026-04-14 07:31:21.036474: val_loss -0.3681 +2026-04-14 07:31:21.038406: Pseudo dice [0.2953, 0.6615, 0.8871, 0.2201, 0.4831, 0.8733, 0.7389] +2026-04-14 07:31:21.041547: Epoch time: 102.03 s +2026-04-14 07:31:22.296198: +2026-04-14 07:31:22.297871: Epoch 3210 +2026-04-14 07:31:22.299784: Current learning rate: 0.00232 +2026-04-14 07:33:04.454765: train_loss -0.4243 +2026-04-14 07:33:04.460886: val_loss -0.3869 +2026-04-14 07:33:04.463209: Pseudo dice [0.5266, 0.6278, 0.6696, 0.5109, 0.4616, 0.833, 0.5788] +2026-04-14 07:33:04.465480: Epoch time: 102.16 s +2026-04-14 07:33:05.720765: +2026-04-14 07:33:05.722895: Epoch 3211 +2026-04-14 07:33:05.725128: Current learning rate: 0.00232 +2026-04-14 07:34:47.496284: train_loss -0.4298 +2026-04-14 07:34:47.502595: val_loss -0.3791 +2026-04-14 07:34:47.504873: Pseudo dice [0.6218, 0.9114, 0.8528, 0.3633, 0.3898, 0.7438, 0.8269] +2026-04-14 07:34:47.507742: Epoch time: 101.78 s +2026-04-14 07:34:48.769540: +2026-04-14 07:34:48.771504: Epoch 3212 +2026-04-14 07:34:48.773871: Current learning rate: 0.00232 +2026-04-14 07:36:31.023983: train_loss -0.443 +2026-04-14 07:36:31.031986: val_loss -0.3708 +2026-04-14 07:36:31.034523: Pseudo dice [0.6457, 0.7975, 0.7538, 0.4957, 0.2687, 0.4738, 0.7562] +2026-04-14 07:36:31.037642: Epoch time: 102.26 s +2026-04-14 07:36:32.296572: +2026-04-14 07:36:32.298633: Epoch 3213 +2026-04-14 07:36:32.301022: Current learning rate: 0.00231 +2026-04-14 07:38:14.744391: train_loss -0.4244 +2026-04-14 07:38:14.751297: val_loss -0.3775 +2026-04-14 07:38:14.753476: Pseudo dice [0.4596, 0.8843, 0.7647, 0.0139, 0.5025, 0.8351, 0.7087] +2026-04-14 07:38:14.755473: Epoch time: 102.45 s +2026-04-14 07:38:16.033973: +2026-04-14 07:38:16.035702: Epoch 3214 +2026-04-14 07:38:16.037662: Current learning rate: 0.00231 +2026-04-14 07:39:58.038650: train_loss -0.4292 +2026-04-14 07:39:58.045207: val_loss -0.3833 +2026-04-14 07:39:58.047042: Pseudo dice [0.4665, 0.7385, 0.8171, 0.5635, 0.526, 0.8687, 0.7417] +2026-04-14 07:39:58.049146: Epoch time: 102.01 s +2026-04-14 07:39:59.346912: +2026-04-14 07:39:59.349806: Epoch 3215 +2026-04-14 07:39:59.352199: Current learning rate: 0.00231 +2026-04-14 07:41:41.817554: train_loss -0.4142 +2026-04-14 07:41:41.823595: val_loss -0.3761 +2026-04-14 07:41:41.825979: Pseudo dice [0.6565, 0.7849, 0.6836, 0.3727, 0.1368, 0.5808, 0.8055] +2026-04-14 07:41:41.828355: Epoch time: 102.47 s +2026-04-14 07:41:43.067393: +2026-04-14 07:41:43.068958: Epoch 3216 +2026-04-14 07:41:43.070872: Current learning rate: 0.00231 +2026-04-14 07:43:25.209978: train_loss -0.43 +2026-04-14 07:43:25.216566: val_loss -0.3862 +2026-04-14 07:43:25.218337: Pseudo dice [0.5154, 0.6246, 0.6866, 0.4383, 0.7305, 0.9246, 0.8068] +2026-04-14 07:43:25.220522: Epoch time: 102.15 s +2026-04-14 07:43:26.479543: +2026-04-14 07:43:26.481771: Epoch 3217 +2026-04-14 07:43:26.484004: Current learning rate: 0.0023 +2026-04-14 07:45:08.523152: train_loss -0.429 +2026-04-14 07:45:08.532396: val_loss -0.3799 +2026-04-14 07:45:08.534590: Pseudo dice [0.889, 0.7782, 0.7431, 0.3474, 0.5371, 0.6556, 0.7549] +2026-04-14 07:45:08.537199: Epoch time: 102.05 s +2026-04-14 07:45:09.787475: +2026-04-14 07:45:09.789671: Epoch 3218 +2026-04-14 07:45:09.792624: Current learning rate: 0.0023 +2026-04-14 07:47:41.256526: train_loss -0.4108 +2026-04-14 07:47:41.263042: val_loss -0.3479 +2026-04-14 07:47:41.266057: Pseudo dice [0.3704, 0.9221, 0.6446, 0.2335, 0.4728, 0.3352, 0.7551] +2026-04-14 07:47:41.268789: Epoch time: 151.47 s +2026-04-14 07:47:42.529069: +2026-04-14 07:47:42.531123: Epoch 3219 +2026-04-14 07:47:42.533378: Current learning rate: 0.0023 +2026-04-14 07:50:49.653353: train_loss -0.4312 +2026-04-14 07:50:49.661261: val_loss -0.3869 +2026-04-14 07:50:49.664173: Pseudo dice [0.7788, 0.819, 0.7175, 0.594, 0.4184, 0.8607, 0.7097] +2026-04-14 07:50:49.666932: Epoch time: 187.13 s +2026-04-14 07:50:52.085962: +2026-04-14 07:50:52.088110: Epoch 3220 +2026-04-14 07:50:52.091527: Current learning rate: 0.0023 +2026-04-14 07:57:13.871333: train_loss -0.4188 +2026-04-14 07:57:13.877306: val_loss -0.3694 +2026-04-14 07:57:13.879586: Pseudo dice [0.4288, 0.857, 0.7628, 0.3289, 0.5158, 0.7392, 0.5069] +2026-04-14 07:57:13.882154: Epoch time: 381.79 s +2026-04-14 07:57:15.231878: +2026-04-14 07:57:15.234890: Epoch 3221 +2026-04-14 07:57:15.236976: Current learning rate: 0.00229 +2026-04-14 08:02:15.683796: train_loss -0.4242 +2026-04-14 08:02:15.690227: val_loss -0.3705 +2026-04-14 08:02:15.693331: Pseudo dice [0.4733, 0.8068, 0.7559, 0.7987, 0.4193, 0.7374, 0.8554] +2026-04-14 08:02:15.697540: Epoch time: 300.46 s +2026-04-14 08:02:16.951964: +2026-04-14 08:02:16.954031: Epoch 3222 +2026-04-14 08:02:16.956794: Current learning rate: 0.00229 +2026-04-14 08:03:59.388992: train_loss -0.4353 +2026-04-14 08:03:59.397101: val_loss -0.3601 +2026-04-14 08:03:59.400029: Pseudo dice [0.221, 0.7043, 0.7217, 0.3376, 0.5943, 0.86, 0.6881] +2026-04-14 08:03:59.403096: Epoch time: 102.44 s +2026-04-14 08:04:00.621298: +2026-04-14 08:04:00.623775: Epoch 3223 +2026-04-14 08:04:00.631714: Current learning rate: 0.00229 +2026-04-14 08:05:45.312335: train_loss -0.4426 +2026-04-14 08:05:45.319589: val_loss -0.3964 +2026-04-14 08:05:45.321752: Pseudo dice [0.6824, 0.5871, 0.7979, 0.7248, 0.495, 0.6011, 0.8316] +2026-04-14 08:05:45.324084: Epoch time: 104.69 s +2026-04-14 08:05:46.581651: +2026-04-14 08:05:46.583491: Epoch 3224 +2026-04-14 08:05:46.585519: Current learning rate: 0.00229 +2026-04-14 08:07:28.881274: train_loss -0.43 +2026-04-14 08:07:28.890424: val_loss -0.3384 +2026-04-14 08:07:28.893052: Pseudo dice [0.7837, 0.9237, 0.8528, 0.4809, 0.4662, 0.3866, 0.7415] +2026-04-14 08:07:28.896800: Epoch time: 102.3 s +2026-04-14 08:07:30.219496: +2026-04-14 08:07:30.223379: Epoch 3225 +2026-04-14 08:07:30.226425: Current learning rate: 0.00228 +2026-04-14 08:09:12.008759: train_loss -0.4485 +2026-04-14 08:09:12.015762: val_loss -0.378 +2026-04-14 08:09:12.018086: Pseudo dice [0.5383, 0.9082, 0.784, 0.7167, 0.5853, 0.6228, 0.789] +2026-04-14 08:09:12.020443: Epoch time: 101.79 s +2026-04-14 08:09:13.304803: +2026-04-14 08:09:13.306429: Epoch 3226 +2026-04-14 08:09:13.308210: Current learning rate: 0.00228 +2026-04-14 08:10:55.209768: train_loss -0.4496 +2026-04-14 08:10:55.217952: val_loss -0.3968 +2026-04-14 08:10:55.220209: Pseudo dice [0.7308, 0.9069, 0.769, 0.5546, 0.4417, 0.8903, 0.7087] +2026-04-14 08:10:55.229569: Epoch time: 101.91 s +2026-04-14 08:10:56.496973: +2026-04-14 08:10:56.499197: Epoch 3227 +2026-04-14 08:10:56.501288: Current learning rate: 0.00228 +2026-04-14 08:12:37.844672: train_loss -0.4386 +2026-04-14 08:12:37.851188: val_loss -0.3859 +2026-04-14 08:12:37.853268: Pseudo dice [0.5322, 0.8668, 0.833, 0.3065, 0.5996, 0.1817, 0.8448] +2026-04-14 08:12:37.855747: Epoch time: 101.35 s +2026-04-14 08:12:39.114159: +2026-04-14 08:12:39.117144: Epoch 3228 +2026-04-14 08:12:39.121170: Current learning rate: 0.00228 +2026-04-14 08:14:20.397617: train_loss -0.4466 +2026-04-14 08:14:20.405147: val_loss -0.3876 +2026-04-14 08:14:20.407917: Pseudo dice [0.8349, 0.4441, 0.7872, 0.3359, 0.6533, 0.5566, 0.7888] +2026-04-14 08:14:20.411392: Epoch time: 101.29 s +2026-04-14 08:14:21.662498: +2026-04-14 08:14:21.665064: Epoch 3229 +2026-04-14 08:14:21.667673: Current learning rate: 0.00227 +2026-04-14 08:16:03.707711: train_loss -0.4405 +2026-04-14 08:16:03.714332: val_loss -0.3616 +2026-04-14 08:16:03.716339: Pseudo dice [0.6786, 0.7129, 0.7139, 0.5055, 0.5008, 0.4397, 0.7246] +2026-04-14 08:16:03.719289: Epoch time: 102.05 s +2026-04-14 08:16:04.963030: +2026-04-14 08:16:04.965501: Epoch 3230 +2026-04-14 08:16:04.968977: Current learning rate: 0.00227 +2026-04-14 08:17:46.611328: train_loss -0.4398 +2026-04-14 08:17:46.618376: val_loss -0.3694 +2026-04-14 08:17:46.621070: Pseudo dice [0.4657, 0.221, 0.803, 0.2253, 0.5942, 0.9332, 0.8108] +2026-04-14 08:17:46.623387: Epoch time: 101.65 s +2026-04-14 08:17:47.877556: +2026-04-14 08:17:47.879522: Epoch 3231 +2026-04-14 08:17:47.881932: Current learning rate: 0.00227 +2026-04-14 08:19:29.585407: train_loss -0.4303 +2026-04-14 08:19:29.592425: val_loss -0.3362 +2026-04-14 08:19:29.594662: Pseudo dice [0.829, 0.8439, 0.595, 0.3199, 0.4776, 0.1569, 0.7303] +2026-04-14 08:19:29.597989: Epoch time: 101.71 s +2026-04-14 08:19:30.908275: +2026-04-14 08:19:30.910715: Epoch 3232 +2026-04-14 08:19:30.913842: Current learning rate: 0.00226 +2026-04-14 08:21:12.623272: train_loss -0.4415 +2026-04-14 08:21:12.631579: val_loss -0.3596 +2026-04-14 08:21:12.634066: Pseudo dice [0.3879, 0.7146, 0.5937, 0.6361, 0.4844, 0.9295, 0.8179] +2026-04-14 08:21:12.637330: Epoch time: 101.72 s +2026-04-14 08:21:13.915060: +2026-04-14 08:21:13.917727: Epoch 3233 +2026-04-14 08:21:13.919814: Current learning rate: 0.00226 +2026-04-14 08:22:55.133361: train_loss -0.4407 +2026-04-14 08:22:55.139246: val_loss -0.3457 +2026-04-14 08:22:55.141826: Pseudo dice [0.5454, 0.3161, 0.7053, 0.5129, 0.6867, 0.2557, 0.8414] +2026-04-14 08:22:55.144165: Epoch time: 101.22 s +2026-04-14 08:22:56.431571: +2026-04-14 08:22:56.433590: Epoch 3234 +2026-04-14 08:22:56.436598: Current learning rate: 0.00226 +2026-04-14 08:24:37.758483: train_loss -0.4473 +2026-04-14 08:24:37.766993: val_loss -0.3737 +2026-04-14 08:24:37.769274: Pseudo dice [0.6456, 0.6544, 0.8396, 0.6182, 0.2573, 0.8185, 0.5916] +2026-04-14 08:24:37.772294: Epoch time: 101.33 s +2026-04-14 08:24:39.103297: +2026-04-14 08:24:39.105541: Epoch 3235 +2026-04-14 08:24:39.107739: Current learning rate: 0.00226 +2026-04-14 08:26:20.659800: train_loss -0.4435 +2026-04-14 08:26:20.666178: val_loss -0.3956 +2026-04-14 08:26:20.668169: Pseudo dice [0.745, 0.5272, 0.8301, 0.2717, 0.541, 0.7649, 0.843] +2026-04-14 08:26:20.670707: Epoch time: 101.56 s +2026-04-14 08:26:21.952917: +2026-04-14 08:26:21.954721: Epoch 3236 +2026-04-14 08:26:21.956762: Current learning rate: 0.00225 +2026-04-14 08:28:02.951102: train_loss -0.4351 +2026-04-14 08:28:02.959481: val_loss -0.377 +2026-04-14 08:28:02.962091: Pseudo dice [0.615, 0.4865, 0.7219, 0.2985, 0.5049, 0.8588, 0.7182] +2026-04-14 08:28:02.964794: Epoch time: 101.0 s +2026-04-14 08:28:04.254321: +2026-04-14 08:28:04.256248: Epoch 3237 +2026-04-14 08:28:04.258138: Current learning rate: 0.00225 +2026-04-14 08:29:46.430436: train_loss -0.4417 +2026-04-14 08:29:46.436293: val_loss -0.3772 +2026-04-14 08:29:46.438383: Pseudo dice [0.5693, 0.8771, 0.815, 0.6289, 0.4615, 0.6575, 0.861] +2026-04-14 08:29:46.440387: Epoch time: 102.18 s +2026-04-14 08:29:47.749342: +2026-04-14 08:29:47.751621: Epoch 3238 +2026-04-14 08:29:47.754285: Current learning rate: 0.00225 +2026-04-14 08:31:29.989496: train_loss -0.4346 +2026-04-14 08:31:29.997998: val_loss -0.3979 +2026-04-14 08:31:30.001934: Pseudo dice [0.6302, 0.7567, 0.796, 0.858, 0.6155, 0.7541, 0.8341] +2026-04-14 08:31:30.005375: Epoch time: 102.24 s +2026-04-14 08:31:31.239330: +2026-04-14 08:31:31.241412: Epoch 3239 +2026-04-14 08:31:31.243371: Current learning rate: 0.00225 +2026-04-14 08:33:53.579187: train_loss -0.4252 +2026-04-14 08:33:53.586599: val_loss -0.3021 +2026-04-14 08:33:53.589316: Pseudo dice [0.4604, 0.8981, 0.6373, 0.1756, 0.5444, 0.3677, 0.7878] +2026-04-14 08:33:53.592487: Epoch time: 142.34 s +2026-04-14 08:33:56.093775: +2026-04-14 08:33:56.095428: Epoch 3240 +2026-04-14 08:33:56.097286: Current learning rate: 0.00224 +2026-04-14 08:35:37.524133: train_loss -0.4276 +2026-04-14 08:35:37.530956: val_loss -0.3224 +2026-04-14 08:35:37.534082: Pseudo dice [0.8326, 0.9167, 0.7441, 0.5925, 0.5081, 0.2959, 0.3151] +2026-04-14 08:35:37.548820: Epoch time: 101.43 s +2026-04-14 08:35:38.811583: +2026-04-14 08:35:38.814075: Epoch 3241 +2026-04-14 08:35:38.816236: Current learning rate: 0.00224 +2026-04-14 08:37:21.571050: train_loss -0.4392 +2026-04-14 08:37:21.578446: val_loss -0.3734 +2026-04-14 08:37:21.580786: Pseudo dice [0.2506, 0.6578, 0.8068, 0.5352, 0.5683, 0.835, 0.5105] +2026-04-14 08:37:21.583780: Epoch time: 102.76 s +2026-04-14 08:37:22.846275: +2026-04-14 08:37:22.847996: Epoch 3242 +2026-04-14 08:37:22.849836: Current learning rate: 0.00224 +2026-04-14 08:39:04.890777: train_loss -0.4524 +2026-04-14 08:39:04.897601: val_loss -0.3685 +2026-04-14 08:39:04.900177: Pseudo dice [0.8789, 0.9088, 0.8088, 0.3929, 0.5273, 0.7343, 0.8141] +2026-04-14 08:39:04.902723: Epoch time: 102.05 s +2026-04-14 08:39:06.139011: +2026-04-14 08:39:06.141219: Epoch 3243 +2026-04-14 08:39:06.143501: Current learning rate: 0.00224 +2026-04-14 08:40:48.365880: train_loss -0.4481 +2026-04-14 08:40:48.373419: val_loss -0.3169 +2026-04-14 08:40:48.378182: Pseudo dice [0.6876, 0.8981, 0.5965, 0.5186, 0.5937, 0.5043, 0.8125] +2026-04-14 08:40:48.380733: Epoch time: 102.23 s +2026-04-14 08:40:49.615796: +2026-04-14 08:40:49.617450: Epoch 3244 +2026-04-14 08:40:49.619751: Current learning rate: 0.00223 +2026-04-14 08:42:31.900060: train_loss -0.4248 +2026-04-14 08:42:31.906956: val_loss -0.3537 +2026-04-14 08:42:31.910594: Pseudo dice [0.318, 0.7413, 0.5828, 0.3727, 0.4689, 0.8713, 0.8215] +2026-04-14 08:42:31.913225: Epoch time: 102.29 s +2026-04-14 08:42:33.154867: +2026-04-14 08:42:33.156616: Epoch 3245 +2026-04-14 08:42:33.158311: Current learning rate: 0.00223 +2026-04-14 08:44:15.169268: train_loss -0.4312 +2026-04-14 08:44:15.175796: val_loss -0.3934 +2026-04-14 08:44:15.178309: Pseudo dice [0.7455, 0.8097, 0.8348, 0.8129, 0.4303, 0.7858, 0.7431] +2026-04-14 08:44:15.180765: Epoch time: 102.02 s +2026-04-14 08:44:16.423026: +2026-04-14 08:44:16.425500: Epoch 3246 +2026-04-14 08:44:16.428168: Current learning rate: 0.00223 +2026-04-14 08:45:58.775289: train_loss -0.4447 +2026-04-14 08:45:58.783109: val_loss -0.3846 +2026-04-14 08:45:58.785497: Pseudo dice [0.6891, 0.8732, 0.8034, 0.8547, 0.6001, 0.5892, 0.8688] +2026-04-14 08:45:58.788439: Epoch time: 102.36 s +2026-04-14 08:46:00.028228: +2026-04-14 08:46:00.030490: Epoch 3247 +2026-04-14 08:46:00.032540: Current learning rate: 0.00222 +2026-04-14 08:47:42.005683: train_loss -0.4333 +2026-04-14 08:47:42.012739: val_loss -0.3656 +2026-04-14 08:47:42.015391: Pseudo dice [0.7961, 0.4969, 0.7946, 0.3174, 0.449, 0.6826, 0.445] +2026-04-14 08:47:42.017848: Epoch time: 101.98 s +2026-04-14 08:47:43.253012: +2026-04-14 08:47:43.256417: Epoch 3248 +2026-04-14 08:47:43.262093: Current learning rate: 0.00222 +2026-04-14 08:49:25.514457: train_loss -0.4485 +2026-04-14 08:49:25.520737: val_loss -0.3845 +2026-04-14 08:49:25.522614: Pseudo dice [0.4401, 0.6796, 0.7855, 0.4781, 0.6027, 0.8642, 0.7581] +2026-04-14 08:49:25.524828: Epoch time: 102.26 s +2026-04-14 08:49:26.786395: +2026-04-14 08:49:26.789837: Epoch 3249 +2026-04-14 08:49:26.791896: Current learning rate: 0.00222 +2026-04-14 08:51:08.615592: train_loss -0.4442 +2026-04-14 08:51:08.622554: val_loss -0.3736 +2026-04-14 08:51:08.625966: Pseudo dice [0.312, 0.9045, 0.6833, 0.8402, 0.5332, 0.3745, 0.8256] +2026-04-14 08:51:08.628477: Epoch time: 101.83 s +2026-04-14 08:51:11.435358: +2026-04-14 08:51:11.439619: Epoch 3250 +2026-04-14 08:51:11.441390: Current learning rate: 0.00222 +2026-04-14 08:52:53.294255: train_loss -0.42 +2026-04-14 08:52:53.301651: val_loss -0.3835 +2026-04-14 08:52:53.304618: Pseudo dice [0.7178, 0.2519, 0.7341, 0.8146, 0.4895, 0.8121, 0.6006] +2026-04-14 08:52:53.307334: Epoch time: 101.86 s +2026-04-14 08:52:54.525894: +2026-04-14 08:52:54.527402: Epoch 3251 +2026-04-14 08:52:54.529226: Current learning rate: 0.00221 +2026-04-14 08:54:36.236852: train_loss -0.4297 +2026-04-14 08:54:36.243012: val_loss -0.3902 +2026-04-14 08:54:36.245988: Pseudo dice [0.6561, 0.9035, 0.7135, 0.5246, 0.5453, 0.7531, 0.7154] +2026-04-14 08:54:36.248385: Epoch time: 101.71 s +2026-04-14 08:54:37.497580: +2026-04-14 08:54:37.499846: Epoch 3252 +2026-04-14 08:54:37.501832: Current learning rate: 0.00221 +2026-04-14 08:56:19.112765: train_loss -0.4483 +2026-04-14 08:56:19.120281: val_loss -0.392 +2026-04-14 08:56:19.122106: Pseudo dice [0.5677, 0.7342, 0.8156, 0.405, 0.3593, 0.6346, 0.8279] +2026-04-14 08:56:19.124501: Epoch time: 101.62 s +2026-04-14 08:56:20.334126: +2026-04-14 08:56:20.338720: Epoch 3253 +2026-04-14 08:56:20.340998: Current learning rate: 0.00221 +2026-04-14 08:58:02.193324: train_loss -0.45 +2026-04-14 08:58:02.200413: val_loss -0.3699 +2026-04-14 08:58:02.202708: Pseudo dice [0.7106, 0.5984, 0.6884, 0.6721, 0.5609, 0.6231, 0.3085] +2026-04-14 08:58:02.204803: Epoch time: 101.86 s +2026-04-14 08:58:03.432889: +2026-04-14 08:58:03.435008: Epoch 3254 +2026-04-14 08:58:03.437233: Current learning rate: 0.00221 +2026-04-14 08:59:45.470809: train_loss -0.4381 +2026-04-14 08:59:45.477188: val_loss -0.3237 +2026-04-14 08:59:45.479455: Pseudo dice [0.7882, 0.665, 0.469, 0.6717, 0.2659, 0.6028, 0.5893] +2026-04-14 08:59:45.482317: Epoch time: 102.04 s +2026-04-14 08:59:46.722178: +2026-04-14 08:59:46.724499: Epoch 3255 +2026-04-14 08:59:46.727802: Current learning rate: 0.0022 +2026-04-14 09:01:42.436946: train_loss -0.4333 +2026-04-14 09:01:42.447533: val_loss -0.3737 +2026-04-14 09:01:42.452563: Pseudo dice [0.6913, 0.8834, 0.8485, 0.406, 0.624, 0.1359, 0.8138] +2026-04-14 09:01:42.455514: Epoch time: 115.72 s +2026-04-14 09:01:43.827769: +2026-04-14 09:01:43.830103: Epoch 3256 +2026-04-14 09:01:43.832966: Current learning rate: 0.0022 +2026-04-14 09:03:26.591831: train_loss -0.4135 +2026-04-14 09:03:26.599163: val_loss -0.352 +2026-04-14 09:03:26.602076: Pseudo dice [0.5612, 0.8562, 0.7062, 0.418, 0.5313, 0.7989, 0.7402] +2026-04-14 09:03:26.604287: Epoch time: 102.77 s +2026-04-14 09:03:27.850260: +2026-04-14 09:03:27.851844: Epoch 3257 +2026-04-14 09:03:27.853746: Current learning rate: 0.0022 +2026-04-14 09:05:09.479902: train_loss -0.4384 +2026-04-14 09:05:09.488474: val_loss -0.3691 +2026-04-14 09:05:09.490799: Pseudo dice [0.5048, 0.88, 0.8139, 0.3644, 0.458, 0.7186, 0.7434] +2026-04-14 09:05:09.493672: Epoch time: 101.63 s +2026-04-14 09:05:10.733408: +2026-04-14 09:05:10.735316: Epoch 3258 +2026-04-14 09:05:10.737541: Current learning rate: 0.0022 +2026-04-14 09:06:52.965127: train_loss -0.4336 +2026-04-14 09:06:52.972002: val_loss -0.3386 +2026-04-14 09:06:52.974184: Pseudo dice [0.6145, 0.5447, 0.6767, 0.5544, 0.4201, 0.3856, 0.765] +2026-04-14 09:06:52.976725: Epoch time: 102.24 s +2026-04-14 09:06:55.340051: +2026-04-14 09:06:55.342101: Epoch 3259 +2026-04-14 09:06:55.343947: Current learning rate: 0.00219 +2026-04-14 09:08:37.426171: train_loss -0.4438 +2026-04-14 09:08:37.432125: val_loss -0.3713 +2026-04-14 09:08:37.434146: Pseudo dice [0.3685, 0.876, 0.7013, 0.3805, 0.5397, 0.6393, 0.8624] +2026-04-14 09:08:37.436313: Epoch time: 102.09 s +2026-04-14 09:08:38.659822: +2026-04-14 09:08:38.661908: Epoch 3260 +2026-04-14 09:08:38.663750: Current learning rate: 0.00219 +2026-04-14 09:10:20.375160: train_loss -0.44 +2026-04-14 09:10:20.381212: val_loss -0.404 +2026-04-14 09:10:20.383822: Pseudo dice [0.7172, 0.5608, 0.7796, 0.3125, 0.5377, 0.7486, 0.8087] +2026-04-14 09:10:20.386362: Epoch time: 101.72 s +2026-04-14 09:10:21.644284: +2026-04-14 09:10:21.645978: Epoch 3261 +2026-04-14 09:10:21.647866: Current learning rate: 0.00219 +2026-04-14 09:12:04.116436: train_loss -0.4412 +2026-04-14 09:12:04.124632: val_loss -0.4011 +2026-04-14 09:12:04.126972: Pseudo dice [0.696, 0.6934, 0.8298, 0.5068, 0.3931, 0.8759, 0.7954] +2026-04-14 09:12:04.129817: Epoch time: 102.48 s +2026-04-14 09:12:05.355719: +2026-04-14 09:12:05.358010: Epoch 3262 +2026-04-14 09:12:05.361083: Current learning rate: 0.00218 +2026-04-14 09:13:47.413048: train_loss -0.4436 +2026-04-14 09:13:47.420339: val_loss -0.3822 +2026-04-14 09:13:47.422728: Pseudo dice [0.5998, 0.7814, 0.841, 0.7082, 0.4566, 0.8605, 0.7132] +2026-04-14 09:13:47.425113: Epoch time: 102.06 s +2026-04-14 09:13:48.644409: +2026-04-14 09:13:48.645975: Epoch 3263 +2026-04-14 09:13:48.647881: Current learning rate: 0.00218 +2026-04-14 09:15:30.722265: train_loss -0.4419 +2026-04-14 09:15:30.728687: val_loss -0.4076 +2026-04-14 09:15:30.730736: Pseudo dice [0.7117, 0.5472, 0.7209, 0.857, 0.548, 0.5935, 0.8586] +2026-04-14 09:15:30.733665: Epoch time: 102.08 s +2026-04-14 09:15:31.959735: +2026-04-14 09:15:31.962087: Epoch 3264 +2026-04-14 09:15:31.963934: Current learning rate: 0.00218 +2026-04-14 09:17:13.813917: train_loss -0.4363 +2026-04-14 09:17:13.819944: val_loss -0.3612 +2026-04-14 09:17:13.822185: Pseudo dice [0.5315, 0.91, 0.7141, 0.4735, 0.694, 0.193, 0.401] +2026-04-14 09:17:13.824807: Epoch time: 101.86 s +2026-04-14 09:17:15.049316: +2026-04-14 09:17:15.050861: Epoch 3265 +2026-04-14 09:17:15.052661: Current learning rate: 0.00218 +2026-04-14 09:18:57.726750: train_loss -0.4405 +2026-04-14 09:18:57.733416: val_loss -0.3812 +2026-04-14 09:18:57.735668: Pseudo dice [0.6336, 0.6172, 0.7439, 0.4601, 0.5324, 0.7614, 0.8281] +2026-04-14 09:18:57.737869: Epoch time: 102.68 s +2026-04-14 09:18:58.962554: +2026-04-14 09:18:58.964488: Epoch 3266 +2026-04-14 09:18:58.966470: Current learning rate: 0.00217 +2026-04-14 09:20:41.176342: train_loss -0.4424 +2026-04-14 09:20:41.182163: val_loss -0.3783 +2026-04-14 09:20:41.183861: Pseudo dice [0.768, 0.6147, 0.8096, 0.3884, 0.5638, 0.8823, 0.7574] +2026-04-14 09:20:41.186107: Epoch time: 102.22 s +2026-04-14 09:20:42.430069: +2026-04-14 09:20:42.431762: Epoch 3267 +2026-04-14 09:20:42.433896: Current learning rate: 0.00217 +2026-04-14 09:22:24.462543: train_loss -0.4389 +2026-04-14 09:22:24.471019: val_loss -0.3613 +2026-04-14 09:22:24.473007: Pseudo dice [0.4184, 0.9071, 0.7121, 0.6448, 0.4376, 0.0734, 0.5518] +2026-04-14 09:22:24.476135: Epoch time: 102.04 s +2026-04-14 09:22:25.722076: +2026-04-14 09:22:25.724838: Epoch 3268 +2026-04-14 09:22:25.727672: Current learning rate: 0.00217 +2026-04-14 09:24:08.073894: train_loss -0.4436 +2026-04-14 09:24:08.081906: val_loss -0.3833 +2026-04-14 09:24:08.083811: Pseudo dice [0.6822, 0.8146, 0.6927, 0.7668, 0.4719, 0.7555, 0.8183] +2026-04-14 09:24:08.086102: Epoch time: 102.35 s +2026-04-14 09:24:09.344199: +2026-04-14 09:24:09.345915: Epoch 3269 +2026-04-14 09:24:09.347626: Current learning rate: 0.00217 +2026-04-14 09:25:50.924090: train_loss -0.4354 +2026-04-14 09:25:50.931652: val_loss -0.3563 +2026-04-14 09:25:50.933623: Pseudo dice [0.5013, 0.6908, 0.7293, 0.2771, 0.2496, 0.9309, 0.802] +2026-04-14 09:25:50.936231: Epoch time: 101.58 s +2026-04-14 09:25:52.168650: +2026-04-14 09:25:52.170660: Epoch 3270 +2026-04-14 09:25:52.172493: Current learning rate: 0.00216 +2026-04-14 09:27:33.995855: train_loss -0.447 +2026-04-14 09:27:34.002237: val_loss -0.3957 +2026-04-14 09:27:34.004764: Pseudo dice [0.6616, 0.6722, 0.8357, 0.6209, 0.482, 0.9286, 0.8828] +2026-04-14 09:27:34.007162: Epoch time: 101.83 s +2026-04-14 09:27:35.228354: +2026-04-14 09:27:35.230122: Epoch 3271 +2026-04-14 09:27:35.232082: Current learning rate: 0.00216 +2026-04-14 09:29:17.322271: train_loss -0.4397 +2026-04-14 09:29:17.328809: val_loss -0.3639 +2026-04-14 09:29:17.331221: Pseudo dice [0.369, 0.6799, 0.7943, 0.4784, 0.4871, 0.8142, 0.6947] +2026-04-14 09:29:17.334594: Epoch time: 102.1 s +2026-04-14 09:29:18.603124: +2026-04-14 09:29:18.604792: Epoch 3272 +2026-04-14 09:29:18.606625: Current learning rate: 0.00216 +2026-04-14 09:31:01.005561: train_loss -0.4427 +2026-04-14 09:31:01.013904: val_loss -0.3784 +2026-04-14 09:31:01.016029: Pseudo dice [0.8551, 0.5141, 0.7993, 0.6451, 0.5872, 0.8513, 0.814] +2026-04-14 09:31:01.018376: Epoch time: 102.41 s +2026-04-14 09:31:02.264579: +2026-04-14 09:31:02.266326: Epoch 3273 +2026-04-14 09:31:02.268380: Current learning rate: 0.00216 +2026-04-14 09:32:52.102618: train_loss -0.4522 +2026-04-14 09:32:52.110101: val_loss -0.3645 +2026-04-14 09:32:52.112083: Pseudo dice [0.6569, 0.8757, 0.755, 0.6293, 0.5412, 0.6295, 0.5251] +2026-04-14 09:32:52.115149: Epoch time: 109.84 s +2026-04-14 09:32:53.367472: +2026-04-14 09:32:53.369371: Epoch 3274 +2026-04-14 09:32:53.371252: Current learning rate: 0.00215 +2026-04-14 09:34:35.326842: train_loss -0.4396 +2026-04-14 09:34:35.333952: val_loss -0.3826 +2026-04-14 09:34:35.336324: Pseudo dice [0.7233, 0.9275, 0.8457, 0.935, 0.7386, 0.1442, 0.3916] +2026-04-14 09:34:35.338973: Epoch time: 101.96 s +2026-04-14 09:34:36.589869: +2026-04-14 09:34:36.591616: Epoch 3275 +2026-04-14 09:34:36.593690: Current learning rate: 0.00215 +2026-04-14 09:36:18.962459: train_loss -0.4512 +2026-04-14 09:36:18.969293: val_loss -0.3808 +2026-04-14 09:36:18.971455: Pseudo dice [0.589, 0.891, 0.7531, 0.4498, 0.5759, 0.6893, 0.773] +2026-04-14 09:36:18.973969: Epoch time: 102.38 s +2026-04-14 09:36:20.187042: +2026-04-14 09:36:20.188649: Epoch 3276 +2026-04-14 09:36:20.190517: Current learning rate: 0.00215 +2026-04-14 09:38:02.379578: train_loss -0.4453 +2026-04-14 09:38:02.385912: val_loss -0.3713 +2026-04-14 09:38:02.387877: Pseudo dice [0.6795, 0.601, 0.8456, 0.1556, 0.3482, 0.9153, 0.8696] +2026-04-14 09:38:02.391001: Epoch time: 102.2 s +2026-04-14 09:38:03.636510: +2026-04-14 09:38:03.638295: Epoch 3277 +2026-04-14 09:38:03.642621: Current learning rate: 0.00214 +2026-04-14 09:39:45.829000: train_loss -0.4496 +2026-04-14 09:39:45.834617: val_loss -0.3932 +2026-04-14 09:39:45.836789: Pseudo dice [0.7038, 0.5363, 0.827, 0.6378, 0.5436, 0.6602, 0.8374] +2026-04-14 09:39:45.839107: Epoch time: 102.2 s +2026-04-14 09:39:47.052436: +2026-04-14 09:39:47.054008: Epoch 3278 +2026-04-14 09:39:47.055811: Current learning rate: 0.00214 +2026-04-14 09:41:29.756226: train_loss -0.4469 +2026-04-14 09:41:29.763345: val_loss -0.3402 +2026-04-14 09:41:29.765168: Pseudo dice [0.3303, 0.8841, 0.7792, 0.5816, 0.2806, 0.7515, 0.1593] +2026-04-14 09:41:29.768368: Epoch time: 102.71 s +2026-04-14 09:41:32.198529: +2026-04-14 09:41:32.200222: Epoch 3279 +2026-04-14 09:41:32.202021: Current learning rate: 0.00214 +2026-04-14 09:43:14.601649: train_loss -0.4277 +2026-04-14 09:43:14.608180: val_loss -0.3769 +2026-04-14 09:43:14.611168: Pseudo dice [0.5702, 0.4889, 0.8036, 0.2714, 0.3896, 0.7944, 0.8312] +2026-04-14 09:43:14.613850: Epoch time: 102.41 s +2026-04-14 09:43:15.857094: +2026-04-14 09:43:15.860035: Epoch 3280 +2026-04-14 09:43:15.862444: Current learning rate: 0.00214 +2026-04-14 09:44:58.314970: train_loss -0.4597 +2026-04-14 09:44:58.321512: val_loss -0.3505 +2026-04-14 09:44:58.323531: Pseudo dice [0.7835, 0.4824, 0.8396, 0.0239, 0.4774, 0.8192, 0.3963] +2026-04-14 09:44:58.325601: Epoch time: 102.46 s +2026-04-14 09:44:59.580474: +2026-04-14 09:44:59.582322: Epoch 3281 +2026-04-14 09:44:59.583895: Current learning rate: 0.00213 +2026-04-14 09:46:41.997845: train_loss -0.438 +2026-04-14 09:46:42.004228: val_loss -0.3752 +2026-04-14 09:46:42.006704: Pseudo dice [0.7058, 0.6148, 0.6273, 0.5674, 0.6103, 0.8136, 0.6983] +2026-04-14 09:46:42.009042: Epoch time: 102.42 s +2026-04-14 09:46:43.255935: +2026-04-14 09:46:43.258609: Epoch 3282 +2026-04-14 09:46:43.260567: Current learning rate: 0.00213 +2026-04-14 09:48:24.584894: train_loss -0.43 +2026-04-14 09:48:24.590980: val_loss -0.3801 +2026-04-14 09:48:24.593806: Pseudo dice [0.451, 0.5505, 0.7701, 0.3242, 0.7386, 0.9087, 0.7115] +2026-04-14 09:48:24.596237: Epoch time: 101.33 s +2026-04-14 09:48:25.810376: +2026-04-14 09:48:25.812259: Epoch 3283 +2026-04-14 09:48:25.814115: Current learning rate: 0.00213 +2026-04-14 09:50:07.778930: train_loss -0.4315 +2026-04-14 09:50:07.784509: val_loss -0.3895 +2026-04-14 09:50:07.786919: Pseudo dice [0.7074, 0.6033, 0.7627, 0.4281, 0.6769, 0.606, 0.7744] +2026-04-14 09:50:07.789278: Epoch time: 101.97 s +2026-04-14 09:50:09.024269: +2026-04-14 09:50:09.026006: Epoch 3284 +2026-04-14 09:50:09.027662: Current learning rate: 0.00213 +2026-04-14 09:51:51.029786: train_loss -0.4155 +2026-04-14 09:51:51.037999: val_loss -0.4091 +2026-04-14 09:51:51.040379: Pseudo dice [0.3969, 0.6865, 0.7359, 0.7818, 0.419, 0.8949, 0.8254] +2026-04-14 09:51:51.042927: Epoch time: 102.01 s +2026-04-14 09:51:52.297758: +2026-04-14 09:51:52.299271: Epoch 3285 +2026-04-14 09:51:52.302064: Current learning rate: 0.00212 +2026-04-14 09:53:34.194137: train_loss -0.4364 +2026-04-14 09:53:34.200349: val_loss -0.3757 +2026-04-14 09:53:34.202379: Pseudo dice [0.5724, 0.5081, 0.7626, 0.2225, 0.3126, 0.6262, 0.8369] +2026-04-14 09:53:34.204741: Epoch time: 101.9 s +2026-04-14 09:53:35.436183: +2026-04-14 09:53:35.438050: Epoch 3286 +2026-04-14 09:53:35.439866: Current learning rate: 0.00212 +2026-04-14 09:55:17.354567: train_loss -0.4406 +2026-04-14 09:55:17.360264: val_loss -0.3544 +2026-04-14 09:55:17.362047: Pseudo dice [0.3871, 0.8841, 0.7742, 0.6811, 0.6496, 0.255, 0.5447] +2026-04-14 09:55:17.364151: Epoch time: 101.92 s +2026-04-14 09:55:18.595049: +2026-04-14 09:55:18.596764: Epoch 3287 +2026-04-14 09:55:18.598757: Current learning rate: 0.00212 +2026-04-14 09:57:00.713434: train_loss -0.4264 +2026-04-14 09:57:00.720516: val_loss -0.3603 +2026-04-14 09:57:00.724382: Pseudo dice [0.5728, 0.9007, 0.7462, 0.4716, 0.2886, 0.3025, 0.7565] +2026-04-14 09:57:00.726572: Epoch time: 102.12 s +2026-04-14 09:57:01.961898: +2026-04-14 09:57:01.963948: Epoch 3288 +2026-04-14 09:57:01.965731: Current learning rate: 0.00212 +2026-04-14 09:58:43.620352: train_loss -0.4291 +2026-04-14 09:58:43.629831: val_loss -0.3718 +2026-04-14 09:58:43.650615: Pseudo dice [0.7227, 0.3225, 0.7381, 0.0852, 0.3332, 0.8462, 0.8184] +2026-04-14 09:58:43.653144: Epoch time: 101.66 s +2026-04-14 09:58:44.869712: +2026-04-14 09:58:44.872498: Epoch 3289 +2026-04-14 09:58:44.874701: Current learning rate: 0.00211 +2026-04-14 10:00:27.085989: train_loss -0.44 +2026-04-14 10:00:27.092574: val_loss -0.3853 +2026-04-14 10:00:27.094569: Pseudo dice [0.6786, 0.8042, 0.7963, 0.2634, 0.534, 0.8844, 0.7733] +2026-04-14 10:00:27.096563: Epoch time: 102.22 s +2026-04-14 10:00:28.356548: +2026-04-14 10:00:28.358073: Epoch 3290 +2026-04-14 10:00:28.359476: Current learning rate: 0.00211 +2026-04-14 10:02:10.643979: train_loss -0.4261 +2026-04-14 10:02:10.651767: val_loss -0.3581 +2026-04-14 10:02:10.653517: Pseudo dice [0.4918, 0.9021, 0.7832, 0.2019, 0.4752, 0.2188, 0.7199] +2026-04-14 10:02:10.656058: Epoch time: 102.29 s +2026-04-14 10:02:11.919324: +2026-04-14 10:02:11.920798: Epoch 3291 +2026-04-14 10:02:11.922192: Current learning rate: 0.00211 +2026-04-14 10:03:54.065849: train_loss -0.4357 +2026-04-14 10:03:54.072881: val_loss -0.3762 +2026-04-14 10:03:54.075705: Pseudo dice [0.6489, 0.4531, 0.7083, 0.3804, 0.5321, 0.8748, 0.8203] +2026-04-14 10:03:54.078616: Epoch time: 102.15 s +2026-04-14 10:03:55.292025: +2026-04-14 10:03:55.304591: Epoch 3292 +2026-04-14 10:03:55.306470: Current learning rate: 0.0021 +2026-04-14 10:05:38.077215: train_loss -0.4358 +2026-04-14 10:05:38.085972: val_loss -0.3868 +2026-04-14 10:05:38.087941: Pseudo dice [0.4247, 0.6813, 0.8132, 0.5899, 0.5195, 0.9145, 0.841] +2026-04-14 10:05:38.091517: Epoch time: 102.79 s +2026-04-14 10:05:39.321571: +2026-04-14 10:05:39.323688: Epoch 3293 +2026-04-14 10:05:39.325357: Current learning rate: 0.0021 +2026-04-14 10:07:20.961556: train_loss -0.4426 +2026-04-14 10:07:20.973428: val_loss -0.3558 +2026-04-14 10:07:20.975256: Pseudo dice [0.1664, 0.8523, 0.8029, 0.5755, 0.5167, 0.1652, 0.5564] +2026-04-14 10:07:20.979395: Epoch time: 101.64 s +2026-04-14 10:07:22.212884: +2026-04-14 10:07:22.214364: Epoch 3294 +2026-04-14 10:07:22.215745: Current learning rate: 0.0021 +2026-04-14 10:09:04.245905: train_loss -0.4482 +2026-04-14 10:09:04.251999: val_loss -0.4036 +2026-04-14 10:09:04.255103: Pseudo dice [0.5587, 0.6554, 0.7553, 0.327, 0.6627, 0.7286, 0.8697] +2026-04-14 10:09:04.257712: Epoch time: 102.04 s +2026-04-14 10:09:05.493412: +2026-04-14 10:09:05.495106: Epoch 3295 +2026-04-14 10:09:05.496492: Current learning rate: 0.0021 +2026-04-14 10:10:47.024985: train_loss -0.4428 +2026-04-14 10:10:47.031385: val_loss -0.3753 +2026-04-14 10:10:47.033583: Pseudo dice [0.7974, 0.7271, 0.8224, 0.2854, 0.522, 0.945, 0.8449] +2026-04-14 10:10:47.036345: Epoch time: 101.53 s +2026-04-14 10:10:48.269137: +2026-04-14 10:10:48.271724: Epoch 3296 +2026-04-14 10:10:48.273419: Current learning rate: 0.00209 +2026-04-14 10:12:30.343760: train_loss -0.4393 +2026-04-14 10:12:30.350573: val_loss -0.3824 +2026-04-14 10:12:30.352520: Pseudo dice [0.6147, 0.1934, 0.7515, 0.1595, 0.727, 0.9214, 0.7452] +2026-04-14 10:12:30.354992: Epoch time: 102.08 s +2026-04-14 10:12:31.562443: +2026-04-14 10:12:31.564213: Epoch 3297 +2026-04-14 10:12:31.565727: Current learning rate: 0.00209 +2026-04-14 10:14:13.114918: train_loss -0.4333 +2026-04-14 10:14:13.121765: val_loss -0.3944 +2026-04-14 10:14:13.123631: Pseudo dice [0.7774, 0.6838, 0.8633, 0.5048, 0.4043, 0.6595, 0.7811] +2026-04-14 10:14:13.126040: Epoch time: 101.56 s +2026-04-14 10:14:14.338255: +2026-04-14 10:14:14.339925: Epoch 3298 +2026-04-14 10:14:14.341598: Current learning rate: 0.00209 +2026-04-14 10:15:56.352159: train_loss -0.4453 +2026-04-14 10:15:56.357932: val_loss -0.4046 +2026-04-14 10:15:56.360433: Pseudo dice [0.613, 0.6984, 0.8367, 0.2399, 0.5977, 0.9379, 0.8631] +2026-04-14 10:15:56.366265: Epoch time: 102.02 s +2026-04-14 10:15:58.904067: +2026-04-14 10:15:58.905849: Epoch 3299 +2026-04-14 10:15:58.907261: Current learning rate: 0.00209 +2026-04-14 10:17:41.060262: train_loss -0.4413 +2026-04-14 10:17:41.068473: val_loss -0.3678 +2026-04-14 10:17:41.070910: Pseudo dice [0.5571, 0.7247, 0.747, 0.0171, 0.4383, 0.6778, 0.7835] +2026-04-14 10:17:41.074145: Epoch time: 102.16 s +2026-04-14 10:17:43.758366: +2026-04-14 10:17:43.760600: Epoch 3300 +2026-04-14 10:17:43.762209: Current learning rate: 0.00208 +2026-04-14 10:19:26.094736: train_loss -0.45 +2026-04-14 10:19:26.102500: val_loss -0.3458 +2026-04-14 10:19:26.104584: Pseudo dice [0.7614, 0.859, 0.7794, 0.107, 0.62, 0.5492, 0.5294] +2026-04-14 10:19:26.107495: Epoch time: 102.34 s +2026-04-14 10:19:27.350338: +2026-04-14 10:19:27.352309: Epoch 3301 +2026-04-14 10:19:27.353875: Current learning rate: 0.00208 +2026-04-14 10:21:09.503061: train_loss -0.4449 +2026-04-14 10:21:09.514011: val_loss -0.35 +2026-04-14 10:21:09.517187: Pseudo dice [0.4946, 0.9049, 0.7941, 0.2192, 0.5859, 0.8226, 0.3737] +2026-04-14 10:21:09.518944: Epoch time: 102.16 s +2026-04-14 10:21:10.750198: +2026-04-14 10:21:10.752227: Epoch 3302 +2026-04-14 10:21:10.753824: Current learning rate: 0.00208 +2026-04-14 10:22:52.175371: train_loss -0.4491 +2026-04-14 10:22:52.181714: val_loss -0.3687 +2026-04-14 10:22:52.183595: Pseudo dice [0.5222, 0.6754, 0.7237, 0.2707, 0.5139, 0.6002, 0.5513] +2026-04-14 10:22:52.186103: Epoch time: 101.43 s +2026-04-14 10:22:53.413867: +2026-04-14 10:22:53.417013: Epoch 3303 +2026-04-14 10:22:53.418934: Current learning rate: 0.00208 +2026-04-14 10:24:35.412798: train_loss -0.4473 +2026-04-14 10:24:35.419835: val_loss -0.3826 +2026-04-14 10:24:35.421851: Pseudo dice [0.6939, 0.8366, 0.8181, 0.3615, 0.676, 0.766, 0.5715] +2026-04-14 10:24:35.424375: Epoch time: 102.0 s +2026-04-14 10:24:36.655464: +2026-04-14 10:24:36.661037: Epoch 3304 +2026-04-14 10:24:36.662817: Current learning rate: 0.00207 +2026-04-14 10:26:19.389931: train_loss -0.444 +2026-04-14 10:26:19.398712: val_loss -0.3549 +2026-04-14 10:26:19.401357: Pseudo dice [0.0827, 0.7805, 0.6637, 0.4462, 0.304, 0.8503, 0.7142] +2026-04-14 10:26:19.404273: Epoch time: 102.74 s +2026-04-14 10:26:20.654914: +2026-04-14 10:26:20.656593: Epoch 3305 +2026-04-14 10:26:20.658065: Current learning rate: 0.00207 +2026-04-14 10:28:02.805933: train_loss -0.4476 +2026-04-14 10:28:02.813037: val_loss -0.3734 +2026-04-14 10:28:02.815476: Pseudo dice [0.6373, 0.5713, 0.7141, 0.6194, 0.6165, 0.6964, 0.8603] +2026-04-14 10:28:02.818471: Epoch time: 102.15 s +2026-04-14 10:28:04.079590: +2026-04-14 10:28:04.082244: Epoch 3306 +2026-04-14 10:28:04.083937: Current learning rate: 0.00207 +2026-04-14 10:29:46.597101: train_loss -0.4523 +2026-04-14 10:29:46.603434: val_loss -0.3418 +2026-04-14 10:29:46.605420: Pseudo dice [0.704, 0.9168, 0.6969, 0.7503, 0.5749, 0.5003, 0.3346] +2026-04-14 10:29:46.607591: Epoch time: 102.52 s +2026-04-14 10:29:47.876909: +2026-04-14 10:29:47.878707: Epoch 3307 +2026-04-14 10:29:47.880538: Current learning rate: 0.00206 +2026-04-14 10:31:29.735596: train_loss -0.4391 +2026-04-14 10:31:29.742818: val_loss -0.3672 +2026-04-14 10:31:29.745085: Pseudo dice [0.6045, 0.6467, 0.7731, 0.4603, 0.5374, 0.928, 0.822] +2026-04-14 10:31:29.748026: Epoch time: 101.86 s +2026-04-14 10:31:31.038984: +2026-04-14 10:31:31.041061: Epoch 3308 +2026-04-14 10:31:31.042512: Current learning rate: 0.00206 +2026-04-14 10:33:12.828996: train_loss -0.4387 +2026-04-14 10:33:12.836229: val_loss -0.3938 +2026-04-14 10:33:12.838867: Pseudo dice [0.6905, 0.7438, 0.7332, 0.7867, 0.5233, 0.8935, 0.773] +2026-04-14 10:33:12.841473: Epoch time: 101.79 s +2026-04-14 10:33:14.151114: +2026-04-14 10:33:14.153549: Epoch 3309 +2026-04-14 10:33:14.156175: Current learning rate: 0.00206 +2026-04-14 10:34:55.776178: train_loss -0.4443 +2026-04-14 10:34:55.782842: val_loss -0.3843 +2026-04-14 10:34:55.784747: Pseudo dice [0.6908, 0.6518, 0.7857, 0.2871, 0.5236, 0.8707, 0.8495] +2026-04-14 10:34:55.786771: Epoch time: 101.63 s +2026-04-14 10:34:57.083689: +2026-04-14 10:34:57.086209: Epoch 3310 +2026-04-14 10:34:57.088509: Current learning rate: 0.00206 +2026-04-14 10:36:39.304607: train_loss -0.4529 +2026-04-14 10:36:39.311589: val_loss -0.3841 +2026-04-14 10:36:39.313324: Pseudo dice [0.6586, 0.3734, 0.7112, 0.4569, 0.3888, 0.8536, 0.8584] +2026-04-14 10:36:39.315699: Epoch time: 102.22 s +2026-04-14 10:36:40.543846: +2026-04-14 10:36:40.545521: Epoch 3311 +2026-04-14 10:36:40.547040: Current learning rate: 0.00205 +2026-04-14 10:38:22.419137: train_loss -0.4436 +2026-04-14 10:38:22.428813: val_loss -0.3931 +2026-04-14 10:38:22.431676: Pseudo dice [0.8717, 0.7711, 0.8648, 0.2931, 0.3571, 0.8173, 0.5893] +2026-04-14 10:38:22.434385: Epoch time: 101.88 s +2026-04-14 10:38:23.658300: +2026-04-14 10:38:23.660053: Epoch 3312 +2026-04-14 10:38:23.661814: Current learning rate: 0.00205 +2026-04-14 10:40:05.845836: train_loss -0.4531 +2026-04-14 10:40:05.855469: val_loss -0.3744 +2026-04-14 10:40:05.857140: Pseudo dice [0.7168, 0.5367, 0.6618, 0.2852, 0.4423, 0.6927, 0.6943] +2026-04-14 10:40:05.859599: Epoch time: 102.19 s +2026-04-14 10:40:07.084244: +2026-04-14 10:40:07.086165: Epoch 3313 +2026-04-14 10:40:07.087847: Current learning rate: 0.00205 +2026-04-14 10:41:49.087954: train_loss -0.4259 +2026-04-14 10:41:49.096650: val_loss -0.3549 +2026-04-14 10:41:49.099177: Pseudo dice [0.7195, 0.7859, 0.7305, 0.7656, 0.6756, 0.6865, 0.424] +2026-04-14 10:41:49.102897: Epoch time: 102.01 s +2026-04-14 10:41:50.302566: +2026-04-14 10:41:50.304829: Epoch 3314 +2026-04-14 10:41:50.306896: Current learning rate: 0.00205 +2026-04-14 10:43:33.074267: train_loss -0.4345 +2026-04-14 10:43:33.082417: val_loss -0.3861 +2026-04-14 10:43:33.084960: Pseudo dice [0.2555, 0.8646, 0.8379, 0.5747, 0.59, 0.4049, 0.755] +2026-04-14 10:43:33.087316: Epoch time: 102.77 s +2026-04-14 10:43:34.336663: +2026-04-14 10:43:34.338946: Epoch 3315 +2026-04-14 10:43:34.340983: Current learning rate: 0.00204 +2026-04-14 10:45:16.121016: train_loss -0.4371 +2026-04-14 10:45:16.133342: val_loss -0.3566 +2026-04-14 10:45:16.135603: Pseudo dice [0.1391, 0.7324, 0.7349, 0.2205, 0.5462, 0.7191, 0.5883] +2026-04-14 10:45:16.140366: Epoch time: 101.79 s +2026-04-14 10:45:17.389627: +2026-04-14 10:45:17.391200: Epoch 3316 +2026-04-14 10:45:17.392594: Current learning rate: 0.00204 +2026-04-14 10:46:58.997795: train_loss -0.4379 +2026-04-14 10:46:59.005348: val_loss -0.356 +2026-04-14 10:46:59.008020: Pseudo dice [0.7118, 0.8606, 0.7636, 0.2096, 0.4795, 0.6728, 0.6517] +2026-04-14 10:46:59.010561: Epoch time: 101.61 s +2026-04-14 10:47:00.261245: +2026-04-14 10:47:00.263415: Epoch 3317 +2026-04-14 10:47:00.265203: Current learning rate: 0.00204 +2026-04-14 10:48:42.664390: train_loss -0.4401 +2026-04-14 10:48:42.672699: val_loss -0.3672 +2026-04-14 10:48:42.674732: Pseudo dice [0.8242, 0.7617, 0.6885, 0.3151, 0.4444, 0.7015, 0.3967] +2026-04-14 10:48:42.677220: Epoch time: 102.41 s +2026-04-14 10:48:43.908069: +2026-04-14 10:48:43.910506: Epoch 3318 +2026-04-14 10:48:43.912374: Current learning rate: 0.00203 +2026-04-14 10:50:26.466772: train_loss -0.4457 +2026-04-14 10:50:26.474573: val_loss -0.3636 +2026-04-14 10:50:26.476833: Pseudo dice [0.8595, 0.7317, 0.5846, 0.1703, 0.5344, 0.6753, 0.5696] +2026-04-14 10:50:26.479529: Epoch time: 102.56 s +2026-04-14 10:50:28.840470: +2026-04-14 10:50:28.844254: Epoch 3319 +2026-04-14 10:50:28.845778: Current learning rate: 0.00203 +2026-04-14 10:52:11.175781: train_loss -0.4401 +2026-04-14 10:52:11.182504: val_loss -0.4069 +2026-04-14 10:52:11.184669: Pseudo dice [0.6511, 0.7907, 0.8021, 0.597, 0.6366, 0.8892, 0.8413] +2026-04-14 10:52:11.187337: Epoch time: 102.34 s +2026-04-14 10:52:12.429380: +2026-04-14 10:52:12.431746: Epoch 3320 +2026-04-14 10:52:12.433460: Current learning rate: 0.00203 +2026-04-14 10:53:54.336519: train_loss -0.4339 +2026-04-14 10:53:54.342683: val_loss -0.3779 +2026-04-14 10:53:54.344871: Pseudo dice [0.6319, 0.6122, 0.7139, 0.3993, 0.5189, 0.7773, 0.4752] +2026-04-14 10:53:54.347385: Epoch time: 101.91 s +2026-04-14 10:53:55.588434: +2026-04-14 10:53:55.590856: Epoch 3321 +2026-04-14 10:53:55.592493: Current learning rate: 0.00203 +2026-04-14 10:55:37.454067: train_loss -0.4324 +2026-04-14 10:55:37.479416: val_loss -0.3895 +2026-04-14 10:55:37.481757: Pseudo dice [0.6177, 0.4116, 0.6402, 0.8165, 0.5124, 0.8915, 0.8646] +2026-04-14 10:55:37.484467: Epoch time: 101.87 s +2026-04-14 10:55:38.706766: +2026-04-14 10:55:38.708856: Epoch 3322 +2026-04-14 10:55:38.711060: Current learning rate: 0.00202 +2026-04-14 10:57:20.780907: train_loss -0.4406 +2026-04-14 10:57:20.786958: val_loss -0.3666 +2026-04-14 10:57:20.789034: Pseudo dice [0.5531, 0.8793, 0.8607, 0.3101, 0.5159, 0.8045, 0.6988] +2026-04-14 10:57:20.792035: Epoch time: 102.08 s +2026-04-14 10:57:22.023982: +2026-04-14 10:57:22.025882: Epoch 3323 +2026-04-14 10:57:22.027542: Current learning rate: 0.00202 +2026-04-14 10:59:04.573061: train_loss -0.4436 +2026-04-14 10:59:04.579216: val_loss -0.3689 +2026-04-14 10:59:04.582253: Pseudo dice [0.6969, 0.7672, 0.7137, 0.1553, 0.3002, 0.8856, 0.6886] +2026-04-14 10:59:04.585714: Epoch time: 102.55 s +2026-04-14 10:59:05.847343: +2026-04-14 10:59:05.849591: Epoch 3324 +2026-04-14 10:59:05.852202: Current learning rate: 0.00202 +2026-04-14 11:00:48.470023: train_loss -0.4422 +2026-04-14 11:00:48.476436: val_loss -0.371 +2026-04-14 11:00:48.479017: Pseudo dice [0.6501, 0.6575, 0.6723, 0.8021, 0.5939, 0.619, 0.6711] +2026-04-14 11:00:48.482034: Epoch time: 102.63 s +2026-04-14 11:00:49.715014: +2026-04-14 11:00:49.717147: Epoch 3325 +2026-04-14 11:00:49.719837: Current learning rate: 0.00202 +2026-04-14 11:02:31.605413: train_loss -0.4437 +2026-04-14 11:02:31.612648: val_loss -0.3869 +2026-04-14 11:02:31.615263: Pseudo dice [0.6973, 0.9051, 0.8108, 0.4411, 0.4382, 0.1928, 0.7208] +2026-04-14 11:02:31.617976: Epoch time: 101.89 s +2026-04-14 11:02:33.166846: +2026-04-14 11:02:33.168823: Epoch 3326 +2026-04-14 11:02:33.170604: Current learning rate: 0.00201 +2026-04-14 11:04:15.081349: train_loss -0.4295 +2026-04-14 11:04:15.088018: val_loss -0.3708 +2026-04-14 11:04:15.090406: Pseudo dice [0.7243, 0.8375, 0.7122, 0.4302, 0.5529, 0.7417, 0.824] +2026-04-14 11:04:15.092955: Epoch time: 101.92 s +2026-04-14 11:04:16.324790: +2026-04-14 11:04:16.326736: Epoch 3327 +2026-04-14 11:04:16.328181: Current learning rate: 0.00201 +2026-04-14 11:05:58.779988: train_loss -0.4358 +2026-04-14 11:05:58.785749: val_loss -0.286 +2026-04-14 11:05:58.787945: Pseudo dice [0.8157, 0.5822, 0.5648, 0.3314, 0.3411, 0.7248, 0.5549] +2026-04-14 11:05:58.790174: Epoch time: 102.46 s +2026-04-14 11:06:00.069622: +2026-04-14 11:06:00.071701: Epoch 3328 +2026-04-14 11:06:00.073539: Current learning rate: 0.00201 +2026-04-14 11:07:42.870864: train_loss -0.4267 +2026-04-14 11:07:42.876939: val_loss -0.3482 +2026-04-14 11:07:42.878755: Pseudo dice [0.6988, 0.7125, 0.6647, 0.2638, 0.4083, 0.8004, 0.6773] +2026-04-14 11:07:42.881107: Epoch time: 102.8 s +2026-04-14 11:07:44.113679: +2026-04-14 11:07:44.116487: Epoch 3329 +2026-04-14 11:07:44.118312: Current learning rate: 0.00201 +2026-04-14 11:09:26.123879: train_loss -0.4253 +2026-04-14 11:09:26.130223: val_loss -0.3545 +2026-04-14 11:09:26.132518: Pseudo dice [0.6202, 0.6822, 0.6759, 0.7342, 0.2212, 0.9012, 0.513] +2026-04-14 11:09:26.134679: Epoch time: 102.01 s +2026-04-14 11:09:27.358953: +2026-04-14 11:09:27.360583: Epoch 3330 +2026-04-14 11:09:27.362231: Current learning rate: 0.002 +2026-04-14 11:11:09.634829: train_loss -0.4333 +2026-04-14 11:11:09.640761: val_loss -0.3494 +2026-04-14 11:11:09.643886: Pseudo dice [0.8125, 0.7496, 0.7631, 0.4696, 0.3967, 0.476, 0.7739] +2026-04-14 11:11:09.647005: Epoch time: 102.28 s +2026-04-14 11:11:10.878252: +2026-04-14 11:11:10.880201: Epoch 3331 +2026-04-14 11:11:10.881770: Current learning rate: 0.002 +2026-04-14 11:12:53.425503: train_loss -0.4323 +2026-04-14 11:12:53.431624: val_loss -0.3171 +2026-04-14 11:12:53.433797: Pseudo dice [0.5781, 0.8553, 0.7848, 0.3438, 0.5868, 0.3486, 0.2468] +2026-04-14 11:12:53.436169: Epoch time: 102.55 s +2026-04-14 11:12:54.687724: +2026-04-14 11:12:54.689906: Epoch 3332 +2026-04-14 11:12:54.691625: Current learning rate: 0.002 +2026-04-14 11:14:36.919450: train_loss -0.4332 +2026-04-14 11:14:36.926186: val_loss -0.3367 +2026-04-14 11:14:36.928416: Pseudo dice [0.6419, 0.9046, 0.8005, 0.4172, 0.6501, 0.0986, 0.7648] +2026-04-14 11:14:36.931771: Epoch time: 102.24 s +2026-04-14 11:14:38.174935: +2026-04-14 11:14:38.177694: Epoch 3333 +2026-04-14 11:14:38.180459: Current learning rate: 0.00199 +2026-04-14 11:16:20.408798: train_loss -0.4377 +2026-04-14 11:16:20.416077: val_loss -0.3472 +2026-04-14 11:16:20.417887: Pseudo dice [0.8163, 0.8981, 0.7885, 0.5781, 0.5318, 0.3144, 0.7211] +2026-04-14 11:16:20.420394: Epoch time: 102.24 s +2026-04-14 11:16:21.698896: +2026-04-14 11:16:21.700814: Epoch 3334 +2026-04-14 11:16:21.702338: Current learning rate: 0.00199 +2026-04-14 11:18:03.931966: train_loss -0.4396 +2026-04-14 11:18:03.938049: val_loss -0.3756 +2026-04-14 11:18:03.940042: Pseudo dice [0.852, 0.873, 0.8243, 0.2647, 0.3462, 0.9344, 0.5875] +2026-04-14 11:18:03.942330: Epoch time: 102.24 s +2026-04-14 11:18:05.184961: +2026-04-14 11:18:05.186807: Epoch 3335 +2026-04-14 11:18:05.188473: Current learning rate: 0.00199 +2026-04-14 11:19:48.112154: train_loss -0.4397 +2026-04-14 11:19:48.119162: val_loss -0.3479 +2026-04-14 11:19:48.122977: Pseudo dice [0.7839, 0.6351, 0.7279, 0.6011, 0.4384, 0.1492, 0.3776] +2026-04-14 11:19:48.125135: Epoch time: 102.93 s +2026-04-14 11:19:49.635100: +2026-04-14 11:19:49.636871: Epoch 3336 +2026-04-14 11:19:49.638486: Current learning rate: 0.00199 +2026-04-14 11:21:32.300140: train_loss -0.4216 +2026-04-14 11:21:32.307431: val_loss -0.3586 +2026-04-14 11:21:32.309244: Pseudo dice [0.8921, 0.6789, 0.7818, 0.4793, 0.3315, 0.5801, 0.4088] +2026-04-14 11:21:32.312077: Epoch time: 102.67 s +2026-04-14 11:21:33.568941: +2026-04-14 11:21:33.570868: Epoch 3337 +2026-04-14 11:21:33.572426: Current learning rate: 0.00198 +2026-04-14 11:23:15.901758: train_loss -0.42 +2026-04-14 11:23:15.908643: val_loss -0.3776 +2026-04-14 11:23:15.911766: Pseudo dice [0.2737, 0.7574, 0.697, 0.5829, 0.6109, 0.8379, 0.8222] +2026-04-14 11:23:15.914174: Epoch time: 102.34 s +2026-04-14 11:23:17.147251: +2026-04-14 11:23:17.149288: Epoch 3338 +2026-04-14 11:23:17.151142: Current learning rate: 0.00198 +2026-04-14 11:24:59.441443: train_loss -0.435 +2026-04-14 11:24:59.447797: val_loss -0.3806 +2026-04-14 11:24:59.449958: Pseudo dice [0.89, 0.531, 0.7858, 0.2778, 0.5577, 0.9298, 0.7332] +2026-04-14 11:24:59.452225: Epoch time: 102.3 s +2026-04-14 11:25:01.788188: +2026-04-14 11:25:01.789930: Epoch 3339 +2026-04-14 11:25:01.791346: Current learning rate: 0.00198 +2026-04-14 11:26:43.779000: train_loss -0.4508 +2026-04-14 11:26:43.784621: val_loss -0.372 +2026-04-14 11:26:43.786628: Pseudo dice [0.8585, 0.9252, 0.7952, 0.6206, 0.4641, 0.6855, 0.8076] +2026-04-14 11:26:43.788898: Epoch time: 101.99 s +2026-04-14 11:26:45.021056: +2026-04-14 11:26:45.022701: Epoch 3340 +2026-04-14 11:26:45.024082: Current learning rate: 0.00198 +2026-04-14 11:28:26.720836: train_loss -0.4477 +2026-04-14 11:28:26.728261: val_loss -0.3773 +2026-04-14 11:28:26.730255: Pseudo dice [0.6449, 0.6918, 0.8573, 0.6219, 0.6516, 0.9262, 0.5934] +2026-04-14 11:28:26.732262: Epoch time: 101.7 s +2026-04-14 11:28:27.962256: +2026-04-14 11:28:27.964699: Epoch 3341 +2026-04-14 11:28:27.967114: Current learning rate: 0.00197 +2026-04-14 11:30:10.627202: train_loss -0.4554 +2026-04-14 11:30:10.634318: val_loss -0.3897 +2026-04-14 11:30:10.635958: Pseudo dice [0.4999, 0.7474, 0.809, 0.6999, 0.4742, 0.8568, 0.876] +2026-04-14 11:30:10.638227: Epoch time: 102.67 s +2026-04-14 11:30:11.909769: +2026-04-14 11:30:11.911354: Epoch 3342 +2026-04-14 11:30:11.912912: Current learning rate: 0.00197 +2026-04-14 11:31:54.346602: train_loss -0.4436 +2026-04-14 11:31:54.352287: val_loss -0.399 +2026-04-14 11:31:54.354695: Pseudo dice [0.3084, 0.6407, 0.7942, 0.4907, 0.5754, 0.7879, 0.8521] +2026-04-14 11:31:54.356647: Epoch time: 102.44 s +2026-04-14 11:31:55.618821: +2026-04-14 11:31:55.621325: Epoch 3343 +2026-04-14 11:31:55.623440: Current learning rate: 0.00197 +2026-04-14 11:33:37.998282: train_loss -0.447 +2026-04-14 11:33:38.005464: val_loss -0.3645 +2026-04-14 11:33:38.008027: Pseudo dice [0.7278, 0.8909, 0.7178, 0.4571, 0.3788, 0.8134, 0.8246] +2026-04-14 11:33:38.012378: Epoch time: 102.38 s +2026-04-14 11:33:39.277651: +2026-04-14 11:33:39.279999: Epoch 3344 +2026-04-14 11:33:39.281719: Current learning rate: 0.00196 +2026-04-14 11:35:21.665860: train_loss -0.4435 +2026-04-14 11:35:21.673498: val_loss -0.404 +2026-04-14 11:35:21.675039: Pseudo dice [0.6234, 0.5047, 0.8005, 0.2727, 0.6693, 0.8441, 0.847] +2026-04-14 11:35:21.676986: Epoch time: 102.39 s +2026-04-14 11:35:22.919648: +2026-04-14 11:35:22.921276: Epoch 3345 +2026-04-14 11:35:22.922657: Current learning rate: 0.00196 +2026-04-14 11:37:05.038196: train_loss -0.449 +2026-04-14 11:37:05.047554: val_loss -0.3416 +2026-04-14 11:37:05.050417: Pseudo dice [0.3397, 0.7828, 0.7429, 0.8367, 0.5843, 0.7111, 0.5788] +2026-04-14 11:37:05.053090: Epoch time: 102.12 s +2026-04-14 11:37:06.295173: +2026-04-14 11:37:06.297264: Epoch 3346 +2026-04-14 11:37:06.299392: Current learning rate: 0.00196 +2026-04-14 11:38:49.492812: train_loss -0.4508 +2026-04-14 11:38:49.499690: val_loss -0.3769 +2026-04-14 11:38:49.501998: Pseudo dice [0.0656, 0.908, 0.751, 0.6165, 0.5739, 0.7301, 0.8742] +2026-04-14 11:38:49.504793: Epoch time: 103.2 s +2026-04-14 11:38:50.751605: +2026-04-14 11:38:50.753443: Epoch 3347 +2026-04-14 11:38:50.754968: Current learning rate: 0.00196 +2026-04-14 11:40:32.709543: train_loss -0.4351 +2026-04-14 11:40:32.717663: val_loss -0.4107 +2026-04-14 11:40:32.719909: Pseudo dice [0.6332, 0.7602, 0.7273, 0.4268, 0.7225, 0.9255, 0.7897] +2026-04-14 11:40:32.722723: Epoch time: 101.96 s +2026-04-14 11:40:33.984965: +2026-04-14 11:40:33.986475: Epoch 3348 +2026-04-14 11:40:33.987964: Current learning rate: 0.00195 +2026-04-14 11:42:16.569119: train_loss -0.4342 +2026-04-14 11:42:16.575803: val_loss -0.3685 +2026-04-14 11:42:16.577773: Pseudo dice [0.8135, 0.622, 0.7548, 0.5341, 0.6354, 0.6997, 0.8714] +2026-04-14 11:42:16.579733: Epoch time: 102.59 s +2026-04-14 11:42:16.581861: Yayy! New best EMA pseudo Dice: 0.6623 +2026-04-14 11:42:19.335112: +2026-04-14 11:42:19.338439: Epoch 3349 +2026-04-14 11:42:19.341005: Current learning rate: 0.00195 +2026-04-14 11:44:01.190084: train_loss -0.4339 +2026-04-14 11:44:01.196115: val_loss -0.3739 +2026-04-14 11:44:01.198153: Pseudo dice [0.5625, 0.8161, 0.8028, 0.1909, 0.6922, 0.2273, 0.7984] +2026-04-14 11:44:01.200828: Epoch time: 101.86 s +2026-04-14 11:44:03.963237: +2026-04-14 11:44:03.965934: Epoch 3350 +2026-04-14 11:44:03.967328: Current learning rate: 0.00195 +2026-04-14 11:45:46.095322: train_loss -0.4331 +2026-04-14 11:45:46.100917: val_loss -0.369 +2026-04-14 11:45:46.103901: Pseudo dice [0.8258, 0.5474, 0.7059, 0.6233, 0.6088, 0.6661, 0.8829] +2026-04-14 11:45:46.106944: Epoch time: 102.14 s +2026-04-14 11:45:47.371797: +2026-04-14 11:45:47.373868: Epoch 3351 +2026-04-14 11:45:47.375717: Current learning rate: 0.00195 +2026-04-14 11:47:30.458316: train_loss -0.4393 +2026-04-14 11:47:30.465726: val_loss -0.3555 +2026-04-14 11:47:30.468246: Pseudo dice [0.6493, 0.7302, 0.6358, 0.5978, 0.6434, 0.5477, 0.8325] +2026-04-14 11:47:30.470757: Epoch time: 103.09 s +2026-04-14 11:47:31.754187: +2026-04-14 11:47:31.756404: Epoch 3352 +2026-04-14 11:47:31.758240: Current learning rate: 0.00194 +2026-04-14 11:49:13.963014: train_loss -0.4267 +2026-04-14 11:49:13.970606: val_loss -0.3722 +2026-04-14 11:49:13.972555: Pseudo dice [0.6595, 0.6812, 0.7673, 0.552, 0.5534, 0.8567, 0.6425] +2026-04-14 11:49:13.975074: Epoch time: 102.21 s +2026-04-14 11:49:15.218031: +2026-04-14 11:49:15.219615: Epoch 3353 +2026-04-14 11:49:15.221138: Current learning rate: 0.00194 +2026-04-14 11:50:57.818453: train_loss -0.4287 +2026-04-14 11:50:57.827032: val_loss -0.3553 +2026-04-14 11:50:57.829631: Pseudo dice [0.5305, 0.757, 0.6568, 0.542, 0.3628, 0.8846, 0.6186] +2026-04-14 11:50:57.832282: Epoch time: 102.6 s +2026-04-14 11:50:59.084733: +2026-04-14 11:50:59.086351: Epoch 3354 +2026-04-14 11:50:59.087779: Current learning rate: 0.00194 +2026-04-14 11:52:40.275037: train_loss -0.4302 +2026-04-14 11:52:40.282050: val_loss -0.3407 +2026-04-14 11:52:40.284038: Pseudo dice [0.535, 0.4557, 0.7079, 0.6501, 0.4717, 0.6286, 0.8199] +2026-04-14 11:52:40.286475: Epoch time: 101.19 s +2026-04-14 11:52:41.569726: +2026-04-14 11:52:41.571607: Epoch 3355 +2026-04-14 11:52:41.573532: Current learning rate: 0.00194 +2026-04-14 11:54:22.963561: train_loss -0.4403 +2026-04-14 11:54:22.970252: val_loss -0.3845 +2026-04-14 11:54:22.972236: Pseudo dice [0.5599, 0.5814, 0.6448, 0.6552, 0.6775, 0.9295, 0.8286] +2026-04-14 11:54:22.974935: Epoch time: 101.4 s +2026-04-14 11:54:24.234066: +2026-04-14 11:54:24.235937: Epoch 3356 +2026-04-14 11:54:24.237489: Current learning rate: 0.00193 +2026-04-14 11:56:06.542718: train_loss -0.4344 +2026-04-14 11:56:06.553342: val_loss -0.3898 +2026-04-14 11:56:06.557764: Pseudo dice [0.1036, 0.8958, 0.8659, 0.5517, 0.6762, 0.6543, 0.5901] +2026-04-14 11:56:06.560184: Epoch time: 102.31 s +2026-04-14 11:56:07.814164: +2026-04-14 11:56:07.818845: Epoch 3357 +2026-04-14 11:56:07.823912: Current learning rate: 0.00193 +2026-04-14 11:57:50.022731: train_loss -0.4268 +2026-04-14 11:57:50.029287: val_loss -0.3427 +2026-04-14 11:57:50.033100: Pseudo dice [0.68, 0.6108, 0.6344, 0.4814, 0.3803, 0.8927, 0.8137] +2026-04-14 11:57:50.036851: Epoch time: 102.21 s +2026-04-14 11:57:51.293433: +2026-04-14 11:57:51.295541: Epoch 3358 +2026-04-14 11:57:51.297533: Current learning rate: 0.00193 +2026-04-14 11:59:32.770192: train_loss -0.4072 +2026-04-14 11:59:32.777143: val_loss -0.3868 +2026-04-14 11:59:32.779495: Pseudo dice [0.7538, 0.4725, 0.8369, 0.6952, 0.3786, 0.8747, 0.7859] +2026-04-14 11:59:32.781767: Epoch time: 101.48 s +2026-04-14 11:59:34.013438: +2026-04-14 11:59:34.015605: Epoch 3359 +2026-04-14 11:59:34.017107: Current learning rate: 0.00192 +2026-04-14 12:01:15.560949: train_loss -0.4293 +2026-04-14 12:01:15.567362: val_loss -0.3614 +2026-04-14 12:01:15.569372: Pseudo dice [0.3732, 0.673, 0.738, 0.4824, 0.6623, 0.568, 0.3036] +2026-04-14 12:01:15.571588: Epoch time: 101.55 s +2026-04-14 12:01:16.794785: +2026-04-14 12:01:16.796469: Epoch 3360 +2026-04-14 12:01:16.797910: Current learning rate: 0.00192 +2026-04-14 12:02:58.196906: train_loss -0.4341 +2026-04-14 12:02:58.203192: val_loss -0.3729 +2026-04-14 12:02:58.207189: Pseudo dice [0.6768, 0.3526, 0.7408, 0.6096, 0.4381, 0.6812, 0.7833] +2026-04-14 12:02:58.211239: Epoch time: 101.41 s +2026-04-14 12:02:59.502734: +2026-04-14 12:02:59.504655: Epoch 3361 +2026-04-14 12:02:59.506302: Current learning rate: 0.00192 +2026-04-14 12:04:40.831761: train_loss -0.4223 +2026-04-14 12:04:40.839252: val_loss -0.3498 +2026-04-14 12:04:40.841273: Pseudo dice [0.7103, 0.7602, 0.5893, 0.4918, 0.5898, 0.4795, 0.6235] +2026-04-14 12:04:40.843844: Epoch time: 101.33 s +2026-04-14 12:04:42.090725: +2026-04-14 12:04:42.092767: Epoch 3362 +2026-04-14 12:04:42.094514: Current learning rate: 0.00192 +2026-04-14 12:06:24.014677: train_loss -0.4359 +2026-04-14 12:06:24.020263: val_loss -0.3272 +2026-04-14 12:06:24.023678: Pseudo dice [0.2312, 0.7293, 0.6158, 0.6267, 0.5128, 0.6126, 0.8571] +2026-04-14 12:06:24.025748: Epoch time: 101.93 s +2026-04-14 12:06:25.270573: +2026-04-14 12:06:25.272674: Epoch 3363 +2026-04-14 12:06:25.274170: Current learning rate: 0.00191 +2026-04-14 12:08:07.477262: train_loss -0.4406 +2026-04-14 12:08:07.493739: val_loss -0.3759 +2026-04-14 12:08:07.495541: Pseudo dice [0.412, 0.6335, 0.7871, 0.6581, 0.4448, 0.1922, 0.8552] +2026-04-14 12:08:07.499237: Epoch time: 102.21 s +2026-04-14 12:08:08.755030: +2026-04-14 12:08:08.757659: Epoch 3364 +2026-04-14 12:08:08.759150: Current learning rate: 0.00191 +2026-04-14 12:09:50.603543: train_loss -0.4444 +2026-04-14 12:09:50.618050: val_loss -0.3824 +2026-04-14 12:09:50.620560: Pseudo dice [0.5615, 0.3438, 0.7483, 0.4583, 0.7044, 0.6739, 0.8045] +2026-04-14 12:09:50.622922: Epoch time: 101.85 s +2026-04-14 12:09:51.863548: +2026-04-14 12:09:51.865070: Epoch 3365 +2026-04-14 12:09:51.866445: Current learning rate: 0.00191 +2026-04-14 12:11:33.736063: train_loss -0.4493 +2026-04-14 12:11:33.744103: val_loss -0.393 +2026-04-14 12:11:33.746264: Pseudo dice [0.5308, 0.5354, 0.7979, 0.4971, 0.4375, 0.8404, 0.832] +2026-04-14 12:11:33.749458: Epoch time: 101.88 s +2026-04-14 12:11:35.000723: +2026-04-14 12:11:35.003033: Epoch 3366 +2026-04-14 12:11:35.004847: Current learning rate: 0.00191 +2026-04-14 12:13:16.668495: train_loss -0.4404 +2026-04-14 12:13:16.676088: val_loss -0.3437 +2026-04-14 12:13:16.678882: Pseudo dice [0.6191, 0.8854, 0.7967, 0.234, 0.3278, 0.2235, 0.796] +2026-04-14 12:13:16.681163: Epoch time: 101.67 s +2026-04-14 12:13:17.935116: +2026-04-14 12:13:17.936613: Epoch 3367 +2026-04-14 12:13:17.938237: Current learning rate: 0.0019 +2026-04-14 12:14:59.584461: train_loss -0.4496 +2026-04-14 12:14:59.591072: val_loss -0.376 +2026-04-14 12:14:59.593920: Pseudo dice [0.8033, 0.6503, 0.6712, 0.5677, 0.4003, 0.7755, 0.7865] +2026-04-14 12:14:59.596590: Epoch time: 101.65 s +2026-04-14 12:15:00.830472: +2026-04-14 12:15:00.832286: Epoch 3368 +2026-04-14 12:15:00.833843: Current learning rate: 0.0019 +2026-04-14 12:16:42.261239: train_loss -0.4409 +2026-04-14 12:16:42.268546: val_loss -0.393 +2026-04-14 12:16:42.270811: Pseudo dice [0.6396, 0.2205, 0.8375, 0.6545, 0.5156, 0.6182, 0.7122] +2026-04-14 12:16:42.273980: Epoch time: 101.43 s +2026-04-14 12:16:43.530921: +2026-04-14 12:16:43.532574: Epoch 3369 +2026-04-14 12:16:43.533885: Current learning rate: 0.0019 +2026-04-14 12:18:24.871787: train_loss -0.4355 +2026-04-14 12:18:24.878502: val_loss -0.369 +2026-04-14 12:18:24.881840: Pseudo dice [0.5831, 0.8358, 0.7524, 0.7392, 0.5077, 0.1307, 0.793] +2026-04-14 12:18:24.884537: Epoch time: 101.34 s +2026-04-14 12:18:26.116051: +2026-04-14 12:18:26.117852: Epoch 3370 +2026-04-14 12:18:26.119260: Current learning rate: 0.00189 +2026-04-14 12:20:07.601450: train_loss -0.45 +2026-04-14 12:20:07.608521: val_loss -0.3952 +2026-04-14 12:20:07.610451: Pseudo dice [0.5564, 0.702, 0.8688, 0.2034, 0.4888, 0.9176, 0.6528] +2026-04-14 12:20:07.613138: Epoch time: 101.49 s +2026-04-14 12:20:08.892587: +2026-04-14 12:20:08.894369: Epoch 3371 +2026-04-14 12:20:08.896826: Current learning rate: 0.00189 +2026-04-14 12:21:50.128046: train_loss -0.44 +2026-04-14 12:21:50.133128: val_loss -0.3904 +2026-04-14 12:21:50.134889: Pseudo dice [0.0888, 0.8838, 0.6856, 0.5682, 0.5075, 0.3982, 0.849] +2026-04-14 12:21:50.137146: Epoch time: 101.24 s +2026-04-14 12:21:51.356860: +2026-04-14 12:21:51.358557: Epoch 3372 +2026-04-14 12:21:51.360121: Current learning rate: 0.00189 +2026-04-14 12:23:32.737987: train_loss -0.4464 +2026-04-14 12:23:32.744130: val_loss -0.3742 +2026-04-14 12:23:32.746460: Pseudo dice [0.8824, 0.8998, 0.852, 0.4628, 0.4526, 0.836, 0.7965] +2026-04-14 12:23:32.748770: Epoch time: 101.38 s +2026-04-14 12:23:34.011658: +2026-04-14 12:23:34.013649: Epoch 3373 +2026-04-14 12:23:34.015251: Current learning rate: 0.00189 +2026-04-14 12:25:15.457560: train_loss -0.4558 +2026-04-14 12:25:15.463558: val_loss -0.3687 +2026-04-14 12:25:15.465370: Pseudo dice [0.6977, 0.883, 0.7353, 0.6154, 0.5205, 0.5272, 0.8441] +2026-04-14 12:25:15.467983: Epoch time: 101.45 s +2026-04-14 12:25:16.724438: +2026-04-14 12:25:16.733616: Epoch 3374 +2026-04-14 12:25:16.735075: Current learning rate: 0.00188 +2026-04-14 12:26:58.083355: train_loss -0.4536 +2026-04-14 12:26:58.090337: val_loss -0.3322 +2026-04-14 12:26:58.092386: Pseudo dice [0.5895, 0.706, 0.657, 0.4174, 0.4257, 0.9029, 0.8682] +2026-04-14 12:26:58.094918: Epoch time: 101.36 s +2026-04-14 12:26:59.325837: +2026-04-14 12:26:59.327673: Epoch 3375 +2026-04-14 12:26:59.329602: Current learning rate: 0.00188 +2026-04-14 12:28:41.234828: train_loss -0.4485 +2026-04-14 12:28:41.241074: val_loss -0.3668 +2026-04-14 12:28:41.243574: Pseudo dice [0.6853, 0.1803, 0.7734, 0.4111, 0.6259, 0.582, 0.7478] +2026-04-14 12:28:41.246553: Epoch time: 101.91 s +2026-04-14 12:28:42.516126: +2026-04-14 12:28:42.517853: Epoch 3376 +2026-04-14 12:28:42.519180: Current learning rate: 0.00188 +2026-04-14 12:30:23.860713: train_loss -0.4527 +2026-04-14 12:30:23.867832: val_loss -0.3489 +2026-04-14 12:30:23.870269: Pseudo dice [0.6428, 0.8506, 0.8087, 0.1919, 0.6595, 0.3293, 0.8579] +2026-04-14 12:30:23.872713: Epoch time: 101.35 s +2026-04-14 12:30:26.211536: +2026-04-14 12:30:26.213160: Epoch 3377 +2026-04-14 12:30:26.214565: Current learning rate: 0.00188 +2026-04-14 12:32:07.983368: train_loss -0.4607 +2026-04-14 12:32:07.992882: val_loss -0.3545 +2026-04-14 12:32:07.994933: Pseudo dice [0.7402, 0.7123, 0.7565, 0.1615, 0.701, 0.9198, 0.8414] +2026-04-14 12:32:07.997497: Epoch time: 101.77 s +2026-04-14 12:32:09.220581: +2026-04-14 12:32:09.223125: Epoch 3378 +2026-04-14 12:32:09.225895: Current learning rate: 0.00187 +2026-04-14 12:33:50.589937: train_loss -0.4382 +2026-04-14 12:33:50.596761: val_loss -0.3861 +2026-04-14 12:33:50.599220: Pseudo dice [0.6773, 0.8097, 0.8057, 0.8079, 0.3721, 0.801, 0.8154] +2026-04-14 12:33:50.602427: Epoch time: 101.37 s +2026-04-14 12:33:51.854239: +2026-04-14 12:33:51.856495: Epoch 3379 +2026-04-14 12:33:51.858407: Current learning rate: 0.00187 +2026-04-14 12:35:32.953214: train_loss -0.4459 +2026-04-14 12:35:32.958742: val_loss -0.3947 +2026-04-14 12:35:32.960811: Pseudo dice [0.696, 0.8131, 0.8308, 0.7614, 0.5329, 0.7793, 0.3487] +2026-04-14 12:35:32.962997: Epoch time: 101.1 s +2026-04-14 12:35:34.197735: +2026-04-14 12:35:34.200597: Epoch 3380 +2026-04-14 12:35:34.202598: Current learning rate: 0.00187 +2026-04-14 12:37:15.662885: train_loss -0.4544 +2026-04-14 12:37:15.668952: val_loss -0.3891 +2026-04-14 12:37:15.671698: Pseudo dice [0.7886, 0.8257, 0.8424, 0.2798, 0.6116, 0.9192, 0.676] +2026-04-14 12:37:15.673908: Epoch time: 101.47 s +2026-04-14 12:37:16.894146: +2026-04-14 12:37:16.895821: Epoch 3381 +2026-04-14 12:37:16.897467: Current learning rate: 0.00186 +2026-04-14 12:38:58.181015: train_loss -0.4487 +2026-04-14 12:38:58.186447: val_loss -0.3979 +2026-04-14 12:38:58.188154: Pseudo dice [0.7418, 0.5172, 0.7773, 0.6157, 0.5648, 0.8264, 0.8501] +2026-04-14 12:38:58.190143: Epoch time: 101.29 s +2026-04-14 12:38:59.432981: +2026-04-14 12:38:59.450830: Epoch 3382 +2026-04-14 12:38:59.452339: Current learning rate: 0.00186 +2026-04-14 12:40:41.096512: train_loss -0.4359 +2026-04-14 12:40:41.102970: val_loss -0.3719 +2026-04-14 12:40:41.106591: Pseudo dice [0.7665, 0.9074, 0.6902, 0.451, 0.4967, 0.207, 0.7922] +2026-04-14 12:40:41.109224: Epoch time: 101.67 s +2026-04-14 12:40:42.360152: +2026-04-14 12:40:42.362555: Epoch 3383 +2026-04-14 12:40:42.364950: Current learning rate: 0.00186 +2026-04-14 12:42:23.853497: train_loss -0.4411 +2026-04-14 12:42:23.859479: val_loss -0.3737 +2026-04-14 12:42:23.861917: Pseudo dice [0.6, 0.8955, 0.8606, 0.7207, 0.2898, 0.7426, 0.4392] +2026-04-14 12:42:23.865151: Epoch time: 101.5 s +2026-04-14 12:42:25.099855: +2026-04-14 12:42:25.101597: Epoch 3384 +2026-04-14 12:42:25.103078: Current learning rate: 0.00186 +2026-04-14 12:44:06.210956: train_loss -0.4424 +2026-04-14 12:44:06.217832: val_loss -0.3725 +2026-04-14 12:44:06.219935: Pseudo dice [0.8694, 0.6853, 0.8401, 0.2091, 0.5418, 0.8683, 0.833] +2026-04-14 12:44:06.223121: Epoch time: 101.11 s +2026-04-14 12:44:07.466241: +2026-04-14 12:44:07.467840: Epoch 3385 +2026-04-14 12:44:07.469255: Current learning rate: 0.00185 +2026-04-14 12:45:49.094566: train_loss -0.4411 +2026-04-14 12:45:49.102779: val_loss -0.3765 +2026-04-14 12:45:49.104690: Pseudo dice [0.3694, 0.6161, 0.7919, 0.1441, 0.5858, 0.9338, 0.7829] +2026-04-14 12:45:49.108301: Epoch time: 101.63 s +2026-04-14 12:45:50.538882: +2026-04-14 12:45:50.540498: Epoch 3386 +2026-04-14 12:45:50.542052: Current learning rate: 0.00185 +2026-04-14 12:47:32.678873: train_loss -0.4401 +2026-04-14 12:47:32.685149: val_loss -0.3888 +2026-04-14 12:47:32.686843: Pseudo dice [0.8346, 0.6671, 0.7483, 0.0413, 0.2392, 0.9445, 0.6841] +2026-04-14 12:47:32.689093: Epoch time: 102.14 s +2026-04-14 12:47:33.918443: +2026-04-14 12:47:33.920085: Epoch 3387 +2026-04-14 12:47:33.921501: Current learning rate: 0.00185 +2026-04-14 12:49:15.702245: train_loss -0.4506 +2026-04-14 12:49:15.716564: val_loss -0.3871 +2026-04-14 12:49:15.718424: Pseudo dice [0.8799, 0.737, 0.7687, 0.6132, 0.3097, 0.8552, 0.8461] +2026-04-14 12:49:15.723055: Epoch time: 101.79 s +2026-04-14 12:49:16.963958: +2026-04-14 12:49:16.965767: Epoch 3388 +2026-04-14 12:49:16.967332: Current learning rate: 0.00185 +2026-04-14 12:50:58.161237: train_loss -0.4468 +2026-04-14 12:50:58.167687: val_loss -0.4198 +2026-04-14 12:50:58.169558: Pseudo dice [0.7406, 0.6948, 0.8118, 0.8352, 0.5416, 0.9266, 0.8769] +2026-04-14 12:50:58.171670: Epoch time: 101.2 s +2026-04-14 12:50:58.173717: Yayy! New best EMA pseudo Dice: 0.6655 +2026-04-14 12:51:01.044787: +2026-04-14 12:51:01.046818: Epoch 3389 +2026-04-14 12:51:01.048324: Current learning rate: 0.00184 +2026-04-14 12:52:43.291445: train_loss -0.4458 +2026-04-14 12:52:43.301012: val_loss -0.3993 +2026-04-14 12:52:43.303324: Pseudo dice [0.8644, 0.8711, 0.8649, 0.0569, 0.3722, 0.6737, 0.8825] +2026-04-14 12:52:43.305907: Epoch time: 102.25 s +2026-04-14 12:52:44.540073: +2026-04-14 12:52:44.541728: Epoch 3390 +2026-04-14 12:52:44.543415: Current learning rate: 0.00184 +2026-04-14 12:54:25.955264: train_loss -0.4513 +2026-04-14 12:54:25.962821: val_loss -0.4029 +2026-04-14 12:54:25.964992: Pseudo dice [0.7572, 0.6423, 0.8418, 0.4975, 0.5281, 0.9305, 0.8153] +2026-04-14 12:54:25.967516: Epoch time: 101.42 s +2026-04-14 12:54:25.969660: Yayy! New best EMA pseudo Dice: 0.6697 +2026-04-14 12:54:28.777923: +2026-04-14 12:54:28.781388: Epoch 3391 +2026-04-14 12:54:28.782841: Current learning rate: 0.00184 +2026-04-14 12:56:10.330377: train_loss -0.4609 +2026-04-14 12:56:10.340649: val_loss -0.3899 +2026-04-14 12:56:10.342725: Pseudo dice [0.7562, 0.4948, 0.822, 0.8024, 0.4067, 0.8278, 0.8489] +2026-04-14 12:56:10.345794: Epoch time: 101.56 s +2026-04-14 12:56:10.347822: Yayy! New best EMA pseudo Dice: 0.6735 +2026-04-14 12:56:13.392628: +2026-04-14 12:56:13.394746: Epoch 3392 +2026-04-14 12:56:13.396152: Current learning rate: 0.00184 +2026-04-14 12:57:54.997735: train_loss -0.4534 +2026-04-14 12:57:55.004700: val_loss -0.3935 +2026-04-14 12:57:55.007077: Pseudo dice [0.7394, 0.9105, 0.7993, 0.5841, 0.6643, 0.7832, 0.7174] +2026-04-14 12:57:55.010115: Epoch time: 101.61 s +2026-04-14 12:57:55.012455: Yayy! New best EMA pseudo Dice: 0.6804 +2026-04-14 12:57:58.222533: +2026-04-14 12:57:58.224829: Epoch 3393 +2026-04-14 12:57:58.226628: Current learning rate: 0.00183 +2026-04-14 12:59:39.687579: train_loss -0.4464 +2026-04-14 12:59:39.693260: val_loss -0.3477 +2026-04-14 12:59:39.696923: Pseudo dice [0.3653, 0.8897, 0.7589, 0.2713, 0.5869, 0.6491, 0.7645] +2026-04-14 12:59:39.699067: Epoch time: 101.47 s +2026-04-14 12:59:40.932002: +2026-04-14 12:59:40.933940: Epoch 3394 +2026-04-14 12:59:40.936378: Current learning rate: 0.00183 +2026-04-14 13:01:22.751279: train_loss -0.4431 +2026-04-14 13:01:22.759184: val_loss -0.3534 +2026-04-14 13:01:22.761152: Pseudo dice [0.896, 0.9103, 0.7385, 0.2221, 0.3397, 0.1043, 0.5926] +2026-04-14 13:01:22.764055: Epoch time: 101.82 s +2026-04-14 13:01:25.120985: +2026-04-14 13:01:25.123017: Epoch 3395 +2026-04-14 13:01:25.124510: Current learning rate: 0.00183 +2026-04-14 13:03:06.923126: train_loss -0.4502 +2026-04-14 13:03:06.929070: val_loss -0.4054 +2026-04-14 13:03:06.931049: Pseudo dice [0.8884, 0.5726, 0.7759, 0.5987, 0.6594, 0.8885, 0.8557] +2026-04-14 13:03:06.933336: Epoch time: 101.81 s +2026-04-14 13:03:08.176248: +2026-04-14 13:03:08.178749: Epoch 3396 +2026-04-14 13:03:08.180620: Current learning rate: 0.00182 +2026-04-14 13:04:49.808372: train_loss -0.4569 +2026-04-14 13:04:49.815871: val_loss -0.3866 +2026-04-14 13:04:49.818269: Pseudo dice [0.6346, 0.5408, 0.8089, 0.397, 0.6381, 0.4176, 0.7538] +2026-04-14 13:04:49.821387: Epoch time: 101.64 s +2026-04-14 13:04:51.098418: +2026-04-14 13:04:51.100032: Epoch 3397 +2026-04-14 13:04:51.102463: Current learning rate: 0.00182 +2026-04-14 13:06:32.686984: train_loss -0.4503 +2026-04-14 13:06:32.692675: val_loss -0.341 +2026-04-14 13:06:32.694830: Pseudo dice [0.7668, 0.9174, 0.7582, 0.2136, 0.5456, 0.2989, 0.7049] +2026-04-14 13:06:32.697114: Epoch time: 101.59 s +2026-04-14 13:06:33.961045: +2026-04-14 13:06:33.963364: Epoch 3398 +2026-04-14 13:06:33.965006: Current learning rate: 0.00182 +2026-04-14 13:08:14.962972: train_loss -0.44 +2026-04-14 13:08:14.969545: val_loss -0.3157 +2026-04-14 13:08:14.972932: Pseudo dice [0.7414, 0.6973, 0.8388, 0.4035, 0.516, 0.4873, 0.1777] +2026-04-14 13:08:14.975083: Epoch time: 101.01 s +2026-04-14 13:08:16.232158: +2026-04-14 13:08:16.238068: Epoch 3399 +2026-04-14 13:08:16.240667: Current learning rate: 0.00182 +2026-04-14 13:09:57.852952: train_loss -0.4525 +2026-04-14 13:09:57.858707: val_loss -0.395 +2026-04-14 13:09:57.860923: Pseudo dice [0.7007, 0.793, 0.8017, 0.1469, 0.4572, 0.4508, 0.7696] +2026-04-14 13:09:57.864123: Epoch time: 101.62 s +2026-04-14 13:10:00.906769: +2026-04-14 13:10:00.908735: Epoch 3400 +2026-04-14 13:10:00.910156: Current learning rate: 0.00181 +2026-04-14 13:11:42.433146: train_loss -0.4483 +2026-04-14 13:11:42.442364: val_loss -0.3393 +2026-04-14 13:11:42.445287: Pseudo dice [0.6684, 0.921, 0.7945, 0.2352, 0.6435, 0.0834, 0.6507] +2026-04-14 13:11:42.448656: Epoch time: 101.53 s +2026-04-14 13:11:43.717897: +2026-04-14 13:11:43.720186: Epoch 3401 +2026-04-14 13:11:43.722097: Current learning rate: 0.00181 +2026-04-14 13:13:24.881338: train_loss -0.443 +2026-04-14 13:13:24.887078: val_loss -0.3837 +2026-04-14 13:13:24.889176: Pseudo dice [0.6259, 0.6016, 0.7653, 0.5622, 0.6884, 0.9122, 0.8771] +2026-04-14 13:13:24.891918: Epoch time: 101.17 s +2026-04-14 13:13:26.153689: +2026-04-14 13:13:26.155571: Epoch 3402 +2026-04-14 13:13:26.157360: Current learning rate: 0.00181 +2026-04-14 13:15:07.324278: train_loss -0.4427 +2026-04-14 13:15:07.330396: val_loss -0.3588 +2026-04-14 13:15:07.332728: Pseudo dice [0.691, 0.9165, 0.7889, 0.146, 0.4331, 0.7528, 0.8292] +2026-04-14 13:15:07.335286: Epoch time: 101.17 s +2026-04-14 13:15:08.580317: +2026-04-14 13:15:08.582023: Epoch 3403 +2026-04-14 13:15:08.583508: Current learning rate: 0.00181 +2026-04-14 13:16:50.816504: train_loss -0.4406 +2026-04-14 13:16:50.821718: val_loss -0.3514 +2026-04-14 13:16:50.823448: Pseudo dice [0.8138, 0.9113, 0.7046, 0.7296, 0.5537, 0.7064, 0.7404] +2026-04-14 13:16:50.826741: Epoch time: 102.24 s +2026-04-14 13:16:52.075789: +2026-04-14 13:16:52.077566: Epoch 3404 +2026-04-14 13:16:52.079110: Current learning rate: 0.0018 +2026-04-14 13:18:33.794908: train_loss -0.4507 +2026-04-14 13:18:33.801864: val_loss -0.3238 +2026-04-14 13:18:33.803855: Pseudo dice [0.3765, 0.9199, 0.5961, 0.0437, 0.7322, 0.6026, 0.3815] +2026-04-14 13:18:33.806034: Epoch time: 101.72 s +2026-04-14 13:18:35.045451: +2026-04-14 13:18:35.047582: Epoch 3405 +2026-04-14 13:18:35.049201: Current learning rate: 0.0018 +2026-04-14 13:20:16.463598: train_loss -0.4504 +2026-04-14 13:20:16.480373: val_loss -0.3738 +2026-04-14 13:20:16.483443: Pseudo dice [0.6305, 0.7132, 0.7779, 0.1518, 0.657, 0.9098, 0.8887] +2026-04-14 13:20:16.488209: Epoch time: 101.42 s +2026-04-14 13:20:17.742632: +2026-04-14 13:20:17.744714: Epoch 3406 +2026-04-14 13:20:17.746637: Current learning rate: 0.0018 +2026-04-14 13:21:58.907032: train_loss -0.4441 +2026-04-14 13:21:58.913347: val_loss -0.3647 +2026-04-14 13:21:58.915055: Pseudo dice [0.4508, 0.7409, 0.6469, 0.2813, 0.4565, 0.8284, 0.8623] +2026-04-14 13:21:58.917087: Epoch time: 101.17 s +2026-04-14 13:22:00.180876: +2026-04-14 13:22:00.182818: Epoch 3407 +2026-04-14 13:22:00.185060: Current learning rate: 0.00179 +2026-04-14 13:23:41.884843: train_loss -0.4268 +2026-04-14 13:23:41.891020: val_loss -0.381 +2026-04-14 13:23:41.893654: Pseudo dice [0.194, 0.2059, 0.8754, 0.4715, 0.6153, 0.6549, 0.3465] +2026-04-14 13:23:41.895925: Epoch time: 101.71 s +2026-04-14 13:23:43.130822: +2026-04-14 13:23:43.133583: Epoch 3408 +2026-04-14 13:23:43.135669: Current learning rate: 0.00179 +2026-04-14 13:25:24.506109: train_loss -0.4391 +2026-04-14 13:25:24.512726: val_loss -0.3716 +2026-04-14 13:25:24.514358: Pseudo dice [0.4946, 0.8935, 0.836, 0.4709, 0.634, 0.4302, 0.3114] +2026-04-14 13:25:24.516831: Epoch time: 101.38 s +2026-04-14 13:25:25.779456: +2026-04-14 13:25:25.781505: Epoch 3409 +2026-04-14 13:25:25.783323: Current learning rate: 0.00179 +2026-04-14 13:27:06.950506: train_loss -0.4415 +2026-04-14 13:27:06.956593: val_loss -0.3743 +2026-04-14 13:27:06.958954: Pseudo dice [0.2909, 0.6032, 0.8213, 0.0535, 0.4782, 0.4441, 0.8297] +2026-04-14 13:27:06.961317: Epoch time: 101.17 s +2026-04-14 13:27:08.238277: +2026-04-14 13:27:08.239962: Epoch 3410 +2026-04-14 13:27:08.241460: Current learning rate: 0.00179 +2026-04-14 13:28:49.640681: train_loss -0.4422 +2026-04-14 13:28:49.649049: val_loss -0.3623 +2026-04-14 13:28:49.651608: Pseudo dice [0.4479, 0.6272, 0.6493, 0.4878, 0.4346, 0.6712, 0.4041] +2026-04-14 13:28:49.654621: Epoch time: 101.41 s +2026-04-14 13:28:50.912360: +2026-04-14 13:28:50.914891: Epoch 3411 +2026-04-14 13:28:50.917094: Current learning rate: 0.00178 +2026-04-14 13:30:32.492376: train_loss -0.4496 +2026-04-14 13:30:32.498149: val_loss -0.3803 +2026-04-14 13:30:32.500218: Pseudo dice [0.6501, 0.737, 0.7213, 0.0891, 0.6578, 0.9168, 0.7099] +2026-04-14 13:30:32.502321: Epoch time: 101.58 s +2026-04-14 13:30:33.757349: +2026-04-14 13:30:33.758913: Epoch 3412 +2026-04-14 13:30:33.760960: Current learning rate: 0.00178 +2026-04-14 13:32:15.239073: train_loss -0.4477 +2026-04-14 13:32:15.244151: val_loss -0.3565 +2026-04-14 13:32:15.246350: Pseudo dice [0.6088, 0.9156, 0.8344, 0.2594, 0.4257, 0.5312, 0.2516] +2026-04-14 13:32:15.249586: Epoch time: 101.48 s +2026-04-14 13:32:16.484187: +2026-04-14 13:32:16.486197: Epoch 3413 +2026-04-14 13:32:16.487755: Current learning rate: 0.00178 +2026-04-14 13:33:58.036826: train_loss -0.4389 +2026-04-14 13:33:58.044945: val_loss -0.4023 +2026-04-14 13:33:58.047204: Pseudo dice [0.8826, 0.6049, 0.8019, 0.5588, 0.6422, 0.7904, 0.7673] +2026-04-14 13:33:58.049908: Epoch time: 101.56 s +2026-04-14 13:33:59.275116: +2026-04-14 13:33:59.277354: Epoch 3414 +2026-04-14 13:33:59.279306: Current learning rate: 0.00178 +2026-04-14 13:35:40.589157: train_loss -0.4402 +2026-04-14 13:35:40.597148: val_loss -0.3701 +2026-04-14 13:35:40.599139: Pseudo dice [0.5917, 0.9159, 0.7656, 0.702, 0.4507, 0.6805, 0.4308] +2026-04-14 13:35:40.601765: Epoch time: 101.32 s +2026-04-14 13:35:42.943313: +2026-04-14 13:35:42.944898: Epoch 3415 +2026-04-14 13:35:42.946393: Current learning rate: 0.00177 +2026-04-14 13:37:24.315348: train_loss -0.4391 +2026-04-14 13:37:24.322135: val_loss -0.3425 +2026-04-14 13:37:24.323949: Pseudo dice [0.3256, 0.7857, 0.6767, 0.491, 0.4654, 0.5794, 0.6245] +2026-04-14 13:37:24.326317: Epoch time: 101.38 s +2026-04-14 13:37:25.577689: +2026-04-14 13:37:25.579769: Epoch 3416 +2026-04-14 13:37:25.581543: Current learning rate: 0.00177 +2026-04-14 13:39:07.493974: train_loss -0.4332 +2026-04-14 13:39:07.500102: val_loss -0.3552 +2026-04-14 13:39:07.502662: Pseudo dice [0.3457, 0.6522, 0.7402, 0.3593, 0.5097, 0.7547, 0.5344] +2026-04-14 13:39:07.504845: Epoch time: 101.92 s +2026-04-14 13:39:08.775392: +2026-04-14 13:39:08.777311: Epoch 3417 +2026-04-14 13:39:08.779118: Current learning rate: 0.00177 +2026-04-14 13:40:49.940278: train_loss -0.4403 +2026-04-14 13:40:49.949465: val_loss -0.4181 +2026-04-14 13:40:49.951921: Pseudo dice [0.6188, 0.7959, 0.7877, 0.7234, 0.6548, 0.6901, 0.8348] +2026-04-14 13:40:49.954650: Epoch time: 101.17 s +2026-04-14 13:40:51.270129: +2026-04-14 13:40:51.272000: Epoch 3418 +2026-04-14 13:40:51.274086: Current learning rate: 0.00176 +2026-04-14 13:42:33.070285: train_loss -0.432 +2026-04-14 13:42:33.076197: val_loss -0.3941 +2026-04-14 13:42:33.079202: Pseudo dice [0.6099, 0.7474, 0.886, 0.495, 0.6845, 0.8915, 0.798] +2026-04-14 13:42:33.081892: Epoch time: 101.8 s +2026-04-14 13:42:34.305222: +2026-04-14 13:42:34.307822: Epoch 3419 +2026-04-14 13:42:34.309717: Current learning rate: 0.00176 +2026-04-14 13:44:15.650312: train_loss -0.4449 +2026-04-14 13:44:15.657954: val_loss -0.3658 +2026-04-14 13:44:15.660262: Pseudo dice [0.6954, 0.4226, 0.6617, 0.8272, 0.4562, 0.5479, 0.7231] +2026-04-14 13:44:15.663051: Epoch time: 101.35 s +2026-04-14 13:44:16.928775: +2026-04-14 13:44:16.930371: Epoch 3420 +2026-04-14 13:44:16.931832: Current learning rate: 0.00176 +2026-04-14 13:45:58.424222: train_loss -0.4387 +2026-04-14 13:45:58.429829: val_loss -0.3937 +2026-04-14 13:45:58.431686: Pseudo dice [0.6664, 0.5645, 0.7697, 0.6691, 0.4808, 0.8644, 0.8166] +2026-04-14 13:45:58.434192: Epoch time: 101.5 s +2026-04-14 13:45:59.668806: +2026-04-14 13:45:59.670637: Epoch 3421 +2026-04-14 13:45:59.672592: Current learning rate: 0.00176 +2026-04-14 13:47:40.855567: train_loss -0.4431 +2026-04-14 13:47:40.862283: val_loss -0.3839 +2026-04-14 13:47:40.864799: Pseudo dice [0.2092, 0.8603, 0.8313, 0.9397, 0.4248, 0.4537, 0.7599] +2026-04-14 13:47:40.867388: Epoch time: 101.19 s +2026-04-14 13:47:42.127949: +2026-04-14 13:47:42.130230: Epoch 3422 +2026-04-14 13:47:42.131710: Current learning rate: 0.00175 +2026-04-14 13:49:23.754582: train_loss -0.4492 +2026-04-14 13:49:23.760993: val_loss -0.3607 +2026-04-14 13:49:23.763233: Pseudo dice [0.835, 0.6529, 0.6143, 0.5244, 0.3857, 0.5893, 0.767] +2026-04-14 13:49:23.766227: Epoch time: 101.63 s +2026-04-14 13:49:25.003748: +2026-04-14 13:49:25.005304: Epoch 3423 +2026-04-14 13:49:25.006930: Current learning rate: 0.00175 +2026-04-14 13:51:07.028425: train_loss -0.437 +2026-04-14 13:51:07.035868: val_loss -0.3569 +2026-04-14 13:51:07.037711: Pseudo dice [0.6632, 0.6652, 0.7131, 0.3386, 0.5685, 0.6963, 0.6429] +2026-04-14 13:51:07.039949: Epoch time: 102.03 s +2026-04-14 13:51:08.273028: +2026-04-14 13:51:08.274746: Epoch 3424 +2026-04-14 13:51:08.276246: Current learning rate: 0.00175 +2026-04-14 13:52:49.491533: train_loss -0.4477 +2026-04-14 13:52:49.498189: val_loss -0.3805 +2026-04-14 13:52:49.499890: Pseudo dice [0.6338, 0.3742, 0.8163, 0.5525, 0.3256, 0.9062, 0.7411] +2026-04-14 13:52:49.502077: Epoch time: 101.22 s +2026-04-14 13:52:50.729493: +2026-04-14 13:52:50.731482: Epoch 3425 +2026-04-14 13:52:50.732864: Current learning rate: 0.00175 +2026-04-14 13:54:32.307192: train_loss -0.4298 +2026-04-14 13:54:32.313397: val_loss -0.357 +2026-04-14 13:54:32.315491: Pseudo dice [0.6453, 0.9123, 0.7697, 0.5439, 0.2724, 0.6003, 0.7521] +2026-04-14 13:54:32.318593: Epoch time: 101.58 s +2026-04-14 13:54:33.558323: +2026-04-14 13:54:33.560240: Epoch 3426 +2026-04-14 13:54:33.561718: Current learning rate: 0.00174 +2026-04-14 13:56:14.788143: train_loss -0.4494 +2026-04-14 13:56:14.796143: val_loss -0.3576 +2026-04-14 13:56:14.798688: Pseudo dice [0.0895, 0.488, 0.8428, 0.1721, 0.5867, 0.9299, 0.6489] +2026-04-14 13:56:14.802630: Epoch time: 101.23 s +2026-04-14 13:56:16.046280: +2026-04-14 13:56:16.047815: Epoch 3427 +2026-04-14 13:56:16.049660: Current learning rate: 0.00174 +2026-04-14 13:57:57.292208: train_loss -0.4335 +2026-04-14 13:57:57.299113: val_loss -0.2294 +2026-04-14 13:57:57.301164: Pseudo dice [0.0868, 0.4797, 0.3475, 0.3084, 0.5059, 0.1039, 0.4944] +2026-04-14 13:57:57.303709: Epoch time: 101.25 s +2026-04-14 13:57:58.521061: +2026-04-14 13:57:58.529514: Epoch 3428 +2026-04-14 13:57:58.531055: Current learning rate: 0.00174 +2026-04-14 13:59:40.154861: train_loss -0.4261 +2026-04-14 13:59:40.162833: val_loss -0.3477 +2026-04-14 13:59:40.165333: Pseudo dice [0.4925, 0.776, 0.7996, 0.5379, 0.4467, 0.194, 0.6371] +2026-04-14 13:59:40.167706: Epoch time: 101.64 s +2026-04-14 13:59:41.424289: +2026-04-14 13:59:41.426362: Epoch 3429 +2026-04-14 13:59:41.429130: Current learning rate: 0.00173 +2026-04-14 14:01:23.146246: train_loss -0.4274 +2026-04-14 14:01:23.152268: val_loss -0.3496 +2026-04-14 14:01:23.154258: Pseudo dice [0.6238, 0.8692, 0.781, 0.3284, 0.5836, 0.7613, 0.7165] +2026-04-14 14:01:23.156853: Epoch time: 101.73 s +2026-04-14 14:01:24.395641: +2026-04-14 14:01:24.397668: Epoch 3430 +2026-04-14 14:01:24.399800: Current learning rate: 0.00173 +2026-04-14 14:03:05.623032: train_loss -0.4389 +2026-04-14 14:03:05.629851: val_loss -0.3764 +2026-04-14 14:03:05.632735: Pseudo dice [0.5619, 0.4917, 0.8425, 0.2442, 0.5062, 0.9065, 0.831] +2026-04-14 14:03:05.636332: Epoch time: 101.23 s +2026-04-14 14:03:06.904195: +2026-04-14 14:03:06.906265: Epoch 3431 +2026-04-14 14:03:06.907716: Current learning rate: 0.00173 +2026-04-14 14:04:48.684618: train_loss -0.4554 +2026-04-14 14:04:48.691041: val_loss -0.3923 +2026-04-14 14:04:48.692961: Pseudo dice [0.406, 0.8818, 0.7583, 0.6376, 0.5797, 0.7876, 0.6652] +2026-04-14 14:04:48.695256: Epoch time: 101.78 s +2026-04-14 14:04:49.919646: +2026-04-14 14:04:49.922129: Epoch 3432 +2026-04-14 14:04:49.924098: Current learning rate: 0.00173 +2026-04-14 14:06:31.180660: train_loss -0.4468 +2026-04-14 14:06:31.186553: val_loss -0.3747 +2026-04-14 14:06:31.188775: Pseudo dice [0.5593, 0.8796, 0.725, 0.591, 0.7346, 0.3327, 0.6064] +2026-04-14 14:06:31.190906: Epoch time: 101.26 s +2026-04-14 14:06:32.426024: +2026-04-14 14:06:32.427601: Epoch 3433 +2026-04-14 14:06:32.429044: Current learning rate: 0.00172 +2026-04-14 14:08:14.532984: train_loss -0.4448 +2026-04-14 14:08:14.539920: val_loss -0.3862 +2026-04-14 14:08:14.542271: Pseudo dice [0.4746, 0.7163, 0.751, 0.5603, 0.4977, 0.8037, 0.7925] +2026-04-14 14:08:14.544617: Epoch time: 102.11 s +2026-04-14 14:08:15.794365: +2026-04-14 14:08:15.796849: Epoch 3434 +2026-04-14 14:08:15.798914: Current learning rate: 0.00172 +2026-04-14 14:09:57.681931: train_loss -0.4555 +2026-04-14 14:09:57.688726: val_loss -0.3819 +2026-04-14 14:09:57.693227: Pseudo dice [0.5065, 0.8555, 0.8236, 0.5282, 0.5101, 0.8537, 0.8022] +2026-04-14 14:09:57.697763: Epoch time: 101.89 s +2026-04-14 14:10:00.006074: +2026-04-14 14:10:00.007733: Epoch 3435 +2026-04-14 14:10:00.009324: Current learning rate: 0.00172 +2026-04-14 14:11:41.316726: train_loss -0.457 +2026-04-14 14:11:41.322788: val_loss -0.365 +2026-04-14 14:11:41.324881: Pseudo dice [0.6659, 0.9126, 0.7908, 0.3726, 0.6445, 0.1585, 0.7302] +2026-04-14 14:11:41.327272: Epoch time: 101.31 s +2026-04-14 14:11:42.547618: +2026-04-14 14:11:42.550953: Epoch 3436 +2026-04-14 14:11:42.552604: Current learning rate: 0.00172 +2026-04-14 14:13:24.376578: train_loss -0.4503 +2026-04-14 14:13:24.382907: val_loss -0.3606 +2026-04-14 14:13:24.384727: Pseudo dice [0.7271, 0.913, 0.7647, 0.4316, 0.5614, 0.5778, 0.8588] +2026-04-14 14:13:24.388012: Epoch time: 101.83 s +2026-04-14 14:13:25.659195: +2026-04-14 14:13:25.661883: Epoch 3437 +2026-04-14 14:13:25.663546: Current learning rate: 0.00171 +2026-04-14 14:15:07.700370: train_loss -0.4542 +2026-04-14 14:15:07.706846: val_loss -0.3974 +2026-04-14 14:15:07.709653: Pseudo dice [0.6841, 0.6969, 0.8272, 0.2392, 0.3893, 0.8384, 0.5593] +2026-04-14 14:15:07.711859: Epoch time: 102.04 s +2026-04-14 14:15:08.959761: +2026-04-14 14:15:08.962276: Epoch 3438 +2026-04-14 14:15:08.963834: Current learning rate: 0.00171 +2026-04-14 14:16:51.114727: train_loss -0.4541 +2026-04-14 14:16:51.121030: val_loss -0.385 +2026-04-14 14:16:51.123437: Pseudo dice [0.7969, 0.3887, 0.7638, 0.3659, 0.5306, 0.8151, 0.7276] +2026-04-14 14:16:51.125952: Epoch time: 102.16 s +2026-04-14 14:16:52.360579: +2026-04-14 14:16:52.362440: Epoch 3439 +2026-04-14 14:16:52.364208: Current learning rate: 0.00171 +2026-04-14 14:18:34.185757: train_loss -0.4529 +2026-04-14 14:18:34.192666: val_loss -0.3944 +2026-04-14 14:18:34.197361: Pseudo dice [0.8176, 0.6672, 0.8536, 0.3936, 0.353, 0.8993, 0.7797] +2026-04-14 14:18:34.200143: Epoch time: 101.83 s +2026-04-14 14:18:35.448447: +2026-04-14 14:18:35.450388: Epoch 3440 +2026-04-14 14:18:35.451754: Current learning rate: 0.0017 +2026-04-14 14:20:17.438926: train_loss -0.4463 +2026-04-14 14:20:17.445820: val_loss -0.3802 +2026-04-14 14:20:17.448364: Pseudo dice [0.553, 0.7859, 0.8261, 0.2759, 0.7634, 0.7402, 0.8448] +2026-04-14 14:20:17.450554: Epoch time: 101.99 s +2026-04-14 14:20:18.729969: +2026-04-14 14:20:18.731743: Epoch 3441 +2026-04-14 14:20:18.733362: Current learning rate: 0.0017 +2026-04-14 14:22:01.038979: train_loss -0.4431 +2026-04-14 14:22:01.044690: val_loss -0.3771 +2026-04-14 14:22:01.046724: Pseudo dice [0.7067, 0.828, 0.8072, 0.35, 0.5946, 0.7435, 0.812] +2026-04-14 14:22:01.049142: Epoch time: 102.31 s +2026-04-14 14:22:02.298903: +2026-04-14 14:22:02.301274: Epoch 3442 +2026-04-14 14:22:02.303232: Current learning rate: 0.0017 +2026-04-14 14:23:43.483957: train_loss -0.4464 +2026-04-14 14:23:43.490699: val_loss -0.4004 +2026-04-14 14:23:43.492886: Pseudo dice [0.7618, 0.5807, 0.8027, 0.7029, 0.6095, 0.8767, 0.6665] +2026-04-14 14:23:43.495426: Epoch time: 101.19 s +2026-04-14 14:23:44.756858: +2026-04-14 14:23:44.759463: Epoch 3443 +2026-04-14 14:23:44.761412: Current learning rate: 0.0017 +2026-04-14 14:25:26.006545: train_loss -0.4406 +2026-04-14 14:25:26.012990: val_loss -0.3545 +2026-04-14 14:25:26.015119: Pseudo dice [0.7441, 0.9261, 0.6503, 0.4215, 0.5893, 0.6341, 0.8177] +2026-04-14 14:25:26.018042: Epoch time: 101.25 s +2026-04-14 14:25:27.238784: +2026-04-14 14:25:27.240335: Epoch 3444 +2026-04-14 14:25:27.241856: Current learning rate: 0.00169 +2026-04-14 14:27:08.493550: train_loss -0.4498 +2026-04-14 14:27:08.500731: val_loss -0.4039 +2026-04-14 14:27:08.502547: Pseudo dice [0.6556, 0.2313, 0.8186, 0.4048, 0.5247, 0.8299, 0.8683] +2026-04-14 14:27:08.505492: Epoch time: 101.26 s +2026-04-14 14:27:09.760605: +2026-04-14 14:27:09.762069: Epoch 3445 +2026-04-14 14:27:09.763488: Current learning rate: 0.00169 +2026-04-14 14:28:51.739063: train_loss -0.4406 +2026-04-14 14:28:51.745749: val_loss -0.3543 +2026-04-14 14:28:51.747917: Pseudo dice [0.4904, 0.481, 0.7344, 0.1833, 0.5298, 0.9024, 0.7218] +2026-04-14 14:28:51.751145: Epoch time: 101.98 s +2026-04-14 14:28:53.018841: +2026-04-14 14:28:53.020667: Epoch 3446 +2026-04-14 14:28:53.022232: Current learning rate: 0.00169 +2026-04-14 14:30:34.733971: train_loss -0.436 +2026-04-14 14:30:34.740254: val_loss -0.3542 +2026-04-14 14:30:34.742063: Pseudo dice [0.7525, 0.6707, 0.7938, 0.5589, 0.4854, 0.5307, 0.7406] +2026-04-14 14:30:34.744935: Epoch time: 101.72 s +2026-04-14 14:30:35.959407: +2026-04-14 14:30:35.968466: Epoch 3447 +2026-04-14 14:30:35.970037: Current learning rate: 0.00168 +2026-04-14 14:32:17.075815: train_loss -0.4482 +2026-04-14 14:32:17.081905: val_loss -0.3804 +2026-04-14 14:32:17.083923: Pseudo dice [0.4987, 0.8904, 0.8321, 0.3943, 0.5463, 0.5908, 0.5589] +2026-04-14 14:32:17.086211: Epoch time: 101.12 s +2026-04-14 14:32:18.349748: +2026-04-14 14:32:18.351480: Epoch 3448 +2026-04-14 14:32:18.353301: Current learning rate: 0.00168 +2026-04-14 14:33:59.566526: train_loss -0.4356 +2026-04-14 14:33:59.573461: val_loss -0.3616 +2026-04-14 14:33:59.575358: Pseudo dice [0.6232, 0.5841, 0.7721, 0.6872, 0.4257, 0.2269, 0.6708] +2026-04-14 14:33:59.577800: Epoch time: 101.22 s +2026-04-14 14:34:00.821290: +2026-04-14 14:34:00.823277: Epoch 3449 +2026-04-14 14:34:00.825312: Current learning rate: 0.00168 +2026-04-14 14:35:42.228741: train_loss -0.4412 +2026-04-14 14:35:42.235973: val_loss -0.375 +2026-04-14 14:35:42.238102: Pseudo dice [0.7827, 0.8761, 0.8057, 0.3299, 0.4581, 0.7901, 0.6569] +2026-04-14 14:35:42.241114: Epoch time: 101.41 s +2026-04-14 14:35:45.118092: +2026-04-14 14:35:45.120047: Epoch 3450 +2026-04-14 14:35:45.121416: Current learning rate: 0.00168 +2026-04-14 14:37:26.374449: train_loss -0.4495 +2026-04-14 14:37:26.380920: val_loss -0.3916 +2026-04-14 14:37:26.383113: Pseudo dice [0.8288, 0.7898, 0.8677, 0.3905, 0.3466, 0.8825, 0.8758] +2026-04-14 14:37:26.385709: Epoch time: 101.26 s +2026-04-14 14:37:27.663111: +2026-04-14 14:37:27.665442: Epoch 3451 +2026-04-14 14:37:27.667567: Current learning rate: 0.00167 +2026-04-14 14:39:09.157647: train_loss -0.443 +2026-04-14 14:39:09.164127: val_loss -0.3587 +2026-04-14 14:39:09.165966: Pseudo dice [0.482, 0.8838, 0.79, 0.5133, 0.3887, 0.2094, 0.3859] +2026-04-14 14:39:09.168575: Epoch time: 101.5 s +2026-04-14 14:39:10.401557: +2026-04-14 14:39:10.405688: Epoch 3452 +2026-04-14 14:39:10.407131: Current learning rate: 0.00167 +2026-04-14 14:40:52.023513: train_loss -0.4469 +2026-04-14 14:40:52.029453: val_loss -0.3967 +2026-04-14 14:40:52.031601: Pseudo dice [0.7034, 0.9126, 0.87, 0.1716, 0.3931, 0.7953, 0.7911] +2026-04-14 14:40:52.034010: Epoch time: 101.63 s +2026-04-14 14:40:53.246015: +2026-04-14 14:40:53.248946: Epoch 3453 +2026-04-14 14:40:53.250551: Current learning rate: 0.00167 +2026-04-14 14:42:34.521877: train_loss -0.4431 +2026-04-14 14:42:34.529510: val_loss -0.3959 +2026-04-14 14:42:34.532753: Pseudo dice [0.3848, 0.7076, 0.8131, 0.4406, 0.6114, 0.9043, 0.7042] +2026-04-14 14:42:34.535544: Epoch time: 101.28 s +2026-04-14 14:42:35.783692: +2026-04-14 14:42:35.785259: Epoch 3454 +2026-04-14 14:42:35.786746: Current learning rate: 0.00167 +2026-04-14 14:44:18.241605: train_loss -0.4544 +2026-04-14 14:44:18.258412: val_loss -0.396 +2026-04-14 14:44:18.260446: Pseudo dice [0.3739, 0.7755, 0.8108, 0.5937, 0.5383, 0.8552, 0.7408] +2026-04-14 14:44:18.263034: Epoch time: 102.46 s +2026-04-14 14:44:19.489040: +2026-04-14 14:44:19.490548: Epoch 3455 +2026-04-14 14:44:19.492064: Current learning rate: 0.00166 +2026-04-14 14:46:01.147021: train_loss -0.4376 +2026-04-14 14:46:01.154381: val_loss -0.3627 +2026-04-14 14:46:01.157012: Pseudo dice [0.4993, 0.6016, 0.8117, 0.5157, 0.4138, 0.8674, 0.7921] +2026-04-14 14:46:01.160160: Epoch time: 101.66 s +2026-04-14 14:46:02.430700: +2026-04-14 14:46:02.432922: Epoch 3456 +2026-04-14 14:46:02.434397: Current learning rate: 0.00166 +2026-04-14 14:47:44.036728: train_loss -0.4509 +2026-04-14 14:47:44.043090: val_loss -0.4066 +2026-04-14 14:47:44.044829: Pseudo dice [0.784, 0.7316, 0.7654, 0.5638, 0.4498, 0.6729, 0.8256] +2026-04-14 14:47:44.046769: Epoch time: 101.61 s +2026-04-14 14:47:45.288877: +2026-04-14 14:47:45.290515: Epoch 3457 +2026-04-14 14:47:45.291927: Current learning rate: 0.00166 +2026-04-14 14:49:26.868671: train_loss -0.4492 +2026-04-14 14:49:26.874708: val_loss -0.3915 +2026-04-14 14:49:26.877520: Pseudo dice [0.8019, 0.8993, 0.7462, 0.6568, 0.5588, 0.5832, 0.5463] +2026-04-14 14:49:26.879921: Epoch time: 101.58 s +2026-04-14 14:49:28.130973: +2026-04-14 14:49:28.132861: Epoch 3458 +2026-04-14 14:49:28.134628: Current learning rate: 0.00165 +2026-04-14 14:51:10.355208: train_loss -0.4419 +2026-04-14 14:51:10.364438: val_loss -0.3528 +2026-04-14 14:51:10.366752: Pseudo dice [0.7519, 0.8911, 0.6819, 0.0659, 0.3974, 0.7942, 0.199] +2026-04-14 14:51:10.369174: Epoch time: 102.23 s +2026-04-14 14:51:11.597318: +2026-04-14 14:51:11.599207: Epoch 3459 +2026-04-14 14:51:11.600813: Current learning rate: 0.00165 +2026-04-14 14:52:53.586244: train_loss -0.4517 +2026-04-14 14:52:53.592883: val_loss -0.3853 +2026-04-14 14:52:53.595409: Pseudo dice [0.5951, 0.7766, 0.8494, 0.2697, 0.7032, 0.8997, 0.6633] +2026-04-14 14:52:53.597619: Epoch time: 101.99 s +2026-04-14 14:52:54.852010: +2026-04-14 14:52:54.853785: Epoch 3460 +2026-04-14 14:52:54.855406: Current learning rate: 0.00165 +2026-04-14 14:54:36.802459: train_loss -0.4477 +2026-04-14 14:54:36.808289: val_loss -0.3895 +2026-04-14 14:54:36.809984: Pseudo dice [0.0447, 0.0918, 0.7102, 0.414, 0.6165, 0.8913, 0.7527] +2026-04-14 14:54:36.812418: Epoch time: 101.95 s +2026-04-14 14:54:38.024141: +2026-04-14 14:54:38.026697: Epoch 3461 +2026-04-14 14:54:38.028467: Current learning rate: 0.00165 +2026-04-14 14:56:19.938000: train_loss -0.4512 +2026-04-14 14:56:19.944208: val_loss -0.3754 +2026-04-14 14:56:19.947496: Pseudo dice [0.6376, 0.8213, 0.8033, 0.7167, 0.6177, 0.7418, 0.5159] +2026-04-14 14:56:19.949964: Epoch time: 101.92 s +2026-04-14 14:56:21.172551: +2026-04-14 14:56:21.174256: Epoch 3462 +2026-04-14 14:56:21.175750: Current learning rate: 0.00164 +2026-04-14 14:58:03.302896: train_loss -0.4392 +2026-04-14 14:58:03.312653: val_loss -0.3818 +2026-04-14 14:58:03.314920: Pseudo dice [0.6893, 0.7053, 0.8057, 0.857, 0.1752, 0.8784, 0.8378] +2026-04-14 14:58:03.318644: Epoch time: 102.13 s +2026-04-14 14:58:04.586016: +2026-04-14 14:58:04.588438: Epoch 3463 +2026-04-14 14:58:04.590463: Current learning rate: 0.00164 +2026-04-14 14:59:46.493327: train_loss -0.4487 +2026-04-14 14:59:46.501040: val_loss -0.4143 +2026-04-14 14:59:46.502923: Pseudo dice [0.8636, 0.5797, 0.7345, 0.7196, 0.6303, 0.8114, 0.8362] +2026-04-14 14:59:46.505989: Epoch time: 101.91 s +2026-04-14 14:59:47.753891: +2026-04-14 14:59:47.755508: Epoch 3464 +2026-04-14 14:59:47.757035: Current learning rate: 0.00164 +2026-04-14 15:01:29.388860: train_loss -0.4524 +2026-04-14 15:01:29.395499: val_loss -0.3816 +2026-04-14 15:01:29.397780: Pseudo dice [0.7041, 0.3491, 0.8067, 0.3767, 0.5309, 0.8325, 0.7275] +2026-04-14 15:01:29.400489: Epoch time: 101.64 s +2026-04-14 15:01:30.653033: +2026-04-14 15:01:30.654997: Epoch 3465 +2026-04-14 15:01:30.656653: Current learning rate: 0.00164 +2026-04-14 15:03:12.082242: train_loss -0.45 +2026-04-14 15:03:12.090792: val_loss -0.401 +2026-04-14 15:03:12.092772: Pseudo dice [0.7033, 0.7242, 0.8227, 0.5832, 0.5626, 0.3682, 0.8013] +2026-04-14 15:03:12.095164: Epoch time: 101.43 s +2026-04-14 15:03:13.344494: +2026-04-14 15:03:13.346427: Epoch 3466 +2026-04-14 15:03:13.348077: Current learning rate: 0.00163 +2026-04-14 15:04:55.378825: train_loss -0.4486 +2026-04-14 15:04:55.386913: val_loss -0.3621 +2026-04-14 15:04:55.388940: Pseudo dice [0.5056, 0.8961, 0.7901, 0.6084, 0.4591, 0.4673, 0.842] +2026-04-14 15:04:55.391457: Epoch time: 102.04 s +2026-04-14 15:04:56.625217: +2026-04-14 15:04:56.628246: Epoch 3467 +2026-04-14 15:04:56.630874: Current learning rate: 0.00163 +2026-04-14 15:06:38.552163: train_loss -0.4465 +2026-04-14 15:06:38.560417: val_loss -0.3844 +2026-04-14 15:06:38.562469: Pseudo dice [0.6818, 0.6945, 0.8478, 0.6011, 0.6455, 0.8665, 0.7025] +2026-04-14 15:06:38.565584: Epoch time: 101.93 s +2026-04-14 15:06:39.835576: +2026-04-14 15:06:39.838383: Epoch 3468 +2026-04-14 15:06:39.840534: Current learning rate: 0.00163 +2026-04-14 15:08:21.689855: train_loss -0.4356 +2026-04-14 15:08:21.696647: val_loss -0.3718 +2026-04-14 15:08:21.699499: Pseudo dice [0.5967, 0.2498, 0.7203, 0.7042, 0.5179, 0.8551, 0.7966] +2026-04-14 15:08:21.701584: Epoch time: 101.86 s +2026-04-14 15:08:22.948820: +2026-04-14 15:08:22.950427: Epoch 3469 +2026-04-14 15:08:22.951965: Current learning rate: 0.00162 +2026-04-14 15:10:05.460625: train_loss -0.4545 +2026-04-14 15:10:05.470069: val_loss -0.3799 +2026-04-14 15:10:05.471899: Pseudo dice [0.703, 0.8398, 0.767, 0.5255, 0.4636, 0.6993, 0.8355] +2026-04-14 15:10:05.474750: Epoch time: 102.51 s +2026-04-14 15:10:06.755944: +2026-04-14 15:10:06.757727: Epoch 3470 +2026-04-14 15:10:06.759968: Current learning rate: 0.00162 +2026-04-14 15:11:48.933949: train_loss -0.4465 +2026-04-14 15:11:48.941099: val_loss -0.3804 +2026-04-14 15:11:48.942896: Pseudo dice [0.5885, 0.1612, 0.7654, 0.2901, 0.6124, 0.922, 0.8002] +2026-04-14 15:11:48.945425: Epoch time: 102.18 s +2026-04-14 15:11:50.202210: +2026-04-14 15:11:50.205583: Epoch 3471 +2026-04-14 15:11:50.207482: Current learning rate: 0.00162 +2026-04-14 15:13:32.280933: train_loss -0.446 +2026-04-14 15:13:32.289218: val_loss -0.3942 +2026-04-14 15:13:32.291283: Pseudo dice [0.9137, 0.7343, 0.7876, 0.8074, 0.6643, 0.2972, 0.7961] +2026-04-14 15:13:32.293800: Epoch time: 102.08 s +2026-04-14 15:13:33.572303: +2026-04-14 15:13:33.574586: Epoch 3472 +2026-04-14 15:13:33.576419: Current learning rate: 0.00162 +2026-04-14 15:15:15.327285: train_loss -0.4475 +2026-04-14 15:15:15.333006: val_loss -0.3942 +2026-04-14 15:15:15.335454: Pseudo dice [0.5807, 0.7133, 0.7622, 0.5258, 0.4895, 0.9195, 0.4795] +2026-04-14 15:15:15.338313: Epoch time: 101.76 s +2026-04-14 15:15:16.574713: +2026-04-14 15:15:16.576440: Epoch 3473 +2026-04-14 15:15:16.577806: Current learning rate: 0.00161 +2026-04-14 15:16:58.207790: train_loss -0.4463 +2026-04-14 15:16:58.213457: val_loss -0.3706 +2026-04-14 15:16:58.215385: Pseudo dice [0.6561, 0.5792, 0.7884, 0.6278, 0.5158, 0.684, 0.8323] +2026-04-14 15:16:58.217797: Epoch time: 101.64 s +2026-04-14 15:16:59.458569: +2026-04-14 15:16:59.460281: Epoch 3474 +2026-04-14 15:16:59.461665: Current learning rate: 0.00161 +2026-04-14 15:18:42.334369: train_loss -0.4481 +2026-04-14 15:18:42.341110: val_loss -0.3956 +2026-04-14 15:18:42.343345: Pseudo dice [0.7158, 0.878, 0.7383, 0.8533, 0.6272, 0.3378, 0.8617] +2026-04-14 15:18:42.346306: Epoch time: 102.88 s +2026-04-14 15:18:43.586017: +2026-04-14 15:18:43.588272: Epoch 3475 +2026-04-14 15:18:43.589763: Current learning rate: 0.00161 +2026-04-14 15:20:25.334223: train_loss -0.4553 +2026-04-14 15:20:25.341614: val_loss -0.3748 +2026-04-14 15:20:25.343368: Pseudo dice [0.7206, 0.8549, 0.7269, 0.8384, 0.4686, 0.799, 0.7869] +2026-04-14 15:20:25.346993: Epoch time: 101.75 s +2026-04-14 15:20:26.594676: +2026-04-14 15:20:26.596243: Epoch 3476 +2026-04-14 15:20:26.597620: Current learning rate: 0.00161 +2026-04-14 15:22:08.433923: train_loss -0.4304 +2026-04-14 15:22:08.441893: val_loss -0.3481 +2026-04-14 15:22:08.443743: Pseudo dice [0.8196, 0.9068, 0.7744, 0.7503, 0.3577, 0.1949, 0.1926] +2026-04-14 15:22:08.447278: Epoch time: 101.84 s +2026-04-14 15:22:09.735446: +2026-04-14 15:22:09.737543: Epoch 3477 +2026-04-14 15:22:09.739306: Current learning rate: 0.0016 +2026-04-14 15:23:51.927632: train_loss -0.4468 +2026-04-14 15:23:51.935440: val_loss -0.3639 +2026-04-14 15:23:51.937425: Pseudo dice [0.3465, 0.9053, 0.7933, 0.5866, 0.4405, 0.738, 0.526] +2026-04-14 15:23:51.940421: Epoch time: 102.2 s +2026-04-14 15:23:53.277887: +2026-04-14 15:23:53.279593: Epoch 3478 +2026-04-14 15:23:53.280999: Current learning rate: 0.0016 +2026-04-14 15:25:35.430415: train_loss -0.4409 +2026-04-14 15:25:35.438299: val_loss -0.3843 +2026-04-14 15:25:35.441568: Pseudo dice [0.6654, 0.7048, 0.723, 0.4603, 0.4024, 0.8325, 0.8691] +2026-04-14 15:25:35.450537: Epoch time: 102.16 s +2026-04-14 15:25:36.701035: +2026-04-14 15:25:36.702474: Epoch 3479 +2026-04-14 15:25:36.704538: Current learning rate: 0.0016 +2026-04-14 15:27:18.758945: train_loss -0.4445 +2026-04-14 15:27:18.765301: val_loss -0.3856 +2026-04-14 15:27:18.767267: Pseudo dice [0.4569, 0.2851, 0.7925, 0.402, 0.6253, 0.9257, 0.7784] +2026-04-14 15:27:18.769410: Epoch time: 102.06 s +2026-04-14 15:27:20.018256: +2026-04-14 15:27:20.020149: Epoch 3480 +2026-04-14 15:27:20.021864: Current learning rate: 0.00159 +2026-04-14 15:29:02.256073: train_loss -0.448 +2026-04-14 15:29:02.265295: val_loss -0.3737 +2026-04-14 15:29:02.267285: Pseudo dice [0.7104, 0.7687, 0.7727, 0.4511, 0.5532, 0.6056, 0.5701] +2026-04-14 15:29:02.269787: Epoch time: 102.24 s +2026-04-14 15:29:03.521836: +2026-04-14 15:29:03.524688: Epoch 3481 +2026-04-14 15:29:03.526678: Current learning rate: 0.00159 +2026-04-14 15:30:45.897223: train_loss -0.4463 +2026-04-14 15:30:45.903500: val_loss -0.3733 +2026-04-14 15:30:45.906473: Pseudo dice [0.603, 0.5243, 0.7532, 0.2994, 0.2726, 0.9027, 0.7697] +2026-04-14 15:30:45.909459: Epoch time: 102.38 s +2026-04-14 15:30:47.221528: +2026-04-14 15:30:47.223381: Epoch 3482 +2026-04-14 15:30:47.225235: Current learning rate: 0.00159 +2026-04-14 15:32:29.488429: train_loss -0.4498 +2026-04-14 15:32:29.494815: val_loss -0.365 +2026-04-14 15:32:29.497093: Pseudo dice [0.8435, 0.9153, 0.7792, 0.4039, 0.5408, 0.5306, 0.7997] +2026-04-14 15:32:29.500665: Epoch time: 102.27 s +2026-04-14 15:32:30.757865: +2026-04-14 15:32:30.760082: Epoch 3483 +2026-04-14 15:32:30.763081: Current learning rate: 0.00159 +2026-04-14 15:34:12.559358: train_loss -0.4522 +2026-04-14 15:34:12.566746: val_loss -0.3713 +2026-04-14 15:34:12.569175: Pseudo dice [0.6713, 0.8723, 0.7743, 0.4243, 0.5286, 0.7556, 0.77] +2026-04-14 15:34:12.572412: Epoch time: 101.8 s +2026-04-14 15:34:13.840742: +2026-04-14 15:34:13.842288: Epoch 3484 +2026-04-14 15:34:13.844891: Current learning rate: 0.00158 +2026-04-14 15:35:55.903292: train_loss -0.4498 +2026-04-14 15:35:55.909710: val_loss -0.3716 +2026-04-14 15:35:55.911750: Pseudo dice [0.5081, 0.3575, 0.8128, 0.1477, 0.5501, 0.237, 0.6867] +2026-04-14 15:35:55.913733: Epoch time: 102.07 s +2026-04-14 15:35:57.220284: +2026-04-14 15:35:57.222060: Epoch 3485 +2026-04-14 15:35:57.223582: Current learning rate: 0.00158 +2026-04-14 15:37:39.453298: train_loss -0.4588 +2026-04-14 15:37:39.459678: val_loss -0.3794 +2026-04-14 15:37:39.461967: Pseudo dice [0.8367, 0.8845, 0.8026, 0.3367, 0.5591, 0.1824, 0.7898] +2026-04-14 15:37:39.464859: Epoch time: 102.24 s +2026-04-14 15:37:40.727663: +2026-04-14 15:37:40.730102: Epoch 3486 +2026-04-14 15:37:40.731948: Current learning rate: 0.00158 +2026-04-14 15:39:22.772811: train_loss -0.4569 +2026-04-14 15:39:22.779114: val_loss -0.3801 +2026-04-14 15:39:22.781104: Pseudo dice [0.7757, 0.8993, 0.7891, 0.55, 0.4996, 0.6649, 0.6592] +2026-04-14 15:39:22.783873: Epoch time: 102.05 s +2026-04-14 15:39:24.063407: +2026-04-14 15:39:24.065006: Epoch 3487 +2026-04-14 15:39:24.066362: Current learning rate: 0.00157 +2026-04-14 15:41:05.954054: train_loss -0.4533 +2026-04-14 15:41:05.960185: val_loss -0.3664 +2026-04-14 15:41:05.962135: Pseudo dice [0.6593, 0.7713, 0.7897, 0.2787, 0.5757, 0.8295, 0.6418] +2026-04-14 15:41:05.964424: Epoch time: 101.89 s +2026-04-14 15:41:07.206083: +2026-04-14 15:41:07.207971: Epoch 3488 +2026-04-14 15:41:07.210517: Current learning rate: 0.00157 +2026-04-14 15:42:49.381410: train_loss -0.4435 +2026-04-14 15:42:49.390282: val_loss -0.3491 +2026-04-14 15:42:49.392415: Pseudo dice [0.617, 0.8961, 0.7326, 0.2384, 0.5607, 0.579, 0.7245] +2026-04-14 15:42:49.395048: Epoch time: 102.18 s +2026-04-14 15:42:50.649981: +2026-04-14 15:42:50.651989: Epoch 3489 +2026-04-14 15:42:50.653881: Current learning rate: 0.00157 +2026-04-14 15:44:32.817225: train_loss -0.4406 +2026-04-14 15:44:32.823375: val_loss -0.3617 +2026-04-14 15:44:32.825969: Pseudo dice [0.6073, 0.7506, 0.6705, 0.0003, 0.6121, 0.3244, 0.7881] +2026-04-14 15:44:32.828371: Epoch time: 102.17 s +2026-04-14 15:44:34.092057: +2026-04-14 15:44:34.094204: Epoch 3490 +2026-04-14 15:44:34.096164: Current learning rate: 0.00157 +2026-04-14 15:46:16.206677: train_loss -0.4523 +2026-04-14 15:46:16.212476: val_loss -0.376 +2026-04-14 15:46:16.214312: Pseudo dice [0.8619, 0.9077, 0.8109, 0.2158, 0.4676, 0.1257, 0.8432] +2026-04-14 15:46:16.216734: Epoch time: 102.12 s +2026-04-14 15:46:17.481422: +2026-04-14 15:46:17.483641: Epoch 3491 +2026-04-14 15:46:17.485929: Current learning rate: 0.00156 +2026-04-14 15:47:59.655200: train_loss -0.4413 +2026-04-14 15:47:59.661250: val_loss -0.3729 +2026-04-14 15:47:59.663699: Pseudo dice [0.5746, 0.6796, 0.6331, 0.3249, 0.4922, 0.9286, 0.789] +2026-04-14 15:47:59.668236: Epoch time: 102.18 s +2026-04-14 15:48:00.950632: +2026-04-14 15:48:00.952554: Epoch 3492 +2026-04-14 15:48:00.954164: Current learning rate: 0.00156 +2026-04-14 15:49:42.754110: train_loss -0.442 +2026-04-14 15:49:42.759628: val_loss -0.381 +2026-04-14 15:49:42.762003: Pseudo dice [0.6303, 0.7282, 0.7708, 0.7109, 0.3573, 0.7966, 0.8583] +2026-04-14 15:49:42.764353: Epoch time: 101.81 s +2026-04-14 15:49:44.016824: +2026-04-14 15:49:44.018602: Epoch 3493 +2026-04-14 15:49:44.020229: Current learning rate: 0.00156 +2026-04-14 15:51:26.133914: train_loss -0.4496 +2026-04-14 15:51:26.140919: val_loss -0.3794 +2026-04-14 15:51:26.144473: Pseudo dice [0.5493, 0.6122, 0.7916, 0.5075, 0.5519, 0.8737, 0.7693] +2026-04-14 15:51:26.147552: Epoch time: 102.12 s +2026-04-14 15:51:27.387241: +2026-04-14 15:51:27.389976: Epoch 3494 +2026-04-14 15:51:27.391527: Current learning rate: 0.00156 +2026-04-14 15:53:10.747645: train_loss -0.4616 +2026-04-14 15:53:10.754318: val_loss -0.3911 +2026-04-14 15:53:10.756629: Pseudo dice [0.7562, 0.865, 0.7825, 0.4873, 0.5624, 0.3769, 0.8259] +2026-04-14 15:53:10.759060: Epoch time: 103.36 s +2026-04-14 15:53:12.006341: +2026-04-14 15:53:12.007998: Epoch 3495 +2026-04-14 15:53:12.009473: Current learning rate: 0.00155 +2026-04-14 15:54:54.123944: train_loss -0.4519 +2026-04-14 15:54:54.129560: val_loss -0.3588 +2026-04-14 15:54:54.131133: Pseudo dice [0.6141, 0.6176, 0.7429, 0.316, 0.5045, 0.6589, 0.6088] +2026-04-14 15:54:54.133628: Epoch time: 102.12 s +2026-04-14 15:54:55.372426: +2026-04-14 15:54:55.374139: Epoch 3496 +2026-04-14 15:54:55.375470: Current learning rate: 0.00155 +2026-04-14 15:56:37.636523: train_loss -0.4494 +2026-04-14 15:56:37.643632: val_loss -0.3497 +2026-04-14 15:56:37.646252: Pseudo dice [0.5078, 0.7847, 0.8018, 0.4855, 0.3713, 0.6077, 0.8145] +2026-04-14 15:56:37.648460: Epoch time: 102.27 s +2026-04-14 15:56:38.902382: +2026-04-14 15:56:38.904217: Epoch 3497 +2026-04-14 15:56:38.905751: Current learning rate: 0.00155 +2026-04-14 15:58:20.966012: train_loss -0.4452 +2026-04-14 15:58:20.992317: val_loss -0.3752 +2026-04-14 15:58:20.994774: Pseudo dice [0.8735, 0.7486, 0.7503, 0.469, 0.7517, 0.9153, 0.8363] +2026-04-14 15:58:20.996900: Epoch time: 102.07 s +2026-04-14 15:58:22.279615: +2026-04-14 15:58:22.282331: Epoch 3498 +2026-04-14 15:58:22.284041: Current learning rate: 0.00154 +2026-04-14 16:00:04.841619: train_loss -0.4563 +2026-04-14 16:00:04.848090: val_loss -0.3851 +2026-04-14 16:00:04.849977: Pseudo dice [0.6746, 0.8131, 0.7586, 0.5707, 0.563, 0.8922, 0.7775] +2026-04-14 16:00:04.852338: Epoch time: 102.57 s +2026-04-14 16:00:06.097569: +2026-04-14 16:00:06.099196: Epoch 3499 +2026-04-14 16:00:06.100849: Current learning rate: 0.00154 +2026-04-14 16:01:48.237913: train_loss -0.4576 +2026-04-14 16:01:48.244687: val_loss -0.3522 +2026-04-14 16:01:48.246519: Pseudo dice [0.696, 0.8245, 0.696, 0.3555, 0.6601, 0.3219, 0.6879] +2026-04-14 16:01:48.249148: Epoch time: 102.14 s +2026-04-14 16:01:51.149615: +2026-04-14 16:01:51.151553: Epoch 3500 +2026-04-14 16:01:51.153292: Current learning rate: 0.00154 +2026-04-14 16:03:34.206114: train_loss -0.4605 +2026-04-14 16:03:34.212642: val_loss -0.3888 +2026-04-14 16:03:34.214422: Pseudo dice [0.7371, 0.7689, 0.7957, 0.642, 0.5836, 0.792, 0.8648] +2026-04-14 16:03:34.217079: Epoch time: 103.06 s +2026-04-14 16:03:35.471200: +2026-04-14 16:03:35.473237: Epoch 3501 +2026-04-14 16:03:35.475102: Current learning rate: 0.00154 +2026-04-14 16:05:17.805062: train_loss -0.4519 +2026-04-14 16:05:17.812008: val_loss -0.3951 +2026-04-14 16:05:17.814114: Pseudo dice [0.6465, 0.9097, 0.6528, 0.6185, 0.5139, 0.2559, 0.6584] +2026-04-14 16:05:17.816441: Epoch time: 102.34 s +2026-04-14 16:05:19.054761: +2026-04-14 16:05:19.056958: Epoch 3502 +2026-04-14 16:05:19.059898: Current learning rate: 0.00153 +2026-04-14 16:07:01.509558: train_loss -0.4532 +2026-04-14 16:07:01.517060: val_loss -0.3983 +2026-04-14 16:07:01.520118: Pseudo dice [0.5779, 0.8993, 0.8331, 0.827, 0.5772, 0.2213, 0.8059] +2026-04-14 16:07:01.524512: Epoch time: 102.46 s +2026-04-14 16:07:02.794399: +2026-04-14 16:07:02.797094: Epoch 3503 +2026-04-14 16:07:02.798600: Current learning rate: 0.00153 +2026-04-14 16:08:44.993982: train_loss -0.434 +2026-04-14 16:08:45.003945: val_loss -0.3816 +2026-04-14 16:08:45.008647: Pseudo dice [0.7399, 0.7038, 0.8074, 0.4272, 0.3467, 0.7682, 0.8036] +2026-04-14 16:08:45.015333: Epoch time: 102.2 s +2026-04-14 16:08:46.292100: +2026-04-14 16:08:46.294017: Epoch 3504 +2026-04-14 16:08:46.295532: Current learning rate: 0.00153 +2026-04-14 16:10:28.603798: train_loss -0.4529 +2026-04-14 16:10:28.612422: val_loss -0.3671 +2026-04-14 16:10:28.614305: Pseudo dice [0.5585, 0.907, 0.82, 0.4803, 0.6181, 0.111, 0.8733] +2026-04-14 16:10:28.617404: Epoch time: 102.31 s +2026-04-14 16:10:29.856451: +2026-04-14 16:10:29.859226: Epoch 3505 +2026-04-14 16:10:29.860971: Current learning rate: 0.00153 +2026-04-14 16:12:11.608476: train_loss -0.448 +2026-04-14 16:12:11.616071: val_loss -0.3631 +2026-04-14 16:12:11.618160: Pseudo dice [0.6019, 0.8211, 0.8637, 0.3993, 0.5572, 0.8049, 0.6321] +2026-04-14 16:12:11.620852: Epoch time: 101.76 s +2026-04-14 16:12:12.872434: +2026-04-14 16:12:12.874212: Epoch 3506 +2026-04-14 16:12:12.875651: Current learning rate: 0.00152 +2026-04-14 16:13:54.898565: train_loss -0.4535 +2026-04-14 16:13:54.904462: val_loss -0.3873 +2026-04-14 16:13:54.907262: Pseudo dice [0.5798, 0.9045, 0.7762, 0.9108, 0.633, 0.7859, 0.764] +2026-04-14 16:13:54.910305: Epoch time: 102.03 s +2026-04-14 16:13:56.154114: +2026-04-14 16:13:56.155784: Epoch 3507 +2026-04-14 16:13:56.157890: Current learning rate: 0.00152 +2026-04-14 16:15:37.788236: train_loss -0.4528 +2026-04-14 16:15:37.795063: val_loss -0.3868 +2026-04-14 16:15:37.798269: Pseudo dice [0.3896, 0.726, 0.7201, 0.6103, 0.5173, 0.8159, 0.8782] +2026-04-14 16:15:37.801126: Epoch time: 101.64 s +2026-04-14 16:15:39.071302: +2026-04-14 16:15:39.073162: Epoch 3508 +2026-04-14 16:15:39.074752: Current learning rate: 0.00152 +2026-04-14 16:17:20.641596: train_loss -0.4686 +2026-04-14 16:17:20.648438: val_loss -0.376 +2026-04-14 16:17:20.651731: Pseudo dice [0.1853, 0.8673, 0.8225, 0.47, 0.7024, 0.8489, 0.7837] +2026-04-14 16:17:20.654496: Epoch time: 101.57 s +2026-04-14 16:17:21.918579: +2026-04-14 16:17:21.920309: Epoch 3509 +2026-04-14 16:17:21.922218: Current learning rate: 0.00151 +2026-04-14 16:19:03.622181: train_loss -0.4456 +2026-04-14 16:19:03.628911: val_loss -0.3467 +2026-04-14 16:19:03.631616: Pseudo dice [0.6494, 0.698, 0.7522, 0.1374, 0.4511, 0.6041, 0.7036] +2026-04-14 16:19:03.633936: Epoch time: 101.71 s +2026-04-14 16:19:04.866910: +2026-04-14 16:19:04.868838: Epoch 3510 +2026-04-14 16:19:04.871186: Current learning rate: 0.00151 +2026-04-14 16:20:46.623379: train_loss -0.4429 +2026-04-14 16:20:46.631660: val_loss -0.3469 +2026-04-14 16:20:46.633584: Pseudo dice [0.4973, 0.776, 0.7863, 0.4775, 0.1542, 0.7779, 0.7933] +2026-04-14 16:20:46.635790: Epoch time: 101.76 s +2026-04-14 16:20:47.858937: +2026-04-14 16:20:47.860850: Epoch 3511 +2026-04-14 16:20:47.862456: Current learning rate: 0.00151 +2026-04-14 16:22:29.765666: train_loss -0.4426 +2026-04-14 16:22:29.774379: val_loss -0.3824 +2026-04-14 16:22:29.777978: Pseudo dice [0.7054, 0.9142, 0.8252, 0.2586, 0.3702, 0.6938, 0.8413] +2026-04-14 16:22:29.780107: Epoch time: 101.91 s +2026-04-14 16:22:31.025626: +2026-04-14 16:22:31.028391: Epoch 3512 +2026-04-14 16:22:31.031751: Current learning rate: 0.00151 +2026-04-14 16:24:12.521448: train_loss -0.4483 +2026-04-14 16:24:12.527529: val_loss -0.3766 +2026-04-14 16:24:12.529082: Pseudo dice [0.6103, 0.8575, 0.7176, 0.909, 0.7059, 0.4076, 0.8617] +2026-04-14 16:24:12.531218: Epoch time: 101.5 s +2026-04-14 16:24:13.799415: +2026-04-14 16:24:13.801250: Epoch 3513 +2026-04-14 16:24:13.802822: Current learning rate: 0.0015 +2026-04-14 16:25:55.157307: train_loss -0.4586 +2026-04-14 16:25:55.163828: val_loss -0.3955 +2026-04-14 16:25:55.165600: Pseudo dice [0.5817, 0.882, 0.7728, 0.8364, 0.568, 0.8746, 0.8398] +2026-04-14 16:25:55.168191: Epoch time: 101.36 s +2026-04-14 16:25:57.544703: +2026-04-14 16:25:57.546363: Epoch 3514 +2026-04-14 16:25:57.547825: Current learning rate: 0.0015 +2026-04-14 16:27:38.809801: train_loss -0.4632 +2026-04-14 16:27:38.822772: val_loss -0.3917 +2026-04-14 16:27:38.825526: Pseudo dice [0.5851, 0.885, 0.7638, 0.8244, 0.4159, 0.1786, 0.861] +2026-04-14 16:27:38.829271: Epoch time: 101.27 s +2026-04-14 16:27:40.079846: +2026-04-14 16:27:40.081715: Epoch 3515 +2026-04-14 16:27:40.084350: Current learning rate: 0.0015 +2026-04-14 16:29:21.393210: train_loss -0.4637 +2026-04-14 16:29:21.400228: val_loss -0.3672 +2026-04-14 16:29:21.403446: Pseudo dice [0.7135, 0.7563, 0.7931, 0.5113, 0.3505, 0.8876, 0.6803] +2026-04-14 16:29:21.407679: Epoch time: 101.32 s +2026-04-14 16:29:22.663393: +2026-04-14 16:29:22.665037: Epoch 3516 +2026-04-14 16:29:22.666538: Current learning rate: 0.00149 +2026-04-14 16:31:03.694074: train_loss -0.448 +2026-04-14 16:31:03.699888: val_loss -0.3718 +2026-04-14 16:31:03.701876: Pseudo dice [0.7248, 0.8993, 0.6954, 0.506, 0.5069, 0.826, 0.6193] +2026-04-14 16:31:03.704339: Epoch time: 101.03 s +2026-04-14 16:31:04.955990: +2026-04-14 16:31:04.958108: Epoch 3517 +2026-04-14 16:31:04.959681: Current learning rate: 0.00149 +2026-04-14 16:32:46.427244: train_loss -0.4487 +2026-04-14 16:32:46.434382: val_loss -0.3615 +2026-04-14 16:32:46.438796: Pseudo dice [0.6055, 0.8845, 0.6751, 0.2972, 0.4298, 0.69, 0.7802] +2026-04-14 16:32:46.441182: Epoch time: 101.47 s +2026-04-14 16:32:47.665179: +2026-04-14 16:32:47.667843: Epoch 3518 +2026-04-14 16:32:47.669700: Current learning rate: 0.00149 +2026-04-14 16:34:28.646833: train_loss -0.4466 +2026-04-14 16:34:28.653199: val_loss -0.4106 +2026-04-14 16:34:28.655840: Pseudo dice [0.6878, 0.7885, 0.8293, 0.4543, 0.5044, 0.9027, 0.7258] +2026-04-14 16:34:28.658320: Epoch time: 100.98 s +2026-04-14 16:34:29.918690: +2026-04-14 16:34:29.920785: Epoch 3519 +2026-04-14 16:34:29.922537: Current learning rate: 0.00149 +2026-04-14 16:36:11.111848: train_loss -0.4516 +2026-04-14 16:36:11.118485: val_loss -0.3717 +2026-04-14 16:36:11.120560: Pseudo dice [0.6032, 0.527, 0.6818, 0.5634, 0.4526, 0.7928, 0.8364] +2026-04-14 16:36:11.122978: Epoch time: 101.2 s +2026-04-14 16:36:12.377493: +2026-04-14 16:36:12.379084: Epoch 3520 +2026-04-14 16:36:12.380897: Current learning rate: 0.00148 +2026-04-14 16:37:54.519818: train_loss -0.4483 +2026-04-14 16:37:54.526966: val_loss -0.3781 +2026-04-14 16:37:54.528927: Pseudo dice [0.4746, 0.9027, 0.7182, 0.8784, 0.675, 0.8129, 0.7937] +2026-04-14 16:37:54.531420: Epoch time: 102.15 s +2026-04-14 16:37:55.816103: +2026-04-14 16:37:55.818947: Epoch 3521 +2026-04-14 16:37:55.821164: Current learning rate: 0.00148 +2026-04-14 16:39:37.772666: train_loss -0.4476 +2026-04-14 16:39:37.779273: val_loss -0.3714 +2026-04-14 16:39:37.781519: Pseudo dice [0.7121, 0.6607, 0.8071, 0.5773, 0.194, 0.87, 0.7834] +2026-04-14 16:39:37.784109: Epoch time: 101.96 s +2026-04-14 16:39:39.020175: +2026-04-14 16:39:39.022468: Epoch 3522 +2026-04-14 16:39:39.024136: Current learning rate: 0.00148 +2026-04-14 16:41:21.263503: train_loss -0.4487 +2026-04-14 16:41:21.272113: val_loss -0.3821 +2026-04-14 16:41:21.274474: Pseudo dice [0.0127, 0.8357, 0.8094, 0.5127, 0.3836, 0.6406, 0.8238] +2026-04-14 16:41:21.277972: Epoch time: 102.25 s +2026-04-14 16:41:22.528683: +2026-04-14 16:41:22.532156: Epoch 3523 +2026-04-14 16:41:22.534196: Current learning rate: 0.00148 +2026-04-14 16:43:04.029604: train_loss -0.4505 +2026-04-14 16:43:04.035934: val_loss -0.3879 +2026-04-14 16:43:04.037999: Pseudo dice [0.6565, 0.75, 0.7816, 0.8468, 0.4098, 0.9423, 0.8696] +2026-04-14 16:43:04.040588: Epoch time: 101.5 s +2026-04-14 16:43:05.278701: +2026-04-14 16:43:05.280402: Epoch 3524 +2026-04-14 16:43:05.282869: Current learning rate: 0.00147 +2026-04-14 16:44:47.409016: train_loss -0.442 +2026-04-14 16:44:47.415656: val_loss -0.399 +2026-04-14 16:44:47.418103: Pseudo dice [0.772, 0.8609, 0.8128, 0.8867, 0.3703, 0.5889, 0.8617] +2026-04-14 16:44:47.421261: Epoch time: 102.13 s +2026-04-14 16:44:48.668101: +2026-04-14 16:44:48.670169: Epoch 3525 +2026-04-14 16:44:48.671798: Current learning rate: 0.00147 +2026-04-14 16:46:30.958477: train_loss -0.4511 +2026-04-14 16:46:30.964584: val_loss -0.4019 +2026-04-14 16:46:30.966440: Pseudo dice [0.6927, 0.6627, 0.8563, 0.3482, 0.6725, 0.8979, 0.8439] +2026-04-14 16:46:30.968993: Epoch time: 102.29 s +2026-04-14 16:46:30.971804: Yayy! New best EMA pseudo Dice: 0.6808 +2026-04-14 16:46:34.213428: +2026-04-14 16:46:34.215250: Epoch 3526 +2026-04-14 16:46:34.216648: Current learning rate: 0.00147 +2026-04-14 16:48:16.214563: train_loss -0.4471 +2026-04-14 16:48:16.221171: val_loss -0.3764 +2026-04-14 16:48:16.223779: Pseudo dice [0.7753, 0.6841, 0.7375, 0.3917, 0.4823, 0.8716, 0.7177] +2026-04-14 16:48:16.226240: Epoch time: 102.0 s +2026-04-14 16:48:17.477916: +2026-04-14 16:48:17.480895: Epoch 3527 +2026-04-14 16:48:17.482559: Current learning rate: 0.00146 +2026-04-14 16:49:59.543555: train_loss -0.4495 +2026-04-14 16:49:59.550691: val_loss -0.3715 +2026-04-14 16:49:59.552988: Pseudo dice [0.8207, 0.8743, 0.8225, 0.1602, 0.5227, 0.1661, 0.8464] +2026-04-14 16:49:59.555367: Epoch time: 102.07 s +2026-04-14 16:50:00.812316: +2026-04-14 16:50:00.815142: Epoch 3528 +2026-04-14 16:50:00.816794: Current learning rate: 0.00146 +2026-04-14 16:51:43.571105: train_loss -0.4661 +2026-04-14 16:51:43.578723: val_loss -0.3732 +2026-04-14 16:51:43.580731: Pseudo dice [0.7378, 0.7851, 0.6517, 0.7462, 0.5872, 0.6016, 0.8383] +2026-04-14 16:51:43.584113: Epoch time: 102.76 s +2026-04-14 16:51:44.836429: +2026-04-14 16:51:44.839551: Epoch 3529 +2026-04-14 16:51:44.843820: Current learning rate: 0.00146 +2026-04-14 16:53:27.487427: train_loss -0.4572 +2026-04-14 16:53:27.504454: val_loss -0.3441 +2026-04-14 16:53:27.528172: Pseudo dice [0.7599, 0.9199, 0.7637, 0.0772, 0.3725, 0.7213, 0.4675] +2026-04-14 16:53:27.530741: Epoch time: 102.65 s +2026-04-14 16:53:29.024574: +2026-04-14 16:53:29.026956: Epoch 3530 +2026-04-14 16:53:29.028780: Current learning rate: 0.00146 +2026-04-14 16:55:12.063484: train_loss -0.4552 +2026-04-14 16:55:12.070271: val_loss -0.4123 +2026-04-14 16:55:12.072274: Pseudo dice [0.868, 0.6451, 0.7368, 0.6759, 0.6864, 0.8136, 0.8654] +2026-04-14 16:55:12.074226: Epoch time: 103.04 s +2026-04-14 16:55:13.343648: +2026-04-14 16:55:13.348070: Epoch 3531 +2026-04-14 16:55:13.350203: Current learning rate: 0.00145 +2026-04-14 16:56:55.776007: train_loss -0.4493 +2026-04-14 16:56:55.782151: val_loss -0.3798 +2026-04-14 16:56:55.784176: Pseudo dice [0.6567, 0.7994, 0.7236, 0.2307, 0.6642, 0.8922, 0.8348] +2026-04-14 16:56:55.786538: Epoch time: 102.44 s +2026-04-14 16:56:57.025517: +2026-04-14 16:56:57.028257: Epoch 3532 +2026-04-14 16:56:57.030002: Current learning rate: 0.00145 +2026-04-14 16:58:39.915852: train_loss -0.4523 +2026-04-14 16:58:39.941477: val_loss -0.4123 +2026-04-14 16:58:39.943939: Pseudo dice [0.634, 0.8937, 0.837, 0.3912, 0.7388, 0.4794, 0.8257] +2026-04-14 16:58:39.946040: Epoch time: 102.89 s +2026-04-14 16:58:41.222083: +2026-04-14 16:58:41.224375: Epoch 3533 +2026-04-14 16:58:41.225859: Current learning rate: 0.00145 +2026-04-14 17:00:25.119243: train_loss -0.4543 +2026-04-14 17:00:25.125750: val_loss -0.3887 +2026-04-14 17:00:25.127878: Pseudo dice [0.5838, 0.8245, 0.8331, 0.3017, 0.4449, 0.8261, 0.865] +2026-04-14 17:00:25.130530: Epoch time: 103.9 s +2026-04-14 17:00:26.369268: +2026-04-14 17:00:26.371781: Epoch 3534 +2026-04-14 17:00:26.373412: Current learning rate: 0.00144 +2026-04-14 17:02:08.532457: train_loss -0.4575 +2026-04-14 17:02:08.539060: val_loss -0.3997 +2026-04-14 17:02:08.541045: Pseudo dice [0.7142, 0.8213, 0.8204, 0.7138, 0.5991, 0.2245, 0.8146] +2026-04-14 17:02:08.546321: Epoch time: 102.17 s +2026-04-14 17:02:09.813077: +2026-04-14 17:02:09.815348: Epoch 3535 +2026-04-14 17:02:09.817489: Current learning rate: 0.00144 +2026-04-14 17:03:51.796858: train_loss -0.4483 +2026-04-14 17:03:51.804623: val_loss -0.398 +2026-04-14 17:03:51.806339: Pseudo dice [0.7021, 0.7658, 0.8208, 0.5635, 0.6759, 0.9248, 0.7441] +2026-04-14 17:03:51.808989: Epoch time: 101.99 s +2026-04-14 17:03:51.810860: Yayy! New best EMA pseudo Dice: 0.6824 +2026-04-14 17:03:54.967778: +2026-04-14 17:03:54.974422: Epoch 3536 +2026-04-14 17:03:54.976581: Current learning rate: 0.00144 +2026-04-14 17:05:36.576863: train_loss -0.4593 +2026-04-14 17:05:36.583585: val_loss -0.3373 +2026-04-14 17:05:36.586276: Pseudo dice [0.853, 0.7775, 0.6293, 0.3433, 0.4675, 0.7009, 0.6746] +2026-04-14 17:05:36.588878: Epoch time: 101.61 s +2026-04-14 17:05:37.834383: +2026-04-14 17:05:37.836417: Epoch 3537 +2026-04-14 17:05:37.838445: Current learning rate: 0.00144 +2026-04-14 17:07:18.987767: train_loss -0.4485 +2026-04-14 17:07:18.998847: val_loss -0.4071 +2026-04-14 17:07:19.000788: Pseudo dice [0.647, 0.7936, 0.8818, 0.4206, 0.3067, 0.6741, 0.8306] +2026-04-14 17:07:19.007454: Epoch time: 101.16 s +2026-04-14 17:07:20.262159: +2026-04-14 17:07:20.263725: Epoch 3538 +2026-04-14 17:07:20.265261: Current learning rate: 0.00143 +2026-04-14 17:09:02.263353: train_loss -0.4522 +2026-04-14 17:09:02.271364: val_loss -0.3704 +2026-04-14 17:09:02.273599: Pseudo dice [0.739, 0.7639, 0.6408, 0.4375, 0.4901, 0.8763, 0.5156] +2026-04-14 17:09:02.276225: Epoch time: 102.0 s +2026-04-14 17:09:03.524558: +2026-04-14 17:09:03.526926: Epoch 3539 +2026-04-14 17:09:03.528639: Current learning rate: 0.00143 +2026-04-14 17:10:44.611047: train_loss -0.4485 +2026-04-14 17:10:44.617475: val_loss -0.3861 +2026-04-14 17:10:44.619737: Pseudo dice [0.8751, 0.7029, 0.8029, 0.3514, 0.4579, 0.5286, 0.8535] +2026-04-14 17:10:44.622900: Epoch time: 101.09 s +2026-04-14 17:10:45.880732: +2026-04-14 17:10:45.882768: Epoch 3540 +2026-04-14 17:10:45.884559: Current learning rate: 0.00143 +2026-04-14 17:12:28.120171: train_loss -0.4653 +2026-04-14 17:12:28.126204: val_loss -0.333 +2026-04-14 17:12:28.128066: Pseudo dice [0.8727, 0.9026, 0.7102, 0.6311, 0.6412, 0.5396, 0.5032] +2026-04-14 17:12:28.130744: Epoch time: 102.24 s +2026-04-14 17:12:29.365901: +2026-04-14 17:12:29.367808: Epoch 3541 +2026-04-14 17:12:29.369482: Current learning rate: 0.00142 +2026-04-14 17:14:10.749585: train_loss -0.456 +2026-04-14 17:14:10.757059: val_loss -0.383 +2026-04-14 17:14:10.759406: Pseudo dice [0.8024, 0.797, 0.8784, 0.383, 0.2796, 0.7074, 0.7245] +2026-04-14 17:14:10.761812: Epoch time: 101.39 s +2026-04-14 17:14:11.994602: +2026-04-14 17:14:11.997031: Epoch 3542 +2026-04-14 17:14:11.999155: Current learning rate: 0.00142 +2026-04-14 17:15:53.605900: train_loss -0.4538 +2026-04-14 17:15:53.613121: val_loss -0.3674 +2026-04-14 17:15:53.616024: Pseudo dice [0.8505, 0.7926, 0.7951, 0.3786, 0.4497, 0.8683, 0.6324] +2026-04-14 17:15:53.618611: Epoch time: 101.61 s +2026-04-14 17:15:54.881001: +2026-04-14 17:15:54.882843: Epoch 3543 +2026-04-14 17:15:54.884502: Current learning rate: 0.00142 +2026-04-14 17:17:36.213491: train_loss -0.457 +2026-04-14 17:17:36.219914: val_loss -0.3664 +2026-04-14 17:17:36.224223: Pseudo dice [0.5588, 0.8961, 0.6937, 0.4003, 0.5642, 0.6397, 0.5596] +2026-04-14 17:17:36.226641: Epoch time: 101.34 s +2026-04-14 17:17:37.454821: +2026-04-14 17:17:37.456281: Epoch 3544 +2026-04-14 17:17:37.458050: Current learning rate: 0.00142 +2026-04-14 17:19:18.873142: train_loss -0.4573 +2026-04-14 17:19:18.879322: val_loss -0.3793 +2026-04-14 17:19:18.881088: Pseudo dice [0.6925, 0.8863, 0.7863, 0.731, 0.632, 0.7362, 0.7684] +2026-04-14 17:19:18.883599: Epoch time: 101.42 s +2026-04-14 17:19:20.159487: +2026-04-14 17:19:20.161503: Epoch 3545 +2026-04-14 17:19:20.164144: Current learning rate: 0.00141 +2026-04-14 17:21:01.506581: train_loss -0.4556 +2026-04-14 17:21:01.512532: val_loss -0.4079 +2026-04-14 17:21:01.514771: Pseudo dice [0.599, 0.7378, 0.7943, 0.6369, 0.5057, 0.9193, 0.713] +2026-04-14 17:21:01.516668: Epoch time: 101.35 s +2026-04-14 17:21:02.759817: +2026-04-14 17:21:02.761577: Epoch 3546 +2026-04-14 17:21:02.762917: Current learning rate: 0.00141 +2026-04-14 17:22:44.452452: train_loss -0.4476 +2026-04-14 17:22:44.458970: val_loss -0.3949 +2026-04-14 17:22:44.461026: Pseudo dice [0.7931, 0.6414, 0.7316, 0.4026, 0.571, 0.9002, 0.8273] +2026-04-14 17:22:44.463511: Epoch time: 101.7 s +2026-04-14 17:22:45.709647: +2026-04-14 17:22:45.711499: Epoch 3547 +2026-04-14 17:22:45.714552: Current learning rate: 0.00141 +2026-04-14 17:24:27.117245: train_loss -0.449 +2026-04-14 17:24:27.125553: val_loss -0.3845 +2026-04-14 17:24:27.127739: Pseudo dice [0.6914, 0.8815, 0.6934, 0.2789, 0.4316, 0.7332, 0.7329] +2026-04-14 17:24:27.130279: Epoch time: 101.41 s +2026-04-14 17:24:28.379342: +2026-04-14 17:24:28.380867: Epoch 3548 +2026-04-14 17:24:28.382355: Current learning rate: 0.00141 +2026-04-14 17:26:10.047788: train_loss -0.4584 +2026-04-14 17:26:10.053673: val_loss -0.3475 +2026-04-14 17:26:10.055491: Pseudo dice [0.871, 0.9235, 0.622, 0.4631, 0.5987, 0.5836, 0.5445] +2026-04-14 17:26:10.057814: Epoch time: 101.67 s +2026-04-14 17:26:11.319440: +2026-04-14 17:26:11.322429: Epoch 3549 +2026-04-14 17:26:11.323845: Current learning rate: 0.0014 +2026-04-14 17:27:52.750566: train_loss -0.449 +2026-04-14 17:27:52.757551: val_loss -0.3782 +2026-04-14 17:27:52.759554: Pseudo dice [0.7937, 0.5063, 0.77, 0.373, 0.4364, 0.9295, 0.7106] +2026-04-14 17:27:52.761578: Epoch time: 101.43 s +2026-04-14 17:27:55.954704: +2026-04-14 17:27:55.959698: Epoch 3550 +2026-04-14 17:27:55.961408: Current learning rate: 0.0014 +2026-04-14 17:29:37.213704: train_loss -0.4574 +2026-04-14 17:29:37.220617: val_loss -0.3954 +2026-04-14 17:29:37.222724: Pseudo dice [0.75, 0.7641, 0.8378, 0.3987, 0.6248, 0.4854, 0.8695] +2026-04-14 17:29:37.225162: Epoch time: 101.26 s +2026-04-14 17:29:38.475143: +2026-04-14 17:29:38.477643: Epoch 3551 +2026-04-14 17:29:38.479530: Current learning rate: 0.0014 +2026-04-14 17:31:19.732352: train_loss -0.4653 +2026-04-14 17:31:19.739589: val_loss -0.3613 +2026-04-14 17:31:19.741817: Pseudo dice [0.7938, 0.8873, 0.8304, 0.1839, 0.6239, 0.8282, 0.6855] +2026-04-14 17:31:19.744223: Epoch time: 101.26 s +2026-04-14 17:31:21.043350: +2026-04-14 17:31:21.045303: Epoch 3552 +2026-04-14 17:31:21.046946: Current learning rate: 0.00139 +2026-04-14 17:33:03.565403: train_loss -0.4497 +2026-04-14 17:33:03.584404: val_loss -0.3683 +2026-04-14 17:33:03.591368: Pseudo dice [0.6078, 0.7139, 0.7859, 0.2214, 0.3108, 0.8014, 0.7162] +2026-04-14 17:33:03.593809: Epoch time: 102.53 s +2026-04-14 17:33:04.835434: +2026-04-14 17:33:04.837142: Epoch 3553 +2026-04-14 17:33:04.838677: Current learning rate: 0.00139 +2026-04-14 17:34:47.101253: train_loss -0.4554 +2026-04-14 17:34:47.113221: val_loss -0.3801 +2026-04-14 17:34:47.117294: Pseudo dice [0.6121, 0.6744, 0.7434, 0.3569, 0.4969, 0.7856, 0.7927] +2026-04-14 17:34:47.120011: Epoch time: 102.27 s +2026-04-14 17:34:48.372789: +2026-04-14 17:34:48.376620: Epoch 3554 +2026-04-14 17:34:48.378736: Current learning rate: 0.00139 +2026-04-14 17:36:30.539591: train_loss -0.4577 +2026-04-14 17:36:30.555341: val_loss -0.3777 +2026-04-14 17:36:30.558291: Pseudo dice [0.7617, 0.73, 0.7163, 0.3451, 0.5741, 0.9487, 0.8332] +2026-04-14 17:36:30.573317: Epoch time: 102.17 s +2026-04-14 17:36:31.828409: +2026-04-14 17:36:31.830556: Epoch 3555 +2026-04-14 17:36:31.832251: Current learning rate: 0.00139 +2026-04-14 17:38:13.471330: train_loss -0.4576 +2026-04-14 17:38:13.478430: val_loss -0.3556 +2026-04-14 17:38:13.480534: Pseudo dice [0.8654, 0.9066, 0.7327, 0.7565, 0.3269, 0.1711, 0.8857] +2026-04-14 17:38:13.483215: Epoch time: 101.65 s +2026-04-14 17:38:14.757800: +2026-04-14 17:38:14.759488: Epoch 3556 +2026-04-14 17:38:14.761053: Current learning rate: 0.00138 +2026-04-14 17:39:56.811922: train_loss -0.4549 +2026-04-14 17:39:56.818312: val_loss -0.3953 +2026-04-14 17:39:56.820005: Pseudo dice [0.668, 0.8936, 0.8428, 0.4873, 0.5665, 0.8205, 0.7996] +2026-04-14 17:39:56.822043: Epoch time: 102.06 s +2026-04-14 17:39:58.042687: +2026-04-14 17:39:58.047495: Epoch 3557 +2026-04-14 17:39:58.049611: Current learning rate: 0.00138 +2026-04-14 17:41:39.808258: train_loss -0.4567 +2026-04-14 17:41:39.814452: val_loss -0.3889 +2026-04-14 17:41:39.817124: Pseudo dice [0.8238, 0.8077, 0.8287, 0.4738, 0.3608, 0.9483, 0.868] +2026-04-14 17:41:39.819880: Epoch time: 101.77 s +2026-04-14 17:41:41.084207: +2026-04-14 17:41:41.086853: Epoch 3558 +2026-04-14 17:41:41.089233: Current learning rate: 0.00138 +2026-04-14 17:43:22.559855: train_loss -0.4648 +2026-04-14 17:43:22.565926: val_loss -0.4017 +2026-04-14 17:43:22.567871: Pseudo dice [0.6494, 0.901, 0.8134, 0.5697, 0.5674, 0.604, 0.715] +2026-04-14 17:43:22.570333: Epoch time: 101.48 s +2026-04-14 17:43:23.814270: +2026-04-14 17:43:23.815824: Epoch 3559 +2026-04-14 17:43:23.817302: Current learning rate: 0.00137 +2026-04-14 17:45:05.498003: train_loss -0.4582 +2026-04-14 17:45:05.506625: val_loss -0.4235 +2026-04-14 17:45:05.509641: Pseudo dice [0.8238, 0.8844, 0.8419, 0.2303, 0.4375, 0.3455, 0.8335] +2026-04-14 17:45:05.512897: Epoch time: 101.69 s +2026-04-14 17:45:06.757888: +2026-04-14 17:45:06.761885: Epoch 3560 +2026-04-14 17:45:06.763725: Current learning rate: 0.00137 +2026-04-14 17:46:48.415663: train_loss -0.4524 +2026-04-14 17:46:48.422500: val_loss -0.3971 +2026-04-14 17:46:48.425927: Pseudo dice [0.7121, 0.6061, 0.8055, 0.0456, 0.6567, 0.8814, 0.7502] +2026-04-14 17:46:48.429185: Epoch time: 101.66 s +2026-04-14 17:46:49.687082: +2026-04-14 17:46:49.688999: Epoch 3561 +2026-04-14 17:46:49.690752: Current learning rate: 0.00137 +2026-04-14 17:48:30.897571: train_loss -0.4461 +2026-04-14 17:48:30.904078: val_loss -0.3854 +2026-04-14 17:48:30.908062: Pseudo dice [0.8717, 0.8972, 0.8704, 0.502, 0.3219, 0.7807, 0.8562] +2026-04-14 17:48:30.910790: Epoch time: 101.21 s +2026-04-14 17:48:32.149068: +2026-04-14 17:48:32.151232: Epoch 3562 +2026-04-14 17:48:32.152762: Current learning rate: 0.00137 +2026-04-14 17:50:13.923476: train_loss -0.4568 +2026-04-14 17:50:13.930037: val_loss -0.3998 +2026-04-14 17:50:13.931877: Pseudo dice [0.6906, 0.7682, 0.8472, 0.4797, 0.6016, 0.8752, 0.8468] +2026-04-14 17:50:13.934220: Epoch time: 101.78 s +2026-04-14 17:50:15.184154: +2026-04-14 17:50:15.185965: Epoch 3563 +2026-04-14 17:50:15.187708: Current learning rate: 0.00136 +2026-04-14 17:51:56.539257: train_loss -0.4627 +2026-04-14 17:51:56.545184: val_loss -0.4074 +2026-04-14 17:51:56.547462: Pseudo dice [0.7203, 0.5973, 0.7536, 0.5078, 0.5808, 0.9387, 0.8436] +2026-04-14 17:51:56.549853: Epoch time: 101.36 s +2026-04-14 17:51:56.552306: Yayy! New best EMA pseudo Dice: 0.6835 +2026-04-14 17:51:59.649480: +2026-04-14 17:51:59.652007: Epoch 3564 +2026-04-14 17:51:59.653402: Current learning rate: 0.00136 +2026-04-14 17:53:41.042652: train_loss -0.4484 +2026-04-14 17:53:41.049931: val_loss -0.377 +2026-04-14 17:53:41.052565: Pseudo dice [0.6593, 0.8852, 0.7763, 0.6002, 0.5456, 0.2557, 0.7383] +2026-04-14 17:53:41.055072: Epoch time: 101.4 s +2026-04-14 17:53:42.289533: +2026-04-14 17:53:42.291003: Epoch 3565 +2026-04-14 17:53:42.292426: Current learning rate: 0.00136 +2026-04-14 17:55:24.325979: train_loss -0.4566 +2026-04-14 17:55:24.331306: val_loss -0.3949 +2026-04-14 17:55:24.333789: Pseudo dice [0.8462, 0.6442, 0.7915, 0.4077, 0.2307, 0.8704, 0.8452] +2026-04-14 17:55:24.337832: Epoch time: 102.04 s +2026-04-14 17:55:25.572233: +2026-04-14 17:55:25.575135: Epoch 3566 +2026-04-14 17:55:25.576999: Current learning rate: 0.00135 +2026-04-14 17:57:06.787355: train_loss -0.4491 +2026-04-14 17:57:06.802843: val_loss -0.3373 +2026-04-14 17:57:06.807526: Pseudo dice [0.4669, 0.9082, 0.8177, 0.3521, 0.5884, 0.7285, 0.8021] +2026-04-14 17:57:06.810125: Epoch time: 101.22 s +2026-04-14 17:57:08.040917: +2026-04-14 17:57:08.042977: Epoch 3567 +2026-04-14 17:57:08.044971: Current learning rate: 0.00135 +2026-04-14 17:58:49.473820: train_loss -0.4515 +2026-04-14 17:58:49.479354: val_loss -0.3656 +2026-04-14 17:58:49.481813: Pseudo dice [0.4237, 0.1983, 0.7985, 0.2479, 0.4867, 0.8681, 0.8723] +2026-04-14 17:58:49.484197: Epoch time: 101.44 s +2026-04-14 17:58:50.767782: +2026-04-14 17:58:50.769759: Epoch 3568 +2026-04-14 17:58:50.771700: Current learning rate: 0.00135 +2026-04-14 18:00:32.174731: train_loss -0.4516 +2026-04-14 18:00:32.181367: val_loss -0.3359 +2026-04-14 18:00:32.183894: Pseudo dice [0.8774, 0.9103, 0.8185, 0.1829, 0.3784, 0.0843, 0.7034] +2026-04-14 18:00:32.187119: Epoch time: 101.41 s +2026-04-14 18:00:33.430497: +2026-04-14 18:00:33.432065: Epoch 3569 +2026-04-14 18:00:33.433743: Current learning rate: 0.00135 +2026-04-14 18:02:14.879499: train_loss -0.455 +2026-04-14 18:02:14.885315: val_loss -0.4194 +2026-04-14 18:02:14.887246: Pseudo dice [0.6077, 0.6835, 0.8323, 0.3667, 0.5475, 0.8274, 0.7667] +2026-04-14 18:02:14.889383: Epoch time: 101.45 s +2026-04-14 18:02:16.121806: +2026-04-14 18:02:16.123513: Epoch 3570 +2026-04-14 18:02:16.124969: Current learning rate: 0.00134 +2026-04-14 18:03:57.657300: train_loss -0.4574 +2026-04-14 18:03:57.665060: val_loss -0.3975 +2026-04-14 18:03:57.667621: Pseudo dice [0.6468, 0.536, 0.8411, 0.4652, 0.6678, 0.9415, 0.8675] +2026-04-14 18:03:57.670508: Epoch time: 101.54 s +2026-04-14 18:03:58.924127: +2026-04-14 18:03:58.925885: Epoch 3571 +2026-04-14 18:03:58.927335: Current learning rate: 0.00134 +2026-04-14 18:05:41.345551: train_loss -0.4615 +2026-04-14 18:05:41.351416: val_loss -0.3951 +2026-04-14 18:05:41.353582: Pseudo dice [0.3442, 0.6704, 0.8107, 0.0641, 0.5635, 0.8597, 0.8403] +2026-04-14 18:05:41.355788: Epoch time: 102.42 s +2026-04-14 18:05:42.623578: +2026-04-14 18:05:42.625655: Epoch 3572 +2026-04-14 18:05:42.628092: Current learning rate: 0.00134 +2026-04-14 18:07:23.848834: train_loss -0.4571 +2026-04-14 18:07:23.854506: val_loss -0.3624 +2026-04-14 18:07:23.856152: Pseudo dice [0.4824, 0.8916, 0.6665, 0.389, 0.6684, 0.2689, 0.8703] +2026-04-14 18:07:23.859384: Epoch time: 101.23 s +2026-04-14 18:07:25.113879: +2026-04-14 18:07:25.115509: Epoch 3573 +2026-04-14 18:07:25.117020: Current learning rate: 0.00134 +2026-04-14 18:09:06.367798: train_loss -0.4668 +2026-04-14 18:09:06.374915: val_loss -0.4212 +2026-04-14 18:09:06.377124: Pseudo dice [0.8398, 0.7553, 0.751, 0.5455, 0.5659, 0.5792, 0.862] +2026-04-14 18:09:06.380213: Epoch time: 101.26 s +2026-04-14 18:09:07.619868: +2026-04-14 18:09:07.621700: Epoch 3574 +2026-04-14 18:09:07.623341: Current learning rate: 0.00133 +2026-04-14 18:10:48.905543: train_loss -0.4657 +2026-04-14 18:10:48.912259: val_loss -0.3636 +2026-04-14 18:10:48.914787: Pseudo dice [0.8292, 0.7544, 0.7948, 0.1548, 0.448, 0.8978, 0.8209] +2026-04-14 18:10:48.917297: Epoch time: 101.29 s +2026-04-14 18:10:50.155869: +2026-04-14 18:10:50.158408: Epoch 3575 +2026-04-14 18:10:50.160171: Current learning rate: 0.00133 +2026-04-14 18:12:31.226353: train_loss -0.4524 +2026-04-14 18:12:31.234775: val_loss -0.3881 +2026-04-14 18:12:31.238079: Pseudo dice [0.7278, 0.6898, 0.8074, 0.3981, 0.5337, 0.8954, 0.7191] +2026-04-14 18:12:31.241094: Epoch time: 101.07 s +2026-04-14 18:12:32.465992: +2026-04-14 18:12:32.467701: Epoch 3576 +2026-04-14 18:12:32.469250: Current learning rate: 0.00133 +2026-04-14 18:14:13.522570: train_loss -0.4575 +2026-04-14 18:14:13.532676: val_loss -0.3883 +2026-04-14 18:14:13.536747: Pseudo dice [0.7478, 0.6754, 0.8101, 0.2669, 0.4922, 0.6393, 0.8583] +2026-04-14 18:14:13.538882: Epoch time: 101.06 s +2026-04-14 18:14:14.770195: +2026-04-14 18:14:14.772838: Epoch 3577 +2026-04-14 18:14:14.774698: Current learning rate: 0.00132 +2026-04-14 18:15:56.125150: train_loss -0.4566 +2026-04-14 18:15:56.131382: val_loss -0.3868 +2026-04-14 18:15:56.133554: Pseudo dice [0.8636, 0.7357, 0.759, 0.5771, 0.6595, 0.8227, 0.8891] +2026-04-14 18:15:56.135999: Epoch time: 101.36 s +2026-04-14 18:15:57.347570: +2026-04-14 18:15:57.349631: Epoch 3578 +2026-04-14 18:15:57.351209: Current learning rate: 0.00132 +2026-04-14 18:17:38.826229: train_loss -0.449 +2026-04-14 18:17:38.832015: val_loss -0.3817 +2026-04-14 18:17:38.833952: Pseudo dice [0.7851, 0.8463, 0.8396, 0.6259, 0.6953, 0.1687, 0.6936] +2026-04-14 18:17:38.836365: Epoch time: 101.48 s +2026-04-14 18:17:40.084685: +2026-04-14 18:17:40.088118: Epoch 3579 +2026-04-14 18:17:40.091082: Current learning rate: 0.00132 +2026-04-14 18:19:21.032392: train_loss -0.451 +2026-04-14 18:19:21.038901: val_loss -0.3481 +2026-04-14 18:19:21.041565: Pseudo dice [0.6415, 0.9065, 0.7847, 0.6674, 0.5474, 0.2311, 0.6401] +2026-04-14 18:19:21.044262: Epoch time: 100.95 s +2026-04-14 18:19:22.271516: +2026-04-14 18:19:22.274290: Epoch 3580 +2026-04-14 18:19:22.276483: Current learning rate: 0.00132 +2026-04-14 18:21:03.237082: train_loss -0.467 +2026-04-14 18:21:03.244626: val_loss -0.3738 +2026-04-14 18:21:03.247068: Pseudo dice [0.8381, 0.6443, 0.8288, 0.2487, 0.5528, 0.6568, 0.7864] +2026-04-14 18:21:03.249316: Epoch time: 100.97 s +2026-04-14 18:21:04.471416: +2026-04-14 18:21:04.474096: Epoch 3581 +2026-04-14 18:21:04.475990: Current learning rate: 0.00131 +2026-04-14 18:22:45.850401: train_loss -0.4654 +2026-04-14 18:22:45.856390: val_loss -0.3939 +2026-04-14 18:22:45.858593: Pseudo dice [0.602, 0.858, 0.7652, 0.739, 0.5275, 0.7912, 0.8893] +2026-04-14 18:22:45.861224: Epoch time: 101.38 s +2026-04-14 18:22:47.079781: +2026-04-14 18:22:47.081438: Epoch 3582 +2026-04-14 18:22:47.083141: Current learning rate: 0.00131 +2026-04-14 18:24:28.701817: train_loss -0.4599 +2026-04-14 18:24:28.707523: val_loss -0.3562 +2026-04-14 18:24:28.709842: Pseudo dice [0.6506, 0.8953, 0.7067, 0.2456, 0.2639, 0.7302, 0.7998] +2026-04-14 18:24:28.712668: Epoch time: 101.63 s +2026-04-14 18:24:29.961272: +2026-04-14 18:24:29.963692: Epoch 3583 +2026-04-14 18:24:29.965649: Current learning rate: 0.00131 +2026-04-14 18:26:11.136966: train_loss -0.4552 +2026-04-14 18:26:11.144860: val_loss -0.3937 +2026-04-14 18:26:11.146826: Pseudo dice [0.8396, 0.6278, 0.7415, 0.3235, 0.3778, 0.8144, 0.8628] +2026-04-14 18:26:11.149341: Epoch time: 101.18 s +2026-04-14 18:26:12.373422: +2026-04-14 18:26:12.381213: Epoch 3584 +2026-04-14 18:26:12.399354: Current learning rate: 0.0013 +2026-04-14 18:27:53.372174: train_loss -0.4429 +2026-04-14 18:27:53.377548: val_loss -0.3893 +2026-04-14 18:27:53.380250: Pseudo dice [0.7384, 0.6396, 0.7855, 0.345, 0.5632, 0.7988, 0.8479] +2026-04-14 18:27:53.382369: Epoch time: 101.0 s +2026-04-14 18:27:54.616558: +2026-04-14 18:27:54.618092: Epoch 3585 +2026-04-14 18:27:54.619488: Current learning rate: 0.0013 +2026-04-14 18:29:35.687660: train_loss -0.4496 +2026-04-14 18:29:35.694386: val_loss -0.3443 +2026-04-14 18:29:35.696624: Pseudo dice [0.84, 0.6635, 0.6598, 0.5409, 0.1898, 0.5026, 0.8143] +2026-04-14 18:29:35.699499: Epoch time: 101.07 s +2026-04-14 18:29:36.912662: +2026-04-14 18:29:36.914172: Epoch 3586 +2026-04-14 18:29:36.915580: Current learning rate: 0.0013 +2026-04-14 18:31:18.082540: train_loss -0.4397 +2026-04-14 18:31:18.091563: val_loss -0.3743 +2026-04-14 18:31:18.094095: Pseudo dice [0.808, 0.7011, 0.8094, 0.6248, 0.3805, 0.9153, 0.8116] +2026-04-14 18:31:18.096936: Epoch time: 101.17 s +2026-04-14 18:31:19.338481: +2026-04-14 18:31:19.340471: Epoch 3587 +2026-04-14 18:31:19.341905: Current learning rate: 0.0013 +2026-04-14 18:33:00.856611: train_loss -0.4508 +2026-04-14 18:33:00.862427: val_loss -0.361 +2026-04-14 18:33:00.865205: Pseudo dice [0.6307, 0.8917, 0.8054, 0.626, 0.626, 0.6686, 0.8159] +2026-04-14 18:33:00.867832: Epoch time: 101.52 s +2026-04-14 18:33:02.115754: +2026-04-14 18:33:02.117482: Epoch 3588 +2026-04-14 18:33:02.118904: Current learning rate: 0.00129 +2026-04-14 18:34:43.154047: train_loss -0.4538 +2026-04-14 18:34:43.160442: val_loss -0.3924 +2026-04-14 18:34:43.163640: Pseudo dice [0.7864, 0.5442, 0.7877, 0.6199, 0.4524, 0.7367, 0.8755] +2026-04-14 18:34:43.166254: Epoch time: 101.04 s +2026-04-14 18:34:44.416071: +2026-04-14 18:34:44.417668: Epoch 3589 +2026-04-14 18:34:44.419336: Current learning rate: 0.00129 +2026-04-14 18:36:25.214887: train_loss -0.4424 +2026-04-14 18:36:25.222572: val_loss -0.367 +2026-04-14 18:36:25.224624: Pseudo dice [0.4331, 0.46, 0.8542, 0.1609, 0.5115, 0.3967, 0.8041] +2026-04-14 18:36:25.227124: Epoch time: 100.8 s +2026-04-14 18:36:26.467565: +2026-04-14 18:36:26.469329: Epoch 3590 +2026-04-14 18:36:26.470695: Current learning rate: 0.00129 +2026-04-14 18:38:08.052690: train_loss -0.469 +2026-04-14 18:38:08.059448: val_loss -0.3867 +2026-04-14 18:38:08.061831: Pseudo dice [0.8698, 0.4152, 0.6155, 0.4966, 0.5776, 0.7533, 0.8225] +2026-04-14 18:38:08.063968: Epoch time: 101.59 s +2026-04-14 18:38:09.320780: +2026-04-14 18:38:09.322552: Epoch 3591 +2026-04-14 18:38:09.324106: Current learning rate: 0.00128 +2026-04-14 18:39:51.420683: train_loss -0.4568 +2026-04-14 18:39:51.426937: val_loss -0.3892 +2026-04-14 18:39:51.429721: Pseudo dice [0.7094, 0.6595, 0.7896, 0.3064, 0.4708, 0.9085, 0.8729] +2026-04-14 18:39:51.432515: Epoch time: 102.1 s +2026-04-14 18:39:52.652704: +2026-04-14 18:39:52.655813: Epoch 3592 +2026-04-14 18:39:52.657883: Current learning rate: 0.00128 +2026-04-14 18:41:34.125117: train_loss -0.4622 +2026-04-14 18:41:34.131279: val_loss -0.396 +2026-04-14 18:41:34.133690: Pseudo dice [0.7998, 0.7411, 0.8318, 0.4109, 0.4568, 0.6571, 0.8474] +2026-04-14 18:41:34.136038: Epoch time: 101.48 s +2026-04-14 18:41:35.387933: +2026-04-14 18:41:35.389949: Epoch 3593 +2026-04-14 18:41:35.391966: Current learning rate: 0.00128 +2026-04-14 18:43:16.687081: train_loss -0.4608 +2026-04-14 18:43:16.693292: val_loss -0.4188 +2026-04-14 18:43:16.695447: Pseudo dice [0.8425, 0.7899, 0.8661, 0.6451, 0.7107, 0.8658, 0.88] +2026-04-14 18:43:16.697942: Epoch time: 101.3 s +2026-04-14 18:43:17.938424: +2026-04-14 18:43:17.940104: Epoch 3594 +2026-04-14 18:43:17.941814: Current learning rate: 0.00128 +2026-04-14 18:44:59.144019: train_loss -0.4637 +2026-04-14 18:44:59.154025: val_loss -0.3689 +2026-04-14 18:44:59.158376: Pseudo dice [0.6495, 0.7019, 0.806, 0.3982, 0.6035, 0.7286, 0.8101] +2026-04-14 18:44:59.166927: Epoch time: 101.21 s +2026-04-14 18:45:00.397251: +2026-04-14 18:45:00.399693: Epoch 3595 +2026-04-14 18:45:00.401487: Current learning rate: 0.00127 +2026-04-14 18:46:41.742566: train_loss -0.4628 +2026-04-14 18:46:41.748609: val_loss -0.3367 +2026-04-14 18:46:41.750599: Pseudo dice [0.7468, 0.6952, 0.7767, 0.1335, 0.4207, 0.8961, 0.6947] +2026-04-14 18:46:41.753842: Epoch time: 101.35 s +2026-04-14 18:46:42.986238: +2026-04-14 18:46:42.988009: Epoch 3596 +2026-04-14 18:46:42.989768: Current learning rate: 0.00127 +2026-04-14 18:48:24.858835: train_loss -0.4547 +2026-04-14 18:48:24.865952: val_loss -0.4005 +2026-04-14 18:48:24.868057: Pseudo dice [0.8273, 0.7331, 0.7747, 0.4864, 0.3204, 0.867, 0.7917] +2026-04-14 18:48:24.870499: Epoch time: 101.88 s +2026-04-14 18:48:26.142861: +2026-04-14 18:48:26.144965: Epoch 3597 +2026-04-14 18:48:26.147289: Current learning rate: 0.00127 +2026-04-14 18:50:07.644004: train_loss -0.4533 +2026-04-14 18:50:07.650944: val_loss -0.4093 +2026-04-14 18:50:07.653152: Pseudo dice [0.7806, 0.8961, 0.6757, 0.5562, 0.31, 0.5459, 0.8423] +2026-04-14 18:50:07.655901: Epoch time: 101.5 s +2026-04-14 18:50:08.897022: +2026-04-14 18:50:08.899126: Epoch 3598 +2026-04-14 18:50:08.901853: Current learning rate: 0.00126 +2026-04-14 18:51:50.376411: train_loss -0.456 +2026-04-14 18:51:50.383248: val_loss -0.3813 +2026-04-14 18:51:50.385625: Pseudo dice [0.5539, 0.8803, 0.7908, 0.2726, 0.4128, 0.8162, 0.8346] +2026-04-14 18:51:50.387679: Epoch time: 101.48 s +2026-04-14 18:51:51.660476: +2026-04-14 18:51:51.662979: Epoch 3599 +2026-04-14 18:51:51.665124: Current learning rate: 0.00126 +2026-04-14 18:53:33.123531: train_loss -0.4615 +2026-04-14 18:53:33.131364: val_loss -0.4036 +2026-04-14 18:53:33.133603: Pseudo dice [0.7382, 0.8886, 0.8004, 0.6489, 0.6026, 0.9125, 0.7802] +2026-04-14 18:53:33.136085: Epoch time: 101.47 s +2026-04-14 18:53:35.943579: +2026-04-14 18:53:35.946584: Epoch 3600 +2026-04-14 18:53:35.948264: Current learning rate: 0.00126 +2026-04-14 18:55:18.017663: train_loss -0.4557 +2026-04-14 18:55:18.027488: val_loss -0.4047 +2026-04-14 18:55:18.029333: Pseudo dice [0.809, 0.8817, 0.8246, 0.6583, 0.4427, 0.6838, 0.8596] +2026-04-14 18:55:18.031593: Epoch time: 102.08 s +2026-04-14 18:55:19.276085: +2026-04-14 18:55:19.278580: Epoch 3601 +2026-04-14 18:55:19.280916: Current learning rate: 0.00126 +2026-04-14 18:57:01.153375: train_loss -0.4485 +2026-04-14 18:57:01.160018: val_loss -0.3863 +2026-04-14 18:57:01.162066: Pseudo dice [0.6082, 0.8219, 0.8309, 0.813, 0.5024, 0.8401, 0.7335] +2026-04-14 18:57:01.165322: Epoch time: 101.88 s +2026-04-14 18:57:01.167722: Yayy! New best EMA pseudo Dice: 0.6884 +2026-04-14 18:57:03.960121: +2026-04-14 18:57:03.962564: Epoch 3602 +2026-04-14 18:57:03.964228: Current learning rate: 0.00125 +2026-04-14 18:58:46.484302: train_loss -0.4408 +2026-04-14 18:58:46.495727: val_loss -0.3672 +2026-04-14 18:58:46.497591: Pseudo dice [0.5686, 0.7333, 0.7823, 0.3566, 0.6257, 0.6801, 0.8306] +2026-04-14 18:58:46.500156: Epoch time: 102.53 s +2026-04-14 18:58:47.751737: +2026-04-14 18:58:47.753639: Epoch 3603 +2026-04-14 18:58:47.755704: Current learning rate: 0.00125 +2026-04-14 19:00:29.223272: train_loss -0.4567 +2026-04-14 19:00:29.231901: val_loss -0.4195 +2026-04-14 19:00:29.234436: Pseudo dice [0.7303, 0.3491, 0.8158, 0.6542, 0.5923, 0.724, 0.8977] +2026-04-14 19:00:29.236950: Epoch time: 101.47 s +2026-04-14 19:00:30.477559: +2026-04-14 19:00:30.480164: Epoch 3604 +2026-04-14 19:00:30.482598: Current learning rate: 0.00125 +2026-04-14 19:02:12.882285: train_loss -0.4609 +2026-04-14 19:02:12.889210: val_loss -0.4166 +2026-04-14 19:02:12.892426: Pseudo dice [0.7844, 0.8122, 0.8646, 0.5446, 0.5543, 0.8958, 0.7868] +2026-04-14 19:02:12.896030: Epoch time: 102.41 s +2026-04-14 19:02:12.898703: Yayy! New best EMA pseudo Dice: 0.691 +2026-04-14 19:02:15.901704: +2026-04-14 19:02:15.903823: Epoch 3605 +2026-04-14 19:02:15.905444: Current learning rate: 0.00124 +2026-04-14 19:03:58.337944: train_loss -0.4539 +2026-04-14 19:03:58.345855: val_loss -0.4017 +2026-04-14 19:03:58.349316: Pseudo dice [0.6061, 0.9014, 0.8191, 0.6038, 0.6197, 0.8142, 0.8685] +2026-04-14 19:03:58.352085: Epoch time: 102.44 s +2026-04-14 19:03:58.357206: Yayy! New best EMA pseudo Dice: 0.6966 +2026-04-14 19:04:01.173430: +2026-04-14 19:04:01.176346: Epoch 3606 +2026-04-14 19:04:01.177922: Current learning rate: 0.00124 +2026-04-14 19:05:43.775525: train_loss -0.4557 +2026-04-14 19:05:43.783643: val_loss -0.373 +2026-04-14 19:05:43.785774: Pseudo dice [0.8111, 0.9027, 0.743, 0.8132, 0.5133, 0.7453, 0.8448] +2026-04-14 19:05:43.788119: Epoch time: 102.61 s +2026-04-14 19:05:43.790353: Yayy! New best EMA pseudo Dice: 0.7037 +2026-04-14 19:05:46.842150: +2026-04-14 19:05:46.845948: Epoch 3607 +2026-04-14 19:05:46.847698: Current learning rate: 0.00124 +2026-04-14 19:07:28.973942: train_loss -0.4541 +2026-04-14 19:07:28.981083: val_loss -0.3481 +2026-04-14 19:07:28.983212: Pseudo dice [0.5271, 0.8864, 0.8191, 0.4104, 0.3566, 0.6738, 0.8237] +2026-04-14 19:07:28.985435: Epoch time: 102.13 s +2026-04-14 19:07:30.249338: +2026-04-14 19:07:30.251346: Epoch 3608 +2026-04-14 19:07:30.253408: Current learning rate: 0.00124 +2026-04-14 19:09:11.928899: train_loss -0.447 +2026-04-14 19:09:11.934961: val_loss -0.3891 +2026-04-14 19:09:11.936985: Pseudo dice [0.6362, 0.7216, 0.7445, 0.314, 0.7137, 0.5524, 0.803] +2026-04-14 19:09:11.941508: Epoch time: 101.68 s +2026-04-14 19:09:14.262462: +2026-04-14 19:09:14.264150: Epoch 3609 +2026-04-14 19:09:14.267044: Current learning rate: 0.00123 +2026-04-14 19:10:56.245514: train_loss -0.4636 +2026-04-14 19:10:56.254725: val_loss -0.3797 +2026-04-14 19:10:56.257740: Pseudo dice [0.7781, 0.9008, 0.6904, 0.4863, 0.607, 0.5922, 0.8772] +2026-04-14 19:10:56.261378: Epoch time: 101.99 s +2026-04-14 19:10:57.507289: +2026-04-14 19:10:57.508992: Epoch 3610 +2026-04-14 19:10:57.511062: Current learning rate: 0.00123 +2026-04-14 19:12:39.595577: train_loss -0.4411 +2026-04-14 19:12:39.833318: val_loss -0.3175 +2026-04-14 19:12:39.835231: Pseudo dice [0.7507, 0.6194, 0.668, 0.1718, 0.6248, 0.758, 0.7203] +2026-04-14 19:12:39.839548: Epoch time: 102.09 s +2026-04-14 19:12:41.081798: +2026-04-14 19:12:41.083825: Epoch 3611 +2026-04-14 19:12:41.085806: Current learning rate: 0.00123 +2026-04-14 19:14:22.732724: train_loss -0.4648 +2026-04-14 19:14:22.739811: val_loss -0.4084 +2026-04-14 19:14:22.742671: Pseudo dice [0.6146, 0.6968, 0.8585, 0.5249, 0.7366, 0.8234, 0.7528] +2026-04-14 19:14:22.745298: Epoch time: 101.65 s +2026-04-14 19:14:23.999937: +2026-04-14 19:14:24.002554: Epoch 3612 +2026-04-14 19:14:24.004944: Current learning rate: 0.00122 +2026-04-14 19:16:05.499921: train_loss -0.4545 +2026-04-14 19:16:05.510669: val_loss -0.3384 +2026-04-14 19:16:05.513796: Pseudo dice [0.1463, 0.8689, 0.7689, 0.5457, 0.536, 0.8078, 0.8592] +2026-04-14 19:16:05.516732: Epoch time: 101.5 s +2026-04-14 19:16:06.738248: +2026-04-14 19:16:06.740336: Epoch 3613 +2026-04-14 19:16:06.750041: Current learning rate: 0.00122 +2026-04-14 19:17:48.337913: train_loss -0.4594 +2026-04-14 19:17:48.344398: val_loss -0.356 +2026-04-14 19:17:48.346702: Pseudo dice [0.7628, 0.6861, 0.7254, 0.3544, 0.4454, 0.266, 0.8911] +2026-04-14 19:17:48.349454: Epoch time: 101.6 s +2026-04-14 19:17:49.568053: +2026-04-14 19:17:49.569643: Epoch 3614 +2026-04-14 19:17:49.571561: Current learning rate: 0.00122 +2026-04-14 19:19:31.785998: train_loss -0.4573 +2026-04-14 19:19:31.792953: val_loss -0.4094 +2026-04-14 19:19:31.796189: Pseudo dice [0.7461, 0.5277, 0.7561, 0.3549, 0.5608, 0.8753, 0.7653] +2026-04-14 19:19:31.800039: Epoch time: 102.22 s +2026-04-14 19:19:33.057556: +2026-04-14 19:19:33.060301: Epoch 3615 +2026-04-14 19:19:33.063018: Current learning rate: 0.00122 +2026-04-14 19:21:14.604372: train_loss -0.4578 +2026-04-14 19:21:14.624177: val_loss -0.4124 +2026-04-14 19:21:14.628493: Pseudo dice [0.7885, 0.8012, 0.8125, 0.6626, 0.5814, 0.8781, 0.8181] +2026-04-14 19:21:14.630845: Epoch time: 101.55 s +2026-04-14 19:21:15.870651: +2026-04-14 19:21:15.873854: Epoch 3616 +2026-04-14 19:21:15.875718: Current learning rate: 0.00121 +2026-04-14 19:22:57.654327: train_loss -0.4524 +2026-04-14 19:22:57.661161: val_loss -0.3882 +2026-04-14 19:22:57.664067: Pseudo dice [0.8578, 0.6556, 0.6438, 0.5449, 0.6533, 0.9398, 0.772] +2026-04-14 19:22:57.666693: Epoch time: 101.79 s +2026-04-14 19:22:58.889646: +2026-04-14 19:22:58.891763: Epoch 3617 +2026-04-14 19:22:58.894182: Current learning rate: 0.00121 +2026-04-14 19:24:40.692134: train_loss -0.4551 +2026-04-14 19:24:40.698378: val_loss -0.4223 +2026-04-14 19:24:40.700472: Pseudo dice [0.6359, 0.5709, 0.8393, 0.5832, 0.7868, 0.8937, 0.6457] +2026-04-14 19:24:40.703328: Epoch time: 101.81 s +2026-04-14 19:24:41.946322: +2026-04-14 19:24:41.949026: Epoch 3618 +2026-04-14 19:24:41.950979: Current learning rate: 0.00121 +2026-04-14 19:26:23.757135: train_loss -0.4604 +2026-04-14 19:26:23.764603: val_loss -0.3868 +2026-04-14 19:26:23.766944: Pseudo dice [0.8087, 0.8358, 0.7756, 0.4846, 0.5852, 0.7166, 0.8474] +2026-04-14 19:26:23.769600: Epoch time: 101.81 s +2026-04-14 19:26:25.007874: +2026-04-14 19:26:25.009906: Epoch 3619 +2026-04-14 19:26:25.011998: Current learning rate: 0.0012 +2026-04-14 19:28:06.730542: train_loss -0.461 +2026-04-14 19:28:06.737834: val_loss -0.3772 +2026-04-14 19:28:06.740455: Pseudo dice [0.8157, 0.6415, 0.8079, 0.3461, 0.6464, 0.7688, 0.8411] +2026-04-14 19:28:06.743096: Epoch time: 101.73 s +2026-04-14 19:28:07.993185: +2026-04-14 19:28:07.994878: Epoch 3620 +2026-04-14 19:28:07.996665: Current learning rate: 0.0012 +2026-04-14 19:29:50.427282: train_loss -0.4454 +2026-04-14 19:29:50.447003: val_loss -0.3863 +2026-04-14 19:29:50.449501: Pseudo dice [0.7896, 0.9101, 0.7969, 0.7638, 0.572, 0.7702, 0.6059] +2026-04-14 19:29:50.452111: Epoch time: 102.44 s +2026-04-14 19:29:51.710647: +2026-04-14 19:29:51.712578: Epoch 3621 +2026-04-14 19:29:51.714646: Current learning rate: 0.0012 +2026-04-14 19:31:33.701214: train_loss -0.4549 +2026-04-14 19:31:33.709598: val_loss -0.3933 +2026-04-14 19:31:33.716227: Pseudo dice [0.6119, 0.6966, 0.6252, 0.5429, 0.6599, 0.8646, 0.7457] +2026-04-14 19:31:33.721612: Epoch time: 101.99 s +2026-04-14 19:31:34.981584: +2026-04-14 19:31:34.985376: Epoch 3622 +2026-04-14 19:31:34.990496: Current learning rate: 0.0012 +2026-04-14 19:33:16.463391: train_loss -0.4382 +2026-04-14 19:33:16.477793: val_loss -0.4114 +2026-04-14 19:33:16.479963: Pseudo dice [0.6712, 0.8254, 0.7417, 0.6008, 0.746, 0.6637, 0.4892] +2026-04-14 19:33:16.489381: Epoch time: 101.48 s +2026-04-14 19:33:17.733813: +2026-04-14 19:33:17.735939: Epoch 3623 +2026-04-14 19:33:17.738384: Current learning rate: 0.00119 +2026-04-14 19:34:59.305904: train_loss -0.4482 +2026-04-14 19:34:59.313255: val_loss -0.4102 +2026-04-14 19:34:59.315396: Pseudo dice [0.8704, 0.8423, 0.511, 0.8342, 0.6821, 0.641, 0.7764] +2026-04-14 19:34:59.317687: Epoch time: 101.58 s +2026-04-14 19:35:00.559984: +2026-04-14 19:35:00.561563: Epoch 3624 +2026-04-14 19:35:00.563315: Current learning rate: 0.00119 +2026-04-14 19:36:42.654087: train_loss -0.4528 +2026-04-14 19:36:42.660973: val_loss -0.3973 +2026-04-14 19:36:42.663565: Pseudo dice [0.7696, 0.9036, 0.8048, 0.2161, 0.627, 0.82, 0.8284] +2026-04-14 19:36:42.665828: Epoch time: 102.1 s +2026-04-14 19:36:43.923062: +2026-04-14 19:36:43.924972: Epoch 3625 +2026-04-14 19:36:43.927180: Current learning rate: 0.00119 +2026-04-14 19:38:26.429923: train_loss -0.4582 +2026-04-14 19:38:26.437835: val_loss -0.4031 +2026-04-14 19:38:26.440382: Pseudo dice [0.8645, 0.5557, 0.796, 0.4312, 0.4947, 0.8112, 0.8091] +2026-04-14 19:38:26.442975: Epoch time: 102.51 s +2026-04-14 19:38:27.681067: +2026-04-14 19:38:27.686442: Epoch 3626 +2026-04-14 19:38:27.689415: Current learning rate: 0.00119 +2026-04-14 19:40:09.469191: train_loss -0.4519 +2026-04-14 19:40:09.477264: val_loss -0.4 +2026-04-14 19:40:09.480502: Pseudo dice [0.5671, 0.749, 0.8286, 0.5279, 0.6017, 0.9232, 0.6602] +2026-04-14 19:40:09.484580: Epoch time: 101.79 s +2026-04-14 19:40:10.736338: +2026-04-14 19:40:10.738892: Epoch 3627 +2026-04-14 19:40:10.741226: Current learning rate: 0.00118 +2026-04-14 19:41:52.200236: train_loss -0.4556 +2026-04-14 19:41:52.209926: val_loss -0.4017 +2026-04-14 19:41:52.212248: Pseudo dice [0.7434, 0.7004, 0.6853, 0.3981, 0.6616, 0.7838, 0.7141] +2026-04-14 19:41:52.215238: Epoch time: 101.47 s +2026-04-14 19:41:53.524511: +2026-04-14 19:41:53.526597: Epoch 3628 +2026-04-14 19:41:53.528533: Current learning rate: 0.00118 +2026-04-14 19:43:34.763821: train_loss -0.4587 +2026-04-14 19:43:34.772195: val_loss -0.4054 +2026-04-14 19:43:34.774232: Pseudo dice [0.676, 0.1689, 0.8166, 0.6099, 0.5029, 0.7284, 0.8675] +2026-04-14 19:43:34.776529: Epoch time: 101.24 s +2026-04-14 19:43:37.082258: +2026-04-14 19:43:37.084217: Epoch 3629 +2026-04-14 19:43:37.086050: Current learning rate: 0.00118 +2026-04-14 19:45:18.522197: train_loss -0.4569 +2026-04-14 19:45:18.534736: val_loss -0.4148 +2026-04-14 19:45:18.537876: Pseudo dice [0.8595, 0.6353, 0.7899, 0.4429, 0.4825, 0.717, 0.8313] +2026-04-14 19:45:18.540864: Epoch time: 101.44 s +2026-04-14 19:45:19.775715: +2026-04-14 19:45:19.777560: Epoch 3630 +2026-04-14 19:45:19.780787: Current learning rate: 0.00117 +2026-04-14 19:47:00.982176: train_loss -0.4628 +2026-04-14 19:47:00.990754: val_loss -0.4076 +2026-04-14 19:47:00.993403: Pseudo dice [0.6051, 0.5369, 0.7278, 0.8092, 0.6529, 0.7816, 0.8527] +2026-04-14 19:47:00.996536: Epoch time: 101.21 s +2026-04-14 19:47:02.239461: +2026-04-14 19:47:02.243697: Epoch 3631 +2026-04-14 19:47:02.257124: Current learning rate: 0.00117 +2026-04-14 19:48:43.448350: train_loss -0.4652 +2026-04-14 19:48:43.456533: val_loss -0.4287 +2026-04-14 19:48:43.459163: Pseudo dice [0.769, 0.7767, 0.8329, 0.4435, 0.6427, 0.9027, 0.7731] +2026-04-14 19:48:43.464707: Epoch time: 101.21 s +2026-04-14 19:48:44.730734: +2026-04-14 19:48:44.733874: Epoch 3632 +2026-04-14 19:48:44.736159: Current learning rate: 0.00117 +2026-04-14 19:50:26.019941: train_loss -0.4614 +2026-04-14 19:50:26.027346: val_loss -0.4014 +2026-04-14 19:50:26.029536: Pseudo dice [0.7737, 0.7916, 0.6897, 0.905, 0.6532, 0.1815, 0.911] +2026-04-14 19:50:26.033328: Epoch time: 101.29 s +2026-04-14 19:50:27.342456: +2026-04-14 19:50:27.344128: Epoch 3633 +2026-04-14 19:50:27.346418: Current learning rate: 0.00117 +2026-04-14 19:52:08.708424: train_loss -0.463 +2026-04-14 19:52:08.715795: val_loss -0.3977 +2026-04-14 19:52:08.718210: Pseudo dice [0.6348, 0.8026, 0.8163, 0.3829, 0.7561, 0.7815, 0.8238] +2026-04-14 19:52:08.719978: Epoch time: 101.37 s +2026-04-14 19:52:09.933841: +2026-04-14 19:52:09.935901: Epoch 3634 +2026-04-14 19:52:09.938172: Current learning rate: 0.00116 +2026-04-14 19:53:51.461923: train_loss -0.4547 +2026-04-14 19:53:51.469757: val_loss -0.3862 +2026-04-14 19:53:51.471666: Pseudo dice [0.8761, 0.9007, 0.7541, 0.4434, 0.5411, 0.8156, 0.8468] +2026-04-14 19:53:51.473920: Epoch time: 101.53 s +2026-04-14 19:53:52.732325: +2026-04-14 19:53:52.734744: Epoch 3635 +2026-04-14 19:53:52.737811: Current learning rate: 0.00116 +2026-04-14 19:55:34.052963: train_loss -0.4623 +2026-04-14 19:55:34.061070: val_loss -0.386 +2026-04-14 19:55:34.065123: Pseudo dice [0.8764, 0.4394, 0.8074, 0.286, 0.6815, 0.9058, 0.8359] +2026-04-14 19:55:34.068007: Epoch time: 101.32 s +2026-04-14 19:55:35.298810: +2026-04-14 19:55:35.300889: Epoch 3636 +2026-04-14 19:55:35.303414: Current learning rate: 0.00116 +2026-04-14 19:57:17.310859: train_loss -0.4602 +2026-04-14 19:57:17.317943: val_loss -0.3736 +2026-04-14 19:57:17.321180: Pseudo dice [0.5935, 0.7805, 0.6794, 0.4138, 0.5748, 0.5228, 0.8649] +2026-04-14 19:57:17.326654: Epoch time: 102.02 s +2026-04-14 19:57:18.602626: +2026-04-14 19:57:18.606793: Epoch 3637 +2026-04-14 19:57:18.611704: Current learning rate: 0.00115 +2026-04-14 19:59:00.780602: train_loss -0.4616 +2026-04-14 19:59:00.786926: val_loss -0.3934 +2026-04-14 19:59:00.790019: Pseudo dice [0.5601, 0.726, 0.8337, 0.2584, 0.5942, 0.8516, 0.8623] +2026-04-14 19:59:00.792837: Epoch time: 102.18 s +2026-04-14 19:59:02.041273: +2026-04-14 19:59:02.043054: Epoch 3638 +2026-04-14 19:59:02.045046: Current learning rate: 0.00115 +2026-04-14 20:00:44.534253: train_loss -0.4644 +2026-04-14 20:00:44.542513: val_loss -0.3889 +2026-04-14 20:00:44.545496: Pseudo dice [0.8026, 0.6283, 0.8049, 0.3836, 0.6918, 0.8865, 0.8674] +2026-04-14 20:00:44.551191: Epoch time: 102.5 s +2026-04-14 20:00:45.816054: +2026-04-14 20:00:45.818729: Epoch 3639 +2026-04-14 20:00:45.821338: Current learning rate: 0.00115 +2026-04-14 20:02:27.455474: train_loss -0.4594 +2026-04-14 20:02:27.463183: val_loss -0.3807 +2026-04-14 20:02:27.465255: Pseudo dice [0.8546, 0.8539, 0.8539, 0.4245, 0.6636, 0.8347, 0.8498] +2026-04-14 20:02:27.467566: Epoch time: 101.64 s +2026-04-14 20:02:28.725035: +2026-04-14 20:02:28.726965: Epoch 3640 +2026-04-14 20:02:28.730649: Current learning rate: 0.00115 +2026-04-14 20:04:10.759207: train_loss -0.47 +2026-04-14 20:04:10.766338: val_loss -0.3621 +2026-04-14 20:04:10.768557: Pseudo dice [0.8156, 0.597, 0.8277, 0.1607, 0.558, 0.8153, 0.7478] +2026-04-14 20:04:10.771099: Epoch time: 102.04 s +2026-04-14 20:04:12.020511: +2026-04-14 20:04:12.026515: Epoch 3641 +2026-04-14 20:04:12.032125: Current learning rate: 0.00114 +2026-04-14 20:05:53.938126: train_loss -0.4485 +2026-04-14 20:05:53.944469: val_loss -0.3487 +2026-04-14 20:05:53.947250: Pseudo dice [0.4568, 0.9144, 0.8326, 0.4999, 0.4345, 0.4455, 0.7458] +2026-04-14 20:05:53.952331: Epoch time: 101.92 s +2026-04-14 20:05:55.219952: +2026-04-14 20:05:55.222357: Epoch 3642 +2026-04-14 20:05:55.224874: Current learning rate: 0.00114 +2026-04-14 20:07:37.398908: train_loss -0.457 +2026-04-14 20:07:37.406216: val_loss -0.3536 +2026-04-14 20:07:37.408305: Pseudo dice [0.5051, 0.905, 0.6674, 0.4325, 0.5562, 0.6347, 0.8303] +2026-04-14 20:07:37.410850: Epoch time: 102.18 s +2026-04-14 20:07:38.674245: +2026-04-14 20:07:38.675989: Epoch 3643 +2026-04-14 20:07:38.678033: Current learning rate: 0.00114 +2026-04-14 20:09:20.443259: train_loss -0.4558 +2026-04-14 20:09:20.450789: val_loss -0.3395 +2026-04-14 20:09:20.452733: Pseudo dice [0.2386, 0.8845, 0.7757, 0.4816, 0.5016, 0.4315, 0.8274] +2026-04-14 20:09:20.455747: Epoch time: 101.77 s +2026-04-14 20:09:21.922950: +2026-04-14 20:09:21.924628: Epoch 3644 +2026-04-14 20:09:21.926586: Current learning rate: 0.00113 +2026-04-14 20:11:02.834394: train_loss -0.4579 +2026-04-14 20:11:02.841975: val_loss -0.3716 +2026-04-14 20:11:02.844454: Pseudo dice [0.633, 0.8662, 0.784, 0.5724, 0.6926, 0.6546, 0.7225] +2026-04-14 20:11:02.848449: Epoch time: 100.91 s +2026-04-14 20:11:04.097223: +2026-04-14 20:11:04.099765: Epoch 3645 +2026-04-14 20:11:04.103520: Current learning rate: 0.00113 +2026-04-14 20:12:46.039708: train_loss -0.4582 +2026-04-14 20:12:46.049316: val_loss -0.3879 +2026-04-14 20:12:46.052379: Pseudo dice [0.5006, 0.8797, 0.8147, 0.6198, 0.3627, 0.4872, 0.8182] +2026-04-14 20:12:46.054916: Epoch time: 101.95 s +2026-04-14 20:12:47.275146: +2026-04-14 20:12:47.276938: Epoch 3646 +2026-04-14 20:12:47.279374: Current learning rate: 0.00113 +2026-04-14 20:14:28.784460: train_loss -0.4566 +2026-04-14 20:14:28.792184: val_loss -0.3748 +2026-04-14 20:14:28.794219: Pseudo dice [0.7813, 0.7172, 0.7429, 0.1712, 0.5701, 0.899, 0.7659] +2026-04-14 20:14:28.797318: Epoch time: 101.51 s +2026-04-14 20:14:30.039483: +2026-04-14 20:14:30.041366: Epoch 3647 +2026-04-14 20:14:30.043190: Current learning rate: 0.00112 +2026-04-14 20:16:12.426134: train_loss -0.4606 +2026-04-14 20:16:12.433114: val_loss -0.3889 +2026-04-14 20:16:12.435567: Pseudo dice [0.7385, 0.8763, 0.7506, 0.5707, 0.6025, 0.5991, 0.8498] +2026-04-14 20:16:12.437999: Epoch time: 102.39 s +2026-04-14 20:16:13.667490: +2026-04-14 20:16:13.669302: Epoch 3648 +2026-04-14 20:16:13.672839: Current learning rate: 0.00112 +2026-04-14 20:17:55.419016: train_loss -0.4514 +2026-04-14 20:17:55.425132: val_loss -0.3777 +2026-04-14 20:17:55.426936: Pseudo dice [0.869, 0.902, 0.8285, 0.7381, 0.444, 0.7628, 0.8613] +2026-04-14 20:17:55.429402: Epoch time: 101.75 s +2026-04-14 20:17:57.755110: +2026-04-14 20:17:57.756785: Epoch 3649 +2026-04-14 20:17:57.758706: Current learning rate: 0.00112 +2026-04-14 20:19:39.426301: train_loss -0.4542 +2026-04-14 20:19:39.434491: val_loss -0.3427 +2026-04-14 20:19:39.437388: Pseudo dice [0.561, 0.7359, 0.7624, 0.4551, 0.383, 0.5508, 0.8131] +2026-04-14 20:19:39.440743: Epoch time: 101.67 s +2026-04-14 20:19:42.338337: +2026-04-14 20:19:42.353964: Epoch 3650 +2026-04-14 20:19:42.355671: Current learning rate: 0.00112 +2026-04-14 20:21:23.964030: train_loss -0.4417 +2026-04-14 20:21:23.978843: val_loss -0.3355 +2026-04-14 20:21:23.999459: Pseudo dice [0.8148, 0.9159, 0.788, 0.4736, 0.5608, 0.6669, 0.8028] +2026-04-14 20:21:24.002857: Epoch time: 101.63 s +2026-04-14 20:21:25.240617: +2026-04-14 20:21:25.242260: Epoch 3651 +2026-04-14 20:21:25.244187: Current learning rate: 0.00111 +2026-04-14 20:23:06.811697: train_loss -0.4624 +2026-04-14 20:23:06.822325: val_loss -0.3732 +2026-04-14 20:23:06.824311: Pseudo dice [0.7782, 0.5237, 0.8466, 0.336, 0.6283, 0.8646, 0.6395] +2026-04-14 20:23:06.826590: Epoch time: 101.57 s +2026-04-14 20:23:08.070467: +2026-04-14 20:23:08.072379: Epoch 3652 +2026-04-14 20:23:08.074592: Current learning rate: 0.00111 +2026-04-14 20:24:50.348145: train_loss -0.4609 +2026-04-14 20:24:50.355076: val_loss -0.394 +2026-04-14 20:24:50.360908: Pseudo dice [0.7152, 0.8845, 0.823, 0.5931, 0.0791, 0.8968, 0.8177] +2026-04-14 20:24:50.363473: Epoch time: 102.28 s +2026-04-14 20:24:51.602159: +2026-04-14 20:24:51.604349: Epoch 3653 +2026-04-14 20:24:51.606488: Current learning rate: 0.00111 +2026-04-14 20:26:33.059471: train_loss -0.4648 +2026-04-14 20:26:33.067424: val_loss -0.4093 +2026-04-14 20:26:33.069530: Pseudo dice [0.5454, 0.7117, 0.8935, 0.8583, 0.5482, 0.9122, 0.7717] +2026-04-14 20:26:33.072321: Epoch time: 101.46 s +2026-04-14 20:26:34.321972: +2026-04-14 20:26:34.323825: Epoch 3654 +2026-04-14 20:26:34.326446: Current learning rate: 0.0011 +2026-04-14 20:28:15.947858: train_loss -0.4572 +2026-04-14 20:28:15.954860: val_loss -0.3871 +2026-04-14 20:28:15.957004: Pseudo dice [0.7482, 0.6885, 0.6653, 0.6717, 0.5287, 0.6723, 0.7674] +2026-04-14 20:28:15.959627: Epoch time: 101.63 s +2026-04-14 20:28:17.216080: +2026-04-14 20:28:17.218301: Epoch 3655 +2026-04-14 20:28:17.220950: Current learning rate: 0.0011 +2026-04-14 20:29:58.786275: train_loss -0.4432 +2026-04-14 20:29:58.793303: val_loss -0.3914 +2026-04-14 20:29:58.795579: Pseudo dice [0.6468, 0.7676, 0.8128, 0.5511, 0.4657, 0.8517, 0.8122] +2026-04-14 20:29:58.798110: Epoch time: 101.57 s +2026-04-14 20:30:00.054314: +2026-04-14 20:30:00.058118: Epoch 3656 +2026-04-14 20:30:00.062376: Current learning rate: 0.0011 +2026-04-14 20:31:41.568897: train_loss -0.4514 +2026-04-14 20:31:41.581179: val_loss -0.3866 +2026-04-14 20:31:41.586077: Pseudo dice [0.821, 0.9055, 0.7238, 0.4884, 0.6117, 0.6535, 0.9039] +2026-04-14 20:31:41.590712: Epoch time: 101.52 s +2026-04-14 20:31:42.864368: +2026-04-14 20:31:42.866693: Epoch 3657 +2026-04-14 20:31:42.868694: Current learning rate: 0.0011 +2026-04-14 20:33:24.832470: train_loss -0.459 +2026-04-14 20:33:24.838764: val_loss -0.3784 +2026-04-14 20:33:24.840979: Pseudo dice [0.7666, 0.8906, 0.7766, 0.3276, 0.5415, 0.6294, 0.8618] +2026-04-14 20:33:24.843743: Epoch time: 101.97 s +2026-04-14 20:33:26.068944: +2026-04-14 20:33:26.070747: Epoch 3658 +2026-04-14 20:33:26.072534: Current learning rate: 0.00109 +2026-04-14 20:35:07.092229: train_loss -0.4594 +2026-04-14 20:35:07.098683: val_loss -0.3639 +2026-04-14 20:35:07.100971: Pseudo dice [0.6409, 0.7615, 0.8201, 0.2376, 0.6303, 0.8646, 0.7346] +2026-04-14 20:35:07.103979: Epoch time: 101.03 s +2026-04-14 20:35:08.328321: +2026-04-14 20:35:08.331908: Epoch 3659 +2026-04-14 20:35:08.333863: Current learning rate: 0.00109 +2026-04-14 20:36:49.896675: train_loss -0.4557 +2026-04-14 20:36:49.903754: val_loss -0.3718 +2026-04-14 20:36:49.906032: Pseudo dice [0.7956, 0.6739, 0.852, 0.0876, 0.5631, 0.8004, 0.7598] +2026-04-14 20:36:49.908659: Epoch time: 101.57 s +2026-04-14 20:36:51.166226: +2026-04-14 20:36:51.167955: Epoch 3660 +2026-04-14 20:36:51.169844: Current learning rate: 0.00109 +2026-04-14 20:38:33.257210: train_loss -0.4569 +2026-04-14 20:38:33.263083: val_loss -0.3861 +2026-04-14 20:38:33.265153: Pseudo dice [0.4896, 0.7959, 0.8242, 0.4726, 0.6141, 0.8115, 0.7925] +2026-04-14 20:38:33.268229: Epoch time: 102.09 s +2026-04-14 20:38:34.530238: +2026-04-14 20:38:34.534168: Epoch 3661 +2026-04-14 20:38:34.536883: Current learning rate: 0.00108 +2026-04-14 20:40:16.807659: train_loss -0.4612 +2026-04-14 20:40:16.816742: val_loss -0.3717 +2026-04-14 20:40:16.819707: Pseudo dice [0.4506, 0.7357, 0.7125, 0.7752, 0.5873, 0.9125, 0.7914] +2026-04-14 20:40:16.822556: Epoch time: 102.28 s +2026-04-14 20:40:18.094797: +2026-04-14 20:40:18.097340: Epoch 3662 +2026-04-14 20:40:18.100256: Current learning rate: 0.00108 +2026-04-14 20:41:59.097716: train_loss -0.459 +2026-04-14 20:41:59.103697: val_loss -0.3899 +2026-04-14 20:41:59.107209: Pseudo dice [0.7199, 0.4558, 0.8188, 0.7202, 0.5935, 0.3811, 0.7776] +2026-04-14 20:41:59.110279: Epoch time: 101.01 s +2026-04-14 20:42:00.326174: +2026-04-14 20:42:00.327801: Epoch 3663 +2026-04-14 20:42:00.329620: Current learning rate: 0.00108 +2026-04-14 20:43:41.975385: train_loss -0.4511 +2026-04-14 20:43:41.982720: val_loss -0.4286 +2026-04-14 20:43:41.984846: Pseudo dice [0.6753, 0.6216, 0.8437, 0.8726, 0.7287, 0.6464, 0.8303] +2026-04-14 20:43:41.987143: Epoch time: 101.65 s +2026-04-14 20:43:43.244752: +2026-04-14 20:43:43.247040: Epoch 3664 +2026-04-14 20:43:43.249146: Current learning rate: 0.00108 +2026-04-14 20:45:24.363913: train_loss -0.4653 +2026-04-14 20:45:24.371987: val_loss -0.3556 +2026-04-14 20:45:24.374574: Pseudo dice [0.7861, 0.8996, 0.6546, 0.2975, 0.5359, 0.6171, 0.8293] +2026-04-14 20:45:24.378541: Epoch time: 101.12 s +2026-04-14 20:45:25.613890: +2026-04-14 20:45:25.617292: Epoch 3665 +2026-04-14 20:45:25.620157: Current learning rate: 0.00107 +2026-04-14 20:47:07.558618: train_loss -0.4646 +2026-04-14 20:47:07.566082: val_loss -0.4186 +2026-04-14 20:47:07.568707: Pseudo dice [0.401, 0.8251, 0.8382, 0.5972, 0.5863, 0.7081, 0.806] +2026-04-14 20:47:07.572048: Epoch time: 101.95 s +2026-04-14 20:47:08.824072: +2026-04-14 20:47:08.826107: Epoch 3666 +2026-04-14 20:47:08.828490: Current learning rate: 0.00107 +2026-04-14 20:48:50.656987: train_loss -0.4577 +2026-04-14 20:48:50.664426: val_loss -0.3272 +2026-04-14 20:48:50.666615: Pseudo dice [0.7075, 0.9166, 0.7422, 0.2867, 0.2958, 0.6392, 0.7681] +2026-04-14 20:48:50.669171: Epoch time: 101.84 s +2026-04-14 20:48:51.915842: +2026-04-14 20:48:51.918446: Epoch 3667 +2026-04-14 20:48:51.920604: Current learning rate: 0.00107 +2026-04-14 20:50:34.073836: train_loss -0.463 +2026-04-14 20:50:34.080489: val_loss -0.3522 +2026-04-14 20:50:34.083513: Pseudo dice [0.7339, 0.7447, 0.6819, 0.6589, 0.3836, 0.8595, 0.7576] +2026-04-14 20:50:34.086104: Epoch time: 102.16 s +2026-04-14 20:50:35.349362: +2026-04-14 20:50:35.351677: Epoch 3668 +2026-04-14 20:50:35.354214: Current learning rate: 0.00106 +2026-04-14 20:52:17.677882: train_loss -0.4595 +2026-04-14 20:52:17.685165: val_loss -0.3597 +2026-04-14 20:52:17.687125: Pseudo dice [0.6627, 0.907, 0.7197, 0.4289, 0.5653, 0.6399, 0.8006] +2026-04-14 20:52:17.689847: Epoch time: 102.33 s +2026-04-14 20:52:20.060958: +2026-04-14 20:52:20.062549: Epoch 3669 +2026-04-14 20:52:20.064425: Current learning rate: 0.00106 +2026-04-14 20:54:01.619862: train_loss -0.4358 +2026-04-14 20:54:01.626056: val_loss -0.3638 +2026-04-14 20:54:01.628129: Pseudo dice [0.684, 0.8695, 0.819, 0.4339, 0.5285, 0.7346, 0.6261] +2026-04-14 20:54:01.630663: Epoch time: 101.56 s +2026-04-14 20:54:02.857847: +2026-04-14 20:54:02.859673: Epoch 3670 +2026-04-14 20:54:02.862041: Current learning rate: 0.00106 +2026-04-14 20:55:44.503659: train_loss -0.4567 +2026-04-14 20:55:44.512287: val_loss -0.3854 +2026-04-14 20:55:44.514943: Pseudo dice [0.7265, 0.8999, 0.8191, 0.3747, 0.6913, 0.8117, 0.6377] +2026-04-14 20:55:44.517603: Epoch time: 101.65 s +2026-04-14 20:55:45.790216: +2026-04-14 20:55:45.792087: Epoch 3671 +2026-04-14 20:55:45.793997: Current learning rate: 0.00106 +2026-04-14 20:57:27.200593: train_loss -0.4613 +2026-04-14 20:57:27.210205: val_loss -0.3699 +2026-04-14 20:57:27.212817: Pseudo dice [0.7882, 0.9015, 0.7988, 0.1889, 0.5691, 0.5094, 0.5085] +2026-04-14 20:57:27.215512: Epoch time: 101.41 s +2026-04-14 20:57:28.461010: +2026-04-14 20:57:28.462999: Epoch 3672 +2026-04-14 20:57:28.465379: Current learning rate: 0.00105 +2026-04-14 20:59:10.374598: train_loss -0.4558 +2026-04-14 20:59:10.381084: val_loss -0.4129 +2026-04-14 20:59:10.386371: Pseudo dice [0.6622, 0.8115, 0.8526, 0.8826, 0.4932, 0.915, 0.8083] +2026-04-14 20:59:10.398237: Epoch time: 101.92 s +2026-04-14 20:59:11.672648: +2026-04-14 20:59:11.674724: Epoch 3673 +2026-04-14 20:59:11.677062: Current learning rate: 0.00105 +2026-04-14 21:00:53.542150: train_loss -0.457 +2026-04-14 21:00:53.571857: val_loss -0.4039 +2026-04-14 21:00:53.574892: Pseudo dice [0.7749, 0.9011, 0.8482, 0.8387, 0.458, 0.7321, 0.8795] +2026-04-14 21:00:53.580337: Epoch time: 101.87 s +2026-04-14 21:00:54.848229: +2026-04-14 21:00:54.852133: Epoch 3674 +2026-04-14 21:00:54.854047: Current learning rate: 0.00105 +2026-04-14 21:02:36.879351: train_loss -0.4497 +2026-04-14 21:02:36.884827: val_loss -0.3679 +2026-04-14 21:02:36.886830: Pseudo dice [0.6973, 0.4608, 0.6533, 0.4955, 0.6483, 0.5282, 0.7887] +2026-04-14 21:02:36.889318: Epoch time: 102.03 s +2026-04-14 21:02:38.130891: +2026-04-14 21:02:38.132983: Epoch 3675 +2026-04-14 21:02:38.134867: Current learning rate: 0.00104 +2026-04-14 21:04:19.781303: train_loss -0.4703 +2026-04-14 21:04:19.787953: val_loss -0.4098 +2026-04-14 21:04:19.790055: Pseudo dice [0.5983, 0.5678, 0.8895, 0.7007, 0.6438, 0.7096, 0.6208] +2026-04-14 21:04:19.792951: Epoch time: 101.65 s +2026-04-14 21:04:21.061716: +2026-04-14 21:04:21.064261: Epoch 3676 +2026-04-14 21:04:21.066442: Current learning rate: 0.00104 +2026-04-14 21:06:02.929641: train_loss -0.4649 +2026-04-14 21:06:02.938287: val_loss -0.4081 +2026-04-14 21:06:02.940644: Pseudo dice [0.7492, 0.6919, 0.8679, 0.5456, 0.4667, 0.9088, 0.8188] +2026-04-14 21:06:02.944246: Epoch time: 101.87 s +2026-04-14 21:06:04.177379: +2026-04-14 21:06:04.179745: Epoch 3677 +2026-04-14 21:06:04.182048: Current learning rate: 0.00104 +2026-04-14 21:07:46.614690: train_loss -0.4682 +2026-04-14 21:07:46.622091: val_loss -0.4035 +2026-04-14 21:07:46.624471: Pseudo dice [0.5149, 0.757, 0.8405, 0.2381, 0.5739, 0.7268, 0.6935] +2026-04-14 21:07:46.626727: Epoch time: 102.44 s +2026-04-14 21:07:47.882084: +2026-04-14 21:07:47.884969: Epoch 3678 +2026-04-14 21:07:47.887986: Current learning rate: 0.00104 +2026-04-14 21:09:29.871393: train_loss -0.4535 +2026-04-14 21:09:29.879926: val_loss -0.395 +2026-04-14 21:09:29.881979: Pseudo dice [0.7174, 0.8448, 0.8171, 0.437, 0.6354, 0.5736, 0.8407] +2026-04-14 21:09:29.884670: Epoch time: 101.99 s +2026-04-14 21:09:31.120477: +2026-04-14 21:09:31.122327: Epoch 3679 +2026-04-14 21:09:31.124162: Current learning rate: 0.00103 +2026-04-14 21:11:13.317792: train_loss -0.4665 +2026-04-14 21:11:13.328251: val_loss -0.3622 +2026-04-14 21:11:13.331237: Pseudo dice [0.6838, 0.9021, 0.8256, 0.2262, 0.48, 0.5454, 0.5341] +2026-04-14 21:11:13.336025: Epoch time: 102.2 s +2026-04-14 21:11:14.588623: +2026-04-14 21:11:14.590653: Epoch 3680 +2026-04-14 21:11:14.593303: Current learning rate: 0.00103 +2026-04-14 21:12:57.502960: train_loss -0.4675 +2026-04-14 21:12:57.511835: val_loss -0.3736 +2026-04-14 21:12:57.514229: Pseudo dice [0.7507, 0.9158, 0.8049, 0.5388, 0.6726, 0.4394, 0.4082] +2026-04-14 21:12:57.516858: Epoch time: 102.92 s +2026-04-14 21:12:58.777236: +2026-04-14 21:12:58.781938: Epoch 3681 +2026-04-14 21:12:58.785389: Current learning rate: 0.00103 +2026-04-14 21:14:40.928629: train_loss -0.4617 +2026-04-14 21:14:40.935034: val_loss -0.3302 +2026-04-14 21:14:40.937793: Pseudo dice [0.622, 0.9157, 0.6879, 0.3817, 0.5968, 0.0197, 0.8607] +2026-04-14 21:14:40.941196: Epoch time: 102.15 s +2026-04-14 21:14:42.187742: +2026-04-14 21:14:42.190447: Epoch 3682 +2026-04-14 21:14:42.192776: Current learning rate: 0.00102 +2026-04-14 21:16:24.037912: train_loss -0.4592 +2026-04-14 21:16:24.046957: val_loss -0.3949 +2026-04-14 21:16:24.049394: Pseudo dice [0.2587, 0.8933, 0.8244, 0.4803, 0.6089, 0.7183, 0.6937] +2026-04-14 21:16:24.052973: Epoch time: 101.85 s +2026-04-14 21:16:25.313466: +2026-04-14 21:16:25.316172: Epoch 3683 +2026-04-14 21:16:25.320124: Current learning rate: 0.00102 +2026-04-14 21:18:07.267803: train_loss -0.466 +2026-04-14 21:18:07.274758: val_loss -0.3953 +2026-04-14 21:18:07.276997: Pseudo dice [0.703, 0.5545, 0.7969, 0.3549, 0.2997, 0.8384, 0.8095] +2026-04-14 21:18:07.279443: Epoch time: 101.96 s +2026-04-14 21:18:08.511234: +2026-04-14 21:18:08.513011: Epoch 3684 +2026-04-14 21:18:08.514856: Current learning rate: 0.00102 +2026-04-14 21:19:50.242856: train_loss -0.4684 +2026-04-14 21:19:50.250964: val_loss -0.3962 +2026-04-14 21:19:50.253622: Pseudo dice [0.8424, 0.6341, 0.8118, 0.742, 0.5375, 0.5156, 0.8117] +2026-04-14 21:19:50.256903: Epoch time: 101.73 s +2026-04-14 21:19:51.495272: +2026-04-14 21:19:51.497394: Epoch 3685 +2026-04-14 21:19:51.502616: Current learning rate: 0.00102 +2026-04-14 21:21:33.800107: train_loss -0.4649 +2026-04-14 21:21:33.806609: val_loss -0.3932 +2026-04-14 21:21:33.808479: Pseudo dice [0.7208, 0.9218, 0.793, 0.8335, 0.5585, 0.1689, 0.8229] +2026-04-14 21:21:33.810912: Epoch time: 102.31 s +2026-04-14 21:21:35.066239: +2026-04-14 21:21:35.068251: Epoch 3686 +2026-04-14 21:21:35.070432: Current learning rate: 0.00101 +2026-04-14 21:23:16.780907: train_loss -0.4651 +2026-04-14 21:23:16.787628: val_loss -0.3756 +2026-04-14 21:23:16.789740: Pseudo dice [0.7619, 0.9011, 0.7966, 0.0392, 0.4095, 0.5235, 0.8636] +2026-04-14 21:23:16.792060: Epoch time: 101.72 s +2026-04-14 21:23:18.054404: +2026-04-14 21:23:18.057397: Epoch 3687 +2026-04-14 21:23:18.060403: Current learning rate: 0.00101 +2026-04-14 21:24:59.346069: train_loss -0.4861 +2026-04-14 21:24:59.352045: val_loss -0.4714 +2026-04-14 21:24:59.354248: Pseudo dice [0.7039, 0.6787, 0.7485, 0.7031, 0.4036, 0.8251, 0.8661] +2026-04-14 21:24:59.356959: Epoch time: 101.29 s +2026-04-14 21:25:00.636154: +2026-04-14 21:25:00.638087: Epoch 3688 +2026-04-14 21:25:00.640002: Current learning rate: 0.00101 +2026-04-14 21:26:43.282198: train_loss -0.5602 +2026-04-14 21:26:43.288923: val_loss -0.5459 +2026-04-14 21:26:43.291092: Pseudo dice [0.6355, 0.7688, 0.6434, 0.1903, 0.4046, 0.6019, 0.7884] +2026-04-14 21:26:43.294660: Epoch time: 102.65 s +2026-04-14 21:26:44.521689: +2026-04-14 21:26:44.526267: Epoch 3689 +2026-04-14 21:26:44.528297: Current learning rate: 0.001 +2026-04-14 21:28:26.931762: train_loss -0.6955 +2026-04-14 21:28:26.938998: val_loss -0.5424 +2026-04-14 21:28:26.941508: Pseudo dice [0.4857, 0.8099, 0.5271, 0.7087, 0.29, 0.4044, 0.8496] +2026-04-14 21:28:26.944827: Epoch time: 102.41 s +2026-04-14 21:28:28.162608: +2026-04-14 21:28:28.165246: Epoch 3690 +2026-04-14 21:28:28.167371: Current learning rate: 0.001 +2026-04-14 21:30:09.829094: train_loss -0.7533 +2026-04-14 21:30:09.835591: val_loss -0.6862 +2026-04-14 21:30:09.837641: Pseudo dice [0.8351, 0.5848, 0.818, 0.6911, 0.4605, 0.7456, 0.7604] +2026-04-14 21:30:09.840155: Epoch time: 101.67 s +2026-04-14 21:30:11.042941: +2026-04-14 21:30:11.044776: Epoch 3691 +2026-04-14 21:30:11.046595: Current learning rate: 0.001 +2026-04-14 21:31:52.579216: train_loss -0.771 +2026-04-14 21:31:52.587063: val_loss -0.6136 +2026-04-14 21:31:52.589599: Pseudo dice [0.6836, 0.8603, 0.6985, 0.0363, 0.6703, 0.3712, 0.8129] +2026-04-14 21:31:52.592031: Epoch time: 101.54 s +2026-04-14 21:31:53.825019: +2026-04-14 21:31:53.826890: Epoch 3692 +2026-04-14 21:31:53.828904: Current learning rate: 0.001 +2026-04-14 21:33:36.269087: train_loss -0.7603 +2026-04-14 21:33:36.276952: val_loss -0.6479 +2026-04-14 21:33:36.279063: Pseudo dice [0.5679, 0.5251, 0.692, 0.669, 0.566, 0.6598, 0.8128] +2026-04-14 21:33:36.281258: Epoch time: 102.45 s +2026-04-14 21:33:37.507332: +2026-04-14 21:33:37.509532: Epoch 3693 +2026-04-14 21:33:37.511285: Current learning rate: 0.00099 +2026-04-14 21:35:19.245960: train_loss -0.7712 +2026-04-14 21:35:19.254796: val_loss -0.6215 +2026-04-14 21:35:19.258253: Pseudo dice [0.5518, 0.8262, 0.6608, 0.5443, 0.4611, 0.7059, 0.778] +2026-04-14 21:35:19.260961: Epoch time: 101.74 s +2026-04-14 21:35:20.479364: +2026-04-14 21:35:20.481350: Epoch 3694 +2026-04-14 21:35:20.483353: Current learning rate: 0.00099 +2026-04-14 21:37:02.272707: train_loss -0.7691 +2026-04-14 21:37:02.279939: val_loss -0.6519 +2026-04-14 21:37:02.282517: Pseudo dice [0.5982, 0.7063, 0.5909, 0.2203, 0.6672, 0.5342, 0.863] +2026-04-14 21:37:02.285788: Epoch time: 101.8 s +2026-04-14 21:37:03.512469: +2026-04-14 21:37:03.514158: Epoch 3695 +2026-04-14 21:37:03.520152: Current learning rate: 0.00099 +2026-04-14 21:38:45.643035: train_loss -0.762 +2026-04-14 21:38:45.651809: val_loss -0.6574 +2026-04-14 21:38:45.655481: Pseudo dice [0.5505, 0.7434, 0.7772, 0.3696, 0.3902, 0.5862, 0.7547] +2026-04-14 21:38:45.657893: Epoch time: 102.13 s +2026-04-14 21:38:46.876885: +2026-04-14 21:38:46.878855: Epoch 3696 +2026-04-14 21:38:46.881006: Current learning rate: 0.00098 +2026-04-14 21:40:29.325535: train_loss -0.7469 +2026-04-14 21:40:29.334502: val_loss -0.6722 +2026-04-14 21:40:29.336949: Pseudo dice [0.7804, 0.6616, 0.6711, 0.4038, 0.4787, 0.7394, 0.7412] +2026-04-14 21:40:29.339202: Epoch time: 102.45 s +2026-04-14 21:40:30.572155: +2026-04-14 21:40:30.574024: Epoch 3697 +2026-04-14 21:40:30.575763: Current learning rate: 0.00098 +2026-04-14 21:42:12.843785: train_loss -0.7562 +2026-04-14 21:42:12.854553: val_loss -0.6471 +2026-04-14 21:42:12.857384: Pseudo dice [0.3232, 0.8461, 0.7216, 0.6737, 0.3636, 0.4598, 0.7854] +2026-04-14 21:42:12.860084: Epoch time: 102.27 s +2026-04-14 21:42:14.100169: +2026-04-14 21:42:14.102378: Epoch 3698 +2026-04-14 21:42:14.105198: Current learning rate: 0.00098 +2026-04-14 21:43:56.498719: train_loss -0.7569 +2026-04-14 21:43:56.505974: val_loss -0.6436 +2026-04-14 21:43:56.508134: Pseudo dice [0.3741, 0.8805, 0.6879, 0.4011, 0.3268, 0.8756, 0.7665] +2026-04-14 21:43:56.510733: Epoch time: 102.4 s +2026-04-14 21:43:57.780842: +2026-04-14 21:43:57.783110: Epoch 3699 +2026-04-14 21:43:57.785650: Current learning rate: 0.00097 +2026-04-14 21:45:40.065187: train_loss -0.7632 +2026-04-14 21:45:40.071865: val_loss -0.6542 +2026-04-14 21:45:40.074003: Pseudo dice [0.6292, 0.9037, 0.7428, 0.39, 0.5202, 0.2204, 0.7256] +2026-04-14 21:45:40.076777: Epoch time: 102.29 s +2026-04-14 21:45:43.185695: +2026-04-14 21:45:43.188278: Epoch 3700 +2026-04-14 21:45:43.190157: Current learning rate: 0.00097 +2026-04-14 21:47:26.101039: train_loss -0.7429 +2026-04-14 21:47:26.109398: val_loss -0.6508 +2026-04-14 21:47:26.111348: Pseudo dice [0.5516, 0.6095, 0.763, 0.7291, 0.3269, 0.3498, 0.8004] +2026-04-14 21:47:26.114013: Epoch time: 102.92 s +2026-04-14 21:47:27.396700: +2026-04-14 21:47:27.399332: Epoch 3701 +2026-04-14 21:47:27.401415: Current learning rate: 0.00097 +2026-04-14 21:49:10.079944: train_loss -0.7534 +2026-04-14 21:49:10.086241: val_loss -0.645 +2026-04-14 21:49:10.088456: Pseudo dice [0.6425, 0.8735, 0.4835, 0.4399, 0.4194, 0.2344, 0.8971] +2026-04-14 21:49:10.090891: Epoch time: 102.69 s +2026-04-14 21:49:11.383483: +2026-04-14 21:49:11.385434: Epoch 3702 +2026-04-14 21:49:11.387647: Current learning rate: 0.00097 +2026-04-14 21:50:53.595551: train_loss -0.7497 +2026-04-14 21:50:53.602641: val_loss -0.6702 +2026-04-14 21:50:53.604538: Pseudo dice [0.7663, 0.1852, 0.7051, 0.7882, 0.4208, 0.7841, 0.8161] +2026-04-14 21:50:53.607199: Epoch time: 102.22 s +2026-04-14 21:50:54.938021: +2026-04-14 21:50:54.939809: Epoch 3703 +2026-04-14 21:50:54.941796: Current learning rate: 0.00096 +2026-04-14 21:52:37.897202: train_loss -0.7488 +2026-04-14 21:52:37.903544: val_loss -0.6636 +2026-04-14 21:52:37.906361: Pseudo dice [0.3508, 0.7497, 0.6441, 0.3933, 0.3757, 0.8961, 0.8659] +2026-04-14 21:52:37.908777: Epoch time: 102.96 s +2026-04-14 21:52:39.172573: +2026-04-14 21:52:39.177114: Epoch 3704 +2026-04-14 21:52:39.185232: Current learning rate: 0.00096 +2026-04-14 21:54:21.054517: train_loss -0.7432 +2026-04-14 21:54:21.066500: val_loss -0.676 +2026-04-14 21:54:21.068917: Pseudo dice [0.7612, 0.5224, 0.7319, 0.5336, 0.5119, 0.7132, 0.7839] +2026-04-14 21:54:21.070942: Epoch time: 101.89 s +2026-04-14 21:54:22.330568: +2026-04-14 21:54:22.332559: Epoch 3705 +2026-04-14 21:54:22.334725: Current learning rate: 0.00096 +2026-04-14 21:56:04.655045: train_loss -0.7665 +2026-04-14 21:56:04.662227: val_loss -0.6705 +2026-04-14 21:56:04.664388: Pseudo dice [0.6803, 0.8141, 0.7679, 0.3975, 0.7387, 0.6633, 0.7144] +2026-04-14 21:56:04.666974: Epoch time: 102.33 s +2026-04-14 21:56:05.900415: +2026-04-14 21:56:05.903351: Epoch 3706 +2026-04-14 21:56:05.906757: Current learning rate: 0.00095 +2026-04-14 21:57:49.373439: train_loss -0.7669 +2026-04-14 21:57:49.381585: val_loss -0.6786 +2026-04-14 21:57:49.383703: Pseudo dice [0.8324, 0.7536, 0.764, 0.353, 0.5318, 0.6095, 0.7173] +2026-04-14 21:57:49.385990: Epoch time: 103.48 s +2026-04-14 21:57:50.625525: +2026-04-14 21:57:50.627671: Epoch 3707 +2026-04-14 21:57:50.630012: Current learning rate: 0.00095 +2026-04-14 21:59:33.036198: train_loss -0.7615 +2026-04-14 21:59:33.043605: val_loss -0.677 +2026-04-14 21:59:33.045706: Pseudo dice [0.5649, 0.4826, 0.7889, 0.3213, 0.6113, 0.7427, 0.4199] +2026-04-14 21:59:33.048278: Epoch time: 102.41 s +2026-04-14 21:59:35.442719: +2026-04-14 21:59:35.445058: Epoch 3708 +2026-04-14 21:59:35.447299: Current learning rate: 0.00095 +2026-04-14 22:01:18.003350: train_loss -0.7531 +2026-04-14 22:01:18.010601: val_loss -0.6734 +2026-04-14 22:01:18.013179: Pseudo dice [0.6542, 0.8091, 0.7573, 0.5478, 0.6006, 0.8435, 0.6409] +2026-04-14 22:01:18.017173: Epoch time: 102.56 s +2026-04-14 22:01:19.247847: +2026-04-14 22:01:19.251782: Epoch 3709 +2026-04-14 22:01:19.253709: Current learning rate: 0.00095 +2026-04-14 22:03:02.416765: train_loss -0.7321 +2026-04-14 22:03:02.424527: val_loss -0.6511 +2026-04-14 22:03:02.427161: Pseudo dice [0.8419, 0.7033, 0.4989, 0.572, 0.4602, 0.8274, 0.8348] +2026-04-14 22:03:02.435387: Epoch time: 103.17 s +2026-04-14 22:03:03.726424: +2026-04-14 22:03:03.730625: Epoch 3710 +2026-04-14 22:03:03.732807: Current learning rate: 0.00094 +2026-04-14 22:04:46.259905: train_loss -0.7436 +2026-04-14 22:04:46.270060: val_loss -0.7269 +2026-04-14 22:04:46.273675: Pseudo dice [0.8783, 0.7788, 0.7052, 0.1822, 0.5312, 0.6997, 0.853] +2026-04-14 22:04:46.276615: Epoch time: 102.54 s +2026-04-14 22:04:47.566745: +2026-04-14 22:04:47.568642: Epoch 3711 +2026-04-14 22:04:47.570545: Current learning rate: 0.00094 +2026-04-14 22:06:29.648458: train_loss -0.739 +2026-04-14 22:06:29.655210: val_loss -0.6691 +2026-04-14 22:06:29.658979: Pseudo dice [0.6368, 0.7816, 0.8937, 0.1267, 0.3561, 0.8213, 0.6282] +2026-04-14 22:06:29.662814: Epoch time: 102.08 s +2026-04-14 22:06:30.896747: +2026-04-14 22:06:30.899040: Epoch 3712 +2026-04-14 22:06:30.901359: Current learning rate: 0.00094 +2026-04-14 22:08:13.162763: train_loss -0.7807 +2026-04-14 22:08:13.169077: val_loss -0.6577 +2026-04-14 22:08:13.171176: Pseudo dice [0.4091, 0.8281, 0.8002, 0.1856, 0.3743, 0.879, 0.7367] +2026-04-14 22:08:13.173402: Epoch time: 102.27 s +2026-04-14 22:08:14.459265: +2026-04-14 22:08:14.461256: Epoch 3713 +2026-04-14 22:08:14.463162: Current learning rate: 0.00093 +2026-04-14 22:09:56.648160: train_loss -0.7314 +2026-04-14 22:09:56.655708: val_loss -0.5765 +2026-04-14 22:09:56.659173: Pseudo dice [0.6339, 0.2849, 0.5383, 0.0019, 0.4576, 0.6039, 0.7074] +2026-04-14 22:09:56.661628: Epoch time: 102.19 s +2026-04-14 22:09:57.918627: +2026-04-14 22:09:57.921531: Epoch 3714 +2026-04-14 22:09:57.924016: Current learning rate: 0.00093 +2026-04-14 22:11:40.249857: train_loss -0.7492 +2026-04-14 22:11:40.256965: val_loss -0.6804 +2026-04-14 22:11:40.258933: Pseudo dice [0.5964, 0.7685, 0.7638, 0.4436, 0.5455, 0.8476, 0.6229] +2026-04-14 22:11:40.262270: Epoch time: 102.33 s +2026-04-14 22:11:41.550436: +2026-04-14 22:11:41.552831: Epoch 3715 +2026-04-14 22:11:41.557806: Current learning rate: 0.00093 +2026-04-14 22:13:23.847433: train_loss -0.7448 +2026-04-14 22:13:23.854205: val_loss -0.6426 +2026-04-14 22:13:23.857482: Pseudo dice [0.4802, 0.7523, 0.8371, 0.057, 0.0762, 0.8712, 0.7802] +2026-04-14 22:13:23.860562: Epoch time: 102.3 s +2026-04-14 22:13:25.098717: +2026-04-14 22:13:25.101148: Epoch 3716 +2026-04-14 22:13:25.103707: Current learning rate: 0.00092 +2026-04-14 22:15:08.278779: train_loss -0.748 +2026-04-14 22:15:08.284700: val_loss -0.6359 +2026-04-14 22:15:08.286957: Pseudo dice [0.6429, 0.8661, 0.8159, 0.4564, 0.4736, 0.2097, 0.7761] +2026-04-14 22:15:08.290658: Epoch time: 103.18 s +2026-04-14 22:15:09.544742: +2026-04-14 22:15:09.546815: Epoch 3717 +2026-04-14 22:15:09.548631: Current learning rate: 0.00092 +2026-04-14 22:16:52.492258: train_loss -0.7418 +2026-04-14 22:16:52.499276: val_loss -0.7097 +2026-04-14 22:16:52.501593: Pseudo dice [0.4449, 0.5517, 0.7876, 0.5158, 0.522, 0.7445, 0.8375] +2026-04-14 22:16:52.504334: Epoch time: 102.95 s +2026-04-14 22:16:53.839222: +2026-04-14 22:16:53.841764: Epoch 3718 +2026-04-14 22:16:53.845218: Current learning rate: 0.00092 +2026-04-14 22:18:36.284405: train_loss -0.7596 +2026-04-14 22:18:36.292873: val_loss -0.6971 +2026-04-14 22:18:36.295212: Pseudo dice [0.6624, 0.8764, 0.7804, 0.2635, 0.5125, 0.8424, 0.8344] +2026-04-14 22:18:36.298050: Epoch time: 102.45 s +2026-04-14 22:18:37.543218: +2026-04-14 22:18:37.545429: Epoch 3719 +2026-04-14 22:18:37.547609: Current learning rate: 0.00092 +2026-04-14 22:20:19.418828: train_loss -0.7602 +2026-04-14 22:20:19.425203: val_loss -0.6763 +2026-04-14 22:20:19.427540: Pseudo dice [0.8838, 0.6473, 0.7605, 0.4594, 0.4425, 0.9018, 0.855] +2026-04-14 22:20:19.430321: Epoch time: 101.88 s +2026-04-14 22:20:20.696942: +2026-04-14 22:20:20.699434: Epoch 3720 +2026-04-14 22:20:20.702828: Current learning rate: 0.00091 +2026-04-14 22:22:03.652124: train_loss -0.7699 +2026-04-14 22:22:03.659770: val_loss -0.6811 +2026-04-14 22:22:03.663081: Pseudo dice [0.6147, 0.8764, 0.7515, 0.2513, 0.5163, 0.807, 0.8478] +2026-04-14 22:22:03.665774: Epoch time: 102.96 s +2026-04-14 22:22:04.985239: +2026-04-14 22:22:04.987491: Epoch 3721 +2026-04-14 22:22:04.990192: Current learning rate: 0.00091 +2026-04-14 22:23:46.898622: train_loss -0.7546 +2026-04-14 22:23:46.905783: val_loss -0.664 +2026-04-14 22:23:46.908435: Pseudo dice [0.7259, 0.7955, 0.835, 0.1987, 0.534, 0.675, 0.7099] +2026-04-14 22:23:46.911805: Epoch time: 101.92 s +2026-04-14 22:23:48.232067: +2026-04-14 22:23:48.234411: Epoch 3722 +2026-04-14 22:23:48.237476: Current learning rate: 0.00091 +2026-04-14 22:25:31.186160: train_loss -0.7652 +2026-04-14 22:25:31.192547: val_loss -0.667 +2026-04-14 22:25:31.195126: Pseudo dice [0.2164, 0.6212, 0.7659, 0.5402, 0.6067, 0.8038, 0.8004] +2026-04-14 22:25:31.197971: Epoch time: 102.96 s +2026-04-14 22:25:32.514747: +2026-04-14 22:25:32.517525: Epoch 3723 +2026-04-14 22:25:32.519778: Current learning rate: 0.0009 +2026-04-14 22:27:15.266572: train_loss -0.7513 +2026-04-14 22:27:15.275390: val_loss -0.6808 +2026-04-14 22:27:15.277323: Pseudo dice [0.7655, 0.8502, 0.8107, 0.4159, 0.4907, 0.5869, 0.6388] +2026-04-14 22:27:15.279560: Epoch time: 102.76 s +2026-04-14 22:27:16.553298: +2026-04-14 22:27:16.555649: Epoch 3724 +2026-04-14 22:27:16.558248: Current learning rate: 0.0009 +2026-04-14 22:28:59.880866: train_loss -0.7499 +2026-04-14 22:28:59.891703: val_loss -0.6491 +2026-04-14 22:28:59.894271: Pseudo dice [0.2115, 0.6749, 0.7624, 0.2604, 0.1227, 0.603, 0.7638] +2026-04-14 22:28:59.897109: Epoch time: 103.33 s +2026-04-14 22:29:01.173624: +2026-04-14 22:29:01.175652: Epoch 3725 +2026-04-14 22:29:01.178026: Current learning rate: 0.0009 +2026-04-14 22:30:43.358593: train_loss -0.757 +2026-04-14 22:30:43.371000: val_loss -0.6889 +2026-04-14 22:30:43.374113: Pseudo dice [0.7591, 0.8905, 0.7512, 0.6352, 0.4291, 0.8498, 0.5865] +2026-04-14 22:30:43.377406: Epoch time: 102.19 s +2026-04-14 22:30:44.701405: +2026-04-14 22:30:44.703530: Epoch 3726 +2026-04-14 22:30:44.707062: Current learning rate: 0.0009 +2026-04-14 22:32:27.239916: train_loss -0.7634 +2026-04-14 22:32:27.246853: val_loss -0.6572 +2026-04-14 22:32:27.249766: Pseudo dice [0.8564, 0.9135, 0.7378, 0.4191, 0.5138, 0.2255, 0.6338] +2026-04-14 22:32:27.252757: Epoch time: 102.54 s +2026-04-14 22:32:28.558461: +2026-04-14 22:32:28.560728: Epoch 3727 +2026-04-14 22:32:28.562822: Current learning rate: 0.00089 +2026-04-14 22:34:11.389156: train_loss -0.7488 +2026-04-14 22:34:11.397680: val_loss -0.7014 +2026-04-14 22:34:11.399931: Pseudo dice [0.7469, 0.6485, 0.7232, 0.5486, 0.5174, 0.809, 0.815] +2026-04-14 22:34:11.402481: Epoch time: 102.83 s +2026-04-14 22:34:12.744544: +2026-04-14 22:34:12.746842: Epoch 3728 +2026-04-14 22:34:12.748904: Current learning rate: 0.00089 +2026-04-14 22:35:55.988358: train_loss -0.7607 +2026-04-14 22:35:55.996376: val_loss -0.6164 +2026-04-14 22:35:55.999375: Pseudo dice [0.3866, 0.9038, 0.8199, 0.5243, 0.1764, 0.5858, 0.7903] +2026-04-14 22:35:56.001639: Epoch time: 103.25 s +2026-04-14 22:35:57.290924: +2026-04-14 22:35:57.292696: Epoch 3729 +2026-04-14 22:35:57.295242: Current learning rate: 0.00089 +2026-04-14 22:37:40.153154: train_loss -0.7566 +2026-04-14 22:37:40.162338: val_loss -0.6268 +2026-04-14 22:37:40.165746: Pseudo dice [0.3697, 0.8801, 0.809, 0.1041, 0.3623, 0.0435, 0.68] +2026-04-14 22:37:40.168640: Epoch time: 102.87 s +2026-04-14 22:37:41.446506: +2026-04-14 22:37:41.449759: Epoch 3730 +2026-04-14 22:37:41.452281: Current learning rate: 0.00088 +2026-04-14 22:39:24.054670: train_loss -0.7585 +2026-04-14 22:39:24.063891: val_loss -0.6651 +2026-04-14 22:39:24.067100: Pseudo dice [0.6047, 0.4852, 0.6391, 0.3844, 0.2821, 0.5344, 0.8503] +2026-04-14 22:39:24.069969: Epoch time: 102.61 s +2026-04-14 22:39:25.417231: +2026-04-14 22:39:25.426718: Epoch 3731 +2026-04-14 22:39:25.429160: Current learning rate: 0.00088 +2026-04-14 22:41:08.216861: train_loss -0.7532 +2026-04-14 22:41:08.224210: val_loss -0.6775 +2026-04-14 22:41:08.226601: Pseudo dice [0.7199, 0.8704, 0.7832, 0.5172, 0.6062, 0.7916, 0.8242] +2026-04-14 22:41:08.229022: Epoch time: 102.8 s +2026-04-14 22:41:09.471513: +2026-04-14 22:41:09.473296: Epoch 3732 +2026-04-14 22:41:09.475342: Current learning rate: 0.00088 +2026-04-14 22:42:51.962003: train_loss -0.7543 +2026-04-14 22:42:51.971494: val_loss -0.5266 +2026-04-14 22:42:51.974063: Pseudo dice [0.8261, 0.733, 0.4565, 0.6568, 0.4235, 0.1095, 0.6526] +2026-04-14 22:42:51.976756: Epoch time: 102.49 s +2026-04-14 22:42:53.264745: +2026-04-14 22:42:53.266578: Epoch 3733 +2026-04-14 22:42:53.268793: Current learning rate: 0.00087 +2026-04-14 22:44:35.664104: train_loss -0.7708 +2026-04-14 22:44:35.671585: val_loss -0.6503 +2026-04-14 22:44:35.675120: Pseudo dice [0.6623, 0.8942, 0.7906, 0.3417, 0.5946, 0.7324, 0.5987] +2026-04-14 22:44:35.677823: Epoch time: 102.4 s +2026-04-14 22:44:36.921540: +2026-04-14 22:44:36.923338: Epoch 3734 +2026-04-14 22:44:36.925781: Current learning rate: 0.00087 +2026-04-14 22:46:18.943213: train_loss -0.7649 +2026-04-14 22:46:18.950281: val_loss -0.6448 +2026-04-14 22:46:18.952543: Pseudo dice [0.298, 0.706, 0.7024, 0.4116, 0.3557, 0.8941, 0.8516] +2026-04-14 22:46:18.955019: Epoch time: 102.02 s +2026-04-14 22:46:20.264218: +2026-04-14 22:46:20.266065: Epoch 3735 +2026-04-14 22:46:20.268920: Current learning rate: 0.00087 +2026-04-14 22:48:02.748544: train_loss -0.7597 +2026-04-14 22:48:02.755387: val_loss -0.5713 +2026-04-14 22:48:02.757481: Pseudo dice [0.3648, 0.8418, 0.7174, 0.5233, 0.4489, 0.6038, 0.7642] +2026-04-14 22:48:02.759871: Epoch time: 102.49 s +2026-04-14 22:48:04.020930: +2026-04-14 22:48:04.023096: Epoch 3736 +2026-04-14 22:48:04.025053: Current learning rate: 0.00087 +2026-04-14 22:49:45.688070: train_loss -0.7483 +2026-04-14 22:49:45.694432: val_loss -0.6992 +2026-04-14 22:49:45.696909: Pseudo dice [0.4524, 0.6571, 0.8105, 0.5382, 0.5799, 0.8798, 0.8458] +2026-04-14 22:49:45.699725: Epoch time: 101.67 s +2026-04-14 22:49:46.949100: +2026-04-14 22:49:46.951328: Epoch 3737 +2026-04-14 22:49:46.953858: Current learning rate: 0.00086 +2026-04-14 22:51:29.402986: train_loss -0.7595 +2026-04-14 22:51:29.410318: val_loss -0.6942 +2026-04-14 22:51:29.412929: Pseudo dice [0.8268, 0.9082, 0.8194, 0.1395, 0.5612, 0.1318, 0.8495] +2026-04-14 22:51:29.416074: Epoch time: 102.46 s +2026-04-14 22:51:30.725766: +2026-04-14 22:51:30.727920: Epoch 3738 +2026-04-14 22:51:30.729829: Current learning rate: 0.00086 +2026-04-14 22:53:12.436914: train_loss -0.7594 +2026-04-14 22:53:12.443953: val_loss -0.7055 +2026-04-14 22:53:12.446179: Pseudo dice [0.7251, 0.6528, 0.8014, 0.1565, 0.6687, 0.5755, 0.6832] +2026-04-14 22:53:12.449948: Epoch time: 101.71 s +2026-04-14 22:53:13.706075: +2026-04-14 22:53:13.707811: Epoch 3739 +2026-04-14 22:53:13.709677: Current learning rate: 0.00086 +2026-04-14 22:54:55.509897: train_loss -0.7608 +2026-04-14 22:54:55.516484: val_loss -0.6732 +2026-04-14 22:54:55.518539: Pseudo dice [0.1133, 0.7688, 0.8502, 0.6205, 0.4257, 0.8762, 0.8132] +2026-04-14 22:54:55.521724: Epoch time: 101.81 s +2026-04-14 22:54:56.831517: +2026-04-14 22:54:56.834551: Epoch 3740 +2026-04-14 22:54:56.844393: Current learning rate: 0.00085 +2026-04-14 22:56:39.018196: train_loss -0.771 +2026-04-14 22:56:39.024137: val_loss -0.6714 +2026-04-14 22:56:39.026545: Pseudo dice [0.6234, 0.8289, 0.6498, 0.8034, 0.3697, 0.4652, 0.8243] +2026-04-14 22:56:39.028895: Epoch time: 102.19 s +2026-04-14 22:56:40.291912: +2026-04-14 22:56:40.293751: Epoch 3741 +2026-04-14 22:56:40.295534: Current learning rate: 0.00085 +2026-04-14 22:58:23.490525: train_loss -0.7843 +2026-04-14 22:58:23.496990: val_loss -0.652 +2026-04-14 22:58:23.499057: Pseudo dice [0.6312, 0.8985, 0.7419, 0.7357, 0.556, 0.7708, 0.7609] +2026-04-14 22:58:23.501143: Epoch time: 103.2 s +2026-04-14 22:58:24.807721: +2026-04-14 22:58:24.810395: Epoch 3742 +2026-04-14 22:58:24.812586: Current learning rate: 0.00085 +2026-04-14 23:00:06.967251: train_loss -0.7709 +2026-04-14 23:00:06.975137: val_loss -0.6825 +2026-04-14 23:00:06.977489: Pseudo dice [0.6901, 0.8953, 0.7641, 0.6977, 0.3581, 0.7844, 0.7169] +2026-04-14 23:00:06.979437: Epoch time: 102.16 s +2026-04-14 23:00:08.202129: +2026-04-14 23:00:08.204999: Epoch 3743 +2026-04-14 23:00:08.207201: Current learning rate: 0.00085 +2026-04-14 23:01:50.437906: train_loss -0.7632 +2026-04-14 23:01:50.445857: val_loss -0.6333 +2026-04-14 23:01:50.448005: Pseudo dice [0.3384, 0.542, 0.6878, 0.4193, 0.6705, 0.3327, 0.8466] +2026-04-14 23:01:50.452442: Epoch time: 102.24 s +2026-04-14 23:01:51.729672: +2026-04-14 23:01:51.732337: Epoch 3744 +2026-04-14 23:01:51.735536: Current learning rate: 0.00084 +2026-04-14 23:03:34.347372: train_loss -0.7534 +2026-04-14 23:03:34.355650: val_loss -0.6777 +2026-04-14 23:03:34.357882: Pseudo dice [0.4566, 0.7157, 0.8742, 0.5528, 0.6326, 0.4644, 0.5899] +2026-04-14 23:03:34.362618: Epoch time: 102.62 s +2026-04-14 23:03:35.673215: +2026-04-14 23:03:35.675941: Epoch 3745 +2026-04-14 23:03:35.678203: Current learning rate: 0.00084 +2026-04-14 23:05:17.907863: train_loss -0.7629 +2026-04-14 23:05:17.918801: val_loss -0.674 +2026-04-14 23:05:17.921448: Pseudo dice [0.7282, 0.8149, 0.6327, 0.308, 0.6024, 0.878, 0.8086] +2026-04-14 23:05:17.923979: Epoch time: 102.24 s +2026-04-14 23:05:19.197050: +2026-04-14 23:05:19.199708: Epoch 3746 +2026-04-14 23:05:19.202071: Current learning rate: 0.00084 +2026-04-14 23:07:01.563827: train_loss -0.771 +2026-04-14 23:07:01.571494: val_loss -0.6969 +2026-04-14 23:07:01.573408: Pseudo dice [0.7073, 0.7657, 0.7177, 0.5155, 0.4008, 0.8907, 0.8914] +2026-04-14 23:07:01.576347: Epoch time: 102.37 s +2026-04-14 23:07:02.801166: +2026-04-14 23:07:02.803412: Epoch 3747 +2026-04-14 23:07:02.805333: Current learning rate: 0.00083 +2026-04-14 23:08:45.413337: train_loss -0.7714 +2026-04-14 23:08:45.420185: val_loss -0.7005 +2026-04-14 23:08:45.422451: Pseudo dice [0.5107, 0.7511, 0.8236, 0.6206, 0.3164, 0.7392, 0.8343] +2026-04-14 23:08:45.425562: Epoch time: 102.62 s +2026-04-14 23:08:46.718441: +2026-04-14 23:08:46.720684: Epoch 3748 +2026-04-14 23:08:46.723145: Current learning rate: 0.00083 +2026-04-14 23:10:30.574678: train_loss -0.7635 +2026-04-14 23:10:30.582264: val_loss -0.6254 +2026-04-14 23:10:30.584493: Pseudo dice [0.744, 0.902, 0.5656, 0.508, 0.5856, 0.4752, 0.7955] +2026-04-14 23:10:30.586932: Epoch time: 103.86 s +2026-04-14 23:10:31.895753: +2026-04-14 23:10:31.897781: Epoch 3749 +2026-04-14 23:10:31.899974: Current learning rate: 0.00083 +2026-04-14 23:12:14.413077: train_loss -0.7592 +2026-04-14 23:12:14.419772: val_loss -0.6284 +2026-04-14 23:12:14.422505: Pseudo dice [0.8446, 0.7214, 0.8445, 0.1673, 0.4064, 0.2312, 0.7766] +2026-04-14 23:12:14.425790: Epoch time: 102.52 s +2026-04-14 23:12:17.529383: +2026-04-14 23:12:17.531290: Epoch 3750 +2026-04-14 23:12:17.533090: Current learning rate: 0.00082 +2026-04-14 23:13:59.657214: train_loss -0.7535 +2026-04-14 23:13:59.664895: val_loss -0.7075 +2026-04-14 23:13:59.666937: Pseudo dice [0.819, 0.5761, 0.8478, 0.5797, 0.3712, 0.3397, 0.8282] +2026-04-14 23:13:59.669204: Epoch time: 102.13 s +2026-04-14 23:14:00.953273: +2026-04-14 23:14:00.967426: Epoch 3751 +2026-04-14 23:14:00.984636: Current learning rate: 0.00082 +2026-04-14 23:15:43.835912: train_loss -0.7711 +2026-04-14 23:15:43.843134: val_loss -0.6328 +2026-04-14 23:15:43.846310: Pseudo dice [0.707, 0.5443, 0.7064, 0.4223, 0.6498, 0.6215, 0.8584] +2026-04-14 23:15:43.850306: Epoch time: 102.89 s +2026-04-14 23:15:45.083649: +2026-04-14 23:15:45.086066: Epoch 3752 +2026-04-14 23:15:45.088953: Current learning rate: 0.00082 +2026-04-14 23:17:27.420673: train_loss -0.7629 +2026-04-14 23:17:27.429206: val_loss -0.6885 +2026-04-14 23:17:27.431576: Pseudo dice [0.8398, 0.7892, 0.8302, 0.1237, 0.5961, 0.8507, 0.7714] +2026-04-14 23:17:27.435982: Epoch time: 102.34 s +2026-04-14 23:17:28.747543: +2026-04-14 23:17:28.749686: Epoch 3753 +2026-04-14 23:17:28.752168: Current learning rate: 0.00082 +2026-04-14 23:19:11.614176: train_loss -0.7611 +2026-04-14 23:19:11.622041: val_loss -0.7103 +2026-04-14 23:19:11.624167: Pseudo dice [0.5346, 0.6757, 0.8767, 0.382, 0.4687, 0.9024, 0.746] +2026-04-14 23:19:11.626681: Epoch time: 102.87 s +2026-04-14 23:19:12.946007: +2026-04-14 23:19:12.947837: Epoch 3754 +2026-04-14 23:19:12.949620: Current learning rate: 0.00081 +2026-04-14 23:20:55.462707: train_loss -0.7774 +2026-04-14 23:20:55.471023: val_loss -0.637 +2026-04-14 23:20:55.473600: Pseudo dice [0.6347, 0.1436, 0.6861, 0.4869, 0.4244, 0.6762, 0.8444] +2026-04-14 23:20:55.476755: Epoch time: 102.52 s +2026-04-14 23:20:56.789596: +2026-04-14 23:20:56.791728: Epoch 3755 +2026-04-14 23:20:56.793619: Current learning rate: 0.00081 +2026-04-14 23:22:39.689403: train_loss -0.7673 +2026-04-14 23:22:39.696229: val_loss -0.6659 +2026-04-14 23:22:39.699684: Pseudo dice [0.5812, 0.7239, 0.8081, 0.502, 0.0, 0.1704, 0.7552] +2026-04-14 23:22:39.702602: Epoch time: 102.9 s +2026-04-14 23:22:40.945760: +2026-04-14 23:22:40.947722: Epoch 3756 +2026-04-14 23:22:40.949862: Current learning rate: 0.00081 +2026-04-14 23:24:23.662047: train_loss -0.7655 +2026-04-14 23:24:23.669014: val_loss -0.6647 +2026-04-14 23:24:23.671322: Pseudo dice [0.7771, 0.8097, 0.8238, 0.2611, 0.4737, 0.9323, 0.7885] +2026-04-14 23:24:23.673474: Epoch time: 102.72 s +2026-04-14 23:24:24.943174: +2026-04-14 23:24:24.954741: Epoch 3757 +2026-04-14 23:24:24.958069: Current learning rate: 0.0008 +2026-04-14 23:26:07.456264: train_loss -0.7638 +2026-04-14 23:26:07.466924: val_loss -0.6892 +2026-04-14 23:26:07.469434: Pseudo dice [0.6816, 0.7781, 0.7693, 0.4704, 0.4045, 0.9369, 0.7842] +2026-04-14 23:26:07.472299: Epoch time: 102.52 s +2026-04-14 23:26:08.756399: +2026-04-14 23:26:08.758750: Epoch 3758 +2026-04-14 23:26:08.761539: Current learning rate: 0.0008 +2026-04-14 23:27:50.638007: train_loss -0.7692 +2026-04-14 23:27:50.645659: val_loss -0.6399 +2026-04-14 23:27:50.649797: Pseudo dice [0.8714, 0.9089, 0.83, 0.3395, 0.1818, 0.2973, 0.4786] +2026-04-14 23:27:50.652364: Epoch time: 101.88 s +2026-04-14 23:27:51.911552: +2026-04-14 23:27:51.913413: Epoch 3759 +2026-04-14 23:27:51.920991: Current learning rate: 0.0008 +2026-04-14 23:29:34.127675: train_loss -0.7774 +2026-04-14 23:29:34.139681: val_loss -0.6971 +2026-04-14 23:29:34.143501: Pseudo dice [0.7677, 0.9028, 0.8055, 0.3628, 0.3688, 0.6563, 0.8607] +2026-04-14 23:29:34.146243: Epoch time: 102.22 s +2026-04-14 23:29:35.450606: +2026-04-14 23:29:35.453405: Epoch 3760 +2026-04-14 23:29:35.455658: Current learning rate: 0.00079 +2026-04-14 23:31:17.974964: train_loss -0.7669 +2026-04-14 23:31:17.982183: val_loss -0.6217 +2026-04-14 23:31:17.984697: Pseudo dice [0.7211, 0.8943, 0.8013, 0.0025, 0.5592, 0.6014, 0.7503] +2026-04-14 23:31:17.987966: Epoch time: 102.53 s +2026-04-14 23:31:19.288990: +2026-04-14 23:31:19.293106: Epoch 3761 +2026-04-14 23:31:19.297992: Current learning rate: 0.00079 +2026-04-14 23:33:01.504627: train_loss -0.7627 +2026-04-14 23:33:01.511613: val_loss -0.7043 +2026-04-14 23:33:01.513695: Pseudo dice [0.6419, 0.7527, 0.79, 0.3578, 0.4083, 0.8917, 0.8378] +2026-04-14 23:33:01.516234: Epoch time: 102.22 s +2026-04-14 23:33:02.847526: +2026-04-14 23:33:02.849277: Epoch 3762 +2026-04-14 23:33:02.852295: Current learning rate: 0.00079 +2026-04-14 23:34:45.021681: train_loss -0.7475 +2026-04-14 23:34:45.028539: val_loss -0.694 +2026-04-14 23:34:45.031035: Pseudo dice [0.6378, 0.7967, 0.5826, 0.5184, 0.4752, 0.9114, 0.7796] +2026-04-14 23:34:45.033775: Epoch time: 102.18 s +2026-04-14 23:34:46.322287: +2026-04-14 23:34:46.324497: Epoch 3763 +2026-04-14 23:34:46.329099: Current learning rate: 0.00079 +2026-04-14 23:36:29.076653: train_loss -0.7659 +2026-04-14 23:36:29.083215: val_loss -0.6804 +2026-04-14 23:36:29.086651: Pseudo dice [0.7472, 0.7345, 0.8418, 0.7648, 0.4101, 0.4651, 0.6192] +2026-04-14 23:36:29.089179: Epoch time: 102.76 s +2026-04-14 23:36:30.401213: +2026-04-14 23:36:30.403074: Epoch 3764 +2026-04-14 23:36:30.405244: Current learning rate: 0.00078 +2026-04-14 23:38:12.452991: train_loss -0.7689 +2026-04-14 23:38:12.460409: val_loss -0.659 +2026-04-14 23:38:12.462655: Pseudo dice [0.1688, 0.7341, 0.8171, 0.6244, 0.6167, 0.7834, 0.6759] +2026-04-14 23:38:12.465040: Epoch time: 102.06 s +2026-04-14 23:38:13.768232: +2026-04-14 23:38:13.770112: Epoch 3765 +2026-04-14 23:38:13.773281: Current learning rate: 0.00078 +2026-04-14 23:39:56.209810: train_loss -0.7598 +2026-04-14 23:39:56.216137: val_loss -0.6805 +2026-04-14 23:39:56.218697: Pseudo dice [0.7663, 0.6371, 0.718, 0.633, 0.4021, 0.7767, 0.7157] +2026-04-14 23:39:56.221462: Epoch time: 102.44 s +2026-04-14 23:39:57.504249: +2026-04-14 23:39:57.506115: Epoch 3766 +2026-04-14 23:39:57.508201: Current learning rate: 0.00078 +2026-04-14 23:41:40.232251: train_loss -0.7786 +2026-04-14 23:41:40.241510: val_loss -0.5837 +2026-04-14 23:41:40.244445: Pseudo dice [0.8337, 0.5634, 0.6233, 0.636, 0.3925, 0.3219, 0.7711] +2026-04-14 23:41:40.247298: Epoch time: 102.73 s +2026-04-14 23:41:41.512671: +2026-04-14 23:41:41.515726: Epoch 3767 +2026-04-14 23:41:41.519210: Current learning rate: 0.00077 +2026-04-14 23:43:24.020361: train_loss -0.7672 +2026-04-14 23:43:24.027720: val_loss -0.6591 +2026-04-14 23:43:24.032153: Pseudo dice [0.6867, 0.8835, 0.7976, 0.5198, 0.4983, 0.8332, 0.8877] +2026-04-14 23:43:24.034925: Epoch time: 102.51 s +2026-04-14 23:43:26.499432: +2026-04-14 23:43:26.501802: Epoch 3768 +2026-04-14 23:43:26.503884: Current learning rate: 0.00077 +2026-04-14 23:45:08.910989: train_loss -0.7811 +2026-04-14 23:45:08.918506: val_loss -0.6753 +2026-04-14 23:45:08.920610: Pseudo dice [0.8208, 0.6542, 0.795, 0.2014, 0.3472, 0.9011, 0.8041] +2026-04-14 23:45:08.923266: Epoch time: 102.41 s +2026-04-14 23:45:10.212624: +2026-04-14 23:45:10.214987: Epoch 3769 +2026-04-14 23:45:10.216949: Current learning rate: 0.00077 +2026-04-14 23:46:52.942509: train_loss -0.7841 +2026-04-14 23:46:52.948561: val_loss -0.7007 +2026-04-14 23:46:52.952862: Pseudo dice [0.5955, 0.8094, 0.8288, 0.1034, 0.4766, 0.7262, 0.8272] +2026-04-14 23:46:52.955686: Epoch time: 102.73 s +2026-04-14 23:46:54.209551: +2026-04-14 23:46:54.211477: Epoch 3770 +2026-04-14 23:46:54.213367: Current learning rate: 0.00077 +2026-04-14 23:48:36.778026: train_loss -0.7661 +2026-04-14 23:48:36.785173: val_loss -0.7071 +2026-04-14 23:48:36.787190: Pseudo dice [0.8243, 0.8496, 0.7752, 0.4401, 0.4283, 0.8892, 0.866] +2026-04-14 23:48:36.790981: Epoch time: 102.57 s +2026-04-14 23:48:38.086608: +2026-04-14 23:48:38.088890: Epoch 3771 +2026-04-14 23:48:38.091113: Current learning rate: 0.00076 +2026-04-14 23:50:20.596711: train_loss -0.7765 +2026-04-14 23:50:20.603450: val_loss -0.6015 +2026-04-14 23:50:20.606045: Pseudo dice [0.6555, 0.503, 0.5927, 0.1089, 0.5233, 0.8982, 0.8169] +2026-04-14 23:50:20.608203: Epoch time: 102.51 s +2026-04-14 23:50:21.854110: +2026-04-14 23:50:21.856451: Epoch 3772 +2026-04-14 23:50:21.858548: Current learning rate: 0.00076 +2026-04-14 23:52:04.337558: train_loss -0.7688 +2026-04-14 23:52:04.346048: val_loss -0.6695 +2026-04-14 23:52:04.348755: Pseudo dice [0.6992, 0.7588, 0.7966, 0.6758, 0.5995, 0.758, 0.6062] +2026-04-14 23:52:04.350930: Epoch time: 102.49 s +2026-04-14 23:52:05.615424: +2026-04-14 23:52:05.617936: Epoch 3773 +2026-04-14 23:52:05.620543: Current learning rate: 0.00076 +2026-04-14 23:53:47.961927: train_loss -0.7698 +2026-04-14 23:53:47.968513: val_loss -0.6909 +2026-04-14 23:53:47.972292: Pseudo dice [0.6345, 0.6931, 0.8218, 0.5077, 0.3892, 0.7972, 0.844] +2026-04-14 23:53:47.976564: Epoch time: 102.35 s +2026-04-14 23:53:49.214519: +2026-04-14 23:53:49.219254: Epoch 3774 +2026-04-14 23:53:49.221598: Current learning rate: 0.00075 +2026-04-14 23:55:31.705124: train_loss -0.7723 +2026-04-14 23:55:31.711793: val_loss -0.686 +2026-04-14 23:55:31.714164: Pseudo dice [0.7507, 0.4843, 0.7278, 0.2502, 0.687, 0.9345, 0.7305] +2026-04-14 23:55:31.716525: Epoch time: 102.49 s +2026-04-14 23:55:33.003646: +2026-04-14 23:55:33.005544: Epoch 3775 +2026-04-14 23:55:33.007458: Current learning rate: 0.00075 +2026-04-14 23:57:15.877517: train_loss -0.7711 +2026-04-14 23:57:15.884915: val_loss -0.6221 +2026-04-14 23:57:15.887493: Pseudo dice [0.8306, 0.7979, 0.5866, 0.7655, 0.6717, 0.4616, 0.7514] +2026-04-14 23:57:15.890048: Epoch time: 102.88 s +2026-04-14 23:57:17.181502: +2026-04-14 23:57:17.183675: Epoch 3776 +2026-04-14 23:57:17.185845: Current learning rate: 0.00075 +2026-04-14 23:58:59.410263: train_loss -0.7749 +2026-04-14 23:58:59.417074: val_loss -0.6116 +2026-04-14 23:58:59.419056: Pseudo dice [0.8099, 0.8759, 0.5988, 0.605, 0.4861, 0.7049, 0.8134] +2026-04-14 23:58:59.421689: Epoch time: 102.23 s +2026-04-14 23:59:00.760772: +2026-04-14 23:59:00.762799: Epoch 3777 +2026-04-14 23:59:00.764701: Current learning rate: 0.00074 +2026-04-15 00:00:43.951308: train_loss -0.7717 +2026-04-15 00:00:43.966201: val_loss -0.6676 +2026-04-15 00:00:43.970687: Pseudo dice [0.7134, 0.9087, 0.822, 0.7863, 0.6941, 0.633, 0.7988] +2026-04-15 00:00:43.974199: Epoch time: 103.19 s +2026-04-15 00:00:45.277618: +2026-04-15 00:00:45.280322: Epoch 3778 +2026-04-15 00:00:45.282714: Current learning rate: 0.00074 +2026-04-15 00:02:28.107863: train_loss -0.7807 +2026-04-15 00:02:28.114796: val_loss -0.6766 +2026-04-15 00:02:28.118009: Pseudo dice [0.7055, 0.816, 0.5546, 0.8026, 0.5654, 0.2146, 0.7655] +2026-04-15 00:02:28.120898: Epoch time: 102.83 s +2026-04-15 00:02:29.386664: +2026-04-15 00:02:29.390191: Epoch 3779 +2026-04-15 00:02:29.392929: Current learning rate: 0.00074 +2026-04-15 00:04:11.432386: train_loss -0.7734 +2026-04-15 00:04:11.441862: val_loss -0.6869 +2026-04-15 00:04:11.448847: Pseudo dice [0.664, 0.8134, 0.8222, 0.5009, 0.3652, 0.9292, 0.7212] +2026-04-15 00:04:11.451894: Epoch time: 102.05 s +2026-04-15 00:04:12.703536: +2026-04-15 00:04:12.707198: Epoch 3780 +2026-04-15 00:04:12.709863: Current learning rate: 0.00074 +2026-04-15 00:05:55.014320: train_loss -0.7695 +2026-04-15 00:05:55.022518: val_loss -0.6704 +2026-04-15 00:05:55.024917: Pseudo dice [0.7967, 0.7425, 0.7679, 0.0008, 0.6451, 0.7216, 0.8505] +2026-04-15 00:05:55.027100: Epoch time: 102.31 s +2026-04-15 00:05:56.363508: +2026-04-15 00:05:56.366950: Epoch 3781 +2026-04-15 00:05:56.369025: Current learning rate: 0.00073 +2026-04-15 00:07:39.360936: train_loss -0.7804 +2026-04-15 00:07:39.368820: val_loss -0.6339 +2026-04-15 00:07:39.371215: Pseudo dice [0.7694, 0.7617, 0.7775, 0.1448, 0.6899, 0.835, 0.7886] +2026-04-15 00:07:39.374031: Epoch time: 103.0 s +2026-04-15 00:07:40.668247: +2026-04-15 00:07:40.670414: Epoch 3782 +2026-04-15 00:07:40.672876: Current learning rate: 0.00073 +2026-04-15 00:09:23.483532: train_loss -0.7786 +2026-04-15 00:09:23.491361: val_loss -0.647 +2026-04-15 00:09:23.493928: Pseudo dice [0.6349, 0.8145, 0.7426, 0.631, 0.4426, 0.7314, 0.8069] +2026-04-15 00:09:23.497444: Epoch time: 102.82 s +2026-04-15 00:09:24.774345: +2026-04-15 00:09:24.776434: Epoch 3783 +2026-04-15 00:09:24.778438: Current learning rate: 0.00073 +2026-04-15 00:11:07.203700: train_loss -0.784 +2026-04-15 00:11:07.210415: val_loss -0.7148 +2026-04-15 00:11:07.213394: Pseudo dice [0.727, 0.8062, 0.7222, 0.8185, 0.497, 0.9165, 0.7119] +2026-04-15 00:11:07.216652: Epoch time: 102.43 s +2026-04-15 00:11:08.502165: +2026-04-15 00:11:08.504448: Epoch 3784 +2026-04-15 00:11:08.506412: Current learning rate: 0.00072 +2026-04-15 00:12:51.298842: train_loss -0.7779 +2026-04-15 00:12:51.313591: val_loss -0.6712 +2026-04-15 00:12:51.317003: Pseudo dice [0.667, 0.7318, 0.7169, 0.3204, 0.4177, 0.4368, 0.7063] +2026-04-15 00:12:51.320282: Epoch time: 102.8 s +2026-04-15 00:12:52.622799: +2026-04-15 00:12:52.624807: Epoch 3785 +2026-04-15 00:12:52.626856: Current learning rate: 0.00072 +2026-04-15 00:14:35.388823: train_loss -0.7761 +2026-04-15 00:14:35.398721: val_loss -0.6329 +2026-04-15 00:14:35.400535: Pseudo dice [0.4494, 0.8915, 0.7406, 0.1856, 0.4992, 0.7138, 0.7368] +2026-04-15 00:14:35.403247: Epoch time: 102.77 s +2026-04-15 00:14:36.710032: +2026-04-15 00:14:36.712521: Epoch 3786 +2026-04-15 00:14:36.717112: Current learning rate: 0.00072 +2026-04-15 00:16:18.931437: train_loss -0.7755 +2026-04-15 00:16:18.939709: val_loss -0.6252 +2026-04-15 00:16:18.941989: Pseudo dice [0.476, 0.8764, 0.7053, 0.1906, 0.3501, 0.8797, 0.6355] +2026-04-15 00:16:18.944688: Epoch time: 102.22 s +2026-04-15 00:16:20.182013: +2026-04-15 00:16:20.184230: Epoch 3787 +2026-04-15 00:16:20.186280: Current learning rate: 0.00071 +2026-04-15 00:18:02.507847: train_loss -0.7587 +2026-04-15 00:18:02.516230: val_loss -0.6956 +2026-04-15 00:18:02.519624: Pseudo dice [0.592, 0.5734, 0.787, 0.1038, 0.4487, 0.8066, 0.8949] +2026-04-15 00:18:02.522357: Epoch time: 102.33 s +2026-04-15 00:18:04.975771: +2026-04-15 00:18:04.977457: Epoch 3788 +2026-04-15 00:18:04.979300: Current learning rate: 0.00071 +2026-04-15 00:19:47.131860: train_loss -0.7781 +2026-04-15 00:19:47.138615: val_loss -0.6766 +2026-04-15 00:19:47.140792: Pseudo dice [0.6682, 0.8979, 0.7654, 0.7134, 0.696, 0.8442, 0.8296] +2026-04-15 00:19:47.143186: Epoch time: 102.16 s +2026-04-15 00:19:48.399355: +2026-04-15 00:19:48.402420: Epoch 3789 +2026-04-15 00:19:48.404185: Current learning rate: 0.00071 +2026-04-15 00:21:30.006366: train_loss -0.7757 +2026-04-15 00:21:30.013483: val_loss -0.673 +2026-04-15 00:21:30.015733: Pseudo dice [0.707, 0.2336, 0.7356, 0.4741, 0.6845, 0.3841, 0.6745] +2026-04-15 00:21:30.017925: Epoch time: 101.61 s +2026-04-15 00:21:31.222847: +2026-04-15 00:21:31.224667: Epoch 3790 +2026-04-15 00:21:31.226708: Current learning rate: 0.0007 +2026-04-15 00:23:13.426961: train_loss -0.7844 +2026-04-15 00:23:13.434204: val_loss -0.695 +2026-04-15 00:23:13.436231: Pseudo dice [0.8999, 0.0733, 0.7632, 0.709, 0.5658, 0.6157, 0.6437] +2026-04-15 00:23:13.438566: Epoch time: 102.21 s +2026-04-15 00:23:14.732306: +2026-04-15 00:23:14.734181: Epoch 3791 +2026-04-15 00:23:14.736127: Current learning rate: 0.0007 +2026-04-15 00:24:56.817157: train_loss -0.7784 +2026-04-15 00:24:56.825883: val_loss -0.7075 +2026-04-15 00:24:56.828573: Pseudo dice [0.5523, 0.6774, 0.8081, 0.6442, 0.5182, 0.7072, 0.8291] +2026-04-15 00:24:56.833525: Epoch time: 102.09 s +2026-04-15 00:24:58.142563: +2026-04-15 00:24:58.144664: Epoch 3792 +2026-04-15 00:24:58.147299: Current learning rate: 0.0007 +2026-04-15 00:26:40.664826: train_loss -0.778 +2026-04-15 00:26:40.671441: val_loss -0.6785 +2026-04-15 00:26:40.673805: Pseudo dice [0.755, 0.719, 0.757, 0.5904, 0.6512, 0.8008, 0.7395] +2026-04-15 00:26:40.676760: Epoch time: 102.53 s +2026-04-15 00:26:41.966107: +2026-04-15 00:26:41.967931: Epoch 3793 +2026-04-15 00:26:41.969780: Current learning rate: 0.0007 +2026-04-15 00:28:24.310184: train_loss -0.7802 +2026-04-15 00:28:24.317253: val_loss -0.6832 +2026-04-15 00:28:24.319329: Pseudo dice [0.3868, 0.9055, 0.7789, 0.3694, 0.2901, 0.7443, 0.6594] +2026-04-15 00:28:24.321527: Epoch time: 102.35 s +2026-04-15 00:28:25.569387: +2026-04-15 00:28:25.571649: Epoch 3794 +2026-04-15 00:28:25.573760: Current learning rate: 0.00069 +2026-04-15 00:30:08.145869: train_loss -0.7636 +2026-04-15 00:30:08.153287: val_loss -0.6837 +2026-04-15 00:30:08.156393: Pseudo dice [0.8009, 0.826, 0.7875, 0.3582, 0.5305, 0.7672, 0.8193] +2026-04-15 00:30:08.158526: Epoch time: 102.58 s +2026-04-15 00:30:09.466362: +2026-04-15 00:30:09.468227: Epoch 3795 +2026-04-15 00:30:09.469988: Current learning rate: 0.00069 +2026-04-15 00:31:52.193179: train_loss -0.7623 +2026-04-15 00:31:52.201196: val_loss -0.7477 +2026-04-15 00:31:52.204061: Pseudo dice [0.8617, 0.8025, 0.8666, 0.7414, 0.2991, 0.8247, 0.8426] +2026-04-15 00:31:52.206630: Epoch time: 102.73 s +2026-04-15 00:31:53.537069: +2026-04-15 00:31:53.538974: Epoch 3796 +2026-04-15 00:31:53.541378: Current learning rate: 0.00069 +2026-04-15 00:33:35.482152: train_loss -0.7777 +2026-04-15 00:33:35.490584: val_loss -0.6498 +2026-04-15 00:33:35.493026: Pseudo dice [0.6597, 0.8319, 0.7404, 0.3435, 0.1329, 0.9242, 0.8652] +2026-04-15 00:33:35.495672: Epoch time: 101.95 s +2026-04-15 00:33:36.739249: +2026-04-15 00:33:36.741461: Epoch 3797 +2026-04-15 00:33:36.743380: Current learning rate: 0.00068 +2026-04-15 00:35:18.958838: train_loss -0.7869 +2026-04-15 00:35:18.964697: val_loss -0.6503 +2026-04-15 00:35:18.966567: Pseudo dice [0.2423, 0.8957, 0.7768, 0.3717, 0.4344, 0.1544, 0.6596] +2026-04-15 00:35:18.968974: Epoch time: 102.22 s +2026-04-15 00:35:20.257428: +2026-04-15 00:35:20.259824: Epoch 3798 +2026-04-15 00:35:20.262608: Current learning rate: 0.00068 +2026-04-15 00:37:02.603447: train_loss -0.7943 +2026-04-15 00:37:02.610085: val_loss -0.6564 +2026-04-15 00:37:02.612195: Pseudo dice [0.8565, 0.873, 0.8052, 0.3073, 0.7274, 0.6752, 0.436] +2026-04-15 00:37:02.615544: Epoch time: 102.35 s +2026-04-15 00:37:03.906745: +2026-04-15 00:37:03.910579: Epoch 3799 +2026-04-15 00:37:03.914170: Current learning rate: 0.00068 +2026-04-15 00:38:45.789013: train_loss -0.7809 +2026-04-15 00:38:45.796090: val_loss -0.684 +2026-04-15 00:38:45.798237: Pseudo dice [0.8144, 0.7738, 0.7083, 0.5002, 0.4916, 0.8857, 0.7563] +2026-04-15 00:38:45.801021: Epoch time: 101.89 s +2026-04-15 00:38:48.779176: +2026-04-15 00:38:48.787404: Epoch 3800 +2026-04-15 00:38:48.790107: Current learning rate: 0.00067 +2026-04-15 00:40:31.239197: train_loss -0.7705 +2026-04-15 00:40:31.246541: val_loss -0.705 +2026-04-15 00:40:31.249714: Pseudo dice [0.6403, 0.662, 0.754, 0.5267, 0.5975, 0.8981, 0.7684] +2026-04-15 00:40:31.252234: Epoch time: 102.46 s +2026-04-15 00:40:32.523073: +2026-04-15 00:40:32.525205: Epoch 3801 +2026-04-15 00:40:32.528124: Current learning rate: 0.00067 +2026-04-15 00:42:14.407437: train_loss -0.7784 +2026-04-15 00:42:14.419734: val_loss -0.7102 +2026-04-15 00:42:14.421887: Pseudo dice [0.5059, 0.7597, 0.7772, 0.3686, 0.567, 0.802, 0.7491] +2026-04-15 00:42:14.424594: Epoch time: 101.89 s +2026-04-15 00:42:15.724085: +2026-04-15 00:42:15.726316: Epoch 3802 +2026-04-15 00:42:15.728545: Current learning rate: 0.00067 +2026-04-15 00:43:57.965125: train_loss -0.7828 +2026-04-15 00:43:57.973166: val_loss -0.6595 +2026-04-15 00:43:57.975549: Pseudo dice [0.7077, 0.9098, 0.8343, 0.4885, 0.4053, 0.7945, 0.7932] +2026-04-15 00:43:57.979000: Epoch time: 102.24 s +2026-04-15 00:43:59.210884: +2026-04-15 00:43:59.212860: Epoch 3803 +2026-04-15 00:43:59.215062: Current learning rate: 0.00067 +2026-04-15 00:45:41.640672: train_loss -0.7662 +2026-04-15 00:45:41.646972: val_loss -0.6685 +2026-04-15 00:45:41.649754: Pseudo dice [0.6453, 0.7985, 0.7573, 0.5586, 0.4475, 0.4642, 0.8693] +2026-04-15 00:45:41.652795: Epoch time: 102.43 s +2026-04-15 00:45:42.940979: +2026-04-15 00:45:42.943532: Epoch 3804 +2026-04-15 00:45:42.945984: Current learning rate: 0.00066 +2026-04-15 00:47:25.467504: train_loss -0.7716 +2026-04-15 00:47:25.474935: val_loss -0.6121 +2026-04-15 00:47:25.477403: Pseudo dice [0.8182, 0.7827, 0.6246, 0.394, 0.5176, 0.0948, 0.6853] +2026-04-15 00:47:25.479495: Epoch time: 102.53 s +2026-04-15 00:47:26.774782: +2026-04-15 00:47:26.776758: Epoch 3805 +2026-04-15 00:47:26.778704: Current learning rate: 0.00066 +2026-04-15 00:49:08.979661: train_loss -0.774 +2026-04-15 00:49:08.986295: val_loss -0.7328 +2026-04-15 00:49:08.988522: Pseudo dice [0.7996, 0.7747, 0.8014, 0.4686, 0.6596, 0.7941, 0.6912] +2026-04-15 00:49:08.990818: Epoch time: 102.21 s +2026-04-15 00:49:10.209526: +2026-04-15 00:49:10.211394: Epoch 3806 +2026-04-15 00:49:10.213454: Current learning rate: 0.00066 +2026-04-15 00:50:52.000670: train_loss -0.7816 +2026-04-15 00:50:52.007589: val_loss -0.6799 +2026-04-15 00:50:52.011151: Pseudo dice [0.7428, 0.882, 0.8124, 0.3541, 0.5869, 0.7735, 0.7979] +2026-04-15 00:50:52.013522: Epoch time: 101.79 s +2026-04-15 00:50:53.240135: +2026-04-15 00:50:53.242737: Epoch 3807 +2026-04-15 00:50:53.244676: Current learning rate: 0.00065 +2026-04-15 00:52:35.378065: train_loss -0.7946 +2026-04-15 00:52:35.384207: val_loss -0.6896 +2026-04-15 00:52:35.386556: Pseudo dice [0.8048, 0.8927, 0.8862, 0.1885, 0.4707, 0.4827, 0.8145] +2026-04-15 00:52:35.389441: Epoch time: 102.14 s +2026-04-15 00:52:37.854311: +2026-04-15 00:52:37.856385: Epoch 3808 +2026-04-15 00:52:37.860001: Current learning rate: 0.00065 +2026-04-15 00:54:20.871980: train_loss -0.791 +2026-04-15 00:54:20.880099: val_loss -0.7197 +2026-04-15 00:54:20.882949: Pseudo dice [0.7218, 0.6758, 0.8329, 0.5672, 0.7745, 0.7157, 0.7349] +2026-04-15 00:54:20.886184: Epoch time: 103.02 s +2026-04-15 00:54:22.151972: +2026-04-15 00:54:22.154432: Epoch 3809 +2026-04-15 00:54:22.157308: Current learning rate: 0.00065 +2026-04-15 00:56:04.436217: train_loss -0.7724 +2026-04-15 00:56:04.446858: val_loss -0.6404 +2026-04-15 00:56:04.462539: Pseudo dice [0.9115, 0.8222, 0.7273, 0.5164, 0.401, 0.1135, 0.4773] +2026-04-15 00:56:04.468978: Epoch time: 102.29 s +2026-04-15 00:56:05.726930: +2026-04-15 00:56:05.728793: Epoch 3810 +2026-04-15 00:56:05.730651: Current learning rate: 0.00064 +2026-04-15 00:57:48.205156: train_loss -0.7738 +2026-04-15 00:57:48.214308: val_loss -0.6965 +2026-04-15 00:57:48.216706: Pseudo dice [0.7745, 0.7425, 0.8109, 0.4328, 0.4866, 0.3416, 0.8746] +2026-04-15 00:57:48.219583: Epoch time: 102.48 s +2026-04-15 00:57:49.530652: +2026-04-15 00:57:49.533302: Epoch 3811 +2026-04-15 00:57:49.535742: Current learning rate: 0.00064 +2026-04-15 00:59:32.244124: train_loss -0.7795 +2026-04-15 00:59:32.251014: val_loss -0.6984 +2026-04-15 00:59:32.253543: Pseudo dice [0.5297, 0.8978, 0.8618, 0.5057, 0.5505, 0.87, 0.6409] +2026-04-15 00:59:32.257515: Epoch time: 102.72 s +2026-04-15 00:59:33.520570: +2026-04-15 00:59:33.522248: Epoch 3812 +2026-04-15 00:59:33.524357: Current learning rate: 0.00064 +2026-04-15 01:01:15.670843: train_loss -0.7768 +2026-04-15 01:01:15.678936: val_loss -0.7059 +2026-04-15 01:01:15.681331: Pseudo dice [0.531, 0.6629, 0.7908, 0.5797, 0.5454, 0.9252, 0.7228] +2026-04-15 01:01:15.684720: Epoch time: 102.15 s +2026-04-15 01:01:16.969715: +2026-04-15 01:01:16.971700: Epoch 3813 +2026-04-15 01:01:16.973845: Current learning rate: 0.00064 +2026-04-15 01:02:58.957402: train_loss -0.7824 +2026-04-15 01:02:58.964205: val_loss -0.6692 +2026-04-15 01:02:58.966691: Pseudo dice [0.5583, 0.6329, 0.8401, 0.6035, 0.4787, 0.3372, 0.7291] +2026-04-15 01:02:58.969631: Epoch time: 101.99 s +2026-04-15 01:03:00.282567: +2026-04-15 01:03:00.284794: Epoch 3814 +2026-04-15 01:03:00.287229: Current learning rate: 0.00063 +2026-04-15 01:04:42.410876: train_loss -0.7771 +2026-04-15 01:04:42.419855: val_loss -0.67 +2026-04-15 01:04:42.422208: Pseudo dice [0.4057, 0.7434, 0.6672, 0.3765, 0.6949, 0.4036, 0.8872] +2026-04-15 01:04:42.425146: Epoch time: 102.13 s +2026-04-15 01:04:43.748562: +2026-04-15 01:04:43.750542: Epoch 3815 +2026-04-15 01:04:43.752651: Current learning rate: 0.00063 +2026-04-15 01:06:26.671890: train_loss -0.7741 +2026-04-15 01:06:26.679949: val_loss -0.7009 +2026-04-15 01:06:26.683065: Pseudo dice [0.8662, 0.8013, 0.6887, 0.2199, 0.3355, 0.871, 0.7859] +2026-04-15 01:06:26.685948: Epoch time: 102.93 s +2026-04-15 01:06:28.008810: +2026-04-15 01:06:28.011251: Epoch 3816 +2026-04-15 01:06:28.013563: Current learning rate: 0.00063 +2026-04-15 01:08:10.734844: train_loss -0.7808 +2026-04-15 01:08:10.742869: val_loss -0.6484 +2026-04-15 01:08:10.745452: Pseudo dice [0.6881, 0.8992, 0.6988, 0.6549, 0.4445, 0.8626, 0.7054] +2026-04-15 01:08:10.748835: Epoch time: 102.73 s +2026-04-15 01:08:12.027040: +2026-04-15 01:08:12.029232: Epoch 3817 +2026-04-15 01:08:12.031157: Current learning rate: 0.00062 +2026-04-15 01:09:53.702962: train_loss -0.7843 +2026-04-15 01:09:53.708675: val_loss -0.7319 +2026-04-15 01:09:53.710748: Pseudo dice [0.7154, 0.7991, 0.7868, 0.6376, 0.5661, 0.8837, 0.862] +2026-04-15 01:09:53.713428: Epoch time: 101.68 s +2026-04-15 01:09:54.995841: +2026-04-15 01:09:54.997838: Epoch 3818 +2026-04-15 01:09:54.999834: Current learning rate: 0.00062 +2026-04-15 01:11:37.560885: train_loss -0.791 +2026-04-15 01:11:37.567011: val_loss -0.7111 +2026-04-15 01:11:37.569619: Pseudo dice [0.767, 0.7017, 0.7581, 0.2055, 0.6658, 0.7362, 0.8068] +2026-04-15 01:11:37.572014: Epoch time: 102.57 s +2026-04-15 01:11:38.812731: +2026-04-15 01:11:38.815113: Epoch 3819 +2026-04-15 01:11:38.817302: Current learning rate: 0.00062 +2026-04-15 01:13:20.812883: train_loss -0.7876 +2026-04-15 01:13:20.819541: val_loss -0.6992 +2026-04-15 01:13:20.824464: Pseudo dice [0.685, 0.9102, 0.7865, 0.5111, 0.6572, 0.1407, 0.8452] +2026-04-15 01:13:20.829005: Epoch time: 102.0 s +2026-04-15 01:13:22.122273: +2026-04-15 01:13:22.124319: Epoch 3820 +2026-04-15 01:13:22.126343: Current learning rate: 0.00061 +2026-04-15 01:15:04.764575: train_loss -0.7951 +2026-04-15 01:15:04.773726: val_loss -0.6818 +2026-04-15 01:15:04.775889: Pseudo dice [0.6704, 0.9125, 0.8226, 0.4441, 0.5906, 0.6713, 0.8044] +2026-04-15 01:15:04.778537: Epoch time: 102.65 s +2026-04-15 01:15:06.037066: +2026-04-15 01:15:06.038767: Epoch 3821 +2026-04-15 01:15:06.040530: Current learning rate: 0.00061 +2026-04-15 01:16:48.482172: train_loss -0.7799 +2026-04-15 01:16:48.488847: val_loss -0.7288 +2026-04-15 01:16:48.490631: Pseudo dice [0.8777, 0.6574, 0.7298, 0.8403, 0.4766, 0.8846, 0.8391] +2026-04-15 01:16:48.493477: Epoch time: 102.45 s +2026-04-15 01:16:49.786598: +2026-04-15 01:16:49.789644: Epoch 3822 +2026-04-15 01:16:49.791816: Current learning rate: 0.00061 +2026-04-15 01:18:31.973102: train_loss -0.7737 +2026-04-15 01:18:31.979236: val_loss -0.7083 +2026-04-15 01:18:31.981111: Pseudo dice [0.628, 0.5982, 0.7404, 0.5678, 0.5062, 0.8912, 0.7021] +2026-04-15 01:18:31.983417: Epoch time: 102.19 s +2026-04-15 01:18:33.258702: +2026-04-15 01:18:33.260917: Epoch 3823 +2026-04-15 01:18:33.263514: Current learning rate: 0.0006 +2026-04-15 01:20:15.839037: train_loss -0.7836 +2026-04-15 01:20:15.846306: val_loss -0.7117 +2026-04-15 01:20:15.850299: Pseudo dice [0.6984, 0.7105, 0.8253, 0.628, 0.3425, 0.7246, 0.8493] +2026-04-15 01:20:15.853263: Epoch time: 102.58 s +2026-04-15 01:20:17.137183: +2026-04-15 01:20:17.139764: Epoch 3824 +2026-04-15 01:20:17.142267: Current learning rate: 0.0006 +2026-04-15 01:21:59.068782: train_loss -0.7909 +2026-04-15 01:21:59.076674: val_loss -0.7442 +2026-04-15 01:21:59.079145: Pseudo dice [0.819, 0.7112, 0.7844, 0.5065, 0.6133, 0.9437, 0.8323] +2026-04-15 01:21:59.082036: Epoch time: 101.93 s +2026-04-15 01:22:00.389780: +2026-04-15 01:22:00.391341: Epoch 3825 +2026-04-15 01:22:00.393399: Current learning rate: 0.0006 +2026-04-15 01:23:43.147760: train_loss -0.7765 +2026-04-15 01:23:43.154067: val_loss -0.7063 +2026-04-15 01:23:43.156439: Pseudo dice [0.6484, 0.7346, 0.7812, 0.3995, 0.5994, 0.8535, 0.7502] +2026-04-15 01:23:43.159742: Epoch time: 102.76 s +2026-04-15 01:23:44.423467: +2026-04-15 01:23:44.425850: Epoch 3826 +2026-04-15 01:23:44.428297: Current learning rate: 0.0006 +2026-04-15 01:25:26.831337: train_loss -0.77 +2026-04-15 01:25:26.838777: val_loss -0.6339 +2026-04-15 01:25:26.841165: Pseudo dice [0.8845, 0.7917, 0.7081, 0.5664, 0.6201, 0.8946, 0.8177] +2026-04-15 01:25:26.843481: Epoch time: 102.41 s +2026-04-15 01:25:28.151586: +2026-04-15 01:25:28.153325: Epoch 3827 +2026-04-15 01:25:28.155721: Current learning rate: 0.00059 +2026-04-15 01:27:10.436494: train_loss -0.7768 +2026-04-15 01:27:10.443733: val_loss -0.6639 +2026-04-15 01:27:10.445690: Pseudo dice [0.7048, 0.8759, 0.7847, 0.5331, 0.4379, 0.6629, 0.8929] +2026-04-15 01:27:10.448142: Epoch time: 102.29 s +2026-04-15 01:27:13.049600: +2026-04-15 01:27:13.051484: Epoch 3828 +2026-04-15 01:27:13.054085: Current learning rate: 0.00059 +2026-04-15 01:28:55.608675: train_loss -0.7887 +2026-04-15 01:28:55.616773: val_loss -0.6173 +2026-04-15 01:28:55.618912: Pseudo dice [0.7539, 0.9319, 0.6652, 0.4589, 0.5544, 0.3909, 0.6165] +2026-04-15 01:28:55.621612: Epoch time: 102.56 s +2026-04-15 01:28:56.937853: +2026-04-15 01:28:56.941257: Epoch 3829 +2026-04-15 01:28:56.943760: Current learning rate: 0.00059 +2026-04-15 01:30:39.613618: train_loss -0.7979 +2026-04-15 01:30:39.620815: val_loss -0.6804 +2026-04-15 01:30:39.623096: Pseudo dice [0.8497, 0.8421, 0.8473, 0.5308, 0.4399, 0.8455, 0.7953] +2026-04-15 01:30:39.625543: Epoch time: 102.68 s +2026-04-15 01:30:40.900865: +2026-04-15 01:30:40.903091: Epoch 3830 +2026-04-15 01:30:40.905178: Current learning rate: 0.00058 +2026-04-15 01:32:23.476657: train_loss -0.7798 +2026-04-15 01:32:23.484425: val_loss -0.7284 +2026-04-15 01:32:23.488454: Pseudo dice [0.7396, 0.9099, 0.7895, 0.5452, 0.3585, 0.8484, 0.7991] +2026-04-15 01:32:23.490797: Epoch time: 102.58 s +2026-04-15 01:32:24.798975: +2026-04-15 01:32:24.800694: Epoch 3831 +2026-04-15 01:32:24.803020: Current learning rate: 0.00058 +2026-04-15 01:34:07.144344: train_loss -0.7864 +2026-04-15 01:34:07.151316: val_loss -0.6705 +2026-04-15 01:34:07.153476: Pseudo dice [0.5015, 0.8763, 0.7702, 0.5016, 0.5486, 0.1147, 0.8735] +2026-04-15 01:34:07.156810: Epoch time: 102.35 s +2026-04-15 01:34:08.472504: +2026-04-15 01:34:08.474465: Epoch 3832 +2026-04-15 01:34:08.477069: Current learning rate: 0.00058 +2026-04-15 01:35:50.834083: train_loss -0.7839 +2026-04-15 01:35:50.841601: val_loss -0.6941 +2026-04-15 01:35:50.843932: Pseudo dice [0.354, 0.8512, 0.6633, 0.428, 0.7199, 0.8098, 0.7137] +2026-04-15 01:35:50.847440: Epoch time: 102.36 s +2026-04-15 01:35:52.143285: +2026-04-15 01:35:52.145440: Epoch 3833 +2026-04-15 01:35:52.147428: Current learning rate: 0.00057 +2026-04-15 01:37:34.531914: train_loss -0.7946 +2026-04-15 01:37:34.538274: val_loss -0.6864 +2026-04-15 01:37:34.540444: Pseudo dice [0.8137, 0.7484, 0.8133, 0.2291, 0.5307, 0.9236, 0.8484] +2026-04-15 01:37:34.542659: Epoch time: 102.39 s +2026-04-15 01:37:35.812699: +2026-04-15 01:37:35.814487: Epoch 3834 +2026-04-15 01:37:35.816993: Current learning rate: 0.00057 +2026-04-15 01:39:17.925614: train_loss -0.7837 +2026-04-15 01:39:17.933417: val_loss -0.6829 +2026-04-15 01:39:17.935745: Pseudo dice [0.7097, 0.0318, 0.6916, 0.5723, 0.574, 0.7947, 0.8159] +2026-04-15 01:39:17.937994: Epoch time: 102.12 s +2026-04-15 01:39:19.162864: +2026-04-15 01:39:19.164948: Epoch 3835 +2026-04-15 01:39:19.167171: Current learning rate: 0.00057 +2026-04-15 01:41:01.510809: train_loss -0.7859 +2026-04-15 01:41:01.517780: val_loss -0.7283 +2026-04-15 01:41:01.520042: Pseudo dice [0.818, 0.6704, 0.8513, 0.2226, 0.6756, 0.9145, 0.8812] +2026-04-15 01:41:01.522324: Epoch time: 102.35 s +2026-04-15 01:41:02.846979: +2026-04-15 01:41:02.865965: Epoch 3836 +2026-04-15 01:41:02.872864: Current learning rate: 0.00056 +2026-04-15 01:42:45.328191: train_loss -0.7818 +2026-04-15 01:42:45.335882: val_loss -0.6771 +2026-04-15 01:42:45.338257: Pseudo dice [0.8869, 0.5443, 0.7326, 0.211, 0.6694, 0.483, 0.8686] +2026-04-15 01:42:45.340871: Epoch time: 102.48 s +2026-04-15 01:42:46.602422: +2026-04-15 01:42:46.605392: Epoch 3837 +2026-04-15 01:42:46.607585: Current learning rate: 0.00056 +2026-04-15 01:44:28.736912: train_loss -0.7863 +2026-04-15 01:44:28.744038: val_loss -0.7025 +2026-04-15 01:44:28.746353: Pseudo dice [0.7829, 0.665, 0.7738, 0.5643, 0.4894, 0.4196, 0.8212] +2026-04-15 01:44:28.749161: Epoch time: 102.14 s +2026-04-15 01:44:30.053189: +2026-04-15 01:44:30.055492: Epoch 3838 +2026-04-15 01:44:30.058021: Current learning rate: 0.00056 +2026-04-15 01:46:11.890160: train_loss -0.7928 +2026-04-15 01:46:11.898257: val_loss -0.6893 +2026-04-15 01:46:11.900447: Pseudo dice [0.4486, 0.3478, 0.8088, 0.4583, 0.4649, 0.8289, 0.7395] +2026-04-15 01:46:11.902789: Epoch time: 101.84 s +2026-04-15 01:46:13.210423: +2026-04-15 01:46:13.212333: Epoch 3839 +2026-04-15 01:46:13.214531: Current learning rate: 0.00055 +2026-04-15 01:47:55.669562: train_loss -0.797 +2026-04-15 01:47:55.683008: val_loss -0.6577 +2026-04-15 01:47:55.685223: Pseudo dice [0.542, 0.8758, 0.8356, 0.0662, 0.601, 0.164, 0.6042] +2026-04-15 01:47:55.689026: Epoch time: 102.46 s +2026-04-15 01:47:56.970156: +2026-04-15 01:47:56.971893: Epoch 3840 +2026-04-15 01:47:56.974629: Current learning rate: 0.00055 +2026-04-15 01:49:39.253643: train_loss -0.7899 +2026-04-15 01:49:39.261348: val_loss -0.6715 +2026-04-15 01:49:39.263983: Pseudo dice [0.4484, 0.459, 0.6919, 0.4035, 0.3123, 0.8979, 0.7192] +2026-04-15 01:49:39.266958: Epoch time: 102.29 s +2026-04-15 01:49:40.540757: +2026-04-15 01:49:40.542868: Epoch 3841 +2026-04-15 01:49:40.544905: Current learning rate: 0.00055 +2026-04-15 01:51:23.100659: train_loss -0.7923 +2026-04-15 01:51:23.106858: val_loss -0.7314 +2026-04-15 01:51:23.108809: Pseudo dice [0.737, 0.8073, 0.7717, 0.8993, 0.4503, 0.9317, 0.8041] +2026-04-15 01:51:23.110965: Epoch time: 102.56 s +2026-04-15 01:51:24.408101: +2026-04-15 01:51:24.409812: Epoch 3842 +2026-04-15 01:51:24.411991: Current learning rate: 0.00055 +2026-04-15 01:53:06.514035: train_loss -0.7836 +2026-04-15 01:53:06.520067: val_loss -0.6811 +2026-04-15 01:53:06.522414: Pseudo dice [0.6351, 0.6118, 0.8446, 0.3942, 0.5543, 0.8109, 0.788] +2026-04-15 01:53:06.525342: Epoch time: 102.11 s +2026-04-15 01:53:07.844235: +2026-04-15 01:53:07.846186: Epoch 3843 +2026-04-15 01:53:07.848542: Current learning rate: 0.00054 +2026-04-15 01:54:50.280590: train_loss -0.7916 +2026-04-15 01:54:50.290482: val_loss -0.6863 +2026-04-15 01:54:50.292739: Pseudo dice [0.6744, 0.8928, 0.8328, 0.5532, 0.6334, 0.8225, 0.8627] +2026-04-15 01:54:50.296268: Epoch time: 102.44 s +2026-04-15 01:54:51.560732: +2026-04-15 01:54:51.562997: Epoch 3844 +2026-04-15 01:54:51.565052: Current learning rate: 0.00054 +2026-04-15 01:56:33.853489: train_loss -0.7646 +2026-04-15 01:56:33.859768: val_loss -0.6751 +2026-04-15 01:56:33.862456: Pseudo dice [0.2776, 0.8567, 0.8461, 0.3651, 0.5612, 0.6911, 0.7996] +2026-04-15 01:56:33.864894: Epoch time: 102.3 s +2026-04-15 01:56:35.199718: +2026-04-15 01:56:35.202304: Epoch 3845 +2026-04-15 01:56:35.204254: Current learning rate: 0.00054 +2026-04-15 01:58:17.677237: train_loss -0.7885 +2026-04-15 01:58:17.684447: val_loss -0.7036 +2026-04-15 01:58:17.687050: Pseudo dice [0.8006, 0.8936, 0.8294, 0.836, 0.5433, 0.7214, 0.763] +2026-04-15 01:58:17.691618: Epoch time: 102.48 s +2026-04-15 01:58:18.949924: +2026-04-15 01:58:18.951725: Epoch 3846 +2026-04-15 01:58:18.953730: Current learning rate: 0.00053 +2026-04-15 02:00:01.807713: train_loss -0.7827 +2026-04-15 02:00:01.815305: val_loss -0.7385 +2026-04-15 02:00:01.817572: Pseudo dice [0.6941, 0.8234, 0.8175, 0.035, 0.5093, 0.9175, 0.8967] +2026-04-15 02:00:01.821344: Epoch time: 102.86 s +2026-04-15 02:00:03.076016: +2026-04-15 02:00:03.078485: Epoch 3847 +2026-04-15 02:00:03.080837: Current learning rate: 0.00053 +2026-04-15 02:01:45.819320: train_loss -0.7931 +2026-04-15 02:01:45.824323: val_loss -0.7174 +2026-04-15 02:01:45.826491: Pseudo dice [0.7896, 0.8164, 0.7821, 0.1345, 0.2882, 0.8152, 0.8647] +2026-04-15 02:01:45.828598: Epoch time: 102.75 s +2026-04-15 02:01:48.298681: +2026-04-15 02:01:48.300459: Epoch 3848 +2026-04-15 02:01:48.302640: Current learning rate: 0.00053 +2026-04-15 02:03:30.606168: train_loss -0.7877 +2026-04-15 02:03:30.612695: val_loss -0.7008 +2026-04-15 02:03:30.615062: Pseudo dice [0.8655, 0.9146, 0.8244, 0.3326, 0.5462, 0.6139, 0.8446] +2026-04-15 02:03:30.617368: Epoch time: 102.31 s +2026-04-15 02:03:31.895089: +2026-04-15 02:03:31.897534: Epoch 3849 +2026-04-15 02:03:31.899712: Current learning rate: 0.00052 +2026-04-15 02:05:13.782480: train_loss -0.788 +2026-04-15 02:05:13.794409: val_loss -0.6486 +2026-04-15 02:05:13.796587: Pseudo dice [0.513, 0.8888, 0.8047, 0.5107, 0.4987, 0.6604, 0.8309] +2026-04-15 02:05:13.799764: Epoch time: 101.89 s +2026-04-15 02:05:16.978131: +2026-04-15 02:05:16.979975: Epoch 3850 +2026-04-15 02:05:16.982686: Current learning rate: 0.00052 +2026-04-15 02:06:59.503665: train_loss -0.7893 +2026-04-15 02:06:59.509864: val_loss -0.6808 +2026-04-15 02:06:59.512942: Pseudo dice [0.6986, 0.7162, 0.795, 0.2508, 0.5957, 0.7923, 0.807] +2026-04-15 02:06:59.515382: Epoch time: 102.53 s +2026-04-15 02:07:00.793206: +2026-04-15 02:07:00.795183: Epoch 3851 +2026-04-15 02:07:00.797196: Current learning rate: 0.00052 +2026-04-15 02:08:43.270266: train_loss -0.7967 +2026-04-15 02:08:43.276468: val_loss -0.6793 +2026-04-15 02:08:43.278630: Pseudo dice [0.6917, 0.4764, 0.8227, 0.2102, 0.5046, 0.6769, 0.7846] +2026-04-15 02:08:43.281098: Epoch time: 102.48 s +2026-04-15 02:08:44.571168: +2026-04-15 02:08:44.573238: Epoch 3852 +2026-04-15 02:08:44.575226: Current learning rate: 0.00051 +2026-04-15 02:10:27.324577: train_loss -0.7902 +2026-04-15 02:10:27.330887: val_loss -0.7261 +2026-04-15 02:10:27.333657: Pseudo dice [0.4463, 0.7634, 0.7404, 0.3811, 0.601, 0.9065, 0.8635] +2026-04-15 02:10:27.336452: Epoch time: 102.76 s +2026-04-15 02:10:28.646834: +2026-04-15 02:10:28.648694: Epoch 3853 +2026-04-15 02:10:28.650816: Current learning rate: 0.00051 +2026-04-15 02:12:10.905726: train_loss -0.7908 +2026-04-15 02:12:10.913944: val_loss -0.6644 +2026-04-15 02:12:10.916981: Pseudo dice [0.7163, 0.9152, 0.8178, 0.0767, 0.7051, 0.627, 0.6513] +2026-04-15 02:12:10.919431: Epoch time: 102.26 s +2026-04-15 02:12:12.180662: +2026-04-15 02:12:12.182934: Epoch 3854 +2026-04-15 02:12:12.185447: Current learning rate: 0.00051 +2026-04-15 02:13:54.111788: train_loss -0.7783 +2026-04-15 02:13:54.119791: val_loss -0.7111 +2026-04-15 02:13:54.123310: Pseudo dice [0.6256, 0.8033, 0.8297, 0.8498, 0.5679, 0.8866, 0.7525] +2026-04-15 02:13:54.125947: Epoch time: 101.93 s +2026-04-15 02:13:55.435496: +2026-04-15 02:13:55.437454: Epoch 3855 +2026-04-15 02:13:55.439644: Current learning rate: 0.00051 +2026-04-15 02:15:37.549772: train_loss -0.7908 +2026-04-15 02:15:37.556354: val_loss -0.6951 +2026-04-15 02:15:37.558604: Pseudo dice [0.7448, 0.7725, 0.8654, 0.5821, 0.4923, 0.9383, 0.7179] +2026-04-15 02:15:37.560988: Epoch time: 102.12 s +2026-04-15 02:15:38.872294: +2026-04-15 02:15:38.874150: Epoch 3856 +2026-04-15 02:15:38.876408: Current learning rate: 0.0005 +2026-04-15 02:17:20.722750: train_loss -0.79 +2026-04-15 02:17:20.732435: val_loss -0.6767 +2026-04-15 02:17:20.734626: Pseudo dice [0.726, 0.8856, 0.8568, 0.3276, 0.5811, 0.7464, 0.6641] +2026-04-15 02:17:20.737010: Epoch time: 101.85 s +2026-04-15 02:17:21.998015: +2026-04-15 02:17:21.999751: Epoch 3857 +2026-04-15 02:17:22.001746: Current learning rate: 0.0005 +2026-04-15 02:19:03.845478: train_loss -0.7906 +2026-04-15 02:19:03.860130: val_loss -0.6806 +2026-04-15 02:19:03.862050: Pseudo dice [0.8289, 0.8101, 0.784, 0.2864, 0.6566, 0.733, 0.8356] +2026-04-15 02:19:03.866005: Epoch time: 101.85 s +2026-04-15 02:19:05.162132: +2026-04-15 02:19:05.163927: Epoch 3858 +2026-04-15 02:19:05.166362: Current learning rate: 0.0005 +2026-04-15 02:20:47.341418: train_loss -0.7917 +2026-04-15 02:20:47.349527: val_loss -0.7527 +2026-04-15 02:20:47.351712: Pseudo dice [0.3247, 0.5146, 0.8015, 0.8769, 0.4706, 0.7829, 0.8998] +2026-04-15 02:20:47.354640: Epoch time: 102.18 s +2026-04-15 02:20:48.689048: +2026-04-15 02:20:48.691315: Epoch 3859 +2026-04-15 02:20:48.693573: Current learning rate: 0.00049 +2026-04-15 02:22:30.705695: train_loss -0.7941 +2026-04-15 02:22:30.711785: val_loss -0.707 +2026-04-15 02:22:30.713946: Pseudo dice [0.6708, 0.7554, 0.7465, 0.5802, 0.5435, 0.8686, 0.8897] +2026-04-15 02:22:30.716549: Epoch time: 102.02 s +2026-04-15 02:22:31.996393: +2026-04-15 02:22:31.999583: Epoch 3860 +2026-04-15 02:22:32.001938: Current learning rate: 0.00049 +2026-04-15 02:24:14.330796: train_loss -0.784 +2026-04-15 02:24:14.337801: val_loss -0.7098 +2026-04-15 02:24:14.339874: Pseudo dice [0.5855, 0.7021, 0.7749, 0.5934, 0.4599, 0.8685, 0.6582] +2026-04-15 02:24:14.342795: Epoch time: 102.34 s +2026-04-15 02:24:15.651978: +2026-04-15 02:24:15.654343: Epoch 3861 +2026-04-15 02:24:15.657365: Current learning rate: 0.00049 +2026-04-15 02:25:57.741933: train_loss -0.7936 +2026-04-15 02:25:57.750396: val_loss -0.6878 +2026-04-15 02:25:57.752844: Pseudo dice [0.8732, 0.8589, 0.6659, 0.5774, 0.6247, 0.768, 0.8075] +2026-04-15 02:25:57.755456: Epoch time: 102.09 s +2026-04-15 02:25:59.045374: +2026-04-15 02:25:59.047357: Epoch 3862 +2026-04-15 02:25:59.049658: Current learning rate: 0.00048 +2026-04-15 02:27:41.169389: train_loss -0.794 +2026-04-15 02:27:41.176150: val_loss -0.6233 +2026-04-15 02:27:41.179113: Pseudo dice [0.6275, 0.8613, 0.7373, 0.1905, 0.6048, 0.427, 0.6739] +2026-04-15 02:27:41.181336: Epoch time: 102.13 s +2026-04-15 02:27:42.469598: +2026-04-15 02:27:42.471969: Epoch 3863 +2026-04-15 02:27:42.474097: Current learning rate: 0.00048 +2026-04-15 02:29:24.206848: train_loss -0.7936 +2026-04-15 02:29:24.212798: val_loss -0.7113 +2026-04-15 02:29:24.215041: Pseudo dice [0.7437, 0.8131, 0.8201, 0.6901, 0.7453, 0.7842, 0.8288] +2026-04-15 02:29:24.218733: Epoch time: 101.74 s +2026-04-15 02:29:25.479888: +2026-04-15 02:29:25.481585: Epoch 3864 +2026-04-15 02:29:25.483599: Current learning rate: 0.00048 +2026-04-15 02:31:07.566656: train_loss -0.7934 +2026-04-15 02:31:07.573630: val_loss -0.6828 +2026-04-15 02:31:07.576250: Pseudo dice [0.7216, 0.8534, 0.7709, 0.6166, 0.481, 0.7254, 0.7967] +2026-04-15 02:31:07.579706: Epoch time: 102.09 s +2026-04-15 02:31:08.850966: +2026-04-15 02:31:08.852538: Epoch 3865 +2026-04-15 02:31:08.854552: Current learning rate: 0.00047 +2026-04-15 02:32:50.890058: train_loss -0.7917 +2026-04-15 02:32:50.896312: val_loss -0.7499 +2026-04-15 02:32:50.898355: Pseudo dice [0.7654, 0.6566, 0.8662, 0.433, 0.6083, 0.9341, 0.7227] +2026-04-15 02:32:50.900995: Epoch time: 102.04 s +2026-04-15 02:32:52.149891: +2026-04-15 02:32:52.152061: Epoch 3866 +2026-04-15 02:32:52.154151: Current learning rate: 0.00047 +2026-04-15 02:34:33.722758: train_loss -0.7945 +2026-04-15 02:34:33.729681: val_loss -0.6644 +2026-04-15 02:34:33.732080: Pseudo dice [0.777, 0.883, 0.7053, 0.1849, 0.7172, 0.4785, 0.8266] +2026-04-15 02:34:33.734815: Epoch time: 101.58 s +2026-04-15 02:34:35.082726: +2026-04-15 02:34:35.084873: Epoch 3867 +2026-04-15 02:34:35.086705: Current learning rate: 0.00047 +2026-04-15 02:36:18.912594: train_loss -0.7912 +2026-04-15 02:36:18.926945: val_loss -0.7405 +2026-04-15 02:36:18.929532: Pseudo dice [0.7538, 0.8568, 0.7491, 0.313, 0.6595, 0.9326, 0.8321] +2026-04-15 02:36:18.933219: Epoch time: 103.83 s +2026-04-15 02:36:20.167885: +2026-04-15 02:36:20.170340: Epoch 3868 +2026-04-15 02:36:20.172961: Current learning rate: 0.00046 +2026-04-15 02:38:02.612751: train_loss -0.7862 +2026-04-15 02:38:02.619148: val_loss -0.6839 +2026-04-15 02:38:02.621161: Pseudo dice [0.7389, 0.6983, 0.7047, 0.6678, 0.688, 0.3521, 0.8344] +2026-04-15 02:38:02.624871: Epoch time: 102.45 s +2026-04-15 02:38:03.931105: +2026-04-15 02:38:03.932756: Epoch 3869 +2026-04-15 02:38:03.934989: Current learning rate: 0.00046 +2026-04-15 02:39:45.730441: train_loss -0.7998 +2026-04-15 02:39:45.738827: val_loss -0.7214 +2026-04-15 02:39:45.741432: Pseudo dice [0.7309, 0.7888, 0.8006, 0.6356, 0.5576, 0.7387, 0.8899] +2026-04-15 02:39:45.744226: Epoch time: 101.8 s +2026-04-15 02:39:47.066143: +2026-04-15 02:39:47.067750: Epoch 3870 +2026-04-15 02:39:47.069589: Current learning rate: 0.00046 +2026-04-15 02:41:29.099972: train_loss -0.7862 +2026-04-15 02:41:29.109040: val_loss -0.697 +2026-04-15 02:41:29.111080: Pseudo dice [0.7154, 0.7315, 0.8077, 0.66, 0.718, 0.399, 0.5883] +2026-04-15 02:41:29.113291: Epoch time: 102.04 s +2026-04-15 02:41:30.402222: +2026-04-15 02:41:30.406445: Epoch 3871 +2026-04-15 02:41:30.409739: Current learning rate: 0.00045 +2026-04-15 02:43:12.919340: train_loss -0.7919 +2026-04-15 02:43:12.926035: val_loss -0.6158 +2026-04-15 02:43:12.928776: Pseudo dice [0.766, 0.4091, 0.713, 0.5669, 0.6048, 0.0199, 0.6722] +2026-04-15 02:43:12.932023: Epoch time: 102.52 s +2026-04-15 02:43:14.215457: +2026-04-15 02:43:14.217290: Epoch 3872 +2026-04-15 02:43:14.219868: Current learning rate: 0.00045 +2026-04-15 02:44:56.447324: train_loss -0.7957 +2026-04-15 02:44:56.453232: val_loss -0.6812 +2026-04-15 02:44:56.455088: Pseudo dice [0.7779, 0.7671, 0.7043, 0.5754, 0.7131, 0.5899, 0.7911] +2026-04-15 02:44:56.457005: Epoch time: 102.24 s +2026-04-15 02:44:57.749714: +2026-04-15 02:44:57.752991: Epoch 3873 +2026-04-15 02:44:57.755119: Current learning rate: 0.00045 +2026-04-15 02:46:40.034341: train_loss -0.7856 +2026-04-15 02:46:40.041052: val_loss -0.6826 +2026-04-15 02:46:40.043698: Pseudo dice [0.6392, 0.9024, 0.8061, 0.819, 0.5155, 0.5508, 0.6934] +2026-04-15 02:46:40.046800: Epoch time: 102.29 s +2026-04-15 02:46:41.323600: +2026-04-15 02:46:41.325486: Epoch 3874 +2026-04-15 02:46:41.327618: Current learning rate: 0.00045 +2026-04-15 02:48:23.113393: train_loss -0.8003 +2026-04-15 02:48:23.122232: val_loss -0.7221 +2026-04-15 02:48:23.124441: Pseudo dice [0.8812, 0.9065, 0.8387, 0.5589, 0.2664, 0.1642, 0.3984] +2026-04-15 02:48:23.128240: Epoch time: 101.79 s +2026-04-15 02:48:24.372279: +2026-04-15 02:48:24.374435: Epoch 3875 +2026-04-15 02:48:24.376353: Current learning rate: 0.00044 +2026-04-15 02:50:06.650274: train_loss -0.7897 +2026-04-15 02:50:06.661314: val_loss -0.7325 +2026-04-15 02:50:06.663597: Pseudo dice [0.6618, 0.4538, 0.7925, 0.2859, 0.71, 0.7999, 0.8072] +2026-04-15 02:50:06.666849: Epoch time: 102.28 s +2026-04-15 02:50:08.020558: +2026-04-15 02:50:08.022714: Epoch 3876 +2026-04-15 02:50:08.024512: Current learning rate: 0.00044 +2026-04-15 02:51:49.918695: train_loss -0.8002 +2026-04-15 02:51:49.925752: val_loss -0.6863 +2026-04-15 02:51:49.928962: Pseudo dice [0.4861, 0.7463, 0.7646, 0.3059, 0.483, 0.8955, 0.8527] +2026-04-15 02:51:49.931294: Epoch time: 101.9 s +2026-04-15 02:51:51.259553: +2026-04-15 02:51:51.261363: Epoch 3877 +2026-04-15 02:51:51.263391: Current learning rate: 0.00044 +2026-04-15 02:53:32.992136: train_loss -0.784 +2026-04-15 02:53:32.998373: val_loss -0.7097 +2026-04-15 02:53:33.001268: Pseudo dice [0.7338, 0.6435, 0.7319, 0.5262, 0.5516, 0.926, 0.8308] +2026-04-15 02:53:33.003727: Epoch time: 101.74 s +2026-04-15 02:53:34.274702: +2026-04-15 02:53:34.276675: Epoch 3878 +2026-04-15 02:53:34.278281: Current learning rate: 0.00043 +2026-04-15 02:55:16.362100: train_loss -0.7879 +2026-04-15 02:55:16.368191: val_loss -0.7179 +2026-04-15 02:55:16.369833: Pseudo dice [0.7204, 0.7617, 0.8313, 0.5994, 0.4389, 0.9164, 0.7992] +2026-04-15 02:55:16.372066: Epoch time: 102.09 s +2026-04-15 02:55:17.628700: +2026-04-15 02:55:17.630369: Epoch 3879 +2026-04-15 02:55:17.632272: Current learning rate: 0.00043 +2026-04-15 02:56:59.423382: train_loss -0.7868 +2026-04-15 02:56:59.430231: val_loss -0.6815 +2026-04-15 02:56:59.432937: Pseudo dice [0.705, 0.8042, 0.7763, 0.2271, 0.479, 0.8651, 0.6244] +2026-04-15 02:56:59.435886: Epoch time: 101.8 s +2026-04-15 02:57:00.687314: +2026-04-15 02:57:00.689596: Epoch 3880 +2026-04-15 02:57:00.692290: Current learning rate: 0.00043 +2026-04-15 02:58:42.594625: train_loss -0.8033 +2026-04-15 02:58:42.601154: val_loss -0.7119 +2026-04-15 02:58:42.603225: Pseudo dice [0.638, 0.9102, 0.8356, 0.4272, 0.5979, 0.7182, 0.7612] +2026-04-15 02:58:42.605457: Epoch time: 101.91 s +2026-04-15 02:58:43.907891: +2026-04-15 02:58:43.910223: Epoch 3881 +2026-04-15 02:58:43.913162: Current learning rate: 0.00042 +2026-04-15 03:00:26.199770: train_loss -0.7903 +2026-04-15 03:00:26.206136: val_loss -0.6447 +2026-04-15 03:00:26.218128: Pseudo dice [0.6227, 0.8753, 0.6852, 0.2098, 0.5472, 0.1808, 0.6829] +2026-04-15 03:00:26.221930: Epoch time: 102.3 s +2026-04-15 03:00:27.540318: +2026-04-15 03:00:27.542429: Epoch 3882 +2026-04-15 03:00:27.544039: Current learning rate: 0.00042 +2026-04-15 03:02:09.722325: train_loss -0.7967 +2026-04-15 03:02:09.729589: val_loss -0.6813 +2026-04-15 03:02:09.732315: Pseudo dice [0.7777, 0.8193, 0.8339, 0.0, 0.7442, 0.739, 0.5485] +2026-04-15 03:02:09.735589: Epoch time: 102.19 s +2026-04-15 03:02:11.052905: +2026-04-15 03:02:11.054656: Epoch 3883 +2026-04-15 03:02:11.056657: Current learning rate: 0.00042 +2026-04-15 03:03:53.018980: train_loss -0.7967 +2026-04-15 03:03:53.024885: val_loss -0.6926 +2026-04-15 03:03:53.026958: Pseudo dice [0.5409, 0.773, 0.8499, 0.4491, 0.3162, 0.5535, 0.807] +2026-04-15 03:03:53.029989: Epoch time: 101.97 s +2026-04-15 03:03:54.334607: +2026-04-15 03:03:54.336438: Epoch 3884 +2026-04-15 03:03:54.338240: Current learning rate: 0.00041 +2026-04-15 03:05:36.734323: train_loss -0.7927 +2026-04-15 03:05:36.742967: val_loss -0.6959 +2026-04-15 03:05:36.745556: Pseudo dice [0.7559, 0.7116, 0.7184, 0.3044, 0.4821, 0.691, 0.8306] +2026-04-15 03:05:36.747966: Epoch time: 102.4 s +2026-04-15 03:05:38.064148: +2026-04-15 03:05:38.066574: Epoch 3885 +2026-04-15 03:05:38.068913: Current learning rate: 0.00041 +2026-04-15 03:07:20.001627: train_loss -0.7851 +2026-04-15 03:07:20.007395: val_loss -0.686 +2026-04-15 03:07:20.009458: Pseudo dice [0.7202, 0.6372, 0.8215, 0.5481, 0.3569, 0.8141, 0.7986] +2026-04-15 03:07:20.011943: Epoch time: 101.94 s +2026-04-15 03:07:21.274625: +2026-04-15 03:07:21.276628: Epoch 3886 +2026-04-15 03:07:21.278544: Current learning rate: 0.00041 +2026-04-15 03:09:03.896516: train_loss -0.8002 +2026-04-15 03:09:03.903660: val_loss -0.6528 +2026-04-15 03:09:03.905834: Pseudo dice [0.8931, 0.8924, 0.8003, 0.2752, 0.6081, 0.1124, 0.8301] +2026-04-15 03:09:03.908146: Epoch time: 102.63 s +2026-04-15 03:09:06.398276: +2026-04-15 03:09:06.402621: Epoch 3887 +2026-04-15 03:09:06.405107: Current learning rate: 0.0004 +2026-04-15 03:10:48.400007: train_loss -0.8031 +2026-04-15 03:10:48.405688: val_loss -0.7414 +2026-04-15 03:10:48.407931: Pseudo dice [0.6515, 0.8718, 0.802, 0.6747, 0.5736, 0.7672, 0.8378] +2026-04-15 03:10:48.410174: Epoch time: 102.0 s +2026-04-15 03:10:49.694317: +2026-04-15 03:10:49.696278: Epoch 3888 +2026-04-15 03:10:49.698360: Current learning rate: 0.0004 +2026-04-15 03:12:31.542954: train_loss -0.7939 +2026-04-15 03:12:31.552792: val_loss -0.6906 +2026-04-15 03:12:31.557180: Pseudo dice [0.8618, 0.8939, 0.8301, 0.526, 0.4608, 0.6597, 0.6734] +2026-04-15 03:12:31.561177: Epoch time: 101.85 s +2026-04-15 03:12:32.829400: +2026-04-15 03:12:32.831232: Epoch 3889 +2026-04-15 03:12:32.832994: Current learning rate: 0.0004 +2026-04-15 03:14:14.606150: train_loss -0.7915 +2026-04-15 03:14:14.614423: val_loss -0.7094 +2026-04-15 03:14:14.617173: Pseudo dice [0.761, 0.8002, 0.816, 0.5252, 0.6636, 0.8926, 0.8259] +2026-04-15 03:14:14.620051: Epoch time: 101.78 s +2026-04-15 03:14:15.884707: +2026-04-15 03:14:15.886992: Epoch 3890 +2026-04-15 03:14:15.888979: Current learning rate: 0.00039 +2026-04-15 03:15:58.422779: train_loss -0.7984 +2026-04-15 03:15:58.429715: val_loss -0.7302 +2026-04-15 03:15:58.431864: Pseudo dice [0.8489, 0.7908, 0.7576, 0.6121, 0.4113, 0.4211, 0.8527] +2026-04-15 03:15:58.434346: Epoch time: 102.54 s +2026-04-15 03:15:59.734883: +2026-04-15 03:15:59.736789: Epoch 3891 +2026-04-15 03:15:59.738989: Current learning rate: 0.00039 +2026-04-15 03:17:41.999161: train_loss -0.7995 +2026-04-15 03:17:42.005561: val_loss -0.718 +2026-04-15 03:17:42.008333: Pseudo dice [0.6833, 0.7608, 0.8233, 0.2081, 0.4062, 0.7274, 0.8339] +2026-04-15 03:17:42.010880: Epoch time: 102.27 s +2026-04-15 03:17:43.268031: +2026-04-15 03:17:43.269863: Epoch 3892 +2026-04-15 03:17:43.271931: Current learning rate: 0.00039 +2026-04-15 03:19:25.401427: train_loss -0.796 +2026-04-15 03:19:25.407941: val_loss -0.6691 +2026-04-15 03:19:25.410699: Pseudo dice [0.9098, 0.9048, 0.8647, 0.1198, 0.4347, 0.6409, 0.662] +2026-04-15 03:19:25.413224: Epoch time: 102.14 s +2026-04-15 03:19:26.666855: +2026-04-15 03:19:26.670229: Epoch 3893 +2026-04-15 03:19:26.672965: Current learning rate: 0.00038 +2026-04-15 03:21:08.349824: train_loss -0.796 +2026-04-15 03:21:08.356185: val_loss -0.694 +2026-04-15 03:21:08.358272: Pseudo dice [0.4596, 0.9067, 0.7785, 0.4529, 0.3501, 0.7262, 0.8218] +2026-04-15 03:21:08.360905: Epoch time: 101.69 s +2026-04-15 03:21:09.618775: +2026-04-15 03:21:09.621080: Epoch 3894 +2026-04-15 03:21:09.623129: Current learning rate: 0.00038 +2026-04-15 03:22:51.627560: train_loss -0.803 +2026-04-15 03:22:51.633076: val_loss -0.7151 +2026-04-15 03:22:51.635082: Pseudo dice [0.7275, 0.8923, 0.8472, 0.4399, 0.6952, 0.1979, 0.8948] +2026-04-15 03:22:51.637646: Epoch time: 102.01 s +2026-04-15 03:22:52.964769: +2026-04-15 03:22:52.967513: Epoch 3895 +2026-04-15 03:22:52.969833: Current learning rate: 0.00038 +2026-04-15 03:24:35.201082: train_loss -0.785 +2026-04-15 03:24:35.208155: val_loss -0.6745 +2026-04-15 03:24:35.210680: Pseudo dice [0.4361, 0.8081, 0.736, 0.627, 0.3907, 0.8193, 0.809] +2026-04-15 03:24:35.213730: Epoch time: 102.24 s +2026-04-15 03:24:36.489111: +2026-04-15 03:24:36.490993: Epoch 3896 +2026-04-15 03:24:36.493550: Current learning rate: 0.00037 +2026-04-15 03:26:18.061995: train_loss -0.8003 +2026-04-15 03:26:18.068301: val_loss -0.7086 +2026-04-15 03:26:18.070792: Pseudo dice [0.8689, 0.9058, 0.7936, 0.8845, 0.6721, 0.8906, 0.8724] +2026-04-15 03:26:18.073190: Epoch time: 101.58 s +2026-04-15 03:26:19.325238: +2026-04-15 03:26:19.327258: Epoch 3897 +2026-04-15 03:26:19.329421: Current learning rate: 0.00037 +2026-04-15 03:28:01.251230: train_loss -0.7963 +2026-04-15 03:28:01.259022: val_loss -0.734 +2026-04-15 03:28:01.261421: Pseudo dice [0.7977, 0.9017, 0.8791, 0.8229, 0.6304, 0.7569, 0.8642] +2026-04-15 03:28:01.270784: Epoch time: 101.93 s +2026-04-15 03:28:02.532485: +2026-04-15 03:28:02.535164: Epoch 3898 +2026-04-15 03:28:02.536798: Current learning rate: 0.00037 +2026-04-15 03:29:44.493304: train_loss -0.794 +2026-04-15 03:29:44.500670: val_loss -0.7529 +2026-04-15 03:29:44.502631: Pseudo dice [0.8101, 0.7546, 0.7492, 0.5357, 0.74, 0.9315, 0.8843] +2026-04-15 03:29:44.505173: Epoch time: 101.96 s +2026-04-15 03:29:45.778869: +2026-04-15 03:29:45.781086: Epoch 3899 +2026-04-15 03:29:45.782874: Current learning rate: 0.00036 +2026-04-15 03:31:27.980388: train_loss -0.8042 +2026-04-15 03:31:27.987799: val_loss -0.7022 +2026-04-15 03:31:27.990196: Pseudo dice [0.8094, 0.6884, 0.7879, 0.3571, 0.618, 0.458, 0.7286] +2026-04-15 03:31:27.994532: Epoch time: 102.21 s +2026-04-15 03:31:31.130719: +2026-04-15 03:31:31.132591: Epoch 3900 +2026-04-15 03:31:31.134457: Current learning rate: 0.00036 +2026-04-15 03:33:13.849246: train_loss -0.8042 +2026-04-15 03:33:13.859712: val_loss -0.7048 +2026-04-15 03:33:13.861891: Pseudo dice [0.7466, 0.6172, 0.7817, 0.0027, 0.7194, 0.8858, 0.8226] +2026-04-15 03:33:13.864030: Epoch time: 102.72 s +2026-04-15 03:33:15.148088: +2026-04-15 03:33:15.150275: Epoch 3901 +2026-04-15 03:33:15.152244: Current learning rate: 0.00036 +2026-04-15 03:34:57.171258: train_loss -0.7943 +2026-04-15 03:34:57.178444: val_loss -0.7424 +2026-04-15 03:34:57.180589: Pseudo dice [0.6925, 0.8993, 0.8813, 0.7086, 0.4262, 0.8159, 0.5623] +2026-04-15 03:34:57.183327: Epoch time: 102.03 s +2026-04-15 03:34:58.474239: +2026-04-15 03:34:58.476085: Epoch 3902 +2026-04-15 03:34:58.477639: Current learning rate: 0.00036 +2026-04-15 03:36:40.374662: train_loss -0.8025 +2026-04-15 03:36:40.383493: val_loss -0.7133 +2026-04-15 03:36:40.385785: Pseudo dice [0.8508, 0.879, 0.8666, 0.0645, 0.2198, 0.8038, 0.3123] +2026-04-15 03:36:40.388089: Epoch time: 101.9 s +2026-04-15 03:36:41.655957: +2026-04-15 03:36:41.658240: Epoch 3903 +2026-04-15 03:36:41.659959: Current learning rate: 0.00035 +2026-04-15 03:38:23.463494: train_loss -0.7945 +2026-04-15 03:38:23.470389: val_loss -0.6259 +2026-04-15 03:38:23.473062: Pseudo dice [0.9116, 0.9019, 0.7464, 0.2529, 0.3093, 0.5296, 0.2978] +2026-04-15 03:38:23.475339: Epoch time: 101.81 s +2026-04-15 03:38:24.710263: +2026-04-15 03:38:24.712412: Epoch 3904 +2026-04-15 03:38:24.714616: Current learning rate: 0.00035 +2026-04-15 03:40:06.678862: train_loss -0.7826 +2026-04-15 03:40:06.684693: val_loss -0.6853 +2026-04-15 03:40:06.686975: Pseudo dice [0.6941, 0.8167, 0.7531, 0.294, 0.2617, 0.8283, 0.8821] +2026-04-15 03:40:06.690162: Epoch time: 101.97 s +2026-04-15 03:40:08.022847: +2026-04-15 03:40:08.024852: Epoch 3905 +2026-04-15 03:40:08.026894: Current learning rate: 0.00035 +2026-04-15 03:41:49.543231: train_loss -0.7922 +2026-04-15 03:41:49.549902: val_loss -0.6518 +2026-04-15 03:41:49.552287: Pseudo dice [0.7832, 0.9144, 0.7853, 0.3005, 0.5481, 0.1201, 0.7731] +2026-04-15 03:41:49.554574: Epoch time: 101.52 s +2026-04-15 03:41:50.808601: +2026-04-15 03:41:50.810755: Epoch 3906 +2026-04-15 03:41:50.812444: Current learning rate: 0.00034 +2026-04-15 03:43:33.616263: train_loss -0.8012 +2026-04-15 03:43:33.623947: val_loss -0.677 +2026-04-15 03:43:33.627409: Pseudo dice [0.758, 0.9056, 0.7868, 0.6152, 0.2405, 0.6039, 0.7025] +2026-04-15 03:43:33.630692: Epoch time: 102.81 s +2026-04-15 03:43:34.888652: +2026-04-15 03:43:34.890583: Epoch 3907 +2026-04-15 03:43:34.892379: Current learning rate: 0.00034 +2026-04-15 03:45:17.091521: train_loss -0.8031 +2026-04-15 03:45:17.099461: val_loss -0.694 +2026-04-15 03:45:17.102213: Pseudo dice [0.7414, 0.3065, 0.8192, 0.3365, 0.6884, 0.6398, 0.2513] +2026-04-15 03:45:17.104575: Epoch time: 102.21 s +2026-04-15 03:45:18.355454: +2026-04-15 03:45:18.358357: Epoch 3908 +2026-04-15 03:45:18.360713: Current learning rate: 0.00034 +2026-04-15 03:47:00.523358: train_loss -0.7949 +2026-04-15 03:47:00.532808: val_loss -0.6767 +2026-04-15 03:47:00.535853: Pseudo dice [0.6762, 0.9206, 0.6691, 0.5324, 0.466, 0.743, 0.8597] +2026-04-15 03:47:00.538662: Epoch time: 102.17 s +2026-04-15 03:47:01.793539: +2026-04-15 03:47:01.796317: Epoch 3909 +2026-04-15 03:47:01.797986: Current learning rate: 0.00033 +2026-04-15 03:48:44.247767: train_loss -0.7985 +2026-04-15 03:48:44.266268: val_loss -0.716 +2026-04-15 03:48:44.268341: Pseudo dice [0.6908, 0.843, 0.8384, 0.1521, 0.2698, 0.9295, 0.8736] +2026-04-15 03:48:44.270609: Epoch time: 102.46 s +2026-04-15 03:48:45.514286: +2026-04-15 03:48:45.515961: Epoch 3910 +2026-04-15 03:48:45.517576: Current learning rate: 0.00033 +2026-04-15 03:50:27.438539: train_loss -0.8102 +2026-04-15 03:50:27.447461: val_loss -0.664 +2026-04-15 03:50:27.449748: Pseudo dice [0.4248, 0.5364, 0.7081, 0.3636, 0.5034, 0.6703, 0.8685] +2026-04-15 03:50:27.452116: Epoch time: 101.93 s +2026-04-15 03:50:28.685480: +2026-04-15 03:50:28.687764: Epoch 3911 +2026-04-15 03:50:28.690050: Current learning rate: 0.00033 +2026-04-15 03:52:10.478260: train_loss -0.7867 +2026-04-15 03:52:10.486223: val_loss -0.7036 +2026-04-15 03:52:10.488056: Pseudo dice [0.8008, 0.9181, 0.7855, 0.3103, 0.4179, 0.8785, 0.7301] +2026-04-15 03:52:10.490206: Epoch time: 101.8 s +2026-04-15 03:52:11.740609: +2026-04-15 03:52:11.743350: Epoch 3912 +2026-04-15 03:52:11.745279: Current learning rate: 0.00032 +2026-04-15 03:53:53.339596: train_loss -0.8014 +2026-04-15 03:53:53.350899: val_loss -0.6907 +2026-04-15 03:53:53.353375: Pseudo dice [0.7507, 0.6182, 0.6875, 0.4972, 0.645, 0.8802, 0.818] +2026-04-15 03:53:53.357684: Epoch time: 101.6 s +2026-04-15 03:53:54.653118: +2026-04-15 03:53:54.654895: Epoch 3913 +2026-04-15 03:53:54.656773: Current learning rate: 0.00032 +2026-04-15 03:55:37.414176: train_loss -0.8055 +2026-04-15 03:55:37.423697: val_loss -0.6885 +2026-04-15 03:55:37.426477: Pseudo dice [0.7261, 0.9041, 0.8364, 0.2494, 0.6747, 0.1187, 0.7613] +2026-04-15 03:55:37.429080: Epoch time: 102.76 s +2026-04-15 03:55:38.674014: +2026-04-15 03:55:38.675919: Epoch 3914 +2026-04-15 03:55:38.677983: Current learning rate: 0.00032 +2026-04-15 03:57:20.838419: train_loss -0.8084 +2026-04-15 03:57:20.848477: val_loss -0.6806 +2026-04-15 03:57:20.850756: Pseudo dice [0.7023, 0.8967, 0.8336, 0.3994, 0.5732, 0.6605, 0.6526] +2026-04-15 03:57:20.853159: Epoch time: 102.17 s +2026-04-15 03:57:22.119904: +2026-04-15 03:57:22.122351: Epoch 3915 +2026-04-15 03:57:22.123956: Current learning rate: 0.00031 +2026-04-15 03:59:03.926605: train_loss -0.8096 +2026-04-15 03:59:03.935762: val_loss -0.6218 +2026-04-15 03:59:03.938190: Pseudo dice [0.6032, 0.9146, 0.7979, 0.4472, 0.502, 0.0467, 0.7699] +2026-04-15 03:59:03.940748: Epoch time: 101.81 s +2026-04-15 03:59:05.191272: +2026-04-15 03:59:05.193254: Epoch 3916 +2026-04-15 03:59:05.194706: Current learning rate: 0.00031 +2026-04-15 04:00:47.166372: train_loss -0.8044 +2026-04-15 04:00:47.174371: val_loss -0.6659 +2026-04-15 04:00:47.177233: Pseudo dice [0.4937, 0.886, 0.7469, 0.4506, 0.4174, 0.3361, 0.8086] +2026-04-15 04:00:47.179404: Epoch time: 101.98 s +2026-04-15 04:00:48.436754: +2026-04-15 04:00:48.439265: Epoch 3917 +2026-04-15 04:00:48.441302: Current learning rate: 0.00031 +2026-04-15 04:02:30.247099: train_loss -0.7963 +2026-04-15 04:02:30.254844: val_loss -0.7 +2026-04-15 04:02:30.258234: Pseudo dice [0.8785, 0.8299, 0.7789, 0.4385, 0.4874, 0.8148, 0.888] +2026-04-15 04:02:30.260632: Epoch time: 101.81 s +2026-04-15 04:02:31.495148: +2026-04-15 04:02:31.497187: Epoch 3918 +2026-04-15 04:02:31.500830: Current learning rate: 0.0003 +2026-04-15 04:04:13.534399: train_loss -0.801 +2026-04-15 04:04:13.542115: val_loss -0.6922 +2026-04-15 04:04:13.544280: Pseudo dice [0.6697, 0.8218, 0.7487, 0.2847, 0.5648, 0.6838, 0.8092] +2026-04-15 04:04:13.546364: Epoch time: 102.04 s +2026-04-15 04:04:14.878967: +2026-04-15 04:04:14.880658: Epoch 3919 +2026-04-15 04:04:14.882322: Current learning rate: 0.0003 +2026-04-15 04:05:57.120355: train_loss -0.8042 +2026-04-15 04:05:57.131320: val_loss -0.6589 +2026-04-15 04:05:57.134449: Pseudo dice [0.687, 0.922, 0.768, 0.2368, 0.3988, 0.7072, 0.7032] +2026-04-15 04:05:57.137202: Epoch time: 102.24 s +2026-04-15 04:05:58.370869: +2026-04-15 04:05:58.372835: Epoch 3920 +2026-04-15 04:05:58.375462: Current learning rate: 0.0003 +2026-04-15 04:07:40.146345: train_loss -0.803 +2026-04-15 04:07:40.153611: val_loss -0.7266 +2026-04-15 04:07:40.155881: Pseudo dice [0.7704, 0.8159, 0.7835, 0.2149, 0.4387, 0.8514, 0.8504] +2026-04-15 04:07:40.158445: Epoch time: 101.78 s +2026-04-15 04:07:41.407043: +2026-04-15 04:07:41.408968: Epoch 3921 +2026-04-15 04:07:41.411130: Current learning rate: 0.00029 +2026-04-15 04:09:23.535799: train_loss -0.7963 +2026-04-15 04:09:23.543165: val_loss -0.6851 +2026-04-15 04:09:23.546365: Pseudo dice [0.7727, 0.9016, 0.8525, 0.4215, 0.2288, 0.2313, 0.8213] +2026-04-15 04:09:23.550219: Epoch time: 102.13 s +2026-04-15 04:09:24.840528: +2026-04-15 04:09:24.842726: Epoch 3922 +2026-04-15 04:09:24.844578: Current learning rate: 0.00029 +2026-04-15 04:11:07.065042: train_loss -0.802 +2026-04-15 04:11:07.072571: val_loss -0.7169 +2026-04-15 04:11:07.074542: Pseudo dice [0.7555, 0.6194, 0.796, 0.3095, 0.2737, 0.8977, 0.8812] +2026-04-15 04:11:07.077461: Epoch time: 102.23 s +2026-04-15 04:11:08.407723: +2026-04-15 04:11:08.410383: Epoch 3923 +2026-04-15 04:11:08.411974: Current learning rate: 0.00029 +2026-04-15 04:12:50.463899: train_loss -0.7943 +2026-04-15 04:12:50.470482: val_loss -0.761 +2026-04-15 04:12:50.473214: Pseudo dice [0.9052, 0.6297, 0.7546, 0.3039, 0.5999, 0.7066, 0.8689] +2026-04-15 04:12:50.475983: Epoch time: 102.06 s +2026-04-15 04:12:51.757548: +2026-04-15 04:12:51.759322: Epoch 3924 +2026-04-15 04:12:51.760837: Current learning rate: 0.00028 +2026-04-15 04:14:34.068274: train_loss -0.7973 +2026-04-15 04:14:34.074193: val_loss -0.7282 +2026-04-15 04:14:34.076462: Pseudo dice [0.801, 0.8226, 0.8402, 0.4505, 0.3387, 0.9403, 0.8678] +2026-04-15 04:14:34.079503: Epoch time: 102.31 s +2026-04-15 04:14:35.359699: +2026-04-15 04:14:35.361745: Epoch 3925 +2026-04-15 04:14:35.364907: Current learning rate: 0.00028 +2026-04-15 04:16:17.735471: train_loss -0.7983 +2026-04-15 04:16:17.741633: val_loss -0.7253 +2026-04-15 04:16:17.743315: Pseudo dice [0.746, 0.6739, 0.8411, 0.6748, 0.5544, 0.7366, 0.7046] +2026-04-15 04:16:17.745543: Epoch time: 102.38 s +2026-04-15 04:16:19.057438: +2026-04-15 04:16:19.059200: Epoch 3926 +2026-04-15 04:16:19.060783: Current learning rate: 0.00028 +2026-04-15 04:18:02.534053: train_loss -0.8003 +2026-04-15 04:18:02.542370: val_loss -0.7154 +2026-04-15 04:18:02.544604: Pseudo dice [0.8214, 0.8955, 0.853, 0.1978, 0.7174, 0.8896, 0.7708] +2026-04-15 04:18:02.547735: Epoch time: 103.48 s +2026-04-15 04:18:03.785014: +2026-04-15 04:18:03.786959: Epoch 3927 +2026-04-15 04:18:03.789900: Current learning rate: 0.00027 +2026-04-15 04:19:46.448869: train_loss -0.8097 +2026-04-15 04:19:46.455273: val_loss -0.6603 +2026-04-15 04:19:46.457484: Pseudo dice [0.7908, 0.9165, 0.7648, 0.361, 0.4667, 0.7198, 0.5751] +2026-04-15 04:19:46.459641: Epoch time: 102.67 s +2026-04-15 04:19:47.765966: +2026-04-15 04:19:47.768654: Epoch 3928 +2026-04-15 04:19:47.770729: Current learning rate: 0.00027 +2026-04-15 04:21:30.335484: train_loss -0.7912 +2026-04-15 04:21:30.341486: val_loss -0.7353 +2026-04-15 04:21:30.343748: Pseudo dice [0.8045, 0.7955, 0.7712, 0.3376, 0.5698, 0.787, 0.8376] +2026-04-15 04:21:30.346556: Epoch time: 102.57 s +2026-04-15 04:21:31.606169: +2026-04-15 04:21:31.608836: Epoch 3929 +2026-04-15 04:21:31.612083: Current learning rate: 0.00027 +2026-04-15 04:23:13.436108: train_loss -0.7978 +2026-04-15 04:23:13.442553: val_loss -0.6779 +2026-04-15 04:23:13.446374: Pseudo dice [0.7294, 0.7294, 0.8103, 0.5655, 0.504, 0.7143, 0.8876] +2026-04-15 04:23:13.448830: Epoch time: 101.83 s +2026-04-15 04:23:14.753367: +2026-04-15 04:23:14.755220: Epoch 3930 +2026-04-15 04:23:14.758056: Current learning rate: 0.00026 +2026-04-15 04:24:57.243469: train_loss -0.8008 +2026-04-15 04:24:57.250211: val_loss -0.7177 +2026-04-15 04:24:57.252357: Pseudo dice [0.7498, 0.8074, 0.74, 0.3164, 0.6986, 0.4637, 0.8721] +2026-04-15 04:24:57.254648: Epoch time: 102.49 s +2026-04-15 04:24:58.597281: +2026-04-15 04:24:58.598934: Epoch 3931 +2026-04-15 04:24:58.600442: Current learning rate: 0.00026 +2026-04-15 04:26:41.025146: train_loss -0.8073 +2026-04-15 04:26:41.032633: val_loss -0.6852 +2026-04-15 04:26:41.035412: Pseudo dice [0.7506, 0.7474, 0.811, 0.5235, 0.383, 0.754, 0.5128] +2026-04-15 04:26:41.038364: Epoch time: 102.43 s +2026-04-15 04:26:42.341390: +2026-04-15 04:26:42.343164: Epoch 3932 +2026-04-15 04:26:42.344896: Current learning rate: 0.00026 +2026-04-15 04:28:25.270889: train_loss -0.8104 +2026-04-15 04:28:25.279073: val_loss -0.7138 +2026-04-15 04:28:25.281536: Pseudo dice [0.4902, 0.6202, 0.8228, 0.6143, 0.6478, 0.9041, 0.7506] +2026-04-15 04:28:25.284450: Epoch time: 102.93 s +2026-04-15 04:28:26.572612: +2026-04-15 04:28:26.574829: Epoch 3933 +2026-04-15 04:28:26.577474: Current learning rate: 0.00025 +2026-04-15 04:30:08.580760: train_loss -0.8085 +2026-04-15 04:30:08.587271: val_loss -0.7353 +2026-04-15 04:30:08.589341: Pseudo dice [0.7996, 0.6488, 0.7572, 0.5617, 0.5412, 0.4602, 0.786] +2026-04-15 04:30:08.592787: Epoch time: 102.01 s +2026-04-15 04:30:09.869866: +2026-04-15 04:30:09.871771: Epoch 3934 +2026-04-15 04:30:09.873493: Current learning rate: 0.00025 +2026-04-15 04:31:52.406805: train_loss -0.7986 +2026-04-15 04:31:52.414189: val_loss -0.7367 +2026-04-15 04:31:52.416678: Pseudo dice [0.7591, 0.8428, 0.8019, 0.7041, 0.5167, 0.9367, 0.8041] +2026-04-15 04:31:52.419745: Epoch time: 102.54 s +2026-04-15 04:31:53.667622: +2026-04-15 04:31:53.669623: Epoch 3935 +2026-04-15 04:31:53.671344: Current learning rate: 0.00025 +2026-04-15 04:33:36.694528: train_loss -0.8056 +2026-04-15 04:33:36.700301: val_loss -0.7024 +2026-04-15 04:33:36.702372: Pseudo dice [0.6319, 0.5942, 0.7843, 0.4468, 0.5797, 0.939, 0.8774] +2026-04-15 04:33:36.704811: Epoch time: 103.03 s +2026-04-15 04:33:38.010207: +2026-04-15 04:33:38.012695: Epoch 3936 +2026-04-15 04:33:38.014787: Current learning rate: 0.00024 +2026-04-15 04:35:20.360258: train_loss -0.805 +2026-04-15 04:35:20.367534: val_loss -0.7538 +2026-04-15 04:35:20.370386: Pseudo dice [0.749, 0.7597, 0.8142, 0.0861, 0.6286, 0.929, 0.7316] +2026-04-15 04:35:20.372904: Epoch time: 102.35 s +2026-04-15 04:35:21.633946: +2026-04-15 04:35:21.635905: Epoch 3937 +2026-04-15 04:35:21.637836: Current learning rate: 0.00024 +2026-04-15 04:37:04.305412: train_loss -0.7973 +2026-04-15 04:37:04.311087: val_loss -0.6837 +2026-04-15 04:37:04.312990: Pseudo dice [0.7102, 0.9152, 0.7839, 0.8157, 0.6215, 0.7381, 0.7257] +2026-04-15 04:37:04.315385: Epoch time: 102.67 s +2026-04-15 04:37:05.621820: +2026-04-15 04:37:05.623738: Epoch 3938 +2026-04-15 04:37:05.626205: Current learning rate: 0.00024 +2026-04-15 04:38:47.414304: train_loss -0.8061 +2026-04-15 04:38:47.420556: val_loss -0.6752 +2026-04-15 04:38:47.423359: Pseudo dice [0.5507, 0.8649, 0.7662, 0.1211, 0.5943, 0.4854, 0.7911] +2026-04-15 04:38:47.426410: Epoch time: 101.8 s +2026-04-15 04:38:48.690413: +2026-04-15 04:38:48.693625: Epoch 3939 +2026-04-15 04:38:48.695507: Current learning rate: 0.00023 +2026-04-15 04:40:31.075261: train_loss -0.8036 +2026-04-15 04:40:31.081908: val_loss -0.6836 +2026-04-15 04:40:31.084071: Pseudo dice [0.6933, 0.7744, 0.8032, 0.3498, 0.646, 0.5291, 0.7934] +2026-04-15 04:40:31.086391: Epoch time: 102.39 s +2026-04-15 04:40:32.330971: +2026-04-15 04:40:32.332798: Epoch 3940 +2026-04-15 04:40:32.334488: Current learning rate: 0.00023 +2026-04-15 04:42:14.262578: train_loss -0.7977 +2026-04-15 04:42:14.268815: val_loss -0.695 +2026-04-15 04:42:14.271322: Pseudo dice [0.8893, 0.7886, 0.7495, 0.3883, 0.4144, 0.7373, 0.7551] +2026-04-15 04:42:14.273843: Epoch time: 101.93 s +2026-04-15 04:42:15.554315: +2026-04-15 04:42:15.556226: Epoch 3941 +2026-04-15 04:42:15.558045: Current learning rate: 0.00022 +2026-04-15 04:43:57.787076: train_loss -0.8029 +2026-04-15 04:43:57.797538: val_loss -0.7307 +2026-04-15 04:43:57.799929: Pseudo dice [0.8153, 0.8458, 0.7933, 0.1496, 0.5668, 0.8781, 0.783] +2026-04-15 04:43:57.802397: Epoch time: 102.24 s +2026-04-15 04:43:59.126293: +2026-04-15 04:43:59.128540: Epoch 3942 +2026-04-15 04:43:59.130424: Current learning rate: 0.00022 +2026-04-15 04:45:41.681215: train_loss -0.8016 +2026-04-15 04:45:41.688709: val_loss -0.6909 +2026-04-15 04:45:41.691073: Pseudo dice [0.757, 0.9145, 0.7637, 0.1568, 0.633, 0.6137, 0.8988] +2026-04-15 04:45:41.693844: Epoch time: 102.56 s +2026-04-15 04:45:43.025231: +2026-04-15 04:45:43.027408: Epoch 3943 +2026-04-15 04:45:43.029277: Current learning rate: 0.00022 +2026-04-15 04:47:25.055163: train_loss -0.8017 +2026-04-15 04:47:25.063086: val_loss -0.7247 +2026-04-15 04:47:25.065062: Pseudo dice [0.7856, 0.5289, 0.7519, 0.19, 0.7248, 0.9275, 0.8095] +2026-04-15 04:47:25.068201: Epoch time: 102.03 s +2026-04-15 04:47:26.360908: +2026-04-15 04:47:26.362953: Epoch 3944 +2026-04-15 04:47:26.364757: Current learning rate: 0.00021 +2026-04-15 04:49:08.268038: train_loss -0.8055 +2026-04-15 04:49:08.275191: val_loss -0.6976 +2026-04-15 04:49:08.277582: Pseudo dice [0.7561, 0.8097, 0.7496, 0.5539, 0.6441, 0.889, 0.8564] +2026-04-15 04:49:08.280487: Epoch time: 101.91 s +2026-04-15 04:49:09.510172: +2026-04-15 04:49:09.512757: Epoch 3945 +2026-04-15 04:49:09.514902: Current learning rate: 0.00021 +2026-04-15 04:50:51.510952: train_loss -0.8009 +2026-04-15 04:50:51.518334: val_loss -0.7097 +2026-04-15 04:50:51.520308: Pseudo dice [0.7222, 0.8349, 0.8037, 0.2747, 0.5726, 0.8908, 0.8039] +2026-04-15 04:50:51.522700: Epoch time: 102.0 s +2026-04-15 04:50:52.813304: +2026-04-15 04:50:52.815465: Epoch 3946 +2026-04-15 04:50:52.817658: Current learning rate: 0.00021 +2026-04-15 04:52:35.973869: train_loss -0.8073 +2026-04-15 04:52:35.980660: val_loss -0.6614 +2026-04-15 04:52:35.983164: Pseudo dice [0.8487, 0.9014, 0.7211, 0.4656, 0.5297, 0.6246, 0.7725] +2026-04-15 04:52:35.985739: Epoch time: 103.16 s +2026-04-15 04:52:37.290421: +2026-04-15 04:52:37.292266: Epoch 3947 +2026-04-15 04:52:37.294233: Current learning rate: 0.0002 +2026-04-15 04:54:19.460441: train_loss -0.7975 +2026-04-15 04:54:19.467062: val_loss -0.7239 +2026-04-15 04:54:19.469223: Pseudo dice [0.5315, 0.4578, 0.7819, 0.7762, 0.4829, 0.8949, 0.8763] +2026-04-15 04:54:19.471484: Epoch time: 102.17 s +2026-04-15 04:54:20.757437: +2026-04-15 04:54:20.759523: Epoch 3948 +2026-04-15 04:54:20.761090: Current learning rate: 0.0002 +2026-04-15 04:56:02.928437: train_loss -0.8064 +2026-04-15 04:56:02.935187: val_loss -0.7168 +2026-04-15 04:56:02.937435: Pseudo dice [0.7492, 0.7381, 0.8183, 0.5421, 0.568, 0.9261, 0.6598] +2026-04-15 04:56:02.939798: Epoch time: 102.17 s +2026-04-15 04:56:04.213059: +2026-04-15 04:56:04.215495: Epoch 3949 +2026-04-15 04:56:04.218068: Current learning rate: 0.0002 +2026-04-15 04:57:46.211567: train_loss -0.8003 +2026-04-15 04:57:46.218235: val_loss -0.6761 +2026-04-15 04:57:46.220683: Pseudo dice [0.7316, 0.4919, 0.7558, 0.3591, 0.5609, 0.8445, 0.8164] +2026-04-15 04:57:46.223433: Epoch time: 102.0 s +2026-04-15 04:57:49.512183: +2026-04-15 04:57:49.514028: Epoch 3950 +2026-04-15 04:57:49.515641: Current learning rate: 0.00019 +2026-04-15 04:59:31.468708: train_loss -0.7938 +2026-04-15 04:59:31.474982: val_loss -0.693 +2026-04-15 04:59:31.477247: Pseudo dice [0.8384, 0.7654, 0.729, 0.2599, 0.4261, 0.9281, 0.6911] +2026-04-15 04:59:31.480065: Epoch time: 101.96 s +2026-04-15 04:59:32.766057: +2026-04-15 04:59:32.768325: Epoch 3951 +2026-04-15 04:59:32.770354: Current learning rate: 0.00019 +2026-04-15 05:01:15.046199: train_loss -0.8063 +2026-04-15 05:01:15.051603: val_loss -0.7177 +2026-04-15 05:01:15.053797: Pseudo dice [0.701, 0.9051, 0.8392, 0.4144, 0.7014, 0.2971, 0.811] +2026-04-15 05:01:15.056418: Epoch time: 102.28 s +2026-04-15 05:01:16.296510: +2026-04-15 05:01:16.298424: Epoch 3952 +2026-04-15 05:01:16.300243: Current learning rate: 0.00019 +2026-04-15 05:02:58.234647: train_loss -0.8101 +2026-04-15 05:02:58.241179: val_loss -0.7271 +2026-04-15 05:02:58.243814: Pseudo dice [0.7127, 0.8075, 0.8631, 0.527, 0.6216, 0.8845, 0.8481] +2026-04-15 05:02:58.247317: Epoch time: 101.94 s +2026-04-15 05:02:59.491433: +2026-04-15 05:02:59.493657: Epoch 3953 +2026-04-15 05:02:59.495737: Current learning rate: 0.00018 +2026-04-15 05:04:41.836272: train_loss -0.8063 +2026-04-15 05:04:41.842866: val_loss -0.6913 +2026-04-15 05:04:41.845504: Pseudo dice [0.8504, 0.9118, 0.8466, 0.2558, 0.5407, 0.75, 0.7307] +2026-04-15 05:04:41.848200: Epoch time: 102.35 s +2026-04-15 05:04:43.104964: +2026-04-15 05:04:43.106997: Epoch 3954 +2026-04-15 05:04:43.108908: Current learning rate: 0.00018 +2026-04-15 05:06:25.663504: train_loss -0.7933 +2026-04-15 05:06:25.669920: val_loss -0.6877 +2026-04-15 05:06:25.673798: Pseudo dice [0.7573, 0.9183, 0.7761, 0.3563, 0.3888, 0.6936, 0.5081] +2026-04-15 05:06:25.676389: Epoch time: 102.56 s +2026-04-15 05:06:26.980685: +2026-04-15 05:06:26.982670: Epoch 3955 +2026-04-15 05:06:26.984380: Current learning rate: 0.00018 +2026-04-15 05:08:09.276753: train_loss -0.806 +2026-04-15 05:08:09.283971: val_loss -0.6852 +2026-04-15 05:08:09.286143: Pseudo dice [0.7439, 0.9079, 0.7987, 0.6184, 0.5574, 0.4405, 0.8631] +2026-04-15 05:08:09.288953: Epoch time: 102.3 s +2026-04-15 05:08:10.615110: +2026-04-15 05:08:10.617422: Epoch 3956 +2026-04-15 05:08:10.619730: Current learning rate: 0.00017 +2026-04-15 05:09:52.621079: train_loss -0.7985 +2026-04-15 05:09:52.628556: val_loss -0.7087 +2026-04-15 05:09:52.630739: Pseudo dice [0.6968, 0.6216, 0.8372, 0.6872, 0.1559, 0.6189, 0.8345] +2026-04-15 05:09:52.633771: Epoch time: 102.01 s +2026-04-15 05:09:53.901173: +2026-04-15 05:09:53.917999: Epoch 3957 +2026-04-15 05:09:53.922605: Current learning rate: 0.00017 +2026-04-15 05:11:35.762266: train_loss -0.8031 +2026-04-15 05:11:35.768179: val_loss -0.6812 +2026-04-15 05:11:35.770784: Pseudo dice [0.6355, 0.7601, 0.7933, 0.4971, 0.3485, 0.6328, 0.6939] +2026-04-15 05:11:35.773166: Epoch time: 101.86 s +2026-04-15 05:11:37.044499: +2026-04-15 05:11:37.046404: Epoch 3958 +2026-04-15 05:11:37.048519: Current learning rate: 0.00017 +2026-04-15 05:13:19.431176: train_loss -0.7986 +2026-04-15 05:13:19.436900: val_loss -0.6965 +2026-04-15 05:13:19.440397: Pseudo dice [0.7792, 0.798, 0.8419, 0.3267, 0.5848, 0.5819, 0.8522] +2026-04-15 05:13:19.443080: Epoch time: 102.39 s +2026-04-15 05:13:20.686434: +2026-04-15 05:13:20.688545: Epoch 3959 +2026-04-15 05:13:20.690167: Current learning rate: 0.00016 +2026-04-15 05:15:02.373684: train_loss -0.8001 +2026-04-15 05:15:02.381818: val_loss -0.7272 +2026-04-15 05:15:02.384368: Pseudo dice [0.7348, 0.7806, 0.899, 0.2443, 0.5508, 0.75, 0.8336] +2026-04-15 05:15:02.386689: Epoch time: 101.69 s +2026-04-15 05:15:03.649746: +2026-04-15 05:15:03.651432: Epoch 3960 +2026-04-15 05:15:03.653080: Current learning rate: 0.00016 +2026-04-15 05:16:45.593827: train_loss -0.8087 +2026-04-15 05:16:45.600667: val_loss -0.7313 +2026-04-15 05:16:45.603617: Pseudo dice [0.6914, 0.779, 0.7795, 0.7276, 0.5855, 0.733, 0.8481] +2026-04-15 05:16:45.605979: Epoch time: 101.95 s +2026-04-15 05:16:46.834265: +2026-04-15 05:16:46.836062: Epoch 3961 +2026-04-15 05:16:46.837560: Current learning rate: 0.00015 +2026-04-15 05:18:29.014900: train_loss -0.7956 +2026-04-15 05:18:29.022911: val_loss -0.7021 +2026-04-15 05:18:29.025060: Pseudo dice [0.6642, 0.9101, 0.7875, 0.4794, 0.6778, 0.1911, 0.8892] +2026-04-15 05:18:29.027145: Epoch time: 102.18 s +2026-04-15 05:18:30.248751: +2026-04-15 05:18:30.250627: Epoch 3962 +2026-04-15 05:18:30.252424: Current learning rate: 0.00015 +2026-04-15 05:20:12.672283: train_loss -0.8126 +2026-04-15 05:20:12.678472: val_loss -0.6592 +2026-04-15 05:20:12.680420: Pseudo dice [0.8356, 0.7816, 0.7787, 0.4391, 0.5841, 0.7061, 0.8107] +2026-04-15 05:20:12.682714: Epoch time: 102.43 s +2026-04-15 05:20:13.967375: +2026-04-15 05:20:13.969583: Epoch 3963 +2026-04-15 05:20:13.971352: Current learning rate: 0.00015 +2026-04-15 05:21:57.018306: train_loss -0.8168 +2026-04-15 05:21:57.027845: val_loss -0.7209 +2026-04-15 05:21:57.030150: Pseudo dice [0.8555, 0.7765, 0.8018, 0.4412, 0.6078, 0.8801, 0.7539] +2026-04-15 05:21:57.032584: Epoch time: 103.05 s +2026-04-15 05:21:58.266299: +2026-04-15 05:21:58.268108: Epoch 3964 +2026-04-15 05:21:58.269690: Current learning rate: 0.00014 +2026-04-15 05:23:40.908162: train_loss -0.8085 +2026-04-15 05:23:40.918562: val_loss -0.7214 +2026-04-15 05:23:40.920620: Pseudo dice [0.8383, 0.5324, 0.8445, 0.5229, 0.5381, 0.7798, 0.7803] +2026-04-15 05:23:40.923159: Epoch time: 102.65 s +2026-04-15 05:23:42.191281: +2026-04-15 05:23:42.192948: Epoch 3965 +2026-04-15 05:23:42.194717: Current learning rate: 0.00014 +2026-04-15 05:25:24.207697: train_loss -0.8077 +2026-04-15 05:25:24.214391: val_loss -0.689 +2026-04-15 05:25:24.216585: Pseudo dice [0.848, 0.8029, 0.8144, 0.5333, 0.3816, 0.6961, 0.815] +2026-04-15 05:25:24.218973: Epoch time: 102.02 s +2026-04-15 05:25:26.772153: +2026-04-15 05:25:26.774111: Epoch 3966 +2026-04-15 05:25:26.775728: Current learning rate: 0.00014 +2026-04-15 05:27:09.032244: train_loss -0.8125 +2026-04-15 05:27:09.039002: val_loss -0.6831 +2026-04-15 05:27:09.040730: Pseudo dice [0.8648, 0.8212, 0.8377, 0.6151, 0.3842, 0.6916, 0.8225] +2026-04-15 05:27:09.042531: Epoch time: 102.26 s +2026-04-15 05:27:10.365138: +2026-04-15 05:27:10.366921: Epoch 3967 +2026-04-15 05:27:10.369140: Current learning rate: 0.00013 +2026-04-15 05:28:52.330483: train_loss -0.7991 +2026-04-15 05:28:52.338188: val_loss -0.6564 +2026-04-15 05:28:52.340485: Pseudo dice [0.5928, 0.918, 0.788, 0.5013, 0.6011, 0.6294, 0.8147] +2026-04-15 05:28:52.342558: Epoch time: 101.97 s +2026-04-15 05:28:53.621571: +2026-04-15 05:28:53.623334: Epoch 3968 +2026-04-15 05:28:53.625336: Current learning rate: 0.00013 +2026-04-15 05:30:35.711204: train_loss -0.8103 +2026-04-15 05:30:35.717199: val_loss -0.6945 +2026-04-15 05:30:35.720547: Pseudo dice [0.7517, 0.9012, 0.7726, 0.2584, 0.5273, 0.9226, 0.8309] +2026-04-15 05:30:35.723829: Epoch time: 102.09 s +2026-04-15 05:30:37.018120: +2026-04-15 05:30:37.020789: Epoch 3969 +2026-04-15 05:30:37.022967: Current learning rate: 0.00013 +2026-04-15 05:32:19.920216: train_loss -0.8075 +2026-04-15 05:32:19.926663: val_loss -0.6909 +2026-04-15 05:32:19.928987: Pseudo dice [0.6375, 0.6333, 0.7769, 0.5306, 0.5046, 0.916, 0.7041] +2026-04-15 05:32:19.931318: Epoch time: 102.91 s +2026-04-15 05:32:21.195238: +2026-04-15 05:32:21.197173: Epoch 3970 +2026-04-15 05:32:21.199534: Current learning rate: 0.00012 +2026-04-15 05:34:02.986997: train_loss -0.8061 +2026-04-15 05:34:02.994779: val_loss -0.7176 +2026-04-15 05:34:02.997614: Pseudo dice [0.8613, 0.8627, 0.793, 0.4722, 0.5336, 0.8315, 0.8812] +2026-04-15 05:34:03.000072: Epoch time: 101.79 s +2026-04-15 05:34:04.238447: +2026-04-15 05:34:04.240400: Epoch 3971 +2026-04-15 05:34:04.242219: Current learning rate: 0.00012 +2026-04-15 05:35:46.115910: train_loss -0.7981 +2026-04-15 05:35:46.124615: val_loss -0.6771 +2026-04-15 05:35:46.126457: Pseudo dice [0.718, 0.8491, 0.8191, 0.4517, 0.6955, 0.6486, 0.6622] +2026-04-15 05:35:46.129467: Epoch time: 101.88 s +2026-04-15 05:35:47.375463: +2026-04-15 05:35:47.378077: Epoch 3972 +2026-04-15 05:35:47.380246: Current learning rate: 0.00011 +2026-04-15 05:37:29.613799: train_loss -0.8019 +2026-04-15 05:37:29.619616: val_loss -0.6679 +2026-04-15 05:37:29.621536: Pseudo dice [0.8198, 0.9246, 0.5865, 0.5306, 0.7068, 0.7482, 0.8513] +2026-04-15 05:37:29.623899: Epoch time: 102.24 s +2026-04-15 05:37:30.894415: +2026-04-15 05:37:30.896566: Epoch 3973 +2026-04-15 05:37:30.899966: Current learning rate: 0.00011 +2026-04-15 05:39:13.338881: train_loss -0.8064 +2026-04-15 05:39:13.346686: val_loss -0.7295 +2026-04-15 05:39:13.350030: Pseudo dice [0.8152, 0.6058, 0.7386, 0.0088, 0.5877, 0.6593, 0.854] +2026-04-15 05:39:13.353073: Epoch time: 102.45 s +2026-04-15 05:39:14.628058: +2026-04-15 05:39:14.630158: Epoch 3974 +2026-04-15 05:39:14.631613: Current learning rate: 0.00011 +2026-04-15 05:40:57.882430: train_loss -0.81 +2026-04-15 05:40:57.888826: val_loss -0.7058 +2026-04-15 05:40:57.891237: Pseudo dice [0.8545, 0.6703, 0.8058, 0.2914, 0.4143, 0.8714, 0.8328] +2026-04-15 05:40:57.893852: Epoch time: 103.26 s +2026-04-15 05:40:59.180776: +2026-04-15 05:40:59.182771: Epoch 3975 +2026-04-15 05:40:59.184559: Current learning rate: 0.0001 +2026-04-15 05:42:41.979971: train_loss -0.8152 +2026-04-15 05:42:41.986285: val_loss -0.6744 +2026-04-15 05:42:41.988485: Pseudo dice [0.7085, 0.9224, 0.8166, 0.5468, 0.5715, 0.5298, 0.8884] +2026-04-15 05:42:41.990478: Epoch time: 102.8 s +2026-04-15 05:42:43.282240: +2026-04-15 05:42:43.283966: Epoch 3976 +2026-04-15 05:42:43.285706: Current learning rate: 0.0001 +2026-04-15 05:44:25.730069: train_loss -0.8039 +2026-04-15 05:44:25.736181: val_loss -0.7142 +2026-04-15 05:44:25.738592: Pseudo dice [0.5514, 0.2503, 0.6961, 0.5415, 0.602, 0.9487, 0.8826] +2026-04-15 05:44:25.741040: Epoch time: 102.45 s +2026-04-15 05:44:26.996223: +2026-04-15 05:44:26.998323: Epoch 3977 +2026-04-15 05:44:26.999864: Current learning rate: 0.0001 +2026-04-15 05:46:09.144571: train_loss -0.8051 +2026-04-15 05:46:09.151500: val_loss -0.7328 +2026-04-15 05:46:09.154157: Pseudo dice [0.5391, 0.8561, 0.8349, 0.5856, 0.6334, 0.8293, 0.8315] +2026-04-15 05:46:09.156456: Epoch time: 102.15 s +2026-04-15 05:46:10.427505: +2026-04-15 05:46:10.429732: Epoch 3978 +2026-04-15 05:46:10.431408: Current learning rate: 9e-05 +2026-04-15 05:47:53.273195: train_loss -0.807 +2026-04-15 05:47:53.278952: val_loss -0.722 +2026-04-15 05:47:53.281118: Pseudo dice [0.7287, 0.8355, 0.819, 0.4479, 0.565, 0.9047, 0.7987] +2026-04-15 05:47:53.283146: Epoch time: 102.85 s +2026-04-15 05:47:54.592803: +2026-04-15 05:47:54.594762: Epoch 3979 +2026-04-15 05:47:54.596464: Current learning rate: 9e-05 +2026-04-15 05:49:37.647406: train_loss -0.8031 +2026-04-15 05:49:37.653781: val_loss -0.7386 +2026-04-15 05:49:37.656150: Pseudo dice [0.6945, 0.8872, 0.7767, 0.6566, 0.6921, 0.715, 0.8945] +2026-04-15 05:49:37.658719: Epoch time: 103.06 s +2026-04-15 05:49:38.945225: +2026-04-15 05:49:38.946937: Epoch 3980 +2026-04-15 05:49:38.948764: Current learning rate: 8e-05 +2026-04-15 05:51:22.132620: train_loss -0.8157 +2026-04-15 05:51:22.139862: val_loss -0.7265 +2026-04-15 05:51:22.142110: Pseudo dice [0.8164, 0.8194, 0.8417, 0.6201, 0.3084, 0.9115, 0.7883] +2026-04-15 05:51:22.144571: Epoch time: 103.19 s +2026-04-15 05:51:22.146246: Yayy! New best EMA pseudo Dice: 0.7043 +2026-04-15 05:51:25.318745: +2026-04-15 05:51:25.320337: Epoch 3981 +2026-04-15 05:51:25.322014: Current learning rate: 8e-05 +2026-04-15 05:53:07.748110: train_loss -0.8133 +2026-04-15 05:53:07.754560: val_loss -0.7221 +2026-04-15 05:53:07.756240: Pseudo dice [0.6854, 0.7982, 0.8151, 0.3879, 0.5731, 0.8027, 0.8869] +2026-04-15 05:53:07.758774: Epoch time: 102.43 s +2026-04-15 05:53:07.761773: Yayy! New best EMA pseudo Dice: 0.7046 +2026-04-15 05:53:10.971905: +2026-04-15 05:53:10.973731: Epoch 3982 +2026-04-15 05:53:10.975220: Current learning rate: 8e-05 +2026-04-15 05:54:53.725488: train_loss -0.8045 +2026-04-15 05:54:53.732307: val_loss -0.7291 +2026-04-15 05:54:53.734284: Pseudo dice [0.8276, 0.6774, 0.7675, 0.453, 0.6811, 0.9178, 0.6575] +2026-04-15 05:54:53.736615: Epoch time: 102.76 s +2026-04-15 05:54:53.740046: Yayy! New best EMA pseudo Dice: 0.7053 +2026-04-15 05:54:56.851557: +2026-04-15 05:54:56.853087: Epoch 3983 +2026-04-15 05:54:56.854568: Current learning rate: 7e-05 +2026-04-15 05:56:39.085486: train_loss -0.8016 +2026-04-15 05:56:39.091013: val_loss -0.6948 +2026-04-15 05:56:39.092923: Pseudo dice [0.7153, 0.9215, 0.8362, 0.346, 0.1481, 0.7571, 0.8434] +2026-04-15 05:56:39.095433: Epoch time: 102.24 s +2026-04-15 05:56:40.406125: +2026-04-15 05:56:40.407887: Epoch 3984 +2026-04-15 05:56:40.409550: Current learning rate: 7e-05 +2026-04-15 05:58:22.750852: train_loss -0.8071 +2026-04-15 05:58:22.756616: val_loss -0.7223 +2026-04-15 05:58:22.758719: Pseudo dice [0.8852, 0.5195, 0.8458, 0.5255, 0.557, 0.9377, 0.7993] +2026-04-15 05:58:22.761324: Epoch time: 102.35 s +2026-04-15 05:58:25.283298: +2026-04-15 05:58:25.285143: Epoch 3985 +2026-04-15 05:58:25.286943: Current learning rate: 7e-05 +2026-04-15 06:00:07.938953: train_loss -0.8111 +2026-04-15 06:00:07.946908: val_loss -0.6896 +2026-04-15 06:00:07.949116: Pseudo dice [0.6089, 0.6436, 0.8005, 0.8498, 0.5429, 0.6666, 0.881] +2026-04-15 06:00:07.952070: Epoch time: 102.66 s +2026-04-15 06:00:09.308442: +2026-04-15 06:00:09.310893: Epoch 3986 +2026-04-15 06:00:09.312829: Current learning rate: 6e-05 +2026-04-15 06:01:52.035313: train_loss -0.8115 +2026-04-15 06:01:52.041511: val_loss -0.7115 +2026-04-15 06:01:52.044032: Pseudo dice [0.6397, 0.8075, 0.8139, 0.4368, 0.4251, 0.9023, 0.7698] +2026-04-15 06:01:52.046782: Epoch time: 102.73 s +2026-04-15 06:01:53.310978: +2026-04-15 06:01:53.312857: Epoch 3987 +2026-04-15 06:01:53.314950: Current learning rate: 6e-05 +2026-04-15 06:03:36.170513: train_loss -0.819 +2026-04-15 06:03:36.178560: val_loss -0.6838 +2026-04-15 06:03:36.180617: Pseudo dice [0.8334, 0.8558, 0.8505, 0.2465, 0.6138, 0.2784, 0.789] +2026-04-15 06:03:36.183274: Epoch time: 102.86 s +2026-04-15 06:03:37.518690: +2026-04-15 06:03:37.520922: Epoch 3988 +2026-04-15 06:03:37.522960: Current learning rate: 5e-05 +2026-04-15 06:05:20.309702: train_loss -0.8108 +2026-04-15 06:05:20.316711: val_loss -0.6726 +2026-04-15 06:05:20.320901: Pseudo dice [0.6536, 0.8989, 0.7365, 0.637, 0.7047, 0.6925, 0.8322] +2026-04-15 06:05:20.323652: Epoch time: 102.79 s +2026-04-15 06:05:21.606405: +2026-04-15 06:05:21.608218: Epoch 3989 +2026-04-15 06:05:21.609661: Current learning rate: 5e-05 +2026-04-15 06:07:03.607728: train_loss -0.8086 +2026-04-15 06:07:03.616503: val_loss -0.7227 +2026-04-15 06:07:03.618312: Pseudo dice [0.8124, 0.8026, 0.7381, 0.2318, 0.526, 0.7252, 0.7986] +2026-04-15 06:07:03.620697: Epoch time: 102.0 s +2026-04-15 06:07:04.888387: +2026-04-15 06:07:04.890283: Epoch 3990 +2026-04-15 06:07:04.891969: Current learning rate: 5e-05 +2026-04-15 06:08:46.516983: train_loss -0.8097 +2026-04-15 06:08:46.524548: val_loss -0.7261 +2026-04-15 06:08:46.527547: Pseudo dice [0.6557, 0.8005, 0.7412, 0.4981, 0.5748, 0.8382, 0.8619] +2026-04-15 06:08:46.529935: Epoch time: 101.63 s +2026-04-15 06:08:47.798905: +2026-04-15 06:08:47.800613: Epoch 3991 +2026-04-15 06:08:47.802797: Current learning rate: 4e-05 +2026-04-15 06:10:29.856317: train_loss -0.8139 +2026-04-15 06:10:29.864961: val_loss -0.7052 +2026-04-15 06:10:29.867966: Pseudo dice [0.7401, 0.8177, 0.8084, 0.4438, 0.5891, 0.8587, 0.7385] +2026-04-15 06:10:29.870756: Epoch time: 102.06 s +2026-04-15 06:10:31.099398: +2026-04-15 06:10:31.101338: Epoch 3992 +2026-04-15 06:10:31.103776: Current learning rate: 4e-05 +2026-04-15 06:12:12.702440: train_loss -0.8074 +2026-04-15 06:12:12.708678: val_loss -0.7116 +2026-04-15 06:12:12.710840: Pseudo dice [0.785, 0.7623, 0.7854, 0.668, 0.4867, 0.7957, 0.7453] +2026-04-15 06:12:12.713315: Epoch time: 101.61 s +2026-04-15 06:12:13.953219: +2026-04-15 06:12:13.954909: Epoch 3993 +2026-04-15 06:12:13.956451: Current learning rate: 3e-05 +2026-04-15 06:13:56.132302: train_loss -0.8123 +2026-04-15 06:13:56.138844: val_loss -0.731 +2026-04-15 06:13:56.141904: Pseudo dice [0.6375, 0.8057, 0.8323, 0.3743, 0.3859, 0.9211, 0.6997] +2026-04-15 06:13:56.144358: Epoch time: 102.18 s +2026-04-15 06:13:57.413010: +2026-04-15 06:13:57.421856: Epoch 3994 +2026-04-15 06:13:57.423458: Current learning rate: 3e-05 +2026-04-15 06:15:39.433893: train_loss -0.7978 +2026-04-15 06:15:39.439913: val_loss -0.7418 +2026-04-15 06:15:39.447838: Pseudo dice [0.7188, 0.7537, 0.8276, 0.4515, 0.5035, 0.942, 0.8303] +2026-04-15 06:15:39.450541: Epoch time: 102.02 s +2026-04-15 06:15:40.686109: +2026-04-15 06:15:40.688135: Epoch 3995 +2026-04-15 06:15:40.689881: Current learning rate: 2e-05 +2026-04-15 06:17:23.033382: train_loss -0.8068 +2026-04-15 06:17:23.042274: val_loss -0.6833 +2026-04-15 06:17:23.044248: Pseudo dice [0.8119, 0.7302, 0.7675, 0.8527, 0.6409, 0.7874, 0.8733] +2026-04-15 06:17:23.047242: Epoch time: 102.35 s +2026-04-15 06:17:23.049044: Yayy! New best EMA pseudo Dice: 0.7074 +2026-04-15 06:17:26.169951: +2026-04-15 06:17:26.175523: Epoch 3996 +2026-04-15 06:17:26.178415: Current learning rate: 2e-05 +2026-04-15 06:19:08.356742: train_loss -0.7988 +2026-04-15 06:19:08.369828: val_loss -0.6733 +2026-04-15 06:19:08.372714: Pseudo dice [0.6653, 0.8964, 0.8283, 0.5049, 0.4389, 0.6347, 0.5805] +2026-04-15 06:19:08.375906: Epoch time: 102.19 s +2026-04-15 06:19:09.661393: +2026-04-15 06:19:09.663359: Epoch 3997 +2026-04-15 06:19:09.665565: Current learning rate: 2e-05 +2026-04-15 06:20:51.490104: train_loss -0.808 +2026-04-15 06:20:51.499350: val_loss -0.7157 +2026-04-15 06:20:51.503258: Pseudo dice [0.6982, 0.8027, 0.7062, 0.1323, 0.648, 0.2364, 0.7661] +2026-04-15 06:20:51.506239: Epoch time: 101.83 s +2026-04-15 06:20:52.762963: +2026-04-15 06:20:52.764488: Epoch 3998 +2026-04-15 06:20:52.766039: Current learning rate: 1e-05 +2026-04-15 06:22:34.983202: train_loss -0.8044 +2026-04-15 06:22:34.989583: val_loss -0.7439 +2026-04-15 06:22:34.992086: Pseudo dice [0.6961, 0.9104, 0.8168, 0.5619, 0.5564, 0.7933, 0.876] +2026-04-15 06:22:34.994286: Epoch time: 102.22 s +2026-04-15 06:22:36.273621: +2026-04-15 06:22:36.275304: Epoch 3999 +2026-04-15 06:22:36.276999: Current learning rate: 1e-05 +2026-04-15 06:24:18.134611: train_loss -0.809 +2026-04-15 06:24:18.141825: val_loss -0.7284 +2026-04-15 06:24:18.144333: Pseudo dice [0.7331, 0.3954, 0.792, 0.6071, 0.6881, 0.9311, 0.8655] +2026-04-15 06:24:18.146631: Epoch time: 101.86 s +2026-04-15 06:24:21.227564: Training done. +2026-04-15 06:24:21.581904: Using splits from existing split file: /data/houbb/nnunetv2/nnUNet_preprocessed/Dataset201_MSWAL/splits_final.json +2026-04-15 06:24:21.585866: The split file contains 5 splits. +2026-04-15 06:24:21.587528: Desired fold for training: 4 +2026-04-15 06:24:21.589210: This split has 388 training and 96 validation cases. +2026-04-15 06:24:21.591285: predicting MSWAL_0001 +2026-04-15 06:24:21.603109: MSWAL_0001, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:25:14.043614: predicting MSWAL_0011 +2026-04-15 06:25:14.058413: MSWAL_0011, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:25:26.768097: predicting MSWAL_0021 +2026-04-15 06:25:26.781461: MSWAL_0021, shape torch.Size([1, 181, 507, 507]), rank 0 +2026-04-15 06:25:39.296679: predicting MSWAL_0035 +2026-04-15 06:25:39.307833: MSWAL_0035, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:25:51.935337: predicting MSWAL_0042 +2026-04-15 06:25:51.946294: MSWAL_0042, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 06:26:00.446775: predicting MSWAL_0051 +2026-04-15 06:26:00.466737: MSWAL_0051, shape torch.Size([1, 177, 541, 541]), rank 0 +2026-04-15 06:26:22.717829: predicting MSWAL_0054 +2026-04-15 06:26:22.729074: MSWAL_0054, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:26:35.124644: predicting MSWAL_0063 +2026-04-15 06:26:35.146233: MSWAL_0063, shape torch.Size([1, 157, 520, 520]), rank 0 +2026-04-15 06:26:49.830066: predicting MSWAL_0088 +2026-04-15 06:26:49.841133: MSWAL_0088, shape torch.Size([1, 177, 535, 535]), rank 0 +2026-04-15 06:27:12.020230: predicting MSWAL_0089 +2026-04-15 06:27:12.035254: MSWAL_0089, shape torch.Size([1, 185, 561, 561]), rank 0 +2026-04-15 06:27:34.575918: predicting MSWAL_0094 +2026-04-15 06:27:34.590565: MSWAL_0094, shape torch.Size([1, 157, 539, 539]), rank 0 +2026-04-15 06:27:49.481499: predicting MSWAL_0095 +2026-04-15 06:27:49.504873: MSWAL_0095, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:28:02.065450: predicting MSWAL_0096 +2026-04-15 06:28:02.078720: MSWAL_0096, shape torch.Size([1, 221, 480, 480]), rank 0 +2026-04-15 06:28:14.709854: predicting MSWAL_0106 +2026-04-15 06:28:14.729215: MSWAL_0106, shape torch.Size([1, 181, 507, 507]), rank 0 +2026-04-15 06:28:27.418233: predicting MSWAL_0109 +2026-04-15 06:28:27.435658: MSWAL_0109, shape torch.Size([1, 285, 611, 611]), rank 0 +2026-04-15 06:29:04.170681: predicting MSWAL_0111 +2026-04-15 06:29:04.192124: MSWAL_0111, shape torch.Size([1, 313, 583, 583]), rank 0 +2026-04-15 06:29:41.267084: predicting MSWAL_0112 +2026-04-15 06:29:41.288753: MSWAL_0112, shape torch.Size([1, 201, 540, 540]), rank 0 +2026-04-15 06:30:03.952543: predicting MSWAL_0117 +2026-04-15 06:30:03.980019: MSWAL_0117, shape torch.Size([1, 369, 541, 541]), rank 0 +2026-04-15 06:30:48.095243: predicting MSWAL_0119 +2026-04-15 06:30:48.117898: MSWAL_0119, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:31:00.950909: predicting MSWAL_0120 +2026-04-15 06:31:00.976992: MSWAL_0120, shape torch.Size([1, 177, 537, 537]), rank 0 +2026-04-15 06:31:23.232902: predicting MSWAL_0122 +2026-04-15 06:31:23.256021: MSWAL_0122, shape torch.Size([1, 313, 508, 508]), rank 0 +2026-04-15 06:31:44.278954: predicting MSWAL_0132 +2026-04-15 06:31:44.293944: MSWAL_0132, shape torch.Size([1, 281, 556, 556]), rank 0 +2026-04-15 06:32:21.322471: predicting MSWAL_0150 +2026-04-15 06:32:21.343091: MSWAL_0150, shape torch.Size([1, 288, 549, 549]), rank 0 +2026-04-15 06:32:58.276079: predicting MSWAL_0152 +2026-04-15 06:32:58.292821: MSWAL_0152, shape torch.Size([1, 305, 533, 533]), rank 0 +2026-04-15 06:33:35.159836: predicting MSWAL_0157 +2026-04-15 06:33:35.186162: MSWAL_0157, shape torch.Size([1, 514, 509, 509]), rank 0 +2026-04-15 06:34:12.766146: predicting MSWAL_0171 +2026-04-15 06:34:12.791584: MSWAL_0171, shape torch.Size([1, 331, 561, 561]), rank 0 +2026-04-15 06:34:50.022391: predicting MSWAL_0172 +2026-04-15 06:34:50.048607: MSWAL_0172, shape torch.Size([1, 358, 608, 608]), rank 0 +2026-04-15 06:35:34.488891: predicting MSWAL_0177 +2026-04-15 06:35:34.510647: MSWAL_0177, shape torch.Size([1, 454, 531, 531]), rank 0 +2026-04-15 06:36:33.326946: predicting MSWAL_0183 +2026-04-15 06:36:33.356744: MSWAL_0183, shape torch.Size([1, 174, 519, 519]), rank 0 +2026-04-15 06:36:56.258419: predicting MSWAL_0187 +2026-04-15 06:36:56.280284: MSWAL_0187, shape torch.Size([1, 421, 613, 613]), rank 0 +2026-04-15 06:37:47.849859: predicting MSWAL_0195 +2026-04-15 06:37:47.876460: MSWAL_0195, shape torch.Size([1, 301, 540, 540]), rank 0 +2026-04-15 06:38:24.764675: predicting MSWAL_0203 +2026-04-15 06:38:24.785868: MSWAL_0203, shape torch.Size([1, 458, 572, 572]), rank 0 +2026-04-15 06:39:23.400574: predicting MSWAL_0204 +2026-04-15 06:39:23.432183: MSWAL_0204, shape torch.Size([1, 248, 605, 605]), rank 0 +2026-04-15 06:39:53.131438: predicting MSWAL_0209 +2026-04-15 06:39:53.155038: MSWAL_0209, shape torch.Size([1, 342, 508, 508]), rank 0 +2026-04-15 06:40:18.329693: predicting MSWAL_0224 +2026-04-15 06:40:18.350763: MSWAL_0224, shape torch.Size([1, 201, 507, 507]), rank 0 +2026-04-15 06:40:31.196804: predicting MSWAL_0242 +2026-04-15 06:40:31.213839: MSWAL_0242, shape torch.Size([1, 296, 571, 571]), rank 0 +2026-04-15 06:41:07.987785: predicting MSWAL_0248 +2026-04-15 06:41:08.011243: MSWAL_0248, shape torch.Size([1, 518, 617, 617]), rank 0 +2026-04-15 06:42:14.690914: predicting MSWAL_0251 +2026-04-15 06:42:14.727208: MSWAL_0251, shape torch.Size([1, 340, 512, 512]), rank 0 +2026-04-15 06:42:40.281367: predicting MSWAL_0257 +2026-04-15 06:42:40.299289: MSWAL_0257, shape torch.Size([1, 310, 496, 496]), rank 0 +2026-04-15 06:43:01.808599: predicting MSWAL_0264 +2026-04-15 06:43:01.823613: MSWAL_0264, shape torch.Size([1, 196, 532, 532]), rank 0 +2026-04-15 06:43:24.057544: predicting MSWAL_0271 +2026-04-15 06:43:24.075559: MSWAL_0271, shape torch.Size([1, 350, 581, 581]), rank 0 +2026-04-15 06:44:08.462917: predicting MSWAL_0284 +2026-04-15 06:44:08.490908: MSWAL_0284, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:44:21.256282: predicting MSWAL_0297 +2026-04-15 06:44:21.270523: MSWAL_0297, shape torch.Size([1, 301, 507, 507]), rank 0 +2026-04-15 06:44:42.702861: predicting MSWAL_0302 +2026-04-15 06:44:42.726690: MSWAL_0302, shape torch.Size([1, 285, 507, 507]), rank 0 +2026-04-15 06:45:03.737118: predicting MSWAL_0314 +2026-04-15 06:45:03.755615: MSWAL_0314, shape torch.Size([1, 189, 532, 532]), rank 0 +2026-04-15 06:45:25.866022: predicting MSWAL_0327 +2026-04-15 06:45:25.891941: MSWAL_0327, shape torch.Size([1, 197, 507, 507]), rank 0 +2026-04-15 06:45:38.677624: predicting MSWAL_0328 +2026-04-15 06:45:38.729898: MSWAL_0328, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:45:51.638487: predicting MSWAL_0333 +2026-04-15 06:45:51.657743: MSWAL_0333, shape torch.Size([1, 237, 507, 507]), rank 0 +2026-04-15 06:46:08.518663: predicting MSWAL_0337 +2026-04-15 06:46:08.535480: MSWAL_0337, shape torch.Size([1, 301, 507, 507]), rank 0 +2026-04-15 06:46:29.493325: predicting MSWAL_0356 +2026-04-15 06:46:29.519744: MSWAL_0356, shape torch.Size([1, 165, 507, 507]), rank 0 +2026-04-15 06:46:38.090490: predicting MSWAL_0357 +2026-04-15 06:46:38.102896: MSWAL_0357, shape torch.Size([1, 361, 585, 585]), rank 0 +2026-04-15 06:47:22.266381: predicting MSWAL_0362 +2026-04-15 06:47:22.300304: MSWAL_0362, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 06:47:31.210105: predicting MSWAL_0363 +2026-04-15 06:47:31.223324: MSWAL_0363, shape torch.Size([1, 277, 507, 507]), rank 0 +2026-04-15 06:47:48.536138: predicting MSWAL_0373 +2026-04-15 06:47:48.549271: MSWAL_0373, shape torch.Size([1, 377, 547, 547]), rank 0 +2026-04-15 06:48:32.916397: predicting MSWAL_0378 +2026-04-15 06:48:32.933932: MSWAL_0378, shape torch.Size([1, 317, 567, 567]), rank 0 +2026-04-15 06:49:09.821510: predicting MSWAL_0380 +2026-04-15 06:49:09.846877: MSWAL_0380, shape torch.Size([1, 247, 507, 507]), rank 0 +2026-04-15 06:49:26.689424: predicting MSWAL_0388 +2026-04-15 06:49:26.702004: MSWAL_0388, shape torch.Size([1, 325, 651, 651]), rank 0 +2026-04-15 06:50:24.027674: predicting MSWAL_0403 +2026-04-15 06:50:24.066729: MSWAL_0403, shape torch.Size([1, 385, 636, 636]), rank 0 +2026-04-15 06:51:08.842624: predicting MSWAL_0407 +2026-04-15 06:51:08.872716: MSWAL_0407, shape torch.Size([1, 189, 532, 532]), rank 0 +2026-04-15 06:51:31.280651: predicting MSWAL_0415 +2026-04-15 06:51:31.297759: MSWAL_0415, shape torch.Size([1, 305, 547, 547]), rank 0 +2026-04-15 06:52:08.098933: predicting MSWAL_0417 +2026-04-15 06:52:08.116592: MSWAL_0417, shape torch.Size([1, 117, 575, 575]), rank 0 +2026-04-15 06:52:23.028050: predicting MSWAL_0418 +2026-04-15 06:52:23.050554: MSWAL_0418, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:52:35.721032: predicting MSWAL_0425 +2026-04-15 06:52:35.742672: MSWAL_0425, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:52:48.381538: predicting MSWAL_0428 +2026-04-15 06:52:48.392933: MSWAL_0428, shape torch.Size([1, 325, 572, 572]), rank 0 +2026-04-15 06:53:25.310329: predicting MSWAL_0434 +2026-04-15 06:53:25.331414: MSWAL_0434, shape torch.Size([1, 341, 507, 507]), rank 0 +2026-04-15 06:53:50.820971: predicting MSWAL_0442 +2026-04-15 06:53:50.842561: MSWAL_0442, shape torch.Size([1, 157, 507, 507]), rank 0 +2026-04-15 06:53:59.497708: predicting MSWAL_0457 +2026-04-15 06:53:59.507064: MSWAL_0457, shape torch.Size([1, 197, 507, 507]), rank 0 +2026-04-15 06:54:12.182544: predicting MSWAL_0480 +2026-04-15 06:54:12.202520: MSWAL_0480, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:54:24.765534: predicting MSWAL_0487 +2026-04-15 06:54:24.777259: MSWAL_0487, shape torch.Size([1, 217, 507, 507]), rank 0 +2026-04-15 06:54:37.757483: predicting MSWAL_0493 +2026-04-15 06:54:37.769457: MSWAL_0493, shape torch.Size([1, 237, 507, 507]), rank 0 +2026-04-15 06:54:54.545825: predicting MSWAL_0495 +2026-04-15 06:54:54.567810: MSWAL_0495, shape torch.Size([1, 208, 560, 560]), rank 0 +2026-04-15 06:55:16.772417: predicting MSWAL_0500 +2026-04-15 06:55:16.784667: MSWAL_0500, shape torch.Size([1, 157, 455, 455]), rank 0 +2026-04-15 06:55:25.284622: predicting MSWAL_0508 +2026-04-15 06:55:25.297767: MSWAL_0508, shape torch.Size([1, 177, 517, 517]), rank 0 +2026-04-15 06:55:47.441078: predicting MSWAL_0516 +2026-04-15 06:55:47.461740: MSWAL_0516, shape torch.Size([1, 165, 464, 464]), rank 0 +2026-04-15 06:55:55.912211: predicting MSWAL_0535 +2026-04-15 06:55:55.934421: MSWAL_0535, shape torch.Size([1, 179, 576, 576]), rank 0 +2026-04-15 06:56:18.099021: predicting MSWAL_0536 +2026-04-15 06:56:18.112976: MSWAL_0536, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:56:30.750713: predicting MSWAL_0538 +2026-04-15 06:56:30.773167: MSWAL_0538, shape torch.Size([1, 137, 529, 529]), rank 0 +2026-04-15 06:56:45.565223: predicting MSWAL_0542 +2026-04-15 06:56:45.586870: MSWAL_0542, shape torch.Size([1, 177, 507, 507]), rank 0 +2026-04-15 06:56:58.194224: predicting MSWAL_0548 +2026-04-15 06:56:58.220877: MSWAL_0548, shape torch.Size([1, 153, 531, 531]), rank 0 +2026-04-15 06:57:12.987938: predicting MSWAL_0551 +2026-04-15 06:57:13.011765: MSWAL_0551, shape torch.Size([1, 249, 507, 507]), rank 0 +2026-04-15 06:57:29.887653: predicting MSWAL_0556 +2026-04-15 06:57:29.908712: MSWAL_0556, shape torch.Size([1, 409, 507, 507]), rank 0 +2026-04-15 06:57:59.165989: predicting MSWAL_0558 +2026-04-15 06:57:59.183943: MSWAL_0558, shape torch.Size([1, 248, 480, 480]), rank 0 +2026-04-15 06:58:15.832408: predicting MSWAL_0571 +2026-04-15 06:58:15.845916: MSWAL_0571, shape torch.Size([1, 254, 560, 560]), rank 0 +2026-04-15 06:58:45.477809: predicting MSWAL_0600 +2026-04-15 06:58:45.493118: MSWAL_0600, shape torch.Size([1, 145, 507, 507]), rank 0 +2026-04-15 06:58:54.049248: predicting MSWAL_0608 +2026-04-15 06:58:54.060612: MSWAL_0608, shape torch.Size([1, 328, 589, 589]), rank 0 +2026-04-15 06:59:31.052160: predicting MSWAL_0612 +2026-04-15 06:59:31.069556: MSWAL_0612, shape torch.Size([1, 344, 573, 573]), rank 0 +2026-04-15 07:00:15.161202: predicting MSWAL_0625 +2026-04-15 07:00:15.195456: MSWAL_0625, shape torch.Size([1, 316, 564, 564]), rank 0 +2026-04-15 07:00:52.236750: predicting MSWAL_0626 +2026-04-15 07:00:52.255772: MSWAL_0626, shape torch.Size([1, 313, 560, 560]), rank 0 +2026-04-15 07:01:29.186068: predicting MSWAL_0627 +2026-04-15 07:01:29.225589: MSWAL_0627, shape torch.Size([1, 218, 480, 480]), rank 0 +2026-04-15 07:01:41.863846: predicting MSWAL_0632 +2026-04-15 07:01:41.877776: MSWAL_0632, shape torch.Size([1, 343, 636, 636]), rank 0 +2026-04-15 07:02:26.053906: predicting MSWAL_0635 +2026-04-15 07:02:26.092052: MSWAL_0635, shape torch.Size([1, 344, 563, 563]), rank 0 +2026-04-15 07:03:10.148338: predicting MSWAL_0658 +2026-04-15 07:03:10.176106: MSWAL_0658, shape torch.Size([1, 139, 496, 496]), rank 0 +2026-04-15 07:03:18.608172: predicting MSWAL_0682 +2026-04-15 07:03:18.628567: MSWAL_0682, shape torch.Size([1, 316, 572, 572]), rank 0 +2026-04-15 07:03:55.384882: predicting MSWAL_0685 +2026-04-15 07:03:55.405227: MSWAL_0685, shape torch.Size([1, 343, 552, 552]), rank 0 +2026-04-15 07:04:39.682172: predicting MSWAL_0690 +2026-04-15 07:04:39.711849: MSWAL_0690, shape torch.Size([1, 523, 667, 667]), rank 0 +2026-04-15 07:06:22.531371: predicting MSWAL_0693 +2026-04-15 07:06:22.574277: MSWAL_0693, shape torch.Size([1, 288, 476, 476]), rank 0 +2026-04-15 07:08:42.525543: Validation complete +2026-04-15 07:08:42.527953: Mean Validation Dice: 0.5208027429800878 diff --git a/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/plans.json b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/plans.json new file mode 100644 index 0000000000000000000000000000000000000000..d7c29249f212bb1ee27bfa432ef6373c1402c8e8 --- /dev/null +++ b/Dataset201_MSWAL/nnUNetTrainer_4000epochs__nnUNetResEncUNetLPlans__3d_fullres/plans.json @@ -0,0 +1,558 @@ +{ + "dataset_name": "Dataset201_MSWAL", + "plans_name": "nnUNetResEncUNetLPlans", + "original_median_spacing_after_transp": [ + 1.25, + 0.75, + 0.75 + ], + "original_median_shape_after_transp": [ + 261, + 512, + 512 + ], + "image_reader_writer": "SimpleITKIO", + "transpose_forward": [ + 0, + 1, + 2 + ], + "transpose_backward": [ + 0, + 1, + 2 + ], + "configurations": { + "2d": { + "data_identifier": "nnUNetPlans_2d", + "preprocessor_name": "DefaultPreprocessor", + "batch_size": 35, + "patch_size": [ + 512, + 512 + ], + "median_image_size_in_voxels": [ + 512.0, + 512.0 + ], + "spacing": [ + 0.75, + 0.75 + ], + "normalization_schemes": [ + "CTNormalization" + ], + "use_mask_for_norm": [ + false + ], + "resampling_fn_data": "resample_data_or_seg_to_shape", + "resampling_fn_seg": "resample_data_or_seg_to_shape", + "resampling_fn_data_kwargs": { + "is_seg": false, + "order": 3, + "order_z": 0, + "force_separate_z": null + }, + "resampling_fn_seg_kwargs": { + "is_seg": true, + "order": 1, + "order_z": 0, + "force_separate_z": null + }, + "resampling_fn_probabilities": "resample_data_or_seg_to_shape", + "resampling_fn_probabilities_kwargs": { + "is_seg": false, + "order": 1, + "order_z": 0, + "force_separate_z": null + }, + "architecture": { + "network_class_name": 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"resampling_fn_probabilities": "resample_data_or_seg_to_shape", + "resampling_fn_probabilities_kwargs": { + "is_seg": false, + "order": 1, + "order_z": 0, + "force_separate_z": null + }, + "architecture": { + "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet", + "arch_kwargs": { + "n_stages": 7, + "features_per_stage": [ + 32, + 64, + 128, + 256, + 320, + 320, + 320 + ], + "conv_op": "torch.nn.modules.conv.Conv3d", + "kernel_sizes": [ + [ + 3, + 3, + 3 + ], + [ + 3, + 3, + 3 + ], + [ + 3, + 3, + 3 + ], + [ + 3, + 3, + 3 + ], + [ + 3, + 3, + 3 + ], + [ + 3, + 3, + 3 + ], + [ + 3, + 3, + 3 + ] + ], + "strides": [ + [ + 1, + 1, + 1 + ], + [ + 2, + 2, + 2 + ], + [ + 2, + 2, + 2 + ], + [ + 2, + 2, + 2 + ], + [ + 2, + 2, + 2 + ], + [ + 1, + 2, + 2 + ], + [ + 1, + 2, + 2 + ] + ], + "n_blocks_per_stage": [ + 1, + 3, + 4, + 6, + 6, + 6, + 6 + ], + "n_conv_per_stage_decoder": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "conv_bias": true, + "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d", + "norm_op_kwargs": { + "eps": 1e-05, + "affine": true + }, + "dropout_op": null, + "dropout_op_kwargs": null, + "nonlin": "torch.nn.LeakyReLU", + "nonlin_kwargs": { + "inplace": true + } + }, + "_kw_requires_import": [ + "conv_op", + "norm_op", + "dropout_op", + "nonlin" + ] + }, + "batch_dice": true + }, + "3d_cascade_fullres": { + "inherits_from": "3d_fullres", + "previous_stage": "3d_lowres" + } + }, + "experiment_planner_used": "nnUNetPlannerResEncL", + "label_manager": "LabelManager", + "foreground_intensity_properties_per_channel": { + "0": { + "max": 3071.0, + "mean": 71.96339416503906, + "median": 45.0, + "min": -932.0, + "percentile_00_5": -93.0, + "percentile_99_5": 1052.0, + "std": 141.6230926513672 + } + } +} \ No newline at end of file